From b556c9b82a730cbab93df4e642f6b83ea8b3d847 Mon Sep 17 00:00:00 2001 From: Wyatt Lowery Date: Mon, 31 Aug 2026 14:48:11 -0500 Subject: [PATCH 01/10] initial cleanup and distillation --- .claude/settings.local.json | 14 -------------- 1 file changed, 14 deletions(-) delete mode 100644 .claude/settings.local.json diff --git a/.claude/settings.local.json b/.claude/settings.local.json deleted file mode 100644 index 91f108d..0000000 --- a/.claude/settings.local.json +++ /dev/null @@ -1,14 +0,0 @@ -{ - "permissions": { - "allow": [ - "Bash(conda activate esapp*)", - "Bash(C:/Users/wyatt/.conda/envs/esapp/python.exe*)", - "Bash(C:/Users/wyatt/.conda/envs/esapp/Scripts/pytest*)", - "Bash(C:/Users/wyatt/.conda/envs/esapp/Scripts/flake8*)", - "Bash(C:/Users/wyatt/.conda/envs/esapp/Scripts/pip*)" - ] - }, - "env": { - "PATH": "C:\\Users\\wyatt\\.conda\\envs\\esapp;C:\\Users\\wyatt\\.conda\\envs\\esapp\\Scripts;${env:PATH}" - } -} From 484d6dd78e2b4bc32317fb00fd9e2fb279ff1a92 Mon Sep 17 00:00:00 2001 From: Wyatt Lowery Date: Mon, 31 Aug 2026 14:48:22 -0500 Subject: [PATCH 02/10] removing old blaot --- .gitignore | 3 + CLAUDE.md | 7 +- docs/Auxiliary File Format.pdf | Bin 132 -> 0 bytes docs/Auxiliary File Format.txt | 13500 ---------------- docs/Subdata.txt | 2527 --- docs/api/utils.rst | 8 - {examples/gic => docs}/formulations/gic.rst | 0 {examples/gic => docs}/formulations/index.rst | 0 docs/index.rst | 1 + esapp/indexable.py | 2 - esapp/py.typed | 0 esapp/saw/base.py | 5 +- esapp/utils/__init__.py | 5 - esapp/utils/misc.py | 55 - examples/README.md | 38 + examples/__init__.py | 32 - examples/dynamics.py | 2 +- .../dynamics/01_transient_stability.ipynb | 61 +- examples/dynamics/02_multi_contingency.ipynb | 52 +- examples/gic/01_gic_basics.ipynb | 89 +- examples/gic/02_gic_model.ipynb | 84 +- examples/gic/03_efield_geographic.ipynb | 170 +- examples/gic/04_b3d_file_io.ipynb | 10 +- examples/gic/05_gic_sensitivity.ipynb | 118 +- examples/injection.py | 117 - examples/map.py | 2 +- examples/mesh.py | 25 + examples/network/01_matrix_extraction.ipynb | 31 +- examples/network/02_network_topology.ipynb | 57 +- examples/network/03_network_expansion.ipynb | 69 +- examples/nonuniform/01_nonuniform_gic.ipynb | 157 +- examples/nonuniform/__init__.py | 6 - examples/plot_helpers.py | 8 +- examples/statics.py | 2 +- .../01_contingency_analysis.ipynb | 28 +- examples/steady_state/02_scopf_analysis.ipynb | 53 +- examples/steady_state/03_atc_analysis.ipynb | 36 +- .../04_continuation_power_flow.ipynb | 420 +- .../steady_state/05_ptdf_lodf_analysis.ipynb | 177 +- .../07_state_chains_and_stress.ipynb | 3 +- .../visualization/01_discrete_calculus.ipynb | 133 +- .../visualization/02_spectral_analysis.ipynb | 46 +- .../03_geographic_plotting.ipynb | 58 +- pyproject.toml | 4 +- tests/test_utils.py | 28 +- 45 files changed, 802 insertions(+), 17427 deletions(-) delete mode 100644 docs/Auxiliary File Format.pdf delete mode 100644 docs/Auxiliary File Format.txt delete mode 100644 docs/Subdata.txt rename {examples/gic => docs}/formulations/gic.rst (100%) rename {examples/gic => docs}/formulations/index.rst (100%) create mode 100644 esapp/py.typed delete mode 100644 esapp/utils/misc.py create mode 100644 examples/README.md delete mode 100644 examples/__init__.py delete mode 100644 examples/injection.py delete mode 100644 examples/nonuniform/__init__.py diff --git a/.gitignore b/.gitignore index 005a64f..7959720 100644 --- a/.gitignore +++ b/.gitignore @@ -155,3 +155,6 @@ dmypy.json .vscode/settings.json tests/test_indextool.py docs/examples/system_health_report.csv + +# Claude Code local settings (machine-specific) +.claude/settings.local.json diff --git a/CLAUDE.md b/CLAUDE.md index 4864d33..98b1b8b 100644 --- a/CLAUDE.md +++ b/CLAUDE.md @@ -8,7 +8,7 @@ ESA++ (`esapp`) is a Python toolkit for power system automation, providing a hig ## Environment Setup -This project uses a **conda environment** named `esapp` located at `C:\Users\wyatt\.conda\envs\esapp` (Python 3.11). All commands (pytest, pip, flake8, etc.) should be run using this environment. +This project uses a **conda environment** named `esapp` (Python 3.11). On this machine it lives at `C:\Users\wyattluke.lowery\AppData\Local\miniconda3\envs\esapp`. All commands (pytest, pip, flake8, etc.) should be run using this environment. ```bash # Activate the environment @@ -98,6 +98,5 @@ Embedded analysis applications accessible from `PowerWorld`: ## Key Constraints - **Windows-only**: Depends on `pywin32` for COM interop with PowerWorld -- **numpy < 2.0**: Pinned in dependencies -- **Python >= 3.7**: Minimum supported version -- CI runs on Python 3.9, 3.10, 3.11 +- **Python >= 3.9**: Minimum supported version (numpy 2.x is supported) +- CI runs on Python 3.9 through 3.14 diff --git a/docs/Auxiliary File Format.pdf b/docs/Auxiliary File Format.pdf deleted file mode 100644 index 1b12f87a7559826b229fda5e2db5001e4a49a8e9..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 132 zcmWN^%MHUI3;@u3reJ{v3;|5G;bRI?TcVoe(CM4g)4S-)eSAcl^Wa^|XP=K(<@L6m zY0cv;`JgN>SVk{`Th!L=t{A6mfa;U7n=TchL~=$448filtername" instead of just specifying the filtername itself. - -The filtername can also be the name of a device filter. A device filter allows you to specify a particular object for filtering -instead of a class of object. For example, you might want to return all buses that belong to a particular substation. You -can specify the device filter for the particular substation and then apply this to the bus objects. The format of a device -filter is "objecttype 'key1' 'key2' 'key3'". - -In addition to a filtername, special keywords can be used to indicate the type of filter desired. These include the following: - -AREAZONE : Only objects that meet the area/zone/owner filters will be selected for the specified action. -SELECTED : Only objects whose Selected field is YES will be selected for the specified action. - - -(Added in October 5, 2023 patch of Simulator 23) FilterName may also be a special string with the syntax such as -"MW >= 50" representing a single-condition filter. The syntax is Variablename Comparison Value1 Value2. These -special single-condition filters are convenient to use because there is no need to create a new filter object, and instead -one is created inside the software for temporary use. Examples for a Bus would be the following: -"NomkV between 220 550" -"MW >= 50" - - 16 - - - -Specifying Special Keywords in Script Command Parameters -There are special keywords that may be used as part of the input for script command parameters. Generally, these are -allowed as part of the file name input in commands that save or modify files, as well as some other commands that take -text as input. These special keywords will be replaced with their actual values when the script command is processed. - -The special keywords that are allowed and their associated replacement strings are shown in the following table: - - -@BUILDDATE Simulator patch build date -@CASEFILENAME File name of the presently open case. This is only the file name without the path. -@CASEFILEPATH Directory path of the presently open case -@CASENAME Name of the presently open case including the path and file name -@DATE Present date -@DATETIME Actual date and time in the format yyyymmdd_hhnnss-hhmm with the UTC offset - -included on the end of the time -@TIME Present time -@VERSION Simulator version number - - -The special keyword @MODELFIELD can be used in combination with an object type and variable name so that any field of -any object can be included in the text. The syntax for inserting the value of a model field in the text is the following: -@MODELFIELD. - -Specifying File Names in Script Commands -In place of the "filename" parameter in any script command, specially formatted text can be used to indicate that the user -should be prompted to choose the file. Depending on whether or not a file is being opened or saved, an Open or Save -dialog will be presented for the user to choose the file. This will not work when using the SimAuto Add-on. - -The special syntax of the filename parameter is generally: -"" -The entire string must start with . The parameters after PROMPT are all optional, must be space -delimited, must be enclosed in single quotes, and can be specified as follows: - Caption - -Caption to be placed at the top of the file dialog that appears. If omitted, either 'Save' or 'Open' is assumed -based on how the prompt is used to access a file. - - FileTypes -List of file types and extensions. The list itself is composed of a pipe-delimited string (|) with the first string -representing the first file type, the second string representing the first file extension, the third string -representing the second file type, the fourth string representing the second file extension and so on. If no file -types are specified, 'All Files (*.*)|*.*' is assumed. - - InitialDirectory -The initial directory in which the dialog should open. - - CancelAction -The CancelAction can either be 'Abort' or 'Continue', with 'Abort' being the default. If the Cancel button is -clicked on the resulting dialog, the CancelAction specifies how to proceed. Setting this to 'Abort' will skip the -command and abort any remaining auxiliary file commands. Setting this to 'Continue' will skip the command -but continue processing the remaining auxiliary file commands. - - -Here is an example prompt using all options: -"" - -The special keywords described in the Specifying Special Keywords in Script Command Parameters section can also be -used as part of a filename. - - 17 - - - -Specifying Field Variable Names in Script Commands -See the Field Variable Naming (Legacy) topic in the DATA Section for general information about naming fields. - -Within select script commands the keyword ALL can be used instead of using the location number of a field when -specifying variable names as part of a field list. This will return all fields with the same variable name. This is intended to -allow easier access to fields when the exact number of fields is not known, such as with multiple TLR (MultBusTLRSens:ALL) -or PTDF (LinePTDFMult:ALL) results. This can be used with SaveData, SaveDataWithExtra, SaveObjectFields, and -SendToExcel script actions. - -Within select script commands the keyword ALL can be used instead of a list of fields. This will return all fields for a -particular objecttype. This can be used with SaveData, SaveDataWithExtra, SaveObjectFields, and SendToExcel script -actions. - -Within these same Save related script commands for any transient stability data model objecttype (Machines, Exciters, -etc…), the special strings may be used to export all the model input parameters for the stability model and the various -primary, secondary or label key fields. These special strings are as follows. - -AllModelParams exports all the model input parameters and references to objects necessary to define -model - -AllModelParamsPriKey exports the primary keys for the objecttype and the same fields as AllModelParams -AllModelParamsSecKey exports the secondary keys for the objecttype and the same fields as AllModelParams -AllModelParamsLabel exports the label key for the objecttype and the same fields as AllModelParams - - - -Specifying Field Values in Script Commands -Several script commands require that a valuelist be specified to assign values to a corresponding fieldlist. Instead of -specifying the values explicitly, special formatting is available to assign values from other fields. See the Special Data List -Entries topic in the DATA Section for more information. - - - 18 - - - -AUX Actions -General Program Actions - -CopyFile("oldfilename", "newfilename"); -Use this action to copy a file from within a script. - -"oldfilename" : The present file name. See the Specifying File Names in Script Commands -section for special keywords that can be used when specifying the file -name. - -"newfilename" : The new file name desired. See the Specifying File Names in Script -Commands section for special keywords that can be used when -specifying the file name. - - -This command duplicates the file named “B7FLAT.pwb” and saves the copy as -“B7FLAT_BACKUP.pwb” in the same directory. -CopyFile("B7FLAT.pwb", "B7FLAT_BACKUP.pwb"); - -DeleteFile("filename"); -Use this action to delete a file from within a script. - -"filename" : The file name to delete. See the Specifying File Names in Script -Commands section for special keywords that can be used when -specifying the file name. - - -This call deletes the file named “B7FLAT_BACKUP.pwb”. -DeleteFile("B7FLAT_BACKUP.pwb"); - -ExitProgram; -Immediately exits the program with no prompts. Note that this command should not be used in scripts -loaded through SimAuto, as the SimAuto instance should be managed from the script which created it. - -LogAdd("text"); -Use this action to add a personal message to the Message Log. - -"text" : The text that will appear as a message in the log. - - -This command adds the greeting “hello” in the message log. -LogAdd("hello"); - -LogAddDateTime("label", includedate, includetime, includemilliseconds); -Use this action to add the date and time to the message log - -"label" : A string which will appear at the start of the line containing the -date/time. - -includedate : YES – Include the data or NO to not include. -includetime : YES – Include the time or NO to not include. -includemilliseconds : YES – Include the milliseconds or NO to not include. - - -This command adds a log entry labeled "DateTime" to the message log, including the current -date, time, and milliseconds. -LogAddDateTime("DateTime", YES, YES, YES); - -LogClear; -Use this action to clear the Message Log. - - 19 - - - -LogSave("filename", AppendFile); -This action saves the contents of the Message Log to "filename". - -"filename" : The file name to save the information to. -AppendFile : Set to YES or NO. YES means that the contents of the log will be - -appended to "filename". NO means that "filename" will be overwritten. - - -This command saves the current contents of the Message Log to a file named "LogFile1", -overwriting any existing data in that file. The NO parameter ensures that the file is replaced rather -than appended to. -LogSave("LogFile1", NO); - -LogShow(DoShow); -This action will show or hide the Message Log. - -DoShow : Set to YES to show the Message Log. Set to NO to hide the Message -Log. - -RenameFile("oldfilename", "newfilename"); -Use this action to rename a file from within a script. - -"oldfilename" : The present file name. See the Specifying File Names in Script Commands -section for special keywords that can be used when specifying the file -name. - -"newfilename" : The new file name desired. See the Specifying File Names in Script -Commands section for special keywords that can be used when -specifying the file name. - - -This command renames the file "results_old.csv" to "results_final.csv". -RenameFile("results_old.csv", "results_final.csv"); - -SetCurrentDirectory("filedirectory", CreateIfNotFound); -Use this action to set the current work directory. - -"filedirectory" : The path of the working directory. See the Specifying Special Keywords -in Script Command Parameters and Special Identifiers for Model Fields -section for information on special keywords that can be used as part of -the directory name. - -CreateIfNotFound : Set to YES or NO. YES means that if the directory path cannot be -found,the directory will be created. If this parameter is not specified, NO -is assumed. - -StopAuxFile; -Use this action to treat the remainder of the file after the command as a big comment. This includes any -script commands inside the present SCRIPT block, as well as all remaining SCRIPT or DATA blocks. - -WriteTextToFile("filename", "text"); -Use this action to write text to a file. If the specified file already exists, the text will be appended to the -file. Otherwise, it creates the file and writes the text to the file. - -"filename" : The file path and name to save. -"text" : The text to be written to the file. Special keywords can be entered that - -will be replaced with their actual values. These include @BUILDDATE, -@DATETIME, @DATE, @TIME, @VERSION, and @CASENAME. - - - - - 20 - - - -Data Interaction -CustomFieldDescriptionAppend(ObjectType, CustomType, FieldString, HeaderString, IncludeInDiff); - -(This command was added in the December 4, 2023 patch of Simulator 23) -This command behaves the same as calling CustomFieldDescriptionModify but the Location is assumed to -be a negative number. This creates a new CustomFieldDescription by incrementing the -CustomMaxOfType. - -CustomFieldDescriptionModify(ObjectType, CustomType, Location, "FieldString", "HeaderString", -IncludeInDiff); - -(This command was added in the December 4, 2023 patch of Simulator 23) -Use this script command to edit the CustomFieldDescription entries for a particular -ObjectType/CustomType/Location. - -ObjectType : The object type to which the Custom Field Description is applied -CustomType : Either Integer, String or Floating Point -Location : Integer value for the location number of the custom field. Entering a - -negative value for Location will increment the present CustomMaxOfType -by 1 for the present ObjectType/CustomType and this newly added entry -will be edited by the command. Remember that "Custom Integer 4" -corresponds to CustomInteger:3 and for that example you would use a -Location of 3. If a location is chosen that is too large, then the -CustomMaxOfType will be increased to accommodate the location. - -FieldString : String shown when field is displayed. Remember that a \ results subfolder -being created under the Custom folder. Enter a value of "_same_" if you -do not want to change the string. - -HeaderString : String shown in column headers in case information displays. Enter a -value of "_same_" if you do not want to change the string. - -IncludeInDiff : This specifies if the field is included in Difference Case comparisons. Set -to YES, NO, or "", where a blank indicates that the value is not changed. - - -This command modifies the description of a custom string field at location 3 for bus objects. The -location numbers are zero-indexed meaning that location 3 refers to the fourth custom string -field. This sets the internal field name to "Custom_Region_v2", the header displayed in case info -to "Updated_Region_Label", and enables inclusion in difference case comparisons (YES). -CustomFieldDescriptionModify(BUS, String, 3, "Custom_Region_v2", -"Updated_Region_Label", YES); - -CreateData(objecttype, [fieldlist], [valuelist]); -Use this action to create particular objects. - -objecttype : The objecttype being created. -[fieldlist] : A list of fields to set with the object. The key fields and required fields - -must be specified. -[valuelist] : A list of values corresponding to the respective fields. - - -This command creates a new Bus object in the case with the specified attributes. The bus is -named "Eight", assigned the number 8, placed in Area 1 and Zone 1, and given a nominal voltage -of 138.00 kV. -CreateData(BUS, [Name, Number, AreaNumber, ZoneNumber, NomkV, -NameNomkV], [Eight, 8, 1, 1, 138.0, Eight_138.0]); - - - - 21 - - - -Delete(objecttype, filter); -Use this delete objects of a particular type. A filter may optionally be specified to only delete objects that -meet a filter. - -objecttype : The objecttype being selected. -filter : Optional parameter – default is to delete all objects of specified type - See Using Filters in Script Commands section for more information on - -specifying the filter. - - -This command deletes all Buses where the Selected field is YES. -Delete(BUS, SELECTED); - -DeleteDevice([ObjectIDString]); -Use this action to delete a specific object. - -[ObjectIDString] : The specific object to delete. The format is the object type followed by -the key fields used to identify the object. Examples: DeleteDevice([Bus -234891]), DeleteDevice([Branch 1239 1234 "AB"]), and -DeleteDevice([Interface "my interface name"]). - - -This command deletes Bus 1. -DeleteDevice([Bus 1]); - -DeleteIncludingContents(objecttype, filter); -Use this to delete objects of a particular type and other objects that these contain. Currently, only multi- -section lines (objecttype = MultiSectionLine) can be used with this command. The branches and dummy -buses that belong to multi-section lines will also be deleted along with the multi-section lines. A filter may -optionally be specified to only delete objects that meet a filter. The syntax is identical to the -Delete(objecttype, filter); action above. - - -This command deletes all multi section lines along with the branches and dummy buses -associated with them. -DeleteIncludingContents(MultiSectionLine,); - -ExportAreaSupplyCurves("filename", "User Defined String", NumPoints); -Use this action to export Area Supply Curves to a CSV file. The output of the file will have 7 entries for -each area for Fixed Gen MW, Fixed Load MW, Fixed Shunt MW, Losses MW, Variable Min MW, Variable -Max MW, Variable Present MW, followed by a set of Bid MW/Price entries represents the supply curve for -the variable MWs. - -"filename.csv" : The name of the CSV file to which results will be written. -"User Defined String" : This is an optional parameter for specifying a user defined string written - -to each entry in the resulting CSV file. If this is omitted, blank will be -assumed. - -NumPoints : This is an optional parameter and is related to converting a cubic cost -model into a piece-wise linear model. If this is omitted, 5 is the default. - - -This command exports area supply curve data to the file TestAreaSupply.csv, tagging each entry -with "ScenarioA" and using 20 points to linearize the variable MW cost curves. The output -includes fixed generation, load, shunt values, losses, and a detailed bid-based supply curve for -each area. -ExportAreaSupplyCurves("TestAreaSupply.csv", "ScenarioA", 20); - - - - 22 - - - -ImportData("filename", FileType, HeaderLine, CreateIfNotFound); -Use this action to import data in various file formats that are not native to Simulator. -"filename" : Name of the file to import -FileType : Parameter that specifies the format of the data this is being read. - -Currently supported are two methods of importing CROW files as created -by the Equinox Control Room Operations Window application. This is -used with the Scheduled Actions add-on tool. - -CSV : Uses CSV Import Settings as specified in the Scheduled -Actions dialog to read in a CROW CSV file. - -PWCSV : Uses the PowerWorld Outage CSV format. -CROW : Uses the hardcoded format Scheduled Actions was originally - -programmed to import. -HeaderLine : Optional parameter to specify if the row of headers in the CSV file is on - -the first line (1) or second line (2). If left blank (or any other value is -specified), it will use the setting last configured in the Scheduled Actions -dialog. - -CreateIfNotFound : Optional parameter that is NO by default. Set this to YES to create -objects defined in the data if they do not already exist. - - -This command imports data from the file "GenFields.csv" using the CSV format. It assumes the -header row is on the first line of the file and will create any new objects from the data if they -don't already exist in the case. -Importdata("GenFields.csv", CSV, 1, YES); - -LoadAux("filename", CreateIfNotFound); -Use this action to load another auxiliary file from within a script. - -"filename" : The filename of the auxiliary file being loaded. -CreateIfNotFound : Optional – default is NO when using the Legacy Auxiliary File Header - -format. Default is YES when using the Concise Auxiliary File Header -format. - - This parameter is only enforced when the Create_if_not_found field -for a Legacy Auxiliary File Header is set to PROMPT. Otherwise, YES is -assumed. YES means that objects that cannot be found will be created -while reading DATA sections from "filename". - - -This command loads the auxiliary file named "ThreeBuses.aux" into the current simulation case. -By setting the CreateIfNotFound parameter to YES, any objects contained in Legacy Auxiliary File -Header DATA sections that do not already exist in the case will be created during the loading -process. -LoadAux("ThreeBuses.aux", YES); - -LoadAuxDirectory("filedirectory", "filterstring", CreateIfNotFound); -Use this action to load multiple auxiliary files from a specified directory. The auxiliary files will be loaded -in alphabetical order by name. - -"filedirectory" : The directory where the auxiliary files are located. -"filterstring" : Optional – if not specified then all files in the directory are loaded. - If specified, only files meeting this filter will be loaded. This filtering - -supports normal Windows wildcard filtering. -CreateIfNotFound : Optional – default is NO when using the Legacy Auxiliary File Header - -format. Default is YES when using the Concise Auxiliary File Header -format. - - 23 - - - - This parameter is only enforced when the Create_if_not_found field -for a Legacy Auxiliary File Header is set to PROMPT. Otherwise, YES is -assumed. YES means that objects that cannot be found will be created -while reading DATA sections from the files. - - -This command loads all .aux files from the "C:\SimCases\AuxFiles" directory in alphabetical order. -It sets CreateIfNotFound to YES, meaning that any objects contained in Legacy Auxiliary File -Header DATA sections in the files that do not already exist in the case will be created during the -loading process. -LoadAuxDirectory("C:\SimCases\AuxFiles", "*.aux", YES); - -LoadCSV("filename", CreateIfNotFound); -Use this action to load a CSV file that is formatted the same as the data sent to Excel in the Send All to -Excel option found within a case information display, or by choose Save As CSV. - -"filename" : The filename of the CSV file being loaded. -CreateIfNotFound : Set to YES or NO. YES means that objects which cannot be found will be - -created. If this parameter is not specified, NO is assumed. - - -This command loads data from the CSV file named "3BusCSV.csv". The YES parameter means that -if any objects referenced in the CSV file do not already exist in the case, they will be created -during the loading process. -LoadCSV("3BusCSV.csv", YES); - -LoadData("filename", DataName, CreateIfNotFound); -Use this action to load a named Data Section from another auxiliary file. This will open the auxiliary file -denoted by "filename", but will only read the data section specified. - -"filename" : The filename of the auxiliary file being loaded. -DataName : The specific data section name from the auxiliary file that should be - -loaded. -CreateIfNotFound : Optional – default is NO when using the Legacy Auxiliary File Header - -format. Default is YES when using the Concise Auxiliary File Header -format. - - This parameter is only enforced when the Create_if_not_found field -for a Legacy Auxiliary File Header is set to PROMPT. Otherwise, YES is -assumed. YES means that objects that cannot be found will be created -while reading DATA sections from "filename". - - -This command loads only the DATA section named "BusData" from the auxiliary file -"ScenarioData.aux". By setting the CreateIfNotFound parameter to YES, any objects contained in -Legacy Auxiliary File Header DATA sections that do not already exist in the case will be -automatically created during the loading process. -LoadData("ScenarioData.aux", "BusData", YES); - -LoadScript("filename", ScriptName, CreateIfNotFound); -Use this action to load a named Script Section from another auxiliary file. This will open the auxiliary file -denoted by "filename", but will only execute the script section specified. - -"filename" : The filename of the auxiliary file being loaded. -ScriptName : The specific script section name from the auxiliary file that should be - -loaded. -CreateIfNotFound : This parameter is ignored and always assumed to be NO. This does not - -mean that objects loaded from DATA sections will not be created. The -default behavior for how objects are created is followed depending on - - 24 - - - -DATA sections being specified in the Concise Auxiliary File Header or the -Legacy Auxiliary File Header format. See DATA Section for more details. - - -This command opens the "Operations.aux" file and executes only the script section named -"RestoreSettings". -LoadScript("Operations.aux", "RestoreSettings"); - -SaveData("filename", filetype, objecttype, [fieldlist], [subdatalist], filter, [SortFieldList], Transpose, -Append); - -Use this action to save data in a custom defined format. -"filename" : The file path and name to save. -filetype : There are several options for the filetype - -AUXCSV : save as a comma-delimited auxiliary data file. -AUX : save as a space-delimited auxiliary data file. -CSV : save as a normal CSV file without the AUX file - -syntax. The first few lines of the text file will -represent the object name and field variable -names. - -CSVNOHEADER : save as a normal CSV text file, without the AUX file -formatting. The object name and field variable -names are NOT included. This option is useful -when appending data of the same object type and -field list into a common file. - -CSVCOLHEADER : save as a normal CSV without the AUX syntax and -with the first row showing column headers you -would see in a case information display - -objecttype : The type of object being saved. -[fieldlist] : A list of fields that you want to save. For numeric fields, the number of - -digits and the number of decimal places (digits to right of decimal) can -be specified by using the following format for the field, -variablenamelegacy:location:digits:rod or -concisename:digits:rod. See the Specifying Field Variable Names -in Script Commands topic for more information on specifying this list. - -[subdatalist] : A list of the subdata objecttypes to save with each object record. -filter : Optional parameter – default is to save all objects of specified type - -blank : Save all objects of the specified type -AREAZONE : Only objects that meet the area/zone/owner filters will - -be saved -SELECTED : Only objects whose Selected field = YES will be saved -“FilterName" : Only objects that meet the specified filter will be - -checked. See Using Filters in Script Commands section -for more information on specifying the filtername. - -[SortFieldList] : Optional parameter – the default is to do no sorting - This allows the specification of a sort order in which the data will be - -saved. The format is: [variablename1:+:0, variablename2:-:1] where -variablename : the first parameter is the name of the field by which to - -sort. There is no limit to how many fields can be -specified for sorting. For fields that require a location -other than zero, variablename can be in the format -fieldname:location. - - 25 - - - -+ or - : the second parameter indicates sort ascending for + -and sort descending for -. This parameter must be -specified. - -0 or 1 : for the third parameter 0 means case insensitive and -do not use absolute value, 1 mean case sensitive or -use absolute value. This parameter is optional. - -Transpose : Optional parameter – default is NO - Set this to YES or NO. Set to YES to transpose the columns and rows of - -the returned data. When transposed the values for the same field for all -selected objects will appear in the same row. Transposing the data is -only allowed for CSV filetypes and this option will default to NO for all -other filetypes. - -Append : Optional parameter – default is YES - Set this to YES or NO. Set to YES to append data to an existing file. Set - -to NO to overwrite an existing file. - - -This command saves data for selected buses into the file "BusData.csv" using the -CSVCOLHEADER format. It includes the bus Number, Name, and NomkV fields. It does not -transpose the data or append it to an existing file—any existing file with the same name will be -overwritten. -SaveData("BusData.csv", CSVCOLHEADER, BUS, [Number, Name, NomkV], [], -SELECTED, [], NO, NO); - -SaveDataEPC("filename", objecttype, filter, GEFileType, SaveBuses, Append); -Use this action to save data in the GE EPC format. - -"filename" : The file path and name to save. -objecttype : The type of object being saved. -filter : Optional parameter – default is to save all objects of specified type - See Using Filters in Script Commands section for more information on - -specifying the filter. -GEFileType : Optional parameter – default is to save with the latest version. Valid - -options: -GE (latest version), GE14-GE21 - -SaveBuses : Optional parameter – default is NO - Set to YES or NO. Set to YES to save any buses associated with the - -regulated bus of generators or switched shunts so that the scheduled -voltage can also be saved. - -Append : Optional parameter – default is YES - Set to YES or NO. Set to YES to append data to an existing file. Set to - -NO to overwrite an existing file. - - -This command saves data for selected generators in the GE EPC format version GE21 to the file -"GenData.epc". It also saves the associated buses to retain scheduled voltage information. Any -existing file with the same name will be overwritten. -SaveDataEPC("GenData.epc", GEN, SELECTED, GE21, YES, NO); - - - - 26 - - - -SaveDataUsingBuiltInAUXFormat("filename", filetype, [List_of_built_ins], ModelToUse); -Use this action to save data using built-in categories. If the file already exists, data will be appended to the -file. - -"filename" : The file to save the data to -filetype : There are several options for the filetype - -AUXCSV : save as a comma-delimited auxiliary data file. -AUX : save as a space-delimited auxiliary data file. -CSV : save as a normal CSV file without the AUX file - -syntax. The first few lines of the text file will -represent the object name and field variable -names. - -CSVNOHEADER : save as a normal CSV text file, without the AUX file -formatting. The object name and field variable -names are NOT included. This option is useful -when appending data of the same object type and -field list into a common file. - -CSVCOLHEADER : save as a normal CSV without the AUX syntax and -with the first row showing column headers you -would see in a case information display - -[List_of_built_ins] : Comma-separated list of built-in data categories to include in the file. -Options include: Custom Info, Network Model, Contingency, Transient -Models, Transient, Model Info, Voltage Conditioning, and Weather -Dependent Limits. - -ModelToUse : Optional parameter that indicates the model to use. -FULL : Full-topology model. This is the default if the - -parameter is omitted. -CONSOLIDATED : (Not currently supported) Consolidated planning- - -type model. This option will only work with the -Topology Processing add-on. - - -This command saves the Network Model and Contingency built-in data categories to the file -"FullModelData.aux" in standard AUX format, using the full-topology model. If the file already -exists, the new data will be appended. -SaveDataUsingBuiltInAUXFormat("FullModelData.aux", AUX, [Network Model, -Contingency], FULL); - -SaveDataUsingExportFormat("filename", filetype, "FormatName", ModelToUse); -Use this action to save data in a user-defined format that has previously been defined. - -"filename" : The file to save the data to -filetype : There are several options for the filetype - -AUXCSV : save as a comma-delimited auxiliary data file. -AUX : save as a space-delimited auxiliary data file. -CSV : save as a normal CSV file without the AUX file - -syntax. The first few lines of the text file will -represent the object name and field variable -names. - -CSVNOHEADER : save as a normal CSV text file, without the AUX file -formatting. The object name and field variable -names are NOT included. This option is useful -when appending data of the same object type and -field list into a common file. - - 27 - - - -CSVCOLHEADER : save as a normal CSV without the AUX syntax and -with the first row showing column headers you -would see in a case information display - -FormatName : The name of the Object Export Format Description to use. -ModelToUse : Optional parameter that indicates the model to use. - -FULL : Full-topology model. This is the default if the -parameter is omitted. - -CONSOLIDATED : Consolidated planning-type model. This option -will only work with the Topology Processing add- -on. - - -This command saves data to the file "ExportedData.csv" using the user-defined export format -named "CustomExportFormat1", in CSV format, and applies the full-topology model. -SaveDataUsingExportFormat("ExportedData.csv", CSV, -"CustomExportFormat1", FULL); - -SaveDataWithExtra("filename", filetype, objecttype, [fieldlist], [subdatalist], filter, [SortFieldList], -[Header_List], [Header_Value_List], Transpose, Append); - -Use this action to save data in a custom defined format. User-specified fields and field values can also be -specified in the output. The syntax is identical to the SaveData command with the following exceptions: - -Filetype : There are several options for the filetype -CSV : save as a normal CSV file without the AUX file - -syntax. The first few lines of the text file will -represent the object name and field variable -names. - -CSVNOHEADER : save as a normal CSV text file, without the AUX file -formatting. The object name and field variable -names are NOT included. This option is useful -when appending data of the same object type and -field list into a common file. - -CSVCOLHEADER : save as a normal CSV without the AUX syntax and -with the first row showing column headers you -would see in a case information display - - Data cannot be saved using AUX or AUXCSV filetypes with this -command. - - -[Header_List] : Optional parameter – default is that no extra headers are included - This allows the specification of user-defined fields that will appear in the - -output. Headers should be specified as a list of comma delimited strings. -A string should be enclosed in double quotes if the string contains a -comma. Header strings cannot be blank. - -[Header_Value_List] : Optional parameter – default is that all values are blank - Allows the specification of the values that should be assigned to the - -user-defined fields specified by Header_List. If specified, there must be as -many values specified as there are headers. If not specified, all values are -blank. Each object will use the same specified value for the specified field. -To use different values for different objects and save these in the same -file, make use of the CSVNOHEADER file format and filtering. Special -keywords can be entered that will be replaced with their actual values. -These include @BUILDDATE, @DATETIME, @DATE, @TIME, @VERSION, -and @CASENAME. - -Append : Optional parameter – default is YES - - 28 - - - - Set this to YES or NO. Set to YES to append data to an existing file. Set -to NO to overwrite an existing file. - - -For the Header_List and Header_Value_List, the input should be formatted in a manner to indicate how it -should be written to the CSV. Any strings enclosed in double quotes will be stripped of the enclosers. -Any strings containing double double quotes will have them replaced with single double quotes. - - -This command exports generator data to "UtilityPoints.csv" using the CSVHEADER format, which -omits headers. It saves the MW and MVR fields for all generator objects and adds an extra -column labeled "P" with a value of "0.0" for each row; the header for this column is not included -because CSVNOHEADER is used for the format. -SaveDataWithExtra("UtilityPoints.csv", CSVNOHEADER, Gen, [GenMW, -GenMVR], [], , [], ["P"], ["0.0"]); - -SaveObjectFields("filename", objecttype, [fieldlist]); -Use this action to save a list of fields available for the specified objecttype to a CSV file. Format of the file -is variablename, field, col header, description. - -"filename" : The file path and name to save. -objecttype : The type of object for which fields should be saved. -[fieldlist] : List of fields for which information will be saved. See the Specifying Field - -Variable Names in Script Commands topic for more information on -specifying this list. - - -This command saves a CSV file named "GenFields.csv" that contains metadata about the specified -fields (GenMW, GenMVR, GenStatus) for generator objects. The CSV will include columns for the -internal variable name, the field name, the column header as shown in the GUI, and a brief -description of each field. -SaveObjectFields("GenFields.csv", Gen, [GenMW, GenMVR, GenStatus]); - -SelectAll(objecttype, filter); -Use this to set the Selected field of objects of a particular type to YES. A filter may optionally be specified -to only set this property for objects that meet a filter. - -objecttype : The objecttype being selected. -filter : Optional parameter – default is to set all objects of specified type - -AREAZONE : Only objects that meet the area/zone/owner filters will -be selected - -"FilterName" : Only objects that meet the specified filter will be -checked. See Using Filters in Script Commands section -for more information on specifying the filtername. - - -This command sets Selected = YES for all Buses based on the current AREAZONE filter. -SelectAll(BUS, AREAZONE); - -SendtoExcel(objecttype, [fieldlist], filter, UseColumnHeaders, "workbookname", "worksheetname", -[SortFieldList], [Header_List], [Header_Value_List], ClearExisting, RowShift, ColShift); - -Use this action to mimic the behavior of the Send to Excel option found within a case information display. -objecttype : The type of object for which fields should be saved. -[fieldlist] : List of fields for which information will be saved. See the Specifying Field - -Variable Names in Script Commands topic for more information on -specifying this list. - -filter : Optional parameter – default is to send all objects of specified type - See the Using Filters in Script Commands section for more information - -on specifying the filter. - 29 - - - -UseColumnHeaders : Set to YES or NO. YES signifies that the first row shows the Column -Header, NO signifies that field variable names are used. - -"workbookname" : Path and name of the workbook to save or modify. If no path is -specified, the workbook will be saved or opened from the current -directory. If the workbook already exists, it will be modified with a new -worksheet, or if the worksheet is specified and already exists, the -worksheet will be overwritten. If using Excel 2007 or later *.xlsm filetypes -can be specified. - -"worksheetname" : Optional parameter to specify the worksheet name to save. If blank, a -new worksheet will be created, if a value is specified it will overwrite the -data in any existing worksheet of that name. - -[SortFieldList] : Optional parameter – the default is to do no sorting - This allows the specification of a sort order in which the data will be - -saved. The format is: [variablename1:+:0, variablename2:-:1] where -variablename : is the name of the field to sort by. There is no limit to - -how many fields can be specified for sorting. For fields -that require a location other than zero , variablename -can be in the format fieldname:location. - -+ or - : for the second parameter indicates sort ascending for -+ and sort descending for -. This parameter must be -specified. - -0 or 1 : for the third parameter 0 means case insensitive and -do not use absolute value, 1 mean case sensitive or -use absolute value. This parameter is optional. - - [Header_List] : Optional parameter – default is that no extra headers are included - This allows the specification of user-defined fields that will appear in the - -output. Headers should be specified as a list of comma delimited strings. -A string should be enclosed in double quotes if the string contains a -comma. Header strings cannot be blank. - -[Header_Value_List] : Optional parameter – default is that all values are blank - Allows the specification of the values that should be assigned to the - -user-defined fields specified by Header_List. If specified, there must be as -many values specified as there are headers. If not specified, all values are -blank. Each object will use the same specified value for the specified field. -Special keywords can be entered that will be replaced with their actual -values. These include @BUILDDATE, @DATETIME, @DATE, @TIME, -@VERSION, and @CASENAME. - -ClearExisting : Optional parameter – default is YES. YES means to clear the existing -sheet - -RowShift : Optional parameter – default is 0. Set to a positive integer to shift the -paste of data downwards from row 1. Negative values treated as 0. - -ColShift : Optional parameter – default is 0. Set to a positive integer to shift the -paste of data to the right from column A. Negative values treated as 0. - - -This command sends data about selected buses (fields BusNum, Name, AreaNum) to an Excel file -named "BusData.xlsx" in a worksheet called "SelectedBuses". The first row of the worksheet will -include normal column headers. An extra header "ExportDate" with the current date (replaced -from "@DATE") is added to each row. If the worksheet already exists, its content is cleared before -writing new data. -SendtoExcel(Bus, [BusNum, Name, AreaNum], SELECTED, YES, -"BusData.xlsx", "SelectedBuses", [], ["ExportDate"], ["@DATE"], YES, 0, -0); - - 30 - - - -SetData(objecttype, [fieldlist], [valuelist], filter); -Use this action to set fields for particular objects. If a filter is specified, then it will set the respective fields -for all objects which meet this filter. Otherwise, if no filter is specified, then the key fields must be -included in the field list so that the object can be found. - -objecttype : The objecttype being set. -[fieldlist] : A list of fields to update. -[valuelist] : A list of values to which to set the corresponding fields. -filter : Optional parameter – if omitted the key fields and corresponding values - -must be included to identify the one object to update -ALL : Set data for all objects -AREAZONE : Only objects that meet the area/zone/owner filters will - -be set -SELECTED : Only objects whose Selected field = YES will be set -“FilterName" : Only objects that meet the specified filter will be set. - -See Using Filters in Script Commands section for more -information on specifying the filtername. - - -This command updates the AreaNum field to 5 for all bus objects that are currently marked as -Selected = YES. -SetData(Bus, [AreaNum], [5], SELECTED); - -UnSelectAll(objecttype, filter); -Same as SelectAll, but this action sets the Selected field of objects meeting the filter to NO. - - -This command sets Selected = NO for all buses. -UnSelectAll(BUS,); - -WriteLimitMonitoringSettings("filename"); -Use this to save Limit Monitoring Settings to an auxiliary file. - -"filename" : Name of the auxiliary file to save. See the Specifying File Names in Script -Commands section for special keywords that can be used when -specifying the file name. - - - - - 31 - - - -Case Actions -AppendCase("filename", OpenFileType, [StarBus, EstimateVoltages]); -AppendCase("filename", OpenFileType, [MSLine, VarLimDead, PostCTGAGC, EstimateVoltages]); - -Use this action to append a case to the currently open case. The optional parameters depend on the type -of file being appended. - -"Filename" : File name of the case to be appended -OpenFileType : PWB – case file is a PowerWorld Binary file - GE – case file is a GE .epc file. GExx where xx is the appropriate EPC file - -version number can also be used. - PTI – case file is a PTI .raw file. PTIxx where xx is the appropriate RAW - -file version number can also be used. - CF – case file is an IEEE common data format file -StarBus : Optional parameter – default is NEAR - Only used for PTI RAW format, with the following options: - NEAR – star buses are numbered starting after the near bus number - MAX – star buses are numbered starting after the maximum bus number - Value – star bus numbering will start at specified value -MSLine : Optional parameter – default is MAINTAIN - Only used for the GE EPC format, with the following options: - MAINTAIN – maintain multisection lines - EQUIVALENCE – equivalence multisection lines -VarLimDead : Optional parameter – default is 2.0 - Only used for the GE EPC format - NUMBER – set the GE var limit deadband to this value -PostCTGACG : Optional parameter – default is NO - Only used for the GE EPC format. Set to YES to populate the generator - -field Post-CTG Prevent Response based on the EPC file’s generator base -load flag. - -EstimateVoltages : Optional parameter – default is YES - Used with either GE EPC or PTI RAW format with the following options: - YES – voltages and angles are estimated for new buses that are created - -when appending data to a case. Angle smoothing is done across new -lines that are created when appending data to a case. These operations -might be necessary if appending data that contains voltages that are not -consistent to the case into which it is being appended or contains no -voltages at all. This is the default. - - NO – no voltage and angle estimates are done and no angle smoothing -is done. This might be necessary when appending large sections of a -case, i.e. such as a new island, or providing voltages that are already -good estimates in the appended data. - - -This command appends the PTI RAW file "B7FlatExtension.raw" (version 33) to the currently open -case. Star buses are numbered starting after the maximum existing bus number, and PowerWorld -will estimate voltages and angles for all new buses and branches, ensuring smooth integration -with the existing system. -AppendCase("B7FlatExtension.raw", PTI33, [MAX, YES]); - -CaseDescriptionClear; -Use this action clear the case description of the presently open case. - - - - 32 - - - -CaseDescriptionSet("text", Append); -Use this action to set or append text to the case description. - -"text" : Specify the text to set/append to the case description. -Append : YES – will append the text specified to the existing case description. NO – - -will replace the case description. -DeleteExternalSystem; - -This action will delete part of the power system. It will delete those buses whose property Equiv is set -true. - -EnterMode(mode); -This action will change the mode in which Simulator is operating. This is especially necessary when -creating new case objects for which you are required to be in EDIT mode. Simulator will automatically -change the mode to the correct mode for script actions that are mode-specific. - -Mode : The mode to enter, either RUN or EDIT. -Equivalence; - -This action will equivalence a power system. All options regarding equivalencing are handled by the -Equiv_Options objecttype. Use the SetData action, or a DATA section to set these options prior to using -the Equivalence action. Also, remember that the property Equiv must be set true for each bus that you -want to equivalence. - -LoadEMS("filename", filetype); -Use this to open any EMS file. This can be a full case, a contingency file, or remedial action scheme file. -Simulator will determine the type of information being loaded and how to handle it based on the records -in the file. - -"filename" : Name of the file to open. See the Specifying File Names in Script -Commands section for special keywords that can be used when -specifying the file name. - -filetype : Type of file to be loaded. Currently the only option is AREVAHDB. -NewCase; - -This action clears out the existing case and open a new case from scratch. -OpenCase("filename", OpenFileType,[LoadTransactions,StarBus,HowToKeepDuplicates]); -OpenCase("filename", OpenFileType,[MSLine,VarLimDead,PostCTGAGC,MSLineDummyBus]); - -This action will open a case stored in "filename" of the type OpenFileType. Different sets of optional -parameters apply for the PTI and GE file formats. The LoadTransactions and Star bus parameters are -available for writing to RAW files. MSLine, VarLimDead, and PostCTGAGC are for writing EPC files. - -"filename" : The file to be opened. -OpenFileType : An optional parameter indicating the format of the file being opened. If - -none is specified, PWB will be assumed. It may be one of the following -strings: - -PWB, PTI (latest version), PTI23-PTI35 -GE (latest version), GE14-GE23, CF -AUX, UCTE, AREVAHDB, OPENNETEMS - -LoadTransactions : valid for PTI RAW format only -YES : load transactions when opening case. -NO : do not load transactions when opening case. -DEFAULT : follow default behavior. - -StarBus : valid for PTI RAW format only -NEAR : star buses are numbered starting after the near bus number -MAX : star buses are numbered starting with the maximum bus - -number - 33 - - - -VALUE : star bus numbering will start at value -HowToKeepDuplicates : valid for PTI RAW format only - -LAST : keep only the LAST duplicate of any device in each section of -a PTI RAW file - -ALL : keep ALL duplicates of any device in each section of a PTI -RAW file, and make them each unique by dynamically -changing their IDs - -MSLine : valid for GE EPC format only -MAINTAIN : maintain multi-section lines -EQUIVALENCE : equivalence mult-section lines - -VarLimDead : valid for GE EPC format only -Number : set the GE var limit deadband - -PostCTGACG : valid for GE EPC format only - set to YES to populate the generator field Post-CTG Prevent Response - -based on the EPC file’s generator base load flag. - -MSLineDummyBus : Optional parameter – default is specified with Simulator Options - valid for GE EPC format only - Specifies how the dummy bus numbers for multi-section lines are - -determined. -FROM : starting at the from bus number -MAX : starting with the maximum bus number - - Value or range – starting with the specified value or will be numbered -within the specified range. If an unused bus number within the specified -range cannot be found, the numbering will start at the highest number -specified in the range. - - -This command opens the PTI RAW file "B7FlatImport.raw" using the PTI33 format. It loads -transaction records, assigns star buses starting after the maximum existing bus number, and -keeps only the last instance of any duplicate devices found in the file. -OpenCase("B7FlatImport.raw", PTI33, [YES, MAX, LAST]); - -Renumber3WXFormerStarBuses("filename", Delimiter); -Use this action to renumber star buses based on user-specified values. - -"filename" : The name of the file containing the renumbering -Delimiter : Optional parameter – default is BOTH - Set to COMMA, SPACE, or BOTH to indicate the delimiter to use to - -separate data in the file. - -The file may be comma or space delimited. The contents of the file should be formatted using the format -below. - -primary bus, secondary bus, tertiary bus, circuit, new starbus number, new starbus -name -or -pribusname_nomkV, secbusname_nomkV, terbusname_nomkV, circuit ID, newstarbusnum, -newstarbusname - -Either the bus number or the name_nominalkV identifier may be used to identify the buses. Each bus may -be identified using either method even for the same transformer. Lines starting with two slashses (//) will -be ignored. The next two lines are sample file contents using different methods to identify buses. - - - - 34 - - - - -11037, 11038, 11199, "1", 11202, "Ki star" -"WESTWING_500.00" "WESTWNGW_230.00" "WESTWG 4_34.50" "2" 99823 "KI STAR 3" - -RenumberAreas(NumCI); -Renumber Areas using the new number for the Area located in the Custom Integer field of the area. - -NumCI : Custom Integer field containing the new numbers. Variablename location -numbers are zero-indexed while the standard column headers are -indexed starting with 1. This value corresponds to the standard column -header value for the custom integer field. - - -This will change each area’s number to the value stored in its CustomInteger:0 field. -RenumberAreas(1); - -RenumberBuses(NumCI); -Renumber Buses using the new number for the bus located in the Custom Integer field of the bus. - -NumCI : Custom Integer field containing the new numbers. Variablename location -numbers are zero-indexed while the standard column headers are -indexed starting with 1. This value corresponds to the standard column -header value for the custom integer field. - - -This will change each bus’ number to the value stored in its CustomInteger:1 field. -RenumberBuses(2); - -RenumberMSLineDummyBuses("filename", Delimiter); -Use this action to renumber dummy buses or a multisection line based on user-specified values. - -"filename" : The name of the file containing the renumbering -Delimiter : Optional parameter – default is BOTH - Set to COMMA, SPACE, or BOTH to indicate the delimiter to use to - -separate data in the file. - -The file may be comma or space delimited. Buses may be identified using bus numbers or using the -BusName_NominalkV combination. The file format is below: - -from bus, to bus, circuit //identifiy multi-section line -dummybusnumber1, dummybusname1 -dummybusnumber2, dummybusname2 - -where the dummy bus numbers and names give the numbers and names that will be assigned for the -dummy buses of a multi-section line. An example of the file contents is below: - -40039 , 40141, 1 // ALFALFA 230 N BONNVL 230 #1 - 49997, "ALFN B11" -40062 , 40699, 2 // ASHE R1 500 MARION 500 #2 - 49990, ASHMAR21 - 49989, ASHMAR22 - 49988, ASHMAR23 - -RenumberSubs(NumCI); -Renumber Substations using the new number for the substation located in the Custom Integer field of the -substation. - -NumCI : Custom Integer field containing the new numbers. Variablename location -numbers are zero-indexed while the standard column headers are -indexed starting with 1. This value corresponds to the standard column -header value for the custom integer field. - - 35 - - - - -This will change each substation’s number to the value stored in its CustomInteger:2 field. -RenumberSubs(3); - -RenumberZones(NumCI); -Renumber Zones using the new number for the Zone located in the Custom Integer field of the zone. - -NumCI : Custom Integer field containing the new numbers. Variablename location -numbers are zero-indexed while the standard column headers are -indexed starting with 1. This value corresponds to the standard column -header value for the custom integer field. - - -This will change each zone’s number to the value stored in its CustomInteger:3 field. -RenumberZones(4); - -RenumberCase; -(This command was added in the December 5, 2023 patch of Simulator 23) -RenumberCase renumbers the object in the case according to the swap list in memory. - -SaveCase("filename", SaveFileType, [PostCTGAGC, UseAreaZone]); -SaveCase("filename", SaveFileType, [AddCommentForObjectLabels, IncludeSubstations]); - -This action will save the case to "filename" in the format SaveFileType. -"filename" : The file name in which to save the information. -SaveFileType : An optional parameter indicating the format of the file to be saved. If - -none is specified, then PWB will be assumed. It may be one of the -following strings: - -PWB (latest version), PWB16-PWB23 -PTI23-PTI35 -GE14-GE23, -CF, UCTE -AUXNETWORK, AUX, AUXSECOND, AUXLABEL - - When saving an auxiliary file, AUX, AUXSECOND, and AUXLABEL are -supported for compatibility with existing processes that users might have -in place, but the recommended option is AUXNETWORK. This option -saves the data required for defining the network model of a power -system. - -PostCTGAGC : An optional parameter, only valid for GE EPC format. If the Governor -Response Limits field for a generator is set to Down Only or Fixed, the -base load flag will be written as 1 or 2, respectively, and this option is -ignored. This option is only used when a generator’s Governor Response -Limits field is set to Normal. If this option is set to YES and not ignored, -the base load flag in the EPC file is based on the Post-Contingency -Prevent AGC Response setting. If preventing post-contingency AGC, the -base load flag is set to 2. If not preventing post-contingency AGC, the -base load flag is set to 0. - -UseAreaZone : An optional parameter, only valid for GE EPC format. YES limits the -entries in the EPC file based on the area/zone/owner filter (NO by -default) - -AddCommentsForObjectLabels - : Optional parameter – default is NO - Only valid for PTI RAW format. YES adds object Labels to the end of data - -records when saving a RAW file. - Label comments will appear as /* [Label] */ - - 36 - - - -IncludeSubstations : (Added in January 30, 2024 patch of Simulator 23) - Optional parameter – default is NO - Only valid for PTI RAW format. YES includes substations, which are used - -for full topology node breaker modeling. NO excludes substations. - - -This command saves the current case to a PTI RAW file named "B7FlatExport.raw" using version -33 format. The AddCommentsForObjectLabels option is set to YES, so any objects that have -Labels defined will include an end-of-line comment. IncludeSubstations is also set to YES, -meaning full topology substations will be included in the RAW export for accurate node-breaker -representation. -SaveCase("B7FlatExport.raw", PTI33, [YES, YES]); - -SaveExternalSystem("Filename", SaveFileType, WithTies); -This action will save part of the power system to a "filename". It will save only those buses whose Equiv -field is set to External. - -filename : The file name to save the information to. -SaveFileType : An optional parameter saying the format of the file to be saved. If none - -is specified, then PWB will be assumed. May be one of the following -strings - -PWB, PWB16-PWB23 -PTI23-PTI35 -GE14-GE23, CF, AUX - -WithTies : An optional parameter. The user must specify the file type explicitly in -order to use the WithTies parameter. Allows saving a transmission line -that ties a bus marked with Equiv as External and one marked Study. This -must be a string which starts with the letter Y, otherwise NO will be -assumed. - - -This command saves a portion of the power system to a PTI RAW file named -"B7Flat_External.raw", using the PTI version 33 format. Only buses that have their Equiv field set -to External will be included in the saved file. The WithTies parameter is set to "YES", meaning that -transmission lines (branches) connecting marked (Equiv = External) and unmarked (Equiv = -Study) buses will also be saved, ensuring a complete boundary representation for the external -system. -SaveExternalSystem("B7Flat_External.raw", PTI33, YES); - -SaveMergedFixedNumBusCase ("filename", SaveFileType); -This online help topic will explain FixedNumBus in more detail: -https://www.powerworld.com/WebHelp/#MainDocumentation_HTML/FixedNumBus_Features.htm - - -This action will save the Merged FixedNumBus case to "filename" in the format SaveFileTypes. A -FixedNumBus is a group of connection points that is defined by the user. Typically other buses that are -assigned to a FixedNumBus represent nodes in a full topology system, and the FixedNumBus is the -common electrical point that is represented if this system is merged. - -"filename" : The file name in which to save the information. -SaveFileType : Same options as for SaveCase command - - -This command saves a merged version of the currently open power system case—where buses -grouped under each FixedNumBus are treated as single electrical points—into a PTI RAW format -file named "B7Flat_Merged.raw". This merged representation collapses all buses assigned to the -same FixedNumBus into a single bus. -SaveMergedFixedNumBusCase("B7Flat_Merged.raw", PTI33); - - 37 - - - -Scale(scaletype, basedon, [parameters], scalemarker); -Use this action to scale the load and generation in the system. This script command should be used in -conjunction with the SCALE_OPTIONS object that specifies additional options necessary for the scaling -that are not set through the script command. - -scaletype : The objecttype begin scaled. Must be either LOAD, GEN, -INJECTIONGROUP, or BUSSHUNT. - -basedon : One of: -MW : parameters are given in MW, MVAR units. -FACTOR : parameters a factor to multiple the present values by. - -[parameters] : These parameters have different meanings depending on ScaleType. -LOAD : [MW, MVAR] or [MW]. If you want to scale load - -using constant power factor, then do not -specifying a MVAR value. - -GEN : [MW] -INJECTIONGROUP : [MW, MVAR] or [MW] . If you want to scale load - -using constant power factor, then do not -specifying a MVAR value. - -BUSSHUNT : [GMW, BCAPMVAR, BREAMVAR]. The first values -scales G shunt values, the second value scales -positive (capacitive) B shunt values, and the third -value scales negative (reactive) B shunt values - - The Scale script command also allows using the [parameters] input to -specify the new value or scale factor through a field with the object type -to scale. To use this option, the [parameters] input should contain field -variable names instead of numeric values. When using a field rather than -value, the scaling will be done by individual object rather than the -aggregation of all objects selected for scaling. - -scalemarker : This value specifies whether to look at an element’s bus, area or zone to -determine whether it should be scaled. - -BUS : Means that elements will be scaled according to the -Scale property of the element’s terminal bus. - -AREA : Means that elements will be scaled according to the -Scale property of the element’s Area. Note that it is -possible for the area of a load, generator, or switched -shunt to be different than the terminal bus’s area. - -ZONE : Means that elements will be scaled according to the -Scale property of the element’s Zone. Note that it is -possible for the zone of a load, generator, or switched -shunt to be different than the terminal bus’s zone. - -OWNER : Means that elements will be scaled according to the -Scale property of the element’s Owner. Note that it is -possible for the Owner of a load, generator, or switched -shunt to be different than the terminal bus’s Owner. - - - - 38 - - - - -Here are two different ways to scale three areas to a particular load value. -Script -{ - SetData(Area, [Scale], ["NO"], All); // Sets the scale field of all areas to NO - SetData(Area, [Number, Scale], [111, "YES"]); // For area, set Scale=YES - Scale(LOAD, MW, [1111.1],AREA); // Do the scaling for particular area - SetData(Area, [Number, Scale], [111, "NO"]); // reset Scale back to NO - SetData(Area, [Number, Scale], [333, "YES"]); // For area, set Scale=YES - Scale(LOAD, MW, [3333.3],AREA); // Do the scaling for particular area - SetData(Area, [Number, Scale], [333, "NO"]); // reset Scale back to NO - SetData(Area, [Number, Scale], [444, "YES"]); // For area, set Scale=YES - Scale(LOAD, MW, [4444.4],AREA); // Do the scaling for particular area - SetData(Area, [Number, Scale], [444, "NO"]); // reset Scale back to NO -} -Script -{ - SetData(Area, [Scale], ["NO"], All); - SetData(Area, [Number, Scale, CustomFloat:5], [111, "YES", 1111.1]); - SetData(Area, [Number, Scale, CustomFloat:5], [333, "YES", 3333.3]); - SetData(Area, [Number, Scale, CustomFloat:5], [444, "YES", 4444.4]); - Scale(LOAD,MW,["CustomFloat:5"],AREA); -} - - - - - 39 - - - -Modify Case Objects -AutoInsertTieLineTransactions; - -Use this action todelete all existing MW transactions and set the unspecified MW interchange for each -area to zero. It then automatically creates a MW transaction between each pair of connected areas with a -MW transaction exactly equal to the sum of the tie-line flows. - -BranchMVALimitReorder(Filter, SetA, SetB, SetC, SetD, SetE, SetF, SetG, SetH, SetI, SetJ, SetK, SetL, -SetM, SetN, SetO); - -This action will modify the MVA limits for a branch. 15 different limits labeled A through O can be -specified for a branch. A specified limit set can be updated to the values contained in another set or a -specific value. - -Filter : Optional parameter – default is to change all branch limits - See Using Filters in Script Commands section for more information on - -specifying the filter. -SetA, SetB, … , SetO : Optional parameters – default is to not change the value of a limit set - For each limit set to be changed, the letter identifying another limit set - -can be specified so that those values populate the current limit set. -Instead of a limit set letter a value for new limits can also be specified. To -keep the value of a particular limit set the same leave that entry blank. - - -The following sets limits A and B to their own values, sets limits C-J to values of other limits, limits -K-N at set to 9999, and limit O is left unchanged. This is done for any branch with at least one -terminal in Area 3. -BranchMVALimitReorder("Area 3", A,B,D,E,G,H,J,K,M,N,9999,9999,9999,9999); - - -The following sets limits A through 0 to values of other limits for all branches that meet filter "My -Filter Name". -BranchMVALimitReorder("My Filter Name",M,N,O,A,B,C,D,E,F,G,H,I,J,K,L); - -CalculateRXBGFromLengthConfigCondType(filter); -Use this action to go through branches in the power system and recalculate the per unit R, X, G, and B -values using the TransLineCalc tool. The branches Conductor Type, Tower Configuration, and Line Length -will be passed to the TransLineCalc tool and new R, X, G and B values will be calculated. This is only -available if you have installed the TransLineCalc tool. - -filter : Optional parameter – default is to check all branches - Check only branches that meet the specified filter. See the Using Filters - -in Script Commands section for more information on specifying the -filtername. - - -This command recalculates the per unit resistance (R), reactance (X), conductance (G), and -susceptance (B) for all branches where Selected = YES in the case using the TransLineCalc tool. It -uses each branch's conductor type, tower configuration, and line length to perform these -calculations. -CalculateRXBGFromLengthConfigCondType(SELECTED); - -ChangeSystemMVABase(NewBase); -Use this action to change the system MVA base to the specified value and update all internal data -structures to store values on the new base. - -NewBase : New power system base in MVA. - - - 40 - - - -ClearSmallIslands; -Use this action to identify the largest island and de-energize all other islands. The largest island is the -island with the most buses. Small islands are de-energized by setting the status of all generators in those -island to open. - -Combine([elementA], [elementB]); -Use this action to combine two generators. - -[elementA] : The object that should be moved. See the format for [elementA] in the -Move script command for information on the formatting of this string. - -[elementB] : The object that element A should be combined with. Same format as for -elementA. - - -Suppose that there were two generators on Bus 2. To combine them into a single equivalent unit -by merging their MW/MVar output, operating limits, and other characteristics, one would use this -command. -Combine([GEN 2 2], [GEN 2 1]); - -CreateLineDeriveExisting(FromBus, ToBus, Circuit, NewLength, BranchID, ExistingLength, ZeroG); -(Added in December 27, 2024 patch of Simulator 23) -This command is used to scale the impedance values of a branch onto a new branch. The idea being to -create a new branch that is the same but with impedance values scaled to represent a different length. - -FromBus : From bus number of new branch to be added to the case -ToBus : To bus number of new branch -Circuit : Circuit ID for new branch -NewLength : Length of new branch -BranchID : Object ID for existing branch in the model using the BRANCH FROMBUS - -TOBUS CIRCUIT format. -ExistingLength : Optional parameter – default is to use length with existing branch - This parameter is used to specify the existing branch length. If the - -parameter is omitted, then the existing value from the branch length field -will be used. If the branch length field is not populated, then the effect of -this command will be to create a new branch with the same impedance -and limit values. - -ZeroG : Optional parameter – default is NO - If YES the shunt G value will be set to zero. The mathematics of scaling - -the impedance values can result in negative g (shunt conductance) -values. This value allow you to set the g values to 0 after the scaling -calculations. - - -Create a derived line with a length of 220, based on the existing line from bus 1 to 3 circuit 1. -The new line will go from bus 1 to 3 with a circuit ID of 5. -CreateLineDeriveExisting(1, 3, 5, 220, BRANCH 1 3 1); - -DirectionsAutoInsert(Source, Sink, DeleteExisting, UseAreaZoneFilters); -Use this action to auto-insert directions to the case. - -Source : AREA, ZONE, or INJECTIONGROUP – specifies what to use as source -Sink : AREA, ZONE, INJECTIONGROUP, or SLACK – specifies what to use as sink. -DeleteExisting : YES – to delete existing direction; NO to not do that. -UseAreaZoneFilters : YES – to filter Area/Zones by filter. - - -The command deletes all existing directions and creates new ones from each Area to the Slack -bus. No filtering is used to exclude areas. -DirectionsAutoInsert(Area, Slack, YES, NO); - - 41 - - - -DirectionsAutoInsertReference(SourceType, ReferenceObject, DeleteExisting, SourceFilterName, -OppositeDirection); - -Use this to auto-insert directions from multiple source objects to the same ReferenceObject. By default, -directions will be created that go from the SourceType objects with the ReferenceObject as a Sink. -Specify the parameter OppositeDirection as YES build directions in the opposite direction. - -SourceType : The type of object used as source object. May be either -Area, Zone, InjectionGroup, or Bus - -ReferenceObject : The specific object used as the reference. Use the identifying string which -starts with the ObjectName and is followed by keyfields or label -identifiers. The ObjectNames allowed are Area, Zone, InjectionGroup, or -Bus. The string “Slack” can also be specified to indicate the slack bus. - -DeleteExisting : Optional parameter – default is YES. Set to YES to delete existing -directions and NO to leave existing directions defined. - -SourceFilterName : Optional parameter – default is a blank string which means to do all -objects of the SourceType. If specified, then will only use those that meet -this filter. See the Using Filters in Script Commands in Script Commands -section for more information on specifying the filtername. May also -specify the string ALL to indicate all SourceType objects, though this is -equivalent to a blank. - -OppositeDirection : Optional parameter – default is NO. Set to YES to indicate the directions -should use the ReferenceObject as the Source for each direction and the -SourceType will be the Sink instead. - - -A direction is added for each bus inside Area 2 with the Sink equal to the InjectionGroup named -"MyGroup". Existing directions are not deleted. -DirectionsAutoInsertReference(Bus,"InjectionGroup MyGroup",NO,"Area 2",NO); - -InitializeGenMvarLimits; -Use this action to initialize all generators in the case so that they are appropriately marked as being at -Mvar limits or not. This could be useful if manually setting the Mvar output of generators or changing -their limits. - -InjectionGroupsAutoInsert; -Use this action to insert injection groups according to the options specified in the -"IG_AutoInsert_Options" object. The settings available with this object represent what is seen on the Auto -Insert Injection Groups Dialog. - -InjectionGroupCreate("Name", objecttype, InitialValue, filter, Append); -This action will create or modify an injection group with participation points of a single object type that -meet a filter. Repeated calls to this script command can be used to define an injection group with -different object types. - -"Name" : Name of the injection group to create or modify. -objecttype : Type of object to be included in the injection group. Valid options are - -GEN, LOAD, SHUNT, or BUS. -InitialValue : Set this to a floating point value to indicate the value of the participation - -factor to use with each point. Special keywords can also be used to -indicate dynamically determined values. (The participation point -AutoCalc field is set to YES). The following options are available: - -Generators: - PRESENT, MAX GEN INC, MAX GEN DEC, and MAX GEN MW -Loads: - LOAD MW -Switched Shunts: - - 42 - - - - MAX SHUNT INC, MAX SHUNT DEC, and MAX SHUNT MVAR -All object types: - variablename can be used to reference a field associated - -with the object in the participation point. - modelexpressionname can be used to reference a - -Model Expression. -filter : Specify a filter to select the objects to add to the injection group. See - -the Using Filters in Script Commands section for more information on -specifying the filter. - -Append : Optional parameter – default is YES. - Set to YES or NO. Set to YES to add new participation points based on - -the current settings to an injection group that exists with the same Name. -Set to NO to delete all existing points before adding new points to an -injection group that already exists with the same Name. - - -This command creates a new Injection Group called "TopAreaGens" that includes all generators in -Area 1 and whose participation factor is set the same as the participation factor specified with the -generator. The filter is specified as a single-condition filter that does not require the creation of -an advanced filter. -InjectionGroupCreate("TopAreaGens", GEN, PRESENT, "AreaNumber = 1", NO); - -InjectionGroupRemoveDuplicates(PreferenceFilter); -(PreferenceFilter added in August 17, 2023 patch of Simulator 22 and 23) -This action will search through all injection groups in the case looking for injection groups with the same -elements. For an injection group being the same means that the injection group would have the same -behavior as a duplicate. This means that the elements must be the same. The elements must also have the -same initial value, participation factor, and auto calculation settings. - -PreferenceFilter : (optional) Specify a filter to use when determining which object to keep -when there are duplicates. If one object meets the filter and the other -object does not, then the object which meets the filter will be maintained -and the other removed. Otherwise, the one with a name that would occur -first in an alphabetic sort is maintained. See the Using Filters in Script -Commands section for more information on specifying the filter. Special -filter keywords of SELECTED and AREAZONE cannot be used with this -script command. - - -Suppose that there are multiple injection groups: “NorthAreaGenGroup_1”, -“NorthAreaGenGroup_A”, and “NorthGen_Combined”. They all include the exact same set of -generators, with the same participation factors and settings — they are functionally identical. To -clean up the case and avoid confusion, run this command. This keeps the group named -“NorthGen_Combined", while deleting the rest. -InjectionGroupRemoveDuplicates("Name contains 'Combined'"); - -InterfaceAddElementsFromContingency(InterfaceName, ContingencyName); -(Added in February 1, 2024 patch of Simulator 23) -This action will create or modify an interface by adding the elements from a contingency to an interface as -contingent interface elements. Only elements that are can be represented as a contingent interface -element will be added to the interface. - -InterfaceName : Name of interface to add contingent elements to. If the interface does not -exist, it will be created. - -ContingencyName : Name of contingency to get elements from. - - - 43 - - - -This command creates or modifies an interface named "Left-Right" using the elements defined in -a contingency called "L_000001One-00000TwoC1". -InterfaceAddElementsFromContingency("Left-Right", "L_000001One- -00000TwoC1"); - -InterfacesAutoInsert(Type, DeleteExisting, UseFilters, "Prefix", Limits); -Use this action to auto-insert interfaces. - -Type : AREA – insert area-to-area tieline interfaces. - ZONE – insert zone-to-zone tieline interfaces. -DeleteExsiting : YES – to delete existing interfaces; NO – to leave existing interfaces alone. -UseFilters : YES – to user Area/Zone Filters; NO – to insert for entire case. -"Prefix" : Enter a string which will be a prefix on the interface names. -Limits : ZEROS – to make all limits zero. - AUTO – limits will be set to the sum of the branch limits. - -[lima, limb, limc, limd, …] – Enter 8 limits enclosed in brackets, separated -by commas. This will set the limits as specified. - - -This command creates interfaces between all defined areas in the case that are comprised of the -tie-lines between pairs of areas. Existing interfaces are deleted. Each interface is named with the -prefix "AreaIF_", resulting in names like "AreaIF_Top_Left". The interface limits are set based on -the sum of the limits of the branches that connect each pair of areas. -InterfacesAutoInsert(AREA, YES, NO, "AreaIF_", AUTO); - -InterfaceCreate("Name", DeleteExisting, ObjectType, Filter); -This action will create or modify an interface with elements of a single object type that meet a filter. -Repeated calls to this script command can be used to define an interface with different object types. - -"Name" : Name of the interface to create or modify. -DeleteExisting : Set to YES to delete an interface with the same Name. Set to NO to - -append elements to an interface with the same Name. -ObjectType : Type of object to be included in the interface. Valid options are BRANCH - -or INTERFACE. -Filter : Specify a filter to select the objects of the specified ObjectType to add to - -the interface. See the Using Filters in Script Commands section for more -information on specifying the filter. A filter must be specified, and this -field cannot be blank. - - -This command creates an interface named “North138Lines”, and if an interface with the same -name already exists, it will be deleted and recreated. Only branch objects (i.e., transmission lines, -transformers) are considered. Using the device filter for Bus 1 means that only branches that are -connected to Bus 1 at either terminal are included. -InterfaceCreate("North138Lines", YES, BRANCH, "Bus 1"); - -InterfaceFlatten("InterfaceName"); -Interfaces can contain elements that are other interfaces. The “flattening” process will permanently -modify an interface to contain the elements that the other interface contains and remove the element that -is the other interface. - -"InterfaceName" : Name of the interface to modify. -InterfaceFlattenFilter(Filter); - -(command added in February 9, 2024 patch of Simulator 23) -This functions the same as the InterfaceFlatten script command, except you apply a filter to the case to -perform the “flattening” process on all Interface objects that meet the filter. - - 44 - - - -Filter : Specify a filter to select Interfaces to flatten. Enter the string ALL to -indicate that all interfaces should be flattened. See the Using Filters in -Script Commands section for more information on specifying the filter. - - -This command flattens all interface objects that have a total MW flow greater than 100 MW, as -indicated by the single-condition filter. -InterfaceFlattenFilter("MW > 100"); - -InterfaceModifyIsolatedElements(filter); -In interfaces with many BRANCH OPEN actions it is possible for some of the open actions to disconnect -portions of the system that contain other open actions and other devices. This can be seen in the -illustration below. All lines on the diagram represent interface BRANCH OPEN elements. However, the -three green lines in the center are completely disconnected from the system by the other BRANCH OPEN -interface elements (the ones in pink). The BRANCH OPEN elements also disconnect load. This situation is -very hard to deal with numerically when calculating sensitivities. We address this by removing the -BRANCH OPEN elements that are contained within the disconnected portion of the system. We also add -OPEN actions - -filter : Optional parameter – default is to apply command to all interfaces - Filter that specifies which interfaces to modify. See Using Filters in Script - -Commands section for more information on specifying the filter. - - -InterfaceRemoveDuplicates(PreferenceFilter); - -(PreferenceFilter added in August 17, 2023 patch of Simulator 22 and 23) -This action will search through all interfaces in the case looking for interfaces with the same elements. For -an interface being the same means that the reported flow is the same. In addition to having all the same -elements, the interface elements must also have the same monitoring direction and weighting. - -PreferenceFilter : (optional) Specify a filter to use when determine which object to keep -when there are duplicates. If one object meets the filter and the other -object does not, then the object which meets the filter will be maintained -and the other removed. Otherwise, the one with a name that would occur -first in an alphabetic sort is maintained. See the Using Filters in Script -Commands section for more information on specifying the filter. Special -filter keywords of SELECTED and AREAZONE cannot be used with this -script command. - - -This script command searches all interfaces in a case that contains the exact same elements, -along with monitoring direction and weighting, and removes them. If duplicates are found, the -preference filter "Name contains 'AreaIF'" tells Simulator to retain the interface whose name -contains 'AreaIF'. -InterfaceRemoveDuplicates("Name contains 'AreaIF'"); - - 45 - - - -MergeBuses([element], Filter); -Use this action to merge buses - -Element : The bus object that will be created when the buses meeting the Filter are -merged. - -Filter : Optional parameter – default is to merge all buses into a single bus -AREAZONE : Only buses that meet the area/zone/owner filters will - -be merged -SELECTED : Merge buses with Selected field = YES -"FilterName" : Only buses that meet the specified filter will be - -merged. See Using Filters in Script Commands section -for more information on specifying the filtername. - - -This will merge all the buses that meet the area/zone/owner filters into bus 6. -MergeBuses([Bus 6], AREAZONE); - -MergeLineTerminals(Filter); -Use this action to merge line terminals. This action can be used to remove a line by merging the terminal -buses of that line into a single bus. The only parameter of the script command is a filter parameter, which -must be populated with either the name of an advanced filter (with the name in quotation marks) or the -text SELECTED (with no quotation marks). If an advanced filter is given, then Simulator will find all -branches that meet the advanced filter definition and will individually merge the line terminals of each line -one at a time. - -Filter : Any multi-section lines meeting this filter will be merged. -"filtername" : See the Using Filters in Script Commands section for - -more information on specifying the filtername. -SELECTED : Merge objects with Selected field = YES - - -This command finds all branches in the case that have the Selected field set to YES. For each of -these selected lines, it merges the terminal buses into a single bus, thereby removing the line -from the network. -MergeLineTerminals(SELECTED); - -MergeMSLineSections(Filter); -Use this action to eliminate multi-section line records. If possible, the individual sections will be merged -into a single line record between the from and to bus and the multi-section line record will be removed. If -a multi-section line contains series capacitors or transformers, the multi-section line record will be -retained. - -Filter : Any multi-section lines meeting this filter will be merged. -"filtername" : See the Using Filters in Script Commands section for - -more information on specifying the filtername. -SELECTED : Merge objects with Selected field = YES - - -This attempts to merge the line sections of each multi-section line that has the Selected field set -to YES. -MergeMSLineSections(SELECTED); - -Move([elementA], [destination parameters], HowMuch, AbortOnError); -Use this action to move a generator, load, transmission line, or switched shunt. - -[elementA] : The object that should be moved. Must be one of the following formats: -[GEN busnum id], [GEN "name_nomkv" id], -[GEN "label"] -[LOAD busnum id] , [LOAD "name_nomkv" id], - - 46 - - - -[LOAD "label"], -[BRANCH busnum1 busnum2 ckt], -[BRANCH "name_kv1" "name_kv2" ckt], -[BRANCH "label"] -[SHUNT busnum id], [SHUNT "name_nomkv" id], -[SHUNT "label"], -[MULTISECTIONLINE busnum1 busnum2 ckt], -[MULTISECTIONLINE "name_kv1" "name_kv2" ckt], -[MULTISECTIONLINE "label"], -[3WXFORMER busnum1 busnum2 busnum3 ckt], -[3WXFORMER "name_kv1" "name_kv2" "name_kv3" ckt] -[3WXFORMER "label"] - - -[destination parameters] - : These parameters have different meanings depending on object type of - -the element. Must use bus numbers here: -GEN : [busnum id] -LOAD : [busnum id] -BRANCH : [busnum1 busnum2 ckt] -SHUNT : [busnum id] -MULTISECTIONLINE : [busnum1 busnum2 ckt] -3WXFORMER : [busnum1 busnum2 busnum3 ckt] - -HowMuch : The amount of the element to move. A value of 100 indicates that 100% -should be moved. This parameter is only valid for generators and loads. -It is ignored for lines and switched shunts. - -AbortOnError : Optional flag that allows users to control if loading an AUX file will -continue after an error is encountered. -Added in the October 4, 2024 patch of Simulator 23. - -YES : Abort processing script commands after -encountering error - -NO : Continue processing script commands after error - - -This command moves 100% of the generator located at Bus 1 with ID 1 to Bus 2 with ID 2. -Move([Gen 1 1], [2 2], 100); - -ReassignIDs(objecttype, field, filter, UseRight); -Use this action to set the IDs of specified objects to the first two characters of a specified field. - -Objecttype : The type of object for which IDs are assigned. BRANCH, GEN, LOAD, and -SHUNT are allowed. - -field : The field that contains the IDs that will be assigned. Only the first two -characters of the field will be assigned. Field is specified in format -variablenamelegacy:location or concisename - -Filter : (optional) Any objects meeting this filter will have their IDs reassigned. -Blank is the default value: - -Blank : means all objects will be modified -ALL : means all objects will be modified -SELECTED : means only branches whose Selected field = YES will - -be modified -AREAZONE : means only branches that meet the area/zone/owner - -filters will be modified -"FilterName" : means only objects that meet the specified filter will be - -modified. See the Using Filters in Script Commands - - 47 - - - -section for more information on specifying the -filtername. - -UseRight : Optional parameter – default is NO - Set to YES or NO. If set to YES, the last two characters of the specified - -field will be assigned. - - -This command updates each LOAD ID using the first two letters of the name of the bus to which -it is connected. -ReassignIDs(LOAD, "BusName", ALL, NO); - -Remove3WXformerContainer(filter); -Use this action to delete the three-winding transformers matching the specified filter while leaving the -internal two-winding transformers intact. - -Filter : (optional) Any three-winding transformers meeting this filter will be -deleted. Default is blank: - -Blank : means all three-winding transformers will be deleted -ALL : means all three-winding transformers will be deleted -SELECTED : means only three-winding transformers whose - -Selected field = YES will be deleted -AREAZONE : means only three-winding transformers that meet the - -area/zone/owner filters will be deleted -"FilterName" : means only three-winding transformers that meet the - -specified filter will be deleted. See the Using Filters in -Script Commands section for more information on -specifying the filtername. - - -This command will remove all three-winding transformers in the case. However, it will leave the -individual two-winding transformers that internally represent the 3-winding transformers intact. -Remove3WXformerContainer(ALL); - -RenameInjectionGroup("OldName", "NewName"); -This action will change the name of an existing injection group. - -"OldName" : Name of the existing injection group. -"NewName" : New name of the existing injection group. - - -This command renames the injection group currently named "TopAreaGens” to "IG_Left_New". All -references in the case to the old injection group name will now refer to the new name. -RenameInjectionGroup("TopAreaGens", "IG_Left_New"); - -RotateBusAnglesInIsland([BUS KeyField], Value); -All angles in the island to which the specified bus belongs will be rotated by the same shift such that the -specified bus ends up with a bus angle specified by the Value parameter. - -[BUS KeyField] : Objecttype identifier BUS followed by the keyfield identifier for the bus -whose island identifies the buses that should be shifted and whose angle -should be set to the specified Value. - -Value : Angle value to which the specified bus should be set - - -This command identifies the electrical island that contains Bus 2 and shifts all bus voltage angles -so that Bus 2 is set to 0.0 degrees. The voltage angles of all other buses in this island are adjusted -relative to this new reference. -RotateBusAnglesInIsland([Bus 2], 0.0); - - 48 - - - -SetGenPMaxFromReactiveCapabilityCurve(filter); -Use this action to change the Maximum MW output of generators that use a capability curve, equal to the -second-to-last MW point in the capability curve if the last Max Mvar point on the capability curve is -smaller than 0.001 Mvar. If the present MW output is higher than this new Max MW value, then Max MW -is set to the present MW output. - -filter : optional parameter that is used to specify which generators are -processed. If blank, all generators are processed. - -Selected : means only generators whose Selected field = YES will -be processed - -AREAZONE : means process those generators that meet the -area/zone/owner filters. - -"FilterName" : See the Using Filters in Script Commands section for -more information on specifying the filtername. - - -This command modifies MWMax based on reactive capability curves for all generators that meet -the area/zone/owner filters. -SetGenPMaxFromReactiveCapabilityCurve(AREAZONE); - -SetParticipationFactors(Method, ConstantValue, Object); -Use this action to modify the generator participation factors in the case - -Method : The formula used to calculate the participation factors for each -generator. It may be one of the following strings: - - MAXMWRAT – base factors on the maximum MW ratings. - RESERVE – base factors on the (Max MW rating – Present MW). - CONSTANT – set factors to a constant value. -ConstantValue : The value used if CONSTANT method is specified. If CONSTANT method - -is not specified, enter 0 (zero). -Object : Specify which generators to set the participation factor for. - [Area Num], [Area "name"], [Area "label"] - [Zone Num], [Zone "name"], [Zone "label"] - SYSTEM - AREAZONE or DISPLAYFILTERS - - -This sets the participation factors for all generators in the "Top" area to a constant value of 1.0. -SetParticipationFactors(CONSTANT, 1.0, [Area "Top"]); - -SetScheduledVoltageForABus([bus identifier], voltage); -Use this action to set the stored scheduled voltage, vsched, for a bus according to how this is defined in -the EPC format. This value is not used by Simulator but is stored for purposes of writing out to an EPC -file. The setpoint voltages for generators and switched shunts regulating the specified bus are also set to -the new voltage. The regulation range for switched shunts is modified for the new setpoint voltage -according to how this is defined in the EPC format: vband = (VHigh-VLow)/2 with newVHigh = -voltage+vband and new VLow = voltage-vband. - -[bus identifier] : This is the key field identifier for the bus being changed. This can contain -the objecttype identifier of BUS followed by the key field or this identifier -can be omitted. The square brackets are also optional. If using a string -identifier such as the secondary key field or a label for the bus and that -includes a comma, the identifier must be enclosed in double quotes or -the square brackets must be used. - -voltage : the new voltage - - - 49 - - - - -This command sets the scheduled voltage value (Vsched) of Bus 2 to 1.02 pu. -SetScheduledVoltageForABus([BUS 2], 1.02); - -SetInterfaceLimitToMonitoredElementLimitSum(filter); -This sets the limits of the interface to the sum of the limits of all branches within the interface. This only -includes branches that are monitored and excludes any contingency branches. All limits A through H will -be set. - -Filter : This parameter is used to specify which interfaces have their limits set. -ALL : all interfaces will be set -SELECTED : only interfaces whose Selected field = YES will be set -AREAZONE : only interfaces that meet the area/zone/owner filters - -will be set -"FilterName" : only interfaces that meet the specified filter will be set. - -See the Using Filters in Script Commands section for -more information on specifying the filtername. - - -This command goes through all interfaces and sets each interface's limits (Limit A through Limit -H) to be equal to the sum of the limits of all the monitored branches within that interface. It -excludes any branches that are only used as contingency elements. -SetInterfaceLimitToMonitoredElementLimitSum(ALL); - -SplitBus([element], NewBusNumber, InsertBusTieLine, LineOpen, BranchDeviceType); -Use this action to split buses - -Element : Enter the description of which bus to split by enclosing in brackets the -word bus and an identifier. The format looks as follows: - -[BUS num] -[BUS "name_nomkv"] -[BUS "buslabel"] - -NewBusNumber : This is the number of the new bus to create -InsertBusTieLine : Optional parameter – default is YES - Set to YES or NO. YES will insert a low impedance tie line between the - -buses; NO will not. -LineOpen : Optional parameter – default is NO - Set to YES or NO. YES set the status of the inserted bus tie to OPEN. NO - -will set the status of the inserted bus tie to CLOSED. -BranchDeviceType : Optional parameter – default is "Line" - Specify the Branch Device Type of the branch inserted for the bus tie. - -Options are: "Line", "Transformer", "Breaker", "Disconnect", "ZBR", "Fuse", -“Load Break Disconnect", and "Ground Disconnect". - - -This command will split Bus 5 into two buses, assign bus number 999 to the new bus, and insert a -closed Breaker between the original and new bus. -SplitBus([BUS 5], 999, YES, NO, "Breaker"); - -SuperAreaAddAreas("Name", Filter); -(Added in the July 23, 2024 patch of Simulator 23) -Use this action to add areas to an existing Super Area. - -"Name" : Enter name of the existing Super Area -Filter : This parameter is used to specify which areas are added. - -ALL : all areas will be added -SELECTED : only areas whose Selected field = YES will be set - - 50 - - - -AREAZONE : only areas that meet the area/zone/owner filters -will be set - -"FilterName" : only areas that meet the specified filter will be set. -See the Using Filters in Script Commands section -for more information on specifying the filtername. - - -This command will add all areas where Selected = YES to an existing Super Area called -"SuperArea1". -SuperAreaAddAreas("SuperArea1", ALL); - -SuperAreaRemoveAreas("Name", Filter); -(Added in the July 23, 2024 patch of Simulator 23) -Use this action to remove areas from a Super Area. - -"Name" : Enter name of the Super Area -Filter : This parameter is used to specify which areas are removed. - -ALL : all areas will be removed -SELECTED : only areas whose Selected field = YES will be set -AREAZONE : only areas that meet the area/zone/owner filters - -will be set -"FilterName" : only areas that meet the specified filter will be set. - -See the Using Filters in Script Commands section -for more information on specifying the filtername. - - -This command will remove all Areas from SuperArea1 that match the active area/zone/owner -filters. -SuperAreaRemoveAreas("SuperArea1", AREAZONE); - -TapTransmissionLine([element], PosAlongLine, NewBusNumber, ShuntModel, TreatAsMSLine, -UpdateOnelines, NewBusName); - -Use this action to tap a transmission line by adding in a new bus and splitting the line in two. -Element : A description of the branch being tapped. The first bus listed will be - -treated as the nearbus that is used as the reference for the PosAlongLine. -If the branch is identified by label, the from bus will be used as the -reference for the PosAlongLine. - - Enclose description in brackets: -[BRANCH busnum1 busnum2 ckt] -[BRANCH "name_kv1" "name_kv2" ckt] -[BRANCH "buslabel1" "buslabel2" ckt] -[BRANCH "label"] - -PosAlongLine : The percent distance along the branch at which the line will be tapped. -NewBusNumber : The number of the new bus created at the tap point. -ShuntModel : Optional parameter – default is CAPACITANCE - How should the shunt charging capacitance values be handled for the - -split lines: -LINESHUNTS : Line shunts will be created (keeps exact power flow - -model). -CAPACITANCE : Convert shunt values capacitance in the PI model. - -TreatAsMSLine : Optional parameter – default is NO - Set to YES or NO. If set to YES, the two newly created lines will be made - -part of a mulit-section line. -UpdateOnelines : Optional parameter – default is NO - - 51 - - - - Set to YES or NO. If set to YES, the original display transmission line -object will be replaced with the tapped display transmission line and -display bus objects on all open oneline diagrams. - -(Added in the February 20, 2024 patch of Simulator 23) -NewBusName : Optional parameter – default is blank - Specify the name of the new bus created at the tap point. If this is left - -blank, the new name will be the same as the new bus number. - - -This command taps the transmission line from Bus 1 to Bus 2 circuit 1 at 50% distance from Bus -1, creating a new bus with number 1001 and name "TapBus1_2". The line is split into two -segments connected through the new bus. ShuntModel is set to LINESHUNTS to explicitly model -line shunts. With TreatAsMSLine = YES, the segments are treated as a multi-section line, and -UpdateOnelines = YES updates any oneline diagrams to reflect the changes. -TapTransmissionLine([BRANCH 1 2 1], 50, 1001, LINESHUNTS, YES, YES, -"TapBus1_2"); - - - - 52 - - - -Power Flow -ClearPowerFlowSolutionAidValues; - -PowerWorld Simulator maintains several internal flags that keep track of which branches are closed or -opened, as well as information to help estimate the generation change needed in a system after making -changes to load or generation. PowerWorld uses this to help with various pre-processing steps in the -power flow solution. This information is related to angle smoothing and generator MW estimation -features of the power flow solution. Typically, this information is a great aid in getting successful power -flow solutions, however in some circumstances you may be using an AUX file to edit information you -know is good and would not want PowerWorld to modify the initial bus voltage and angle nor the -generator MW outputs before a solution is attempted. To clear all this internally stored information so -that PowerWorld does not do any of this, call the ClearPowerFlowSolutionAidValues script command. - -ConditionVoltagePockets(VoltageThreshold, AngleThreshold, filter); -The goal of this script command is to find pockets of buses that may have bad initial voltage estimates -and to get a better voltage estimate of these buses based on assuming that the voltages on buses outside -these pockets are good. It will identify pockets of buses bounded by branches that meet the condition -that the absolute value of the voltage difference across the branch is greater than VoltageThreshold or -the absolute value of the angle difference across the branch is greater than AngleThreshold and the -branch meets the specified filter. - -VoltageThreshold : Per-unit voltage difference (absolute value) that determines if a branch -can be considered when determining groups of radial buses. - -AngleThreshold : Angle difference in degrees (absolute value) that determines if a branch -can be considered when determining groups of radial buses. - -filter : This is an optional parameter that is used to specify which branches are -checked. If omitted all branches are considered. - -ALL : All branches will be checked -SELECTED : Only branches whose Selected field = YES will be - -checked -AREAZONE : Only branches that meet the area/zone/owner filters - -will be checked -"FilterName" : Only branches that meet the specified filter will be - -checked. See Using Filters in Script Commands section -for more information on specifying the filtername. - - -This command checks all branches in the system. If the absolute voltage difference across a -branch exceeds 0.1 per unit, or the angle difference exceeds 10 degrees, that branch helps define -a "pocket" of buses. The tool will then determine the buses in each pocket and estimate voltages -better using known good values outside the pocket. -ConditionVoltagePockets(0.1, 10, ALL); - -DiffCaseClearBase; -Call this action to clear the base case for the difference flows abilities of Simulator. -In Version 21 and earlier this script command was called DiffFlowClearBase. Simulator 21 patches after -January 20, 2021 will handle reading either the DiffCaseClearBase or DiffFlowClearBase. - -DiffCaseKeyType(KeyType); -Use this action to change the key type that should be used when comparing fields when using the -difference flows abilities of Simulator. - -KeyType : String that starts with ‘P’ changes key field type to PRIMARY. -String that starts with ‘S’ changes key field type to SECONDARY. -String that starts with ‘L’ changes key field type to LABEL. - - 53 - - - -In Version 21 and earlier this script command was called DiffFlowKeyType. Simulator 21 patches after -January 20, 2021 will handle reading either the DiffCaseKeyType or DiffFlowKeyType. - - -This command sets the key type used in PowerWorld Simulator's Difference Case comparison to -PRIMARY. -DiffCaseKeyType(PRIMARY); - -DiffCaseMode(diffmode); -Call this action to change the mode for the difference flows abilities of Simulator. - -diffmode : String that starts with ‘P’ changes it to PRESENT. - String that starts with ‘B’ changes it to BASE. - String that starts with ‘D’ changes it to DIFFERENCE. - String that starts with ‘C’ changes it to CHANGE. - -In Version 21 and earlier this script command was called DiffFlowMode. Simulator 21 patches after -January 20, 2021 will handle reading either the DiffCaseMode or DiffFlowMode. - - -This command sets PowerWorld Simulator's Difference Case mode to DIFFERENCE. -DiffCaseMode(DIFFERENCE); - -DiffCaseSetAsBase; -Call this action to set the present case as the base case for the difference flows abilities of Simulator. -In Version 21 and earlier this script command was called DiffFlowSetAsBase. Simulator 21 patches after -January 20, 2021 will handle reading either the DiffCaseSetAsBase or DiffFlowSetAsBase. - -DiffCaseShowPresentAndBase(How); -Call this action with the parameter of either YES or NO to toggle the difference flows options “Show -Present|Base in Difference and Change Mode”. -In Version 21 and earlier this script command was called DiffFlowShowPresentAndBase. Simulator 21 -patches after January 20, 2021 will handle reading either the DiffCaseShowPresentAndBase or -DiffFlowShowPresentAndBase. - - -This command enables the "Show Present|Base in Difference and Change Mode" option in -PowerWorld Simulator. -DiffCaseShowPresentAndBase(YES); - -DiffCaseRefresh; -Call this action to refresh the linking between the base case and the present case. This should be used -before saving data that identifies objects as being added or removed, especially if any topological -differences have been made that affect the comparison. -In Version 21 and earlier this script command was called DiffFlowRefresh. Simulator 21 patches after -January 20, 2021 will handle reading either the DiffCaseRefresh or DiffFlowRefresh. - -DiffCaseWriteCompleteModel ("filename", AppendFile, SaveAdded, SaveRemoved, SaveBoth, -KeyFields, "ExportFormat", UseAreaZone, UseDataMaintainer, AssumeBaseMeet, -IncludeClearPowerFlowSolutionAidValues, DeleteBranchesThatFlipBusOrder); - -In Version 21 and earlier this script command was called DiffFlowWriteCompleteModel. Simulator 21 -patches after January 20, 2021 will handle reading either the DiffCaseWriteCompleteModel or -DiffFlowWriteCompleteModel. -Use this action to create an auxiliary file that contains information about objects that have been added or -removed when comparing the present case to the base case when using the difference case comparison. -Fields that have changed for objects that exist in both the present and base case can also be written to -this auxiliary file. This auxiliary file can then be used to modify cases with these same changes. - -"filename" : Name of the auxiliary file to create. - 54 - - - -AppendFile : Set to YES or NO. YES means to append the saved information to -"filename". NO means that "filename" will be overwritten. - -SaveAdded : Set to YES or NO. YES means to save the added objects to the file. NO -means to exclude the added objects. - -SaveRemoved : Set to YES or NO. YES means to save the removed objects to the file. -NO means to exclude the removed objects. - -SaveBoth : Set to YES or NO. YES means to save the changed fields for the objects -that exist in both the present and base case. NO means to exclude -objects that occure in both cases. - -KeyFields : Optional parameter – default is Primary - Set to Primary or Secondary to specify the key field identifiers to use for - -objects in the resulting file. -"ExportFormat" : Optional parameter – default is blank - This is the name of the Auxiliary File Export Format Description to use for - -defining the object types and fields that should be included in the -auxiliary file. - -UseAreaZone : Optional parameter – default is NO - Set to YES or NO. YES means to use the Area/Zone/Owner filter for - -including objects in the file. NO means to ignore this filter. -UseDataMaintainer : Optional parameter – default is NO - Set to YES or NO. YES means to use the Data Maintainer filter for - -including objects in the file. NO means to ignore the Data Maintainer -filter. - -AssumeBaseMeet : Optional parameter – default is YES - Set to YES or NO. YES means that areas/zones/owners and data - -maintainers that are in the base case and not in the present case meet -the Area/Zone/Owner and Data Maintainer filters. - -IncludeClearPowerFlowSolutionAidValues - : Optional parameter – default is YES - Set to YES or NO. YES means that the ClearPowerFlowSolutionAidValues - -script command is included in the auxiliary file. -DeleteBranchesThatFlipBusOrder - : Optional parameter – default is NO - Set to YES or NO. YES means that branches that have the order of their - -from and to terminal buses flipped will be included as both removed and -added objects. The branch with the old order will be removed and the -branch with the new order will be added. This is always done for -transformers, but it is optional for non-transformer branches. - - -This command generates an auxiliary file named “B7FlatChanges.aux” that captures the -differences between the Present and Base cases. It overwrites any existing file (rather than -appending) and includes objects that were added, removed, or changed between the cases. -Primary key fields are used to identify objects, and no special export format or area/zone/owner -or data maintainer filters are applied. The base case is assumed to meet any filtering -requirements even if certain objects are missing in the present case. The file also includes a -command to clear solution aid values before applying changes, but it does not treat non- -transformer branches with flipped terminal buses as removed and re-added. -DiffCaseWriteCompleteModel("B7FlatChanges.aux", NO, YES, YES, YES, -PRIMARY, "", NO, NO, YES, YES, NO); - - 55 - - - -DiffCaseWriteBothEPC ("filename", GEFileType, UseAreaZone, BaseAreaZoneMeetFilter, Append, -"ExportFormat", UseDataMaintainer); - -Call this action to save any elements in both the base and present cases as determined using the -difference flows functionality. The elements are saved in the GE EPC format. See the -DiffFlowWriteRemovedEPC script command for the descriptions of the parameters that are common -among all script commands used to save difference case change files in the EPC format. Parameters that -are specific to DiffCaseWriteBothEPC are described here: - -“ExportFormat” : Optional parameter – default is blank. -This is the name of the Auxiliary File Export Format Description to use for -defining the object types that should be included in the file. Only object -types that are allowed in an EPC can be included and others will be -skipped. Fields that are specified with the object types in the format will -be used to determine if an object has changed based on those fields -changing between the present and base case. If any field has changed -for an object, the entire object will be written including all fields that are -required in the EPC format. - -In Version 21 and earlier this script command was called DiffFlowWriteBothEPC. Simulator 21 patches -after January 20, 2021 will handle reading either the DiffCaseWriteBothEPC or DiffFlowWriteBothEPC. - - -This command saves all elements that exist in both the base and present cases (and have -differences) to a GE EPC file named "B7Flat_Changes.epc". It does not apply the area/zone/owner -filters (UseAreaZone = NO), but it assumes the base case meets the filter -(BaseAreaZoneMeetFilter = YES). The file is not appended (Append = NO), and no custom export -format is specified, so all valid EPC object types and their standard fields will be considered. -DiffCaseWriteBothEPC("B7Flat_Changes.epc", GE23, NO, YES, NO, ""); - -DiffCaseWriteNewEPC ("filename", GEFileType, UseAreaZone, BaseAreaZoneMeetFilter, Append, -UseDataMaintainer); - -Call this action to save any new elements determined using the difference flows functionality. The -elements are saved in the GE EPC format. See the DiffFlowWriteRemovedEPC script command for the -descriptions of the parameters. -In Version 21 and earlier this script command was called DiffFlowWriteNewEPC. Simulator 21 patches -after January 20, 2021 will handle reading either the DiffCaseWriteNewEPC or DiffFlowWriteNewEPC. - - -This command saves new elements identified by difference case to the file -"B7Flat_NewElements.epc". It includes all new elements regardless of area/zone/owner filters, -treats areas/zones not in the base case as meeting the filter (YES), and overwrites the file if it -already exists. -DiffCaseWriteNewEPC("B7Flat_NewElements.epc", GE23, NO, YES, NO); - -DiffCaseWriteRemovedEPC ("filename", GEFileType, UseAreaZone, BaseAreaZoneMeetFilter, Append, -UseDataMaintainer); - -In Version 21 and earlier this script command was called DiffFlowWriteNewEPC. Simulator 21 patches -after January 20, 2021 will handle reading either the DiffCaseWriteNewEPC or DiffFlowWriteNewEPC. -Call this action to save any removed elements determined using the difference flows functionality. The -elements are saved in the GE EPC format. - -"filename" : The path and name of the file to save. -GEFileType : Optional parameter – default is to save with the latest version. Valid - -options: -GE14-GE23 (must specify with the version number, GE##) - - 56 - - - -UseAreaZone : Optional parameter – default is NO. -Set to YES or NO. YES means to save only those objects that meet the -Area/Zone/Owner filter. - -BaseAreaZoneMeetFilter - : Optional parameter – default is NO. - -Set to YES or NO. YES means that areas or zones that are not in the -difference flows base case will be treated as meeting the Area/Zone filter -if that is used. - -Append : Optional parameter – default is YES. -Set to YES or NO. YES means to append data to an existing file. NO -means to overwrite an existing file. - -(Added in the October 30, 2025 patch of Simulator 24) -UseDataMaintainer : Optional parameter – default is NO - Set to YES or NO. YES means to use the Data Maintainer filter for - -including objects in the file. NO means to ignore the Data Maintainer -filter. - - -The command creates a file named "B7Flat_RemovedElements.epc" to store removed elements in -GE EPC format, using version GE23. It includes only elements that pass the current -area/zone/owner filters (YES), excludes areas/zones not present in the base case (NO), and -overwrites any existing file with the same name. -DiffCaseWriteRemovedEPC("B7Flat_RemovedElements.epc", GE23, YES, NO, -NO, NO); - -DoCTGAction([contingency action]); -DoCTGAction("contingency action"); - -Call this action to use the formats seen in the CTGElement subdata record for Contingency Data. Note -that all actions are supported, except COMPENSATION sections are not allowed. The action must be -enclosed in either brackets or double quotes. - - -This command takes the branch from Bus 1 to Bus 2 Circuit 1 out of service. The branch is -identified by using the secondary key fields of the buses. -DoCTGAction([BRANCH One_138.0 Two_138.0 1 OPEN]); - -EstimateVoltages(filter); -This will estimate voltages and angles at the buses that meet the filter based on voltages and angles at -surrounding buses (these are buses that do not meet the filter). It is assumed that the voltages and -angles are correct at the surrounding buses, and in order for this command to work, there must be some -buses that do not meet the filter. - -filter : See the Using Filters in Script Commands section for more information -on specifying the filter. A valid filter must be specified. An error will -result if a blank filter is specified. - - -This script estimates voltages and angles at buses in the Area named "Top" based on -neighboring buses outside this area. The filter is specified as a single-condition filter. -EstimateVoltages("AreaName = Top"); - - - - - 57 - - - -GenForceLDC_RCC(filter); -Use this action to force generators in the case onto line drop / reactive current compensation. The -present voltage at the point at which the generator is controlling based on the line drop/reactive current -compensation impedance is calculated, and the setpoint of the generator is set to this value. If the -absolute value of the line drop/reactive current compensation impedance is less than or equal to 2*10- -6*MVA Base, the generator will regulate its terminal bus and the setpoint voltage is set to the present -value of the terminal bus voltage. For a typical case with an MVA Base of 100 MVA, this value is 0.0002. - -filter : Optional parameter – default is to set all generators - See the Using Filters in Script Commands section for more information - -on specifying the filter. -InterfacesCalculatePostCTGMWFlows; - -Call this action to update the “Interface MW Flow”, “Contingent MW Flow” fields on all Contingent -Interfaces defined in the case. The fields will be updated according to the option chosen in Simulator -Options > Power Flow Solution > Monitor/Enforce Contingent Interface Elements. If “Never” has been -chosen, this script command will have no impact, i.e., the “Contingent MW Flow” field will be zero, and the -“Interface MW Flow” field will equal “Base MW Flow”. - -ResetToFlatStart (FlatVoltagesAngles, ShuntsToMax, LTCsToMiddle, PSAnglesToMiddle); -Use this action to initialize the Power Flow Solution to a "flat start." The parameters are all optional and -specify a conditional response depending on whether the solution is successfully found. If parameters are -not passed then default values will be used. - -FlatVoltagesAngles : Set to YES or NO. YES means setting all the voltage magnitudes and -generator setpoint voltages to 1.0 per unit and all the voltage angles to -zero. Default Value = YES. - -ShuntsToMax : Set to YES or NO. YES means to increase Switched Shunts Mvar half way -to maximum. Default Value = NO. - -LTCsToMiddle : Set to YES or NO. YES means setting the LTC Transformer Taps to middle -of range. Default Value = NO. - -PSAnglesToMiddle : Set to YES or NO. YES means setting Phase Shifter angles to middle of -range. Default Value = NO. - - -This command resets the power flow case to a flat start. It sets all voltage magnitudes and -generator setpoints to 1.0 per unit and all voltage angles to zero (FlatVoltagesAngles = YES). -Additionally, it adjusts all switched shunts to halfway between their current and maximum Mvar -values (ShuntsToMax = YES). LTC transformer taps and phase shifter angles are left unchanged -(LTCsToMiddle = NO, PSAnglesToMiddle = NO). -ResetToFlatStart(YES, YES, NO, NO); - -SaveGenLimitStatusAction("filename"); -Use this action to save Mvar information about generators in a text file. The information saved includes -the generator bus number, generator ID, Mvar, Max Mvar, Min Mvar, AVRable flag (user specified), and -internal AVRable flag (set by Simulator). This information is useful for debugging. - -"filename" : Name of the text file in which to save the generator information. - - -This command saves information about the reactive power (Mvar) limits and AVR (automatic -voltage regulation) status for all generators in the case to a file named "GenLimitReport.txt". -SaveGenLimitStatusAction("GenLimitReport.txt"); - - - - - 58 - - - -SaveJacobian("JacFileName", "JIDFileName", FileType, JacForm); -Use this action to save the Jacobian Matrix to a text file or a file formatted for use with Matlab - -"JacFileName" : File in which to save the Jacobian. -"JIDFileName" : File to save a description of what each row and column of the Jacobian - -represents. -FileType : One of: - -M – Matlab form -TXT – Text file -EXPM – Save Jacobian in Exponential form (ex. 1.2E-2) in Matlab form - -JacForm : One of: -R – AC Jacobian in Rectangular coordinates -P – AC Jacobian in Polar coordinates -DC – B’ matrix of DC power flow - - -This command saves the Jacobian matrix of the current power system case to a file named -"B7FlatJacobian.mat" in MATLAB format (M), using polar coordinates (P). The file -"B7FlatJacobianIDs.txt" will contain descriptions of what each row and column in the Jacobian -matrix represents—such as voltage angles, magnitudes, or power injections at each bus. -SaveJacobian("B7FlatJacobian.mat", "B7FlatJacobianIDs.txt", M, P); - -SaveYbusInMatlabFormat("filename", IncludeVoltages); -Use this action to save the YBus to a file formatted for use with Matlab - -"filename" : File in which to save the YBus. -IncludeVoltages : YES includes the per unit bus voltages in the file; NO does not include. - - -This command saves the Y-bus matrix of the current system case to a file named -"B7FlatYbus.mat" in a format that is compatible with MATLAB. By setting the second parameter to -YES, the file will also include the per-unit voltage magnitudes and angles for each bus in the -system. -SaveYbusInMatlabFormat("B7FlatYbus.mat", YES); - -SolvePowerFlow (SolMethod, "filename1", "filename2", CreateIfNotFound1, CreateIfNotFound2); -Call this action to perform a single power flow solution. The parameters are all optional and specify a -conditional response depending on whether the solution is successfully found. If parameters are not -provided, default values will be used. - -SolMethod : Optional parameter – default is RECTNEWT if the case is in AC power -flow mode or DC if the case in is DC power flow mode. - - The solution method to be used for the Power Flow calculation. If the DC -method is selected, the case is switched to DC power flow mode. If one -of the other AC methods is selected, the case is switched to AC power -flow mode. It might be difficult to solve a case with an AC power flow -method once the case has been switched to DC power flow mode, so -take this into consideration before using the DC method. The options -are: - -RECTNEWT : for Rectangular Newton-Raphson. -POLARNEWTON : for Polar Newton-Raphson. -GAUSSSEIDEL : for Gauss-Seidel. -FASTDEC : for Fast Decoupled. -ROBUST : for attempting the robust solution process -DC : for DC power flow calculation - - -"filename1" : Optional parameter – default is blank - - 59 - - - - The filename of the auxiliary file to be loaded if there is a successful -solution. You may also specify STOP, which means that all AUX file -execution should stop under the condition. Default = "". - -"filename2" : Optional parameter – default is blank - The filename of the auxiliary file to be loaded if there is a NOT successful - -solution. You may also specify STOP, which means that all AUX file -execution should stop under the condition. Default = "". - -CreateIfNotFound1 : Optional – default is NO when using the Legacy Auxiliary File Header -format. Default is YES when using the Concise Auxiliary File Header -format. - - This parameter is only enforced when the Create_if_not_found field -for a Legacy Auxiliary File Header is set to PROMPT. Otherwise, YES is -assumed. YES means that objects that cannot be found will be created -while reading DATA sections from "filename1". - -CreateIfNotFound2 : Optional – default is NO when using the Legacy Auxiliary File Header -format. Default is YES when using the Concise Auxiliary File Header -format. - - This parameter is only enforced when the Create_if_not_found field -for a Legacy Auxiliary File Header is set to PROMPT. Otherwise, YES is -assumed. YES means that objects that cannot be found will be created -while reading DATA sections from "filename2". - - -This command attempts to solve the power flow using the Rectangular Newton-Raphson -method. If the solution is successful, the auxiliary file "OnSuccess.aux" is loaded with object -creation allowing for any missing elements referred to in DATA sections to be created. If the -solution fails, "OnFailure.aux" is loaded, but new objects will not be created during the load for -DATA sections in the Legacy Auxiliary File Header format. -SolvePowerFlow(RECTNEWT, "OnSuccess.aux", "OnFailure.aux", YES, NO); - -UpdateIslandsAndBusStatus; -Changes to branch and generator status impact islands and whether or not buses are connected. Islands -and bus status are always updated at the beginning of a power flow solution if necessary, but this script -command makes it convenient to update this information without requiring a power flow solution. - -ZeroOutMismatches(ObjectType); -With this script command, bus shunts or loads are changed at each bus that has a mismatch greater than -the MVA convergence tolerance so that the mismatch at that bus is forced to zero. - -ObjectType : Optional parameter – default is BUSSHUNT - Set to BUSSHUNT or LOAD to indicate how the mismatch should be - -adjusted. When set to BUSSHUNT the Bus Shunt fields at each bus -where there is a mismatch are adjusted to eliminate the mismatches. -When set to LOAD, a new load is added to a bus where there is a -mismatch to eliminate the mismatches. The new loads are identified by a -unique ID of Q1. If a load with this ID already exists, a unique ID is -determined by incrementing the ID until a unique ID is found. - - -This command checks every bus in the system and identifies those where the power flow -mismatch exceeds the MVA convergence tolerance. For each of those buses, it adds a new load -(with an ID like Q1, Q2, etc.) to cancel out the mismatch. -ZeroOutMismatches(LOAD); - - 60 - - - -DeleteState(WhichState, StateName); -Apply this script command to delete a specified system state that is currently in memory. This script -command will fail if the specified state has not been set. The following options are available for specifying -which system state to delete: - -WhichState : Optional parameter – default is USER. This determines which state to -restore. - -USER : Delete the user set system state that is set with -the StoreState script command. - -BEFOREFAILED : Before the power flow is solved, either through -the GUI or the SolvePowerFlow script command, -a system state will be stored in the event that -the power flow solution fails. This pre-solution -state is deleted with this option. If the power -flow is successful in solving at any time after this -pre-solution state is stored, this pre-solution -state is removed. - -LASTSUCCESSFUL : If the power flow solution is successful when -solving the power flow either through the GUI or -the SolvePowerFlow script command, a system -state will be stored with this successful solution. -This post-solution state is deleted with this -option. - -StateName : Optional parameter – default is blank - This option is only used when WhichState = USER. This specifies a - -named system state that was stored using the StoreState script -command. If no name is specified, the unnamed system state stored -using StoreState will be deleted. - - -This command deletes a previously stored user-defined system state called "PreContingency". -DeleteState("User", "PreContingency"); - -RestoreState(WhichState, StateName); -Apply this script command to restore a specified system state that is currently in memory. This script -command will fail if the specified state has not been set. The following options are available for specifying -which system state to restore: - -WhichState : Optional parameter – default is USER. This determines which state to -restore. - -USER : Restore the user set system state that is set with -the StoreState script command. - -BEFOREFAILED : Before the power flow is solved, either through -the GUI or the SolvePowerFlow script command, -a system state will be stored in the event that -the power flow solution fails. This pre-solution -state is restored with this option. If the power -flow is successful in solving at any time after this -pre-solution state is stored, this pre-solution -state is removed and cannot be restored. - -LASTSUCCESSFUL : If the power flow solution is successful when -solving the power flow either through the GUI or -the SolvePowerFlow script command, a system -state will be stored with this successful solution. - - 61 - - - -This post-solution state is restored with this -option. - -StateName : Optional parameter – default is blank - This option is only used when WhichState = USER. This specifies a - -named system state that was stored using the StoreState script -command. If no name is specified, the unnamed system state stored -using StoreState will be restored. - - -This command restores a previously stored user-defined system state called "PreContingency". -RestoreState("User", "PreContingency"); - -StoreState(StateName); -Apply this script command to store the current system state to memory. Use the RestoreState script -command to restore the state. Multiple states can be stored by providing a unique name. - -StateName : Optional parameter - Multiple states can be stored by providing a unique name for each state. - -If StateName is not specified, an unnamed state is stored. - - -This command stores the current system state and saves it under the name "PreContingency". -StoreState("PreContingency"); - -VoltageConditioning; -Perform voltage conditioning based on the Voltage Conditioning tool options and case voltage targets. - - - - - 62 - - - -User Interface -Animate(DoAnimate); - -Use this action to animate all the open oneline diagrams. -DoAnimate : Set to YES or NO. YES means to start the animation of the open oneline - -diagrams, while NO means that the animation will be paused. - - -This command starts the animation of flow objects on all open oneline diagrams. -Animate(YES); - -MessageBox("text"); -Use this action to open a dialog box that will display the entered text. This script command will fail if -using the SimAuto add-on. - -"text" : Text that will appear in the dialog box. - - -Opens a message box with the greeting “Hello”. -MessageBox("Hello"); - -ObjectFieldsInputDialog("ObjectIDString", [fieldlist], "DialogCaption", "DialogExplain", -[LabelCaptions], [TabBreaks], [TabCaptions], [RowBreaks], [RowCaptions], [ColBreaks], [ColCaptions]); - -Use this action to open a dialog box displaying the list of specified fields for the specified object. This will -allow the fields to be modified in the same manner as they can through case information displays. This -script command will fail if using the SimAuto add-on. - -"ObjectIDString" : The specific object for which display fields are displayed. The format is -the object type followed by the key fields used to identify the object. -Examples: "Bus 234891", "Gen 16445 'A'", "Branch 1239 1234 'AB'". - -[fieldlist] : A list of fields to to display for the specified object. -"DialogCaption" : Optional with default of blank. This is the caption that will appear on the - -dialog. -"DialogExplain" : Optional with default of blank. This is an explanation that will appear in a - -text at the top of the dialog underneath the caption. -[LabelCaptions] : Optional with default of []. Inside brackets, you may enter a comma- - -delimited list of captions that will appear with the respective fields. The -captions must be enclosed in double quotes if there are any commas in -the string. If now label captions are specified, then the concise variable -names will be used to indicate what each field is - -[TabBreaks] : Optional with default of []. Inside brackets, you may enter a comma- -delimited list of integers that indicate that a tab break occurs before the -field at the particular index. The fields are indexed starting at zero. The -dialog that appears will be created with the first “tab” representing a -panel at the TOP of the dialog. This top panel will be made a fixed -height so that all rows of fields can be seen. Any subsequent tabs will be -placed inside a Tabbed control. The tabbed control will take up the -remainder of the size of the dialog. - -[TabCaptions] : Optional with default of []. Inside brackets, you may enter a comma- -delimited list of captions that will appear with the respective tab break. -Each tab break will represent a TAB on the tabbed control. These will be -the captions. If nothing is specified, the captions will simply numbered. - -[RowBreaks] : Optional with default of []. Inside brackets, you may enter a comma- -delimited list of integers that indicate that a row break occurs before the -field at the particular index. The fields are indexed starting at zero. Each -tab of the dialog will be drawn with controls optionally grouped into - - 63 - - - -rows and then these rows optionally grouped into columns. A particular -“cell” of this table can then have multiple fields inside it. - -[RowCaptions] : Optional with default of []. Inside brackets, you may enter a comma- -delimited list of captions that will appear with the respective group box -that starts with the field at this index. The group box will contain all -fields up until the next Column or Row break. Blank captions are also -allowed, in which case a group box is not drawn. - -[ColBreaks] : Optional with default of []. Inside brackets, you may enter a comma- -delimited list of integers that indicate that a column break occurs before -the field at the particular index. The fields are indexed starting at zero. -Each tab of the dialog will be drawn with controls optionally grouped -into rows and then these rows optionally grouped into columns. A -particular “cell” of this table can then have multiple fields inside it. - -[ColCaptions] : Optional with default of []. Inside brackets, you may enter a comma- -delimited list of captions that will appear with the respective group box -that starts with the field at this index. The group box will contain all -fields up until the next Column or Row break. Blank captions are also -allowed, in which case a group box is not drawn. - - -The following image depicts what the resulting dialog would show for the following script command. -Note the field list is abbreviated but for this example there are 21 fields listed in the same manner as -other script commands. - -ObjectFieldsInputDialog("Branch 5 6 1", [Field0, … Field20], - "Add Caption Here", "Explain Stuff Here", [], - [4, 12], [My Cap,Another], - [5,11,12,17,19], [EDFG,"Test,Cap","Heref",ABCD,""], - [8,14,15,18,18], [HIJK,,LMNO,,XYZ] - ); - - 64 - - - - -A few things of note -in this example. - -Column caption that -goes with Index 14 is -blank so no group -box is drawn. - -Column index 18 is -listed twice in the -ColBreaks which -results in the empty -column between Field -17 and 18. - -RowBreak index 12 -would seem to be -unnecessary but -provide the -mechanism to add -the caption “Heref”. - - 65 - - - -OpenDataView("ObjectIDString", "DataGridIDString"); -Use this action to open the Data View Dialog to a particular object using a particular set of customized -string grid options. The string grid options determine the fields to show as well as whether to have any -Tab, Row or Column breaks on the dialog in the same manner as is done for the ObjectFieldsInputDialog() -script command. - -"ObjectIDString" : The specific object for which fields are displayed. The format is the -object type followed by the key fields used to identify the object. -Examples: "Bus 234891", "Gen 16445 'A'", "Branch 1239 1234 'AB'". - -"DataGridIDString" : Optional. If not specified dialog will open with the first customized grid. -This is a reference to either a DataGrid or a UserDefinedDataGrid object. -DataGrid objects store the customizations used on various case -information displays in PowerWorld Simulator. A part of the -customization for a DataGrid includes information about the Data View -Layout (allows tab, row, and column breakers along with captions for -tabs and group boxes). The format for this string is object type string -DataGrid or UserDefinedDataGrid the key fields for that object (only a -name for the DataGrid, and name followed by object type for -UserDefinedDataGrid). Examples: - -"DataGrid 'BranchRun'” -"DataGrid 'BranchEdit'” -"UserDefinedDataGrid 'My named grid' Bus” -Note: you may also simply enter a string showing the name of either the DataGrid or - -UserDefinedDataGrid. If you do this, then Simulator will first look for a -DataGrid with that name. If a DataGrid is not found, then we will look for -a UserDefinedDataGrid that matches the name specified and assumes the -object type matches what is specified for the ObjectIDString. - - -This command opens the Data View Dialog for the branch connecting Bus 1 to Bus 2 with circuit -ID "1", using a customized grid layout named "BranchEdit". This layout controls which fields are -shown and how they are organized. -OpenDataView("Branch 1 2 '1'", "DataGrid 'BranchEdit'"); - - - - 66 - - - -Oneline Actions -CloseOneline("OnelineName"); - -Use this action to close an open oneline diagram without saving it. If the name is omitted, the last -focused oneline diagram will be closed. - -"OnelineName" : The name of the oneline diagram to close. - - -This call closes the online diagram named "B7FaultExample.pwd". -CloseOneline("B7FaultExample.pwd"); - -EditMultipleOnelineAction("Path", LinkType, SaveFileType); -Use this action to convert all files with a PWD extension in a specified directory to a new format. This is -useful for converting files from a newer version of Simulator to an older version. The files will be saved -with the same name but with an extension appropriate for the SaveFileType. - -"Path" : Specify a valid path where the files are located. -LinkType : Specify the key field identifier to use for linking objects in the oneline - -diagrams to a power flow case. Options are NUMBER, NAMENOMKV, -and LABEL. - -SaveFileType : Specify the new format for the oneline diagrams. Valid options are: PWB, -PWB16-PWB23, and AUX. - -EnumerateDDLOnelines("InputDSET", "OutputList"); -(Added in the February 29, 2024 patch of Simulator 23) -Use this action to analyze an Areva/Alstom/GE DSET DDL file and write out a listing of all diagrams -contained within the file. - -"InputDSET" : Specify a valid path to the DSET file in DDL text format that is to be -analyzed. - -"OutputList" : Specify a valid path to a text file that will contain a listing of all the -diagram names contained within the DSET DDL file separated by line. - -ExportBusView("filename", "bus key", ImageType, Width, Height, [ExportOptions]); -Use this action to export an image of a bus view oneline diagram to a file. - -"filename" : Name of the file in which the exported image will be saved. See the -Specifying File Names in Script Commands section for special keywords -that can be used when specifying the file name. - -"Bus key" : The specific bus. The format is the object type followed by the key fields -ImageType : The type of image to save. Valid options are: BMP, GIF, JPG, EMF, WMF, - -and PDF. -Width : Width of the saved image in pixels. -Height : Height of the saved image in pixels. -[ExportOptions] : Optional parameter - -This is a comma separated list of options based on the ImageType that is -being exported. - - -When exporting an image of type JPG, the following options case be -specified: -ImageQuality : Quality of the image specified from 1 to 100 with - -100 being the highest quality image. The larger the -image quality the larger the resulting file will be. -Default is 80. - -ResolutionScalar : The resolution can be changed from the default -resolution by adjusting by this scalar. To increase -the resolution set the scalar to something greater - - 67 - - - -than 1. Increasing the resolution will also increase -the file size. Default is 1. - - -When exporting an image of type GIF, the following options case be -specified: -NumFrames : GIF images can be animated by introducing - -multiple frames. This value specifies the number of -frames. Default is 1. - -FrameDelay : Number of seconds to wait between frames. -Default is 0.1. - -ResolutionScalar : The resolution can be changed from the default -resolution by adjusting by this scalar. To increase -the resolution set the scalar to something greater -than 1. Increasing the resolution will also increase -the file size. Default is 1. - - -This command exports the one-line diagram view centered on Bus 1 to a JPG image file named -"bus_1.jpg" saved in the H:\ directory. The image will be 800×600 pixels, with a high quality -(ImageQuality = 90) and a resolution scaling factor of 1.5 for enhanced clarity. -ExportBusView("H:\bus_1.jpg", "BUS 1", JPG, 800, 600, [90, 1.5]); - -ExportOneline("filename", "OnelineName", ImageType, "view", FullScreen, ShowFull, [ExportOptions]); -Use this action to export an image of the open oneline diagram to a file containing the specified image -type. - -"filename" : Name of the file in which the exported image will be saved. See the -Specifying File Names in Script Commands section for special keywords -that can be used when specifying the file name. - -"OnelineName" : The name of the oneline diagram to export. The oneline diagram must be -open. Use the OpenOneline script command if necessary to open the -appropriate oneline. - -ImageType : The type of image to save. Valid options are: BMP, GIF, JPG, EMF, WMF. -"view" : Optional parameter. The view name that should be opened. Pass an - -empty string to denote no specific view. -FullScreen : Optional parameter with default of NO. Set to YES or NO. YES means - -that the oneline diagram will be open in full screen mode. If this -parameter is not specified, then NO is assumed. - -ShowFull : Optional parameter with default of NO. Set to YES to open the oneline -and apply the Show Full option. Set to NO to open the oneline and leave -the oneline as is. - -[ExportOptions] : Optional parameter - This is a comma separated list of options based on the ImageType that is - -being exported. - - -When exporting an image of type JPG, the following options can be -specified: -ImageQuality : Quality of the image specified from 1 to 100 with - -100 being the highest quality image. The larger the -image quality the larger the resulting file will be. -Default is 80. - -ResolutionScalar : The resolution can be changed from the default -resolution by adjusting by this scalar. To increase -the resolution set the scalar to something greater - - 68 - - - -than 1. Increasing the resolution will also increase -the file size. Default is 1. - -When exporting an image of type GIF, the following options can be -specified: -NumFrames : GIF images can be animated by introducing - -multiple frames. This value specifies the number of -frames. Default is 1. - -FrameDelay : Number of seconds to wait between frames. -Default is 0.1. - -ResolutionScalar : The resolution can be changed from the default -resolution by adjusting by this scalar. To increase -the resolution set the scalar to something greater -than 1. Increasing the resolution will also increase -the file size. Default is 1. - - -This command exports the currently open oneline diagram named "B7Flat" to a high-resolution -JPG image called "main_view.jpg" in H:\. No specific view is selected, and full-screen mode is not -enabled (FullScreen = NO), but the "Show Full" option is applied (YES), so the entire diagram view -is captured. The image is saved with high quality (ImageQuality = 90) and a resolution scaling -factor of 1.5 for enhanced clarity. -ExportOneline("H:\main_view.jpg", "B7Flat", JPG, "", NO, YES, [90, -1.5]); - -ExportOnelineAsShapeFile("filename", "OnelineName", "ShapeFileExportDescriptionName", -UseLonLat, PointLocation); - -Use this action to save an open oneline diagram to a shapefile. -"filename" : The file name of the shapefile to save. -"OnelineName" : The name of the oneline diagram to save to a shapefile. The oneline - -diagram must be open. Use the OpenOneline script command if -necessary to open the appropriate oneline. - -"ShapeFileExportDescriptionName" - : Name of the ShapeFile Export Description to use when saving the - -shapefile. -UseLonLat : Set to YES or NO. YES means that the coordinates of objects on the - -oneline diagram will be saved using longitude,latitude. This will only be -true if a valid map projection is in use with the oneline diagram. -Otherwise, the coordinates will be saved in x,y. If this parameter is set to -NO, the coordinates will be saved in x,y. If this parameter is not specified, -YES is assumed. - -PointLocation : Determines where points are specified – object centers, or the upper left -corner. Specify “center” to define points as the shape centers, or “ul” to -define them as the upper left corner of the shapes. If not specified, upper -left is assumed. - - -This command exports the open oneline diagram named "B7Flat" to a shapefile named -"grid_map.shp", saved in the H:\ directory. The export uses the shapefile export description -"GenAndLineShapes", which defines which objects and fields are included. Since UseLonLat = -YES, the coordinates will be saved using longitude and latitude, assuming the oneline uses a valid -map projection. The point location is set to center, meaning point-based shapes will use the -center of each object (e.g., for generator or bus icons) when written to the shapefile. -ExportOnelineAsShapeFile("H:\grid_map.shp", "B7Flat", -"GenAndLineShapes", YES, center); - - 69 - - - -ImportDDLAsTranslation("filename"); -This loads an ESET DDL and converts some definitions into translations within Simulator. This currently -only works for keyset definitions that set links to open onelines. - -"filename" : The name of file to load. -LoadAXD("filename", "OnelineName", CreateIfNotFound) - -Use this action to apply a display auxiliary file to an open oneline diagram. -"filename" : The file name of the display auxiliary file to load. -"OnelineName" : The name of the oneline diagram to which to apply the display auxiliary - -file. If the oneline is not already open, the OpenOneline script command -can be used to open the appropriate oneline. If the specified oneline is -not open, a new one will be created with the given name. - -CreateIfNotFound : Optional – default is NO when using the Legacy Auxiliary File Header -format. Default is YES when using the Concise Auxiliary File Header -format. - - This parameter is only enforced when the Create_if_not_found field -for a Legacy Auxiliary File Header is set to PROMPT. Otherwise, YES is -assumed. YES means that objects that cannot be found will be created -while reading DATA sections from "filename". - -OpenOneline("filename", "view", FullScreen, ShowFull, LinkMethod, Left, Top, Width, Height); -Use this action to open a oneline diagram. When using SimAuto, this action cannot be used to actually -view a oneline. This script can be used in SimAuto to associate onelines with a PWB file. Any oneline that -is opened using the script command and while the case is saved will opened in the GUI once the case is -reopened. - -"filename" : The file name of the oneline diagram to open. Wildcards are allowed -when opening a DDL file type. This is useful for loading DDL files via -browsing patch searches. - -"view" : The view name that should be opened. Pass an empty string to denote -no specific view. - -FullScreen : Set to YES, NO, or MAX. YES means that the oneline diagram will be -open in full screen mode. If this parameter is not specified, then NO is -assumed. If MAX is specified, then FullScreen is NO, but the oneline will -be maximized when it is opened (Added in July 25, 2024 patch for -Simulator Version 23). - -ShowFull : Optional parameter. Set to YES to open the oneline and apply the Show -Full option. Set to NO to open the oneline and leave the oneline as is. -Default is NO if not specified. - -LinkMethod : Optional Parameter that controls oneline linking. LABELS, NAMENOMKV, -and NUMBER will link using the respective key fields. - -Left : Optional with default of 0. Value between 0 and 100 that indicates the -location of the left edge of the oneline as a percentage of the -Simulator/Retriever window width. - -Top : Optional with default of 0. Value between 0 and 100 that indicates the -top edge of the oneline as a percentage of the Simulator/Retriever -window height. - -Width : Optional with default of 0. Value between 0 and 100 that indicates the -width of the oneline as a percentage of the Simulator/Retriever window -width. - -Height : Optional with default of 0. Value between 0 and 100 that indicates the -height of the oneline as a percentage of the Simulator/Retriever window -height. - - - 70 - - - - -This command opens the oneline diagram file "B7Flat.pwd" from the H:\ directory. No specific -view is selected, and the oneline is opened in maximized mode (FullScreen = MAX) with the Show -Full option applied (YES), meaning the entire diagram will be zoomed to fit. Objects on the -oneline will be linked to power system elements using the Name and Nominal kV -(NAMENOMKV) method. The oneline window will be positioned to start at 10% from the left and -10% from the top of the screen, and occupy 80% of the screen width and height. -OpenOneline("H:\B7Flat.pwd", "", MAX, YES, NAMENOMKV, 10, 10, 80, 80); - -RelinkAllOpenOnelines; -Making modifications to the power flow case could cause objects on a oneline from becoming unlinked. -This action will attempt to relink all objects on all open onelines. - -SaveOneline("filename", "OnelineName", SaveFileType); -Use this action to save an open oneline diagram to file - -"filename" : The path and file name of the file to save. If a full path is not specified, -then the file is saved to the current directory. - -"OnelineName" : Name of the open oneline to save. -SaveFileType : Type of file to save. Valid options are AXD, PWB, and PWB16-PWB23. If - -omitted, PWB, which is the most recent version, will be assumed. Note -the use of "PWB" instead of "PWD" is not a typo. The version of the PWD -file corresponding to the PWB version will be used. - -OpenBusView("Bus key", ForceNewWindow); -Opens the Bus View to a particular bus specified in the first parameter. - -"Bus key" : The specific bus. The format is the object type followed by the key fields -ForceNewWindow : Optional with default of NO. Set to YES to force a new bus view to be - -opened regardless. If NO, then if a bus view is already open the -command will update that bus view instead of opening a new one. - -OpenSubView("Substation key", ForceNewWindow); -Opens the Substation View to a particular substation specified in the first parameter. - -"Substation key" : The specific substation. The format is the object type followed by the key -fields - -ForceNewWindow : Optional with default of NO. Set to YES to force a new substation view to -be opened regardless. If NO, then if a substation view is already open the -command will update that substation view instead of opening a new one. - - - - 71 - - - -Connections Tools -CreateNewAreasFromIslands; - -Use this action to create permanent areas that match the area Simulator creates temporarily while solving -the power flow. New areas are created if and area is on AGC, spans multiple vilable islands, and only one -of those islands has more than one area in it. - -DetermineBranchesThatCreateIslands(Filter, StoreBuses, "filename", SetSelectedOnLines, FileType); -Use this action to determine the branches whose outage results in island formation. Note that setting the -Selected field will overwrite the Selected fields. - -Filter : This parameter is used to specify which branches are checked. -ALL : means all branches will be checked -SELECTED : means only branches whose Selected field = YES will - -be checked -AREAZONE : means only branches that meet the area/zone/owner - -filters will be checked -"FilterName" : means only branches that meet the specified filter will - -be checked. See the Using Filters in Script Commands -section for more information on specifying the -filtername. - -StoreBuses : YES to store the buses in the island to the output file -"filename" : file to which the results will be written. The format of the file is based on - -the auxiliary file format. Each branch that was checked will be followed -by the list of buses that are islanded. The branch and bus information -will be written in appropriate auxiliary file DATA format. If this is left -blank, SetSelectedOnLines will be assumed to be YES. - -SetSelectedOnLines : YES to set the SELECTED field to YES for branches that create islands -FileType : Optional parameter used to specify the format of the file. This is AUX by - -default. -AUX : The saved file is based on an auxiliary file data format. Each - -branch that causes an island appears in the file in the auxiliary -file data format followed by a auxiliary file bus data section -containing all of the buses that are islanded by the preceeding -branch. - -CSV : The saved file is a comma-delimited text file. Each unique -bus/branch pair appears on a single line. A unique bus/branch -pair is determined by a bus that is islanded and a particular -branch that causes it to be islanded. A header appears in the -file specifying the fields used to identify the branch and bus in -each record. - - -Evaluates each branch to see if its removal causes part of the system to become electrically -isolated. If so, it sets the branch's Selected field to YES and logs each affected bus in a separate -row of a CSV file. -DetermineBranchesThatCreateIslands(ALL, YES, "H:\branches.csv", YES, -CSV); - -DeterminePathDistance([start], BranchDistMeas, BranchFilter, BusField); -Use this action to calculate a distance measure at each bus in the entire model. The distance measure will -represent how far each bus is from the starting group specified. The distance measure can be related to -impedance, geographical distance, or simply the number of nodes. - -[start] : The starting location. The starting location may be either a Bus, Area, -Zone, SuperArea, Substation, or Injection Group. Format of string is - - 72 - - - -[Bus Num], [Bus "Name_Nominal kV"], or [Bus "label"] -[Area Num], [Area "Name"], or [Area "label"] -[Zone Num], [Zone "Name"], or [Zone "label"] -[SuperArea "Name"] or [SuperArea "label"] -[Substation Num] or [Substation "label"] -[InjectionGroup "Name"] or [InjectionGroup "label"] - -BranchDistMeas : is either X, Z, Length, Nodes, or a field variable name for a branch. -X : means use the series reactance, -Z : means use sqrt(X^2 + R^2), -Length : means us the Length field, and -Nodes : means treat each branch as a length of one -FixedNumBus : means treat each branch between different - -FixedNumBuses has length 1 and each branch -between the same FixedNumBuses has length 0 - -SuperBus : means treat each branch between different -SuperBuses has length 1 and each branch between -the same SuperBuses has length 0 - -"Variablename" : Otherwise use any Branch object field variable name. -BranchFilter : is either All, Selected, Closed or the name of a branch Advanced Filter. - -This parameter is used to specify which branch can be traversed at all. -All : means all branches can be traversed -Selected : means only branches whose Selected field = YES can - -be traversed -Closed : means only branches that are CLOSED can be - -traversed. -"FilterName" : See the Using Filters in Script Commands section for - -more information on specifying the filtername. -BusField : is the variable name of a Bus field. This field is populated with the - -minimum distance from the Start Place to that bus. All buses in the start -group will have a distance measure of zero. Buses which cannot be -reached from the start group will have a distance measure of -1. - - -Calculates the shortest distance from Bus 1 to all other buses using the series reactance (X) of -closed branches only. Open branches are ignored. Results are stored in each bus's CustomFloat -field: Bus 1 is set to 0, reachable buses receive a positive value based on the shortest path's total -reactance, and unreachable buses are assigned -1. -DeterminePathDistance([Bus 1], X, CLOSED, CustomFloat); - -DetermineShortestPath([start], [end], BranchDistanceMeasure, BranchFilter, Filename); -Use this action to calculate the shortest path between a starting group and an ending group. The results -will be written to a textfile specified by filename. In the text file, the first bus listed will be in the end -grouping and the last bus listed will be the start grouping. The result text file will have a line for each bus -passed. Each line will contain three entries delimited by a space: "Number DistanceMeasure Name". - -[start] : same as the starting place for the DeterminePathDistance script -command - -[end] : same as the starting place for the DeterminePathDistance script -command - -BranchDistanceMeasure - : same as for DeterminePathDistance script command -BranchFilter : same as for DeterminePathDistance script command -Filename : is a filename (may need to be enclosed in quotes) to which the results - -will be written. - - - 73 - - - -This command computes the lowest-impedance path between Bus 1 and Bus 7, using impedance -magnitude 𝑍𝑍 = √𝑅𝑅2+𝑋𝑋2 as the distance metric. It evaluates all branches and saves the path -details—bus number, cumulative impedance from Bus 1, and bus name—to the file -"SP_1_to_7.txt". -DetermineShortestPath([Bus 1], [Bus 7], Z, ALL, "SP_1_to_7.txt"); - -DoFacilityAnalysis ("Filename", SetSelected); -Do Facility Analysis (Minimum Cut) is used to determine the branches that would isolate the Facility from -the External region as specified in the Select Bus Dialog in the Simulator Tool dialog. It is assumed that -the user will set the options before using the script command. The script will be used to identify the -minimum number of branches that need to be opened or removed from the system in order to isolate the -Facility (power system device) from an External region. - -"Filename" : The auxiliary file to which the results will be written. The results will show -the buses of the different paths in a data section consisting of the buses -that form the respective path. It will also show the branches of the -minimum cut. - -SetSelected : (Added in the October 19, 2023 patch for Simulator version 23) - Optional parameter – default is NO - Set to YES or NO. Set to YES to set the Selected field to YES for the - -branches contained in the minimum cut. - - -Identifies the minimal set of branches needed to isolate Facility buses from External buses and -saves the results to "cut_results.aux". The file includes bus paths for each isolating route and a list -of branches in the minimum cut. With the second parameter set to YES, all cut branches are -flagged with their Selected field set to YES, highlighting the lines to open for isolation. -DoFacilityAnalysis("H:\cut_results.aux", YES); - -FindRadialBusPaths(IgnoreStatus, TreatParallelAsNotRadial, BusOrSuperBus); -This online help topic will explain radial bus paths in more detail: -https://www.powerworld.com/WebHelp/#MainDocumentation_HTML/Find_Radial_Bus_Paths.htm - - -Use this action to calculate series paths of buses or superbuses that are radial. The following fields for -buses and branches will be populated with the results and indicate unique radial paths: Radial Path End -Number, Radial Path Index, and Radial Path Length. - -IgnoreStatus : Optional parameter – default is NO - Set to YES or NO. Set to YES to ignore the status when traversing - -branches. -TreatParallelAsNotRadial - : Optional parameter – default is NO - Set to YES or NO. Set to YES to treat parallel branches as not radial when - -traversing branches. -BusOrSuperBus : Optional parameter – default is BUS - Set to BUS or SUPERBUS. This determines groupings to traverse. When - -using SUPERBUS, any branch that has both terminal buses in the same -superbus will have blank results because it is not part of the path. - - -This command scans the network for any series of buses that end in a dead-end (radial path), and -it assigns RadialEndBusNum, RadialEndIndex, and RadialEndLength values to all involved buses -and branches. -FindRadialBusPaths(YES, NO, BUS); - - 74 - - - -SetBusFieldFromClosest(variablename, BusFilterSetTo, BusFilterFromThese, BranchFilterTraverse, -BranchDistMeas); - -(This command was added to the January 15, 2025 Patch of Version 23) -Set buses field values equal to the closest bus’s value. The parameters for this command are as follows. - -BusField : variable name of the Bus object to set (and also the one copied from the -closest bus) - -BusFilterSetTo : specifies which Bus objects the should have their variablename -overwritten. See the Using Filters in Script Commands section for more -information on specifying the filter. - -BusFilterFromThese : specifies which Bus objects that can have their variablename used to -overwrite another bus. See the Using Filters in Script Commands section -for more information on specifying the filter. - -BranchFilterTraverse : specifies which AC Branch objects can be traversed when searching for -the closest bus. Value is either All, Selected, Closed or the name of a -branch Advanced Filter. This parameter is used to specify which branch -can be traversed at all. - -All : means all branches can be traversed -Selected : means only branches whose Selected field = YES - -can be traversed -Closed : means only branches that are CLOSED can be - -traversed. -"FilterName" : See the Using Filters in Script Commands section - -for more information on specifying the filtername. -BranchDistMeas : is either X, Z, Length, Nodes, or a field variable name for a branch. - -X : means use the series reactance, -Z : means use sqrt(X^2 + R^2), -Length : means us the Length field, and -Nodes : means treat each branch as a length of one -FixedNumBus : means treat each branch between different - -FixedNumBuses has length 1 and each branch -between the same FixedNumBuses has length 0 - -SuperBus : means treat each branch between different -SuperBuses has length 1 and each branch between -the same SuperBuses has length 0 - -"Variablename" : Otherwise use any Branch object field variable -name. - - -This command assigns buses that do not belong to a substation equal to the substation closest -to the bus based on impedance magnitude (Z). -SetBusFieldFromClosest(SubNumber,"SubNumber IsBlank","SubNumber -NotIsBlank",All,Z); - - - - - - - 75 - - - -SetSelectedFromNetworkCut(SetHow, [BusOnCutSide], BranchFilter, InterfaceFilter, DCLineFilter, -Energized, NumTiers, InitializeSelected, [ObjectsToSelect], UseAreaZone, UsekV, MinkV, MaxkV, -LowerMinkV, LowerMaxkV); - -Use this action to set the Selected field of specified object types if they are on the specified side of a -network cut created by specified branches, interfaces, and/or dc lines. - -SetHow : Set to YES or NO. This is the value to which the Selected field will be set -if an object is within the network cut. - -[BusOnCutSide] : Specify the bus that is on the desired side of the network cut. Objects -that are on the same side as this bus will have their Selected field set. - - -At least one of the following filters MUST not be blank: -BranchFilter : Specify a filter to select the branches that define the network cut. See - -the Using Filters in Script Commands section for more information on -specifying the filter. A blank filter means that no branches are selected. - -InterfaceFilter : Specify a filter to select the interfaces that define the network cut. See -the Using Filters in Script Commands section for more information on -specifying the filter. A blank filter means that no interfaces are selected. - -DCLineFilter : Specify a filter to select the dc lines that define the network cut. See the -Using Filters in Script Commands section for more information on -specifying the filter. A blank filter means that no dc lines are selected. - -Energized : Set to YES or NO. Set to YES to only include branches with a closed -status when traversing branches to determine which side of the network -cut each bus is on. This option does not apply to the branches that are -specified to define the network cut. Those branches will not be traversed -regardless of their status. - -NumTiers : Once the network cut has been defined by a set of branches and the bus -defining which side of the cut is being examined has been chosen, this -value indicates that buses will be included within this number of tiers of -the network cut boundary, on the opposite side of the cut as the -specified bus. If the number of tiers is set to zero, the buses examined -will only be those on the same side of the cut as the specified bus. - -InitializeSelected : Set to YES or NO. Set to YES to set the Selected field for all objects to -the opposite of the value specified in the SetHow parameter. - -[ObjectsToSelect] : Comma separated list of object types enclosed in square brackets. These -objects will have their Selected fields set if they are within the network -cut. Valid options are: BRANCH, BUS, DCTRANSMISSIONLINE, GEN, -LOAD, and SHUNT. - -UseAreaZone : Optional parameter – default is NO. - Set to YES or NO. Set to YES to only set the Selected field for objects - -that are within the network cut that meet the area/zone/owner filter. -UsekV : Optional parameter – default is NO. - Set to YES or NO. Set to YES to only set the Selected field for objects - -that are within the network cut and within the nominal kV range -specified. - -MinkV : Optional parameter – default is 0. - An object’s nominal kV must be greater than or equal to this value to - -have its Selected field set if it is also in the network cut. Branches can -have different nominal voltages at each terminal; the largest nominal -voltage must be greater than or equal to this value. - -MaxkV : Optional parameter – default is 9999. - An object’s nominal kV must be less than or equal to this value to have - -its Selected field set if it is also in the network cut. Branches can have - - 76 - - - -different nominal voltages at each terminal; the largest nominal voltage -must be less than or equal to this value. - -LowerMinkV : Optional parameter – default is 0. - This value is only used with branches. Branches can have different - -nominal voltages at each terminal; the smallest nominal voltage must be -greater than or equal to this value. - -LowerMaxkV : Optional parameter – default is 9999. - This value is only used with branches. Branches can have different - -nominal voltages at each terminal; the smallest nominal voltage must be -less than or equal to this value. - - -This script sets the Selected field to YES for all BRANCH, GEN, and LOAD objects that are -electrically on the same side of the network cut as Bus 2. The network cut is defined by the -branches with Selected = YES (BranchFilter = SELECTED). Only energized branches (Energized = -YES) are considered when determining connectivity. The InitializeSelected = YES parameter resets -the Selected field for all objects to NO before applying the new selection. Voltage filtering is not -applied (UsekV = NO), and no additional connectivity tiers are considered (NumTiers = 0). -SetSelectedFromNetworkCut(YES, [BUS 2], SELECTED, , , YES, 0, YES, -[BRANCH, GEN, LOAD], NO, NO, 0, 9999, 0, 9999); - - - - 77 - - - -Sensitivity Calculations -CalculateFlowSense([flow element], FlowType); - -This calculates the sensitivity of the MW, MVAR, or MVA flow of a line or interface to a real and reactive -power injections at all buses in the system. (Note: this assumes that the power is injected at a given bus -and taken out at the slack bus). - -[flow element] : This is the flow element we are interested in. Choices are: -[INTERFACE "name"] -[INTERFACE "label"] -[BRANCH busnum1bus num2 ckt] -[BRANCH "name_kv1" "name_kv2" ckt] -[BRANCH "buslabel1" "buslabel2" ckt] -[BRANCH "label"] - -FlowType : The type of flow to calculate this for. Either MW, MVAR, or MVA. - - -This command calculates the sensitivity of the MW flow of the Interface Left-Right to real and -reactive power injections at all buses in the system. -CalculateFlowSense([INTERFACE Left-Right], MW); - -CalculateLODF([BRANCH nearbusnum farbusnum ckt], LinearMethod, PostClosureLCDF); -Use this action to calculate the Line Outage Distribution Factors (or the Line Closure Distribution Factors) -for a particular branch. If the branch is presently closed, then the LODF values will be calculated, -otherwise the LCDF values will be calculated. You may optionally specify the linear calculation method as -well. If no Linear Method is specified, Lossless DC will be used. - -[BRANCH nearbusnum farbusnum ckt] - : the branch whose status is being changed. Can also use strings - -[BRANCH "nearbusname_kv" "farbusname_kv" ckt] -[BRANCH "nearbuslabel" "farbuslabel" ckt] -[BRANCH "label"] - -LinearMethod : The linear method to be used for the LODF calculation. The options are: -DC : for lossless DC. -DCPS : for lossless DC that takes into account phase shifter operation. - - Note: AC is NOT an option for the LODF calculation. -PostClosureLCDF : Optional parameter – default is YES - Set to YES to calculate any line closure sensitivities relative to post- - -closure flow on the line being closed. This is known as the LCDF value. - Set to NO to calculate any line closure sensitivities based on calculating - -the flow on the line being closed from pre-closure voltages and angles. -This is known as the MLCDF value. - - -This calculates the Line Outage Distribution Factors (LODFs) for the branch that connects bus 1 to -bus 2 with circuit ID 1 using the lossless DC method. -CalculateLODF([BRANCH 1 2 1], DC, ); - -CalculateLODFAdvanced(IncludePhaseShifters, FileType, MaxColumns, MinLODF, NumberFormat, -DecimalPoints, OnlyIncludingLinesIncreasing, "FileName", IncludeIslandingCTG); - -Use this action to to mimic what is done on the Advanced LODF Calculation dialog in the GUI. -IncludePhaseShifters : Set to YES to calculate the LODF/LCDF values assuming that phase - -shifters are allowed to operate and will see no impact due to an outage -or closure. Set to NO to not enforce the flow on phase shifters. - -FileType : Either PROMOD or MATRIX. For PROMOD, save only “Monitored Branch, -Contingency” pairs for PROMOD; and for MATRIX save Matrix as comma- -delimited text file. - - 78 - - - -MaxColumns : Maximum number of columns per text file. -MinLODF : Only Save pairs with an LODF whose absolute value is greater than this - -minimum. -NumberFormat : LODF Number format, either, EXPONENTIAL or DECIMAL -DecimalPoints : Fixed Decimals Points. -OnlyIncludingLinesIncreasing: Only include monitored branches whose MW flow increases. -"FileName" : The name of the text file to write. -IncludeIslandingCTG : Optional parameter – default is YES - LODF values cannot be calculated for contingencies that will cause a new - -island to be created. When including these contingencies in the results, -the LODF will be reported as a very large number to indicate that these -values were not actually calculated. Set to NO to completely omit these -contingencies from the results. - - -This command performs an advanced LODF calculation allowing phase shifters and includes only -monitored branches with increasing MW flow. Results are saved in a comma-delimited matrix -format with up to 10 columns per file, including only LODF values above 0.03. The values are in -decimal format with four decimal places. Contingencies causing islanding are excluded from the -output. -CalculateLODFAdvanced(YES, MATRIX, 10, 0.03, DECIMAL, 4, YES, -"AdvancedLODFResults.txt", NO); - -CalculateLODFMatrix(WhichOnes, filterProcess, filterMonitor, MonitorOnlyClosed, LinearMethod, -filterMonitorInterface, PostClosureLCDF); - -Use this action to calculate the Line Outage Distribution Factors (or the Line Closure Distribution Factors) -for a particular branch. If the branch is presently closed, then the LODF values will be calculated, -otherwise the LCDF values will be calculated. You may optionally specify the linear calculation method as -well. If no Linear Method is specified, Lossless DC will be used. - -WhichOnes : Specify the type of sensitivities to be calculated. -OUTAGES : Outage sensitivities will be calculated for those branches - -meeting the filterProcess. -CLOSURES : Closure sensitivities will be calculated for those branches - -meeting the filterProcess. -filterProcess : Specify a filter for the branches for which the outages or closures will be - -implemented. -ALL : All AC transmission lines. -SELECTED : Only those branches whose Selected field is YES. -AREAZONE : Only those branches meeting the area/zone filter. -"filtername" : See the Using Filters in Script Commands section for - -more information on specifying the filtername. -filterMonitor : Specify a filter for the branches for which the impact of the outages or - -closures will be determined. -ALL : All AC transmission lines. -SELECTED : Only those branches whose Selected field is YES. -AREAZONE : Only those branches meeting the area/zone filter. -"filtername" : See the Using Filters in Script Commands section for - -more information on specifying the filtername. -SAME : Same as set of branches to process as specified by - -filterProcess. -MonitorOnlyClosed : Set to YES to monitor only those branches that are closed. Set to NO to - -monitor branches regardless of their status. -LinearMethod : Optional parameter – default is DC - The linear method to be used for the LODF calculation. - - 79 - - - -DC : for lossless DC. -DCPS : for lossless DC that takes into account phase shifter operation. - - Note: AC is NOT an option for the LODF calculation. -filterMonitorInterface : Optional parameter – default is to not monitor interfaces - Specify a filter for the interfaces for which the impact of the outages or - -closures will be determined. Using this option will add the individual -lines in the interface to the list of lines to monitor. - -ALL : All interfaces. -SELECTED : Only those interfaces whose Selected field is YES. -AREAZONE : Only those interfaces meeting the area/zone filter. -"filtername" : See the Using Filters in Script Commands section for - -more information on specifying the filtername. -PostClosureLCDF : Optional parameter – default is YES - Set to YES to calculate any line closure sensitivies relative to post-closure - -flow on the line being closed. This is known as the LCDF value. - Set to NO to calculate any line closure sensitivities based on calculating - -the flow on the line being closed from pre-closure voltages and angles. -This is known as the MLCDF value. - - -This command calculates Line Outage Distribution Factors (LODFs) using the lossless DC method -for all AC transmission branches in the case and monitors how all other closed branches are -affected when each one is taken out of service. -CalculateLODFMatrix(OUTAGES, ALL, ALL, YES, DC, , ); - -CalculateLODFScreening(filterProcess, filterMonitor, IncludePhaseShifters, IncludeOpenLines, -UseLODFThreshold, LODFThreshold, UseOverloadThreshold, OverloadLow, OverloadHigh, DoSaveFile, -FileLocation, CustomFieldHighLODF, CustomFieldHighLODFLine, CustomFieldHighOverload, -CustomFieldHighOverloadLine, DoUseCTGName, CustomFieldOrigCTGName); - -Use this action to do the LODF Screening calculation. This calculation uses LODF/LCDF factors to -determine how significant a branch open/close action will be on monitored lines. The significance of the -action can be determined by LODF/LCDF magnitude or line loading on monitored lines. Significant single -contingency actions can then be combined to form pairs of contingency actions that will be used to -create new contingencies that can be saved to an auxiliary file. - -filterProcess : Specify a filter for the branches for which the outage or closure impact -will be determined. - -ALL : All AC transmission lines. -AREAZONE : Only those branches meeting the area/zone filter. -CTG : Only those branches included in any currently - -defined contingency. -LIMITMONITOR : Only those branches meeting the Limit Monitoring - -Settings. -SELECTED : Only those branches whose Selected field is YES. -"filtername" : See the Using Filters in Script Commands section for - -more information on specifying the filtername. -filterMonitor : Specify a filter for the branches on which the impact of the outages or - -closures will be determined. -ALL : All AC transmission lines. -AREAZONE : Only those branches meeting the area/zone filter. -LIMITMONITOR : Only those branches meeting the Limit Monitoring - -Settings. -SAME : Same as branches to process specified by - -filterProcess. -SELECTED : Only those branches whose Selected field is YES. - - 80 - - - -"filtername" : See the Using Filters in Script Commands section for -more information on specifying the filtername. - -IncludePhaseShifters : Set to YES to calculate the LODF/LCDF values assuming that phase -shifters are allowed to operate and will see no impact due to an outage -or closure. Set to NO to not enforce the flow on phase shifters. - -IncludeOpenLines : Set to NO to monitor only those branches that are closed. Set to YES to -monitor branches regardless of their status. - -UseLODFThreshold : Set to YES to screen outages/closures by LODF/LCDF magnitude. Set to -NO to not screen by LODF/LCDF magnitudes. - -LODFThreshold : Threshold above which LODF/LCDF magnitudes are considered -significant. - -UseOverloadThreshold - : Set to YES to screen outages/closures by monitored branch loading. Set - -to NO to not screen by branch loading. -OverloadLow : Threshold above which a monitored branch loading is considered - -significant. This value should be entered as a percent. -OverloadHigh : Threshold below which a monitored branch loading is considered - -significant. This value should be entered as a percent. -DoSaveFile : Set to YES to save an auxiliary file of new contingencies created by - -joining pairs of significant single outage/closure actions. Set to NO to -not save the file. - -FileLocation : Specify a directory path where the auxiliary file containing new -contingencies will be saved. The filename will be determined by -Simulator. - -CustomFieldHighLODF - : Optional parameter – default is 0 - Integer indicating which Custom Floating Point field for a processed - -branch will store the highest magnitude LODF/LCDF determined for any -monitored branch. - -CustomFieldHighLODFLine - : Optional parameter – default is 0 - Integer indicating which Custom String field for a processed branch will - -store the identifier for the monitored branch that has the highest -magnitude LODF/LCDF. - -CustomFieldHighOverload - : Optional parameter – default is 0 - Integer indicating which Custom Floating Point field for a processed - -branch will store the highest overload determined for any monitored -branch. - -CustomFieldHighOverloadLine - : Optional parameter – default is 0 - Integer indicating which Custom String field for a processed branch will - -store the identifier for the monitored branch that has the highest -overload. - -DoUseCTGName : Optional parameter – default is NO - Set to YES to use the available active contingency names available in the - -CTG Tool. If a contingency in the CTG Tool has the same branch as the -only contingency element it will use the contingency name in the tool to -create the new CTG Label for the new contingency combination of -branches. Set to NO to only use the branch info to create the new CTG -Label for the combination of branches. - -CustomFieldOrigCTGName - : Optional parameter – default is 0 - - 81 - - - - Integer indicating which Custom String field for a processed branch will -store the name of the contingency from which the branch originated. - - -This command performs LODF-based screening to identify significant single-line outages among -selected branches and evaluates their impact on all monitored branches. It considers both LODF -magnitude (threshold 0.05) and monitored line overloads (between 95% and 120%) as criteria for -significance. Phase shifters and open lines are excluded from analysis. When impactful -contingencies are found, the command combines them into new two-element contingencies, -saves them to an AUX file, and logs detailed results—such as max LODF, max overload, and -related identifiers—into designated custom fields. Existing contingency names are reused when -applicable for consistency. -CalculateLODFScreening(SELECTED, ALL, NO, NO, YES, 0.05, YES, 95, 120, -YES, "H:\", 1, 1, 2, 2, YES, 3); - -CalculateLossSense(FunctionType,AreaSALossReference,IslandLossReference); -This calculates the loss sensitivity at each bus for an injection of power at the bus. The parameter -FunctionType determines which losses are referenced. - -FunctionType : This is the losses for which sensitivities are calculated. -NONE : all loss sensitivities will be set to zero -ISLAND : all loss sensitivities are referenced to the total loss in the - -island -AREA : For each bus it calculates how the losses in the bus’ area - -will change (Note: this means that sensitivities at buses in -two different areas cannot be directly compared because -they are referenced to different losses) - -AREASA : same as Each Area, but if a Super Area exists it will use this -instead (Note: this means that sensitivities at buses in two -different areas cannot be directly compared because they -are referenced to different losses) - -SELECTED : Calculates how the losses in the areas selected on the Loss -Sensitivity Form will change - -AreaSALossReference : Optional parameter - default is NO - This parameter will only be used if the FunctionType is AREA or AREASA. - -This option specifies whether or not the Cost of Energy, Losses, and -Congestion Reference for each area or super area that is used for OPF -calculations will be used in this calculation. - -IslandLossReference : Optional parameter - default is EXISTING - This parameter specifies the loss reference that will be used when the - -FunctionType is ISLAND. -EXISTING : Use existing loss sensitivities -LOADS : MW value of all loads within an island will be used - -for weighting in the loss reference calculation -"InjGroupName" : Name of injection group where the participation - -factor of each participant will be used for -weighting in the loss reference calculation - - -This command calculates loss sensitivity values at each bus in the case, showing how much losses -in the bus’ island will change due to an injection of power at that bus. -CalculateLossSense(ISLAND, , EXISTING); - - - - 82 - - - -CalculatePTDF([transactor seller], [transactor buyer], LinearMethod); -Use this action to calculate the PTDF values between a seller and a buyer. You may optionally specify the -linear calculation method. Note that the buyer and seller must not be same thing. If no Linear Method is -specified, Lossless DC will be used. - -[transactor seller] : The seller (or source) of power. There are six possible settings: -[AREA num], [AREA "name"], [AREA "label"] -[ZONE num], [ZONE "name"], [ZONE "label"] -[SUPERAREA "name"], [SUPERAREA "label"] -[INJECTIONGROUP "name"], [INJECTIONGROUP "label"] -[BUS num], [BUS "name_nomkv"], [BUS "label"] -[SLACK] - -[transactor buyer] : The buyer (or sink) of power. There are six possible settings which are -the same as for the seller. - -LinearMethod : The linear method to be used for the PTDF calculation. The options are: -AC : for calculation including losses -DC : for lossless DC -DCPS : for lossless DC that takes into account phase shifter operation - - -This command calculates the PTDF values between Area Top (seller), and Bus 7 (buyer) using the -lossless DC with phase shifters method. -CalculatePTDF([Area Top], [Bus 7], DCPS); - -CalculatePTDFMultipleDirections(StoreForBranches, StoreForInterfaces, LinearMethod); -Use this action to calculate the PTDF values between all the directions specified in the case. You may -optionally specify the linear calculation method. If no Linear Method is specified, Lossless DC will be used. - -StoreForBranches : Specify YES to store the values calculated for each branch. -StoreForInterfaces : Specify YES to store the values calculated for each interface. -LinearMethod : the linear method to be used for the PTDF calculation. The options are: - -AC : for calculation including losses. -DC : for lossless DC. -DCPS : for lossless DC that takes into account phase shifter operation. - - -This command calculates the PTDF values for all directions where Include = YES. PTDFs are -calculated for all branches and interfaces using the DC lossless method. -CalculatePTDFMultipleDirections(YES, YES, DC); - -CalculateShiftFactors([flow element], direction, [transactor], LinearMethod, SetOutOfServiceBuses, -filter, AbortOnError, BranchDistMeas); - -In Version 21 and earlier this script command was called CalculateTLR. Simulator 21 patches after January -20, 2021 will handle reading either the CalculateShiftFactors or CalculateTLR. -Use this action to calculate the Shift Factor Sensitivity values for all buses on a particular flow element -(transmission line or interface). There are some additional options that are set with the TLR_Options object -rather than through parameters with this command. - -[flow element] : This is the flow element we are interested in. Choices are: -[INTERFACE "name"] -[INTERFACE "label"] -[BRANCH nearbusnum farbusnum ckt] -[BRANCH "nearbusname_kv" "farbusname_kv" ckt] -[BRANCH "nearbuslabel" "farbuslabel" ckt] -[BRANCH "label"] - -direction : The type of the transactor. Either BUYER or SELLER. Shift factors are -calculated between each bus in the case and this transactor. - - 83 - - - -[transactor] : The transactor of power. Shift factors are calculated between each bus in -the case and this transactor. These are the possible settings: - -[AREA num], [AREA "name"], [AREA "label"] -[ZONE num], [ZONE "name"], [ZONE "label"] -[SUPERAREA "name"], [SUPERAREA "label"] -[INJECTIONGROUP "name"], [INJECTIONGROUP "label"] -[BUS num], [BUS "name_nomkv"], [BUS "label"] -[SLACK] - -LinearMethod : Optional parameter – default is DC - The linear method to be used for the calculation. The options are: - -AC : for calculation including losses -DC : for lossless DC -DCPS : for lossless DC that takes into account phase shifter operation - -SetOutOfServiceBuses : Optional parameter – default is NO - Set to YES or NO. If YES then set the sensitivities for out-of-service buses - -equal to the sensitivity to the value at the closest in-service bus. The -"distance" to the in-service buses will be measured by the number of -nodes. If an out-of-service bus is equally close to a set of buses, then the -average of that set of buses will be used. - -filter : Optional parameter – default is to include all buses - The filter that will determine the buses for which sensitivities will be set - -when SetOutOfServiceBuses = YES. In addition to meeting the filter, only -out-of-service buses will be included. - -blank : All buses -AREAZONE : Only those buses meeting the area/zone filter -SELECTED : Only those buses whose Selected field is YES -"filtername" : See the Using Filters in Script Commands section for - -more information on specifying the filtername. -AbortOnError : Optional parameter – default is YES - Set to YES or NO. If YES, the script command will fail and the auxiliary file - -containing the script command will terminate without processing the -remainder of the file. If NO, an error message is printed to the message -log but the command is not treated as failing and the remainder of the -auxiliar file containing the command will be processed. - -BranchDistMeas : (Added in September 27, 2023 patch of Simulator Version 23) -Optional parameter – default is blank. If omitted or set as blank, then the -old behavior of taking the average of the closest nodes measured by the -number of Nodes is used. Otherwise set to either X, Z, Length, Nodes, or -a field variable name for a branch. - -X : means use the series reactance, -Z : means use sqrt(X^2 + R^2), -Length : means us the Length field, and -Nodes : means treat each branch as a length of one -FixedNumBus : means treat each branch between different - -FixedNumBuses has length 1 and each branch -between the same FixedNumBuses has length 0 - -SuperBus : means treat each branch between different -SuperBuses has length 1 and each branch between -the same SuperBuses has length 0 - -"Variablename" : Otherwise use any Branch object field variable -name. - - - - 84 - - - -This command calculates Shift Factors for how a transfer from AREA "Top" (SELLER) to each bus -affects the flow on the branch from Bus 1 to Bus 2 circuit "1", using the DC method. -CalculateShiftFactors([BRANCH 1 2 "1"], SELLER, [AREA "Top"], DC, NO, -,YES,); - -CalculateShiftFactorsMultipleElement(TypeElement,WhichElement,direction,[transactor],LinearMethod); -In Version 21 and earlier this script command was called CalculateTLRMultipleElement. Simulator 21 -patches after January 20, 2021 will handle reading either the CalculateShiftFactorsMultipleElement or -CalculateTLRMultipleElement. -Use this action to calculate Shift Factor Sensitivity values for multiple elements. There are some additional -options that are set with the TLR_Options object rather than through parameters with this command. - -TypeElement : May be either INTERFACE, BRANCH, or BOTH -WhichElement : There are three choices that represent which elements of the - -TypeElement specified will have shift factor calculations performed. -SELECTED : Only branches or interfaces with their Selected Field - -= YES will be used. -OVERLOAD : Only branches that are presently overloaded using - -their normal ratings will be used -CTGOVERLOAD : You must have first run the contingency analysis. A - -branch or interface is included in the calculation if it -has been overloaded during at least one -contingency. - -Direction : The type of the transactor. Either BUYER or SELLER. Shift factors are -calculated between each bus in the case and this transactor. - -[transactor] : The transactor of power. Shift factors are calculated between each bus in -the case and this transactor. These are the possible settings: - -[AREA num], [AREA "name"], [AREA "label"] -[ZONE num], [ZONE "name"], [ZONE "label"] -[SUPERAREA "name"], [SUPERAREA "label"] -[INJECTIONGROUP "name"], [INJECTIONGROUP "label"] -[BUS num], [BUS "name_nomkv"], [BUS "label"] -[SLACK] - -LinearMethod : Options parameter – default is DC - The linear method to be used for the calculation. The options are: - -AC : for calculation including losses. -DC : for lossless DC. -DCPS : for lossless DC that takes into account phase shifter operation. - - -This command calculates shift factor sensitivities for all branches where Selected = YES, showing -how a power injection from AREA "Top" (SELLER) to each bus affects flow on each of those -branches, using the lossless DC method. -CalculateShiftFactorsMultipleElement(BRANCH, SELECTED, SELLER, [AREA -"Top"], DC); - -CalculateTapSense(filter); -(Added to June 4, 2025 patch of Simulator 24) -Voltage to tap sensitivities are calculated as needed in the power flow, but the field is not always kept up -to date. For example, if you want the voltage to tap sensitivity values without solving the power flow, they -would not previously have been calculated. This command forces the voltage to tap sensitivity calculation -so that the dV/dtap values are current for the system state. - -Filter : Optional parameter- if omitted the sensitivities for all transformers will be -calculated. Branch filter returning the transformers for which sensitivity -values are updated. - - 85 - - - -ALL : Calculate the sensitivities for all transformers -"FilterName" : Only sensitivities for transformers that meet the - -specified filter will be calculated. See Using Filters -in Script Commands section for more information -on specifying the filtername. - -CalculateVoltSelfSense(filter); -This calculates the sensitivity of a particular bus’ voltage to real and reactive power injections at the same -bus. (Note: This assumes that the power is injected at a given bus and taken out at the slack bus.) - -filter : Optional parameter – default is to calculate sensitivities for all buses in -the system - -“FilterName" : Only buses that meet the specified filter will be included. See Using -Filters in Script Commands section for more information on specifying -the filtername. - -CalculateVoltSense([BUS num]); -This calculates the sensitivity of a particular buses voltage to real and reactive power injections at all buses -in the system. (Note: this assumes that the power is injected at a given bus and taken out at the slack -bus). - -[BUS num] : the bus for which sensitivities are calculated. - - -CalculateVoltToTransferSense([transactor seller], [transactor buyer], TransferType, TurnOffAVR); -This calculates the sensitivity of bus voltage to a real or reactive power transfer between a seller and a -buyer. The sensitivity is calculated for all buses in the system. - -[transactor seller] : This is the seller (or source) of power. There are six possible settings: -[AREA num], [AREA "name"], [AREA "label"] -[ZONE num], [ZONE "name"], [ZONE "label"] -[SUPERAREA "name"], [SUPERAREA "label"] -[INJECTIONGROUP "name"], [INJECTIONGROUP "label"] -[BUS num], [BUS "name_nomkv"], [BUS "label"] -[SLACK] - -[transactor buyer] : This is the buyer (or sink) of power. There are six possible settings, which -are the same as for the seller. - -TransferType : The type of power transfer. The options are: -P : real power transfer -Q : reactive power transfer -PQ : both real and reactive power transfer. (Note: Real and reactive - -power transfers are calculated independently, but both are -calculated.) - -TurnOffAVR : Set to YES or NO. Set to YES to turn off AVR control for generators -participating in the transfer. Set to NO to leave the AVR control -unchanged for generators participating in the transfer. - - -This command calculates how bus voltages across the system will change if you transfer real -power (MW) from AREA "Top" (the seller) to AREA "Left" (the buyer), with generator AVR control -turned off during the calculation. -CalculateVoltToTransferSense([AREA "Top"], [AREA "Left"], P, YES); - - - - 86 - - - -LineLoadingReplicatorCalculate([Flow Element], [Injection Group], AGCOnly, DesiredFlow, Implement, -LinearMethod, UseLoadMinMax, MaxMultiplier, MinMultiplier); - -This command calculates an injection change list containing the injection changes needed to alter a line -or interface flow to a desired value. The user supplies the element to manipulate the flow on, the injection -group, and the desired line or interface flow. This command calculates the injection changes that will -result in that line or interface flow. This command can optionally implement the changes, or the changes -can be applied separately with the LineLoadingReplicatorImplement script command. - -[Flow Element] : This is the flow element we are interested in. Choices are: - [INTERFACE "name"] - [INTERFACE "label"] - [BRANCH nearbusnum farbusnum ckt] - [BRANCH "nearbusname_kv" "farbusname_kv" ckt] - [BRANCH "nearbuslabel" "farbuslabel" ckt] - [BRANCH "label"] -[Injection Group] : Injection group containing elements that are available to move to - -implement the desired line or interface flow. Multiple injection groups -can be specified by using a comma-delimited list of injection group -names: ["IGName1", "IGName2", "IGName3"]. - -AGCOnly : YES indicates that only elements on AGC control in the injection group -will move to implement the desired line flow. A value of NO indicates -that all elements in the injection group can move as needed. - -DesiredFlow : The new desired flow on the flow element. -Implement : YES indicates that the injection change should be implemented after it is - -calculated. NO indicates that the injection change will not be -implemented. The list is stored in memory and may be implemented later -with the LineLoadingReplicatorImplement command. This option lets you -implement the change with one command if you have no need to check -anything before implementing it. - -LinearMethod : DC : for lossless DC. - DCPS : for lossless DC that takes into account phase shifter operation. -UseLoadMinMax : Optional parameter - default is YES - This option indicates that the maximum and minimum values specified - -with the load records should be used to limit the load movement while -calculating the injection changes. A value of NO indicates that the values -specified in the MaxMultiplier and MinMultiplier should be applied to set -the load change limits. When set to NO the Max and Min multiplers are -required. - -MaxMultiplier : Optional parameter - default is 1.0 - When UseLoadMinMax is set to NO this parameter is used as a scaling - -factor on the present load value to set the maximum limit that bounds -the load’s change. - -MinMultiplier : Optional parameter - default is 1.0 - When UseLoadMinMax is set to NO this parameter is used as a scaling - -factor on the present load value to set the minimum limit that bounds -the load’s change. - - -This command calculates and applies changes in generator/load injections (defined in the group -"GenShiftGroup") to adjust the MW flow on the branch from Bus 1 to Bus 2 circuit "1" to 100 MW, -using the lossless DC linear method, while respecting load min/max limits. -LineLoadingReplicatorCalculate([BRANCH 1 2 "1"], ["GenShiftGroup"], NO, -100, YES, DC, YES); - - - - 87 - - - -LineLoadingReplicatorImplement; -This command takes no parameters. It applies the changes in the injection change list calculated by the -LineLoadingReplicatorCalculate command. - -SetSensitivitiesAtOutOfServiceToClosest(filter, BranchDistMeas); -This will take the P Sensitivity and Q Sensitivity values calculated using the CalculateTLR, -CalculateFlowSense, or CalculateVoltSense actions and then populate the respective values at out-of- -service buses so that they are equal to the value at the closest in service bus. The "distance" to the in- -service buses will be measured by the number of nodes. If an out-of-service bus is equally close to a set -of buses, then the average of that set of buses will be used. - -filter : Optional parameter – default is to include all buses - The filter that will determine the buses for which sensitivities will be set. - -In addition to meeting the filter, only out-of-service buses will be -included. - -Blank : All buses -AREAZONE : Only those buses meeting the area/zone filter -SELECTED : Only those buses whose Selected field is YES -"filtername" : See the Using Filters in Script Commands section for - -more information on specifying the filtername. -BranchDistMeas : (Added in September 27, 2023 patch of Simulator Version 23) - -Optional parameter – default is blank. If omitted or set as blank, then the -old behavior of taking the average of the closest nodes measured by the -number of Nodes is used. Otherwise set to either X, Z, Length, Nodes, or -a field variable name for a branch. - -X : means use the series reactance, -Z : means use sqrt(X^2 + R^2), -Length : means us the Length field, and -Nodes : means treat each branch as a length of one -FixedNumBus : means treat each branch between different - -FixedNumBuses has length 1 and each branch -between the same FixedNumBuses has length 0 - -SuperBus : means treat each branch between different -SuperBuses has length 1 and each branch between -the same SuperBuses has length 0 - -"Variablename" : Otherwise use any Branch object field variable -name. - - -This command assigns P and Q sensitivity values to all out-of-service buses by copying the values -from the closest in-service bus, where "closeness" is based on impedance magnitude (Z) between -buses. -SetSensitivitiesAtOutOfServiceToClosest(, Z); - - - - - 88 - - - -Contingency Analysis -CTGApply("ContingencyName"); - -Call this action to apply the actions in a contingency without solving the power flow. -"ContingencyName" : This is the name of the contingency to apply. - - -This command applies the actions in the contingency named "L_000001One-000002TwoC1". -CTGApply("L_000001One-000002TwoC1"); - -CTGAutoInsert; -This action will auto insert contingencies for you case. Prior to calling this action, all options for this -action must be specified in the Ctg_AutoInsert_Options object using the SetData script command or DATA -sections. - -CTGCalculateOTDF([transactor seller], [transactor buyer], LinearMethod); -This action first performs the same action as done by the CalculatePTDF([transactor seller], [transactor -buyer], LinearMethod) call. It then goes through all the violations found by the contingency analysis tool -and determines the OTDF values for the various contingency/violation pairs. - - -This command computes OTDFs using the lossless DC with phase shifters linear method. PTDFs -are calculated for a transfer from "Area Top" (the source) to "Bus 7" (the sink), and then OTDFs -are calculated from these PTDFs for the violations under each contingency. -CTGCalculateOTDF([Area Right], [Bus 7], DCPS); - -CTGClearAllResults; -This action will delete all contingency violations and any contingency comparison results. - -CTGCloneMany(filter, "Prefix", "Suffix", SetSelected); -This command creates copies of any contingencies returned by the filter. If neither the prefix nor suffix is -supplied, the command will default to appending “- Copy” to the name of the contingency being cloned -to define the new contingency name. Integer indices contained in parentheses, " (0)", " (1)", etc., will be -added as needed until a unique contingency name is found. - -filter : Optional parameter – by default all contingencies will be cloned - All contingencies meeting the filter will be cloned. See the Using Filters - -in Script Commands section for more information on specifying the -filtername. - -Prefix : Optional parameter – default is blank - Prefix to prepend to the existing contingency name -Suffix : Optional parameter – default tis blank - Suffix to append to the existing contingency name -SetSelected : Optional parameter – default is NO - If this value is YES, then as new contingencies are created for these - -clones, the Selected field of the new contingencies will be set to YES. - - -This command duplicates all existing contingencies, naming each new one by prepending -"Clone_" and appending "_v2" to the original contingency name. Each newly created contingency -will have its Selected field set to YES. -CTGCloneMany(, "Clone_", "_v2", YES); - - - - - 89 - - - -CTGCloneOne("ctgname", "newctgname", "Prefix", "Suffix", SetSelected); -This command creates a copy of a single existing contingency. If newctgname, prefix, and suffix are all -blank, “- Copy” will added to the end of the existing contingency name to specify the name of the new -contingency. Regardless of how the new contingency name is specified, integer indices contained in -parentheses, " (0)", " (1)", etc., will be added as needed until a unique contingency name is found. - -ctgname : name of contingency to clone -newctgname : Optional parameter – default is blank - Name of new contingency. If the special keyword @CTGName is - -specified it will be replaced with the name of the existing contingency -that is being cloned. - -Prefix : Optional parameter – default is blank - Prefix to prepend to the existing contingency name. This will only be - -used if newctgname is blank. -Suffix : Optional parameter – default is blank - Suffix to append to the existing contingency name. This will only be used - -if newctgname is blank. -SetSelected : Optional parameter – default is NO - If this value is YES, then as new contingencies are created for these - -clones, the Selected field of the new contingencies will be set to YES. - - -This command creates a copy of the contingency named "L_One-TwoC1", naming the new -contingency "Backup_L_One-TwoC1_Test". The new contingency will have its Selected field set to -YES. -CTGCloneOne("L_One-TwoC1", "", "Backup_", "_Test", YES); - -CTGComboDeleteAllResults; -Deletes all results that are associated with contingency combination analysis. This includes violations, -what occurred, combination summary results, and injection sensitivities. - -CTGComboSolveAll(DoDistributed, ClearAllResults); -Call this command to run contingency combination analysis for all primary and regular/secondary -contingencies that are set to not be skipped. - -DoDistributed : Optional parameter – default is NO - Set to YES or NO. If set to YES, distributed methods will be used to solve - -contingency combination analysis if the Distributed Contingency Analysis -add-on is installed. Distributed analysis requires the proper -configuration and security settings to work. - -ClearAllResults : Optional parameter – default is YES - Set to YES or NO. If set to YES, all existing contingency combination - -results will be cleared even if a primary contingency is marked to be -skipped. If set to NO, only those primary contingencies that are marked -to not be skipped will have their results cleared. - - -The command runs contingency combination analysis using standard (non-distributed) methods -and clears all existing contingency combination results before running. All primary contingencies -that are not marked to be skipped will be run. -CTGComboSolveAll(NO, YES); - - - - - 90 - - - -CTGCompareTwoListsofContingencyResults (PRESENT or "ControllingFilename",PRESENT or -"ComparisonFilename"); - -This command compares two different contingency result lists. The first parameter is to set the Controlling -List. The second parameter is to set the Comparison List. - -PRESENT or "ControllingFilename" - : PRESENT wil set the present contingency analysis results as the - -Controlling List. If the results are in a file then you can set the path to the -list as the “ControlingFilename”. See the Specifying File Names in Script -Commands section for special keywords that can be used when -specifying the file name. - -PRESENT or "ComparisonFilename" - : PRESENT wil set the present contingency analysis results as the - -Comparison List. If the results are in a file then you can set the path to -the list as the “ComparisonFilename”. See the Specifying File Names in -Script Commands section for special keywords that can be used when -specifying the file name. - - -The file types allowed are: Simulator Contingency File (*.aux), Simulator (Ver 5,6,7) Contingency Files -(*.ctg), PTI Contingency Files (*.con), PTI Load Throw Over Files (*.thr;*.dat), and GE Contingency Files -(*.otg). - - -This command compares the currently loaded contingency analysis results (as the controlling list) -with those stored in the file "B7Flat_ContingencyResults.aux" (as the comparison list). -CTGCompareTwoListsofContingencyResults(PRESENT, -"B7Flat_ContingencyResults.aux") - -CTGConvertAllToDeviceCTG(KeepOriginalIfEmpty); -This command is intended for use with full topology models, where breakers and disconnects are defined -in addition to generators, loads, transmission lines, and so on. This function would have no affect on a -traditional planning model representation, which has no breakers or disconnects explicitly defined. - -The purpose of the function is to allow the user to take a contingency set that is defined with outages of -breakers and disconnects in a full topology model and convert them to outages of the traditional -planning model elements, such as generators, loads, transmission lines, etc. This would be used in -conjunction with the ability to save a full topology model as a consolidated model. A consolidated model -reduces the full model down to a traditional planning model by examining the breaker and disconnect -statuses, and reducing the system down by consolidating breakers and disconnects that are in service. The -resulting model is a smaller model with the traditional planning elements represented, but breakers and -disconnects have been removed and nodes aggregated into bus representations. This function will also -take the breaker and disconnect statuses and convert contingencies defined with the breakers and -disconnects and convert them into contingencies of the planning model devices affected by opening the -original breakers and disconnects. Thus you could create a contingency set that is defined for the -consolidated model, and can be run on the consolidated model with the same results as if the original -contingency set is run on the full topology model. - -The parameter KeepOriginalIfEmpty is a YES or NO option to retain or not the original contingency -definitions for any contingencies that do not end up isolating any devices. This is an optional parameter -that is NO by default if it is not specified. - -Note that the contingency set generated depends on the statuses of the breakers and disconnects, and -that the contingencies created will be different for different statuses of breakers and disconnects in the -full topology model. - - 91 - - - -CTGConvertToPrimaryCTG(filter, KeepOriginal, "Prefix", "Suffix"); -(Added in the April 19, 2024 patch of Simulator 23) -Converts regular/secondary contingencies to Primary contingencies that are used with CTG Combo -Analysis. Not all actions that are supported for regular/secondary contingencies are supported for Primary -contingencies. Examine any messages in the log after the conversion to determine if actions were not -converted. - -filter : Optional parameter – default is to convert all contingencies - Contingencies meeting this filter will be converted to Primary - -contingencies. See Using Filters in Script Commands section for more -information on specifying the filter. - -KeepOriginal : Optional parameter – default is YES -Set to YES or NO. YES means to retain the original contingencies. NO -means to delete the original contingencies. - -"Prefix" : Optional parameter – default is blank -The newly created Primary contingency will be named with the original -contingency name including this as the prefix and the specified suffix. If a -Primary contingency already exists with that name, an integer will be -appended to create a unique name. - -"Suffix" : Optional parameter – default is "-Primary" -The newly created Primary contingency will be names with the original -contingency name including this as the suffix and the specified prefix. If a -Primary contingency already exists with that name, an integer will be -appended to create a unique name. - - -This command converts all regular (secondary) contingencies into Primary contingencies used for -contingency combination (combo) analysis. The original contingencies are retained, and each -new Primary contingency is named by appending "-Primary" to the original name. -CTGConvertToPrimaryCTG(, YES, "", "-Primary"); - -CTGCreateContingentInterfaces(filter, maxOption); -This command creates an interface based on contingency violations. The contingency elements are -included as contingent elements in the new interface, and the violated element is included as a monitored -element. - -filter : This is the name of an Advanced Filter. Only violation objects of type -ViolationCTG that meet the named filter will be used to create new -interfaces. - -maxOption : Set to BRANCH, CTG, BRANCHCTG, or leave blank to select all violations. - BRANCH – for each branch, this selects the worst violation out of all - -contingency violations for that branch. - CTG – for each contingency, this selects the worst violation out of all - -branch violations for that contingency. - BRANCHCTG – union of violations selected in both BRANCH and CTG. - -CTGCreateExpandedBreakerCTGs; -This will convert any “Open with Breakers” or “Close with Breakers” contingency actions into OPEN or -CLOSE actions on explicit breakers. This will permanently modify the contingency definitions. - - - - 92 - - - -CTGCreateStuckBreakerCTGs(filter, AllowDuplicates, "PrefixName", IncludeCTGLabel, -BranchFieldName, "SuffixName", "PrefixComment", BranchFieldComment, "SuffixComment"); - -This command creates new contingencies from contingencies that have explicit breaker outages defined. -New contingencies will be created by treating each breaker as stuck in turn. The new contingencies will -be comprised of all existing elements, minus the stuck breaker outage, plus open actions for breakers that -are identified to isolate the stuck breakers. Only branches with Branch Device Type of Breaker will be -considered in determining the stuck breakers. - -All of the following parameters are optional. If not specified, the defaults will be used. - -filter : Only contingencies that meet the specified filter will be set. See the -Using Filters in Script Commands section for more information on -specifying the filtername. Default is to process all contingencies. - -AllowDuplicates : Set to YES or NO. YES means that contingencies with the same actions as -existing or newly created contingencies will be allowed. Default is NO. - - -"PrefixName", IncludeCTGLabel, BranchFieldName, and "SuffixName" are used to name the new -contingencies in the format: PrefixName_Contingency Label_BranchFieldName_SuffixName. - -"PrefixName" : string that is used as the prefix of the new contingency name. Default is -blank. - -IncludeCTGLabel : Set to YES or NO. YES means that the name of the existing contingency -will be used as part of the new contingency. Default is YES. - -BranchFieldName : variablename of the Branch field whose value will be used in the naming -of the new contingency in the format -variablenamelegacy:location:digits:rod or -concisename:digits:rod. The Branch used to evaluate the -variablename is the stuck breaker. Default is blank. - -"SuffixName" : string that is used as the suffix of the new contingency name. Default is -"STK". - - -"PrefixComment", BranchFieldComment, and "SuffixComment" are used to create a comment for new -contingency actions in the format: PrefixComment_BranchFieldComment_SuffixComment. - -"PrefixComment" : string that is used as the prefix of the new contingency action comment. -Default is blank. - -BranchFieldComment : variablename of the Branch field whose value will be used in the naming -of the new contingency action comment in the format -variablenamelegacy:location:digits:rod or -concisename:digits:rod. The Branch used to evaluate the -variablename is the breaker in the new contingency action. Default is -blank. - -"SuffixComment" : string that is used as the suffix of the new contingency action comment. -Default is blank. - - -This command creates new stuck breaker contingencies from all existing contingencies that -contain explicit breaker outages. It avoids duplicates, includes the original contingency name in -the new names, and uses the breaker's label to uniquely identify each new contingency. The -resulting contingency names follow the format "SB_OriginalCTGName_BreakerLabel_STK". Each -new contingency action comment describes the breaker involved, using the format -"StuckBreaker_BreakerLabel_Isolation". -CTGCreateStuckBreakerCTGs(, NO, "SB", YES, "Label", "STK", -"StuckBreaker", "Label", "Isolation"); - - 93 - - - -CTGDeleteWithIdenticalActions; -This action deletes contingencies that have identical actions. The first contingency alphabetically is -retained while all others with identical actions are deleted. Messages are added to the log detailing which -contingencies are deleted. - -CTGJoinActiveCTGs(InsertSolvePowerFlow, DeleteExisting, JoinWithSelf, "filename"); -This command creates new contingencies that are a join of the current contingency list and a list read in -from an auxiliary file or the current list itself. Contingencies with their Skip field set to YES will not be -included in the join. - -InsertSolvePowerFlow : Set to YES or NO. YES means to insert the solve power flow solution -action between the joined contingency actions. - -DeleteExisting : Set to YES or NO. YES means to delete the existing contingencies and -only keep the joined contingencies. - -JoinWithSelf : Set to YES or NO. YES means that the current contingency list will be -joined with itself instead of contingencies specified in a file. If set to YES, -the "filename" parameter does not have to be specified. - -"filename" : Name of auxiliary file containing contingencies to join with the current -contingency list. This does not have to be specified if JoinWithSelf = YES. - - -This command joins the currently active list of contingencies with those stored in the auxiliary file -"AdditionalContingencies.aux" (filename is specified and JoinWithSelf = NO). A SolvePowerFlow -action is inserted between the two sets of actions, and the existing contingencies are retained. -CTGJoinActiveCTGs(YES, NO, NO, "AdditionalContingencies.aux"); - -CTGPrimaryAutoInsert; -This action will auto insert Primary Contingencies. Prior to calling this action, all options for this action -must be specified in the Ctg_AutoInsert_Options object using the SetData script command or DATA -sections. - -CTGProcessRemedialActionsAndDependencies(DoDelete, filter); -Remedial Actions and any Model Conditions, Model Filters, Model Expressions, and Model Planes being -used by the specified Remedial Actions will be deleted or have their Selected field set to YES. Model -Conditions, Model Filters, Model Expressions, and Model Planes that are being used by other objects or -not being used by a specified Remedial Action will not be deleted or marked. - -DoDelete : Set to YES to delete remedial actions and dependencies. Set to NO to set -the Selected field to YES instead of deleting. - -filter : Optional parameter - default is blank - Only Remedial Actions that meet the specified filter will be processed. - -See the Using Filters in Script Commands section for more information -on specifying the filtername. AREAZONE is not a valid filter. Default is to -process all remedial actions. - - -This command deletes the Remedial Action that matches the single-condition filter identifying a -Remedial Action with a particular name along with associated dependencies—such as Model -Conditions, Model Filters, Model Expressions, and Model Planes—provided these dependencies -are not used by other objects. -CTGProcessRemedialActionsAndDependencies(YES, -"Name='VoltageViolationRA'"); - -CTGProduceReport("filename"); -Produces a text-based contingency analysis report using the settings defined in CTG_Options. - - 94 - - - -CTGReadFilePSLF("filename"); -Use this action to load a file in the PSLF OTG format and create contingencies. - -“filename” : Name of the file to read. See the Specifying File Names in Script -Commands section for special keywords that can be used when -specifying the file name. - -CTGReadFilePTI("filename"); -Use this action to load a file in the PTI CON format and create contingencies. - -“filename” : Name of the file to read. See the Specifying File Names in Script -Commands section for special keywords that can be used when -specifying the file name. - -CTGRelinkUnlinkedElements; -This will attempt to relink unlinked elements in the contingency records. - -CTGRestoreReference; -Call this action to reset the system state to the reference state for contingency analysis. - -CTGSaveViolationMatrices("filename", filetype, UsePercentage, [ObjectTypesToReport], -SaveContingency, SaveObjects, FieldListObjectType, [FieldList], IncludeUnsolvableCTGs); - -This command will save contingency violations in a matrix format. Multiple files can be created for each -type of violation as well as a file showing all contingencies that have violations and the objects that are -violations under each contingency. - -"filename" : Base name of the files to save. It is possible to save multiple files. This -file name will be appended with an underscore and name of the object -type that is being saved in the file. - -filetype : There following options are available: -CSVNOHEADER : save as a normal CSV text file, without the AUX file formatting. The object - -name and field variable names are NOT included. -CSVCOLHEADER : save as a normal CSV without the AUX syntax and with the first row - -showing column headers you would see in a case information display -UsePercentage : Set to YES or NO. Set to YES the values will be reported as a percentage - -loading. If NO, the actual values will be reported. -[ObjectTypesToReport] - : Comma delimited list of object types to include in the results. Options - -are BRANCH, BUS, INTERFACE, and CUSTOMMONITOR. If saving results -by contingency (rows show contingencies that contain violations), the -columns will only contain objects of these specified types. If saving -results by object (row show objects that are violated under any -contingency), files will only be created for these specified types. - -SaveContingency : Set to YES or NO. Set to YES to save a file containing all contingencies -that have at least one violation. The results will be reported as -contingencies in rows and the violated elements in columns. Only -violations of the specified [ObjectTypesToReport] will be included. -Contingencies that have no violations because they failed to solve can be -included by setting the IncludeUnsolvableCTGs to YES. - -SaveObjects : Set to YES or NO. Set to YES to save a file for each of the object types -specified in [ObjectTypesToReport]. The objects will be in rows and the -columns will be the contingencies under which the objects are violated. - -FieldListObjectType : Optional parameter – default is blank. - Additional fields can be included depending on the object type that is - -being saved in the rows of each file. This parameter specifies the object -type associated with the FieldList. Valid options are BRANCH, BUS, -INTERFACE, CUSTOMMONITOR, or CONTINGENCY. As an example, - - 95 - - - -suppose that this parameter is set to BRANCH and a FieldList is specified. -If SaveObjects is also YES, a file will be saved showing branch violations -in each row of the file. In addition to columns showing contingencies -and the violation value of the branch, columns will be added showing the -value of the branch in the present system state for each of the fields in -the FieldList. - -FieldList : Optional parameter – default is blank. - Additional fields can be included depending on the object type that is - -being saved in the rows of each file. This parameter specifies the -additional fields to save. See the FieldListObjectType parameter for an -explanation of how these extra fields are saved to file. - -IncludeUnsolvableCTGs - : Optional parameter – default is NO. - Set to YES or NO. Set to YES to include contingencies that have been - -processed but did not solve either because the power flow failed or an -abort action took place. The Solved field will be added to the file that -lists results by contingency. Set to NO to only include contingencies that -solved. This option is only relevant if the SaveContingency option is set -to YES. - - -This command generates a detailed report of contingency violations, including one file that lists -all contingencies causing branch overloads and another that shows each overloaded branch -along with the specific contingencies under which the violations occur. It also includes additional -data such as each branch’s name and its LimitAmpA value. Contingencies that failed to solve are -also included. -CTGSaveViolationMatrices("Violations", CSVCOLHEADER, YES, [BRANCH], -YES, YES, BRANCH, [Name, LimitAmpA], YES); - -CTGSetAsReference; -Call this action to set the present system state as the reference for contingency analysis. - -CTGSkipWithIdenticalActions; -(Added in the November 6, 2025 patch of Simulator 24) -Contingencies that have identical actions will have their Skip field set to YES. Only contingencies that -originally have their Skip field set to No will be processed. The first contingency alphabetically is retained -with Skip = NO while all others with identical actions will have Skip = YES. Messages are added to the log -detailing which contingencies are set to skip. - -CTGSolve("ContingencyName"); -Call this action solve a particular contingency. The contingency is denoted by the "Contingency Name" -parameter. The system state remains in the post-contingency state following the application of this -command. - -CTGSolveAll(DoDistributed, ClearAllResults); -Call this action to solve all of the contingencies that are not marked to be skipped. - -DoDistributed : Optional parameter – default is NO - Set to YES or NO. If set to YES, distributed methods will be used to solve - -contingency analysis if the Distributed Contingency Analysis add-on is -installed. Distributed analysis requires the proper configuration and -security settings to work. - -ClearAllResults : Optional parameter – default is YES - Set to YES or NO. If set to YES, all existing contingency results will be - -cleared even if a contingency is marked to be skipped. If set to NO, only - - 96 - - - -those contingencies that are marked to not be skipped will have their -results cleared. - - -This command solves all contingencies that are not marked as skipped using local (non- -distributed) processing. By setting ClearAllResults to YES, it clears all existing contingency analysis -results before running the new analysis. -CTGSolveAll(NO, YES); - -CTGSort([SortFieldList]); -This command sorts the contingencies stored in Simulator’s internal data structure. This is different than -sorting contingencies in case information displays in the GUI or sorting data when it is written to an -auxiliary file. Contingencies are processed in the order in which they are stored in the internal data -structure, and they are not sorted by default in this structure; contingencies are added to the internal data -structure in the order in which they are created. This could be significant for other actions like -CTGJoinActiveCTGs if the goal is to join contingencies alphabetically. - -[SortFieldList] : Optional parameter – the default is to sort alphabetically by contingency -name - - This allows the specification of a sort order based on the available fields -for the contingency object type. The format is: [variablename1:+:0, -variablename2:-:1] where - -variablename : the first parameter is the name of the field by which -to sort. There is no limit to how many fields can be -specified for sorting. For fields that require a -location other than zero, variablename can be in -the format fieldname:location. - -+ or - : the second parameter indicates sort ascending for -+ and sort descending for -. This parameter must -be specified. - -0 or 1 : for the third parameter 0 means case insensitive -and do not use absolute value, 1 means case -sensitive or use absolute value. This parameter is -optional. - - -This command sorts all contingencies in the internal data structure alphabetically by their Name -field in ascending order, ignoring case sensitivity. This affects the order in which contingencies -are processed or joined using script commands. -CTGSort([Name:+:0]); - -CTGVerifyIteratedLinearActions("filename"); -Creates a text file that contains validation information relevant for using the option to Iterate on Action -Status when using an linear method with contingency analysis or running ATC. - -"filename" : Name of the text file into which validation information is written. -CTGWriteAllOptions("filename", KeyField, UseSelectedDataMaintainer, SaveDependencies, -UseAreaZoneFilters); - -Writes out all information related to contingency analysis as an auxiliary file using concise variable names -and headers. Data is written using DATA sections instead of SUBDATA sections. - -"filename" : Name of the auxiliary file to save. -KeyField : Optional parameter – default is PRIMARY - -Indicates the identifier that should be used for the data. Valid entries are -PRIMARY, SECONDARY, or LABEL. PRIMARY will save using bus numbers -and other primary key fields. SECONDARY will save using bus name and - - 97 - - - -nominal kV and other secondary fields. LABEL will save using device -labels. If no labels are specified then the primary key field will be used. - - UseSelectedDataMaintainer - : Optional parameter – default is NO - Set to YES or NO. YES means to save only the information belonging to - -Data Maintainers where the Selected field is set to YES. NO means to -save all information. Default is NO. - -SaveDependencies : Optional parameter – default is NO - Set to YES or NO. YES means that all relevant objects that are required to - -define the selected objects will also be saved. NO means to only save -the selected objects. - -UseAreaZoneFilters : Optional parameter – default is NO - Set to YES or NO. YES means to save only the information for objects - -that meets the Area, Zone, Owner filters for Contingency Options and -Limit Monitoring Settings related to the Area, Zone, Bus, Gen, and Shunt -Objects. (Opt2 and Opt3 of CTGWriteResultsAndOptions) - - -See the CTGWriteResultsAndOptions script command for a list of the option settings that are considered. -The equivalent options are set as follows for this script command: - -Opt1 = NO , Opt2 = YES, Opt3 = YES, Opt4 = YES ,Opt5 = NO ,Opt6 = NO, Opt7 = NO, Opt8 -= YES, Opt9 = YES , Opt10 = NO , Opt11 = NO, Opt12 = YES, Opt13 = YES, Opt14 = YES, -Opt15 = YES, Opt16 = YES, Opt17 = NO, Opt18 = YES, Opt19 = YES, Opt20 = NO, Opt21 = -NO, Opt22 = NO -The UseObjectIDs parameter with the CTGWriteResultsAndOptions script command is set automatically -for this script command. The equivalent setting is YES_MS_3W. - -CTGWriteAuxUsingOptions("filename", Append); -(Added in the August 18, 2023 patch of Simulator 23) -Writes out information related to contingency analysis as an auxiliary file. The CTGWriteAux_Options -object is used to specify what object types are written and other relevant user specified parameters for -how data is written. - -"filename" : Name of the auxiliary file to save. -Append : Optional parameter – default is YES - -Set to YES or NO. YES means to append the saved information to -“filename”. NO means that “filename” will be overwitten. - -CTGWriteFilePTI("filename", BusFormat, TruncateCTGLabels, "filtername", Append); -Write contingencies to file in the PTI CON format. - -"filename" : The name of the text file to write out. -BusFormat : How to identify buses: - -Number : Using numbers -Name8 : Using BusName_NomkV strings truncated to 8 characters -Name12 : Using BusName_NomkV strings truncated to 12 characters - -TruncateCTGLabels : Set to YES or NO. YES means that the contingency labels will be -truncated after 12 characters. - -"filtername" : Optional – default is blank and all contingencies will be saved - This filter will be applied to the Contingency object type to specify which - -contingencies should be saved to file. - See the Using Filters in Script Commands section for more information - -on specifying the filtername. -Append : Optional – default is NO - Set to YES or NO. YES means to append the saved information to - -"filename". NO means that "filename" will be overwritten. - 98 - - - - -This command exports contingencies all contingencies to a file named "contingencies.con" in the -PTI CON format. Bus names will be represented using Name12 format—i.e., truncated -BusName_NomkV strings up to 12 characters. Contingency names will also be truncated after 12 -characters (TruncateCTGLabels = YES). Since Append is set to NO, any existing content in the file -will be overwritten. This is commonly used to generate PTI-compatible contingency files for -external tools. -CTGWriteFilePTI("contingencies.con", Name12, YES, "", NO); - -CTGWriteResultsAndOptions("filename", [opt1, opt2, opt3, …, opt22], KeyField, UseDATASection, -UseConcise, UseObjectIDs, UseSelectedDataMaintainers, SaveDependencies, UseAreaZoneFilters); - -Writes out all information related to contingency analysis as an auxiliary file. -"filename" : Name of the auxiliary file to save. -[opt1, opt2, …, opt22] : Each entry in the Option Settings parameter is either a YES or NO entry - -corresponding to the following options. These are all optional -parameters, so if they are not specified or blank, the default entry given -for each will be used. - -Opt1 : Save Unlinked Contingency Actions, default = NO -Opt2 : Save Contingency Options, default = YES -Opt3 : Save Limit Monitoring Settings, default = NO -Opt4 : Save General Power Flow Solution Options, default = YES -Opt5 : Save List Display Settings, default = NO -Opt6 : Save Contingency Results, default = YES -Opt7 : Save Inactive Violations, default = YES -Opt8 : Save Interface Definitions, default = NO -Opt9 : Save Injection Group Definitions, default = NO -Opt10 : Save Distributed Computing Options, default = YES -Opt11 : Suppress Gen and Load options when writing out Options, - -default = NO -Opt12 : Save Contingency Definitions, default = YES -Opt13 : Save Remedial Actions and Global Actions, default = YES -Opt14 : Save Custom Monitor Definitions, default = YES -Opt15 : Save Model Conditions, Model Filters, and Model - -Expressions, default = YES -Opt16 : Save Advanced Filters used as part of Custom Monitors, - -Model Conditions, Remedial Actions, and Global Actions, -default = YES - -Opt17 : Save Limit Cost Functions with Limit Sets, default = YES -Opt18 : Automatically Convert Contingency Blocks and Global - -Actions, default = NO -Opt19 : Save Voltage Control Groups, default = YES -(Following added in the August 9, 2023 patch of Simulator 23) -Opt20 : Save Primary Contingency Options for Combo Analysis, - -default = NO -Opt21 : Save Primary Contingencies for Combo Analysis, default = - -NO -Opt22 : Save CTG Combo Results, default = NO - -KeyField : Optional parameter – default is PRIMARY - Indicates the identifier that should be used for the data. Valid entries are - -PRIMARY, SECONDARY, or LABEL. PRIMARY will save using bus numbers -and other primary key fields. SECONDARY will save using bus name and -nominal kV and other secondary fields. LABEL will save using device -labels. If no labels are specified then the primary key field will be used. - - 99 - - - -UseDATASection : Optional parameter – default is NO - Set this to YES or NO. If YES, data that by default is specified using - -SUBDATA sections will instead be specified using DATA sections. For -example, the actions that define a contingency by default are specified -using a SUBDATA section. If choosing to use the DATA section instead, -each action will be specified in a DATA record belonging to the -ContingencyElement objecttype. - -UseObjectIDs : Optional parameter – default is NO - Possible settings are YES, NO, YES_MS, YES_3W, and YES_MS_3W. - -YES : Any input with YES means to use the ObjectID field when -writing objects with contingency settings instead of using -multiple key fields to identify an object. The advantage to -using ObjectIDs is that you only have one field to be used as -an identifier rather than a changing number of fields that -depends on the type of object. This will simplify your auxiliary -file. - -NO : This means to use the specified key fields to identify an object. -MS : Any input with MS means to write out a multi-section line by - -identifying it by the from bus, to bus, and circuit ID of the -multi-section followed by the number of the particular section. -This follows the PSLF format. If not writing out in this manner, -individual sections will be written based on their from bus, to -bus, and circuit ID. - -3W : Any input with 3W means to write out a three-winding -transformer using the buses at the three terminals of the -transformer followed by the circuit ID with the first bus listed -being the particular winding that is desired. If not writing out -in this manner, a particular winding will be written with its -terminal bus, the star bus of the transformer, and the circuit ID -of the transformer. - -UseSelectedDataMaintainer - : Optional parameter – default is NO - -Set to YES or NO. YES means to save only the information belonging to -Data Maintainers where the Selected field is set to YES. NO means to -save all information. - -SaveDependencies : Optional parameter – default is NO - Set to YES or NO. YES means that all relevant objects that are required to - -define the selected objects will also be saved. NO means to only save -the selected objects. - -UseAreaZoneFilters : Optional parameter – default is NO - Set to YES or NO. YES that to save only the information for objects that - -meets the Area, Zone, Owner filters for Contingency Options and Limit -Monitoring Settings related to the Area, Zone, Bus, Gen, and Shunt -Objects. (Opt2 and Opt3) - - -This command saves a comprehensive snapshot of contingency analysis data to -"CTG_Export.aux", capturing components like contingency options, limit monitoring settings, -power flow configurations, results, remedial actions, custom monitors, and model logic. It uses -primary key fields for identification and stores elements in DATA sections rather than SUBDATA. -Dependencies are included, and the saved data is filtered according to area/zone/owner filters. -CTGWriteResultsAndOptions("CTG_Export.aux", [NO, YES, YES, YES, NO, -YES, YES, NO, NO, YES, NO, YES, YES, YES, YES, YES, YES, NO, YES, NO, -NO, NO], PRIMARY, YES, NO, YES_MS_3W, NO, YES, YES); - - 100 - - - -Fault Analysis -Fault([Bus num], faulttype, R, X); -Fault([BRANCH nearbusnum farbusnum ckt], faultlocation, faulttype, R, X); - -Call this function to calculate the fault currents for a fault. If the fault element is a bus then do not specify -the fault location parameter. If the fault element is a branch, then the fault location is required. - -[BUS num] : This specifies the bus at which the fault occurs. You may also specify the -bus using secondary keys or labels. - -[BUS "name_nomkv"] -[BUS "label"] - -[BRANCH nearbusnum farbusnum ckt] - : This specifies the branch on which the fault occurs. You may also specify - -the branch using secondary keys or labels. -[BRANCH "name_kv1" "name_kv2" ckt] -[BRANCH "buslabel1" "buslabel2" ckt] -[BRANCH "label"] - -Faultlocation : This specifies the percentage distance along the branch where the fault -occurs. This percent varies from 0 (meaning at the nearbus) to 100 -(meaning at the far bus) - -Faulttype : This specified the type of fault which occurs. There are four options: -SLG : Single Line To Ground fault -LL : Line to Line Fault -3PB : Three Phase Balanced Fault -DLG : Double Line to Group Fault. - -R, X : These parameters are optional and specify the fault impedance. If none -are specified, then a fault impedance of zero is assumed. - - -This places a single-line-to-ground fault at Bus 1 with a fault impedance of 0.001 + j0.01. -Fault([BUS 1], SLG, 0.001, 0.01); - -FaultAutoInsert; -Multiple fault definitions are inserted using the options in the CTG_AutoInsert_Options object that are -relevant for fault analysis. Faults can only be inserted for transmission lines or buses. - -FaultClear; -Clears a single fault that has been calculated with the Fault script command. - -FaultMultiple(UseDummyBus); -Runs fault analysis on a list of defined faults. - -UseDummyBus : Optional parameter – default is NO -Set to YES or NO. If YES, dummy buses should be created and inserted at -the specified percent location for branch faults. Faults will be calculated -at the dummy buses. If NO, the fault will be calculated at the branch -terminal bus that is closest to the specified location. - -LoadPTISEQData("filename", version); -Loads sequence data in the PTI format. - -"filename" : Name of file containing sequence data. -version : Integer representing the PTI version of the SEQ file to open. - - -This command loads a PTI-format sequence data file named "sequence_data.seq". The file is in -PTI version 33 format. -LoadPTISEQData("sequence_data.seq", 33); - - 101 - - - -ATC (Available Transfer Capability) -ATCCreateContingentInterfaces(filter); - -This command creates an interface based Transfer Limiter results from an ATC run. Each Transfer Limiter -is comprised of a Limiting Element/Contingency pair. Each interface is then created with contingent -elements from the contingency and the Limiting Element included as the monitored element. - -filter : Optional – default is blank and all transfer limiters will be used -This is the name of an Advanced Filter. Only objects of type -TransferLimiter that meet the named filter will be used to create new -interfaces. - -ATCDeleteAllResults; -Deletes all ATC results including TransferLimiter, ATCExtraMonitor, and ATCFlowValue object types. - -ATCDeleteScenarioChangeIndexRange(ScenarioChangeType, [IndexRange]); -ATC scenarios are defined by RL (line rating and zone load), G (generator), and I (interface rating) changes. -This command allows the deletion of entries within one of these change types by specifying the indices of -the changes that should be deleted. - -ScenarioChangeType : RL, G, or I to indicate the scenario change type to delete. -IndexRange : Comma-delimited list of integer ranges that must be enclosed in square - -brackets. The indices start at 0. - - -The following will delete line rating and zone load scenarios for indices 0 to 2, 5, and 7 to 9. -ATCDeleteScenarioChangeIndexRange(RL, [0-2, 5, 7-9]); - -ATCDetermine([transactor seller], [transactor buyer], DoDistributed, DoMultipleScenarios); -Use this action to calculate the Available Transfer Capability (ATC) between a seller and a buyer. The -buyer and seller must not be the same. Other options regarding ATC calculations should be set with the -ATC_Options object type. If the distributed ATC add-on is installed, the optional DoDistributed flag may -bet set to indicate that the ATC should be solved using the distributed methods. - -[transactor seller] : The seller (or source) of power. There are six possible settings: -[AREA num], [AREA "name"], [AREA "label"] -[ZONE num], [ZONE "name"], [ZONE "label"] -[SUPERAREA "name"], [SUPERAREA "label"] -[INJECTIONGROUP "name"], [INJECTIONGROUP "label"] -[BUS num], [BUS "name_nomkv"], [BUS "label"] -[SLACK] - -[transactor buyer] : The buyer (or sink) of power. There are six possible settings, which are -the same as for the seller. - -DoDistributed : Optional parameter – default is NO -Set to YES to use the distributed ATC solution method. - -DoMultipleScenarios : Optional parameter – default is set to YES if scenarios are defined -Set to YES to process each defined scenario. - - -This command will calculate how much real power can be transferred from Area "Top" to Area -"Left" under current system conditions, ignoring any ATC scenarios or distributed processing. -ATCDetermine([AREA "Top"], [AREA "Left"], NO, NO); - - - - - 102 - - - -ATCDetermineMultipleDirections(DoDistributed, DoMultipleScenarios); -Use this action to calculate the Available Transfer Capability (ATC) for all defined directions. Other options -regarding ATC calculations should be set with the ATC_Options object type. If the distributed ATC add-on -is installed, the optional DoDistributed flag may bet set to indicate that the ATC should be solved using -the distributed methods. - -DoDistributed : Optional parameter – default is NO -Set to YES to use the distributed ATC solution method. - -DoMultipleScenarios : Optional parameter – default is NO -Set to YES to process each defined scenario for all defined directions. - - -This command calculates the Available Transfer Capability (ATC) for all defined transfer directions -using local (non-distributed) processing. -ATCDetermineMultipleDirections(NO, NO); - -ATCDetermineATCFor(RL, G, I, ApplyTransfer); -Call this action to determine the ATC for Scenario RL, G, I. - -ApplyTransfer : Optional parameter – default is NO -Set to YES or NO. Set this value to YES to leave the system state at the -transfer level that was determined. When using the Iterated Linear then -Full Contingency solution method, the system state will retain the -transfer level but the contingency will not be applied. - -ATCDetermineMultipleDirectionsATCFor(RL, G, I); -Call this action to determine the ATC for Scenario RL, G, I for all defined directions. - -ATCIncreaseTransferBy(amount); -Call this action to increase the transfer between the seller and buyer. - -ATCRestoreInitialState; -Call this action to restore the initial state for the ATC tool. - -ATCSetAsReference; -Call this action to set the present system state to the reference state for ATC analysis. - -ATCTakeMeToScenario(RL, G, I); -Call this action to set the present case according to the scenarios along the RL, G, and I axes. All three -parameters must be specified, with no defaults allowed. All entries must be specified as integers indicating -the index of the respective scenario. Indices start at 0. - -ATCWriteAllOptions("filename", AppendFile, KeyField); -Renamed to ATCDataWriteOptionsAndResults in December 9, 2021 patch of Simulator 22 - -ATCDataWriteOptionsAndResults("filename", AppendFile, KeyField); -Writes out all information related to ATC analysis to an auxiliary file. Saves the same information as the -ATCWriteResultsAndOptions script command. Auxiliary file is formatted using the concise format for -DATA section headers and variable names. Data is written using DATA sections instead of SUBDATA -sections. - -"filename" : Name of the auxiliary file to save. See the Specifying File Names in Script -Commands section for special keywords that can be used when -specifying the file name. - -AppendFile : Optional parameter - default is YES - YES means to append results to existing "filename." NO means to - -overwrite "filename" with the results. -KeyField : Optional parameter – default is PRIMARY - -Indicates the identifier that should be used for the data. Valid entries are - 103 - - - -PRIMARY, SECONDARY, or LABEL. PRIMARY will save using bus numbers -and other primary key fields. SECONDARY will save using bus name and -nominal kV and other secondary fields. LABEL will save using device -labels. If no labels are specified then the primary key field will be used. - - -This command saves all ATC (Available Transfer Capability) options and results to an AUX file -named "ATC_Results.aux". An existing file with this name will be overwritten and primary key -fields will be used to identify objects in the file. -ATCDataWriteOptionsAndResults("ATC_Results", NO, PRIMARY); - -ATCWriteResultsAndOptions("filename", AppendFile); -Writes out all information related to ATC analysis to an auxiliary file. This includes Contingency -Definitions, Remedial Action Definitions, Limit Monitoring Settings, Solution Options, ATC Options, ATC -results, as well as any Model Criteria that are used by the Contingency and Remedial Action Definitions. - - -Contingency and Remedial Action definitions will always be saved along with dependencies and only -those object types that are dependencies will be saved. This means that if a Remedial Action definition -uses a Model Filter, that Model Filter along with any Model Filter Conditions, i.e. other Model Filters or -Model Conditions, will be saved. If a model criteria object is not being used by a Remedial Action or -Contingency it will not be saved. Objects that are saved if they are dependencies include: Model -Conditions, Model Filters, Model Planes, Model Expressions, Model Result Overrides, Interfaces, Injection -Groups, Calculated Fields, and Expressions. - - -Dependencies for the ATC setup are also included. This includes Injection Groups that are used as the -seller or buyer, Interfaces that are used in ATC Extra Monitor definitions, and Interfaces that are used in -Multiple ATC Scenario definitions. - -"filename" : Name of the auxiliary file to save. See the Specifying File Names in Script -Commands section for special keywords that can be used when -specifying the file name. - -AppendFile : Optional parameter - default is YES - YES means to append results to existing "filename." NO means to - -overwrite "filename" with the results. -ATCWriteScenarioLog("filename", AppendFile, filter); - -Writes out detailed log information for ATC Multiple Scenarios to a text file. If no scenarios have been -defined, no file will be created; this is not treated as a fatal error, and an auxiliary file containing this -command will continue to process subsequent commands. - -"filename" : Name of log file. See the Specifying File Names in Script Commands -section for special keywords that can be used when specifying the file -name. - -AppendFile : Optional parameter - default is NO - YES means to append log information to existing "filename." NO means - -to overwrite "filename" with the log information. -filter : Optional parameter – default is blank - Log information will only be written for ATC scenarios that meet the - -specified filter. See the Using Filters in Script Commands section for -more information on specifying the filtername. Default is to write the log -for all ATC scenarios. - - - - 104 - - - -ATCWriteScenarioMinMax("filename", filetype, AppendFile, [fieldlist], Operation, OperationField, -GroupScenario, [RLFilter], [GFilter], [IFilter], DirectionFilter, DoGroupDirection, filter, -PreferIterativelyFound); - -Writes out TransferLimiter results from multiple scenario ATC calculations. The results are grouped based -on the input parameters, and the minimum, maximum, or minimum and maximum limiter from each -group is written to file. - -"filename" : Name of file. See the Specifying File Names in Script Commands section -for special keywords that can be used when specifying the file name. - -filetype : There are several options for the filetype -AUXCSV : save as a comma-delimited auxiliary data file -AUX : save as a space-delimited auxiliary data file -CSV : save as a normal CSV file without the AUX file - -syntax. The first few lines of the text file will -represent the object name and field variable -names. - -CSVNOHEADER : save as a normal CSV text file, without the AUX file -formatting. The object name and field variable -names are NOT included. - -CSVCOLHEADER : save as a normal CSV without the AUX syntax and -with the first row showing the normal column -headers seen in a case information display - -AppendFile : YES means to append to existing "filename." NO means to overwrite -"filename." - -[fieldlist] : Comma-delimited list of fields to save. For numeric fields, the number of -digits and the number of decimal places (digits to right of decimal) can -be specified by using the following format for the field, -variablenamelegacy:location:digits:rod or -concisename:digits:rod. See the Specifying Field Variable Names -in Script Commands topic for more information on specifying this list. - -Operation : This is the operation to perform on each grouping of transfer limiters. -The OperationField specifies the value to use when performing the -operation. - -MIN : Write the minimum limiter for each grouping. If -there are limiters for the grouping, at most one -limiter per grouping will be written to the file. - -MAX : Write the maximum limiter for each grouping. If -there are limiters for the grouping, at most one -limiter per grouping will be written to the file. - -MIN/MAX : Write the minimum and maximum limiter per -grouping. If there are limiters for the grouping, at -most two limiters per grouping will be written to -the file. There will only be one limiter if the -minimum and maximum limiters are the same. - -OperationField : Variable name of the field to use for the value in the operation. Valid -entries are TransferLimit or any available ATC_ExtraMonitor field. - -GroupScenario : Indicates which type of scenario to use for the grouping. -None : The results are not grouped by scenarios. All - -scenario results are included as one group. -Direction grouping also applies. - -RL : The results are grouped by RL scenario. For each -RL scenario all results for the G and I scenarios are -included. Direction grouping also applies. - - 105 - - - -G : The results are grouped by G scenario. For each G -scenario all results for the RL and I scenarios are -included. Direction grouping also applies. - -I : The results are grouped by I scenario. For each I -scenario all results for the RL and G scenarios are -included. Direction grouping also applies. - -[RLFilter] : Optional parameter – default is blank. - Determines which RL scenarios are included in the groupings. If left - -blank, all RL scenarios are included. Specify the scenarios using an integer -range list enclosed in square brackets, which is a comma-separated list of -either a single integer or a range of integers such as [1, 3-4, 7]. This -parameter is ignored if filter is not blank. - -[GFilter] : Optional parameter – default is blank - Determines which G scenarios are included in the groupings. If left blank, - -all G scenarios are included. Specify the scenarios using an integer range -list enclosed in square brackets, which is a comma-separated list of either -a single integer or a range of integers such as [1, 3-4, 7]. This parameter -is ignored if filter is not blank. - -[IFilter] : Optional parameter – default is blank - Determines which I scenarios are included in the groupings. If left blank, - -all I scenarios are included. Specify the scenarios using an integer range -list enclosed in square brackets, which is a comma-separated list of either -a single integer or a range of integers such as [1, 3-4, 7]. This parameter -is ignored if filter is not blank. - -DirectionFilter : Optional parameter – default is IGNORE - A single direction or multiple directions can be run for multiple scenarios. - -This parameter determines which set of directions to write in the results. -IGNORE : Use only the single direction results in the - -groupings. Scenario grouping also applies. -ALL : Use all of the multiple direction results in the - -groupings. The DoGroupDirection parameter will -determine if groupings will be formed for each -direction or if all directions will be combined -before determining the scenario groupings. - -"Direction Name" : Only use the specified direction in the groupings. -Scenario grouping also applies. - -DoGroupDirection : Optional parameter – default is YES - This parameter is used only if DirectionFilter = ALL. Set to YES if - -groupings should be formed for each of the multiple direction results. -Within a direction grouping, scenarios will also be grouped based on -specified parameters. Set to NO if all directions are considered together -and the only grouping that should be done is based on scenario -groupings. - -filter : Optional parameter – default is blank - Groupings will be formed only for TransferLimiters that meet this filter. - -RLFilter, GFilter, and IFilter will be ignored when specifying this filter, -but all other options will apply. See the Using Filters in Script Commands -section for more information on specifying the filtername. - -PreferIterativelyFound : Optional parameter – default is YES - Set to YES so that transfer limiters that have been iteratively found will - -take precedence when determining the limiter that meets the selected -operation for each grouping. If there are no limiters that have been - - 106 - - - -iteratively found or this is set to NO, the first limiter that is found that -meets the operation will be reported for each grouping. - - -This command writes the minimum and maximum Available Transfer Capability (ATC) limiters for -RL (Rating & Load) scenarios indexed 0 through 3 into a CSV file named "ATC_Summary.csv". The -results are grouped by both scenario and direction and include the specified field list for each -limiter. -ATCWriteScenarioMinMax("ATC_Summary", CSV, NO, [DirectionName, -LineZoneChange, GeneratorChange, InterfaceChange, TransferLimit, -ObjectDesc, Contingency, Sensitivity, ValuePreTrans, Limit], MIN/MAX, -TransferLimit, RL, [0-3], , , ALL, YES, , YES); - -ATCWriteToExcel("worksheetname", [fieldlist]); -Sends ATC analysis results to an Excel spreadsheet. This script command is available only for Multiple -Scenarios ATC analysis. - -"worksheetname" : The name of the Excel sheet where the results will be sent to. -fieldlist : Optional parameter - If specified, the results will be saved including this list of fields. If not - -specified, the results will be saved with the fields that are specified with -the TransferLimiter DataGrid. It is possible that the results could be blank -if the TransferLimiter DataGrid has not been initialized by either opening -the ATC dialog or loading the DataGrid settings from an auxiliary file. To -make sure that results are stored and the desired fields are included, it is -suggested that the fieldlist option be used. - - -This writes ATC (Available Transfer Capability) results from a Multiple Scenarios ATC analysis into -an Excel worksheet named "ATCResults". -ATCWriteToExcel("ATCResults", [DirectionName, LineZoneChange, -GeneratorChange, InterfaceChange, TransferLimit, ObjectDesc, -Contingency, Sensitivity, ValuePreTrans, Limit]);); - -ATCWriteToText("filename", filetype, [fieldlist]); -This is used with Multiple Scenario ATC analysis. Multiple files are created with "filename" as the primary -identifier and the Interface scenario label appended to the end of the filename. Separate files are created -for each of the Interface scenarios. Results inside the files are separated into sections based on the -number of Rating/Load scenarios. - -"filename" : Primary identifier for the name of the file in which to save the results. -"filename" gets appended with the Interface scenario label to complete -the filename. - -filetype : Either TAB or CSV. This indicates the delimiter to use when writing out -the file(s). This is an optional parameter with TAB being the default if -omitted. - -fieldlist : Optional parameter - If specified, the results will be saved including this list of fields. If not - -specified, the results will be saved with the fields that are specified with -the TransferLimiter DataGrid. It is possible that the results could be blank -if the TransferLimiter DataGrid has not been initialized by either opening -the ATC dialog or loading the DataGrid settings from an auxiliary file. To -make sure that results are stored and the desired fields are included, it is -suggested that the fieldlist option be used. - - - - 107 - - - - -This script writes separate CSV files for each interface scenario in the ATC analysis using the -specified field list. -ATCWriteToText("ATCOutput", CSV, [DirectionName, LineZoneChange, -GeneratorChange, InterfaceChange, TransferLimit, ObjectDesc, -Contingency, Sensitivity, ValuePreTrans, Limit]); - - - - 108 - - - -GIC (Geomagnetically Induced Current) -GICCalculate(MaxField, Direction, SolvePF); - -Calculates the "Single Snapshot" using GICSolution Options -MaxField : Maximum Electric Field in Volts/km -Direction : Storm Direction, Degrees from 0 to 360 -SolvePF : Select YES or NO to include GIC in the Power Flow - - -This command calculates a "Single Snapshot" Geomagnetically Induced Current (GIC) solution -using a maximum electric field strength of 5.0 V/km and a storm direction of 90 degrees -(eastward). By setting SolvePF to YES, the GIC solution is integrated into the power flow analysis. -GICCalculate(5.0, 90, YES); - -GICClear; -Clear GIC Values - -GICLoad3DEfield(FileType, "FileName", SetupOnLoad); -Loads GIC data including time varying fields. - -FileType : Type of file to be loaded. Options are CSV, B3D, JSON, and DAT. -FileName : Name of the file to be loaded -SetupOnLoad : Select YES to run procedure to setup time varying series after loading file - -or NO to skip the setup process. - - -This command loads GIC data from the file "TimeSeriesGIC.b3d" in B3D format, which contains -time-varying electric field values used for GIC simulations. By setting SetupOnLoad to YES, the -command initiates the procedure to configure the time series data upon loading. -GICLoad3DEfield(B3D, "TimeSeriesGIC.b3d", YES); - -GICReadFilePSLF("FileName"); -Added in the August 27, 2024 patch of Simulator 23. -Reads GIC supplemental data from a GMD text file format. - -"FileName" : Name of the file to be loaded, with extension GMD -GICReadFilePTI("FileName"); - -Added in the August 27, 2024 patch of Simulator 23. -Reads GIC supplemental data from a GIC text file format. - -"FileName" : Name of the file to be loaded, with extension GIC -GICSaveGMatrix(“GMatrixFileName”, “GMatixIDFileName”); - -Added on November 19, 2024 to Simulator 24 -Use this action to save the GMatrix used with the GIC calculations in a file formatted for use with Matlab - -"GMatrixFileName" : File in which to save the G Matrix. -"GMatrixIDFileName" : File to save a description of what each row and column of the G Matrix - -represents. - - -This command saves the G Matrix used in GIC calculations to a MATLAB-compatible file named -"GIC_GMatrix.mat", with an accompanying identifier file "GIC_GMatrix_IDs.txt" that describes what -each row and column in the matrix represents. -GICSaveGMatrix("GIC_GMatrix.mat", "GIC_GMatrix_IDs.txt"); - - - - - 109 - - - -GICSetupTimeVaryingSeries(Start, End, Delta); -Added in the April 19, 2024 patch of Simulator 23. -Creates a set of Branch series DC input voltages in the "Time-Varying Series Voltage Inputs" Calculation -Mode from the Active Event(s) in the "Time-Varying Electric Field Inputs" Calculation Mode. -Note: Set all arguments to 0 to create a complete set of Time-Varying Series Voltage Inputs with the -source time offset values. - -Start : Start Time Offset (seconds), (optional) default is 0.0 -End : End Time Offset (seconds), (optional) default is 0.0 -Delta : Sampling Rate (seconds), (optional) default is 0.0 - - -This command generates time-varying series DC voltage inputs for GIC analysis by converting -active electric field event data into voltage input series. It starts at 0 seconds, ends at 3600 -seconds (1 hour), and samples the data every 60 seconds. -GICSetupTimeVaryingSeries(0, 3600, 60); - -GICShiftOrStretchInputPoints(LatShift, LonShift, MagScalar, StretchScalar, UpdateTimeVaryingSeries); -Scales, shifts, or stretches the active set of Time Varying Electric Field Inputs. - -LatShift : Latitude Shift in degrees, default is 0.0 -LonShift : Longitude Shift in degrees, (optional) default is 0.0 -MagScalar : E-Field Magnitude scalar, (optional) default is 1.0 (to NOT scale) -StretchScalar : Geographic Stretch scalar, (optional) default is 1.0 (to NOT stretch) -UpdateTimeVaryingSeries : Select YES or NO to update the time varying voltage input values, - - (optional) default is NO. - Added in the April 19, 2024 patch of Simulator 23. - - -This command modifies the active set of Time Varying Electric Field Inputs by shifting them 1.0° -north and 2.0° west, scaling the electric field magnitudes by 1.2 (increasing intensity by 20%), and -stretching the geographic area by a factor of 1.1. Setting UpdateTimeVaryingSeries to YES -ensures that the corresponding voltage inputs are recalculated based on the adjusted field data. -GICShiftOrStretchInputPoints(1.0, -2.0, 1.2, 1.1, YES); - -GICTimeVaryingCalculate(TheTime,SolvePF); -Calculate GIC Values using the "Time-Varying Series Voltage Inputs" Calculation Mode. - -TheTime : Current Time Offset from Reference (seconds) -SolvePF : Select YES or NO to include GIC in the Power Flow and Transient Stability - - -This command calculates GIC values at 1800 seconds (30 minutes) into a time-varying event -using the "Time-Varying Series Voltage Inputs" mode. By setting SolvePF to YES, the calculation -integrates GIC effects directly into the power flow and transient stability analysis. -GICTimeVaryingCalculate(1800, YES); - -GICTimeVaryingAddTime(NewTime); -Adds a new input values at specified time - -NewTime : New Time for new input values -GICTimeVaryingDeleteAllTimes; - -Delete All Input time varying voltage input values -GICTimeVaryingEFieldCalculate(TheTime,SolvePF); - -Calculate GIC Values using the "Time-Varying Electric Field Inputs" Calculation Mode. -TheTime : Current Time Offset from Reference (seconds) -SolvePF : Select YES or NO to include GIC in the Power Flow and Transient Stability - - - 110 - - - -This command calculates GIC values at 900 seconds (15 minutes) into a time-varying event using -the "Time-Varying Electric Field Inputs" mode. By setting SolvePF to YES, it ensures that the -calculated GIC values are factored into the power flow and transient stability simulations. -GICTimeVaryingEFieldCalculate(900, YES); - -GICTimeVaryingElectricFieldsDeleteAllTimes; -Clear all the time varying electric field input values. - -GICWriteFilePSLF("FileName", UseFilters); -Added in the August 27, 2024 patch of Simulator 23. -Writes GIC supplemental data from a GMD text file format. - -"FileName" : Name of the file to be loaded, with extension GMD -UseFilters : YES – to user Area/Zone Filters; NO – to insert for entire case. - -GICWriteFilePTI("FileName", UseFilters, Version); -Added in the August 27, 2024 patch of Simulator 23. -Writes GIC supplemental data from a GIC text file format. - -"FileName" : Name of the file to be loaded, with extension GIC -UseFilters : YES – to user Area/Zone Filters; NO – to insert for entire case. -Version : The version number of the GIC file, 1 – 4, (optional) default is 4. - -GICWriteOptions(“FileName”, KeyField); -Calculates the "Single Snapshot" using GICSolution Options - -FileName : Name of Aux file name to write out the options -KeyField : KeyField indicates the identifier that should be used for the data. Valid - -entries are PRIMARY, SECONDARY, or LABEL. The default setting is -PRIMARY. PRIMARY will save using bus numbers and other primary key -fields. SECONDARY will save using bus name and nominal kV and other -secondary fields. LABEL will save using device labels. If no labels are -specified then the primary key field will be used. - - -This command writes the current GIC solution options to an auxiliary file named -"GIC_Options.aux", using primary key fields (like bus numbers) to identify system elements. -GICWriteOptions("GIC_Options.aux", PRIMARY); - - 111 - - - -ITP (Integrated Topology Processing) -CloseWithBreakers(objecttype, filter or [object identifier], OnlyEnergizeSpecifiedObjects, -[SwitchingDeviceTypes], CloseNormallyClosedDisconnects); - -This action is used to specify which objects are to be energized by closing breakers and to actually close -those breakers. The status of an object will be set to closed if necessary in addition to closing the -breakers. If only the status of an object needs to be changed to close an object, that will occur without -requiring any breakers to be closed. - -objecttype : Objects that are valid to be energize. Only allowed for Buses, Generators, -Loads, Transmission Lines, Switched Shunts, DC Lines, Injection Groups, -and Interfaces. - -Filter : The second parameter can either be a filter specification or an object -identifier. When specifying a filter, the following options are available: - -SELECTED : only objects whose Selected field = YES will be -energized - -AREAZONE : only objects that meet the area/zone/owner filters will -be energized - -"FilterName" : only objects that meet the specified filter will be -energized. See the Using Filters in Script Commands -section for more information on specifying the -filtername. - -[object identifier] : The second parameter can either be a filter specification or an object -identifier. When using an object identifier, the objecttype is applicable -and no further specification of the type needs to be included with the -object identifier as is done with some other script commands. The -following describe the possible objecttypes and identifier options: - -BUS : [busnum] - ["name_nomkv"] - ["label"] -GEN : [busnum id] - ["name_nomkv" id] - ["buslabel" id] - ["label"] -LOAD : [busnum id] - ["name_nomkv" id] - ["buslabel"] - ["label"] -BRANCH : [busnum1 busnum2 ckt] - ["name_kv1" "name_kv2" ckt] - ["buslabel1" "buslabel2" ckt] - ["label"] -SHUNT : [busnum id] - ["name_nomkv" id] - ["buslabel" id] - ["label"] -INJECTIONGROUP : ["name"] -INTERFACE : ["name"] -DCLINE : [num rectnum invnum] - [num "rectnam_nomkv" "invname_nomkv"] - [num "rectlabel" "invlabel"] - ["label"] - - - - 112 - - - -OnlyEnergizeSpecifiedObjects - : optional parameter, default is NO. - -YES : No extra objects in addition to those specified in the filter can -be energized. Each object will be evaluated individually. - -NO : Extra objects could be energized in addition to those specified -if a group of breakers required to energize a specified object -also causes other objects to be energized. All objects will be -evaluated collectively for determining which objects can be -energized, i.e. breakers that cause one object to be energized -might also be needed for another object to be energized. - -[SwitchingDeviceTypes] - : optional parameter, default is "Breaker". This is a comma-separated list - -naming the Branch Device Types for switching devices that should be -included when determining which devices to close to energize objects. -Options include "Breaker" and "Load Break Disconnect". - -CloseNormallyClosedDisconnects - : optional parameter, default is NO. - -YES : When searching for the specified SwitchingDeviceTypes and a -Disconnect is encountered that is open but normally closed, it -will be closed and the search for open switching devices will -continue past the Disconnect. Additionally, Disconnects that -are in series with any open devices of the specified -SwitchingDeviceTypes will be closed if they are normally open. - -NO : Only switching devices of the specified SwitchingDeviceTypes -will be closed. - - -This command energizes the generator at bus 1 with ID 1, allowing other connected elements to -be energized in the process. It uses both breakers and load-break disconnects as valid switching -devices and will also close any disconnects that are normally closed but currently open. -CloseWithBreakers(GEN, [1 1], NO, ["Breaker", "Load Break Disconnect"], -YES); - -ExpandAllBusTopology; -This action is used to expand the topology around all buses in the case according to a topology type that -is specified with a custom string field (currently Custom String 5) for the bus. New breakers and nodes -(buses) will be inserted as necessary. - -ExpandBusTopology(BusIdentifier, TopologyType); -This action is used to expand the topology around the specified bus according to the specified topology -type. New breakers and nodes (buses) will be inserted as necessary. - -BusIdentifier : A bus can be identified in one of these formats: BUS busnum, BUS -name_nomkv, BUS label. - -TopologyType : These types of breaker configurations are allowed: -DOUBLEBUSDOUBLEBREAKER, MAINTRANSFER, RINGBUS, -BREAKERANDAHALF, SINGLEBUS, and SECTIONALIZEBUS. - - -This command modifies the topology around Bus 1, expanding it into a Breaker-and-a-Half -configuration. It inserts the necessary breakers and additional nodes (buses) to match the -selected topology type. -ExpandBusTopology(BUS 1, BREAKERANDAHALF); - - 113 - - - -OpenWithBreakers(objecttype, filter or [object identifier], [SwitchingDeviceTypes], -OpenNormallyOpenDisconnects); - -This action is used to specify which objects are to be disconnected by opening breakers and to actually -open those breakers. - -objecttype : Objects that are valid to be disconnected. Only allowed for Buses, -Generators, Loads, Transmission Lines, Switched Shunts, DC Lines, -Injection Groups, and Interfaces. - -Filter : The second parameter can either be a filter specification or an object -identifier. When specifying a filter, the following options are available: - -SELECTED : only objects whose Selected field = YES will be -energized - -AREAZONE : only objects that meet the area/zone/owner filters will -be energized - -"FilterName" : only objects that meet the specified filter will be -energized. See the Using Filters in Script Commands -section for more information on specifying the -filtername. - -[object identifier] : The second parameter can either be a filter specification or an object -identifier. When using an object identifier, the objecttype is applicable -and no further specification of the type needs to be included with the -object identifier as is done with some other script commands. The -following describe the possible objecttypes and identifier options: - -BUS : [busnum] - ["name_nomkv"] - ["label"] -GEN : [busnum id] - ["name_nomkv" id] - ["buslabel" id] - ["label"] -LOAD : [busnum id] - ["name_nomkv" id] - ["buslabel"] - ["label"] -BRANCH : [busnum1 busnum2 ckt] - ["name_kv1" "name_kv2" ckt] - ["buslabel1" "buslabel2" ckt] - ["label"] -SHUNT : [busnum id] - ["name_nomkv" id] - ["buslabel" id] - ["label"] -INJECTIONGROUP : ["name"] -INTERFACE : ["name"] -DCLINE : [num rectnum invnum] - [num "rectnam_nomkv" "invname_nomkv"] - [num "rectlabel" "invlabel"] - ["label"] - -[SwitchingDeviceTypes] - : Optional parameter – default is "Breaker" - This is a comma-separated list naming the Branch Device Types for - -switching devices that should be included when determining which - - 114 - - - -devices to open to disconnect objects. Options include "Breaker" and -"Load Break Disconnect". - -OpenNormallyOpenDisconnects - : optional parameter, default is NO. - -YES : When searching for the specified SwitchingDeviceTypes and a -Disconnect is encountered that is closed but normally open, it -will be opened and the search for closed switching devices will -terminate along that path. - -NO : Only switching devices of the specified SwitchingDeviceTypes will -be opened. - - -This command disconnects the transmission line from bus 1 to bus 2 ckt 1, by opening associated -breakers only. Since OpenNormallyOpenDisconnects is set to NO, the command will not change -the status of any disconnects that are normally open. -OpenWithBreakers(BRANCH, [1 2 1], ["Breaker"], NO); - -SaveConsolidatedCase("filename", filetype, [BusFormat, TruncateCtgLabels, -AddCommentsForObjectLabels]); - - This action saves the full topology model into a consolidated case. -"filename" : The name of the consolidated case file to be saved. -Filetype : Optional parameter to specifiy the type of the file to be saved. If - -omitted, the latest version of the PWB will be used. -PWB : save a pwb file with the most recent version -PWBX : save a pwb with version X -PTIXX : save the file with PTI version XX, where XX is between 23 and - -35 -GEXX : save the file with GE PSLF version XX, where XX is between 14 - -and 23 -BusFormat : optional parameter used to specifiy the bus identifier format in the .CON - -file used to store contingencies when saving a PTI file -Number : identify buses using number -Name8 : identify buses using the Name_kV identifier truncated to 8 - -characters -Name12 : identify buses usingthe Name_kV identifier truncated to 12 - -characters -TruncateCTGLabels : optional parameter used to specify if the contingency labels should be - -truncated to 12 characters when saving the contingencies in PTI format -YES : truncate the contingency labels to 12 characters -NO : do not truncate the contingency labels - -AddCommentsForObjectLabels - : (optional) YES adds object labels to the end of data records when saving - -a RAW file. (default NO) - - -This command saves the consolidated case of a full topology model to a .pwb file named -"B7Flat_ConsolidatedCopy.pwb". -SaveConsolidatedCase("B7Flat_ConsolidatedCopy.pwb", PWB); - - 115 - - - -OPF (Optimal Power Flow) and SCOPF -SolvePrimalLP("filename1", "filename2", CreateIfNotFound1, CreateIfNotFound2); - -Call this action to perform a primal LP OPF solution. The parameters are all optional and specify a -conditional response depending on whether the solution is successfully found. If parameters are not -passed then default values will be used. - -"filename1" : The filename of the auxiliary file to be loaded if there is a successful -solution. You may also specify STOP, which means that all AUX file -execution should stop under the condition. Default Value = "". - -"filename2" : The filename of the auxiliary file to be loaded if there is a NOT successful -solution. You may also specify STOP, which means that all AUX file -execution should stop under the condition. Default Value = "". - -CreateIfNotFound1 : Optional – default is NO when using the Legacy Auxiliary File Header -format. Default is YES when using the Concise Auxiliary File Header -format. - - This parameter is only enforced when the Create_if_not_found field -for a Legacy Auxiliary File Header is set to PROMPT. Otherwise, YES is -assumed. YES means that objects that cannot be found will be created -while reading DATA sections from "filename1". - -CreateIfNotFound2 : Optional – default is NO when using the Legacy Auxiliary File Header -format. Default is YES when using the Concise Auxiliary File Header -format. - - This parameter is only enforced when the Create_if_not_found field -for a Legacy Auxiliary File Header is set to PROMPT. Otherwise, YES is -assumed. YES means that objects that cannot be found will be created -while reading DATA sections from "filename2". - - -This command attempts to solve a primal linear programming optimal power flow (LP OPF) -problem. If the solution is successful, the auxiliary file "LP_Success.aux" is loaded with object -creation allowing for any missing elements referred to in DATA sections to be created. If the -solution fails, "LP_Failure.aux" is loaded, but new objects will not be created during the load for -DATA sections in the Legacy Auxiliary File Header format. -SolvePrimalLP("LP_Success.aux", "LP_Failure.aux", YES, NO); - -InitializePrimalLP("filename1", "filename2", CreateIfNotFound1, CreateIfNotFound2); -This commands clears all the structures and results of previous primal LP OPF solutions. The parameters -are all optional and specify a conditional response depending on whether the solution is successfully -found. If parameters are not passed then default values will be used. - -"filename1" : The filename of the auxiliary file to be loaded if there is a successful -solution. You may also specify STOP, which means that all AUX file -execution should stop under the condition. Default Value = "". - -"filename2" : The filename of the auxiliary file to be loaded if there is a NOT successful -solution. You may also specify STOP, which means that all AUX file -execution should stop under the condition. Default Value = "". - -CreateIfNotFound1 : Optional – default is NO when using the Legacy Auxiliary File Header -format. Default is YES when using the Concise Auxiliary File Header -format. - - This parameter is only enforced when the Create_if_not_found field -for a Legacy Auxiliary File Header is set to PROMPT. Otherwise, YES is -assumed. YES means that objects that cannot be found will be created -while reading DATA sections from "filename1". - - 116 - - - -CreateIfNotFound2 : Optional – default is NO when using the Legacy Auxiliary File Header -format. Default is YES when using the Concise Auxiliary File Header -format. - - This parameter is only enforced when the Create_if_not_found field -for a Legacy Auxiliary File Header is set to PROMPT. Otherwise, YES is -assumed. YES means that objects that cannot be found will be created -while reading DATA sections from "filename2". - - -This command clears all data and results from previous primal LP OPF runs. If the initialization is -successful, it loads the auxiliary file "LP_InitSuccess.aux"; if it fails, it loads "LP_InitFail.aux". In both -cases any objects contained in DATA sections using the Legacy Auxiliary File Header format will -not be created. -InitializePrimalLP("LP_InitSuccess.aux", "LP_InitFail.aux", NO, NO); - -SolveSinglePrimalLPOuterLoop("filename1", "filename2", CreateIfNotFound1, CreateIfNotFound2); -This action is basically identical to the SolvePrimalLP action, except that this will only perform a single -optimization. The SolvePrimalLP will iterate between solving the power flow and an optimization until this -iteration converges. This action will only solve the optimization routine once, then resolve the power flow -once and then stop. - -SolveFullSCOPF (BCMethod, "filename1", "filename2", CreateIfNotFound1, CreateIfNotFound2); -Call this action to perform a full Security Constrained OPF solution. The parameters are all optional and -specify a conditional response depending on whether the solution is successfully found. If parameters are -not passed then default values will be used. - -BCMethod : The solution method to be used for solving the base case. The options -are: - -POWERFLOW : for single power flow algorithm (default) -OPF : for the optimal power flow algorithm. - -"filename1" : The filename of the auxiliary file to be loaded if there is a successful -solution. You may also specify STOP, which means that all AUX file -execution should stop under the condition. Default Value = "". - -"filename2" : The filename of the auxiliary file to be loaded if there is a NOT successful -solution. You may also specify STOP, which means that all AUX file -execution should stop under the condition. Default Value = "". - -CreateIfNotFound1 : Optional – default is NO when using the Legacy Auxiliary File Header -format. Default is YES when using the Concise Auxiliary File Header -format. - - This parameter is only enforced when the Create_if_not_found field -for a Legacy Auxiliary File Header is set to PROMPT. Otherwise, YES is -assumed. YES means that objects that cannot be found will be created -while reading DATA sections from "filename1". - -CreateIfNotFound2 : Optional – default is NO when using the Legacy Auxiliary File Header -format. Default is YES when using the Concise Auxiliary File Header -format. - - This parameter is only enforced when the Create_if_not_found field -for a Legacy Auxiliary File Header is set to PROMPT. Otherwise, YES is -assumed. YES means that objects that cannot be found will be created -while reading DATA sections from "filename2". - - - - 117 - - - - -This command performs a full Security Constrained Optimal Power Flow (SCOPF) using the OPF -algorithm for the base case solution. If the SCOPF completes successfully, "SCOPF_Success.aux" is -loaded with object creation allowing for any missing elements referred to in DATA sections to be -created. If it fails, "SCOPF_Failure.aux" is loaded, but new objects will not be created during the -load for DATA sections in the Legacy Auxiliary File Header format. -SolveFullSCOPF(OPF, "SCOPF_Success.aux", "SCOPF_Failure.aux", YES, NO); - -OPFWriteResultsAndOptions("filename"); -Writes out all information related to OPF analysis as an auxiliary file. This includes Limit Monitoring -Settings, options for Areas, Buses, Branches, Interfaces, Generators, SuperAreas, OPF Solution Options. - - 118 - - - -PV Analysis -Changes were made with Simulator version 14 to eliminate the need for a PV study name. To maintain functionality with -any existing processes that users might have in place using older script definitions, scripts from older versions of Simulator -will still be supported if the name is specified. However, the name will just be ignored. The script formats given here -reflect the changes for versions 14 and later. - -The PVCreate script required in previous versions is no longer necessary starting with Simulator version 14. Versions -starting with 14 will still recognize this action if is included and will simply set the source and sink for the study. This does -the same thing as PVSetSourceAndSink. - -It is highly recommended that for any new processes the new script formats specified here be used. - -PVClear; -Call the function to clear all the results of the PV study. - -PVDataWriteOptionsAndResults("filename", AppendFile, KeyField); -Writes out all information related to PV analysis to an auxiliary file. Saves the same information as the -PVWriteResultsAndOptions script command. Auxiliary file is formatted using the concise format for DATA -section headers and variable names. Data is written using DATA sections instead of SUBDATA sections. - -"filename" : Name of the auxiliary file to save. See the Specifying File Names in Script -Commands section for special keywords that can be used when -specifying the file name. - -AppendFile : Optional parameter - default is YES - YES means to append results to existing "filename." NO means to - -overwrite "filename" with the results. -KeyField : Optional parameter – default is PRIMARY - -Indicates the identifier that should be used for the data. Valid entries are -PRIMARY, SECONDARY, or LABEL. PRIMARY will save using bus numbers -and other primary key fields. SECONDARY will save using bus name and -nominal kV and other secondary fields. LABEL will save using device -labels. If no labels are specified then the primary key field will be used. - - -This command saves all information related to PV (Power-Voltage) analysis to an auxiliary file -named "PV_Results.aux". It overwrites any existing file (AppendFile = NO) and uses primary key -fields (like bus numbers) for identifying elements in the output (KeyField = PRIMARY). -PVDataWriteOptionsAndResults("PV_Results.aux", NO, PRIMARY); - -PVDestroy; -Call the function to destroy the PV study. This will remove all results and prevent any restoration of the -initial state that is stored with the PV study. - -PVQVTrackSingleBusPerSuperBus; -If the topology processing add-on is installed, then this script command can be used to reduce the -number of monitored buses. The script action examines each monitored value for each bus and -determines if that bus is part of a super bus and selects monitored buses so that only the pnode is -monitored. - -PVRun([elementSource], [elementSink]); -Call this function start the PV study and optionally specify the source and sink elements. - -[elementSource] : Optional parameter – default to using element already set - The source of power for the PV study. Only injection groups can be used: - -[INJECTIONGROUP "name"] or [INJECTIONGROUP "label"] -[elementSink] : Optional parameter – default to using element already set - - 119 - - - - The sink of power for the PV study. Only injection groups can be used: -[INJECTIONGROUP "name"] or [INJECTIONGROUP "label"] - - -This command starts a PV (Power-Voltage) analysis using "SolarGen" as the source injection -group and "UrbanLoad" as the sink injection group. These groups define where power will be -incrementally injected and withdrawn during the study to analyze system voltage response. -PVRun([INJECTIONGROUP "SolarGen"], [INJECTIONGROUP "UrbanLoad"]); - -PVSetSourceAndSink([elementSource], [elementSink]); -Call the function to specify the source and sink elements to perform the PV study. - -[elementSource] : The source of power for the PV study. There is only one possible setting: -[INJECTIONGROUP "name"] or [INJECTIONGROUP "label"] - -[elementSink] : The sink of power for the PV study. There is only one possible setting, -which is the same as for the source. - - -This command sets up the source and sink for a PV study, designating "SolarGen" as the power -source and "UrbanLoad" as the sink. -PVSetSourceAndSink([INJECTIONGROUP "SolarGen"], [INJECTIONGROUP -"UrbanLoad"]); - -PVStartOver; -Call the function to start over the PV study. This includes clear the activity log, clear results, restore the -initial state, set the current state as initial state, and initialize the step size. - -PVWriteInadequateVoltages("filename", AppendFile, InadequateType); -Call this action to save PV Inadequate Voltages in a CSV file. - - “filename” : Name of the CSV file to save. See the Specifying File Names in Script -Commands section for special keywords that can be used when -specifying the file name. - -AppendFile : Optional parameter – default is YES - Set this to YES or NO. Setting this to YES will cause the data to be - -appended to an existing file. Setting this to NO will cause any existing -file to be overwritten. - -InadequateType : Optional parameter – default is LOW - Set this to HIGH or LOW to indicate the type of inadequate voltages to - -save to file. - - -This command saves all low inadequate voltages identified during a PV study to the file -"PV_LowVoltages.csv". It overwrites any existing file (AppendFile = NO) and specifically filters for -LOW voltage violations (InadequateType = LOW). -PVWriteInadequateVoltages("PV_LowVoltages.csv", NO, LOW); - -PVWriteResultsAndOptions("filename", AppendFile); -Writes out all information related to PV analysis to an auxiliary file. This includes Contingency Definitions, -Remedial Action Definitions, Solution Options, PV Options, PV results, ATC Extra Monitors, as well as any -Model Criteria that are used by the Contingency and Remedial Action Definitions. - - -Contingency and Remedial Action definitions will always be saved along with dependencies and only -those object types that are dependencies will be saved. This means that if a Remedial Action definition -uses a Model Filter, that Model Filter along with any Model Filter Conditions, i.e. other Model Filters or -Model Conditions, will be saved. If a model criteria object is not being used by a Remedial Action or -Contingency it will not be saved. Objects that are saved if they are dependencies include: Model - - 120 - - - -Conditions, Model Filters, Model Planes, Model Expressions, Model Result Overrides, Interfaces, Injection -Groups, Calculated Fields, and Expressions. - - -Dependencies for the PV setup are also included. This includes Injection Groups that are used as the -seller or buyer and Interfaces that are used as part of interface ramping options. - - -“filename” : Name of the auxiliary file to save. See the Specifying File Names in Script - -Commands section for special keywords that can be used when -specifying the file name. - -AppendFile : Optional parameter – default is YES - Set this to YES or NO. Setting this to YES will cause the data to be - -appended to an existing file. Setting this to NO will cause any existing -file to be overwritten. - -RefineModel(objecttype, filter, Action, Tolerance); -Call this function to refine the system model to fix modeling idiosyncrasies that cause premature loss of -convergence during PV and QV studies. - -Objecttype : The objecttype being selected: -AREA -ZONE - -Filter : Specify a filter to limit the objects that will be included. -Blank : Select all objects of specified type -AREAZONE : Only objects that meet the area/zone/owner filters will - -be selected -SELECTED : Only objects whose Selected field = YES will be - -selected -“FilterName" : Only objects that meet the specified filter will be - -selected. See Using Filters in Script Commands section -for more information on specifying the filtername. - -Action : The way the model will be refined. Choices are: -TRANSFORMERTAPS : Fix all transformer taps at their present values - -if their Vmax – Vmin is less than or equal to -the user specified tolerance. - -SHUNTS : Fix all shunts at their present values if their -Vmax – Vmin is less than or equal to the user -specified tolerance. - -OFFAVR : Remove units from AVR control, thus locking -their MVAR output at its present value if their -Qmax – Qmin is less or equal to the user -specified tolerance. - -Tolerance : Tolerance value. - - -This command refines the power system model by fixing transformer tap settings in all areas -where the difference between the transformer's voltage maximum and minimum (Vmax - Vmin) -is less than or equal to 0.02 p.u. -RefineModel(AREA, , TRANSFORMERTAPS, 0.02); - - - - 121 - - - -QV Analysis -QVDataWriteOptionsAndResults("filename", AppendFile, KeyField); - -Writes out all information related to QV analysis to an auxiliary file. Saves the same information as the -QVWriteResultsAndOptions script command. Auxiliary file is formatted using the concise format for DATA -section headers and variable names. Data is written using DATA sections instead of SUBDATA sections. - -"filename" : Name of the auxiliary file to save. See the Specifying File Names in Script -Commands section for special keywords that can be used when -specifying the file name. - -AppendFile : Optional parameter - default is YES - YES means to append results to existing "filename." NO means to - -overwrite "filename" with the results. -KeyField : Optional parameter – default is PRIMARY - -Indicates the identifier that should be used for the data. Valid entries are -PRIMARY, SECONDARY, or LABEL. PRIMARY will save using bus numbers -and other primary key fields. SECONDARY will save using bus name and -nominal kV and other secondary fields. LABEL will save using device -labels. If no labels are specified then the primary key field will be used. - - -This command writes all QV (Reactive Power vs. Voltage) analysis data and configuration options -to an auxiliary file named "QV_Results.aux". It overwrites any existing content (AppendFile = NO) -and uses primary key fields (like bus numbers) for identifying devices in the file. -QVDataWriteOptionsAndResults("QV_Results.aux", NO, PRIMARY); - -QVDeleteAllResults; -Added in October 31, 2023 patch of Simulator version 23 -Deletes all QV results including QVCurve and PWQVResultListContainer object types. - -QVRun("filename", InErrorMakeBaseSolvable, DoDistributed); -Call the function to start a QV study for the list of buses whose QVSELECTED field is set to YES. - -"filename" : Optional parameter - This specifies the file to which to save a comma-delimited version of the - -results. If not specified the QV curve results will not be saved to file -during the analysis. - -InErrorMakeBaseSolvable - : Optional parameter – default is YES - This specifies whether to perform a solvability analysis of the base case if - -the pre-contingency base case cannot be solved. -DoDistributed : Optional parameter – default is NO - Set to YES or NO. If set to YES, distributed methods will be used to solve - -QV if the Distributed QV add-on is installed. Distributed analysis requires -the proper configuration and security settings to work. - - -This command starts a QV analysis for all buses where the QVSELECTED field is set to YES. The -results are saved to "QV_CurveResults.csv". If the base case cannot be solved initially, setting -InErrorMakeBaseSolvable to YES will trigger a solvability check and fix the issue if possible. -QVRun("QV_CurveResults.csv", YES); - -QVSelectSingleBusPerSuperBus; -If the QV tool is being used on a full topology model, this action can be used to modify the monitored -buses. This action examines the monitored buses and sets the monitored status so that only one bus is -monitored for each pnode. - - - 122 - - - -QVWriteCurves("filename", IncludeQuantitiesToTrack, filter, Append); -This will save a comma-separated text file with the QV curve points. - -"filename" : Name of the comma-separated file to save. See the Specifying File -Names in Script Commands section for special keywords that can be -used when specifying the file name. - -IncludeQuantitiesToTrack - : Set this to YES or NO. Set this to YES to also include any Quantities to - -Track along with the QV curve points. -filter : Specify a filter that is applied to the QVCurve object type. Specifying a - -blank will select all curve results. See Using Filters in Script Commands -section for more information on specifying the filtername. AREAZONE -filtering is ignored with the QVCurve object type and will return all -results. - -Append : Set this to YES or NO. Setting this to YES will cause the data to be -appended to an existing file. Setting this to NO will cause any existing -file to be overwritten. - - -This command saves QV curve data to the file "QV_CurvePoints.csv", including any Quantities to -Track (IncludeQuantitiesToTrack = YES). It does not use a filter, meaning all QV curves will be -included, and it overwrites any existing file with the same name (Append = NO). -QVWriteCurves("QV_CurvePoints.csv", YES, , NO); - -QVWriteResultsAndOptions("filename", AppendFile); -Writes out all information related to QV analysis to an auxiliary file. This includes Contingency Definitions, -Remedial Action Definitions, Solution Options, QV Options, QV results, as well as any Model Criteria that -are used by the Contingency and Remedial Action Definitions. - - -Contingency and Remedial Action definitions will always be saved along with dependencies and only -those object types that are dependencies will be saved. This means that if a Remedial Action definition -uses a Model Filter, that Model Filter along with any Model Filter Conditions, i.e. other Model Filters or -Model Conditions, will be saved. If a model criteria object is not being used by a Remedial Action or -Contingency it will not be saved. Objects that are saved if they are dependencies include: Model -Conditions, Model Filters, Model Planes, Model Expressions, Model Result Overrides, Interfaces, Injection -Groups, Calculated Fields, and Expressions. - -“filename” : Name of the auxiliary file to save. See the Specifying File Names in Script -Commands section for special keywords that can be used when -specifying the file name. - -AppendFile : Optional parameter – default is YES - Set this to YES or NO. Setting this to YES will cause the data to be - -appended to an existing file. Setting this to NO will cause any existing -file to be overwritten. - - - - - - - 123 - - - -Regions -RegionLoadShapefile("FileName", "Class Name", [AttributeNames], AddToOpenOnelines, -"DisplayStyleName", DeleteExistingShapes); - -(Added in the August 12, 2025 patch of Simulator 24) -This action loads the shapes from a shapefile with options to add the shapes to any open valid onelines -and to delete existing shapes. An example, is -RegionLoadShapeFile(“c:\tmp\cb_2018_us_county_500k.shp”,”US_Counties”, [STATEFP,NAME,GEOID], YES); -This example files is available at www.census.gov/geographies/mapping-files/time-series/geo/carto- -boundary-file.html - -“FileName” : Name of the shape file (*.shp) to be loaded. -“Class Name” : Class name field; though not recommended, it may be blank. -[AttributeNames] : Set of up to three shapefile attribute names, which will be used to set the - -proper names; at least one must be set to a valid attribute. This -command will fail if any of the specified attributes are not found. - -AddToOpenOnelines : If YES then then new regions are automatically added to any open -onelines the have a valid geographic projection; default is NO. - -“DisplayStyleName” : Optional parameter to specify the DisplayStyle to use if regions are being -added to open onelines; default is blank and the field is only used when -adding regions to open onelines. - -DeleteExistingShapes : Optional parameter to first delete any existing shapes; default is NO. -RegionRename("OldName","NewName", UpdateOnelines); - -(Added in the April 21, 2025 patch of Simulator 24) -This action will change the name of an existing region. - -"OldName" : Name of an existing region. -"NewName" : New name of the region. -UpdateOnelines : Optional parameter – default is YES - Set to YES or NO. If set to YES, any regions on all open onelines with the - -old name will be changed to the new name. -RegionRenameClass("OldClassName","NewClassName", UpdateOnelines, filter); - -(Added in the April 21, 2025 patch of Simulator 24) -This action will change the class name of all the regions with the OldClassName names. - -"OldClassName" : Class name for some existing region. May be empty to select those with -no existing class name. - -"NewClassName" : New class name for the specified regions. May be empty. -UpdateOnelines : Optional parameter – default is YES - Set to YES or NO. If set to YES, any regions on all open onelines with the - -old name will be changed to the new name. -filter : Optional parameter with default apply to all that meet the original name - -criteria. Filter is used to limit the regions by their initial names. See the -Using Filters in Script Commands section for more information on -specifying the filtername. - -RegionRenameProper1("OldProper1Name","NewProper1Name", UpdateOnelines, filter); -(Added in the April 21, 2025 patch of Simulator 24) -This action will change the proper1 name of all the regions with the OldProper1Name names. - -"OldProper1Name" : Old Proper1 name. May be empty. -"NewProper1Name" : New Proper1 name. May be empty. -UpdateOnelines : Optional parameter – default is YES - Set to YES or NO. If set to YES, any regions on all open onelines with the - -old name will be changed to the new name. - - 124 - - - -filter : Optional parameter with default apply to all that meet the original name -criteria. Filter is used to limit the regions by their initial names. See the -Using Filters in Script Commands section for more information on -specifying the filtername. - -RegionRenameProper2("OldProper2Name","NewProper2Name", UpdateOnelines, filter); -(Added in the April 21, 2025 patch of Simulator 24) -This action will change the proper2 name of all the regions with the OldProper2Name name. - -"OldProper2Name" : Old proper2 name. May be empty. -"NewProper2Name" : New proper2 name. May be empty. -UpdateOnelines : Optional parameter – default is YES - Set to YES or NO. If set to YES, any regions on all open onelines with the - -old name will be changed to the new name. -filter : Optional parameter with default apply to all that meet the original name - -criteria. Filter is used to limit the regions by their initial names. See the -Using Filters in Script Commands section for more information on -specifying the filtername. - -RegionRenameProper3("OldProper3Name","NewProper3Name", UpdateOnelines, filter); -(Added in the May 24, 2025 patch of Simulator 24) -This action will change the proper3 name of all the regions with the OldProper3Name name. - -"OldProper3Name" : Old proper3 name. May be empty. -"NewProper3Name" : New proper3 name. May be empty. -UpdateOnelines : Optional parameter – default is YES - Set to YES or NO. If set to YES, any regions on all open onelines with the - -old name will be changed to the new name. -filter : Optional parameter with default apply to all that meet the original name - -criteria. Filter is used to limit the regions by their initial names. See the -Using Filters in Script Commands section for more information on -specifying the filtername. - -RegionRenameProper12Flip(UpdateOnelines, filter); -(Added in the April 21, 2025 patch of Simulator 24) -This command flips the proper1 and proper2 names for either all regions if the filter is empty, or for the -regions that meet the filter. - -UpdateOnelines : Optional parameter – default is YES - Set to YES or NO. If set to YES, any regions on all open onelines with the - -old name will be changed to the new name. -filter : Optional parameter – default is to flip all regions. Filter is used to limit - -the regions by their initial names. See the Using Filters in Script -Commands section for more information on specifying the filtername. - -RegionUpdateBuses; -(Added in the April 18, 2025 patch of Simulator 24) -Updates the buses in all the regions. - - - - - 125 - - - -TS (Transient Stability) -TSAutoCorrect; - -Runs the auto correction of parameters for a transient stability run. If there are still validation errors after -running this script that would prevent the stability simulation from running, then the remainder of a script -will be aborted. - -TSAutoInsertDistRelay(Reach, AddRelayAtFromBus, AddRelayAtToBus, DoTransferTrip, Shape, filter); -Inserts DistRelay models on the lines meeting the specified filter. - -Reach : Zone 1 reach -AddRelayAtFromBus : Add relay at the FROM bus: YES/NO. -AddRelayAtToBus : Add relay at the TO bus: YES/NO. -DoTransferTrip : Do transfer trip: YES/NO -Shape : Shape: 0 - Circle; 1 - Rectangle; 2 - Reactance Distance; 3 - Impedance - -Distance. -filter : Lines meeting this filter will have DistRelay models inserted. See the - -Using Filters in Script Commands section for information on specifying -the filter. - - -This command inserts impedance distance relays with 80% Zone 1 reach at both ends of all -transmission lines meeting the specified area/zone/owner filters, enables transfer tripping, and -uses an impedance-based protection shape. -TSAutoInsertDistRelay(80, YES, YES, YES, 3, AREAZONE); - -TSAutoInsertZPOTT(Reach, filter,); -Inserts ZPOTT models on the lines meeting the specified filter. - -Reach : Zone 1 reach -filter : Lines meeting this filter will have ZPOTT models inserted. See the Using - -Filters in Script Commands section for information on specifying the -filter. - - -This command inserts ZPOTT protection (Zone 1 permissive overreaching transfer trip) on all lines -that meet the area/zone/owner filters, with the Zone 1 reach set to 50% of each line's impedance. -TSAutoInsertZPOTT(50, AREAZONE); - -TSAutoSavePlots ([PlotNames], [ContingencyNames], ImageFileType, ImageWidth, ImageHeight, -ImageFontScalar, IncludeCaseName, IncludeCategory); - -Added in November 21, 2024 patch of Simulator 23 -Create and save images of the plots in the same manner as the AutoSave an image in the transient -stability dialog works. The script will ONLY plot the plots defined in the [PlotNames] and only will plot the -result of one contingency as defined in the [ContingencyNames]. It will NOT PLOT Multiple contingencies -in the same plot. Files are saved to the directory specified in the Result Storage -> Save to Hard Drive -Options. Filename is by default “ContingencyName_PlotName.jpg”. - -PlotNames : Specifies for which Plot/Plots will be plotted. If left blank it will plot all of -the plots in the case. - -ContingencyNames : Specifies for which Transient Contingencies the plots will be plotted. If -left in blank it will use all the contingencies in the case. - -ImageFileType : (optional) ImageFileType choices are: JPG, EMF, BMP, GIF, PNG or PDF. If -an invalid string is entered, JPG is used. Default is JPG. - -ImageWidth : (optional) Image Pixel Width. Default is 800. -ImageHeight : (optional) Image Pixel Height. Default is 600. -ImageFontScalar : (optional) Image Font Scalar. Default is 1. -IncludeCaseName : (optional) Include Case Name in the file name: YES/NO. Default is NO. - - 126 - - - -IncludeCategory : (optional) Include Category in the file name: YES/NO. Default is NO. - - -Here are two examples of saving the plot named "The Plot" to file for the contingency named -"My Transient Contingency". The first uses all default settings and the second specifies the -settings. -TSAutoSavePlots(["The Plot"], ["My Transient Contingency"]); -TSAutoSavePlots(["The Plot"], ["My Transient Contingency"], JPEG, 800, 600, 1, No, No); - -TSCalculateCriticalClearTime([branch] or filter,); -Use this action to calculate critical clearing time for faults on the lines that meet the specified filter. - -[line] or filter : A single line can be specified in the format [BRANCH keyfield1 keyfield2 -ckt] or [BRANCH label]. Multiple lines can be selected by specifying a -filter. See the Using Filters in Script Commands section for information -on specifying the filter. For the specified lines, this calculation will -determine the first time a violation is reached (critical clearing time), -where a violation is determined based on all enabled Transient Limit -Monitors. For each line, results are saved as a new Transient Contingency -(named CritCTG) on a line, with the fault duration equal to the critical -clearing time. - - -This command finds the line that goes from Bus 1 to Bus 2 with circuit ID 1 and runs a transient -fault simulation on that line to compute its critical clearing time (CCT). -TSCalculateCriticalClearTime([BRANCH 1 2 1]); - -TSCalculateSMIBEigenValues; -Calculate single machine infinite bus eigenvalues. Initialization to the start time is always done before -calculating eigenvalues. - -TSClearAllModels; -Clears all transient stability models. Load Model Groups are considered part of the case and will not be -deleted. - -TSClearModelsforObjects(ObjectType, Filter); -(Added in the October 17, 2025 patch of Simulator 24) -This action deletes all transient stability models associated with the objects that meet the filter. - -ObjectType : Specifies which object types to act on. The ObjectType is the object name -of supported objects such as GEN, BUS, BRANCH, etc. - -Filter : (optional) Only objects that meet the specified filter will be modified. If -the filter is not specified, all objects of the specified type will have their -transient stability models deleted. See the Using Filters in Script -Commands section for more information on specifying the filter. - - -This is an example of clearing the transient model of an individual generator using a device filter. -TSClearModelsForObjects(Gen, "Gen 123 1"); - -TSClearResultsFromRAM(ALL/SELECTED/”ContingencyName”, ClearSummary, ClearEvents, -ClearStatistics, ClearTimeValues, ClearSolutionDetails); - -Clears results from RAM of one or more specified Transient Contingencies. -ALL/SELECTED/”ContingencyName” - : Specifies for which Transient Contingencies results are cleared. -ClearSummary : (optional) YES to clear, NO to leave them alone. -ClearEvents : (optional) YES to clear, NO to leave them alone. -ClearStatistics : (optional) YES to clear, NO to leave them alone. - - 127 - - - -ClearTimeValues : (optional) YES to clear, NO to leave them alone. -ClearSolutionDetails : (optional) YES to clear, NO to leave them alone. - - -This command clears specific transient stability simulation results from RAM for the contingency -named "Contingency1". It clears the summary, statistics, and time value data, while retaining the -event log and solution details. -TSClearResultsFromRAM("Contingency1", YES, NO, YES, YES, NO); - -TSDisableMachineModelNonZeroDerivative(DerivativeThreshold); -The transient stability solution is initialized and state derivatives are calculated. The state derivatives for all -generator models are checked, and for any where the absolute value is greater than the -DerivativeThreshold, the machine model for the generator is disabled. Some models may have state -derivatives where it is valid for the derivatives to initialize to a non-zero value. These states will be -excluded from the derivative check. - -DerivativeThreshold : Optional parameter – default is 0.001 - Absolute value of a state’s derivative must be greater than this value to - -disable a model. -TSGetVCurveData("FileName", filter); - -For a synchronous generator a curve of points for field current and field voltage is created from a fixed -terminal voltage and MW (P) power output and varying Mvar (Q) output. - -FileName : Name of file to create. If no file extension is specified this defaults to -CSV. - -filter : Specifies for which generators curves are created. -SELECTED : only generators whose Selected field = YES will be - -included -AREAZONE : only generators that meet the area/zone/owner filters - -will be included -"FilterName" : only generators that meet the specified filter will be included. See the - -Using Filters in Script Commands section for more information on -specifying the filtername. - - -This command generates V-curve data (field current and voltage vs. reactive power output) for all -generators with their Selected field set to YES and saves the results to a CSV file named -"GenVCurves.csv". -TSGetVCurveData("GenVCurves.csv", SELECTED); - -TSGetResults("FileName", SINGLE/SEPARATE/JSIS, [Contingencies], [Plots, ObjectFields], StartTime, -EndTime]); - -Use this to save out results for specific variables from plots, subplots, and object/field pairs after a -transient stability simulation has been run. If StartTime and StopTime are not specified, results for the -entire simulation time are obtained. - -FileName : Name of the CSV result file to write out -SINGLE/SEPARATE/JSIS - : Determines whether the results are all saved in one file (SINGLE) with - -name “filename” or whether results for each transient contingency is -saved in a separate file (SEPARATE) with name "filename_ctgname.csv." -A separate header file is also saved out, with a name of -“filename_Header.csv”. If using the JSIS format, a single file with the -name "filename" is written in the WECC JSIS format. - -Contingencies : A list of contingency names for which results are saved -Plots, ObjectFields : A list of plots and object/field pairs to save out for the specified - -contingencies. For plots, write PLOT and then the name of the plot. For - 128 - - - -ObejctsFields you need the Obejct name followed by the key fields and a -vertical line ( | ), then the field. - - [Added in November 17, 2023 patch of Simulator 23] - May also specify the syntax - "ObjectName FilterName | FieldName". - For this syntax anything after the first space and before the | character is - -considered a FilterName. The FilterName can be All indicating that all -objects of the type ObjectName are used, or it can use the syntax -available for defining a FilterName as described in the Using Filters in -Script Commands section. - -StartTime : Start of the window of simulation time for retrieved results -EndTime : End of the window of simulation time for retrieved results - - -This is an example of saving transient stability results to file using a combination of plot -definitions, specific fields for specific objects, and a specific field for all objects of a particular -type. -TSGetResults("D:\Cases\Scripts\Test.CSV",SINGLE,["My Transient -Contingency"], ["PLOT Gen_Rotor Angle","Bus 4 | frequency","PLOT -Bus_Frequency","GEN 1 '1'| TSGenMachineState:2", "Bus All | -TSFrequency"],0,10); - -TSInitialize(CheckInitialized); -Initialize the transient stability solution. This is useful to calculate the initial state and state derivative -values. It is not necessary to call this step manually as it will be done automatically with any other -transient analysis, but it is useful if analysis of the initial states is desired without doing any additional -calculations. Any validation errors must be corrected before initialization can be done. - -CheckInitialized : Optional parameter – default is NO - Set to YES to check if the transient solution has already been initialized - -and not to initialize again if it has. Set to NO to initialize whether or not -the transient solution has already been initialized. - -TSJoinActiveCTGs(TimeDelay,DeleteExisting,JoinWithSelf,"fileName",FirstCtg); -Added in March 21, 2024 patch of Simulator 23 -This command joins two lists of TSContingency objects with a specified time delay in seconds. - -TimeDelay : Time delay in seconds -DeleteExisting : Set to YES or NO. YES means to delete the existing contingencies and - -only keep the joined contingencies. -JoinWithSelf : Set to YES or NO. YES means that the current contingency list will be - -joined with itself instead of contingencies specified in a file. If set to YES, -the "filename" parameter does not have to be specified. - -"filename" : Name of auxiliary file containing contingencies to join with the current -contingency list. This does not have to be specified if JoinWithSelf = YES. -It must be specified if JoinWithSelf=NO. - -FirstCtg : Optional parameter which can be Active, AUX, or Both and defaults to -Both if not specified. -If FirstCTG=Active, then each existing active TSContingency object is -joined with the active Contingencies in the AUX file specified by -Filename. All TSContingencyElement objects in the AUX file are shifted -by the TimeDelay value. -If FirstCTG=AUX, TSContingency objects in the AUX file have the same -timing and they are joined with the existing Active TSContingency -objects with the existing elements time shifted by the time delay. - - 129 - - - -If FirstCTG=Both, then you get both options of list (A + BTimeDelay) and -(B + ATimeDelay). - - -Here are two examples of joining two lists of transient stability contingencies. The first example -joins the contingencies in memory with the contingencies in the specified file. The second -example joins the contingencies in memory with each other. -TSJoinActiveCTGs(10.0, NO, NO, "c:\temp\Myfile.aux", Both); -TSJoinActiveCTGs(10.0, NO, YES); - -TSLoadBPA("FileName"); -Loads transient stability data stored in the BPA format. - -FileName : Name of the BPA file to load -TSLoadGE("FileName", GENCCYN, EnableOutOfOrderModels); - -Loads transient stability data stored in the GE DYD format. -FileName : Name of the DYD file to load -GENCCYN : YES to split combined cycle units, NO to leave them alone -EnableOutOfOrderModels - : (optional) Default is YES. If set to YES, models that are specified out of - -order in the file will be enabled. If set to NO, out of order models will be -disabled. - - -This command loads transient stability data from "case.dyd". It splits combined cycle units -(GENCCYN = YES) and enables models even if they are specified out of order -(EnableOutOfOrderModels = YES). -TSLoadGE("case.dyd", YES, YES); - -TSLoadPTI("FileName", "MCREfilename", "MTRLOfilename", "GNETfilename", "BASEGENfilename", -“MODREMOVEfilename); - -Loads transient stability data in the PTI format. -FileName : Name of the DYR file to load -MCREfilename : (optional) If not loading a MCRE file, specify "" -MTRLOfilename : (optional) If not loading a MTRLO file, specify "" -GNETfilename : (optional) If not loading a GNET file, specify "" -BASEGENfilename : (optional) If not loading a BASEGEN file, specify "" -MODREMOVEfilename : (optional) if not loading a MODREMOVE file, specify "" - - -This command loads transient stability dynamic model data from the PTI DYR file "case.dyr". All -optional file parameters like MCRE, MTRLO, GNET, BASEGEN, and MODREMOVE are left empty, -indicating that only the DYR file is used for loading data. -TSLoadPTI("C:\TSData\case.dyr", "", "", "", "", ""); - -TSLoadRDB("filename", ModelType, filter); -Loads a SEL RDB file. The RDB file is a Schweitzer format for describing a relay. This command will load -the file and translate the relay settings into a PowerWorld transient stability model. An attempt is made -to match up the protected lines in the power flow model with the SID field in the RDB data. A SID in the -format "FROM SUB/BREAKERS/TO SUB" is expected. If the SID does not match this format, or a match is -not found in the case, the objects can be linked to the transient stability model in the user interface -manually. - -"filename" : The directory path or filename of the RDB file(s). If pointed to a -directory, the script command will load every RDB file in that directory. If -pointed to a file, the script command will load the single RDB file. See - - 130 - - - -the Specifying File Names in Script Commands section for special -keywords that can be used when specifying the file name. - -ModelType : One of the following: -DISTRELAY : create a Simulator DistRelay model from the RDB data -ZPOTT : create a Simulator ZPOTT model from the RDB data - -filter : Optional parameter - Lines meeting this filter are searched to find ones matching the SID. See - -the Using Filters in Script Commands section for information on -specifying the filter. - - -This command loads relay settings from the specified "substation_relay.rdb" file and creates -PowerWorld DISTRELAY models for branches with their Selected field set to YES. -TSLoadRDB("substation_relay.rdb", DISTRELAY, SELECTED); - -TSLoadRelayCSV("filename", ModelType, filter); -This is a quicker alternative to using the TSLoadRDB command for loading RDB files. Relevant relay data -can be exported to a CSV file so that a single CSV file contains multiple relay models. This is much faster -because it does not contain the unused data that an RDB file contains. - -"filename" : Name of the CSV file to load -ModelType : One of the following: - -DISTRELAY : create a Simulator DistRelay model from the RDB data -ZPOTT : create a Simulator ZPOTT model from the RDB data - -filter : Optional parameter - Lines meeting this filter are searched to find ones matching the SID. See - -the Using Filters in Script Commands section for information on -specifying the filter. - - -This command loads relay model data from the "relays.csv" file, specifically creating Simulator -DISTRELAY models. Only relays for lines meeting the area/zone/owner filters are loaded. -TSLoadRelayCSV("relays.csv", DISTRELAY, AREAZONE); - -TSPlotSeriesAdd("PlotName", SubPlotNum, AxisGroupNum, ObjectType, FieldType, "Filter", -"Attributes"); - -Added in February 8, 2024 patch of Simulator 23 -This command adds one or multiple plot series to a new or existing plot definition - -"PlotName" : A string to specify a certain plot -SubPlotNum : A positive integer to specify a certain subplot of a plot -AxisGroupNum : A positive integer to specify to a certain subplot of a subplot -ObjectType : Specifies which objects to set. The ObjectType is the object name of - -supported objects such as GEN, BUS, BRANCH, etc -FieldName : Specified a transient stability field name that can be plotted -"Filter" : (optional) Only objects that meet the specified filter will be included. See - -the Using Filters in Script Commands section for more information on -specifying the filtername - -"Attributes" : (optional) A comma-delimited list of other attributes for the PlotSeries. -Blank by default just uses the defaults for a new PlotSeries - - -Here are two examples of creating new plot series. -TSPlotSeriesAdd("Gen 2 1 | Rotor Angle", 1, 1, Gen, TSRotorAngle, -"Gen 2 1"); -TSPlotSeriesAdd("Area 2 buses", 1, 1, Bus, TSVpu, "Area 2", -"ValueType=Actual Deviation, LineDashed=Dot"); - - 131 - - - -TSResultStorageSetAll(objecttype, YES/NO); -This command will allow setting which object types are stored in memory during a transient stability run. -This will affect all fields and states for the specified objecttype. - -objecttype : Specifies which objects to set. The objecttype is the object name of -supported objects such as GEN, BUS, BRANCH, etc. ALL can be used to -set all supported object types. - -YES/NO : Using this command will toggle all the “Save All” fields to YES/NO. It will -also toggle all the “state” fields (such as exciter, machine, governor, etc.) -to YES/NO. - - -This command configures the transient stability simulation to store all fields and state variables -for all generator objects (GEN) during the run. -TSResultStorageSetAll(GEN, YES); - -TSRunResultAnalyzer("ContingencyName"); -Run the Transient Result Analyzer for the specified contingency or all contingencies. - -ContingencyName : Optional parameter – default is blank - Name of contingency for which the analysis is run. If the name is not - -specified or blank, the analysis is run for all contingencies. -TSRunUntilSpecifiedTime("ContingencyName", [StopTime, StepSize, StepsInCycles, ResetStartTime, -NumberOfTimeStepsToDo]); - -This command allows manual control of the transient stability run. The simulation can be run until a -specified time or number of times steps and then paused for further evaluation. - -ContingencyName : The name of the contingency to solve. -StopTime : (optional) This is the time to which the simulation will be run. This should - -be entered in seconds. If NumberOfTimeStepsToDo > 0, this field will be -ignored. If not specified, the stop time specified with the contingency -will be used. - -StepSize : (optional) Simulation step size in either seconds or cycles. If -StepsInCycles = YES this should be specified in cycles. If not specified, -the step size specified with the contingency will be used. - -StepsInCycles : (optional) Set to YES to specify StepSize in cycles. If not specified, the -units of the step size specified with the contingency will be used. - -ResetStartTime : (optional) Set to YES to reset the simulation start time. Default value is -NO. - -NumberOfTimeStepsToDo - : (optional) Number of time steps to run. If NumberOfTimeStepsToDo > 0, - -StopTime is ignored. Default value is 0. - - -This command runs the transient stability simulation for the contingency named "FaultBus1" until -time 5.0 seconds, using a step size of 0.005 seconds. The step size is interpreted in seconds -(StepsInCycles = NO), and the simulation start time is reset (ResetStartTime = YES). Since -NumberOfTimeStepsToDo is set to 0, the simulation runs until the specified StopTime. -TSRunUntilSpecifiedTime("FaultBus1", [5.0, 0.005, NO, YES, 0]); - -TSSaveBPA("FileName", DiffCaseModifiedOnly); -Save transient stability data stored in the BPA IPF format. - -FileName : Name and path for the output file. Typically this will be an *.swi file -extension. - -DiffCaseModifiedOnly : (optional) Default is NO. When set to YES, it will only save models that -are either new or models which have had a parameter modified as -compared to the difference case tool base case. - - 132 - - - - -This command saves transient stability data in the BPA IPF format to "export_data.swi". With -DiffCaseModifiedOnly set to YES, it includes only those models that are newly added or have had -parameters modified from the base case defined in the difference case tool. -TSSaveBPA("export_data.swi", YES); - -TSSaveGE("FileName", DiffCaseModifiedOnly); -Save transient stability data stored in the GE DYD format. - -FileName : Name and path for the output file. Typically this will be an *.dyd file -extension. - -DiffCaseModifiedOnly : (optional) Default is NO. When set to YES, it will only save models that -are either new or models which have had a parameter modified as -compared to the difference case tool base case. - - -This command exports transient stability models in GE DYD format to the file -"output_models.dyd". By setting DiffCaseModifiedOnly to YES, only those models that are new or -have been changed relative to the difference case base will be included in the output. -TSSaveGE("output_models.dyd", YES); - -TSSavePTI("FileName", DiffCaseModifiedOnly); -Save transient stability data stored in the PTI DYR format. - -FileName : Name and path for the output file. Typically this will be an *.dyr file -extension. - -DiffCaseModifiedOnly : (optional) Default is NO. When set to YES, it will only save models that -are either new or models which have had a parameter modified as -compared to the difference case tool base case. - - -This command saves transient stability data in PTI DYR format to the file "output_models.dyr". By -setting DiffCaseModifiedOnly to YES, it restricts the saved content to only include models that are -newly added or modified compared to the difference case base model. -TSSavePTI("output_models.dyr", YES); - -TSSaveTwoBusEquivalent ("FileName", [BUS]); -Save the two bus equivalent model of a specified bus to a PWB file. Initialization to the start time is -always done before saving the two bus equivalent. - -FileName : Name and path for the output PWB file -BUS : Bus can be specified in three ways: - -Number : [BUS busnum] -Name/NomkV : [BUS "busname_nominalKV"] -Label : [BUS "buslabel"] - - -This command saves a two-bus equivalent model of Bus 1 to a PWB file named -"TwoBusEq_Bus1.pwb". -TSSaveTwoBusEquivalent("TwoBusEq_Bus1.pwb", [BUS 1]); - -TSSolve("ContingencyName", [StartTime, StopTime, StepSize, StepInCycles]); -Solves only the specified contingency. - -ContingencyName : The name of the contingency to solve -Times : (optional) Pararmer is a comma delimited list of strings enclosed in - -square brackets. This string then consists of 4 optional parameters which -override the corresponding property of the contingency. - -StartTime : (optional) Start time in seconds - - 133 - - - -StopTime : (optional) Stop time in seconds -StepSize : (optional) Step size (in seconds unless StepInCycles = - -YES) -StepInCycles : (optional) Set to YES to mean that the StepSize is given - -in cycles - - -This command solves the transient stability simulation for the contingency named -"GEN_TRIP_BUS1". It runs the simulation from 0 to 10 seconds using a time step of 0.01 seconds, -interpreting the step size in seconds because StepInCycles is set to NO. -TSSolve("GEN_TRIP_BUS1", [0, 10, 0.01, NO]); - -TSSolveAll(DoDistributed); -Solves all defined transient contingencies that are not set to skip. - -DoDistributed : (optional) Set to YES to use Distributed Computing with the transient -analysis. Default is NO. - -TSTransferStateToPowerFlow(CalculateMismatch); -Transfer the transient stability state to the power flow. - -CalculateMismatch : (optional) Default is NO - Set to YES to calculate power mismatch when transferring transient state - -to the power flow case. Any input other than YES will set the option to -NO, which means no calculation of power mismatch is done. - -TSValidate; -Validate transient stability models and input values. This command is useful for examining model errors -and warnings when preparing a case for analysis. Validation will be done automatically when running -transient analysis, so this command does not need to be run manually prior to analysis. - -TSWriteModels("FileName", DiffCaseModifiedOnly); -Save transient stability dynamic model records only the auxiliary file format. - -FileName : Name and path for the output file. Typically this will be an *.aux file -extension. - -DiffCaseModifiedOnly : (optional) Default is NO. When set to YES, it will only save models that -are either new or models which have had a parameter modified as -compared to the difference case tool base case. - - -This command saves only the modified transient stability dynamic model records to the -"Modified_Models.aux" file. It includes only those models that are new or have parameter -changes compared to the difference case base model. -TSWriteModels("Modified_Models.aux", YES); - -TSWriteOptions("FileName",[SaveDynamicModel, SaveStabilityOptions, SaveStabilityEvents, -SaveResultsEvents, SavePlotDefinitions, SaveTransientLimitMonitors, -SaveResultAnalyzerTimeWindow], KeyField); - -Save the transient stability option settings to an auxiliary file. -FileName : Name and path of the file to save -SaveDynamicModel : (optional) NO doesn’t save dynamic model (default YES) -SaveStabilityOptions : (optional) NO doesn’t save stability options (default YES) -SaveStabilityEvents : (optional) NO doesn’t save stability events (default YES) -SaveResultsSettings : (optional) NO doesn’t save results settings (default YES) -SavePlotDefinitions : (optional) NO doesn’t save plot definitions (default YES) -SaveTransientLimitMonitors: (optional) NO doesn’t save transient limit monitors (default YES) - - 134 - - - -SaveResultAnalyzerTimeWindows: (optional) NO doesn’t save result analyzer time windows -(default YES) - -KeyField : (optional) Specifies key: can be Primary, Secondary, or Label (default -Primary) - - -This command saves transient stability settings to the file "TS_Options.aux". It includes dynamic -models, stability options, stability events, plot definitions, and result analyzer time windows. It -excludes result settings and transient limit monitors. The data is saved using primary key fields. -TSWriteOptions("TS_Options.aux", [YES, YES, YES, NO, YES, NO, YES], -PRIMARY); - -TSSetSelectedForTransientReferences(SetWhat, SetHow, [ObjectType List],[ModelType List]); -Set the Custom Integer field to a corresponding integer or Selected field to Yes/No for objects referenced -in a transient stability model with extra objects. - -SetWhat : Either Selected or Custom Integer number (1, 2, 3…). Default is number 1 -for the Custom Integer - -SetHow : when SetWhat = Selected, will be either YES or NO. Otherwise it will be -an integer to be used with Custom Integer. Default is 1. - -[ObjectType List] : A comma-delimited list of objecttypes. These are the objects on which -the custom field will be set. - -[ModelType List] : A comma-delimited list of transient stability model types on which -references will be queried. - - -This command sets the Selected field to YES for all generator objects that are referenced in the -transient stability model, REPC_A. -TSSetSelectedForTransientReferences(SELECTED, YES, [GEN], [REPC_A]); - -TSSaveDynamicModels("FileName", FileType, ObjectType, Filter, Append); -Save dynamics models for specified object types to file. - -FileName : Name and path of the file to save -FileType : AUX or DYD. -ObjectType : The object type to save - Gen, Load, SwitchedShunt, DCLine, VSCDCLine, - -Branch, among others available in Transient Stability.. -Filter : See Using Filters in Script Commands section for more information on - -specifying the filter. -Append : YES or NO to whether replace an existing file or append to it. - - -This command saves dynamic models for all generators with their Selected field set to YES in the -file "gen_models.aux" in AUX format. Since Append is set to NO, any existing file with the same -name will be overwritten rather than appended to. -TSSaveDynamicModels("gen_models.aux", AUX, Gen, SELECTED, NO); - - - - 135 - - - -Scheduled Actions -ApplyScheduledActionsAt(StartTime, EndTime, Filter, Revert); - -Applies any scheduled actions that meet the specified filter and are active during the specified window of -time. - -StartTime : The beginning of the window in which to apply actions. Must be -specified in the current locale’s date/time format. - -EndTime : Optional parameter – default is the same as StartTime - The end of the window in which to apply actions. Must be specified in - -the current locale’s date/time format. -Filter : Optional parameter – default is to apply all actions - See Using Filters in Script Commands section for more information on - -specifying the filter. -Revert : Optional parameter – default is NO - Set to YES if actions should be reverted rather than applied. Use this - -command with Revert = YES is the same as calling the -RevertScheduledActionsAt command. - - -This command applies all scheduled actions that are active between 8:00 AM and 12:00 PM on -June 1, 2025. No filter is used, so all eligible scheduled actions during this time frame are applied. -The Revert parameter is set to NO, meaning the actions will be implemented, not undone. -ApplyScheduledActionsAt("01/06/2025 08:00", "01/06/2025 12:00", , NO); - -IdentifyBreakersForScheduledActions(IdentifyFromNormalStatus); -For each Scheduled Outage, identifies breakers that are necessary to implement OpenBreakers and -CloseBreakers actions. New Scheduled Actions are added to the Scheduled Outage if new breakers are -identified that do not already exist as actions. - -IdentifyFromNormalStatus - : Set to YES to return all branches to their normal status before searching - -for breakers. If using this option, all branches will be returned to their -current status at the end of the process. Set to NO to leave all branches -at their current status before searching for breakers. - - -This command identifies which breakers are required to implement each Scheduled Action -defined using OpenBreakers or CloseBreakers and adds new breaker actions as necessary. By -setting IdentifyFromNormalStatus to YES, all branches are first reset to their normal status prior -to breaker identification and restored to their current status afterward. -IdentifyBreakersForScheduledActions(YES); - -RevertScheduledActionsAt(StartTime, EndTime, Filter); -Applies the opposite of any relevant actions (e.g. Closes breakers if an Action is to Open them). - -StartTime : The beginning of the window in which to revert actions. Must be -specified in the current locale’s date/time format. - -EndTime : Optional parameter – default is the same as StartTime - The end of the window in which to revert actions. Must be specified in - -the current locale’s date/time format. -Filter : Optional parameter – default is to revert all actions - See Using Filters in Script Commands section for more information on - -specifying the filter. - - - 136 - - - -This command reverts any scheduled actions that were active between 8:00 AM and 12:00 PM on -June 1, 2025. Since no filter is specified, it applies to all scheduled actions within the time -window. -RevertScheduledActionsAt("01/06/2025 08:00", "01/06/2025 12:00", ); - -ScheduledActionsSetReference; -Set the reference state that is restored prior to applying a time stamp. - -SetScheduleView(ViewTime, ApplyActions, UseNormalStatus, ApplyWindow); -Sets the View Time for Scheduled Actions. - -ViewTime : The desired view time. Must be between the currently configured Start -and End times, and fall on a valid view point given current resolution -settings. - -ApplyActions : (optional) Set to YES or NO to override current “Apply Actions” settings -to either apply actions at this view time or suppress the application of -actions at this time. If left blank, current “Apply Actions” settings will be -used. - -UseNormalStatus : (optional) Set to YES or NO to override current “Use Normal Status” -settings to either restore devices to their Normal Status after they are -restored, or to use whatever status they had before the action was -applied. If left blank, current “Use Normal Status” settings will be used. - -ApplyWindow : (optional) Set to YES or NO to override current “Apply Window” settings -to either apply any actions active in the window starting at this View Time -and extending forward one Resolution step forward in time, or only apply -actions active at the specific View Time specified. If left blank, current -“Apply Window” settings will be used. - - -This command sets the schedule view time to 10:00 AM on June 1, 2025, and applies scheduled -actions at that time (ApplyActions = YES). It uses the current device status instead of restoring -them to their normal status (UseNormalStatus = NO) and applies actions that are active within -the window starting at this time and extending one resolution step forward (ApplyWindow = -YES). -SetScheduleView("01/06/2025 10:00", YES, NO, YES); - -SetScheduleWindow(StartTime, EndTime, Resolution, ResolutionUnits); -Defines the window of interest for Scheduled Actions. - -StartTime : Defines the start of the window. -EndTime : Defines the end of the window. Must fall on a valid time relative to the - -Start Time as defined by the Resolution (e.g. if the Start Time is 5/20/17 -8:00 and the resolution is 1 DAY, the End Time must be at 8:00 as well on -a later date.) - -Resolution : (optional) Decimal value defining the time step between the Start Time -and any valid View Time. - -ResolutionUnits : (optional but must be specified if Resolution is specified) May be -MINUTES, HOURS, or DAYS - - -This command sets the scheduled actions window from midnight on June 1, 2025, to midnight on -June 7, 2025, with a resolution of 1 day. This means valid view times for scheduled action -evaluations will occur every day at midnight during this week. -SetScheduleWindow("01/06/2025 00:00", "07/06/2025 00:00", 1, DAYS); - - - - 137 - - - -Time Step Simulation -TimeStepAppendPWW("FileName","SolutionTypeString") - -(This command was added in the December 19, 2023 patch of Simulator 23) -Loads the specified PWW file into the Time Step Simulation, appending it to any timepoints that already -exist. - -"FileName" : Name of the PWW file; it should either have a ".pww" extension or no -extension; if no extension then it is set to ".pww". - -"SolutionTypeString" : Optional solution type string; when the file is loaded, all of the time -points are set to use this solution type. Valid entries are "Single Solution", -"Unconstrained OPF", "OPF", "SCOPF", "GIC Only (No Power Flow)", -"Weather Only"; if not specified the default value of "Single Solution" is -used. - - -This command appends the "SummerScenario.pww" file to the current Time Step Simulation, -adding its time points to any that already exist. It sets the solution type for these time points to -SCOPF (Security Constrained Optimal Power Flow). -TimeStepAppendPWW("SummerScenario.pww", "SCOPF"); - -TimeStepAppendPWWRange("FileName", ISO8601StartDateTime, ISO8601EndDateTime, -"SolutionTypeString") - -This command is similar to TimeStepAppendPWW in that it loads timepoints from the file and appends -them to any existing timepoints. However it only loads timepoints for the date and time values between -the start and end datetime values (expressed in ISO8601 format). - -"FileName" : Name of the PWW file; it should either have a ".pww" extension or no -extension; if no extension then it is set to ".pww". - -ISO8601StartDateTime: The desired start date and time specified in ISO8601 format. If string is -empty ("") or before the start datetime then start at the beginning. This -datetime should either be a UTC value, or local time specified with the -time zone offset. - -ISO8601EndDateTime: The desired end date and time specified in ISO8601 format. If string is -empty ("") or after the end datetime then go until the end. The datetime -should either be a UTC value, or local time specified with the time zone -offset. - -"SolutionTypeString" : Optional solution type string; when the file is loaded, all of the time -points are set to use this solution type. Valid entries are "Single Solution", -"Unconstrained OPF", "OPF", "SCOPF", "GIC Only (No Power Flow)", -"Weather Only"; if not specified the default value of "Single Solution" is -used. - - -This command appends only the time points from "SummerScenario.pww" that fall between June -1, 2025, 00:00 and June 3, 2025, 23:59, using OPF (Optimal Power Flow) as the solution type. The -date and time values are provided in ISO8601 format with a time zone offset of -05:00 (e.g., -Central Daylight Time). -TimeStepAppendPWWRange("SummerScenario.pww", 2025-06-01T00:00:00-05:00, -2025-06-03T23:59:59-05:00, "OPF"); - - - - - 138 - - - -TimeStepAppendPWWRangeLatLon("FileName", ISO8601StartDateTime, ISO8601EndDateTime, -minLatitude,maxLatitude,minLongitude,maxLongitude,"SolutionTypeString") - -(This command was added in the November 3, 2025 patch of Simulator 24) -This command is similar to TimeStepAppendPWWRange in that it loads a range of timepoints from the -file and appends them to any existing timepoints. However, it can also optionally only append weather -data that is within a specified geographic rectangle. - -"FileName" : Name of the PWW file; it should either have a ".pww" extension or no -extension; if no extension then it is set to ".pww". - -ISO8601StartDateTime: The desired start date and time specified in ISO8601 format. If string is -empty ("") or before the start datetime then start at the beginning. This -datetime should either be a UTC value, or local time specified with the -time zone offset. - -ISO8601EndDateTime: The desired end date and time specified in ISO8601 format. If string is -empty ("") or after the end datetime then go until the end. The datetime -should either be a UTC value, or local time specified with the time zone -offset. - -minLatitude : Optional minimum latitude for the bounding rectangle. Must be greater -than or equal to -90, and less than the maxLatitude; default is -90. - -maxLatitude : Optional maximum latitude for the bounding rectangle. Must be less -than or equal to 90 and greater than the minLatitude; default is 90. - -minLongitude : Optional minimum latitude for the bounding rectangle. Must be great -than or equal to -180 and less than the maxLatitude. Hence rectangles -spanning the international date line are not allowed. Default is -180. - -maxLongitude : Optional maximum latitude for the bounding rectangle. Must be less -than or equal to 180 and greater than the minLatitude. Hence rectangles -spanning the international date line are not allowed. Default is 180. - -"SolutionTypeString" : Optional solution type string; when the file is loaded, all of the time -points are set to use this solution type. Valid entries are "Single Solution", -"Unconstrained OPF", "OPF", "SCOPF", "GIC Only (No Power Flow)", -"Weather Only"; if not specified the default value of "Single Solution" is -used. - -TimeStepClearResults(ISO8601StartDateTime, ISO8601EndDateTime); -(This command was added in the November 1, 2024 patch of Simulator 23) -Clears all the Time Step Simulation results, but does not delete the time points. - -ISO8601StartDateTime: The optional desired start date and time specified in ISO8601 format for -the clear. If not specified, then start the clear at the beginning. This -datetime should either be a UTC value, or local time specified with the -time zone offset. - -ISO8601EndDateTime: The optional desired end date and time specified in ISO8601 format for -the clear. If not specified, then finish the clear at the end. This datetime -should either be a UTC value, or local time specified with the time zone -offset. - - -This command clears all Time Step Simulation results between January 1, 2025, 00:00 and January -3, 2025, 23:59 (Central Daylight Time), but retains the time points themselves. -TimeStepClearResults(2024-01-01T00:00:00-05:00, 2024-01-03T23:59:59- -05:00); - -TimeStepDeleteAll; -(This command was added in the December 19, 2023 patch of Simulator 23) -Deletes all of the time points in the Time Step Simulation. - - 139 - - - -TimeStepDoRun(ISO8601StartDateTime,ISO8601EndDateTime) -Solves the Time Step Simulation either for all time points (when there are no parameters) or for the -timepoints between ISO8601StartDateTime and ISO8601EndDateTime. - -ISO8601StartDateTime: The optional desired start date and time specified in ISO8601 format. If -not specified then start at the beginning. This datetime should either be -a UTC value, or local time specified with the time zone offset. - -ISO8601EndDateTime: The optional desired end date and time specified in ISO8601 format. If not -specified then finish at the end. This datetime should either be a UTC -value, or local time specified with the time zone offset. - - -This command runs the Time Step Simulation for all time points scheduled between June 1, 2025, -00:00 and June 3, 2025, 23:59 (Central Daylight Time). -TimeStepDoRun(2025-06-01T00:00:00-05:00, 2025-06-03T23:59:59-05:00); - -TimeStepDoSinglePoint(ISO8601DateTime) -Solves the Time Step Simulation (TSS) for the specified date and time that should be specified in the -ISO8601 format. The command returns with an error if the datetime is not within TSS start/end datetime -values or there are no datetime values. - -ISO8601DateTime : The desired date and time specified in ISO8601 format. This datetime -should either be a UTC value, or local time specified with the time zone -offset. - - -This command runs the Time Step Simulation for a single time point: June 2, 2025, at 12:00 PM -Central Daylight Time. -TimeStepDoSinglePoint(2025-06-02T12:00:00-05:00); - -TimeStepLoadB3D("FileName","SolutionTypeString") -(This command was added in the September 2, 2024 patch of Simulator 23) -Loads the specified B3D file into the Time Step Simulation, deleting any timepoints that already exist. - -"FileName" : Name of the B3D file; it should either have a ".b3d" extension or no -extension; if no extension then it is set to ".b3d". - -"SolutionTypeString" : Optional solution type string; when the file is loaded, all of the time -points are set to use this solution type. Valid entries are "Single Solution", -"Unconstrained OPF", "OPF", "SCOPF", "GIC Only (No Power Flow)", -"Weather Only"; if not specified the default value of "GIC Only (No Power -Flow" is used. - - -This command loads the B3D file named "GeomagneticStormData.b3d" into the Time Step -Simulation, replacing any existing timepoints. All loaded timepoints are configured to use the -"GIC Only (No Power Flow)" solution type, which focuses on geomagnetically induced currents -without running full power flow calculations. -TimeStepLoadB3D("GeomagneticStormData.b3d", "GIC Only (No Power -Flow)"); - -TimeStepLoadPWW("FileName","SolutionTypeString") -(This command was added in the December 19, 2023 patch of Simulator 23) -Loads the specified PWW file into the Time Step Simulation, deleting any timepoints that already exist. - -"FileName" : Name of the PWW file; it should either have a ".pww" extension or no -extension; if no extension then it is set to ".pww". - -"SolutionTypeString" : Optional solution type string; when the file is loaded, all of the time -points are set to use this solution type. Valid entries are "Single Solution", -"Unconstrained OPF", "OPF", "SCOPF", "GIC Only (No Power Flow)", - - 140 - - - -"Weather Only"; if not specified the default value of "Single Solution" is -used. - - -This command loads the PWW file named "HourlySimulationData.pww" into the Time Step -Simulation, replacing any existing timepoints. All the loaded timepoints are configured to use the -"OPF" (Optimal Power Flow) solution type. -TimeStepLoadPWW("HourlySimulationData.pww","OPF"); - -TimeStepLoadPWWRange("FileName",ISO8601StartDateTime,ISO8601EndDateTime, -"SolutionTypeString") - -This command is similar to TimeStepLoadPWW in that it loads a PWW file, but only for the date and time -values between the start and end datetime values (expressed in ISO8601 format) are loaded. Also, like -TimeStepLoadPWW it deletes any existing time points. An example is -TimeStepLoadPWWRange(“c:\tmp\test.pww, 2024-03-06T00Z,2024-03-06T23Z), which loads data for -March 6, 2024 (UTC). - -"FileName" : Name of the PWW file; it should either have a ".pww" extension or no -extension; if no extension then it is set to ".pww". - -ISO8601StartDateTime: The desired start date and time specified in ISO8601 format. If string is -empty (‘’) or before the start datetime then start at the beginning. This -datetime should either be a UTC value, or local time specified with the -time zone offset. - -ISO8601EndDateTime: The desired end date and time specified in ISO8601 format. If string is -empty (‘’) or after the end datetime then go until the end. The datetime -should either be a UTC value, or local time specified with the time zone -offset. - -"SolutionTypeString" : Optional solution type string; when the file is loaded, all of the time -points are set to use this solution type. Valid entries are "Single Solution", -"Unconstrained OPF", "OPF", "SCOPF", "GIC Only (No Power Flow)", -"Weather Only"; if not specified the default value of "Single Solution" is -used. - - -This command loads only the timepoints for May 1, 2025, from the "dailyLoad.pww" file into the -Time Step Simulation. Any existing timepoints are deleted first. The solution type for all these -timepoints is set to "SCOPF" (Security Constrained Optimal Power Flow). -TimeStepLoadPWWRange("dailyLoad.pww", 2025-05-01T00:00:00Z, 2025-05- -01T23:59:59Z, "SCOPF"); - -TimeStepLoadPWWRangeLatLon("FileName",ISO8601StartDateTime,ISO8601EndDateTime, -minLatitude,maxLatitude,minLongitude,maxLongitude, "SolutionTypeString") - -(This command was added in the November 3, 2025 patch of Simulator 24) -This command is similar to TimeStepLoadPWWRange in that it loads a PWW file for the date and time -values between the start and end datetime values (expressed in ISO8601 format). However, it can also -optionally only load weather data that is within a specified geographic rectangle. Also, like -TimeStepLoadPWW it deletes any existing time points. An example is -TimeStepLoadPWWRange(“c:\tmp\test.pww, 2024-03-06T00Z,2024-03-06T23Z,30,31,-88,-87), which loads -data for March 6, 2024 (UTC) within the geographic region between latitude 30 and 31 and longitude -88 -and -87. - -"FileName" : Name of the PWW file; it should either have a ".pww" extension or no -extension; if no extension then it is set to ".pww". - -ISO8601StartDateTime: The desired start date and time specified in ISO8601 format. If string is -empty (‘’) or before the start datetime then start at the beginning. This -datetime should either be a UTC value, or local time specified with the -time zone offset. - - 141 - - - -ISO8601EndDateTime: The desired end date and time specified in ISO8601 format. If string is -empty (‘’) or after the end datetime then go until the end. The datetime -should either be a UTC value, or local time specified with the time zone -offset. - -minLatitude : Optional minimum latitude for the bounding rectangle. Must be greater -than or equal to -90, and less than the maxLatitude; default is -90. - -maxLatitude : Optional maximum latitude for the bounding rectangle. Must be less -than or equal to 90 and greater than the minLatitude; default is 90. - -minLongitude : Optional minimum latitude for the bounding rectangle. Must be great -than or equal to -180 and less than the maxLatitude. Hence rectangles -spanning the international date line are not allowed. Default is -180. - -maxLongitude : Optional maximum latitude for the bounding rectangle. Must be less -than or equal to 180 and greater than the minLatitude. Hence rectangles -spanning the international date line are not allowed. Default is 180. - -"SolutionTypeString" : Optional solution type string; when the file is loaded, all of the time -points are set to use this solution type. Valid entries are "Single Solution", -"Unconstrained OPF", "OPF", "SCOPF", "GIC Only (No Power Flow)", -"Weather Only"; if not specified the default value of "Single Solution" is -used. - -TimeStepLoadTSB(“FileName") -Loads the specified TSB file into the Time Step simulation, deleting any timepoints that already exist. - -"FileName" : Name of the TSB file; it should have a ".tsb" extension. -TimeStepResetRun - -Resets the run to the beginning. -TimeStepSaveFieldsClear([objecttype]) - -(This command was added in the September 2, 2024 patch of Simulator 23) -Clears all the fields associated with the saving of the result values during the time step simulation. - -[objecttype] : Optional parameter that is a comma-delimited list of the objecttypes -whose save fields should be cleared. If omitted all objecttypes are -cleared. - - -This command clears all configured save fields specifically for the BUS and GEN object types used -in Time Step Simulation. -TimeStepSaveFieldsClear([BUS,GEN]); - -TimeStepSaveFieldsSet(objecttype, [fieldlist], filter) -(This command was added in the September 2, 2024 patch of Simulator 23) -Sets the fields associated with determing which result values are saved during the time step simulation, -first clearing any previously selected fields and objects for this objecttype. As an example, -TimeStepSaveFieldsSet(Gen,[MW]) sets saving all the generator MW fields, while -TimeStepSaveFieldsSet(Transformer,[GICNeutralCurrent],AREAZONE) sets saving the GIC transformer -neutral current for the transformers with their AreaZone filter set. - -objecttype : The objecttype being set. -[fieldlist] : A comma-delimited list of the A list of fields to update. -filter : Optional parameter – if omitted the default is ALL - -ALL : Set data for all objects -SELECTED : Only objects whose Selected field = YES will be set. - -In the time step simulation the selected field can be -set before calling this command by loading a TSB -file. - - 142 - - - -"FilterName" : Only objects that meet the specified filter will be -set. See Using Filters in Script Commands section -for more information on the specifying the -filtername and other filter options. - - -This command configures the Time Step Simulation to save the MW and Mvar output for -selected generators only. It first clears any previously saved fields for the GEN object type, then -applies the new configuration. -TimeStepSaveFieldsSet(GEN, [MW, Mvar], SELECTED); - -TimeStepSaveFieldsSetByObject(objecttype, [fieldlist], [objectIDList]) -(This command was added in the October 21, 2024 patch of Simulator 23) -Sets the fields associated with determing which result values are saved during the time step simulation by -individual objects. This command is similar to TimeStepSaveFieldsSet, but rather than using a filter to set -the objects to save, a list of the objects is provided. Also, in contrast to TimeStepSaveFieldsSet previously -selected objects and fields are not cleared. Note, in the time step simulation all the saved fields for a -particular objecttype must be the same. Hence executing this command multiple times on the same -object type results in the union of the fieldlists being stored. An example is -TimeStepSaveFieldsSetByObject(Transformer,[GICIeff],[1 2 1, 1 3 1]) saves the GICIEff for the two -transformers. A second example is TimeStepSaveFieldsSetByObject(Bus,[Vpu],[1,2]) saves the per unit -voltage at buses 1 and 2. If this command is followed by TimeStepSaveFieldsSetByObject(Bus,[Vangle],[3]) -both the per unit voltage magnitude and angle are saved at buses 1, 2 and 3. - - -objecttype : The objecttype being set. -[fieldlist] : A comma-delimited list of the A list of fields to update. -[objectIDList] : A comma-delimited list of the key fields for specific objects the type - -given by objecttype. - - -This command sets the Time Step Simulation to save the per-unit voltage (Vpu) for Bus 1 and Bus -2. -TimeStepSaveFieldsSetByObject(BUS, [Vpu], [1, 2]); - -TIMESTEPSaveSelectedModifyStart; -(This command was added in Simulator 25) -This command is associated with setting the power system objects whose fields should be saved during -the time step simulation. The objects themselves are specified by calling SetData with the field -TimeDomainSelected (e.g. SetData(Gen,["BusNum","GenID","TimeDomainSelected"],[1,"1","YES"])). -However, before using the SetData command for this field you must call this function. Then, when the -changes are finished, you must call TIMESTEPSaveSelectedModifyFinish. - -TIMESTEPSaveSelectedModifyFinish; -(This command was added in Simulator 25) -This command is associated with setting the power system objects whose fields should be saved during -the time step simulation. The objects themselves are specified by calling SetData with the field -TimeDomainSelected (e.g. SetData(Gen,["BusNum","GenID","TimeDomainSelected"],[1,"1","YES"])). -However, before using the SetData command for this field you must call -TIMESTEPSaveSelectedModifyStart. Then, when the changes are finished, you must call this function. -Changing the selected objects will delete any saved results for that object type. Note, the selected objects -are saved in the *.tsb file, not the pwb. - - - - 143 - - - -TIMESTEPSaveInputCSV(“Filename”, [input field list], ", ISO8601StartDateTime, ISO8601EndDateTime) -This command saves the input fields listed below. These include the regular input tab fields and the -weather input data. - -Filename : The output filename. -[input field list] : The list of input fields to save. See list of valid fields below. -ISO8601StartDateTime: The optional desired start date and time specified in ISO8601 format for - -the save. If not specified, then start the save at the beginning. This -datetime should either be a UTC value, or local time specified with the -time zone offset. - -ISO8601EndDateTime: The optional desired end date and time specified in ISO8601 format for the -save. If not specified, then finish the save at the end. This datetime -should either be a UTC value, or local time specified with the time zone -offset. - -Valid fields that can be specified in the input field list: -LOAD_MW WEATHERSTATION_WINDSPEEDMSEC -LOAD_MVAR WEATHERSTATION_WINDSPEEDKNOTS -GEN_MW WEATHERSTATION_WINDSPEEDKMPH -GEN_MWMAX WEATHERSTATION_WINDDIRECTION -BRANCH_STATUS WEATHERSTATION_CLOUDCOVERPERC -AREA_LOADMW WEATHERSTATION_WINDSPEED100MPH -ZONE_LOADMW WEATHERSTATION_WINDSPEED100MS -INJECTIONGROUP_MW WEATHERSTATION_WINDSPEED100KNOTS -WEATHERSTATION_TEMPF WEATHERSTATION_WINDSPEED100KMPH -WEATHERSTATION_TEMPC WEAHTERSTATION_GLOBALHORZIRRADWM2 -WEATHERSTATION_DEWPOINTF WEATHERSTATION_DIRECTHORZIRRADWM2 -WEATHERSTATION_DEWPOINTC WEATHERSTATION_WINDTERRFRICTCOEFF -WEATHERSTATION_HUMIDITY WEATHERSTATION_WINDGUSTMPH -WEATHERSTATION_HEATINDEXF WEATHERSTATION_WINDGUSTMS -WEATHERSTATION_HEATINDEXC WEATHERSTATION_WINDGUSTKNOTS -WEATHERSTATION_WINDCHILLF WEATHERSTATION_SMOKEVERTINTMGMW -WEATHERSTATION_WINDCHILLC WEATHERSTATION_PRECIPRATEMMHR -WEATHERSTATION_TRANSMITTANCE WEATHERSTATION_PRECIPPERCFROZEN -WEATHERSTATION_INSOLATIONPERC WEATHERSTATION_DIRECTNORMIRRADWM2 -WEATHERSTATION_WINDSPEEDMPH WEATHERSTATION_DIFFUSEHORZIRRADWM2 - - -This command saves a CSV file named inputs.csv containing the LOAD_MW and GEN_MW input -data from January 1, 2024, 00:00 UTC to January 2, 2024, 00:00 UTC. -TIMESTEPSaveInputCSV("inputs.csv", [LOAD_MW, GEN_MW], 2024-01- -01T00:00Z, 2024-01-02T00:00Z); - -TimeStepSaveResultsByTypeCSV(ObjectType, "FileCSVName", ISO8601StartDateTime, -ISO8601EndDateTime) - -(This command was added in the January 8, 2024 patch of Simulator 23) -Saves the Time Step Simulation results for the specified object type in a CSV file. - -ObjectType : The name of the object type being saved with only the first three letters -significant, case insensitive. - -"FileCSVName" : Name of the CSV file for saveing the results; it should have a ".csv" -extension. - - -ISO8601StartDateTime: The optional desired start date and time specified in ISO8601 format for - -the save. If not specified, then start the save at the beginning. This - - 144 - - - -datetime should either be a UTC value, or local time specified with the -time zone offset. - -ISO8601EndDateTime: The optional desired end date and time specified in ISO8601 format for the -save. If not specified, then finish the save at the end. This datetime -should either be a UTC value, or local time specified with the time zone -offset. - - -This command saves the Time Step Simulation results for generators into a file named -"gen_results.csv", including data from January 1, 2025, 00:00 UTC to January 2, 2025, 00:00 UTC. -TimeStepSaveResultsByTypeCSV(GEN, "gen_results.csv", 2025-01- -01T00:00:00Z, 2025-01-02T00:00:00Z); - -TimeStepSaveTSB(FileName") -Saves the Time Step Simulation results using the specified TSB file in the latest TSB version. - -"FileName" : Name of the TSB file; it should have a ".tsb" extension. -TimeStepSavePWW("FileName") - -(This command was added in the December 19, 2023 patch of Simulator 23) -Saves the existing weather data in the Time Step Simulation using the specified PWW file. - -"FileName" : Name of the PWW file; it should either have a ".pww" extension or no -extension; if no extension then it is set to ".pww". - -TimeStepSavePWWRange("FileName",ISO8601StartDateTime,ISO8601EndDateTime) -This command is similar to TimeStepSavePWW in that it saves a PWW file, but only for the date and time -values between the start and end datetime values (expressed in ISO8601 format). An example is -TimeStepSavePWWRange(“c:\tmp\test.pww, 2024-03-01T0Z,2024-03-01T23Z), which would save data for -March 1, 2024. - - -"FileName" : Name of the PWW file; it should either have a ".pww" extension or no -extension; if no extension then it is set to ".pww". - -ISO8601StartDateTime: The desired start date and time specified in ISO8601 format. If string is -empty (‘’) or before the start datetime then start at the beginning. This -datetime should either be a UTC value, or local time specified with the -time zone offset. - -ISO8601EndDateTime: The desired end date and time specified in ISO8601 format. If string is -empty (‘’) or after the end datetime then go until the end. The datetime -should either be a UTC value, or local time specified with the time zone -offset. - - -This command saves a PWW file named "march1.pww" containing Time Step Simulation data -specifically for the time window from March 1, 2024, 00:00 UTC to 23:00 UTC. -TimeStepSavePWWRange("march1.pww", 2024-06-01T00:00Z, 2024-03- -01T23:00Z); - - - - 145 - - - -Weather -TemperatureLimitsBranchUpdate(RatingSetPrecedence, NormalRatingSet, CTGRatingSet); - -Updates branch limits based on a temperature limit curve and weather station temperature if valid -temperature limit curves and weather station data are available for a branch. - -RatingSetPrecedence : Optional parameter – default is NORMAL -Valid entries are NORMAL, CTG, or blank. A blank entry is the same as -NORMAL. This is used when the same rating set is specified to be -overwritten by both the normal and CTG temperature limit curves. - -NormalRatingSet : Optional parameter – default is DEFAULT -Use this parameter to specify which limit for a given branch should be -updated with the normal temperature dependent limit. Valid entries are -the following: - -DEFAULT : When updating limits for a given branch, update the -Normal Rating Set that is specified with the Limit -Monitoring Settings with the normal temperature- -dependent limit. - -NO : Do not use the normal temperature-dependent limit to -update any limit. - -A through O : When updating limits, update this letter-specified limit -with the normal temperature-dependent limit. - -CTGRatingSet : Optional parameter – default is DEFAULT -Use this parameter to specify which limit for a given branch should be -updated with the CTG temperature dependent limit. Valid entries are the -following: - -DEFAULT : When updating limits for a given branch, update the -CTG Rating Set that is specified with the Limit -Monitoring Settings with the normal temperature- -dependent limit. - -NO : Do not use the CTG temperature-dependent limit to -update any limit. - -A through O : When updating limits, update this letter-specified limit -with the CTG temperature-dependent limit. - - -This command updates branch limits based on temperatures using predefined temperature limit -curves and associated weather station data. With RatingSetPrecedence set to NORMAL, the -update process prioritizes normal ratings. The NormalRatingSet parameter set to "A" ensures that -the temperature-adjusted normal limit is applied to limit set A, while the CTGRatingSet set to "B" -applies the temperature-adjusted contingency limit to limit set B for each branch. -TemperatureLimitsBranchUpdate(NORMAL, A, B); - -WeatherLimitsGenUpdate(UpdateMax, UpdateMin); -Updates generator MW limits based on a weather limit curve and weather station temperature if valid -weather limit curves and weather station data are available for a generator. - -UpdateMax : Optional parameter – default is YES -Set this to YES or blank to update the Max MW limit based on the -calculated weather-dependent MWMax limit. Set to NO to not change -the Max MW limit. - -UpdateMin : Optional parameter – default is YES -Set this to YES or blank to update the Min MW limit based on the -calculated weather-dependent MWMin limit. Set to NO to not change -the Min MW limit. - - - 146 - - - -This command updates generator MW limits using temperature-dependent curves and weather -station data. -WeatherLimitsGenUpdate(YES, NO); - -WeatherPFWModelsSetInputs; -(This command was added in the January 8, 2024 patch of Simulator 23) -Sets the inputs for all the case PFWModels, but does not apply them to the power flow case. Usually these -inputs require the availability of weather measurements. - -WeatherPFWModelsSetInputsAndApply (SolvePowerFlow) -(This command was added in the January 8, 2024 patch of Simulator 23; the required -SolvePowerFlow parameter was added in the December 31, 2024 patch of Simulator 23) -Sets the inputs for all the case PFWModels and also applies them to the power flow case. Usually these -inputs require the availability of weather measurements, which can be loaded using the -WeatherPWWLoadForDateTimeUTC command. When the PFWModels are applied to the case, some -existing case values can be changed, such as the generator MaxMW fields. Use -WeatherPFWModelsRestoreDesignValues to restore these values to the design values specified by the -PFWModels. - -SolvePowerFlow : If YES then it also solves the power flow using the default power flow -solution method. If you do not wish to solve the power flow, or wish to -solve it using either a different method or another solution type (e.g., the -OPF) set this parameter to NO. Then optionally call the another solution -command (e.g., SolvePowerFlow, or SolvePrimalLP). - - -This command applies all PFWModel-based weather adjustments—like generator MW limits— -and solves the power flow using current weather data. -WeatherPFWModelsSetInputsAndApply(YES); - -WeatherPWWFileAllMeasValid ("PWWFileName", [field list], ISO8601StartDateTime, -ISO8601EndDateTime) - -Returns true if the specified PWW file 1) has the all the specified fields, and 2) all the measurements for -those fields are valid. This command only works with a version 2 or greater pww file. - -"PWWFileName" : The pww file to check. - [field list] : The list of fields to check. At least one field must be provided. Valid fields - -are given below. Note, in contrast to commands that are providing -numerical weather values, like TimeStepSaveInputCSV, here the units are -not included in the field names. - -ISO8601StartDateTime: Optional start datetime specified in the ISO8601 format. If provided, then -the command only returns true if the starting datetime in the pww file is -at or before this datetime. - -ISO8601EndDateTime: Optional end datetime specified in the ISO8601 format. If provided, then -the command only returns true if the ending datetime in the pww file is -at or after this datetime. - - -Valid fields that can be specified in the field list: -TEMP DIRECTHORZIRRAD -DEWPOINT WINDGUST -WINDSPEED SMOKEVERTINT -WINDSPEED100 PRECIPRATE -GLOBALHORZIRRAD PRECIPPERCFROZEN - - - - 147 - - - -This command checks if the "weather_data.pww" file includes valid temperature and wind speed -data throughout March 1, 2024. Returns true only if both fields are present and all their values -are valid for the entire specified time range. -WeatherPWWFileAllMeasValid("weather_data.pww", [TEMP, WINDSPEED], 2024- -03-01T00:00Z, 2024-03-01T23:59Z); - -WeatherPFWModelsRestoreDesignValues; -(This command was added in the January 8, 2024 patch of Simulator 23) -Restores the case values changed by WeatherPFWModelsSetInputsAndApply to the design values -specified with each PFWModel. - -WeatherPWWFileCombine2 ("SourceFileName1", "SourceFileName2", "DestFileName") -(This command was added in the March 4, 2024 patch of Simulator 23) -Combines two PWW files provided the PWW filles have the same weather stations. The combined file is -stored in DestFileName. The files names should either have a ".pww" extension or no extension; if no -extension then it is set to ".pww". The datetime ranges for the files cannot overlap. - -"SourceFileName2" : Second source file name; it must exist. Should be the second file -chronologically. - -"DestFileName" : Destination file name; it does not need to exist and can be either of the -source file names. - - -This command merges two PWW weather files—"weather_march.pww" and -"weather_april.pww"—into a new file called "weather_combined.pww", provided both source files -reference the same weather stations and have non-overlapping date ranges. -WeatherPWWFileCombine2("weather_march.pww", "weather_april.pww", -"weather_combined.pww"); - -WeatherPWWFileGeoReduce("SourceFileName", "DestFileName", minLatitude, maxLatitude, -minLongitude, maxLongitude); - -(This command was added in the March 4, 2024 patch of Simulator 23) -Reduces the geographic scope of a PWW file. The minLatitude, maxLatitude, minLongitude, and -maxLongitude contain the bounding coordinates for data in the destination file. The files names should -either have a ".pww" extension or no extension; if no extension then it is set to ".pww". - -"SourceFileName" : Source file name; it must exist. -"DestFileName" : Destination file name; it does not need to exist and can be the source file. -minLatitude : Minimum latitude for the bounding rectangle. Must be greater than or - -equal to -90, and less than the maxLatitude. -maxLatitude : Maximum latitude for the bounding rectangle. Must be less than or equal - -to 90 and greater than the minLatitude. -minLongitude : Minimum latitude for the bounding rectangle. Must be great than or - -equal to -180 and less than the maxLatitude. Hence rectangles spanning -the international date line are not allowed. - -maxLongitude : Maximum latitude for the bounding rectangle. Must be less than or equal -to 180 and greater than the minLatitude. Hence rectangles spanning the -international date line are not allowed. - - -This command extracts weather data only for the geographic region bounded by latitudes 30.0 to -40.0 and longitudes -100.0 to -90.0 from "weather_full.pww", and saves it in -"weather_region.pww". -WeatherPWWFileGeoReduce("weather_full.pww", "weather_region.pww", 30.0, -40.0, -100.0, -90.0); - - 148 - - - -WeatherPWWSetDirectory("PWWFileDirectory", IncludeSubDirectories); -(This command was added in the March 10, 2024 patch of Simulator 23) -When getting weather information from PWW files, this command specifies the directory to search, and -optionally its subdirectories. - -"PWWFileDirectory" : Directory that contains the PWW files. -IncludeSubDirectories : Optional field indicating whether to include subdirectories in the search - -path. Either YES or NO, with YES the default. - - -This command sets "C:\WeatherData\PWW" as the main directory to search for PWW files and -includes all its subdirectories in the search path. -WeatherPWWSetDirectory("C:\WeatherData\PWW", YES); - -WeatherPWWLoadForDateTimeUTC(ISO8601DateTime); -(This command was added in the March 10, 2024 patch of Simulator 23) -This command loads the weather for the specified date and time. It does this by searching the directory, -and optionally the subdirectories set with the WeatherPWWSetDirectory command. - -ISO8601DateTime : The desired date and time specified in ISO8601 format. This datetime -should either be a UTC value, or local time specified with the time zone -offset. For example, for weather on March 6, 2024 at 18:00 UTC the value -could be in UTC “2024-03-06T18:00Z” or in local time (with an offset of --6 hours from UTC) “2024-03-06T12:00-06”. - - -This command loads weather data for March 6, 2024, at 18:00 UTC. It searches the directory -specified earlier using the WeatherPWWSetDirectory script command, including any -subdirectories if configured, to find and load the appropriate weather conditions for that exact -time. -WeatherPWWLoadForDateTimeUTC("2024-03-06T18:00Z"); - - - - 149 - - - -Distributed Computing -EnterDistMasterPassword(Password); - -Use this action to enter the master password used to unlock distributed machine login credentials. -Password : Password that must be specified to unlock the credentials. - -VerifyDistributedComputersAvailable; -Allows distributed computer status to be verified. - - - - 150 - - - -Trainer -ResetStatusChangeCount; - -There is a field with each branch called Status Change Count that tracks the number of times a branch -changes status. This script action sets this count to 0 for all branches. This field is only used with Trainer. - -SuppressCommandDialog; -Tells trainer to only send status change commands instead of opening a dialog, which in the case of -generators and other objects has additional options. - - 151 - - - -Customer-Specific Actions -ISONEInterfaceLimitCalculation("filename", variablename); - -This script command has been developed specifically for ISO-NE to determine the limit of a specified -interface based on a set of logical conditions being met on the given state of the power system. - -"filename" : Name of the file containing all input data. This is a text-based format -with the description of the contents given below. - -variablename : Interface floating point field that will contain the calculated limit for each -studied interface. - - -The file containing the input data contains specific sections to properly calculate an interface limit. Each -section is identified by a keyword. Any sections with keywords that are not recognized will be ignored. -The data for each section is enclosed in curly braces, { }. Curly braces must be on their own lines. -Comments can be added using the double slash, //, notation at the start of a line. Only entire lines can be -commented. The contents of an example file are shown below: - - -// Put comments here -Interface -{ -MyInterface -} -ModelExpressions -{ -Branch1_Online -GeneratorA_Online -GeneratorB_Offline -LoadGroup_Online -} -MatrixM -{ --0.5,0.5, 0.5,1.5, -9,9, -9,9 --0.5,0.5, -0.5,0.5, -9,9, -9,9 -} -VectorL -{ -123 -456 -} -MatrixA -{ -0,0,350,100 -0,0,120,80 -} -VectorB -{ -700 -400 -} - - -The section keywords and data contents are described below : - -Interface : Name of the interface for which the limit is being calculated. Only a -single interface limit can be calculated per input file. - - - - 152 - - - -ModelExpressions : List of Model Expressions that correspond to the columns of MatrixM -and MatrixA. These Model Expressions must be specified by name as the -key field for identifying them within the power flow case. There must be -as many Model Expressions defined as there are columns of MatrixM and -MatrixA (m). - -MatrixM : This is an nxm matrix of floating point values. Each entry in the matrix -contains two values that represent the minimum and maximum range of -a power system value. It is possible that there will be no limit in either -the minimum or maximum direction and -INF or INF can be used to -indicate this instead of a floating point value. - -VectorL : This is an nx1 vector of floating point values. This vector must be the -same size as the number of rows in MatrixM. - -MatrixA : This is an nxm matrix of floating point values. This matrix must be the -same size as MatrixM. This matrix is optional and does not have to exist -in the input file. If not specified, it will be assumed that all entries are 0 -and it is the same size as MatrixM. Previous versions of the script -command named this MatrixL1. To maintain older functionality, -Simulator will look for either MatrixA or MatrixL1. - -VectorB : This is an nx1 vector of floating point values. Each entry is the upper -bound limit for the corresponding rows of MatrixM. To maintain older -functionality, it is not required that this vector be included in the input -data. If it is not included, the calculations will be done without imposing -these limits. - - -Implementation of the limit calculation is determined by the following: - - -Each column of Matrix M corresponds to a Model Expression. All defined Model Expressions will be -evaluated in the present power system state. Each row, n, of M is evaluated for the logical conditions -defined for each cell entry. The logical condition for each cell entry m is: 𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚 ≤ 𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀 𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝑚𝑚 ≤ -𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚 . There is only one row, call this p, of M where all logical conditions are met. -The interface limit is then calculated based on row p using the following equation: - -𝑚𝑚 - -𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝 = 𝑉𝑉𝑉𝑉𝑉𝑉𝑉𝑉𝑉𝑉𝑉𝑉𝑉𝑉𝑝𝑝 + 𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑝𝑝,𝑗𝑗 ∙ 𝑉𝑉𝑗𝑗 -𝑗𝑗=1 - -where 𝑉𝑉𝑗𝑗 is the value of 𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀 𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝑗𝑗 . -If VectorB has been specified, - -𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿 = Min 𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝 ,𝑉𝑉𝑉𝑉𝑉𝑉𝑉𝑉𝑉𝑉𝑉𝑉𝑉𝑉𝑝𝑝 -otherwise, - -𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿 = 𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝 - - -If there is no row p where all logical conditions are met, the Limit will be set to 99999 if the input is -specified using MatrixA. If the older format that uses MatrixL1 is specified, the Limit will be set to 0. To -access the calculated Limit value, the field that is specified by the input variablename parameter is set to -the Limit. -During the calculation, the Selected field will be set to YES for all Model Expressions that are used in the -calculation and to NO for all Model Expressions that are not used as part of the calculation. The Selected -field for the interface for which the limit is determined will be set to YES, and the Selected field will be set -to NO for all other interfaces. - - 153 - - - -DATA Section -Concise Auxiliary File Header -PowerWorld defaults to using a concise header that starts with the object_type string followed by the -list_of_fields as the argument between the parentheses. - -object_type DataName(list_of_fields) -{ -data_list_1 - . - . - . -data_list_n -} - -Simulator Version 19 and later support reading both this syntax and the older Legacy header. Older versions of Simulator -only support the Legacy Auxiliary File Header below. All versions of Simulator support reading the legacy header format. -When writing out an auxiliary file there is an option starting in Simulator Version 19 called Use Concise Variable Names -and Auxiliary File Headers that determines whether to write out the DATA keyword and other arguments or to write out -the concise auxiliary file header. See the online help documentation for more details: -http://www.powerworld.com/WebHelp/#MainDocumentation_HTML/Auxiliary_Files.htm - -When using the concise format, the data between the curly braces is always written using space delimiter and -Create_if_not_found is always assumed to be YES. - -Legacy Auxiliary File Header -DATA DataName(object_type, [list_of_fields], file_type_specifier, create_if_not_found) -{ -data_list_1 - . - . - . -data_list_n -} - -Immediately following the DATA keyword, you may optionally include a DataName. By including the DataName, you can -make use of the script command LoadData("filename", DataName) to call this particular data section from another -auxiliary file. Following the optional DataName is an argument list. The argument list is contained inside left and right -parentheses "( )". There are 4 arguments in this list which will be described shortly: object_type, [list_of_fields], -file_type_specifier, and create_if_not_found. - -ObjectType -The object_type parameter identifies the type of object or data element the information section describes or models. -For example, if object_type equals BUS, then the data describes BUS objects. - -There are some special object types that start with the keyword REMOVED. If these are loaded into Simulator while in Edit -mode, the corresponding objects will be deleted. For example REMOVEDBUS will delete BUS objects, REMOVEDBRANCH -will delete BRANCH objects, etc. Not all object types have a corresponding REMOVED object type, and simply prepending -this keyword to the front of an object_type will not allow this functionality. The objects that exist of this with this -functionality are the ones that allow comparison of topological changes through the Difference Flows tool. - -The list of object types Simulator’s auxiliary file parser can recognize will grow as new applications are developed. Within -Simulator, you will always be able to obtain a list of the available object types by going to the main menu and choosing -Window, Export Case Object Fields, and then exporting the objects to Excel or a text file. - - 154 - - - -File_Type_Specifier -When using the Legacy Auxiliary File Header, the file_type_specifier parameter distinguishes the information -section as containing custom auxiliary data (as opposed to Simulator’s native auxiliary formats), and indicates the format -of the data. The parser recognizes two values for file_type_specifier: - -(blank) or AUXDEF or DEF Data fields are space delimited -AUXCSV or CSV or CSVAUX Data fields are comma - -delimited - -Required Fields and Create_if_not_found -Each ObjectType in PowerWorld has a set of Required Fields that must be included in the List_of_Fields when using an -Auxiliary File to create a new object. These Required Fields are highlighted in green in the user interface. If these Required -Fields are not included in the List_of_Fields, then new objects will never be created, and the Auxiliary file will only edit -existing objects. When using the Concise Auxiliary File Header, if the required fields are given, then new objects will be -created. There are also some restrictions for objects that define the network topology and these objects may only be -created while in Edit Mode: Bus, Gen, Load, Shunt, Branch, LineShunt, DCTransmissionLine, VSCDCLine, MSLine, -3WXFormer, MTDCRecord, MTDCBus, MTDCLine, and MTDCConverter. - -In addition to specifying these fields, when using the Legacy Auxiliary File Header, an optional field -Create_if_not_found must be specified. In the Legacy header, objects will only be created if all Required Fields are -given and the Create_if_not_found=YES. If the value is NO, objects will not be created. If Create_if_not_found -is omitted in the Legacy header, YES is assumed. If loading an auxiliary file using the LoadAux script command, the -Create_if_not_found field for the data section will override the Create_if_not_found field with the script. Only if -the Create_if_not_found field for the data section is set to PROMPT will the field for the script command be used. -To repeat, regardless of the choice of CreateIfNotFound, a new object will only be created if a particular list of -Required Fields is in the List_of_Fields. - -List_of_Fields -The list_of_fields parameter lists the types of values the ensuing records in the data section contain. The order in -which the fields are listed in list_of_fields dictates the order in which the fields will be read from the file. Simulator -currently recognizes many different field types, each identified by a specific field variable name. Because the available -fields for an object may grow as new applications are developed for the convenience of our customers, you will always be -able to obtain a list of the available object types and fields by going to the main menu and choosing Window, Export -Object Fields, and then choosing to export to Excel or a text file. Certainly, only a subset of these fields would be found in -a typical custom auxiliary file. In crafting applications to export custom auxiliary files, developers need concern themselves -only with the fields they need to communicate between their applications and Simulator. A few points of interest -regarding the list_of_fields are: - -• The list_of_fields may take up several lines of the text file. -• When using the older heading starting with the keyword DATA, the list_of_fields should be enclosed by - -square brackets [ ]. When using the concise heading these square brackets are not used. -• When encountering the PowerWorld comment string ‘//’ in one of these lines of the text file, all text to the right is - -ignored. -• Blank lines, or lines whose first characters are ‘//’ will be ignored as comments. -• Field variable names must be separated by commas. - -Example: the following are equivalent representations - DATA (BUS, [NomKV, Number, // comment here BUS (NomKV, Number, // comment here - - VAngleA, VAngleB, VpuA, VpuB, VAngleA, VAngleB, VpuA, VpuB, - // comments allowed here too // comments allowed here too - - // note that blank rows are ignored // note that blank rows are ignored - AreaNum, VAngle, ActB, Equiv, ActG, AreaNum, VAngle, ActB, Equiv, ActG, - kV, MargCostMW, LoadMVA, // more comment kV, MargCostMW, LoadMVA, // more comment - LoadMW, LongName]) LoadMW, LongName) - - 155 - - - -Concise Field Variable Names -Variable names within Simulator were overhauled starting with Version 19. Most no longer utilize the special location -integer and instead spell out such information in the field variable name. In general, the variable names have been made -more concise or at least more understandable. Therefore what was once called for a BRANCH object LineMW:1 is now -called MWTo. Similarly LineMW:2 is now called MWFromCalc (representing the MW flow at the from bus of branch -calculated from the terminal voltages). The only fields that continue to use the location integer are those that represent -fields for which a dynamic number of fields are available. Examples of this include the CustomInteger, CustomString, and -CustomFloat fields which use the location integer to specify which value is used. Other examples include the multiple -direction PTDF results fields PTDFMult:0, PTDFMult:1, and so on. - -When writing out an auxiliary file there is an option within Simulator Version 19 called Use Concise Variable Names and -Auxiliary File Headers that determines whether to write out the DATA keyword and other arguments or to write out the -concise auxiliary file header. See the online help documentation for more details: -http://www.powerworld.com/WebHelp/#MainDocumentation_HTML/Auxiliary_Files.htm - -Legacy Field Variable Naming -When listing fields, some field variable names may be augmented with a field location. These are in the format -variablename:location. One example of this is the field LineMW. For a branch, there are two MW flows associated -with the line: one MW flow at the from bus, and one MW flow at the to bus. So that the number of fields does not -become huge, the same field variable name is used for both of these values. For the from bus flow, we write LineMW:0, -and for the to bus flow, we write LineMW:1. Note that field variable names using a location of 0, such as LineMW:0, may -simply leave off the :0. - -These field variable names have been updated with Concise Field Variable Names starting in Simulator version 19. These -are described in the next section. The Legacy field variable names can still be used to support existing auxiliary files or -files needed for loading into earlier versions of Simulator. - -Special Naming -There are several fields that can be referred to by the user-defined name for the field rather than using the location -number. These are fields that might have their location numbers change when different auxiliary files are merged in the -same case. Referring to these by name can eliminate this possible confusion. These fields can be defined in the format -variablename:location_by_name. They can also be referred to by location number as well. - -Fields that allow referring to the location by name are: - -• Expressions – "CustomExpression:my expression name" -• String Expressions – "CustomExpressionStr:my string expression name" -• Custom fields (Floating Point, Integer, and String) – "CustomSingle:my custom single name". Using this - -format for custom fields requires that Custom Field Descriptions be created for the fields to be used. -• Calculated Fields – "BGCalcField:my calculated field variable name" - -Key Fields -Simulator uses certain fields to identify the specific object being described. These fields are called key fields. For example, -the key field for BUS objects is BusNum, because a bus can be identified uniquely by its number. The key fields for GEN -objects are BusNum and GenID. To properly identify each object, the object’s key fields must be present. They can -appear in any order in the list_of_fields (i.e. they need not be the first fields listed in list_of_fields). As long as the -key fields are present, Simulator can identify the specific object. By going to the main menu and choosing Window and -then Export Case Object Fields you will obtain a list of fields available for each object type in either Excel or text format. In -this output, the key fields will appear with asterisks *. - - 156 - - - -Data List -After the data argument list is completed, the Data list is given. The data section lists the values of the fields for each -object in the order specified in list_of_fields. The data section begins with a left curly brace and ends with the a -right curly brace. A few points of interest regarding the value_list: - -• The value_list may take up several lines of the text file. -• Each new data object must start on its own line of text. -• When encountering the PowerWorld comment string ‘//’ in one of these lines of the text file, all text to the right - -of this is ignored. -• Blank lines, or lines whose first characters are ‘//’ will be ignored as comments. -• Remember that the right curly brace must appear on its own line at the end of the data_list. -• If the file_type_specifier is CSV, the values should be separated by commas. Otherwise, separate the field - -variable names using spaces. -• Strings can be enclosed in double quotes, but this is not required. You should however always inclose strings that - -contain spaces (or commas) in quotes. Otherwise, strings containing commas would cause errors for comma- -delimited files, and spaces would cause errors for space-delimited formatted files. - -Special Data List Entries -When specifying values in case information displays, AUX files, and script commands, there are some special formats that -can be used. These formats will allow the value of a model or object field to be used instead of specifying an explicit -value. Formats that are allowed include those described in the Special Identifiers for Model Fields in Data section as -well as others mentioned below in this section. - -A string of the format "@Variablenamelegacy:location:digits:decimals" -Or "@concisename:location:digits:decimals" -will be treated as though the value of the named variable is entered in the field, with digits total and decimals digits -to the right of the decimal point. This format may be used in case information displays, AUX files, and script commands -that set values. String fields that can be converted to a valid numeric value can be used to populate either floating point -or integer fields. Floating point values that are used to populate integer fields are truncated before populating the integer -field. - -When parsing an AUX file while reading, the treatment of concise and legacy variable names is automatically handled by -the parser. - -There are specific fields for specific object types that allow referencing a model field definition as the value of the field. -The model field definition is specified in this syntax: @MODELFIELD. -The model field definition and not the value of the model field itself is used as the value of the field until the field is used -for its intended purpose. The fields that can be specified in this manner include those that specify directory paths and file -locations. When an action is carried out that requires using the directory path or file location, the model field definition is -converted to the value that it represents and that gets used as the location of the file or directory path. - -The following object types and fields (given by variable name) can use this special model field syntax: Transient_Options -(RSHD_Directory), PVCurve_Options (PVCOutFile, PVCStoreStatesWhere), QVCurve_Options (QVOutputDir, -QVOutputFileName), CTG_Options (PostCTGSolAuxFile, PostCTGAuxFile, HardDriveFileName), Sim_Environment_Options -(SEOSpecifiedAuxFile:0, SEOSpecifiedAuxFile:1, SEOSpecifiedAuxFile:2) and MessLog_Options (LogAutoFileName). - -Special Identifiers for Model Fields in Data -The following special formats can be used in case information displays, AUX files, and script commands that set values. -They are also used in some script commands as part of parameters that input text. These formats will allow the value of a -model or object field to be used instead of specifying an explicit value. - - - 157 - - - -A string in the format "&ModelExpressionName:digits:decimals" will be treated as though the value of the -named model expression is entered in the field, with digits total and decimals digits to the right of the decimal point. -If no digits or decimals are specified, 7 decimal places will be used. Trailing zeros will be removed if no decimals are -specified. - -A string of the format "&Objecttype 'key fields' variablenamelegacy:location:digits:decimals" -Or "&Objecttype 'key fields' concisename:digits:decimals" -will be treated as though the value of the named object and object field is entered in the field, with digits total and -decimals digits to the right of the decimal point. If no digits or decimals are specified, 7 decimal places will be used. -Trailing zeros will be removed if no decimals are specified. -This syntax can be used when specifying the values of key fields used for identifying objects when using SetData and -CreateData script commands. - -Example: -Use a SetData command to open a branch that has the highest loading in the case. -Create a calculated field that returns the maximum loading for a branch object and -then apply this to the PWCaseInformationObject. Use the ObjectID for the branch -identification and the CalcFieldExtra field to return the string identifier for the -object that meets the calculated field conditions. - -SetData(Branch, [ObjectID, Status], ["&PWCaseInformation -'CalcFieldExtra:HighestLoading'", "Open"]); - - - -A string of the format "&Objecttype '@variablenamelegacy:location' -variablenamelegacy2:location:digits:decimals" -Or "&Objecttype '@concisename' concisename2:digits:decimals" -will use a specific field for one object, variablenamelegacy or concisename, to determine the key field value for the -objecttype object. This will be treated as though the value of the named object and object field, variablename2 or -concisename2, is entered in the field, with digits total and decimals digits to the right of the decimal point. If no -digits or decimals are specified, 7 decimal places will be used. Trailing zeros will be removed if no decimals are specified. - -Example: -Set the CustomFloat field for every load to the MW value of that load’s zone. - -SetData(Load, [CustomFloat], ["&Zone '@ZoneNumber' LoadMW"], All); - - -(Added in the August 1, 2023 patch for Simulator 23) -A string of the format "&@variablenamelegacy variablenamelegacy2:location:digits:decimals" -Or "&@concisename concisename2:digits:decimals" -will use a specific field for one object, variablenamelegacy or concisename, to determine the objecttype and key -field for identifying another object. This will be treated as though the value of the named object and object field, -variablename2 or concisename2, is entered in the field, with digits total and decimals digits to the right of the -decimal point. If no digits or decimals are specified, 7 decimal places will be used. Trailing zeros will be removed if no -decimals are specified. - -Example: -Set the CustomFloat field for every load to the MW value of the zone identified by the -objecttype and key field identifiers contained in the CustomString field for each -load. - -SetData(Load, [CustomFloat], ["&@CustomString LoadMW"], All); - - - - - - 158 - - - -Using Labels for Identification -Most data objects (such as buses, generators, loads, switched shunts, transmission lines, areas, zones, and interfaces) may -have an alternative names assigned to them. These alternative names are called labels. Labels allow you to refer to -equipment in the model in a way that may be unique to your organization. Labels may thus help clarify which elements -are described by a particular set of data, especially when the short names employed by the power system model prove -cryptic. Furthermore, since labels are likely to change less frequently than bus numbers, and since a label must, by -definition, identify only one power system component, they may function as an immutable key for importing data from -auxiliary files into different cases, even when bus numbering schemes change between the cases. Labels must be unique -for devices of the same type, but the same label can be used for a device of a different type. - -Information dialogs corresponding to buses, generators, loads, switched shunts, transmission lines, areas, zones, and -interfaces feature a button called Labels. If you press this button, the device’s Label Manager Dialog will appear. The Label -Manager Dialog lists the labels associated with the device. You can delete a label from the list by selecting it and pressing -the delete key on the keyboard or clicking the Delete button. You may add a label to the device by typing its name in the -textbox and pressing the Add New button. You will not be allowed to add a Label that already exists for the same type of -device. A single power system device may have multiple labels, but each label may be associated with only one device of a -given type. For example, a bus could have the label Bus North while a generator could also have the same label, but there -could not be another bus or generator with this same label. - -You also may designate a particular label to be the primary label for the device by checking the box Primary before adding -the label. Alternatively, you can select the device from the list and click the Make Primary button. A device’s primary label -is the one that is listed first in the Labels (All) field (variablename = LabelsAll) in a Case Information Display. This -field lists all labels assigned to a device as a comma-delimited string. Any label can be used to import data from auxiliary -data files. - -Labels can be used to map data from an auxiliary data file to a power system device. Recall that auxiliary data files require -you to include a device’s key fields in each data record so that data may be mapped to the device. Labels provide an -alternative key. Instead of supplying the bus number to identify a bus, for example, you can supply one of the bus’s labels. -The label will enable Simulator to associate the data with the device associated with that label. This mechanism performs -most efficiently when the primary label is used, but other labels will also provide the mapping mechanism. The Label (for -use in input from AUX or Paste) field (variablename = Label) is used for importing data using labels and is blank -when viewing in a case information display. Keep in mind that all devices read via an auxiliary file using the label field -should have a non-blank label. Otherwise, information for that device will not be read. Even if the primary or secondary -key fields are provided with the device, as long as the label field is present, that is the only field that will be used to -identify the device. New devices cannot be created by simply identifying them by label. Either the primary or secondary -key fields must be present to create a new device and the label field should not be present. -Again, it is important to remember this: a single power system device may have multiple labels, but each label may be -associated with only one device of a particular type. This is the key to enabling data to be imported from an auxiliary file -using labels. - -Saving Auxiliary Files Using Labels -All devices that can be identified by labels will have the Labels (All) and Label (for use in input from AUX or Paste) fields -available in their case information displays. In order to save auxiliary files that identify devices by label, the two label fields -should be added to the case information display prior to saving the data in an auxiliary file. Because the Label (for use in -input from AUX or Paste) field will be blank when saved in the auxiliary file, this field must be populated with one of the -labels in the Labels (All) field before loading the auxiliary file back in. Keep in mind that devices with blank labels cannot -be identified when loading in an auxiliary file, so avoid saving auxiliary files by label if all devices do not have labels. Note -that when saving out an entire case as an auxiliary file, the field "AllLabels" is included for each object type that allows -labels and has some labels defined. - -Many devices require SUBDATA sections. These sections have custom formats specific to the type of information that they -contain. When saving auxiliary files with devices that require SUBDATA sections, the user can choose to use primary or - 159 - - - -secondary key fields or labels to identify devices in the SUBDATA sections. The user will either be prompted when saving -the devices, or there is an option to change the key field to use when saving subdata sections on the PowerWorld -Simulator Options dialog under the Case Information Displays category. When choosing to use labels, if a device has a -label, it will be used. If it is a device that can be identified by buses and bus labels exist, bus labels will be used. Finally, if -the device does not have a label and the buses do not have labels, the primary key for the device will be used for -identification. - -Devices that have SUBDATA sections that contain other devices that can be identified by labels include: contingencies, -interfaces, injection groups, post power flow solution actions, and owners. - -The setting to choose which identifier to use for the SUBDATA sections does not just apply to SUBDATA sections. Often -when saving groups of options, this setting will apply to everything being saved with those options and not just the -SUBDATA sections. This includes contingency options, ATC options, limit monitoring settings, and PVQV options. In these -cases, there will be a prompt asking the user to decide which identifier to use in the auxiliary file. - -Loading Auxiliary Files SUBDATA Sections Using Labels -The various SUBDATA sections that represent references to other objects can also be read using labels. Examples include -contingencies, interfaces, injection groups, post power flow solution actions, and owners. When reading a -SUBDATA section such as this, PowerWorld makes no assumption ahead of time about what identification was used to -write this SUBDATA section. Instead, an order of precedence for the identification is as follows - - - Identification Explanation Example -1 Key Fields assumes that the strings represent Key Fields BRANCH 8 9 1 -2 Secondary assumes that the strings represent Secondary BRANCH Eight_138 Nine_230 1 - -Key Fields Key Fields -3 Labels for the key/secondary key fields for some objects BRANCH Label8 Label9 1 - -component consist of references to other objects. An -objects example of this is the BRANCH object that is - -described by the From Bus, To Bus, and -Circuit ID. This assumes that labels of the -component objects are used. - -4 Labels Assumes that the string represents one of the BRANCH LabelForBranch -Labels of the object - -Special Use of Labels in SUBDATA -There are a few special cases where objects have fields that identify other devices. These devices can be identified by label -but not in the conventional means because the label field applies to the object that contains the device and a SUBDATA -section is not necessary. These special cases include: (Note all fields given below are by variable name because the use of -labels is most relevant with auxiliary files.) - -ATC Scenarios -ATC Scenario change records usually contain primary key fields to identify the devices that should be -adjusted during the scenario. If using labels, these primary key fields will be replaced with a single Label -field. The use of this field is different because the Label field refers to the device in the change record and -not to the change record itself. When labels are used with ATC scenarios, device labels only can be used. -Bus labels cannot be used to identify devices for which no label exists but a bus label does. - -ATC Extra Monitors -ATC Extra Monitors identify either branches or interfaces to monitor during the ATC analysis. These -devices are identified in the WhoAmI field of ATC Extra Monitor records. Usually, the WhoAmI field is a -special format that contains key field tags. Optionally, this field can use the label of the device for the - - 160 - - - -extra monitor. If the device label is not available, the standard format will be used. There is no option to -use bus labels if they exist and the device labels do not. - -Model Conditions -Devices in Model Conditions are usually identified by the WhoAmI field which is in a special format that -contains key field tags. Optionally, this field can use the label of the device. If the device label does not -exist, the standard format will be used. There is no option to use bus labels if they exist and the device -labels do not. - -Model Expressions -Model Expressions contain Model Fields. Model Fields are identified by the WhoAmI fields in the Model -Expressions. Usually, the WhoAmI fields are in a special format that contains key field tags. Optionally, -these fields can use the label of the device associated with the Model Field. If the device does not exist, -the standard format will be used. There is no option to use bus labels if they exist and the device labels do -not. - -Bus Load Throw Over Records -Bus Load Throw Over Records are used with contingency analysis. These records have an option to -identify the bus to which the load will be transferred by either number or name_kV combination. If -choosing to identify objects by label, the BusName_NomVolt:1 field will contain the label of the bus -instead of the name_kV combination. Bus Load Throw Over Records will be saved in an auxiliary file if -choosing to Save settings on the Contingency Analysis dialog. - -Injection Group Participation Points -All participation points and the injection groups to which they belong can be listed on the Injection Group -Display. Load, generator, bus, and shunt devices that can be assigned to a participation point must be -identified by bus and ID. The bus can be identified by either the number or name. When identifying by -name, the BusName_NomVolt field is used to provide the name_kV combination for the bus. If choosing -to identify devices by label, this field instead will contain the label of the device. If the device does not -have a label but the bus does, the bus label will be used instead in conjunction with the ID of the device. -Even if the device does contain a label, the ID field must be included in any auxiliary file that is going to -be loaded because it is a key field. Injection groups can be included in other injection groups. Injection -groups can be identified by label, even though this is not a normal thing to do. If any injection groups -have labels and these injection groups are included in other injection groups, their labels will also appear -in the BusName_NomVolt field. If they do not have labels, they will be identified by the injection group -name that appears in the PPntID field. - - 161 - - - -SubData Sections -The format described thus far works well for most kinds of data in Simulator. It does not work as well however for data -that stores a list of objects. For example, a contingency stores some information about itself (such as its name), and then a -list of contingency elements, and possible a list of limit violations as well. For data such as this, Simulator allows -, tags that store lists of information about a particular object. This formatting looks like the -following - - -object_type (list_of_fields) -{ -value_list_1 - - precise format describing an object_type1 - precise format describing an object_type1 - . - . - . - - - precise format describing an object_type2 - precise format describing an object_type2 - . - . - . - -value_list_2 - . - . - . -value_list_n -} - - -Note that the information contained inside the , tags may not be flexibly defined. It must be -written in a precisely defined order that will be documented for each SubData type. The description of each of these -SubData formats follows. - - 162 - - - -ATC_Options -RLScenarioName -GScenarioName -IScenarioName - -These three sections contain the pretty names of the RL Scenarios, G Scenarios, and I Scenarios. Each line -consists of two values: Scenario Number and a name string enclosed in quotes. - -Scenario Number : The scenarios are number 0 through the number of scenarios minus 1. -Scenario Name : These represent the names of the various scenarios. - - -Example: - -//Index Name - 0 "Scenario Name 0" - 1 "Scenario Name 1" - - -ATCMemo -This section contains the memo text for the ATC analysis. - -Example: - -//Memo -"Comments for the ATC analysis" - - -ATCExtraMonitor -ATCFlowValue - -This subdata section contains a list of a flow values for specified transfer levels. Each line consists of two -values: Flow Value (flow on the monitored element) and a Transfer Level (in MW). - -Flow Value : Contains a string describing which monitor this belongs to. -Transfer Level : Contains the value for this extra monitor at the last linear iteration. - - -Example: - -//MWFlow TransferLevel - 94.05 55.30 - 105.18 80.58 - 109.02 107.76 - - - - - 163 - - - -ATCScenario -TransferLimiter - -This subdata section contains a list of the TransferLimiters for this scenario. Each line contains fields -relating one of the Transferlimiters. The fields are written out in the following order: - -Limiting Element : Contains a description of the limiting element. The possible values are: -"PowerFlow Divergence" -"AREA num" -"SUPERAREA name" -"ZONE num" -"BRANCH num1 num2 ckt" -"INJECTIONGROUP name" -"INTERFACE name" - -Limiting Contingency : The name of the limiting contingency. If blank, then this means it’s a -limitation in the base case. - -MaxFlow : The transfer limitation in MW in per unit. -PTDF : The PTDF on the limiting element in the base case (not in percent). -OTDF : The OTDF on the limiting element under the limiting contingency. -LimitUsed : The limit which was used to determine the MaxFlow in per unit. -PreTransEst : The estimated flow on the line after the contingency but before the - -transfer in per unit. -MaxFlowAtLastIteration - : The total transfer at the last iteration in per unit. -IterativelyFound : Either YES or NO depending on whether it was iteratively determined. - - -Example: - - "BRANCH 40767 42103 1" "contin" 2.84 -0.0771 -0.3883 -4.35 -4.35 -0.01 "-55.88" -YES - "BRANCH 42100 42321 1" "Contin" 4.42 0.1078 0.5466 6.50 5.64 1.57 " 22.59" NO - "BRANCH 42168 42174 1" "Contin" 7.45 -0.0131 -0.0651 -1.39 -1.09 4.60 "-33.31" NO - "BRANCH 42168 42170 1" "Contin" 8.54 0.0131 0.0651 1.39 1.02 5.69 " 26.10" NO - "BRANCH 41004 49963 1" "Contin" 9.17 -0.0500 -0.1940 -4.39 -3.16 6.32 " 68.73" NO - "BRANCH 46403 49963 1" "Contin" 9.53 0.0500 0.1940 4.46 3.16 6.68 "-68.68" NO - "BRANCH 42163 42170 1" "Contin" 10.14 -0.0131 -0.0651 -1.39 -0.92 7.29 "-15.58" NO - - -ATCExtraMonitor -This subdata section contains a list of the ATCExtraMonitors for this scenario. Each line contains three -fields relating one of the ATCExtraMonitors. The first field describes the ATCExtraMonitor which this -subdata corresponds to. The second and third variables are the initial value and sensitivity for this extra -monitor for the sceanario. An optional fourth field may be included if we are using one of the iterated -ATC solution options. This field must be the String "ATCFlowValue". - -Monitor Description : Contains a string describing which monitor this belongs to. -InitialValue : Contains the value for this extra monitor at the last linear iteration. -Sensitivity : Contains the senstivity of this monitor. -ATCFlowValue : A string which signifies that a block will follow which stores a list of flow - -values for specified transfer levels. Each line of the block consists of two -values: Flow Value (flow on the monitored element) and a Transfer Level -(in MW). The block is terminated when a line of text that starts with -‘END’ is encountered. - - - - - 164 - - - -Example: - - "InterfaceLeft-Right" 40.0735 0.633295 - "Branch251" 78.7410 0.266589 - - -AUXFileExportFormatData -DataBlockDescription - -This subdata section is used to define the objects that should be included in an auxiliary file along with -their fields, subdata sections, and any filter used to specify which objects should be included. Each line -contains the following: - -ObjectType : Name of the object to include in the auxiliary file. -[FieldList] : List of fields to include. Must be enclosed in brackets. This list can either - -be space-delimited or comma-delimited. -[SubdataList] : List of subdata sections to include. This list must be enclosed in brackets - -and can be either space-delimited or comma-delimited. Include empty -brackets to not include subdata or for objects that do not have any -subdata sections. - -"Filter" : Description of the filter to use for determining which objects to include. -This must be enclosed in double quotes. If no filter is to be used, empty -double quotes should be included. Valid entries are: "", "filtername", -"AREAZONE", and "SELECTED". See the Using Filters in Script Commands -section for more information on specifying the filtername. - - -Example: - - // ObjectType [FieldList] [SubdataList] "Filter" - Area [AreaName, AreaNum] [] "SELECTED" - Gen [BusNum, BusName, GenID] [BidCurve, ReactiveCapability] "" - - -AUXFileExportFormatDisplay -DataBlockDescription - -Same format as for the AUXFileExportFormatData subdata section. - -Example: - - // ObjectType [FieldList] [SubdataList] "Filter" - DisplayArea [AreaName, AreaNum, SOAuxiliaryID] [] "" - DisplayTransmissionLine [BusNum, BusNum:1, LineCircuit, SOAuxiliaryID] - [Line] "Nominal Voltage > 138 kV" - - -BGCalculatedField -Condition - -Calculated Fields allow you to define a calculation over most network and aggregation objects along with -a few other types of objects. The calculation can then be used to show an aggregation calculation on -objects that link to these calculation objects in some manner. Part of the definition is a filter which -specifies which objects to operate over. This subdata section is identical to the Condition subdata section -of the Filter object type. - - 165 - - - -Bus -MWMarginalCostValues -MvarMarginalCostValues -LPOPFMarginalControls - -These three sections contain specific values computed for an OPF solution. In MWMarginalCostValues or -MvarMarginalCostValues these specific values are the MW or Mvar marginal prices for each constraint. In -LPOPFMarginalControls the values are the sensitivities of the controls with respect to the cost of each bus. - -Example: - - //Value - 16.53 - 0.00 - 21.80 - - -BusViewFormOptions -BusViewBusField -BusViewFarBusField -BusViewGenField -BusViewLineField -BusViewLoadField -BusViewShuntField - -The values represent specific fields on the custom defined bus view onelines. Each line contains two -values: - -Location : The various locations on the customized bus view contain slots for fields. -This is the slot number. - -FieldDescription : This is a string enclosed in double quotes. The string itself is delimited -by the @ character. The string contains five values: - -Name of Field : The name of the field. Special fields that appear -on dialog by default have special names. -Otherwise these are the same as the fieldnames of -the AUX file format (for the "other fields" feature -on the dialogs). - -Total Digit : Number of total digits for a numeric field. -Decimal Points : Number of decimal points for a numeric field. -Color : This is the color of the field. It is not presently - -used. -Increment Value : This is the "delta per mouse" click for the field. - - -Example: - - 0 "MW Flow@6@1@0@0" - 1 "MVar Flow@6@1@0@0" - 2 "MVA Flow@6@1@0@0" - 3 "BusAngle:1@6@2@0@0" - - - 166 - - - -ColorMap -ColorPoint - -A colorpoint is simply described by a real number (between 0 and 100) indicating the percentage -breakpoint, an integer describing the color, and a field indicating if the color should be used or the -contour should be transparent. These three values are written on a single line of text. Each line contains -two values: - -cmvalue : Real number between 0 and 100 (minimum to maximum value). -cmcolor : Integer between 0 and 16,777,216. Value is determined by taking the - -red, green and blue components of the color and assigning them a value -between 0 and 255. The color is then equal to red + 256*green + -256*256*blue. - -cmalpha : Integer between 0 and 255, where only 0 and 255 are valid values. A -value of 0 indicates that the color point is transparent, while a value of -255 indicates that the color point is opaque. If the alpha channel is -omitted, a default value of 255 (opaque) will be assigned. - - -Example: - - // Value Color Alpha - 100.0000 127 255 - 62.5000 65535 255 - 50.0000 8388479 0 - 12.5000 16711680 0 - 0.0000 8323072 255 - - -Contingency -CTGElementAppend - -Normally when reading in contingency definitions, the CTGElement SubData section is used to define the -list of elements. When reading a CTGElement SubData section, all existing elements of the contingency -are deleted are replaced with the ones read from the file. Using the CTGElementAppend as the SubData -section will modify this behavior so that the elements are appended to the existing ones instead of -deleted. - -CTGElement -A contingency element is described by up to the following entries. All entries must be on a single line of -text: - -Action : String describing the action associated with this element. See below for -actions available. - -ModelCriteria : This is the name of a ModelFilter or ModelCondition under which this -action should be performed. This entry is optional. If it is not specified, -then a blank (or no criteria) is assumed. If you want to enter a Status, -then use must specify "" as the ModelCriteria. - - - 167 - - - -Status : The following options are available: -CHECK : perform action if ModelCriteria is true -ALWAYS : perform action regardless of ModelCriteria -NEVER : do not perform action -TOPOLOGYCHECK : perform action if ModelCriteria is true following - -implementation of other actions and before -solving the power flow - -POSTCHECK : perform action if ModelCriteria is true following -implementation of other actions and solving the -power flow - -SOLUTIONFAIL : perform the action if ModelCriteria is true or not -defined following the failure of the power flow -solution. - - This entry is optional. If it is not specified, then CHECK is assumed. -InclusionFilter : This entry is optional and will only exist for elements of RemedialAction - -or GlobalContingencyActions objects. This is the name of an advanced -filter or device filter that gets applied to each contingency. If the -contingency meets the filter, that contingency will include this element. -Otherwise, the element will be ignored. - -TimeDelay : This entry is optional. If not specified, 0 is assumed. This entry will only -exist for elements of Contingency, RemedialAction, or -GlobalContingencyActions objects. This is the time delay in seconds to -wait before the action takes place. - -Persistent : This entry is optional. It not specified, NO is assumed. Normally after a -contingency action has been implemented it will not be applied again. -Setting this option to YES to mark an action as persistent will change this -behavior. Any action marked as persistent that also has a Status of -TOPOLOGYCHECK, POSTCHECK, or SOLUTIONFAIL will be applied in the -appropriate section of the overall contingency process any time that its -ModelCriteria is met. An exception is that a SOLUTIONFAIL element will -only remain persistent until a solution is successfully achieved. - -ArmingCriteria : This entry is optional and will only exist for elements of RemedialAction -objects. This is the name of a ModelFilter or ModelCondition under -which this action should be armed. If it is not specified, then a blank (or -no criteria) is assumed. If you want to enter a ArmingStatus, then use -must specify "" as the ArmingCriteria. - -ArmingStatus : This entry is optional and will only exist for elements of RemedialAction -objects. If not specified, CHECK is assumed. The following options are -available: - -CHECK : action is armed if ArmingCriteria is true -ALWAYS : action is considered armed regardless of ArmingCriteria -NEVER : action is not armed - -Comment : All text to the right of the comment symbol (//) will be saved with the -CTGElement as a comment. - - -Possible Actions: - -Many actions have a value field that can be specified. This value can be expressed in three ways: - -1. A numerical value that will be used directly. -2. The variablename of a field for the object in the action preceded by the tag . This field - -will be evaluated and that value will be used. Including the keyword REF in the appropriate place -in the action string will cause the field to be evaluated in the contingency reference case. -Otherwise, the field will be evaluated at the moment the action is implemented. - - 168 - - - -3. The name of a Model Expression preceded by the tag . Single quotes should -enclose the entirety of the tag and the name if the name contains spaces. The model expression -will be evaluated and the result will be used as the value. Including the keyword REF in the -appropriate place in the action string will cause the model expression to be evaluated in the -contingency reference case. Otherwise, the model expression will be evaluated at the moment -the action is implemented. - -Transmission Line or Transformer outage or insertion -BRANCH | bus1# bus2# ckt | OPEN - | | CLOSE - | | OPENCBS - | | CLOSECBS - | | SET_TO | value | LimitMVA | REF -Takes branch out of service, or puts it in service. The contingency rating of the branch can also be set for -the duration of the contingency using the SET_TO action. Note: bus# values may be replaced by a string -enclosed in single quotes where the string is the name of the bus followed by an underscore character -and then the nominal voltage of the bus. These values may also be replaced by a string enclosed in single -quotes which represents the label of the bus. Also, the entire sequence [bus1# bus2# ckt] may be -replaced by the label of the branch. - -Generator, Load, or Switched Shunt outage or insertion -GEN | bus# id | OPEN, CLOSE, OPENCBS, or CLOSECBS -LOAD | bus# id | OPEN, CLOSE, OPENCBS, or CLOSECBS -SHUNT | bus# id | OPEN, CLOSE, OPENCBS, or CLOSECBS -INJECTIONGROUP | name | OPEN, CLOSE, OPENCBS, or CLOSECBS -Takes a generator, load, or shunt out of service, or puts it in service. If specifying an injection group, the -status of all devices in the injection group will be changed. Note: bus# values may be replaced by a string -enclosed in single quotes where the string is the name of the bus followed by an underscore character -and then the nominal voltage of the bus. These values may also be replaced by a string enclosed in single -quotes which represents the label of the bus. Also, the sequence [bus1# ckt] or [name] may be replaced -by the label of the device. - -Generator, Load or Switched Shunt movement to another bus -For the following set of actions, all of the object types can use the same action keywords which are -associated with the value keyword following the actual value. The move is based on specifying a bus: -GEN | bus1# | MOVE_P_TO | bus2# | value | MW | REF -LOAD | | MOVE_Q_TO | | | MVR | -SHUNT | | | | | | -For the following set of actions, all of the object types can use the same action keywords which are -associated with the value keyword following the actual value. The move is based on specifying a -particular device: -GEN | bus1# id | MOVE_P_TO | bus2# | value | MW | REF -LOAD | | MOVE_Q_TO | | | MVR | -SHUNT | | | | | | -Generator actions that move a generator by a percentage only apply to the generator MW: -GEN | bus1# | MOVE_P_TO | bus2# | value | PERCENT | REF -GEN | bus1# id | MOVE_P_TO | bus2# | value | PERCENT | REF - - - 169 - - - -The following set of actions are used for specifying a load move by maintaining a constant power factor: -LOAD | bus1# | MOVE_PQ_TO | bus2# | value | MW | REF -LOAD | bus1# id | MOVE_PQ_TO | bus2# | | MW | -The following set of actions apply to loads and shunts and are used to move a percentage of the entire -MW and Mvar output. The move is based on specifying a bus: -LOAD | bus1# | MOVE_PQ_TO | bus2# | value | PERCENT | REF -SHUNT | | | | | | -The following set of actions apply to loads and shunts and are used to move a percentage of the entire -MW and Mvar output. The move is based on specifying a particular device: -LOAD | bus1# id | MOVE_PQ_TO | bus2# | value | PERCENT | REF -SHUNT | | | | | | -Use to move generation, load or shunt at a bus1 over to bus2. This can be used on a bus or specific -device basis in specifying what to move. Note: bus# values may be replaced by a string enclosed in single -quotes where the string is the name of the bus followed by an underscore character and then the nominal -voltage of the bus. These values may also be replaced by a string enclosed in single quotes which -represents the label of the bus. When identifying specific devices, the device label can replace the bus -number and device id. - -Generator, Load or Switched Shunt set or change a specific value -For the following set of actions, all of the object types can use the same action keywords which are -associated with the value keyword following the actual value. These changes are based on specifying a -bus: -GEN | bus# | SET_P_TO | value | MW | REF -LOAD | | SET_Q_TO | | MVR | -SHUNT | | CHANGE_P_BY | | MW | - | | CHANGE_Q_BY | | MVR | -For the following set of actions, all of the object types can use the same action keywords which are -associated with the value keyword following the actual value. These changes are based on specifying a -bus: -GEN | bus# id | SET_P_TO | value | MW | REF -LOAD | | SET_Q_TO | | MVR | -SHUNT | | CHANGE_P_BY | | MW | - | | CHANGE_Q_BY | | MVR | -The following set of actions are used to set or change the MW output of generation at a bus by a -percentage: -GEN | bus# | SET_P_TO | value | PERCENT | REF -GEN | | CHANGE_P_BY | | | -The following set of actions are used to set or change the MW output of a particular generator by a -percentage: -GEN | bus# id | SET_P_TO | value | PERCENT | REF -GEN | | CHANGE_P_BY | | | -The following set of actions apply to loads and shunts and are used to set or change a percentage of the -entire MW and Mvar output. This based on specifying a bus: -LOAD | bus# | SET_PQ_TO | value | PERCENT | REF -SHUNT | | CHANGE_PQ_BY | | | - -The following set of actions apply to loads and shunts and are used to set or change a percentage of the -entire MW and Mvar output. This based on specifying a specific device: -LOAD | bus# id | SET_PQ_TO | value | PERCENT | REF -SHUNT | | CHANGE_PQ_BY | | | -The following set of actions are used to specify a load set or change by maintaining a constant power -factor. This is based on specifying a bus: -LOAD | bus# | SET_PQ_TO | value | MW | REF - | | CHANGE_PQ_BY | | MW | -The following set of actions are used to specify a load set or change by maintaining a constant power -factor. This is based on specifying a specific load: - - 170 - - - -LOAD | bus# id | SET_PQ_TO | value | MW | REF - | | CHANGE_PQ_BY | | MW | -The following set of actions apply to generators and shunts and are used to set or change the setpoint -voltage of the devices at the specified bus: -GEN | bus# | SET_VOLT_TO | value | PU | REF -SHUNT | |CHANGE_VOLT_BY | | | -The following set of actions apply to generators and shunts and are used to set or change the setpoint -voltage of the specified device: -GEN | bus# id | SET_VOLT_TO | value | PU | REF -SHUNT | |CHANGE_VOLT_BY | | | -Note: bus# values may be replaced by a string enclosed in single quotes where the string is the name of -the bus followed by an underscore character and then the nominal voltage of the bus. These values may -also be replaced by a string enclosed in single quotes which represents the label of the bus. When -identifying specific devices, the device label can replace the bus number and device id. - -Bus outage causes all lines connected to the bus to be outage -BUS | bus# | OPEN - | OPENCBS -Takes all branches connected to the bus out of service. Also outages all generation, load, or shunts -attached to the bus. Note: bus# values may be replaced by a string enclosed in single quotes where the -string is the name of the bus followed by an underscore character and then the nominal voltage of the -bus. These values may also be replaced by a string enclosed in single quotes which represents the label of -the bus. - -Interface outage or insertion -INTERFACE | name | OPEN - | CLOSE - | OPENCBS - | CLOSECBS -Takes all monitored branches in the interface out of service, or puts them all in service. Open actions will -also open all generators and loads contained in the interface including generators and loads inside any -injection groups or other interfaces. Note: the [name] may be replaced by the label of the interface. - -Interface change specific value -INTERFACE | name | CHANGE_P_BY | value | Option | REF | PPREF - | SET_P_TO | | | | -The following Option settings are allowed to set or change the MW flow of an interface by or to a -particular value: - -MWMERITORDEROPEN - : Value will be interpreted as the amount of MW flow change or new MW - -flow. The element in the interface with the highest participation factor -will be opened, followed by the second generator and so on. This will -continue until the amount of MW flow opened is as close to the desired -amount as possible without exceeding the amount of MW flow open. If -an element will cause a change in flow that is not in the desired direction, -that element is not opened and the next element is examined. - -PERCENTMERITORDEROPEN or %MERITORDEROPEN - : Same as MWMERITORDEROPEN except that the value will be interpreted - -as percentage of the contingency reference state MW flow. -MWMERITORDEROPENEXCEED - : Same as MWMERITORDEROPEN except that the amount of MW opened is - -allowed to exceed the desired amount of change. Interface elements will -be opened in merit order until the desired amount is met or exceeded. - -PERCENTMERITORDEROPENEXCEED or %MERITORDEROPENEXCEED - - 171 - - - - : Same as MWMERITORDEROPENEXCEED except that the value will be -interpreted as percentage of the contingency reference state MW -injection. - -MWEFFECTOPEN - : Value that is specified with the action is the desired MW Effect that the - -action should have. The participation factors defined with the interface -elements will be interpreted as effectiveness factors akin to transfer -distribution factors. These factors are supplied as input by the user when -defining the interface. The effectiveness factors are multiplied by the -present MW flow of elements in the interface to determine how much -effect they will have if dropped. The flow of an element is determined by -its MW flow multiplied by the Weighting factor specified with the -element. If the effect of a particular element is in the opposite direction -of the desired effect, that element is skipped. The action will find the -smallest number of elements to drop that results in a total MW Effect -that is within 5% of the desired MW Effect, but does not exceed the -desired MW Effect. - - This option is not valid with SET_P_TO actions. -MWEFFECTOPENEXCEED - : Same as MWEFFECTOPEN but it will ensure that the total MW Effect is - -within 5% of the total desired MW Effect and also meets or exceeds the -desired MW Effect. - - This option is not valid with SET_P_TO actions. -MWBESTFITOPEN - : Value will be interpreted as the amount of MW flow change or new MW - -flow. When using this option the participation factors of interface -elements do not impact which elements are opened. The flow on an -element, which is determined by its MW flow multiplied by the weighting -factor specified with the element, is used to determine which elements -should be opened. If the follow on an element is in the appropriate -direction to achieve the desired flow change on the interface, that -element is eligible fo being opened. To determine if an element will -actually be opened, the best fit algorithm attempts to determine the -combination of elements that will achieve the desired flow change by -opening the least amount of elements and achieving an actual flow -change within 5% fo the desired flow change without exceeding the -desired amount. - -MWBESTFITOPENEXCEED - : Same as MWBESTFITOPEN except that the MW flow change is allowed to - -exceed the desired amount of change. -PERCENTBESTFITOPEN or %BESTFITOPEN - : Same as MWBESTFITOPEN except that the value will be interpreted as - -percentage of the contingency reference state MW flow. -PERCENTBESTFITOPENEXCEED or %BESTFITOPENEXCEED - : Same as PERCENTBESTFITOPEN except that the MW flow change is - -allowed to exceed the desired amount of change. - -When using an action that requires participation factors, an optional parameter PPREF can be specified. -This indicates that the participation factors should be determined in the contingency reference case. - -Interfaces can contain other interfaces. The treatment of interfaces within interfaces is to open the entire -contained interface when using the MWMERITORDEROPEN type actions. - - - 172 - - - -Notes: The [name] may be replaced by the label of the interface. - -Line Shunt outage or insertion -LINESHUNT | bus1# bus2# bus# ckt | OPEN - | CLOSE -Takes a line shunt out of service, or puts it in service. bus1# and bus2# identify the line that the line shunt -is on and bus# identifies the side of the line that the line shunt is on. bus# values may be replaced by a -string enclosed in single quotes where the string is the name of the bus followed by an underscore -character and then the nominal voltage of the bus. bus# values may also be replaced by a string enclosed -in single quotes that represents the label of the bus. The sequence [bus1# bus2#] may be replaced by the -label of the line to which the line shunt is attached. - -Injection Group outage or insertion -INJECTIONGROUP | name | OPEN - | CLOSE | value | REF | PPREF - | OPENCBS - | CLOSECBS - | OPEN | value | REF | PPREF -Takes all devices in the injection group out of service, or puts them all in service. -The OPEN action will open all devices in the injection group if no value is specified. If a value is specified, -only that number of devices will be opened in the order of highest to lowest participation factor. The -CLOSE action will close all devices in the injection group if no value is specified. If a value is specified, -only that number of devices will be closed in the order of highest to lowest participation factor. When -using an action that requires participation factors, an optional parameter PPREF can be specified. This -indicates that the participation factors should be determined in the contingency rerference case. This will -only be done for participation points using an AutoCalcMethod that indicates the factor should be -dynamically determined and the AutoCalc field is set to YES for the participation point. - -The [name] may be replaced by the label of the injection group. Bus participation points will be -completely ignored in this process. - -Injection Group change specific value -INJECTIONGROUP | name | CHANGE_P_BY | value | Option | REF | PPREF - | SET_P_TO | | | | -The following Option settings are allowed to set or change the MW generation/load in an injection -group by or to a particular value: - -MW : Value will be interpreted as the amount of MW injection change or new -MW injection. Each participation point in the injection group will be -changed in proportion to the participation factors of the group. - -PERCENT or % : Same as MW except that the value will be interpreted as percentage of -the contingency reference state MW injection. - -MWMERITORDER : Value will be interpreted as the amount of MW injection change or new -MW injection. Both generator and load points will be modified in the -injection group. Elements will be adjusted in order of highest -participation factor to lowest before moving to the next element. This -process continues until the desired injection is met. Generators will not -be opened in this process, which means all online generators will -continue to provide Mvar support. Loads that have both their minimum -and maximum MW limits set to zero will not be allowed to increase. -They can only decrease towards 0. - -PERCENTMERITORDER or %MERITORDER - : Same as MWMERITORDER except that the value will be interpreted as - -percentage of the contingency reference state MW injection. - - 173 - - - -MWMERITORDEROPEN - : Value will be interpreted as the amount of MW injection change or new - -MW injection. Both generator and load points can be modified in the -injection group. If the MW injection change is negative, the generator in -the injection group with the highest participation factor will have its -status changed to Open, followed by the second generator and so on. -This will continue until the amount of MW opened is as close to the -desired amount as possible without exceeding the desired amount of -drop. If the MW injection change is positive, loads will be opened in the -same manner. If an element would cause the desired gen drop amount -to be exceeded, that element is skipped and the next element in merit -order is processed. If the change requested is positive and there are no -loads in the injection group, generators will be increased toward their -maximum MW output in the same manner as MWMERITORDER as though -the OPEN option was not specified. If the change requested is negative -and there are no generators in the injection group, loads will be -increased toward their maximum MW output in the same manner as -MWMERITORDER as though the OPEN option was not specified. - -PERCENTMERITORDEROPEN or %MERITORDEROPEN - : Same as MWMERITORDEROPEN except that the value will be interpreted - -as percentage of the contingency reference state MW injection. -MWMERITORDEROPENEXCEED - : Same as MWMERITORDEROPEN except that the amount of MW opened is - -allowed to exceed the desired amount of change. Generators or loads -will be opened in merit order until the desired amount is met or -exceeded. - -PERCENTMERITORDEROPENEXCEED or %MERITORDEROPENEXCEED - : Same as MWMERITORDEROPENEXCEED except that the value will be - -interpreted as percentage of the contingency reference state MW -injection. - -MWEFFECTOPEN : Value that is specified with the action is the desired MW Effect that the -action should have. The participation factors defined with the Injection -Group will be interpreted as effectiveness factors akin to transfer -distribution factors. These factors are supplied as input by the user when -defining the injection group. The effectiveness factors are multiplied by -the present output of generators (or loads) in the injection group to -determine how much effect they will have if dropped. The action will find -the smallest number of generators (or loads) to drop which results in a -total MW Effect that is within 5% of the desired MW Effect, but does not -exceed the desired MW Effect. - - This option is not valid with SET_P_TO actions. -MWEFFECTOPENEXCEED - : Same as MWEFFECTOPEN but it will ensure that the total MW Effect is - -within 5% of the total desired MW Effect and also meets or exceeds the -desired MW Effect. - - This option is not valid with SET_P_TO actions. -MWBESTFITOPEN : Value will be interpreted as the amount of MW injection change or new - -MW injection. When using this option the participation factors do not -impact which elements are opened. All generators or loads defined with -the injection group can participate if they are online. Specifially which -generators or loads depends on an algorithm that attempts to get the -actual injection change within 5% of the desired injection change by - - 174 - - - -opening the smallest number of generators or loads without exceeding -the desired amount. - -MWBESTFITOPENEXCEED - : Same as MWBESTFITOPEN except that the MW injection change is - -allowed to exceed the desired amount of change. -PERCENTBESTFITOPEN or %BESTFITOPEN - : Same as MWBESTFITOPEN except that the value will be interpreted as - -percentage of the contingency reference state MW injection. -PERCENTBESTFITOPENEXCEED or %BESTFITOPENEXCEED - : Same as PERCENTBESTFITOPEN except that the MW injection change is - -allowed to exceed the desired amount of change. - -When using an action that requires participation factors, an optional parameter PPREF can be specified. -This indicates that the participation factors should be determined in the contingency reference case. This -will only be done for participation points using an AutoCalcMethod that indicates the factor should be -dynamically determined and the AutoCalc field is set to YES for the participation point. - -Injection Groups can contain participation points that reference another injection group. The treatment of -injection groups within injection groups will be to drop the entire contained injection group when using -the MWMERITORDEROPEN and MWEFFECTOPEN type actions. - -Notes: The [name] may be replaced by the label of the injection group. Bus participation points will be -completely ignored in this process. - -Series Capacitor Bypass or Inservice -SERIESCAP | bus1# bus2# ckt | BYPASS - | INSERVICE -Bypasses a series capacitor, or puts it in service. Note: bus# values may be replaced by a string enclosed in -single quotes where the string is the name of the bus followed by and underscore character and then the -nominal voltage of the bus. Note: bus# values may also be replaced by a string enclosed in single quotes -which represents the label of the bus. Also, the entire sequence [bus1# bus2# ckt] may be replaced by -the label of the branch. Keyword SERIESCAP may also be replaced with BRANCH, which will allow -bypassing or not bypassing any branch and is not limited to series capacitors. - -Series Capacitor set impedance -SERIESCAP | bus1# bus2# ckt | SET_X_TO | value | PERCENT | REF - | | | PU | -Changes the impedance a series capacitor either specifying a new per unit value or specifying a -percentage of the value in the contingency reference case. Note: bus# values may be replaced by a string -enclosed in single quotes where the string is the name of the bus followed by and underscore character -and then the nominal voltage of the bus. Note: bus# values may also be replaced by a string enclosed in -single quotes which represents the label of the bus. Also, the entire sequence [bus1# bus2# ckt] may be -replaced by the label of the branch. Keyword SERIESCAP may also be replaced with BRANCH, which will -allow setting the impedance of any branch and is not limited to series capacitors. - -DC Transmission or VSC DC Transmission Line outage -DCLINE | bus1# bus2# ckt | OPEN - | OPENCBS -VSCDCLINE | 'Name' | OPEN - | OPENCBS -Takes DC Line or VSC DC Line out of service. Note: bus# values may be replaced by a string enclosed in -single quotes where the string is the name of the bus followed by an underscore character and then the -nominal voltage of the bus. These values may also be replaced by a string enclosed in single quotes - - 175 - - - -which represents the label of the bus. Also, the entire sequence [bus1# bus2# ckt] may be replaced by -the label of the dc transmission line. For the VSC DC Line, the identifiers are replaced simply with the -name of the VSCDCLINE instead. - -DC Line set a specific value or insertion -DCLINE | bus1# bus2# ckt | SET_P_TO | value | MW | REF - | CHANGE_P_BY | | PERCENT - | SET_I_TO | | AMPS - | CHANGE_I_BY | - | CLOSE | - | CLOSECBS | - | SET_TO | value | OHMS | REF -VSCDCLINE | 'Name' | Same options as for the DC Line, except that - the AMPS option are not avalailable for VSC -Use to set the DC Line setpoint to a particular value, or puts it in service. Note: bus# values may be -replaced by a string enclosed in single quotes where the string is the name of the bus followed by an -underscore character and then the nominal voltage of the bus. Note: bus# values may also be replaced -by a string enclosed in single quotes which represents the label of the bus. Also, the entire sequence -[bus1# bus2# ckt] may be replaced by the label of the dc transmission line. (Note: for the CLOSE and -CLOSECBS choice, only the units of MW or AMPS may be used.) For the VSC DC Line, the identifiers are -replaced simply with the name of the VSCDCLINE instead. - -MTDC Converter outage -DCCONVERTER | rec# bus# | OPEN - | OPENCBS -Takes multi-terminal DC converter out of service. The rec# specifies the multi-terminal DC line record, -while bus# specifies the AC bus to which the converter is connected. Note: bus# values may be replaced -by a string enclosed in single quotes where the string is the name of the bus followed by an underscore -character and then the nominal voltage of the bus. These values may also be replaced by a string -enclosed in single quotes which represents the label of the bus. - -MTDC Converter set a specific value or insertion -DCCONVERTER | rec# bus# | SET_P_TO | value | MW | REF - | CHANGE_P_BY | | PERCENT - | SET_I_TO | | AMPS - | CHANGE_I_BY | - | CLOSE | - | CLOSECBS | -Use to set the multi-terminal DC converter setpoint to a particular value, or puts it in service. The rec# -specifies the multi-terminal DC line record, while bus# specifies the AC bus to which the converter is -connected. Note: bus# values may be replaced by a string enclosed in single quotes where the string is -the name of the bus followed by an underscore character and then the nominal voltage of the bus. Note: -bus# values may also be replaced by a string enclosed in single quotes which represents the label of the -bus. (Note: for the CLOSE and CLOSECBS choice, only the units of MW or AMPS may be used.) - -Phase Shifter set a specific value -PHASESHIFTER | bus1# bus2# ckt | SET_P_TO | value | MW | REF - | CHANGE_P_BY | | PERCENT - | SET_TO | value | DEG | REF - | CHANGE_BY | | | -Use the MW and PERCENT options to change or set the middle of the phase shifter MW regulation range -to the specified value. Use the DEG option to change or set the phase shift angle in degrees to a particular -value. -Note: bus# values may be replaced by a string enclosed in single quotes where the string is the name of -the bus followed by an underscore character and then the nominal voltage of the bus. These values may - - 176 - - - -also be replaced by a string enclosed in single quotes which represents the label of the bus. Also, the -entire sequence [bus1# bus2# ckt] may be replaced by the label of the branch. - -Keyword PHASESHIFTER may also be replaced with BRANCH. If the branch is not a phase shifter, no -change will be made. - -3-Winding Transformer outage or insertion -3WXFORMER | bus1# bus2# bus3# ckt | OPEN - | CLOSE - | OPENCBS - | CLOSECBS -Takes all three windings of a 3-winding transformer out of service, or puts them in service. Note: bus# -values may be replaced by a string enclosed in single quotes where the string is the name of the bus -followed by an underscore character and then the nominal voltage of the bus. Note: bus# values may -also be replaced by a string enclosed in single quotes which represents the label of the bus. Also, the -entire sequence [bus1# bus2# bus#3 ckt] may be replaced by the label of the three winding transformer. - -Area Control Type Change -AREA | area# | SET_TO | 'OFF' - | 'PARTFAC' - | 'AREASLACK bus#' - | 'IGSLACK injectiongroup name' -Specify to change the make-up power for an area so that it is different during a contingency than the area -control settings used in the reference case. The area may be set to toggle the control setting to OFF, -PARTFAC, AREASLACK, and IGSLACK. The Area Control topic provides more information about these -control types. If selecting Area Slack is chosen, then a bus must be specified which will act as the area -slack during the contingency action. If selecting IG Slack, then an injection group must be specified by -name. -Note: bus# values may be replaced by a string where the string is the name of the bus followed by an -underscore character and then the nominal voltage of the bus. Note: bus# values may also be replaced -by a string which represents the label of the bus. -In order for the Area contingency action to work correctly, there are contingency and power flow solution -options that must be set correctly. Simulator does not automatically set these options so the user must -make sure they are set. - - -• Area control must be enabled in the contingency base case, i.e. the Power Flow Solution Option -for Island-Based AGC must be set to Disable (Use the Area and Super Area Dispatch settings). - -• The contingency Make-Up Power option must be set to Same as Power Flow case. -• The option to Disable Automatic Generation Control (AGC) found with the Power Flow Solution - -Options must NOT be selected. - -Another suggestion, although not a strict requirement, is that the area should be on area control prior to -contingency analysis if a control type other than Off AGC is going to be set during a contingency. If a -large ACE exists in the base case with area control off, switching the area on control during the -contingency will zero out the ACE in addition to compensating for required make-up power. - -Substation outage -SUBSTATION | sub# | OPEN - | OPENCBS - | SET_P_TO | value | MW | REF - | CHANGE_P_BY | | PERCENT -OPEN and OPENCBS will take a substation out of service. The set and change actions will set the MW -output of online generators in the substation to the specified value. sub# is the number that identifies the - - 177 - - - -substation. sub# can be replaced by a string enclosed in single quotes where the string is the name or -label of the substation. - -Abort -ABORT -Include this action to cause the solution of the contingency to be aborted. - -Execute a Power Flow Solution -SOLVEPOWERFLOW -Include this action to cause the solution of the contingency to be split into pieces. Actions that are listed -before each SOLVEPOWERFLOW call will be performed as a group. - -Calling of a name ContingencyBlock -CONTINGENCYBLOCK | name -Calls a ContingencyBlock and executes each of the actions in that block. - -Make-Up Power Compensation -Only valid immediately following a SET, CHANGE, OPEN or CLOSE action on a Generator, Shunt or Load. -This describes how the change in MW or MVAR are picked up by buses throughout the system. The values -specify participation factors. Note: bus# values may be replaced by a string enclosed in single quotes -where the string is the name of the bus followed by and underscore character and then the nominal -voltage of the bus. -COMPENSATION -bus#1 value1 -bus#2 value2 -... -END - -Example: - - // just some comments - // action Model Criteria Status TimeDelay comment - "BRANCH 40821 40869 1 OPEN" "" ALWAYS 0 //Raver - Paul 500 kV - "GEN 45041 1 OPEN" "" ALWAYS 0 //Trip Unit #2 - "BRANCH 42702 42727 1 OPEN" "Line X Limited" CHECK 0 //Open Fern Hill - "GEN 40221 1 OPEN" "Interface L1" CHECK 0 //Drop ~600 MW - "GEN 40227 1 OPEN" "Interface L2" CHECK 0 //Drop ~1200 MW - "GEN 40221 1 OPEN" "Interface L3" CHECK 0 //Drop ~600 MW - -Note: ContingencyElement object types can also be directly created inside their own DATA section as well. -One of the key fields of the object is then the name of the contingency to which the ContingencyElement -belongs. The Action string will remain the same. - - - 178 - - - -LimitViol -A LimitViol is used to describe the results of a contingency analysis run. Each Limit Violation lists nine -possible values: - -ViolType : One of six values describing the type of violation. -BAMP : branch amp limit violation -BMVA : branch MVA limit violation -VLOW : bus low voltage limit violation -VHIGH : bus high voltage limit violation -INTER : interface MW limit violation -CUSTOM : Custom Monitor value - -ViolElement : This field depends on the ViolType. -for VLOW, VHIGH : "bus1#" or "busname_buskv" or "buslabel" -for INTER : "interfacename" or "interfacelabel" -for BAMP, BMVA : "bus1# bus2# ckt violationbus# - -MWFlowDirection" -violationbus# is the bus number for the end of the branch which is -violated -MWFlowDirection is the direction of the MW flow on the line. -Potential values are "FROMTO" or "TOFROM". -Note: each bus# may be replaced with the name underscore nominal -kV string enclosed in single quotations. Or bus# values may be -replaced by a string enclosed in single quotes representing the label -of the bus. Also the entire sequence [bus1# bus2# ckt] may be -replaced by the label of the branch. - -for CUSTOM : "custommonitorname deviceidentifier" where the -deviceidentifier will use the key fields or label as -specified by the option selected when saving - -Limit : This is the numerical limit which was violated. -ViolValue : This is the numerical value of the violation. -PTDF : This field is optional. It only makes sense for interface or branch - -violations. It stores a sensitivity of the flow on the violating element -during in the base case with respect to a transfer direction This must be -calculated using the Contingency Analysis Other Actions related to -Sensitivities. - -OTDF : Same as for the PTDF. -InitialValue : This stores a number. This stores the base case value for the element - -which is being violated. This is used to compare against when looking at -change violations. - -Reason : This will say whether this was a pure violation, or is being reported as a -violation because the change from the base case is higher than a -specified threshold. - -LIMIT : means this is a violation of a line/interface/bus limit or -simply a Custom Monitor - -CHANGE : means this is being reported as a limit because the change -from the initial value is higher than allowed - -CTG Specified Limit : This specifies if the Limit originated from a contingency action or from -the rating specified with the line and Limit Monitoring Settings. - -NO : the Limit originated from the line and Limit Monitoring Settings -YES : the Limit originated from a contingency action - - - - - 179 - - - -Example: - - BAMP "1 3 1 1 FROMTO" 271.94031 398.48096 10.0 15.01 //Note OTDF/PTDF - // values can also be specified with name underscore nominal kV string - // enclosed inside a single quote as shown next - BAMP "'One_138' 'Three_138' 1 1 FROMTO" 271.94031 398.48096 10.0 15.01 - INTER "Right-Top" 45.00000 85.84451 None None 56.000 LIMIT NO - - -ViolationCTG object types can also be directly created inside their own DATA section as well. One of the -key fields of the object is then the name of the contingency to which the ViolationCTG belongs. - -Sim_Solution_Options -These describe the power flow solution options which should be used under this particular contingency. -The format of the subdata section is two lines of text. The first line is a list of the fieldtypes for -Sim_Solution_Options which should be changed. The second line is a list of the values. Note that in -general, power flow solution options are stored at three different locations in contingency analysis. When -implementing a contingency, Simulator gives precendence to these three locations in the following order: - -1. Contingency Record Options (stored with the particular contingency). -2. Contingency Tool Options (stored with CTG_Options). -3. The global solution options. - -WhatOccurredDuringContingency -Each line of this subdata section is part of a text description of what actually ended up being -implemented for this contingency. This will list which actions were executed and which actions ended up -being skipped because of their model criteria. Each line of the subdata section must be enclosed in -quotes. - -Example: - - "Applied: " - " OPEN Branch Two (2) TO Five (5) CKT 1 | | CHECK | | ELEMENT" - - -ContingencyMonitoringException -Each line of this subdata section contains a string identifying a specially handled monitored element for -this contingency followed by a string indicating how this monitored element should be handled with this -contingency. The elements can be identified by their primary or secondary key fields or by label. The -element descriptions should be enclosed in quotes because they contain spaces. - -Example: - - "Branch '2' '3' '1'" "Exclude" - "Branch 'Three_138.00' 'Four_138.00'" "Include" - "Branch 'Line_2_5'" "Default" - - -CTG_Options -Sim_Solution_Options - -These describe the power flow solution options which should be used under this particular contingency. -The format of the subdata section is two lines of text. The first line is a list of the fieldtypes for -Sim_Solution_Options which should be changed. The second line is a list of the values. Note that in -general, power flow solution options are stored at three different locations in contingency analysis. When -implementing a contingency, Simulator gives precendence to these three locations in the following order: - -1. Contingency Record Options (stored with the particular contingency). -2. Contingency Tool Options (stored with CTG_Options). - - 180 - - - -3. The global solution options. - -CTGElementBlock -CTGElement - -This format is the same as for the Contingency objecttype, however, you cannot call a ContingencyBlock -from within a contingencyblock. - -CTGElementAppend -When a subdata section is defined as CTGElementAppend rather than CTGElement, the actions of this -subdata section will be appended to the contingency actions, instead of replacing them. This format is -the same as for the Contingency objecttype, however, you cannot call a ContingencyBlock from within a -contingencyblock. - -Note: CTGElementBlockElement object types can also be directly created inside their own DATA section as -well. One of the key fields of the object is then the name of the contingency block to which the -CTGElementBlockElement belongs. - -CustomColors -CustomColors - -These describe the customized colors used in Simulator, which are specified by the user. A custom color -is an integer describing a color. Each custom color is written on a single line of text and is an integer -between 0 and 16,777,216. The value is determined by taking the red, green, and blue components of the -color and assigning them a value between 0 and 255. The color is then equal to red + 256*green + -256*256*blue. Each line contains only one integer that corresponds to the color specified. - -Example: - - 9823301 - 8613240 - - -CustomCaseInfo -ColumnInfo - -Each line of this SUBDATA section can be used for specifying the column width of particular columns of -the respective Custom Case Information Sheet. The line contains two values – the column and then a -column width. This is shown in the following example. - -Example: - - "SheetCol" 133 - "SheetCol:1" 150 - "SheetCol:2" 50 - - -DataGrid -ColumnInfo - -Contains a description of the columns which are shown in the respective data grid. Each line of text -contains at least four fields: VariableName, ColumnWidth, TotalDigits, DecimalPoints. The remaining -fields are used when showing a Data View based on this DataGrid object. See help website for Data View -or the OpenDataView script command for more information about this. - -Variablename : Contains the variable which is shown in this column. - - 181 - - - -ColumnWidth : The column width. -TotalDigits : The total digits displayed for numerical values. -DecimalPoints : The decimal points shown for numerical values. -TabBreak : Optional. Default to NO. Set to YES to indicate that a new tab should be - -started immediately before this field. -TabCaption : Optional. Default to blank string. Specifies a caption for the tabbed - -control for fields occurring after the Tab Break. -RowBreak : Optional. Default to NO. Set to YES to indicate that a new row should be - -started immediately before this field. -RowCaption : Optional. Default to blank string. Specifies a caption for a group box for - -the fields occurring after the row break. -ColBreak : Optional. Default to NO. Set to YES to indicate that a new Column - -should be started immediately before this field. Also, a special feature -for column breaks only is you may specify a number after YES to indicate -multiple column breaks to skip over a column. For example "YES 2" to -skip a column because there are 2 consecutive column breaks. - -RowCaption : Optional. Default to blank string. Specifies a caption for a group box for -the fields occurring after the column break. - -Example: -DataGrid (DataGridName) -{ - BUS - - BusNomVolt 100 8 2 - AreaNum 50 8 2 "YES" "Tab Caption" "NO" "" "NO" "" - ZoneNum 50 8 2 - - BRANCHRUN - - BusNomVolt:0 100 8 2 - BusNomVolt:1 100 8 2 "NO" "" "NO" "" "YES 2" "Col Caption" - LineMW:0 100 9 3 "NO" "" "YES" "Row Caption" "NO" "" - -} - -ColumnContourInfo -Contains a description of the column contour settings - -ColumnNumber : Contains the column index of the contoured column -UseAbsValue : Contour the absolute value if YES. If NO then contour the signed value. -IgnoreValuesAbove : If YES values above the maximum percentage are ignored. -IgnoreValuesBelow : if YES values below the minimum percentage are ignored. -AbsMin : The minimum value for the colormap -LimMin : The break low value for the colormap -Nominal : The nominal value for the colormap -LimMax : The break high value for the colormap -AbsMax : The maximum value for the colormap -ColorMapName : The name of the color map the contour is using. This must reference a - -color map that exists in the case. -ColorMapBrightness : The brightness or color saturation. The values range from -0.8 (darker) to - -0.8 (brigher). -ColorMapReverseColors : If YES, the colors in the colormap will be reversed. - - - - 182 - - - -Example: -DATA (DataGrid, -[DataGridName,BGDisplayFilter,FilterName,NonDefaultFont,CaseInfoRowHeight,FontName,Fon -tStyles,SOFontSize,FontColor,VariableName,ConditionType,ViewZoomLevel,FrozenColumns]) -{ -"Bus" "YES" "" "YES" 13 "Segoe UI" "" 8 0 "" "High To Low" 100.00 -1 - - "BusNum" 75 8 2 - "BusName" 75 8 2 - "AreaName" 75 8 2 - "BusNomVolt" 75 8 2 - "BusPUVolt" 75 8 5 - "BusKVVolt" 75 8 3 - "BusAngle" 75 8 2 - - - 5 NO NO NO 0.996563732624054 1.01118695735931 1.02581024169922 -1.03790521621704 1.05000007152557 "Blue=low, Red=High" 0 NO - 7 NO NO NO -1.17415904998779 0.0616339445114136 1.29742693901062 -3.84944224357605 6.4014573097229 "Discrete 20 Red/Blue" 0 NO - -} - - - -DynamicFormatting -DynamicFormattingContextObject - -This subdata section contains a list of the display object types which are chosen to be selected. Each line -of the section consists of the following: - -DisplayObjectType (WhichFields) (ListOfFields) - -DisplayObjectType : The object type of the display object. These are generally the same as - -the values seen in the subdata section SelectByCriteriaSetType of -SelectByCriteriaSet object types. The only exception is the string -CaseInfo, which is used for formatting applying to the case information -displays. - -(WhichFields) : For display objects that can reference different fields, this sets which of -those fields it should select (e.g. select only Bus Name Fields). The value -may be either ALL or SPECIFIED. - -(ListOfFields) : If WhichFields is set to SPECIFIED, then a delimited list of fields follows. - -Example: - - // Note: CaseInfo applies to case information displays - CaseInfo "SPECIFIED" BusName - DisplayAreaField "ALL" - DisplayBus - DisplayBusField "SPECIFIED" BusName BusPUVolt BusNum - DisplayCircuitBreaker - DisplaySubstation - DisplaySubstationField "SPECIFIED" SubName SubNum BusNomVolt BGLoadMVR - DisplayTransmissionLine - DisplayTransmissionLineField "ALL" - - - 183 - - - -LineThicknessLookupMap -LineColorLookupMap -FillColorLookupMap -FontColorLookupMap -FontSizeLookupMap -BlinkColorLookupMap -XoutColorLookupMap -FlowColorLookupMap -SecondaryFlowColorLookupMap - -The values of the lookup table for the characteristics that can be modified by the dynamic formatting tool. -The first line contains the two following fields: - -fieldname : It is the field that the lookup table is going to look for. -usediscrete : Set to YES or NO. If set to YES, the characteristic values will be discrete, - -meaning that the characteristic value will correspond exactly to the one -specified in the table. If set to NO, the characteristic values will be -continuous, which means the characteristic value will be an interpolation -of the high and low closest values specified in the table. - -The following lines contain two fields: -fieldvalue : The value for the field. -characteristicvalue : The corresponding characteristic value for such field value. - -Example: - - // FieldName UseDiscrete - BusPUVolt YES - // FieldValue Color - 1.02 16711808 - 1.05 8454143 - 1.1 16744703 - - - - - 184 - - - -Filter -Condition - -Conditions store the conditions of the filter. Each condition is described by one line of text which can -contain up to five fields: - -variablename : It is one of the fields for the object_type specified. It may optionally be -followed by a colon and a non-negative integer. If not specified, 0 is -assumed. - -Example: on a LINE, 0 = from bus, 1 = to bus -sgLineMW:0 = the MW flow leaving the from bus -sgLineMW:1 = the MW flow leaving the to bus - - Note: this value may also be the string "_UseAnotherfilter" which would -then be followed by either meets or notmeets and then the name of -another Filter. - -Condition : Possible Values Alternate1 Alternate2 Requires - othervalue - - between >< yes - notbetween ~>< yes - equal = == - notequal <> ~= - greaterthan > - lessthan < - greaterthanorequal >= - lessthanorequal <= - about yes - notabout yes - contains - notcontains - startswith - notstartswith - inrange - notinrange - meets - notmeets - isblank - notisblank -value : The value used for comparison. - For fields associated with strings, this must be a string. - For fields associated with real numbers, this must be a number. - For fields associated with integers, this is normally an integer, except - -when the Condition is "inrange" or "notinrange". In this case, value is a -comma/dash separated number string. - -(othervalue) : If required, the other value used for comparison. For conditions "about" -and "notabout" this is the tolerance with which the value should be equal -or not equal. - -(FieldOpt) : Optional string with following meanings. Unspecified means that strings -are case insensitive, use number fields directly (older files may have had -an integer 0 as well). - -ABS : strings are case sensitive, take absolute value of field values -(older files may have had an integer 1 as well) - - - - - 185 - - - -Example: -FILTER (objecttype, filtername, filtertype, prefilter) -{ -BUS "a bus filter" "AND" "no" - - BusNomVolt > 100 - AreaNum inrange "1 – 5 , 7 , 90-95" - ZoneNum between - -BRANCH "a branch filter" "OR" "no" - - BusNomVolt:0 > 100 // Note location 0 means from bus - BusNomVolt:1 > 100 // Note location 1 means to bus - LineMW:0 > 100 1 // Note, final field 1 denotes absolute value - _UseAnotherFilter meets - -} - -Gen -BidCurve - -BidCurve subdata is used to define a piece-wise linear cost curve (or a bid curve). Each bid point consists -of two real numbers on a single line of text: a MW output and then the respective bid (or marginal cost). - -Example: - - // MW Price[$/MWhr] - 100.00 10.6 - 200.00 12.4 - 400.00 15.7 - 500.00 16.0 - - -ReactiveCapability -Reactive Capability subdata is used to the reactive capability curve of the generator. Each line of text -consists of three real numbers: a MW output, and then the respective Minimum MVAR and Maximum -MVAR output. - -Example: - - // MW MinMVAR MaxMVAR - 100.00 -60.00 60.00 - 200.00 -50.00 50.00 - 400.00 -30.00 20.00 - 500.00 - 5.00 2.00 - - -Note: ReactiveCapability object types can also be directly created inside their own DATA section as well. -Two of the key fields of the object are then the bus number and generator ID of the generator to which -the ReactiveCapability point belongs. - - - - 186 - - - -GeoDataViewStyle -TotalAreaValueMap - -This subdata section is used to define the lookup table for determining the total area size of geographic -data view objects based on the value of a selected field. Two values are entered for each mapping: - -FieldValue : Value of the field selected for the Total Area attribute. -TotalArea : The total area size of the object. - - -Example: - -// FieldValue TotalArea -1.000 0 -4.000 23 -7.000 46 - - -RotationRateValueMap -This subdata section is used to define the lookup table for determining the rotation rate of geographic -data view objects based on the value of a selected field. Two values are entered for each mapping: - -FieldValue : Value of the field selected for the Rotation Rate attribute. -RotationRate : The rotation rate of the object. Entered in Hz. - - -Example: - -// FieldValue RotationRate -1.000 0.00 -4.000 0.10 -7.000 0.20 - - -RotationAngleValueMap -This subdata section is used to define the lookup table for determining the rotation angle of geographic -data view objects based on the value of a selected field. Two values are entered for each mapping: - -FieldValue : Value of the field selected for the Rotation Angle attribute. -RotationAngle : The rotation angle of the object. Entered in degrees. - - -Example: - -// FieldValue RotationAngle -1.000 -90.0 -4.000 0.0 -7.000 90.0 - - - - - 187 - - - -LineThicknessValueMap -This subdata section is used to define the lookup table for determining the thickness of the border line -around geographic data view objects based on the value of a selected field. Two values are entered for -each mapping: - -FieldValue : Value of the field selected for the Line Thickness attribute. -LineThickness : The line thickness of the border line around the object. This should be an - -integer value. - -Example: - -// FieldValue LineThickness -1.000 1 -4.000 2 -7.000 3 - - -GlobalContingencyActions -CTGElementAppend - -This format is the same as for the Contingency objecttype except that the SolvePowerFlow action is not -allowed. - -CTGElement -This format is the same as for the Contingency objecttype except that the SolvePowerFlow action is not -allowed. - -Note: GlobalContingencyActionsElement object types can also be directly created inside their own DATA -section as well. - -HintDefValues -HintObject - -Stores the values for the custom hints. Each line has one value: -FieldDescription : This is a string enclosed in double quotes. The string itself is delimited - -by the @ character. The string contains five values: -Name of Field : The name of the field. Special fields that appear on - -dialog by default have special names. Otherwise -these are the same as the fieldnames of the AUX file -format (for the "other fields" feature on the dialogs). - -Total Digit : Number of total digits for a numeric field. -Decimal Points : Number of decimal points for a numeric field. -Include Suffix : Set to 0 for not including the suffix, and set to 1 to - -include it. -Field Preffix : The prefix text. - - -Example: - - "BusPUVolt@4@1@1@PU Volt =" - "BusAngle@4@1@1@Angle =" - - - 188 - - - -InjectionGroup -PartPoint - -A participation point is used to describe the contents of an injection group. Each participation point lists -six values: - -PointType : One of five values describing the type of point. -GEN : a generator -LOAD : a load -SHUNT : a switched shunt -BUS : a bus -INJECTIONGROUP : another injection group - -PointBusNum : The bus number of the partpoint if the type is a GEN, LOAD, SHUNT, or -BUS. Value will be blank for an injection group type. Note: bus# values -may be replaced by a string enclosed in double quotes where the string -is the name of the bus followed by an underscore character and then the -nominal voltage of the bus. These values may also be replaced by a -string enclosed in double quotes that represents the label of the bus or a -string representing the label of the generator, load, or switched shunt. - -PointID : For GEN, LOAD, or SHUNT type, this is the id for the partpoint. For an -INJECTIONGROUP type, this is the name or label of the injection group. -This is blank for a BUS type. - -PointParFac : The participation factor for the point. -ParFacCalcType : How the participation factor is calculated. There are several options - -depending on the PointType. -Generators : SPECIFIED, MAX GEN INC, MAX GEN DEC, or MAX - -GEN MW -Loads : SPECIFIED or LOAD MW -Shunts : SPECIFIED, MAX SHUNT INC, MAX SHUNT DEC, or - -MAX SHUNT MVAR -Bus : SPECIFIED -Injection Groups : SPECIFIED - - - All PointTypes can also set their participation factor based on a field - -associated with the device. To specify this, the tag should be -followed by the variable name of the field: variablename. All -PointTypes can also set their participation factor based on a Model -Expression. To specify this, the tag should be followed -by the name of the Model Expression: ModelExpression. - -ParFacNotDynamic : Should the participation factor be recalculated dynamically as the system -changes. - - -Example: - - "GEN" 1 "1" 1.00 "SPECIFIED" "NO" - "GEN" 4 "1" 104.96 "MAX GEN INC" "NO" - "GEN" 6 "1" 50.32 "MAX GEN DEC" "YES" - "GEN" 7 "1" 600.00 "MAX GEN MW" "NO" - "LOAD" 2 "1" 5.00 "SPECIFIED" "NO" - "LOAD" 6 "1" 200.00 "LOAD MW" "YES" - - -Note: PartPoint object types can also be directly created inside their own DATA section as well. One of -the key fields of the PartPoint object is then the name of the injection group to which the participation -point belongs. - - 189 - - - -Interface -InterfaceElement - -A interfaces’s subdata contains a list of the elements in the interface. Each line contains a text -descriptions of the interface element. Note that this text description must be encompassed by quotation -marks. There are eleven kinds of elements allowed in an interface. Please note that the direction -specified in the monitoring elements is important. - -"BRANCH num1 num2 ckt" - : Monitor the MW flow on the branch starting from bus num1 going to - -bus num2 with circuit ckt. (order of bus numbers defines the direction) -"AREA num1 num2" : Monitor the sum of the AC branches that connect area1 and area2. -"ZONE num1 num2" : Monitor the sum of the AC branches that connect zone1 and zone2. -"BRANCHOPEN num1 num2 ckt" - : When monitoring the elements in this interface, monitor them under the - -contingency of opening this branch. -"BRANCHCLOSE num1 num2 ckt" - : When monitoring the elements in this interface, monitor them under the - -contingency of closing this branch. -"DCLINE num1 num2 ckt" - : Monitor the flow on a DC line. -"INJECTIONGROUP 'name'" - : Monitor the net injection from an injection group (generation contributes - -as a positive injection, loads as negative). -"GEN num1 id" : Monitor the net injection from a generator (output is positive injection) -"LOAD num1 id" : Monitor the net injection from a load (output is negative injection). -"MSLINE num1 num2 ckt" - : Monitor the MW flow on the multi-section line starting from bus num1 - -going to bus num2 with circuit ckt. -"INTERFACE 'name' " : Monitor the MW flow on the interface given by name. -"GENOPEN num1 id" : When monitoring the elements in this interface, monitor them under the - -contingency of opening this generator. -"LOADOPEN num1 id" : When monitoring the elements in this interface, monitor them under the - -contingency of opening this load. - -Note: bus# values may be replaced by a string enclosed in single quotes where the string is the name of -the bus followed by an underscore character and then the nominal voltage of the bus. Labels may also be -use as follows. - -• bus# values for all element types may be replaced by a string enclosed in single quotes where the -string is the label of the bus. - -• for GEN or LOAD elements, the section num1 id may be replaced by the device’s label. -• For MSLINE, DCLINE, or BRANCH elements, the num1 num2 ckt section may be replaced by the - -device’s label. - -For the interface element type BRANCH num1 num2 ckt and DCLINE num1 num2 ckt, an optional -field can also be written specifying whether the flow should be measured at the far end. This field is -either YES or NO. - - - - 190 - - - -Example: - - -Note: InterfaceElement object types can also be directly created inside their own DATA section as well. -One of the key fields of the InterfaceElement object is then the name of the interface to which the -interface element belongs. - -KMLExportFormat -DataBlockDescription - -This subdata section is used to describe the objects and fields that should be saved to a KML file. Same -format as for the AUXFileExportFormatData subdata section. - -LimitSet -LimitCost - -LimitCost records describe the piece-wise unenforceable constraint cost records for use by unenforceable -line/interface limits in the OPF or SCOPF. Each row contains two values - -PercentLimit : Percent of the transmission line limit. -Cost : Cost used at this line loading percentage value. - - -Example: - - //Percent Cost [$/MWhr] - 100.00 50.00 - 105.00 100.00 - 110.00 500.00 - - -Load -BidCurve - -BidCurve subdata is used to define a piece-wise linear benefit curve (or a bid curve). Each bid point -consists of two real numbers on a single line of text: a MW output and then the respective bid (or -marginal cost). These costs must be increasing for loads. - -Example: - - // MW Price[$/MWhr] - 100.00 16.0 - 200.00 15.7 - 400.00 12.4 - 500.00 10.6 - - - 191 - - - -LPVariable -LPVariableCostSegment - -Stores the cost segments for the LP variables. Each line contains four values: -Cost (Up) : Cost associated with increasing the LP variable. -Minimum value : Minimum limit of the LP variable. -Maximum value : Maximum limit of the LP variable. -Artificial : Whether the cost segment is artificial or not. - - -Example: - - //Cost(Up) Minimum Maximum Artificial - -20000.0000 -10000000000.5801 -0.6000 YES - 16.2343 -0.6000 0.0000 NO - 16.5526 0.0000 0.6000 NO - 16.8708 0.6000 1.2000 NO - 17.1890 1.2000 1.8000 NO - 17.5073 1.8000 2.4000 NO - 20000.0000 2.4000 9999999999.4199 YES - - -ModelCondition -Condition - -ModelConditions are the combination of an object and a Filter. They are used to return when the -particular object meets the filter specified. As a result, the subdata section here mostly identical to the -Condition subdata section of a Filter. See the description there. There is one exception however with the -FieldOpt which has additional strings - -(FieldOpt) : Optional string with following meanings: -ABS : strings are case sensitive, take ABS of field values (older files - -may have had an integer 1 as well) -REF : Means that the variablename is evaluated in the contingency - -reference state -ABSREF : means that both ABS and REF are being used - - -Nothing specified means that strings are case insensitive, use number -fields directly and values are evaluated as normal (older files may have -had an integer 0 as well) - -ModelExpression -LookupTable - -LookupTables are used inside Model Expressions sometimes. These lookup table represent either one or -two dimensional tables. If the first string in the SUBDATA section is "x1x2", this signals that it is a two -dimensional lookup table. From that point on it will read the first row as "x2" lookup points, and the first -column in the remainder of the rows as the x1 lookup values. - - - - 192 - - - -Example: -MODELEXPRESSION (CustomExpression,ObjectType,CustomExpressionStyle, -CustomExpressionString,WhoAmI,VariableName,WhoAmI:1,VariableName:1) -{ -// The following demonstrated a one dimensional lookup table -22.0000, "oneD", "Lookup", "", "Gen11", -"Gen11GenMW", "", "" - - // because it does not start with the string x1x2 this will - // represent a one dimensional lookup table - x1 value - 0.000000 1.000000 - 11.000000 22.000000 - 111.000000 222.000000 - -0.0000, "twod", "Lookup", "", -"Gen11", -"Gen11GenMW", -"Gen61", -"Gen61GenMW" - - // because this starts with x1x2 this represent a two dimensional - // lookup table. The first column represents lookup values for x1. - // The first row represents lookup values for x2 - x1x2 0.100000 0.300000 // these are lookup heading for x2 - 0.000000 1.000000 3.000000 - 11.000000 22.000000 33.000000 - 111.000000 222.000000 333.000000 - -} - -ModelFilter -ModelCondition - -A Model Filter’s subdata contains a list of each ModelCondition in the filter. Because a list of Model -Conditions is stored within Simulator, this subdata section only requires the name of each -ModelCondition on each line and whether or not the condition is using the NOT operator as part of the -Model Filter. - -Example: - -// ModelConditionName NotCondition - "Name of First Model Condition" "NO" - "Name of Second Model Condition" "NO" - "Name of Third Model Condition" "NO" - - - - - 193 - - - -MTDCRecord -An example of the entire multi-terminal DC transmission line record is given at the end of this record description. Each of -the SUBDATA sections is discussed first. - -MTDCBus -For this SUBDTA section, each DC Bus is described on a single line of text with exactly 8 fields specified. - -DCBusNum : The number of the DC Bus. Note DC bus numbers are independent AC -bus numbers. - -DCBusName : The name of the DC bus enclosed in quotes. -ACTerminalBus : The AC terminal to which this DC bus is connected (via a - -MTDCConverter). If the DC bus is not connected to any AC buses, then -specify as zero. You may also specify this as a string enclosed in double -quotes with the bus name followed by an underscore character, following -by the nominal voltage of the bus. - -DCResistanceToground - : The resistance of the DC bus to ground. Not used by Simulator. -DCBusVoltage : The DC bus voltage in kV. -DCArea : The area that this DC bus belongs to. -DCZone : The zone that this DC bus belongs to. -DCOwner : The owner that this DC bus belongs to. - - -MTDCBus object types can also be directly created inside their own DATA section as well. One of the key -fields of the object is then the number of the MTDCRecord to which the MTDCBus belongs. - -MTDCConverter -For this SUBDTA section, each AC/DC Converter is described by exactly 24 field which may be spread -across several lines of text. Simulator will keep reading lines of text until it finds 24 fields. All text to the -right of the 24th field (on the same line of text) will be ignored. The 24 fields are listed in the following -order: - -BusNum : AC terminal bus number. -MTDCNBridges : Number of bridges for the converter. -MTDCConvEBas : Converter AC base voltage. -MTDCConvAngMxMn : Converter firing angle. -MTDCConvAngMxMn:1 - : Converter firing angle max. -MTDCConvAngMxMn:2 - : Converter firing angle min. -MTDCConvComm : Converter commutating resistance. -MTDCConvComm:1 : Converter commutating reactance. -MTDCConvXFRat : Converter transformer ratio. -MTDCFixedACTap : Fixed AC tap. -MTDCConvTapVals : Converter tap. -MTDCConvTapVals:1 : Converter tap max. -MTDCConvTapVals:2 : Converter tap min. -MTDCConvTapVals:3 : Converter tap step size. -MTDCConvSetVL : Converter setpoint value (current or power). -MTDCConvDCPF : Converter DC participation factor. -MTDCConvMarg : Converter margin (power or current). -MTDCConvType : Converter type. -MTDCMaxConvCurrent - : Converter Current Rating. -MTDCConvStatus : Converter Status. -MTDCConvSchedVolt : Converter scheduled DC voltage. - - 194 - - - -MTDCConvIDC : Converter DC current. -MTDCConvPQ : Converter real power. -MTDCConvPQ:1 : Converter reactive power. - - -MTDCConverter object types can also be directly created inside their own DATA section as well. One of -the key fields of the object is then the number of the MTDCRecord to which the MTDCConverter belongs. - -MTDCTransmissionLine -For this SUBDATA section, each DC Transmission Line is described on a single line of text with exactly 5 -fields specified: - -DCFromBusNum : From DC Bus Number. -DCToBusNum : To DC Bus Number. -CKTID : The DC Circuit ID. -Resistance : Resistance of the DC Line in Ohms. -Inductance : Inductance of the DC Line in mHenries (Not used by Simulator). - - -Example: -MTDCRECORD (Num,Mode,ControlBus) -{ -//-------------------------------------------------------------------------- -// The first Multi-Terminal DC Transmission Line Record -//-------------------------------------------------------------------------- -1 "Current" "SYLMAR3 (26098)" - - //------------------------------------------------------------------- - // DC Bus data must appear on a single line of text - // The data consists of exactly 8 values - // DC Bus Num, DC Bus Name, AC Terminal Bus, DC Resistance to ground, - // DC Bus Voltage, DC Bus Area, DC Bus Zone, DC Bus Owner - 3 "CELILO3P" 0 9999.00 497.92 40 404 1 - 4 "SYLMAR3P" 0 9999.00 439.02 26 404 1 - 7 "DC7" 41311 9999.00 497.93 40 404 1 - 8 "DC8" 41313 9999.00 497.94 40 404 1 - 9 "DC9" 26097 9999.00 439.01 26 404 1 - 10 "DC10" 26098 9999.00 439.00 26 404 1 - - - //------------------------------------------------------------------- - // convert subdata keeps reading lines of text until it has found - // values specified for 24 fields. This can span any number of lines - // any values to the right of the 24th field found will be ignored - // The next converter will continue on the next line. - //------------------------------------------------------------------- - 41311 2 525.00 20.25 24.00 5.00 0.0000 16.3100 - 0.391048 1.050000 1.000000 1.225000 0.950000 0.012500 - 1100.0000 1650.0000 0.0000 "Rect" 1650.0000 "Closed" - 497.931 1100.0000 547.7241 295.3274 - 41313 4 232.50 15.36 17.50 5.00 0.0000 7.5130 - 0.457634 1.008700 1.030000 1.150000 0.990000 0.010000 - 2000.0000 2160.0000 0.1550 "Rect" 2160.0000 "Closed" - 497.940 2000.0000 995.8800 561.8186 - 26097 2 230.00 20.90 24.00 5.00 0.0000 16.3100 - 0.892609 1.000000 1.100000 1.225000 0.950000 0.012500 - -1100.0000 1650.0000 "" "Inv" 1650.0000 "Closed" - 439.009 1100.0000 -482.9099 274.5227 - 26098 4 232.00 17.51 20.00 5.00 0.0000 7.5130 - 0.458621 1.008700 1.100000 1.120000 0.960000 0.010000 - 439.0000 2160.0000 "" "Inv" 2160.0000 "Closed" - 439.000 1999.9999 -878.0000 544.2775 - - - //------------------------------------------------------------------- - // DC Transmission Segment information appears on a single line of - // text. It consists of exactly 5 value - - 195 - - - - // From DCBus, To DCBus, Circuit ID, Line Resistance, Line Inductance - //------------------------------------------------------------------- - 3 4 "1" 19.0000 1300.0000 - 7 3 "1" 0.0100 0.0000 - 8 3 "1" 0.0100 0.0000 - 9 4 "1" 0.0100 0.0000 - 10 4 "1" 0.0100 0.0000 - -//-------------------------------------------------------------------------- -// A second Multi-Terminal DC Transmission Line Record -//-------------------------------------------------------------------------- -2 "Current" "SYLMAR4 (26100)" - - 5 "CELILO4P" 0 9999.00 497.92 40 404 1 - 6 "SYLMAR4P" 0 9999.00 439.02 26 404 1 - 11 "DC11" 41312 9999.00 497.93 40 404 1 - 12 "DC12" 41314 9999.00 497.94 40 404 1 - 13 "DC13" 26099 9999.00 439.01 26 404 1 - 14 "DC14" 26100 9999.00 439.00 26 404 1 - - - 41312 2 525.00 20.26 24.00 5.00 0.0000 16.3100 - 0.391048 1.050000 1.000000 1.225000 0.950000 0.012500 - 1100.0000 1650.0000 0.0000 "Rect" 1650.0000 "Closed" - 497.931 1100.0000 547.7241 295.3969 - 41314 4 232.50 15.45 17.50 5.00 0.0000 7.5130 - 0.457634 1.008700 1.030000 1.150000 0.990000 0.010000 - 2000.0000 2160.0000 0.1550 "Rect" 2160.0000 "Closed" - 497.940 2000.0000 995.8800 562.9448 - 26099 2 230.00 20.90 24.00 5.00 0.0000 16.3100 - 0.892609 1.000000 1.100000 1.225000 0.950000 0.012500 - -1100.0000 1650.0000 "" "Inv" 1650.0000 "Closed" - 439.009 1100.0000 -482.9099 274.5227 - 26100 4 232.00 17.51 20.00 5.00 0.0000 7.5130 - 0.458621 1.008700 1.100000 1.120000 0.960000 0.010000 - 439.0000 2160.0000 "" "Inv" 2160.0000 "Closed" - 439.000 1999.9999 -878.0000 544.2775 - - - 5 6 "1" 19.0000 1300.0000 - 11 5 "1" 0.0100 0.0000 - 12 5 "1" 0.0100 0.0000 - 13 6 "1" 0.0100 0.0000 - 14 6 "1" 0.0100 0.0000 - -} - -MTDCTransmissionLine object types can also be directly created inside their own DATA section as well. -One of the key fields of the object is then the number of the MTDCRecord to which the -MTDCTransmissionLine belongs. - -MultiSectionLine -Bus - -A multi section line’s subdata contains a list of each dummy bus, starting with the one connected to the -From Bus of the MultiSectionLine and proceeding in order to the bus connected to the To Bus of the Line. -Note: bus# values may be replaced by a string enclosed in double quotes where the string is the name of -the bus followed by an underscore character and then the nominal voltage of the bus, or the string may -represent the label of the bus. - - - - 196 - - - -Example: -//------------------------------------------------------------------------ -// The following describes a multi-section line that connnects bus -// 2 - 1 - 5 - 6 - 3 -//------------------------------------------------------------------------ -MultiSectionLine (BusNum, BusName, BusNum:1, BusName:1, - LineCircuit, MSLineNSections, MSLineStatus) -{ -2 "Two" 3 "Three" "&1" 2 "Closed" - - 1 - 5 - 6 - -} - -BusRenumber -This subdata section allows renumbering of the dummy buses. The entries in the subdata section must be -the new bus number that should be assigned to each dummy bus followed by the name of the new bus. -The entries can be either space or comma delimited. The bus number must be specified, but the name is -optional. If the name is not included and a new bus needs to be created, the name will be the same as -the number. If an incorrect number of dummy buses is entered for a multi-section line, none of the -dummy buses will be updated for that line. If a dummy bus number is specified that matches an existing -bus that is another dummy bus, the other dummy bus will be assigned to a new bus number and the -current dummy bus will be assigned to the number specified in the data. - -Example: -MultiSectionLine (BusNum, BusNum:1, LineCircuit) -{ -1 2 "1" - - 3 "Bus 3" - 4 "Bus 4" - 5 "Bus 5" - -22 33 "1" - - 14 "Bus 14" - 15 "Bus 15" - -} - -Nomogram -InterfaceElementA -InterfaceElementB - -InterfaceElementA values represent the interface elements for the first interface of the nomogram. -InterfaceElementB values represent the interface elements for the second interface of the nomogram. The -format of these SUBDATA sections is identical to the format of the InterfaceElement SUBDATA section of a -normal Interface. - - - 197 - - - -NomogramBreakPoint -This subdata section contains a list of the vertex points on the nomogram limit curve. - -Example: - - // LimA LimB - -100 -20 - -100 100 - 80 50 - 60 -10 - - -NomogramInterface -InterfaceElement - -This follows the same convention as the InterfaceElement SUBDATA section described with the Interface -objecttype. - -Owner -Bus - -This subdata section contains a list of the buses which are owned by this owner. Each line of text contains -the bus number. As an alternative to specifying the bus number, a string enclosed in double quotes may -be used where the string represents the name of the bus followed by an underscore character and then -the nominal voltage of the bus, or the string may represent the label of the bus. - -Example: - - 1 - 35 - 65 - - -Load -This subdata section contains a list of the loads which are owned by this owner. Each line of text contains -the bus number followed by the load id. As an alternative to specifying the bus number, a string enclosed -in double quotes may be used where the string represents the name of the bus followed by an -underscore character and then the nominal voltage of the bus, or the string may represent the label of the -bus. Also, instead of specifying the bus and load id, the label of the load enclosed in double quotes may -be used. - -Example: - - 5 1 // shows ownership of the load at bus 5 with id of 1 - 423 1 - - -Gen -This subdata section contains a list of the generators which are owned by this owner and the fraction of -ownership. Each line of text contains the bus number, followed by the gen id, followed by an integer -showing the fraction of ownership. As an alternative to specifying the bus number, a string enclosed in -double quotes may be used where the string represents the name of the bus followed by an underscore -character and then the nominal voltage of the bus, or the string may represent the label of the bus. Also, -instead of specifying the bus and generator id, the label of the generator enclosed in double quotes may -be used. - - - - 198 - - - -Example: - - 78 1 50 // shows 50% ownership of generator at bus 78 with id of 1 - 23 3 70 - - -Branch -This subdata section contains a list of the branches which are owned by this owner and the fraction of -ownership. Each line of text contains the from bus number, followed by the to bus number, followed by -the circuit id, followed by an integer showing the fraction of ownership. As an alternative to specifying -the bus numbers, strings enclosed in double quotes may be used where the string represents the name of -the bus followed by an underscore character and then the nominal voltage of the bus, or the string may -represent the label of the bus. Also instead of specifying the two numbers and a circuit id, the label of the -branch enclosed in double quotes may be used. - -Example: - - 6 10 1 50 // shows 50% ownership of line from bus 6 to 10, circuit 1 - - -PostPowerFlowActions -CTGElementAppend - -This format is the same as for the Contingency objecttype except that Abort, ContingencyBlock, and -SolvePowerFlow actions are not allowed. - -CTGElement -This format is the same as for the Contingency objecttype except that Abort, ContingencyBlock, and -SolvePowerFlow actions are not allowed. PostPowerFlowActionsElement object types can also be directly -created inside their own DATA section as well. - -PWCaseInformation -PWCaseHeader - -This subdata section contains the Case Description in free-formatted text. Note: as it is read back into -Simulator all spaces from the start of each line are removed. - -PWFormOptions -PieSizeColorOptions - -There can actually be several PieSizeColorOptions subdata sections for each PWFormOptions object. The -first line of each subdata section, the first line of text consist of exactly four values - -ObjectName : The objectname of the type of object these settings apply to. Will be -either be BRANCH or INTERFACE. - -FieldName : The fieldname for the pie charts that these settings apply to. -UseDiscrete : Set to YES to use a discrete mapping of colors and size scalars instead of - -interpolating for intermediate values. -UseOtherSettings : Set to YES to default these settings to the BRANCH MVA values for - -BRANCH object. This allows you to apply the same settings to all pie -charts. - - -After this first line of text, if the UseOtherSettings Value is NO, then another line of text will contain -exactly three values: - -ShowValue : This is the percentage at which the value should be drawn on the pie -chart. - - 199 - - - -NormalSize : This is the scalar size multiplier which should be used for pie charts below -the lowest percentage specified in the lookup table. - -NormalColor : This is the color which should be used for pie charts below the lowest -percentage specified in the lookup table. - - -Finally the remainder of the subdata section will contain a lookup table by percentage of scalar and color -values. This lookup table will consist of consecutive lines of text with exactly three values - -Percentage : This is the percentage at which the follow scalar and color should be -applied. - -Scalar : A scalar (multiplier) on the size of the pie charts. -Color : A color for the pie charts. - - -Example: - - // ObjectName FieldName UseDiscrete UseOtherSettings - Branch MVA YES NO - // ShowValue NormalSize NormalColor - 80.0000 1.0000 16776960 - // Percentage Scalar Color - 80.0000 1.5000 33023 - 100.0000 2.0000 255 - - - // ObjectName FieldName UseDiscrete UseOtherSettings - Branch MW YES YES - - -PWLPOPFCTGViol -OPFControlSense -OPFBusSenseP -OPFBusSenseQ - -This stores the control sensitivities for each contingency violation during OPF/SCOPF analysis. Each line -contains one value: - -Sensitivity : The value of the sensitivity with respect to each control in -OPFControlSense or with respect to each bus in OPFBusSenseP and -OPFBusSenseQ. - - -Example: - - // Value - 1.000441679 - 2.447185E-7 - -1.1109307E-6 - 1.6427327E-7 - 0 - - -PWLPTabRow -LPBasisMatrix - -This subdata section stores the basis matrix associated with the final LP OPF solution. Each line contains -two values: - -Variable : The basic variable. -Value : The sensitivity of the constraint to the basic variable. - - - - - 200 - - - -Example: - - // Var Value - 1 1.00000 - 2 1.00000 - 5 1.00000 - 6 1.00000 - - -PWPVResultListContainer -PWPVResultObject - -This subdata section contains the results of a particular PV Curve scenario. The data consists of two -general sections: the first three rows of text contain the "independent axis" of the PV Curve. The first row -starts with the string INDNOM and is followed by a list of numbers representing the nominal shift, the -second row starts with INDEXP and is followed by the export shift, and the third row starts with INDIMP -and is followed by the import shift. Following after these rows is a list of all the tracked quantities. Each -tracked quantity row consists of three parts which are separated by the strings ?f= and &v= . The first -part of the string represents a description of the power system object being tracked, the second part -represents the field variable name being tracked, and the third contains a list of all the values at the -various shift levels. - -Example: - - INDNOM 0.00 500.00 1000.00 1500.00 1750.00 1875.00 1975.00 - INDEXP 0.00 500.00 1000.00 1500.00 1750.00 1875.00 1975.00 - INDIMP 0.00 -417.23 -701.58 -890.58 -952.60 -975.35 -990.43 - Bus '3'?f=BusPUVolt&v= 0.993 0.983 0.964 0.939 0.926 0.919 0.914 - Bus '5'?f=BusPUVolt&v= 1.007 1.000 0.982 0.956 0.940 0.932 0.926 - Gen '4' '1'?f=GenMVR&v= 19.99 245.27 523.62 831.13 986.84 1060.6 1118.7 - Gen '6' '1'?f=GenMVR&v= -6.59 -120.84 -131.37 -39.53 48.35 103.8 154.5 - - -LimitViol -This subdata section contains the limit violations of a particular PV Curve scenario. This subdata section -would only exist if using the option to monitor limit violations with the PV tool. Each row consists of an -identifier, either VLOW or VHIGH, to indicate the type of limit violation followed by the bus identifier -based on the key field identifier chosen. The bus can be identified by number, name and nominal kV -combination, or label. The bus identifier is followed by the limit in use to identify a voltage violation and -this is followed by the voltage at the bus. - -Example: - - VLOW 3 1.00000 0.99017 - VLOW 5 1.00000 0.98245 - - -PVBusInadequateVoltages -This subdata section contains a list of buses that are considered to have inadequate voltages at each -transfer level for a particular PV Curve scenario. This subdata section would only exist if using the option -to store inadequate voltages. The data consists of two general sections: the first row starts with the string -INDNOM and is followed by a list of numbers representing the nominal shift. The second and subsequent -rows list the buses and inadequate voltages for any bus that has an inadequate voltage at any transfer -level. Each row starts with the bus identifier followed by the voltages at that bus at the corresponding -shift levels. If a voltage is not inadequate at a particular transfer level, a blank entry will appear instead of -a voltage value. The bus identifier is based on the key field identifier chosen and can be number, name -and nominal kV combination, or label. - - 201 - - - - -Example: - - // INDNOM ShiftLevel1 ShiftLevel2 ... - // BUS Voltage1 Voltage2 ... - INDNOM 0.000 100.000 200.000 300.000 400.000 500.000 - "Bus '3'" 0.99269 0.99278 0.99282 0.99280 0.99273 0.99262 - "Bus '4'" "" 1.00000 "" "" "" - - -PWQVResultListContainer -PWPVResultObject - -This subdata section contains the results of a particular QV Curve scenario. These results will exist when -tracking quantities with the QV curve tool. The data consists of two general sections: the first three rows -of text contain the "independent axis" of the QV Curve. The first three rows start with the strings -INDNOM, INDEXP, and INDIMP and are followed by a list of numbers representing the setpoint voltage -representing the V of the QV curve. Following after these rows is a list of all the tracked quantities. Each -tracked quantity row consists of three parts which are separated by the strings ?f= and &v= . The first -part of the string represents a description of the power system object being tracked, the second part -represents the field variable name being tracked, and the third contains a list of all the values at the -various setpoint voltage levels. - -Example: - - INDNOM 1.100 1.093 1.083 1.073 1.063 - INDEXP 1.100 1.093 1.083 1.073 1.063 - INDIMP 1.100 1.093 1.083 1.073 1.063 - Bus '1'?f=BusPUVolt&v=1.05000 1.05000 1.05000 1.05000 1.05000 - Bus '1'?f=BusKVVolt&v=144.89999 144.89999 144.89999 144.89999 144.89999 - -QVCurve -QVPoints - -This subdata section contains a list of the QV Curve points calculated for the respect QVCurve. Each line -consists of exactly six values: - -PerUnitVoltage : The per unit voltage of the bus for a QV point. -FictitiousMvar : The amount of Mvar injection from the fictitious generator at this QV - -point. -ShuntDeviceMvar : The Mvar injection from any switched shunts at the bus. -TotalMvar : The total Mvar injection from switched shunts and the fictitious - -generator. -ReservesMvar : Total amount of Mvar reserves available at the bus. -ReservesTotalMvar : Total Mvar injection from the switched shunts, fictitious generator, and - -available reserves. - - - - 202 - - - -Example: -QVCURVE (BusNum,CaseName,qv_VQ0,qv_Q0,qv_Vmax,qv_QVmax,qv_VQmin,qv_Qmin, - qv_Vmin,qv_QVmin,Qinj_Vmax,Qinj_0,Qinj_min,Qinj_Vmin) -{ -5 "BASECASE" 0.880 0.000 1.100 312.490 0.480 -221.072 - 0.180 -86.334 191.490 -77.373 -244.075 -89.562 - - // NOTE: This bus has a constant impedance - // switched shunt value of -100 Mvar at it. - //V(PU), Q(MVR), Q_shunt(MVR), Q_tot(MVR), Q_res(MVR), Q_tot_res(MVR) - 1.1000, 312.4898, -121.0000, 191.4898, 0.0000, 191.4898 - 0.9800, 124.6619, -95.9656, 28.6963, 0.0000, 28.6963 - 0.7800, -96.6202, -60.7808, -157.4010, 0.0000, -157.4010 - 0.5800, -206.9895, -33.5960, -240.5855, 0.0000, -240.5855 - 0.3800, -207.4962, -14.4113, -221.9075, 0.0000, -221.9075 - -} - -QVCurve_Options -Sim_Solution_Options - -This subdata section contains solution options that will be used when running QV Curves. See -explanation under the CTG_Options object type for more information. - -RemedialAction -CTGElementAppend - -This format is the same as for the Contingency objecttype except that the SolvePowerFlow action is not -allowed. - -CTGElement -This format is the same as for the Contingency objecttype except that the SolvePowerFlow action is not -allowed. RemedialActionElement object types can also be directly created inside their own DATA section -as well. - -SelectByCriteriaSet -SelectByCriteriaSetType - -This subdata section contains a list of the display object types which are chosen to be selected. Each line -of the section consists of the following: - -DisplayObjectType : The object type of the display object. -(FilterName) : This field is optional, but must be given if either of the following fields is - -given. See the Using Filters in Script Commands section for more -information on specifying the filtername. - -(WhichFields) : For display objects that can reference different fields, this sets which of -those fields it should select (e.g. select only Bus Name Fields). The value -may be either ALL or SPECIFIED. - -(ListOfFields) : If WhichFields is set to SPECIFIED, then a delimited list of fields follows. - - - - 203 - - - -Example: - - DisplayAreaField "" "ALL" - DisplayBus "" - DisplayBusField "Name of Bus Filter" "SPECIFIED" BusName BusPUVolt BusNum - DisplayCircuitBreaker "" - DisplaySubstation "" - DisplaySubstationField "" "SPECIFIED" SubName SubNum BusNomVolt BGLoadMVR - DisplayTransmissionLine "" - DisplayTransmissionLineField "" "ALL" - - -Area -This subdata section contains a list of areas which were chosen to be selected. Each line of the section -consists of either the number or the name. When generated automatically by PowerWorld we also -include the other identifier as a comment. - -Example: - - 18 // NEVADA - 22 // SANDIEGO - 30 // PG AND E - 52 // AQUILA - - -Zone -This subdata section contains a list of zones which were chosen to be selected. Each line of the section -consists of either the number or the name. When generated automatically by PowerWorld we also -include the other identifier as a comment. - -Example: - - 680 // ID SOLUT - 682 // WY NE IN - - -ScreenLayer -This subdata section contains a list of screen layers which were chosen to be selected. Each line of the -section consists of either the name. - -Example: - - "Border" - "Transmission Line Objects" - - -ShapefileExportDescription -This object uses the same subdata sections as SelectByCriteriaSet. The only distinction is that only buses and lines can -be exported. - -StudyMWTransactions -ImportExportBidCurve - -This subdata section contains the piecewise linear transactions cost curves for areas involved in a MW -transaction. Costs are only for areas that are not on OPF control. Curves must be monotonically -increasing. Each line corresponds to a point in the cost curve, and it has two values: - - - - 204 - - - -MW : The MW value. Use negative values for imports (purchase) and positive -values for exports (sales) - -Price : The price in $/MWh. - -Two different cost curves can be entered for each transaction. One is for the cost curve relative to the -Export Area specified in the transaction, and the other is for the cost curve relative to the Import Area -specified in the transaction. The first curve that is listed in the SUBDATA section is the curve relative to -the Export Area. The curve relative to the Import Area is denoted by the keyword REVERSE. Either or both -of the curves can be blank. - -Example: - - //MW Price[$/MWh] - -20.00 5.00 - -10.00 10.00 - 0.00 15.00 - 10.00 20.00 - 20.00 45.00 - 30.00 70.00 - REVERSE - -25.00 7.00 - -15.00 12.00 - 5.00 17.00 - 15.00 22.00 - 25.00 47.00 - 35.00 72.00 - - -SuperArea -SuperAreaArea - -This subdata section contains a list of areas within each super area. Each line of text contains two values, -the area number followed by a participation factor for the area that can be optionally used. - -Example: - - 1 48.9 - 5 34.2 - 25 11.2 - - -TSSchedule -SchedPoint - -This section stores the schedule time points used in Time Step Simulation. Each line contains seven -values: - -Date : The date of the point. -Hour : The hour of the point. -Pointtype : An integer specifying the point type. - -0 : Numeric -1 : Boolean (Yes/No, Closed/Open) -2 : Text - -Numeric Value : The numeric value if point type is Numeric. Otherwise it is just zero. -Boolean Value : The boolean value if point type is Boolean. Otherwise it is just false. -Text value : The text value if point type is Text. Otherwise it is just an empty string. -Audiofilename : The audio filename associated to the point. If none, it is just an empty - -string. - - - 205 - - - -Example: - - //Date Hour PointType NValue BValue TValue AValue - 5/8/2006 0 1.00 NO - 5/8/2006 6:00:00 AM 0 1.10 NO - 5/8/2006 12:00:00 PM 0 1.25 NO - - -UserDefinedDataGrid -ColumnInfo - -This follows the same convention as the ColumnInfo SUBDATA section described with the DataGrid -objecttype. - - 206 - - - -SCRIPT Section for Display Auxiliary File -The syntax for script commands in Display Auxiliary Files is the same as for Auxiliary Files. See the SCRIPT Section and its -sub-sections for details on the proper syntax. Any differences for display auxiliary files will be discussed below. - -AXD Actions -The following script commands are available for AXD files - -AutoInsertBorders; -Use this action to insert borders according to the settings in the AutoInsertBordersOptions object - -AutoInsertBuses(LocationSource, MapProjection, AutoInsertBranches, InsertIfNotAlreadyShown, -"filename", InsertSelected); - -Use this action to insert buses based on specified location data. -LocationSource : "Bus", "Substation" or "File" -MapProjection : "Simple Conic", "Mercator", "Alaska" or "xy" -AutoInsertBranches : YES to insert transmission lines when finished, NO not to -InsertOnlyIfNotAlreadyShown - : YES if only buses that are not already shown should be inserted, NO to - -insert all buses. -"filename" : (optional) path to location source file (if LocationSource is "File") - - -FileCoordinates is no longer used. It should not be included when creating new auxiliary files, but if it is -included in existing auxiliary files, it will be read and ignored. If MapProjection is set to “xy” and using a -file, the file coordinates are assumed to be in x,y, otherwise, file coordinates are assumed to be lon, lat. - -FileCoordinates : (optional) format of coordinates in file "xy" or "lonlat" (if LocationSource -is "File") - -InsertSelected : (optional) Default is NO. YES is only insert buses that are selected -(SELECTED = YES). - - -This command inserts bus display objects using the latitude and longitude stored with each bus, -interpreted through the Mercator map projection. It inserts only the buses that have their -Selected field set to YES and also adds the connecting branches, regardless of whether the buses -are already displayed. -AutoInsertBuses("Bus", "Mercator", YES, NO, , YES); - -AutoInsertGens(MinkV, InsertTextFields); -Use this action to insert generators. - -MinkV : Minimum kV level to insert -InsertTextFields : (optional) insert text fields (default=YES) - - -This command inserts generator display objects for all generators connected to buses with a -nominal voltage of 140 kV or higher. It places only the generator symbols without any -accompanying text fields such as MW or Mvar labels. -AutoInsertGens(140, NO); - -AutoInsertInterfaces(InsertPieCharts, PieChartSize); -Use this action to insert line flow objects. - -InsertPieCharts : (optional) Insert pie charts as well (default=YES) -PieChartSize : (optional) default size of interface pie charts (default=50.0) - - - 207 - - - -This command inserts interface display objects along with pie charts that visualize flow or -loading. The pie charts are included (YES) and are set to a default size of 50.0 screen units. -AutoInsertInterfaces(YES, 50.0); - -AutoInsertLineFlowObjects(MinkV, InsertOnlyIfNotAlreadyShown, LineLocation, Size, FieldDigits, -FieldDecimals, TextPosition, ShowMW, ShowMvar, ShowMVA, ShowUnits, ShowComplex); - -Use this action to insert line flow objects. -MinkV : Minimum kV level to insert (default=0) -InsertOnlyIfNotAlreadyShown: (optional) if existing line flow objects are ignored (default=YES) -LineLocation : (optional) where to insert flow objects (default=0) - -0 : middle -1 : 10%/90% -2 : after stubs - -Size : (optional) size (default=5.0) -FieldDigits : (optional) total digits in field (default=6) -FieldDecimals : (optional) digits to the right of the decimal (default=2) -TextPosition : (optional) position of fields relative to flow object (default=YES) - -YES : above -NO : below - -ShowMW : (optional) show MW field (default=YES) -ShowMvar : (optional) show Mvar field (default=YES) -ShowMVA : (optional) show MVA field (default=YES) -ShowSuffix : (optional) show field units (default=YES) -ShowComplex : (optional) show complex form (MW+jMvar) (default=NO) - - -This command inserts line flow arrow objects for transmission lines with a nominal voltage of 100 -kV or higher. It inserts arrows in the middle of the line (0), with a size of 5.0, showing MW and -Mvar values (but not MVA), with units displayed (e.g., "MW") and text positioned above the -arrow. It does not use complex format (MW + jMvar), and skips lines already shown. -AutoInsertLineFlowObjects(100, YES, 0, 5.0, 6, 2, YES, YES, YES, NO, -YES, NO); - -AutoInsertLineFlowPieCharts(MinkV, InsertOnlyIfNotAlreadyShown, InsertMSLines, Size); -Use this action to insert line flow pie charts. - -MinkV : Minimum kV level to insert (default=0) -InsertOnlyIfNotAlreadyShown - : (optional) if existing line flow objects are ignored (default=YES) -InsertMSLines : (optional) insert pie charts for Multi-Section Lines (default=YES) -Size : (optional) size (default=5.0) - - -This command inserts line flow pie charts for all transmission lines with a nominal voltage of 50 -kV or higher. It skips lines that already have pie charts (YES), includes Multi-Section Lines (YES), -and sets the size of each pie chart to 5.0 screen units. -AutoInsertLineFlowPieCharts(50, YES, YES, 5.0); - -AutoInsertLines(MinkV, InsertTextFields, InsertEquivObjects, InsertZBRPieCharts, InsertMSLines, -ZBRImpedance, NoStubsZBRs, SingleCBZRs); - -Use this action to insert lines. -MinkV : (optional) minimum kV level to insert (default=0) -InsertTextFields : (optional) insert text fields (default=YES) -InsertEquivObjects : (optional) insert Equivalenced Objects (default=YES) -InsertZBRPieCharts : (optional) insert pie charts for lines with no limit and bus ties - -(default=NO) - 208 - - - -InsertMSLines : (optional) insert MultiSecton Lines (default=YES) -ZBRImpedance : (optional) maximum PU impedance for bus ties (default =0.0001) -NoStubsZBRs : (optional) ignore stubs for bus ties (default=YES) -SingleCBZBRs : (optional) only insert a single circuit breaker (default=YES) - - -This command inserts transmission line display objects for lines with a nominal voltage of 50 kV -or higher. It includes text fields, inserts equivalenced lines, skips pie charts for zero-impedance -bus ties, includes Multi-Section Lines, treats lines with per-unit impedance ≤ 0.0001 as bus ties, -ignores stubs when identifying those ties, and inserts only a single circuit breaker for each zero- -impedance bus tie. -AutoInsertLines(50, YES, YES, NO, YES, 0.0001, YES, YES); - -AutoInsertLoads(MinkV, InsertTextFields); -Use this action to insert loads. - -MinkV : Minimum kV level to insert (default=0) -InsertTextFields : (optional) insert text fields (default=YES) - - -This command inserts load display objects for buses with a nominal voltage of 50 kV or higher -and includes text fields showing load details such as MW and Mvar values. -AutoInsertLoads(50, YES); - -AutoInsertSwitchedShunts(MinkV, InsertTextFields); -Use this action to insert switched shunts. - -MinkV : Minimum kV level to insert (default=0) -InsertTextFields : (optional) insert text fields (default=YES) - - -This command inserts switched shunt display objects (e.g., capacitor banks or reactors) -connected to buses with a nominal voltage of 140 kV or higher and includes text fields showing -shunt details like Mvar values. -AutoInsertSwitchedShunts(140, YES); - -AutoInsertSubStations(LocationSource, MapProjection, AutoInsertBranches, InsertIfNotAlreadyShown, -"filename", InsertSelected); - -Use this action to insert substations based on specified location data. -LocationSource : "Bus", "Substation" or "File" -MapProjection : "Simple Conic", "Mercator", "Alaska" or "x,y" -AutoInsertBranches : YES to insert transmission lines when finished, NO not to -InsertOnlyIfNotAlreadyShown - : YES if only buses that are not already shown should be inserted, NO to - -insert all buses. -"filename" : (optional) path to location source file (if LocationSource is "File") - - -FileCoordinates is no longer used. It should not be included when creating new auxiliary files, but if it is -included in existing auxiliary files, it will be read and ignored. If MapProjection is set to “xy” and using a -file, the file coordinates are assumed to be in x,y, otherwise, file coordinates are assumed to be lon, lat. - -FileCoordinates : (optional) format of coordinates in file "xy" or "lonlat" (if LocationSource -is "File") - -InsertSelected : (optional) Default is NO. YES is only insert buses that are selected -(SELECTED = YES). - - - - 209 - - - - -This command inserts substation display objects using the latitude and longitude coordinates -stored in each substation, interpreted with the Simple Conic map projection. It inserts only -substations that are not already shown, adds branches between them, and includes all -substations regardless of selection status. -AutoInsertSubStations("Substation", "Simple Conic", YES, YES, "", NO); - -AutoInsertAreas(MapProjection, InsertIfNotAlreadyShown, InsertSelected); -Use this action to insert areas based on the latitude and longitude of the area (which is calculated as the -average lat/long of buses in the area). - -MapProjection : "Simple Conic", "Mercator", "Alaska" or "x,y" -InsertOnlyIfNotAlreadyShown - : (optional) Default is NO. YES if only areas that are not already shown - -should be inserted, NO to insert all areas. -InsertSelected : (optional) Default is NO. YES is only insert areas that are selected - -(SELECTED = YES). - - -This command inserts area display objects based on the average latitude and longitude of the -buses within each area, using the Simple Conic map projection. It inserts all areas, regardless of -whether they are already displayed or selected. -AutoInsertAreas("Simple Conic", NO, NO); - -AutoInsertInjectionGroups(MapProjection, InsertIfNotAlreadyShown, InsertSelected); -Use this action to insert injection groups based on the latitude and longitude of the injection group -(which is calculated as the average lat/long of objects in the injection group). - -MapProjection : "Simple Conic", "Mercator", "Alaska" or "x,y" -InsertOnlyIfNotAlreadyShown - : (optional) Default is NO. YES if only injection groups that are not already - -shown should be inserted, NO to insert all injection groups. -InsertSelected : (optional) Default is NO. YES is only insert injection groups that are - -selected (SELECTED = YES). - - -This command inserts injection group display objects based on the average latitude and -longitude of the objects within each group, using the Simple Conic map projection. It inserts all -injection groups, regardless of whether they are already shown or selected. -AutoInsertInjectionGroups("Simple Conic", NO, NO); - -AutoInsertOwners(MapProjection, InsertIfNotAlreadyShown, InsertSelected); -Use this action to insert owners based on the latitude and longitude of the owner (which is calculated as -the average lat/long of objects in the owner). - -MapProjection : "Simple Conic", "Mercator", "Alaska" or "x,y" -InsertOnlyIfNotAlreadyShown - : (optional) Default is NO. YES if only owners that are not already shown - -should be inserted, NO to insert all owners. -InsertSelected : (optional) Default is NO. YES is only insert owners that are selected - -(SELECTED = YES). - - -This command inserts owner display objects based on the average latitude and longitude of the -elements owned by each owner, using the Mercator map projection. It inserts only owners that -are not already displayed and only those with their Selected field set to YES. -AutoInsertOwners("Mercator", YES, YES); - - 210 - - - -AutoInsertZones(MapProjection, InsertIfNotAlreadyShown, InsertSelected); -Use this action to insert zones based on the latitude and longitude of the zone (which is calculated as the -average lat/long of buses in the zone). - -MapProjection : "Simple Conic", "Mercator", "Alaska" or "x,y" -InsertOnlyIfNotAlreadyShown - : (optional) Default is NO. YES if only zones that are not already shown - -should be inserted, NO to insert all zones. -InsertSelected : (optional) Default is NO. YES is only insert zones that are selected - -(SELECTED = YES). - - -This command inserts zone display objects using the average x,y coordinates of the buses in each -zone, interpreted with the x,y coordinate system. It inserts only zones that are not already -displayed and only those with their Selected field set to YES. -AutoInsertZones("x,y", YES, YES); - -FixFlowArrowLineEnds("OnelineName", "LayerName"); -The unmoving line flow arrow indicators that can be displayed on lines may not always be setup correctly. -This script corrects the flow arrows so that they look at the end of the line to which they are the closest to -determine the direction of flow. - -"OnelineName" : Optional – default is blank - The name of the oneline to which this action should be applied. The - -oneline must already be open. When not specified this action is applied -to the oneline to which the display auxiliary file has been applied. - -"LayerName" : Optional – default is blank - Name of the screen layer in which this action should be applied. When - -this is left blank the action is applied to all flow arrow objects regardless -of the layer. - - -This command adjusts the direction of all unmoving flow arrows on the current one-line diagram. -It determines the correct flow direction by checking which end of the line the arrow is closest to -and orients the arrow accordingly. Since no oneline or layer is specified, it applies to all flow -arrows on the active diagram, regardless of screen layer. -FixFlowArrowLineEnds(,); - -FixFlowArrowPosition("OnelineName", "LayerName"); -The unmoving line flow arrow indicators that can be displayed on lines are difficult to position properly. -This script is intended to automate as much of the work as possible for positioning these objects. - -"OnelineName" : Optional – default is blank - The name of the oneline to which this action should be applied. The - -oneline must already be open. When not specified this action is applied -to the oneline to which the display auxiliary file has been applied. - -"LayerName" : Optional – default is blank - Name of the screen layer in which this action should be applied. When - -this is left blank the action is applied to all flow arrow objects regardless -of the layer. - - -This command repositions all static flow arrows on the currently active one-line diagram to -improve their visual placement along the transmission lines. Since no oneline or layer is specified, -it applies to all flow arrows on all layers of the active diagram. -FixFlowArrowPosition(,); - - 211 - - - -InsertConnectedBuses("BusIdentifier"); -Insert oneline display buses that are connected to the identified bus. - -"BusIdentifier" : Identifier for the bus for which connected display buses should be added. -Bus can be identified as a data object "BUS Number", "BUS -NameNomkV", or "BUS Label". When identified as a data object all -display buses that are linked to this data bus will have display buses -inserted. - - Bus can also be identified as a display object "DISPLAYBUS BusNum -SOAuxiliaryID" or "DISPLAYBUS BusName_NomVolt". When identified as -a display object only display buses connected to this bus will be inserted. - - -This command inserts display objects for all buses directly connected to the bus identified as -"BUS 1" on the one-line diagram. It places those connected buses visually, allowing you to easily -expand the diagram from a single bus outward by showing its immediate electrical connections. -InsertConnectedBuses("BUS 1"); - -LoadAXDFromAXD("filename", CreateIfNotFound) -Use this action to apply a display auxiliary file via this script command that is part of another display -auxiliary file. - -"filename" : The file name of the display auxiliary file to load. See the Specifying File -Names in Script Commands section for special keywords that can be -used when specifying the file name. - -CreateIfNotFound : Optional – default is NO when using the Legacy Auxiliary File Header -format. Default is YES when using the Concise Auxiliary File Header -format. - - This parameter is only enforced when the Create_if_not_found field -for a Legacy Auxiliary File Header is set to PROMPT. Otherwise, YES is -assumed. YES means that objects that cannot be found will be created -while reading DATA sections from "filename". - - -This command loads and applies the display auxiliary file located at the specified path. If any -objects referenced in DATA sections are not found on the current oneline, they will be created. -LoadAXDFromAXD("G:\Diagrams\SubstationView.axd", YES); - -PanAndZoomToObject("ObjectID", "DisplayObjectType", "DoZoom"); -Use this action to pan to and optionally zoom in on a display object. This action will find the first matching -display object linked to the model object identified by ObjectID or the exact display object identified by -ObjectID. - -ObjectID : The object identifier that uniquely identifies the power system model -object or display object. For example, to identify bus 123354, the -ObjectID takes the form "BUS 123354". - -DisplayObjectType : (optional) The display object type to find. When a power system model -object is passed in for ObjectID, this parameter helps this action narrow -down to what kind of display object to pan. For example, to find the first -bus display object, use "DISPLAYBUS". (default="") - -DoZoom : (optional) Whether or not to zoom after panning. (default=YES) - - -This command pans and zooms the one-line diagram to center on the display object for bus 1, -specifically targeting a DISPLAYBUS object type. The zoom is enabled (YES), so the view will both -move and magnify to focus directly on the bus's location in the diagram. -PanAndZoomToObject("BUS 1", "DISPLAYBUS", YES); - - 212 - - - -ResetStubLocations(ZBRImpedance, NoStubsZBRs); -Use this action to reset stub locations. - -ZBRImpedance : (optional) max P.U. impedance for bus ties (default=0.0001) -NoStubsZBRs : (optional) Ignore stubs for bus ties (default=YES) - - -This command resets the placement of stub lines for the one-line diagram. It affects lines with a -per-unit impedance ≤ 0.0001, and with YES specified, it preserves the existing layout of the bus -objects, adjusting only the stubs for cleaner visual alignment. -ResetStubLocations(0.0001, YES); - - -General Script Commands - -The following script commands defined above in the general SCRIPT section are available for display -auxiliary files as well: - -ExitProgram -LoadScript -LoadData -SelectAll -UnSelectAll -SetData -SaveData -SaveDataWithExtra -CreateData -DeleteFile -RenameFile -CopyFile -SetCurrentDirectory -SaveObjectFields - - - - 213 - - - -DATA Section for Display Auxiliary File -The syntax for Display Auxiliary Files is the same as for Auxiliary Files. See the DATA Section and its sub-sections for -details on the proper syntax. Any differences for display auxiliary files will be discussed below. - -Key Fields -See the Key Fields topic in the DATA Section for general details. - -Display objects have an additional key field used for identification because multiple objects can be present on the same -one-line diagram that represent the same power system element. This extra key field is SOAuxiliaryID. This is a field -that is unique for each type of display object and other key field combination. If there are two display buses that -represent bus one in the power system, the SOAuxiliaryID field will be different for both. Simulator will automatically -create unique identifiers when these objects are created graphically. They can also be user specified but are forced to be -unique. This field does not need to be present when reading in a display auxiliary file, but if it is missing, Simulator -assumes that the ID is "1". This field is the only key field identifier for objects that do not link to power system elements -such as background lines and pictures, and therefore, should always be included when reading in these objects or the -expected results may not be achieved. - -By going to the main menu and choosing Help, Export Display Object Fields you will obtain a list of fields available for -each display object type. In this output, the key fields will appear with asterisks *. - -Special Data Sections -There are several object types that should be noted here because they can impact the reading of an entire display auxiliary -file, overall look of the resulting one-line diagram, or require special input to properly import/export the object. - -GeographyDisplayOptions -Most objects supported in the display auxiliary file have coordinates that can be specified in the appropriate data sections. -What these coordinates specify can be controlled by the GEOGRAPHYDISPLAYOPTIONS object. This object has only two -fields available: MapProjection and ShowLonLat. There are four possible settings for MapProjection: "x,y", "Simple -Conic", "Alaska", and "Mercator". The choice of projection will determine how the x,y values for display objects are -interpreted. ShowLonLat can be either "YES" or "NO". If ShowLonLat is "YES", the setting specified for the -MapProjection will be the longitude,latitude projection used when reading/writing the object x,y values. If ShowLonLat is -"NO", the x,y values will always be interpreted as x,y regardless of the MapProjection setting. This object should be placed -in the display auxiliary file before any other objects containing coordinates are read. If this object is not included in the -auxiliary file, the coordinates will be interpreted based on the current settings of map projection and whether or not -coordinates are showing longitude,latitude. - -Picture -PICTURE objects represent background images that cannot be stored in a text file format. To properly include a PICTURE -object in a display auxiliary file, the file containing the image must be saved and read along with the auxiliary file. The -FileName field indicates the name and location of the image file. If the image file cannot be found when reading in a -display auxiliary file and attempting to create a new object, no PICTURE object will be created. If attempting to update an -existing object and the image file cannot be found, the object will not be updated with a new image, but the FileName -field will be updated with the specified file name. - -PWFormOptions -One-line display options that affect the current display settings can be changed by using the PWFORMOPTIONS object. -Usually, this object specifies named sets of options that can be selected and used to change the various one-line display -options through the GUI. By including a specially named object, the current options can be changed through a display -auxiliary file. PWFORMOPTIONS are named using the OOName field. Setting this field to - - 214 - - - -"THESE_OPTIONS_ARE_APPLIED_TO_THE_CURRENT_DISPLAY" will apply the specified set of options to the current one-line -when the file is read. When saving the entire one-line to a display auxiliary file, a PWFORMOPTIONS object with this name -is added to the file by default. - -View -Different views can be specified in the display auxiliary file using the VIEW object. Usually, this object is used to specify -named sets of options used to select and change the view through the GUI. By including a specially named object, the -current view can be changed through a display auxiliary file. VIEW objects are named using the ViewName field. Setting -this field to "THIS_VIEW_IS_APPLIED_TO_THE_CURRENT_DISPLAY" will apply the specified set of view options to the current -one-line when the file is read. When saving the entire one-line to a display auxiliary file, a VIEW object with this name is -added to the file by default. - -SubData Sections -The format described thus far works well for most kinds of data in Simulator. It does not work as well however for data -that stores a list of objects. For example, a contingency stores some information about itself (such as its name), and then a -list of contingency elements, and possible a list of limit violations as well. For data such as this, Simulator allows -, tags that store lists of information about a particular object. This formatting looks like the -following - - -object_type (list_of_fields) -{ -value_list_1 - - precise format describing an object_type1 - precise format describing an object_type1 - . - . - . - - - precise format describing an object_type2 - precise format describing an object_type2 - . - . - . - -value_list_2 - . - . - . -value_list_n -} - - -Note that the information contained inside the , tags may not be flexibly defined. It must be -written in a precisely defined order that will be documented for each SubData type. The description of each of these -SubData formats follows. - -ColorMap -Same format as in data auxiliary files. - -CustomColors -Same format as in data auxiliary files. - - 215 - - - -DisplayDCTramisssionLine - -DisplayInterface - -DisplayMultiSectionLine - -DisplaySeriesCapacitor - -DisplayTransformer - -DisplayTransmissionLine - -Line -Line - -This is a list of points defining the graphical line used to represent the object. Each set of coordinates can -be enclosed in square brackets, [ ], or the brackets can be eliminated. The brackets will be included when -Simulator generates an auxiliary file. The individual coordinates are separated by the specified delimiter, -either a space or a comma, and if the brackets are included, the same delimiter should be used to -separate sets of coordinates. The list of points is in a somewhat free form and sets of coordinates can -span multiple lines. Each point should either be in x,y coordinates or longitude,latitude coordinates. -Which coordinates should be used depends on the current option settings for map projection and -whether or not coordinates should be shown in longitude,latitude. If the display auxiliary file is -automatically generated by Simulator, a comment will be included in the subdata section indicating the -coordinate system in use during file creation. - -Example using brackets and a comma delimiter: - -//Coordinates are x,y - [14.00000000, 63.00000000], [14.00000000, 60.00000000], - [20.00000000, 45.00000000], [20.00000000, 42.00000000] - - -Example with no brackets and a space delimiter: - -//Coordinates are x,y - 14.00000000 63.00000000 14.00000000 60.00000000 - 20.00000000 45.00000000 20.00000000 42.00000000 - - -DynamicFormatting -Same format as in data auxiliary files. - -Filter -Same format as in data auxiliary files. - -GeoDataViewStyle -Same format as in data auxiliary files. - - 216 - - - -PieChartGaugeStyle -ColorMap - -This is a lookup table by percentage of scalar and color values. This lookup table will consist of -consecutive lines of text with exactly three values: - -Percentage : This is the percentage at which the following scalar and color should be -applied. - -Scalar : A scalar (multiplier) on the size of the pie chart/gauge. -Color : A color for the pie chart/gauge. - - -Example: - -//Percentage Scalar Color - 85.0000 1.5000 33023 - 100.0000 2.0000 255 - - -PWFormOptions -Same format as in data auxiliary files. - -SelectByCriteriaSet -Same format as in data auxiliary files. - -UserDefinedDataGrid -Same format as in data auxiliary files. - -View -ScreenLayer - -This is a list of screen layer names that are hidden in the current view. Each screen layer name is on a -separate line of text. - -Example: - -//These are hidden screen layers - "pie layer" - - - - 217 \ No newline at end of file diff --git a/docs/Subdata.txt b/docs/Subdata.txt deleted file mode 100644 index ac09e7b..0000000 --- a/docs/Subdata.txt +++ /dev/null @@ -1,2527 +0,0 @@ -ubData Sections -The format described thus far works well for most kinds of data in Simulator. It does not work as well however for data -that stores a list of objects. For example, a contingency stores some information about itself (such as its name), and then a -list of contingency elements, and possible a list of limit violations as well. For data such as this, Simulator allows -, tags that store lists of information about a particular object. This formatting looks like the -following -object_type (list_of_fields) -{ -value_list_1 - - precise format describing an object_type1 - precise format describing an object_type1 - . - . - . - - - precise format describing an object_type2 - precise format describing an object_type2 - . - . - . - -value_list_2 - . - . - . -value_list_n -} -Note that the information contained inside the , tags may not be flexibly defined. It must be -written in a precisely defined order that will be documented for each SubData type. The description of each of these -SubData formats follows. -163 -ATC_Options -RLScenarioName -GScenarioName -IScenarioName -These three sections contain the pretty names of the RL Scenarios, G Scenarios, and I Scenarios. Each line -consists of two values: Scenario Number and a name string enclosed in quotes. -Scenario Number : The scenarios are number 0 through the number of scenarios minus 1. -Scenario Name : These represent the names of the various scenarios. -Example: - -//Index Name - 0 "Scenario Name 0" - 1 "Scenario Name 1" - -ATCMemo -This section contains the memo text for the ATC analysis. -Example: - -//Memo -"Comments for the ATC analysis" - -ATCExtraMonitor -ATCFlowValue -This subdata section contains a list of a flow values for specified transfer levels. Each line consists of two -values: Flow Value (flow on the monitored element) and a Transfer Level (in MW). -Flow Value : Contains a string describing which monitor this belongs to. -Transfer Level : Contains the value for this extra monitor at the last linear iteration. -Example: - -//MWFlow TransferLevel - 94.05 55.30 - 105.18 80.58 - 109.02 107.76 - -164 -ATCScenario -TransferLimiter -This subdata section contains a list of the TransferLimiters for this scenario. Each line contains fields -relating one of the Transferlimiters. The fields are written out in the following order: -Limiting Element : Contains a description of the limiting element. The possible values are: -"PowerFlow Divergence" -"AREA num" -"SUPERAREA name" -"ZONE num" -"BRANCH num1 num2 ckt" -"INJECTIONGROUP name" -"INTERFACE name" -Limiting Contingency : The name of the limiting contingency. If blank, then this means it’s a -limitation in the base case. -MaxFlow : The transfer limitation in MW in per unit. -PTDF : The PTDF on the limiting element in the base case (not in percent). -OTDF : The OTDF on the limiting element under the limiting contingency. -LimitUsed : The limit which was used to determine the MaxFlow in per unit. -PreTransEst : The estimated flow on the line after the contingency but before the -transfer in per unit. -MaxFlowAtLastIteration -: The total transfer at the last iteration in per unit. -IterativelyFound : Either YES or NO depending on whether it was iteratively determined. -Example: - - "BRANCH 40767 42103 1" "contin" 2.84 -0.0771 -0.3883 -4.35 -4.35 -0.01 "-55.88" -YES - "BRANCH 42100 42321 1" "Contin" 4.42 0.1078 0.5466 6.50 5.64 1.57 " 22.59" NO - "BRANCH 42168 42174 1" "Contin" 7.45 -0.0131 -0.0651 -1.39 -1.09 4.60 "-33.31" NO - "BRANCH 42168 42170 1" "Contin" 8.54 0.0131 0.0651 1.39 1.02 5.69 " 26.10" NO - "BRANCH 41004 49963 1" "Contin" 9.17 -0.0500 -0.1940 -4.39 -3.16 6.32 " 68.73" NO - "BRANCH 46403 49963 1" "Contin" 9.53 0.0500 0.1940 4.46 3.16 6.68 "-68.68" NO - "BRANCH 42163 42170 1" "Contin" 10.14 -0.0131 -0.0651 -1.39 -0.92 7.29 "-15.58" NO - -ATCExtraMonitor -This subdata section contains a list of the ATCExtraMonitors for this scenario. Each line contains three -fields relating one of the ATCExtraMonitors. The first field describes the ATCExtraMonitor which this -subdata corresponds to. The second and third variables are the initial value and sensitivity for this extra -monitor for the sceanario. An optional fourth field may be included if we are using one of the iterated -ATC solution options. This field must be the String "ATCFlowValue". -Monitor Description : Contains a string describing which monitor this belongs to. -InitialValue : Contains the value for this extra monitor at the last linear iteration. -Sensitivity : Contains the senstivity of this monitor. -ATCFlowValue : A string which signifies that a block will follow which stores a list of flow -values for specified transfer levels. Each line of the block consists of two -values: Flow Value (flow on the monitored element) and a Transfer Level -(in MW). The block is terminated when a line of text that starts with -‘END’ is encountered. -165 -Example: - - "InterfaceLeft-Right" 40.0735 0.633295 - "Branch251" 78.7410 0.266589 - -AUXFileExportFormatData -DataBlockDescription -This subdata section is used to define the objects that should be included in an auxiliary file along with -their fields, subdata sections, and any filter used to specify which objects should be included. Each line -contains the following: -ObjectType : Name of the object to include in the auxiliary file. -[FieldList] : List of fields to include. Must be enclosed in brackets. This list can either -be space-delimited or comma-delimited. -[SubdataList] : List of subdata sections to include. This list must be enclosed in brackets -and can be either space-delimited or comma-delimited. Include empty -brackets to not include subdata or for objects that do not have any -subdata sections. -"Filter" : Description of the filter to use for determining which objects to include. -This must be enclosed in double quotes. If no filter is to be used, empty -double quotes should be included. Valid entries are: "", "filtername", -"AREAZONE", and "SELECTED". See the Using Filters in Script Commands -section for more information on specifying the filtername. -Example: - - // ObjectType [FieldList] [SubdataList] "Filter" - Area [AreaName, AreaNum] [] "SELECTED" - Gen [BusNum, BusName, GenID] [BidCurve, ReactiveCapability] "" - -AUXFileExportFormatDisplay -DataBlockDescription -Same format as for the AUXFileExportFormatData subdata section. -Example: - - // ObjectType [FieldList] [SubdataList] "Filter" - DisplayArea [AreaName, AreaNum, SOAuxiliaryID] [] "" - DisplayTransmissionLine [BusNum, BusNum:1, LineCircuit, SOAuxiliaryID] - [Line] "Nominal Voltage > 138 kV" - -BGCalculatedField -Condition -Calculated Fields allow you to define a calculation over most network and aggregation objects along with -a few other types of objects. The calculation can then be used to show an aggregation calculation on -objects that link to these calculation objects in some manner. Part of the definition is a filter which -specifies which objects to operate over. This subdata section is identical to the Condition subdata section -of the Filter object type. -166 -Bus -MWMarginalCostValues -MvarMarginalCostValues -LPOPFMarginalControls -These three sections contain specific values computed for an OPF solution. In MWMarginalCostValues or -MvarMarginalCostValues these specific values are the MW or Mvar marginal prices for each constraint. In -LPOPFMarginalControls the values are the sensitivities of the controls with respect to the cost of each bus. -Example: - - //Value - 16.53 - 0.00 - 21.80 - -BusViewFormOptions -BusViewBusField -BusViewFarBusField -BusViewGenField -BusViewLineField -BusViewLoadField -BusViewShuntField -The values represent specific fields on the custom defined bus view onelines. Each line contains two -values: -Location : The various locations on the customized bus view contain slots for fields. -This is the slot number. -FieldDescription : This is a string enclosed in double quotes. The string itself is delimited -by the @ character. The string contains five values: -Name of Field : The name of the field. Special fields that appear -on dialog by default have special names. -Otherwise these are the same as the fieldnames of -the AUX file format (for the "other fields" feature -on the dialogs). -Total Digit : Number of total digits for a numeric field. -Decimal Points : Number of decimal points for a numeric field. -Color : This is the color of the field. It is not presently -used. -Increment Value : This is the "delta per mouse" click for the field. -Example: - - 0 "MW Flow@6@1@0@0" - 1 "MVar Flow@6@1@0@0" - 2 "MVA Flow@6@1@0@0" - 3 "BusAngle:1@6@2@0@0" - -167 -ColorMap -ColorPoint -A colorpoint is simply described by a real number (between 0 and 100) indicating the percentage -breakpoint, an integer describing the color, and a field indicating if the color should be used or the -contour should be transparent. These three values are written on a single line of text. Each line contains -two values: -cmvalue : Real number between 0 and 100 (minimum to maximum value). -cmcolor : Integer between 0 and 16,777,216. Value is determined by taking the -red, green and blue components of the color and assigning them a value -between 0 and 255. The color is then equal to red + 256*green + -256*256*blue. -cmalpha : Integer between 0 and 255, where only 0 and 255 are valid values. A -value of 0 indicates that the color point is transparent, while a value of -255 indicates that the color point is opaque. If the alpha channel is -omitted, a default value of 255 (opaque) will be assigned. -Example: - - // Value Color Alpha - 100.0000 127 255 - 62.5000 65535 255 - 50.0000 8388479 0 - 12.5000 16711680 0 - 0.0000 8323072 255 - -Contingency -CTGElementAppend -Normally when reading in contingency definitions, the CTGElement SubData section is used to define the -list of elements. When reading a CTGElement SubData section, all existing elements of the contingency -are deleted are replaced with the ones read from the file. Using the CTGElementAppend as the SubData -section will modify this behavior so that the elements are appended to the existing ones instead of -deleted. -CTGElement -A contingency element is described by up to the following entries. All entries must be on a single line of -text: -Action : String describing the action associated with this element. See below for -actions available. -ModelCriteria : This is the name of a ModelFilter or ModelCondition under which this -action should be performed. This entry is optional. If it is not specified, -then a blank (or no criteria) is assumed. If you want to enter a Status, -then use must specify "" as the ModelCriteria. -168 -Status : The following options are available: -CHECK : perform action if ModelCriteria is true -ALWAYS : perform action regardless of ModelCriteria -NEVER : do not perform action -TOPOLOGYCHECK: perform action if ModelCriteria is true following -implementation of other actions and before -solving the power flow -POSTCHECK : perform action if ModelCriteria is true following -implementation of other actions and solving the -power flow -SOLUTIONFAIL : perform the action if ModelCriteria is true or not -defined following the failure of the power flow -solution. -This entry is optional. If it is not specified, then CHECK is assumed. -InclusionFilter : This entry is optional and will only exist for elements of RemedialAction -or GlobalContingencyActions objects. This is the name of an advanced -filter or device filter that gets applied to each contingency. If the -contingency meets the filter, that contingency will include this element. -Otherwise, the element will be ignored. -TimeDelay : This entry is optional. If not specified, 0 is assumed. This entry will only -exist for elements of Contingency, RemedialAction, or -GlobalContingencyActions objects. This is the time delay in seconds to -wait before the action takes place. -Persistent : This entry is optional. It not specified, NO is assumed. Normally after a -contingency action has been implemented it will not be applied again. -Setting this option to YES to mark an action as persistent will change this -behavior. Any action marked as persistent that also has a Status of -TOPOLOGYCHECK, POSTCHECK, or SOLUTIONFAIL will be applied in the -appropriate section of the overall contingency process any time that its -ModelCriteria is met. An exception is that a SOLUTIONFAIL element will -only remain persistent until a solution is successfully achieved. -ArmingCriteria : This entry is optional and will only exist for elements of RemedialAction -objects. This is the name of a ModelFilter or ModelCondition under -which this action should be armed. If it is not specified, then a blank (or -no criteria) is assumed. If you want to enter a ArmingStatus, then use -must specify "" as the ArmingCriteria. -ArmingStatus : This entry is optional and will only exist for elements of RemedialAction -objects. If not specified, CHECK is assumed. The following options are -available: -CHECK : action is armed if ArmingCriteria is true -ALWAYS : action is considered armed regardless of ArmingCriteria -NEVER : action is not armed -Comment : All text to the right of the comment symbol (//) will be saved with the -CTGElement as a comment. -Possible Actions: -Many actions have a value field that can be specified. This value can be expressed in three ways: -1. A numerical value that will be used directly. -2. The variablename of a field for the object in the action preceded by the tag . This field -will be evaluated and that value will be used. Including the keyword REF in the appropriate place -in the action string will cause the field to be evaluated in the contingency reference case. -Otherwise, the field will be evaluated at the moment the action is implemented. -169 -3. The name of a Model Expression preceded by the tag . Single quotes should -enclose the entirety of the tag and the name if the name contains spaces. The model expression -will be evaluated and the result will be used as the value. Including the keyword REF in the -appropriate place in the action string will cause the model expression to be evaluated in the -contingency reference case. Otherwise, the model expression will be evaluated at the moment -the action is implemented. -Transmission Line or Transformer outage or insertion -BRANCH | bus1# bus2# ckt | OPEN -| | CLOSE -| | OPENCBS -| | CLOSECBS -| | SET_TO | value | LimitMVA | REF -Takes branch out of service, or puts it in service. The contingency rating of the branch can also be set for -the duration of the contingency using the SET_TO action. Note: bus# values may be replaced by a string -enclosed in single quotes where the string is the name of the bus followed by an underscore character -and then the nominal voltage of the bus. These values may also be replaced by a string enclosed in single -quotes which represents the label of the bus. Also, the entire sequence [bus1# bus2# ckt] may be -replaced by the label of the branch. -Generator, Load, or Switched Shunt outage or insertion -GEN | bus# id | OPEN, CLOSE, OPENCBS, or CLOSECBS -LOAD | bus# id | OPEN, CLOSE, OPENCBS, or CLOSECBS -SHUNT | bus# id | OPEN, CLOSE, OPENCBS, or CLOSECBS -INJECTIONGROUP | name | OPEN, CLOSE, OPENCBS, or CLOSECBS -Takes a generator, load, or shunt out of service, or puts it in service. If specifying an injection group, the -status of all devices in the injection group will be changed. Note: bus# values may be replaced by a string -enclosed in single quotes where the string is the name of the bus followed by an underscore character -and then the nominal voltage of the bus. These values may also be replaced by a string enclosed in single -quotes which represents the label of the bus. Also, the sequence [bus1# ckt] or [name] may be replaced -by the label of the device. -Generator, Load or Switched Shunt movement to another bus -For the following set of actions, all of the object types can use the same action keywords which are -associated with the value keyword following the actual value. The move is based on specifying a bus: -GEN | bus1# | MOVE_P_TO | bus2# | value | MW | REF -LOAD | | MOVE_Q_TO | | | MVR | -SHUNT | | | | | | -For the following set of actions, all of the object types can use the same action keywords which are -associated with the value keyword following the actual value. The move is based on specifying a -particular device: -GEN | bus1# id | MOVE_P_TO | bus2# | value | MW | REF -LOAD | | MOVE_Q_TO | | | MVR | -SHUNT | | | | | | -Generator actions that move a generator by a percentage only apply to the generator MW: -GEN | bus1# | MOVE_P_TO | bus2# | value | PERCENT | REF -GEN | bus1# id | MOVE_P_TO | bus2# | value | PERCENT | REF -170 -The following set of actions are used for specifying a load move by maintaining a constant power factor: -LOAD | bus1# | MOVE_PQ_TO | bus2# | value | MW | REF -LOAD | bus1# id | MOVE_PQ_TO | bus2# | | MW | -The following set of actions apply to loads and shunts and are used to move a percentage of the entire -MW and Mvar output. The move is based on specifying a bus: -LOAD | bus1# | MOVE_PQ_TO | bus2# | value | PERCENT | REF -SHUNT | | | | | | -The following set of actions apply to loads and shunts and are used to move a percentage of the entire -MW and Mvar output. The move is based on specifying a particular device: -LOAD | bus1# id | MOVE_PQ_TO | bus2# | value | PERCENT | REF -SHUNT | | | | | | -Use to move generation, load or shunt at a bus1 over to bus2. This can be used on a bus or specific -device basis in specifying what to move. Note: bus# values may be replaced by a string enclosed in single -quotes where the string is the name of the bus followed by an underscore character and then the nominal -voltage of the bus. These values may also be replaced by a string enclosed in single quotes which -represents the label of the bus. When identifying specific devices, the device label can replace the bus -number and device id. -Generator, Load or Switched Shunt set or change a specific value -For the following set of actions, all of the object types can use the same action keywords which are -associated with the value keyword following the actual value. These changes are based on specifying a -bus: -GEN | bus# | SET_P_TO | value | MW | REF -LOAD | | SET_Q_TO | | MVR | -SHUNT | | CHANGE_P_BY | | MW | -| | CHANGE_Q_BY | | MVR | -For the following set of actions, all of the object types can use the same action keywords which are -associated with the value keyword following the actual value. These changes are based on specifying a -bus: -GEN | bus# id | SET_P_TO | value | MW | REF -LOAD | | SET_Q_TO | | MVR | -SHUNT | | CHANGE_P_BY | | MW | -| | CHANGE_Q_BY | | MVR | -The following set of actions are used to set or change the MW output of generation at a bus by a -percentage: -GEN | bus# | SET_P_TO | value | PERCENT | REF -GEN | | CHANGE_P_BY | | | -The following set of actions are used to set or change the MW output of a particular generator by a -percentage: -GEN | bus# id | SET_P_TO | value | PERCENT | REF -GEN | | CHANGE_P_BY | | | -The following set of actions apply to loads and shunts and are used to set or change a percentage of the -entire MW and Mvar output. This based on specifying a bus: -LOAD | bus# | SET_PQ_TO | value | PERCENT | REF -SHUNT | | CHANGE_PQ_BY | | | -The following set of actions apply to loads and shunts and are used to set or change a percentage of the -entire MW and Mvar output. This based on specifying a specific device: -LOAD | bus# id | SET_PQ_TO | value | PERCENT | REF -SHUNT | | CHANGE_PQ_BY | | | -The following set of actions are used to specify a load set or change by maintaining a constant power -factor. This is based on specifying a bus: -LOAD | bus# | SET_PQ_TO | value | MW | REF -| | CHANGE_PQ_BY | | MW | -The following set of actions are used to specify a load set or change by maintaining a constant power -factor. This is based on specifying a specific load: -171 -LOAD | bus# id | SET_PQ_TO | value | MW | REF -| | CHANGE_PQ_BY | | MW | -The following set of actions apply to generators and shunts and are used to set or change the setpoint -voltage of the devices at the specified bus: -GEN | bus# | SET_VOLT_TO | value | PU | REF -SHUNT | |CHANGE_VOLT_BY | | | -The following set of actions apply to generators and shunts and are used to set or change the setpoint -voltage of the specified device: -GEN | bus# id | SET_VOLT_TO | value | PU | REF -SHUNT | |CHANGE_VOLT_BY | | | -Note: bus# values may be replaced by a string enclosed in single quotes where the string is the name of -the bus followed by an underscore character and then the nominal voltage of the bus. These values may -also be replaced by a string enclosed in single quotes which represents the label of the bus. When -identifying specific devices, the device label can replace the bus number and device id. -Bus outage causes all lines connected to the bus to be outage -BUS | bus# | OPEN -| OPENCBS -Takes all branches connected to the bus out of service. Also outages all generation, load, or shunts -attached to the bus. Note: bus# values may be replaced by a string enclosed in single quotes where the -string is the name of the bus followed by an underscore character and then the nominal voltage of the -bus. These values may also be replaced by a string enclosed in single quotes which represents the label of -the bus. -Interface outage or insertion -INTERFACE | name | OPEN -| CLOSE -| OPENCBS -| CLOSECBS -Takes all monitored branches in the interface out of service, or puts them all in service. Open actions will -also open all generators and loads contained in the interface including generators and loads inside any -injection groups or other interfaces. Note: the [name] may be replaced by the label of the interface. -Interface change specific value -INTERFACE | name | CHANGE_P_BY | value | Option | REF | PPREF -| SET_P_TO | | | | -The following Option settings are allowed to set or change the MW flow of an interface by or to a -particular value: -MWMERITORDEROPEN -: Value will be interpreted as the amount of MW flow change or new MW -flow. The element in the interface with the highest participation factor -will be opened, followed by the second generator and so on. This will -continue until the amount of MW flow opened is as close to the desired -amount as possible without exceeding the amount of MW flow open. If -an element will cause a change in flow that is not in the desired direction, -that element is not opened and the next element is examined. -PERCENTMERITORDEROPEN or %MERITORDEROPEN -: Same as MWMERITORDEROPEN except that the value will be interpreted -as percentage of the contingency reference state MW flow. -MWMERITORDEROPENEXCEED -: Same as MWMERITORDEROPEN except that the amount of MW opened is -allowed to exceed the desired amount of change. Interface elements will -be opened in merit order until the desired amount is met or exceeded. -PERCENTMERITORDEROPENEXCEED or %MERITORDEROPENEXCEED -172 -: Same as MWMERITORDEROPENEXCEED except that the value will be -interpreted as percentage of the contingency reference state MW -injection. -MWEFFECTOPEN -: Value that is specified with the action is the desired MW Effect that the -action should have. The participation factors defined with the interface -elements will be interpreted as effectiveness factors akin to transfer -distribution factors. These factors are supplied as input by the user when -defining the interface. The effectiveness factors are multiplied by the -present MW flow of elements in the interface to determine how much -effect they will have if dropped. The flow of an element is determined by -its MW flow multiplied by the Weighting factor specified with the -element. If the effect of a particular element is in the opposite direction -of the desired effect, that element is skipped. The action will find the -smallest number of elements to drop that results in a total MW Effect -that is within 5% of the desired MW Effect, but does not exceed the -desired MW Effect. -This option is not valid with SET_P_TO actions. -MWEFFECTOPENEXCEED -: Same as MWEFFECTOPEN but it will ensure that the total MW Effect is -within 5% of the total desired MW Effect and also meets or exceeds the -desired MW Effect. -This option is not valid with SET_P_TO actions. -MWBESTFITOPEN -: Value will be interpreted as the amount of MW flow change or new MW -flow. When using this option the participation factors of interface -elements do not impact which elements are opened. The flow on an -element, which is determined by its MW flow multiplied by the weighting -factor specified with the element, is used to determine which elements -should be opened. If the follow on an element is in the appropriate -direction to achieve the desired flow change on the interface, that -element is eligible fo being opened. To determine if an element will -actually be opened, the best fit algorithm attempts to determine the -combination of elements that will achieve the desired flow change by -opening the least amount of elements and achieving an actual flow -change within 5% fo the desired flow change without exceeding the -desired amount. -MWBESTFITOPENEXCEED -: Same as MWBESTFITOPEN except that the MW flow change is allowed to -exceed the desired amount of change. -PERCENTBESTFITOPEN or %BESTFITOPEN -: Same as MWBESTFITOPEN except that the value will be interpreted as -percentage of the contingency reference state MW flow. -PERCENTBESTFITOPENEXCEED or %BESTFITOPENEXCEED -: Same as PERCENTBESTFITOPEN except that the MW flow change is -allowed to exceed the desired amount of change. -When using an action that requires participation factors, an optional parameter PPREF can be specified. -This indicates that the participation factors should be determined in the contingency reference case. -Interfaces can contain other interfaces. The treatment of interfaces within interfaces is to open the entire -contained interface when using the MWMERITORDEROPEN type actions. - -173 -Notes: The [name] may be replaced by the label of the interface. -Line Shunt outage or insertion -LINESHUNT | bus1# bus2# bus# ckt | OPEN -| CLOSE -Takes a line shunt out of service, or puts it in service. bus1# and bus2# identify the line that the line shunt -is on and bus# identifies the side of the line that the line shunt is on. bus# values may be replaced by a -string enclosed in single quotes where the string is the name of the bus followed by an underscore -character and then the nominal voltage of the bus. bus# values may also be replaced by a string enclosed -in single quotes that represents the label of the bus. The sequence [bus1# bus2#] may be replaced by the -label of the line to which the line shunt is attached. -Injection Group outage or insertion -INJECTIONGROUP | name | OPEN -| CLOSE | value | REF | PPREF -| OPENCBS -| CLOSECBS -| OPEN | value | REF | PPREF -Takes all devices in the injection group out of service, or puts them all in service. -The OPEN action will open all devices in the injection group if no value is specified. If a value is specified, -only that number of devices will be opened in the order of highest to lowest participation factor. The -CLOSE action will close all devices in the injection group if no value is specified. If a value is specified, -only that number of devices will be closed in the order of highest to lowest participation factor. When -using an action that requires participation factors, an optional parameter PPREF can be specified. This -indicates that the participation factors should be determined in the contingency rerference case. This will -only be done for participation points using an AutoCalcMethod that indicates the factor should be -dynamically determined and the AutoCalc field is set to YES for the participation point. -The [name] may be replaced by the label of the injection group. Bus participation points will be -completely ignored in this process. -Injection Group change specific value -INJECTIONGROUP | name | CHANGE_P_BY | value | Option | REF | PPREF -| SET_P_TO | | | | -The following Option settings are allowed to set or change the MW generation/load in an injection -group by or to a particular value: -MW : Value will be interpreted as the amount of MW injection change or new -MW injection. Each participation point in the injection group will be -changed in proportion to the participation factors of the group. -PERCENT or % : Same as MW except that the value will be interpreted as percentage of -the contingency reference state MW injection. -MWMERITORDER : Value will be interpreted as the amount of MW injection change or new -MW injection. Both generator and load points will be modified in the -injection group. Elements will be adjusted in order of highest -participation factor to lowest before moving to the next element. This -process continues until the desired injection is met. Generators will not -be opened in this process, which means all online generators will -continue to provide Mvar support. Loads that have both their minimum -and maximum MW limits set to zero will not be allowed to increase. -They can only decrease towards 0. -PERCENTMERITORDER or %MERITORDER -: Same as MWMERITORDER except that the value will be interpreted as -percentage of the contingency reference state MW injection. -174 -MWMERITORDEROPEN -: Value will be interpreted as the amount of MW injection change or new -MW injection. Both generator and load points can be modified in the -injection group. If the MW injection change is negative, the generator in -the injection group with the highest participation factor will have its -status changed to Open, followed by the second generator and so on. -This will continue until the amount of MW opened is as close to the -desired amount as possible without exceeding the desired amount of -drop. If the MW injection change is positive, loads will be opened in the -same manner. If an element would cause the desired gen drop amount -to be exceeded, that element is skipped and the next element in merit -order is processed. If the change requested is positive and there are no -loads in the injection group, generators will be increased toward their -maximum MW output in the same manner as MWMERITORDER as though -the OPEN option was not specified. If the change requested is negative -and there are no generators in the injection group, loads will be -increased toward their maximum MW output in the same manner as -MWMERITORDER as though the OPEN option was not specified. -PERCENTMERITORDEROPEN or %MERITORDEROPEN -: Same as MWMERITORDEROPEN except that the value will be interpreted -as percentage of the contingency reference state MW injection. -MWMERITORDEROPENEXCEED -: Same as MWMERITORDEROPEN except that the amount of MW opened is -allowed to exceed the desired amount of change. Generators or loads -will be opened in merit order until the desired amount is met or -exceeded. -PERCENTMERITORDEROPENEXCEED or %MERITORDEROPENEXCEED -: Same as MWMERITORDEROPENEXCEED except that the value will be -interpreted as percentage of the contingency reference state MW -injection. -MWEFFECTOPEN : Value that is specified with the action is the desired MW Effect that the -action should have. The participation factors defined with the Injection -Group will be interpreted as effectiveness factors akin to transfer -distribution factors. These factors are supplied as input by the user when -defining the injection group. The effectiveness factors are multiplied by -the present output of generators (or loads) in the injection group to -determine how much effect they will have if dropped. The action will find -the smallest number of generators (or loads) to drop which results in a -total MW Effect that is within 5% of the desired MW Effect, but does not -exceed the desired MW Effect. -This option is not valid with SET_P_TO actions. -MWEFFECTOPENEXCEED -: Same as MWEFFECTOPEN but it will ensure that the total MW Effect is -within 5% of the total desired MW Effect and also meets or exceeds the -desired MW Effect. -This option is not valid with SET_P_TO actions. -MWBESTFITOPEN : Value will be interpreted as the amount of MW injection change or new -MW injection. When using this option the participation factors do not -impact which elements are opened. All generators or loads defined with -the injection group can participate if they are online. Specifially which -generators or loads depends on an algorithm that attempts to get the -actual injection change within 5% of the desired injection change by -175 -opening the smallest number of generators or loads without exceeding -the desired amount. -MWBESTFITOPENEXCEED -: Same as MWBESTFITOPEN except that the MW injection change is -allowed to exceed the desired amount of change. -PERCENTBESTFITOPEN or %BESTFITOPEN -: Same as MWBESTFITOPEN except that the value will be interpreted as -percentage of the contingency reference state MW injection. -PERCENTBESTFITOPENEXCEED or %BESTFITOPENEXCEED -: Same as PERCENTBESTFITOPEN except that the MW injection change is -allowed to exceed the desired amount of change. -When using an action that requires participation factors, an optional parameter PPREF can be specified. -This indicates that the participation factors should be determined in the contingency reference case. This -will only be done for participation points using an AutoCalcMethod that indicates the factor should be -dynamically determined and the AutoCalc field is set to YES for the participation point. -Injection Groups can contain participation points that reference another injection group. The treatment of -injection groups within injection groups will be to drop the entire contained injection group when using -the MWMERITORDEROPEN and MWEFFECTOPEN type actions. - -Notes: The [name] may be replaced by the label of the injection group. Bus participation points will be -completely ignored in this process. -Series Capacitor Bypass or Inservice -SERIESCAP | bus1# bus2# ckt | BYPASS -| INSERVICE -Bypasses a series capacitor, or puts it in service. Note: bus# values may be replaced by a string enclosed in -single quotes where the string is the name of the bus followed by and underscore character and then the -nominal voltage of the bus. Note: bus# values may also be replaced by a string enclosed in single quotes -which represents the label of the bus. Also, the entire sequence [bus1# bus2# ckt] may be replaced by -the label of the branch. Keyword SERIESCAP may also be replaced with BRANCH, which will allow -bypassing or not bypassing any branch and is not limited to series capacitors. -Series Capacitor set impedance -SERIESCAP | bus1# bus2# ckt | SET_X_TO | value | PERCENT | REF -| | | PU | -Changes the impedance a series capacitor either specifying a new per unit value or specifying a -percentage of the value in the contingency reference case. Note: bus# values may be replaced by a string -enclosed in single quotes where the string is the name of the bus followed by and underscore character -and then the nominal voltage of the bus. Note: bus# values may also be replaced by a string enclosed in -single quotes which represents the label of the bus. Also, the entire sequence [bus1# bus2# ckt] may be -replaced by the label of the branch. Keyword SERIESCAP may also be replaced with BRANCH, which will -allow setting the impedance of any branch and is not limited to series capacitors. -DC Transmission or VSC DC Transmission Line outage -DCLINE | bus1# bus2# ckt | OPEN -| OPENCBS -VSCDCLINE | 'Name' | OPEN -| OPENCBS -Takes DC Line or VSC DC Line out of service. Note: bus# values may be replaced by a string enclosed in -single quotes where the string is the name of the bus followed by an underscore character and then the -nominal voltage of the bus. These values may also be replaced by a string enclosed in single quotes -176 -which represents the label of the bus. Also, the entire sequence [bus1# bus2# ckt] may be replaced by -the label of the dc transmission line. For the VSC DC Line, the identifiers are replaced simply with the -name of the VSCDCLINE instead. -DC Line set a specific value or insertion -DCLINE | bus1# bus2# ckt | SET_P_TO | value | MW | REF -| CHANGE_P_BY | | PERCENT -| SET_I_TO | | AMPS -| CHANGE_I_BY | -| CLOSE | -| CLOSECBS | -| SET_TO | value | OHMS | REF -VSCDCLINE | 'Name' | Same options as for the DC Line, except that - the AMPS option are not avalailable for VSC -Use to set the DC Line setpoint to a particular value, or puts it in service. Note: bus# values may be -replaced by a string enclosed in single quotes where the string is the name of the bus followed by an -underscore character and then the nominal voltage of the bus. Note: bus# values may also be replaced -by a string enclosed in single quotes which represents the label of the bus. Also, the entire sequence -[bus1# bus2# ckt] may be replaced by the label of the dc transmission line. (Note: for the CLOSE and -CLOSECBS choice, only the units of MW or AMPS may be used.) For the VSC DC Line, the identifiers are -replaced simply with the name of the VSCDCLINE instead. -MTDC Converter outage -DCCONVERTER | rec# bus# | OPEN -| OPENCBS -Takes multi-terminal DC converter out of service. The rec# specifies the multi-terminal DC line record, -while bus# specifies the AC bus to which the converter is connected. Note: bus# values may be replaced -by a string enclosed in single quotes where the string is the name of the bus followed by an underscore -character and then the nominal voltage of the bus. These values may also be replaced by a string -enclosed in single quotes which represents the label of the bus. -MTDC Converter set a specific value or insertion -DCCONVERTER | rec# bus# | SET_P_TO | value | MW | REF -| CHANGE_P_BY | | PERCENT -| SET_I_TO | | AMPS -| CHANGE_I_BY | -| CLOSE | -| CLOSECBS | -Use to set the multi-terminal DC converter setpoint to a particular value, or puts it in service. The rec# -specifies the multi-terminal DC line record, while bus# specifies the AC bus to which the converter is -connected. Note: bus# values may be replaced by a string enclosed in single quotes where the string is -the name of the bus followed by an underscore character and then the nominal voltage of the bus. Note: -bus# values may also be replaced by a string enclosed in single quotes which represents the label of the -bus. (Note: for the CLOSE and CLOSECBS choice, only the units of MW or AMPS may be used.) -Phase Shifter set a specific value -PHASESHIFTER | bus1# bus2# ckt | SET_P_TO | value | MW | REF -| CHANGE_P_BY | | PERCENT -| SET_TO | value | DEG | REF -| CHANGE_BY | | | -Use the MW and PERCENT options to change or set the middle of the phase shifter MW regulation range -to the specified value. Use the DEG option to change or set the phase shift angle in degrees to a particular -value. -Note: bus# values may be replaced by a string enclosed in single quotes where the string is the name of -the bus followed by an underscore character and then the nominal voltage of the bus. These values may -177 -also be replaced by a string enclosed in single quotes which represents the label of the bus. Also, the -entire sequence [bus1# bus2# ckt] may be replaced by the label of the branch. -Keyword PHASESHIFTER may also be replaced with BRANCH. If the branch is not a phase shifter, no -change will be made. -3-Winding Transformer outage or insertion -3WXFORMER | bus1# bus2# bus3# ckt | OPEN -| CLOSE -| OPENCBS -| CLOSECBS -Takes all three windings of a 3-winding transformer out of service, or puts them in service. Note: bus# -values may be replaced by a string enclosed in single quotes where the string is the name of the bus -followed by an underscore character and then the nominal voltage of the bus. Note: bus# values may -also be replaced by a string enclosed in single quotes which represents the label of the bus. Also, the -entire sequence [bus1# bus2# bus#3 ckt] may be replaced by the label of the three winding transformer. -Area Control Type Change -AREA | area# | SET_TO | 'OFF' -| 'PARTFAC' -| 'AREASLACK bus#' -| 'IGSLACK injectiongroup name' -Specify to change the make-up power for an area so that it is different during a contingency than the area -control settings used in the reference case. The area may be set to toggle the control setting to OFF, -PARTFAC, AREASLACK, and IGSLACK. The Area Control topic provides more information about these -control types. If selecting Area Slack is chosen, then a bus must be specified which will act as the area -slack during the contingency action. If selecting IG Slack, then an injection group must be specified by -name. -Note: bus# values may be replaced by a string where the string is the name of the bus followed by an -underscore character and then the nominal voltage of the bus. Note: bus# values may also be replaced -by a string which represents the label of the bus. -In order for the Area contingency action to work correctly, there are contingency and power flow solution -options that must be set correctly. Simulator does not automatically set these options so the user must -make sure they are set. -• Area control must be enabled in the contingency base case, i.e. the Power Flow Solution Option -for Island-Based AGC must be set to Disable (Use the Area and Super Area Dispatch settings). -• The contingency Make-Up Power option must be set to Same as Power Flow case. -• The option to Disable Automatic Generation Control (AGC) found with the Power Flow Solution -Options must NOT be selected. -Another suggestion, although not a strict requirement, is that the area should be on area control prior to -contingency analysis if a control type other than Off AGC is going to be set during a contingency. If a -large ACE exists in the base case with area control off, switching the area on control during the -contingency will zero out the ACE in addition to compensating for required make-up power. -Substation outage -SUBSTATION | sub# | OPEN -| OPENCBS -| SET_P_TO | value | MW | REF -| CHANGE_P_BY | | PERCENT -OPEN and OPENCBS will take a substation out of service. The set and change actions will set the MW -output of online generators in the substation to the specified value. sub# is the number that identifies the -178 -substation. sub# can be replaced by a string enclosed in single quotes where the string is the name or -label of the substation. -Abort -ABORT -Include this action to cause the solution of the contingency to be aborted. -Execute a Power Flow Solution -SOLVEPOWERFLOW -Include this action to cause the solution of the contingency to be split into pieces. Actions that are listed -before each SOLVEPOWERFLOW call will be performed as a group. -Calling of a name ContingencyBlock -CONTINGENCYBLOCK | name -Calls a ContingencyBlock and executes each of the actions in that block. -Make-Up Power Compensation -Only valid immediately following a SET, CHANGE, OPEN or CLOSE action on a Generator, Shunt or Load. -This describes how the change in MW or MVAR are picked up by buses throughout the system. The values -specify participation factors. Note: bus# values may be replaced by a string enclosed in single quotes -where the string is the name of the bus followed by and underscore character and then the nominal -voltage of the bus. -COMPENSATION -bus#1 value1 -bus#2 value2 -... -END -Example: - - // just some comments - // action Model Criteria Status TimeDelay comment - "BRANCH 40821 40869 1 OPEN" "" ALWAYS 0 //Raver - Paul 500 kV - "GEN 45041 1 OPEN" "" ALWAYS 0 //Trip Unit #2 - "BRANCH 42702 42727 1 OPEN" "Line X Limited" CHECK 0 //Open Fern Hill - "GEN 40221 1 OPEN" "Interface L1" CHECK 0 //Drop ~600 MW - "GEN 40227 1 OPEN" "Interface L2" CHECK 0 //Drop ~1200 MW - "GEN 40221 1 OPEN" "Interface L3" CHECK 0 //Drop ~600 MW - -Note: ContingencyElement object types can also be directly created inside their own DATA section as well. -One of the key fields of the object is then the name of the contingency to which the ContingencyElement -belongs. The Action string will remain the same. -179 -LimitViol -A LimitViol is used to describe the results of a contingency analysis run. Each Limit Violation lists nine -possible values: -ViolType : One of six values describing the type of violation. -BAMP : branch amp limit violation -BMVA : branch MVA limit violation -VLOW : bus low voltage limit violation -VHIGH : bus high voltage limit violation -INTER : interface MW limit violation -CUSTOM : Custom Monitor value -ViolElement : This field depends on the ViolType. -for VLOW, VHIGH : "bus1#" or "busname_buskv" or "buslabel" -for INTER : "interfacename" or "interfacelabel" -for BAMP, BMVA : "bus1# bus2# ckt violationbus# -MWFlowDirection" -violationbus# is the bus number for the end of the branch which is -violated -MWFlowDirection is the direction of the MW flow on the line. -Potential values are "FROMTO" or "TOFROM". -Note: each bus# may be replaced with the name underscore nominal -kV string enclosed in single quotations. Or bus# values may be -replaced by a string enclosed in single quotes representing the label -of the bus. Also the entire sequence [bus1# bus2# ckt] may be -replaced by the label of the branch. -for CUSTOM : "custommonitorname deviceidentifier" where the -deviceidentifier will use the key fields or label as -specified by the option selected when saving -Limit : This is the numerical limit which was violated. -ViolValue : This is the numerical value of the violation. -PTDF : This field is optional. It only makes sense for interface or branch -violations. It stores a sensitivity of the flow on the violating element -during in the base case with respect to a transfer direction This must be -calculated using the Contingency Analysis Other Actions related to -Sensitivities. -OTDF : Same as for the PTDF. -InitialValue : This stores a number. This stores the base case value for the element -which is being violated. This is used to compare against when looking at -change violations. -Reason : This will say whether this was a pure violation, or is being reported as a -violation because the change from the base case is higher than a -specified threshold. -LIMIT : means this is a violation of a line/interface/bus limit or -simply a Custom Monitor -CHANGE : means this is being reported as a limit because the change -from the initial value is higher than allowed -CTG Specified Limit : This specifies if the Limit originated from a contingency action or from -the rating specified with the line and Limit Monitoring Settings. -NO : the Limit originated from the line and Limit Monitoring Settings -YES : the Limit originated from a contingency action -180 -Example: - - BAMP "1 3 1 1 FROMTO" 271.94031 398.48096 10.0 15.01 //Note OTDF/PTDF - // values can also be specified with name underscore nominal kV string - // enclosed inside a single quote as shown next - BAMP "'One_138' 'Three_138' 1 1 FROMTO" 271.94031 398.48096 10.0 15.01 - INTER "Right-Top" 45.00000 85.84451 None None 56.000 LIMIT NO - -ViolationCTG object types can also be directly created inside their own DATA section as well. One of the -key fields of the object is then the name of the contingency to which the ViolationCTG belongs. -Sim_Solution_Options -These describe the power flow solution options which should be used under this particular contingency. -The format of the subdata section is two lines of text. The first line is a list of the fieldtypes for -Sim_Solution_Options which should be changed. The second line is a list of the values. Note that in -general, power flow solution options are stored at three different locations in contingency analysis. When -implementing a contingency, Simulator gives precendence to these three locations in the following order: -1. Contingency Record Options (stored with the particular contingency). -2. Contingency Tool Options (stored with CTG_Options). -3. The global solution options. -WhatOccurredDuringContingency -Each line of this subdata section is part of a text description of what actually ended up being -implemented for this contingency. This will list which actions were executed and which actions ended up -being skipped because of their model criteria. Each line of the subdata section must be enclosed in -quotes. -Example: - - "Applied: " - " OPEN Branch Two (2) TO Five (5) CKT 1 | | CHECK | | ELEMENT" - -ContingencyMonitoringException -Each line of this subdata section contains a string identifying a specially handled monitored element for -this contingency followed by a string indicating how this monitored element should be handled with this -contingency. The elements can be identified by their primary or secondary key fields or by label. The -element descriptions should be enclosed in quotes because they contain spaces. -Example: - - "Branch '2' '3' '1'" "Exclude" - "Branch 'Three_138.00' 'Four_138.00'" "Include" - "Branch 'Line_2_5'" "Default" - -CTG_Options -Sim_Solution_Options -These describe the power flow solution options which should be used under this particular contingency. -The format of the subdata section is two lines of text. The first line is a list of the fieldtypes for -Sim_Solution_Options which should be changed. The second line is a list of the values. Note that in -general, power flow solution options are stored at three different locations in contingency analysis. When -implementing a contingency, Simulator gives precendence to these three locations in the following order: -1. Contingency Record Options (stored with the particular contingency). -2. Contingency Tool Options (stored with CTG_Options). -181 -3. The global solution options. -CTGElementBlock -CTGElement -This format is the same as for the Contingency objecttype, however, you cannot call a ContingencyBlock -from within a contingencyblock. -CTGElementAppend -When a subdata section is defined as CTGElementAppend rather than CTGElement, the actions of this -subdata section will be appended to the contingency actions, instead of replacing them. This format is -the same as for the Contingency objecttype, however, you cannot call a ContingencyBlock from within a -contingencyblock. -Note: CTGElementBlockElement object types can also be directly created inside their own DATA section as -well. One of the key fields of the object is then the name of the contingency block to which the -CTGElementBlockElement belongs. -CustomColors -CustomColors -These describe the customized colors used in Simulator, which are specified by the user. A custom color -is an integer describing a color. Each custom color is written on a single line of text and is an integer -between 0 and 16,777,216. The value is determined by taking the red, green, and blue components of the -color and assigning them a value between 0 and 255. The color is then equal to red + 256*green + -256*256*blue. Each line contains only one integer that corresponds to the color specified. -Example: - - 9823301 - 8613240 - -CustomCaseInfo -ColumnInfo -Each line of this SUBDATA section can be used for specifying the column width of particular columns of -the respective Custom Case Information Sheet. The line contains two values – the column and then a -column width. This is shown in the following example. -Example: - - "SheetCol" 133 - "SheetCol:1" 150 - "SheetCol:2" 50 - -DataGrid -ColumnInfo -Contains a description of the columns which are shown in the respective data grid. Each line of text -contains at least four fields: VariableName, ColumnWidth, TotalDigits, DecimalPoints. The remaining -fields are used when showing a Data View based on this DataGrid object. See help website for Data View -or the OpenDataView script command for more information about this. -Variablename : Contains the variable which is shown in this column. -182 -ColumnWidth : The column width. -TotalDigits : The total digits displayed for numerical values. -DecimalPoints : The decimal points shown for numerical values. -TabBreak : Optional. Default to NO. Set to YES to indicate that a new tab should be -started immediately before this field. -TabCaption : Optional. Default to blank string. Specifies a caption for the tabbed -control for fields occurring after the Tab Break. -RowBreak : Optional. Default to NO. Set to YES to indicate that a new row should be -started immediately before this field. -RowCaption : Optional. Default to blank string. Specifies a caption for a group box for -the fields occurring after the row break. -ColBreak : Optional. Default to NO. Set to YES to indicate that a new Column -should be started immediately before this field. Also, a special feature -for column breaks only is you may specify a number after YES to indicate -multiple column breaks to skip over a column. For example "YES 2" to -skip a column because there are 2 consecutive column breaks. -RowCaption : Optional. Default to blank string. Specifies a caption for a group box for -the fields occurring after the column break. -Example: -DataGrid (DataGridName) -{ - BUS - - BusNomVolt 100 8 2 - AreaNum 50 8 2 "YES" "Tab Caption" "NO" "" "NO" "" - ZoneNum 50 8 2 - - BRANCHRUN - - BusNomVolt:0 100 8 2 - BusNomVolt:1 100 8 2 "NO" "" "NO" "" "YES 2" "Col Caption" - LineMW:0 100 9 3 "NO" "" "YES" "Row Caption" "NO" "" - -} -ColumnContourInfo -Contains a description of the column contour settings -ColumnNumber : Contains the column index of the contoured column -UseAbsValue : Contour the absolute value if YES. If NO then contour the signed value. -IgnoreValuesAbove : If YES values above the maximum percentage are ignored. -IgnoreValuesBelow : if YES values below the minimum percentage are ignored. -AbsMin : The minimum value for the colormap -LimMin : The break low value for the colormap -Nominal : The nominal value for the colormap -LimMax : The break high value for the colormap -AbsMax : The maximum value for the colormap -ColorMapName : The name of the color map the contour is using. This must reference a -color map that exists in the case. -ColorMapBrightness : The brightness or color saturation. The values range from -0.8 (darker) to -0.8 (brigher). -ColorMapReverseColors : If YES, the colors in the colormap will be reversed. -183 -Example: -DATA (DataGrid, -[DataGridName,BGDisplayFilter,FilterName,NonDefaultFont,CaseInfoRowHeight,FontName,Fon -tStyles,SOFontSize,FontColor,VariableName,ConditionType,ViewZoomLevel,FrozenColumns]) -{ -"Bus" "YES" "" "YES" 13 "Segoe UI" "" 8 0 "" "High To Low" 100.00 -1 - - "BusNum" 75 8 2 - "BusName" 75 8 2 - "AreaName" 75 8 2 - "BusNomVolt" 75 8 2 - "BusPUVolt" 75 8 5 - "BusKVVolt" 75 8 3 - "BusAngle" 75 8 2 - - - 5 NO NO NO 0.996563732624054 1.01118695735931 1.02581024169922 -1.03790521621704 1.05000007152557 "Blue=low, Red=High" 0 NO - 7 NO NO NO -1.17415904998779 0.0616339445114136 1.29742693901062 -3.84944224357605 6.4014573097229 "Discrete 20 Red/Blue" 0 NO - -} -DynamicFormatting -DynamicFormattingContextObject -This subdata section contains a list of the display object types which are chosen to be selected. Each line -of the section consists of the following: -DisplayObjectType (WhichFields) (ListOfFields) -DisplayObjectType : The object type of the display object. These are generally the same as -the values seen in the subdata section SelectByCriteriaSetType of -SelectByCriteriaSet object types. The only exception is the string -CaseInfo, which is used for formatting applying to the case information -displays. -(WhichFields) : For display objects that can reference different fields, this sets which of -those fields it should select (e.g. select only Bus Name Fields). The value -may be either ALL or SPECIFIED. -(ListOfFields) : If WhichFields is set to SPECIFIED, then a delimited list of fields follows. -Example: - - // Note: CaseInfo applies to case information displays - CaseInfo "SPECIFIED" BusName - DisplayAreaField "ALL" - DisplayBus - DisplayBusField "SPECIFIED" BusName BusPUVolt BusNum - DisplayCircuitBreaker - DisplaySubstation - DisplaySubstationField "SPECIFIED" SubName SubNum BusNomVolt BGLoadMVR - DisplayTransmissionLine - DisplayTransmissionLineField "ALL" - -184 -LineThicknessLookupMap -LineColorLookupMap -FillColorLookupMap -FontColorLookupMap -FontSizeLookupMap -BlinkColorLookupMap -XoutColorLookupMap -FlowColorLookupMap -SecondaryFlowColorLookupMap -The values of the lookup table for the characteristics that can be modified by the dynamic formatting tool. -The first line contains the two following fields: -fieldname : It is the field that the lookup table is going to look for. -usediscrete : Set to YES or NO. If set to YES, the characteristic values will be discrete, -meaning that the characteristic value will correspond exactly to the one -specified in the table. If set to NO, the characteristic values will be -continuous, which means the characteristic value will be an interpolation -of the high and low closest values specified in the table. -The following lines contain two fields: -fieldvalue : The value for the field. -characteristicvalue : The corresponding characteristic value for such field value. -Example: - - // FieldName UseDiscrete - BusPUVolt YES - // FieldValue Color - 1.02 16711808 - 1.05 8454143 - 1.1 16744703 - -185 -Filter -Condition -Conditions store the conditions of the filter. Each condition is described by one line of text which can -contain up to five fields: -variablename : It is one of the fields for the object_type specified. It may optionally be -followed by a colon and a non-negative integer. If not specified, 0 is -assumed. -Example: on a LINE, 0 = from bus, 1 = to bus -sgLineMW:0 = the MW flow leaving the from bus -sgLineMW:1 = the MW flow leaving the to bus -Note: this value may also be the string "_UseAnotherfilter" which would -then be followed by either meets or notmeets and then the name of -another Filter. -Condition : Possible Values Alternate1 Alternate2 Requires -othervalue -between >< yes -notbetween ~>< yes -equal = == -notequal <> ~= -greaterthan > -lessthan < -greaterthanorequal >= -lessthanorequal <= -about yes -notabout yes -contains -notcontains -startswith -notstartswith -inrange -notinrange -meets -notmeets -isblank -notisblank -value : The value used for comparison. -For fields associated with strings, this must be a string. -For fields associated with real numbers, this must be a number. -For fields associated with integers, this is normally an integer, except -when the Condition is "inrange" or "notinrange". In this case, value is a -comma/dash separated number string. -(othervalue) : If required, the other value used for comparison. For conditions "about" -and "notabout" this is the tolerance with which the value should be equal -or not equal. -(FieldOpt) : Optional string with following meanings. Unspecified means that strings -are case insensitive, use number fields directly (older files may have had -an integer 0 as well). -ABS : strings are case sensitive, take absolute value of field values -(older files may have had an integer 1 as well) -186 -Example: -FILTER (objecttype, filtername, filtertype, prefilter) -{ -BUS "a bus filter" "AND" "no" - - BusNomVolt > 100 - AreaNum inrange "1 – 5 , 7 , 90-95" - ZoneNum between - -BRANCH "a branch filter" "OR" "no" - - BusNomVolt:0 > 100 // Note location 0 means from bus - BusNomVolt:1 > 100 // Note location 1 means to bus - LineMW:0 > 100 1 // Note, final field 1 denotes absolute value - _UseAnotherFilter meets - -} -Gen -BidCurve -BidCurve subdata is used to define a piece-wise linear cost curve (or a bid curve). Each bid point consists -of two real numbers on a single line of text: a MW output and then the respective bid (or marginal cost). -Example: - - // MW Price[$/MWhr] - 100.00 10.6 - 200.00 12.4 - 400.00 15.7 - 500.00 16.0 - -ReactiveCapability -Reactive Capability subdata is used to the reactive capability curve of the generator. Each line of text -consists of three real numbers: a MW output, and then the respective Minimum MVAR and Maximum -MVAR output. -Example: - - // MW MinMVAR MaxMVAR - 100.00 -60.00 60.00 - 200.00 -50.00 50.00 - 400.00 -30.00 20.00 - 500.00 - 5.00 2.00 - -Note: ReactiveCapability object types can also be directly created inside their own DATA section as well. -Two of the key fields of the object are then the bus number and generator ID of the generator to which -the ReactiveCapability point belongs. -187 -GeoDataViewStyle -TotalAreaValueMap -This subdata section is used to define the lookup table for determining the total area size of geographic -data view objects based on the value of a selected field. Two values are entered for each mapping: -FieldValue : Value of the field selected for the Total Area attribute. -TotalArea : The total area size of the object. -Example: - -// FieldValue TotalArea -1.000 0 -4.000 23 -7.000 46 - -RotationRateValueMap -This subdata section is used to define the lookup table for determining the rotation rate of geographic -data view objects based on the value of a selected field. Two values are entered for each mapping: -FieldValue : Value of the field selected for the Rotation Rate attribute. -RotationRate : The rotation rate of the object. Entered in Hz. -Example: - -// FieldValue RotationRate -1.000 0.00 -4.000 0.10 -7.000 0.20 - -RotationAngleValueMap -This subdata section is used to define the lookup table for determining the rotation angle of geographic -data view objects based on the value of a selected field. Two values are entered for each mapping: -FieldValue : Value of the field selected for the Rotation Angle attribute. -RotationAngle : The rotation angle of the object. Entered in degrees. -Example: - -// FieldValue RotationAngle -1.000 -90.0 -4.000 0.0 -7.000 90.0 - -188 -LineThicknessValueMap -This subdata section is used to define the lookup table for determining the thickness of the border line -around geographic data view objects based on the value of a selected field. Two values are entered for -each mapping: -FieldValue : Value of the field selected for the Line Thickness attribute. -LineThickness : The line thickness of the border line around the object. This should be an -integer value. -Example: - -// FieldValue LineThickness -1.000 1 -4.000 2 -7.000 3 - -GlobalContingencyActions -CTGElementAppend -This format is the same as for the Contingency objecttype except that the SolvePowerFlow action is not -allowed. -CTGElement -This format is the same as for the Contingency objecttype except that the SolvePowerFlow action is not -allowed. -Note: GlobalContingencyActionsElement object types can also be directly created inside their own DATA -section as well. -HintDefValues -HintObject -Stores the values for the custom hints. Each line has one value: -FieldDescription : This is a string enclosed in double quotes. The string itself is delimited -by the @ character. The string contains five values: -Name of Field : The name of the field. Special fields that appear on -dialog by default have special names. Otherwise -these are the same as the fieldnames of the AUX file -format (for the "other fields" feature on the dialogs). -Total Digit : Number of total digits for a numeric field. -Decimal Points : Number of decimal points for a numeric field. -Include Suffix : Set to 0 for not including the suffix, and set to 1 to -include it. -Field Preffix : The prefix text. -Example: - - "BusPUVolt@4@1@1@PU Volt =" - "BusAngle@4@1@1@Angle =" - -189 -InjectionGroup -PartPoint -A participation point is used to describe the contents of an injection group. Each participation point lists -six values: -PointType : One of five values describing the type of point. -GEN : a generator -LOAD : a load -SHUNT : a switched shunt -BUS : a bus -INJECTIONGROUP: another injection group -PointBusNum : The bus number of the partpoint if the type is a GEN, LOAD, SHUNT, or -BUS. Value will be blank for an injection group type. Note: bus# values -may be replaced by a string enclosed in double quotes where the string -is the name of the bus followed by an underscore character and then the -nominal voltage of the bus. These values may also be replaced by a -string enclosed in double quotes that represents the label of the bus or a -string representing the label of the generator, load, or switched shunt. -PointID : For GEN, LOAD, or SHUNT type, this is the id for the partpoint. For an -INJECTIONGROUP type, this is the name or label of the injection group. -This is blank for a BUS type. -PointParFac : The participation factor for the point. -ParFacCalcType : How the participation factor is calculated. There are several options -depending on the PointType. -Generators : SPECIFIED, MAX GEN INC, MAX GEN DEC, or MAX -GEN MW -Loads : SPECIFIED or LOAD MW -Shunts : SPECIFIED, MAX SHUNT INC, MAX SHUNT DEC, or -MAX SHUNT MVAR -Bus : SPECIFIED -Injection Groups : SPECIFIED -All PointTypes can also set their participation factor based on a field -associated with the device. To specify this, the tag should be -followed by the variable name of the field: variablename. All -PointTypes can also set their participation factor based on a Model -Expression. To specify this, the tag should be followed -by the name of the Model Expression: ModelExpression. -ParFacNotDynamic : Should the participation factor be recalculated dynamically as the system -changes. -Example: - - "GEN" 1 "1" 1.00 "SPECIFIED" "NO" - "GEN" 4 "1" 104.96 "MAX GEN INC" "NO" - "GEN" 6 "1" 50.32 "MAX GEN DEC" "YES" - "GEN" 7 "1" 600.00 "MAX GEN MW" "NO" - "LOAD" 2 "1" 5.00 "SPECIFIED" "NO" - "LOAD" 6 "1" 200.00 "LOAD MW" "YES" - -Note: PartPoint object types can also be directly created inside their own DATA section as well. One of -the key fields of the PartPoint object is then the name of the injection group to which the participation -point belongs. -190 -Interface -InterfaceElement -A interfaces’s subdata contains a list of the elements in the interface. Each line contains a text -descriptions of the interface element. Note that this text description must be encompassed by quotation -marks. There are eleven kinds of elements allowed in an interface. Please note that the direction -specified in the monitoring elements is important. -"BRANCH num1 num2 ckt" -: Monitor the MW flow on the branch starting from bus num1 going to -bus num2 with circuit ckt. (order of bus numbers defines the direction) -"AREA num1 num2" : Monitor the sum of the AC branches that connect area1 and area2. -"ZONE num1 num2" : Monitor the sum of the AC branches that connect zone1 and zone2. -"BRANCHOPEN num1 num2 ckt" -: When monitoring the elements in this interface, monitor them under the -contingency of opening this branch. -"BRANCHCLOSE num1 num2 ckt" -: When monitoring the elements in this interface, monitor them under the -contingency of closing this branch. -"DCLINE num1 num2 ckt" -: Monitor the flow on a DC line. -"INJECTIONGROUP 'name'" -: Monitor the net injection from an injection group (generation contributes -as a positive injection, loads as negative). -"GEN num1 id" : Monitor the net injection from a generator (output is positive injection) -"LOAD num1 id" : Monitor the net injection from a load (output is negative injection). -"MSLINE num1 num2 ckt" -: Monitor the MW flow on the multi-section line starting from bus num1 -going to bus num2 with circuit ckt. -"INTERFACE 'name' " : Monitor the MW flow on the interface given by name. -"GENOPEN num1 id" : When monitoring the elements in this interface, monitor them under the -contingency of opening this generator. -"LOADOPEN num1 id": When monitoring the elements in this interface, monitor them under the -contingency of opening this load. -Note: bus# values may be replaced by a string enclosed in single quotes where the string is the name of -the bus followed by an underscore character and then the nominal voltage of the bus. Labels may also be -use as follows. -• bus# values for all element types may be replaced by a string enclosed in single quotes where the -string is the label of the bus. -• for GEN or LOAD elements, the section num1 id may be replaced by the device’s label. -• For MSLINE, DCLINE, or BRANCH elements, the num1 num2 ckt section may be replaced by the -device’s label. -For the interface element type BRANCH num1 num2 ckt and DCLINE num1 num2 ckt, an optional -field can also be written specifying whether the flow should be measured at the far end. This field is -either YES or NO. -191 -Example: - -Note: InterfaceElement object types can also be directly created inside their own DATA section as well. -One of the key fields of the InterfaceElement object is then the name of the interface to which the -interface element belongs. -KMLExportFormat -DataBlockDescription -This subdata section is used to describe the objects and fields that should be saved to a KML file. Same -format as for the AUXFileExportFormatData subdata section. -LimitSet -LimitCost -LimitCost records describe the piece-wise unenforceable constraint cost records for use by unenforceable -line/interface limits in the OPF or SCOPF. Each row contains two values -PercentLimit : Percent of the transmission line limit. -Cost : Cost used at this line loading percentage value. -Example: - - //Percent Cost [$/MWhr] - 100.00 50.00 - 105.00 100.00 - 110.00 500.00 - -Load -BidCurve -BidCurve subdata is used to define a piece-wise linear benefit curve (or a bid curve). Each bid point -consists of two real numbers on a single line of text: a MW output and then the respective bid (or -marginal cost). These costs must be increasing for loads. -Example: - - // MW Price[$/MWhr] - 100.00 16.0 - 200.00 15.7 - 400.00 12.4 - 500.00 10.6 - -192 -LPVariable -LPVariableCostSegment -Stores the cost segments for the LP variables. Each line contains four values: -Cost (Up) : Cost associated with increasing the LP variable. -Minimum value : Minimum limit of the LP variable. -Maximum value : Maximum limit of the LP variable. -Artificial : Whether the cost segment is artificial or not. -Example: - - //Cost(Up) Minimum Maximum Artificial - -20000.0000 -10000000000.5801 -0.6000 YES - 16.2343 -0.6000 0.0000 NO - 16.5526 0.0000 0.6000 NO - 16.8708 0.6000 1.2000 NO - 17.1890 1.2000 1.8000 NO - 17.5073 1.8000 2.4000 NO - 20000.0000 2.4000 9999999999.4199 YES - -ModelCondition -Condition -ModelConditions are the combination of an object and a Filter. They are used to return when the -particular object meets the filter specified. As a result, the subdata section here mostly identical to the -Condition subdata section of a Filter. See the description there. There is one exception however with the -FieldOpt which has additional strings -(FieldOpt) : Optional string with following meanings: -ABS : strings are case sensitive, take ABS of field values (older files -may have had an integer 1 as well) -REF : Means that the variablename is evaluated in the contingency -reference state -ABSREF : means that both ABS and REF are being used -Nothing specified means that strings are case insensitive, use number -fields directly and values are evaluated as normal (older files may have -had an integer 0 as well) -ModelExpression -LookupTable -LookupTables are used inside Model Expressions sometimes. These lookup table represent either one or -two dimensional tables. If the first string in the SUBDATA section is "x1x2", this signals that it is a two -dimensional lookup table. From that point on it will read the first row as "x2" lookup points, and the first -column in the remainder of the rows as the x1 lookup values. -193 -Example: -MODELEXPRESSION (CustomExpression,ObjectType,CustomExpressionStyle, -CustomExpressionString,WhoAmI,VariableName,WhoAmI:1,VariableName:1) -{ -// The following demonstrated a one dimensional lookup table -22.0000, "oneD", "Lookup", "", "Gen11", -"Gen11GenMW", "", "" - - // because it does not start with the string x1x2 this will - // represent a one dimensional lookup table - x1 value - 0.000000 1.000000 - 11.000000 22.000000 - 111.000000 222.000000 - -0.0000, "twod", "Lookup", "", -"Gen11", -"Gen11GenMW", -"Gen61", -"Gen61GenMW" - - // because this starts with x1x2 this represent a two dimensional - // lookup table. The first column represents lookup values for x1. - // The first row represents lookup values for x2 - x1x2 0.100000 0.300000 // these are lookup heading for x2 - 0.000000 1.000000 3.000000 - 11.000000 22.000000 33.000000 - 111.000000 222.000000 333.000000 - -} -ModelFilter -ModelCondition -A Model Filter’s subdata contains a list of each ModelCondition in the filter. Because a list of Model -Conditions is stored within Simulator, this subdata section only requires the name of each -ModelCondition on each line and whether or not the condition is using the NOT operator as part of the -Model Filter. -Example: - -// ModelConditionName NotCondition - "Name of First Model Condition" "NO" - "Name of Second Model Condition" "NO" - "Name of Third Model Condition" "NO" - -194 -MTDCRecord -An example of the entire multi-terminal DC transmission line record is given at the end of this record description. Each of -the SUBDATA sections is discussed first. -MTDCBus -For this SUBDTA section, each DC Bus is described on a single line of text with exactly 8 fields specified. -DCBusNum : The number of the DC Bus. Note DC bus numbers are independent AC -bus numbers. -DCBusName : The name of the DC bus enclosed in quotes. -ACTerminalBus : The AC terminal to which this DC bus is connected (via a -MTDCConverter). If the DC bus is not connected to any AC buses, then -specify as zero. You may also specify this as a string enclosed in double -quotes with the bus name followed by an underscore character, following -by the nominal voltage of the bus. -DCResistanceToground -: The resistance of the DC bus to ground. Not used by Simulator. -DCBusVoltage : The DC bus voltage in kV. -DCArea : The area that this DC bus belongs to. -DCZone : The zone that this DC bus belongs to. -DCOwner : The owner that this DC bus belongs to. -MTDCBus object types can also be directly created inside their own DATA section as well. One of the key -fields of the object is then the number of the MTDCRecord to which the MTDCBus belongs. -MTDCConverter -For this SUBDTA section, each AC/DC Converter is described by exactly 24 field which may be spread -across several lines of text. Simulator will keep reading lines of text until it finds 24 fields. All text to the -right of the 24th field (on the same line of text) will be ignored. The 24 fields are listed in the following -order: -BusNum : AC terminal bus number. -MTDCNBridges : Number of bridges for the converter. -MTDCConvEBas : Converter AC base voltage. -MTDCConvAngMxMn : Converter firing angle. -MTDCConvAngMxMn:1 -: Converter firing angle max. -MTDCConvAngMxMn:2 -: Converter firing angle min. -MTDCConvComm : Converter commutating resistance. -MTDCConvComm:1 : Converter commutating reactance. -MTDCConvXFRat : Converter transformer ratio. -MTDCFixedACTap : Fixed AC tap. -MTDCConvTapVals : Converter tap. -MTDCConvTapVals:1 : Converter tap max. -MTDCConvTapVals:2 : Converter tap min. -MTDCConvTapVals:3 : Converter tap step size. -MTDCConvSetVL : Converter setpoint value (current or power). -MTDCConvDCPF : Converter DC participation factor. -MTDCConvMarg : Converter margin (power or current). -MTDCConvType : Converter type. -MTDCMaxConvCurrent -: Converter Current Rating. -MTDCConvStatus : Converter Status. -MTDCConvSchedVolt : Converter scheduled DC voltage. -195 -MTDCConvIDC : Converter DC current. -MTDCConvPQ : Converter real power. -MTDCConvPQ:1 : Converter reactive power. -MTDCConverter object types can also be directly created inside their own DATA section as well. One of -the key fields of the object is then the number of the MTDCRecord to which the MTDCConverter belongs. -MTDCTransmissionLine -For this SUBDATA section, each DC Transmission Line is described on a single line of text with exactly 5 -fields specified: -DCFromBusNum : From DC Bus Number. -DCToBusNum : To DC Bus Number. -CKTID : The DC Circuit ID. -Resistance : Resistance of the DC Line in Ohms. -Inductance : Inductance of the DC Line in mHenries (Not used by Simulator). -Example: -MTDCRECORD (Num,Mode,ControlBus) -{ -//-------------------------------------------------------------------------- -// The first Multi-Terminal DC Transmission Line Record -//-------------------------------------------------------------------------- -1 "Current" "SYLMAR3 (26098)" - - //------------------------------------------------------------------- - // DC Bus data must appear on a single line of text - // The data consists of exactly 8 values - // DC Bus Num, DC Bus Name, AC Terminal Bus, DC Resistance to ground, - // DC Bus Voltage, DC Bus Area, DC Bus Zone, DC Bus Owner - 3 "CELILO3P" 0 9999.00 497.92 40 404 1 - 4 "SYLMAR3P" 0 9999.00 439.02 26 404 1 - 7 "DC7" 41311 9999.00 497.93 40 404 1 - 8 "DC8" 41313 9999.00 497.94 40 404 1 - 9 "DC9" 26097 9999.00 439.01 26 404 1 - 10 "DC10" 26098 9999.00 439.00 26 404 1 - - - //------------------------------------------------------------------- - // convert subdata keeps reading lines of text until it has found -// values specified for 24 fields. This can span any number of lines -// any values to the right of the 24th field found will be ignored -// The next converter will continue on the next line. - //------------------------------------------------------------------- - 41311 2 525.00 20.25 24.00 5.00 0.0000 16.3100 - 0.391048 1.050000 1.000000 1.225000 0.950000 0.012500 - 1100.0000 1650.0000 0.0000 "Rect" 1650.0000 "Closed" - 497.931 1100.0000 547.7241 295.3274 - 41313 4 232.50 15.36 17.50 5.00 0.0000 7.5130 - 0.457634 1.008700 1.030000 1.150000 0.990000 0.010000 - 2000.0000 2160.0000 0.1550 "Rect" 2160.0000 "Closed" - 497.940 2000.0000 995.8800 561.8186 - 26097 2 230.00 20.90 24.00 5.00 0.0000 16.3100 - 0.892609 1.000000 1.100000 1.225000 0.950000 0.012500 - -1100.0000 1650.0000 "" "Inv" 1650.0000 "Closed" - 439.009 1100.0000 -482.9099 274.5227 - 26098 4 232.00 17.51 20.00 5.00 0.0000 7.5130 - 0.458621 1.008700 1.100000 1.120000 0.960000 0.010000 - 439.0000 2160.0000 "" "Inv" 2160.0000 "Closed" - 439.000 1999.9999 -878.0000 544.2775 - - - //------------------------------------------------------------------- - // DC Transmission Segment information appears on a single line of - // text. It consists of exactly 5 value -196 - // From DCBus, To DCBus, Circuit ID, Line Resistance, Line Inductance - //------------------------------------------------------------------- - 3 4 "1" 19.0000 1300.0000 - 7 3 "1" 0.0100 0.0000 - 8 3 "1" 0.0100 0.0000 - 9 4 "1" 0.0100 0.0000 - 10 4 "1" 0.0100 0.0000 - -//-------------------------------------------------------------------------- -// A second Multi-Terminal DC Transmission Line Record -//-------------------------------------------------------------------------- -2 "Current" "SYLMAR4 (26100)" - - 5 "CELILO4P" 0 9999.00 497.92 40 404 1 - 6 "SYLMAR4P" 0 9999.00 439.02 26 404 1 - 11 "DC11" 41312 9999.00 497.93 40 404 1 - 12 "DC12" 41314 9999.00 497.94 40 404 1 - 13 "DC13" 26099 9999.00 439.01 26 404 1 - 14 "DC14" 26100 9999.00 439.00 26 404 1 - - - 41312 2 525.00 20.26 24.00 5.00 0.0000 16.3100 - 0.391048 1.050000 1.000000 1.225000 0.950000 0.012500 - 1100.0000 1650.0000 0.0000 "Rect" 1650.0000 "Closed" - 497.931 1100.0000 547.7241 295.3969 - 41314 4 232.50 15.45 17.50 5.00 0.0000 7.5130 - 0.457634 1.008700 1.030000 1.150000 0.990000 0.010000 - 2000.0000 2160.0000 0.1550 "Rect" 2160.0000 "Closed" - 497.940 2000.0000 995.8800 562.9448 - 26099 2 230.00 20.90 24.00 5.00 0.0000 16.3100 - 0.892609 1.000000 1.100000 1.225000 0.950000 0.012500 - -1100.0000 1650.0000 "" "Inv" 1650.0000 "Closed" - 439.009 1100.0000 -482.9099 274.5227 - 26100 4 232.00 17.51 20.00 5.00 0.0000 7.5130 - 0.458621 1.008700 1.100000 1.120000 0.960000 0.010000 - 439.0000 2160.0000 "" "Inv" 2160.0000 "Closed" - 439.000 1999.9999 -878.0000 544.2775 - - - 5 6 "1" 19.0000 1300.0000 - 11 5 "1" 0.0100 0.0000 - 12 5 "1" 0.0100 0.0000 - 13 6 "1" 0.0100 0.0000 - 14 6 "1" 0.0100 0.0000 - -} -MTDCTransmissionLine object types can also be directly created inside their own DATA section as well. -One of the key fields of the object is then the number of the MTDCRecord to which the -MTDCTransmissionLine belongs. -MultiSectionLine -Bus -A multi section line’s subdata contains a list of each dummy bus, starting with the one connected to the -From Bus of the MultiSectionLine and proceeding in order to the bus connected to the To Bus of the Line. -Note: bus# values may be replaced by a string enclosed in double quotes where the string is the name of -the bus followed by an underscore character and then the nominal voltage of the bus, or the string may -represent the label of the bus. -197 -Example: -//------------------------------------------------------------------------ -// The following describes a multi-section line that connnects bus -// 2 - 1 - 5 - 6 - 3 -//------------------------------------------------------------------------ -MultiSectionLine (BusNum, BusName, BusNum:1, BusName:1, - LineCircuit, MSLineNSections, MSLineStatus) -{ -2 "Two" 3 "Three" "&1" 2 "Closed" - - 1 - 5 - 6 - -} -BusRenumber -This subdata section allows renumbering of the dummy buses. The entries in the subdata section must be -the new bus number that should be assigned to each dummy bus followed by the name of the new bus. -The entries can be either space or comma delimited. The bus number must be specified, but the name is -optional. If the name is not included and a new bus needs to be created, the name will be the same as -the number. If an incorrect number of dummy buses is entered for a multi-section line, none of the -dummy buses will be updated for that line. If a dummy bus number is specified that matches an existing -bus that is another dummy bus, the other dummy bus will be assigned to a new bus number and the -current dummy bus will be assigned to the number specified in the data. -Example: -MultiSectionLine (BusNum, BusNum:1, LineCircuit) -{ -1 2 "1" - - 3 "Bus 3" - 4 "Bus 4" - 5 "Bus 5" - -22 33 "1" - - 14 "Bus 14" - 15 "Bus 15" - -} -Nomogram -InterfaceElementA -InterfaceElementB -InterfaceElementA values represent the interface elements for the first interface of the nomogram. -InterfaceElementB values represent the interface elements for the second interface of the nomogram. The -format of these SUBDATA sections is identical to the format of the InterfaceElement SUBDATA section of a -normal Interface. -198 -NomogramBreakPoint -This subdata section contains a list of the vertex points on the nomogram limit curve. -Example: - -// LimA LimB - -100 -20 - -100 100 - 80 50 - 60 -10 - -NomogramInterface -InterfaceElement -This follows the same convention as the InterfaceElement SUBDATA section described with the Interface -objecttype. -Owner -Bus -This subdata section contains a list of the buses which are owned by this owner. Each line of text contains -the bus number. As an alternative to specifying the bus number, a string enclosed in double quotes may -be used where the string represents the name of the bus followed by an underscore character and then -the nominal voltage of the bus, or the string may represent the label of the bus. -Example: - - 1 - 35 - 65 - -Load -This subdata section contains a list of the loads which are owned by this owner. Each line of text contains -the bus number followed by the load id. As an alternative to specifying the bus number, a string enclosed -in double quotes may be used where the string represents the name of the bus followed by an -underscore character and then the nominal voltage of the bus, or the string may represent the label of the -bus. Also, instead of specifying the bus and load id, the label of the load enclosed in double quotes may -be used. -Example: - - 5 1 // shows ownership of the load at bus 5 with id of 1 - 423 1 - -Gen -This subdata section contains a list of the generators which are owned by this owner and the fraction of -ownership. Each line of text contains the bus number, followed by the gen id, followed by an integer -showing the fraction of ownership. As an alternative to specifying the bus number, a string enclosed in -double quotes may be used where the string represents the name of the bus followed by an underscore -character and then the nominal voltage of the bus, or the string may represent the label of the bus. Also, -instead of specifying the bus and generator id, the label of the generator enclosed in double quotes may -be used. -199 -Example: - - 78 1 50 // shows 50% ownership of generator at bus 78 with id of 1 - 23 3 70 - -Branch -This subdata section contains a list of the branches which are owned by this owner and the fraction of -ownership. Each line of text contains the from bus number, followed by the to bus number, followed by -the circuit id, followed by an integer showing the fraction of ownership. As an alternative to specifying -the bus numbers, strings enclosed in double quotes may be used where the string represents the name of -the bus followed by an underscore character and then the nominal voltage of the bus, or the string may -represent the label of the bus. Also instead of specifying the two numbers and a circuit id, the label of the -branch enclosed in double quotes may be used. -Example: - - 6 10 1 50 // shows 50% ownership of line from bus 6 to 10, circuit 1 - -PostPowerFlowActions -CTGElementAppend -This format is the same as for the Contingency objecttype except that Abort, ContingencyBlock, and -SolvePowerFlow actions are not allowed. -CTGElement -This format is the same as for the Contingency objecttype except that Abort, ContingencyBlock, and -SolvePowerFlow actions are not allowed. PostPowerFlowActionsElement object types can also be directly -created inside their own DATA section as well. -PWCaseInformation -PWCaseHeader -This subdata section contains the Case Description in free-formatted text. Note: as it is read back into -Simulator all spaces from the start of each line are removed. -PWFormOptions -PieSizeColorOptions -There can actually be several PieSizeColorOptions subdata sections for each PWFormOptions object. The -first line of each subdata section, the first line of text consist of exactly four values -ObjectName : The objectname of the type of object these settings apply to. Will be -either be BRANCH or INTERFACE. -FieldName : The fieldname for the pie charts that these settings apply to. -UseDiscrete : Set to YES to use a discrete mapping of colors and size scalars instead of -interpolating for intermediate values. -UseOtherSettings : Set to YES to default these settings to the BRANCH MVA values for -BRANCH object. This allows you to apply the same settings to all pie -charts. -After this first line of text, if the UseOtherSettings Value is NO, then another line of text will contain -exactly three values: -ShowValue : This is the percentage at which the value should be drawn on the pie -chart. -200 -NormalSize : This is the scalar size multiplier which should be used for pie charts below -the lowest percentage specified in the lookup table. -NormalColor : This is the color which should be used for pie charts below the lowest -percentage specified in the lookup table. -Finally the remainder of the subdata section will contain a lookup table by percentage of scalar and color -values. This lookup table will consist of consecutive lines of text with exactly three values -Percentage : This is the percentage at which the follow scalar and color should be -applied. -Scalar : A scalar (multiplier) on the size of the pie charts. -Color : A color for the pie charts. -Example: - - // ObjectName FieldName UseDiscrete UseOtherSettings - Branch MVA YES NO - // ShowValue NormalSize NormalColor - 80.0000 1.0000 16776960 - // Percentage Scalar Color - 80.0000 1.5000 33023 - 100.0000 2.0000 255 - - - // ObjectName FieldName UseDiscrete UseOtherSettings - Branch MW YES YES - -PWLPOPFCTGViol -OPFControlSense -OPFBusSenseP -OPFBusSenseQ -This stores the control sensitivities for each contingency violation during OPF/SCOPF analysis. Each line -contains one value: -Sensitivity : The value of the sensitivity with respect to each control in -OPFControlSense or with respect to each bus in OPFBusSenseP and -OPFBusSenseQ. -Example: - -// Value - 1.000441679 - 2.447185E-7 - -1.1109307E-6 - 1.6427327E-7 - 0 - -PWLPTabRow -LPBasisMatrix -This subdata section stores the basis matrix associated with the final LP OPF solution. Each line contains -two values: -Variable : The basic variable. -Value : The sensitivity of the constraint to the basic variable. -201 -Example: - -// Var Value - 1 1.00000 - 2 1.00000 - 5 1.00000 - 6 1.00000 - -PWPVResultListContainer -PWPVResultObject -This subdata section contains the results of a particular PV Curve scenario. The data consists of two -general sections: the first three rows of text contain the "independent axis" of the PV Curve. The first row -starts with the string INDNOM and is followed by a list of numbers representing the nominal shift, the -second row starts with INDEXP and is followed by the export shift, and the third row starts with INDIMP -and is followed by the import shift. Following after these rows is a list of all the tracked quantities. Each -tracked quantity row consists of three parts which are separated by the strings ?f= and &v= . The first -part of the string represents a description of the power system object being tracked, the second part -represents the field variable name being tracked, and the third contains a list of all the values at the -various shift levels. -Example: - - INDNOM 0.00 500.00 1000.00 1500.00 1750.00 1875.00 1975.00 - INDEXP 0.00 500.00 1000.00 1500.00 1750.00 1875.00 1975.00 - INDIMP 0.00 -417.23 -701.58 -890.58 -952.60 -975.35 -990.43 - Bus '3'?f=BusPUVolt&v= 0.993 0.983 0.964 0.939 0.926 0.919 0.914 - Bus '5'?f=BusPUVolt&v= 1.007 1.000 0.982 0.956 0.940 0.932 0.926 - Gen '4' '1'?f=GenMVR&v= 19.99 245.27 523.62 831.13 986.84 1060.6 1118.7 - Gen '6' '1'?f=GenMVR&v= -6.59 -120.84 -131.37 -39.53 48.35 103.8 154.5 - -LimitViol -This subdata section contains the limit violations of a particular PV Curve scenario. This subdata section -would only exist if using the option to monitor limit violations with the PV tool. Each row consists of an -identifier, either VLOW or VHIGH, to indicate the type of limit violation followed by the bus identifier -based on the key field identifier chosen. The bus can be identified by number, name and nominal kV -combination, or label. The bus identifier is followed by the limit in use to identify a voltage violation and -this is followed by the voltage at the bus. -Example: - - VLOW 3 1.00000 0.99017 - VLOW 5 1.00000 0.98245 - -PVBusInadequateVoltages -This subdata section contains a list of buses that are considered to have inadequate voltages at each -transfer level for a particular PV Curve scenario. This subdata section would only exist if using the option -to store inadequate voltages. The data consists of two general sections: the first row starts with the string -INDNOM and is followed by a list of numbers representing the nominal shift. The second and subsequent -rows list the buses and inadequate voltages for any bus that has an inadequate voltage at any transfer -level. Each row starts with the bus identifier followed by the voltages at that bus at the corresponding -shift levels. If a voltage is not inadequate at a particular transfer level, a blank entry will appear instead of -a voltage value. The bus identifier is based on the key field identifier chosen and can be number, name -and nominal kV combination, or label. -202 -Example: - - // INDNOM ShiftLevel1 ShiftLevel2 ... - // BUS Voltage1 Voltage2 ... - INDNOM 0.000 100.000 200.000 300.000 400.000 500.000 - "Bus '3'" 0.99269 0.99278 0.99282 0.99280 0.99273 0.99262 - "Bus '4'" "" 1.00000 "" "" "" - -PWQVResultListContainer -PWPVResultObject -This subdata section contains the results of a particular QV Curve scenario. These results will exist when -tracking quantities with the QV curve tool. The data consists of two general sections: the first three rows -of text contain the "independent axis" of the QV Curve. The first three rows start with the strings -INDNOM, INDEXP, and INDIMP and are followed by a list of numbers representing the setpoint voltage -representing the V of the QV curve. Following after these rows is a list of all the tracked quantities. Each -tracked quantity row consists of three parts which are separated by the strings ?f= and &v= . The first -part of the string represents a description of the power system object being tracked, the second part -represents the field variable name being tracked, and the third contains a list of all the values at the -various setpoint voltage levels. -Example: - - INDNOM 1.100 1.093 1.083 1.073 1.063 - INDEXP 1.100 1.093 1.083 1.073 1.063 - INDIMP 1.100 1.093 1.083 1.073 1.063 - Bus '1'?f=BusPUVolt&v=1.05000 1.05000 1.05000 1.05000 1.05000 - Bus '1'?f=BusKVVolt&v=144.89999 144.89999 144.89999 144.89999 144.89999 -QVCurve -QVPoints -This subdata section contains a list of the QV Curve points calculated for the respect QVCurve. Each line -consists of exactly six values: -PerUnitVoltage : The per unit voltage of the bus for a QV point. -FictitiousMvar : The amount of Mvar injection from the fictitious generator at this QV -point. -ShuntDeviceMvar : The Mvar injection from any switched shunts at the bus. -TotalMvar : The total Mvar injection from switched shunts and the fictitious -generator. -ReservesMvar : Total amount of Mvar reserves available at the bus. -ReservesTotalMvar : Total Mvar injection from the switched shunts, fictitious generator, and -available reserves. -203 -Example: -QVCURVE (BusNum,CaseName,qv_VQ0,qv_Q0,qv_Vmax,qv_QVmax,qv_VQmin,qv_Qmin, - qv_Vmin,qv_QVmin,Qinj_Vmax,Qinj_0,Qinj_min,Qinj_Vmin) -{ -5 "BASECASE" 0.880 0.000 1.100 312.490 0.480 -221.072 - 0.180 -86.334 191.490 -77.373 -244.075 -89.562 - - // NOTE: This bus has a constant impedance - // switched shunt value of -100 Mvar at it. - //V(PU), Q(MVR), Q_shunt(MVR), Q_tot(MVR), Q_res(MVR), Q_tot_res(MVR) - 1.1000, 312.4898, -121.0000, 191.4898, 0.0000, 191.4898 - 0.9800, 124.6619, -95.9656, 28.6963, 0.0000, 28.6963 - 0.7800, -96.6202, -60.7808, -157.4010, 0.0000, -157.4010 - 0.5800, -206.9895, -33.5960, -240.5855, 0.0000, -240.5855 - 0.3800, -207.4962, -14.4113, -221.9075, 0.0000, -221.9075 - -} -QVCurve_Options -Sim_Solution_Options -This subdata section contains solution options that will be used when running QV Curves. See -explanation under the CTG_Options object type for more information. -RemedialAction -CTGElementAppend -This format is the same as for the Contingency objecttype except that the SolvePowerFlow action is not -allowed. -CTGElement -This format is the same as for the Contingency objecttype except that the SolvePowerFlow action is not -allowed. RemedialActionElement object types can also be directly created inside their own DATA section -as well. -SelectByCriteriaSet -SelectByCriteriaSetType -This subdata section contains a list of the display object types which are chosen to be selected. Each line -of the section consists of the following: -DisplayObjectType : The object type of the display object. -(FilterName) : This field is optional, but must be given if either of the following fields is -given. See the Using Filters in Script Commands section for more -information on specifying the filtername. -(WhichFields) : For display objects that can reference different fields, this sets which of -those fields it should select (e.g. select only Bus Name Fields). The value -may be either ALL or SPECIFIED. -(ListOfFields) : If WhichFields is set to SPECIFIED, then a delimited list of fields follows. -204 -Example: - - DisplayAreaField "" "ALL" - DisplayBus "" - DisplayBusField "Name of Bus Filter" "SPECIFIED" BusName BusPUVolt BusNum - DisplayCircuitBreaker "" - DisplaySubstation "" - DisplaySubstationField "" "SPECIFIED" SubName SubNum BusNomVolt BGLoadMVR - DisplayTransmissionLine "" - DisplayTransmissionLineField "" "ALL" - -Area -This subdata section contains a list of areas which were chosen to be selected. Each line of the section -consists of either the number or the name. When generated automatically by PowerWorld we also -include the other identifier as a comment. -Example: - - 18 // NEVADA - 22 // SANDIEGO - 30 // PG AND E - 52 // AQUILA - -Zone -This subdata section contains a list of zones which were chosen to be selected. Each line of the section -consists of either the number or the name. When generated automatically by PowerWorld we also -include the other identifier as a comment. -Example: - - 680 // ID SOLUT - 682 // WY NE IN - -ScreenLayer -This subdata section contains a list of screen layers which were chosen to be selected. Each line of the -section consists of either the name. -Example: - - "Border" - "Transmission Line Objects" - -ShapefileExportDescription -This object uses the same subdata sections as SelectByCriteriaSet. The only distinction is that only buses and lines can -be exported. -StudyMWTransactions -ImportExportBidCurve -This subdata section contains the piecewise linear transactions cost curves for areas involved in a MW -transaction. Costs are only for areas that are not on OPF control. Curves must be monotonically -increasing. Each line corresponds to a point in the cost curve, and it has two values: -205 -MW : The MW value. Use negative values for imports (purchase) and positive -values for exports (sales) -Price : The price in $/MWh. -Two different cost curves can be entered for each transaction. One is for the cost curve relative to the -Export Area specified in the transaction, and the other is for the cost curve relative to the Import Area -specified in the transaction. The first curve that is listed in the SUBDATA section is the curve relative to -the Export Area. The curve relative to the Import Area is denoted by the keyword REVERSE. Either or both -of the curves can be blank. -Example: - - //MW Price[$/MWh] - -20.00 5.00 - -10.00 10.00 - 0.00 15.00 - 10.00 20.00 - 20.00 45.00 - 30.00 70.00 - REVERSE - -25.00 7.00 - -15.00 12.00 - 5.00 17.00 - 15.00 22.00 - 25.00 47.00 - 35.00 72.00 - -SuperArea -SuperAreaArea -This subdata section contains a list of areas within each super area. Each line of text contains two values, -the area number followed by a participation factor for the area that can be optionally used. -Example: - - 1 48.9 - 5 34.2 - 25 11.2 - -TSSchedule -SchedPoint -This section stores the schedule time points used in Time Step Simulation. Each line contains seven -values: -Date : The date of the point. -Hour : The hour of the point. -Pointtype : An integer specifying the point type. -0 : Numeric -1 : Boolean (Yes/No, Closed/Open) -2 : Text -Numeric Value : The numeric value if point type is Numeric. Otherwise it is just zero. -Boolean Value : The boolean value if point type is Boolean. Otherwise it is just false. -Text value : The text value if point type is Text. Otherwise it is just an empty string. -Audiofilename : The audio filename associated to the point. If none, it is just an empty -string. -206 -Example: - - //Date Hour PointType NValue BValue TValue AValue - 5/8/2006 0 1.00 NO - 5/8/2006 6:00:00 AM 0 1.10 NO - 5/8/2006 12:00:00 PM 0 1.25 NO - -UserDefinedDataGrid -ColumnInfo -This follows the same convention as the ColumnInfo SUBDATA section described with the DataGrid -objecttype. -207 -SCRIPT Section for Display Auxiliary File -The syntax for script commands in Display Auxiliary Files is the same as for Auxiliary Files. See the SCRIPT Section and its -sub-sections for details on the proper syntax. Any differences for display auxiliary files will be discussed below. -AXD Actions -The following script commands are available for AXD files -AutoInsertBorders; -Use this action to insert borders according to the settings in the AutoInsertBordersOptions object -AutoInsertBuses(LocationSource, MapProjection, AutoInsertBranches, InsertIfNotAlreadyShown, -"filename", InsertSelected); -Use this action to insert buses based on specified location data. -LocationSource : "Bus", "Substation" or "File" -MapProjection : "Simple Conic", "Mercator", "Alaska" or "xy" -AutoInsertBranches : YES to insert transmission lines when finished, NO not to -InsertOnlyIfNotAlreadyShown -: YES if only buses that are not already shown should be inserted, NO to -insert all buses. -"filename" : (optional) path to location source file (if LocationSource is "File") -FileCoordinates is no longer used. It should not be included when creating new auxiliary files, but if it is -included in existing auxiliary files, it will be read and ignored. If MapProjection is set to “xy” and using a -file, the file coordinates are assumed to be in x,y, otherwise, file coordinates are assumed to be lon, lat. -FileCoordinates : (optional) format of coordinates in file "xy" or "lonlat" (if LocationSource -is "File") -InsertSelected : (optional) Default is NO. YES is only insert buses that are selected -(SELECTED = YES). -This command inserts bus display objects using the latitude and longitude stored with each bus, -interpreted through the Mercator map projection. It inserts only the buses that have their -Selected field set to YES and also adds the connecting branches, regardless of whether the buses -are already displayed. -AutoInsertBuses("Bus", "Mercator", YES, NO, , YES); -AutoInsertGens(MinkV, InsertTextFields); -Use this action to insert generators. -MinkV : Minimum kV level to insert -InsertTextFields : (optional) insert text fields (default=YES) -This command inserts generator display objects for all generators connected to buses with a -nominal voltage of 140 kV or higher. It places only the generator symbols without any -accompanying text fields such as MW or Mvar labels. -AutoInsertGens(140, NO); -AutoInsertInterfaces(InsertPieCharts, PieChartSize); -Use this action to insert line flow objects. -InsertPieCharts : (optional) Insert pie charts as well (default=YES) -PieChartSize : (optional) default size of interface pie charts (default=50.0) -208 -This command inserts interface display objects along with pie charts that visualize flow or -loading. The pie charts are included (YES) and are set to a default size of 50.0 screen units. -AutoInsertInterfaces(YES, 50.0); -AutoInsertLineFlowObjects(MinkV, InsertOnlyIfNotAlreadyShown, LineLocation, Size, FieldDigits, -FieldDecimals, TextPosition, ShowMW, ShowMvar, ShowMVA, ShowUnits, ShowComplex); -Use this action to insert line flow objects. -MinkV : Minimum kV level to insert (default=0) -InsertOnlyIfNotAlreadyShown: (optional) if existing line flow objects are ignored (default=YES) -LineLocation : (optional) where to insert flow objects (default=0) -0 : middle -1 : 10%/90% -2 : after stubs -Size : (optional) size (default=5.0) -FieldDigits : (optional) total digits in field (default=6) -FieldDecimals : (optional) digits to the right of the decimal (default=2) -TextPosition : (optional) position of fields relative to flow object (default=YES) -YES : above -NO : below -ShowMW : (optional) show MW field (default=YES) -ShowMvar : (optional) show Mvar field (default=YES) -ShowMVA : (optional) show MVA field (default=YES) -ShowSuffix : (optional) show field units (default=YES) -ShowComplex : (optional) show complex form (MW+jMvar) (default=NO) -This command inserts line flow arrow objects for transmission lines with a nominal voltage of 100 -kV or higher. It inserts arrows in the middle of the line (0), with a size of 5.0, showing MW and -Mvar values (but not MVA), with units displayed (e.g., "MW") and text positioned above the -arrow. It does not use complex format (MW + jMvar), and skips lines already shown. -AutoInsertLineFlowObjects(100, YES, 0, 5.0, 6, 2, YES, YES, YES, NO, -YES, NO); -AutoInsertLineFlowPieCharts(MinkV, InsertOnlyIfNotAlreadyShown, InsertMSLines, Size); -Use this action to insert line flow pie charts. -MinkV : Minimum kV level to insert (default=0) -InsertOnlyIfNotAlreadyShown -: (optional) if existing line flow objects are ignored (default=YES) -InsertMSLines : (optional) insert pie charts for Multi-Section Lines (default=YES) -Size : (optional) size (default=5.0) -This command inserts line flow pie charts for all transmission lines with a nominal voltage of 50 -kV or higher. It skips lines that already have pie charts (YES), includes Multi-Section Lines (YES), -and sets the size of each pie chart to 5.0 screen units. -AutoInsertLineFlowPieCharts(50, YES, YES, 5.0); -AutoInsertLines(MinkV, InsertTextFields, InsertEquivObjects, InsertZBRPieCharts, InsertMSLines, -ZBRImpedance, NoStubsZBRs, SingleCBZRs); -Use this action to insert lines. -MinkV : (optional) minimum kV level to insert (default=0) -InsertTextFields : (optional) insert text fields (default=YES) -InsertEquivObjects : (optional) insert Equivalenced Objects (default=YES) -InsertZBRPieCharts : (optional) insert pie charts for lines with no limit and bus ties -(default=NO) -209 -InsertMSLines : (optional) insert MultiSecton Lines (default=YES) -ZBRImpedance : (optional) maximum PU impedance for bus ties (default =0.0001) -NoStubsZBRs : (optional) ignore stubs for bus ties (default=YES) -SingleCBZBRs : (optional) only insert a single circuit breaker (default=YES) -This command inserts transmission line display objects for lines with a nominal voltage of 50 kV -or higher. It includes text fields, inserts equivalenced lines, skips pie charts for zero-impedance -bus ties, includes Multi-Section Lines, treats lines with per-unit impedance ≤ 0.0001 as bus ties, -ignores stubs when identifying those ties, and inserts only a single circuit breaker for each zeroimpedance bus tie. -AutoInsertLines(50, YES, YES, NO, YES, 0.0001, YES, YES); -AutoInsertLoads(MinkV, InsertTextFields); -Use this action to insert loads. -MinkV : Minimum kV level to insert (default=0) -InsertTextFields : (optional) insert text fields (default=YES) -This command inserts load display objects for buses with a nominal voltage of 50 kV or higher -and includes text fields showing load details such as MW and Mvar values. -AutoInsertLoads(50, YES); -AutoInsertSwitchedShunts(MinkV, InsertTextFields); -Use this action to insert switched shunts. -MinkV : Minimum kV level to insert (default=0) -InsertTextFields : (optional) insert text fields (default=YES) -This command inserts switched shunt display objects (e.g., capacitor banks or reactors) -connected to buses with a nominal voltage of 140 kV or higher and includes text fields showing -shunt details like Mvar values. -AutoInsertSwitchedShunts(140, YES); -AutoInsertSubStations(LocationSource, MapProjection, AutoInsertBranches, InsertIfNotAlreadyShown, -"filename", InsertSelected); -Use this action to insert substations based on specified location data. -LocationSource : "Bus", "Substation" or "File" -MapProjection : "Simple Conic", "Mercator", "Alaska" or "x,y" -AutoInsertBranches : YES to insert transmission lines when finished, NO not to -InsertOnlyIfNotAlreadyShown -: YES if only buses that are not already shown should be inserted, NO to -insert all buses. -"filename" : (optional) path to location source file (if LocationSource is "File") -FileCoordinates is no longer used. It should not be included when creating new auxiliary files, but if it is -included in existing auxiliary files, it will be read and ignored. If MapProjection is set to “xy” and using a -file, the file coordinates are assumed to be in x,y, otherwise, file coordinates are assumed to be lon, lat. -FileCoordinates : (optional) format of coordinates in file "xy" or "lonlat" (if LocationSource -is "File") -InsertSelected : (optional) Default is NO. YES is only insert buses that are selected -(SELECTED = YES). -210 -This command inserts substation display objects using the latitude and longitude coordinates -stored in each substation, interpreted with the Simple Conic map projection. It inserts only -substations that are not already shown, adds branches between them, and includes all -substations regardless of selection status. -AutoInsertSubStations("Substation", "Simple Conic", YES, YES, "", NO); -AutoInsertAreas(MapProjection, InsertIfNotAlreadyShown, InsertSelected); -Use this action to insert areas based on the latitude and longitude of the area (which is calculated as the -average lat/long of buses in the area). -MapProjection : "Simple Conic", "Mercator", "Alaska" or "x,y" -InsertOnlyIfNotAlreadyShown -: (optional) Default is NO. YES if only areas that are not already shown -should be inserted, NO to insert all areas. -InsertSelected : (optional) Default is NO. YES is only insert areas that are selected -(SELECTED = YES). -This command inserts area display objects based on the average latitude and longitude of the -buses within each area, using the Simple Conic map projection. It inserts all areas, regardless of -whether they are already displayed or selected. -AutoInsertAreas("Simple Conic", NO, NO); -AutoInsertInjectionGroups(MapProjection, InsertIfNotAlreadyShown, InsertSelected); -Use this action to insert injection groups based on the latitude and longitude of the injection group -(which is calculated as the average lat/long of objects in the injection group). -MapProjection : "Simple Conic", "Mercator", "Alaska" or "x,y" -InsertOnlyIfNotAlreadyShown -: (optional) Default is NO. YES if only injection groups that are not already -shown should be inserted, NO to insert all injection groups. -InsertSelected : (optional) Default is NO. YES is only insert injection groups that are -selected (SELECTED = YES). -This command inserts injection group display objects based on the average latitude and -longitude of the objects within each group, using the Simple Conic map projection. It inserts all -injection groups, regardless of whether they are already shown or selected. -AutoInsertInjectionGroups("Simple Conic", NO, NO); -AutoInsertOwners(MapProjection, InsertIfNotAlreadyShown, InsertSelected); -Use this action to insert owners based on the latitude and longitude of the owner (which is calculated as -the average lat/long of objects in the owner). -MapProjection : "Simple Conic", "Mercator", "Alaska" or "x,y" -InsertOnlyIfNotAlreadyShown -: (optional) Default is NO. YES if only owners that are not already shown -should be inserted, NO to insert all owners. -InsertSelected : (optional) Default is NO. YES is only insert owners that are selected -(SELECTED = YES). -This command inserts owner display objects based on the average latitude and longitude of the -elements owned by each owner, using the Mercator map projection. It inserts only owners that -are not already displayed and only those with their Selected field set to YES. -AutoInsertOwners("Mercator", YES, YES); -211 -AutoInsertZones(MapProjection, InsertIfNotAlreadyShown, InsertSelected); -Use this action to insert zones based on the latitude and longitude of the zone (which is calculated as the -average lat/long of buses in the zone). -MapProjection : "Simple Conic", "Mercator", "Alaska" or "x,y" -InsertOnlyIfNotAlreadyShown -: (optional) Default is NO. YES if only zones that are not already shown -should be inserted, NO to insert all zones. -InsertSelected : (optional) Default is NO. YES is only insert zones that are selected -(SELECTED = YES). -This command inserts zone display objects using the average x,y coordinates of the buses in each -zone, interpreted with the x,y coordinate system. It inserts only zones that are not already -displayed and only those with their Selected field set to YES. -AutoInsertZones("x,y", YES, YES); -FixFlowArrowLineEnds("OnelineName", "LayerName"); -The unmoving line flow arrow indicators that can be displayed on lines may not always be setup correctly. -This script corrects the flow arrows so that they look at the end of the line to which they are the closest to -determine the direction of flow. -"OnelineName" : Optional – default is blank -The name of the oneline to which this action should be applied. The -oneline must already be open. When not specified this action is applied -to the oneline to which the display auxiliary file has been applied. -"LayerName" : Optional – default is blank -Name of the screen layer in which this action should be applied. When -this is left blank the action is applied to all flow arrow objects regardless -of the layer. -This command adjusts the direction of all unmoving flow arrows on the current one-line diagram. -It determines the correct flow direction by checking which end of the line the arrow is closest to -and orients the arrow accordingly. Since no oneline or layer is specified, it applies to all flow -arrows on the active diagram, regardless of screen layer. -FixFlowArrowLineEnds(,); -FixFlowArrowPosition("OnelineName", "LayerName"); -The unmoving line flow arrow indicators that can be displayed on lines are difficult to position properly. -This script is intended to automate as much of the work as possible for positioning these objects. -"OnelineName" : Optional – default is blank -The name of the oneline to which this action should be applied. The -oneline must already be open. When not specified this action is applied -to the oneline to which the display auxiliary file has been applied. -"LayerName" : Optional – default is blank -Name of the screen layer in which this action should be applied. When -this is left blank the action is applied to all flow arrow objects regardless -of the layer. -This command repositions all static flow arrows on the currently active one-line diagram to -improve their visual placement along the transmission lines. Since no oneline or layer is specified, -it applies to all flow arrows on all layers of the active diagram. -FixFlowArrowPosition(,); -212 -InsertConnectedBuses("BusIdentifier"); -Insert oneline display buses that are connected to the identified bus. -"BusIdentifier" : Identifier for the bus for which connected display buses should be added. -Bus can be identified as a data object "BUS Number", "BUS -NameNomkV", or "BUS Label". When identified as a data object all -display buses that are linked to this data bus will have display buses -inserted. -Bus can also be identified as a display object "DISPLAYBUS BusNum -SOAuxiliaryID" or "DISPLAYBUS BusName_NomVolt". When identified as -a display object only display buses connected to this bus will be inserted. -This command inserts display objects for all buses directly connected to the bus identified as -"BUS 1" on the one-line diagram. It places those connected buses visually, allowing you to easily -expand the diagram from a single bus outward by showing its immediate electrical connections. -InsertConnectedBuses("BUS 1"); -LoadAXDFromAXD("filename", CreateIfNotFound) -Use this action to apply a display auxiliary file via this script command that is part of another display -auxiliary file. -"filename" : The file name of the display auxiliary file to load. See the Specifying File -Names in Script Commands section for special keywords that can be -used when specifying the file name. -CreateIfNotFound : Optional – default is NO when using the Legacy Auxiliary File Header -format. Default is YES when using the Concise Auxiliary File Header -format. -This parameter is only enforced when the Create_if_not_found field -for a Legacy Auxiliary File Header is set to PROMPT. Otherwise, YES is -assumed. YES means that objects that cannot be found will be created -while reading DATA sections from "filename". -This command loads and applies the display auxiliary file located at the specified path. If any -objects referenced in DATA sections are not found on the current oneline, they will be created. -LoadAXDFromAXD("G:\Diagrams\SubstationView.axd", YES); -PanAndZoomToObject("ObjectID", "DisplayObjectType", "DoZoom"); -Use this action to pan to and optionally zoom in on a display object. This action will find the first matching -display object linked to the model object identified by ObjectID or the exact display object identified by -ObjectID. -ObjectID : The object identifier that uniquely identifies the power system model -object or display object. For example, to identify bus 123354, the -ObjectID takes the form "BUS 123354". -DisplayObjectType : (optional) The display object type to find. When a power system model -object is passed in for ObjectID, this parameter helps this action narrow -down to what kind of display object to pan. For example, to find the first -bus display object, use "DISPLAYBUS". (default="") -DoZoom : (optional) Whether or not to zoom after panning. (default=YES) -This command pans and zooms the one-line diagram to center on the display object for bus 1, -specifically targeting a DISPLAYBUS object type. The zoom is enabled (YES), so the view will both -move and magnify to focus directly on the bus's location in the diagram. -PanAndZoomToObject("BUS 1", "DISPLAYBUS", YES); -213 -ResetStubLocations(ZBRImpedance, NoStubsZBRs); -Use this action to reset stub locations. -ZBRImpedance : (optional) max P.U. impedance for bus ties (default=0.0001) -NoStubsZBRs : (optional) Ignore stubs for bus ties (default=YES) -This command resets the placement of stub lines for the one-line diagram. It affects lines with a -per-unit impedance ≤ 0.0001, and with YES specified, it preserves the existing layout of the bus -objects, adjusting only the stubs for cleaner visual alignment. -ResetStubLocations(0.0001, YES); -General Script Commands -The following script commands defined above in the general SCRIPT section are available for display -auxiliary files as well: -ExitProgram -LoadScript -LoadData -SelectAll -UnSelectAll -SetData -SaveData -SaveDataWithExtra -CreateData -DeleteFile -RenameFile -CopyFile -SetCurrentDirectory -SaveObjectFields -214 -DATA Section for Display Auxiliary File -The syntax for Display Auxiliary Files is the same as for Auxiliary Files. See the DATA Section and its sub-sections for -details on the proper syntax. Any differences for display auxiliary files will be discussed below. -Key Fields -See the Key Fields topic in the DATA Section for general details. -Display objects have an additional key field used for identification because multiple objects can be present on the same -one-line diagram that represent the same power system element. This extra key field is SOAuxiliaryID. This is a field -that is unique for each type of display object and other key field combination. If there are two display buses that -represent bus one in the power system, the SOAuxiliaryID field will be different for both. Simulator will automatically -create unique identifiers when these objects are created graphically. They can also be user specified but are forced to be -unique. This field does not need to be present when reading in a display auxiliary file, but if it is missing, Simulator -assumes that the ID is "1". This field is the only key field identifier for objects that do not link to power system elements -such as background lines and pictures, and therefore, should always be included when reading in these objects or the -expected results may not be achieved. -By going to the main menu and choosing Help, Export Display Object Fields you will obtain a list of fields available for -each display object type. In this output, the key fields will appear with asterisks *. -Special Data Sections -There are several object types that should be noted here because they can impact the reading of an entire display auxiliary -file, overall look of the resulting one-line diagram, or require special input to properly import/export the object. -GeographyDisplayOptions -Most objects supported in the display auxiliary file have coordinates that can be specified in the appropriate data sections. -What these coordinates specify can be controlled by the GEOGRAPHYDISPLAYOPTIONS object. This object has only two -fields available: MapProjection and ShowLonLat. There are four possible settings for MapProjection: "x,y", "Simple -Conic", "Alaska", and "Mercator". The choice of projection will determine how the x,y values for display objects are -interpreted. ShowLonLat can be either "YES" or "NO". If ShowLonLat is "YES", the setting specified for the -MapProjection will be the longitude,latitude projection used when reading/writing the object x,y values. If ShowLonLat is -"NO", the x,y values will always be interpreted as x,y regardless of the MapProjection setting. This object should be placed -in the display auxiliary file before any other objects containing coordinates are read. If this object is not included in the -auxiliary file, the coordinates will be interpreted based on the current settings of map projection and whether or not -coordinates are showing longitude,latitude. -Picture -PICTURE objects represent background images that cannot be stored in a text file format. To properly include a PICTURE -object in a display auxiliary file, the file containing the image must be saved and read along with the auxiliary file. The -FileName field indicates the name and location of the image file. If the image file cannot be found when reading in a -display auxiliary file and attempting to create a new object, no PICTURE object will be created. If attempting to update an -existing object and the image file cannot be found, the object will not be updated with a new image, but the FileName -field will be updated with the specified file name. -PWFormOptions -One-line display options that affect the current display settings can be changed by using the PWFORMOPTIONS object. -Usually, this object specifies named sets of options that can be selected and used to change the various one-line display -options through the GUI. By including a specially named object, the current options can be changed through a display -auxiliary file. PWFORMOPTIONS are named using the OOName field. Setting this field to -215 -"THESE_OPTIONS_ARE_APPLIED_TO_THE_CURRENT_DISPLAY" will apply the specified set of options to the current one-line -when the file is read. When saving the entire one-line to a display auxiliary file, a PWFORMOPTIONS object with this name -is added to the file by default. -View -Different views can be specified in the display auxiliary file using the VIEW object. Usually, this object is used to specify -named sets of options used to select and change the view through the GUI. By including a specially named object, the -current view can be changed through a display auxiliary file. VIEW objects are named using the ViewName field. Setting -this field to "THIS_VIEW_IS_APPLIED_TO_THE_CURRENT_DISPLAY" will apply the specified set of view options to the current -one-line when the file is read. When saving the entire one-line to a display auxiliary file, a VIEW object with this name is -added to the file by default. -SubData Sections -The format described thus far works well for most kinds of data in Simulator. It does not work as well however for data -that stores a list of objects. For example, a contingency stores some information about itself (such as its name), and then a -list of contingency elements, and possible a list of limit violations as well. For data such as this, Simulator allows -, tags that store lists of information about a particular object. This formatting looks like the -following -object_type (list_of_fields) -{ -value_list_1 - - precise format describing an object_type1 - precise format describing an object_type1 - . - . - . - - - precise format describing an object_type2 - precise format describing an object_type2 - . - . - . - -value_list_2 - . - . - . -value_list_n -} -Note that the information contained inside the , tags may not be flexibly defined. It must be -written in a precisely defined order that will be documented for each SubData type. The description of each of these -SubData formats follows. -ColorMap -Same format as in data auxiliary files. -CustomColors -Same format as in data auxiliary files. -216 -DisplayDCTramisssionLine -DisplayInterface -DisplayMultiSectionLine -DisplaySeriesCapacitor -DisplayTransformer -DisplayTransmissionLine -Line -Line -This is a list of points defining the graphical line used to represent the object. Each set of coordinates can -be enclosed in square brackets, [ ], or the brackets can be eliminated. The brackets will be included when -Simulator generates an auxiliary file. The individual coordinates are separated by the specified delimiter, -either a space or a comma, and if the brackets are included, the same delimiter should be used to -separate sets of coordinates. The list of points is in a somewhat free form and sets of coordinates can -span multiple lines. Each point should either be in x,y coordinates or longitude,latitude coordinates. -Which coordinates should be used depends on the current option settings for map projection and -whether or not coordinates should be shown in longitude,latitude. If the display auxiliary file is -automatically generated by Simulator, a comment will be included in the subdata section indicating the -coordinate system in use during file creation. -Example using brackets and a comma delimiter: - -//Coordinates are x,y - [14.00000000, 63.00000000], [14.00000000, 60.00000000], - [20.00000000, 45.00000000], [20.00000000, 42.00000000] - -Example with no brackets and a space delimiter: - -//Coordinates are x,y - 14.00000000 63.00000000 14.00000000 60.00000000 - 20.00000000 45.00000000 20.00000000 42.00000000 - -DynamicFormatting -Same format as in data auxiliary files. -Filter -Same format as in data auxiliary files. -GeoDataViewStyle -Same format as in data auxiliary files. -217 -PieChartGaugeStyle -ColorMap -This is a lookup table by percentage of scalar and color values. This lookup table will consist of -consecutive lines of text with exactly three values: -Percentage : This is the percentage at which the following scalar and color should be -applied. -Scalar : A scalar (multiplier) on the size of the pie chart/gauge. -Color : A color for the pie chart/gauge. -Example: - -//Percentage Scalar Color - 85.0000 1.5000 33023 - 100.0000 2.0000 255 - -PWFormOptions -Same format as in data auxiliary files. -SelectByCriteriaSet -Same format as in data auxiliary files. -UserDefinedDataGrid -Same format as in data auxiliary files. -View -ScreenLayer -This is a list of screen layer names that are hidden in the current view. Each screen layer name is on a -separate line of text. \ No newline at end of file diff --git a/docs/api/utils.rst b/docs/api/utils.rst index ef049e6..5624619 100644 --- a/docs/api/utils.rst +++ b/docs/api/utils.rst @@ -77,11 +77,3 @@ Binary 3D electric field data I/O. .. automodule:: esapp.utils.b3d :members: - -General Helpers ---------------- - -.. currentmodule:: esapp.utils.misc - -.. automodule:: esapp.utils.misc - :members: diff --git a/examples/gic/formulations/gic.rst b/docs/formulations/gic.rst similarity index 100% rename from examples/gic/formulations/gic.rst rename to docs/formulations/gic.rst diff --git a/examples/gic/formulations/index.rst b/docs/formulations/index.rst similarity index 100% rename from examples/gic/formulations/index.rst rename to docs/formulations/index.rst diff --git a/docs/index.rst b/docs/index.rst index 6cee5c9..dba1db3 100644 --- a/docs/index.rst +++ b/docs/index.rst @@ -6,5 +6,6 @@ ESA++ overview examples/index + formulations/index api/index dev/index diff --git a/esapp/indexable.py b/esapp/indexable.py index be280cb..8d67bf4 100644 --- a/esapp/indexable.py +++ b/esapp/indexable.py @@ -1,6 +1,5 @@ from .saw import SAW, PowerWorldPrerequisiteError from .components import GObject -from .utils import timing from typing import Type, Optional from pandas import DataFrame from os import path @@ -22,7 +21,6 @@ class Indexable: esa: SAW fname: str - @timing def open(self): """ Open the PowerWorld case and initialize transient stability. diff --git a/esapp/py.typed b/esapp/py.typed new file mode 100644 index 0000000..e69de29 diff --git a/esapp/saw/base.py b/esapp/saw/base.py index 822c359..92a95d3 100644 --- a/esapp/saw/base.py +++ b/esapp/saw/base.py @@ -357,7 +357,10 @@ def _com_call(self, func: str, *args): PowerWorldError If SimAuto returns an error message (e.g., invalid parameters, operation failed). """ - self.log.debug("COM call: %s(%s)", func, ", ".join(repr(a) for a in args)) + # repr() of variant payloads is expensive (renders the full data), + # so skip building the message entirely unless DEBUG is enabled. + if self.log.isEnabledFor(logging.DEBUG): + self.log.debug("COM call: %s(%s)", func, ", ".join(repr(a) for a in args)) try: f = getattr(self._pwcom, func) except AttributeError: diff --git a/esapp/utils/__init__.py b/esapp/utils/__init__.py index cdbb774..74321a2 100644 --- a/esapp/utils/__init__.py +++ b/esapp/utils/__init__.py @@ -4,11 +4,8 @@ Provides tools for: - Binary data formats (B3D electric field data) - Analysis modules (GIC, network topology, bus classification, dynamics, contingency) -- Function decorators for debugging and profiling """ -from .misc import timing - from .b3d import B3D from .gic import GIC @@ -18,8 +15,6 @@ from .buscat import BusCat, parse_buscat __all__ = [ - # misc - 'timing', # b3d 'B3D', # gic diff --git a/esapp/utils/misc.py b/esapp/utils/misc.py deleted file mode 100644 index 3681606..0000000 --- a/esapp/utils/misc.py +++ /dev/null @@ -1,55 +0,0 @@ -""" -Power system utilities and general-purpose helpers. - -This module provides: -- Function decorators for debugging and profiling -""" - -from __future__ import annotations - -from functools import wraps -from time import time -from typing import Callable, TypeVar - -__all__ = [ - 'timing', -] - -# ============================================================================= -# Decorators -# ============================================================================= - -F = TypeVar('F', bound=Callable) - - -def timing(func: F) -> F: - """ - Decorator that prints the execution time of a function. - - Parameters - ---------- - func : callable - The function to wrap. - - Returns - ------- - callable - Wrapped function that prints timing information. - - Examples - -------- - >>> @timing - ... def slow_function(): - ... time.sleep(1) - ... - >>> slow_function() - 'slow_function' took: 1.0012 sec - """ - @wraps(func) - def wrapper(*args, **kwargs): - start = time() - result = func(*args, **kwargs) - elapsed = time() - start - print(f'{func.__name__!r} took: {elapsed:.4f} sec') - return result - return wrapper diff --git a/examples/README.md b/examples/README.md new file mode 100644 index 0000000..f31ba94 --- /dev/null +++ b/examples/README.md @@ -0,0 +1,38 @@ +# ESA++ Examples + +Example application classes, reusable utilities, and Jupyter notebooks +demonstrating advanced usage of the `esapp` package. + +The notebooks add the `examples/` directory to `sys.path` +(`import sys; sys.path.insert(0, "..")`) and import the helper modules +directly (e.g. `from plot_helpers import ...`). Run them with the +notebook's own directory as the working directory (the Jupyter default). + +## Application Classes + +| Module | Description | +|---|---| +| `statics.py` | Continuation power flow, state chain management, ZIP load interface, and generator limit checking | +| `dynamics.py` | Transient stability simulation with contingency definition, execution, and result retrieval | + +## Utilities + +| Module | Description | +|---|---| +| `map.py` | Geographic visualization (borders, lines, vector fields) | +| `mesh.py` | Discrete geometry, Grid2D, PLY mesh I/O, spectral helpers | +| `plot_helpers.py` | Shared plotting functions for all notebooks | + +## Notebooks + +| Directory | Contents | +|---|---| +| `dynamics/` | Transient stability simulation examples | +| `steady_state/` | Contingency analysis, SCOPF, ATC, and CPF examples | +| `gic/` | GIC analysis and sensitivity examples | +| `network/` | Network topology and matrix extraction examples | +| `nonuniform/` | Non-uniform electric field GIC analysis | +| `visualization/` | Discrete calculus, spectral analysis, geographic plotting | + +Integration notebooks expect a PowerWorld case path in `examples/data/case.txt` +(see individual notebooks for details). diff --git a/examples/__init__.py b/examples/__init__.py deleted file mode 100644 index 157648d..0000000 --- a/examples/__init__.py +++ /dev/null @@ -1,32 +0,0 @@ -""" -ESAplus Example Implementations -=============================== - -This directory contains example application classes, reusable utilities, -and Jupyter notebooks demonstrating advanced usage of the esapp package. - -Application Classes -------------------- -statics.py - Continuation power flow, state chain management, ZIP load interface, - and generator limit checking. -dynamics.py - Transient stability simulation with contingency definition, execution, - and result retrieval. - -Utilities ---------- -injection.py - Normalized injection vectors for sensitivity studies -map.py - Geographic visualization (borders, lines, vector fields) -mesh.py - Discrete geometry, Grid2D, PLY mesh I/O -plot_helpers.py - Shared plotting functions for all notebooks - -Notebooks ---------- -dynamics/ - Transient stability simulation examples -steady_state/ - Contingency analysis, SCOPF, ATC, and CPF examples -gic/ - GIC analysis and sensitivity examples -network/ - Network topology and matrix extraction examples -nonuniform/ - Non-uniform electric field GIC analysis -visualization/ - Discrete calculus, spectral analysis, geographic plotting -""" diff --git a/examples/dynamics.py b/examples/dynamics.py index 55fe932..f091236 100644 --- a/examples/dynamics.py +++ b/examples/dynamics.py @@ -10,7 +10,7 @@ ------- >>> from esapp import PowerWorld >>> from esapp.utils import ContingencyBuilder, SimAction, TSWatch - >>> from examples.dynamics import Dynamics + >>> from dynamics import Dynamics # with examples/ on sys.path >>> pw = PowerWorld("case.pwb") >>> dyn = Dynamics(pw) >>> dyn.watch(Gen, [TS.Gen.P, TS.Gen.W, TS.Gen.Delta]) diff --git a/examples/dynamics/01_transient_stability.ipynb b/examples/dynamics/01_transient_stability.ipynb index 3798ae6..75d8062 100644 --- a/examples/dynamics/01_transient_stability.ipynb +++ b/examples/dynamics/01_transient_stability.ipynb @@ -14,7 +14,13 @@ "id": "bb2c3d4e", "metadata": {}, "outputs": [], - "source": "from esapp import PowerWorld, TS\nfrom esapp.components import Bus, Gen\n\nimport sys; sys.path.insert(0, '..')\nfrom plot_helpers import plot_dynamics" + "source": [ + "from esapp import PowerWorld, TS\n", + "from esapp.components import Bus, Gen\n", + "\n", + "import sys; sys.path.insert(0, '..')\n", + "from plot_helpers import plot_dynamics" + ] }, { "cell_type": "code", @@ -26,7 +32,15 @@ ] }, "outputs": [], - "source": "# This cell is hidden in the documentation.\nimport ast\n\nwith open('../data/case.txt', 'r') as f:\n case_path = ast.literal_eval(f.read().strip())\n\npw = PowerWorld(case_path)" + "source": [ + "# This cell is hidden in the documentation.\n", + "import ast\n", + "\n", + "with open('../data/case.txt', 'r') as f:\n", + " case_path = ast.literal_eval(f.read().strip())\n", + "\n", + "pw = PowerWorld(case_path)" + ] }, { "cell_type": "markdown", @@ -40,7 +54,16 @@ "id": "ee5f6a7b", "metadata": {}, "outputs": [], - "source": "# Set simulation duration\npw.dyn.runtime = 10.0\n\n# Watch generator fields during simulation\npw.dyn.watch(Gen, [TS.Gen.P, TS.Gen.W, TS.Gen.Delta])\n\n# Watch bus voltage\npw.dyn.watch(Bus, [TS.Bus.VPU, TS.Bus.Deg]) # TODO remove TS.Bus.FreqMeasT (parsed wrong?)" + "source": [ + "# Set simulation duration\n", + "pw.dyn.runtime = 10.0\n", + "\n", + "# Watch generator fields during simulation\n", + "pw.dyn.watch(Gen, [TS.Gen.P, TS.Gen.W, TS.Gen.Delta])\n", + "\n", + "# Watch bus voltage\n", + "pw.dyn.watch(Bus, [TS.Bus.VPU, TS.Bus.Deg]) # TODO remove TS.Bus.FreqMeasT (parsed wrong?)" + ] }, { "cell_type": "markdown", @@ -58,7 +81,16 @@ "id": "ab7b8c9d", "metadata": {}, "outputs": [], - "source": "# Define a bus fault contingency\n(pw.dyn.contingency(\"Fault_Bus1\")\n .at(1.0).fault_bus(\"1\") # 3-phase fault at bus 1 at t=1.0s\n .at(1.153).clear_fault(\"1\")) # Clear after ~9 cycles\n\nprint(\"Contingency 'Fault_Bus1' defined:\")\nprint(\" t=1.000s: Apply 3-phase bus fault at Bus 1\")\nprint(\" t=1.153s: Clear fault at Bus 1\")" + "source": [ + "# Define a bus fault contingency\n", + "(pw.dyn.contingency(\"Fault_Bus1\")\n", + " .at(1.0).fault_bus(\"1\") # 3-phase fault at bus 1 at t=1.0s\n", + " .at(1.153).clear_fault(\"1\")) # Clear after ~9 cycles\n", + "\n", + "print(\"Contingency 'Fault_Bus1' defined:\")\n", + "print(\" t=1.000s: Apply 3-phase bus fault at Bus 1\")\n", + "print(\" t=1.153s: Clear fault at Bus 1\")" + ] }, { "cell_type": "markdown", @@ -77,7 +109,11 @@ "id": "cd9d0e1f", "metadata": {}, "outputs": [], - "source": "models = pw.dyn.list_models()\nprint(\"Dynamic Models:\")\nprint(models.to_string())" + "source": [ + "models = pw.dyn.list_models()\n", + "print(\"Dynamic Models:\")\n", + "print(models.to_string())" + ] }, { "cell_type": "markdown", @@ -96,7 +132,14 @@ "id": "ef1f2a3b", "metadata": {}, "outputs": [], - "source": "meta, results = pw.dyn.solve(\"Fault_Bus1\")\n\nprint(f\"Metadata shape: {meta.shape}\")\nprint(f\"Results shape: {results.shape}\")\nprint(f\"\\nMetadata columns: {list(meta.columns)}\")\nprint(f\"Time range: {results.index.min():.3f} to {results.index.max():.3f} seconds\")" + "source": [ + "meta, results = pw.dyn.solve(\"Fault_Bus1\")\n", + "\n", + "print(f\"Metadata shape: {meta.shape}\")\n", + "print(f\"Results shape: {results.shape}\")\n", + "print(f\"\\nMetadata columns: {list(meta.columns)}\")\n", + "print(f\"Time range: {results.index.min():.3f} to {results.index.max():.3f} seconds\")" + ] }, { "cell_type": "markdown", @@ -110,7 +153,9 @@ "id": "ab3b4c5d", "metadata": {}, "outputs": [], - "source": "plot_dynamics(meta, results)" + "source": [ + "plot_dynamics(meta, results)" + ] }, { "cell_type": "markdown", @@ -140,4 +185,4 @@ }, "nbformat": 4, "nbformat_minor": 5 -} \ No newline at end of file +} diff --git a/examples/dynamics/02_multi_contingency.ipynb b/examples/dynamics/02_multi_contingency.ipynb index 0fa0016..f3d19e1 100644 --- a/examples/dynamics/02_multi_contingency.ipynb +++ b/examples/dynamics/02_multi_contingency.ipynb @@ -17,7 +17,13 @@ ] }, "outputs": [], - "source": "import matplotlib.pyplot as plt\nfrom esapp import PowerWorld, TS\nfrom esapp.components import Bus, Gen\nfrom examples.map import format_plot" + "source": [ + "import sys; sys.path.insert(0, \"..\")\n", + "import matplotlib.pyplot as plt\n", + "from esapp import PowerWorld, TS\n", + "from esapp.components import Bus, Gen\n", + "from map import format_plot" + ] }, { "cell_type": "code", @@ -29,7 +35,15 @@ ] }, "outputs": [], - "source": "# This cell is hidden in the documentation.\nimport ast\n\nwith open('../data/case.txt', 'r') as f:\n case_path = ast.literal_eval(f.read().strip())\n\npw = PowerWorld(case_path)" + "source": [ + "# This cell is hidden in the documentation.\n", + "import ast\n", + "\n", + "with open('../data/case.txt', 'r') as f:\n", + " case_path = ast.literal_eval(f.read().strip())\n", + "\n", + "pw = PowerWorld(case_path)" + ] }, { "cell_type": "code", @@ -62,7 +76,25 @@ "id": "eee5f6a7", "metadata": {}, "outputs": [], - "source": "pw.dyn.runtime = 10.0\npw.dyn.watch(Gen, [TS.Gen.P, TS.Gen.W, TS.Gen.Delta])\npw.dyn.watch(Bus, [TS.Bus.VPU])\n\n# Contingency 1: Bus fault\n(pw.dyn.contingency(\"Bus_Fault\")\n .at(1.0).fault_bus(\"1\")\n .at(1.153).clear_fault(\"1\"))\n\n# Contingency 2: Generator trip\n(pw.dyn.contingency(\"Gen_Trip\")\n .at(1.0).trip_gen(\"2\", \"1\"))\n\n# Contingency 3: Branch trip\nbranches = pw[Gen].head()\n(pw.dyn.contingency(\"Branch_Trip\")\n .at(1.0).trip_branch(\"1\", \"2\", \"1\"))" + "source": [ + "pw.dyn.runtime = 10.0\n", + "pw.dyn.watch(Gen, [TS.Gen.P, TS.Gen.W, TS.Gen.Delta])\n", + "pw.dyn.watch(Bus, [TS.Bus.VPU])\n", + "\n", + "# Contingency 1: Bus fault\n", + "(pw.dyn.contingency(\"Bus_Fault\")\n", + " .at(1.0).fault_bus(\"1\")\n", + " .at(1.153).clear_fault(\"1\"))\n", + "\n", + "# Contingency 2: Generator trip\n", + "(pw.dyn.contingency(\"Gen_Trip\")\n", + " .at(1.0).trip_gen(\"2\", \"1\"))\n", + "\n", + "# Contingency 3: Branch trip\n", + "branches = pw[Gen].head()\n", + "(pw.dyn.contingency(\"Branch_Trip\")\n", + " .at(1.0).trip_branch(\"1\", \"2\", \"1\"))" + ] }, { "cell_type": "markdown", @@ -80,7 +112,17 @@ "id": "aab7b8c9", "metadata": {}, "outputs": [], - "source": "ctg_names = [\"Bus_Fault\", \"Gen_Trip\", \"Branch_Trip\"]\nall_meta = {}\nall_results = {}\n\nfor name in ctg_names:\n meta, results = pw.dyn.solve(name)\n all_meta[name] = meta\n all_results[name] = results\n print(f\"Solved '{name}': {results.shape[0]} time steps, {results.shape[1]} channels\")" + "source": [ + "ctg_names = [\"Bus_Fault\", \"Gen_Trip\", \"Branch_Trip\"]\n", + "all_meta = {}\n", + "all_results = {}\n", + "\n", + "for name in ctg_names:\n", + " meta, results = pw.dyn.solve(name)\n", + " all_meta[name] = meta\n", + " all_results[name] = results\n", + " print(f\"Solved '{name}': {results.shape[0]} time steps, {results.shape[1]} channels\")" + ] }, { "cell_type": "code", @@ -113,4 +155,4 @@ }, "nbformat": 4, "nbformat_minor": 5 -} \ No newline at end of file +} diff --git a/examples/gic/01_gic_basics.ipynb b/examples/gic/01_gic_basics.ipynb index e61a2b3..dcae355 100644 --- a/examples/gic/01_gic_basics.ipynb +++ b/examples/gic/01_gic_basics.ipynb @@ -24,11 +24,23 @@ ] }, "outputs": [], - "source": "# This cell is hidden in the documentation.\nfrom esapp import PowerWorld\nfrom esapp.components import *\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport ast\n\nwith open('../data/case.txt', 'r') as f:\n case_path = ast.literal_eval(f.read().strip())\n\npw = PowerWorld(case_path)" + "source": [ + "# This cell is hidden in the documentation.\n", + "from esapp import PowerWorld\n", + "from esapp.components import *\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import ast\n", + "\n", + "with open('../data/case.txt', 'r') as f:\n", + " case_path = ast.literal_eval(f.read().strip())\n", + "\n", + "pw = PowerWorld(case_path)" + ] }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "id": "670cab8a", "metadata": { "tags": [ @@ -58,7 +70,9 @@ "id": "fe5f6a7b", "metadata": {}, "outputs": [], - "source": "pw.gic.storm(max_field=1.0, direction=90.0)" + "source": [ + "pw.gic.storm(max_field=1.0, direction=90.0)" + ] }, { "cell_type": "markdown", @@ -76,22 +90,17 @@ "id": "a07b8c9d", "metadata": {}, "outputs": [], - "source": "gics = pw[GICXFormer, ['BusNum3W', 'BusNum3W:1', 'GICXFNeutralAmps']]\ngics.head()" + "source": [ + "gics = pw[GICXFormer, ['BusNum3W', 'BusNum3W:1', 'GICXFNeutralAmps']]\n", + "gics.head()" + ] }, { "cell_type": "code", - "execution_count": 12, + "execution_count": null, "id": "a18c9d0e", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Maximum |GIC|: 15.440 Amps\n" - ] - } - ], + "outputs": [], "source": [ "max_gic = gics['GICXFNeutralAmps'].abs().max()\n", "print(f\"Maximum |GIC|: {max_gic:.3f} Amps\")" @@ -109,21 +118,10 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": null, "id": "3869bdf3", "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "gic_abs = gics['GICXFNeutralAmps'].abs().sort_values(ascending=False)\n", "plot_gic_distribution(gic_abs)" @@ -145,33 +143,24 @@ "id": "a52a3b4c", "metadata": {}, "outputs": [], - "source": "directions = np.arange(0, 361, 10)\nmax_gics = []\n\nfor d in directions:\n pw.gic.storm(max_field=1.0, direction=d)\n gic_vals = pw[GICXFormer, 'GICXFNeutralAmps']['GICXFNeutralAmps']\n max_gics.append(gic_vals.abs().max())\n\nmax_gics = np.array(max_gics)" + "source": [ + "directions = np.arange(0, 361, 10)\n", + "max_gics = []\n", + "\n", + "for d in directions:\n", + " pw.gic.storm(max_field=1.0, direction=d)\n", + " gic_vals = pw[GICXFormer, 'GICXFNeutralAmps']['GICXFNeutralAmps']\n", + " max_gics.append(gic_vals.abs().max())\n", + "\n", + "max_gics = np.array(max_gics)" + ] }, { "cell_type": "code", - "execution_count": 15, + "execution_count": null, "id": "18590645", "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Worst-case direction: 130 degrees\n", - "Worst-case max GIC: 19.50 Amps\n" - ] - } - ], + "outputs": [], "source": [ "plot_direction_sensitivity(directions, max_gics)" ] @@ -198,4 +187,4 @@ }, "nbformat": 4, "nbformat_minor": 5 -} \ No newline at end of file +} diff --git a/examples/gic/02_gic_model.ipynb b/examples/gic/02_gic_model.ipynb index 5edd250..579f37d 100644 --- a/examples/gic/02_gic_model.ipynb +++ b/examples/gic/02_gic_model.ipynb @@ -16,7 +16,13 @@ ] }, "outputs": [], - "source": "import numpy as np\nfrom esapp import PowerWorld\nfrom esapp.components import Bus, Branch, Substation, GICXFormer\nfrom examples.map import format_plot" + "source": [ + "import sys; sys.path.insert(0, \"..\")\n", + "import numpy as np\n", + "from esapp import PowerWorld\n", + "from esapp.components import Bus, Branch, Substation, GICXFormer\n", + "from map import format_plot" + ] }, { "cell_type": "code", @@ -28,11 +34,19 @@ ] }, "outputs": [], - "source": "# This cell is hidden in the documentation.\nimport ast\n\nwith open('../data/case.txt', 'r') as f:\n case_path = ast.literal_eval(f.read().strip())\n\npw = PowerWorld(case_path)" + "source": [ + "# This cell is hidden in the documentation.\n", + "import ast\n", + "\n", + "with open('../data/case.txt', 'r') as f:\n", + " case_path = ast.literal_eval(f.read().strip())\n", + "\n", + "pw = PowerWorld(case_path)" + ] }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "id": "01173666", "metadata": { "tags": [ @@ -66,7 +80,11 @@ "id": "e5f6a7b8c9d0", "metadata": {}, "outputs": [], - "source": "pw.gic.configure(pf_include=True, ts_include=False, calc_mode='SnapShot')\n\npw.gic.settings()" + "source": [ + "pw.gic.configure(pf_include=True, ts_include=False, calc_mode='SnapShot')\n", + "\n", + "pw.gic.settings()" + ] }, { "cell_type": "markdown", @@ -85,7 +103,16 @@ "id": "a7b8c9d0e1f2", "metadata": {}, "outputs": [], - "source": "pw.gic.model()\n\nprint(f\"Incidence matrix (A): {pw.gic.A.shape} (branches x nodes)\")\nprint(f\"G-matrix: {pw.gic.G.shape} (nodes x nodes)\")\nprint(f\"H-matrix: {pw.gic.H.shape} (transformers x branches)\")\nprint(f\"Zeta (per-unit): {pw.gic.zeta.shape}\")\nprint(f\"Effective operator: {pw.gic.eff.shape}\")\nprint(f\"Bus permutation (Px): {pw.gic.Px.shape}\")" + "source": [ + "pw.gic.model()\n", + "\n", + "print(f\"Incidence matrix (A): {pw.gic.A.shape} (branches x nodes)\")\n", + "print(f\"G-matrix: {pw.gic.G.shape} (nodes x nodes)\")\n", + "print(f\"H-matrix: {pw.gic.H.shape} (transformers x branches)\")\n", + "print(f\"Zeta (per-unit): {pw.gic.zeta.shape}\")\n", + "print(f\"Effective operator: {pw.gic.eff.shape}\")\n", + "print(f\"Bus permutation (Px): {pw.gic.Px.shape}\")" + ] }, { "cell_type": "markdown", @@ -103,7 +130,13 @@ "id": "37fb8c5e", "metadata": {}, "outputs": [], - "source": "plot_spy_matrices(\n [pw.gic.A, pw.gic.G, pw.gic.H],\n [f'Incidence Matrix A\\n{pw.gic.A.shape}, nnz={pw.gic.A.nnz}',\n f'G-Matrix (Conductance Laplacian)\\n{pw.gic.G.shape}, nnz={pw.gic.G.nnz}',\n f'H-Matrix (GIC Function)\\n{pw.gic.H.shape}, nnz={pw.gic.H.nnz}'])" + "source": [ + "plot_spy_matrices(\n", + " [pw.gic.A, pw.gic.G, pw.gic.H],\n", + " [f'Incidence Matrix A\\n{pw.gic.A.shape}, nnz={pw.gic.A.nnz}',\n", + " f'G-Matrix (Conductance Laplacian)\\n{pw.gic.G.shape}, nnz={pw.gic.G.nnz}',\n", + " f'H-Matrix (GIC Function)\\n{pw.gic.H.shape}, nnz={pw.gic.H.nnz}'])" + ] }, { "cell_type": "markdown", @@ -121,25 +154,27 @@ "id": "a3b4c5d6e7f8", "metadata": {}, "outputs": [], - "source": "# Apply 1 V/km eastward storm\npw.gic.storm(1.0, 90)\n\ngic_results = pw[GICXFormer, ['BusNum3W', 'BusNum3W:1', 'GICXFNeutralAmps']]\ngic_sorted = gic_results.reindex(\n gic_results['GICXFNeutralAmps'].abs().sort_values(ascending=False).index\n)\n\nprint(f\"Total transformers: {len(gic_results)}\")\nprint(f\"Max |GIC|: {gic_results['GICXFNeutralAmps'].abs().max():.3f} A\")\nprint(f\"\\nTop transformers:\")\nprint(gic_sorted.head(10).to_string(index=False))" + "source": [ + "# Apply 1 V/km eastward storm\n", + "pw.gic.storm(1.0, 90)\n", + "\n", + "gic_results = pw[GICXFormer, ['BusNum3W', 'BusNum3W:1', 'GICXFNeutralAmps']]\n", + "gic_sorted = gic_results.reindex(\n", + " gic_results['GICXFNeutralAmps'].abs().sort_values(ascending=False).index\n", + ")\n", + "\n", + "print(f\"Total transformers: {len(gic_results)}\")\n", + "print(f\"Max |GIC|: {gic_results['GICXFNeutralAmps'].abs().max():.3f} A\")\n", + "print(f\"\\nTop transformers:\")\n", + "print(gic_sorted.head(10).to_string(index=False))" + ] }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "id": "1b42e9af", "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "gic_abs = gic_results['GICXFNeutralAmps'].abs()\n", "plot_gic_bar_hist(gic_abs)" @@ -163,7 +198,12 @@ "id": "8028ece3", "metadata": {}, "outputs": [], - "source": "plot_spy_matrices(\n [pw.gic.zeta, pw.gic.Px],\n [f'Zeta Sparsity Pattern\\n{pw.gic.zeta.shape}',\n f'Bus Permutation Matrix Px\\n{pw.gic.Px.shape}'])" + "source": [ + "plot_spy_matrices(\n", + " [pw.gic.zeta, pw.gic.Px],\n", + " [f'Zeta Sparsity Pattern\\n{pw.gic.zeta.shape}',\n", + " f'Bus Permutation Matrix Px\\n{pw.gic.Px.shape}'])" + ] }, { "cell_type": "markdown", @@ -201,4 +241,4 @@ }, "nbformat": 4, "nbformat_minor": 5 -} \ No newline at end of file +} diff --git a/examples/gic/03_efield_geographic.ipynb b/examples/gic/03_efield_geographic.ipynb index 27e9899..abd27b7 100644 --- a/examples/gic/03_efield_geographic.ipynb +++ b/examples/gic/03_efield_geographic.ipynb @@ -23,7 +23,19 @@ ] }, "outputs": [], - "source": "import numpy as np\nimport matplotlib.pyplot as plt\nfrom matplotlib.colors import Normalize\n\nfrom esapp import PowerWorld\nfrom esapp.components import Branch, Bus, Substation, GICXFormer\nfrom esapp.utils import (\n Grid2D, B3D,\n format_plot, plot_vecfield, plot_lines,\n border, darker_hsv_colormap,\n)" + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from matplotlib.colors import Normalize\n", + "\n", + "from esapp import PowerWorld\n", + "from esapp.components import Branch, Bus, Substation, GICXFormer\n", + "from esapp.utils import (\n", + " Grid2D, B3D,\n", + " format_plot, plot_vecfield, plot_lines,\n", + " border, darker_hsv_colormap,\n", + ")" + ] }, { "cell_type": "code", @@ -35,11 +47,22 @@ ] }, "outputs": [], - "source": "# This cell is hidden in the documentation.\nimport ast\n\nwith open('../data/case_B.txt', 'r') as f:\n case_path = ast.literal_eval(f.read().strip())\n\npw = PowerWorld(case_path)\n\n# Configure geographic border shape ('US', 'Texas', etc.)\nSHAPE = 'Texas'" + "source": [ + "# This cell is hidden in the documentation.\n", + "import ast\n", + "\n", + "with open('../data/case_B.txt', 'r') as f:\n", + " case_path = ast.literal_eval(f.read().strip())\n", + "\n", + "pw = PowerWorld(case_path)\n", + "\n", + "# Configure geographic border shape ('US', 'Texas', etc.)\n", + "SHAPE = 'Texas'" + ] }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "id": "6f7d9a1e", "metadata": { "tags": [ @@ -69,7 +92,15 @@ "id": "e5f6a7b8c9d0", "metadata": {}, "outputs": [], - "source": "# Get bus coordinates\nlon, lat = pw.buscoords()\n\n# Determine geographic bounding box with padding\npad = 0.5 \nlon_min, lon_max = lon.min() - pad, lon.max() + pad\nlat_min, lat_max = lat.min() - pad, lat.max() + pad" + "source": [ + "# Get bus coordinates\n", + "lon, lat = pw.buscoords()\n", + "\n", + "# Determine geographic bounding box with padding\n", + "pad = 0.5 \n", + "lon_min, lon_max = lon.min() - pad, lon.max() + pad\n", + "lat_min, lat_max = lat.min() - pad, lat.max() + pad" + ] }, { "cell_type": "markdown", @@ -87,17 +118,7 @@ "execution_count": null, "id": "a7b8c9d0e1f2", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Grid: 40 x 30 = 1200 points\n", - "Edges: 2330 (horizontal: 1170, vertical: 1160)\n", - "Resolution: 0.281 deg lon x 0.364 deg lat\n" - ] - } - ], + "outputs": [], "source": [ "# Grid resolution\n", "nx, ny = 40, 30\n", @@ -113,21 +134,10 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "id": "2ac51215", "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "fig, axes = plt.subplots(1, 2, figsize=(6.5, 2.8))\n", "plot_geo_grid_buses(LON, LAT, lon, lat, SHAPE,\n", @@ -159,7 +169,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "id": "d0e1f2a3b4c5", "metadata": {}, "outputs": [], @@ -173,7 +183,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "id": "f2a3b4c5d6e7", "metadata": {}, "outputs": [], @@ -204,21 +214,10 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "id": "eb66f969", "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAAAoUAAAC6CAYAAADYmjeFAAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjEwLjgsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvwVt1zgAAAAlwSFlzAAAPYQAAD2EBqD+naQAAVJxJREFUeJztnQeUE+XXxh9YWJbei3QQECmCFJFeBRQQUGkWsPwtIDZUmgX0swuKFMVeaaICCkhHEVhAEFBApPfeWdqykO8872RCNiTZTDJJZpL7O2fObiaTySSZZ947970lk8PhcEAQBEEQBEGIazJH+wAEQRAEQRCE6CNGoSAIgiAIgiBGoSAIgiAIgiBGoSAIgiAIgiBGoSAIgiAIgiBGoSAIgiAIgqCQRBNBEARBEARBjEJBEARBEARBjEJBEARBEARBjEJBEARBEARBjELBUixatAgdOnRA8eLFkSlTJkydOtXv9vv378fdd9+NSpUqIXPmzHj66ae9bjd58mRUrlwZSUlJqF69OmbOnBmmTyAI1uDNN99E3bp1kTt3bhQpUgSdOnXCf//9l+HrRCuCEN9jkySaCJbhzJkzqFGjBsaMGRPQ9hcuXEDhwoXx4osvqtd5Y+nSpejRowceeughrF69Wg2OXNatW2fy0QuCdfj999/x+OOPY9myZZg7dy4uXryI1q1bK435QrQiCN6Jp7Epk8PhcET1CATBC7wbmzJlihJJIDRr1gw1a9bEiBEj0q3v1q2bEvT06dNd626++Wa17dixY+W7F+KCw4cPK48hjcUmTZp43Ua0IggZE+tjU5aovbMQE5w/fx6pqalen+P9BgXkTrZs2dQSKZKTk9GvX79069q0aZOh+18QrKSlUPV08uRJ9bdAgQI+txGtCLFEOPVkBlbVmxiFQkiiy5E9O3y5mnPlyoWUlJR064YMGYKhQ4dG7Fs/cOAAihYtmm4dH3O9INhFS6Ho6fLlyyqmqWHDhqhWrZrP7UQrQqwQTj2ZhVX1JkahEDS8C6Po8tGl7vEc159IScHu3buRJ08e1/pIegkFIRa0FKqeGFvIOKXFixebfNSCEH96inXEKBRCJsFLxtJl518Kzl10kaZYsWI4ePBgunV8zPWCYActhaKnvn37qpglZk+WLFnS77aiFSHWMFtPZmJVvUn2sRAyWX0sVqB+/fqYP39+unXMxuR6QbCLlozqifFSNAgZEL9gwQKUK1cuw9eIVoRYwyw9hQOr6k08hULIJPrxFBqB8R1btmxxPd6+fTvWrFmjguNLly6NQYMGYe/evfjmm29c2/B5/bXMsOTjxMREVKlSRa1/6qmn0LRpUwwfPhzt2rXDxIkTsXLlSnzyySdBflpBiKyWgtETp4zHjx+PadOmqVqFepxS3rx5kT17dvV/z549UaJECVXTkIhWhFjDLD2lxNPYxJI0ghAMJ0+eZGiGoyzgKO+xcB2f4zaBsnDhQvUaz6VXr17qef5t2rRputd4275MmTLptvn+++8dlSpVciQmJjqqVq3qmDFjhvzggm20FIyevOmCy5dffunahlrStaUjWhFiAbP1tDCOxiapUygEzalTp5TnoYIzdsOdSwC2OEthRDOmUBDsriUiehIE0VMkkOljwRQXvTejUBCE0LUkehKE4BA9GUeMQiFkMnuJ25A2OYJgjpZET4IQHKIn44hRKISMeAoFwRzEsyEI5iF6Mo4YhYIpJ5HnieStYKggCMa1JHoShOAQPRlHjEIhLC56KYApCOZoSfQkCMEhejKOGIWCKS56zxNJjEJBMEdLoidBCA7Rk3HEKBRChtXhZfpYEMKjJSLhGIIgeooEYhQKppxEVmgbJAh2R7QkCKKnaCJGoRAyCV5qqwXT5k4Q4h1vWiKiJ0EQPUUCMQqFkLFKg3FBsDuiJUEQPUUTyQcIkrJly2LEiBGux2w4f8sttyBnzpzIly8frMD999+PoUOHGnrNb7/9pj5bMAOZ5yLE1jluJs2aNcPTTz8NO0KNZMqUCSdOnDB931lN0BM1T+0Lgj94jnTq1Mly15UdO3YofQVzTfnqq69M11O8EVdGoa+BiCeSUUPuzz//xCOPPOJ6/P7772P//v1Ys2YNNm3aBCvCz0mxeS5JSUkhn0QJHktcnVg2v7j7Ov89z3GeK1OnTkW0WbVqlTqWZcuWeX2+ZcuWuOOOO8L2/g0aNFBaZ69is/GmJXc9PfbYY1413LZtW9OPRTBXo/pvlTVrVpQrVw79+/fH+fPnw34zohtZHJvc+eCDD64yoqwKDUlv5/1bb70Vkp6Eq5Hp4yApXLhwusdbt25F7dq1UbFixWB3idTUVCQmMok+fOTJkwf//fdfunXB3JW54+3OS9rcxd45bhWosxo1auCLL77AzTfffNUAuHDhQvzyyy+G9+twOHDp0iVkyeL/skiNFitWDOHAlxfDXU80AL/88st0z2fLli0sxyOYh/67Xbx4Ud3Y9OrVS11733777ah8zeG4qQknr776Kh5++OF063Lnzh2ynoT0iMHsx/MybNgwXHPNNShYsCAef/xxJWZvLnD+/+OPP+Kbb75RItenbnbt2oWOHTsiV65cyhjr2rUrDh48mG6ap2bNmvjss8/UnaPuseM+Pv74Y7Rv3x45cuTA9ddfj+TkZGzZskV5OzlFTW8FDVGjcN8c0NyXokWLIhTEPW9t3nvvPVSvXl2dN6VKlUKfPn2QkpLi8j488MADOHnypOvuWw858DzHSefOndU2+mNvXkp643me6pw5cwY9e/ZUOqCehg8fftUxXrhwAc899xxKlCihjrNevXrq2Hzx0EMPYdKkSTh79my69fR88D04AH/77beoU6eOGjh4nt999904dOjQVZ6XX3/9VRmaNKy+++47ZM6cGStXrky3X34PZcqUweXLl6/y2Oie1tmzZyut8nPy/elN1ElLS8OTTz6ptuP1ZMCAAcoo8PzuApnu4nF6ajh//vw+vyvBGui/GzXI371Vq1aYO3duOg3wHClSpIgaCxo1aqS89frNTvPmzdX//K3dx5lZs2apbfVzi+OG+9jAsYXceOON6nW6Nj216+/9iX7ez58/X+mKYxPHIXcnA9+XYx7HFOqgbt26mDdvninfn65j94XXCn/I9LFxxCj0Ab0NPMH59+uvv1YXfl+udgqHgwCNPg4EdMtz8KA4jh07ht9//12Jf9u2bejWrVu619LQo0H5008/pXPv/9///Z8aSLmucuXKakB79NFHMWjQIDVg0avRt29fWAFv7nlvGZRCdKCRM3LkSKxfv16dywsWLFBTV4QXdRo8vGnhucuFxpkn+uBATwe3cR8sMuL5559XGpg2bRrmzJmjBpe//vor3TY8l3njM3HiRPz999/o0qWL0tTmzZu97vOee+5Rg9gPP/zgWkdN8PNxsEtISFA3cdTR2rVr1bQ3B1ZvsXYDBw5U01D//vsvbr/9djVYe3ri+Jiv5XfpDRqnvImkIbpo0SJ1Q+j+PdIbNG7cOLWfJUuW4NSpU16n4n1pSfQUW6xbtw5Lly5NNzNETXIs4DlMfVSoUAFt2rRRYwgNST5HaITp44x+09WvXz81LtBg4znKmzeOQWTFihXqL40zvo5jjTf8vb87L7zwgrqx4/vRq/7ggw+6nuPN5m233aaOY/Xq1UrDHTp0UHqIBqKnIHDEEU2bNnU89dRTV63/8ssvHXnz5nU97tWrl6NMmTKOtLQ017ouXbo4unXr5nrM599//33X444dO6rX6cyZM8eRkJDg2LVrl2vd+vXr6bV2rFixQj0eMmSII2vWrI5Dhw6lOx5u8+KLL7oeJycnq3Wff/65a92ECRMcSUlJfj8vj4fv4f45uZ+cOXOmW9q2bevaZuHCheqzBcLJkyfV/h4FHE96LFzH57iNEF74O/P8C5TJkyc7ChYs6PP893WO8/ecMmVKhu9NjVFr5PTp047ExETH999/73r+6NGjjuzZs7u0uHPnTqWVvXv3pttPy5YtHYMGDfL5Obp37+56HzJ//nx1jJs3b/a6/Z9//qme5zHp5zofT506Nd12kyZNcuTPn99x/vx59XjVqlWOTJkyObZv357udcePH3d9f3y8ZcsW1z7GjBnjKFq0qOsx/3/33Xddj3ltKV26tOu786cldz316NFDfVeeGn799ddd+6bm3a9FQvTh76H/btmyZVO/ZebMmR0//PCDej4lJUWNBePGjXO9JjU11VG8eHHHO++84/W888Xhw4fVdv/88496zPOWj1evXn3VMennn5H3nzdvnmubGTNmqHXnzp3zeTxVq1Z1jBo1yud1xRP9eN3ha3gd8TzvFy1a5NqG1wJq0YieZHy6Gokp9EHVqlWVt0GHU1L//PNPwMY2vQ68u+OiU6VKFeXi53N0qxNOSXmL3brhhhtc/+vTu5wCdF/HIGV6HOjlMeKC9/TSZM+eHaHAe13PSEiJ2bAO9BC8+eab2LhxozpfOJXJc4feLU4BhRN62xkry+lgnQIFCuC6665zPaauGMtXqVKldK+lJ5DTYYRTUTr33nsvxo4dqzwU9GTwPa699loVY9i0aVPl4SCM2+JUOD2Fx48fd3lO6LWgFnU4FeYOp9QYLjJlyhR0795dzRBw6s5fVj6/Rx6D+/VCn6rm1DzDRm666SbX87y2cMpaPyZ/WvLUE4/lo48+Svc8v1PB2ui/Gz17TEykl+3OO+9Uz/Ecpme7YcOGru2ZkMJzhuOFP+hNf/nll7F8+XIcOXIk3XlerVq1gI7NyPu7j008zwnP9dKlSytPITU3Y8YM5ZXktebcuXOmeAo54+Dp6We4iT8C0ZOQnrgyCmk88QLtCWODPINuKQh3GEvheQE3A18xEe7vryeCeFtn9Jg4taAPmuFsOi5xCdaAU6aMMerduzdef/11ZTwsXrxYxeTRWAvVKOT5pDkRr+AeexsIHEhoJNGIc78RczcG3UMr9JsgZhlzIKLRxgGD02KMxSUceGkwcuG0LW+8ODDxMT+3Pw1ySo+hG5zqZRbz+PHjXVN1vvB2vfD8XoLVkr7e/XjN1rAQftx/N97AMFnq888/V1oMBU7P0rnw6aefonjx4mpMoDHoeZ6bhb9xiCETDJViKAU/Kx0Od911lynHUqhQIcPnfSB6EuL4u6F3wtNLRrjO00sRKgw43717t1p0NmzYoAxQdy9FLCCJJtaFhhYv2IwBYqYuz/N9+/ZdZQTRUxfIYOC5HY0t94QKTwOO3jO+jl4MHXrt3Ms2MQCe+6W3gRd990XP8nVfx0B43SBlkgxjoGi48XNwACL0ih49elTFCjZu3FjF5bonmWTE//73P+Vh/fDDD5W3I5QSN7zhpGffPQ6Tn9fbtUgC4+MDnruDBw/Giy++qDxp1AnPX8abut9c8ZzRxws9/tBdgzzHGWPI/fAmieMO9eWOt9d5Esj7BwJfT28eYxo5s0X98sY0WoiejBNXRiG9JRyMmGHFYHaKiZmZEyZMwLPPPmvqezFYnaJgQDwv/gz2pfeB01ue01WRhN4LFtr2XELxgmZ1c9PrixQHjSz0gNMYc194Q0Ijihf3UaNGqUQnJkJw6tUdTovSW8fgcE4/eWb0um/HbXi+6ANPixYtVMA5M+85jTVkyBAVRO/u6aMnhJ48JrjwOc+EDRqq1An1QW/f9u3blV445c1pKH/QKNy7d68aYHv06OEKhaAHkYOc/rl//vlnlXQSKBxcaUQzS9h9v8HyxBNPqM/DZBted5566in1HXqWg/KmJU89cVrdU7/83QR7wWQqesbHjBmjvIgcn6gTZhPTgcDyK9Si7kmkN5Dny/Tp03H48GGlWWYiM8Tik08+UUmL1BiTTtzhTRTPX+6XYQzeZssCef9AYEk2PWmSYRtMkDRrhu306dNXnfcMh/FHIHoS4tgoLF++vMoMpBeBRhvjnL7//ntMnjzZ9OKvFC8HAIq2SZMm6v34/iyjEU0oIsaBeC5GvCi+XPSeixA5mNFLj5v78sorr6gpKt74MPuVU0qcSqVx4g4zkFkUmZnx9Py98847Xt+D3kZODTFOlvsnnI596aWXVOYi42R54aZx5867776rvHWc5qIOWOqC8XTucKqWr+PNGT36jOujl4LGnT/4PPdJA8s9C5Kfg9PK1DY9HfQYckrLCPoUu/t+g0U3LvkZ69evr4xlfneeheMzB6AnDtqe+uV3KtgLxhQy6556Y7gDz1HGGN53332oVauWMvJY5kgvN8T4OWqa2fL0PPO1vLlixj5nBKjvZ555RunN831YfYChFZxeZlUMb2T0/oHAaw235zWFeuc5zn2ZAeMmPc97vYqCLwLRk5CeTMw28VgnxAj0yNC7Y6TVHY0Lvi4Qlz8NTE6NvQLAsycK6/QPcXqwjCTCCIJVoGeRRiVnFcyG3hN6I1nGiu/jT0tG9US9U7926VYhCO7w3GVtRaOmCesvcuziYqae4o24SjQRwoO3qvEZR6gJgjXhtBwHptGjR+O1114zZZ87d+5UNRoZPsLpX+6b0+ScXgukA4PoSRCMI3oyjnhRhZAR97wQS3BajtPb9DyYMXVMOM1Hzx2n2Fn2g2V4mMhCb2G67URPgmAaoifjiKcwhmFcFusiGoHTzWxTZgQ9mNcd8WwIdsVf96JgYRyme2anES0Z1RONWb0FnyDYDY5ZTFgzCqeN2TbWbD3FGxJTKASNHrcxkgWwPZ47B+BJidkQhJC1JHoSBPPGJtGTf8RTKISMt96s0qtVEMzRkuhJEIJD9GScmDQKmdnHAr1s6eZZB0wwDrPAWGqE5Qzc68v5C+ZNC+J9WC6I5RRYXoEFkdlmjFPgGWVLsy7X+vXr1RQdi7i6t0JiJibLOLjDkicsSyQEhugpcnryFRgfjJ4EayJ6sp+eFsXR2BSTRiENQveew4I5sBhyyZIlvZ5EnsIz1uhMg7W6WFePwf2BdJBg9ma7du1UjT3W32NhZXaiYP0q1sdy72PNoH7X8WaJydM+bIieIqcnb1oKVk+CNRE92U9PZ+JobIr+EYQBegjJ7s5AniBKl9ecAXQsBbxype+3KWw9DdT6FZjZHGhYGLbh1EWg1JQr32u4po9vvfVWtQQKO3OwnhWLKhNmcrKvL5vNuwuPQtPbpQnG0X93xuYY9bszoPsCgGxhCClIdd7xh9a9OfI4nDG33vQk012xj2t86uhjfNKdXV4agQz9G5iyF1gb+GXS9349HjecC9QvCAwLtda0r+MPpNaJr+YnmX1vp8anaeHV061xNDbFpFGoTxlTcHm8pR5lQEJmIDFzcK/1Ry7nBSBnFvP3HQl8TcV7c9Hrjz3bEGXLlk0tZpCcnKy6WbhDwXlmT7P9GqcW2D2C3STY0SOjThnC1b974SBqWNEgZGfkAk7D0EyYX8uzS+uEbB84nu3yoSdf013SlitGx6esfiwWLymySVm0G7NgnB1X7dfjcUImIDEhyH37ex/P9f7wlRackPF2wegpXONTso3HJqlT6AXp8WJO03HCaXxmgemLZ4u1UGDvS7Z7coePKXQ2mSdsZcjyImwN9tFHHym3PluuMQZFEOyiJTEKBcF8PYVrfDpg47EpJj2FZiD5KcbuLBJ83G0wzsO9jZBZXsJAcXf533DDDUqIbCzPntdGGr0LmqfQ6AXjjNNTWJAecpO/RE4dp/Bia7MfJ83pKQxUS/p6QaDDQsamwMlIT9Ecn2616NgkRqEQMv6mjym4cPWWZCzGwYMH063jY75f9uzZfRZGrVSpkmr2LghWQ6aPBSFyegrX+FTMxmOT3ID6CASXQjaBE63pLsZgMKvLnblz56r1/vrabt26VWWBCYLVkOljIaOxSbC+nurbeGyKuqewdevWav6d9YWYPTRy5EjceOONqt0aXbm6VT1o0CB069YtYsclRmHgmJV9TFG43yUxxmLNmjUoUKCACr7lObB3715888036nmm+48ePRr9+/dXpQIWLFigXO8zZsxw7eO5555Dhw4dlFuepSDYPikhIQE9evRALBJOPRUN4mKqN1vj9LGxhosZc9J5nlkrdy9j/JXDkOxja2HF8UnGpsAxS08pcTQ2Rd0o5Bel9+dlQUgWd1y7dq16PGnSpKt6GUYCuRszhrdaUMGcWCtXrkTz5s1dj1n4k/Tq1UsF5LJo6K5dV6KxmPJPkT3zzDP44IMPVI2qzz77LF3K/549e5TIjh49isKFC6NRo0ZYtmyZ+j8WsaKehMDxVVct6hfqOMVqepKxKTp6WhlHY1PUrzW64MjJkyeD6kBy4cIFtcBHmrkQvZhCIzRr1kxVp/cFxeftNatXr/b5mokTJyJYTpw4oQaCP/74Azt37sTZs2eVYOkpoLgbNGgAq2FVPYl3I7ZjCu2oFTvrSYisnprF0dgUdaOQ9OzZEwsXLlT/z5w5M916/hA33XQT3nrrLZ8WNNPIPdvFCJEj1nof05X/8ssvq0r0rCHF848eAU4VHTt2TJ2rw4YNU25/uvwjGdYQTT2xHmBikEHLrFNYCOay17l/u2Ufs+h2rEwf210rdhufpFyaMeymJyvozRJGoT4P//XXX2PAgAFKeOw1yLn6ixcvqp6BdNO6C9Idzufr7lz9TiyUNnfhTvuPtSkAszyFVoF3Wzzf2OeySpUqXrdhrampU6dixIgRqqwB40OsgtX0JMSup9DuWrGbnjguidc9dvVkBb1lcvjziUYBWrycay9YkKHpGpyvZ6p2oEUdKToWojzZNbjOIeWmAHeXA143OVxk8ymg0s/AhzcBvSvBNpxKBfJ+r02fuKfv69/zGrZu8ngNfyl+fZ6vsQOM8XA//8ze3q56Yit3o3I6BGAqgDvC4Cn8i7E+jOsJQ7eUcHsKv/LQhj8tBaMnGi3vvvuuGjz4e3OqqVOnTj63/+2339LFTOnwtf7acMWSViI+Pt1lvKNJ/9XA1L3ApvbmdzSpPF37+2+7EJ0iEe5owjZ3eX8Ir56sQiT0FlVPIefFORdONyihdcsPwJYvfE6P55gwYYKykO1ekobt7UifFUDRJKBxEWBHCvDPCaBneSCLTQsE8bA9D92mH0VhVERWGeTCraeiQRhf+rW7UBiyhPVv/WenYcj2cQfYCgxAWViXK9FlgWlJX2+EM2fOoEaNGirz8Y47aJIHxn///ZdukCxSpEhMasWu41M4PThFkoA/DgMPLgdG1gYOnQfmHABuLwGUsFuDcZP1ZBUiobeoGoW00rt06aLcnUz5Z0zG9OnTVZHHO++8E5cuXVIxG+XLl3e58CNFOIzC4jmAg3cBT/0JdPkDuOym8IeWAae7XemPbCdibfrYWxwHm5kfOnQIly+n79j+5JNPwirEm55uAsAuod8DGOHx3PUAuiB+p7vYLcG9Y0Kg0Ah0T66IVa3YWU/hmj6e2RT4cTfwvxXAV9uvrO+zEpjSGOhUErYj1qaPI6G3qBqFDIZcsWKF1+f8Ze3YGd6NjWsE1P8P2HgK2HoamMM+YHRzTwL6VAJeqAYcvgCUyQmcvgjkzgqcSAVK5wQyWzCgJNHLtGIQs/aWhFlljz76KBITE9Vdl3v2If+30kAXb3riL8Eyrw8DWAzgrLN24U5OgQH4AUAJALUAHHFuewxAXue2/GsHLenrvWWuslaema25GLTOTNlq1aph6NChaNiwYUxqJRDiTU90SPQqD1TMDXyzAzh8XhujNpwCOv8B3FsWeND5/IVL2lh2LFWbAcuaCcibaD892ZmvwqQ3SySaxBs07J6snD5mr8YMYMcZ4MNN2uKNL+sD918LyxFr08fuvPTSSyrbi8Hi9BbEM5yI8N6gyX/vY5Lf2Ts5XJRx+389gLEANjiXuT4Ghf8DEI1ZsXMhTHd5Jigww5DGW6iwi8LYsWNRp04dZRSyphpLaixfvhy1atGszhjRSmzQoLC26PT+Exi7Bfhuh7Z4g1PM9CZazWkRa9PHkdBbUEZhcnIyfv75Z1XR+/jx48ifP7+6w2zfvr2hO0urEunzmskw2ztr/288Cfy6T7sT+2QLsOes5jkc+jfwQDIwbQ+w7yxw7hLwdGWgbC7g6AXt8WdbgLdvBLalAC2KAte4jXjrTwC5sgArjwFvrgN6lNW243udSQMO3Bl8cHFWL3deseKeZ0xR9+7dw2oQxrqeSCSz2aoCGOV8T3oMdwNIAzDL+RyTXw4DGACgJQD6gsoBaOVczyi6Jc7Yv7bOdVXdLpaXnd5IBrD/zSQNlidxTmPTE9kMQDB5AL60pK8nzCZ0j/kzy0t43XXXqUWHdc7Ycuv999/Ht99+axmtxLuWeImOdGboR3W15XgqMHMfsOsMsO4kMH4n8Hxl4N2NwM97gfK/AE0LA/MOAneXBe4pA6w4ClTNC7z8D9C5NHBtLqBwElDHLdSNs2DbU4C0y8A764Hzl4Ce5YCBa7Uxak1boEah8OjJzpwNk94MGYVs1cL0ZsZaMFPtlltuURcoTmls2LBB1W1ixg/r5LRo0QJ2JfUykBClO57KebWFDKx2ZX3LYkDjOcBUjnBucYieNJh95f+JjYBf9gDjvNzdreLo5UbmcVpGNDOjjRLLMYUPPfQQJk+ejIEDB5q+73jQ0+Uo1gWjhFm0QS/c0M75l4Pq+2xVBUDvTvq3c/FEnySkh/QhliehZ9/Ldh+4/T/bufSlsWVyDBTPj0hlS7IGGuOVrKCVjIgHLeljU5YojU35E4F73DK4xjnrIz9/PVBkCrDzDPCNc2pg2L/a4s6Cg1f+H1wNKJlDS7r0xvR9V/6vOQvImxXY1FGbsjZCLMcUPhQmvRkyCt9++21V+6ZJkyY+t2GF7TfeeMP2wstmseqWjYoA69sD0/cCd5YGsmUGBq8Bxu8ALjlvHZ+7HvjvFPDnUeDAeaC72/W8QCLQ6hogRwJwTzkt6/nQBeDOUpoHMvkI8NEmbSHL2wI3BXh3FsvTxyw8Sy/DrFmzUL16dWTNmv5y8t577wW9b7vpqWgQ062M5YMz89hKPYrfchp8zFZu7Zxm/sn5V6ercyp6m3PKd7Tbc/Qc8hLR1PlcLqdHkQkuLzq3cd/+a7fvjvGMdpjuoreN08pW0EqsaSmkscliF1d6/o7cAfy0G7gmO9CyuDaOfLEVWM8gX2gGYKtiwNLD2tj0xrr0+2As475zwJCqwPKjWqmegomap/C5NcDJi0BRBgkDeKQC8HHdwI7NSnoym3DpzZBROHu2mxvKB40bN8avv/4KO3PxMpDVgmdNlXzaovNNQ22ZsQfIkQVo7jbqpl4CnlypibHvdUA+P5G1S9sC8/YDt+huEwD1ONfm5NEK8ZtoQuHxvNen1jyDeUMhHvR0yaLBy5R3bbfHNZxLqnM6mM8VdMtg3uU0Gus5s5793TNOBvAqgH/c1rFkjs7ACATGp6SkYMuWLa7H27dvV0ZegQIFVNFlxiHt3bvXlTVLg4r9WqtWrYrz58+rmEJ63+bMmWMJrWREPGiJXHQAiRZzWJCC2YCH9XEiAehXBeh3HbD5NLDokDYdrNssrLrx1VZt/aCqwHXuGV+XgIYewcfcL+sQ6jCsigthDOPRO+Iz0eTNMOnNatdqy9yNWdEo9EU7L6UCeOEYyxEsQOhFdNyrxTLSIFx7/Mpz9EZmeBZ5noMOZyCXzRk+fDi++OIL3H8/SzfHN4y1y2nwNXrh2GJhKF4dLu7z4SUN0DmhGOP8u9PL/tiez5CWgtDTypUr0xWj1jtqsBsCsxZZcHnXLpq6GqmpqXj22WeVoZgjRw7ccMMNmDdvnteC1r4QrYQf3uwz09cuMFOZizs05B6soC2BQK8hxyby3gbgWVatxxUD89iF8OvJioRLb4aNQvcLiS94J2p3T2GijYxCM+G0+Ro9+IoD2hKtbA6nl32SELtGIQP5wxmgHut6Sovzu09mRS9ye9wqI8Pam5aC0BMzh/01q6Jh6E7//v3VYmWtxLuWdE+hnRwWZqM8kAwSvgT8she43V1cYdSTFQmX3gxfq8uWLatck54XHN1dyb9pafb9tnnnkeaw191YOGFGWIYXoaw+jEJ/d3A24amnnsKoUaMwcuTIsOw/1vV0Mc6NQk/SMvouvGnJJnoKt1biXUvx7rDw9l0Qv2VwbKynaOnN8LWaaf6esLI7m4UziLdChQB9whY/0awYt2FZfBmFMQCL1zK2ip0MGG/lGcz700+MNAseO+mpqDOhwgi6V4wRDgYTB2OSTD56sQY0iMW5VmJJS/ES2hROAvLb2FhP0dKbYaOQaf3usCbU4MGDVYuVjz/+2FCfTSuit54T3Rk8izy/sPQdd2wLW36F85yOeT05/4qeQtCS+xcZx1qJdy3p41O0yqVZDUeM6ylaegt6Vof1qwYMGICdO3eqqvqsmRNLHR/CnCwXW9Cr6vnTx8j39+WXX0bkfWJdT0KA0vCmpQxfFF9ayQjRkhALeoqW3gyPOuvWrUOHDh1w++23q4VlDx5++GEZwOKZrD6WGGDChAk+n3v++edD3r+d9FTErd5goIvuuzH6ulhdSJ5gtGQDPYVbK7GkJSFC2FhP0dKbYU8hWwax1hWbLWfPnh2ffPLJVdvYrfG5O+IhDAIKLMFHgTqb07t3b+Wmv/XWW9Otf+aZZzBx4kS8++67Ie0/1vUkmKAlm+gp3FrJCNGSEEt6ipbeDBuFjRo1Ullcv//+u9fn+ZwMYnGGt7LxMRDIS8aNG4cePXqoYF6e++SJJ55QQbwLFy4Mef+ip/jDEUwLBhvoKdxayQjRUnwR0AywjfUULb0ZNgp/+431/oV4gZ5TP+XOfN+NxciMTbt27fDhhx+q6ai5c+fi888/x7Rp05ToKlWqFPL+7aSn3NmB3AZjcbKzAkiq9trEGIjjCZmzQCKvumkGPRs20FO4tRJLWhIihI31FC29xXT5MPZSPGvQTXw+BtzKZkKDkGUQ/JIYu9PH5O6778aJEydUodDChQsrL3kslLcwyv7LwFmDF9P9MXBHbiaURSajWtJfaANEK4FzkOOTt5uDBN+/OfsGF88e7K8Th9hcT9HQW0wbhdf/Evxrk6ROoYI1sdigPJ6mj/WWYJ5QdLVq1VJ3Z6E2HbcjlUMo9hoDN+amjVH90mJnuku0EjyVZgT3uq72bspiGnrR6t4rY0dPVtBbTBuF4xsCuTPIMvJ2187C1U2ZaingieuAb7dn8EVk9XIm2bhxwOrVq72u5x3YqVOnXM+H0nTcjkwqCOTIwLrz9o1ckwBkyRauo7IX/zsKfJhiUEsW1pNoJXgmNQByePutdY1d9q6nWgVCeNMYou012t9Z+2NHT1bQm2GjsG/fvurNW7ZsqZqrW5l2JYA8vDUXgqZuIeCNmsDgNQaFZ2N7KRJB8XbUU9scQB5x+YXEmIJBGoUW1VMktRJLWiJtiwN5shqbPhaukC0BOHUXkOeH2NGTFfRm2CjcuHGjaqY+aNAgWwhPCJ2uZTIwCr256G1uPJQuXRodO3ZUQbzNmzdHlizhcarbSk9FYn1uITLcdQr44eqObP6nuyysp0hpJaa0JJhCRjOBdtRTtPVm+KsZOHAg3n//fbzyyiumHohgXQplNPWX6GOxMd9++y0SExPRp08fFCpUCN26dVMlABjUayaip/jjeee0lyEtWVhPkdJKRoiW4pOnKsWWnqKtN8NGYatWrTB8+HCUL1/elAMQrE+GnvYsXqrFB3HzsmjRItWRoHjx4iomYurUqQGVoWCAbbZs2VRcBT0FnowZMwZly5ZFUlIS6tWrpxqJZ0TTpk3Veb5582YsWbJEFcYdNWoUihUrhhYtWmDEiBHYtm0bQkX0FH9Uzm5QS0HqKVJESisZIVqKT169Ifx6WhRHY5ONnaiCZUjwsRjkzJkzqFGjhhJKIGzfvl3VaqILfc2aNXj66afxv//9D7Nnz3ZtM2nSJJWxxX7Cf/31l9p/mzZtcOjQoYCPq2rVqmpKatmyZdixY4cqGDp//nxUq1ZNLTNmBJlGaDeKWqBPXCws/B6Naskm1RBEK4KlMElPZ+JobMrkcGRYmth2MNg4b968ONlVEk1M+T5TgbzfAydPnkSePHmu/p47Xh0wfeoikHfa1a8JFN6NTZkyBZ06dfK5zYABA9RJz56nOt27d1du9FmzZqnHvPuqW7cuRo8erR5fvnwZpUqVUpXfOd3ki2PHjqn2c/44e/asEnnu3LmVlyJWcf3Ot/kIjBeMfZ/Uxsz02vCnJTP0FE5EK8Zw/dZ3RTjRxHO/kXofz/X+8HUsfurgKm38EDk9ZYrxsUk8hULo+Gk4TnG6LxcuhFDszoPk5OSrTnjeaXE9SU1NxapVq9JtkzlzZvVY38YXnCagiFkp3hc5cuRA586dY9ogFCyiJQsb46IVwa56Ctf4lGzjscmQUcgD2bBhg99t+DxdmFZg5bFoH0GckNmLa955ZvHOh3ds+vLmm2+a9rYHDhxA0aLp5+L4mOI+d+4cjhw5gkuXLnndhq/1x6efforDhw+jbdu2KuZj6NChyj1vJnbT08l8zqlPWUL7DooY1JKbnqxIJLQSa1oisTdHZ0Ey0FO4xqcDNh6bDIVb0l3KYMuCBQuqgMbKlSsr1ys/KMsBLFiwAEePHsUbb7wBK9ByHjCrBdCmeLSPJMbx5slwXvB2796dzj3PoFs7cN9996mFsSEMEP7666/x2muvqRgRxobwLowZYKFgNz2VnwAc7SUhGWHFl1fQEd9aiTUt6Z04xjeI9lHEt57sOD7dF2a9GfYUbtmyRQVGHj9+HB999JGa++ZfPn755ZdVRgxTpK1C2wXRPoI4wI97noJzX8wUHbOtDh48mG4dH/N9smfPrtL1ExISvG7D1wZCuXLlVPklCpCxIEWKFMGDDz6Ia665Bk8++WRIx283PaU5gAd+i/ZRxDg2nD6OhFZiTUtkwk5gjr9uHEJAXLwcvJ7CNT4Vs/HYZLjQAYMsmVXDxQxat26t3KWcT2dQ5MiRI3HjjTcqAbMAKd2sdOvSImamjRFq5gfWHAeGrgWG1jDlcAVveMvmikC2ZP369TFz5sx06xhnwfWEd0u1a9dW2Vh6UDCDefmY3Q+MwvgMLj/++CMeeeQRlYnG8zUU7KQn8tMOYE8OoKS1ch3shb+wJV+ZkTbJPg6nViKtpUjoqc1vgMM6M9q25Mdd1tNTfRuPTVGPVPn+++/x999/q7Rtpmfff//9av2jjz6qPtymTZtUJo++3ggTG2l/X/kHOHDO7CMXzK5TmJKSos4DLoR3QPx/1y5N9Uy979mzp2v7xx57TNVj6t+/v5oiYjNwnk/PPPOMaxueU4zBoIv933//Re/evVV5gQceeMDQse3cuVPFbvDujN4G1p9iwVCrEU49fewca6t9ZPZRC3auU2hXrURbT/ULan9fX2/2UccXBbKFX08pcTQ2Rd0ozJeP0esaTA/n3R7r9KxcuRL33nuvWn/nnXequX9ODxjhmuzAg9dq/4/bbu5xC26YNN3F35x34Vx00fB/Tv2Q/fv3u0RIKAKm/fMOjDWeWNDzs88+U1leOhTJsGHD1D5Y5JNCpqvdM8DXG8xEGz9+vLoDu/baa/Hll18q4fM85HtyyspqhFNP3as592teArkQI9PHdtRKtPU0rYn298vw1/WOaW4pFn49rYyjsckS95/8MHqjZ7pcKTDOi+s9/ShE9vvjl87K4N6+IPdUcgYX6yQ53cS/7AGerRL8MR67ACw6BLQrAWSNuikdWRwRmj5u1qwZ/JXN9FYRnq9ZvXq13/3SHW/UJc8WQhMnTlT1nthnkuflLbfcos5FqxM2PRW+ss3lwiyhEPwxrt8HnDgHNCjP40FcsdmC012hYGetRFNP2Zy/6daU0I6Pl8xZ+4Hq+YCSORB3vP1v+PXULI7GJksYhd988436SzcqXfH/93//Z+j1TCP31Yt5UFXgw03A74eA1EtAosGT4bcDwKj/gJ92X1mXKwuQkgbUKQAUzAZsPg28eSPQtQxikgF/ZbCBtzsvf8G/NmDx4sUqaJ3eAGY02olw6unuOsD4lUCT94HFzxo7rnOpwBfJwLcrgOUeFRRyZQPqlNY0ev4iMPVRoFR+xBxnU4E7PvGzgS8vhoX1ZGetRFtPrvfYDvQsZ+y49pwF3t4AfLQFuORhr5TLCVTJC/x3Cmh9DTCqtgWmBcPAggPAmxtiS0/R1lvQHU1orXKe3BNavXqF7mBgZg5r7lSsWFFV7ubdGA+Rd2b8MgK9E2P9Ib2jSePZwOLDQNviwK8tAj8WTjnfu+TK4+vyAAmZgA0nvW8/sg7wRGXEBDwrlh4GvtkGfOKcFfHZ0WQgkCcp/etPnQfyvmXNDgxWxBZ6+grIkwPI1FVb/88woFrpwI+l7evA7LVXHje+Hth7DNiWPgHPxaYPgIrXICagQfzDMuC7P4A5zu/AawcGL1pSz4ueoq4l0/V0l2bYVXXmI1zsBmTR6+pl0GnkRCqQ/8f065oW0camw17COwpnA/bd6dx/DHQ02XMa+Gob8NI/V9aJnswh6JuH7777zut6znMHClu+7Nu3z/WYTaZp+TK1msGS+nswo6ZkyZJeBUeYRu6ZWu7Oglu0v7P2AU3nAEsOBZbmrhuEq28DLt8DbLwdWN8BcNyrLVx3tjsw22loPrkSSD4MW3PwHPDWOuD6X4BGc4C5B4A2GQ3MNu7V6o233npLFRgNhOXLl5vS+9hOevrWOdtR/TngkY+Bw1eiNXzy80rNIKxaCjjyOXB5ErDoFWDrKMDxvbacHwekjgdur6O9ptJTwP7jsDWrtmnf0TWPAD1Ha8bhtTHU+zgaWomEliKlJ3rzGjvDMkpMBUZsDOzYei3T/r5xg2ZMXu4O/NYSOHSHls3M5WwXKMOT0FB8fIXmibcrPPZJO4C284EyPwNv/QvcWSqDF9lMT1bQm+Hp459//ln9ZTXuX375Jd08+9atW9MF5mYELfsuXbqoD8mU/8KFC2P69Olqbvzjjz9WGV0sNkoRMZAyWBgDeKEH8OJa4N0NmrEzvRnQrqTv1zzxp/b3kQpATR9tBjmFnz0L0Lo48HglYMwmoMFsYFgtoN/19ouVevJPbao8ewLQuRQwui7QohiQwj6R3xt00dv44sPOB4wR4rnJgrh16tRR5yZJS0tTz9MrwEGBg4Y+vRQMdtTTvU2AhpWBe0cBn84Hpv8FrHwTKO6nHWfHd7S/c14ACub2vk025zn0Qz8g533AxUtAjeeBmYOAOs6EMbtw+TKQ4Iz1LlkQeLwN8EBzoEIx4NRZIO/9Bqe7LKqnSGolklqKpJ4WtQKm7QE6/QE8sxpYdwr49GbA1/Bx4RLw894r4VG+4NiUncZBa6DeHG3GZ9dZYNzNGWTsWpBVR4E6v2r/NygMjK0DdCuj9TXWex/Hgp6soDfD08fMqiEMquXB6VA0zJoZPHgw2rdvj2jimopxTh+7s/wIcLPWjxrFkoASOYDfbgFyuZ04/EYyOzO6994BFA8wePdUqlYsO/kIULsAMK2Ztn+rc9kBfLoZeGyF9vhYFyB/tvSfi0ahz+njN3xMHw+27/Tx2rVr1VTTDz/8oD4nC43yjp/BvYSZZ6wez4EhKcnLfF8s6ulXIE/O9M+9+hUw5Asgdw6gYB6gXX1g9JWqC4qte4EKPYAKJYDNEwJ/3xUbgGZPAedTgb53ACOfgi04fhq451Xg1+Xa47SFQIKbZ+LUGSDvrT6mu7xoyep6ipRWYkFL6X7ruzSjxt0TVnMW8O8pLVadU75v1QA6ejgvHlwGfLkd+LAO0LtiYO/JMe2tjcDgNUD+ROCresDtZewxfbzqGFBntvb/wKpa/L77drpRGCt6irbego4p7Nq1q6q7Y0X8GYXkXBrw2RZg6N/AsVRtXblcwPK2QKFsQIt5wG8HgQq5gc0djb03v016Iwes1gzDpW2MJ7dEknUngLsXA/+c0B7zeOu7ZZkGZBS+5cMoHGhP0bnDgqKsU8ZaUPQYsBI9ywfwr5nYQk9ejEKyYz8w+idg+KQr6x7rCIx5BthzGCjTRVv3yoPAywbLuZ0+C9z4kGZYDusDPGvxyiaf/gI88q72f5ubgAlDgPwenlG/RqEXLdlFT5HSip215M8o1Jl7CBjzn+Y9JIxj/74h0Lkk8NlW4BHnLNbujgazjRPSO0X+7QBUzmtdo/D0ReDJVcBXznJy79YCntVn4AI1Cm2sp2jpLWij0MpkZBS6M3Mv8MrfwIqj6dfzLo0xhMG62UduBJ5aqRXQ7lYWlmTsJuDZVcDZS8Ci1kDjIt63y9AoHAbkye7xmnNA3ufsLTohMKNQJ/WiZhz2H8spvPTPPdsNeLdPcCEVZ88Dt/QDlq4DLi4AnJVALAU/e/ehwJQ/gKIFgDWfA8V8JAb6NQq9aEk9L3qKGTIyCnXjacdJrdzKWC/lDxk/yMQSQzj3u/wgcOvvQJMiwNRm1jQKVx8D7k0GNpwC3r8R6Hu9M0nGY7sMjULRk2FCurwmJyfjt99+U61+3G3L9957D3bhthLaMnkn0PWPK17DzbcDCSHk8D9xnWYUdl8MdCkDZLZQfOHGk8D7/17JKj7SRZuuCBoa3omxEbMRTSyvJyZI5PL9NE+Bfk8BT/UFeg0AxmkhXuj3ADBsUPBvS2dIr66aUfjIGOCLN2EZ+DP9NAd45CXg2Ang9WeAgY9mUMMxxaCWiOgptrQUAGVzAR/VBZ6/Hmg+X4sHZHz8rKZBGIRu1CsEVM2reSJXHAFuslDpJ3oHaQT31xqHIPkW4GY6voIdi0VPhgna7GFvvZYtW2LFihXqf7Z9YfAt+0TaERpuelbxtk6hGYSEHpEOJbT/WdbFCmxPAR5ZBlSdDszYB3QqBaR0D9EgJJl9LEJc6onxc98NAxybtGV4CAahTq87tL9f/giknEHUoZ0xfylwcxfgrieAEkWBt58HBvcOrai3Ty2JnuJSS6R8LmBnRy2jOLWblvwXKoOdjRz6Z1SDNkKcTQNGbQSu/QV46W+gfiFgmW4QhoLoyTBBX2pGjBiBX3/9FVOmTFG1m/h38uTJKuBR0PipKZCYGXhkOXD0QvQGL06Rd/4dqPwzMHU38H5tYGtHYEpTIKcZU3E2bMtlNURP/smWCExz9lzu0Q9R41QKMOoboHZnoNX92s3f/K+Bv38B+j8cv23urIRoKWNuLQ70KKs1dfjeX4edMLPpFNBvJVBuKvD0Ki2pZnN7YOktmkczZERPhgnaJDh48CCaNm3qyu6ii/7WW2/FfffdF+wuYw7GQLCsC43C7n8Ac1tFvtr7wNXAn854SRqDj1QEcpgdk5XVy5RXmsnvEePYQk9MQIpieGiHHkC1UcD0hcD3S4CuTu9hpErMDBsJDB8NHHLWIp03DWjRNIg4SS8xg361RERPsaUlC/BBHWDCDqDbEqB98TCMC37YdxZ4YY3WyYXVL9g+dlRdoJw/bQSD6ClynsJixYq5CnuWLVtWxW+sX79eiVC4wsMVgYq5NcMsLUKtdVja4LPNQMt5WuX7+a20QttPXx8m4ct0V8iInjKGxtc0Z/3hOQsQMfbtB9p1AQYMAdq3AXZvABwngZbNwlCLVKa7Qka0FBiFk4DB1bT/2ao1UrBTFpsjjNuh1fQ93wOY3lyL5Tcd0ZNhgjYRevfurSpmd+7cGf369UPr1q3V+hdeeCHYXcYsb90I3LlISzqh57CY2XdDHoJ7YCmw6bTW1m9CIyBfBhnYIeMtmNfGno1FixYF9TreHLnXRzOC6CkwypcDunYGPv8WqF0TePj+8GUj0zv4wUfAi69pcZKvDAZeHoDw4isw3qJ6ioZWMkK0FDgvVgOGb9Cykac2Bm4KY+vqI+eBPiuAybuASrmBj+qZEx8ZS3qygt5MK0mze/du1QuSRuK2bdtsU5ImEvAbfmktMPxfoHQO4K92JsXyefDtNuCBZE3YH9cDqpuUVZZhSZrJWk/cdK9h14Yu9ixJoxfBNQK7HDz99NN48sknEbN6OskWXdFPoz91yoEOHc5g0aJLeOaZRAwfnqS+fzM5e9aBhx8+h/HjL+LJJxPx6qtJyJs3k2nHnzfvKe8lNLxoycp6soJW7KQlIyVpwl4qxvl44T6t/Muh88C8FqFlNnt9HwDbUoBm87XyZ5y2Zjyj14ocAfY+Drgkjc30ZAW9mWaasME3e0KykKLg+aMAr9UEWhUDms8DWs0DktuaX3Ow9wrg/vJai6R0NZ3CDcXt+X7Rtx2ChtmK0Ub05Bsapr//ngt9+57D+++nomTJzOjXz7wEt7Q0B+644yz++CMN48dnR48eidHVkr7eglhBKxkhWvJP86LApvZAxelAqwXAxnbAtT5aUQabTNJgrlbz94/WQJlwTBPHiJ6soDcJAIwgzYoBj1UElh3RYv7MYtlhzSAskgR8UT/CBqG7i95zEYQw8v77SShSJBOeffY8du82L2C3cuUUzJ6dhmnTckbWIPSnpUTj00zsjVq8eHHlKZg6dWqGr2FceK1atVQFiQoVKuCrr74K/nMItoIzV5MaAGkO4J5kLfnDDNin+boZwNFUrWNYRA1CE/UUT4hRGGGG3gBkyQQ8vBxoNFsrJB0KTCphv2WS3CYMge+BEIMlNNgmKzXV2QMRwJ49e1RbIR32mXznnXcQX3Dq4lrLLFmzVsCIEVoD19KlT6NXrwRcvFg+pH3Onl0YW7deRo0a2dGq1XVhOvZyYS+hcebMGdSoUUPV6QvUA9GuXTs0b94ca9asUdNN7J86e7az6awfRCuxATta3V4CWH4UKDUN+GJr6Pvs7WzJN6IWkD8a1episCTN92EemwzHFI4cOdLnc2lpaXj++edxybPHVZzHFHpyMlXrP7nxlPaYmcHBGnMf/gc8/qfWo5m9msNBhjGFc65uf6ZaebW2X8yGDpuM79+/X4VEEH4GDpbly5d3lb2gFybUc91WejpZA3nyWK+R95IlKWjUaJP6/777CuCbb4LrK8lLYf36/yEtDVi+/DoksOlsGDh16hLy5l3rPQbKi5ZC1RM9hawj26lTJ5/bDBgwADNmzMC6detc67p3744TJ05g1ixns9woayUWtGTFmEL396E18MxfwAeanLCwBdCsaHDvs+e0Zly+fgMwuKrx3sdej91oTGEY9BRtwq03wzGFvLj4o0mTJkEdSDyRNxH493YttnD+AeDAOeAaI43NnbDEzVvrgXvLhc8gDPgs8ry4WbA/rRE875XC1SLcXnoqbclb7IYNmSlcFZkzT8H69bxjDs4oTE4+iuXLz2LatJuRkFAc4eMigLWBa0lf7xzs3OFUrxkNA9gWrlWr9IVU27RpozyGVtFKbGnJmtA5MaI2cEcpoOl8YOWxIIxCJ8P+BQokAk9UQvTIQE92xBFmvRn+ahYuXGjqAcQzenu5HWeCMwr/OATsPqv1WY4q3tzx1rMdLInoyVz++usE0tIuI0sQgbXffbcLZcrkQPv21yBq+JraynolacKdIUOGYOjQoSG/LVvAFS2afvTnYxqh586dU12rrI5oyTyOOWcnf9gNPHe98dczJnH8TuCB8kDuaI4FGehJuBqJKYwiT1XW/v64K/iahKxBWCeMtaUCIsHHIggRglOk99yjGUyrV58Iah9Llx5D69ZFkNlrrYwoaynhSnkVTnnpy6BBJjSWFgQP2hTTWrTuPRfcV7P1NHD4grafqJKBnoSYcqLan7pOY+63g8G9nqIrnt1HvadIEqOeQgbZMy6FMJB3/vz5rpgrxlrFH0yQsG5v80aNTmPcuN34888sqFuXCR3GOHRotorF0ZJBwsmFoD0bjB8KRwwUu4AwFskdPuZ7BeIlFK3EFtmzALULAMlHgKMXrsxqBQprHpISQcyAmUqMegpnh3FsEqMwimTNrAlv1THgrXXAQGfLISNlBE4zPCnaxGBMIenVq1e6x48++mi6x2YXTBZCo0uX69C791w8/vg83HZbeZQtq100AyVnzqw4ffpKVl88xUDVr18fM2fOTLdu7ty5an0giFZij55lNaOw9mxgewdjyZB6cwYmgUSVGIwpDLfeZPo4yixtA9QvBLy+DrhosNRatbxaTOHBIF38phFjKf/63VdGixUyGYUrFCyYHevWPYCkpCwYMmSJ4a+mevXCWLnyQHS/UpNKaKSkpKiMRC56yRn+v2uXFqvCaeeePXu6tn/sscdUt4/+/ftj48aN+PDDD1Xpi2eeeSbD9xKtxCasqftOTWDnGWDmPmOvrZRHK71Gh0dUicGSNJfDPDbZ3F62P4kJwBs1tU4njC3sbiBxsqUzHv6jTcDQGoge3mI0JGYjBmHJA2snHFStWgH33rsZ48Ytx+jRjyB37qSAX3vLLXXRp8947NyZH2XKhDNQ189dnK94J4N6Wrlypao5qMP+9LqHgUWpWdJCNxD19lksSUMj8IMPPkDJkiXx2WefqQxkIX55rjLw+nrgi21AuxKBvy5HFqBxYWAES6ZFM/vYJD3FE+IptEinE8Zs9Fis9S8+H6CRzw4mbYsDC4OMSTSNGLsTE+xNr171ce7cRVSs+BKSk7ciLS0wQd11Vy0kJWXFL7/8Dbt7Npo1a6ZKVXguepcS/mUHE8/XrF69GhcuXMDWrVtx//33m/nJBBvCWci7ywA/7QHuXgrsPavVMgyEu8sC289oJdeiRgx6CsONeAotwvBawP3JQM+l2tKwMPBOLaBBYf+vYz2px1YAxy9EqWI8nALzLBJuY9HRaxJMTIaRpuNC+GjUqAJuu60aZs5chwYNtMr+jz/eDC+/3A5FivhO0ihcODcaN66A6dP/Rt++V7xsUdeSvt6CiFZin5eqaZ7CCTu1hXxVD7ivnP8kx84lgUf/BGbsAx4Kd+5WjOjJCnoTo9Ai9CyvZSN/tBkY/R+w5DDQ0Nlh6rUawAvVvb+uZTGtJhT7Kd9qwL1vur85c+z4oIPt+Vq2bHBFk+2VfeylPYAF+fHHdzB37iqMHj0Vc+aswpgxv6mFrFgxGnXrOutBedCyZUO89to4OBzlw5hIdMaYlvT1FkS0Evtckx3Y2wn4fCswdB1w7hJw/3JtqVMAWNwKyOZlOpazXzXzAYsPR9EotJmerKA3MQotAsefKvmAUXWB92oDwzYAg7UYcby4Vlvc6VwK+KkpUNbZYPybbVE0Ck0sScNere+++64qpsveraNGjcJNN93kdduLFy/izTffxNdff429e/fiuuuuw9tvv422bdu6tmFh31deeSXd67gdg+l90bRp0+AOXrAMSUmJ6NChvlp27TqIJ54YjZ9/TlbP3XRT36u2nzjxBXTr1hy5cmVHSso5rFq1CXXqRKEqvM1KaIhW4gMaeP2raMuCA8D/VmhTw+x4kvT91dsf6QIUzKIlQv51HPjy5mgctbl6GhMnY5NN7eXYL1UzqBrguBfY1gmo7GXGa8puINN3QMI47TGbmEeNRB+LQSZNmqQC4tml4a+//lLCY6D7oUOHvG7/4osv4uOPP1bi3LBhg8qg7Ny5s4qLcqdq1aoqsF5fFi9eHOwnFWxI6dJFMW3a/8HhmIcJE15A5sxXX/a6d38dmTK1Qt++o9TjQ4dOWEtLFuzhLsQnLYoB224HLnUHHtLa7V5FoclApglaLd2oYpKeJsXR2CSeQotTLpfWJ9mdYywmOvnK41uLA8NqIXr4mT420qv1vffew8MPP4wHHnhAPR47dqzKiPziiy8wcODAq7b/9ttv8cILL+C2225Tj3v37o158+Zh+PDh+O6771zbZcmSRRXnFcyYPo5mk+3Q6d79WnTv/r9060aP/hpPPHGlVdwTT/RCixZ3hrFQ9+m4me4SYhfGE35WT1vcKf8LsD3lyuMJDRA9MtBToOPTe3E0NsmlxoYUyKZ5EcfeBPzSDJjZQpt6jhqZEoFM2TyWRFevVlZe1xe61L2RmpqKVatWoVWrVq519OjwcXKyNu3nCbMkk5LSlxxh9wXPu63NmzerThXly5fHPffck64UhyD07dsLZ85swLPP/g+7di3ByJFDkZSUzUJauqInQbA6nN1a1hZ47QbNm9i9TBQPJgM9BTI+pcbZ2CSeQhvzaDTrP6WDJ79nUD7rFqSqXq3ubbl8eQmPHDmiCm4WLVo03Xo+9hVjQfc97+CaNGmCa6+9VrX6+emnn9IV7qxXr54KzmWsBt3zjOFo3LixagmUO7e9vV6Rh1d381usWYEcOYBhwz6N0Lul905krKUrehIEO1CvEFAvPyyAfz0FMj4dibOxKSaNQtbjskSLnRhB/x717/VqErw4nS+HtVcrYZFduvQrV66sMkUpPrr36dLXufXWW13/33DDDUqIZcqUUd0aHnroobAcV8zqyWOqRQgO/Xv0ridvWiIG2x0J9h2fLoXpJ/fcb6Tex3O9P3wdyyXf2/kfn/zrKVzj0wc2Hpti0ig8fVqL2Sk1JdpHEnvfq96E++q7Me9GYaAUKlQICQkJOHgwfSVuPvYVc1G4cGFMnToV58+fx9GjR5UbnvEddMX7Il++fKhUqRK2bNli6PjiGZeeSpWK9qHEgZ68aYmIURhzepoW7SOJLcKlp0JxNjbFpFHIH4AZP1WqVLnKPWwFLwEHVysdV0bHxDswCo7fq3eyhWwUJiYmonbt2srN3qlTJ20Ply+rx337Xl1CxB3GbpQoUUKVAfjxxx/RtWtXvz1h2a3hvvvuM3R88YxV9WRFLYWuJ29aImIUxgqiJ3M1Hm49JcbZ2BSTRiGDQPlDhHv6MhSseFz+jsm7h9Bfg0njDbmZ8s/erHXq1FH1n0aMGIEzZ864Mr569uypflc9GHj58uWqBlTNmjXVX9Z9olj79+/v2udzzz2HDh06KLf8vn37VEkB3vX16NHD8PHFK1bXkxWPKXg9+WrWGnyDe8FaiJ7M13i49dQvjsammDQKhUiTZIpR2K1bNxw+fBgvv/yyKhBKQc2aNcsV4MvMLPcac3TNsx7Utm3bkCtXLpX+z1IAdMPr7NmzR4mMLny69Bs1aoRly5ap/wXBHloiYhQKQrT01C2OxqZMDt/ZA7aGbmfePZw8edJSXgQrHlewx3TldbWRJ0964Z06dQl5866y1OcUgieWzlsrHpc/LWnPi55iCSueu7F0TKKn4IlZTyFTy+mO9VUCJVpY8bhCP6aEWD6VhJg9b614XKKleMCK525sHpPoySgx6ykUws+Vu7EmyJMnvVF46lQa8uZdZKm7TkGwo5a050VPgiB6Cj/i3hFMIJuXDuNSJFIQzNGS6EkQgkP0ZBQxCgWTTiPPU0kc0IJgjpZET4IQHKIno4hRKJiU4eXp3fCW8SUIgnEtiZ4EIThET0bxVtXRdsyYMUMVl2Qw6tNPP53uOdYGeuKJJ1SbmQoVKmD06NGu51g7iOnfTC/nwobUVjguf8+ZCSuy33HHHarFzvXXX69qL+mwJyNjnPTvpnnz5hm46JM8FusEKwv215PVtWSenrxpSfRkZ0RPwSF6ig4x4SmsWLGi6ik4efJkVRXcne+++051Y9i0aZNKerjxxhvVBblq1arqeQ5c7hdvKxxXRsdsFizIyS4VbNTNQpwNGzZUS926ddXzfE+26skYbwVCxVNoV6yoJ6tryTw9+Sq2K3qyK6Kn4BA9RYeY8BSyX2CNGjWQJcvVNu6kSZNUY2pWCi9QoIAqQjlhwgRLH1ekjnnt2rWqqCbJmTMnmjRpogpsGifJxyLYESvqyepaMk9PvrQkerIroqfgED1Fh5gwCv3BSuNsI6NTtmxZtU6HngcONi1atMDChQstcVwZHbNZcDpu/PjxaoqN1dpnz56NHTt2uJ5fvHixmupq0KCB+p58I9PH8YIV9WQFLZmnJ5k+jidET74RPUUHW0wf169fH5s3b/b63OrVq1XD7GB47LHH8MILLyBr1qxYsmQJOnfujD///DPdIBKN4zKLjI5v+PDhqv8ip9SKFCmCZs2aqcGMtG/fXjXvzpEjB/7991+0bt1afZ6bb745wAwvactlVayoJ6trKXJ68pUtKXqyKqKn8HxvoqfoYAujMDk5OejXli5dGjt37lQnIOGdO9eRYsWKubZj7A8v5itXrgzYKAzXcfl7zgiBHB8D4N0HdT3WqlChQq71DJrntBgHeu9GobdA+EyGj1eIXz1ZXUuR05OvpBLRk1URPYmeYomYnz7u0qULPv30U1y6dAnHjh1TMUaMK9IbUuvwjmXNmjWoXr161I/L33NmwkbcFy9edN2ZMQi+T58+6vHevXvTZYEtWLBADfLekenjeMGKerKClszTk0wfxxOiJ9+InqKEIwaYN2+eo0SJEo7cuXM7cuXKpf6fNm2aei4tLc3Rp08fR7ly5Rzly5d3jBgxwvW6nj17OqpWreqoUaOGo1atWo7Jkydb4rj8PWcmM2fOdFSoUMFRuXJlR926dR2///6767lBgwY5qlSpor6b6tWrO8aMGXPV60+ePMkK1Y6TJ19xOBxvp1u4TnvuZFiOXYgvPVldS6HqyZ+WRE/2RvQUHKKn6CC9jwUT+rW+jTx50mdHnjp1HnnzDpDex4IQopZET4Jg3tgkeoqBmELB6uhTXu5ImztBMEdLoidBCA7Rk1HEKBRMwFvGpJxagmCOlkRPghAcoiejyMgtmECil4xJKaEhCOZoSfQkCMEhejKKGIWCCXjruHBZvllBMEVLoidBCA7Rk1HEKBRMQKaPBcEcZLpLEMxD9GQUMQqFMAXzyvSxIJijJdGTIASH6MkoYhQKYXLRi1EoCOZoSfQkCMEhejKKGIWCCcj0sSCYg0x3CYJ5iJ6MIkahECYXfZp8s4JgipZET4IQHKIno8R872O7MHHiRHTt2tXUfbZu3Rrz5s1D5Fz0nosgRAf76smXlkRPQnSwr5aI6MkoYhT6oVmzZhgxYgTCzeXLlzF48GC89NJLVz03d+5ctGzZEn369PF6fNmyZUOuXLlcS6FChVzPv/DCC3j++ecj6KL3XAQh/fkqegpWS6InQbRk3tgkevKFGIUWYObMmShQoACqV6/u9bmvvvpK9XKk8ejJ22+/jZSUFNdy5MgR13NNmjTBiRMnsGTJkggVCHVfuE4QIo+99eRNS6InITrYW0tE9GQUMQqD4ODBg8qdXrhwYZQuXVp55NLSrsTQlS1bFu+88w5uvvlm5M6dG02bNsXu3bt97u/nn39GixYtvD7Xt29fDBo0SHkLM2c29nNlypRJ7Zf7Dy8y3SUEj+gpEC3J9LEgWjJvbBI9+UKMwiC4++67kTVrVmzfvh1//PEHpk6dqoxAd7777jtMmDABhw8fRs6cOb1ODeusWbMGlStX9vrctddeq/b1wAMPBHOoqFKlitp/eDHPPT9mzBhlVCclJaFevXpYsWKFz20vXryIV199VX1H3L5GjRqYNWtWSPsUIo/oKXzTXUbOfXp9eCPpvvB1gn0QLYVPT2PiZGwSo9Age/fuxYIFC/Dee++pGL4yZcooTyEvqO4wBrBcuXLqx77nnnuwatUqn/s8fvw48uTJE9QPSC9ivnz5XMstt9yS7nnul/uPTIaX++Ktf6t/Jk2ahH79+mHIkCH466+/lJDatGmDQ4cOed3+xRdfxMcff4xRo0Zhw4YNeOyxx9C5c2esXr066H0KkUX0FIiWIqMn/Xqxf/9+17Jz507D7ytEB9FS+PQ0KY7GJjEKDbJnzx5l6BUtWtS1rnz58mq9O8WKFXP9T0/h6dOnfe4zf/78Ki4jGN58800Vm6EvTExxh/vl/sOLb/c83999uXDhgs+90NB++OGHlVeUHs6xY8ciR44c+OKLL7xu/+2336oEndtuu039Br1791b/Dx8+POh9CpFF9BS+6a5gzn16B3nt0hf365xgbURLxvUU6Pj0XhyNTWIUGqRkyZI4f/68ioPS2bFjh1ofLDVr1sTGjRsRDniXwv2Hk1OnznpdSKlSpZA3b17XQiPWG6mpqcqb2qpVK9c6xlDycXJystfXUMCe01vZs2fH4sWLg96nEFlET4FpSddToINYsOc+EwI4+0HdduzYEevXrzfplxbCjWjJuJ4CGZ9S42xsEqMwA5hAQiNQX1jypXnz5njuuedw5swZ7Nq1C6+//jp69eoV9I/QoUMHLFy4EOGA+23fvn1Y9p2YmKi8CZ7C4sJ1fI7G88mTJ10Lp7u9wcy0S5cuXeWZ4OMDBw54fQ1d7bzb2rx5s8p+o5f0p59+UtNewe5TCC+iJ+Na0vXEcJVAb7KCOfevu+465aWYNm2aimOmpho0aHDVLIhgDURLoekp0PHpSJyNTWIUZgDr/NHC1xdeOMePH49z586pO+qGDRuiXbt26N+/f9A/At3KPEnWrVtn+LUDBgxIV6eQy9GjR9VzTIJhjFDjxo0RDngnxGQbd1G5L3yuSJEi6hj0hXUVzeKDDz5AxYoVVZIOLwLM1KYr3miWthA5RE/BaYkLjTPPdb5usoKhfv366Nmzp5pZYMUEDmKssMDYKMF6iJZC01M4x6cP7Dw2OQRLMH78eEfXrl1N3Wfr1q0dc+bMcdiBCxcuOBISEhxTpkxJt75nz56O22+/3e9rz50759izZ4/j8uXLjv79+zuqVKkS8j4FeyN6Mufcv+uuuxzdu3cPwy8k2AXR0oW4GptsYLbGBz169FDZSGYye/bsq7KRrQrvpmrXro358+e71tHtzsf0YGR0V1iiRAk1nfLjjz+qWKhQ9ynYG9FT6Oc+p7f++ecfXHPNNWH8pQSrI1pKjK+xKaomqSC4MXHiREe2bNkcX331lWPDhg2ORx55xJEvXz7HgQMH1PP33XefY+DAga7tly1b5vjxxx8dW7dudSxatMjRokULR7ly5RzHjx8PeJ+CEKsY1dMrr7zimD17ttLTqlWrlIcwKSnJsX79+ih+CkGIPhPjaGySBoCCZejWrZsq9v3yyy+rYFvGNrHgpx6My6Qe95gMJv6wHtS2bdtULCVjM1kKgPUaA92nIMQqRvXEeqYskcFtWcaKnoylS5eqchmCEM90i6OxKRMtw6gegSAIgiAIghB1JKZQEARBEARBEKNQEARBEARBEKNQEARBEARBEKNQEARBEARBEKNQEARBEARBUEiiiSAIgiAIgiBGoSAIgiAIgiBGoSAIgiAIgiBGoSAIgiAIgiBGoSAIgiAIgqCQRBNBEARBEAQI/w+/u3zTBIb6iwAAAABJRU5ErkJggg==", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "fields = [\n", " ('Uniform', Ex_uniform, Ey_uniform),\n", @@ -245,7 +244,18 @@ "id": "6c63c324", "metadata": {}, "outputs": [], - "source": "lines = pw[Branch, ['Longitude', 'Longitude:1', 'Latitude', 'Latitude:1']]\nmagnitude = np.sqrt(Ex_varying**2 + Ey_varying**2)\n\nfig, axes = plt.subplots(1, 2, figsize=(6.5, 2.8))\nplot_network_efield(LON, LAT, magnitude, lines, lon, lat,\n Ex_varying, Ey_varying, SHAPE, ax=axes[0], fig=fig)\nplot_efield_vectors(LON, LAT, Ex_varying, Ey_varying, SHAPE,\n ax=axes[1], fig=fig)\nplt.tight_layout()\nplt.show()" + "source": [ + "lines = pw[Branch, ['Longitude', 'Longitude:1', 'Latitude', 'Latitude:1']]\n", + "magnitude = np.sqrt(Ex_varying**2 + Ey_varying**2)\n", + "\n", + "fig, axes = plt.subplots(1, 2, figsize=(6.5, 2.8))\n", + "plot_network_efield(LON, LAT, magnitude, lines, lon, lat,\n", + " Ex_varying, Ey_varying, SHAPE, ax=axes[0], fig=fig)\n", + "plot_efield_vectors(LON, LAT, Ex_varying, Ey_varying, SHAPE,\n", + " ax=axes[1], fig=fig)\n", + "plt.tight_layout()\n", + "plt.show()" + ] }, { "cell_type": "markdown", @@ -259,7 +269,18 @@ "id": "b0c1d2e3f4a5", "metadata": {}, "outputs": [], - "source": "pw.gic.configure()\npw.gic.model() # Cosntructs GIC Model\n\n# Uniform storm via PowerWorld (baseline)\npw.gic.storm(1.0, 90)\ngic_data = pw[GICXFormer, ['BusNum3W', 'BusNum3W:1', 'GICXFNeutralAmps']]\n\n# Top 10 transformers by GIC magnitude\ntop10 = gic_data.reindex(gic_data['GICXFNeutralAmps'].abs().sort_values(ascending=False).index).head(10)\nprint(top10.to_string(index=False))" + "source": [ + "pw.gic.configure()\n", + "pw.gic.model() # Cosntructs GIC Model\n", + "\n", + "# Uniform storm via PowerWorld (baseline)\n", + "pw.gic.storm(1.0, 90)\n", + "gic_data = pw[GICXFormer, ['BusNum3W', 'BusNum3W:1', 'GICXFNeutralAmps']]\n", + "\n", + "# Top 10 transformers by GIC magnitude\n", + "top10 = gic_data.reindex(gic_data['GICXFNeutralAmps'].abs().sort_values(ascending=False).index).head(10)\n", + "print(top10.to_string(index=False))" + ] }, { "cell_type": "markdown", @@ -278,7 +299,27 @@ "id": "d25b8b37", "metadata": {}, "outputs": [], - "source": "bus_coords = pw[Bus, ['BusNum', 'Longitude', 'Latitude']]\nxf_geo = gic_data.merge(bus_coords, left_on='BusNum3W', right_on='BusNum', how='inner')\ngic_mag = xf_geo['GICXFNeutralAmps'].abs()\n\nfig, axes = plt.subplots(1, 2, figsize=(6.5, 2.8))\nplot_gic_geo_map(lines, xf_geo, gic_mag, SHAPE,\n xlim=(lon_min, lon_max), ylim=(lat_min, lat_max),\n ax=axes[0], fig=fig)\n# Top transformer GICs\ntop = gic_mag.sort_values(ascending=False).head(15)\naxes[1].barh(range(len(top)), top.values, color='#4C72B0')\naxes[1].set_yticks(range(len(top)))\naxes[1].set_yticklabels([f'XF {i+1}' for i in range(len(top))], fontsize=7)\naxes[1].invert_yaxis()\nformat_plot(axes[1], title='Top 15 Transformer GICs',\n xlabel='|GIC| (A)', plotarea='white',\n titlesize=11, labelsize=9, ticksize=8)\nplt.tight_layout()\nplt.show()" + "source": [ + "bus_coords = pw[Bus, ['BusNum', 'Longitude', 'Latitude']]\n", + "xf_geo = gic_data.merge(bus_coords, left_on='BusNum3W', right_on='BusNum', how='inner')\n", + "gic_mag = xf_geo['GICXFNeutralAmps'].abs()\n", + "\n", + "fig, axes = plt.subplots(1, 2, figsize=(6.5, 2.8))\n", + "plot_gic_geo_map(lines, xf_geo, gic_mag, SHAPE,\n", + " xlim=(lon_min, lon_max), ylim=(lat_min, lat_max),\n", + " ax=axes[0], fig=fig)\n", + "# Top transformer GICs\n", + "top = gic_mag.sort_values(ascending=False).head(15)\n", + "axes[1].barh(range(len(top)), top.values, color='#4C72B0')\n", + "axes[1].set_yticks(range(len(top)))\n", + "axes[1].set_yticklabels([f'XF {i+1}' for i in range(len(top))], fontsize=7)\n", + "axes[1].invert_yaxis()\n", + "format_plot(axes[1], title='Top 15 Transformer GICs',\n", + " xlabel='|GIC| (A)', plotarea='white',\n", + " titlesize=11, labelsize=9, ticksize=8)\n", + "plt.tight_layout()\n", + "plt.show()" + ] }, { "cell_type": "markdown", @@ -298,33 +339,24 @@ "id": "a5b6c7d8e9f0", "metadata": {}, "outputs": [], - "source": "directions = np.arange(0, 361, 10)\nmax_gics = []\n\nfor d in directions:\n pw.gic.storm(1.0, d)\n gic_vals = pw[GICXFormer, 'GICXFNeutralAmps']['GICXFNeutralAmps']\n max_gics.append(gic_vals.abs().max())\n\nmax_gics = np.array(max_gics)" + "source": [ + "directions = np.arange(0, 361, 10)\n", + "max_gics = []\n", + "\n", + "for d in directions:\n", + " pw.gic.storm(1.0, d)\n", + " gic_vals = pw[GICXFormer, 'GICXFNeutralAmps']['GICXFNeutralAmps']\n", + " max_gics.append(gic_vals.abs().max())\n", + "\n", + "max_gics = np.array(max_gics)" + ] }, { "cell_type": "code", - "execution_count": 14, + "execution_count": null, "id": "5e7fcc21", "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Worst-case direction: 10 degrees\n", - "Worst-case max GIC: 184.07 Amps\n" - ] - } - ], + "outputs": [], "source": [ "plot_direction_sensitivity(directions, max_gics,\n", " title='Maximum Transformer GIC vs. Storm Direction')" @@ -366,4 +398,4 @@ }, "nbformat": 4, "nbformat_minor": 5 -} \ No newline at end of file +} diff --git a/examples/gic/04_b3d_file_io.ipynb b/examples/gic/04_b3d_file_io.ipynb index 4047c6b..ebbf25f 100644 --- a/examples/gic/04_b3d_file_io.ipynb +++ b/examples/gic/04_b3d_file_io.ipynb @@ -22,7 +22,13 @@ ] }, "outputs": [], - "source": "import numpy as np\nimport matplotlib.pyplot as plt\nfrom esapp.utils import B3D\nfrom examples.map import format_plot, border" + "source": [ + "import sys; sys.path.insert(0, \"..\")\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from esapp.utils import B3D\n", + "from map import format_plot, border" + ] }, { "cell_type": "code", @@ -181,4 +187,4 @@ }, "nbformat": 4, "nbformat_minor": 5 -} \ No newline at end of file +} diff --git a/examples/gic/05_gic_sensitivity.ipynb b/examples/gic/05_gic_sensitivity.ipynb index f879bf0..dadf284 100644 --- a/examples/gic/05_gic_sensitivity.ipynb +++ b/examples/gic/05_gic_sensitivity.ipynb @@ -24,7 +24,14 @@ ] }, "outputs": [], - "source": "import numpy as np\nimport matplotlib.pyplot as plt\nfrom esapp import PowerWorld\nfrom esapp.components import Bus, GICXFormer\nfrom examples.map import format_plot" + "source": [ + "import sys; sys.path.insert(0, \"..\")\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from esapp import PowerWorld\n", + "from esapp.components import Bus, GICXFormer\n", + "from map import format_plot" + ] }, { "cell_type": "code", @@ -37,11 +44,19 @@ ] }, "outputs": [], - "source": "# This cell is hidden in the documentation.\nimport ast\n\nwith open('../data/case.txt', 'r') as f:\n case_path = ast.literal_eval(f.read().strip())\n\npw = PowerWorld(case_path)" + "source": [ + "# This cell is hidden in the documentation.\n", + "import ast\n", + "\n", + "with open('../data/case.txt', 'r') as f:\n", + " case_path = ast.literal_eval(f.read().strip())\n", + "\n", + "pw = PowerWorld(case_path)" + ] }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "id": "718ac5d2", "metadata": { "tags": [ @@ -74,7 +89,14 @@ "id": "d7e8f9a0", "metadata": {}, "outputs": [], - "source": "pw.gic.configure()\npw.gic.model()\n\n# Apply a baseline storm to get signed currents\npw.gic.storm(1.0, 90)\ngic_baseline = pw[GICXFormer, 'GICXFNeutralAmps']['GICXFNeutralAmps'].to_numpy()" + "source": [ + "pw.gic.configure()\n", + "pw.gic.model()\n", + "\n", + "# Apply a baseline storm to get signed currents\n", + "pw.gic.storm(1.0, 90)\n", + "gic_baseline = pw[GICXFormer, 'GICXFNeutralAmps']['GICXFNeutralAmps'].to_numpy()" + ] }, { "cell_type": "markdown", @@ -94,25 +116,21 @@ "id": "f5a6b7c8", "metadata": {}, "outputs": [], - "source": "# Compute dI/dE Jacobian using H-matrix and baseline currents\nJ = pw.gic.dIdE(pw.gic.H, i=gic_baseline)\n\nprint(f\"dI/dE Jacobian shape: {J.shape}\")\nprint(f\" Rows: {J.shape[0]} (transformers)\")\nprint(f\" Cols: {J.shape[1]} (branch voltages)\")" + "source": [ + "# Compute dI/dE Jacobian using H-matrix and baseline currents\n", + "J = pw.gic.dIdE(pw.gic.H, i=gic_baseline)\n", + "\n", + "print(f\"dI/dE Jacobian shape: {J.shape}\")\n", + "print(f\" Rows: {J.shape[0]} (transformers)\")\n", + "print(f\" Cols: {J.shape[1]} (branch voltages)\")" + ] }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "id": "6801577a", "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "J_dense = J if isinstance(J, np.ndarray) else J.toarray()\n", "plot_jacobian_sensitivity(J_dense)" @@ -130,29 +148,10 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": null, "id": "f7a8b9c0", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Rank XF Index Total Sensitivity \n", - "------------------------------------\n", - "1 8 14.8116 \n", - "2 7 14.8116 \n", - "3 9 14.8116 \n", - "4 1 12.5756 \n", - "5 0 12.5756 \n", - "6 2 12.5756 \n", - "7 6 12.5283 \n", - "8 5 12.5283 \n", - "9 11 11.5955 \n", - "10 10 11.5955 \n" - ] - } - ], + "outputs": [], "source": [ "# Rank transformers by total sensitivity\n", "sensitivity = np.sum(np.abs(J_dense), axis=1)\n", @@ -176,29 +175,10 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "id": "da92e31d", "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Top 5 most influential branches: [18 17 12 13 14]\n" - ] - } - ], + "outputs": [], "source": [ "col_sens = np.sum(np.abs(J_dense), axis=0)\n", "plot_branch_impact(col_sens)" @@ -221,7 +201,21 @@ "id": "d3e4f5a6", "metadata": {}, "outputs": [], - "source": "directions = np.arange(0, 360, 5)\nn_xf = min(5, len(gic_baseline))\ntop_xf_idx = np.argsort(np.abs(gic_baseline))[::-1][:n_xf]\n\ngic_profiles = np.zeros((len(directions), n_xf))\n\nfor i, d in enumerate(directions):\n pw.gic.storm(1.0, d)\n gic_vals = pw[GICXFormer, 'GICXFNeutralAmps']['GICXFNeutralAmps'].to_numpy()\n gic_profiles[i] = np.abs(gic_vals[top_xf_idx])\n\nplot_direction_profiles(directions, gic_profiles,\n labels=[f'XF {idx}' for idx in top_xf_idx])" + "source": [ + "directions = np.arange(0, 360, 5)\n", + "n_xf = min(5, len(gic_baseline))\n", + "top_xf_idx = np.argsort(np.abs(gic_baseline))[::-1][:n_xf]\n", + "\n", + "gic_profiles = np.zeros((len(directions), n_xf))\n", + "\n", + "for i, d in enumerate(directions):\n", + " pw.gic.storm(1.0, d)\n", + " gic_vals = pw[GICXFormer, 'GICXFNeutralAmps']['GICXFNeutralAmps'].to_numpy()\n", + " gic_profiles[i] = np.abs(gic_vals[top_xf_idx])\n", + "\n", + "plot_direction_profiles(directions, gic_profiles,\n", + " labels=[f'XF {idx}' for idx in top_xf_idx])" + ] }, { "cell_type": "markdown", @@ -259,4 +253,4 @@ }, "nbformat": 4, "nbformat_minor": 5 -} \ No newline at end of file +} diff --git a/examples/injection.py b/examples/injection.py deleted file mode 100644 index 41c9e42..0000000 --- a/examples/injection.py +++ /dev/null @@ -1,117 +0,0 @@ -""" -Normalized injection vector for power system sensitivity studies. - -Represents a pattern of power injections across system buses, -normalized so that total supply equals total demand plus losses. -Useful for computing power transfer distribution factors (PTDFs) -and line outage distribution factors (LODFs). -""" - -from __future__ import annotations - -import numpy as np -from numpy.typing import NDArray -from pandas import DataFrame - - -class InjectionVector: - """ - Normalized injection vector for power system sensitivity studies. - - Represents a pattern of power injections across system buses, - normalized so that total supply equals total demand plus losses. - Useful for computing power transfer distribution factors (PTDFs) - and line outage distribution factors (LODFs). - - Parameters - ---------- - loaddf : pandas.DataFrame - DataFrame containing at least a 'BusNum' column for all buses. - losscomp : float, default 0.05 - Loss compensation factor. Supply is scaled up by (1 + losscomp) - to account for system losses. - - Attributes - ---------- - loaddf : pandas.DataFrame - Internal DataFrame with 'Alpha' column for injection values, - indexed by BusNum. - losscomp : float - Loss compensation factor. - - Examples - -------- - >>> inj = InjectionVector(bus_df, losscomp=0.05) - >>> inj.supply(101, 102) # Set buses 101, 102 as supply - >>> inj.demand(201) # Set bus 201 as demand - >>> alpha = inj.vec # Get normalized injection vector - """ - - def __init__(self, loaddf: DataFrame, losscomp: float = 0.05) -> None: - self.loaddf = loaddf.copy() - self.loaddf['Alpha'] = 0.0 - self.loaddf = self.loaddf.set_index('BusNum') - self.losscomp = losscomp - - @property - def vec(self) -> NDArray[np.float64]: - """ - Get the current injection vector as a numpy array. - - Returns - ------- - np.ndarray - Injection values for all buses in bus number order. - """ - return self.loaddf['Alpha'].to_numpy() - - def supply(self, *busids: int) -> None: - """ - Set specified buses as supply points (positive injection). - - The injection vector is automatically normalized after this call. - - Parameters - ---------- - *busids : int - Bus numbers to set as supply points. - """ - self.loaddf.loc[list(busids), 'Alpha'] = 1.0 - self.norm() - - def demand(self, *busids: int) -> None: - """ - Set specified buses as demand points (negative injection). - - The injection vector is automatically normalized after this call. - - Parameters - ---------- - *busids : int - Bus numbers to set as demand points. - """ - self.loaddf.loc[list(busids), 'Alpha'] = -1.0 - self.norm() - - def norm(self) -> None: - """ - Normalize the injection vector. - - Scales supply and demand so that: - - Total supply = (1 + losscomp) * total demand - - Supply buses sum to 1.0 - - Demand buses sum to -1.0 - - This ensures power balance accounting for system losses. - """ - alpha = self.vec - is_supply = alpha > 0 - is_demand = alpha < 0 - - supply_sum = np.sum(alpha[is_supply]) - demand_sum = -np.sum(alpha[is_demand]) - - if supply_sum > 0: - self.loaddf.loc[is_supply, 'Alpha'] /= supply_sum / (1 + self.losscomp) - if demand_sum > 0: - self.loaddf.loc[is_demand, 'Alpha'] /= demand_sum diff --git a/examples/map.py b/examples/map.py index eb9a20b..602eb4f 100644 --- a/examples/map.py +++ b/examples/map.py @@ -148,7 +148,7 @@ def border(ax: Axes, shape: str = 'Texas') -> None: ax : matplotlib.axes.Axes The axes to plot on. shape : str, default 'Texas' - Name of the shape directory under ``esapp/utils/shapes/``. + Name of the shape directory under ``examples/shapes/``. """ shapepath = _SHAPES_DIR / shape / 'Shape.shp' shapeobj = gpd.read_file(shapepath) diff --git a/examples/mesh.py b/examples/mesh.py index ffaf8ac..eee8927 100644 --- a/examples/mesh.py +++ b/examples/mesh.py @@ -32,6 +32,7 @@ # Matrix transformations 'normlap', 'hermitify', + 'sorteig', # Mesh utilities 'Mesh', 'extract_unique_edges', @@ -827,3 +828,27 @@ def hermitify(A: NDArray | sp.spmatrix) -> NDArray: dense = A if isinstance(A, np.ndarray) else A.toarray() return (np.triu(dense).conjugate() + np.tril(dense)) / 2 + +def sorteig(vals: NDArray, vecs: NDArray) -> tuple[NDArray, NDArray]: + """ + Sort an eigendecomposition by ascending eigenvalue. + + Eigensolvers such as :func:`scipy.sparse.linalg.eigsh` do not + guarantee ordering; this reorders both the eigenvalues and the + corresponding eigenvector columns. + + Parameters + ---------- + vals : np.ndarray + Eigenvalues, shape ``(k,)``. + vecs : np.ndarray + Eigenvectors as columns, shape ``(n, k)``. + + Returns + ------- + tuple[np.ndarray, np.ndarray] + ``(vals, vecs)`` sorted by ascending eigenvalue. + """ + order = np.argsort(vals) + return vals[order], vecs[:, order] + diff --git a/examples/network/01_matrix_extraction.ipynb b/examples/network/01_matrix_extraction.ipynb index 0fabb97..88595e7 100644 --- a/examples/network/01_matrix_extraction.ipynb +++ b/examples/network/01_matrix_extraction.ipynb @@ -20,11 +20,23 @@ ] }, "outputs": [], - "source": "# This cell is hidden in the documentation.\nfrom esapp import PowerWorld\nfrom esapp.components import *\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport ast\n\nwith open('../data/case.txt', 'r') as f:\n case_path = ast.literal_eval(f.read().strip())\n\npw = PowerWorld(case_path)" + "source": [ + "# This cell is hidden in the documentation.\n", + "from esapp import PowerWorld\n", + "from esapp.components import *\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import ast\n", + "\n", + "with open('../data/case.txt', 'r') as f:\n", + " case_path = ast.literal_eval(f.read().strip())\n", + "\n", + "pw = PowerWorld(case_path)" + ] }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": { "tags": [ "hide-cell" @@ -59,7 +71,12 @@ "id": "e5f6a7b8", "metadata": {}, "outputs": [], - "source": "Y = pw.ybus()\nprint(f\"Y-Bus shape: {Y.shape} (n_bus x n_bus)\")\nprint(f\"Non-zeros: {Y.nnz}\")\nprint(f\"Density: {Y.nnz / (Y.shape[0] * Y.shape[1]):.2%}\")" + "source": [ + "Y = pw.ybus()\n", + "print(f\"Y-Bus shape: {Y.shape} (n_bus x n_bus)\")\n", + "print(f\"Non-zeros: {Y.nnz}\")\n", + "print(f\"Density: {Y.nnz / (Y.shape[0] * Y.shape[1]):.2%}\")" + ] }, { "cell_type": "markdown", @@ -77,7 +94,11 @@ "id": "c9d0e1f2", "metadata": {}, "outputs": [], - "source": "A = pw.network.incidence()\n\nplot_incidence_and_laplacian(A)" + "source": [ + "A = pw.network.incidence()\n", + "\n", + "plot_incidence_and_laplacian(A)" + ] } ], "metadata": { @@ -101,4 +122,4 @@ }, "nbformat": 4, "nbformat_minor": 5 -} \ No newline at end of file +} diff --git a/examples/network/02_network_topology.ipynb b/examples/network/02_network_topology.ipynb index 925f272..00173ee 100644 --- a/examples/network/02_network_topology.ipynb +++ b/examples/network/02_network_topology.ipynb @@ -17,7 +17,16 @@ ] }, "outputs": [], - "source": "import numpy as np\nfrom scipy.sparse.linalg import eigsh\nfrom esapp import PowerWorld\nfrom esapp.components import Branch, Bus\nfrom esapp.utils import BranchType, sorteig\nfrom examples.map import format_plot" + "source": [ + "import sys; sys.path.insert(0, \"..\")\n", + "import numpy as np\n", + "from scipy.sparse.linalg import eigsh\n", + "from esapp import PowerWorld\n", + "from esapp.components import Branch, Bus\n", + "from esapp.utils import BranchType\n", + "from mesh import sorteig\n", + "from map import format_plot" + ] }, { "cell_type": "code", @@ -29,7 +38,15 @@ ] }, "outputs": [], - "source": "# This cell is hidden in the documentation.\nimport ast\n\nwith open('../data/case.txt', 'r') as f:\n case_path = ast.literal_eval(f.read().strip())\n\npw = PowerWorld(case_path)" + "source": [ + "# This cell is hidden in the documentation.\n", + "import ast\n", + "\n", + "with open('../data/case.txt', 'r') as f:\n", + " case_path = ast.literal_eval(f.read().strip())\n", + "\n", + "pw = PowerWorld(case_path)" + ] }, { "cell_type": "code", @@ -63,7 +80,16 @@ "id": "e5f6a7b8", "metadata": {}, "outputs": [], - "source": "bmap = pw.network.busmap()\nprint(f\"Bus count: {len(bmap)}\")\nprint(f\"First 5 mappings:\")\nprint(bmap.head())\n\nA = pw.network.incidence()\nprint(f\"\\nIncidence matrix: {A.shape} (branches x buses)\")\nprint(f\"Non-zeros: {A.nnz}\")" + "source": [ + "bmap = pw.network.busmap()\n", + "print(f\"Bus count: {len(bmap)}\")\n", + "print(f\"First 5 mappings:\")\n", + "print(bmap.head())\n", + "\n", + "A = pw.network.incidence()\n", + "print(f\"\\nIncidence matrix: {A.shape} (branches x buses)\")\n", + "print(f\"Non-zeros: {A.nnz}\")" + ] }, { "cell_type": "markdown", @@ -81,7 +107,16 @@ "execution_count": null, "metadata": {}, "outputs": [], - "source": "L_len = pw.network.laplacian(BranchType.LENGTH)\nL_res = pw.network.laplacian(BranchType.RES_DIST)\n\nprint(f'Length-weighted Laplacian: {L_len.shape}, nnz={L_len.nnz}')\nprint(f'Impedance-weighted Laplacian: {L_res.shape}, nnz={L_res.nnz}')\n\nplot_spy_matrices([L_len, L_res],\n ['Length-Weighted Laplacian', 'Impedance-Weighted Laplacian'])" + "source": [ + "L_len = pw.network.laplacian(BranchType.LENGTH)\n", + "L_res = pw.network.laplacian(BranchType.RES_DIST)\n", + "\n", + "print(f'Length-weighted Laplacian: {L_len.shape}, nnz={L_len.nnz}')\n", + "print(f'Impedance-weighted Laplacian: {L_res.shape}, nnz={L_res.nnz}')\n", + "\n", + "plot_spy_matrices([L_len, L_res],\n", + " ['Length-Weighted Laplacian', 'Impedance-Weighted Laplacian'])" + ] }, { "cell_type": "markdown", @@ -99,7 +134,17 @@ "execution_count": null, "metadata": {}, "outputs": [], - "source": "lengths = pw.network.lengths()\nzmag = pw.network.zmag()\n\nplot_histograms([lengths, zmag],\n ['Branch Length Distribution', 'Impedance Magnitude Distribution'],\n ['Length (km)', '|Z| (pu)'])\n\nprint(f'Length range: [{lengths.min():.3f}, {lengths.max():.3f}] km')\nprint(f'|Z| range: [{zmag.min():.6f}, {zmag.max():.6f}] pu')" + "source": [ + "lengths = pw.network.lengths()\n", + "zmag = pw.network.zmag()\n", + "\n", + "plot_histograms([lengths, zmag],\n", + " ['Branch Length Distribution', 'Impedance Magnitude Distribution'],\n", + " ['Length (km)', '|Z| (pu)'])\n", + "\n", + "print(f'Length range: [{lengths.min():.3f}, {lengths.max():.3f}] km')\n", + "print(f'|Z| range: [{zmag.min():.6f}, {zmag.max():.6f}] pu')" + ] }, { "cell_type": "markdown", @@ -184,4 +229,4 @@ }, "nbformat": 4, "nbformat_minor": 5 -} \ No newline at end of file +} diff --git a/examples/network/03_network_expansion.ipynb b/examples/network/03_network_expansion.ipynb index 14e6232..8ed0371 100644 --- a/examples/network/03_network_expansion.ipynb +++ b/examples/network/03_network_expansion.ipynb @@ -26,7 +26,20 @@ ] }, "outputs": [], - "source": "# This cell is hidden in the documentation.\nfrom esapp import PowerWorld\nfrom esapp.components import *\nfrom esapp.saw._helpers import create_object_string\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport ast\n\nwith open('../data/case.txt', 'r') as f:\n case_path = ast.literal_eval(f.read().strip())\n\npw = PowerWorld(case_path)" + "source": [ + "# This cell is hidden in the documentation.\n", + "from esapp import PowerWorld\n", + "from esapp.components import *\n", + "from esapp.saw._helpers import create_object_string\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import ast\n", + "\n", + "with open('../data/case.txt', 'r') as f:\n", + " case_path = ast.literal_eval(f.read().strip())\n", + "\n", + "pw = PowerWorld(case_path)" + ] }, { "cell_type": "code", @@ -59,7 +72,10 @@ "id": "e5d6c7b8", "metadata": {}, "outputs": [], - "source": "Y_before = pw.ybus()\nprint(f\"Y-Bus before: {Y_before.shape}, nnz={Y_before.nnz}\")" + "source": [ + "Y_before = pw.ybus()\n", + "print(f\"Y-Bus before: {Y_before.shape}, nnz={Y_before.nnz}\")" + ] }, { "cell_type": "markdown", @@ -77,7 +93,14 @@ "id": "a7f8e9d0", "metadata": {}, "outputs": [], - "source": "branches = pw[Branch]\nb = branches.iloc[10]\ntobus = b['BusNum']\nfrombus = b['BusNum:1']\ncircuit = b['LineCircuit']\nbranch_str = create_object_string(\"Branch\", tobus, frombus, circuit)" + "source": [ + "branches = pw[Branch]\n", + "b = branches.iloc[10]\n", + "tobus = b['BusNum']\n", + "frombus = b['BusNum:1']\n", + "circuit = b['LineCircuit']\n", + "branch_str = create_object_string(\"Branch\", tobus, frombus, circuit)" + ] }, { "cell_type": "markdown", @@ -93,7 +116,18 @@ "id": "c9b0a1f2", "metadata": {}, "outputs": [], - "source": "new_bus_num = int(pw[Bus, \"BusNum\"][\"BusNum\"].max()) + 100\n\npw.esa.TapTransmissionLine(\n branch_str,\n 50.0,\n new_bus_num,\n 'CAPACITANCE',\n False, False,\n 'Tapped_Substation'\n)" + "source": [ + "new_bus_num = int(pw[Bus, \"BusNum\"][\"BusNum\"].max()) + 100\n", + "\n", + "pw.esa.TapTransmissionLine(\n", + " branch_str,\n", + " 50.0,\n", + " new_bus_num,\n", + " 'CAPACITANCE',\n", + " False, False,\n", + " 'Tapped_Substation'\n", + ")" + ] }, { "cell_type": "markdown", @@ -111,7 +145,18 @@ "id": "e1d2c3b4", "metadata": {}, "outputs": [], - "source": "target_bus = 1\nsplit_bus_num = int(pw[Bus, 'BusNum']['BusNum'].max()) + 1\n\npw.esa.SplitBus(\n create_object_string(\"Bus\", target_bus),\n split_bus_num,\n insert_tie=True,\n line_open=False,\n branch_device_type=\"Breaker\"\n)" + "source": [ + "target_bus = 1\n", + "split_bus_num = int(pw[Bus, 'BusNum']['BusNum'].max()) + 1\n", + "\n", + "pw.esa.SplitBus(\n", + " create_object_string(\"Bus\", target_bus),\n", + " split_bus_num,\n", + " insert_tie=True,\n", + " line_open=False,\n", + " branch_device_type=\"Breaker\"\n", + ")" + ] }, { "cell_type": "markdown", @@ -128,7 +173,17 @@ "execution_count": null, "metadata": {}, "outputs": [], - "source": "V = pw.pflow()\nY_after = pw.ybus()\n\nprint(f'Y-Bus before: {Y_before.shape}, nnz={Y_before.nnz}')\nprint(f'Y-Bus after: {Y_after.shape}, nnz={Y_after.nnz}')\nprint(f'New buses added: {Y_after.shape[0] - Y_before.shape[0]}')\n\nplot_spy_matrices([Y_before, Y_after],\n [f'Y-Bus Before\\n{Y_before.shape}', f'Y-Bus After\\n{Y_after.shape}'])" + "source": [ + "V = pw.pflow()\n", + "Y_after = pw.ybus()\n", + "\n", + "print(f'Y-Bus before: {Y_before.shape}, nnz={Y_before.nnz}')\n", + "print(f'Y-Bus after: {Y_after.shape}, nnz={Y_after.nnz}')\n", + "print(f'New buses added: {Y_after.shape[0] - Y_before.shape[0]}')\n", + "\n", + "plot_spy_matrices([Y_before, Y_after],\n", + " [f'Y-Bus Before\\n{Y_before.shape}', f'Y-Bus After\\n{Y_after.shape}'])" + ] } ], "metadata": { @@ -152,4 +207,4 @@ }, "nbformat": 4, "nbformat_minor": 5 -} \ No newline at end of file +} diff --git a/examples/nonuniform/01_nonuniform_gic.ipynb b/examples/nonuniform/01_nonuniform_gic.ipynb index 5ec9eef..ba581fa 100644 --- a/examples/nonuniform/01_nonuniform_gic.ipynb +++ b/examples/nonuniform/01_nonuniform_gic.ipynb @@ -25,7 +25,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -36,29 +36,21 @@ "from esapp.components import Branch, Bus, GICXFormer\n", "from esapp.utils import B3D\n", "\n", - "from examples.mesh import Grid2D\n", - "from examples.map import format_plot, border, plot_lines\n", - "from examples.nonuniform.nonuniform import build_L_matrix, stack_efield, compute_gic, bus_gic\n", - "from examples.nonuniform.plotting import plot_efield, plot_gic_heatmap" + "from mesh import Grid2D\n", + "from map import format_plot, border, plot_lines\n", + "from nonuniform import build_L_matrix, stack_efield, compute_gic, bus_gic\n", + "from plotting import plot_efield, plot_gic_heatmap" ] }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": { "tags": [ "remove-cell" ] }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "'open' took: 13.0517 sec\n" - ] - } - ], + "outputs": [], "source": [ "# This cell is hidden in the documentation.\n", "import ast\n", @@ -72,7 +64,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "metadata": { "tags": [ "remove-cell" @@ -103,19 +95,9 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "H-matrix: (861, 4137) (transformers x branches)\n", - "G-matrix: (3250, 3250) (nodes x nodes)\n", - "Incidence: (4137, 3250) (branches x nodes)\n" - ] - } - ], + "outputs": [], "source": [ "pw.gic.configure(pf_include=True, calc_mode='SnapShot')\n", "pw.gic.model()\n", @@ -138,18 +120,9 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Grid: 50 x 35 = 1750 points\n", - "Edges: 3415 (H: 1715, V: 1700)\n" - ] - } - ], + "outputs": [], "source": [ "lon, lat = pw.buscoords()\n", "\n", @@ -169,20 +142,9 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "lines = pw[Branch, ['Longitude', 'Longitude:1', 'Latitude', 'Latitude:1']]\n", "\n", @@ -218,7 +180,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -236,20 +198,9 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "fig, ax = plt.subplots(figsize=(12, 8))\n", "\n", @@ -341,18 +292,9 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "H-matrix: (861, 4137) (transformers x branches)\n", - "L-matrix: (4137, 3500) (branches x 2·grid), nnz = 27796\n" - ] - } - ], + "outputs": [], "source": [ "# Build the line integration operator (computed once for a given grid)\n", "H = pw.gic.H\n", @@ -366,22 +308,9 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Transformer GICs computed: 861\n", - "Max |GIC| (transformer): 2265.40 A\n", - "Mean |GIC| (transformer): 60.68 A\n", - "\n", - "Buses with GIC: 357\n", - "Max |GIC| (bus): 2265.40 A\n" - ] - } - ], + "outputs": [], "source": [ "# Compute transformer GICs: |I| = |H @ L @ E|\n", "gic_xf = compute_gic(H, L, Ex_field, Ey_field)\n", @@ -408,20 +337,9 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "ranked = np.argsort(gic_xf)[::-1]\n", "top_n = min(30, len(ranked))\n", @@ -458,20 +376,9 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "fig, ax = plt.subplots(figsize=(14, 9))\n", "\n", @@ -498,19 +405,9 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "B3D grid: [50, 35]\n", - "Locations: 1750\n", - "Time steps: 1\n" - ] - } - ], + "outputs": [], "source": [ "b3d = B3D.from_mesh(\n", " lons, lats,\n", diff --git a/examples/nonuniform/__init__.py b/examples/nonuniform/__init__.py deleted file mode 100644 index 384f149..0000000 --- a/examples/nonuniform/__init__.py +++ /dev/null @@ -1,6 +0,0 @@ -""" -Non-uniform electric field GIC analysis examples. - -Uses the esapp GIC model with geographic plotting and mesh tools -to compute and visualize GICs under spatially varying E-fields. -""" diff --git a/examples/plot_helpers.py b/examples/plot_helpers.py index 932b690..6fb470f 100644 --- a/examples/plot_helpers.py +++ b/examples/plot_helpers.py @@ -19,11 +19,13 @@ from matplotlib.colors import Normalize from matplotlib.cm import ScalarMappable -# Import plotting utilities from examples.map +# Import plotting utilities from the sibling map module. Notebooks add +# the examples/ directory to sys.path, so the sibling import is primary; +# the package-style import covers running from the repository root. try: - from examples.map import format_plot, border, plot_lines, plot_vecfield, darker_hsv_colormap + from map import format_plot, border, plot_lines, plot_vecfield, darker_hsv_colormap except ImportError: - pass + from examples.map import format_plot, border, plot_lines, plot_vecfield, darker_hsv_colormap # --------------------------------------------------------------------------- # Standard figure dimensions (inches) for 6.5" LaTeX text width diff --git a/examples/statics.py b/examples/statics.py index 73ca50b..92e0066 100644 --- a/examples/statics.py +++ b/examples/statics.py @@ -9,7 +9,7 @@ Example ------- >>> from esapp import PowerWorld - >>> from examples.statics import Statics + >>> from statics import Statics # with examples/ on sys.path >>> pw = PowerWorld("case.pwb") >>> s = Statics(pw) >>> interface = np.array([1, -1, 0, ...]) diff --git a/examples/steady_state/01_contingency_analysis.ipynb b/examples/steady_state/01_contingency_analysis.ipynb index 21b8f80..fa94a89 100644 --- a/examples/steady_state/01_contingency_analysis.ipynb +++ b/examples/steady_state/01_contingency_analysis.ipynb @@ -26,7 +26,19 @@ ] }, "outputs": [], - "source": "# This cell is hidden in the documentation.\nfrom esapp import PowerWorld\nfrom esapp.components import *\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport ast\n\nwith open('../data/case.txt', 'r') as f:\n case_path = ast.literal_eval(f.read().strip())\n\npw = PowerWorld(case_path)" + "source": [ + "# This cell is hidden in the documentation.\n", + "from esapp import PowerWorld\n", + "from esapp.components import *\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import ast\n", + "\n", + "with open('../data/case.txt', 'r') as f:\n", + " case_path = ast.literal_eval(f.read().strip())\n", + "\n", + "pw = PowerWorld(case_path)" + ] }, { "cell_type": "code", @@ -59,7 +71,11 @@ "id": "e5f6a7b8", "metadata": {}, "outputs": [], - "source": "pw.pflow() # Solve base case first\npw.auto_insert_contingencies() # Create N-1 contingencies\npw.solve_contingencies() # Solve all contingency scenarios" + "source": [ + "pw.pflow() # Solve base case first\n", + "pw.auto_insert_contingencies() # Create N-1 contingencies\n", + "pw.solve_contingencies() # Solve all contingency scenarios" + ] }, { "cell_type": "markdown", @@ -77,7 +93,11 @@ "id": "a7b8c9d0", "metadata": {}, "outputs": [], - "source": "violations = pw[ViolationCTG, :]\nprint(f\"Total violations: {len(violations)}\")\nviolations.head()" + "source": [ + "violations = pw[ViolationCTG, :]\n", + "print(f\"Total violations: {len(violations)}\")\n", + "violations.head()" + ] }, { "cell_type": "code", @@ -110,4 +130,4 @@ }, "nbformat": 4, "nbformat_minor": 5 -} \ No newline at end of file +} diff --git a/examples/steady_state/02_scopf_analysis.ipynb b/examples/steady_state/02_scopf_analysis.ipynb index ae00c2f..06c33bc 100644 --- a/examples/steady_state/02_scopf_analysis.ipynb +++ b/examples/steady_state/02_scopf_analysis.ipynb @@ -26,7 +26,18 @@ ] }, "outputs": [], - "source": "# This cell is hidden in the documentation.\nfrom esapp import PowerWorld\nfrom esapp.components import *\nimport matplotlib.pyplot as plt\nimport ast\n\nwith open('../data/case.txt', 'r') as f:\n case_path = ast.literal_eval(f.read().strip())\n\npw = PowerWorld(case_path)" + "source": [ + "# This cell is hidden in the documentation.\n", + "from esapp import PowerWorld\n", + "from esapp.components import *\n", + "import matplotlib.pyplot as plt\n", + "import ast\n", + "\n", + "with open('../data/case.txt', 'r') as f:\n", + " case_path = ast.literal_eval(f.read().strip())\n", + "\n", + "pw = PowerWorld(case_path)" + ] }, { "cell_type": "code", @@ -67,7 +78,14 @@ "id": "af6a7b8c", "metadata": {}, "outputs": [], - "source": "# Capture pre-OPF dispatch\npre_opf = pw[Gen, ['BusNum', 'GenMW', 'GenStatus']]\npre_opf_online = pre_opf[pre_opf['GenStatus'] == 'Closed'].copy()\n\npw.esa.InitializePrimalLP()\npw.auto_insert_contingencies()" + "source": [ + "# Capture pre-OPF dispatch\n", + "pre_opf = pw[Gen, ['BusNum', 'GenMW', 'GenStatus']]\n", + "pre_opf_online = pre_opf[pre_opf['GenStatus'] == 'Closed'].copy()\n", + "\n", + "pw.esa.InitializePrimalLP()\n", + "pw.auto_insert_contingencies()" + ] }, { "cell_type": "markdown", @@ -83,14 +101,39 @@ "id": "b18c9d0e", "metadata": {}, "outputs": [], - "source": "pw.esa.SolveFullSCOPF()\n\nproduction_cost = pw[Area, \"GenProdCost\"]\nprint(\"Production Cost by Area:\")\nprint(production_cost.to_string(index=False))" + "source": [ + "pw.esa.SolveFullSCOPF()\n", + "\n", + "production_cost = pw[Area, \"GenProdCost\"]\n", + "print(\"Production Cost by Area:\")\n", + "print(production_cost.to_string(index=False))" + ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], - "source": "post_opf = pw[Gen, ['BusNum', 'GenMW', 'GenStatus']]\npost_opf_online = post_opf[post_opf['GenStatus'] == 'Closed'].copy()\n\nfig, axes = plt.subplots(1, 2, figsize=(6.5, 2.8))\nplot_dual_bar(pre_opf_online['GenMW'].values, post_opf_online['GenMW'].values,\n label_a='Pre-OPF', label_b='Post-OPF',\n xlabel='Generator Index', ylabel='MW Output',\n title='Dispatch Comparison', ax=axes[0])\n# Redispatch delta\ndelta = post_opf_online['GenMW'].values - pre_opf_online['GenMW'].values\ncolors = ['#55A868' if d >= 0 else '#C44E52' for d in delta]\naxes[1].bar(range(len(delta)), delta, color=colors)\nfrom examples.map import format_plot\nformat_plot(axes[1], title='Redispatch Delta',\n xlabel='Generator Index', ylabel='\\u0394 MW',\n plotarea='white', titlesize=11, labelsize=9, ticksize=8)\nplt.tight_layout()\nplt.show()" + "source": [ + "post_opf = pw[Gen, ['BusNum', 'GenMW', 'GenStatus']]\n", + "post_opf_online = post_opf[post_opf['GenStatus'] == 'Closed'].copy()\n", + "\n", + "fig, axes = plt.subplots(1, 2, figsize=(6.5, 2.8))\n", + "plot_dual_bar(pre_opf_online['GenMW'].values, post_opf_online['GenMW'].values,\n", + " label_a='Pre-OPF', label_b='Post-OPF',\n", + " xlabel='Generator Index', ylabel='MW Output',\n", + " title='Dispatch Comparison', ax=axes[0])\n", + "# Redispatch delta\n", + "delta = post_opf_online['GenMW'].values - pre_opf_online['GenMW'].values\n", + "colors = ['#55A868' if d >= 0 else '#C44E52' for d in delta]\n", + "axes[1].bar(range(len(delta)), delta, color=colors)\n", + "from map import format_plot\n", + "format_plot(axes[1], title='Redispatch Delta',\n", + " xlabel='Generator Index', ylabel='\\u0394 MW',\n", + " plotarea='white', titlesize=11, labelsize=9, ticksize=8)\n", + "plt.tight_layout()\n", + "plt.show()" + ] } ], "metadata": { @@ -114,4 +157,4 @@ }, "nbformat": 4, "nbformat_minor": 5 -} \ No newline at end of file +} diff --git a/examples/steady_state/03_atc_analysis.ipynb b/examples/steady_state/03_atc_analysis.ipynb index 0e1d521..2e0477f 100644 --- a/examples/steady_state/03_atc_analysis.ipynb +++ b/examples/steady_state/03_atc_analysis.ipynb @@ -26,7 +26,18 @@ ] }, "outputs": [], - "source": "# This cell is hidden in the documentation.\nfrom esapp import PowerWorld\nfrom esapp.components import *\nfrom esapp.saw._helpers import create_object_string\nimport ast\n\nwith open('../data/case.txt', 'r') as f:\n case_path = ast.literal_eval(f.read().strip())\n\npw = PowerWorld(case_path)" + "source": [ + "# This cell is hidden in the documentation.\n", + "from esapp import PowerWorld\n", + "from esapp.components import *\n", + "from esapp.saw._helpers import create_object_string\n", + "import ast\n", + "\n", + "with open('../data/case.txt', 'r') as f:\n", + " case_path = ast.literal_eval(f.read().strip())\n", + "\n", + "pw = PowerWorld(case_path)" + ] }, { "cell_type": "code", @@ -34,7 +45,15 @@ "id": "cd4e5f6a", "metadata": {}, "outputs": [], - "source": "areas = pw[Area, ['AreaNum', 'AreaName']]\nif len(areas) >= 2:\n seller = create_object_string(\"Area\", areas.iloc[0]['AreaNum'])\n buyer = create_object_string(\"Area\", areas.iloc[1]['AreaNum'])\n \n pw.esa.SetData(\"ATC_Options\", [\"Method\"], [\"IteratedLinearThenFull\"])\n pw.esa.DetermineATC(seller, buyer, do_distributed=False, do_multiple_scenarios=False)" + "source": [ + "areas = pw[Area, ['AreaNum', 'AreaName']]\n", + "if len(areas) >= 2:\n", + " seller = create_object_string(\"Area\", areas.iloc[0]['AreaNum'])\n", + " buyer = create_object_string(\"Area\", areas.iloc[1]['AreaNum'])\n", + " \n", + " pw.esa.SetData(\"ATC_Options\", [\"Method\"], [\"IteratedLinearThenFull\"])\n", + " pw.esa.DetermineATC(seller, buyer, do_distributed=False, do_multiple_scenarios=False)" + ] }, { "cell_type": "code", @@ -42,7 +61,16 @@ "id": "ce5f6a7b", "metadata": {}, "outputs": [], - "source": "results = pw.esa.GetATCResults([\"MaxFlow\", \"LimitingContingency\", \"LimitingElement\"])\n\nif results is not None and not results.empty:\n atc_results = results.iloc[0]\n print(f\"ATC Results:\")\n print(f\" Maximum Transfer: {atc_results['MaxFlow']:.1f} MW\")\n print(f\" Limiting Contingency: {atc_results['LimitingContingency']}\")\n print(f\" Limiting Element: {atc_results['LimitingElement']}\")" + "source": [ + "results = pw.esa.GetATCResults([\"MaxFlow\", \"LimitingContingency\", \"LimitingElement\"])\n", + "\n", + "if results is not None and not results.empty:\n", + " atc_results = results.iloc[0]\n", + " print(f\"ATC Results:\")\n", + " print(f\" Maximum Transfer: {atc_results['MaxFlow']:.1f} MW\")\n", + " print(f\" Limiting Contingency: {atc_results['LimitingContingency']}\")\n", + " print(f\" Limiting Element: {atc_results['LimitingElement']}\")" + ] } ], "metadata": { @@ -58,4 +86,4 @@ }, "nbformat": 4, "nbformat_minor": 5 -} \ No newline at end of file +} diff --git a/examples/steady_state/04_continuation_power_flow.ipynb b/examples/steady_state/04_continuation_power_flow.ipynb index 76c9319..968697a 100644 --- a/examples/steady_state/04_continuation_power_flow.ipynb +++ b/examples/steady_state/04_continuation_power_flow.ipynb @@ -15,7 +15,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": { "tags": [ "hide-cell" @@ -23,30 +23,23 @@ }, "outputs": [], "source": [ + "import sys; sys.path.insert(0, \"..\")\n", "import numpy as np\n", "from esapp import PowerWorld\n", "from esapp.components import *\n", - "from examples.statics import Statics" + "from statics import Statics" ] }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "id": "dc3d4e5f", "metadata": { "tags": [ "hide-cell" ] }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "'open' took: 9.6130 sec\n" - ] - } - ], + "outputs": [], "source": [ "# This cell is hidden in the documentation.\n", "import ast\n", @@ -59,7 +52,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "metadata": { "tags": [ "hide-cell" @@ -90,17 +83,7 @@ "execution_count": null, "id": "de5f6a7b", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Base case min voltage: 0.9749 pu\n", - "Critical bus index: 10\n", - "Critical bus voltage: 0.9749 pu\n" - ] - } - ], + "outputs": [], "source": [ "s = Statics(pw)\n", "\n", @@ -134,370 +117,10 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": null, "id": "e07b8c9d", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " [0] Predict: lam=0.05 MW (step=0.0500, dlam=0.05) OK (lam=0.05)\n", - " [1] Predict: lam=0.12 MW (step=0.0750, dlam=0.08) OK (lam=0.12)\n", - " [2] Predict: lam=0.24 MW (step=0.1125, dlam=0.11) OK (lam=0.24)\n", - " [3] Predict: lam=0.41 MW (step=0.1688, dlam=0.17) OK (lam=0.41)\n", - " [4] Predict: lam=0.66 MW (step=0.2531, dlam=0.25) OK (lam=0.66)\n", - " [5] Predict: lam=1.04 MW (step=0.3797, dlam=0.38) OK (lam=1.04)\n", - " [6] Predict: lam=1.61 MW (step=0.5695, dlam=0.57) OK (lam=1.61)\n", - " [7] Predict: lam=2.46 MW (step=0.8543, dlam=0.85) OK (lam=2.46)\n", - " [8] Predict: lam=3.74 MW (step=1.2814, dlam=1.28) OK (lam=3.74)\n", - " [9] Predict: lam=5.67 MW (step=1.9222, dlam=1.92) OK (lam=5.67)\n", - " [10] Predict: lam=8.55 MW (step=2.8833, dlam=2.88) OK (lam=8.55)\n", - " [11] Predict: lam=12.87 MW (step=4.3249, dlam=4.32) OK (lam=12.87)\n", - " [12] Predict: lam=17.87 MW (step=5.0000, dlam=5.00) OK (lam=17.87)\n", - " [13] Predict: lam=22.87 MW (step=5.0000, dlam=5.00) OK (lam=22.87)\n", - " [14] Predict: lam=27.87 MW (step=5.0000, dlam=5.00) OK (lam=27.87)\n", - " [15] Predict: lam=32.87 MW (step=5.0000, dlam=5.00) OK (lam=32.87)\n", - " [16] Predict: lam=37.87 MW (step=5.0000, dlam=5.00) OK (lam=37.87)\n", - " [17] Predict: lam=42.87 MW (step=5.0000, dlam=5.00) OK (lam=42.87)\n", - " [18] Predict: lam=47.87 MW (step=5.0000, dlam=5.00) OK (lam=47.87)\n", - " [19] Predict: lam=52.87 MW (step=5.0000, dlam=5.00) OK (lam=52.87)\n", - " [20] Predict: lam=57.87 MW (step=5.0000, dlam=5.00) OK (lam=57.87)\n", - " [21] Predict: lam=62.87 MW (step=5.0000, dlam=5.00) OK (lam=62.87)\n", - " [22] Predict: lam=67.87 MW (step=5.0000, dlam=5.00) OK (lam=67.87)\n", - " [23] Predict: lam=72.87 MW (step=5.0000, dlam=5.00) OK (lam=72.87)\n", - " [24] Predict: lam=77.87 MW (step=5.0000, dlam=5.00) OK (lam=77.87)\n", - " [25] Predict: lam=82.87 MW (step=5.0000, dlam=5.00) OK (lam=82.87)\n", - " [26] Predict: lam=87.87 MW (step=5.0000, dlam=5.00) OK (lam=87.87)\n", - " [27] Predict: lam=92.87 MW (step=5.0000, dlam=5.00) OK (lam=92.87)\n", - " [28] Predict: lam=97.87 MW (step=5.0000, dlam=5.00) OK (lam=97.87)\n", - " [29] Predict: lam=102.87 MW (step=5.0000, dlam=5.00) OK (lam=102.87)\n", - " [30] Predict: lam=107.87 MW (step=5.0000, dlam=5.00) OK (lam=107.87)\n", - " [31] Predict: lam=112.87 MW (step=5.0000, dlam=5.00) OK (lam=112.87)\n", - " [32] Predict: lam=117.87 MW (step=5.0000, dlam=5.00) OK (lam=117.87)\n", - " [33] Predict: lam=122.87 MW (step=5.0000, dlam=5.00) OK (lam=122.87)\n", - " [34] Predict: lam=127.87 MW (step=5.0000, dlam=5.00) OK (lam=127.87)\n", - " [35] Predict: lam=132.87 MW (step=5.0000, dlam=5.00) OK (lam=132.87)\n", - " [36] Predict: lam=137.87 MW (step=5.0000, dlam=5.00) OK (lam=137.87)\n", - " [37] Predict: lam=142.87 MW (step=5.0000, dlam=5.00) OK (lam=142.87)\n", - " [38] Predict: lam=147.87 MW (step=5.0000, dlam=5.00) OK (lam=147.87)\n", - " [39] Predict: lam=152.87 MW (step=5.0000, dlam=5.00) OK (lam=152.87)\n", - " [40] Predict: lam=157.87 MW (step=5.0000, dlam=5.00) OK (lam=157.87)\n", - " [41] Predict: lam=162.87 MW (step=5.0000, dlam=5.00) OK (lam=162.87)\n", - " [42] Predict: lam=167.87 MW (step=5.0000, dlam=5.00) OK (lam=167.87)\n", - " [43] Predict: lam=172.87 MW (step=5.0000, dlam=5.00) OK (lam=172.87)\n", - " [44] Predict: lam=177.87 MW (step=5.0000, dlam=5.00) OK (lam=177.87)\n", - " [45] Predict: lam=182.87 MW (step=5.0000, dlam=5.00) OK (lam=182.87)\n", - " [46] Predict: lam=187.87 MW (step=5.0000, dlam=5.00) OK (lam=187.87)\n", - " [47] Predict: lam=192.87 MW (step=5.0000, dlam=5.00) OK (lam=192.87)\n", - " [48] Predict: lam=197.87 MW (step=5.0000, dlam=5.00) OK (lam=197.87)\n", - " [49] Predict: lam=202.87 MW (step=5.0000, dlam=5.00) OK (lam=202.87)\n", - " [50] Predict: lam=207.87 MW (step=5.0000, dlam=5.00) OK (lam=207.87)\n", - " [51] Predict: lam=212.87 MW (step=5.0000, dlam=5.00) OK (lam=212.87)\n", - " [52] Predict: lam=217.87 MW (step=5.0000, dlam=5.00) OK (lam=217.87)\n", - " [53] Predict: lam=222.87 MW (step=5.0000, dlam=5.00) OK (lam=222.87)\n", - " [54] Predict: lam=227.87 MW (step=5.0000, dlam=5.00) OK (lam=227.87)\n", - " [55] Predict: lam=232.87 MW (step=5.0000, dlam=5.00) OK (lam=232.87)\n", - " [56] Predict: lam=237.87 MW (step=5.0000, dlam=5.00) OK (lam=237.87)\n", - " [57] Predict: lam=242.87 MW (step=5.0000, dlam=5.00) OK (lam=242.87)\n", - " [58] Predict: lam=247.87 MW (step=5.0000, dlam=5.00) OK (lam=247.87)\n", - " [59] Predict: lam=252.87 MW (step=5.0000, dlam=5.00) OK (lam=252.87)\n", - " [60] Predict: lam=257.87 MW (step=5.0000, dlam=5.00) OK (lam=257.87)\n", - " [61] Predict: lam=262.87 MW (step=5.0000, dlam=5.00) OK (lam=262.87)\n", - " [62] Predict: lam=267.87 MW (step=5.0000, dlam=5.00) OK (lam=267.87)\n", - " [63] Predict: lam=272.87 MW (step=5.0000, dlam=5.00) OK (lam=272.87)\n", - " [64] Predict: lam=277.87 MW (step=5.0000, dlam=5.00) OK (lam=277.87)\n", - " [65] Predict: lam=282.87 MW (step=5.0000, dlam=5.00) OK (lam=282.87)\n", - " [66] Predict: lam=287.87 MW (step=5.0000, dlam=5.00) OK (lam=287.87)\n", - " [67] Predict: lam=292.87 MW (step=5.0000, dlam=5.00) OK (lam=292.87)\n", - " [68] Predict: lam=297.87 MW (step=5.0000, dlam=5.00) OK (lam=297.87)\n", - " [69] Predict: lam=302.87 MW (step=5.0000, dlam=5.00) OK (lam=302.87)\n", - " [70] Predict: lam=307.87 MW (step=5.0000, dlam=5.00) OK (lam=307.87)\n", - " [71] Predict: lam=312.87 MW (step=5.0000, dlam=5.00) OK (lam=312.87)\n", - " [72] Predict: lam=317.87 MW (step=5.0000, dlam=5.00) OK (lam=317.87)\n", - " [73] Predict: lam=322.87 MW (step=5.0000, dlam=5.00) OK (lam=322.87)\n", - " [74] Predict: lam=327.87 MW (step=5.0000, dlam=5.00) OK (lam=327.87)\n", - " [75] Predict: lam=332.87 MW (step=5.0000, dlam=5.00) OK (lam=332.87)\n", - " [76] Predict: lam=337.87 MW (step=5.0000, dlam=5.00) OK (lam=337.87)\n", - " [77] Predict: lam=342.87 MW (step=5.0000, dlam=5.00) OK (lam=342.87)\n", - " [78] Predict: lam=347.87 MW (step=5.0000, dlam=5.00) OK (lam=347.87)\n", - " [79] Predict: lam=352.87 MW (step=5.0000, dlam=5.00) OK (lam=352.87)\n", - " [80] Predict: lam=357.87 MW (step=5.0000, dlam=5.00) OK (lam=357.87)\n", - " [81] Predict: lam=362.87 MW (step=5.0000, dlam=5.00) OK (lam=362.87)\n", - " [82] Predict: lam=367.87 MW (step=5.0000, dlam=5.00) OK (lam=367.87)\n", - " [83] Predict: lam=372.87 MW (step=5.0000, dlam=5.00) OK (lam=372.87)\n", - " [84] Predict: lam=377.87 MW (step=5.0000, dlam=5.00) OK (lam=377.87)\n", - " [85] Predict: lam=382.87 MW (step=5.0000, dlam=5.00) OK (lam=382.87)\n", - " [86] Predict: lam=387.87 MW (step=5.0000, dlam=5.00) OK (lam=387.87)\n", - " [87] Predict: lam=392.87 MW (step=5.0000, dlam=5.00) OK (lam=392.87)\n", - " [88] Predict: lam=397.87 MW (step=5.0000, dlam=5.00) OK (lam=397.87)\n", - " [89] Predict: lam=402.87 MW (step=5.0000, dlam=5.00) OK (lam=402.87)\n", - " [90] Predict: lam=407.87 MW (step=5.0000, dlam=5.00) OK (lam=407.87)\n", - " [91] Predict: lam=412.87 MW (step=5.0000, dlam=5.00) OK (lam=412.87)\n", - " [92] Predict: lam=417.87 MW (step=5.0000, dlam=5.00) OK (lam=417.87)\n", - " [93] Predict: lam=422.87 MW (step=5.0000, dlam=5.00) OK (lam=422.87)\n", - " [94] Predict: lam=427.87 MW (step=5.0000, dlam=5.00) OK (lam=427.87)\n", - " [95] Predict: lam=432.87 MW (step=5.0000, dlam=5.00) OK (lam=432.87)\n", - " [96] Predict: lam=437.87 MW (step=5.0000, dlam=5.00) OK (lam=437.87)\n", - " [97] Predict: lam=442.87 MW (step=5.0000, dlam=5.00) OK (lam=442.87)\n", - " [98] Predict: lam=447.87 MW (step=5.0000, dlam=5.00) OK (lam=447.87)\n", - " [99] Predict: lam=452.87 MW (step=5.0000, dlam=5.00) OK (lam=452.87)\n", - " [100] Predict: lam=457.87 MW (step=5.0000, dlam=5.00) OK (lam=457.87)\n", - " [101] Predict: lam=462.87 MW (step=5.0000, dlam=5.00) OK (lam=462.87)\n", - " [102] Predict: lam=467.87 MW (step=5.0000, dlam=5.00) OK (lam=467.87)\n", - " [103] Predict: lam=472.87 MW (step=5.0000, dlam=5.00) OK (lam=472.87)\n", - " [104] Predict: lam=477.87 MW (step=5.0000, dlam=5.00) OK (lam=477.87)\n", - " [105] Predict: lam=482.87 MW (step=5.0000, dlam=5.00) OK (lam=482.87)\n", - " [106] Predict: lam=487.87 MW (step=5.0000, dlam=5.00) OK (lam=487.87)\n", - " [107] Predict: lam=492.87 MW (step=5.0000, dlam=5.00) OK (lam=492.87)\n", - " [108] Predict: lam=497.87 MW (step=5.0000, dlam=5.00) OK (lam=497.87)\n", - " [109] Predict: lam=502.87 MW (step=5.0000, dlam=5.00) OK (lam=502.87)\n", - " [110] Predict: lam=507.87 MW (step=5.0000, dlam=5.00) OK (lam=507.87)\n", - " [111] Predict: lam=512.87 MW (step=5.0000, dlam=5.00) OK (lam=512.87)\n", - " [112] Predict: lam=517.87 MW (step=5.0000, dlam=5.00) OK (lam=517.87)\n", - " [113] Predict: lam=522.87 MW (step=5.0000, dlam=5.00) OK (lam=522.87)\n", - " [114] Predict: lam=527.87 MW (step=5.0000, dlam=5.00) OK (lam=527.87)\n", - " [115] Predict: lam=532.87 MW (step=5.0000, dlam=5.00) OK (lam=532.87)\n", - " [116] Predict: lam=537.87 MW (step=5.0000, dlam=5.00) OK (lam=537.87)\n", - " [117] Predict: lam=542.87 MW (step=5.0000, dlam=5.00) OK (lam=542.87)\n", - " [118] Predict: lam=547.87 MW (step=5.0000, dlam=5.00) OK (lam=547.87)\n", - " [119] Predict: lam=552.87 MW (step=5.0000, dlam=5.00) OK (lam=552.87)\n", - " [120] Predict: lam=557.87 MW (step=5.0000, dlam=5.00) OK (lam=557.87)\n", - " [121] Predict: lam=562.87 MW (step=5.0000, dlam=5.00) OK (lam=562.87)\n", - " [122] Predict: lam=567.87 MW (step=5.0000, dlam=5.00) OK (lam=567.87)\n", - " [123] Predict: lam=572.87 MW (step=5.0000, dlam=5.00) OK (lam=572.87)\n", - " [124] Predict: lam=577.87 MW (step=5.0000, dlam=5.00) OK (lam=577.87)\n", - " [125] Predict: lam=582.87 MW (step=5.0000, dlam=5.00) OK (lam=582.87)\n", - " [126] Predict: lam=587.87 MW (step=5.0000, dlam=5.00) OK (lam=587.87)\n", - " [127] Predict: lam=592.87 MW (step=5.0000, dlam=5.00) OK (lam=592.87)\n", - " [128] Predict: lam=597.87 MW (step=5.0000, dlam=5.00) OK (lam=597.87)\n", - " [129] Predict: lam=602.87 MW (step=5.0000, dlam=5.00) OK (lam=602.87)\n", - " [130] Predict: lam=607.87 MW (step=5.0000, dlam=5.00) OK (lam=607.87)\n", - " [131] Predict: lam=612.87 MW (step=5.0000, dlam=5.00) OK (lam=612.87)\n", - " [132] Predict: lam=617.87 MW (step=5.0000, dlam=5.00) OK (lam=617.87)\n", - " [133] Predict: lam=622.87 MW (step=5.0000, dlam=5.00) OK (lam=622.87)\n", - " [134] Predict: lam=627.87 MW (step=5.0000, dlam=5.00) OK (lam=627.87)\n", - " [135] Predict: lam=632.87 MW (step=5.0000, dlam=5.00) OK (lam=632.87)\n", - " [136] Predict: lam=637.87 MW (step=5.0000, dlam=5.00) OK (lam=637.87)\n", - " [137] Predict: lam=642.87 MW (step=5.0000, dlam=5.00) OK (lam=642.87)\n", - " [138] Predict: lam=647.87 MW (step=5.0000, dlam=5.00) OK (lam=647.87)\n", - " [139] Predict: lam=652.87 MW (step=5.0000, dlam=5.00) OK (lam=652.87)\n", - " [140] Predict: lam=657.87 MW (step=5.0000, dlam=5.00) OK (lam=657.87)\n", - " [141] Predict: lam=662.87 MW (step=5.0000, dlam=5.00) OK (lam=662.87)\n", - " [142] Predict: lam=667.87 MW (step=5.0000, dlam=5.00) OK (lam=667.87)\n", - " [143] Predict: lam=672.87 MW (step=5.0000, dlam=5.00) OK (lam=672.87)\n", - " [144] Predict: lam=677.87 MW (step=5.0000, dlam=5.00) OK (lam=677.87)\n", - " [145] Predict: lam=682.87 MW (step=5.0000, dlam=5.00) OK (lam=682.87)\n", - " [146] Predict: lam=687.87 MW (step=5.0000, dlam=5.00) OK (lam=687.87)\n", - " [147] Predict: lam=692.87 MW (step=5.0000, dlam=5.00) OK (lam=692.87)\n", - " [148] Predict: lam=697.87 MW (step=5.0000, dlam=5.00) OK (lam=697.87)\n", - " [149] Predict: lam=702.87 MW (step=5.0000, dlam=5.00) OK (lam=702.87)\n", - " [150] Predict: lam=707.87 MW (step=5.0000, dlam=5.00) OK (lam=707.87)\n", - " [151] Predict: lam=712.87 MW (step=5.0000, dlam=5.00) OK (lam=712.87)\n", - " [152] Predict: lam=717.87 MW (step=5.0000, dlam=5.00) OK (lam=717.87)\n", - " [153] Predict: lam=722.87 MW (step=5.0000, dlam=5.00) OK (lam=722.87)\n", - " [154] Predict: lam=727.87 MW (step=5.0000, dlam=5.00) OK (lam=727.87)\n", - " [155] Predict: lam=732.87 MW (step=5.0000, dlam=5.00) OK (lam=732.87)\n", - " [156] Predict: lam=737.87 MW (step=5.0000, dlam=5.00) OK (lam=737.87)\n", - " [157] Predict: lam=742.87 MW (step=5.0000, dlam=5.00) OK (lam=742.87)\n", - " [158] Predict: lam=747.87 MW (step=5.0000, dlam=5.00) OK (lam=747.87)\n", - " [159] Predict: lam=752.87 MW (step=5.0000, dlam=5.00) OK (lam=752.87)\n", - " [160] Predict: lam=757.87 MW (step=5.0000, dlam=5.00) OK (lam=757.87)\n", - " [161] Predict: lam=762.87 MW (step=5.0000, dlam=5.00) OK (lam=762.87)\n", - " [162] Predict: lam=767.87 MW (step=5.0000, dlam=5.00) OK (lam=767.87)\n", - " [163] Predict: lam=772.87 MW (step=5.0000, dlam=5.00) OK (lam=772.87)\n", - " [164] Predict: lam=777.87 MW (step=5.0000, dlam=5.00) OK (lam=777.87)\n", - " [165] Predict: lam=782.87 MW (step=5.0000, dlam=5.00) OK (lam=782.87)\n", - " [166] Predict: lam=787.87 MW (step=5.0000, dlam=5.00) OK (lam=787.87)\n", - " [167] Predict: lam=792.87 MW (step=5.0000, dlam=5.00) OK (lam=792.87)\n", - " [168] Predict: lam=797.87 MW (step=5.0000, dlam=5.00) OK (lam=797.87)\n", - " [169] Predict: lam=802.87 MW (step=5.0000, dlam=5.00) OK (lam=802.87)\n", - " [170] Predict: lam=807.87 MW (step=5.0000, dlam=5.00) OK (lam=807.87)\n", - " [171] Predict: lam=812.87 MW (step=5.0000, dlam=5.00) OK (lam=812.87)\n", - " [172] Predict: lam=817.87 MW (step=5.0000, dlam=5.00) OK (lam=817.87)\n", - " [173] Predict: lam=822.87 MW (step=5.0000, dlam=5.00) OK (lam=822.87)\n", - " [174] Predict: lam=827.87 MW (step=5.0000, dlam=5.00) OK (lam=827.87)\n", - " [175] Predict: lam=832.87 MW (step=5.0000, dlam=5.00) OK (lam=832.87)\n", - " [176] Predict: lam=837.87 MW (step=5.0000, dlam=5.00) OK (lam=837.87)\n", - " [177] Predict: lam=842.87 MW (step=5.0000, dlam=5.00) OK (lam=842.87)\n", - " [178] Predict: lam=847.87 MW (step=5.0000, dlam=5.00) OK (lam=847.87)\n", - " [179] Predict: lam=852.87 MW (step=5.0000, dlam=5.00) OK (lam=852.87)\n", - " [180] Predict: lam=857.87 MW (step=5.0000, dlam=5.00) OK (lam=857.87)\n", - " [181] Predict: lam=862.87 MW (step=5.0000, dlam=5.00) OK (lam=862.87)\n", - " [182] Predict: lam=867.87 MW (step=5.0000, dlam=5.00) OK (lam=867.87)\n", - " [183] Predict: lam=872.87 MW (step=5.0000, dlam=5.00) OK (lam=872.87)\n", - " [184] Predict: lam=877.87 MW (step=5.0000, dlam=5.00) OK (lam=877.87)\n", - " [185] Predict: lam=882.87 MW (step=5.0000, dlam=5.00) OK (lam=882.87)\n", - " [186] Predict: lam=887.87 MW (step=5.0000, dlam=5.00) OK (lam=887.87)\n", - " [187] Predict: lam=892.87 MW (step=5.0000, dlam=5.00) OK (lam=892.87)\n", - " [188] Predict: lam=897.87 MW (step=5.0000, dlam=5.00) OK (lam=897.87)\n", - " [189] Predict: lam=902.87 MW (step=5.0000, dlam=5.00) OK (lam=902.87)\n", - " [190] Predict: lam=907.87 MW (step=5.0000, dlam=5.00) OK (lam=907.87)\n", - " [191] Predict: lam=912.87 MW (step=5.0000, dlam=5.00) OK (lam=912.87)\n", - " [192] Predict: lam=917.87 MW (step=5.0000, dlam=5.00) OK (lam=917.87)\n", - " [193] Predict: lam=922.87 MW (step=5.0000, dlam=5.00) OK (lam=922.87)\n", - " [194] Predict: lam=927.87 MW (step=5.0000, dlam=5.00) OK (lam=927.87)\n", - " [195] Predict: lam=932.87 MW (step=5.0000, dlam=5.00) OK (lam=932.87)\n", - " [196] Predict: lam=937.87 MW (step=5.0000, dlam=5.00) OK (lam=937.87)\n", - " [197] Predict: lam=942.87 MW (step=5.0000, dlam=5.00) OK (lam=942.87)\n", - " [198] Predict: lam=947.87 MW (step=5.0000, dlam=5.00) OK (lam=947.87)\n", - " [199] Predict: lam=952.87 MW (step=5.0000, dlam=5.00) OK (lam=952.87)\n", - " [200] Predict: lam=957.87 MW (step=5.0000, dlam=5.00) OK (lam=957.87)\n", - " [201] Predict: lam=962.87 MW (step=5.0000, dlam=5.00) OK (lam=962.87)\n", - " [202] Predict: lam=967.87 MW (step=5.0000, dlam=5.00) OK (lam=967.87)\n", - " [203] Predict: lam=972.87 MW (step=5.0000, dlam=5.00) OK (lam=972.87)\n", - " [204] Predict: lam=977.87 MW (step=5.0000, dlam=5.00) OK (lam=977.87)\n", - " [205] Predict: lam=982.87 MW (step=5.0000, dlam=5.00) OK (lam=982.87)\n", - " [206] Predict: lam=987.87 MW (step=5.0000, dlam=5.00) OK (lam=987.87)\n", - " [207] Predict: lam=992.87 MW (step=5.0000, dlam=5.00) OK (lam=992.87)\n", - " [208] Predict: lam=997.87 MW (step=5.0000, dlam=5.00) OK (lam=997.87)\n", - " [209] Predict: lam=1002.87 MW (step=5.0000, dlam=5.00) OK (lam=1002.87)\n", - " [210] Predict: lam=1007.87 MW (step=5.0000, dlam=5.00) OK (lam=1007.87)\n", - " [211] Predict: lam=1012.87 MW (step=5.0000, dlam=5.00) OK (lam=1012.87)\n", - " [212] Predict: lam=1017.87 MW (step=5.0000, dlam=5.00) OK (lam=1017.87)\n", - " [213] Predict: lam=1022.87 MW (step=5.0000, dlam=5.00) OK (lam=1022.87)\n", - " [214] Predict: lam=1027.87 MW (step=5.0000, dlam=5.00) OK (lam=1027.87)\n", - " [215] Predict: lam=1032.87 MW (step=5.0000, dlam=5.00) OK (lam=1032.87)\n", - " [216] Predict: lam=1037.87 MW (step=5.0000, dlam=5.00) OK (lam=1037.87)\n", - " [217] Predict: lam=1042.87 MW (step=5.0000, dlam=5.00) OK (lam=1042.87)\n", - " [218] Predict: lam=1047.87 MW (step=5.0000, dlam=5.00) OK (lam=1047.87)\n", - " [219] Predict: lam=1052.87 MW (step=5.0000, dlam=5.00) OK (lam=1052.87)\n", - " [220] Predict: lam=1057.87 MW (step=5.0000, dlam=5.00) OK (lam=1057.87)\n", - " [221] Predict: lam=1062.87 MW (step=5.0000, dlam=5.00) OK (lam=1062.87)\n", - " [222] Predict: lam=1067.87 MW (step=5.0000, dlam=5.00) OK (lam=1067.87)\n", - " [223] Predict: lam=1072.87 MW (step=5.0000, dlam=5.00) OK (lam=1072.87)\n", - " [224] Predict: lam=1077.87 MW (step=5.0000, dlam=5.00) OK (lam=1077.87)\n", - " [225] Predict: lam=1082.87 MW (step=5.0000, dlam=5.00) OK (lam=1082.87)\n", - " [226] Predict: lam=1087.87 MW (step=5.0000, dlam=5.00) OK (lam=1087.87)\n", - " [227] Predict: lam=1092.87 MW (step=5.0000, dlam=5.00) OK (lam=1092.87)\n", - " [228] Predict: lam=1097.87 MW (step=5.0000, dlam=5.00) OK (lam=1097.87)\n", - " [229] Predict: lam=1102.87 MW (step=5.0000, dlam=5.00) OK (lam=1102.87)\n", - " [230] Predict: lam=1107.87 MW (step=5.0000, dlam=5.00) OK (lam=1107.87)\n", - " [231] Predict: lam=1112.87 MW (step=5.0000, dlam=5.00) OK (lam=1112.87)\n", - " [232] Predict: lam=1117.87 MW (step=5.0000, dlam=5.00) OK (lam=1117.87)\n", - " [233] Predict: lam=1122.87 MW (step=5.0000, dlam=5.00) OK (lam=1122.87)\n", - " [234] Predict: lam=1127.87 MW (step=5.0000, dlam=5.00) OK (lam=1127.87)\n", - " [235] Predict: lam=1132.87 MW (step=5.0000, dlam=5.00) OK (lam=1132.87)\n", - " [236] Predict: lam=1137.87 MW (step=5.0000, dlam=5.00) OK (lam=1137.87)\n", - " [237] Predict: lam=1142.87 MW (step=5.0000, dlam=5.00) OK (lam=1142.87)\n", - " [238] Predict: lam=1147.87 MW (step=5.0000, dlam=5.00) OK (lam=1147.87)\n", - " [239] Predict: lam=1152.87 MW (step=5.0000, dlam=5.00) OK (lam=1152.87)\n", - " [240] Predict: lam=1157.87 MW (step=5.0000, dlam=5.00) OK (lam=1157.87)\n", - " [241] Predict: lam=1162.87 MW (step=5.0000, dlam=5.00) OK (lam=1162.87)\n", - " [242] Predict: lam=1167.87 MW (step=5.0000, dlam=5.00) OK (lam=1167.87)\n", - " [243] Predict: lam=1172.87 MW (step=5.0000, dlam=5.00) OK (lam=1172.87)\n", - " [244] Predict: lam=1177.87 MW (step=5.0000, dlam=5.00) OK (lam=1177.87)\n", - " [245] Predict: lam=1182.87 MW (step=5.0000, dlam=5.00) OK (lam=1182.87)\n", - " [246] Predict: lam=1187.87 MW (step=5.0000, dlam=5.00) OK (lam=1187.87)\n", - " [247] Predict: lam=1192.87 MW (step=5.0000, dlam=5.00) OK (lam=1192.87)\n", - " [248] Predict: lam=1197.87 MW (step=5.0000, dlam=5.00) OK (lam=1197.87)\n", - " [249] Predict: lam=1202.87 MW (step=5.0000, dlam=5.00) OK (lam=1202.87)\n", - " [250] Predict: lam=1207.87 MW (step=5.0000, dlam=5.00) OK (lam=1207.87)\n", - " [251] Predict: lam=1212.87 MW (step=5.0000, dlam=5.00) OK (lam=1212.87)\n", - " [252] Predict: lam=1217.87 MW (step=5.0000, dlam=5.00) OK (lam=1217.87)\n", - " [253] Predict: lam=1222.87 MW (step=5.0000, dlam=5.00) OK (lam=1222.87)\n", - " [254] Predict: lam=1227.87 MW (step=5.0000, dlam=5.00) OK (lam=1227.87)\n", - " [255] Predict: lam=1232.87 MW (step=5.0000, dlam=5.00) OK (lam=1232.87)\n", - " [256] Predict: lam=1237.87 MW (step=5.0000, dlam=5.00) OK (lam=1237.87)\n", - " [257] Predict: lam=1242.87 MW (step=5.0000, dlam=5.00) OK (lam=1242.87)\n", - " [258] Predict: lam=1247.87 MW (step=5.0000, dlam=5.00) OK (lam=1247.87)\n", - " [259] Predict: lam=1252.87 MW (step=5.0000, dlam=5.00) OK (lam=1252.87)\n", - " [260] Predict: lam=1257.87 MW (step=5.0000, dlam=5.00) OK (lam=1257.87)\n", - " [261] Predict: lam=1262.87 MW (step=5.0000, dlam=5.00) OK (lam=1262.87)\n", - " [262] Predict: lam=1267.87 MW (step=5.0000, dlam=5.00) OK (lam=1267.87)\n", - " [263] Predict: lam=1272.87 MW (step=5.0000, dlam=5.00) OK (lam=1272.87)\n", - " [264] Predict: lam=1277.87 MW (step=5.0000, dlam=5.00) OK (lam=1277.87)\n", - " [265] Predict: lam=1282.87 MW (step=5.0000, dlam=5.00) OK (lam=1282.87)\n", - " [266] Predict: lam=1287.87 MW (step=5.0000, dlam=5.00) OK (lam=1287.87)\n", - " [267] Predict: lam=1292.87 MW (step=5.0000, dlam=5.00) OK (lam=1292.87)\n", - " [268] Predict: lam=1297.87 MW (step=5.0000, dlam=5.00) OK (lam=1297.87)\n", - " [269] Predict: lam=1302.87 MW (step=5.0000, dlam=5.00) OK (lam=1302.87)\n", - " [270] Predict: lam=1307.87 MW (step=5.0000, dlam=5.00) OK (lam=1307.87)\n", - " [271] Predict: lam=1312.87 MW (step=5.0000, dlam=5.00) OK (lam=1312.87)\n", - " [272] Predict: lam=1317.87 MW (step=5.0000, dlam=5.00) OK (lam=1317.87)\n", - " [273] Predict: lam=1322.87 MW (step=5.0000, dlam=5.00) OK (lam=1322.87)\n", - " [274] Predict: lam=1327.87 MW (step=5.0000, dlam=5.00) OK (lam=1327.87)\n", - " [275] Predict: lam=1332.87 MW (step=5.0000, dlam=5.00) OK (lam=1332.87)\n", - " [276] Predict: lam=1337.87 MW (step=5.0000, dlam=5.00) OK (lam=1337.87)\n", - " [277] Predict: lam=1342.87 MW (step=5.0000, dlam=5.00) OK (lam=1342.87)\n", - " [278] Predict: lam=1347.87 MW (step=5.0000, dlam=5.00) OK (lam=1347.87)\n", - " [279] Predict: lam=1352.87 MW (step=5.0000, dlam=5.00) OK (lam=1352.87)\n", - " [280] Predict: lam=1357.87 MW (step=5.0000, dlam=5.00) OK (lam=1357.87)\n", - " [281] Predict: lam=1362.87 MW (step=5.0000, dlam=5.00) OK (lam=1362.87)\n", - " [282] Predict: lam=1367.87 MW (step=5.0000, dlam=5.00) OK (lam=1367.87)\n", - " [283] Predict: lam=1372.87 MW (step=5.0000, dlam=5.00) OK (lam=1372.87)\n", - " [284] Predict: lam=1377.87 MW (step=5.0000, dlam=5.00) OK (lam=1377.87)\n", - " [285] Predict: lam=1382.87 MW (step=5.0000, dlam=5.00) OK (lam=1382.87)\n", - " [286] Predict: lam=1387.87 MW (step=5.0000, dlam=5.00) OK (lam=1387.87)\n", - " [287] Predict: lam=1392.87 MW (step=5.0000, dlam=5.00) OK (lam=1392.87)\n", - " [288] Predict: lam=1397.87 MW (step=5.0000, dlam=5.00) OK (lam=1397.87)\n", - " [289] Predict: lam=1402.87 MW (step=5.0000, dlam=5.00) OK (lam=1402.87)\n", - " [290] Predict: lam=1407.87 MW (step=5.0000, dlam=5.00) OK (lam=1407.87)\n", - " [291] Predict: lam=1412.87 MW (step=5.0000, dlam=5.00) OK (lam=1412.87)\n", - " [292] Predict: lam=1417.87 MW (step=5.0000, dlam=5.00) OK (lam=1417.87)\n", - " [293] Predict: lam=1422.87 MW (step=5.0000, dlam=5.00) OK (lam=1422.87)\n", - " [294] Predict: lam=1427.87 MW (step=5.0000, dlam=5.00) OK (lam=1427.87)\n", - " [295] Predict: lam=1432.87 MW (step=5.0000, dlam=5.00) OK (lam=1432.87)\n", - " [296] Predict: lam=1437.87 MW (step=5.0000, dlam=5.00) OK (lam=1437.87)\n", - " [297] Predict: lam=1442.87 MW (step=5.0000, dlam=5.00) OK (lam=1442.87)\n", - " [298] Predict: lam=1447.87 MW (step=5.0000, dlam=5.00) OK (lam=1447.87)\n", - " [299] Predict: lam=1452.87 MW (step=5.0000, dlam=5.00) OK (lam=1452.87)\n", - " [300] Predict: lam=1457.87 MW (step=5.0000, dlam=5.00) OK (lam=1457.87)\n", - " [301] Predict: lam=1462.87 MW (step=5.0000, dlam=5.00) OK (lam=1462.87)\n", - " [302] Predict: lam=1467.87 MW (step=5.0000, dlam=5.00) OK (lam=1467.87)\n", - " [303] Predict: lam=1472.87 MW (step=5.0000, dlam=5.00) OK (lam=1472.87)\n", - " [304] Predict: lam=1477.87 MW (step=5.0000, dlam=5.00) OK (lam=1477.87)\n", - " [305] Predict: lam=1482.87 MW (step=5.0000, dlam=5.00) OK (lam=1482.87)\n", - " [306] Predict: lam=1487.87 MW (step=5.0000, dlam=5.00) OK (lam=1487.87)\n", - " [307] Predict: lam=1492.87 MW (step=5.0000, dlam=5.00) OK (lam=1492.87)\n", - " [308] Predict: lam=1497.87 MW (step=5.0000, dlam=5.00) OK (lam=1497.87)\n", - " [309] Predict: lam=1502.87 MW (step=5.0000, dlam=5.00) OK (lam=1502.87)\n", - " [310] Predict: lam=1507.87 MW (step=5.0000, dlam=5.00) OK (lam=1507.87)\n", - " [311] Predict: lam=1512.87 MW (step=5.0000, dlam=5.00) OK (lam=1512.87)\n", - " [312] Predict: lam=1517.87 MW (step=5.0000, dlam=5.00) OK (lam=1517.87)\n", - " [313] Predict: lam=1522.87 MW (step=5.0000, dlam=5.00) OK (lam=1522.87)\n", - " [314] Predict: lam=1527.87 MW (step=5.0000, dlam=5.00) OK (lam=1527.87)\n", - " [315] Predict: lam=1532.87 MW (step=5.0000, dlam=5.00) OK (lam=1532.87)\n", - " [316] Predict: lam=1537.87 MW (step=5.0000, dlam=5.00) OK (lam=1537.87)\n", - " [317] Predict: lam=1542.87 MW (step=5.0000, dlam=5.00) OK (lam=1542.87)\n", - " [318] Predict: lam=1547.87 MW (step=5.0000, dlam=5.00) OK (lam=1547.87)\n", - " [319] Predict: lam=1552.87 MW (step=5.0000, dlam=5.00) OK (lam=1552.87)\n", - " [320] Predict: lam=1557.87 MW (step=5.0000, dlam=5.00) OK (lam=1557.87)\n", - " [321] Predict: lam=1562.87 MW (step=5.0000, dlam=5.00) OK (lam=1562.87)\n", - " [322] Predict: lam=1567.87 MW (step=5.0000, dlam=5.00) OK (lam=1567.87)\n", - " [323] Predict: lam=1572.87 MW (step=5.0000, dlam=5.00) OK (lam=1572.87)\n", - " [324] Predict: lam=1577.87 MW (step=5.0000, dlam=5.00) OK (lam=1577.87)\n", - " [325] Predict: lam=1582.87 MW (step=5.0000, dlam=5.00) OK (lam=1582.87)\n", - " [326] Predict: lam=1587.87 MW (step=5.0000, dlam=5.00) OK (lam=1587.87)\n", - " [327] Predict: lam=1592.87 MW (step=5.0000, dlam=5.00) OK (lam=1592.87)\n", - " [328] Predict: lam=1597.87 MW (step=5.0000, dlam=5.00) OK (lam=1597.87)\n", - " [329] Predict: lam=1602.87 MW (step=5.0000, dlam=5.00) OK (lam=1602.87)\n", - " [330] Predict: lam=1607.87 MW (step=5.0000, dlam=5.00) OK (lam=1607.87)\n", - " [331] Predict: lam=1612.87 MW (step=5.0000, dlam=5.00) OK (lam=1612.87)\n", - " [332] Predict: lam=1617.87 MW (step=5.0000, dlam=5.00) OK (lam=1617.87)\n", - " [333] Predict: lam=1622.87 MW (step=5.0000, dlam=5.00) OK (lam=1622.87)\n", - " [334] Predict: lam=1627.87 MW (step=5.0000, dlam=5.00) OK (lam=1627.87)\n", - " [335] Predict: lam=1632.87 MW (step=5.0000, dlam=5.00) OK (lam=1632.87)\n", - " [336] Predict: lam=1637.87 MW (step=5.0000, dlam=5.00) OK (lam=1637.87)\n", - " [337] Predict: lam=1642.87 MW (step=5.0000, dlam=5.00) OK (lam=1642.87)\n", - " [338] Predict: lam=1647.87 MW (step=5.0000, dlam=5.00) OK (lam=1647.87)\n", - " [339] Predict: lam=1652.87 MW (step=5.0000, dlam=5.00) OK (lam=1652.87)\n", - " [340] Predict: lam=1657.87 MW (step=5.0000, dlam=5.00) OK (lam=1657.87)\n", - " [341] Predict: lam=1662.87 MW (step=5.0000, dlam=5.00) OK (lam=1662.87)\n", - " [342] Predict: lam=1667.87 MW (step=5.0000, dlam=5.00) OK (lam=1667.87)\n", - " [343] Predict: lam=1672.87 MW (step=5.0000, dlam=5.00) OK (lam=1672.87)\n", - " [344] Predict: lam=1677.87 MW (step=5.0000, dlam=5.00) OK (lam=1677.87)\n", - " [345] Predict: lam=1682.87 MW (step=5.0000, dlam=5.00) OK (lam=1682.87)\n", - " [346] Predict: lam=1687.87 MW (step=5.0000, dlam=5.00) Q-LIM [347] Predict: lam=1685.37 MW (step=2.5000, dlam=2.50) Q-LIM [348] Predict: lam=1684.12 MW (step=1.2500, dlam=1.25) OK (lam=1684.12)\n", - " [349] Predict: lam=1686.00 MW (step=1.8750, dlam=1.88) Q-LIM [350] Predict: lam=1685.06 MW (step=0.9375, dlam=0.94) Q-LIM [351] Predict: lam=1684.59 MW (step=0.4688, dlam=0.47) Q-LIM [352] Predict: lam=1684.36 MW (step=0.2344, dlam=0.23) Q-LIM [353] Predict: lam=1684.24 MW (step=0.1172, dlam=0.12) OK (lam=1684.24)\n", - " [354] Predict: lam=1684.42 MW (step=0.1758, dlam=0.18) Q-LIM [355] Predict: lam=1684.33 MW (step=0.0879, dlam=0.09) Q-LIM [356] Predict: lam=1684.29 MW (step=0.0439, dlam=0.04) OK (lam=1684.29)\n", - " [357] Predict: lam=1684.35 MW (step=0.0659, dlam=0.07) Q-LIM [358] Predict: lam=1684.32 MW (step=0.0330, dlam=0.03) Q-LIM [359] Predict: lam=1684.30 MW (step=0.0165, dlam=0.02) Q-LIM [360] Predict: lam=1684.29 MW (step=0.0082, dlam=0.01) Q-LIM [361] Predict: lam=1684.29 MW (step=0.0041, dlam=0.00) Q-LIM [362] Predict: lam=1684.29 MW (step=0.0021, dlam=0.00) Q-LIM [363] Predict: lam=1684.29 MW (step=0.0010, dlam=0.00) OK (lam=1684.29)\n", - " [364] Predict: lam=1684.29 MW (step=0.0015, dlam=0.00) Q-LIM\n", - "Collected 351 points\n", - "Transfer range: 0.0 to 1684.3 MW\n" - ] - } - ], + "outputs": [], "source": [ "# Collect PV curve data points\n", "mw_points = []\n", @@ -525,30 +148,9 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "plot_pv_curve(mw_points, v_points)" ] diff --git a/examples/steady_state/05_ptdf_lodf_analysis.ipynb b/examples/steady_state/05_ptdf_lodf_analysis.ipynb index bad6809..beddeef 100644 --- a/examples/steady_state/05_ptdf_lodf_analysis.ipynb +++ b/examples/steady_state/05_ptdf_lodf_analysis.ipynb @@ -31,22 +31,14 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "id": "a3", "metadata": { "tags": [ "remove-cell" ] }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "'open' took: 8.2118 sec\n" - ] - } - ], + "outputs": [], "source": [ "import numpy as np\n", "from esapp import PowerWorld\n", @@ -61,7 +53,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "id": "a3b", "metadata": { "tags": [ @@ -92,31 +84,10 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "id": "a5", "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "pw.pflow()\n", "\n", @@ -149,21 +120,10 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "id": "a7", "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAAAwIAAAJ8CAYAAABN4xa9AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjguMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8g+/7EAAAACXBIWXMAAA9hAAAPYQGoP6dpAAEAAElEQVR4nOzdeXxV9Z34/9dZ7n5v9uQmELawL6KACLggAoqITt1raxdbq9OptevM2NZ+u1intr/pMqPW1uk67XSqrdXWEVcURAVEQJBVlrAkQG729a5n+f1xc6+5WSBgyA3m/XxMpubm3HPOPfeSfN7n836/P4pt2zZCCCGEEEKIYUXN9gkIIYQQQgghBp8EAkIIIYQQQgxDEggIIYQQQggxDEkgIIQQQgghxDAkgYAQQgghhBDDkAQCQgghhBBCDEMSCAghhBBCCDEMSSAghBBCCCHEMKRn+wSEEEIIIcTwEY1GicfjWTm20+nE7XZn5dhDkQQCQgghhBBiUESjUQo9fsKYWTl+aWkpBw8elGCgkwQCQgghhBBiUMTjccKY3MpInIOcoR7H4o81R4nH4xIIdJJAQAghhBBCDCoPKk5lcAMBzR7Uw50VpFhYCCGEEEKIYUgCASGEEEIIIYYhSQ0SQgghhBCDSlMUNEUZ3GOigKQHZZAZASGEEEIIIYYhmREQQgghhBCDSlVAG9wJgeTdb5kRyCAzAkIIIYQQQgxDEggIIYQQQggxDElqkBBCCCGEGFRZKxYWGWRGQAghhBBCiGFIZgSEEEIIIcSg0rJQLKwN7uHOCjIjIIQQQgghxDAkMwJCCCGEEGJQSY3A0CAzAkIIIYQQQgxDEggIIYQQQggxDElqkBBCCCGEGFRSLDw0yIyAEEIIIYQQw5DMCAghhBBCiEElxcJDg8wICCGEEEIIMQxJICCEEEIIIcQwJKlBQgghhBBiUCkM/t1oSQzqSWYEhBBCCCGEGIZkRkAIIYQQQgwqKRYeGmRGQAghhBBCiGFIZgSEEEIIIcSgkgXFhgaZERBCCCGEEGIYkkBACCGEEEKIYUhSg4QQQgghxKBKpgYNdrGw6E5mBIQQQgghhBiGZEZACCGEEEIMKikWHhpkRkAIIYQQQohhSAIBIYQQQgghhiFJDRJCCCGEEINKVhYeGmRGQAghhBBCiGFIZgSEEEIIIcSgUrNQLCx3v3uSayKEEEIIIcQwJIGAEEIIIYQQw5CkBgkhhBBCiEElxcJDg8wICCGEEEIIMQzJjIAQQgghhBhUsrLw0CAzAkIIIYQQQgxDMiMghBBCCCEGlcwIDA0yIyCEEEIIIcQwJIGAEEIIIYQQw5CkBgkhhBBCiEEl7UOHBpkREEIIIYQQYhiSGQEhhBBCCDGoNLJQLGwP7vHOBjIjIIQQQgghxDAkgYAQQgghhBDDkKQGCSGEEEKIQaVmoVhYHeTjnQ1kRkCIs9R3vvMdFEVJfxUXF7N48WJee+01ABYtWpTx896+brvtNgDGjh2bfszhcFBSUsJll13Gf/zHf9DR0ZFx3EOHDvW5v02bNp32+bvdbqZOncr/9//9f1iWNWDX6UT279/PZz/7Wc477zx0XWfGjBl9bvvrX/+aSZMm4Xa7Offcc3nmmWd6bNPS0sLtt99OQUEBgUCAG2+8kePHj2dss2nTJj71qU8xdepUVFXl6quvHvDXdSJD4boDPP3008ybN49AIEBZWRk333wzlZWVGds8/vjj3HDDDZSXl6MoCj/60Y/63N+GDRtYunQpgUCAnJwc5s+fz9atWzO22bNnD5dffjk+n4/S0lL+9V//lXg8fiZenhBCnBVkRkCIs5jH4+GVV14BoLq6mu9973ssWbKELVu28Mgjj9Da2pre9nOf+xxerzdjMFVcXJz+7xtvvJGvfvWrmKZJbW0tq1ev5t577+WRRx7hlVdeoby8POPY3//+97nssssyHps6deppn38kEmH16tV87Wtfw7Isvva1r53Svk7Hzp07WblyJfPmzcOyrD4Hwo899hh33HEH9957L4sXL+bxxx/nuuuu47XXXmP+/Pnp7T784Q+zc+dOfvGLX+B2u7n33ntZvnw5mzZtQteTv27feOMNXnvtNebNm0ckEjnjr7E32b7ua9as4brrruMTn/gE//Zv/0ZDQwPf+ta3uOKKK9i+fTsejweAJ554gsrKSq6++moeffTRPvf3yiuvcNVVV/HpT3+ae+65h0QiwcaNGwmHw+ltmpqaWLx4MRMnTuTJJ5/k6NGjfOUrXyEcDvPwww+f8dcshMiUlZWFZUKgBwkEhDiLqaqaMRC94IILGDt2LL/4xS96DG5ycnLw+/0Z23cVDAYzfnbdddfxqU99iosvvphPfepTvPTSSxnbT5w4sc99ne75X3bZZWzfvp0nn3xyUAak11xzDR/60IcAuO222/qc0fj2t7/NLbfcwve+9730eb7zzjvcd999PPvsswCsX7+eF154gRdeeIErrrgCgMmTJzN16lSefPJJbr75ZgDuvvtuvvjFLwLJWZtsyPZ1f+yxxxgzZgy/+c1vUDqn6ktKSli8eDGbNm3ikksuAZIzAqqanLjuKxAwDIPbb7+dL37xi/zwhz9MP37VVVdlbPeLX/yC1tZWnnrqKQoKCtLP/dznPsc3vvENRowYMeCvUwghhjpJDRLiA2T06NEUFxdz8ODBAdnfrFmzuOuuu1i1ahXvvvvugOzzZAKBAIlEIv39mjVrek07uvbaazMG0tXV1dx8880Eg0Hcbjfjxo3jy1/+8gmPlRpknkhlZSV79+5ND+RTbrnlFl5++WVisRgAzz33HHl5eVx++eXpbSZPnsx5552XDhb6e8xsGMzrnkgkCAQC6SAAIDc3FwDbfq+/X3+u1apVqzh06BBf+MIXTrjdc889x9KlS9NBAMDNN9+MZVm8+OKLJz2OEGJgpRYUG+wvkWlo/kUSQpyW1tZWGhoaBvTuZuru9oYNGzIetywLwzDSX6m0mtQA8ne/+12/9p96fltbG08//TR//etfufHGG0/5PD/xiU/wzjvv8OCDD/L888/z3e9+F9M0T3k/3e3ZsweAKVOmZDw+depU4vF4Oujas2cPkydPzhjcprZL7eNMiUajfO9730sHJf2Rzet+2223sWvXLh555BFaWlqorKzkG9/4BrNmzeKiiy46peNv2LCBwsJCNm3axOTJk9F1nUmTJvH73/8+Y7s9e/b0eA/z8vIoKys74++PEEIMVZIaJMRZzjAMIHlnNpXjfzoDur6MGjUKgJqamozHP/zhD2d8v2TJElatWoWiKGia1q+7uR0dHTgcjh77PZ30lI0bN/LAAw9knNcnPvGJU95Pd01NTUBy0NhVfn4+AI2Njentum+T2i61zZly/PhxHn30UdatW8dTTz2F2+0+4fbZvu6XXHIJTz31FB/96Ee56667ADjvvPN4/vnn0TTtlI5fU1NDR0cHn/rUp7jvvvuYNm0a//u//8snP/lJgsEgy5YtA7L7/gghxFAlgYAQZ7HuA7r8/Hwefvjh9OBnIKRSNbrf6f7hD3/I4sWL09/n5OQAcOmll6aDk5PxeDysXbsWgFgsxubNm/nWt77FHXfcwW9+85tTOs/Zs2fzox/9CF3Xufzyy5kwYcIpPX+oO3LkSEbxa3ePPvooH/nIR7j66qt5+umn8Xq9fW6b7eu+bt06Pv7xj3PHHXdw9dVX09DQwPe+9z1WrFjBa6+9li4W7g/LsohGo/zwhz/k85//PACLFy9mz549/Nu//duA/lsQQgwcKRYeGiQQEOIslhrQKYpCUVERo0aNGvAc9OrqagBKS0szHq+oqOD8889/X/tWVTVjHxdddBGGYfDVr36Vr3zlKyds59nd448/zr333su9997L5z73OSZPnsz3v/99rr/++vd1jqk7/y0tLRnXIDVTkMo5z8/Pp6qqqsfzm5qaMvLST9cnPvEJXn311ZNu9/LLL/PXv/6Vj3/8431uk+3r/oUvfIHFixfz4x//OP3Y/PnzGT16NH/4wx+48847+3381PvTNSiF5AxV14L5/Px8Wlpaejx/oN4fIYQ4G0mNgBBnsdSAbs6cOYwZM+aMFKK+8MILACxYsGDA992bVAvSnTt3AqTTXLr3e08NxFPKysr4zW9+Q319PRs3bmTy5Ml8+MMf7tGb/lSl8sq755Hv2bMHp9NJRUVFert33303o9g1tV333PTTsWbNGmzb7vPr9ddfJxAIcPvtt3Prrbee8v4H87rv2rWL8847L+Ox8vJyioqKOHDgwCmd9/Tp0/v8WTQaTf/3lClTeryHLS0tHD9+fEDeHyHEqZFi4aFBAgEhRJ+2bt3KI488wrJly5g4ceKgHHPHjh0AFBUVAaTXL9i9e3d6m/r6erZs2dLr81VVZe7cudx///0YhsH+/fvf1/lUVFQwadIk/vKXv2Q8/vjjj7NkyRKcTicAy5cvp6mpiZdffjm9zd69e3n77bd7tLIcaHv27OHKK6/kYx/7GL/85S9PKyAczOs+ZsyYHvs5fPgw9fX1jB079pTOe9myZTgcDlatWpXx+EsvvcScOXPS3y9fvpxVq1bR3Nycfuwvf/kLqqqmC+KFEGK4kdQgIQQAoVCIDRs2YFkWdXV1vPLKK/zqV79i1KhRp5Q3/uqrr7JkyRJ+85vfnLRo1LKsdDeieDzO5s2buf/++5k2bRoLFy4EkgPSefPm8d3vfpfc3Fx0XeeHP/xhut0kJO/sLlu2jI9//ONMnjyZeDzOQw89RF5eHrNnz+7z+OFwON3a8/Dhw7S2tvLEE08AyVqH1IJr3/nOd7j11lsZP348l112GY8//jhvvvlmOs8ekjMmy5Yt49Of/jQ//vGP0wuKzZw5MyNNpq6uLp3iU1dXR3t7e/qYV1111Qlz+/syefJkfvOb33DTTTf1a/tsX/fPfvazfOlLX+KLX/wi11xzDQ0NDdx///2UlJRktGndtWsXu3btSn+/fft2nnjiCXw+H8uXLweS61984Qtf4Jvf/CaKojB16lT+9Kc/sWHDBp5//vmMYz700ENce+21fOMb3+Do0aP8y7/8C5/97GdlDQEhxLAlgYAQAkiu4vrEE0+g6zp5eXlMnz6d73//+3zmM5/B5/P1ez+2bWOaZp+r9HYViUTSKUe6rjNq1Cg+9rGP8e1vfzujCPqPf/wjd9xxB7fddhulpaXcf//9PPbYY+m7u263m3POOYeHHnqII0eO4PF4OP/883nxxRfTd7h7U1tb22PwnPp+9erV6X75H/nIRwiHw/zgBz/gBz/4AZMnT+app57qkS71+OOP85WvfIU777wTwzC44ooreOihh9KrCkMy9aavYx48ePCU74hDspC7v0EAZP+6f+ELX8DlcvHzn/+cX//61wQCARYsWMBf/vIXCgsL09v9+c9/5rvf/W76+9///vf8/ve/Z8yYMRw6dCj9+A9+8AP8fj///u//Tl1dHVOnTuVvf/tbxp3+/Px8Xn75Ze6++26uvfZaAoEAn/nMZ/i3f/u3fl83IcTAURUFdZBTdQb7eGcDxe6e0CqEEEIIIcQZ0NraSm5uLo8Hp+JVT61d8PsVtkw+HNpNS0tLutPdcCczAkIIIYQQYlApmoKiDu4d+u5tsIUUCwshhBBCCDEsyYyAEEIIIYQYVKqmoA7yjIDUCPQkMwJCCCGEEEIMQxIICCGEEEIIMQxJapAQQgghhBhcmopyGosfvi+KNMrs7gMbCFiWxbFjxwgEAlIlLoQQQohhybZt2traGDFixGmtOi4+2D6wgcCxY8cYNWpUtk9DCCGEECLrqqqqKC8vz/ZppCmqgqINcvtQ5MZwdx/YQCAQCADJD74sGiGEEEKI4ai1tZVRo0alx0VCdPWBDQRS6UA5OTkSCAghhBBiWJM0adGbD2wgIIQQQgghhiZVU1AHOTVIldSgHqRqRAghhBBCiGFIZgSEEEIIIcSgUtTBbx+q2NI+tDuZERBCCCGEEGIYkhkBIYQQQggxqKRGYGiQGQEhhBBCCCGGIQkEhBBCCCGEGIYkNUgIIYQQQgwqRZOVhYcCmREQQgghhBBiGJIZASGEEEIIMaiSMwKD3D4Ua1CPdzaQGQEhhBBCCCGGIQkEhBBCCCGEGIYkNUgIIYQQQgwqWUdgaJAZASGEEEIIIYYhmREQQgghhBCDSlEUFHWQ24daMiPQncwICCGEEEIIMQzJjIAQQgghhBhUqqaiDnL7UNWW+9/dyRURQgghhBDiBH7wgx+gKApf+tKX0o9Fo1HuuusuCgsL8fv93HDDDYRCoeyd5GmQGQEhssiyLG644QYOHTqEqvYelytKMqfRtu1+7TO1fUpvz+u+TX+PoShKn8/tbb8net6Jtk9JnU/38+rtPG3bzthf9236e6xTeU5/pParqirf//73ueyyy973PoUQQgyet956i0cffZSZM2dmPP7lL3+ZlStX8pe//IXc3Fw+//nPc/311/PGG29k6UxPnQQCQmRRLBbjb3/7GyUlJdxwww3pQaOiKBkD0+6D3K76GsSf6Dmpn/U3uOhr/yd6fvfzP5XnnijY6O37E/2s63XoT6DT2/O6PnaqUvv41a9+xeuvvy6BgBBCkFpZeJCLhe1TP157ezu33norv/zlL7n//vvTj7e0tPDrX/+a//3f/2Xx4sUA/Pa3v2Xq1Kls2LCB+fPnD9h5n0kSCAiRRalB4o9//GM+9rGPZflsxJn0zDPPZPsUhBBCnKK77rqLFStWsHTp0oxAYPPmzSQSCZYuXZp+bMqUKYwePZr169dLICCEEEIIIURvsjkj0NramvG4y+XC5XL12P6xxx5jy5YtvPXWWz1+VlNTg9PpJC8vL+PxYDBITU3NwJ30GSbFwkIMAQORiy6EEEKIkxs1ahS5ubnprwceeKDHNlVVVXzxi1/kj3/8I263OwtnOThkRkCIIeB0cs+FEEIIceqqqqrIyclJf9/bbMDmzZupra1l9uzZ6cdM02Tt2rU8/PDDvPDCC8TjcZqbmzNmBUKhEKWlpWf0/AeSBAJCZNGpdgQSQgghPgiyuY5ATk5ORiDQmyVLlrB9+/aMxz71qU8xZcoU7rnnHkaNGoXD4eDll1/mhhtuAODdd9/lyJEjLFiw4My8gDNAAgEhhBBCCCG6CAQCzJgxI+Mxn89HYWFh+vHbb7+dr3zlKxQUFJCTk8Pdd9/NggULzppCYZBAQAghhBBCDLYsFAtzGu1DT+SnP/0pqqpyww03EIvFWLZsGY888siAHuNMk0BACCGEEEKIk1izZk3G9263m5/97Gf87Gc/y84JDQAJBITIIqkREEIIMRypioKqDu6MgCod+nqQ9qFCCDEIpEWsEEKIoUYCASGySAaHQgghhMgWSQ0SYgiQ1CAhhBDDiaKpKIPcPlSx5P53d3JFhMgimREQQgghRLbIjIAQQ4DMCAghhBhOVE1BHeT2oaolN9+6kxkBIbJIugYNL/I+CyGEGEqGRCBwxRVXMHPmTM477zwuueQS3n77bQBisRif//znmThxIueccw4f+9jHsnymQghx+iQVTAghxFAyJFKD/vznP5OXlwfAU089xW233ca2bdv42te+hqIo7N27F0VRqKmpye6JCnGGyJ1iIYQQw4mShZWFFUkN6mFIBAKpIACgpaUFRVHo6Ojg17/+NdXV1em7aKWlpVk6QyHODLlDLIQQQohsGRKBAMAnPvEJVq9eDcCzzz7LgQMHKCgo4Pvf/z6rVq3C4/Hwne98hyVLlvT6/FgsRiwWS3/f2to6KOcthBBCCCFOjbQPHRqGzBX5/e9/T1VVFffffz/33HMPhmFw+PBhpk2bxqZNm3jwwQf58Ic/TCgU6vX5DzzwALm5uemvUaNGDfIrEEKIvkn6lxBCiKFmyAQCKZ/85CdZvXo1I0eORFVVbr31VgBmzZrFuHHj2L59e6/P+/rXv05LS0v6q6qqajBPW4jTkkoNsiwry2cihBBCiOEm66lBzc3NhMNhRowYAcDf/vY3CgsLKSkpYcmSJbzwwgtcddVVHDx4kIMHDzJ16tRe9+NyuXC5XIN56kIIIYQQ4jSoGllYR2BQD3dWyHog0NLSwk033UQkEkFVVYqLi3nmmWdQFIVf/OIX3H777dxzzz2oqsqjjz7KyJEjs33KQgyYVLqIqg65yTkhhBBCfMBlPRAYM2YMGzdu7PVnFRUV6QJiIT6IJG9cCCHEcKSoCoo6yO1DB/l4Z4OsBwJCDGepQEDaiA6O6upqQqEQwWCQ8vLybJ+OEEIIkVUSCAiRRZIaNHiqq6t56623iMVibNq0iRUrVkgwIIQQWaKqKuogtw9VTflb250EAkIMATIjcGbZto1tWYwdMwbDNIknEoRCIQkEhBBCDGsSCAiRRVIjcObZlkUikcDtdrPxrbdob2/nnHPOYcaMGdi2LUGYEEKIYUsCASGGABmMnhmmaWIaBrrDQXFJCStWrEjXCAT8fuKJBLquo0lqlhBCDCpFU1AGuX3oYB/vbCCBgBBDgMwMDCzbtjESCQAcTmc60CovL89IB3I6HCQSCWxVRdM0CciEEEIMKxIICJFFEgAMPMuyMBIJNF1H07QTbqsoCg6HA8M0SSQSOBwOCQaEEGIQKJqKMsjFwoN9vLOBBAJCDAHSNWhgmIaBaZrJAX0/r6miKDh0HbOziPjwoUPU1NRQVFTEtGnTzvAZCyGEENkjow8hskhmBAaGbdsk4nEs206mAp1GYKVpGocPHiQv4EO1TF577TV27tx5Bs5WCCGEGBpkRkCILJIFxd6/U0kF6o1tmViJOFYiRnlJPm3tYaZOmciEinFUVh89A2cshBBCUdXTumnzfo8pMkkgIIQ4K9m2jWmaWKaZURDcr+eaRnLwb8STwZiqY+sujtXU8/LLL9Pe3s41y6/kvGlTMBNxNIfzDL4SIYQQIjskEBAii1KDV0kROjW2bZNIJFAVpV9BgG3bycG/EcdKxFEUBUV3YDs92CjomoaqqkyYMIFYLEZDQwMGCk5vACPagWUk0N1embkRQogBompZWFlYioV7kEBAiCyS1KBTZ5kmRufaACcqsk4O/hOdaT9xFFVDdTjRvQEsO7nGgKYl1xDoev2nT5+esR9FVTGiYeIdrTg8ftTTSD8SQgghhiIJBITIIgkE+s+2bUzDeK8guJdrZts2duddf8tIoGg6qsOJw+UFRcGyLBKGgaZpOPuZTqTpOorXTyIaJhFuQ3e5UR0uec+EEOL9yEL7UGRGoAcJBIQQQ9qePXtoampi8qRJ5OTm4tD1jEG4bVvpu/62aaDqDlSHE83jT29ndhYUq4rS7wCgK1VVcXp8xKNRzM4gQ3f7pPBMCCHEWU3+igmRRTIjkKm6uprNmzdTXV0NwP79+2ltbaW4qIiXVq1i3759KIqCbVmYsQiJjhYSbc3YpoHmdOMI5KN7A+k79pZlEYvHMU0Tp8PxvhYMUxQFp9sNmhMblXhHK5aRGMiXL4QQQgwqmREQIoskEHhPdXU1zz33HKNGjaKosJBwRwdFRUU0NTbyzMqV+H1eHIpFor0Z27ZQdReay4ui6T2un2XbGIYBto1D1wdswTZFUXC4XJhGAlNRMKJhVN2B5vLIeyiEEKdAUbOwsrDM4vYggYAQWSSBQHIdAMuycDgcXHP11XSEw+zYuZPi4mI8Hg+RjnZu/tBVKIqKojuSKT+q1meNgJGqIxjAAKArRVHQHU4URcUAbGwSHa3oUkgshBDiLCOBgBBDwHAIBPbu3UtdXR3BYJBx48ZhmSa2baMoCqqmYRgGu3fvJicnh5EjRzJyxAiOVFUxbdZcDuzZgdfn55xzJvS6b9u2MUwTy7LQNQ3HIAzItc5ahUQijuZ0Y0Ta0JxSSCyEEP0hC4oNDXJFhMii4bJ+wN69e1m9ejVej4e83Fz279vH4cOH0R0ONE3DsiyKi4qYO3cuEydMoKSkhLr6erbv2EFLSzNbt73T66rBqRmAeDy5NoCzc3+DRdU0nE4XpmWhun1YRgIj0o5tWYN2DkIIIcTpkhkBIcQZV1dXR3t7OzZQEwrh9/kwTJPmpiZycnKSvfx1HafLRTwWIy8/nxEjRqBpOg6Hg0suuYRp06al92fbNpZlYZxiK9AzQVFVnC43iXgMRXehYpMIt6K7fai6IyvnJIQQQvSHBAJCDAEf9FSSkpIS/H4/bW1tbNu6lRtuuAGv10tLSwv5BQUZr1/tnCHQNI2K8eOJJgxGjyhN/9zsDABUVc1qANCVoig4nC6MRBwL0D1+jEiHFBILIUQfFE1FGeS6KkUzB/V4ZwMJBIQQZ9zEiROxbZtoNIrL7eaxxx9n3NixLF68mFg0iqbr6J0596qqYnbe6bdtG7VzEJ1aDCyVAjTUBtfvBQOJ5MrH3gBWPJouJBZCCCGGGgkEhMiiVI3AcKgVmDRpEgC6rlNfX09xcTF5+fmYpomRSBAzjHTNgG3byfSfzssSj8cBzlgnoIGkOxyYpkIiHsPpdKPqDoxIG7fcdGO2T00IIYYMJQsrCw/6SsZnAQkEhMiiVAAw1Ae3A6lrrj+ApmlompYOCIxEgvb2dqqPHmXEqNF4PV50p/OsukaapqMoKvF4DIfDicObw6KFF1NRUYFtWdK5QgghxJAgf42EyKLhMBPQX5qm4XK7OXr0KAClZWWEQiFCoZqzKghIUVUVp6uzbsCy+OzdX+Lg4SMkwrIisRBCqKqalS+RSa6IEEPAUMt3z6ajx47x37//PTt37MDn89PS3JztUzptiqLicLkxTYN/vPNOtu3Yhe4JYETDGNGwBIJCCCGySgIBIcSQUlhYiN/vZ+vWrTicTkpLS0/+pCEsVUQcCPhZdsUVKKqKw5cDQKKjFcuULhZCCCGyQ2oEhBgCZEbgPdOnT8e2bRobG3E4HOQF/MkuPPrZ++tKURR+9OOfMK6igvNi5+JwudDd3s4FyNrQnB5Ux9BohSqEEINBioWHBrkiQmSRpIb0bsaMGSxcuBC324PToScXD/sA3DnfuXMXusNBPBbFsixU3YHDm4NlxJMrEtuyIrEQQojBc/beYhNCfODZ2MnUGoeDeCKBQrKo+GymahoOxUWis6OQqmnoHj9WIpZcc0BWJBZCDAMyIzA0yBURQgxJydkSBUVR0ouIGaaJaZ39d82THYXcGIk4ZuciaZrTLYXEQgghBpUEAkIMAZIb3pNtg9rlsqSDAcPA+gAEA4qidHYUMknE48lVlDWts5DYJtHRim2d/elQQgghhi5JDRJiCJC7vz1Ztt0jQEoFA/FE4qxYZfhkkh2FnJhGIpkq5HShKAq624dlxEmEpZBYCPHBpCjqoC+uqChn99+MM0GuiBBZJAFA32zbRu1l8JsKBhKJxAdmZkB3ONE0nUQs+t5q07qzSyFxhxQSCyGEGHAyIyCEGJLsXmYEUlIFxIlEAqfzg3G3XNN1FEUhHovicLpQ1eTdMikkFkJ8EEmx8NAgV0SIIeCDMJAdaMnUoL5/rqpqupvQB2VmRdU0HM5kRyGzs13qe4XEfikkFkIIMaBkRkCILJIBXd+SxcInDpBUVcWh68QTCerq6gjV1BAMBikvLx+ksxx4qY5CiVgM27LQHckZAFXTcfhyMGNhEuFWHB4/inp2t1IVQgxfMiMwNMgVEWIIkBmBnnorFu6Nqqo0NTYSiUTYvn07q1atorq6ehDO8MxJdhRyYdsWiXgsHTCmCok1p4dEuA2zy8+EEEKIUyUzAkKIIamvYuHeHD16lB07dzJ50iQ8Xi8up5N4LPbeBp1rESh0Bl3Ke+sTQM9ArLq6mlAolNXZhVQRsWkYGR2FADSHE1XTMaIdWEYC3eOVbhhCCCFOmQQCQogh6UTFwt0Fg0E2bdrEuvXr8fv9rFixAqfLld5P6n9t2wbbxrYsrNTPut1RD0ci1NXWsm3bNhKJBCtWrMhyMODANJJFxE6nK91uTwqJhRBnM1VTUQc5VWewj3c2kEBAiCFAUoN6smxOWCzcVXl5OStWrOj1Ln5fd/17Y9s2h3bvxuvz4XG7aWpqIhQKDVggcLppPJquo6gK8XgMh9OJ2lkbkCokVjQdI9KBqjvQXB75PAkhhOgXCQSEEEPSqaQGQTIYeL8DdkVRKB85kr379nHs+HH8fj/BYPB97XOgqKqG0+kiHo+h6w40/b1f36lCYiMaJhFuw+HxSSGxEGJIU1Rl8BcUU+UmSXcSCAiRRVLo2Tebwb82lmWRk5PD6NGjcblcA14j8H7v1CupjkKdRcKptQdS+3Z4fJiJ91Yk1pyugThtIYQQH1ASCAiRRV27wYj32LaNgjKo18W2bYxEAofTOSCzC2eKoig4nC6MRAIjEUd3ZC6oliokTkQ6sMwEulsKiYUQQvROAgEhskhmBHpnn2QxsTPBSCQy7rAPZclgwIlhJEjEYjhcrozzVlQVh9ePGZdCYiHE0CTrCAwNckWEyKJUIKAOcp7kUJcsFB68AXlqFV9NO7vy6nXdgeZwEI9FsSwr42eKoqC7uqxIHItI4CmEECKDzAgIIYacUy0Ufr/HMg0Dh9M5KMcbaJqmoSiu5FoDDidqt2BGComFEEORzAgMDXJFhBgCzoZ0lMHU31WF3y/btknE4+gOxxk/3pncv9pZRGwk4piG0euxHR4fmtNNvKMNMxHrZS9CCCGGG5kRECKLJFWjd4NVI2AaBqqmfSBSsxRFwZHuKGSh6T2Dm/cKiduTKxK7fRKECiGyQlHUwW8fKo0TepArIoQYcmybM54aZFkWlm2fdXUBJ5LqKASQiMd7DTSThcQBFFUj0dGC1csMghBCiOFBAgEhhgC5K5vpTKcG2bZNIpHAMQgpQV1FIhG++tWvcvfdd7Nt27YzcgxFUdAdTjRNIxGL9h4MKAq6y9NZSNwhhcRCCDFMSWqQEFkkg6/encli4XQQMMitQi3L4oEHHkBVVQKBAL/+9a/55je/yde//vUzch6pVqjxWBSH09Vr+pMUEgshskXRtB7NDQbjmCKTBAJCiCHnTM4IWKaJoiiD/gfo5z//OXv27GHZsmVMmDCBb33rW9x7772Ew2Huv//+M3JMVdNwdHYUSs0SdNd1ReJ4Rxu624PmkBWJhRBiOJBAQIgskhmB3iVnBAZ+v5ZlYZpmVlqFXnfddRnf/8d//Ae5ubncd9991NTU8PDDD+N2uwf8uKmOQolYsohY72NhMSkkFkIMJmkfOjRIICDEECADrky2DceOHSMUChEMBikvL39f+6uurqY2FGLChAn4A4Ehc72/853vUFJSwpe+9CU8Hg8PPvjgGTm3ZEchF0Y8TsLqu11qqpDYjEdJdLSge/yomvyZEEKIDyoJjYTIIpkR6J1pmjz//PMcr6lh79691NXVYZgmZvcvy8K0rGQHoK5fto3d+XX06FGee+45YrEY6zds4NixY9l+eWmKonDXXXfxgx/8gIcffpif/vSnp72v1tZWPvzhD3PDDTfQ0tLS67F0pxNFUTpbjPb+2csoJI5IIbEQQnyQya0eIYaAoXKHeiiwbRvTNBk9Zgwup5OGhgaam5vJLyjIGJDayY1TT3rvscydoek6F154IcePH2fHjh0UFRW97xmGgfaVr3yFw4cP89WvfpV3332XH/3oRwQCgR7bNTY2AlBQUNDjZ/fddx9//vOfAYhGo/z2t7+lpKQkY5tkRyEHppEsIna6XH321ZZCYiHEmSSpQUODXBEhskjutPZk2TaqqjBu7Fi2bttGXV0dHo8HXdPQdT395ej65XDgcDhwdv9yOjESCV5//XW2bduG3+8nGAxm+yX2oCgKDz74IL/4xS/4/e9/T3l5Of/xH/+RsU1zczNjxoyhsLAQn89HXl4e3/72t9m9ezdAOgh49NFHWbNmDcFgkAkTJvCDH/wAy7Iy9qXpOg6nk3gshmWZJzyvzBWJ4wP7woUQQmSVzAgIMQTIjMB7bBucTiclJSVcfNFF77tGoLy8nBUrVgxYvcGZ9I//+I8sX76ce+65h3/+539m165djB8/nsLCQkKhEO3t7fzzP/8zwWCQtWvXct9993HfffcxefJkqqqqcDgc3HTTTYwcOZJnn32WRx55hK9//esUFhZyxx13ZBxLVTWcThfxeAzd4UA7QS1AZiFxXAqJhRDvm6JmYWXhD8Aq8gNNAgEhskhmBHpKXROfz8ecOXMGZJ/l5eVDOgDoavTo0Tz66KN4vV5efvllfvnLX6Z/9tGPfpQf/vCHqKrKP//zP7Nt2zaWLl3Ku+++C8CXv/xl8vPzWbFiBStWrGDBggV8/OMf584770RRFG6//faMAbyS6igUj2FbNrqj945CqW3fKyRuRff4pJBYCCHOcvJbXIghQO6uvseybRTsXnveDxc5OTn8+te/BpItT5uammhsbGTixIkZ25177rnU1dVx+PBhSktLcbky+/9/7GMfo7i4mCuvvJI77rgDr9fLRz/60YxtFEXB4XRhJOLp9Qb6+jymCokt3YER6UB1ONGcbvn8CiFOmdQIDA1yRYQQQ4pl2YAig8tOqqpSWFjYIwjoasyYMbhcLvbu3csbb7zBvn370j9btmwZb7/9NgCf+tSnen1+KhhQFPWEHYXS59RZSGxbJolwG3a3GgQhhBBnBwkEhBBDimVZqGdiNbEPuL1797J5yxZM02L9+g3s3bs3/bNUl6F4/MTFvrrDgabrxGPRkw7uk4XE/s5C4lYpJBZCiLOQpAYJIYYUy7ZxDOO0oNPV3t7OBRfM4+0tW1hw4YXYtoVt2yiK0mu70b5omo6iqMTjMRwOJ+pJ3gutc5tEpKNzRWKvzOYIIU5KUZXBTw2Sm0w9yIyAEFkkxcKZbNtOtg+VgeQpMS2LcRXjeeP11zh8+BCrX3kZv99PLB7HNC28Xi+FhYUA6QXaumtra2Pnzp1AMh3J6XJjJOKYhnHS4yuqhsMbQFFVEh2tWObJnyOEECL7ZEZAiCFA7qAmpQIjVVq89ZtpWiSMBHm5OcyfP5+6ujpKSkoYUVaGZdskEglA4Vvf+hZf/OIXmTx5MgAlJSUoisK8efPweDw8+eSTJBIJJk2axA9+8AOuvfZaHKmOQraFpjtO+DnNLCRuR3W4pJBYCNEnaR86NEggIIQYMkzLwrZBZm/7xzRNEoaJy5ns9DNp0iQmTZqU/rmqKLicTkzT5DN33EmwtJSXXnyRt99+m9bWVnJzc3n55Zfp6OgA4NZbb+WPf/wj119/Pddffz0PP/wwpaWlGIkERjyO7uy7o1D6mJqOw5eLEe3oXJHYL398hRBiiJLfzkKIIcOyLGxkhqQ/jHQQcOI79QCapuFxu7j+uut5+GeP8NZbb7F69WoeffRR9uzZQ1VVFYlEgv/5n/8hGo1y+eWX8+STTzJixAgeeugh1m/YQDgSIRHL7CgUi8VYtGgReXl5fOpTn+IPf/hDui4hWUjsyigk3rRpEx//+Mf5l3/5F958880+z/e1117jrrvuSqcqCSGEODNkRkCIIUBqBTqvgW0DtgQCJ2EYJqbZvyAgRVEUHA4dzbJobWunuvoomzdvQtd1VqxYga4n/xy4XC5efPFFrr/+ep566im++MUvpp9/ww038P37v4fmcPDaa6/zwAMPsH//fn7+85/jcrk4fPgwl1xyCa+88gpOpxPN4epckbiDxvpaLr30UsLhMKWlpfzoRz9i3rx5jBw5kkgkwi233MKNN97IunXrWL58OYZhEA6H+e1vf3vGrqMQInsUVUNRB7cxxGAf72wgMwJCZJEMeN9jmiaqpqEg1+REDMPAtEycpxAEdKWqKgf27+PIkcMsWbqUC+bNp7W1tUcw+sQTTxAOh6mqquKFF17gJz/5CQ6Hg+UrrubIoUP88pe/ZOTIkbz11lvJRc+amykuLmbZsmVceOGFHDp0CEj+4dU9fn7yk/9g7aoXeHnVS1RXV/PHP/6R8vJyqquraW5u5pOf/CQ+n4/LL7+cyy67jIKCAhwnWOlYCCHE+5f1GYErrriCmpoaVFUlEAjw4IMPMmvWrPTPf/vb3/LpT3+ap556imuvvTZ7JyqEOKNMy0LTNAmOTiBhGFiWhdNxekFASjAYZNOmTax85hlGjizniiuuIBaPo2kaeud7oKoqHo+H8vJyysuT2wDs2rWLvXv38uwzz+Dz+9i67R1mz56NPxAgEY+TSCT48Y9/zIIFC3jmmWfYvHkzDz74IDt37qTq2DF+8+gj2Eacj3zkIxmrHL/77rts3LgRt9tNPB7npZde4uqrr37f10wIMUSpWvJrsI8pMmQ9EPjzn/9MXl4eAE899RS33XYb27ZtA+BQ512n+fPnZ/EMhRBn2nt3oxUpFO6FbdsYholt2+87CAAoLy9nxYoVhEIhgsEgBQX52LaNaZrE4nFURUXXtV67N02bNo1p06YlzykeZ8L48eyvrOTZlSspLStj+ZVXcujwYb55770sXLgwnQr06U9/Ojmr4MvptZB48uTJ6Y5Gf/jDH9KvO1VzIIQQYuBlPRBIBQEALS0t6V/4lmXxmc98hoceeoivfvWrWTo7IcRgsCwLTVVl0NcL27ZJdPbydzj0Abs+qTv9KYqioOs6mqYl244aBrYNuqahaWqP4yqKgqrruNwuJowbh0KyJWlBQQG2bfP9Bx7g7i98AQUYO3YsTqeTyspKdu3aRTAYZMyokcQ7WmnpiFB19BjBYDB9PjfffDOf+MQnuPbaazlw4AAVFRUD8pqFEEJkynogAPCJT3yC1atXA/Dss88C8JOf/ISLLrqIOXPm9GsfsViMWCyW/r61tXXgT1QIcUaYloWuaSRM66wJBKqrq9N31LsOqAeSbdskEkbnIH1w0qYURUFTFDSnMz0TEYvHUVUVXUvOElRWVlJbW8uUqVPJycnBoTuYMW0qDpc7vY+cQICA349l25iGQVNTEwcPHqSpqYnm5mZcbjdOhwMrEcNOxHj++ee58sorKS8vx+Vy8ZGPfIQ//elPrFu3jlgsRjwe56233mLGjBlccMEF/V5rQoJLIYYoVU1+DfYxRYYhcUV+//vfU1VVxf33388999zDjh07+Otf/8o3v/nNfu/jgQceIDc3N/01atSoM3jGQgwM0zSB4V00nEr/UDtnBIb6qsK2bRMKhWhoaEBRVRobG6mvr+9cA2Hguj/Ztk28MwgYyJmAU5E6tsvpRFNVEgmDxqZmdu/ejdPl4vXXXuNgZSWarqM7nMRjUSzLApKB0jvvvENDQwO2DQnDxOfzk59fgKbrdLS309LSSm1zO4qqsnzJIlqamtLH/s///E9mzZrFxz/+caZNm8Z5553HHXfcwYIFC7jssstOeu7Hjx/n/PPPR9d15s+fzzPPPHPGrpMQQpythkQgkPLJT36S1atX8/e//51Dhw4xceJExo4dy4YNG7jzzjv5+c9/3udzv/71r9PS0pL+qqqqGsQzF+L0JFd9BafTmeUzyR6ry+Dfsm2GYhxg2zaGaRKPx4knEkQikWQv/ldeYf369TQ0NGBZFolEglg8TqyzaNY0zeTaCKcYICSDgASqmhyIZ5uiKGiahsvlpPLAfgKBHHJzcqg6coTa2loAVE3D6XSRiMc4cvgwx2tqUFSNbdu2UV9fR3tbK9u2bWXr1rc5WFmJruu43S6OVlfT1NrBm5vfZmx5GUYsim3bFBcXs2XLFmpqalizZg3r1q1jz549wMnb7dq2zRe+8AWOHDnCT3/6UzweD9dccw0f/vCHiUQiZ+QaWZbFD3/4Q372s5/x4IMPsmzZMjZs2HBGjpUtXWfdT8XGjRuZNWsWt912G08++aS0SxYAKJqWlS+RKat/YZqbmwmHw4wYMQKAv/3tbxQWFvKNb3yDe++9N73dokWL+NKXvnTCrkEulwuXy3WmT1mIAZUKBIZzm0TLNNE6fznbNqhDpFo4VTxrdt7h1lQVR2ehrq7r6dkcAJ/Ph0PXM55r2zaWZWXMFCiKgtrZkUdRlB53+VMpN5MmTyYnkIOuD70/WgUFBWze/BKVlQdYvnw5apc/rIqqUl/fgKLAhIoKtu/YQVlpKTU1NcyZM4elS5dSW1tLMBhM5/0rioqm6xiJON68AoxomEQ4gcPjQ1FVgsEgwWAQgG9961sAGasnd3fbbbexdu1aDh48yJ/+9CduueUW7r77bj760Y/y2GOPce+99zJz5kxs2yYWi+F2uwfkurz11lt87WtfA5ItWsvLy7nllls4ePDgCWdzotEoTqez36lO2bJlyxbmzJnDiBEj+OpXv8pXvvKVfj2vrq6Om266iSNHjmBZFv/93//NqFGjCAQCPProo5SUlDBhwoQh//qF+KDK6r+8lpYWrr32Ws455xzOPfdcHn74YZ555plhnSYhhheZEUjNAiT/zWc7n9uyLBKGkb6jrygKTocDl9OJrr+XnpPqurNo0SJWrFjRo0Yg1X5T1/X0850OR7o1p2maxDtnD+LxOAnD4OjRo2zYsIGc3Dze3PAmhw8fysIVOLmKigqWLl2a7PCjKJQGgxlBUX1DA80trcRjUUqKi3jjjTfSN2nGjx/PggULMop/R44cQV5uLiNHjkRR1M4ViZ0kwu+tSJyyY8cOoPcZAdu2ufPOO/nv//5vLMviueee45ZbbgGS70eq/enbb7/NPffcQ1FRER6Ph0WLFrFv374+X28kEuETn/gEhYWFfPSjH+XYsWO9bjdu3DgAcnJy2LVrFw899BCHDx/m+PHjfe578+bNFBQUcMEFF/DGG2+wfft24vF4n9sPBtu22bp1K0eOHCEcDvMv//Iv/MM//APnn38+APn5+Xz1q1/l2muvZc2aNSfc144dOygpKeHIkSP85je/Ydu2bTz77LPk5eWxa9cuLrnkEiZPnpy+ESCGmVT70MH+EhmyGgiMGTOGjRs3sn37drZt28aqVas477zzemy3Zs0aWUNAfCCl/ugP1xkBy7Iy7oxbgxwIpO7ap1J6DNNE7Rz8O53OE65rUF5ezpw5c/pdKJwKDjRNw9EZHLicThwOB6qqEo5EmDJlCl6vl+bmpnTKzVCUGtCPHTsWh9OJaZpUV1ezdu1acnJyyMvLo7aunpycHNrb22lsbDzh/hRVyRjcaw4XDm8AMx4lEelI/yyVaqOqKm+//XZ6e9M0+cpXvsIvf/lLZs+ezZYtW7jyyiszjrF8+XLGjx/Pbbfdxs9+9jM+9alP8V//9V9UVVVx2223ZWz7+uuvs2DBAv7pn/6JF154gT/84Q9cc801rF69ms997nO9voaioiIqKiq46aabmDx5MmVlZQDs3LmTXbt28de//pW9e/dmPOeRRx4hPz+ftrY2Lr74YmbOnJkxGz6QotEor7/+OolEgjvuuIOioiIefvjhHkHVF7/4RWbNmsWYMWPw+Xz853/+J9deey0rV67k0KFD/PGPfwTg73//O5dddhkLFy7k0ksv5dixYzQ0NLBv3z6+9rWvMXfu3IxajtT6QMuXL+ell17iqquuyjhudXV1ur5EiKHigQceYO7cuQQCAUpKSrj22mt59913M7aJRqPcddddFBYW4vf7ueGGGwiFQlk641On2B/QZL3W1lZyc3NpaWkhJycn26cjRK9ef/11LrnkEnbt2sXUqVOzfTqDLmEYqJ355wAtHRECHvcZTQ/qnrKTOn5vqTqDqbKykpdeegnTslgwfz5jx40jNyfnrEiZ2LlzJ60tLeTm5tLW0YHP68Xn9dDc0srGjRu5+OKLmT59ep/PTxgGCkqPVCjbtjFjESwjge7x8ebGt/jyl7/M/v37aWho4KabbuKKK67g8ccfZ9WqVVx44YU88cQT6UF4d7Ztc/ToUfLz8/H5fADccsst7Nu3j82bNyfPJZHImKFzOBzk5+dTU1PDD3/4Q+677z6OHTtGXl4ev/zlL/nTn/7Epz/9aV5//XUeffRRXn75ZRYvXoxt28ycOROAW2+9lREjRnDkyBHq6ur4z//8TyC5oKau63zve99L33F/7rnnegQxp8MwDN544w3KysqIRqOce+65vW5XUlLCT3/6U55++mkOHDjApk2bAPh//+//8eabb/Lv//7v7Ny5k2PHjuH3+7nyyiu5+OKLqa6u7ve5PPjgg9x99909Hv+v//ovfvvb37Jx40YsyyI3N5eysjLuu+8+brrpptN74aKHoTYeSp1PzZ9+SI53YFLz+n3scJTSj9zT72tx5ZVXcssttzB37lwMw+Ab3/gGO3bsYNeuXenfIf/0T//EypUr+d3vfkdubi6f//znUVWVN95440y/nAGR/So0IYax4V4jYFkWepfXbtmckWJh27YxLQvLNLEhoxXmUJFKuamtrU12P8vJwehcP0DX9SF1rt01NDTw1ltvoaoqlmVx6aWXEiwpAUXloosuIhqNnvD5mqpiGCaQGQgoioLu9mIZCYxIO3Nnz2L9+vWYpsnvfvc7vv/97/OXv/yFc889lyeeeIIbbrjhhMdRFCVjBse2bf7+979zxRVXEAqFCAQC3HfffQDMnj2bH/3oR7zyyissWbKEo0ePsnr1aiKRCNu2bePiiy/mc5/7HIZhsHr1ahRF4Ze//CWLFy9OH+t//ud/ePXVV/H5fLS2tlJcXIyu66xatYpwOMzChQsZMWIEK1euBGDmzJm43W62bNmC1+tlypQpp/pWsHXrVq644grq6ur63Oaaa67hgQce4MCBA3z961/n1ltvBZJpP7Nnz+b666/nX/75n4Fkqtf555/Ps88+S3t7O/X19elmHJZlEY/H2bNnD1VVVUSjUQoLC2lra+OCCy7ANE0CgQC5ubnpY3dtu3vnnXdy55138uSTT7Jhwwa2bNnCyy+/zF133cWTTz7JN7/5zRMGkOIsp6pZWFn41H6PPv/88xnf/+53v6OkpITNmzezcOFCWlpa+PWvf83//u//pv/t//a3v2Xq1Kls2LDhrFgQVwIBIbIolRo0HGsEuhbQvvf96acGVVZWpgcYFRUVJyz2HarGjx/P+PHj0987nU4sy0oGBJ1FykOxvWpqSry9vR2/38+o8nKqqqvZt28fzc3NJ233qSgKlt13WoiqO5IrEkfCtDa0sLfyMIsXL+b2228nHA6n78ydKkVR+PznP89PfvITSktL04//4Ac/4J577gFIn/unP/1pNm3axLe//W0uuugiFEVh1KhRHDx4EICVK1eyfPnyjP2fe+65JBIJdF1H13UcDgearrNr504qKyspLi7miiuuoKioiPz8fH71q1+lj6frOtu3bz+lYODll19m6dKlQHKwf+TIEb70pS+xf/9+XC4XN9xwAy0tLel/I9OmTeOq5cvZ+NZb5AQCVIwfT31dHaVlZel/K5ZlUXP8OCPKymhsakoXbkMyoHa73Zx33nm9pvV2V11dzcqVK9Ofk1R9zfXXX8/1118PwMMPP8zdd9/NY489xmOPPSYdhsQZ0X2tqf42nGlpaQGSTRMgWeeTSCTS/+4ApkyZwujRo1m/fr0EAkKIExvOxcJm52rCXSmcfhDw0ksvJbv5WBb5BQX4vN5kS8shPvg/GVVV0wFBqoDZoWdnXYG+TJ8+Hdu2aWxspLi4GE1TicVi1NbWEo/HaWtrO+HzFUUhGo2xe9cuiouL03ft092XbBvbsmmJxIlF40ydMI6q47XU1NRQUlLSo9bkVPz7v/87d999N5s3byYSiTB+/HjmzZvXYzuXy4WiKNx9993ous6uXbs4ePAgn/nMZ7j22mt7BAEppaWlNDc3s2bNGhwOBwsWLMDn93PxJZfg9/mwbBuHw8F1113HggULUBSFSCTC4sWLeeihh5g1axbvvPMOU6dO5c4778worLUsi9ra2nQQ03Ug8/TTT2ecR2VlJRvWryc3Lw+fz0dzUxNenw9VVZk/f376+o0eMybjeeXl5WialiyG7jajcqpCoRDt7e1cvWIF1UePEgqFeuzvzjvv5M033+R//ud/cDgc7Nu3jzFjxtDe3k5ubq4UFn+AKKqKMsgznanjdV9r6tvf/jbf+c53Tvhcy7L40pe+xEUXXcSMGTMAqKmpwel0kpeXl7FtMBikpqZmwM77TJJAQIgsSs0I6Prw+6domWZGStSpFgqncv0t28bj8XDZZZeRm5OT7PgTj4PXi21ZmJ37VeicfchyLcDpUlUVV2dhbjyRQO2cIRgqryX1h9FIJGhqakJRFPLy8giHwxl3kXtTXV1NdfVRNF2joaGRQE5O56A22W5VURVUVeHI4UNs2LCBhZdcwqiyEuIJA9NMziTYtk3q3nHqvVZUJd2yNXWdFEXpsSp0NBqlpKSEkpISJk6c2Os5fvrTn+YXv/gFDz74IN/97neZOHEiBQUFlJWVsWLFih6vp+v+3W43559/PoWFhXg8HtavX097ezuBQIBr/uEfaGpupr2jA03XaW1pYeLEidx222088sgj3H///cyfP5/Kykp+9atf8Y//+I8AVFVVMXr0aAAuueQSPve5z6W7Kj344IPpa5Kqh/H7/cydN48jhw6xbt06Os49lwULFvT5fnQ9/9LSUhLxOM732aI7GAzi9/t5adUqPvqRj/R6t9/pdPKHP/yB8ePH893vfpdJkyYxc+ZMdu3axYc//GF27drFokWL+PGPfzxkPvvi7FNVVZVRI9Cf2YC77rqLHTt28Prrr5/JUxt0w2/0IcQQkgoEhtsaGKlBW9c/5MnC3RNs32XgnxpAqKqKqihEo1FWr16dTjm4/PLLKS4uTj8vdVeZLs9NSd9J7vxfpdugMaX74ChbtM7ahlR+tqpp6bak2ZZMxzIoKi7GBopLSnA5nSe9XqFQiPXr1zFq1GhaW1swTYPZs2f32C4vLw9N03jhxRcZO3Ysy5Yuxo51oHv8aPp7QWXGTIJtY3QWhts2xGJRmpqbURSV4zU1oChUVVWzZ89udF3Htu1e1ymYMmUKs2bN4tFHH2XZsmWUlJRg23Z6Vi913Lq6WhobGmhva0vXErS1t7N161YURWHFihWsWLEi/VkqKy1l8+bNrFmzBk3TGDVqFJMmT+ahhx7iO9/5Dq+88grHjx+nuLiY2tpaHnvsMW655Rb+67/+C0imMeXm5hKPx8nNzeW73/0ud955J7HO3y1aZz1Me3s7G9av5/y5c9l/4ECfwVl1dTVVR47g9njYvXs3qqom1/pRkp2ddu3aRUNDA4WFhaecv59quxsKhYjGYhTk55OIx9F7mbX7zne+w1VXXcW8efN45513ANIdi95++20mTJjQZwcnIU4mJyfnlAqnP//5z/PMM8+wdu3ajN9npaWlxONxmpubM2YFQqFQRrrhUCaBgBBZNFyLhS3L6lH8miwUfq9ewLKs9MAfuvTm72UxrnHjxvWxWNWJB8epAWMqQLBTgUbyh+ntwpEINcePs2//fjZt2tTr2gGDKbXSr6qqmJ0BgaZpJ2x3OhiS72vyHIqKisjLz8fZj892MBjE6/Vy+PAh/H4/JSUlvW7XtaA6GAziy8nDiMcwIm1YDje6y50RzPWWdLBzx3bWrl2Lx+PB7fag6zrhcAdXXbmMF19aRU1NDV6vt8f7GwgEWLlyJVdeeSUXXXQRkEwv+Md//EdMw8A0DbBtOto7eGPdOspKS1m08BJsFCoPHGD27Nm89tprhEKhHm1nU3fKU8W4pmHgdDppa2vjnHPOoampifb2dmbOnMm1115LcXExW7duZc6cOdx66628unYt4ysquPjii8nPz08Xl3f9LKTqZlAUrrnmmvRCnt3V19eTk5ubHvAoQDwWwyaZBvHWW2/R0NCA3+/Htu30TFBXJwqay8vL049ZlkWic82O3up3LrjgAg4dOsTjjz+ertlIueuuu3jwwQdZs2YNb775JpMmTRqWndfOakoW+vorp3Y827a5++67eeqpp1izZk16vZCUOXPm4HA4ePnll9PNCt59912OHDnS54zbUCOBgBBZNBwDgcrKShwOB7ZtM3r06PSgv6GxEVXTiEU6CPj96Z77ej9TeboX2vZHf/LKbdvm0O7deP1+cnNzOXbsWK+5zdmgKAq6pqGpajJlKB5H03W0boPAwWIaCXSHM31u/S307Hqn+GQzLt3fZ4fLjeVwkIh0EO+I4/D4M1Y77i4VdLS3t6MoCm6Xi2PHjlFXV8eVy65g89tb2b17d6/BXiQS4ec//zk+n4+W5mZmz5qF0+nAti10R3J1YEfnKsEHKisJ1dayYsVVXHD++VgkV0Tu7U58X6/f5/NxsLKSyZMnc/DgwXQAkSpM/Pd//3caGhqYO3cub7z+Om+88QaLFi1izpw5fV4727aJJxI9Fu9LzdKNGjWKvXv3UjZiBIGcHBwuFxbJYm6v3881H7oWM5H8nDl0nUg0mqzs6Uy/i0SjhGpqOHzkCFVVVWiaRjAY7HWWTVVVdIcD0zBIxOMcPXaMmpqajGB+zJgx/Ou//iv/+q//CsATTzyRbi367rvvZrSKvfjiiyktLeVXv/pVRqciIU7XXXfdxf/+7//y97//nUAgkM77z83NxePxkJuby+23385XvvIVCgoKyMnJ4e6772bBggVnRaEwSCAgRFbF4/H0gHc4SBX1/sOHPkQ0EqG1rQ2n00l7Wxvtra1UHz3G3j27Moqxsk1RFEaNHs2uXbvYvXs3fr//pDnvg03prBfQNA2jMyDo7a7wmWR3dmdKzfSc6mrRXe8UnypV1XCmFiALt6I6PehOF0c7C1K7Dq57G3RrmkZNTQ173n2Xc2eew4svreoR7FVWVrJ9+3ZGlJWSmxOgrDSItzNg7f46uu+/uroaIxplyeLLiEQibNmyhZKSkoz99/b6U9+7XC6Ki4sBePbZZ2lqaqKgoIBp06bR1tbG888/T2NjY8ZnMxWEvZceRzpdyrJsItFYZ6/e9yorFMDj9TJx0mSCpaX4fD7y8vLS71+0o4NYPEFbawtr1qzh4osvTt+FT6XeNTc1kZ+fT3t7OwnDIJFIYBpG+ue9SXX48no8rFu3Dr/fz9KlS3sN7G+88UaOHz/OJz/5SY4ePcrOnTvTP0vlbj/xxBOsWrWKJUuWYNs2NTU1fa4tIbIoGyv9nuLxfv7znwOwaNGijMd/+9vfphci/OlPf4qqqtxwww3EYjGWLVvGI488MhBnOygkEBAii1JT4sNFqmvI21u2JPOhIxEmT57M0aNHqW9oJL+wqF8r0Q4mwzAIBAKMGzsWr8eT9RqBE0l1FLI1DcMwMEyTpqYmjlZXn/HzNgwDrVvRu6IoyVqQM3bUzGPpLg+qnpwdCMcibN60if0HDmS0qoSeg+5Jkybh9XpZuXIlx4/XsGL5lcQNk/Xr1zOqvJxgsIScgJ+S4iLy8gv465NPceGFF1IS7D0HuPv+U/8dCoVoa2slLzeHygMHMn6W0ltazYEDBzh08CCbNm1i2rTpHDlSRVt7OyXBIDk5OSy9/HJisTgerweXy0W4c90GBYVIJEx7ezsejydZY9FZM9DW3kZVVRX5eXkZqTqmaVJUWEBRYUHGjM6+ffs6V5G2OLB/P8uWLaO8vDy9NgedMwr5+fl0dHQwduzYdN2CbdvJz4CqZswepBcNsW1aW1vZs2cPAO3t7dTW1vY5w1daWsoLL7wAwFNPPcV9993H7bffnrFoWdd2jgC/+MUv0oXWQvRXf2Y13W43P/vZz/jZz342CGc08CQQECKLuq9i+kGXyoVO3VmfOHFiMv1H1zlwYD/Lp00nLy+PwsLCbJ8qAKZpYlkWDofjfd2xHmypGYL6+noUkoO4M1nbkEzvMjMWhwNQFSU5UzCIM16qpuP05VB/5BCXXbyAhoZ6mppbTprO1fVOfjxh4HQ6KA2WEKqpwbItDMPkne07uOyyy057Vii1Gu/rr7/GxAkT8LpdGIlEcm2BXbuIRqNs2rSJaDSK3+/nqqtWEI3FcDhdLLn8ClRNxTRMykaM6FwwLFkrkpeb26PI3bZt6urr2fvuu7z55psZwVB1dTVr165lxowZtLa20tLSkhGs2e4uq712Dto9Hg9ulwvbSgZ87W1tmYX2nds6HA6i0ShRy8Lj9fY7Rae9o4OysjKCwSAdHR39vr7XXXcd1113HQA333wzM2bM6HUxtdra2pPuy7btdDcnceZls32oeI8EAkJk0XCbEehe7JnKA071oY9HoyxavITx48Zm90RJrnNgmOZZtQ5BKsXCMk0A2tvaePGll2hvbwc4Y7UNlmn2WqisdHY3GuzEN0VRUJ1uahqauOryJezZt79fA8tUsLd3716O14TSqSYXXnghCxYsYOnSpegOB8uXL0+37jwVwWCQUE0Nfn+AquqjzDw3Rk4utDQ3YZoGYysqmDh5Mh0dHbS1tmGYJjk5OWx9+21ef20tbrebiRMnYts2582aRW0oRNWRw+nZg1SBvWlZYNs4HY70ALi9vT39/odCIY4fP059fT0Al156KZs2beLiiy5izauv9ggYTdPE6/PxzP/9Hz6vh7lz5hCOxfpMaSwuLqagoOCUfrdVVFRQWVnJsiuuIByJMKbbegb9UVJSQm1tLYlEgt///vcUFBRw5513Ul9fz3e+8x0CgQAHDx7k/vvvx+v18ve//53HH3+cd999lx07dhAIBGhubsblcvGjH/2IxYsXc8cdd/DFL36Rm2+++ZTPR4izgQQCQmRRPB4fVoEA9F3UO2PGDOKGSSxhZOGsMlmWhdE5WzOUg4B0m8zOmQsUJbmCcud5O7rMNp3OXez+tkw1DQNHLy1wVUVJr+w82AqLilAUhZrjx5l93rl4PB5syzrpHUHTMCgqLGT16tVA5nUbP348pmn2K12g+7WzbZsRI0bg8/kYUV6Ox+3G7fFgWBYWCmPGjqO+vp43N2zg+PHj6Tv4JcXFzJ8/P72v5uZmcvPyiEQiaJrGwYMHsYFATg4ejwe1s1uSrSh0dHSk7453fR1duxT5/X4ikQiGYdDc3JwRMEAy5cuyLAry85kzZw6NjY2YNowfO7bP+o9TKRTvqqKiArtz4bz+1pb0xuFwcPvttwOwfPlyfve73/GTn/yEL3/5y0BynQWXy0UsFst4XnNzMwCxWCwjzWjdunUUFxefdIVsIc5GEggIkUXDLTXoZByaSkc02cJTzdIAPNUbfqgGAak2p2bn3V9VUVA1rdfVhk+lG0931dXVrFy5Mj1Y7CutKBmA9N6qVVEUItEo27dvH7Aahf4EJ6lrFAwGKS0tTQZLiTiJcCu624eq9x58m6aJaZrkFxT0OnMFyWLormsH9HWOK1euZMGFFxKORGhpbcXtdqMqCj6/H5/Xi6Jp6RWRkylcCk8//XfOO+88xowZw5gxY3qtaSgtK8O2bd7Zto2dO3dSNmIE2995BwWYPXs2iS5rS4wcObLX97/75wJg27Zt5OblUVBQQDAYTP47MAwUSLf27FrAb5kGiWgYh9uDonRbIVxR+iwMPhmls5NQIh5Prz7c/T04FW63m89+9rPccMMNGW1puwcBTqeTeDzOvffeSyAQ4Gtf+xpXX301b731FqFQiP/3//7fB24hqaw7C4qFhwMJBITIouGWGnQyiqLg1DUShonLMfi/nmzbTs/SDKUgIL2ugmkmg6RUa9V+rCx8urUNbe3tLLviCv765JM97hJ3ZRoJ9M6B9d69e6mrq0svtlVXV8eEiRNZu3YtXq/3fdcoVFdX88477xAOh09Y82BZFmqXVCVFUdCcLhRNx4i0o+oONJcn49pZppmc2egMAPuauUoVQJ9IKBQiHA4TiURwOBwcra5m2rRpyb75hoFt2+iKkk47Gz9+PKGaEHPnzj3hQl2HDx/G7Xanc+g3bdrEO9u24ff7KR85Mr04V9dORn29/90fX7FiBR0dHfzDNddQUFhIPJFIFhb3seq5qunoTkhEI70GA9FolO07dpxWAKiqKvX19bS3t5+0i1B/FRcX09raymuvvUY8HmfRokW43W7cXeshuvjqV7+aXmBu2bJlQ+r3gRADSQIBIbJIAoGenLpGNJ4Y9EAg1V+9+0AqW7rn+6uqmuzdPgjnZhgmZWUjaGlM5pD3lVaUClB0h8revXupra2lqLAQn9dLR0cHebm5vL15MxdddFF6Ma33EwiEQiE0TeOCCy7gL3/5S6/7q6ysxOPxEIvFGDt2bMbPVE3D4cvBjEVIdLSie/wcO36clpYWRpWXE8jJ6deALxwOs2fPHgoLCnp9Pam1CuKxGI0NDUydNo1INJrsm69ptHV0sPvtt9N3ui3LoqSkhBEjem9xmWqBaZoma197jcaGBpYuXZq+qz9u7FgCgUCvK/T2Vyp9qa2tjYbGRgzDoPQkqWR9BQPV1dVEwmEOHz5MXV0dLpeL/Pz85GtJvqAe/211mUGwbRtfIIDX50u2Fz5JF6H+CgQCXHXVVf3aNhUAKYpCXl4eTU1N7+vYoheqmoUZgez/bh9q5IoIkUXxeFxSg7rRNRXTsjMGBmdaKh0otTjXYKqurmbz5s1UV1enaxPisRiJeBwAh9OJ0+UalAAleR0MTMskJ+AjNy+PRYsW9Xnn3TQNNC05YMoJBGior2fls8+yY+dOnv6//+P5F15g565dRCKRAVl/IRgMEu3MZx87dmyP/VVWVrJ27Vqam5t54YUXONDZorMrRVHQ3V50t5dIaxP79+4hHovx5FNPcfTo0ZOeQ3V1NVu2bOHw4cOsXLky3Qmoq/Lyci699FI0XackGGTb1q3UhkIoikJzSwtNzc2sW7eOl156iQMHDmBbNqqqpPef/jyYZnLl3Xic1pYWXnrpJSZOmMCMGTNoa2vj2LFjVIwbR25uLg6nk6NHj6afezpCoRAJw2Db1q38/W9/69d+ksGAm0Q0kl5LIhQKsfa11/B4PCQSCZqamt5b7bnbIn5255fSWd+i6zoup5PGhgYef/xx4vH4kFi7Q2YExAeVzAgIkUUyI9BTshe+RiJh4nKe+V9RqVzobCzsVl1dzWuvvcao8nKMRILWlpbkaq79SPkZaKkgAAWcDgeWZeH3+/tcpda2bUzDQHc4ScTjxGIxakIhIHk3tWsxamFh4YC0Li0vL08ukgYsXbKE3Ly8jJ+HQiFmzpyJx+3GMs0T3kVWdQf7DlUxbvQodF2n4wTpT13V1dVRPnIkr7/++glTphKJBBUVFfzxf/4HSOaqjxkzhiOHD6fbU6budI8sH4WiKOnagvLycgry82lubiYvPz+9YjHAyy+/zOLFi8nPy8Pj9VJ99CilhoHucNDY0EAkEuFodTVut5v8/Pz3+vZ3SZOqrKxMHnfkSMrKypJpZ7aN1+fjrY0b2b17N7FY7KTXo2u9xoiyMhKxCA5Xcq0NVVVpbGykrKyMouJijM6ZLaXzHBRVRes8r94+6+PGjeuzTkMIMXAkEBAii2RGoHdOXSMSTwxKIGCYJgr0mQt9pti2DYrCpEmT2Lt3LxvfeotLL720z4H3mT6XeCKRHHB2XgfbsnrkfXd14MABvB43breH/Px8xowdy2WXXZauESgvL0//98SJEwfsXMvKykjE4yiKkqwF6DJLMnbsWI5UVbF69WquXL78pMFUUXFx8s565X5uuvYabK3voLy6upq6ujrGjh3Lq2vW0NzcfMI71WPGjmX9unVAZmpVfn4+7R0dPR5XFIVQKEQsFqNi3Die+tvfkp+HzjU1urbeDYfD+AMB1r7wArFYjHPPPRen08m6detQFQV35+Jh+Xl5WJ0LfdmdK/uGw2FM0yRYWsrRo0dRFIVgaSmqZdHa0sL+/fuJxWInvQufClpMy2LkiBG4L7qIQCBAPBqmuLiY6667jpaWluS5dFvnoL/6qtPIhtPpgiROTNE0lEG++TLYxzsbSCAgRBYNx/ah/aFrKpZlY3VJmTgTjM5WkI5BDgJM08QwDBwOB5s2bUrfOc9G+oNt28TiybQoXde6PG6har1flx2738Xt8dDWEea5519g3rx5zJgxg0mTJjFp0qT0dl3/e6CkBpOarmMYRjqQtiyLgsJCwuEwU6ZOTS60lZfH1q1bKSoq6vXO9siRI2lrbU0OjlWdwoAPIxruUUhcXV3NCy+8wMUXXcSra9Ywbfp0Ro0e3Wch7PHjx/H5/YwZM4acnJyMO9qpPv4XXnghwWCQsWPHYhjJu+XBYJCSkhKam5vx+Xw9Pg+pgfGx48fZtWsXtbW1+P1+8vLykq1DVTW5ZkRnd6LuKz0DbN6yhY0bNzJ//nx0hyOZgmfb6UXzTtZlKlUXoigKl112GQ0NDXSEwzQ0NFBQUICta5jxGLk5OeR1m7E5W72fVqZCDHUSCAiRRZIa1LtUelDcMHA7B/76VFdX097eTllZGTn9LBAdCKlaBBQFp9NJsKTkpAOv/vbyPx1WqkuSrvdIi0oOfnrOCLTHDOK+QkxFITcvB83porGxcUDPqz9SMwGpQWnCMHA6HOnBcmrwPn7CBDZu3AjQ4/qlFspKzcLYtp1RSKx2XpPa2lrOPfdcNm/ZwvHjxxk1enSfMzfV1dUYpsnON9/kwIEDPVKi2traqK6uTgcHhmGmg93y8nIuu+wywuEwo0aP7nMgXlhYyOhRo/C43RkzLv1pFRsMBnG73Wx4803+4R/+gby8vGS6lfLeOXR9bmqtilS7WgCtc/vVq1eng9gpkyd3vicqilNJpwl1X7dhz5491NXVUVxczJQpU3o9x6HG4XAQ76zZEQNIVQe/eFeKhXuQQECILJJAoG8uh0Y4lhjwQCA1QLxk4UKeeOIJli1bdkZW2+0q1QHINE0c/WzvmDrX/vTyPx2WZRFPJHD2UYTc213QpnCcI01hVAW2v/4Kc+YvYMlVHyLS0jAg53QqbNtG13VaW1qIxGIoipLR5SYUCnHkyBFqa2uZv2ABqqr2eE2WaWYsupYqJLaMBEakDc3pRtGdjB8/nldffTW90NeJZm5aWlrw+nyEQqEeNQQHDx4kEY/T0dGRLBK2bUaNHp0RhBUWFlJUVNRrvUp1dTUoCrqm9Zh9gf61iq2oqOCKK67A5/cTj8UI+P0kDAPTsqirraWmpobS0lJKS0sxLQu7c00PtbO/f+r6jRgxos/AQ9U0dJe7RzCwZ88e1q1bh6Zp7N69G9u2mTp16gnPdyjoOvMkxAeNBAJCZJEsKNY3TT0z6UGhUIi2tjY0TaO1tfV9t7Q8mVTveFVVT3mRslAohG3brLjqKg5XVVFfXz8g52qaFgkjgdPh7PXa2raN0u37UHuMmtYoHodGRWEO+jlTqauqxD1hCt68Ipo6ouT7eu/JnjJQsxupa3js2DHa2tqoratjz+7dGYFS19VzN731Fh/60IeSvfE7O0MdO3YMXdNIGEaPc1F1B8fqmvA4VPz+AD5/gNmzZzNy5MgTnnt1dTXFxcXU1NQQ7qVTUjQWQ1dVysvLmTx5Mg6nE9M0URUFuzOHvqWlhVAoRE5OTsZxqquree6555g1ezabT7CGwslYlsWIESMyAlKnw5E+Z5/fT2trK06nk8KiohMu7HeiwENV3wsGdNd7aUsLL70Un89Ha0sLXq83+fq7zEiIYUQWFBsSJBAQIosSiQQ+ny/bpzEkpRYXG+j0oGAwiMfjYd++fZx73nlnLC/ftm0M08SyLBy6flqtP4PBIMGSEhoaGlAUhbHjKojF4snBrHZ6gyfDNDEME9cJgpLjx4+jYGNaNiNHjqSqOUJjOE6OW2dMvg9NzVxltiUcoy2aIGGEKQq4e32tAzm7oShKMjgJhVizZk368a5BXfd899QKw4Zp0trWxtGjR9MFwN3PpbKykpdeeom5c+firG9g7OhRjCg9cfBSXV3Nmxs3Ul5eTnFREbPOO4/S0tKM55QGg9SGQjyzciWTJ0/m4osvBpILlMUTCSKRCM3NzaxduxZFUTLOKxQK0drais/nO2G3ohMxTRPDNHsNSI8ePcqaNWtQVRXLsli0aBHFxcUn3WdlZWWfq/8mgwEPRiyCojsZV1HB/z39NJMmTyZUU8OMGTOS6XKdaTeqpqF1WQhuqEh93oT4IJJAQIgsisfjH5iCujPBeQbSg1IDxNraWiZMnEjA7x+wfadYlkWi8+6z830u8uTQdaLRCJruIC83Jz2YjXV2ztE1rd93VBOGgWVauJx9n1N1dTVb334br9dL5aHDLFj2DyQUnSKfk5G5nl6fl+t1oWsqTR0xQq0RgjmeHsFAKBRi0qRJeNxu3li37v3NxHQOzLre9e8tZaf7Hetk7YnO4UOHiCcSHD58GMuyepxLKBQiEAhQXFTEE3/9K4sWLmT6ZCcdkSgHj1RT0nmcrrMbdXV1jBs7ljfffJNFixahKEqP4KKoqAhFVdOFwoFAgHg8QVNDA1WdPfu3b99OR2dXoa7nlXqtCn0v8HYihmFgWVafn8eTXcveVFZWsnPnThSgubkZp9NJWVlZumUpALaNrWrYiRh5OTnMmzeP5uZmFl56KYUFBcltOlfwTf27oXP1bFXTOHToUJ+BxmCRIEB8kEkgIEQWSY3AiaXSg0zLGtCFvlIDRMMwME1zwFqHptYkwLZPOQ2oL0VFRcl8+M7PSWow69D15AJkpplMPVJUdF3rtU3je+cFzhMEAZAcfDY0NjB1+gwCY6eSQGNErptin+uEz/O5HDg0hbrWKMebwxTneHB26UIUDAbJy8vD6/UyZuxYzM6+8l31N3UodYe2P11uelNQUMD69evTayV0H/QGg0Fs2+bgoUNceuml5OblUdvcRuhoFUWFBax99VUqxo/njTfeYNasWRw/doyJEyfy8iuvEA6H0XQ9Y5+VlZW8++67xONxXn31VZYuXZosFDZN2jvaOXbsGIcPH0ZV1XRRavfzSr1W3eFgxdVXUz5yZL9ea+q9V0gWvfb1Hp7qtbRtG4/bzcgRIygpKWHv3r1EwuF0cXFq1WCzs5gbzYERj1IxbiyKomLZNrFotNfzSdXURKNR8vPyOHjwIC+99BJLly4dMu1ExfunqBrKIKfqDPbxzgYSCAiRRfF4HJfLle3TGLIURcHp0IgbJh7nwHd70DSNeDz+vtIRUoPX8lGjyMvNRe+lA8/71sepqaqKs7MI1rJsDMPEsi00NZk6dOzYMUKhEBUV4/H7/eiOk7/OYDDIrn0HqLdcOF0ugh6FPEeysFY9yXVy6jrBPC+1LWFqWyMU+Fx4XckApqWlhV073mHOrPNobQ8zelxFMjghOfALd3QQCoV44/XXcbndJ0wdOnDgAA5dJxyJMH369FOeWejPoHdcRQUH9u9n5MiRuFwubNtmxKgxuN0url1xJaaikpeXl3yvbZuXX3mFc845h4kTJ5ITCBAOh9P7CoVCxOJx9u/fn15EbPz48ViWRX1dHavXrOGqq67imWee4bLLLkvPdnQ/r/Lycmrr6qivqyMei530DnlqfYjUir39uS79uZamaWIaBgnD4K1Nm1AUhSWLF+PxeqmpqSEUCjFixAgKCgtxOp3p2SHbciRrBhxOFFVNFqv3USNl2zb79u3D4/Fw6ODBjOsmhBg40kdJiCySdQROzqnrxBM97x4PBEVRkv3oe7k73R/V1dWsWrUKh8PB0epq6urrBzwIsLFR+ooEOimKgqapOJ2OZO6/qtDS2kYsFsft9rBp0yZCoZp+BTuBwiDnXHw5TqeTPCVGWWEeusOJbVvEY1ES8RiWZfaZLqGrKqW5XpyaSmN7lNaWZhLNdVQUuPnQwgvIdShMK8vFGWnE6mhGsUz0zhSQw4cPM2/+/HQOfG927tzJxrc24TLDWC217Nu94+QXsRfl5eXMmTOn14FvKBRCVRSmTplCJBJBQSFh2uw/XMXOfQc51NBGh6kzPkdltF9lZK6LWVMn4HVq7N6zm4bGRl566SUOHDgAJIMrh64zfsIE5syZQ15eHjt37qSurh4Ar9dLc1MTo8rLcblc6fOqrq5m8+bNyW5BJGcWtm7dypEjRzL239s1Wr9+PU1NTZ3rQ7z/e36VlZW8+eabNDQ0JOtenE5Gjx7N0qVLOeecc1BUFbfbTX1dXbo9b31dXUaKmKKqOFwejHi0c8E6Jd2StDvTNBkxciRrXn2V5paWrK2zAQy5moUPDEV9r4XoYH2dYJHE4UpmBITIIkkNOjlNVbAZ+PSg9/afTMewT2NWoK2tjUsuvpj1GzYQCoVYtGhRv1M2+s2mzxmB3qTqBioP7GfdunXouk44HKagIP+kd3vr2mMcbYng6uwM5OpM7VFVFVV1ouk2tmVhduabJ4uWdRRVZefOnTQ0NBAsLmT86HLyjQ7sRBQ7AXHNRUvU4qVXX6e+oZGyYDGLFlxAsTOCFWnB1l2MLs5j5/Y23G43FRUVfQ76GhoaMCwbAsX421sYlevAqD+C6stHcfvf16AtWX9hUT5mHIrDzaGjx8gvLKTD1tAcOmMrxtNYX49DAWLthF257Nq6hUkVYyh0K/icBtdddC7huMmcyWMwO5rZt3snx+samDZtOvFEgnEV43G6XLjcbnRVIc/j5OMfuRnbhvEVFex6910OHjyI2+3mxRdfJBwOU15ejtvtxul0MmH8eEKhEPv27XtvZqEzBcc0bWrr6nD7cxkzIoilKHSEI+jdPtup/+x6pTKuW7dr2NHejsPhIDcnh7Vr1zJz5sz0nfmuq/9u3ryZgwcPUlhUxDPPPMPChQsZ2e3fQyoYSMQiaA4XCcPA1WVWwLZtjEQCRVHIz89n0aJF1NbWZrVGQIqFxQeZBAJCZJG0Dz25dPeghInHNfCBgKIo6J2zAv1dYdi2bUzDYERZGX998kmamprO2B3L2toQtbV1uNzuU+q5HgwGcTqd/Sr+tG2bYy1R6jpi+J06Ywu96L0EXYqioGgaqqYl05FMk0QiTltbG7YRY/r4sfg1g0RbI7ongO7PJ2yqNEcT6Pl5zDl/Lo2NjRQWFjJifHIxKTMRxwi3ElCi3LT0YqKGycTy+bhzCno918LCQo5UHyWme3nl7XdZuCCPijIXVvNxUDRUXx4Ha+qpqa3PWGyr++s1LZuEaWGYFoZlkTAtTCvZM193ezAtC5/biZKIUlZWjG1ZtJgxjjQ3UFpYQXX1USrGj2fDO3swnH5KS0uh1cDr0jm0bz+lRfmMLPChqwpFxVMJGzb5hV6qqo7Qcfw45SNHkpuXx9s7d1NYUIDX48KwbI4fO0ZxURG6rnP+3LmYhkFTczMNDQ24XC42bd7CvPnzuWjhIsaOq6CpPYIN6KqCqioomooRjZJIaDitdtoSNnlFpe+N+hWF1DcWNsnxbXJ14fT7nAo+04uJ2Rw9epTDhw/T0dFBY2MjFRUVPYKuYDDIpk2bOHz48Ak/c12DAdRkrUtqnYdEPI7WJb2ua6CRLbKysPggk0BAiCyS1KD+cek67dEYHteZuVaqqmL0c1YgNVhRVZWc3FyuuOKKM7by765du2hubGTfgQNEo1Fs22batGn9em5/iz8ty+ZwU5iWaIJ8j4NR+d4T9o5PSaVVabrOu5u3oDuctGg+Opwu4tEIBXoAFyoet4bu0Khvi5I/cixTpkxD198LMjSHEzWnsPOagi8RxYq0YzZUY+oOVJcf1e0DPVl8PX36dOzO81tw4UVMnDIZADunCDvcSrS5lnKPRUSJsuPtTaAojBozNjngNy0SVvKuv6KAQ1Vpa2ulpakRr9vF+HHjOHToIJqmsXPnTqqqqli6dCmKonDw8GGqqqqoqBiH0+miuroaj9fLjTfeyKFDh2hrayMvL48DVdXo/gJcJaOx8/Npbm/DhYlL12msr+XF558jPz+fCRMmADZvvvkmEyeMZ8G8eTS0tFFcXIxl26iaztFjx/F4vRSXjaSgtAxN01i4eAlGPE7FuLHk5+WgaSqqomCYJpFYAjMeZ82qFxk7ZgznTJtKjlNBNyKovlxAwe5cITh1h1vVVBRV5ciRI9TU1DB61Kj3zkFVO3P54yQSBiNGlhMIBCgfNYpILHOlXQUoKi7hxptupq29DZ/HS15+HoZponR+XlL/thRFyQgGmpvbOXCgkgnjx5PbudLxUJIKVMTAkmLhoUECASGySFKD+kfTkn+Ez1R6UHpWwDBO+H6k2hs6dB21845lfwssT0d9fT3FRYW0vt1KPB6nvr7+lJ5/snNLmBYHGzoIJ0xKA26CgRN3BupLbm4ur7/+Ou1rX6U4WMq8Cy/CzgnQEonT0GGjqwpuh0YkYXK8NUxHQw21x49RWFjI9OnTk52QnM7k3WCXH92TQyIWRTXj2PEwRkMTaDqq24/i8jFtyhQON3bQ2tbGzp07mT59OqYFMc1Nu7MIh2IwrsLD5LHlGIpKvK0Jy+lH13U8TgVdU8G2OXbsOKpt49ZAw6KlqRG/z0tLSyt79+7FNM10+k0oFGL37t2ce845PPm3vzFnzhxM0yQ3J4ctW7bg8/n40LXXUlJSQl5uLrm5uTQ1N/N///d/LF60kBHFhdi5ucyZM4cZM87B7XLS2NCA3+/nvJnn0NbejsPjZfyUaXi8XlxOJ7PnnE9LczN+n5fCgJ9jx46mAzuXx8WmtzYSDAYZUT6KcCRC9ZHDFOTnc9FFF9HY2EhuQSE7duxg0mgbvxFHDxSi6g5SQ6FUy86G+noUkt2UDh8+TMIwGDt2bPr9HVFWRiQcpra2lpyAn6KC/Iz3PxVU2LaNIy+X3NyczkkGG8u2sDv/O91NqItwRxSPUycvN5e/P/00559/fsYaFUKIM0sCASGySAKB/ksVDZ+J9CDonBUwzV7TAFLtDK0+FmM6U4qLi/G4XcTjcfx+f78WeOqvaMKksqGDhGkxOs9Lge/0U9SmT5+ObdvptJ/JFWPTPzMsi1jCJGaYaKqCYdl48op4e+UzuD0ebNtmxowZ7wUDiURyQOlyE4+rOL05YFvYsTBWtAOr4xgJ08J2FFFSNgqv101Nczs2CpoClqqyc99hGhvrOXfaFIIBF55YM1akkVBLGDy5lIwox7Is3B4P0WiUykOH2bVzJxdccAEjR47kldWrMU0zI70lGAyy+LJFvLJmDRdfcgmRcJjm5mby8/KYPXs2x44do76ujrKyMpwuF22trbS0tLD8yiuTaS6ajhaPk5Ofj6ZpvLtnD80tLSy65GICXg8uj4djdU0888zTnDNjBgsXLiTX6yZYlEyRqq6uZsObbxLwJwOaeDyOPycHX04e7R0d7N29C03XqTdNxowdS7C0lI5wmKbmZv68bRv/cOXlFFoWmj8PzeUFUkXmGsdravB5vbz00kvEYjEuvPDCjEAATpyi0/VO/4l0bQ87YsQIjEQC00jQHguzevVq2trbaWxsPI1PoDgrqWoWVhaWmZ3uJBAQIovi8bjUCPST06HRHjlz6UGpItvuswK2bZPoLF50DGIQADBlyhSaGuqZO3cuxcXFp1QjcCJtsQSHGsLY2FQU+QgMwDXt6y6urqroLhVf5zFef2Md544fSY7fS2NLW8bAT1EUHA4HhmFgGAaaptHU2MjBQ4cIBoOUlZVhGwlaW9tRsYlEO9j/7i5yAn7mzT2/873x0daYh2olSBgmroIy6uvq8TosivNUdBIkGqpw5ZdwpL6O1ra25OrJR45QXFxMRUUFS5cuzShQ3bNnD5Zpkp+fnwwMbJvKgweZMH48hmGgOxwUFBSiaTqqptPa0orT5aQkGMRIJNB0nVBDM4U+F+NGj8ZWVSzTZFz5CEaUBemIm4RjBkerjqBrGoWFhaxfvz5d5xEKhQiWljJy5Eiqq6pwOBy0tncwZuw41r2+lpEjR7Jr167OO/DJQDYajRKLRmluaqK9vZ3K6uMEL5iL0daAnYih+fLSXXvGjhnDk089RSwWO+Val/6u/ZBaWTovL4+A309rays5gQBNzU14XQ7aOmtZCgsL+33swSTFwuKDSgIBIbIokUgM2GJWH3SplCDTtNKpQgOt+6xAKhXojKwN0A+2ZRHIyWXhwoUDts/GcJyqpjAOTaWi0I/bMbivKz8vF802Cfh9xE27x8CvazDQ3t5OXV0dXo+HeCxGc1MTmjdATHHy5ppVVB8+iN/v5+KLL84I0CZNmsSkSZPS3+/bv48NGzbgdDopCHhZcuH5FGEw2q9QFbEpKA1SUlKCaZq88cYblJSUUFhYyPHjx4lEIuzevYsLzj+fZ1auZMGCBbz99tsES0pobGzk4MGDTJk6jbq6OjRNpaW5iQ0bNhCLxVixYgVerxdVVZOLedkmHtskZttMqhiDoqpYqhPdmUx5mzJlCpMmTWL/gQO0trURiUQIBAJ4vF5279pFR0cHDQ2NnD9vAY2NDTz1xJ9xu91MnTIF0zTTheGFhYVEIhEMw2DbO+/g9/spKSlBdThx5JVgtDURrjvK0cY2SstGkJOb/Iydanee6upqXn31VUrLyjAtC6/Ph9/vTxYZd9vW5XYz94ILCHd08Mrq1cydO5c5c+YwoaKCmprjzJ07N50qNtRIofAZkmrpOdjHFBlkBCJEFpmmmZUB5tnKqevEDAOvdmZmUVKzAqFQiIb6esrKysjPz0fJ0h8Py7ZQ1YEZhNi2TagtRk1bFK9DY1yhD8cZCqhOZPr06URCh5kxfTqm6uhz4KfrOnV1dVi2zdrXXqO9vZ2rPnQ9ftVNaa6P6VMmUVZS1K/BY0lJCX6/n/b2dtpjTtoUL8HgOEJ73qEk4MQXb+Ly+bOIKE7WrVtHLBZj3759jBw5kslTpjBu7BgSCYM555+PaZrMu+ACOsJhIpEIe/bsYfyECYQ7Ojh69CgzzzmHqqqq5B1k2+ZIVRWlJSVs2bqVC+fPA9PCiUXU6UmmxiQSPPPMM0QiEebPn080GiUvL49IJEJzczMOp5MtW7YkX//VV3PRwkUY8Rh5AT8XXHBBeuDu9/t73Jlvamxk9uzZGd2TFFUj1BYldKSSaRPGsfa1V5l+3uzT6s7T3t7OBfPm8fbbb3O0uhqP2830GTN6bUsai0Z5a+PGHl2sLCPByPLRlI8ee0rHFkIMDAkEhMiyoXa3qb9T/f197p49e6irq6OgoIDGxkaKi4uZMmXKKe1379691NXVUVpWhj+vgE1vbjhjdw/37NmD3x8gGo3y1yefZMGCBWe0eLGysjJ9zbrfibUtC3UAZows26aqKUxTJEGu28GYfO+ABRinQ3c4mTC+At2bc8LtPJ7kYmhLliyhurYeX34RJQEPLod2Su/JxIkTsW2burq6jEGx4Qzwl1dWE8zzM2faBEoLcrnlyoXsP1rLOTNnMmHCBMxEgmONjWzfsYPZs2cTTyQI5Oayd98+Jk6cyJIlS7CB5cuX09HRQTgcxrZt/H4/hmmyft06br7pRs6dNhnNMgibNrrDSbS1Dc3tpaW1jZkzZ3L48GECgUCyDSlw+MgRdF3H6/EwafJkxo4bj8/no/rIIYqLinoM3LsXhh89ehRN0xgzZkyPf8ehUIiDx+uwVZ0LZk6lI9be7xaZqc/ruIoKSsvK+OsTT9Dc3Izf7yc/P7/PjlO9dbGyOxcTy1agLYSQQECIrBtKgUAqj1fTHfj9fi655JIe+cJ9ne7x48dZteplwh1teL0+Lr/8cjo6Onj99deZOXMmGze+RXt7G06nE9u2+53vvnfvXlavXo3b5UJXbPw+P8ePH2P37t3pQtOB1NjYSF5+AXX19bS2tg5Y8WLqDrHd5b/r6uupb2hg/fr1+Hw+li5dmjG4sy0b5X2uhGlYFocawrTHDYp9LkbkurP+mUsYBq21tRh66wmDzdTPwoZNxZTpFPvduE4zlSmVLpRarTcYDKYDhI6ODqIuP1Vhi1wHTCsvIhY3cKgGbZZCNBajsLAQh64Tj8VQFYX5CxYQjUbZ9NZbqKqK1+OhpaUFn9fL3LlzKSgoIB6LceXlS9BtA18gl4Rts3fvu5SOKMftcmIlYiiaji+Qw+jRoykpKUm39pw/b14yRcqyQNUIt7VhxKMk4nGaGhvxer3kBALQ2Zaza8Hu0aNH2bJlC16vlwMHDrBixYqM6xwMBtmxYwcOr5//W/0G1y9fitFajx4oOGF7xcrKSlatWsXs2bM5dCgZkCxbtqzfNw66ByumkUDTpVnCcKVoGsogz4gP9vHOBhIICCHSQqEQsViM6669kba2VmzNSVsk1S/c7vL/e1JdPi5atIR8NQqKgtsFOU4vl196EbnBcsrHjMWKR8n1+1BVjVi4HUVJ9ilXtWQ/6d56dYdCIc47Zzq5OTkcqDxIPLGb+XPP57U31p2RDiOjRo3mYGUl58yYga7rPXLYu7ZKTP9vtwF+co2mbldKUZIpE6mBG9Da2sbEiRN54/XXaW9vT7eqTB+L97eQUcxIdgaKGRYjcz0U+12nva+BUl1dTaKlnnjCYM2bW3oMUrsLFJZgR+IU+10otvW+erqnAt1UesqKFSsAqDpyhIrx43njjTdYcfXVHG2oZ2RhDnoiiuIuZszYAEXFxSi2jc/nY+WzzzLrvPPIyc1l/IQJ6ZmAnJwcnnvuOaZPn05OwE++34Npe4ij0VTfiGWZna1OLSxFo6O1GSMWp6C4mMLCAjo6ZxPcLhdVVVWUjixHVVWee/ppGurrOeecc9i5cyculyvZVagzmLbt5IrPqfacLqeTiRMnsm7dOtrb2wmFQhnXuLy8nCVLluByuyksKMBXPBIz3EKiOYQeKORQ1dGMWapUm1GHrnPJwoVEo1FeffVVLrzwQhYsWHDa7XMtI4HD7T2t5w4mKRQWH2QSCAgh0oLBIC6Xi53vbGVsxXhIRMj396+LR3VzA6+8kDnIam/v4NVX13L++XOoDYUoKshHHTECr9eL0+XEtsEyTSzTyBw4KyqqqtLU3MzIsiCWYfD3lc/h9/uZ6A/w7Asvcs1VVxGJx/s+odNUWlZGONxBNBqlID8fXdNobmrC6+0yYOkcyCuKkvHfqqqiAEePHTvpXVLbtikJBqmrq8WyrB7dWg4dOojL6SQSjfW7eLOrjrjBwYYOLNtmXIGPXM/QuPMaCoVQY20UFRb2OkjtqiUSpzUST6cD9bbybF+6p6lZloVpmixYsID6+nq2bt1KKBTCMAzmzL0Ap0MnLy+PDevXc96sWVQeb6CstBQjEqHNNCnND7BuwwbKR45k0aWXojscxGMx3tywAZ/Px+w5c8jLz+eWW24BI4ZtWbRHE7h9PqLhMK+9tparrroKDZu2cBjd7aO6upoZM2ZgmiaGlQwMwx3tjBk7ltHjxhOPx/j7k3+lra0t3T7Wsiw0TSMvL6/PaxCLx1m7du0JV5UuLy9PLmhYWJisjfHlYekuYk211B09TF1TG16PJ/nZ9/lQVTVZr/Hqq0ycOJGrVqzA7Tr9wNK2LFDOnrSgbM+ifSCpWhbah8qMQHcSCAiRZUPpblPXPN683By8Xl+/c4f7WsnWtm3q6+sZNXoMTU1NtIajlJWPxjKNzgDASs4M6A5UTU/eWbfMZH68oqDYNu2RKOeff34yB1lV0XWdjkiUcWNGYVkm6gD9crc6c5aLCgvZum0blZWVRKNR5s2bx4IFC/q1j9Rd53A4jNfr7fWOt23bxBMJvF4Pfr+fCy+8MKNGoLKykuNHq8nJzeOd7duxbfuUCjmbI3EON4XRFIUJRX68zqHzqz4YDFK1rwF/Z4eZvlpVdg8CgIy1BhobGzly5EivwVbqPbAsi+kzZhDIycHjduN2u3nhhRcYPXo0H7r2WmzLwu8PsHPXTirGjmXOnDmsXLmSqVOncuDAAfw+L9FYguDocbTFDKbPmEGkowO3x8PGN99k3vz5XH311Wi6jmWZmLEIDhU6YnFMJXnO7W1tOBxOpk2bhq6qmLZNbk4OURPKRoygtaUFbJuW1lZ8OXmMDgaxLIvGuhDHjx/jkksuobq6Gsuy2LhxI5dffjkFBQUnTanqz6rSiqqmAwsAxekm1BFnxuQJjGxuZdUbbxKORNKf/TFjxqRbqwa6BCanM0NzNqUFDaXf0UIMtKHz10GIYWgo/oFJ5fHatk1LOEZHLIHf3b8uPb2tZNtXLYCmOtEcyf3aloVlGpiJGLZloigqqqYTjkZ58ZU16Tubl19+ecbdcduySMQi6C7PaaeLpPdl28TjCVAgHIlw8OBBOjo6TrmveigU4qILF+Dz+di1a3evd7wN00RVVGxsgiUllHUWiAJYpkGu38sx22bfvn3MmHEOfn8g3Vf/REGZbdvUdcQ41hLFrSfbgzr100+jOd2i8RMpLy/Ho1roisWHPvShdHFsV70FASmKolBbW0tNTQ0dHR1s2rSpR7AVCoUoLS2lvLycqqoqDuzfz+zZswkGg1x11VWEQiEs08QfCLBjxw4qKw9QkJfHxIkTWXjppSQMg5KSEoLBUlra2iAWxvb4aWhuY+e2LSy74gomTZxIa0sLXp+PN9a+ysUL5tHU3sHRmhAbNrzJ0iVL8Pp8eLxeIuEOJk2ahENVeHn1Gi655BIURUNRVHJycti1ezf5BYXJNUUsi//5799x7rnnUlxcjG3bzDrvPP7+9NO0t7fT0dHBzJkz+3WdT/a+qZ3rGWialg5O3R4fuw4dxq/DNYsvJmxnDta7FimnnqOpKkeOHOmz6L03Z0taUIrMCIgPKgkEhMiyofoHRlEUcrwumjuiROIGnjN4V1lR1XRgkM53Ng3KSoq59cM30tbWjqLpBEvLejxPd7kxYhEcLs9ppxmYpplc0RYFt9PJ2LFjeywq1V8jRoygsb6ep//vGWbPmsWkCRMwjASapqMoCqZlYZoWTodOIpFA7Vy8zLYsjHgMgPZwlJ279yT76NfXc8UVV1BYWEAsnkBRQNeS9RRdPzu2bXO0JUJ9Rxy/S2dcgQ9NVU7Ylagv1dXVPP/887S1tfU5q/F+5BcUYoZbcPsLevysJRKnJRyj6XgVSl5Or8cNhUK8+uqrzDznHC688EJqa2szc+BHjcLn9/Pcs8/i9Xo5//zz3/tZeTnhcJhwJEJ+QQHbtm0FoK29ndbWVkaPGsWql19m5MiROBw6r65ZA8DChQvJLyzm8itXEI92sOfddykpLuKcqZO59JKLCMcMGppbKSwoZMKECex5910WLVpER0eYpqZG3C4HitfHrNmzMU2To6HjlI4YgaVoTJoyDSyTxqZm4tEICy+5hPyCAgKBAJqq0tzcTPw0Fvs6GVVRMDpXzTZME4euU1paSiKRIBGPk1A1gl4HZrQd1eXr8btKURScDgd1dXW0tbezbt06/H5/j6L37mzLTKbUnSVpQeIMkdSgIUECASGybKgGApAcKOR6XTR3xNBUBad+5n+JKoqS7OygaWi40N1evIFcLNMkEYskU4lUDbVzFVdV1dBdbhKnEQzYto2RSADJvvW2/d77cTp91QEKC/IBm4svvphgMIg/JwfTMIjHomiajmGauFyuZMqVqmLbNmYijmWa6E4nqqYzplsgMm7cuM5zTKYvGaZJwjCSaVKaxq7de4g4/Dh8ORR4HZTnusG2OFZ9jOojh7EVlYbGRvyBHHJycrCx6fy/XrncHi5ZeCnhcAc5ubkoqkY8YaCpSkYActqzBqoKtk00GuWd7dsJBoOMHDmSlnCM1miCRGsDHW0tbHpzPcuWLeux72AwiN/v553t24nGYlx66aXYlsW+/fuJxWKMLC/H7/OxcOHCHueW6kJ1y0c+wgvPPw+A3+/H7XZz7PhxJk2cyIULFnQWdCssXbKE7Tt2EItGicZD2EVBFAvmnHsO+bk5HKw6imnD/v37ueCCCwgEArg83mTaUSAHVdPwej143C5QVPJy87CAkmApiqLR3tLMiy++wI033kB+QQHtzY1UVFSwa9culBEjKBsxAsuyOG/WrB7pY6ca4PXGsiwMwOlwpN/X8vJyEvE4TpcLy0h0rkYcR/Pn9ehipSgKLS0tFBcXA/Ra9N7d2ZQWlDIUZ2+FGAgSCAiRZUP9D4ymquR4XbSGY+R6XeiDvAhVMjBIDvrB1TljkCwwTkTDgJ0OClqbG9m5Zy+5ubl9rjGwa9cu6uvrGTt2LKXBILquo2oasVgch+P9DU5SNQZlZSMoKxuRflx3OFA1jXgshoKNaRo0NDYRDYcpyMvB6wvgcDszgsK+AhFVVXF2BhCWZdNYV0tRYQE1hgufFSHXSmBEk0FGPB4jHA4z/ZxzefXVV9E1naJzZwJKug1sb4FoY30da1a/kk7Juuaaa1AUMEwLK2FgYxONRDlQWcmePXvAtk9t1kBRMY0Eu/fvxuVyEY/FqG1uI26rRJpqee6Z/2PkyJFcumgRLS0tPfbbPQfe7/fT0tLCsWPHKCsr48m//pWFCxcyZ86cHoeuq6sjHo8Ti8XIzc2ltLSU4qIi8vLzAYgnEjidTt5++23mnj+HaCzG9OnT0TpnYdxOnZimgdtLm2Wxc+8BjHiMhRdfhMfrZe1rr7Fn926WLVtGPBajqbGRksJ8TBRMw8QwE3jcbjxOJwkbDh2sJB6PEw5HUXWdg4cOseXtt1mwYAFul4va2locus6oUaPS16GyspI33niDWCzGzp07M2pIUmtudF0voTeWZaWDSYeuZ3wOUv9t2zaq7sCRW4LZ3oTRXIseKETpMoi3TJOiwkLqOzt49WfWwjISODy+E24zlAzlmzVnM0VVB31WSGahepJAQAhxUg5Nxe920hKOkedzo2VxMaqMwMDZ2TrRNKgN1aCpCtMnT6I9Gqeuro7c/5+9/w6S67zuvPHPjR1nuqcn9OSAwSADBEAiBwIkmCmSkiU5abVbDuvdsl+Xw3plV/3KLnurvKVaOcl+y0G765XstfRKtERSIikSFAkxIQciDMIAM5hBT+jJnbtv/P1xuxszmIAZBAKk+1McDqb79n2ee7vv7XOec873BALFxgcCjhE4NjZGVWUl/ZEIyWSSVatWFZ2x222yNd9Kp2lZSLKCLEsMR6PoWo6B/ghv/cRJIVlM9MG2LexcGjuTxC/k0GUPkeOHuXSxk4ceeojdu3cDYCHSey1CdbgORZEJBMoXVEtxo6FdX18/7Xnbtrnc1UVscpI9u3byo9den1f950YEQUTA5r333ydUUUG4uY1VgWpqAx5G0y48Hg99fX2k02mef+EFdMMgOjQ0LfpQ+LHy9SW6prGsYykv/uv30XWdkZERli1bNmPsmpoaVFVFkiSuXr3Kzp07efe999j00EPU1NTQPzCAx+NhdV7Nx+PxYJgmvrIyMjkdTTdQJYFsTsMSJXY8vBdbEDE0jZwJhmVTHgjS2tpGJHLNUdZR3JiWjWkZpGIT2LqXkbExGlvaaO9YRiAYxO3zMTw0yLKOpfT2XaOnp4e6+noUWSGeSJJMJSkrK8flduEvK2PdAw8QqgiSTCQp8/vRcjlisRjX+voYika5cOECtm1POweFKEJrWxuhUAhVURgdHWUyFsPjdk97/8QphcSCKCKVhbCyKfTYCJI/iOTyOipOhuE4UaI4o+h9NizLBEG87f4YJUqUuDOUHIESJUosCJciYdky8XSOoM9136ySCYJAV7dT2OtyuTj90fvsevhhZEVFzPcoKEiTDg0N0T8wwJkzZ8hkMmzatAkAwzCRbjPtqVDbICozDRzTsrBMC1V10i+6e3r48MMPi8/fLJWiOIahY2WTWNkUgiSjyR6Sgp9opI+LF87h9/un9T1YsmQJ+/btI5PJsHfvI9TUVC/4eOYrNhUEgVBFBYcOHmTtqhWLz10XRSftrLycmqZWVqxdj6SlcCvlM5yQQHk5o2NjZLJZjuabdz333GeoDFU6ilGC8x7HEgkAfOUB/GXl1DQ0EctomJbtRF80DY/Hi6uyjn2f+3kyNjzy2OO8vf9NduzYQSwW48TJk2zYsIEyvx/V5cI0dBKpNBYCugXpVJLYxDj14bCjhNXYSHR4kJymEa6tR5REtu18GMEycCUGWFIugWBhpYYdZStBxKVaDF67TG11NWJyFLcnQFl5OTbw+muvsXbtGtav38i1SATTNDnf2UkikSAej2MaBhs3bmQgkeDQwYPs3rWTg4cOs2fPHqpraujt62N8YoKmpiaqqqsJBINomoYoigwODvL+++/zwPr19HR3k85fLx98+CGNDQ2cPXt2WlRHlCTMfCFx4T2XPH4EWcFIjGPrOSzZg6I6kSyv17sgZS3rE5gWVOIuIdyDGgGhVCNwIyVHoESJe8z9nho0FY+qYFo28YxGuUe9b5yBkZERGhobuXTxIv0D/fzoh6/wxOOP43a5EAwTVVUQRRGPx0NscpJMJlPUZbdtG9MycckLU0aaje7ubgRsXC439Q0N056zbRtd13Gp189XIZ1lPp33qa+3tQxWJolt5BBcXuRADVlLIJXT8aoCsiSwadMmKisrZ6REtbe3Y9s2uTvcc6GxsZGnn34ar0tdfDFxfjV46+49lFWGEbUUTfXXC8EbGhqoq2/AtGwyukk8nUXxlvHEZ17AtCGNjJXWMW1nhdm0NQg45/DhZz6X34vNeMrR87dFyZGexSlPUG0L27LwVlTzhZ/7BQRTd9SLBAFFkhBlmUgkQjAQAFHG5VIYjw4Rm5xg6bIObNOifckSdF0nm81iWRa9PVfoaF+CSxbRBJFhXQFTx63KpNNpBAEE2yZUESSr62i5HNdyMoZmELJMTMNA13VOnDgJCFy6dImHH36Y3t7e4uekpqYGuO7gYds88fjjNOTPfSAQKMqq+v1+HnnkEWryEp+GabJl61YunD/P5cuX2b59O6qqkkmnEUVxRk8HQRAcrf8bEBUXSrAGPT4GuoagVGLbC78POGlB/oV/VkqUKHFXKTkCJUrcQ4R8l9lPEj6XQjyjLUpW9G5TU1PD4UOH2L17N263m8rKSlrb2ooGUDanIYkiy5cvL/Y1qK6uZuXKlZimNUOBZzF0d3dz6NAhtm/dwo9efZVHH310uryipqPIyrT9Fwy5QjEwwMGDB6elVdimmV/9T4IgIHrKkMqrEESRnG6QyOaYHB3G8nlYuXLlnDKtUPicibfVlXc2Ghsb0TIpQtU1i3pdZ2cnVZUhXBUVDPb3UxEKEZlIY9o2pmVj3egce8rRDINMJo2Wy1IZCiFLEi5RQBIERx3pymUuXbxIY0MDyUSc1pZmmpcs4dSpUxw+fBhN07Asi61bt7J6eQcXe7qxBImGtmW4JAmfW8U0DQwLrnb3OIpUHj/x0WHSiTger5flK1Zw8uRJ3C4XA4OD9Pb28sgjj9AXGWDH5gcRsBlPZXG73PgDFQiWwfhknKwp0NXVxcDAAD/7xS/StHI9IyMjDI0nCLor8PvLiE1OAE6OfSgUKkrl+v3+WQuy29vbsUwT0zSLj3V0dDgSsjfUCEiShG1ZHD92jAceeIChoSHC4TCqqjI+NsbY2NgMh1QQBDKZDOc6O6murp42tmUDngCSmUOfHEbyVyxopdUyHWngT9o9r0SJTzMlR6BEiXvMJykiAHlZUY/KZCpHRtPxqPc+zF8wgARBoK6urmgAFQprZUlC1w2yOY2lHR2sXLmyaIwYpoEi39qt0DJNPG4Xa9esJp3JUFVZOS3NxzBMRFFEmqXAulAM3N3dzf79+693ZH7iMWoryrC1LILLg1RWiaBcT8XSDINYOsfFc2eoDgV54833eOihh1i1atW8c5Ulsej03GvGx8eRgjXouQy6aZLLZqisCIBtI2Ah4hj4iiyhKDKSKNLf309iMkpdOExj7UzZ0cpAGeMjUfp6ruD3+1m5YjmSJFFeXo6UT3Px+/20t7WQ1XQuXrxEfX09RiqGWBYkntXREpPolk24rh5JlhEtnWQ8BjgG+okTJ7jW18eOHdvp7etj2crVlNfUsypYTSpnUOUrw22lyWbS+IIBkpqFv6wMBIHlK1YQjUbJZLN4vF6am5sxyzLENBu3qqLnZLZu3UogECAYDBKNRolEIvOnaIkitj490rNs2bJZayOWLFmCbdvIsswzzz5LYz5y5fV6GRwcZP369dPGiUQiXLp4kZHRUY4cOVKM+liWhWkY+ZQgF4KsYiTGERQ3tlIxr5FvGTpiKS2oRAFBKEYHP9YxS0yj5AiUKFFi0UztMSAKAi7l3t9Kli1b5uRqz5JmI4oiLpeKZVlomk7WyKEoMl1dXQQrQoyPjc6pMnQjtm07K7GGgSAKaLrBhwcPkU6neebpp/H6HDUU07SwLKcuYD6i0SjPPvUULtFCsQ1kWUCQVaSyED1Xe4lGu4qRAsOwiKU1sukklRUB+vv7GRkZYXR09KbzFkUR3dBQ7oPbfmtrKyPJJDnDQEhP0rKkEZdooRs60eFh3G7PrEpBt9JNd+oqeV1tLRWBckZjSZ79zGd47733eOn7L7J9x04al64gYQqUeT1cudzFqhXLURWF9iVLMCyLo0eOcObMGSRZJlgVZte+p7Ask3R8knfeepNEIkEgEOBnv/hFBGxOfXQaW5Robmri6tWrdHV18fgTTyAKAiPRKBUVFWA7q+hv/eQtHli7jppwGI/Hywfvv08kcs1xDOdJu3KMbmHB3b8LaWKarhdf4/f5WLVq1YzXR6NRRkZHCVVWFmViGxoaMHS9WBcAIKpupEANZmIMIz6KXBZCmCXv27ludBS1lBZUosT9xL3/RihR4t8wn+QQuSQ6PQZi6ZwjQfgxy4rOhiAI2DgKQbMhiiJutwvDNEmn0lRWVXH0yFEikWvYts2aNWvm3LdtO7KfpmEgihKKy1mlb2lpKab5KIpCTXW1k+9tmtPqAmbdp66xqrUBt2iTs+BizyDty1aiyG7GRsc4cuQIg4ODtLe3U1ZWBi4fsiiAnuPI4cPFlI6ChvvNzk0mk6Hr0iUqKiruaIOwxdDT04MiyzRUVzBpiFTVhvD5/cVCbrfLzaVLlwEWPcebOQtVoSAmEi6Xixe/9z3i8Th+v59cNotg5AgGQ+RyWfRcjqs9PSxZ0kY8nuDDgwdJZzKsWf8gy9esRbPgzMmjrF6xnHBlBU899RTxeBxZltE0DduyOH32LKIo0rF0KWvXrWPVypWMjIxw9Px5BgYH2bx5M2KoHlQf/f0D1NbUcO7cObZs3cqS9iUIgsC1a303VWOajMWIDkfx+fwL6icgCAKSKGKaJnI+EjbbZzQcDnPx0iXK/P5i2pCu60g3SI1CXpLRV4Ggp9Eno8hllYiKa9o2tmWV0oJKTEcQ70FE4N5/T91vlByBEiXuMZ+01KCpyJJImUctKglJ9zjtRCwUOErz5yvLksSpUydZsXw59XW1XLhwnvG8DvqN2LaNaeh5BRUZ1eWeYcxM1fy3LCvfk2CmweTsz5H+tDJJbNPA6/UyEk8zmUzTumwloaoqbNtmeHiY5cuW8dCmTQwMDGDJblRRoMLvocLfzObNm6fVOtyMSCTCRx99hNfn48MPP7zj3YIXiiiK2ILABwfeYeX2vVwdHuaBQBCPx8vQ4BD9/RHWrFmL2+NFNwxkSUIQhFtuXnbp0iUOHz7M008/5XRfHhhg6dKlPPLIIwwPDxOqqsbt8yNi89HRD+lYu5721Q+gJydQVZWxiUkaWttZ0rGcdDJBLj6B7HGzYe0aqmtqOH78OCdOnHCiNm1tyKJAZGiEz77wArlcjsnJCaprwpw6eZLLly+TzWbZsmULS9ra+KhvGF+1D3/e2N65cyeBQIA333yTPXv3ksmk5y0k7+7u5lpfL16Plw8/PHjTjr4FJElC03XEWYqBCzQ2NrJ71y4UVSUUCmHoOol4nMqqqhnbRqNRpyeBIFBfXYkRH0PylCF6/MVr4JOcFlRyXkp8mik5AiVK3EMWGtK/n1FlCa9LKfYYEO/h8QgF7fMFbFtZWUkqlSIcDhMMBqfJboKzgmkYOnZe/191KQt6r0zTQpIlLNPEEsViTr5t6liZ69KfoscPqgdd06hrrKReENA0zek3IIp4vV72f/ABD+/eTduyVViWScB7vQnTzWoCbiQajdLb28umhx6coRDzcWFZFlVVVbz00ksMDQ3Rsno9leF6LNsik0kzNDRIJpPh/PlOlixpIxAoJ6dppJIp3n77bUZGRm6aLnMjIyMjKIqCR5H45+98l9Wr17B06VLa2pZQU99IOp1m/+uvURmqoKOjg6BHIZHTkP0VJDWbyoYWgrkM1QEflwb7GBsbo66ujqq8w9bY0IBlmgSDQcrL/ExMTHDo8BEMw2DXrl289957rFi5kg0bH6Sqqor6ujp6enr4p3/+Z3Z95vMYwO6HH6ZjSVtxzo8//jixWJznX/gsgfKyOc+l1+PG7/OTy2XZs3sn42NjC3IEBEFAliQmJiaITU5iWtaszccaGxuJxeN8eO4cS9vbeePNN2ec+0gkwpEjRwiHw0UJ0oa6Woz4GJaRQ/aHQBBKaUElStynlByBEiXuMZ90RwDAo8qYlsXIRIxIz5VFr9reKURBwJhnlXMqq1evZnRkhGw2y5NPPklDQwMXLlwgmUzS2tLsFJnKCqKy8HSGG+sCtFwOGRM7m5om/SkojtqSme/sCk6XYEmS0DUNZJmW5maeeOJxyvxlZCyBH33/e2zbtm3e9KX5CIfDhMM16LqxeN3/Oeju7ibg9xEbjC4oLUXTNFRVZdu2bUiyjOQtJ20JyLI8Z46/LEn0jl5l3QPrOf3RKUZGRhblxNTU1KDnsqTSWR588CHKy8vJ6QaZnCPtWRMKsm3bVoaHh6mursbv92OOjRHpv0ZrWxsVbglcXuLjY3RduUImEUcSRRobGtA0jcrKSmxgcHCQunANqUyWVatWFes6ZFkmk8mgaTlqqqv58RtvsG7dOvx+P26Xm6QNzc3N0+bspDiBrhsYhokypQbHtm0MXcO2bDTd4NiJEySTSZa2t/PInt1YppHvwj0/V65cQVFVRsfG+Oijj2Y0HyuQy+XI5gucfT7fjHMfjUbp6emhtbWVLZs3Mz4+TmNjI3LQ6UasT0aRfMEZaUG3GuEp8enBFkTsjzlV5+Me75NAyREoUeIe80lODZrK5OgwIxNxxmJxjh07dk9STxbrVFmWhWEYlJeXc+nSJWzbRhIFfvL226xb9wArV650dPwL75FtY0/9jZ3/zwbbdhqTSSJGLgNaBkHLYAJZS8SWfXgUD5Zpgpkpji+IIrqmISCAAKIA6XSKxMQYdT4V08pw/MQ54vH4nOlLC6GxsRGXupt4PE5zS8ttvzcFtaN9j+zlp++9j23bc65GX7p0CYDaujq8skx9fQOyImNacHUiRUYz8bnkWXP8BUHA5/Nx7NhxNm3ezLs//eminJilS9uprarg3MXLrFm9BluSyekGZd7rqWyF1C7DMLCBiYkJyn1uDn/wLhndZOPW7RiCyrYNa6lID3Kkb5J0Ok0gGESWZWpra6msCDIyOlo0bguO0bJlyzh06BBut5tjR4+wfetWJmMxnnnmGXIeH8lEjrnuALIskdN0RoeGGOzvp7m5mbIyP7KiICoSzc3N02Ro/eVB9FwWUTKRlPnrU0ZGRhgcHGT1mjXs3bsXWZZnlZdVVZVUKsUbb7zBrl27aG5qmhbJLPTEeOedd2hqbOSJJ55A1zT6BwYYHBykrbGOkDWO4Ckrvi4SifDqq69eV8q6R2lqi+HTcp8uUeJGSo5AiRIl7gjRaJSr1yK0tLVz5uSJe5J6Ak6h8HwpV+fOnWNsbIyqqioEAXxeL4lEnLraOizbYmQ4yoMbN1JeXo6WzVzfq+D8Fm74PTg0SDwWp76+Hr9bRcgkHelP1c1I2uDs+Yu0LVnCtWsXaG1tvd4nwLbRcllcbs+0+UUiEWQjQ6XPzVg8yeVIlPVrVhDp75+RvrRYCilQ4k1qKBZCNBolmUySSKV44oknGBoamnW7S5cuceLECR57/HFikzFSyRS1teF8egqokkhS0/G55v46amxs5OGHdyOIIs8//7zT/GuBGLkcHl8ZGx58iHROQ7ZtfG511noWSZLQcjlaliylp3+I9o3bQJSwRQEjMclHZ06yoy3E1qYyMtkJTN3HxYsXyWYytLU20z8wyJGjR3G7XNPy9VetXs2xY8f46KPTDAwM8vRTTxIIBLgwOAnIDA0O0drUMGM+giAwNjqCaVlUVAQ5e/YsSzs6ZvQTmOqAKW4Ppq5h5DL0D0YZzPcMuDFiU1NTQ2dnJ6Io8pOf/IQnn3yy6AhJkoSU762haRoPPfQQmqYRCoUoLy9H1zRESUKSpGIkZ3h4mLraWvxlZYyPjeHzepmYmOD06dN86YufQ8mlMC0DyR9kbGyMrdu2IQD79++/Z/eKEveYUrHwfUHpjJQoUeKOEA6HaWtfSl9P9x1LPbkVCnUCs3Hu3DlOnDhBTXUVwfIyPG4PP37jTQ4dPgKCwMjICD/80av85O13GJ+YRHV78j9uVJcb1eVCUV0oqoqsqFzq6uKt/fuRTA0xPYEeH0WQVfqTOkc7L2Mg0tDUxIEDB0glk/i8XqcOwDQZHBigv7+f7u7u4vwsy6JcNAh6Xbx3/AzffulVJFkmkcrw1OP7FixxOhe27UQg7gSFleB3330PLIv29qWY5szzPjY2xsN79pBOZ3jttVfp7r4yzUnzuWSSOeOmK66NjY3U1dYRrAgtaHX23LlzHDt6lEQiQcawyekG5V43qiQwNjrKBx98QFdXV3H7nGEylsrRn9Tpm0zjKgtQ4fPQGHBT71cIumV8lWGu6D5ytoQ3OQSjPYyNjdHS1IisuGhqbGTH9u0kk0mGh4cB0HUdVVWpDYfZtGkTmzZtoqq6homJSWzbOV/vvPMOkUhk1uMYGhri5Zde4tVXX+P4CcfBng9BEJBVF8OjY7gUiav5yM2VK1embdfR0cHDDz9Mmd/Pvn37aGlpQVVVVEWBvMTo8PAwvVevcuHiRS5cuFB0FIS86lAumyWXy+H3+airq0NRFEeqdXSUDz/8kFAohM/rIZ7K0D+ZYWxslMzoIG2tzVw4fx6Xqt7Te0WJEiVKEYESJUrcIRoaGlD9QTA0Nj+08Z6t8ImCMKuhWIgSPPzww4iSzA9/9EP8Ph8PPfQQzc3NJBNxqquq2L59O5IkoSjzK5zYukZAhZ9/5lEsUeZcVw8Zw6ax0eLQoUM89NBDXL7syGDG43EsyyKVTlNTU8PI6Cgej5v+/n4udXVh2zZtjfUYiTEURebF139CdGQUv99PS0sLDQ0NaMmYI8F4G4b8nSxOn9odWZIlfF6PYyjaYlGW0rZtli9fjiCKvPLyy4CzEj0VvyozkdbIGRZuZf5IhSgKiILgFITPE9U4d+4c77//Pk899SQ5ZHKJOBXBALquO+o2ksShQ4eoqgljqV5cZQFyhoUkCPhdMqKepetCJ1ZNDTUdHfT395NOpRgZHubs8DChp5/ClxqiPDXGhioJ3bb4p3/6J9auW+d0IF6+nHA4XOxs7VLVGcXd1yIRKK8GRSWdycy5Kl7oAFxIo1mI0WwaBl6PByFfdD4wNDSt0V2BZcuWYRjGtCZLguDUbEi2TX9/P+Xl5WzbsoWx8XEs08S27XyTPOf8T05O0t3dzbvvvovP52Pfvn243W5sIJvJsGrFcjTDZGBwkOqaGkYjgyxpCPPIrh1Yosyzzz5LQ8PMaEiJfwMIwsff4OtTUJN3pyk5AiVKlLgjaIaFS5XZuGHDPZ2HKIrouj7tMdu20XWdYDDIsWPHiMXibH7oIfxlZYiSSDw2SV/fNVavXk24to533v4JK1asmGE42bbFYG83HtHG63YRDIY4ePocV3v7eP6557Bsm57ubnbs2MHp06e5dOkSW7duZfv27dPSM65cuUJ9QyN1dXWEQiGqy9zo8VHSukWgtok9jzw6s2jW5cHIpVE8t6a8Ytv2nP0VbpVCWkohzUlRXRiGiabrKLKMYZq4PV4y6TRLly6lpqZmhjqNR5EQBYGkZtzUEQBQFJl4IsHlrq45C03HxsZYurQDf3kF2UwaAbuY6nLqo9MMjI6z46nnqaiqwTJN3LJEtd+NV5Ho6uoim82SyWR4++23sW2bRCLBgQMHeO6557jyyitEh0cQRZll1UE8ySju2FUaKsuprKwkmUyya9cuRkZGiCcSpJLJoqHb3d1dfF/bWls4FxlFCvjw+XxzGvhzFVFP5cKFC4yMjNCxdCnBQDkIAllN45VXfnhTB0KSJOd6ucGxEgQBv9/PhfPn8Xi9XLlyhUceeaTo5BW4ePEizc3NrFm9mjNnzzI8PMy2bdsQBIFcNktjXRhLkjEsm9dfe41cLsdjj+xhRUs9OdOiOzpKLpdbULH5vaRUI1Di00rJEShR4hPI/ai4kTMMVPne31IKTcUKmKaJYRgoikJ9fT1Llizhww8/5O0DB+jo6KCnp4edu3bj9fvp7u5GVVUe3rMXl6oW91GQ/tRTcTwS2KqPM5FBAmXlhGvrkGSF8YkJGhsbWblyJRcvXWLlypWMjo5SV1c3w8gJh2sZHo5y9MghfvGFZ3AJNleHxpA9jmJNfV0dDQ0N9Pf3c/z4ccLhMA0NDZhadsGqMDfS19eHlO+EfKeNLkEQkBUV09BRVRe6YZDLaZimQTyeIJvNsmPHjjlf63fJpHI6VT7XrNtMpb+/n8GhIXq6e+YsSq+srKS+oZFXXnmJbCbDzl278QQriWc1KlqWUd5oMHStl0jXedatWkFtneOcWJZFRchJPdI1jW3btjExMUFDQwN+v5+xsTEaGxsJh8N4PW5e+vEbfO7xPSjjvTy7vg2xNoRYsZqrV6/iLytjYnycN998s1iMe/78eZa0t3O1p4cHHlhHMBgkYcPDDz9MY+Pcq+LzNUq7cOECpqHzwNo1jI6NOQpCHR3UebzTConnes8L18ts0aKOjg4UWca2bVpaWmaVGG1ra6Ozs5P29nZ6+/oIh8OOYzI8zJo1q7Fsm+hQlLfffttJI/L78QcqGIhlcNsaDUEvP/7pe/MWm99rPg3KbiVKzMW9/9YuUaLEoigobuSyWVxu911X3FiI02HbNpph4nepsz7/cZPNZOg8d47Wlhb8fj/qlA6/a9asIRgMEo1GKS8vZ3BwkAPvvM2aNWvZtHkzkfEkk/FxqgNljET6qPC7sY0c8azOaCxBdX0Tk+MTVIcqGYpG2bJlS3HcK1euUBMO09TYiGlZPPvMM1y4eJFMJkM8Hi+meYyMjFAdCvBLX3geBIGro3Hc5SFisUmnSDlfR5BIJDh69CiCIPDMM89QXxvGyKZRvGULNk5s26avr5fhoSGy2SydFy4uuPHUYpAkCdPQnbQdUSRn5kgmU4yNj/HB+++zd+/eWeUpwUkPGsjqGKaFfJMO1dFolLNnzrJq9apZO++eOdfJ0Hic6npY+cBGykLViC4Pw4ksPpdMXcDLYF8PLiPDA6tWFI1b3TAwDAPLsnjpBz8gmUzS1trKI48+SllZGc888wypVIqlS5cSqqjANAw2btxITIfy8HKMSCe+6GWsbJKB/iiirDIyPEwymWRkZARRFFm1Zg2JeILNW7eRTqedlXhZIZFI3tI5N02TUGUVpmUxNhHj9NlzVIZCLM0f042FxHMhFvpvzJJu1dzSgmkYKOrMa9u2bSpCIVpaWrBMk6eefJLo8DDpdJqcpmGZBm//9F1qwrUznJKzZ89iuFwohs7PPPkIExljVtWiEp9iRNH5+bjHLDGNkiNQosQnjGg0imWZfPa5Z0ilM4znVynvBtciETq7evCVlfPBoSNs3wpNs4ylmxayKCKK937lLBKJEB0epioU4tChQ6xdt27G+Zm6wqqJKh/0jHNYU3n9wDUMy2azMMjzsReRXC5yLheCJOEBmgC6oKBZUw1kXjpYjEDUAfYZ8BeMdEFgg23DmFNgKYw6ObEhQBgBw7axEajUNFwuN55sDtc1Nygqhq4Ttiyeq8xhWhbej15Du+zHtixMSUIQnY67ReWN4r+dMZw5CdiCQEDTsSdjBIJB/AET6cox9NwQiJLzxSiIzr8FAUQRQcg/XnxMzj/ufHEL+b8RRJDk4utFID4+ii1IpDIZXnnlh+i6hmEYjIyMzOkIeFVn1fnk2fO4JehYsQLdtDFMC92y0U0L3bTRLYuMt5KUp5JRsRyxcSVDUoi3L4+gmzap+CQVgRBSbRU9cRNvdQN6NkVLVQV+l4KU/3yWL1vG8vxcLMtCy6eSuVSVcE0Ne/fuZWRkhJqaGgKBAIZhUFNTgyzLxGIxhqNRcrrOypUrudzVRSAQ4J8OnOKxdW20Aw9UKrx89Dxbd+1heDhKTU0NHo+XkdFRAmU+vv+vL7Jv3z7K/T60lMaFixcRLX3BPSIsy0LXDQRBQNd1zp09y/nznWzfsYO2tiVYls35852MjY1RWVl50yJzSRQxTHNWR+DGCNtUjHzn5/b2diKRCIauc/jwYXbt3k0qmURVZBobm6gJh2lpacE0TccBLyujNhzmpZdfJhaLsWxJK4/t3IKRnATVW1QkulOF7bdLKSJQ4tNMyREoUeITRjgcZt2a1Zw7f4GcpvPonocxtNxNdcNvhVgqw5IVqxgZHGDzw4+iGTqJrCP1OLWDcE43cS0gv3supkp6LrZj7o1Eo1F++tOf4nK5yGaz1NwQychoBh9eneCjgRj9k1lypgVIgFnc5opZDpaFmclAJoMgy4huN6I62zmennsvABT7DnC9y/EsSkaF52QJMLKUyYCewdYzFNZfPUp+r3oKezI1ZVTmNNBuxAWEAUbGWOEG0gMYlwYW+OrFUUjucdnwH+rJnwsRRo6QeenI9Q2L59Ep7m4C/k/5o0zaLohOV7iZQfVyTsWAqqWcGtNhbAKAcskkWCEhmzkwBXKCjM/SOHvi6Iz6hEuXLqGoKjU1NaiKMq04fNmyZdOcFkVRsPIRmmw2y/DwMIcOH2bfvn0MDg0RCAZRXW5+ePQiLzy8mVa/zs9sWkp/OsYzzz5LbThMJpvD7/PS29vLzp07uXz5Mp7qBixPkI6Vqwn4XTddEbfzvSpMy0JVZARBoCJUQXNzEx6PmzK/n1BFkInJSUZHRzl+/Dg+nw/btud1MoQ5CuynDDzjoYGBAWRZRtM0Ghsbi52rd+3eTXRoiNbWFjLpDD6fj0wmw+jICKIg0NLczODQED6fjx07djA6Okp1dTWuUC1GYhxyCfAG8oXn9nWn4B4b46UagTtPqaHY/UHJEShR4h6z2C+4hoYGgn4vXT1Xqa6uwReowNRy6JkUsstzRzTiAUzLpqwyTOep43R+dILKqioeeeJpYmmNiXQOv0uhzK1w6cJ5KmobiI9EWbFi+Yz92LbTbMu2LbCsGb8TiTgeyaLSp9LXfRmwWbXq1mUyw+EwPp+vWCRZWV3N4d5xjl+b5Op4hrRuzvt6VbQJChq2vwIh6RiYtmFgJpNogOYNIlbVE6xrQrBtotEhDEOnKhTkancPAA0N9UgCCNjo2RyGaeBWZBTBMd8tREcByGl6gGUaZDMZREFAUeSihKOh65imgVRQabFtTMPAtk0EnM9O4bdjrF1vfJb/x5SHrj//cZhU1+38OUabMkchPyfJNrjuSiwcUQDBtvApIj0H32TLhgeYmEjjrltKxlVBZY2Ny60STyRQFCWfUlNJPB7nu//f/8eOHTvmjFYUx5Akzp8/jyxLnDl9pigR2rFsGWfPOg3f/H4/ur8asSmM3H+BFuIYYgVXe3spLw+QSaV4YP16wEmlGp5Mggd6+iL4WuvQDRPbNlBkCVEU6e/vL6bl1dXVoRsGsiTjUhUEQXBW8UWJFStWsGLFiuJcz509g6Ko7Nq1i3ffffemjegEQSCRSHCut5dgMDijnsC2bUzLyl/HNrF8c7vjx4+TzWZ55plnCIfDHDt2jJ6eHsrKyli6pI1EMlWsP+i6fBld15mcmCCdTrNjxw5cqoqmafT19REMBqmpqcFKx9Anh7kaHacsGHKOW9fhBqfg466TKjkCJT6tlByBEiXuMYv9grEtE5fbzYYNG4uPyS43lmViZDPOl6Xqvu0VtFhGQ5Fl1qzooKai3PnCra3Gtm3Smkkiq9E/oWGqPnLpDKODEYZ8LqpCIcfIty3svPGAIDhhfkGc9luQZK5GBhkdHWFJSxNLWxppqA6gJ8YQFTei6kYQF+fY1NfXs3LHYxy5FqdHl/ja0Ulgcs7tJVEg7HexutbPdtcolS44nwhxNraV5jIVZbAL4dpFPIKJBHjSk9A3SW5iCKVjA1LbA/hDNXzvpZfYseNxvF4vlttNQtNJxGKMRIdY2dGIy+sia9h4KmudSEC+OLPr8mWOHT3K1q2PoJsW8ViMbdu2zTnfgwcPcuTIET7/2ef56bvv09bePu/2s2GZhlNr4PHPSL+wLMdBwzKv/9gWmIbz27KwLWOaM4dlgG0zPDjIuXNn0HM5PG4XK5cvpzJUUXxdcV+WjY0FhoGlZbANDd2ysXqv4FX9tLW20FRfhyIJKKKInP+tSAKKJKKIAqZp4HOrqJKEJAqcO3eOMq+b7x0a4oMPkuzcuZPYaB9WZROTeLhw4hjNTY2sX7+egwcPcujQoeIxz5e2NJWamhr2799fdDJbW9sIVYSorQ2zefNmqquriwa52Loeq78TeewqNe4KuvvjfPDBh+zatQvDMGhpbSOeTKMDTUvaWbbUMWZt20Y3DJKpNOfPn89HEHYRi8WpqAhOu651w8A1i8xtZWUlAwMDjnKS3z9vIzqnhqSP0dFRXG43uVyOiclJvF4vkG/Ql/9cFJzOwYEBDhw4QC6XAyga5Fu3biWTyVBWVkY2neJi12WGhkfYsWMHVVVVKIrC8ePH8fv9JJJJ3nzzTSorK6moqCCXzWIaBmlTJJnK0VIdJJ7VSMbjeH0+bJyaCNMw0HSdixcvcurUqY+lM/FNIyYlSnyCKTkCJUp8wjB1DUmZWbgnihKKx4dlaOiZJJLqRpLn18KfC80wSWR1wuUe3MHGaXUBgiDgc8n4XDJHjh0nXN+IXzQoX7YUzTAQZAVRkor55E4X3rmdEpfXR3fvCdral3Lw6EnWr19PwO+lvqYSPRNHEGXHIVDcCJI86776JtJ80DPOxeEkY2ktv9gsMlvyjCBApVdlRY2f7W0hmiscg8eeGMAezyJULyMWOUd90MtYIsXbZwfZueNJyjPjVKeHUYavgmViJcbJnfgJHsCuauK5DasRgwECVdWIosjEhU7qvBKtHU3Iqsp4xkRyefCLYv78OMfhdrvJ5NNN2pa043G7531vwuEwbrebH732Y559+imMWzBQRElGdnnQM8kZzoBYLOBb/NdDfcMyMhWNxaLQ2jmUamzbxs4mMRPjyIqLnFrGQCTCGjuaz2lfedOxNE1Alpy6lO7ubt5//31+9mc+x6ZNm4p58V1dXZw6e4zWdZsJtK7E73WOr6amBr/fXzTob+xtMBdTeyc0NTURCoVQFJmVK2fOV1A9iC3rGT7zIVVMEIgnsPQco6Oj1NbV8/prr7Jjx04ytoDqvS4JKwgCqqJw9sxZbGDN2rUcPPgh69atIxR6sLidZTnRntlSiVavXk2gvBxFdbFz585pNQJ2flXfsizH6bMdadX+/n6Gh4dJpdNs3rRpmnOp5XIoUxTBgsEgLpcLr9dLOBymvr6e6PAwDQ0NTg8CUSSdSbOrsZmRaJRAIFDsN7B06VI6OjooLy/H5/MxOjpKNptl48aNKKrK5TNnOHDgAIEyP0/v2YGWsCgrL78eWbJtevv6GBsbAyCZTH4snYnvdWrSp5JSZ+H7gpIjUKLEJwjbtrEtc85VckEQkBQXoqRgaFksQ0dW3YsqurNtm/FUDq8qz6vrblsWK9qaOHjkGGc7z1MbDrP34d2Yhg6miaQoiHnt9vlYtWoVlmWRzWb5zLPP8K1//r9s2LCB5iVLneM1NCwti5l00htExcWEIfJBX5LOoSTRZA7Tmt8YDrhlllb52NISYkWNb4bxZOs57IkB8FWAu4y1S5u52hfhnQ+P4Pf7CQQrWLHVMYysXBr9yhnipz9ATY07q6Sj1xBHr8Hpd8g2tKOEG2hzuxEMgd7hNB9evMZQdJhNmzaxe/fuaWMXjMvR0REC5WW4vbXMx1Rj1ATqaqpvqVGYKCtIgJ5JoXh9juN2B7iZUo1t6JiJUWxdQywLoQkqqiyxYvlyViyfmVo2F1NXaaPRKJIkkchkOXbsWNGI7ejowLZtxmLD+CtqEDxO59vC44WC4NlkMefC5XLh9/spKw+g5lN05qLnai9DKRHJ66euwuYXd63hmuFC1/W84R3FU7+UlGbMeA9raqo5evTIrH0AIpEIhmnicXvwuGd3YlRVxcJ2fueN/oLhLwgCoiiiKM78NV1ncHBwxlgFpyGTzdLb10ewooLy8nKqa2r44he/SCKZxOVyMTY6yunTpykvK+Oll17i8ccf54MPPmDLtu00NDYyMjTIG2+8Uez43djYyKpVq2btj1DoWB1LJHnt3YN84dmnMGIjyOUhRNlZAPF6vUxMOGl7pc7EJUrcHiVHoESJTxCWoSPK8xsf4CjUKG4vpqGjZ1OOc7CA1wGkNAPNsKivmHtl2rYs9EwKX3mQxuYWPD4/VVVV1Dc1O/M0TScykU4iiBKirMw7vizLmIaBZej8/C/8IuNjo85xCAKC4iJrS3wQyfFRf4yBeBbNnN/w9yoSrSEPDzUF2dgURL5JAaY92uv8UVGPHh/FWx5E9ifZtGnTtHQPANHlxbVqC+dHc/R3fsTupgCuaDeKbYCeQ7/aiX61E9EfJB5q5r3uEUaS2XlTNArGs65pmLqOfJM6j6nGtmUa6NkMituzaGdAkp1aBD2dQvH67+qqp23bWOkYVnISwe1DrqrBsGwwrZse72xMdQTC4TCqy8Xg4BCrV6+ZZhguW7YM27bpnUgzktTom8jQXOGZURC8ECKRCG+88Qb7HnucN378Otu3b5+2Ej2tYVhbG7phEAgEyXorONs7waoKhQ4ly6joIlRZSTgcJqdIJHLGjM7KczUSi0QivPbaa+x7/AleeeVlnnrqqRmr4ZcuXQLbpiJUiSSKjI2NEaqoKBr+U7Ftm9bWVp599lky2SwVwSD+sjJymgZAbHISXdM4ffo0mUyGzZs3s2zZMlyqSiAQACCXzZJJp7lw4QIPbdpEMBhkx44djI+PU1VVSWNzC/UNDUSuXZtmuM/WH+HG4w7UNmJmUxixESRfEMntW1CTtRKfAEoRgfuCkiNQosQnCFPXUNzeBW8vyQqiJGNqWfSs5hQTz5Nzb1k2kymNgFed03guOAGS6kJS1FmlCUVJQpQ82Kob2zKxdA09l0WQJERFRbwhxceyLDo7OwlXbufS5W6WdCzjYM8YxyMx+iYWUOArQYNf4YH6Mra3VeH1zJ9eM430pPMTrMdIJ5A8ZUhu36zpHsVzoOdYFg6wzL+GmoAPe9kyMmOjCBPDGENXwbKwkpP4k5M8Jwsk6kPo9U20LZ/f8JRkGS2d5tLp01SEQgsycERJRpJtjFwW2bX42hAnzcxGz6QYHp+8K8aVrecw4qNgW0jBMKLLg5mX7fS6XbfkgAiC4BSw4jS1qojF6Lp0iS1btxAoL5+xbUuFl3gyzXASMqkEK5qmryJPNeLnar4VjUaZnJxkcGCAVavX4HJ7yOY0BEFgYmKcrq4uysrLEQSB8YkJysvKePPNN9mxcxdHz3UReuRhasxxwlaC57etJW3bePLGf0Y3Z0TgZjOUo9EoHo+Hvt5e4vH4rGkxIyMjrFi+HFGSSWcyRIeHaW1tJZFIEKqspLy8fFrOuygIhMNhx/EWxWJRsGlZWLaNrCisWrWKo0ePzlpPMdUwX9rRQSqZLHYLbqyvJ6frPPXMs/Rf66PM77/pZ+vG45bcPgRZwYiPY+s5JH/FvE3WSpQosXBKjkCJEp8QLMu8/kW9CARBQHZ5sMxCMbGMpM5ufMUyGoII5e7ZawscJyBffzBLncJsYwuSjCg5OvG2aWDqOmbWkeSUZJXunh6Cfi91qx/iexeTXBpR0Qf7mU/bRhYFwmUu1tSWs2NJiAqPjK3nsLQsVnYSXRMRCsXG0tyRCNsysUd6QXFjCBKyL4iozu5E2HoWkhPYqXHIpaiRIVNWxpjlIiu5adq6FQArm0a/8hFa10mssQEEbMrTY3D5Q+J9J1CXrENZthGpqmHGvAYGBhz9dVXh1VdfXXARpKQo2LaFqeWQXYtwgoqvdzExMUlsbIQDBw7csQJM27awkhNY6QSitxzRH0QQRGzbJqtpuF23LnnrCCU5xqxpmpSVlbF582Y0XWd4eJhIJFJ0aGzbZnBwkMELnZS1LCfh8dM9OEpzTQUCznk/cuQIw8PDuFyuORuuFdJWPvjg/eI5qqoMYVkWpm7Q2NDIiZMnOHTwINu2bWPbtm3s3LkTQRR46qmnaWxsoP9aH9q1TppDkB08T8ZaBXhI6yYVCzjucDhMPJ4gHo/NmRYTDAbZ/9ZbPPX0M07evqKQzKfxdF+5QmNTE9lMZlr0wrIsTMvCNh1FKlEUkUQRVZY58NOfMjw8zPbt26lvaJhV5rRgmA8ODhKLx2d8jkRBoLG5BZdya2aHKKsowRqM5ATG5DByeQhBurUaqBIlSlyn5AiUKPEJwdI1xAUY33MhSk4xsak7xcSO1KhzC+ju7mZsYpLqlg7C5bOnmCzWCbgRQRCcQmLZydPuGYlz4FKEy+NZ4nqaouEvzlIIXSjwDfvZ2VZJQ9Azc/+qB1H15OsKdCw96zQosi1E1Y2ouBEUdVouvD3ej21qmL4wclnljPPrGP/j2Mlx0NLOgy4fQmUT+EL4FBcey1EyKc7V7cW1ehuu1dswx4fQLp1Av/IRdjYFWhbtwhG0C0cQgzWoHRtQlq5H9JYBha65Z3jqqSeprq5eVBGkpKgYWg5D15Bv4f3pvRbB71apDFUwNj5x2wWYVi6NmRgDQUQO1SEojixod3c3oizjUlV8tfPXQ8yHkxp0XdpSzavnjIyMkMvm6O7uQdN1/GXluN2u/PtuMXT+JHWrHmLMdhPM6JS5ZEzLYtPmzUiSzPf/9UWGh4dndQRmS0mxbRvTNJEVmfc/eH9Gnn17ezuWZWEYTlRraHiEAwfP8ciGZaytD5LO9BN1L6XnWj+TkdxNm381Njbi8foYHOhn44YNs75HY2NjRKNRDMPg//zj/2bTpk0cPXoUl8tFS0sLjU1NyIpSrB8YHx8nEAgg54vYC9e/oeuUBwKsWbOGkZERPB4PoYoKdMNpZqbIM4v3Jycni2pCUwt5nW1FMrkclg1udfFGvCCKyGUhrGwSfXIE2R9EdC08Qno7lIqF7wKl1KD7gnvuCDz++OMMDQ0hiiJlZWV8/etfZ+XKlfzcz/0cnZ2deDweampq+Nu//VuWLl16r6dbosQ9wc5rzStzrFYvFEEQkFUXtqxg5DKYgkZkMMo7733Amo2biPZfw4y5ZhhB150AD9IscoULYTSZ472ecTqH4gwnNMxpajc3fMnaNn5VYFm4nIfqy3GlRxgdHaXGL83qBNx4jIKiOka9txzbNJxi42wSO6kjKC5E1UVvTzctcgZT9aKE6ouri7aWgdTEDca/v2j8Fwza6+OJWJY1a8GuFKrFs/Vp3Jsex4h0oV06gXHtItgW1uQw2aNvkD22H7mxA7VjA+HqKo7lcnx48BCPPrKXi5e66O7unjNV5cbjllVX/n0VFq0YFQ6HuXS+kyWtLeQ0/ZYLMG3LxEyMY2dTiP4KRG958bx0d3fz3vvv88QTjzMUiSBYFjXhGsdIF+ZXl7oRZ1tH/Uac8tqhwUH6+weoqqqit7cXt8vFhg0bME2Dqz09JJNJYok46x5+mp6JDCtq/Pi8Xq729JBIpVi1atW8xz41JcW2LHRdR5JlwuFwsYg7HA6jqirHjx8nHA7T0NBAJpvlzJnTlJeX4/f7efvkJYZjDexZ2YRtGhiWzfsH379p869IJILqdhMMBud01CorKwkGg5imSbCigsrKSvx+P6IkUZOfW1dXF12XLhGLxdi+ffsMCVrTNLFtG1mWZ9RTuFS1mNoliiKyJNHf308sFqO+vp7vf//7wMxCXlkS8bldpLI5bMvC4158zwhBEJA8ZQiyipEYR9Q1JF/grhvqJfnQEp9W7rkj8N3vfpdgMAjAD37wA/7Df/gPHD58mP/4H/8jTz31FIIg8Dd/8zf8yq/8CgcOHLincy1R4l5hmcaMvPrbQRBFZLeXeDrLhOCjedsTmGgMXjyNVVUxzREoOgEuz6KMy6RmcLBnfGEFvraNZBs0lKl0lMP2JTWE86vFfX3X0GSFM2dOF9NKFlPkKUgykseP5PFjWxamlmF4LIa3PISWHuDld0+wcYNFR12lk/ajZZwXuv0Ilc3gr0CQ5zZYCgos8yn3CJKM0rISpWUlViaJfuU0WtcJrPEhsC2Maxcxrl2k3OXhc0vaGXL56e3tI5lIcPLkyTlTVWabi+zyoGfTjIyM0j8wsOB8/8bGRgRBwKtKLOlYvuhogCMJmsJMjCEoKnJlA8INn5doNMr42BhaJsPZzk48Xi9VVZXYlu70JEBAEAUEQUQQJafXxE0cBMMwUNXrEZBCY6uuLmdlvrq6unh8U1fzK6rLuTCc4NJIiuVVAUKVldhAx4aNVAQD8x5rJBIhEY/T0NjoGNj5NJlCEXckEuHw4cNUVlWRTKZAEMhkMpzr7CSTTrN3714SiQThcJifXosyqGRYEXDzM08/RiydxbLMWWt5IpEIb7/zDps2b+bdAwfmTN9avXp1sfHWw7t309rWRmNTE6lUClEQiMdiXDh/flZFInBqdkzDQJm1k7aDJIqIioJpWSSSSa729hIOh3nppZfYtWtX8fhunJ8oOs5AOqeRymTxuNR5uynPhai4nFShxDhGbITuwVGiI6NF6dgS9z+2INyDzsKlyM6N3HNHoOAEAMRiMQRBwO128/TTTxcf37p1K1/72tfuwewWhmEYjIyMTFsxKMiuTdVsLlBYPZxtm8JPYV83yi8WvhRv/Jn63Fzb3vjYjdvN9ZrC4zce240UDKKPm4UYx4tZzZn6Xs02zp1YGSrsI5PJ3HTb7u5ufG6VnG7S3NJy22MDXLzSw0BGwHT5AAHJNvAoIms378CIj/Lee+9RWVmJrmssbWnC7S+/qROgGRZH+yY40T9J30SGjD77eSzgkkUaA24aVI1qc4LG2pqirOPkxARHjhyhoqKC4eFhenp6eGLfo0zG4iQScTo7OxkdHaW6unreot6p2LZNxrAYy0BO9qNkJjhzpZ9HVjdR5c5iT/SDuwyhqtlZ+ZcXnl4jSpLjrM2S1jRjW48f15rtqKu3YY0NonWddFKHcmnsXAa17yzNwLgpUb1sI+dT8TlTVWZDEARGxyexTYNTJ09imOaC8/0bGhrQ0gmClb4FjVXANnXM+JhTyFkWQnDPrkAUDofxer1c7e2lqqoaxeVGVq87WcX7oWU6Reamjm05HZGFQhM6USz+O5VKkcvlSCWTRQnQ+RRlCqv53d3dnD11nMYlyxjI2FwZz7CytY329nY0Tce0LKRZ7mW2bTMUjRLp78fn9fLiiy/y+OOPz1rQ275kCdciEXRdp7qmmv5IhJHhYQASiQQPPvgghmVzIGpg2iIfXe5DDtqs6mjDiI2ApCK6PEiKCyHvaBqmybbt2/ngvffm1c+3bZv2pUvRdAO/3we2TXlZ2bQi6qnRi6kRJzvf0Xo+J6CAIAjIkkRXVxfD0ShnTp8mnU4Xj28uRFHE61LJ5DTSOQ3vLToDgighl1cx1HuF+oCH1984gcfjuWlUZdHjlBqKlfgUc88dAYAvf/nLvPPOOwC89tprM57/q7/6K55//vl595HL5Yp5iQDxePzOTnIOent7efLJJ7lw4cLHMl6JTx8u19yrzd3d3Rw9coQdWzfTdaUHQRRpbGiYlkYRiUQWpfTS3d1Nz4VzyB1b0FJx6v0KfkknnUjiLfOQKa9Cj/Rx8dwZHtq4gQPvfcADGzbO2LdlWZwdSnC4d4LusTSJnDHHiA6yKFBb5mJNXTk72kJUeGc3mru6ujh16hRLly7l7bffZufOnaRSKb7/8g9pX9LGwzu3oaVTTAykOH2yD9u2WbVq1bxjZ3ST8WQWI5emwkzgzk0i2waN7XUMTiYZ0lXqlq5alPE/FVGU0HUdFpGNIwgCUlU9nqp63JufwLh20UkdinSBbRGSTLhylF8ICBjDZ9CvBpCbliFIN79tDw4N8dFHp9j00Eb2/+SdReX7i7KCZehI6s3TNhxJ0DhWagLB5UWuapy3E3ShB8L4+Djr1q0jWBGa9nxxAeLGPg9OMQB2vlu1aeqkkkm0XI7xyTgnjh+bFimaT1Gmu7ubQ4cOsXnTJo68f4DlDzxECj9doymW1/iRZYmJyUkuXbx4vZA2Xwdg2zamYXDl8mWGhoYAZj234XCYgf5+4rEYyVSKtWvX0t3dDUxPl+mMJtBsEQGbdj+4yytwV9Zj6TmsXAorNYklStiSiiXK+LxefvCDH5BIJGZdyS84C4XFDCkvLiDLMz8zs/V7sG0bXdeRZ8n9n49wTQ3Hjh4lnU4vWNdfFEV8HjfpbI5UNofP7bq1hSTbwuPxIqkqkiiSTCYZHx9f/H7mQRAE3nnnHb761a/yla985Y7u+980pRqB+4L7whH41re+BcA3v/lNvvKVr0xzBv70T/+Uy5cv85Of/GTeffz3//7f+eM//uO7Os/Z+O53v1t0Al599dViekDhJipOCW1PXXUXbwh5i6JY/Jm6/dToATDt940/Uyl8Ecy2zVz/LrxutshGgbmiBPF4HJ9vcauIs7HYlZf5tr3xvbjZuHP9fWPKx9T93nhuFjL3qfuSZZk9e/bMuW18cpLd27dw5MSporFpGEZxnGw2y8T4OKdOncIwjAWt/EajUTyCyVr3JO8cO0SmrYP1+fzgDz74gKalK9iwtJGyZbWASd3GDiw7jjF4iasJm4NRk66YyUTOnqVv73VEAaq8CiuqvexoraA+6F1Qp+GRkRFCoRA2UFVVRSqV4qnHHkEwNCrL/WBkmUwkWN7ewqbVHaRzOmZyAtHjn6EiktMNYvE4UnqCSj2OauvYgOAuYzCepW8sQXlFLatX3F4qgSAIIAjYtsWtNOYSJBmldTVK62qsdAL9ykckzx5CTk8iYqOO9pH+yb8guL0o7etROzYgVdbNub9wOIyuGxiGwdL29kXl+4uyQio2waUzZ+d1Lm09hxkfxbZMpEDNgos2CwaolsvOqj4zG4XzKyCCBBJw7thxDh8+zJL2dnbtfhhg3v0Voq4+r5ft27Zz/sJ5uru7qa2tpXnVegZiWbpHUwiJYUzDZHRsDJ/PRywWw+fzFQtpwSmChbmbWTU2NuJ2uQgGg3h9ju69x+OZ5rBndZOPhmIArK0N8FDj9WifpLqRVDeGoWNkkghGFkkQCbhkfuHzn2NsMobidlNf31A8LiOv9iNJErKiOOk9glWUWIX5JVIjkQiWaeL1eqnKp1MtlPmiMDeTZfW6XWRzGqlsjoFrfUSj0QWl99i27dT+5DKoXj8v/+hVpzh8nn4dt8qv/Mqv8C//8i/8/u//Pk8++SQPPPDAHd1/iRL3EsG+z+JdHo+HSCRCZWUlX/va1/jOd77DW2+9NS2FaDZmiwg0NTURi8Uov0FT+k6STqeLBvB9dipLfMKxTIN0PMarb+wn0t+P3+/nsccem/Zlevz4cfr6+qiurubw4cPs2bNn3pA8OF/M+/fv5xcf34ZpWlya0AkGgyxpayObSTM+NoZLFjh0+CjReIbJskZS3homshY36eNFUBVoD4hsrRbpmO+yEyTHKRBEyKd6FP6dTKXRDJOuy1fw+/20NdYjKQouXzmC6uHilR7ee+89Ht33GB+8d4B9O7dRU+bF1jMIogyqSv/wOG5VJiDqqJaGDWjIDCdzvHvyAg9u3nLTKMJiMQ3HyZAXWaQ7F7ZtY44OkD1/BLP3HGjZac+LoTrUZRtR2tchumc64ZFIhImJcVqaGikPhmY8PxeRSATJ0vnXl3+EoqoznEtHEnQSKx1zJEF9FYuWtAWn6ZxpGigLiDzMRldXF2+//XYxz/2pp5+hsrqqKHl5+fJlRkZGqKuro6mpCSuf7hPp72dwaIie7m4SiQSPPfYYbW1tXB1PM5rSsJLjaOODyLLM2bNnWbN69YxC2oVE4UaGh0kk4qgu96zbHOwb5/xwErcs8vm19ajSFDUr2yan69i2jSvfBMzWc5i5FLahI8gqtiBhI4AoIcqy0zNkyvtgGCa2baGbFl63q3jdZzIZKisrefjhh2loaADbZnJykp6eHgCOnzhxR6RjwbnXnOvsJBSqRFFkWlpaCE2NAuUlYAXbJhaL4XKpfP+lV8hm0uzYsWPO9B7bMomPDaPrOu7yStweNxfOn2d8fPyu1QjkcjnWr1+PZVkcPXr0rtoVd5p4PE4gELjr9tBi5zPa9RHlZWUf79iJBFUdD9w35+J+4J5GBCYnJ0mn09TX1wPw0ksvUVlZSSgU4s///M/59re/vSAnAJz0ivlSLO4W/f39H/uYJT79WKaBkU3jDQR5aNMmmltaZl1RC4fD9Pb2YlnWgkPyS5YsYc/mzZz8T7/Psl/7EiO2iKqsJJtJkzMtjo2anByIExeWY5TbgACZ2fP9/apEa8jLpuYKHmgon9aEzHGMbbAssK3ib9s2pz1mWSa2aYCpg5bBa+uI2QRLQh7K/D4GrlzE7XFTH67GNnSWSQZND7Uhxy7zs2vrkGI9MGlN0x2qcgdwo5IxZdKSn8FYhvcOHiKVdmoyRkdHF/uW3BRRktG1HNwhR0AQBOTqBjwVn8Ha/AT2wBX0rpMY/V1g21jjg2QPvUr2yI+Rm5ejdmxEbuwopuYU0mP0bAbLNBEX2L03Go0y2H+NR/fs5u2fvjct9cXSMpjxURAEpFA9Yl5BabHpaeAUrFv67GpLC6FQTzIyMkJNTQ3NTY351BiLiXicnKZTFQqh5XIMDgzQ1NyMIAi0trbi95ehKioVFcHiNdUa8qKbFjFCVPl9/Phfv43X651zxb+xsZFIJFJUBiqeI8tmdHSUsbFxurouMjg4NMOwjmV1Low4UYUN9YGiE2DbNrphYpgGqqIUOy4Xzm9tbS011VVYuTSCnkaQFETRhaVrGHruegdvUaJ/oJ94PE5dfQOKJOL3+/nCF7/IQCRCLpfDyqc6CcDI6Chnzp4tptTcjnSsbdtOKpVhUlZWTlVliJamJq5c7kJPJ7H93vx9wHbuAeAUjJoaY2MJHn78Kd744Q9mTe8pRAGyqTgnT59l+eq1XDpzmqrKyjtaEzAbLpeL7373u6xbt45QKMSv//qv85WvfKVov5S4BfKRvo99zBLTuKeOQCwW4wtf+AKZTAZRFKmuruZHP/oR/f39/O7v/i5Llixh7969gHMRHj58+F5Od1b+63/9r4CTmlSixJ3AMnSMXAbF40MQpVlzeQs0NjYiyzI5TXO6eC7gy9tIJhn5L3+AGYtx/r//NfKv/jpvlMl862ovueKSf+HWMP2m6ZZFGoNuNjQE2dIaxC3LjtqLaYCexTZ1bEN3/jZ1MPXrRr5pgDHLY6Y+bQwB8OR/SKRp8wNksUevXZ+HKIEkY4tukFUnb15WQJK51j+EYcPxjy4RGYyyadMmqquriwbyVCWZO4kgCGSzWc6e66SmpuaOdT0VJQnDEFDb1qAuWYuViqNfPuU0LIuNgGViXO3EuNqJ4PGjtD+A2rERKeQYsJKiYuoaojS/7GqBguqObdn8zPOfcVafLRMzOY6dSSH6g4je63KNkUiEd3/6U2ycCNXTTz+9oGMXBAFJkjENA/kWJWlvlLV0tO0lLl+86Bhopk7nhYv4y8qnFdr7y8rYuHHDjFTH9io/pyNjJHGxdc8+wgHfnNKtkUiEV199FUVRaW1rxef34/E4PTiSyQSHDh9ifGwMmGlYH41MYtsQcMsoiREOXjlLU1MzoapKZEnG43JNO7+vvfYaTzz5JEI+Yub3+wE/GDksLQOWCYoby7Ixc1kMLYdHkVArK1FVhVQ6jWXb/OuLLxKPx4vRxULtgN/vR9O04r8XsqBg2zZ9vVeJTcaorqqkqqoSy3QcfSG/COCVoaGmCkvPEa6pRlFVJEUtFnxPrXW63Bvh/fffp7FtCY888TR6MjZ9PNPEyCQQRJHLfYM0Nrdy4O23WbV2HV5/2YLTzG6HtWvX8k//9E98+ctf5utf/zpf//rX+fM//3N++7d/+66OW6LE3eSeOgItLS0cOXJk1uc+KWk2Lfkvly1bttzjmZT4NHDdCfAvON2iuroay7ZRZikInI0Pf+ZnsWL5L1kblH/4f5n4lT8iVz1zZUsRIeyBtQHYHjIpl3LYRhxy/dBpYJi6Y4QsFkkGSQHVA1IZSAqWICIqLgRZQZBkhscmmIwnKasIUVtXh6XroKhI3vJiU7KcpuG+IRKYSohc7emmvqGRyUSKyspKVq5ciW3bi1YaWgyRSISrPT1Eh6McPXr0jqVXODVFEpZlIkkyoq8c1wO7UdftwhyJoHedQOs+A1oWO5NEO/sB2tkPkKoaUDo2oLSvcxpvWdaCPlNT870tUaLKq2CMRsgaJl0DowRDFm2t5ZimiWlZuF0uli9fzmQsRktrK1o+pWUhq/ySLKPlskiLLE69GUvb2/npu+/S29vL1s2bWLVyxYyoyGzjXbnchaTpJKVy1Io6skZi1v3bto1hmOzavZt0Ks3g4ADX+vpYu3YtgiCgqipaPlW1YFgXcuVdoTB9CedabXMbdF26SEtrGyOjI2i6xpK2tmljRaNREokEx44eRdd11q9ffz2lQfWA6nEccC2DnY0hSCrZnIbLpXL42EkU1UVFMMj69Q/wyCOPzKoUdGOOf0NDA7ZlOepNeRUn8oXaTsG2jWEYiJZBKFiGokhkMlk8Xi+i5HKkTwUBlyAwNB5jfHScpe3tVITmTlFbvXo1tm0zPj6OIktUtizh3LlzjI2N0d7SRHVFANnjwxZlmpubefmVVxgbGyOeSPDkk0+R0TRkUUJV7uxn6Ua+9KUv8ZnPfKaYqfA7v/M7/OzP/mwpMnArlIqF7wvui2LhTzK/9Vu/xTe/+U2ef/55fvKTn7Bp06Z7PaUSn1BMXcPUcotyAhbLuc5Osju2o7zyo+Jaf7RtFYm8EyBiUyUbLPdk2FmWIqxOUQJKki8OFoqr7yj5At2CYS/L0/+WHKPeeS7/uDj9i9q2bTRdR5HlaSt6tWGY2ndWtG0sPYeRSSDKKpLLWyyon/q6Qn5wuCpEqKq6+Pedrgm4kWg0ypmzZ9mzZy+9fb0MDw/fsaiAJMsYuoY0RTFIEATkmibkmibcW55G7+3Mpw5dAWzM0X7M0X6yh19HblqOtWQdrtaV86r6FGhsbKShrhYzMYadS5K2JSYzmvNa22Z8YoJgIICiKGRzOY4dP04ymeTKlSu88NnPTms0NZ9RVuzBYFkIC0xduhmmaVJeXs6aNWsIh8NU1dRQHqzA0HKYhkBkYBCvz89AMjFjtX9kZISKQICuIwdYvv1RkkoZac1gKNKXX9VvoiZcg22Dx+vhzTffKCr4bNhwPcJwo2GtaRp9vT3Uhus5k3Y+q2GfSoWYYUQQ8Pn9HDr4IStXrpzhCITDYfx+P1evXsXv9+P3+5FuPFeShK24MHQNK5fBJZiIloVo5mht6SCnaZw7d47Vq1c7x2zbWIZRVGHCtgiHgtRUlGNbFlrSkfIuyLUiiAiSjChf//vEoUNcvtzF3j17iCUzKKqJrzwwQ/K6WByuaTd1EAvpPbZtExkZJ6XpNNdWEZ+cIJZKs2zZcqx8k7Tt27cX08JaWpqLaVXpbA5VkbnW1zdvkfLtEAgEsG2bQCBAPB7nr/7qr/jqV796R8coUeLj4r4rFr5TfJzFMR988AE7d+4E4A//8A/5tV/7tdLqwKeMW8mBXgzXnQDfop2AaDRKJpNBluVpcyvOubae8lAlOd1AM5xVvjf/4du0ff+bjDYu5aNf+l06/LC1RmRphXrdcM8b9EXDXs4/Joh3bMWtIFcoSdJM42ae11haBjOXJZXVmEikQWDGl72eTSOr7rvmVN1IIVUknU7zwAPrefChh/B5PcXGTreLls062u43OR4rOYl2+RR610ms+Ni05wRPGcrS9ajLNiAFa2Z9vW3bWJkEVnIcQfUglVVy6MhRFMFmdHyCc+cvzOhEe+P1MVXJRhSEeeUoLcvC0DVU1+11zS7MXdM01Dk08AciESzTwAR+/OM3ZjRq6+rqYmhwkLGxMRI5gxXbHkXAZrL7LK3NTcTjcSpDFcVI8ELvC+fPnaOhvpZvv/k+SvsGAJ5bWUs8GmH//v2IosjjTz6FbRq0trbOeP184zjn2sTQdSRJRpJlLNNgYmQYTA2vSyapWWSzOUIVQRRFnWnkT+nNsNDru7u7m49OnSRcXc2ZzvM89dRT1NXXX5cunaKEB46DNjY6ypXu7lmN88JnplBjMD42jsdI0Ds0ylvvfsjnPvtZGhsbbxo9sm2b4dExMpkMP3z5JVRVXXBDvsUyNDREXZ2j3hWPxyn7mAtfF8p9Wyx85ey9KRZuX3PfnIv7gZIjcId4/fXX+dVf/dVi8XBnZ+ddST8o8fFTMPCeePxxp1tmba2Tc36HjGFTz2FqGorXt2jpyUgkwsGDB1m+fDldly+zZfPmYhHjq6++ytYdOwnVNeESbVySyPjoCAcPHqR12+NEP3yfJWuX3DMpvEgkAjj1P7eSs3+1p4fY2Ajl5WW8+c67M77sTd3JeZaUW+sNcCtMNdgaGhowTBPTNJElGUm6PQfKzK/gygs8Htu2MYf70C6dQO85C3pu2vNSdSPKso2obWsRXE79gG1ojiSoaSCVVRbViLq7u+m6dAGv20PnxUsz1Kvmm8NCHAItm0FxuW5JerVAd3c3siQhybKjhjMLBw8e5MiRwzy652GGolF8ZYEZikBXe3oQBAFN16lubOHScBLJNgmLGV778eusXbtuxmvmwzRNtHSK1/a/xXjNSiS3j7Bq8cy6VgCuXLnC8PAwDY2NVFVXT6sPmO9Yo9EoDfX11NaGneiQct35MXJZxiYmeHP/Wzz92KNE+iP0RgapCIXYtWvXgud+M6719TrqTzYzmpJZliNdWogCjAwPo+VyvPTyy9TX17Nr165iWiM4tUGCKCLmo0SdnZ3UqTojSQ3B60SfFFVd0ELMla5LyJLEtYEhDh8+NMNxvZP80i/9Ev/4j/8IOMpCU7tc3y/cr47ASHfnPXEEqpesum/Oxf3ALaUGHTx4kFdeeYVTp04xMTFBRUUF69ev59lnn2XHjh13eo6fCJ566ikikQgXL15kxYoVfO5zn+Mf//Ef+Zd/+Rf27dvHvn378HoXprNd4v4iGo2STCZ558ABHtm7F13XneZRtg35L60b+0IsFFPLYRoainf2TqwLmVtfXx/ZbJalHR14PB40TUPPZXh42ybqK30YyQEik1nWPbCeMp+HqmAZw7pCaOt6Vixtu/kgd4FIJMKVK1eQZJlzZ8/eUj794NAQV7uvsGblcpLJ5Izuu6IkY2g5pDsj4rMgbmxkpcgysiRhGCY5TUOWZYYGB28puiRKEolYgnPnjy8o3UEQBORwC3K4BXvrM2hXz6FdPI4VvQqAORLBHImQPfQa6YoG0koFDevWopRXIgVrp0UelixZgiSKuBWJhuaWBadaOAXBTrTHtCw0XS92o52aziXJSr5oePFGlG3bXLt2jWuRCA0NDRzYv5+9e/fOugIcDodxuz3sf/sAzz75OOXBihnbtLS2ouWyuNyOc+S30qQkHyO2h2eefBJRlhdcA9Hf30+wzEcmqxFavoFYRkLE5uEV19/3qWIAhmGSzWm4XXN39S3IgG7ftg1ZlojF4wSDFehT+otgGgiSzJrVqzENnbff/YD29nZC8+To3woNDY1Ypo6sTo/mTH3fC71ostksCAJPPf00E+PjxONxamtrkee4b65evZrJq+dxqxaT2Sw//NGP2LNnz7zXjG3bmFqOimCQF3/wEo/u20cgEFhUH43F8r//9/9mYGCAN954g1/4hV/gxRdfvGtjlShxN1jU8svbb7/Nxo0b+dKXvsTIyAiPPfYYv/Irv8Jjjz3GyMgIX/7yl9m4cSNvv/323Zrvfc/y5cv5wQ9+QG9vL9u2bePv/u7veP7552lubuYv//IvMc1bKKwscU8p5OiOj4+z/6238Hq9CICiqiiKgoCzWqtrGlouh6HrRWm++TC0LKahOzUBt7hSXJjb8PAwXRcvIpgaZJOEKysIVNZwtrufXE6nMeTn4vlzpGKT+AIhPC6F2nuUvmZZFoqiYBgGhw8dIplMEo1GF72fcDhMc1MjQ8MjsyqdCKLo5EDf46CnIAgoioxLVYlNxshpGpcuXeLVV18tRkUWQk9PDxcuXmRwYID9+/dz5cqVhc9BUXF1bMDzxJcx9v0HxmpXYnnyq2GmgT06BG+8yJW/+R9cOnp01vSj5paWaXKbi0USRVyqmneMDDRNK6aRiJJU7Nx7MyzLzjtWOplsjkwuB4JIOp2mf2CARx9/AsXlxphlf4XOxlu3bkVyeQgFAxja9EiJIAgIXBesWNXWiNvMYAgycaWcylAII5e96VwjkQh6NkP/wCD/+sNXuZZ1Ut/W1Abwq7OvwcmyhCxJ5DR9zv1Ho1Gy2SzBUCUHDx1mcHAISZJQZBlVUfK1NhJaLodtaJw8fQafz0d7e/td0deft6sg0xtovrV/P6/+6EecPn0aVVVn1BPMeK2iYuQyvPXWWzdVM4pEIowMDRJPJKioqubRRx/FNEyeeebZO14jcCO/9Eu/BHBHmmr+myLfO+Zj/SkVC89gURGBr371q/zlX/4lu3fvnnOb9957jz/90z/lkUceue3JfVJ54YUXOHPmDG+++SbPP/880WiUv/3bv+W3f/u3EUWR3/zN37zXUyyxCG4s/KuoqMA0TXRNQ1YUJFmmkN1eCIlbloVlOIW2wg1do7u7uxEsg4pgkEDl7aUYNTQ08NnnPoNgGfi9HhSPF0l1o4oS48kIVeFalGAAt5VGiKd58aVX2LFzJ8G6JvSbdQe7wzhKKwZW/hxdvnwZWLhc4Y0sWbKEUJmX3sgg7ctWzPplL0oytmkiLFBR6W4iCAJDQ4O4FIWqqioGBgYWpdcejUY5ceIEv/CzX6Cn5yoT4+OwyLznoegwgqBwcBJWrNhHe9BNtvMwsfffQwXcmSS88k1GxgaofO7nEd3Xo5iCIKBpOucunqS6uvqWa2VEUURV1WLKkG0YReN1cGCA3r6+YsTDtm3M/PU0Nc1EEkUkSSwqxBi6Rl9vL8lkkgudnTz19NMYpuMsSJLo7D+/8jx1Bd62bYxsGlMDaUpjs2QqxfDVXvx+P42NjaxprSs2HBtImTQF3OjZNBOxOJH+gVmjO8lEHEkQyOU0wmu3MG478rvr6uZPR1AUGUvT0XQDlzoznOVENdzE43FSeSnRqdEV09ARJZmG+noqfG6QFVavW39XapsWQ8EJm021aC7KgpW4MNmzZ8+8EbT+/n4mRkfoi0SI9A/wzDPP0N7eXlQWu9VeFQvlscceo6GhgW9961v82Z/9GVVVVXdtrBIl7jSlGoGPkV/4hV/g29/+Nv/zf/5PfvmXf/leT6fEbWJbFrquI+ZD4LN90RTC4gXnIJNOMzQwgCjCgfc+4JFHHrmlIjbbsrC0LKaeRRAlJNXtSHDaYJgmumlhmBaSKDDQH+HIoUOMDDur7lu3bmXV+ofIGiZ1gbufrlbMEzcM5LwykCAIt12Abds2WjKG6g/M+SVvmaYTdbkDhai3Q+Gzkk6n+OGPXmV4eBi/37+olKhCSkiZ38/uXTspLyvD6/UWddkXwvHjxxno76ehuZlLFy7Q0dGB1+2m5+Xv0D58Bdm+3jhOCoao/sIv41vjdKqORCKMjwxz6fJlxsYn7pg8asEhSKVSpJIJPjp9hoaGRppbWvC43YiimC88FeZNvyvk2k81MguOhG6YWLaFLEko+bSkQp59OBymqS6MKMlIqqvYJKyhvp5znZ08+uijNDY2Ytk2l0eSxLIGDeUu7NQEqXiM6Ogoo6PjbNr0ULE+Z3xsjNbmRv71pVeYzOgENj0BgsDWxgDlRuKmn3vHgNWRRJFodGjG9leuXEHTdCpCFdTe4ERrmTSK242ZyyKIonNvuEtYpoll6Mh36fqycmmssWtIdcvmKTY3iY+P88ZbbxHpHwCY1mFd0/W843hnVKnm4r/8l//Cn/3Zn/EzP/Mz91160P1mDxVrBK5eorz8Y64RiCeobl1235yL+4F7v0z2b4h//ud/xjRN/uRP/oRf/MVfxO2+t8ZJidtDEEUUVcUwDHRdd9KEbviyKhguhRW748ePc+nSJbbt2MnK1WsYGR1dsCNg2za2qWNpWSzDQFRciJ4yDAtyhoWeyyGJArIk4lZkZLdjcEexyaRTgLP6XlNTgyyJ6Dn9JiPePpZloRtGcRV46vm5MZ9+0dhWXu1kntQCUSxqod/NFcG5sG0b03BSxWRFJVgRYu/evbfkAE1dUZVkBbfXiyBK6LkMoighqepNi20LzcIudXUVIzGNjY3Yws8z2ttDzbkPEa85KUfm5DhD3/gf+Ddup/Jz/55oNMqpUyfZt2c3oigh2GZx9fl2zq0oiqiiyEddXaTTaQRB4NTJE2TSqUUVeM7WeK9QjyDnc9UN0ySr6WSyGXp6e7l86RIA+/bto7m+FjQn8nL58mUEUWTzli2oLje6YSCJEu1Vfi5EE/THc0jJNO+8/jrh2lqWLFmCy+NlfGKSzs5Olra18sb+t9ixYwen4wJxBAIumTqXyaWrET748EN8Pt+czpQgCLhUhclYnI8+Os2FC+enOY5TV7unUlzXs8EyNBRfYJHvxv2FIOdrRkwd5Ov1IwUnrqW5iapQBWlNZzIWB2ZGGGVJQteNu+4I/Nmf/RkAn//85+/qOCVK3GkW7Qj09fXddJvm5uZbmsynHVEU+cM//EM2btzI7/3e7/HXf/3X93pKJW4TJ/9bwTJNNE1zUhDm+cIJh8OcPn2aH778Ehs2PsjqjRsxTBN5ntfYloWlZzG1HAgCtqSiyyqGaSNYJorspEj43LMbxR0dHdi2XdTc7ujoIKMbmJZ91wxkR9PbANtGURTEuzCGZRqI0vy3sIJMom1bCMLdNQSmYts2lmnkoyAKiuu6k3g7DtBUY1fXNGxAcXuxTAM9m3GiU8rcDsGNaW6FeaxatQpWrcJ+8mmSx95n9Affwko5zbSSJz4kffEMVQ8/i6EbvPjSDwkGg3zmmaexTQM9n2MvSs5nf2AoekuOTmVlJSdOnCCZTN5yuth8OF2HZRRZpuvSJVasXEVFIIDH48Hn92OJMpahsbR9CROTk4yOjHDixAn2PPwwAgLxeBzdMKiSYciS0PyVbNm1B68scPToUZqbmpiYmECSZQ4dPcrg4BA1S1YSJwjApqYKuq904vP78Hg8xdqYuc6RIAhc7emmubWFCxfOz9i+sMgwtY+Gc01ImFoWSXXf8Wt7tije7aYUzBsZzDcmsw2t6BQUImMrly8jGY+TzTld1Wf7XAPXz83H0HkY4Atf+MJdH6NEiTvJoh2B1tZWBEGYUchUuOEIgoBhGLO9tASOEsJf/MVf8Ou//ut87nOfY+/evfd6SiXuAKIkoYqikypkWXNqXd+YJxso85PVdXTDxK0qdHZ2MjY25nTDXd6BmctimTqWqKChgiAiCxKqLOKVpQUb2MuWLWPZsmXFv5X8F6Jh2SjSnTMWbNsudpyVF9Eb4JbGMg2nx8FNECXZiaCod9cRKKxSNjU2UlNd5XwmXHfeGCsgKwq6lkMwBSRZcY6z6BDIeYdg5tjzOSKCIFC2aReeFesY+/43SZ74EAArlcB67ds81racyYcepqZ9GTW1dcXX2baFZZrEY5NItslIdIhjx44tKn3oVnLIbwXDNGlqaebHr73G4MAAfr+fxx57jHBNDbatIJDmoQ0PMBFPEggE8Pl8pNIpeq724HG7cXs81JdX0Je08VfXM3LxJA/v3lU8zg8//LDozAzLQTCgrsxFU8CNVFtLTtNIp9MLcnbC4TCJ5PVo3o3bS5KEad7oCMiYWuaORwMKksSFY3vmmWeor6+7+QsXsE+P18vAwCCqqjrdh22wnf8hiApGNoMlOI6Az+/nkUcexeOSeesn77B8hVMfNN/nWpYd5S5VvTuOQDweL/77bkcePlWUOgvfFyzaEZiYmJjxmGmafPOb3+RP//RPWbp06R2Z2KeZ//yf/zN//Md/zDe+8Y2SI/ApohAdKBQSK3M0NboxhcGjqhimyXgsTjQ6TLDMS1NNiEwigSWpiIofWZYokyRE8c4YlZLoKKPopoUi3ZkbYyENSBJF1FnSpO40lmGgqJ6bbidKMrqeuatz6e7u5uCHH7J2zRrGx8fRDYO2GzrE3mkEQUBRXei5LIIoOOlB0xyC9LwOwXzIZQHC//438T+4g5Hv/S/MyXEA7J6LBPuv4n/257Dr64u1CYIgIskiV6728e677/Jzn/8s585fWFQxNMye3nOnKESpDNOiorycLZs3z3A6BEFA8fjwCQJlZWVIilNAfPbMGQ4cOACALMts2ryZsZRG26r1hEKVhCvK0eLj1IYCfP6F5xgcHiHtruBszFkw29xUQX9/P7puoCrKTYtfC1RVVyPJ8pzbS6JIztCQbcf4tC0Ly9YQlYU7oHOtyBfrm2wb27KRFYV9jz3G0OAQhw4dJBqN3rYjEI1GqautpaGhkfHJCRKJBKHKSgSnqYCj3qS4ABNFde4pA8kkb7/9EyRJYteOHVRVV900simKYlFe9W7cl/7hH/4BgOeff/6O77tEibvNoi2AQCAw7eenP/0pu3fv5hvf+AZ///d/z8GDB+/GPD9VCILAH/3RH/Gd73yHCxcu3OvplLiDCPmmSbKioGnaguRiC3nMI9Eh1qxdS01NDYdPneWjC5coKy/D53HhUuQ75gRcH1PEMK2bb3wTbNtG03UM00RVlHm7yN4pCkbKQopkC3Ox7ds/1rnmgmXwyMO70E2T/W+9xdDQ0F0Z60YEQUBxuZw0ofzxCYITIVDcXgRBRM+mMbTcLcmo+tY8SPMffI3yHY8VH7O1HGPf/yb9f/VHaEPT5U/D4TBer5fhkVGWtLXekfSeQvHuYqRWb8S2bbKajmXbePIa/e3t7Wzbtm1G5EEQBGS3F9MwMPNN2MLhMO3t7bS2ttLS0kKgvJyljbUIgsDFq9cYGIuhlAURFReBQDntLU10Owv5tAddGLER3vjxj7Fxmk8uNG3KMC1qqqt58MEH56wlEAXRuR4sCwQR29CnKSDNR2FF/vDhwxw/fpyR0VFymk42lyOnaeiGU18jigKmYfDW/v2UlZfT1Nx8R97bcDhMR8dSjhw9QveVK7hcrmIksdCZWFBUJzUofx0XIkerV69GcbkIVoTQclkMfW7J1cI91rhL8t0FJcWCHG6JBVKICHzcPyWmccuqQe+//z5f+cpX6O3t5Y/+6I/45V/+5Y8l/26h3G9V8jcyNjbmNEyZnOQb3/gG/+7f/bt7PaUSdxjbtjF0pyBXnmOFvJBKY5kmQ0NDCLLCd7/zbfx+Pzt37rw7ut95hmJpFEmk0r/4ovXCKmJLSwtl5eV5+ceP7/q3DB1Ty6F4/Qva3tC1ooF8Jygcf0N9HaFAOZPxBPvfeps169Zx6OCHC+68e6ewLBND0xgeHZuZw23bWIaBaWjFCEFPT09xu4XOM3PlPCPf+Qf04cHrD0oSFY99lorHXihKtEYiEdKpJNWVlVRULb5j9FQikQivv/46jz76KINDQyxpW1LspLtQLNsmm9OQJQlVWXgQ3LZt9Gya0bFxenr7sG2bzs5OADasX09Hexv9mgufkaClsX7anD4ajHG8P4YkwGeXV2Cm4rgUhXgiQffVPqpqwnSsWDFvcbdlWeR0HY9rfqO+kIon2hZWvnh7oSo+nZ3nwLKxBIGxsTHqamtZunTpnOpMkUiEiYkJmppbCAbKsSwTU791VS7btknEY3RdvjKnc2Rl4lixYeTaubMNnKJ8A9M0HElnSZ4R6SgUV7vmiNTeLs3NzVy7do1vfetb7N27F7/fTzAYvOPj3Ar3mz1UVA3q6743qkHNS+6bc3E/sOhv7rNnz/KZz3yG5557jueee47Lly/zq7/6q/eVE/BJoLKykq6uLh599FH+23/7b6W6ik8hTuqGiihJ6PnmSd3d3Rw8eJDu7m5HbSiv+qGoKvWNjUiSyKZNm+66EwCgSCKGtfh1gEgkwocffkh5IMBHp08zMjz8sV//Vr5z6kIp1AncCSKRCG+88QamliWTSjI2GaOmto6t27ZSW1f7sTsBAKIoMRmLk4zHOXDgwLRmZYIgICmFCIFAMjbBQP8ABw8eXFRjMk/7Shr/61cJPvaCU8QJYJpM/PhFrv2P3yd7tQtwahA6li3H53XfdjO3aDRKPB4nNjnpSMFaTofmnKZhGPM3IItEIpw5e5Z4IomqyItyAorjj4yhyCJNTY2Ul5ezZ+9etmzdysjoKOfz0VxJdaGnYpi6cy1ndJPTg07O+OpwGS7VBS4f//rqG7z+kwNouk5tdSV6YhI9MYGRSWJqWWxr+mr12PgE13r7bhoJEUURy7QwDR3bNBYUDbAtCy2Xpb6ujuMnT/LGj3/MhfPn8Xq98zb5amxsZO3atXg9nny08/YMatuy8Hi8c0Y8IK8cZJkzzs+0bQQBWVFQXW6nVmVygvffe2/atVBQb7tbq/bHjh1DVVW+/OUv09TUREVFBV/72tfueUPDEiVuxqK/vdevX8/hw4f5zd/8TTweD//wD//A17/+9Wk/JRZGWVkZf/AHf0B3dzdf/epX7/V0StwlJElCURTisRi9V68yOTGBIAiMjIygqGoxlaaQBrB79+677gQAt5waNDExwQPr1/PGj3/MqZMnb6kr8O1iL0AxaCriHewyPDoyws5tWxgbH+f7L/+QgUEnDai9vZ2ysvKP1Qlw1IlMdC2HJIkM5d+L2bo1C4KAICukcjrBUIg9ex8hlUoxPDy84PFERaXy2Z+j8b/8Ka6m68epD0Xo/8s/ZPT738TKZfNqTdK8xttCKHTOfve992hpakIUwO1yocgyNjZaPo1F0/VpnYnPnTvH6NgY4bo6Xn/1RwwNDt5kpOsUInlaLoth6PzglVfRcxofffQRV3t6uHjhAlu2biVc14CATXR0gt6BKKaWRU8neOfcVXTLRhVheciDLMtUBIPs3buXdQ+sZ+mKVZRXhVHLQ8i+cgRJwTYNjFQcLT6OkU4wFh1kfGyE119/7aYdqAVBIJ1Oo+WyJDPZOY34SCTCiRMnGB8bRddyyLJSlLPds2fPogq7FUVGN0y4zevJNM2bF9dKedlQ4+Zyx4W6mb5IP2vXPQBMvxZkWSaRTN12qtls1NTUMDExwf/9v/+3+Njv/d7vlRb55sEWBGxB/Jh/Pn4Z6TvF7t27mZycLP79yiuvkMncfv3bopdIdu7ciSAI/PSnP531eUEQSp1zF8HOnTv5/Oc/z9/93d+xbNkynn32WTyemxdAlvhkIYgiV7q7aW9v59y5c7z00kts3bqVurrrxXamaeK6hVXLW0URRXTTWlQBnW3btLS08PLLL5PL5e6KzONC5mDdRHJ1NsS8YbqYSMLMsS2WtrVw4N33uNh1ecbxC/n53fVCacvCNAwsy3QabkkymWyOi3ld/NneF8M0MQwDLZfjg/ffY9mKFTz+5FN43QvLJ5+Kq6GFht/+b8R++jrjr30XW9fAton99HVSZ45R/bO/gmvpqmKqyq0yVe5UcbmorAjmc9ad/HHkQsM6G9My0Q2DZDKFKEm0NbfwyssvsXzZsgUVLdv5pneWZSLJMqrLjZG/Pt7cv5/PfvYFIv0DlJX5eefHP2Lfrm0kyHL5Wh/Hey+zc+dOfB43Qa9KNG6hJEbp7Y6zZs2a4rHcOAenGaAEOO+BbVvYhoFhTFBd7uPLX/wsw6NjWLkMlqEjzNKzIRKJMDTYz8ql7SSSKRTXJO5iOpEAAmTSGWITEwTKyzh+4gQrV64qzuVW5Gyd1XUB07K4HQFRyzKRlfnT9QRRBFF26gQW2Byttq6OkydOANOvhYH+fmLxOKdOncIwjDvWFK+A1+vl53/+5/nFX/xFAPbu3VvKlihxx3j//ffRpvQO+dKXvsSpU6due/Fp0XfognJCiTvH1772NV544QW++MUvsmXLFl588cV73g6+xJ3FNE2Wtrfz6muvMTg4OMNQsyxr3q6pdwNZErBxcqilBYxbKAr2+f233BTrjmBbCOLiz5Uo52VEb9EwtW0LPZPG6y9j/cYHqWtonHH8gigs+HwuhKl5zvX19VimgWk6xr8oydNqT+bqEeC8bwYC4FJVmpubizKdlZUhgsHgLTkvgiQRfORZfOseYuQ73yDTdQ4AY3yEwb/97/g37aL8ic8hVd2ehOpUQ9XQdUxDR1auN5cSBAFJEpDy6lcnjh9n+arViILA88+/kE+/s6fpyE/tKtza0oJh6GCDpEw/p4UeHOlEjEhXJ+2NtXgVDw81PUQ8m+by6TNcvngV27YZGxsjVx5gTAphIxDHxej4TJW9ec+p4BTHqr4Ar7/+GsPRIVqamnh41w7MXBrbNJ0merKCKCkIskx/fz+tDbWc+Og0R46fZM+ePWzcuHHafjvPX+DwoUOk0mls2yYUqrzt61aRZeLxBKn4JFndWLQxYi/inifITsHwgvZr2/j9flauXEkoFJp2LUSjUaJDQ1RVVXL58pVFq1otBEEQ+I3f+A3+5m/+hnfeeYcvfelLfPvb376jY3xqKMmH3hZ3Ku2s1Fn4PqCxsZFjx45x+PBhnn32WVpbW6mvr+d//a//xWOPPXbzHZS4bykUsVm2TXkgwJYtW2bVSTdMa9Er3LeLnDecdNNmIQqiRl4aVBLF2+8KfBsspJHYbAiihGXlbmnMghMgqy5EWZnz+EVBwLbsW0i6nEkkEuHtt9+mob6eUDBAPDZJWXk5qjy3NOuN8zIty+l6LcvTUjCmynRqukEmpxXVdBaLUlVL3a///0gceoexl/8ZK5MGIHn0PVLnTpLY+DDyyg2szq+M3w6SLKNrOSzTnLVxX3d3N1XVNZiGwTf+9/9i5apVbNu2HZfLRTanIQDZbIYLFy6Qy2WpDdcwMTFORahy2sqtbdvYWho7m2RJQAKfn1RGITIURfEFCNU2EssmuRQZKRqetXX1BKtq6PzJO9hN65B85aSlxRfRWpaF2+1m965dRWclVFN3fV6WiW3oWHqOXHycjqZaFJeLE6dOU1ZWRjg8s5i6qqrKacyVn+udiOL19PQwMjxCuKaa/fv3s2/fvkVJv46NjTI5GcPt8dz8XiKrsEBHQNcNFFme9RoNh8PEJifRk/pdjWb+9V//Nc8++yxPPvkkJ0+evCtjlChxp7hl1aD7nfutSn6hJBIJvvOd7/Dd736XAwcO0N7eTmVlJdXV1fzWb/0We/bsuddTLLFAbNt2GowJwpwNxgqkszncqvKxh5H7xpOEvC787vnD84ZpYlkW6k3C+B8HRjaFIMlFjffFoGczyKprQbKjBW50Auadm2Fi2XfmPF26eJF0OkV3Tw99fdfYtWsXDz744ALnbDtzsSxU9eY9HTTdwDDNW3YGChixCUZf/EdSp48UHzPdPt5rfZAtu3YX02RuB9u20XLZWZu1nTp1irLyALGCJn0oxJo1azBNE8MwESWRyLUIkihy9WoPnZ2drN+wgW3btmGbOnY2hZ1NYudSYFugeBDdPobjaV5+9cfFZlqf+cxnqKoMEY/F6e7poTwYJNzQjN+tcvHCebrjOqNqJQBPLauhrnzhDkFO05AkaUELA0cOHaSjsYZYziZj2sQnxtmyZcus287bwfcWOHjwIIcOHeKpZ56h89w56mpr2bZt24JeG4lEyGXSvPf+B+Q07aYpOlZyAisdQ65pBaCzs5PR0VGqq6tZuXLl9e0sC103cLnUOfbk1PiMT4zj9fru6mJGKpWiKd9t+u/+7u/4tV/7tbs21s243+yhwnyG+/s+9vnE43FqGprvm3OxGERR5Jvf/CaBgNMs8Od//uf5y7/8yxkO7XPPPbeo/ZYcgfsUTdP4+7//e65evcrY2BinT5/mo48+4ktf+hJ/+Id/eNea7pS4M9j5lVjphpXYWbe1bdK5HD73rUnw3Q4Dk2k8qkSFd26jutAo7ONoErYQtFQMxeNHEBcfQTF1HRt7WmrJfCzGCQBnBd4wDFzqwvY/F4auk0om+d6//iuxWKzYyXUhhotlWWi6gSSJyJK04PdMMwwM4/adAYDkR0cY+Je/Q8qmcW14CLt1OeP1a1na0ohXXfic5sI0DUzDQFFdxX2ZpkkikeTll19idHS02DG4EHkrOEepdJrDhw5y7txZ2hrr2b3lQQIeGfSc08TK7Xd+XL5p9SSzGdJWvnlg2rTJpVP0X+sjHA7T0NDAjy8NM5jI4VclXlhdh7qAsJtt22RyOTwu103PkW0apMeHOHLyDCfOdLJtx06WtLcTrqr8WK7T7u5u9u/fT07TePYzz6EqMo0NDQt67fHjx7na082StiX4y/xUVFQQKA84KX+i6KRITUkbsrIpjLFrHLoyTGVlJRcvXiQajSKKIrt27WLVqlUA5HIaijK/lLGh6wiigHQbtSsLJZPJUFZWhmmavPDCC/zgBz+462POxv1mD5UcgVtjIQuFgiAsqH/RVBZ9JfzGb/wG8XicRx99lH//7//9Yl9eYoGoqsr/8//8P8W/TdPkT/7kT/jqV79KOp3me9/73j2cXYn5MPNFmYqysBV+w7SQb8GovRMokjCvclAhqqHeJe3txWLnu5zeap6nKEvouSwswBFYrBMATmqQdQuSrNfHLPSesCkPBnnyyScXvIrr9KSwMEzHaVtsdEmVZQS4rTShAv4HNqObItZb36bajqON9ODu2MZALIMogM8l43cpeFUJ8RbGkSTZkcw0DWRZyRdCmwQC5ezcuXPW9DtBEJBFKJMtdq5dyt41LYjYIMsILj9ioBZUz4LTrsApZM3aApahYWg5Dhw4UHTadrXW8oNzgyQ1kyPXJtjZWnnT49INJ61lIU6AnhjDXRakur6JTW4fgTI/FcEA47EEvT1XqKmuvqsr3oXGXsPDwwi2RUVlFbphosg3v5eFw2GOHTvG1d6+4vmqVBQs28K2bCxLv94AUBAZHxmhSoQlbS2UBysJBivw+X2cP3+B0dFRAEzTgrxE6PzY3JHcvQXgKcqsQjab/VjGLPHp5W5J3y7aEbhw4QL/5//8H/7gD/6g5Ah8jEiSxB//8R/T09PDO++8w5//+Z/zn/7Tf8Lr9d7rqZXIM7U52GIMZ9M0kRfw5Xk3kCWRjDb76kGhOFi5TyIBAHY+N/xW5yMIItg3V/a5FSfA2b9ANpvl1MkTi2rW5Yxpo2s5RFEqRiwWWotReK8EQbithklKvilYOqfhvU1nYPXGh7gWvQRDF1Ayk7TVBBBEiWTOIJkzGCw4BaqM3y3jVeVFOQWyoqDlstg2mJaNK58CNbX+wbZt0HPY2SRWLglaBgQRt8sHrhCm5MYSJcf4lsRpRcQ3e+8syyKZ1bAtm1Qizqs/+hFwXa6ysbGRLc0VvH91nEujKZqDHpqDc9+vI5EIbq+XbDo973tuWyZ6YgzJ5UNy+6elW0UiEfoi/WQzGY4eOXLHVXFu5MZznc7lEOCm97O5CtslRJjyUtu2wbYxUzFyqs3Js5cI19Zy8uRJNE3jM889TygUorunB6/XRyIeo6Oj46bz/rjuZoN52do1a9bwwx/+8GMa9RNEqVj4vmDRjsDv//7v8xd/8Rf88R//8d2YT4mb8Ad/8Ae8//77/O7v/i7f+973+J3f+R2+8IUv3Otp/Ztnaj2AsghDzLZtTMvCJd6b3HtZFDGs2fW5dcNAkqT7Sv5uYnyURCKB7IrdsoEjShKWaczaZbi7u5ux0VFWdCzBVxZElBd3i+zu7kaUZE6ePIkkSQsuoLRtGz2Xc1LJFjmmaZrFleSbarIvAMcZEEjnU1RuZcW+QHjpSrJDFxBMAz0xSSyrMzg4SDgcpq2ufopTkEXIOwVlLhmv6+ZOgSAICJLspGJNSaWxLRM7l8/1z6bAMkBWnXSfsmoEl7e4rcT11LdYPMWxY8e4du0afr9/xntXSA2qra2lpiZMVtOxgHKvi+Tk9ZXoqUWoHZU++iYz9E1m+ODqODWrXbiVme9RJBLh4MGDtLS1cfL48TkNeNsy0eNjSC4vkmdmV+2xsTHqGxrZ/+PXpjkkHweCIOB1uUjnciBw0xqHhTi5giBgmzo1XpGTXX1c6upiYHCQHTt2MD4+TiadYgKbiooQw9Eo77//HoZhTKsbuJGPMxn6nXfeAWB4eJjR0VFqa2s/vsFLfGp5++23+f73v8/Vq1cRBIG2tjY+//nPs3v37lva36IdgX379rFv375bGqzE7bNy5Uq6u7v5m7/5G77+9a/zcz/3c3g8Hp599tl7PbV/syymHuBG/v/svXd4XOd5p32fOh0dGHSQIAl2ihRFiVWiRKpb7o7rWraT9caJ1k6869j54thpTjbZjXdjx05sR/baceK1LEeyLaoXihRFikXsnQRAYFAGgzK9nPr9MUUACZAACBKghPu65mKZmXPeOXPmnPd5n+f5/aycJvp0rbgrkohp2dlxDBuDYWTlJq+3ktHlCAQCDPR2MxSJcLa1nfvvv39SkxxRljF17ZJAoLW1lR2vvsrmTRt4ZcdrLF22fMK9OMFgkCKfj6333IvD6UASJTK6XlBbGu17ti0LTcugKOqoSjhjYds2umFg2/ZVZQFGQ5ElBIFCvfpkgwGxuKLw93h3O0l3Ba+++ioej6cw2S1xqxim9VZQEH0rKPA6ZDyjBAX5en/bzp3D6QSYWnbyryUBAcHhRvSVZwMAeexSMFEUcagqJ44fZ9mKFdy++U4SsRi+oqKs0pCQXeVvbWvD5XSh6QZDkQiK002x24koCmOucAuCwIamMvriPaQMi9c7BrmzueKS7yoYDNLT08OKm1bi91ePOoG3LQsjNoCoOhGdI4OA1tZW+vr6mNeyiF07dxCNRqfF40MQBFwOB6lMVp1rKq4f1lA3oqLi8TeyZo2P8vLyEYaLJ0+cYGhoiI7OTjbfeRdaJnNlOdzrdLn90Ic+xLlz5/j6179OQ0MDX//61/nqV796fXZ+A5A3+bre+7yR+d3f/V2+//3vU1paSktLC7Zt8/rrr/Od73yH3/u93+Pb3/72hLd5Yx+RdzCPPPIIJ0+e5J577uHhhx/m6NGj0z2kdySWaaLpOrKiTGo11jDNgozndCBL2Tvinjf2cuLECSDb8GpaFvIEV6avNcFgkL1vHsTn9fCe++9BtAws05iwlrIgiNjWpS7DwWCQrXffQ3ugm7Pnzk/IcTeP3+8nlUrxxuu7eHbbNsxclsgwTZKZDIlUmlRGQ9MNenp6OHbsGPFYDEV1TCgIsCyLjKYjCuI1a+KWJQmHopBKZybd9yANCwRiXe2cOXWKlatuvsT5WJZEStwq9aVu5lZ4qPQ6MC2b3mia1lCcnkiKWFrnxImT7Nixg2BvEDsTR070w2AnwlAAKz4IigOxvB6ppgWpohHRW3bZIGA4xcXFvLZjB8eOHkGUZVLJBA5VQZFlgr29hEIhTpw4zquvvootyhS5HYjiW8e9vr6e1atXXzKBdykS65vKAGgfStE6mLxk336/H4fDwbPPPM2SZcuob2gY8Xw+CBAUB5LLN+L7zjftqi43x44eYWHLggk7BU8lYi4YyOg6xgSbFi/GTkYgHUMsrWPpsuWXuK4bhkFFZSV79+7l7JkzvPzSixQV+choGsG+vjEchG2uVySgKApf+9rX+PGPf4xhGPzpUSOV/AABAABJREFUn/4psVjsuux7lqnlO9/5DnPmzMHpdHLbbbexd+/eK79pinniiSf40Y9+xA9/+EP6+/sLyl2hUIgf/OAHfP/73+fXv/71hLc7GwjcwEiSxL/927/R2NjIli1baGtrm+4hvWPIrkgaGIaBqqqTLp8xp9A/IBAIjHHjG5vTJ09iGgZt7RfYuXMnp06dyn6mGdQXkMfv92PZsP213fz62RdQVAdGOomeiGJk0tjjbKQSBAFBlC55/Zw5c3A4HRw4cGDSq6nNzc3ZCeEtq7n77rtpampEkWWcqorH6cTtdKDKMuFwmIGhMHUNjQxGYwyGw+iGgTlKgDKcfBZA0w1URUaWr16B53LIkoRDVUhlMpNqVBMcLgSnB4ASyaKzs4PKykoWtLSMeXxlUaTYlQ0Kmis8VPocWLad7Skoraa8ph4tFUMId4OhIXpKESvnYpbUIRZXITp9E5KHzdPc3MyWLVvwuN1IAlRU+dENk+7ubgzDoD8UIpVOc9c992FmUkgT2MecUjeVSnZSvKutn4RmjHg+n1HYuHEjbqeDoqJidCP7GtvOBQGyguQqypbL2DaWYWBoGbwuJ/fdvYWaEi9nTh4nHA6PGpBcT4YHA525sqfW1tYJbcO2TKxwD4K7BGFYBiR/nevv78e2LCoqKti0aRO33normzZtoq6ujoH+ftrb2rGBV1/dMaFr4rXgk5/8ZEE+9KMf/ei0jmWWifPzn/+cL37xi3z961/nzTff5KabbuLee++d1GLR1fCjH/2IL37xi3zqU58acd0XRZHPfOYz/MEf/AGPPvrohLc7Kx/6NqC/v5+1a9cyNDTEq6++OiVa3bOMTUHZRRCQx6HwMRZdXV3IDgd6On3VN+1AIMALL7yAYZqUlpSwadMmKioqsio72UGP/DPH4OAgAjZDXRd4bucePvLRj1JVWTmj+gKGM5qEo22ZmLqGpWcQRAlRyTb4Xu57MQ0D2zKR1axsqmVZpDMaob4g3d3dE270vWTbto18GS+BAwcOsHv3bu6//wF27HiV29auY978eViWhWVlyxokUUTMlRS1t7fR3z/A/Pnz8fq841KWmUoM0ySj6YT6gvRM8Pgknn4UM3gBqXYeQ0u3EAqFmDtvPkVez4TOs52v7aJnMIJz7nJsUcab6GPLrTcVnrdME0PXUcYhvTkebNtmcChMd28vzz/zNDevXs3CpcsRLJMaf9WEtnX27FlC/QOcFsqxZQclksn7Vs4Zc5y2bZPWNELBIG5BR5RkiiqqsW0rG8AKAqIoIYgSnZ2dPPf889RUVXDrqptI2SJz507u3J1q2trbESSJwwcPkkomWbduHQ0XZTtGRRCwwz2QGEKobinIuHZ3d/Piiy9SX1dHeXk5FWMoIx04cIDt27dTUlLC7bffgSiJNM+diyAI2V6cSahqXS1tbW00NzfzW7/1W6xatYr6+no+9rGPXZdxzLT5UH48wd7eaZEP9VdXT+hY3HbbbaxZs4Z//Md/BLL3i4aGBv7rf/2vfOUrX7mWwx1BfX09//Ef/8Gtt9466vNvvPEGH/jAByYc+E7oDPzIRz5SKB8YixMnTsxGvNeZiooKXn75ZWKxGL/3e7/HP/7jP2IYxpXfOMuEsS0LXdMQJemq1HQCgQCHjxzh5PETbNu27apXrILBIM3Nzbzn3e9m48aNqA4HhmFkV3Fz9bJSbsyqouBQVRyqSiadRrBN5tWU8fEPvIdUMjljgwAYvfxCECVkhwvFU4ykOrEMDS0ewUgnsczRfwf5huE8mm4giiJNTU2sW7du0kFAdjzCW9KHY+D3+1EUhVdeeZnNm++kuMiHQ1FwORx4XE6cuSyTZVm5enQX1TU1HDiwn86OjuuerZEliYH+EJKscPz4cV544QXOnz8/rveKxZUAWJF+6uvrWbVqFT6Ph4ymT6isq6K8jKGeTvpOHUIQBBJeP+cHEm/tR5IQJZG+YPCSFejxZsuGv043TGygq7MDQRAp99eQjMcnHATYlkV5kQdTS3NThQuAsClxKhS/7Pu6u7pwuT2IkkxHbz9d3d1IigPF6UZ1upFVB5IsM2fuXLZu3UpldS2S6qS+qvyKn3cy2cOJYlsmomXgEizqaqppmjMHcmVyV3yk4tjxASxfJYadFS7QjexvdMWKFeiGwUsvvzyivGw4fr8fr9dLOBzmlVdepri4mIymY9k2iWSCo0ePXPcswZw5c/B6vTz22GP88R//Mf/pP/0n3njjjes6hlneIhqNjnhkMqO7zmuaxoEDB0b0xoqiyNatW9m9e/f1Gi6QXfS93KJhfX09AwMDE97uhIqA3/ve9/LQQw9RXl7OXXfdxaJFiygqKiIajXLq1ClefvllBgYG+Ou//usJD2SWq6OxsZGvfe1r/PCHP+QLX/gCTz75JD/5yU+ora2d7qG9bbDy6ixTsJoUDAbp6+3l9g3rePPA/qtW98jrcu/fv7+gy11cVJTNCOQelmFgDp94CQKVFRUcO34Cs6qUClXHVK6/qdlUIQgCgqwgykr28+oaRjoJto2kOhAVNSsfmn+tIGBbFqZtY1kWzsu4kU5sHOIVJ7jDm0tdLiclpWUjGhxFMbviCxJvHjjJ66+/jt9fzfKbVuD2+jAME0m6vk3mXYEA6XSKe+66gwNvHso2p46jmVoszurn24kItq4hKGrW7MyS0HQDhzo+xazFixdj2zb9/f04SBDEw/FgDMOyaanwIAgCHZ0BJFGgoqKcZDJJd1cXTqeTvmBwhMb/aL+1QCDAtm3bqKzyU+mvJhKNkEmnudDezrqNm9A1DafPM6FjZpkGRipOMqNz4tRpzBMnmXf7g3SlYF8gTF2Rk6Jhrt7Z0i8T3dBx+Uro7O5h+4vPYds2a9asoWnO3FH3k5fxtG2baH+QSKiXV1/dgc/n4/7776e2ro5cSpCenl5e3bGDYG/viKbtqSB7ndExdQ2wUR1OfvPMswVTPH9V1RVdt23bxhoMgupCKqoccY6bpsnevXsLDs9jlZdd3LxdUV6OZVnEYnEi0Rg7d76GoijXtY9CEAT++Z//me985zuFCeTwfod3IrYgYF/nRY38/i7OTH3961/nz/7szy55fX9/P6ZpXnKu+f1+Tp06dc3GORqapqFc5vcjyzKapk14uxMKBD7ykY/w4Q9/mKeffppf//rX/NM//RNDQ0OUlpaycuVKvva1r/HAAw/M6BXFtzNf/epX+epXv8rjjz/OJz7xCerr61m7di1/+qd/yv333z/dwxuTiWh3TweT9Qe4HH6/n/2aRndPL2tvveWq1T3GUi0Zi7zc6eDgIJlMmoQl0N/eSXNDLbZlTarGeiYhCAKS6kBSHVimiaVn0OJRRElGUh0IkowoyQQ6OykqLWNgYIDmuXOmbgDjWOkeLp9oGGah7v/i8yu/uhkM9pJ4Pc69992HaVloho4syTmFn2t7M7Vti0Ut8+no6OSlV3Zwz52bcLo9V1Zn4a2MAIAVHUAqrwGyWvMZTac3GKQrELjieRsIBEilUoU+jO5omgOBMKdDcQzLYkmVj2AwSHFxMXMaG+ju6eHEyZPMmTsXjy/bYHs5Sc1gMEg8HmfrPffy/LPPsmrVSlavXs173vcBjJwfwXgmjfnytYa6Goo9bhS3l/rGEu68805CoRAVqkbEdBHXTJ49EcAf76SmuprGOXOwLBtZEjERsSyT/Xt2Yds2Xq+X8vIrG5IJgsC5ji7MTJrf+vgnsS0LRVWJJtOF9lhBcXDb+o20njvH0cMHr2oRIv9Za2uqqSwrwzJ1RElBdrgQJYkat48777xzVIO3sbATg6ClEP3zLzm3JnKdu1ieVBRFzp87S3d3N5qmkclkrqu8KsDHP/5xPv7xj/P+97+fJ554Aq/3UgnYWa4PnZ2dI0qDHA7HNI5m/Pzpn/7pmP5RyeSlQgTjYcKyIIIg8OCDD/Lggw9OaoezXHs++MEPsmXLFn7961/z93//9zzwwAN8/vOf56//+q/xeCa2onWtyStelJWVEQgEEASBOXPGrp293gzvB5iIP8CVGH5DWzhvLm5f8ZRsc7w3tbxRjyCKnD59mpraOvadPE/L3EasaB9isX/GfAdXS7ZkxI3kcGGbOqaWwbYSDEViuF0OBvr7eeGF59mwYcOU9NcIgsBEG69kWcLWs5KYijLysjzcwdXv9zOnqQkA25bRDZNUOpMt+5KvjeeDaRiYeoai4lK8RVEWLVlCBhl/cTFGPIzsLkK4TMO7VPKWcpAVCRUCAUEQ6A/1EY3HURwOBgaHcLrd+Lw+BCGXtUEAAUJ9IV55ZTt9fcERq/pyYyn7Ooc4P5DEMG2qqvy0tp6n48IFFi5cSF1tDdWVlex+Yw8APp9vzGtgPuA6evQIt65dS1lJMRndQFId9LSdo7KyctT3DSefVZjbWE9dZRkDpk2trwSAlpYWWlpaACiOpXn6dB8p3aQqOYCuV9DZ0cG85maSmoFl2dRVVbB+/XoGBwcvkcy8HH6/n23btrFz9xuFY+UvLyk8Hxsa4MDeN1i6/Cbazp+d9CJEIBDg2OFDLFwwn3QixpAkUVF16XVjuOnYlbBNHTvci+CrQFBdo75mIte5i6mqqmLfvn2F4Op6y6sCPPnkkzzxxBMA7/hF09xt6LrvE6CoqGhcPQIVFRVIknRJGVreU+R6cvvtt3P69OkrvmaizCx9wFmmjNLSUh5++GE2bNjAN7/5TX7wgx8QiUT40Y9+NKMmeMFg9sa+ZcsWzpw+jUNV0S9KbeXLOMj9Wfh37rlrRdbpVZuUP8B4yN/QLMvESKdQXJ7r8t3kgxtZUaivr+f+++/H4XRx733346isxOzvxFZdCO6rD05mEtnSIRVRVrFti6ELAaqrq3j++edIJBIMDg5O3b64snvxxciyhJaTXLxYSWq0yZQgCKhKNiNgmlk5UUEQcsZiVz/BsG0bQ8uAbaM43Ze49gJYegY9EUFyepDU0VfUBE8JSDKYBmakn+GJ7WAwyI4dO3A4HKiqiiyto2RRcdb9may/BTmlpHW3reE320YaZVV5HaxtKuONjiEuhFPUFZVRV5dh584dBLq6+OAHP0gskaB5bjOrVq4k2NfHuXPnUFWVZDrDQH+IqspKFi9ePCI4r6urIx6PE47FsVIxjp84TiKeuGIpSTAYZMWSRXjcbh7/1VOsW78+W5ZzEdU+J3MHTrK4dRdiIsKuY0cov/VOahua0A2LYk+24XkygemVVs3r6+vZsH4dqts36dIYyzSy5m8+Ly9u30E0FmPz5s1U+q9uYmSHe0GUEIom1ocxXiaaOb0WDA8+Pve5z/FP//RP130Ms4wfVVVZvXo1L730Eu9973uBbLPwSy+9xCOPPHJdx7J9+/Zrst13djj6DmD+/Pl897vf5c/+7M/48Y9/zOuvvz7dQxpB/qLY3dNDcXEx6UwG1eEoPBRVRZLlrOkW2cY70zTRdR1d09AymexD09A1DUPXsyuYpol1BSnGy2GZJpqmTdofYCKIooQkK5ha+pruJ49lmgg5RRrI3hy9Xi91dXUIigOxuBIrEsLWR2+eejsgCCJOj5eu3lChiW88pRcT2f7E/Q0EVEXBMExMc/xSnYIgIMsSLqcDRZbRDYNUOpMz3Jrs+W+gp5PZpnina8yARlQcKN5iLC2FkYyNuj9BFBF9WR19K9I/4jm/34/b7SaVSmGaJsXFxciShCxLKLKMqsioioIogKrIWJZ1yUpuuVtlfVMZqiTQFU3TbXtYtHgJDz30EJFIhMFwhEBokINnL5BUi/E0LaXb9jGgVjDPoeG1koQGB0mk0pSWV7Bw8RJkWcbjdpMc6OXChQ623HP/CO+D1tbWSxqSbdtmwZwGinw+XtrxGoqqXnbFuabYjZDMasrfIkSor64ioxsUe67OzRnG9jQY/ryqqtSNEqRcDtu2MdIpjHSKjGFx7ORporHYlKyu2+k4djKMWFqLIF67a+6Vjs21Zt26dfzLv/wLAP/8z/886d/oLNePL37xi/zgBz/gxz/+MSdPnuRzn/sciUSCT3/609d1HM3NzZNqBr4SsxmBdwhf/OIX+fKXv8wPf/hDNmzYMN3DKZCvGXW5XIiieMlkbPjq/1jkL6T5plhsG9uysPL/HrnBEVkF4aL/CwQCaJpGeXk5RUVF1y17IipqVuXG0BHl8TVPTgbbtjFyvQ4j9i8KWJaNJAkIriIELYU51INU0XjD9wuMRW1NDedbW1m1ahUNDQ1T27gnCJPKeQuCgENVyGgagjDxpnRJEpEkFcvK+Q1MsI/Atm1MPZOtLXe4xvXdC6KE7CnGzCRzpUK+gtxjHrGkEivcd0kgMN4V2vr6euKRITZv3jzq60pcChvmlLPjXB9JQUUuaaQ9bmEKHnTVA1UVqEASEN1gWQbOcBdVxiBWeIiEtxjR7UEUs+pa+/ftY9euXdnPJwjMXbi0MNnNlzPW19WhpdO4HA4qqyowM2lcbg/l/hru3rqVsvIyampqCtegi4//orWb6I90YR3djcM2cZ7aQ/H8hVcdBIwXSRSyxoHjWOgoNAFr6axykcNLtcc3Zavrtm1hDXWB04fgmn55y2vJvn37OHfuXOHfp06dYvHixdM4ounDsnNZv+u8z4ny4Q9/mFAoxNe+9jV6e3tZuXIlzz777HUvLWtvb8e8SpO+0ZgNBN4hyLLMvffey89+9jMeeughHnzwwct2n19PmpubyWgaqqKg6zqWaU7IZXW8ZUIXBwwXP9LpNIl4nFAoxIsvvsgDDzxwXRUlZIcLPZ1AkaSCus1UY+j6qN4HgiBi2RYSWSUasagKc6DzbdcvkMeyLARBYPHixei6TmVFxZXfNAGyEqKTu8HlMwOaruOYZF+KKGYDCtuWMQyTVCaDJIoouezaaFimiaGlkWQFaYJa/IIgIDs9WJKCnogiOd1I6lsKVGJRNsC3IgPYtjXi/B5Pzbdt26gOB6tXrx7zNT6HjHuonSFnJZVlxaRtEQyDdDyCraWpKPZSU1GGUxawTIEzoQxHI7C82MbddRhnZRWCMzsJ9fl8eL1e4vE4Ho8H27Z417seoq6ult27d2OaBretXomBiKkbmOkkgqwgSBLlZaWUl5WATdboLqfWczGZTAaW3Eb61GGcehL7zEHMjrVIcxaN+7hfDaIoYlo28hiX2nwjcE11NZWlxdk+KZd3RHB4NfX6w7GjITB1xMrRVZHeTuQ14BVF4X/+z//JokXX5/ue5ep45JFHrnsp0PVi0oHA7/3e7/Hd7373kv9/5JFHCqYLs8wsfvjDH7Jx40be9773AbBt2zYeeOCBaR7VWwiCgKIoaJqGKk69NOKVAoajx46NqMG73ooSgigiqU6MTBrZMXY5xmTJl0qNVuokisKIchRBFJFKqt+2/QKmYSDJ2cufJElZN+UpLAHLS5NOFlEUUWSFjKbjUCfvV5H9TWUdiE0r20eAAGouIOjq6iIYDDK3qRGvxzPuLMCY41ZUFKkkWyZk6HT2hugNBmnExgdg6tiJKIK3ZELbvTh4OHPmDKFQiKqqKhYsWJB7TbbBttqy0dKDzCktodjrRrecDA6G8Xo9FBe5UXPqIKG+EvoNg7DToCTdj35iB8ryLQgOF0uXLs0aiuUadQ1dJxQKEQ4P4ff7qautpaOzi96+EHduWg+y47IlVCM+i2ViaBky6TRvnjrHok3vg5f/DYDoC49R/qmvIChTI2V7OYYGB0il00jYl6j5BAIBnnnmGRY0z6W2spzBaAx/dc01GYetZ7CjIYRiP4J87T/3TOFv/uZv+PznP/+2W2SZCDajhcjXfp83Ms899xzFxZe/H7/73e+e0DYn7Syc9w+4mLKysiltupssM81Jb6ZgmiaPPvpowe78jTfeGNOl7nqS0TQcuXIVy7KyDqFTqNIzHvKKH3mN6uupMT2cbG22jDSFk4F847M8hgfC+fPnUR1O0qlkYWJ14sQJhEyCef5ilMomBOXGkFe7EvljoQ6Ti9MymSk93yzLwjR0lDEaaMeLYZiYljWqrOhkMS0LXTdIp9O8eeAAcxpq6Q320Thn7pTqyff3dmHpGs+8tB2PnuBeJQSA+57/hFy3YELbs0wDy8w6QZ85c4bt27ejqCr19fWsWHFT7sZoAwKpTIbWc2dxOZ0sXrQo2/AM9PUPYJoGhmmNmPjato159g2sUAeCpwR52eYRE9Ljx4/T1z+IZVucO32KDRs2UFFWimVoFHm9uIvLsMmaXkmShCyNXoZlWxaGlsa2LGTVSUd3D6Lq4qVnt7EsfJ4mI5I9PrduwXv7xG7kl8O2bbBMbNPAtgxs0yQeDZNJpUigIqlOvD4v6rDrTSqdYmign2CgkzcPH2HTpk2XzcZczdisUBtY5qhyoW9Hvv3tb/P5z38egN/85je8613vuub7nGnzofx4Aj3T4yxcXzMxZ+GZwnhKRQVBmHD50IQzAr/+9a+B7ITyN7/5zYj09/nz5ykpKZnoJme5jkiSxGc/+1nKysr40Ic+xG233cY999zDM888M+1SZnmVFVEUEXOrtNezfGkmKEoA2RKhVBxRkqesPt8yzexxHWV7+YnVhnVrcbuc9PcFsx4DoT4isSiimaHeAnft/MJ4jh8/zsDAwIRkDWcKw7MBeSRZZnBggPYLF6bku79w4QJul5NEMnVV3hiXkxWdLJIoIjlUTp08QSqVZMeu1xkYGMThck/ZOS8IAufaO2lvPc89997Lgb1vwFA2EDDD/RMOBGzrLQWmUChETU0NN61cxfFjR+kKBKiqrEDIuda6BAerb7658F7F6SLQ2QGWSSaVYt+bB7Ftu6B+JAgC0vw12ZXpcJD+PU8TKpnHoiVLMQ2D2mo/jQ11vHnoCPF4nMHBQVrmNoJtobiLCr8JURQxDJNMzvTnQnt7trSmpoa6Gj+2aWaN7SQZ07LxlVYQGwqxaGEL5SW3ILz8b9ipBMl9r+BYtBqlanyNvNm+KCs30TfBNAp/t00DLBMEEUGSEEQZJJnQYISDh48QGgqTyWisW7eOdevWFbYZGexn+8svX9G462qxkxHIJBCrmt8RQQDApz/9af7X//pfdHR0cOTIkesSCMzy9qG3t5eqqqlV1ZrwneULX/gCAOl0uhDVQvYi6Pf7+da3vjV1o5vlmvHBD36Qs2fPsmDBAp5//nkkSeKll17irrvumpbxXHwTkCQpqwBkmtdctWc4U1XzejUU+gUySRTn1UuKjtUgnCcUChGLxXj2+RdwqCr33nM35aUlNM+dgw34vF5ULYo+2A2eUoJ9Ifbt3cvA4CBerxfbtqdEf/96YOdchJWLAoGenh50XefVV1+9arfVfDPp3Vu3cL61HUVVqautnbTkbVZW1MAwTOSxCronQUVFBW+88cY1m+z5/X6OHDlCd0+QRctXYu89i5BOYEX7r/zmi7BtK+e0nNWCP3bsGKIosXjJ4qwfRu6Y6oYxqnNtZ6CL119/neY5Taxbsxq3U8UyjOzkWBAQRImzdiklqS4qXKBmeknHG5EUhWgshoXIkcOH8fl8LJ7fjG2ZI4IAeKsMS7IkwuEwsVgcCRsZi75QP7V19blgxSKSzOBzqZT7GpnT2AhA2n4/0af/FWyLgad+QteKrfira6ivr8e2LTDNwop+fnWf3N/BBklGEOXsZ5IURNUFucn/xQsKksvLUCxBOp0Z9bu/HositmVih3sQPGUIjpnlb3Mt+cd//Ec6OjoA+OxnPzvNo5leLDv7uN77nGUkEw4E2traAPit3/otHnvssSkf0CzXj/nz52NZFt/97nd55JFH2LJly7RJmV2suy4IArKioGsaoiC8bZVrxkKUZERRwtQ15KstLxmjQThPVVUVVZWVzGlqYH5zM7KiMBCJ8vLLrxQmiVs3305TkYWtpfC43Tz04P28vP1VOjoDM6IUcLxYlpWVor3oWASDQSLhMBUVFYRCoavqD8k71EqKg1Q6RSaTk/LEHiEkJAggMFzFaqRK1vA/VUUmo+n094fo6uqaksnZtZ7s5Y3QorEYpaUlSCWVWL0JzHBowtuybRvE7PFYsGABtm0TCoUQEKipq8UwTc6eOUNpeQUDob5LslR5s7DW9gv09Q/wwP33ZdWRMlZ2lV5WiCfT2OULKde7caWHSLUfRmm5hdqaGsKRKGtvvZX5cxtBEMloBqFIz6jHTBQFTp8+xcDAAE0Ndfz8l0+wfv166uobMK1sEOB1KqgXBXWOxatRT+xDaz+FONiLN3AKSotIhgLIAiCKuYm+DKKEqLoQcpN/JthTdbFJ3WhZq2u9KGKHewEbofj6m3pNJ48//jgAzz77LBVTLFIwyyyTYdKzq9kg4O2BIAj8/u//fqFkRBAEfud3fodEInHdx3FxEFIIBnT9Ham1LKlOLCOrojRZLMvChlGzKnlJwDn1tbz7wfupqKwintaorK5lwYIWNm3axJo1a7j99tuZt3AxYnEVQjLMwEA/x0+c5PYNG6Zcf/9aM1pZEGQnirZtU1RUdNWr4/lJp6ZpBHt7EQUBVVVwqCpOR/bhUBVURUGWJcTcBNeysiVAmqaT0XTSGY10RiOjaeiGQTwWw0bgzYMH2bZtG4FAYNJjzHOtNdXnzZvHqpUrcbndiMVZZ14r0o9uGBP7TV/ULNzS0sKGDRuYO3cODlVlcGAQWXVw6NBBXnvtNY4dOzbi7fmJ7/r167n77rtpaGwiODDEqXOtxKIRtESUhfObOXGulX/d10rasHDFeqGvDcs0Kfa6WbG4BYfqIJNO09HZednvwO/309XVhdebdS/1+/1Ylk0kmcHtUHCMUuYlCAK+rR/CymU+itsOYQkyQ4KXtKcSobgaubgS2VeG7ClGcnoQFUchqzFR5s2bx7p1666qdG2y2JkkdmIQoaTmEqnZtzvxeByAVCo1zSOZfsZS8LvWjxuVj3/843zve99jw4YNrFmzhq985StTch5d1S9w9+7dbN++nf7+/hEH95vf/OZVD2yW68v27dv54z/+Y3bt2sWjjz7Ko48+SnFxMf/+7//OPffcQzwev7b9H4Iwaje/KIpIslxoHn4nkS8RMjKTcx3OOwgP77MIBAL0h0I01Nfi87gRRBlJdVLs8lBcXjni/UuWLBnxb9FdhK0lmSul0YxKvF4PW+66i/kLJlbvPV3kJUNHO4719fWoikI8kUDNNaFOlvyk0+lyc/fddzNnzpxLXjMZydv+gX46OjpIxOOYpnndVa2uBkWS0XOmYqTiWVMqxUFHext9fX1UVlZeVkbxcvduQRBobT3PwkWLOHfWLtTxX8xwV+S8MMBNN91EX/8gvqIiKstLufeuzQxFIgwmo9TG2rE7j5PSTJTyGpzeYo4dOcqOHTuA7Pk01neQPwcs2+bBBx+guqaWcCKNU5FxqWPfdqWSCqzlGxAP70A0DYZ+9UPKP/wItsfFYCKFJAg4FRmnItN6/twlykk3ArZtZz0DHB4Ed8l0D+e6s2DBAk6fPs3hw4cLTrWzzDIeWlpa+LM/+zO2bt2Ky+XiH/7hH+jr6+OHP/zhVW130hmB73znO2zZsoW9e/fyne98h7a2Nr73ve/R29t7VQOaZXrYtGkTr732GpqmsXLlSgAikUjBb6C0tJTPf/7zpNPXxv02Xxo0GvnV7GthpDHTESUJcRKuwydOnODUyZP09/dDrokyNDBAMpNBkUWOHTtGaDCC4nRNyLNBLKoCQWBRfQWqw0VjXe1EP9K0MVY2IE9FRQWNDQ1TMrmeN28e5RXlNOTqvydLPnARRRGvx0NbayumaV7TBs5rgSJL2J7Swr/VVJS+YC+yw8GZM2d59dVXOXny5KjvDQQCxONxurq6Rn3etm0WLlzIYH8/CxctwufzXTFLFQwGSaVSVPv9PP/CC3R3dyOrTnxlFdQ3NFLbvIB07WJswBU8zf6dr9De0VlwQh7N4fhi5s2bR/O8eZSXlhFJZnAoMm7HlcUPau56L1ZJNiivMeNUxIMUuxxUel34JBtiIVKBU1QRIxEZZPfruzhz5swVtztTsOP9oGcQS+veMQ3CeQ4fPsxTTz0FwIc+9KFpHs30k+8RuN6PG5V//dd/5bvf/S7PPfccTz75JL/5zW/4t3/7N6yrkKqGqwgE/s//+T8888wzPPHEE7hcLp544gl+8Ytf4HC8PSQG36koisLBgwcJBoOcO3eO97znPYXnvv3tb+NyuUZo7U8VwhWcWGVFwTSMqz7hb0QkRcWyLAKdHezevZvW1tYRz9t5J2XTxDIMOtrbCA/2U1pWhmZaDAyFMUyTwYEBXnzuOZ59/kX2v3mI3mBwwmPJ+wvYqTiimc46glozP0DLp4Qvq4w1Snna1SBO8fbyNf2bN2+eNmnbySIIAlLJWxknMxKi48IFME3u2rIFp8uVDVovIq9nH+rvH7UUx7ZtNF3H63EjSiKWaXL33fdcUcnK7/fjcrnY+dpr3HvPPYUJvSAISIqK6vbRNpTmAsUI2KwusYgHA5P6DpKGjSwKuC+TCRhOV08PoXlrChnS+AuPYbQdwm7dh9zxJq6BVlzJASRTY25TI++++w7K3ApmOplVDZrB2IaGHelDKKp828gRj5dYLFZYZPvqV796ScZ1llmuxIULF0Z4P23duhVBEOju7r6q7U46EAgGg9xxxx3ZjYgitm1z//33F+RFZ7mxqaqqYt68eTz55JMYhsGBAwcKF7E777wTQRDGXKGbDKP1CFz8vKIoGO/AfgFBEOgJhhAtE9E2SSdiRAZCaMkYWjKGnoqjZ1KYegbL1Mlk0tTXN7Bv/36efOI/OHHsKE5VxeN2F7IrV7OiLCgOxOIq7Gg/IjZG5tpkiaaSK2UDIHcOTuE+BaY2EIBrX9N/LVGKSrHF7HfQ13qayspKLMviuWef4Y47NlNf33DJe4LBINFolL6+PtavvY2+vr7Cc7Zto+s6siRhWRZz5syluXku/pqaKx73/IR+yZIllJSU4K+qIhAIcODAgUKw4Ssq5pVj7ezqCKNKIi3CAHYqPu7vwLZtoikNSRJxiPa4Vr/zJUsJQYS52YmilUrw6PPH+fdTGmfxY9ctpV2qotUuY9/RUzz21AukDBvb1NGjA+ixAcx0AtsyL/lM040V7smqGxVVXvnFbzM+85nPAFnX6q9//evTPJpZbkQMw8DpdI74PyXXR3k1TLpHoLq6mu7ubmpra5kzZw7bt2+nsrJy2rXoZ5l6JEni5ptvZv/+/XzrW9/im9/8JoFAdnUsHA5f0eVuvFxpyiQM6xeQlcm7rd6I9PT2UllZSduFDlKpNMuWZbhlzZpRj4HbW0RbaytnTp/G6/VSWZm96U6lSky+X4DEIJa7tKDGMxMZSzJ0jBdP2X4FQcCy33kZrLE4c+YMlQ4vjlSYcOd5XPNuwe12s3TpUrRMmvrGpkskUvON13v3H2DJ4kVs2rCe1vPn6entZf6CBZSXlWVLp4Ssao4ASKKAaVnIVyh5G66KMzg4yJnTpzl46FDBTLCgrBMMEnNp+JJ9OffhuxBU52W3bds2sbSGAHidKolYlGPHT1BVVXXZ311eccoua6CrcQXVwQBCIsovwpWkhnT++VgAj6OH5XXFzC1zUFkxlw0rS2iY01zYr21oWFqa9FAfg8FuDh4+hqnr055FslNRSEURK+eMaPx+p7B3797Cn/J4rkXvEN5Zy3pXh23bfOpTnxpReZNOp/nd3/1dPJ63JHj/4z/+Y0LbnfTZ+LnPfY433niD973vfXzxi1/knnvuAeBP/uRPJrvJWWY4kiTxh3/4h/zhH/4h73nPe/j1r39NSUkJv/M7v8PWrVt58MEH8Xq9k9r2eCf1Um71zzTNd9TFtKmpiZMnT9LV3ZNV6qmoGPWYWZZFZUUFkUiENWvWUFlZyeLFiwvPT6UkoFhUhTnQiZiJY4gyqntmaoGPJRl6MVMdWAoCzMYB2VXuYDBIIpHAiKWZI0O5IhA1TaqqqqipqQGyN7l0RsO27YJ52sXBa2hgCFUSkSWRQCBAJBxm7pw5iMOuBYqclVqVhn3nra2thW3kVXLy5WKWZaHrOpU5k554PF5oAs43GNu2jXl6D9ZAJ8bJnVn3YWn0en/btomndSzLptjtoKuri75gkDNnzrBv377LTsjzgc+el56lrnkB0op7CPf1kTrz1udLZEz2tA6yJ1chqBxMsHh/jJsaSripvoTldcV4PcUcPnWOjs5u7r7/IQ7ue2Nam8tty8Ia6kZwFyM4fdMyhukm72K7cOHCaR7JLDcqDz/88CX/94lPfOKqtyvYU5S77uzsZHBwkPe9732X1DBPBzPNUvvtyG/91m/xi1/8ovDvlpYWjhw5Muk+kYym4RiHMpBt2+iahqwoM3YVeirJ10EHOjsvq/tt2zaapqGq6nXLlth6BrO/E8vpQy6emRlBLZNBGecx0TIZ1Cnqc7IsC003cDpuPLWr/OT9ajNHgUCAZ599FlmWWbVqFfF9z7NcimELItZ7/4DioqIR54xt22Q0HVEUUEbxvti1axdvvnmAj370Y/z0pz9lzZo13LL6ZlSHs/DaQCCAIGZlWWuqqwsGb/F4nLq6Ou7YvJnysjJscj0Bokhvby9PPfVUwTdjtMm6bZkYx3dgR0MIJdXIizeO6m+SSGtopkWJ25HzO+invb2dN97Yg67rbN68mdWrV4/r2EciEZ7ZeZB9yTIC2uWzEHlEAeZVeplXKpMMnMSvZHjgvrsp8nrxl5dMSybVCvdgxwcRa1rGDKDerpw7d47PfvazvPLKK3zpS1/i7/7u7677GGbafCg/nvOdPfiu83hi0SjzGmpmzLGYCUzZkmpDQwNVVVVcuHBhqjY5ywznscce4/Dhw+zcuZNHH32UQ4cO8cwzz1yVJNpwU7GxEAQBRVWv+6R3usivaA+XP7yYQr30ZYzDrgWFfoFIEFN1IXpm1oX1cpKh15or9b3MVPINunPnzmX//v1XVVLS3x/ijk2bMAyDc+fPs3TBUmjdg2BbKFqKjO7COew3LAgCDlVB03U0XUe9qASwqqoKVXWQTKVZsnQp9XV1I7I9+Rr7VTffTGPTHMLhMG6PhzvuuANZURgaHGRocJBqv3/Eduvq6q5YNieIEvLiDRhHX8EO92Ke24e04NYR20lmdDTDpMjtQDcMLMvGskwOHTqIruvj6s0ZnrXLugrbrOsPUhdt58yAwbG0l0N6BW0peVQFFMuGs31xzvYBZLMtO7e1s6KhlMXVUW5tLqep/Oody8eLraWxY/0IJbXvuCAgmUwWpF03b97Mn//5n0/ziGaZ5VLeObUVs1wTbrrpJm666SZ++7d/G7fbzbe+9S3WrVs3qX6RidyWBCG7Yqhr2rhXe29UDNNEVS5/AzVNM7u6OQEp0KlCdBdhZZLYsX52Hz5GRWXljNE1H0+T8MWMJxgd97ZuwArYfJ36okWLcLlcky4psS2LeXPn8vyLL9LR0cmDD9yPVzCgdQ8AUmIIuaySZCaDS3UUjNWyTsoKumGQ0XQc6lvBQN5V2NB1Vq++hSKvG2mYIVUwGETXdfx+P0NDg9imidPp5NVXX8Xr87FixQqcTueo3+94yuYEWUVecjv60ZewQhdAdSHPWQFAStNJ6wZeh1wIyhVZpKam5qp6c5YtWwYsY8/ru6gO72FjRRgIEHVX8S/tAmL1QrozKse7o2jG6LVovTGd3hN9PH+iD14+T5lHZU6RwOZqg4V15bl9TD0FzwDVheAtuyb7mMl89KMfBWDjxo288sor0zyaWWYZndlAYJYpweVycf/99/PMM89QXV1NZWVlVpt7IpOw3ArqeCdhoiQh2jamYSBfYaJ8ozKeFW0rJx06nYZrbaEo9R6BhTWlvHmuDZfDQW1tbS66y40/93e4fD3+VJWljEsy9CIKMrZTEAjcqMFpXis/Ho8jCMKohmhXwrIsdC2Dx1fEbbetZc6cubg9XopLfGTyL4oNFMp/UpkMA6E+urq6CqVvqqKg6wbpjIbT8Vaw39LSgmVZJJJJwuEwfaH+glyo3+9HURT2793LokWLqamvo6ykJNv029eHv6oKTdPYtWvXpI24BIcLZcnt6Edfxuo6RWAwTL9cRk1DIz6XioCA46LFianozamqruGFox4GMjFuK7EpSvbx2QqJ9lI/vpom5n94Jad7YxzuDHO4M8yRQIRYxhh1W4MJDcMQ6UhJeNr72NzzJh+7fQVex9ROCezEEGhJRP/8G/b3MFlCoVBBRfGRRx6Z5tHMTKbD6fdGzNJeayb8q//Wt7415nOGMfpFZ5Z3Br/61a946aWXuP/++wmFQvzJn/wJf/u3fzvu909GvlGSJHRdxzLNCRlj3SgYhnHZYCpfEjTdJVJ9oRChPpNb5lbjL/aQyaSz/gJ2bk3ctgE7J8oz2rcsIAiQTKUID/QTDQ8RHhpEliT81dWT+myTyQbkHa6n8khOZYbhepBv0FUdDrxeL0VFRQWlrvFgmSa6rqGqDgRRHDEJtm2bjLsIklH6Wk8Tl8tzQaKEr8jH+dY2zpw7jy2INDQ0Zr8PQSSaTOfKhLJnT3tbO5VVfs6cPcexY8ewbZtly5YVxj40NITP58W2bE6ePEUo1EdVVRXhcBhBFDl58mThfS0tLRM+RoK7CHnxRrSjr1CZ6qWoroJoXzuDCNQ1NIIpZaVSRQkkaUyVnBMnTtDf339JU/9oZFWM7mYoHCHuAvfxl/CKJovDJ/nFufbsMViyhGV1xXx8bROWbdMWSnA4kA0MDgcihGKFMAy/T2HAEokh85u2DM917Gf93HLuW+xnSbXvqs9Z2zSwI70I3nIE1XVV27rRsG2bqlzzOWTFHmaZZaYy4UDgiSeeuOzzt99++6QHM8uNjaIo3HfffViWxapVq/i7v/s7vvKVr1BaWnrlN5ObfE0wWs/7C2iahjoOZZgbibx52uVWtI1p6AsYjaqqKgRJ5tU3D7Jp1RJ6MwKyOr6m27dWaGwunDnLm2++idPhoKysHNs00NMpwEYQRARx2EMY+/uekGToMKa6rl+Y0pDi+lFfX09G0wolaYZhXDEohWzwZRrGiObd4XR1daGW1qKW+pGrWnBVveUdoAPr732o8O9AODHyzam3FprU0irCaZ2aBUu50NXD4OBg4blIJMLrr7+O1+tl7dq1+IqLOHfuLP7qavx+P5Zts/Xue3jzwH5CodCEAgHTMtEyGpphotsO7PpVCMkwJWYCr5L9vHbv2UveZwsC5AOD3CMaT2CFehnoHeDsmTPYtn1Fk6k5c+fijaWJDvXTWbqEedFznCtuIdrbcYkhmygIzKvyMq/Ky/tvrqezs5Pz3f2E8XKwM0JrTx84SsiHvZpps/1cP9vP9VNf4uKeRVXc1VJJsXNy2VY73AOCgFB84zhgTxX79+8Hsp4BZ86cobq6eppHNDOxco/rvc9ZRjLhQGC2zm2WKyEIAp/+9Kf5gz/4A+68804OHTo07vdNZhKWDwbebv0CxhUkUk3DgGnqC7iYefPmMxgewl3mJ2VJ1PsUbMsaVVXlYt76vgTKysrJaDoDg0NEYnGWLFuG6nJnz4vc5N62sg7Kdk6b860AQcoGTYIwbsnQS8YCU+4lcKNlBPLkxy6KInLOv+NywYBhZDNzisMx6ucNBALs3r2bu9euwaNHCWdkytL9DCZSnGy9wOBQmOa5czANg/r6BhqbGvOFZAiAaVtomoZTVTl16hRHjx5lzaY7WXvnPdhaih07d1JVWYkoCGy58058xcUcPHiQ1bes4Y477uDgoUMk4nH27duHpmmsXbeeec3Nl/XAMAwDTdPQTBPdAtMWkAUbVRTwOmTaRR9JxcGPf/ULSop8bFy3jpYF88AywDTByj+MrOtv/mEaiKZOc00F5S034RRtjGQcMxVDVF0I0qXH2LZthhIZnIqEf04jhxJJnilaiCE78RRFC34hoxEIBNj3xh5uu2UVbxw4xH9eu5Zw2El7cJBzmodDAxYDybcCrUA4xQ/3XOAneztYN6eMexf7WV5bhDjO89hOx7GTYcTyRgRx+q9P15vnn38eyLoJu93uaR7NLLNcnimTD51pzDS5rHcamqYVZERN0xxXnXbeH0CZZL2/YRg5DfIbv18gLxk6lpyqZVkYuj5jAh9NNxCErIa7bVmYA50IsgOxxD/h8U2kRyBbY2phW8MetoVt2YiyhChKhSBhPOMwTRPbtqfMoyKj6ciSOCOCtYmiGwbisEAz36Qr5oz98ti2jWlkHb9lZezz8cCBA7S1tvLALQsZGBzkse0HaJnbxB23rgTToD04QMqWWLxwIb7i4lHLaSzLIq1pOBSVkydPUFFeRiytI3qKKZYsHIpMKp2mo7OTrkCAivJyblq5El03eOyxn7NhwwZUVSUUChXc0zU9/znFbLOxYeD2+rAlGTs/8ZdEFEVCVdTCtSytm1wYSmBE+hnoCVBeXl7oVRgPHZ0BYvE4Z1vbWXHLWtT0ENUlXmwtDZKMqDoRVBeCkvUiyFgCLo+HmrJiBEHgcHeEI70xFDPDCo/GkiVjlxYdOHAAlyzw5pFjBPtCl0iYmpbNgY5Bnjvexf7uxKhqRDVFDu5e5GdrSyWl7rH7kU4cP0aD08ASJYrmLJsR16frzXCfm5/+9Kd8/OMfn9bxzLT5UH48py90T4t86MKm2hlzLGYCs83Cs1wTVFXlXe96F0899RTJZHJ8RmOT6BEYjpxTETJN84aceA3HMM0xnVHzE7KZ5K5smiaOnF6+IIpIJdWYA53YKReCe2LO0xNprMw2UufKLXLkgyQpF5RYhoFlaZCr/hfzgUGhxOitY9jd1YVpmli2PapXw0SJRiMMDg7icbun1dV1MlzcsyMIAnLezt4w6OntJRgM0jx3Dj6f77JBAGQbedvOn0O1dSRvKWvWrKG8vJxAVMNMJ2iuq84qBMkCeiaD6ry0rry9vZ2BgUHmL1jAosWLEWybWGsrgyf3IZeXki6twef1sXjRYirKKygtK6O/fwCP18PmzZsJBoOUl5ezYcOG7HliGOiGjm5Y6DaIbh/EY3QHOqmqrMTv94+5iDGQzKBIIi3z5yIsmNi5YhgmFZWVJOIxynxubD2NUFSF4HMjCTa2lsHWUpixgWzPRWQIq8jPc795kru3bqW2ro5zA0kAVjb5WVR1+eur3+/HKdr0hfpHlTCVRIFb55Rz65xyQpEYLx4L8GJbjL6kWXhNTzTDT/Z28G/7Orm1qZRF7jRlZoTKimwAZFkWHa1n8ZFGEWV+ufMAK5PihIKjtwuSJLF8+XKOHj3KokWLpns4s8xyWWYDgVmuGbfddhtPPfUUgUBgXBdDgavv6JdzJUKiIIyrLGUmkq9vH6s5Mz/JnSnmXVbuOxteNiAoDsSiKqxIH4LiRFCmxqRrPORVpERRBEkC3jqO+eyBZVlYhp4t1yBbXhSORDh/7hzllZWcOXMGWVGyykf2WzKg+Wbnt07Tt/5uZ1+YfyWappHOaOx9Yy/JZOKq9PinAyFXYnXx/ymKQiQS4fixo9TV1nHw0CFaWhZe8bPV19ezee1qBDOK6C7m9ttvGvG8bdvY6QRmYgjBjhKOSJwP9OL3+6mtraW7u4cLHR0M9Ic4duwo73nv+/B5vVRWVlChmMQ1g/DQEKFQiAULWnIqWjYI0Hmhg+b58/H5fAyGwwQHhkBSEAQbRQBVlujv6sThdPHLxx/Htm02b95ccD2+mIxhEs8YVPtG74W4HKZpkdF1XE4HixcvZvHixdi2TTCaIhRL4y9yIjpc4HAh2jbHjx6m9cxpuvp3k0wkCAaD4CsnqZtIAjSXXbn0pLbaTzwyxB133HHFTFtlsY+PrF/EB1dFOdQW5IULafZ1JzFyaQLTttndPsgbWHyuIYFPLCbV14EkitQUu7FsN6/tP0xvaICBgYEJHZu3E7/zO7/DF77wBT73uc+xd+/e6R7OLLOMycyYSczytiRvpf7JT35yXK+fitXt4auWN2rVm2maSGPUt5tmduI6kzIepjF6BkZ0FyG4vJhDPdjW9WnRupJkqCCIiJKMrKgoDieqy4PidCOrDkKhfgQBEokkNbW1GIaRDRhsqyB0JAggCiKSJCJLIrIkoygyqiLjUFScDhWXQ8XlcHD+7Fke+38/o68vq8sfDAavyzGYKsbq2REEgba2Nm5etYqu7m4OHjw07s9W5lKwBImuvgECgcAl2xVdXuTyeqI6OKwMi2tLkS2dSCSCpmkk4gnu2nwntm1z5vQpDNPEsGw8TgVfSTkej4eamloymTSiKHDk8GHisRiGaSJJ2dIap9NFdLCfcq+TqmIfpUU+3C4n/upadr22C9u2r2j8NZDIoIgCRRNspM2XNrkc6sjAWRCo9DkxLIuhpDbi/0vKKugZCJNMJArjOtufbaRuKnWjyuMouzQNPL4iVq9ePa5gVBAEFHcxtyyex5c31vL9e6t4eGUltUVvuRvf6k2yscxEFASCg1EEXzlxycu5YJi2rh68Xi8VFRUTOTxvGzKZDF/4whcAaGxsnObRzFwse3oes4xkNiMwyzXj/e9/P5BVCrme5GuY8zX0NxK2bWNa1qgGYrZlYRrGjPtMhmmO2csgFlVhDnRiRfom1S8wUUzTnLBkaFa+UsDpdnPqzFni8Ther5dqv/+KRm6Xw+/34/V6C9u7kqPsTGOsDJ1tWcxvnsvre/ZQV1dH+4UL4/pstm1jxIeI6TZvvvkmlmWNmiURBIH2nhA7d+xgectcblm2ECHej1OS6OoKMBReyJ2b7yCZSnPu7BkcqoNiH1iiRGVlJQODgwwOJHC5nLhcLlKpFEuWLkEABkNBXnrpJTZu3Fio4TZNE90wKPJ5uf32TVfsT8kYJrFJZAMs2yaVyTY7jxaoSqJIpc9JMJJCkUR8uSAjL4maH1dpVTVdx3oBWFDhueJ+W1tbKXapxDMGTRP0hBBECamognKXj/d6Bnj3XJWTSQe/eu0I9xonMbpsth3q4t777sNCIpVMUOX3s2LFCnw+3xVVkN6OaJqG0/lWwPTDH/5wGkczyyxXZjYjMMs1QxRFKioq6O7uZsmSJSMk/q41kiSBIGSVdW4gxlK7yfsFzKS+AHhroph3hb2YfL+AnYljp6LXfCzWOBvTRyOr076V9evXc/fdd191j0B+Ard58+YbriwIRs/QWZaFljMKW7p0GSWlpbz3Pe8Z32fTU0hYHDx1ni133UlJcfGYmQS/34/L7ebQqfM8/sIuDEml0iXx0Qe3Uuxx4nK7KS7y4fF6eWPvHhLJJEf37SYWjVJRXoHb7aasrIwlS5awaNEiDF1Hy/1+Nm7ciMvlYvfu3XR3d6MbZsEArL6+ntLSUjo7O2ltbR11bAMJDXmC2YBAIEBoYIBYNIIkjX1+OmSJcq+DwUSGtP5WfX59fX1hNf/8QBIbKHbKVHouvyjQ2trKzp07ME2D555/nvPnz497zMMRFQdSaQ2yt5Ql7gxfmhNjkRQGUeL9H/gANTW1hEJ9bNu2jZ6eHg4dOvSObcT86U9/Wvi7ZVnv2OMwHvIZ3Ov9mGUks4HALNcMQRB44403ADh58iTPPffcuN43VT9UWZazjZ/XqSxlKhirSdgwjKyT8gzpC8hjjKMx+61+gRC2nrnsa6+GyUqGDmfevHmsW7duShqFYeQE7kbHMk10LYOqOhBzRmF1dXWUlpaO6zdrJ6PYQEcozLann2HlTTcxp6lx1PcOD6Luve8+ymqbkCvqiaYy+AQdIT6ApWUoKy1l8ZKlZCQXty1vIZPKTpJLiotIJJL0BYNZ6VHTZMerr6KqKi6XixdeeIFUOk0sFqenuwub7Ip9W1s727dv5/XXX+eFF164ZOKsGSaxjE65Z3SJ1NEIBAJ0BQK8uf8Av3ryyUtKoi7G41AociqEYmkMc+S1y7Jtzg1ky4IWVHiuOIZkMsntm+8k0Bvi3q134XO7MHQNK6eOlR/fgQMHrjiubOmWD7m8rvB/siThcjhQcopL8XicgYEBmpqabrhSuKniL//yLwF44IEHZtSizSyzjMVsadAs15Tm5mZeeukltmzZwsc+9jFWrFhxWRWJvFLJVFw+BUFAUVV0TaMvFBq3JOV0YVpWtsn5opvHVEtaTiWGYeJQr7wyKrqLsLUk5lAPUkXjNWnknollU28Hss3rJqauX2IUJggCoiRhjaMky05FEZxe7rn3PoLBIB6fj+LiYnQtg6JeOrG+WD1KECVSKPSbCrFonLk1biwtRnV5GWc6OrllbiVlTgFREEim03h9Poa0DKfPnMHr89KyYAGyKOB1O7n/nq2kdBPTsnG43KQz2bp8p9vN2nXrqa/xk0qlMEwT0zA439pKKBSiomk+stM9oWzA4MAA1ZVlvLZrFwDBYPCK16ASt4pmWtnm4WJXoZ+gJ5omoZmIAjSXXVoWlJfera6poay8HH9NDU8/9RT9/Vm1oHvvvYdyQcjKvVom6XSG82fP0N5+gWOiyJYtW0aMbTQpX0GUSGY0HAAC2JFeYikvhmHg8/k4fOgQD7373ciSxIEDB2b0NfdakG+Qfvrpp6d5JLPMMj5m3sxilrcdd911F48//jgf/OAHWbZsGXv37mXNmjWjvrZg6DRFKymCIDAUDhMOh9m+fTter3fGlmnk1W6GY9s2hmGgzhC/gOHYdlZLZ7xZimvZL2BZVk5KdGYdoxudWDRKOBymvKyUktKyUY+vJElZpS5pbK8G2zIhHUcoraW+pHrE7880DbRMGiWXabgc0WiUgYEB0uk0slenptRBlQvKFjeDKONKDRJLxgua6bphUl1ZjtPpxLLhQkcHvqJitm/fXujduPvuu3FXZ3scervj7NzxKvF4nJb589mwYQOpZIIafxWCJGHKDox4BLHCN+5j2FBXy76c0+x4e0UEQaDS66QnkmQwnqHcmw2UhjcJOy5qEg4EAjz99NM0z5tHeWUVkaEhqqur2bhxI319ffj9fubMmQuAJGevM6fOniORSDCvuZmyslJcqoKWyu5D0zRsQ+fM6dPs37+/cN20bRsp9z0PROJo4SRNVQ5KPC6aGhspLStDEkUs22bHjh243e4Ze829FuzevZtly5YB2e9xVq9+bGadhWcGM6vOYJa3LR/4wAf42c9+BsCtt95KOp0e9XWTdRe+HIFAgEQySVFR0YxVb8mXLw1XErFtO+uWPMP6AvJky5jGfwm5lv0CpmFMuEl4lsvT2trKyVOnMDSNXzz+yzFr5gVBQJLly/fj5L5vwXXphEiSZBTVga5lMM3L9/T4fD6OHj2K1+vFFkSCsQzdMQNN05G0OABuCQTLpMjrxeVy0jcYJmPa9A8Ocb61jXg8PmYvyPA+kaXLl1NWUcHho8d47vkXkL0lZNIp+rs7rnToCti2jcftZMmy5RPuFRFFgaoiF0ndIJrWSWomgUj2ujlak3AwGGTR4iXIsswTv3y8INJwuXK3srJyOru6eWP/fna+vpuUpqO6PKguD6fPtfLkb55i/dpbC9dN27bJaFpBdjeVySArMqbs5OSZsyyc30ytv5JEPE5HRwd+v3/GXnOvFUuXLuXRRx8t/Pvd7373NI5mllmuzOydc5brxkMPPVT4++LFizl58uQIdQW4NoGA3+9H13VisdiMVW8xhjlR5jFnaF9AHsM0cUxQVeda+AtcSTJ0lskRDAY5dfIkRbfdRiwWo6+vj3nz5o36WlEU0XPO3qMFrXYqCpIC6qUmYfn3qw4nupbBtix6g32jlvLFYjEqKyupqKwkFovRUF+PlslwobeX5lIVpyRCKoKZSSE6vRT7vBQVF/H6rtepq6tDEITCNsf6LPPmzRvxXFVVFdU1NWSQ6Tx3hIVN41/ZNnUNUVYnZJI3HEUSqfQ66YulOd/Tj42MQzCpGqVJuLq6Gs0w+Y/HfzHu69zFikTDx+j3+1FVlWBfiIfe9SBD4ShD4TAlxcWgqhi5fTr9Vew6cIjzbe0EQ/3cvXULDXXVNDXUcc7nIxaLzchr7rXkM5/5DM899xyPPfYYr7766nQPZ8Zik/dmub77nGUks3fOWa4bHo+HZDLJJz7xCdrb2/na17526Yuu0l14NGpqavAVFXHHHXfMyBT1aBNZK+duO5P8AoaTHfP4y4KGM9X+ApORDJ3lyvj9fqyclG15efllJ3N5/w5D1y95zrZt7GQUwVV02cxWtqfHQSQSIRIe4rXXXmPbtm0jmlj9fj/BYJBfPv44u19/nWg0yokTJ6itKsMlWujuCjKOYlRZRMlEsuVIhs6mjRuoqa6moaGBSCQyoeOwYMECHCUV2KZBS1M9CxYsGNf7bNvGMjQk5er6VlyqjBbqxIqEAIh3nOH48eOXvK6yqgqPyzXhzMNYDe35IKGouASnqlJRUcHT27Zx4sQJopEwAJIsc+jkGc6fb+Oee+5h8eLFpDMar762m2BfP6XFPj74vvdQO4Yx29uZX/ziF0A2Az7LLDMZwX6bailFo9FCnehsfd7MwrIs/H4/oihekjK2LAvDNK9Kv/1iMpqGOkPLawB0Xc96H+Qm/baddaWdiX0BefKrv5P9nmzLwhzoRJAdV9UvUCifmsHH6kbm/PnzZDKZKwYCecJDQwS6uigpKSlMLG0thdV1ErFqLoKn9IrbOHDgAG1tbSxevITnn3+O22+/ndWrVxeeH97A6nK7kUQJd7o/20/jqyaZTOB0OMDQUM0MWAaW4sJWnGx75jkGw2E2bNhQqOO+EumMxoVImkqvgzL3+DJYgUCAVCJOZUU5JeWV43rPWJiDPYSf+RGSZXJi2fvxVFRjZxLMqavGq8rIkoht26TSGVzO8asZjZcdO3Zwy6qV9PQPousasiRTdOx53PE+MmUNDM1bz4E3D+J0uUin06y86SZUh4NINIrT4UBVFOpr/QgIdPUG6eruoaqqatwB1Y1K/nvIZDKo0yxiMNPmQ/nxHG3rwue7vuOJxaIsn1s3Y47FTGA2IzDLdUcURX7/93+fvr4+jhw5MuI5QRCmNFc405tIbdvGGpYNyPsFKLI8Y8cMY8ucjpep6heYCsnQtxvjlYMcD/PmzWPx4sWUlJRcsWQvEAjw1LZtxKLRESv5djL3/TrH12Dr9/sZGBigrb2NLVu3XhKA5FewtUwGURRJDoUQjTSmw8eBfXvp6Q1i2TYD8RRJ2UNbKIKWjGGEg9y1/ha23nUng4ODo66qX8zx48fpGYoi2DYlrvFN5gKBANu2bUMS4ennnr+q78HoOkfy2R+hmhqCZWIc34lDT+Lx+uiNpjnXH6dtIE5fLI1uT91voLW1ld27d3P8+HHm5XoLnvyPX7Jr506wbZyubEmnJCu0d3Ry++2buOP221m2fDkvvfwy58+f56UXX2T37t24vV5sSSUST2CbBhWlxRw9cpgzZ85M2XhnImvXrgXgJz/5yTSPZOZi5e5/1/sxy0hmA4FZpoWbbroJyCosXMxU/kyvdsJ6rTFzOvz5iaxpmog5ScaZylTV5E+Fv8Bsk3CWvJnaQH8/iXgcTdd5/vmrm4TmEQQBURSv6McRDAYJBoOUlpaOaBC1UxFweBGk8X1P+ZKUmupqamtrqfL7OXXqFDt37uTUqVMYhkE6naa8vJxUPIaXFAlDIJ7S2PfmQXbv3o1pmvSHQuzYuZO+oSgZtQhd8eB1KDQUqZR6VIJdnXS0nsPMpEZ9dLadp6+nmzQKPYGOcQUO+ePg83oYGgrT09M76UZZ7eybpF7+NzA0kGT6562HxiWYqSjNlT4afTJ+r4KeThJOavTEMpwLxemOpIimdUxrclfS1tZWDN1g0cKFNDY04HQ6sGybNWvWcNttt5HJpDH1bFO3ICvZcqtoDE3LUFtTgyRJ1NbWsn79eu64446CQ3doYBBvcSlOt5eN69dRVVaCoWtvW4Onv//7vwfg937v96Z5JLPMcnlm76CzTAvbtm0DoLy8fMT/T+XK7kxvIrVtGzNXgw3Z1W3LNGe8Fr5pWlMWXF2Nv8BMz/ZMJa2trYVymObm5py2vzXCGEoQRUL9/bzwwgtUVFRwx+bNJBKJKdm/JMsYun7ZnhW/34/X6wXeksnMyoYmEEonViOeb661bZu+vhC9wSCCbeOvqiKZSODxejl//jxatJ/K+goCCQtTT7FmzRoqKytJplKUl5VRWlJCKpViaGgI1eFg0DRxSzbVNbV4PF4s0yAWCeP1eS8Zg2XouJ0Kom1SWtOIaRp0R1KUulVcyuWPQ1mRl52v756UOIFt22iHX0E7sgMAwenGdefHmFdZz/D2ZpdLIhHqQ9USWEKK4ydPs3DZCtKil2g626vhViSMdIJwqJfK8jJ0TRtxHo2GoqocPnyE1tbzLFu2jJIiH5V+PydPnmTjxo1Eo1Hqc8s1iVSK6ppauru6+Nn/+zn/+Xd+m/vuu5eOjk4qK7MlUQcPHqSpqYmKigrCQ0M899xzFBUVsXXLFly6hqllQJJBELPiCLnAE6b2fnC9yd/b9FH6ZmbJYnP9m3ffnmHn1TEzZ0izvO3JOw7v2LHjmu1jpmcDLMtCypW1FEqCboBa9/G4CU8EsagKBAErEpzQ6uA7JRvQ1tbGgQMHiMViZNJpwuEwuq5n9dxlGUVVUR0OFEXB6/Wiqird3d1sf+UVqqqq0HKvvRrEXBP/5baTX8kvLy9/q1k1FQPsUWVDx4MgCFy40M7CRYtomjOHJ558ksNHjiBJErFolAXVZSQsidLySvpC/bS0tFBWWkpbWzuJZIp9+/czODSEZds8//zzZNJpnnr+ZXbu2Y+guth78DA/f+LX9A5EkF2+EY+4ZnLo+GmOvfYSVaqBaOn0JzTOhuKcCcXoj2cwRsmS1NZUU15RztJlyycsTmCbBuldT7wVBPjKcN//O0iVl25DEATOnW/l+RdepPXcOSpLi/HKUOtTqS9yUOaSMQyDjKDirp5DxFJpDw5xtq2Do0ePsWfPnksyRoZh4HQ46evLZjF8Ph9z5jThcrnZuHEjr732GslUirykQ6h/gJ/9+79RXVPDmjW3YtrgUFRaWhbgcDrRcpPgZ555hmgkQnlFBatWrWLdunXU1tVxIdDNsZOnSUSjCKYOtoVlWeiGgabrZDQNXdcxcg7x+fMvX7o0lqTtdGNZFu973/uA2YzALDOfGXEXveeee+jt7UUURXw+H9/61rdYtWoVZ8+e5eGHH6a/v5/i4mL+7//9v5d1pZ3lxkHTsk6eg4ODoz4/lgzheMmvmF5s0DVTsG270BSdDwLkGd4XAPmeBmuE38HVku8XMAc6s+6z7uJxjWMmZ3smQ75fxLas7J+5SY/T6eSWW27h8OHDvLJ9e2EiNRoXy0GWlpZimiaapiHLMqdOnWJgYICKigqWLFkyofHJkoRhmiiXCb7qc3KeqiPbVHsl2dDLYds2pmkyf8ECDh48iCCI6LpeWGlurilHlXRSoptMOkNZWRl79uyhpaWFEydO0NQ0h0WLF+Nxu1mwYAGqqtLX18eGDRsYHBzkN09tY+vWu3nllZdHdftdunQptm0zODiIauksqvQiONwMJXWGUhrd0TQ90TRFToVSt4LPIXPixAkqS4uJJpIjGpxH42LXXltLk9r+c8zeNgDEygbcd34Uwekecxt+v58jR45w+PAhvF4vVVWVOB3VAHicNr3t5zlx8gQ1DU346xqpndNMbfMCsC1S8Sin2jqxEEgnE8TjCebMnUNFRTlbt24lHo+zZ88e5s1p5Oz5cxQXFaFpGrfccgv29tMApNIZRNFHe1srS5ctI5lM4ikqorOzE1EQ6OjoIJ1K0dvbi6ZpRCIRSkpK6OvrQ9M0XnvtNeLxOPu9Xu7eupWGuhpsy0JSHZw6c5aBgQGqq6tpnjcPwzCwbJtMJoMgCPT09HDkyBG2bt06phTsdHHo0CFOnjwJwFe/+tVpHs3MxbKzj+u9z1lGMiMCgccee4ySkhIAnnjiCT71qU9x+PBh/st/+S989rOf5VOf+hSPP/44n/rUp9i3b9/0DnaWKSHfKPYXf/EXlzw3FVPMmd5Eatk2Yq6sxTCMrCnTDM5e5An29ZHJZJBEcUplWCfqL2BOcVbiepGf3OdXN4dP+AWyQZEoCMi5c1cQBFKpFJZpcv78+XGVmlysWS/lvCj6QiF0w+DQoUM4HA5s257Qwoooihiahn0ZB+GLP6udjFxRNvRiAoEA/f39zJk7F4/HQ2lJCXW1tRSXllJbU82iRYuwLZMy1SItuDBNeH3PGyyYP5/z58/j9/u58847CYVCVPurKSsvA0b6AwQCAU6ePMmJE8dZu24d/qqqUceSVxayLAs9GSPU3Ul7R4DKqirmNc1lKKkRTulE0jpYJoMJHVEIs2PnLtIZbUxlonxDsWmaeDwetm64jZITL2OF+wCQm5bg3PA+BFm5JGAYTt4ALe8cPLzcRxAEyspKiYTDdAUCzJvXy7z58+kJhkjqJnVNzdQtWEJSEDBdKkWeEl7Z8RrLFi9kYUtLoX/L4/EwNDiEJIrZYCoYpDr3dS5YuIjFq7Zy8tQp0pkMLnfW6Gznzp0sXrQIp9PJksWLqa2rIx6PE4lEKC4qYk5TE16vl3c9+CAuVzZIVBQF0xYAESOVpL7GX3CrDofD+Hw+RFEkFAqh6zoer5e2trbL+ltMFwsXLiz8/Qc/+MHoUtmzzDJDmBGBQD4IAIhEIgiCQF9fH/v37+f5558Hss60jzzyCOfOnWP+/PnTNNJZpgqHw0Emk2Hu3LmXPJcvlbmaSfxUS5BONYZhoCrKDdMXANnJS08wSCKeoCvQyR133DGlwUC2XyB1xX6BfGPsdB+zy03QbNvOltJMYMI/Fs3NzYSHhli/fv1la7svhyAInD51it5gkHc/9BBd3d3EYrEJb0OUpGxJ23iCMD0Npo7gHn9ZUCAQYP/+/TQ2NfHySy9x6623Ul9fz6JFi0ilM1TnJux2fDArX+srRxsMMzQ4SCQaZfXq1YVj1NLSksu2GeiGMSKTMTxzMn/efHyj9AgMRxRFgv2DlJYUcb61jWQqha+oiLLiEopVgZhmEsuAv74JQRRoWb6KoXB4zO0Fg0FUReG2DRsQk2F8B36Dlcn2cwgtt2LfdBcZ0yYRHeRCRwf9fX20tbWydu26S841h8OBqqqjSlRenCHyeDzomsb+/fs5f/wwxaWlrFi1Bg2Rytp6lq69o9ALUVHbQO1AP5KsMG/+PATg/vsfQDd0qGpC8BQhV9RlDcOq/GRSSdwuJ1omw8c+9nF6e7rZvn07dbW1CEB9QwMOh4NAIEBHZycdHR1EIpGC0eNdd93FggULsCyLPbsP0jS3mePHj3OhvZ01a9Zw++23A6AqCrqucyznMD3TzMpM0+SLX/wiAB/96Ed55JFHpnlEs8xyeWZEIADwyU9+kldeeQWAp59+ms7OTmpqagpuq4Ig0NjYSEdHx6iBQCaTIZN5S3kkGp28JOEs1x5ZlslkMvzkJz/h4YcfHvnkVboLz/Qm0vz4AAxdR5nBHgfDCQaD7N+7l7lzm1m2YgVOl5t0RkOSRCRRnJIyHbG4ErO/EysSRCypHvW4zIRsT35FN5FI4K+uZuvWrZSVlWUn/rnXCIJQyPrIkpQNACYxZtu2cbvdY5YDjZeKigpOnjzJztde487Nm7M13JkMkiyPejxHC3RkSUK7QtNwYdyp8cuG5vdVWVXF3OZmdrz6Kpqm0dTUVNi3lCtNkgWwYv3YzmIEWaaxqamwKr5o0SKKi98qLRMEAUWR0XQdwzCR5bfGnc+cmLmadMcVAsvOrm46Ozq4/8EHSSYTWFY2qycrCi6ng6GTJ9m77wALlq2gpnkhAhCKJinzupDEkcfW7/ezauVNdB/ezc1GN4KQPWsca+5HWZQ1oLJtm/M9PXQFArhcThqr63CpCloqgSCICKLEwOAgL734Iv0DA3i93lF7EoZniDKaxs0330xVVVXhu02lUrz88sv4iorYdOcWLFkhY5iYziIW3HI7/YkMc4tkwrYDQVEwTRPbNJC0BLooEejowOPz0djYmF3YsCxi0Qi1dfU89NBDRMJhMpkMR48epbS0lL6+Pjo7O6mqqmLDhg2Ew+ERvgKiKFJcUoKuaXjcHrxeb6Hx1rZt/NXVdHd1XVVgfK0Ih8N84AMfYPv27fzhH/4h3/zmN6d7SDMb+/o7C892C1/KjAkE8lq7P/7xj/nyl7/MX/7lX07o/X/zN3/Dn//5n1+Loc1yDfjEJz7B9773Pb7xjW9cEggIV+kuPNObhPPjM3QdSZYnpJQzneRN4I4fP8aFC+08+OCDlJaWYJoWGV3HtuycMdrkAwNBuHK/gGkY054NCAaDxONxHnr3uwmHwyQSCSrKyxFzWaipVr+aiu0tWbIE27YZGBjAME2qiouRZTlb9qJp2V6NXBlRIBDgueeeI5FI4HA4ChPM/DiuNKa8mzAOzxVlQwOBANu3b+eWW27h/LlzRCIRNE27ZLVXlkQ03UBMDYIgYqseZDWrZ58v+xnNjE8QBFRFIaPpDAz0EwgERgQ3kihiAGaueX8sqqqqePnll9mzb39hBbuuprrw/PCeAnOwm/LaRgZSOkMpnTK3QpnXVeitqauro3iwnWYzFwRIMs5NH0BpXFzYniAIFBcXMzQ0RGdnnO7uHmrrGylzusG2sSwTLZPm1ltWk8ho7NyxY9Reh9G4uHzMtm1CoRCybVFW4kGwbRAluvv6celJXKl+nIBp+8h4yslkUqhAT18fi29aTyqTQRIEBElC13X6Bwbo6elh4aLFHD9xgqbGRiKRCK+++iq//ZnP8C+PPsrKlSvHLE1bunQpoVA/C1oW0NjYUHidYZpIosjcuXNHzSZPJ4ZhcP/993PkyBG+8pWv8I1vfGO6hzTLLONiRjoLu1wu2tvbWbBgAYODg8iyjG3b1NTU8Nprr407I5C3k591j5t5nD9/vvA9XnwKmjlJRHkSijC2baPp+hVX96aL/PhkSZoR5S0T5UrlMJZtY5oWppX9DkVhcoGBlYxiRfqQKhpG9AtYljUjAoF8RqChoYGi4mLmDFu5nmpM08S+Bo3vhq5jk63NzjdfmznH6EB3d2HfZ86codrvLzS/mqaJZdtjNg1rmpZ1u+04ilBajVgytnSoZVn0BoMMDQ6yZ88ewuEwmzZtQpKkS84x27ZJJeOoQ51YnjIEpw/F5Rl1m3qu9G54sBIIBNA0nd27dxOPx0asnluWhaYbONTLZ+fOnDlDKBQatzOuZpgMxFJENBNJgDK3Sm97KyU9xyjqyTaTCg43rrs+ilTZMOo2LvebK5yHjU1UVlZQU1192fMwo2mXvTZmAymd/v4QiizjcDo519pGd+tpttyyDIcWRbAttJ4LkIpjLNyAUDUn+17LwhYEIuEwgUCAOU1NyIqC0+XCMIyCBO7tGzfyxJNPctttt7FgwYIxj2kmJyiRH+9Y3+tM4eDBg9x888384he/4IMf/OB0D2cEM9VZeP+5TrzX2Vk4Hotyy/zZueFwpj0jEA6HSSaT1NbWAvDkk09SXl5OVVUVN998Mz/96U/51Kc+xS9/+Uvq6+vH7A9wOBw4HOOzf59l+rlcc1dfXx+6YSAKwoQnVzM+G2AYSJI0Iyazk+HilcThCIKAJAi5VVV5RGCQyUlYvhUYSIji2DfzsfoFZopk6Iga8/nz8fnG55o7GWzbviZZo7w3QP6YCoKAqKpYlkVFRQWDAwMMRSL4q6qYv2BBYcX8Sk3DyUSCga525nhsBNdbGZ3hE9ra2loMI2dKBQX1GK/XS21t7ajnmCAIyKkhbEnBlp0oDueI50+cOEF/fz+VlZUsaGnBMAyUYcFTMBhk9+7dbNq4kZdfeWXE6rmY69kwrct7ZLS0tNDS0jLuY6zKEjWlXsoNk/5YilBCwyVYhSBAd3gpeeAziL6yMbdxud/c8PNwbvM8ii5zHo5nzU8QBHp7exAEkV89+Tgf/cR/or6ulkwqyZBSirekHjMcxF1hojlKkIorspO6oiJMy8K0sopi9XV1OJ1OYrFYrpx3AeVl5aRSKURR5O6776akpIRINEpJaSnJXHmSbduF4ysIAlZO4sW27UKfx0wMAgBeeOEFnE4n99xzz3QPZZZZJsS031EjkQgf+tCHCheIyspKnnrqKQRB4Hvf+x6f+tSn+Ou//muKior40Y9+NN3DnWUK8Xg8JBIJfvd3f5d//ud/BrKThVdeeYXFS5ZwYP/+Celw3wiSoaZlIVjWDdMXcDWMHRiYZDQNm8sHBhf3CwAzSjI0P0GzcvKv12ql8lp9ZiFX365rWqERGLIrry6XC1mWUVWVuro6nKqKoesYgoAiy6M2Ddu2TW9vL0ODg5gDvSTlMiJ9A9Q3uAsr10XFxRTlViaLi4sRRZGampoRDa1j/d5tPY2QipFUijBTGVRvcaHZfnBoCLfHg97Tw7Fjx7Btm/kLFhTq0f1+P36/H0VRGBoaYtGihZc0mSqKTCajFbw9phJVlqgt9dK2ew/NZU6EpiWEuy5woWolGy4TBIyH4eZrqUwGURQmfb5YlkVZeTnbX3mFxqZGLMvm8KFDrFy1CllRGYpESRhOvOVL8CqAKFFcrGKZJn19QaqrawjGYsiSxODQEPF4HNM0SSbiiJJEY2MTNhCNxZAkiVAoxOuvv8573/MeXorHCYVCbwUCCNi2RWtrK7quU1JScsXG4MtlT64l8Xicf/mXf+Hee++dXWWe5YZj2u+oTU1N7N27l6NHj3L48GFefPFFVq5cCWQluHbv3s2ZM2fYv38/y5cvn97BzjKlHDt2DIDvfe97nDhxAsiu2vX39yNJEqlUimAwOO7tzYQm0sthmiYC2YbL6ZrM2rbN//gf/wNBEFi6dCn/7b/9N77//e8XVmevJUIuKFBzzZUuhwMlV/aX0TSS6TRpTUPP6YXn+wXsTIKu86c4ffo0PT0913ycE0XMfS7DNK/J9qeqR2A0BEFAUdXsMc81euZXxevr61m+fDllZWWoDke2VM+yyGgapmkyFA6zc+dO2lpb0TUNXdOwTJPDR45QX15Ee28/wb6sHGa+p2LB/PkMDQ3R1tY24jdQX1/P6tWrLzt5CwfOE4rEEBweRNVBPJEko2kYpkUymURVHYQjEerq6ykrLyeZTGLZNkeOHCk4mT/44IOUlpWyYd36S/aVl/Dt7x/gwIEDl5htTQWlRV5K7CSRohrSt76XluUrr9rsLY8gCDhUlbQ2uoGcbdskE4lRP9uJEyfYt28fyVSKaDRKd3c3LQsXEw0P0dTUhGYYxBMJFG8JlqHx/G+eoCvQhSSAaYGNSFlZGZIk0tDQQJXfT1FREcePH6e7uxvdMBgaHMDn82Lb8PS2bZw9exZZlgvyyVkfhLdkXAVByHoN7NqF0+Viz+7ddHZ2Fs7Tix/d3d28+OKLbN++nW3btl2T72802traeN/73kdnZyd/+7d/e132+XbBtqfnMctIpj0QmOWdy5w5cwpa1UuXLiUcDuP3+3G73bS1tnLH5s0TkoabyWVBeQMxQRCmtbTlL/7iL/jjP/5jAIaGhvjmN7/Jf/kv/4UVK1bwwAMP8A//8A/XbSzZiddFgYGUCwwy2cAgYwsMaCIVTgGXIvHc888XAsiZhJRbIbdGcZq9aq7xnSvfTKtrGtoYmY38JNnhdOJ0OIhEIqiqit/vJxAI0NrWhupwYAOiqVHkcdEbThZ+v36/H6/Xy6uvvsrAwADzczKR48XOJPEpcCLQT2ugm0cffZSDbx7A5XTidDpQVAfHjx+jva2No0eOMDQ4SFtrK/v37SMWixGPxwulQDfdtBKnyznq/oPBXlKpFLt27bomk8nFjTU4FJl+TaC0rIKy0lK0XGA1FUiiiCJLBUff4fT19dHa2kprWzunT58h1N+Prhs5Kdk4ixYvzjaS6zorVqygosqPaRr4a2oIR2JYsgtLz1BVWsSG9etpbm4m2D+AYGQNviTFQVv7BWzLRBYFqqqq+NjHPsZD7343ZaWlNNTXY5kmNjb/6ZMPU1xcQk9PDxs2bEBVVZYsWTLiOJxvPY9t29y5eTMd7e34fL5CsGmZJuZFD8uyuHXNGgRBKHzf1xLbtvn2t7/NihUrOHPmDD/72c9G+AfMMsuNwmwgMMu0snbt2sLfN2zYUKh5bWpqoqmxkeqasRsNhzOTJUNbW1s5e+4chqaNqFm+3ti2zQ9+8ANuueWWrJZ3RwePPvooAKdOneKZZ57hD/7gD/jXf/3XUd8/NDTEY489RiQSGfH/hmHQ0dFBIpEY1zgCgcCIxv48YwUG4bRB2JSJWgqpdHpMN+rpRMiVzOi5Zttrsf1riSCKIIpZtZhxYNs2B998k47OTvbu21eYdNXX13PPpnXYts2S1bcVVt3zv+vNmzezfNkyfF4vumGg66OvXl+8LzMSJK5DdeN8Thw7NkJS0rJtSstKqautLejNL168mMrKSrq6utB1/RIFIkmWMUfJggWDQQ4fPkRLy8JrMpm0YyGQVVbcuoHi4iLC0SjHjx8nEo2iadqUnDvZLBt0dXeze/duWltbsyWJokhNbV2hJHNocBBRFOgLBjl77iyH3nwTyzAoLS1l+bJlyKJAdU0tgihR5q/Fo4ADE0kQaGtvxzAMXtv1OggChpYmGglT39CIjUA0FiedTpNMxGk7f45MTpVKdToRBRHVodLQ1IRt2wW51/Pnz3PixAna2ttpa29ncGAASZJ4Zft2urq7OXvuHOTK2WRFQbnoARBPJCgvL78u/gJf//rX+fznP8/DDz/MkSNHeO9733tN9/d2JO8sfL0fs4xkNhCYZdrpzimUnDhxgn/6p38qlAmUlpYWVnquxEzMBti2zYULFzh16hQlxcU8+9xztLa2Xpd9JxIJgsEgg4ODhcnFzp076erq4qtf/Sp1dXXIssxnPvOZguTiU089xfLly/nkJz9JS0sLn/zkJ/m7v/s77r33Xurr6ykrK+PDH/4wlZWVLF68mA9/+MN86EMfQlEUmnJOoc888wxaTu1jNP78z/+cefPm8elPfxqAo0ePsn///lFfmw8MDF1n20s7EVQnq9bdXpgAzjREMauONFWruzC+Bs+pwMxlq+ScWdNY+82q6+hEIxHOnDlDY2PjiEk5QJEqIDi91DU0AtnA78CBAwCF8p98FkIQRTRdv+xv3E7HQUviq23GX11NfV0tGzduLEhK6rqOKissWbKkEATAyODj4l4jUZSwcspWw/H7/fT19bGgpWXKJ5O2oUEqCr4KBEEg2NtLdGiI1157jV889hiDQ0NTlh3o7u5C0w1S6QyCINIf6mdocJATx49x8sRxenq6cbvdSJKE0+kkEY+zd98+nn/hBWKRML092XIeG7C1NB7RxCUJ+NwOvE6VLXdsQlVk7ti0CUsQcKoyxUVFnDp5AkEU8RYVoygKbq+Xuc3NuBwqkqJgGiaGZTI0NIRpGqxevRqv18tQJMKCBQsoKytD0zRcDgfzc4ISob4+amtrufvuuy/rGVBfX09TUxO3b9o0od6yyaDrOn/7t3/LH/3RH/GP//iPI7wrZpnlRmNGyodOBTNNLmuWy/PGG28UsgPpdLqgAJWX27xcI+ZMkAzNyy9allVwkwXoC4U4fvw4HR0dRKNR1q9ff9XGUGORTqf567/+a97//vezadMm4vE4AF6vl/vuu4/HH38cyErtjuZCClnpx7//+7/nT//0Ty+ZkKxZs4bTp08XzPqWLl1KMplEEIQRAU5xcTFz5sxh7dq1JJNJbr75Znp6ejh48CAvvPACkC2l+aM/+iP+5m/+BoCf/exnHDhwgD/5kz8Z4TSe59ixYyQyOuWN86kpcuFxTLvOwajkz0UlZ9I1FdszdP2aKkxd/BszclmNi7NXRq4EI/+6Y8eOUVRcTCqZJB6P4/f7qautwbpwBKGkGrG0hkAgwNNPP52d+KsqW7dsobaubsRvOb///v5+zp87Vwg088+ZfeeJJjIEdQW3201Tw1sTPMuySGs6Loc64ayJoWvZPpSLSvUCgUD2/yWR6urqMd49ceyhbuzBANQvw0IkGo2wfcdO2tovALB582Zuvvlm9Fym4moUcvK9dWtuvZUXX3iBVatWEQwGWbBgAcFgkOrqalasWFF4/bFjxxgcHKR57hz8lZV0Broor65FtAwee+wxTMvi/gcexO3xEI+EcagqXq8HLDMrFWtnA3fdBkmUsAQBEDBNA0USsWyIRGMIWJQUFZPRNRLxOP/xxJOsvOkmli9fzvMvvMDKlSspKS4mlU6zb98+HnjwXfz0X3/Chg0bAFi2bNnlj3G+cf8a3wtM0yyUsf77v/87GzduvKb7u1pm2nwoP549p6dHPnTtwln50OHMBgKzzBg+9KEP8fjjj/OVr3ylMEGE7EXXtKwxb4x5qdHxuJ1OBYUJf+7PfA23kJMgFHINy/kJ8gsvvFCQRrzSqtZECIfD/O///b/5wAc+wG9+8xu++c1vXlI2c/fdd2PbNq+//jrJZJJ3v/vd/OpXvxr35zx58iThcJi1a9cWJrbhcJjXXnuNBx54AFEUsW2bXbt2sWPHDkpLS3n55Zd58sknL2lAXrNmTcEX5PTp0wiCwLp163j99dcLr9m2bRsPPPDAmGPqi6VJZAway9yXNX+aTqZS79zKZcSulRLWaIFLPvgQxewk2bbtbHDApZPTYDBIoKuLA/v3Y9s2779/K2VmBLF2EYLDzYEDB94yzhME6urqKCstvWQcsVgsKw+pqrS3t+NyOlmyZAlWIow52MUvXtnH+jvvZvvLL3LL6tWFCWE6o6HI0qR++7ZtoWc0VKfzkuey36GJQ730uE9Gmca2bezOI9iCjFkxF1lR6e7tZdu2bYVrw/BVbNM0MQwDWVEmdZ7nrzt3bdnCKy+/zO23384bb7zB6tWr2bVr14h9HT9+nNdee43ly5dTU1ODIAh4PG4UlwcJG0kUGRgYwOP1oqgqmqZj6Dput4t0Oo1hGASDvSxsnkNWDBYEbGwbTBsQJWwETMskkUhSUlyEbWUXGSKRKLF4HEmWqaqszGZK+vqQcu7CLkWm9UIHzz33XKHs60pomQzqdZASP336NA8++CA9PT10dHTM2EwlzLz50GwgMLOYDQRmmTGcO3euYChz8Wmp63q2dOGi1bvxZAwmS36Vf/hKfx5RFLMT/9xN+nL7Pn/+PH19ffj9/ikLAgA+8pGP8POf/7zw7w0bNiDLMsFgkC996Us8/PDDhQlSvo6/vLz8uqSx86VJoVCIN998k5tvvpnbbrsNgJ6eHs6cOcOKFSvweDx88Ytf5J/+6Z+wLIsf//jHfPKTnxxzu5Zt0zmYQJUlqoucM7InBCgEQZMxxRtOvo79WjSYBwIBNF2nyOejoqJixHO2bRONRunp7aW6uhqvx1Mwdiwotdh2oV/kQns7xSUlVKsGHnSEhmWIosiZM2ewLYuntm3D5/Nxyy23UFVVdYlR2JmzZzFNkxPHj3PnnXfidLlQZAmz9xwJA3CXEk1leOI//qMwITQtC03TcTknP+kb7O/nfFsbJSUllxiEhcMRTp8+RWVlJc3Nzdi2TTAY5PiJk5w6dRJZksZVgmJZFmZ8ECnUCuWNCMX+wnl7JYM+XddBEDh/7hyhUGhEtuRKnD9/HlEUURSF+vp6AoEATqeToaEhotFoNoNTV8fhw4eRiqtwuNw4lEvPs6z/h4AAWJZZkCfNrvlnH6IAggAyFk5ZxAY03UCVJbCz105RAJvsQgmCiGYYyJKEQDZgsCyL3t5eFEXhjTfeYPOdd+JWZf7lRz/G6/WOKAe7HLqmIV8nx/b/9b/+F3/0R39EQ0MDJ0+exO12X/N9ToaZNh+aDQRmFrOBwCwzivwNsr29naampsL/j1VycSWX0/EyVmmPkFvhH77SPxMIh8OUlpayadMm/vt//++Fv9+oaJpGeXk5RUVFvPjii5ed7KR0g65wiiqfkyLnzPWM0HSdgf5+enp6Jq1rnl+ZFye44j3aBHN4YDs0NEQgEMC0LPbv23fJhDYQCLBr1y7uu+8+LnR00NDQgNvtzk76cgGwIAh0dXUxMJCV29R1nU/fuxbR5cMsrScajTIQCtHR2YmqqoTDYc6fPz9i9TvvwRDq62Pbtm2kUinmzZvHunXr8Io6VjJKylGGrqUIx5Ls2bOHNWvWsHTpUlKZDOokV8wh6xLc3t5OZUUFHZ2drFq1itq6OrChL9TH4OAglmWjOlT8/mqcDgehUFZ5p7i4hJdfepHNmzcXHJeH09raSiQcZk5jA16vBynSA4khhDmrEMSJfZftF7KlQ7typmubNm1iyZIl43qvZVkYuXIugIHBQXbu2MGFCxdYsWIFq2+5hXAsQRgX7SeP0HPuJBs3beTo0WPcfd/9HDp0kNrqGmRFwev1EY1FKa+oJJVKozqdGLqevT5KctZwTgCvImLZFpFIhJKiYmTRRrBMbMASlWzOQMjL4oo5nwyBPW/spbe3l6amJpYuW0ZkaIgir4dAT29B7ng8GIaBwLUJnvP9VIqiFO5De/bsYf369XzjG98oKLLNNGbafCg/ntdPd0xLILB+YeOMORYzgZmZW5/lHcuXv/xlALZs2TLi/wsShxc1Mk6mSdjOmVoZhoGmaWiZDHquSS9/A1FUFdXhQFFV5JyB0kwJAoCC2s8XvvAF3v3ud9/QQQCAqqrs3LmT7u5uvve979Ha2jqmt4FLkSlxqfTH0+jmNZDrnAIEQWCgv594PH5VuuaxeJxjx49P6L15865YPE4sFiMSjZLRNHRdxzSzDbKh/n727t3Lq9u3j6qOEwwGqW9ooK29nYqyMroCARyqipr/PeQCgfr6enw+H7ffcQdb7tiIZJsMJHXSqRSWaeIrKuLo0aNouVXaB9/1LtxuN4ODg9l+k0gESZKorq7mgx/4AB/9yEdYu3YtPq8XIxkjIPvpzEgENYV0JsPdd9/N0qVLc79V4ZIgoLW1taCUkw/s8791XdfRNI1M7uErKmJhSwuqQ8VXVEQ6k8ldW2yikSjnzp3j+PFjbH/5ZU6dOI7L6UAUBE6fOoXH6ymYlF1Ma2srsfAQ/qoKXnl1B51d3ZAMg6d0wkEAQHdXAJ9D4b47b6espIj+/v5xv1cQhELwB9AXDLJs+XI2bdpEMpnk3NmzmA4f2BalDpGNGzcQjURYtnQJWCarV62iqb6WYo8LpyLSUF2FWxbwKAIeVcTrUihyyMhWBrcsoFg64aFBBMugosiDgk4qmcSUFGzFycDgIGldR9MNbBu6e3qyamO2nQ0Cm5tZtmwZlmlSXFxMNBZj165dE2qaF0VxymR8k8kkv/rVr3jkkUfYtGkTFRUVOJ1OVFUt9Fl84xvfwLZt/r//7/+7Ln4ss8wy1cwGArPMKL70pS8B2bT2E088MeK5i1VNriQZOnwioOs6WiZTmPTbufcqivLWpF9RkIZNcmYq27Zt46/+6q8oKSnh5ptvnu7hTBk33XQTt956K//wD//AvHnzWLBgAQMDA6O+ttyjIosifbH0dVPWmQh5F+W+UAhgUlKUgUCA/lCIF198cUKBRN68q8bvZ9++fZw7e7YwiVcUBVmW8Xm9hQniaOo4fr+fY0eP8tKLL7Lr9ddpbGgY9TifOXOGttZW/FVVVPmc2MChU+f4xS9+QSKRIBKJsGzZMiorK1m9ejXdXV1svvNOysrKcDidRMJh4rEYpmni8fkoLS8nmUrR036OXqUSTUvTduIItupCKKqkvKIip1xkoF5UxtLW1kYykaCsvBwz5zacD3zyPgiKouBQVRyqikC2mf/ZZ5/j7JkzCGR7ILKOygrB3l66u7oQBKFwfPJKRLZpcd8DD1xazmNZFHnc9ASDHDh4mOU33ZQNAiwToahyvF99AdPQWbl0Md29QZ7Y9gx3bVxP3TgllSF7zQyHwxw6fJju7m7q6uvxeDy88sorBAIBSqtqiGUMqnwuGhvqOX36NG2trXR0dBQagVvb2kimUvT39xdWxB1OJ4JlkUkmMQFBUpAxcSkCFcUeJEHAkhQs2YnTVwyCiGWaBINBDMPg8OHDGKZJbV0doiTR19fHyZMnkVWVVCqVNYoDFKeLO+64Y0QwM57PfDXXBNu2efXVV3n44Yfx+/28973v5bnnnqOxsZHPf/7z/N//+3/59re/zec+9zk2bdo0Yl979uyZ9H7fiVwP87DRHrOMZGZKb8zyjqW8vJzvf//7fPazn+X9738/GzZsYPv27YVaa0kUsUSR7u5uTMtC1zTmzZt3xdIeSRQRrkKFY6Zg2zYf+9jHiEajnD59mrlz5073kKYMQRD4+te/zv/7f/+PZcuW8eUvf5nq6mpuuukmfvWrX1FXVzfitf4iJ51DSSJpnRLX9ClGXczwvpXjOfOzyUhRBoNBpFwz9nBDrCuRN+96+ZVXuPeee0ZtnMxPaMeqT7/4eY/Xi67rKIpCV1cXfX19NDY2UlJSgqqqCKKIbKQZSqRpXrCQU2fO4XA6KSktxV9djSgIDA0OMq+5GY/LhShJ9A8MMDAwQDKdpmXBAmwELly4QDAYpGbu/Gx/RGyQcqeIkI5hO320hSKoWhy3203NMEUfy7Lweb2E+vsJnD1LW1vbZRW6DF2nvLycaDTK2rVrqaqqGtEj0NzczNatW0ft7amvr8e2bVK5DEL+mmKZJoaWJq3pXOjoJB6PE4lE+K3bV2JbBji8jPfqY9s2ppbB1DOoLi+S6qBl4SIiKY2G6nJsyxxXduHMmTMEAgGWr1iBoesYhoHX6+Xjn/gEsqwwaCpIpkWxCnpJCUuWLCEWi9HY1EQkEqFUkmhsaGBoaAhREN7qiYJsj0Ymg+py48JAQMCwBTJ2tqdLlm1UVSWZSBANhykqKaG6uhpFklhzyy1omsbBN99k+fLluCoq+fjHPobD5SIWjSIqCmePH6eyooK9e/dy5113oeXK5OQrZGfzz03Ukbu7u5uf/OQn/PCHP+Ts2bPMmzePL33pS3zkIx+hpaXlsu9Np9P8xV/8xbjLl2aZZSYx2yMwy4zk8ccf5z//5/9cUKz5+c9/TmNjVpf81KlTCKKIz+tlcGCAurqsSc5oqj1vJ2zb5q/+6q/42te+xhe/+EX+/u//frqHdM34/d//fb773e8W/v3LX/6S97///Ze8biiZYTCh0VDqzjYmTjOmaWKYZqGXZTIKM3kCgQCDg4MF9+2JaKPn99vQ0EBpaemUGNkZhkE0EuFXv/4169ato+PCBRoaGxkaGsKyTNbWeTnU1k1P0mbxkiVUVfmJRSM4ndmJvyRJhMMR3B43lm1jmRaiJBX6e7IZPpAlmUQ8iiwrKIpMoLOTc+fOMX/VWgzZSZmQYfuLz3HzzTezbNkyTMPANE26e3o4cOAA1dXVHDt2bEyFLkPXsck2cl/NNUI3DMLhCB0X2mlqqKfI50VxuBBEsSAQUFtVQQNDWMXVmEXV4xI1sG0LPZXEti0khwtZHvndWXoGM51E9hYjCGMn9W3bpqOzk1AoxO7XX0fTtGxwEwqhyDLuknKcVY34PQoVXgcXLlygtbWV+oYG3tizh9WrV5PRNBa2LCSRiOHx+hAEgVQigUOREEURQ5AxM0mS6QyablJWXo6uZUglEpSUltLT08PZs2dZumwZJSUlKJKEruv8+je/4V0PZuVILdvGtiy6urt56je/4c677mLR4iW0njuLKIp4vV4aGxsLCz15h3ZZksaU6M33LlxJTcowDLZt28aPfvQjnnrqKRRF4QMf+AC//du/zebNm98295CZNh/Kj2fXqenpEdiwaLZHYDizGYFZZiQf/OAHee9738t3v/td/uZv/oZFixbxt3/7t/zX//pfC0o0/qoqwpEIS5cuveFr5MdDV1cXX/va1wC49957p3k015aLlY3GqosucakkMgbBWJr6Eve03bht28YwTWzLGjHZq6+vn7SxUX19PS6nkzvuuAOv1zuh7Qzfr65pWKY54Ybji5FlmVQ6TW1tLZIkEYvHSaVSqKpKb0cbRvMq5q+8jfmyE0HM6snohokDGBwcpKqyEq/HldWb///Zu+/wuKo7gfvf26Zp1MuoWbYly022ccEU2xgbTDWEQCiBkLYBNuUNm+xuKqls+rIpb/JCEkgvLAmBBHBoCxgbMAZssI27LcvyqIx6GWlmbjvvH6MZJEu2Jbmb83kePWBr5s6ZcTu/e34lEae3p5dAwE9HNEpXVxczZ8xA03Wefvop9u3bRzAY5OKLL0bVNBacfTYZfg9Ry6S4bRvXnFXB1s4OIpEIebm5GB4PkyZNwnEcgpmZlJSUHDoIEAL9GHQZizQ3I1AQtkVLS4S4aVFengFAVVVV8qSysxHR0YmWXYSq6elaiUNtUF3Hxo71I1QVw5cx4q+ZangRrovd14uekTXi+3BcN1lorihseOMNTNMkGAzS09ODbZrU1MyiIWrj2iZmby9v7W2hcvJkzjrrLPbs2cMFF1yQ7pTU0dVFRiCAKhxc08Kvg4uCpRpEe7rJCASIxbrJy8/HdR0cV9Ay8Oc1KyuLadOmUZCfnzzNdV0ira0sWrw4fVrQ0dlJfn4+Tz/1FHl5+UycNBmfx8OsWbPSp2tAOr1L07R0QCBsO/lzB6VzpusEDvE579q1i9/85jf87ne/o6mpifnz5/OTn/yEW2+9VQ4HO4HcgRTKE/2a0lAyEJBOWbquc+edd/KRj3yEj3/849x5550UFhYyd+5cfD4fDY2NBINBCgvHnnt7Ovr0pz8NwJ///GcuvfTSk7ya4+szn/kMb775JtOmTeMnP/kJO3fuHPFxyRQhP/UdfXT2m+RlHP/+4QdLbVY0VT0mG8zBcnJzyc7JOao2pLphYJkmxjGofRFCMH3aNFRVpbe3F1VVmVJZyeyKIizVQPP46WhrxRGCUChEfl5yOnhRYQGaAtH+Pjw+P6qi4A8kTwpc12X27Nns3rUL07KYOnUqBQUF5OfnEwgEUGP9ZAczcAER7cfxZpHR3868bIPXN79JfumEdEpGVVUVCdOkaIS/E45lEACpGQphMgMZvPjSyyxbtmxYW1TR2wq+TBTDh0KyKN6y7WFzUYQQuJaJbSVA0zC8/sMOpNO8foTr4MSiaP4giqIQDodpaWlh0uTJBDMy8Hg8TJgwgeXLl9Pa2kpRUVGyBazr8ta2nZTPmIvW38kLr6zhvPPO47XXXqOmpmZIOpWiKLS1tlFQFCKgOnT1RsnOK8B2BR2RZnJyctixfTsZGRk0NzVRUVFBzO5n6rTpKEKgqQrZ2dnYtk1HVxeZmZnJ1CrXpTcaxUwkCIVCdHZ2MnfuXKbNmElWZiaqqqRfX1EUXNcd8nmoqopnIG3OcZxk2tBAe+lUOqh7UOFuW1sbDz74IH/84x957bXXyMnJ4eabb+aOO+5g7ty5R/37QZJOVzI1SDottLa2cvnll7Nx40amTp3KL3/5SwByc3OZNWvWMZnieip75JFHeN/73odhGDQ2Ng7r+36m+uY3v8k3vvENbr31VqqqqqipqeH6668ftpHrjpm0RhOU5wTwGScuRch13WQ+9FG0sTzS9Z2BQVtHdZ2BqcDjuU5tbW16Gm15+QS6e7rxeX24wiXaG8WOdpCX4WNHYwdTpk1HUxUS8Th9/f3k5uSkO26JgfaSff0xXn7llfQ8AV3XWbVqFd3d3SxcuJDCwkKmVlcne+gj6OuPUVdXRygUoqurC9NMMDEAWfE2ErbL3oSPsxYvT6/XtKxhaSO2bSMGBrMdq0At1Z1ppIFgACLWi2jcjlI4eVihcKqTkWEY7N9fh9/Q8fn9+AMZeLy+UfXAF0Jg9/eiaBrNbZ28+uqrzJ03j107dzJnzpyR5xKYJvXhMFE9E103KNAt+vqirHnxRTo6Ooa0QxVCEDcton39uJaJJmx8WTlYtovfo9Pb00N/fz+u62IMFKJ7vV6am5spLS3FdgVvvP4a++vquOY978Hv93MgHGbz5s1MnjyZoqIicrKz8Xg89PT0kFdQSEdnJ3t27aSwsJDp06cnPyvXxXWcw6a3DW4MAcnOb73d3Wzbvh2v18u3v/1tnnjiCYQQrFy5kptuuolrr70W3wjD5M5Ep9p+KLWe1Vv3n5TUoGU1E0+Zz+JUIE8EpNNCYWEhGzZs4G9/+xv/+Z//ybJlyzAGOgjV1NTw4IMPMnv27JO9zOMmNTistbX1XXV0fdlll/HnP/+Zt956i+eee46mpibuvfdePvGJTwx5XJbPSKcITcgNoJ6AFCF74M6ux+M5bilJR9sBJUXVNJyBjdJop/AKIWhoaCAWi1M9dSqqqhHtixLtjdLd1UVhYSGZAT9eNUBU9VM5JY/O7m78gSB+rx+nt5fOzk4yg0Ec1yUei6U78uTk5NAz0Nb0lVdeobu7m8zMTConT6agIB/LMtGNZKtSnz+QntpaXl7Otm3bePjFtRT6FK6YXcHMoIXbVo9akKwhSqWFpAKBIwUB463jOFLBtehtA0WFYN6w52oDgUp3Vxc5GQF27dlLYSiE0t3L5FEOHVQUBT2Qid3XjUeFyZWVPP3UU8Tj8RHXk5rwjDeIqnrJ0iyCGQHq6vbR0dExpKDdsh1iCZPurk7CB+qZM6sG4bqYlkMs2suWvXsoKipKd2IydJ3MzEwikQiu69IXjdLW3k5pWTmZWVn886mnmD9vHhMrKrBsm30DtQixeBzXddm8ZQvnnr8IxXXo6elh27ZtCCGYMWMGqqJgDzSAONSfs4PThiItLaiqyqZNm1BVlVtvvZULL7yQW2655V1zgixJoyUDAem08r73vY/3ve99QPJu6a9//Wtuv/125syZwx/+8AduvfXWk7zCY08IwV/+8hc0TXvX3cE477zzhqQFffCDH+TOO+/kkUce4bzzzuOss87i6quvxuv1UpTpo76zj46+BAXB43enTwiBNTC06HhMtB7sWF5b13Us0zxse1zHcXGcZICDgIyMIM3NzcnWp0IggJqZM2lva6O+vp4poRwc3Utvnwm2ghHIRNF1+myBkZmPrrgIVUGI5KmJz+fDo2t4vV4MXScrK4uioiIWzJ9PSUkxuq5jGJ7D1jPMnDkTIQTt7e00eLOYpPTgNuxAxPtQy6alN45w+CBACEFTUxObN29OdiFznDEVZMOha0CE60C0HYJ5I3b3SaUCYSdo7+klEAzy+BOrOOecc0YdCKS4ho/MgE3TgXri8fiIHapSd8obm5roMnV0NU5L2wHU4mKmTZtGfl4efr+f0rIy+uMJHFfQ3Bjmheef59YPfjA5SVrRcBJxsrMyQVE4cOAA06ZPpyEcJpiZSUYwSFFREQrJU5mgaZKVmUlJSTEzZs4k0d8PikKoqAjXccgIBEiYJrphcO6ixTTU1zNpYgUTJ05kx44d6bogRVHSKWSHC2Jd12XVqlX8/ve/557/+R9279pFf38/AHPmzBmx2YB0cskagVODDASk05aqqtx2223U1tby3e9+l+eff/6MDAR++tOfAslOOmdKF4vx+vnPf04gEODZZ5/ljTfeoKuri7lz57Jx40Z0TaUw6CPSGyfg0Ql4jv1fb67rYtk2+sDdxxNlrK0QR5Kaw9He3s62bdsoKChg+vTp2I6D67i4QgxMDk4O71NVlaamRjZs2EA0GiU/P58rrrwyXXha6prgmHQrGeDzEfB68GmgaArChQQuaAYJRyBUDc3rQdGSQcjEigo6uzrBdTln4dk4jkt3T++opzAPbtMorARO3SZE+wHcRB9KxRz6+2Psb2khLzeX3Ly8ZC656yJcgStS7YWTBc2Gx8OKSy7lbw//ddQtWo+orxOEi5I5PIVPCIEd70/+mho+du3ayPbt28fcYjaVlmYYHhQ9l4uXLmJyZSXZuXnp97Br1y66u7upnjKFrOxs+lwNj89P867NbNnwGsuWLWN+WRmBQACBQrQ/jsfQ8Xt1mpuaiEajbNm8ma1bt3L1Ne+lvb2NvJwc5syZw7Zt2+js6KBsoKg9Fouxe/dutm7dyo033EBPVxcZfj95eXk4gKEbWIk4Ho+Hqqoq+vv7eeyxx5g95yyqqiqZPHkSHR0d6Lo+rPZL07TkicYIf+Zc1+XBBx/k7rvvJhQK8b3vfY+uzk727t0LjK91ryS9m8hAQDrt3XXXXaxbt47f/OY3XHzxxXzgAx842Us6pl5++WUAvvzlL5/klZx8GRkZ/OIXv0j/+L777uOTn/wk99xzD5/73OfI9Bn0mTYtvXEqcjPSRYdHI5U6Ul5eTk5ubro16ImiDBRFHosgcPv27bS1tVE1eTLBzCz6+vrxeAx0PZmqcvD7OriffkF+PpZtE4/HycAiZmRiCZ2ABh7VxTKt5KA+nw+fx8B1HHwejQMNTfT29TNhUhVRW+APBJmQkUFvNMqf/vwgF198McJ1eeWVV/B4PGO6M68YXrSqs3HDWxGdTZjbXyaaUU5GMJOe3iiqbiSLjhUFVVXQFANFYaAI1aF27178fj/V1VOP2YZR9LaB7gVf5pCfdx0HK94Hioaie8jyZzBt2jRyc3OHzSsYSer34sSJE8nMyhqSlubNzKV6ko6RkUwd3LVrF6tXr+aySy+luaUFW4Cl++lpb2HLhtfSG2QhwHJcXNcl4PWkC9MLCwsJBoMEg0Fs2+aF55/j4hWX0NzYwOtvvMG1115Le1sb5WVlJEwTr8dDV1dXMjWor4+GxkYCGRnkFxSgOg5mPI5q6HgUhddff53CggIuXrECj9dHSyTCg88/z0c+/GEMj4cLli7FcRzWrFlDQUFB8hSI4QHxhg0buP3229m8eTO/+e1vuWj5ckpLS1EUhWAwOOIcCEmShjqzKyyld4WMjAyeeeYZZsyYwW233cYjjzxyspd0TO3fvx+A9evXn+SVnHo+/vGPc+WVV/LjH/+YpqYmAAqDyQm3rX2Jo75+qiC0PxajqbmZ1oHc4xPpWNUJALS3t7N582a8hkZjuB4zFkVNNsE8ZKBRVVXFeeedR3l5Oa7rUrdvHyLRh6Po7D7QhO4kMPv7MHSdv//jH8m1DtxxN00TFUj09bJuzQuIWDcaDt1xm9q6Ovbs3cuKFSvo6uoCkoOoxjOFWVFV1AmzUEuq0V2L3M49bFj9DI888jd2bN+G15OcSZDMzX9nxkgqzz8rM5MLli49JqcBwkpArAclsyD9OuFwmNrdu0j09YBmYPj86U18VVUV559//qiCgKeffhp/IMD27dtpbWkZ2jJTN9B9Gdh9PQjhEo/Hue666+jr7+eVl19mX1MbAoXy3AyWLVvGypUrKQwVE43FMXSdaHcX3d3dNDQ0ADBjxgwuuOACOjo6WLx4MSXFxQPpidlUVVbS2tJCaVkZzc3N9Pf3s3fvXqqrq1m+fDldXV0sWbyY6urqZB2Bx8OWLZs5sK+Wnu4udu3cSU9vL/kFhSTiMV5++WXmz5tHrL+fTW+9RSAQwHVdXn/9ddauXcvWrVvRNC2d4gTw1FNPsXjxYkKhEOGGBm6++WbKysrSn8loP1fp5HGFwDnBXzI1aDgZCEhnBMMw+PnPf048Hud973sfgUCAJ5544mQv66glEol0APBuTwsaiaIofO973yORSHDLLbfQ29uLpioUZfrojVv0JYa2EAyHw2zYsIFwODyq60ciEaLRKOWlpezeuXPMG9RjQR3ouHMsFBQUJFtYmiZrX1lHpK0DTTcQrosVj2HG+rHNBK7jIIRg69atbNmyhe6eHlxXYNsOJaWl4M3gQGcfa557ht07d/Liiy+SME2uueYaurq6aGttpauzk7b2DqLRKNXV1Vy5ciVZ2dm0tLbhmHFeffVV8vPzqaysJD8/n3giQVlZWfpO9Vh/rRRFQS2aTFugBFVVuGx6iIVVJUe8y19eXs6cOXPw+/3HJuDqHZh5MZAWFA6HiXZ1kpebw+qX1tHa1j6uYDISidDT0wNCUBwKoakqjm0PWfOO3XupCzcS6+qgavJk1q1bx5NPPgmKijengCyfzqTyUubOm0dOfgG27RD0+2iNNPPCCy/w9pYtxBOJ9Gc+c+ZMli5dyqxZs1i6dCmZGQHyCwq44IILqK6uJtLcTEYwyMsvv8zzzz/Prl27mDp1KjNravB4PMle/gPy8/PJzc7iqaee5pJLL2XK1KmsfuF5fF4vy5cvT74PRWHmjBkYusHkykpWXHwxCskAVlPVZO0K0Nvbywc+8AG+/OUv88ijj1JUWIh+AlP1JOlMIgMB6YyxdOlS6urquPbaa4nFYlx99dV87WtfIx6Pn+yljcsf//hHqqqqAPjoRz/K1VdffZJXdGqaPXs2d999N6tXryY/P5877riDhrpasnwGLb3x9OYhHA6zb18d+fkFdHf30NbWjm3b2I6DM5AaIYQYsrEKhUIEg0Eee/xxFi1eTEFBAXv27Dmh709R1WN2F2vmzJksWbIEf8DPBRdcwMyZM1E1Dd3jxeMPYPiSk3Ed26Kns51QYQGVkyZixuN0tLfh9XoIHzhASyRCUWEh5WVlTJ48mbMXLKCnuxsFyCsowO/zYVomDQ0NdPf24rgupSUl4Lpk5eSz/e0tLF68mBkzZtDQ0IDjOLR3dBAqLubKK6/E4/Xy1ltv8dJLL7Fq1apRBwMAJVNn0xqswEJl0eQCJnoSCHHkQErXNGzbOeLjDic5O6BtYHaAF+E6ZHh1eqNRtu3aw7bt28cdTIZCIQKBAE8++STPv/BCMlWG5CmKmUjQ0tJCX18fRSVl9ET7iEW7mTZtGueddx7nXnQZKCqhoJe4adEXS+AxdDL8XlRVoaWlhXMWLkQ3DMxEAt0wRgyKNE0DRUkP+WpoaOCB++9n965dRKNRWltb049VNQ17UC//GdOn4/F6mTJlCl6vj3h/jP6+PiKRCNXV1UyaNAlFUQlmZRPICNAQDvPWW2+x/KKLqK6uBlLpXC4vvvgiX/3qV/H6fHzi4x8faDUrnW5c8U7B8In7Otnv+tQj5whIZ6Suri7e+9738uKLL7Jo0SJ+/vOfn3btRS+//HKefvppvv71r/P//D//z7tmdsB47dmzh2984xs88sgj+P1+vv/9H7Di2pvw6hrFWT42btzIq6++yoSKCkqKS5hcWUlGRnISrKKAgkKyL87Qk5d4PE48Hid84AA9vT1MnjQJRVGYNGnSCXlfQgi6u7rYvmPHMct3NvujGP6Mw54yrVmzhj17dlNYUEBRQT6VkyeTnZ2FoqjE4gn6YzEygkHa25OtJy3Lore3l1AoRHdPD/F4nFgsRlEohMfjxTQT+AMZdFsKiY5msjKD5OXns7+ujo0bNybv6KsqM2fOxDAM6vbto7SsjHA4TDAYZOLEiWNK3UkVEdPfhRLMQ514Fop++F70qVz38Z6+iVgPonEHSlElri8Lx4zT0RPlH489zvLly1mzZg2XX375uFOQDtXqVAjBhg0bmFBejs/no6mpidygj/zCIlzdz86WXrJ8Onk+DU1V8Hk96Ra7qd9fa9auZffu3cyePZvzzz8fr8+HZ4Te/aZlY9s2Pq+HvXv38vzzz6dnKVw0aNPuui5mIoHP7wfANuMoqk5Layv9CZOnVj3BBRdcwKTJk3Hd5IyDt7dsoaOzA8dxOPfccxFCEIvFmDp1KrZto6pqug6hubk5PQissbGRkpKScX2m7wan2n4otZ5nNu8jIzPzyE84hvp6e7l0zuRT5rM4FchiYemMlJOTw+rVq3n44Yf51Kc+xZw5c5g9ezbz5s1j5syZTJkyhaVLl5Kfn39KDiOLx+M8/fTTfOADH+Ab3/jGyV7OaWHKlCn88Y9/ZMuWLdx6663cfvtt3PziGu7+4c/oTdiEQiF0XWf3rl00NTZSUlJMfl7uwDAiMXAikLyWqiY3pZqq4vUY7Nq5g/6+PgxN45///CfXXnttui/78U7ZqqurQ1EUuru76e3pwePxJO+uK8qQ1x78/0fqjZ+sO3BRlEOnU+Tn57N9+3Z27d5DY1MzwexcCopLEcJFxBM0NjSQk5NFwO9HU1ws12HXzh2sW7eOq66+mpzcXBRFIRaL4fF6UDWVtza/TcmkKlY//xwXXnghxQN5511dXenNpM/nw+PxULtvHwWFhRSHQvzfc8/x9ttvH1URsbNnPdrkeSjejJEfPxCIHKlN5eGkZgc4mg/XMjH8QYozsli5ciXxeJxrrrnmqPrYH6pdaaoT08uvvMK+fftYtmwZEyZMwDUT1Lb1IRQPHmHh8/gw9OR727p1K+3t7VROnkxxcTHz58+nrKwsffJgWRaWomAcNNXa0DUs28Y0LaqrqxFCpCcXp4IASHZ1S5gm++rq0HWdieWl6LqHrJxc1J4err3ufWQEAsRiMaLRKH6fjz1799DT00MwGMRxHCZOnIhj24TDYRobG6meOhWv18uf/vQnVFVl9erVNDc3yyBAko6CDASkM9r111/PVVddxeOPP86f//xnHn/8cX7/+98PecxZZ53FwoULWbRoEddeey05OTknZ7GDPP300wBMnTr1JK/k9DN79mzeeustvvOd7/CVr3yFCy5awcVXXE2WxzviAKjkBhAguUEaHBiYjoUQUFlVhbAtnvm/5/B4PPT19UFREdZAH/RjHUwOnpTqDwR4bf16LNPE4/USi8WSQUvygcNSOOLxOB0dHWzdupU33nhjxM2zoqrJc/nDLLumpgYhBB0dHeTn56dbdtbV1ePYNn3xBD0NzRQVFZGrGvT193Peuefi83pwHBs73o/h8RDMCBBuaCQvv4AJkyeTSCRYsmRJenLswZ2JUiceK1euZP/+/eiaRjAYTBcRj+VueqqIWPiCuE27cXavR514Fmpm/oiP17XkJnc8gUBydkAHri8TNB3D4x1SlJya7HusOkAN5jgOOTk5TJ06lby8PHw+Hz6/n47OOI6iobk2nQ0NuP05FBeXsK+ujvXr1+PxeCgqLGTnrl3U1NQM+2xNy6KttTU92bmysjLZFUrXBmZOOEydOnXEv6d27dpFOBymvKyMt7dsITc7C7+ioZCcCJ/qVLVt3VbWrVtHdXU1l11+OfFYLDmkzDB46623yMrKQgEqKiqIx+O8vWVLej6AqqrcdNNNx/SzlE4cx01+nejXlIaSgYB0xvP5fNxwww3ccMMNQLIA9y9/+QubN2+mra2Nl156iQceeIAHHniAv/3tb6dEkfGqVasAyBvogS4LhcdGURQ+//nP09PTw3/d9UXKJ1fR0d7GOTOrWbBgwRGfq2kKmvbOLllTMuno7GTRosVk52QTCGTgugJFVbFMk9a2Nvbt20dhYWF6gzseQoiBvv4Oqqqi6zr9fX00DfR0DwaD1NTUoI+QspGy5e23WbduHZdceikbN2wYefM8cCJwJLNmzRryY9d1yc7Kor6+ntdeew2ACy+8kLVr11JWXs6M6dPpivbx3P/9H/PmzsVMxJk9exZlRQVomopwdZSuWjbuqkcIl1mzkul6VVVV6XqYlNSae3p6sCxr3P3gFUVBKZoM3gzc+i24tRuhbFp6EvFgqqqSbHo09j9zbk8rinBRs4pQvcMH2imKkqz3OIoTh5EIIbBtG4/Hw4wZM3AdB8dxcGybxkgrPf4i2ur3EK3fyfx58wiFQvg8BhctXYKqqpimicdr4NgWqqqlT5o0TSMSDhPw+3nttdfw+XysWLGCqqoqDF3HdhKYloXvEAPq+vr6mDd3Lk2RCOcsPBvV8KAAfp93yONDoRAZGRns2rWLpqYmrrrqKjKCQba9/TavrFtHZmZmOq2lt7eXs846C2DYrAFJksZHBgLSu47X6+WDH/zgkJ/buXMn06dPZ9WqVZx77rm88MILBAKBk7TCdzZgn/70p5kyZQqXX375SVvL6cowDL7//e9z11138ctf/Zorb/wgP/n+t/np/3wf4zAb6YMJIRCuk5yaOiivOjWkqrs3iuHxUlBQyI6dOxFCMGPGjDFdP3X3X5C8K60PylM/1B3zQwmFQhiGwVNPPcUVV1xB9gh5sIqijrpDTm1tbbp3fTAjAyEEb23aRDAYpKqqitKyMjIzM6mrq+Mf//gHF154IStWrCAWixEIBnntjY3U1tYyc8YMZleW4vdorJg9CdOJ4rYfQMkqQjG8I752eXk5Pd3dnH/++aMaNHY4anYRypRzcPa9OWQSsaIMPRZJFQ0bxpH/edy2bRttbW1Mq6ok3+1C0b0oGTmHfHxquvOxCgR27dqF1+PB5/ORm5cHQqAOtOtUFAWV1K+xSn8sDqqG7vHR0xdj7dq1RKNRQkVFXLBkMcJxsC0z2aFKVVFVjVi0FysRRwhBNBqlpaWFqqqqdBqVAiN+Vnv27KGgoABl4LMMBAIIRcXv9Q4LGg7+/V1cXEx7WxvRaBQgXXeSqhUpKSnB6/UeddAtnXxysvCpQRYLS9KAr371q3zrW98C4Nvf/jZf+tKXTuideNd16e/v54033mD58uVAskvHa6+9xvz580/YOs5UD/39cWrmn8vLTz/Gv95+26ifZ5kJVE0/5OZt7dq1vPZackDTrFmzmFJdTX5eHpqmHfb3z8F3/zVdTxdwHq3BNQJFRUXJzkCD1u/aFq5jo3v9h71ObW0tzz77LLNmzSIQCNDV2UlVdTW6ruM4DtrAsLMnnngifWKxcuVKAP75z3+y/KKLeOH551m0aBEZfh/Rnh7+74XVLJ4/k7mTStHtZIoHgRyU7FDyvwd9BmYigcc7cqAwHkcqIh5t0fC2bdvYtm0b82bNpLe7izkFOkpeGUpu2WFf3zRN9GMwlC41MOzyyy4jHA4TKi4edqriuIJX6joQ/T3kK/1DJjKnagRSA7tSUnMgXNeho70NY+BUKmGaZGZmkZuXh6JpCJEsHFYUqNu3j5aWFiZOnEhRUQhXuLREWnjyyX9SVFjIRZdcSnZm5pBTtoMJIdInGa+9/jrbtm1L/5668MIL6ezslMPBxulU2w+l1rPqzdqTUiy8cl7lKfNZnArkiYAkDfiv//ovvvnNb3LllVdy11138eMf/5ibbrqJW265BZ/Px9SpU9NdZo61xsZGFi9eTF1dXfrnLrroIn75y18O+8ddGp/rVl7OI8+sZuKMOaNO/Ui1FD3cHdzUBNZoNMrbb79NQUEB+Xl5mJaFqijouk5DQ0N6Y15SUnLIu//HyuCiUiEElm1jC5GeGssoTwQikQimaVJRUUFrWxvRvj6eevJJent7WbZsWTrN6uDaizfffJPFixaxadMment7SSQSTKuaTL0QLDj7bDLy8zEmzsSJ9UJPK2p/J6K/C3QPZIUgqxBF9xyzQWqDHamIWFEUVEXFdQWaNvKvi+u6FBWFKCkpZf/+OvpaGxD5FSjBI3f20nUdx7ZRPZ6jeh+tra309vby14cfBuC8884b9ndFarB2Xn4+NSWTh3xvcFAwmJIcu4ymqnR295Kbk8O+Aw2UlZWSm5+PcB3chJUsNncFfbEYuTnZlJWV4wpBZ2cHfX19PP/888RiMc5btAjTTKBp2Yd8L67rYlsWu/fs4aabbmLbtm089NBDzJs3j9LS0mMy6E069aSGfJ3o15SGkoGAJA2iqioPP/wwd999N5s2beJnP/sZP/vZz4BkHrOu65immcyJLjv8nb+x+Otf/0pdXR033HADfX193HfffVRUDM9hlsZP0zTu/tJ/8ufHnuLxZ57nPZddfMTn2JaJbhx+wzZ9+nSEELS1tVFYWMi0adOSrzeQ8tPd00N3Tw99/f1Ytk1XVxd5eXkn7LRJGej8Yts27e3t1NXVUVwcojA354jPDYVCZGdn0xKJULd/P+ecey4er5f9A8WjKYMDD9d1qaqq4tlnn2V/XV06t184DpVVU6ia8k5nGT2QhesLYpslqLFu1L4ORMcB6AgjgrmQVQTa4U8txvWZHKGIWNc12tra2bFj+5BCadd1MU0LIQTxeIzGpmZq9+7l8ppS+oVO5iFSnAZTVRV7ILXsaH4PFBUVpQPQYDBIUVHR8PepKKjK8M3PSF2lUicBQggEguamZnq6u3Btm23btlFYWIimG8A7pyeWbZHo7iEjI8hjj/2D5uZmFi5cyNKlS5OtP+NxsrKzCR4izVIIgWPbxGIx7vrKV/jpT3/KokWLeP31149YyyNJ0rEhAwFJOkgwGOQHP/gBkDz+/8c//kFBQQF/+ctfePXVV5OTZsvLeemll1i8ePFRv15DQwOf+cxnmDJlCv/7v/97SrYzPROoqso/H3+Mn93/c/7lk//Gb37/R1ZefumIGyhIdmJJ5UIfyUg1AamCy7179rDxzTfxeb288frrXHjhheTnj9y15nhRFIVIJMKBAwdo7+jgjTfe4Nb338CR7klXVlaiqiqqqiZTQki2aT3vvPMIBoPDHp9K7cjMzOTcc89l0qRJyVOQUBFi4PM8mKqqGF4/ru7B8ueguxZKtD05oTfagaZ7ETnFkFmIoh27f7IOV0S8fft2DMPD1q1bk2lbmsakSZMRQqDrGpqmUV5eji+QgU8kyAoYKEUTR/3a2kBqla6P//0crnXnkNdSFNxBU5TC4TA7d+4gVFSEZSbo7e7C4/Ump2cMFAorikJPbw91+/ezZPFiLrvkEvpjsWHBi67p+DIy6Y/HaG5uJhgMpn9vV1dX0xeP41VH/jOUOgV47fXXueaaa1AUhV/96ld85CMfkX8HStIJJAMBSTqMmTNnpvNnb7/9dgAeffRRrrvuOpYsWcLLL7/MokWLxnXtvr4+HnjgAT7zmc8A8Lvf/U7+A3icTZw4ka9/8XOs3biFkspp1NTM4vnnnxs2bE4IgWOZGCN0fxmrUCiE6zi0tLSMu/PNsRCJRHjllVe45j3vYeeOHUOmvh5ORUUFjuMMKbBOpRs5jkNjQwNNTU1MqaoiJycnXaiautMciUTICvgIZucc8jUURUnWSGgatqUhsorR88pp37+LDKsfX9t+aK9HBAtQsovAGzxmJyojFRF3tLdRu6+OSy69DISgKBSivaOdkuLi9J9RIQSBQIBZk0og2gEZuaN/zYFuU+IIdSRHcqjWnUNfS8EZiANc18Xv9eI6Li+uWUs8Hh+S4jWYrhu0t3fwf889z7x588jPy8VMxNF0HU3T0wGD3+cF4bJw4cL06YkQgrhpogPaQYX5qS5HiXicL3zxi9x7773cdNNN/PSnP5VdgN5lXDjhk35l99DhZCAgSWN07bXXsn79es4991wWL17Mf/3Xf/GVr3xlXNd59tlnAfif//mfcQcU0tgEg0EuWXwu+zuifO6r3+Sb3/wmDw/kWae4joN6lJu0lPLy8hHnF5xooVCIYDBIU1MTVVVVaJo+6vSUgx+hKAoew+DAgQM4jkMoFKKlpQXLsggVF6MAkZYWnnvuOaK9vZQV5dPTHz/ie1cUBcPjwXVdujo76BEeXnp7FyLey7L5NeRE2xC9reANJGsJMgtQ1KPvwKP4M9Gqz8Wp24RoP8CcHC97LJPNmzexcMHZ1O+vo6Ojg7LS0vRzXNdFw00GAcH8Ma0jddLU2NhIfX39cS2C1RQFxxXYlonrOCQsi9p9+4jH44cNTIecOBQWkjcwfNGxLcxEHF032LFzZ3JQn+HhggsuSP9esmwHBQUVN9mSdEDqFGDL229z9tlnA/CHP/yBW2+99bi8d0mSjkwGApI0Dueccw6PPvoo1157LV/96lfZuHEjv/jFLwgGg/zqV7/ioosuGtKJ42D/+7//mw4CnIGuMdKJo2sqRZl+rn3/B1j7wv8N+V7yjqWF5xicBqQcaiLsiZQKSDo7Opg9ezZer+eI04UBOExxXTgc5pVXXklOo/X7qampoSgUSud+nzVnDrG+KLv21JKRlT3qz0BVVbZu287kyZMoCRXx8rpaCsr6uGDxIuhtRXRHEK37oK0ekVWAkhVC8R5du9/BRcQZnU3ctLCSt3s1dEPnwIEDTJo0acjjHddFi/eAcFEyj1wkfLAD4TC2ZfHKK68QDAbTPfoHO9KE6NFQFTCFCygYXh+lpaWjDkxTJw5CiOS8AY8H3fCg6YLGhgYyMzIwdI2t27ZRUlLM5EmTcFwXy7Hxqird0T7qtu0gFAol60SEoKm5OT0E7E9/+hO33HLLuN6XdPpzXIFzgo8ETvTrnQ7k7kOSxum9730vdXV1lJWV8eijj1JUVERVVRWf/vSnqampOexgsueeew6AZ599VgYBJ0mmzyC8bw//8dW7MS0r/fOObaHr+hk5xK28vJzZc+YkZ2QoymE3+SkCko8dQeqUIRKJ0NrWhuHxoOt6cuCZovD21q1Mm1pNuKlpzClRU6dN4623NhEqKqSoqIjCwmSNgJJTglJxFkrpDMjIge4WxIHNuOG3Eb2tyT7445QqIlZLqvHgcFamReu+nZx77rlMHygCT3EcF7WvAwwv+IbXSxxJa2srKAorr7qKC5ctIxAIYFpWssOT49Dc3Mzq1atZvXo1q1atIhwOj+n6QiRPARQErgDdMIZMOl6wYMGogwtloPuVPfDnRFEU9tbW8o/HH6evr5/ZM6aRm53N2rVr6ezuxufx0N8f5cmnnmbnjh1YpklnRwd/+MMfmD17No7jsHbtWhkESNIpQO5AJOkoTJw4kbq6Ov785z8zadIkWlpa0t+7+uqr+fjHP87evXt5++236e9P9kx3XZe1a9dSVFTE0qVLT9bSJSBDTea+v7VjLzAw3MtxUI9hUeqpSB1oh3o0m2Z4ZxjUokWLuOSSS4akt5SXl3PR8uV4vR4uvHDZmO5o27ZNXm4ulZWVdPdGueLSS4YUZCuKghLIRi2uRpk0DyVvAtgmIrIXUbcRt20/woyN6z0pioJaNBl10lw0VWVmhkW204tlWel2pkIIsOIQ70XJLBxz0CiEYNrUqWzevJlVTzzBi6tXE4/H0TUtPUsikUiwcOFCDMMgGo0SiURGfX3XdTETcUBB1zTcgWnJR0PTNMTAtQHy8/PxeDw8+L//S8w0MXSNmTNr2L9vH7t27CARTzB12jQqKip4YtUqXlm3jttuu43rr7+et956iyVLlhzVeiRJOjbkQDFJOoYcxyEej/Pd736Xb3/728O+X1JSgmEY1NfX873vfY8vfOELJ2GV0mAXXXo5v/zTXzGsPnTXRtd1QsXFJ3tZx5UQAjPej6bp6J7Dt7xsaWmhp7sbn98/5vQU24zjugJVN0bdIcd1XSzbxjPoDrYV70fzeIfkm8PQ1JmysjLo70J0t0B/Z/IB/uxkcXFG7rAJwqMhYr04+95MbvpzyxDFUzA8XhzXxW07gNYbQZl4Foo++oFnQgisgaFi++rqDjkxOhwOk0gk+Pvf/54e1Hakzz+ZkmXhOi66x4OqqmyP9NLWZ7JoUh6aenSnXEIIor297N69m6JQiK6uLjo6OsjKCFBeXEjUcuhs72BSRQVCUVj78ivs2bOHYDBIT08PU6ZM4dprrz2qNUhjd6rth1Lrefj13QSCJ3agWH+0l+sXVp8yn8Wp4My+7SVJJ5imaWRkZPCtb32LYDDIunXrmDhxIsFgEL/fz89+9jMmTJjAxz/+cT7+8Y+f7OVKwAvPPk1rQz05BSFETwdr164d1abrdKYoCv19MXp7u+ntjx9yuFSy1eROenp6aGlpGfPn4loWui9ATzTK9m3bjlgUm+pGNDgIANA8Xhwzgep7pw6goaGBNzZsoC8aZcuWLaxYsYLy8nKUjFyElUD0tEBPC6J5N2gGIqsIJasIZRS9/lMGFxHT2QCJPpyJc3DRUPs7wZ817iBA1TSqqqoOOTCwrKyM3p4eli1bNqoaAdd1sEwTTdMxvN7055c6YXCEQBtW9j2yYQGWEAPDwjrp7uqivr6e1994g2vfcxXTJpbS3x/j5dfeYPuu5KbfBdaufYkZM2awcOFCtmzZwo033sjEiaNvsSpJ0okhAwFJOk6++MUvDvu5r371qydhJdKhWAM5z+E928nIzsWXVZBOwzgTA4HksCjYu2cPthknLzubZ19djRCCWbNmDXt8JBKhtbWVktJSamtrx/S5uI4DikLd/v10d3eDorBu3TqEECNufoUQmJaFMUJ9hqpq2CjJlpuKgnBdNF3H7/ORl5dHTnY2Xp+PhGmiAIqqoeaUouSUQH8XidYDeDsbcDsbUDJyUbKKIJAzuo5Jg4qI6WzC3fM6fcFishyThm6FCaVHvET6/VmmmW6TOprH+/3+Iw7WGnwKYHi8w2qOtIEfuq6AUTQ2CofDrFmzhhkzZmAmEvT29OD3+1EUhfb2dl5+6SWKC/O55tLlqK6N5g+SFcylYnIVwexc8vPzeemll3Ach7q6OhYvXsyHP/zhEedOSO9ujiDd2vZEvqY0lAwEJEl61zIMgwULFrBzxw4cobDgwktYtOxiQqGxd4E5EYbdqYUheeupjT6D/38w4aI4FiW5QRzHT2dPHysvvRjHFbi2lSzGHbQ5DoVCvPHGG1RWVjJ79uwxFfy6tolmGEQiEfJyc6ndt49Zs2YRzMzEtm20gfasqQCjsqqK/Lw8VFVNvxfXdXFdN/keFRVhW+heH+pA4WpdXV16su7KlSvxDAyzSj3HEYKY8NBkBVAsFa/ZQzndqH2doHshuwgyi1B047DvZfAkYqdpNxlddSR0P0+//BbnOdqIQdSQj31QEKCNIghIPUc5QiOBQ50CDDb4RGA0WlpaOPecc1i9ejVd3d3pOQNCCApyMrnmsotojLSw5rUNLFp8AaqeHEuX+gy6urpYvHgxHR0d5OXl8bOf/Yyf//zno3ptSZJOPBkISJL0rvbXv/6VpUuXcscdd1BSMYnyymqKcjNO9rKGCYfDrFq1ioULF9Ifi9HT24vf53tnGiykhzyl/j9F2CaOGUPYNqrhpamjG0XV+eeTT+Pz+Vhx0UU4lomIx1B1A9XwoA5Mz021mjzn3HPJHOVdXSGSgYURCFJSUkKsv5+6fftoa23l0ksvJT8/H9Oy6OvrY/uOHUSjUfLy8xGuS25eHvBOr33dMNKbWdskGcwo2mHnMwzebG/ZvJnVq1cTCASYMGECvpL55HlB62uH9gPQHqYXD/VdcQK5RVQeIlUnNYl4Z12YSdke4pklJMzNdHR0HPGzGGsQAMlC7pE6ioXDYVoiESZNmkgwIzjiKcBg2sBn546ybeLkyZN5+eWX6eruJhgMUhwK4cT7cBIxsjMzael0MdFZtPiCYadDe/fu5bLLLqO9vZ3f/va3LF26VAYB0iG5AylnJ/o1paFkICBJ0rva5MmTOXDgQHJI1re+xdt1TUR6YpTlBE6p1q6RSIRoNEogEODxxx475ETYFOHYOGYc14yjaDqax4cSSN41rpoylUhLC3PnziU/P5/KKVOSzxnYwNuJGAiBahiUlZZSXl6eTt1JbdAPR7gOiqqhKCrl5eVEIhEWLVpEKBRi8uTJAOiaxpbNm/H7fFRXV9PV2Ylj24RCoUOm7GiGByvejzpwcjGa+QypFqfRaJSGhgbmzp2LkVuGyCnGiUexOxoJxLuZmaPQGT1A655+CibPQDlE5yhf0QR6MoJYjsv1t3yQrtbmQ752bW0tgUAAx3HSJzijUVtbi8/nw0wkmDR5cvqEpLWtjf3795Ofl8vmzZuZUj31iO9fVVMnAkd+XcdxCGZkMHfuXCaUlzOxNETAayCEwMjMRVE1SgOZlJYlX1MIwV//+lf+53/+h507d9Lf309OTg5vvPHGIWsfJEk6tchAQJIkieTdzKqqKm68+jL++s/n6Oo3yQseu6FiRyu1oXVcl6ysrBHTdIRwcc0ErhlDCIHm8aU3cIPZtk2oqIiSg7ojKYqCZnjQDA/CdXFsEyvWBwM/b+g6lm3T3tZG08BsgJE2oq5log6k27iOQ2lp6Ygb4by8PDZs2MCbb76ZnPh8ySWHzdtXFAVVN5KzHgzPqD63Q50cKIqCGsjijU1bycnKxO/2U+QP4KMPUbcREcxHyQ6hHDQjoLiklN7eXur27GLGrDnkF5eRsGy8xjv/nAohaGpsxHUc3nzzTerr60ccGDaS2tpann32WS6+eAWKotDVlezYpCoKfX19NDY2sXHjRkzTJHMUQ9q0gY9z85Yt5Gd4D1kYnvz1ttE1leK8LIoyfWheP6rXP2LHpRdffJHPf/7zvPbaa1xyySV86UtfIj8/n6uuuoriM7zrliSdSWQgIEmSRLIn/ic/+Unuvfde/vDAz/ng7Z8g4DXwGaNP5zieUhtagKuuuoqSkhJgoDbAtnDNGK5tohpeNH8QRTNG3FSnjsaPdFdfUVV0jw88PlzHwbVNHDNOImEiHId169ZhGMawTkLJUwUbw+sfyO1XDrm5T80hOFQLzZFouoEV70foI7+/kRzu5KC8vJyO9nb++dxaJldWcskF5xOwe1GibckBZd4MlKwQZOajqBqu65CTncWSJUtwXUF/PE5vzKQ50kJDfR0TJ1ZQkJ9PLBbjlXXrOOfcc9m2bRstLS2jCgQikQh9fX0IIXjiicc599xzOf/88wHwGAYdHe2YpkkwGDxszYawLUS0A2vvboKBYrY3NFLb3Q6Qnnqe+vx27NhBfm4OBg4Bvw/NG8DwZ474+W7ZsoUvf/nLPPHEEyxcuJDnnnuOiy666IjvS5IOJicLnxpkICBJkjTg//v//j8KCwv5r69+iQ/fdgctPTHKc0+dFKHy8nJs20ZRFITr4JpxHDOGomioXh9GIPOI/fId20YbZU//FFXTUDU/wuOjdv82hOvwoZtvJJYwQdMxTRNVVZPrchyUgULg5uZmOjs78Xq9h9zkH66F5khSpxaOZR5xBsJohIqKsG07nboUzA9hWfmI7DKMeFeyBWlrLbTvRwQLwJuN4ctNfi6qgs/jwYr34M/IYEJ1DR0dbfSbreiGl4Rpsr+ujrPPPnvUhdahUIiioiJ6e3vx+/1Dnneo0w3hOoi+LkRPO25vO6K3HdHXAwiKAc1OEJg3F4+h4ff56eyLgWBgQJiDPysHxYrx2uatTJhcNWR4W0ptbS3f/OY3+cMf/kBlZSUPPvggN9544ynzZ0OSpPGRgYAkSdIgFRUVuK7L9772Jb7wXz+goy9BQab/ZC8LGLj775jJAMB1UT0+jIycQ+azj/R8Vwj0MU7CTVEUhWBWNqtWrSIeizFj+lTOP+cchBVHaDpC03EtEzSNzq4u4rEYzzzzTLIgeZSpMaOhajrd3e3s2LWR7JycETeuo+E4DoqiMHny5HTtAoDHY+A4KqaSj55ZhGb2IbojiJ4W/EQQvZmIzAIcTxAhXBLxflrbOzF8fhoO1FNZPRVfdi5XXPd+rEQcv89L0O/FtB0MTT3sSUZlZSWG4cG0zGHTmiE5X6A0LwvR0469+w3cnjZEtBPEQVOiNQMlM4+EJ4Mtb+9h20tvEQwGWbJkyZD0oLVr17Jx40Zs2wbAn5k95DJNTU384Ac/4Gc/+xkFBQX89Kc/5fbbb8fjGV1qliQdiiwWPjXIQECSJGmQ973vffzkJz/h/l/8nLu+cTddcR8ZPhu/cXL+ukxu/m1cM45rJZKtLnUfhs8/6tQYSHabsSyLjIwMioqKxr2eg+9KZ+YVDOSXWziJOAgXzTDYtXMnHsPA4/EQjUZHlRozpBVq8n8Qyf8AAiFAIGhrbaOnp5uigjy6OjtpPLA/eedcGdhkKyqKqgCHTktK9t+3MQ6xodU0DVVVMS0bRw+wt9MhN6uUbCWG1+xFad2HpuqQVUhPZw+27eBD0NPRihUtoriiFFcITNtHwnboT9h09SeSt+GFSzDgw6OpGLqGrg5dpxCC/v5+VEVBJPpxe5J3+VP/xbGGLlZRUTLzUbMKUDLzULIKwBfEsm0ydJ0JbiYZxR3k5+cPqxEoLCzE5/Ol27AWFhYC0NnZybe+9S3uvfdePB4P3/jGN/jsZz9LIBBAkqQzhwwEJEmSBsnOzubOO+/ktttuw4334c/JINITZ0JuAO0EpkEI101u/s0YKEry7r8vDxQl3b1ntMLhME8//TRLly7l73//O1deeeVRDUw7OOc+WU+Q7Ejk2hYIl5nVlfRG+5h/9tkIIZgwoSKZSjRoQz980ME7eeupNqgooKCgDPxAVRRaW1tpaYlQkJvDgYZGMoJBigoLEcIZmKHgpmcppK+pKMm0KUVBGZhVAMnuSmLw9wZ9roqi4PUY1NbWUlZajKGpdHZDd7/JjEmViO4IdDUxxQtRXcPxa1x00UVs27aNbdu2UVNTg9+j4vfotLW1sXfrVnbs3EVp+QTmnb0Qrz+A1W8CYOgqHk0j0tSA29lEoGUvuQaYrW8N/4wC2ShZ+aiZ+ShZ+SgZOcMKwk3LSgczh5tzMH36dIQQtLW1UVhYSEVFBT/4wQ/4/ve/TyKR4Itf/CL/9m//Rk5OzpF+W0iSdBqSgYAkSdJBUnnPmcEgOZk+6jv7aI8mKMo6vilCycJfEycRQzg2qseLlpGNOsrUn0Nd00wkuHDpUl5Zt47e3t7jNjnZtS00jxdV03lz/etoqoI/I4ht20SamyguKkpv6FP77bEENClej0FhQQFvvfkmpmUxdfoMdN/Id6qTG36BcAWI5KAx4bq4jo2qqjhWAuEODhzEO6cKA//1GSr90SjBnFyefPIpzjv//OSE4ozcZFpUTwvBnlaIR3AT7RRrcXqb9tKkmRSXlIGq0tlQR7ZX5dILF2PZNiLWS15eNigKjgDLcbEcl4yAj2xTwQgkfw8m0PAXlg3c8c9HCeYdcQCabdsoJFu0jsaMGTPo7e3lvvvu44c//CHt7e187GMf4+tf/3q6KF2SjjU5WfjUIAMBSZKkg1x++eWoqsoFF1zA97//fc5fupzuhE1GwiLDe/hN2HiM2PPfGHlSLIDCwOTZI2yiHcfBsW2ysrJ45NFH6e3tPWK3mXG/B+Gm5wcA5Ofn89JLL6VTTpYsWYKmHZsTlenTp9PZ2Ulvby+FhYWHrRFIfkYKigaQXJtj22iqhm4M/7VMnRQMDhq6unvo7e2lTFG45OLleL1eHMtMzjMwfCj5FYi8cra//gp+t5eJoRwU10XXLERbHQBVQSDoBaLJf3ntOOxvQSgqmqIm1+MKvLaJrsELTQnKZswF49AtPw8WDofpjUYpKSkhOytrVM/Zt28f9913Hw888ADRaJQPf/jDfOlLXxpVBydJkk5/stxfkiTpICUlJTz44INEo1GuueYavvX1r5Dh0WjpjeM47pEvMApCuDiJGFZvB1ZfN4qiYGTmYgRzUD2+I/bTF4cpenNdFzORwHVdDI+HgsJCrrzySpYtWzas3eex4loW6qCWnjU1NSxevJiFCxcOK1A9WkIIMjMzWbp06ZgLhYUQ2I5zyM5J6enMqoaq6cmZCopGW2c3e2v38dSzz9Hd2weAbcYxY33YZvJUQWTk8symWn797AYO6EXsIw9l0jyUijko5TW0Gfnsjap06jkoBRNR8spRsoogkI0wfKBquKrGW202Wxo7ae+JjikIWLt2LYZh8LeHH6ahoeGQj7Usi8cff5yrrrqKqqoq7r//fj760Y+yd+9e7r//fhkESCdEqlj4RH8dD3V1dXzsYx9j8uTJ+P1+qqqq+PrXv45pmkMet3nzZi644AJ8Ph8TJkzgBz/4wXFZz1jIEwFJkqQR3Hjjjbz3ve8lMzOTX/3qV3z6zjvRsotojcYpzh5fweTIPf8zUQYm5Y6Woqq4Qgy7kyOEwLYsBKAbxpDWjqOZwns0HNtC9w5NnTpcbvpRvZbjoI0y7WWwcDhMIh4nOyeHgoKCUT+vpqaGzKxsenq6WbJkCdOmTweSk46TxdwOrm1TXTmJUEE+La1t2KbJ5MpKHKGwc9de2tvbk8W6Z52N6yZPalzHAgGqrqNqBjt37OCVVzayfNkygsEg+fn5o1pfavLwnDlzeP655w6Z/vXGG2/wpz/9iYceeoimpibmzZvHL37xCz7wgQ/IImBJOgo7duzAdV1+8YtfMGXKFN5++21uv/12+vr6uOeeewDo6enh0ksvZcWKFfz85z9ny5Yt/Mu//As5OTnccccdJ23tMhCQJEk6BI/Hw69+9Stuu+02qqdM4dEn/smsc5bQG7fI9I0+RUg4Do4Zw7Xig3r+Z40rPx5AVRQcx4GBzXCqA47ruui6jjqOTfLRSOXYn4jXTaXrKGOchRAOh3nyySe5aPlyHn30Ua644ooxBUZFRUWUlZYMC0AURUHRddSB9eR5/eTk5eE6NjgW0Wgc4bo0hA+Q4fPS3d5KIBBA1Q0MbwBlULBWU1ODEAKvz8eyZcuYNm3aYdckhMC2bYTr4vF4hqRipdK/TNPk0Ucf5d5772XNmjUAfOITn+D2229n3rx5o37/knSsua7APcEDvo7X611++eVcfvnl6R9XVlayc+dO7rvvvnQg8Kc//QnTNPn1r3+Nx+OhpqaGt956ix/+8IcnNRCQqUGSJEmHceutt/LMM88AUL93N5lendZoHPsIKUJCCBwzjhXtxIp2AmBk5GBk5qJ5xtb682CKotDb28u6detobGzENM1kapHHc8KDAADHNlFHyLc/Hpqbm2lpbT1s6stIIpEI1dXVbHn7bXp6eohEImN6vivEkE37oaiqim548PgCGL4ABw6Eyc7KZE5NDXX797Nl2w4Mfwaa4RnxerNmzaK0tJSqw6TnCCGwLAtrYJCb4fEQCoVYuXJlOv3L6/Vy9913U1FRwfvf/36EEDz44IP09fVx7733yiBAko6z7u5u8vLy0j9et24dS5cuHTKD47LLLmPnzp10dnaejCUC8kRAkiTpiPr6kjnhXq+XwkwfsY4+WnpjlGQHhvV/T/b8j+FaCVTdg+bNQBmUO38s7Nu3D1XTyMvNpW7fPmKx2DEb1jUermVh+DOO++uEw2E2b9pEqLiYTZs2cemll476rv6ECRNobmpiw4YN4yqYFkIw1l9BRVFQdZ1oLM7ql15G13Wmzzxyzr+qqlgDA74OXkPqBEAbOIUY/PuqvLwcy7L47ne/y69+9StUVeXDH/4wn/rUp45bmpYknY56enqG/Njr9eL1Hv2k8pQ9e/bw05/+NH0aAMmbGIMHFwLpv4eam5vJzc09Zq8/FvJEQJIk6QguvfRSrrrqKj7+8Y8zf948nvnHX+m3XHbtq2fNmjXs2L4NJ96P3duBE+tF0XSMzHz0jGxUw3NMgwBI3t2ONDXR3d3Nq+vX09LSckyvPxau6yR7/Z+AGQuRSITtO3awd+9ellxwAZZlHbZo+p01uuRkZ5OXnz+ugmkhRPI9juPXcebMmYSKipg9e/aoi6ZTcw9c102/vm1ZmINOADRNG7KeTZs28aEPfYjq6moeeughvvzlL1NfX899990ngwDplOSKd1qInqivVGbQhAkTyM7OTn9997vfHXGNX/ziF99pIHCIrx07dgx5TkNDA5dffjk33HADt99++/H+GI+aPBGQJEk6Ak3TePDBB1m5ciVr1qzh3z71STZfcjlaMIdQbgehnAzaWlsoLC0/qp7/oxUKhXj22WcpLy/n6quuwjhBaTkjcS0TTR95Ou+xFgqFCAaDHDhwgM7OTq697rr04CxNVUfcqKfSaDwez7gLpl0hUJXxBTpCCIIDHY7GQtM0Ojs6iPb1kZebSyAjA48+vKi8ubmZr3/96/zyl79kwoQJ3HPPPdx+++1kZBz/ExpJOl0dOHCArEEtdg91GvAf//EffOQjHznstQZ32WpsbGT58uUsWrSIX/7yl0MeV1xcPCwlMfXj4uLisSz/mJKBgCRJ0iikeuGvWbOGj370ozTV7SEY8FEU9PG7hx5l3vz5hCZMOiFrqaysZMWKFbS0tODz+SgKhXBdd0iXoBNBCIFrWxiB4Al5vfLyclauXEkkEiEUCg1MExY4joNpWegDk3RTm2UhBJZpYhhHl5rlugJ1nM8fy69LOBwmEolQHArh8/vRdZ0333yTSCQy7BTDtm1+/OMf881vfhNd1/nBD37AZz/7WfQxFlFL0slyPNt5Hu41AbKysoYEAodSWFhIYWHhqK7d0NDA8uXLWbBgAb/5zW+G/bk///zzueuuu7AsK33z5tlnn2XatGknLS0IZGqQJEnSqN199928//3v58EHHyQ/L48NG94kFo9z6dLzR93q8Vipqqri/PPPZ0JFBR6PB9uycEbIKz+ekgPEVJRx3i0fj/LychYsWJDeFCuKgq7reAwjOT/BsnBdl9raWsIHDtDW3n7UAZIrXFR1nIGAGF0QEQ6HWbVqFaZp4vf7UVWV+vp69u7dSzQaHXIncdOmTSxdupQvfOEL/Mu//Au1tbV87nOfk0GAJJ0kDQ0NLFu2jIqKCu655x5aW1tpbm6mubk5/ZhbbrkFj8fDxz72MbZu3cpDDz3ET37yE/793//9JK5cnghIkiSNmqZp9PX1kZuby8yZMxFCEOnsZHbVBDKyckY17fd4SHUMsm0b1zTRj/IO+Gglh4idmLSgI1EUBcMwEELQ0dGBbds0NzWx5e23WbFixVEVUwtXoOrj68Y02t8TkUiEaDRK+MABduzYwaxZs9i4cSNAurhZCMG3v/1tvvGNb1BWVsYLL7ww5pQjSZKOvWeffZY9e/awZ8+eYemHqTqm7OxsnnnmGT71qU+xYMECCgoK+NrXvnZSW4eCDAQkSZLGpLGxEb/fTzweTxdhCtfF7m7BjalogSMfNx8PqY2w4zjJdBjPsS9SHiydFuT1HbfXGA9FUdi1axe9vb3E43Gi0SgtLS1HFQi4QozrRCDVaWg0vw6p+of6AwcIBoOUlJQMSYMqLy/nd7/7HV/96ldZuXIljz766EmtDZGko+UIgXOCU4OO1+t95CMfOWItAcCcOXNYu3btcVnDeMnUIEmSpDH4yU9+wr59+7jzzjvTP6eoKnpWIU68DycWPYmrS55a6IaBZZq4jnPcXkc4NspBnWtOFaFQiHA4TPWUKeNqFXqw0XQmGslY6gNS9Q+DuxoNToMyTZMvfOELzJkzh/vvv18GAZIkHRPyRECSJGkMFi9ezJe//GW+/vWvc/PNN3PRRRcBoGgaRnYBVlcrqCqaN3DS1phqMWlZFqoQNDc3D7mzfCw4toVmDE0LShW7HsvXGY/KykqEEPj9fi677DImTZo07msdTetQ13WHTSI+nEN1NdqyZQv/8z//QyQS4emnn6akpGTMa5GkU82ZNFn4dCZPBCRJksboc5/7HMuWLeOGG25I93oHUDQDPbsAJ9qFa8ZP4grfSRXq7uqiu6uLl156iVWrVhEOh4/62kIIhG2jDGqVGg6HefLJJ4nHYuzatYtIJILruuO+m360qqqqkgFJWdlRXWe8rUPD4TBd3d00NjaO73VdlyeffJIrr7ySOXPm8Pjjj3Pvvfdy1llnjet6kiRJI5GBgCRJ0hh5vV4qKyvTA2UGU3UPemYedm8HrpU4SStMUhSFuv372VdXx7z584d1nxkv17ZQDuppH4lE6I/FKC4tpauri/7+fhzbxjJNzEQi+WWaWJaFbds4jjNioBAOh9mwYcMxCVhUVaU/Fjuq642ndWg4HGbnzp3U7t075uArHo/z85//nOnTp3PllVcSiUT43e9+R3NzM5/4xCfGunxJkqTDkoGAJEnSOFRXV9Pe3s6Pf/zjYd9TPT70YC52TzuubZ34xQ0SCoWYWVPD9m3bjkm+PCQDgYPTgkKhED6vl/a2NmzbxjAMDI8Hj9eb/jIMIz0RN9X/f3Cg0N3VRSQSoba29picXjQ0NLB71y62bt067uuNtXWo47r4/H5QFF555ZVRB19CCB566CGmTZvGJz/5Sc466yxeeukl3njjDT70oQ/JmgDpjONw4icLH7+qqdOXDAQkSZLG4fOf/zyf/exn+fd//3fKysr40Y9+NOT7qtePlpGN3dOGcE5sf//BikIhghkZnH322cOGUo2HEGJgfsDQ3PdUsWsgEOCSSy4Z8XUURUFV1WRBs65jGMaQQGFvbS2bNm2ipKSEOXPmHPXpRSQSYeObbzKrpmbcpyHJE4Ej/1Ppui6maeI4Dol4nD27d+O67qiCr9RcgPe///3MnTuX7du389e//pXFixefksXYkiSdOWSxsCRJ0jjdc889VFdX8+tf/5p77rmHO++8c0hxqObLANfF6mnDyC4ctnk+3pyBrkFFRUUUFRUdk2u6toWqjzynIFXsmjDNcc1UKCoqwrIs1q9fz4IFC5g+Y8ZRzWYIDUxcbm1tZdH55x9xQz5SsbM4QutQIURyfoMQGLqOqqqUlZUNa/05kng8zt13381///d/M3XqVJ5++mkuvfTScb1XSTrdnMzJwtI7ZCAgSZI0Tqqq8olPfAKfz8e//Mu/MGXKFH74wx+yaNGi9KZTC2QihIPd3YaeXYhylFNuR8sVAttx8BzjlBLHMtG9/sM+RtM0HMcZ86Tb1KlCagOdmZmJaVl4xjkgbfD1qiorycw69IyHxsZGamtrSSQSvP3221x88cWUl5cfMhBJpTal3qeuqkMed6gOQCkbN27k1ltvZc+ePXzta1/jC1/4Ah7PqTGcTZKkdw8ZCEiSJB2lD3/4wxQWFvKd73yH6667DsMw+OAHP8h1113HypUr0QLZOG4ndm87elbBcU/3EEJgWRbGQQW9R31d1wUhUI/QElNTVUzLQhvH3fyDN9AKHHUwkNrQW6ZJOBymsbGRUCjE5MmTcV0X23FQNY2W1lY0VWX+ggV4PB4s+52Urtra2nSAMnHSJGzbRlVVPGMc3Oa6Lj/84Q/58pe/zKxZs3jzzTepqakZ8/uSpNPdmTRQ7HQmawQkSZKOkqqqXHXVVaxZs4bXXnuNz3/+87z44otcddVVvPLKKyiKghbMBUXB7m0/7i01bdtGU9VRD7MarVRa0JEoioI6UBB8tDRNw9B1TMs67PVqa2tZt24dtbW1h1xTU3MzjuOwe/duOru66O7uRgiBxzCwLYv2tjZqa2t55eWXsSwLMdBzvL2jg63btrFlyxbiiQSdnZ14DGPMgVZLSwtXXHEFn//857nzzjt59dVXZRAgSdJJpYiT1eT5OOvp6SE7O5vu7m6yDnMcLEmSdDzYts2cOXOwbZt169aRn5+fzCfvbkXRdLRg7nE5GUilqxjjvIN+OGZfL4Y/Y1TpTak77ccqNcl1XSzLorm5ecgdfYADBw7w6quv0tDQQCAQYMWKFVRVVQ27xrp165KBhWGwZcsWqqurOf/889PfP7hGwBrI/d+yaROO65KdlcX69euZOnXqkOeNxvr167n++usxTZM//OEPshZAOmFOtf1Qaj0/eHYT/ozME/rasb5ePn/JWafMZ3EqOKknAvF4nPe+971MnTqVs846i0suuYQ9e/YA8Nprr3Heeecxb948ZsyYwQ9+8IOTuVRJkqQx0XWdX/ziF+zevZuvfvWrdHR0oCgKelYBwrZw+nuO+WumClePRxAgXCc5YXeUpwyqqiY7DB2je02qqtLc3IwQgrzcXFzHoauzE9uyQAiqKiv5wC230N/fT0tLy4jXqKioIBAI8PzzzxOLxYYVD5eXl7NgwYJ0alJymJhCfkEBRUVFPPfcc8Tj8TG3YP3d737H0qVLKS8vZ8OGDTIIkCSSHbmcE/wlJwsPd9JrBO644w6uuOIKFEXhZz/7GbfddhurV6/mjjvu4O677+Y973kPHR0dTJ8+nauuuoqZM2ee7CVLkiSNypIlS/i3f/s37r//fnbu3Mlzzz2Hoqro2QVYXa04iooWODZ3xMLhMIbHg+s4lJSUHJNrDuZYFpo+tmJWfZxFwyMRQuD1evm///u/dBvQRYsWcf7552M7Dm9s2ADA+2+6iVh8+FRn13UpLCggkUiki7krKysP+5quK9B0ldLSUiLNzcyfP39Uzxu85q985St85zvf4aMf/Sj33XcfXq93jO9ckiTp+DmpgYDP5+PKK69M//i8887jnnvuAZL5nF1dXQD09fXh8XjIy8s7GcuUJEkaF0VR+PGPf0xpaSnf+MY3kgW8hoGiahjZBVjdraCqyTajRyEcDtPY1ER7Wxv19fXHZF7AYEIIXNvE8AfH9Dz1KIqGD359y7JwHIe+vj6AIf35KysrWbFiBS0tLfj8/mH/VqSLpw2DysrKUW3kt23bRm5BAYoQFBQUUFFRQUVFxajX7Loun/jEJ/jlL3/J1772Nb7xjW/ImQCSNEjqLv2Jfk1pqFOqWPgnP/kJ11xzDQC/+c1v+OpXv0pFRQVTp07lO9/5DsXFxYd8biKRoKenZ8iXJEnSqeDSSy8lFovx29/+Nv1ziqajZxXg9HXjJmLjvrbrunh9PhobG9m2bdu4B2cdjnAdFEUdc+vTVNGw67rjfu1Ul5+u7m7KyspYsWIFixYt4pJLLhmyoa+qquL8888nNzcXADORSK59IAjQB3r8j8a2bdtYu3YtCgoNDQ3s2rlzTGs2TZP3v//9PPDAA/zmN7/hm9/8pgwCJEk6JZ0ygcB3vvMd9uzZw3e/+10Avve97/Hd736X+vp6tm7dyl133cW2bdsO+fzvfve7ZGdnp78mTJhwopYuSZJ0WHPnzuWWW27hjjvuYP78+Vx77bW0tLSg6gZ6Vj52tBPXSoz5urbjYNk2lmkSaW4GGNUk27FyLQvVGF+Pe03X04PNxiocDrNz1y527tzJo488QjgcTm/4D3dX3/B4EELQ0tLCnt27aWtrGzLo7Uja2tro7+8D12HNiy/S1tY26ufats3NN9/MP/7xD/72t7/xkY98ZNTPlSRJOtFOiUDgnnvu4ZFHHuHJJ58kEAjQ1tbGo48+yi233AIkj33PO+88Xn755UNe40tf+hLd3d3prwMHDpyo5UuSJB3RAw88wP33348Qgr///e/s3bsXANXwomfmYfe049rmqK4lhMA0TYTr4jEMSktLWblyJcuWLTs+aUHO6NqGjkRVFMTAdcYqEokQKipi06ZNYzrpUFWVfXV1KCRTUB9//HG2b98+4mO3bt3KmjVrhtxoKiwsJDs7B0OFkpISCgsLR/W6ruvy0Y9+lMcee4yHH36Y9773vaN6niS9G53oQuGTkYp0OjjpgcAPf/hDHnzwQZ599llycnIAyM3NJSMjg+effx5I3p1Zv349s2bNOuR1vF4vWVlZQ74kSZJOFX6/n9tuu41PfepTAOzfvz/9PdXjQwvmYHe3IRzrsNdxXRfTNNNtMFMpJwd3vDlWhOOgqNpRpbbomoY9jlOBUCiEx+PBdd0xn3S0trby5wcfxDAMlixZMuJd/a1bt9La1kZ7Wxtr165l69atAMyYMYPFixfhCsGyC5cyY8aMUb3mF7/4Rf70pz/xhz/8gauvvnrUa5UkSTpZTmqxcDgc5j/+4z+orKxk+fLlQHJDv379ev7yl7/wuc99Dtu2sSyLz3zmM2Pu2yxJknSqufnmm7nnnnu4+eabaW9vTwcGmjcArovV3YaRXYRyUCqLEALbcXBdd8zTbI+GY5vjTgtKUVUVe2Ag2FjWXV5eTnd3N8uWLUv39h+twsJCfD4ff/rzn7nu2mspnz0b13WH1AnE43HmzJnD9q1bqZoyhb6+PtauXUthYSHTpk7FNhP0xWKsWbOG/Pz8YcO/Bs8dWLVqFf/93//Nj370I97//vePep2S9G7luCe+eNcZf7nSGUsOFJMkSTrBurq6qKysZO7cuemTzxS7rxthxtCzi9LFuUKIZPcdVUXTju7u/FgIIbD6ejEyMo/6NS3bpqO9nXA4PKZNfcI08XrGF4hs376dtrY2CgsLqaqsxHEcPF4vqqriui59/f2sfuEFdu3axYQJE1i6dCnGQJDl8RioQmC7gqamJurr68nMzKSoqIjKykrC4TCrVq0iGo0SDAZpa2ujqamJn/70p7IwWDqlnGr7odR6vv74RnwZY+tEdrTifVG+efX8U+azOBWc9DkCkiRJ7zbBYJC+vj4yMjIwTRPPoI2uFsjCES49zfVs2XuA8gkTKCkpwTCMUXe9OVZcx0bR9SNubA+eyDuSlkiE3miU1atXEwwGj3ktw0gGp/SEw2EsyyJUVERDQwMer5eMYJDKykqys7MJBoMI1+Xxxx5DVVUuueRSAl4PL6xdy6SJE5k/bx7NkQjOwCAzr8fDkiVLeOqpp4hGo1RXV/Of//mfMgiQpFGS7UNPDSe9RkCSJOndRtd1fvazn/HEE0/wwAMPDPmeoijsqm+iKRKhoriA7p4eamtrT3gQAOBa5hGHiIXDYVavXo1wXTo6Ouju6sJMJIZ9eQyDzo4OgOPS4vRIa1y1ahVNzc30RqOUlJSQmZXFo488ghCC/Px8otEomVlZnL9oMXPnzcOxTfpj/USam9m7dy+//d3veOqpp3jsscfYvmMHpmWlf02CwSBNTU1yWJgkSacdGQhIkiSdBLfffjs5OTm89NJLWNbQAuG2tjaeXP0KamY+XT1RWltbT/j6hBDJ+QFHaLsZiUSwTJOcnBxeeeUVamtrMTwePF7vkK+EabJ582bg+LQ4PdIao9EoHe3tqJrGvro6mhoayMrKQghBbk4OEydOwnZcXMfGTCSwbYfCwkIWL17MhAkTyMzMHLZ2r8fDggULaG1tPaGBjSRJ0rEiU4MkSZJOkszMTB588EE6Ozu54447uPbaa4FkoWsgEOD/nn6KK6+5jq6WpmP6uqNJ5XHtZMvQI6W6hEIhTMviwIEDVE+ZQmFR0YjPKS8vZ+XKlUd83cGOVQlbKBQiGAyyc+dOpkyZQjAzE9dx6OrqorOzk6qqSuKxOLqu0drayuuvv85ll14CipLuVpefn09LS0s6CIg0N7Pl7beJx+PceOONTJ069ZisVZLeLWRq0KlBBgKSJEknyVtvvcWHPvQhVq1axVNPPcWmTZuYM2cOM2bMQAhBW1sbZryfwtLyMXfcOZSDi1wPlavvWiaa13/E6w3e4FdPmULmYQrwysvLx1wXcCwy7gevsauri1dffZXrb7iB91xzTbIFqwDLcfAqCgUFBaxYsYKc7GwUBuYouC4TJ05kQkVFsn1rIoFj27S3tyOEoL29/RisUpIk6cSTqUGSJEknSV5eHr/61a/47//+bwBuueUWfvzjHwMwc+ZMli5dSlmoECEEscTh5wuMViQSYfGSJay86ips2x4xpUW4LkK46a5FR5KaYRDMzMQyzWN2Jx+AY1R8O3jOgt/v56H//V9aW1vx6Dq2Y6f/MQwVF5ORkUFmVhb98TjNkQidXV0DqVIumqKwY8cOnn/hBYQQJzzNSZLOFO5JGCbmyhOBYeSJgCRJ0kkUCoX4z//8TyoqKvj+97/PZz/7WaZPn87ll18OJIuHM/1euvsTeAwdXTu6+zcTJ03i7S1b2L9/P1euXIlvhALXZFrQ2GcVqKqKoqq4joOmH/0/L0KIY3IiMNjg04Henh5s18WMx2ht76DQdYn29dHY2EhbawuqotLd28uUqioYKCrevXs3F154IY899lg6CKisrDzGq5QkSToxZCAgSZJ0Crjxxhu59tprufLKK7nuuut49dVXqaioICcnB0PX8BkavbEEORm+cacIua5LZmYmU6ZMITMzE49hkJefj+046IOKgh3bxPAFxvUauq5jmSbqMZh3EIlEsAa68xzLVqOpFKXdu3fjOg4NDQ20tXeQn5+P1+OhqKiIooJ8dI+HxsYmHv3737nwwgvJy8vjiiuuIBQKsXjxYgKB8X1GkiRJpwoZCEiSJJ0iDMPgscceY8aMGZx11ln4/X5uuOEGfvjDHyZTZAwfMdMm4DXGfG0hBJZl4fF4huTqCyGwbRvTdTF0HYQLAhT18N2CDkVRFHTDoLu7m7179455InBKOBxm/fr1lJWXs2XzZq644opjGgzs2LEDIUQyGCooAFWjJRLBYxh4vV46OztpaGykKFScvvP/u9/9jtraWp5++mkZBEjSUXLESSgWPjNn6B4VWSMgSZJ0CvH7/Tz66KPMnTuXWCzG73//ewoKCijIz+euz/8n/QkLx3HHfF3LstBHGA6mKEp6WJlpWTiWiWaMb5IvJAOLSCRCJBJh3759rFq1inA4PObrRCIR6urq6Onu5oKlS1FVFcd1x11/4LpuMuBJJIjHYpSVlqJrGt1dXbS3tRPt7WXTpk1seftt2tvbKSwspLKyioL8fIqKiujt7eVLX/oSN954I5deeum41iBJknSqkYGAJEnSKWbevHm8+eabbN26leXLl5OVlYWu6/zi5/fx97/9lX3hxjFtiG3HQVEUtMPMBNA1DUPXcSwTNI1t27axZs0atm/fPuyxqU46juNg2TamZZEwTRKmiWlZ2LZNbW0tzc3N4x4elmr5uWXLFl5cvRoA13EwLQvLsnDdwwdDB2/8U51+FFXF4/XS0NjI8y+8gGmZbNiwgfz8fBKJBOFwmLc2baKrq4tgMIOenh6mTJmS7jh0zz33jPm9SJI03IkuFD4Z7UpPBzI1SJIk6RQ1c+ZMnn/+eQCamppYuHAhH7/9Y+yorafuQAMH6mrJz8+npqZmxOfX1tbS2dlJZVUVOdnZR35BIVBUjc6ubiKRCPvr6+nv7ycrO5vCwsIhwYeqKCiKgqqqqAOnDMqg/9bX15NIJMbdVefguQOlpaUDSxTJdCbHQdg2qqpSu3cvkUiEigkTKC0txRUi+V4UBVXT8AyceAw2ffp0NE3D4/Fw4YUXMn36dPx+Py0tLRQUFJCfn88/Hn8i/bnH43E+/elPM2HChDG/F0mSpFOVDAQkSZJOAyUlJYTDYUzTZF/dfjLyCvB4vaxbtw4hRHrwVUptbS0bNmxg/oIFPPnPf3LuuedSVVV12Ndw7WRa0LY3NhJPJKieMoWu7mRQUFpSAjCqAuDxDA871HUOfq4yEIB4VBUhBHV1dbhCMG3qVGzHob2jg1AoNGzjP5LKyZMRQqAbyZqLyspKKiZMoKe3l57eXpqamggGg+Tl5bFy5Upqa2vH9T4kSRpODhQ7NchAQJIk6TTi8XiINDdRf+AAc+efTVZWNvX768jJyaY4FMIdyKMPZgSYUF5GY0MD8xcsIBgMYtk2mqqmN9ODCSFwbQsj4KOwsJDta9akh45VTp485g5A4xkeNlaKonDgwAFs2+btLVuor69n4cKFlAwELUfiuu+0ORVCpLsdZWQEiPb2snDhQvLz8zn77LP54he/SGFh4fF8O5IkSSecDAQkSZJOM3l5ebS1taGpCvkFhXh0FTORQFFVDE0HRSHa18SmzVvSm/lLL72UgoICHMdJps4Amqome/8rCrV79uD3GnTWHaCmpiY92biwsJAZM2ac5Hd8aIWFhXi9XjZt2kQwGBzTZl24Loqi4roulmVhGEYy/UkIKiZOZOKkSdx+++0Eg0HuvPPO4/guJEmSTg4ZCEiSJJ1mWlpaKC8vR1cEUdPkQGOEiRPK0LR3/kqvrKxkxYoVtLS0EAqFmDx5MkC6YDhV8GtZFvFYP7nZWeytq2Nf3f4RU41OVdOmTaOtrY0FCxaMKWhJ1Ts4joPrOHg8yQFq8UQCVUkGR/v37+e3v/0t3/nOd8jNzT2eb0OS3nVsV6Cd4FQdW6YGDSMDAUmSpNNMaWkpL7zwAvPmzWPGjBnMPmsuWcHhfe2rqqpGrAtwXRfhODi2Ca4LrkNPXx/dPVEqJk6koLCIeMJEVZPFwKl0olNRZ2cnHR0dVFZWjikVKZlClTwVMAaCANd1UYRIpwv993//N9nZ2Xzyk588XsuXJEk6qWQgIEmSdJqZPn06ra2t9Pb2Jgt5S0vpiyXQAj40beQi2cGbf+G6oCiomoHuNdhTV093dzfNzU307e0jMxgkVFSI6wpc4WJaFiLVhUdVURUVVR1eZ3CihcNh2tvbee6558jIyGDlypWjCgaEENimiaqqGJ53ZiZYtg0IVE2jvb2dX//613zxi18kIyPjOL4LSXp3ksXCpwYZCEiSJJ2GXn31Vb72ta/R2dmJx+NBxBP0xuI01u+nra2V/Px8ZsyYccjN/+CuOjU1NdTX15ORkYGu6+l2pJqmoA0aNyOEwHFdHNfBspP/oKqp4GAgMDiRwUEkEiEej1NUWEikpYVIJHLEQCCVDqWoCpphDPl5eKcr0a9+9Stc1+UTn/jEcX0PkiRJJ5McKCZJknQaSiQSuK6Lruuoqorf5wXHIa+wiJ7uLrKDARJ9vdhWAkXVMfwZeANBDK93xNaaPp+PjIwMsg8zb0BRFPSBvvw+rwevx0DXNQQCy3aSQ8USJpZl4zjOuKcAj1YoFKKhoYHq6upRzStwbBvbspKB08AJR4pt26gINE1HCMEvf/lLbrjhBtkpSJKkM5o8EZAkSTrNOI7D/fffzwUXXIA+kM+uqiqbN73J1GnTWbLofDZt3kLdgQaWLFlyxOuFw2HWr19PTk4Oe/bsGXWKjTJoqFhKqgjZdQWWbQECRVHRBuoNjuWpQXl5OYsWLcLn81F2mHalQghsywJIpwIpvLOO1GmA67rohod169axd+9eHnjggWOyTkmShnNPQmqQK1ODhpEnApIkSaeZv//979TX1/PhD394yM/n5+ezdctmotEo0b7+UXe6iUQi7N+/n9LS0uScgkhk3GtTFAVN0zAMfeDUwIOha4CCbTskTIt4wsS0rOR04KM8NSgvLycnO/uQswNS8wGUgXoARVGSbUMHBS+24wyZr/DQQw9RWlrK0qVLj2ptkiRJpzp5IiBJknQaeemll7j++usB6OnpGfK9mpoa/H4//bEESxadR2bO6AKBUChERkYGL7zwAldcfjker/eYrfedUwOAwa1LkycHpm0hGFSIrKqoYzw1UDUNx7bTrVFTBs8HGHxq4boO6kBRtRAi+TUoLeiRRx7hfe9736imE0uSND6OEDjHOX1wpNeUhpJ/y0mSJJ1GampqWLx4MQCf/exnaWtrG/L9SZMmUVlVRWZOLnYiPqo77uXl5axcuZJzzz0XfyBAYWHhcc3vT54aqBiGjtfrwef1YhgGiqLgOINODUwL23bS05IPRVXV9IY+ZXA9wMEbetd1UdVk0GDbNrqu4zgOqqaxadMmwuEw73nPe47Pm5ckSTqFyEBAkiTpNJKbm8tLL73ERz7yESzLoqioiB/96EdDHiOEQFU1NMOLlYiNOhhYsGABxcXFqKqKY9vH6y2MSD1sIbJ9xEJkVVWp27ePNWvW0NzUhDtoPsDBUoXCQghcIVAGfqwoCs888wyBQIALLrjgRL11SXpXSrUPPdFf0lAyNUiSJOk09PnPfx7LsmhsbORb3/oWn/3sZ4Hk3fbUP3WariNcB8dMoHt9o762pmnYloXjOMPSbU6UwxUiO66LZTuASM802F9/gEQi2Up027ZtFIVCI05HFsJNBwe246BrGq7rpKcyr127lsWLF+M9hulRkiRJpyp5IiBJknQamjFjBn/84x+ZO3cuXV1d1NfXv/PNQXfLNSPZKtMZ6JozGoqioBsGjm0n5w+cIlKFyO+cGnjQ9WSgkpGRwYQJE3CFYPeePXR0dIx4Dddx06lEyRQhNZ0WJITglVdeSadeSZIknelkICBJknQa+7d/+zdc1+X5558HGJYKoygKuteHY5u4jjPq66aCAWtgqvCpKN3+03EIZgR49plnyAwGufDCC5kxYwbOCLUFqfoAe+C0QwxKC6qtraWjo4Nzzz33ZLwdSXpXkalBpwYZCEiSJJ3GsrKyyMrKYt++femfi8fjbNiwgXA4DCQ3zIbXj23Gx3SHX1VVNF1P9+A/lezatYv169fT39cHQCAjg6nTptEciVBUWEh+fj6u4yTblNp2OiBITVh2B1qGuo6dTgvas2cPANOnTz85b0qSJOkEkzUCkiRJp7Gnn36anp4eJk2aBCSHg8VjMVavXk0wGEwPB1NUFd3jxUrEMXz+Ubfn1DQN13VpbW2lvr6eUCg0qmFjx9OuXbuo3buXc889l9dee43yCROYOnVquibAcRxs28YwjPSPTctCAfr7+2jct4/MYJAJEybgOA4eb/JxDQ0NAJSWlp6U9yVJ7yYn4w69PBEYTp4ISJIkncaKiooAyM7OBpLDwRoaGyksLCQajQ4ZDqZqevIO/yjbiqZEIhE6Ozp4++23WbVqVfqk4WTxeDycc845PPy3v/HmW2/R2to65PuapqGqKrZtJ1OcdB2PYdDY2Ihl24RCIZ566il27do5ZNJxU1MTBQUFeAamD0uSJJ3pZCAgSZJ0Gtu/fz8A/f39QHI42IEDB1gwfz7BYJBQKDTk8ZrhAUXBsUef7tPZ2Ul/fz+TJk4cFlycSK7rkojHycvN5W+PPEJPTw/BYDAdDA2m6zoIkW6DqigKfdFe6g+EMU2T884/H1VRaGhoTD+nra2NgoKCE/Z+JEmSTjaZGiRJknQa+81vfoPf7+ecc84BkvMAli1bhqooXHPNNRQXFw97ju7xYsVjuIqKqo/8z4AQAuG62LbNhPJynn7mGcLh8IjBxYngui6maaKqKlnZ2Vx44YW0trZSVFREdXX1iM/RDQPLNGGg21BBQQEJy+aRv/2ND9xyC67j8LdHH+X8/n5mzZpFV1cXubmjm8YsSdLRcUSyFfCJfk1pKBkISJIkncZ0Xee8885j6tSp6Z8rLy9HCJHcBI9AURQMnx8r3o+uqiP36rdtFFXF8HjI8XpZvHgxkUjkpNQIOI6DZZrouo4+kPc/derUIe95JIqiYHg8WKbJ3r17yc/LRSgqH/rgBwdONprp6elJtxq1BiYRS5IkvVvIQECSJOk0NnHiRH77298ybdo0Xn755XRqSyr33R3okX+wVFtROxHD8AWA5IY79fiDp/KWl5eflCJhe6Drj+HxjGu4maIo1O7bh8/rxevxorsuLS0ttLS00NDYQDAYJD8/H0h2SXJPobkJknQmc09CsbAri4WHkTUCkiRJp7FvfetbfOADH2DPnj1ce+21HDhwIP093TCwB3LkR6KqGpruwYz1YyYSKIDh8aDr+qi7Ch1PpmliOw4er/eoJhwnEgkKCwuxHZvf/+EPNDQ2Mn3aNEKhYpYsWUJNTQ2QLEI2D3GKIkmSdCaSJwKSJEmnsbKyMv74xz+Sn5/P//v//r/89a9/5d///d+B4acC4XA4nd5TWlqanBwsRPrEQDtEvcCJ5rpuOq3J4/EMSV0aC8dxsC2L6ilTsG2bX/36NwSDQYqLi8kIBrnggguGPD47O5uurq6jXb4kSaPguAJVtg896U6Nv/UlSZKko5IagmVZFlu3bk3f5U4VzLY0NbFq1SoCgQAZgQA93d1kZWejqipCCOxEHMe20HTjZL6NZFFwIoGm6+k5AKO1a9cuWltbKSsro6S4GAEgBKqqUn/gAAsXLiQ/P5/KyZNQRgguCgsLaWlpOTZvRJIk6TQgU4MkSZLOAB/5yEd43/vex1133cWsWbO4++67gYFTAVWlt7eXaDTKvLlzWf3ii+ytrU3faU/VCziWies4J3ztu3bt4uWXX+bAgQOYiQS6YYwrCHjhhRcoLysjMxikpbWVVHKT4fEwa9Ysli5dSk1NDY7joKrDU43Ky8vp7Oykb2BasSRJ0plOBgKSJElnAL/fz8MPP0xXVxczZ87ktddeS39P13XKSksJBoNoA/n/B7cAVRQFw+vHNuOIE1gwm9rAO45DMCODxqam5AyAMWptbSUajZKZlcUbGzYQzMhACDGsyNh1XVRFHbEGIjWded++feN+P5IkjY7tgu2KE/x1st/1qUcGApIkSWeQYDCIz+dj1apVLF++nF27dqEoCv5AgPdecw0F+fmsXLlyxA5AiqomZwyMcfLw0WhtbeXsBQuYWFHB7//wB5qamsZ1naKiIoLBIA8++CBLlizB6/WO2GnIsW1UfeTC41R61datW8e1BkmSpNONrBGQJEk6w/ztb3/jgQce4MEHH+SWW27hjTfeQNd1cnNzEULg9fkO+VxV09F0F9tMoHu8x7170PRp07Btmz8/+CCBQGDEKcGjUV1djRCCosJCEAJF04YFM8kZCU56FsHBCgoKmDBhAhs3buSmm24a1zokSRodWSx8apCBgCRJ0hlm0qRJfOtb32LmzJl84AMfYO/evVRVVaGoKmIUNQCa4cEdKB7WjeM3YMtMJAgGg+yvr+ecc8457JTg0ZhYUZFOB1IUhcRA56FUqpEQ4pBpQSlnn332kLQqSZKkM5lMDZIkSTpDXX311UyaNInbb78dSG6IhRCjSvvRPV5c28Z1Dj2H4GiYiUR60z516lQWL158VEFAIh4fUhOgqipejwfLtrEHgp/DpQWlLFmyhFdffVXOE5Ak6V1BBgKSJElnqMzMTD71qU/x6quvAqTvhI9meq6iKBg+H7aZOObFw4ODgPHOCBjs4CAgJR0MWBaWbeO6I3cLGuyCCy4gHo/z+uuvH/W6JEk6NGdgsvCJ/pKGkoGAJEnSGay8vJxYLMaPf/xjhBAoipIeJHYkiqKie3xYidgxKx4+UUFASioYsC0LUI5Y8zB//nxycnJ49tlnj3ptkiRJpzoZCEiSJJ3Brr32Wv71X/+Vz372s7z22msoioKqqrS0tLBhwwbC4fBhn69qGprhwT7KTkKu65KIxwHw+nwnJAhIUVUVTVVxhcA5wumGpmlcdtllPP7440e9PkmSDk2eCJwaZCAgSZJ0BvN6vfzHf/wHAL29vQBEWlroi0ZZvXo1q1atOmIwoOkGiqriWOPLm3ddF8s0URQFj9c7rmscLB6LjSoIAAbqIlwMw8A0zSOmRl199dVs3LiRhoaGY7JWSZKkU5UMBCRJks5wEydOJBAIsHHjRgCam5tpbGoiGAwSjUaJRCJHvIZmeBCui2NbY3rtYx0EOI5DPBYDGFUQACCEi6qq6LqOYRgkjhAMXHHFFWiaJk8FJOk4ck/CaYArTwSGkYGAJEnSGc7j8XD55Zdz77334jgOoVCIrKwsXNclGAwOmzI8EkVR0L0+HMvEHUULUjg+QYA10M1ntEEAgGM7qFqyhaiuaUcMBvLy8rjooot46KGHjnrNkiRJpzI5R0CSJOld4Ac/+AE1NTXU1dUxY+ZMMoNBbNumqKhoxCnDI1EUBcPrx0rEMHx+FGXke0m1tbVEIhFmzZqF1+vF4zn6WQSDgwCf3z/q5400REzXNBCChGniPUTR8s0338zHPvYx6uvrqaioOOr1S5IknYrkiYAkSdK7QFVVFevXrycnN5etW7fi9/uZP3/+qIOAFEVV0T1erPjIxcO1tbU8++yzTJ48mdraWg4cOHDUa08FAYqijCkIgHfSgg7uFqTrOoaukzBN9uzZw8svv8zu3bvT37/++uvx+/388Y9/POr1S5I0nCwWPjXIQECSJOldYOvWrbz66qt0dXWxe/duOjs7qa2tHde1VE1H1XVsMzHse5FIhGg0SjQapTcapa2t7ajWPTgI8Pp8Y3/+oLSgg6mqSl9fH5mZmbz66qs8//zz7Nq1C0jOYLjuuuu466676OnpOar3IEmSdKqSgYAkSdK7QHt7O9FoFNuymFpdTTQa5dlnn2Xv3r3jup6mG4AY1kkoFAoRDAb5xz/+QUdHB9OmT8ce5dyCgx1tEJBKCzo49cd2HOKJBAnTxLYsnnzySQCi0Sitra3px33oQx8CkMPFJOk4EEIg3BP8dYzmoZxJZI2AJEnSu0B+fj7BYJBdu3fT0dHBvPnzWXjOOXR2do7reoqiJIeNxWMoqpq+615ZWcmKFStoaWkhFAqRnZWF47qYloWmaWgjpOmM5HBBQDgcJhKJEAqFDpvatGfPHjyGTrSvnxkzZmDbNo7rJteuaaiqSm9vL4lE8mQjGAxSVFSUfv6KFSuYMGECjz76KBdffPF4PiZJkqRTmgwEJEmS3gVqamoQQhCLxWhvb+fF1aupqKhgxSWXYNk2uqaNaoM+WLJ4ODl52PCqKAN33quqqqiqqko/Th8IABzHwbSs5I81jV27dtHa2kpRURHV1dXpxzuOQ6y/H9u2aW1rG/K9cDjMqlWriEajBINBVq5cOWIwsHXrVvr7ooDCxEmT6Ovvx+v1DisOrq6uRggx4joUReH666/nz3/+Mz/5yU9G3aVIkiTpdCEDAUmSpHeJWbNmAcn2mLW1tbS0tJCTnY3rupimiabro75jn5IsHh4IBnyBQz5XURR0XUcTAtu26e7uZtOmTdTX1xMMBhFCMHXq1HQQ0NfXxx//9Kch34NkDYLP52PRokWsXr2aSCQyLBBwXRefz8eE8nJa2tp45plnqKioYOnSpSOuberUqenrH+ymm27iRz/6EWvXrmXZsmWj/lwkSTo89yT09ZdzBIaTNQKSJEnvMtnZ2dx444089thjKIqCpmnJFp9CYFoWzijnBKSomoZmeLATI3cSGkxRFAzDYM+ePdTMmsVHPvpR3nf99RSXlBCLxTBNE1VV0QyDm97//mF5+6FQiMWLFrFjxw6uuOKK9AwEMRBgmIkEtm0DYNo2jmXR19dHfn7+GD+lpIULF1JWVsYjjzwyrudLkiSdymQgIEmS9C4TDofp6upi69atPP3001xzzTU89thjRCIRmhobkzn9R5i+ezBNN1BUdVjx8KHk5ubi8/n4/e9+x98efphIczMKoCoKDY2N/O3hh3Edh5kzZw7J2y8pKSE3L4/KykqCwSAFhYWYicSQQWMej4eqqiqEEGTn5HDFFVdQU1Mzps8oRVVV3vOe9/DEE0/IQkNJOoaEECflSxpKBgKSJEnvMnPnzuXee+9l69atXH755axZs4b3vve9VFRUMHnyZLweD9///vexLAvTsnBdd1RBgWZ4EK6DY1tHfGxlZSUBv59zzjmHiy66iAnl5enC4OrqapYvX040GmXRokXpvP1wOExnZyeJRIKz5swhNzcXFXBJBgC6rg9JTcrJziYnN5fiUGhMQc3BVq5cyb59+9i5c+e4ryFJknQqkjUCkiRJ70Kf+MQnmDlzJvF4nPPPP5/77ruPWCzGN7/5TQC+9rWv8cMf/pAbb7yR9998Mw8//DDTpk7lzjvvPOQ1FUVB9/qx4v3JTkLqoYtrbcchFApRWFg4YnegqVOn4rouiYGTicbGRsLhMJMnTaKrsxNN0ygqKkJRFBzHobOzk507dxIKhaisrARA0zRi8Tjbt22jesoUmpqbaW9vp6CggJkzZ476s1q+fDkej4ennnqK6dOnj/p5kiRJpzpFnKHnJD09PWRnZ9Pd3U1WVtbJXo4kSdJpYfv27bz88ss8+OCDvP766yxatIiOjg4WL1nCVVddhW1ZhEIh5s6de8hruK6LnYjR2tFFc3Nzus3njh07aG1tpbS0lAkVFWiqetg5AUIIEokEZiJBLBajs6uLnu5u+mMxMjMzmT9/PkIIIpEI3d3d9Pf18fobb7BixQoqKysJh8Pk5OTQ3d2NaVkE/H7+NFCAvGTJkjGlC61YsQKPx8M///nP8XysknTSnGr7odR6Fn3rCXRfxgl9bTvexytfueqU+SxOBTI1SJIkSUqbMWMGt912G88++yzt7e089dRTvPbaa9z8/vfzvuuu46WXXmLfvn20DCrgPZiqqnR29+JaJpGWFlatWsXWrVtZt24doVAITdOo27eP/r4+TNNMBwG1tbWsW7eOuro6bMvCNE36+/pIWBZrX3qJ1tZWiktKmDF9OtOnTcMyTVzHoauri/Xr1yOACRMm4PP5sEyTrq4uFFVl7dq1tLe3EwgEuPzyy4knErS3t4/pc1mxYgVr1qzBNEdXAyFJknQ6kKlBkiRJ0jCqqg7pt3/OOeewc+dOiouLKSsr42tf+xoXX3wxPr+f5qYmQqEQ/f396X78PT095GZlMm/ObPw+H+3t7eTl51NUVISmaeiahjWwwZ80aRKdnZ3k5+WRm5NDLBajORKhrKyMjbt2MW3qVHJycmhsaMAwDMrLy/H6fHS0t7Nj504KCwupnDyZ3JwcJkyYQEtLC0KI9PobGxvp6enB7/ORk53Ne97zHhzbpra2Nj2YLJVOdCgrVqzgS1/6Eq+//jqLFy8+rp+9JL0byPahpwYZCEiSJEmjEgqF2Lx5M/Pnz+df//Vfuf/++znvvPPwejy0trSwd+9e6g8cICMjg8WLF+PLCGImYpQWh8jOy2dfbS0CUIC2tjbe2rSJhQsXEggEKC0tJRaL4fP5iEQiGLqOZZrMmD4d13WZNWsWvT09FBYWEggE6OrqIhaLUVZaim3blJeX0xyJ0NfXx+49e2hra6OwsJBJkyezYMECCgoKUBSF9vZ2SktL0XSdHTt2sHPnTrZv344QYsgQNBg6wfiss87C7/ezfv16GQhIknTGkIGAJEmSNGqzZ8+mra2N73//+3g8Hp5++mm8Xi9nnXUWZWVlLL3wQgJ+PwJobm7mxRdf5Ppr30t/IsHMmTPxGAbxeJz9+/czY8YMGhsbKSos5KG//AWAq6+6imhvL16vl1BxMR6vl7q6OnJzc+nv7ycnNxchBNu3b+fNN99Mzw9YuHAhGRkZPP/CCyw6/3xqZs6kuLiYaDTKhg0bWLhwYXqgmGWa7Nq9G4TggiVL6O/vJ5iRgZlIAMmi5+6eHt5++20OHDiA67qsXLmSuXPnsmHDhpP10UvSGUW4ya8T/ZrSUDIQkCRJksYkOzub73znO6xZs4ZYLEY0GqW9vZ2WlhZeWbeOvLw8zjvvPOLxOBcuXUrCsvFoGoauI4TAdV1KSkqItLSQnZ1NX18fAMFgkFg8TkVFBXl5eQghUBSFSZMmEY/FmDRpEpDcqOfl5eH1eolGowSDQfLz88nOzsbn89HY2MjUqVNpjkR44oknCAaDFBYWptevGwblZWW88cYbeL1eIi0tBDMzCRUXp1ulOo7D7NmzMU2T3bt3E4lEqKmpkYGAJElnFBkISJIkSeOydOlSKisr0+kz/f39NDc3k5eXx5o1a9Kb9Llz52KZJvPmz0Og4AsEyPz/27v34KjqM/7jn92QC2STDYSQCyFclltIgEQEBCoKqFRUxp+daglabwUdb2WQS5EB7c8qFa+11Kodrf2JYryPo0UjIApKJVWQSyKIEAnQBMhtsyEJ2Zzz+wOyNYCpJuScTfb9mjkzbM7mfJ99Zo377PP9fo9hqF///tq2bZvSBg7UmDFjFBkZqWPHjikxKUl+v1/+hgZ9u3evjhw5oqFDh8pRU6NvvvlGiYmJysjIkGmaKi8vV3x8vDIyMmQYhnJmzJBpmvLV1KixsVGjR49WQkKC0tPTA3E7HA7FxMZq+hVXaNfu3Ro/bpxcMTFq8PtlGIacDocMw9C7774beA2JiYlKT0/XSy+9dOI5TvbaANDxUQgAAFotNTVVqampgceDBw+WJHXt2lUlJSXq1auX6uvr9enWrfp882bFx8fr5z//uWJiY1VXX6+BHo/CnE6lJCfLGRamQ4cOaefOnSorK1PftDRFRkaqtrZWhYWFGjlihD777DO5XC5ddNFFyszMlHRit6Fdu3apT2qqorp2VXh4uGLdbqWkpPxg3E6nU66YGJ2TnS1Dkt/vV5cuXRQRHi6Hw6Hk5GRddtllgSInNTVVHo9HtbW1Onz4sJKSkto1r0BnZ8edfjvpjvltQiEAADjrPB7PaYtvy8vL1aNHDyUnJ+vqq6/WihUrVFJSov4DBigpJUWGYSgpMVGmaSohIUGRUVE6evSoUvv0UXh4uBoaGgLTgQ4fPiyPx6O9e/dq27ZtGj9+vN586y2NGzfutHF3794d2M1o4MCBgak/hmnKYZoKP/nh39/QoLCw/94E7dQip+nfBw8epBAA0ClQCAAA2l3Tt/dN+/e/+uqrmjdvntxut8rLynTo0CFVV1crIzNTVZWV2rdvn8LDw7V37175fD7FxMRo+PDh8gwcqP3ffSe32y1JKi0t1b59++QZOFDh4eGBAqHJ7t27tX79erlcLrlcLlVUVio2Jibw4d8wDPkbGhQeEaGwLl3U0NAQOHeq3r17Szqxm9CoUaPaO2VAp8b2ocGBQgAAYJn4+Hjt2bNHAwcO1OHDh7Vx40alp6droMejOLdbTkkREREaOWKEorp105gxY+StqpIpyd/QoH79+6tXQoJ2796tmJgY9e/XT42Njar2ejV58mSFh4fLW1Wl+vp6RUZGKjY2VhdMnKjE5GStXr1aPp+v2fafTqdTYWFh8vv9Cg8PD+xCVFpa2mxakCT16tVLkSd3MQKAzsD21U51dXW68sorNXjwYI0cOVIXX3yx9uzZI+nEXK777rtPgwcP1vDhwzVp0iSbowUAtJXH41FlZaVSUlLUrVs37dy5U2vXrVPNsWPaWVCg2tpafbt3r0pLSvT444/r2337VFpaqli3W7U1NUpJTtaY0aPVNSpKm/PzlZycLLfbrdLSUjmcTn2Ql6f/9+KLWvXKK6qoqND6jz/Wxx9/rEsvvVR90tJOi8cZFnZiN6PGRnXp0kW+6moV7Nyp9evX67333tOBAwdOPM/pVH19vebMmaOjR49anTYAOOuCoiMwe/ZsXXrppXI4HFqxYoV+85vfaP369XryySe1bds27dixQxERESopKbE7VADAWeB2u5Wdna2oqCgdOXJEtbW1OvfccwPnPR6Pvv32W0nS73//e0nSmjVrFB4ersTERDX4/aqpqdGcOXPUu3dvDRgwQB6PRzkzZsgzYID2798vn8+nwsJCjRkzRseOHVNFebmSkpNVUlqqb/fsUa9evTRo0CA5HA6Fh4fr+PHjinA6tXffPiUkJCgsLEw+n0+lpaXN1gpIUrdu3axLFtAJmYYp0+KpOlaP1xHYXghERUVp2rRpgcfnnXeeHnnkEUnSww8/rHXr1ikiIkKSWJwFAJ1Menq60tPTZZqmysrKVFdXp9LSUmVnZ+vxxx/Xc889p8mTJ2vJkiXq2bOnCgsLA7v2xPfooW3bt+vQoUP6+9//rgceeEBOpzNwzwCXy6WCggL94he/UE5Ojl566aUTU4piY5WYlKSCggIdP35c5eXlSkhI0KBBg+RvaFBiYqJqfD6ZphnYOrTJ/Pnz9dZbb1EIAOgUHGaQ7aV03XXXqUePHrr//vsVHx+vBx98UK+//rokae7cubrmmmvO+Hv19fWqP3lXSEnyer3q06ePqqqqFBsba0nsAADrGYahxpM7AVVVVqqoqEgVFRWqrq7WzJkz1dDQIEnasWOHKisr9a9//UtDhgzR+JNrBXZs367CwkJNnDhRAz0eOU92Avbs2dNsjYAkPfroo1q6dKmqq6u5lwA6BK/XK7fbHTSfh5riGbXoLYVFRVs6dmNdjb5Y9n/aNRf19fUaO3asvvrqK23ZskVZWVmBc9u2bdPtt9+u/Px8JSQk6M4779SCBQvaJY4fy/aOwPc9+OCD2rNnj9auXau6ujr5/X7V1tbq888/V1FRkcaPH6+hQ4dq5MiRp/3usmXLAu1jAEDocDqdcjqdCu/SRREJCeoRHy/DMORwOFRbWyuPx6PvvvtOmZmZ+vzzz+VyubRr1y65XC5lZGaq/4ABys/P19GjRzV06FA1HD+uxpNTjyorK/XFF19o7dq12rlzp9atWydJKi4uVt++fW1+5QCCzYIFC5SSkqKvvvqq2c+9Xq8uueQSXXTRRXr66ae1fft23XTTTYqLi9Ps2bNtijaICoFHHnlEb775ptasWaNu3bqpW7ducrlcuvbaayVJ/fr104QJE5Sfn3/GQmDRokWaO3du4HFTRwAAEDqaigLpv52CXbt3a8Mnn+iPf/yjJkyYoC+//FIVFRXKPuccVVdX69XcXLlcLiUnJ8vn86m2rk5hDofy8/Plcrk0duxY/fnPf242jsvlsuxJAzIAABFFSURBVOPlAZ1G0308rB6zPa1evVp5eXl64403tHr16mbnXnrpJR0/flzPP/+8IiIilJGRoa1bt+qxxx6ztRAIir7mY489plWrVunDDz9UXFxc4OczZszQ+++/L+nEjWg2b96sESNGnPEaTdvEff8AAISupi5BZESEJk+erHPOOUclpaUyTFMjs7JkGIZc0dGaNXu2Zl57rRKTkpT/73/L5/Np9QcfSJJ8Pp+8Xq8Mw1BFRYUKCgr09ddfKz4+3uZXB6C1vF5vs+P7U8tbq7S0VLNmzdKLL754xjVEmzZt0sSJEwPrXiVp6tSp2rVrlyoqKto8fmvZXggcOHBAd999tyorKzVp0iRlZWVp7Nixkk5M93n//feVmZmpiRMnauHChRozZozNEQMAOhqn06nly5dr544d+nd+vvZ8843+tWmTvv76a23+/HO5oqP17DPPaMrkyfJWVanG55N04pv/+Ph4ORwOxcXFKT09XUOGDLH51QBoiz59+sjtdgeOZcuWtel6pmnqhhtu0K233tps97PvKykpabbxgKTAYzt3xbR9alBqaqp+aL1yfHy83nnnHYsjAgB0VvHx8SosLAzcbbh3794aO3aslixZorVr10qSsrOztX37dpWXlys+Pl4ZGRk2Rw10PqZpw/ahJz9vFhcXN5s5EhkZecbn/+53v9NDDz3U4jULCwuVl5en6upqLVq06OwFaxHbCwEAAKySkZEh0zQDH/IzMzMlSffff3/gOTfffHPg5wA6nx87hfzuu+/WDTfc0OJzBgwYoHXr1mnTpk2nFRTnnnuuZs6cqX/84x9KSkpSaWlps/NNj+3cHp9CAAAQUr7/Id/n8+mpp54KbOF32WWX6S9/+YtdoQEhoyPcUCwhISFwX5KWPPnkk/rDH/4QeHzo0CFNnTpVubm5genu48aN0+LFi9XQ0KDw8HBJ0ocffqghQ4aoe/fuPymus8n2NQIAANglOjpa8+fPPzFNwTT17rvv/uA0AQA4k7S0NGVmZgaOwYMHSzpxh/Sm+5Dk5OQoIiJCN998s3bu3Knc3Fz96U9/arbjpR3oCAAAAADtyO12Ky8vT7fffrtGjRqlnj17aunSpbZuHSpRCAAAAMBihiE5LJ4aZBjWjNOvX78zboQzYsQIbdiwwZogfiSmBgEAAAAhiI4AAAAALNW0LsfqMdEcHQEAAAAgBNERAAAAgKVM48Rh9Zhojo4AAAAAEIIoBAAAAIAQxNQgAAAAWMowTBu2D2Wx8KnoCAAAAAAhiI4AAAAALGUapkyLv6G3eryOgI4AAAAAEIIoBAAAAIAQxNQgAAAAWIqpQcGBjgAAAAAQgugIAAAAwFKGacphWrx9qMXjdQR0BAAAAIAQREcAAAAAlmKNQHCgIwAAAACEoE7bETBPzgPzer02RwIAAGCPps9BJvPjcQadthCorq6WJPXp08fmSAAAAOxVXV0tt9ttdxgBpmnD1CCKodN02kIgJSVFxcXFiomJkcPhsDucNvN6verTp4+Ki4sVGxtrdzgdDvlrG/LXduSwbchf25C/tuuoOTRNU9XV1UpJSbE7FAShTlsIOJ1Opaam2h3GWRcbG9uh/gAFG/LXNuSv7chh25C/tiF/bdcRcxhMnYAmpmHKYLGw7VgsDAAAAIQgCgEAAAAgBHXaqUGdTWRkpO69915FRkbaHUqHRP7ahvy1HTlsG/LXNuSv7cjh2WWapuWLd1ksfDqHSVYAAABgAa/XK7fbrf43vyhnRDdLxzaOH9O+565TVVVVh1vn0V7oCAAAAMBS3Fk4OLBGAAAAAAhBFAJB6L333tOoUaMUGRmpOXPmNDtnGIbuvPNOeTweDRw4UCtWrGh2/o033tDw4cOVmZmpzMxMFRUVWRd4kGhL/iTp8OHDSkxM1JVXXmlNwEGmtfl78sknlZmZqeHDh2vEiBFauXKlxZEHj9bm8Me8P0NNaWmprrrqKo0YMULp6el64oknAudqa2v161//OvD3bvr06Tpy5Ih9wQaplnIoSR9//LFGjx6tjIwMDRs2TJs2bbIn0CD1v/InnXgvDhs2TFlZWZbH11EZJ7cPtfpAc0wNCkKDBg3S888/r9dee00+n6/ZuZUrV6qgoEC7d+9WVVWVsrOzNWnSJGVkZGjLli1avHix1q1bp5SUFFVXVyssLMymV2Gf1uavyS233KLLL79cZWVlVoceFFqbv4yMDH366adyu90qLi5Wdna2xo0bJ4/HY9MrsU9rc/hj3p+hZu7cuRo2bJjefPNN1dTUaMKECZowYYJGjx6tZ555RseOHdP27dvlcDg0a9YsPfzww1q+fLndYQeVlnJ46NAhXX/99Vq9erXS09NVX1+v2tpau0MOKi3lr8nChQs1YcIE5efn2xgp8NPREQhCgwcP1siRI9Wly+l1Wm5urmbNmqWwsDD16NFD11xzjVatWiVJevTRRzV37tzA3QNjYmLUrZu1C3GCQWvzJ0nPPfec+vfvr/PPP9/KkINKa/M3ZcqUwE1r+vTpo6SkJBUXF1sae7BobQ7/1/szFH311VeaNm2aJCk6OloTJ07Uiy++KElyOBw6duyYGhoa5Pf75fP5OuWNJNuqpRw+9dRTysnJUXp6uqQTO+PExcXZFWpQail/krRmzRodPHhQM2fOtCtEoNUoBDqY/fv3q2/fvoHH/fr10/79+yVJBQUF2r9/vy644AJlZ2dryZIlamxstCvUoNRS/vbt26enn35aDzzwgF3hBb2W8vd9a9asUUVFRbNvzHBCSzn8sfkNJaNGjdLLL78swzB05MgRffDBB4Epj7fccotiYmLUq1cvJSYmqqqqSnfccYe9AQehlnJYUFCg2tpaXXTRRcrKytKdd96pmpoaewMOMi3lr7KyUgsWLNBf//pXe4PsgEyj0ZYDzVEI2GDcuHHq2bPnGY+2fIPq9/u1ZcsWvf/++9q4caM+++yzTvnHqT3yZ5qmbrrpJq1YsUJdu3Y9yxEHl/Z6/zXZvn27brzxRuXm5io6OvosRBx82juHoeR/5fLRRx+Vz+dTdna2cnJydOGFFwY6LXl5eTIMQyUlJfrPf/6juLg4LV261OZXZL225NDv9+uTTz7Ra6+9pvz8fFVUVOjee++1+RVZqy35u+OOO3TPPfeoV69eNr8KoHVYI2CDtizESktL03fffadx48ZJkoqKipSWlhY4d9VVVwU+yF511VXatGlTp/uGrD3y5/V6tW3bNl1zzTWSJJ/Pp2PHjmnKlClau3btWYk7WLTX+0868e3i5Zdfrueff14/+9nP2hxrsGrP/4Zbym9n9GNy+cILLwT+feuttwbWTDz77LPKyclRVFSUJGnmzJl68MEH2yXOYNaWHKalpSkrK0vdu3eXJM2YMUPLli1rlziDVVvyt3HjRm3cuFHz5s1TXV2dysvLNWTIEO3atau9wu007PiGno7A6egIdDC//OUv9be//U2NjY0qLy9Xbm5u4MNrTk5O4Bsyv9+vvLw8jRw50uaIg8sP5c/tdqusrExFRUUqKirSI488oksuuaTTFQFt1dL7r7CwUNOmTdOzzz6riy++2OZIg1dLOWzpXKgqKytTQ0ODJGnLli16++23ddttt0mSBgwYoLy8vMAdSt977z1lZmbaGW5QaimHOTk5+uijj1RfXy9JWr16Nf/fOEVL+Wv6f0ZRUZFeeeUVDRs2jCIAHQodgSC0du1aXX/99fJ6vTJNU6+//rqeeuopTZ8+Xdddd53y8/M1aNAgORwOzZ07V8OHD5ck/epXv9KXX36pjIwMhYWF6fzzz9dvf/tbm1+N9VqbP5zQ2vzdddddqqqq0sKFC7Vw4UJJ0kMPPaSpU6fa+XJs0doc8v483ebNm3XXXXepS5cuiomJ0auvvqrk5GRJ0n333afZs2cHPvwPHTpUzzzzjJ3hBqWWcjh+/HhNnz5d2dnZCgsLU0ZGhp5++mmbIw4uLeUP6OgcpmmyqSoAAADandfrldvtVu8Zz8gZYe2aPON4rQ6uukVVVVWKjY21dOxgxdQgAAAAIAQxNQgAAACWMhsbZVq8xbnV43UEdAQAAACAEERHAAAAAJYyTRu2DzXpCJyKjgAAAAAQgigEAAAAgBDE1CAAAABYijsLBwc6AgAAAEAIoiMAAAAAS9ERCA50BACgnb3yyiu6+uqrz+o1L7nkEq1Zs+asXhMAEFooBACEpAsvvFBPPPFEu49jGIbuueceLVmy5LRzH374oaZMmaLbbrvtjPFFRkbK5XIFjp49ewbOL168WPPnz2/X2AEAnRuFAAC0o3/+85/q0aOHhg8ffsZzL7zwgrxerwzDOO38Qw89JJ/PFziOHj0aODdx4kRVVlbq008/bdf4AaA9NE0NsvpAcxQCAPA9paWluvrqq5WQkKC0tDQtXrxYfr8/cL5fv35avny5zjvvPMXExOiCCy5QcXHxD17vnXfe0eTJk8947o477tCiRYs0ZcoUOZ0/7c+xw+HQ5MmT9c477/yk3wMAoAmFAAB8T05OjsLDw7Vv3z5t2LBBb7/9tpYvX97sOStXrtSqVat05MgRRUdHn3HaT5OtW7dq6NChZzzn8Xi0cuVK3Xjjja2KddiwYdq6dWurfhcA7GQahg0dgdM7r6GOQgAATjp48KDWrVunxx57TC6XS3379tXixYv1wgsvNHvebbfdpv79+ysqKkozZ87UF1988YPXrKioUGxsbKviWbRokeLi4gLHxRdf3Ox8bGysKioqWnVtAADYPhQATjpw4ICioqKUmJgY+NmAAQN04MCBZs9LSkoK/Ds6OlrV1dU/eM3u3bvL6/W2Kp5ly5Zpzpw5P3je6/Wqe/furbo2AAAUAgBwUmpqqurq6lRaWhooBoqKipSamtrqa2ZlZenrr78+WyE2U1BQoKysrHa5NgC0J8NolCxevGuwWPg0TA0CELL8fr/q6uoCR8+ePTVp0iTNmzdPNTU12r9/vx544AFdf/31rR7jiiuu0EcffXQWo/6vjz76SJdffnm7XBsA0PlRCAAIWfPnz1fXrl0Dx5AhQ/Tyyy+rtrZWffv21YQJE3TZZZdpwYIFrR5j2rRpOnr0qHbs2PGTf3fhwoXN7iPgcrlUVlYmSdqwYYNiY2N1/vnntzo2ALAL24cGB4dpmqbdQQBAZ7Zq1Sq9/fbbys3NPWvXnDp1qubNm3faAmIACGZer1dut1s9Lv2/coZHWTq20VCn8tVLVVVV1epNHDobCgEAAABYoqkQ6D71XlsKgYoPfk8h8D1MDQIAAABCEIUAAAAAEILYPhQAAADWamyU6bR48W4ji4VPRUcAAAAACEF0BAAAAGAp07T+hmKmSUfgVHQEAAAAgBBEIQAAAACEIKYGAQAAwFKmYVg/NcgwLB2vI6AjAAAAAIQgOgIAAACwlGnYsFjY4vE6AjoCAAAAQAiiIwAAAABLnVgjYO2cfdYInI6OAAAAABCCKAQAAACAEMTUIAAAAFiKxcLBgY4AAAAAEILoCAAAAMBSdASCAx0BAAAAIARRCAAAAAAhiKlBAAAAsJRhNMrB1CDb0REAAAAAQhAdAQAAAFjKbDQkh8UdgUbuLHwqOgIAAABACKIjAAAAAEuZpg3bh5qsETgVHQEAAAAgBFEIAAAAACGIqUEAAACwlGk0Wr9YmO1DT0NHAAAAAAhBdAQAAABgKToCwYGOAAAAABCCKAQAAACAEMTUIAAAAFiKqUHBgUIAAAAA1mpskGnDmGiOQgAAAACWiIiIUFJSkkoKXrVl/KSkJEVERNgydjBymKZpeUEGAACA0FRXV6fjx4/bMnZERISioqJsGTsYUQgAAAAAIYhdgwAAAIAQRCEAAAAAhCAKAQAAACAEUQgAAAAAIYhCAAAAAAhBFAIAAABACKIQAAAAAELQ/wdK0wS5sq3vwAAAAABJRU5ErkJggg==", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "buses = pw[Bus, ['BusNum', 'BusPUVolt']]\n", "lon, lat = pw.buscoords()\n", @@ -197,31 +157,10 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "id": "a10", "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "most_loaded_idx = flows['LinePercent'].idxmax()\n", "branch_key = (\n", @@ -243,31 +182,10 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "id": "a11", "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "lodf_df = pw.lodf(branch_key, method='DC')\n", "\n", @@ -292,21 +210,10 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "id": "a13", "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "plot_sensitivity_dual(\n", " lines_geo,\n", @@ -338,39 +245,17 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "id": "a15", "metadata": {}, - "outputs": [ - { - "ename": "KeyError", - "evalue": "'LineLimitMVA'", - "output_type": "error", - "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mKeyError\u001b[0m Traceback (most recent call last)", - "File \u001b[1;32mc:\\Users\\wyatt\\.conda\\envs\\esapp\\Lib\\site-packages\\pandas\\core\\indexes\\base.py:3641\u001b[0m, in \u001b[0;36mIndex.get_loc\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m 3640\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m-> 3641\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_engine\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_loc\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcasted_key\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 3642\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m err:\n", - "File \u001b[1;32mpandas/_libs/index.pyx:168\u001b[0m, in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n", - "File \u001b[1;32mpandas/_libs/index.pyx:197\u001b[0m, in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n", - "File \u001b[1;32mpandas/_libs/hashtable_class_helper.pxi:7668\u001b[0m, in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n", - "File \u001b[1;32mpandas/_libs/hashtable_class_helper.pxi:7676\u001b[0m, in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n", - "\u001b[1;31mKeyError\u001b[0m: 'LineLimitMVA'", - "\nThe above exception was the direct cause of the following exception:\n", - "\u001b[1;31mKeyError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[1;32mIn[8], line 6\u001b[0m\n\u001b[0;32m 3\u001b[0m post_mw \u001b[38;5;241m=\u001b[39m flows[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mLineMW\u001b[39m\u001b[38;5;124m'\u001b[39m]\u001b[38;5;241m.\u001b[39mvalues \u001b[38;5;241m+\u001b[39m delta_mw\n\u001b[0;32m 5\u001b[0m \u001b[38;5;66;03m# Estimate post-outage loading (approximate using same MVA limit)\u001b[39;00m\n\u001b[1;32m----> 6\u001b[0m limits \u001b[38;5;241m=\u001b[39m \u001b[43mflows\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mLineLimitMVA\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[38;5;241m.\u001b[39mvalues\n\u001b[0;32m 7\u001b[0m safe_limits \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mwhere(limits \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m0\u001b[39m, limits, \u001b[38;5;241m1.0\u001b[39m)\n\u001b[0;32m 8\u001b[0m post_loading \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mabs(post_mw) \u001b[38;5;241m/\u001b[39m safe_limits \u001b[38;5;241m*\u001b[39m \u001b[38;5;241m100\u001b[39m\n", - "File \u001b[1;32mc:\\Users\\wyatt\\.conda\\envs\\esapp\\Lib\\site-packages\\pandas\\core\\frame.py:4378\u001b[0m, in \u001b[0;36mDataFrame.__getitem__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m 4376\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcolumns\u001b[38;5;241m.\u001b[39mnlevels \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m1\u001b[39m:\n\u001b[0;32m 4377\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_getitem_multilevel(key)\n\u001b[1;32m-> 4378\u001b[0m indexer \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcolumns\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_loc\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkey\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 4379\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m is_integer(indexer):\n\u001b[0;32m 4380\u001b[0m indexer \u001b[38;5;241m=\u001b[39m [indexer]\n", - "File \u001b[1;32mc:\\Users\\wyatt\\.conda\\envs\\esapp\\Lib\\site-packages\\pandas\\core\\indexes\\base.py:3648\u001b[0m, in \u001b[0;36mIndex.get_loc\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m 3643\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(casted_key, \u001b[38;5;28mslice\u001b[39m) \u001b[38;5;129;01mor\u001b[39;00m (\n\u001b[0;32m 3644\u001b[0m \u001b[38;5;28misinstance\u001b[39m(casted_key, abc\u001b[38;5;241m.\u001b[39mIterable)\n\u001b[0;32m 3645\u001b[0m \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28many\u001b[39m(\u001b[38;5;28misinstance\u001b[39m(x, \u001b[38;5;28mslice\u001b[39m) \u001b[38;5;28;01mfor\u001b[39;00m x \u001b[38;5;129;01min\u001b[39;00m casted_key)\n\u001b[0;32m 3646\u001b[0m ):\n\u001b[0;32m 3647\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m InvalidIndexError(key) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01merr\u001b[39;00m\n\u001b[1;32m-> 3648\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m(key) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01merr\u001b[39;00m\n\u001b[0;32m 3649\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m:\n\u001b[0;32m 3650\u001b[0m \u001b[38;5;66;03m# If we have a listlike key, _check_indexing_error will raise\u001b[39;00m\n\u001b[0;32m 3651\u001b[0m \u001b[38;5;66;03m# InvalidIndexError. Otherwise we fall through and re-raise\u001b[39;00m\n\u001b[0;32m 3652\u001b[0m \u001b[38;5;66;03m# the TypeError.\u001b[39;00m\n\u001b[0;32m 3653\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_check_indexing_error(key)\n", - "\u001b[1;31mKeyError\u001b[0m: 'LineLimitMVA'" - ] - } - ], + "outputs": [], "source": [ "outaged_flow = flows.loc[most_loaded_idx, 'LineMW']\n", "delta_mw = lodf_df['LineLODF'].values * outaged_flow\n", "post_mw = flows['LineMW'].values + delta_mw\n", "\n", "# Estimate post-outage loading (approximate using same MVA limit)\n", - "limits = flows['LineLimitMVA'].values\n", + "limits = pw[Branch, 'LineLimMVA']['LineLimMVA'].values\n", "safe_limits = np.where(limits > 0, limits, 1.0)\n", "post_loading = np.abs(post_mw) / safe_limits * 100\n", "\n", @@ -398,21 +283,10 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "id": "a17", "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# Pick three distinct seller-buyer pairs\n", "bus_list = buses['BusNum'].to_numpy()\n", @@ -451,21 +325,10 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "id": "a19", "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# Pick three most-loaded branches\n", "top3 = flows['LinePercent'].nlargest(3)\n", diff --git a/examples/steady_state/07_state_chains_and_stress.ipynb b/examples/steady_state/07_state_chains_and_stress.ipynb index b37edd2..3b041e1 100644 --- a/examples/steady_state/07_state_chains_and_stress.ipynb +++ b/examples/steady_state/07_state_chains_and_stress.ipynb @@ -43,10 +43,11 @@ }, "outputs": [], "source": [ + "import sys; sys.path.insert(0, \"..\")\n", "import numpy as np\n", "from esapp import PowerWorld\n", "from esapp.components import *\n", - "from examples.statics import Statics\n", + "from statics import Statics\n", "import ast\n", "\n", "with open('../data/case.txt', 'r') as f:\n", diff --git a/examples/visualization/01_discrete_calculus.ipynb b/examples/visualization/01_discrete_calculus.ipynb index 9574e92..01c3a86 100644 --- a/examples/visualization/01_discrete_calculus.ipynb +++ b/examples/visualization/01_discrete_calculus.ipynb @@ -17,33 +17,22 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "id": "ec99918e", "metadata": { "tags": [ "hide-cell" ] }, - "outputs": [ - { - "ename": "ImportError", - "evalue": "cannot import name 'sorteig' from 'examples.mesh' (C:\\Users\\wyatt\\Desktop\\GitHub\\ESAplus\\examples\\mesh.py)", - "output_type": "error", - "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mImportError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[1;32mIn[1], line 5\u001b[0m\n\u001b[0;32m 3\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mmatplotlib\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mpyplot\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mplt\u001b[39;00m\n\u001b[0;32m 4\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mscipy\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01msparse\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mlinalg\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m spsolve, eigsh \u001b[38;5;28;01mas\u001b[39;00m sparse_eigsh\n\u001b[1;32m----> 5\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mexamples\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mmesh\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m Grid2D, sorteig\n\u001b[0;32m 6\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mexamples\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mmap\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m format_plot, plot_vecfield\n", - "\u001b[1;31mImportError\u001b[0m: cannot import name 'sorteig' from 'examples.mesh' (C:\\Users\\wyatt\\Desktop\\GitHub\\ESAplus\\examples\\mesh.py)" - ] - } - ], + "outputs": [], "source": [ + "import sys; sys.path.insert(0, \"..\")\n", "import numpy as np\n", "import scipy.sparse as sp\n", "import matplotlib.pyplot as plt\n", "from scipy.sparse.linalg import spsolve, eigsh as sparse_eigsh\n", - "from examples.mesh import Grid2D, sorteig\n", - "from examples.map import format_plot, plot_vecfield" + "from mesh import Grid2D, sorteig\n", + "from map import format_plot, plot_vecfield" ] }, { @@ -86,19 +75,7 @@ "execution_count": null, "id": "a3b4c5d6", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Grid shape: (30, 30)\n", - "Total nodes: 900\n", - "Total edges: 1740 (horizontal: 870, vertical: 870)\n", - "Boundary nodes: 116\n", - "Interior nodes: 784\n" - ] - } - ], + "outputs": [], "source": [ "nx, ny = 30, 30\n", "grid = Grid2D((nx, ny))\n", @@ -126,18 +103,7 @@ "execution_count": null, "id": "2a9e478f", "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# Build coordinate arrays\n", "x = np.linspace(0, 1, nx)\n", @@ -173,18 +139,7 @@ "execution_count": null, "id": "12ca26eb", "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# Visualize directed edges on a small grid\n", "small = Grid2D((4, 3))\n", @@ -237,18 +192,7 @@ "execution_count": null, "id": "f84afb9e", "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAAAn4AAAESCAYAAABjDtjcAAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjEwLjgsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvwVt1zgAAAAlwSFlzAAAPYQAAD2EBqD+naQAAWSpJREFUeJztnQeYVNXZx8+dtjPbWdiFlSpVxIKADTX2EgsYiVEjEYwIGtEY/AxgNHbxS9RojEawYIgajaDRqCHYPwsqiI2igKIUgaVun36/5z1kJrM7570zd9ndKef/e54DO+e2c8+98857zluOYZqmKQAAAAAAQN7jyHQDAAAAAABA5wDFDwAAAABAE6D4AQAAAABoAhQ/AAAAAABNgOIHAAAAAKAJUPwAAAAAADQBih8AAAAAgCZA8QMAAAAA0AQofgAAAAAAmqCV4rd7925hGIZ4/PHH43XBYFBcfPHForKyUm67995749tWr14t69avX8+ec/jw4WLixIki2+nXr5+YOnVqh17jpptuEsXFxSITfPvtt/L633//fVb2z9lnny2OO+64Djs/AAAAkA4uoTnz5s0Tf/3rX8Vf/vIXMWDAAKkAxPjnP/8pDjroINGnT5+MtjFXmDRpkjjjjDMypvjdfPPN4swzzxT77LNPRtoAAAAAZDvaK35ffvmlVBQuvPDCpM556aWXpCIBrAkEAsLtdotevXrJAgAAAIDsJK9NvQ8//LCcwSssLBQnnniiWLt2bYvttO3uu+8WGzZskCZdKjRzFDMLv/vuu+Kss86K7//++++LkSNHCq/XKw444ADxr3/9q8X56urqRN++fcWPf/zjFvWXXXaZ6Nq1a0oz5OzZs8WQIUNEQUGBbNttt90motFofDuZqKmNn3zyifjhD38oioqKxKBBg+SsZbo88MADso1lZWXS/Lht27YW27/77jvZftpO5z/11FPFF198oTSL/u53v5Pn8vl8YufOnUmmXjJtxvo1sSSaPO1cj2v7W2+9JY4//nj596GHHhq/DtHY2CiPpX6l94DORc+jtrZWtIWXX35ZHH744fKeyT3g8ssvl9dIZNWqVeLYY4+V7wnNItNssornn39etov2O+KII8SyZctEeXm57Ec71wyFQuLaa6+VM9P07lRXV8v3tq33CAAAII8x85R//vOfJt3exIkTzYULF5q333672a9fP1k3d+5cuc+yZcvM8847z+zRo4e5ePFiWfx+v9z2t7/9zayqqjIjkYj8vHnzZrO4uNg88sgjzRdeeMF8/PHHzb59+5rdunUzJ0yYEL/u66+/bhqGYc6bN09+fuWVV+Q1n376acv2/vGPf5T7XXnllea///1v88YbbzSdTqd5zTXXxPehdtM+Q4cONe+55x5z0aJF5rnnniuvt3LlSsvzU1t79+5tnnLKKbJv6Fzl5eXy/mPU1dXJPurfv7/51FNPmc8995w5cuRIud/69etbnIv67JhjjjGff/5588UXXzSbmppkm4uKiuL7rVixIt6vVObPn28WFBSYkydPtn09q7bX1taaDzzwQPzZxq5H1NTUmJdddpn57LPPmm+99Zb517/+1dxvv/3M4447Lql/rrjiCss+pHM4HA7zkksuMf/1r3+Zjz32mHxHEvuwubnZ7NWrlzlkyBDz73//uyx0vX322cc89thj4/vRu0fP9+yzzzZffvll88EHHzQHDhxoer1e2Y92rnnzzTfLd5P6gO6R+pn6eOvWrZb3AwAAQD/yVvE7/PDDpWKSyA033NBC8SN++ctfyh/91lx44YUtFLrp06ebJSUl5u7du1soeXS+xP2Iq666yiwrKzM/+eQTs7q62rzgggss2xoOh6UCef7557eonzlzpunxeMzt27e3UPzoBz5GQ0ODWVhYaN56662W16B7JIUkptgSpGC43e64cnvfffclKZE7duyQyty0adNanKtr167y2om0VvwSIYVo1KhR5sEHH2w2Njbavl6qtr/55puyb5YsWWLZD6FQyHz33Xflvl999VXail80GpX7tH6WpIzRPSxfvlx+/vOf/ywVtdWrV8f3WbNmjaxLVPxIYSdFL9Z+gpRSaldM8Uv3mmeccYZ5zjnnWN43AAAAQOSlqTcSiYiPP/5Y/OhHP2pR39oEa3U8mXETzbwffvihNCeSmTHGCSecICoqKpKOv/POO6W5jcx3DodDmihT+Rlu375dnHvuuS3qzzvvPBl1/NFHH7WoP+WUU+J/k3mUzJ8bN25MeV9kfiRTYIz9999fmglramrk53feeUeasIcOHRrfh+7v5JNPlmbvRMhcS9e2E/ixbt068Y9//EOaXO1eL1XbraDgnUMOOUSaockX8eijj45HbacL7Utm6Z/85CciHA7HC7WLnvHSpUvj7wndE5ngYwwcOFAcfPDBLc63ZMkS6T9Kx8YYO3Zsm645YsQI8corr0gTMZ030T0AgHTQOeNBZ0DfTXIVag314V133dXu10OWAmBFXip+5PtFP5BVVVUt6rt3757W8fQFbWhoaKFgbd68Oel8hKqOfLHIB42CHn7605+KLl26WF5v165dyvbFPpP/XCLkB5aIx+MRfr8/5X2pjiNix1I7VH1Eda3bkG5fEuQL+PTTT4tnnnmmRdS0neulajsH+dFddNFF4rDDDhN///vfxQcffCDr0jk2EVLMCRpMkPIYK6TE0kCB/ESt3pPW90n70Q9qIiUlJdLfz+41f/Ob34jp06dLX0K6zx49esgIZ5rRB2BvMx6Qwrd48WJx/vnnx7ch44E96PuoUvyoX1WBhQB0JHkZ1Us/qC6XK2k2aOvWrWkdT9G8P/jBD+QPcQyawVPNLqnqPv/8c3HPPffIWab7779fjpoTZ7VaE5s15NqrmlXsCOg6X331VVI9taN1G2LBE6mgmdOZM2fKUS0F2LT1em3l2WeflTMPFDgT4+2337Z9nlh7/vSnP8lAi9bEUsjQe0JBGqp7Ki0tjX+m/VoH1tTX17dQRtO9Js2E0owCFQpgeuyxx+Tf/fv3Fz/72c9S3htdk2Z30oEU7kTlFOQvyHiw9zQ3N8uJAA6yCoHM4NdY7uXljJ/T6ZTmr9jMToz58+enrfglmnkJmkl58803W0RKvvHGG0kzU/Qi0Y8t7U+zS2T2oxknmoHkoMhOUlZJSUmEZqjohaNzdQZkAqWI2kRljGblXnvttbh51A50ngsuuECOaH/1q1916PW4GUASvLFtMZ588kmbdyLEfvvtJ1PVfPPNN2LUqFFJJaaE0bNavnx5iwhy+vuzzz5rcT6KPqb3LNEsS2bwtlwzETIr33HHHVJppOjiVFB/dfUVSxeGdMq+++5ra6YUZCednfGAZgjpHGvWrGlRT993UowefPDBjGQ8IFM0tbc19N2k8yXKJroG5XWle+zZs6ecaaeZ99ZtoFk8clehNlC0fWyQHPubCmUi4Ey9FMV/1FFHyWdD1iJyq6H7IpCloH3way738nLGj6AvJflM0WwbmSjI54/MFqmgH9mVK1cm5e+7+uqrpa8eCZUZM2ZIgXXjjTfKNC2J/Pa3vxVff/21/KEnhYMEDymhJKhap+lIVFRvuOEGcdVVV0kz4emnny6Vxv/93/+V1219jY6C+uoPf/iDTMJM7SUBd/vtt8vZU2qHXcaMGSOF+s9//nN5PzFo5ot89NrzeoMHD5b9SLNddDwVUo5IAF9xxRXi1ltvFUceeaT0hXv99ddt3wsJaJrFJdM9CV9qMwl28sEjQU3KFrWBfkjoXuj9oWvG3gkyvyZCs6Ck/I0bN05MnjxZnod+AKgPYn5/6V6T3AroR5dmmGk7/cjS+0k+qKmggUqTiIiLRE/hSTEODIqomLdlkzwmn0a/ukFKDb1z9K7GZGNr/2IaNJP8odnx2ACaZqmJhQsXyoFFbEC6ZcsWmYbpwAMPlINVevdiKYdotp0gmUbKEn0/Z82aFb/OU089Jf+nd5yDrCYkG6+88kr5vSIlk2QpKaCtlSYaZF566aVi2rRpUrmle6TvGWdxoYEpuUjQYC1RAfzb3/4m5TYpmwR9D3/961/LASwpxDSoiil+5NOdCN0L9e91110nlbfx48dL2UPtj90nyT8V5A5DbaLfLuob+g157733xKZNm+T3u6mpSV6T5CRNFpBiTn+TDKCJCTvQRAj5kZMcJlM0uZ/EftvINYcgZYdcnkiuxH4/SZ5R6rJEP2ZSTOkdosEAyXSSUXRucneye016Px566CH5/g0bNky6vCxatCjpXHtDUHe5l88xLg899JBMA0IpMiii8sMPP0wZ1UuRppQuRcX//d//mcOHD5eRtrTPSy+9JKNUY1G97733nozepOsm8oc//MF0uVwpI04pInTQoEEyWrVPnz4yUjcx6jMW1btt27YWxyW2gUMVtUqpWOh869ati9d9++23MkKUIpgpWvjkk082P//885TnUkX10rlVJTG6ta3XU7Wd+p1Sw1Bfx15tipimlDiVlZXyGj/+8Y/NDz74QG6nVCmp7qk1lEKH2k/3SWXYsGHy/InR3hRtSxHl9J7su+++MgXL2LFjW9w3sWDBAnPw4MEyxQ2lsaFoY2r7vffea+uav/vd72TENEWS0/YRI0bI9DjpQKlwqC+mGH3MKx39LAvtQ/vSMSB3yVTGg+uvv16mNaLvZAx6V3/6059mLOMBRfmTbLjuuuvidZR1gNIj/f73v4+nnaLPdM3W8trn8yW14c4770y6DtXHzsfVUxQ/ZS849dRT2faq2o8sBfap1Vzu5bXi1xZI8bj22msz3QygIa+99poUMJSLr7OICcCpzr7mNa59LQvtk28CUDdIkaLBBeUBTeSzzz5LS/Gj4ysqKmSuyBiUE3PMmDFJ16L9EhU/GqRRGiIaMCdek5REDhpE0T40SErk008/lfWUJzVR6aLUSYnQAH3KlCmWffKLX/zCHDBgQPzzM888I9u5YcMG+ZnywNK5KfcmKVqxQtdK/L7G2vDFF1+0SfFbtWqV/EzXt4JyxNIEBA3yEgfUlOM03YHsl19+KY+hAXTiPVE+VsovSnlqCcqDe9BBByUdf8ghh7QYyFI+1quvvrrFPqQwJ6anSveaNAiJ5TP96KOPWkx+tBe1msu9vPTx2xtoSpmiUAHoaH7xi1+IBQsWSH8fciMgMxWZc4455phO73ynkV5pC3Rv5JdFZhIKUmmdnigRMs/R/ZNvE5WTTjrJcn+QOxkP6B0g14tHH31UfiazL/lOxVbdyVTGAzKtkntO7D0jMy+9g7HlJ2PR9WT6TYyuj5k6Y9H1rdtmlx07dsj/rdYaR5aC3JB7D2S5zMtbHz8Ash36USO/H/phIQfi0047TfosJeb26yychiGL5T7CvgQknyXytyKfHRKAlBqE/MHIaV6lLJASTD/Eo0ePlkKT/HxIyVixYoX0EQO5nfGA/O/Iz4181ijIinz3rDIEdEbGAwqk6N27t/QxI58+CkxJzFkYu8Zzzz0n92sNKa9tyXjQmpgvt9XSnshSkDpLQabl3jM5IPMw4wdAhqCZBRLy5DRMMzHkPN3W2YJsHfmSUzz92JMzNzm0kzAkh3ea7VFBygDNhFJQAEU1P/LIIzJ6sy0BOSC7Mh4QFLRAsxqk/NH2VAmeOyPjASlqFORC56QZeAqeSEz2T4EZ9M5SknxVdH06wXc0Q5hq5pHulWYZ586dy+6DLAXZL/fuyQGZhxk/AICtkS9F9CVCKTYSV1WJQQotRYxSBHMMms0kUwalvEgHimKkFVo6K5elDmQq40FMAZowYYL4/e9/L2dBVDNomch4QDMu1Ca6Fs22dOvWrYX5+JZbbpFRvaT8UXoVahf1xwsvvCCVxdhqRBwUVUz7kkmPImRJyUucNU1M7UJtoWh/SgNG3yv6rlBkMvU7shRkt9wL5ojMw4wfAECKNkeKEhOP9GOdmOMqMT1HImTCptkTlX8WpQBJB1qRhHyeSHCC9oHSLNEsBM0oUBoQ8msm81Q6s32kwFBi8ETI1EvmUZqNopQepJSRIhjzkWtNbClNSvOUDuQO8ec//1mmYiLlh3wEKZ1Le/pik28tKWM0A0+KV2uuueYaORNHM5uklNF9zpkzRypkrfOEqqD+oFkcUo7pGFIOVFCqE1IQyRROSjm1hXImxvpyypQpsi2U4uacc86R/oWxlDh2oXugPqVE3XQdei8oVQ35psW+s5SOi94PUropLQ19H0kBpnto3X80Y0oDA3q+NGtFaXLInzRxmdN0rkmm9xdffFFej2aXKaUQzYp1hAww2lnu5YrMMyjCo8PODgDIamgUS0LsRl9/4TWsx4F+Mypubv5G/tgkrkLCzfjRjyj5qFBAAJnLYtAPBwlzWtfYCsqPRj/u5ANDiXNBZqGZMDJH7a3CRXngKGEzKTeq9wbkBzSwIOWFvr+0vrgOcu/7HJF5eW3qpREWPQiaUm+rwy0AuQSN42jpNxox2gkS2ePLksrksQcSfokCkIPMZWQSax04QJ9bJ7RuDZm8SAjSKi5Q+rIDmvnZG8i5nQrNVlFSdSh9+QX5qdEqMGR+p8AESmCfqSwFmZJ73XJE5mVM8SOfDZrOpQzflPU7luG9NTStT51BShytREAjRfITSQdS+lL5kACQj9DolDO1qUjHiTkmANOFTGC0okjMpEjEnJanTp3KHkcjXlqN4N///rd0ngf5AZkpyT+PotcTfaBAfpBNWQoyJfc8OSLzMqb4UdQUTX9arcm6bt066WxLi96TjZwcksmvgkaL6RBznr2QWZal1KV+Ifct4hXLAX3+66+QSM8jeAWz+piRyvqC4cexx9RWDFTWf1HTxB7z5to9+aZa8/7K5LQKMbZ+tydPVmt2r+fXeW3evU1ZHwk2s8c4PeqFyn3llewx5X3UyywR3ft2UdaP3j85XD7G8QP/67CdyIFVvGN22c6Wa5jGCHy6Z61NFZvfUfvvbPqgZb6vRL5e/9+IyETWNYbYY+rC/12rNHF5oSfFpiTH8Uylc6G0BuTMT8KMIjAptQEt5UWBBQQ5sJNpJOYvQ/5hZAokvyXy+4n5xRQXF8sCcpfY+rQgf7MU5BrODpB7uSDzMqb4US6oVFCKAXL+jE2RXnbZZXJ9Uk7xo7X8EtfzI5MXQUqfSvErYGz7PoPX8Yuc6m0lHl5ZLC1Ur+9XUMI/VJOZUi5q5ttWUKhOF+DyNrLHODzq9Q8NF78moeFUOzMbzoj9Yyyu4/DwCpnLW6SsLyjk+7SIUYZKS9XnkttC6vMFmGdKNDDvAvfuWL1zBQbfp1Yu5XZdGxxpjHzbMm4nZ3VKVUOCjQQazezTWq8x5+f169e3mBEgJ36KjEtMp0FQlCi31jUAALQFRwfIvVyQeVnt40cd1Ldv3/hn0oapjoM0aFr4GQBgD4/DEJ4UymLEbJufLJk4ODNH61mgb7/9VuQrneHeAgDIvNybmuUyL3uN722A/EYoiWistF5KBwDQ+Uu2gT3QiJ5ScyQOZjn3lnfeeUesXbtWOoWTewsAoP1xair3snrGr0+fPnINxUTNmOo4uLQSAIDM+PiBjnVvUbm40EwhrYpB0ZXIZgB0oO3ZDAwt5V5WK36UKJOCP8jOTfZxSjpKSS3tQlO5qulcn5Px8bNQ8Z0etS+Ww813pcH5drVzihknOSyo6l38dRxMHzgLfPwx7tQJS9M9xvI6TNus7onrgzbDPCP2mVq8C9y7Y/XOce8o0RxRpOAks0QbMnNSy1JGtyHjZ9a5txBwcQGgjdkMhJ5yz5XJ0P6XX35ZOj/S0j0UhUimjUmTJskRLxXKEE8+e5TJm6Clcug4AED74khj5Ev7gOx0caFIwhjk5kKWkbVr1iQvCxYNq08SUUePG0y9hDkXe432XCvA6l1kgvZMB/Nzx9XTMU63rfqoRWBgKKq+/4jdeotuZA6RM2LtBTeLbDFGZWULk1hDuCwG8KoxP832DRw0yHY2A4emci9jit/s2bOV9bTUSyK02DEVAEDHkVY+q/yTfznv3mLl4kI/gq0TztpX/IL8hSM5pvg526L4eTpc8QtrrPi520nxS9U2vl1CS7mXV8EdAIC9iG5Lo4COd2+hyF+yhNCPdVvdWwAAqfFoKveg+AEA4k7OqQpoO+SmQv5HGzdulO4tAwfuSdJO7i2k7BGJ7i20vbKyEu4tAHQQTk3lXlYHdwAAOgfyY3Fo6OvSmcC9BYDswqGp3IPiBwAQhtMQRgqTBlKDAADyCUNTuaeF4tfD6xJehcNvBZNeo8LHZ8kv7KpOP+KxWPbL8DDLe1nkG+LeRS8XBkVr+3nVj9NrdT8l6ryHgS578ojZIRKwWKuXSdvis7gO1zare+L6wKrvLL/3zDNin6nFu8C9O0TF9w3pp2z5D15Fw/1mVAj+MbA4nIZwOPQb+eqGEVQv3+hoUq/Z7fDvWfZSRbRup7q+qU5ZbwbUS0ruOYhfmlCJi08p5ShSR3Y6i9TLYIrCcr5ZPvXa7NFC9VrhIQuZXh9Q32NDKGpr//ogEzxD5wqqjwlF1Ndoy/edk6PFHl72lvvU24qY6I4Si9RXxZ7kY8w2yiaHpnJPC8UPAJACp0MYqRKfGnmY0AoAoC9OPeUeFD8AgHC6HcJplY+BZKRVHgkAAMgxnJrKPSh+AAA56k018jXaMw8bAABkGENTuQfFDwCwx9clRaZSRx6uWQkA0BeHpnIPih8AYE90WwoBaOShAAQA6IuhqdzTQvHbv7RAFDqSo4TcxerI0NJe/Hp/Jb3UUV6+Sj4yzCgstbUckBWFbj7aqVuR+nx9u/ERx/5mi7U4GQqKipX1UYvIMQfjR2EVuVteWchu4+6J64NUfcfBPSPumVq9C9y7Q4T86kg910Y+qnKfhuRn10TRkc1tFYApTB7CfmQgyC6MgDqq16jfrqwPbVzLniu8bZOyvnmbOkI4WNfEniviVy8Nx72T7iI+qt7bVf0981RWKetd3fkl8Rzd+ynrzQK1/AkLXv5w0bsb6wLK+k116ijozUw9UcOcqyEQtrUsHOFj5GV5ofp3s0c5/0yqi9Vyvl95oe0l21RRvW3F0FTuaaH4AQCscbocsljuw6yBCgAAuYhTU7kHxQ8AIGdkuVnZGA4z/wQgAEBfHJrKPSh+AID0fF3M/PN1AQDoi6Gp3IPiBwDQVgACAPTF0FTuQfEDAGhr8gAA6ItDU7kHxQ8AQB7MKUe+Ig9HvrphhNURoREmQjewYR17rt2rNyjr69bXKOubdvDh5uFmddQp9056S/lsAMXV6oj7sgHqCHk+h4MQnhImW0OpOkI4YrG8F7f2Lhe9u+J79ZrHqzar64ldu9TnCvjV2RtMi6heFxPV6ytmskdUqbM9EMN6qp9JgUt9jTKv/QwMbcKpp9zTQvGrHtlDFLuTb9XFLBxdWMWn6ijp3V1Z76tW1xPOsq6207kYNhfIJqqYkPlB3fkvJMd3PnXIvlUKmEiYFyJOF3M/FtexSkPD3RPXB1Z9x/W11TPinqnVu9CFSVlBuHzq6xR3L7L1Y9kQCgvxyiphF6fLKZwp0t04Rf5lsAcA6ItTU7mnheIHAGiHDPbR/Bv5AgD0xaGp3IPiBwBIz8k5DwUgAEBfDE3lHhQ/AIDMXp8yg300/5ycAQD6Ymgq96D4AQAErWiY2uSBjgIA5A8OTeUeFD8AgDAchixWpNoOAAC5hKGp3NNC8dvnyMGixJsc7en0qiMpC7rwAf5cNKersid7jFGuDv2POvmIVm4Q4rOI6u1Zwi+SzdGFiartX8lHkzb47S/47WS+PMVe/hXsVsRHPXPRu1Z9wPWd1YDPZJ6Rg3mmhLvXQGV9SYGPPaawWv1eBXapU1BwC9vX+wNCvPKmsAtFtjk9qaLb8nDoqxlGKKCsj9TuUNbXr9/KnmvX11uU9TvX7FSfa0sje66GsPrd8nByg0kpQvjr1PfIYSXvXdXq1ClGhJGBFlbB+qD6mM1MOhcubcuWzbxMqNvepKz3N6rroyGrTAPqzAnF5Wo5Fo3w8sHHyJYejLwORjonktapqdzTQvEDALSDr0uK7QAAkEsYmso9KH4AAGE4HLJYkWo7AADkEoamcg+KHwAgvaWL8nDkCwDQF4emcg+KHwCAnDBTmzTyUAACADTGqafcy9gdrVmzRowePVoMHjxYHHrooWLFihVJ+0SjUTFt2jSx//77i4MOOkgcf/zxYu3atRlpLwD5jMPlEg53iuLCOBEAkD84NJV7GbujKVOmiMmTJ4uJEyeK+fPny/+XLFnSYp8XX3xRvPfee+Kzzz4Tbrdb3HbbbeK6664Tf//73zPVbADy2MnZOrrNcKoXmQe5gxFRR3FG63cp65u3qeuJug3qqNOaTeqo002KtaXj52Kiet3MOtqVQf5d7MPUe0vVmQBKd9Sy5/I2qu/FYarba5HYQDQwba5hopB37fLbityV27ZsVNb7a7cp66Nh9brrhLtIvWZ9JKjOYOFy8/NIm8vU0bu1AfX1w1Yd2Y4Ymsq9jCh+NTU1YunSpWLRokXy87hx48TUqVPlbN7Agf9NhWEYhggEAsLv9wuXyyXq6upEr169bF+vfPQPRGlRYVK94VKn6jAK+JQghled5sQs4NOfRD3qsHjTk9ymGA5G4BVafLm6FaofZ5HFdfowX8imEP+y+xkh3Ra8FulpCi0Wz+aOs0p3w/Ud19dWzyha2IU9xrGP+r0q6NqDv45fnerCG/DzxyiEtlOmbZgt7KJrdFsmLB0TJkwQ27dvF2VlZeLxxx8Xw4YNS7J0/M///I9YuHChlHtdu3YVDz/8cAvZCADYewxN5V5G7mjDhg2iurpaCrWYgtenTx+xfv36FvudddZZ4rjjjhM9evSQ+7/++uvilltuYc9LSiIph4kFAJAah8ORVgHtY+lYvXq1mD59urR0tCbR0vH555+LE088UVo6AADti0NTuZfVd0SzgsuXLxebNm0S33//vRSAl112Gbv/rFmz5Cg6Vnr37t2p7QUg10e+qQrYe0vH+PHj45YOGgS39ltOtHSYptlmSwcAwBpDU7mXEVMvKWSbN28W4XBYzvqRcKPZPpr1S2TevHnihBNOEOXl5fIzmUhOOeUU9rwzZ86UwSAxSGBC+QMgNbqaPLLF0pFoxiVLx5tvviktHSUlJaJnz57i7bffZs9LSiKVGLB0AJAehqZyLyN3VFVVJUaMGCGeeOIJ+XnBggVyRNvah6V///7ijTfeEMHgHofkl156SRxwwAHseQsKCkRpaWmLAgDIbHTbAw88IPr16ye8Xq84/PDDxUcffcTuS9H9NBNG+5NidO+992r3+GDpACC35d4DWS7zMhbVO3v2bOnfcscdd0gFbe7cubJ+0qRJYsyYMbJcccUVYtWqVeLggw+WUb00An7ooYcy1WQA8paOGvk+88wzchaevrckAEmonXrqqeKrr76SA8DWNDU1yQHfueeeK371q1+JfCIbLB2qgCBu3Wci1MgHF/l3q7ftDKqDv7YGwrbX6nVaBF5xVDSo76WMiZ61ukcRZtayNdVRp/RMOULMWrYNTL8E/CFb6+5aRe8271KvuWxG+SC+cLBZWe/yqNfq9TeV2V7fvZmJdA510lq9RgfIvVyQeRlT/IYMGSIWL16cVP/II4+0mMGjaDYAQG5msL/nnnvEpZdeKi6++GL5mYThyy+/LB577DExY8aMpP0ppycVQrU9l0m0dNCg18rS8corr8jIXo/Hk5algwoAIPNy754ckHn5l5lQgTnwcGGWliTXG/Z/yNhjrM7lUKclsbq+Q6hHPEUW6Vx8LvXoOGLyaVGsRqgc7ZliyWF/QC+hKXEVTovzcWlbuL4mTJf6BzVa3I09JhplUr0wub8Iw2Ib37jkY8w6dd6xVBgOI401Kw2lDxmneJCLxscffyxnpGJQhNxJJ52kHPTpACwdAGQPRjvLvVyReVoofgCA9jN5tDYj3njjjeKmm25K2p9y1UUiEdG9e/cW9fT5yy+/1PKRwNIBQP7Kve05IvOg+AEAbAlAik5NDJyCmREAkIsYmso9KH4AAOFwuYXTrV5xJIbjPw746UbMd+vWTTidTrF1a0vHcvpMgVoAAJBPcq9bjsg8KH4AgA6JbqPAhJEjR8oVd84+++z4cmT0mZZoBNmPaRFdGWW2BRknYKtATX6bekPIwjeZuz53LyYTbdtZRLj2MvXREBNpbLH2Lhe9axXVa0bU2yJMpDPX3lTb8knueXJE5kHxAwB0WDoXSmtA6UhGjRolDjvsMJnaoLGxMR7xdtFFF8kExbTqTsw5euXKlfG/adWeTz/9VBQXF2OtWgBA1su9aTkg87RQ/L4LFYjioDep3iGYyFCL5+xiwlDdFuGpHuY6BRYhqC4mytMZDvDHBNX5nYyIehS4Z1vQVr0kyoyQrSKEuXxcFhFVptNje5vp5KftTU+hrchdIszkOA9E+WcXjKiPCVmMesPcLInFZERUMRvSYDEbYAVFtqWObrOv+J133nli27Zt4re//a3YsmWLGD58uFi4cGHc+Zny2CWuhUlLMx5yyCHxz3fddZcsxx57rHjrrbdsXx8AADpT7p2XAzJPC8UPAJC5pYvIxMGZOVoLNspe35YUQwAAkC1yb2qWyzwofgAA4XA55fJEVjhC/MoLAACQazg0lXtQ/AAAwnA4ZbEi1XYAAMglDE3lHhQ/AMCe1WVSCbg8FIAAAI1x6Cn3oPgBAPYE2aRyYm5DcAfILjhHdYdH/VPg9PLBUm6v+phiZunIYpf994eLfyu1OBd3HZePu0dPp/zoc0tG+tzqa7i4el8xew13kTrPXDjYbCtlizwXcx23t9hWe4kCj3qbh3lWbXQnto9DT7kHxQ8AIAynUxYrUm0HAIBcwtBU7mmh+H2woVb4ipPzYrgZTb7QzWv4ZcwIuFshPzIuLVC/OGVM/Z62qfN4OJpr2WMcTbuU9ebuGvaYSO0O9TFNLRekbrEt6Lc9euS+PIYnOc1OfFshnyXdWdZVWe8or2KPiRZ2UdcXd2OP4dK21Ab4e61jtm1v4tPq1PrV25pCfD6XkCKtTnNDvWgTmpo8AAAa49BT7mmh+AEArDFcLmG4rJcuMlz5F90GANAXQ1O5B8UPAEBTsqlHtrQPAADkC4aecg+KHwBAW5MHAEBjHHrKPSh+AIAOW7INZBnMkoYOr3o5w4IuJeypiroXKet77FL7AFvREFavXOBhlsKs8PDvYkkPdbsKu/rU1yhV3zthFDA+yEyErsEtTSmE8DIRrOWMf7ivWB1tXFyuvg8iEuyprHd51MdEwvwSj1z0bmHXPUuPtaaolF/6sitzL0VMJLDTYgnU9sTQVO5B8QMAaDvyBQBojENPuaeF4vfvFVuF29eQVF/CROhWFPF5nfp2U48Oe5Xy0akDKtTH+CzzWqkjQx3B5PuIEa1Zr6wPbVzLHtO8eau6fttu9phgXaP6+hZL23DL4nhK1aNzwldZzm+rVo863b0G8m3YR/28o1F1tC8RjDhsRe4SX+9sUtZvrONnQr7brj5mZyM/Iq9XRAKHmtXPJhWG2y0MtyfFPvnn5AwA0BdDU7mnheIHAEiBpolMAQAa49BT7kHxAwBoa/IAAGiMQ0+5B8UPAKDtYuUAAH0xNJV7UPwAACTdUps0aB+Q05hOtT+To0Tt51rUo4I9V/m+9laJ8W3l/U9DfrUflYNZrNdbzvtUl/ZWr/hT2ke9qo+vkvfxdTCrB0UdzLq/Fl+RYmY95B7MvfStUkfVRiP8aj4uZtUpf1OZst6MmhbnctqK3u1axftr9+qi9nMvZfzs3Z0U1Ss0lXtQ/AAA2o58AQD6Ymgq96D4AQCEcLloiJ9CWkBcAADyCJeeci//7kjBN2t3CmdBcioNj89lOxElm16jL596pIxLG8NcX+JQT+cbAd5cEt62SVnfsG4De8yu1ept9Rtr2WOadjQr6yNBPsWJ0+O0lVSVKOmlNk8QXfzq51BSwJ+voGsP9QaTN52EGFPI9qbkVCqp0rZ88h2fIufbrWqzWWNdgD0m2JxsHosE1GlhUmE4nbKk2gcAAPIFQ1O5p4XiBwBIgabRbQAAjXHoKfeg+AEAtBWAAACNcegp9zIWrrJmzRoxevRoMXjwYHHooYeKFStWKPf74osvxHHHHSeGDh0qy3PPPdfpbQUg34mtWZmqAABAvmBoKvcyNuM3ZcoUMXnyZDFx4kQxf/58+f+SJUta7NPU1CTGjh0r5s2bJ44++mgRiUTEzp07M9VkAPIXI42RL+0D9nrAO2HCBLF9+3ZRVlYmHn/8cTFs2DDlgPfKK68UW7fuWVLx9ttvF+ecc85e977pUfvAuhj/16I+u2xfo6BLibI+sItP/xJRLD9IGEw6F3cRn86FS89S0ke9zKOrsid7LkepOp1N1MmkczH4NCTljE93dbHap3xYT3UqGR/jL01sLlP3SwOTLscqnUsBc52uxR5bKVuIAZXqVC/dmeVRC5jn3u4Yesq9jKiyNTU1YunSpWL8+PHy87hx48SGDRvE2rUt15R96qmnxBFHHCGVPsLpdIrKyspMNBkADUwerhQl/wRgpga8q1evFtOnT5cD3tbEBry33XabWLVqlVi+fLk45phjMtJeAPIah55yLyMzfqTkVVdXC9d/wqQNwxB9+vQR69evFwMHDozvt3LlSlFQUCDOPPNMsXHjRnHQQQeJu+++m1X+AoGALDHq6urk/zs2bBIOd/JI112kjhr1V6gTZ1pRwYxciF6l6lFYOMqPWg0m0tT081G9kdodyvr6DXtmDVTsXKs+ZucafqS/s1k9Om+O8KNHHzOCq/i+wXZSV8LlU/d3YXVX9hiu77i+JsLMiLiWmaEgvtveZCtyl9ixWd0P9Tst+qcxOfI6GlJHXKfCNByypNoH7P2Ad9GiRfEB79SpU+WAN1Hu2R3wcnIPAGCNqancy+o7CofD4rXXXhOzZ88Wn3zyiejZs6e4/PLL2f1nzZolzSex0rt3705tLwA5Cwm3dArokAFvIokD3uHDh4uLLrpIbNu2jT0v5B4AbcTQU+5l5I5IIdu8ebNU7AjTNKXwIyGYCH0+/vjjpcJHQpJMwx988AF73pkzZ4ra2tp4IUELAEgD8k1Kp4CsG/BC7gHQRgw95V5GFL+qqioxYsQI8cQTT8jPCxYsEL169Wph7iB+8pOfyICPmOnilVdeEQcffDB7Xholl5aWtigAgDSgyLV0Csi6AS/kHgBtxKGn3MtYVC+NZsmx+Y477pAK2ty5c2X9pEmTxJgxY2QhAXjdddfJtC8Oh0MKwjlz5mSqyQDkLabDJUuqfUD7DHhJ9lkNeB999FE54CXZmGrAawdT4etMOCqqlfW857IQjpIutiKBo35+VZlokPfnVeH0emy3y1mm9v91WkT1Rr3qCGXTqb6+20JHKGI29itXR8MWuNRBBT1KeN/w2gDjf22xqhKHx6Vub5Fb3a5SZoUqq+jdSqbey1y7vTE1lXsZu6MhQ4aIxYsXJ9U/8sgjLT7/7Gc/kwUA0IGk48uSh74unQ0GvABkEYaeci//VFkAgH00FYCdDQa8AGQRhp5yTwvFr2HLOmG4kpNkehiTgBD7sufyFqmns3c2BtljmkLqdCERPosIixnws9u4BKlNNXx6h7qN6mM2NfD38z2TZqXZ4oZ8ToftFDAupm1Ecfci20livRZ9x8HdEvdMrd6Fxrr/ptxIN21Lw9Z17DHB+mSTmhnmr2GFaRhppDXIPydnAIC+mJrKPS0UPwBACjQd+QIANMbQU+5B8QMApJe2IA9HvgAAjTH0lHtQ/AAA2ka36Ua0wN6qREaB2qWCcHdVR8MaEcZNJMKvdmNG7fm9GC4+gpSLuDXd6jVxoy4+Stbk7t+pvr7LQkkoYda+dTnUx5R51fsHLdxjuJWGQhbHcDDeOcLJtNfN1FutvctF7/pc/LkM00yrLh1MTeVe/t0RAMA+Rhr5qvLQ5AEA0BhDT7kHxQ8AoK2vCwBAYww95Z4Wil+gYZcwmOl/FaHiCnZbsFmd0LPez5sxQowZIyospqdN9TFmmL9OxK82sYSb+eSooQb1+XZaJPzktgUZM4NV9K7XwjywD9M2q3vi+sCy75i+tnpG3DO1eheCVs+hsTbtyN0YgfqdSXUmZ2ZLhaYCEACgMYaeck8LxQ8AkAJNBSAAQGMMPeVe/t0RAKDN+aysS9ui2x544AHRr18/4fV6xeGHHy4++ugjy/2fffZZsd9++8n9DzzwQLlkGQAA5JLcy2ag+AEAhHA40ys2eeaZZ8S0adPEjTfeKJYtWybXnD311FNFTU2Ncv/3339fXHDBBeKSSy4Rn3zyiTj77LNlWb58OZ4SACAn5N4DWT7YhakXANBhJo977rlHXHrppeLiiy+Wnx966CHx8ssvi8cee0zMmDEjaf/77rtPnHbaaeLaa6+Vn2+99Vbx6quvij/96U/yWLB3mG6fsj7C1IP2+0Etcatnjkrc9hULkL1y75n/DHZJXpHSd++998rB7ldffSWqqqrYwe6sWbPEmWeeKZ566ik52KWB8gEHHCA6AtuSfNiwYVKYcyN2AEDukdrcsacQdXV1LUogoF4mLhgMio8//licdNJJ8TqHwyE/L168WHkM1SfuT5DQ5PbvLCD3ANBb7rVlsLv//vtLBbCwsFAOdlUkDnaHDh0qB7sjRoyQg92Owrbid8MNN4hFixbJaUzSSl944QURifARoACAHBr5pipCiN69e4uysrJ4oZGqiu3bt0vZ0L179xb19HnLli3KY6jezv6dBeQeAHrLvbo0Bry5Mti1rfidf/75YuHChWLNmjVyGpPMNT179pTa6sqVKzumlQCATnByTl2IDRs2iNra2niZOXNm3j8dyD0A9JZ7vdMY8ObKYLfNwR2k7JHAp2nM6upqOb1JiuCxxx4rPv300/ZtJQCgQ4lEzbQKUVpa2qIUFKiXwurWrZtwOp1i69atLerpc48ePZTHUL2d/TsbyD0A9JR7G/JowNsmxe+7774Tt9xyixg4cKCYMGGCGDt2rPjmm2+k39/pp58uxo0b1/4tBQB0GGaaxQ4ej0eMHDlSvP766/G6aDQqPx955JHKY6g+cX+Cgju4/TsTyD0A9JV7pWkMeHNlsGs7qvf444+XocljxowRDz74oDj55JOFkZDnZvr06eL2229v73YCADoQGtRaLLwS38cuFN1Gg8NRo0aJww47TEa4NTY2xqN8L7roIjmLFjOb/PKXv5RWg7vvvlucccYZ4umnnxZLly4Vc+bMEZkkX+Sen1lBpzmsrveH+RVqAsy5QsyLEps5UW8TtnA7+dxqLmY1II/TXj3hc6nnRnwu9TFuk1+dxwg2qusDTH3Yr64PqYOp5DZm5R6rFZ/Yc3Fr2Drd6mtYrI5leny2osyjBcX8udoxAj3aznIvcbBLMRCJg92pU6daDnavvvrqThvs2lb8fvSjH4kFCxaIigp+WbPdu3fvbbsAAJ2IaZqypNrHLuedd57Ytm2b+O1vfyt9VoYPHy59hGM+LevXr5fOzzFGjx4t0xlcf/314rrrrhODBg0S//jHPzosrUG6QO4BkH+YHSD3cmGwa1vxu+qqq1LukyjIAQD6zvgRNNLlRrtvvfVWUt25554rSzYBuQdA/hHtALmXC4NdLRI4FxR3EYYr2R7vKemi3N9dVMaey+NTd1mJVz39Lc/HKMIOYbEUDJM7yHDx13F61VPtLqbNsm3F6vNVNPOmAT/zTWi2sNf4nOr7qfA4bbfN6p64PrDqO6s8Tdwz4p6p1bvAvTtW7xz3jnKY4YCwb9T5z7FtPA4AAHIVswPOme2DXS0UPwCANYnRa1b7AABAvhDRVO5B8QMACJqrTeVfb9P/HgAAspqopnIPih8AQJD/ciof5jbEdoAsg4ve3eVXr760rVEdJUpsZbbV+dXOBo0hfoWnoEX0sAqfhYtIWYHa1aKLT11fXazOQ7nnGPV13A6mPso7WjiadinrjfrtyvrItk3q+tod7DWi9eprRPz8c+RweNTqgcNbqK63cEtxdVWnJnFUVNtuV3uuK21qKveg+AEAOjS4AwAAspGopnIPih8AoMPSuQAAQLZiair3oPgBALT1dQEA6EtUU7mnheJX3GNf4VD4BXApNEoq+KzhRaVqf5CKIj6NSKFbnfqDyXBiiVHgZbcVdClRX7+qlD2mtJc6a3zPZj4DvY/JdN/MZPK3OqaC8bvZ0zb1/VjdE9cHqfqOg3tG3DO1ehe4d4fws+/cvuwxoeLkJOrRULNoEPaJmGlEt+XhyBcAoC8RTeWeFoofAMCadNbizT/xBwDQGVNTuZexJTbWrFkjM1YPHjxYHHrooWLFihWWNvYTTjhBlJeXd2obAdDNyTlVAXsH5B4A2UNUU7mXsRm/KVOmiMmTJ4uJEyeK+fPny/+XLFmi3PcPf/iDGDBggFi2bFmntxMALUgjrUFeDn01k3t+Jm0Kl7ZlzQ61Kwjx9Tb1to27mpT1Oxr4lCKBoDrVi+FQu4gUe/mfrupydbqP/pVF6muH+TQzHpf6mBImnUxhhL9Hh79eWR/auFbdrg3rlPX167ey12jepk7nEmr0K+tNC/ccJ7MCEedOU9Qj2fUkvq2Pul2cg5RRoO73dsfUU+5lZMavpqZGLkI8fvx4+XncuHFiw4YNYu3a5C8AzQTSunUzZszIQEsB0IOoMNMqIPvkXiAQEHV1dS0KACA1UU3lXkYUPxJ21dXVwuXaM2ozDEP06dNHLl6cSCgUEpdeeqmYPXu2cDr5hJ0xIAABaBu0zHI6BWSf3Js1a5YoKyuLl969e+MxAZAGEU3lXlYHd9x8883inHPOEUOHDhXffvttWgKQjmlN1949hbMgOdu4x+eyHX3Zr7t6mrtvN3U2c6KMmTJ3MWYMwjTUOrnh5afAnWVdlfUlvbuzx4Sb1aYJt4UppWRHs7I+wphrZNs400hXPgt7Sa8yfhtzT1wfWPUd19dWz4h7plbvwk6LVRA4vEX8dYLNye9iJNAktti+ir4Z7PNB7s2cOVNMmzYt/plm/KD8AZAaU1O5l5EZPxJKmzdvFuFwOB68QaNeGv0m8vbbb4v7779f9OvXTxx99NFSoNHf27ZtYwVgbW1tvNAIGwCQGl1NHvkg9woKCkRpaWmLAgBITVRTuZeRGb+qqioxYsQI8cQTT0jn5gULFohevXqJgQMHttjvnXfeif9NI9/hw4dbjoBJAFIBANhD15FvPsg9AEDbMDWVexkz9ZL/Cgm/O+64Q45Q586dK+snTZokxowZIwsAoHOImqYsqfYBuS33AkwU51bGDYGL3CU+27BbWb+jRn1MY12APVc4ZC+q11vIu0DsqlBHsDYzrig+xg2FqCpSTyRUFjI/nRE+8X20bqeyPrxtk7J+92q1xWrX17wzR90GdWCPf7e6T6IWUb2cu09Rd7XLTPm+6qhlKxwlXdTX7tpTdAZRTeVexhS/IUOGiMWLFyfVP/LII8r9ydSxe7da0AAA9o50nJjz0cm5s4HcAyB7iGgq97I6uAMA0DmEo1ERikZT7gMAAPlCWFO5B8UPAPCfDPWpTB7oKABA/hDVVO5pofj1H1gh3L5kv4QSJiVHRZHHdqqOXqVe9phujD+K2yKdi2BSjJgWGc1dlWq/iOKAOv0K4fSq77WomjerB+vUPjzREO/f4nCrXzVPKX8/vkp+iT5fdXdbfWDZdxbpXLhnxD1Ty3ehL38/3DtnlQKm3h9Kqgs1ewW/+CGPriYPAIC+RDSVe1oofgAAa3R1cgYA6EtUU7kHxQ8AICKmKYsVqbaD7CfE2K3qFLPHVuvuWkXv7tiiju5s2sGvMRvyNyjrnS71TLinhF8XNhxSb3M41TP7vbrwSeQHdCm0NQtkRC2iepvqbK2vW7e+Rlm/c406Opio2aTu+51BdYODFnbMYpfa2tFjlzpC2Ap2fV9mDV/DYs3j9iSiqdyD4gcAENE0fFny0OIBANCYqKZyD4ofAECEI1ERSuHMQvsAAEC+ENZU7kHxAwAIyuNqkctVkmo7AADkEhFN5Z4Wit+pw7oLX3Gyj4Hbofb5KHTzUZ5lTCSwVZRnaYE6M7zHaRHV61AfE/UU84dU9VFfx8NHHDu7Vivrixh/FMIMqn08zIg6Mz5hONX3Y1i0zSjk1xx1lnVVH1NexR7D9h3T14RHGLaeKTGgotDWu2MVCdwU4kebqvxTzQ314p/CPro6OQMA9CWqqdzTQvEDAFgTiZqypNoHAADyhYimcg+KHwBA25GvbnA/Yo3MWrk7GvjoSm7tXS56t3Gbeu1ZItTcYMtS4Ata5Cb1qKN0i0rV6+7ublJHNBP+sHrG3eS+CxbfETOgtpQE69SR00071PdYv4VfP3lTszqqeGsgbNuMWeziLV8qfFv5dgV2qaONo34majzCP5P2JKqp3IPiBwCQaT64VB8xUm0HAIBcIqSp3IPiBwAQ0agpixWptgMAQC4R1VTuQfEDAMhcVqmi1/JQ/gEANCaqqdyD4gcA0NbXBQCgL1FN5Z4Wit8RvctEcUlyahAHk6qDWdlH4nKoj3Ez9VZpWwos0rmYQt2IqK+MP8apThdiFHZhj3F2D9pfMkeRRmRPAyy+IAZzr0xKHXk6p8f2tijTB/IYjzrNimnwbeCeUZlFOhcf4xRd4eO/buGoOp2LVe7QqEju74Z6ew7Zui9dBADQl4imck8LxQ8AYI2uvi4AAH2Jair3oPgBAETINIUrVXRbHo58dYObQQ4yaUsCQT4pe5hJARPyN9hK2SLPxRxjMMnVQ0zKFnku5jrhUHISf6KZuY92N/NF1deJ+NXWlTCTmqWBeVZEHbONO6Ytq1I0hNUHhfzq9srr+NXpWaJB9TEmZ1VqZ0Kayj0ofgAAbU0eAAB9iWgq96D4AQCkOSOiockDAKAvUU3lHhQ/AIC2SxcBAPQloqnc00Lx6+sOiFKPYrkci2hODjYC1OpcjJ8KF7lrdb6IWx2ZSkRdPtt+HOzSQxa05/fAIhjaEoOJErYIlBYO5hiHIjo2hstU+5q4HVY+KIzPkMUxBnMdSxTH1LnVy2ilQlcBCADQl4imcq9tuR8AAHnn9B8TgnzpuOvv3LlTXHjhhaK0tFSUl5eLSy65RDQ08MEAxJw5c8Rxxx0nj6GBwO7duzuugQCAvCOiqdzTYsYPAGANRXU6LKIFY/t0FCT8Nm/eLF599VURCoXExRdfLCZPniyeeuop9pimpiZx2mmnyTJz5swOa5vOGBZT8tw2p0udY9Nw8rkvuehdrt7h4vN1Otwee+1tq9mhnTCYxLEGY77wWLTXzVpCuGP42SzOesJd32FhbuHuJdMENZV7UPwAABl1cl61apVYuHChWLJkiRg1apSsu//++8Xpp58u7rrrLrHPPvsoj7v66qvl/2+99VaHtAsAkN9ENZV7MPUCAPakNUhl8viPP2hdXV2LEgi0za8wxuLFi6WZIyb8iJNOOkk4HA7x4Ycf4ukAADqEiKZyD4ofACANP5f/jox79+4tysrK4mXWrFl71YNbtmwRVVVVLepcLpeoqKiQ2wAAoCOIaCr3oPgBAGwJwA0bNoja2tp44fxMZsyYIZ2PrcqXX36pVe+vWbNGjB49WgwePFgceuihYsWKFUn7vPHGG+Kwww4T+++/vxg2bJj49a9/LaKdtJIBADoR0VTuaeHjZ6z9UBhFyWlQDMZB2Cjw8ufyFinrzQJ1PRH1FKvrfWXsMVzalsYQ/wPQxGxrtnBO9TPbmiyWMeKOaQteFz/2KHQ7bR/nszyfelsRU084w+rpfEdzLXuMI8gsPxVoZI8x/eptZkCRhii2LZy8DJLR2CTaQoCeaYrnKvcRQkaTUUnFNddcIyZOnGi5T//+/UWPHj1ETU1Ni/pwOCwj3mhbPjFlyhTpvE39Mn/+fPk/+fgk0qVLF/H000/LvvH7/dL8M2/evJR9CQCwR0BTuefK5Mh3woQJYvv27XLa9PHHH5ej29YjX9KeKbyZtOQzzjhD3HnnndIGDgDIbifnyspKWVJx5JFHypQEH3/8sRg5cmT8u0+zXIcffrjIF0jIL126VCxatEh+HjdunJg6dapYu3atGDhwYHy/Qw45JP631+sVw4cPF99++227tMHNRFf6POpBVrGX/4nwFqoHzp6SCvU1gs3subi1d7noXW8Z/155FYN8osCrPldxAX+Pbi7ilouSZaNn6ddWHW3sLlJPNHhLC5T1xcXq8xCVFmsr212HtpQZRFd41PXecn7ChLtHp5eJwLaI2m5PoprKPUemR76rV68W06dPV2rIsZHvypUrZee8//77cuQLAOiYNStTlY5g6NChMjXBpZdeKj766CPx3nvvSYXo/PPPj0e2bdq0Sey3335yewzyg/n000+l4kR88cUX8jONmLMRMhVVV1dLP56Y8tCnTx+xfv169hi6R5oZPPPMM9l9yMm8teM5ACA1EU3lniOTI9/x48fHR74kFGM3kjjypSnRjhj5AgDa5uvSETz55JNSwJ144okyncHRRx8tE5XGoBxXX331lcxhFeOhhx6SMoIEJ/GDH/xAfn7xxRfz4tGSAnfWWWdJH7/EyL/WkJN5otM5OaEDAFIT0VTuubJt5Jto8lCNfF966SXLkW9iiDVGvgDkxtJFFMlmlbS0X79+ScsL3nTTTbLkCqSQUbJW8uMh2Uf3QzKPZF9r6uvr5WzA2LFjxbRp0yzPS07mifuQ3IPyB0BqIprKvZxwlsPIF4D8HvnqAKVuGDFihHjiiSfk5wULFohevXolDXbJpzmWmf/6669Ped6CgoK443m6DugAAKGt3HNkeuRLtOfINzHcmmYWAQCpCUUiIhi2LrQP2Dtmz54tC6VzoUC1uXPnyvpJkybFTTX33Xef9Ol57rnnpHsLldtvvx1dD0A7E9JU7rkyPfKloI72HPlSac3u9/9PRLwFaUcUFXQpYa/hLOuqrHdV9mSPcVQlK7SE6eQjl6Iun62ULcT2pj2KdGs21fMpQWoa1OlKtjcG2WMa/OrrWI2MuPUwraIGuxXx0WtVxeqIt54lfGRZt0L1tXwuPhLPFVSnR3E07WKPidaonfXD2zaxx0RqdyjrA7vq+WP8yc+o3t+2bPL07BwZNHnowpAhQ2TG/tY88sgj8b9/85vfyAIA6Fgimsq9jKVzoVEvKX133HGHNE0kjnzHjBkjS2zk29jYKEe/xLnnnguhCEA7Q6mqjBQCrgPXKgedhIsZgJUVqAeh1eXqASixq0I9oAyH1OlcnEzKFnlMszr3pcPtsZWyhSjtpt7WpYt6UFjFpE0hipk0N0w3CmHwRjRHkXpCwdtVnc+1uFptsvfX8YM79RSDEBUN6oF80OI7X8ykcynpoc5ZW9qbdzHwVXZR1jtK1PWmkx/0tydhTeVexhQ/jHwByB50HfkCAPQloqnc02LlDgCANboKQACAvkQ0lXtQ/AAAIhSOCjOFTSOcjzYPAIC2hDSVe1D8AAByVGtoOPIFAOhLRFO5p4Xi9/3i1aLYnXyrLp/69gureCfVkt7dlfXFAX4dSo9H7VRsFKodW4kI8641W4w+uOjdr7apHaeJNVvV277b3sge428OKesjYYuoXiZy1uvjI5v7dlM7ERODuhcLuxR51E7fEVPtwE0YEfW9mrtbLq6dSGhjyxVoYjSs49ML1W/YqqxvquGX3wo3J0dXN4TUEdepoJRKZgoB1zqRKAAA5DKmpnJPC8UPACBSLkQebefFykH24XGqB2BdmAFY/0p+8NUcVOc3czjV0aBFFtGz4ZA64tVgwmcLvPyAkYveHcpEyVaX8imgSjzqn0imG4Xp5H9SnUXq63sqq5T1ZQP4VE4cXqaPy5hIYJObYbCaGOmqjs4u7aO+D6KkT3db6dFMN/+utCdRTeUeFD8AwJ6Rr6nfyBcAoC+mpnIPih8AQJo7Upo88nDkCwDQF1NTuQfFDwAg/TMNCx/NVD6cAACQa0Q0lXtQ/AAA2po8AAD6Ymoq96D4AQC0dXIGAOhLVFO5p4Xit/njLaLQkZyyw12sjgwr7cWnMgk3q9c8dHr5tQWdXavV9d3V57IaZfgt0rnUNARspWwhvtqwW1m/e1sTe0xTvfo60QjfNi7Sr7CkwHbaGCu46ESiT5nX9ojOiKifUaR2B3tM82Z1apZdq/l0LjvXqs9Xt5GP7As1JPdPU1QdaZkKXX1ddIOL6q0uVn8PA2H+ffIx69j26qKO+tzdxH+fm0Pq6ziZqN7iAv6ni1t7l4ve7WkV1VvgtNUuYVr8pBaWK6td3dUr7KrjnIUo6MJtEaJ0R62yPtSoTvVlWshs7jfNU1poaz1ewlXZU30Npj7q4p9Je2JqKve0UPwAAClIQwDSPgAAkDdE9ZR7UPwAACJqmsJI4ctC+wAAQL4Q1VTuQfEDAEgzvZFiTUorUz4AAOQaUU3lHhQ/AICIRkXKNStpHwAAyBeimso9KH4AAG3TGgAA9MXUVO5pofitrAsIr5EcVVrBRI32bOYXund71V1WVK2OjpXbmupsRYxa0cREvxHbG9Xn+247H6XMRe/u2mIRtbpri7I+Emhmj3EWqCP9Al16iLbwXRvWFrXqOw7uGZnMMyWat6nfhfqN6og7YueaXcr6TQ38O7JTsVaq32zb8JQOS3VoG08NAABZiamp3NNC8QMAiJS5qlKbPPJv5KsbPpc6rVIXnzpticfFD6SqitRpUwZ0KbSdisquA72bSQ9FFDNpZko8LlspW+S53OrruJh0Lqbg03pFfWXKekf3fsp6TwmT/qWaH3R6G5n0T2H7kwxCkQKNMArUqVYchaX8qUorlPVRrzo1jVnAv3ftSVRTuQfFDwAgouGoMJwpnJxTOEEDAEAuEdVU7kHxAwBom9YAAKAvUU3lHhQ/AIC2GewBAPpiair3oPgBAPZEt6USgHk48gUA6IupqdyD4gcAkMIvquHIFwCgL6amck8LxW+LPyw8inQufuaB+piFzImSHeqUJcE6PmWKGfTbzgzJvWtWkXENfnUaGj+TtoZoqg/YStkij9nxvbI+GuIjxxxuPtqNo6ComN3G3RPXB1Z9Z/m9Zp4R+0wt3oUm5t0hdjL3873F/ajSuQTbms5F03xWuuFzqWWbm4ngLGEiZInKQvXPB7fQQXu+P4bBy2gm4FZwYt3JHWARvevmonoNdZopIlrYxV4Ea2mVstqI8DLBwX3/2/O7y/R91MGrE1GnepvpZH4XnHw/tiempnJPC8UPAGBNJBwVpkO/6DYAgL5ENJV7UPwAAMKMRmSxItV2AADIJUxN5R6fBRMAoJ0ATFXA3rFmzRoxevRoMXjwYHHooYeKFStWKPd79NFHxaBBg8SAAQPEpZdeKkIh3l0DANA2TE3lHhQ/AIAwo9E0BGD+mTw6mylTpojJkyeL1atXi+nTp4uJEycm7bNu3Tpxww03iHfeeUesXbtWbN26VcyZMycj7QUgnzE1lXsw9QIAhBmJyGJFqu3AmpqaGrF06VKxaNEi+XncuHFi6tSpUrkbOHBgfL/58+eLMWPGiB499qxjfdlll4k77rhDXHHFFcrzBgIBWWLU1u5ZE7q+Pnn5rjDjpx5mIpys3JsijNN7PgV3OG0GdzhM/jtiREK26kVUHcRhMPWSDAZ3mBbBHYLZZnJBHBbBHarrxN51u++Yqancc2XS5DFhwgSxfft2UVZWJh5//HExbNgwpcnjzjvvFNFoVJxwwgniwQcfFG63vYifIL0MiheimZFQzRH+5YkoIimJaChs/8Vp52ihCCO8I5y0p3YzfRAJ8BGoXPRuJMgfw2F5He4XxOKeuD5oM8wzshIG3LvAvTtW7xz3jhJBxb0GRdvu3zTT8HWx+FEDqdmwYYOorq4WLpcrrrz06dNHrF+/voXiR5/79u0b/9yvXz9ZxzFr1ixx8803J9UPHDQIjwVoBSmApE+ki6mp3HNl2uRBpg4a4dL/S5YsUZo8li1bJrp37y7Gjh0rTR7cyBcA0DaitIi74Uy9D8g6Zs6cKaZNmxb/TIPknTt3iq5du8ofwt69e0uls7S0VOQCdXV1OddmAu3OXH+XlJTId32fffaxdY6opnLPlc8mD3oxAACp0TW6rTOhH6nNmzeLcDgsZ/3ILEUzeTTrlwh9/vrrr+Ofv/3226R9EikoKJAlkfLy8hYmUVKgckmJytU2E2h3Zvrbzkyf7nLPkW0mj0TaYvKghx8rJGgBAKnR1cm5M6mqqhIjRowQTzzxhPy8YMEC0atXrxaD3dhA+MUXXxRbtmyRyuFDDz0kzj///Ay1GoD8xdRU7jnyzeRBjs2xQgomACA10WgkrQL2jtmzZ8tC6VzId3nu3LmyftKkSVLZI/r37y999o466iipFFZWVkrXGABA+xLVVO658t3kAQDIfpMH+aRdeeWV4p///KdwOBxy1uu+++4TxcXF7P433nijdBch2UHK0dlnny1uvfXWNpl8OoshQ4aIxYsXJ9U/8sgjLT5T7j4qewvJQ+qnXJKLudhmAu3Ovf42NTX1ujJt8qCgDiuTx9FHHy1uuukmGdxh1+QRC+0OCvVUbYAJgGy2iOJpZKI564N8gtW6JvW6rgX1DfwxbrV/YmN9E3tMoEl9vrCfX0c4GlSfzwzza9GakaCt+j3bnLavw7WNCPsLbPUB0VjvVdbX+SxSMDDPKMA8U6t3gXt3rN65gMXau6r3OlZnO61BiI50pNyno7jwwgvlYPDVV1+VyYovvvhiGfz11FNPKff//vvvZbnrrrvE/vvvL7777jvpA0x15BsM9kA/iiQ/c4lcbDOBdudef5sZlnuZGvAaZoZWIP7qq6+k0rdjxw7pmEkmjwMPPFCaPCiggwrx8MMPS5MIcdxxx0nlL910Lt98843MfA+AbtBMOZkMU0EBUCQwyk+cKQyXWjFOVNJ3vz5LulG0p9P9qlWrpPJGUf2jRo2SdQsXLhSnn3662LhxY9qRes8++6wYP368aGxsjPsPAwBANso94oc//KEc8JL7R2zASyv6cAPe5cuXS8WPdKfEAe9BBx1ka8CbMcWvM9i9e7fo0qWL1IyzyfyTrWH/aFfu9xcJJ3KH2LVrVzyyMx0BWHb8dGG4rE0mZjggat/836T73VsXi8cee0xcc801ss0xyA3E6/VKZe5HP/pRWuchcyn5+W7btq3NbQEA5D91bZB7+TTgzethMU2dEvSAs+WHORfC/tGu3O+v2LufLtKPJU1fl9bR8jQC3RuTC0WvkvtHIiTAKioq5LZ0oETwZO4g8zAAALS33KtrlR5ubwe85OtLg/OY0kecdNJJUnZ/+OGHaQ94YwqpHStHXkX1AgD2Jq1B6kLQjF9i9DzNsqmYMWOGTNVkVb788su9fmQkkM844ww5es5F3zAAQPbLvd69e7dIF0fp4/aGTA54ofgBANLIZfXf6LfYDGescKNeMt+SOcOqkB8iJWinpO6JkKmXHJljyds5KFv/aaedJjP3P//887aXc8xVaMnL0aNHy7Qw5BO0YsUK5X605OWgQYOkrzNFCZMfUTrbMtnuN954Qxx22GFSkadlPH/961/L1UhimR2cTqcYPnx4vCRmfshku9966y3h8/latK25uTlj/Z1Om8m3PrG93bp1E+ecc05G+/qqq66SOXtpYPjpp5+y+7XHu23akHt5NeA18xi/32/eeOON8v9sAu1Cf2XLu1VbW0s+vqbv8MvNwqOutiy0D+1Lx7QnK1eulOddunRpvO7f//63aRiGuWnTJsu2H3HEEeaxxx5rNjY2mjpx/PHHm3PnzpV/P/vss+aoUaOS9vnmm2/M6upqc/PmzWY0GjXPOuss809/+lPKbZlu97Jly8yvv/5a/t3c3GweddRR8WPWrVtnlpWVmZ1NOu1+8803zYMPPlh5fCb6O502t2bYsGHm/PnzM9rXb7/9trlhwwazb9++5ieffKLcZ2/f7doOlHs1NTXmqlWrLEsgEDAfffRRs7y8vMWxoVDIdDqd5nPPPWd5jbq6OvPII480TzzxRPkdsUteK34AAGtiAtA7aorpO+JKy0L7dITiR5x22mnmIYccYn744Yfmu+++aw4aNMi84IIL4ts3btxoDhkyRG6Ptfvwww83DzzwQHPt2rVSyMdKOBzO68e+detWs6SkRP5IEPTj1r17d3PNmjUt9vvd735nTpkyJf755ZdflkpUqm2ZbndrrrjiCjmYyZQykm67rRS/zu7vtvT1Bx98YFZWVprBYDCjil8MK8Vvb9/t2iyQe5kc8MLUCwAQppmGycMiv+Xe8uSTT4r99ttPnHjiiTKqjfJ3zpkzJ76dTDWUAqqpaU9ux2XLlkkH6C+++ELm/6QlIGMl31fsaY8lL+0uh9mZ7U6EfJ0oTcWZZ54Zr6PoRTJdUi7YW265RUQs8mN2drvJFErtovY9+OCD8frO7u+29DWZR3/2s5+1cJfo7L5Ol/Z6t80Myr2hQ4dKNxUyRX/00UfivffeE1OnTpW5imMRvZs2bZJykbbHzLunnHKKfC70vOgzfUeo2Hk2eR3VCwBID+nHYmQugz05NHO5q2LCOzHzFOX0zONMVOA/P3JnnXWW9PGLRT6SMkM/huQUTz6g5513nrj77rvlPpmGlCNKw0GO//Q/DWDIZ+4nP/mJyHZIkXj66afFBx98EK/L5r7OF7n35JNPSmWPBryxBM5//OMfUw54idYLXqxbt07KyXTAjB8AwJaTM8gsiUteElZLXlKCV9WSl1bbMt3uxKCdsWPHimnTpsXrKZAoFglJg4Wf//zn4p133smKdlOgUyxfLK1EdcEFF8Tb1tn9baevY7ngKJCGAgUy2dfp0l7vtplhuRcb8NL7TgEjlNM0cdWO2ICXBrqJA15VSVfpyxvFrz0i3LIxgi2T7YpBL9QJJ5yQVjLgzmoXmffoC0BT5VSee+65jLaJnhf9ONEzpAzqxx9/vFi7dq3I9si3RMyQX0RDzZaF9gGZJ3HJS8JqycsXX3xRmoHoe5y45KXVtky3u6GhQSp9VK6//voW2yj6O/YOBwIB+d0/5JBDsqLdpGjFZDf9kL/00kvxtnV2f6fb5kQ5cckll2S8r9Olvd5tU1e5Z+YBexvhlq0RbJlsV4y7777bnDRpUqc4+abTLnJm3Xfffc133nlHfiZHfoqiymSbnn/+efOwww6LO0Xfeuut5rnnnmtmc+RbDHrnevToIZ2M0ym0b1uiyED78uWXX0oHbwqCGTlypPn555/L+ksuucR84YUX4vvNmTPH7N+/vyw///nP4+9oqm2ZbPdtt91mulwuGSgRK1RHLFiwQEaeHnTQQeb+++9vTp06tVOyNqTT7vvvv1+2KdY2Ckih712m+jvdd4T2Ky4ulpGiiWSqrydPnmz27NlTRrdWVVWZAwYMaPd3u1lzuZfzil97RLhlsl1WEWyZbtfy5cvNY445RkZNdrTil267Hn744RbRntnQpn/84x/yx4kEJ+1z7bXXmr/61a+yOvItERJoFCmWTskn4QcA0JdmjeVezgd3WEUvJU5rZ1NUFTfdHotgIxNBpttFU/xkGiQTACXx7GjSbdfKlSul7wlF+ZEDNZlWyeG4srIyY20iB/Q333xTJhumRMI9e/YUb7/9tsg06b7ztCYuFQAA0AWvxnIvL3z88jWCLZPcfPPNMoM7+dBlE+Ss/Nprr4nZs2eLTz75RCpZl19+eUbbtHTpUrF8+XIZAff999/LCK3LLrsso20CAAAA8lLxa48It0y2yyqCLZPtohmr+++/X84SUU41Ukzp723btmW0XfSZgidI4aMZuPHjx7dIQZCJNs2bNy8eAEMh+RMmTJAzgJkmE5GbAAAAspucV/zaI8ItWyPYMtkuCtsnpYGUhXfffVemKqC/O8KkaqddlBNryZIlUhElXnnlFXHwwQdntE203ixFZweDQfmZTPUHHHCAyDSZiNwEAACQ5Zh5QHtEuGVjBFsm25VIZy3dk2675s2bJ6PNaLkuWupr/fr1GW0TRbpR5PN+++0n23TyySfHI7WzOfINAACAfhj0T6aVTwAAAAAA0PHkvKkXAAAAAACkBxQ/AAAAAABNgOIHAAAAAKAJUPwAAACAvUj03q1bN/Hqq6/KzxTdT9kAKBcqANkIgjsAAACAvYBSPU2dOlV89tlnYtasWWLZsmUyxVNnrHoEgF2g+AEAAAB7yZQpU8TixYvlCj6ffvqpTAAPQDYCUy8AAACwl/ziF78QX3zxhfjpT38KpQ9kNZjxAwAAAPYC8us78sgjxYEHHiief/55aeYdOXIk+hRkJa5MNwAAAADIZWbMmCGKi4vFY489JkaNGiUuuOAC6edHdQBkGzD1gnYD0W0AAN1YuHCh+Mtf/iLX9HY4HDLIY+jQoeLKK6/MdNMAUAJTL2hXEN0GAAAAZC9Q/EC7g+g2AAAAIDuBqRe0O4huAwAAALITzPiBdgXRbQAAAED2gqhe0K4gug0AAADIXjDjB9o1uu3CCy9skbV+7NixoqKiQsydOxc9DQAAAGQYKH4AAAAAAJqA4A4AAAAAAE2A4gcAAAAAoAlQ/AAAAAAANAGKHwAAAACAJkDxAwAAAADQBCh+AAAAAACaAMUPAAAAAEAToPgBAAAAAGgCFD8AAAAAAE2A4gcAAAAAoAlQ/AAAAAAANAGKHwAAAACA0IP/BxIxATPplLMnAAAAAElFTkSuQmCC", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# Vertical edge midpoints\n", "Xmid_v = (X[:-1, :] + X[1:, :]) / 2\n", @@ -286,17 +230,7 @@ "execution_count": null, "id": "1b018537", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Grid spacing dx = 0.0345\n", - "All horizontal gradients = dx? True\n", - "All vertical gradients = 0? True\n" - ] - } - ], + "outputs": [], "source": [ "# f(x,y) = x coordinate\n", "f_x = np.zeros(grid.size)\n", @@ -380,18 +314,7 @@ "execution_count": null, "id": "18036f2f", "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "H = grid.hodge_star()\n", "\n", @@ -433,26 +356,7 @@ "execution_count": null, "id": "168bf698", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Boundary values (should be 0): max = 0.0e+00\n", - "Interior max: -19.6140\n" - ] - }, - { - "data": { - "image/png": "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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "L = grid.laplacian()\n", "rhs = -10 * np.ones(grid.size)\n", @@ -498,18 +402,7 @@ "execution_count": null, "id": "bdaf64d7", "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "L = grid.laplacian()\n", "vals, vecs = sparse_eigsh(L.astype(float), k=9, which='SM')\n", diff --git a/examples/visualization/02_spectral_analysis.ipynb b/examples/visualization/02_spectral_analysis.ipynb index c37c5a0..e11c9ce 100644 --- a/examples/visualization/02_spectral_analysis.ipynb +++ b/examples/visualization/02_spectral_analysis.ipynb @@ -23,7 +23,16 @@ ] }, "outputs": [], - "source": "import numpy as np\nimport matplotlib.pyplot as plt\nfrom examples.mesh import (\n pathlap, pathincidence, normlap,\n hermitify, MU0,\n)\nfrom examples.map import format_plot, plot_vecfield, darker_hsv_colormap" + "source": [ + "import sys; sys.path.insert(0, \"..\")\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from mesh import (\n", + " pathlap, pathincidence, normlap,\n", + " hermitify, MU0,\n", + ")\n", + "from map import format_plot, plot_vecfield, darker_hsv_colormap" + ] }, { "cell_type": "code", @@ -131,16 +140,7 @@ "execution_count": null, "id": "e9f0a1b2", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Cycle: L == B @ B.T: True\n", - "Path: L == B[:,:N-1] @ B[:,:N-1].T: False\n" - ] - } - ], + "outputs": [], "source": [ "# Cycle: B is N x N (N edges), so B @ B.T == L directly\n", "L_cycle_check = B_cycle @ B_cycle.T\n", @@ -189,16 +189,7 @@ "execution_count": null, "id": "c5d6e7f8", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Singular values: [7.1369 5.0195 1.5215 0.8544]\n", - "Reconstruction error: 9.38e-15\n" - ] - } - ], + "outputs": [], "source": [ "# Create a complex symmetric matrix\n", "np.random.seed(42)\n", @@ -272,16 +263,7 @@ "execution_count": null, "id": "c9d0e1g2", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "MU0 = 1.256637e-06 H/m\n", - "Used in GIC: E = -MU0 * dH/dt\n" - ] - } - ], + "outputs": [], "source": [ "print(f\"MU0 = {MU0:.6e} H/m\")\n", "print(f\"Used in GIC: E = -MU0 * dH/dt\")" @@ -309,4 +291,4 @@ }, "nbformat": 4, "nbformat_minor": 5 -} \ No newline at end of file +} diff --git a/examples/visualization/03_geographic_plotting.ipynb b/examples/visualization/03_geographic_plotting.ipynb index d087d67..78d0ea1 100644 --- a/examples/visualization/03_geographic_plotting.ipynb +++ b/examples/visualization/03_geographic_plotting.ipynb @@ -16,7 +16,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": { "tags": [ "hide-cell" @@ -24,9 +24,10 @@ }, "outputs": [], "source": [ + "import sys; sys.path.insert(0, \"..\")\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", - "from examples.map import (\n", + "from map import (\n", " format_plot, border, plot_lines, plot_vecfield,\n", " darker_hsv_colormap,\n", ")" @@ -34,7 +35,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "metadata": { "tags": [ "hide-cell" @@ -52,22 +53,14 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "id": "z6a7b8c9d0", "metadata": { "tags": [ "hide-cell" ] }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "'open' took: 8.2832 sec\n" - ] - } - ], + "outputs": [], "source": [ "# This cell is hidden in the documentation.\n", "from esapp import PowerWorld\n", @@ -83,6 +76,19 @@ "SHAPE = 'US'" ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Branch endpoint and bus coordinates for map plotting\n", + "lines = pw[Branch, ['Longitude', 'Longitude:1', 'Latitude', 'Latitude:1']]\n", + "bus_coords = pw[Bus, ['Longitude', 'Latitude']]\n", + "lon = bus_coords['Longitude'].values\n", + "lat = bus_coords['Latitude'].values" + ] + }, { "cell_type": "markdown", "id": "j6k7l8m9n0", @@ -97,31 +103,9 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "ename": "NameError", - "evalue": "name 'lines' is not defined", - "output_type": "error", - "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[1;32mIn[8], line 5\u001b[0m\n\u001b[0;32m 2\u001b[0m vmag \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mabs(V)\n\u001b[0;32m 4\u001b[0m fig, axes \u001b[38;5;241m=\u001b[39m plt\u001b[38;5;241m.\u001b[39msubplots(\u001b[38;5;241m1\u001b[39m, \u001b[38;5;241m2\u001b[39m, figsize\u001b[38;5;241m=\u001b[39m(\u001b[38;5;241m6.5\u001b[39m, \u001b[38;5;241m2.8\u001b[39m))\n\u001b[1;32m----> 5\u001b[0m plot_network_map(\u001b[43mlines\u001b[49m, lon, lat, SHAPE, ax\u001b[38;5;241m=\u001b[39maxes[\u001b[38;5;241m0\u001b[39m], fig\u001b[38;5;241m=\u001b[39mfig)\n\u001b[0;32m 6\u001b[0m plot_bus_voltages_map(lines, lon, lat, vmag, SHAPE, ax\u001b[38;5;241m=\u001b[39maxes[\u001b[38;5;241m1\u001b[39m], fig\u001b[38;5;241m=\u001b[39mfig)\n\u001b[0;32m 7\u001b[0m plt\u001b[38;5;241m.\u001b[39mtight_layout()\n", - "\u001b[1;31mNameError\u001b[0m: name 'lines' is not defined" - ] - }, - { - "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAAAjYAAAEICAYAAABMLr51AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjguMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8g+/7EAAAACXBIWXMAAA9hAAAPYQGoP6dpAAAbBUlEQVR4nO3df2xV9f3H8VdbuLcYacF1vS3sYgMG8QfQWeSuIiEudzbR1PHHYiem7YjK1M4oN5u0IlwVpcwxRgLVRqbiHzpQI8ZIU6edxKg1JIUmOn4YLNrO7F5oHPeyIi30fr5/7OvV2hY5l97b3k+fj+T80cPnc++7wHnl1dPe2wxjjBEAAIAFMkd7AAAAgJFCsQEAANag2AAAAGtQbAAAgDUoNgAAwBoUGwAAYA2KDQAAsAbFBgAAWINiAwAArEGxAQAA1nBcbN577z2Vl5dr2rRpysjI0Ouvv/6De/bs2aNrrrlGbrdbl112mbZv357AqABsQY4ASBbHxaanp0fz589XQ0PDea0/evSobr75Zt1www1qb2/XAw88oDvvvFNvvfWW42EB2IEcAZAsGRfySzAzMjK0a9cuLV26dNg1q1at0u7du/XJJ5/Ez/3617/WiRMn1NzcnOhTA7AEOQJgJE1I9hO0trbK7/cPOFdWVqYHHnhg2D29vb3q7e2NfxyLxfTVV1/pRz/6kTIyMpI1KoDzZIzRyZMnNW3aNGVmJv9H9cgRwD7JypGkF5tQKCSPxzPgnMfjUTQa1ddff61JkyYN2lNfX69HH3002aMBuEBdXV36yU9+kvTnIUcAe410jiS92CSirq5OgUAg/nEkEtGMGTPU1dWlnJycUZwMgCRFo1F5vV5Nnjx5tEcZFjkCjG3JypGkF5uCggKFw+EB58LhsHJycob8KkuS3G633G73oPM5OTkEEjCGpOpbOuQIYK+RzpGkf3O8tLRULS0tA869/fbbKi0tTfZTA7AEOQLgfDkuNv/973/V3t6u9vZ2Sf97GWZ7e7s6Ozsl/e/2b1VVVXz93XffrY6ODj344IM6dOiQnnrqKb388stauXLlyHwGANIOOQIgaYxD7777rpE06KiurjbGGFNdXW2WLFkyaE9xcbFxuVxm5syZ5vnnn3f0nJFIxEgykUjE6bgAkuBCr0lyBECyrskLeh+bVIlGo8rNzVUkEuF748AYkI7XZDrODNgsWdckvysKAABYg2IDAACsQbEBAADWoNgAAABrUGwAAIA1KDYAAMAaFBsAAGANig0AALAGxQYAAFiDYgMAAKxBsQEAANag2AAAAGtQbAAAgDUoNgAAwBoUGwAAYA2KDQAAsAbFBgAAWINiAwAArEGxAQAA1qDYAAAAa1BsAACANSg2AADAGhQbAABgDYoNAACwBsUGAABYg2IDAACsQbEBAADWoNgAAABrUGwAAIA1KDYAAMAaFBsAAGANig0AALBGQsWmoaFBRUVFys7Ols/n0969e8+5fvPmzbr88ss1adIkeb1erVy5UqdPn05oYAD2IEsAjDTHxWbnzp0KBAIKBoPat2+f5s+fr7KyMh07dmzI9S+99JJqa2sVDAZ18OBBPfvss9q5c6ceeuihCx4eQPoiSwAkg+Nis2nTJt11111avny5rrzySjU2Nuqiiy7Sc889N+T6Dz/8UIsWLdKyZctUVFSkG2+8UbfddtsPfmUGwG5kCYBkcFRs+vr61NbWJr/f/+0DZGbK7/ertbV1yD3XXXed2tra4uHT0dGhpqYm3XTTTcM+T29vr6LR6IADgD1SkSXkCDA+TXCyuLu7W/39/fJ4PAPOezweHTp0aMg9y5YtU3d3t66//noZY3T27Fndfffd57x9XF9fr0cffdTJaADSSCqyhBwBxqekvypqz549Wr9+vZ566int27dPr732mnbv3q1169YNu6eurk6RSCR+dHV1JXtMAGOc0ywhR4DxydEdm7y8PGVlZSkcDg84Hw6HVVBQMOSeNWvWqLKyUnfeeackae7cuerp6dGKFSu0evVqZWYO7lZut1tut9vJaADSSCqyhBwBxidHd2xcLpdKSkrU0tISPxeLxdTS0qLS0tIh95w6dWpQ4GRlZUmSjDFO5wVgAbIEQLI4umMjSYFAQNXV1VqwYIEWLlyozZs3q6enR8uXL5ckVVVVafr06aqvr5cklZeXa9OmTfrpT38qn8+nI0eOaM2aNSovL4+HEoDxhywBkAyOi01FRYWOHz+utWvXKhQKqbi4WM3NzfEfAuzs7BzwVdXDDz+sjIwMPfzww/ryyy/14x//WOXl5XriiSdG7rMAkHbIEgDJkGHS4B5uNBpVbm6uIpGIcnJyRnscYNxLx2syHWcGbJasa5LfFQUAAKxBsQEAANag2AAAAGtQbAAAgDUoNgAAwBoUGwAAYA2KDQAAsAbFBgAAWINiAwAArEGxAQAA1qDYAAAAa1BsAACANSg2AADAGhQbAABgDYoNAACwBsUGAABYg2IDAACsQbEBAADWoNgAAABrUGwAAIA1KDYAAMAaFBsAAGANig0AALAGxQYAAFiDYgMAAKxBsQEAANag2AAAAGtQbAAAgDUoNgAAwBoUGwAAYA2KDQAAsAbFBgAAWCOhYtPQ0KCioiJlZ2fL5/Np796951x/4sQJ1dTUqLCwUG63W7Nnz1ZTU1NCAwOwB1kCYKRNcLph586dCgQCamxslM/n0+bNm1VWVqbDhw8rPz9/0Pq+vj794he/UH5+vl599VVNnz5dX3zxhaZMmTIS8wNIU2QJgGTIMMYYJxt8Pp+uvfZabd26VZIUi8Xk9Xp13333qba2dtD6xsZG/elPf9KhQ4c0ceLEhIaMRqPKzc1VJBJRTk5OQo8BYOSMxDWZ6iwhR4CxJVnXpKNvRfX19amtrU1+v//bB8jMlN/vV2tr65B73njjDZWWlqqmpkYej0dXX3211q9fr/7+/mGfp7e3V9FodMABwB6pyBJyBBifHBWb7u5u9ff3y+PxDDjv8XgUCoWG3NPR0aFXX31V/f39ampq0po1a/TnP/9Zjz/++LDPU19fr9zc3Pjh9XqdjAlgjEtFlpAjwPiU9FdFxWIx5efn65lnnlFJSYkqKiq0evVqNTY2Drunrq5OkUgkfnR1dSV7TABjnNMsIUeA8cnRDw/n5eUpKytL4XB4wPlwOKyCgoIh9xQWFmrixInKysqKn7viiisUCoXU19cnl8s1aI/b7Zbb7XYyGoA0koosIUeA8cnRHRuXy6WSkhK1tLTEz8ViMbW0tKi0tHTIPYsWLdKRI0cUi8Xi5z799FMVFhYOWWoA2I8sAZAsjr8VFQgEtG3bNr3wwgs6ePCg7rnnHvX09Gj58uWSpKqqKtXV1cXX33PPPfrqq690//3369NPP9Xu3bu1fv161dTUjNxnASDtkCUAksHx+9hUVFTo+PHjWrt2rUKhkIqLi9Xc3Bz/IcDOzk5lZn7bl7xer9566y2tXLlS8+bN0/Tp03X//fdr1apVI/dZAEg7ZAmAZHD8PjajgfefAMaWdLwm03FmwGZj4n1sAAAAxjKKDQAAsAbFBgAAWINiAwAArEGxAQAA1qDYAAAAa1BsAACANSg2AADAGhQbAABgDYoNAACwBsUGAABYg2IDAACsQbEBAADWoNgAAABrUGwAAIA1KDYAAMAaFBsAAGANig0AALAGxQYAAFiDYgMAAKxBsQEAANag2AAAAGtQbAAAgDUoNgAAwBoUGwAAYA2KDQAAsAbFBgAAWINiAwAArEGxAQAA1qDYAAAAa1BsAACANSg2AADAGgkVm4aGBhUVFSk7O1s+n0979+49r307duxQRkaGli5dmsjTArAMWQJgpDkuNjt37lQgEFAwGNS+ffs0f/58lZWV6dixY+fc9/nnn+v3v/+9Fi9enPCwAOxBlgBIBsfFZtOmTbrrrru0fPlyXXnllWpsbNRFF12k5557btg9/f39uv322/Xoo49q5syZFzQwADuQJQCSwVGx6evrU1tbm/x+/7cPkJkpv9+v1tbWYfc99thjys/P1x133HFez9Pb26toNDrgAGCPVGQJOQKMT46KTXd3t/r7++XxeAac93g8CoVCQ+55//339eyzz2rbtm3n/Tz19fXKzc2NH16v18mYAMa4VGQJOQKMT0l9VdTJkydVWVmpbdu2KS8v77z31dXVKRKJxI+urq4kTglgrEskS8gRYHya4GRxXl6esrKyFA6HB5wPh8MqKCgYtP6zzz7T559/rvLy8vi5WCz2vyeeMEGHDx/WrFmzBu1zu91yu91ORgOQRlKRJeQIMD45umPjcrlUUlKilpaW+LlYLKaWlhaVlpYOWj9nzhx9/PHHam9vjx+33HKLbrjhBrW3t3NrGBinyBIAyeLojo0kBQIBVVdXa8GCBVq4cKE2b96snp4eLV++XJJUVVWl6dOnq76+XtnZ2br66qsH7J8yZYokDToPYHwhSwAkg+NiU1FRoePHj2vt2rUKhUIqLi5Wc3Nz/IcAOzs7lZnJGxoDODeyBEAyZBhjzGgP8UOi0ahyc3MViUSUk5Mz2uMA4146XpPpODNgs2Rdk3w5BAAArEGxAQAA1qDYAAAAa1BsAACANSg2AADAGhQbAABgDYoNAACwBsUGAABYg2IDAACsQbEBAADWoNgAAABrUGwAAIA1KDYAAMAaFBsAAGANig0AALAGxQYAAFiDYgMAAKxBsQEAANag2AAAAGtQbAAAgDUoNgAAwBoUGwAAYA2KDQAAsAbFBgAAWINiAwAArEGxAQAA1qDYAAAAa1BsAACANSg2AADAGhQbAABgDYoNAACwBsUGAABYI6Fi09DQoKKiImVnZ8vn82nv3r3Drt22bZsWL16sqVOnaurUqfL7/edcD2D8IEsAjDTHxWbnzp0KBAIKBoPat2+f5s+fr7KyMh07dmzI9Xv27NFtt92md999V62trfJ6vbrxxhv15ZdfXvDwANIXWQIgGTKMMcbJBp/Pp2uvvVZbt26VJMViMXm9Xt13332qra39wf39/f2aOnWqtm7dqqqqqvN6zmg0qtzcXEUiEeXk5DgZF0ASjMQ1meosIUeAsSVZ16SjOzZ9fX1qa2uT3+//9gEyM+X3+9Xa2npej3Hq1CmdOXNGl1xyybBrent7FY1GBxwA7JGKLCFHgPHJUbHp7u5Wf3+/PB7PgPMej0ehUOi8HmPVqlWaNm3agED7vvr6euXm5sYPr9frZEwAY1wqsoQcAcanlL4qasOGDdqxY4d27dql7OzsYdfV1dUpEonEj66urhROCWCsO58sIUeA8WmCk8V5eXnKyspSOBwecD4cDqugoOCcezdu3KgNGzbonXfe0bx588651u12y+12OxkNQBpJRZaQI8D45OiOjcvlUklJiVpaWuLnYrGYWlpaVFpaOuy+J598UuvWrVNzc7MWLFiQ+LQArECWAEgWR3dsJCkQCKi6uloLFizQwoULtXnzZvX09Gj58uWSpKqqKk2fPl319fWSpD/+8Y9au3atXnrpJRUVFcW/f37xxRfr4osvHsFPBUA6IUsAJIPjYlNRUaHjx49r7dq1CoVCKi4uVnNzc/yHADs7O5WZ+e2NoKefflp9fX361a9+NeBxgsGgHnnkkQubHkDaIksAJIPj97EZDbz/BDC2pOM1mY4zAzYbE+9jAwAAMJZRbAAAgDUoNgAAwBoUGwAAYA2KDQAAsAbFBgAAWINiAwAArEGxAQAA1qDYAAAAa1BsAACANSg2AADAGhQbAABgDYoNAACwBsUGAABYg2IDAACsQbEBAADWoNgAAABrUGwAAIA1KDYAAMAaFBsAAGANig0AALAGxQYAAFiDYgMAAKxBsQEAANag2AAAAGtQbAAAgDUoNgAAwBoUGwAAYA2KDQAAsAbFBgAAWINiAwAArEGxAQAA1qDYAAAAayRUbBoaGlRUVKTs7Gz5fD7t3bv3nOtfeeUVzZkzR9nZ2Zo7d66ampoSGhaAXcgSACPNcbHZuXOnAoGAgsGg9u3bp/nz56usrEzHjh0bcv2HH36o2267TXfccYf279+vpUuXaunSpfrkk08ueHgA6YssAZAMGcYY42SDz+fTtddeq61bt0qSYrGYvF6v7rvvPtXW1g5aX1FRoZ6eHr355pvxcz/72c9UXFysxsbGIZ+jt7dXvb298Y8jkYhmzJihrq4u5eTkOBkXQBJEo1F5vV6dOHFCubm5CT1GsrOEHAHGtpHIkSEZB3p7e01WVpbZtWvXgPNVVVXmlltuGXKP1+s1f/nLXwacW7t2rZk3b96wzxMMBo0kDg6OMX589tlnTiIkpVlCjnBwpMeRaI4MZ4Ic6O7uVn9/vzwez4DzHo9Hhw4dGnJPKBQacn0oFBr2eerq6hQIBOIfnzhxQpdeeqk6OztHttUl2TdtNJ2+QkzHmSXmTrVv7n5ccsklCe1PRZaQI6OLuVMrHee+0BwZjqNikyput1tut3vQ+dzc3LT5B/uunJyctJs7HWeWmDvVMjPH7gsryZGxgblTKx3nHukccfRoeXl5ysrKUjgcHnA+HA6roKBgyD0FBQWO1gOwH1kCIFkcFRuXy6WSkhK1tLTEz8ViMbW0tKi0tHTIPaWlpQPWS9Lbb7897HoA9iNLACSN0x/K2bFjh3G73Wb79u3mwIEDZsWKFWbKlCkmFAoZY4yprKw0tbW18fUffPCBmTBhgtm4caM5ePCgCQaDZuLEiebjjz8+7+c8ffq0CQaD5vTp007HHVXpOHc6zmwMc6faSMyd6iwZz3/Xo4G5Uysd507WzI6LjTHGbNmyxcyYMcO4XC6zcOFC89FHH8X/bMmSJaa6unrA+pdfftnMnj3buFwuc9VVV5ndu3df0NAA7ECWABhpjt/HBgAAYKwauy9pAAAAcIhiAwAArEGxAQAA1qDYAAAAa4yZYtPQ0KCioiJlZ2fL5/Np796951z/yiuvaM6cOcrOztbcuXPV1NSUokm/5WTmbdu2afHixZo6daqmTp0qv9//g59jsjj9u/7Gjh07lJGRoaVLlyZ3wGE4nfvEiROqqalRYWGh3G63Zs+ePeb/n0jS5s2bdfnll2vSpEnyer1auXKlTp8+naJppffee0/l5eWaNm2aMjIy9Prrr//gnj179uiaa66R2+3WZZddpu3btyd9zqGkY45IZEmqkSWpMWpZMtovyzLmf+9n4XK5zHPPPWf++c9/mrvuustMmTLFhMPhIdd/8MEHJisryzz55JPmwIED5uGHH3b83jipnnnZsmWmoaHB7N+/3xw8eND85je/Mbm5ueZf//pXymZOZO5vHD161EyfPt0sXrzY/PKXv0zNsN/hdO7e3l6zYMECc9NNN5n333/fHD161OzZs8e0t7eP6blffPFF43a7zYsvvmiOHj1q3nrrLVNYWGhWrlyZspmbmprM6tWrzWuvvWYkDfpFld/X0dFhLrroIhMIBMyBAwfMli1bTFZWlmlubk7NwP8vHXMkkbnJkgtDltifJWOi2CxcuNDU1NTEP+7v7zfTpk0z9fX1Q66/9dZbzc033zzgnM/nM7/97W+TOud3OZ35+86ePWsmT55sXnjhhWSNOKRE5j579qy57rrrzF//+ldTXV09KmHkdO6nn37azJw50/T19aVqxCE5nbumpsb8/Oc/H3AuEAiYRYsWJXXO4ZxPGD344IPmqquuGnCuoqLClJWVJXGywdIxR4whS1KNLLE/S0b9W1F9fX1qa2uT3++Pn8vMzJTf71dra+uQe1pbWwesl6SysrJh14+0RGb+vlOnTunMmTMj/ltNzyXRuR977DHl5+frjjvuSMWYgyQy9xtvvKHS0lLV1NTI4/Ho6quv1vr169Xf35+qsROa+7rrrlNbW1v8FnNHR4eampp00003pWTmRIz29SilZ45IZEmqkSXjI0tG/bd7d3d3q7+/Xx6PZ8B5j8ejQ4cODbknFAoNuT4UCiVtzu9KZObvW7VqlaZNmzboHzGZEpn7/fff17PPPqv29vYUTDi0RObu6OjQP/7xD91+++1qamrSkSNHdO+99+rMmTMKBoOpGDuhuZctW6bu7m5df/31Msbo7Nmzuvvuu/XQQw+lYuSEDHc9RqNRff3115o0aVLSZ0jHHJHIklQjS8ZHloz6HZvxaMOGDdqxY4d27dql7Ozs0R5nWCdPnlRlZaW2bdumvLy80R7HkVgspvz8fD3zzDMqKSlRRUWFVq9ercbGxtEe7Zz27Nmj9evX66mnntK+ffv02muvaffu3Vq3bt1oj4YxiCxJPrIk/Yz6HZu8vDxlZWUpHA4POB8Oh1VQUDDknoKCAkfrR1oiM39j48aN2rBhg9555x3NmzcvmWMO4nTuzz77TJ9//rnKy8vj52KxmCRpwoQJOnz4sGbNmpXcoZXY33dhYaEmTpyorKys+LkrrrhCoVBIfX19crlcSZ1ZSmzuNWvWqLKyUnfeeackae7cuerp6dGKFSu0evVqZWaOva9Fhrsec3JyUnK3RkrPHJHIEoksOR9kibMsGfXPzOVyqaSkRC0tLfFzsVhMLS0tKi0tHXJPaWnpgPWS9Pbbbw+7fqQlMrMkPfnkk1q3bp2am5u1YMGCVIw6gNO558yZo48//ljt7e3x45ZbbtENN9yg9vZ2eb3eMTm3JC1atEhHjhyJh6ckffrppyosLExJEEmJzX3q1KlBgfNNoJox+mvdRvt6lNIzRySyhCw5P2SJw2vS0Y8aJ8mOHTuM2+0227dvNwcOHDArVqwwU6ZMMaFQyBhjTGVlpamtrY2v/+CDD8yECRPMxo0bzcGDB00wGByVl3s7mXnDhg3G5XKZV1991fz73/+OHydPnkzZzInM/X2j9UoGp3N3dnaayZMnm9/97nfm8OHD5s033zT5+fnm8ccfH9NzB4NBM3nyZPO3v/3NdHR0mL///e9m1qxZ5tZbb03ZzCdPnjT79+83+/fvN5LMpk2bzP79+80XX3xhjDGmtrbWVFZWxtd/8xLNP/zhD+bgwYOmoaFh1F7unW45ksjcZMmFIUvsz5IxUWyMMWbLli1mxowZxuVymYULF5qPPvoo/mdLliwx1dXVA9a//PLLZvbs2cblcpmrrrrK7N69O8UTO5v50ksvNZIGHcFgcEzP/X2jFUbGOJ/7ww8/ND6fz7jdbjNz5kzzxBNPmLNnz6Z4amdznzlzxjzyyCNm1qxZJjs723i9XnPvvfea//znPymb99133x3y/+o3c1ZXV5slS5YM2lNcXGxcLpeZOXOmef7551M273elY44YQ5akGlmSGqOVJRnGjNF7UgAAAA6N+s/YAAAAjBSKDQAAsAbFBgAAWINiAwAArEGxAQAA1qDYAAAAa1BsAACANSg2AADAGhQbAABgDYoNAACwBsUGAABY4/8Aa1b7hfMwrokAAAAASUVORK5CYII=", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "V = pw.pflow()\n", "vmag = np.abs(V)\n", diff --git a/pyproject.toml b/pyproject.toml index 00f2f73..0c3f1e8 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -86,12 +86,12 @@ docs = [ Homepage = "https://github.com/lukelowry/ESApp" [tool.setuptools] -packages = {find = {}} +packages = {find = {include = ["esapp*"]}} include-package-data = true zip-safe = false [tool.setuptools.package-data] -esapp = ["py.typed", "utils/shapes/**/*"] +esapp = ["py.typed"] [tool.setuptools.dynamic] version = {file = "VERSION"} diff --git a/tests/test_utils.py b/tests/test_utils.py index 630f786..d693f7f 100644 --- a/tests/test_utils.py +++ b/tests/test_utils.py @@ -2,8 +2,7 @@ Unit tests for the esapp.utils module. These are **unit tests** that do NOT require PowerWorld Simulator. They test -the timing decorator (esapp.utils.misc) and B3D file format I/O -(esapp.utils.b3d). +B3D file format I/O (esapp.utils.b3d). USAGE: pytest tests/test_utils.py -v @@ -16,34 +15,9 @@ import pytest from numpy.testing import assert_allclose -from esapp.utils.misc import timing from esapp.utils.b3d import B3D -# ============================================================================= -# timing decorator -# ============================================================================= - - -class TestTiming: - - def test_preserves_return_value(self, capsys): - @timing - def add(a, b): - return a + b - - result = add(2, 3) - assert result == 5 - assert "'add' took:" in capsys.readouterr().out - - def test_preserves_function_name(self): - @timing - def my_func(): - pass - - assert my_func.__name__ == 'my_func' - - # ============================================================================= # B3D file format # ============================================================================= From 3b306bb55010427bb0ceee1278caf2955ddeee26 Mon Sep 17 00:00:00 2001 From: Wyatt Lowery Date: Mon, 31 Aug 2026 14:56:14 -0500 Subject: [PATCH 03/10] cleanup of old example code --- .gitignore | 3 + examples/README.md | 12 +- examples/data/case.txt | 1 - examples/data/case.txt.example | 1 + examples/data/case_B.txt | 1 - examples/mesh.py | 854 ------------------ examples/network/02_network_topology.ipynb | 9 +- examples/nonuniform/01_nonuniform_gic.ipynb | 469 ---------- examples/nonuniform/nonuniform.py | 304 ------- examples/nonuniform/plotting.py | 247 ----- examples/nonuniform/sens.py | 143 --- examples/plot_helpers.py | 534 ----------- .../visualization/01_discrete_calculus.ipynb | 436 --------- .../visualization/02_spectral_analysis.ipynb | 294 ------ 14 files changed, 18 insertions(+), 3290 deletions(-) delete mode 100644 examples/data/case.txt create mode 100644 examples/data/case.txt.example delete mode 100644 examples/data/case_B.txt delete mode 100644 examples/mesh.py delete mode 100644 examples/nonuniform/01_nonuniform_gic.ipynb delete mode 100644 examples/nonuniform/nonuniform.py delete mode 100644 examples/nonuniform/plotting.py delete mode 100644 examples/nonuniform/sens.py delete mode 100644 examples/visualization/01_discrete_calculus.ipynb delete mode 100644 examples/visualization/02_spectral_analysis.ipynb diff --git a/.gitignore b/.gitignore index 7959720..95e1f78 100644 --- a/.gitignore +++ b/.gitignore @@ -158,3 +158,6 @@ docs/examples/system_health_report.csv # Claude Code local settings (machine-specific) .claude/settings.local.json + +# Local case-path config for example notebooks (machine-specific) +examples/data/*.txt diff --git a/examples/README.md b/examples/README.md index f31ba94..811a8d1 100644 --- a/examples/README.md +++ b/examples/README.md @@ -20,7 +20,6 @@ notebook's own directory as the working directory (the Jupyter default). | Module | Description | |---|---| | `map.py` | Geographic visualization (borders, lines, vector fields) | -| `mesh.py` | Discrete geometry, Grid2D, PLY mesh I/O, spectral helpers | | `plot_helpers.py` | Shared plotting functions for all notebooks | ## Notebooks @@ -31,8 +30,11 @@ notebook's own directory as the working directory (the Jupyter default). | `steady_state/` | Contingency analysis, SCOPF, ATC, and CPF examples | | `gic/` | GIC analysis and sensitivity examples | | `network/` | Network topology and matrix extraction examples | -| `nonuniform/` | Non-uniform electric field GIC analysis | -| `visualization/` | Discrete calculus, spectral analysis, geographic plotting | +| `visualization/` | Geographic plotting utilities demo | -Integration notebooks expect a PowerWorld case path in `examples/data/case.txt` -(see individual notebooks for details). +## Case Configuration + +The notebooks read a machine-local PowerWorld case path from +`examples/data/case.txt` (and `case_B.txt` where a second case is compared). +These files are gitignored — copy `examples/data/case.txt.example` and point +it at a case on your machine. diff --git a/examples/data/case.txt b/examples/data/case.txt deleted file mode 100644 index 512a6a4..0000000 --- a/examples/data/case.txt +++ /dev/null @@ -1 +0,0 @@ -r"C:\Users\wyatt\OneDrive - Texas A&M University\Research\Cases\Hawaii 37\Hawaii40_20231026.pwb" \ No newline at end of file diff --git a/examples/data/case.txt.example b/examples/data/case.txt.example new file mode 100644 index 0000000..43a0981 --- /dev/null +++ b/examples/data/case.txt.example @@ -0,0 +1 @@ +r"C:\path o\your\case.pwb" diff --git a/examples/data/case_B.txt b/examples/data/case_B.txt deleted file mode 100644 index 37df999..0000000 --- a/examples/data/case_B.txt +++ /dev/null @@ -1 +0,0 @@ -r"C:\Users\wyatt\OneDrive - Texas A&M University\Research\Cases\Texas\ACTIVSg2000.PWB" \ No newline at end of file diff --git a/examples/mesh.py b/examples/mesh.py deleted file mode 100644 index eee8927..0000000 --- a/examples/mesh.py +++ /dev/null @@ -1,854 +0,0 @@ -""" -Discrete geometry and linear algebra utilities. - -This module provides tools for: -- Linear algebra: matrix decomposition, eigenvalue analysis, spectral methods -- Unstructured meshes: PLY file I/O, graph operations -- Structured 2D grids: finite difference operators - -Both mesh types support computing incidence matrices, Laplacians, and -other discrete differential operators. -""" - -from __future__ import annotations - -from collections.abc import Iterator -from dataclasses import dataclass - -import numpy as np -from numpy import block, diag, real, imag -from numpy.typing import NDArray -import scipy.sparse as sp -from scipy.sparse import csc_matrix, csr_matrix -from scipy.sparse.linalg import eigsh -from scipy.linalg import schur - -__all__ = [ - # Physical constants - 'MU0', - # Graph Laplacians - 'pathlap', - 'pathincidence', - # Matrix transformations - 'normlap', - 'hermitify', - 'sorteig', - # Mesh utilities - 'Mesh', - 'extract_unique_edges', - 'Grid2D', -] - - -# ============================================================================= -# Unstructured Mesh Utilities -# ============================================================================= - -def extract_unique_edges(faces: list[list[int]]) -> NDArray[np.int_]: - """ - Extract unique edges from a list of mesh faces. - - Each face is a list of vertex indices forming a polygon. Edges are - extracted by connecting consecutive vertices (including last to first). - Duplicate edges are removed, and each edge is stored with the smaller - vertex index first. - - Parameters - ---------- - faces : list of list of int - Mesh faces, where each face is a list of vertex indices. - - Returns - ------- - np.ndarray - An (M, 2) array of unique edges, sorted lexicographically. - Column 0 contains the smaller vertex index for each edge. - - Examples - -------- - >>> faces = [[0, 1, 2], [1, 2, 3]] - >>> extract_unique_edges(faces) - array([[0, 1], - [0, 2], - [1, 2], - [1, 3], - [2, 3]]) - """ - unique_edges = set() - - for face in faces: - n = len(face) - for i in range(n): - u = face[i] - v = face[(i + 1) % n] - edge = (u, v) if u < v else (v, u) - unique_edges.add(edge) - - return np.array(sorted(unique_edges), dtype=np.int_) - - -@dataclass -class Mesh: - """ - A 3D mesh consisting of vertices and polygonal faces. - - This class represents an unstructured mesh and provides methods for - loading from PLY files and computing graph-theoretic properties like - incidence matrices and Laplacians. - - Attributes - ---------- - vertices : list of tuple - List of (x, y, z) vertex coordinates. - faces : list of list of int - List of faces, where each face is a list of vertex indices. - - Examples - -------- - >>> mesh = Mesh.from_ply("model.ply") - >>> L = mesh.to_laplacian() - >>> xyz = mesh.get_xyz() - """ - vertices: list[tuple[float, float, float]] - faces: list[list[int]] - - @classmethod - def from_ply(cls, filepath: str) -> Mesh: - """ - Load a mesh from a PLY file. - - Supports both ASCII and binary_little_endian PLY formats. - Extracts vertex positions (x, y, z) and face connectivity. - - Parameters - ---------- - filepath : str - Path to the PLY file. - - Returns - ------- - Mesh - The loaded mesh. - - Raises - ------ - ValueError - If the PLY format is not supported. - - Notes - ----- - Supported vertex property types: char, uchar, short, ushort, - int, uint, float, double. - """ - import struct - - with open(filepath, 'rb') as f: - # Parse header - header_ended = False - fmt = "ascii" - vertex_count = 0 - face_count = 0 - vertex_props = [] - current_element = None - - while not header_ended: - line = f.readline().strip() - if not line: - break - line_str = line.decode('ascii', errors='ignore') - - if line_str == "end_header": - header_ended = True - break - - parts = line_str.split() - if not parts: - continue - - if parts[0] == "format": - fmt = parts[1] - elif parts[0] == "element": - current_element = parts[1] - if current_element == "vertex": - vertex_count = int(parts[2]) - elif current_element == "face": - face_count = int(parts[2]) - elif parts[0] == "property": - if current_element == "vertex": - vertex_props.append((parts[2], parts[1])) - - # Parse body - vertices = [] - faces = [] - - if fmt == "ascii": - lines = f.readlines() - for i in range(vertex_count): - parts = lines[i].strip().split() - v = (float(parts[0]), float(parts[1]), float(parts[2])) - vertices.append(v) - - for i in range(face_count): - parts = lines[vertex_count + i].strip().split() - vertex_indices = [int(x) for x in parts[1:]] - faces.append(vertex_indices) - - elif fmt == "binary_little_endian": - np_type_map = { - 'char': 'i1', 'uchar': 'u1', 'short': 'i2', 'ushort': 'u2', - 'int': 'i4', 'uint': 'u4', 'float': 'f4', 'double': 'f8' - } - dtype_fields = [ - (name, np_type_map.get(type_str, 'f4')) - for name, type_str in vertex_props - ] - vertex_dtype = np.dtype(dtype_fields) - - vertex_data = f.read(vertex_count * vertex_dtype.itemsize) - v_arr = np.frombuffer(vertex_data, dtype=vertex_dtype) - - if all(n in v_arr.dtype.names for n in ('x', 'y', 'z')): - vertices = list(zip(v_arr['x'], v_arr['y'], v_arr['z'])) - else: - names = v_arr.dtype.names - vertices = list(zip( - v_arr[names[0]], v_arr[names[1]], v_arr[names[2]] - )) - - for _ in range(face_count): - n = struct.unpack(' csc_matrix: - """ - Construct the oriented incidence matrix for the mesh graph. - - The incidence matrix B has shape (|V|, |E|) where each column - represents an edge with +1 at the source vertex and -1 at the - target vertex. - - Returns - ------- - scipy.sparse.csc_matrix - The incidence matrix B. - - Notes - ----- - The edge orientation is determined by vertex index ordering - (smaller index is source, larger is target). - """ - edges = extract_unique_edges(self.faces) - num_verts = len(self.vertices) - num_edges = len(edges) - - row = edges.ravel() - col = np.repeat(np.arange(num_edges), 2) - data = np.tile([1.0, -1.0], num_edges) - - return csc_matrix((data, (row, col)), shape=(num_verts, num_edges)) - - def get_xyz(self) -> NDArray[np.float64]: - """ - Get vertex coordinates as a numpy array. - - Returns - ------- - np.ndarray - An (N, 3) array of vertex coordinates. - """ - return np.array(self.vertices, dtype=np.float64) - - def to_laplacian(self) -> csc_matrix: - """ - Compute the graph Laplacian matrix. - - The Laplacian is computed as L = B @ B.T where B is the - incidence matrix. This produces the combinatorial Laplacian - with diagonal entries equal to vertex degrees. - - Returns - ------- - scipy.sparse.csc_matrix - The graph Laplacian matrix L. - """ - B = self.get_incidence_matrix() - return B @ B.T - - -# ============================================================================= -# Structured Grid Utilities -# ============================================================================= - -class Grid2D: - """ - Graph-based discrete operator generator for regular 2D grids. - - Constructs an oriented incidence matrix for the 2D grid graph and - derives all discrete operators (gradient, divergence, curl, Laplacian) - from it. The Laplacian is computed as L = A^T diag(w) A where A is - the incidence matrix and w are edge weights. - - Also provides boolean masks for boundary and interior region selection, - useful for applying boundary conditions in finite difference schemes. - - Parameters - ---------- - shape : tuple of int - Grid dimensions (nx, ny). - - Attributes - ---------- - nx : int - Number of grid points in x direction. - ny : int - Number of grid points in y direction. - size : int - Total number of grid points (nx * ny). - n_edges_x : int - Number of horizontal edges: (nx - 1) * ny. - n_edges_y : int - Number of vertical edges: nx * (ny - 1). - n_edges : int - Total number of edges. - - Examples - -------- - >>> grid = Grid2D((10, 10)) - >>> A = grid.incidence() # Oriented incidence matrix - >>> L = grid.laplacian() # L = A^T A (unit weights) - >>> Dx, Dy = grid.gradient() # Extracted from A - >>> u[grid.boundary] = 0 # Apply Dirichlet BC - - Notes - ----- - Grid points are indexed in Fortran order (column-major), so point - (x, y) maps to flat index y * nx + x. This matches numpy's 'F' order. - - Edges are ordered with all horizontal edges first, then vertical edges. - Each edge is oriented from the lower-index node to the higher-index node - (left-to-right for horizontal, bottom-to-top for vertical). - """ - - def __init__(self, shape: tuple[int, int]) -> None: - self.nx, self.ny = shape - self.size = self.nx * self.ny - self.n_edges_x = (self.nx - 1) * self.ny - self.n_edges_y = self.nx * (self.ny - 1) - self.n_edges = self.n_edges_x + self.n_edges_y - - # Build and cache the incidence matrix and region masks - self._A = self._build_incidence() - self._build_masks() - - @property - def shape(self) -> tuple[int, int]: - """Grid dimensions (nx, ny).""" - return (self.nx, self.ny) - - # ----------------------------------------------------------------- - # Region masks (formerly GridSelector) - # ----------------------------------------------------------------- - - def _build_masks(self) -> None: - """Compute boolean masks for boundary and interior regions.""" - idx = np.arange(self.size) - x = idx % self.nx - y = idx // self.nx - - self._left = (x == 0) - self._right = (x == self.nx - 1) - self._bottom = (y == 0) - self._top = (y == self.ny - 1) - self._corners = (self._left | self._right) & (self._bottom | self._top) - self._boundary = self._left | self._right | self._bottom | self._top - self._interior = ~self._boundary - - @property - def left(self) -> NDArray[np.bool_]: - """Boolean mask for left boundary (x = 0).""" - return self._left - - @property - def right(self) -> NDArray[np.bool_]: - """Boolean mask for right boundary (x = nx-1).""" - return self._right - - @property - def bottom(self) -> NDArray[np.bool_]: - """Boolean mask for bottom boundary (y = 0).""" - return self._bottom - - @property - def top(self) -> NDArray[np.bool_]: - """Boolean mask for top boundary (y = ny-1).""" - return self._top - - @property - def corners(self) -> NDArray[np.bool_]: - """Boolean mask for corner points.""" - return self._corners - - @property - def boundary(self) -> NDArray[np.bool_]: - """Boolean mask for all boundary points.""" - return self._boundary - - @property - def interior(self) -> NDArray[np.bool_]: - """Boolean mask for interior (non-boundary) points.""" - return self._interior - - # ----------------------------------------------------------------- - # Indexing - # ----------------------------------------------------------------- - - def flat_index(self, x: int, y: int) -> int: - """ - Convert 2D coordinates to flat index. - - Parameters - ---------- - x : int - X coordinate (0 to nx-1). - y : int - Y coordinate (0 to ny-1). - - Returns - ------- - int - Flat index in column-major order. - """ - return y * self.nx + x - - def grid_coords(self, idx: int) -> tuple[int, int]: - """ - Convert flat index to 2D coordinates. - - Parameters - ---------- - idx : int - Flat index. - - Returns - ------- - tuple of int - (x, y) coordinates. - """ - return idx % self.nx, idx // self.nx - - def iter_points(self) -> Iterator[tuple[int, int, int]]: - """ - Iterate over all grid points. - - Yields - ------ - tuple of int - (x, y, flat_index) for each grid point in column-major order. - """ - for y in range(self.ny): - for x in range(self.nx): - yield x, y, self.flat_index(x, y) - - # ----------------------------------------------------------------- - # Incidence matrix (core data structure) - # ----------------------------------------------------------------- - - def _build_incidence(self) -> csr_matrix: - """ - Build the oriented incidence matrix of the 2D grid graph. - - The matrix A has shape (n_edges, n_nodes). Each row has exactly - two nonzeros: -1 at the source node and +1 at the target node. - Horizontal edges are listed first, then vertical edges. - - Returns - ------- - scipy.sparse.csr_matrix - Incidence matrix A of shape (n_edges, n_nodes). - """ - n = self.size - nx, ny = self.nx, self.ny - - # --- Horizontal edges: (x, y) → (x+1, y) --- - # For each row y, there are (nx-1) horizontal edges - ex = self.n_edges_x - if ex > 0: - # Source nodes for horizontal edges - all_nodes = np.arange(n).reshape(ny, nx) - src_x = all_nodes[:, :-1].ravel() # left endpoints - tgt_x = all_nodes[:, 1:].ravel() # right endpoints - edge_idx_x = np.arange(ex) - else: - src_x = np.array([], dtype=int) - tgt_x = np.array([], dtype=int) - edge_idx_x = np.array([], dtype=int) - - # --- Vertical edges: (x, y) → (x, y+1) --- - ey = self.n_edges_y - if ey > 0: - all_nodes = np.arange(n).reshape(ny, nx) - src_y = all_nodes[:-1, :].ravel() # bottom endpoints - tgt_y = all_nodes[1:, :].ravel() # top endpoints - edge_idx_y = np.arange(ex, ex + ey) - else: - src_y = np.array([], dtype=int) - tgt_y = np.array([], dtype=int) - edge_idx_y = np.array([], dtype=int) - - # Assemble COO data - rows = np.concatenate([edge_idx_x, edge_idx_x, edge_idx_y, edge_idx_y]) - cols = np.concatenate([src_x, tgt_x, src_y, tgt_y]) - data = np.concatenate([ - -np.ones(ex), np.ones(ex), - -np.ones(ey), np.ones(ey), - ]) - - return csr_matrix((data, (rows, cols)), shape=(self.n_edges, n)) - - def incidence(self) -> csr_matrix: - """ - Return the oriented incidence matrix of the 2D grid graph. - - The matrix A has shape (n_edges, n_nodes). Rows 0..n_edges_x-1 - correspond to horizontal edges, and rows n_edges_x..n_edges-1 - correspond to vertical edges. Each row has -1 at the source - node and +1 at the target node. - - Returns - ------- - scipy.sparse.csr_matrix - Incidence matrix A. - """ - return self._A - - # ----------------------------------------------------------------- - # Discrete operators derived from the incidence matrix - # ----------------------------------------------------------------- - - def gradient(self) -> tuple[csr_matrix, csr_matrix]: - """ - Build gradient operators for a scalar field. - - The gradient operators Dx, Dy are extracted directly from the - incidence matrix A: Dx consists of the horizontal-edge rows, - and Dy consists of the vertical-edge rows. - - Returns - ------- - Dx : scipy.sparse.csr_matrix - X-direction gradient, shape (n_edges_x, n_nodes). - Dy : scipy.sparse.csr_matrix - Y-direction gradient, shape (n_edges_y, n_nodes). - - Notes - ----- - For a scalar field u (length n_nodes), the gradient components - are Dx @ u and Dy @ u. - """ - Dx = self._A[:self.n_edges_x, :] - Dy = self._A[self.n_edges_x:, :] - return Dx, Dy - - def divergence(self) -> csr_matrix: - """ - Build divergence operator for a vector field. - - The divergence is the negative adjoint of the gradient: - div = -A^T, applied to an edge-based vector field. - - Returns - ------- - scipy.sparse.csr_matrix - Divergence operator of shape (n_nodes, n_edges). - - Notes - ----- - For an edge-based vector field f (length n_edges), - the divergence is -A^T @ f. - """ - return -self._A.T.tocsr() - - def curl(self) -> csr_matrix: - """ - Build 2D curl operator for a vector field. - - Returns - ------- - scipy.sparse.csr_matrix - Curl operator of shape (n_faces, n_edges), where n_faces - is the number of grid cells (nx-1) * (ny-1). - - Notes - ----- - The discrete curl maps an edge field to a face field. For each - rectangular cell, the curl sums the edge values around the cell - boundary (with orientation signs). - - For a grid cell with corners (x,y), (x+1,y), (x+1,y+1), (x,y+1): - curl = bottom + right - top - left - """ - nx, ny = self.nx, self.ny - n_faces = (nx - 1) * (ny - 1) - if n_faces == 0: - return csr_matrix((0, self.n_edges)) - - # Edge indices within the incidence matrix - # Horizontal edges: row y, column x → index y*(nx-1) + x - # Vertical edges: row y, column x → n_edges_x + y*nx + x - face_idx = np.arange(n_faces) - fx = face_idx % (nx - 1) - fy = face_idx // (nx - 1) - - bottom = fy * (nx - 1) + fx # horizontal, row y - top = (fy + 1) * (nx - 1) + fx # horizontal, row y+1 - left = self.n_edges_x + fy * nx + fx # vertical, col x - right = self.n_edges_x + fy * nx + (fx + 1) # vertical, col x+1 - - rows = np.tile(face_idx, 4) - cols = np.concatenate([bottom, right, top, left]) - data = np.concatenate([ - np.ones(n_faces), # bottom: +1 - np.ones(n_faces), # right: +1 - -np.ones(n_faces), # top: -1 - -np.ones(n_faces), # left: -1 - ]) - - return csr_matrix((data, (rows, cols)), shape=(n_faces, self.n_edges)) - - def laplacian(self, weights: NDArray[np.floating] | None = None) -> csr_matrix: - """ - Build the discrete Laplacian operator. - - Computed as L = A^T diag(w) A where A is the incidence matrix - and w are per-edge weights. - - Parameters - ---------- - weights : np.ndarray, optional - Per-edge weight vector of length n_edges. If None, unit - weights are used (standard combinatorial Laplacian). - - Returns - ------- - scipy.sparse.csr_matrix - Laplacian operator of shape (n_nodes, n_nodes). - - Notes - ----- - With unit weights, this produces the standard 5-point stencil - for interior nodes: L[i,i] = degree(i), L[i,j] = -1 for - adjacent nodes j. - """ - A = self._A - if weights is None: - return (A.T @ A).tocsr() - else: - W = sp.diags(weights) - return (A.T @ W @ A).tocsr() - - def hodge_star(self) -> csr_matrix: - """ - Build the 2D Hodge star operator on node-based vector fields. - - The Hodge star rotates vectors by 90 degrees, equivalent to - multiplication by the imaginary unit in the complex plane. - - Returns - ------- - scipy.sparse.csr_matrix - Hodge star operator of shape (2n, 2n). - - Notes - ----- - For a node-based vector field [u; v] of length 2n, - returns [-v; u]. - """ - n = self.size - I = sp.eye(n, format='csr') - Z = csr_matrix((n, n)) - return sp.bmat([[Z, -I], [I, Z]], format='csr') - - -# ============================================================================= -# Physical Constants -# ============================================================================= - -MU0: float = 1.256637e-6 -"""Permeability of free space (H/m).""" - -def pathlap(N: int, periodic: bool = False) -> NDArray: - """ - Create the graph Laplacian for a path or cycle graph. - - Parameters - ---------- - N : int - Number of nodes. - periodic : bool, default False - If True, creates a cycle graph (first and last nodes connected). - If False, creates a path graph. - - Returns - ------- - np.ndarray - The Laplacian matrix of shape (N, N). - - Notes - ----- - - For a path graph: L[i,i] = 2 for interior nodes, 1 for endpoints. - - For a cycle graph: L[i,i] = 2 for all nodes. - - Off-diagonal entries are -1 for adjacent nodes. - """ - O = np.ones(N) - L = sp.diags( - [2 * O, -O[:1], -O[:1]], - offsets=[0, 1, -1], - shape=(N, N) - ).toarray() - - if periodic: - L[0, -1] = -1 - L[-1, 0] = -1 - else: - L[0, 0] = 1 - L[-1, -1] = 1 - - return L - - -def pathincidence(N: int, periodic: bool = False) -> NDArray: - """ - Create the incidence matrix for a path or cycle graph. - - Parameters - ---------- - N : int - Number of nodes. - periodic : bool, default False - If True, creates a cycle graph incidence matrix. - If False, creates a path graph incidence matrix. - - Returns - ------- - np.ndarray - The incidence matrix. - - Notes - ----- - For a path graph: shape is (N, N-1) with N-1 edges. - For a cycle graph: shape is (N, N) with N edges. - Each column has +1 at source node and -1 at target node. - """ - O = np.ones(N) - B = sp.diags( - [O, -O[:1]], - offsets=[0, 1], - shape=(N, N) - ).toarray() - - if periodic: - B[-1, 0] = -1 - - return B - - -def normlap( - L: NDArray | sp.spmatrix, - return_scaling: bool = False -) -> NDArray | tuple[NDArray, sp.dia_matrix, sp.dia_matrix]: - """ - Compute the normalized Laplacian of a matrix. - - The normalized Laplacian is defined as: - L_norm = D^{-1/2} @ L @ D^{-1/2} - - where D is the diagonal matrix of L's diagonal entries. - - Parameters - ---------- - L : np.ndarray or scipy.sparse matrix - Input Laplacian matrix. - return_scaling : bool, default False - If True, also return the scaling matrices. - - Returns - ------- - L_norm : np.ndarray - The normalized Laplacian. - D : scipy.sparse.dia_matrix, optional - Diagonal scaling matrix (sqrt of original diagonal). - Only returned if return_scaling=True. - D_inv : scipy.sparse.dia_matrix, optional - Inverse diagonal scaling matrix. - Only returned if return_scaling=True. - - Notes - ----- - The normalized Laplacian has eigenvalues in [0, 2] for - undirected graphs and is useful for spectral clustering. - """ - Yd = np.sqrt(L.diagonal()) - Di = sp.diags(1 / Yd) - - if return_scaling: - D = sp.diags(Yd) - return Di @ L @ Di, D, Di - else: - return Di @ L @ Di - - -def hermitify(A: NDArray | sp.spmatrix) -> NDArray: - """ - Convert a complex symmetric matrix to Hermitian form. - - For a complex symmetric matrix (A = A^T), this function produces - a Hermitian matrix by pairing the upper triangle's conjugate with - the lower triangle. - - Parameters - ---------- - A : np.ndarray or scipy.sparse matrix - Input complex symmetric matrix. - - Returns - ------- - np.ndarray - The Hermitian form of the matrix. - - Notes - ----- - Useful for converting admittance matrices to a form suitable - for eigenvalue algorithms that require Hermitian input. - """ - dense = A if isinstance(A, np.ndarray) else A.toarray() - return (np.triu(dense).conjugate() + np.tril(dense)) / 2 - - -def sorteig(vals: NDArray, vecs: NDArray) -> tuple[NDArray, NDArray]: - """ - Sort an eigendecomposition by ascending eigenvalue. - - Eigensolvers such as :func:`scipy.sparse.linalg.eigsh` do not - guarantee ordering; this reorders both the eigenvalues and the - corresponding eigenvector columns. - - Parameters - ---------- - vals : np.ndarray - Eigenvalues, shape ``(k,)``. - vecs : np.ndarray - Eigenvectors as columns, shape ``(n, k)``. - - Returns - ------- - tuple[np.ndarray, np.ndarray] - ``(vals, vecs)`` sorted by ascending eigenvalue. - """ - order = np.argsort(vals) - return vals[order], vecs[:, order] - diff --git a/examples/network/02_network_topology.ipynb b/examples/network/02_network_topology.ipynb index 00173ee..feb13b2 100644 --- a/examples/network/02_network_topology.ipynb +++ b/examples/network/02_network_topology.ipynb @@ -24,8 +24,13 @@ "from esapp import PowerWorld\n", "from esapp.components import Branch, Bus\n", "from esapp.utils import BranchType\n", - "from mesh import sorteig\n", - "from map import format_plot" + "from map import format_plot\n", + "\n", + "\n", + "def sorteig(vals, vecs):\n", + " \"\"\"Sort eigenpairs by ascending eigenvalue.\"\"\"\n", + " order = np.argsort(vals)\n", + " return vals[order], vecs[:, order]" ] }, { diff --git a/examples/nonuniform/01_nonuniform_gic.ipynb b/examples/nonuniform/01_nonuniform_gic.ipynb deleted file mode 100644 index ba581fa..0000000 --- a/examples/nonuniform/01_nonuniform_gic.ipynb +++ /dev/null @@ -1,469 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Non-Uniform Electric Field GIC Analysis\n", - "\n", - "This notebook demonstrates the complete workflow for computing\n", - "geomagnetically induced currents (GICs) under **spatially varying**\n", - "electric fields. Unlike a uniform storm analysis, non-uniform fields\n", - "capture realistic conductivity gradients, coastal effects, and\n", - "geologic heterogeneities.\n", - "\n", - "**Key steps:**\n", - "\n", - "1. Build the GIC model from a PowerWorld case\n", - "2. Construct a geographic E-field grid using `Grid2D`\n", - "3. Define a non-uniform E-field pattern (Gaussian hotspot)\n", - "4. Build the line integration operator $L$ and compute $|\\mathbf{I}| = |H \\, L \\, \\mathbf{E}|$\n", - "5. Visualize transformer GIC distribution\n", - "6. Map bus-level GIC magnitudes as a geographic heatmap\n", - "7. Export E-fields to B3D format for PowerWorld integration" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "\n", - "from esapp import PowerWorld\n", - "from esapp.components import Branch, Bus, GICXFormer\n", - "from esapp.utils import B3D\n", - "\n", - "from mesh import Grid2D\n", - "from map import format_plot, border, plot_lines\n", - "from nonuniform import build_L_matrix, stack_efield, compute_gic, bus_gic\n", - "from plotting import plot_efield, plot_gic_heatmap" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "remove-cell" - ] - }, - "outputs": [], - "source": [ - "# This cell is hidden in the documentation.\n", - "import ast\n", - "\n", - "with open('../data/case_B.txt', 'r') as f:\n", - " case_path = ast.literal_eval(f.read().strip())\n", - "\n", - "pw = PowerWorld(case_path)\n", - "SHAPE = 'Texas'" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "remove-cell" - ] - }, - "outputs": [], - "source": [ - "# Plotting style constants (hidden from documentation)\n", - "_C1 = '#4C72B0'\n", - "_C2 = '#DD8452'\n", - "_C3 = '#55A868'\n", - "_C4 = '#C44E52'\n", - "_C5 = '#8172B3'\n", - "_CG = '#8C8C8C'\n", - "_FS = dict(titlesize=11, labelsize=9, ticksize=8)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 1. Build the GIC Model\n", - "\n", - "The GIC model extracts substation, bus, branch, and transformer data\n", - "from the PowerWorld case and computes the **H-matrix** — the linear\n", - "mapping from induced branch voltages to transformer neutral currents." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "pw.gic.configure(pf_include=True, calc_mode='SnapShot')\n", - "pw.gic.model()\n", - "\n", - "print(f\"H-matrix: {pw.gic.H.shape} (transformers x branches)\")\n", - "print(f\"G-matrix: {pw.gic.G.shape} (nodes x nodes)\")\n", - "print(f\"Incidence: {pw.gic.A.shape} (branches x nodes)\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 2. Geographic Grid & Network Footprint\n", - "\n", - "We extract bus coordinates from the case and build a structured 2D grid\n", - "covering the network footprint. The `Grid2D` class from `examples.mesh`\n", - "provides the grid structure and discrete operators." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "lon, lat = pw.buscoords()\n", - "\n", - "pad = 0.5\n", - "lon_min, lon_max = lon.min() - pad, lon.max() + pad\n", - "lat_min, lat_max = lat.min() - pad, lat.max() + pad\n", - "\n", - "nx, ny = 50, 35\n", - "lons = np.linspace(lon_min, lon_max, nx)\n", - "lats = np.linspace(lat_min, lat_max, ny)\n", - "LON, LAT = np.meshgrid(lons, lats)\n", - "\n", - "grid = Grid2D((nx, ny))\n", - "print(f\"Grid: {nx} x {ny} = {grid.size} points\")\n", - "print(f\"Edges: {grid.n_edges} (H: {grid.n_edges_x}, V: {grid.n_edges_y})\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "lines = pw[Branch, ['Longitude', 'Longitude:1', 'Latitude', 'Latitude:1']]\n", - "\n", - "fig, ax = plt.subplots(figsize=(6.5, 4.5))\n", - "border(ax, SHAPE)\n", - "plot_lines(ax, lines, ms=6, lw=0.6)\n", - "ax.scatter(LON.ravel(), LAT.ravel(), s=0.8, c='#aaaaaa', alpha=0.3,\n", - " label=f'Grid ({nx}$\\\\times${ny})', zorder=1)\n", - "ax.scatter(lon, lat, s=18, c=_C4, zorder=6, edgecolors='white',\n", - " linewidth=0.4, label='Substations')\n", - "ax.set_xlim(lon_min - 0.1, lon_max + 0.1)\n", - "ax.set_ylim(lat_min - 0.1, lat_max + 0.1)\n", - "format_plot(ax, title='Transmission Network & Computation Grid',\n", - " xlabel=r'Longitude ($^\\circ$E)',\n", - " ylabel=r'Latitude ($^\\circ$N)',\n", - " plotarea='white', grid=False, **_FS)\n", - "ax.legend(fontsize=8, loc='lower right')\n", - "ax.set_aspect('equal')\n", - "plt.tight_layout()\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 3. Non-Uniform Electric Field Pattern\n", - "\n", - "We define a spatially varying E-field with a **Gaussian hotspot** to\n", - "model a localized conductivity anomaly (e.g., a geological boundary\n", - "or coastal effect)." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Normalize geographic coordinates to [0, 1]\n", - "Xn = (LON - lon_min) / (lon_max - lon_min)\n", - "Yn = (LAT - lat_min) / (lat_max - lat_min)\n", - "\n", - "# Gaussian hotspot E-field\n", - "cx, cy = 0.4, 0.5 # hotspot center (normalized)\n", - "sigma = 0.15\n", - "gauss = 2.5 * np.exp(-((Xn - cx)**2 + (Yn - cy)**2) / (2 * sigma**2))\n", - "Ex_field = (0.3 + gauss) * np.sin(np.radians(90))\n", - "Ey_field = (0.3 + gauss) * np.cos(np.radians(90))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "fig, ax = plt.subplots(figsize=(12, 8))\n", - "\n", - "im = plot_efield(ax, lons, lats, Ex_field, Ey_field,\n", - " shape=SHAPE, lines=lines,\n", - " cmap='viridis', title='Gaussian Hotspot E-Field')\n", - "\n", - "fig.colorbar(im, ax=ax, label='|E| (V/km)', shrink=0.7, pad=0.02)\n", - "plt.tight_layout()\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 4. Computing GICs from a Non-Uniform Field\n", - "\n", - "For a uniform storm, PowerWorld's `storm()` method suffices. For a\n", - "non-uniform field, we construct a **line integration operator** $L$\n", - "that maps a gridded electric field to branch induced voltages.\n", - "\n", - "### The line integral\n", - "\n", - "The EMF voltage induced on branch $k$ connecting buses $a$ and $b$ is\n", - "the line integral of the electric field along the conductor path:\n", - "\n", - "$$V_k = \\int_a^b \\mathbf{E} \\cdot d\\boldsymbol{\\ell}$$\n", - "\n", - "We approximate each transmission line as a **straight segment** from\n", - "$(\\lambda_a, \\phi_a)$ to $(\\lambda_b, \\phi_b)$ (longitude, latitude).\n", - "\n", - "### Cell-by-cell discretization\n", - "\n", - "The E-field is known at the nodes of a regular $(n_x \\times n_y)$ grid.\n", - "Rather than evaluate $\\mathbf{E}$ at a single point, we **trace** the\n", - "line through every grid cell it intersects and accumulate contributions\n", - "cell by cell.\n", - "\n", - "For each cell $c = (i, j)$ that branch $k$ passes through, we compute\n", - "the **directed segment** $(\\Delta x_{kc},\\, \\Delta y_{kc})$ —\n", - "the signed length of the line within that cell in km:\n", - "\n", - "$$\\Delta x_{kc} = \\delta\\!f_{x}\\;\\Delta\\lambda\\;(111\\;\\text{km/°})\\;\\cos\\bar\\phi_k,\n", - "\\qquad\n", - "\\Delta y_{kc} = \\delta\\!f_{y}\\;\\Delta\\phi\\;(111\\;\\text{km/°})$$\n", - "\n", - "where $\\delta\\!f_x$ and $\\delta\\!f_y$ are the directed lengths in\n", - "**grid-coordinate units** (fractional cells) obtained by intersecting\n", - "the segment with the cell boundaries.\n", - "\n", - "The E-field inside cell $(i, j)$ is approximated as the **average of\n", - "its four corner nodes**:\n", - "\n", - "$$\\bar{E}_x^{(c)} = \\tfrac{1}{4}\\bigl(\n", - "E_x^{(i,j)} + E_x^{(i{+}1,j)} + E_x^{(i,j{+}1)} + E_x^{(i{+}1,j{+}1)}\n", - "\\bigr)$$\n", - "\n", - "so the voltage contribution from cell $c$ is:\n", - "\n", - "$$\\delta V_{kc} = \\bar{E}_x^{(c)}\\,\\Delta x_{kc}\n", - " + \\bar{E}_y^{(c)}\\,\\Delta y_{kc}$$\n", - "\n", - "### The $L$ operator\n", - "\n", - "We stack the gridded field into a single master vector\n", - "$\\mathbf{E} \\in \\mathbb{R}^{2N}$ ($N = n_x n_y$):\n", - "\n", - "$$\\mathbf{E} = \\bigl[\\,E_x^{(1)},\\ldots,E_x^{(N)},\\;\n", - " E_y^{(1)},\\ldots,E_y^{(N)}\\,\\bigr]^T$$\n", - "\n", - "For each cell $(i,j)$ that branch $k$ traverses, we place weight\n", - "$\\tfrac{1}{4}\\,\\Delta x_{kc}$ at each of the four corner-node columns\n", - "in the $E_x$ block, and $\\tfrac{1}{4}\\,\\Delta y_{kc}$ in the $E_y$\n", - "block. When a branch passes through multiple cells, the COO $\\to$ CSR\n", - "conversion automatically sums contributions at shared corner nodes.\n", - "\n", - "The full branch voltage is:\n", - "\n", - "$$V_k = (L\\,\\mathbf{E})_k\n", - " = \\sum_{c \\in \\text{cells}(k)}\n", - " \\bigl[\\bar E_x^{(c)}\\,\\Delta x_{kc}\n", - " + \\bar E_y^{(c)}\\,\\Delta y_{kc}\\bigr]$$\n", - "\n", - "and the transformer GICs follow as:\n", - "\n", - "$$\\mathbf{I}_{\\text{GIC}} = \\lvert\\, H \\, L \\, \\mathbf{E} \\,\\rvert$$" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Build the line integration operator (computed once for a given grid)\n", - "H = pw.gic.H\n", - "n_branches_model = H.shape[1]\n", - "N_grid = len(lons) * len(lats)\n", - "\n", - "L = build_L_matrix(pw, lons, lats, n_branches_model)\n", - "print(f\"H-matrix: {H.shape} (transformers x branches)\")\n", - "print(f\"L-matrix: {L.shape} (branches x 2·grid), nnz = {L.nnz}\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Compute transformer GICs: |I| = |H @ L @ E|\n", - "gic_xf = compute_gic(H, L, Ex_field, Ey_field)\n", - "\n", - "# Aggregate to bus-level totals for geographic plotting\n", - "gic_bus = bus_gic(pw, gic_xf)\n", - "\n", - "print(f\"Transformer GICs computed: {len(gic_xf)}\")\n", - "print(f\"Max |GIC| (transformer): {gic_xf.max():.2f} A\")\n", - "print(f\"Mean |GIC| (transformer): {gic_xf.mean():.2f} A\")\n", - "print(f\"\\nBuses with GIC: {len(gic_bus)}\")\n", - "print(f\"Max |GIC| (bus): {gic_bus['GIC'].max():.2f} A\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 5. Transformer GIC Distribution\n", - "\n", - "The absolute GIC magnitudes $|\\mathbf{I}|$ are always non-negative.\n", - "We visualize the ranked magnitudes and their distribution." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "ranked = np.argsort(gic_xf)[::-1]\n", - "top_n = min(30, len(ranked))\n", - "\n", - "fig, axes = plt.subplots(1, 2, figsize=(12, 5))\n", - "\n", - "# Left: top transformers by |GIC| magnitude\n", - "axes[0].barh(range(top_n), gic_xf[ranked[:top_n]], color='#DD8452')\n", - "axes[0].set_yticks(range(top_n))\n", - "axes[0].set_yticklabels([f'XF {i}' for i in ranked[:top_n]], fontsize=7)\n", - "axes[0].invert_yaxis()\n", - "format_plot(axes[0], title=f'Top {top_n} Transformer |GIC|',\n", - " xlabel='|GIC| (A)', plotarea='white', **_FS)\n", - "\n", - "# Right: histogram of |GIC|\n", - "axes[1].hist(gic_xf, bins=30, color='#4C72B0', edgecolor='white')\n", - "format_plot(axes[1], title='|GIC| Distribution',\n", - " xlabel='|GIC| (A)', ylabel='Count', plotarea='white', **_FS)\n", - "\n", - "plt.tight_layout()\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 6. Geographic GIC Heatmap\n", - "\n", - "Transformer GICs are aggregated to bus-level totals using ``bus_gic()``\n", - "and interpolated onto the computation grid to produce a continuous\n", - "heatmap of GIC magnitude across the network footprint." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "fig, ax = plt.subplots(figsize=(14, 9))\n", - "\n", - "im = plot_gic_heatmap(ax, lons, lats, gic_bus,\n", - " shape=SHAPE, lines=lines,\n", - " cmap='YlOrRd',\n", - " title='Bus-Level |GIC| Heatmap (Gaussian Hotspot)')\n", - "\n", - "fig.colorbar(im, ax=ax, label='|GIC| (A)', shrink=0.7, pad=0.02)\n", - "plt.tight_layout()\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 7. Export to B3D Format\n", - "\n", - "The non-uniform E-field can be exported to PowerWorld's B3D format\n", - "for time-varying GIC simulation. `B3D.from_mesh()` converts a gridded\n", - "field to the binary format." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "b3d = B3D.from_mesh(\n", - " lons, lats,\n", - " Ex_field.astype(np.float32),\n", - " Ey_field.astype(np.float32),\n", - " comment='Gaussian hotspot E-field'\n", - ")\n", - "\n", - "print(f\"B3D grid: {b3d.grid_dim}\")\n", - "print(f\"Locations: {len(b3d.lat)}\")\n", - "print(f\"Time steps: {len(b3d.time)}\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Summary\n", - "\n", - "This notebook demonstrated the full non-uniform GIC analysis workflow:\n", - "\n", - "- **GIC model** construction via `pw.gic.model()` produces the H-matrix\n", - " that linearly maps branch voltages to transformer neutral currents\n", - "- The **line integration operator** $L$ traces each transmission line\n", - " through the E-field grid cell by cell, accumulating directed segment\n", - " lengths to discretize the line integral $V_k = \\int \\mathbf{E} \\cdot d\\boldsymbol{\\ell}$\n", - "- Stacking the gridded field as $\\mathbf{E} = [E_x, E_y]^T$ enables\n", - " the clean matrix formulation $|\\mathbf{I}| = |H \\, L \\, \\mathbf{E}|$\n", - "- ``bus_gic()`` aggregates transformer-level GICs to bus-level totals\n", - " for geographic plotting at known bus coordinates\n", - "- ``plot_gic_heatmap()`` interpolates sparse bus GICs onto the\n", - " computation grid for a continuous geographic heatmap\n", - "- Export to **B3D format** enables PowerWorld integration for\n", - " time-varying non-uniform GIC simulations" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "esapp", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.11.14" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/examples/nonuniform/nonuniform.py b/examples/nonuniform/nonuniform.py deleted file mode 100644 index 5fe24c5..0000000 --- a/examples/nonuniform/nonuniform.py +++ /dev/null @@ -1,304 +0,0 @@ -""" -Non-uniform GIC model formulation helpers. - -Provides the line integration operator **L** and E-field vector -assembly for the non-uniform GIC computation: - - I_gic = abs(H @ L @ E) - -where: -- **H** is the transformer-to-branch transfer matrix from ``pw.gic.model()`` -- **L** maps a gridded E-field vector to branch induced voltages -- **E** = [Ex.ravel(), Ey.ravel()] is the stacked master E-field vector - -The operator L discretizes the line integral of E along each branch by -tracing every transmission line through the 2-D grid and accumulating -the directed segment length (dx, dy) within each cell. The E-field -inside a cell is taken as the average of its four corner nodes. - -Example -------- ->>> from examples.nonuniform.nonuniform import build_L_matrix, stack_efield ->>> ->>> L = build_L_matrix(pw, lons, lats, H.shape[1]) ->>> E = stack_efield(Ex, Ey) ->>> gic = np.abs(H @ L @ E) -""" - -import numpy as np -from scipy.sparse import coo_matrix - -from esapp.components import Branch, Bus, GICXFormer - -__all__ = ['build_L_matrix', 'stack_efield', 'compute_gic', 'bus_gic'] - - -def stack_efield(Ex, Ey): - """Stack 2-D Ex and Ey grids into a single master E-field vector. - - Parameters - ---------- - Ex : np.ndarray - East-component of the electric field on a (ny, nx) grid. - Ey : np.ndarray - North-component of the electric field on a (ny, nx) grid. - - Returns - ------- - np.ndarray - 1-D vector of length 2*N where N = nx*ny, ordered as - [Ex.ravel(), Ey.ravel()]. - """ - return np.concatenate([Ex.ravel(), Ey.ravel()]) - - -def _trace_line_through_grid(x0, y0, x1, y1, xs, ys): - """Trace a straight line segment through a regular grid. - - Finds every cell the segment passes through and returns the - directed (dx, dy) length within each cell in **grid-coordinate - units** (i.e. fractional cell widths). - - Parameters - ---------- - x0, y0 : float - Start point in continuous grid coordinates - (x = (lon - lon0) / dlon, y = (lat - lat0) / dlat). - x1, y1 : float - End point in continuous grid coordinates. - xs : int - Number of grid nodes in the x-direction (nx). - ys : int - Number of grid nodes in the y-direction (ny). - - Yields - ------ - (ix, iy, frac_dx, frac_dy) : tuple - Cell indices (ix, iy) and the directed length of the line - segment within that cell, in fractional grid units. - """ - # Clamp endpoints into valid grid range [0, size-1] - x0c = np.clip(x0, 0, xs - 1) - y0c = np.clip(y0, 0, ys - 1) - x1c = np.clip(x1, 0, xs - 1) - y1c = np.clip(y1, 0, ys - 1) - - dx_total = x1c - x0c - dy_total = y1c - y0c - - if abs(dx_total) < 1e-12 and abs(dy_total) < 1e-12: - # Zero-length segment (co-located buses) - return - - # Collect all t-values where the line crosses vertical or horizontal - # grid lines. t parameterizes the clamped segment: r(t) = r0 + t*(r1-r0). - crossings = [0.0, 1.0] - - if abs(dx_total) > 1e-12: - # Vertical grid lines at x = 1, 2, ..., xs-2 - ix_lo = int(np.floor(min(x0c, x1c))) - ix_hi = int(np.ceil(max(x0c, x1c))) - for ix in range(max(ix_lo, 1), min(ix_hi, xs - 1) + 1): - t = (ix - x0c) / dx_total - if 0 < t < 1: - crossings.append(t) - - if abs(dy_total) > 1e-12: - # Horizontal grid lines at y = 1, 2, ..., ys-2 - iy_lo = int(np.floor(min(y0c, y1c))) - iy_hi = int(np.ceil(max(y0c, y1c))) - for iy in range(max(iy_lo, 1), min(iy_hi, ys - 1) + 1): - t = (iy - y0c) / dy_total - if 0 < t < 1: - crossings.append(t) - - crossings.sort() - - # Walk through consecutive pairs of crossings - for i in range(len(crossings) - 1): - t_a = crossings[i] - t_b = crossings[i + 1] - if t_b - t_a < 1e-14: - continue - - # Midpoint of this sub-segment -> determines which cell we're in - t_mid = 0.5 * (t_a + t_b) - mx = x0c + t_mid * dx_total - my = y0c + t_mid * dy_total - - ix = int(np.clip(np.floor(mx), 0, xs - 2)) - iy = int(np.clip(np.floor(my), 0, ys - 2)) - - # Directed length of this sub-segment in grid units - frac_dx = (t_b - t_a) * dx_total - frac_dy = (t_b - t_a) * dy_total - - yield ix, iy, frac_dx, frac_dy - - -def build_L_matrix(pw, lons, lats, n_branches_model): - """Build the line integration operator L. - - L is a sparse matrix of shape ``(n_branches_model, 2*N)`` where - ``N = len(lons) * len(lats)``. Given the master E-field vector - ``E = stack_efield(Ex, Ey)``, the product ``L @ E`` yields the - induced voltage on each branch. - - **Discretization.** Each transmission line is traced through the - 2-D grid cell by cell. For every cell the line passes through, the - directed segment length ``(dx_km, dy_km)`` within that cell is - computed. The E-field inside the cell is approximated as the - average of its four corner nodes. The voltage contribution from - cell ``(ix, iy)`` for branch ``k`` is therefore: - - dV = E_x_avg * dx_km + E_y_avg * dy_km - - which translates to weight ``(1/4) * dx_km`` at each of the four - corner nodes in the ``E_x`` block of L, and ``(1/4) * dy_km`` in - the ``E_y`` block. - - Parameters - ---------- - pw : PowerWorld - Live workbench instance (used to read branch coordinates). - lons : np.ndarray - 1-D array of grid longitudes (length nx). - lats : np.ndarray - 1-D array of grid latitudes (length ny). - n_branches_model : int - Total number of branch columns in the H-matrix - (``H.shape[1]``). Rows beyond the number of geographic - branches are left as zeros (transformer windings, GSUs). - - Returns - ------- - scipy.sparse.csr_matrix - Sparse matrix of shape ``(n_branches_model, 2*N)``. - """ - nx = len(lons) - ny = len(lats) - N = nx * ny - - dlon = lons[1] - lons[0] - dlat = lats[1] - lats[0] - - # Branch endpoint coordinates - br = pw[Branch, ['BusNum', 'BusNum:1', 'BranchDeviceType', - 'Longitude', 'Longitude:1', 'Latitude', 'Latitude:1']] - - lon_a = br['Longitude'].to_numpy() - lon_b = br['Longitude:1'].to_numpy() - lat_a = br['Latitude'].to_numpy() - lat_b = br['Latitude:1'].to_numpy() - - n_br = min(len(br), n_branches_model) - - rows, cols, data = [], [], [] - - for k in range(n_br): - # Continuous grid coordinates for endpoints - gx0 = (lon_a[k] - lons[0]) / dlon - gy0 = (lat_a[k] - lats[0]) / dlat - gx1 = (lon_b[k] - lons[0]) / dlon - gy1 = (lat_b[k] - lats[0]) / dlat - - # Midpoint latitude for the cos correction - mid_lat = 0.5 * (lat_a[k] + lat_b[k]) - cos_lat = np.cos(np.radians(mid_lat)) - - # Conversion: 1 grid-unit in x = dlon degrees = dlon * 111 * cos(lat) km - # 1 grid-unit in y = dlat degrees = dlat * 111 km - km_per_gx = dlon * 111.0 * cos_lat - km_per_gy = dlat * 111.0 - - for ix, iy, fdx, fdy in _trace_line_through_grid(gx0, gy0, gx1, gy1, nx, ny): - dx_km = fdx * km_per_gx - dy_km = fdy * km_per_gy - - # Four corner nodes of cell (ix, iy), each gets weight 1/4 - corners = [ - iy * nx + ix, - iy * nx + (ix + 1), - (iy + 1) * nx + ix, - (iy + 1) * nx + (ix + 1), - ] - w = 0.25 - for idx in corners: - # Ex block: columns [0, N) - rows.append(k) - cols.append(idx) - data.append(w * dx_km) - # Ey block: columns [N, 2N) - rows.append(k) - cols.append(N + idx) - data.append(w * dy_km) - - L = coo_matrix((data, (rows, cols)), shape=(n_branches_model, 2 * N)) - # COO allows duplicate entries; converting to CSR sums them automatically - return L.tocsr() - - -def bus_gic(pw, gic): - """Aggregate absolute transformer GICs to bus-level totals. - - Each transformer in the H-matrix corresponds to a row in the - GICXFormer table. This function sums |GIC| contributions at each - bus (using ``BusNum3W`` as the transformer's primary bus) and - returns a DataFrame with bus coordinates for geographic plotting. - - Parameters - ---------- - pw : PowerWorld - Live workbench instance. - gic : np.ndarray - Absolute transformer GIC magnitudes (length n_transformers). - - Returns - ------- - pandas.DataFrame - Columns: ``BusNum``, ``Longitude``, ``Latitude``, ``GIC``. - One row per bus that hosts at least one transformer, with - ``GIC`` being the sum of |GIC| over all transformers at that bus. - """ - import pandas as pd - - gic = np.asarray(gic).ravel() - - xf = pw[GICXFormer, ['BusNum3W', 'BusNum3W:1']] - xf = xf.iloc[:len(gic)].copy() - xf['GIC'] = gic - - # Aggregate by primary bus (BusNum3W) - bus_total = xf.groupby('BusNum3W')['GIC'].sum().reset_index() - bus_total.columns = ['BusNum', 'GIC'] - - # Join with bus coordinates - coords = pw[Bus, ['BusNum', 'Longitude', 'Latitude']] - result = bus_total.merge(coords, on='BusNum', how='inner') - return result - - -def compute_gic(H, L, Ex, Ey): - """Compute absolute transformer GICs from a gridded E-field. - - Evaluates ``|H @ L @ E|`` where E is the stacked field vector. - - Parameters - ---------- - H : sparse matrix or np.ndarray - Transfer matrix (n_transformers x n_branches). - L : sparse matrix - Line integration operator (n_branches x 2N). - Ex, Ey : np.ndarray - Electric field components on the (ny, nx) node grid. - - Returns - ------- - np.ndarray - Absolute transformer GIC magnitudes (n_transformers,). - """ - E = stack_efield(Ex, Ey) - gic = H @ L @ E - if hasattr(gic, 'A'): - gic = np.asarray(gic).ravel() - return np.abs(gic) diff --git a/examples/nonuniform/plotting.py b/examples/nonuniform/plotting.py deleted file mode 100644 index d56005b..0000000 --- a/examples/nonuniform/plotting.py +++ /dev/null @@ -1,247 +0,0 @@ -""" -Plotting helpers for non-uniform GIC analysis. - -Provides standardized functions for visualizing gridded electric fields -and transformer GIC results on geographic maps. -""" - -import numpy as np -import matplotlib.pyplot as plt -from matplotlib.colors import Normalize -from scipy.interpolate import NearestNDInterpolator -from scipy.ndimage import gaussian_filter - -from examples.map import format_plot, border, plot_lines - -__all__ = [ - 'plot_efield', - 'plot_gic_map', - 'plot_gic_heatmap', -] - - -def plot_efield(ax, lons, lats, Ex, Ey, shape=None, lines=None, - cmap='viridis', vmax=None, title=None, **fmt_kw): - """Plot a gridded electric field with one arrow per cell. - - Renders the field magnitude as a filled heatmap and overlays a - quiver arrow at every cell center. Arrow length is proportional - to field magnitude so both direction and strength are visible. - - Parameters - ---------- - ax : matplotlib.axes.Axes - Target axes. - lons, lats : np.ndarray - 1-D grid node coordinates (length nx, ny). - Ex, Ey : np.ndarray - Electric field components on the (ny, nx) node grid. - shape : str or None - Border shape name passed to ``border()``. - lines : DataFrame or None - Branch coordinate DataFrame for ``plot_lines()``. - cmap : str - Colormap for the magnitude heatmap and arrows. - vmax : float or None - Upper limit for the color scale. If None, uses data max. - title : str or None - Axes title. - **fmt_kw - Extra keyword arguments forwarded to ``format_plot()``. - - Returns - ------- - im : QuadMesh - The pcolormesh artist (for external colorbar creation). - """ - LON, LAT = np.meshgrid(lons, lats) - mag = np.sqrt(Ex**2 + Ey**2) - if vmax is None: - vmax = float(np.nanmax(mag)) - - # Magnitude heatmap at nodes - im = ax.pcolormesh(LON, LAT, mag, cmap=cmap, shading='auto', - vmin=0, vmax=vmax) - - # Cell-center coordinates and cell-average field - lon_c = 0.5 * (lons[:-1] + lons[1:]) - lat_c = 0.5 * (lats[:-1] + lats[1:]) - LONc, LATc = np.meshgrid(lon_c, lat_c) - - Ex_c = 0.25 * (Ex[:-1, :-1] + Ex[:-1, 1:] + Ex[1:, :-1] + Ex[1:, 1:]) - Ey_c = 0.25 * (Ey[:-1, :-1] + Ey[:-1, 1:] + Ey[1:, :-1] + Ey[1:, 1:]) - mag_c = np.sqrt(Ex_c**2 + Ey_c**2) - - # Quiver: arrows proportional to magnitude, colored by magnitude - ax.quiver(LONc, LATc, Ex_c, Ey_c, mag_c, - cmap=cmap, clim=(0, vmax), - scale_units='xy', angles='xy', - scale=vmax / (0.8 * (lons[1] - lons[0])), - width=0.003, headwidth=3, headlength=3.5, - linewidth=0.3, edgecolors='k', zorder=5) - - if shape is not None: - border(ax, shape) - if lines is not None: - plot_lines(ax, lines, ms=1.5, lw=0.3) - - ax.set_xlim(lons[0], lons[-1]) - ax.set_ylim(lats[0], lats[-1]) - ax.set_aspect('equal') - - defaults = dict(plotarea='white', grid=False, - titlesize=11, labelsize=9, ticksize=8) - defaults.update(fmt_kw) - if title is not None: - defaults['title'] = title - format_plot(ax, xlabel=r'Longitude ($^\circ$E)', - ylabel=r'Latitude ($^\circ$N)', **defaults) - return im - - -def plot_gic_map(ax, lons, lats, Ex, Ey, xf_lons, xf_lats, gic, - shape=None, lines=None, cmap_field='viridis', - cmap_gic='YlOrRd', vmax_field=None, title=None, - **fmt_kw): - """Plot transformer |GIC| bubbles over an E-field background. - - GIC magnitudes are shown via both bubble size and colour on a - sequential (all-positive) colour scale. - - Parameters - ---------- - ax : matplotlib.axes.Axes - Target axes. - lons, lats : np.ndarray - 1-D grid node coordinates. - Ex, Ey : np.ndarray - Electric field on the (ny, nx) node grid. - xf_lons, xf_lats : array-like - Transformer geographic coordinates. - gic : np.ndarray - Absolute transformer GIC magnitudes (non-negative). - shape : str or None - Border shape name. - lines : DataFrame or None - Branch coordinate DataFrame. - cmap_field : str - Colormap for the E-field magnitude background. - cmap_gic : str - Sequential colormap for GIC bubbles. - vmax_field : float or None - Upper colour limit for E-field magnitude. - title : str or None - Axes title. - **fmt_kw - Extra keyword arguments forwarded to ``format_plot()``. - - Returns - ------- - (im, sc) : tuple - The pcolormesh and scatter artists (for external colorbars). - """ - LON, LAT = np.meshgrid(lons, lats) - mag = np.sqrt(Ex**2 + Ey**2) - if vmax_field is None: - vmax_field = float(np.nanmax(mag)) - - # E-field magnitude background (faded) - im = ax.pcolormesh(LON, LAT, mag, cmap=cmap_field, shading='auto', - vmin=0, vmax=vmax_field, alpha=0.4) - - if shape is not None: - border(ax, shape) - if lines is not None: - plot_lines(ax, lines, ms=2, lw=0.3) - - # GIC bubbles: size AND colour encode |GIC| - gic = np.asarray(gic).ravel() - gic_max = max(float(gic.max()), 1e-6) - sizes = 10 + 250 * (gic / gic_max) - sc = ax.scatter(xf_lons, xf_lats, - s=sizes, c=gic, cmap=cmap_gic, - vmin=0, vmax=gic_max, - zorder=8, edgecolors='black', linewidth=0.4) - - ax.set_xlim(lons[0], lons[-1]) - ax.set_ylim(lats[0], lats[-1]) - ax.set_aspect('equal') - - defaults = dict(plotarea='white', grid=False, - titlesize=11, labelsize=9, ticksize=8) - defaults.update(fmt_kw) - if title is not None: - defaults['title'] = title - format_plot(ax, xlabel=r'Longitude ($^\circ$E)', - ylabel=r'Latitude ($^\circ$N)', **defaults) - return im, sc - - -def plot_gic_heatmap(ax, lons, lats, bus_df, shape=None, lines=None, - cmap='YlOrRd', title=None, **fmt_kw): - """Plot bus-level |GIC| as a heatmap interpolated onto the grid. - - Interpolates sparse bus GIC values onto the regular (lons, lats) - grid using radial basis function interpolation, producing a - continuous heatmap of GIC magnitude across the geographic domain. - - Parameters - ---------- - ax : matplotlib.axes.Axes - Target axes. - lons, lats : np.ndarray - 1-D grid node coordinates (length nx, ny). - bus_df : pandas.DataFrame - Output of ``bus_gic()`` with columns - ``Longitude``, ``Latitude``, ``GIC``. - shape : str or None - Border shape name passed to ``border()``. - lines : DataFrame or None - Branch coordinate DataFrame for ``plot_lines()``. - cmap : str - Colormap for the GIC heatmap. - title : str or None - Axes title. - **fmt_kw - Extra keyword arguments forwarded to ``format_plot()``. - - Returns - ------- - im : QuadMesh - The pcolormesh artist (for external colorbar creation). - """ - LON, LAT = np.meshgrid(lons, lats) - - points = np.column_stack([bus_df['Longitude'].to_numpy(), - bus_df['Latitude'].to_numpy()]) - values = bus_df['GIC'].to_numpy() - gic_max = max(float(values.max()), 1e-6) - - interp = NearestNDInterpolator(points, values) - gic_grid = interp(LON, LAT) - gic_grid = gaussian_filter(gic_grid, sigma=2) - - im = ax.pcolormesh(LON, LAT, gic_grid, cmap=cmap, shading='auto', - vmin=0, vmax=gic_max) - - if shape is not None: - border(ax, shape) - if lines is not None: - plot_lines(ax, lines, ms=2, lw=0.3) - - # Overlay bus locations as small markers - ax.scatter(bus_df['Longitude'], bus_df['Latitude'], - s=8, c='black', zorder=6, alpha=0.4) - - ax.set_xlim(lons[0], lons[-1]) - ax.set_ylim(lats[0], lats[-1]) - ax.set_aspect('equal') - - defaults = dict(plotarea='white', grid=False, - titlesize=11, labelsize=9, ticksize=8) - defaults.update(fmt_kw) - if title is not None: - defaults['title'] = title - format_plot(ax, xlabel=r'Longitude ($^\circ$E)', - ylabel=r'Latitude ($^\circ$N)', **defaults) - return im diff --git a/examples/nonuniform/sens.py b/examples/nonuniform/sens.py deleted file mode 100644 index 5e8eeae..0000000 --- a/examples/nonuniform/sens.py +++ /dev/null @@ -1,143 +0,0 @@ -""" -GIC sensitivity analysis for non-uniform electric fields. - -Provides standalone functions for computing: -- Interface flow sensitivity to transformer GIC currents (dBound/dI) -- E-field to GIC Jacobian (dI/dE) - -These functions operate on matrices produced by ``PowerWorld.gic.model()`` -and require a live ``PowerWorld`` instance for bus category data. - -Example -------- ->>> from esapp import PowerWorld ->>> from examples.nonuniform.sens import jac_decomp ->>> from examples.nonuniform.sens import dBounddI, dIdE ->>> ->>> pw = PowerWorld("case.pwb") ->>> pw.gic.model() ->>> H = pw.gic.H ->>> J = pw.jacobian(dense=True) ->>> V = pw.voltage(complex=False)[0].to_numpy() ->>> eta = ... # injection vector ->>> PX = pw.gic.Px ->>> sens = dBounddI(pw, eta, PX, J, V) -""" - -import numpy as np -from scipy.sparse import hstack -from scipy.sparse.linalg import inv as sinv - -from esapp.components import Bus - -__all__ = ['jac_decomp', 'dBounddI', 'dIdE', 'signdiag'] - - -def jac_decomp(jac): - """ - Decompose a power flow Jacobian into sub-matrices. - - Parameters - ---------- - jac : np.ndarray - Full Jacobian matrix of shape (2n, 2n). - - Yields - ------ - np.ndarray - Sub-matrices in order: dP/dTheta, dP/dV, dQ/dTheta, dQ/dV. - """ - dim = jac.shape[0] - nbus = int(dim / 2) - - yield jac[:nbus, :nbus] # dP/dTheta - yield jac[:nbus, nbus:] # dP/dV - yield jac[nbus:, :nbus] # dQ/dTheta - yield jac[nbus:, nbus:] # dQ/dV - - -def signdiag(x): - """ - Create diagonal matrix of signs. - - Parameters - ---------- - x : np.ndarray - Input vector. - - Returns - ------- - np.ndarray - Diagonal matrix with sign(x) on diagonal. - """ - return np.diagflat(np.sign(x)) - - -def dBounddI(pw, eta, PX, J, V): - """ - Compute interface sensitivity with respect to transformer GIC currents. - - Parameters - ---------- - pw : PowerWorld - Live PowerWorld instance (used to retrieve bus categories). - eta : np.ndarray - Injection vector (n x 1). - PX : np.ndarray or sparse matrix - Transformer to loaded-bus mapping (n x m). - J : np.ndarray - Full AC power flow Jacobian at boundary. - V : np.ndarray - Bus voltage magnitudes (n x 1). - - Returns - ------- - np.ndarray - Sensitivity vector (1 x n). - """ - buscat = pw[Bus, ['BusCat']]['BusCat'] - slk = buscat == 'Slack' - pv = buscat == 'PV' - pq = ~(slk | pv) - - dPdT, dPdV, dQdT, dQdV = jac_decomp(J) - - A = hstack([dPdT[:, ~slk], dPdV[:, pq]]) - B = hstack([dQdT[pq][:, ~slk], dQdV[pq][:, pq]]) - - Vdiag = np.diagflat(V[pq]) - - return (1 / (eta.T @ eta)) @ eta.T @ A @ B.T @ sinv((B @ B.T).tocsc()) @ Vdiag @ PX[pq] - - -def dIdE(H, E=None, i=None): - """ - Compute Jacobian between mesh E-field and absolute transformer GICs. - - Parameters - ---------- - H : np.ndarray or sparse matrix - H-matrix (e.g., from ``pw.gic.H`` after calling ``model()``). - E : np.ndarray, optional - Electric field vector. If provided and i is None, computes i = H @ E. - i : np.ndarray, optional - Signed neutral transformer currents. Required if E is not provided. - - Returns - ------- - np.ndarray - Jacobian matrix (rows: transformers, cols: E-field components). - - Raises - ------ - ValueError - If neither E nor i is provided. - """ - if E is not None: - if i is None: - i = H @ E - elif i is None: - raise ValueError("Either E or i must be provided") - - F = signdiag(i) - return F @ H diff --git a/examples/plot_helpers.py b/examples/plot_helpers.py index 6fb470f..2d20a4b 100644 --- a/examples/plot_helpers.py +++ b/examples/plot_helpers.py @@ -104,79 +104,10 @@ def plot_dual_bar(values_a, values_b, label_a='A', label_b='B', return ax -def plot_hist(values, bins=20, title='', xlabel='', ylabel='Count', - color=None, ax=None): - """Simple histogram with white edge on bars.""" - if color is None: - color = _C1 - show = ax is None - if ax is None: - fig, ax = plt.subplots(figsize=(_W1, _H1)) - ax.hist(values, bins=bins, color=color, edgecolor='white') - format_plot(ax, title=title, xlabel=xlabel, ylabel=ylabel, plotarea='white') - if show: - plt.tight_layout() - plt.show() - return ax - - # --------------------------------------------------------------------------- # PTDF / LODF / sensitivity # --------------------------------------------------------------------------- -def plot_ptdf(ptdf_df, n=20, figsize=(_W2, _H2)): - """PTDF bar chart (top-N by magnitude) + histogram (2-panel).""" - vals = ptdf_df['LinePTDF'] - fig, axes = plt.subplots(1, 2, figsize=figsize) - - top = vals.abs().sort_values(ascending=False).head(n) - colors = [_C4 if vals.loc[i] < 0 else _C1 for i in top.index] - labels = [f"{int(ptdf_df.loc[i, 'BusNum'])}-{int(ptdf_df.loc[i, 'BusNum:1'])}" - for i in top.index] - axes[0].barh(range(len(top)), vals.loc[top.index].values, color=colors) - axes[0].set_yticks(range(len(top))) - axes[0].set_yticklabels(labels, fontsize=7) - axes[0].invert_yaxis() - axes[0].axvline(x=0, color=_CG, linewidth=0.5) - format_plot(axes[0], title=f'Top {n} PTDFs', - xlabel='PTDF', plotarea='white', **_FS2) - - axes[1].hist(vals.values, bins=30, color=_C1, edgecolor='white') - axes[1].axvline(x=0, color=_CG, linewidth=0.5) - format_plot(axes[1], title='PTDF Distribution', - xlabel='PTDF', ylabel='Count', - plotarea='white', **_FS2) - - plt.tight_layout() - plt.show() - - -def plot_lodf(lodf_df, n=20, figsize=(_W2, _H2)): - """LODF bar chart (top-N by magnitude) + histogram (2-panel).""" - vals = lodf_df['LineLODF'] - fig, axes = plt.subplots(1, 2, figsize=figsize) - - top = vals.abs().sort_values(ascending=False).head(n) - colors = [_C4 if vals.loc[i] < 0 else _C1 for i in top.index] - labels = [f"{int(lodf_df.loc[i, 'BusNum'])}-{int(lodf_df.loc[i, 'BusNum:1'])}" - for i in top.index] - axes[0].barh(range(len(top)), vals.loc[top.index].values, color=colors) - axes[0].set_yticks(range(len(top))) - axes[0].set_yticklabels(labels, fontsize=7) - axes[0].invert_yaxis() - axes[0].axvline(x=0, color=_CG, linewidth=0.5) - format_plot(axes[0], title=f'Top {n} LODFs', - xlabel='LODF', plotarea='white', **_FS2) - - axes[1].hist(vals.dropna().values, bins=30, color=_C1, edgecolor='white') - axes[1].axvline(x=0, color=_CG, linewidth=0.5) - format_plot(axes[1], title='LODF Distribution', - xlabel='LODF', ylabel='Count', - plotarea='white', **_FS2) - - plt.tight_layout() - plt.show() - def plot_sensitivity_map(lines, values, shape=None, title='Sensitivity Map', clabel='Factor', cmap='RdBu_r', symmetric=True, @@ -372,32 +303,6 @@ def plot_bus_markers(ax, lon, lat, indices, marker='*', color=_C4, ax.legend(fontsize=7, loc='lower right') -def plot_solver_comparison(results, figsize=(_W2, _H2)): - """Compare solver results: mismatch vs iteration (2-panel).""" - fig, axes = plt.subplots(1, 2, figsize=figsize) - pal = [_C1, _C2, _C3, _C4, _C5] - - for i, (label, data) in enumerate(results.items()): - c = pal[i % len(pal)] - if 'mismatches' in data and data['mismatches']: - axes[0].semilogy(data['mismatches'], 'o-', color=c, - markersize=3, label=label) - axes[1].bar(i, data.get('iterations', 0), color=c, label=label) - - format_plot(axes[0], title='Convergence History', - xlabel='Iteration', ylabel='Max Mismatch', - plotarea='white', **_FS2) - axes[0].legend(fontsize=7) - - axes[1].set_xticks(range(len(results))) - axes[1].set_xticklabels(list(results.keys()), fontsize=7, rotation=30) - format_plot(axes[1], title='Iterations to Converge', - ylabel='Iterations', plotarea='white', **_FS2) - - plt.tight_layout() - plt.show() - - def plot_snapshot_comparison(base, modified, field='BusPUVolt', figsize=(_W2, _H2)): """Before/after voltage scatter + difference histogram (2-panel).""" @@ -476,74 +381,6 @@ def plot_voltage_profile(vmag, vang=None, figsize=(_W2, _H2)): plt.show() -def plot_branch_loading(branches_loaded, figsize=(_W2, _H2)): - """Branch loading bar chart + loading histogram (2-panel).""" - fig, axes = plt.subplots(1, 2, figsize=figsize) - - labels = [f"{int(r['BusNum'])}-{int(r['BusNum:1'])}" for _, r in branches_loaded.iterrows()] - axes[0].barh(range(len(branches_loaded)), branches_loaded['LinePercent'].values, - color=_C1) - axes[0].set_yticks(range(len(branches_loaded))) - axes[0].set_yticklabels(labels, fontsize=7) - axes[0].invert_yaxis() - axes[0].axvline(x=100, color=_LIMIT, linestyle='--', alpha=0.7, label='100%') - format_plot(axes[0], title='Most Loaded Branches', - xlabel='Loading (%)', plotarea='white', **_FS2) - axes[0].legend(fontsize=7) - - axes[1].hist(branches_loaded['LinePercent'].values, bins=15, - color=_C1, edgecolor='white') - axes[1].axvline(x=100, color=_LIMIT, linestyle='--', alpha=0.7) - format_plot(axes[1], title='Loading Distribution', - xlabel='Loading (%)', ylabel='Count', - plotarea='white', **_FS2) - - plt.tight_layout() - plt.show() - - -def plot_gen_dispatch_and_voltage(online_gens, bus_data, figsize=(_W2, _H2)): - """Generator MW bar chart + bus voltage scatter (2-panel).""" - fig, axes = plt.subplots(1, 2, figsize=figsize) - - gen_mw = online_gens.sort_values('GenMW', ascending=True) - axes[0].barh(range(len(gen_mw)), gen_mw['GenMW'].values, color=_C1) - axes[0].set_yticks(range(len(gen_mw))) - axes[0].set_yticklabels([f"Bus {b}" for b in gen_mw['BusNum']], fontsize=7) - format_plot(axes[0], title='Generator Dispatch', - xlabel='MW Output', plotarea='white', **_FS2) - - axes[1].scatter(bus_data['BusNum'], bus_data['BusPUVolt'], - c=_C1, s=25, edgecolors='white', linewidth=0.4) - axes[1].axhline(y=0.95, color=_LIMIT, linestyle='--', alpha=0.5, label='0.95 pu') - axes[1].axhline(y=1.05, color=_LIMIT, linestyle='--', alpha=0.5, label='1.05 pu') - format_plot(axes[1], title='Bus Voltage Profile', - xlabel='Bus Number', ylabel='Voltage (pu)', - plotarea='white', **_FS2) - axes[1].legend(fontsize=7) - - plt.tight_layout() - plt.show() - - -def plot_gen_load_balance(total_gen, total_load, ax=None, figsize=(4.5, 3)): - """Bar chart comparing total generation vs. total load.""" - show = ax is None - if ax is None: - fig, ax = plt.subplots(figsize=figsize) - bars = ax.bar(['Generation', 'Load'], [total_gen, total_load], - color=[_C1, _C2]) - format_plot(ax, title='Generation vs Load Balance', - ylabel='MW', plotarea='white', **_FS2) - for bar, val in zip(bars, [total_gen, total_load]): - ax.text(bar.get_x() + bar.get_width() / 2, bar.get_height() + 1, - f'{val:.1f}', ha='center', va='bottom', fontsize=8) - if show: - plt.tight_layout() - plt.show() - return ax - - def plot_contingency_results(violations, figsize=(_W2, _H2)): """Bar chart of violations per contingency + histogram (2-panel).""" if len(violations) == 0 or 'Contingency' not in violations.columns: @@ -1194,382 +1031,11 @@ def plot_comparative_dynamics(ctg_names, all_results, figsize=None): # Discrete calculus / Grid2D utilities # --------------------------------------------------------------------------- -def plot_grid_regions(X, Y, grid, figsize=(_W3, _H3)): - """Three-panel view of grid: all points, boundary/interior, edges. - - Parameters - ---------- - grid : Grid2D - Grid2D instance (provides .boundary, .interior, .left, etc.). - """ - fig, axes = plt.subplots(1, 3, figsize=figsize) - xf = X.ravel(order='C') - yf = Y.ravel(order='C') - - axes[0].scatter(xf, yf, s=5, c=_C1) - axes[0].set_aspect('equal') - format_plot(axes[0], title='All Grid Points', xlabel='x', ylabel='y', - grid=False, plotarea='white', **_FS3) - - axes[1].scatter(xf[grid.interior], yf[grid.interior], - s=5, c=_C1, label='Interior') - axes[1].scatter(xf[grid.boundary], yf[grid.boundary], - s=8, c=_C2, label='Boundary') - axes[1].set_aspect('equal') - format_plot(axes[1], title='Boundary vs Interior', xlabel='x', ylabel='y', - grid=False, plotarea='white', **_FS3) - axes[1].legend(markerscale=2, fontsize=7) - - axes[2].scatter(xf[grid.left], yf[grid.left], - s=8, c=_C4, label='Left') - axes[2].scatter(xf[grid.right], yf[grid.right], - s=8, c=_C1, label='Right') - axes[2].scatter(xf[grid.top], yf[grid.top], - s=8, c=_C3, label='Top') - axes[2].scatter(xf[grid.bottom], yf[grid.bottom], - s=8, c=_C2, label='Bottom') - axes[2].set_aspect('equal') - format_plot(axes[2], title='Edge Selectors', xlabel='x', ylabel='y', - grid=False, plotarea='white', **_FS3) - axes[2].legend(markerscale=2, fontsize=7) - - plt.tight_layout() - plt.show() - - -def plot_incidence_directed(grid, figsize=(_W2, 3.8)): - """Oriented incidence matrix as directed edges + matrix heatmap (2-panel). - - Parameters - ---------- - grid : Grid2D - A small Grid2D instance (recommended nx, ny <= 6 for readability). - """ - from matplotlib.lines import Line2D - - A = grid.incidence().toarray() - nx, ny = grid.nx, grid.ny - - fig, axes = plt.subplots(1, 2, figsize=figsize, - gridspec_kw={'width_ratios': [1.3, 1]}) - - ax = axes[0] - for xi in range(nx): - for yi in range(ny): - idx = grid.flat_index(xi, yi) - ax.plot(xi, yi, 'o', color=_C1, markersize=14, - markeredgecolor='#2c3e50', markeredgewidth=1.0, zorder=5) - ax.text(xi, yi, str(idx), ha='center', va='center', - fontsize=7, fontweight='bold', color='white', zorder=6) - - shrink = 0.22 - for e in range(grid.n_edges): - src = np.where(A[e] == -1)[0][0] - tgt = np.where(A[e] == +1)[0][0] - sx, sy = grid.grid_coords(src) - tx, ty = grid.grid_coords(tgt) - dx_a, dy_a = tx - sx, ty - sy - length = np.hypot(dx_a, dy_a) - sx_s = sx + shrink * dx_a / length - sy_s = sy + shrink * dy_a / length - dx_s = dx_a * (1 - 2 * shrink) - dy_s = dy_a * (1 - 2 * shrink) - color = _C4 if e < grid.n_edges_x else _C3 - ax.annotate('', xy=(sx_s + dx_s, sy_s + dy_s), xytext=(sx_s, sy_s), - arrowprops=dict(arrowstyle='->', color=color, lw=1.8, - mutation_scale=14)) - mx, my = (sx + tx) / 2, (sy + ty) / 2 - perp_x, perp_y = -dy_a / length, dx_a / length - ax.text(mx + 0.15 * perp_x, my + 0.15 * perp_y, f'e{e}', - ha='center', va='center', fontsize=5.5, color=color, - fontstyle='italic', alpha=0.85) - - ax.legend([Line2D([0], [0], color=_C4, lw=2), - Line2D([0], [0], color=_C3, lw=2)], - [f'Horizontal (0..{grid.n_edges_x - 1})', - f'Vertical ({grid.n_edges_x}..{grid.n_edges - 1})'], - loc='upper left', fontsize=7, framealpha=0.9) - - ax.set_xlim(-0.6, nx - 0.4) - ax.set_ylim(-0.6, ny - 0.4) - ax.set_aspect('equal') - format_plot(ax, title=f'Oriented Edges ({nx}\u00d7{ny})', - xlabel='x', ylabel='y', plotarea='#f8f9fa', grid=False, **_FS2) - ax.grid(True, alpha=0.15, linestyle='--') - - ax2 = axes[1] - ax2.imshow(A, cmap='RdBu_r', vmin=-1.5, vmax=1.5, aspect='auto', - interpolation='nearest') - - for e in range(A.shape[0]): - for n in range(A.shape[1]): - if A[e, n] != 0: - label = '\u22121' if A[e, n] < 0 else '+1' - ax2.text(n, e, label, ha='center', va='center', - fontsize=5, fontweight='bold', - color='white' if abs(A[e, n]) > 0.5 else 'black') - - if grid.n_edges_x > 0 and grid.n_edges_y > 0: - ax2.axhline(y=grid.n_edges_x - 0.5, color='#2c3e50', linewidth=1.2) - - if grid.n_edges_x > 0: - ax2.text(-1.2, (grid.n_edges_x - 1) / 2, 'H', ha='center', va='center', - fontsize=8, fontweight='bold', color=_C4) - if grid.n_edges_y > 0: - ax2.text(-1.2, grid.n_edges_x + (grid.n_edges_y - 1) / 2, 'V', - ha='center', va='center', fontsize=8, fontweight='bold', color=_C3) - - format_plot(ax2, title=f'Incidence A ({grid.n_edges}\u00d7{grid.size})', - xlabel='Node', ylabel='Edge', - plotarea='white', grid=False, **_FS2) - plt.tight_layout() - plt.show() - - -def plot_scalar_field(X, Y, f, title='', clabel='f(x,y)', cmap='RdBu_r', - figsize=(_W1, _H1), ax=None, fig=None): - """Pcolormesh of a scalar field with colorbar.""" - show = ax is None - if ax is None: - fig, ax = plt.subplots(figsize=figsize) - im = ax.pcolormesh(X, Y, f, cmap=cmap, shading='auto') - if fig is not None: - fig.colorbar(im, ax=ax, label=clabel) - ax.set_aspect('equal') - format_plot(ax, title=title, xlabel='x', ylabel='y', grid=False, - plotarea='white', **_FS2) - if show: - plt.tight_layout() - plt.show() - return ax - - -def plot_field_panels(X, Y, fields, titles, cmap='RdBu_r', figsize=None, - suptitle=None, equal_aspect=True): - """Row of pcolormesh panels (always >= 2 panels). - - Parameters - ---------- - fields : list of 2-D arrays - titles : list of str - """ - n = max(len(fields), 2) - if figsize is None: - figsize = (min(_WFULL, 3.2 * n + 0.5), 3) - fig, axes = plt.subplots(1, n, figsize=figsize) - if n == 1: - axes = [axes] - fs = _FS3 if n >= 3 else _FS2 - for ax, data, t in zip(axes, fields, titles): - im = ax.pcolormesh(X, Y, data, cmap=cmap, shading='auto') - fig.colorbar(im, ax=ax) - if equal_aspect: - ax.set_aspect('equal') - format_plot(ax, title=t, xlabel='x', ylabel='y', grid=False, - plotarea='white', **fs) - for j in range(len(fields), n): - axes[j].set_visible(False) - if suptitle: - plt.suptitle(suptitle, fontsize=12) - plt.tight_layout() - plt.show() - - -def plot_gradient_vecfield(X, Y, f, grad_x, grad_y, step=3, - figsize=(_W1, _H1), ax=None, fig=None): - """Scalar field background + gradient vector field overlay.""" - show = ax is None - if ax is None: - fig, ax = plt.subplots(figsize=figsize) - Xs = X[::step, ::step] - Ys = Y[::step, ::step] - Us = grad_x[::step, ::step] - Vs = grad_y[::step, ::step] - - ax.pcolormesh(X, Y, f, cmap='Greys', shading='auto', alpha=0.3) - sm = plot_vecfield(ax, Xs, Ys, Us, Vs, scale=150, width=0.003) - if fig is not None: - fig.colorbar(sm, ax=ax, label='Angle (rad)') - ax.set_aspect('equal') - format_plot(ax, title='Gradient Vector Field', xlabel='x', ylabel='y', - grid=False, plotarea='white', **_FS2) - if show: - plt.tight_layout() - plt.show() - return ax - - -def plot_div_curl(X, Y, u_field, v_field, div_uv, curl_uv, step=3, - figsize=(_W3, _H3)): - """Vector field + divergence + curl pcolormesh (3-panel).""" - fig, axes = plt.subplots(1, 3, figsize=figsize) - - axes[0].quiver(X[::step, ::step], Y[::step, ::step], - u_field[::step, ::step], v_field[::step, ::step], - color=_C1) - axes[0].set_aspect('equal') - format_plot(axes[0], title='Vector Field (u, v)', xlabel='x', ylabel='y', - grid=False, plotarea='white', **_FS3) - - im1 = axes[1].pcolormesh(X, Y, div_uv, cmap='RdBu_r', shading='auto') - fig.colorbar(im1, ax=axes[1]) - axes[1].set_aspect('equal') - format_plot(axes[1], title='Divergence', xlabel='x', - ylabel='y', grid=False, plotarea='white', **_FS3) - - im2 = axes[2].pcolormesh(X, Y, curl_uv, cmap='RdBu_r', shading='auto') - fig.colorbar(im2, ax=axes[2]) - axes[2].set_aspect('equal') - format_plot(axes[2], title='Curl', xlabel='x', - ylabel='y', grid=False, plotarea='white', **_FS3) - - plt.tight_layout() - plt.show() - - -def plot_hodge_rotation(X, Y, f, grad_x, grad_y, rot_x, rot_y, step=3, - figsize=(_W2, _H2)): - """Gradient vs Hodge-rotated gradient quiver plots (2-panel).""" - fig, axes = plt.subplots(1, 2, figsize=figsize) - - axes[0].pcolormesh(X, Y, f, cmap='Greys', shading='auto', alpha=0.3) - axes[0].quiver(X[::step, ::step], Y[::step, ::step], - grad_x[::step, ::step], grad_y[::step, ::step], - color=_C1) - axes[0].set_aspect('equal') - format_plot(axes[0], title='Gradient Field', xlabel='x', ylabel='y', - grid=False, plotarea='white', **_FS2) - - axes[1].pcolormesh(X, Y, f, cmap='Greys', shading='auto', alpha=0.3) - axes[1].quiver(X[::step, ::step], Y[::step, ::step], - rot_x[::step, ::step], rot_y[::step, ::step], - color=_C2) - axes[1].set_aspect('equal') - format_plot(axes[1], title='Hodge Star (90\u00b0 Rotation)', xlabel='x', - ylabel='y', grid=False, plotarea='white', **_FS2) - - plt.tight_layout() - plt.show() - - -def plot_eigenmodes(X, Y, vals, vecs, ny, nx, k=9, figsize=(_WFULL, 5.5)): - """3x3 grid of Laplacian eigenmodes.""" - fig, axes = plt.subplots(3, 3, figsize=figsize) - for i, ax in enumerate(axes.ravel()): - if i >= len(vals): - ax.set_visible(False) - continue - mode = vecs[:, i].reshape(ny, nx) - ax.pcolormesh(X, Y, mode, cmap='RdBu_r', shading='auto') - ax.set_aspect('equal') - ax.set_xticks([]) - ax.set_yticks([]) - format_plot(ax, title=f'Mode {i}, \u03bb={vals[i]:.3f}', - grid=False, plotarea='white', **_FS3) - plt.suptitle('Laplacian Eigenmodes', fontsize=12, fontweight='bold') - plt.tight_layout() - plt.show() - # --------------------------------------------------------------------------- # Spectral analysis utilities # --------------------------------------------------------------------------- -def plot_vecfield_gallery(X, Y, fields, step=3, figsize=(_WFULL, 5.5)): - """2x2 vector field gallery using plot_vecfield. - - Parameters - ---------- - fields : dict of {name: (U, V)} tuples - """ - fig, axes = plt.subplots(2, 2, figsize=figsize) - for ax, (name, (U, V)) in zip(axes.ravel(), fields.items()): - sm = plot_vecfield(ax, X[::step, ::step], Y[::step, ::step], - U[::step, ::step], V[::step, ::step], - scale=30, width=0.004) - format_plot(ax, title=name, xlabel='x', ylabel='y', grid=False, - **_FS3) - plt.tight_layout() - plt.show() - - -def plot_graph_operators(matrices, titles, cmaps=None, vranges=None, - suptitle='', figsize=None): - """Grid of imshow plots for graph matrices.""" - n = len(matrices) - ncols = min(n, 4) - nrows = (n + ncols - 1) // ncols - if figsize is None: - figsize = (min(_WFULL, 3.2 * ncols), 3 * nrows) - if cmaps is None: - cmaps = ['RdBu_r'] * n - fig, axes = plt.subplots(nrows, ncols, figsize=figsize) - axes_flat = np.array(axes).ravel() if n > 1 else [axes] - fs = _FS3 if ncols >= 3 else {} - for i, (ax, M, t, cm) in enumerate(zip(axes_flat, matrices, titles, cmaps)): - kwargs = {'cmap': cm, 'aspect': 'auto'} - if vranges and i < len(vranges) and vranges[i]: - kwargs['vmin'], kwargs['vmax'] = vranges[i] - ax.imshow(M, **kwargs) - format_plot(ax, title=t, xlabel='Column', ylabel='Row', - plotarea='white', grid=False, **fs) - # Hide extra axes - for j in range(n, len(axes_flat)): - axes_flat[j].set_visible(False) - if suptitle: - plt.suptitle(suptitle, fontsize=12) - plt.tight_layout() - plt.show() - - -def plot_normlap_spectrum(L_norm, evals, figsize=(_W2, _H2)): - """Normalized Laplacian image + eigenvalue stem plot (2-panel).""" - fig, axes = plt.subplots(1, 2, figsize=figsize) - - axes[0].imshow(L_norm, cmap='RdBu_r') - format_plot(axes[0], title='Normalized Cycle Laplacian', - plotarea='white', grid=False, **_FS2) - - axes[1].stem(evals, basefmt=' ') - axes[1].axhline(y=2, color=_LIMIT, linestyle='--', alpha=0.5, label='eig=2') - format_plot(axes[1], title='Eigenvalue Spectrum', - xlabel='Index', ylabel='Eigenvalue', - plotarea='white', **_FS2) - axes[1].legend(fontsize=7) - - plt.tight_layout() - plt.show() - - -def plot_hermitify(M, H, figsize=(_W2, _H2)): - """Side-by-side |M| vs |H| images (2-panel).""" - fig, axes = plt.subplots(1, 2, figsize=figsize) - axes[0].imshow(np.abs(M), cmap='viridis') - format_plot(axes[0], title='|M| (complex symmetric)', - plotarea='white', grid=False, **_FS2) - axes[1].imshow(np.abs(H), cmap='viridis') - format_plot(axes[1], title='|H| (Hermitian)', - plotarea='white', grid=False, **_FS2) - plt.tight_layout() - plt.show() - - -def plot_colormap_scales(scales, figsize=(_WFULL, 1.8)): - """Show darker_hsv_colormap at different scales.""" - fig, axes = plt.subplots(1, len(scales), figsize=figsize) - if len(scales) == 1: - axes = [axes] - gradient = np.linspace(-np.pi, np.pi, 256).reshape(1, -1) - fs = _FS3 if len(scales) >= 3 else {} - for ax, scale in zip(axes, scales): - cmap = darker_hsv_colormap(scale) - ax.imshow(gradient, aspect='auto', cmap=cmap) - format_plot(ax, title=f'darker_hsv_colormap(scale={scale})', - plotarea='white', grid=False, **fs) - ax.set_yticks([]) - plt.tight_layout() - plt.show() - def plot_colormap_2d(LON, LAT, theta, scales, figsize=(_WFULL, 4)): """1D gradient + 2D angle field at multiple colormap scales.""" diff --git a/examples/visualization/01_discrete_calculus.ipynb b/examples/visualization/01_discrete_calculus.ipynb deleted file mode 100644 index 01c3a86..0000000 --- a/examples/visualization/01_discrete_calculus.ipynb +++ /dev/null @@ -1,436 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "a1b2c3d4", - "metadata": {}, - "source": [ - "# Discrete Calculus on 2D Grids\n", - "\n", - "Demonstrates graph-based discrete differential operators on structured 2D grids\n", - "using `esapp.utils.Grid2D`. The notebook builds a grid and its oriented\n", - "incidence matrix, then derives gradient, divergence, curl, and Laplacian\n", - "operators from this single matrix. It also covers the weighted Laplacian\n", - "$L = A^\\top \\text{diag}(w)\\, A$, the Hodge star rotation, Dirichlet\n", - "boundary conditions with the Poisson equation, and Laplacian eigenmodes." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "ec99918e", - "metadata": { - "tags": [ - "hide-cell" - ] - }, - "outputs": [], - "source": [ - "import sys; sys.path.insert(0, \"..\")\n", - "import numpy as np\n", - "import scipy.sparse as sp\n", - "import matplotlib.pyplot as plt\n", - "from scipy.sparse.linalg import spsolve, eigsh as sparse_eigsh\n", - "from mesh import Grid2D, sorteig\n", - "from map import format_plot, plot_vecfield" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "5ddf9133", - "metadata": { - "tags": [ - "hide-cell" - ] - }, - "outputs": [], - "source": [ - "# Plotting functions (hidden from documentation)\n", - "import sys; sys.path.insert(0, \"..\")\n", - "from plot_helpers import (\n", - " plot_grid_regions, plot_scalar_field, plot_field_panels,\n", - " plot_gradient_vecfield, plot_hodge_rotation,\n", - " plot_eigenmodes, plot_graph_operators, plot_spy_matrices,\n", - " plot_incidence_directed,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "id": "c9d0e1f2", - "metadata": {}, - "source": [ - "## 1. Building a 2D Grid\n", - "\n", - "`Grid2D` represents a structured rectangular grid as a graph. Internally it constructs\n", - "an **oriented incidence matrix** that encodes all edge connectivity. All discrete operators\n", - "(gradient, divergence, curl, Laplacian) are derived from this single matrix.\n", - "\n", - "Points are indexed in column-major (Fortran) order: point $(x, y)$ maps to flat index $y \\cdot n_x + x$." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "a3b4c5d6", - "metadata": {}, - "outputs": [], - "source": [ - "nx, ny = 30, 30\n", - "grid = Grid2D((nx, ny))\n", - "\n", - "print(f\"Grid shape: {grid.shape}\")\n", - "print(f\"Total nodes: {grid.size}\")\n", - "print(f\"Total edges: {grid.n_edges} (horizontal: {grid.n_edges_x}, vertical: {grid.n_edges_y})\")\n", - "print(f\"Boundary nodes: {grid.boundary.sum()}\")\n", - "print(f\"Interior nodes: {grid.interior.sum()}\")" - ] - }, - { - "cell_type": "markdown", - "id": "e7f8a9b0", - "metadata": {}, - "source": [ - "### Boundary and interior regions\n", - "\n", - "`Grid2D` provides boolean masks for selecting boundary and interior nodes, useful for\n", - "applying boundary conditions. These were previously in a separate `GridSelector` class." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "2a9e478f", - "metadata": {}, - "outputs": [], - "source": [ - "# Build coordinate arrays\n", - "x = np.linspace(0, 1, nx)\n", - "y = np.linspace(0, 1, ny)\n", - "X, Y = np.meshgrid(x, y)\n", - "\n", - "plot_grid_regions(X, Y, grid)" - ] - }, - { - "cell_type": "markdown", - "id": "a5b6c7d8", - "metadata": {}, - "source": [ - "## 2. The Incidence Matrix\n", - "\n", - "The core data structure of `Grid2D` is the **oriented incidence matrix** $A$ of shape\n", - "$(m, n)$ where $m$ is the number of edges and $n$ is the number of nodes. Each row\n", - "has exactly two nonzeros: $-1$ at the **source** node and $+1$ at the **target** node.\n", - "\n", - "Horizontal edges (left → right) are listed first, then vertical edges (bottom → top).\n", - "\n", - "### Directed edge structure\n", - "\n", - "The plot below shows a small grid with every oriented edge drawn as an arrow from\n", - "its source ($-1$) to its target ($+1$), alongside the dense incidence matrix with\n", - "annotated entries. This makes the one-to-one correspondence between rows of $A$\n", - "and directed edges visually explicit." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "12ca26eb", - "metadata": {}, - "outputs": [], - "source": [ - "# Visualize directed edges on a small grid\n", - "small = Grid2D((4, 3))\n", - "plot_incidence_directed(small)" - ] - }, - { - "cell_type": "markdown", - "id": "c3d4e5f6", - "metadata": {}, - "source": [ - "## 3. Gradient of a Scalar Field\n", - "\n", - "The gradient operators $D_x$ and $D_y$ are extracted directly from the incidence matrix:\n", - "$D_x$ consists of the horizontal-edge rows and $D_y$ of the vertical-edge rows.\n", - "\n", - "Applying $D_x$ to a scalar field $u$ gives the forward difference along each horizontal\n", - "edge. The result lives on edges, not nodes." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "grad_setup", - "metadata": { - "tags": [ - "hide-cell" - ] - }, - "outputs": [], - "source": [ - "# Compute gradient operators and a test scalar field\n", - "Dx, Dy = grid.gradient()\n", - "\n", - "# Smooth test field\n", - "f = np.sin(2 * np.pi * X) * np.cos(2 * np.pi * Y)\n", - "f_flat = f.ravel(order=\"C\")\n", - "\n", - "# Gradient on edges\n", - "grad_x_edges = (Dx @ f_flat).reshape(ny, nx - 1)\n", - "grad_y_edges = (Dy @ f_flat).reshape(ny - 1, nx)\n", - "\n", - "# Edge-midpoint coordinates for visualization\n", - "Xmid_h = (X[:, :-1] + X[:, 1:]) / 2\n", - "Ymid_h = (Y[:, :-1] + Y[:, 1:]) / 2" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "f84afb9e", - "metadata": {}, - "outputs": [], - "source": [ - "# Vertical edge midpoints\n", - "Xmid_v = (X[:-1, :] + X[1:, :]) / 2\n", - "Ymid_v = (Y[:-1, :] + Y[1:, :]) / 2\n", - "\n", - "fig, axes = plt.subplots(1, 2, figsize=(6.5, 2.8))\n", - "im0 = axes[0].pcolormesh(Xmid_h, Ymid_h, grad_x_edges, cmap='RdBu_r', shading='auto')\n", - "fig.colorbar(im0, ax=axes[0])\n", - "axes[0].set_aspect('equal')\n", - "format_plot(axes[0], title='df/dx on horizontal edges', xlabel='x', ylabel='y',\n", - " grid=False, plotarea='white', titlesize=11, labelsize=9, ticksize=8)\n", - "\n", - "im1 = axes[1].pcolormesh(Xmid_v, Ymid_v, grad_y_edges, cmap='RdBu_r', shading='auto')\n", - "fig.colorbar(im1, ax=axes[1])\n", - "axes[1].set_aspect('equal')\n", - "format_plot(axes[1], title='df/dy on vertical edges', xlabel='x', ylabel='y',\n", - " grid=False, plotarea='white', titlesize=11, labelsize=9, ticksize=8)\n", - "plt.tight_layout()\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "e1f2a3b4", - "metadata": {}, - "source": [ - "### Verifying the gradient on a linear field\n", - "\n", - "For a linear field $f(x,y) = x$, the x-gradient on every horizontal edge should be\n", - "exactly 1 (the grid spacing), and the y-gradient should be zero." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "1b018537", - "metadata": {}, - "outputs": [], - "source": [ - "# f(x,y) = x coordinate\n", - "f_x = np.zeros(grid.size)\n", - "for xi, yi, idx in grid.iter_points():\n", - " f_x[idx] = x[xi]\n", - "\n", - "gx = Dx @ f_x\n", - "gy = Dy @ f_x\n", - "\n", - "dx = x[1] - x[0]\n", - "print(f\"Grid spacing dx = {dx:.4f}\")\n", - "print(f\"All horizontal gradients = dx? {np.allclose(gx, dx)}\")\n", - "print(f\"All vertical gradients = 0? {np.allclose(gy, 0)}\")" - ] - }, - { - "cell_type": "markdown", - "id": "a9b0c1d2", - "metadata": {}, - "source": [ - "## 4. Laplacian: $L = A^\\top\\, \\text{diag}(w)\\, A$\n", - "\n", - "The discrete Laplacian is constructed from the incidence matrix:\n", - "\n", - "$$L = A^\\top W A$$\n", - "\n", - "where $W = \\text{diag}(w)$ is a diagonal matrix of per-edge weights. With unit weights\n", - "($w = \\mathbf{1}$), this gives the standard combinatorial Laplacian whose diagonal entries\n", - "equal the node degrees." - ] - }, - { - "cell_type": "markdown", - "id": "e5f6a7b9", - "metadata": {}, - "source": [ - "## 5. Divergence and Curl\n", - "\n", - "The **divergence** $\\text{div} = -A^\\top$ maps edge fields to node fields.\n", - "The **curl** maps edge fields to face (cell) fields, summing edge values around\n", - "each rectangular cell with orientation signs.\n", - "\n", - "These satisfy the discrete exactness property: $\\text{curl}(\\text{grad}(u)) = 0$\n", - "for any scalar field $u$." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "479e3beb", - "metadata": {}, - "outputs": [], - "source": [ - "D = grid.divergence()\n", - "C = grid.curl()\n", - "\n", - "# Verify discrete exactness: curl(grad(u)) = 0\n", - "L = grid.laplacian()\n", - "A = grid._A\n", - "grad_f = A @ f_flat # edge field\n", - "curl_grad = C @ grad_f # should be zero on every face\n", - "\n", - "# Divergence of the gradient gives the (negative) Laplacian\n", - "div_grad = D @ grad_f\n", - "neg_lap = -L @ f_flat" - ] - }, - { - "cell_type": "markdown", - "id": "a3b4c5d7", - "metadata": {}, - "source": [ - "## 6. Hodge Star (90-degree Rotation)\n", - "\n", - "The Hodge star operator rotates a 2D node-based vector field $[u; v]$ by 90 degrees,\n", - "returning $[-v; u]$. Applying it twice gives $-\\text{Id}$." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "18036f2f", - "metadata": {}, - "outputs": [], - "source": [ - "H = grid.hodge_star()\n", - "\n", - "# Interpolate edge gradient to nodes for visualization\n", - "# Average adjacent edge values to get node-based approximation\n", - "grad_x_nodes = np.zeros((ny, nx))\n", - "grad_x_nodes[:, :-1] += grad_x_edges\n", - "grad_x_nodes[:, 1:] += grad_x_edges\n", - "grad_x_nodes[:, 1:-1] /= 2\n", - "\n", - "grad_y_nodes = np.zeros((ny, nx))\n", - "grad_y_nodes[:-1, :] += grad_y_edges\n", - "grad_y_nodes[1:, :] += grad_y_edges\n", - "grad_y_nodes[1:-1, :] /= 2\n", - "\n", - "# Apply Hodge star to the node-based vector field\n", - "grad_flat = np.concatenate([grad_x_nodes.ravel(order='C'),\n", - " grad_y_nodes.ravel(order='C')])\n", - "rotated = H @ grad_flat\n", - "rot_x = rotated[:grid.size].reshape(ny, nx)\n", - "rot_y = rotated[grid.size:].reshape(ny, nx)\n", - "\n", - "plot_hodge_rotation(X, Y, f, grad_x_nodes, grad_y_nodes, rot_x, rot_y)" - ] - }, - { - "cell_type": "markdown", - "id": "c1d2e3f5", - "metadata": {}, - "source": [ - "## 7. Boundary Conditions and the Poisson Equation\n", - "\n", - "Use the boundary masks built into `Grid2D` to apply Dirichlet boundary conditions\n", - "and solve the Poisson equation $L\\, u = f$ on the grid interior." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "168bf698", - "metadata": {}, - "outputs": [], - "source": [ - "L = grid.laplacian()\n", - "rhs = -10 * np.ones(grid.size)\n", - "\n", - "# Apply Dirichlet BC: u = 0 on boundary\n", - "L_bc = L.tolil()\n", - "for i in np.where(grid.boundary)[0]:\n", - " L_bc[i, :] = 0\n", - " L_bc[i, i] = 1.0\n", - " rhs[i] = 0.0\n", - "\n", - "u = spsolve(L_bc.tocsr(), rhs).reshape(ny, nx)\n", - "\n", - "print(f\"Boundary values (should be 0): max = {np.abs(u.ravel()[grid.boundary]).max():.1e}\")\n", - "print(f\"Interior max: {u.ravel()[grid.interior].max():.4f}\")\n", - "\n", - "fig, axes = plt.subplots(1, 2, figsize=(6.5, 2.8))\n", - "plot_scalar_field(X, Y, u,\n", - " title='Poisson: Lu = -10, u|bd = 0',\n", - " clabel='u(x,y)', cmap='hot', ax=axes[0], fig=fig)\n", - "plot_gradient_vecfield(X, Y, u,\n", - " np.gradient(u, axis=1)/3, np.gradient(u, axis=0)/3,\n", - " step=3, ax=axes[1], fig=fig)\n", - "axes[1].set_title('Gradient of Solution', fontsize=11)\n", - "plt.tight_layout()\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "e9f0a1b3", - "metadata": {}, - "source": [ - "## 8. Laplacian Eigenmodes\n", - "\n", - "The eigenvectors of the discrete Laplacian are the vibration modes of the grid.\n", - "The smallest eigenvalue is always 0 (constant mode); subsequent modes capture\n", - "increasingly oscillatory patterns." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "bdaf64d7", - "metadata": {}, - "outputs": [], - "source": [ - "L = grid.laplacian()\n", - "vals, vecs = sparse_eigsh(L.astype(float), k=9, which='SM')\n", - "vals, vecs = sorteig(vals, vecs)\n", - "\n", - "plot_eigenmodes(X, Y, vals, vecs, ny, nx)" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "esapp", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.11.14" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/examples/visualization/02_spectral_analysis.ipynb b/examples/visualization/02_spectral_analysis.ipynb deleted file mode 100644 index e11c9ce..0000000 --- a/examples/visualization/02_spectral_analysis.ipynb +++ /dev/null @@ -1,294 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "a1b2c3d4", - "metadata": {}, - "source": [ - "# Spectral Analysis & Linear Algebra Utilities\n", - "\n", - "Demonstrates spectral graph tools, matrix decompositions, and visualization\n", - "helpers from `esapp.utils`. The notebook covers vector field visualization,\n", - "path and cycle graph Laplacians, normalized Laplacian spectral analysis,\n", - "Takagi factorization for complex symmetric matrices, the Hermitify\n", - "transformation, custom colormaps, and physical constants." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "hide-cell" - ] - }, - "outputs": [], - "source": [ - "import sys; sys.path.insert(0, \"..\")\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "from mesh import (\n", - " pathlap, pathincidence, normlap,\n", - " hermitify, MU0,\n", - ")\n", - "from map import format_plot, plot_vecfield, darker_hsv_colormap" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "hide-cell" - ] - }, - "outputs": [], - "source": [ - "# Plotting functions (hidden from documentation)\n", - "import sys; sys.path.insert(0, \"..\")\n", - "from plot_helpers import (\n", - " plot_vecfield_gallery, plot_graph_operators,\n", - " plot_normlap_spectrum, plot_hermitify, plot_colormap_scales,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "id": "c9d0e1f2", - "metadata": {}, - "source": [ - "## 1. Vector Field Gallery\n", - "\n", - "Several vector fields plotted with `plot_vecfield`, which color-codes arrows by angle." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "X, Y = np.meshgrid(np.linspace(-2, 2, 30), np.linspace(-2, 2, 30))\n", - "Xc, Yc = X - 0.0, Y - 0.0\n", - "\n", - "fields = {\n", - " 'Source': (Xc, Yc),\n", - " 'Vortex': (-Yc, Xc),\n", - " 'Saddle': (Xc, -Yc),\n", - " 'Shear': (Yc, np.zeros_like(Yc)),\n", - "}\n", - "\n", - "plot_vecfield_gallery(X, Y, fields)" - ] - }, - { - "cell_type": "markdown", - "id": "e7f8a9b0", - "metadata": {}, - "source": [ - "## 2. Graph Laplacians: Path and Cycle\n", - "\n", - "`pathlap` and `pathincidence` construct Laplacians and incidence matrices for path and cycle graphs." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "graph_setup", - "metadata": { - "tags": [ - "hide-cell" - ] - }, - "outputs": [], - "source": [ - "# Build path and cycle graph operators\n", - "N = 8\n", - "L_path = pathlap(N, periodic=False)\n", - "B_path = pathincidence(N, periodic=False)\n", - "L_cycle = pathlap(N, periodic=True)\n", - "B_cycle = pathincidence(N, periodic=True)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "plot_graph_operators(\n", - " [L_path, L_cycle, B_path, B_cycle],\n", - " ['Path Laplacian', 'Cycle Laplacian', 'Path Incidence', 'Cycle Incidence'],\n", - " vranges=[(-2, 2), (-2, 2), (-1, 1), (-1, 1)],\n", - " suptitle=f'Graph Operators (N={N})')" - ] - }, - { - "cell_type": "markdown", - "id": "a5b6c7d8", - "metadata": {}, - "source": [ - "### Verify L = B @ B.T\n", - "\n", - "For the **cycle** graph, `pathincidence` returns an N x N matrix (N edges), so `B @ B.T`\n", - "matches the Laplacian directly. For the **path** graph, only the first N-1 columns\n", - "represent real edges." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "e9f0a1b2", - "metadata": {}, - "outputs": [], - "source": [ - "# Cycle: B is N x N (N edges), so B @ B.T == L directly\n", - "L_cycle_check = B_cycle @ B_cycle.T\n", - "print(\"Cycle: L == B @ B.T:\", np.allclose(L_cycle, L_cycle_check))\n", - "\n", - "# Path: only first N-1 columns are real edges\n", - "B_path_trimmed = B_path[:, :N-1]\n", - "L_path_check = B_path_trimmed @ B_path_trimmed.T\n", - "print(\"Path: L == B[:,:N-1] @ B[:,:N-1].T:\", np.allclose(L_path, L_path_check))" - ] - }, - { - "cell_type": "markdown", - "id": "c3d4e5f6", - "metadata": {}, - "source": [ - "## 3. Normalized Laplacian and Spectral Analysis" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "L_norm, D, D_inv = normlap(L_cycle, return_scaling=True)\n", - "evals = np.linalg.eigvalsh(L_norm)\n", - "\n", - "plot_normlap_spectrum(L_norm, evals)\n", - "\n", - "print(f'Largest eigenvalue (eigmax): {eigmax(L_cycle):.4f}')" - ] - }, - { - "cell_type": "markdown", - "id": "e1f2a3b4", - "metadata": {}, - "source": [ - "## 4. Takagi Factorization\n", - "\n", - "Decomposes a complex symmetric matrix M = U * Sigma * U^T." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "c5d6e7f8", - "metadata": {}, - "outputs": [], - "source": [ - "# Create a complex symmetric matrix\n", - "np.random.seed(42)\n", - "A = np.random.randn(4, 4) + 1j * np.random.randn(4, 4)\n", - "M = A + A.T # symmetrize (M = M^T, not M = M^H)\n", - "\n", - "U, sigma = takagi(M)\n", - "\n", - "print(\"Singular values:\", np.round(sigma, 4))\n", - "\n", - "# Verify: M = U @ diag(sigma) @ U.T\n", - "M_reconstructed = U @ np.diag(sigma) @ U.T\n", - "print(f\"Reconstruction error: {np.linalg.norm(M - M_reconstructed):.2e}\")" - ] - }, - { - "cell_type": "markdown", - "id": "a9b0c1d2", - "metadata": {}, - "source": [ - "## 5. Hermitify\n", - "\n", - "Converts a complex symmetric matrix to Hermitian form." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "H = hermitify(M)\n", - "\n", - "print('Original symmetric (M = M^T):', np.allclose(M, M.T))\n", - "print('Hermitified (H = H^H): ', np.allclose(H, H.conj().T))\n", - "\n", - "plot_hermitify(M, H)" - ] - }, - { - "cell_type": "markdown", - "id": "c7d8e9f0", - "metadata": {}, - "source": [ - "## 6. Custom Colormaps\n", - "\n", - "The `darker_hsv_colormap` creates a darker version of the HSV colormap, useful for vector field angle encoding." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "plot_colormap_scales([1.0, 0.7, 0.4])" - ] - }, - { - "cell_type": "markdown", - "id": "e5f6a7c8", - "metadata": {}, - "source": [ - "## 7. Physical Constants\n", - "\n", - "The `MU0` constant provides the permeability of free space, used in GIC electric field calculations." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "c9d0e1g2", - "metadata": {}, - "outputs": [], - "source": [ - "print(f\"MU0 = {MU0:.6e} H/m\")\n", - "print(f\"Used in GIC: E = -MU0 * dH/dt\")" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "esaplus", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.11.14" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} From f74839a66859931f793ceb5f6d4e23c547a05223 Mon Sep 17 00:00:00 2001 From: Wyatt Lowery Date: Mon, 31 Aug 2026 15:20:18 -0500 Subject: [PATCH 04/10] cleanup --- docs/dev/tests.rst | 2 +- examples/README.md | 1 - examples/network/02_network_topology.ipynb | 80 ++------ examples/plot_helpers.py | 175 ----------------- .../03_geographic_plotting.ipynb | 180 ------------------ 5 files changed, 14 insertions(+), 424 deletions(-) delete mode 100644 examples/visualization/03_geographic_plotting.ipynb diff --git a/docs/dev/tests.rst b/docs/dev/tests.rst index 8b51fd1..93ada1c 100644 --- a/docs/dev/tests.rst +++ b/docs/dev/tests.rst @@ -26,7 +26,7 @@ Test Coverage * - ``test_dynamics.py`` - Dynamics module: ContingencyBuilder, SimAction enum * - ``test_utils.py`` - - Utility modules: timing decorator, B3D file format + - Utility modules: B3D file format * - ``test_buscat_unit.py`` - BusCat string parsing: all known PowerWorld BusCat variants (Slack, PV, PQ with controls/roles/limits) diff --git a/examples/README.md b/examples/README.md index 811a8d1..f4d1a7d 100644 --- a/examples/README.md +++ b/examples/README.md @@ -30,7 +30,6 @@ notebook's own directory as the working directory (the Jupyter default). | `steady_state/` | Contingency analysis, SCOPF, ATC, and CPF examples | | `gic/` | GIC analysis and sensitivity examples | | `network/` | Network topology and matrix extraction examples | -| `visualization/` | Geographic plotting utilities demo | ## Case Configuration diff --git a/examples/network/02_network_topology.ipynb b/examples/network/02_network_topology.ipynb index feb13b2..3bdda3b 100644 --- a/examples/network/02_network_topology.ipynb +++ b/examples/network/02_network_topology.ipynb @@ -5,7 +5,12 @@ "id": "a1b2c3d4", "metadata": {}, "source": [ - "# Network Topology Analysis\n\nDemonstrates graph-theoretic analysis of power system networks using the\n`Network` application module. The notebook covers bus-to-index mapping,\nweighted Laplacian construction (by length, impedance, and propagation\ndelay), branch parameter distributions, spectral decomposition, and Fiedler\nvector visualization for identifying natural network partitions." + "# Network Topology Analysis\n", + "\n", + "Demonstrates graph-theoretic analysis of power system networks using the\n", + "`Network` application module. The notebook covers bus-to-index mapping,\n", + "weighted Laplacian construction (by length, impedance, and propagation\n", + "delay), and branch parameter distributions.\n" ] }, { @@ -19,18 +24,9 @@ "outputs": [], "source": [ "import sys; sys.path.insert(0, \"..\")\n", - "import numpy as np\n", - "from scipy.sparse.linalg import eigsh\n", "from esapp import PowerWorld\n", "from esapp.components import Branch, Bus\n", - "from esapp.utils import BranchType\n", - "from map import format_plot\n", - "\n", - "\n", - "def sorteig(vals, vecs):\n", - " \"\"\"Sort eigenpairs by ascending eigenvalue.\"\"\"\n", - " order = np.argsort(vals)\n", - " return vals[order], vecs[:, order]" + "from esapp.utils import BranchType\n" ] }, { @@ -66,8 +62,7 @@ "# Plotting functions (hidden from documentation)\n", "import sys; sys.path.insert(0, \"..\")\n", "from plot_helpers import (\n", - " plot_incidence_and_degree, plot_spy_matrices,\n", - " plot_histograms, plot_eigenspectrum, plot_fiedler,\n", + " plot_incidence_and_degree, plot_spy_matrices, plot_histograms,\n", ")" ] }, @@ -151,65 +146,16 @@ "print(f'|Z| range: [{zmag.min():.6f}, {zmag.max():.6f}] pu')" ] }, - { - "cell_type": "markdown", - "id": "e1f2a3b4", - "metadata": {}, - "source": [ - "## 4. Spectral Analysis\n", - "\n", - "The eigenvalues of the Laplacian encode the network's structural properties.\n", - "The algebraic connectivity (second-smallest eigenvalue) measures how well-connected\n", - "the network is." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "k = min(10, L_len.shape[0] - 1)\n", - "vals_len, vecs_len = eigsh(L_len.astype(float), k=k, which='SM')\n", - "vals_len, vecs_len = sorteig(vals_len, vecs_len)\n", - "\n", - "vals_res, vecs_res = eigsh(L_res.astype(float), k=k, which='SM')\n", - "vals_res, vecs_res = sorteig(vals_res, vecs_res)\n", - "\n", - "plot_eigenspectrum([vals_len, vals_res],\n", - " ['Length-Weighted Eigenvalues', 'Impedance-Weighted Eigenvalues'])\n", - "\n", - "print(f'Algebraic connectivity (length): {vals_len[1]:.6f}')\n", - "print(f'Algebraic connectivity (impedance): {vals_res[1]:.6f}')" - ] - }, - { - "cell_type": "markdown", - "id": "a3b4c5d6", - "metadata": {}, - "source": [ - "## 5. Fiedler Vector Visualization\n", - "\n", - "The Fiedler vector (eigenvector of the second-smallest eigenvalue) reveals the\n", - "natural partition of the network into two clusters." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "fiedler = vecs_len[:, 1]\n", - "plot_fiedler(fiedler)" - ] - }, { "cell_type": "markdown", "id": "c5d6e7f8", "metadata": {}, "source": [ - "## Summary\n\nThe network module provides graph-theoretic tools for power system topology.\nWeighted Laplacians encode connectivity under different physical metrics,\nand their spectral decomposition reveals structural properties such as\nalgebraic connectivity and natural clustering via the Fiedler vector." + "## Summary\n", + "\n", + "The network module provides graph-theoretic tools for power system\n", + "topology: bus mapping, incidence matrices, and weighted Laplacians that\n", + "encode connectivity under different physical metrics.\n" ] } ], diff --git a/examples/plot_helpers.py b/examples/plot_helpers.py index 2d20a4b..065ce87 100644 --- a/examples/plot_helpers.py +++ b/examples/plot_helpers.py @@ -514,48 +514,6 @@ def plot_incidence_and_laplacian(A, figsize=(_W2, _H2)): plt.show() -def plot_eigenspectrum(eigenvalue_sets, titles, figsize=None): - """Stem plots of eigenvalue arrays (always >= 2 panels).""" - n = max(len(eigenvalue_sets), 2) - if figsize is None: - figsize = (min(_WFULL, 3.2 * n), _H2) - fig, axes = plt.subplots(1, n, figsize=figsize) - if n == 1: - axes = [axes] - fs = _FS3 if n >= 3 else _FS2 - for ax, vals, t in zip(axes, eigenvalue_sets, titles): - ax.stem(vals, basefmt=' ') - format_plot(ax, title=t, xlabel='Index', ylabel='Eigenvalue', - plotarea='white', **fs) - for j in range(len(eigenvalue_sets), n): - axes[j].set_visible(False) - plt.tight_layout() - plt.show() - - -def plot_fiedler(fiedler, figsize=(_W2, _H2)): - """Fiedler vector bar chart colored by sign + partition histogram (2-panel).""" - fig, axes = plt.subplots(1, 2, figsize=figsize) - - colors = [_C1 if v >= 0 else _C2 for v in fiedler] - axes[0].bar(range(len(fiedler)), fiedler, color=colors) - axes[0].axhline(y=0, color='black', linewidth=0.5) - format_plot(axes[0], title='Fiedler Vector (Network Partition)', - xlabel='Bus index', ylabel='Fiedler component', - plotarea='white', **_FS2) - - axes[1].hist(fiedler, bins=15, color=_C1, edgecolor='white') - axes[1].axvline(x=0, color='black', linewidth=0.5) - n_pos = sum(1 for v in fiedler if v >= 0) - n_neg = len(fiedler) - n_pos - axes[1].set_title(f'Partition: {n_pos} vs {n_neg} buses', fontsize=11) - format_plot(axes[1], xlabel='Component value', ylabel='Count', - plotarea='white', **_FS2) - - plt.tight_layout() - plt.show() - - def plot_histograms(datasets, titles, xlabels, colors=None, bins=25, figsize=None): """Side-by-side histograms (always >= 2 panels).""" n = max(len(datasets), 2) @@ -1037,136 +995,3 @@ def plot_comparative_dynamics(ctg_names, all_results, figsize=None): # --------------------------------------------------------------------------- -def plot_colormap_2d(LON, LAT, theta, scales, figsize=(_WFULL, 4)): - """1D gradient + 2D angle field at multiple colormap scales.""" - gradient = np.linspace(-np.pi, np.pi, 256).reshape(1, -1) - - fig, axes = plt.subplots(2, len(scales), figsize=figsize) - fs = _FS3 if len(scales) >= 3 else {} - for ax, scale in zip(axes[0], scales): - cmap = darker_hsv_colormap(scale) - ax.imshow(gradient, aspect='auto', cmap=cmap) - format_plot(ax, title=f'scale={scale}', plotarea='white', - grid=False, **fs) - ax.set_yticks([]) - for ax, scale in zip(axes[1], scales): - cmap = darker_hsv_colormap(scale) - ax.pcolormesh(LON, LAT, theta, cmap=cmap, shading='auto') - format_plot(ax, title=f'Angle field (scale={scale})', - plotarea='white', grid=False, **fs) - ax.set_aspect('equal') - plt.suptitle('darker_hsv_colormap at Different Scales', fontsize=12) - plt.tight_layout() - plt.show() - - -def plot_borders(shapes, figsize=(_W2, _H2)): - """Side-by-side geographic borders.""" - fig, axes = plt.subplots(1, len(shapes), figsize=figsize) - if len(shapes) == 1: - axes = [axes] - fs = _FS3 if len(shapes) >= 3 else {} - for ax, shape in zip(axes, shapes): - border(ax, shape) - format_plot(ax, title=f'{shape} Border', - xlabel=r'Longitude ($^\circ$E)', - ylabel=r'Latitude ($^\circ$N)', - plotarea='white', grid=False, **fs) - ax.set_aspect('equal') - plt.tight_layout() - plt.show() - - -def plot_network_map(lines, lon, lat, shape, pad=0.5, - figsize=(_W2, 2.8), ax=None, fig=None): - """Transmission network on geographic background.""" - show = ax is None - if ax is None: - fig, ax = plt.subplots(figsize=figsize) - border(ax, shape) - plot_lines(ax, lines, ms=8, lw=0.8) - ax.set_xlim(lon.min() - pad, lon.max() + pad) - ax.set_ylim(lat.min() - pad, lat.max() + pad) - format_plot(ax, title='Transmission Network', - xlabel=r'Lon ($^\circ$E)', - ylabel=r'Lat ($^\circ$N)', - plotarea='white', grid=False, **_FS2) - ax.set_aspect('equal') - if show: - plt.tight_layout() - plt.show() - return ax - - -def plot_bus_voltages_map(lines, lon, lat, vmag, shape, pad=0.5, - figsize=(_W2, 2.8), ax=None, fig=None): - """Bus voltages colored on geographic map with network overlay.""" - show = ax is None - if ax is None: - fig, ax = plt.subplots(figsize=figsize) - border(ax, shape) - plot_lines(ax, lines, ms=3, lw=0.6) - - sc = ax.scatter(lon, lat, s=30, c=vmag, cmap='RdYlGn', vmin=0.95, vmax=1.05, - zorder=6, edgecolors='black', linewidth=0.4) - if fig is not None: - fig.colorbar(sc, ax=ax, label='V (pu)', shrink=0.7) - - ax.set_xlim(lon.min() - pad, lon.max() + pad) - ax.set_ylim(lat.min() - pad, lat.max() + pad) - format_plot(ax, title='Bus Voltages', - xlabel=r'Lon ($^\circ$E)', - ylabel=r'Lat ($^\circ$N)', - plotarea='white', grid=False, **_FS2) - ax.set_aspect('equal') - if show: - plt.tight_layout() - plt.show() - return ax - - -def plot_vecfield_map(LON, LAT, Ex, Ey, lines, shape, - figsize=(_W2, 2.8), ax=None, fig=None): - """Vector field over network with geographic border.""" - show = ax is None - if ax is None: - fig, ax = plt.subplots(figsize=figsize) - border(ax, shape) - plot_lines(ax, lines, ms=3, lw=0.4) - - sm = plot_vecfield(ax, LON, LAT, Ex, Ey, scale=30, width=0.003) - if fig is not None: - fig.colorbar(sm, ax=ax, label='Angle (rad)', shrink=0.7) - - ax.set_xlim(LON.min(), LON.max()) - ax.set_ylim(LAT.min(), LAT.max()) - format_plot(ax, title='Vector Field over Network', - xlabel=r'Lon ($^\circ$E)', - ylabel=r'Lat ($^\circ$N)', - grid=False, **_FS2) - ax.set_aspect('equal') - if show: - plt.tight_layout() - plt.show() - return ax - - -def plot_format_showcase(x_data, figsize=(_W3, _H3)): - """Showcase of format_plot styling options (3-panel).""" - fig, axes = plt.subplots(1, 3, figsize=figsize) - - axes[0].plot(x_data, np.sin(x_data), 'o-', markersize=3, color=_C1) - format_plot(axes[0], title='Default Style', xlabel='x', ylabel='sin(x)', - **_FS3) - - axes[1].plot(x_data, np.cos(x_data), 'o-', markersize=3, color=_C2) - format_plot(axes[1], title='Colored Background', xlabel='x', ylabel='cos(x)', - plotarea='#f0f0f0', **_FS3) - - axes[2].plot(x_data, np.sin(x_data) * np.exp(-x_data / 5), 'o-', - markersize=3, color=_C3) - format_plot(axes[2], title='Custom Ticks', xlabel='x', ylabel='y', - xlim=(0, 10), ylim=(-1, 1), xticksep=2.5, yticksep=0.5, **_FS3) - - plt.tight_layout() - plt.show() diff --git a/examples/visualization/03_geographic_plotting.ipynb b/examples/visualization/03_geographic_plotting.ipynb deleted file mode 100644 index 78d0ea1..0000000 --- a/examples/visualization/03_geographic_plotting.ipynb +++ /dev/null @@ -1,180 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "a1b2c3d4e5", - "metadata": {}, - "source": [ - "# Geographic Plotting Utilities\n", - "\n", - "Demonstrates the geographic visualization functions in `examples.map` for\n", - "plotting power system data on geographic coordinates. The notebook covers\n", - "border overlays from bundled shapefiles, bus voltage visualization with\n", - "transmission network overlays, vector field quiver plots, plot formatting\n", - "options, and custom colormaps." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "hide-cell" - ] - }, - "outputs": [], - "source": [ - "import sys; sys.path.insert(0, \"..\")\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "from map import (\n", - " format_plot, border, plot_lines, plot_vecfield,\n", - " darker_hsv_colormap,\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "hide-cell" - ] - }, - "outputs": [], - "source": [ - "# Plotting functions (hidden from documentation)\n", - "import sys; sys.path.insert(0, \"..\")\n", - "from plot_helpers import (\n", - " plot_borders, plot_network_map, plot_bus_voltages_map,\n", - " plot_vecfield_map, plot_format_showcase, plot_colormap_2d,\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "z6a7b8c9d0", - "metadata": { - "tags": [ - "hide-cell" - ] - }, - "outputs": [], - "source": [ - "# This cell is hidden in the documentation.\n", - "from esapp import PowerWorld\n", - "from esapp.components import Branch, Bus\n", - "import ast\n", - "\n", - "with open('../data/case.txt', 'r') as f:\n", - " case_path = ast.literal_eval(f.read().strip())\n", - "\n", - "pw = PowerWorld(case_path)\n", - "\n", - "# Configure geographic border shape ('US', 'Texas', etc.)\n", - "SHAPE = 'US'" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Branch endpoint and bus coordinates for map plotting\n", - "lines = pw[Branch, ['Longitude', 'Longitude:1', 'Latitude', 'Latitude:1']]\n", - "bus_coords = pw[Bus, ['Longitude', 'Latitude']]\n", - "lon = bus_coords['Longitude'].values\n", - "lat = bus_coords['Latitude'].values" - ] - }, - { - "cell_type": "markdown", - "id": "j6k7l8m9n0", - "metadata": {}, - "source": [ - "## Network Visualization\n", - "\n", - "The function draws transmission lines from a DataFrame with\n", - "endpoint coordinates. Here we combine bus voltage scatter with\n", - "transmission lines and geographic borders." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "V = pw.pflow()\n", - "vmag = np.abs(V)\n", - "\n", - "fig, axes = plt.subplots(1, 2, figsize=(6.5, 2.8))\n", - "plot_network_map(lines, lon, lat, SHAPE, ax=axes[0], fig=fig)\n", - "plot_bus_voltages_map(lines, lon, lat, vmag, SHAPE, ax=axes[1], fig=fig)\n", - "plt.tight_layout()\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "t6u7v8w9x0", - "metadata": {}, - "source": [ - "## Vector Field on Geographic Coordinates\n", - "\n", - "The `plot_vecfield()` function plots arrows colored by angle, useful for\n", - "electric field or power flow visualizations." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Create a synthetic vector field over the geographic area\n", - "pad = 0.5\n", - "lon_min, lon_max = lon.min() - pad, lon.max() + pad\n", - "lat_min, lat_max = lat.min() - pad, lat.max() + pad\n", - "\n", - "nx_v, ny_v = 20, 15\n", - "lons = np.linspace(lon_min, lon_max, nx_v)\n", - "lats = np.linspace(lat_min, lat_max, ny_v)\n", - "LON, LAT = np.meshgrid(lons, lats)\n", - "\n", - "Ex = 0.3 * np.sin(2 * np.pi * (LON - lon_min) / (lon_max - lon_min))\n", - "Ey = np.ones_like(LON)\n", - "\n", - "fig, axes = plt.subplots(1, 2, figsize=(6.5, 2.8))\n", - "plot_network_map(lines, lon, lat, SHAPE, ax=axes[0], fig=fig)\n", - "axes[0].set_title('Network Topology', fontsize=11)\n", - "plot_vecfield_map(LON, LAT, Ex, Ey, lines, SHAPE, ax=axes[1], fig=fig)\n", - "plt.tight_layout()\n", - "plt.show()" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "esapp", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.11.14" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} From 884559ed57c58418744d98bb7424a651e73e5c4c Mon Sep 17 00:00:00 2001 From: Wyatt Lowery Date: Mon, 31 Aug 2026 15:26:51 -0500 Subject: [PATCH 05/10] removal of unrelated exampels --- docs/conf.py | 7 - examples/README.md | 2 - examples/dynamics/02_multi_contingency.ipynb | 2 +- examples/gic/01_gic_basics.ipynb | 190 ------- examples/gic/02_gic_model.ipynb | 244 -------- examples/gic/03_efield_geographic.ipynb | 401 ------------- examples/gic/04_b3d_file_io.ipynb | 190 ------- examples/gic/05_gic_sensitivity.ipynb | 256 --------- examples/map.py | 326 ----------- examples/plot_helpers.py | 406 +------------ examples/shapes/Texas/Shape.cpg | 1 - examples/shapes/Texas/Shape.dbf | Bin 282 -> 0 bytes examples/shapes/Texas/Shape.prj | 1 - examples/shapes/Texas/Shape.shp | Bin 1446492 -> 0 bytes examples/shapes/Texas/Shape.shx | Bin 108 -> 0 bytes examples/shapes/US/Shape.cpg | 1 - examples/shapes/US/Shape.dbf | Bin 830 -> 0 bytes examples/shapes/US/Shape.prj | 1 - examples/shapes/US/Shape.shp | Bin 112648 -> 0 bytes examples/shapes/US/Shape.shp.ea.iso.xml | 254 --------- examples/shapes/US/Shape.shx | Bin 132 -> 0 bytes examples/shapes/US/Shape.xml | 531 ------------------ examples/steady_state/02_scopf_analysis.ipynb | 2 +- .../steady_state/05_ptdf_lodf_analysis.ipynb | 13 +- .../07_state_chains_and_stress.ipynb | 5 +- 25 files changed, 23 insertions(+), 2810 deletions(-) delete mode 100644 examples/gic/01_gic_basics.ipynb delete mode 100644 examples/gic/02_gic_model.ipynb delete mode 100644 examples/gic/03_efield_geographic.ipynb delete mode 100644 examples/gic/04_b3d_file_io.ipynb delete mode 100644 examples/gic/05_gic_sensitivity.ipynb delete mode 100644 examples/map.py delete mode 100644 examples/shapes/Texas/Shape.cpg delete mode 100644 examples/shapes/Texas/Shape.dbf delete mode 100644 examples/shapes/Texas/Shape.prj delete mode 100644 examples/shapes/Texas/Shape.shp delete mode 100644 examples/shapes/Texas/Shape.shx delete mode 100644 examples/shapes/US/Shape.cpg delete mode 100644 examples/shapes/US/Shape.dbf delete mode 100644 examples/shapes/US/Shape.prj delete mode 100644 examples/shapes/US/Shape.shp delete mode 100644 examples/shapes/US/Shape.shp.ea.iso.xml delete mode 100644 examples/shapes/US/Shape.shx delete mode 100644 examples/shapes/US/Shape.xml diff --git a/docs/conf.py b/docs/conf.py index 8392b42..1c9e90d 100644 --- a/docs/conf.py +++ b/docs/conf.py @@ -82,13 +82,6 @@ def setup(app): exclude_patterns = [ "_build", - "**/*.cpg", - "**/*.dbf", - "**/*.prj", - "**/*.shp", - "**/*.shx", - "**/Shape.xml", - "**/Shape.shp.ea.iso.xml", "**/PWRaw", ] diff --git a/examples/README.md b/examples/README.md index f4d1a7d..34af9e2 100644 --- a/examples/README.md +++ b/examples/README.md @@ -19,7 +19,6 @@ notebook's own directory as the working directory (the Jupyter default). | Module | Description | |---|---| -| `map.py` | Geographic visualization (borders, lines, vector fields) | | `plot_helpers.py` | Shared plotting functions for all notebooks | ## Notebooks @@ -28,7 +27,6 @@ notebook's own directory as the working directory (the Jupyter default). |---|---| | `dynamics/` | Transient stability simulation examples | | `steady_state/` | Contingency analysis, SCOPF, ATC, and CPF examples | -| `gic/` | GIC analysis and sensitivity examples | | `network/` | Network topology and matrix extraction examples | ## Case Configuration diff --git a/examples/dynamics/02_multi_contingency.ipynb b/examples/dynamics/02_multi_contingency.ipynb index f3d19e1..cf4644f 100644 --- a/examples/dynamics/02_multi_contingency.ipynb +++ b/examples/dynamics/02_multi_contingency.ipynb @@ -22,7 +22,7 @@ "import matplotlib.pyplot as plt\n", "from esapp import PowerWorld, TS\n", "from esapp.components import Bus, Gen\n", - "from map import format_plot" + "from plot_helpers import format_plot" ] }, { diff --git a/examples/gic/01_gic_basics.ipynb b/examples/gic/01_gic_basics.ipynb deleted file mode 100644 index dcae355..0000000 --- a/examples/gic/01_gic_basics.ipynb +++ /dev/null @@ -1,190 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "fa1b2c3d", - "metadata": {}, - "source": [ - "# GIC Basics\n\nIntroduces the GIC module for computing geomagnetically induced currents\nin power systems. The notebook walks through configuring a uniform E-field\nstorm, retrieving transformer GIC currents, visualizing the distribution,\nand sweeping storm direction to identify the worst-case orientation." - ] - }, - { - "cell_type": "markdown", - "id": "fb2c3d4e", - "metadata": {}, - "source": "Import the case and instantiate the `PowerWorld`.\n\n```python\nfrom esapp import PowerWorld\nfrom esapp.components import *\n\npw = PowerWorld(case_path)\n```" - }, - { - "cell_type": "code", - "execution_count": null, - "id": "fc3d4e5f", - "metadata": { - "tags": [ - "hide-cell" - ] - }, - "outputs": [], - "source": [ - "# This cell is hidden in the documentation.\n", - "from esapp import PowerWorld\n", - "from esapp.components import *\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "import ast\n", - "\n", - "with open('../data/case.txt', 'r') as f:\n", - " case_path = ast.literal_eval(f.read().strip())\n", - "\n", - "pw = PowerWorld(case_path)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "670cab8a", - "metadata": { - "tags": [ - "hide-cell" - ] - }, - "outputs": [], - "source": [ - "# Plotting functions (hidden from documentation)\n", - "import sys; sys.path.insert(0, \"..\")\n", - "from plot_helpers import plot_gic_distribution, plot_direction_sensitivity" - ] - }, - { - "cell_type": "markdown", - "id": "fd4e5f6a", - "metadata": {}, - "source": [ - "## Calculate GIC Response\n", - "\n", - "Compute geomagnetically induced currents for a uniform electric field. This calculates harmonic currents in transformers due to a 1.0 V/km electric field oriented at 90 degrees:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "fe5f6a7b", - "metadata": {}, - "outputs": [], - "source": [ - "pw.gic.storm(max_field=1.0, direction=90.0)" - ] - }, - { - "cell_type": "markdown", - "id": "ff6a7b8c", - "metadata": {}, - "source": [ - "## Retrieve GIC Results\n", - "\n", - "Extract GIC neutral currents from the transformers to identify which components experience the largest impacts:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "a07b8c9d", - "metadata": {}, - "outputs": [], - "source": [ - "gics = pw[GICXFormer, ['BusNum3W', 'BusNum3W:1', 'GICXFNeutralAmps']]\n", - "gics.head()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "a18c9d0e", - "metadata": {}, - "outputs": [], - "source": [ - "max_gic = gics['GICXFNeutralAmps'].abs().max()\n", - "print(f\"Maximum |GIC|: {max_gic:.3f} Amps\")" - ] - }, - { - "cell_type": "markdown", - "id": "a29d0e1f", - "metadata": {}, - "source": [ - "### GIC Distribution\n", - "\n", - "Visualize the distribution of GIC magnitudes across all transformers." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "3869bdf3", - "metadata": {}, - "outputs": [], - "source": [ - "gic_abs = gics['GICXFNeutralAmps'].abs().sort_values(ascending=False)\n", - "plot_gic_distribution(gic_abs)" - ] - }, - { - "cell_type": "markdown", - "id": "a41f2a3b", - "metadata": {}, - "source": [ - "## Storm Direction Sensitivity\n", - "\n", - "Sweep the E-field direction to find which orientation produces the worst-case GIC." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "a52a3b4c", - "metadata": {}, - "outputs": [], - "source": [ - "directions = np.arange(0, 361, 10)\n", - "max_gics = []\n", - "\n", - "for d in directions:\n", - " pw.gic.storm(max_field=1.0, direction=d)\n", - " gic_vals = pw[GICXFormer, 'GICXFNeutralAmps']['GICXFNeutralAmps']\n", - " max_gics.append(gic_vals.abs().max())\n", - "\n", - "max_gics = np.array(max_gics)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "18590645", - "metadata": {}, - "outputs": [], - "source": [ - "plot_direction_sensitivity(directions, max_gics)" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "esaplus", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.11.14" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/examples/gic/02_gic_model.ipynb b/examples/gic/02_gic_model.ipynb deleted file mode 100644 index 579f37d..0000000 --- a/examples/gic/02_gic_model.ipynb +++ /dev/null @@ -1,244 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "a1b2c3d4e5f6", - "metadata": {}, - "source": "# GIC Linear Model\n\nExplores the internal structure of the GIC linear model built by\n`pw.gic.model()`. The notebook covers GIC configuration, construction\nof the G-matrix (conductance Laplacian) and H-matrix (linear mapping\nfrom induced voltages to transformer neutral currents), comparison\nwith PowerWorld's own G-matrix, and storm application with result\nvalidation." - }, - { - "cell_type": "code", - "execution_count": null, - "id": "3b21282a", - "metadata": { - "tags": [ - "hide-cell" - ] - }, - "outputs": [], - "source": [ - "import sys; sys.path.insert(0, \"..\")\n", - "import numpy as np\n", - "from esapp import PowerWorld\n", - "from esapp.components import Bus, Branch, Substation, GICXFormer\n", - "from map import format_plot" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "c3d4e5f6a7b8", - "metadata": { - "tags": [ - "hide-cell" - ] - }, - "outputs": [], - "source": [ - "# This cell is hidden in the documentation.\n", - "import ast\n", - "\n", - "with open('../data/case.txt', 'r') as f:\n", - " case_path = ast.literal_eval(f.read().strip())\n", - "\n", - "pw = PowerWorld(case_path)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "01173666", - "metadata": { - "tags": [ - "hide-cell" - ] - }, - "outputs": [], - "source": [ - "# Plotting functions (hidden from documentation)\n", - "import sys; sys.path.insert(0, \"..\")\n", - "from plot_helpers import (\n", - " plot_spy_matrices, plot_gmatrix_comparison,\n", - " plot_gic_bar_hist,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "id": "d4e5f6a7b8c9", - "metadata": {}, - "source": [ - "## GIC Configuration\n", - "\n", - "Before building a GIC model, configure the GIC options. The `configure()` method\n", - "sets sensible defaults." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "e5f6a7b8c9d0", - "metadata": {}, - "outputs": [], - "source": [ - "pw.gic.configure(pf_include=True, ts_include=False, calc_mode='SnapShot')\n", - "\n", - "pw.gic.settings()" - ] - }, - { - "cell_type": "markdown", - "id": "f6a7b8c9d0e1", - "metadata": {}, - "source": [ - "## 2. Building the GIC Model\n", - "\n", - "The `model()` method extracts substation, bus, branch, transformer, and generator\n", - "data from the case and computes all GIC matrices." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "a7b8c9d0e1f2", - "metadata": {}, - "outputs": [], - "source": [ - "pw.gic.model()\n", - "\n", - "print(f\"Incidence matrix (A): {pw.gic.A.shape} (branches x nodes)\")\n", - "print(f\"G-matrix: {pw.gic.G.shape} (nodes x nodes)\")\n", - "print(f\"H-matrix: {pw.gic.H.shape} (transformers x branches)\")\n", - "print(f\"Zeta (per-unit): {pw.gic.zeta.shape}\")\n", - "print(f\"Effective operator: {pw.gic.eff.shape}\")\n", - "print(f\"Bus permutation (Px): {pw.gic.Px.shape}\")" - ] - }, - { - "cell_type": "markdown", - "id": "b8c9d0e1f2a3", - "metadata": {}, - "source": [ - "## 3. Matrix Sparsity Patterns\n", - "\n", - "Visualize the sparsity structure of the GIC matrices using spy plots." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "37fb8c5e", - "metadata": {}, - "outputs": [], - "source": [ - "plot_spy_matrices(\n", - " [pw.gic.A, pw.gic.G, pw.gic.H],\n", - " [f'Incidence Matrix A\\n{pw.gic.A.shape}, nnz={pw.gic.A.nnz}',\n", - " f'G-Matrix (Conductance Laplacian)\\n{pw.gic.G.shape}, nnz={pw.gic.G.nnz}',\n", - " f'H-Matrix (GIC Function)\\n{pw.gic.H.shape}, nnz={pw.gic.H.nnz}'])" - ] - }, - { - "cell_type": "markdown", - "id": "f2a3b4c5d6e7", - "metadata": {}, - "source": [ - "## 5. Storm Application and GIC Results\n", - "\n", - "Apply a uniform electric field storm and examine the resulting transformer GICs." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "a3b4c5d6e7f8", - "metadata": {}, - "outputs": [], - "source": [ - "# Apply 1 V/km eastward storm\n", - "pw.gic.storm(1.0, 90)\n", - "\n", - "gic_results = pw[GICXFormer, ['BusNum3W', 'BusNum3W:1', 'GICXFNeutralAmps']]\n", - "gic_sorted = gic_results.reindex(\n", - " gic_results['GICXFNeutralAmps'].abs().sort_values(ascending=False).index\n", - ")\n", - "\n", - "print(f\"Total transformers: {len(gic_results)}\")\n", - "print(f\"Max |GIC|: {gic_results['GICXFNeutralAmps'].abs().max():.3f} A\")\n", - "print(f\"\\nTop transformers:\")\n", - "print(gic_sorted.head(10).to_string(index=False))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "1b42e9af", - "metadata": {}, - "outputs": [], - "source": [ - "gic_abs = gic_results['GICXFNeutralAmps'].abs()\n", - "plot_gic_bar_hist(gic_abs)" - ] - }, - { - "cell_type": "markdown", - "id": "c5d6e7f8a9b0", - "metadata": {}, - "source": [ - "## Per-Unit Zeta Model\n", - "\n", - "The zeta matrix converts the linear GIC model to per-unit form, suitable\n", - "for integration into power flow studies. Each row represents a transformer's\n", - "contribution to reactive power losses." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "8028ece3", - "metadata": {}, - "outputs": [], - "source": [ - "plot_spy_matrices(\n", - " [pw.gic.zeta, pw.gic.Px],\n", - " [f'Zeta Sparsity Pattern\\n{pw.gic.zeta.shape}',\n", - " f'Bus Permutation Matrix Px\\n{pw.gic.Px.shape}'])" - ] - }, - { - "cell_type": "markdown", - "id": "e7f8a9b0c1d2", - "metadata": {}, - "source": [ - "## Summary\n", - "\n", - "The GIC model expresses transformer neutral currents as a linear function\n", - "of induced line voltages through the H-matrix. The G-matrix is a\n", - "conductance Laplacian ($A^T G_d A + G_s$), and the per-unit zeta matrix\n", - "enables integration with power flow studies. The Px permutation matrix\n", - "maps transformers to their loss-modeling buses." - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "esaplus", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.11.14" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/examples/gic/03_efield_geographic.ipynb b/examples/gic/03_efield_geographic.ipynb deleted file mode 100644 index abd27b7..0000000 --- a/examples/gic/03_efield_geographic.ipynb +++ /dev/null @@ -1,401 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "a1b2c3d4e5f6", - "metadata": {}, - "source": [ - "# Electric Field & GIC Geographic Analysis\n", - "\n", - "This notebook demonstrates the complete GIC workflow on a geographic coordinate\n", - "system: building a 2D electric field grid over the case's footprint, computing\n", - "spatially-varying E-fields, overlaying them on the transmission network,\n", - "running GIC calculations, and exporting the field to PowerWorld's B3D format." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "247ecbe5", - "metadata": { - "tags": [ - "hide-cell" - ] - }, - "outputs": [], - "source": [ - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "from matplotlib.colors import Normalize\n", - "\n", - "from esapp import PowerWorld\n", - "from esapp.components import Branch, Bus, Substation, GICXFormer\n", - "from esapp.utils import (\n", - " Grid2D, B3D,\n", - " format_plot, plot_vecfield, plot_lines,\n", - " border, darker_hsv_colormap,\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "c3d4e5f6a7b8", - "metadata": { - "tags": [ - "hide-cell" - ] - }, - "outputs": [], - "source": [ - "# This cell is hidden in the documentation.\n", - "import ast\n", - "\n", - "with open('../data/case_B.txt', 'r') as f:\n", - " case_path = ast.literal_eval(f.read().strip())\n", - "\n", - "pw = PowerWorld(case_path)\n", - "\n", - "# Configure geographic border shape ('US', 'Texas', etc.)\n", - "SHAPE = 'Texas'" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "6f7d9a1e", - "metadata": { - "tags": [ - "hide-cell" - ] - }, - "outputs": [], - "source": [ - "# Plotting functions (hidden from documentation)\n", - "import sys; sys.path.insert(0, \"..\")\n", - "from plot_helpers import (\n", - " plot_geo_grid_buses, plot_efield_comparison, plot_efield_vectors,\n", - " plot_network_efield, plot_barh_top, plot_gic_geo_map,\n", - " plot_direction_sensitivity, plot_b3d_roundtrip,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "id": "d4e5f6a7b8c9", - "metadata": {}, - "source": "Import the case and instantiate the `PowerWorld`.\n\n```python\nfrom esapp import PowerWorld\npw = PowerWorld(case_path)\n```\n\n## Extracting Geographic Extent\n\nWe extract bus coordinates from the case and determine the geographic bounding box\nthat will define our E-field computation grid." - }, - { - "cell_type": "code", - "execution_count": null, - "id": "e5f6a7b8c9d0", - "metadata": {}, - "outputs": [], - "source": [ - "# Get bus coordinates\n", - "lon, lat = pw.buscoords()\n", - "\n", - "# Determine geographic bounding box with padding\n", - "pad = 0.5 \n", - "lon_min, lon_max = lon.min() - pad, lon.max() + pad\n", - "lat_min, lat_max = lat.min() - pad, lat.max() + pad" - ] - }, - { - "cell_type": "markdown", - "id": "f6a7b8c9d0e1", - "metadata": {}, - "source": [ - "## 2. Building a Geographic E-Field Grid\n", - "\n", - "We construct a 2D grid in latitude/longitude space covering the case's geographic\n", - "footprint. Each grid point will hold Ex and Ey components of the electric field in V/km." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "a7b8c9d0e1f2", - "metadata": {}, - "outputs": [], - "source": [ - "# Grid resolution\n", - "nx, ny = 40, 30\n", - "\n", - "# Coordinate arrays\n", - "lons = np.linspace(lon_min, lon_max, nx)\n", - "lats = np.linspace(lat_min, lat_max, ny)\n", - "LON, LAT = np.meshgrid(lons, lats)\n", - "\n", - "# Grid2D for finite difference operators if needed\n", - "grid = Grid2D((nx, ny))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "2ac51215", - "metadata": {}, - "outputs": [], - "source": [ - "fig, axes = plt.subplots(1, 2, figsize=(6.5, 2.8))\n", - "plot_geo_grid_buses(LON, LAT, lon, lat, SHAPE,\n", - " xlim=(lon_min, lon_max), ylim=(lat_min, lat_max),\n", - " ax=axes[0], fig=fig)\n", - "# Bus coordinate density\n", - "axes[1].hist2d(lon, lat, bins=20, cmap='Blues')\n", - "format_plot(axes[1], title='Bus Density',\n", - " xlabel=r'Lon ($^\\circ$E)', ylabel=r'Lat ($^\\circ$N)',\n", - " plotarea='white', grid=False, titlesize=11, labelsize=9, ticksize=8)\n", - "axes[1].set_aspect('equal')\n", - "plt.tight_layout()\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "c9d0e1f2a3b4", - "metadata": {}, - "source": [ - "## 3. Defining Electric Fields\n", - "\n", - "We define several E-field patterns on the geographic grid. A **uniform field**\n", - "with constant magnitude and direction serves as a baseline. A **spatially\n", - "varying field** whose magnitude increases with latitude models conductivity\n", - "variation. A **rotational field** whose direction varies with longitude\n", - "demonstrates non-uniform storm geometry." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "d0e1f2a3b4c5", - "metadata": {}, - "outputs": [], - "source": [ - "E_mag = 1.0 # V/km\n", - "E_dir = 90.0 # degrees from North (90 = East)\n", - "\n", - "Ex_uniform = E_mag * np.sin(np.radians(E_dir)) * np.ones_like(LON)\n", - "Ey_uniform = E_mag * np.cos(np.radians(E_dir)) * np.ones_like(LON)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "f2a3b4c5d6e7", - "metadata": {}, - "outputs": [], - "source": [ - "# Latitude-dependent magnitude: stronger in the north\n", - "E_magnitude = 0.5 + 1.5 * (LAT - lat_min) / (lat_max - lat_min) # 0.5 to 2.0 V/km\n", - "\n", - "Ex_varying = E_magnitude * np.sin(np.radians(E_dir))\n", - "Ey_varying = E_magnitude * np.cos(np.radians(E_dir))\n", - "\n", - "# Rotational field: direction varies with longitude\n", - "lon_center = (lon_min + lon_max) / 2\n", - "angle_field = np.pi / 2 + 0.5 * np.pi * (LON - lon_center) / (lon_max - lon_center)\n", - "\n", - "Ex_rotational = E_mag * np.cos(angle_field)\n", - "Ey_rotational = E_mag * np.sin(angle_field)" - ] - }, - { - "cell_type": "markdown", - "id": "a3b4c5d6e7f8", - "metadata": {}, - "source": [ - "## E-Field Visualization Gallery\n", - "\n", - "We demonstrate multiple visualization styles for the electric fields overlaid on geographic features." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "eb66f969", - "metadata": {}, - "outputs": [], - "source": [ - "fields = [\n", - " ('Uniform', Ex_uniform, Ey_uniform),\n", - " ('Latitude-Varying', Ex_varying, Ey_varying),\n", - " ('Rotational', Ex_rotational, Ey_rotational),\n", - "]\n", - "\n", - "plot_efield_comparison(LON, LAT, fields, SHAPE)" - ] - }, - { - "cell_type": "markdown", - "id": "d6e7f8a9b0c1", - "metadata": {}, - "source": [ - "### E-Field with Transmission Network Overlay\n", - "\n", - "Combine the E-field visualization with the actual transmission network from the PowerWorld case." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "6c63c324", - "metadata": {}, - "outputs": [], - "source": [ - "lines = pw[Branch, ['Longitude', 'Longitude:1', 'Latitude', 'Latitude:1']]\n", - "magnitude = np.sqrt(Ex_varying**2 + Ey_varying**2)\n", - "\n", - "fig, axes = plt.subplots(1, 2, figsize=(6.5, 2.8))\n", - "plot_network_efield(LON, LAT, magnitude, lines, lon, lat,\n", - " Ex_varying, Ey_varying, SHAPE, ax=axes[0], fig=fig)\n", - "plot_efield_vectors(LON, LAT, Ex_varying, Ey_varying, SHAPE,\n", - " ax=axes[1], fig=fig)\n", - "plt.tight_layout()\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "f8a9b0c1d2e3", - "metadata": {}, - "source": "## 5. Computing GIC from the E-Field\n\nWith the GIC model built from `pw.gic.model()`, we compute transformer\nGICs. The storm function applies a uniform E-field and PowerWorld\ncomputes the resulting neutral currents in each transformer." - }, - { - "cell_type": "code", - "execution_count": null, - "id": "b0c1d2e3f4a5", - "metadata": {}, - "outputs": [], - "source": [ - "pw.gic.configure()\n", - "pw.gic.model() # Cosntructs GIC Model\n", - "\n", - "# Uniform storm via PowerWorld (baseline)\n", - "pw.gic.storm(1.0, 90)\n", - "gic_data = pw[GICXFormer, ['BusNum3W', 'BusNum3W:1', 'GICXFNeutralAmps']]\n", - "\n", - "# Top 10 transformers by GIC magnitude\n", - "top10 = gic_data.reindex(gic_data['GICXFNeutralAmps'].abs().sort_values(ascending=False).index).head(10)\n", - "print(top10.to_string(index=False))" - ] - }, - { - "cell_type": "markdown", - "id": "d2e3f4a5b6c7", - "metadata": {}, - "source": [ - "## GIC Results on the Geographic Map\n", - "\n", - "Overlay GIC magnitudes on the transmission network map. Transformer locations are\n", - "plotted with marker size proportional to GIC magnitude." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "d25b8b37", - "metadata": {}, - "outputs": [], - "source": [ - "bus_coords = pw[Bus, ['BusNum', 'Longitude', 'Latitude']]\n", - "xf_geo = gic_data.merge(bus_coords, left_on='BusNum3W', right_on='BusNum', how='inner')\n", - "gic_mag = xf_geo['GICXFNeutralAmps'].abs()\n", - "\n", - "fig, axes = plt.subplots(1, 2, figsize=(6.5, 2.8))\n", - "plot_gic_geo_map(lines, xf_geo, gic_mag, SHAPE,\n", - " xlim=(lon_min, lon_max), ylim=(lat_min, lat_max),\n", - " ax=axes[0], fig=fig)\n", - "# Top transformer GICs\n", - "top = gic_mag.sort_values(ascending=False).head(15)\n", - "axes[1].barh(range(len(top)), top.values, color='#4C72B0')\n", - "axes[1].set_yticks(range(len(top)))\n", - "axes[1].set_yticklabels([f'XF {i+1}' for i in range(len(top))], fontsize=7)\n", - "axes[1].invert_yaxis()\n", - "format_plot(axes[1], title='Top 15 Transformer GICs',\n", - " xlabel='|GIC| (A)', plotarea='white',\n", - " titlesize=11, labelsize=9, ticksize=8)\n", - "plt.tight_layout()\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "f4a5b6c7d8e9", - "metadata": {}, - "source": [ - "## 7. Storm Direction Sensitivity\n", - "\n", - "Sweep the E-field direction from 0 to 360 degrees and track the maximum GIC\n", - "at each direction. This reveals which storm orientations produce the worst-case\n", - "GIC for this network." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "a5b6c7d8e9f0", - "metadata": {}, - "outputs": [], - "source": [ - "directions = np.arange(0, 361, 10)\n", - "max_gics = []\n", - "\n", - "for d in directions:\n", - " pw.gic.storm(1.0, d)\n", - " gic_vals = pw[GICXFormer, 'GICXFNeutralAmps']['GICXFNeutralAmps']\n", - " max_gics.append(gic_vals.abs().max())\n", - "\n", - "max_gics = np.array(max_gics)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "5e7fcc21", - "metadata": {}, - "outputs": [], - "source": [ - "plot_direction_sensitivity(directions, max_gics,\n", - " title='Maximum Transformer GIC vs. Storm Direction')" - ] - }, - { - "cell_type": "markdown", - "id": "a1b2c3d4e5f7", - "metadata": {}, - "source": [ - "## Summary\n", - "\n", - "This notebook demonstrated the full GIC geographic analysis workflow, from\n", - "extracting the geographic extent to constructing E-field patterns, visualizing\n", - "them with network overlays, computing GIC from spatially-varying fields,\n", - "identifying worst-case storm directions, and exporting custom fields to B3D\n", - "format for PowerWorld import." - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "esaplus", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.11.14" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/examples/gic/04_b3d_file_io.ipynb b/examples/gic/04_b3d_file_io.ipynb deleted file mode 100644 index ebbf25f..0000000 --- a/examples/gic/04_b3d_file_io.ipynb +++ /dev/null @@ -1,190 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "a1b2c3d4", - "metadata": {}, - "source": [ - "# B3D File I/O\n", - "\n", - "Demonstrates the `B3D` class for creating, writing, and reading PowerWorld's\n", - "B3D binary format. The notebook covers constructing B3D objects from scratch\n", - "and from mesh-grid data, round-trip file I/O verification, and E-field\n", - "visualization from loaded B3D files." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "hide-cell" - ] - }, - "outputs": [], - "source": [ - "import sys; sys.path.insert(0, \"..\")\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "from esapp.utils import B3D\n", - "from map import format_plot, border" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "hide-cell" - ] - }, - "outputs": [], - "source": [ - "# Plotting functions (hidden from documentation)\n", - "import sys; sys.path.insert(0, \"..\")\n", - "from plot_helpers import plot_b3d_components, plot_b3d_roundtrip" - ] - }, - { - "cell_type": "markdown", - "id": "c9d0e1f2", - "metadata": {}, - "source": [ - "## 1. Creating a B3D Object from Scratch\n", - "\n", - "The `B3D` class stores time-varying electric field data at geographic locations." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "a3b4c5d6", - "metadata": {}, - "outputs": [], - "source": [ - "b3d = B3D()\n", - "\n", - "print(f\"Default B3D object:\")\n", - "print(f\" Comment: {b3d.comment}\")\n", - "print(f\" Grid dimensions: {b3d.grid_dim}\")\n", - "print(f\" Locations: {len(b3d.lat)}\")\n", - "print(f\" Time steps: {len(b3d.time)}\")\n", - "print(f\" Lat: {b3d.lat}\")\n", - "print(f\" Lon: {b3d.lon}\")\n", - "print(f\" Ex shape: {b3d.ex.shape}\")\n", - "print(f\" Ey shape: {b3d.ey.shape}\")" - ] - }, - { - "cell_type": "markdown", - "id": "e7f8a9b0", - "metadata": {}, - "source": [ - "## 2. Building B3D from Mesh-Grid Data\n", - "\n", - "Use `B3D.from_mesh()` to construct a B3D from regularly-spaced geographic arrays.\n", - "This is the most common workflow for custom E-field creation." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "c1d2e3f4", - "metadata": {}, - "outputs": [], - "source": [ - "# Define geographic grid covering Texas\n", - "nx, ny = 25, 20\n", - "lons = np.linspace(-106, -93, nx)\n", - "lats = np.linspace(25.5, 36.5, ny)\n", - "LON, LAT = np.meshgrid(lons, lats)\n", - "\n", - "# Create a spatially-varying E-field: gaussian hot spot\n", - "lon_c, lat_c = -99.5, 31.0\n", - "sigma = 2.0\n", - "gaussian = np.exp(-((LON - lon_c)**2 + (LAT - lat_c)**2) / (2 * sigma**2))\n", - "\n", - "Ex = 2.0 * gaussian # V/km eastward\n", - "Ey = 0.5 * gaussian # V/km northward\n", - "\n", - "b3d = B3D.from_mesh(\n", - " long=lons, lat=lats, ex=Ex, ey=Ey,\n", - " comment=\"Gaussian hotspot E-field over Texas\"\n", - ")\n", - "\n", - "print(f\"B3D from mesh:\")\n", - "print(f\" Grid: {b3d.grid_dim}\")\n", - "print(f\" Points: {len(b3d.lat)}\")\n", - "print(f\" Ex range: [{b3d.ex.min():.3f}, {b3d.ex.max():.3f}] V/km\")\n", - "print(f\" Ey range: [{b3d.ey.min():.3f}, {b3d.ey.max():.3f}] V/km\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 3. Visualizing the E-Field" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "plot_b3d_components(LON, LAT, Ex, Ey, 'Texas',\n", - " suptitle='Gaussian Hotspot E-Field')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 4. Write and Read Round-Trip\n", - "\n", - "Write the B3D to disk and read it back to verify data integrity." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "a7b8c9d0", - "metadata": {}, - "outputs": [], - "source": [ - "b3d.write_b3d_file(\"gaussian_efield.b3d\")\n", - "\n", - "# Read back\n", - "b3d_loaded = B3D(\"gaussian_efield.b3d\")\n", - "\n", - "plot_b3d_roundtrip(LON, LAT, b3d.ex, b3d_loaded.ex, 'Texas', ny, nx)" - ] - }, - { - "cell_type": "markdown", - "id": "c5d6e7f8", - "metadata": {}, - "source": [ - "## Summary\n", - "\n", - "The `B3D` class provides a clean interface for PowerWorld's binary E-field\n", - "format. Fields can be constructed from mesh-grid arrays with `B3D.from_mesh()`,\n", - "written to disk, and read back with full fidelity. The round-trip test\n", - "confirms that all field components and metadata survive serialization." - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "esaplus", - "language": "python", - "name": "python3" - }, - "language_info": { - "name": "python", - "version": "3.11.14" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/examples/gic/05_gic_sensitivity.ipynb b/examples/gic/05_gic_sensitivity.ipynb deleted file mode 100644 index dadf284..0000000 --- a/examples/gic/05_gic_sensitivity.ipynb +++ /dev/null @@ -1,256 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "f1a2b3c4", - "metadata": {}, - "source": [ - "# GIC Sensitivity Analysis\n", - "\n", - "Demonstrates the E-field-to-GIC Jacobian ($dI/dE$) for identifying which\n", - "transformers and branches are most sensitive to E-field perturbations. The\n", - "notebook builds the GIC model, computes the Jacobian, ranks transformers\n", - "by overall sensitivity, identifies critical branches by column analysis,\n", - "and profiles directional vulnerability for selected transformers." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "32cc9e7a", - "metadata": { - "tags": [ - "hide-cell" - ] - }, - "outputs": [], - "source": [ - "import sys; sys.path.insert(0, \"..\")\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "from esapp import PowerWorld\n", - "from esapp.components import Bus, GICXFormer\n", - "from map import format_plot" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "b9c0d1e2", - "metadata": { - "nbsphinx": "hidden", - "tags": [ - "hide-cell" - ] - }, - "outputs": [], - "source": [ - "# This cell is hidden in the documentation.\n", - "import ast\n", - "\n", - "with open('../data/case.txt', 'r') as f:\n", - " case_path = ast.literal_eval(f.read().strip())\n", - "\n", - "pw = PowerWorld(case_path)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "718ac5d2", - "metadata": { - "tags": [ - "hide-cell" - ] - }, - "outputs": [], - "source": [ - "# Plotting functions (hidden from documentation)\n", - "import sys; sys.path.insert(0, \"..\")\n", - "from plot_helpers import (\n", - " plot_jacobian_sensitivity, plot_branch_impact,\n", - " plot_direction_profiles,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "id": "f3a4b5c6", - "metadata": {}, - "source": [ - "## 1. Build the GIC Model\n", - "\n", - "The sensitivity analysis requires the H-matrix from `model()`." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "d7e8f9a0", - "metadata": {}, - "outputs": [], - "source": [ - "pw.gic.configure()\n", - "pw.gic.model()\n", - "\n", - "# Apply a baseline storm to get signed currents\n", - "pw.gic.storm(1.0, 90)\n", - "gic_baseline = pw[GICXFormer, 'GICXFNeutralAmps']['GICXFNeutralAmps'].to_numpy()" - ] - }, - { - "cell_type": "markdown", - "id": "b1c2d3e4", - "metadata": {}, - "source": [ - "## 2. E-Field to GIC Jacobian (dI/dE)\n", - "\n", - "The `dIdE()` method computes the Jacobian of absolute transformer GICs with respect\n", - "to the electric field components. This identifies which E-field perturbations have\n", - "the greatest effect on each transformer." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "f5a6b7c8", - "metadata": {}, - "outputs": [], - "source": [ - "# Compute dI/dE Jacobian using H-matrix and baseline currents\n", - "J = pw.gic.dIdE(pw.gic.H, i=gic_baseline)\n", - "\n", - "print(f\"dI/dE Jacobian shape: {J.shape}\")\n", - "print(f\" Rows: {J.shape[0]} (transformers)\")\n", - "print(f\" Cols: {J.shape[1]} (branch voltages)\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "6801577a", - "metadata": {}, - "outputs": [], - "source": [ - "J_dense = J if isinstance(J, np.ndarray) else J.toarray()\n", - "plot_jacobian_sensitivity(J_dense)" - ] - }, - { - "cell_type": "markdown", - "id": "b3c4d5e6", - "metadata": {}, - "source": [ - "## 3. Most Sensitive Transformers\n", - "\n", - "Identify which transformers are most sensitive to E-field perturbations." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "f7a8b9c0", - "metadata": {}, - "outputs": [], - "source": [ - "# Rank transformers by total sensitivity\n", - "sensitivity = np.sum(np.abs(J_dense), axis=1)\n", - "ranked = np.argsort(sensitivity)[::-1]\n", - "\n", - "print(f\"{'Rank':<6} {'XF Index':<10} {'Total Sensitivity':<20}\")\n", - "print(\"-\" * 36)\n", - "for rank, idx in enumerate(ranked[:10]):\n", - " print(f\"{rank + 1:<6} {idx:<10} {sensitivity[idx]:<20.4f}\")" - ] - }, - { - "cell_type": "markdown", - "id": "d1e2f3a4", - "metadata": {}, - "source": [ - "## 4. Column-Wise Analysis: Critical Branches\n", - "\n", - "Which branches (line voltages) have the greatest aggregate impact on GIC?" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "da92e31d", - "metadata": {}, - "outputs": [], - "source": [ - "col_sens = np.sum(np.abs(J_dense), axis=0)\n", - "plot_branch_impact(col_sens)" - ] - }, - { - "cell_type": "markdown", - "id": "f9a0b1c2", - "metadata": {}, - "source": [ - "## 5. Direction Sensitivity Profile\n", - "\n", - "Sweep storm direction and track individual transformer responses to identify\n", - "directional vulnerability." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "d3e4f5a6", - "metadata": {}, - "outputs": [], - "source": [ - "directions = np.arange(0, 360, 5)\n", - "n_xf = min(5, len(gic_baseline))\n", - "top_xf_idx = np.argsort(np.abs(gic_baseline))[::-1][:n_xf]\n", - "\n", - "gic_profiles = np.zeros((len(directions), n_xf))\n", - "\n", - "for i, d in enumerate(directions):\n", - " pw.gic.storm(1.0, d)\n", - " gic_vals = pw[GICXFormer, 'GICXFNeutralAmps']['GICXFNeutralAmps'].to_numpy()\n", - " gic_profiles[i] = np.abs(gic_vals[top_xf_idx])\n", - "\n", - "plot_direction_profiles(directions, gic_profiles,\n", - " labels=[f'XF {idx}' for idx in top_xf_idx])" - ] - }, - { - "cell_type": "markdown", - "id": "f1b2c3d4", - "metadata": {}, - "source": [ - "## Summary\n", - "\n", - "The $dI/dE$ Jacobian maps small E-field perturbations to transformer GIC\n", - "changes. Row-wise analysis reveals which transformers are most sensitive\n", - "overall, while column-wise analysis identifies the branches that carry the\n", - "most GIC influence. Directional profiles show how individual transformer\n", - "sensitivity varies with storm orientation." - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "esaplus", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.11.14" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/examples/map.py b/examples/map.py deleted file mode 100644 index 602eb4f..0000000 --- a/examples/map.py +++ /dev/null @@ -1,326 +0,0 @@ -""" -Geographic visualization utilities for power system analysis. - -Provides plotting functions for transmission lines, tesselation grids, -vector fields, and geographic boundaries using matplotlib and geopandas. -""" - -from __future__ import annotations - -from pathlib import Path - -import geopandas as gpd -import numpy as np -from numpy.typing import NDArray - -from matplotlib.axes import Axes -from matplotlib.cm import ScalarMappable -from matplotlib.collections import LineCollection, PatchCollection -from matplotlib.colors import Normalize, ListedColormap, rgb_to_hsv, hsv_to_rgb -import matplotlib.pyplot as plt -from matplotlib.patches import Rectangle -from pandas import DataFrame - -__all__ = [ - 'format_plot', - 'darker_hsv_colormap', - 'border', - 'plot_lines', - 'plot_mesh', - 'plot_tiles', - 'plot_vecfield', -] - -_SHAPES_DIR = Path(__file__).resolve().parent / 'shapes' - - -def format_plot( - ax: Axes, - title: str | None = None, - xlabel: str | None = None, - ylabel: str | None = None, - xlim: tuple[float, float] | None = None, - ylim: tuple[float, float] | None = None, - grid: bool = True, - plotarea: str = 'white', - spine_color: str = 'black', - xticksep: float | None = None, - yticksep: float | None = None, - titlesize: float = 12, - labelsize: float = 10, - ticksize: float = 9, - spine_width: float = 0.8, -) -> None: - """ - Apply journal-standard formatting to a matplotlib axes. - - Parameters - ---------- - ax : matplotlib.axes.Axes - The axes to format. - title : str, optional - Plot title. - xlabel, ylabel : str, optional - Axis labels. - xlim, ylim : tuple of float, optional - Axis limits as (min, max). - grid : bool, default True - Whether to show grid lines. - plotarea : str, default 'white' - Background face color. - spine_color : str, default 'black' - Color for axis spines and ticks. - xticksep, yticksep : float, optional - Tick separation for x and y axes. - titlesize : float, default 12 - Font size for the title. - labelsize : float, default 10 - Font size for axis labels. - ticksize : float, default 9 - Font size for tick labels. - spine_width : float, default 0.8 - Line width for axis spines. - """ - ax.set_facecolor(plotarea) - - if grid: - ax.grid(True, color='#cccccc', linewidth=0.5, linestyle='-') - ax.set_axisbelow(True) - else: - ax.grid(False) - - ax.tick_params( - axis='both', color=spine_color, labelcolor=spine_color, - labelsize=ticksize, - ) - for spine in ax.spines.values(): - spine.set_edgecolor(spine_color) - spine.set_linewidth(spine_width) - - if xlim: - ax.set_xlim(xlim) - if xticksep: - ax.set_xticks(np.arange(*xlim, xticksep)) - if ylim: - ax.set_ylim(ylim) - if yticksep: - ax.set_yticks(np.arange(*ylim, yticksep)) - - if title is not None: - ax.set_title(title, fontsize=titlesize) - if xlabel is not None: - ax.set_xlabel(xlabel, fontsize=labelsize) - if ylabel is not None: - ax.set_ylabel(ylabel, fontsize=labelsize) - - -def darker_hsv_colormap(scale_factor: float = 0.5) -> ListedColormap: - """ - Create a darker version of the HSV colormap. - - Parameters - ---------- - scale_factor : float, default 0.5 - Factor to scale the value (brightness). 1 means no change, - 0 means complete darkness. - - Returns - ------- - matplotlib.colors.ListedColormap - A darker version of the HSV colormap. - """ - hsv_cmap = plt.cm.hsv(np.linspace(0, 1, 256))[:, :3] - hsv_colors = rgb_to_hsv(hsv_cmap) - - hsv_colors[:, 2] *= scale_factor - hsv_colors[:, 2] = np.clip(hsv_colors[:, 2], 0, 1) - - darker_rgb = hsv_to_rgb(hsv_colors) - return ListedColormap(darker_rgb) - - -def border(ax: Axes, shape: str = 'Texas') -> None: - """ - Plot a geographic boundary on a matplotlib axes. - - Parameters - ---------- - ax : matplotlib.axes.Axes - The axes to plot on. - shape : str, default 'Texas' - Name of the shape directory under ``examples/shapes/``. - """ - shapepath = _SHAPES_DIR / shape / 'Shape.shp' - shapeobj = gpd.read_file(shapepath) - shapeobj.plot(ax=ax, edgecolor='black', facecolor='none') - - -def plot_lines( - ax: Axes, - lines: DataFrame, - ms: float = 50, - lw: float = 1, - color: str = 'k', -) -> None: - """ - Draw transmission lines geographically using a single LineCollection. - - Parameters - ---------- - ax : matplotlib.axes.Axes - The axes to plot on. - lines : pandas.DataFrame - DataFrame with 'Longitude', 'Longitude:1', 'Latitude', 'Latitude:1'. - ms : float, default 50 - Marker size for bus endpoints. - lw : float, default 1 - Line width for transmission lines. - color : str, default 'k' - Color for lines and endpoint markers. - """ - cX = lines[['Longitude', 'Longitude:1']].to_numpy() - cY = lines[['Latitude', 'Latitude:1']].to_numpy() - - segments = np.stack([ - np.column_stack([cX[:, 0], cY[:, 0]]), - np.column_stack([cX[:, 1], cY[:, 1]]), - ], axis=1) - - ax.add_collection( - LineCollection(segments, colors=color, linewidths=lw, zorder=4) - ) - ax.scatter(cX.ravel(), cY.ravel(), c=color, s=ms, zorder=2) - ax.autoscale_view() - - -def plot_mesh( - ax: Axes, - gt, - include_lines: bool = True, - color: str = 'grey', - tcolor: str = 'red', - talpha: float = 0.3, -) -> None: - """ - Plot a GIC tool tesselation grid. - - Parameters - ---------- - ax : matplotlib.axes.Axes - The axes to plot on. - gt : object - GIC tool object with ``tile_info``, ``tile_ids``, and ``lines``. - include_lines : bool, default True - Whether to overlay transmission lines. - color : str, default 'grey' - Grid line color. - tcolor : str, default 'red' - Tile face color. - talpha : float, default 0.3 - Tile transparency. - """ - if include_lines: - plot_lines(ax, gt.lines, ms=2) - - X, Y, W = gt.tile_info - - segs = [[(x, Y.min()), (x, Y.max())] for x in X] - segs += [[(X.min(), y), (X.max(), y)] for y in Y] - ax.add_collection( - LineCollection(segs, colors=color, linewidths=0.5, zorder=1) - ) - - tile_ids = gt.tile_ids - refpnt = np.array([[X.min(), Y.min()]]).T - tiles_unique = np.unique(tile_ids[:, ~np.isnan(tile_ids[0])], axis=1) - tile_pos = tiles_unique * W + refpnt - - patches = [Rectangle((t[0], t[1]), W, W) for t in tile_pos.T] - pc = PatchCollection(patches, facecolor=tcolor, alpha=talpha, - edgecolor='none') - ax.add_collection(pc) - ax.autoscale_view() - - -def plot_tiles( - ax: Axes, - gt, - colors: NDArray | None = None, - alpha: float = 0.3, -) -> None: - """ - Plot colored tiles on a tesselation grid. - - Parameters - ---------- - ax : matplotlib.axes.Axes - The axes to plot on. - gt : object - GIC tool object with ``tile_info``. - colors : np.ndarray, optional - 2D array of tile colors. If None, uses red. - alpha : float, default 0.3 - Tile transparency. - """ - X, Y, W = gt.tile_info - - patches = [] - facecolors = [] - for i in range(len(X) - 1): - for j in range(len(Y) - 1): - patches.append(Rectangle((X[i] * W, Y[j] * W), W, W)) - facecolors.append(colors[j, i] if colors is not None else 'red') - - pc = PatchCollection(patches, alpha=alpha, edgecolor='none') - pc.set_facecolor(facecolors) - ax.add_collection(pc) - ax.autoscale_view() - - -def plot_vecfield( - ax: Axes, - X: NDArray, - Y: NDArray, - U: NDArray, - V: NDArray, - cmap: ListedColormap | None = None, - pivot: str = 'mid', - scale: float = 70, - width: float = 0.001, -) -> ScalarMappable: - """ - Plot a vector field colored by angle. - - Parameters - ---------- - ax : matplotlib.axes.Axes - The axes to plot on. - X, Y : np.ndarray - Coordinates of vector origins. - U, V : np.ndarray - Vector components. - cmap : matplotlib colormap, optional - Colormap for angle encoding. Defaults to a darker HSV. - pivot : str, default 'mid' - Quiver pivot point. - scale : float, default 70 - Quiver arrow scaling. - width : float, default 0.001 - Quiver arrow width. - - Returns - ------- - matplotlib.cm.ScalarMappable - Mappable for creating colorbars. - """ - if cmap is None: - cmap = darker_hsv_colormap(0.8) - - norm = Normalize(vmin=-np.pi, vmax=np.pi) - colors = np.arctan2(U, V) - colors[np.isnan(colors)] = 0 - - ax.quiver(X, Y, U, V, colors, norm=norm, pivot=pivot, scale=scale, - width=width, cmap=cmap) - - return ScalarMappable(norm, cmap) diff --git a/examples/plot_helpers.py b/examples/plot_helpers.py index 065ce87..9d9bdb6 100644 --- a/examples/plot_helpers.py +++ b/examples/plot_helpers.py @@ -6,7 +6,7 @@ a hidden cell near the top of each notebook:: import sys; sys.path.insert(0, '..') - from plot_helpers import plot_barh_top, plot_direction_sensitivity, ... + from plot_helpers import plot_voltage_profile, plot_sensitivity_map, ... Figure sizes are optimized for PDF documentation rendering via nbsphinx with a LaTeX text width of 6.5 inches. All figures fit within page width @@ -19,13 +19,17 @@ from matplotlib.colors import Normalize from matplotlib.cm import ScalarMappable -# Import plotting utilities from the sibling map module. Notebooks add -# the examples/ directory to sys.path, so the sibling import is primary; -# the package-style import covers running from the repository root. -try: - from map import format_plot, border, plot_lines, plot_vecfield, darker_hsv_colormap -except ImportError: - from examples.map import format_plot, border, plot_lines, plot_vecfield, darker_hsv_colormap + +def format_plot(ax, title='', xlabel='', ylabel='', grid=True, **_ignored): + """Minimal axis labeling (replaces the removed map.py styling engine).""" + if title: + ax.set_title(title) + if xlabel: + ax.set_xlabel(xlabel) + if ylabel: + ax.set_ylabel(ylabel) + if grid: + ax.grid(alpha=0.3, linewidth=0.5) # --------------------------------------------------------------------------- # Standard figure dimensions (inches) for 6.5" LaTeX text width @@ -60,28 +64,6 @@ # Generic chart helpers # --------------------------------------------------------------------------- -def plot_barh_top(values, labels=None, n=20, title='', xlabel='', ylabel='', - color=None, figsize=(_WFULL, 3.5), ax=None): - """Horizontal bar chart of the top-*n* items sorted descending.""" - if color is None: - color = _C1 - top = values[:n] if len(values) <= n else values.sort_values(ascending=False).head(n) - if labels is None: - labels = [f'{i+1}' for i in range(len(top))] - show = ax is None - if ax is None: - fig, ax = plt.subplots(figsize=figsize) - ax.barh(range(len(top)), top.values if hasattr(top, 'values') else top, - color=color) - ax.set_yticks(range(len(top))) - ax.set_yticklabels(labels[:len(top)]) - ax.invert_yaxis() - format_plot(ax, title=title, xlabel=xlabel, ylabel=ylabel, plotarea='white') - if show: - plt.tight_layout() - plt.show() - return ax - def plot_dual_bar(values_a, values_b, label_a='A', label_b='B', xlabel='Index', ylabel='Value', title='', @@ -109,7 +91,7 @@ def plot_dual_bar(values_a, values_b, label_a='A', label_b='B', # --------------------------------------------------------------------------- -def plot_sensitivity_map(lines, values, shape=None, title='Sensitivity Map', +def plot_sensitivity_map(lines, values, title='Sensitivity Map', clabel='Factor', cmap='RdBu_r', symmetric=True, figsize=(_W2, 2.8), ax=None, fig=None): """Geographic network map with lines colored by sensitivity values. @@ -120,8 +102,6 @@ def plot_sensitivity_map(lines, values, shape=None, title='Sensitivity Map', Branch data with 'Longitude', 'Longitude:1', 'Latitude', 'Latitude:1'. values : array-like One value per branch (PTDF, LODF, etc.). Length must match ``lines``. - shape : str, optional - Shape name for geographic border overlay (e.g. 'Texas', 'US'). title : str Plot title. clabel : str @@ -163,9 +143,6 @@ def plot_sensitivity_map(lines, values, shape=None, title='Sensitivity Map', ax.scatter(cX.ravel(), cY.ravel(), c=_CG, s=8, zorder=3, edgecolors='white', linewidth=0.2) - if shape is not None: - border(ax, shape) - ax.autoscale_view() sm = ScalarMappable(cmap=cm, norm=norm) sm.set_array([]) @@ -182,7 +159,7 @@ def plot_sensitivity_map(lines, values, shape=None, title='Sensitivity Map', return ax -def plot_sensitivity_dual(lines, vals_a, vals_b, shape=None, +def plot_sensitivity_dual(lines, vals_a, vals_b, titles=('PTDF', 'LODF'), clabels=('PTDF', 'LODF'), cmaps=('RdBu_r', 'RdBu_r'), @@ -190,17 +167,17 @@ def plot_sensitivity_dual(lines, vals_a, vals_b, shape=None, figsize=(_W2, 2.8)): """Side-by-side geographic sensitivity maps (2-panel).""" fig, axes = plt.subplots(1, 2, figsize=figsize) - plot_sensitivity_map(lines, vals_a, shape=shape, title=titles[0], + plot_sensitivity_map(lines, vals_a, title=titles[0], clabel=clabels[0], cmap=cmaps[0], symmetric=symmetric[0], ax=axes[0], fig=fig) - plot_sensitivity_map(lines, vals_b, shape=shape, title=titles[1], + plot_sensitivity_map(lines, vals_b, title=titles[1], clabel=clabels[1], cmap=cmaps[1], symmetric=symmetric[1], ax=axes[1], fig=fig) plt.tight_layout() plt.show() -def plot_sensitivity_triple(lines, vals_list, shape=None, +def plot_sensitivity_triple(lines, vals_list, titles=('A', 'B', 'C'), clabels=('', '', ''), cmaps=('RdBu_r', 'RdBu_r', 'RdBu_r'), @@ -210,14 +187,14 @@ def plot_sensitivity_triple(lines, vals_list, shape=None, fig, axes = plt.subplots(1, 3, figsize=figsize) for ax, vals, t, cl, cm, sym in zip(axes, vals_list, titles, clabels, cmaps, symmetric): - plot_sensitivity_map(lines, vals, shape=shape, title=t, + plot_sensitivity_map(lines, vals, title=t, clabel=cl, cmap=cm, symmetric=sym, ax=ax, fig=fig) plt.tight_layout() plt.show() -def plot_flow_map(lines, loading, shape=None, +def plot_flow_map(lines, loading, title='Branch Loading', clabel='Loading (%)', threshold=100.0, highlight_idx=None, figsize=(_W2, 2.8), ax=None, fig=None): @@ -274,9 +251,6 @@ def plot_flow_map(lines, loading, shape=None, ax.scatter(cX.ravel(), cY.ravel(), c=_CG, s=6, zorder=3, edgecolors='white', linewidth=0.2) - if shape is not None: - border(ax, shape) - ax.autoscale_view() sm = ScalarMappable(cmap=cm, norm=norm) sm.set_array([]) @@ -539,362 +513,20 @@ def plot_histograms(datasets, titles, xlabels, colors=None, bins=25, figsize=Non # Direction sensitivity (GIC) # --------------------------------------------------------------------------- -def plot_direction_sensitivity(directions, max_gics, title='Max GIC vs Direction', - figsize=(_W2, 2.8)): - """Line plot + polar plot of GIC vs. storm direction (2-panel).""" - fig = plt.figure(figsize=figsize) - - ax1 = fig.add_subplot(121) - ax1.plot(directions, max_gics, 'o-', color=_C1, markersize=3) - format_plot(ax1, title=title, - xlabel='Direction (deg from N)', - ylabel='Max |GIC| (A)', plotarea='white', **_FS2) - - ax2 = fig.add_subplot(122, projection='polar') - theta = np.radians(directions) - ax2.plot(theta, max_gics, 'o-', color=_C2, markersize=3) - ax2.set_title('Polar Response', pad=15, fontsize=10) - - plt.tight_layout() - plt.show() - - worst = directions[np.argmax(max_gics)] - print(f"Worst-case direction: {worst} degrees") - print(f"Worst-case max GIC: {max_gics.max():.2f} Amps") - - -def plot_direction_profiles(directions, gic_profiles, labels, figsize=(_W2, 2.8)): - """Multi-transformer direction sensitivity: line + polar (2-panel).""" - fig, axes = plt.subplots(1, 2, figsize=figsize) - - pal = [_C1, _C2, _C3, _C4, _C5] - for j, lbl in enumerate(labels): - axes[0].plot(directions, gic_profiles[:, j], label=lbl, - color=pal[j % len(pal)]) - format_plot(axes[0], title='Transformer GIC vs Direction', - xlabel='Direction (deg from N)', ylabel='|GIC| (A)', - plotarea='white', **_FS2) - axes[0].legend(fontsize=7) - - ax_polar = fig.add_axes(axes[1].get_position(), projection='polar') - axes[1].set_visible(False) - theta = np.radians(directions) - for j, lbl in enumerate(labels): - ax_polar.plot(theta, gic_profiles[:, j], label=lbl, - color=pal[j % len(pal)]) - ax_polar.set_title('Polar Response', pad=15, fontsize=10) - ax_polar.legend(loc='upper right', bbox_to_anchor=(1.3, 1.0), fontsize=7) - - plt.tight_layout() - plt.show() - # --------------------------------------------------------------------------- # GIC matrix / sensitivity # --------------------------------------------------------------------------- -def plot_gic_distribution(gic_abs, n=15, figsize=(_W2, _H2)): - """Histogram + top-N bar chart for GIC magnitudes (2-panel).""" - fig, axes = plt.subplots(1, 2, figsize=figsize) - - axes[0].hist(gic_abs, bins=20, color=_C1, edgecolor='white') - format_plot(axes[0], title='GIC Distribution', - xlabel='|GIC| (A)', ylabel='Count', - plotarea='white', **_FS2) - - top = gic_abs.sort_values(ascending=False).head(n) - axes[1].barh(range(len(top)), top.values, color=_C1) - axes[1].set_yticks(range(len(top))) - axes[1].set_yticklabels([f'XF {i + 1}' for i in range(len(top))], fontsize=7) - axes[1].invert_yaxis() - format_plot(axes[1], title=f'Top {n} Transformer GICs', - xlabel='|GIC| (A)', ylabel='Transformer', - plotarea='white', **_FS2) - - plt.tight_layout() - plt.show() - # Keep backward compatibility alias plot_gic_bar_hist = plot_gic_distribution -def plot_gmatrix_comparison(G_model, G_pw, figsize=(_W3, _H3)): - """Compare model G-matrix vs PowerWorld G-matrix with difference (3-panel).""" - fig, axes = plt.subplots(1, 3, figsize=figsize) - - im0 = axes[0].imshow(np.abs(G_model), cmap='Blues', aspect='auto') - fig.colorbar(im0, ax=axes[0], shrink=0.7) - format_plot(axes[0], title='|G| Model', plotarea='white', - grid=False, **_FS3) - - im1 = axes[1].imshow(np.abs(G_pw), cmap='Blues', aspect='auto') - fig.colorbar(im1, ax=axes[1], shrink=0.7) - format_plot(axes[1], title='|G| PowerWorld', plotarea='white', - grid=False, **_FS3) - - if G_model.shape == G_pw.shape: - diff = np.abs(G_model - G_pw) - im2 = axes[2].imshow(diff, cmap='Reds', aspect='auto') - fig.colorbar(im2, ax=axes[2], shrink=0.7) - format_plot(axes[2], title=f'|Diff| max={diff.max():.2e}', - plotarea='white', grid=False, **_FS3) - else: - axes[2].text(0.5, 0.5, 'Shape mismatch', - ha='center', va='center', transform=axes[2].transAxes, - fontsize=9) - format_plot(axes[2], title='Difference', plotarea='white', - grid=False, **_FS3) - - plt.tight_layout() - plt.show() - - -def plot_jacobian_sensitivity(J_dense, figsize=(_W2, _H2)): - """dI/dE Jacobian heatmap + row-wise sensitivity bar chart (2-panel).""" - fig, axes = plt.subplots(1, 2, figsize=figsize) - - im0 = axes[0].imshow(np.abs(J_dense), cmap='Blues', aspect='auto') - fig.colorbar(im0, ax=axes[0], shrink=0.7) - format_plot(axes[0], title='|dI/dE| Jacobian', - xlabel='Branch index', ylabel='Transformer index', - plotarea='white', grid=False, **_FS2) - - row_sens = np.sum(np.abs(J_dense), axis=1) - axes[1].barh(range(len(row_sens)), row_sens, color=_C1) - axes[1].invert_yaxis() - format_plot(axes[1], title='Transformer E-Field Sensitivity', - xlabel='Total sensitivity', ylabel='Transformer index', - plotarea='white', **_FS2) - - plt.tight_layout() - plt.show() - - -def plot_branch_impact(col_sens, top_n=5, figsize=(_W2, _H2)): - """Branch impact bar chart + top-N detail (2-panel).""" - fig, axes = plt.subplots(1, 2, figsize=figsize) - - top_branches = np.argsort(col_sens)[::-1][:top_n] - colors = [_C2 if i in top_branches else _C1 for i in range(len(col_sens))] - axes[0].bar(range(len(col_sens)), col_sens, color=colors, width=1.0) - format_plot(axes[0], title='Branch Impact on GIC', - xlabel='Branch index', ylabel='Aggregate |dI/dE|', - plotarea='white', **_FS2) - - axes[1].barh(range(top_n), col_sens[top_branches], color=_C2) - axes[1].set_yticks(range(top_n)) - axes[1].set_yticklabels([f'Branch {b}' for b in top_branches], fontsize=7) - axes[1].invert_yaxis() - format_plot(axes[1], title=f'Top {top_n} Branches', - xlabel='|dI/dE|', plotarea='white', **_FS2) - - plt.tight_layout() - plt.show() - - print(f"\nTop {top_n} most influential branches: {top_branches}") - - # --------------------------------------------------------------------------- # Geographic / E-field # --------------------------------------------------------------------------- -def plot_geo_grid_buses(LON, LAT, lon, lat, shape, xlim, ylim, - figsize=(_W2, 2.8), ax=None, fig=None): - """Grid points + bus locations on a geographic border.""" - show = ax is None - if ax is None: - fig, ax = plt.subplots(figsize=figsize) - ax.scatter(LON.ravel(), LAT.ravel(), s=1, c=_C7, alpha=0.5, - label='Grid points') - ax.scatter(lon, lat, s=12, c=_C4, zorder=5, label='Bus locations') - border(ax, shape) - ax.set_xlim(xlim[0] - 0.1, xlim[1] + 0.1) - ax.set_ylim(ylim[0] - 0.1, ylim[1] + 0.1) - format_plot(ax, title='Grid & Bus Locations', - xlabel=r'Lon ($^\circ$E)', ylabel=r'Lat ($^\circ$N)', - plotarea='white', grid=False, **_FS2) - ax.legend(fontsize=7, loc='lower right') - ax.set_aspect('equal') - if show: - plt.tight_layout() - plt.show() - return ax - - -def plot_efield_comparison(LON, LAT, fields, shape, figsize=None): - """Side-by-side magnitude heatmaps for E-field patterns (>= 2-panel). - - Parameters - ---------- - fields : list of (name, Ex, Ey) tuples - """ - n = max(len(fields), 2) - if figsize is None: - figsize = (_WFULL, _H3) - fig, axes = plt.subplots(1, n, figsize=figsize) - if n == 1: - axes = [axes] - fs = _FS3 if n >= 3 else _FS2 - for ax, (name, Ex, Ey) in zip(axes, fields): - magnitude = np.sqrt(Ex ** 2 + Ey ** 2) - im = ax.pcolormesh(LON, LAT, magnitude, cmap='hot_r', shading='auto') - border(ax, shape) - ax.set_xlim(LON.min(), LON.max()) - ax.set_ylim(LAT.min(), LAT.max()) - fig.colorbar(im, ax=ax, label='|E| (V/km)', shrink=0.7) - format_plot(ax, title=f'{name} |E|', - xlabel=r'Lon ($^\circ$E)', - ylabel=r'Lat ($^\circ$N)', - plotarea='white', grid=False, **fs) - ax.set_aspect('equal') - for j in range(len(fields), n): - axes[j].set_visible(False) - plt.tight_layout() - plt.show() - - -def plot_efield_vectors(LON, LAT, Ex, Ey, shape, step=3, - figsize=(_W2, 2.8), ax=None, fig=None): - """Heatmap of E-field magnitude + vector field overlay.""" - show = ax is None - if ax is None: - fig, ax = plt.subplots(figsize=figsize) - magnitude = np.sqrt(Ex ** 2 + Ey ** 2) - im = ax.pcolormesh(LON, LAT, magnitude, cmap='YlOrRd', shading='auto', alpha=0.6) - border(ax, shape) - ax.set_xlim(LON.min(), LON.max()) - ax.set_ylim(LAT.min(), LAT.max()) - - sm = plot_vecfield(ax, LON[::step, ::step], LAT[::step, ::step], - Ex[::step, ::step], Ey[::step, ::step], - scale=40, width=0.003) - if fig is not None: - fig.colorbar(im, ax=ax, label='|E| (V/km)', shrink=0.7) - format_plot(ax, title='E-Field Vectors', - xlabel=r'Lon ($^\circ$E)', - ylabel=r'Lat ($^\circ$N)', grid=False, **_FS2) - ax.set_aspect('equal') - if show: - plt.tight_layout() - plt.show() - return ax - - -def plot_network_efield(LON, LAT, magnitude, lines, lon, lat, Ex, Ey, - shape, step=4, figsize=(_W2, 2.8), ax=None, fig=None): - """Full network overlay: heatmap + lines + buses + E-field vectors.""" - show = ax is None - if ax is None: - fig, ax = plt.subplots(figsize=figsize) - - im = ax.pcolormesh(LON, LAT, magnitude, cmap='YlOrRd', shading='auto', alpha=0.4) - border(ax, shape) - plot_lines(ax, lines, ms=4, lw=0.6) - ax.scatter(lon, lat, s=12, c='navy', zorder=6, label='Buses', - edgecolors='white', linewidth=0.4) - ax.quiver(LON[::step, ::step], LAT[::step, ::step], - Ex[::step, ::step], Ey[::step, ::step], - color='darkred', alpha=0.7, scale=30, width=0.002, zorder=7) - ax.set_xlim(LON.min(), LON.max()) - ax.set_ylim(LAT.min(), LAT.max()) - - if fig is not None: - fig.colorbar(im, ax=ax, label='|E| (V/km)', shrink=0.7) - format_plot(ax, title='Network + E-Field', - xlabel=r'Lon ($^\circ$E)', - ylabel=r'Lat ($^\circ$N)', - plotarea='white', grid=False, **_FS2) - ax.legend(loc='lower right', fontsize=7) - ax.set_aspect('equal') - if show: - plt.tight_layout() - plt.show() - return ax - - -def plot_gic_geo_map(lines, xf_geo, gic_mag, shape, xlim, ylim, - figsize=(_W2, 2.8), ax=None, fig=None): - """GIC magnitudes on a geographic map with transmission network.""" - show = ax is None - if ax is None: - fig, ax = plt.subplots(figsize=figsize) - border(ax, shape) - plot_lines(ax, lines, ms=3, lw=0.4) - - sizes = 10 + 120 * gic_mag / gic_mag.max() - sc = ax.scatter(xf_geo['Longitude'], xf_geo['Latitude'], - s=sizes, c=gic_mag, cmap='Reds', zorder=8, - edgecolors='black', linewidth=0.4) - if fig is not None: - fig.colorbar(sc, ax=ax, label='|GIC| (A)', shrink=0.7) - - ax.set_xlim(*xlim) - ax.set_ylim(*ylim) - format_plot(ax, title='Transformer GIC Map', - xlabel=r'Lon ($^\circ$E)', - ylabel=r'Lat ($^\circ$N)', - plotarea='white', grid=False, **_FS2) - ax.set_aspect('equal') - if show: - plt.tight_layout() - plt.show() - return ax - - -def plot_b3d_roundtrip(LON, LAT, ex_orig, ex_loaded, shape, ny, nx, - figsize=(_W2, _H2)): - """Side-by-side original vs loaded Ex from B3D (2-panel).""" - fig, axes = plt.subplots(1, 2, figsize=figsize) - - ex_2d_orig = ex_orig[0].reshape(ny, nx, order='F') - ex_2d_load = ex_loaded[0].reshape(ny, nx, order='F') - - for ax, data, title in zip(axes, - [ex_2d_orig, ex_2d_load], - ['Original Ex', 'B3D Round-Trip Ex']): - im = ax.pcolormesh(LON, LAT, data, cmap='RdBu_r', shading='auto') - border(ax, shape) - ax.set_xlim(LON.min(), LON.max()) - ax.set_ylim(LAT.min(), LAT.max()) - fig.colorbar(im, ax=ax, label='Ex (V/km)') - format_plot(ax, title=title, - xlabel=r'Lon ($^\circ$E)', - ylabel=r'Lat ($^\circ$N)', - plotarea='white', grid=False, **_FS2) - ax.set_aspect('equal') - - plt.tight_layout() - plt.show() - - -def plot_b3d_components(LON, LAT, Ex, Ey, shape, suptitle='', - figsize=(_W3, _H3)): - """Three-panel plot: |E|, Ex, Ey on a geographic background.""" - magnitude = np.sqrt(Ex ** 2 + Ey ** 2) - fig, axes = plt.subplots(1, 3, figsize=figsize) - - for ax, data, cmap, label, title in zip( - axes, - [magnitude, Ex, Ey], - ['hot_r', 'RdBu_r', 'RdBu_r'], - ['|E| (V/km)', 'Ex (V/km)', 'Ey (V/km)'], - ['|E| Magnitude', 'Ex (Eastward)', 'Ey (Northward)'], - ): - im = ax.pcolormesh(LON, LAT, data, cmap=cmap, shading='auto') - border(ax, shape) - fig.colorbar(im, ax=ax, label=label, shrink=0.7) - format_plot(ax, title=title, - xlabel=r'Lon ($^\circ$E)', - ylabel=r'Lat ($^\circ$N)', - plotarea='white', grid=False, **_FS3) - ax.set_aspect('equal') - - if suptitle: - plt.suptitle(suptitle, fontsize=12) - plt.tight_layout() - plt.show() - # --------------------------------------------------------------------------- # Dynamics diff --git a/examples/shapes/Texas/Shape.cpg b/examples/shapes/Texas/Shape.cpg deleted file mode 100644 index 3ad133c..0000000 --- a/examples/shapes/Texas/Shape.cpg +++ /dev/null @@ -1 +0,0 @@ -UTF-8 \ No newline at end of file diff --git a/examples/shapes/Texas/Shape.dbf b/examples/shapes/Texas/Shape.dbf deleted file mode 100644 index 7ccc00651103838bf1d96de07eebc4056682aa34..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 282 zcmZRsV3A>DU|@L2U;!jCft#lbOwbR+0I5X=Zk_?bP(E0m30V@zb`ElN3~`MQ4R&R4 z21zla%lrC;K!w2a=;jA|I0m@J`=sWjGl0#P;72t8BJWs~nuuMVSHaN4A|xO{0Rl|T p^el``jV(;f4Gk;|jEoE{A>wAHdgex!Mn;y#re-D<24=8Inm@B#VA5iczvujG`nAsUaG|P=wN?6cs}lg;6Ml zMVLw^VI>;E5PhHbKHvBCeqZ0~^T*d8uIu6Xdfn%o`<&hFoC6AFL@&z!?N{%fj+(9% zN;hP(Jg1t#Gm8{jrCWgi^GR%Rn!|?#{sM*0vh1`Gdpi67*8czbGXqL*s_y^KSO#dc z3L(GaSHB6ShKz-ZXy1z&f2K`m>jwJg=MGt@kpI~*vjZ-p`GvZ`Vn>72zi`1Q|K$?P zg$hk)#8HxqFF0K6K5(HzEeO@S;J9%JK8*%!U* z>r=)A!{YoJeZ6MTsf|DSEL3o+pZ}l!bBx!=;s0!(|NHQ#eZ3bdv{(P>`-N9}2E_GR zsL<`Yr{BNw_Pe|7VdlePz18k&*(2r-f zT{-PaH_YD~%O9j(Ioxc9BOKke>ufR~aq*$LErtsfjN^h&Wd4OwW-)fKput&xe#DNt z&i1Yg6=`#=r^Iy*IOT`AMq(cN>C$o3UI16==gy0Mw@iFL%(@ST~d?QZN= zqjBwYjlr@3!jaOVzq@ZhMm(JF@hc{`l4NZX9f>b7QA$3+dJa{ep23{as(W zv4uyr>&NF^Yc$EKabsr}74;Kkr*3T4jU9R`DJv}w!c5yB z{dkm^y`76;*V8NX`y(70Q}gYK8$0oNr{XGZC_SAJvcutyi&*o=_S|Fgg2mO6zWL4O~v-ii^8 zy9(pn*wk)rddseD`lO6@V^@uup*L+z`cY!{>;(P!U`_1m`#!>roqn`vA~~OFYzObK zm2PZxwUvn>v3X72$rYHd=GFT7;2%ogZVdA$Owf-fA9yn?cM0}){VctegJ05!(`+5} z`_JvqlCy%{*iNzD|L1y4>2o45$c-)U=b+EoA=5te4|HSK^ylbvfm`Ovr3*2iuiW+4 zG}D+NJj~~kx%&B0?frbmeZl`Y!Nh#_{QsKTz2?A>fdAUASKAa~k&>tHXAK*m`ZN#c z)6_?Ad1}_M+_`RS6~$X`-uI$?tNh&9x=(ZTre%KXOXcGF;701_i=Fo{L+FF!eX~SA zAMz)wRYBfv?C9Rp^z|D5ZGU@v;rb}|)SIf!IYa!P@u=I%>#okm_1Sa#9x~seL&f!h zo;ZJ1VgKjz5k1%U4abd5ubZv!rw`1a+??gc)?_rF`Pa!)$V(L0uEQMPZ&&`ntb)xU z#cX_@+d{x9<_-(ltU2Xhq2)yf;^zrjLv^sL*kdrcPY z3#_edZn*=O^k3QW5$3+TJ+%}zT6e3D24-Da?NSC)?`5UEgT+5w8;Lu5{Y-CyHKzwT z--XkL3T@uP%z1BL5f^l^*xLZ}zH;`FdSRAPwF;IkdaK`_e9QRV&tR6@xHhsstlZ1A z8dxRqJ5H>5?zQR(tVnj&=eCBvn;ygJr4}>D{)!fSE_etF=N29$`|BT@*s}_zm@)PH zSFp-{ei_Wmbl-ZP)ZaU6s(`r%Z;ZVMQ?&NSZo<^}KX;M+l_&VlD~8kTB=5<1wDpBy zS7Gho&p&U$<%9Zfm%*&V;?;6k+-u|F^RTGLe!bJzA7U4f`sO?Lt|I5m>GdxcmP~4l zA&!gKQGANjFFLgOGVxBcpmbPQbR_jWoLzH&%@LTNwX5q{*u8blg@dprJos)l932>+ zM67!fy@oj3?)$_8u-&K?Pfo&=xRjOqVMVUuxfHg&|JQvVEOfrEw-v|b>~2`rb!<{5 za{6#Na~Di)^c+vDmCBdzgjwemZ!=)Y@&v0ySp0S0o)fTp^Ove^uqG?&HL<9SzB2)) zh4gup1qY=r@3tPM)P~M3gypgWl~J%RI4``BE8T~GI`T?z}Tehen_ zqmXYX3W52*9XF6%k{acTbuX%(><{nV`&dtyIi}a73o!NDp9LOpdHl$C zGPvC*k~$L>JmMT6X1|+yV>&FF95{toFs{PF4Ypf6U7t44?)#Fuz08OwPakhKJ|I{g3nHDaRaOrNH?sna`%d6CRF+DVf<> z1#nekcF_np+Og)-IXG=a-~(&;Kl^8U>RjDm_&>)N7XK{85@s1ZQIq2pUhtbc0H!ZH z=S1eCqcd}_De>8h`uWsl1xNOS8H};|^U+jlyv`V=eSW3Co~8Hq|LFtEgeCgxU9@bx zqY?a{&jWL@eRxlp{wM4X`Md~&epvK?84vAOUW4uCwQ&t$8h_wT;`Xp1Y6>ja__ZHd z?@G>pO8NH>F_^y&2a08meDUs88&lzJ~?P_4@Tm96dq(7XHtA zn785B{nx~C5A^F}bm@T!wQ#iPoxa|^kz=MJ9`IRj3PZB72L8|bn*Dyh;}ckJF4u2g z?wDh9H!If=+|HMGT#1+#1pqyRbaf1UrWvuz&cK#=|kAx;c-nC z%-Q{dQv*w4%x9&;b~@dM=WvrCmX`)IS8tf8B6%14)rVls_|LU3VcBTY#}b&k`GsRG zY(Mtv!hNv#W|iL2%#yyz#J~1DCpoR8)N&WBTzOFMFvGv19k3wbtA4yPwx~xU%r@zf zM8-E7(P!T_SQqa0nry%0W_V^itmR$b|AOQ_|4}!Qe9KPvXRzI~Q=D}q&%c>Yjz=~4 zWOCI1d?M?Ofy-g$c)5PONV%*f6jqLTs^5RfAW=auO#8a0;uY$x z0xnJtgsBH9t6sy=iRWT?Fn>^gqehr#EOMF$OA0q-y@#7TE|&Vj^f!~Hw7??UR9kOY z`6f!-3NxBr7R-UAFDEfS!DiRT?&HAGv$lhJ`*}<#7!jVU;%EyvC;o!A)xK(k&he`U+ zzLWic74-JGV_Z;0CoevCv=}qn1N5H~d&C&j_F01^811!CE$;T8<%Q(>D3>yjE z^ZUW7!}sl6;hI0P9W+=wtKt4sI4(bMUtgHXdVJjtZq2E|HvoLzyBrCa3CpZ~yeTl- z%k%9VSj*jL`ui_FFRDWixp3m)!2EXD?p0IST$sAX%c2bygf(0ZfW@Dl6tuvc^;iD! zV5u@C?H%d=zVT=fTpqJ7wiad%-#s`Smd}XHe*x!RzP4gH?B4dL$8%VAhkZ=|+ilps zuLfpYR#8{NqWO2fDPew9V{RmD6tHk{6|7m)ur~(gT7<2vApL%!%Jp#E-yiqx!{rfc zqBg>ezkw(3!IbxJO*X@-ea9qsVeb1`7q`Gx(t)BfSZHnRCnEi?Ke*k3wY{CUiQ(wr zuER=T!Rr-@y>L3ijCu{`D<%I9!kp`sgNjJM`{C`!VOqCiPcFa`rFxYVt{URpO$M_v zON&myVK3LMEP$!zem`?zM#Hy}`7pm_Br8&3V}W z?jN@-SiL*>{zX_w3%5E6b6cQ(+ z;<~V_U2#98Qu(fphMD~5t-WCFtfB)eV4>;$Rc0`Mx`S^xtY}D{H;~kq9OQ?<>J7nr z8L-u?=ll3Duj&!g8cxg`_8^GlA2`cK!u38CJ$Z1n?B>4lu)B4DWIikodFJ2@hrMX9 znG0(?-dvnYazl^aKCrHEiDo9O5LKP@>zr-SjD}c5A0zc1&ng3?(UJY}T;*JrE!z8h5Vb0|ueZ6Q( zjCUMd^YKPG=@$i^-Ln-=KifQ;Sa#%E)_yoeHS`qOzS6gJ;BmMkzi{L%n0vpFauQar zZeQR5t3#*E$brrFc?8Xb<)gPS3gO7p!#_-iquWmUU53-|3O~BRd47-gUnl*$w(F*n z{_Ab0%VGKaCEHzL)}!%>N|@PVP&yG#`}|M#5*8I78sZFVH=1(mVTNeZTql@*XIxtY zOmA7(do1Y>G#vR37N0R4Y7gs1%g%g;JLWwx8bLf;^Whs@7TzV@hV)Oo>G%^4+fq~||*-ymwdmPQa!;%2nfgUhT>^k%-9PRhLiI~z{RST?$iyvVG$68K2^B(4IR1EG5OZP;w-ok8)33rU)#9E~6N~0yr zXxhefbpv@>HU`rU7i@sER9t=nuFWc>LzWt(8inZ0^5S3i6Z3rFv{9XABId~8;7 z6wI8KGz2{d}pZLM;!@d$LG>ewd}7qzmA*6}zvI z^C!G>H6Q>M@-#PSaM)Wx@I2VA|3Gu%xV6Kl_`zy{|7vpnW3!TcykV{FNSi)z%7T84 z98y1bG_w~Rxun2s7R(bLRg>$@Upb&{CM;>C>#t7{+dF;2l6W)VcM`>-zlX3gO6zoEPwXu z%AY?t|1{^=4{&|wC*^mTYs+_Nf}@q5jbC8dh?)D|z=l32-)mvbz3uPSFtv2Vsy3Lt z@>R=gn9&e?_cP2i=re{ zH?+Qk)kX%{Wc}6Fze{g|<<5ik>#^M-_Q|)f@)Wb94!JteK=cOIc1@)a+XYrSy@uJe z_lC8wuEO=*E12%m^#WPnJ4WQ*s)zrx-n&+GKUN1T=f;PqkkbnPDOE6I$>Z=BaDiBH z{y8iNK05FjT-5Mj6|wkAMi*k+ir~f?Si0q^+}MdpYn$mKp{b=@I@xa*S3 z#6h3>jZ25ii`VJTx4LtK(Q%klb^RLYXIA%Iag=z%MZL>+dp=5qrKXko>p`&lTwV&S zIQLnPyFaquz8)4bx_DH=LF>odkA}Pvjru@4wPm+2r5|)kHO)G;-E_a9{;5^l`3eQxRIdFI{y#1%&^Uy`;>gyaBya6ub7%>yGYxrill0$c zT(cOKDG!b!=6*LbSVZz`D_Z3+ZM9|J1+el@<=-MWZ$#kQd2o3gHS_{p&YfPvh3UH% zE|93MDPTz=EL;BiS5>~m}9zyxJ=&t3P_>QY7z?b zA2JO|u3b~8zrXPM{KlvkOjqzd79bBQcrl{~Ebh}lpg| zr5%>J{OxK1r~fj{{0h^bwS<|&?did%zQFQ}5AT}7b|tc0Elf$zPU;J@cNi74!MaP$ z`uk7DlBk`ZVe!Q+^SdL@8)~_~70#397O< zDbW^zbueS~%R~(vHq=I-f|WfE9D54~?U^z387v%Q6kZQ&Ke&ZICiQQq>Zh<3Z$o<} z%=#<%@)%Z&O2?GLlC8V1R=`fOTg&dk0`o7=?vUJbL!1Jpf4bI9PE30_{}wU(rpaZv zLwb0^4OnLtVJ9Q~gXftQ!&H0smb0+eIodZltl8u+ClAi1S5;qu*#)x=bKoj33ktEk z<-Y#;O4^gtK}B$$2P-@axw5X?Y8lBT%10TnyN;4m2rImD^v`P&x4AzpfK~Cjq+`gF zBmLB8V5Y3U+Yva@qv(1b%s930ODb#|!u@m_=AM}H`w(nMX&;gUiz157lIKC$a~JnG zNxc1+@qU;RG3DS1nE#|PIvJLQI9D8pg(JI++6BjbC>?eT=1sAGy916bWsEusD>f@X zZikcSHBgVh;(I5gTj8qqDQ{EZv>W68Ccu%sZ>>lnxy7l%c(}yxh1UU?IqXQ^I9T;S zn6Mw#owbeF1m{g^8M6RPFrV9mKU{qsDpg7|PDOn`K1>UYzKDQ=JS&jSkzr@OC* zSx-EnwTUTXYS_bpZ2Cnl%y&eApJrrhe94C6gdAn9*hQOk2lF8(GX~DS( z{!2*CUz^T>-A|gn4kCFd@9#{Qw`=KjV*T?P*#4y&Z%FX{cz$)EJ6tyCSPT!AeK+n- zoV`6WApoY_Ke==|Tz^i~YaT3;t{A|Eoh)3NxiIJB#HuN9P-a*Jv37}#!(=!m;8v6` zOe?FHFbVd5bT`fmW<1UMIUY`)vn_BooOgJ`HAlE9bm{w9FgrOj!48(j|D$`r+Dijx z4~4`0J!Z`$dDOK_R&cBNSKk>hbKxht1?=938#0a52d;Z=1~;WGSnUdP_8#2d59a-T zacdGRYaV4ngTp5BHxnya)~&{HK}GUxVnNq=Ma0yJcLP{(`RL>6ePQL+L%p0~@oT>R zd3vkaaa$%#>C7qZhMYh4aG(P$>vhbwE1dR0yTl&m+w^~C02f%ehmD5m8+;vzDT$ra zN5TTmn_c92KW-$q*}~-~9{eQl4+QTwOtgk&CZayyVQNf*`w&`&eoMyI`W>;dZz-_-wo3*pU2=?1W@rrQwmewrO9 z%<24v`kBw4lK0C(@8Vt^u)_JR{{1V*cx?GkIQsiCc`kCV_1z-c;qnv5Zk~b@4PqUL z)8b#QBQ}b0Z~Ox1HH7Kk-#aB5A8CWt)0?(Qk(V1RKmHk}H(J+a!ja_;+*Vk`xUYXd zU+?sm*FyR&*S8)=uJ}1JsTo!@RcnvIaYdA-g7hmwd0LyFkFA5@gYsQ^?nD4o{cp+Tgb!+Gum}zctbOBto@Al?Al8+ac&xdLE zN6ycM=}C7O#8$KU?@qzmu~y^e!baoPmP=vX=Az@?u)KTAj&xWtw(5>MT-4~X`#3DH za$Y+FP8rMmm40kd+bb8TU>MTwmvIL&Zk@1d}&&+MXjSm@|~l>wK14?40L z*5*o#iR1cNZ(k2b2PNw*?;5U&fn^U5>h0PPFn<-Cm$_B%`t7B&myz6gx84lSqXnU` zo$TK7AsEkUxK~36OsRcqN-X@m=14Hi&0iNi2=@OuVf#Xo7w12-fWt=aO5(w3tgf|m znC<_#)gM-l@|e~iR^RX!abeZJB@2y7zv<_CAJQM%eQ6&!Q609|8@3C+ys|f3G`IMK zCtN=76|);`CRyI#4)dBf9;3iD)YTu|VBNi-KYu;2etoaL%Z8PguXlF9DO+Q8Q(^Yv zF|t2!*q6RbU0_y2M1LLJ`j9c#369o$d##1J^76!SFm3T`_Gg$CbXsrilYG6!9q*@% zL9U*#=4>-^J74aw(Xjl%i}FU8S$Ms3B+T!(r&d(fgq4rqC04>IX#vyw!BmHs zpxdy{`^k(Rur8pd&jmPc`pBbyb=ZEy`kmQud5q_j->_=t$e2u+6R_>g4_FcRZ}Blw z-@GWY9Tq3|i8um#@v5x8!5sF}A*pcmk4c7YFxTwUr2TN~H)-rxng{(9JhnuE5L#X^vxI!R2+eMZ`_%lO18#)-{9A6W_lQWeu~_uX>$>StF)b zF<@S}sb3*%$N5$>73M|jQa^)jzc=z8U2`peMrmEp4_m!o)V19CSObsk!=Pub!+`nCM2aa`g z`W6pMv!AA4h3!3+!?wW8PFe6p(jOYh+YGA=__2ks!oq&yMp&_~|L#1v{e9Ml4KOR; zyDb~0|5}y44(1HJ*yjYy80_M=7S8)^Wq$;=5-;i%4Rb%JeD=fj>po;ez*>Wc6+2;; z)t*<&V7lP0dpyi;+dMT4R(DJ5z7dYRkUTU5mJ0G2Yhmsrv%Dp6o_cfRD%kxxw@(l( zezt0HILw;7H--o6K8zMEhC4#!qXS@u^XpU|%)Iec;s;aT&70>>`ZHF#abdo=aKSv- zs^Qd8Us#>?_555oI?ZCX56pUCyuuew?pGY=1uKt5Y?=e-`Q6;*2{S_vjOW0t{FsBY zVA_V_R(Du+s_D=aSml>lJ_$BU=v>JLc$mM8yR{wW-st&$4V?X@M4*LvyO|?G zVaB+?kT=aJ13*y206?*C;Oy(85VYgc5Q;0s?J|Bn3HVx7~#Zp0K;&!m@d zGXL=9bFg68pZ2G4Io-sy5Kc?mbgK$x{jxP7R=&DD_8uJR*I%D=A3vI-fSDr}uPi{$ zo{<<*3{xFm49tgBtb!4jVbLw_$un@Yls4%Ctn9WT@iff*-O%F<%=5o`D+`vM${BSM zHvBZx>LjdI=1x2TTU|NHkivq;lTRLn*#^D;oPagsc62`sCpOG=J`T&)^BZ@=qEmv1X#u0Ts|L`PZBq7fyFK38vJ0pHysD#NUoN&dBaX^1;;m$egmUIPq-kV zn{*?rHFI9(0edB^+$4lK9`lCGfUBZ>>ej*1bl1I8VE)DT6JueqY?+4(oaj4x*;<$u zJDEBGW@OGbAr`oX+#USiqTonDdJ_44U^71l)Q$l<~SHkq=L;LoD({~P}3t)=- zBO5C0U$pmBhYer}RpcB3i(*=d@abC@gQRnH>z zmwfFW!cD$=MiMKMGWS-%$%Ts=+(|#R{_9|)^MGBbz&}IEJSk*>*dJWEQ z@{~-1l`T<`XJLh{cZM@e-MV69E=)Pl-FX7cvAuiuG|VZO^lv;Y?@~XKm}_)*(KtBo zdg{1rxLqVG9s>*I-?$mDAS3^)J+YE?99P1 z^ayr!)rc#-21#m}_ zU|erlZm>3BDQx9hVblv2aE1r-;gqVO{d&SI>E@+NV7BQqiyknyq;72>tg13g=n6Bg zPdexi%Z_S$8o-o8*{8U09ChjS&L7D6(+hlHAtQH42h7XaHJAgZrytz=11@jp1-ih| zKgT}&0*h36wM`P z^HkFYttR#78hs)j{@v4NQ`!*SThWU3Ej;?@tk*96n!&Jsd^*xwBar3uVa9o$gEMoPu?UXt= zGWemU4A!}82G_tk?T`V)JkG;}YFKe^`OH${3QzBi=ieX`g-N`GkUz`6gv{TVr~AX3uoc9 zk26-L!|W{yeb2y*yS;}TgT*H*UGqqPN#^D>*lyHQ&m34{va9(Z9Q(4-J`0u`?+)7y z^M43&#nu+5e@ zI@Hl=FX``5w<8MXM1CD2hI!fxvsS_#qt>^GV7^0XNEj@j*nEqJ#i3(OLSg#0)Kgnv zMa1%zOW~Ag-nPxKy0UV02+U$}wCiBu>b4QVa2YT7X$&ks{>3Q}=KJhC8VNItopb$R zb`Qy=WiV&d@w?tIPxDeS&^0GQ$2H{TIf{5HMOA1)JbI5-NXe_8XWADljQ;ug)8SCoFwv^V%9_oSt{KJFNAp{lI_~+nswG!qn_v(m}+>H%C+8ydO)& z7BJsyBI%f7sxcuqJp&;@|HWPxb5dQ@HB=>0TXh^qGJ! zk6~NCrOm%!$rIPP4`JP)sj)v{X@&A!CET7A?9dLgBHNdj!_g%%!@j_Fj;gXUSTbtc zdoAf-I(H+n*SG0+Kf|0W$)eD!7k1#JLWan)-sOo-t6HI+}Tz4IIwY>264a|*L zT_}g0JevcE%T3-cz6zJE=wvm(nthKdFTi1rIT7`+@_a(gdE%{SgfC&{l=tuQVZ#~| z{xg_%f8XabaQZ!M%M)1FU+9|)yUx}9d<@eKrPOTL&ZT!<6)ZhJCOiwa>fOcU0nD+r zyq5|4-wrabfYm2s9-M&db^CJe!bR)$R5eL(DEITt3j$2wYXcMeiTUO`}+nOW{7s9H?PQf!s9_P|+ zJxuL>s_%5tf5$2#8Wyi;T{{(~oE;-t1?Md=bC?WQ(N8iXVL|WOH!L`>N2gN+OrN{A z#TnKNdY397ez3S;EG$0LniUSqZ|#T~4ZHsx{wEC9BzIVkg4MM>gO5gV_n#V4SV3@w^#N>W3tM?eMAXt#l;nfG$4y%k`1j_|a z`%+=*74c#otX$1@Fo4Z`FTP#?>z=%y&^ZnB?K*AkJeX~K$M7ed7QW}4FU-uF@wg2x zI<;z&H%v2*No|ImQv8n1Ci$r>ff|-JB#xX#`aPZoRKv+G-(Svz#qBLom9YIQ`l0DC z)v!AJIvjb@Mmi1F|9d@HyXm9<6j<78jyxBRwP+7{rDH#=tsuXSfK? zo7(%~Xjt>*Zt6Oi`}y*-k+5L!!3EK<|A{|ciS3>&Fj@_(`&P+^!`ydsHVNROy4)GI zFeRa@Bor>$^J$F@EN*-19s+Y0$GBR-lI+Wj`EbzcNRc_QYHOP}oK|CW$qZ&St>eyt z)yG5r^nuG4)oyl!P6~x1yfvMSuiE0J51}+Suzgp7<2HEA*s)sVm$`dHb*Y( z0t=!YBqQOHXq%|N?YN#qp49_k>Z0FF9n4;%WOgI@fPm@kuu#6Dziuk#x1jv%SJ-Z^ z`pGvqY@O}TFEG9DAeI&`IqfF<3=4K#J*R=w(wf_vVag%f+6Gwb8d>-b*2bT`Py_pG z54$$P{4d{_m2kAk-%<_JyT(y(!!*GUG-|f%*$#DHT`%eC)KilkY0W4Y=wfPC`v}?kr)3DNSNauYx=!K?B7A$@_Z1^oW?@xSW2FzKxCQS~< zsWXF)z_NG4i)FC z+H;V{0l0kdk@i@aH+2Yq56qbpdL@d~lkdB*fbz*R0+x%Gd&j}F<_W@;FvtGqnN7s& z4Z_0V^3<9gQLvM*wJ(5`o5#C)z=jS%z2?E1 zr2;D#I8Sh9rY|hs5_DuNoH%%etvAe^>DA31*7OcMI16SE`JFl#jvRbD(gRl9=yJ*u zE(v?-I|HWo2)}Ix)1UURae-w6mxXqPnW1+LSg@wLnbGNr&%3454<>O@*MM(uS=h>% z4zO7BVn!>>Jo%rLbx?+R0FI#1-m>OGD&6q0|n4bOt(9Ou?|e8c?L z-K{$YS9QK+{ehKTH+)HjJ7R)o|Ays9ha?|@X`O|ibTIe(_TCa$DVxjq2}>ImJ=_hq zj%=U%9TpY$c)J7U%p7dd1}jFaidzpeUIxa0gc*mA`>uk;lTU4HA%5WO7y%1z_e^Sr zxmV2ltbpx<4))Q&{K=~ahr{UuZC1XAqjz){^I_epJLyfN|H{3LKv?wial#vzk}s#v zgHukN>Gc|xXaCvl1q(kH+rES)7sf@f;V|P>4liKAx0Eg=<~`tGauf9|YSkDE?api@zB>=?|ODZ%Mulb2q<{_JMVCbw_W)vg!wY zsjxuRqAZ5<3<6$KU?-!Dm)GF(E$mr;CgbP#`5&+3FeQ3>!B1E@$;F76ow4IuJ51H4 z(5}LYc~3`whT}w^%&x%E-sUGhz_fqv7MEdWgn7gJFD@bVdzU}C0JHYau(<*=FFh)d!IaVF?_@Al zbavu7k{7OClMP#4{5!c2R(?O`ejH9S8>}gS)ABDy9E8P#FS+N#oEt7P#IXA$-wkJA z{_^0S+u)L^j$LwLt#4<-COGb@lW{got7`ca3pdT1@l^`b>q^f@!A6cpU5>$mxxvE) zFtrbRQ!31v#XY$M7CZ|de-M^@UtYWr_8afx04x2~6O-UIV&xY=`H*Tiv? zl3)SrM*UP+d#NII2h5LmIpzZMxGLc`Seh{Z_E^}kW&ge{a9UDm7aO=l^61+}SoC-0 z?g4Q6n3&;0ShpZ5yC1B+-q0r&raU>I>ITavQtn3+FU{@ja6x^)(34Rx-TJ2d7cBcy z>$e&fZf#xj1C|eZF?|)x@4e0Y3*0_(hCOlG-xsg7Fyp9i7h<`=w>`wRo~g`8nEEtP z^BJxh+YlT9Gj120{0JAV$TwX9OZtwAY=WizpQj37MbDS9jj-L$)3cYsEXD8-b+F+& zL*H;x|7C_$MJ)8$9RgE(9iCJHx4NXt`Ec~)XkNWwK(^XA^31u%~l_U}9_r6dGrC)A) z!qMAgokp|-%o!N6unTNw^xD-8W_5{m?fi=C@tvp76qx-i zETaR?`=ASD!6}1Ibom9dvqH9wg(YX$6Mn)BcVF#jk}o=M^$k`9ytqG<Ti1DfI3 z*_5$$upF;9-oUyvpU4+5<dPhxHs#1`C?=f0vW`e}Ququ;%92{QEHF zM(eqou)1|cS7PPp;3YR;=CA8L?!lUETj-q z&MRLF(+)e^Cc$>o?ogJ)d5(iB;z|9-Rf$2Q{{D$W8%X`4^ab-^`=d6&(XiZZei8@n zIJ`A(1uQf=-F+HNe-_$fIn3R4|JG#KkfRL^hv_w`v6JA$z=WTCn7V4fv~h5Ka=L6W zEFB?QJeu?;yzajU&bykLXbl&bXBY&)@{Rw_)8W`-J?rO@{y7q(0kG|$oSD9`it~9C z4d$*KljRN5`}~^N2X3ADMKlMNOdp%k6PAn#70iOA>z|u;fz|itXS%}-!CQw8{M)JRP}pdgK5d1y3Z^cK|8c%7E-B zHXR>fyKyf=17MM()%a#uuz~U12d0i%7NUXq_Zl0#Nc|q}op-R(XNaE%Ofyf>Ho*+W zg+g~&Y`J!GBP^U;w{|MbN!ciV4NH96Fx57$#TiZ>@;<5#);-bYj)Uu8 z1~O{lv|063CLDSD>8oe3>VBj3DA+4~$(m|d{k*uz7G|HB+OrDQ_#~emLVSMl>k3kz z+PBOKHY#47Ru0Q=4yRedQi>n56c#()-e?Xphb}i&zzT-pYZJKKbmoa0q+dNS%@{7a zb1m>1tmTy%^oGqkT^p~$$~j@Zdcq~CXG^ZYc5OHMQej)qXLByW%;=|<-C#i$R}r@#L>kn0Kr&`6KM!c(N)J zrdlzkw7_vog7Y(AiQm}SA7JWixdX9$;Ibd@VXO78$EL%odmDX-Yqt9>JqG9PNIU%& zrZKOV93^?f%}b52)1wjDsj%kv;JXd5;l&|!2Vw3a@Ax`6$g1v|1XdrC?tcO6ex|4G zgE>EG-cMngc+rMrSjP4;eF)o)aq6-g=AYWR;~wlq4LPtA&P!fA>ozPc?fktJre|8N zyar1Gc}DTDl=^GtIk+ajo7YCtzuoU{HZ1@BxnMoa=KrhBgk3piV~FV<15(oAa)EtPw>Gh0v1MWyu1}oOZedv4y*ixv)975@w?@GSZwFE zNdOx*?Z3Mi)>J!wUjp-4ybC;-_vn{x0C6naWIil!w)yA-J5{Bc2f#Ybr%W$cRry;x zkJPt5q0ENkDwkBwg*8E^F3*Bvb4wok!4e~%n=@g4`6`t!Ec?A~!*tllF4))`W<_|| zvtinoenB%~LBri2EYhD7o;?*-vmVgK!_xl8$4`d&FP;oy!itJNeO%zQY|lrIF#Fq< z6c((gy&CTTrx%~{o&XEXnkJ5d+dnnmawPrNTm5Wdtt9V+Jsf>Obj}*)J{&!4G|ZmZ zxA#yuZ|&o-;V^w;wACP(GtZDU2o~IV;$lwvA55w-hpE0@4QOzY{N8dCI9i==-W$%Y zznyOci-)S}4PnaI-A8)BX*Xl%{dLBAG%Ivjrxx>V-LH-&_)?SM5yS9`X?HRBUQ zf57~dtL2Sw?CfqW?XYvAs*EJ(Qet{fKI4Uba8X=6AKiebZ?f-dziwaq|%1x`$rguaAXvt4Iig3A{k zkA4mdL*^_ggk=x8JF8(%^C-t0n09#mv`4U#d*z4}wz`=-rW~ekII%VrE_?fa!);i{ ztoBHT)$2bWC?@^RV`8?!uJf5cufmd;UT&M<_IGn-MX=Im(WSMp`;4S%7hu}56%SXD z`oOB}Lel>}>ceulB++Md0ja+=F+Cji$_eXx22N`?3FgD%C(A5P!`zYmg^OUTdj8x~ zu%ugf%RHDHJ?WJcmX{b)eBtb6M?R;+`tQ$hN6`1qG}!K1?HG5GUoR^<3{%z=OWa7l zc<{AUm}k~Ml?`jsCY;$%@_NIr{|{65AJ@_s@Bw@%R*E5nFoY;6QcEFQPK>Tywy7jP}=2+ez zHmIiQ*1)tezKMO|5~HHXa9En0oz@G^SwGGk?7?n+sewhkJx)8og4m_vC$O2{I>%8kYyaBAC9wNg$_n z$_SXz`H=5JSX?qFb|}nuS9H1$m*qU!H3ZglTgWJYX&ZGbnJ{O3-J?6OLiOfSU)T`# z*zE?)+)?$X8?5^46m$ia$Rw!Vz8OH~i!kUz)nu~DKwnvJNu=e7G#ItaT zi_KMIn31EaKLa-;Uve{oMGNL0PbY53Vz+~te}n<4aE^=U_n*($Uu zQP-O0YFJleK5rA;^4dB`2Mc2x?nS|>P0Z&nVGbv>Z3XNzdD64Tr2eoUx*%Aa6L?n* zYp2iu>knJy4B2=emj1TNnGJirJG=8bERM8xp9bsuH><9~Hu|n7JmI$9&$tR$)}P(p z16FN%zwQFe@uge3!6m)7e>e?u+ex=M!|Krwd{4oaR~I@v!J1VX#}ruqv?*~6Y}fO| z$RwB-vYulPE3R4E?SX~n$-Qh~%c`%xw!sW_yK#eHp9%B&Y=%8%M}4(|jRN}_Z-AN0 zPG0N-OSszMt6}x*pvT={r*&x`_@sWijl>KV(Kno24ofY5dYQs;(|=_I!mQDs?zAKM zBhjomr2eRFBYr!fy#k9199U#ou&oVN^|#+Q3vRi?8q*4Eqh?K;0c)pMjsFQtg^x)ym(iNP!A>>l&nCbQQx4v4ge{i2ipIj~J+}_N zhw*|et5LAv!_~i)u#(@+&lVPDvx48k1?@xk42N5KZqI)Omx*fvn6UoC-LYk`_-0{4 zUy_eqaN-GUnKt17fr`Pxp)2ouLnqXG)qlMRCk1}Jo2AJj| z8<-7i%maP&FmL=$(PdbAuy9x%ti6)2&4hXOYev6^>DyTGmte`Ogn^Z?sQZm+=i%TN z4{vH=MmJwYIvjWT`;6zrr@AdW4LA48@GpgRH_zqE;F86o>mI|}QoJ|~R%BYTOJMz2 z;f~|5;7b1P2e54I9sN<*t2A}sU07`s_9qe6hKmcX!@9gI?)17Wn6BABDGpAE zb)B0*@@`)^BG~%xtE6+VR5>~~29}o>w?7T5iicQlgc*v|f)rRjx5REeY!~BgB!%gQ z>-*NhUS`JChhgURo{4MW=7<5M2Vicw#BUX>;xAYe4{M7~tXu&H9}7Psf)$@0xG#bY z(^uDRgvIB6b)5^#4u#vUgEhBJM*5Mwd`FixFstsyq#3Z8|Io>+V4Y~)+^Mi`ZNt623z-v zxi-L=Puy)>o^>1M1 zyxlWRVUE?Cc2D7&KKXSWh{xx?DTKL&@`Tn;*k3Cq56LB-J@d{_nBD!jQ#Q=m{%FoO z*rT(2!Fky1r9!QIVZi{GDZv@rA3=&BB|cuxGz7qGN-=c*rLaD7bJm{$t3 zrWswSgGHr<_0M2NuaVJJFn_Bdf>_X}>#Mi0T||KQQ`qMBqQK{*en5N96WGH=I_*BJ z&P&O71hX5W2j7H+#%o=QiO0WD%V8Uf%cTV{BQNi<2yT9EeNY7pDoeIRz@lm2rrw72 zD;F$X3`>{JoqvPW-z8*Cfqjz9ot3b>U*Q-B*v#C*^D6QF=6(I(lAV1y*|6mOy_cqN z+saEB3YamaZ|EOKe108>UXlqbHr3szhq*gnuDbxYL@`XtVO8z0%(JjMN|js;r;lTF zO^4;ZGCSReIi5qm%3z^|*|0k>uU)?pCrR#8!oCJO2olOuV8-C+*;iqa$@zyzU|N^I zHWy)e^`CAMnE(EF+G)5fU|~=^%pJdZvlO;A94pxl>#tjXmB2~CoaZ8#)1v;hANDEk z^H>NgWA5?e;Q*ze*E(2d>^y8I9An(NP5=vc>>C*etA`7MB4ECo_t@<)y{P5W3X;D# z=&}v=a!6(_hb?biuNJ`(PdT28U^#!f$5uFl`TSup?65_XvIS;lje5KQ=2!X6ih-lT z(_({QMZfa%n_!3C{IIzse-#`R35U+3o$!Yx$9$d(h$Rm;&xUP;qn%g7no)my&xD!X zvS=${L1~1U7jf$?^H5mg+|OqcthPF{gg9?i_SNw)jdzSP<8wT|5#V~TGlep3_q*9n zus(Cg-6bfm4X8V856c>ghA)D7Z<~d-r2bWdE(oTj9lJP!A=7r_zhymUPyxd{`vdZ>)w5!yhcX23Oo0#kx=OugC2!!;F#;Ng>Qk z^7lIjmyPoMau*gj+}LyicJJH!tO^!pJ=uK>wg`^CmPhhc#YsnDea??4B`jIcG%E?t z*|2I%4$QsXk-s0-)t=5*zz+NE?8UIx@ML#6OpmeEY=HBgiK2J<>oe+(=lu+ zu}|Eo))-i_Q9wCi@%@}=xaD>rRi6D>ziI<4??*dK+LPNIzO)v0c(c7`5lpKONREKH zJ4fjj!zl^EIV)lP>7ToKuws_7U@5FPc5gIUzdUQJzJajp`&g=c+rExsf7qt;O?T2> zg63q+Y}ljg3q4t0QeH`#<)MM zDjc$4J{(hO+_5jLZ#~#~9$X{O?qUfudEN2?V1vmD>mIPf{4Q)@9s={U$|}Wn#n(5>DGCa`Ju~q zHo?rv~Brc6N9C$jzq)47}9!TME? z=W$_?ZTO-}m^Hi~wY|Y>8o#_EdEcVxWPex`%3KVb4-%g zYv6zT*J5m!u!pdab%ZLfO8g_c4|AP1Q~TTOT9WBqSer2F6X}2Q+8vj#!vf!Usz0fZ zn|oe`nNwI)|KK`oYq|pe>n}Acu54Gp|N2|=BJavfSgvlR`kMpo-1SQ^?Oo|>(*K;= zY1UnU+53}skpAb@_TpYDEPcAdmYhEcF7f3lFh4Pe>Yx1RkM&ZRS=&gpm$b3Q{t&Dj z*TspnXDHgSgk6IrqdmYX4<${XTDk=|SmKdxEBy>!V@W zYNOMnzm`?XMs9?4=AOR-;fe{JjW@uYz}0hztpdI*i-a92%c$e8IxsPH4Jps_rus)& z&pY9KSTgQ*0qH+dh44P-horwI7$-##ixX}45X)Cj7!e9Hy6%eRz=GzYol9X_ z`1cW{KO5cm<1B!6o=eF2`Ip^MpUSh49yM?aVwa`uuJ zY*qO1@JyKbVXUbqtfQY<>jCqhnU+j|1G1*Pc7x@A&C18af|xr?#={<4Lf^Q;5lKc5 zoMGJ;zlkoewc=s6BPs7bpvei=R&6^t8g9WrgR!t~QbV{sYug>&*ZjD*Wp1f>sxb(5~tSi=q7&keDJmAlVJ^nsD}M;XkSm;)(+PG%9+&) zt4*@w|BlA@qta>DzQeMa7ROuRlKM-1>S2CS=72A7#jn`(I=E&1#Lb^!-j1Z(RWN5_ zs8a(RoXanG0b5o$f3Jl-I<)>&!RqY4Ln~n6m!y0Jtem;cPzJMZoqBx%=C#`XdLLb&4bTdCwk?U|ETl7Y+Yg zzqqq*ZbF#ezsIanKDv$TL5d@j#8Fy4e9n@+r59(G7q_C*#RoI@oUWT zhiP5L@71C{|KvK>99Z>{>UtB&-A>HYFw5hr9VKkEZKh%@Y8 z!Qz0SJ7>V$MF+MMyYG4v!iHt0b4R`=?k+kqjpWI`l#P;|GbY0xcJ8qxkL!7B(gc`y z_IWfp-af-WJ$8i^AJPWBfz9lWbQuTJ_>ODe!h+eq^d7=h?jdfDv>@dLzaokCE~jF;!y@ME2gCyBv9;!;{Ge{s`AFlg+ujML zw>vVoGxD+xBbFG$yo3wQ=CCaPN6!whN65WH448g2eLy={bJMrF8!2BjZ}{I2IDZ$v z9n*v4!wx32!ZOvVSHu<9F0E;Snd2*{oZU`2^}qI6Jec_dxwP>mmGg5amp8$TBR*97 zn%)O346wLsyc=1b%xU=AM&h!E)cT~H5>(g0OzRJm$oi$vq0Op>`BBRbkn*&1YSvql z|KIYBN|HXkhW{-uso{b~3)4<1caim_#b3Yj92WZLP;MztnOF)-_WQiBKyG;Ky!I)~ z=|7FKz;S+mV(|}8%4Un3{hs_MxBi@E{|HtjuGmiMx7^%!y$EK{jI}40(7dH;SZMj} zZFktK&t3Kd_}}*1%|7tEfYg5?Od;1(-hhso_elBMzgx`U=IGa~d{`ZwvXl-dL|ngj z8+KT+pXwhhV>a^^tb1m@pY*2~;hNKV#KK;3(%+&gr&75p!DGoc*lPWr$#;-z*A96? z`mbn_$xRi^4=R|dhb=02cSz1ua2TZj8;oPai8-$K9+C4aW#r{wH(@&WSu?pFWmxY^ z%!OO%$<_~H%hsgzlvu+>E zpV#YiK5~a%i)ZYBnU$M`Wc>;rmK}(Mb%R2^$n`RBRo?K`uqf#_b^W6EFEI^=X`&4; z$n}Un=#}*{So&VPo?K6yT9dpN!VZiI>Ut5N81R}4v;A^aiX!_J>ctf*v8}ob-kw9_j&6DdmPSXIPgN9bN**?7M!(33m7y-H7_LVX@d z=T*{}Fk_?XSaN;03O_o_64phVC`td}{9U8%2@9Sjb*hG&3!V4%fcfi8{`Y!XTX(n{ z%zpX)54pZ-rf{b*U@^OavZ{yQ7IRp-X;KH0)13=KO<;|8?v;12Vu1M}I;>}OrUvn_sDlH*a5WpSxJOyBpLx*t$WZZ`bZV}IPb0Y(PwH%hrF8px^4!BT0K?^%9AKFIF7k2Ie zs~51~ajhZ`mfN>>ct*vz}aqMSE_0Dqx{Q!PXo&u4>%Wv#@2Dft3X}bH>HXVC~GtshP0c+g^17 zw#gBn&VU1|-anVZ(hsUm=V96-;iM#3cd6LoEG+u|t1uDPn79|F!#NLX!uG)OnI~V! z;E10o)5Wml`LxQDFn?B2*X=N~eFOU#ta@f~KnN?ZbhSDNr~D3Tj)J8Ln?~(}6>pxU zN5b6Af8+PUahz+@!(m3Yf3K~u^oOL34_m$*SR4Z!q4Au>5+`0*-$7U2Ph1KtR z*bz6B93IMpW$SIU(XeF8%DN!9rEtrW&2aO!jqT>b(z?(gq&>bUuaCdk+Awgn(zRum|j+319Kc#FePw_ujR<0u;lbA$~LF3 z1rLEWG2xV(r#~3;Ul(+qU>Ja$XVV%(+7IBnIrSmF(_srSZC}K_o-p0&0<}D0(2J@r zFn`?k60&@{=SFTPm@$7P)qYIYtZF0J;o2i>lE-9kNNERi`bAO4i|KL7_D>y-$MPxf z_8_k?iNDtZt6q)rB-_hMX3TGf^)^-99dNKq>_h|c=J(Y8Ybj6t(E!uK%m)cW%RM#vwN`t>35IOGO>3ilD*A{fru2`A~>jV*!y9sh!oqyt5;(4K#MH}WXo zP96_o`tByGKg4|vIavrx;*%5Nk>}l=znPeRH#?D7RX4=p9<2ZPWFj%|PkL@Xtm^rR zs;^7AbniASTsfTD--b))I$bB_pI1=*kGpN`{%f$Mw`n?AzZCDq{c~Ui^CFF`zt8^e z!?IzyZ#A`j1&Gb57JfBD^L11v3yoiBjR>VHtC z+kdu*fR)ou(x~!IiLonSBhHzJ(I~I3?&ux@H{7YKC+!>FUiDf6hyHeZN0u)ex^mWH zxFwtJzYUgaTXKV7hc_zg?J&nFbzx{wV^!oDPgA*bcsUF`)c25x39hk0L@Z=3=P zq}xV?z^oa0n&SXkPrY68iR`rq_{)s@`$?y%LWCda9;_C>>FH&|}DW4|YC z$!?+?)3&M517>WnJvJUWg&R|eg`@q7$HBT)->CW)JZD!2 zn7JeS18Kh`CwYb)ELm1d<#u1Jo{oft!^|2dR00W(|PF#tW$J;jBw<=nHdFUpq}C>*Mpw$pRLgc_Jn*5J*mTg=s;>l=GDK zZ%twD1*XUYIVKn6(P4&bU&=MbgMEx)UW|S?*`H<)o(6P)b>V4Gh#N-H`u(oO@mf2w z`!tyU%R}@N)*oF;9j}0Q{vqFCM!(7FGm%@38f@7NOXKg{l?;!twnExa2{}1TDJ5h0m*)&tXO6W%F&w z(@jSEmBIX|_tf=?dt#-cl=$Yw>*V_5!Bjkd2LHQWSbo~e)4+@itEkVb6t`E$OJLfw zZe5d5UKN_(_K?(Xm;3uDtZj~c{ea{v)G5bc2lK_t3t{=m)72;7&{UT%cVS6{A9a6Y zedgN6e3&)n)2ma+%Qnq#ybia_|4iAb>F)EZr2hZiA93fhgL7a#Z$WG-%C`hua=rpf z157uahNA{G9={C#yWcW=N&S`%E9$%_D3E(aMvG3vGTTUTHf(os+Bg|3PEHz5?*9ZA z81*M$zTndRYp@SbqdpD`o3g3zcL^IS-yMb>v`eV_U+%i5!bF(Glux{e^4j5+TMxj3 z=jHL_ewh_|+;~5%+;+hiZ9e1rx81O0Y(qct z`VKANT3QLVgiig2gmycg^ zfo0vZUJi%p8lT*8usF_Q^9Z;te)#0Eu;A;szIJfB>$B;OF#plYW%jU<^`@B7urNaz zOny)1ZR~S-6wK@Uiu%2opYlG$mgH;BO>stU_Stvla2PMf>Ng3lxpShZKTLZtX*+p7 z#K>EpU`y(fDErIdo5T8=i*)k*ka3Hj(he4c#mKh7+_+mc zzusf}q(;>9NcU~UUw*(27rXyDgxqPvGVAZKcA|6g5mG<(V*FQFIK-?p1#X^_{G$Q3 z;n*0Rfh{6L+-kqX+`tO1u7mlDXOFuCTbuNM@(z}JOzDyfYaH|EXkiJD)~Og4 zOb<^ig|((p?$6+u%*2!ju%;$w<40H(u-`flW=n6aXpiU1x?roq99Y2GzNs5*9b6lo zP3o^0wz4Oj*UL|Ok(8ejr5y|lraL&Cg_RF)jUNjq_(mN*4J-cW!`i3J z>Un!}d-a>+uy91evl+-Wso{)cuqupBp93@XJDQHb;`(-t^I?H#%@e0_Iz8{9JZTk=6zdHtD13@lkY$XO0oTwHZ`EzB8o znfr>=Z$3MGHOyQ)AgT^FRE@p464oitoo$ctC=IVy9$yHHTL)ZY;FE@ zIIqV6X#mOj>c|DKk+`yy18eS&T)!H&=-ex07R)+cvuXz%XS95U56s%cjqz zme1hMxjhxuBzL9OpX2Iz)ECyjP*dx}o%-^{a#+D!?cj&{qW8x?tcN3xjBHP~pMA2u z@eY{NKmN&VSUq5j^Iljb?m5sO<~e$(C9uUceG0L7^5-jZ*sF`ed_E~}BJW)SDquggu_!ZJI>tk27~9<1y`~`f(=09(}9Ba$uid+Gj2 z<9RVrK5r=OusLj6f0$9*#b+=q509h98*_se?;ZfR)ZH0H#w)A7uL|o2OXu~b#xuut z+_KRUR=it6jdu=Ee|55e)#t;LrlLIaUPsGru;sCFqo>1dovwtM!1A7M`M$9DQ1=^u zYtX(&K?gG4S|VE6{wK^*-}*im7MaUD_0Jpp^ryLyg;qE<>k9a~^{P3Ifzvj=_GwnKZmON2T#*@4EUj8(f)PI!pzpgZd zX2TlZ`2dm!GyB_Sz=}anXvAJg&pgf%*LDqD4$Gywpj2388M1I0$rCsCOoC-MIK^ap z)OQ4uy|7TWGAuz3Vo@iYv~?<|Ak83(_ND znegO{d2sXbcd22p#&n*408IDHv*W}3dlA(5cg{64ekdt_B8;*?9vi$6wiKP5PmWjU z@MDvLVBxnr)bZ1;V6EelyrS;%9OQNhk6-)4>V-i(GJZZveg48snA5g&6*>OOGhg0& z!>s(G{Te4Ccyx^1yiOpz|&(?Vn z2j3f6JqQ-2WpFWvuxGw@uV_`v!MY0CwX1$(l59{{EPbcH~a|W9B917d)_5VZ0@B5^dnfE94V@ooN zVJEx@un(+!^f8FoCbM%R17?4`)}EACHTRlChxyB9Q}y+aJRcju|Ju|2ZS4E|-T%8H zjJ5AO%v`>kTE1wEO{)Qx&Zd1L>(}t5iqQb8ox&u~V1u_q;CopAUNrhSTvj@=tOBOz zh3p{a^H0XThS^7!P)_>l+FDBLpFR7U%wH&*e0$q7*d}q>OH!UawBMn}FoVY5poNVZ zU#~5JmBC%sk^P(T!dg}gv$~m54pz8yz6JC9codQH=^dl<6|kgywCxMh-rytO(qUnU zmYOf&WO`nk3b#~PB#`3~@VdS*k>rmDoh9=nbQTj|?uQM>mUb?JwMR=b<6v?1{rFn-S<#<2xx&KOrTwmu_L7YkIm0$?Ns-xb`kbyp2Ur%} z%{mKi7=KG-4@()7V-zrR0c+w&n0MqcHGiWe<(S_v*kgWk9JznszM6Jm5G=F~A4leM zDBsu`2EfWce9C!q+p_w=nhBoNd=HI9ztcTn_30L>zDL)j#a&@>hZ}ZezKCU^|3q_G zZRABowpaHc8DZQW5u<@??~rNb>dm)el^O9~w5Vhrp2yHe{P@#Tr+SJnUTZ->5C zCEwwH$A@>{HO~P5+rQGqna$O(Lnn3m4YcQVYPEGatl;0F=Etbk432#a+Z5&QA^pkf z@A+;;Fuh{`Q!>BC;#)O;PA&od6+fpuw*sq>$`O1%CG zOgnm%x}P$On&XlQ^J2dhlJiAX7;a9i8}o&-lhYLGMOd0&Pg$W*+n$4kA3IU!6H~B# z@@e?r`BauVo+*RXKe*KSC`<9}Z~|sLtbaz%FT3aGXC5K>|DDg)MV_%qu*~)5ZgPGr zE+4xtf&ZOv4g5(z_re~LbspsU=OJ}-h==7n{J)Uvoz2IiJGa8zcQY50^Dpnkc9$rU z+h$Vpk32r;yG6pxGxg8!B5!NpOV`5MC06#t?nc7waF`LW_|R=wy+6};8EhGC%O>-Y zDlT;4ErIzvW2pH@+>L&P3t?W@KW*2Mr>At$&n5YZ{48?*%4ORsd|}p&`PBK@roS3t;D7hK8Na6VnG6fB54uOr53z9H-tn+vW_d9=ADZu~`ntf{ZQj)R zVCS*rrxR?Lkw*2;l*=&_M#Gj-d#W-}Ue(;MWf08Wo7{R97LN2TwuGh2j#9P=G{4pZ zX7!w6bOw1;@YmkmV2ScLoy>QVEwJcGC;6z|D^uZ!KilJ4bm-3;2bQM57VX9yYl2y& z2X>J8O#y}O=M1o6Skdcauw6!MpL$poJ5ZMlvl~mLI#?IsoqPl~>&+~yfH~!DsY$R` zRK~?;ut0L|+5uQF-eHHD)X$kPXfMp~dMxucEIDHFLJUW!=tpkA%7gdb?t;ax7t~5v zai`Up*rU&G|6Eu+hf%!~&JpiAs(|SsbKQuogvq-vkUTSxvd`1(uye5Li!bFk$M`MD zu#KPXcCx(S`=5^|!rCdXwvhQ*c5QcuNnq~76GQjFhJ}}Q?}ODdr%~%G+vioY7v`+& z{*bJH+gSaO-LUe&+K7EHKepeM9kA%<#>Eo2p=4#_R+#T#wcsFJR+8@=4a=|f^d|uug_uHFbuD03A23GsHI!3`QBNrK2!!)B? zjcZ_eb<1EwQOcigFg->2hYKg1 zUG%0a+_JMFWGLf=Qm~Ea86m($M&#V zD{D6uW}lhx`%fjd&$FJIpB51tme2|dA7mbJMjm&p=)@0L|KMiPNVqIK_}4esVDzZQ z8ZPm^{-+sk8Ihb!zMscDw7uK}vl`RR4us9J(jq>?%txo~EnpEdP4NMiRfw%Sz%32c zJ!@h1npsP};rqVoulu4(xaGO6vI-7(vL^Q}Os8+n)4?8JrVGnqn=TEn%Hg;pR^Crx z-PXTd9>es)@tX@_X>jn>n=mi@?F|)dnYyZi%ok&ana{WdYaFlpX252VBVw~)>i<08 zHc#f&OjsNe5k-E#pg)-BbrCk~?Z1coz7g>_GdUI39?;+40@pNJ6&{7D|KEW1Uk_ZD zz?SUANlRf_!!z4GusX0VoC^y~R*JwIX>lB$^*7k8yWfUy8F!y7@ zyr!d0;jr#+@M~LG$9iV57#1#N?y`gh%o)#uV9RAsj`x5weAi0@VEXL2k>vLxk3gx| z7gnFGn$RB3=`cTPI;o%AJM9mCUlI>-G4mp}*N6Tjzdv>S`C}q1wPFnT4ztAf49;*% z;cw|D*dkGJ*dEq;tu3gA>Gm5_rQ>WePHVSt}y@ZsR|32?X3xW3VR)DT+o^1T~yj)STiwJX#zV; z?c_*YHd)F*RS#D0$Dt&#=dS4^}R6W_P={pJ49%6!)vJkbrNirSMV$YR&P%%ON5yrCl_9Tc}Kr$55gRFP~;hy5n$~|%zk)8n+nsn z2ggg`fS1#%PQm(%(c_3CRv4*Hz{>GHZ-@nH4b0=PgwCYuOGi!&J_^g$8mRU&p7yRw zf<4+E+mYqds*RH4VY#PLL>#L5-Z2g~jGUl31j{N;`NzQeqO8cnu(WGb;96Mqq<#2N zIBEFx+pA!~?DhSQz@p=;%lNQ#cXct@UjCB56GCBetF(&z{u=is&uS@b*fN<~-{xua zqIocvR!^;8Ox;%cLRfOOAGN>qXI%$yVgA-j)biB_1Afhi86W$tBkSWdpe%_4v%23M zNUh(INijY!-8A6=v3y^tb_Q&tTTQKB#zx(HZM-#q3l>5q)55oyC5b z1Hp?)`)xPFoBI>@XIhZ{+ES_r>5ohmpFTQmio?GDTSsJ5JiWy{weF@u>)O{>ZF zM711@Hh~qZhNhf?6+@GDb%gmAq3uZj)tL;hHiB7>zZM>Yy{0#(|9$iS{j0W-est=UX`c>k5>u)g|(eiN*iZ?yI)EXho_-S^<}Z50a{3dY6L0C2&SYkl_K`622=Y5Kf^N z%kIM(%P}-R*ydBqU}AcM`|xRSo0sqKLRh?G&Vuo9`lzFG3t;Kj+~~2ev~H~~A7%z8 z&9#9wq4sy~z^qNv%KO7!W@&3~llpeDAH85nuhK8KV8#hSh6OBd3AfIJ9cmmtbcEGG zUoPB$c~y?o{9O5}uap(a`7Yn^{Dd>lt^7KvU$*UcBV1FT8Lxyr%%(8O^OcsOVdJmC zx{n*0-oP0G_u5=o6W4O<13r!^xWH9fe-##1{GOwRRXx8o=D;>1rarq3 zC&^8Y6Z2h{^iaTY6D-#fi%rvLsjw)(sXwvI{sT21m;1OgRbFG3P0goG54n1Tv@bJd z%p>z*Ip-%)>!W0Bq2|L@j9WuW?#H>R~8{fxoOR{6sJ)|a;UL?xNe8Zo%alB~a+aV%&O%)I}63bA6{#t|;?zx_dX zx?o}pM~P-OT|us~-bOvYD;S@jmJM^=g3k3t-Ygg$kOiv;>eiC^sjBaJipwxB_OFXM z$uoLi&4k$_#(Xh>`E#!hxBy#jO0uHCOqVOR=U~=N)w`c~zL@cA_oq}?axFWo5w`g- zqvRAUD6SYnodn$%hZoDU!(sl(oO~HmsDnNU=NnbKxf!cW}j9?>TBBOj)gt`ETiTx8=aVNb2Q8se5K|) z2jrJp+rru(ZW1%}w}wqU_6&!`^r!Rtz$($c@xx&CJ|We=8FL06wT7jB)|18{FB`t1 za0sjj8dESH&a1!FG?>&+%53n0B~v=I3?k*_Ha-1db@Qmz#5OgpA^vbl)npDa^?#zo zt$$g>w8$L~=EB+#zhT7sn-O2yEhX0n`7_mvfw0?D|Q zh}7R;KAo8VAys@B*1cUh#1b}GEg6ywtG1ZW>jiscRV_O~^2)>t3*tp@xH3}T_*ZTZ zSfKT5BiB!>WEU$2EFSTC;#pX&>+#kU7Ov{JhdfVDTAn)I1ZK7UjVCTUl;+(L*1j7~ zJ#XjslgK;3)c;u__vm}ag$7F^Bju!h%erHCe^=o8bG5A-SsyE@$%UUVJ)v-NBJoG_ zYu{nUh^5LsuvezHzk&D&UZ?{*DJw2F!m{OonbEM>%R~DcV6kV%f+$k|NOo*3tbalC zSqW=Pe^$SPTOvJ}fSpxAHX{;j>A58>arR2#zrobj>4v+L0O$qFvZ^$_-duQDAi+d9Yx;)pc08 zu-*2%a9O(_&H>dKR{vH}#4Hj#!pGD;;LpZ;IYS$}@8ho`lu*yt!N9f>IsxIIQiM7b%1*K6{Kg z3e);3s@B0#=S4>k!Sqn}%+;`A@U72@uzsH2KLR%DFiv(5X4m*GUl|3>8kSW#+4%}3?^F7gb7nTK9bFl!d^@rTs>6yEE` zARk!n$C|D~dG*zUqM5|+m-K%N3x4jHL|IsL?hP!y71ufix&Fp&YW__@`;t}euyRG% z_yXik>x#3SVac8(YJP^({vDm|VEXvaXD=eBUm9OO0+v^F=gHxO(>GeIV8tNQ5Hj9S z_~xm;7i?o2OpTWhec(LN9Bz4hml|&@e`9sZ6z0%$rJKm|?%(R!0cIt<@+IRXV=NP1 z{(g=9RjA1$uRl+lku*? z?Gfv*z?#nz6S8~{`md*1uw}|TYJHr1Hy*eI%bTlcWPKR}?{`UuIe&J1Cfiezsed4Y zrSIvJ$aq+JQ<6n8Eb<@umh2zp;>$k{!+J&{HGZ1We@(T7c>CiLGG5kbr0?dvux9z{ zn}M+9)8QTCVaEK$nPmTK4jx~+o8)gUQR8P>cl_?`BtD+pumrhoT=41buzq;Wi{-G@ zj6vH(uzXzVT5^0XqPHi;z=9;kxwUZ8n|rFwFxw&QBpF|B!wl}f3D%DLqT2}PZFZDz zgcV7yx5@k&-M+Tq^{}Pk9yPz9OtjuS3a0xHr{?2m7BwAO2Qyt)HXKKJS+VEcwJ<9v zEc6`Qaz1!9v23_w+!dIoJeMMXRed@oy?}FSH}xgC?t1ar_ppI6wt-licxh`REL~Mz zN7`3(`rt>-?>x^uw@6qRc;U@on71tL05S7moOK6WFIxKFpmO;FpF8CG@vl9Jyqdbc zFvjLHk&pI{`dJ_(3J0MOY%Rh zRPNqZZ!r{BJeXfY#(Qcm6l4s9|E+(UzjR4|l2@ioe}Y^!LU+Il<{95leGI1y9~%0= zs#90mm%!W$+n)4>nLVFplJ8d$Z+<8AfDO+FT`7XiPN&`J20JJQQg+&`@$CXz(tQ_G zxuay18LUjSqwaQ?!X4B_9v?uCur{oLFu?&;lgj>>jKQ+MO_d3oEn04c~V`Fgd0GAx7O(l9c@TL^_mlgdz}(r7 zLJpAjZC@<8Ps*RPsNMfFwN!2rC69ZF!A6`*mD2X<(pyI zFOlpzEE*~8PVUD9r`1d)$&2rc$oZ8}-CUamYZr8HBKKn=o9Q1Fuzt>2zg4i#*4`%* z79{-St%PHOnqAJr>_I&0{Yui(!(r!OhfZ^-_tB`gUI|KtxpI>ZT$Ja&7Qa6UGc4j& z{;=A6Vq*%dmt3IUhZ3x_K9oet_j6rD-d~d5@1^_z%rs++BG;n=>;3I_!UEr|>uli^ zW$}t_uyjeJZYV6=Svi_=zg==-ef+|Lt;prvP7|zQohQq03-J_-8G~S&YRIjPu*hQ_ zzb_m$`^=_DSUqC;$eyrGUTf?+nEJm&SeX{-z8a>-^ls=1YY!g(76!A5irRIClLp<+ zTmf_6++Jf0YgBC=iRo$^>U|0#lcxtmVaxLmsrQjYjNE=~DJ)K#;@yhR1GC8;1}!1= zd%RJ6hiOhRPZz_qUJ3WVz|0TE%ND>MqEnyru%WsSn+qEhi>dj_g6VOi17KC=E$V#~ z%;N8rb6`c%W$JwwF$0ctpAD-&KBnd)+jtp*X22Xt_?;58$5y>ody%~JFKRx7>{NgI z$uO&9BsG6ReA4IE1el&xLd~a-+jKtP9TssXo8+Os*9vn?Ir;zl#SC@uOBYyh@-{U; zL$GD$)G;v2{U9};qwU4tSq{X;7VpV?4~H@1uh^0D_GOU!}<_2e{w%-rQ-ZBfhAUL)cviY(&2y+u`1g)19?)v*diKi`8M`DamHW%nwD~0 z@Ai0|BhuTGvwLed7Df zeg`uZ)2Q|&uj{(Mfn}%KO(WYM(7s1WIjrzJc=$4$Gg-H<3>Nk}Kx<|HEd5{?q(Sb$n2?tX+kC!4y>Eu!C*@H#tS#zK z&7Wb#JG*3(`sDw>NO|Q^_63-IKzO+tZV8w?TMmnRtvg*y@@KzCpN09K_Duc&v&Lpm zO@;NXTd4P`FeaR;m%^<2Z`A!6&*rH8A()f(mwI1Hp3-~sUYNJ%2K7FS^q6Z)cf*zy zr>Xfd0fl=%#lflzJE`}BDC@sI*a0hdUZ>{E(0A1S5yA9^c7>L>9#mL#c((=Cb=~`G zARN`HDq=JI@A}2LlmAEvYsS4>HYr) z9#TUX!Vrcqgi)BJ7ReBXFcpTcT&M#T_@FbYGMM5AOVh7iI^WfTqlSQMjZ2;awb zy*_T=_n-IM^SaK?IXmY%*SXI1BY&zhZ1kNsA{;jFJ-VH|&+@y*n5D4t+i+TbvTq7b zhr#-x%F2l({qmX@p|DmSPUk;!yBpU$1g8Dt5f5zjy=)0AY_W{a*QNHJcS}OzlhW1% zBJP%b3o23nj+mi*!bRY5_x_)X+|Z?g=Is&#*pW&Iq~@F09ap^ z+*Au|E0^{6gQX)wSPx*9;}?wx=52q4m88(#f8xTPSum&Z5X~D{-fd}^FU)?Sr|m6V z+qA?Puqjt?{WIc;f8KT@j#y%7sD_(m51T*L|Eki>)Il?MDlGMG3UVAcgx_iIHl(#n4Kd$LFWIooW9tR zm_K&H-(4^(YHtX!VDVkr-sXE|!f3L6-smf|{jlUE!$9^=CmMV+k)-#KO(Vx=>hL6m zv=_!C+npfCFW8Y-LD~n6x9_eb=SMqW1N#EZjy-*!SaHTDn0%iR-CyTL&aZH+Q>QyH zBXhzXV(ApuV~=3RAC2Q@!iNtFJ85|Sl$fq72cB=~Z*)*U7P2c0| z{#rP^y2Dap^T@2UzcBme*lMzUjol48o~OiTG&N^JMVT%6-en#_^UE@NsN2JKOT+&A z|1W(g=ms-qOsD%NS$|M4fPDWt6W5t+Pg0$~e;~}Nj-8T~Fi4^=b8d{hAxFFe|8vTu<`eE2n0|J_+xalk1WFE9}8Dm^nQ3y8xCReD|(| z`1ce2cvv6#DYzU~?e*O34YRhK|Mn5)#dNpxf^*o`E5F0$V>jH#z!GE2deT2dsM^$( z534G^lvtDgEDfKZxWl6OUR|2QKDE*PN0aowPSEjC%7HDnjUwrvwRlDP$7n)6w;2hG zRagCb!ckwM!bZRhhlBAPSh=*@iD9s5?1HC*V8`N5Go49(+pT|x!zG#33%RiT*|8W8 z*dgzd=O8#jckhlDY!z==J`grf{nm0kY!Dax><=q#_J5uX$8{TQ*B2HgZpxVj>t`LR z=nXS%F0`8i^Z&Fs*g@Cr|MZG+%RLIwi#^v zIHWu24<$YK%bo%22hUx)31*gdw6=wLfx{FsIPl7)+NQAJR@{}%u&j3f#wM_p?v7Oq z?8A?~Vg<{!cWSr7s^y!ie;ZJq)m^RF4s#U;p8bS#s;|C`gAFx-F&3B|ksGlGc3~{u z`wceFo_1qDEGk`b>?SaGpgo*5S0JbYRSTR*mDe1dgldFCUq?%9R$Rd7yj z9GySh-dSU*ggNbVw;o44(Ce_e0v6SmZc)MU>uu+h!>UuK`=5ZreV00y!RCrp`NZPy zudcjBi0L8@>H-_16yuNa|&V6i*!0ay3e|bhys{l)60;Cc*Eqm z!}YMTMRI2?9H;oU?>Xk+n?JWm`oP$i_hH@s9WggybMLqM9N6oO zd0rN5- zo1SHm{(o_APljt@-Q@)~dRUOWXhsIi-yr;v4<`*jasCP{*zrP80Eb2^-d%=OK1+5Q zVA<#7d#P|vbKCVLaG8Bu)kT<_6V~+&Z1E0Cx&TXGR?+$FRo`1*I1fucm!!QzT-fJq zvt(HBbM0q29H34wJqPpRPfn?TjejbZ6RQ<1j#a|(^IDgwVQI{MI$wT#?-e)C!pzMV zjUN#g%w%*q1Dl5adSr$hZq4g{8Wt`*eyt9cIjSZn!RFM^+Mlpv%c{szFfXIu=0@mW z$#~j|L(FxbbIS&9nAB)G^?mv87O>>%t`j7`vIV=eEi5hO9wpo7x4-kG1DrX+Z544& zG@nV<8_@KNTT1qiJ7dmCvK~Q6^TM6vc;xpvC4J!VVVr1kJ{W~v?l{5m2V;cfd?^^k z0bJP8`Os!^KILh)Bb{M!;}m+nSr?1Okp8Cnw+HC|!P^q-I2yK-o!?CUFMi3q=3_~I z!JFKxu=#2Z?a#{H-DC{C9_D_0(VsGUy`6IM_Jz}9Uq$K= z=M9qD&W2O2J>7H%=FH^mp94DvWh?K(rV(ww`@z1~mu`CivyXVVh~b#P;Z1Xh3;z@o z$0hr(C-;LcuM-_lDp4x#k^6_ab206|o2?x1@d=4G@mlF`vO2pF4EDiNFKi zJIMWL^?2$v3E~0L^@aJc{J~z$QrNB4w<+ZQ75(gN6$;xKwjLw*x9sG-U&JxN8CB%| z7rS?_4TBl$YMZ};6+N~el)@6`AzFS|jdG>yU&!UHlS%oKv+VBMm5ArwnnKGTqfgtH zq`$JlLvV?dU#!tha@WGXlbiaONP6z&2N5vuT0z7Ym|uQ@TC@3NRUIs!l}??$WG*d_ z87H;WcGoXQlJZ*Zbg0WZbst}mj0c;AyeA9JEJ3RYwPxNGOq=PBoX8C_q*E;y}k zN5mOLG4y=%1Py{tu%10*68S%(0#hFLgqgy#j{@NWk4V=(Bz@ld!3*Hr8I#ZVgH=3% z=Uh0>`k~_hSeN)|fWs@##Jo2-&q#l9erdDDw0@H_S3>RwhYR8t#M*_ToEdPnWBfE1 z*j#WilvuKHODVDO+N(TrKS|5JbsG*F=FMI`4HjQox4;$VbpB57KkIp~2D!odhpI}_ z|Gc0k>9_|Ru~$LYLlKN=Kh_i0^<6~oXTx)M?O52XDSSZIM^P_o){oQ|jYHZF90e=p z-MdTbm#XD+_Kkou(?Twj`l%ptG9CXdiCIqT%krCJsIz05)Atqmk46c@kiJgUmcB3i zD_^YQo%6~2B6HyFVIvWbx*pP%ykDwk+tc4G`F8IEdH)n`-J*9xTry>vs~Bdqam^*= zk#T5l@d8*Jm3p7-FK_A1*Tm-WZ|MGOjGCY1{a15y-@-A7^TU6ASq$^H`~4>U?O8SF zGM2)EZf$mw@=9;ra3K`7xVSzf=QI3LKx7y!YyWK)`G0d*PrFHBZtgzbe7IrG$wXq# z+56Gt`f=>E#A^lo@A?WD+oQuuSTj*tzYKBftM9XjIS$k5{qoPu5zA+8LOgeMYd(of zmv5?!fxT=bbbE{}A3DAx@!V|z`MsQ_8|ion&dh1FJwc=MqvKlws`_!s{;Mu+evyFu zNqH}Ll!+~QTEz+hFo8a5#^m;A-%lu1^;odI)q7Mub>JJcdxM~mMOmX`UrE! zEZa%$50z)jyEQOFP&taek8Eh1VTP^y1l=L;BbxUURo`Gu{a!C(aocNjy)0kjtCi$_ z6LV6A|3o~n-^5a^#F|RW_b#wqyG&}~Pn+#zJaX=i!}Ca7F{mM|FYJ43^JSVpu2rLf zBz|{94B5Wy>~uPQI`NDREl-q>P8X8-0|eCyT7Iz4ix-WC)6c%3`>$=)gO2ZJw4Ts` zhy3QVk5`cK;OrNX|CJx(v~;|=O!xB@d4E%!j6X)kud_Z*rstdKF=-@Ozl}NV*FjQ# za26ixMaIjAuO3Ou5B=eYrDVLn^_ZT8cECc%T9OANR z;s2Fqf8$Q3zn)rPv7@dr;Kq=VT(&bEV;kr zuVU!>hb{@D=>H{K|6@QN;>KH{TSLBFZ!6iXbH5=ATR`?df zst10}y20{6ruq_?^EXb}8D?%aPt1o zX%l*@3ig#en?mYCmRG-9eT0?4hnv{J+G&^1eS&@RKijr|HDCHnHpAHq1$ND0tMjY8 zzQV%jd7o{FAD!P)52qCU?$MOwHx}p`V9n9_4W$02%h}^^h4nX+6owR1zf)~nbif7< zooYRpSkc%nra4@)`KyT7tT__Y1~x7&qUojQj)?4Gmfy1?5|@WAtRU-w#+-Ud<3>S0 zb`RK%d$~XPJ$bgRA6aiSKqRI0M}9F^=>&(ib9icvxWLxj+Zh(8DOQmEmu+IsChMR2 z%-iQm>XTv(yJRe^ZJd=#j-L^HGixGDzmLP5CEMzSaQ66X^n4q_!+CiV3(RUGsyKR?J`-i0=6F0)q%868PZI%R>LKOrw5bkn>Fg)ga|nF zZXzx3EkCYAZ-jX_7SsDfII%^f3=Uk>hL-P)NuKw~`nGnutMqxp?yty-fm0?Pq~*QX zV{%X|9CiFeXY%}F$80;b12#5npyj)M{+E$^VAX{m^nNzE^M~w(1<#DMzR3O9^d+(V z!^a}>{LFb%?6eP#vtbKKdBA*4jM# zF|h8F>$yZYrE?OK)-P>y1;^ok?Tx%UVU;If{py0(UPy1HAF$;#to1ue%PUKHx7BKx zJAcM(@;u=`T%b#aL$|k}_0yaY#>S~6e@{Iv?}TUO(e;JB+)qv+?VpBY^LQDEi_Yzj zAdW~__d*M2+8(Ahw++m?2{QtdXnJP1m17>jzF`G)dyM)SUGy;PVEuGbUr|h$RA+!C zs=oC3CM+HC2e1&If5PbK9ot1z{36>Jw}|9~7HE3BsQBkc2;Ih4@0Gr!zQbAb^O-Hyf6k6Bq5l`t@5oKE zJ~Zdt*ks)r00|n^H;xVQbGi z+Ws3c<=ew0aDeE%3#ktowIXpCobzf1-5-;x-LDm}#(Di|^1dXp&n$_6tuEVsBgZ2M zd)0X({O@^XShZm87FguV z^-0Faua0|Qp8YUqa{guW`%Cwd{L?gZ2v>AC%wzkUKwy)(|T(DtGER>VD;7EpGkgm?^!CiR_=%tSMK%!JN0+ z)x?T!*{}b?_G=!{xF&FK^JcvNe|(muQ_neYkidxxX|^)WcT6NI>r|#g0u>2Xh{@AZt95@75AMR`*_ls4*u{FnFzC#!KJTMnH z4!HHnxtpJK z9Tq#DqxBuu_)8zN-~xUUt?%f2cG+?VRxU9Q>W=hc&dKciB>qgXpexLI5FC&VQkg}mo*lK9SPV$0azK;nPjKQw;|wiUpc zt&R-sh&c08-d_Wp_@gQP9%5J$wxk$#NgH>A)Q2n&WWS8Cxp^(Ezo-rE@7BQK`${DY z#05-qvoA1f&Y7;{`7h5`C)L3v*A?`8g{ak94-3r5@YvP}aZy$Bj-PO*iQS#FZ?RhX zvl@*=eZS)tF8MxT=*&v9hO6VfeZF8qu(dgKNG((;Lxp;`jPJwR+GL~ zH-}{dd#BgHmJf>+c5un~KDR%>sx7(BtzgwTW>3;S$KO<$&xEsE9HieTSVLU0+7rth z2A3nwbN>Ck3tZ;1p&4o4PkH3g9~X- zltz~;z2KBS8}=Gtz2r;R$*{xIFP^0Rk-4yMz;sy7>Yeu-Rs{t(&4t~fBWZi4q4Vyw z^I>ICXGR|4viYA|2EtLjt(TDR50dqEswJ?OG`}nT{;+BPyJawE;xG$oU(Km0ez_dB z52>(v2A**3tGhb6StX({G)_PcZeeSp0u+%;tmaD z9u9ary(ww$<^(SGOM}I;Uefk(?NI5DD{x%gcsJ6%Z}8BYufi(J0@{AhDG)km!kN4v zr;~`Yn?(g?!!gm$Z;!)j$M1(9!w#cYc_)&1tFDiq!g(4dO76!PMFb3v-&3-@nFs>IV`SlIp4??^}E#2S+TIjXTt#- zIH#{+^k&#xwImXA5n??!i)#cd{X5mQ3oMVeY+MOPMBQH488Ou?dDbJgf7gVB?{|p^Hd*yQMB;VZ(jJhWW5` zm1)UDSbM^ujX$jZ$ViwDM|AGhO$1w}O^clkJJv+Z@rBJVWp@K$-ONJg88CaxfLn9n zlr>x3ropVa^P7udQNheFQ;3`U|6WM)AAEdqBFxEt?GX%1uXcPr0gf0GvPS|t{CQM9 z9#+(D9uxvshq@i~hSmA{-pk<3?fGB5V8OGG*~?+p>lMAn!tz<=S69N8jDo+OFi+RE z$!b{WH^`behjncF8rZ5tmF@wn((5Ngz^bo3M!Ca`$@_UxaC-jXk8UvM{OL!VVcNeA zmc73cuno@bcP(io@!LfocENV;8>2@MTP1S#!|@MxE_Q)+3m-04!klsMmpa3YiBmET z!$O76xS_CN%JB;cu%cA|m z-Q9l@mx#8tXbqdZ_|yU8Cw{PlWp@tI{4oc9|Fwm6i3g}1-t>F^=LO2&%hS)0{ONkz zc@41O`T}Z=E2rZRSfcQv+cS1AbE<<`ZLcPi{W10&Br(JP_BVHIsq_;puUwRN0dZmS z$~Kj-cFZwWGVIp%@3L}OF{Hua9LyW$_53YteDE}noIhE4)be7O^-cfg6s*i~_b-A~ z%|xTf`Hm~>TC0b(hw2WgU`zLDTb{t?NvG1}>#$@5-!UGx()#Snfcd^fvVE{!r$*{au(?n$WH0O%CtH~e z%X>>+D`4XcsWAywOmx||8@9eK@lS*qC0C~IfRn_38T;UfqCqXU!n&ZbVY}g+cbnS8 z!cpAb&vwGB^fqD9Fgxbz@)%fRIQwEFoOo}e|0Y=V=!07%96m;69|a5B92>U|E;v^B zGLpoLhP5L$1lxXIPu%r0y}vAF%|EPzIbj(La=%rN-*lW<_QwAETA2TER{ymyb4L=r zKMglru7tx@8@kf_H|oaWJ*!||>a%qZ>Y%&g{}BDb_-zfy4E|Fz_kAn ziBI#Iwg`4|-F8v{=bUQWZXWDamMj?qn{t1w@`tl0v=_R=n(5p0bKvx!#haXA$xWNx zGhxP;k$VTj!qTe;XTWU1oQX~_Gwc#|!*5o<{;;U?T6bT>b33QTa$s?_dj&CTFMF&b zoa4-KCEF8bHhS6vwp!bj+G%93ZQWo&OYHsS~KY!r` z8*dFMZwng+q@Hnu1qa^LFk$A3@2uf))WVs^T9fpBy>fc59Kc^4+`UuvHFK%8B zv#Sp%wXnIj&)?6)J=0&FgZZI>@2X)bced~(Tvi|QxeC^eu>G_bcIu?+}9MCDtzC*}_6_+kRm zJ5^{Uj3j26ury%&s9T693i>R137eV;7n1tF_3RaU4Y1|c7}05% zJIeY~5o~znX*>)&jypWD5H=?!w@|>MS4Va~gJnBQEiyRpV|@NYSbyn3!D`rAu-^`V{vz4$iW@?jeaNVJ{TMxkkn>%R^B>lOw zpZCGqdyx-2!X|FKPyt7{Zz-~et&@jE?}pi*oJ!lm;h`f7V&R;DHLV!1y32{bkualX z7JdH|#{czQ2@B``nO=_fPfPy6$4g;}Dr%6 zoaUK13t-yc1$LYtlO6!;?L7(~z@e9>ubB;tlZ)N&!giO9&1S%I%WmJButP({swpt1 zUG%dnu)vx5dID@YvhdgiIJ3jDPvc2=uq3Fom z(*qVBuz0RUT-yBYwob5&owZ^qERWjrwiRr6+%V4{RyJm)*pmFCnY6ve_%N`}isbJh zZtIP>?A?eOOCf&mg5R#uu(8}>%6C{)1KPV=AdDuB&XKPI$;CHGe8@=5+<1r1DClRbL1 z9u|bOU)U0k(yedv2-b*f^|rA5;FSw^VP$S+d1E;7{h{eMV6*Ve`3AJ7`98F!7TV1I z&ncoI*AN$H?N9%Sxa?<(@#(P9Kk;@wtp0X=>SdTaa8S4JaFTM}APvl${xiQ8HqC#! z<{T`s8aa-%=c#O3mYjxJb_36RfQ8%D<4(Y$7WIB6IQ?R4whC6X8<;14~SwTM|;<8h7-YejU<9!E} z!pd9M>f&J8-^)8*!;({%2JChER{lqVR>8az!tIX` zPoK6wQ3^){`5z?ht4jM$w?&=8*Jr*3$Os zhQHx0XTzE^b(d&+_mKyJ8L({0e$IZRPY-jqnF?F1&IN3PMe=4j6JXY+3b#l&@%9n_ zaU}n@iS+wG;5@Iuv2f1&l|9J!1IF{g_xL1#X!)BZu*=vW-_bDd#)`xs*t{v)idb=Y zhk#iA`<1mDtaK^#A>SwBR-eB=9OhWgwVDr$Oz+skU~}+Ob^yuW=gMz4ARPy~KsYj2${a|I=DV-<4ZpGrjKCmvW>1a<_a&*P` zUa+iqPO2-MnY(Nwo5cOa4_#o>p0k}h!-|wUb^T%Pug%n|pcrbe_ui9Phzm3G8gUTk zaT2C=gxN2H=k+1!D@^*9u+-SGp*_sLT58(@)>&Vq-=BPsWgc$|OEwK0ZHqYT;^UOY zB>mgwc7O4Gi~ao20xMYkabFSn{uMBi7yqXK$GdPK{a%*Rd(Pf(u>ReC`h7IT<5t8M zSQMD?kbHklf7vFz29~71JC_bK0@_+CVcp+@u4m!UtOd?xu;O=}NCi8Z$2@*RysNqF z5N!T1>tZo1d_1k3d{0*IyO~=6izJ`?&Ib5C6 zvFBr0Y`ocZ8_EA5&G#Yk>A$PD!bdPBrV26~E-S5E0yvHLp!`5?l`Q3zd z<=_7M9{xQ~o&__%dVh;TJePUD$rV`Uk$r15EIyZ_P9gClCpMGs@4~10)2Cp?{bgfA z;D+mQISDX3wbk`Su=?DRW&2@H%VauVzOv??dJn8>5#&7^@$A&1opG@Gy)IA)>!u%A zx&v0u$)WSh>vnH=ybTr|J(V*O@%RT7-HCI;)!sv4JI%u%Tj7Wy1qoa@BFt}AEUapM z)X)>oT;|v!8fI+06yE}Nx%Z|@2Fos#(D~E(b^}?||JNVH=*6YhRP5<#K!1+9561t} z-(E%MALm3ZAGrnjr5AqC`Fo{_`!2@7nvN}hUqgEP3AK@Af4BkN=={r?UU4VL{#r(V zqw_@z?zk=930q~{qy1@epRe1t3sy{8Naw$GtLY!T8)k{C#HX-5`S&3{dtud@JA-Ne zo!I*;_Q8z6^rHt57o5FtkC^*8;>ixUU}y1oVoS1P2$}CRM?aY6H=dA)LjK3HAIoY2 zV0Nm&qCk4~hB>sqnWWcYXR<%$#T{(C5VzkfF_HhnJiiz1f2I94V}cwuT9WEX|4_FL z{A6;zMKjK|83gABYJ)e!?2OC-99U~uwjm1EcI`3R5mv?C)<(e675)c$!7gtPj$H#Q zRTjl0IrlXD2xE>Ex+nVfENmDMR4!pu*3i;D`e*t~}UlLEE@tu;|@m4(Xr9JG*DX z5ZG+jolW|qSt{8d`oWUKDIt7V)_umhKCrU5QLrbhnf2ja514Uy3GIIuT49&i4dx`b zp#O(#S4AHOSn;K94*7pX2b0@&gw^`7)4bt=NzWd)gDq3?X#d-?r%nBtu&gj%K<1xS z#)(7iVCB~HwEv%8zp-~SnBmoq_II<|kl4NnEL_-r<5Z*%*eI^Ef~7o59x?CN<_o`{ zWBWI6tP{eLF29%lfUWjM_aOb_a&7N-uZIPrC(!6@cYhmWk8Pthgmz=7GHMhRd z{=cSAKmUG!Bd*?{{Tb7HUSL$g;!pGG^_ViPRp)Zpl=|)}=^vTictfMN@W1}S0U1aB zynrno7SQWCrQNe#1+dbPSvC*pRq-uq^)R;yvxM{qF1r{gcmVSsb)x-!<*8qb?~wS# zYXPJ`Z*^kXge+LSx#kF&571%ZfHXPnU`_KCRN>iO99yIbd>Cb0be!S6H*it$*aSN<9oo#wUDU{m>;tP^m2bi#*7SfAGG+DVu(fSIr!HpG6U<&{tWS5qTk!STd^B*b%P1Qe}- zIS;SV{)K_wt_#9p^@c0d-1XHJ?vKd$E#uNAPeYyXtf$S+SBS`q@Q_(`8g|HEvv z@PGu?ADH<10@;4x=J3U^;Z)!M=EL{5qgFN9Ppx;Y`m+de$*%x9U%A)2xv2|bVZEnb zL$>#=ddxgnoRHW3G8|R-@QFWc*?pF-2jKGb{ZqtkV|CR}%|(`P;`41Lo2 zA#DG)^ocvnVf3|q46E0MTaAWUZT2VU!ihs8yhgxE`3Aav1!rmUb7wf>`!TwHg|FmP z$3d`&v6jwv?{&{@b3fQHDD^h!PnmOk;OAbjuI*IK8}fVis@pigobkp<~ z`hvK+;+u72*c^JW=~p;zIHP+*K8|N2uTd>5Zc=vc2W*f;&LMH_vg7i4_+R=m?n}on zFyEcCkZfOW$=UZ2RmnVYoNL2nVJ{ zZqA2QYX?}9{-+u{4f7fC0$v-^KQ;8|uPr$w{r~+Rq2t%?y#Y&Yzud?{T$$IFkq+xN z?xg)?MdR=1EBx;E2{Q=z0vMq1ShwgLA^1=z19J zDtm<*Ha%7Rx8BAc?Xy#`=4?+||6n}$Fi!=u^v_48VEc6!e|sKyVKMSu;|ZCT3+inOLnY- z*)N1s{IEUW)VWPpz=~GIy~*>Y#5Kz=7#6Gwrq4%Vd8GG3Se(9}_6PUbzUWN=ti0KU z_AeL4UFb9i7Ha3U9E|+33)2+SVBLlkIv=`Ke?zayu<3o0eP_f?M|X`Bz=EpcN+xXa zkCu*uEzH@Mo52pwdp3-LxlOCL|0UypvijG#lK5g39Umkc#f%#U3;C>VWd3vIxk+D! zkn}6NIFRvDtOQXDCz#n<(klZN?n;nyVAk}O>16%2lI$mc9ZC8>TdEUam!#G~Jz(kg zIdr|YlAj;>9bi>+Pj50FP|AO5V8W)?j_t|#LdQRx4|cFPIcU8Ec2Y+6ZU$RT-tHU( zYkrs8HH9O5xAPamWj5owH6rn#r*u3c_w(02fAdfuub$y!qz||LGZz-LT;Av{td5(o$Pf0aasO!`@k{Ml zBADBE;E*DiGhn)OHk|pq+&d4pXi9Tu!YSNMnp~LW(_|fS!{{H&a$s2x&E^@f#^r0$ zJ=o~qcI$LF@%`e3w_&+=)bMGrWyIhqnXu`5zz$-0Q%}eLwa@jz`!W#cN*2*L^G?r} z7h$2h_ZyNv{d|1gIau?l&rq`c+)nyAXJFB~ru6qr`&Psy!MxQUUXnOBvipIPaL&FA zx_{}$?NP^IRsFs~vcG~4(P;^=EMR#*U)ZT}j|F>Sz0b&Oa{TevZkBC@r7`VV_``8) zu>dZ~zd?FpKCFCMHFPyB={ekO5uCg1PS#3T)+m<#zfm)%Eh1Jd84yDL@3P;U5~Z-d zLzif>USLuqt6s}tZAQk%V3Pm#=jc#aQtsH3j8_e{pA#a1rGchQay>;KP)bUdo6VP58ZSg5~B*B>-z6ok%$IY%apB;!-rpF)b}z&Vv2R*~_i z>CRKad|~5PM@ua1czwydDX_(Se&8;+>_^_XiLfXzGMJ1HRo&U=D1ccb*7+TTnM-X8 zyaWRQr|oNQwSgl##C9O#bB)@T4op}ttn9oIwu|ic*N&tgZdE|W`-b0}w!0au^t?&W zzul(2wM|KW#o39g5SO5G(-@XicckNq`N!=pHG&y#BhIcsJTZ9a(m#5fU+2e7$p2gY zxA%!(u>N*7wXjcyOFb;Ho+}PR+^OTR8{c3p^J3sKIQJXB@H1?>o}5pvr|h|WyBe6) zp?pLjY&<%)(+60#_lREroO$}qBoiFb@Sfh^+&{P9zk`+R{tw0?o-;Nn@--};(pEDJ zF4*N(Zh%$ZRXh8`CY|S@*<&?u z&iRO;Z{Wm)n_W|3!O>W{egbRC;RIsK@X-sOBQC$z`y)wj8aF!X32afeW~9UVgAsH* zzG3)|&1Cz!!9P1?As#quE&V-y4_lY3Fk{bpYK`+fI{&-6)11E~Jx}LA$N$$|Z2T(& zHuy}Y^U-?^R7}*uIl9Kvjv{~fkJp~pVJq`2I$yu}y!I9`?ca;I{^m7mONYd%JKz*k zQ3#3C_DHxO>~GCAvb~qC_sM+gLf4K*h;?5!4cG)*T6j4SYgBb~zV*x@-qhw3Oy)|& ztxow+N3{6#aWTnn8s$yW3xf_UoDYjnGqQ=5Uwh{I!Euw*=>CX8`n{YDTQ!P3|pMdh%}^PuE6nJ?o9E2$D@#tgdm3$}9%O{jud=YFpH0gDE%3ap0NPwx+^ zhk1`{Ueo)l;m_9ZaMJMg1Ihho?(N`AoTCqCe}y@Nrtc*4!3)lQJzNWO@A*-)?#>_m z4Q6~^OXrJc-YfO}4x5%}&exIjzMtZVWqIfK5Z5WE_9Ep0??WFxncqWO|5?xg%fGMn z`U?A=4v+p#(of6Y_66qnh^vWN;m-ubQB5zr{|k%SwK`#joh(E9Sfl)5p0uS-eBm(G z7N+g3h_ikVC?w^ZGfe{ucvEKzruKk+inZ-4j5@t&|Ssww^bIIGVGN%_qkJzz5Vy?^EVKZ}I( zE)PN6{MeDkbCaIVB+n0Z_xYzkBfT)A#oA#o`)~rCZ{H{F_Nb9C^SX~s4dNQ+u`O;e zw_9zik8u3K=LbDurPjFI1aoKG(B~P;p?@KnUqu(NtK%fZdCjNqehX)w@5Y@93m2>y z{{~LK-KN)cm|gkjR0-_B$j_Py8;@ku^^{H0)OvqdWoU7;2yu4ndk4w$IO4&QJB6@4 zYly2D&Ut?QegPac?@PfV*y{f80eQsvQ%;iSy=cj7t{x7MKI{?yK|aeFgqkKjPc*E~de{>UVRt zBW@MzenSIGzQ+i5!J@XOb5mf(#!j?;B_GPB^JOQAE^pn3c*Jgn>siFZ!@Kv7hb14Z zKAwgn^2C!4!ir?`q*Jh=`H_Q4So-57oo^>au$|W5cveYa35bg?XZ$&exTbrRF&?fS z`&UNldy*$LN6GxzUX>#wj>DXy;WKu_nWG;cRl%kyqs2Sn5+9$Aq`oNOPh1}h>yFsn zB9<4dpG@Wp5 z>H6GJ8F|TM|G2Nh%I6?nH@H<3V%?qA0n=fv!^+nQu>N~GT@O5^$VYM*mKIf=_C(yU ziQV-O%ox@zbQBzC{hC3}pQ#&t%d1t+8p(G2KY|QMWjo5!crQwQmn+kAE^X0v5FM_+tZGRkl#Cg_UJ` zzoxLh$w})qu$9-}c4WPD{`UA$t6*Ms(?JcG4@y1H(`yCHcPsk*9oD6JM3U>1vpRR~ z7noCGRxE+lb%{<@us$axjnrp_KaQr8`S7dbr`HC+`VYx;K2S$fV1ZZqcj3gJ zwtIbGLH{m$$a)Aa>EpkX`mKt+j?Q-(S00!;4wfxn*odr$;C0W_bu7tWbB)fY72aSt zJKws zdb}p<*JnD^&+iNK&SdN&^JB*Clra0i+U~XG=i%&TQ`Br&8CK#=)+f-mx8Bwb=Fjo2 zr}K~RURYuei(D(kr$~HRk|!yj)h*U#k@XOy#{<}{VN1tHbbd|2Ob1$j72fDY*He%u zsbX6ouHlYbN!DNB`c9(tS@pNrN5|lB)~o~@#0`tndJ>zTtoLmKM`YwbJql|!b>Bwn zv;QoOKHky_R!;lAp2W+JAMqm3qln0MeTf?ei|F%=>C*o>ncvfJa%_9@d=s?dP>W~Y z`uziDJ|0RP#deFaz&YCj>3pD;LNR@QT6~L(k05__=PRCdh+8^2xDc0Rt@`izE8J10 zgsmE8o+r;|&96?Lhv0%UshJ z?oqN;1+4AKlk6tx_v>kWg4yXHozLA~l{$^omxVw2()o7NGq|*Vq6>O$A@hxfCuN1a zKwNkk z!0hn-ckYt-{-Gm$;JW>*{7C)GF!pX25BOjGlUdR#VHnIz>w1>dSB2g^?>NDV(=*mz zfX)7^i|k?3r21=WSX|NMhAoNzu60qtqJiP^YRm_y8C~yr6ppBnGT*}T>#SdgVbd`F z}<(;0D7v>*O0xz*b+}uLk{(Eh6_%3t{7iQ~gQYWh?*Je33PJGhi9on zoBVz`7pC(kA-!Si)7}2?zwOuE&r1-&>eb8g$@Uyq-p`*2i}Kq&CHteZ|FCm9%zQq9 z9-s7JfZH@!JJ@Vbj$bdcUOtuN=LKlVeBM!=_CKBs^EqbQbFf)w8|6dt|KIsF_2&$m z0E_xx^Ca_Yn(c>;@rHHXj?w=sGy2R|FIaflw292G>7eVkZY->Ru<_XyxL~p5jR(xk zpGB_+xA)%HU5JlV%*;bv_q}j37uMd<(fL?eM+Q$H3`_j_^eI8yeqGVt{;+ZE^aK{a`(rPY8}*a{X&x5+A_$S_wC}2mk6#;x6F_$o!U0O4*y9uyn{Cx;_A-{mDb! zVSXShrUr3t-(@~sVb;CN&p*NXzQ0OYFh`(WN#>U|mYX|tfVCrh?tF#4<^}v|1Iw2d z()9?E7VXbtz{&}Z&B*+;qLknO8(7!fb}q5?xbIqPSmODK&R<(MWoP>)aL$*f*7YRb zaNR1y;qpr`vOAk#DFaHagMY z&wkz4-VAHn%36NI_ELiN4xeE2rJF%y{e*(`**i?IPA! zb+7!xWIkQ-#<~+PVg38t^nCkvToam4T=JC8r)yU~z5W@@Jn?eSE2Q_?a_h}wSXzIF z&Q~hDdDi10%rhOP^P6h6KC#?`RWUE!$^58^??#`?hPB0(`de^jPtT0ou%>zP{9%ZM);+rjP^}7kKBv<_0re-ykmc?9~F8(P>&+Z^VU*ZDWgIrhC^1W8uua z{RJ;zUii+nV_-LRnaJ- z`Bq}#&&Pf0V4>}rSYoTN@Rr1)M)IM=+Pp_6KEmu5&%|VVf^YqGWpHLeyK9v&_YiAP zDJ+w}Tt@a!T`)ZA3GB6cw4e-T3-5W~hZWw>=`L zf9FSZey|pfYPtQvE3*9-qYkIRWq)e=J%u?AyLz96(}#UnmILz!EUr8OtFKOtBlm;y zOqb?IVB^$;7UX);<@vr=z^2uTb(dk2--9D@usr97_##PvV*RNdu+M;&k!o1__|Q~h z>GJ5PV{v9Q|5}()dpi3ZoSiFhCO);JGh(wc*9l`f>`8wi!r>O?*;4mgX#5`esiz; z7+7<;{+?TMN9MGh!y=d_k$%PCLVl1e4)*|KCtX&ZKo_)EEO?{ znO)l%GhqIZjhVekd~jalRG4*srYnhCc)hlqhI7oN+xxF3uT=DA93(qN0YbU70?uHz0@!^W1* z=j~u^t3$pg;HXjh*Ue#}^{vhcuqdczJF!%jEI$Cp6#SS^%xqaaWe=PyyJgz~R)_qw z-%ipW+nGwNDjK_O3vA&mZ9=v$o!4vDdN}jWLw!q_eK*y087x0JrCnQCSh0NlB$(N+ z?G0kX4!b{mxZ(TFjCQcdcDJD?T&MIcv4^dWOmE*Fw)V9$kmJ*cib`9;#tmI#h>hD% z_51CF_8tDiIFesoGW$U_%r`zMY!MVu|`*5e+W?cQ=*BUo`&ZMhDY z>~C@N&r|FVd4GoEE5~&prti-%=ZnYWUofxr#a$_|mCnu20;@OI+ns`qy~hN9Bk6<2 z#~p^nafP;@;2invQL(VB=~~xnSezH#DhgIKG(YqKmKp=}YvAyazs_U@4Yq1Bh`+X%r*A#PqJTv^dyOFbUl6p3zXMjMUQ>;SwVwvdVqtB_ z?wRA@^e08PWU#E5bBhoAJ{fgn6U>{Nu-pSK*;9FP11#y^mhBFQ7d~{2fW>2G$GXA| z^Q~U2fRzjL#|(!h!!DFAhvhRT)w;ln%s-t&;hZcx^-wsoRqx(n*usrlIRH-VIrU8d zEM4BLSAWG94UZ#3C9ojggKQRG<=eOr20&E*s0fFraR1;Xg|9f9J(_4kQ;I9nWh3S0xu+!61hJmnZ(2*V;{~uHLAJ+2s{||f! zE2UAG6r->RqcDU;7{XE*ibXOMp)@HLA(R$jh>Ea?7GV^UVv(9kM#T`8iXmEr@BKXA zAK&Zs=k0o&^ZapsJDV9E1kJKz!t_(F2U)Q6S-9AS zxVqhj-Xwne?{#uNsq<}tEo=-pf5jFSj$63H2G*UJO+BCBWshY{SmIFshdh5`)9?Il zu)zP9t`BGn(> zg=u@+!d?e!`amTx`AFyg;$b`{wYoDe?-(X3|`8{J`b7ofXCpd=oeIprP5r%WwdRVk{ zyysMy>1OEp4raXajh_j-$SuoX!>mqrc4U6*&Ny6q3G+8y`y+${J+r&kz_hT2>5Jit zrGI(VubrcP>g(URr=NpW>2!BeKhb@B zk5e#X)-vjTPGeWp9EVfvZZF)5IM2Aq`Y0^DbK!vmu5h*fb{H1b+q_>7s|t>9J_OSP zj@=^dmH0{3pV-hbW-wW=1%sbGNhIDTq}o5$_Ddu&V^?7(vRVI#m1A%flrAQzdk2tNjFZDc1$Lfl4aKYCtd&u)Lq=nS@vp#?B&k2}w z@P?9%-`ozff1ZSSbM~(o3a4$7^-6^$?{-t;o$KTqn+A&wQ)0+^VtXZL;aNDZ?{YUX zf0Aa04<_x!*!!EBUj=;H76r`PnL({5s+7(%Vs3FenD2W_dJ|^+7(>N#UC*7k1vAxc)TDgM=;hqIu-vibCb>Q< zr#h_=W)GA8?gX1x`8VB%Ic@l7%;D_d^lnA4*7sA21zd4>>bQq6liNT!S};`j7?$XZ zn@GIj@TadDSlIgGB6%PFkv+^ZSo3la^?uxx?>Xgg%!n0BOcAg4n7`*KtUtHQjF`pX z))4dU6xZl*u}!<3m9Vt`%g)5H59d+ym0N5ZLf(fLe|ne>aZ#3b&)>ngzk^=z1{NGQ z|NIN~cRbneEr}mUnfViT?NJ^?)(>%M)`}+BJ;XZx1FU!u<5mX;FW5Sw4mPfEd!>b~ zWl@x+AC|Ko!m%I5bs^=2(aM2WVWZazu0Hi5oZMf&nQNH{XP*zfNS;qw zrzp3sgoVjL2Z?q2RqvM)|9+WBEC}^WC62aX z^dRO-`&?fHCnni7lJ!dzJ$5-Ui+^$Y3s|~gne76Yt6k03!rGS;BE8{%>zndG&GWf% zQhwLPqc zqzQ=+oI}lLSy@>2&p|lvb(%)4m%M~uTff07ose2DxUKGA8epbfb=TA6e%5>E*TEhG z#yKRD>xFNBqJu-$r%~&XkhQP=1?&{d8=izXE4lwV4g7EYk_V}#-iK?~*yWP-R>V*0 zmjhRKu0D1IPU&@S`em4LI-6QwQ|wxDF2L+N)}s>;XPg~5^bBlVwU!?T8($7iN`_^J zJ{tGI+-Z%16EKt2_s~vQoVJ;L5LV~3I7wkXyA5Q8w z54pb`rj4U-4TIG;+r~t~KKsupLt$Qi+e6{7vQpP=11#w{qhkmhQ@!YZFiac2*m^z8 zGBX<2!oszZ$3d{QAoSX5Sn&Mr#Fa39Yz>U^Uj{cUT()=_te)BFjxUMVMO!b0 z+1A4)i{Qj3N17MI!Q2P0=aG1P+|Y%je(v2l0yx`0xyyW*zj2h!bU4j+p^Z08cbx6U zgBe2#AI^n2@l$h0!*aG|izlo}A3J&&+?4I%Nz8oTt;0Z=6F-BpNZs;@38%fi*+lB| zwmaop!i<3B1}|9fMW&;}x+u-Td9ZA8T1OhJ>2^&ig!9G(xqo%UcveOA^&ws`mGuFR zz4zghFU)l<{`d+GwA@X-uk=cQpB8TIAv{3dKW6Y~`*PSR-<~0Yg_SawQrO{H@3F+( z@ppczVV_stRCyZnbmcAB<&gDya((sVo4>Q+G_Ny{N&5*B;_qa_1rf1S`_WGKIC2IS z-f`Hs0P(!^*TNK7Ys7G|!xQ&agE7Qt1*n=_apk zC~Q<-s$L9N>}~VZ5e|<0>?VZ$`-JXf!+(S?IAz&| z1zxcAt>k!HSU%5zJ{Q*P6s@&^8KokTC!CuXw#1sm-+suQ14oS6x~2=v@tb&eHtb@1 zW_Tx<)w89@0}kwOGPxrx^LOY+thIG6?C>84_iZWouhmzU+5OkdCbPHg|5M)kcxWmEZB0VYZ6xV$jtZ&*g=sm0)V;w;H z+xv>m?poOR?zsgS4~o`^Gc|DVv?sKcus?HBf)?i5lzt=Q$tTy*u^g72d3%72H=U1{ zsubo`c#c{NYXbDvC9rM@XD_jJWJ%~Fm~YwTC2{O@%c=*&XM0lS%JSb9{wGfT{|&HV z&GqN&P~MP#dP^RxV&_v9$_3H4VR_uiA;E|jXV1*Zfu*e2C}R28=RI!1l07^A=eGVA zuEOF4oXGWv+wK*ZUx5uFtqvPtMU{U01(ty7^Qm)*3^slY4|B&R+YI$M!<@F{g)G~ zi=I9YhZ*IauM=})0!D2n_A6UM)}NRy;pW8JKWS8d$O5*#k-+RnhwRDv!3rAolEkI2 ziWy`+2>batlIwGlC)W>$tv?!PN5aBMv!wR0&UX9Bt)#xt(5@$3JHdoWo=^0GFEfY# zJ%8}SI;*xY*QPvX2jcu=mM?y>FMxbDef%BRKc->!F~k|QAvdqW!I}|MlVHWk8to;R^X;Qq3QXTw9h3r#jFr^* z5Kpz7od|P(5A1UWap5Z&GY-}X&-jt?NB#enA4>8Q(H>bex4OU`At&2IlurH0oi=uE-x1F#Xc* z$`^2jv$f@OSnB&`-6Pn+{;J^>v8KzTYovacoqlg&>i_SAqdRrVBI}VheGlUtvDusE z4{+Yd=zS+)!Pvg$jc`nMZ_7luShR2fSr0WQzH9cw@`G>A8emT9i{Y_w(v}l$O)%?k zl6N$m+fD95)?5AOjrI|6#74{1U$C-$k8|tc*j3@~zhUN3b#5T+RMl%WS%RZ z)Afh44c2?!lP8`_;EV^cm1Ozy_ePPALaFolC7?v}d2{$nbyn8AY8dFwfFTI=Jl(RL=lJn2d5 zdu8N2nHme{db}F?4sm6Yz$6AvIj)X>Lwwiv#!lFH$Dgt>K3_$w^>Xk28gcRD_*Ky` zt?G~``JSZ&Cv1v>#i2Vczks<{9Lu-C)wafEWPBPr_zxzQ{Aw3TzHg#7HTrP4;>DCV z=H|3vr?MYswAcj6>x6m3RChb$#xAodcP_`iwy8{^~{EPs#U8 zR^oM(Jdg16gPqwhcks_d^1Kxx66d-nmfa`!Y0)_6dAC#f-z7E z>k=kY-#evs-}~F)kjmB-3d9xJhp6^TOS(yY@5H5t7^Hn^IfE!0M2ou+GoS5kA>TKa zlXT8rxXGq-9QA$kb{J0jv-*Lb`Z(e#>rMv9Ve2P(&yT={rrz#`!wj;IyZVtmpKk-(@#o(e!9)y_Xn*Hs!#rmrYNQ?}K#rCd%UV$69v5vVM2N zZz3L(b%OeSO1q66OzKN}o)}NQpE03@yUF#<3!RQ_gY}sMW6Ay56H1E7_e^;*L!S*- zEc?5dd_RpXcfM!A#Yb+R3nTGd?+WsM=C5nNg}{oEQCBX)PCn-p8(>cOeaj437t(Lx zdRX0TXHDi;c5#?*Ff3&pEgkh;Ej@xtmVPmU_BoX$$-<7!>mR?W%MCPNa ze)Cs9QvQCBayMN3=*uY)ar$m*ywNHK{#pw2x^<++d+-6(f+cY9s@X%=puBXCN45{l z*WX>}2d9aIcNfB1k)&Aw=T7?aZ2@e28XP+b*7RGIHlNgQ?zny=9Mb7>**p?2w^nq6 zwVa+8y;2Ut z@;rHJd(-D|#_;A$UsyF=wDu|S8ShB)K62l^1IyrG$=u*&uwcRCqmN*Z7qu|~Fs-|% ze*r9iTklEQTkv<_fIBeXaZ@Vk56&LV&|7d`z(aW;tRH5XcmvKB+Q`)7=13|!kj;48JC>UcZ(G@QL?J+=Q6x#VS}z_k8%joVQ^FZPZu88*>A^(5n8Xvn#7 z0ya+%;q4{Y>;L%vF_^pemLJ(~${3@=j*|KoZFcO3d5)I{%HfbC4z)j({9tPj!^I6d z3(5Rsy*?0g2zF{@)sy+m8egqTgxw>qGRS^PC+j|jm{xfshU||Ns}!f=VP%__2C^Sg z7CHZogKh6jNhJI4e`b7i&LaDD#>9CSNL(>zs(|c|q<#0aBWCnVr`A8#dPQa|%r`na zUPD}cAfAdR-rq7q1@pJhpz0SqXxc#bKf-z&ul*=5d1;qL&MRC&^I2m4nv?IT{m=hB zpG)_Y%qOIN)2<)nc{QE+i>hGS_guFFFnxWfTn7s-nNshUSVYs4?~}CG_gM12I^QVj zJjTeJ7){#CJZz$>0de`}j(tdbv*Mko^PW_B!R9#Zo>HPA`y1}7p0|@=m$L@$4_NZM zR~G3%mi{Mo-lQi4C{khCuFx$%5jQ+AOFIRJ>{1EIenn_;YG*p!bf}`4I?ozysPV-9 zG02VVSA;z}O(5e-=^IF$j}4>0M-T^h)>Hczk>^=S1{~qQtg%OyT!uMRS71g(&Ny=ZH{#Eo$at5{H~(x8bK9gE zuEXjG&*H%_m}38nHSd9N2!_J8P|IpsfbW)O=t9sU=$-M?8k3)W~qQ`a~8AJ`^< z|J~o*{q-i2ztZNnQqL#)J!CDFACqmAt8hP!=SeDm#=ZZVPu_>IV(e&=4`Sa~)N&Qh zm{{&Z@>zy+ujY~W*Uw$Io#fxR=hDqcd$9vLDHg#BtEE^GAC(hpiYVco)`hsgNlUUiqOfK|tTQRClZVFi_66tbOyVh|6U!U_;0p0eQCsBN%J zW>rTlvGki20S8|+KfDSyW_epn;8^d$F>Bzw?p^BFz}8ith6Td%G2?D8gTtTNX{pLMj z#rW$7Nj{a66|=iDY`*M!b_lHdUD}~NELSbvz6ln6u4esp!1z3G?M?Etv;(vkzhLj2 zYjY$pquuq@pJ3y+8e(rGH6YZ&riz%#h1OccKn^@B(E zJo^}Sshesb`P|^PpF#>@t?vlxeb}v>54T~KZ)+++b0-=6Z#*#j+H8-3J(M$x$avw%wx&eE zf&Lk%$#|lj?duQ*XYVvVA>)m^ef{BJnDP9G2^o*fx@4c#a9*F87G(S?#&XM-5x>v) zK=QM4=~dYhxOHd!L}EqQ^hO_;Hfs3)=EIhuvlqd!HcP4euB`q~TOpj{ahb}8az!7P z&x4COQ>gr~-o>x4CoG^h>>&BkysYgnX2I@z*mX1E;OD>7ros}tHX&rb(p;(n+~C+f zy%tP``F5Atu5iZqh~Z>@2S*8qkA+3)zdn)tDEr!?nJ%!Qs7u*+Sf_eJ=fHte)@6|O zgI_fJ#!xu1{Zb2(FV(DEaDOmd>!015tT*}{*Pji9n=~bDNPbk&daKrfl>aa%W;m?; z=ILe+D;o|r4uLtpJZH0DSJ9=KfiTmBpKAka=l}KT4U2;9_jQH!)!PR4fLURU9u_dy zUT#L#PyOATHl1Ky*(9s3uu(Nyf(7(eBdpq{YWxO^W+w@oVWD(t6giKJdi1&a9ae|mnLzT9x@Gp1Io&QM z>;DtiU4E8c4T~=gq~Z-O@3s*uV)|32{c!mD^gr<=hk7dCpl=yW#l^xC|N9>QU-{N6 z8`c-2Jpa78=?~nW=jT^O@)y#^Hk1?3OgT!v=Z0<$x5)D)zTx^3|GS^4d;N++m_DWh zRo-E~*Q~p+%IT^Jc|X?=W8US!oYzXq4)4VwWdERTE2rL{ce!)+6*yS>*@xU;)a}-{ zOR#tfhk9QBc4H`~e6gj>nJL?vfjCd=?njinhdbKI;HPAT=CO!jBGE01alVeaS~-sJp5%PZb+9do zw^HY^yj6llhv8V2jLK)K6O}t-;s2lCv|iL24I4A>Q2WmmHy_jOu)Y(gE4iPn+leh( zVEV`&8_9W2f1HuM7OrMmp5F;`o*2`8;k=bbvmGSvzCKu5xb75|R3YyWLeHj{l%MUNpytiG@MEQS+xuZL&D?5Ncd3t^`J;*(^)GPb2o@`9UU=OmE% zEb*Q_)024XN&j20hEeu*4yq!Cwe5(km>MG;a&p9L#> z7g6~k)~EJ<9&p3(701bXq_d(^R+=o|UkdZ%)^wSPxW@6>&vIB0Ch5(Gtvh$6@};U| zN0-SkEAA<^J_gqWADsy2Ry|63Meg^ze*QSvMHjV}oG*+iQx=bfO@i8{eSmeEACV(q zeTt8N0~|cKV&V`|epCl4-jLjYX330jKB*S1>sLG+%^O!(dhkZRzCUh|5mrW|Tla@q`%QS?U?1mcKL*3v46BtN z;Ush0@nru``*lC!9n5yTGnjmT)%7Q4*1*!u*7oH0OZnkupXacx+N?Vl7B4(p{scCE zvgro-y;JwGN5W&+wQ=iq^1V+n9jGcG@yO}adBJcd?(I!jzGTSQ$%qR(39OWG;Ipbf zWPhP?ck)e!Ym?L0&46jg3m3=2X@dUL{v>5e>F;p3*s?+9fw=f>>YY$na%s1YSkyaY z?FKk7q0WMwFPJOl?hS@n6Yg{-)=O4I5y$Q>swL%P4qaI52UojIJVf?CoGl?L0i0KK zzZbcGio>`nK1{zH;y`{M4*u}m(+w7UtfroqQJ8yq0xVfSlzJb@v+$>*V4u7m$H{(3 zUU+K1GaUPBRWfy6QkqX30lW0N{$d>B%> zbc97-VZTX#D7%%e9SDoF2V0T;(zI(H=m6`=gQ?$#={e!=`oK-$GZ+I9=X}v0>J5AJ zPNVu?7GO4sIPaV(uNUHM=Fo24VAdkn4rF}k=7olLfor$NxRKw3#lyxAvxNOK%+!{o zyp42sXV@d1N#(nFZ>#oMkn)Z7FHIKa>|$OO@B`C_nlQrun>~Y&7dno!?{ZR_Mw7 zVy*j7zh5Z6O>;GeBYJ0x$^J-t{Of4)eE##J?CRi@gSoGW6JtL=Ci9us`?xcCpO|gp zt?x;^WDE6vxmIqb$*x(e@KCUcUX9I>;Hb=*)=D-2h830T0_<^ zL+-asCM*v*L*+wM50x`);oL4Csr?$Q?(%rj-{QC3MH+IycaN+4!lIue%!wJHzp3%z zIxf(ktdGGLEvWHSaKW|bW0Tgbx9^i+)?A%X z4RbUPM^A%O40>umCvl7|n*smJ$4h^Ch0lZw_RpvGckF{V>}J9IraUSiue&?&?rb>l zz);&8xE_6VS?(N|)5MG<`6F)ENNWE5%fB-_FEjT$Mh5DsbdF^cREV92dthmk~^Ev5Y;2V+;lX`UMM(*d)o$iqUi!7X|=keIF zf%<-{>fM9L^J&*uT_^cF>H4xF57@!hgUbIYhUX6=?_1pd_E@qX;nsNWChJ4=yJLl+ zu%hu6)jqCXedn3yY~YG*=L?0fKx?m8D~GMe=eH1RLT_Dq z1UGyU_L&VUtLsAwVb|dIYsvK#?S?nyz^w~uo#(=wiIrhlaLoAjRK8dgcKBF2tmgV0 zS%|o{ykWozIJVQGy(B-Z8eW;22)q8iW<}mNujoluB+Rr}t6d3;OUlM9D)6q~|7>I?uwvyf;*SH^qf(HW{|v z(eDY#=W2fDyyn6Mt0zq*`@fhQ6ElXxiFamMlK!VXkM2E~)IS_>DH>Lb{2Tkh4LfKR zWdA3cAA7|Xmg~-zlKifcHF$JaIArp&)ZHYW_IaThsefqvcVf<*>1#T`>URek_Q1@0 zf23_-6UNNjBp=KgS30S+5B3i|8#DG3-=EyB7A{akQNOq7r*c}#VEyDX)bHu^?yQi9 zut~0+QHJu`ZW@zYFvnm0jf`)J)ypdiIHG0!XOb`G4=ztX0yEl94mt?O^lxq639FXW zQNIsyq9!M7g0*>Tsq#GE$%)Hg$+OR)NuL3k4i@5>v))B(VhBzfVRP5#ZEXQf9&)%xE{A%WT%ZV&An@dnA|V(>*N49 z(R%GWGCpaqyqX2Da;|{-y@C_C`shTMKCAC0^81Q#$Kj*xVQJ50Rb>99blQ>h#SZ(a zBJ)~Lm^C(4^A4t;UUypnD|-I^RRtHT*HdPgx>c9LioruaEW%NOyyimyG! z!RD_o&F~}kr%xOK<6i?(zgMwtP3|)Qmb(0K2|!%jT)f2rmT7lJlliEQc8+7g5k?ED zedy2H%;^QkUb#`a3UU2g?l4PO_d0ggYT^m@{r}ivztI#&Svu@|v>sODUq6%bimQ)j zmB21L-&4Qu2syL*7r;K6IZmYg*|+mnXT#DrHm_H}!rglcVqvaz`5p56ncnEJXCvGi zdW%Kghi6%-_k|nE=2P!4Ye~N82CEDH6p{WBa9<1?2n*j?Q@?KsSHwQ<4qKnAi6#BT zsoZk2D@-$+>Pp&Qd-u&kGuZILbt75-Xg^Oyw1orDMQUfj%-l{2V=t7KJg=Jy^KZ=_ z_!;&-_mt%h>%y*2(Zj{eVO5i1wQ^J52ROIWx+AVIZ^^Y6&tZwEOu>bPj{cryF#kwN z!w6WgPb?{djZC)_!(qj@wof0x4tq8^4TWh@tKQ#(&8yPY17UvBO_N)2MPu78ePOoR z(f%6DEZ*4O23E)SOi;q2?7p3uu&REKTPB=hQaZ~DmWeO?yZ~qTFFN0a_^Z=}^RS6& z%}NWHekX`=4z}L!ve^_CcG#MG7FHY?s_X#M8qbJ{b*G=T{&|G<^W09wBc?g5Gs3KU zF_fE5zMS;~=2r|-k?Uz2A6)uI$~#@_m<~JH6}&URoXAdp$^B)famPNv5`Wq33^>E@ zskt5&+|v%uhP`)nNUDXUUK8?FFrW8&z#BNPTeD9tEWFp_+DllqY~{B+*rns~=`Ud8 zVV~#sVR>rI`Dd^q<(ZDC%e?n0Ik0<>^85){ zw7cuep&&L!B~(T7jsDLoxN+=h!^(6;O& z@eusC4KTk~^2I%{&gJ!jSFonQA|?h_y&p2E77n~&erPAmf4gqXdssXr^T7_7+fY^g z0Z!yC(L}+9thv%USif#j$8E4WW=)?4m^Jb1m`GS`HDe=jV&cBTTVPq8(*t5faWjor z`KL=gv1&w1WEjkyTAtqsn`dk2p)h^3PuI_|chc4_8{ri7mfN4;nB100qZHpIX;>=JLS7*EPK7KERGWiIhUUE!3!+fN*V&5vZh z7zgtP7p0N@4?cgiikO|yCzjZiJz)^3Py5Mqqtcouq3(C2=gEuzv25t8Wr?slYo^6ml-IQ1 zzjiCB@40uy7?`PX`nnP3T^}SL4X0?jmWW}N9p{V-Eb7l24 z^4ejr>hJk|gJ5aj33rCVF>{wX+Q2fegnL6^X|?V-6E-$(br=lOYIX0s!LcK_?HpnD ztN71WFk{Nent`zP_}yv58h&s80Wi1H-lHp=mYUSJA1psPy}cz&kKZ3}OX{EZzugIz zCI;Eqz^ZvoHfFF^U^0sd3(9BqF@+QN&%A66%gVY{bRd2sk#vT&hgq-N!J;!gK6fDH zm-|hn5gU88wu7Y{?T5d_9ae{|R$9zTeXb^Y)%C`~j=johE;TRciZ9 z-(kV|r{mth%ITu2FR*b@`tNF3^r&o~0hV%W&z8et552q*_SQdNQ3&U0OqYIuwY%PI z$cF`1r*huG)qCfy%z-(wdm(RNhx;8PufdF08BbrqoQmr^vtimeyIn6~liYq;=V9^k zlH<=w+;5ipILz+(K3@ZCj%|*T!(2vqpHkT4^r>kFVBXJNPQ|eCL3!nVQh&*ikq_af zplU-5Ol#x$={_tnA3t>)%wP;s-i33I7d#J#gRLyP+<`fr3hY8*j(zXmxv*-{xUOqq zo+5OZ3TDeThOLHU&P}Yn3afQF)d8?@$YWkM+}bYc=@MAeVsTap2e%E{?FCDO9jq_F z#))fv=ECB*bDPe?oQR}ZvtU_f|HtQG#>lfPr@?{=eJIC{FCF6cAN$`lydMuUemPR{ zf)i(W&agbHF+_p-iTuBw!(ij^5;bwgiWplq%$girkPau+C9<>8+WXu@ z^8Ugz79T&u3gP@6q`gAkk5Sgad>Q9q4%{%oj$aGYi~HZY4Hx%JuX+WuB3*q*e`t%j zQ>x&U(;?^X!QR__|2%^QWp8E_!c1;x|58$4YQ2Z_uQKXkRz4h)_S3H%_Ft5}{5q`O zSwFTK7M|YXlL>QA%qVyZM+aOrDqvyWiA#D|Fu=^^3@n)(@x2}{9%ANp3TAgt<$Z$P zcQtC0V8hJ-YCZ&y=u~tVrkj7YBl9Qm(vMLIaBx~2HNTYXyZvHe?sTtnzY$lfwzk;| zv*WgY{R=x(C?4#9jV~uk$$VxnKQwYHta`u+Foz2!EU=D%=~0osondX?p0qGH=Hak< zYuJ0au6{jisOZ?g7aY60YFuP6M`#o^rmIdxMaPUL!qQkJ^m!P;aEM7O;pX@iIO>Z}wkn;bxKQ^qD4*aRc z^DSQazy0*M0V}`5sxr^wB9vz|B+mW<^R499O5oUSqxUz!hS%Z0pTI)5i!=3b@Z=4K z3b^591^*SScYnEy+TR;vJZoSz?;f?^w_euyofb|pn>VNmarj zg?t|)(pZkyV6{fuhCHvk+r#16F!R*vC8e(1YU9PrJ78Jo^QTCEx)#`-i-475 zD|gO?3%X2pB<9ZXeItNdO){s2!VFuNY!}!`d3x&xSks<)-X6Br2RjE79~$z6d|z!X z#_NM%ZU4!}E^uBHd-@uf*R!bF1m-KNlXB7c+FKZzZK<+-}%E- z{s2zdWK*yVHdekZy8#y@x9Kl}^A>2R^{;r0x5x*UPwQJFLtJ)z!rg^%@OD|ZXjrgv zdX*Q69}YXX5@wHHmpThJ-q=0K3ziQ3v1Tf)dB(^bOT7AJ@no33Id!u$Ok=g%#e;Pw z_dEu{wZFCATv*!IbZjq}9~ShTLww+UKMUAswQedK7G7T_{@oMx`}LVKfW*V*7X5@} zW_g9ZVZ+>m{4cP>%G;(rVYXS14vnz%rmt%cnA_&UvU=EB{cxoPEY7T}sD)GJwlV1l zYuBFs_zIS7YMs`G#BH3Xy@YM$<7$6C!24vg6jgBIwFM`dVO?KmZ(>^9kfmQ?-Vtva zvD3Z*ryF2yVXW{4tSOl5QwP&-&8OmeZ^pO}F#F&bs(#G5&HdiO!4v=fBK1{0GP7R6 z!cC`}t6|11NA62l;<0OH4IJI|USc(@j`#jXEM|AsXko2$?IiO2wu?WnEQf3aG<=3Z$HONKR5l23O>JY(^(*T-PRy4*R$P99q)$zlEUo0KKoTM(zs%G|ULmWPM0 z=mIkamTnAyInD`Ph`T?Ru)d`kyPFM>7a!cP^yb)1+JA zVm2(iwoXXeGh?4*LVr@f(TaNCyfubT_OSTSZK^%2m(8ZJNd3c2$CxPZvTvlH9n9R_ z>$x2qcyP@PTUhbxEV~a}Jyy4>7o2kQ#m2s{!A59f18ZjR%m%GHZr;<@%DTu*$KWc_Yjen8i$n^-o!?@8P_$A>2u@d}J5rT2ek+DI@o%-FWli zH7txYKg5GGdbU2Rf(?B*v&R#kIcfF+j?qs2=}PLyWi?j93hsqJ<6x7)b$c|hHeEZP zID(_LE`fzhDkz5x*hecO<^QieIM*)TD1@cG3$AlfzWSx-_#BwS>^Nuy?67utPbDnq zFzoG6m=p23?Ht_&yO-+U++s$-GB~g3@Eif+5jR3k z`oT=jt1KECGIqQ_EO{qt@`p2Wx=Y2drbmtC1~~ES%`K~7>C~B5 zHp1%SsPC)cV8f4!P&hmG>RR$Vnir40hrwx+?Aot~mER6i-%rJ7rg0;zwO>DHFXGC( zy5tbpm^*0?`QD~=7@0=mEM=$kB-ni-m$IzHmpWf~k32n%yua~O50iYvt8)TA6Z02! zrS|uNoxVLc!TLZ}FAd^~f&2X>uBz?xHiG+k?$Rdg*7|EiCNDlI+F5Afmb#;AGhxR+mZAa-BDZD2Ky2A#sk#- zf-gwlwS{9cyVaBDVbTXu=Wl)3IO=^A$JcAyBd)Fd9={fG`Ou|KWPk7Nxj2=yC#T;? zYQOLFPTpZ9Oxwh#tXH09k^Yh#97oyu;+8jyVbSIx)b#~JwztlM8QiQ3t$04C-febx z!{VWqw~TOsc2(*uSa~MhnL4kZ(Rq5nyjbIC1L86Ff4EGCITzEZ^Ngq@VdXSfU+D0q z0dd8oH>W1UDd+nAse^Mvmxw09s*!gVk@JzvtZ*wAR$TjarxuPdvwJWc4)!jIBIkGR zv5C^5aLk29>b#X_#u(`YYu)lbll}?zs=ea~OTVl(K7(C&{9|lb(7DaLN)qq2&Z|G1 zcQv&AQ`q{=NV+{Nbbf4F0W)^lrP#r=w_#n&U}HnNLv0tT+;fjZSM_R&pQ}$8$0Y%pGb96Z6_nNpO#Qh($54M9jJd=+PVB^V) zzO99bcgy-p&YNPttV`eie}stN-IBKfl)% zFn5K9D(`boY5N4`jh0U(`H0xy=E4VXuuWU){soO!(h5m^(_0B@#BDEZl;y$nDf3>F z=Z!tnFe?`p-h0D&1T%&_zjOmO#z?966W#xQ@(RqEa5;>$M?{B~3raYKxrfS^gt!bh zAx>Gad?U%9Xy1+PauNR5o~<*?%Fn?(@2ymO*XD#?JPXsWPP|FlzxZaF>uH#oxIc^Z zkH71>k;h4W^7|ev`SVPaK;l^;kviCrey9B&n6a|jisXa%dj~mehvk9G=acbP-B)iJ z3G+9#pV9~`!vh{m;D6&;<{bQC1FTrw<>7b48D)~Db)>$3WKc8gZoO%65S-GRru+r# z+^m+Yf%UF!Pyd0dSvOAvz|uL4k>q?qfBR6n9F~=omv)33UTNb*utpZUn&jhj4cGfG zhK;}aQ~5jTf&Mn$#7uK$Z^UKYe9{H5`1^$x2iPM}=js8A20a@z1UB?seQySw(hy(B zfurk^dJ!8|+jScOdv|b_@L~Q^N9w$!pV2UXD$Jpq{Thw9lV$Vr$;4ONe;Nx{zYXd; z5zbrn;?Ovl9nPzCg=K>V9wTvq`dj^InET?%DU#phNfMTENc}0jsQbAt;GP-`%U{tq z4MW`L#x3?BSo2{*$xv9=@3=`HShrU1N#4h2tNK|tSUjWcW|Cj9URu(NLFyZMRQ|zm z$}_VQtm@0A+DEab>wZ&Mki1Pm&U3L*zD4axeQ}jH17`d7QM7}Ff6XqE{3mzaq%%JY zaJ{NURDQFfOTdz5n9gUgNq;aFT+|rgf9q}Fm@(_?V0I50)t{{k=Xk#-aecyHvK}g9 zISo3PDR-rl{%`oI>HP|pg$IksdS37=(d-2*5iPq*#!ExR-J_Lo3TtjZGM-|Ot}L#A zImNHHeuKsPEX_6Wzwsx`G!s08bwh2b^)gpv?fU>`+%2cRFYFJ)X5EGV<&!w(t!?hW zisj9hKj3;vT$cG2*wDrD@oU(B$EQ3ctgYNc%@6v_mqRa*`Y$)_twFqLcKVtVaB%Hg z1z8^jKSKQv!Mrza56FDYZcU@b!v@}1D!*wm`PSQgB>sQviRz-2elLmlZA-03`h>IY zdti>;UMinf{mj1YPMH0CXLcU07dUd}-z{*8^WKu1u>AI|+%T9oqWRS|xam&R@laAe zH(8$zSM=Yl*Z>QAyqJ{*Yl>A{R>8qPiyhCxbpI;1CQXRSeKfC!J)GSf_tXIv zKdh7XhAS3Y`Pjp(gQq~bRELm{34{UstA4txl?%uIIi46s%)cG|y=J~YVFlWz;6mq`xp2^x| z2PyCGdRT|r^_=~@?q474ltjSxuF6!-mdXC zfoaio56fZy#SPQiz?!NFrDd@Eb3xgkd$_;-oGrwy)z^3}Fs8$6lwAbH14-n70(4pojY%KjrnQ=pU>n=yB&Mx z!;<^<2I;Zt$N{~o3J2#gFCV6RIc_0 z9PEDBa2+;kKAgM)>jymK6C3(Ia?FCo7vrdSL9xTQi!iJD*TEZzXZuHbq{EDe;L$m7 zw8gcmGcbGcouRkL^|A-Wror?DY1@fe%IArvU_%^#>uuPzW6P{$nCU;bjJT=vhSLc+ z<>RN#D;^fsI~3kUJbJ(yfL$_QuwY#69FmXnF3B(Og*nk<#=eFFv!azkQh(`b%{w^5%fj88_(NG|l5b-^dujB9 z4Q*ai^GkK&%*8pdYT2oIpAb*9c@W?M%ZoIE7dE#+Jx| zWV}lY?`;|h^GXY@koJg3TYZfKtFFwT`bTiSHeeV`n_=fn#uMk+%-MtCyt!jmDq+ns zuf=SbdvSc!c~}>+N!SP0SEy#5hS|Ojes-|PYE)|~9PF?;y*o_XR^8f0 z+v0o}&Tjkh1To8YE{9mwlu%;^^W@`C%3z1lgT|V|n#qrU?uS*`Q4{E}-rVtT9IWY- z+H3+Vb`CS&2V3{sEo=)1ce*9s0~-%~9nl76t3Tz%z`^6Qdbj4Ie@%Ps-VPgvefaVf z7KT6R5en=3pRZ|z#XZ*U4}zoBj}L!@V-7BCT?xC^9@zd4W?sKx;|H_byYG7o^TN`% zEP<_gi?`Iks!PsZ-moDz!sP|5H(jd|z_vp!->ZbVRe!DLz_R65Z%Se6?@eLj`Xvlv zW8tKr*>JSor@+TBzwF@pS+I1Zb6FA0?i*P)19mF8J5){T7gSbGhpR(}557-)>b4sn z*52@z--CH=d%I4B4Kv1ZZo?|m-qR<;Y}xix*Z*T7O*K55)E~y8;<{0HJO0-qzrbmk zh|^~^7)X7m%;?eSF!lGW#O*HsJ_QS!mkyW-*QOhmB@?eoqMl#SWdHRz%)9s?m%M-V z$_Z}{!K}dJKjy)r@U+}SSm9XhCWP~@4V-)s&U^7yw+QyX7Hf9^4({U}wv@!TUas5^ z>#uG)z8Y>FHPkl_=3RNPa~+%!PEST4Rc^F*#HcGw+=BvaFTVd|WW_}DTPMhFN z#zRWRq_Ewv(EUo~4wz~3d)`jiSQ~A(8*UmpIVXzLpZU0BER4U(p1KXz=F1KpfLr66 zH4(66`!Sp2u=mf)nc?ui`Cx46v3E19@*6|VAJ^!`DhVtY6SF209%?*S3&s#fZ z!6BPY+J(Z#Lqn-2l^ae)hVAxY~HqZyl@)8S*&|ZkjqbY&9$wMy@6IXA81o z0$|au6${9GNpkLdY#A&KIyO2ArY#H&@q=m8mQn4Y?eF<_39P#)yDUMx_1^9Gi%9v? zD#->|l%@2Z2XjvR`nU#;PHl>}e?-Ffa9;=F4;sqrm5U0CM`3)gYJT!uG_78tsHmtqrLfORR z)pL6|iSO2RG-C)MsL;IcK2CmcjE-=63oy|a)X7kv}0v5OQ{$vMhVkQ)Hf;sMur+UG{ zmd=|^N%`aEK1^8sZTG15F!k>N;M&}`V@+V)8gY;{Osfj})fN`}KZ&z~TdmqP(TJbb(yVy?xH<5io?6W8HZ2$w!-qR=gJr``A8$_(hFKzh_Q~$mJrmbtN z`%KEWUU_8!^VjJXe1xS&xpQY&V)JzW2UuY`ZkQ#kkGatE9jxzdtRybh-g15g8e zQO|SWx4a6b{yhoe1)n)bhoyN5r$Ps9=5*W5h@} z9Cr1`HUOPO_<&}4gNP?Sa$Avj>BTb zrb4nlOYVJ8ABL41LoSfyI_96g3o0A z6108zeLJk`$aP!~b2pCEM8V?qD?&EH)ope&x4=5PE&Is)^%(UaI|Nn+eLPFPkJjdv zZ#Tk{l#M^);gHICy@O%yC{d{#7TcCrt%3g^Q}-X&()<4pe5j0)A(^CxWE6&E2t#QI z>j#F^BrJtVvJygNQ4G=i9zuvFVJS5ULoo`Y)Q~Jf2#fH2T<87qy}kat-=5Fw?40Xd z=Q`Ip*EwgjYd+ggk^BM1LNRQJxj!=())YtgSOSY8Mp5H4^?+!%dx=8(^ZW9FYI~JDf{zqO5yrF@{`E2*>}R%*_d)=A*2#roC8z^6|P^N4g;{ zkO|=TWoS9Wd&41~AEo9SS z-Q(Q5WPU7Q_M6T=u<J0`3M^?D?n~VGbz8z@m|2ti%#!5qE?Y&cNnKt} z%w5Kr&W8;PefyF8zzx4zPlEX~K2!6lI%mm6Pgp@8Ye(jnm<8Rly zpATpHzNY4@SpNOQ!La)3#U8y;{$KmxYeoe*!#)vjzAZxBc&xB5nXgJ2bXXn+%cW^v z$tc7sq zoN>LD!?g`b!%6!zJuQ{2fa7XHHKcv=ihQa216H)P$4Pw{;?Gg{53D>Envwcz)P6l5 zg?xkKywRk7*MP0qHFW7*z5x!{ zm2s7{U)ibQk(*$VGJtBovTN)`i6s7i?NwMD6SxIt=Op-%_9i_)<2acQQ8~<&34%B z`1BnkVIFhV-c(pKFwtcg9PiAc)+eIA@akY#FTH)A++X2;*>rLMtQoiQvlN!iTV~T2 z=A{0~UkNK4{IeWkNrB&JF>Lk6*tG{Nj#XX_f>pn#HFk%2LpSyaB=OY8o4dlorus>K zu-g)sQfpXlKlKA?KP;OKZ7pHJqY!F-izQehro;d4uXto;KW_!Iq&2#>9=`H9mzLs z{Mj0&7yKGp3v&iFo@oWQoR50_3g&MQ$fm)Xry7SEm?xS@+3eswmnu>|$&0dNZlLTr ztU2pV*?7Tri5^zPG*Qc=-H(=5z~a}vsP)O^mc^CBYSR@jvc3|JX>A^nct7WQ3phZq zrr3A~fpVW2v8;W2$85$NNe9;`4?(%Sri6eP_}h;}hc)$*{`m!PYQ1R77`8goTHd)bU?B{F2*d z*w{7HD+ck5);o*hVa>Y%)bX3O(ChJ9n7w4&4|06QZ|=J+8rGT^PH8a5T+wqitmn<{ zu@`2%c(ytU7VmqtG#!?Y?~$>J#6S31?}I~wF3=)j&E8=%55id;uD@Om%Y#-8&44pJ zdVgCAvkO1ZJx|K3p0;01;;IDddD7hCV?_&L*5|w3uOZ$b{e2?@R`+nAo=3HJ`tmav z=Io|Z_j7Y?b&2y~K3hS}_oOur6$O&~aX+Z%qvF`Ku5)3AY%w(-D&57pKMNLRPo?g6 zM?8&sG!xdih^XfiLoYsj5&+9~+feu4rMo9@@rPA^&b70~{dm*nfkpw$Z}>z#|6H3< z-8daqI?<^4fs%=v`b>jGkA_@fAwTM7Q~6X_KiU3}GaMJ}-H}i7qdQUa4^<(q5-*a! z|9TsD#08IBa>v1jD5ISxtQ)cSz-UbWk7iJffB8$N2uR?%-bPd2)|| z%fIx4O^&On=Mm#SOaD5-`fC2CEyy>!$II%OyS7g_$O%AO9e z)HIoTUeRhp^AkH*UgY0!0CBOE$Y=}mH%ohFz^iEcK_8@{`Vty#Xsin(NxYiZzYa_hH@A z!Q+V8mZL3na6sO=*uOu!C9jmzv~8emaXaEEWOBKN_@cd&lUY?=vn${97Z9&WTh`J)-; z?#g-f7Un(q^6?j3`ZO)}70kXYct*3w`0w_MOVu#*=US&WF!w_4nrE=?u(+EAOnmUAT`i`Jb*>CkvZ+)is5(8mXZAX6MU^;dK+F~DJ+*C z=9Bkr)CoCO_h4R;RYONOE!(v6Hq07THM9#Xu4}q<9j3o7h#~KnWuEz4TuAa2A4{09 zpw8#dRhZk}h0%k=eTVG51Z$rOrX3?5@3^)Sh4Qs!cKKbcl}75e7^~nex=E# z!@4b-Y=~3#Kkw`V8@C zRZbWWtEYQV??WkbUz^;Bt9^sW{?5&QH`x^yR>V;4M^_g9q93e}+%$x=FK$HXPzPAv z=KgWg9!su;p0*|N*THsQ;M%uQHyAMgcz4bRSTl9bbZc00Sj#2vgRyU0ue5;aZ<{P% z!BGljnHemau|sTt#nnBx(O~7#`PB2kqJ?F(-wLsRIBoOG5O4VCI;sKImL+${hbunJ zyJLiPilC|-IOCx|tqzv;KSRAA=P~1~*&CRy&7q#xEv@xlZh$!*S1Cw;Qt_^`Ru8Ma z)=>Rbmh0H>6)-R8*!M{Cd-rc8>tL48QN;q-wDrK)`>>%^UXc)1Eh{$D!pgBnspr4# z*AKdM3#NT8myJdIV8rL$C9p7c6g5BX5ix7|4LCl$&1ce|3s>$hFM{=+Yu&rSf-g_F z1u%C?4t2f=-KV;dM_jz=9-S<&WasoN@c)0FC4XyTF04+QO`U&~w=0t`!kL%o)cn1T zB!GaRgCkau^1`qm0$4ilS-BYIz8~l|5mw5E^$&&_)ADx@giYVL6X(GH#>bgsx|#>T z)c0i&_xySIgb!?ZoEm8jA6&F~j5o|rX-!?m%n)PiPQa)oKwLTu{!q%N&q08sKWPN2#DLXsB?4a1yFgYoTVaEPqx3sUM%n!9FxtGyG04sXw;fZO-p&IN$kyp~my- zqAc5HV%o<;W~gs^?S%Lyn9UA!YX@`XBFRVCFgB6ef38zT|9THA#^qmQAkNt56j%>4 z7JsGguLNGZdb1Xm9dn@W$M8>nUr-Hm$FI538~F+4rsb6+|Lcu&{owfQnX8_|#ve8( z`jg-5`90?`te-0#M8@a3jum6dVQtJ)YJAR{_@1RB`OCjh&u1DY2!7os`D=ox=R3J| zr~j70qLDAD=T+^0_iVZaD=eEuK3HDb>k?iOEUh~13X9gUmt{*wiyJ{}M z;)---DB`)f7K2nUOHuGJ0_G=%jLU)*6>q8M5v%TcK0OKl8^4$KtL7bpO;yXDu17xo z<)-*UuretjECDV}?es_i(|5YB*aqwJ7YFVl`O199J#g0F`cJ7O|5r!q`9p?t?B-;c z_Oi#I6NpPb9`Q&b`CYw(&%srH-A-sDN zFD?|8-fyJF@AA&7R1qw)e%?Z!|4>Nm#KCapbNUtfdsfJCx50`uX*5t{swxuxkD&q8$(`+WdycP7HD!A(RG3Iz! zWxMW7HY`h@JIDi;j(uo<8a4|(u$v1DPmg~~`roJ^8(%kA-A!^;LE=~ID~G`Hi8Xq1 z|3khghRcEf-R}tfv8TsCm}B=ql!Unc`4`vzupK&3n>M?^Zt53{iJ6gG_{610`p0)7@iVq-$?@x%z48VFZk(-b#fF`dD-Lyp<8#(g z$G>pa)alkRt8C?1JH(rYbQov_v#)h7?g%$#yT51;tG!YLR>V&lQrp2i*EI$@T=0;# z(gM~;9luEWvy2H5Q_W%iOCitC=7SLeTh0ve|RQ*UBnyQS3o9BgjG923m^$t!-0c*~CQK|f%T+g)mYUUVsw`yJMd$fln6D4_Gh zn_%hjL1W60U!gFc@&(qnp8uUZ&mnkTI{X7E-`$;h-k?e^I%0&m9wLPr@%U*zcoh-L z`|CFMBHWO^VaF@jz+1pN2U}fV+*ku=M*P@&0%mV5ZmK5vH*5vRh*PaNm9Tsof9Gzv z;=-7sr!ezNtqqyKmOA%mJ%*(}dKfprI>(0*WyIwZCX)F#^_gS%rwpu5RS-3wpnoXt za0~u7U#dxOj=m1Fh7Gzt9r^YmM@}Iu8UK))Pt#rYtG^73BAu^{KwPl##z|tK$L#); zD{f!B2y6OJP9K7}hpG6-IZ~c{9}1>#i0N>ec>CkEPH?GV;qsHP+9{Jm=3nGxxA`Yv zdE9TeUT{gsos=W6jz^#101HoT3O@w%uX^q$^I;{QFa6vHn?_%mLgshk9_~s^hXs84 z3^G6F>TmA87nZzs`DF)l7hD>f2D6m&{fPzYiXU=VmbK+?cbNZnc)Qg9Sb9&pXd6ts z8%xD29K?!bm_1@KRbFy^n{N`_IEX_nzbT}8-v-z);vhBO#JstXwI1g8@u1c>bIwy; z94t(+qPEAZ_wVEwl3$y?(-FTP|8dNx)vzwdGnECKzAwJC0%kGzu6ESj0Mbo+_b|D)`VouHHTe`S{)h!OYgfdTfwS_OV)5;y3b~N8mtV{$XsFm*jpQ0 znCO4&;+716872PJ_#^gVBP^*|m(c{X?k2bE4##U8tQ$#u z>aFEnVN*)*ss{MrB*q~dIMe!3pZBmt?;76;R`2dV{4K0)H)W(X%+q}P{t~XLzg}+z z8}HT6u7M-g58rPIbM>PFt6_T9qtbRTyZL=+6>Pe+q(2>&++BD5IqcN%_?ZPMPye;@ z8S#fdyV}4eank*#aHw4?B@N~W*VdH7DM$S(Tk>)K?mX@D1GwONnZg81BK6bm!|~^b ztoi|K1)rPCV2=i={wr+gKJ8B_oZEiS&PJHkG=;J_+Ga)rEI&|m{vP5!H-Bf>!;+g- zbBUE3R*8(TZqy$O;D{EneX4}F$u>Fibw3o0pe^kV6I4V$wwVw*$xU*T0%V6qz z+ptd$?{RlX{x-&qi!gKGn)^3l*>Jbyb1*|U{M-$gH+apjlW^3xgIkJWew&?LkHThK z8Qklz;>ZtPI&3v+)?y8;PIB$F4UWiO)0vpJqAY#$f2{9UJCWGaMMA|zDXcs(oHa6^ zSA=|7iJ?*i+mvrG64Tp#X)T1=?<35~`Y_!yl-_X5TG3!)Nm!TCF)-(v+yB-}P=?Nj@_D`CP7L$o( z3t^`=IZH|Xh|fkWSHo)W@X6O;V^Y$dY}oW5^;-c<-H(8I6ECeH=Kf(>Wx~Rsh6nku zWTJTeQ8;th5Ra=cy{kptewfMF*_xPV(e~Ruk{{ePSxxetH|6YsGpzeYT!Q7tma*h; zd}O;HIk3$5?!`7(9$xrZMat)X`;iETmU!&WBKgl>l*hs)w|h06h8sDz$FC;&of|Vx z!TOB2*%Fw)ct9&+&7i)b<#1HTKbJD#%wx}2hmrEj{ckH_dh?Cf3t@Jk*YE?dX;zX& zAS??WuvY;q|+d|=7$C@-TEPFqgs;`6{F@F}qhLOYGL?hl1>fAO2*4r%M#K1mB zPp67te%MoLe<+e??+t=YH`hH_i#X>>lQt06m@FLE!STu2)w5xm*Z8Fy;Ea%}WdX2a zMcRUmut0ORw;!xcy_U5ZmgdiO5x}hK&KYF?+q`3Sn+h9lwb@GAM_Q4-6>)s`&!>qS z2FzVC1*We!pO^wiU6NaR!psB9y6lF1^4k|ofW_T^g`~k+ck$lwuI`vb5bsb=daSS4Ay?J%5Rr|!xn<-hVDk@j!D@$0e?r2L|+ z)yLtqq(>$KWvR}vG~vR4C5ShKq{Z}txyLoWL2y>({v-!j=@3n|Z*916kv;scJ-Q93zitO> zm#=M^f_!E7dz6hcr&HEHjpTPnT({f%0v~bv%Q-$wICH?^1>Uft^2_~hFgL6zZxWoc zr)*AFn7aQ6tIgZgcY>w)#cRjI;%9;n44AJM>c_$IeZ1+`B);I@p3$(|z7y;Yu;G{b zI~Q)8GG(46Z0gn>pE^hTcUtqVEzET35Iq=nD!kLz4376N+%^EFSJ*6|!Qv%G&wemt z-#gXct2my2HqUW}S%)Nxhy|{KZ+&6jt=YF)V9|zwXZnzQ_b-D@Fl*S?pkA=mlBgSB zVOg1>$N@IE#kBnh3ttX-Xa`5hr|)_XH*TJ}wHutaK6xaLv^*i>^fsTS5eX~%907nJs2@ET?v8T_&hocnV@-Yb|N>^PwniJv?e z_7dhs&es05MSbjyp+i-2f#ZJWWPo5mU z35Qx1I26Nzj!mIOFoW~qTp=l6U9jZ}ENdCqB_GawB)yUaE3WpLqK37LPj5XAi-g}T zFT%>|{x9S(M=M`_9;Ux*a*l^nUhUj;7G~gUWh-E-tK3VcVP1zzi-X`Q_L)VQuplky zoj)wdYrXUoELo&{>IvKQ_;5D^mK}LkGMtp}q`G_rre_yjb%Hsm2b}i9%;aTLyTSZ7 zulMeT#jWSMwS(p77ATTonk>E7&#vg-o-aI+1gp}^lD@+MPtKJmlKl5=Yretab^~^9 zgyVPCD;r^-v|oO4u%xrI#Rpi@eAi|T%xUV=km^RI2_RuAjnz8Sq7*3uIw z*M8gM837x;gThJNN`10l7%W*dfGTemxiM!k+}Pcja?@Jdj*DRG{S@S9?adj#5a!-` zf0g{6#}%!U2o@f^Z~Y!t4tBE&gw_3CpCjv+R&Sj?2d0mS`TY^D>iceyFDzE^N09AP z=nfQ3f|cwr@o(5*IsNf?nEzvHHjUKhiKRD3!`yQhz06@5U*|CrR9S->gHLoc|) z%!Ja~w#3)3c@Bk@UNflr_7u0A9t2CKC|xWOH?(Ihbb)#6`meQuwQ1V${;;4|-rWvx z%afbOoMA(Zr(H)lp<%VU7p$6dC$J05@b-_jhxO-IQ2V!0Fs-UPEStJ)KNInQ`6DZd zspr#R(a@=dwlLE@Y^)two=$zDGfX{i2Kx;EFy0C_d5=%*2?sV=4z+*{59?EVz^)6| zy)uJo%Q}oAZi+g$vK7p;^4Ud}CunW6`F9?cCvA0r{C>jTh7P}A&6$8Br2Pe$2NgEM z|Joz{&8W#gU{<~9R9D1P3S0kaf=vh0&1~R;Z~K-s!n(m(Dzd%OqRoHb!JPU;c%SNsqTz5z1^JpM!S`AdYWuEEsv zKd^eiLrxw{-S3AZ(sHyHVS!mWm0y~k-v0uu^gepBHR5Jnh9zag!YBJFr+o5^J`Ib0 zEuKf(pU288&Y7_KuqOB~+Nbtz_~4VUF?I^->=PYv%UN5gXx7wGq%Ajfy3WaShwzb z%@&x}G{0seoYD9^VFS!vabs5mEa+-w69+eT3s@RL;+9MMN=d$#1>Fyp&Gk=O4YU8W zmQR8uyC*lTfQ_rqj2Z?zF)a*BVN*)j8%Mb0c!v*5VA)+!r8R7lBpg}{3l9DbZw1Fk z+`qR7HY`8)>AMa3Q|+^(#F8_9s~X^va7Fw=*raipPz#&o+qVsYnXQHNYPg`G;!Q9q z|Ga+XGuWwxUp5bJJm`1m0i5}mv1ty>yKc0&2Xn*Ey$^u3Pp>~Nh8eefTm&$)F4QL< zHp{zNIi18^nKSc9eEZYFsjzXGXOD|;R({my$*`fzwBhGq*<7X*AC}SD2~WWMba$OM z%+Z=|9)Y>jZ#$BFjcT8V0yf(ySTO~b&-*$p4OX%Sd>}U7NIkO)R;pS7prUy9&H z4_~Uh{z>u~vVWv=dpfN_dDpSOrjh+8+IgM&J*C}*_F|av*F2D{kEFMI2x%Xz{TZo6$4AAdQx#+@Y}hh z{fZYR-zM8vwe6%a5td)@c(Dq04eXdg+Pm>u>dQ!&H9dH9GOS$IY1c~FG&=1*IX;xX zDkiOf_0O#Nsj!3@eta2Rn|5^~Ii8f$pZQ0?X8E(J(XG;M*fBnxHiV+ zIO#7~>watz!5(*JzuXV&!Y-Z>!iv^AT@S&+;}!)0u*T=%v?H*jXmObUcI$ei@EEMj z=>2*sTw>?k>J+SS(wZ7zcCIDT=y~b9L%5Lnm7v9 zFO_XQ4>R`v9La@kbbs5ZV54v4{^4+3xBA({!hW*j!{FNY5vNH1ET;GGFc{|A@%rY# zinW&L````SdW+54L~a>N1Io zSLl!U!19gJi<4mK`U?xZ;f4;mg=G1SQ@-4q2z&S+r!vwY`m&iCTL&VwJ7N9T5AAFXCpe~6N5O2vroSznQUCSh zsPm=L-K(n!X4K_Wh9j=}KJR`L>^c4ekMx&(UCV&4u%L2WTqrEPcXrrk*ebR5JURc# zxP>*1u+P^7sz0W_rwMbnl(9*F&2H0o&3jm$r;^Nv^_IuZ8%exp-V9<*@WsW%axeem zXO;VG65nL;yc)L8TSlF4nB%xZiL;({j~;=8qjXz7za!e--Ry zJhO!9AM&XEmr`cc)8apI_VAU1TElY1WDin4qfL3&KWfzfr5eQtSXFfD?k`vr%lg~^ zv%L1VYKH$U-z;r&$anZ(dkUD|-1!@9l-ytW9r*&+HT%B8Y`>x@e_=*-kJ8Vuw$q&l zZP8v?2RpU-1WQs+wIl7lWb%RJ4{&4upo~tik6&RVv3Azcq2&1RT-B4&04w~s5AEPa z%dCiZu)xMr-XG2kt}CgBh09w<4~8`t^Cyrvqo()%QLx9+nd0{_H*syB39xqPh8JY{ znx~O1Q()HPY<43o5B)lX^e?g=k{e|Gg>%D~%z-oGO66qxXtA`(q(4j9p7GQ~;!e|? z#jq-=-HD&Da(AoT<*=t#%5Q-=lM<-@Q>r_!B=xD8YUxV)S5AF@Uy?6v{NX_QWATxM znWTQX)7BKPCth4Pm@J=`Uz8UQS2(RnA@wcqGPT_%SpH!|coUqNe2kU=E1oU-N!DLX zn|)vltY7;vmDHEYVb1oga6mzdhSUddP3?$_jVvGK zo`IX5tlgxAIg4)XI}h{c#N8@}S*wO8T!d5pOy6^rl>brI=_+hLZ`!k5I6g9(RtSqH z{(6}M(?W)15;xp^J3$3AEUy13f~7kxc&A|9#|tmW`I#SZ;_?xgv)NL67f#D*{Z|2V zPbRj10EapizS|9}+OGTk5N?RHa+bq_JNl+_61T`+v<;>Yv*3~OfK}X$4T&V4a{Ku+ z*uWTYcs(3%KX7F=Y`W5>e;kR|6>4hWrac?8V_|O3h1B@M)0fpf8kS}RX1_+9*%C7# z5@tNeKKTX~jd0~HgN2{J9(oJ6Je$!R28(1Zlmjcf=$HJ*O=Hk zn!d*rQeHjn(vjJ)aY2b6Sw5}UywV^3_xsZBnmk`v@+hq>S)bfTq0K(TefQar@sRy1 zy@3y#=AP;G0&ZYFcA5waqAT8#`d}uv&m9MAo_U^l1RM5`n&<(`4R5LO5@SQzkx{TJ z^45xbh(|c_okzmLycp>nVi9MHJ1qG7of=P7?e<wFg5=H7Zk{`}!urE;+Xb-DtFW{k$?rTgV=62!jy>BJ<{$7b^n&@7-{x7sqA!crkp7geo#So} zOFaG>#=r%Ie#=_J)c5*ffz~JE?-lG{%TM!$!&br-^IKqD+NkS8V5u$b>Q6Y{_S{Mi zoVLty)el(jeRse>So0#FbrZ~P6PCt?BcfjieIey@x*Q_ov)Z-A^`D6E3S0Mqt0aN; zA7S-UHQX55lNztFFPy2VhK*^bsPUPweMxa8tQ=jpmYmMpEEp4|Hz;?i&7=~`GfOY?|~_e#3A zZdC$HEy63wcu#d}riX^aPnS^Rg{V(Q*A$TQgZ-%SK~w6PggjW(5J)+zC}iOkxUoYx zH697IxfOL0mgki>knsc4`;^srlK+3>hmzm>Kc0gb#-U%ycp~ljWS`S8{a{-!l3(Q% zz&J^K_uxBX*It&CnWl4;b(bXOBu>p=ARg6s35{6)=G_O9FDd=JEd!P~sCZ<2$Gk4tV6J{wP5fU#I63cx_AIK&aGTe>c=e4{Kp>H zFtznHGJY0UM?6e}b*d}sX4n*I*tnaNj~qqq4`uIdTX({ubIsXg|A^i4yKjfZmF-o; zB{SaCY=PzbG7ppSP}9qU#za`}wR{rU|FSWWt2V=$xXu$=!OS4ptaz9gQC3fE6J3@c z2kThJsPTTvH?0v*7?J6}2Ig8#pxRf$4!-qLSpOw#Bx!H1W^OB%z_dF) z|BIh_dOid;4e_KLaN_;WU|8V&yvhvu5{J~1`LNb~Pl7otfArySAlzsr)f4O0v3=*k z)b}xAMFj7;5S9+`jJF`=M|nB;!@__CuEe@A12za?&fC>g`HCEs`3(5q@-vK^jUdw+;e|{xa(cl)xBZr{|{i#tedA;aOR{= zZ!6)Zyyshc!lrJ=Cz9(Q6LYykcUVaVrZ8>nsB&9a@hy=u{qr4<&an7t;GD;Zi@&7y zV36|H&d#rZ;~trpS`%+-xKs|~--!NL{l~mtzHKdG$p|AAZ;UIs(hjCK{#iisxu0#m zn#1gEy{(C@3LDq8A#UnUxg>P(>%W(=eR=W=(lbeX6;Xy9y0nI zSw96M=bQk`~o9oc(3EZ$VnfqUBf)?HQ zig@FE78MT-x_jsgEbeSiSwFsC_D5JZ$9yI!@Ae>7^A46?J}V&g$scp4?^{^wHj=uY zq_?XW_YxLrH&fT28MS*HYGD3e&Tg`Q5~dt*se-vj4pRG5x%Dvj8BF~@3gV`wIo+PZ zoLJpP(mt3%`s_!ras;>UOIUUA`fwert9eDWr_!pD31zV8L9~RlH{SIr9d5z;pp*IJ zdefxrr?>%gvVY~gg_|bUMPG+$xjtv=VYBFwYc;Ue#+qu+DZl$xU4wa}CmkaFu_v?G zx&Z!nJct*%)L$juoRUWRQ&Y#Y&Y}zmyzL8hZgY{G36ZUu3`TtIojj2YQ8( z{*rz8OV}y+-}x%D&3xbEuu|^v>o?+ANVytEIhS{I^NCdjGqs{ z|N58Ew^_PQDGZABVaab zhKR(4YVnCM*f@XhO;X;cPWf953xypwyTTFWM<|PQHJ<%owRmzf$!9Omq1LZ)SF3wV zVXo4g8s9iM$?eGU1%r-J_oD*h;;G+bNL;AvWufb>$Z*7IYwPRC_H*xrQNJ&AmkG%A zwoLyzWD(->uScl<-YxQgR0JD)-VG)Fr%c?O90+qatfbEOo}2d+%!P$-!l?exuzy_3 zOqlxKCGr`Q+=ltXrbQO-Zo>`5GaF{W;)(kzufX(H+%?lk++~!X3eM=3_j@W#{T~i2 zjyWDQnZzq&Z|{K(y7^DBsb@PCfI9SB_V>SX7^QzNgVME&!BZk8wbH@)c zaP4{@-Y}BCEN`+DW?bC2d??BP{i6{!w# zg|&+AtCz#Zj@wxMVCwyM*xoSJsyECkl_^8vxZ;xqEZF$1G-DwtZ|}Cu5vG3_c{&8n zx>|j}0nS{}rY~`WEni>{s}9y)5WzxS1h)sQnA$j=xTQl?mL18zYu}!@bo};9Ojvns z&($DUHA^*u*mU6hVq)Xf!A4tH-rXX6J{a_*FRyyk(lEGI4!f< z&ID`w_s*IQd#;5;oG-&py@xdx znPJ3+RVQb^gY^&EET0U=%@DgAVO0pzj@V|Mc~~7R`{sI!4~w%}DPP01=6Cle!Buwp z0WV?WHNOpBu!`kZSp)NjMY#}*j+JbyhJ}l65B7uuR^=C0!jeIK+$O^GA+{AyVcyg{ z`FJ>Cu43*JSnB`D$b(CU7j-FzwZXg(3@eL0B<_^@aMWE`uVqknn|gcY zZJ7Fh0K`KF&%J*W=Ei)cmZuJyWqt#u{?7pMh9#G5i(uoa;FJllFvoZzAEs?NznQGB zSW;c3h6Q)ysO?dH*q?tH&U_s}ZLgX7<@X#|aiP^(QXhd{e0nyl?@g!7-Zf%e)_>v# z_nEylb%uCUY%Je>>7Z>dUMm=wmFbweq0sbKs(T z6e-_zDrH@C+5B)=AmmXNo6=`3gq2UqsO2li)Mx|YfAuNhemy$}{#T#Oy~U#0Fq3Aa2J$41orN3wR8!|y8_j{ynJ}ZX1+~A8OuOdeFe`mN)qXg>o63*Dw50r1xYA|Ea~VBa{g%)uAaUh)<|n}$?;+QHEf^)Hdbw=jwh*I9|vM> z`lJGK{Lz?cW7FVx^8YZfPn(oY+hIfd8`SYxlGcH@6_!k!7fFt14R5zz z?+<6*G_UyvAKdNW-xt=eXg#eFZmPSM*axP*{|z%#-M99J>GwQx8sN0#mt$G5;I74z zcO<{nG-rF5c4VDX9jvbJ+fP$m&Mbldt&ePO z$c18<-S_ARQXdN6?;Wng+9(ZGKM|*=cGkeM{l%0kCXQTn4dx`eP9fWu_Tx{#d{~?s zLT$g#)5;t*tm-p-_HD#@J?BVrVblD=i#K5>!;!-mVP2ZK6|wBD_rnV${&rkw39O6? zPd^K*`K_q9aExp8X*jd;V=&1Nom#d26wD6EX?qKH-Fs%(30Svz-#lWWFyHnVteq*N z%=UlKd>B@pyG-Rfoh-LF2-CjZqShxXEK|Fe_-b3~_nd6=oVWeQ4gKmbZGkhxa%^s3 z`G%J_c!{uKZ*DIQENT6`&nB2(v}i;=%zkzG{08Cy*{5^iicj};#=&fM-HP*Y;QOwc zwXiTKfOQTQCy7d8VS#U%?kwCAaKL{J%o!A0cM?vyurGZj%sn|J^C)b;uhYzBF!O5E zg2OP)`18_IQoi_1-XS=&Lx?;K7S6dy+0}8E<6=1TDX-lT#2fmI4qgCj_f-El2J1#; z`_CouP4AL3;mpS6Lxr%uC2-&w*gn|n+Dw=^IbE6sx0Fn|>j(2)TiKKCiCgIZbOx;Z zusJ*%R*2I*d|<_y?UV%xfoyMB^rBn73USc{zhF<;)HXaP2M&0CM#6)IyEph=BFo#q z|KS)|BY4|%nK+l0HxgzZ^=MGTJlpr(N5C|_;cXr)4{Y;$7_4i*l&XK)EdN%-Qdi$? zWPdbu9F)R=xv8Cp6~F~^TX=(DO_wLniOqJ0_815&JLV6%1`CTtegj~Z``4Hvm_6iC z>;7<~IPMPF->R?cEc?NtYYh>_u>Hjmy?Vo@w9!<1=db3QIl|QU<`K_`wt8(3$G5pk z9X~0wOYiOAfBliE(2L(4ZX6m!9luQO+t_Zfu<<6Gwx3Yz)|2M$$5;objw}Gku zLx7u}UR>N6rtg&4J%+X4mwGW^T2Us|KdP2IaqI}QZrpGn{bg-2BheCOT=sZC`pYb* zUvKE}|Ns5h+a|PybsN3wNq<#qJ9er$Y`9nBr-d_icnVv?%qL5FUWZxjHmX{Y_`Gja zeTmib-G6e>9}g|0+P~*=L1YW8Y;UI}?Y(0A;+f4bZ7O5p2~z%IOJNg<5Ad=*2#3b{ zw*3gJgWjd2!IFp=tKrOI zo^v8x`fPHyr*NZ9N8<*V`IkQG0c=XCIUNfdLKpA22`d5~bXyHuO|a;a4>Qj094LX) z%quG{!IJaQ+n2%e+Ko0B;CREGT@f%xlHBV&thMw1v=pWp`fNK5%NO;^5W^+&qRNlK z%mYI&EQ0ahfZ>N=$xeCk0{CF#^Unuh*7RaK;wp>le*0ifd{;*i%=c}2xd*1cCk$KV zwr$w~)AdXH1j7>7Rj;?fm@qD%54&={hHWL~6>}2;;fVR)qPGyg5862gb{g;XeI3m0 zyiP5EQ@$z2u7TBKC&x~OS$}t}j)FA{yAGZVdmf_Am%!BbkYVL#EkB%;5AW(R38sBJ zcUlZ94DLoRIKpCLmr$57cNLGgBH2c}fY>ot;R%b+A38D{<}~;ood5^a=kD-@jRzk4 zj)xnL_dGuZ)-r#z9}9CfZ9g^A23se4iJWU0}Lk$SP+z;@yCzMgoNI3{2Sj-4y62J8D(*8 z{Q4d+_i(MO7vci@>BG9i^dZe|#91Sx&7EOk>uoz7VPW8DSw~p6_IAAkoK`$JzXQyf zYuH0<${yXH4)cS4jVJaQ(k0g%rYHQSEXmpb;M)b%-|4@UHB+w?eS)d?{gF?*badc5 zIMZv+NETe0eTnfF{>8)L?fMHZ=V1Spf6brXD>4tA3UUuEMIWYg-?Jj{h`u>1p4%{-bQ-lJh(fY)xVEyHxefPlhzMnYfVU~Wqe;OR-TPBCc;9~ zqrWGJ|DHa-0p@j2pl85QX^b){DIe;0?I_H%JKTQ_%pWVD9O(FJ`zqL&x+09ktN5W^ zBVomqqt}(N(~!;%64+E!yo@+Bes9KdSYKb$otU%xQ^OLNoxd{rFkHHA!N>V9)8l;Q zepnXJyKEjTP!3Gl2ggr&qn-t4KAhsU7dC|ra1p>-_R>GQh!gVe@nN=YU_>fh690Y4 z1Xyj;_*e$(`{}-nBl(sIL$|zN<_6fbdE1sN-&jet3(Br9hCLIrN}XY5;n~w6u*YX(eqWgX@{l|TF4;eOuM^C%OZy{) z8w15Ly5>xqPOJEzDSFv)mP?&&xCKN_@4i%o&!1nYr1( z+W*JY{l~TR{{I6Xl_p^+wG@Uh5<@ZyLoq5AVNwiXQ4C=chGGa&vh>4BG!#QJ3X3p_ zhA<>UC4`|Eh414!pYQ8-d;fXAJ+A9?c6N5wxz6*tme%WFFIZcD*{2gMj4N&D3iAS6 zmfFGUdwu$MhK&n1e(ea0=dMcc0M~mhpVl6hdn|Bj1M^2tKW_u8Tz2VO!wn-ed)mVM ztNA0K)ynQCM z9#$*}wfm>W`(ZAhRtxj|kB#{Yvu=&IsDUekkF0Hm^@lHOUy=UCc5{Eig1;7hs$jF9 z-LghlJfqhu16;pr(TeY|@NCk)r?7#Q@%IZXce4-C!wyr9wEG0BrUrQ2hWT&SrF?)B zmuNm*C;g9iy;BeCO%{S{u&wWcpYLGW)fn1UIH%-Na~;eQ%${Ea=h5l?iPg1DWrf6Z zY=^&v4aaKT3t*p%uS<-uZlCjhH5^xXYHc+vzbE>U2h+<&+B}CfpI)3-!HEfVoeVHr z-_e#>W6zyZ3A1jQAI1mr`nLJMq8(vsM9fJdEi<$Re@%3pdbKrFM z$IW+Oc6~5q@!17J-Tye+Gxc35EX~RilkHWxJx(lwRhPU*k^QZCJYIScPV{_5y`J*Q zRn`SCHGcs4fbFpkXJJ-e_NB{kBKhYH z2X4T$c#DSPFz0I7io38fPJHVaEU~dFx(EBl-Baek>DcE9q1KA^5MOg4uuVc zCEo*Jj%Mx19_gs|0#1@{+_|{$mLHT4p;r&&|rqywfi(f zVN1rOC4;=3C@SKM9Kbza8 zC(MZ{d4B<}A901{3P+DF|8b7^-Kk!5SkwBcg&Njd6yN;O0iPGam+vRw#C1I)Yha$y zF8?@ew3i=x4vQXa%Q`~(li&A+1HO5l%z(w&gW30B&CL6(6j(aue&$WM(aN^EC3wv8~_3!J9r#uY;-jH`wg=qQnSu3euf_!uY|wlMmtgbtRMk!SvQK z_sU?6>yFyiI3BE!>tjk`i7?h+1v6sPTU{gh*-@+8!fH#m{&_HKvt?3eIQC7@*bJC= z^TD-lB)=>3-w%s9%d&dI330m??S|#Ow-0rP*rY3ts*jJr$1C?#l}%-qkdC8mw(vmpvY)=G(xc zNjBqsU~$R&bP*id?rxzwEUWR^u?Xg^ja=Cc*7O}h*>zEJd0SZFn7BI}dGtqXt3Ua$ zWXH;-Fx|MWYcotw@O-rlP8cs(_XC#ntemrgw2xY~;yX+WdHj1N+;V5x5)&-4Z%&Pd zeUoiI*24M@k1okz{M?O|5w2&Xf8P!l^IRr9hXpRt9(&-}rnVO=VI!+~#eP^d{z`cT zEbpH&_aLm`M2205xt?PJQ{je#+aIf8;i%?$^FRpQ?c! zZkFty2eaq=PW%9KZRQ6|BhJXPYlMA&x6)69ifR<&(1+ZV8wf{atq|fZ_Al(FrRsBg%$CH^y;oKrzF0R4s$<^>E;Y;_wAd} z4lb>5Tw?|E2K_8$!t}MB{`|_r`)z%6T6b77sP*@HSW-2vZ$G%|oo>(zSemB^<-oS* zt~K9*%^MH1ec;fio4Q?xMcp?n7zgVo=`u7hC$IU?B$(0r%ghU~;qBw<0Jwhh-V`;g zyb;oa{6A&tVzc`MOus*5&Meq)JM7jmSY_2dI|MHOob8tm>$_&J4TpoRVjYrU)BfD% zNSLcS;Jq8xwt0JZ9UPs}y=oh*ez8@#1*SJocoqZ81y>~ru;}~E$hEM{K7S_pf0nOw zR;_}C1HbYP!Q!q1D2ncUwLKWbu+gVp|TN7cfT zHcz!aur9LZf(Z^NPB}6X<_&+OBKJ=GHm?!@aPO z?=WlUI>A7g89G__31)_$O5?yv;gvJ*VbPUHFHcwzIP1+jSe`U>7qRNn%9}MX^?OvX zYrmCkU%{%)y{YjG>G)NSRWNtOCDsV!#rLcKJcSw2HcW3g;7g;i5*B#fI7h}qic=!? zKY|mt{`td$8}0i@9>C&1<-TNm#kJ2@w+dJ>ZS_z->~QCd#XVSKvFqVvnAJbrSqGb@ zmUaz<`3=!6rLd^|fRZUN+o@=N3H;yqP)_-)E>~g6+=~{|k!ufyW);IKL%-Z$V%xWU zi(u35sLYwLwD(-YC0H@b;qWY2;PJ~wL;8mPV=+z{Dkg+5cW_LeyP6{`6n4Fso zYiytGlflLHmSqXBSh6a77c7Y?xv&G)y&apG26>0C-T00k}4chta1T4D!c*|_!s$@qCgaFB8stj(EBJ^yJqd8B?IT>r~NjW@IHcrG3=qg@j9yv=fr+wuOeag;0d zyv}n~BZmzO!-J{uYNPMuNHkfZZKZAMcp-C<6lVAhJBaLpod zSu-r3=ov(wHy4Ov^UZMm_Tk0D;TC@X!XL2iNb4$6pOiCrbxjkTcrb>lr=cfGDRW~4 z{-j=pKiF`!5jkhnn8CAP*WuGvd?#kEjwSUkq7|1XeS>8wwDJ&G@8N&k1oQWnQS}L| zk?AMDz^c)wZd3IN=SRK#1e-ocsCowThQCE0VYUAdCsN-RT#@nlJYFZ z1z~}y>yazBe4Bm;=5x1D^&G+N zewPKaj;#>xg@x6T%#(1z+QyzcVEO?2-)dO-XivgcSa$T!t3p^9Q~q@m%-y_XZ4pch zAN?$bIP*w>7LGoAm#3@%^)I#dGdymohb2*;gD^-7{norF?I(}W=58SK`CLt%{<*=NiEsV4lm2^MYZtjVEeHDbp>nlX!(-&han(PGHCbK{U%>oy&X;bm zxW?~C9c;-qo+p1#*qF2GD_m9bj_VGyK5n4a!-$-2cbdFD@#dC*_E;aI;9OEZvB-ah zn7DCgmpEdE-Tn2BaD8l&=KxrGB0}5+=B@tfM_xbwZEd0(TpaUtT5nia=v>hc=5idV z*Gmg-bAhbi!Rg!jtTXaN%i+>durhk{hEA}sy07mfnD+6Gu>;v&^1kPRaCm0pigvKn ztw}Ns4qox`I2{&`54N5KCr|DqvVtYQlKn#A@;70xnor<-dH*<ocZquafcYsOuafm*;%3EnYJ&A|H&l`JV1#eFv~Gme${S`G?CZ0X^PQM+`oT_^ z#`{qC6=t#9&)E<2+c-}64682OcySnRh-sDk5tdKAD>@2S#idw$fQ>s|d{e=Z#$|io z!OCIn2cL%Be$|bxgVj@A^lG>z!7b+v%x!WkEQHlRV^ptU)ubJ>HN;n|wi;oYMWx^} zT;6iEeKl0QM|;nVdjd1Zx3?zi7ts#d z?5QN}H?{tK59T$Wy!H?l8h9A@WExJ)em{HIGC%-`6rg9%m~c4@bXNq<1*-j{P4|Of^Ql8;zoNa!?$#~h#JRp|;>7*sfqYn}^Pslxwtzj)7yfU5 z8XsKOje!m9OG`haz3ywaXe3PkG05c;EE7e}9zya?x2V?_URD#rh9z%%?0kn@e0Jrw z&M^15_8&Q(HEFv`+rYe)2k$?H9j*-W{-?s{h3%a1hueY$_k#Kz}Y;-#@`vpu3yX&Qe^G?lM@dW1gcryM9j5?Dkm9X@4KqFb- zDt0Bm$6eU?V)O#)d@Y-Ase^g1dfhmW+@yMF*23nK17GIDnkScEpM&}Ho<}L+;PN-Q zc`z%>XLCAS|G2991T24L+(AB1rr)ZhTv*t$wnhQx?QXQng7IMfcM@!zbKz?mtnrDO zmk2YgPqI^CQG=6sFU-_183$p#@SY?A9+39Dc^~nq2OG)fS+(_Kt9V!&GQm3@*3jL{ zH^R#0lEy8t>!6}}>tWUXW&yb#sP|gDS^?{}gj4H9(MtoDhrx=_a~GnKGY-7nJ{^{Q zUzZ|*IjQ{RQ(pcv3jVGdX$dR4U^Xdo`lb30tzq_{ zT`R`GZadE8{mI4aITJB>Bpm6P*}fSz4;)Dw0uLB5G4KaWYY~m@1=H^5FZ)8;M~@y( z)?>mtpBq2I{JQ@3*03yd{>>U#?fX08JHDR`6`MA_h7|{gjrssHyIWWpVY%a~Z1R2P zx-sfiHO%XyqShlw{5XDT6|8!9e&18%$!R+kFJQ^%jX`9+0nR%Ab(ablnfv zM7lJQ?d#@@7sSKCfdlUnOSfI^vWc{>>@t9CPcqi_a5QWi@x8DTX6NsoC4uEDT{qr` zdFRb*$b9~Qxw2XJU`Dhb_5TMmPciW}$&D~&%PQw8P4mo>~uP;=`+k~ z5^QPxr2HVsQ!1AG!o?#lh9|{|B4nmd3+0WslDzk!uUr zJlqDGPgJ}c0n7ZlR?1+0_<-i&aC-P{)_Pd@ej7C(kFn+YthI2xyKBS{Zr`c-b~)Tr`{%)WbuVgu9Xqr0`Z+N3@ug_8o>ThPwDG}k{X-{e zK4Ij>Gkt0ihuN(oJK4ep zt~PTFEV9X_<}(KO-L`QQ%=NrRtuH_;8JWa|`GzTrNqgnfBIn^SeF>kk?%~p&!(hqj z!Iw$yQ+RvmV3@hlp%nwxY^A4qkeuF6N%oI7;bjOL)@#~Q`zxKcJ(mTWB3RVxF{d25 z?*tq3{i~hO-gDK}9S*Rtb@3Kw*zo6OvK=gs9Z0R8K#OGr@Ye~;t+wUF*` zhod{0zO=xcMlrQMm(K>Jsu`BduNp|^PilW>n192fM~TjZVZ*LDPtCA2E>|;@cz0Z4 z6KOy1g#}sf%hl=Yy064*?*HJzl~)G6`~>T0Zx4FI%t`5`?_utY=_AN`7TV*>*1v=0 ziXN|txvTT4-;(zE)ztk;`nmPWYFL!9$}k3bxFBG`Ggx91A4kmJI`wB6%$X2Ft(T!& z9cp(IR$CNZC+EL(@Wq}buxzEp@TRtpwle=7k1&(*9&(i>3@%i(x zMmFFhb-$zF961fEy^^;~M|=4}!`3`lIyQC-x!=io7P?3UbNqHt>lyiOE}NYN8)NF< zE<|qj>Aft2wEyvmy588<{B1f6^U}^U$oRYBuCx52b2 z+o|ff%SB$<8)3%1N!0b!5a&2j3af^Me%XOMU|Z{kNSJB5abPFR z8Wdl+i1hc?QP!O4^Fst1^6pcvnsOy&Hf*ZyaY&B#`ZcW`1+d5=q$LiP%ozTD0&M2} zTSWG+ylYtYc#=EjEhpD+jWGJaC|G!?o?0)@H`&>7D9lZGK4lg1xW5BN^@llIj;&k@ zyJZP}^ns0c4{urmD@v~R=n9K-3#j``cGQ$jF0g5uZ|gAR!aaAqonZQk9Q)a@s!yG} z9n2K(dLw|1KP&WfSU!hwdpulU)5p0rX}@bAb^ViNF5dL_7~a30G1TWpTM+Q785Zwt z6G=Xg3DH?y%rG~=GM21Y!Y^xS`~e%Qb=3M6j42TnO{9O9Zuef}e!pRK{ftIf@j^h& z$G6Rjs`>^?thIOVBNzV7TK0u_`h$PhVb5{B|9yZH&ppXH3)5Q1jC=Dk=GBK>$M-&L>5!yarzdg**l5lK3@Tgv!gSmM#=TpzeH;ak_~Fn!$iLNY(bC#Gh1AZ)yv zOO0+O3}IsIh)sNS%;qfHuVuW_IAxflH3_^Udk_+NLJcD#z3Zxi{U zTH^|HA619iqy2!VzXAU6d7c5l}EBjg* zf4~~CBJVlOJ8%2xJ1n>VMvZ^7_9|pwU_q}|cgc8pRgJ5|M_9NmftsJ9TK1@49c*aP zZ6@~6x0Z-OOiI5z6YwyUtwy72>fKB76Cl~@GJxG$*ZE36ySju*lrr{~6GtVVVZvD_LHQ4(ww5TaPkxz-{Y{vZ!`6LNg6(5b2iLLYd3o_a!b1o2Qy&$vcXh6 zxB5i-*ECq#>39Ws-sO8fg`El;I`8I?dTn9P<_QO2_Q)R8^E36U%R~0SisMhRXCO~# z*FR`G%$W0#GW|ut*G;f6Bz@L&R2J2q!eC&n1 z;lcG`p|GI3>zE;MtoyprA*8>R&tg(PZP~XZayBg8wthlam=iXrs}MF^?!T5i-;KOc zF?A+v-W|En0#5jFa9}V@dpC3TH#|>n*>Ce|8q88oe_cnOFEfYECsxEgJM{|YUs$&< z2xhLWyZHgHdcIwmdd!aGs>M;%^KdObhs0v7cViLS%W@=?S(TkfpMo0} zv~nc<4GNFV>9EA-PFWyKPrpt*zc&xJ{}lkUmPp>kAure+7(}f2k~e!3oV-Zw?hn&O z^>-un067oJI`CmpeFRkxYI`;5h968nyf|SVa&3lW%6M3Rv!yku_tbSB%NPwa4}3CD zfXi>4`8fjCrhJxp!O55 zUgz4vDp72g&amWXc?VM8QH-Hp_Pi0C!k4kX|4W*Dglbyc+aW&_v%a=*|7 zC*1A&*c#S)rhTf1*qhcMe6hN9t2PR1j}P{-migUH$HW!gGC+(sQMyxdOzn{ShnZKnh4}^ z11Ge+hSfpr7o=WE*Rj=rr?6pX{hu({VVHJG1EqW##uo70hoHq%UJKpltz;s9DC?D82*Dm)gOiLX|)$8%S{yOBrf?)@FY~-A`K>`&lxpULL zJDgbOb@mvnT{K_}ssD-|#_DtwHavM#VGXNhq*Y|X%Gv`lji@iH`jBp)3Y%3<_g=%& z;GVh3u&URgoTqSNEq%;Bm@`tcx&n@Tvg76+Sbus>WjV}j)&%b&xzF`l9V~7edUq=< zuRSBu!oj|)i(+Bn+xD9;!IsnHnd@Ois@)3*(*@=r znDtN|5eHW~G@SE=&6C;|uY`Tph3AffMbWmti{a7{47E3D|EW{ZLb&D2(t?q2V)Bkw z3t)$dyP}4}yokS7!(dD2lVO8lSy8!62rKftcHqFw5pMCb;HrNgEBeF2&e1;waKfbb z_Pt@V-<(DMuqtR>L06c+zvGWlFpdBCrZcQ88u4T>tUGWt#tG)uSC8?4V|S*Xb%5Cs zF30-8krmD>7_fNGT$?_yV5Rj-8BfQ^?A=qWVcipZk8Y&>sC?Hpr2WU$gS*1P zYhoT)z_izCKOJH1wR{nc*y%Et4hv>XE^E%l>m$Ff-5%SUH0h}sR)qK+`vNl#o;&*! z=KK-QZGhQlJKgvWvo{^PQ4hQAOPur#);71?eh0fQ`xN*UHk1m>>tNdDC#OHd%+j7) zYG8)6y!In3znV7M2n)K*SpN>zsjH7x!J2Vx_Pv2=M|-q=1{=(p7tdiKN8aTzT-D;P ze*}wh9l8&zu9ZzFhb6Lt-#R$JKI~m7%n7*Wa08}I7`^!#%*~s;vILF|9Gy@M8xO4N zN*u~A>#l)?wrAF8VasP3ug}5e2LV+3^lq~C`7p$+#kS-UB%7&V8QK+t198r-(Pnnz>>)+m!84~&AXDFFs<!}a}_dz^$FT16Kwg0;E(>W;$!eg{@8fTi{wbFyJY1p8SS zZ0geS>=D>k{o?f;(!afYekM#?pRSz^8zRhh8L(SKZSxG6_awhd8ZmyCcq%O0zq;xm zEI(<a$$4n)UZXcr8?iv6Bb%4CeMWH^NP9+hSj>V{*&RV#VIES!iwOV z$Hu}!*P;dPuzcod$w=6>|PU&*}P2e!oTrZD09c7+;8xT-@@ zLI;?4S{-5q`#d%JwS&baIl6zgXdkvbyDhQ9iN`IlDy(Ig6)f?--SQoF@O=35Ulv~f ziEDB7u&Dgzz(26;htJg4aQ*ai`+mcT8|xlEfg5JFl>LD9x{@LtEE)WX`xREMyLR>> zY>Roo4KUYz*w0+J=BStE9jvQ9>7#%nkI3rkV8-C}e-FTxcUw|wVe0odiSs|%y@Z+n z4or=LRrMnSpTW|eNB;`pXw^RXV_4!p(!_`3F6}I@Ao*#Vihgjw-Ai-IVSZ`;wC=Ex zZzsA7D-Mo1(-rn%PrrE^HmM`lI1y{a4Od}7p_1PL7D;@H3Ss$_hTE2~Qv3e*Sy*ZA zxc6^6{6F}4ecDNw)p|kuCb;EC_WT@}_H(+t0cN(;-BQA;U#&CW!CdJ>&or2kGHzux zTs1oNy#h93WcE3nxa#Q0M3|bt04L;3*&&A$UrbP4g)LFHzY!KKvlXe}N>#!84Y1m( zhx0MG)H^a+3@c`5E#CyQFZQ@7g6n%92#VBy8cs9;$4C&PX=tbcO$RUj*_^~rA;<#_`2IIl^>b7ujVh49exc-gJ6|)VF zmy5B10UM_t&-w`KZYx^b!ZLH%10$@ByP~rqCcjS)Cvtd6f0a1j;^;5S;N+;Fd^0TW zc+XY~+m1ZYsR^c!YxC(mocHQqo6j(-v19rXSb8LQ+y_`a=tB2{aBvN6-y335-vdk3 z$M3&{Iq#aX*1@vA@nO$lrFikB<#6M!Lp}yr5&p+30&b9nuphyy^-EWT!aQ%i;Q=iE zCW)O5*Yt9>CRSd1H$VuBcW(P!4pZxa!R8aG5AMMAQ*P4(Vb_ikNu{vn>ikuF*rbeK zTLQBiQ=j_6y!?>z@*Xy4h zIu4tyU$1Qsm)E&H%7*y~tw&kI^zhebl`wPpv#*x0BI-qb2CT4s^S2e;=%9Likn~@0 zH?pNI&d>9m@9c;5IUbwLu-g1nlmH9zJa#w1rAHoo+zA_IU3vE%=D7+t$HT_4WsN2{ zC;7(WEiij*@`=x|#3d#+7B*EcUiKc=mv7s-9@cvZ%In~UUPs5Rg<0#)eRu`e{QEay z4a~KgIV@4)QoKT9G>K71ad z6b^5W?r8dmIjTQ>x5bf0o(B+Q;O#yk)iC|WU|ZYWPPVYPic7ipR@$d_FuS!k<(8@~Q`-KIlT%90TfqWD*R`a-spr3T#Eie) zDf?`;d1LuMxo#mhoY?fj{c~5eXRbWgg9dBL)RSG|@*jt8{XBx>G58gO?4S5kz29e8 zwJ2^!2RP4qTv`pxJl|@XHC!O`cC3Qcs(#E?Fy_w8eGc+md!0GM#WfjA$4h<8D-8TK2 zaT%62ST;7nk{(juvm~FgK-CCqZ}sj+th%%zhq(Ayxj65C+Q(gN`Kf}b`P0Y)ydxgw z!Ze!)X=MAFxYervwTy1udK@{*xTyUpH|^P-1K0D`9Quj&G;Qyz*|0jxYYf@{mi{X^ z>98WhC5f2hd4H^e$g}i=fX6dYUTkrowwu7Ojz{xp=$!1d~044 zA7%%{p-awc$ShCL!Rs}w)=nrdy_4zii#xJq9H%!gHA^o=(pXyG``B2#k zjy8=Q;{xkntRGB+<-M-9IKqs=b(CW{8Oe6UU%j4@oL*zvLTs+t`GnZ$x_DAYSo%j4 zPaGMnFtmfI`M%aT9%FY+Xamz)8b3F~$vGJ!8Z7)ajb?^v{Sy!U$;9iO`HTJo)@G;V z{Dx^uM$d18E7PNWnqb!5nuzbPU@&jkCs=pZ){&SwUQ+rVHeTp^{Ts=b2TgqkYr9xd zdANAcyVo!^p9y)ufFt)`lJ?Fgz`Taq*MDKb(xYZAtX*|t=RdegJbXbhtme(6j)$ghOXUU9|KKfl zYaE{eQi}s;VTpaP)B>(QaHRAMEUR^4S;B7e0gF_meb*n0tzoriZOl}{S+-cPYI zoO=YOW!e0)g=NcLU(JB)pPp`P4|B?YpFTwLBbx2@Fn4j_ha^~bNVDD%ra!)V_yDYb zzP*PNtd2hrx(8+&{w{QZW4Bb8x5Mf)fm2wpY14tPn_%VBuY>x++yPsr#lnfkut#iI zwf?W`8j?Rga>WDoF>iV_Iy}p8>RPw4Zhv7MKHZ*f9WXL zQ^XgCkoNB7i^szC-O7hdhZCcF1WkYyjmu8>!^TalxBf8eqSLuaFy~MHkU+S_bAj7< zSW@J6CkQs2%S-Wr>21H^7Z!2;>JL~BC;c~X_6vne7ffzH4Au?EPMiy~Hu`rN0xQK5 z_XTjHXnfEhn3X>0)*{m0*IPaSW?wr&UkWRK|6bf5)=W9q90{jy&q!s%%)GthB&5Gd zDD49?GA_v0z;Sjf=JbL^>R(|SVMEf2BVA#(@+Eb>68?I&-5KWBot$(Kxq9KjMNY8r zMZ?g;u=Mo8E}dce#hGQ9F#p85KX$O*OQ6nyx#w*|7_cs2{Lx&Pxo3rEM_9BjllG3i7Zk#=$+ve}!AjlAfFd|jbN$}G417KwZGLbAPL`e< z_y;x*5SQM99h{21f5V2y{B~t<=(MPyCRiM3o2rM|;TguSFo%A#&r{gf@gIgjJv%)!o|aSp85bwZgZ*c8{@=J z^Xg%P!Giie7N>Zp)xg~1E7boN>HQu0yn*Z6hEE{h_v{4|E>y#EOOMrk;JCsLSboAYeUVp#n6QxvhNdCrgsI3a#!co}T+9O<r7(SH z$+9)DFzwg7YcMTcL)myJvZ4qUT280hOPz=7&clMJ$&{1xm!zG9m1?FW8vTtA{#YLW zpS;2Ny| z+_(~MY~Ab7Uf7U5=dlD91+AA?(T-w`&&6JKd+d2$mOy#RS2uKOJRZu;sSQSAno;Yev-^*mGG9 z#}8(#ym^qkzpAPIn@7Qk9q(t)fNArc@`u384hw99V8N}yy?emyuHN_kVfOnuW+&2r z{i#ipVQB{!7X~bvJn97>PG9|@%7*mMTsM@M7cG^vA?^2@-jegpbKvx53s^PDnmYgD zyzgoLrK5kw*m^&*J$|0xH>?jiv3nxy_GaPYCYYKJ2p6=Dne>gc|NnoFG1EEL1hY^5 z-8u=m>d10>4Xn#`?im34NXndzq<`L0=P5ADe`fv*n6>uahIN9;ewRr9wdoU=!ty(UX@zj&q{Abl z;NYp%rwd^14O{B`iZqr^I0LhWS2>Z-k0SeVzdV?K`SM&ToIcG(cnaoxog1wzz+H{j{dTR+!Z{!i@#zL@!ve8I}+F@^JvH z^Iskt3kyEvt|#LU0dp&7Zh$$$J4`QFm^eg-;%Fi4YPNhxjhFC&3jd_0+#mf^;Qfs)$_cU!vfxtdr`14J~tu?R^8nY zDS>0p`=&30Wtts-*TAxjpc7(P5$NQ(5iS_q-*Yj{I&`OG8*Ce6Jh_PYB`-T3rp-81 z6Am*12Mgt}!?2DA7s8^kqkis!8y}s!Js;+3%0}#igX4=U=fd^N#V#qZc6{LAFyfiL z3RB?(*H?#yaAJMqn{3#a`Ko#vZ1zYfJpuE3o=p@4Bt^X*e=7 zy_G+Vc@V$VaQ*a>%M)R({$B$b@6vg=9h?9QwMjk~;9^#WpC4>wc26jTTegpqj)(Pi zcXwQbZ7(Ipjw9}|dyocJ`j2cH3+s$NWyFdF|7H>!Z+6*y2`(;Ay?{wan4kaY6|wfr z&hukn`qSc=%dlif@2=jkX5EsAt8gsuaqkf@&CSE^8f-|477m9c#~)MUb(M*GCVIhK z^X*+^{7yZ|bNDdWz}`eJfqj~8Bn^cX-@MesmItEJ2g7vR`cv0oact*`L8Se9&e2jh z=goyy1L4FcXC~Z&xqVj{-C<2nyAQYFP{GWx{b06Hy5|n;mge@O56m+L4I|@;vd=fz zyIs~)!r!+uOq=^lMXZ}JE2tC9_N;0lra3W3 zwTBtYWM(q{SryxLvJEV8nIB+)3nu-lw1#=L-_oAJ<0@Hi ziJEC2Nqgm~envPixhka=*3+V>@mkK5Xx|qw`&Il#k|(#;B~-%FF#CJ2;o_anIz4R6 zX<@yAYtm|B@Bfe0;~S^_uNztv2ksyjwQ01kMSI4Vp}cD_v!M614`ll_=ck;5^<`5M zzreyGH?PyMpvExtD_r%?^iu^V8rr#khZB}2%+G=8--`P*!VZ_7rz>IA#gLv&a45si zc0a6$x9;`>&bb!+We?1>y?c|Gw>~*|7c84z;QW&~=1Th=uxR50Di05~pDBa?+g|vJ z>>pB?F^#|N8`|5xdwO6MtRCcEL-se>YwC_=u(9M#!zb93voT}=T)*)mH6HHP>a}zZ zO!uF0@;&n4yvJvRuzb$p*2I>BRyqg65?1T;?_k68?{5Q0``_6e>xd(aK@(wi&~+U- z-m=?g+W5hO9h~*#{owHm?Z?5gt*^F__cy@t>lts7@7(&4oIlFB{X27EZNoR}{B&q@ ze-ohm!o%+STOqK^N>>=MEcl$5Nk9+QY_!ZZMO# zr)LpzRng*&ELeQx%}{bZ($l{_>qhdrgVgn_q5ZHiF0jB}G3gv~R&`TC2lKqrsOzieW~&ryShg_Z!Exkt`|-^d zFgL5l>nI%BX4V5@k=5=oS#aW`ds|w=%)C1}8F2884Tt_5#`WNO_UePM`9@p!U$AN8 z*q4cL<%0aMpRgwK7WI9=X&aRB4VF8x{C6X_d@_J-g4L4(sP7Mc@3W^r!;+KjkC41{ z%Ftn-V3G9l;RLwl?ic%yu;J^EZzPv3HM8nrQ?JQXe_hnDvT9g)tT=Zka(%5;!ZTQ( z|Lq_7z6wa|@Am|zvlksD-&eAiePbWN3a4Mx_Oz*vKg)=pzccJd&OgiCd=2L4`cdCk z1H4OiT!FQ7qVv;`H%`_kUxsA^S1B`LcJ9#g=V9*ipvaT3&+{{qGcYa0e;WCI;;-#B z@D$A3%i|Wp=}{-mM`2~@-&y4P9lUczO(v`lJ28S>k9FJXvkt@BSh}8EZwqo#Pby&f znf=|#`4s*sYFG-a>iypF0bKPhK$`?hgsT>l>#s`s-1Pv=3h1$bTz@sgf7vF$^ewBY z^Evvq%Pl!<+}oDAzBMF;y2r!n6M@Vcv@eLrnH>j<#-1-C*Bg#-33n?j4R3RtTyH$* zoL#*MW{k9;t`E%9?e9dxSofqA1JAb{g35DOk-X;8UPto$>ojfn3RwNmHnJP6_}0y| z3>E}FKjsGKjEL$+%yNos>J7Ud`+F$@7M+=q%!ZR~!e@wKgU7+4?l5c9Ccg!+EU;RJ zR!@Vq9ZW|8VD^+RB~xLub)V`W*tXw>8UC7;DZxjQ^=vlRG8hwqD&9z(nQ=av)zyJ z$dxw!np~Js{QTl*IPs;Tiw3sz{&CnFu8&WOzXb~(y??+Z+q>=8Mh~0bFBJ}lIfEB> zCC^`NKgU}VOBhyhpJ4N(9|gmRkMvDzhQ(uh52qZ_HpB+cfBC_ED2pV$c6Wk9U#C9z zLVJ4ari(7HkmLS+Byr)}ZGBK!6J)B^8}csc(vLWmLJu$ zn+cm29`N&n1$`TvLg2>F?XHpIp>(>MI2V?lj!GcM&oFvG--U3)o>?cz@zFCpu(?%)lAHdAFe-4rJL2#XAR|RwZwsj+apPC;F7teZc z4uGlov9Rf5$+pQb=g_-RwXm^RbD6yVvg;DrdsuUQ$TISJHS4nPeuQ1?Q>o)2Kk4rB z3674cDJAc(Vd7U6saKG{=-!{azv36($|l(M%jqjaVPmW4k3V7R|3O%l(KzxKtaoZQ z4}zsjw(t54=h>8h?F%PvN$&Rtt~?nsrx&rt?%rQGAt+Vb9hQgcqDlP(XIkH27g#;y z6je{rvc0vVBh1cw$09kKvF4E-EOXepk!-K@qu05Pupsj37E*5^_)zB34rbK8+)MU{ zUF4kB3g&C-FPPw(4Bq_aRQ$c!>#5_xUf{pn4Aae9_S7K{*mvy4XINXDdjBb0|7$wq z9n5n!h1`W1Y3+u-f)kG{Ou7nJ)wRxg21_d(3Jc-TU*Zi0nBQ=+(*;=gw64`tSd-NK z#(7wC#J1=WY>r>RR>J|cpQqi2O1D$ zT6G1Mbd`qYAmlNXF2e$shl3Bm`tu&{q@E?z^wwb?%omLxR|8Ah{{FBR z*7&^bMb5|K0ZY>Mz=_-H(tf~lk#o*&nBC1SvK8uWDjCyw@v!WzTDp1`8)NKbb`G*7HI(!^YYBdd-71zu#^n){oy*5Ci+lg5roxfz^MAiy0^7 zn~1MRj7oxA<_+w)5vJeT|Lg?Jt{7Px3v&;2j=2WM?o=P#0Q0`)-y-#5HHVsS#K5B5 zMN~bVE@a%d^(4m%+ux9L8rvKux%6g_>m5P59tA5RdAgFe18k?q;}ohHXalv=vQ8TG0E9Us-@&JkpN zN_om~>h-g2y9^=g2g=)BT}Jj-leyBFtnb(OD{Ba`cIu0XWIfIQUcaEn(zx}oRvt5% z91o$#-y39oJ_D26ZyoW^S6h{EPJgjxHLSNcuDS>pizkm?3G-dYQ0srj4jquQ9Oi8l z{CbEyFTroeGMF>+l-Nidtnab}mNTl|Kf|2MBU>*d?ISLIXo1b6a;xUU(rzEAdT3ha zkP#wSmT{U|PtSKpatN{Ma6%=sy(>UTK`` z3v1pivz`Uh+9{&Pkp8?)siCm6|I0cqY#KN5(E`})YJYhUEbY~Legs^T)Tv!xn0a8f z|8lsXti#kEB)94pPu6SpU3WX&71nXi-Il`K~{o%=tBy$JuO!2XO4zOf$d%l9UY{{!=C^QrC2Pi?7hgtblI zx|6?0U*w$f0p`Z{TowbnEe&g~gQfFWl#`WLosIB++iMB#wBQ+>xX{~)v=^Ru9#{!8 zM_H7U`2>ld%xxZ!yl@OPzrZzBzOVvT2QAdEC4aB|==EhVE1+r*S)V%EDy85Stol%M zcm-@5-@*GP%=UUuS(epy)pb}oy^1npg$w_GotLhO(IQtcyM8CfR~p*Buo#v{?>QO; z^Fx+ZUVyo>EtX{c>#F{@YtO=3cRT9+DP53!_zY|wI`I-&4?Eb*u{;HNHuT-Xp6PC0f$jq`C>y<-d2zG|Y%d=yr8I~%bC?HM`koHAfh;gUry7?fpu#4319dvnCp5ykS0(!{K3F^H##f zk__hmW9t6nTKfP0@sElj8loXg!up}2(j<&Zqc90eVG^doBn-t6D#;KPVJJdrNR6VQ zG*pH#l!juF6k!p*x5s&ZUf;{}&+Fy>c%1z@=W!nAoX75WlJA^1-|pHfxOp=xXg@4= z7VyG}j|kF8zI2|4rOPr{`CuQFZ{4u{p+_hzn>_H^DZ~SMI#?_r?N78(`QR--tn&_m zdCfC^o<%%jWDUuR-v)4FaXpQP%8(F-cRx}5sI zO-Yws{-i(eYTB)rh|3fatI77`f*yQ*3%6FdN{LaP;mAAKPhxzCydQb}ovFJ9jo28*jwsQif4HVb=_?^9{lIoKa@C(Ef0 zO@4p%P~*Mi|75Y#(`DOWZNm23B)?y>XJ;3(zoZsyIGOQHbqg%LyrQ-cap|{=^P6GDw_K_|jn0uhio73M`vB_ya%%a(Ze;(G?OR6W z*GnEgM6?0s&8ea4Aw`vzP@jiZ+;z4Z+hd6Iv&rYxed(_y`SYw}c}vOnBe>Omx|%rT z?xzTtzai9}ZQbR*fsp|3v!xc8nwYpW1e%Ey-_}F<@aD`Tg|wM~x@{U(DrnaD*b5|1dE)WQ}x$4o6j%< zNq-+&Dfz!?==wQB=E0ghi-X)@_0gi=b6`c=_Fo=wV&uqovxtkRA?@EAg8StA;jH_3gO67Kf(5f_H64{L#Pli;Kh)*3FVo^&xd3x2h(=BC~5${YPoY z@LVozIAz2l`R(*GzATvl3m>2DLGsn{#-<$hfK$!7q%MQYegBBZ!OTttOfk&Xy5Dn$ zvnuT;k$S(`!-T%X>?rmKl7Ei*+cba!D>p3oN%Fl3-(3$L0}F;k#mnGi#i;JCur9#a zIvLh&yR*U>mdn@{DX@Lmn#fVGroPhTC@j75L}m*MrKZm^;pmC$@33M1n5YPn|1Be- z_SguRS$Vl28!p!#xMBsX>t21Q{y**dSVY`kL3n zUcS#a{!78ZCR+ZGSKf$8gE(5ya|K!5I zUEY3xB{Q~C`7S&*+a0ZgwY&CF`QHqWavgNAJi2qm0JNuD@={;HOuN-{hr+2I3pZdR z_Qq`~*>_+@$PX&tTb=3cr7D=UUYs0)c*%QfZZXUWz8p&O9W?ikTy+y>r~ah!6{K!p zhLN~Lx9sU&#I*@|`$&Inobcf_KF3zcVKX0MCe$?M6Ne~T=D1BOk@DuH<} zu|Bups9sYkYgUaZtbm0(pBj_)lB5UeRiyny1+{(kwSZbJO#kaV{~F@zf{rU|VGXaN zSplq=)~q6C4Sap(5}f*RPmvCmRi_kX!<tPP_{GrXT%&py@*y~6^_{`vv&fOkWd2w`R7sYwm66C-|FFf9GNq=A_8YC*pMn6+(pTq7(^EM7GY=5MMS zMDp9kZR}g%3Clc=U2BD_j%BkZ!pu9u#ou9O^uRtG*y*o9@dIX;y>@dV9;+1mgk#^- zdX0n?u|<^A2j6~Z3uj$!rP?beL|6`m4ZO0YWczVBHRVHK{s>)9Vo~pRF9yN9Im|GU z&nUnp`K=|)p7rMju{7Myj|rzf$)w(2*4OCW=5W>q*DmD!WG^@B(VO`Hy`IYn*JEZd zFV~FUhPYtBkZxT`|B=T|{37kA4V>H=X5|Tee#5L&&siogt>5^g#2K9@(>uWQPsX0@ zuxM04d)r}rA8%8s{<^p|uYSU^pr=2{_VeaUU-lhVm3O3GPg-N|`WbE>`kVUv_V@Mk z>S6Jp+tlaJcAP!&4NR-+Nqt`9O|SbthdGzx`hUgtBHB7Rs9`}#H1&J6`pj8T0*eY6 z^(4Pjz^j=pN|?XkFSY+jDn1^+2&+1kuB$;j_=0NX8MyiWkC!jt^yyPOoPue$6Wqx0 zVtR}@AsH5y-Rk-fc3SUUA%oS=M^eX|ey(rNJ+Sh|V9N)HM_*>8$HDydW7PNIlBVBb zQdrcux40Z`|CLc7fi>-IRKCTM#*B$8VX0@1Hfw9hbxO#T!{< zd$RSJvQYScuh%M^ekTOxc)hzrUf)z15xwX?wm#}0nhzTshEwk^uh7eP9<2TnNo~)i z{_dkWu;|$l>h(N&blU6(D_xswO0hl7na06hFt@GbgbJ=2u=dPk*x>i?XEE$GGa-H= zOwUOOxJf+Sd7(Y58vpDr8LzTGhW#1_>rX~Myb5dA))WkZnXA8T&4bgQH9r~vYj&@4 zx&phbe!jjptj%_>SHPmqLw|H3anYEsIdJpbBOg1$Lb2J|3vgY2%)VcTuzxo6$;*NR zDlVS+1jpUHU33O!hx93}gE=PaOEciSG@pPsus9=cAhEK0Sn*X@YSnLU3hc4A>dYnB z@XVaDxlQuUT$sxnK)K|CnJ7h z*fUfutJNod1i^-h9qX3BqE+2L1;C1X^J4;F@!;skd{|nhJUShgZMW^^ z2g`mh=tc4i(efvK=aKf--fKN!eqUAbB$y+fN?8)R_Nm8zY@FZC&lOho@RxGYo-Stw zj)s}je^B|4tXF=RWef9abM|{6Zaq_5J_Odp1rbRevannvZP5%nF}X8_EZ zz2vML;--BMeCbEpciS|ZIQ@b{U>}&Fi50rQQTOZJdJ`WirSdr`GEG~|VD7Qs3}?i} zgL?lph1KJX2a$YGvHWEniM1=|SvkTI&)UmfVe$5Y;SR8>z_FFcDv#5Maby@dJW_E7U9u4bTx)sm(BZo z6^`3H>hxdC_e`fYJLSQeO~2=n`5!%R?CBhsx=iPwR04ZuWDU^9-gp-PfkT{LFAF zzj5-gKg*M0Rp>C^QpBSbl{WieiIdaPVpv@jdN2W2@Gn@Ae95g|N33?k!XjH8$)}vh z$n@9-%UVBn%!S$CZ98v*Q{7|-Pr@$uJwC1{u1U=y^LJ50PcgB0|M>32+!uYa*TC!! zZ4b!&-};O2!%A2+^0+IxK2SRMzZ(v7U5`)R3)>%mu_z2?oYV)B{L;~tr~HUh`D!Y^ z5p&I5ljX2noqC_-H&V1L_YQ$YdJqv(Yg{f41d3}v8 z!5`KcYN>ofv{}kyUa(V2>Q6q}>+)}$pG5pI-FOC^;p~>}21|DtIeEcO&aIV>uq1yk zb^R81EH`H)%v#1V8i%;czm~byFuPboT@UgneqS;KcH+LL>TMe)SB&llE9Vwd^|eJ6 zK^CT@zuB%67U*xhr^lKOFmucUD&J0H-!6@YgBaib#7^pjxJPcg)eo4lXUK75SQI&? z_zO%oU1-z+uAW;IuZJ~r?UjFVecN2}v#Acwy11?A56nFr@B0pxS6?$H_R9?E{u*Yn zZ&K!7o|LJD`5F;}^tU!%H|a4fs@g&Im)6xTdH`$AIhFoKToxv@Eg|iBW2t=cC3o-e zZou-0hvQliPY<$Po=@7(+B5459I)Yw|0UQEy0y3g=EYWiya201YHHrX$-hO5GhoKo zqHE7#=luRmGAx^rN8L}5Y)dQJ2XiJZq3$;rOGggh1555-r1Jl# zuba~t3o}*Ex?aZi_;0GtNnx5xB30km=}C0MW>`8bjjBJO{c}Bh6Y0+l`9|{ZBo})h z-w2DQOrz?-vOB*1wEtN#|oHX-mzNO;d0BA-jiWnUVS*JAFKb+ z`v@0qe$Nq;`$Kx`VR`N_KkCFbF>DfP}W?NWl z?L^hPa9VV3-e6cCI-IJP5&ZtwfdMe5Wb@a#=wCHFt+XG^Ei{`+@&o17o~rE)izGq;|k^7{l# zKYXzXcG}samDE>Fw{RU@M{H-&!xoNa*LHdYt7dGV?!Q?p1Lo{y!<3`n=p>Oo^PlJ`)rfmF+ z`-wVfZEOl@&s_iL4=iyX{P7^He)H%lsb3RNz&$M^e(?Hj8*JIpOppXOJMR4S6V4ht z=|ckPKQYd_{4N!2`C!Qs(dwQa^{gZ6YrmRyn#xe}o0!KA#lA zyjLY!JuJ5C)ZHHz&r?hx_d^wv8duJQ)%$z-lKZ8Wv$s8;2J_FPoz=muxsmY`N&mE7 zf?8PO-<04Ar+P#LzlH@{=R}Tz4VU)Pi5qM0p0|c2cK3xfuqf)lX-injnWZ3c<@NKs z`oZ*Kb836+t3$o|z~V_aW|P-bj32wu4CY2nIsFC>c(C|$H<L+pVOZVJPuwm>C z>ivt1HVOXh$Nur9X+Nna6cEU2_yVi8kB$ET`@McRxgJ*B4XtW`Y0p|l*TTGf!_ZG~ z)%QpDU%;s@yVf?6_6f)CJc4CsZr>#Lhb{Y*Rg}WIbsf)9^@uiE4=sZA-jdhkesL;i ztBVqrYaCzxhGW+rw!REE$467Yw|?;@-*Yg1#RK&}#7&dlemDg?`MFa2OVpQ9Lr=i0 zS}PT)Ulhz{?@xo}l{L#d!gb@dXOdxY>gocrzh#?GO_IUP?|rTq!@?ekv;@oK7(p|8A#EFiier?sYwVB)CxXUqfiL-ir4T*tiEx)Mtb^9((+WepX5qAqL{%dAq zPhk||41E%nFUxpZ)%FNj^|OTfJk4fH8<)e(CM)XmS3RkEvIG{~kM#RaK5vb4{UTW6 z6kbNY-;4!Mt%G3hkjaPZV3p10(7CWUu;aCASk(KouRpAeNww0#($OiY(_p!mzqba~ z-5O*+1(qp02R(qbUEfUj)Hb9#6U%vnBl-%iA{qk}!PFnub^YX@w9j{WyB ztZ+_fCH23=#XS~jVA1XpKVqkxqhBBV$Hsw!Zj`{hMmbe4%+ftRCL89~_M1hX2eCTs zHaQ7vDs!l~tbsrTo{;+&SjhOs`| z$@3)cwQDQmVWr}?Wg^^ubI-DEFelcL`utV?_a4N+GBdAV2N7>vWmB^eb~0T=Jzq+m zUo>SM=|6E0_5Ehle0{nGmh`e5LB9Whs!yh?VMW>W$P+NF%`!cl#E=5;N~jpQ5RwP_KKJ}Fhe&wKObi0rcLC-+za!l z{B6pS{r~vF`lmY%-b7qrI^1Fk%yNvN>M=8?7Zgl@v)Y`8lp-GNw=Kd0Hf*>hCi`br z`!)I)SZXezo@ceL4cO`cYkS_K>OEWj&b)35Gt+MCNxd}wy9?c{U`FoMS5|I8HGnlH{YyKDQ~Y8=Tr9Cy$K3{54^nJHdkSr>OC~ardXa#;_`O z&u=o`vMfuO9f^ylQR6Lb`@LcR_Tls9no#Ehk8!J({(=P`k5SLhie9Xb{7K^f@A!=f zYOwkNGkq6LCdYHIZSnJO|80M5O86I;vA_BsIe%F1`?mHo{J-->wNy!2P(6n_f27av z#v*a;tMkGIB$d>>in@#mhy`s-~}K0opBYn&=rWZ80@od48IUmk`o(d9f#%0k5T0O zp-Ws*oCrt4?;O5i zJFFPIf~s#`oq5t_3#@E$^6!lH$=BEVh+%!&E2>^z>;P$ID9run9=Fy2Tlm$JkZ;#rLBP z*;VTVXPx@Fjm)oNf17(a!cH@GQ|D{Vh3Q&0tntjvAirNLW8jgYF#Ay->i5m4KR9nN z%m}(ko&RN>yoCc{l|^%3G9R!%C+gQ9PJNI>%@-1*wB37?_Nfo4`IT`+VP<#uf5(IA z*WL5G!So0Qbv(&`Z`)6Y|2LoF>Br~MV0D!JR&qSc`vtB1lZ1G*ec}^1TH|SAfEkxM z&!~je(&&>du<+@zf_rd;Z~inrEDrV=R}5##{uI4|1?x@1^I(HnUTH0?oM2~7p0C?G zZ@l>&7QJ4t$bdPMY|5U(>Ni0>$@xyaOFZ@=oVsG1E)ABBkY=c0;l~lw`KbNoA#ENk zIIx{s&!X3@K9UP7_2Ja|giysV_biy%bc$Nvpr2{q;S}7=sG#au<~8&Froy}wZZ27W zV=d%OJ_yTtyc))bquN48?}dejuebWZtOMVY_K^0hCDi&Kdy#|PPFQK&P&NT^8b59B zX4nw>JMirdhEA`nT|Q1X2P;{>n9C`H9Zp6Plx43y^jxpGpduW`@oWm5xE0lvCWniFPLwa zBE_Q{hZr=Yi8aDh|Qrrn{zqYZ}3Fhq|6J-q3 zrY;MygS8uQ!Lr?R zh75#tKgSJihFhb2_F2H{DZ^qv!)n3AyXG+MLWWBt9Or#n-WyI`QmFd`o5wtLWWZU2 zWrrGIryuJ>dcgdyzr0C4&i3e$k}k0B<(zq>er#0N8fIr$ygfaR)R&FRThzG|thSk` zd;@2{UOj|H`hV`dshT+MljFC&7!Nx?KKBS#(oemtg{2Kwsruf0|DVgAz(V(yrKCQ% zuxHY-eiz5m6*Vp#O$@g!pPn;~t5u%aoQs<$2?UOVY3=^yx> zs&^h|v^gjjPW`&v`2^Z?oO>yAVEvu8eaB#PBhHqyuzY*W{KGIa@66KEFy~6BS_WJH zZasL4v_DoU*#|p$g?gQYg=+(Bl1P82Q!C`8|6{YCq&~Qi{ZoDd7NxRY?}3XpAAWKS z<_x;%PV$p-jdr*kg;n>)r^LZpACKN?uwvLnpWQI0&6{-?*47`(iiJZ{-AfO`a(CaA z+hKLTU?XBJ=hW40aI3?bZ;5d8+tSUOVOdw_#|g0R$X05-WT<@SlsFRaar?!3#5sOJM%~X`g1m0mbiY=E1TVA%i_(g~Nm}UlO0Ug{oiPex}0_F5Eok%5Vq7 zBUYGva)VQcuBPgVD+-@vk0$+BG*b1%Qw#0B+rf;WHmaUDt=^<$D9l;)IjJxDx3}H* zV!&cMlRI7E?8kXOjA41_=)Fd;%&Ls>FA<;T=0B?bwmkFCiQh0cIeYdWtXGxo-?5hxw#^PJi=yILcz9 zI2Vo!JTHC+Yi?L7&XYK8@Q}A~>&NvbCt=#~Pfu%MznsU%_rvV>FUQot)rZ#hNr3fL ziw?em^}ELVN5hJ`RdOxN_%e6qT9`4k_qk_qk@=;R2$;Y7K;To@IKIOZ5v*Eq_fi!s z-(r&%0&_QY>q_jG@PY3Ki;cFFk^He5`z8L9VZ+7l)|GIunfo~hSor+r*n4nQ&$OAA zu*NfLav7{FXuD(vOO%UFm%t_Am#aF%&9QPisn;GAa-o|E%-;Kq$~POE6XEb@5BjIx zzjXs~P1;_&R+!nN_uV2mpY&nS+RB*GjwTVYbOxt3o)|DE~$^EJ#^sNAlG= zb;<2f1&i-pa#X@9hs7uE!dXAAc#!fNm?PQlXi{%KcWVOmxEeptWt-^oib^WneX zIGA;(m2&#qq~;wk?Q_o=B%Zz4^6grkr1WV|z~_4$(|R27s6BlH4Y20GeZw)BZ5LAi3D$-lkdwH&n`%uhY2ST( z`UzN(_G;BL*pPi`;z_tVxKB3?ta~?S+$lKOC_r2eGZ(5~pN2)jk4LIuq4#u$OxVvo ztGyUjE{dh{3;W%j=6I9XRQjC!elCA4>Iz|2STObbtJaLJx(d_B6r8<-c%w^%I2RUd zaIU!zXRrIi$b$LV3wu0)b&rIOCt>-KXVmdiGVX;_3M@@}dh!$EqKTLOCBc#x-%82& zkSIzOCc;iHZF;uBReg5fiii1oxQgF!QM||VSXlM_^b{J#AKk{Cy|%$pqs4E?c%){T zIjn(cK3fev;izrwLE)smqxiK2oSJNzxD+<*KV~@)ru_&nTMVn|c8i9<46gC01@QmI zOQ-L9O9Zge%v)xIcvb7yyJ(fEBsdob5>a8_9HH z`TV(|#EH-QEu94$=$k9a_}uRKaOF&xd42!NQE|l3U_taW zx+|O=;E>3JIUmE%y1}XHcPBkz(K|Jj|JuCE_Nqy6Tr=Bs0^_m|k{ac_1u_9X){sGnsDZ$@wgGY|*0uuqeBraWQPo;g7Q*@jE)(<#6)| z+W~!HwOkP^g5{s1T6@Fv3Gvkb73im1zITBcg})||{Ni4zTMl=GX*VCQ+5)R~OR{oz}PD=H?U{wD_HzmdbJRi z#gET@2504cdszzmT`$cc=5A}PA!f+BS3f233BlXSU~#!DqzaDfvyOEi4j8cM&Ldbo zHLQ~cR@s}4tbmofJG^=UXKXg?y${o7|5Me%)+VpzT6Fot#A4X#LhI=^ zIO3dceG#nLcfW?@msjpj+Ik&k#M3NIFuxYX?anWNrSe6Yrf~29%ij5D;Hs{(yM`74+tA;W@f?44;|Y_!_D3Y1ZQBz`Q!<1u-B#a&2kbS zI;4{q%y2WFegdW|Px5%M;NP|xhhX`ny;OdGr};yYWU#vJFtt8jIX8J-BCMYnIw}h7 zX*o~rq{R2%F4+ew>{}G!FsDQJXSZSf*gN?`xOrB`Y;wOwHq`ZaFsxoMg?gUg*VOl4 zAnbIyWR1uRlj8# z;>>qJT}Hu7moHSkc)@7hK1b4j`9-Qee9=6gFb9}(pu=GS+Gl?`vDp?@zPd7W5nL{s zbde3~o#yvi0_)5Mj2#Y3>F3&)!?e)!qxiXVHkEI%4jd* z+@F(oo0B+gk8B9XQ=0$<rWi^{}VI*uRi)56TjcY zA>;p*AufFq%>M-|-EX@*giX8ctNj7fqbGztgJtzwr?aEA9o5kne-NRO5JvdJP*&h5VTSU%gctvc7?6; zVx3>YaRU^-3^ACNY-n|J{i(f4s)j3)sp%@8MmgMy$6fmTTtt@E#D;d zy94Xzt#Tpt$0aMv*W4!UpZ(rJp8w~W%`v$N#~mIQFca2tJeC#0YG=0l9Jq?V?Lh&o zeY%vY7cSl=8haU*DR1uzLp*eLoJ}^YTH2$5tfy{Xd))LK%u|}&-T@m@!)~2{ou-~V zod8F8+T@&srIFgThhX}Jy*H1M_Ae7#kHf-*heMCRy8cgIpN6wLgdI5m3wv%`lmqkE z&-uL%rdjW6xlH2sT1O_qS@D}VWPL|<&hCUnSe)Ya$bXUX z98P?ByyIqAakT9c3%crb^IdzL8>R`5YYtkxMmT}VK3oI5s zT(Arl-PQgh>uK~`KJ{G!ODsYscfk6a=$St3MX*!pA!>b2>b$4wd9b2kbD$aGUilSc z{9%6OEUJDVt#JMN>9FWf5LKVA@doqPWS9}s*k+CP;-Ndz%U&Lty?QO%fk**~6#Dhy_-Ww~5*1HD<)}ngy>({YZA7%cX;0;o86KIdFjb>Z1WL zV__auuTIMOBxJ%o)f=i_n&jGuyXLUkb`Z7x&6Jb1g8}RBd>I&q{;~Cn^PS;1r|(pK zI_||a@g}h3eK1vzE_%A+jWMhcK76tPudFa9cE;W$2JKX2pG^Q3>m z>L{|lm1ljy_zbMgOQhDP+OJJsdK%_C+@sdVT0S)HdkSuj(OZ-CvCXy>J&wT4w@D+* zU{McS)?wm}DU>rj=Dgnr3uYgpochCY&0bg}w4~mj^{L{DJ+OFm9ko48S--wJ;kb9d zsP(jZ0qx>enC|^GzY^O|ZPj{jf@R~so_hp`=8fF89_GJ@Yq; z`O=rvdgaD-7c3US!pkE!Ril5#ymQt;u-@vg!%J9qW9qy>nBzFpnXG5di!&C^gB8#2 zyd$s2yD+YFHmvz~-s>sc>huGISD*3dyV z<6&**{nYOfJ)4;{7H0OfH6`nfGZNFjxx$QM^G~FHF3a=7Qb$-nZcGMQZyfRU2hX1L zPdP)aH?AudE*}Qd1c$GZ{Z*x?{X7(A|NOo5I&5v@`*bjzHQ)O|J}g_iCDf9%pLM$W zGMwEwIHf-u%I<;emA%|HIh1>^!yda zO<;Y%s(zV>C)Qqn|92PsYrXRcSWD~Nhgj)f?sgnjKlZ|G0coN}V=>8Vg?|oD)LHq2B znOW5^ul*mjzSq+AR_Zf2Yu_)bzOTAlg8BihdLyCg?W(QIe%yyOTGJ0R(O)^uJm)4X zKfQm7JDlD<=+zCFKg{?isc$M)<~3d;?O)BJ>fbi6@jH?W3tcr-y;g%&;kFAf@4|C_ zPqb(HoqC%^oYWF)0%v8;{d9)3Z)FI``N_EL`@l>%tE_c+D;!|5lfc>IO?l66<2pwGS9<};oCQn{=8$G zUuM8OL-FHNFw4KYPas$40MC zI0qYY8)uUBkg~W6)_<}$1~c+Q#yOKPMi3t)N2db@6LRp0B>_h)bo zm`BzlCYQM`D@0sv_*&71`JAYH*8=i=>z2=J{0ifO)sg%j+;9)7K44wTp)a>#S*5FI zE#h7;KjoH@_^qAQWIidpv!SpOW+ry^xDGe!JeNF#d4by+m9XWSb8SyxZSrvOMOgji z(JHck7})cu^?Ul|)AY|_=`&8RV~CfSlrDZj`j?r#A^(3Yf1cRoC9E17C?xCcS`W{@ zL@bSQ@gVbaWzihUvh(j>tcKgCq}7uCvVR^_J-zbu3Tk`&1{Ss6P1@;q_AA6SuUH#7 zX#ciV*)uy&8P{bLgMoug5}QOi4b)4)pQ zbOBl4VDF@EsDQIpn8%UzD0Pda)bYc7_^g0j@6mG_PLbnBJlo{8d_jI6c2pqse{jvu0xB*yc~wRJkE|(tNF9Gp4?Vk+|2y+5Oqt~M)YS^kB3Mzr zCH4_4xz@eb4;H@J#U;NFZ~m~Myj6d81{M8ybi@E0?8Gm$dL#X?|>P?JY=V7|J(>iiL)!x4U z!Lu-9b^q`b*m_7_DH(s5*6mXgVU?>nHU5aLxRW-)I;-B4oeVjA4;x zjw8v(;&Pz=i7Bk${we3c20QPQJxKh+xY4d~`jWx=o-i-oM(zkJCaP&=4Q?>dC!AENy(yG!OP z!XKkv{)Tz?%=8YV|MXc04RB+bZH*bK$uCwYRAJSNqn#5WrbGkG32_JoYqiS}=*9w{Y792WO>CBJ*EWjMc&z*lYID zu8T=~`LGvJu=K>!IZI$Y%l_Lcxc&9ChoLaXBEdKu&d3?rm&~sXzh3NE4D+&AxP`%j zS$A53U@YY65)P||i+=^e0p>l2uOjg`TIOt+-!wqE7N#A1dT<)dXiKu#Oxho9DVqR? zKI%PvJ8alBDPS~gy3FzFZdlxw@PrLF3qQ@=13LvD|7-;(d*6!R4|Dw{u3^H`XTjmA zFwaBR+8y@rZQhYiJpY=eBb;8WUw?x1C;ul!KC~j1x7SHnV6p8`1Ke&|dV^daiBjCS z&*0`G1s{lM0bez0n4O^LNv@|1Bb+^N!-k&*dM3=w7<8!!4j45$;4Do2{|@GTd^aqM zc)pE39hP@E`jgmj6LO~gSxe%=$@FZJ z&ro!-q%I%M`l4Dw^7qP(2UL^mSAEd%Kk2aQ>Zr|yu)JdID3Wi{smsQtDp<^Fs+7VK zu5#CHSmxz&kJOuzN10r`1*@W4KP`jVe|x3ggqx3rT=Rvk`wISChlQVNcaZu}?E@a? zT!Xo9hpusjLp|qH*U!pd>$(nyX$Lpv7m)bO*+v~;wm)|vd4Cd)&xJDNdrloa+l5%% z_99yjN4YmGBcDgrc6iNhSP)q_AP?3bJJ>e_PX4mt*Cm+N+;auV|6Tojn2`d`I(usV zMA-D-^Pv|>e^J*%B!7O@rn)!e_oDtE0E-;vk2ntNuXK9y4Eg$5&u)3A!0fu|RDA*U zu0x#u+&F?@1{z+hR&U0&FPj>b48E=BYpIgz2Z0Wu#tq+?@@e-tQXe9;YxKRxLnoofecy#seAGmqF@j#J%qPm5eXSHO%>>euqKc%`*Tt zm>Fiapvqke z91d;xaE4s(%74vEDuG*5KmH`+d)B<U_grSMhBZ%>5xq5SGLzjsHaAnn}Y3z|!@3)b%F&TiUUHux{bFDdc>g)%rZC zFD(4yIh)J})bll6dcfKBEkSi~s&mNNF0jYuq|#cLwYu}qzn$Q&o1I^h_#6KE@33S> zS|hpMV61)Y+ydv>sHpk4*04zS1&+}C?Mmj~lBV5r8(@*5(umA|G?z@zJ%c%exp`#1 z$=Y$;RO9;!OYC?*>eF-%8CVg-sbA zSK)|_erL1MUf-0!JP%t3+KeXiMN#2{m@}}k%hmszA6`YmO*pzXJ?t3b{O7L*AA)Ji z;~yL${Riet55QH^AB<0iS?meEdtmE}Q|WtQRZPpu9k4cI^27v~cctTIDJ+W}e1A8b zmH**Y49xEI@%C0&W2u|F4sLw$vNRl)XJ__U1dHApB`$@9mdiDPu<^~(uqANw!hiMx zSi$k090)rl&CZ?yo8G-Jdk)MmaPRI1yHq!{`@(F$lXl)P{iV+UZ&-AE_2-Fj(KYKc zlVL$I>&JN5a%%S239upEXx=zjax7}tSXdZ*BVr7!D;+k@1*ZAek9LBy(hplX!R)0k z-U)rOV&nP429J?Q|sZdGIB~e3(k0) zw`2&MHKZ`!63+f9We$efZHtTg!&P1UDlCb28h>HJ0a*br`@wN{vVQi3qYHUFb6C1L zgw_Z4aKU9KEYM8|Al5#9Y)71W-Gj2|bP(nL#l4bWUNZYn`{YwGY&xtz-McRX{j(X@ z_8Py-zvAmWVmd)Q_2`y*vAvp##&JJ@hq^ScC=4Yhsy25vk&HTw?C(*?N` zYbQrJ5G&~2`PH!LjrWg8dtu_E$SPQ$?Q!BROyvuLlkJw8--9(pqBXZ*uQgwm+=o*; zyA3af^GX+WE{6?WEq52ejD%k04`KDD{N@6<=wROUC&UG1T`$AI50W}OhouWUi7&#* z4)q2ttnDC-%7L|>r{phSMe_|wHtZA>82J*GWgUNU4%U?aN_hp--B~G_aQWNM3FQ0J zOkb0A5|%|z7wBM-tc!^p4$iqc>@A5e&F_93PPUoq`wmve{Ej>V3%mZDQU?p7_f;Q) z)fWAV$nT>_xprzFOt-uG<^xPy;rnkdY;Yd3vjJ|7Z||H4CoYVN{seP^l0p;U#>Ww> zn_yP`n3Zv`s_8|qW|-@Jbj@y9b6`bC3oM(}xNaAmygcstH`uUPFnlK*>+1TX6&AL8 z@7@m6iwv8-!{Xpa@1!tej@2I$cY1EKWCLt};8i7wYZ!AaqhMN!{oS81J7#pu8n}AY z0^>h0r|kaw2srp)z|ViM>;~H)g7fBtbf97XbjoXaNStW>Zb}E3$?SA}3EXV9V{%7W z=)bRT2%Pn_t=@>l+bT9MgsUEIbv1#-=_8~I;B@~xb34QG$A_GPVe@$#spCO-LmL+a z^TYbg=z_Si>H8!BiI49+$&_rbV8HJHSiCR(dUsgY;bX2pEbJa#M=bDbIYKOFF}srE zO?GntotV?B;uSIf+(O|j*zW{)D>+{2906s{w8_-*&FYXB$|wCz;#c%VT;sERS)%w8dH?B2Sq<@ahF;6(OsIsFq%^*b;KSVV)YmBxZ#> zT$uxulYbesf>I8Egom|Zr7I)6z$x@;t7)aU4@AT9};^vDg?9I^@F!TdjS zVu-6wEL%Sv7V@1(yTZv9UiXQ0#*UO-vyz)YSsWu4e$=`2`a>Ns&U+KY7ALVsAh zGdyJ!>0dVGz-(CJa>|U@uaDD9;#Bd)FHSHwFlg9ZnD4%vYA=heTRtC_il6$Bc-{dI z_9D2sI)U#18^`|LL(cE1iLQb6aO2V#!wQ(*_HnT-tWH|mQi zmkozqPVX8kfdyZ9Yl$NU_g}Q}KW<+rl5U1M{gw?Taj7`-WDKkf4Wn$I@7S`1#9yD; zFpR`4C9ihCv{09-p>SU1qEGQKZ^_@dA+SVLtlR_3I|=r(VA1(|+mc}RvJ%@*U;x?@c-`T@t%#WGlgl(lVbBoe5OlF z7nma(cuWb)2g^Ekg0oh>cO&yX!-uPd9pR{*y7wip`h39X-^S>Fa=v9btUUa(OA9Ou z=tSKgle*+Q_yoHo>SmGq!QxG}@gHE#NzPz$zgQ8mvgsY{<>*7*4_BPqLVpdX4jFCo z3UR@|QFhN@ew1fM4J>omb-o-<@A8?tKP?#8t@8so;883!A6Cb!%rvpP$xyE|cPI;OMSj~<7zvltLsX1F<`(>Y~`_HP} z^y{19|2uCzVVBlh%iyZ8#_$T5J~gi;2re1-j=G;NDtKGs57WYW ztRTNci6q@0P*2hCw9S3qVj3`VP>^eFDcwu zqw4o)wVwI#<09mW=6&Xanw?)?;SB-THiTN#tXsh z(DMy&(a0N5*TD2%`lz>XghDWJC24>E;-xCMdR4KRPXdxwE&)uEG(K{jLPVj5`B&bXZ`zYLa z7EV8qEEd4Z11gvEI`T!3GXTLB?y^0$J4oSX^Or zj$9wHT2yD_;r2j_^FFYs)w4PlR{xtyoo_TzJMV9UBa)6%=PUNj5Bg28K(UUxK9Vkf zx+D^A?B=T&gZ|CK_ibDW^A>%4>q55o?cwVcuyN=?>UxP4!867yjhIp+yJfKj75BYRv`M_=))lGJlqp4D-x{^{l7Vd|I$+d{{c% z9(CT2v}b>O{v-v?{`Q#Ko+dh-kpL&3_~S{oFN!RAwu8hA7tA5^bJkq*xtrj;eriWD zzgOS?m%jnFpTCTn|5No6Vg2N@-d_;sq=;`XgF~NNO(NC|`#X@T$vKahP6)$$}}*& z+W7DwnAM{<>YVV>1OUvmAZKl-4yJ8U_5!aEhLIbc@tmxl3dN7%XRF!%4$ zwT*CVs3^YxW|ys@zlPIiygYUVW@+yvKZ22VX=g5p@68n7CHn7!HOT% zZ*oZd@Ag0?oGtuwZh!R zT{ax;ybk8o%~°a0;8UPIzPCqD^+1El_o!(gFgD`ydDKg6Q12&V0=^;ihoziN>$ zg(VMqi6z?_FPp*yTF^Zw%vTjo4-7!C6_oa-?erv1_L>`8koBi01CyhlGR z8&(}|l8%F;oTn`x1}jU3zi@}Eey?U(!TN|tRV1$Ss@1Syx@R8S1NIt~wQ~@xRZrSU z+RLY1y2pe?X~zzafs1DKy<`qIcUk4@0>`G;E$s#C-OikLhIL{MLjy*`)(tyGc7&;V#c_EOcAsNCfa%6Q zwtZmMg)S3IV70gAAOp^F9vpa+^mnY0cZW;XS}iMtRePq-GKJ|0^fD!E_)^}X8{B;R zugevf*)K1I4k!Qh73adzCSiCd*riX!j~tlsG*9{cor5o-HGl9^Pam}Wxzt& zj2ASRJt0(o45pj7yZ`;ijuuqOZZ_+(h!&A8nFH>OvH?txYM_-n1C zef-;p(XiOJ#I6YzjTzHzBWW)=q5A}@uFrcE3FE=sXFVKzBr#(JEUNT3s)Oaj+V(7k zm7&9wZ%F?c-erqn?OxMC zz5gG;7sU`JN$A5$X$UJ}NJe24MyW{>g(R$mAuN(f7$qxVh=ynqhOllhiiU26Fe*i1 zC>F)+IZI=ej*I7p|YZ&a)hj82ww}1skF#wLFE*+wvDqhr|5W z%^}v$;8N&c>?F97GC6%^37XNXVCKW%SQ3`g{HKH~tZ6KY~Ku+-DK<3O10GJn{8IAim}c>`eP0dB{8u({=_%zm&j z#_?Sd%$Sp#-wT$1weTo_ljTFNc7s(sm)6^`_~);`Z9C9jET1&+DlEVEaZ@wQC_U|( z1BZp)ax;+liljr?F#o&B_i9)Ww3CquEA#kEU%`^=ch;SPoiv5Zp24i_y75P0t-pKN z16XEuZjKD5=bnf!fR%v{Iv*nCn-bRK!u+{kwfkU6`1RQeSbSG_WEULRTJCZhX1{Lm ziicgz()!C`-Q3Eo9k6%9=_!X{R*uu{ZE$^`2Wa|SH6l(z)IuJc|GoeEnIcJx~aa}JG- zb%SO1M}!2x87`6kjE3cnzk3Vd)*hF)yTID(Q5ruuJ!tL>JD6{|;T<0i=Ffd&R@*>K{=YEd58Q9{r2N!P8z#esNI&yx5}(km zj0-EJOGmzd<(qS=++of=?%_h1@$tI7E^S=vW>jKrgPZi8V#!^RV7uzW%W zmjxSM`8&yA<+-bkj&NY^u+Il!bSo0sZX4gcngjJL7P{((>_wZ>L{O|AUtzB<~!qTaX8ghQFJoeYc zFk^J_o*{5!NOM&n9Gok7ITWsmp0s_4{Qh=a(5^ z!G|5@nQV}a#%)78Z;c`xxcJd zK7}QL?Wy|5|9bAe7N%8996{=%%xU4PVpt!UO66;gS7jE#D$|Qkhaq0?yKKA~7A9p< zR<*9{@n6f9Z0>&xalY@Ptt3D6gR`p&u76^`gB+jv`Ni7HFmHs(LsCCf`C|2XSm896 zdOr4DU(rv)(qmVs`=Mc6sZ57y-yNv?t1{ldkqYbm+s)>X<4@dNco@k_!)M=YW0OKWAr zDOXq>JK%rU`+kf^F`O!12dccax->#%RbDDOmcSl&B zGeS(-JM|oWM-y1uyLR&=Sf}`_ZWE(@i{3-Ek4-Ksbw=3Wo_3G4pBW>rRuJ=IE&kWO z+FlL%1ncqzmq>eDuPQ$H7N&`R9QTG5#*v#|!NSzcBc#38ukT}_gN*@08fL-9`7hg^ z!c6C`6K9kBus&%eu)2u-dJdepHMCqq$_J*z`oOG|ZoP_N@%UQNJXqJfspl=&z+kcc zVdk6iu6eL-&p07*^x}#+S7GT5MbG)Lq|@_5N?6YsP4!>)Yl8-7!StPnI|L$5@8x~? z3{3m^Y!2!FWSp!k$6=9|XY(SMoi~2iQJ7s)6cr4+E{{5q1_y7iHxNe%pX8*#()=Th zi(x@>$qy-skEp2*foqa>_t^~##cKwI!EPM$$~gGn{#rbDsncdszR#rK)rhmsPNECp zFxQN91Ln_wMJImJU~5J2m7B2sT2jAU;@duo+c3SO@Z)({5=T2s#=D_rxizUU zYukguVpw}7Xk8+#&}^p0>s2X#jj^QstzcO>;>-!Z#p_|gwmu`td?0jvqGc4U=caFe z0V^~C6%nx9)qYSF9BsAs-ZEI6{XOChY~%Cc#v)i*v6z~FaK=yb<-;7N<$;fg2U|(A zT;SkUWht#NXKiedBP=-i_r59SOHF=HUi|~B3LZ9*`C8S1`bWKCNkZqRWPV4>GuHKh z#h+F!_y<-BesMa%xFfb#IF0^R4-1@~ z=1heH2W<>}2h$y1dXxD*U%0}mlEhy-dQOMwE@Ny;U}IPZ88Lgx(1d$1H?kLHH$R8R zYT}|kr^)ex8D8CUU}Z(+ee(O>8OkN6U-K~>g`oL{r zec{l&ckdHmp_SQ8a{Z#A)6C*vY28$6{wR3(am;o&Z17pHU$}pPwoAr}V0C}RuUeSC zee>WbSkmLk_)6HV_m))=u-s@y&8MR4Cd^+B%WRK!CG|(%zr0`x%x-0GBK5yDGP*gK zg9O%`nSE^m{BL^@qwAP5A68~<9T0-HiA}pM!yfO_|4_4GpARc_Hz!SEvmnMvY z!%lVhU;pf`&vk*hL${Zb@rz|&x5{C#{AmEy|2C#9ln#N7{ccg?PgD0X{aCPK*p#Aw zknh=UGRpxDzIJqBH<))|c*+2h?-ym){0HOh+OW_4iEBnxeuEp6J1p%D>s#i^b+D;J zkw-65e&fJ3TDa=#@y8Z0cckCs`*5Dr!=wkyittwE!;+%rPTgVoJ^sU+u-o*U=1#Dn zp!VWLSj_0?-X3P#*`^Pxk(}7gnb{z5NB2?72wa zMam!9{O2Rd9~}EI7Uriux2YjMF()wwZt8k>%Uf8z{!j8In7zM=uOq(PJv$oa&_5TK z!W=Q*GLppGcKvxo%Ezr#3t{U~^DY;|LP>lCvCRdCb@yOy_(I#&uinZu4XHi_tFmmU>ua)`oi2yj zCVMxoLU}>lvC4EfZ07xMKMX+X2lx-p`t(i(a zpFF*H)@~B-xvz37;^rH<)$y?A%&ZU5pFUU`;OjA0ftGM2&QG9cyfm1pAv`` zz+9$+vb^g?X928zJH%FjxWuWG$`3YN>$;AZv+mVoUs#mZmvY|h$I4l-u)PK4ulnc@9YWdE`QUI;~8a=-jiTecBRc(*h#!Jdpyib8ARo? zWalh7FgiTM)Mjo<^Lz2|+g&)#oTMl)_B@_a4nK-KA$CX|FhXrQsW_ahcxj8O#^1E;YdFu3P4p z!=`dGr!O!!nQKx3$4NgVHp2DuU$+voRT)vlT9*G$VsVX?&wE(s?SFxkZ{2YH;9J;w zVw%NM*th?}r&X|~AZXM>*ix1#tALpn^QiVvJgUf5OX7J!5$6!6x5$1xgu^Q5)}4UW zJx2|G02|Chn~uRX(&>CP$^W$^g|vrpHgguF{U{urqCHpyYg4yZ9fJ9Higw{nV2C&2##Bb`z|7xjecUmY%LY7(vQ!p7f2F5$J5T3^wHr>0V0Wc@GW= z;M@tn5{U&Rx|8$ZtPwNfp1{WS?>s$Wo~onYQ~Je*wL_O$JEuN zeyA=Ee#?ZbBF=Yx4$JgT>n-5KvTGGDVR5UQwFykiJ@NfDOiRhQ{kaw6Tl$4OQvYQA z-kpB~GyLfZ)v$ig?&+m4w?5^04a{F*GyV>2II;Cd9n5$Y`!);a{GNEA0p>j0zvBoj zr!VR78CK^`u-yZzhyNW)-VcN0t%e=2XGq;PVpY^0c`WQ(Q=CrTFJ8kW_ej_{fwk}_ zOy4|wRsbA$#5CgTS?ke+26Ag5zO!Blj8`}nq%LD z!otrD4~LTSSyzVn!1c$E?jHej+s$_3!Zr~N@y@WkerCQioT1}Q8x5-_=d?25z))kC zaj=H(6!`lW#+$B-srFuBe(6L5tj;f+I{|TK#=Y`4aPDRM853daGQL~~Ylj{!Aj!~6_4Cc}oD_2Lp(+<#z)DX<`6-S0w}k;*Qe1}nQA&c6k7 z{2$-)f|*loQ!kV9^?_Yy!jh3aJ6?p@E9Jvy!!(io@YAsT=sp3l%w@0lF<5%$*Ka;7 z_;Yxf47Mq{cWMqCb|a!+3T%F0#`}4&Ml#524_q_lZ5Mx-ew8JVz-7A{%I3qogQADA zaFM2@W)aM>@0b?}i`W18U;mw|Glsw{vAhTA--G=Ji2`8#;cxVna9HQ7Z|A|j!Rn2x zVe5UHOlQHy((c2^_@Vynr9b0gW%9z_n_*S{nWiCd;H)XGTVdJe=l5-4c6d%D8L!9_ za(3ImdB0zJZzu88oKse?bkCVvJ7Iw;|6x~HakrOm0<37iegO>@=wC;Z@e+OV+|4a5 zs6VzzDf?ilkR$sBJH6U=Ckf^)WAuFkOXo>q$oNa2+cLWfj_|m3KLr-*=C3Y?t=lqc zQemTmQvVos>(Z8S3>F#V@(N*rcqldAUVA|5mzDHo2cCi;KVCKGkn`LlK zT+w4Pz8BgrW~acGRzCe-z+%5#*F&&Ac2iOnTt9U2gd|v-@P71bShwrRrvtDJQ|j^t z7Raoo5|=#RF#8>>PdS&J2=o5NzpaC%w;eX^gx$VY=sv)bBj?A&!ZCxIH#Cy`F%}^k zVCD6U>7PjaL$}wfVT0M5svMu=2V{d=?4oRap=~t$nI7x6Xv@vf7}Ch4a(6CgaygphA>Ec zey3D2zvWiH+|w0iewlfL%zt@X2T9FgPH2pA1kC(tHl;JHZ8z-JNLV6V(M*Sh_s)(c z^J}{GwVA}$3+^5p2Q%J|J=X#LcRsENI&jepHWjtMOy=wI#0MRSYyK=2Pl8pA!@8Kl zF-v@olKDONUh^Fjm`CqnLgxRB(bBr@l}s*1~^VKvq%8z4tu_R4>K!0sPzXy`{)tnq`bx`2}E2t?^|pE%uDIKh`4@D z%HAy4$nQ6bSmkeW{Un?=xXP=tl$bqw*Nx4v;hU0LFHv8dAXo|0dD#a75a+Uk zQv+e&K9fF^>j|FgYdZ&S+I}zA7gpN^7fysVFFhxc`@!9Ib@oWulJ-(T?w_jg{`w&> zFZuGw=`eqOsNMlKT^MaK4Q86&{%Q*cJERqRz=E5Tvj2g_&wrhs42uIY7umoi`HG8N zn0DJxWDV=tT_25wt;3hKb%C{e&rtP&8)P@k1U5Zv#&brz{^#?_tv}H}U)bnI>K8rw zuJQ-WUJ!QF5th!}yR-?8c)9wTEi8)P@$564JYLt4tf%Pu^K%+u&8=Vlec^g>tMntx zSouLk)>rs`XH)A*eq)zkbeP$P$9M~?57V5<`^b5B%2E$2n^M!r`>WnNGV(QCBZ+eO zi}pc1H~PUV*wnpM^$TX$cNkPf^1n@9^BLB68t_I3msnV>t0ws)-S!YOZFf-ZF(bXA zt^%&H*zqtAadH1`)s=9v@&dJc4_-b`Sfeyreu9VR`-HBzC_orYX+=rXig%(r*a-;AnOUztbSZ#>D{P#M`7#u z>ZWI~O=P>ABXIpIPRmo6HYzIhFdW7-UeLmV?*Z=nVMBGvnqoNXgKG93SQUHR`5w%7 zvm3Ju7W;ikDS!=rRym}94ikhez6mpeA9aa_b;b{zOC)~p_mfqy+F5_*JRHigku8TM zr8!||;AETZrlqhx%6!#nm^J9^&=5GRy2|zx+_)<#F_@H}eSNzews-$5CF2ifLcd+d z;k=*2evtJ6`D3SxDX>lJc!nR$*XQm`gqgH#Q!*YBre5kA3n%V8G2R>0rLvDC3hB-weTgZBW?CvW= zFwEHWy^;k>zxSF>oLdrjhm3a_6Z=w5>~=@Xgf+jtPAo#a#QwB%e^{I_g>qHDvHNUb z?m9_J5aQ}^J8C^ab=mCPLfCpnsIL{`)}6of7QlvAC$3w-9AVwT*|63l^ImtDeb~uj zD(trJ#XNIZmOmnG9ISufcz_OTii-9OfwS74r*8Z%hBxwN1&OrNu{ z#1uAkdpP6o4~$=}*HacmCvIwjjkUX|^$8)f-Q_yCjOP(e%CponC%%OLJ-%^ihmZGR zUPvQ{tZyhhue8gC8T6cIlam-}Mx+ z?0th1%v{}(nx7~S)h)A!S=`c}WW9yCVAS{CFlUWUP39|_oN1M$mdadH?vC@*`|Gl$QCw_tVLkrO2hqGYq_a)b<@qgLe zu}ruzW1}Df<;8sW%)Z1oymyiIK-y!Qwl^%8p7Dsx2WXSM@-1QSKkv@1hYi=}PVWS_ z_9(cp4OTsBF8R}h@sOBn*Z~J0svXw?3#Olqmk>Yw;rksHo3@Q5>!XSsNpS-#yb(&R zpYpxAZ{Nc%ZI9-G=#} zTfW^R@t+-dc_iQU{s1x_H#pv3cnz*k*!Q^xmY*Hl?JCSYVbhU}hr>GmzCx^eEu_}x zwR37Zk$h&_wE<*2%x-s=Dld;`C4Pl<1G6rXxb@jj?@0f}>!7FdTP1y8nUVf5XcTpP z?+pjd7;xB$_E*XAg)Z-@{!?XA#)qOSTVAV4`BSO8$oNHYf1X1TY}#p> zHyIDIe!Y&q3ricldXe!D=h*6^dvMIRRa?k-P}@E>2?=fEkYyEZ#$@%$OwXR$bOL|_@kn^wco9wq4 zt{?E0x}G?nh0R-F+TQO>63^=KyE+yYuxC=&&v}`8R|4|}-YYLaylTb=krd{x@JJ%{ zk8yW_%|VzmJdV1bo(=MrR9L#`1ob?G{qL?$hqZ~PsQP1RTD15$%u7CXI~V0cV_C*i zF#YMrftO%;(veUFtedoRZx*c0J~iw-tiCetR3@yyFP(7_<}xPUIsxm3xl{X@)(<14 zGFV;F-|q_Iyml^^rLfcF^QW)EoJ(hfi7@j{wABq*v%|M37M30h8hHz5?cQ@=1dAiO zjJORmbkVH!aD7hkgM1R7_I&CZIAXwHYJZjEY0`HY99((n84Gs(ZVNvD$fG2RU z&ot+8u(ZbmLpe-akv7E+7SO}rJSX`g@%SFF#^1fX7S<>)Tz>Z*{hM{grw_2?Nw=BL z;NTH^?|g*S%jp4H*pp!y(n#X_!c&XkjN{=QKEo;pzvuU0)z#tQO|Wv}CuJVYe;d2_ z2TYIb9DW_9*851jw)x>zxT+|ANDC~yM5|T8v?D9F5{o(K!p^|0i_TRTVeQ0#4k<9B z!vY0~GwQZZ+Xagw>wf-%^#jto#=?O-+Tk|XTI=*}Eh*n+3*#>wb}ym55RN{QwZH`X z;bC_U4_OYYw%q&F0Twk**Z9Hd6Kj@`{c^e6Y`zz)-j~o!tlF}D!c@3wu2|9;*0y`* zI0?3yO>1WkOL?^QBVgB4<=coQZS->k;nv1Yo4dj~ZKJ3kTy~STyKUs zgH~BQg%g)ta{UCe^0~8%V8*)M-O2N^e)rYC0Os0``b(ZSz0;UV6`a1I|0+F9yC3J5 z4HwOczC!AUV*d1`6R>jatPN$bq-V@B8O#oK(UJN^8=0>=0PA%Bj4g$O&$<3gAU?jW z=VO>YJ~Mv@$={YQBK4Ca8=1Nh)|)JRcNZ3{SQj4-yT&aXq$a+ma}vO`gBa3c(_r?9>Kh;7 zqUDZz55ZyghmU&;n_fHLGZ|LN)G2RZInz0FKg_qu>QM__#h)v$8blOwZW#=I)86|m*I z;(=uTs(jIBYyg})DyV2Y%(Td0@L~N6$xG5dY2I#J?gm$Ro=qnGS(wG*^3iZ`$)V;! zFmKSFeIwzpZwVo`#3f0JVX!q#K9$Tz#XEX08w|T@Rh!8Aj4e;?wUv()~)Xt8u; zH<&xZYANZDmEY>0|HdCP&pV%PY=&jaj(zz7yB$4iVSqJW0cPLeD$f-g$$A<8Mf;Q* zxMszu;d+=mI(lj~Dc=@Ft-rDT9cI6US-dlL&k>gwzVLqoyRrH|tAvdwzPBf4=G=Q( zPL4NaCjB*BQt>nVDXbrwZvG0^fAf!d3>y}HVN}7vHK8dQSQ0h6*Grf?BmC4|SoZWq zw7WI1Qp@XX$_XZL(dI;=@`N{35@efV0Yf~j`ZtrmK7UGJ}<93w82Ifd= z|D7K)sOBl`WTjqv8FAs89&;ZNpImb05;?x<>kh@RAaw2Si?Bi4KjJ-HDMV=!GSyL1iKMQ`V%!Q%AiR=F@u zvi=NN@8v(q)m?;zU01w10JE9L#%95;Yka8rv9=)pn*weX?|4Mbk0VDbGhok?rN7B~ zGylup+*7b>r&CulUlzW)vPuq%8+I?*0!wy1ADIRhJsikb3#$(-{&Ns^vwQtn2pfWQ z7Z1QS-O{M_bXDs)VIs^*>(CO4IAgeG@NQUJ?&G=`4og`QzYCUM_gWbQ>n2o=j)x6@ zLuQfrF{}HEq&S$tUA{m7i~8L-yaQHpn?L)&Y}Zk|?XYl>sTJ98;Mm2@-wHRL>P5{z zl$XtVZ-UMH>se%eLfd(Gtq7KTCTu738Ntk3PU~P}y*YO{ES>i~E*dtxIuqpt3%424r3Kh^Dzc^d`WgiT;~LB9BiaIz4luY7Zv z23z;hOkD#r3mpfM{V(J5Pv2IKbie* zAm#c+CF{t3pEjc1aS{*XE2;fJ<&)Rc@$wA2sr_S4PO6w(pYD3?FS4I0N<2(iZkn4- z_B+*WM=A4XO2*wK<}75bM}EzI=3%m5%IN8;CAJg|s=omX7+DsZ$?sY2rS^|?k*Cba z{myvgOzjsNr^QqE-`@4^_YCB#R!J!HPd#}+{BOKOIKKWUtTsJB#c5`iYmdOs51$u_tsG*ht?W)~P+iCv#<8M%YxcY4Pc!8@9ll z(rwygRq(&(7t^&}<#O2Yq+{nTr2NW= zOUqzQsLj(PhpLW_pqs}NA6pG}Nxt>L9&;y?`2XKOrPrKm?!+p;Zshmmnfc2nz_QpJ>iH#qa(5UD|NB1h9`#d> zhK0+Ei#H(O;K52735NxE#*pW4-{ZK43(TE`^SiN;v(dV0}+GOuFROayZw^YF-am-|2WS;;a~1y*Vto zKE3}^SnHF~+zAd|Rlg|&ZX7UUlo=_1Fn7daI9dD8&%c{-|Lv2cK`?F9wN*wqOvXDi zAJ(n!v#p6(xwnfS9NlLE=PS%`*cjmh*Q+npe}UyEwZgfuc{=-JEi74)kTe69YFf|h zVR3fHIaA@h*6ASk}R@vj?2JEIg@-#GB)~O@=eZEPnVL);v3U zV-j4mG*S8t7CY)Jh#M0GQ=Y)85#3gj|GdPy;LF7lSlT~g%|uwfvq*R!)*4q(7WHXY zQ3$K=SyB0N%S*5GiA^e~<3&ews8++gnfTu(P(Fj7I`Ix{~?sz0%p%SJ$od~>ma&*8kP^BFBk#0iY4*H z^?CMPDIe+Wc#Py1Z09&29#J~H^ATA0`{Gwym>)nZIRvX+PcO28^G*j^?uUiL>!0+3 z-R!C}6Nt+U4c+13Ro>foll;@$dEH>k&?(pBVfpqEL7iZGyYYq{a9HhRQF~aIc%W%J zESPke)sB>Z`DVm6ST+1~O6wPlPkp?4Z-#ZQ7FU{KamgHe5p3Awy!spLbUbrWG;DOw zulxkltA~7F12gAdnNkneI95&!gH?;{B45Lax^pHWu(V0CzY^9w{IqNl%sQOKD}#A# z!K?*vecAU5S~zZDzyblxIkU5KDa_gX^_m}vI~s31f&-`Ajh_XJ|6#`7hm~&LbEc7a zXNxcSu+x^0JEp)&>2WVLoYn8UbTVwc%-i=i?3=_I<_63B=dV_gc!7QBSUC8k$cmbF}VPK0yk=Lw9PaDNB8x5mMZN3QR0h6T~)m$t)- z8=9+)u((&zvrTX`{q33eaMwKBWdv-)iY$Ew%P+my9|8yFb`X}p zhC5q21j72?i=W&J2(zl+hGoL)&w{_B zVc*x%$kVXq+^}3%xOLvWuE$CF@$#+1V42tFXEIpMIniPd^B(V>kPIst*da`~ME|9C z60D!C>^T6YrzQLEg9YbuhxLJ7Q?L5&gvG~%3%kKuzZDlZ!G=#0(`azC|IbP5VcD*q z*}p&I{0e4HT}R?W=H&i@CHEEtuYrRv?;X?x3)=iMR>0~cqvt0$@c?_oa+sD>(DD&x zIzEyuf%!d8-mHc51h4MQhqdRfeR&R>e{0G1f$RI&bbAH|uYEFZ4jgQ2(@zUG9h=!O z8G^uhrMGVDFS;=A&R~w!@=Ln0>** zWH`(*d;aA(iBHhJ848OYRJW$XH6t#D4~C7)8#|@J_FaFRc7S<_+t(f<`GQ4ady-#Z z7?=z@{a%;Z50=lo;+_8R;7&dJ9qUZrzpX;KJfotL(bIsx4{Jy`| z!`5@BeC-SizpwhU4p#llscjE)XZBjR8djL_Cbxq%i_@P(z{wv19R6;^^EiCr^$OTj zxV~2ltms#o5dv2^jBfe?%brOtEQXbxh7WCm*?mL0ErQj43~d9fJoHuP4_nSUoK^$V z`}RCO2bRPcRj**yu#R25;o!F)!k@zO7u{Vw;Knsczl&hQ_-ofVa8q5#tvj&vRKEwV zu!g>>+cj9yZRz;oaAMpoKP60id0IXMj*!)LzW~$6JGZf5U8^|$G|cor(9Hq%-upZO zTMTMdS-pilEVj+*brRMT$E>k~g;OgmQepWFLz*?rd(l&L2$r^l3M}Ek8+%yEuwdJ_ zTnpGVqpiCX<|QZ}c7+XdqaN*n8LS!0_EL{p_8urm$yvSwkGGO-)g^e!}xm zmD+8GX*YUoHNr*XJLGJIIpeHl-(XKw;*?FW^i5&623Rq7)0qvhvEw@X_i*%4-hy>7 zzp&@Nw=jR+;Jhf9@$l8`D%hZBKUf1R4?Mb60Wj)0OrT;`-^Hdx*2%bpH?p z%dZsjuEFL9`utb`hxw#*&VgC>whTX5AblKj9ySgL%9>5gtT}ZO*6cU-@`Aa^sn?If zh7r5AO@;ZVqBhIm#A(iVCc$FK@B>LCpH}h54GtS~YSw-@bY+~^C|DQA>?48eJM9&? zz={)B*TuuF8#K3u!^(Txhs43WX=7!>;QGg^?K|Ma)Q$lxSo$KXS`6pDKX7#rEMNDt zZW}32?|96Ply@#k-vVnd2WR&u_LI%o467Nxtoy?3AX$(ImfrLkVg+mNwyueWJ#)Ml z_k`>HUuq-atb8@EJIu0q|7tayyKdvkj<9GcuXQ=hw?1lb3d%z~qnXR@Eb!lJg}(_!D=n}0ln4NE$>O@&(pUgtHidRw~86V3}PcD@e_l3UX~ zU=F_Gz{ja-iC}m;F5H+C%FcypQq}WOurEKoW8p z%76{uyR97r=MC6)}%CmWZ*3gzhe z-(c3P8>Ne2Mtt>R18kGjtt<#uPwsW&3tSe}5EKZjemySu1ltc=+_hTWF59CCwc?Y0HKf+Mb6$EWp{HsIl-#h$8Vm&CC%<-_OQWh#?ewZrg2OcJCg7D z`Sc^0@42_TEpew2e@fu`{Ft>l!vuwBhGoOSiGEiY04vU~z!d?<364 z>K>znn=Z{5RSz?ctP*F#zK8zqdj;38)g3qw8@EQTe+~;Ji$0%)8B0bkDI@WFRmT*t z{KpxKXR!SCBg)znN2Y6G#k>m2%(cVLJcjj6mOnF*A2Z%(Q86rh8S?uK?0x=|=H7p7 zh|Jwm1gpHmsd(edveEf4KYe#5$#3K@xOE4v_m%q)D<|AJeEUDn^I9X%gSGsfRK6-C z*X%k>Hx&Lojr>5}@nu(GNyZ(@inGpzx&JBe8u6+)2j<9DQ@>x|bIQl_Ft1;98o7Rb za_npciHirPoP#}MXPBOW^_}S-iN$O8t~d!ZX56eL_b+Cy>)E5QQM~AF7K!h(>3jrc z4(|Nw0*Uv%DACN-IyR4_4M|5R%yChhg+n;(~5gk`Q+5>A{vWhPu z-+R^C7ZO;~8cIE1)lkzhyZ&Q^+M+A*zt5jv>yf?#78KV53hMaAx=+SXIN0QBcrH2Lcg500uzKLLPS@bjnMzgwi8DVduETK$ z1`XlE|JHAzS=B@@_}}*-)N6j*RG4=^W=kRRjRRQi-C(xBdeK8zbC%^h5?0&kTgdyI zK7Ps=Hq7iMZhj1xb>jy)!t%G5N0z}H_579gu=L}A%5u2MbxMgXtO-51sS>u{l<3|M zraOA|dI=Yu=-9~;*1FP_q&;ekiSOJQW(FVlM%uHy;H{H7!eP^_CfC4xm-gl~;-(&> z>R{j2i?+Yl|x%Yh${BQph_^|N$O;}*!MD<_h%kOr+40HYID|(Us zO4Bj-ENnQk;%^^VI9l;N17_4nj*p8^^Fl)}lRl7-flQrQi*nWn0vKZ$6DyEVCy=d`|XPZg+-DOn&AASCN@AYuKa>cum zh|59^$HQS(ZIEUxtf^cR9s(P8+lNem-I~Hr1jFjQ3pws^Ov-oHKv?dF1uOs7e)1=tANtaXcvmkc zG9HuneAMCqOUttQE{45r-&NVc){nBfhQbjQD~0`GaVwu1?=>j{Ut7cUX8xCO#05*0 zgDha)wN43Sd?-tCV9{Ykdav#q$noyw#dU!H9d8=ir@S(Q!xB0^CgaaCN!VUfnA?tZ zaU&cm)X$*7no)H;GJcKlcyhjN9onbwvl_R;=C--!zhR;2!~J4dzAPo98D?w<*-FOC zT8rG|FEF>H(<(B)Ze@L&@E+D~O*~1)-x<&E{?!vFZ##4lmV_S8e+e6U2UQ({iw+j- zDu!%sVU@gm|r;>PH z^=%n!e6Kgjg|m9^zn=ojJ$AJb^Ak6*l41Us(JfbC`wKfZ?tv8rGh`}Q-cgdY6AqT- zkH0}&YT+0QYdeRJA@iZIzHeV{g&9o8H^j_ex0c1gqUDT{c`#db`RGPiGIi0A+pu7w z+4FTU%VX06H7tHxn;Q<(rf!_|0Os(sFD!wzPg{0q;j99q(|i&yKeDuvxX||fJeYgf zm-Z4?$M*imhs49@et!kG)?J%D2WHKPZpB(oZLT48Sh}_jOiQ6dQgP(G({E6?4Hql2@Yd?uB#;R%0B(` zV8ter?xisI);f!)aG9IMw_;fMtKtY*|4J-zJ+FaT3i^xJu!;gba$N7sJ0fOA*XS!Tl0t^Kc&^~79tgKY*Z>V8A! z2-AeWO;5n8$hrzASmfpweiY`qWk<5%!~^3_q`@>%M(9X5{iJJcGF(sRP8|o!6DNH; zK+4w_N!;OxW0A4@VBX*^Ydv7bu)+tsNPfT^53+t5T*uiI53?s%9Gnf){#>W+gf$U^ z6Xw8q?LxR>n6<&A$RCyls64m9dj0V30dR7ud+`=n8vDF$AzT$R%xN?6*TRZraE6}K zT?BJ!9i7PfFEeGKCYr=6#(voabMJT#j)HYnKVtU5{4eXzN5Y1=v!bQ2@AbWbLYOt{ z?ozV;9L#yYfLOWDV>z*kfA9S&m|w*>L9JJJ_xFf^!{&y`$a;31*NX??u=?b!{YPQ( zm0#PJ!`eXZ(-W}unsN0Zu)aPa?+h$$-{I{7SgCxNdJz`9Sg-Mi1@#&FOC-PVn`|Fg zce-|HF5INOKW!E)?cQ?(S-~XduJojlu-)^!fm^pV17YuzhXEdHNejv7IrqfN%kXx z-x?1MfJMC%sr?Gt&MEXhu;x&o0J5JE8rkiZ70j6Y#^ecXzCfH|0jrMfY$NtIiE-)( zYrinK6)-((SbXbR^k;=DD_+2YMaqTWV1D%Wb+6zi_geM`nAU4p=v&x2tmMFZn6Yr} zf@+x2Wsk517GJ9VQ$xzDHa>X^8%}Ge{TV~#_0g|kh0m`YpAe7sifwoa>pdFQ8{in> zO!;$I#dD+fd+7gkxLO7W_Z~p)2l-A)AE1Rf{v&*i$mg85w7mzjo9FDLVLyp}?uhXw ztavTDN{2appX^eR_*w28bGT|=|H53DxmQ1t0n@gb+2_Ezy;gfH;NYLaE*D|$vkMV@ zVDpFfrk;ZZaYbFoepp5~v4a9uw41x&ADBOHW(^FtU?6%!xe~;H3$x4RVBTLr0!#1-FwfkZD z&p`$rtctn%B!QG?jafGbHtg(G9S^H%)$Igu*gp5OV&aV!jv=sXSWB@8W^I>!Cf{$A zO*MNT1&iK!FCqKup(kGmg)pNzpeYt+S6*>hN%9N*7fIo)*Hz4*m-~R;Si5$I(pR!1a#8GXbzHd~(?b z*x28+{d}0`8DMOJtIq7%?GH=#O@7h_%kmxb=E1>>?zeQr_gp$g)?{Cp?bxxhJ1k=K z_{Rs!TeW^nS40xOy9Rmxb>sSo|&*wU*b!?AFHt|o#+ME8-_j^12g(>v^zjuggmwoc^He>1o6T3(e=il64$M$`AOp^Zyg4K z&S>&|9J6Zewy7wuN=x_L3P-fvxiAH0T-4jf!|HEMX2fFK-R<|n=56*8PuO}!g5(ga z2np7Ez_hFLuN;H*zMB$><)s143$VR)AQcyOs^ndRIWw|rCL_)roR?Y*H~seMJqecX z7IxLaZfiEDa$&=JZ{L@2iDtw}cXGTVvch_}M(|1O1{*sMYWV`oX1~8S7S@|yfAIr0 z&y=T(fu;T4S+~Ni`>R}>VVPa|S5tiNC{M~Y84R;eKML##2bh~mLg`(Cl_V57KiS8teqwBNqJQFwlN-D$Jo#P)gWHkgsI>L(u#y}tSQ zZ#Yc0F~x`2T%2Wu6*ZUN&V%i}>yw&bX7o?=i1r#-cI?4pF>IOX z(5Dcldp4TK!|aO*v^#L{qzTmbua<3V7w5r}xo^JhK|HwDHv9%`Tq*sW2p5^3yQhLV z6Q-Xc-^(^WD$wV^I?;XV_)SxiPF{ey&ilHoMO-oA%E3%n8D?D_1}FN^9-o2*w~Ngd z!=|?3jp=YO=kr){Kk#RfT~lDKZcPs_IPhJ1i4@kS>D(s4mKotscfzs{k;BRJ)~@mm z7Zc}BZDYZlr=Rp9ShRYhmeilB#Y&oFgtSq_5Cep;E{qQu(Yn% z3{pS6i;s3#2+J$4K58NLcjj230G3UralgY&kJpv^!kQ(U+djh*??=yl;9&hz6Jq*= zOXKE{^1S4$4KV+b$@-aa{q;1;fp;!#^@799j#G}g@O%sp*4I7lM)FOCJGoO~W5Xxv z`(NKJf7>R((iKzgd_X+!^{lb(u=a%)WhaYeHsfL0(_YJ|?}aC}pFI|4IL3EwL_Vi( z!{0Hm;ht@WPq6gf^-xz>=YOk?T#uYF_wfjrc~+KchU>54-B1iCKH%fp38ob+c4se9EvuZw*_qFxrftyTNoU^6S4h|L8w(koo^k#cTz&6%1?Rvt(SHe{C ze(|iPF6#FmH_S%T8FgwmU zur(6xpJ3T2DcqFAUC{zdMlIc#N#fs<`WRuFv1g_d_C4(I@CQs!Z+m(jE^*xYxEW^b zo?li1Yby&|n_yMR{eXIy5&T|3tX*ehw7{H5Ne0Oe^J4GKxZKsTiU~6q1!_5f-IU zScD-AVF*JqL_;(rL)a_P^n>N+5H0#K3X{rEgi4VN{TM~x$8|nmx7+*A`|WXE=bW9L zb9Q#N>w0d^A=@KPE*H%vd6@4gvi;g!9s7jC>O6V-ADG{L#h^&o-}Xaq^7^@-QWhSE zRXuCVJHr;Hy!z9y;_d4G-C&k|$g~X7o~3!$1D4#}_EQaO{W^E(3Dd29*^==Mj7^6p z^?^kfFAu1I%awcF>9Bg;yyJDmbpt|eU{=2e{l3FSYGWmF<>y&Hf5Vl!l|hZyaLC-8+zYl?F<`qB^4RR0 zVzPbQhQ4c^VVV)YvjwdF(0<(oRxWV4Lyiw!=b&?Lu-#wf5wbt%GwM3~!rb+))cbGb z5&47*I~2YwChwQ)@jk~Fz=@er{lCKW+!ra~u>5AF0XKmJrWa^!O~SEK=St zhQ+D}{PU#!-r0Yjz^aH((U)P?$Nh&M!;GET)OelnElcPcShur@8m|>Oh8LIz3#+?a zx{da32k!E7VDbJF1~MMYmy>zoJ}k+dLygDs>KHpP3)Y*m8_D=AcIUDMDp)3{`9{WP zCI2WWyb7yjY9})OO?YGZ#5CCOLD8`Yu3BK6ei61<_p@FLt7r@FCBeE|>s(4v#&rJRFY%yk>{2|0Ct1j{>2FR6p+gU5*`z(SKmYW$+MaBGDp%uQ-~@D;i1 zz@^j2z_h6!Zs=j%sT}z*nBlV7&!$3ufN=)(o>f1~w0X*|z@P2Ds9CgoPcf z&|UjL#!tqIrW#qnwRI!*wZies&T`C&9U6wW!K~%Z<=tS(m7-n5^6KE9twK1W`)krZ zbhCU}6D-RLdj1FY+I=~;9@e<$N3_H7Wt&BxVAuGhyB#r}Gjv><<$Kt`63j7yBj%b8 zsen}x3#jp)4$Bx_U&9u8SJ|e><$WhVD1&(+qxYD@ZVukRUcln=o{xLM^o`7U&*56r zn<^_#Je-qZ;IqNYIR^I;8;Rf;3v3`@_#3YZimth)i zE6MoUu&-T$(qY%czk)zGYwp95sj$rL)Lb$?SNk_HD~06ezx$B!yTmqE zurU0%Zz3#SSAS_6TpRdfwhXRhIP(v}ZOQuB6j`7c`aLH9-8ME{b89&X*>)nxTA1!j$jPbB*YWXE%`Xi6@8F10P z-c6@q*Rg4L7Q%XyVGGFXwWye~b0_I9?YHebtVqZ@6bIYIPf;mI-ttMKfc>{9=ac=X z{=WarRakc{Xd2nyl>yW4-G{@J&8(|1vt3#E6qa^A<4E2wRm8wgWIXwG$@aD6{T0tP ziPggq2aCtuh1r+iQ{&T(O)8D=lYCZA*B%(Z9^Sj-HZ`o<{pIQaII(%P{R3FKwE4;y z*l>61X<`feV^))4qsJ}hNqhb8vxm8`JmodDJXVKLySZ?A@!dGGK27RLlTbL_XE^%~ zTsdEPfsEG|CbQ>Vhed*+Ijdpchb`3WQMaXb62k1Hv!V>-LcXtVD=hYPG`j>V)~)Hf z8;(dHGU+18Rdv(%!LDf z4k4F4J7|#t`>t7g_#mugSO<~$8xp6-coM5U2mi=`EBAb}JOJzC3M!vw;qK59Zwb? z&V}qG9_8IN3psPWOa5}wzM*0RIX;zv$C&eASL?hN*Wn^*c0&*>9%Mxw&*B+`(X}=nlJ}#&Fil$n zbG>gnJc9WXngT0e7TcSe-=?JI=xrU$NU*hehg>?|I-mrWZT|DO3YHCBd;AIP+B&tI z%=c3{JZ(ZQX>XdsYk}QRz2`d2TKu8|nQzE>za%Mxs0Bf9P zyPkmc>@N*tNc*=6krd`%YU||#n`ybKgQR`h*~Y1G#Kj@o_QBeB$6g1(viB$6M#Jj3 z(#k;C!Xmxw?bIdM?8>gu&M<9cHTMq8 zd0TpQAS}o^HKzzJ|MaY`Kdiq0h5Z~(5eJ(u+f=*whb`HWfV1^TJE8yx@uT6=rA>ZTVlfP(n^?dsmFIR-N8D9(=27YlQ^UI|lcoOi8lZS`F*=T`+$P%j!Pu zT?c0+>Zj$y7B70-BJV)V4wCuqG)*aM@4?c{gM%b+(dFJ8Vyb>QENNa8 zodwfcxvB$j&Se4qL;S{uM&42mPzIhR5j#Rr^C9q zK~8CK?BcWo$*?kWUcCx-H6PRSJj|-LiOGT!2mBm;8dgs*_8{{c);hRW9Dxlz9#?#T zMI34C0l3nfOU<9C%-?iuFRa*NY}1CEvDZYu2i7ocRexc&CWy5IW`}z`GsAq07VBdR zw!%gohba2MoSQE^Hp23eeN7x;<6_z1wWNP@>XXs1u(Oi3n&i@}v%Fw+Sm(YgV1{Gq z=BaSyg^`tfSXBL>gh%?D4V}+}`CrEg$b6NG2F1$huNy-7Gpf5QEIsw-a5-!|$n?Tc zlFQE4Ho%G@A2$w!m2LO7&@jJf6@AF7ez2kc;^%!~b+@PVzA*Di`siV>>*JUaePG6z z%s~@j<=uFWIq6?tlFft7%8t)AgZ0wuQ7hmupKV`FV9wgteWPGYqrbP>!}0zN_%lKd zN5t)&-U?SbrFXvzm+MRRwZODRj+eEtZqln`jj-f^5ueQ8TD~)<@jI;k{AO4KY23T4rOK*W$4c`iCVVd}^wqqN%uZMQgN0|54zPJxupT6uWP1P!V=B484Ot1n)9RrX1_ZhI~We$;}={Ga~7^}bb_V*X_`V=*YfJi#c;%*iiC%-^mN+eFxYLvy0KZX#O8cP6dZXqdxHwDJ+@4J2rmD&`sy`U zn=t-s0?D^;%vQpz{n{Kke7#q~-c*>|;-gilS`!ZCpe(R$37f9ar!Ceh& zN*!07fi(rLu`ghG*2?k(ShoJep&FR(e$kCs(D$5k9c&gk=*JP5_jrSU6P*6+#?b>X zyY_9d1?C@S&8r^~4Kvoq7Y>H&Js#cO1y`EvN*)0-=J{^fLHZXz{pABED~?{>3R{@G z?GyxC%5&U^h0IoJzGS_WeRm5?-xKJ(0J)&{_sLDLYce-k0J|L-?X-d9EJyyxf6BNru~ys!v1 zOn<)j5-eG&IvzqCmFla4S^a-F%zu*z4mGf9SGglG`y9=#77o4Vv~&XODs`Fn6;5ux)9gX= ztcFiNVd3qNHKSmzDN{w}%g*g^tat>hzdm7_G3L|u-@kLTGiZhjunL>N_SMKcgkJH4+fM9WV}Kgo#{ zt7Qv*z^*seznDz=Z{1k?4VHE;csv!>P6^!j71qY8?@fdGKi)q30#~j!zd`0Nmw$`t z_!(wpg{9AcnT|hy)WV{dN10r>)~V+rV&VFP7UKHe{lcqZ#SqQJAXsC2`|L+p))+A% z7!KaFCgnXWelWmuHY{xD@%kOCy>xN^960`95RI7q*J?NqZY(G}Tmj4H|J*nq4)1?t z%^R4pswQMHY!M&%R0kVH-j|2LUU9=-mcf;?UV5*9jebn{u7&xF9}ivw7fl$!do3!Lr_Ne<3WXQc(3PjQwu@$cJ4cdsu8hdy#W*lyVVunfeo!)nzz8=nzvn5Fh5H9oz!2COd|=R@jm6s2d`rNT7HtV&X!BjWkjw2Lr(USA2BuimIDW88UI zSUti~3@e7ue0&bpX`fQ{AaZ{-cRdYrv!>?nMjpN4kChB&9cawi16zE2Qg|E|pUYes z4NJxsSI5D8&QWCyte!egDS_4Eq={ty{MfGAHPJ9rIb|fN7r{*bIb;{C;nJyk5$um2 zbeo9hn$A3fTo`BcXdSHUdH5KapTFeklLM<@#_GTkq&`CA@O(oUEXcKZMCvgJJugTX z!L+u|=IO9k&isq>Nc%S)T`m*9$vHF!=1Eu2Qo_M*qi_4eI>~CP9)h&X1WZ+c*JmlN zx`DjNwo|qbOnckY?G{Yieg3;QEOS4zM+MhwV`q(r`74LKxDB(!iDNur-Ms{=oE7M9*G z%DIR3jo(w1mZblxqbtesG(8V;%t?Qf-nYr}uKzkS%nX*CH0rK~E%V)9b$|szoAd{8 zrA6n%?W?doW~W*o!feaW4Q;Uafa7#h-+@2=dEjqYc6ee-E-YW&)~6Asox7Yu+Uviz zg*3ndk3?Dy>?+;+wGOV_VneOpKXlmeYM3$0nlkS`t*smu7qFI*?JMz&i_?+*5ACVk z*fTTs8BCkeW=6JG^P|_d2e2q9oT_KzwWQbRd$3#-XiT+bqexzg9CI0<%bzDR9f@Q@RZC*gnlLlhgAB!vx~Z&L4v%*Vz(7N-4Y)R*jE-#6On zF|e#3uU{5iyJ~vC4p^)(a=ZfzcRAQ?f(3_v#E|2G`^m{^18ihAE%qkdX8tsN1jmP0o)vO>}n11_e$x+xWBKCtd?E0niD{?+cw#R<6gvCMfF;Y0|<+tuVVD;pW zr($95`u5cwVc~2WBT^qGK2XSSU5Wj%X5q5Ea5Q7Z2?PA^`oOa0KW!qpddUWI{YcjD zEUbs+TWVaQV3FTnNiD2D9Z6lkT%898ejtwYolol7a7rAHmBZ4TLd*5A!G7?|GFaU+ zjjD&k|M~n~3C!X$hlV2;zyBqB4hyEgr0VUoc{zq_U`fFP>iS+@QL`c)rukLBC!ZhD zZ2>P6VYTy^#pLrUIrHTz8O(6)`@jzl&Y@Xvhc&~Q>&L)thvG-Ah6SUXemTJoul068 zuw>4`nR$+GBiSc6<9{a)02J{q*^0xN_;WE=F*~Ca1DduzW`I zqITSGxJvVABVn!W8meALbasyS2w2gr(DD!Rthn97oM6LB#*kmIe$f1zLrAWCF`@+) zh2H$zA7<@xVUznAaZlN6OOkgDzgiE|965S(Sao1mb{%YFZ(80Brav02A@x8icU#nT zfth=MM^?j$`=skkU_*J}t}2q3jla?XmS&_Kt%PHRO}T9Xy#73W3%MV&$ev{M3)b#$ zHGU0;zw+(g46_y~y6a#w)7frcVd=+5FP_74m$#-LiK}g=6vA0!!_(ius!IWB1#oC? z;)YUKeQcg87p|T0qQ^6sp0{Ov4$QhT^Hl-NNgW-N1v5VNJDf+_*DA-7`Xsz%#zQdq zFy>$Vns^Hip0qptK575S_rIHP{TTk(ELim6=cVg#Xhq4VJ1~tu{qi-KE7J|T0rPrY zC`yMT2Sy}iz_q_8Pr3j{pR`R&gLPNir<42JdPUsP6qvz2bmJ`SW?%W_Jgn)mW6(*s zWc1?TBv_U0?tB7P1o_k_!j=4n8F4U6^!VXXxVHI3eH5JK>Y+MFJiTG{Cb+!ji*Yo} z8W%#{pF6Lt=S0EMwVG!Ok^7#z>a!VE_Y1i`6V|XhA6N$)wpSmV3UdPf^IZb#g1&o> zhNBbarv($QdO5y7Ty<(?-VD@3|P?1qx(vjbL0AtOE67P??S#WJB%tzy9o0)$6XA8S+8~9Sjj{9z=2d*1mNESfZEyaOC*o>Lu1`tST}Y6r7M?mKk= z=Gm3KHit{LUD!dal5%ClH1jB$1QskSm~9Gkh8!BWo0#){e>>bui6mM)4b%qtrRAfjPVd^`$W5hv&GJ zuvo8HT?Cg;P3R+l)%IzkM{rSlV87+W$G3at!j-><-{ixZ;@IH(u<@Wi)(c^VaVquv zB>s8BWgaY8bgwU|UsHky{d3_;pYI!P!Sc!iVGyh+h4%k=I7uc7Y9BBD?N}HD%{kj({zO zRa4KmjK{MRongU%NzU7lM_g)39!m1NzkN2rCG*-d9bx9sMf|le+co{pVA#k)WV0IP z^_u*35G;%wK5!*mbUr_|Kg_!^NV*gbJvc#N3u`{lZ(0OL_mZBshIJQRQy0LpA6@YJ}SwJ%-4nA``!<6*z-JP;s zZOoFcq*`?n>-4T1INI0f@CUed`Zda8ZIZh3fAYlAn!yz?KkwV(sc5e`(L#F-a{^_n zCd0v#VwUM(-mWieec|g1WdZ8;qPj+V#P0;y#f}88EL@HTwq4pE-9Fo#bsFdt8OpBOh>k!lHw#lQLjg zTv{P{{uTwfrcU_jw76*tv9g^ftJ*xYL!(uoo@WXEQAF6TD6)o_~49 zMwnhElaqQ=k$>Djtc8vAbF}37I^Ul$DIC@>HLE!cS7sgf3gNv+kCjR5LWKx#|dC*;0?D>SU+_1LOyAK z7Qc?ein^Q35IA-he^)R}ORQ^{McR*gCg8%t_K@@$u;`wA=ycd<-q^K%uyIrI?WwT3 z`=5d2_XJ57kN7DtJH})C6j>D45aMdW(fTGCS+1E37lyA{Yx-R{O^|!-`wlt>pI(fx-|s z6qcv@4IKp=Pq*`Pg!v22-?@-w95Z6lMM!^32o-P~+Yai!+8czJW)xi#CyzEo& z3_D2gcIpe)wpmkF=I6e$ffWP$QZ~Lf@Jk<Z0Tc1)4}OOgoBC1h70XH&|F3BY8V4$OjlAqy2OE9yzCf+tD)C4Su~PhpiS4QU>vyUO)^#cCKwOj>VMnaz zbkvY~WWg8jPx}bVre{`=dSuR@T9Q7%k|ePs`F$%o&mxdmDfSB^zkh|VANTw{tZ~jx z{0=v+3fn;}HBz3dfV1@9U%i9HVJ6h?ZIUbYp~SW8p6|YgTwF2x`dirOeN1msKaB2s z#Eh7pzly3KW|6ky05L6L<+o(yQqI)3#KNBad!2y|?@jlS<>_KesQO)&#|922+sC+; zWkG&VtUOR(L@a&C%pt!&YH9D9i4_Ht)(|V#+@)SGfAqtl(J-_B8uL%cxn1X&lixFG zO`%QoFzfx|`%7TS*7m*)upvZHxCmz4i(`L-nY5JgA#ko$y-_2q5-)hefy1kxP9^WR z&}TG#9Le2U_qLJtQMR^JeXEdlKa6nv@TPnZvqElkf|=U^HjJ|wM(S5_qB44Sf@x_x z{JO%|GiG)q$D=4q_HU19bF<`Pm5%HO|ZK{r@fzr3UlHas5cMUHQg+0lVI z*y7lN7;{*WHLyz|teo@Os3+-fJMl>_EYW605wk|ejLLzt65BWRhSh%j4!2=p;Tjuq zKC$ROb(i5}@wqT-m=o12{Q{g`QMs3#f0F*^H=l)ddk^j+=cjgl)MQdWDtb=bG<(>h zY87WUY)EbY+7ISk?pm~k^q}b?D)7)8S5ae&|5{pP^dRFrU0$k<@V) zsRzaC)4Puqtnq!(vl0L2sBipsj_g0aSHQGSFwNgQgj|0F-$zjOm;Sw9jIPro$p4Xq zpVwU`*B7G=h3WU;cuB=^a(xkvweiS=izcv6k>i8+?bpf6aDCJ`>UvW-^qhS%tUhb& z{R_ue<>0?J<6$0eesUA6e&>4NAZ&DVXvj}k8d&&~{GZMtXkE+?n4Q>b;||!O{bW~S zz0tY3`Av{|LBmhW5g!-V?{cj04|4lH=Rp*)()GT(UD~aSxbzIihJW?0faf zL1Im1235}|yr%6ob-kZ>Fu5mk)kK<YCIsRxkn@Fh+Gyq(;r5BH_fay~Pog&K@ zhwWPz3UeRa%qH!1q3%Ct!m*{Zs>%7I?Hj#;1KW*w>qb7GjUGLy@`3qh?`|IqE1oVCz0swx+D+wA=|iTBgSAl&yZ6BU>Ql|6y*fO< zXamf9I*q#jG4Q&sB>#U7PS@;pNACLb;ym(yYS$%_FgKWUuW79#EO_TTiQFI2Y^;=Y znD*|HJ=uPqR5_$4Tt1dby?(lB*Na_X`QRICnaIV5lQX~L|K_nWpTZ%q!Rq}WQvXNw zOh@f+#=DzqUX%Z)&&@kd-XGVo}%F)X*g9})#?x3>6@>lHKR^q7rsc}&VQBiLwePAeHt^6&mgDe!x- z1g@WahPpn8ht>MchojreYsmFWy^|Id2uF0?Fp0XJoF3)t2Z#E28_4yHwxoW!4{T<2 z^9H$J;`(X>N5iVkz&LBfnA2FzVmR#%N&^f&vH1*{GWOdP=AGT^eHoU#i%KE&PG~M+(WhZ?mEu(_tl2y2{6Uy@@Wiw2urb>xaStq) z8Mm${?cIJ@ZHEmeqn`?3g>vGe2$;@U6u$&EvU6-%3ri;IW-f$7(_c%ignWVl7yJtis0~W@c zjF|wt)l{Td!F>JZYA=|(wK~TFX4lX67!M~4!(+R`Dt1vhY2WtRadj71%u0y%hE=Pk zIT*u=O178{H-6dL@;eOsry%bcSzpzz%zZ7eKGuX_qC=@(eZ>p5{8maokTbNB?aQZK1S!+Oml(~mGcto-0m zSYGzF;Two92? zlLCuBl(}|>rB-u?Cc%cw)(IV8dW_?{bFltD=@cWlHtYAwGcez;bIV_hAFSek=pch# z8yC`AVc+4~uN;E8V-7xTgpJpHOxO?e=FQ#n1E&2k+!Mp}AxqBG!`F|;^xX+7--`T* zB}UaI4x4{a*?_Zx`(X`EytuXDa?M33E_zwJaFe}jSZyj9Uysu^r zEX!S$O6>o8_0-icXG#S%{*jfEy?X^LuXA7Y3Ath3-m%MIL#%XZ4e9TE!Eq5R40*2k z0LNE|>Ox@FyM=?_!@{R6>v*sxFT<%4uF4GF6%32a7LR%hbCxR}1;Dh5tVyq7J9Qv? z3e5elfcp~GpP#yA66xR9_)-~cnDer70<7-7Bcv3*9^Y@uIGEY5u4@S#IXSao3{3CU z^%AKcR5dTjek3eam92VC`g`_$>H-TaoY!e#-$lx?!(h?Gog1IQJd*f3$EC}v}ru5eXwodq&_fn z|ENgPziQmFA-!PX5g*EJg#$ZV5EmIaJVhSw7-~#h8yifys+0b9clh7-G|tE@@BBZm z9lz^a%TjDlMH02W>2HEEf580G@B*^^TH|rO^f24g&z8L2WcG_rm9TJYn-$p~`ZHaQ zmBZS(C;$(OuH90+ge!9$sr^&_U-7f2Fs&ts>QB4%DO&?G!j@CZ51(1e$cO)}FRZxd zz6Y=*Es$zo8*wN`1=n77qh4?1!u%?LT_m7}LwJ((Qq+kZd1Aa4Hp6nH-?@ zr)&SVP8VU0!yIaVmJBQ@J_G-Iy{;jLJf!fy*B4tVO% zEr^2oviTY0_@SMY_1_AME4TUU$o`P&S8RY8S*GL4;fRgzOT%ILb<+{=V6))^#;t@6 zYk1W0&ETIgguzmm>vkWJH<~gYE`V7p4pZlc+09A&=fe7=J#)zURkBHz8w7Jgwbc3Q zcI3f<0GQ_Tl-)r3%Wia@46ARAe%u6yjsMCpl!t@hf7eU5BQZ<{%-nw3-5NPPrFf|wtg&6Q zqc60U*fA7qY5wP+4=d_k3INlO?DP!T#^VZw-Fz?2#@#OQ)*QHnUCs>^ww`C$MeLLV) z70e3Een37yeS0|%EhnzpMAa|TwS65}0`o1o2WBD{l@Dxw3=6kE&E~<*Wy=J)u;$uZ z>hquLweaIzSpQLVFbsLPPZCE3)BfgD_0lAz#kN;qnbMEH7P)fEwU+a+TJzRT1V`o- zemVnd?=kxCfJ387JIG*7w<9-p!Phf=haZN;PjA%hhkc9ZSsa9AzTMv*gwr1++};JN zBR*Llg(Z`Qt=IzBCg;o~_dmWb0vHjnQM85MDOfwHU+H?-;%vX#M7Vy>fDdb7RjFZw z9Io9ax`DUduEN3rZIQF#%COZp?!sn$*G5f;4cC(Q=EJ#? zv0Em=+JN+K&tS#UhuyqkP3ap>30&*=)Y=QydCiM@13O>yY#$2?x(u{=2e&C@_9IFA z8_M6+utYhV?F2I(HNN}`%N~fvGf4jZsc{ROJo!qI9jw0+PK_TI>1l82u>4@z{&tdI zo4BnP%;BwA*cJD$Mo%Zjn8T_V(}3=<{-y3?cUZZ^TV@Ui@Bg6h25YPB-}QpkJxU#E zFw=keBWu{9E&q5&m|HfsZV;Tj$fEV{Vyy4NmKRJ|Jfb_T4K`H7EE@sqrhGm63uZ0b z#TyBy_kLvG0_zqhwvhYp>#XC&jWGRk*+h3ZBJ=dS@35L7r0Nm!9hhEvSet&w)*E^J z@R)#ln5q72m;%?InCSc&wpjVjdm7v}o3->4%#RoBo&gItj6PKb8{9k}&4d%zd`S8L z(`&ebV3_yEIP@JX?$b1f2gjO(t$9QGpX^#k#<#b1FsXV0*T&sixdN7&8reRF^*y#5 zuZHE1nr1$QdEs-XuZ4N78LmaJD=l-P5H1-UA}oMqa{rMLaPE|DTk~MKTTqu0dqnRQseE}eO9ET!1NdNti#Al2Bg{i2lIEtem@R#Z*J~&64twzJD!2- z<99nBgSmqKx07IjZ$;i7xc0NPLndsovd3&OEE)J?)pgiwSbx?I*uwS)Re!aX*v*B?qm685cfN^*Tcj1{@Q>8di@@ zq`s$MvBYHwOjCCekouwFWj${4VPRUz|J5&@JKc5xEDjt?)gKKXzVFr?(tb~Kz*Ds6 zMy{g=!`d4=&pd1VAx33 z>{<(Rzv%63U?n5;&NrBO>fXQ}q`%EkYW%v~a*$g$n8CU`sRem#%oQmO=J+k3#;-SS z`Qp*e$NoQl;=pg@iHjEuXoXdx4kO6;^r9EnMzz2iyCXZuc=VEA3i)?fe{Nl_5uO*Y zJkQzI!4`vNm>R?B)%)<@92{R4ooA8f3ECcO2fT&lOXHhpFk|*U<2SIb!sKK((!YAr zM;$RepBkSotDE_!7*-8E8`A^1!RJs#J}mxodK-COA>?05cnIsQ0ydNJ>K3A)viqdH zUs+yn*z3w??>n&KuE)wgaD>Wl=1o|%w~!joE`BuWxDwW-)Xud*UNYVHRVvIH{ioR$ zPA>HwnnZH){~p*s`)1Fxus*uFd;l!HFU~&=vpa2c9t6{O&z2;>(h+Uc^A^4?VZs4e zP{|1)^_=Oq6L&|$vUzvghQsWmk9zKcji$^ujDWQhn`}41hNFdt$@qC*D1FFE(*Mth z!7R9_(0bKUn7$%Z=n0DtoOTO^`Fj^G9S_S*Vv^>-!mteL`H$P)F8o<=<$yka*~qg_ zSB{tlGu8%D_b0qH4DaaMy_hoy*Qr*>eQ(<1g5ce^##q+Dh(_q7zdmo3w zg0ZbxLuX2T`N_k>x)dh46h=D-^3y4@BqJ7LnP zxv(l?cfBcWSky+1&kyz5FwX>*@A`P0jMsPVI-|N1%n_JQBo3XU^XUlxTc0{oQ2J*P z-mki;DP;X>71zBL7CoFynU_9yTNAAC*gS=-k2i}S{|#0S-dG(1tBwqx^BHC+Y~Rm= z;}-|nRl!1wo8w9DJbNDhE&OkL>Bi6OU&FEjeh^t7<7IZ#OIS3sYv?RkynJe}7qI@r z0h<|c`kA-epTgq7gEo`-3Y2o@t;cX}xY=W3=P`{}9+37ah3f-g*Noit_h8rEk0`sH zcph~J=GuR?CwZ`-cX%2snf7fcc|T~2hYwc3f(Wyhez0Vgr*{&pjTXglV9uO7d2(1a z`Rr)otPMBX&XD#UwFk-jtE=BxD}$9A%P6cxeYpyD(_uotPt^5cq^Vvf!&v}1M z?>Lw>CgcsZ{KvgsOJLm`whLLme!^eReX!bkIh9L~tQfc({#N-hj7Jn2rk@+uJa}UUSz?ylc;iP`N zs_AHkDakubdp;JHi^JBLz>?CXUq-^UE60uO0CUQRXOQa+tD}qH??RZ2$An9gXANkD z)vX))4}p2bQxku{m2u~(`8*=mzxDb8i>8_Nv_c-%x^HqFtdl5hEa7N}wHIq(#j2R6 zBg{l7&!iwbqi+987mxHK!?1>@yJ$Jz({n_O= zkk=38t=$O=KU!Op`wv5e{^d5({_IWh6_`^pxo$Hoz962M3Ok2BS+@z+jtgob^|%@9 z-;5?^Wbj@PhoZvmMp##Nru8DMzjsT!0j77a4kA`NJMCHz>%%;G-%i@+cQc;=EAPzHlKV@>y|b&mVDZ{t&YNKVi|8T`SToAla|5h3Nxb3?*CwP= z_pADy<3G5;Y)|8<>yTG@(ASKDnT%#?ekY4RGrKy&;$-W!%aMyt?Y}S>=6OWLE`oE< ze(Pcbi*DTw4}xX)F4kDVMo0DxAot%Q*{E&3VBx{GxBhVCw@ce;FkSkGdOja{%gx*v zHiV}}dLggR%qVJKfaCvy{4@D}kiK+Q`yZJ4{T1dsd3p2~%>8M9!v&7*%wA)F^%KW0 zCYBT!z5fK$V*@R0Nq%_A-5Qv`;C;9atiS$a#s^pzIfVMYQ=Zs))H_%huv^~)xzXcA zF{QAp?K^5dE`8>PRmCvvMRo=GeieV}uIxF?duQ?CFYNkgS9B4qF6zi7zYny%pn3zy$n)(D613EYtj(~UN4{{}m}++X?-mZa1SYk+-Yd#}uf4N=t> z$@f92@>1~~SYh{0O};Nyt?Iw$Hq4kGy8bO3x__M4O;{AWbtw6MSzoinBZK5ewp}IP zN7J_%YLZ}!72QuffOAdXMV*1U35%_6!_K2uyg3c)H~Mb532S;d-8l&xT|BY<2CVvh z`A8frjhSq70Zt5aJhm6EEdF)r1T5LtVYV1%M}BgMg9XwR>5M=EBS^OR3)zq9=V(`NNus^+DwM2<_OCjLEP>dnBF-GsZ3Z z?hUKzY_^i;;i~35-8fk6?kOkFx4jZt&W?fgs8G`xj-K3Q_b8Zu*JmPm9v$&!LQfai z!rNpSnZHhC?YO}iX5=o|Or9?}ziQkvfV7V+p`O3;f2|v15C409M$0QcVF7bvhIF}% z_J-q+o_2>_8-kyZ`Sm!-;gd~Zk$K|8L$G*&lY=p=&q`hy4JYrpoZ1nly)xcT=9^R& z+0X9)8-Cr`L+0@ae%Mab1r>@`JV2c zwy=0ZS?30r8t)FPe;YEt!iJZsSQ^ZIKlNrk%*$GK#RwLpp1<>j^#8c0tOfHW$_~3* ze}*->`NOMVHh<~KI#{5R8j<-@>qkm$iFwIZIxTFG;O_8=wC`4AeFb(4w-FI59S%%Q zhC^@do>c>LjkEinhKo)dXA-jtMdOdb;@|>tHOU=53--gV=DIn=wb_0BcEHXh^ea^` z?b3gzSHa2WT;~(3``&oA5SE<_lX4d_|f1egJd(g=oq8 z4b{(1yTY_TW=UjwD?f3ldg@}&#nkH+^4mMvAdfs?yPE6|b=xu*GT*NMn$NYw%)!=^ zyTOLY057utgu&DNNPTsseIaGeBJL>S#H+7*k-YNQo9J4&KCA0-(qDe{a~hcs*v)k$ zwZGLUr}bAEpw^c{`|j#f(Fb zsrQ3km`~LUR+rZ~lU$sXN7dieT+XEWi<=(NR{hWNj54|%^oJwvp7~4K3sj{g<6zmG z9BTOthuKHR!s3qg)bi95{HS`YM$61zk@u6~E3PE`Or%O zE^H>}BgcIeH6MFuiJ%`je+|mI3Hy;N$Gy(^3G3I6y}uJ?xwpDE!EdXva5u&8zP&91Ph*W%Vcn9pC# z+w{_u^#8irkL)WY^J-x`8%3XBA^gOu4<#~`5 z$%pLRqk_{FJL!F4ZAj~fB$%;b^F(51pHu#4VTX9bU0agpClrwSmFl%e-rK>N?DGw= zaN7&o{C==XxT@c7SU$C|V}F>JZ|}1WR$k3=CN@~dE!qetuMCJ}z=EI8sQQz&!%VCl z;M%^e9Dojz_8bkZn$f{D;9TeJ-;< z^6>ey?u~#ePd%mTHHKBCUU!A%+#0IB1;=S}CNVp;DudK(j99KZK>26av<@(9zm(5gt4ZB0^$!wVUbI1`=Pciz`k~atSo!)u>n?|{Jou6&s4UC!$s42+Ma+#CU(acz!8Mr@3?)#FqqZQ>m@PE_{7P9 zaNCR$>V8o%sNB>J_Ej9W`H$q^5^mC9`RG*Dd01_K<8#{=>_3+{vt*c-HZj-$N31_t zdJ(obGiKT+I9#_bC+@z!Si#d?IH8^!jcvn`8njS0Us?d!nr4tsMl+t#a%rP|Jz@R zzSaj0!jbcZ9@PHMY~HW~)*c8K9zuU{_364zuztdmY2^KqHb$fgN&n%)0uPeB)26&t zaM*ke_5P~&KAE`?md5o_N|3AcR=IOwTIifU3aj?SdIIS%=2Mx~k#S z@g)wnZ}5bbKK&O(Ag7HSI@lIwPq(3tS6Ys@e=k@aaoSFZoZl^Jl_{Jo$tw*f%WK@r z|NR-~J5nQ|FmaY)i_?n$u0JGDE7(alc zWmCR~!u)MkJ8#46#QZJf{NtGP{H=uLI={amu-bg-))cr+zV6l>nEpxPe-`E^2h)RL z3+vw2C*byg&-ux%*Z$cReyf?1CK6N2IR^u4z{U`}rBb$^(ja`Kcr$rr5JIT=noS>8_W z-v!z_zX`DL$2{u#S-Wp+)>v3l^79+He>dzXq`Sk)fhnDtaOHRLh>ubi?$F0C2pX$vz?Ol+}+U2{91ro;5|gjVwT!-&j2Zv|`W(%$wa%iFzwSx?w( zpylmeuuhqC&jOYWc>bmv=^wzXGl3(%H~&ZO_qCSvxgB7C_wx;H`21zwdHCd49X`)C z4B6BQ8}+g&Ho%s$LxI0x-8lCsVs<1W-2kiF<_{%~?#SOm?0TzV6mjg_9w*4>zxa;y z6tQ#I=(r|WJ=T_59yedrhkQSv&-^)6Y!4%_}}^sPdZWePf~lg zgH2?8Pt>jDq`go4r3Sd9=~2jYSRT_d@f}>N*QRP8D?S2o9*SU9D239%sD zsVD>H3Okz|6tGJvv6)sRlyO`K5x{PlW^Ad>{)TJtV`jG6EN>6 z`_&;>Yu>^6|1ov{aV@?7|G?7^4aE>f(V{dYLl}i2glJTZq9GZDrDQ4@qDcs02rFSo zhG>YXF5kTUc?|6JQa(5y=G}H^9}RPx>-S0ERNMK7qDcEMDQ99}O;3xH zkuZPB%&bkYi$|C^0;cKmmWW|NnWtGeY*@8oWfaV4>d+ht>mmni4u|7MkB<|=f;k_D zuY>7tODaQ1`(M#}*TC7~)2jnv&EDF~5ZG_m7h68bcO_pBhBMi>ZFn$$0g#Rd^F6m_#yNmb{;;`1vUvfQ8OJ*TDb9y z6Ktq?<>?8Vj4!|8K>8mSXHA4P3F)EsusG;KcMn+Hp@&^RSn*}4-8h&wc*ws#qq}yLX*9ySh)}A6T0^CS(xI&**6U4tu4B z-e$qH@-)8|SlvU>li0+Lo6-!^8^%Tqgd=xP)P8`4t~2e4t>XGFZ-C_o9z1b`OIB|k z{~ng9+p&l}MFPz`Sk&0Bd;qNS&3{z`^K3jM#KGdv!fIH3OUdpJ^NyYhuY|>2K1v;6 zPSc#7c`&)GbOj{6tI|eqfV2>gW z`x0)y3Fa$yjeQS`|2eQW!n_xE_SeI8KX@0S{>Rc8H}|iHHHrEEmj~A^2!o|&g(u&l zz3{-RXUk!I@?hl~Sl;FEm8CGfSSEN4C)Zw23m_)P%doLbv)`Yz5BL}N0_IzLGyGu1 zw8<|k;3l8C$a%2j^i6KC z()$JF*rGnJBVd|g*t}BY#z|>+hQeIG%cdo8Qn$8egJ53sulA4N$e9lloMBO7-)j%y zmbbJG3|K9RDZCGRcIx)r3YMpw*?bSK9;>}=2`d(}D(}E4Yumxvn`6PW2f4yqVb`HA3t(1lDrKuy#m;<~IWvW_$UVwS1*?~|?7xWiyjAT#C}DldvdI@< zbNbXdmtflW`_s>O!#~}gM%sJE}4Q@5vo*D=1brDe~VS1auZ4+#KY56=APA<1xxe=Bo zRY#wIv)6B!69IExK210VE2f0)5RvwGY;;HAc(;$8*1`NSqmzhbRfUJcVD`hA9Aa)e z`xsHG_Wz!NRJdD)Rq4_q(-U4D)9X z+Cn~Gxs&2X0O@~w7-g3QMeF#mcz_l4`FS@ZF8jiq=lixFM|;VuqHFVD+LPnMh_mJN z;JGlvBa528W@oky50-dosqg0&bdf(3*87>dk^e9GzS-IfmcRQml9=DxSUL>mEcK)*igNT4?gWl>?5U| zwERMLcbIoPi88Yn^S2qXpmg&t{6F)M4gu|8iO}qY6mAOUG`8_EKi@B^On`Okx$fU# z1?$`Bov<>zxZ6iq7pkWmdECRY4%WDIezya;YT)_LHL!lX@(pRvsvkS+4J;0b-ASe= zDsAwshGoNpsOc+Q8|)v!qQVF&FIn(dqJh2kT2P-y-+z{6F-+h2g!;TXe|zgZ*kqr( z!){EkF7t5bt1yE*kD6ab+dnQBVcLw_%gOwdPv|sG0UJC|=Mcwk+go}XrrYkM)-S)F zB_rgp%3}?+{@GY;+aiOd>waYIM|;OL-wLHLKh!jU*pPE)(@t11^_3}EpY;xJM{I`; zUrti%cbUVum{?dfPcZKw@;X~a_l>aHk@cH6Pu!s>l;nk}C&=$Z;k4L92peB5q<-JQ zss}9sn7PfI+8$_AeF_%B|LqSv$1X{q4+}l>sqJ<7fo>Q5V0ph=l&inpSNp*KZNEfG zmsU)NrG4bo_P%ai$Bk29#r1ptx4n0_4x{{d>Kn2>m*raC8jswN>P2lo8TRYb$HFwN zj@o|e-D&^Ez#{3L$7FxV4K*J<3ikSRqCeSxO3&;$FcQ{pjHULcvf#{ZLt*u`nO&*< zu`%9y2yF7elG;C8%@{FzAS|4v%qIJ9kCuV)17NTI52*b;Z(y{vKPg4i)BYVN$76Z_QcC_U!1hC+JdzK$o}TdbH*6wHe4~a<)_isS2D63-GK%5I!c6Ng zFw-h|`2$!sa70QoOz+W39Y4BFsD9o^@{oiXPe}j5RqPM&f5*2;>wYCQz`P$#)bTK9 zvXAB+to_{d>;>98ez~`!23DCA^{#~Nm`+EkV6IzyHaQ+oQEon73Df`fFt35LyP5TW z1#AA!@~I`BFgNZc%-+%D`VN*jj=WLss&Mub*lWp`CylV6_Huy+Rtg_< z_yRXE-p(n4RZovn=QBwe9|G>ejE)zk{6x<9dm>m(91!^CH|)J3dB9cJq!asZdz^12 z_3E8-3FemSsq?kEVZSC_gmn+hIM&GNo421l4;$ZgeM!#uWD|#-I|nmY8v2s+Lv5e= zf(%&j`2)Qt9GE`&P8zXbU?78d_^@HAu;kazL;Xnq#^-B~z~X5_7jk||H(dRd0+(5N zQ0Kpl9$T^wz-$?>8##YYI`i`CewcdS5}cYE+9erQpLppv6wbV~!Z#5XY`e6VobL;C z(>}(-nz37Nk?{eW!Jfmmz=n)IC#JwW`=?Jf!d@#Dt(gO}NB)sU!s71*X1;JqMZd}6 zuwsW3HQwRKtz@nv?eqWX79sch_;^7W%ns2xFM+dfNq&aHOmovNWITmt8mX3UiI zTn48$%B+a#jV-y$VfL!t4_ClO=Ok*pMxW=~pO`)E`YSS?Q~ug~$a0wH+v6!2?@{yi zCoF}_03TFN+SmSWD&v7_) zgwyuq-1`VqG$Fzrm#LKcid0?(hq(kWX)oM7?ZW0{pC z_xZZg7B2Iac5H%~HJ7LLhDE}vhHtQW`j1Ektlc|~8b8gu8Sc~z7ToU%rcu*>8Ad?U;-YkfKb7WJQ!)C8*(YpL;bPC-*jBP@vtTknND z(rHhz9_G$jy>}B-gNjLtef>&yC1fjMmH&d8MN+gsj#qDdSE`R?t1;B9F{jM^HRaAKJM2p!Gh^7 z&NpC{{oU_Z;P^2cmR*H4JGn#1^$#2V?x~kyWv^;0HL?9C`z%<<=*B14OKdKiPCo-v z^&MdDg)2U%VNS-W%{n;pp^Nn?*f=(6{s%aANNxXPuz1?ghu`6h@tH4=z{y)8&b7gc zW#XhmuweSNcne(LN%E))NrBnUoZ=3!-Ls8l`(Up7)#mObFEQ-d4YM}QDC`TD*lw^% zgq7hla+q*fXo`9l%rRO8Ilz9s3MLb4BD#GU2)i)1yT+58JZ}suZ|`>B3hScAQP-zZ z{q8;749lOcyge0p@RzXXF>qPu2h(T6+6|KMXjq>m>F5jFaG2GbU@v2{*dKP;H}Oyu z%$?Htga9_Ocg$K1YkM&)w!>~g%C5n%bXx{>eJ=3Qa_1n}WZUf*a^zA)H}+y!|MD39 z92{sObPI%4)gLX-ll;8fXO955O#Gr7uff<+m54s4Pyuo>zO8#*|5aDl1km*B|SQuo2IbmZd^v*Gx3>#PAV z+j5>R2jsy3Otaii z@+ljZc81k6eY>W^+QvzX+QYoWv()uxTENB+rZBTdMb;zaiVdN`zy0xjg<&DhFzxE+ zMkCDKc<9JaIGEY@b~CKw5A?Cc^>F55_tnJAe+wi7VDn7JM@^)CJBOR(db)mX=&z5& zrG6iW!-f>W%J;D5Vt)IHui=|C7PcK2Ca0aN#1!HnJ` zZa;(7zYd2dz!`;i`aFeI^1q+#Qm6m(XQj^1BI(ABR|X zgIRc&Rc8_2lKQik8$vb`We+tv%q9`k88q_r~qgp53 zkDOl^D{X>3HaX=a!)3mi&p*S`?fzqlnbyA^8sMavIoEykzsvyS1 z2y2|)lIM-zr$HucfpDc?+&-ZqA%QGTVSsA3(FMum$f{k)y2X*m#tzmm^NlW z@g~wf_Qg62xO%Vnkr>tnal2c>QfEunMpzseaJ2);_up;50akY1)7A+tn-SY30+#!$ zyvY4*;$H(9;c&A4Oe(SCf$Nd$V0J`2b-$a}cIQ<>(%)G^Id*RGv*obS*mbrw+B0pn zcNfFV{=Y7DgjGlRl7%o;?-n+0d=NPg7LD0Q{eN+KWcF;Bs*i=-u9u+4ELd>xAJw0+ zRq}W`EFUqUJ(*tF@^dMZV78w(^?mePEYFUI6{XUSzVksO0bUfyj-`clW*gpByhn zUh)LatT|Ri`j@So^0pAB=|;611glo%W#+>r+@A{Kw{} zM!_7$pXzb2Xw&+tA+Xi?&+Xk|>E=BN#F9dFJhAMuZuekVzU0=%3B*@M{2c&$dfr@3 z))$E{GprwMBDuWSllYNgXm2?9_3jL^ehEg{w{(LuUUlj>1JPHV2lO?8^R)?PdIcg->~;eZE}(8J141nn~7gvEh~GEiBw0E%Jp~jdt$U zu+1P{cYj#^zG3xKxXyES&V1O|uG{cOu;cy&Q}TOZ{wNiwN&92p&MboI`+SDv!rYWi z-veOf+W5bh;cB}v#euM{*6e`-X8d%ctntr%a~c-5TQP&mtCuY!`wRB0j#J6+lXLRW zWU@bDeLA&z331O5b1AG*;_*7-Ut2h_Fvsrv$fdA&)6RKOu-$3PcI5Z2j!xgShPe97 zz+jl)_x_00aM+@eQ7d3$x6j7quuaaZ@DN!3@4RgwEb!jGW+hx!w_^ALSodMs9b$fU z`@1vXXv+j(UF?$$fVNWPCa z<=BSb*#D~wKl_pIOFQUV^BvAh533^cgW-RqV-uXzCBFkPx3c)29%lMQ>B;;Qk1`!p z5C6A36s5BjRj_!ZYBpJZ3|*&3&*Acd@6_@Y%^7#{AsqX=H;XK9uhC~7kmD6v&GZXo zePFLUKIRs&-(xl(*7vAP&4I-`^A-`)>L2vFO!^mXSxBt=w~j`v^yn3|fcT4vF&*al z%IeAbr`)8we+*99ZKC#rB@ZWmO@o@z00!->|J_D{OM|_FHdQ zHfhhV7})cK^a%NV@NXV$zlqq8ME#ybFTVBK02`#X?WQ3&_^?Hhu;1m2|F`{KIWci9 z9KT5Zi2PnfLuMWcg(HJUvB>sZwc`1=Rj}ZcH-l^+l#>@;SpnO0Dybmb51qJpX)qkv z@o*v=R;Sll6aTk8<9{`~xg4(k+f+lgzp|2cl=ZTSwgX_{zr+q?dM&n57wloff}RUQ zV0DY7jV-L?duIq?&hPgp88Guw-t^UQe80^!8(2Mi@7^_VQm1mBHEc4yuw*?f(T$Ha zgSBU@E=0rHf-SZG{4jquZ=>#C)toy}*a~~)hmPEaye@)cZY25DOMS`lY|C(0_wTU2 z+N1}W|2BGE!Y7#ZVzlpG*lJ$8NsXj`!fa}Jsq+?0{Qw)@eWQ+l17*D(8(@>2!yg?* zdsWnkviGp`blZBeykoti|JIY-xTa?+TsQ8J(`%BycrpAu$y>@3U%~8o*F(wrBZ%94 z@FlF;5lP+OYuw#NuMf!m)_8ro3>$Zx+t&gI=ji5R09q~U_%*DR^tVU}I}0o0ep2@{ zyY!uqnFf1JXt&Xt{GK}motME{!P*^t;rN=PQxCxEO=Hgtf)$>tEcd`7-?=fBVzy zo$`+TVS0V%E#HtQ?R6i|glT`;t^Wb1T(kVz7nWU$KS!PyNIsA?yf>__vRc;>`|s3m z%ck4FqKRjx*udUVf0bQe#h-^O?O}fRdj!yW8v8Gu+C6X_SYBf z6BMm8NxpQ_#Wq-#cH{VLxFyhV?GH?w7(2)hc5`27BYe1n<%>V6sNzw45#4IDXg-1S5CjV92OTwQ7-GI*t-Vy804{>eJx~#DOc`s$top;di94-aD&r z{{Psou|D_4|JdWm*zZ?iZd%=gouvIR&D<-nq5lGE`m(5)MVDdqyVG&;B;WqM*F}<# zORd=o=kcDVD`2nQ*LM?pMEedp2QxDtj3E{sd;C6~v^TT&Am2w|Ip-a*=#*?MvCZHa zdKt{>e6IZ_*!!GC@nP7j^>M)lIPm1r0f%7mi1DIGSoY{R`v5He>hd(4^xvxfuotG; zzIh)8M{bMPB*B7rX7W|AbV}j4ov=FTOP{4MZ+_qHTVP$z;Li(S$@t%WqG3+EJyvsJ zla;Y)Vpuc9X_XI5Uqb&I1+%7iq@Fia)KvahOY%)qE^v_-{*9lq5~k{vz~wWhg#^L! zBPBgiBbUplxH>xkW_&GU_lLb&%;znF%Q|}3GD-i|p$tEme_0>g6IP!N`ZyET_F-Hz zhwD1`sq?=9A7S;-i(f~<%&N#$RdC?bpTWamL#5;IQrLyo zq8J1#!_D6nz{>fP>zrWG_%3a^uwY1Tngg7i`+GHczED`y>2+UNRq|Pz1$*>eT5AWh zBA4&F1UC(iD(nqwOj3N#!I91%F7|*GOEy0|35#>>{jFf3n|fkA%n2<_=m_hNbT}h{ zm7}Xi5;HoCY1;y~{C>~1B<*`uMu=gldF@7XSbDIic0H_F+j8>HJj{={&plScp6xq6 zZ-(`e1L*#+Oxm$mBg{G8sc8l*->@|39n7FTMqw-L&z$~MRKenF);)*9em!4gJSY8= z<(A}m!`6s13!lO&6ZdcioZ833vKZ$7^i*>H{@?6?`M-L>A1N)z(6`|FIQI8_$w_PUv1T{ty;=bkk{AxHwcn#>WC5 z#r8W6OBpxQGhwBC>$(Flzl)PT6_&|g3nh zokJwZ*&7u35?EaHEQX9f_@$e6-w1o1o%vV@x4!n769F^znKi+1M)93d>qz^8XIBGY z)rIX2t6|;Yr$c>VE0=)Gm9V=0=3X+M&^pRM3nAtdQ2XaH|1!A%Rt1^cdLoyp-rZaT z)6R!b`}dZk)6(a|GBZzV{6T(ZoS7f2?c>{H1lkA3Y#Z(iOCGlMCC3MLS1y^)gr(2k zweJtB&ADeLlRSC&PV&5?{>`CJ?l8UNMTgFC<(U|VuLk`i-d!bsBIY-z0- zEQ${p>u)7xIK^5IKrd_imP zaoh&>IzF=U4RW_g^Mo$2D9hRF6&$PgyW0ttK1^6x0V`vN&h7xSCPv(R1}oMZIOecK zTA?N54}~B1hnm9bgj0JSz?qHe-+$*~{(tL7Jx|GKi|hLn7S?3+zlA&_;d-tC*4-QY z=^AXFbnkg1%)x>CcYHAnnIp zeenX;AFj_h0ZY3s>`(#A7<~U!xFsw3%ww2utLsLNXA2iL-gpS}*4Eh{f{g>0u6zJ1 zx=wIRfd#GBqGDKNa!+yqj@4Yde3#^3E#B{iBiYy97r-Wt15WLR<2Us`avSCs$DU4v zlcj(7xv;^tHhU-Rz3x!=Op>pANgaQC-#yAaPx{|Tq{d4G#-QG5uY+C{AYlDApGC>jij(>ygw{n@|7CT(H~tu%@_Xf{Dl!` zae6K+`Wuo;rXTzJUEH+)ZSUEu^AwmKF|TbIa-AQyb}XE{%=!!&AEH(7w;K&>s<%*| zuPIJ3WhktEVx<0GS%+u~7Hs(BK%F1CDEm$w0E;_){z%T3%%{dLw}Y9&l!ldL`n%Xl z8`x{YMrwWtSYz|M!lL&MV^$#-3|bb|8RoxjPhD>jW#uMxgyj!*SCaEBMq~SX?P1l# zcTdUk5UydX+jyA2w?z(Qd09yv-M_>1U&E;7CuerjHNo77^e_=}QSzA?dRU$mttOVN z8tm|fc))hb+)<<4wXlh4D%IX&cC@@4md(AzAnoa`)6PDC>F+#Vkmb$Z6i|wrBh;Ex zYpLZQpXjyaI{e@A))}MbWx{gttBK_M`*pWnav0|Qo<%Kx{kq=i$?$*ki{^BCR1!?R zKM3t(C!Gx21^+iazpjNNw!@s^25SC#^*jA=GyLE6DNWwRm(j37kV4Hbmr=b6qDUT> z??lD}OT6#=4TsB8l+^c$Y)esx!HjEHJCXIjE-b}-CCnYYpBhgr`Qc%`0;a9%lfDr- z%V^TH45r?H1n2p_h!Vi+4n@f^uwC-Pwk5E#+ip0FVe&Kz*ceLAf3<=R{%)64!j zaT@7AVqd3AFuN_p;0fyo+?a6{_BOrfKM5AI*(=EPJIUegx(Tp)*>h@ptgg3QIUc6o zpM~6Sc~GbaOt%PO6;5@BRh_GE)x##z>_5LVEiQ4`!vTsOdbJ?)3$fB!v08kl<~|JHnhIq+Y^@D$Z{PDi&PcV`LbVU^N}tv zz5VGlC*-_F!I&Yiq9}8qE1crqo;wJ}i=TgRV2<*Od?2j3TKj}t|4Z>0_R9(8MF!hX zfmt_qwmHJo`_f>yeKMT`OufGiPQFB=EO}dy;sfi?b>Gh<(>riuE7|{h-L*N{7p4_o zT)7Yy@7q1c4rb(in!5z%wiGn>fd9L`D4F6JZUdM7Xr|7`W9`#6bcdxsog~p{FVCw# z-3_LPWZaE|mC;p|U0_XYsYyIsy&>_PH7wqjJuVRz_Mh^L26NtzPdEjaOuz8l5>^h$ zqLcG+t0l>979{TYK{cGA&Py!oF z&IPm*2a07CFt=jcw;yns|KF*kegyZ$#msN8`mx{LuW)?Re(Nu=L3L$98=N#!=Wc-6 z%0txkF~yia4b3ofNiUTxjvv+YMsSH4nl*h~h`3x(p{X@AhBedJ2CfMr;_woW* z_1)0#6Ks^8-~_=eR*e^NSxPNce?wiYx=*aWIa0h4xp>Xv^~8*?yx#|5TJg4+kFaY0 zu;JwR-LU>rTO(;-S)Qnd?cTjjYJk0t2S3!ps+YA#>tJPD?M8AwL631i`3@GTj#+$# zlZNl2zlMz$&CE%Cl-6MRw^y*b&&9<<$oa|gC)-}YWhc7?O@OQUtFD*B|D9hedL0@= z{NMSt>cutZ$1u&}<2Q1C#_{Oa>0 zEcz<${s0zp#*a-QUY>M@OwXh+jjBHrTYof$SVVU*%|_nZ+0L{S=FU_Mz73}&MYJ!0 zc}?|}_hGS=zld0re!^M{H?2;(`UqwUTr~}F(y$Tr)^>zMhuL;}T#)6HX?o!`JXv6C}3idL7rM?fN{iU-WuqM#$>wWZ> z?(>S7278#FPcMRLC*7BN!+~GR0`9`9zJaIa!jUr!HU%(${vodwuztnIS$VKmaQeI` z*iYH7N(HM|U-n6aGxFBeNv`uhurmWzn%%Z0d2;TM08$@GSLSy6 zI&AVZvFJStFD{Sb;F2Gt7R`PP#?bmoaV)24;J;Gq~p51$Omr%zz?R#;MD)9ohA^qr@Vf$6UD zrMKYn_>RS!V6}tK;ygGn?`6zJSi%3s&xbXc;(uWZJ6VhuUHNnz4E`QVSR_i z_JOcWa%5Z~oKdlI?0n+5shWpyUHjMLykW0>mHS@8CP$9-o(Z$>$L-d_yw_J-r@@jc zo2pl&|8sihDX{pG8D$e^C3h0cXzQOqa!q*L_X#j}b!RGfY5Y(<4wlY6NYzuLRSB1m zCG9Wti>^ldvLzMcIk4bUlf!E`^LyYMHmsTXK&XQ`K3yAJVZ+ym;96KRr|P&1Ogmzh z{T8-b_a$x+EFV)r)%*5aduyaK%uh3&rbn*pJuBG>E=!8}@Bz*%8_8nAoK?1u46uxM z%C0x*AD(ug4bI$dTWJH!R^2+^3H9qr>g9s&qNbFGLKX-h5aQ1GYQn{lx;-S|1no zf!T+AC%4T(`$ZMk>|n*xlykpe!_oSA_OSUETlep<=uh?3{xHio?anutYvMZI5w??k zFZv2Ax4qyv!6m(gQD0#7QYW@E$pw;ypW)<57uFAftycHA(FAkodvaajmT&pD8)4aq z3sk*vPK(l|9;S~dr0NN3hRs)2!ivt>r98CfxiLqawLO0JSV`K4`o=zhjk{Wpk$Q>xees>{!(4IXy-jfW#v`Ba z!UA@j`wp1?W{krfSa(k7up8#6_N=-I^C#?{dKgZMaCv?m7RU3~pMu!~URGU$O;o+C zPs4Q?Gm@^tf=ahcQjgMWf450WxNOUUMpExmlyqlC7My&lDB>!d_u;$t0<7r%h^oi! z*k$px^RTRTW6Le%s-)~31mi10KP= zrQ7pQ5D)P^S_Yd>5_laajvcewYqc$lO6ypg!>%$RpuVWE9`L=zlX=$je` z>pL%|>Zi)v>V`$b+@P!7nvu7}<=xx}GYj{I5Q`G(CE>8wn+=z~lKy{=9S(!#T@F$? zzq{^_5T>W>DgS_+Z5!jd66RXmav>I4-#)mUv_GZT^B%6NbiNu0tKxpflX~Mc?$3w? zu%>^)|Mj1Dm*o%Z2Bu3$|IEi;c|4eTU#BJQ4Vk&h86@9ymYSYCbbQQYm?JVj^%gnD z+>tpBHa_c>TMaAAf_IFD|C`^ONu$QO{f~{g?$d|E%(~ocvOLuD>wSj7UN=*zdg_e3 z4;lu+!n5mr$nq&s-R<(*DOyYW^7xFjg{Q&4F-gew7*fM)rjT ztJOzHy>z$w_bgkOo3mi&ADFRb?d9Gu|7&waD=Y}(1owhzMK%uq;KGH2pLK=HR#o0I zMSa|)f&^z8tm)trMCzj_C3L%G1}jE}QT5Vwp3lzyosI7w9h+l@Tw3m}Xn{>u?4s(c zM;fO$eTJp<-qiGEPkeuUgw+h6O@HzKY)-v7S_kX9|J_PHkCpz+fohn3)YbhL%sSpV zLkn{_DIdxA5e+K3Uk1wp&Dy>ZtFE{`f*HYP9lyZJlBn(vVCl3m%M37o%Jv^c@PF%% zW8VqAZ^M$LY+pZ;i(|X3?JvXJ=Hjtree&w=eJc~@ z@C*LWAw13b|FLHL&)?^W-*Tzt#iC6ZodFyEww)yFi_zUMAsrTukD->Y-Z`qL3|8-a zO}&q$qkqKe7+5gFg?gWW-Rt>%D7!43UWD9hd-k5<{q=2b_vd0BQ#wIOE}Z1 z_w|);8JRO579_AwDUg?4d1K}SmrZNTCi@eW()!>m*s$(tk2F}9xV*{}_8KDEk_wl{ zSxn`U{`qrsM`6|l{u?&T?tG@KT;QZjckkrF z$?ZQr91I(p_HECG4X&v=C)mWKdaMFgjZxW<;~^GX^6(fe>6S_ze+jZyEIS03jUKV6 zBl7Ca2GhN;>~_~drf_z_Jex$AzI)zYQcsw(B)}m7)>}qS{{sj1;-8Np`OYiOq+T%B zwd6!3Z1fJD*#K(;R+X)X<*hw@>tXMHYaCX?d|M~#cvXKV_uz7vt=vt$f3T(FAkSs6 z$mgMfyq`wVu76ArEZf0Jdk$MQcmJ>u)}>CrRSKJPs%q!LoF&o6@4zl|dhhmv#T%=f zNqt~#)`*zNFmHOh&si|-V9`r1tp2>=bZ#*f=<(gL&Sw|kQFHM`%XUY}rt z|Ht5waBxu9OCMmiXSA6MT&9}a{XKD8m$Qy=WXs7pZ(-VjsKUOm)%qXvYGK*<_73EH zQQkazfevO#PEzlG@-$R6R>5?o4^>|{lDb={rwZR07Mcu#ybO+1n2u(!hfBf7JWrbUo+R7s6ggI}IoAe`BBQKl%)fNTcC-34 z57ve~>~jQ`oZF+D3_JRZ2ONTx>K;X8JVSPGPSydEcYnCd2`+!xeNQq>+k9?9AGl@S zi%EN7UDwWwyTg((=7oD;#zgvRIvhA~nI;KVw>QytgyqUJKX${4=V9%qU9f>tV z`~7PNb5_>}NMTi-_)Ig#U)+8q?A-~|jyjHh3!AU_HDL#FPFvTPa8kvCZ`)ww&sV~G zuv9F4CV};TGR|CwUBsU6w#^s5t61XP7{-ScCTUq z^2p|d0mS^ypUiw=TK>^r8)2H+zAoco`uuw_#M;EQZ`@#}w5KErmT)go<8Q%P?Y0t^ z73X&&<8hkvZO@6B=gqg!VaL(#F2r=p;xuwU4ZW|^d}70#{6TGGyzl>8H9flaJ?!!% zIBNrP_R0cZ9h~y|a&;uEI`lfBoQwx{(z_9hVzyBAiCaQD=S0BFJ(hECBG-gVSFVR; zJhM=8KS6oNs~^H)laGc+F-$DBMsL6s1dJ)S#Q5=-UX-b7yb)_1;H~r zZicOP9ob5(3jDo?+z;RuKP_=JEWfgGg%D2q+-{YSQ$r&HVS&fJ&XZyK--aa% zVDSLs=TR^%{mGBHFuTJss(!Kjy|lsyR$Uq$OvZcpdy2<-!_;^H>`|>eJ_FWNkBcPt z6R7rA_)UWu6W;z`y<$0Y`&76raS-)9L<)OTf+wtve)zi{u*njjh+9LR+I7Q z^DnE%!NRA%R@{Ug3;Hw0!17;govfuV~PB8645p})av&1`unEqrxZx-4! z9Me}j!mN*9mQIH?XKZW-z^YrbBgplKU@uO&JuLc~_K!TT)HG$ADY1}apHHs;tNgF- zw}k~S-KggY7~DmLy@_A#6!k?ej(t6o0kfJ1QO^&=ev9|*NpkNSk7%U7XwnE9*yLuG zFS$Qa9rtBiH*FOEEKmzk3}tp+xfMir3zgIj&%o2y}wO(nk)E(sf5SqV#m+mC$=o0~6` zmy`aN82Vh8|NF+3GMKqR_5LiJ;w1BU3>&vLQ}?^`1~KlHz+CUWGs*q#c8ecAdI(eZ zhru?z&dk@qvVPrjHo{u*zMLYMx*h=Qg7c@{h4n4&w0W?6u7krJ(w@A32Tt)k_Ot-j zSXu^6fb~V;f%&jvvy;x%$(xtehU`&sI9Svo1%16a$&EpJzZPz zyd_8GvR(xnCoH-270!$nJj{Vv7H{MAFne$F;%l%h@rh{(%vDcbb`@3@(hPa9aq!WQ zD=_1xZrXWRb1NyCn7&Ig?;y0C-sxx>_&2})zyc=u$M?m<+SCq9?XRq z_w%T{>{9PIA6RSN%2lGh#&!O?S+Fi%pF+&uw=r`#EH>pHy$q{FPBR@~*$sI_7R+7E zm~9PbaI$6)a~`jK--hRDOMXUvAr>p=J!yvR2K*gKzMpu}zf$u2ExpH_A=hC6Bl65M zSmtGYpUfZ0)PIe4VP1J&BALIE8`aNAJz&+Ets|)UZ&xuV6K;LIWRn`XcAsDGGq9q+ zx(ivp;tO6Wsjx=!ERC!Wg2MKN`(ehvpf8We^gexZ+y~bU|G+JUz3z^u>IIwFZty1S zpK$+B>iK0Ck*&KH)-?2tAoYW7{B8{*>#t%LZXAOR1DBT9!}M&+Rbepq^>XU>!SdpG zEQhVWFP_+fT&*}CIUkl^7)<>h)w{w+&xLiv9Z&y8E*db%+Z$#TZeRWn)|E7`^@JTO zqvo69_s-Gx9yI|rzA2tfwh!u^{U^J_dgim9WcwjK7U4SvW{j__=>Q9=D}u@M-d4i~ zza-lqiA8=98|DqUKy9DsQG23YVa@6M<}S$Pod(wpg0pM=sO=&C=xz_UuHXI0f#kW2 zh90myyts*MZ@s>G%1OOqX|erhvVBgT+pcR@lJ~nuZO=6&R{nHY^1Ui<5OPKQr|Z^m z`4Ov!Lty%%W4B3ty!|UIWV=4)!IGK;k7igYy?>SLPvxbTzkh@|r@idS z{#G|C`~rDipKFrHT>`5dpJeG_da~b@r7-P>pi=`Z*|~_?zw?T}uB?ZfVg*xHBG-4= zUHA^x_-=Z;3Z|Y9gJp{Ni{$u$wsv@FEi78}^bk27QDxUv)W9YCRcp!dnVfa(V?SDSLx;uhtPKd!K@SO1R0+KX2YkAT@9u7$|pWRc0% zF)+Vw*}B89$LD_@<6)tEjAII1cDm#eIp5b#&n!OxiwhIIJYm(pq*2MR*W6BVvta2u z=Tp03y?5JkGM->uB@ax1gEOC(`@xb*QP&-?i;dKO3CukkGh_>#shnU~3Y*w(V4CNgPn%%Y^rbdyV9~kT7dDXornEv4%$#|vQv|FDu81JxC7kM`VInv=e3d^L zPmv^E*}0nZ-#Roi9@ZIa7A}PYe@7kO1xuL$j*DP#vv*^6!!pTx2_NQkbPd=8Yi2JV zz5s5q+QHff8?0v^AoWco%kF!mz`Q{lsd^|jJX)VrSaIac5UL*e%t8B3lK$$Xq{(Pc zKXr7399DLT`ZN({_4qO<9o9ZsF=sq%ci`r+GjN$^{RTHUZ*K)P-lUMpsCq7$M<%^K zkKE`QY8ZlC9hF>u5tdoanM>-wxU4yRA`8}ickJ#6%kQ+Gq=Xd~ZcGPQ!F^bDjrjea zT2jwEV_!f^4lGRV-l03};cAt83pQAMsk4MtwrAgv@iN{D`;+EyneCjz_h9bKtEpd5 zpT^iNks5DP6z2|q2O9*tZ z>kVt!oz7aqDMMR6kn!2%71ru@aQwQzSNpd@pY2cWfPB3rwp{JytElU?=-yb&oJQ;Bnj`Y~}-2pC(>UN6M zb9K8eH6`Q6UL_l-dUx5ypR39L*G{ga>h;Mgn<>jLZvB5u-G5w5@BcsWQ4*4&C`yae zN-;!3F{CCT2_cNaP%Mf?7%D?)2tyc!Q5gLYDvM|m4W+1<6qB$hjf(H%I-l?Bc6+VB?=#sO76hw@zOSH_yD+&kk|ExBkm? zSZnCFimVS~YJ%Dw*2~{zk^9$-%(b_N!!hx7I~-wcx9KGXKE3vAJbK6H)ee{dBGi*HZbjNeJdEK*9#Nrs* z@%yki;;f|$>}NOhN%sHbGk2SMU4!F450#PSvHIABU4Z#-Qtiq5FxBCWr(i>;!WU$H zCAA}ZCBbI4fg_kOJFQQ`7TD_f=_L-Z{8CMBD9jqVsft+XV|rx?+&H`RHL;@o<33`H zqPh|5W~Q8)3A6K)srLHAC(n$7%a&}W+Ox;sd*=kl%=KDGK2KA!$I2RJDH;RF`s8GO z^X?0)_Q!4|>uWr`iq!>{FF!+lUuJ^l#m|p8UiZX?k?+qv@qTqZtOzyyG#q9Pt*)(x zxkCpYAy!9zJM$10PVqG-)?7Yfo(~&W+*_`PX5Y;SxRhJU{yMdC4$6yiX^x zKH@g%`$QOTRZ;6Jk&X!z!|8p;Qu`;p%6n*fXi(H`(7Uwj_00 z0cZO2splIKyVJ~tFe_=q6mq;M8#iv`!oFprX7`8vz6Q?nAkK+&H->Y32WItw?fRNW z8^E-YbLfBTu|0-vo8KAM#y

6ISh|)sXXn>Q%S94RC~Q&W|>nFN{A8ZLcE!JSC2L z9%Ak3@e~$Y-JALoacP(OjEAr~V@^yHIp5s4eW?Jp@F=&}!J@89cjUv$eY;y4VcOcS zMYmvCQI7|oVZW}Xy>7s|mqA5Z*qE{J-gVe>K;#v2K2+x|-gO0Tt|-0x9%lcY(|!(a zzoN7v=TB)=SDTZta@qC$(q zQ4wrB*67;;nBE#OTS?*uE+eMHth92+dobr{otrltzp1U3Tz}|Crp)J%_LGb{vS7i) z-BU-xw6xeeH(=5Go2-q*6;>M`w4#yx`A)mf5pSwE_`43j zZ=awyfm7`c8MrwlXI!TK{zTTj4#?~H0b!LoBQ z3Ax_08a?u=NPF$v6Nh2CtLrp1Y;kGlKw{RCfft{`F)Qgp2`rZEfBpdW^pEf-{Rd6! z!zVXLd&OI4@_dMz{~DGbY^@~y4Z-MjvoFEIw1hG7BtF&Y>oHg|VFva5NYk?BQw;3h zQ%Ln+EDmO^+6G%)bDu$;CowxLVdyF$T&8Nv^j_^|TO%mL1D&|3SRq<_cj_&LG>_B{Wy zfIP2KeDCF92b;bA-en0aJ$}fxKP-Ioe$xV&J9}{7uCN{N#6EwR(c@BJ>j$(y_i(l^ z%qw`3Q40s%^ZMWobG%;8dk%|#9_dZYXy`NcAskV4v3V*?i|zR&8&(!>&z=bDHrAV6 zB=HetcS!$NXj#_%D6F46{-`@_VJ`9B3)8BC`xC3PUA!V-XMNI%39!ub+<_1{_59q} zu`oN)=6L|@9(1H(6wK;#P3{AG_Ktky0E^t*29JRKY=)K&h52j7-(tYrf;-Lju-N)o zWfwRpv2#C1n7e34RZ}g#4>$7@u_$I_b0wU~rBP#5qm3*v2qpbA0qXSa5Ql=a2VTUwh+U^I;Lk&FCxaTGapELRjYWky#J> zCi-*_g6T_AXVk!jhbsp2;7a*QdLdkGJYoRZKLyz@CftGL?DfM}6HoSTKL?BBhQA_a zTMer`M&fSMBG(cp7#E1)LhWK|{Gy2}{2+n@&oE=j@xq?#?idaS?KOE#%zsN;xC&OM zbfvF{B|CGrEhX)@aCejC=M272_k)!NUoxrVr!4<~H*9s~U^7`?ow+`C3@lHqA3=^E znPkh7VQ}ug2Sw!gVdq&D*~4aQy0`?uBG+3UHn6V^Z{%E9FpuwL4JR$?<4v|_<@tJg zKbX&G(@%l@`tRy&1gF;zzeo0e-g3*(POz>{ziB+oFGyAW)8P2`{CtJ{-qN{2&pY7I z9@6rxC=NOzK`;TzxYB7Bg!96%FXu+yYx&xH*iB zcg6J$_rJoGg1l*iVD+on?FPAAWA)kT4yV&Fne%;z*%;Fj`! zBb@#&Lnwzezk*uB;E=c=k`$KdWBnJxO75&J z8`(X8)ke5uDwyklbm@ID^!(b51>|~y~7pA++`_LQK+V`i-qm8@S4Gxjb zH6d~J&-`QU?{NODGx-t&vujh#G_dEC$y;~9T9${A8rCLVS-zckhb%o04tW(mQwR(1 z53{@g3%AdxB+nOR&z!FvgBdQrH01fA?1oqEeptN4Q9zy#GP{g?5)HFsC(aFn#mvcD z`LH}^-q^JyUl_J1h~$^-{Jsk2uRC^XCY(NUHg&&HcBFOk1X%FVgSx*cZJk*)3|7q< zd}}`P<-dv;Rv)ULACV>Zf{pJ^+VcYrFfSm)dlILTJ5m`1z5%*p3%fKAY3D{M37#7e%zR+QPep1^$I3j#<8M&YB$65U} z40ew>Y1NMV-4>Tkm-0wF;kSz(&Uw)J^#a({h&G`G76#0kF$cEu-A+B9lHYhU*c;aO zNer$f?Kke_4uea6tIMlM`%~L$tYGKAZ@-trfjI@mJz-y?FbR2n#D6;6pc9-~J;nbq z%#!zc*HVN2@bl6iMN_gxCIJb^TJ?k>4I9oshSj57splaY zF8CT)-VFrtmVeJr~kV|lKW#d$0nMvq)VqLnb>KIIu zt-5~-7R-3AJpxnp#9;Zr!(|6y*6nW>55nSK*8Kgj@?4_zURduqX2xDvJZZ(dU9g|l zTDJpMSP!#`fUT_5`e<0)V{W+s&P)zx3t{dssrOnq*gvn|D#}6do@fGXWPQ!v8R_b!G>`!##q2Qo~*te z&b`}V+zVFp%Q2~em17tl1~8-h#HLa>^x3r&onU#^)0xj;_s936|4v8#**A-xz~ZbA zQ7y1!Vb9_BVfv9lLmFYhtbg*`aP@${O&?%UUU1P(IP-c4y#^K@N*$LDd#bnod<}D| zH-5VeH{PjBsU-1jH@t|0wiRtu!!*MpUJ9IfwP*cfSiH`^_zbK{TN<7Rb3b00eiY^$ z?vkGc^H&_Zl?W%zOm(ah;iS9m8>3)aV(GJB zShH?SuT3!fx=;Kvm@%U?mk(=4^}43xQfIl@iOMa`isZ2c(D3nVWB@96nO6b z5?EE%ZbjVKXtpT`X8H_zJO?g0kaJ-X?02p3`D~bddS5=O?H6sEy1=T_kzK9fP+E?IaNbG!Jzu}5uD;y-AWOxn^lrA3d19qOp%svWR$k#f2hf7?R?2Ct)#=rJ_ zg-sSOJGKwzrdYCw14V;w?}GgdF2sL<1x{0TZi6|?%sm?5q)xNqg>X*goTHy%{?P+X z5wPxpYMK^qY#Veq3>J7k_pgWTj8$(}!hT2YWYoej>aWLFz{>F>Lf*qx7LEETuqZBdc4qd?QaH%Fd+AV^*Ef7AamawS z5kug>$(OUA!I@j5{tSek4aMO=A=z zuAH@GU1!*$Wj&Q2dRUUE^Tz%^@8yOkh-z4`C$8Rq)AltiUmru|3l^J>E{By>$NH0a=Cv-@N?^YyPSpB~ z_I=)00BiS5?n}Opy0lYRK5Vf%jrzW3*ETM{1q-GXt{~rE+TYJ39S(F1nezgUm&Tu$ z!|aE7+%lNA{p#WKu>Sbn31s{8mHtDH!m6A9hE~JsMVy$!uyH~MWzC#2jRe-&e2F4) z#@+l`2Vizox|s%cZ*MS*g*nStU;hBJw1UR1F!z@=wg1Q#2w0J@a)CLuzxg&fcnM%$ zoVja|>x$XGVb0hshvvaKPQiye zV7Yqu^hqRs_b+uk>2qg$jfEN3zgBg{@nq6x*7nh`!eoE8F|5hGbZ`V5_`=~PIUeg= zg?>&jXPYr~yvCSa9byY>mc1R-7jai1eRhACb#4)V0Gu1KXKY{CBKFVxLB#H+#ip>| z)bOu^VfHjPi7{MxM$m@G-pL1t%pjLp88?B{y*foO@-Jc?D@dJpa}_SeoI~=>?qAC#h*M%$T_R z^b?r%v1r6{IN;RRhyqwUUpqGxc3y2|norulA0rpQ#?PLa!c~AMS2AX;aelYcMrlg5?9&m!-i}{U8!IxFWa!Q}v+W&}AFfoQ8A! zOf@H9P4`8nN8!NQcP1&YMaqSQ12E0@s`GW&WN?8%3^U*P49tMz_YMn;fq6!^Z`^`& zM==t2!NSFH`a7^)(uU&gusX1!_8u&op0sBxtQX&~dIT4yS$2L|g!qPokr<4AKq2-p)P+0fozU3QO_pta9A7*8&zodapo|yJr3Db%N z2_IqO@cs_VV7A%9Ro~&zC7P-r*l*XmKEyn~h4G8w%3vYoBx|+%B3QY*LjDtRNs4^7 zFRYrI&gp^vk-Wh1gEuUx_~b-~s|QSdNG|%9;GpusnC{myvMUt8oLEu<<>nyBCSK&hD^-Sx)z@XOi|+wF`&9wC>)#Ik37( zb%z1VGM4<94@dX=Gu;|i4QhS41ePwar}u~1l9^N2!Huu&1A3GE#rJjFVcxD&hICl+ zYAw~j&EeIoF@e=T2Jj9b9ysaq9wWH&VBw>aFsoND9~yDN>DbF;c}`_x+NNRqgoj@s z{dYNk;Hg$vbkufqF04rSG`j`X{fl`_`v0jfCImFY0{c9(2XIo#r~^$T-_Fyg7!K@W z-S`b=HkA!3gH;FE)mm6PT~SjD$4B={)4;|yGTT3}w)9%fThg9klJggq4k&+9PV(1! z(#$cQ5%vgJUIyo^>9lJQT&pUYy)dtF{mpAI$724m1URDW#qPv82BqT? zVY5LFhtgrixu&(p;Xs?IIVmus$BL$#aFWx58E0X^LCK{DaEyS{?G!9LGSc)VEd0K> z@i@$U7rR{ptJ>0a$6(sTXN$D3OwzZP1U8Pfoze;8akC*OTL6QQYvUF|agp&?+yuWPSCF-7xdoj@1j{Iybi8E?8h^|BT#E zP~<&K-wCsl$5HniGHVYl-wxA;FJ66wEbm<2hiKB?OE=*(Oy5v?n^-@AAD#+}H^m+x z`RcQCvhKoMMT#3Sz1zl;$1snxyx%rB(AnI+n&c;Z*%(FIe;Jfk2fHq_Xb}>-JRkNA zj=8|9jD#!e@}oOz@cS<)?;Zj38>Zbef+M#0sWuQ-(uO#}G0JZZi?)_|=YF z2j{Tw8Lx%S-j^>X*7q*fi(uK`a|WTXqBSxo6%N|Ga{;mZgjd}QIG|=;IkDDZwBjvX znQgs)EzIBW&$=(}FG{BV>PnVJ)sujw=K0r&HC8*it%L&;PEpHOCB2V~gHs1j`$v|? z?3F4$3a2-lTndAEwB{N)Y<&CPx^P%Bp#Io9I4Rh^*Ctrz+;gT54rwaw*i3x6p~x2Z zlj|%UpOEdvs`Yv73R@(dTE7(*JeY9a1CB@bKDQ>&kfjp0am!CEIkBslkFRdU~a>P(j-_ka6EN?Us}D0+J99u zYgX4IuJmqxdWN(QH;V510sC{!OSAJZ>x_BSK)8BO%tvDV`hwqfaGAO}`VuTIAJygt zGx~&6$D3wJXNy2MwHvoF6LHm%3$85f-hq;KN>>&&LK80Djk`v_o!GH1V(3`Qk%etzqr5Lc>?EpQn2EXxKPz zxl;wqR}Y|`KWT?lr&Pjv7jGRG@!ZoHwy$BD;J3gZwi`Q}UIR1z1%~V3z|YHGza#DM zN(M%exYcLod(vLnf9ZBuxO-&ZkFbRIE9p2)UnJ^ifO%yT=iP?O?o7V$g}7lPHy3W! zye)2o^#!6)1+d|nH11beCLCm@g406}ChA~i)pL^?SZq5qi(C(cKmPdC!kYSuD02Of z%pUOcFPxfF9;%1w&sq=k#PiLh>CxT)z!q*d?^?ie^Zo~k)%GuatYGEcqGjazq@RB^ zhCDBAe-<3uP8_hfk0Y$O@)Q$zWB+7NboYXNV^>Y?4D0+Of6RwX#@603fR!~VSs}1R zQ$n^OES3gMT?gm(_MKx4Tg*-+3z9FpGP21KA$Dsj;noutksN)8u;36Dp|x14yQa&-#b$r@9o-e+lA#W6PO; z$@c7n`S4-6OwyHXU&)Ks?V)h>;ahDJXqcg(+`|0 zI}GbPv8nz5Yx2T3sj&K1>zeO~tFJEgSHQfp8yv{~#m!tdyAY0X&zwj0KW3@;{bN|( zSGnR7Ow9)f`<^Y@PzQ^~Zyfa&E@PJall@s3E1CZp4vctBS<<_T^9>f=kLOn-t{;4H zZU=02W|sFGSer305_rp2WF7Ncnn zFeA2V13BLP#!L4OgCn}zQ~d*NuH?r!SZi-4Ne6b`72GGW1fN34=w!IkzZZ)aFOXyoYEuxEJND<;gCwq{2aoU6*pvWMAr zIgvH6HhkY&JJ|RzE$kg>e>CRWAmT~w%WB~q0e#8r72atRJ7CGe_1k~L9Jfga|H8ZkMqDe*y_#3^4^EA5 z7~cZRmz)_%!~esC3m-lF0p~ng|C*Tb?1sG#rd|8V>DwL>OIoJ35C_E;R$hU* zj}<3Kd&4U&t1rRo{i8n*A^9V{Q&M1Bmy7X3;YQaY$MbOIGq-AcxUf@7=2@6!lkk!W zi{9UgCKgN*_Hcw%_8ZomhP5^i1`UJNz9ALIVO8j&o+IE8L1BvoX5?Kr90}K*+%P5{ zw&+wfdo*0RUU4aw#6@Qs#=?Trn~#WK?jg=cH<u$A_JY1h_#4TAj&W}W{x z73at7a>-KI#Ny?cHkciJioFbGv?ba8f~9?<3$tsd> zmNs{?ek!Y`-NZSu=O+@2YBU!w!T+sq{LLTxQvbf(8ZaOKx1YW*XgzTS8Yrl+J; zEJyp0rm}1a%+0jl77Qof&tEKtl{?JWuY%(jbL@A)j8&Uf^5K{{27RJo_S%EJiKBbG zO^$-~i4w~0F?E+W!$Qw^%E=uV)kP{xDs6C?yQmne=+@2eVza zw-Ph9d-7(&+QnZ^Zh$Suud(+ge(LZs9M;Q@H*#R+hq{D~u)?vZ#1)os3d$m3lRH~1 zT}Zx1pL<*3LSFU`N4RpANB8Zpvs*l#5#V^Cb-Eyeo28r2*^;=Yvu-yW^x-mV5NW?i zzdsJn{2Auh59S=ors`!|6;J&_hXn>xSDr_l7qHioIM8U|;WSt~`?+^7*jT@1U`oFWPBJf#H#N|r$ zk|x9pzeQ{%Ry%DOLDe7jD&AoND>e@_b;AFtn_qOT=mBe6Mf+Xh&@(oBh#8s#Nz-66 zr4be9cUB+ph9d-@oJf0%+hJ7w-}Hus)aS8{MfD3253*E+ll52i$jShTaEp5%Vqe|8`@k?jy;UJZA1E|A%*8 z|8hKu3paLqPW~@%va5`;GGg#nQa?ATtF)HHCG`WnNIl#fi}7#!!p18u9oE3|j~`!~ z!P4Y~Mqgps*mG&-u=;PD=MOldD5;!ScRX;19@h63SXsi1Tbf}#@IHamLG^h6%F_jRh* zSXf^$VJQ!ep3>ktp2VMrB=TX;zfZP#z?_aD)*E2y>yy;+z1E+&aU!AoIYmO zOY%Mo&1Tdya{O3qp#|)Nlgj>1oB_)YZ{0`Umr3VMcO=K3H2z~B^8QWc#r0>%@mcBk zl6v2Vu{_?09M2r9RZq$LOAN1$mCIm(Rkk~M|0;S|w=b(#eW_ba~Rf&_|*G$nVLxHahM;FocRa&B?*>O&yoBFaRPZ?&ybn> z^$N^$4PRvfhYrM(XjmO=PQ4GPEGyhd&fkpM^Qq*0L20Zug`Dq=H%*MQfTQ1>vU^B8 z)+KZhOq;UAtqj(lYnM8}W|(of3Jx^kjc|t9e@s_@glXc5YevC5N9CvQu&lgC3LDn3 z&dlqI>zS0{R5TTK_ZVt00OpC`QuS?p`&ass>!o1E#d1>LR&mw;wi6tf*DZ?Fx0QVl z+U){scEqd5``@;Gfo0jh^B2O*I}v+V!tG8!ZUn)sc=r*j;B2($u5!pp1`XY^?s}?dwewMKdSl8qOFMk*B`YwAlMQOyISoYd<${9 zZYcGBZR5bi%v`cO?v4995N9m1Y*oVIJ7%jyu!Oeq+kLpQqqgTRSa-F}i1d%S{&R2a zhBd*#r=R?fC7TS-5KEiZQgP>zySqGxb=Q8U??JwC)U~ms|1IqylgGfJ%vH~;Vf|fE zJ8^Q(lhyBGRfbP$ENrFvWK;)he%R@WtDld&`VHoqPdP4zO&lsuk@12gkUnW2?7FC( zPR0|8;|+(2)feO{G9IC~JbE2Z+AkZ{gNE^otk02M39xadoYNInx3p95CpT`oFuFS| zx;cFNLBvb`7<3}zBSz<0j>KGBcVAOjHDnAGPs++Y*atRN|DoC|wHgC+*l%*Dq2zsM znw$G`GXDC{p;fEAEMeZJ0BU)5tNZn^hB+&4x{&qZd7SG<#&=ZxNjPHO!O;$|MSLl> z{>o$B*9?b+npNc_Usaj=*$w`0{r?+}TAaUMLOwq^?%2Djh*R|?VA+QXnis6ET&0qb z<^S`f#<%=ifmVm$fP0gV`ykF^_)+FYUUnhlWqFA?^*%Qy1f|B)bhCf`4L&2KGB~QF;n)REqNbZv3{*bA}nFZ4xE8iV^538{a;4Zu&T2#&CZh}g~hMB4o-pDap5~o!-6}P zsQTEIyW_28u*`KprW|p}iLAvJVEJS9(9_zUc#=;lMX6jwY>A(dbmut$c5bBz<)8naG?_M%~L zD;!|%c8&*m-lSG6rHz7Fj_dA`IP1yL>?ts#;DNdjrghJl76dbGhEw;OwS#x85Aa+powT z_FTMY`AL}DH_dN2Z1%+Z$x)cG=$dLA>~8s7L!MVD1GcnGfeU{Y%sK$;6!g5=FhBJu zJq}hlJX}EPZ(D2~vv&uqH_m1S!G^)%Lxix5IinA$r&zd40W_LP5>T`4c8ogZ$ ztM8@k62K;TL&x%ANu}G0O)%Sq|9utA4;o!a>OTf{h=!2+UB-v+N_W5^!OuUE`&H_7 z=^^{!x`4OT@y&VFtBuqFe7u|CQ{!} zP?Hxb4HggegunF zlvC&Lz)MBxWWEurk9QsKAug!DrfY&Dsx2qng#$-<{a-!M&_??cFn5bOp3G+=EIhI4 z5Udkz+-3s%*|`4M2utrxI%NTeW`sOl0}KAm9zPIH9qF?&1ZEBOrz}hz-aUxqhfX+1 z;tcDAW&W__}%1MCSZ%jrsg}g$Ggmjj#-m(AYG8W4uHhXl;r1L|4$I#Ab$bm*EDc=$91fhR zepCj>XUxAq?hoj`|D@`<%OtbH^I!|x0;+y0z36&kHmvHoy)g&*hSH0VNPp7!#opgp zaP`bp7f64TUmm(B1Ez22ZbkZ|v=6UiNj=QazlW}0fHlb`3uPo8mZ3Wa=Zsplm(=eR zcK;GDf$83-Bgp<9eTQ|I^oMEx=5G&&D|uyq_QS>k*Hz^BY0P>tlJuXMjw8P*W@!ei&UP_w@xGZVwOl*hu1$EfN37`Q_Bl2Lf1^()aRD zn6+?LIT;_Y&bYU}hm&i#Q_1*&bIthiL%48Qd*BLKxwhfPE!afnOWki!*_0%t!=hJL zZ!RG5zU)~^B>#KOtU0huI!U)1<{E_6`ob1HtKM&feT}XVrop{qLJ=oyhp$En!3I) z)IEDx!vekSzdBrRv|*Z({;>AYK5G6LGyhH0{R8dgXWdH?56vBJOpRB`{Nk|Hq+)7( zt>HP|$c5dT=XN3E^_(!?AqC8O=;Lk-Q}rI;)WT(#eow*of0EdZ^e^kqU8-$?<(qnc zi+}@kGgp0q`9{}YtcIiON6oGw@duBLf?&nBL%i3p)YBj|5DvL=)ayR1^fT<_1LyQ% zr6^&G<|SRG!=Ao-GIL>B$nZN}a18UMeKv`&7mf3P8}BRkX26UizTd{fimks{S72fF zH4|6Z;@%3`Nmy*!x%X(;s^6P)5;)M&tBuSL6A@z^xfkYdeeU57hi0sa+XJ)22P-GR zeCz#_L@>=IU=tZHi9Xg(-3e=h_0)Vc{KND|J7CUt6H{-*HJ^)9qhW<{em5@cep9z3 z3KmW9UP!)A^P#8O&BWR%)ciJ*dus-)ADOGQ63v9;WJlz})IUWhksMHl@a2 z(yI+t>tJ>7tyT*V*JzVRt%7Auf0r(XeMhScR*-z5ehwLLxf>Sgm%;q>TaC-%h+TUs zmcYEdU);!eE7WdE_F`DDYW)9=uO1aoUjVZsS|6`L-0#r{yZNwj6t{qkuaqT$ALhYg zpI+aIOOBhkabeml?a@eBnjY6_GR%5A)0vEK_*S;-++npjlN!%N&pyp$!}Qa()c8d> z+><{V){6=P$oL{hx**XB7VLKULdFm2Z_9k`VIKP^H6EyoZ0*B<`A=u`N^v+jlo--sw&`7}|Jb-Y>T*|D>sRc0 z6LGVno=dyH#wR{g^DA*Z)AzMa#`gUG$A|Cvej$J0K=Yy0@nUt_$-5P%=KDu`<*?{P z9V{E|ka`bJFUT156;^BuJ5>OOj=wXk0T%Z!I{X0E8nsD2!b+PCYJMlq#&^v3uw;w> zcarZOKaE}kYX_wNRKmGijlaKvHQc%x#N6cze!hlT@i(csN&CB~N?6DiQlA$fU}crT z|IHs|b@%U@7w~_dU+H5zq?EKzYZ&Oa9t#u`E$+)I5 z1+aj1_$E1@s8Ywv@4);Xk2;>i$>IG*=fJ$Q@#jeYnK9$-r!10B=3j;7-g8QCkbGJ6 zflAoU?6B)KSlcOLM-^n6G2R!!*Oo-Mhgm-$gG(uqba@xCLBh8=AEp=3E@RU=ZBAr)feo ztT{jD7Xvm~o#C(rrfY8QWWr+k*1j7_{^+jlq(9z%pu%oFthji+W*p4OF)mmQ^F&3R zroqNp{ho!utf2|X>&>CIhYHp5*FzVe$P7QVuNM*7)o+ zT=+F^$V8aO*Yi?fGkN<pYga zUh4wu&-aHF3x}y25Ldk#Vcr{7%0px=u;z9X%>?H0*AM**^Rp(5=mGnE=x5d${ewdF z$`(Udo-)7A1PdmwDwbxW!arme1*yTH0S-%VQB?|Q&34>-Qxl9TV? z9HmXMFU(!v{Zj?WcPpaC6Yd)e4H!+$$Jab$>wFc-@839w)Sr(qTYBpuiT|s1BlYP6hrLM4hxM^X(h^`@ zUGLs^VPVJiLr36fD^J-iSaEXgmJ=}FqeY$t^EQ|DJOhi8%vRilMZ0PbUxc~&`{t*? zdKdlWOxW2c=J|P;@33P5sec}kUuJR^rhVF5T?+g9_t|&~X8POazk((HT{K5wxpr#p zdpL7jBj*S#j4Y(;pR@EE%MQYoM-x4NAg*fgTNV#XcZi(Ge2AiAmxg_?-|BGsU)b0( zdT1Ppzgzp+i1g2=eK6S#D=cn2GKEuDd0yNI=ahbnW5H&g6~|Y={IGzZb6~s9PoFJ= zne(T;UI6>1#565{h0a2%eqWN`CjWV`bpL*;-dsp~i`6Wc7qacoX5@!n4KehAW$q4Q zQhzN}^)qz}%(&1yU>_{wO|lzD;zPbu^%+$gyT2X_(+~PzCi77m-#@r)3@nNBPbT%# zoE?_hvf!Lx=3_amr&)S3VXoQ2_-vT#JN#$|LVhlg>m&>k6`7h%_j!I zip{UaJ%$6W?rJcHS$CSLdgKD(Ux69S-(a6i>R~FUOP=AHwmBD`%>l~ zSe}y_{TJr${hQVf`_+{9XoKZr5)w$gPm?fxVJnHx(X8zObHDz5+eG3YKGl+XYr^H1 zR(ygvuZpR9Y8t^npARtaePtS%pS04s{{jun=(3NhUm72DE#ob$sfcstAa0n+KU@uq zeHc`|Q$r_<7Zorw#5T?!aX)i%!-~^{REZ@CnRiv zr8NotRj~HTEjg)wrVP$LQ3Uh;IrzrGzP-GDJ%s61hiXYZG*wmH;`=Zs_j2DOu%d4O zT?y-}`ky`tbDtiG%!P#w-S^0dj}GdV19KC0q+Njn13Z4;fOSSaHr;~tYx8$1V4m@o zZn<#G?8Z*S`ccI+QlHkZwXNbhOzU!(s#lxTvzC1o4xD?`rHCv~zV==kEF4nTy%csX zvORMdrvKLLeglWx4l%e0=PY8@H^AJgaK(czm`^vN)5XaLVQogVlR3;@m*ScsY!>UEC)cnm6 z>(v7|#MU3f-XKnoopWv?%(n42db(cOt$1w)%Q_xV_1&!`(I$OKzV$+?{&-36@43BUeV)NGA=)!aLc-{<;zSrV-?#qd zzq6(!e%5Kv9>kq%mfq+NYlcVskid%Xg$KI9el3jmq<$TJQt z*;SYuKWHe~9=e&a(=tf=#}&VbHIa@EcVM38=Xql7?*!&kI6d<8nKoGZrQrsduRkXH z6_wBG=;GQ0d#?5uv?I={c~jpR^#v5iZ7e!S``z{v`oVS?O-G3}#)e%7!-35Y`~HR1 z7QuT+{Q^-&{9R&^quys6Y;j?70{Q*eiXJPxVQ$CBTg3cjm&cO&3AFv)$I$S5%Pt@4 zwFqV#omtcgmO562gpv0C9-_{0&ba-4v9O>y>1U8B8TxSf6EMphc!OYutv&jC&?`tvc3(VpARgwKuys&;{Gl{!c?&(4D zhsTa5^)dV!-#MY?gHoct-`@!JGn${jweAJ;wmMo`!v7r)%2^+Jllov0>yPI2L7e^} zIA9daKe4CW9M(5ffAWE2d`?DKz=Ccgd=|s;nkedcR7F^vBK5tRmtMa=ge?DD(dIL- zZg3HGK42JVMWjAU-C4;CN5r+}Uc2wZF~L23hrtY5%HI;0W47<1Gc55h7(wb;X)3x* zBIhI7?MW-EVYMc_mYlDoMv68K%)LBm?r4~PN<`H=ipi}Ky22cvLA$B?K~v8%#=xwq z+rgwBQMJiP0XZLPqWWlh!qPI;Q8&2qb9+ud*zEp_yKK_F_uYIZ9KF~fcpNO))nYUW zj(=p4K%8^2;e`+E8nomZIp1n8j-L_;^9+rzxRdth$K45rbwNjdd%%j@(y8m<=G$e| z`CD6miL(&^lr)2`9Cmqz1JbQZgha3H{!;2i806E=og0OI&V<#IMmyDMV+KsaL1oU~BHgB>BvZ5=Yqe#OG;OVEyXIHaA#ITl_f<4$ND(a5~KFh!kIgX@|ZW1;Oft zRx#H}eD&bsHL%~W7lSfj`SRhT;^6>`dD2YSFSx<&44hmzfRhDRew*cW4Q9}7C@Zs9 zKgotA+tL!V5ocxZ`d$Dl<{bP$>^F;N`3~k>u^dhMo0Z8KJzHQ+*s=86uzHAYLT^2O z-_5I??vVJOKp$I}R{A6AF3gh!upHnzs|;Jxf5pUDrc+=+{37c-n6|tAlMfsiKI9>> z__qJ+2v{FBF@*GgbqDS&lfdH6H_h(B?CVQzpNA!7QMr^mZOG1sUCZ7c%ST+osyPTo>N$ z+)Y?sx~p(JoO&bt|F%CZJ!}?<$4e)Z-$xMMxn?nJn7#A79Hx&nSrGyUvPIPIO_zV} zyBQ9O;8Xj9AfZLNgT!mwnvWu1(fq*U2#F7>n|B1(&TrZ#g`E%XxOWhiJDhGm2S+?+ zmG6VOJFf4#0t>gE&5ecGKHYoVgvA}@Gj_w3!!@2cu*KEN=%S{Qn$|Q)oH2W%l@``U^ren}!MwK1-$?%I;6;ImTQr7wbp3i%eUDqh@m8m)^PP~pAi@?lxqIQjRK&G6?{v3^EgsKHBK==l z$n(ogIL76B_wg_@Dec2>IP@Yr^3GSnnN}N%~hh{eaqeFfGh( zO*1TsIQJcIem(4G@N-}g%#X4N)euLHSiKbXTzmY-TbMpc zYq|`kKUiN*EFGsFOq?`m>YnFt&g%OU_^|PWiFcmCjJYe^*1$oz=F>`GZkx&IwIu)0 z#<#h!gkJoHScYNvt^YA~zXoP6*#3bmFGjj|ej2PC;C~?)jyP|XaT(Uzz1cw4S3P`D z4`NNg0qXm6N7HMFWu+f_lkG#@?}2GEPPk5m<(i8vS7EMbm(wWNcP^*y2CRJR=x75s zUwybF16Ft2A7BoQdoVjpcb4q0p7c|cb$v&4c?0V%(OQ)x-mKjA0+v{wx4I8Yf49zh z3@f(&{rmu?ExWTLA5PWl`WC{Phg#}*5|wt#cmx~2DZPIR@c>>&H8I0^mia!|tnv-3 z2o4OmewQ5Y|K;acl}BuXBWAW!`J!)|x~zgTlSRP~$@0Cf*Dr(($3&bY>qGS)U~O(2 zqX1U>^Lk8#`L-Uj$@;74*Y>-@xm7p+k?$`r)+`wYr|zxUMa=yD_mB-7QPZ2+K5}6x zuRqM&=N^}bxWf1I*50tXmjh+->myObIjiT5B5_^G_oN=Mnb&%1|H;XF6l?%Xi2WGAiJuM{dXUtqfY!No`!#&ur|4MUW2D|UgTf}pm zsr^}heAZbx>=$t&H5+k1;qAP0aNYJJRb>C7Z>_aRfF*@q)c&W9v-~ZBtpu+)3dA+p zDG#FHq#}vUHCXMXnIeE`akkOvuxj(#o@-!>+1o|r_~4pv8n6l$_cW#UfA&5mYdK8s z7Dyc*w6b4yOJTFyr^m^WFXPru3xc)z{TnXAN|%Qj^I?(uhHn>0eDe3uxv+ETh+JaP zpss`bVb74m0%CSuAA3L8FHLhM71r&abHEq2SZiWSoKtSYA=l541A((s;J}Q|=e%M0 z{=tUlU}5v{)stc0hhA|qm@}zk;3QaH4swn$*VQ6Fsk!^(FJhxWs?AjQ&d za8kSIn;6z#%Uov!D-3%j?jrG9Cj<28Pq?P^Pu@Y=_tULxhSk$1%-aTYK7M=o4W?@! zXg9LO^1~) zXCA+gc;Q@Q&uRZ-LE|ymR9NKNYZ7U1cQa5-%u4aw+WC{~h{4%0zHSR_Ljg%FDHdtLYM@p(MHe?A}Y z_jPZ(PrDD-`??}*Va0)JH?lrxtr_y+u(pAsx!_K*3zHrR;Op{Oe?n;3hWY+uQgr&1GG&WVuqAltupQ=k#dvu+-10o!bm z2mf`&{`2`GwZFJwTFvh;P3V?97;)Co-TZH``0nIr8(1#ozix&N!x=hqJ*gO#KJX)~ z_F?TJ`(x0s+Z{f@%{T9D7z;OCy25@3%Z^;h7zgt<`+B^BX=QaE#>4uBue;X6_6PUW zPJ&sbjKwW_;hm(@hBnfCs;Yohb=Nb|Gfh%he#iLz|yrADmBd1|J+TEZ$IA-t5hWZ zCM4ez*5x=gWy9Qg(JPn1Mps_9T!mYwaF+35^~et$l(71#I&C>j^C{6@Ao=Gd(^kQ% zlAKQGNqz@AS8_fIZ`(*e2a5;&rp`}7?!s+ySYdx!zXoxax?_)%;DGhRsq>lonsetw z5`QjxxDIhs<^Ea;%=rG(YcrgG+{-%xmdp_v`N5Hao{3?w%J#vzEpS{7?Meu&J3BJb zA7+|Q=In*#^zAX*V4C*Qh+QzJThusmz2CB*o9qt@(pLNKfy18-7`X*j+BD_ug;R|i zI{Cs}3vriyu+e}w7DAFg_SnV!u+;VUPj6V2dzKptyM$*Mt%q6NdpCu_o}(p41;p=X zO^ATQ=RQ5Y25!Boi#P-~@rMhf3ho#nJCQVXeEqE4lv=O?tS}1Lhv?IyauQ z5BDvZ14}}uNfKao)l}YWSh#$}h|@5y>yT$|Fz3|f(iGUXs7v5fn7;cnEfsEdU3rrY z^SO54$p2SHN9%;~B>y@uCLK1~_C(|a%cgA@mj$!jm+l!wyi>Z4-0!s57+w#94Rt#A zo3Q8U#TA2LRsO2uw_&=E#icLP-yp#)+J%g#&~YvW~Fkk!WBoOv}o&?*I$> z+@{sTHAN$Sx4Gc@b1^gK4Xo*WZ`)T`-Bcb(t`|f`Ws1+R%=THY&#>vg6}=i^_SE;O zEwHWb(3y9z(fqWj*1Flcp`#opY!%U9<8dF#z z-nY087O#u^+YzP@pCP>m3md2X>InaL&W@17rz$5$|QxEEzHWkI3t(jw=_Q*52t<)u*-%eX$k9H z;pQlBpDVC**y_I=*lCe}U>3}59(KwNwymj&z5uh-iTCEg76}`EpC$2b<{Nmh#_WwM z8CDyT7JI_(z6<0Du;gFz#1(K$|Do&RNqlYPt~D_2U&DMU{NMek`BJY15il=&VuU~9 zLCtfNVK7(YeSaHVSMo!30A>qosq59^(p`DGU{&Pp`N4?CIlte$6K2jG@ns*J8p7|t zgTyD^_YT6UX&?S=gjo|Gc^`pYr0mJwuxw0TdK66SQ+je8thr#j z;5ghoKh|9U%ipoH}(Q<%MVIdwlQ=(5P@2`sSvxo#NZ^cR|24`AM_)omkTVb31+m9VO5 zyrVsAaCyxtCh-i8iVeHS)5qq*v{z@S`!#JqCr%E`NV3^A8}a;@mmQR_?2^^0`LOhA z;i*foeB`sAJlK0x|MWCiw5jpLVz~9}pXq1dfG_>1``zp!=b`bie&CjcXN)*89&%2j+iU`u@L56ySO9?_Fl0veE}?tX?0JATSwi< zp9k|F50%Mbf#wr`Cami2G%5|&hqzQsfsHJ`+{l2{8I`^hVC|W0AFshJ)zRK|u%6aD z}8SX)40gHE z^LHQEzU;ZU63#zgQ)>as=R^co!D{Q^SH0i>9pC02%)T=FSr?c;H@~bJHj=1{I+FN7 zPpkWIq{oxne=lmeDcI>#lJjEtXk z9_FSE>6i$67tWL?z(&54W=F!QRd@YQ!p$_7Cx_s;SMU48!t|BTsQpi0esWMWEIGyB zD@L5Q=>3W)SobRL)NWYy=|g%r%s%!jf;?|g^m_X_1a8jgoV6A&s42AHvbO8R8j@1lCp zG7>-3C141wcwA)b32PtSIyeAkWo=rs2sYS0pVALD><-%P4y*P}-AsqIqI0x4usV+y z-UY5aJE?3o+-eB!K!crnM{k-1YwnJmWd<`oew#59){e|MYXaA}osqi2GVA)+EjYi` zZQ3$(G90j)^Sv1krkj4Vhee^8v&s3BeJZU4ocqhTA1o<2)+S6H*|_8~6f z#dZa;pGiLb%u@1yov-}W+6aq3ZHjdu79EUy2TP+9#6w}>>FfO)VD^lYzx%;)dGAiY zhM84UP5Z*~r7KsyfVnG{rgeh7RWtMJh!?M~`ituse$i9LGg#n%r~Vgg#Jp6cBkctV znO|YOdTZ!oShMFC>jRv6*{1p)EaBa!o_D)Nb+WI7Sp|28QO{2%{gB>;l_M^6$VGm0 zf^~0V!NtDR^Al#5k?%FI>X5@~@;oKgEYOOW-dvfN12bn&GPy&%zTNT+agUM{1+a9& zQ19cg)AVVcd9XSve_b%F^{ywar+oDhFdgy+bS(#XM0oI7WD?>>9-3}Y_zAbXg@}v9U)`#O}N5ZNjN4gO+cmr}G zh!-}!+Y8IPFQjjW+vNdU_rS7}^y_P2gY$kjV!_zOv#Ixe2TdwOu>FBfZ>Axh`giJ3 zVjb`KWk&&N6oX3}ELZiU5tejA;ETdp=< z+YHke$HtNOwVv0t7D8BE?OneWmX^nV+W@o1IytX|l_5W>ykU;zCd&n|&U6ruxOKg8 zC-VMSb*?CM9ZaW-raHikz71oEHQ&mp_sx}$EDx%` z_1rbYpZf}p;iTMd1*>6!X63qOybt$w-6LBCi+A-IPzvYwk98xK#aLBdfxUdMHhaOW z!G&I_uqb*#AJSgA^Usaruq?!bGOyIyn^?zpr_8k4fBYcq_N=8R`F!PZ=P^5A&u=fO z@6Y!9^Oq0X`bnwvp?B$8F$$KejZSVvzTSAMV{h0rLmorcUvpj49W%Hl(&N}BnA7j( z*|+GgpkDF&Fj?Q?pMPxb!VN#Csz_WD@S)pHn09Z7If+Z-qtY+I!8=aoljZ47CcQcW z7aIzu5Oar&F55@)Ph_T$?L(ipeU1Rmk4$?^*0*{|C+>Wh%O3AY);H}&R>gF<_3Nz8 z#Js(^x~Xus-G&^p{zW^5FJ0lvxk=rK6(?RNll~!|-MG~H;dJAc+rhH?i-xX5ob&Zs zzyQ*IZp)J8urSs)))Hp4|5>*bR<-o9>IyUCN4!}Avo?C2=m2L_*xw|U*Pgd*CH+|( z{CX1$UKP%1f?Jw`-+RIVn|!NY!jjE39~Z-#eP{C?zzw$onTudi-;C*HaO>$tEf40o zJhm)=)q#%x7Qp=Mv?nUq)}eT%2g$dOq59uQdUhJMZZu&>$`W*`4aJA}CQhvOSItnfQR^&hf#(%U$} z;z8vt-iT|m4|v{zfauWX(b;wsxqTZh70<-w3v^snOHPgf3s`5hnJY=P-BkF+siTGFE~O|Z0( z9%T);9?tZ91~b~GTla8|tV!IfPLOo#&{h8MHp z{4?KUT3}hW#hnqb^n(A4Z?H6NY){fZPd|12o@Q8B)vo!6{(PEU_I96O_R4_n&2SL? z_R%J|`NEB;x5Pf_0Ybg~#Be6R$iUz_i1!91g)P zM#+v^Sh%xBegLLd%?PZ3Sr1yM{+HHPb#S49rZrgO2<`cZJ3g)b^@k=B5_7!_`;L1wNbLU`ja+^m6Y-__RPk?2~KB)&`l~(^E z9_BdiTf7aXolE>Kg|%ltonH-$bPrcV!4iq|qzCLZ;?~V0FymiKksF*py5dndEHz1c zHX7!~+PxFQ+;d+x4uWaIwA5f&805Hw4$Bq{TqlCHw!fl{Va>m+S-!A+t9R8)^q&qc zaOmj`%jq|K9>8uxH)gJa>4J!J#juN0?!<*8et(WI9hSW5cYHS7%quU8heg+Wmb$^B z1*^wJ!=C>A^;2MpDW_Wm95=?R%8tYzjXSytj=Wsh$bvP+Nq4;9{2|k>4~E$bMk^M; z>{)Hr7O>7cB!2>2d>~og8P;BXxo#M&I-d5$2xb}Ue)fSg<{SwAI|18o%d(taux+4; z$sagC`MGCLSlYPtLo3W-9--=mDYH*m0x-Q^d=qjTQsNqxYqt@CP0`=)nPeXM4#)zKQ5pY9u7g}8-(W=l1!EmxY5 z`URUPQ(Izt(T43MFx&j=5Mq6yn}C=n{`%<_+3J!$yC8$=u-VN82Squ)x^CZ!&E3i}uwYR-0K> zu}NHP#q)+~Mx6d5;G~Cv=T{KFUQX4YaFR1U7Q(EgrB6sbJWsUp#SB<9smBgd&)R;Y ze~*bUv+CiMFL2TbpUxv-j@!W8IyisT$mhLDe)Ol(Qdk)m*lrFB=7mu8{H#v5_L{)F z38uHR$?`^&R{k1~{dM5P6a~!Ldu#SrSTHkWUiZqjSux^gsgb3KCq*_)9i+_*4x)1g~$B8O|c`x03_rRvYx3}-W^vXkbNWBuf zyg_FIkFfq46}O=Zhq?ua6)9;<2i)Zitsa;c9M z`MplT%XYiN%)+~wA+WGl=*}51+qv4|5bW-q)5{e$%F!^Q;F{eVSZuhp!^F$cu5g8d5DA7&8mX)7SxGqt=Z!2%Zk?Y1xvmeDzUI*ET{ zy}k|e|5i43CBAM&)jNxIL!8WDQRwzXRfucLqWoH&vAs=KAAScH%fionhq*uUdVPlN z%_e1hgEgjEtt~L4|G{^kNq+R$vF)(tG;?7ita|c$g&Fo=<)e>g?_uTrja0oA`~Czc z1I)g6@mY7o`GsQt23VlKG?58A?c#X6f)!J`O?HI+TH0;(u;lx%qqE_R83(S_llG#= zbTXboyG3vMb68a-*{Fn#0{4A*2DdW2`xe2b29s@1VXjN3KvGX1-ZSE-4wm(rH2xKA z&OKg5ELm3kx&=-xd3*l}%qrSN)gQ@^j2K1S`Z|jmKOi{U?eSw+)%Zj^2&Hu*T-ZBEIH|mq<$!4c;dSU#8y`E*>GK#v2OQC{^Ea&@4(sfHYRD|*5!QmOFZDxcRq z-okzxZ6y^jE0MLK4fafFbGZwP_s9b~T=_#9hX2D+LD`RtD`#*bI=fMG+xC^hq9QPc}QdqJ5!n#aY zKCW>78kjc4YFQer+hM8nhh1jx6rP1e)6>Ft!*p-+5;-jO${rdF^UVBGQeffmS%LfD z)+dEe6JhSMrLE-u+D3pxcL2-O_=&vt@!-q?O`}SstWu4#UnLvWpJO(t6|mh(d+NQ_Ex49D`ED{AxrPWT>YBr6|m8#^z#o${Kc`= z%V6ct`Eie7^LP)}C2)XQS36SwBfPv|?jpEZH)YEUlK*SKzXh-&TVc`wyTk=da)-qp zN=XxmpIPu=7EJX=hfTMRUgZXB&JVis1NNJt+c2Hv`=0P5^+A;n*0j08(z$Qu{en5H zwk0mGZq37D)VR)EbU%4?LF-M>ua+C zRy-Lp%K*1d$*6t{vlgyNeh2G~t#7=7WvvfA-ogPx9THx^^zHNuZ(t|&v$M}(A#dbj zVn*V|@wKqD(fuD;|Mq!3!|ucCZ=uVGHJOaE3Rn=bHk~-YX4oYStoXcCJ?HXJGL!_r7F* zaM4#BOD6eUW46`9;hxsz2{7O4^^WJTYFJ1A7?R&BCgd4h6FiJ{9Bw^&?=jiGWN8W3 z5?IsI%dr-Axxi9}!FbU7`U$afllK8w`pv;W_Rr#ZC62pcrnkxI8koDrvn&Xvs{`jh zB>8%&?KW7`JB!-Ch3CpOo8f>v{46cv`l23_ye=6 zqUa5uATJf)hN=QJ*u$?4$cn$G5}T`pObnU zHgqt~?*p?Y6nK()zRJ3&1-)SVlXI?+dcVpSPmXklncs5P-h{&?!gZ#w!T9c072J?k zxBizSw*TI0>i$NPKO*G^tmDqI%|={pGve_#n7%ML{2J_3{ig5}%;eIk^9}cy=~V;V z?49PSM4Z`mxbGX-=w#cp%dq(2JeyarisAQ_oUbaw!qv}Ubq(i2I_z#5!qt*|X~#`z zr2VRPvr3Y$IMDYzZ0noRy&P6P&fS&@^W17rm%_52oA;cB#WQ{<+=5wAov8Dr`Q9<3 z@?b%{s|z`Qwucu*s9?EMa2z?m1~E32UV|k|#!&T?PVCcZ7hprcvh{I@E3AiZlf#&G z+nSuu8H-jvPJ)$l-d7%nRo^dgPr$PDwP)Uu zc*f&s7g#dv_ro`^s($2&$#9GR*!8bqp&)72B$&}XX>L8tXDnU7hTC^pxYxj}XDuc6 zaIk$Crwab>{=LO{T}3&pj$hR<5^=9R6_akl^n)7f0dQSG-P)@#Z!P0^AGqd=a^nS9 zwK>|y9BwxWwmuIFuekYlfi1F9UM0iQV)k=W(%z@T%otd)`lxZ8;`&`>BxuQ5SR5f>v@2*|M2q+soz$dI{0ii%n84IgIwRW zAAjAn0~TMoQv4duk6j-a1UK`C?|20>ybjxLgOw+?JgtY_RZVj@!*aXNsZZeG^)C-> zgk?j7OCH1C-s{3v!MwP=hs)tg&tzLqn4L8-_cn=t48K1QW+mK~=D<>A!IqgY|H1xi znQ$EIxE%*>F1xV%0-Sxs;`$WWuzzjndDy+WD$WJA&-+k+4t5zMz2O9NeFRn~VSCry zBcoxxXS}Hd_I{9eZy2n4NV5-t^Y2bOH4tWeFZCnW&pg)JH4IoI)wX!UrtzI0(_zV; z><^1c{$%dtZZOk%KhUlyQJQB02UNbQawPGK z=T95K;!XSAY+)6zTYlR(Y`^u}XOr>9wEFUdpRk;t(2YF*&_7xI^E=GF5ko!i@OHgh z{{?Q{I&=#eKg`?AbNfd)AYv!zfRtV zQJD7C8cucE&eg(_S?98?;JOc$?$t0|HhDA|&x`XUzC#skzxW6>o`)B2f>Q;|oJ#*g z#`DtM4U8;@r3>#3YJw&6-n!g{TdzwOknz1*I`2AF2J3e&JEevBcJ+a!uo3rEL?K)^ z&CR0(Za%;AVlJ%mUHhvT)-0Vfj@)016#LVNMNhkVrjh)rx8ggn`1ohLMA*x>w1!yI z@km-MoEq87gqV7M3TL?R#}&bXbt^=>V3psIYlWo!+tJnQ;r1U@FV!T!Xy%UCg2fF05Zy>56#1V^b8ddUi8=7%Z@M&c6-oGY!|Q$?`%2EQndJwHLd> z4Pz5e6u_Dyvwg;}^@RJMZo#S(vp)aE_&L^Zz9&%5I_lX9tLBaSmXCOISBC8;*swY5 z=S^6Vv_APA+}?9lXf8}$|G~vxqbJ;eMLP{vWPG5w$`biinDxALs|uFr_U>20TI)l} znQ+Zk54Fh!q`hN?}u6ui@1ySv_CQTLtbxU`sv-jF2M4L!PNM%T)&=U z(qLwf3CqcNwG!^yloVJOokWfQ+dBQJS0XI7S-X^sKWrpS>v;+e2pvj|pT%%;Ul<3g zvr;OiBc5@5a9uR<(qGHQ!|eBu8jh3p36C5{!~Bu8Um{`Ns-R0lVT+B-Wrtzm$;^`$ zFfAi!r3B`tKjHO+IXMsg!r|7MRBHU^xIxF~g~FPXGfdktJ~X4?B!55LeDt5=7c#!| zoRkg0u)=@AwRf=iEqCi4nA^U8_e(g)wsiDPm}y)ru7s0D>~IQ#Rl{sjO5mm~QC8bv zqpue6*|4IxXSyFOvU7ZW7N%c$x@I%1+*w%`4>J{8^Mx?8cuveQSTdTk%?B17SX&wj z%icRH*2AODz{zygT3FKQAT|CsXIR+lH6-q|aN&ByGcN7uw+iOEZHe)Oi`l() zt%U8Tr}8~uRjx^+E|+?moy2*3ojtT!NK~`iI7_Alr=^Z(exR zETP>VUNJxQEaLj{AE&gye0llWWVk7J=)GoGtsLL$G|V#2+4~k|+m_VD!$FNlU0=eY z_;$t!RO17aJzKjo`_01h&geC z^kPvZ++0E5bQpFCXdH1D))yS^D}j|(0$mx*K7F?25Zt<`*QX-bzT)WsV)mw14>in< z8$TohR>ht>b`#dVm_*t8M!NU^+H*p)QO^J5dp>XNcNrGByrh=r{_tkUORzfg9c9fq ze?batkS;7F-$x%Y&OHIPceSFvZ}I8#oLE?C`+$Cgd_U(ew-3Yr{T@&ClcpUoeWQ~l z`F%ml7X22&|1Gcbca6#$)_fmB#SO>m9;_zq*Y%~AZ&{*A?)`d z1I&E5_(}@g-aFUlEzG?*-b@bLJ2~0Bf@#%k$~B7;V;{h}_raf25tnWi?5~2ghrh(6 z!RC!`d)$d*Mi(sP)qre(i z7;xQU9xOM0miq+O?D+U@8qAyRLLL8n<)*8XV9{{z&-I8G7yj)(9&Y8;Pa*R=(1R8q zu!N1ws=746iZQWod%`;TaN|#~``E#Ix{>xBC!Qha7r#s9hEA~9W50~d{}5j4*Jc8% zG+!pSz*@5ylh(0V-;Hk6`APb1;K^^WxDoB>z1sbJ;;E;ZkTdgOfDbXaqKMQOJ`2lZe9n)AZQATCPs8dE zmnK_ST5a~^By5lkb+m`umkch8h7~<@RgN&t)@%Jym{*@i%}>#w*k>()IYZvAnTR+q zuUAAE%&mU8$b~rcbl?53{QA_nQ((hVzd=DT!)u*n2CTiP3EB*^7w+pfhr~-*5k4^U zj{eqMICa3|2djw#>Xh?g&#&&%6|m-4o+b9%6p39DG&s5md@_lmx-^hOD_ywty|cK3q02SPH) z=W+7s4?F#j**hConZeCxHyABIyi$1AU<^xQ+7|HO_Kl5i+QwjioBf@#ls)_B_y2Ly z+6Z|Q%x)i^xCr@jpCKNv;O2I(x8(m7vE4eqUK5RNB4dx8Y zs@ejp_Lnivz$)@SgT(y?u1z5R^0I3nY{`{_GyqoJ@xHWEnCkZUWbmseD_oL4l;V^Gv|7tPJxxeD^ewcQS+b;~RIdlEpURYk0 z*z+JPD>oi0g6XR&ybi&pmgBDm!ODRnsQE{{`?^(bh1pT(PedW^o@#k)6KOwe+QDcz zyR!70H%#@9fz98fja>(G&kVYK5_WQ2;kF8997yeu1UDtuc34i@&+|)4fm@Drie3V% zCrqcVzxc|nzMe2oTIG<6c*bIl|6*9+v?}yGoa)`LeHKjh-+?7l(~eJvl`*%&RB(Qe zjeA{T&GNgJ1+cgz!I^a=mELxl0dg zSm)XG?km{*<*gmvVO7}OXXN@**ml~;95(3u%fG{>^g)NZz%p2G# zgIvE#*XghSv&H8p^dD!2>s{rA{oB66@-Q!Iz7nH2`PTQabnEB&!w_dV^P^vq{I#kr z_xOtVU+#PPu(7nC~D<)nTzYsQ59d4?G>6_*aBlC}_ zADW!5fHe<90c8AJ#_o`zjS02$sg#JR$Ri)cs)A=fm2gv~$~GdY`~o zH(}xRl?g$x(o&b72MeN)zajH=$p3suxdF>JO{T`HZ4p*axC%2*`cU&(1dm)%poH10 z1E~2SR7<@oGGU{>BX7i^J%4M*t`~``tit55cgU0YG+6ws%ceBgts*5T6;==XM$J#a zUf+694*z$*)I9wWJsDPn7I_vRKNZ7LB*3a|0n~WSHqV&SI5?nu-l1B=Y3mhzVqxj^ z!T0K5{_n*3Qkdtx*!&gT@_3c)F<4aH^w9uwewEin!jkvDA{${|?)%CJV%OE}b-{uFU$ zS&PTQysO8{$nyn7;Ct05SZ8{O8gJA+XqWqNSmF8OrZJvZ1Y}l?BUUaML^FYHJ)RC9 z25X+w7!yuR&Ww)UZY%{a8!kWaJYTgWjnU~*&_l50WY|I-8 z>pyw;S;D$AOVCGku!;&EnyUyNYR;@2i*yit*VGzm`4y3LBYfC1iY1`}*w0PcUQX{$pf3P>V6i?oF`l=%y8D@33CLTSBb&TjBTv=DnRB`3`0am#25Y^EBa3Bi}c$;gerc54e4nSMW<%9jd3^ zzcbwBFQ3EqH--%-&+95j@AG>G8+|$B;R<_>C`)(>>%Pe!&VWVdAUL&SO8j)wZe-bU`*wJ_b~Tx2jD(7k=nJrXxtU_{0P4d^kc^mH86vWx5+sBKHwG{@SVG^8=fy)*WbUN2kU#zS!4q< zHV2vJ!pes&Do5CXWJ%8tXDVa(v+OgxlC*n3SM_R7J+HzkzAJ}x<*b!G? zemL#K4%mEZz1?M4_;okcf6lx@ye<=_UE799Y|>vaDthK6*j|uj;|g=uEdNN%eU=vL0n;x2HKfBv2fjWb{Sg)C`%1E4 z>6U5d#c+POaK93!UG6qF9`?KTcnJCZn(T((X|O>az`X|RCMEh`gEJ;+Ey?;4m-icW z2iCpX)AKs4y19A(V_0~3{aCWT*{h1g?_p`%xdk_2#ZvCrc35WGWdPY;9Lcf29ZCOC z%M3;V9B{sPA``awdTu(gVe-3GWV}VEG*c>HKkl+I2d2+dP|GtK)R4Xy=B0B($>+(= z)b&{h2XUj6Dp*mKm@0&W`-f29Pydr|N5((&nw?K=Z-$M~_9RRz>$Wu;`2w@$W^&?V z{?zYPPw(d$2h2?&JJ=@@b?Bd-ANPg-pemg9Ep8hlhRj=|3U7WWSjvKF-O3a_y+CKoM5B4160CP7zJ{$U;(ZHne4@#lnKqM;0<+X8lTX5-Rat&Fm=46zJnEba==$upIdXUC#9k8)2>Z{Ei^q`?!nFG`QN9*o{U?2@51_`p^>v;y8pBu zC9ue3^G*GM(ig+DnKj>Tz=|~{Qmu)rNwBow_N-@gVg=((^e8nafw`JTt0O@)Q0=bl>vYwvt( zW5eou8@(36?K;{A2hzUN>Th=!mISOV-6I^D&7P?Top~NvwS)~4lb?Zcw72In3 zZU)(2O#xohEn)SpoYt|hXn-Z34r>bMuNwt>ew;M03#<>j9!0jdyr6Oq4W_xJeHs9J z|6A3+1I(Ouuh0TEs^8@NdnA6}f^BRvzNMgW;PGa-)qc=pG9IS&qRkyXz~Y4VD_>yR z-A#*L!U|Ezr`NEOlk({)%<{9@`vmq*|Nga>wEuGD2^o)*y550}JddZ5{av@EZN`0A z(tQwBZ{&4A@}v^hSr^3?AkLAsSCo)^MZ}$JaMB)$UjfPQ>7Am08>T(az5#1{iAz)9 zx`xEq99U5<7;zdlvNzkB4b#l-j81|xUJvn2C%&>K{}}A9(44*q+izPiAQEQ$&_|sk zwmY2^3cKxdxSImYmU&#=2g^?#^gtD}kY)!R3~y7Ob>kHJRIC)=-u z6_$7Z9)|hDlRZ|z&Cgn*B1l}l#Cr*BGx5NNV3-qs^$$5eS)Axsy$d#s-Q0T`oIi(M zxfNz`zy2iWvzC5Cw)qhIR6JzCTJ6gP-mqZQWBoALTbMF=4IFT4(!YVQ&46PUmy>vE zt3^MUZx;892eTvBbLggv@drJPekbGbN_EwnZkgT5w^DbUr`yIB<_59%k z3l~2f@fnt{xG-fb%R@crKydqB6xOFHj8Xo^b2Meu&0%V2ln!+(EnrM_t6gTISWhPxF-LGdEr5ZB$)1; z6Vn2#0#zC*+@ikRt%-QhlI3BrXxNv@jj*!Uz0RSq@FuI`9f^DS1d{qN_nG$}z9Df7 zX7FBExtX2S0JFV)#Cu3Qm~rnRY{*{NupZ{FyxpxD7Iplv!V34ch|7A{$K=5QM|`JGhRq+TM&-abj|!Y%`XOFLCaljh zp2mWkhqe4kC+^nCV;J1f<`i%qZm#y<-5>UDoOUDyW}DCB_J&b>P?!YEPF-2v1E$Xp zcRT?b0-o4*gavPQZH|Ri`fXIbS!;@35(%@OyOjRG^+&??CZ z^9``Pt@O)P*wdTexDFO}e*E_`Z0q-Jz*<=UsE>CB+~Bij&N5hA`%<0^vxjj%dBB{W zej-x8ru0e6m=CiKssypH+wFvr^I)Czp>ff${!xGB99UdAZqjjBRsSN<4L0NsRgm$o zTW6Pdo=WnIU)~CbIbw$fS6H*MXCGpxN!|P>!vg24+e2ZSlWDr~FuQ-n=>2d*xTBu~ ztX~`Av=0{VS?OR4%b$i%+XF}Tss20yR-Mz&+zkgLFWosDwl920xqWVr$ic8?%ILWw z#8V@@$_BtRVI+m$=cn_+7F3(|fn7%Pqq-2CFf8w&<1G7573QITY z_-Ni&8~JZIK9Aoknye3Xg8%zJuqyW8n7#DKLvlV~>@;b82rK+2SCj1- z@O%EQa#(b>cpy1{Fu%^qDuadT_SEr6xAILdfVE?4sr}1zmj~}UOncphIzE;3jy_jl z*@I-h1not9&+#d+a`pS^|%UbvzyFCXIuo&c?6E`OefZTzm{R99&jQ z&WEl0_eURr?YGpi$oW!wdF%L4Sa&znhMZ4LV=HEdz}(l4%y^iwurOpVtZ45VMC|sb ze(MgndFs>t5lK-{8TmT#T zb>B!_Il8QE2`s9UUOxrb99a>y5N;h2Pr0S)!lSt)9(anf&7P7HH(1f%jhdezczLJM z(_p4{DrJMi2o@Vw-?8kNh~-IodHo&>3;aX6B#`BA-FW|ht-RKI;0VNJR|BcI`cL|y zA@F~nZ%f~NsUOU^qgqG4pZ)y3#TF!gS2ksK^qlUdFl~h)kNh6t$(Da6FgN1gP2%uQ zH!J=P!}i%*-JP`Ogvgivf&XjYvh)b;EBs%3{jSM9n_*tZENc0T{N?3MaKM@SRQuNV z?>E2spZpB#3S&LY`regVpQgP>TGYXs{U50B<8&vt^&ZSA*hp;;I?ZQh8OhIczfHDJ z%d0QCB3QC{47Gj4OU>`*!>SE+)b?%W9gI-HhAq9Q?e7;oH1|4eG+`_C|I7JsX6IFy z<~3KDmzH@yIY<|q-$5B|)ZSPx8IDDI+lLY2vru3TwOHSD4AB07{W$_%?^X0FAFqpIF znZIIgfE< z{TW@mFmfAA-}P=zB+R;)Q?UkCmOOVM>t8v4(Sjw!+iUG&V3n~GH6M)qoMjhdVQM@* z#NBm`_u^o6%$!Bj;F>XGUY~;LeuXE={4zCP+(MIJ-Qd~+7L0ics*+)QSCfN7VDEx4 zDJd{Do-*97ojmS5teCoPG#%y)I{BCEUy`}APj!LmnVgSge^h9~cmKunS!?;)B}!PC zx6A({9Q^F&5VAirbDC|*^V~Sbjuv8`GR65FEa_6xm`xnL@#jmp*wNE8m*ksxJ|xeB zo1K5WxecrOEZI>4x9{!0k{n<9k#k<(hJ}CcEWHCOyE0|BNPcTWMF||x>%(#t%t)RY zSq`&)Osvg_b}tf(%XAr zTIcSKA7F87+&B>|YOUin!T|-rqFto@`kfA6VO`{Sg&!Qg(h%JSt9_mA$@6!W`2~Y9 z&WFNy<1LF}eN!oq2HQu}PM$~FKh}Tl0yCXG+GfFid-mP$2{&8kQqT9LhyTv)4U2w= zYbPNdTzpgYCI%=68eD zfdcUaIN;+rYchXT%aspBlVR;1*_aNnaJA9XIWWC6u+KNVui!fO-R(}iqj2wMm|xJB zMgFgZuRl}uUi3%PeOAD76JPUJh}+}E!)7>O(AkcpUdiaf#vbJVkkS13HmN^K`sN}g z|CdU&!+la;l)Z7f*&dj>e$}Cns-2x~k@_52^5FFfn0YHY>jZ3jxUX3@Y}kJBQ4}o5avpgb<_Z#Lkop(R z*?s{<#Gh7E^_1;xFZCsG^SZfIJ&bNuh0SwV8J7Mv5c&UI4@pY8nFhc{I|XfT5tmz! zq3Tr{F6QMn!g~FLa#Ej?(QLV=85T7y3M22AD*tYo^c~h^i(J;j471Ui->`75Wf~cO zL9y@cKr?c^*Ke{PsXwvJbSUWo)1TMA;=wfTdJ{|7zGLAGQg0$K@1tSB;-pxr{#1CT za>*cANT1VvHsZqCX+=Xx-1g$28L+H=%nNdTs$QN>%_qm;40|vVX0J`B=9|mbygD}u z)|{I`)t5*b7R^Puc=H3XsG>mN1h-B&IgVTpH=kO_ z9tFE6zvw?64!AvPurFT6~e*K_IC1xrHkL#sbJbB2T2fY^g~6}e_-73cY9&`v^lF2 z5U)G3XK^e3YI>#zbt_j9#;m2!vEdhDE|BnBlR9ME3c1Hh_gmj%-jvDv@^Oy z6USSqw!@w~-(5Hj3!P^%$@|>)_~65-FfT}J>H%Zk36~65*8ls=sjy${j?qe(>2S}2 zjBnBWoVWcN%s6dzh|DJ`Iv2dA5at^nq3S!N=WMLV{iATv^$}#gdy5{+2WtP1se6xW z`TzgNKZGGnl11q#Q8Am*4Bp>*e;iKhJv}9(x{OoEIp__bNk@Zx)d2Pl+-;iTs}(GUS@UM_9Wq z^NI%MpZw_a8UEk@-`yE7Yxdux|^`oxeZR=`gX2XWZ_rB?e>vxWG;L-iC-x7~41~9Yiu7W%tU)*i| zS3{T<<-BPZtZ;CeHb4s=V=6S~JhQr!9 zCvPo-YflfSk^T#|;Mir|tckEs(}Rn4$hj%+n@Il+dnCMcQ|@ z^Co8Q82j=rtatu}C+QE1oA&whO_()5&V%YN%ea2y3T*phP?$Gz)#m5a{V&}2hLjbv z4XNi_)B?>wALR1#lXe+suX}NC6*1@9^pokZwEbKmv4HodI2rakuB38t{QKd1VDrGH z1~Post`F4vE+ixL4iT#j3?s?=J^Z5FsQ$a=ciq0Nga0?Z>|t+ju7XV_rBVHNslIa? zf=GLXH?=%yA^Q3YVO{U*-$?%*V_%rF7aV=yG`0N2c^A);?=P#qy?RU5pF-gh=>~Ur z8qOf=Up?Z)0cTjca_p8dB##NNwjuT1Uz{h~Tim$u`>o-&`mr}h!m6RY=bORY#<{8E6iKy(e)`@F>cC&M%Z@$m5If$U*MgpdXguH zOuq|u6goK}l4M)}ABKu#tCwC0FpN>8MiCGCueJ=|x3w*fn9xMtz z!pVT^M@qa4iSM1!Mb+Zr$ka8&YCNOrSI2J_y1u#n3e_eBd+$^j@(DvbLml-@pi(4ZLs*6XUQR0 zqWBvX1=D7v&mhMyS<}hZ2w0x7Kaup;(LT`hgmAUb$*=@iB}n0~CGDqA9c`u%= zg0p)&P{&hleef7^Kchg_Icpsmo;;y`8SHN3StNi(2Lg`sNj;rQ9iP<`u4FBN%k=Vw z1tMn$ue|0D$N7}iQvH8HtS}xNXz6ZB&L4zh*S{h6Tk@Y@(wjksf3P{l2UgDTD|3X^ z>sKwF1^XGvYgn-6(D;5cVfovC-sF5KPQB#8bXYv;0oM{vP2Cj7fraeM+&(ZnHet{> zIK(68Z&z6B5xK)2wtnq;UWfBPMy%r*Yna|5&uk%X*Atk*x=^dScjWv~;mjQf%PUWw zBv?)P94_gZwvNfEfc=U%tS4{(^*au=!)Xlb>Ow>pstHSl4eA z^}Lp#Id>-Me@hMDMLn;jopoJu0H#}4e147gfu}!)Cc?~)F}*8b>)u1@m_{Uw_*#*3L&TbN+i8>2DJ+ZFPA_a%NOk1T1`!<6R8PRCB5RHD#*D_1kdM112*J zIcw$jJ2&CxZe6ylhDFXhhZVuPkSi%6uv&1klH5=1bNCM}nB)(R&bSNgrRm_7MX+F!Pe2}AoL0h`4^!_GgmE8Rm^WN+AGA>hS92W4k?SRS=@{ozuu7ek zkyXZQ$H>=@;^0dd{Ya zLt*W{{-Nh#&V!bABe=Gw)}HhS@L$)h7y!%94yXDXw4Jx+c7yfrdsF=rq8lT^JMjEP z+2n;+Q_-FoG@E)}LmIq<>OZorZ97Yz|1dnL93GFHmH$xm5w10B9J3MT#`RfAo;OiU zDSfaG7Iyrko=@SKbsk*_i)CT@_mNBMMvYnyvkjXbDq(sNttp7qcb!yK1P5LU{<|19 z|K8|(h2+~E{XAg$^%>OjVUn@VrzgYI_a>1GyY8}fB=xCn)bmraK6MjVFf(PIYaH@a z^dsBC@|Q9FH^U~ei`H7f+PJ7~>tWWE)oXjeK1Y={v*GAI{vkiCu|2$;y=wyTlS%%y zuustGqo%M_tI2o-D}@Um|EtILvXrkVglP+sZ5v_NzTBY8uts~~j}~tGw$|zb@%zs3 z*RcMKaQ}0#Ph;oSN?0#{@4y^5F8bPz7jQ!C)bI?LIiY+Eaftt8ngr(bxk%+1m5FZ; z!iw9ul+z!ch+G4+{*833Kzq@?_s4`lX?J2gByw-B(n@8CK?p2Emf%BKjM+VCxib1DJVQK{?&{LpeWt^69auc-Nw(ifHvO1xP`%xXQ>8`gQ8*+jl z-0%giU$rRqC2|e!Yx)GUT^g#$`l4m{46TKAR`%5TwJzaLcmvB$pQt74%Q0bLSQX6p zvYT3;+S+x2FJSt@Sjx;%*UHObzR7%Q_>nW-rK@0FpHJmvdXtwvAS^Ub5~f=R#`x{H}_h*24}drRzK!;DPp19foR$*`BUa4OAYXFbVF%GxZ5Cso-uz&u})q zVP>(AhW#D8&)7MiU|Z?$!mhAxhqHezEE*>6qX)-*o&4k7E^@nxZB$&J4_C-55IzXV>4GT5L zb)#Vsb8kvKEV%r1`dCO4uK1(k??DNg&%v4zL@!yCAuxfXbs|W1blfK0Zj$yeHn7ICJJ|Z@A#{dj|(t({!zKJ{-Mg{-BYtLOh^-0m*y&kFkQ) zF1CkwFlYLe%_gvUVAx)NnC&83J{YE%9&jPnU5pVLlKS&Wlry%hebW!td6w>9jGUfq z-=!z432yUTNQU?Mm4Al?j=%1G^~m^B$F@%X1#@G!yUHnVqWuxv}dUD$nx>aH8iY-r2{YYpA1WCqrbj}m3ityvOHD$)(_Xf%z2}f zlVJV+e$Sr4+FjkZJHfUQHnERM{oW_k`ZqbbJM|%HZ|2*@0lCSKq~j&9?&a8JqhK#V zAHi*yWwNEh3f4Q;)Oj7I`cL43{dG>)VEV;ZF+h^J){x2m@N#qUq#zHQ41Sc0Ay9jvD~yjlXwFOIYR3^z6J ztvmq#@A$?zyY$Q+xY@)d;1P1`>YC-d;QH1_Q*ObE#S;WOVbQ;jx*KrusmD*_h(ov6 zT!4K}E}axjT)m_u6D~7(;3a~EUoOr|gH?aRlOtfI@#xcWaQ2iIvvsiQqukK&&~6J1z{%r>9Eg@P`BBz_>slMpP%siTD^APG+6iM*@sHl z$3XwrWSD)zbY1McN5D+ei7kAXw!ZwPH7v0=|Ktq|Jfcc0Va}K*9iFiLyKZwVU{zL&)il@^{Yt}N zt-Ixb@o;*tE6YsaxaZlkY+%h))5$|&=EJi7=CBI=PE6S6U~N!8I5q!fv=OYBbN(oe zw3np24S;EsrRYFUY5UC_k<-Ie;yaX{H_~@^ngXT-6Shv#py@Gy2Eji=8*xg z=)jm?-C*hS;YOaY;jcBm#M+2OG2`IuF0N0AMI#>HvxF<^%I&(s@;hs;7{D2YM*dx3 zP3HJkJ(#xdq_)!>$GeTGNk8gvzJTvI5Sz1lym=2Z20mpI%ci<{Jc1qn^7<3A2Rm)J z4!awF>_^PKDCQ8e4{NCQypkWHT$pd#rSKneR)d+>ssFU6-meaGQ^F4ZMJ|j8z9WWZ zZgU!lRllD8ih||Knro^0k0ISx!jfm>dXVW?FUaqlPug#~yMWA}`d`N}7OWO>Pm}rA zwe@y0gL$V})beURzoJ$jPX23drAKm4zsLV-vHdt%Qx+TE?A{795^76H&SA`JtcB}~ zO{tuEe=?k4IGI|$44WU}uVL$uz$h|4^YdP@FJb$CdDQY_W-mVTn6w{Lwyp#7W7|Jk z`2g1{t7oI3cehIYqP?gn_%&?bmd;SIp37i2(!byUW#GiYgKF=%sk;8 zx)rwWdv356)}^QMLP`6cw|-Z{bmjLQOJPaNnD-i(e1}y$x3Dx|rp`%xA~C)xsgAyBf~Gws&23zk-VUg_|EVX_H{vH`imbV8x2tCwCFAeR5g?(~NX(aWLJs>y~{mCpfWb zBV50@d%t)%-DJc5a9HSj=0pTs-?Hg_2rP?;C>Ow~ry_y^VcQy?b3w4*)>?NS@hxT; z53cRU`>_z#=#@n-gbinuoL&I)H)f3VgM}4yTzp}sqj{SbEcrAwXEw~Xt+tv*+(ni$ z6HZ<4Hf<8ji>maSO4^GD4;T+?H(Bi9koNUagT}%2$G$kb!SwR<^3ibF7xM~dSYi=- z%^Z%N@b}<2QqSh?V#111dz|cuhip+sX z9ZCHLGuuvfGGMxF$&4?seZ>%Ye^`6y=By^zF!-LLFD(CRF!wX;waT)z7c3DKE^L4~ z7JvPElKMK~l235K#RZFdz_fv9uYDwW!|wX7urgu|mB%G9qW_s;{W+%gA?;JQXKOm( zxChx(d-K7}#6PfkLlTusa)f(-!~A=RlxbHB3xC1t8RL4^qdt3;GKW}x_-A)w-NevA z-(Xem2x@$eCmp*q!kjN>sNw5t#yPS)^r9XV5HrJEWyH~2O?MJA zHZ^AxSM=*NQo+2QSE%J@xU{149xV8L>{lZhUgYY3#Og$+-^8vR%?Wp5TRXSpW?1~G zUR4CwUw5W#{<>uTjsG~#$n&)V7G}6pxoXeRL04eR0 za#%bk!0tO4-^I)Gmljp>;nS-rm#1TPMUJ)F#>lmy|^`YE+B0fz5YZm8H!&AQ4 zR(SxX-lu^2wj%#^d;jBHjah&guJ_?k)5kfNJv<&xts15!(JZAbP5D!z zVD1_wW%+LB!Rul9rfF1tpv5=)Fw*}2&0l0U@sc&LSh|Fo-`aJP1p?T7@upg`y)#0a zgezg&<`~L)7gvv72J>|Vt|V{c-klQspZ3|J#TkLHEPE@}Ub=51_rDe!5A*d$PP;Xa z8ecNLOXLgx@As8EGi%A5|5$dZ@u>%lKNP6nUo+iaJQ3#Ed`bU`@mG(mNoD^hPcX2# z?F@6rET)FfZ8&shJp8}yAtRxJ=>QA5yHd-8)osB`TUhqig|d%(e1;_~I{T=q1;ZEL znH*&fOJnn>^<^^UV5}Kz-eW^0*&gKa4%MbGZ`LZx#arLs7z&H8&ZE|M+swtGL*V+A z<73J8!C(BfVh}7$=(K5r{g&?-YY2;O-zq1T9f%bSAngZ^q4q!W*53E~!~feKg%v(J z)fZ;O4=Ex0C#iS#=sqxe(O+Y-|0*+hQ>q8k@6Q^psuYHX6PT$4S+NH@XhbQx`HiJ2Em4Qrw`nKB~A0G<3W=)WWC})&KUoo z_$sNN^>4rsw3qA-^C6Bqw{jb??CfyL@@3~Kb30O;FCiD~H?}ZAu2enA$b&U?iN&U{ zV_oU<^KgBlv7s3pY4BJpgLyOKspFG$dspFUSo+;#i3M^=W0#zh#G`xkw1lM@f<;-d z?0!GWK4qPkGhu;^I*;V`+ixE@4ySfp>S_gdthn~@D9rs^KW8}H7O?foL6V=%`$6m) z;JzjeHt#xd#t4|5$K9L?YnT0LBCgJR**lr!jcsp7!S*vtrzXM@w-r0=VWuF;b2m)8 z(>K-u_A4K{Z6_?A`P7CSf8`d1`8!}?%tp&Gu~AFY9GID-xDl=gfD8^*`|wgfQ)t<0&?&KR)oOfV3Yyi#k7O$|_=n zz^w5{T_z)^WgL|S!?yj(6Q;l!kIy|>085VleKHl+HT<3H1^bkRMofp(-wq$-LE4{6 zFz|qzc0Tl*3NzP_?KcyaPkI-{hQ*_gT;#$U!+w1xkUY2f3dyM<5pwVt{)11_@L-swQ{imXTE{6_U@VSyd2?Wdh%Wgf*_NwxoSocUG!7EI9aJGZ|j-?t^*8FvDxb;#sh}&51%ISaxxz0>*A4nathtrI?+0t9)};8rkp;OnbeNM^@}9V0-}AS6F!eqhxOkA? zxxc2EKhIg?=E1d_U$Hu1pF?vfv+qpu{sC)x45NlG7_wkoD=ZI=_&gWwD|!hmn@IhL zC*S74+^=7nYGKANz85(k>v9W=A9Tnl(_cntf3EJX7L2docj^)l(6{B zYHEAXT=NaPMe@W=l$#5~nEy4s@|5rhFo2*~&y%FYG z_k2%&pN6>`*%7d;^EEX*-rayBLO9ha_UJ_9>XeLsVX(5>Pfs%bu(8T(t6^r2w>PQp z@O^o7<$o+G3>du()|B~DIYXS077VM7{-f$m0`|5Az^MmR)clFVEQ1%p<~9Qx$^4sK z();O4>TPCCaDyYAY>H;X+FAFg-;c0n(Y|T0{8&PF^81mDY_xQP|F=DcakIL(!pwDV zkCE-QquTJ&M3{QtIL!OCYV25IWBV-&V8L|SF?*QqzhSvQ9AXh;VF!!y-uDQEnG=6K z9SKX8{5l;B>-TAz&^8K|k6a`g467G3 zE{%sHi{Jhm1PkIOFWv!rm2Dks2=j^s**oD3gEYedF!g;Bk_W8H><4pL?58PkI(;Ec z9~L|P2tEi4_~-TdkoNoH@(#hx0YAp|hNV8;{vC!n4!if!VQt3bSw~@wzxCZ7#Isyn zkHh@;3@bgD{bEj4Caj0=vUPzKPBY3+!iHJtUph?id+3+0$bse46=#0I_2;bqoQHi5 zS)}}c8TS-Vu8{V+KHpklg=yc#H{trKZZ^$ubL_;!cj45eywP7_LHrqq2QXW-#`hzv ze$1+W30F_?zpaI7AMY1w;pjzromH^preRJUtQj}yVp`!{ciOA0at(YVV#3%GlF~lgv~!qKamapuYbWhzkI9|=6zgo><{vc zZ($)PV8NarjU8~C#pRcW;redlrW0#A+}5PQ{B3Kg_C*GJPVa+NdtcA~jl5{(wa`Qu z&tXtz8DuizVad9SG#zrrmr>c#F!j9?Shu-j#b#LW!!3wRUq{qBe<94b+I)pfe};cl z&NZ!t6*C1Ov>EC^pFtP_N*Q?|BA2YXD)@s!)$JmxcQ2HfH_fenF)X#5N-b|6c9#l2SU98)H9X~0k9~7tM!VXcv=7Ny(K-j_ z_F7FXe|z;s2T!>9`?Hc3g?X4t-< zZO9~;<`&8#>%Vw>c$N!HeXoY(<^ElrVc|J*m(MWIEp`8RQvYQT<#gj~o-DY&{&+(J z^5lNL+eX7$qnA^O73!sYJCbi&dg2pYCJMNVwbm~&43um+Z1I=!YV%;;)U zUJWx3b&dWv6#G9`unlo)<-=9KVDn`cM^(YD#XI-3z?>vWk_N6e`nLWHOxslTp%M;! z614IoEMPoze*t@~IQZi|oa*8H<2kY7NLMXPeV+xUGiG{gNc~hf)t_ct^6Quy*805; zEkmx1b?NdLX80sVmXdnAhe3~Edd!`&hj8@Eo9QZ8^TOz<3a*#;m|X<_Z~szo*Xgwa zrfn|&u0$^2n#9UsUbuaCvOf|o`oTF5vx^3;ya5;Ok2!u8R=b3+xeh1zKG&Rvg^9^V z#Cqk6!?IwVqe;XyIM;5ZMJCK!7qj^)EV}g8=_t&!a2RE`+3^yrG6?RH21`b{yIh2|RoA;8fED*w)yd)Xz8h-y!JPH&Q}bbyK&4qSEZV=J z^BkPo#I#O=Wx);kXJNU+jK&05c&FpxDY!ZMp>PKr_uZS?f0zAg92p0vHhvhDg}fkK zIwcCGzNZ4K{{{`)0{dM3>V6CsS?;^A5vDy^{o@E+-9LV)5SC=#?{Nq=`J7V`M)Hjt z`lrKT3B|=LVcn0s$W-F2$WOsA&*}8H-LQ4M?}k8_-ClZ(xcW$$WdJOCs-m2{;cA69 zOiShŜNs4kubOKI*A+hAFbPdOfNs>P2eay+e77D(M-#X8ykO|au)S>+^{dx`Tk z61KiOZO=ql>AiC%G1tXhOiWw1!GlhI=K{iTkn-=2;n?Hfw(uR+dPIclvH zEOtDyRRH^O?@29SZDniBD!AIRpq~lMT-%fs0?SNQElij{C8mBET$`}JW*}T&v+Kta zIHU`6eSesDS6|5^d8>6&FPQrO3Z}cItf0e!Sy!i%^KC}vw5V>dw7reGUSWER&vu1% zN9W!k=gZBH&Mxa5g6;qRU0>vWT08eQEG-_|PR_^NUwgf3hvV!#Z<6aXy%d9#AF%Cr z7dr>o`ids(J1ltqC~G)rzh)Wj8=UIfYr8QV7VU7Q3HJF`HQf+S?-tu!53@Q9tm&{< zUG(2Nm|nZhoAe*l|C<)^f#h3i)|38;WG{~fEo}awo$9X0#+JX?|cM{3%mL}hvkhS?Zt3H@SV|5VeJ$1 zzDk(Y?w#}m=GkvPav63lTe`N4)ZbojA%opt57=A^b3?ntoPn9QdgVQUg-?I!j>GC# zKa&QeeN!hvq6_t=q`~JK^e$(-z%=o9FlU6v5fQ^2XkTxyRQF*27-!+m{rP zd_d5{wQyT0C#4XUDvpf~g-ts57Ttg)zNWvH!v)u}1;jr4LW22ly6L%%*GT)t-QF*N zIZ<0iU4c0t$IHA)`{0+8F2b^we=Tk>t*G~-3$UU`*r;)EU;-=Q94xQ?*Kati4brh? zuzFg1q6r*UuKIEsjvI41*AUJ&_;ltZtb5bfqdVNj>rBjqZA*im>E2=cvyNt*fSYeP zW;MfXrx(B<~ zzgdwEi;KT+RKn5D%bO0ss_!#@T!r-)`52|a?3Mc?FTfn@2POMqW}Tk&6kP1&kd+E+ zY_(Am;`5hB?1P0)b8=E(X6(uydtrH%*`{4^k$rP=63pmKzr=3NxPn}@bA1%&TA52@b2^KO=E^dVldsmf2 zlJ;9-Cwzh#7VR5|`QG+!b+CzGMpgvOx?Pf^C3!=94%gZJ!5Sy$Y8#o?99K zOIuIc%Hd*d_?(3>^}Gn&=5wlg9xRjuJkNmBtIsuf!^(FzTMoj}hN2U5VBNls<@<;e z|5oClVQownw_&?pL?PEcEYTgi=DWzXn@)2IJjW(ZN^Mk-fda$Z7{!ePWTL1 z-Rp_%COB+kLdR5?<$3>9IIMSLa@7=AlPM}$0o!)Zx#9-P;=QAm!wF`xyOT)0WzQ1< zBtM`3gbgcy&SWfrnOEHHoniUlU(ES%q8?4W zVd=Ik>&b9BzEd(1<`?{svtaJ%J%%=Ls>#rTk+4_WofvD_wp8M111C@S=xzm@&wg>l z64uzY^|gR`jRRXPVA@x`7E@T-sityssrxKqh5QqHIC6RTLNnsj3gKFN(tc}>l?iFj zFXWAdGai;a847DBUZ>2+{XOEpPJSL>KLk0)?ZoxbXzwTNK7k3#|1Nq$rq{0{XO$7G zE}Q8|riWv?SZD|<4es|eh1s+Bs|Ld6M&|@W;A+Qi^8TdWDAm0`oSgpsqdsiw^x`TV zHsAepeQ#KB+uWo(TyGV%l@7DlY<1`Y%YWUK^@RBr!>4z=MSq~AOBZ6E%Nm&uF6tR^ zu{+E$3+(k1Hn|>st1HYpa{6Ec9QibG7LC+fuetvQ7Mz?P_tzN1zpnmJ2}|yrPUwIc zPOTXgaM?TaX*yW*yg2v?+%{;fupLfqn$o`AhS64^fumb|zrKd+ z_s5RUgsXonp}&Ar55*Ox!I7J~aMiH+v*A+`VaKR*yC1>YW$Q0)hgnZ5=iG;duU=G( zV3YD5HFt=~|Hp7y@}8UuFusA+ToI{pkfT^JCGRC9wKKm$PzM zQB^x|G3@tq)sB3apR~_p5nO8&voe?D%%2YPVUu2W9?4*Nlatg77UhTZ$%Z-0k3E_O z^8*zgQn=oG(btKvV?p29nXu@uP(vB+m~=@_`$Zu%z) zY<{@;nFGwqn{Jl|Q~xi+^;+wDdtue}Qm4T%d(`fCyWr;gj~>zC+-KIax05{f--*sQ zxE|T^??o)k3UAi`4Huc*EZ7D!`oG!Q2J4UPcRd!FGZJ!q?VafA#r$S)P(ubCXaL4SbJ$zVr zByq-NSRB?89t7(So95@i^x^zv3rTL7o0bK;#-9A=4YTj69~^|+_|iUeV8z#r#8lYP z^+EA0n9=)z_a3-8Epos#nAYz=ax|Q37CmeVZ0>z)-X_>9;_4MO`DTJ+CXbsM=`1~a2)iAxMm$f78b9#~Qa#%8AS2ZS4Y2Km9QRgOeRJrqkFY4c zW5HKA)yc9)EouL;bZ`TloE0jnA)aJ$_$};r`B=}_F!g;BSg3d#_7b+OKDqVCty|XeN!P2C^JxgFmaZuDlnEidJ z@m*N1SKIGC95;hiRRot^wOUmSH#cP`6~eXFOUjfm$EoMJ0yy&fk${`9V#wae3vl!A zBZ3>SdCcFjXGuLjbK^Cbwn#AjG|XRfB)5RL&Q5m{mR>g%ObIa0hNbUaa#COxW8R`HI8GovzZW(< zW@VfKHxC=MX&0$?tQvh7R_&bb8V7S(!-EdN{BFlMv2a4HOE}c;mBvDFM~+Eb7`vy$w%(I z5&%ngZlg2d zryAE2zTdyhg*j7SIsJxdJ-5Ys!t8l=_rAdT{}^w$u;xub;wM-bXhfNzAAG15&TjrH z@IWpbbE}{RmM1bl&w!bCrfhfxS5(B`$CpEusXsSYzJO_JR{1oNll$f1umx7#IWTMB zt>O~6t-Alp$uML3x=*)YiSM71iLkWU=jv71{o?1p&M?0}zvcqWwb_v11k)D|{CFN_ zJ|Q@kHyl>3(M};2et1W-hBbFOb#XBFNY+~mm|;3GcpEIv-_0?D#d(E8x4^phC#+0h zN$F6Bjj;A~o$pXMRX2ZUB&^Qgnr95h`4vxyfOG9LRvE!;<@E1sVH5sk&w(({#&zvV zSkbkz8v|y}n0+t^E@tf3?+2@l@6KCH>URuWtq(V!XzjBQHmqBgO^3N$?XEd+fpE>m z?y!8qv$mPA`~JA}u5kT`LH_Qra4&D+-@(Y=t=%~h_L|{x?Kdn*&T?~xIZ0O|f5Lo) z^OZ4h$VltJR+8Vn9cv5Mrld^&3TtrPZV5B)RjvO53zViOEa2=k=N%0&!^}wEggCFn z>OCxLxwmfsEMeJCtAeGC>Z^1(cU14!FW}UsS@B(9^O@ZTJcpSqe@$6Z+RGrqbB)}#LzcpFyN7f)?~ zT|HYpi(vVRkH72S>ft-&BFz2XH1-)RE`He|C+!Em`1=&jUGiH- z?6bV>EHO_f?w$`z9e4Fm!|o}{^z*RTsZ*_j3wAS(oP#-rg|A9r`oTR5&%j*0z@Q>H zcbZ{MHq4CtS#k}|u6tH^64rW&S(jnK%$p`sSQGI$|12yEI<@UM%-eG&CJPq+_S$s} zR?Xt4XTU{6j}#n*8T~@Dj=(05Pu@8U3$s>?Oo2H+6D<$I%y+{V?|>Nt&12JG+JdUJ zF|fLG$KnI9tcRvyBkcNuwJa6pe$>Ai2J5?@G}s68Q&~G!!o`QH*QCJmrFY+kzy+`S zf7uIj`p$D*4(HOjGlrm znLQhp$1!_~VQF|o_gQeVlcsJbEZDTqWiqTOe`K-)R%ch-XTh?`lOM;!{LYKJ?O=D~ z&hxP(PvnFRgWHa-4~&6ncWL{K;No(t711!`3;%{b%-pbTbrh_-Jfubs&fPlq6EWYo zM*sI~T#qwHPa^HPE%k+UaM_>@X(Xp@cO3nexcYhcR#<+iZTf3iFKzUOEim(Y6|l5N+V-(`oNOI zzDMT6+%1-!U0}}4jx1kT=X~bj?^jsgs|>S5KhfcBX%-zI718@OYoB2r<87?5UwJt78JSJhVqA z%9(I9Zz?rDjnT`z?l9larkRXiY#Y4mj|RgV96pR#q4~PzE3B!FX(Q9mk3Vzt11uk| z@FEsGa?^VYNB-&DO6FH?+PCBt%(m!C&A+Y3;TVl#wX8} z!Mq*2yAb>AU8H;fm$d{XyTGCBqW?T_rY~x^)6v zKPKv<6!u!AiXirx+jxgKH*NOs@h~rMY?s=`h0ijKf*A zD{LFxYmpA-4L#=O40k*a4rqr{t)3oof>}m^2ijnrZd;}!tY>&b{sop-KOSZWOX}RB zKEkZX5A)36+HK=5eSoEF0~teMdXDkHcf`Earc9XIUNWy5<}Vrl)&Oo>crEc2%!vq+ z^oQ;H8l-4oiP5!;elWl4V*7KLK7i%l8}4v-PI>|}oI3i^;bx}juL>4-ogAr099|xM z8?NWsy#H5;;Z>~7ze)1&T_69#?6YwPZostMZihQy?v1T43t&dr5T9Q#tx^z`3rngS z_1a<8q~n9nz}$;V@3z9#LkFp{Vb0qlLx{t=?cFJb1+TeQ&9GvG)wg4C{rsI#-{4}W z4*4O{zWq(?SGaal=gl-&bjVcm1!i&g4>?WGpz9aaHJkq&CeOKk=V$(^%G1VaC`1*SXA)y@_U%~@m?k$)(#vV{uVYl zcByXw$;tT_oU41)dmgNO)V;hKuJG7vJ_lw+Y>lZR`3m<=PhxiA%hx2oGHdq~SZq|W zo!H!9LC}BgcztxeD{{{1$5hUeycofT>q`o#`r5Ac_Rg^S?vYVs`0Rvk*BoKq(BWIj z_@nJ?b;IGf6W#8R>CG;CJ7gHFU~UR|2RquNTbaPk0il$&Oh0d=`d&44|@twp4 zi8*ieVBwlzPqKXD7{Na}2VwrTSEe?=wdZCp{0lcP3#Qgjky~QnFIacnw2rJV?JA*3 zJDhryoj}%av_cX89X4MghZEP5?MqiBtgnOX_nq2F z!}guLSk+Vu`^@PxvMZdf&s(U4Z95kY>-U& z3zTWgqLE8thSltbxkaL@#F36~3U|Vy*I8X+;pD;6oH*FFc6#%6*!AJv=onbHFsdd2 zrl;FXiiUYEjc<0r3ELZtx5C_M3%845<-`?I5zN^5_Gu!l&1kgQ40BBPJl+HAR(RNL zf|clpOo9cU*~=ne`dx1UIUY4%s9m$3R2i zXE`jpbN$a5m|2rLZYiwDDEWF8798&JVF|3UpI>tURvrC3JqT9i{CRW{j@zm#_lJ4U z=H9yuOM36Rz=Ko0?yS55o78O@Iv+OQ+tc?3tS~T~>kG5S9+nou?zc{z^M=LoO^0v7 za(bnU7c3gx|HEy#IA2~o3%0eq^+pK`tTTqsgcSqkjlK(OqhoJQg@sYETZ&=P9d^bP znAX2kSOQmDiYq6<+H+O{6`Wy151t6uUpsg0A?&p<^9CFCnK=J)Da<+c{gyK<`N#}- z1oJ|??>WKB`Fi=pK56obaj@)rCUriecVBRs1#_wX~CU{N#YlwQot&~G#@6YLA z4*#?z86J1XTDLY>DQu*sCqXWp-2&5Q*HaE$$r|+y7Ser&k@H6hFJj+kn0IXQ2IA_} zs@09KTEU{Mck0ZrdN^*qId#72-a6{^N0|Sz!}vAYOC{yC?_lxt`fy^whQyw)VQJ7$ z{TetrY*l6j%(%2t{sv|qb4YqZ>VG)$-oj0*dKo>2<9bxi*TMx`TuwiP)d?Y|-oZsD zy8AzXMellNzK7X+RqXquo|#XbA7^xTG`|DW-*_FWMXpz+wY?2D3nw-b7u~pdxd>*P z(hk(YCheSwg{1!b`#NGt=W5y&m|^Lg_zBidG%=FHve&C75EsR&mdRjl<@SmOSSH;P zdIr{&j#W3p{Fw^%Ntit}&#VbHlqpKOkmZn>XmcOcY#yyT=M6Sh#7Ivj~>x3H}Tr^^8c#Mws*5 zle(U%t=k#30j9s}S!01b%(3F?T3D@2HnV~CCoJ<^1FJT*T^R+(onpUO1@o(WEO3Ao zPo^0!gN4_-y`BI!jr6;}l(bKK9q$BlY9DUo!_qb1&0S!%o|YE`o4buz;sz`GZM+fy zH}gVgO^0PSGM$OJXO)~8ur6tA`eK;9#I|x4ocnXPga>OK`V5-`*G9N+UI?qtEImrD z_o5vVs^`J<0`}sS#Mf>5`@&*J!Jf4+d)6$~99Xllmb#u)eDZza33IhG7j7lFW`YwJ zZf*s$iQ^Bx(T{&EY; z3I5;pbl`=?!Q)`*!e=jUB9DIgR5S+W7*z+9z!Ha+vG$~1ZfyS)W|==J8VM^$3T0Jr zvB8CzBVZM4d3-HgeSOz6Ygk&krpFgJ<4j|&70mt~n)Cy%o&Tz@1uQwP5d0;6e?Mv{ zY&*As>aQrW>U=%~*3LEC+!y^7%*`iy7{m1+Ug(kjjj*wX*9MdNgt+g6VeyCg{fSkp z8V?!6^u8|-4uZMI&W<&Oiz~1FG=Q03*4(y&y(H7N4S?x_fq5ffQS^&f{b8-{#U}^Y zhcR%9J}mkc$RPbQ$q%o^^n^9-?<-0FPT}7fuR;2W;{RsQqJJWIr zF3z~wR7=`tGQ^}mE^tAGgqW9RGU5iT{WVeh9+u7hG3hoeyF|BrN6b9#T}<-ARW3C! zE4xc@1*~aQdA@>EoqD|_{e=~qLbp}I@+YtRe}tR2?ixvK`_$u1BgrQ}Ui1>yeE3_YC9>c1a@~&2}yq`&B87!{+IA=JVYGF#5dOi(K z59-b?Mb7FlIW!3le7D}VwnFXZKNM8_ncr$ zEC_WhTnuNMSW{MB$fd04IHtLa+~;fS>0snOS1u(Gi#6Vst6{$tx=VN9=8eglqhaQQ z_XBUk+Sonkprt{#caC)Yex3_ng?qhCCw+6 zy!dkc25h^li{5pZy1yB2+OXoyHJJJNx#%Ix?{@3`Raju}lSlghL#CXWUO?Kn-GBKS zPMx^G>k_QUj|q7Pv!C9zxd`*8`s{Cn-G_{;%qR6bx*D{>iaTd^=fUC>#^DawcJ)y9 z1z2d7E9zE_^D)hce#HO#|D;S1XOIh3Q^3ikQ;w{fkWWsd8T*d-8`muid30OWcB482Bm>u%xILx0BnHvf-9l72lSG%XE zH^DMjS34be)mh=$bnd7t;!b$$MPT+jPHJ3Hq- zXZN|!*?p+_?_kfZ`Gd&%7^bVU^stZh1T$hy@tPy`u;g`dYZ}ZIds6pT8Gp|Fue`g& zjogoQ|Fk=c`aUBYHLbAdS3hd~8703}J#qi`-{%`nZT-_54nJQ`ZJ&SllO+<>H?qFM zLMCT2avRfvFLGG6C?a((9MRQQO|~z?F=$;VtoEw)ItfQ749;Bw(?YB+WWW;N+^@?? zo?F&4o0QKP!dMS$`D1cPd!nAdgyWV(^d;?&uN&QC4=MlP!_f<{%Hioxa(|wAF|5Z$ zlB>GjO@&pJ7cEKq7P|ECJq1V4O$;SgJe?Gq4gYKZw2tkLU4q4%M+U2qn|9<<&mY8f zA4ggD+-CxLJ|aF_NF6Vnlb4MRu*@fcTAu!3+4|qGed(4P>oy0tQ%1}j;y?a23&rrSOs&ws@9J7gf=UoJ?s>Q2h{-b=N2;gY!u z^1O)tX!B#z-nq3aM%%#^=NVM{7kHfRGyrxnb;u;SVxYrt^8AT!;t)S#?U6Io^DBZL z9xN&sJ9V6n+}OdE+WrdlHrx5IBG+XB*&k+go#ris3)qXO{mEhUo=cwBiJ4s8ne1O> zVC{19d=F>BJq_9aGS0^xTj1b?ZndPno7uEv?SPZ|c6KH0Po5I1O@u2ByD5*tq6JrT zkHWnD3*Cri2UDs~z{0PeR7YU?=iiHR;9&PLS7juR&ab)%i|6<4lMHhnIPQD^vxd%; zA138hLu{WA7d1RhghOW^cY6z4C!To_ARiPJWnahFZLi7BrZJA0&}0&YZen%m;CLl$N8>3a~iRFYiJ#L zo>R6-v}++OyR#_V4i5MHG+{n0$N%)m^P>e;(efae#xAQJNy-N=Y!e8F_Ry~$2lL`h z^XJ0R)=N)Mf;lyZ?)t&p(uZpDysaj-UGywi(*I!4Tv$4LdL|#%?RzmX7#0jVTs9Nt z5Bt4iAuNmT6*L282JE;jgeAOc9c4*ybQD~Axt_ADWOrc<9AEjBvYF)?>Um;zX5oHc z)Ym>!R>vVPeq*qoLj3BKdOs|8c^W$jmX3HrJ7g~D6JWuRDFcteb$ns9H*8>Qmz{t$5BJE&!mP!&XJ*0dQzOrK z!m4Kz60%|a{K;F1*(1j6%!6rtM(I6Z?!w>QFT$el9}J^l+T69gV%VZ&-0$JAtk5XB z1Lx#OSj3WHM;6>CIsd(j8%)h-3mZj=BiOJpzMp#;920Qk(omRr@OF>au*%A7tShYf z7E)UU^G?0Y8UzdHcQ^io{eAd3ELbhxIr29wpER7ZWQu`uT*pnX1|m1S@pfuOo?|!h zV1JmJ?+?$z=SUy)aE8UvTN^sU{u^z4`oZk?Ii8lVJls;^07r|C&=|1Uvfd|cVM(0z zu)c6y%$X6^u+)Fgl0h)N;B`q?nAf%_!xb*pS^8VT-0$z+4~HX#L#;Z(tSQ&*N5c{G z?~UsK3p+>s?|J{&%DxsPKe0$U3Av1EUSS3sC%jPcVZC5QVQY7^$F|wu=D@lct|x!P zs?m9TAsj847t{o^{H$KBhMgnpOTNLHZEZN)U|+@P?+q}m@TbFWxOmBfnMU~E{~rp1 z7H|IoN7s&7mx|n!_%OI0rmx<`KM996et%mBn|k!X5yHba0-jP{vv#Pta*-LYfMA6WEo z-Cqr<-y4*%WpsLF09Omt7#AG&GzYU!v^Cu_l~goRsEF`*i38X(it{MdDm~j z!k-b3En(m8%&9kEX?fDBuCTNJ_ModUr}s|_@_(2#<}LMQnD%?qWCm<#@OMzb;u)W7 zd%-s2DmNCw?AX8qcCc^UfVy)eSG?Cc!utKpe!0XSPDC@|f_crkXJLWi+v>h>MqxzA z8CW7cwxS=*NWHiEG|VeJPMLp-we18cKc*>$EHB~^e{?!5`m%ufyk<{*avH3dlKR~i zx%A4Yqeo%0&6lYE^U$jafFtcLAsKdlFj&3H~qwZK; zqZC&4|DoT= z{%Oqole`tCTUy@;DxG2$_*!a!z!8%yR-WuM5_Q5|@-zO5*FQ?sTChaBaoRL`8JDzGkx~|hI z$?|wFuHO5NJf=81XA`Wx+~7o9>Or4C){ogZd%!O^G)rDZ<46?U%J0-)-+gsva@3sI3uYu2RRJUeeJt9@ZOl)5!6u=O^tO3;#Qx6u8IV z90T(+xnIfoC8E;Hei*6$UAfZ%4&_eW)Su+9S1_Gm`+jSXG~d(Mp}xi{NHgH=8inVv9na@QU0VNqVkZ{+`m?q3X67O>*?1TSw`YqyKr z7UsM4iSvPN?D#)gyJ7pw)9&zKVfpRTEwIT@w0IiK&bBK13v)*NR8EJ*i+98|!$#5P zQ+zl{*6M8{CIXyqZ z@~y7a|1-5~9zT8uYuZ|Imm;^H7ptp;dEf3<6B`EvE_wrt$F?2591cG`^vf%lu`FP4 z1Z?@e<)jW~^D>;s|3Q63Mcbakp*MTpUJb`hdq4UaO#5x@ybfk(KV74R&B~qUu7@=Z zZ*Ja&1y|S=v9RvkvtK2!Ca*`oZLm;VQ*i?pZ5zF62ORuH6Il%N1+%So!Af!8jYY8X zVdUkVu!gRmdl^=R$5QrxwlDM&tXNqSyc@Ysgw+rw%ro6p5-S{RIk_;a??)=9%}c$O z4U1FScO#7j3zvAEdmTyY7v5HjV8)+ScUQxBZKng-{<8kPJFI{W zN8fH-Ny;~63QJVZOG98)(UMb> zVdlsfs=Z2QX3%_Kn%X%s2)XFRkb~o4we^Bh(!Nb~uQR-0qp3G_JS1t3?HB{|n_I`v zM(z{7!EPk1`<<9G11``GadwC43#{jp<1gpVm##xe`B`&rPJ!Juv9DcWQ)GyY*njf( z<1VnYu;1g!uzIB?l{nhB-2-At=cB;`VXjxjk#Vp%FK|qM*ig6Yq!(N_?a+%puz19U z_3p4umy=iQVBO7LyVEV}8EMZapj7W0+^m!g-(HZ{NpJMf2xR?g3Qu~i2 z|A$uen0Tih$)jm0q<@!2ySBjs7OqaC`d4)?XFh8S8@9fq&gYzVNqNMy_%K^?elKwD zBQb-y`7Koc(KBzziobLm53~DTBmJ$2gll*I!2kN2@joIuG{JJm|8%6kRhsf6^&2c` zGvs3jvb?@`gX`gc{oRa7H|5oE=ptG{D}E0n?z?XO1e-+*Riyt@uyJh22Ur#`trzM4 zs0wFBzk!)Utsjy8Ou?AeCr@Eh&n;AcA$X;DFtMnKvyk)`{LM~VJb`Jad(R=i-!=uy zFF%6WkJ?=N3@fH|=yxAhUhnecBkVqGtNu33`L*!3im!UZ!_g~Qrh^=)!}r`+3PWf;sbOzBJh&+g9{+Z+OO z6&^cwz}%Lh^MgtKhwZ5AL(90Um*>HX@dK;G$b%1!ikbs6rw(i&|9{ua)I9TtweE*b z#K3X02I^+R^cA{an_-bz+Toe7f!}aG8jkdg`{@gt-tO-j1q(_#$4-N#DH%l@NO`*< zBY80I^IL1;y2H}RJ}}2pY99$R7qd2vhv|Rnnd@NTF{!^dteL^uxR&HgW(^+)o7o>9 zv<7xwc68Mk*bvo~y8g9U$9~{J%AauAMXrbWFSUP1!J&?EdsmV2^oQPVu-3o$>vFiH ztDF5WSfcnjnYb=_$K3y5dat|9OX24Iwgp2-`G__F#DaH)p9aC9wSvGUFk{};?*m|3 zs(DyAtY^Ev?h8x)`rZwNBlC7vI>EY*=?@mbRS`WsnJ_QEdrSx%ZBaJR4(2u;^q5D? zEtW7~&C<{>b75`?YmqH1pL316{C9|J$hw&!}gD93==Gv7TUsv4SG&iBTT)216Bl%D`$=%^!X=>@N9$qD)ibf9 zVCUzzCld>;?osn2M2`*4_zWv2`|TQmTv(T_sD){tv-i5gF{S+iYv9lW?G6ry8!d0{ ztA=@uyp&_?SN6oJg+4AW0J6Ive=uXCaR5xFqe+!FK+dKD% zb2_+QdIRgrge+$`;_}m)*Ra5r^~MQ~n`gW7p7&sfJG+FBnP|U_{Gik3UuqlqYxF;-a`%!im)|n?sEn&&DoI}@P)zv$T z%whS8ttkbttlibmO?qt44>JSLk(`lF&G(=`=HQnF(|wsc4aoJEc6gkE8Lj8Oe1PT4 zSP>^+Nm+2yJ6PhfI#muw-z}%+e=t4!F!eaef3b2Zkqb8q{v3mahF!|Hu;;Vf)-;%U zzY}aK{JSL)W>(o9egX@=i`yl@hGl0O9>YHJGVTFbHkNsg%okC$nc{C73P~j@u0X8_$e%nl*1KXPPI#6PTR0M$6^1&uQiKd z^;);hWPCO!(!n(h=5BJ6q{0>%y%sMZ`J2u4WV|=peeJRkSljh6H6Gj;Nly!gIUhIw zPDHL=?YuP*7QMK(Knj=5OdBeI#gF`o55UdF*?xRjcRHGy@51-y$GjP^ad?lq9mr)@ zGVV`@^+(;Sx5FZ z#YODj!(rXb{s-2=zJtE}B9=_*9$<2!U30+~~$jF2>FmL7i+>VA|>nPX@s}YbQknoMZ1LV8QHP-8(Lajlbd&onf=${HB!So41<^q(L#6(Xd zR{U9WZZ6ECiT!%Q|L#8|oxb#?E38z`-sg+F@!jb4U0}(>n(C==sQkebOIW<_Tbqe+ z#eoCLPOyCA0_Iq_c&pW@jp z3*Ty(wro^hCphNBLj8N#wC=~Pc5r0uwW@cpq3`l-ZD6jsueTmnhnFV&twejx<3_)M zSsG9NFIXOB1HnD)%pm)vhN)U+dqBY4r?PKdO=19-(zkD<_gXxz4Hx z%d;4_&)*xSiwbV4V1xa_ejYG2-yLkjpu3EMnO3tr@?qh^wNu$}=#!vHd2md}^#=yS z0zX+cneW7AZ_h{lVbPom|C|4Wb;ZmX=8LuFB)4GBnBNy>$6Tax+LL0RKExk$C~IEq zYTpZ1o~%EagYtCW7v4Q!v*6LG1C)Twg?*EDUKA|7d+BaKa z{dgWGvcFB;7Jd5(i@Y|!C;P+K@y)q!u-SyR{mK1Q_r!$T^{{sSBx?Q_<+gx~&#>{? zvG!Wz;r9m(t$`I~&t^S^xyL`B)WP(~uwIogZEUrJmgEaFevs)|=!>`?G5=!rYae$5OuUvEv*pZ__K)fIMU7p`>hBAni_BQDSR%77h)2Mb+n& z_U&{AW=|`>PU@@u%YS9T^f`OH$ofc@x{f~q>m}Xyll3j@IktZ~so(XvdmY@^ecM+V z%)8$oC#3`TnWA9vo2SQr!_H6sh7t44huk4H&Mr<` z0~=CDQ}YLzrTe!;z>1?H>i)Lp#?6kZ2IAc|*2edT?VDyUronoD zKJ|Oyd-a)TizV9o7pLX_As73Vi%qcT>wYsgSW=Vqq6#*hjd?j5cHZAO6%n(lR!l2{X=lv4j)&z@UG_bO z^#$oU*WG|+Ih;!J z`^MwduJ+HvOIzRdpEDs%+P5Kw1 z7keK_htizU#46LX)eQP7^-syAWI+$&8{6*$V3(h`i8$s${ zAGB{5-1t&xz7*!B&VMd}O%>M>4(Z^7Z?F0N`rY_NB@1KUXU{A%fp_EYw45xIVn4J^8Df;F+v z&X&S*$?Ed&uyF(1`w6M9c~Sfo<{WmTp2zTE)IP6)#e*(T^ZO~kT$feBqKu%$6)0a) z9U1i=*0teK*MIJgtuu76?V=Bhz-p7nB!-r+0h+i$CZ2{(v>lj!nM} z^D>vd`U4wt?pNF(_5WM=um#q3*VbHv^{Vv|ZE!s+8{qf30M^yZkCEp!%&hI?=V5kM zeNtzb{)GPG94x)p$-N8AaJyZ17UpxWKk5csWNvDk2^;fzUMAPis&2oppCIL(lv%xD z_ONkoM`6Jb5 zoyWr3@D+WdNj}u9o{X=!OnlB=0}FbLze>hyn61hOt6=f7N@{#Z{x!)Z0{%BX#G4-e zZW*k9bLnFs${U-z)Gmb;%8R;SIFgp7Q$L(J02PT(gd2N1;e3Z zi#25Yt*&Y14gqWk9NDr7R%u)7X2bg8BfGc55&7+g&48uiYpT6)jL6xE2Q#7~Jd$AJ z+uzv}VYRpmi_E{tn^L%U0&Fm^Uq!}01)Dn_91n|Kxl4}0WyRV}Ua(FVKjAd&YqKwK z3~Xw3awFrZ;cvUzjv{uSz9=7-JQDZ~hgA+^r(A;FGfbKxu+a4xy#&@iIs9i3Ec0!q z#)ElbUs_o(%TtLn5+GAI>Yq*yu;-A8E)c=SDj!@rM(9Yws36>?FftV z(5&IKwYlRuz*^Od%jAA|8 za`x^YShsr~bwAtU_>(EbrgiKA!;x2HO#A%{79>xIcU6CilOXrt#|=VB_>P z3w>e1`qtTBVMDq6_$;{bWZJHJnDvZ7-G7tK7V!+QV18{IazDAkN$f%_{Z#5f>^Zx< zZ#Ar`>qXrk6z=Xj>l3V7@rJse>{%HV@DXM=HS8kylU;aU=kDK!HN*bu+QGiMft&Baj5c1kI>GGodjm8ubD}r( zeyX%HmtWk4IZyVv&*RWM6$DyqKk!m0Nz!ThOi@q>}O)I6D42+Mk~srNO}Z@CY?2pbm-r{)7M z-S>lb0aj(&-J;&7GiU9=e3%o~DQ!CHb3dG`R>I1cjoW6!!ezQ;#HOa7)cYU{_BzYX z!TO$aItL-=znb7fEY{_>oexXiZ%oUBL;Fu@2!%DnTKeX~iVGvIM!?~`t!J`fRom73 zqhM2yLtBXzn{RjA4rgdj1Q7H3S5Wi8N4UioDqxzp`m!(dEacjGUZtmC?#G?f zeEJo_`mz(SevFKoufIfoWzTVvf2vwoM3!&-rMt!~s)4ofl zrNWZhnX%-31WdZ$Fk;oc!|llX3Pf$nHXea>TeR27`wf&!BXd$%X)3Yvtc&g5pyf3w?dA;!v%*`MA!wU9vO+0gexKJD90Q)z6 z$=wfYi{_>~!OpKgRqcgM0~)rnV7Ah9cQ>rL7p`!FWjjx6cfw{%XC2_c?y2p{<4F1b zEvv`D(&#?hheTFeI!i@I&w83|i%7nyB_jk%+k z(J=pt?$t(Eb6#Rg-ghfn<#90@7Du;U*$js-Jij;!)|uPP+XDN3p0{c}@xBSN9Wckz zGHM-cusyJyygxTGd+Ou0u-d+I<6c;CK|GL{$4|F80GHL;9$E>THOK0Yki0`){R)`V zuWmy+%vSZjzZ{P4Z1pPx7B+U%5Q_($u{aH@JlcFCW_n&qJ`2-S2fQO-+Q*bPxp39# zTk)&l(D#PB=V8wuU+e2ZY8b$fF*!7O(BHFw|=`f_Uf zh{FHODTUdN7hOrNN$Xtm1ZF&_RFdr_Ec-aLoRm+q98N6S_O7oEb`F?+ifli1?sY>2 zTzubWAF-tJd%+txV~mp8o<>jYA3dr6mCoIOoXH%0{5>oU*=~%4`KdYM$@`TRb$zB& z+qd=FyH9YPS4qGc3yrYx!n(s`dx+i+{QI4FQ~7GLeN{J> z^M1nqa?h#M_T@N!_(i-l-FX>u{mWK+vybTSOdpcH6lVA_XV74m_vt}PU|n7AekNSj zE^8REyw|BEgJ7jF%$$^G)<4g1g@u!QmJ{>kg|VYxPv51bWcjjfS#!8BJ1U@ttRLG# zeq#n4IeL^AX%FI|gDlAVs(l>V7ZIznri=)JB?l}i(`{cY5Bon>p1638*hTM4El(B1 zX|og-^QtG3?`zgRXXGll%3b+e2+JeQoz}tfzoM69{dp5UE!_Zf!gHwaqqZuaw-IJG zKP+B^T-)KCF%~YpzKyb|W^4m7uf}{r7;^Ec8^gE5IbBvbEF|S0P2I2yZr<-SU;%9C zaA@`cn9Eq~9|ALnw$>!T%C1Sz=ac;W=&}@;=QXTjFwA?_uFVNp5`XdFJXpE+<$q`4 zg5BXV0nClI>wF#-+3ubd0BiK^UR;I6UF`B^!;;X};#;u)>z|xiq<&^;O(`6Hq;B;L znC?-wPzPfoi}IP+ub%NI_16qLF&Jhs zuBOd}g-K<k^cFO}{iSOIBJW}4P)Rk->@x0G5fpFx<*A~Pw!P@hou=3d4eL*nO z$?$49?EB@okt|Q=-&D03RyEi5A!ZB5+}r`zwVO(!s=wNd1U#D);m|8!Y1L0ImB}N1Osv8?A26xZd^CnD`L$LYJUk{d3k?<`IjT9 z&*!>oXn){3N2M+KJlc@qwwtc@cqzm-9m>#>Y87>=pGLG2&@^beu# zu;um<>16+@H;s5R7LK^4a`uHWXKOVSbLkk zX*?|claeBVWBTb`xupJwlP8YAhTdKC$HCf|$k)eVskxwXEU6!Nto1C+Xgy&?th+kg zR}D8yruOuNLrovAm%=V9->e-CGnTcldI4AYUimu;7KF&WYT@F5Ej4a1@9S{K2DsEZ zlF5d-&I|azVBd{zJGhXX-{);BY@aKo4~7*zzd4&5aQG$%gX>!Ce%QmI zzOzqQ!OpR5_S?eIf0uFTFikuEy%j79eR$6nHrvZt*%>xD@q6`xwJX^fG?=q$Z@3+- z$XnLj0hV5?403=)rY}Wrw~`fT{it?9-yT zMUHolX4_PfN9VtcC+8QVT`{%%h*N{&Nc-mfE}KC~GETg`dPQ zJ>+#XX&uyKb`R$7B1+ehDK-&S<8b(srcj99)$!%b+|E-PW`cGPIWA+#4PhP6w ziwZUbOnCMkmV5RZO|Bp5+hQ_nVRcyHf?`;^dy?=Y9Fv~ycO90jNUnYj8v?(ry8$bO zDPhlG`u?6Zx8Tq|&)?mHON!#C>s@`<4}WgKE?yRo?;&UTUb|leOM>z?K7*z9_an~2 z_EURN;|2UNFB4C|W>2EtyhP5PX+Aw2R-GGi?iFk*lb%U~eY^vl-@tm_r0eAOOTf&q ztAyp#9>pZW1#5dB*2DDT+k2#NwB@ESGG1cjRFB^eXVl-^Q3bPax+(UM^7f^^)v)H_ z^o6@%b@x$5158s)qb!U_RMwMx(-ePFzOgwm;ybDTw_)a9*zD^*yJlFsTYHLF+SomW zj9*2cxzt4Le75ot8UNDtc}e9lzuoVU@iA%cQ_Aw7Ly2TOjUM)hazUP#fQ-ipo|RF~ zFiv(N<9F(S*yH=Kyygcz6Uca!cqcC@aTI2#VhMyX95-x3j!^I62hfN-w377OvJT?MW z`*9r3!13XnBO_sUL=A)NKj#BsFUa_<;Klaeq`#nQnB6!Q<`(s>DS+**d9Qq6{-C6$ z5;%0~?mv@Y#lZ@yy~KYT)-)Mr{2mwm2zkt(W2sZ%&;x3!zYyW2SmX;E*ROJVkDS$x zyJtGg(j8q;4JV08b!2>+wKK)97LJ_!F=`e}?>XGP0p@;rkmL`Wif>T;2VeJwj&osl zX|0Djjt}0B?R|q_?%VFv@nv)D(S-T1B%-y5^e6o9Wu*{DuV1mi31*%RNDP71n_2Y( zVfD(VmZ7kC$E*1?KF%ms≫^4g#L+3jIVo_}mb3YhFs*nXb^dft zS{k?>=H@W&pF-|F(>h=S%qaU{PWms6kv%B$R5=}sVgA9M)bc~qzr4HyMwZ8gCLhAQ|RoB5rh+IByPtWhjHBnui7L)S2c~t);y!UYGensfemE~>l zdnBA=H)a8Ho|;DWSDI7HdXfDh%M(|2C%<3w1_cDd|9(&9VI{lC{uQ=-{Z0B05sz&O z=a8H=f%-icj6cym0Oo%F6W<@X-s>}$v=7cc5qluqTo}L34`zAm?hJu*T0>iA!l7?V za)!Y^-#$L7yafl$?{W17kR@jm(uPsV9nQq*U9&h=l9rY1M@QWQ8vAI8%xed z+8cA1lJe@;{g0FL6=yAns&A&9a1=kJ8sa|N*UTag<%pUZ+jW(LCgThkAIhIJp_9tnn>*LYIr zf7R0OL&D&HzXt|4#`2|bS>c^=wPg7&GZYc9@#j72_d`EE>eCvy?ucZ_N93mO`*%hX zmy4+JdRd^{ISRH>-YI*HoMV2eFa|EZkba8%9`a5%CdR?K#{0`2!baw^hkN1XKCh|q zW!}wK3#70nL#V%v+;HtjO9Cv&3!}!5l^xc4r;z&TlcLqgrRx**M_|QMllU4e+*I}A zC~T8(KKU}NtD3w@4u=-<%r3$#`V(aa9B|1$)9kl&6dA8FJFBC<|G)k>=X{p~IjA3=oJEb-nbKx`%7z7dr_5c7@|yYS zwPgD#+4ad}e1!Yr)p%msQO7u9)~i1gi8a2{736wAvO`Xlk9f6y6uF+KpNL2x%kv4? zu!@X#$ilw9BcInS8be)AtIu|!zE8>1{zc?^{onSEelU(2pO6=Sx=Z#)^N2|u$@M6E zuCwea99lh-y56LJk3D!D&iK^pfd_K4<{Gyfa8ic{&&c%zEElf74aayF%(8=-J{_Nu<&|ZxX7_@X zi*2dp$GLV+wShIR>$cxSuJ5{vx*nAHucsV0C3UnFa_L6zQEKEiZ>>I)>rdg6&p)T55* z!KT#Kb!2-9>rbtzhnYcNsrG8-HlP0)7P)L2M%o`eJ!-fC{?}eCsv3!i)D| z_K0JPPm=mwZayx7dGW=4j>FP5qONLEepTYLG*}$`@W0Ejv9=(d9PiHOM)tS}o9$|q z%3$A7`5p6M#i8#Fhhb$fqbdjHPVJOVEWPQ_a0Zs0&-k4PH|Bjhods)u9HH#8*!yfI z%=b>G?Az;P+-aB@IdCeeZx&~gWWYwR%SV!7LE+dx$4L3k;>%?H<9A%lPJ=b&t8z}m znv_j@Q(=SY26cXk?o~hf2=V@C>U^Qrd+$hq+4n*}ko{XC-JO33W{fG>McSv}nPV3z ztY{bSMB0~cR;YLnEXmxp^*L;+x8AcGmb*P!_8JyeFWSEgmTqV+cmtcgWGo;#|4Y~N z4KQ!G`2}KSm)CE9!vzhN=e)l%eIKmPI_Ne6rf&*a7Y_@?;V-7Z%uz!ch*j-Aw$6sxudi?qz=|j1 z_5{JKSpJfOFzs8X!f@DzKK2SRvqy-0865dyG-bt(iuWsFov3vo*&cF1%%v!}Ds~sO zy;zYy>$bq2hrOun$#+Y7y&YEE?ldHkEU$a7KD*%(O|wHX%sD;hT@ozpw>>2VHu^~% zQ(>O9E}ZNi+KxT>$6<>-Nh8VrGu>PKBpWVUv8aHU{@$sj04|-B(n|KXUg=+O8xCJn z`!gMuMQ;4~5H8r%aQy_VkaO0R!6kjRbv*?$RG3Z<<}Kez+03$UiGkFA|CefCn)x4Q zeIY&vq+Cu#o9R9gocU?LTgYxzC%wpGVH{+mB z6Zt${w04gaj?TPVnM;;ezHHVp;+2k9$odLyz1x@tvv;K~C*OxTB>A@zZk|kMWx@I* z;W;T2%UG?28{67MknJ6vIQrguxMK7>=5bi`S32Z7{BQrTTJCK40T+yq znN5yg^=F#@U)ap1i}EO}oDuQH;xpDKV+pmt896*>8my4~qLcO^?0%?=6)bgkPL#ot zTUohX;f&XRsrIFD3m(%GuKIejnY2Igl${&xVE+1x!^!b28oB&}6C64`dpS8C#pMjxWeBub0w^v zw5-cyxN7s@hvB6DYsa+daPicEuNJ~uL+0lhq zH1}8K99X?*<|{wAuKVA9vtZVzrt;aagx0@yDy$sVOgX2wF`Wl9zAoArfSi5Vls%DH z5JWkiCm!bw3s*L1<{-DJeY$%zOfx;Up9?GFe{x5`lAH&WW$`oiuwhd7{V{V;bUO^)_3@%%A_j7xgrF@x0ES>Zv-wfvODmD;j#J*Yd zg9fjP+OY(d&(966hb4xZ0YaFzD`c$!W?FmoSq>}0m&d>VKi2~`wf9-;eY$5tgmLlaadhAyLu*apF`=jX|Sql-#B9N)Cq%S zFm3t(s{JHQTIU`Qn*|RjB<-uDVd;uJFgIZ()!rn%3o>`Y#+Vn^CL)hHYV5onRv%eu z=L1XUb#2}ZYp!`{z2Q>zIoD0FA#i>SF`t)qFbWo&mXvW}pMcE+*TTxCp<_w=*IzR$ zS_#vv=UI^BC2rcVZA)Rv-2lq!`pY(p;ON6`%|;<-2-bfOg@x&>PH|wK`SQUbupsNz zq|tEMy3v(EuqaWvAD7m+A7ZmS5N3oWQlDQo=JwjTu=uy@EwcU$GwoJ?n3*v#U@C0v zu;RQQth=%*k!&xS@q&I9{O|ZNyAxV76V}gRQ2Wc@_0Pl^F#pD8=^~VG&gd{`8mu-t zZe0qqpzCEeamQz$F(uP8tS_ z`Coc&g^fzpjG-{2(5!G9tS`HeFbL+vf9SX!mfu=d=>+T7cBXOx``u#Z|H<7y$$k33 za?g_aqTS+ZXQ5FBQJ1pt6CU6JJ+pufKTEeE50QoLBiTUt& zM>up_%hBC1*Y*AO_OL>@$CT3SYeWN7b9m4j*ol^b23<~x4ektDbcyuAm>h**=znKZSL(jqLXC4=VP(I#T zyDt~!OyAI%SQ2aWISU&S2T|u&%R^grr(xry%d!5*?f1`5qS1^_JlKZiH#7le3@mdY$GiRXK2--{`iI9JFLE^7VQ$MA=^(f_@9KN@r zzrkF+_|GDk*RlK$>3;}6TpSq+(^8qMnqc|)>B~Z3MU?sDMwm9zu}?6pYM!Sz!lpUe z0rOzfALqkgV97ui&Ky#|bIY6>*xs~ziXSW*elDU47Ua9^nGQ=kSGm1~3+|72I2jhm z*PM9{ON$C-Pa?TRLtr^9$&VW6P3mX9z55teySn!o2g@sbqaVRliRO9_*l@ew=zFkY zn{?d>Sl9VPQwc2ptaNdM87&`zZ@_5>R(EET`e)W%z7D&5UO96xEPMHPhYD7u3|QPB z7A&9gGaojUu-5j4nNQMfvf+#n{cZ92pHJ=W^;b{#M+QU-rk>mSdTDLbD z<}j__!=}A(NsnDsE$#7rDo^A{V8t=36@OvwmJaiG!=iIuzkk8Bd*$7B!qH(57yW=W z-W7ee!%0I12^(SY;hs_3VC}dslfJ>SBg6U;yKgO`)f4~Nv{?*uicT%7g$>JZZQTmT z#gDjSfHgyEh7h~l%f9jsrUicH#K0LHr`A`(igDId|2yYLR7?e|dNsdh6Y{E-=lgZA zxW7w!G~D=OdF^wUacalOC^#*1Y|ayycdKQ=I#?02k@tYqA6g(>1M7c{7XN*B?q$ zu=IZ3-!M3s$+>q4W>qkG9hLgB{$PB-Slrf&$hZIzZ|d^!Ts62xj;`p|01Ijn5{ofa%@3r_4rPe77W1Na`!R zzxu(A;$@!}!CcE5$7jJlr(0((fcfSD%`;(7-Zj_xuzc!b1+k>*VYhiOW6iPeqcx+A*MR32YfwmN^YJxo&Y-4cq5L&?m!! z!Lf%)dt^T0-1dRlwtEJW_Fm?fy?z3$`?zA&4miT{^Q&<%tL#Jaepr`3wj;4=Cwp50 zoVM8FnkO7>Kl?Pfe*9F`RL#BLhFKdK0xlt z5^V8CPXBB4)xr8Q)ikm`>U_`QkFYkiTRJi8@~OvlaEbQVabntcRReK2^D0%I(P4OR za{l)D?KogOssHVdOMCntSTOri7OZM>y)&$fiW*ElPda`D&l(O7KArRbn7aG87Qg=w z;6s>-hGe>76hau?2t^c@!Xhlfbi+~@C6ikyuD#D`dBKd&*?)15!c+F} zzx@#mOT052<{jy`j2sW{vfI@2Fz`$DShByGGN+T|eUlz+x}CJA_oUA!@Apjw>=yF* zyglCTVKB#L-Mh)KGB2(n24*K;>F5jpdw(!+N1mPvYf_g{?;}kQtu)hM?FH|~1IW*< z@bAurz33tRj>5Hm`~HyYg)ydi@fBE^&}Z5lSll|ZrVMt9DqKC6w9mNtvjUck*lQ%_ zJZT)IgH?ZCzYl_$j1~OXaOIhw+k#=nhZ& z=7?!y!brUHpqjsMV9|~9;jprk&xW@37*D6oY`2u;yPsp3z+9iNjmuz8&ZcRdVcv8$ zb${u{mDn+1e=qT$Rfx;#jq~l`oYzWD49p07KEWQ=bT1gY7Ur6EjvfLl`loD;gM|s* znw{b5#;5DZ{jA^MWIG<_#B5$e?sxgf0Ox72ikVlp5q9i)dHOuqOA`2wj1Q9UWsgE& z+jWnkNL)7Q@6qLO>8Ij*q(6S%d55)dR5xSrI+D+v)*%75ym6y9**`NM+0dzE|COq)|MSNYbII}H9Ue{P7bJd&3L)*w$5tdE zpXGCOGZ`o()37#78TXd>sI`NlGv!~>m9 zQsas7eYk83?7lwDWhRLqZ?}Cr?C75qNyZbgWA0|M{kaQ2Pnr(%dYV%4l+o*-lIxT4 zvm^ESfrAEpCgX{cA9aj;o<{uU9=ZR#YHpm|2-9{^ z1~{F5BXbOCe_lx0FskIyDA+JF-;|2yH?152b2QqE@uYv#;g5&H?gvUJE4}-BI}$(i zm`D0&x-72m4b#-us5pn&#Ib|LW;ZF*UKI?qf%W3|A*4TPicexM(%xah()F-^BPWoU zkvx7eabUx+w^p#WdTw{hjUrZe*sCNtG7fQ>*Pr8EVSSg=J&9Gr`t!TMB6~C2buiDj z*YozUm{X;Vg$?XfSsPgWzQ-WqsKVptez(W@9UnP#4Qx_&EwUNr2e;V~4VPO;S>NI2 z4Hj<1;<$GX-(c+tr`M}tR_fBzUrGD4m1BtYomQxfuyG=H7jc1wLGT%7_Hw53U1n*Y ze1L@(sb5zh9_F4>UkkJP+~P&TCRQt_y(aBxS)C(@d&)Yzge8Rqg#y?;`nQ9Qn0u9S zLBVy#BUrI}Kb0Tm=6dZRtZmu6n6$5N*57^ri+V4+KrGi?4ZH^{7cZi0+bekcU6`F0 zN;zhPN4MKB^CYL!GUWT0a*H)EZ_8xLVLNlL--2Zed?;%f32Vz>&dZC`_Qv zzo_d=&|Wa!CsYj!=vmgp{(3sI7;c{Gwl)l|95#2>WmrGi<}Pv6zAIrDVfl&b)!{IE zOP}!c9L!aff3t?e#dyQT-en z(2yy6s|db|-&txfm7& zO1_ivRI*U?aRDsZ_WV~6?5?jn7z}%*JK7LOz5Bb0n7?N9Umlz*k+kQ*y1C2e5?6-Q zObdWzdkcyJVRgycOa8F-`o-G;uzy#^ut~5{S+jXM95&*O^B7nZbKb`n=8Y9e-C>Q` z#FqoJRT;jnaC5h!fXT2Zp-eXdmi#oGGYRH@W}j!ntaE+a68n_D&7{75I289P??@cn4U1C3+LFbi?@G{a|7D+gr!L;-aORmGrV2(Pb6C19cc9d=kD}=Kb z5KG>T2&cm|%lyT|;h1^vl6?dX~09+-C=u>4kLe0yw8=ZPzxNBkk z0e0NHvy4gN_5J_6h3We)CG>#3-V_Ga!qQXYo_B|R&g^omhP6)nmv)4?oL8Yb_}}}B z<*B=+k73m?#z-3Ci2+_~DoK006>j8xM{WAH-~p^Tztp=07F?+HCX+ovGX@ruGtY^LfX@duYZBVE;P(3hUNV3w;JK1bav5YnAdTp zQv>WXL7YzwjGLe+obrJa9HCT0q{Q~6On%llu1 zS*5{L|3&#F!RKM&(u?d*=+Cx(;+wOup!Kb!9%jx>nSGkXISF6h!-l}R=p0z;pOHn( zzC9*i4yy_isknbfyD?d?vR^!9MnuPh2VvGRhXw=kdHU?G>F~e)atE2Pcf!r@w*9R| zJePiYr36+-Sq8s>%SWVXx57H%a5^z}Qs1ymukeG7Wov*tv3}5=8?evt9%tNOU2@^)Yq0x=&+aa;;{rc_HSD-~RlA|E(e=dz@_ixr zNWIz-rVU-y?J_JFpERm3?CyGESs^S8PdH`+v*z%kFOl|QYh5o`^(cSsML7M=w53)s z{n&)c0+{}ulhqy8?>VeH4>tup{?!#WmVK!{2YYzu7Ma5|M)Kq{u<)?O^Y*Z^Wpc!E zIB?zS3ns8QFmw4a*m6iuLtB`;YUCaAeMfjXYkg}wj9*7)Y>~q*iXj%iU{%JOmszm3 z>)9jUV5xkYFZsUYFu(h?23UD}`KkSIOK4T@2Uzd!qHt$L`tk`~K&@MQ$qWRM^Sl0XFrk$`jFLBcoSff0$ zgVd)@w)?wP!pc<|sy>~#@T;JLc#Pj3QqOK~FX;6U=BSQP^={pX+1u~JYF~%hq~4sH zJgry*8?6qu-vSq1m0!OF(`_!ECG~pg4eRKer2XY4sy<(o(sebl%4Iv{uoW?h*I`-m zwk4!K9eB5M!&O+fgKLujSC6eZas`%{-=O9f0^eosx(Ksg^nM{i+_50LLP>1R{;?Vs z%x;sH4+{ru`n?LSH65@d7xr2;L%9-eIp=ulIP5ie?#S8QB5%nrXIoQwWM&t|nxgPH5z4hn?hUmWt<1$9Yu&2}0!G6B}sH7_6z zp5Wh41k+S2srfP8)U50kr2X-G-6kWR+cVB73if)tikcr2w;Zrt0!!tLt>Y1QGz{Ig z7#7-%q~^c0`*ph(!RoE$2gv-D$7{3R!7x8G|Mw`;pOydR*{~qOdo`(#hb7kkU<-MQHn+~(0?hbb%Ue#|a2UZ4MSx@4Msy8vj#;m}N#6IVb&6*5rHLIxhvXkw? zC&ILcC$^CGlA!G8V@Uq<@Qi_^Kh}497g)2cgsN|Zh3B^&35)0>sd~JV2di!<%&<#a z)ED{8jKW!ih=u#8`h3fn6W8ovru~2?y%2BiHN8zA(*C30ZY!9Z*hy~-Yl>ccw1nM@ zo}^pDwEfR18wahe>;X5gvKZA9ag&W}%(}x8kE(~(ur6fzLAnofP5BxuRH&afb9 zg}OK5lDG6+Gg$UL$g&UI5=b|;hgtIdt@d!i%A9^Cuw$S3)c(d#s^_+YRcqfF91$1E zj0(ggSrCu6LW?!SZ=~Zh0e~Ib+}D zSFo-~-JV=u#!Kv;didYvLG}r-N}CTspW%Q5CFql#ZMU zyBx5jRlr=U3X6HL;PURP_u%H*f;nWqxc0hg%Pm;=jz!%sDQEmXU5DxZ&MBmxlsoWK zpKGv4esuT}SbSyh)~m34_ET!UI!chBE`$Yh%x*42ToyX?_9c=Zn${xNqb$;DOk0CJ9U5if7%+NfF*Yre$j{r-%z(^!&2YMx3RF#s94KGu(JG|QyiSW za86-5%nYASjV~?D-qvaGzwyU-uJ^Is@W1h^#ebDP8J5K7QS)tzdE0JCV9tg&pUM1T zfxxYN8{B+4fEq7TT)WLpgr!}>My4R%^pG`X6Rb*~xQ>j^nov)>1Xw(pZn+1}+}!kg zJaX4p`2$q%2Y$XoNDtsRc^9EQRm%$!`3qn?qxT|Ls zdET&VcYTV46_zRYkHFIR(bFT~=6kxb9N3oU({3ru3bH%9O`-LXfb8+eB!LN)brPW^xXBcVXwtYnoE%Hl|AR&OjsJgrk?M7>*iW6OtX-= zk@}v{@=3fOEcraM^e!Bg@ZFUIv)V46^AHxFUH#P?Ru;TTCiOm_8IvxIgB60G`EOu( z#N_K^VPK3Gcs}x_d8%!0zS!hg-sxuNIFP z466jyw|l~ws*1h?U_;M1>iwP7+p|+Yn8~)Fz8~n`{dwO9)=YLkOTK@w@&>K7g}I%q zLnp#~376dq)<5vE1YVnDtqq&w$+x z$#$l2^KjS67{7o85nO|ebY38cRk`wn|#q`f_N%T?Ip=B@$haPw!Wp%9k! z8WxlW8~oZ(+aJFp!#Nd}A0FFy332Lu0hX%ke_ntYXMP6kfCZji&Yy$B1S{Jn!K^t+ zp=V)T$)H!;V9}ur3t~EZk#GyFt@_iq<bej%T%1a|yd5`sC9LWqr~3C8wRBDtEZTmYIzMuIw%Ib+tFF*Zf%XM= zI_+Bu>$b>pj=&~e`>hLuwGFSVGGWUHJ(Y`L_nZ3EJ+LNa^|b}C>e8pvJ7J%Wx{4su ze%p%yTVYk5OYLk}c4kCI0<4~@#&7uId~Do#Dh_5RRM`i>{4eHfqDlL@#tzeAX2pdQ)l?PfpBoA%vof6 zS#k|^f3pwl-auLX)x#WdcKx!4#PVBTeLKMhk@pO;zcTCQIUQj7l8KZBFZSJQ#rWZ0 z#i8sjclG%Jmkxav!$CenIN!7pR<5k|^n(=}G_LRA6o>Vp{;)FVU2qLt-}zVdG?jsr%m~=q0@E)IiLFM^;^%twLudu zlk?4v?o5q`I=|Gjb7A)bLptRk?!Pp2I1i?aeXh%4>EKCQh&5M}2OffjZ5H;M4>P7k zwo8L!_IpMwge51h?cNG2Z&+O=rZ-ej^>%%)cVUZQ?x#$uUhZ@LlsO+38T?{I$oJTJ zAuF7Cr7f4tui!Tn8kWG49<5zTz1i4wZK(jJ3EMi6`lE|en1K^+HzUHLF#yN}(7$1M$+DEycvF_x{v; zP-SJq;%J!h#gwWS%50;b$B_2BUzmC$Kd{!cO)M;$dr#yE>t~&7A!hFAK-Je$GCcf6 zu(9{@wSy5?KV#e>rv1?DwTGpfe!N=`dll%Z=VRrDwR|!@xR;HbV}^Lsta;RUL3{rA zDXA~TOlW$VfVg(qLF##|%eD?CrtcJ7BlQc~fPR#{_|XPZUr3CeQbUb5!$U(!y}-f! z6C<9qkJk^q1qa%Sm&d`f^PNM;_kH)si2G|{vD=MQC9LE44_^b*N_3}B!HK*7jv~iH zcIRu@379{rggSogk-XTWaOPi{??^zZtuW=0(B=-M;6C5s&HUIFXE3?x!5x z$@g*ps%dm;e7e5;&;i&y>B|^$eJEoiD3_1fznqL$+Bp1W2IAVY<6_D6X6&K(mH|sr zZQ79Wi|KDgy^qlr1}`Dkvmz&(iZ>m7N{v@Ke}8IzRJSSr3%TFC(%w{LqkZk1YmdqO z?rz<^E7>0(7K1Y$X5IKYm+Y^2n=;N57N6NO>ohDpxw-vVSmoH^2YFv>X*;URXjs>3 zn3@OYoHx}v!~g0R_2()RN5J$Gd#HK_-+O2h8#W#h9U%1$(W0FPoM7%Q=DvJ5^M>=$ z!LaVBEoJ@hgBu3HQh^m^$KT;22f~V@XWo+Y6Qu~;;{eNYt&>Ro#N1Eh-xqfD3Kx@l zi}`kbUT>J;F_n6sOsug^u_f)fr>OdigS}oz>~+7Rp44Mvlw;zpU}l$Y@fTo^>7B=Q zg;l0QZB%f|`$5CZVE)oaR;2!u`#9izM_9>Xbs^VJp!&-G4zO%)X{RDs;PfP}9jsn; znoHhSop!08(@6a66{`M}esNXfZyN4zr!Z6{FLSn@*NI$6Ewl1tA9(% z;Y!w4=2sG*9Zp@(OUvmRx8>?Rm_EL}%@i9Qq1`xlHXWW(g9W_e-x@< z!;8EQrZ8=mSK(RMxH>GI0po`e{PW;{^=!kf-jb6rJL*6Msdq;$8Mya2Ondg48h;9U zj1nCs`6E~K>ViYphe5olKrfauYQS%Q)hi9*Xg;(sm4MV<<;OeAESY`Q^n(xSY z74R+;=J(!y&=v9W&RaGwfLZh6caixO!}NU4T;h%*YQCm+=p4yRnD%zdPck1w)9=*> z!1N10hmrXtla-sN`@`(n#S<35F;+iXr^3wEENZ?hFuui%0~^X}!pZYWtn?i-1#Yeg zr{*(--wutN4AajQG|J{R!4%VWB=ec^jO#z#`EN z)_G2>NP=@ygU`9b?gx8Q>kV9b`F3}PjRyn6b|c=DI)lZAWs@Smr^6{F^)rVN4;_#y zgFWuGaT@~vdmk|VeSNP#%qdUkd=&Apm9M+9V8!_9Q%=JAkAh=1uuA$gDG$!+QIlZ_ z^OY+i3t-x^<6A9YrQE%P3N9GfX1^KCZp;388TJX;u-F7vZ*ZmFcVz9W!rQ@|Sl5Ib zh+A&#UfdRz4d1+`3}&)7A8Y+PS1mffuj5@<_V)O~->|mV%f<(AuFdkfUtvaZv68%B ziDN!meulY2qsP64`7_ok>R{uOByAmu?~c3h9#)mN>F@<+`d+rKg<0RbCj5aV-p;z0 zuvcl94`z7(OOIN;=?Toa&%HPp4tBa#Q~@hG2lgEeJKPqQX<=30`@Y_A=KUGm`>;Ny z-_2>T`iI-uyD)FVCfE6}WkdS)Td?86D>w4KEqU1Z=mxCmv#4%oHbTc z;ezp6%F6v`-UY%!rExy_e8#sOLwRs;!*=#{STXNV`8-%$Ci|{~wPkK+7Q4{FoO7sRO+C!Nd({{Y|2yC6PtSG>NWSDw z&t}Axi^e%GgG&R?yO8Tc{`cIINZ9{R^XxycaHPk8<*;gFb^y7aFk!G}1?&)e!nzHv zH~IG0maAa*+26a8>yc4CQ?(kFst33ev*b5F5o`8^Um({nCpEF%8rZNUjIuDr**qGi zuL`9s8#tG0?~=29a7W}T;~oBz{FtsbZe3t?{rim@VXu($rc9XLIP+i%%sg`Aq&3Vv zZ{{Y2X-}6@_Zv^0wlx*jKWtmv2XWDbyq^2v;Ns}P4zPG?*s(*fXyuOE17Q7d;o##i z`^U|U5wOZ8Dfc=o*_irg6zp}g|IaEo<$Ba8GX6BLJU_FJcw6EVGCoO{j9K3V>+8$a zWPH<1pR~38XPkdy6E$9ndo9>)1*g;unmP?}+G9UKAK2&n`Ux{&qx^W|0JyfW`7#%7 zK9O-}I25|w8*vnT;jpsc6le_V-akukaGM=;MOmCA2mk+B-BICL7^?kQwSYZEz z8n3DMPuQtz=->$?etX}wW3afOXc*alMYBox30V1YmW+(QN{7-fd9b`_>H~M!V0q!K z64u=BL5;_X9v|7~;jnilH^}j(E%9=^01Kb+rwoLRdV^IV+_G4mX$MP>Z|im$HoCU4 z=n1n&gfAghapuiskhsg#TbJN}*B9%~JF0&jGdh`Ee}>Nn%0mH(e}v8VpyD9zL1Q6a`VjS4X~E~FrSPs zJmbeN(Zt4gPx4`X(CzyIIJc$g&v6nj?>2oAF*9#&ChVnsu_FM^ls;Z8g$)DW^znw( zL#?H|VHUfWh+JO|ZH`;*g1LWoJ5GRw$_qbtz>Kj~J3V1-y}LLGW*!Ww8%yGi$GZ}< z-`u=IEM8gpAraQtK0Yu8X2vsv6JXZXl|S5JRYz9DdYC4%ppAn0;h*i+5r^1zCfC2N z=wwSQtW-ScF%oWhD_sx+^9N77$%YwCx9g&bv+Amd3(f?1tcE!Q23n7R>zzhbMiDQ5 zca7LG^+okEnA_Yy*>TnEoe?no`iThAza&rBT>!KGj4yPC6$fee!btl^{iyA!lxgRN z5O;Z9K(;UV{;RM>Fu%%^%8yaT?Oy;h(pPpR?Q^u>0)t?~$5Lwhb23h52f)p*d8^6x zR{CV=ro+-7s#tRW7r5Fjng(+U>h2GN#mkym9N2LUV?A*}*~o1^u;55H$q>^1{JilK zV1t*9D4>Fys;3?3^&ZBdl25VOcRuAJQ$eJT?>baXc_80=DgMbS8EeMa0a9>65cP zT3d0wZ<@H=4K{vApv<1RD3Vz3XY;8AadA~acL&((#sV8+{iEXtnXr#AsLNkiz4Pe` z3s{%2#oq+|vu;&0TJe6s4GFkP_J=j$ug@1)adyLRbNJu;1@lh(ab026;5~OAA)YB2 zHAABVEbDhilLy!C`u)-tHZmQborHw}Ck*8L z$ir6#$>GwWhr8L3{L}SRJ(Tg{%(tGf(0+MD65=NP+EM3E^~vlLsdt*}PZE>!r>`>? z#KN`0`gxWx?_;mbWw0?JGqfAb5BJb6gmWznkIDIDm1a}*M4z@d`JG@!>Srf=#A$XR zd&vIlC54s5{GhkJI=~$55l07@o?vy7oUi6qMtOf&o$);V4~{2CwcrM+KWd6k`2HsT zY8&YYv(p1-{)8pt=Th&J>Vjpi-{Iz#;vh2L;PC9B#Wz@No!n+9tR0nI`UMu+|Hv5z zH;?4rGr}78olR7|v#D%HJ&BL5+dl$vgKOyLI?~?sWiL`6Wsa>`{sI2Cf9B$ms|~Q0 z#o6tQxWay8?JHQb>erxAu+yK(882adZrVC`(jVr#sy7D20_RZT3!r6BF}ZU4eNWa<=%wfkWR3RWQ49Z51(t(RtK4 zShBB^^>ny&@0aH%iH$TGsTZfW-#O_R%x%4TU@jb{d^aZ>7H!IWH4jc~>}GcmmYe16UQHg815+y|DU_e+Zd>QB6PGB!xLgALR((cxBS;U9i{t*3abn2pcnU zVG68M%zIDj--%`nmRbbwdIynQ}gKEouMuN&k45 zmTBK{9qe(j|FPAu@#XZH>tR;vS@#vNz@_9{JnZtf{?Ia5CmBxF>%Dxpc3cAUDxE1S zY`g9XgViOE(l;VMv9r`N6n2~#FBQWo!JT!BV3d8fY=JqW+II?u={KeQw!taWK1Kw= z9QiKF68Y!YIsY;3+JYnguxNSvM~TQU>iOE01IxV))OfTV8t^d<(MO(5?9zz zC7|vFqxJ&ZC&S!OpX)^}7K7c)9{w(SOa};$CDqy;8XSe^_ zq~xxpIpUJd1}g3}scE0tf6Q`UKE)K)PX1a=`ZG+Pb&!~6s>!+on=Ch~sr>M^CbwX( zjN4Ry%??G>e3q?bU@sD<&$Z}%4e_EZ#od+XMAq<_hvij#l-;C}UTn{gZFb);Qufc2i+N8g95Um1sefMw&ZCTd~Z z#C6weVeQujYX8!E+P2lh?qhZKWcoTldKZ&y7)#}gZVO`!TDnI68VuccJKATefgyau? z+&K^C6u-Fs7?z#*e)A;ES{>hqd|uelM0*9y3oD_vzxud({RvpC3ExTfue7JroZ}>a z_P$vfn057dbq?$}X;c-N4-};IymS_J?=!Wryi7H`au5?TKiiFRZ%yiP~SE)4Ye1VZL>`@C@>O z+Wkx(4eLA2VCKW}-u5${VWXrQHGf)p?sqjC7UWNDCC8U2gConO`<%ND2nP;@ChbdEUzdC+u;6>DPIbbDvC)AlAH}M_Ib?$>)Cmu~SNPzrO!* z{FH7fwy?abh`%Uh*~hLs7m@ueQY#N&tZwykD5Q1|LT-h z1^*il{po$yRKoPzei3ARSld-|nv6H1ckj>MB>9i*kCO4mM}4HS82&fDl>RJUR0InGTL+Ty)x4r2^a{+l zUr&uU%Fe9Amq_~|x2?$iFaJJ&>{+2o|ljZEb)Zokkr#0Lu)~-;A)pdvakq?D*sYR$^j26|Ci? z5@+P7T45)@(Jm=)bJ5U-_IQ8I{V-ud63pXF+)0O7_p)YgCGm~6m1eN0CfOtoW+o2~ zvV_?u{NJo4`SSO)9x#{H&?6S6r|)Ya#;?k?Sp|zP-=9L>mt!nXO^PJ>p-#n9-R2A{=%cDsZ!ft34AfLtz#@b5n0P-EQ$=J}fP3*Plh&-?+ag2xc;W z|8a!rw>dsM;#rrE4T57XT^~TqxaaVQxTVX@Hi0n9AarKK>4A!j>9BBl<9t%DOZl-x zhu4GMz&{|0^ahUuTb zUt9z;7W)KFfED|$Q1!I1<$HNvFn^xDV+`W5GZ9aS1>ciaY=Omn9xKO__=CtPq@E=W zaJ(=M7XBXmI1SDjArJS2S&7nx2VizYMbl_lQ^<7AfsL+?xg~J@)LGfw(qweutLd7*7OIlNQ73lDijw!NSks?qP7tfW1e)!HkYQ zMy!L22KN}$1RGxr#Sd;{|NC~~H}U}hPyCeC>98tgRS(&2iTCth1q2Fq4nTSKh+ zI+9AKf>zrHW`m$jfeXHVuL&_t{M)0c>2i)SZ3lh;~gxj8T_1>BN`$mu0An{ zYOlYLw%|LQa_Hwh(q1^kWL-NW-haL)+L7%MnUwrAhXYxj)b<-Sy<&U9nwY$_&xlK` z-sjuFs)_SE*ez7@&{~OI9j(D=KDRmK`b1(+-fN-jH^ze z&L7(*XF2S(&zDWkFK0m1rZq6<>yCg{SpH&3hXj~b*yh_`STnNow@q;QB16}9xIWyA z6t|OMe)il82y@L|XY7MBKaStk8Kw{6+GoMcr%%s!A?+4p^BFz;P+YdLJ*XYMLfSYCf2FN%xOWBF9}eSZDXkI5*C{HHSnI7?WCBOMj~`5|>RVc60sqTqee1o# z686dL&Ly8m>r%Iq1;-3Ma+utI%%&j=hrsM_qsz$lIns}BbA_cvZj^bfvGd2n4!L(M z%@OD3FIYVdmfgxsBc^pdR=MCmR#=|*k09RF8b$VBG&NZ&CibxJMXc9`BGe=@U)!HA#F`N-zZQBvpgXzUi`wL)MC&{aHSeV{6C?7TmUVM?l^6@`*=fZryu71SIq}@MH z5WDu~?Sw1&2Rj^t-PejEl3?T4Bdo))_VMB2TVQpl=Z*s;pS_`R11zrg-oGDao!rtI z2YV={pG}38sSazRVfyAyl}RMOCbEfKKcb7i!Y#01j>p9?*h{nS??#w$cVN#DSejuL zoB;DO3#0k4&)pki;z|CVY1)M_w_CS}^|0gn?T*AQk9qs!U}K=R56PEp9sQA5YUM=r zr}m-kA^HD4FL33o9UDmdv=JwlB0qPVDfM}d*!1 z6VDSnt!*eG{mFPW`zech#)^pFWmETu_-6wx7FNcLqxw(r@*KVfmYc0;Lq4zc>d(&6 zFm2QByd|)sTllvXu(A5@f^axxe))stup!%vIzFn@G2$rVC7ZgCHD$K75!8OS>j3XTqf%_x@9e4^I#0!kjZl%)Mb@!>F5NylA>|>GOEf-r~c3 zKiHo?CT}b(?{a>NFRb)$7cv@FRdtbY;NU#lna;!;dLEew=WeW8It=z|ct6Jrray{3 z-~=mMXLTgwlgH8-{RhMRY72%ZoH*mpP)C@vM8zBn=Xmyi?*P*(Mm=$Z;~$3j_l3D5 zcD^48JAB{s+zysr*7*;Ig+2A1i22=y1q_2t3}bD2!R)KsLk7bdhGb3;ShOi^6f1;r%>h>@$5(?#;zCcbVI!PDOdRkmNr+%5=(w9*;5B|{Chs=3S0jC)$$e=u4x@^ z0k^OuHx%XP}yi}aIUhn``gvf`rz>YefvG-wl z@6543Vb;Bx<>j#C9^+>dEbbK2bO#pyIdJ0>T)p?OKd~ky?(l1v9yju&24-K{Ft7>^ z-nJ$07AzlsIYSHAexDUm2GfV;cDxIx9B*BA16Fywj=BXqWK@4s!-jV?&#u8fx|q7< z!TLu>WAk9i>FHs)uw}OOz+94_zCZ0WoWA@|-YMe!%BiPd-R&hI3YaXA$rs)G zMBcX)r#r4lBk^Moe(izPF`=tdVfIDKS;V^6Syek>#)@y}$@^MP&$tW;tQZ$imIBj? z?VfId=@Z=s?0{>{JC4}|^WN?roCG_4E10+u)&xv)-VTeH{l0F1W!=(7ZG(C3FVN#) zT57I*Gt6GT)r43!xGIy_=jq}1v9M@Bk&#$4pe%xz_os6AR#?G(-Gf+GvXlBe|FV0r z(XcS7PaN4ElUHdWD`0iUJnH?^WV!3!D46-KYz=uIZ9bcn5ee&G?6xEO)3W%%t!1#{ zaS*kCI$HEE0c@zVTD}wUuvOmqOJEWGjpZ(w%Th&!z|8w%#%|dBgUF8$d*y_N5nKA2 zC>E0TWm8qe{xd@h=fUa%MGC2q&07u{>=R>~zum^4(PagklwK-lQTZ;666ebUwpfZ1My&n|=c(}pV?VC9s-#pL@$SdYtNS+J($(ZEnx zRJ7-&9W1GqR`cPaDYP@aU|p~@`N6w{nFXyb6|(j;f&6({!1?pKe(mA;j<~+ z>^u6~6qtEAdrc>p-l5B>NpMWXCR#^WXcafl2Nn*#8;n zu7g<*XWX}f)2oLpdkb?rF&ewTPW`&H)x+ZW-B&uo^#?31Uck)SujS?3J{z#nZxl99#D|A!e!;IXs0i`hg zZ;ttE*dW?jeGR7dtXowB$7CEWDJH)9C0Pfnix|SIu;Yf;_0;!g#kjwPusn0%q6dhV z+m&rq!R(Qxnme$G?{UUOSh%gWr5Fxw@9A9tD}z4UUxXc!PTHM^=^8s~y+_OK*K5wf zTNKoHETXg+8?%EodZ`3wkeb_?fJED8L-beX#(YD;SowU`D%k7qoycrh>gpW695(EApDc%IM-0m& z;mQXCHXMTWy-f#_^&z@K=N$(~oYS~H9Co~!xGMuzk95(KdJ1EdQ6PgEuF-vu{&wVnxv9ajySlSJ}+Mc{9^a6Zl_FWn0B z`bONF4VMaz?uv)`T}=c0VE#U@YjGrh#`ZQ-;c|U6cP+`EyS>X4n7MB0_83^mdedz( z>_53?R5UDcXf2)u2S%Q%7s8soCMCq~o4yTP{-3zz>28%#|8e@;hPq|2AR%`%=`Tgu zxRAKnGygKNL-n@Y2$*M+^OM-t{>|Q{u&mv*i)4F(Pq&}=Ukj5rdN#d$9`~5Th|$bVfngidwRfvOZHbCVC~sW7dylH zEn)ro!n`$eM0D8UV6=w~%<n8 zX>UL2f9vTUkGa|r7AJXrC-p=5^a*!NNdBY8i{8VMYxf4UgJs*oZ@-59gXiS6fwlc} z`VzBOXB+-BBmOkv$xGOB+R*r)Fl+12ku`8_(XF>lu%vci{WI9%QQqV)@W1+|WyIUZ z4Wxa<#y@2JillqawGXgr?umuZ3#%WdEO`ZU&i1z=>t7181dTPYY(#sV3eJ4udb}DI&V1p1 z5#~L+;Pf0;UtYVn0M6}nE>s7r#uoe{>t$GH2Uu3Y^v;v#l6r7XW$=?nu=35$=+m$} zeeykG{d9$ZIM-o<$wSzES;eqpux35~>OELKw>0xG>|`~)_g$DV({tWISbxCO^d>C! zzeug86c+g&FM(C<#!&TY)}4h(MX=)BnY3NV=YCM`yaHHv3YQF@&H`|%$X6CaSWDinw}+sZC8#SA&2pg0nd~5qs<@Z z)n&q*_T8@uVcEFShcejk`deSJUe#sxB;I~l)PGk(6wGdJH)=1ebBumT)(45A0#~QP z{MqBF^|Oot=~X*nm0kEevi>%(E~|D2iN~4E34v994Slx5>^={v^|gWPl4ov(wJVI7 zWWB8*s9n26*s$8nm)Mvzv2ZQS*!Gv2AE0X-eAd9U*DIGUM4WvjE@cJG>%Nmk)+c%N zKWh^SE7lL(K;{=DofkPoz^sMyAM@Z~C#!Z#VfTTTp3a43y@Ldyut70s#~ip`VmW0o zENSMr5vRLl-CG3XhpBeWhI5+sm@N2D-1b^!L=fz_WgykQDX`mv*|3VXk;*q5dU|Ub zOdEBRTE854R(*6LEFQ3lip$i^X=9059*u$MKWzE}YhqPd36)>}x;StQtUKCvE{Th8 zwfc{S1ye@up9Rw{#juE(uX%~Ytk+*|v0=VuMavAheEjwJVX(e?H07d1t4%{;R)df- zeR*lp5SYf?r6BpDrJhp;!m3COjaW4?jOIY%p7Rz=gSqZ0H|=0|S^9NfxW4=JV>WOz zkpyccQjK5#;hKbX%;Y?tdt+B5necK!}4vU@qVhb5c7 zm3)Bt-)1G7z(!3CHGe4h5WbRFo~dyo^NCGKx5l=E^$(|B`Uo>0v#r{~+}-EqeS(eE zcR4hed423DGT&Gf5_GKf2hO)o?Xk}=_gG__Kd|I^J!KOHr$Y;@c-e6#iAz3MT=@xW z5C1q<4+|vcKm8za+g?#*`+U}ojQI|WQqDiFg*g|jAAg1Q>yCN9B>w#Bd?U%<`91JC zY0tcE`59JSJ4mgcqnVvJTo3EoYEthZ&K~^p=tr2lrYp6cM4+FgsDlm5MpNq}j7xfQ zsd)Q=?qoh%(q~u;iF3Av9wPJeazhf0Sa54M_4mu}Rs)?4u;g&*AhLcye4|JDTbL`F zv*$S6(yrj<8`!`(NBuo=ZGH`hm=#-a_Auh<-%|r#!8+To)cPKs`0++POlKbHa{zJe zLzQU_EUcREx)f?W7&_6gY-&I;#qHw;9!S2h0^5+xi&x zYVXr~JFGigWmy3W?%tx-ODQ;vockny*OI6;h;tUD4!#FFwzb_XgjF3nJiiMIKYsC! zgrimkzq|vp)Ddl#!gA@EH@9Klx!fh8u;sfcUK&_;v{1YNuBz6rj%);{nOJS;B2%DICCtimg+b=em1-pa>FD-$^ZV6Xs zz(w^DQ;SIZS(em#H~xjqtIos5mp=L_i06E&^E(H7)$gsH4D&a1Zl4b;wr!c@1E-Ad zZ8#0fUVr^I5w2gKz2X!sGVgkXSle#UHw7#xy>rTos3V7>lJmlE6O5q zkAqQTVSnGy$CvhQa>vZAK=*itVwphr*?tiTmSV z<2EnTAuwZv@vsQiDi=`eBV8UW*d7CmelSYN`bnMX>FYw+YvRl30kCvYm&z#EF;zpY z&#XL?HZKy^=--R&5ifeT$RYyP*R~x>tXtG(;!=`7rxRr->$P9PVfPz0Px~QmyXfxs zFj(g9xs{lcJ9}0r%s991MPIle(jsCZEFE&umALwF@csp`d_ku%ELeth>%_|A82jO{ zhEo+muweG1SHzY=yDdDJeK9?Wv^VlIJI#gv_3xv2af}N$=PevTKA#`6<*gqq&32)- zr@6_Y{}fpDh)=}}e%=e42-6&1Q2SeLmeI=#)}G6!)~D7se_lKq<~)5*9WVZx*l#Yd zq#=j;`*>xSJ#UAR{6(k#=Ry6p4TOzOz7AwPtJ5;Isz1q}aFXgTzW<_reM$cMk<|87 z?zV2z2bL~bLmi*=2_?>UuyEi(YX8cM*LSmlIhXUP`fEPVEXy%@cmuz)EAn33FJtG}WXRteKLBIOw^jBkZ-yc)}Xy zlsYt-!0i3OezvgtUYj9pVZ;8keWZW(AM236-*LZgF{93xRI_7FGprxgk#df3=YcOU z$7ck!eVJKhUm9U?@8OhfHB;6Z;eXq&yL;|eYS6&^!h+NES%cj`1uptyd~|QHc|W2l32R^4eV7nnnw0dx&CUg z9_DUMq>cw~+-|+je{?C{rbU5ZYx9JWnxMmU74$dig!@W&xX}O99`+QJWYheDSej()dR0~5*BG!FD8EzjBGuIxU*;V8_D zTu%MHr66^K;}H@cE~0*4A*K3V+iX}D9LFGkpGhmRo-c>>MeUdGghex=7afKT$GgSt zfE`teS6MLQ$TjNsn=84a#vOv$cNK5Q?=$51`Sm>jtND|rQNIVB-Y0iIta`PH`u$~p zVZ3r5%(FQ|{a!i36rD;UXeo!YB-35<-|1Luo05@O|F*=li}ce?G3~eV?7}oSk!apWUw`m#8Aq-sj7k z`^hkG{x>>bRvzd0dpq3d+d%8>8d93e6JgWo5mmF$K9aw?G681Y7#I)?=W#~Y$YJ3a z-eOV@h#UUKVGFEs%-rS=TVC_%y_xhs!~N<9H&|eTDa?<*PV2qeYVOG5VdbIf&V1yG zBT2J2z^rM*XnoeSZiB@#SbcB-t%u6bJM6uVIRC_T7qpk84L-C67OOwIlljcTWofJ8 zV1@ByPfb4Q zDr^jkmXP{U+H!4f0Bjiblh%*mj*ZEm44W-_v%Ls;$hyh}LRj*2PwI8p$7hYzSeVXN zgX4RC{xu3Vy^Bja4cqVzwi^KpUOqoa>L)1kyFcK;((8NjNPR5-7n5cUhMBv*(E3@8 z%U@j_1heYb(E9cbuZ8cNVac4F|J8d~Y`fnHR{DznSHE$>*d^VG!$#5P1F@Xi^|FVh z>z|I6WBFWDq*GUzH~2u~CRoxUK+b|gFW#f|6N{&JjqU(*>p!lHLtfCY{Rt+lijN#D zg{8hHU$%icy;jiYH%Z$JUD^_s*!WwLdTE-y-=|r_rbFgSm%#j;+2e`%JD-ITb3`8J zEMemUA+3MN?7HTy1+1F9D<&K{??t4KIjoP3eM;)L8D}z1HhsnZXD_DDJImsHZ1WT5 zCGmdFLaw~4s`(Dn@kO%yTVJ{otFB9C&ww>2Bc6SM744>DP78kDghs!sg&D8*IZlN= zx6bgZfu-Yw0|Ma6cYT{a!n&>%36o*vsZY<1BtH;1TnMw34~D;k8;8#nkot8gZ6B;N zz)bHu3rKy$c)PIjSFp)FBFYDrtSC)<2{Q^?6nn#2EuZM0!rVh^_6#TOB`;oGOh=(ciWbrwv{7orm1yd-~7=Si7Y=eg2+d%EGnxNdK94JGvo{?BvPU!Rm=+v_7ID z@0e2&Y}Rrmt@oETW8;}?u=sVa!;WaL9M<FNeNOd+8!nlz zxd`iyj-~Yv1ApWeoFn!+`uxF^dDq_Nz=HFx zliQ(vaY3B%1k5cwd5j6mRUN5iSLaiE%D*f-hFtGt)}k%)g45AIjuOXOcCdk?o1dCY ztRCK%KEI+kDuSC0>lUvmY>8Z7Y0A%p866U>dEUzQ|9cyz|z>>E!+=)5Debo}! z*yCAcEu5IV@Z=)cRM6|gXX36c--N@W+Y{*Xl{^o`?GTfEeb^|Hho$w~w*VHpjiEVz z;wNbs+!)|O`?G>hh0Xpa*M$}Ng}}V}2-;uT(;-*{%l}>)Q-kFdwffd20Os5Xrn&Lh zyk?VN#b_;$Jnt#4#a!R9@PF%{m{`H|hE?ZpTqf_|M|HHx6E?0|D{6v6b3e@)3Y#`J zr=Op)(~}2-V8uqZANjm(9xrflBmJG!d(2^%iHzA?SaWyeyJoQEw9Fj?;Ii^JZ^-si zr%&7G0?UWW>GsrjY%J~zi@ctdlI>kwzaqdHW-RuTGT>4}@&2B$H2u>8vj4bf+n77R z8oj~4J_Iq97;`Q#6TEmJl_T9R{3Xfiwtzc=KmPP7w7IewCYeDi$`)K`6{bqer zGq|x?Jgwi!b|@NT2Fqu(WwOcg`71pCG~oRoe0iQ&Q)aQZ36_SfrqA!>zN+Z;6J`ds zwIJW$JePs9zrmWGU+D8ZeJ1#M*TbgvZ#|unR~}M3*TNzz9j$-L-dVr>6U^O^N}u1N ztqh5I4;y-VUl@e;WkYA=R+7BsEyiHDX?&l#6|hVmDDr^i2dfY9SUOj*n>Gx%P)NdGa7cJ$-8GVDMA7FzGVW}8a|zapr_kqXiavK8 ze;#I+_n`H2o1DWppM~lDDfG8Lx+eEHEFH+YuoyP+_9>3S8cn+#a{fyB8#*Q%mfgJX z8x6yr7(lhK?R>Pu)Kkdo-bCxkf6|ioF<4*Ga<+oZ~$$&L$cMRSDs}oacJvWth z|DSl+IP~p%@_WU_Z}t)Lc?z?9X?>JZ<)sA)r2P-i@>1kV8UGmB9$YCN%miC@+8IK& zpQOirtvPIaHX>CL0C4t z;~6p@Be&tdJOpbTmkl7}JGRbm?`Fc>i{5m+D)D&=-QOBlfA$SR&f56=^fBbpqqiE# z{L0XX+2$u;vuEyfzNGfGjP9=n>oG@GBQJ29~m#? zMf)Ei`zy!vq-q!3U=cN&tdAn_NOL7z+DSsKxjOPa8Nbw=ceg!&+-&P7S|7#z$oDV% zU|H%bKAAse8r&r(71qAFN$2zVvqtya4VPJ;Idv0x)?hUC@s7pO(vWheftASD3JV^{SeIu=>mP z2x8eE+o1li@oMv~6c{!Q9MbNlCTLk7&;>NC_EdC2j1KM%mddEV5~iZ4BO|6@<3x$90?Q}xlE%$F+N`T9aK zEc4Bw^QUYJ`!(B2tT8%~`)g*&>q}x_)$N2{ez5zeN>dbUHuVs_A6Ghc*9bA3R`Ttp z0D04u$MrK{?)zzXNqgql+h-=g>P@rDd`SP{VWFd8?fEnG^~z37y2gj4(Uo+4s*m*# z9u9N9O{*gJ^8#fST|Hn`i*)*aMFpQ$4}t~LE`1=Mhi9wDX#ts~k#c{U}?d+1{ov@JAJ$?`E>^IpqiZ-~PbWi-Hs`E3SUqkUF*80!ga zy!FY#98O7^9ioR7$uE}v!tqe{vGK-Tn6*9X1-ZW%r}7Rc!KVcN&&_Z}NKu7bV|(aSWC(m_z4FH;!mNF%uTwOLigW z3&V<%W*IQcb~K&8&SkQ4QelzbGfffl#0BPEcEYM#8d^^|UA=c>5^U!GoSv_=&yOA0 z0?V3l=zQF!2OeHBnD1+md=&i~Zv1dw4J+sLpPB(jPMb0y78breM9+^Bt7{!&V9BLY zT2DHp_EF<9nEiIro}FlKGwkQra9C#COy~0#__cku05&pt>*A5Sq|Ey`7Z$ZT?J0xv zn$Hx?gc*ZeXng~@IzK#^STcg1-+R|Qxib~!2L+u9M|=Ng&9aGEZb$ry*^3o#1!ZY|-o$&2LVda@z2$*nnTc8`6c z7`E>>afvx;zpQ>$5zN}X#OY5Rmai+P*Qfpm@9}=Z+K|?l&LNjw5q$d!iw8aVcpO%D zp8M_J}Lt5qLjX^S0)Q(&{5zT4iz zs(Ib$czBs#i-;^lx#J)`!&a zWJB)2Y587syxK)EtdoxPj~yE;MEkfr{klR}od5av7`Qkp>cbUSG)bcKfg5laCS?RvaevJ#EVRRUL+fkLpf!g9F>2 z8&ph%;38?uKyG-NUA!I+J(5G8zhzi>sUFv0TA|a2VhiMEZ$kR4ghf~C zsHN|QpN@vjvh7xpoLAF)c@!+UZbL2hb(j_jn_?K$SqUTBh7+H@#kNFy@tl+2!btzF zX*%K%x9t9N;j-e&GvxUOmElhdX2IMg)jn-u#;z;fLSUJ7D1HC#o;yo|N#5lvt!J86 zTQFxDY|u=|8ie+$cfu=yF#F6#`nT ztWs3d{UNNy)`J3AkYsg0fZXy}PS6FEWFAC=Pj*F~=5MtH*N&F(wN{S%QZTfu`xMZajh?1CjVlLx_Nsjl?-3XGJT z=zgUCf&Bl~J9TuK(ii3*3Uio?{?aKU9y-IyDMtFdK)!v-`F^M+^1eJvwS1HKa}TpXK7E;e_FS!#c1!@GA5P{^MB^h z=T$P(ch2kv%U&EPUWvRRrh6R=mQ3@dzn^njj5^*C*4uY!whlSx(fYRSVQuPl6RD3{ z(0TahwlG)mVK})TCSjb2AQqP|xl7!*<^{VAOy^I)N}DEWYnZo&IbIGk{f0OaH@csq z&v#`-T0CzFm-%k#mWW)i?`#tT=8u0PN`_rJybL4$@ArIUp65s_*zjV+iJi!WAB{_! z6Hkc!-~GS2zhf+6Cih(^Ild`6e_GoNHXUp}bT3@`vQwMCCbSP+{NMl_?^yrf2Wm@Ao&!F|@ZDMAYz98-SLR!Do-aVkz zGuZI@lR3RVda%vMC$Q@2xnsnVxJ1cgm^FP?HmUDg9WiOrBUmRd78DRKk}~u#d;Ba~ z@72ed*Kh|GcG`IR26D^8UgkQOGi{ex2b-?fTHl1VvUjuZz|86=5rwc};O{@=e5AX3 z_N$ikzveRT0bHJB-{U;YD7a6b{~@S^hK=NOaby@I#=g;^LDaSKl&C1sF{AxQUV(=lj?B^$X{w=TY{Jxj8Pq|F*Z?nd} zbWVeHH+|{(+Wpx+#vYQ7y72rLmhUd!5~F~H71~HL9;4vqx+cNecJpU6ha2AHdnUl_ zs8RI(yXL`Y{U%t!#6%M0yr#B0Ww0hcc77+=xMqX*I+9!mBduaU(Vs+=f>BNl=mliCBg-K6F zy1}xp&38w_26m4j@ICiM0v z`C2Z$ey;p*p_KsE9v^9$ggkLpcH|gXB=d|*gUv=gEAoM*+s4uQzmYtKAF(!aIIRyP zKYeVKHynEH7p=d;b~Rr+isaj`+uT5Z_s6Fvk^H~kAJGp7-X0ArT`xyGL0*u*dk=X% z+5W^-^8duV&U{;6SigJo)(Y74J)By0dR+o>&gOa4;@u9b$nPWDoncmE(Vp{t4vUd^j z6JcJ0HysZ$Eexz7)`{QEA>%jvcW*p}Fe_M1|8MHjA|jD|{@mqPXgxy)cMIJf%uRbL z$oNU)(jEWXe!I5P@et2r`krL_iu>=FKz_gSyA3b}!Gc?9C2nw_htEx75vzB{A+Y8A zGvUE7Z>;;ik+A<$wOa_RNFCxpe$V?eCyB`ZC0+frwLgqM(CnK7E4!xC|38;Ks~H*w zi?6h$|DP7V%4{{C^l#y6T!{9DAUXX$m<~L>xd9fx8ed4hA7-NI)?475!cz+JeG+fV zq4j^c`}(&e-$%LPV#|HVwJ*}?_mzt>{ik64L#xTlkcU3Gv#bCX9t~+nzW?GR#-LYl zdEwu`U$=#L)|z8XEHa+a~90L{v<0I zmR;|(whJ6~bl9>TFyF|c<|SC0h|St`rMCRWou2};<_{av8SN#~XQ6n)y;f^mMVGI; zGwC@w-u`EweBByyJXU%&tF}dZ`JDNY>2O(sC+#1SFQLct#+deW`8L6xM-C&G-L?+u zh4#HGXFfd+Yg*a(k$SFXes;%Bz^szjv>t3_$}@U?ko9lQ zl;??X+Q|*eNnUpRJ*~g%|I*ZnoKMXr{Nbk~FZEArUj(yv-CdjwXB{q1A-T}FG~^g8 zI6N?lERVB#GlMwmt3HFAf7$$7v|g~KTOj8u$=fH>JTc=}FOstcem+R*B{vMy-X-Q{ z*VFlEA*JzjeHbxPe=Ty=t2}x>*Iay7QACy>KHz_^_pIL-GXJfZ^OdfzBH(jSCG2xy zLRYdr>guaIKEk5CH|X-E4rAYt`FH=lK0l)Ko)2(Q&@{S!rcYaajikNPR@oKwPiwt& zAF<_S?Mh<#0M~oOY}Y*%{6eXP*W(W*-{V0WPTTF(42AZ~mh_H#_ePtoCF3yCV-{1zaZk4|7W6 zreQFvt$#x24teC6>_&3^ zq#pK#&fm7={%#_#$DT4d>^5>!QL9J9CcoQsKDg!gS@iXc7n)aR+m7hAk z0%o3R`M>rr5<7o}?XT|WNnVeUx#w6tT+lG?J$e7!Oa0dVhS`RX^!uY-eDbU%=DWM} zVT#D-$Cz{|u{E5w(nEUzE-QQj4@h+ZhFaC~kiJadtt>&vOh9@O#R&-wtPK_?oT?$@w8ro)-8eVpG@yxwElu}{qj=MU-PJ# z)^CXWlafTfpE~tz2B`<(*{mb|{)%@W@+S2pe8$f6ChMnc+x}fJEIVbf;yi4a;Xg10 z7Tv!Xa1Lg4`AF+iaN}d$$ninxGlJIVkfe=lM~)w=#ErDRg=BWdr&FZ=U5}Qe9!ARX zmN!q5_AZU5iOVG(bbnwToY;fZ7vX=%@Fdr_s+*IJtS0RffANpN+^duJu7wK@-u#+L zZ0ENw9uAwd@BBen&@s+?C#?jXJ16-L`8ABD39xYOgcsQ#(*3D|7xV0wJl%^Xha$K)KbbRx%pQ`cuF&m#96 zE`6E=%U`-5IS0GH^2px?3qRHIFTnPPj~pQ97tPn(u~%V({e2g5K9Y~WXLAFNTil6W zA2&KJ{(KiUo*&y|4RU_?hhC3h%eLXqSHYr%lHljCjQ1&23M+!<_`if1?t_!b`O)Nc zb@XdEaO(QYF{J-x|6K-{)$jJF6|i2Inot3&#=pK#&aZmy`gvGIzTDB9UO(%eL{Iw+ z_kQ#zX(@8<`Bo*wX8n4&lk>Gsvp@C+Y-1imuV0ZWGqT>7 zTtBEz5BKj3%O>}t*RNum@HGx_x!u@ga{bER!Yk?t$KNXH7!2#*epf zy*aR%&jtA!nBU#r&x!Q64vJq38;~72Npy{t6YGHz_0iEzFXNmmY(2E`DtD z64r7>H;6+|za8@&7JXPkb3?|hBTq^Dwo9pfRs{5a0?T*UEk2I+5=k?UQj&knDmVfA zbKdTG2&*TZpK%iAyGiRGz@nU)L8oB*hh6*JhuK+9e~IHBJM(q0Qq+rDKh6B^4Vaax zJVp8&-(5LY2t`QZl*2RajNqkM=j3PaIc3-1pNFHTsvA_^iJG z>&))|BsOqQ&CtMH!TE`&Vao^hqx0aj#?-^a@+jXDHLO*7A3OsWcwXLc3g%b)QY(T3 zUg!Sfl!v`CRIq7d&Zs=JFMHUjZ6?W^4cl=JRxZ(Y&VW_%6^Z#U!}_H3Agt^i#MQv* z6BY)g!(|KosT=3}RqTbe^9II~yz*sKbrQ^Ae(!eywkhdnzY*43U3)}a9enoidYFmF zL|ueU?2c<>a3gEp1LC+8*Tu20V8D;=1thm{Jsl0p8Q-ZBUz?Jb!K~Uby)GfQ%o+G~ z3Cz`GIFbJHO+SuB!pw>*^z{mEeKUl^Wwrv^UOVmW#4wm~`3qehcXEhL=s%VYZP{@a ztbX-u8F~HcH%He7!_4H1H;LWvOx+R$%SY!^yJ%U;X+swe_`m%{ zpAp{H6&4F?=>Es;^7va{*l^pK?w>j4*-Z|xAgps}F8YV6-p;Xy`8y|Ra$wJzx+&r^2{NMLCapDlSmN0|cGnRb68-_b8E#WeM%@gwdm&+>FHiOe%oc^Ev ze1gqkZijwdzg{&ckW)2Kl073 zggNG63)A4x!>?Xfz=ptsS5x68eb(KVut3vh4LRN^*AAFcPWop~e!LS-Gfr&v3}#xq zq{qjygr^5fVRh#b9tz}P9t(e!z`}(g70Gac%MF_cusmf}Z6eJ1G&TGlthrNsXd7JK zZHT!JW{L)#-U93AZfkxE)_Nb9vl$NYyRhXZX>aw49?#jwGL40>nfJm@@yK09f9s_s z{r?QVyB-#u?RT~SmSw9~%iyA|zbr4p!UeH^$oY!p;-YeI(9M(EnGsyWnU478=D6HF(61EIxru>?a33GdH zxDf>xB{akI(q9Nm=RX_WD#5*nRNajY~uKDiGYh^dfFa<1;yFh$n`{u z$I3~2Vby|N)eB&If2a8=a9T&k@wsr;qYgKBz#6aGmZ7k+59`r(n5&oQW{`aU!QdpA zUDxAbFw8Rq@wUM_W%08h;z1!}m;!TM9#`=`L7 zOAl){!tClvu|m>+%hpyKVE*y$!4qL_-Layzq`m6Q^|3JLMEs;U(%#?ZmH?*XF|gr~ zuLChtRF)uxwfp-mC-&5}?y>?F*==0u2bUGtI4vXn4>3!}z@?>=6PNsBNsGB>=fi>( z^-jLXh2ITB!(d6bgS|+9WlXB~T-fluIeq;ymoL6^VDaVl)QKfl^`Wql(V1F4_|X?) z_BjjcoFDfd&VogO-gJE>xw|%pz-a^c)Z9M%b_K)#y&uc^72Sj2|K5LepCt1@Shw+A zYk#bdf4w+yGOS6Qa(V(RPth$968D-x?cP7Aa3X9rltn*JK}p+Ue^@{DiQ{YEsM=z{VlouEY)TmP|jG)%?P1SC~DZtjGrzw`*BB0G3;9y5|jtj`a2< z4zr#+oDVbVEj;?co?f9(rPwogkQL2@g%;~Y4vr)!ttu&&dn^Nw(&`|M*w zU{S}%>>jYb$K=j#Fzar$9}5n2KVQd%g?*fIZQ=Ac*;59<#-wdmn6Mz${geyLzp7f< z8dml5DsYCytwWL+q`xp~d>>fH*&NUe=5@$l

$DMlW3;UlKu>J@O z4rv|R3+r!IF6)D7Uaymn!U4Olwf4ijKPwGm;J(3G4&Pwm;?>$2u$q^G-*=c2^!VOQ zIQ-Yqm*jnq)poM%DNK78^^m*|vi|IjdJR{{R5X+QCDunfW(Ul!8~!{7=AQ4=?T0B_ zmv_s;*l(};0y}2)HIVm9nxmiFAY40RakMJTY3INC2lv`7p^*I_MnR28=?L~eLsuUq z@2_-cx`zhLR@~k{4;BqJubc{-ClrjQ!ve0c4GnhQ=lOC0EbeNauM5}Sh?}qw<_$}; zXT#y5Xn_^Xcsfo=A6D#ElDv;ItYSCMgXwgpu03+9aPt@gn3vKLv;tqeMvJX&Ir?!41mU~%-t#}+tVlW750 zvR`DRKYca%ypv#_rw(M;(k;I7r$zMCxvZ2i*+%qhP+r5N`*}aCrWV?DvSq zCK~L4y}Li!ko_LUv0pQS;n+++KG~n)$Q7xE!Hy$0ye_~3LH*Z5aLux*H?v{!F80F{ zaIeagX~m>|>6=HXaCB5+Tq*H+VL>L$Sn)ozg0$ZqZjlEEyc({rhA9?$g*RZwx@G&G zlf1S*@*XU@eca_WOchPq`Uqy<>w4D)vmUHi@*M7a5+Bn`>ch2mG{F^lA63LKx4_w6 z1Y2_anHvNPQ;D8Q7u(c`2daeixWu2O-;pS-h1!;S~8Febr_ zOGOvP!nKtaj#@CccBn}aW~?5{oC$MOef7q}L&|n>b77wQ(MMD`*@4zz08=K_-&KYM zR_?tEV1AYK-^rxCTJu&Dm~r%&qdLqSzihM_EO1klxx~^c=lx;!k_#0@Fm?FEx-eLEI{QZ{OplnP zcLXk}QAw(X`8(R~ori7ZBh4Pdq78A^(qMh7SqmP)oKO5!JlNgCaQHFIEuR^g1GAQW zJX;3~6YHthVDYhyi=M-j$uT2EaKpH=u`gg&tB2hUSjN%Xp%JEs#!kNjt1k7r-b`|1 z>Xjy#qS!Oo2J_Ns>-yl9_L0Q*u;AjDPtsB7&&H4ZPExOZ?4&vzu*h>xH_Vi37^@4{ z1f_)Z!rbPzpNnAQ?HNlx!So3a4c5bK{JZsiu-Nl>#U@yk|9-6)=C6PM=n&kQl-fT4 zbGC)P%z|a#wT%Bk>cs++5||diy#AAT??ovAEHJE_`wM1NtxcjeFOP7yNwi%RmIwEeNLW2LT&GR*uF zzdZ}){_-+bg&BGc*Kffbi@?8=i2J7fc@E2V;-6K9MWd(tw!wPt`pFukJ!5$OM>uTt z=+}M_2Q*M39%IIk}=GkZW7iATYktMG=cfii#j^szLs@jQ;vt z{|63!9qh9Z7X8q4pFhwy)K@aYYDmJl&>G7Mbt>KDQ znzi=C%2hV&;V7%1Lrj=eH-*0!=G68LIl;`xNV@~DY_Qzc4KSnsP3}opPyW=FtuUqW z^}$?N_2|OYKBT^PiaQ^cQPMk=f~3{FfE-vx*ECbw-?ICNN(hu{Sc07w77X37VhjD zcm&hjjwL3*%-jd}Uc#I&ukFsk^w*K~?XY0cho)2*>*pW+FoU|}dHm6#7x7eD?z6L##moKpnTt6V1;!@}(})tj*3)5a7_*s?M5%59i` zbWzPRIQ-eI-g_`h$Ew~1mKn7#vI=H9UhDUQo9gDSuZAg8%3^(C^K7{3+H3)_DC4+K9_-#RLG>jp ze9z9$hx3c~mo^X=b-yo$YgdI_c}?o~xc?QvQM_X-nqh{^B&rauF4o!gmRNoCk4D&i z?aKx4V8Pq&oF3Bt&X!0KEVTA%{S3!)J@$6M)Vb`Q5x8@Qeor?n9;fwC;{?XzGI!pm?Q;C#;`mqW2wU**yKS z8?FeMx@-s*sTPz+z}a0a-`}vXT-7`p?mIPe{2!RI}S?*>|L2{t|4h zz?6ijn9;EC?A_&E@UZ$n>9Me=?)vos*wTOF3t5=sVYE{^2J5p*n;CL2Yt1_!HCXMb zY8EjyJB+3W2g|JftN_z}K3kZ;tT9zL$HStvzT=m|*)*3@Vn*3EDsjyZa|IB1IVL?#;ZI~KRS|;)Dj>vnk z_k|c!9pvoA136D%|H!}?V(R;ls@Jf-OF+a7ShRAV`A@jyZ*_0_N{Jk{t_k!=;`q zBJI!0Z%KvoIhuQkc{-QMuEF%NA}vxcZec3lhef~R)h$V0`Y5>t)@xVUwixERoum)K zWjz3@+=;yagVCQ2i*~>^?R3+~Kb9J{W*~7w1_H=((KmYqI zN0{BET+fAvVzZ{Nf@#I06F zxgKhAb6^XcE$dja6c(R2?)Me0{t>j3^atBteB>V-V01s%92TsdnIjjE@h4sO&IG2d zdw5hA<_ZnCuX4>t!=MDe&eKXVr(?Ve5bKh-^ zkW(99Pl$!X%dCRtllq@i&1_9!)vLC(K_ z=*~h|aG=*)2Ikei{ll#18f{p$gV_;s+wB`>m<6Fy@csRVt{~j@|!m2X?*0hu=?S;92F0Vfi z`_GLpA?AJ63Lq{yem;hndg1HAT-b8FhY@MdJlsxu0ar)J7?987N3?1Df-A0C9+MnD zE#aLy7xyX7G^keBn=6_)k5_?A51s8a5WePF#~M=e{)@zUQ; z*ad6axy8MKDOx6n!byF}#MVZb$3HJV0q3#<KWL6>TDVE zJZGr*jk^N7X6HS70J9TJ&NRW|4<~&EF#qUGr$4X&e}?1^%=%#$GUgPHe?di10SnG# zwaCM~fLX$FnBh4oQwiql8%-kX4~oVfV->h%My*mQ%-(->uR5&T`1V^dEG%{3WdO4t zO?BnNV*UFr%i)H=MA<@^uCngz7Fd6EAFl$Ds?E;wktu6Z`O#fFxae@C^&+&Sjiuc38 zcSF}6M$UYB?IsV_oAXmCf_U74)n#y)+3vb9n9}>T;U&yik>MT!b2r{o_zVXKzn$An z>Q~#&S4_n7VwSR#WW6~zv6Bu{{P~dq$a&$>w^zY~ZnGupQ(kFtNeC?ZVkS@4s|>5W z?E<)lmDRZo7Qa%E)lI^9`dM#DEGU$#wu7mxo#*{v=J*A}K`?KdpU@i?e!TJX8tnZ< zFN~~@Sr^YGJcL`GwXN`lxyLfkzlH_t7f&YZW$tN@`Cs5@@9C2DGiUFsM*}dsf+krn zix&FCs-D69t9P(x59&E@Y^n@k|Jp-YlKwD^;jV%?bxhT8@_BI&7H);}Lp4tmb8?L= zf?>;pwQ;0B`M0mIPQ&gyo=VKj`8BTy7We8%>KVDN-1~4+&*tKTsF%#=u(RCfyGLQ} zxw=KYFk|^q$$FdqWr>c|S@d6O;Fc8RlqKc!X27-cgJJS@tz zh21B=nJrmQKR)q%72Hr0^SK1MV08SAbue?kWpNoyXMbJk3Ww#J?Yc|M@H@PP*yY1a za{sWN?Z31gc6>ZGl&qh5%g<%(fQ9e>K6nHRZZxF^z~1Mcm_CJ>{ckLG!4=&Bb4n~?jNc3FKS99C=$Oe5=Irae9CD4ZO5u#L<=4A%q-7j8Ip(1px@+%lWs zRCrM7{ue1S-x}OV$b;FJJ5a($O$dr2?Z3U9oJR8A zMN%hW#j1CrbXathl5h%kx7tb1fGNE321#&~#lWFVn76>F?kr4OGv{eG%m}+(egU@K zeAV_cOucx2ANjCYy;`vlu8Dm%zXWEREU~@`)0IN!-GG^k5*C)h)b51ZQkXH}FsB@D zd7!FP0gHYm-xa_mk1bc-hdH-0Z63q<9$yryU~Xmqsan{6p27BNSbRQe_H$To>6oHN zF#Xs9wU@-92efKo%C5hc-oX6D!m3xK-lKkZ7fdZm|Jwo!3KK^@k-Xq`>N{BUO7HU* z*#32Ab0_ii^HGDuQKGsYn0?~v^q(-dC9Lo>%l?+V4R_`qbIR5I2_X?7~# zN&SG-v9YlE6ualYVfy6jD&t{l(qQ60n5%HkZ34{SuzMV33O=vYeUvh++rx7m1@q!l zf2qLv74IKO!_2fj+7n^b*U#%@V6N-fuf)|G)E|t21xt&QC&7AF$xCEO{_H4YGF+mY zHdcYOH(cIJtVX~1d))uzn(uD!q7wVWOX@Y%HlCRPvtGV3Q%8MR)=pzpV%qX&T5#w2 zpudw~;k06J9ay(OX}TKBbevZ*9oGE&c#xQV{DJfgxXiO9aSCZ)eD%W|SaHc!8Eu%> zb9=x74%m|ZQJ2`$GKK*+T-tI}4;C+zeqjR#H-F(8!qnTcE{<@rF8%s^n43CLV%1mg z_nMIUCci!=@ zi>&!)2lE0>O6nDh1Nt3^_bI<6^=0&cJSNPjN~5~NiqkhdTnDobTa0XhW%u;9Z6NiI zti?WX?ciLB8>tsXw)nxEb3fT0B-ebkCjd4MjuC8zsYc7T2f|dnA$>1cz0@DsuHKoD1A*OqX=^M2LH%b2F^2B^t)cE_r8#u}OcF8s3svGZy z;m(!!BZ^@0tS_ghUBLOypRuwSrZPJ^OkmlFYIX_CJaTMIs+ntsQk6Id3j&(-)Z8=)Sfb=Jy-F zQA{E07jD`en65tlTNRqguyK<&8!t+ z(eiKK_QTH6o}39V%ctxA{QKfl6_`=-Y#phu@pDX5g~h)QyYGc7#@=U4g4uS*B(9$8 zZ8`aWT$1PF`@h!jx#FQt@(P*!P_)-9G2m&z%rCxK``{>(Ywgou+RJ$#!{L(3$BXB{ zy!SNW5jb~T?^YAie&(j}(QxqMS$h}3;+|HI6Y%hZrcqld!7vAB{CIeZ%Qb z32?shz1^;`h-KWI2#5P-9Nr9L!%UL*%4IF~hFL9@*|Er3_3UeYu=rrN$B@gaM$55brfgo!X;}A)lI(t%{aVJg2ySwG8x{exdb}FSVd43eN=IOR?b)I# zxVL!YwJ4JBwBkL2Yj$bZ7;(VX|rA@n5#eFlM8b*Cu#@4gAYo_<-z=0Z5FYlKE8N6 zG2=*pLn7SA`{a5R=2`8pErSIPv)|^E`n=EXZ(*_OikSs4w_15pJIwy{F#Q@#R}-np zrs4kBI`tf%I3>(;KAgPa#G>mk>)M)+Tj0U=v^OOrkIcKD00-NisV#-MX66AmVYPW} zzi-0q4L`h}!fc`0$PJkCYn5*s9IZPpg5;cE=}Eug+*O0E#jq&dK2GT(*1Ln}v*k6_Bd4&4hdcf*^5Be1zZwCNm7y}i)RE))IZJoP>q-%PuP zbXS)tgD)`D|P)%-DQjtq;ryK7TL)W{G@H9D-eo)-t)UsK|c(N!Yx6 z&pu-QKNtNgu%qF!{lxSU*C)iHpntC9erI=ml&gZ9w%wO}9{=K#tDj)@nZ@79`G{ZC z9+u6<{j#H6a{avdmAkcJVMO8$ay|m_-O2?d&m5~m`j0tVyV?$pu5(i-{mZ@OG0G9n z_Evat9_AQOFL=S#(Jqql#_)Ey8wfY>Y%<7vz)UM02!R>X`y}I&=OXhd6xJ^}evpiR zPUXwVhv968E1BeeV+O{ajDvYE__GACpu)fGEUE9G^n@Hy;3Fg4@~i6r|gB3KW^Si(Zc7?T9_RSiyOT> ziD^gIXA#T9`8!J!+fNw12TrwhSw04)TEyI7!-|EsTIFGOjrd^zoP4NDXB^4d*A2GA z0<|7JV&T)qS;V>bAMK)&{Qc?Y#QfTMiOmJ+wrJ&!mBcbx5+`rEK7JK(ysqJTe&PHyTiY$!o%O2UxdOG zwfkcx5Kl5b9S)18E>;@{w`{%gj{`HLl4}%T$9;0q(Xcqfx>N}sDqoQr2h(o9KB*3~ zq~1p)!NSBZlJjGAJGG|3f_cK0dB|mLhMH1g-Yfp}`LKTLx2NgEVG}o+!%fe_#%IIq z$;%^bV8!v$PPs7UHB&Mls7;CC<&oTYo}~Z9&uI^@z)ZW*+_k75Ufs#MM)J)UyxmEA zzZVfj#M4el#s~YPQhG5g>~TNkgFM{*T3jh{eDydozK0CkpH{$hyW~at;LbJMr`(0{ zhcDic`>|nkeNiPWzFD?83T{a$8hQZpY*>9UFr{GK;6wPo`Gwlze&;dFwKG>wLM~Hc zSNELc)$d|?a8yhBg%_~cA-p964j1oMXoPuNFKOh!e1qXtZ(w$@Z*Bq1)-c=k7Uq}+ zEa$^RzrPN@hZ&7&JIY~th&QhT=Kg%|c?WJWaJOT3QY4eolhTw@QVddhV(38< zMpCITNJ^#OdC&XUzwhVI*KuCgTK8J_!^+-vJF^$&Em%Ttfm6nIs|>)x^;J^su=?6n z69!>+@t$8_V0};X*TXP=kwc&u?oD!BG*$!Sy=gXA>L%ttVQQ@$%!;5Ejf4FI#bc>3 zlkI3Q8O~2t(3?i`mG;+X!7gSIEKOqn>S^=g{`75SI;5U*vUU|*|Mqjnd{}s~X22XS zidvsZ%vk95m$*fF_K$@yomtMcf;+#8jx2}8b6mT2!?q$%vvs6Cf5h}C%x4a)-Ar<4 zZd(X!u6J=iljQ2}-kyWw()%b|Vfw$hb}?}M$v_1MnD)}QI0bHt*81j5`s1hkErFkH z>7TO?X7=8G@DAqR(ztej)T^`ycEj}XD%?Xb^~>V6LD=B>=~iEuZd@6+I2GrQG(F89 z=Eksg*}~ypgWUpQfpy!b!?2gBb5aQD*M6mS73R7(jt_?^+O97i!Kw+D(j#HM(T1=# zSbdTA_lvNo;{GH`8s2~E!PZNppL?}d6K;uka^nijvYYqD4EBhkWZr=3+k30ez%C!$ zOfY zgxRLo#uO0SED_YfWf9w~9>F5_pKkSV+?b2gYha=M0acQ>%vh7y0Mk#rZmou0%9N|$ zlRVAevV!z~md$O0nGU`Z=U-*7{{r(gEu*WDD|F~a{f4=36vIEjDZ$NK2Z&GjJZgt6 zZDKx+z+#oMpk8u4zG|!73|#Mh-#poL{QR`y-;aqfrD(zyC0Ojm5v!5>Xz?v=*fP^> z{VbUN<@0z$cx~SWEj^gC;Y#2dILPTmsS(UOIa$*VPG}q+TtV`8yGC}y;i|Gv88AEg z=_MDKR(s2TJz2 zor4|BZMvQp1gBS7Htv8$dB=7J!{V;P8y#S_{OHWHa1+Ba*cqnY*4}dlRxNvV#slUh z?3CP3fZwXZCjH-ZB;$FOsO01Yi+60)-$&}Vs!aETX$I^dcevBw>h6;;cWsM-8_c_T z|I9g<;U78c0Bkn@IGg?}*-H^Ab> zv2P0CQ1ylJZ(*UcZBH4g|2{wbJxqNi>sJkz{c79w0p{@()t4j+x0pGsCVtMxxxeToT4U=Skk$TI2lhxtg2dmcg!wiZjdoirv`Fz(9 z$z%At*TEix`_Bx+d<)gS4RFmTf$3-(uGg5i*#T~H4tg&QvpNHu55VemPTF#?Kzo5b z2TsWNkT?!zF3LD|0j569RhNgUCGw|pVUb8fpa9e6)danU)Au-koB%WUDjK6Rv41_D z2T@_+%r;wfINf=fjv~xm`fKhom^b=%wGvFxF}Sb+uF15kP=*ENuDQ0PUZMMgDlD>9 z;GBhryaS}C!s1om@A6;+@%Qv;Fz=o1tjDnbhFsGbFeTXGWCv{5p;tl;{VG ziFJebWy9g$4oNSC*^2ZnZ(x)3ZJNd~V@bKsA2@FDfk!4V$8O(Cxh$;TJ(&%wVS3;5 z6?(7~RX*7irnnxvyaG1(Ww1kJcR(>NEDum76PU_7$6U|``b0F&|+@rst zkeK$(Q7wkJ@G;#2X4i}DvS5eHSyomsJ>11b2(z_lKQ_UFWaEN1xXqUv!-QE$`FH=o zF^hb2ZHQ|)29vY#{uoOvx5NDYu>(uswthy29Za>@R=f@_(anEKOi9%nvmRDgTD)o} zEKF^hybab`+GuPK(~8x9?Sgq)Y?cG*pWQRV4UW0te4be3o*nH8%X;j{C+6NNDLe_s zjU?Z4gn8w<_oCo>U59|(FugVTTO90eo8#p~tj<2mhg}wYdbkIsoG98;1S?QFLifV_ z^&5;H!{Jx_llH@+1-5JIV7prB9|vHLXjk?}IKP&sau60~$j$r)ubr*G_YllYj4)HU zjedFVVm2(Olk1pCeCjLZC`_3ZJ=+-8%4GC=!qk-%9V=M*PS#a#nDcbQ`CV}6*xq12 zSUlZ5C>XApRr`=b`X`UIDTK`vM2=@*e%ba($7*`ycaQ-kjgV!KD7*l3xtC zw?e8S6sD>?h~5Sp+!!q}d%!2v6*d|%?L3d1t+iI`4D1lU^6dpuA2MlmGAw=)^(qn; zg-*Zw81ARl{fZ*qXI<3>x2PAbzX?tHz+G#VBdPS&Br zO`;d=F)(fJ{K5^edv?*7IGFRVBgYXIefhNd8cdmB-0ub3R@OT5VCu!hfe<)9&$1~A zX82`{iHBQ`C~!YtuIav5kO2!EL$heP zn9ng5A(^CqbLE7EuwCqc-7T0#AJ(*iI}DZi0>u!zYYF(eLFc***kc@HCB>-n$(!ZGhmZ3KUe0Wo=*+f zqz^M&8HRUYTBG5-^|0`VLqh?~n3~tI4R$#cMk|7;Bd@vpVR6{@XT!=m?7M#jT*MV-+PN&n=LB|5O`ndp{MSnxIT$$Xfnko2$^rhE5luYxHS zRwLy31u~Z7?co1^J-j(3!+|>sjee8$EJ|*=a20+wR&yI!??T3=9+|&d<89h2rjWOmz)>W7yDPuyo;Z=_KlR2^F^dP(Z~>1-@I)! zIe%!(^AD_HkK4DD$ayA``B&);|95_ITfLZ3aPOxQ8adCH0i(GYaHq?F`nr#oZnze&U1!suW2R8P51ke^PExr@^>@L6Z)DSg9Z2EFZ_gEJO(89r<_?g zY6SKdJg531=jyEPkSfCYX&BZ@%nCVIrU1tn&Xy(TKV!zK*VEwiO-kAUB!8U$zyOZZ z_;`buKVk1nQ&@b+@CrHKsRxt%w!{B@FWC3CT-XcKw(wSv_s5AI#|?)q|5_{~-wRe- zZb2q&^g&7T{oooZY1Y8LH+~zC?~7<4sl5k|Nu^415!2R!dhh?4pSAtw61bi-L6TE; zOt&(GYlP48NIz?W{LX_gPe-GW+>dJSu<y6i2PKf0sM`O9$JQ^jLm zVlQz6S@6K7oZN4*qZcH&h;Ig4rit{cnAE8iss^ef3n&93lNJf`tnA z|DUNXHZ6GQZsDyZ?a^()LyqX)e?N)4AmqSR2iRL4U1bU*OT=xeCoe!FYF$fBH0hz zD%RTwI7_Yb7r7q8MRQN-|1mWs(Y6Do9kRPj*0&h{)sSo{=I5C1WU{}63MCl^a8R|u zd-6WHJ3WRE!FIynh7+*h!lAk__`mnh`4{mg7XEL2Gk3kNPlQ?XD*DL!<|T}Z%YyS` zjINON&7PVee-Bm;h}aoK?8*H1P@*2-%KX`%XSLiz?5cZ$@h-xIOSqJ+(O%%NWLHcx#sH^ zQ3?Ft^5P9c$915<2IlqF zxQ@V-YZpfRVK2d{rIeYt|CCQ9d9c=+pBqP<5*(BnP6*GxF#@fN-s>+%v`+y?w6Ia5yQgWgQ~k> z{f(o}e}x%EYDyt6cktsoV*1m?<8$Dw?3sqd?5hKQpGdvp9+w`NXK?%ZbOHA7Y8lD( z#I_2lR&cK$Bayrh_JghU`{1IzCmxdfvz*jC0^t_^eNi9Gn7U&@BQ##dqf0@|h$STJ$i z)i^lo`nWkyVSa&P!A1C)@-o*dn3}~dkAPFUt)*&VVeB0Z;x@xQv9Dmp^hAkW%uX$7 zfbsoM;UXW(bf5MXX5LJdih((smz%f2Y|h+=$;1z}XMTl6QNLg2!TF!}|LcYSd!8DN z+_yupAbVnaJ#vTc`2G=?q5ZeI8Rid0Uy;{De_7M$-!OM$n95X`WqLYG1Mh!mX5a#1 zzSP!UVux|BziYsP?@RTT!J)_E&uhYz(R}81xc(H+MjK{iMK%S)$~zM!7Fe>>U&AiS z%2Ha$xjUYf55wtm|GLbCIc}?G89qV%tS!9hFkSP4{T^az(`l1ofvQFy59SuT)lY&& zV@A!p59iNV8mjLf>Tl;eIPS4k=@?S4G3re(ob|iXk37G)XV`R1 z8UDPmaLV05m>wEeI1#2L<<9s6^M8E&tOvK8Y5FXNMN0QVY+;eX`r00nm*^iq35Ull zmFj|Ng*oZja4$3MeJ4ym9yGfe=4sZmB#zc*_re}&Mv{8!7kTyhPx0q-(~E=MsAuk& zp|B37URe5vT%Wn`WawsC+|;Q44Hmw1x3z~oth?@$@vuw{!@Nmu{^)lf%$L#1ONHCo zKg}l3N4HAM{s=#NzxC``JU{pI&2I|j*pC&r%*cFm<70|dVWR`fCGU@~J2OKauA#}w zP?1wsxSrL5DRtjfRA7###hrPiK6IwDDok~Im9!A{ZSW^9JN z?#QGrfcdY3*?V9+8YO%Q%rTo+-Ln(i8yF*33=6YgD_6J!Ak zkLbUwhl@-%UfTrYf2h3aCv1{)DrFl?{bHh^Q-S?{u&Br$7G*DWvw~@9y0;xj{~OV^ z?XY>0kIinFFSq>hQ8=N761xZH$c`?^fooxc!uv? zvz^4kh0_vDVDUiaPEya`Kh1wX@!vWn7t%jj*60G<(%Rm~f(5cOD^lT5Q%c89ay`C| zW(_R5-F9*-%&1h~(GIJ-pL@WBxw7lkd*QZ~e{WjCLgTJTN+sTZZgy5Kb<+OGc_V0pN#VIh4@qxJ=HaD9RPtFHw`moD!n0s%f zZ~K-Rag=(oQ)te`eIoZL@f z@>S5JDKm@_Z+ zgla9u)4SM1m-HK@)#$^v5z))^NWIyGlPlr>-XHU0al#R}DR{r|1*TF2+ ztK22r?9NSGrfmmL8Mjc?w+4jXw;hsU0&NP_4d;PX|aQK20<2>Tyo!7^{ zz|ThmYt-+-th?bZbKs_J#i6CJ_{dMG6)@{?*Xd_4#Yv%>4U2>yy{lp2Y14?4uwAii zS{=;ja=O8TRaH4kO)$-K%GClmrgCA^TUan_=GZdWLGY^l1MvkP_j;HXExgwXbN@^` z@d+*p3J&`Oi%-ivZihS19Z7G8X;%|t#V~!zgv(tp<-*2DX(4_d7=A1B4QBU`51k4( zMeN`H9cCKXbj^iX|0dapVbQ)r`|RPY==+;~z}&z-xg)TYVNd)on6owcc@)fX(E9uv zW;>TYy9Ym8`|2ynxyipM9kBT!Td$uma};~F^h@l&Ys_2Zeu7s1p=t1c_oF0mscT_w zYf6q}e7(2FdB8dir;U3^eMd!nFr04ub!I0_)vcG4=MhahHp3Ko{OgP2R zZyp&ht*six$Y8LXXNjLVwCFy&qG}&kM$+4 z>M-|li>nvBc0iUn6XvaJmGXi0r?98bf+@)lHu}LP3VAX*Bp(n8Ps8j3m&Pq5-hE~h z4^~~aIc_P;d1Ey1KAe7R<9Y@xSo-*EE!-84@1ig7;Acta z>X^hm6AQk;CL6crIK!MtGj3AqaXXv?El8dVq-@&}_ zncnN*Cf>dNR+w(Dr)&-<+|sw}g1K*^NN-E z59mss-!6Bhhc3)itSOv`yzO{?%wkgigAz6tes*MbuQ5ztYCb%I`6x+Hp0pAc{W`vt zxW{eTW>c8mcvfPeLRyj)OntJ(_YZRYmpk@a!~CH`3wz-qmey-qSlAWYC5DYY*G1UF ztj_8mZ(-j*W`ny)K4;IkCvcPM@{g`CTlLAL+pygo;lMspzq~yBCM>+WOYsm)i8`Kh z4K~VD<9WdXsSjJjV3taYu`j7l8$B@;*3pV@I03W&&3qOFC&W81J5BocW-sKx22a9$ zLSX(_I~vJ_3od@Y2r~?8lZ0JfD``8E^gP0#PB zf@w4-Q$8#zwZ72|s}9CQ;)iB@cGItBC-&9fZ9Hy+9c-kK>N#VbFN$L$8 zc4Wcsv)*i~hiT1!rq{y$e9NB=Fttk0@(ZjGuw>#JnDg04e@X+!$1-kuORT#-$_no7 z-sJNh7C=?7=|{9E1%v)9kq&xOMev#)-EnO}?N=fWOl=Zt$`(J4ihD!AxI43(H0 z-S@HwW?nifsi&{LU#!}Q=c`@C>_ARSueo9d_c}PswZV)ne_wgRUXN?UA7IL*O+QlM zmbe>v%_RTl>(>bj7itC6!Mw$DQdYgjezgjUcmazpPGmX5Mg8w2?~C`NtAh*czuU9F z47u=+G3@~?a-wZ}2(#TH!#}_}jqIrVF!g$a_P8e8Z#eB`G0eQVDOL$?bB(qrg1J&B z4ljoNXVm57lbrp|Y!6H=IIKqIpMFs9a2j0lIA|yfrvAG6`3)Si?Xgn^sXx5`pzIr5 zuYPkbSwGZk4$tPn>>od`-zJt-ys!xln!7CEE-acY_53(&yJoRI8Lx?#Wi4P&k1n zj2wX}vlLTP;LfStT{7Bu-!1&=$1vZ8+b#>UZWZr;3M&tevl$PIDivxQU{++sIP7KUiHl^z;sx@^SvNAXv)r_8)s#OjDg03TGvpnBopIiv(Y9 zz&-X|#}316QU1z8INwP7qz}wts(h${X+PEGabVgeuIK}7u*Uyx2+Y;Ac+(3Pc`vsP zgT-qmkNpSxDp-HwlK!XTk0`X@dr|lM%4L`_HBC<)_L4gAH<9G(g8Bt;!eObFn=p-A zq`ell6=_j&i97b6bA#JPU!3p==7w%vk_4|EU3b13Wtx+G?P3YWOw!Km&z za`7YDydTK3c5aoJ9Z@4OCBW%U1G(Ox$B(DI!+A9H(6bq4M(4P!h2w1b*N7=QXPtM3 zTRx}at);yxMs3JdEo(dw8(Uj5U!}FyGK_&3%$&K_Om!6IBK8%-GT}exMJWO3!@%K9NlJ)sBlwrn`lVcJ| z{cfd3*7y_BqQhU6JK2p zQ$J2>ErP=nr`4L0d~ZzUOPHcJ>fHvI_Ac+wSGeauYtLpktVV>r^ zUplatOY4U%F#AZxKTDWVXTv2q=lIc4o^Vj?k~U(dPjl3DxMs~@7^$ZhZaY&6D_lIK zNOG~}y`2NF*RSVRR-|6$)eF^+INzmQ3e92Oo>s?2u=}XjiEClu&9+S&VA-^_(JNqD zaY%#{Y&7kw%3@gL;$;y;@-1I>&VxB&LsBuYL;aV;ImC_@ZYeN*p1Z>=m~P}8mIId^ zwdkD=b2rROd_eNopZ2T3wCstN&UnS`(E_-6!(XGfJGZvl72y=_(2OS z9H}UkLVsIyLmJs{tQ_ehy0EY7&Z8A1=bGJH1rJq~?RWx<6&Hzjz(xb#XOzPHjQ?3atb+-GJ5zKm3oW2rP zb{0phg!#13!DeucubSk%VC6;bWWpTofwFbT8D(xsHgJor#yN6+2qt<+Tr_vWVRC+m zIq?Hqkn70YW^RM&$JvtS;mNybZii`o`whwS)ZCe%VMpraO4hH2^Bt)jJ7HFgg}wo- z{^2sqf!O|CpFT`8c@yXe(+6^j=D_tOn#WnN$o+_)Cd|J3t7R`t9n?8PgE_e)?LIKW zW82#qu*dh<`++da$AzT>(@d1x!eH8)p#95W-f4fsSePf}sJ;GrQ-QGxlM>&V%s z)OyCWVLztN{zk4((G+f;13N5w(RYPhZ~UFJt6`TCrqPZo@R348MsD4H9LajJ=qs{a9s5B$LC;Xk@VIMSfM*KDTH|R!hLd|Q9mcm zikR~E!hJd{6*jz<)N?bc4{e3@gU`gAhj}r#Zytu}10ttzn3KY*;lX7&jbX%sSJ&-} zVUOK`l6v~S`LB9l6Vv0>`)8nSLV5!|LLx(3*4f4 zJMkp(-p$Sz;Lg*_2K{07CM%;%(*Jd?b^t6q>{Ias7O)SQog(#Xp6b@a{zp=x$oz`) z@gFR};`W2T$^BV&n*8sut?A4&#Ih}!$_s6xRPx}W8Ew(Qs^Fw`;#FXy9 z`RP94*lk$!?&o?L*rQIZ>>lawI(}IeHeZs}QVI*HTUW`!I`;&Jp1>Sa=?>!lpC*@U zVgAbe@1&n)RPS34|93s#)S2JTM8%JjE4o)9X^j?1DQ2ZiZHjlFX<-iJ38Ka z3h|6c?W=J9qG_gDFnitpT4EkMZ_0d_{vj?i8E#@Fk6Q>++P+BowRrzZjbL&6qvyAg zvsNnaS`KqUiVx-z=b9g3z*PGRn?kt9uVc%4V!ajT9>C%`pVnEx^cEYPM{v!fuBt7t zXyTqluVC7>d&{@M_#a~knusUV*4o4T)dNSmV1KiByPaU(>m|kHeerCU(Dstt{>ptN zxaqF5;{lS7loTw7gAAOf9)a1usVyFGXjJlYPgv;i&^jE>;%OCl!L+ukj!$6!_42QL zNFK1O{ReCmq-xcO9---D@boOr`Og}%o%@S5fRvCLv(jU^{3n#P%XPk!x4^VQkrfYz?ia5~hFNbsO|QVzWrNbGFrR1Hl?b=AT<*w%@#n{x_h64!jk&onqwiv2 zDID&vHa#B}{mkAbfGzhfF1ZU+Qp$cmhUsRkyN_UQ%~QW>n4#%Kk{s zRuTW&x$gxWR2kv%3KstOnAix%?7LOb3{!&Q)9PWD_wAQI!!+3g|H%Ce9vo=?0`qJR zOYHkQbFlmWa&S+0ST9AJaTY740_<$89N z9o#hV%SVayKm2UD9M-wRI6N7~|Je4J2CQIw-DC3xkALDM3MEt-&Nct(>wzFelWwZHD$atx5JqO78HW+Yi zBje>f9C#88vjZk4Xpr39SpN`gr(LV74bxSBy0b{Vf&CJFk{>vAn+c~6j`S>o8B5lk zUk4}5{w8M#vnz%!n!=$)c`3_aR`$1F47jXHd(ujn!c@#&0W(aO@fk2JzeutlL(OZ0 zEMOtet7$RvlC#?@Ho^2)`@|2~?%4Q71%aYG*#{!yG{5AzhA z@^xUNQIDfsVD1S!iQT(3CLD&D7XyEi{(jTUU2K?@6&Xc`%^ywZ^&+`;`*?Cbc=$V~ z`NN{Ccf;)9l=qRJ17Ts@5xHaV(7cjaA;gaq55~hSQVoyK!CZ!#>0Oxe$a+BpObOuE zwZgqp0e)OkKWV1RM68dpOY9V4!Iee_E!btcbZHFfU(?;a0v1FK=Uj$GoI$faaA=bq zI{_9y%a)uE?0vn~$uN_lC)v*_HrCIxNk6l6w)7Xg-{z8e1u$p7BYQq9J5%-BJ(&GV zX`d;~yyJ8G0m&ax>#bobwcM#3X0*>JbBFaqSH7))xw0GHw4qe1O@`acaeKn5w!2HWy#2d$+ zPk_U1zAXF(^Uk*>+=63F1&)7U`nI%t)o|7+rq3WuU1l5C4)=ba_uwDQ`8VEYR2RM% z3s&wLrHlSA;_PYgGxwI$qhVHQx8`yt+XH^GdilgF>Z zVi}`HwJ`rp*F+xdaoy_oBa$yVwmS)S2w^pm_m2sY?AeWB#G49Mt!S z64jky`sX;$C$LM|;5e*og)7lnK*n*s5l~B8u)DL!jAZA&aWbKCeyHqvohz|rdpMYg= zADp}sW;nenJOkSmdcAib?lr9A!X-*N9(!P+{^j~)*r=dI(%SC z`>|s+aLS_6TPI+#RHMR6*z505=oy&zQyle%^h>il&l4McR%wL=Mhni6^M|MInB5Nl zcfJVbcr5x32c2{}AB$Xgtt(IrcPh8^U4a>fyH$U~eEERSi7>ZSOF-NyxJ^%jIr&Nw zuT3p3$bfk_mLLC#yjNJ@Le{5X?UsLkVd{cslJ(4syzD3a72_Q)d?7$CHYo{Gfd$7h zB<}-WPkCG-9B;xE_?aBlI%a`Vo$FmxMkerG%?AW z{a%#A)c*UD^Mbl2{!=F`H7oPPU*z0?my@Kw;r%~ydo>2<0ejc7Lo492g!g~P!~FUU z8Cm4cUe&3mI2?tiKM=Z{?7njJG(AB6=oPV zv}}frLdT!XC6;pi83YTz&X}GLb57MyNruJE8WM9)7UvYh{YK~h3e=+y`%=7N`r-g8f#CXk;kb{>4@JQInde$h!glR#+2>$}?d}gX zaQ?C&Q3%Z0{<=#H+cKry&cdQmTF?4nq4ZqZ8JNCk(-DPU{Cv#Zm2(QFDJOhYfh{eT zulIwgi#|?U40ERB{UP(iO1hM>4CYQSv-Kf%35r|=m&pBg@P_$5&pg({Lq|VUk@*!f z>l_^682uS^50cBJR$YYYvL7exhq-dgyzjyOu9FkV`Vl@>oze>HcZ6(mg=u9|?8g7b zem>Mb%?+kr_dmD>=D&9gCF_Yv{r+$t%%knIJ_NH=iylY9%x31HBd}0*K#K>Pq}B7u z_&Bj6%7vtVak1b8sZaf}yc2ersI{F#>{DJhLUJZAoV-7l;iLw|K0N=;TU*I`6F&Mk zO$B}yPQMuji=GD`m;y7l{+k>L(-m!%RN;~nhLZW>Y}LF^O#PH5C0U<8DxKBf`oeO_ z`lL*`Ic_R!H~NNv%s+d{ACI}P|D;*QS75G3_iH1VaqF~90?e|W6le*X2Uz3~(;}Vi z{Nc74huq^~5wCVbDm+vfCD{+Oo!Q;bVcR2)bI5p^fg$yOVZN%~|K{J*pmyRPjOT8g z44HrSIp%8`ER0c!Ap1{5SzbCDW~kdQ^@8aPSB?Sfk=mC{=94;*V!8%)nZ%Ine|~w4 zp99Q|i?Vk{PI(>O5D07S`|9Bc^JPmP3gDJA=dL@z%#=TqI^l$jFp24I$8RY2V?VvP zATisbe76PsUp>zta=t$tH?Z!=F7&f&lasH)Ov~+57V!lunTN2KY`wn=%=PZR-v`I+ zZI`S!YUh-#GY8PWDJkP1az>lBU;*s^d%;Pvo@tiWvS#pq>s{1!THhK@xFGkP><7-L zfp!+`RjVd>KZ0V7!kci4WBR+3BwrGk{sNY&;|a-m#d@oLPz*ab790+Og{37^ru@bC zAUOrU2^RHwaraii=Ho;^$bJ-BT%Nz1?X@?2-0?*XB-oN}fls z;BX8VR@eKohU^D^;W+J5*m9!3q@TI;!T2vQ#qU)z+5ZC7k*y;zNA;~_KZ*Ia>vf0l z^S$BbJhC6T))z%Ka7oXEsC}?#@O`O0-084ZvOdJxmT%l)URB#}C*(}Gq`PeRzxfpK z{SuDBP4ymgcOvJfY{?0R?ZyQ=+QCBGOB=7j{#O@&*ba-W>gtnWqbBPxVpg2|^kO)C z)sy)3%MsYUU*;A$PlT&4 zzY8MvaP>V++_uv@0X8pu+eXe~c8|*1=dhOApQFvPSa#p;j&pZh@sHi5xB>7sh|}Q5&^_LESF>Du$RgH+2c@p0=|x78ci>?stIy z``$5LTuoxbG0i7elJkq=WOmFCZnAI)Cg+9_P)^1WvndKQcwy#jxq$|r+-k6G+h zRvIwpNZYC`nC_XWwFyo!OKi>|c5)3p0*f+wUrN63owvIJVc8VFnRk#=RYtjm!{Kv! zFBHPO&->i3!$HS_Q}4sn%q;QpxXS|#Lq^0N;-g?%|w#}vbyP(kK9Sa$dIvBdOP zhVFaVV_8i-F?EEiGYHe(3H}jN9-o>pYRn4!zDnn>4~QQchikwhW1|BPi4|^X&VtkT z`a>NW`53$-~C2luAXM-Ggz?c#;P%MF32P7Qv!3S4#FP3neT~3Z~m>i1gkE(IH*nP{|^2B0Q2)8x5LNDZ>g3~=sMs0_YN?B;Oiv;%;ArAwTBsI*8M`*>%DR5PMGtcQ~de=n0@h5!Ps#t z82-!sCH?F~rj-V)sxn#15%tWWlaE)xtU=$GyJ4#RP74p%U6wM_2^JjukbDx(zm}oy zO!90ww{X~(oi@=GW@t=wX(n!ydbgKYTOmh99?vV^WVIjWv0jJlfT`1jwK}XWhNwr|CgblW8OUzjHb9WFd(oJ|k?$2TkZ+HR=yqC8fB0jms_XC`6 zbXt*^*Ohx_%=i_Ip}kwV#1!4f?{(ms39fsIsUh!MonZ4L!IFALpZ2s!SXI>b*aJDg z%;GNizwvPO%dMwR!1-URH$n0~Qm0L{fm@Dm zliZ(GXEVziX1JOEb4S0}NoMv{ST?HkCz&7GqwgO}Vd@X_%`BMjd34HC*db(^WPU|u zUNalvpz&+Z?m{k@tiHY%w%qv>-&-n_g=c2MCMnt=jrw)Sbt+@4|#r?dRUYybp>N>h_Cb!m|Ff`BO2C! zXS>P=rp(x*w{jBR=dXsR-Z0%{qFx^SU(Vj4!x@2lMD>!Kc9q|5u88>@)o<*Jdhw@; zrhzd1JadsBO#A!bPZ4Z8=Mw8U%wGS(qY2h;(K}0WhC)@vFx+|mY=A$pS?un~N*MoI zIROXezErVZ1yiI!yffU^2CBWm$!w%S;C^AAea}s zc1bF%JmvU|P?%D2=>0vCe@$d^Vdkt;2kT(})q$*7nEF5=yPH@#ek2YS^X`R>RmS?Y zeYzqMX6!ii)C^A1*nZ(U%sR*}*aZibyd0YZ({)2{dcn_rQFW4G=J_vrTv)g-(kF$u zAnV&TSnA^JikmR|v`yYE*k49GHw&h{bawB9gDk3D@?i0?sMjkd<9o;zzsZMrDJt*I z!Rou-^cBLCol;!_*j@L?)WVT{XN7?%{Ha zUcdsXwEB5iH2K*5CYX`iu=_3CKUu@`1F3fj)6klN`I`E%@iWYtvT2eNEO?Q<{tL{T zTCN^U>}zQD3+5I%Dt5y4x&E^TN&QTJwmgS$P`Lyv zl*pxxhv`%QZEheAQJ$d&vki;ZD5~K+pPNge!L-W?mzKd^x$3Mrq`#xr&>0?DDw5HG zIX5qA?}f|U%^P%K>NKgXR>+|5U z1F{Vq;ItR*O?r+o3n=nmb!{8gZO{h4K4l_U7bc^93hh3ktVQNfU*jROp zKdky}4$RmsS0xYUcPgak!rZkcX-Y8n&y_nxB%g6--U4{&E6@Kv%>Oh#a~aILGRmTq zICUs;6!}r1(-efP3Ju6rf~d(JXo{yMAj)i5u)yxNN7@pFdlVOrOG4Lg{7(kk^ZOfO&DybGqsZJKlvj&pqR z%n4?{@BJJCvrDI>?t?jDZoJDdmvZ&;F_=2Z$S4QyFD@E60rP%TAG{0eycRq=2@3-4 z9ExH8E~(VBFx&qxy$mi|qGA*d3nN!fsDay*$9KdKH!O;-hkeKR2E-F$EyhDOEYDAVUO!4Ki`KLMXy#2!mKKf(GOwqBQO0CI9#J^X*o=h-Ipt)f&JKL zq*)D%k{_qb!RE)O?0ZgZTrF{U#Zf~c%%0vPaf&AX5)EoUg__+zDtn;fL z1;@4NrM-puxmy>L`;ossL@c%oklf#(z@hp*$u$k@Np7ngr}7bI95R+zKYY4oC;Z>_ zO;X0K>Ly;OTP%(1KMR>C`;FAEzQu+aaF*NATMDG!>?~s@+{0M^ zb0VpaTmO6x9Mc%4q)76hz7joHIa}_663nhVd20cj?%X)6LL9|zGJ;F|o=lwzGrX71 zFoBDtSHw(%xkb_y#7%n5_tatL=?8~az_ux0?Pd_GRY)wm{$8afOqsQB*J|X-qg|iQ zf;ny7S!-aHFsogg+tG6n~9u|%ChtpwERzT5S*uOa{Zz0T9(*8K%6q^*RSrgqE|mkbbqVPtL=3GmblLhpFr8GPv+i72n$d7L@(! zxCAG7RmeEQoTJ*aufqDWiX(eq?x`&A1mbVErntjm?aE-~0aR*@jt+lt3VJ(Wj z%2Al<+i)ryZgMmF-~|g>ja2erYNd%w0L&U%7APR~5!zm7V2aA}p69U1HoJ$xFpct1 z&;)bJmyA6JbEL)uwZq(b`OCR5^?+CK035Sv{7?)m$Uc)HH3R#3p`}wS%#hFdJ{IPl zJ?$P3Gb@MR%fn@1hk_GG&ik%C5!Rx(`Cf;4o)#rau#V=JA3RbobI4u==2uGZOoeIQ zcekj*^o{dub7A_I`>$!R?ate^c`#L7s2IDj4d4@@lMF%>O(aQ} zyrt+g5+%hT6_q9`kunHLhoPd98XX9wkthj+q!K0_6sbw2BASj$sfkJ`zH9fbZ>{go z-@5L5KhNI#dG@5$yl*zl|9S73I$Wf?a3~+<+Wx$<4DPx9g)4xC#dkDxVM%TO>;jm* zX~+jf@#rZ83Qmo-b<3?{P{Gc>aM=4NPvl{ihFgUU4WA!<9*?HL{N@M6axkwhOk*0%TQ=WuDon9D zr8EO(%*(zr4d%@M7dI28??@bTeT%W1DlGhcb3`3^qwl;-HJG-4XT(x6-gD+7Etu1$ zoMZ?~J(OGXAL)OtQ(z7=q6l2QYQ6?pvdhuL{oW!hoMxFeiUl7F0TISSLV zlOCLhxrgr$EKtRH_v&=SMVLPtaM22`Ul^~PNczhL#(BfamDF$^%#fNEa~8Jx+Lv$( z7FRj7-Gg17=hfYTDV*gJm2l^{Qp+5e9v`%A2xf2ddhiMsbyS~QFdygJ^Iw!Qm^(UB zrU82-XGK+#{wAYoCUDkPuPF^M|J;p)op4Xjl)M(0w#DiAQMmNwzvNC*&%E&B9Goyc z!RI^7H55f=z(vcBhYS-xdS;vtTfL9%8HG6)_s^|^#et`aCoaeSwED#i!4!uxr{rL^ z`;k$}1sMNRS&BSNzjN0>1{UWYo2dkg^1e-54XYhYcbN+d%+1AXVCug)5sP5v&yXAL zu$=BeA1%_KSTNx@tY|t^xRmr89vJY0b(Wm2)FJt#hiNyb2!6w^&-YqxAoj+-hRLzLpPlHo*dqs>x1pP12UVRxt18(RTqb?aaS}q@TBH74s@= zF?q|K^`t*dRXqph>y9fR_eUSpzFJ3|5oXSS1?_6qT`+5@+5-bv)D!jXFU-3rud)*6 zhu+vdZz0xCcb3+FFw6h?R$aJF_2k4AFz;}Tygf{vt2u=Z3)=&h_`(c{XP(Po=HG{( z6XBlA8%xRhvOAcuHL%$k{CEX(>y~{WR@gC(P1c)v?)+dIOkev-M;m6C+VuW{)x07T z^kC7u6|!^GkzYNKZba&1yHd1aN>(&=Elk(8E1+prNHaa5c_)|1v+ zM0JM^mCt(Hz+%;wV@_TIB}xR&I2%Qjn9i>Sk6vc`Y6o$)0%T6Dxjgj)y7Kp=NE^ zmmcYH85XtW%NxLL&UCvZSX_`ByB6*g9}#E3LQ36yC(@slp!x`=c~9%u4^vIkzCI@T zw-st^c;w1k=Ny>*)w=L7EOP9w$%9$3%VdthsW;Zgzaaj$P<#?*oQw}Dg6US?Z-ZeC zXI<}7m@;XRLj+vvz?}Ss)UQ3!9|hMJD+X4;Jf~Hn7|KkmIb3kzpOmXiBR9rtE`1kCfbX8#8d-*`6?O&tH9p$Y6^ zF?uM5xX^6O{DGIVV`0&~xUqg=bHSJyeGzLl$#`0>nsKE5`@z&j@W7IB2JtYr-etl< zI8ohX3o+yL>E*=rUu%7?l6=s-iVlmW&6mGT@}P`H8q=p2 zV)p1=QvWjLS}iPCeX_g>rmvy*zK2D1Q8MlD@JmbEMwqX+(sC3o%2W7D%o`nXlv|AZ zYj7V&EC>wOo(Y%U7~b*;78*Es&`G}WhV^HdVXW@54$iz8xrmr$VtU&Wro>Mh8;>&a zSLYFuFTK9=BXXMk&h7DVXL-}Z55%AKRIkIOp26+)F#Ws-uL`zFOY0;S%=+F>toW6x zJ61oVfA=e5Z#SH;X~bYtlE?0cbLrENHau`<_aM0+o^0#M)v#D&c#a6BP)c7K z!OV>_@>*cp-%Z12@UUW~c^Ayu>3nt1gJpN&BDpKyi@dT#lDE13HxH&?x;S(b&gpurNrM>%>g&^BWe)w48q8d@ zp#3)NyY$t44VW@)ckLE@AcITOf@vK=b~oTs-J36#!2;Ra_fudUw+;7J!0fG#IXqbM z(w}Z!PxO07g>spw9z5Qnn+&I$q+nm(9 zo>?k@6=d5FZ-Y7eL-eX(s-(u+T`<>oe^(1^aqacDy)dggN* z7@T&Gb3P>3ELf)mv$|_jiRqzhw<*JFbJxB*4)dPmNf^U9@20Fh3Dfo+3UYvXrdwSD zV8M*K3CD^1tnE0kC`~o@DjarYGcB0(OPdyE!crNYFG6ANy0Z(P!NMmRJHug)V(rgWvgXHFsc4uHKn;F;0_=1Yk z&=k`DO*`re%$OB6UI2T%>fy)09D&s0&u~a=jQBjvUlHH>4Gs$u1VzB?8;>Xbg2l7% ziO#};>|bq^r8tj+6idl^P-GTK%fZx3FDC`TLV>|U6_`7H!&N_+qwV-!6IR%L>HRU7 z9zU3&0}C3;53^zR{62R(xRW*S-T|0#cdn)z+?KV`#~r3NJ=zxl>%T14-c3AmSvMRO zt@`uU0Tw^Y-*Xcd@D|5yhgs|0G)my~(}(TW!<;_fVVPxkzx8q_ljl>AZEUp;&N^Zk zr%&pYw{)AsnKSsRu2%5uDKm&dP=MJ}$*A7H?Af6ZY@Qvb>^*9F!| zuGJh53$2!JIROi%zH*_!BE1_c@?gcsH1~ga|0&sDLz@3z&MIQhkf-D4p)|IW1R3AF zv_%V6ymFyyxHhO(33sz*|Zi``y1WmCQ$2#u-(G6E>Y6yFTym zK<`Far_S=yB;+E)e?=a!z3RCfX_&QL$M-xesVUh@h3RXBE8oM365Hx$z}yMzV&zxh zd>W_yk(_TL=Y!*H;m&S5xkWJL%`{ssOqKj5qQflL6TjZTQaAM9=)$6|4HsHr+AL$H zF-#jN$d>pI=bPTh*EKN1ta-a6Z2vWowgG1Qj>byEBPW^GTVY}D)H zHT3v>az9po)*reAQ{H`9r$ll_l^}uCo6wC&zu-^!res(UHqyHcKJai6eeArswPx-{ zxNT8?Y$kHLJWJaP_E~<}o)7ayJm+wD!1(dchcKrlvnCx@(8?M!e+AS3HtZXbJ7%ie zO_L`uRs81SW7M;D%++p&eHtvOPhe*7+@A_Mcs{Ks=MXd2C!bvdTSYbN=8)V)F5CvT z-(t6dn9*gPMl2T_ar6;Px&B@=01lfnRr)?Gc(_J64OTE;n{Wr_ZnV8n0_UervnJ;y z^I7ez7C2JjZgwi^_f=(1(Z%@cpVnO`?%p|f8LV7*W^8@B~|Bu;R4o_lO1e*GNdOG-5D>{6un|GGuQCOedC7 zmnP>e_pE5A5^UzSSCi}?$H<~z1*Q*;le-Fw=Y_9Y32U%+c$4|?@k5tCT$7q7N%lj` zdJuR8&T)M*w%&|q1(UPj;jd@4bCHXR0vg+4!MP80g~aMr2le%E9@=y-tc2N48@HT* zxrLHZH89^%#Ww@4QQW4}1Pf0uz1;&#&M9~7A}%qIU!ZTqU~O&e?S)y@j~CNnvokC2 z|9}MxWK~zdiiIQ7e#6u~x@{(Kh~`Z1aVxO?)fP3juyR?+iit4SxpnD2m`&GzKMAHj zNh#*QzBV7Lq+r$+$NFfPo<2j9m{~_j;*hp-4zWXrt3TzdpI!6Vj zx6mm0aQ+clz4@fyV8`xqI6aycqX`SOE$8*YqJC@Pa#%Fw)&!~5I8RP@Jy;1digftP zV9EBGVgp#PH1M+}%(vhdt%G?FqrV@3S*s^+vWBTQgf;;%XG7GhO(c(TO$~%a&(7p+ zA^F)2-KSuG>HiMf!{XW%Rza{%e9KBlSajve2)KnUz@ldb z%0VQLnyvj8^Vd<6nG^!EFF8Kzhf@t>HiQvR4eDvg$$e+22@WBo`3a~fKu&%>e> zGvC+4h8vfDi6eQiuS6xR=HdD0GU;bz?5KbTg6pqcg$2Kcm_k^XS$^RfEN;JPQUlja zEIOVFQzCSuYvBV2-p@>f>4%zEzK3;cA2IL3jO6cQ9+}f&_ZViUX^ho7`o6rD^Z)u2 zKJ+y{gEZIW#g8V!7K&4vHkA};>ihN(c^n6s<%Q+d{YP zN6t2UX4ePP6m<V!DRhNWmbAEVL!^0grQi(J{EP9Z2mF?s!lG5#zkb8qtW|fNVfN1Q#-HT+!&!R|!uBzjFAT!G!~54q zzzV9dW9GzYm?Xor)H2$4!h)=SqE|4R`*m&?vFt;$ZkRK}ba^`& zugcf%H|){)XUiv;dCqO=EF*kAda+`>(u!85xxSt>5@(wiveXFC80?ec^iVFPK{!UjGsO zq8Ya;78zqdO87II$@MB1ZuW!|eFFTx!0ffp1FyjLYb;g2k^1_D8Bbt2=0Hk6Om_?G zseqYYZ^zb;$@E;%1;=@M*AJ8NT2Dp&ggu(yxlqXd>9+n>G{JeryLELUEGVhixD;;O zy0~gG%&bg3Z4TS`6noEssYV<%Hf(>cw@wA7%sN^X3~O{zOcuiYq?%)w;K+ADIqER? ze&(|)aO&l7OD&kS`XeVD<|f~Hs15U&KO=Hsimd*)|6t+Gq!Z6zrf$Ny)i8BXJ+~IN z?-9nB!kn-BcXz=wr7zzsVSF)2c9irlwqk6D=~}FLatxdoo{=^?N&T+zF^aITZd0EV zOg$?(OBMDpzn0<#i_#4~EQ3?6B3^jFyl+PpbztA?@nN2%UbFO`KFrQCW*mk&z4Mo> zh2sv!{5Sy%9d`9HVb;tO)Bu=)uWH!BiQlwpIj~5%MtT=KGI>}c0%lpcUH+#>$ z1dCZM-f^)0VSmpQm}jtK@IE}8<*IiNrtMvkRsdV*(0&!e!YgYYyn_dJDWzAyj0rc* z>)^zk;~v$dzh|6D11$MZZ+smr@Q+^l3C_BftN$6MTDU)LCH0!Q2l` z_usI1Us7N{sUI+HmDIug#|OLqgvIxbH%*5HZ3l`6;NkDH!)C+m)wIqY*s7v)SQ%!9 zOKN`B(2NE{0i73-+|Z%<6DwEm)MkXv}SbH-etj(_<3$>rdS*m&{j|B{`fW3r%htd``41wAHE`iZRWnjAd0@2yY~M6DnOG<{ zAXpAty}P)1EzH-DuiXNt8g3oyr+r;ec>=bv+&k5n^q+chf(NVR))yGUylFXq(_wq- z2Ue>|t}Ww|3v10eU#ACCk{Zsxga^VS8+2j*m05{zU<;M1vH7sK>WJULVFf#c%aL1!g4Vd@YWkA*x|Gt^xwoHxm?=Bgf2aDI-;mw7~FIY|B{OCgNESO;=RCj^7 zoomMSgD(EQIv5slbVI4gDOEoIZorZ%Hx9^=`g4!Qe}a8J{Z}Rfb4=%alv|6>my(C3 zB&SN)r!RqX{=1_w4HhlveY*kX1TfXeeslf>=3B#U)8C9;pL5L7+Zm4QmGxFZPN#1$ z^M@OsJZWA+>aVr#y8?&(su-I;r6FJU7927ZB0=_(Bel9b1LhvzH_rg3yv|q3g!#26 z`3#twP?C2K9*NlgXag)PI8#XS#>=hGnK1uM?~yyO@zz;||QH zX+iX1E;{i0vOKd@uPdTueP51tz_3|kErQS^`hrg7~huPO6&dI{*Aup)v zu((um?0KZ@J+*TM%-ZwaKnA)0w(lh?Vg3%L^+cH8YUgACbIS_H)<^Qd{DL(kFX`6) zh3gC7(*n$4N`o+y+^_xGKqD(y^uXYG4@@7p?rj6pqR!DfV8OZg@q1vdOGn99QvYP8 z*GW>}J;_!Q*V7tk6rO_V--}ww{g@5jT@wlmU3a~chv~m>iO-Yz&X?7yaAfGyXf7cS3I+-1y+I*&X8Ib(M~= zPR62Bd9YB+wKWjVw<&sC3DeqMkG&s^X*C-4u-Lz8c?EKd^hXCj!u0#6Z+w93WA04- z0<)FA-}_7anikki`c3`z$(!N%sR}I|Ci$Z83}aaI?Le61O7t7b9QK9tSFxjsX`e60 z5tr^|Cd-iAId}SPI866p+bmf4sNJ^-j%*H@HxK6Dj5#y_D@43Dp~2!ioB#cW#jMuP z^I>jtUA5#ood1)z7;D0`PwG$S!oHH{w=Rbnd$Qka!aB}*!FsSL?n=oTn9n<-VMXdA zzqa|nYG?P%vxj+}4|m7F`oRg!ZY1C7_xm=ib@*D6CoI^b+;tbWx%qOMH;iA5Z6$fw zA*u|U^cPHUy$Q<&TQ4~b)0-CrCd2&3+ug@tmaeBvEKEOcB6$)P4!f(Jhb>GFZw`PN z^6!o(=O(C>$&#{XUc4hQa)ULDPz1j!oCYC|K@K?1NXtmHRiu z!t4nLy5GX|X?|YUU}nK!R~^h~=}Wl@H*Oia`3V-;gkQJ~Tc!N4=^*v<8@kfrqRlH7 z_LF+Xxv~Dnh|3|vFn_t%n8m{~u~K@dPy91C3H3G0@&jqG*f#6V4LHO}bN+miA2_rv z6&_X%y`}~8%B-)X!9K2jBSx@5+!A&NW_vt-WCJtIS1f!E^P9emnYG$!%!)50m+wH% zKciArfLvi1&a@-n;n8F_0^g+u$t*HS}-gs<1FZcEzX?}3xj#z=nwkf zo`ZW{M!^iXw;e-p$ex2QQeZ)TWT3pc5rbAPvGy^{X};>c5ca59EB+ghdqzL^(Stxo1u@qTK;MZtlZ^Ptpw8! zM;*+BhlgG+REK#{dnXmaTFojcbeOShz42#QP~j4!4>KEQIsAoL-~L`QBre|=KF0$4 zJG9@xnDmd<#2LelNoQ}Yg+&jBy3WCI?^ifj!otnbO}!)^)SAVF86lr<>2AP!IMqzB zf!QuJb$3|2K4%9pzpUHp66{#U+P)p;sBe;a3yXHx*o^t+-{ZtZb$5bEJ@bG^+k8tS z1}Eov2(jpd;mieay6i-=9kAF|ll>PCd)*M=4D%Vkds8;z{;2=HyTI)9oaQFD)3JGQ zKTK6vBkyE|emR}&gD_p)>hpD&{;hZIF<4-=cWpf!_p>CxALcA{`}Z5}>Ry=lhxIhf6TCT_?vAW=+1iCzp8&mqdoa7WGN~Q5 zIp3S}5N1fEXDDyN{VZ3Jd<+X^KCLx}OaOu%IMwZwXA>^7MKE%#HeORsrWviYq)$RtaMR)L+BwZ>IC# z!)5{Vzf{2d-Vu)mxajlj4^^;Gkd*TQW*(e#v5wR~%dz_e%cU1ve};KG%i~+%xXmq( zTVdv(V<8=IkJW#?PM8%Ow7&;dxM(TW3)77Chrh$V&$VBTzyBxyFWL9ERZ z*k`@_b0wJGRTM*q8&}P@nh#S`ByEjhCNs)jjnvOZSlX!2^9^=4gyKWr6wOx>7x`zwo+aC*_1oVBoU#NgKn z*yrh@G;^5QYcbsi<`~KqS(1AFX8(gQd&x8o6J{Ttv!4Y=&Uj^J12YQ!<89&el25I+ zFz@_;zzWXdu+$x3X2$A!MliGEaIq8dm5PdfXPl z)POL%JutoebBP+9u&~0{?f;qbeBa)EuxRJMc_a_{_IR=n%ssXwRSPbai8l9xh4uC= z+A#Cso%=yBgSoa;50<*`Juw`n6zu6Sh52hEyrW2e<0n;1SmV@ZH!l2t^BE9YpGtx` z(hJ7s?-Oab^A_nZmsCEC`X0(BNj@x`J-;{Ei>o!orS_b(@JxVmhqhq79uDw!{anQY>J}{;6dyu*j{-nBC=`QO}K7#cRd}E}+jM5^09-PDS zGkE}upTFc)!Q83W*tszEkbX)VOr>40eF0PQByEP^`VSl0ieX`LxbvK?I8UlK)Rn@# z(FvjYFvVOhsEpXfMBfSKu%jlBoWG3SeF{#XSdI1bOqRV)ggdJ~F^Z6L4;-2H2u?iu zZa*u)T{#?+;QRtdSE2OMa|*OfkUqbyh661oK~0 zpGm=-(~gv6I8y6ItsE>!RPIZGImZir&4PJ{T9qQiw_;0y#}TfH1LUO1}AFQ z!@No7bFadkgW7wnNgn^>HMw6wi_zd#lJn$4;@~#R^H-f<+U^V0@vv57VTv0pEY?gX z^N~FNrP>Q-RZyC8;jqD~hd*%*bJiD_^Z1x$FzG*aRCO3`Ysm5p zC3(U%=TTVngE>3A!t4|_hd684$f|91dp*?GkEz+txyx_-fw>p#kVkbc{y z*ztyVe(S&JjN6X&?2cEJAzslmM+&ATnRiTv8Df8FIe6fdSF|e3Te@tVDm)y<^3a6o zMT;x6;4tMo&RQ_@D8p+xEP2v?)_*X~a?%xjILEHrU59w`P>?aql{+1&OZwygx|zX_ zuS2%$lm7DW!mV&)18-n8EL!SvZa2(1Bk|G@7N*VI9{?-*X2%+l{DpDXUD$BbUS>vYTKF^)t`T0?Y6a70T#BoN2lhv<+f2Oih4}#N zYI$t;R$}t`Wry?ks(a9Om}&fXi32>K<1M)hrkR$yU4_e4b||>QqIP}iOE~ckpX&_^ z-@oDY!i|OBl!IWNvx1qXJ>IXslAaMTw`8H3J#4?@%h~g=!2IE`3*1;xwe2#@u2PUZ z1M8d#QA~mvKQ_LKg@^5SY`p=Cs!iyI4JAGtFMve>yry2bbo{KKGFV`@ zf7>6J`ztC-Ncv;es!ZB}>!sAxzJpoJ=E<_KAZ@(x6HFCKSW)5B>D410q<_-m)e5li zb~yD5EZpj*O-#!eq$xP_P3M~+@C_d$H#dvPx;;W->}-r%0mlC|EyDT!*EYnq}(Ey9^&LR z2)iC>99jZXN?ru^!{v+5?$?I}vwy!A!#p>&8KyArU$-7v51rB;UkjMIa0d4q%#;kc zVD~n%S1D_tY!;ule zKJKJmyRW}iA9 zfA4L|hiPf7vHQ(4%NMl5^v&rM3+zX|ze-L&EEJv@?+r_xmaQEi z{njr1`Dd6oSYAjs8a5S!2BOaee~e^%_YNJm^Q8Sy*W(v$=#C-Q`03?c9HsCm#sHQ z{ok3X?r_n_yl=N)%7hwiFW5IU7mx9O` zSn}uTq?a)LT3Bcr%rj%wmBQ@UIZe65nqT|MVeucyJH>F#aMZLG(toRM#Q^M@(HYeP zbC!Kfo9<}D;Mhg38iGaJZY$B?gz~)Oe~1^TY*_>kFI0Oy!367Bx$x);Sni>BmJG3F znyen27-&~D8K&`bGgiT=R+%9)V5Ya-Wh2;Urg6Qchgn0us zYh7X1=x&x7Ok?T`yy2or23IU$(dfRvM_?;H=cyIU`5lsU3>J(py}50eTYPQ#)JC3gg{&WUo*P?%j)X;}zsEY}$q0n<-QgqOmM8>`O7!2G)(R)2sS zuQqEZlAN92(+%5%O6^I7nZ~(xzhT4cbDw9De9eWB@lJT&Bh31;V5)SLi!7|4EHPLD zGcL7-&V&yz1sjDhfB6n&B{=awsX{fW=iHk<7na)KG1LIFKSk`M!NRC3qs=g_Bqd=X zOgj}irya)s=t@rm_7JnLcEEgUf#+gaD^sbjm(*WXnn8z$Go)GHVa~bdb~?nHbA*GW zUQ6MMJ}fD&S@IWVaqoJY!h-Khe*J?5-@+fSgNv*dG)-Wjeza4Y3Cn5k`XK|e0;Jj7 z;l#slFUrB(VvQ;Gu*VsjEy^&>Le6BJHGwL zSqAft{9fWq^5L+;6);^rz$FOgK5-n?hbg|seW4`3KJ}#`=^yXcaTaFGV@x)InN*3D z;c!o)$LTdNP3p}j;-VR|%-6x3GupN1;1PaH#zxZr#&h}wIIP@Jv=!#P&Wyea%dscg zIK%7_)5=G%{lhz^dtiZYVCGZU_gU^MPnZ&YCA%C>h+e$t2+Y*TcNW2Mn=7jPVVJ2Sf|5oT+t`x?L_k5V#HVA0~<#ny1m^}4}Sn4)pi*#;iS zZ1=kha~2e>_kt6p_B?w4^ET#OItANj$NkBM`N6VoJUB#Vn)Y*;9o9Fo43-kP_rHXx zy4%mU!OXj%J4#_r`KaeVI7h2(_FLk-oXYvmIM3xIPrrltx*CHFnA-E3{t;$ouVw_m zS#r5=KEr}#Az`sF^UgkQ3oKL;h2Me`7}FzLi9c5y%!D}xmFiz$M)PHl0yy!C%;7GW zRBdqpjg9b5$e=mg%XR2lmlKC_K>9vHx_5s@ZWWKaor$Z^Q z*-yULSCY@sk9r9^{I^VL;DFz?N&?1{UL7_|GZj{kwFSLRXZaA{@e zw11>N=!UEf?D6LKu;d!tUxT;eIrxA^?(9i0<;9N$_u-spyJtwj)aeyF%izXIH&)8P zqAlHBEwF{2`9gV^zn(osVh=vwSFDm%AWoWpL=EmaH%Wgk%vFf3bc7wlDIYY5k5k@U zf|U>VzgPy-wr>v1higW8AuC|PQ$bKAoY4PMZ#8jNo#JmeG3oUo2F$tQs5Z|P*PGQ| zZUM6gI+kMX zeMIW>+fTc~r3&75d89tI)&3OR&q(vDUZlm4uhN4H?c!Sp$$q<)cq zN(RjSH~B$1$*W3(9>T20N6o8XiuALu0@(NQ+<$d2uRYVC7FK*PrTr5u>M32)12g8n z3u=bh^*I-R!&aI*DqBc?XZq5Aa8AtC)-N!1-0Z;V?s$H!b2om2#Ss<9G~h<7l26}Z zuFtwz#xO%=@tpx!Fw-${J)Ehvk@gd&ncY*|0>@Fi<^RBp+ROXgVEVd~bHWGc+7df&Sn7PnsdIt$i0d&Ais=0AyX)`J;w zn)mk-8&{s+1{>O&ZrTsiULD`+1B*)Y^1NZnEsj+ptQ@v@&M}x_tfb!ym;2p!^dtQ* zRTP!?;eETPO!0?BdEwXeV2zWOr%%GP&sxJ9U`hO)%n9OC;%FB*M76hojK@s#oEZS? zpZ5wn4D+0hoJ)lL6+gI>>x;@-E(&0aYkLb1z^vCg#f>myg7F$Jm^$}{+jrRKX~H^^ zi|kJxUc4Xc$6oG9a?WA@PkQi3ubt>1EGWJ$u@+8{a6fz$W_?IoxC!RS_ zZiC~(`DDy{Y)|+`rLh&cf!iU(|-h#d~3g#E9`$juHzKU&6?_a6qej1 z)(V9g8Zu?!aHPf5l(R5pA8&plJi@x%b`ECSEYPolhr6fkIuFyc*P6D#A(rM^QLs3# zKkWxRVDNT1F~3=PfLP8>GK|#ovi93bdg6U87wjN8wM2K{bU3}KV9YFMyJ?HynoSjB z{gj8r4-H|qzvu2q)YDuIvNpq=)eF}|!0d0MB6~P8&-z<9%y7K9+7ss0I7E)sr*D{j zhV+X|$Ho)7eW;FyeWuPA$Dm$JkI659EnfU-zXmg9_K5Hw2V%Igr-fdJMY+6KH#oDb zTID7zn7_n05^f8e;F$(Qsj6I#9F|v9TK8en6WuOX$L%o(Z`7}`|Qg@PO#9ZEb%d%xv-wff<*?* zclof@MJB$`hV_`?G4u+Ka~=`1VMg%U1TmbRU|Qiz>N9TyNwblQO(FtG{?TTa8tltA z3^)Y~zf_#ihB-qn--BVwK8H;Pu;RVYs4Fn*!fA~QaQdSC*LX0!P|@Qy?DJ+KCk3gGGUJ3NKrG)zkcD|J(xFPQ?tY&JpUW6yAV^7hF&Pc{F#c=?!t6mKR+#a zct%S}2I-HSn`Z{+j9Y*GHY_w{?LPJ8xPKliQ4$wi0p zd^%K#Nlv>o&-W-y-Fmo*+&_KCkHHu?#LLX-KFs8VwOxd{EVtq;nB^NfqXD*WP4CEt zISy_Cx<_#SHEFPNU~$-!Una2633|H#7HXai%!hN@4h2@gj2CNjgMIKkz77~)2eaN3 zg^WLHgs*d0@BIW*C(#XEV7cvHYyLCC{kRsNu7?d@uhlbwnU7yZcEO2p0;SC`D^0!j zAFO7VcE%4DAG~$Y=ot2+W$0`MEGnG7B@oV8?!2y$)eL)@Zrdzky2Q(^#fMy4jX!uoo10-a&Oo%;v8;kL=0?%pup zW!Cs3u-e4I?qjex*x|PiEOb*UKMRXe^0+5p=D2t6=SaTZQiNZ$F>0DjbK{98f65^J zj9Qhlt1zd#V-FkV{i+!J4AZyE7?}{SI9&4!rg;5Zu?!B|+-<34j_bb*TqFZCEm~dI z!iZ!L@M(vIcf;--fTMvia-Eso^eT({_3l{!<(zF}S^laPy7v{{d`!5Nm z)93dXTHt*+wEO60KkU!HzE_)IaYIx0DL6ls_mc(l?(SIR0=MlxVG%>zFMm%8W?O1` zJcUKFKYu>Mc)o`=Ta?3`zm;Xz;ry)()mm7{Fqskq4`{s6YJ~Znf*&EUS(?57Czv+p ziOp$PZp!4*4&v1wR*7_rtghouwa{kqru}gkoEJ^GKSL~zNkyWwAu~l4PcJN zj~r=OoS&7W4U3($1yf;GRgv`qII^P4+zF=N&JQhu2Y&wbJq=T)efTgH^GRi`%DPJO zpmdA%FsJE3!X22l;*FyRoVDuLx92b~qRW;GGh#xF1TZJDASssk^{?ASFm+kp{6tt$ z{!vFY%+M~b?|>b5*H<^gtUtSVXr44;P$&I4I1KYA>P@=}$FZm1n`4RfT)($uy+59x z?~j(PfyHJYLdFN+dC6%~+6;43x@;_9;j-7x?y&Ip($r@#<;L;EqcG2AV-qzH`^9^t z6i(`wY>_z$o6Q=(IvEzE4D=1aH4=Y5X26uCw-@UM;pd0XwFPg91;5_@gz3@OrFvoN z_;L3$Ie5PU)C)&p&P(m^ai{RSNY8eY-iY=3^i_us(-NPKn+XdXmYAJ7jpreu^}l&A zb)7?-OfWtllNwvpVPUCn`vur$Xq^3GnE5C%AsZfvo9VR~W=X2hy5Uk$jMGt)HyHV- zgy4C&{M!5y%uDukH)H?Zo~uC=3|mvw1> zH1YL#S}C06cdzFT%sKyEbRRa{Vf~<-^mE=kjD<@bq7U`LqRNVsCt$^mx^h#PSg#4k z#@0vbm8{odn7;T->IUR(c{25SFzs_xy%FqT@-TZVspoxa(tv%MDyBNX|F^yx^rA1v zVcrLgJIc6Uf72O{FT(UqlS4aTJ~glI4orEzXd(M7`VG(gd;;^`mlX(L$JLQnB(_n#oTG&{JtnVw&p*|5-kruAi*z4HBpa9F2n+niXK8#Z3= zIjJw_FNuaZD#^`laAx*1{YaSFH>vd}tY|Y>6ADv~{EN~)hxuP+>YadT>#|;6hP8C` z8rd*A`LlL2?9nS(>j868cRy5%#Cl(xG-rc^t7q)nI#hX8h6j!^rhJB3+ur}%0+%aE`F?~2 zQ;jVh;F`Uin;KxwWTEsPSgL5F*E^WKddAihuy}?a^C8T=xN)Tj?r~qVA`zzasNGx0 z#r4vT4o1Sv*)p}Z@B!OTv|yMMIoN*;)>3y^ehlUv(J{LQYaE*)aU2!|nwH&yS@otS z{xEgHgp@8=FjzkaQ>v8Tzm~? zOEjDagDHu>=aBP-vN~pmW-NaGunlm!4|AlWCb+`v?h5J?m>a(-s}N>n>7_k|>FeiZ^g@?6F#q^d(_%Qmc*n|GnDTY9woyFZ&-@>=8ez^2nqwN= z*-*Zv1s2FlXGmYfd9hT%@C!`iEedgl^(lEBV$y%qJo+ch9ciBR3ubKFm*IN}>)obT zKMIRo_pUF48P)eGQcR5BrM=TR0sGgtb?kZLyE~g*gR`o?eZtFH7=# z3JS{DN|*8Zc-{QMESS~dDZ2wUi+($-2=lJ^uJDB;Qx?Y2V6Iv7H`*0=v+CIeB(Lt6 zw+E(8=>4e$x1Y7)mCnDnuAAM-?yo&SY zSz5|;m}(up@)k^g`8a=+^QqfJ*1n45^tOLK~$B+RmroL~hDf9j8| z2Sq+4G#aj-yKMb1)`uO`<}QSNM^>@Ell3wY4NpnJ`VT#vOWqIK%vqu^TXV;o*I3FV>^p3qR|7~2b z8+MH<9ot`#%K7|AIIh*3(SdqO)X|vRu%T~6I(Z)j`nxxk!}UeZv-@Dq6A!g6xOBpr z#e<|?m{d188RzY}V@AJWmaWoh9hiPV`wa#2XY0Oy;{>OACApCEgV(%lUMS2?d^EOx z0x!q;&q=>t5RL3N>-WW--LRZ+&J?o#)cJwOWqCM%7_ldnVTMfjDP>qA;CZepOdHYl z(}ro>y@xbl@zV0#4RD-s<=FYid;0D+3zp1qu+&B_XxW?K3X50&{NMA`meu78H&!%S z7?7NDw}b;n{-!)&z#OUfU(dny1_2%9yrz73l6?tQIOFz$2~+)UsNRA{ray5bpU;d> z%U?c)S+}LeJ|9?jw3fbxbGB+3c_SA{xQl@N5)I{+b(|{|2};FqnCXCaX!o~n*&?CJ)xQdvp?{UtbrxXT-_>RvGXa}9WYyu z_M?{ckFLM$4A(Dy^r{IKuBkk`2X4$8Vts>Ifz@97;4u9m?i#vVgmoctiPJC$#_&*ag=l+}|)8=H$pZ?1bxoRCX%CtVu&p?cmaM`H}^&@b^icB}}g|5iBG1 z4B>ixSpV#VY9msA?6^P~7WR54ZG-9Dt?xf!{bk#?j%Goi2o&VLfry$fb*AK!Ez zPCV}a*##Dn{f0tIzh8eF8ZaYPC8Qan6uyV+@Kg6O5>z>QQ z>5ZQ)gJGe|WADju!`!y9#c<@)xxLR}PRQl4^QLFYp23DdefzN81H3S9Yz#c$mkT__qk=E%CnH zkMpWW;Vh#Jrq4Fhc?Wa7TPYPVt$5$)6WD%nVpI+Bma4a5@NjqErUsbp_jd^k_Ar|H zTC+_7(YCx58qc;Nc{gr}@mg9i}}sNNdG;JTM`$sS{?; zeZTr8Jba=n^DE5g`5TxFdsOUT_P~N0CK9LNoI2L*KGOf;$Gx4fZ&+iD80IuRU#w9CtG0N3EE;inBVnrR}(DKTjHey zQ@;sbG?H9$@ugXCQEToHSwEq_i7okj%t?wnRR_}-AKq31C-wxiko6WzDfnN6H4-M9 zRKqNdZ&y9w1e4Q!RWR4wv(6k2+c4{Yn7Z@0nA-Sn;DaKqgS1f0og_&OLMR4NQ4B4T zXj*PUGLaUFDQ$>`iYPU-NQG&mw2g{NVI&ojiBy!D6ov47uK7Lp^ZfpNzdqmZIcLst zoomh}4{C*7OAqD`p5tR3D`BymuHp$eHmanxg7}l3>O@#7{XmN( zKXRe#73L?Qak?DRPkHfla|N7Q{4nhy@u`#*JhL%x-AThy90cPzT{y?^$r4z&$zU`m+nqVX`Rq0Q?q zEE9d+T#k5*Y;zJU%3tw!63k0Eo5O+YgUeT|z;p$(#lA4_4Kwz_b_GJ`dQ0i?v$y^t6`psON9wcoAI2RNAj)3 z(JNuzW#=8)aN~6AcdLk-*hkV~{rv%&>xiWj-=2d6hV}`7#VYn7h-%qdQ&lH#+_l(m8>#9~vPlts|B2wPLCFh@YWWbW~ z12!=TKamZKC)9F)T-ABkNtqV2vwW#SbuhAmDcp?75}ng56JEUnmFDkG0BNhwHgjHxyuIN0>tj?Eht7 ziX!R%)U-Jb4$V+WR)Tp`wj`&+KC?wiQ((cQmd0B!tu<&s9cFAd7T|Ky;Fi|DBuiL$#b(Srn57iPVZz)k9+@lQRQ+A|?P0d6 zVW~CDs8c-Q2-E*Op_A*UYDw^R_FwK9^yXOMe39G_w0o`W~~W#CG~mJoqb_Z zmUq=2n4$NB8VQT1E?>N#xOAs}9Gn>Int6cqd;X#%!<;cYxQB_;tK;v&+TNLKkHP%S z7G;gFPxIIL9GDZ__Pdwl_R~{BV9Kl|fB(W7r(*j>?Kur zG~bPL8WvaI+hGi|gBA>&CH)OQbhp7&Mp(`T(l68Q>%=_R|McMX5}5wr%XmlP3J;|R z#CJ0G1;L`2>$4ug;=CyNbXcCB*;@lMnhkds!oA^@XX;4(Jt?)vu)~0kmXJ8VJgy72 z4X|guglVBQN2cQb8GGC3Z!^q#v87->+`4^I-y2dt-h7)qtbNqTtqrCgKGJ!Rc%{|Y zc9{7r?8qrttxb^j9_FO~yD=T677d9zNdK_oKb5e9c+uo8n6;_Au?tqdcIQkt%)WkE zZY=KqYMZi-_rT%<0}U2%p;cG%7g#8DHs~NMezbjUKP-A)N{@wW^3R~Uh|!^proShz9KTn^?1_S^h| zrN%tHpg{VM{3u+2`B=@B`79-vF|u~66D)JPHjGN@7xHV)!EE}W;7KsGYQ;z?tgW-= zFELx;OUPu*|HKzA%qOPZxGlRHR#Pv&L;3};ndSbl`|=5GWcjS4hTkv1R-FqZ>tlrp zXIH@gZ4c+{W!-L=t)%`y9`*EhUszKyA054)p-Hx%@%?VlVwk;gjFv3Sc{OeYd7dOV zuP!|SruvO;$%9Sg?01fbDU{p|H83mV${A@`zLwu;2mk3k8UV2837iG($$O2gE zBbD=q9M90jyBgs7;QNuIFe`iRolclPag7o=-pn<-heu&sp-dGyA9%VWOQz!c2TiKD z{yR+fdKWPluJTWxKLpdhO~1Pi_Wv-tU=U{g;j``Fs7jyro~tU&UhfBIm|e>F{8Aned69h%J;9i{PPEHf5c}A#c)zFn!_0 zSs!3p0(JX0xTB>p@*OPfkv5!+?`KhwC(gfx`S^#*b6}}+yEMr4NwXh4XbLw*9+F(Y zTt&mb_OR%pmP`|JwuYO_9(d%->IV(5Sfe2NBpf>1hxv>w@92VpYcMZq>d7ZCrTTp} zAC6z$=t9O1wyWWpGPq=4aRnJ)SjvMYLU<@H(VC1uxW3+gg!TV5;#3h^x=6miMs04* zt$>-wq%{8^_bg~HFNf*F3m;9w_g$t`9jz1=T3*{d2aXJm_rxE0U0j^eOi^yu*uV>$8(5t*>b+HTIji*cVLEXjr=J% zYEO?>7R)toPq+?qTfM*Cgz5K>o=%6|-}j_t!2D0cv@AGVuy}ex>^)ct??tD= z9Cpaxhp=GtK>2l;YTM`11c&Ol-bf<#OA;i{I|TbUtOQuJ#=b=c&qGQq=kPAVl+=5> zw8`_475k)PU}n~fO)Fpv%Z`e3FoPD}xfRaNT=a+BzeIk+PxiyMGiN$S!eZkClIMR> zlU%FF{g1g!p(X~o)LsqA{gF}rIVKsdS?T^f7`gDma6lUD;}*v{4$~a|%9O)qFAN_C zkbWtL9i4FGuEd-pFx$SUdIFwLDa+YavtfEw>|Q$D@nq0+FUfDTOP-Iht{!i5fd!7K z>M6)gYO2=mf|(UQL62akfvnUWFz@pCln$67-L%tz-c{@nBDH*Uc6%BmE1f3$CGje!muGZyvKnKLz_I(tGb3n7XZW<}SGVX#Ufc zFmH2pXe>-GFr8-#vs}-&JcYT*A9t=Gc}V}AKXA)r1<`VtKbBuVBNgZGlMwDQQr{L< zumKM3SmsJ{Mw=ST8+IRFF0p9vNM{smG$ljY1i5IBqDl!YHDou2tdC(j$Ge@>8}cRF zBOZUaQYOum!S}W+CELqPXqveiPVJjvu%3AD?!~_Ff1j7O|4?QeJakrir4@2&pX^W> z%=Wj_-2#hDJ6#50lbI&#Y+<(Bb$9i2pC+ZoK?0-?#P}ELX<8|U` za=iFm^_OGedg%vWcf(9MYQqDVcTeH%eo`;zN@;_8ORh=I2VP2M>69Dzyi=FQ1R@tS z1_vyG1!>DShrqnmd>JMzQ#s`nIX_v&d6)LW?n^#M&S$2jXLB%YVdXBlKA4M6+UJt~ zt=_4*sON9-aQg(Sd7ijN#uKr&eX4SXDT6f{)&HC<->=F{4-V{(f7cBQUWLxLhlSRK zR5Bh5y7s->1M9lZzCrH)wB(iY-muy0tF&Q~AF#+e408`vOYYzFo19nCuwaaoAziqC7 zjV@d=Gl3ax=d|l#vER!6btHFuJf;y=`_L@3f~k8;yI#TUcYnv&kz91+$varEs>t9N z%kOs_HCGz(UXo4utA=ASCQstxCVm~QqNX1rhjWj36hBIp~0c?%Oe z=D@m-N0xmj{npRzqD0b*KymwL$wpD-qU;@lB^O-jfHnuhj zk%xs5YhDodUe!)kf~j@XWBTx@R7#@?EHd7H$N(1o-F0vpEPm!9aieR+$UiXai><`G z)iXlo!nD=VkLRPFm%BjO5a!KFlvw1=JG=yDik@hZoR%K9-2|ow&Y5Ni(_bcOt%4b+ zZttbT-Isgi&0xU>{269A_KfAljWBEZLzzYJ(9(O~w!&1Y<9uR=Co(r3VRoCXgArWS zt~vZKOiKut!hMuVik&}ENR0L*imNLfslr{Q)l0%lZ9PhJWez1#9Di8xF) z%oG;>Tk4n&v-(FRNqw=|O$(TxSLogd^D=!u%6@rT6Q5Y``5i48(><>`<)!P@%+M?O)%wa zU0D!ZsOr&b0}FZ-7x=-g7W<#sle}hE3LCB&^o-w5EX^+62Wwv&m$MTVEjcXt{24uM z!z`G|^9yGp&)*yHY7Z z)I56~xW+nJ6a_PVtd@<3dv^@;&cp1uwX522|A^f7Df|-Yw?6+ZALd`1n{gQynXbB( z1V{O=F;9f)vh&V#nXLuwV-9I7>q>fyA)^m#{m&0&rx__Bz&P0D^Ptp9mdWCtv0jW1sbx6E7dp%-Rf z^%|wau~Y3r`eD|OwroR~uIr}!4d$M?STG-sEYI2b8>WY@c{Ud=2|RX?SY-3z5wY?0 z(__YML!N3}HV<|%2^*4z1u^vsq`y08=+Xp|&%AF-*5^6KOG+MQj^2G`42K>$d`cPS z{&^N+1^ese?$&_$pK^0j~QMh~WsGk=x< zQ(Oi<8p2%f_MswJu-|>%QkW)`l^m}jHq&)EOl_}`8;j#D@3Y3v6c)X2^QWF z^h+1nuY~>g{CU3?7OM9%f?)RD=wIeAqqaP$2DTVFuC@`TS1!IX4(Dg1m8^#qO!-## zYC7B^*t2*O%nlG1S;F1(wmVCl|0|EoS5gPXp_`G5l8@J>!%;0o`b=2zdpz72dZLk- zX0QIK7j`gfzqS>otd)`6KmRkc|A`5?-~K;8yWVFEXD_KQvqL>^hPQ42>`%YfO)UQN zu`mr5`TCvQ4)X^>%LZU(^xua&VbOc(wxyZap2^`=dtip`O808m>R_YaLE^tVZ_dlY z=l4*xI0{qkn*W}GZ5cO}B48HV@3e^Is{3|xVe#Kn{VLg}44O%g`xRI;_jJJqSXY*z zb{*#K8u`Z)w)ib$d>iK9Iop&17fzZHUPRoi+|UL)e0w~;5~i$?n<9J5ltI6tI#3PM zpv zYlGP;Ze@ymoKNlE8gF1~f_k4RT$5mRgM5CDQu!7a*x_WAdm}9Va$hwL?ih?!Z-50C z4JMYu*#TOuY<5qmAjLeVtHKa8q6*_ zEivzP@wMl$vNE;#30eNcTc%pMn4fg`AAJUkCcnJy0EgYsCZ1x!}uPcuvZK-6jMsAVZrv7k)yC+>MfJw zu#uzMZYc+BU%;}rF)(*Vot_LVSoC$@6m@byxh|b_eLL2 z(1lsvO}%20Kc{OfggIj?Or`Vj{3Z0e!D?7UKdeE6TML=}>tLFFG-Do24NK}YC;gir z9;d_p{*FGIV5;Eb^%byap?|9lOsOn=W(H@^Rj=Dd9J5H;3U2*(OM)vb3Kjik!QHiC zaR*@GN#*r^@W{xyU2IseEU@($Z1uuO#gF9Ye-1~&+_~9DjuT(o?h^y+N0%uF!!(ci z#TQ|U)X$DHFt^`V^9D?x^DX=m%(yk7^J&jGFmBU>U2NuBR}4BkzfNfhj{ce&3OoFh|*HwGM3ihjHf(%uGHiWd!qNYn=LE zF7=E34!COZ_)mXe+9%`6K$xF3Sx;d*`lnSq<-s%+U%@1pq1E#>i{un9S5268(kh@D zRz8ukcQ#DjzhQF+tmdv{i*km~Y#EKDrX7T$8?K2#+cq zy|o=?-AdCu4O>05;k&}zXMyFpaQt}T$GtG;aB|UW*#C5g-$BxU=+To=*r{x#u@6k= zYrmr1!}aIy#0!80XAeiMgFX05x&mR|E7koh*hpiS(+QYUKkHE#954O$B?qRy{4tsg ztF=0A4k7(9pVS2~eSg2tNz%Wy`r!}KZ~LN#n4((Ht5=BScW2oI!+hPm$YXH)>W52? z!(utg`W#s5M#S#JFz3`0MY$rJZ!=2A9w52mf-RPC>v@wW|HAARrA?P$!MP2u$>*p4 zd{taW+`g&H8RmJNnAs1TXnj%NMf#5|8(DB4*Zv{{mVw&S_PAy|GvOA%Q<+hCa*k&Zts(y(B8!-15`>`<7o|K8GTaLclW;&7P%K=nWd9DB=T`ZxWISSnA<;5iH8K3E2YEqso4~hXoe50&L*anomo+Nq*JF zV;h`LKd;kE`Y#yYafGWbDLD$uvRk1x77A{@Do(c!naT8$WN17)8ko@e9(p0$Ht~*;BrYC>2xe3ej zbf4Ut)5HpS6Us~(4E0;nw!@T} z^$%5G&jWf)7R=d@=BEo=6xauQz|5~jVcSVj43D=C0bQ6A8P2RhB;q)8{rmx(atJOIJNj>LbRQl)_R9tLW!o z;o$LOFX5<__YrLm2$a_!p{{xCoAU-u4}6X6x_3$vd3T>nJ!|GR&A1FECHVCtEq zJA06eerDg5dw}cr-)K!&m~sC5LKV1mPl|#QOrh&;`v=ZWuv2k_sit?*wcym1%HOuZ zwDy(XXTfF{(&Lygd-dR6IxLuN`I4M3bW4qME8sxAugc~ylQAcEJvL%Gp+58Qotc)@&9?^yHeAT0b-GdPFjXL57{;6~re1zIqt-uCVZctm-^ z)fq6i)HX2;uKE%`o?M@TKf`mQ;FgI$R8?Sh@^v*XELi=hehR7YdT{RotX=GQKptki zcZ*7dJrw2|PJsFTw`6X>^$!C5D5Rg$q@N4ZADuq`6XS_s;^pndaQDsE)B%{)8JYPE zjz7NTDH*TW&5_)fFl~(vw~Lti{b4KI@m={(JE^DHt?45Di*(dlVcyn}f8_%Lv;(ju8 z+VU*S)SBX>4JW=>a4M4I1+vQ>;Guw*lKBWVvF}C{EV>f@JQ%syrQm)7tgCoiGC$$G z2pK4VjaP~!^Bw+rFU2Ofgt?wi=0hTb0JScd(LB+C%!gQh#uufk{(nCu;n>p!a7$E^ zWPO5l9;a>L(I%D8VW_9GRAdgp{#AUX2w2e685RvwMh<44hItkD%NxArs;`_#=oOYBm);WaF< zy(Jd|Gs>6WYlCT)7q(u6xsJCRdPw~Q`syoi)ZfZha)0B`OP1vF%?E3gaefMeoo|L9 zH)-h>lj}j~9w1qt(VU|bXTh|O4$0og<3sJ40v(=XV+PEy=0hhf?Zf$B53g}TQp2Ils?Id&YT$j#c50W-^H=$gU# zxB8m1U{>+^U&~?Rk=hw|U`9%g4mlrW+SG!}VZrv8_2c0BnJqC-VV>Gf$@!(fD|elU zc);jL7S6BO)-J1VSR7=ceF?Uj8Y9&YbDTfpf4sr<|_h{OfaXYYm zi$DL`3bPwbKTL$h4+b(?E)|&KBJMVoO&h{>`XTtp7PTx+z@!bQ=9WZTv+<7NBR_c!1 zKA2nUFU^($3}!5xf9f;F53}#9FNP7TMXh=Ow`3_!I7Rw3jyIC=%P5O;_$(~8 z{e6@N(@Zq%Vqsy!B(rch)kwqQ5-i%iMl}egtNkR5idK2mQUN}7q7Oq=5eS=%}*Zbbq zz=_KGPEDl#qR0Uc%^C4-r9Q7=-r78)0@5#^W&HsbuD($o2g`>jHGhU#tlfSm;Eb++ ze80f7ft*kNutiowVYtBcy)l`~Dqp>X@?ezez62 zJ!eDuZ+)9K)(QFGrHLD1nYHf+Wnp?ko#`r=@isI`8D=@iK3)jRmriU~gW3Ap9r|!a zkjcy$F!#xu@v~vi@SXDdFkd-wttwn){wQt{EX=4^QG~^P<}FK!Kfa~Q!1Br^W+pII zDQVewxMbzMK{J@y{CUGzSfny=$N}c1E>9Ul@{1R!|HAyW)meYbP4PaX@uH)!$k6x7 zIOLvDSH%}#`j3AUsjztX`S2B(?a`E=0yDR)*pLP@g2I(%!I3?0hi=2dzsHX*fK6;Z zm*x;#^;4I?sqw+dg`|GchO-P<-nK??9~RtQcX&OiXA4i2!qms7n43s`M%kwVrW|~f zy9;jFvv|%USfr3>~w?p2^z*OB_6Qz{|w=qIU(&tbuvh~6YvCN*N? zOIS=DcQzk3p3cvEP3mvoJR*XvtX}_Vg@txyQa|CuapQg7!My$jt#fg{y8ldf{XqJK z($99oo^|WCbdh|zH?It?sT28j!>qibokMVB!Oy8ZFtcYNhlT66Fy6hW7v}3Hu6_oK zq~_R)VK(F8usX&M>Qg=HcbJkH?d%Ivl;mlnuprCeLXIh{hxIpgR%^%1s5(foh<-x=L9 zgfl!v2~NDoGp3N`P1L{i5RQ+rmmFV;PUoqywKzV$ zvyVw37ay{xO@OP$ET1+3rYkYO&w_=V-}$mIb;nTSO4!|N)dlkT*bUzQ?uWaR@(0NA zX6z4teHylvPLu3U(VUHM`J|t^X{8}@icw)zEgU;T?$tuFyo4n`g>d4WnMNx}-Z3`& zJ9F%CyOS*Rx5wR*N}d-+*n1DKR6`)gw(rk_%jjqEOqZ`fW`9-1X8et z$=he|iLJLQ4&w7@TgAq9!i=SK;TPD4>nQyh79`e>UqvEdrKA10?{MH1n&y-(w5T^HQXDo)JWJ1#plm3}^4jI5Ky<(4GQs3ySrv{h2 zXp4%1S-nL1x(;S^R!rPT z`hOjo_5>Cg^tY~tDZvU8pTpv`WgYr(@2f?}U%)iQ(C3=4zS36BCX!F`-K_-k3a%=* z!0gENE{EdgILx@u*Goya|M_Y`(1Pi7FXSLR)$3}_G5$KsN^+a zQ%U|$>68FCV|3oG88AyTJk%MknqeQU15-3R)68H&yV((am^~O)HV@WFA6`a>X?m|W z%!HK<=J+p$#dXX9dE(|fzN=u)_)C+&V*IBYUEXW~b0@exegoI!nV)bZ{jQD6?!es< zin2#Y9yxb^5FEiX>zX>T)1gRa}24!ns>?sj{lm>z6Mk0 zD(a}ivAb05a!7sLw$^^!?_y;?pSwpqY4CL`ob7f|;|(mbJev^#E89_wyJ1S)K&>b7 zVWm1e{nyLnxLYl0t-5~2Xw$fzrkJVFg?>QrVSply7fZ?<_$OO zY=+f#NZgw2OJ7rmQ{;ri@ZmMdUxQR_}_F#1bobQ(QTFwQZ{~g`# z73>hKzI+PIdO!6pdHzu7v3~V*m~rL1N+ZnI_8*=Hi}Z_a9>GRY%Vbx;{PQ$X5u9Du zq0fYAN6am*!|p4c0=;04>6klyFng05{{&1=7`^}@ZF;@{nPIJuOwelJLm})E|Z@=))nhpbyVIK_L<ce$9Oxxjd$p|ia9@M`Mrfzy|z6f?N-xF#J3*Uumn8GdevOyP^67hPD3vB!F zT-sq+RP}b#1=wTh;fz3-Z`fX$13Lr+d7pq;dMD$H;lPNO3qoPex8=cIB;VC142PK` zX%4E`pCuvsUn61OH%h@~xHY3z^)$?$ozoZx8{sd55OYJke9K@?hJmD>(O7Z22X-G; zwIR#nU5Jr9Z%|9JFARYhr%%p0f$LR1qenTI@SqJ|qRl|5Kwzl%~fkg|(9n^zu z59e(m$4l(=tk)b4-M0R=H|bB3o_z!k3_Ctv;z_H%#=tVp-K}iog6ElUYvGKc(-PBj z4sLr4bG%#~Nj+`E?$bov-ze)uFAl*>?Sl?mV2`%cV`P0Cy>jkx*y{DEXT&VaoaZ(ci)HP}^61as$^VAMJZ30aALW_()w#G|Ym@~`wwK{> z)n_~W-}Z2|GB)_aEn({Vq@Te(*_#AM=0`}@Pg^oGryMr^IccV3f9-CS`U!W`t-MC| zmtb39?KI49C`G0c)Bc{+X23j|oz^60eVpp(4|zGY!^WrQtoMXD=^ZEl@Q%V;NtcP2QmmY9~xdZ(QdtmogYbFb( z#-@9pCLV4TJHSE*Bflm%)%-M_T;Jja4U?4XFhAJTugZjJb>aVRf=j+O-Pr*1u7pgA zgu4TDd)C7gg9tf3Eb^+*B;y6WNA^M;EU=h8vKHp_gtTMmx zFW}z$Ik`44U3JO3sZaj@=M$TxE--~X)RyGzL{43Iz{m|&)@n&6;}^ZcPWvd_ve!wS z>@UFs3vn3i=`AJMp8`91uM|?>TO>JtLi(&1uV79!J%U`n{P2kpqQKPR1Kuz41k*F?K5IO~cpbgWF$?AlU3|M8)^|IqcNZ4>CzM9Ov7&JQ z2QXubMr{^s@z(O$W0?22|64xX{mR|z3CZbWWSG;FgE8GsLhctl-`qm_BuD z=Qom1Jo@?u?0zmm_!AaxdSG=8j>cQLh`t#RkLAn#cPWp>~Goy`yw6EAEu$v1DgcO9oK_J^$D#FFl(vvf`z0$ zc+zej$uAFBE`j+O<9$!Tyi&vc#>854GlO7#mE!x$h>J6Qd|{7#;dT?45~p>4H_W`a z`4IzV8RQh&!0h3ym^HA_>&Bvua4%PF&=Tf487j_)`7bEtb})bMth@=ZcH&dR?J&n| zLi8xkPY2%l7-yKuw!YW}n*?{Q-vbM`FNkV_HKbn5WW$sravv&Sq0N<(zA)Es?SdSb zm%7k_1Jl)IBCo=sR-ZqH!@LNkadEKjuWM}yupo9^#z8o7)2A99O!2mvwikB#_{T9F zrdbb|xWF1u`!OfS^0$sDbbzz3wCubC^Y7je*u!coU#l0u;tRtInQ*FOkW3ND#}*B3 zfIHfKe-*^J46|UBo}uNX8SNE@R#onD=_Y4<+O! zc?O%mll~dy+T-Cuzx911BsYKZb}SsZt&aYi^q2dZ|H1g=U>u8fJZt|Qt z#vR+Ea*^^2=DdkGIuYjkoH+Is_MC97j0$sQ+>1ZMtr6c|5wl!YBoWu#2%0+?W+%S* z(gVw<{=Pm1rp`F<{S(~M6#i8i#>Dt<7i=|eI8Fr?k8|Ee9B=ezmpV*I*dsAZ{4Pxc z78oyAe~;WEq1|gHEQ;RV&;~o@Jw2mEa`WYjT4CO-?1tGe?fsnCS8(hixy^K#G9mR< zElitob=GoNIFB~-KJ3FV?>2?`rzT#>fwk>6r>-SFd+T^A?78f8p#`bW`yd+++iL3F zup)V_^OAG01?Mc?7N)qP1>3{4Ga2TtupoEb zL35bupnrNdEK06_xCm~o&|B|G>Z70RQ-n=Ent${nd7Ay>5!|n0GoFi&z!ard8L;o`osxW6ztxf~gh1qhZGE9^QP?Kfv{hf%(5q7m@pMVN&z={>5pM=XoWY62i)0 ze(5yH^SM}?-arAdz2550=Rcx=#985l1gO!@cQe?vV9y!@MN(IgPEHPO?3;2y^NTfVLljT^&{mTOlPh3|dN9EljtLSegfO6=vw) zgSn?oA^Ew-Go{4KjK!xPHqn+ z=D)J&GyPxYPP3BCfBl(Nl3Z}E%ufzEttwn{yx0pihqqT_d)13&$?>BNJY-eE!sC~A z5wm>V4qk@&=i?=&q+9RsgDF&<+vNDNYX02V3QPT%IF@W5b>&;NWw2WEeaZIz_k2rq zG4vQY{>+rkuV~0kZm*DhK5_8u4HMvwZ6b|3$VHml7IxzMLVoVR3}WHVmv2A99-0=j z$o`=1_CHew*RQ$rTe3gq>=;djJ9c*35p%|6c_qP8g8i*z``J3^$@)xocSXPq@s}s${9rBAp0*bj<*q(g0@Es} zdRt*?!H0vDFf-!PiFvTbFPV=IVUcn1RxOyjIUur{ zVbS7xn7*deRStGiKB)JC(jeoLhhfneBgy*&9-oR2 z{wDR_-H!*6Gv?MSQvSv9ePAltekt~xXHu}Z;-<-qLhVq&%^vUO{xQOwe9X>VoCir zp7VA%G*U)<31&Mt-6Q#E)gptdFlEgLi9J0Qf4&Y&-oHdHwQ9%oEYfejTCzR-ku}D* zVQR+MGnt<_9dKZ!{p!JG4cSWB~49xVKyxCNcwdXrsBV$ z)P*JQbHY{cbXV!Y^pCGva$vK*>9grD|88+=DQvvu#O#h^ibFam>uQQ_8a-gxUDu;F!gJA5akh;*FER# zR+u5Hu~ioKxx3)>Zc<-Vq@xVeIkY!NVM;E&eHL89H~kt(>PMOi=EL$kx`Zh()3bH? z5;!A0CNdMIZiruN1Uszjl`4SQ)E5=5^p(n4i=;P6Lj7^>^SM%=(xtKMl5ZRan#qGjC@} z#@G5JWA8y&;C-W58hPTpqV%7{J+=Km9^&)0hpGO7Ie}AM2jSS2uDizU!RJ$OY3zkV z9$fXLu>&up<9(Y1$#|a;{dN0HSTJQo zJ{>v5Hf_c%SV*Nw&Nok&>X_LuZ{4qJmyp+7p}d=Gh*KWyl(OokpzAD(gK99jO( z{;K&fzk6PEB;5L9eTgB=if_6U4%fHlIxm2!C)LGaaH#&#%B3(xNm=syiBW8L)N+{7 z%nA%Z?l0y3!W3o)P4D!9tzr$#R>Pd?yv=O5bzP)jBh2&LFL_@q)+y%?ll1pxNX8pA zyPN?Vn5lYCGCsxL&FHWv^#;kW$@OpXQ6XUEQ&sGcO9JJH-q9% zY)=VZ37fns%{%}LeJBMBVRqZiYBo&2d{FYfnM}d65r3GOGUJmDaz@q2++dht!poZp zhc3ExFM{Nnt0nit(G1_|=V11q_HY&Ci7L;gM#IzPNkQ@=6eNIpj_xnJ^&_5ZyB zvlQO*sK~{-0`@hSE}d;L2^KDozn2Pg-i1h>-}(o&)ZK*tyWg5@z9r3v8F9u-r=p&1 zds!0ySm^<6=$TU*F`?2{t(*J+wGaSmps>IIX|Duh6B@O?j3}wPTBn`u+fc`V-LaH^etz{!#vlpSw1k` zT7BMk{GMPIGEMpzENZBf{GP?4cXX`!Da$f!%FSyNAG{hs?N%aHN}D8|fFAPh9&RzlYdkl1V5=1$ zFIU3CaOI(q2N=)%s4X=xzvH);7&e+dIrb^c+_6IPzO?=~4V^}q`a?nLJ@V`vqrz9P znBV9A4rVm>jOm1FpKeDq!;Qb|r21h-XOrVoIPhanzyQoseNtQpSM@#@3=!)*dHE2Q zm#+`}N$Ri4-YkLZInHr^VD?7K>33lb+WIu9y~uUu4`-5kmpKKp#Jf+%M8G_iq1lQs z%}ZUu6HXmHyK*ut`S<;>?(pqAWs+ysN#1|wTz>aQ1?DBRv~5PtJbL!0D$KDdT4fEZ zc^iD14vU3?M)E!-FZIjGnK0ig=HfD#Ww-Y?4aVQ!NnQ$9$(!ow!PMR>^X9>vb=~cB znD^NrP6wuLJgB`G794cmMBd-eKHlWJiu8L8-kSl}>|R!DNpfwE5LLMG!iYT+7CKWJ z)L{Af31b{dee-8Yz13xDTX$G=^uPwVP*yEvNIf*`0_?D%!s=! z`S$^4+s-YHB=tY@b{|JBKiRzU985E)WJkje@9*s9!qg7E39)dLla}lSm~UV8?Gmi~ zzGNhh;|3(tbjjocM9~#g1d0vMFL$!=XJp-oAIySQ(u5bUl=ngDQ`B^T7^W9S|=RpC?T`-0}9d0qV;}^lKTZWSB zXXL=wm-k8DzESe~Y3tNXw+AqD)@qw=sL%dpkp2)B`bW@w;n=I4i|S#1MrvFfT(#F| z-cy+K{ek57-x{0M#&x8=Yv`63Ih8U&M9kjt z|G5h8o}F{4fz)?bPql_AeIMR85@%1{}K~YU}sh`vvnhG9>R$$V6CFkJ*R)`O!;*hI-CTg&A@% z!|dLVIWUW7Uo{nGW-T@}gmt@eLZ`uE#b@J|!SuQA(%LYuyv55LPOLMLnNRvBn=9>v zd6&-@8^LT_%fE-=A!Gl#QCPYWMN1(Rqo_zNDj`gwQZngYYTY0diy}meQWSFT!c$b$ufsM|`&IcGHTi(ux&9QuDcGuwR96_{)1L)VA5Qc=uxSaRq+yt`PeX<~?0~xDl?}mp`BzmU#4QxJ=qFKds#ZGwNLF_z_-9(3U?i|FD#v z4_Kw`TH9v}KHt^PHfK=Zv1Zn-0mM5JJ;LGWJ9igqz?@}2?<|6O{f=g_NWNNTq7QRM zEZnLMOBQ{xCg%^1BR=U&Be}!db~)K!&VPE%hIt2W(({GjfsC-3u+Z`T12TTW>vN^9 z5zK9T!ruXNPk+xQxy(3qzBAmrg{4pHf4ZO5fK3OxM;Ma!H%|=c2RC!y*OHvih`an5 z$A_e*_yiMJIx%DYb2wA$#?U#iT*0QA^gl6Dyj^b&3r;uF{wF1|DJlzKmh$+Da^ww{ z&L3O`OU}2_^9vE*ZIlx%Q#-Py0J)xn>10>p7vqh|`9{N@;f`LgI4a0G12!);p0|T| z`aX|yFn4j-v;dfYY{9@3xJjkUAq-}$U7DE+D||kG_Xy1X>N%ni?$|i-cPy!o{u6l< z4)5{pI0f^n-aWhp`;4wDi-)DgwqAGPrmmAZXJNTY^|*RCGtqEBI!xyuAhx>RUkr02 z2k1}0`cmp1Vp31Yxh^ghH0(QaM3%rKd)i&M%IB8m~$*> z!#kLDs%CvDELyY4QV#RqzNY=vBsVIQzrnm=m6A8eW!suAbimRzsh`OA-EB{V^B-8K ztv+H9_Mg<^qjAbUSbu>n^!&S_@!)KA_}}@wm-?C++QbQY`@K-_a&nSrGV!(9MxL

aNllOm^jXd6EfwL1V=Ga#$!rrSUtXc~* z_DfXA{u;jE>DYCoy}1UxzQ>=uOL+q<%2gaqK99gmZp0_`uhrh%g*gl7H138usoK7o zu(EmMy%3nWrGML5m}kFzv;dYJenIz7w~b!P`(b(K-*s`wRnN8#Is%KbzI+ORy)7PU zoPgOG+CSF8RW@mk2{7+%RPR#QU(Nl^S(sZ~62gX=PrWy0lDz-v%|l_E+r!4^!SXC) z`uoLrQ9Q347Ehhn{0-Z`V~>K~J(!{TEA103Q10LMh}eFxgp7a7G`sBGKb-Y=ZBX`C`%VT$q*n+kr>EkNJa*@<{!X4Eldmx6hNfOR!i& zEGNH5@{TFCrNleRF4w?qd)U$nn74Dc1^NBdHg)Qd!VCi)`v2TpmXXl_bGr`H{$#!L z`Ga4Pd@w)q5bAp^eyg>@bbLD;*sy8WCs_8O{>?3zedY@98}X4u+MkRunBmd^3(fV$ z$dL;)G`l)sQAxWA6W8xdef3ZMgqdff-;nE#;buFp^uXe%e*5j(aOQ|S<6*F1PfAog%nPqxtpW301LkDIN_Sc>X~EotSsM%Ccr%xp z8SuaRLrvyE`5aPza>3&=FD$IcGYU)ujig`y*Ia^%`e zaHTXhU?nUrO%2S0;dva{aVO zd8GMCSiEaJy&fu_$MHP_%ezZsYmqA#RBXv0-X6B0nABSjI+8`|`#z-CyEXgS=U#yM zJC4%polyo=ZwpAS)jdgsdOfw2u~$jH;Yr_fB4o|-Z04=lavORtAYKD545+J^RLMXSP4 zulMJ1@F-ZG$}Kzr%a)#&jfI)wwRC)wd_n*4@x)24w7;b=f99phr2hWs4;N9-n)+q1 z0W7RP(f>N!{_yZ86If|J5uy4TNd!X-hAHP*t+uKeg4IBCUxGaf89n49?$wqnh_w*lswo>pbt z#rAlg=Dr2y@AWqz_kUfSXK&d7vyQubnh*1MyFxJmk3@JQd(%?l+9!5FFvs1A_764x zeb+J=W``7gc0w*NDAm~q^YmBH@f6Vmmw!46Gprq^Z$-}jtUdW8EKN%sybD&SFD*X@ zOU8XWAb@MVHWsFl_EU$<+Y7V8J+@sS?LS}34kX^(b07~EE!g^y50`vBCMqQDn|0)5 zycj>IihqOn>Z%>KaPP@&?zc#L{eosIxF{$7%w3pyqUMSjoLL`v?LN#-*+0Pq&heRR zQv+kZIXit=;k<#$S6H%m{p4Y=(sBjcE?5@&YsL`R?P=N%1|RD~qfIyn=8j>X9|6lQ z4rY_w?yyF%CM+KMu%1{cntONLe{!1x83!i9!j2oJgON+;ZJDi0>O1qz$nxB5H@?(^ z*+V?$k^Z^0xbixaIPT3?a(`Z6!Tw_k%e&4eD8UW({*4@1@LWvI=)Ca81{Us4QW%B2 z{Mcae5?E|H=eq`MmuZ&mM0~+dLiz`k_Zc7T3d>%$l-t3zJyY#Gh+i+I+mn;@rfM_H z$rAYZAy;a9`8@;{Y*6W|Eg=sNPW5G zK+>OBu<+UCxBtlF6l2Kb$a6R zg|vV4c^a|ZNt?%Vn7vg${4(qv`ukJ|EctRLx(F6rKa$rC^IolPx&+$zIF768Iygj%VE2{v(=PfLGnud`><)9@#7&d&ubj#8C<;b zK*UJm?$@QAFn8bc;8Cz-@T9~)u%44(@&s7?&e?nHJ*@vlFT}dA@YfL)12}W=lNCm= zsNsG0Vz@ow<~R`_aOxqjeM8*6ZS8PbWel%`)8c!fvx5ZIGF>>k81_> zxsUa=Wp#8e%ncIo`@xzU^X&>ry=bKS0NBydMd2FEx?*ug8BWp?>6E~d^vCT(;AqdV zS+`)ZjS1}^9{ZCQTmjST_sF&V7DhjS`JLW84Y*zZ&*;Z6D{jQ{aj@!XNl_gv2-&`f z1U3n)IYj2mzbmUYNjI0nCVMjFT8!U&0tubw=rWZ^2)hCerm#^3WNQ_VgCAb z-zhNnO~C)w&(`th*)VhFbGm-Lj?Pz~3G*T{X@B|J&2N6rh6PpLr(3bUwf8h7E`<5R z;ykP1?m-#Ki%EO`C88U!UVTSDTVk^po39YtuBfpi?JbYe{`*R~L(VUS#kUxNyOD=u z7#qzmis|@D-Oo(%66CxYo1RQVo)ogFh%8^c(xmzu_7Bb#A8Mx3P&(cqw#Cwr)N|tp z{76DB3zGORgJo9-&)5XpU?#{Fuq1!J-U3*<@l3!u<>%r}Xp<2@9Pj=d61a@T$-yODPUPxOm{xvS&o?{`kM(eRV7+)7u~AGu@ls8#W> zaO4{L`&_QH=SvdI>fS`Hxk}oa42yEsQ;VWBk7U7I&B@2Mpnc`H=u3sL)MD&Ya=xJY z@>)t6EHOA0=?iN{ii(KYZiP|A!hZLS?!tnYmGt}~+W*C!D$;)AG`c)aqvYvhSoUWo zwd{;)TsyF!m_ElTTVj5sh-_i8^LHunz?>Q_m<4UqsF?WY;HZ z%3!Xq*&t82>DsS#Z%O-m7p{50F0T!9TVQ_8>*2&=wtM>rSaN7OtuNUX?(vCORZQ#I zmMb31VV-g&t#`S_9^C=UHCw4O_v~eM!fYMQEj%nQc;PT!7c441LfdQWzeGfg`3 z$@}3wu=4LEzPO{$dboX}^!9I=G{7zZ=6^+Wd&$JCCS2=hMm(C?q9Uz=DLrt>u-7u?*Q zX9$ZAl@%R?+t<{W&WG6vMW>I!%7-laabd|DOX(@t<=Er;Rj}yEkqwE&ei|WOB!8Ls z@jM*f$312f%rXz8`-fxZ!aN_C^XVuZ&*VQjzfS9sNcd!K!WEbi(eYK%)180o zi3>l6?j?D%@r36v_s4=Y5wPY+6;~N7-K|QGcfqro4!nnjZC_4RAeV{nI{bh+KabM! z@Nc)%3hr{7wo5Er0u(-%w|02xT{C6(NH!uobhXg1mpxxD<8Le>`>L zkWYPK+1hCq$Klw&>;L@0`Vhnx4#$)pmW_ zuLI_%ZU1@+xwQ6H+!x~5MWtnMm9BgZ*&fVXYh5YqHOol)2A2B`HG2hHd6x4VVc8-# zI-bktxOM0w;^1(uY8B4!B5TLrg;@ckzfOS_mj7zF4GUAZ_!_~&15Oi4VMbBur6q8* zV4Y17ESXnM`|lSIo*bA(+V^+ZybpQKs29!|q8%FJbC3g<&ItMEZn{tzwb@bZ!OK?-~nn62ZK}b(n1!+I+nPo7{PJff!0=qo; zaXK89b=Ld$eT3r+o~THUC;ar0x|89Oz4{tteQ`PrYvvG_oOCD04`z}z&k;_#|0_R_ zc!bVyGCs*_ug+Pr{di{{2Ag2!{s$FD`o0} zOxSU?%fn}|z~+frDQtSQ;K)l@e7AMsBbd#$DSQRX3r`x6@m2Bf#>m=8`@23RjB0GZ z5dY%iyMabEc8~pU&3{oA$LW9RkZ&J0IHu|J(n#83R&w z!_0+^92Rn1I4C*@>s~%}NDr3wF5Q*@vn=jZ8o=y~({y}V&V&cYjbKq?=!sO4x9SYF zgjsg=x%qH$iLIqIEY{o7Tnu-gF<0lpocyNIWc*yaLu#%g@eR*SWw6&h-{G#L{p}*& zZCEtI`J*RsvxUknSaV0e^P5Tit53AOZT4xFf8Dd(D8g72?~+n zBtO_zNyhv28b!>EBffa0CmI&iUN6Xo*(KUrcyLmw^^JVursY@3`m^zGIa>^i7I)Xo zf`gw_rj)__iF<7|;M$yp=N^#!sPgqs*gmlfTxZsh_8*#}YhbrIy%XwSq4LpZIk5cl zRNs1%7j&vdlX~5%mk9|IJmY` zm6)Yd-%k^+N{ib56c&6tk>7#uyZ`;Gj3=;Mhi_O;zW+vseec0|J^>x?X>)(m@d}u~ zZp<7rjJ7s!=!Y+@z28aIH(j%|o!*?Def7@W1UVyw8%n!|^z3l-r^G$mQ>& z56NIR6LYN)vb^s0ikq--<&{@Ku=Klm#Gkf4l-E-mFVcw&wyOzNI zzemmYh1tXVW~#$(y^Cr#!SsGAey=i{JDzQX`A62!-#6xWOC@54d(NOs$dl@Q4{d;@ zw+f8nVMf%lm&A0wcvzq_-H_A^7MDER0V|B{??YZsP?Ye~3KqLB6zqUmML#vjc;@D% zBfpaO%P9VsN9G%f`nB^$0L(k~Vd@Cjt1WxrZdm&JYuX?*}QU?cP4-?ug%e>g?xWS z1xD$okmr2PD7XvD*8W-=2g~f%^^)&D@7kLC=U}~~FAW+=u4QxTB3%1!&bL>@mebc= zh1s#0_U~YEh%LST;nMTS;s@79cB$$syP=faa+|ijv1 zgc(PbJmcV`!9T)(!=jp9?+akf6>I7T>_U4@H>(EN?V0oaL9qO5t%Qsx6}O+x7*728 zm<0pJS3O%B_t7wKphXE8Z_7An60Awu_hgys!konHHWn=CV0|}%dxu%{(SiS+e~E*( z*i9y0eC;9`58PfJ{8Eq9zckt54SQ=lEN8>wyXRgWgvD07$_-)8j2-hY!kOc{<09})DGBh z2m35~9%c)R#&I63gKcE56&+!oL#|0UY;|yR|FxujoaT+Qu)2NroAtzf{8(bRd;wc$ zGifggPAeuJU{me`b2(?;lJlkbtFgJ;Nv^nkY8kB8({F_zEFRGvbPHx5e;dCGX7)I3 zFNc|DZ9WQMp~!yaeK>gc%@K!TwxU;O6&#&a*>r@oxA&&jOS^XK#DC1$eEx9)EPFft zFKM5{%Sudwg(vP0xCh(0C5ENIq7UyS6)?~2LtrY*Ua^zDKbea6w=`0}f6L(;$i3Va z%+7)tdesN7?lKTB8f*eSDEdg@`%vd%!Z85CW;kaZp%xo^A&;a%pNIH@E5nPl4_Z%;F9OEKdmCWh?j-~kh`pn{Inewg-qnHhT~g8W&W^W&^UVjB-NRg76?n( zigylGc7ij{lf89xV*6^8RnmT*Di;{`!9$& zPx7`A^!%+o@>XpI%=^_nhy1^8J{`O80xV1vuc?RI_AQs?!t%!ShDWeEbBoy(m@)P3 zq*6F(&)NAmNqtH}c@E4@30+tTvwl_NpMmv~mF=rxDO-bHFVGv>HKhS&pF6!j40&v& zYRn6gzrW)g2#c=I`t=5uwSJzm4UV>}X?zcJ@;5lGhf||GIzGW%ug)7hxGA*g{uh|F z)%WZg*odLCuLBl-uRHAoH#n3uDhA^7zw<6MhsB@UM=Qfp|LSs6*ednvpq9C%c1fCkJ;QtM{{^PjJ2o&bw>hmNy_YyTwn*M z2lcuKUbiO0oP@Zd+ptiPlW`uFurIbp^a6EJG^-P#|`9mFHf@(49b+l%x_Ld6tVxCPB$>_ zg5}vK>GiO{Nfy`t!jf6#BealrjA)QCg7AKxIe1Ql1zk;9{b2c_&3z}qRTp2a9|-fJ zpT69oq2Ha&lT^6z@lXd^!lWPS-4;*EU%q$m&`YkB*-rs2J;tr zE?oyp`U!i6!<@QVKZtA7JCBco#lhLMeS665*XsYVAR)MR%zvy~yd`83X+Nd1m9+1j zakiV7ZJ|$>=hN}i-vAcywzqpB&x{$SYDC->L@nwrX)u8$r;4peu3YDK%>ov+oTIPb zX8h&xe3g-Xzu(i!4NsGo!E*mFx_yd$x)mKs zduJ`W{j`%y16IPaA^q%@qrIqi^Nh8y_=j)6LfGhIUBx<>KlT>=J{C!)Ms9>TTea!` z4QcPSccl5Kd_s$j}2Nxeoy6t7Au9rg6801@3H^6{BCSI0!yXw4&?Y_ z`sBf_6R_m1`HDi=%{hBu9LyY~L$7yp1>1bj!0d+tIzLN$(ci#SSST|{@4e{-%3*%T zDeXL1xFCAs1DKtafA1iyoHDzy24?ux-|>V4YhK)|Bl*$%v0T{2#XPPaX6`>9Zw)IY z-)U`th5q7#d9b?Dv6W4*e084nOt?~Ad;EJ+-*RT@VAB5n-o)>)ta@qBTl}9d4p1%X zf+d@6=={X$gn>)L5G%SyQ?)DS+Z>&y@IG_tNEV)%x zaCup~=^R)x?A!Acxa3>v&c!hQ^IhA6aEC+IFMF6f&*^v&++^(h%mmi#g~q7bX~4f0L*q8(M|40#UC?>-vdjp>CpMP{WmNz34yt145=MIti87%mb4D0 z^98$Ei#|laV!!pP>HOW?qJ>9^zv~2qVtJ-_)T>XD`b@vHM3}X*NSp-Ag&}!4uubH8 zgLG2wJ(l+Gi5lXrokj9)SK5E3=+Zz|Hfg^h6<3bua+se-7v#aB*M4+97)eH->H?T~ z>Cu=+$eX9iMqY*)tygU7VeNuvN>^c7`X1G{u&N+Z;Tp_;8~Upa4t5;mcpVl74r9w< zhN{nmQdsVKZ~kAn=(0(S1g6g)$M#{X4eeJBOU(?0gOi%#itha8BRQZI!TmOW2usnCTHt&Dy@h?-H?Y>ta&Rd16TW$8>zq z3%mpK6$Yu3`M0~{?X`)S<2Td(AGIU>Ufd$}3tQ;*9aX(m7Uca(6RMt)`GErMCF(a} ziQDxOa{Z=KYsF6T`8d(04l7|= z0QNsS@n#Om@5G%v2`i1=bNd2W{v+QivcGbyXPzXv^ylm)4`J@yWYb(&UKDfrGhCvg z6-BlmYwwO7DmXrHLNoj1!$QM{wG-g*%kQ)b;eY3^ISgH^OE6nG@VosjLoSGs#1yF%I@NTB!gl;7{A+n2H4IQSIu@{12X-$2ee|19hc%(3wpa2u9&riUxx zco=-)${(^lS?c4_%*%5 zUcyY3p?y!oM%!n)%3x_*j%yBFqRzeD0<*jA9Lr$G%k_`iVcFZyLu=sh^*hgZ65Gt5 zO6CKL*Hxa^FBspS>qorb!sRE@vT=}zM>ahI8Y&t(# zy!+8J<4Ap)%G^)L^+vi5)q*kquhADcc)+4*I;8#+yNNik-xFg!m}$(V^P|~ZoNa0e zbF}_$>O}56XrhV@EC`+A(G9E48YkhxjCID;vS~MS9bwU0dph5mIP~0&)i7_&ewSaU zZ#(uq(;b#63wLzDF29^DyQ> z!on-T^nOBW|4TD>!;B9#U$yc7Td>jEE(HGff4$_mfr|i^DbD^zu5Y>Bd*-zdmV9!i z^VbO?oNFRsX2yA4OXO`2)O-%XB6Cj69GKyH;rL-#7%!&R1A1q@YC1yNm-s3ek@}jW$w*0Y$rKfP7YLlR&n_iOnWxR}w^nhfK4 zjB*v2cgjrZ9LzVCuNngDzPhp`4QA{(`M=M<;8j63sW;0_Z^QaYox8qo4$Q3jss09b zw72Sii8$Q4x)|1TKG}O2mTf8cl?(g)Wp26-3o8cF@2BnxEu|a8<5D7yAy>ZTHAMmo z8ZI&p!0MMGj#t1uzs!??aBRn~L3d$>TGg_huoY{(UllCAG^)`XF5f5)c>@1CKXmN* zwX+Up{>q=@Klw13MCYKGk|zkbqhFFY@2 z!3(%}(#FR_U^@N`PP$leb{H(O^*((M<_T`c42PvdvVA3RL-$jIktF}2O8+0$p7vR( z2D7-M3&qHbVqRVz3p1KV(DNZLLteW!sn_Q8Ig8x;mFug?uyE8>dc7b=Es-%5mPEA< zB>%q@8kbh+!_uHu`hT^uIc|$7sqZ>M|IZ0W?SN1HAae+>UogWTIK zWcoFj5!t^_C(I2hnST@JEI4>T1=l+Tv56}xU`e05j%=9Sn=z)6v|oE_qa`d(IIxYn z&Tq6mT)c0~ZDP*QS`7?Q!1==7GMW$g$faJZl|?KQHvJ;~0dmCW>GI@PJm`F;|6Y&5 zP^H(mqq+P6w^1*bXof#Qz3K=}W*Kp(l}R_;9Ck!P-Y-jk{G4&Pzg+dL`}7T1(z!W$ zA*|%WSCx=_f#z99I48fVwj5^LwwxpP@4E-Czx;rB@!zmaxP&X&C54$M&(r(qKJs}v z^{~|UoMtQXhH<5@Uy|JY*zx{S?4Lu|-fn?eZJp1?!0|;{*WSV0DW^AT!Q$Yi{vSwv z{e!g0uv=tE$0u0maP=`8=5$I%%3;y1>r7)Z=m>81s0~?wfBHcixzi{ zfaRBG_T|B)bX8J8_*n8-4yU8#|GHW=gH!9Gd zKAq%|z1l8ts^;->Lzv-uo4#N9hlUm-m~X34v>mzLAOFF4z%kBex2Wxd8z$_1Jqs3W zl#V(H7rzdwHHD?S_{?lLXYGQ8<}mm1ljtJY=+OtxT$tDXdfFA3mp5~rC2^iqr4+VV zJ!{&0n91G4D1(bHxSp_v~e1`GB{Z$EqHbjpP}cKBPXuy5KR~ zP`lT82P|0>B(H}VC&nz?1*1R5yJv8;na74ek{9`zG{P<_ql1arZyKi&$A(2V?uJD{ zjkMnF*n(Xl|7p)KPhB8@c@IZ&$nu!420aglB^&#ldkRb9zjYoU?Z-FwsfCSBuRRhC z^Gh$&&+mP9N%k>Vu=io`1LR)oW(!Zk;r=--EJPS4Abz{0!hXZcL{{d!qKh;_SdoNd{Irq9D zoe!J;-ld@hIdA)bB?FL02lKv>_s89D*ZLO6YwxW}hK(@mx(~g-=jdxvRu6L;ZqoV6 zOXNzs>R?7t{jvS1HwyFEP1X-vc8mU=XcqUUIWw{C_X6a?tJ{^IBbPn#~ zlh4n%#NPB0zh|PVTYJdoWoD{vy9}3H4&Z!*#ajou#=^0iTXMd@!jCt~$b8c|T`t_O zuykVkY|_7nUlmaC9TwFbdN>;HIQ%T;2l3HHdOqJafAgD8SnPXa+avsbi)Rn6`2};Y z&Zx+M<#rjJJtY6BL;LSoeW{=K2bL!(?LCg%e}Pj#|4^*2(fN5h;D)98`GaAJk1f6a zsB8H}bvP_^SozJIEdPRCof^!l8Y0w#Ir&dUje$j_8rkIfuj-{9Cl<^$uCZ2w8F~iX zX{5dBN_ss^Q)FB>6BauUq~}NSGT-=lFq6?`MeZ+oy9i6{VDwLj{Yt!ZSCk8BZ?~GB zkM>UfS?Uf;p8Gx+jOA<3)7a%f>LZp9VZy=}71KRoS<4=J{W`oTGQx+{SL)L9C+YNa zGJlfiZKT%&+0R=dLy5`we6;t!!AcB=dCfiZE8ry0FxP`H-^h^OZ|pek_>7o0SeyQz z>kv+39f4UvfA^|m`!Ec*8^yq!MQ;QaaLLVidrrc_l|2i{`I5XfPvtZ$bG00>8cquC zFie8E0e0^Wz&`G`#l+IHGPNskGjr!o5zNErmPVM9bWrs?%=psFAm>ZsB9)aHq+VY` zI1Jyvj*r~HESUA+)mSx{zvRyhJm6L$Epomw4OUk#F1-x%OqSF8%h5i%9>uWaw-$dc z^0phv&##jF_@ZGP*nj!iQ#WD8-A8X1!(N&bpWTLKqWG20uwDdX#C@0>wwv~cGxz9U zT@4HFB{Z!i?Z+D&eN1xS(8)Zw&0t_WG2^hN9&uDkm_j`)wq6$Q4zmxP3wj2#i<*o` z`wnUE?3b`0c*>&HaQT|O+geDzsl|H*Y&G-oj1MFiJe;}|_GvJbe}-9?I+GT`@q?!4 zbilmun>Q_p!v?Qa6kvP2HxNvM4V*{)e+0*OJJd{l-`dQm%Oc92J?&VJG&ycIpr|Rfq2*I=VX3* ztE}GkRj}Yd#L$JXJaONQwXnRl@3XmZ^BumO2XW%b^Tx1J($sfeFr)7!`hVQJblk(u zFn5-A%0%RD5BDzK3Dfy^;9ke!>w;kB@Gu+l|65qYxwr?G-}9jTlZ^(B=?Ee9Ya(ba z^C_x~g2jK#w+uvmb98gH5N7#WwUYievHL!Broxi1TJ-viY}@P&1u$FSH0Lh%SFeVN zhBrw2D!-IGxZ_2(|1FrY>G{Jnn0eP&a1WMZc9dAS;fGyg4J>G3n#V?fU@ShCe%G3jrvE_krz56nC8#C|gDAN)N@ zF%0?tAHR}fqqZ|)?&=9e!y)xI6l|iYbnWj zza3`5ZUUoZ2UzmHz>Yj0A>Q)*@6|AmX-1Eab{fsk*TLMkQJXfC^R?G1<1*VCI3;C&vJ2CSm}<;!qvd)>?B(_aMf_f23VHCHCsm7^UY#jz)ZiLw7(8-<+3LqVbQFW)7B$z zf4A(|SJM9S+rMOei_}UDzr(!3Kgru+3C>e`VEN5Jy1tthS!y%(Vf`$Nrt2#+sM4>V92?$ez;;-~dPw_E`Bw+{?Sy#`&(hM?1qKQ zyZw*CMSr9hB4EKz4(Mn zIv%Ka>=O?8yz=~|M#`vf@-`o{3ub3kT$JPfL_E8e=9ur0_Qw_uR*DWnEk4w! zey=J*Vb-VJw12C@-nBiEFmGSL%|PVB_y+b-m=hJd)eBBtQ-1erK?)!ROns#IjCKo?pe9Y43Rl=9$ZnErv@L zMden)Y@=%?OJMPB`|(w<$o?WVtLd4C6lM(mHJ#i~l6NiY{}|?6xk>LYX-2H$Jty^6 zlW2e8;Ge4HF=@yBH!T^*3y2 z|7GF&-Cw@LLSv4x30|M66%+Oo=3G-)H3L@qT~XBo^9*xuk^b%N*Ao=`?#J;hv7nQT zzpky%^%w-pE`(I}g*#dlwT8jmedAt}=hGUcC(juHv$r_W=go>AT&z+j_2w#OQ6yJ8 zI6@N^?>C^&w@`8nWREBH<3HvvLM}H^A3qUhHN@T5hf_C1-qMEU$G`p{&l{G!{9`r= z7WFB-G7io$(aG0^x#@ilkA&kDvcBuV{5ghem0`}>{zV3)z4e{fec;OeTAC&>BWA7b zZ`_YG9hbe<43@5$KcpLu-W=^=1@j&|-symaBaPoIg;`=I9UtAnGO6Rj@)PwV$@~Js zU+MFlhy%De{DHR zNUoChaycvtuUqF23&$U(&&QQUEIS+mi#{Eo_dk1!cQC?8{kMj1+fg6;KoAoNiyajW z$n!%KGFkpdh>wr5B{}29h>b#+vD(Yc7uG%N78L`_EuH^&KYi`cigUzT{I6?~YwKP! z&4qabI`EPemA6Z`+34~u^drSDHKr<%7I7QC!G?vHvUYsT4Suw+)o=^b#< zird3hz?`2cMLw|IG_yhnSX88O#~YT^L}oh?rwC_w!9EqU(w$-Xj4lotZ)vl;>cL7_ z^6(zLpPcl1?zUAh%lnEac^;nNylb;7$}m`*)(^U-*$?Qmycc@`_U5?{205_ z5%ui(MZ66#=jKyd?>6ywm^W!JSVcdtJn8lEtt21)h`ye%TlD=P5q;X>m7|2~+i zuP?g>O9q^x>z~KGf3yVVshA$zh5C+i-$l1!saC(F2w3JcQ1?D8oD@Z$|7_kj;6M$` zGV)k;9eM1bc^~Uxf$vt|7jWex#GTud`)Kv8n=56KeR>koo_3KU6E0{mc zkdEI_9}<&HEYj{tSdF}w)4sMD<~~?JpAV>HH017kSP(zSUyQuD<^bm-EUubga0xCM zx-9i8%vu=z>j5k`IuZ60mhaM<(+r32>Z{WOGn23Aw!x-%?nU*9!0V^@7@=`cmB98JrEZ1yPXwafkN*-Wmslhb!GtUa%p^#DlFB! z@rUHaPe+{AAnmVQpzV2EX7pvj%x%$q<%rNTUlh=!VJw0C+mQV7X@Edl~ zJ+xgP7ML8SR(|00h7F4|i-UTRYoCbHpGor2ZrZ*b)3%$!|K4xOi@|zxVdlkN^Feq& zZs(%XEMVDwbNc(;JfwB~0$9|iIYS+}xJspMKB?zQXnz84mK}#!ys3=-{#Ty#ZJ!5A zrkc3UMSan_uhSR7yuE2XD`4TMC`W5pZktE@+se~lcGnUZYx4#MI~>|{R7RN6@U zW2I&YPVRt(d5xnZk(-C4-`Nc_rfqHxfo-aCG?%g3rEkUV%% z!hE>)uJNTrSTgWpwgoJTm>rS~OBL((&Vu9P-^@ybnZD`t`CNjj8PZHxz)ra~nJn*{ z|GXTMU%f)NXZb0WiG{><78YZXyV$)varr;)zOHY51Lm9@U_Ayo*Qz_a9OerzFCPut zDKVGag?a6h807mRvvD@6f`v~W)9;Vl+mCiq(mr*7$4KP<7skauhX3_fVcv9lTT9yK zUfw?hxvDJp{4-+5n{@v%+MRW(31&9zr~8L&>%P7(Ve!g)ZRGnNt@->lF-uExY7Z>! zW05C=g~2S^AC8S_uv=j6+)lwe2Zf3wYCn7Mf;t#1$ddT1D_-+G5GKd0WiYXqrJKK|r1 z>WhrB)zx4g-*6&%f8oaYwPRrZcavMmF#C~O;8>V>z}4d%>|+1p1`8IBmv*JW9V)uN zbzsr#m!~pG`O&4#(H3&xW5U0X9HYd*|6yI)oVGc${d7Q^Db?L9AH_5k@`TbOs?=!ZA3 zsGp>5H7xv~akv|1XdfQw1&a(tOdu@zxG`7@j*7AD z6Aa5{9vGtoJ62S3_rh%4cO`DR`Mx5Q)aN>{(}nr1_bQ`EF0-R~{NbqwkHCVb*>rz0 z|8qOwI4n=jnl&EvcEc@1i7+?+s8|j5S!EP?4i+(+%gFdU$?&kf=V9sW)cZ`>ZBlV% z8q8m@=*du+H`LEF3zq%7M)wa%(>n74n5ou5kKdArk8?_3%pXLbx94b9eBmZ6Oi-lb z{dD_vN8N=vV;>m)#QKq(%Ut>ZmXuq*?Sf-p#ch5BGh&X>`5tPAd&Skm(l=i>d_rD) zrRM!>SU#oT{3}?n<4`~w@yly;|FOE-V)6-QAJg`_i#+w5S@&1cUUi*)F)XvRkL!Si z1x6zZVTD_YIz2EW@)+Hptgf4TG7jML?U6;^M9ykvJNJXRd+q4=!EX6|lR+>;;ySMy zIp2Cle`Q$Y5K6xvjD{IyDzIejk@e4!t4?c*7zr~QE9aB%ht&_Io>4HykFNa!+u2X> z91F8r2GRB7FYJLRPS62%g-#K+uv>uXS6M;H#Mcd&t7IKhnB$%oep}wq1_(cj|+2$K2Rs; z4=x-2rrX0phv9U8U|wY|a)c#g8|d~F9y@x|iR3E^>G_U%yU}%Ln8V&?(2Dl8f43;E zf_azD()~*?Z@-5J$%6%S{pPGWJk{&}n7aG8mj3^L;FEMgF%+e+QVG$bONDiTN;0|- zg+&pPQ7TD>N+qmRLKvb@8AVaD6iQ)TsYpge2#c;Ld>^m#{=9#`_n-IMjo-bwPpGu=O6eV^2+p)g}|1NHaC$+OOK zFU(RC@a?dEWdTUKc298bfkRN9ukhyEpu(SeRG( zeJ#2F$=GrTm9zE@Up9nXANj&MdI&jVyvUpU{^VypPKoq$RyrJ-1uIl_uOsFu-jA_{ zb-pK1)8o%ENOy+KllwJ}A{U0rl?0IbPMuABSkk9nNuF2rcbra{ZL?x*3LNxnd-5^l z(n&TxS7EV` z_eZkBXAY9}K@-IrRlw!@l@9HL`OBkjllvvPTik9%z>LSe)#QFj{uM(-lCy)8sQcp# zE;~}|lPwtXpa;2AWwA;$a{Aj78Va~R73u4JpUf|R+RBrljiFrzkZjRev)_9j2kdN+j-$SQeWaT;TBAv@R}MgC4O#>>|I!LNcRiL`3(Ds z#s8_-ykXx|N^G~1>MuC__D>m1TkrUf&0lvthS{kJXV#*hTNZ!25@y(b&RGp-;W_LY zSj@=CC+`~${d_&885U-pr>+OOj$ZBD3S&Hf>UuBj*s9?lN&oe!)cuz_2bZ*Vk$jEr zRC2wrdE`Jw56N%(=T3$PTk~vw!knwN$5i07@tVm)V{p83dQ9Dq%5@uYQw|nx*-gEl zw>;fUIs#^9p0Iq0?VUbjN9)9M~3Qd@1&!*mY&JTa# zFp1QUSEqiDu5yVRsSR^4_iK>fV>mWF>3XC;zE2nh3ymcu(_qn2?w$ykUu|??7R*24 zyD|`FMCRq2zyj&-m*oD)!Ab2sb77%h2KD@if`R`+E0{U)B4vHcFU9jBC{xu=Kqr_56v!hx`4uFz>}T%Z=QD)b^Hq*s5?Ird3x`_s{a1$`dk)S6-oh zukl(amAeKD`&_8|ah0xJU6Th(b3>{7c?F9`e<+0c!w*x>ugBbWQ79&PR|mB}#y4b@ zKY+!%7f`=9@cW0wK86|hlv~N~53Y&6?iHlpw*MWm!h{QYb+Dk4R^bXOWsTh30`nG$ zM^XF#p{Vm6#IGAEE3Gzf>4Q1ngw*jNre?|BU$E%KNM|yhQECp`f)b=u&Li`3>=I0Xp!7FVgdPmzRi1N;1pQe zyf!+H%%9w%uo6~h`*RwjX2b04pi^0Jf5C?OIk0GaBl8BVvUz!-1uT4acG@G@ z=ju}_i}dqLR=$G=?IUj4!SoF>)bGm%V-Dt1nj1`84okc%LM`!ow4i_O zCU?^B@YtC=KOXtL%YF^a(Ea@30E`!0A6gHK$|@$2@r?MDSrr>$-o5YoIWT*{_-O&8 z{=*9TW7z6W%GzBp_sr_3_i%@4%7i^IP4oH)8FIhPqMLX2!UFfXQ%Auu#nb2PBYE|p z1-buG|3F3-4`!dr%$`p2^s*%fNxgg<*PQf|zmG7-qiK;F+&o;_l$hT5GjcVokehE5 z3-jiz8R82|wSTQR4D(ODGWCa3_X(WiVTp_WtN^%3W?%9#Sa|7UVG!}$?jcDq^NT~n zez+huYC4k;|wXo0ahVEQgvb#dD z0cK{0w&oL;n67Gsy<{fW6vE;q1ys(N5}GK2>7RSOUm#bSIbnYpEVY+3KZW`8S6+BT za$j}o^U^0?K3NWP$^tSTk?98;eXM}_CTV{k!kyQLt$qq~N6J(8TZM;z4HlFB3EEGJ zk^AH(?yQDIFI=>VlL{h^)xpejE2!o3QI7Lyfd5_IOvT|fjUP!4t|2A(QImcC>2>R z?}SA|7=l-*XXftp?SeTa({$Tl{>^XIJ+L?`^88maz2$H$HkV2?*P zzRrgE51b3h{jkhV&&w9Dr21ud=z zD++ztW9d~QhV%R^voVyHWTEq`*hy8xPl5r#bqbui}fi->ZkMSh=_Me$Wu#Zto z7zY*@3h}=;JT&Df(uA}&FAtRFhACAb0Nw1RE^pN zr>$;Xc^_u&HNU(Y=HNYpC9uT5swWKAKdB%pgSqKXllH|JGU`D_EmvGoMWn_0f z%#OQH)%$Np{byx+NpU7vm&p*K2%pJSA zB$sRR{zCdM4{O~D`Dg=DoYR(HrjfzOjQ2bA}##ycSm3>?Fg0S;zL;xWn{<*AsML=7HYt zuCUbdd)72kPp)UeoB+uSVu{gt%FV~W`5BU&TpvarURiN{HcY+W1kOtC)iHqunr$v5 zck*y-w}AOyYbbZdzm&3IdYOmpa^!US9Wy(axAke>GC0P_t)2}t_5UgnGyk+-Sxu}F zdT%Mr`10A814|mNs1m1o$J4jK!scI8y-uTEdmzkRszL29bu!vX;jr}9u-{~V=}fqN zXD>|uqm$+bv%e+M_QU+oN2<5OMU%|FMZzNM%Kie}rW!aj4wmxR z`~tY++6u=bu+Y5hbTe#Ms;GIC)Mq|f`Ipo$?buGtE>+7jtDDCX&Pv&O45qD!kXr|L zv_8I_0E-4jaCX3)z`h|#Fo%ECI}%RPpSCmw7DO=$Q{cLw4XLRxd!6~4^RPg}@z!aQ zzX~k60()g@&q;$NOAcj;V9n%cWs-}oK94Pj`Bnq>PQpSv;q7WTR4A+^pT{y7wE6)Y##$LZ{nGYWYu@TI<1TlWSC>(Jy@{bKe&B_x;p& z0A^PHNaDb(>8pPbi^Xq?hTgm!o6h-SEfqB!41VK4Q8^+B*;rK|QU z*nY^!MLS6SGaW%c+`RCy&UTnKts+(V1@`|Rx1#)Eao46sUAV2+L}@GWz?DmOux2)& z+CQlK8Q~(c?LK6GVJbL|I|pYD)8vqP+R8^-rLcWPY7?0r?a0@<53p3b=Op=jahha6 ztpV%9=9^mx%-}w=vV+C`HcNNIcprAg3OKJhGGGtP45JU<0J}DwQ;vYSH*S~hgoQqN zSv*)WJ~%iMj-Q*JNY)=OJRtil9HZL!n(S}Xc#kkQ;LnP~q+X_T!^ZM-^n7;3_-xatsc>SK6 zFw3$sJ`ZNCFzUPo^Q~hh6vBh*6{GWE=KPCy?!kgU-I_v}#`jrV1~U$*6_mh?(OnwP zVD?$Y{xZ_P$Mt3nY`^Yn+Y{m;vm;tyfB%V{HKg9};=V38Ek^fNJuErwqTCNhzHT&m z1G8Tob{pA**TkdFKzFaDvMpmI#K!!r5%ZDI-w22`GZ$MOz_w2he$GY)Ov`W#j;nxD21rf26<+ei3tmFyCj`99%CEb>gB zOJA449HU;9?XZ3QL{C?kH%7M37ETj(R<4AlFYnD&hx23~IC#Kpug!VCv47F$4)_kt6c{(Vq!O*Ci`PhmbN!clmDHN09W1c?Ixz)mjZ_# zObn*-&cXJTuvo7%jDwu}H(SjD7AO|>6N{eP{Fne|m2IT@+4s~U2JrplkC~db9ywhl z)#EiBR5Rt6FD%qc3%w3Ii9B9zf+e+kVp8EoL)r^Jn0ar7<8hex(5`O_EInbW7Xvf* z8K?vhGgbu!!{rlSH|~HLN>^xGVEdUrLxN!Of%ml>*zc&@!eAKh$6B-+W=t41JdE_q z6--|Wr;l#7<&pf1cexcD>9s-_2}|T_5=~%*jG29hV9t#lYi7Z9j!zno!mLrA{~XU) zCgFTou*b||3Uc$z%aapfwsgQ+3r?G_6`un0kIg;BfU`0`4xWU?C%E4y!whqE`!rZO zFm7NXoXJ$TJV)}}K2Hs}sJk&C6Xq_~pmO{}QknCge&e!}ukzr3=clfzEAuwYUonlR zhyFB0qkDxgBjDn#nQ&**NzOf(bC5$V54Yx|iwMU1(z6W7=Pmp5u?(i|*z?X5u6t!) zR{=|O3f5Y~>3z3CDoMYqNzX!<{N*_UqjYsmW5w`9ZXNXxW^k-n(|@C3m9*) zyx~Xk$ugsxV2Rw4`aoFnd6~j17~`{^-v`r{X4N;7eu>+|I5@42^;H5(le#Wmf_?bf zCT%cl^4K}|;ksW@l^rnWcff>3*eT15-%0XPlbPi2gA;d|;Wt=Rtv~h)taN1pPaaMkUjM@Y<|PZ0^5LWfG+h%|TIwBG1E<|PC1(Y5MxH9{fQznhmo9+CKO#E1 zV13{G0Be|^V;0;GJALpUTnr1W(-MZ^cwu$+_YwzK6kEttfCbJi=ZJZq!W)TQ&pj)0 zf`yqA9xK5~y61XVz|69w!Le}n$rB&lV8-ca+wrjfDDS0Suw?42>WOfk>ZDieVe0uw zxbw*}Jzr9P;@w&uSUP2rO90GSFMnnloO)+{5EsV%X?D}$s-MahK`^T{ENKQ@aJZ&6 z1g7;)w=#f*8|5a4k^F+RmALKAGR1wcP{Wm~&pM@WA`0gG97RpPGkM+0I9P0UWT`&t zC6T^k`7k?p_#Ztul6huoGR(Ejl+%UNpPZPGO8TEp%bx=C*X}ht4KoV-M-ivbJ@7*S zv!aHpX~ANdHN970=?A;G32>fi{_PynFSPwk){oc9UCVF6Jnrgr71)1Hm~sIudZ52? z6wC~(e_907??i`;fUDko@V^hUmdxiV!kz2FLm!d+n7Y>=Y){##(Zj1?VSuw?4{Q~i zYhMjBul~6C3ATUUc&`@b{F%FeoPTit-t224`Rx3&<#5%`14a^4c6uTAlUQIp?T$0tsr+_< zGV!@3wf1oL_(`r~VA@TlGYghv@WVA>iPMZ$BUm)+o5LiSf4t?hApw0U`77NPW-Yt%b`tultX}3hklZOkOpYHxBkm12 z!D8*=$2ug}TB5QXW|!Z|o(|`Ed>gfbp z=fRCmZ#!0#`YXSB$oeT*d&h7s%nd&L#1WPZogiHgGsY&?dBUOHmlQS;t8|(A!>nq_ zsO_-eVMbmo%+vgSJ_wecei?ECcGa>y8BFT`$lp$b#V6g(cf%4Jo9^>)`U}mpeK1q$ z>!utyb8GFiXp;Z$d^kAhLf&r`Ev|rJnfzN4vx3K z-H`;dWIv}$;kt2u6i>j+$4wEv@L=uE1!rOD_~)+Vc+}x*_VPR|^6mUhzRymjW3?`k ze2;>HIjnzjsQfipqLp6dLFx}oyqpd5I}2ZIhDBz1l{v(U7cT6A{cZLIUWf6#WXL|! zKYaX2A?d&UC@Y%u|6rQu!aO|<^-S3H#O&?@SX|V%pb3uI!25g`7OFp9(E%&vc+M_{ zrFq_ux?ztQqVfkYbA7aU7|s_%V{2BI!_;^^a8}2yHPtY`Gh^LkIB#KJL@mr&Ep^v| zC7q!q4Y1hL>kHoa$rAK^X1syflBe1;VOe#FTsz7CcRrn^eW0fU7Ral5&qnTZ(RD{B zOl$sqi&(`^z2O@yiKYWusEcZS|5yLYm2!gztTkI;s?=F z7QoWzWRWK7n-#KBtzm9@4CTgQ>(?xVg)g70kz8q!vbF=v$LL5C;4B;ck|i*2AY->W ztkc~TwT$>HW4apLs5P1E3NuXVW{-o@RD-8@!5rzyZaQqYy{44|OO~Cd{=ZS0S)a85 zX6o*`J%aRmsg(G^toFm(72tTw^2E)=jj{}RIOy8&ysa?aXQxm0548lvi~yMbY`xJ? zxPMk8gA23oT#CgLW-RvI)cZSO!K;rvSvbac#D*Z4X}pWtzarCnEce1zt=)lw(F7CN>`a-trW3j$;BfJ#kxv_i32B zQjJ=__8*Psq`~6)2raTb+RSF?og?*K{nYj@_s=;efJKb5Gdjp+*PKhd2s75J?=gVA z+Bf%Jfq7ZWOKo83Dbr2auyoF8T^E=mS9&{#^hbH+u7bM@k4?P^)8F~q1j3S7t0}p# zq%wJS9Ne!~ZJ!6zYX8=pgGE#G(}>wtCvMz@!)KUL^{l^YHXq=Ega)@8WP0WK`~Sio zu3mCNGQEe_3e{d>dnOlApD$gi-){sDYTC^s^C#S3_RJaf7>v3==3lB7csLSveP!@2 zomeMuVi~M&e2rRuhJ3d+{T1fN+rH*3a$f)O4@}rtJZTMCf6{d~2UfsJFGt9b?ZZiP zh~Eb@L)gsYFsm?AI}(=a_pB$|Q#!gZBOY$s=CFxR>dE+6TJre<*C|)^n&+|T&YPbn!+4(e zg&{0(bC{nBO9uJ%v*9Avx6x$(pvDJ;)vAWxAj>1P-gnIc{&#<%FDeaK3^%S>%msD z&5oA9^su!Oec1T!#;Ql8fBzv_W4QUH&ZZ}@f zb!SENz;wo@U)snur?)NZg?V>fJsB|1S$ju6%(=ek#dx@^_;3CIEa7(t%ERpOhvk36 z(x=0Y4uLzv&s_dX`WF?==)(GCwT*Nf!pHKT%}8s2{l?F^KNMy-ct0oGhxH_L{V-V4 z@HIREj=3@OrW`EJ-tQd=ch7l|Ef3S?+?=qV^v5h(t_brA9~jf6RR=6}il z;MA=eF^1%1JWY6T_0J<}F!jD}*ysEmeN9qdIAq9JSnbXT#w3^-HT6#?`9548PFb4x zQtuw@Iyq|vsb}q35uE{R+V@k_lU%jh83o%H%$%r-oF2MZ>;(tqHtUXsMI(h>*8ef{ z#WLwEn6a(QW*n(^8Maau_9>i9C-cV-O0(=E$BU|rg_B{ygfZ8i!2L%DhUvi4_z{J< za9Yr>em$7+cE#chnEx{4-gJ_uHN>BQrA_M74N1R$cI!bne5>s_BUs3K{Ch9S@8{ag zfkj$=n|8zWtk_snl23VSv=feee|(`8%+UGjyAdw)_t|Fya~)q;d&2z2!`*fyKTAKg z8dmF>)oKq@KsoaV+*0Sh7xq-vJmV2Z>$aak%>a!;QUU?s;+b$j40?STxwkd-}kGcB;VTt1E zx(b-~Yi!RxIH|pK%QKjW%Y zsx15j^KA^48^E;EgyPSzq<_w%C2-aLdn{sJ*HxY;tY7aH_z@PJeIDlz`&jvn})c8|3^^4oBqheQwr% zdaW5|_J&b^U#fnTz9Y*oC^U|9CEx$-q4qDx^!|5z33Zc;C+kbH@0Mp4^8V`h$yFq8 z-_%qAYhFn;AnTjg>{}s$BdZ;#?I9gLK63zOuWUH`0J-$co>B!I|I)nWwTL;LE2X1h zk7?%?mBJ#?=#S*@iL9n>>|qGBhPi6eL=(8^?W@If znC8<>Ss^6Kb~Mbp-cQ*lgt2)H%y)@-XNvmNaHVKfSgO|=Y6**5EXpRpj8uB{T)105 ztX&gkKGLG>Q?=*3HcXA@fn4E>g_RyGuC|IZgAFo|e47cgOC8dU;K=@W?+szDeb|~= zFr#*lk`YYz*4$_S*U?IPO<>-+ljCNvE?t10O zE;w@B>_hute*ePJTj3b9!;2$f+UiGE-f;O>jR#S%#BF4u7cA2CEI34R$4w%4SjS(( zR_gr7C*tV+W3aSo7=xVusA(VkNG$j} zi#osJC@NSa!2h2ANWG88B*OF)c3I^7tZZZ3z9g8}RuViDR+&WaNr72PM%5T^Y*sr9qRqkOYN&+7E_=_ zj#ot~Ilgr;Ey0^Qe#shmc-O;p_dS$@#0Oq9!2G}f%8Wydf)-e+>@~dw{q{BGyL!T)um?Je}y?R);r!JZ_8#+?;-VzD%Q1=detpa zzhRNK(%DaN-KfLA{=)2-N%OwIY9rp=l}*I-Tx|DzhowfhW+=eiofk&;!E6oVONy}M z_c4!OFx~?sI~r!6=jg~`eVcDD_8$kc{ur8(^Lg`(IZYE_;fw>hlVQzSiI+7={|^0_ zX>iizOA9BHe9(P~8Eo9VcK8%n`mQ5x8O&ieFPIJs%vI5GNr0POo+~)Q{2gz*@4#NummXLGvwCI29uS|sx@;-Q*Usy$g8iT7 zsC}K4%2pQ+S&=*MKm~gz=BrVYAGyI+qT{dW;*Wl z9Qp>=1Dzggdy~A(IA9E%56to;9dC9wcOPKE;S9c8NiKZAYf@K4Gwr-I%nD!DUnA5Un^I2G+JMEk+EEsK{k^xKJE-hUL*NrpIy-NBQbJnki zefVmnLYTQWRL&$9!b%HIy{d*83-0B`!ewFyK|M^n8$S>a7iA3ZYk|eDN{dgy z+`j77?WBH5sMC2EFGW=R3Udo`M_q?SLsu#ElYal?j7PAh@X@BfFy7zf{{d#}e@K-- zj`@pOc50{u-`8C$4V7TwsFQwsR}HP{M~8*o1fyfYZANF z?6HGopDLP6g1Lol6Bfg~AJ#kuaYQX`DNH-skg5YS-FL2D2M4)dQq+efFS>pO!RGp5 zwzFVbi|6S$*x>QQnR8&lQD3=}u-fF(kLED_we_3}aHzE;#uDbWWLI2<`)4liSpf6B zbN#NsqBVTQg)pnmX=OHCw`;GSJuKP8?Y#~ct(vInMDmAD^KxPS#}845=CfmriB%qZG_Hba{ex|!Ub=VDw1EHAcQ>s~2!xpdGb3*!7e(k8guvW`pJwF4 zK6^P!cmKysZM}|gm~s15Ey>LdHVp2Eg^vm-Td7Ps5d}-_w(TiEu5@X1$$y=`y2U6K zIV~`P$~$w;`TYhJocF^%L;4q7N zq8bS`zf$&>&B`z}UMT8mi#?P_k@~hJH)~+=*G;V>NnWwT^F1uP`DHX6X51P(t`|1= z+O%ve@rCW<72jg{Kdq`zA^jI$Wh=w3lk#HKVd0KOHBH!KfwJQyn6pG`H5JZ!J1?05 z^L9qfm;pEMxHvQxxbq5b4)z&fiR_wR-2EqS}u;qIK85+2Ol&^~({%v{>RIS6CCnB!yNf}&$y zkuWV))J=!2!n>wL!$R)v+aq9us5MUy!5r(H?FumM{1a|0%yj$9lZ8|Bh7=u!*}bZ07>tl&!y*I`yoSdK0{XplMV7RgPt zgZpv()j6Ry>pm>7V6_#%;Ypj-%1C{u;r5eoyqP$soYar6*su%E>rHX5fVszaq`1RD zb8g8Hi#j5yzlY5V>qk5$)_)aDN3N9TZbPOg)+h<@z~AR2UAC|o=8cq3zXbQ|n~W}k z`7>5OjDsZ{nMZeEfwJm}0Jwaek=t#Uc2+cfDeSuLYe)|K|Ng&Axa^(D$9<>zu7{yc|6E$l=Yq<_8kr))TVTf*1NuqbByFY^C`UH^6)lJnGLhx6c|S557i zF#Xet8NRTnO_FmBmilj@&i6e=8DGvO^<^pg3y?D^Rlf*fE^GEw1DF@~^iUowG*=v| z1#{2aQ_D+#NWZBBOL()NmLTWfo>)cB_tdt_%zp@T4h*5LKl7bYm(46i1S0{ zgB)u8QsZ~SnSW(|Jwg8O`DK6CmvPTvsX(;wGji7rY0v6l!I5ykR#+r=Htr=Xyd6iK zPvgBg8LvtI#t+o_Z<^rJs8*OWV;Ob6+&y*i-A)+S505vYzv$iH#Xn$1(JAWsXSrjB zX&>o7+vD^CdDhp@&t;QQzp*>J7FOA&)u05^)kZ8OxqWukX*$feiK6-iSGudzU`b8s z%Vy*T%SW3`g2l-euQJjYv`K5>=Tn#et;P>R&dQ=>irKe-MDzH1*solMEyUK zdEz?L3KsMRQtOZX!yu4Fatj+O7oAwyVFNR7@BKp7chZL8cWq&gK@K&)9d11@7QytQ znTyHwdAlcHDjZr2t&u)W(JDQtEk{h{ruOgQaQ(cGn^pNzi_b6@?iAm*N$60#Kb zc=z%>spl=N1jh~goR3@E{ov~nJ(({r1bOZui+&7Jybpa zwb9r2uz2U3&!nIAb4}nk_}}@FZgSa6!}m*Rv_qUTa%#Lx*zQqSJ)6`!`G21bD=5E6 zc7qx7)ju1DUCsu1XXvE|YhiZMBr6A4IA$211Jmo= z<}87IZi#6=FyH@4#WFbkg?6SHX56 zI+Md-L79EK7c5(?e8F1w4T0^Z z%r4A^MK*l1NVu~gxcNG%|ICdz1eaYp5O9m+<=ckxVdHweHFsdn!Odwkl_`-tSXV`5U_HaXSyCouO-ighv^q}$~7jQGb%sy{Qb$fE&f3jC?hWBiIL zdJWTQ`^WA^JugGE{w>V-t98Q<&TJZ=@Bya9-J{mOU)=HGA7Os`k<)XK3$MK~?||7> z%E78|=V;#6_b}`91M2_7=511qc9=^~YVW}Qk% zIBU``!Z=BfV8%Gx=bbRSBxHbG4{YmTJ@^K*lw%Fg!aCf!x4x6=?N0+4f}X3ELK!uQ*rY=UEKE4zooGz+Wv^{}Ae=WIH$vioIn zeMh`d^{5)ma2?l5uICtUak)MLW+qJiO@7Z3&H2vPg7J6u2l@ZnYY}bAR9Lzti@F|D zb&C5)9~M}?HJpw-FWJD`02Z$pe_;w-mGZmV5T+Y$Qke+5<_bh}i2Xxnk>8`ZQ!g*H zA})wsKLYNIa`|lov+sSNuD_JMFYnZDSnzCIIX=PaOd!QGE%kHS7Os?_H(d@ojS zhefW3{kNf>m)U|h(_?%1J;>hK$PcG4gt()fq6KXmqJQpzNc!S+xfieE8ja6}e_GbN2wuO)_dnFVXP=RSC2^8DXJF<|Un@nJ@j9pH1k9yB93U3@?^u%p zH%pJ)7(wd8_U9+U=BIAdk0bf`o^g9&)|ROwCXu{|J;@Jts=mb1g_#Z>Ps#ln25xt2 zro+6#<*%*avit+4Mx=i6D(e3GyltPVOkmd5&6ec;6HacBrWs7zIp;TdzQAYQV)uEl zc<7R;xiEm+y^KWJc^J%rz^9?N5?P2yX^Gzo8yhQ(Hp867!Kg`g& zjQW_s&8E(z|F&DxF?eu=i_3CYxHn^J1ROb#8stLyyJueugzcZDY;q^{i$zlMJcqtp zkFf{LPw+kJLi)|hZW8kzD3%z*MIZgzNj*c;yL>V%I+-(;Sh7J9BTxE`)2Qi7cP{OJ z*?|48bpuzRr1Slc`@f9&lhBgy@G@g3iaoM553Gj;#5!0O@Lr7)wWxjq27 zfAkh=dBmE=)cu-~?jQ6!d;PQ{skgaDJx{|44LCQ6oD*KT$(hUvzMQ@+CK6|46WGc+~3i(!u3?rFrr3xN))u+nI|YGSs1 zb7Tmtkao|}6lT$;TROwsD{tRzj3zvYFJ9=PHTm&{O_c$g~i54luBWC!_^1GINl$+3;Q`W(R^Uqd-{-b zFi%CvX(P-txznBiH_j-XxCv(89MX3L#yIf{Hp8N&h1Bz*v?+g${9(@I;neekN}F{u z0$?$BhGZe?L)T5`?toc5^I67lb5DBnE?D?QvPc6ilHWUJH>uAl%^w1Hcug6z7iPre zJ^oaK^VyMeZ|sAm3oVpi!Rf=@*au)qh}DHExY3cO8V%FjhrKC=X*O2V4#VvEd*@$) z!+(7FaunuI+L4?L^J}cy`7qwE=ob%{9SUF`ho!M!j~{@O?)L3Vg1KX4bHZVc-sGMX znC1Q^b_Xo0G^yhxajwSqRdA~5jyI=aM*E~gOJMV;O*Lmpew!0y1vlEaEfK&{wbSoS zV0KC0rwcId|9dbU&ZO&1%Y>RzV87Y9#@As<)a%ZZaM4MfMj_1Ge{xzp%zE0oGY`i5I+yH#J6M+|+#>ms z9l@Jmul!WA0+_o^^_Vj(b!?q=7iJBQ{b~-kSW9W(dFz3b-)m}0BgEA9EFfB6ltX3uAL{*4 zvZH?qU%jA1?#kK zrnkThzv#fVFt1hC^bIVMDzqBG1v*W+#LTM?43%NyF@a4In10jaO4l>|Kbxdc|CZFN znbBI{Ea$W1-;;dx;?{exlKI4h4={VBmfUSvWq7-J2h8vC8h;b+=iADCf+dNMVz0rO z_itSJ46_n4FJ6L$3A>kf!qNbrtc!3=b*%I&>2E*3?-cARJC-GdnPV1zOolZ@-O)c_ zT9I|?emL^!xWOLc1G{KjNZ!BsM<2|dyCK;V4yrZx8Gr@6VCwx6?5oR%{(-sm;m@Zc zuiKm}{!3htGL*a@rYhyZc9~T4|2%n99;P3+`8X7o6pP*uJjMRT3o(|1MTe^Pe1XdX zvi9LsY9dx>*c>vxxze%c-NRw7uWei@9Phg{KnWHeuphVr+ZC);9tBJ375|JUKIO9) z9hTTsDe{n;w>nK91M?s84MJgd+r-nVuz>bNeKRa8I%G5frn9`M@r9brO?xN8wAayZ zEs-m@&A-opxt+(U_b+00Q z#1d}m1xtAF+qhfAtm6*f$opoz1bqrKN&is~^|v$*eJFN0Im=dstxdzHaf za=)qhVeP+0y|3P2{kBK?WcuS(QuxTZr|Jb{{<+7tR&R$x=ls!rk^t}JT&y3k9O0n=bwOF1?EqwDIL<79n^3%m4# zQ2(#F`Psf3;f{uBsidCe#+&L5i%0c2k%!88_77xyb64#(Q}tj$ zM&W!iK6%Urqf5l%)xZ0UV9}ehC)D;3*ln2s`{iVeChMQ`ZSG%9IPJ5->DjQb;pSR- zn0dZ_gelC`I68N*0>>xY^WhdS&w@pb&)XUMsLK*&9{V97<9mm@Umv*uriH9klEBR3 z!Uk(tWLKD43wIQEY1@(hS?7*EfCXnRbrbVt?qn9h{R>_!SPTpA_ZbUe&C!D3abPZ=<+^dL3fcv6SO>#HzbYx05d$o;)L&gZ~_8XLEfFt>PGsSxJwP1`se zF1UDvf0OiIwH6P9`x_1f7r?@sk<|OB%NJH|y9=}IwW#sN)74K^i%9NeNgxJ>4v2~d!{pCk95xk zJ*2*S(_D2}CG*w8AFyQGYtKKAu|Fhs)JRGHmo&7wT%4|?mx&ML*|F3 z^>RWj{O|lr^tnm-aG7`UG_ri$U&Xi5V8P`r!SyiXt_eQ^<{BmODq!K~)J1Nv{yAe2 zS-%qPUvl$crp2KFvi?P8@S>ond9ff#&S7U#EiRA{kgEh4S8p>zln!_%$)#REtE7~g9REbR}^8NmDi43fu-GU z-+n%v$C8GWaLN8G*t0IF4vy(j+jR+M>`~rW0Ehm~uD%GrcS_XWQ6heQ*x6eT{?Z-}%(~6;;1H7!EURH9hVl=Ly%_ z`M`c*p1Vt6*3kKLUEoMT|Jetybd>Hj3%D$-mohueV72ala$)rIT1A+rD5Y}#+E)j9 zO0mCHE=eKVpX0Wuyd7>#Y%P2U3+r{`B(Rl1=7vWw&1BE%W>_|O={oZL6Thj;Y9#$b zOJvCY#P<92sD|Woeg6=PN77VF;MDvgDwnSEF}(~o-;T~F(_^YXYd#Kp`CsFf!W_0C z?zqJH;^5P1WdGy*dUe_lHrKrsNA^EP+x>)xWL(LG-c<54%V=A z9@CPTUUg#3RJbvy&Xn}?24{^{fy-z3Mi;{(RsI=y(r;W%eI7k!`#Kqzxl>n*tWVZm z1Fhc=uzeQU{j+}ZUAuq4p%WjBBJAnLsizD5>0@=x$?-rurhxkgZoXZjPL3~(33VJ*%zx;7_u2i#+^OsJVK1{T$3d8H z8F~`m|E7!T(XghqWtSYxOYzgpg414i9UcxduR1k8 zgVpAh4=Tg_u=Sl%I8=Aiy)mS}-}9(C_ODJ^g_o)@BdYhq46;A&y?s_47O}iytl@Yq z7ajwaik@|^hdIG(9j3rc*1JD}aGQx}oGvUJUG`)j>}31y@KjiGu7Ub~*j+iso&mGn zW-mXDT<81{;cVh#e>%wbOTWY@e=f{08yi{$cktT-tYOjB5fu_Ra{TgV_Aq^f$m$Cm zGg0HKE6E$!qx)c*yv^h_u(-Zq(I2>ov*?5$$-7No%i#On-1{_nC(KweR!0u@Xq_(; z@*kIHJ}`@bB^p(gB~aS_&)w{yyZW;QP)N@>yeub?POoQ4;{qfS!Hn>r(GQI<*bH{#u4U6-~g?xbp0U54M zu-9n~mK3JFI(X{^%<@#5^8;pvJy50`zT-z9%y==oxDI(+=f`z}uq5{@b$rqsw>y+} z3iEFg^IVMFLua>i#DC0qSeY^orl}=Uc}&3}ory5x%)^+csJHif*`@{aii}(y!RC`} z_UMs*x7>;M;n0?*;c?UHfN6MUr$ItcnMW(l7Tc8{)@pw(mzm=0i@F&A$zqQRhp=S>Z=3Y(yfIQD#_K5@WM*f~Juus}i^(8RBF7AO8_7Za9oMG-52kLx~ zld7S+0%m_0r8yMmi&YcK7%rq=;5Bv_?BBvV-Qq zb?;#<{{J7q7oAWH=^QJek_=I(tdxpms6@qdAW23cR2E4HqmU$vBqT#oN!E!rvL8N!dCdm%iePr>n%GxxiTXtC zGMK3^*zz6@)IGoQB`o@NL$Lu?(m87O2Ij8&T38FaeD(7E2y@cVYM42KV;EIeDE|f5YOt%lB`DS=RTK^}^iK2dVoL2hpxCe_(pWifgtcPvnKtl5o65 z@~2HBuAZ1I12fcSoYsRAcCGj}oVapssVZDM_p6K|ENb09i?8N=Odm2oOT~D14iaP%WX2SF&0mMH5aw+@7^9En z5ga~!z=qV1s5@!~OJ44EUk3AAQxsg`*64d1R+Ii_Y3DZBw`GIcI+*d#OG=KX_>iRT zjj&|c=b0B^|Eb?TZi1OYg#~2)XuGaC=no4E{!9|WY=uQ!bW-9M+E_|=jQ(-kvr%A#JF zdpGzFad*0-0$ld1m;DfC`Mph%C;s{3d>+g(l2VTM`mOZ%e_X2=Hz0x;*UhQvH*^}S zl)^N%jFP`(`H#LVcmZ=$7Tov=JG9;_dudc z<*@#hw&ks`AkMbn9z?Q9zDrSqOixke&w6)Qcq}K@3)U~C|6K<&N>5Yc zbyE4$mmV)SlegLOdnW$t9W-3%rwwmw1wnH(&F;rP^C4i zgJ97=ruzqYFf?m?FwDLaG>Sa0(M~qt?IHcP$&>V9iwqtsj5ufBiq&xF=*);nn0xSG z%0Ae^C)YlTc!~HMALdN&I1^3k*IXRD45yBncKtXkebeOpjN}fl*Pnrfz78p!aM>{F z)APi0b>_?C`4-J^Zt7*2`P5N<3bEp?E&<6m*<70jYu7DXd9bsY*@yH(>Ta zN!kK9){rqj6XwtG*R+R~Xvz2P!1SjL!ey{@>$b)mSU6bx)enwWf02;~GsF`k0!aU_ zS}PGOQk(TH6b{^@daMZM9;T}8tQL844Ve!SYZ6t5_yP@MXsrPd}6ANc(R-LMW1xEgqL!-N8-@!EHVyfT#R@JgP zm_tv$8iRV4_3bksVBsN}>OsA>9fW@|-qy1s^ z{^F!g(jRR{J)i7QnZ=UAv@eM_ILJNyLydZ1#te@yt4RHp%w4^(XxEWkM|jXDfIa{V ztm2#(!d$<%Y5!pQcBAD6upsi|_F>8JH>XH#*jLNSQWlnO7G;csnFB=!6k!(ITxkgV zzhkyt#5kDS{Km8jjv5*7uLesLzI@JsnJaq!YQY?hiB2hSn2sf5B1~J=e&q!0EAwHr z9_f#s8odps^W>uRiGO)3vSCRLefcz)v2c*dfNMU4Ney6ulj#LTxa^!sksx)|ja%pDqxj8V$Nrq0YZ|IS}Urb?^y%RM)iVru;od?s-&iMQoIsfzOD+^%WV2)ER ztjIVZSwvjCOzj?=^k&)0#W4Snw>T5_oKyVR9u{x7XOj*aAGubwoaFaA60gHK{zp|- z!n9Y7l-U>3rf-0y#qP_}kav$_Y2r&z{J+;`DlglhXS9X%8*XhQ)8jM;D(rx{p)ORt z%D`E>-SB_cM*`DHv}lrN`}qk_?-|s^Jqq)jf+)v6{hau}7Oa%3JdK=|u#?IKe@;|f zg#WufY<-q7I|Zg+>(3(Bi-AjrTGC8|y@5AzP!Q_JuF zuIkuh_`mBp_YXgQ6~Us2vF|fbpA#Ig;Tg;qzut5oW~I@rUy%Bnd_RkW?HwyB_8-2O3A=_zFy& zY0?0*yiZf(spotvSz@6ALr9KK{bfOMwIr`@uH?c|bv;vSVEXTUzfQs#8|}TtuxO`S z=3Q9DtUG`#FJobA?i<)(i~aU0l3UwN?Irb>W)>53jI|7PaK0rxemL$WOn=Y2V-5#K zGpO|w?*5{+4)!0-{z~T0fBkLu0l0GI(gRQ+vPYJmi+Lz>49AE{6ph3nCBYgNdDhzh>e|xAuPN&`x9~Z>=lQI zMF|$v`?4Bw`JfRj9T7m)8)PJGGa>a}sVQEl58b$vYX(cs`cm^#+$8R|BK^-cueC>B zcH>ioJSvnEkLPFFfQXsju0-D-bS;-OUt|dQXl1jWB2Vji78;u+X{E9Trw7%jLmz zM$NgEu)_-Xrb1Y9U|xeYY+jbRz7l55C|hU@$8?#ch+*FBshjm+>GA4QH87JENWDLE z*nEXn3$wRgQ>G(Nve~htp5&bKF$(bDgDt)vNxx&z(qXX6er>}hn3lTJcM#`S{E1U% zzrdoK7S#F1OnEA4g#~<*Bi+ayRC^|N!t9Mcte>!Y4?DdFW@PG8Ip>Jb{|~9}pF+9M z>+OYM0_^XdVZ;BSKBsr?W?7giFSsIu`5TN5G#dj;)Ta!NhoxUVhik#?W&57!!mLa4 z|RfpbGs;fB_x$40PVeL<2ToS`7Lvw%4VgQ)k3G|l!c_N0EpR%$%RINvs9In3Ci zoNR&mEWbtXU0`XwJGFgdQa$~HV5UbPbv@8AKT~Trsh2uZ`zOIs(t}Ufw)K`bRyi+6uEDJX@PUY;cKsU&=psYHupcVa*PTM6Sepa5DoI z-1#za1djQaua^V=cfAzi{`q_!>F;R!bON~xKEHed)5LcTPQ$!Mowa4ef@$j$V6J(N zVFya~zA)YX~5!p1dQ|YbEoXGc`(O{Q`(Q?l{xL( z`1vsJH9tWL8X1*aGmb^rNh4hyd3 zT)PkFSEo?(lT5w$@*Lcp@gj$boPOXED;!QAovT5Xm)lq{!UqlwH>xm(87D_lzo#N2 zjp=0lB=W(RtdXf6uF0&k20U{*@e^MBT5_&E2EP{%87}r>;+U z1;M+2!MxFbTgmgI+T3k(n@RrTZ}4uI<}t;+0Txt8S#5_y_XSkEfoYztivF-ub?cE= zFuUPdg*VLnHmA4>roSC&=?UvI>*ha&nL5v@`$@q+-?{==8p)KmLEc=v{p%y*0&9i^ z%ok1{nMM4nWuhT$arnx!J1|`%%|i=j6&^FV4KprF!)S2q8STTji2d|iTJbzcpB{4b zCd|zD&pZlXH6Bf*k3kZj6?(`JgC9cYT?MIwhDVGiNV(PQ) zVB_Ee71a4XQ+UJ>ZVk?!K$cguW~_-CES~uA<71f7y=Sd5+#rpJChNzW5|lR*jyGD} zTL??cR#WfGVyr?O3vi@ouB*zo$&q1T##WWnm5az5#f-EssL@x1fMc*r+$>DdR% zhT$Iq!J-MNkwI|0!dH)}K;aI%^Ot(>`-haExEb=7dAL~cYjiUbY{j{G>UWA92`O}|&cB2_hya@cppVQm+fle<9WDQup0ce5MJ z4Xg++g!#YaH@L&J3){cu!yU>6%(bxOtGh!U?7nv?ZyhW=Jbh#~tZwwQ-vgFz+#i++ z+v+MhWTSZ>G;E}MSL$o`NKOe?bcy#S_# zw8}H!_&wt`Tft(F)w5OMj74_$STKwAesDCLkg#>yVi?y4&lKROx5EaVV9Bi2flVUh z7cN{_3p2CSo$tUj^_PN;uy}3c(UY*shlj$=FyDTJFBk59v#NY6%;LSx^MpgR&Uo*I zIk$UmPk}r59#0Rz?3WjwD#8B0R^=XqdA3`J|9gV-?X1hL7+9hlRoM);a+#s0VeyL9 zbMj#Eu%2;eVNq|5+HKfaG?yiVV?1t0d?4YRfgUhSX%x3mKsd|=<&4{7VBjzO@I^r{C(2^(aVwyj)lvvzFXW4^DHJ;4Hn@1S8gvGg!#rL>|b!`{ueF(VD2g5IQ-{QR!C^n z&EZ!ueKnO2EwJ(4ydF82F>^3k0>?);1Sk>*D(BY0`L_GkD#7$)%HLnW9Tyl5bePtr zdr$<+Ui_Ie9%h!c72Jb!5?zO>63_23$be1D6kn*p{Ik7-X|P2J?-emaW$+iVP_68d zHmTP=WRni7=m~oCV4m;FHsUaqITgmFepVATecPAqYv#a$+0L#Hk@u}x`Dp>^_gGQ) z1lB*k%gqvIYkV&&hNFU?tJ=Uc1x4pdSbJPpy&WtJNYQ%>vtv6W>|ybr8Dq)%s@#26 zx|HNYa#o*V2cxmV6)?9<$Dtcea83NR8Ws#Cw2r{`w~%=)zn1j3T>VA%uf=@(U@w?1 z%q%m51BZvSZh(cubha#q{hxZj@Pj4o(Qkra;{-FsKvJJGa(5Igls#Lk_Oz^U{a|#OBY>q{1=lt@{qb%*&2tw_s1B?(#!0 z&ti<*Jvjcv(!#^A$Yb>LS1{YP=}R;$opxsHSJ?l?L*FAX+jY+wbrH_*19Lydz%094 zB2zd%R#)*TEY|xIycTx4mXt&4CDC)HMZiKGMFE)~BRMnfGI8^kePnrfoR2+Zd@B2P zyD-bN*VD_y|wQv(iT5={GGPW`^b7_Kx-hlRTG zFY1u{?~*#-fW;0M^FPAeC8sQJll}sw+IF~G?&rumu#|g>`h8@k%HO<8`VTLP{D-`z zULwtc@xAt(u|?S4rGHL6gxRf1$&+Ar`w4yd#PX{u7s84QE-om9C9hP!dB84VEW=Wm zzxdqtJ#g%m!}3+Ipi_^!e&O`rUHKAbJs(G1AGseK>G%rfeKPT@L%nhGo{rZrV@D?S zeWd>0TV`)y&ZojLGPs_rys+hUE$P44yhaU9J(8?b4~q@5-L;9EPOeA!I~0xEVTNwYsT^4JG*j~jap?K3dbl;pX+#$+(Tk$Kud@Anb6Yn| zUnMpo-`AR+T>ZKS7TMUOkB1F-SIc{0ZdyI{{T0t+=Yj#4d6PqZf5jGt5C2R0AD{Pg zM1A_d8`i^9u|6rnI`X|Vyr(mqsjeb{9fPp zX0RkwTip%L$nO5R5SA8?Z{7$8zB@Ez19Q*z+}Q@(23gy$Aocs|8g|1qOMeMGVMb@g zR`NZP%JujM#G+xdsPB^ke~Y>|!W^yY{dN6&G?u7e}{LqUa_1yQzz2T5ANp(>$JHj|_IqZLFhEX)k zeY4DIDa`OIU2z2d@BW$RYnFeU_(<65xyWmVPpmu*GfKXnRDoI7C&iqH`EwV5}hTv$lWlON41J%c$?g@+(a=FTw0jx82D7{zWkoROd~)D*m=`@lnH(RU>rSk?4Kr3PJn#?}J=}6Z2(wkf zS7pMLJ13mIOY*s{)c1Q?H*)#+V3z7{?d!hh@Z)+*s;-l=^+lBK^_{ z0o47jV1et-T;d(}k1}!ovQ8AN&4%d*7IKcl3Hf={@(Ina5BkB31DmMzVQ9ag?l)Dw zSg4Wp;eX!dvk-Y|dok579?89~4rhHb8G3-6bELv>0OM2CQLAcV_8S-Keo<=waweG{ z?cUuNZ;-1@s<$B9gR{Tl{3Dpo_7aor$y>2f;S`+jwe$W>Qg3_p>Q*?g*O1yDjJvl! zn8EQ8`(>|?`WY^C4OmitCoUP5hO2sYV|;ekc7B=!bIfNdN#NLpf{Kg8X5t$!Vg35O z4kTwyZp_VrC92yOUWREyKGgk(|5B5;WcuQj0gIzaf0dF>8p$sh@j_wYm8TDHz>@IC z5q@yrwvtPCV9u)M%GEG^$i0{x540Wy>i(mn?ZoK&Fw39Q=ZKuAlH)}7H+!?px%n_B zq4FcyUyK!jg)?DcO7(TJzxirg^=81*BqRS)lB@nZPJU1MukwN`NuKnLvbb`r=_^>g z(4cu5a&5i(wrZHxlgA>K3}_A!^QUgLAdZn)+WQfvANWo6=ii^D&;s+mt)=E?%u-Wp zhb1GXteA&-w)6IPKVX(*%{W^)WU}GTE?BTma|&5sB~#Ud{lv>ON0Hy_4!h2>L71CS zPyK$=U6ve`xrY6-ZT&D0&{(LcsKnBC>SX(Z{d z*E?rU%-qM5g`2-k2(u#n2_@TlM3yYEjm1wEEL?qTrWE#1cjhmKX*FAZw!vAMqb4qe z#oQ0VYTci%o$(p8vygJ?p4_i z#}Cc?6av$-?}n{`LwZtJVKDch*?wENVOU9a1kBDA+F8Qloas)7NWJAQ|GBXCfOx?X z;?Lm+XTte(L&c-8XlUuRX>fP&fq%zf{=~`hc=E+XjtOtiR4OyQe^=_XH zbVy!5WO@b`zNoWNgInL^zB~(a)pGsDz@hAC2?;P`U)MWDIN|Q(O^GnmBi>pT=IL%a zl0>OBPamTa@jv2RgYCg=@+-mt8Ru8Dk7s0}>ic8Aj zr0+`%OJPy>sk0Su-^NASWiZcmB~>pTA1hx4v#qqK>vgV!_P83DF|EbD7WLBR*BS3% z=C=1AByeRHC+8z9Y%VVT0;>n@AO97moua37!t4?Dd%lx;-Le}Ua5qc0KnjZ%eh&K# zXRu7zze)X0BWnF~p8RaD8l$Yk1+{mobQ=93Z~tpFE@atmJJI>!$Reoy0c;Ro5^p-l6t@0 z-^uY5do-p?1?D=lsN>C_lX6B4X1_f*))9H=shb{}uxMkHyAxb1Otxmgv{&X|RuRX4 z`D#S`>AbfW?0fgSIc)>=cj`>wz!epT5@1ewn3^ukJU7Mm5-j@OPMuHr?VH@M zzimlJoN1Z`3nu+|Jeo{zjzjeWm^(H1zzEnSpyS0ul1n3~ z^Qj|zvSuF4)^wuICwE%qghw!gdzU(XxMhDik74N=?Rn&Q6*S8miC})YTTwNfy6+3O znDn2&@BEB-@8gG0;s2g52`t~ZmBWG&x7Q1i$J|y6eNO66?1)T<4cz2b*1-5NRZfDl z{x04578V9`RTE)T$EmsRVSdrtJ?CLz;e7W7Sd#NFCJv5S(5fvV^%{Gb@o?bwt>-?$ z(o(BhV)OBep{+1eT{-a-tbI^d@*UxJo-M;i~oeLHTu4Zn`%d8Ssf z6OL&=y=er@QI(lOo@WW0s`ibB>D~D=$n$`P3BP{OVgAKg9|K|D=Ud@wFhj;Rd^@b| zSfH)>KmAb_`))Fb=X{m!M_y+3DqspM&SS2Qg$-UTduIs$w?0{!@_tNMux)%f7pHMc|(Yw(_c&C6CdA3BCq{(>8>3tQ5d}_0HzCH4%(Ccgtd2lU?Ia{ixaV2 z5B0pQqrgjX1uPnqn&XT-w4T0Urm)L%!UL3Fe7tdpC9S@xMhgp3+MeT6I&KqyG z!Gf~}Y0qKdu#hD?NdM!bPPuUUDiigcFw<(w=qy-!>w|T>VQI>C^Ybt_fir0z>3{52 zyayJU+0W#`;#uXF){@+3>-oblKgJ<+A!3!9#3u_YF?-8o1so_=mb63jh(Q>_9H+re0S36{K` z)>H&*2QBQo4D)|%dwCx=pS0@y6_S^=sHDOjg*u99#48nj&cOa(jT3Ld(!K`M-LOMr za{oP8w0tpR%C1)tc8X7qqd%fL;tyGG{C&`Kb9PWV?SP{e}qM=vK0@*o`2|B zjWAo)XD1JqT47GhUOQrb^EPH1@%Dk9U2w_w(1igaRUodxR8He=qUN4=~1@k2a&cqE~8(#Oo zoZ|;K?1018?c31@^Y-3U4}t|noX1s3OK8Z}Pu6BY0 z>ScG&95DlyGKuJjE6kDmz$P!J&6In>oU?-igEK!~A>Z=}xfZiBW7jL?Hh(3;d(2TxyF_B|4cmC19x|Mo?Qpi<5pe!4omtY zhBm;`PYdPS;Z`@%L~mGltNS*wMZv5hADHi7>)HX^y3Ibm31(f486s9%dekZira9&< z{6%v2qYw7N%)GC0KVg?A3LQL{5z;mK8*HpIjUEdN+9J+;g!}q8s>Kr*BxKaWO3Oz# zoQI_W>s{W%84k3fOR&(}V361;`P+#buz2QESyFFM5@#nQ{SKO5?_h`f-?+svQ%#?$ zS8B}Wzl1qA>Z$tp0(-yrFnwusS3T;RgLVCzU|#XJt97u840Bcs%*r&T`rY-8{`>~h zn4#42Xxn?6e}}nU!9mri5AE(_|AZy1pXQZtRObG;9+(ku*SZ4E+H+vV0P!C4)6d}! zY1w?*4J>cUJqf8-5kF~Fgn7rpsOe?Nvb)E?!rjxU^-=dWUN|0Rd>cnCPhjR~D{WZZ z`*7AL^fOA$!gOKD*r(#paOJwNOOs*g5nDzJT%xJ#Fas7I7+ppz$TFy!3v&{LR6RFa zUTg)6E}o^PN59*bZAc7)j*7GG?DIhzM|Ed3wb z(i?J?!{YIasJvuH^({8ZXU(Q;Z2WV)8!Yu$PffozEiHZnOm}TEAj{7bWt|R&`I(h< z-Ee5(n;lWG=qUTFEROGn;gi)*!OZUa)5-Dfq;)Xm9O=Ilzf%e38V%Pe!p>Fw1GSy((O`&+GYpQa|s376TS6AHMrBEY`Z{U z7S0GhWegi^n!_!F8R>JcEQC9@hpSe@v|GwAZApEqvR)l5B7X=TwoTQ{YJ|DRe`FD} z-rKc&C3)dIDi7?|@A(chx7>P6#+SkVTV}stfzEZxQGODuKQMcwkA#I>B>XA<2lG8= zQS%QQHEEdQO-z4o(tR`JjI%XKqhQ*}Y1H^3Jy-8O78d(%{<8piO}P?BgXDS2-^h4l z>Zwwz4>PqFP9w`7_I+uiF)VHAdO@6XoD(q<=9ST?_2Vh;|6&S@M!lrQv!vhhzZSwQ z<%QJx@GgHfUjz&HBwuw#f9x5>6kAxZ;KWfE*x&y0Q#(>`mQIb=fgd829btCz)b;C; z+kQ%0<_t^NH{$%@hVBpN*s#cyTjvk+Cp#@z1#`;c_HKo9K7BKAgQcI9@9czm<5vA% z1JhzI{SAh-zqe_vg}K-Gl)1;#cY6O%z0$IKy#cUz*f#3-N_e&9?@pL?a$W2`^mlar z=7++P2OeV&!K{-X1Q9S}UaA1v#@CCz4^~z{UhN!lVHKW1G;!)(r#fDyP}iO+B6We^9&XludUsn*ta ziJiS(7{e-|b!)R>rv4$XC2)R1LU#_#xUO}5J6w5kctRd5l+=xkgL5X23nHdvPs@A( z2R@Uha#rKBRXVu-tEG>na_%g{CsuGMhp9>GS>s23*$zwQe$XS+rq3x_nV=SN9zh;Y0mS1ACM=c4J|2#S#sgj z`(B|W@WV5h^EK>aA9AsE`OR`zvPjY74@`IU6IZ~@j;c4r+EGCls)(O2osGXKiZ<9f!hL`gD?4~Mz*bPJ&~mwZFE9=_|8L#=*8@<#sM5u6r9s zgZr}jFA}qs$-eoA^Q~qJzi=7MKAU7U05h+aD>=jb9Ua1MSp4(JKQ_rJyQs0UD$JC4ROnJ$sM>mWBh%0m~&FeF#!$@+{If5Gmg!z z_aXfU{e!(=VcDcl3t>~&O`A9*SMvBc0XDXO;N=ZVCIskpVLVdbY<1fQ=2;63Kf=wg z{q#1G{9}_+0bDz@deIhGP#++Z2s6&qL~escCpU}8^}cpm+Al6FT$l6C1`gG{+!73n zH)n+Dz|9vY8ic~MK}YI(RzD#*WiQN1G^DOK8g{P=-bZZv_Ch(XCu$c2j0-3I9je3b z!6kA0BL`rnai-=4*z}+FJ02|h>ydmC#@88wLnL3ePks+fAAPIo2+U4c^UDQx&$WMb z92T_y+&UkQ8r}Ki6!E7!$(pcw{o95!uw-6wQ*R!om!CKJ9L%!(UiuC$ksq*4fO!R9 ze&oR+qNa@(iI?5hOM(ae4Gvv`xu0Jii-Hwp44nipZELhm5NvR9@}jFSp4Z2$hnpk2 zuH1n6bAI*Nl6(gL^lj4b*K=YXoaH!&cbC-P+vz?NrjPz&n@#-V&`v`*P}%)%E-cP! z*E4{5uUUmpNdL6MV~ycdNkU`^alr}7twTE=RltI}rxm2WNlFy_e=U) zVe_A*8Dn7C+lG5?Oty^ zSlY4aV?8W=CB88QX0C|W{sb$H-dJP^Grom5w!x1 zMBdleb#xvv{~NV@Dmwy)o5O;-NS|ipiUvcg=EIy%%chX!%c${UEQCeRmgb3Jr-Zr- z7NlQ$WkoLBp_lueSeo5fmIm_#^j}o{yvl;Juwv3Ws-O0L#f3d^%}XUiGJR&Am%9_p zpKKpV=Fb_{=41dHFDO$mgK7J$V-(HRv#bYcvhL-G#3^I9=THj4{qB> zCl)9=4_=1J`@A`@)Zgg27o5}|sYBLZx^V_i7Z#Uku$eI5j1k@c0R4lTB4)zOqL>Mn z;e5Rfo9C)yWQ@bqLo`Q?pg#NJhh7QV{H}X%cU?&^5bkfh?=1?gE2hMb*%rO1^ z_+d7Fj}n)>COOS`#-r1)_GH;qGJm#x$mTV$O62QwGCJ~+4jlg= z?elchbDkDj(%`Jd6Sic3)13Yp{mjDeSFUuT0nAFS>S%|xs}9_oOgv4xpbl0v($Jm+ zGiE5amBO^$cEe#)MV&`hBu^~g+j}45QKtVr7R>0Wiu(z7kKMe{5$5+tWYog! zrF*)ZVQFKybp;%?!iT;J7F%x4FNAYWjeg?_OPtS0vtf7I5$km@%SQc8I_#pd_MR8a z-L^9QGHlV;C)fxx?;1zN!_D*QgIi!mwK!=%Z1DNGe-KRj=2W%=7JN~U+zInG)Gpf$ z>!3^AX&jMDrUTw>R zX@B2-VZ!hopNe|GBEe9}K+$H6PaPV460f?bBf zbg#i2lL-o^;EdX3XVPJA%wC%yQlF(cJOgGd+*0KXt5hm5?!e;j?(Q?;R*%>Gd$7o9 z^l>`e7yRUQHq6vpVg2=h~d1ZLcmQP+kAdpoa-U}@6b(rzKfr_l*l3t`E3-jWhn{h`g} zBA7P)U-o&J{Uo%PR0yl~Uxqkq0Ze@PE^zoiUGE2h$=3sqG`4%`P*8J8T~`Q``66sat9=XTKV? zKg8yBm4lgB{_@AsDH+)Q7w0sKVfv>^!9`g5IzBZAWM>tZ-|@{yziq~Cq-eNCAAcN}v+%)NR2+@IUH zKVlYy1;c`4Ze0a%!{x`KZKS_B&m5s5&nJ45eiI(k60V%@Z|egK9WD+x zB_2BPnM|Ly{ZOL;9J+YqoXs$=H=$n-u33D|-5+LO`89G99CdVNIywKvgTM7P;FvGm z@~tpmf7$tQu))pmHe`Lcn{KC%hRp@;U�%^|c;167FNyY~YgqF&8Jx!~cyJY?*XR z8l024tdfiuw1Kue{TVp^#wsN5f+Yd#3wvPxv_Je%m~L=s`8W8#@j`mk=yo;iKgVk0 zA>{1x!qPlgc8L=;K8Stye@uZZSC2Rui<~)5*(w%hkKgKX66TI<)`)~_E|?5IMV$2Q zbvW#=Vo`aTOfROdX%pOcJ-Rp!7DQg$Z3kDH@B2`>O;JpiJ9){&2q45{<15?jMQB9KesTxch0Bg$K80dyA!7GXnJ-AIr~N2 zQ3)*0NM1{(FLcYD{{aqJY)2ihoDMtXw{XMeLl4d&=j+QeU%(>oMJvg8$4akQ^%Sm| zX0TBJb2a*Imcq1`H?F3_oVA~RmcimSRr@qp5R`nO9Co-6A-)aMHZ%^rhk4FIEg{Tm zI@d<#Z(g@Sa-UdBmeB&6j(g*j1B;ZoNk3p?*(tVU{F0c;9q)o&(mj`v@rGA>B#nmk zZ`QT;D1+(8M>Z(HE;7?YpTojU7b`}==5r65i($e2Q5)%S?1q-YcQDgV_KP~4kyd~G z11#CjJgf!RzW=8!A#Ta6(T263__Te3xn++i#}`S5eSw7`kzqQ>ZLKvzTZ#FK)bv7s zFD-6^`Her9s3NBsja=VJa?u&#SeR>bM(_*fuJ_xi1Xu36GP@6E#j#Au@{2DH{2GMW zA8IcRgI!u~^M>C>zs^f)|ArkKZLJ9Zw|!*$V-G38f??ijU$K2!bB^vF1M@h`&U}O$ z=C1xa4rVtNJg$M=#jXETN>P{6g6O-_J4)QorP;LpB`iIJ-@Y)R*dn+=3IzdZH%5 zj3pxj65%La?LWFO{cybV30QC;K6o}f zVg8XFyNzLP-&$1;?DDeb#|)V6tUTWxR(b98cqYs*zc_p)tgZBbvgGV98RAxN)>o2? z7L5(O{RAX4Lw!*cmLMAxD{5>i*O|a*jHCBVp1yM!H0W!>tIIAcl$iJ&!(&2 zgXF%JdG}$SrONj8Fu!!vxpX)t(Us4E1!0St&%u(mn@7A!eenjHAh@RCPlPYb9kwQH z0X#TnQuij9dG0G$6*l%OZ3=+ty%U0@H?aOgoL50GKe;!!4366PeS9!1zOmE!CS2*T z(=j|c={^4hiz#*!}Klj0s{`mX=*p#+}A5QXvBl{d-!OXUZNSHn8 z_;CZ+c>E9hD440P+ddA?Ie2dIVVJh%km?Ay!>sJ&5tyetd0-$N^FOpXD+ZP>SDo-1 zHtySS{WvUik9zbOZXSKKg%7g}>l@18hI2WS&yo7ecbDIRRSw6*Um!mCJn||m`*cg{ zWmx>Pk$DPM{95Fj3QHDg9|(uzhi_V)4l_5(sBDM(YA$cS4GXk1i`T)i#{z4Gu(WpM ztt`OWwyxMx{NjQG}Q{`VU zWAC1~k+AuJsPrC~WBAE?7u+yyXXIa)Ieb&YTDW8Q9!uFwEbo5peU5O=x@9b7;+1#S zTfhSIJG5~y!!Yyx9N0gv++U64JL)owU}l?QxHimm#>lbG@L(MN~}y_PJYMbE;ww|I^Vf4?YH_`DV#K` z_vk!W=)U^-H@L>*j)xh{>il8a0;h9mtLMXvZ9bfOSpB)rH%nN0d()U_aE4NK>>`+^ zan!I7E;(ovWewx{@nRm#u`wO7l=SE4s@{VeOs?&6{vVgT^&IX>`XhCp36ZBqsGRnI zCEEK7NWHpvK|crPUR4{N1zQBV1^K|Nu^hPvaNox=S$|l1cVu@iY_P6#1TojwTS_cB zT2U1M3o0@OAHrELSRc2-;tP_z`>_9P`j0@EHtOo0J0#aQYZV0ZmpBZh!hOZ_eYcbT z;6DlRaOJ6vxm=k3HzxcDtQ62jnR|?H91M4-JZ#%W`j39r@P!%UzV9XTr%m3ez7lq5 z*BG%GW?59H+QR%AX~jn3(w<+lVfD$2qx)K)7kG}|3|Z_ex|z&^Ddk-Uli+!oOfF#;s%#ZD0T3LS@tux&4gtQ%(FMa(g|rBN5PzqHfsGC zU1Kc1r{etbyFhI}$*c2&HL#+|us1tVPxITd?GbEiIbD7~EHwX|o(SvDz2tY0%+?@5c@2m3R-ryPMI$EA zlGLw_;km)?&nva!U_or}Eho6;T8UJaym#EGl{U!WQ;@TvBlnrmYI( zFM@?D?UV&D%d=|OeAsh@Qu07tMqJ5M697(-fd3!f38MrlnHLSm| zY1r^PnBN$wst3$IwZVKOOxG>*S`Vks;vZ6i*@l0!z2LG_S0;>uS=xIxdBUyU3r$pE zuDOuff7J%(QQlYrl&9@2K-(A!nE<$^qEX$FtwMRwMt zfBhFLCTy-~kP_dGY#Tu&10$qa#dzA5=Mb1;?-TYmH^FfW57$ z7w*`4teu$E#lP4MhsMmPTS5Bg(mplA>QR$Kt}xf+9P1?<(x-Oc9p=1Mn^FWDaP-4G zNPXevyV)>b)25jNON0990yy@{4!unzU;Z*On$#!zCLFw(zFnmQ6T|7O@64s%Y`W_6`t`+MoV z zIXt*j#cn^$9BUqB345;4+pr6k7^T@xftilahPWi(@$>L-m|=P67ctlP0FyYRAUZq< zrae|)Js`k%OLtwgh2%2#@3p{Lml)H1VZp(ShBDZpzbAVmEZHGgeg}?m*k?huC%xi$ z!CBbdkA7i2%&#w=8Uc%fJ)U_Hn|j_1hMVbv9#5DPY8LGe`%jh^ll{Zh@(*4M>o0o# z$PK1Nn{P0KO?TSwT}AR+!3H|8=R4Jf%Sr#dG1jthb7K3TJuLYkUD9wF_fsQ^sN;=$ zB}ZKZH^?eIuthGaw0(aa7O5#ymgKLxxBGugKfh-ramOFqN#uAHWH-BU;Qx+a)|iv! zOJR$>vNy>5#EZ`8jD>|&Ax_ls`z5!bB^kdrgZ34~BBMKuV)(yYs%|^;0-Q6waVk0f zITkJ-qhN958_J9&K6>78*y>@_^x3v+Zri{vRqv^soBV%F-FsZi@BcsWNmPVMDnyec zBugQb7NLk1NffP=iquj%s8|O{QloTGSW0Ia9VwbbMP*WyiXnt(=!EclT>IYMzuV`} z`|a_(uGi~zo%UKtMbB)wvO{A#xjz4vFtR}g>C@Jz!0L)(4TTbR*7<4OTJ1qUWe7+S- zU3mYV0W2$}@QIuULe{M>Ghm_ngZ&P$IM!h8z2KDA zgSlJgdcm~#-TmX?oUIO$^GOtJ^66I!u6KI!ELY_86;JoRf;$U8zasmSzjOTiyKu17 zcFFz|`0bI&hRfXsevo3C zi54(t&b1R{{Al-IZ=VfQ=P!Lo&QErz&*L#L|8rFI0a!pk`m-k)^S|!hMsnWJ+T|65 zaLkez$@5aH*A5iIElRaY-pFZk);3RwTpvc?FgwCCt<$1tCSDSU+bKG3Z^#)s~&`Tx+ZxEFt<`I z(F*Rha#l-%MQ>ydt%sx9hnJ_4T+3cXA5NBO-;fUTs&mgP!GeMnvRNdLwrLzn!g@CN z*pN%Sd-Czuu)?k^;9~lq)eVvl=zXb&73pqwN@3o!7MuI9ua$jfB}_}+ zk@WyhW_8WH1B-`Mw$u_k6g%IA8RnkrNP9CWPHPR!$xjV$f(>s{=RSa$)4GocVcFeR z$2P)3mknJaxVLuOk#^GF+4GwedA{Gb8s5T!y5hDOu-U_PkG{ZsrHChVxWFOl=m5;y zTCZn9>bLk@GN{J&Cmp zX2$A&z6h&2d3-d2Spzp{S+L=iz84!|LC>SYD&o5ZUv|P=m5&;)-~yNDPWCWE{nV7V zaMa3+aW1eZGyLc`cx30a{6nPuq80vQv3}*Zb_AY)nT^WVXt47UwZ<5d-@YZ*fY}LJ zw6id~ZLZ#Yn6`y#bdJ=2`V*)PQ^uWNbqN-yaSHWd*(m*)S){!(b8s0fZPs2}2vfgn zUR(|iSxaI4h{?&fV4-7=!xFeR^iMW6vw&-{uw0kqi_Rz3z?4%tZgkk1 zwzpIWvxXw-7s0_9CV!s7ynnZ56SpzjrnbSNrBX-8{q?%01->ME={)Q>P<{un@ z`L@^cMqkHz_V)D^!F36%@5#Wd<~7V#I1T@JMV`3CKB6Ah&=~or1apm=t8!p2eV{@W z=DQZq<6vg2ht_PE;eVj%DD3=N<%o5 z4vtBkx)>IW7B#Ph)4mM7p~KYa?n`vwVneZrn3?qv-|Pu(M;5|Dw-|?e$W^oBxSB9`Y4ph~ICz<|hB{37Ge0UG z*5)X>k>_Ko7mW&l%`9$ZE5mH50olE9`41giG9KdDQ|9c3qe2$vP9V9H-;YHwYk09} z94rj@w^aqEJX0`{B`&@`P6{?WY?VUBo3p`hLw}+XJ|7y)#}dE3SN{yw+U*=H4bxXv zdR~VOjXJf-e6cDOe`msVJqJ#XBEF_$9uHg7E~@;+{8Duji{Bw_Wo!Sjk zChS@@2-D6??Oz8sFyu}9iRV?FSqj(9ihlSV7O%1OP=?d|UNw>RBrtHc96E>hIjx9M zVwe{){%tEP>$QB`S7JR;QxR-9ntJXt%*bCpE*_@1{2J(i*)p{b9x$ESr~RIIYoIO@ z=H$HD`xd4y+xl?@oP1TY{uL}~IS2Qzog zQ9lOrW6zwdh4~d3n-9ZLvhI>xd}Knu6Rf}GhYe}Z(EA-_4O{!T-fM(~p{C=P!vTZq z#yx?lQ|lhhBEGddm+WtPz@$U+aEp#j+B2AG_;AulJnY(k_c_c}nI1L(_mAo;egX5I zKWTdn2ef(Ubi$NZyJj`RoOeG$x?uLC@2XYs@W)(B$@u6dF3p0aE4_z^xhF%jVqn`( zriaORvVC=}UEuCU^VM%)e!Xe(8raOaCXdVqzc6FVG+3M^T}9@P=^g*$$5~vTH*$o` zKSg##u^n!Z@tpO8P0)0FFI|K+#a zC0Ki%nlf4ctaH(!k#K@{;KNC<;PwUo!|;FmiRrZR_kK88>h|85$QjN>_xHk?mBv}* zd|~NYH95i&!GKy1HAyj7WYZdEQFFx#BG_tHUP03+~~Ss9M8(!O5t1uy$D1 zfIUnr@qF9^)Au`aS+Hm--Tx!Z+_TNyow&e2V)jH6OD~w6-yrHnEt2fMRiTU^j z=Ek<}@*{aq*RPi(4_aS)1Qs4PmG6LyQ>C4b!c^UHO|39%`l9C?n69?uL^C|ne}5zt z<`g6+H^F9q8}3KI%s;>G)xg{xv8*UqSiEX|HXIPU#Qr2qKWaVcG+eHm6$kS?jygVpqjW6G z&cK|{-(qTEt$p+VCcvWNaW+?BcGaikWRmNg&X0sgF84X6!r~9>haF-5(VfGWNPYN( zbw+Uc(zE}vVP2YQh9cb7w0Ffdm>DutXCMyyQ`_%R5pjW8Vi&Ca^ImlcEZ{b=p1~2J zv6_`6S2?aCfV*GrKUf8`mMu>!fh*ruINyb7wP6zrVfz`0`86=r;fh}ptfJa5_yFei z26TDA5iZXfhy@&_u|{xDv{CkbSoGqEi7N5Gdy7bWVQJEWmRNi~&VSv0kJMLe_b-G! z6c)%5^956CqG8dZk>kYNZhH29*mT=oRy9n0ly+t*Y!<67sb^c5jh_xDhdCsX`xl0d zlz%^Mg#RBn^Zp%}QP|hq3o|ySFiJ@t9`08R8zi0lo(GHj7oYQii}xwUXTYMVhc|76 z+4R2)F2eNEV|IF^zA;-b4W>qan5zw|c%4g6Bzf}6Mar6J`%E88=`JxvNK9i0j4=r^5W;+WihNU+i)5B+S0~ zPKlVecql9wj(HVkNX`pZg8d{m9HpbU-5I7&H6P;u_xI@DCg%q||NG@F@c+M_{N1B# z;gL)GYsmS*+THqdCCuDeR^dbHy*0-#CmvgLjhrVeHJ1=N-2W-6Cm5#L+?3OR%ZJxV z#zQd8)kh81(E8*}_8;3$dFgbRa&J1HoCm^Vk?WOUQOozi7+4U0GJ6Wiy}o=T`-{C+ zpFbJyPw5yN57Sru-Jt-xeNq3G1d9(|`$z2TJKv87({}CtO8TQEmWPu4BdGE^Jq@<- zYx`ILbN!i;+@jEL64_6Jo)F3PHToj2@re)p8&o9qcIM{AFs*37TOQ8YRZ&t7QEmwqFRw{8m!y>gqrn0bEz<&QPFngcRA89zVHnHS8Oj)f__a_?b zDRkQKAS|9W<;@T*I^gF!>IPg~dF%sR^L$E3b*7vfM zcfqud0U=3#MG(eNGk5Jo0>zi+=C1yz#QzyeM-;C>tMe|NZQ(?DdKeGiSKi;hQh73*rC9Q~0lP-WfP}$^0AS{upx_p9RBa&+f-pz@pYl$K52q=OGy%I>lUl9o!Z< zXj6}zui90m4@ZnqYj{Mw^J}{fsaL5BZ-zNX_p7PFzRr^zU%)i!9C<3N`g1}3D_EH5 zVJ-zn^}e@x53`GIEc)HSJKo2aqpLG2b?3RCv+Y5`{nlBZ>{r{Rj_rt8z=(1`! z$|=?IH_Z5>tb7$Vi&$VMRfPUsybR95%g6%GAXe&}EKEDV&KjZ-@9(Nj>fH2RS&P%WO!6TO)sq(%!Ks;_q?n$76A8FOxj%)VtrX!4u|_9GKD2dT|&IcyzRlPwG2b z%fG{Ir$_3q!=jVACg0%TfFqBJV4+Nq`d660Z=+!u%s=$G_dRUJ^*vGvv&xO*TVRIO znwQlub&L7dT$q^>)mj5H5?j|Fhcy-i|C9LL!asJfw)ewD#8mSo^Nish%9r;7nA5(I zIuv2V;EC$J@4(y!y3%7&Y;e>)KNai$Z3zB4B|A z!>5?EUpi?+5llvDv$T3<9TRhTnC#D|$P^bg5-n-l->r3F= zV=V`#JwJAkm|l{X-W`hZlpp;;G9Gmm19>pN&m)@j&lT>;2#0xDegKM;kT*-Veax!PhzyWFn7s>c=yJ8dvIrw~b&wWGY zgS8}H;}vYW)KZ;C>YWpB3gL#KUkff0r)NKX2$xT5?Irt1ocqb)4xBvB+9wI-i7&WS z!rC2$5oEuK)_5!^gC*bJVD5J%y%Lx{>dN=iBm<#!&$!>qWX-N%SUO`&<;2%{ip)5pu)Xw3EcVE zic8FP*tudF+~CaGM@&`EVMxQ=zg2pqKdSzhPSG*!XWP6J#PsqzdW~?mcKmf>QRWew z`*1;z?{FB*dHwlJCOmx6zc`AtKiQZR478b2FCB&ds8gd z!3@Pg$5e7ZeLJ_&VOmj2LJG_ZTUet6>+kYWlgxKT*~Q@yd>^XO`AeQhR2`e}0T$jH zzHyd#)Acv4aN5LLSMvN!rql0A*e$uX`~*yIo}POS?p9dzFdSx|iLeZZJ7sD|LSfpe z+PlGUz}u(}GX5O>5<_3uFlmcqKB;=}Hr#^mE5Sm{l?{$`BUs@|5);?cFYZTTbfLf2O*^ zLa%wtwP04x=VoV^I#FhkGCa~6HD)hy()^$?aLXyPZWc_T=db%4jQx1&(1qQwSY2aq z1WtZNO(f=Zu}1%a8(JoH*^>4;OC;@`DKV#a{Er3O77Df#mlrLRCjI}C-n5N4$GY$* z+Q+1?@cfxw`KH+%kMs zjeLG{jwkEK!f9pg>Bg{dK;_6$SoBrpo(ar-HR5#;R^((WkoK&6k**uuovK)B3iEcV zWjez4ZN~lPFtxn)@)kJ4R4Q&W%(m8PGKCF3H2!14++(ikt6-HSrXJ+^gcCP}&|!Id zIiVfQ8*`(Y2D3&~Que@n%Z=KquvzwWCr6l7HU6s-9N@j-IT?S!!I8qra0~n9S~r+7 zzuH?K)}Hbyc0b8)PUs#3YZUESLgrUEI&1SNnDy7I#v5js)TH(W;eM<__xi$|nTiuX z!ZFh?gs) z9}-7gaxy3Tj~%0RoAlQ(Jg1wSAMBJud2)S@rhC&Rm}b69ay{#Xx9+FIjOAfXLgZ9! zF_oMr+{EXv8ep#9qaEb)lr9{dQV*-lxEMj+Z#av0=G})qbly0Y!@T%^H}1h{=65CU z7qsT!z16Vf`!{m_rn3^~s0V+qg+)tNk0ivMl3=PTnb+Z-JhhXl$&64XSKOfNj z1M?F0NX(eNY`s)5ywY`X6Z*4A7S5A{xubn*s^H#T8a)$;?Z=yyz|^g*5e1mFyl+k( z?BTB5GzF#yS}o0j*$-^{rox=5<*YQA<~sW2G?;qJy88?~oHZpxmDI1+Ty+%gA8gz* z6BZgp+V6!`5)>pkb>9amD_FcfiZ%TuMk!67TrMneBmxN0L0}6Mq{WWDMIM9&JPJ zPw3;mY93rOSz(1b%+(P6o(L!J=MIwb<1Ps;8V<_(g@v>#hh^o*8kqOhA zDw0RT5neSi)-cPO-AZ8rca-j~6 zk`6kz3uet2`{EW{-ZSAlY0rz@V^Iu?#cL(~iT5tKdL2$jSuES)5`HEHtzIm;)D&p0cBWzh-%=%CP3oWUQJh-P>wB!!V zjsKi;4wjA>dLn@NW?9;&;HY!@KdMQtI&ml*j#;R<@&Qc0*P7r1o37obTL-g`rrLPI zmFnBi6N_?oeei%Sm|r;cFsDvW;11in+}-*R7P_*9Zm@@6bQZDr!c8{u@H^ehk694xl3{%gCz1{)~HosSIg_#?B53Yfe zr9#Y}!NLhWCk$c6^}k8aVNOL*)>6`*clJXY@$!-y9ayFP*IkkepI5ag!<~OeB<8v3 zO_zl!X(nRQA3M6xVb~Y_X*j15GndMF_rv|Mw|M0G4Bzg za{E(~FK{!t5BnawIEt7$`)One%ss9qEpfvdd*aH4&bh=){tc%*xVX4d(m$?*l(_ea>V0#glNt!3Vbya)nuB|5Fyr1n0tT%4I_*U{-y9XBOP%vO6e*IKS{(G;DBLG%6To$1qhQ;ehdv zy!=Re&%Y&yVWwi7oi{8Jociqor@f<a?-(?b+Dlk<+dY`=#EoVXZmmN_Whr!oI09B-0qq;bFg38qz7PKY=cUPw+r4l?_6{*QA4N)$vp&HQR|9RUVEV=Z za}k_O(VR)vKXvSpI3ZkIi>%0bK|TCC{v6y(XFee3 zi%_9yI~VRev@5P1W*%(ziiTs#yI;P58QW@vCt!oWo;sbReOJ8haky9`JMbm(In6iW zu<5tB#Mdy}KlE%E%>9+<&<#^|#Y!BoczXN0|FO00#D*T2^CqJp81=#5>{wr6>Icgu z-Z16ageYS1rW_F)w!Ypu{0-(FZ%H`_3nxYMhhes~+5Nq+$WQkDKT==T?X(THH=lAq zwglr5aO=`Kc-Vep={T5jVg7X!IL&nb!U-^QL&Tcan2f4b?4zrAO9Gro+sJLM{cajG7Tg%qV&9 zBVyxzA`V$Dh3U^0E+~VYM?aag5*D^ErpLj8*mu_qNqyGopdGN5R-TO!sW%yLQHQl7 z6?I9@oZDeVfooPT>{~CuM0S!WOZYpXd{R z?CcAIRZf-`>%xp-!-aOlQ+IwK&%?O>aqSvdRV71R8x~%yYEp+~>li5-#9w_s$ibZj zjwffqEZ2p{e|qBn|0c{;ftj?n3nG}G&`_*MoT|9;Hk`~DT1HHJs(nES8lN!BJIVOF8#5A4d*tprxUN*w{Zz)j2+zIuE z*OB&Z&PAhOk=I^14VX5{LXoU@zIjiWEUcP2?#BqsJ5~0v>i~R3uiz)y5AnN_N@1$Q z#N|UU>-o6^E*#O=eYciNS*x;1$Q!&i5oR+&9rn{y+?}NFa zs;lWR)#TrGvOhWe4OuF1rnzu2$?49C@p5p~x{`2WzOtU~&;9s5I7Zou>|a*d(c8T+ zBc-o{v==%hZG8!gBYwOkrWFiybiuxB=dLF0DfqQGZLqE8-ecr`DCdV29>7|S=4n4* zR>CjY5?DIZY1JUi6pc5^g)0{;P<|1+>zqo0yXVfelOp4*5IoNprse;6GzMlF>)hW9 zvsurZCc=VCma*nAYfJGHavlg{&lKvwys+FdvL2}KM%$>unU~w|%!N4>8J?qItx!=LCHtR~_W7q5 zOl@gtBm0|fktpQ}moL&yF@Z$}Tj%bB3*ukyUI(+fzUJ7$ElRgD&0uD zYh{x5zpcYc;Y!b2EOLG3^P$%~IAD2@u>~xk9@>-!t85v$zmc@p&X-Pshqa=1t|#@i z$u5a-O?{^1JQTc_rpLkE0xaJXs!lMhzfA29`Mja=Xa4kshaD2W zjfL5JCRTXCl*yyp$@=7$Eb{e$>(uq!$o{6=S}k&ish>_rKJR!pwr|)A(>s1cEfZK3p<-ch1KENKct~d>*r!Sg7Eav^iuTsGLl$+lq z?+>&&J9hfR7N_1k>4JI6xdYo^;oi_j^1i@%-SmACoc!TSHpvBY^pqJe*KL8MKjyT) zW#eIw_rAx-_Y=B*+jS|JRq-;9y#KJ){OOZ{r7Jxo??=4mRXP)3UhlSoDw40Y->L!^ z{2E`|60KY zky<(Aefq!gNVps{FNEZ}WeGOOdmL6x34poFWMgbef7-$S_kRB(sbeQh|N3ny8#(7} zfZ-0(pX+D7D=ZAMl=RQtVDp#-GY{IUSRgkYb6xWD6j6Zx6EoOe!*0=b#8VEK;Q1v)Ty)7d;_SbCh=odq!dU~sP@+&2H;_PH>%CA(%atayk!dIo7fX|oxb zPx%*dD^+0e^OT*VVf}g2laxq4YqQfR*zni5;VG~{{_u>y`*6Q!|LRPJDGqD9hT-0( z(2_|oo4aE5Ak4Yc^ml!i?X4WXR8(DGNt!%!12n`XxVq7N&if zn+ivX99u~}d-aXr6R_Ett}Ud0*299Kqi|*Mh{QsJ4}-)73oZ|mT<~>3=5YlJK(4lbSYU_wD5eu7Pw{S zHve%jN5v*}J?z}VVvwJIvtBJzS`GKiGCVZHfza`_#d1~jg7?!8M3?t)B zmoBo?f%Uc4G9~jNwc-AJIO=uWhN;K}#ka8OxJ+<6Tb$$fK?q=`_F^LUX|95;cn00lNZ9gNwTl2VYhwF zn{;8eeuQQbY?yi1O`rHh4C5*++!@=x9A>0^ypaX>oNXyJhMDvI((~ZpNu#C6dKRmU z`<4&OZ_(Pn0j55raj(HF%@b{#U|M0K z`iTisrhG9Gz#6ZnNPd1Q`1MJ-3)WwMR_sLT1J0iM2&au|pX@^V|Ge zQqo>S$y|T!T;S4y>GkKd1B+aHgAw~C(S-e z&NtR0wI}Vctc68o4lGK&ys{NGO^%ewhba{EwNGI|#E?%RsXtvPnXk^5iIShsurI!d zx`(`boXw?bF8z?<#kg8@b}_8)9Zsv=i*CBt2U4dD(umjR{NseGPKDZAa?@*#5#e$@fwIOsR2d zaPb!h$@{42&P{D)*v)!voaFuU)wgRuow1%qW!jVPr}SF;7kw~&Q+LQOSe(u%?S@Sk z=p82S>&(FwXWQYP@VKSq{hn`St5^w-Xbwu=PkAfjhEn0Wu|X5b_Xol8A1a|R?Tz^{ z@;*TizH`h5?$)|utN=5X#2?UyMgGfd$@d}J%K7h9V0l{`$@dj*vF+wju%b)XB=SDX z!(SsB*^7F)j6535TDoqn7;bQ?SDOQK>JKY@fNSE~B=5UItw;0T!ol2$c!*!73s*74kky3#P3M zhbv?1-;wuO>Uh@j-EhL0>0P!YcXpv_!}_W}={sS1&HG=2jyPX--jLb_bJiDh-h=W2V;ixc^8GB%6VONeFth!?PI$~;p%Z24| zOy<1=2bg}LxlIX{@4MLHMB2ArT09ySAO4f$OzLNRNFQOL{-l2YKH|H56@#$8x3#-F z%-&+t_#KulTpQ^Dvv%8j=!N@@U%C3gLX*zx?_kl>ng~Cb(OIwX7G?(Y{S1Ibr+jC; zhUxz{*B*fdFLnpNgjH>Q4MSj>Tg~YgFk?zxUj)o^dvdo4t~}Kddjh89)*3z_?X7eF zMZ?1B#VQqWz}5ZdV@b~X9bF9P)F0Y=24>!PK@TI#EtiNy~ONcvY< zZu9aAv7@iV<=fID_%O5Y#lBqh7nMQf-G=Ge6}4Hg$Hj(~cVYhbDv5nxXZzN|j7^)A zbC8#-d^lGR3%_^@GhxFvv%QaCwwG-CWw^%q*@8BhJIDFg30P~(_RMxzkdsgp0kb^= zrgg%c!aawN!7TyIZEs+PV$CQYxcu*N*AKAx)qsx&OnqV9_KDQXr*^r*8m@Igy|5@{ zRNr1$Rc=rE7t;RGkfkGBtfD?V2-B;g-OONCF@43DQq0G}`6HUJ{lhuhvas;>sd+PD zcAUK7M3{dr>6t9de=)OZ3duJob&4D?9+ubNE5qDOrLt;bL%VKbUbH)nn6r3(k`hd- zvDfy5ne99Krjk64H)#t@p-mVj{qd&fhD?M-KfRh2U`p#(`mSJ&S^<5-nD+ZKP-QFy5#;SQNv~XV1teK>lINiEb#qf1FNpjn@O%u zW$svMPJC1?dK%0%&Wclq#S=dLQiYjMmqq;Djq~QP-Njk3;QfcvS8&~ly8$yv9$Q~o z2GjPv&>&{ZmFJv;StWb&XOQ~LD(@gT;K`jzG9L6n5zQ55*M{bh=Mm~$^WO&dyS0eN zlX~V>?n+p!sm_*yMHdzatH3d5gq6QAUt*abdQxzkiQLY9m^-?V-EVKi;7vQoBznmECe9dc zxctJp0v3!aJER8lFIMFc^JQ9QkB6-{(fmlgux;w35j%|k84;EA$8LGD=_~9!Z|;(N zFufyn!biAx=yWT&KKI`^wRdpxcXM_fX>Ypq%uATJbZ%Y~Of{N+@F}cOQI*^bbE31B zH^2tLON(E@tn~{+D&g{OHO^<4lJ@daF`RQfqq+|k$L?Sj!Bo9DW61c>?_bQh0dpqZ zdr!uXejvNL0M=f4=iF$FFKgO+{VbS2P~a&K(-!losj%DSk{@KfP%83HohA8_dnPn8 zAAXCUgu%AkLObWc%*ivCaA1#2c{MGVCljH? zQ_hjRogLW>JHOl#bRMQ(jpjAL?SG0R}n0i!Q6FhBN{!y?!%&OkH{rcA1u ztp(>)OHWXQSyJo9&VY-ZX1l4v)F!_GMObTIaJMGRJYqzfd3xr#-V+a|q@)Egg}Ac|%vE z{9&Qec1eE$GnuzSV3EDKpeR}ww>8Ll=pUpo>tSohnJU>^;{mCg<@bU5= zRhT1Q*OCA;XJ@9V!5V2HJt?rzBS&IW&o3L&|HnB2_d~N`?ndV;Gf^MVDBPF_)7v8? z{n;wERG0iuy*Ti_V>!%wA&#Go`p#pr4{yVQQ!;uQa9Vm&YYoit+jwdYOnq5BZOI}TXE-FfYl0hnfFW#-o4jFu$={y%09IW6M|oQ!*06 z3t*~Ou=f&}cEjvTHr$h-Ie!Jrs2rL^u4g9X46cMl19K$%ms`?(&k*K}G7d{Y&UF%% z8pAx7chO1k$eD;MYhmu2SD(rL4OVQm-2@Aj>t2V$luz@gF<}<>=rJES%J{&d?J$4R zzn8n=;Wr*1cfq0+?UtsnS?i9mdtlDAiIVY^S1o$rMDoO`<=Uiu!>VR?k|!Q+C+ofP z=$toR#KA3bJv%WT23Z=uFlEXq#|Aj+VRl1SXiuKbPi@;`<$u)M>P29C&A1_o7(9x^+}j^GR&bK7fHbuN~&~X zF@8@laa3@nQzFcDUewiLi~jG8J45;t*-PIP!klGEU&!@YaSxu9!TSB*{>G8~XX5R1 zuxSys?<7opPD%EM>!hOIhQSosD-Ug9!@QCBP*}MCOw2ae;AFP!{CeE!R!ZzTSH;rHuQV?Mj+v)fXG_{Q(2gI6746jV zwp#K$7R<8TR=E%^FE*Lu2=iJk7b(E}87k?-Oxuz#KeuCkZVvSmb1fISzJ%R8=QEsP zO7VBaGPtuk<*f_MKB9L$9O)ntBnR{t)WllCFMw0vOtCsFJ?SbuTB7c!qB1xt51Si`%tlsF(?G)N6vN;!aZ_d9v&t2awq1N!!`r=;| zt9iiorbCet$b}Va=k0*Ii!O#8C;f@4JT}1EzhdMfNq>J$ey)KvrYVgOi}YoFu7u6f z`qxCm+#HWfT5yCus114c9$n zzpy7w(-aTDL?K;-;C8%U}`PxAPus-`C+@L2{iUw=%dq?NC@1%xQo0CkJ+W zGx9_L^Nxhm(qXD`M&n(W9;h6h26rwyc(@v7KYF(*2~Jp^A*dmFNxexVtjHQU{Qzca zE1G-5l!0;1#0;-B3Oitx+eU?Tus}0BVFk=TG>+Cl>OXJXHxuSPOLP*#w49Gq2AM_- z(W;4^Pe?woV%b+X;KdzYE6n(@d1(hs374*ZPMntdqzoR`IRBvyrt9BX!TX<_?s_^h z2o}HeJ@kz9XYkm^17?@bxY9!EsU7OJaLw``i<@Ci)jNw-uy3f?huj}UcxUSrSlAMm z^$-?VTP_-~L_Q&2V%FM->F;6guQL*}*~`jX;p9j3b4{pc-Vu~m!wK71PoKh!mG&it z@PF6mrd)rJ4^vuSO(pkF-D;J85!PrlecKLmE;Suag86*&f==SkuX0bpq9cB@U%`R_ zhU#&+=Xu$h*CcQH*>wz#O4YA?1Jjl*PYQ+^MUh3_Fy%EjCjf3pJQe%_7QK=4@g^Sk z(flLHYw|ihU=>BNLNClZqL%Ffo4r(x`U=y|_15o(MY}dV6~lsUNxj?Q%&F3jLol;p zbM!j6W@SL*Z#RoSaG#=y+E?u}Ve_X1F}IUQPP%=V;A_DNOutEO1&`OV3o#%_qr&rPjCD^BR<#?N2jF*c{*_^VrWg%uT|EYf?Id9m4+!wl}{@TTR#9ZE}S{?Ym^GK}9T2D;x=>IIqw=cRd z9cDXyeMzp**6pJI+=%`9ZRUb5m_t+B`VyACUn<$Z?8t7T=tj#35!~5hlH@b%cGh?M#uXf3&5V&E| zeFaUJd*0pG6&5Ua@7IO}DV2`4u&iy2`(l{s|6uxdn3kX#rboO_IPMtWJU4}cX?%)K4X-d z4J=yYzilGyY*(PY3#JAw_Zv&{+NbIEFyrjOo_}U||Jit>$ALI(qQ?L%;2sL!3sW2p zD7=MxeL6K=V9`AP>=rm}QO#O6l6N-O-Gqgr7Wo4(>${y+CQQ3-#P@)CjTQ!{;kpUk z`3GVC^aUCRVD|p#Y)@F+rtfA3Gd3Ayc)`@SZ=)G-@W;(-4#AYhKp`ELJ{!N+7iQdN z7R-eW`vvKNFt=%I+c49URdl5=aBZcVXMEvw9j&85yW2?c8XxL zSvt2PVczs7>pEdA`dH~`Sm>Zq-U_<~Gc03ZK}D2X3vBPnT^~#Q_*DH9STP9N*-$IrVegURh)MmBAlHave8g7#@kiAFR8<{9wg*EQ1tf_-(w`?xD!Zo+H z?|lR_E*eI!gB4HR8QVy5qmI!FVEdh8wI0J_C058pm~Pm5g_w8C@@xNk?B6)wHd4=x z3uixv3l=0t5c4isuBm`K%|Aso5LX@H=E7}`i@%cVbBiYAa^VD1o8oGivgGx^0oeAZ zsXBQc%CO=dYq-wMbYB@P4&(c+f+>eG{KP*xx;$7g z_RUv$*y7W^>>QF)&rBWzGltd~XOLWOo_F6m%>R#>qb|Zi{_izUVeRa zxR(F_fBd89pqPYIG?f%#Dio!4PNGFAiH0at7NMxD6G^cW9V9~(l1Y&cOb0?~9S|y$ zP?Ux!N&Rk*y}z&D<@4wDa=YK3hsX2reD1mDbF#zneIlvfFtm>W<3?~27Z&XuKCcB= zi;XQ$l3eCw@`yFAmkSjI#5Co~!VWk#E_DLwry2E1HNq~xA4H#msdE+5D&gqYdb+1c zo?ob540CH7xaqKXp%p(B{%?QSTFj?Ou(H!xtBc54b^A^Q!|o4)<1WK&TS59(n3c#d zxB_#w*hzcA>SLQ6$awOdm+iBGrT6vZ--Id4-R^0^?A8U33rMb45F!r?RpOHGz}yXY z#}8Rydk)MgCdZG$FniqxH>a#xPz*D_UYhX{?%toFQwq}$WTv&i)Faa+^O-U^aKkG& zc1fN0L*(q-BPXB1ypd6SGM||--+7NozT3LK3KprJkgtH7%x?re{yz>ft)BE0=8dV} zQjR=XH`9a6Pf>TMr2fD8&2FK3R3cYqFWBBh>aV{JErmJf8(o@VitFS4+px<1KQSVh zNtth!1=k#Fj_ra4n_ezUgc*;d9)E-BUEZ{4m>W7ast=}Zqi6(@ety;T0azUROPK{% z{&MpF1B?2)=X=1Rwv4zTl6Njlv4oYE)Lf(#WBupHM$uruqchV-!-Dt|8%M*r4pQHU zS-ZTScUodT4o~ip`80S zrovR`dG+lu<@mLi2Vl?r-9O*Jf`5EfSGap-DDySR1LYD{!_3Vql*sYpjG>!?4Q@)h6;Vm*d*iMv!Uf8^JIQ<%Nz1&Ff~f;qmgIWO)Es$D69m=U{ne-a$p zr6jT@^&x^=fpFI9_6z2)Ff8z?J3JKOT)POSSf-WDgt?Cmx6xsyaN3fwuyu2i(JHwg6LFqa#J~l5;m0K34>G5J+zH3W*hZ53Kl*1`Q#aUm-RV)}`<(E5Ws(&v z+nzC=+|N_DO&?kmc{Ba5;lLQ!^Umn@c;t-p6_W9>zvD6I2uz6^ zE2;0a*s>%FX8mrbkmYrsubCJL(;TL+mxALh#s7QWr`;#XRSMsa3MI=6%$F?Bde+p$ zV3_AlHJ3wuRzarZ`TKu1isTQH=Z{oH1xd~r^Z6K?EI&(GV*X{{&D-Jsw!cZZ*5S{PpW( z$n#_N8o}5WSS95Ca`L>G73oml1ZSl+&0I~ayY}mIxNqn7a7S2d7?AZ0&RG0JWDfJQ z8y)V!IT3ME#w1s~?0*XmwxT>VgelKUbn;;Om*qNim}anLK`v|@ow!*a7G2RXz6LXS z-}mUkJgwa;uE12kp?FQ0-TtiPJY1kITs<8YrZSGC!ou8yd1|mA+~D(RxJiCMMipkw zT*pg-GYqY*rognfH=o6l`jgq4C%_D=!J-gSFB7aK5A&nMhJmp4BUz3tOfCGB902>3 zi!7vIVV<6hFPuB~+0B1h z5sNNpMaRLp>5c`Yo_%!RSbtdZdk2_OUt-}1(?2mKIe)JsQxjI82K;aNVTv6Tm_MO$ z=|j}>O{m*1fdvU@3CyyC?5|EW`cX>8il{q~G0fNe$dG_pKBeKbFAyHxKq3sGyPY z6{g4jJP7Bcj1GAOi!3%)ZGx-k$V>4!}l ztllTW)Y(faU%@=XQ|c#3zOl`$6gCdoR}c#`5`F5^VZMS&Ulh!|V0bAU&N&gLMBYF1 zuD--Cgv=O%K=WbpewsgZd5_E%%he;85xIv(YgAffHo|9Q;@q z5eL&YHGEh~@{RoJ1emJ%cA*{|zgErfB+O8nKZXYL`mdF6Nxz?ghAJF1^fx~V<_BEw zQi3aQ&(=Ln@?DLka&Xqaptl(?TSN2OpfTpxL_0?w%y@dJm-X3VXKysJRLYo0cejfVs(UT5gg&cy`ni*uDclq=lI+^KRtAxfbD)`O5uu z!h{Da$1c|-@6S2ss54H%0!{5}&tdAR`FqyG#!W8*8eoxQ>^Ubme(8+$uZWrYk(RJ} zu&S&G<~Al;&LRDtVvA2O)2_dBDygrYdbbl6BnRE5!o2M5d0&W^hSkc$en-Dbu0Pbx z+pdl*F=KEtZ@Tenx9)`oFC z9(5eneLv^fY?yZWXbcDD9_*ETUI^N5FAsx5(+`yEk^bmX_XDt)y7L|ReBpUk%WQ>N z&+8-S!_3=u?bdKdf+3T99x*kdy=TKNe;%A)2y=O1C*~O9xUwSi)~<0lGdwSSk$JLYzK3T92XD^H++;{ z2~+*;C2k5`E_t8J;!ly(vusn@R2u6pl(vna$|lN^%kJ!e!*a zE5=y)!|X3kmFHke=vm4Aitxyt-cxW#Oa@~=azTD^DiYn8NK1;K8qYLc4aBXY~>czCc$4+p(_kn+DFmtnyr5!AK`rtEpUrCi}>okJ3 zA5F=*1oJ9Jy_^MiDxRH5-sjQgCYaBJ`&2apufX&(H`l9?Tw#xI4ovwxF=irMaDc6y z2Q!=E=Z-AG{%ww{;FEgR(JQ}Uj?4AAcVMo{zGa_q%$G)3c=kKz2`qL~aH)f-!cW`lV1~tq$xq>?9kxc~{Tjd1?NK#s z&2s!l-cPYMs@FY&V++$SwUK?hwv~v{QCDUSd_kWdLb-qHv1nY2AuqFigh2%=)XHEht!`rgzbJa6k4@;qADA{QM8mnug|-gmIpX-G^B?Het15933(ZaGHk#m+)$ znD6%bX#!ldB9BX+Pt&dx_Md@O`mL`jl6rlf#Gdn`pH6@|SJT~-k@L1ye4PkW3kxTb z?P-o*7&!(0Z~X#xJzEuKPc-};MRH^5QgxVL|93b7?(|WNA@7@LQa`*76F1YQ&VqRx ze}{y?c<%7O_eZw1%YtCf*IOmezlEpoWCX(MOWv{;qMoZXa>F0?Wwh%Vk>yin`|O09 z7I*$wOmZh`wGV7e^YJr=IoCPC+u_o-^Bgmn9$L6?E37?>rDj3u52q)wVCA=uZ`r`S zm-kj~giSYwnLEPN!0cQP*q)gw`M&#~jgxZNF32ghQ#YFLJGQ4M1M-^G#JW>7Qa1c!>1Zesqf?o_ewIIBcvF zn0*|kPxDkg2{&CUkv#9?+a%Aw1^X&ScarCi;%~~5^P?!PGdvR(q{Q0%LEci|=AA|I zbIoBoIA8TEc#`L#{BZ>ue%NKX6j=ypA_!j#?Z%s3!(u1(X(L6sE+$aaA^wd*KC+SRq>w* ztS}|dS`TJ^nv!7(C$I2-r%%inFS%doR-|RoNj>wRfdz7^OOMb57UpJ1Txv^-3w0yqQ%Ju<)UG3NlY?}0D#?3pOYU#_uCtueN#0=V z=8b&7-P8IU%=x^P<^YSTGq;_G#TP>B7QwlHXBYBdD#u1a7p6Xsi@XRk4xY}R4Cg#B zcew;pz74ud!9jQBhKNN<2Qq(ReoG79gk%wGU2OOa=Ng`yd71RLoRr)T{b!-P+V?BS zJJZZ0%j2lpb?k9Mn4p5*JD^2vAzb{vqL|9x>XiKk)l*Sh*{a{bW%HGC4LY&&&H2v-g(_mbly3gP?R zf&V*x>=&Hn=`hELQO-fmQ?%He1Pi|v?m0~AIcbvXQ=hZ9_^To z3gTy61v6F(b59W55vCi|0->vV7sdl8bNP%Aqnzzkur@Ujh3X zrOHd@yOfJ+E^KuE%p)=${9uFQNwDb*kIhlUsT*H&VCvGx(lIbwq2h1=>|V#F9D&70 zetcdBGm`GkCpl$%#*r?MW$;-3OGvJo2DnT4sUeLjEW4JSnV<9;oC>1^P;S8Fi z|8!*f0dxk3PJ8EQmJ}j8y zls5%tO|E}cNb0YJ(x|Z5&oH1E<}IsEl7o#O^~v6c>Gj@2(y&ms%&(I8fd7;c1Dx-o z&i9XCT6VvvAJ*;Jy|5bQmdNbyf(Mch%MjBzb#}EdPoePALs;~v_i`5OxbTyKWcfFJ z#+-zu2d_*lMb2{#TN?)Fw)dKsz|7W<9=>qu3Dt74eSH0v-u1BTL)mU(`i4QR)i6Wb z>jUX$4c)S}h3WI29U;qS>YGk9hl9@L)ROfJO@zrtq+j)w3fUjd$*8;faIj;QB{6?~ z`2=Fm;!l$O6BO<`HIvj+0ww!Lec*pd9($a!BpYPQ3ym=ZqOUfQX%MouvA>;=hq zu)X5vEQfW2T@ANtvG>p#KGYu8#lM=soSulzk6>|`^$3Fbt%s}ghE*GIO% z+)VMk4{%@N(wL7h^~Q(uU9k9sctsaXfBbzpSw3^LAgUK;yV1kG!$#$$`+vY9p4`@c zxMf{)TR(AI$h%S4e(tOv*Z;u0(=oMUVA&-Dw*O!jf5UY}Sk${jeq0HTPs{t=^I@u0 z{E^A9@Vc4VD%g9SaO-qfT)8mFhghm+9S!CsmfHuy!swN0+9V(TH|{WORQ+&ZF3enc zbH)jnKg+g%5iCqmipYe!*>1;{!J;n-t~qdO@8MxHm{KQ5&e|wBAOpjgrArBVk zotf-NJX~+Uhh_ObN1R})_Ki2h?2CgQ>tWu8ypY>)(BJB#TVUGpxzr*!wYQkH3ufdC zhD+cq(Xn+0VCFjJ>M}T2$co~S`VALs9>BukPKN}TbN#pJLz3Hcm#30?zMWA894g=L zokc8fuKENnxO}7f8Z6%JeD4`tU6%gw227pl>;9bdTmQ+w35%NTCAlx>+vR+itF&)V zHFA}`L7jJC{`y5Xi(zBM-EZ%a{{Odseo7k#N@12@(%5X|tXo&wAHuXry?;-`+7Gsd zRl&UG$@Dll^=0|kTGH=&f6FeIKPj}Cm~Qn+&6ni;YLDw-zIO6GH&{B~WWg(#7E>5$ z23zOd%V~z0hJAgDVeOa(ySFg=?DF>6u<(gt+dE>HuY==ZmxuBypJ1WM@p)o0KYuS; z@eLMxL>gAZSz02+A0#(8(S94IY@D{fk9cyp!bQ&WrN_hk!GUdFaIXFw zC1T3ppc}DJYrD=Qm|HE~zZup(7UQM{(__bn?u6-u)*ok*dRpa)5V(VzcSr{osBYgH z1^2~>x6OscPo#_!VA)CDa`Q-T-5Ab=yX%)&8It~mk^afBf}g?S#W3^BbMG^xKbGyV z45s=_-JeAA@;kGwU{Psk=W$rNX|c{KVvE1dWIVdhvFEwM48i6V$6)Ia^GtWr?_~6y zm^bUjKQH3>*BGQ(0NkOuq3Zy#ipMuH-s%(QdK`k8+g}xV z!s5xH&S5ZP#68Lt?preaG#sX$n`^^_RTPW1MZyB}(zQBpN4IIt5tyrf#HnWi?iVUt z?T!&wHGX{pJKo=4dK?y-*gm=ryF6(*mH;#34d+F`)g5^A9GE;c85Yv8^j%_VT$!?k1525yI-ZkoNXVM%fTY9-k)@sx>eP-Z$9Sd z*^`elV45AppaEvwwoN@pa&@DE0=RoX_&F069bVd&0xQh0ak&IDV^6*fgi9T7_h-SZ zZ+UsXa8ZW;lgltAdZmgt$ye+s&4C3oXS~@2`$;*!z78|48TPruq5jhxZo=F>oH4Gj zO7XYie32wOYZ_BpAH&>be|k0GpyCs0wXl%4i!TFnTi-`Dz`Tin^t$Ka zcuh~Re+e@VGFR5athNV7-okW)%uzY8{p!&3&y6sfRqILW zS;1fXHQ~yqvCCe=A|>0rKl;eGPECD9Tyo0cB^+&aQnEhw)1NVgu)EHtM`Zn!u6(oe za9^I9q@I81z`;Z~Rvaf;9!IDCcmnKupi5S=zL90;j=>yrbr-Tdtp8_q)xwZAm{~tZ zk~3>Ej&;ENnVjU~sMo!-Nz?-ihoqd&!nEqPwtiTU#7MaX>z9n!{v-L41uH6Gmf-7F zN*O$fHKiSvou1@13TDsP?DHEA{S-RWQ zD5L1^5A)34P51%3uQI6J4U6;c-z8RA;^?;r=D!c{`U&e-oLP5(_}j0%KA5N4o^l9g z#Hd;SflI5WrxEiT0&Iwb)D{?r!@{VTj=yknq2AmGnC`HC?g;7c*)$;%7F6v#N8Dj} z_aL$Onrb7ldRJ#vG|Z*W+cO&Hlj+aeL@rD_HLH~hmzrH|K1K2wleCrLj8&|2DKLHT zl9CGCG&-p>4HkOcxupt+hR)z$fmuHk(+o-dg(UUsuy|S8^QExovL9W!q`o6$@^Uy? zthMMS%uL%S@xbegd3;hIE4R%YIc5F!6$K;@`*xjNKavmII+nrg_sInexcb3&>O+{H zd+&!0%x`sXD~Bn`Q@@bw&(Oa^YpY$+vQ;ksh@Re+5woE$$jx2=Fx9u9)_)@m#2S( z8AmI$V&Uk(`pez0u;yA&2F$(tTeTM!zbg8Z1s9m)=KLa_7?W5E`wkw-9CaVtYvsA> z9Zb=R)slxPI&%7hq(4n_U=s1#)j?EzKJ-m>A5nwp8V$-iFyrHXpP4Y%KhI(TY}&4` zKMQ6hZmL@Z=e&|LolEjN2Y)hPibD45B``Z@cDF0(cPrXuMqH&>v>9&R*?Y&F`0A0> z+hLU-omVYk-nfGJU2v22puG)D-9J@}4eMWhQD_g-^vtjBhvUPa-y;^LJ?0Y&A}J-r z%nd#H#IeZ_b63LLwo$!@VC65PA2Lbqq&ev*99&}h!WHI!+;uV$F0x!)?G96-7EVuv zTmBt8#3J>+-h=017U#`;Uzn;jJ@yK$D>Z|;lhj9Z59h$b6qBOeFx{!J>;^o*@3+|x z^BZ-37Qh^J{Z-*4_d9#+9^Ax>Dm@O1WULkz!yT3n(@w$MoF0w)a2C&aY${Ca`z~?w z8rqahm?d}fG0CH4PcP4bnIE|$2XN&wX zSouX!Su^RUc~gR6Z;B7X+;M7CylYhbg-QNVY2F?FN`j6-B(?YJS zG-fH~0oGSJPkkb+u5P({49q%Z-75o|hGnYBzyjKJiZtwgzS?mDslU?ML+*ckPaBG< zF#l@p>K|~8&zUkMSeWulrWdApM7)^{^DLdFeTA(TEZa8?=Je#u`3!S<8xp3&)F_+e zcDShZ;xG*s*U$e&&NtfE%Dq}JH{$ibR#%onU1Yy! z9?UNdl$@`fQxZ=tfZ5XxCCkf|dyudc7CH_u`+$1kPPGlDq<+c{!;f%tko#^6ShQ<` zW*6MF#Lm?YX5?%x>VXUTt?V6O=3E~)F>I9dnzfpEf=DtRwA8=VYhl*y-Dba$_ic%J z?*_BK$t3i{3g`L`c)|k4OUZb3f8N=_g2nO~lJVf4x;U^6<^>*%XheNSOv2r{E$N!LtyUv8w+#8xG#@ z^oj%1J}2FGhV7+<3bC-r=dk4Z&1jQyk0bTJK30+YLG?oQ{&<-8)#{Zo+;MD~-$|I; zR(f;^9ID$*KSkiTh&J+E zyP&qqq`$cM8TmXDj5Atv6BY#iUDc1zLzem3qxWE5x?1oXIHQ%XQwobG5BwnalcC-d z`pQVXLt{Y=EL=B8eF$^p=#u*h<8#H=D@p&y+7KXutZZq571he*C*~fr0DuZvmgas*X+~@ zF1FJdF16~ABK7<^PHKj*NTh2(mdDpkT&DyJZ0DP|!qkj)Gd|A6{g~~*PqIC{a|`4v zVC5(Cqh7&+*)ynT;R2V%T(Uo$k7~4NICW>lGa<~hulT$UPL6y;uZ6i^Y|D+|4%K2G zGG0uE&r*4~Gg#a52~08To&RYLK7Tge&3p{=d>1XQhm+5_)>py&WBR67VcLq1^T_y% z)=zkR3@-gD`&j^UCv_H?!`0CSJBVqk>JBLXA2U85a}w&|`%~Rns|O@kZ`hs%_xV;; z-G{{`arS|5^`D|%VxDudj4j+`-sVk?KlMo6msv2?_-hy0UPkMNKYw-2@c$2m{382H z3GD891?SGU&?Mu7H{l{8Nir`SCpb}}A=C|%-BCMTL)G`)kEXaS#hGkon&W$7W-&sEyu=wTkB@1p`?fJqO z<{uJPnZh|rBk|@WUqdZh4tJLv{9^@kf1g)iz`}#84IN;L?ioqH^5cPRt6*`E=;|`$ z1$j9?SHsNYJFaO*&BFC+m1)Bfk{?`nV zeEua4rXE-y(gRb5rr5>9yojpYH?SapUPMe8{VTBvu53?;IROjwPaNUH8J^puPQo<9 zq8(hAA)I}km>+m?w=Zn8TJ?n_k4@FqhqErm1t*f_t+_vQEZn)xuZEc2E0+GGjePsX zsmU;JaM!!Xu)EE=E@GD9mE#Gpx{6Cn3M}{$dejA`D4WnzNqx|wMe|^~L$>G)%oPRR zod7py?>|lY>Am$ozG>lkjOQMSMRg5fb+G8!w8YcM*_YCH+<-aK(r`5rKHyzIqHSa@}_ z<{H>Ks;7*Yteq-Y+bTt z&Lx-{UNmtjT%gk%k_9srQ+wvaL*v%kU4g|nORmg?Ib(xLb6}oEn6D13pHb9$4W?Rm zhiDR0r(Dm4X=le7YQRQUeto|QQ(FD4)!>?OvWfXHr})6Xsc`l4-}{ST!9m8@DX=KC z;>~@SC0cV?1u?;Xozdy@3(n);<3-eDk8X+(8ZSi~y|5rb7XUUvanDfWB#en2I=O^!Av2CSff4u`;liFcc zvPGaa@`8-ErcW?qe7-B$e%g#pQ@_GAwt3+wxKe7P{5P26(LVo^Cbs9zo2Xuxzr<0l z8dkpFP~1)2VHTGH3w<_h_y{x4JkR%mwY3H-MKCR~wZ#Dr9c?PvKK?r$uX(WOoAkRk zB;T>_{RFsg{cl;aKeSI}DW7KIdaLtpRvRq7LYb2fvlgHE@}A_ii|<^9OCMf4)&VmH zb|1?i_1536k^P}GuHBXh=Z=m~`vMDW?~OYQC!d;q=sQe}_iuEDTlkd1ewfc1bzmA? z9TwgH7iO$TJ=8|S_&CTer94ExyOPEgSb60ouTe15Y>as@-09S}P6}qH>^SHK^XBL1 zO2f41S?8SKq6HyoGBB0a-8Bc6)%3n24~tz-ZJhvH3qo8KVfx0M>xVTkpIx<_$HQ!4 z`Lljl)X3*efSFly&WPcblxlM&m@{ptp&d?5c~L)=^mo+pjh>gv8o-o; zao;b%e&c;>7m<4Pyla^-Cq4h}QsO!@<#d=gZHBxJ%%Ao$>@>_wnf}NDrXI;}OM>G+ zxJ_IIvmz!fAr5-nKf;7LcXQ0Su**c7+wQRFM0W8}*t_8RijAb6Q_vIvS8lf4vKgk& zIC>@&ZfW>;-5(aLs6FQcoA!NA+)L~knBf8|OjsNm3^Tp1ezAm&o)2k-z`SQMD;Y3r zf40U!n6v+#rzxCM=UW^`d?|5oF5IDd&@l?;bN)6d!liEbFY#dE)WA8PXW)9$W^ySW zrWme_XoO3H)7U3SeZv{W$FTAF`Vub8SpH~3Asi&VVo?$-k{hVaBd%sOC6l~!kH?0MhSeQBC2W|H)ZTSS|@0on(9US^Yc1<13cG}}v3o}as zI~!rDdts*l*8am9^9trHDA-yA3-qhq-V>J|Jd*}DSr-O%5bu_!oQ7L2{675&<~`E8 zmk1}%yW;VM==A z*_o^ebL^s|J>XcatQ;j$Z@TZWGweCL=F?PII4Nk*p7fX5u2qM*w`@zTU|qAzlV-xA zg*xHOVY;H)3SF3U^fhlW%pU7}bRI0Gm^m+kjg6M;nUGv8l;oAn_fySa?msv26676P z2k921zN7H72|VN_zrqHloT)it3j3bSTe1QcWoOlz6RYapa)9~g1NJds(`!Fwt|p$` zQ)vMw8%=!36 zL@X$KJFE!TSbVxe%uJr=^IP4FAqv_O!WEOtto=z$E(JzMNTV| zO}Pa#7!AJ8FypYp&Kx*HL;3kCn7Y64W-6SsBUiG2g7cBxad5|m-VCz;%vplhhhX1D zcb%=s^7dJqEyJ*UHz z5B-HhYM7ssnqSU`xtDFrKEtvDW7ZS1Kn7rW4bDRx}2{qTGydQqP(GabOKx^lkX$Tv(LJ-LnK1$IM(e z3+Bz6L8HPh!|`ThK2hG@JUpn1@#y=qdOFOV;wmMEMZ(hkI+WZ%jL*M@8*t7Fi|3O_|EN7q^n4Q(~U=G}5?;EWR)04c@6<}_p>95%^b)MIP;c2+u zue^L*pY%)bYU_d(5=uWA!NN;&?$2S_^3Rg!gx+g-3m&N*-JB@N~YBMZs- z!nnrc9)*2R#DqJ-ToaC8D4bC<;UN>IUcOnp6HczwYu-fi4T*0|;Vd6+z*f>9vuWxA zxM1d_TRy}azW<#Gn?7}K*a1_xHD&5>C{s^?4GWJfky3S(9?@!#hnW^-G7Ye?t72X%OdBblS_1pdn7A_&7CTM1 zx&?Rs+&1SD%rd&9cnvOXogI-2vr8Nk&%>$w+PQZ~|3peb3e21I+OHC(eojh_gZuX7 zZ4r|Gs6Q1PSd_0G-b(tDwFiP=_ogDXFEFEU$bAo-6I9*N4KwE+%GnDypUm$20gG-; zmDKCE=JN-LTmQ8lM6MEB5b~GwH?(Vq!$Xm5ji4lLeRdCjcEUp#lk66{Z1(r<;=)P03A0 z?y_oY*$$HbQO~#z({KKI;SV!*U0qiKcMO-B1i>65*QbwRcJ}BE`(a+?`=V#C_C)%X zV3=O8Pqr2gGQHOs3X9q>^P^Mm2i`6!oYc$f5#{*Dxr(;}y2;GFNm_zk=} zcmEDd*V8quhGP@^mp&x*CZkMvaA{pcSQRWdYIgJl96as-yBZe1jh++<+p{7~p20L_ zo5ke#W_k5pu7i1&TXzP)IYs3S%`ofd>pAP-ppCKqEyU(uH#xwbvT=*w!Q!J+4J=@e z6G#0c%t+X`aS5z_wWXyCrcGlv&xbp|jqmslv!A^7)`aN_ozn+k&LMd}MYv%4Eb~F) z*OwQMg_Bzbf+>~Qo{-_+JyUUhxsA1yg6TQxbKBwS6QbC$u<%l|PZR9;x};7H=02@G zPzLt}P`-~N^+wTu^I=cfs#FD-5w5;1kC?G^%tV+q`ddjlO#XS7DomNUx<3Tw;Abt< ziC23~3?dc{Po=@k=gS)d;DYDRW3^zOg1nI*tS?-up$l_<{7v2ghdvyNoDU0ht$KWk z4XBfiNdGN&nXRz*mPsVS(AbDh$xkVq5yKhvpM-l!{|}eF1F*X2^_>t>ubc6#AC_jSHyf| zShlT!9ADbiq|)Q0e(+0o3tTOt-&geN;N)eN-REG|{v(q8ZCX|6evw#jifa|} zmaa_?Zo#yOzQnt*-`S#NrLfrUg~ClZHpZVKfJK#c{<(0qI4`XhrazsRb`iFo`njWy zzZJpl*gL$q&{{0yEicN*LJ@&Se7Q+&F8D@?+ZP0^-MnyJXV20+}mo(VX&8&!s3{$TU? zH?UFi*SnM|tpAzn&{G(HnE8q{Ow%abcN3=iByN$1`Nv{5C&2OXyuJxA_3M~~D42e{ zWwH{fzpie}fd>{`guuaBhSD%xN&?)X~8Gu)fhYWp$Vy z{_5WjnE&(9`{^*Vi2Bt7c0B!1V#+Ftr5&s?T8T<>&K>Ff%V2lkd`mT0ux$cw2CVF7 z@PsU%|NdI$I9R&+Vu%V^p77oAp~=|(%E_CRVaB6#_gi6wIUIW`%&|-96u{LsHb&!M zUYo-5beLisn+7TZ5T`id}qCx+Ub766(3atw{fA@JSDY)CX@jJ1A z`8WUNB#du^!Go{FEnA|p;c6XOvu>D`xkfh<9(rpMOSVU#F??Y=EZATeK<1Z7|A(yw zTv^9m_8S%!bw4zQ-S7XcC-aMaXxS4jxK#C~*C>n!lk1)&2M0^>Y{>X=78RuRDdGB` z{j^w)jMv#lzpt?0kISPJiH`?~MQ}W2_~AsD75Q#aJ>2qb-xqRxIc`-lg>cr4!eJUr ze=>3^2UfYBK7KYVI`%@C4tEZR&X@;NerVi34o6Qs&Nm>rme$%ZSafm6ZVQ-Mx~6wM z%$(a2w;C3x2Njvay3R)|T}fWCY~2DlCqHbFJ4`Q{SFZzyiv8AXfEg1Xxv0bX*IagQ zBK4`enjQ!Yfv^o}=UFxb1k zlYZewTH#|@R)3W-SsyRhV_q8Z-AA{`@uC)ex8cAw@#94#7o`3WvS3cXcSjh^NU-)d zgT;lvB-_WVO_x)J3#@mYCC8svx~<@kBF^^(25H2iw}aNi?*H17$^Ou;d5?JyM~mzw z>lfcxm)-z-eoR}yLC$&eSL!+3X*TOH86Uove%VtvwM1Jop1f);iA$GDNyeL(t|YO( z)TUu_JQ=qOs;W^>T~c&E4Q96lJ%0qNj8WWk0cOtKw4f5EJfAMeg@p|b@5ST{X~SwjYo-y-L?-DNbA zy!32dAIyEVX8kLe*)? z!-eA;?p%KlvpfrCRKQK)p=;YoeckzGA7Jf&a+f~9!kBFSXv`nw^#>(6ea_BfHn8ua zJz=Dtzg4ft7mgq2T2D-yW&W89bN*#jcfgD@BVBjkqU9mBA7SyQz6Xu4?jEilsTZ4N zYxTpWVb|N+h~=AKs^R>Qo||qZ*?zfOlJm!}S7c8tyq1STSw!&@dr!uHmbV_h&Uf6UPnaAnr>@?MxXS50Em_kSqAV1`WY zz9i)0glIhb-ff7?}9n{tw&2=m-oby={i;J6_brU||p z^I)bZFGmR$n;JP}z}-JT{#1sk>;0w3_LzPWQdLO((((SuFxyM@uR2T#WruU&jHKmk z4VdwKIpa92-nVG0Cd^kWm5+w20~cxNlKj%1a(~!(#Hw=+EE;<9#GT|b7AnmpF8{aC z0(Ln$>M^MoXz4E?$BUBDBPQm~Shju`$8+dwcDWu*bBJJd5qpi{l73o9Zgo95{uZlm z&4W3dMdQn1`+JY(EPyHfg2cP9c6;%31DNSieDn$|94~8bMC#9v8axjt8@`WO0#mIs z!%o9NKMEe05zj8VbqrRa>S@@(jNhE52$;Wb*?l{h_+l`4wBq@e(a{H1~M`7`-cUv}-dY6aA z#H<~?d$z#bA+s&9aOVq0Lm!y6VkqZ0Y})>Et{>?in$UKF^uIf1N6e&s>m@d(+$aqo z`Q;f`NqvFNN0~ram?I@m_OE)3(a0W{oAi0)036-yFhtC|>&qq%dc}$$X6CvIyx`dI zr3!3V@HWBI6t1~*@e^4-um4nhKhAgln0U$hc~O5otI72t@&;uGa(d&l3_m#8WnT`l zsJXCw3fy^8n6?Y1+59;(NUldJ36g%g+p7m+nA7P|MfQjO_^uAQ-qJ7aI!i2AK4Hsc zxaG!TD}PvM*VgF>({f)eC)-2cd2*31tk0XtBHPQVKPZreqidgO2f@_Srw_L(U_Q%v zngx@7?)`~ZVa^kG`XN$pd*n$HOs#vj=P>d5In{^ZmZdVC5v1OXpA-xi=$aZv!-AI! zHwMCf1z-D*!rc2Au|9C{pU<}wU`FSTs`aoVd*AOwnD^Y*VpXlV*xW> z{27%7vo^WzG=i%w+{(|vv^$2`I&jc~b>Wv_rtQ41QgDm(H?M0j)pA`}-8d}IOD!=U z=Cs$gC&Io5CNoM%|M5e-wQ%=`Jrn`V>OC<$2i8u@T3HVBo@@Oa3pWRiZ+`^SP5Rn8 z+X3*l6=(xM-EV)DxJpwyy6oOru>FK7~0KR1U<#HQN@^YG9_*U3nikxii?} zIZWq&l(B%NPgWWWVcv&_u~b-l>k;{SnDuYLiUB!%o|o)5BIevWzOxn1ikD5SgBf{~ zyzarule8nq`q?(W<^168_7!#&uxRO)@e5(flti4d5wU^pZT7VP~scL&^L6g)>V z{&GpDyI623!$_ZxFrte=2$A0UITuJJG zXo*!}(~s@<>|v%#@wuO4F~4%W3>}D_=G8rfgD)R!aD@3hx#$?!n;CkREKijAZ~S&R z^igAyGjZ9IK`YWwV)wXp5wODE#Le4b`j_^h06120_Q9Pnd-O-0 zHL!Y)x5GY|+J8w!7PgO#&OZpV8d;(@(pcZ(J7RKv^B;t27r_e8{KAjG!aX4$;$ccz z={|D&ST?hLBH{Q|WeZQhg7FNxFHB8M4JPL^?bq_E^)Syn;&d|1eQV~ilH_4>&8aYb z%(xUYn0_(Lnw-znIY(RO!SPPY8!y1341$oWifjXFCT z*43BJDE3dFDFGkM$eu~})%iiogav$b57^U~Z3U}JQ z9>b!Odv!j*ojaH{&tQsn)}Pm;|H1EKa=x+88&o#H?EAj6n@Db5XIBjysf$iE!`vG& z&&uGIuC$r&NPSG=!K<*R|D+QiNUmG%lMZVi66$^?{eo9#Prxok3-iCh!ZgpxdtvK` z=Ul}wGraxXF4A8f@aQKjlA_gm!uHkN`F~)BTd}PL?7pkN@*gZ7yn1&woEo-s;poS3 zU8{*a>_}UDaSSZ%7#uAgjqj_qHN|o;zjSQ=TR2`VVT(M>9rmqw1q+$iM=6rr?e>}H za6$B#@`@B7qg;LF!k#BzXzG)GXL*hioNLqEwh$K8=I8$$h2!H>Q@I4Du@@)2 zfCuhB;h4dc@%yu`!s=rM+pS>E23F-EI7ofm)NLp?R%X1y!tHp1echeE$o@cFXy=#8x~i>-aL0j7mNi`xa$-`~=`3zse*u4cnR zeXVdV9Gvf~8%*jyE-3PZz2&!egu$Z2aRG*~ctVd(BuruY|Ct1PJ~OnBCYDoG`a5FE z$hf>vV&(={y*ijDm7y6$`mbhQzx97|%BI1ZGqCB7yoWK!asME93}!s)Z9fXL{_6Ah z!_qyf8D#m~<}-2bu)TTL#=|h*;BVCuSTJr*#UWVi;psPvWPCV2 zUxq_r+Ij7j#579H7Y|t7@`GLg%x>{P+7XzwE~>*AYm{N79N-AFF8 zBl|S`-|=V87+D%b>gOKdJ0TamDmv-}w@i?i%opm0)@S6K-)G^LqNH)|FlWqi+F_Enjt%yNnJVmR3=H6tc0XkDN11~DWV}1VbK-B z5QWN0DHJQ|LSZFcsZ3Y8k|BzcMF^?i<8?m2_wDxm^L~3gpRb+0&THrFoYqD?U$+W9 zC%7-}&I~W)%vZlJo5MlN%c%9pE-v~q2d=YJq2` zs!p5S0W*Tx+LvI_qnd`du>Yk;kFUVI53^Raz{(asYT{wWV3~ar%=+kb@dhkZSr#sV zZ9S#_w_%n?bpaW#toZR=AxwX%l=TT_2+pWy!Gbx7Bf4N`QQ}gU$G<&~ zVR2uHg%p+~d%k=Iiykj9{stRdw=8{0@`>r=2H=34W9#0*w5+EWWXSXHYMRtR>Y4IM zH1hm4*YdhZ{WZPEe^Bq-;icXK^VGjf$an*+r*FQ&4ApznK9KQr4QzkG!Y8W}nqj`) z$VY!*{<}45H89KnocsVRo_+c3bJ*x9VoneuggL9OmPNyk(?1=S zgPE_!jgNqBSNBhqhuM0%r6Dkby-Hy`$&V;$ABWi`)`d#M9H!d=m^Wm&ekO7GY|3el zudU{g{)sF91|ScqbZ0YRR_WdIN8nbq+?D#UXp1H_UgeleHl{Fr-}aHGkUK}uP_Tv> z%^yt8lll?78e5ocHYxBj?BAR`!U^U%rrHVMAT{TZ%`j~l_d^aGHEqt1tuV*KRG|=# zIq*wkCoH_UEx!)7@^-wl7yfTPE8X`q_QQ0EQuTY}?H()62f(!2b4TC7-ck9|W3Wij zJ*EN9s$V=SoYd1SsriFW#LOJE(R z`I4=$aPfv`!(rvk4IlV0(`}(n<8SPb^N9*qVbT39D&=tdbeD@aV7_1b<~+DN_PhUW z(%+P#bOYvWUDKNdOKM^^@?cNBLtRf`wos$l430_dK3fRWw#Ho5f|dy5o~U zufXHa4xFrp@#vd2uTz8Rc9)E2!vk5a6K0THyYZk7%&kq|qYv{xdh3(zs~r1ygb^(A zoJMW0Ro+0YCFxJ?3N}Zcx@Jwd4a``r=BEcsgomH4g=rP%sORHde&@+XnCaTdQb4Zu z?_v2)SWx~oUkdyg|+Kkg3pou%acq^;K_>H z$+MTQhP$6_E{cPBI?}B>VMQA+%JfZkQ6ku2pPlA43ZKUz($s0>pWO&wU~0J@AF@Uuw-QVc+wwRaKW_RbbuU;5*`9PHg(*|Qkt7Iesy z*R%M}s1fF{D6p}UY;V+&7PDorfVr{b9UPFc=-*11`N>P;9_-tyIdwHGUGs7BX;|Ja zHf$}-eVk|Q3o}3E{I!Q!p;w=Jz){Eaz1NfeH8-7|VE?7_U0q0jSHM&@Oi#A><4Wq6 ztZcP`BSw79aEC?TEGav`-`D3w^7^%u2hQG+_5DA2npfiOgD|)0?b;3K7bd+mjDRIw zW1k&>eb4@_KLyiv#a!XRVoke~7hsm$1;030dtYE_G|U`j_vIn%_N8mb6_~f8L7@q* z3sTL#1~cmX(v>h@w!tsMV@dt0x~TcEfx<4I8?cZo=e`COn8$nEh555&ZUn-j0=ev5 z(m!b(HwKnh_04$>vyzq+roqCrfEB|lvEKeHqs}LlnV+XkhQ)D;ZI#H&K6YPXz!I5@ zlgRl+w79-$G0giXN1ab}uJ;(QNWSHh>u2OrlVn{dm^RK$iJb4%QZJeK5wFyy-Y+R9 z%~)}O^gmXJnu7NSiY-Zr=V1ZepJoipsD))l!=m}2AD6(L;ZAP^r2n+xBx~61Wa0i~ zm?>!;W(%8*4Y`y7b5sVx97(;X|4}}vkLcxZgUjMtf+}F?(kb1iV487?L<|dOWp9aw z>&o`n*TJGQ2AAUD_MMF#jimnT*4i6z(CDRmBrtdNv$c2O2+p6&Utr0(Nm~fj1AfhVP4QO zT}y0_XwY|)4e9^5<>^D>yPXD`V3FnJ6b)P-r9RvG&JPy2Rb1Nx%WPmJ9EIu6Rw^XH zT!ru=9?UivxPBWpd+_J;DdGh=)5>7lfYY6Guqbls)f(8dIyc}v%q;h%uJ;UFjqk?5 zLK%guN>Xp1@%}n2uH;hJdzC(QtCL~L5 zVCkn<)cT2Sskl-Fb7%iltHS&!Iy}l2!>lXs`U~NZ0)|~Z%Dri6<8)Gvq~A}U%&rP0ao6q;yI1f zzuHG#FY;fGu+fAWdxop`V?DI?-Mq1g=o@fViqw>Bw!qV+|%1$I-S`)GY?mXl!c7-K5lUVe6Bak^%9#y&?;f>42-Ck;FH?nMxTy*` zq`yN%z2A_rRW~h!Sz|_0?=L)+V%1;59Nr?QDWv~>sY)yS-}@0|n>%yf!9x5z9eKaP zHadLu1F082QK!Rc(}j(ni7lTs%EO{vpHhF2dhc}V`htDu!-heae|tj7Kb(IJ*2R60 z5tG;F;4@-=TSmxenErDTl}D-d(v@Mpxj*&(Nk^ctNE7DTj=nh$3ukzA&V?nRE?N8Gki&12XTkhhIawjh)A;;^0Sog}sOx#P+bQQY zNPfI(!Dy@}sqx<7Ghy+-jIU;J{|oz7+OVKEMbQ;@^HJ(07SAkp;J`5#lI3(^X4}uV zfpAcV^C?4^t@(2B3M|Wyzhgw|=_UpVFw5i3;bkyu!dB}1VRZN3^);}tm-hV=a>ty! z%uU3fr$j2^^%~^bo41WPzgv^s-$^wyY1s~o(z@ly>)p(~SIZyfNH_BB;BJ~t!cmw# zFj;#W+&<~Mb0{pGGqsNk2OY>SI1MxO(zYLgB|lV-@Jao;veOZ8?(Q;+D3}-7K|c>? z=~^vJAoYf_lPlo%DGB%PkbK?QCsNqW#B0<8So$!nRsr|Fq%ryXi%9(?rCH>DSzTw1 zbpy<{tZv=|Ya2vfcn6Dab@yC^W0ihh{6XqzlIlX3omVLHo4EH4b-#|eX;l2Emzdv# zxv>)DmBG=0vM?vuX&t%0Cw9>=mxHB2yP7}1Ni$B>j)QqI9TUF5ItLT=lwkI+`PBWv zqyq&G8YI72RXB*;ic#A>i#T-IL>jrjD0}wUTvE@OP1W}s4$RPjY40P`TT#yrx1Xv< z>RAn|^5A0KxQR?yl)Z1sC75Pmym$f3QGdSW5bWqD|A3g687t<%zD2o#i(uA)oO6rd z)@D|LG0af>byE(`&2VuwgW2kPgS#-lw6Ur`m%`$P%n{YFplWxv6)c^f+ZqcSSO*~;Hj6-lij^<%&SQ0YNato~JdW5Q{D}_+!4}YtHev*rt;;HkY zXkM_j4a}Dsn9@-1+4y#{EiATXq_BN8akUYy35m2c}IR z$LCn*&3yvWKlo{Y9B=6_*b|drc2_cWJnf78dp?~wz}KM{$46$Pyw-hE|F9+cEv&;a zbI&Dtdum-REL&84sDSvUk#jj*DLdD+5T*yKQP;C+HY#(yKgAC80KwD z9L$Cd20oOP!s64XsOw{^BNZPiV19xlbv>Wdd{d_y779}EABNc8%U|rou;};F+2OEr zHvMT0$&D(;9)kVnEgjYfv*z7+y$N>AjH_;fxgH+0^)TCUVP7*$`#t1i4_m)J-PH;+ z`|j>t3(KBqyYm(n7B8T#4@wk#|1oVSa`gw{mVSgJU@l6TZMK+N&)r zI3iD?BqjOBGLbp#sk(1|A1s}d6sr#l@*=8#!lG;V#S_?jgkHrjn7u&v-3(aNHem9X z2>lx|b-8gGHQe zl#Bh+@+Ogb^7jGsN1fGmn+kJ3M2wJyS$W!HHHqgPiJ+659}=z$Gq*%u(12rRnoc$# zxoSqX4$S@>F=PZwf2-6O!-_sGI+ifQ{IJ$mm@eItVFR;TZK?gk?#kY?78bKEQukNV zI!C)X!lI1^)Ov{sJ6`Vub25{clKTTf`8yjZ&wNAOPY{h*nzar&&3C3~Ce~}8=0j>c zNpw7$tbeIF!(}yc;So*her{~tnT6!>8OP3jJA}N^M9<3_mKa;}g5gfZ)}JP@$o1H= zbFg^Q*s#TO)AkPI;XKlxQDc>e`m*kh zy4i&8RS0o_!e%)j6z-#iCyuUo!!3e4%u zJ6r%q{ytDm_7}tCu-sGF`<`L?L|7UglS%I9*6k{lqmz2N1$F!>E?@j`Jj}B{cuxz* zFTr<5FJjtNL+bkDzg#NQ-Rq9LJ=>6~mmK=nMXsMchnrHS8LoFugJYLXFq(jV(M!ue zb#PW~CAFTo6UW8=BmGAU?ZzM%+$%OT#qrr}hN^=cOt09RM;+!pYS$P>&R1J&M`;o7 zKD%=+TyT0JpUgi?OKk)Bdzq|@LqC)B)2=(@z(F4t%aZ-cT-2jN&abv6%U&A8!p0Gj z1vtNYce!xQVEV_o6u>Vu3)E(wVshV$tS)s8T_QD(|tr1t?;$`(a z55l}7%8u!9$TrV|M@gR6;8g%;t%)&-gr&WYa>Ve!n`(d|NF6nnj&YBK0HQF?aV6L)nsV*El zrTud;$&Y;=Fn|qar&?6Q%yGTltKqWkJ@aZ|*5b{wF0hqTgnb<>iRGra!bU#^`s!iP zUF)9Br2fx|#mzAH*5@K`IOa)U(+8OG=S@r~+)n#B{tIz)mQ6It^-O>F!u&3T->>LJ1j{v3tS3If4eH~gE?oWZ&^;pm&#oVf?2JjMV4@^vx{js%w6Pg(HL%h zdEJsnYOq`G?Amyk z*HSus9IQCjcy9tM*1cRj8fGfg4bOm?%En8&u|9*=v5j(Jt|W9VS-(mB!u|qSuq|8m z1FU^`SXwbme=X?w49lE4m-8HEoIm&QCoH@a@$wxkY1#gEHpZu0R2}?6>Yu%|TLl-t zkLi@cBJG_s*1?>UW?}zeeBNMYAe>sdnZ^vi5~5J$vYjU_l~d_bFK7dAZCKX57;? zcns4{FYGacX`$<=&x`nauIpMz+&XghUou|or13g1|8V=J$@u(9_rUo^E#d}~oAY47 z{C!+?m=UV-YbngVKCXExES+He(2jgw<@^H`vi-c!y!wrBU*&Ko1(=;8jq!#%!(P9c z0Mm37r|*H27MwF433D>^y~2qLlQ;upex4{RUxE8`hVSi%`LEKu6JgtDLG$}yTCA*3 zI_y1?XVwc#{;*d(fIX)@OCa+v`lr3`F|7E_bjv4Lpw2O@gmuQpazDV#vFwCqnBCc% z+eW-x&G;MKJ@e|{R+u+AQ#uISmd5lq!Q7h3KNMwfJZh8wQUkNhRdyJ_K`Kv%SHm>F zd$(-i{_6Mn6|hMB;{GOhVA&4Ka+uM3RpJH9B>jjlg}GK`GJD}ZV_iM6zIZdVx=+Dn zF5^lb6JOetcMDGQa~_rtGu>XcmBO9%{R{JmM;N=-z!LNR9ofY1$Bk=;+gX-3GhxZy zkid~6aXelaI`$sSsvM)G19RuRSGxm?MqJ{0q$!4JI?E4-!;LrRR=tP0Q^!rZ4rleOAMAoT8-Kba!dj!>u*mCAni+iS z2CR1W!!xp;7*}=eli>eekIb?qlx1bUjT(*hD9}&rNJVaDv5r3;=2ib~yA8(%rEkJ- zZ;DvSrVVLuAB!_X5oR>TRo#VKHL~kfNqz8X>zgqD;|Ls^Fn_~#j!7VSS9H`|SYnej zF%owCs+Fz>i}25!$6;<$(;j`2Z?dM7=WEqA*T(=BgvK7<3#Wx%ts<{)zSSM8T`)aX zSAQ`q+;2Y08}2Nk-7tX}Qs3Qdxb9rYhb6Gss!-1eF1~cr&WiYD_*NCzajRE`4a`1S z_Wm#SpOM`3rS>rI?S1O=jFrsUf7g?GjzLx>a?ak%@y;+;{Y^tLtfO)y-<9<5^`+K_ zux@9RCoFt>W+)1ItM;ezox~}H+mFBjW{k7X)b`1vQPY30@LD~sqNuh_m^0~l6#rd_9|B8Rjh)k&qtx& zTk5dttt? zxG8hGFYy=UzVtVfpRs8AtH=igMbfaO0X zdgTy1tt;IQ2Mx}>l@D_+wRVTXWrY&wC!{~cQ6ULd4!tb<6c*=IE-r$#WH#O?A$dZ@ zxHqumVDa#3;&OYrsd)YN2VC0slDI@Zd@)>^9m21Jr83jrxsm)z&%y>+u($sN`FwNb zoV=tam>0iCF%#CI_q=O^IgY+gui=5~x$EA+qUG{a2Vv)ocCgbze^jd?7 zq+XI;sip;cmS33B3=5nFS@Yq#fmA~#1r~$Sa9cKg#}eGUFkvN z3Yg7s_bn&=BUfo#lUy(&tr!+6h!3uUb#fI-9>bEPaNbH-ahumLGJov%G7Ct*?49?I zvx$dJU$uZe&-iV*2MhML%bUV#Hg50JVcK!qU?W(k(Oi+3J8jAI#jug_w>haWKW-AGXIDK+Ianp-vRpbTV*6ioPoYW@6>9E=P z4aav8H+-g?y5rz6Pgs0)`F(BVMpF-oU14dPXcu{Ywv&T3){#7#M$M0K+HmFySol6^ z!dB#24BMTSFy~RhLSL9pJ1brS(>2!19)Ly1=a`tl+-u6Jp>Tn=zM}yw5MRu{3iBVX zS-b$2J}8Mvgu7=u+RuYIJ2n1h!i}rfUYZH>5+{2V!rHe^Xp!RuXTIOEa@d-8Zh<;1 z*s73IPdqDTwF=4q4LT3PQ3X3_Q()=YU#~~WVt<*-vlL*FiO_x$%zGjqA`kQH+VoW6 z|Bhe6`=hF-!TuI|6GtK!(hhpgfIIJ5>yCiM1{V`&!(4{SI~ptqtdi&ucN{7h!tqj4 z`A5bG7TT$PCC5)`$!jNDIEfW?eE?>z3%Iu#?l0UqmmF_7Z%@~f|DQ1XQ!DeE^wngOl%wK$7>nO~vh?0JXg+KHr9f#GlJ(GJ$Uh!k;X}B}JcNjUoi&x3n#KPXs z4BWoLB015kLRia=xsh06Ut?PYmx;bn_56U1qibO9`o)^xkkf63_b2W60dt4Z zX&rF4W$L+~u-J0j>mFG4M@I+AX+4{(M#^Em@El)|?UBf=S-KBS+Pv#9G5dRJa0cu@ z>WDqD$gFhh8(1dhW(j#dG@f?Ws4>{S-@~KGc)XNN!82hQt3Vo=AKKJYQ`s=<-C1h> z7;+O0H^KaI%Kw}HhBea;z&f46(Zk99jLg1&2##sXrp{OVTd~K&VBZw=?d1H#_Rl&Q zP5QG1k}<^9cl_euf}@Aulk=5?Gh#zF%zjc>r3{NK?Q2D_*~!0k449WJUiuD}(T&NT z4GRsHmHdNaZU&X;!ZhcP>h!UgFZpLeebT@EqOA(7?7s6lIiE4z0xqh-0XBtOOku{_ z-esyVYviFX7BFug$7u>Yka=g`N?5pi!sf}aq9x1Tn$&OZ-9)VX)zoS=@sHn>Wtr0) z$@z}fWn(%Gxm(cC`i(F%aPt;z*m-G>83(5O-7{mt!l{qaJz(+Q^%aJ&cBbsM!44j{>9%ek%voXKz6Oq{sm<|+CEWaswQ$zpwLEhC;VZ^ju;HZq zEl&@_wD~U7|5sTjY|bXftN%>Dr)qy3<~3BY9Z)Y`>F~ef-$XB}J~h;_oTnzx7?@A-!|{qON3O4PGp=@H9e_Q(h7QfEAZC7P>MSHmH) z-j75uuVi-}nJ>?EZF8Q%+%(Rj^Cr?bc26K!~b^V4py_}Rxm{EP8cz6SzpTmo-MR4RIzWP|0 zxx=RVJ?wdRN&9%1;i7)^D=fO(Bu6Ltzq_BLu$oCvssb$Wvu^Bz{nIaAw^ zRsqW%db@QM$(LVtx&e18heoZ01>X%m1(Q50l|{_ie)XImoO=E=_4o|g%vxLczwKeD zuCFnM{n3)xieUcEmQMou=b8?sbQr5 za?8~!y#EwV%%w-doQodkZox*^cb~rmvqwG>9fT`)Znzsm>bvr(`v)=ouijsW@&6x2 ztB@;?eqNpob6X=abzxg8optH3#6Ne<7+5ysPLt zSB(gkPM&gcA9CBH{hy18pFBO!fm6l1JxfS^#}t{3uz`C_QyDDY_?Y^CJ(`PgStZQ) z95;L=a?#xJ!)u9oD+PuyYtI44*RVi!Sl%?)Gw$`3&oHlHR_s4qU&!B(kNN>K4=GXi zcW7VN?fD1u^<@(4k(Uk7<%hk(_Uo)vd;u5CYVaEkO9l>CJ%_EO{`TWxLG$8X6X`c{Y#vVLBVyU?Z7?HAsdfr^e(dRUcf))W4t2eh zcI!>?9+(#{{xA!9vEe%JeI$?Cut*zrJb1++fVgLksvaDavM=%|EQ;&?x)?UAw5<(> zxnVMT=CJb-kG&!Af7f3OBQ@G-QvWeGVHN40rX6|_W`Eazv>vWw`nq3(1?Ag#p0HJH ze?}}U?Yec)3vRz^P#aI`SA{+HfeZfDw&cM~uOWk2SjO<;+vlWy!GVNFaF+F|d5xsL zar)I7n5DC(uNh{Y4P(B7MJ4a$-;n%=j8_|6$2AY?hUxqZ$v@$NA7;zH!Xl=5)E}68 ze1_8xnDKB(Gy?0@v8Ok95SBh>=Z=Ic8!dcCG-CUIj!2P%t?$_Oj)lcp375vfA@re2 z6XO4Wzene9{46_|+cJke4Y~Y}?HSu)_6;B1nXuU-%PcObx862s9!xtorRM-F+FpEX zJ{-xg@CbzIxhe?@;C9D0S{TeQRmX2{uC0+=cm&=1$4F4b#Hd zjbuK#>(VRl!u+}`8y~_U`U6jeF!ldquw(e>GxuS}v=ZG6IOyTL;n}cYCF@8g%pY_1 z^dp#^Z&Y0Z3za{vE+YB=zrSfT@#B(bB##_N?JsNX+lkL%9@qc$0O}2D@9eCG|C?Xu z7GM4gSUNq{%a}Z$bgT2k90RAL#I!7z?Zos6l`>9n|J(CVNI!pJ!P@h1Uw=9^9_L?P zZysz_v9zobIpcxuKXQE@^nF>?`Au!=TbLX0b@^zR zp0qIUJ=Z~;Xm)E74#49%JmB1WN;jy`}AoT5eaz2e5t6!uC zGseY?e*}vUPmt3m^*0;c?!c9!P99$fv#Spsy8#PL^Snm3Oue zhy5S6YA=ELhWbUj;Qr?-HY}L2p53|~mYBNjv?RHT?a!^S^OteV6)O+7z~OpS^V_$*)ap z)PtpM|2+I*zUe0FdRo>vZdfqPEf?P+|Ld$%eYmY#_@{1fZP zvwe#Fby%>u%HTWPsaw&O4AbVdlz!Y3i_W^WtrV zFzfnHBl7+uqG0{WN?2r(9=jH{T^2J)%-S9Ca~vgH=gH8dZUV7`&K zlUNYOqSl}AicWSt%xb#p@*BD6T(rw8SYkXPjX~az)tFhgz-(2kK10}=>(KEUmgYSz zW5JDPrZR6~x`Ji99b7i1bBOXG^`LFAQO3ew?~#jUg$@sfd6%P$KalZCJdCfwoLe!J zrNQM*LfF5htE?S4KeA%(Ggxu^_j)oOt*>)Q2OODlURDB2mMs|m1C~u&*-Xsw8$(^s zNAA3p@(yO(J2NNZ{p`Sr1)o2{toX8$$*|E2%b;$U`+fHV6*%dc_42PU(~-GpDjc-r zm)v()x?~@fclz#K`Ue*OT&FYzxvj7>epoY}Z|x20`!XbRs_u@4*)#Xrj72W;U#2P# zGmM|Qj)p_><6G!3E5rBMaN=*D&nlDr=4RF(Y`?_g$_jPT-<6#CojhN@+GtIfYrA61 zSD0n{b(9v#&)=>50;@3|@0bhomVTk;!})QooGz)K`Q~~%a$2eFQUg+-(KMC3U#D;P zxoQRr7UZ=y!0m6QY+=E|jir}r;kvYI305S3Rrpg3hbX4*SP8R=9EPf4=7p45cCa{< z=2HP{-wcdq!_s})GfH9plF4C?u&C}+IkDTjjP0((Hr^`buv%7F!d95ZnOR88sDHE7 z3ziu1Ysmi9exr6^CoGPlohXJo_v_2FEe4D?)s!~m^RC04l&DNe%C4Dwk~SDnf=ZA z6a|ZFPi@aZ-dN9`a2e*iZ<>$`3!4*0#lY;1vvY|19=A@7gISw~-jnT9w#?rufEnf0 z^yhGYoBWJqSU{hz(+Ha_3JFewxs@x_$o>?4FSwLJ>JuMQ>!WYM%EkvUeH@pejrC#V z6ZSg?W;)oITf=o@g8%2?m$bIR{cj^jk$P$W1;qf^*FMtb9vLs-=Jb=Wzxl(aJ7m0) z{Zr!M+!@nT(qaC_73KF}w%wk1QqMP0JzNNZb$_UDt*cTa%v9eVI0Jcq`^2O+ShU_-c_u70Xd3+iW}g^$oB@Y?ocZk&ssI1? zuLM!woTRYyw+(f_s&08k+HY9=_5p7k>OG%aDj0(4&m*bp>r_1(n^7&;A2af(?PX3F z*(eXwm6j@YWBWOC+dC9sj(ptAPFRrfx_L6GSFAnK0S{IWV z7mfFSf*sYfo@&AL#pT81dOdaLU>u%ILy#^L2A9QcVc*?qIA}5&hv9_m*jKBQKYiHss z9?s=(|Gnrdn_*^pmd`V|Gd|AA6J~eX=9R#n#c3TruxR&U$^vV*>s(U*M~k|@;5@4% z$Dia|7GJMKecAmVyo01)<018UZV8JwAA>m&Mq2gA=`Vt;LtyTJ`K{M*)Y-PlVI)tU zPOTT7tGn|ln8A5P&8OqblDFq!$?K!N-%%gYkU1Dd@>f~Z{)x#rJ1idlZ#|jGCWPID z#Zxu26|sL=bvH`0U}=;3=sB=BY}UVg;^w11#&G|)&gdtwXj?jUKhb~ZfMF5L(YvZf z?%zn&`~T$w5jn~!<$bh!@hHDvU8C$ zBXTy7`$e9&!&D!@_C{!4JoAljZc0ZEPww`bRsPJ^IqBl zvugJ{k>|_WTix&$7VE8+TqF72nt~2E*8N}S6_N{=tm}s9WAiuw1bJcBXKKE`7mX*ixugVu`f#hG>)}rN z8y6ld)V*Hf3wMjOBFK7?xKG`F9OnO?O_@Gkal;kZ%0@Z)IC9pck-FJ1v+ZADDCu8g z?pgy&9nLqOBvv?mqZ95{x)m4+)6dvt|AHCi{F4`8kvQLM(m2f5oGnku{IESHY3abC z#W^S9V9Cx;8&|;AuQPpaz^slj;%#u_`sl!0Fh5RVc_>U@zWs9=%xKuVA_fi`{^HDC z($Ak)7Y}m^6RGtl^0N2HCAs&v0;JS4Vze-@~(8!!` zu=c~NZ=aLAsKZ-kJkIyFTY_G|v@zBp6XB3~tFF|*q7B=0XTn-vpDEPA3}0F5`#1;Y z5ACQW{aZ8!)+5(x`JNyq`LxVzFE}VIbg-KA?<+R-gFRiEjLGA%Caakrhn;O5K9|G7 zM_<>ThuI@#3MydU<5+hATshBdbQR2au5>gVRumPidI^j5gbq2d{1#b*2AFpKHNOCM z_MY&s5f=RXI{yirTa+Lxftjqs+e%=wBipsRVSc&!g%_|?CaM1?EIj16pay2oE{y(7 z>TBQdYT=|aa>oZ@QIxy54lb^3xI4NPf8I=hg?h{W zy@uU(bv>I5^R^sn{s7bbmjzCPrHb`tKVf!n%7huP=*5|e0a)hHxGQr=|Ll#IWhdbH zTH!Zi0Zcz&_;ey1bawVaeVAkW+-nkXPx`WjFim~Rn8|Qwxt6av%xW>!W5BHB@2xDD z;c=l@3y#WN{LmT}bdR!_4>w-XzGx4#OQvr#fs4J;>YZS^Kl{c~IBlyp*9E4{%{8`y zt@EDl<-j}x(YDoanN8neV*YY{KVof%$n_}KLc};zg(z*IknyQ&ca;Xp8;an zKhrdYPwdjVxd9eNlw?Me{+XPiE|`0P?ve=euI>3fiH_}=Gpj3w)E}MRs0Y(57*~Zb z-FnAd^8KE5M?U_`hWS+{)b~?rJ6RqogeBYatIi`=ysUYu6y`0uBa;TR``KlcuxN|| zT?|`kF8%NV7Jhl$^BL~5Pmg#Bi$~p`IdUR7fA443kop-_#FNX{=7CXP|S{#~H|yq@&SoqjSG7MA)@<1^-%g{+2CC&*r{BIDUz(b@|03ta2V zVO|02=svh^ti-hpW|Tj?!Xx#sF6fe+8J#0@4*qXGnDw)RufYAr-kRj`cn>#jPJor` z+?B}oiHs(UN`$rjkL{7bELAni(qk(fJ7M+}Zn*%tuq?Wgm^J=VHZiRx{!$mrJytZH zn8o zTt?n!B;2n8ORYXxM!~IRGvYPj|K_VPqSRU!rdv)_BHzEwy+4&TALe}uEjbBWC#*fa z02Z_xyaUukwRMmv2+w58fJe`SV`lp#;jl4_4jq zC-qMsMU(Fj=dr2>55k<8&$Wqd7yErV3^PWAQ}e}LA@w=}v&TN7#!vdIk$DWJmG!(O z^P}T^Y;Oq69}%@J63%iLbcDfz-PzRh$z2^{8V-vd94H7tE^e`y8VWN;?y}zjYahEd zo%GWOPJVQSt@_REkHg}=T|cd0$;{>xJdz(QIc@~YJe^KGJ};|XOunDIvTT0hQRJc} zJ5-fnPVqZF*?&IU7dD8Tv70=9)>mon23VM}fOZ-dXbd}U4oiA2oV);wmN?#+0jE8$ z;$MZOH%dIlz(ISn(&J!pTK>`@a=iNStNJ?0?edaTZ5`vTH$;T8K7W?#09eGD@%3HplQb~<}o zA?d%`+ePZ>^VVJD)9Zdg2gf`tMclP%cSL%eeo<;o;(doL`x8Bcv*ycTb%(+`-vW)hV< z|HknLcQtjp}nEh(eIU2S{d3s#uT$m=Zt0L#Oh?8@SbYQ_)nQ5cp zZrO-_Cd?h0BTLH zcLxfVk(^;NB#12aFZRVLfZ3f=3S32WV$d&7i`=bAiE1gGUT=PrV!0rMI@ z!hJmFhx15(U-ul1Nw}YA5~Hq7a&r9$7kr3ZKNA+u(zJGjwR|>@)r4_dPE3A(g6%m@ zQ3Gc08c4nlb7qWKJssxlUKL#qM^4LJrv|eQzMS>}&axj0R)sl!rb#qKbC%FsRgYL2 zz%?hohoLCh08n5O_stM8S5huzeSDGS>l$I&O7vuI9clm+9Tcv!*g`p*NT zp8CF4xKXZ_O~&Kc`Nf66b$7fEkjLXYaP}p@TCaL@m0@AD3BM3Fxc}3idj20a7}vwX zd1I*g5qPzadjorB`NdK5dop}`FP!>0bfg}6ymbp({=v3SqHGrsJN8W*u7vZu*~*Is zu=ub6XA~S!>RoIG^R_okRf4;YQunNwK5@(NrdC|#v zez0iZVDflUugLlx4x7ygU#tj=4t+b24AYu_oSY6zMDu>u!!bhD{8=z9D8&62>?qW> z(;<1OwgvgUxB)Ax2Mb7ke=PNTX=Z`O*A~N!JNpM$BDcoZ3Y)-Em1)i!;n;vXqs?HZ z&&}w)u=cWjiAzcSd)EVJVCHtEcoxiWFO|OlvyP3(Uk1}hO!Z5I4Gb(}Rui9SK3zn- zOY+DTragUH^NjQ-JI%F+IX91dsDpV9-y0lYW@s9(5pFfPx_TYSPyIaH1gq`#3UGs2 z0Ym5h!t|rh`n+IaZ;Hu`saXFsIVB%hu*!dw9;{|$y?7_guK0eF{61sS&e1iyVM$o- zEgx8suD@~*ET-*_2!Vs9ITq}LX?)3e0bG1w@`i&jqyFJM5iEPxF(42Yn(FA*!rn9N zb_T&x`bDE(aO}0qPQkEXPsge0s<=LDTs!+XEUr~7*M$3GI+ljPq8jUoCa_gvkL*d9 z%WrzV3YK;RIG-XeUop&{T6 zI^P_B4Q73cPKt!JJXCfk!W{LzDVebB{@$!)SR8O$?kSvf`*z?hSU7*r@J2Yqdgk}r zF!OZX(RXn7*$-##!UDZyn;uwtK%kuo^LL*;-wOx5m=|y#=2Z+@NMS1nzbg-5>7ISx zNPWL<)BK09D71J)7hG2N#P$))c_J+!zc-pDJ9S(U>9^82&2kxcftN?@Rc`r zzF#ma{zFYF%r($B_ZQ|2k8HUK+m4yIf0zXIv4LhuuyFgAd>L3`I%;_$+#gO~Fd7!j zS{0W7vv&Cp%98qQy?;~StX(-RbmB`%wkmK`uCDcDm^0xaW#-Z=DQYl}ze0noC$kpI z@jAqhPWu?cGV;Qxl`vPqIsFvOPHOUZCjBl;mR7*x4QctVF!R-i%m&!;siepQmW&pf zj8wz=^}D&llk`8Uy>1HYEP4BzL;BlN7m(kd7W(eQWQ+ zQqKlC8o5pHTwNy!ZfVU$)y=&ek6vBG5WAkt;SP%m=P|UMt;B8w#QkOnEpfQ zRWYe=)sCD1vsEu_lE7+*o7j_Jp6(dx_nf;cuePbcoWQ$VRi@+huBZEpJf1{Pu6Pcd zm2zUAHmP6!EX@gaRvvgm=9^P}D0v5L_2Jlz1+Z{1Y0XhMW`X-m6Vkts@s<2uHFx^% zoTV@;!8J1hrk}Doum%mzG><`|A zjMveycSL!O56L$$hXKL6Ev)@Z?-@x+g4Z{0LZW}T058OU0 z;)(2AjQ_)RiJUs-)BBy%T$ro<+-C^6n@(cdLYQvwamD}~Ik~~lg47>98u<%m=`7l? z5oTSUv~?Kz)0Xt>`;z)QPpRK0x3(YkY!57TPg*ngr`#m`}5{KMwOZ zbN2qjcrtfSX`F$%pErK#gM-W;`ko`X(s%0dTR+XNybSZw=I>NMe_gTblq)d%*XRq& z;0R_*T@=h(Y8Q49{%<_S(8;LRu>bZGHK&m??v69;g*ylK9Y00-ExSI-Ft9$?pV<}; z3+zw7QGty##&n0mV%nT6P1w1A@^7-etf94K%VD>$rgR?6l$*&q4O{I@bP9%fDq7cs zFn5w2_57F?V{^rDMB>{6Cy>)VAHDSlmMk|tOvYn&J-@1@f!BZjFI$pJC$yY0g?;Dx z{3GMhWtUIf0DC`pNA>?drtUo~rtf_p_@ofRpj0G7NjjMbMKPU27%7ryN)d{QP$~_D zBsEcVWK&#%Lx{r-p8aK@%% zwU0~8tZtAvqqA#dK62slI*C~aOG5s2Z?wn$B9hBFrTU|v@#Bqx0A^ZjF$;u~^gGha zU{1TwlTf(ognMHZEDC&R9tDd74jaCPS!z=wuENesiZ;K6Y1ZY+neeck$m%0Z5xtA7 zgpDsxJJ$g-)mGzB}J^fg9d6MlXj&{jH)M zu-AIO9X2pMcZ&I8SZB6+$9h=!`0Tb&Shhgh%L(S6w)Z&?XNVsjc7?^3{;`+fioeER z-C*vz>7glb(zIjmcf$0SQ@-%wPGQJJZ&)zbso(`n*;;*UKk-pHS|1#Eym5*jEHWA6 zGkOlzQ)=Sv0GL^3_ev4&oYVXw2xjg2Fk2Z;bGgSj1~Wz)Pn!<+r~2Osg#{0mNG$po za^w`uxg%9V^1O>e_XwD(d{&!`XOp}soeT4Z9{m{)2kyFh=^V^zmDxnB6x7fa12Z$c zM$5s*l!x><;uQxardzZJB#?UB;f7i{899p=IxHah^}1v#Y+t!w zxfEvQ^hu7-?U!0m0rSnAJjWncT4iqa9OfKeq$LB#-CSxUgt<#>TPbko>x7cGq+X`G z=MUyzw9Mwzdzk)iU(#<_W~j8U85ZSgU;PD(f46V>NOF$f{XtlB?jheUn6_iL;sDG) z|H|MqO!?-1t{0ZwmvZ$hspqB2w8O)RcP#p0);_PfO>k%47sns4C^P(E6&x{1jV0BJ z`eoxJ>t#4+&1-3xGWrYuCUTkK{i{a7OmW%%Yp@|@@ohPnn>Sgq-t3RhFDIroe2paQ z&-2ittqL%|IbBjulPd63frY+HGHxMfR(3Y3!aV=5%ULkPVujN@n8~9$6~p{3ms;sC z>sgTG{Hngr`LL9D!ItrD$Q>V+J~oAgAHyAf!otBXQr0lPVu^tQ=9m6Ah_W8$j<|Se z1M%r`V>ghKycT-XPHkdbmQSuO67p395 z6{dZushX>S{c_rFNj?AOg%@jJ=ah)Gq+jr@>;4Yd@cg*fWPH)~8xJC2L8OB;G4n_L z^V@Ld;pod7NWL!kcOzW4pOHb%m#=Q6)dAO*S>&uG_4)$Kewgd9bMItvDdw#ntgN21>HEm!o*>q1M znEOZVxD#v;F~!^vrrma36#TD~RN_@3Dwq+qC1?zsC$Bfv7N&8oPniH$)lb#1gQ>p9 zG-kl^-^;IUfw`BR>hxe=#fK5DBsb6wXOR5;l2O}9UUivf33I323U-Gn9`d;>VfT@7 zQM*Zf+d-eru;cu#eQcQdOWb$_W<^|*KLGQu#J`DxD_(BPIs`Km$9+qJZ7a8>_>=mF z38t4|zuOg-0kG(XUi4*BpZj9MQCK+YvLw$~*2W8g#b;jpOd$1@w4Puks5@D{HD%%Pc$XISmBJ~mX>rG*= z5bsx)VA09#??Sk9U(e! zUc#1BgAEQPKEF=tuZ2D&_fu|rx=R*J_x&939qWOkeNcE8W}jUr+YNh#CR*kYUw`BC z5pLLi!YvPGG~HP!g56&{*d>5DPo)H};Qm@hW+^P18XR5)4-Le7J%y=xi*J>}X_-sb z3Sln$O>{1tdEeLVHB5Uj!s9l~USt{71PedBxWa=&z1{A0!Gbm$uXxx~*X3F-%+?RP+@M_@KmW-i4u<@ZZNvC$ z7?Sbh8l()Uuqf7LToUR#D^nB{Va~ZDH!i_(C&Cv@gQ>2eE3d%rR^vrFB$xg1<1$=p zKI*t3ObcH8`~vJZ_udFIm>n836aj}lQ;xTS1(5^0Pr}YlZDUr!bi1#i9JsE&`Kg_EK;*Sf&Wv0uX-VcAE&OSZ$jiReEj5eK6a_F4zF}R9^1vL)`Uh{z6#j^5?rR zEO6j+>9FW*)2INL5uaD`$`Od02F@%2XES zs&l>{g)0KQ=yI^I*)q=`7Vponm;`g`!?X^-qPU;;RAFYe*C@_$%`fRan-CYGg$bS zEg9eD!;0S3FsD&xBKf?m8k}>}38pp({9Iu}jVmHon3nWbe;X{9G;;SYnC|?!+ll18 z6K@`dd1u>JJHnhGtGmI(xAzWjglj)|w4Q?L5q>!vV7c5EUQsY5`e~*ETw2=`k_>Y* z%|AH99BRuU9?Usjv-CNh4ztEZ6uZNoR_EOEV9w9U?|tBc&2Lr+NUphGa{a^h zx|A}QHYG=L{duY?>+9kF=7$=wNxlRAZ+;Aq@80+2pMHMY{PH1K{H|YcSOed`x$)zp zOgrYI{Ow>goX3AWauUqWJzQ`XmiJvVNeSl6bg+L0E7e3Um|a>*=(!=T;;R;-4dAlCh6o% zxX#_h%mk)H40e*|vnpzh3j^j&5KEpHTH&CY1M$@_## z;ewMsu-H<%#vHj$@3M8iq~GJwQ%9H`6FDLf78Lak`ofe~_jVtH=~`Lh)36FX**qNP zer<_QhO4RvierfRI*EMZUKz_|m~UrJdqDah*co4j1>axp%P0Mc9@S|u`(n_xA~?`+ zbM_5bxOM&2M{t0vW z1$*oe9Qa}4oJyE}E%o`xc_s{=4%6y6$>)@g8wb0e5$~yn1xu3bCcw01<~pxnzP7K& z47lHTRc9T^i9~MT98WfWL#k7$>U`pCxdk5_0B0uyC=DUVn8H9P0hDVR+!0}SU zqbF!#y)*@{8U-^?RMxA*VLP(I#=*4RUymEYHd9Beq{3oZwRCg1HeI`DGRbSLKia@m zR);ebVg8p#)0nWa%ir$lq(1Rf39)xy(|O|=%hbo zedua9NpHsDWiU7Id6p?Wte3aN80G{mVlm(fSJRs&Fuk#&wQ|SHk^05+j1Ms z5`XGbg=HKU>}0`|G2CGlSkBd|#1p0^3tmitLyMjX_QQ;z5pVxser@jz#jfrpZ;OM!=kx3ey|lx{E=cT$r--O6Xfy=kchUF)(k3)#sOR=l;!m z;z@s;@j7yU`@gD;N`@I){hO*`+4~L7*I@P}-d|#a3&W*1VeSQCUL`Eh_|3Tki{o>Y zpTZ5+e})Q3{nk4RAH!+SOs5r-d_~d!JR zo;yA#cO=ZW?vTu{LB7I?(J;r>P%{4o{)g?xz~Zt*$$Uq|whfFW{dw|ya{Pk$^v#oC zcH%6FLnG&wEBs@No`e1~VEXwU$@q>A%e!eXZz)T1{&|PDoK}OGPdSqFv7H>hSr2Ad z1w116&)vgPbpi2@vD##QxcNPDi(&4EmR@pyJQpU^7{S6KeU}Hs$_CvgBwrU)a~G}~ znlaiGrcci@C;LaQTl--PnD^~gQ7Y^ge0qlsOevhx9S_qKs!y#V`SljLb1+@!Tn`gw zrIe`zz`QMs-`T_b{Z507p9In;Q0d14hY>^1PkPZwPLvQJy*FDW;JaW|AzZJ6?c`9 z{`~$XDO|tot+&P1u;}KMw^QMmrIGj>*~l}p%;vzty42=(q@S_>v?k2y**vcUrrwY3 zUkuZ)np$;}{)pE-hH$B>#zryBOPReTji4={G2yLp~oJFB@LbftkvyB=h4a zh<4S78OCe&3}C+4Q+4B5 z19Ntc9!G@>swf|#VOE(~^8VJ_DSI>KAJ^qXe2RmadAk;mLcJq%_NZi7D7{x=mAGrc zm;R|wGT1sT1*VEs=8Q$XOu&qst1w;huH^eoLtfsQ=`ho!<}NwD?Do2K*J0W_-j6A8 z*kZZJ+oXTP;6YWmK<%Do4)OQ=@nn96PL46jg;~Rg6OG}vYta#TuwduXnDsE@L(k@X zn0+&2nm3%r_$o2W^rPhd34ND`JwVQRDZFJ{g}P$5ng? zH>_mtxerTz9tK;qnN;0_MR&9t-owJmoYPsb@VvicJKW!&)SU@4jSepFg=^0jt;CFE)1)P^OfTJ<^s^^cOPv zydU&GG@4sa+_ijj3*2kI%cBVvl)8Nzr)9#R{w+{yhZz%&y(I5jUXS-RcEOa6qmjOmO0M0g2wDgexK*%l?K%?DXfIu&jLHufH(uQPjRk+E_17KU0GGD*&;v+)!4k>%oq1O)kHX9fyU#hu zd0&|!Ct%91-wovWb$cFboPxQJ!gL;zenabBXW;+F=Oxr1iz0c?fOQ4(VTCmV(Xh}T ze<2C(%pd+v%%sy#5?egHsB#__?bM$mgz54{(XlY=;_ZjEuycRoj|7-KeM(^m9JVsF z^AgO6&!_jpax?2QQelq5_wmCpm9bz~226|l>^D}&guyL2uEK|Tag1~gI8e&`#(kJ- z%l={vC%IidoCh-$V|C5oyw%zv#U!6$dCDGE8jAZ+4%4Go$!&$b=uI59diwkyFz2_1#7R_{ z6@Ovcr~HB~$Sc15{4}};{l;lCHp8`hi^9gitT)j|H^NL|PQV11yVOuJe};2*4J*RD zp0ygAkk`fJWz8i0<#VcuSyEHk>af^JI-ATded8>nxiB?#_68Q5dGG2HeVAL6B$;2w z0#$EA(m&omhmBk~eV}m}Ec#u(`XF5QY|@71q~CY2`!LMc3h6Q<^C8ak@6o|FfG4FQXey}$-x~KW}meP zM?J?~rg8@?NOhxLfcwQ=@4R4^^+)YgxXt_CYBtHg>W{ez^QM11eURjLmPOu!#a4T( z{9#eCp6>(LHona=0H#wz_dJAa9j&GW!9uyqV~gMhw~)EVN&lsbQRQ%zQcvMYl2>+( ztAQEbTU^6nzV9=;S~yAhsUQmGo{}2wfc;h+@VfxBXXZ{AgyqYd-X;^TRM(Qx#n0b0 z)0d^eqHzDwam-JFFmp*%x92vM|##<#{>GyYx(nm@!?T`2uF0 z9`7?64qKYvR!j2HC6c`MbE4{dSlkjP>Cb-hdPO75@UG1ri~9ak-i|FKU&g&Q0e08k zC*J{!W;D%KhMkX4bbDZquQ+`cOquKYvlnJ(*MC-rdAA29e~G2<{pCyU>%gK{mo8?)nlTZ(4B-EcFL!IxX$I*J`5@{>t6QV2&B5a3dV*vVV3M%t$r$*aRotRqZ(Q zkE;S^r$)nU`F)al^{T@O=V7|%bp_I28r44~7G|-Vy&Yh7{+E(uSdez>*A}=fzP_0U ziyTT5wh>!qDyPGgpv|Y<;JST2LAPL`Mf{H4aJE&r3ZL|c9>3)cH_TqbEr8i(Q#^xU z{_?biMWp{!;>KgJC{XWE39+8^t1~deBXsc-n14L!Y$B|rSVOBKPHghch8xr)AHIOO zJ~A@ZaP8ElyEUYrt*s=6>kiDUeFd{GHGChdhxy1q5>p2=)jp5ag;`~v`kG%E7$l zM^u-qR#EZ>GZ!u+qzV>MyE+cSwdRz0t@V5-TL z1)Gr%pJ1)gfw^H(1W_bW0@;EIrb zc_WhVTsn6Vto~+qHzxVL)m!Gla(x*oD`1|??UOTMo9H4_Gnkt^ zUowAH>joXHVYb^vDMjSQS7sF1!qkfEJZ0Fx=|$8USm0?NH3LpFdXUK^Rxy{1Z?U*` z^#+)Az(I2SKvu*Idzf=}d$0lO-DNFqI+46!);V+7wzoBY8_c-7f8T1DbE)#WJ1pb` zPFM@ebX^ngge5{52ua%c40qExnZnm3wM5eaQ`UDUwBBy^Xp;! zI1Y23%$MYKE=Ij4V0OH!#Pm*SX2d_a>>I7_%7r2cfz6jiu1|9%89V+K<)U&5|phf6R^XT%S( zUS+=A+AK&B>747=zQTql z-fP~41>qjQ8)2OhSL1VFc3XkWQ#iDI)3ygNwM|9iHf*~xG58_Oy!@df1Ma6k>@S3A zwT0JH;W}|{hX59<(_Y8JwBo#IkZkT|GZwL1%iGwI250uEv_buyAIQ{YJQIluEnIXUxxo!r!~#%(M$G zqhQwEzGeI1up=iepKi7<0R&An_`#j|3zJWREp%!$5z+{k^0{Q6D?qyk`u$Zx7SGVg?dn_Q{*Fn_)reOv_}LvMW#GG;c}-4`$9DN!fzD;nq!N z8mUi>u62hEZ(AL@3e&0!5A1|B-EYg?fH~D$?T*8}i!=Xb!mMiXa0V>Y_e|i!lAlMw z{pU(|-X;CQD3vd;+!z&?Y?wD#+BJRw*568p+Aa9qIn3wL13=o<~IV8LH` ziK)9obzNXj=ZDdf@g^Qnv;sei zbh!7|S86%T`7zI=0M5P~`K<~Tu4=ka4g2|Q*Lel=Kb*hb2-m6pw0H~C!=j>E;ZXO` zm^zpdJGHruTJK6@?G`7Q-}4 z(T{pKuYB%BLzr`N(c2bSbN9Rr%SnARYvdoeHb#AfHOx9RoTYAn_2t$xYXeNtO_O$k zd+*#mu^Hwa=)NBU%d?rjTVdKhqf1G!jhd?o3l`o@SI>jhZ5`;_VS(lYRS|5wM&rs3 zm_Nri={L;OIT+^&Gb;BTmtKVRIY7xLrgVAoWnlXf`{q3)|Gtbe3)T#eDL4Rg=a!AL zgbV!s+WEoa=iV|-FuS7s{!xxjZeW1$Jn_0bJGn{3w6X19Zo(=xD|%1B%%B>%D%dt>XyPeY5V=sPo%GKaO(GVl@Xd+o za};);gn1VyFQP6c-#?ERlkr4TS_+oKtg$u0$6(r^?h#+uHfU=DIX}_uD<|UM3MG}> z9MXT`@|GO9vvPyve8pZbRUgBl`kO66NG|{W zCAnZh)x;$@pIb-z&LZb_YRpiFlZrBC5wmajrEA0T=f*S1_~J^@{Dp8t*Au;PSU7!N z9|LBY&RBRF=6QE>tl`YbSyAMCnV-fM+rmjLp%!Gmd3{f8H^4Egul^?UC7QDbzoHG- zzc5ck?k{yYC3Y8F7dY+jVUoYg`y2olFv8o(`k;=}w~2&Bjn(qRl-1LsqT%d)J%jsT z{?U2yDKMwtot`%=Xdk7Y4^v`}xsvrpk1M?W7WPW}G-)?1vQS&p2QxjU6zzg3xmjOE z7~*;Ac=&+ilz`t$lwr9mvUj{-zJ`pmI&64+{mZ>Dcc<-0Ls&hfeij>ME^wc?685ZD zGT#rghYMoeN&aqaq%X`k)#JDy=8eeMatNj*B~~4U3woVtM@YWoT1q0^E9=n}2-9`E zb8f)mOBQspegwZ>-!Ft!tjhP3^~ZR({N*z^v`k$x|H4TOpXadctCr{FezA5}3aa4@ zt2?95lm5u1l6uM()k_y)e)o;zPmwEKynR0r=Iy-NS`OzqY15NQ{f$SF{qo@6c|XQwk$SmxiF~+J zZY}gR?>LJXx zy}INq%pPTWwE(6(I2IQUC*3OP5WtMI8g(+iRZpMsAH#xiTO*8U! zR_g%j5QH zSpLZJtnPm-yZ-OUewclA>;h9V-aWUdUoi8`@VVu%jZJIKUzoRHW!h5M{>e2jX)!(z zoXT<*!`yo1`q41cHR`ZFY&)WzDqU zu-bdIb6{Gr-#1OTVfxQ`MldrxnXX6b$A7Ccg&7NvO2!Wqr75f@K6`NM667&02T$6= z+^h%oM(~ivc+V{)FaIDp|1j~*m2M;tT~K3A#;-h*?*Vg;kIp5}Q{WRX-X55@^^DJE z*!P`+<9=AE5?103_h;=^ItpG{*+g~N zKF>}-j?api)u;(GjWgJXV9}_dnYwVs^zPT>eCgASe;dH`-G-C)!NToN78t|-U7v8F ziKjVCwfeMmCvu9n^QYA?t@U)R3(VG?a$-BIeozp!5vGonesdV+E=sjs3)9sG<<7wt zeZRM^f*G$&`)|N}V_5@YZh~)e5iGoPHs1yo4`o@sg#Avv>9mGffexi#;Ed+-$t34h zHc87a#s0J^&zP896S;L7oVh_=w35`XwlH1<8&~UJw`>3rFmN z``es4$a>&S*l{xu7WPldbRdpRsgEP|hjir0dSIL6dftT#3TOXh!7SCDz*@NC@Ks~7 z9=LUFCqKgO2QJPg>w)>Z@6a$zDK>xS15*Qk+A1%@{FwgO=?CNI0j|s7sssnkAkzQH z*Txjp73FYYYyaK)q|V!^zM5MuU&!5;~*Fy;NnT-cUNjZcOdo8C$s zmJ%{D1*XW4Sx$29nVp;ewew(Pb=p6(ugKuJaJv+w-@*9##+L1Hg#DIBEigAkrfMae{nkkKBgt2sp0pHpAC+;l9p;4! zLl%;Hqbgb#%y>S$Q43a|G=1D>n4`Auw<6r1oX8k}*$<;VC@@36@98ki-LPXuH`b4_ z-t|bzSM;}QIW@pZDOZk3!@_kfjWw`mLbP}k%=xlA^bwq$y8j!oaP9E`V*WSjapPd- zz{RIoFt_{Q?#U!KZ&`f~?u@vWBM*z$aQBA8G_yL2BFtvZH41?R_xv@KNd0!X1P*NR z$KHE7$<0l@50hNY{Vxq>xtd(;@w}qjoKa8+2Dz5(`x?zchlmu?{iwVOHyov>C8( ztAa5dra#;7Ee-QT<06S^zN1;a*e|lL4Mq_&90ad>U|Cmh2I*(B6LjieO@-=ddc+&D zSyk|`w{6`#Se(D&*%P=b{DXle%zf4JvJ9r&Fff`+T<#Q73cFj(PEdzgvV}1M;)@#T zvtY^`>En0cQaPIq#6rj5mPA;+Q1gGe?`Fd=m=d3!I~zHpqw7&1+`Ga{GJnjVEjB)I z(uwbiWd10k*!jP)<;5^p?t|r9*rKg(xe@XIy>HRRcj_C%6!j%rZz6AN{;0kJ z7GFB>;}k6X>+Q>cIcGE4{oslzjF09pb#d2PS2*nV=1?nGFd;?qK2spGQ*9;8bsrt# zfIRcz;Qm#x*vf68Jp zesXHC@u^S2?lAYlVQ(57+I+%w5B%TzIpz7xQ(iFT@McNB;J%T@K3JfBCR!WyLwol4 z_>lVKmj9j4k;nRnVA|z1lJ}?Vvp)m@ga3QqstZnNJw@{Wdw=P* z2`x-GgfFe_)5SlqZrA#`^ zKGgiw5&aRek&49Jy+7~=WwBp6mEX&PIfBEI{-GJ>x_3zbke{SK&{Nen8x|Vh```Wh zqPQ~`=E?MZ+KzrkmcPLRSnQ*B$A^p`^vK~M$#aq<@6Q&(m;Hq>%}`VFJ};BsdtLww z;{qk?k;M_bdkl+~YP?Xwdam-lI_n9{`msG}37q|MyV+CHe{1zrGuZaz2Z@>c^OcB) zp8QCzLC$ddY_$!R@tpdj9_A-&96SQsB#moshK2rP&LzMq^)+VgWIVO-+&s9}{n+Sk zQa|CyY_ebI1WcrThFP7_R^~*zHLgB0enh{1Z$a`MKsVERxc2 z9*zA==}m+{`WxoMPkMqftTR_hS{CMb*u7r@*A)d_8v_eh?qP0%jc0^NOuraVcIcma z`uo#bw_w&o@!656r*E6>`-b$N((jXknVxf4>=zZ!(aidIcsg}FPL(I z?Y0)~{T@4sSp4E=9S8RP*1X~;OgG-$mIf#J$(H;e^&cBwSHtWv!#%{@xtuhLu?a(b z?Y*R)(iMA26Xwp3myE|u<2kK{lZ%5Q(&4)Qs!=~0S#Z^`E{L$}RTV)9|MjSQ0kDSq*D=}Se z$|dr=vK};~SRjw{cB|*Z?2bs|T{p?dfT^X8lIwF;J3=Amxoxd*Lw)EB3iCQlD-u|E!!c9#NctIS&@ zP2{4hMbpAzUkxuw&P^&9djd2T6a6eOV;Wg4f$v)s0 zaq4tNDoj~D>ZaBTd|q`gRO7+I6pvY3VcU?cS1-fNS<4Bdp_pZ`mW5-~R2vKA5v% zb066+IaUcjPQkra3X=84qxJ=-!p4Sg+iFRD%M-LX@)6kdD^x4BMhp9KrD2AZ2;&yVNrG+=7BQR*i+lS?<%BHn$)=o_iGbc@h|#R2}D z0l2`kM0N=*N_W}u3+_Ku!ZCyCu9;hZ!}eRQ+OCCJ&RZn8@2=l+8(`+T+NnR0Yu1c; z;{bC%Pu}+fw)h@o<^(g$);t-4WerUncESH0&v@L?IleG;hQ8$doOf|^{b45ev_c2! zX@}R?1;M;W&ox?MIoYm-p~UfL<_KZ-rR}^hSm?C(!hN{v&4C4xF#TDS{{^^C#@;Lj z79=($1i+dTwv4+7GZei&d|{iGKD9)cvb2c18;`j>a@^YXJoS}MEg%68IPRv+N`a?gB%7Li~D*_h5RrNzJ za$%-hM1(%v_Up@tNxku72Sr#t`1;?c#5O%< zvT)pl`c+l1xbak`G|Zl&WmXLfW_B0;#d?h}db0BcObuS~;uoCcDWt!IIiIXn^}*c3 zqxzd+;gH^u+c1BURL2LHdsS*-I>!9wFU%0}3?`lS0NQa^flESVqfn9q;5!Mvn1jJV`T1u+BVr{T6ubH5#dIkS@Oxv<5^ zqOrj+f4r@E44iq=WK9UnQv58rzZQ467AIi3%8Gdj$esNkN1Y-$emm(UnEEul?<~x_ zAZ2`=^q(%&KM%9>4FBGTMGoWU#lo~JOp^jwxb1M&1(@r!F0>d9>(+=)Ai2YeF%>Z7 zUcb~O((m`d?Hz1e@aq{57TP9l`9k^`m&8|K$@?wVcV2JBeqzeh_x`hC8R}v+QqP@9 zow5d&XLw${40BHJIJgz&_HUP%f9d{{P}rSXV?+8`pKX^#!_F>lu2*3i)7vry_B$4F z?iT6S;7Rt^xDCT!vtjDlNn;-)mn{x6y$|zi;)lv%$L>j?`7r(4)Pps!`=0N-LRj2y zY*!Dn9`g4W!MvHfzP^WTwtG^_h^Lpyb;0Zl5eJ_T|B5dn`)6-Kso*J0zpV0Zv<23; zT=2PPFz4X9WCh{`GwTYN9XtMl7EDiElv4sTOlHrqfH@gi|2v*uNtZj^`|X3&L*zn5 zosB19+xa`f9>5gcz>HgP?bk`sxn#Vi1saub!_+3r9GI#wZR%A)_;L{ zpVsQ$fyL%^`}$!{)AM^-Fry{;_fI%*pv#4reR)~$ADDmB!u>YP|K#y>l;!`o;Dtx* zWVm6&7fC%o<=)_ISm~S0Uot+kGCNQk&SU(N%s=NseLfv_G?^+nU)rah6-Kc9=jgHI zdRVW5g4e@Ce|N1ffJM_ye(!~q65K6{VTSF_YscZRxd+CR`Qn1l^MU7JJtVV-7?Z5v$csde=`%=)&dpLpodagSf5zvZbpsTUZW(E9_6`mTg`!Eqfw zYyQIQBg-Y@ce=ZH%JgG=7oXg3$UBp2Yh{T~H@*A|vxVpP$-$zXOY@biu-_U#Y?}ac zk8U5D0W&}Bxjzx+Gi7XM!qg)-q$ZR8S7SP6!8YGb=1ze*4h}1b8I~_rD8jtieb%Jj zetG^AO;Z2uwd8odRIRymn0=v4a{hU`(yoh1|1Qq27dRhB^DTOYFx}c%rWy_#AC|fl zrqa?Cp259Sm#AA18|wv^!Q6ejwQXVczT_JZ;fSJN=1f@pMpHf)PAjg;-T;g0t~6!A zJi5qgGpWD1&h08}yY%>?tuUpd|6>BooNXdx!Sw5kA4J2{2X<~dU~bf+wIOin7S&g~ zNPV}FXAoTbxa^TPESMDe?hq_D=b6;|*y!ug?Zm?pm9UN-4qwP4%*?HT} z2JV%*HSz?^9GDen2seyXzJ3xG_2ozzkb3R6=HaA&+@;5Qa2j(pKa$utR$^Z}JFghx zC85_zy*T>G$5@zuJWOI{c3Ws1%(Fi%G4;&>?qyiW=DX>mJ|-qJEsfL{$4ka5-O6X( zfazQnT`lA)PIb|DiBB1oOo!w8EWSQ~8J`>_&vWL>^PLZ2;n0D#6OoJF29GR;dAjSU zV~HmY_&kQixAb1hz%i_kk>w=sZ+SNY4xBA5^Ar|53T~y4{Gz^DHOv;@S0(vws~SV z+?EoxxD96LX6cjtO*5=dx*g^P$;|r-dnQKB>wwwE9cX|pMyO7b0pDI;N)`L=cq zVy%g0vM}XqV=bBQw&|r)#*qAD$#inR^D=Wyi2u9)!>!-9je|LxD|m~M^Cx?Z7!Qm5 z&p%)BkC{@9^O>-p^VmNmr|-{7b%xz@=m)5tX7d&_zjbLqpBEjj-tG4axqVAvY;~3vt1;gW1UY z9S_C1!psfkhaSLkid`aN>P(w^#5Os9g56>6-+A%HaK+#H@|~n!{+(PEoH4=kbA;i@dBO);eYrqs;GFk9U!FB+z>ofDtKIx9YON5Qnct-@NE zV(FYnj?c+0xc?3o-h00KEa_iSmL!6`q(2Qtz(PeItq!=eS|*K{so_rjL~@4;Nj*-iK)#;gWjlv(FRqV8&aXHs!@|jC5Nd05|<`S4@&eper<;QHYFN66DWR6+G)GEuR zl`z{QThj)P%cNA)z>NNrLgEPP`ocz7Z2d@*QwmdNwZYt5ub!+zo@Zm%-T~7_xhUF_ z`ogpKzrmu$-|THL@AZ*a1H`=EjqY%E!uHl*Fu(QIGd67d*xgg+JC1i^WK{^s$IOwI zg;{e|)%U~0N?R5XlVrx`|t*)qy+V&J@qUuMh0T-CV;FTxp0TgnxPwQrZk z!RmhcOO#>$4lDTtSf+&LKNA*vaQ|L~BTUt+h&kGs$=6^n;Zr3VssC|femWc@{rT){ zSkQe#;T9}EDKB4})Q1JVC3)EDl0*71P5TNr4X(0MUa}Y#ulE0X1vZG_E1AG7cISvo zFr~@onmH^~Z`DSmBSI`c7`D& zXJx$?!4*l3=HW2Q-E`e2IA+`F8}TsH_}mc12A}s4yYerSewNwE(Xjek3#n9?XSPma zgXkyb=`c5Fk;H!O3f*^LO7ImGIn;ZdaIL*h>H`WMPJlDzwmf+N(`&8AQDK>^Gm{HQ z?zBK+6~1#wG0YD$mv}h6`a?O)ejc<^0rkf7D_%b(PVPIZ0(-t$sZ>q+RXH0p;JCV; zAGI)}hpI`3dzTxu)x*q1*Q2%JhP->qEwDg&zOy%`?Ow|K5N7LAEbYtXGjb2^Etcz!UEH+{zACc z|Kwt+LENu110O13k->rmBVgK4jrmj9ulrBRNSITya$hkl`Mibcb3$A4V1scU0~2BP z83&m=u;-PSn>1Mb`sWoMoE=dTrw0qwPVmmch9PWaL(>0wxRN{%Vvh5?DnoX#V4b=42bgxH;qnI3Z}jAE56pPtUuIAG<3?Qn3)fY4j(3DPhx3)m z`beu;|JMoTJ3e42!nQlSce4H;Q}-U%;ve@9e9}?TRCG`bl~OdFgfJac3PUA?by6uA zDwQxDR78tV38RBfvM5Sn2uWoqLS-n0u&5--@AcXJyYI*M&*$UydSAPCeXi@;(i&Kx zl&>@$uCN*Fj`*2)Hvp!s6}irXTbX_e!LaD)aodHk-SpZQ+hEq~Qxco= zQXO`{g6bbX7a(UITB;NV3yY8Mo&yhl?2!wH#dB)tvtgyf(Q9{;e2j_BOt?6yA|evz zpESHV71pr-m$8S~+VmwkUuq5|eTs$|IX5NypSE?J@d21Ru~2gUC)?ZXj)g@HhxRI< zUlFmTCm!a!%C(e*?G*g;50ifJnj3#`d=kH&ujazyxhpJw!k#vl;*Z1pi^{*q^_lNn zw&En2-x3_y1!rk>%VogqgERZz!X7eNLBzs$jvej9S59rpf~mLC-CN+Cr5fjXFn^ov z<`=MbTyXjYn6~e-ax*M_zOeWrOqZ?be+_pm($g=1DWR0Ft+08!qUB|hXK0V_CJv0h z%qMx+X36~{{olVy!hP~wa6{U>bDuBg(S0T&_V8~%pjic=*`2IVCLj_IXRfC`nb3lR+2xLrvNjA z!_vgQN7d6O!t9CJugc+^pcB#hFr{MXbuH{NSrBLr(`P1}Xn~tF-nCnj{DCO>HLMY0 z62Al%=^Ac*4~xnU*)N5;&(`O6!LC2rtsTj{)i#IEWd3c0w-d~N+wLu zsn;xq6Sw}`#w7E1OhSLcE#1jCSHU9bkx6~9XRz!{cbHlETjDrx&G=2EKeyx_$<;Tw>&<=(z|?4?uME5XSCkIJt^l4qhX<$ zVO%R*T{LdRK9YCqN^JMGq5A;LUngzchP>>To6$j-XY|uZVtqgb`vQ5IV*Hsbm_I4)@-x!!X}EP37E1^1 zXn>n^1{Kc3T*{1++r%m6ZrQ{Ot!gV_>oe|kxiE9?p7~c{kGpl2g)r^@xBc0$%i^?) zmtdY8b;1ca+KyAhhgl<0>*L{Im05yPm}>WgvWxV4QZh-->?&R82V37f_>|0Z9XXoo z;dG}OLt=gd|Cu{nwn9Fofb_SA8QQ|N{qHm{!YniTLsMAbS#gS(KJ&EX{>O+P???Kg z&uq!-UvlkRC9=QtWe?VmM_+gLkD_9jruyu=6wF#UsjUnasF+HwPp*yJ&~=zKGweFK zzH6(P9n~;px4q=`rYY>X{vDWUYw1^l+;`9V!26`Xce~{MMD4lsLl23kM+9de56HS| zSO-(7=92eQEql%^ZGgGyP6daNt7%>Bd_sKD>0Ugnoxc3x3z+{+ff50CAN^MT8m6Zg zNZ!BYM990f!GeG0=E2Ct=|**LVTQlpj6Yl*!B^^l#RrZEHp5oE_liEk%x{5HH^QQW z@(tavaQ)!Z4KP*Xl=f$s>N9&L@!%gz#cwdnP-+KxKiP5ag}4`{e81~P9#6?7Bdwpz z3kp|8!tA3T=Tm;;dZ6(ZbK&8Xp+rTP_AMp65LV+}V5q=6eev#_aOg?7gHvE`)~KDG zaB`z=;Y^sNBtKgo>%pAB*=hna=1!Bm->8|`Lop}TYrE}$yyNoC`SW3lxbKk%JovD1 z?LsntaM4m9xb}nkpG7duZOEM54*3ap1ed$GNC`{GmE|?DsX74z#8>UJ1Oj`rHsDv+zBJ=Fy(SES@n|9GY zV%4>3d*K>0mF|PEaPOt?Bd`Xi?{f+)-eX>z278L1&N&VFO zW6dS6SCsbPuZA%1{0_d_*Wx$J=-s=(ZN|Vd0bSLgJ3Zj;I8f8eJYy0mqsCQ91(u@9Re^XC(Cm%=xIHd=t57 z)=*qJEDjzmadk@anJk!HAt$l%_V63$V7lg`Yos3?kZT^ld$d`Og)q?a~tMq|J?TvrrAw$ya#hn=5N$mhV`A!JM@tB zYt~s?z}0-3K=OQMD&1HOM?AayxS8a>EvvS|>B|i4U&10SMt2C@SM_4J4QA>nzKbRO ztCuW0VD4<)oK(2pvbCp=IFD6v6&|L|3;YMOCdjKiAoIU|-5Ke+ILfGR`Z* zf<@N~+F-GNNBVe}UY9Kv!7VTKZ>Yk|=F+fFaBYy%%Zadf?*3y6&Un3*L<}mNAe!UYcuNoX>0dosd?>WP+M-DbGg!xNzN3DVr$9()vEM`>v zWx~4e0y=GpJ3p)=HjjI<#}Vc zSZLmGV>#^laP>Afn6YbFkPEDR;Qf-#Fg@&^g*|NUudA{Z=IG73Yeo8$0cSSMjGefU z4vP~G`G&&cqjLs~VcxYtxp0^t8M;DZR^jbvn4vtrVg~XYN5AzPGH;(J(1WeJCQONg z1#gc}olfS55}tFFO^>x*Gp{4~k=9p_lm;RuU8?w{~rRt@& z0hWAT2)1L(Q9WVK@Hv}kGH>rGWRd=Ki#Z&)r*^)&FU)m6We@`o2iI-!gK0~RB4T0A zcP*`Lu-Nd&t%GoDlm59dm~MCF(jl1U{kb#_W}oD2kB0?c&bcJQ!iYCh;$XIVP1zAx zq}(BK>(YSiRFVg|3d!SHEq$$g5*G9~a}K~w4Iu`ZFcse>OLE8cu2wwKm)a=lQzwjl zkO%XN7OIo|k(S$dGaqJ1KiZN2YfM@8x(McG9hU5$d~+$A57X-9?NX4tX8%sT0<+`Y z7No+wqL=3@U}oa1fJ`{eYq8Z$m>%3$at7vPJB<>+f(?lyXWNyOXg84j2L5mzPIOmRdjbnJ)>~8(yM0{$4CY2fEv|#9_m8+ghb5m^ zhC@s6MYu4dT~)0Kb`^9?Y$5$-VS)(m*>vK~8<-_4@7E2dUl_@6Bl(7sH9hd~k+V7N zFk3n$^E+%F9WvoPObamB{u>TfpC8!`^Xfy~Rb23X=)ww(FEGC#=#3VfyotW~8<{Un z`=$mrG{|RjMisEu&_4z4h=Q1!wrSqlx$IG9C+a->SI%!~XI-tCuUo z{D%4ChT)tiVlOqAMGxcj!xWp^XX-Fx*uzE)JC;2-twrXIbd!D(zi^zO4fCE{ZRvw6 zmNfh7z|@-bDTA=MzinU|%x}v$B#XzlF1$@Qghf)0G>di9$ni+m)DE{K^XIB7 zrV+17eLo-Ozjl?^g$GNv87_iF9twP7-IOD#wlJ@P~`}F z9#CAj4Q6yConHY5?+zahfw^a1q%+~ZLW@&7VWHAt{k5>*phM?gSR@;=I|!~Xo6#Q& zbC|!2cfu7*dr}f$`dA|w;-+6xCWm1D9es)Usbk8H!PJi@CWatalHy*;g1J8pGksuY z_jB(YSiH5le-rHKchoBvX5Aj^NnD#|$}E6ss*YtFVQG==`x2NVHB;h%+KdZVVSInv z8W!^E>;Tp4B>#9}nKzvC>&NSRu&`%yO8{(O5HSBSOrzwKvth>&3;!mVsV8`~2d=4q zKlu&JKlUdp2{!NC>h%`p4X^r_0bB14n)x1PDAa3Rgu7Mv&K)qV{g?wE?pso+)&+Af zhpJY?o@(0fyJ6*mWNckX1|V{ZC&^v5@V9 z83Qx4Z;HB*RYa$OGt2A8=W<@SBm4i8%`yWhz`M+s>qu>^6{&)>o@_AcaPrkrk>l+6JaYjw`qiCuTRsI+OX(o2B>QG}hC?%`m0%*Y{d@FiyGN3ufAxN#<+L zn40^;9Qym_M&voWrfF@1`QP@}kjImMI>KVZf^#Q*nqj5cjF_D;+w6u^3(S03^(`Fc zo#|oc6;MYN6zQE70e5#E^Nj_3wTlSJPPSm;^KH+%Ap4X%(fM44B*6Ah{pt zW*1D$CHck9)>$}y>4_URmy!8i9X&fZG%T>U5~j*{OS!-iWlt?1!u)ruBbR9O2}#G%Qg zzvRctX>gOT!OCecC-UbvU0770t7rgAK2HZ*nQ0gpk$#hVkO^GDc<*Ws3u^K0SFj){ zp=2p>0pDgb?7CsR={lIcSmb^H<}8?#xe2CKSUx{Y^3XViV3>0HLuno?os@ik2h1Ja zxc3U&I%>h8FjzEEWy1~FF7ri1B+Oo{D!L0BjM2>81Jj(!8y~>I4*JWYVagn3uUa@j zr~EcC-RIu6M{wfx%UAZp;>ez&Mz}ig)ZKWPp?+mvGwfo?d7B8cnpYaUg5@ob>^KT@ zXAVqkgJ~YuFPtPf`TQK5d}oH?6=Kh1_kNhOs5he=7MH2#4Ztpi8p;(gJ3O=h7tFHs z`CSbQOuBl0!)jKOrVC(^)w2VCVC{~yxpzr_y=w9>JbXa8^d5}wN0V5%EW)7{=69}$ z9YLP+=X8V+rtPq<8?^%K8)>=zCCqKq7%u~-p9~FYCHa|Yd^tG6%`yKyOj$bm*BIEq zvGMaqShURbn-W}Gv2^Vhn9bjEM48O*wf;Q-v$ix&S0nwH%$O14o}%wl;A*yv=4i@# z{Cl)^@J!hCg?fbyOx;+oY6Pooo3%_4rj7FSF@~k*mnJL2?6XsQP2uDMk3=fWnL2}O z0asIKzg0<|xmSq+D_x&ft_h1|7&2~fjY06Ssignj#61w!NGXffC;cBspY4J}wH{3} zgjx9m0THmHw(#X_m^pQMXADfAH?GHw^iQnbdKhl%45_q$g-LTukHTfa4F~7K^gk2V zrNF-W<=OLL$}@LnD%>OE5Mf8=mFU`M;O_TNzpW$lO2VP*a1$-?o)65~E1rChI5pZW z5EeN;EPezBw;7CO!>k6Y_d?j*uB<5-wPB{6o+x~;F39CbmBaiMhd2^-z-;{rMGbW z16X+6PtFI9%b(=&i1edPP6Wa=**i5FVaBA(fxF;AxAg{1u)r^9Of$C&Gf8;|khf(TyX#BXH94+zukT+1s-1i=jaERs#Dc|49Rm*kmTgtInhKkk7kqob>@z(V=vxbHB{Z<|3i96D~a#ZO|R ztFkr3`c54KFf&Bz!(+ItSab4UnEhTZpb2g<8T6%$!t*cp9%+H=r#gB{!SrZL`&PKM zrsRh#Oq+XtS_dp${aIEC=7(Ik^AR4rG=F3)%y>9!d^g;r+%QoE79{dnpWu!!PoJri z`Q#Ile(;pkZIj5nOp(M&lVaCQg?WrriHp_G{+JF+ejiP?-z9re55^ZuNw#0FH*m!O zru8RF`tmE|9~;B0a<@9NfAlJcEp(V-7cSXfp~9kdW-#wdmc$w@@lh5qC+6p~_vo7^ zWocT${6mID#CEn*Jgv$6fduPzSP;1O;v&*_AN!RY53xn=S;l`%-Jq#x4^uCvOZqJn zo~1j(|9dRWPNbKCTZoV3TjdMY6HZ@zbJaPR z@iVwN8Wzv5epCnxUDhTXg$&2AEa+c4|GWt5~7_9OgU^r3zt^Z!mO0e_AoLZJ95SV79}!xdkw<$5nU^rfI2|Tf?H0jqiDIZJ^O;8`8gJnNC25?aTCsOP;*!Z^B*WZzYnX;JAZOL zEZjYPXDu9Y)u$kk^z)ZbYK0vc9qnvbyuT;(8%&v!&fh`u0d2KjczDu^urT5u5hf#W zjmmeQNSJZy>FFt}aXzoiw}^u2uP1g`z;v7AtM|f`gHP7l!1db35&K}4OZHiN*hW>jQsA#1d==EG;bz(;D(YTFkNxmrEPGHOVuAP%+FK_ z4}&!p=V_#oT;=bEJ#gH%sVh#y%-vEClVF1mpVvjOpq=)n2u`f8%;dwYqSDLdaQ9~) z`x`Llc`l_6ZrbPMeG{fQb3ZPS%(Ur!*sPL z(xca4|CZ|to5*~O`mi+I6g?~BCCt`JKBfSBd^)tQmGm!~(WtO!($obXNX~wHTpOle zPjeT;0@06HGq`N}kBR-T=;hywi(qPa0Cxc9DVLtKA@goupAEsRx=tQpx+A!njGWCNb zcR9FJ7p7>hk~;%?8tv^jCjIiAd_FAy!In#hCExD@hralyGZ$u0Y_hC}Iouf50$B3% z9;`ODv}hsBdp5ZKC0w!BP{Rfmn_g=C0Lu?w^S6aLqpolJ2oIJ{9&v!_9*=%}gNt21 zhd9Fm_2;{P!C5vBceujL%9)X4TyekF++*nhi{D-`(}D$mwe&n;QLxhZX)uo?JK{_7 zzrVaJVd<3vFSfyyFKc|9V5YS2RX8kI7`lc958HQ{?}r&D%)ab~X}dqqh#@{Z(fuII z&A*%;Pv%!Szc~u4{XFzG5#~BCV4Z?p+~2)81T(jP`FaNCFJ>{5VS%eq>muBDdEu!P zSh(V9P!-8pmDE&{PaW0x7!Hj;wl)o>{n9k-h12DCoIOSQ+50amxnVsf&YpJ~7MTZ8 zrotTEgTK!a@11+a6t1r{>F2>LhNiC_T#?W!J`eM=41aDU`Hu#z3#1>YG!P5(#>Y$M zsgL?f^I-G$qA^*>xnjfp_u=Z*rMEL+p8V>Oepo!kfSnF=-oE%g#U1Zg9*CLwAOI-quSVk8wV^)*Fs4E}xr=oKjIP8vttr->$v{bLW@r2!}bF z_be+T+q1H`xEIz{yt=6p<}@8WyB{{FC|Ola`tK%uM`F z>%zsS4c}_Q+^>edGhnp>XIE{Q6+NtI08{37SxkqS)t#%2V2$pp8HTWsu{O~ZZc3}3 zYzi|hte;rGHC_xaOIVb-BE$-AHQZHf1=C$m`^|@|`A;L~6MIc7UjQ4JMTISZC7*wX ztp#5-7Q)oOdx{sr#-pAX*pghw;nh-@?_9WjDJ=QCIGn}Ip1cxf{tOOa!OUga^F3i+ zL1^24SnyS6zzb%DCU=Ur=-z9)Dq+g5XJd(L3t3h-VRr1ITlsKK`ia=vFx6*Q>Nz-T*Q3Te zu;^-Y^$D2ObSV5TEX;VQa~$R=6im2J<|Bv2NpRex1D>^H-hQ@EENuNjBjypzc-a|r z0PY*w8u=KeJT+*Kf{poEH4QM|E0h@uNBmfF^(oArw(Q6jnD4s1_XR9Eyy6Zye&#LR z8{fb}UE$$1u$}#!tKUe!;^$0v*u%4bVn57_vKVoJ84+W4{)NTt08$7&B7G@pzciRDW2`Rdz0&^n9bgYIe3?Augz~ToVsh+ULnLC=3V1B5^N-sET zN!n6fn5mjRJqV`Nbo@4gSu5zfg5esr{li%>#o1Y9J1jh__tFfOd_ETDzSgRvrdoeAl!~ zOjvX)yr%}1@3>gBp5#;KHjCj3lVPm@n3c2rulYLMpIn>0f=KQb8DI<3Hq4r^i_G(M z95%tNA6s9A!?eR|f&$^3Ly=W`VX?}>;xt&JU630C3pc$xbQw;5YiykWvw}9{Rl}hx zmgXeGlr&GH`!G{)vuz5@J2RsB2v*VyZcc**rbkx_VM@}VdIrpKU)1&j7R(Dx&4d{z z!y)v)8_wy=jVRq=S0A57cyRjrP=@xb{Yn0l^JR|r$a zJRBVjd(4^L*#y%~KFmmfYrTKEwZQz@FMb|_?M6yxyn>k#UWS>lsAbvm*D%X%Q^^I` zH+<%KV#ax?BR9$X>C`=>&uNnOc@6WG9a72m1zsbQ|H6WGZ-<`1yh|7Us&BykMxVy2 zhuM!UT20|@GYi{CFlEz`d(Lo&b5#C)lJ{F&*aF+tbf(-P4pw!IfVsuT(Lt7vZ$aZO^MiB7-|0I9Y3z+(3-pUzpT+3z2`UoqdFY3dC#`w!bbn5*EtPa78SFVXhH5ubKP%pmjo1C?W8k9VJ5&L(}UYqIfhS#DOADaDDwM^8O%P3DVM_7l@86Xz^}xgWdi4#6(0F`@P_&2t^=6zrO3^2q@f zPFQML2KNYKL{2d8jLGh|a9ZW#al}-GAKUxj;!RymWS%L@+hDvAZZ@J3i_KQ0TfzBP z3-WAXp-qS9I#{i!#ekTVk(8VW3ya1{9-qUhZNCgBca8RUL{6JL+xaQ1QF_5~C7J)C z6Y~X@_aF0i4avhqC4I2R++8tlFhl>>@?S7T*8PzOEH=ro{sZ@AM+~uG?!lrvqc`Dv zdly;|2y;%3Su_sLdT+0_4W_+0W~>5Nmz76`!t^Y8$0@L*LSs`j%&ZwsHzT?IZzVIxZH;`@ zZ475DOcQ;(MXZ$S6FU*+CiqDDj;eQRr^5n+Nyn}u7i);`&W7=M*~C0pm$7M`8O)6P z;&U3NG;M9PfN392Og#lhh+Osz>j`9Rx#n6vTi zG6pP+SV)P09fPtuZDHQr%$+-6f%b|*2a-Q+j@<@(`ajKIMRHcYQ3zaC^tG6nz0JUo zIMJb(x)#QNq}s6+_AQ7t+(7!WrS<`^uw{nEW>~OMIN}Ri%h${Lz|6oe*S+AL_evdG zVR2rS+bWn^@nByFOqtrAWDm>B%$=|cX4Km~v49!=zH1^#zr~254eNfpON)Y;U#L~8 zu(7kWO*F~t?2nFtvu1wVv>#^w_PINP^R@d{$oW`U6#FBu7fuVeuudTTFI~LnQv(> zg(<}!f63tauoE3Sufx5_xCe8hVwae}l#xoxeOOex zIm8N%kYYb07RXLMy#x+WYz=z|3#T5=a)mke&Pw$#pZZfpa((IkYm?YNH@pyenr`Ok zM&yiCnN9cLiaYId8c6?lOjAEh`7o13`qWgTt5g=wuN1Ky$*J-Re$(LA^v{dxV9LuY z?-#>^NHn_Ma;kgvi+ww6*)-;lMeaO)ynEGI4?zejYp_)_w!i{xtwUCI4Ow zOK-4@>x0Evw>*!-ypU~A24K{ z8SL>eR%{AWZ(Gx55g$ICVnOU~FL6)#w}W$G%9V1V5pwaFf~gD0ywz$+o>Q-W-u(70V|L+0-;nrMRg;$>IzY+<^b=WXJ|$p^+PgIUiwf6d|0@O=+f z!lF?llKo{aKjpWYINSRX19|P=DLdA|f@>K&U15Ii%aP3_w~8+afpa_qR(Qd*(^tB7 z!H(sp-2!38uxdyOY*%XF#fCYIB@sM0G|E^foaCimSw%2!YRTp(nBBSeH6Nz6Wm&}% zFAfu2gLBdkk5496{?dOFHdkR!JqFV`idv6gHa}jF26KlRj9Xw&g<^|SFm;!D*f+Ro zthZb`%=yB)DeaB>YoGHRVm_^PqZS-6(JWqKN~y9j9Qw4KlYyMQqOoEzZ2U~E{xosF zg~c*hY5SIB9?V;6wRi)ZUUQL`3$yY)oPyzwb*r-SV8#tPcRQJn3LRen)6Ei;qTsU7 z6ZoH?aQwE;|FahsC(J1>hWSgPe(Z;HuAN(10(0isWpd#9Pxos0Fmv-1n*(sZvwK?^ zaj&jqzK0VUeVuquUQsl1kKIxBH(-{h!-NP}ce0oFO_;6xa^Ehv^?ixUU6}T^xOp=i zSJ!x_1{M_+{Oa3;0jq6V>HQj}3k9ZReKpR{%o4%0yfy0CFzY2l`XkJJwsg)J zIKW-{+$S5oz=1iXZ`3ubYk|}X0HR0V?m`CxD^ovK_SN9T!zu=rj-#TJ}_b-^E zLYJIxE*I^LhG6jnBZ-9-2V92#V-4qbN~09;d@HPuWnx}8RW@cc%y3Yge;Q6>i_gl! zT+iBf#KSYk$tl4iTm4sLe|5uF7>tAI>pn;xU-#0zG!>ZTr76k#!iSS* z51XJ3a~@2#E5>~Am;R$Vq`x9ea{has->5f@^f!E$obREt@|9=6?B`aCn$V9Ej>|KG z1rJs=e1&}{#jDU^+QZ%=d8`lHt1!V778j|zX~PEJVr0z7{6{g(05+fdqkIm`KR&@| z9&9)Jpk$sSbu-QxZcV$h)r9mbbSE+4#2?%#vtUY&+QCgQugP#Vd3@@3#noG3rFxI1 znXn+`8Y2X*J{VX?Ec~bbE*93E<5?uh)gQ^k5i6)j_Mctd`k?@3pRGD?j6Ob(cA^UQ zyk*-<9*>uF`{NV1+SIy(JU{w6gYNfm{kSDOOJbo+W*@9{a{lpoFg4C*;y<`G>~`}) z;`s9s%04(BPdMl;h8cx7v$Wu}@O~NtrpV8~YXv*z`qV6e`6>%kmy`bYMT+(?r%D-(H@*I_c}l~xRj}ZR(lTO3mg}@N zusFo|!WLL$A5`W6vo0^uA=}HR=-OGdlFS&j&@92y1^I_fnqeg|mw4Ny? z6JhbEf7QES(dY>|G9>^0A z&?ycUeAzE?N1&5R0-1N+Epe;e-i=8xWvlt_Vf3Y~D|Z})spD!MknMHE%j%sZdAX6T zJRYz1$H^+GEHb_(X9!bv zs!X^9GuF$;nZaeRCbwUPIc(jdc5py1ySWsmQ*s<#VXl+@5{T+mch)4 zYyKWap032~ybiPdws+OR?1M9WDoEd3XHFm7A}`!jNo@1uy^Jr`M|75VgY=UMgH>V5 z8Y-s>rce6&Oq)0~kRc%X>FtXb!D7bOm>O6(J9OR_m>co%(L>TVQPVg88?;t@tb>`p zDHSJSrI(=x8p-^ciA~vXu;Ay=6IfhFxlsV;%v;#m1ao^6Lo4B?@UtPU#7{TsKZXUi z(@I6KC}eusQ#f=d*Y!QjX|h<;1bgItR_lWK=hmmbf?Y4{rhS7c9}MQbh9gce7(*~) zuv0`V9y2C~G6v`0-ietn;WVS#Mba?aW!APvxaD+iyc{gpIl<{3to^(rPl0$|zG^ib zvFW$H5-dLQ{oG}eKiP4G3NuUptvC&HPc5FV2Gc)CbtJ={yKDS3V5)bxax6@tU0F00 zrpa6!-UZj3jR~3w^RlYx0q`I@GscSKe|$~c;NXzsx^}Rr!%@!;j`kgSXb&@!Z#LV& z`5f6)M_9Zdpq&BNYPQs^fLSlxBV*Ek-R1cWz*Y4 z|Jxh9_o~+$=4ubCIwE)U5sV5XeakUdm%;TP=Y?#8sc9d7FNcR0j(W?6g)7b_xWe51 z?3kS}yR1}yEi7;hT@(ScvJd|7hN<1_SL}o7Dzja}V3F0&X^AkeBP1#nrkOAMkPI{0 zI?d0+!;!idPQ%>4wn}9E<=4EOmJ9P==^OUK-Qsmwc_eoo*xm;l&tOXz!>sUGW>S7Q z9$gn~N{QX2I#uEPTUGC_5~uC-nha-sDj2y2^P;5BTf#kC#+|5wX`+J9n_>3zAU6Tb z(b@1J6b|jHnQ$K#pVAqKh3%9TD0Q%CL(rm3xV~{~!(%f4W}jmb+;?P7SUt?l%Ra~_ zeWlwq4KVfbmGFnKy!rd3#Ppwe58uE{ol5yyGM~FO@+-_zTkiLO%nwlmdf}EJIlCH| zcj`s|FIYQxsPq=hNs9fZv<25+8uda2%s-}4pbdAVS4y6*VEcsGdT_GSk?a!W?B^v? zCa`exF3IuY4%zma!g126UHQnl?bQi$VgCGor!K;b{r=$#VDsgEJIV1DF5fX>5%DXz z+#Hgt)TcVaefNX*XA|erFS)@r+xEB-v;LOUdc&?q0wlTcPjhP=EZraKDB0fGjLlApjeVCEF6`5Ra` zmLN=GlNbaj*_Xze}mM7l{Qy(slX@tdAAPl!-?M&V@AWw1&Rw6!`6+r*GR+SR2_i>Y{yIZLrmLfGvWf5 z*4#pMp#jJF%?J zP#Gpn6Z*SOg*9rJE^EkqYSz!`u+lZ2ts600#a0iFo;vHeJIvSonPdRBp3pnE4yGG? z+&&xjm5N=u31+HnwX=dlMfq_)Fnf<`+C12JaA=PoEL_s{+8WM3#!U-^S$%<2JDAVv zbl3s&2VyGM!scl???YjZ#*Jt{nEGpTSr|;4&^##sPW~XIMUj5TLY+OZb!Ldv0pj~R z=A41;g2wj7!K_Q$VJvHTtFkNF;^edRX?eeq3u;2yXX%MEfWqC(P-&y{z_EtPz zb?*J6Fzw?BYYUkD>)$ahnNJWYxWb&%DeqHB{#0>tI9zexq3jsU=-+VY6wLp*Ux}Fi z@{jo~I9caR1?lsY&s=!}r+@clkzBm|(%4Z!IKE$;B=h2&%{z2qL5Y$4apY_*-?<*J z#{~cX>GvlHW8hi`w?kx}dvCYg894OF+gGG7^2+*k6&9-o|2YA3^!_C^z`jpDT{uZR zzuTu7HVAZLr^A8|0ZBjML8o6q88CJ4Ayc_v{Qlu;Qfem5$d;*`2G?9)>~k8XDRF(A zVDrl%og}9!))Z}qgL#RPzL>w6&4K%x*pggu%C^1iKYh`@7Q14iMW&9$z%?4hRY`A7-A+3x!YUV&J?9pcIUIlZ4lNM#d6&eZCZ^AUU^U>$v#3Ofq zK7>UZ4M$hOaRCF1>R?7>Z?^zutm@JglHBmmnkR7eg1O4gFf-@omUfuc6*B!bOqtMr z=o2j3+MUu){M&u%H(1v{KtTjk&wONmhwVoB48JFqZI?LC!sC56%-jD{;-0AQ_rAj% zxn**F=!?I(r1rv;NA>Rgur>YBgF({oZFut+PH)@%b{OVgvHdkB1iv3Nc7OJd6Wv%GvQ!%p*a;6=tT7gk$DZV zn+nW3>Jk_MTdSw8Qib_ZPV#$TjmYU+)nSfV(&I!pz(ikP2c~TH-kVAC=VOH0fo_DaI>4lpKnRooR^Dk^|+308ri|*cuSK5x((+|F0mat%~oRm5| zXz}LtTrz)t=6OxnWqhIY0+^!2NSFfGyxLv82xhx!ZqtJ)Usc~PCV5FtwLYvpv*9ZK zs9ivrH+ER5DH zXTx;&v#IA`y3dNAVZ`zm7rugBCtt|h3-j-tOPsI+>orQVH4YZP2&dY?`CG0eC&ILL zDU($&&n{&_GE80P?XU^1*S(ybO7h>!uLQ&LcTXpsf&~?8=Z3*n?b)j{VbdXaGc z)+BEp%&Ri--3Ql7$GK(0^h5JTAArRo`lHLR==hu7IGD9U&8`M!$2Df3gnhSflYT_( za5E_j7Ntd>Y=i~I;w?F_{vYCvDfv!!XACcrfI_LyvLbJJF))b>RJq8N`1l^ zQ#e0$W8gf}cV2LC6D(;>d;=Qw#aJOYp z<|bHBzE=AstfZW)xf2$*9HCKn;ePG@bXgS1UuT>!fi;R&a1Oxq{g3WB!#PK0auZo+NgS`Fj}V zoK=2!9u^KnwpGG3&guI_FfDA?{`YY3_Jih^V7{O6!OyVc=qRPjFl(#Kt3KFehK@WR z=32~DAA)@izC~Om^I4}g#)jhlaA57@o3QBXcjkP!qa80nFvYV)e=W>k7NGfvn6c{S z2DrFtPf;Vx8E=;45BomL`Scv7wcp#g15U0}xZX_q>yGDy!i=M{yW3#K_RA&lusp`Opj{Dk@cuF`(OEXJr6HL?mUQ-Si$F#*a zz|^ZxdT+tHYMX7z@!~7I4}S<(Z}8rD4;G(@7QTZ2?|5&p{m>1oO$x2Lj+}DI{_Ag; zK8o|cg!DgsD%A?d{d$L%OCc=0t-Qn>W=+#wcah|@9S>K+gIn6>=ECgd1#fo1^}%bq zvtiDy!1hzH>y7BG#LT5@rrd*zm)^L00T#T8G3$hr=gxDIoL`%}%m?Ab;mGqm(toGg zt-YI^|DrwTV8*+=W45sG*TEUY)T;&yx56Hp)RD6=|G@4SX>i#ugF<5FD5Ki~SiJF3 zYZgrLqFsIov+B?JXTsurer-Qtm&l(N$obB={Y^?G0_XSGIr8LsU_PP?>2R@~&na@f zuu2=_0^qVh6PF{zN_q|ZV1ED8eTT_>&g-{F;HLVv!{mD76rS3a28%sUZ#_u7xARF3 z@$|oT@vu1Pl7BUlOmeaJl3-#kVS4h|R>91ZhXW6s&bo;$N^ z$^D4qJyFdM=J#bQg^~G<9uJe?KE;F&bBM~brl{we@Z`@Z2$N%k5ag& zK3;Oa71!SyPqyEhp>U9#U*eQYl78@;2*VSkzgzJ;*&g$Sc}+SjXshErgxSMSKV`tY z`I%Q=!16w0ZHQULn)83a5hwlx5esRz&rFEMdd}EjOwND)=2l}9SWUD~a{Y0Br7m6s z_nnpxB=>9P$$LIdFy$yk;~JUQvdCZ(cmMiZ1=IQ!m#l)T*DZNN?jNFCHQmJ8d)+1X z7s1#Pr`Czm%pXJj)CR}7-8v>c4*efdYE5uc z^M$dRu-Imy*Jaqi`pYjJm}1u6kptJP_?q}waA$C zV~_tmjJ($MUV#Zr({-lB!NsblK3I@`X5!B%IN(n0)CDj;urM|Z9vr2t#31v-?oYPE zu1BU9*}&p=j`VGCQ`X{T_At+2*eVEScinYd2{R8{7Wu-=x90ioFxTVYXyW1S7bn-l z0@I=mK5%h@iK-vT{SWGFf_(+A>w;kFlko}b;H=}5PKU$7ivDOvn0C*9$pO;8=QG_B z?vT%POoA!8@ja7Z*LA0UrNXROhc&&p-mJr$6;Hw9^RkL>h^Idt$$;tdEwi4(_1`Ne zoq;)8H}mUY&awDp9?4UO_S}KfLJm3R!7P*4JNU4{)78brFn4Tr$pv`0De7}6EHXQM z=oFkY`(@!Zn9^)>DFu!gN{YTtyt!uMQ8@pwMtud$kNaAF1g_;LTit}Y*H=pV+Q}OV z1hA+=q3;-Sit3Hv@6Y}F-G4qG zuj^WS?X}llGwS_D^mtRI3f8YO@AmOVn04u}6*+%&^Q~{bCqB1+GaF8dU+_!<3%5#> z1K}DwcZC*MP%Cr!D$EL&qkV?i3pYy3;KE6lRlgC-MSPXQE~Zk=?=b7V_DC7>KGk`z z>w@Vs>=nrS<8tzQ#ZQdk70``s+JMe^UR)T9{K@t4HNuR#pFl_2&P%O6Dv1Z9Q+s5xmb26sYl; zQ*FLl!46yByd(1wjk%b+6Rz3&{PACy(H|2R4YQIgevO!b`SUN;7s2xPJhqO7*(Wsr zzK6?ns~=5-#Va`#L$LWtp5J7cQFUyy%2AxRuBkVs!!)&-i}hjo>Yz(=VL?#$275T@ z>${moFz=zsKX+I(ZQ$u5SQPU)au1xds%X|SSmtSD1Z4BYGp8w%Jy7D=hfs)3E_gI{4plrC9R`bL?hH?beKh_o3Ws^CRa9=n4g!VeQ7~7sZc8z2EdQ}D z78VBC8cN`#2$jE=VfN1AnqE?`^6beqSfXk<<`2xeu`T5$@s$wc$vm7N%^Tw~NdMpG z%G2SXd)qYb!GfKAu8Ux~In|4vz|yDu-OjL!|Ggy@Fnj#vIDc4spx^lgslT;-#u1qD zh(7od7TxgXN5ZWAIu~EV;!TDA*I^%Rr%yF7eZ#*J4umxsaKDH$l*0?sh%ncS=^ikLE_y2v{gXCJY zva84gR?8ZA!}KA~hD&hSm#-`QVbLw+=0uq1dvVlZSek9Kgg9vONyoFKeuo=XFGrIf zd5u`tzKGm!beZZM7@r5C`>>vSdu}!{f8M47 z*zss-X+EjH80=UD^Y_LXKZJ#PWn;Tx>BqBkL@@VO)yD}Tc;35DO)Z9LGRj%=;H0f* zKR<=}R?cqg;j+VV!ZKL0bMu=6u(sL3dF3!O$J6#aoT9(!>@%2qbLe^sT*C~EB-dl# zHZXbwYpAi;n^<6OY_hpL*Fe76?XAq7b z`S?L0>A$~f*7)NXKc&v82$nwY@R$kPo}Fo40<#BICN6<9&OY8v=Fd#|<+v2?(OQu~ z&3BQD&uZAkbC}f&e1^EjD`{;zEcM$%PlQ_w|JHYs z{!Mm$0$5@`Z&EMJ^LCC&hRxT!kNXSL96TuJ6&Z|@SHSot)uIICN!?MUbXYoTFfbN& z+|@r*6{h)IXgLRWoRMjn0!t=*e|rJW$bLI(I`O8>vtvk({{e3S%xH*TL#}Vle}2da zX1QGcehp4K7TIJ1^M7wYd=u8ye_C$|3sUQh@?ml03UwQpuCi3|G29<|qumx3P5U78 z6qa~cy2;%JdKAAAiC*`og^%kc+uK_lHQmO4to& zSb8|bWJD;Qe{Ijm?J&31Ym@>UbZ@%MJ~G~hs5u&NP3FJ8K;rf;%UL8}slN3n%ot_= zelF~u@GJfVEY1m0H-MG?-RV37ORk5=EQX~VbGZvJ*C$e70t<{y(l5hI%|}fv*o$rC zbc57C@pxeli&ilF(noIJ{IDI4*pB*IqdfG{hAFx1n%c)&3?IJC`7iL!P()A?P zw)Y$$X4F602TRh<+#7_IKIr1Eq-?XxW4U44=m)Eewadp~8 zm|@sCyc%ww^(A&Q%-5}1T?g}M6Uy9Rp^G&99W0(85qiUd5wBi0!2v0Qs{W+Ebp135 z+_=Q=#9^3SI%5Av*yXBu_*Gc^%7NPhSLPma%z*h@hW-q|oV`}%SuoG&hxuPP(r%aj zJ(#<#X144Jyw4x@-4qeK8AK_;jPo1wpTV4Wr$$eM<;n+gN?|^e*{=!bj6We;4&%2D z3w2?Uy1c`4So(A8W&=3f)@OPpENNS`cLkinKKr5q=2nL-*#_qwAA6dN$A0}F#2+?w ztzP~V<}6;EcLWv&R{1@FnKFSfVK7b9=2S%LW92HM;L0Nt8y~^+;2X;>!vCGW*ugSB zgVg_8FpiARoBwcX8JrgBKab3pwoE8n1=pY3ZbRnBEHZ6)13T!3?SBJvWR{ozfWzk= zhb)0Y}j1lq+_ zepd~qPjHU>1#{#j{ZnAk=BzdJ zMdPXkaO>&0UO})#AWWYR=d9nmDwOowj47N0yT}ZMC&41-+A(DQ^|N#GGD&V$_Cp@l z82?N$7v{WH+%^(+I5X@`3Ca5hO$V`FLiYAqFJXpd;3%?wT<5y*y8p3H(EL^JVDYe9 zTf31f$(C&U0Mi%$%>4;V8?voF!MwX0Ji6eX8*+mkuwcN48qa6NWyLO-la^}n8#(`h zO4Lu7akP!PzWm2E^*yj8Yyveuy(+u^c=iHneQSdIB~<;cNl8waf66xLBq{1e`>H)p z!}44c%TAbnp>}2_Y(6t~^$(aP=w#IWj|J7|YR8?#`#8x)gVc-LJX}qQ*94=Zs8rz61|#t{OK4Ge5*n62bjb9(Kzp;ePpRW8TB! zmAh`q!So@8Ge2S7O8p5FU{Snk{P1x6`Qu5_T{_ItkD4b3OB;UNoCtF|XHSubU4o@M zRAKS?!S+dTkkP<+Ry*B9QV$c)&EnU^24L;vm{fP?fv@eW?0%*J8c2+Ca>XJVeW6Qud`v!30uV-#Aj8) z)nK9Dc>g`{f7c^4K-s_>7TQg({Dt+Cbc~zn1B<6_&u@oAUBk`?kenw~t%hmGW3-RJ z>=7@Mv*E^8ouwhLVEn7>Bsk}_OL!>ESlaoS4~JVnR)~a|%-ZiBF#YhR?(?uz+@Ws? z_h=a}i6!+fC51CdeYfZN7-F>xXU4#+YO5(1U~X_^=x^+=9{#m?XJOXgfJ5!D-h}XH zr(izw)XFN@bfkt>IH}*se0Z1CJ8e9C5|*f2&Par1|9*N-a>4eepF&{O_l8d*V2+ll z$Q^c3@@XOSW%+t0o5OOOa(&ml8|Dx>tbFfI}YIY9HNj|G~7B*g1*_BWF zvj?A(^W5=@Zcza&e7$+wc{s((O6M^#y@DFQ{S;$cDe32mUXbetG~W`I!R&`j_dK}7 ze?vw&Oe=l8t`P3H$@X{&3nrU&zk-9>eZ@7fl>T>T6YO58TvShTHNl2vIIS$k_$@5d z(Yf9NXIQ#DZh~3)H{9D`Cikx31I%Ci?;EkZ@8yTh#96^6?QqG89q)g@jE-Z|$>)cv z+GD5RuwYRbs&{1k8rHR+aWH$+ z3^{pX&gzp$++FtzSpr`ahe0+rc91sjBs`?Lx(sn_*7FkYPV8Shb1a zM(XwUP@gAU_gC46V8*(@k!+j~j@4h)d9bwcMmhOBY1FD{Erj`7e@`5Q4a*NJy@bW@ zN-xgB`NtJ)mAr)|=~^Dvut#u_+^|XL-*0c5Mb1Zoo0}5MJg!^wp46XOZ>jyEYyM-D)td$VZLE{r4GqmWG{?{9q*SI&V>b|wxo=JnablP8jZC#Y3E$nvWwIgy`t8`yzhIgKP*|` zddnU4q4!++j=!^!4%;8{4UX1} zU%nV-x#$+oIfL)#sbgA}!1T;7s}{g=`N3Wz_$|X%?Bv9OIUcRuC)m^oHJ(3T3EDf`rSXUk{{1+JuHZ`l}zU2yh)N{ny1=xH>Bl#~y!Q#OoMDDvl)x8uO!swhBK@2@>o3A=%|iy16GLpW;gpfb z77)`y-Zc|5y5@F`4ma-(+XGUi2 z9hm3E40{bbJXhB(gxM=LDtEvc3+F9(0dr_4GgU5Nea94ee}aWh>k8~(uNOyV($p}% zf@*IR%;l~6sS9&0>>N7al!+sASTH|%x8saxtY@J1OdnX{ruY6T%s904bUNvOKE;U< zgP%Y2g(mdCJWi_3E7&xw(^6X<`}x#f*%h&vPw%YnHn7lATj>g1U*})C3+A17z9fMq zTcZDRiLY!q(+8Wg+U<^$eyfO?nsL}~nydZNVRqQtWx;Ux>yxuy!s0Ip9yPE}U%$S# z2G&1K{4*&Y-&Z7VEJK*tv+GhZte3Lm^+s5DwTP*bfX|n&Z}0DfX?Gu;Hi1oRlE!gi z){zx@_HeYT*x~`q(c7y-yNL16d^+9;^FDnzxE3z-6$Ev`!l>wv$BF%a-H@Gv>p!`s zCxYqgE^QqP(}wGG={#j?=0IaU%-(m5vgFewH^M0W+uV2>6BdXpP%`<0Z`JeWIRck4bl*7#4L+ z93tx(v{1>b8kTIxm;DAizA{g*huI%fsr@n(eBJ*oEFL>Ou@gDnrQ+TP(m(!pX+JFQ zbnH+IO#d|J^hh$lqYmHNV2R3;=Tl+rs;su!1afvkt*$}FBeoYJ8Apa~XtB&ckJO)F^@ zjWB)Q(i9gs(!J+!11#_~i*SRJv`uG_=OKw|ZSjE{WkNZXB!7Qs(=j+_)L%Mzp8OY? zCoaKx(vR-M9I5%O0@$$Wa0IDm{d=>&5mxF7qRdgPOznfq+HB=sAg6UrQdGT+{Vl(i zGWWHk^E_B~pnYx?a_-%^<5)1qb}O}Cgohh=wlFP3^*{S#N!=?BZ1|*@`3CijUjw~+ zVB58eYRG=!3a!5Hh3g$2sl0~;hrQ+;gdMlv8EPi=N*^El!-DZ^sQp2knJ;%37TLdQ z>?Zw}l7H}Ej?#=v{VX`T8Hsb9+cx_Cs{%u&H=H?-*B(qMn;HbY2$bdz$?^ z0#_#31&@X$KG${!z^&VOWn+o?v#QB_f=bWt8V~<>zVa_7r%xpQ?l{T=d70dVOUlH8 zcJ6k#{YsI82FzMkuwV;pexjm58x~YdH{T4?b(asDPx83+_t(Sq&A-kXk^IdneH)l_ zdg+}-FmH0k3M)7x+WgQGShVMm>d|QmvrorQHHAx@*T}PAq1m`EhHxaiD!?56 z@AGnGt}n2L1q=Ue*F&DS=XUsNm^t%8_FP!(b>z1l%=i;Jz=Yj3Z9MH^iPN=*%iyx3 zI(Hmk-Vf~}dpL6P$s-$Kwr|!YFSurLdbcC7RZCnn%ro~*bb>iO?L*IDxeDWzn_=mX zDM5qqkp6_n#M}n6`HU;5ACp<=OzJsDTutGgxWW@#V9C+>ny#>e{Jnr}FfXM2=4m)- zho9mOm?>MDaR;UquiNYai|$_k^At{Lq4(|~{gZBa)WCX8`kwnp{=2`m33k{CUiRthEo7f7_$r0*Ci|Q1x`JiIsccykyr+WIX8#aS0E0_sgcP#}Cg{ zj)6^&ta<8(T+;iq{u;?uuh>#)cb_=-!Ilb zOzfNbxC-VRU|lBj5#&z~YJw$JFXf<-5%QrDB;QIT*7W>sra4iazrav7G+bX`v7Bky)> z%T-vCyh-o{Ha>DnCk5us=t}!cu9qC2e-ox#Oep?H>W}W4eH-RJI&e?|)4X%1XTgGJ zX%!#f=&hQ0*|79NzZP+tR`d9WFwHk1>MfjPcjG`Y%u3j}wiXt?Ra1Ei3*S`#tA<@1 z)m}e`xlNa-^>#Uz_p5^VpU#07$W7-Pc)x)8l}`^Fj(GQq$ zF3OhV9OoW1~p#JSa|Wtrv}oW`O0=O@u9f5Z*a%`ESG68-^-j&yNdA&69YA2 zk%n2R8a#CO{F#|BYqrrtQ@D{i|2#3{`^I;~B@;9M&W7pZlxyr^*6o#(=9B*C*UxN& z9cj{c3t;i93G@A7_kzSX2CyKw?AmcSMeKOg5T+gRICdFUy0d2kv83<>Jr8EH({Gb{ zn&#=l&*1ur8}IAG-0;tWTvzIJjwUUaTf7u<1#n=lV%l|7p}>>Bp(yTkj584nIl zngkD-ET!frxtlay7w-S(K#j+5&~97=n{pTYA=ejfz9TdvHe8*w2W=&ift)FlLmYmhIC)abS z^*Hxtx3OT;+mE7?VP6071Ez3>s!eMO%zR?1wiJ%{_IA1fi&9om4miF_ zkWTtPCQ{G0^+#>zEt0>#zm_~-7pYm85SEO%C2)k@7w#Io3$u<@DhI%V#3r8{nD)eN ze*|o((y=rbW}hD45(~?IaHuID{d=ydUWOwt-@p2hID3M}6S^gYmwk}r4hF#u_R;YkE zizWZ=!w!L07FNRak=`{TxMaQcR${)g&8And+>~ezv3S?HCSr-}i#*cLJu^AsBg_$f zcPAE(o16R-mNgX0lX~u_iXqu#?2mFMxfd{V^1G2Lu$S5EV^uJ1SAW(lSQ=UPAHS@% zGJ{iA>t7)Ar5#B4;0Sm8n$}Ya^N%PF?tqQeqf*HIii_@td%?o!@*%Pw;*$KA`(Z;b z?)(y1^ip|*KkUGGHM$sPOV1xW2A7#6pCo4P{_^KEoM%2^4(XS!4fl?RIf_5M%V4^5 zY@YzG4BkM^pLVD>@;dPkkBhHK{d`xgbXfOL@ZwrnY?!H*0kb-9`M)LoCVSHE!r|3z zr$4|iEC5|a$)?W`Qx1GBeRt|9YJORIa?2TPOsM_nTM zWwV3*uy`9MFA?S#hD8s+!pUZn6X3!}-iK%mtp80$Zxn1eW!i#K@PF5@J#^0CXjt%c zXY&!{9oIt(#>2wYQMQL*smvuUC0OJ;b(%MoaO%X2Z;pi{!|D zEHp6Kt_Slv$354Am5Qu}&4&g2ryc5W*-VEDeVBgF@b4s8>J-2-gc)m*9gk!FsJ#evl-0VevqpG8^;|xwgRSmlGVLbA zs;LvAR>8t!PhU@m*+CcM*)TiT?#3LLK38SpI#@8EzE%&`wirHt159fUw44XayP2)r z40D{{_mR9%eHO)%^(y{z ze`+z3A$%VQS6*#E=0^)*tnMe@4;F_z9E2I$A7^*N?*5H70aBERw`FU8PW>iGJpXknKZ%TkUp+~+Oz|t9A7MEb2 zW*7DOS1FnvdX3b7I=apid1ZgBG?kccD?SN_e>_>20W~7jffcYs*0}T!so#}oQwh^2 zdheuRzi2=HHTD(BCr+i_kEB1EtLtE~%c>}S+l?^)^0HM9aO2$TCM_^~=)t}N zu%tLNqz$G$u$dJCJC-eR_)PNSzmm?tE&)6$7oWLKBj>Nc#I261_ZN;Sh8uUNrIC7m z? z=LtzYJ+`)HKCF7Eb@@Mx$9%GP%p%y-UTQl8(^C!Ym%!2jOZ!1`edc|c#c+nr?lJu^ zTNqPp0BgJs9U$|Sd|P5Q3znPLn@;XWddr?R6PDG^i>2;2YvDF6xQ4Z+`wQv+KWBsp zWyyNcy*%{W~uiuZKG{o4u-F(Ia&S4$N@B z|CLyHTRP1P_E~~-M9hA`s3Gq6I#gEh2@&Uy!+synl>9>(69-rqFYW5SD~0ONB6h`A^|3nEs`pKNIF&|Mf79^k4ajGrgDS)B~?+K#7-VENGD_2hiu>mT;3glWV2 zWJoS>+FvSxGpu;_DKM8F%I$&ko~`U4<8zc}#fb=s1V5A%bORr*>BVM&og6|wPl zCbNimqbp_A%+#b}SSobLu|O_JD^(!RgLU$x|9Y5z$KYNm%yApLU_0#6S}04NA3rYa zbpYH`Xqfh#^qW;@@nFNTd|z@tiAF0%g~Lgzcd7j*xZ7QQ8qO#*TOvj-)IFdT1e z(@k0o#$)v9^!{D^`Fx7rQ7AnHXc%8n-K&nLs& zoPXB0P%mle{H9KFwiD|%+<4-I!W39AI(1PNY^xakb{Z_MdG4D7*Z0LIYm)xxnt?)6 z@4_u1mW(sM^AH~5Ux=MS@)!fx7o`9Ak!)R9YWel;S6J6rxnUN}-+Lft0M;JT3(zC| zzvVmSQ?cLGqAli;diM-@^f0*Yq?1L6udCoh5QeoRj`htuk&w9O>_EhOBE zV`3pC=8RC%FM>_EbIr;1Y0bk!s^J=4b1D~Yz18~xmj7^>8c(?Jl4%P}dp(J|9%tIU zL+vo%d~_q(9|F~HdEa5xdkuU=ay{Eozkb3J`{M~@KZ%~Y9{dAKc{>w{S*{)mf8m;! zzSMqWo-J57_6GJd{=o<(n1$F+f_f}azasHfR`PgnzY-2XJffQ(n;m&t}% z=X3lP!d%Vu@0{QgdlhqIm?;jOycO2|m*HYcywUBGC)_i+%hC*HXY5*d6t?Xx$~7nX zx6r5YaJ$^EVOB8zfr)=Qtg*U+N6c~Bn|P1p_kQ-Sf~C53No6qq(!@QsFw0c!kQip& zRnu8V>iZN!Kf*nc&a#_f$+|kXA=uPAR&^`PUsbbCF%6%OiEF+ROPA&+sKBiAo%P#c zo`&WFFA@vOH$An0O@|NPvm2&ex&7P` z4wXsS=?OD)S6p#}QxY#b?Irc}x%NIJ|Gf0*KA7F{aCrdCRx7RA5A)Z23l4_8D&Frs z05fFvWrxDi#VbsFV0v!ts#CD8?5P=nu;hrc&Nh<#!PL+H;gkZ|F{fbR;;NiD zm@RYGDT4Gbo0gFP8yg#JI1P)>_#`L78Iz_QI0JJO+v=~u-0Hu1=U~Z*bsKKONr!Ld zoQGN4=OqbY!F>;x3#7k{Ws(j16y&duff*J-ss(WT@P?mpq(9)aW+hy2ZzxKD>GId2 z-olInn~aiR=9#FH9@sRCKI#&wf7t(J;!V6?pA{+dowsZ>gw2oDXOf&7p|N`5zJchTK)$dy`eMhDRK4sH*K)BzSHvOFsCz; z-vS#idD>h7Gx9BrTj7+Z&P`SSW9E|{#w(aFw;OTyAGDVEOiKgh0fb}WIX9Y z%ZQ_JP-%GYTbL2q5pW!qUBv8q2lE=uxF_KHfAJ?Ju)wQo%PE-lXrW&#=~q!VJqOEw z?H&FNW+vZ@NrtV5Jh+`Oec0avx8M@Zs|8&!t)VkN6Hc4ry0MpdMBSAfSS&EP)DN?C z)Z*^LL&vYP24Ifc-L_nq{Y|BHkofll%Ha)d4*y_w#ZAg`StiP|n*aZJ0cU0`7zOj` zU5Etd>t4)36?CLni~#x zaL*d3!T(*afO56t>M+BsH^U!!`09UGrjmZEi7wk=>y3$dnlRVSbookHwAnsF3+B%m z5o82wEcRST%zDe#)Pj3{%u=3C>Kl?LD3N+!$!rZ$|9|_#>!QK~RhX&4pxzJsn|7we zg6T|IDfZLQyc3>ez6{N!)czFg3a8Ax*;deiJaWwe>VBktE3IF^K3)ymCZnEhnx`j% zGh)wC_a|~)tB?)zr$}kk^)v67-h{aYo`;L6o=xt63~%g^ElY&g>AoHfkiw!OOrNB=0dw}Y7(bCwmt zW!?Aov0?F3y_?VB*7=qv9boCh*!kqV(AIlK+eqpo)Wm}@V`X#32AEwG!&SiZOk1Y+ zj?~jXT%9}vR{B%8Xg$p9syJf~|95=J-=BpXxTah>W;Jqt*#5M;aO)at4KiQO-t{V< zVb0mMHRiD7$I6YE*#dOzXcZf=kyN29{HyfcaU8CL2L4D?58!Xt9wYl%idLs;Qn5h z(cUmqVYKc#ShC~F?1LoFo_2dP9Bvrm?MG}GTDA+04{aR?hWT3q{vCtOHCA6f3bQ`M zFFgs1MqUgLg(YXNrNzL!?d>L!ut?SMYC0UfZNK*g(*M$H^&^<|yGAL7z)mZQ&ibwGV%S^l9zQ4 z7Hoa>o(}6a+b9;2`dq%RE*$>x;ZYGxEA8xB0CNw9sFlFNtWiv3xW>8uZ5hm4_Vx{N zMxWf|DpDWO^dHYVa^f}2>KLoC3_0i3s=iv{m5&0q!D(|0SJx3oC~P|ftKOM;tQr>1 zGdmkgJj%Dc5*GP&*B8N!yTe?cQem<0cZX%LxHYuH!Wx)ai87`01bC|c6!kp=6h6fQV zic}WEIq!z&9D?azEJ_!_#@nSz0WjjmZ zY4?)-EiijUjpuVve&cRfxW;kk667WE6#?5webXE2{)ASng|0BCamz+~U}i|b|1ccyB^GKeK7notQXN6paY8!$@0=*Rd!f2 z`8?xWXhh`0?5aJ~=YiPSIQltUGo@vp3UcAHPX|81hW{)!PK22^UM}l`)6~tPCct#X zD|-fDNvF@=;V_qzC7CS5di~y)LWB7qP3~#Ig54MhC8B`P~TT* zL$w8c@c%cz-mwFNFnwHV=P=xlL_IoZ>>Ygm?BBP3G|Y^Dc}@|wzF~b)9>(tzc#~k( zgD!hT;xR6^T5x}`%74CJ+>GMRgH7d!uO-iedthfAqQaI+!=!*F!RhIHhEu!we-#yxXi@zuCE>=f&1AHi^=&U@KBkhco*-N-b)2?o-s0BZ=DOv zZY%j!LGrW9o-BoH?!6R|^He-#U7Iz`sQYR33Z^TzE_a6I`wmg(D|eFa@ZGR&zDol+ zZ^ccUGlJlx;z(y=N&V74d^qF(&SSdG^IQS!a9W!>Z&?GoBGX~hlcQ^?^VhvXGYi)J zF!Edj`lWOIV+&yGA1BAWgK1BXQl=Zv9oa~(x1cQQAsO#u+5rhHI%=%_1a8+L2#&i7_l}yz%uL8IOG5fol#azy1sh#)zJ7fi;$_a{C0+v}OP8 zhSM(g?k4vqY*)YS562t$85iM!}UTEln1MV3nzCb;nVmSZ_AiTP}y0(Wff4kMqx z;-#6|`f$+P5w66H&+}HVh5y?xm3^Y?32RI^N9_k*_~gEDn18QvT@C6vY^Q2I$&VhU z=Fe)2QNIC~sEnKN9yzaLDC9OA`qboQ6HFhpIGYW((qfaS=e=}8_9IxrX7BrmoIi7# z*;6=g*@^rXShDKL#W%2FH;=M(QqhPuSU0XDx-eG^6F1= z$$k;+K6_y7J$ye3pH1x_MlN4Y9yV@Jqn?jc!ANF09FSP;MV=pxp`N_}HfJSJ&tG7f zymtj$x#U4m7jkLprjKh#zrM(#2WIa+xpFJq|IwtVmt2oyp1TL`Ne!Ar%$ODu8Ugcm z>oU4waff$73M^cBMNGz%aQs=fVfo@lU1IhImy<%cJ=-~p>^J6L)2eJZZ3nHH>^JtA z^1@u06PG6<^A%XBUDnFxxwuE5?Ed- zf_fkM({{POfZ018D{D#pGmVHk*z3dZzvO*pjNMfB4rZ)o4<}|GtDW>7?pgaho17=& zvdUr^BUV4!TcdYcL-gl`ZXCMW3@6}8s@3$y7?M((;__Ayr zSwE42;Ne}EcRD?RJRjaW#~s2eR|o;`GyriJ=FamjhGT;p1rEG+qv zw4wm+Z!Jh30dt#}=L_Kyc8jVUENuNgtr(7+G3?A3Sm0`&^a76lRcrokgQX*%KK=$b+Kuh_OYUdAk)sq2T@+kR?oX<9E9w`l`*&q{Kg^u%q5c=n zIKliv_9HJfxqn11=KJu^C2BuraKhwan%PTg{rI~kD=ET5)BMhp^OAA=!YVbG_j{=o zwcaYZ?HZ(BOZ0-Aw*qxJ$yB)gr6TqIahB)podK(k458jvR_S=z*>H4^Iko>dE|(YU z!-a?Ts*(N3ZazF>Da@VfW=_niTd!&hOG<8=koTXG+9wC#vdP;D$$Yuq#}*!iHLMM(^PPW6W$Y}nJopZc+j+&|}* zT~#dXb!!2&KHP>al%wYq?K{kfuhBywKv zgL_$UtM8^0^00VCU&bA{Y?1H4crxD7>r`Gg7@42|OA?fKW+G>t;N4e(+3X1B4cIij zM{_dCS3Yr1gbT-ZC~J^>k@c!Gu!p_hjw!IX$aQ--Twzz%;Gv zqy0$#B}b2$F!TDKnBB0;jPJbJu*g&UIe9+9(kYdCFx|lWofFI*u2?i5W@}hqav=Q= zmKiP}p77XiJzQ4$^tb^`+dg=G9h@iD*D)e_YB`nHJZSr73`-Y(@7RDmT6K%#Qdl@Y z@{kK`x>%!|1#`p;cXMDVd*9a;u)z5M#|;iWms4W})4e}#a)(PIzD~4*X>aQ(S2~}P zv4@%Sk6L;l7jWM8uY>u$>!S9+9_dGW*27ZesSkW$miDFt4lw(;6SY3kDl5)zf*DPJ zeB+Sw7CvusggIpf7q7tqc5M;F%=K=rLYS@gM0q1D^v)>~!D+vTA12om?%Th#iMT%f z0hu4qp~rey9{xO8?Nn?7i>3$)$HQ{=D%V!Qv?zzw%CPS9TW;)3g= zY~Yldq3t@Pe{`{M3mjkSt3>vru)z4PH>|N^1GPU{`spu@z{aP4Mv;2)QtdCN;s1`u zFQ{;dhrQ0;>mt|Z{h}A8!E&B!CXxAZW*@wt4x6^b$&mXIkJ*qUg!RUnwk&}ey%C}U zIMOm!XbN++H;yWTho+BfAnVUtV45$6wM{B^lIO{IQgBcL%RQ!1&zsg27}g1AsJWVu z{X<(~X)KeE{kk zlnv*&8vBxZW>aE;C+t(VEMzlGo2y!S2Ij1)_|N{7d0mtYGv^jl`%%FARGtIdhBjA` z{YbZ9Sr)-w&OhF*g+*x%YUOay^)L&vABEGIan-Q7)M3CHma6Tnt%t)$nmAMYuPs8c z369LYxq`fp62Z!=tuTFgZ1QrLaWU}27g$3nEsU(6M0jmVC#<)HC0zt_x6fGL2fMS6 zQ~OCOH+}ICEL}5+I#1{kx5tfqfS)hkNwSzn>hl8}M#H+(>eI=2#FOPRl;QZ(QQ5jM zr|n_VG~#ug)P7(N?xpL&V!74l$^K#e?y)d{<%&{bXTZYkj*W{*z1pGaS|sPb{$&n_ z`+a>i9TtCDskjavTK;j6Sjaqe)&&lImXfRq^Vf;idBXDhzm{mj+^n-Z0%4b&h9ATN z+WGwvaC`hHZ(`nF(cfe^Ag|{l<=|thDV22BR zKM%ni>pyuq@Q}_#XWsv~vF%fI7|eAW>qyqOaNfjcXGs2T8?}Bpqb(KA!T(*ql%m@= zVo3cLt1wH{bBZ1;PK5b2vmbI`bB{#cWs=X`_$>$yU6DXp^07508CF&7J$Df~@8N?Z zFX5zst%0#HZO@BI18{xQzgy8TWA<~8@rC%lS=#cCn6CCWb~Y>;Tq;U{#dhPan!t3O z(qaFPse6x$>HFV@KanIICPF1lL`5;Flq7=?qM?IIG!jB(C<)P&C`2QjB#cT*#gwQh zjTA|TiO>my5K2QuQMs?R-(UCd^XKz%z1G@mueJ8tJ4bRo{CYOa3O2a3Am|dz%>4X` zSh%;iM*#D_ET2f`!I^W#$qud#-o`=I`A!+7ynEmVU^G zr76|Fbm7R~fBTAH#^tM;3|MEX{r7t?d(`tilVG~o;m&>1J~i^-1Xz+#%zg-S$~{{& zNq(zb<1xwCX?;+KlMZ|SsfC5|#$n@O#oA5R8eq{B!O@v;f^J7&Bk{-6J56Bo8zG}! z!?b~GdzZlV>>$dLaa(oQ!7%}cDNCo?HhRH=;wsiF)C=CXoDGC~zkR0qW4dOm#=(k{ zx8^@5`AE;Gn{d(2&D3~UnTsA)!~RVM-H(uq4`e=)z!eE&&XvHz#;(*h*rav$+X9l` zR3G?6>KmR{fa{Pinx{_EjK$@9i~ zy2HH}cJqvVdl=>~oYXN$^0a96AmZSt?IVhDzK-;0BG0dAMc!3qIC9(cXXHGP>|P!> z3O4zbTJH&Sziy=LJc4`N1E!xJafyyxwxzsmBh37v=d1+(JKs3tKXXRHw#nb^*CS`; zE>Kl~)o!gwa)3D9S? z`=0wZz96;5S@V&LJxiCWB3GYLHk+Ks{DS_AWPGVF?4vATL08N-lJkGbCYZy_TjQ)q zev-ita?c0cYias!HVW7m889>p!8`VtToGw`u?XE%f%go#ph1VFd_MdoUjvc z;Bs^7dr}-X@y;3e-}{QSe*c^#m=!xKnf&}AeR9tADon2^px$Tr1*DprFz0>A7$$O_ zTJ`xgI?~=*)fi-~ICn+H@bn5gVJS`}t?@4|A#SL7ujR z%6nyA)mfpvXkb=i3F@^*2UnBt9r}i&ndEwI?uF{qdYw`U$b-Afp5$AT>u>8?lL1!@ zxl4V|ieTnXXgmHu3tYCwwu9^kX8PwU0daW-_45L2q8F93ZZ4LrCjI?i zdjrKCj^yVX;m}pfGf{8y+Gv27zHo4%2(FlPSwMciVPEty{sPlhxKKae2!CCP{S9l4 ziY#_Q&ewS5EPoH@@xmu{WWUn>-ao1eJG70ZzE2s#1|2ona~va@gIxHj@xfSFYW#pl zo)4+A_7E+YeKqgNLWVT0S{r$orGGS>9(l95!r~>^@jL z|Bc!Vm@`K9F?oN|+9y!$MaTQ^9)g8qzEandJr-md46}U3CHV^PtF78CC#OMuzI83?@(B@(CTjwx&P1+3(0vSnYFdx2VAj0QQ=X{E4d(T2h5&pL7m5<=6wpM;ULFF=2wtQmvbc7VA;WNm#eUZrrTHo zr~A?#lJi{pd&Km5n6EJ;>N@F9VmZ78Zn>U$g1kQk8&d{f!7&!=?QX(Cx9KJy;MB34 zk%cgQ%HV-ESh(=#n^IV6(>VDv@!)vr17hXnmBfK%b7jh5PRoitU*H&>{s3a`oXTio z_3%^o$@`Qy?*09*Fh`>!q=w|Wi>!Xa@)ns|PhqB4L%e(m=KHv%=?mf-{Dl|Tk|U+w zhk~W`8!Ta#O=iq1c2G? z5KCo8$4N=O(=*DgAuV#bImv3UAhrIZ6-<+VA<=-_U0)_!!|e4#hfRfPW-S|6!1NxYcLs3% zJVPg2SoqyGWEL!Lz2)Epi`?^ z(jeRjhi!A(w-aWmdwtvjv(|9O`jR}%Z1gTzeP}Vw52lNP_6EcKbH?4@2TPOn9TH&^ zPFmwZSl~8xdOqBsIQ;4%SoDqasu|`i?EFoP-#;t*N!nkXZcpXhH}|ye|G)P1Z~F&L z;Pk~qzmon1g;wJoVK1Ak>E!xSIXk{TT(+D$VmHj3uru)}EbnfZLoA$E+#d_`hx`qz zJ%uAbYK++KT7s}d^>zU*R z`Fp^$A7#65!Hj2yhd8igmFeU>Sj}O_J2%q)j_Q#dm>+9Yvkn&dDp3}FTan`c3zz(T zOzOju>08&rG_S??Z^KE(zXr&DU>^*>cn?mOAMtiM%s0y_CF4=k*n7r?)F-RB)WGc@ z*jh`8Q+)5fhUF9Z{!3t{QRth`uuf7YgP1lpJz@an46(Ie3^SgmHma85`J7SHxDb{m zwcgf%-89b>EPzEe7p6~x>p#{}`RjsaGc3966`$;3gJarck|U4$KdTI=T|J z_UTX}X3jaM-~dNx+`L81l8xKz0r!S27)ILDOI8ov4m-TOb(lO)?44?v#L_SAkLSUR z-yhfRfXzqJ7m(+P?zE1o|2IB%PwaXx=6!Qp%;~L$a;3I~ryyzP&;2pLtO3X(C)SAwXk3O#k$JFa_oZbiW|u6-oDX zT!p3k8_mf52qNoRGvMwSyBo;)!B+e+`39VK?^Ts9%;>o*yhXf%_jf8Rd{Ayz02j@< zetI&@Y5I1m80I!V*-g$Xp(JyB8C;?Gc{(}Iq;{N)O4#zk!dc^ChVo#14IK77V}vHm zDp515gUfVg^3`FTa3LN4nvC14`V%C1f9CT>mYq{io9y|N4TH&;4K&Qr!Qi!3RuZnivc3e3D= zv!w%O+*Y(B_s{(hRoo8CPqX9d!-9_TF~ltki$>2Nc~AnC=gs=Q(GZrNJ4v}AxAqD- zfB9wkZk?zvd%CX2h_ttuLtQUHWugmN55`uNjYA$_zGh6DO!kZ5&d&-uEQ;>GMD3qf z?J8<;!m_jFUvc7@>dWIEDb9&uH>S@0ivYv2N=}f9W-jLdD;c&zn zz5n_jH|frGIMPBffa?G8bF(TqCVmywpIEY?;Vmqk>@kN$`jb1*-wwC!?xOZ5%j>Ge z4>+*$@I~@G^0m8-x?y&~&Cg_i(tUa<7kygSu=0N#F)qHH4Krg-F?vzY-%zy4p5%`@ z$Nh#ax0=Sg!o0*4*9YO4TbrJ{!!&P$0}5qW|6%!6d8SoYV6EXj(d7B&j5A8e zf&J&sqs|B3&XKiG;7G^wV@;6rayQ;?h26>&AI*ljDl;E3BQ8({R0(^CB`{?xUT|3$TAw$xm|L@bvY*XTsjeUf0Qb(eCvo7sH&Gf-9?F;m+xA zOJP}-w2++d(hJoNwXkrO!F{q``~Z#nQn=T4*dMYU{FsfMiVrdVX?wSAfh8-pdK$o< z2YE~{n78Gj(MmY5*6}*A^!lZ7Yv72%jnw;|KRRLcPT0iy^FwdsEQ#Kfy>RPsKkEH2 zU0nL>FwAS%PQ7m>W9hC@aFFgF>U}EEj6HXi)Tg<;+mH7Ay7kdTuy7UYZxGD<>NZdb zJNpiv^RQ&(T>8_$o^ZWYhhqvX&C!<$gi~Ez z`%+=%Nt5rV;V}Ci)eO?##!&l9FxT(UrfV?2;=|_@xb;zoY!)m!tDu(wcmMoZeI1qt z7mm$_wY~>U5W>8LmRqmGws&}qw_t|Vx?wr6=-iH<1u%0`uWuIYc}MR|5zJMv`9rQJ z)fYz=llGgo?*xZ=+4`Ft}0w1Lc%;%!>S&w01gF@n1II#22{wJ_FE^WTi|!1ne}x&=)zLHHh`sq~-(lW3J$)ujudJ2%4b!4Vsmy@`ZHA2) zG8OZ4K63hE*qOD@YXr=$J#XRwhkfe3rUZ+J_nU5mMe_!JD#Oe@C#HqKCWc3T6VtS% z-o(yp8V~1mnDRt8RkuH10l7GTcA6=ywlMqVNLcc0aD)r&mRD~|Ebxt7 zz8h|@|DrBW{9*f_P}ut5Y3HG^D8SJ+9Yv(ro#YnM zsl#C2&ZgbBU^>I0OAeOk=x)!4BNfZt$@S=}FV@|K+r;~HRA6SU-_jD;{DH^9Q7~si z_>M|A>m)OG49pGhKKc@F-A|jM0SlY7uf2n$2lGrtYGeS`Sg5PkF`y0G0Z<6>G6oPpPA&o1QygEnfVM( zzr1nyQsR}HB&{%0Rc`1qQZK!*s0Z%OJbj$hv)A6Um3xBv^~5F~%u!uaNGy&r-G2m@t`)gi!u+(2`4Modyj~XzmTs8cdIn~WnpH&Z zSL|`5`7Fs<7pVIc?^2i%3kS_!Yeeps>wfV-9IWVnznNL0_>Ijd(sM+;mWcT zz%A$EnJZ!8{{DzGn5$s9&=%%IzAw#!`KfbE>|l1Iy-N;UHR5_U8TAgTYA z5MGU(L0hJM0H)txGph!!U<@ld1hdWuP&wz%g|~-Ed#{I7`@CtoI!9p9Ifd^PsCVWa zX$^;&*8(m-f|;VTM^2D><=Vc7aG-|c#M7`~&t7W01%lP8XJGbZ#>fZA^{m;|aj+=z z2-Tk3*?u<>7Bfdv{o7_;^1BQRgZtNz{(_=Lj7x!~6X#tc_aDP=3QvRC2^U?<;MCvA zy&15GbKx$Tzf|uxi?d)Z-KMMrmW-&<&xHkZ<*Uhj%Q`5o%!BFrU#f`Jz5BN1!;E!* zC>!)HURwl9@{9cMA?IAk*mw_SJ)L=zjK3H)?NX8-FN+;lh4aeRExQb6|6#Lr;e;uBZ6Cq-VRokp93ikOu7r8#9!M== zliOxJ#Nx}D3G-oFhIxt@mKw$+uZF|2&dAomLir%qwXj!$Q0pnoxPMJ)1Kg_G;ZJgI zU4~aEoO+@wq=vLtR|t!O**cl=k4gUJtKE6nlJk{C?vHlC_h>RK*tR(CHq29V*_jSY z{fq6%d@|C56f$A8my4Q&uw=JsF0sKjTH$q=yXmn^HXNZ`okP}(x&6n~0=V~3RgVA` zb!=W=0_)w{?4AVEPwIazhgl&bF2%zVcj=f~xId}pL^RB9%GG-f2R&p{`-ygN!?<^_ zbizm}*>8M{ZAo9?hPMY^lKm(yJdpMWRy#jw+%aM|E347fSf89>fn+}mKXlE}gUg%` z=!L?}$koACaD7wwU-h{-4G zVDl0F3#k1V?ddFqBUIPgk^M(A9bYuM7VD|sbfz9=SRQrLhZ(Vce$6mPjz4=T%#I)9 z_>S~HUNy&12qM!+{gMm-mwT{cWiWU>$w4@5Fri0!J&jA==aO56oG6 zd8-917?ZwX0OlGm5}U&8LwVC^({O)}U(Ypyy$h`u$ihsSOVs>0=!bLUV9`qJ-|EPd z?lIkl!;+h)KaGGJZmf_eW_`RU?8JOq%Ii>OuUdA4xGMQU!7$`J#qh{h*m=yY&LJ>! z*Zsj)ux*U^>0dIw9o*q9q`j1NWDu52^rjpmudUnlB%9p18jLH(X($A|mTS zd-LcH4fkWf>}HVlVlj5w42LasBMy=EWFGgR%y4&{{e|TDew1UXXB2;e8FCvkWKpl< zamnW+%nug1425H2Z?oUSv_GqL`f-XfOB9S?m5t0!@86Z06agNeQNUk|7Ho3Yfo2KHXYx=74!yhbN3+rK>f zEvYXzzVd|RDyM76cMSv9$$j%{@W1|~nU|vC;DS^$s(*fBJ@x!r&iJ#NJWo}Gr-{qgKWVtrwC_2IJQ`QnMR4s3#ZbDw5(zygLV&6Tu| zR;#95`6S5&ZqrcyZ#|u7Uw4FUhj{XOP|y9Fw$TB$`Kz&n>=#j_5|<6*Ka^`F&xeFz zwRjm!`#bF=wf|V#&zQn`%eVEC=Y##iK64h#$ttGyucT*pxjr13wwQWexCya`r@>ln zx$nq+7k$}Goqv*$Q9|;3&O8`>$rl3g)<6JQ7B-V!C48QmbA1bgO&tEiBA z?M$jZ(AoSgF?(@m)MVtAGv?Hff~B@+sOuHcPR5Lb1yO=zBjoIFRl6s_%rBpB+rxRH zSqrpbZuCfM{zP()c~eOJ|DC4}As&aO!EC>$)cQrtiK);dx#(T?W3)Hd*BogG^HZXz z=a;`AeTosxqAOOB{cNt}kz+>cR~473V!umu^3vzRQu_mI$$7zI&;L4~*w{{XI$ZyG z%@AAS^)LQ7BcyPV+>^k=Fu!l~;qSx;^p^5qhWoS$-Edf0^}-O+{&}i*53CqA zOXUbmYwFqj3pTIMP78yjzkb;E!8D!JTw>1jmFtOH`(Hgf28&Bez7N0&t7@djVgApW zKgxBOuOT$cNSHpX^sO4~Jg4ME6fAn(IE(?OcaE$%19O_YQzsKYzw$8_#?Onh_2I1L zL-XQc-p3)+XTS!G5d6t`^dGPB*bo-JdG|Y!wD)xp&4Se`%Rskzdw4G0<+J!&C^BB+I44un0GGv>@?V5{a(>YePcmQPmZCahc1g+_V$tCjNok~hWM6L^>|m0jbPbk9CkMTT z8LJ-dy#WiSeEaqi?w8qcUid$@EVt_XU-w^kK7I3l@`mNds|rYef6h_;S+{Shy${oP zgNMj^yXD1bmJxgIQ~L?q{Fu>M4bwNeZc)JawL*Wo*TIq-#Ab1AF#fcqoCHrR_h9;mFR$rVk`{FDWR1(;H7;`V5OKQev`Uy?Mu?+hNA2WR)v0 zW9*$*-(dDlm)RF!hw*8@zr*w?TY&o`WfdAp`C`$+qR)%#AtYB|x* zeiJu5r<@cMpELlomk$+3BIjIE=$F;S^_4c-lKy(<-xbNj!eejT8R6*FHX z!+Fy+?G#|ihn|ddIQ>`htC28&LHEfUaR291zm;LBoUZ8uSY9Lkfhx?j&>ty*wZbAg zh}pYRGkaiZ^Q-T4SoET_WZYAXN2RA;iR1<&PR)VK`ador*B4mIx2=b{3jI@uk=$6W zVGrzS$DU5cFYfttJPf8y|7=Hc+Ll?RIdE$Z_r*|{{%il<7cjr%Xdw+|h1!h!3v)vL z1oV^nu$~aB^$g=HS=7=C(?_1UV*!U{jy&@drUfhwUJqBi_uJV6Gp|mVcNn(a$*KAQ z^RE_boP$}yJTX}xaZ^d>E!b<$tF^?Eotdmk*dYEQwSJsYa>pgG{BIYkJw1BbqYrSa z+n9@8r2VZI4(+hcoG-29`ofn=>c2_-PM?ire5^;8xBY?hguk+>_2!0NAB3$(92p?% z&C{>FLaWDkV*083V|InFl7Wlvn3N8Z{`c!#7y=i#Ret&lOP7{X^%eg4vShx*5jy$( z=#SQ^pHA%$?X-{{SX|dj%|HEy;@R(TgfQzDwH_zkhJA(ECK^vkz2MTF70obbt2Z_O zob!4wYhaUK?lh7!?(H8^4tsx?wRF*Qk6Z^EkBQ80e~yL%tbI#eY1>VQMS@rVsE*!vvWy)bum?MuzA$7g)pO}!SgwC z;o0ys8)Eq@V?V(rSIQo(AnpHezFI6se71#I4+cWzG2eM|MR!)g!nbxG)Zwbddm~rF zJoYcE8F2skEw_nja%(TogLw@6jXGFRzJ8iD?B7_J&4$@)hvuz-<)e;Gbb!TY-aK@H zb!2b3xxnZdw_lAI9ZZI>Hp}zxeKU!P15oQUEC~L9%=Wc>IU8_R2Be$%vdgcpD zd|Os}z+B$6_x`Zd?$E6buui<4_5srV##8EgZbv6YA0qV|57n+i&Zs)DHWU_TZ)G~d zK~eaR) z`1J)Pa{VIXR;>j1-}pooiPtW|jLX3#zQ|b?3oHe&=&RKL7p9GQ-a|}V!I9eoGc^)i zuEMN4em^7NEV9F*{m=BR~w|kdH7HMBr`s+NbwQknT9GE+H9P=?;pTVlWN$m6Z zY&)z@`>b_~)F1NrNPCX;Nn7uI8)i1AnvQ|Rv(J^0oENfc=5&}#51LBavkwHA&x2(( z1Kfl#i}|`{0bD=eb2gjQ>#Hd(f&YzHx~JpE3b>&%rk>m{GvZp}N;t+cU7pO3r1O)# z4Xi%yT)-*P{>aEKQm>`?K8Kore)J++xai84d&iK|*N%x^1(!Lwq>=UFj`TQg2e%q- zxfu*gJA5f;O|%T%2Qz-AzFm!6@$pM@Vz%?mcH)W^SLYDZyJS0v`@2}w{$YAgF=E3l z;kBd4e&W0+^(Cg2j7i=KOXBRrYhf?#({b)FFL;}>GhAPsw4dx(ZVB6QJ>1sVf5I82 zpMSg99d^S%i)9as@13dJ0tX)d+f1Gh#-X<=J7Kkga_ae$a)&<)gvHC+c*~IUn5xbp zu@ie|WVN^a96SYS72@;6wr?~Q<*Kg>y&rpUa&{v5u|WDLxB zu|;bvY@MIwp+?#d?_WO?=8u!{q`WJ&nYg{->}_I}qhj4Exc7sm968@4R}bvo4C`5q zq|Q63mo$k{u(A;5 ztG)g2eZKdCQ3=e7v!`;lgWRerILvk94Mo(8J^E%ph3kK;`8Wa=`q%A$4s&Zpms9V9 zO9Sc+aH{@l>itf8ezfB)OuKzDl)Rr99h#+IVavz5)cZ-gZ~4(aSkSTMu^e*A0qxu& zFL6IZQ~!Hkq>LFl4(`<+x{aEjYgW3GVTPs`wH}hXXJe+p-ZxaK`D9y~Wle`G_I?N; z>nD1^Q=SL&=DBH(huL|4B3n59Xwtk1uy9+7{ARdc@8<$7l3Sm3*$30Myr$M)YN+r# z5-uoE?^8$4eedaZ0dCRQ`%shkjcDIB*mKuyYX7i|LWkwRif=!5ll{ba-11!ri+)$` zpA1X)nJV9ai;_5(rjY!z$JXnxx2%uXG~&xWW3%BP;{fXY##4Gq_1}uWu{#4fXP%K> z2J-aquC3Jm_B}f(6K-Jt`fvXa@vJL?X<_3N%t?KF)9$CR|K?%0=D{?3TfYx5V?sxr z6^#El=+IBtty>Vch}8c&cv+?a@4Lm-_2l{E9+V`j!GW&~l9$8GfY5yt;eYQ-y5;f{ zhA?NGBUZc$D;unR&^6zi9 zu0k%DxWhMwpip{79H8uNZh?H-kY3P9Nvk*DtKH?wb-3!A!|k0gPiEYz8*os%C3RkMJcp`WgE?y^XzoES>g&);h5wzuoFYqR z6s*?h@+Am4^MC~_5Vn1!zn%y4e=U5z9Zqn2<`e>R3?{^T!m_W|o;wPQZ)YA{2e+wa zyPP0-f{Vs7*x}IJjFT{HciHsCaMkj~V0ln9cTo%iT>^*?{6tB$HD*(tDo~A8DV40Dw9()l+z~bsi&APR^K~WE^Th4GH;S~Ex=7zdBd0}-P$1Wr{Mdc%BwWAd zJas+V&hcvDaFs!AY6j^)r$*~2>CbeYO(rZp+2tPw8(e-APrf$s3DM-lmSzE%tk?#w>KRcV87uMk;yz*g2NM*%6 zI5KUhPBAPIyr7=nh?&M7rLbVSbzX1~mTNX$F`>~9S$X_oQ+4SRQq@7BY-isI8hU`v~pu$M6F_}M3| zaM0u?%91lL)-}M^z1lZlAg3FjJ|KqO2DYd_BW{d~sf5jk$*in{1@6YJPhi^*fq!aY zQQ{NhYS_Ss7D}$qYZ@}67OqIVkXHqBl(tao8@74l*-BV4-I+4S;T3g1lKULW1>VJ} zhQ4aG4_WZd7`~SI3wHF9d z+W4@^2k~NRevCR@Pr%HKws%iZ&kvYWeFWAD@SZA$r9&^g4u<)TPK(I>^5$JU6bduq z%Ri9$6OY+DmDIEK989VCF)yU{M_BOqv__K4?Hzd>x%|y0)+-plp7#ATthH_j|1~Ty z%c91^KO0-~9_EBEqTV;Xp0)4VV0w!0+bgK|Kk?fC8_d&mc|^|tRQq?#Zde*tw1K>D z$~IYi?}a7dF((R1z2oQhKA3r}A>=L`;qzj|0L(7+aU#!8%d{yne_@W}Q)<82Y+}%e zC93(Oo*}QeYxHsu=J`HMZ--OO?fHLT!GvGB6Y%`<8C(6x_1G2fsq^1$`~jyP_}}?u z5_jO#517gFP3NGVx8wx36P9{uAKC#|6gFjkg<0#HGsydaaZXE>+^?YZ!iykS(lz>A z3oO}WJ~kHibUDmxfO(6mR+9HamDi7LWd7NHL-on|t8?DGshZ@shH2b|&GF9;ll2vt zeYbl~>PPtoJc4O66yAJ;C5PMvl>1)?$Ti}Ava0{v4}-rZX~Ldgia2C{vK3#~&47Dn z7|4?SOEcaYIS;N-{u4sX8Oyk|0+!u-p4!h$$N4io;Rb){Nzz_o7?v0So3vg%K`hcq zo3#&CV=fbt@d`O#&xFJ2u8CL4^TH_$+j<60IvP>-6y|BnaEyf&7fY%8l|0>N8xKpr zTE=lePxd>NXvKmk3i>2+bUjQ=>Dn-5_d4KD+RM=DJcrV#sys;mj=EDA(-RD2R z!WBbQ3t-u-3Do@aE9)=bgGDjQzdA^M#o46>cDt%|ybBguc{w`(xMdD#P55-*P2z(U4bisxTw&+}+o(IEsC36scF(Sk?loz0TaH1`ExL z{hMKa;6lDSEZSN_Sv`w3Lks3H!?j3#mfoy@i7?yAgK8gBr&6p93%==$B-gY4IBvf# z%&pwEoQ$v4_(QHCEM47rxE+pstY1G1W|WVZ*a=r`Ti`bvX7^2X>xLbU6sFH5?dvnf z$~IwsK7=T;Nc+E`6=Pt=@`j;{VCIvna^qo#KP97WV9C2#SD7%)d8^8Dn17@x)&w?5 zP_4IxIm#nk7QqHt@2%M|%Qf3<1MFDN3rCW7c~7&3!~UooA!h5CUEc#o#>^c? zOy6p-BNqPu$6wgzeHRYgKO~!~7oHg22a6xCOC|jaM8`cQzCwTAQ^ru^Jtnocfm!M5 z@*9y$*Oh;Dhpiv)uJC}xDcdU#z(ogbmu`lI@7JmxhIO{JFZ3i z`upuBx!1Vf(LWEpN&oNOm`#D}Z*Je=3-i{5pD~5yd*-(hGwZI_TEM-glY0GO{0Hr! z?r__Ui-tiYZyb6k9G2a5tLiXJmoJTqgT0K^6M3-Y=7FIpuzaZUsUtAQX63CsIPawJ zU^vXUxq5m%%-d;t?U}ecY7YGKey$)kmMO#=2^o!SBux*g8BBj>b9i*kwlyi3)7v4vtf40T4o8% zUe?g;0$ZN?J3|C>d%qoUgP8>#HVO<20HSdl;$)z5*9>M&Xj}L{y zVH4*VSHP0Y&BckZ+p{X?$E5w?oJpCm`J9O9m9U8O_xvrm>hV+UDwsFEK)(pqtMpk` z4GS9`)kSd770UuKOjFiLdk7nReK`Cn%$eWW{uq`TO|EzWi=S^kQUlZ1#hEk{UtLSt zf0M88TUZj&Mb+Edr4+u0*)O>HrKpcN_O9szanSxca((sSchzmMuxOWU9`WHqwRZU5 z_}y;xntp>tyy;(3kjsWO2YrWm^`47}8U6M@f56=NZj`mcj+peqe3gMSsmM)^I$Zff z+W$iI)y@gUojihiUq@**S1nRpM`Dm~l}UQwa<7!=8+F;Tur&L$bq*}` zz3#pQX6(NidK>0T?2aylxw4)63t+7>ht~ME4Cd~9XDogt3b9Oi{gVW2sk{n=Wz@yh5a7*hQfjuk?yHn-` zhb=ncxCR!rm`nD;(z%5%$@sY$kMn|I$%t?&=RKPvj)c=AD+5S-QPR7`=U_cuPSiS> zru5TT2&*%zX1l;r^_(Y<;D6&`u5?&X54X%eUhRgQG5gApcW~ru<01~s^{CD4fu-~4 zLp@>MJU!cy?{NP`_v(rHlCgtYF#X^cCt{lN_5-@G*dXe-2eB82V+gB{nIBF16OD z6>Wh$Xg4v}fYW-oo%}3UT!mRB-RtCcnFApv`O6tEHW*jH=GA84Wk$lVQZIQ57 zoL%rq(*D9_lT0|l-G1H~Seh}OT>uyPWd4bTg}M(Ol)-|gl(*+$zEjAHXRy}Lf$JAw zUdngx23V(VjmH&OBzIWp8_eDMXG0n+{wve}9Tqu%Jdi=!51+vpfE^MYCuYL5HbuFi z@A1AfGu=bXTg(krg5|4}2GU8sc=KuvSoX|nM`G#M#j#p&l5(2MRhV6JafUW5%yXJS za>2YNd40HO*AQKDJx2WeqsFj$#yqNj_S7{QR&YZ8Oy?ZrOvPkxTe!7E(JP-=OXjl^ zTyXqiegVv?9v9&Pvx-d4m5{vc>D-;LCC}Ep6lS0Je9sT&&pkJ*jN}zpzXriN($g-) zY@;>v4#Pql&5UxA2gT(_z}%zej>O`3!wb*DsT!&h(jUz+#xE0=1m}#p5A%nQ%gBXg zqk|sZg9Qh*X5NLhDi(=u!>sXtJfFjsw|DHn2{TJQ-oAkaZ09B+EWGh#>?hd!#fLdJ zU=BNA*jJdb&~n~&(*8(8e>?1E94xp-av!(4pK!#?nHgk0>B5~oGOh5Au?l28`JKz; zRbe)3qd8e`sa9gxcsTuizGW)R4IH3NgJXuWB1z6lG$}KN|E-rqu_o6HRtqoeCF>)Y z@=Iz7>)bBAp9M4Q{lgc+?2SIf*~FYbFBZXBpF`Zp{fNGItzQX?y*Vl5ex;RzD?MOo zbrJU#EQ}rNUGk^F0SVTo?d#@(o|$UjWYFa6w~+gw;a zZ;&NI{?FVnm81$-(|1ZD3=Ov*v-uFmzd;-LPEB~ z_=kzPPhkdo)-d<~$yutS0+zr!N_(f0divM6PJP&at*0I3*u~$*!Zzz}EhX*gGdfPn z!3ob7#XTdo=(s|IZNIEo+X&O>yc1nmznF!04!nn1dw;BGhWmR@U2TV@A&qzM!_1E+ z?p?4b&{~Gx?VE!Bu~`2D4zs?ol!$KH_`ooM}Dfw$5Bl zxFGZV^_j?p$Cu?9!gVSdLz$#j@kC9h`z8>~BGPv(m;%EkedG05(^ZizDOVl;`d|2`Ai3qQ;M3pFNWSiyuCf zBlp9cJVyUM>{%#0GXoZU;JSePoX`Uz|0 zmY-3EIbZFmoF;SQ$4HodevoS4_Hcn4+24XRjS-}ONsqv1D6v+8vlM11hS$(w+Am+) zcQ|n0&aHpQ^QD{isu%WjKfJsb<{U2Ykokn?*)M%a4=mYyAxZ&ehd7TU&m-qinA&K# zD#BS^N_@~yG7UvZ>Wmm$oYck=aTk{e8zZUd_g)+rK;U2Ehw!vdfE@?T*7 zbLQ_zI3jg|>{nPa=FX%{Qa@+e{tlSk>^E2rw0x8tenwQN#f zI~ta1S_!_9Tx5S?49pPtx_pJrRc`Dc>r3lgcc%?@&dAt6)}QC}eo8A`6>;_e*&lqf zuv2ef&!kFf|Im)gI5)uk>V4GnAf0ezavhxaYUv-cABAVyD(=CNPYy2Cho!dHPZYv| zI>~p*{$+W_+2+Cjo@f3lALSdcPV(h_M#woYEq7(X%(jQ^#>Bl3sr68=+S+UevmMOD z8ORIf#wxOi$4$FU+S3OnsxN~Dn_p7b=X8!RT>*=tc2VmWHn6E@H7uFb-~Jf&q76@{ zu7kO64_iKgd9zPVBc}aGqFnJxf6RJVYH;K~AManiiFlP#2Wek+EO+!4m{DS}gt$BT zPRlk}Vs|XR6qXMiQNNwkpPQ&n=A-w--hDfXGrs(|OxiEM+_wYfbw}~f!1XLsCsNNi zP<<#APUnpIwwcs#X*snMwhk;YbcZ?1hyJvO|Ba6`M5sIuE)pk~yO92lg>2C!?eil$ ziRo8Z6I9{u->&hlq`kbwXc}yjclERzEEMUcljo7fT4&FJrJ4IoKEMUCSLT!Z6A#KB zcn-@hTVX)PC!QH)T?5PWG$YCVi1#c^tAt~=C|~e~@%yE6k72H*uH`ORa@lm-L)iMj zSeHF8{hlx50c`Na`G7yn<4BI*gVh%YcLuKS7@(%X@nYh0kN$DxNVAJT^Lv>fiE`X4fD(KxtQYl1-v>Y20rPZ+{&9iyrK z==H+(`EcO#lZ+(fqE9jD9N0XZN!>4BS2ghjsh3X~dJehxXku3qEdTQ|WvRcm$~BS) z(dx+jFkY@+bpuW?d9?E)%r@wmcazkQj;JN$5v~`7-iGUcD2~d2c~fYddvL4v*yc=_ zUl0{rPU^j#`m;&fM!0%v|63m?}!@UEujE@?Pg{4LlRElBIlfFlqu=k{VlY6ju*YR;$q@G3Z zDTO(8zv^bd731G*c?k2HA|vO)g7uxUk70?$LYfWicKGV!D$>4o;>T66!MU$D>tOn9 zTOE5?Ji?Rlg5*k39?m2`>^8a)W^QbCafRu_?n;_q;iPRD>tW}3&C73KiN8{UJ8UC3 zX8jiC$OnJoz*3R*n^u_d(&B|D+;XZy`!h@{j99x1W_?ZdYKOT~+S~l#es}W_DNKK; zlFWsj&+V@I0gDtLT=OOM3-w+5VAhJay>77oojulrq`lK!Q#)AdAirVA474A!)p;4* z;?K_-3NsVqRIOq2xr=uXhxxh#lUZ<16WXLNH-$&e=|IcNc*S=YQ5_1{<;{$qFKfV7a&(t@h!A~dBt;gI>CICLn~RZ zl=bGn`Lc;Vw-ROtcP>7MTwv(+Yz-{fBmXQHmOW{$=?HTJ3k=Fhd*9{n*TKU1qPwr* zB&ky&F>`-$I56D!)sjCv2 zTwq$VQOmEt`TH`C;$7xfhuZN|VW^dAi-NL+MAb$!NNpCwfk_{4d(|(VTrjQ?*;5o zpug}KO#S>1NBV{>AvyEw&?Q6L|DSnj$}-bnwH-Bnq(62bKhqWtODeiR%xjI_6b}bR znXWzpOK(4mx(TPQS!%?Cg&|+hmBI3XFUrXHS!*25zl2RJK3%2m_nEESM>uG5tS|sM z?TzcnZ*W!DG$k@W>>n4se!|>Y{x)R(1SdtR3LQ9~ew~x?g+({>JSM^hVJ}^LV6p%7 z!!uxZ?0!d*i@y$M&WD-a(cN5Fvi8^(YxrM(oZ+FA8P}7F{Yd`#H06Ke<7A%*wnBYY z+iL25{<+{4e>^px6|%RMkp5<(IGJp8Aa!VPjv8(-)Y#hZ9TPqLU&_@vv5LQ#vRfh zt@cy=Ik;c-n9NDiU-F`v7vUL^>q2rTbjwJV5I_eXieauNC?#wMqhCSuDGU>2bF^zJ= z*~69B{wHsUc2vC%(>c%DvQcmQsXXrn@ke1^A#5<)QacY8Enji`0UV^GYn2akIew2S z;Pk4xxdpJ)$nk9b|9V%s${Sc-d^_&7)BK?Vmuz7Rg?o9_x3t`S&vwVBlyXkcO zU6Ma=Ug`szpS$BkEM4TyJ`A(<1F3p$n*H-*uybSaW-@+(R+#^BI5jhnx}G55plcMY z7PrHlj8|%?ZbKXsv`Z+2S(_9$pMsg!6yKBipht;Pc@*E2Pg2}rDnh) z@tas*xYdiFeU-F#RZ(99w-0&WmO|PW%PX_siltkFF2T~6;m6HjhpvgyiLmg%vP5H; z$z`pKhXp>O6|-T>!&3WLnAJNu%zMvOiNRy%xe*aK7&P0C*p!oFBKj**amaydu|59 zykQ<6f5Lhzmg^Us;_p01o@aKBrt45xyuEqY379o_LOL8yy5rYG?w2oX zb66RU7@cSr2Q!|nnV<^)n?FX`nxW%hYf*CjMdYF*>mw$?wv}(F`4?wbq!_?iH#d4* zK`!07HJS;#y>_9V55C@vSSOg{Ki4@2IqSC3Q6E@P8Z(vb4(Te0Lp$-PoL)Nief-<^RXj zy~nlq{(k@;LI^`82`eEA(}9po(t*?@O2Q-wr6EMoBt!>8XJH5-3`s~WN+AqU5f;fQ z3`GdP*ZcD8`}q9%e7vvwx=+{nzE%~QYP4T-Pt;I*BHfWVy$6iNrvS3R&3`WsF8f~2 z+8cGVQrGZ5=~ZW~PY!@JRyek&_J^Zqt7i#UzH7WCgTGSy!a4Gr!v{(Zz>0>i>2lc8Ht`R=UrzOY`!g_ao&&pH!Ld&)ZoFq$ z0M#df#qz1ADZjaGhEVJO9)XBRrcp)-?Bs6SCTG!K~w zN4t91QvGRpo}ni`u>Myv;QpS$i2Jyl^`ZV(HeNNx4CWN>W&NRiUFi0qaMqo(GU`7? zQTl5~P<)%ov|w1CH>A@j*kj^8)*nj+KjD3&F_v2Kp8{;l5$L-uekHohy!@@q=o5aGRg-|Hcm%_UAi&Mt`;G6nh?=`#CcVVSeU1Hs0VZ zIC#X09CZ04jYp(@UB-=pwHxi&c#!AUs)GYuI`CI_cD{nMR&KEB)6AaqJfzyQjHkl^ zXIt~=dGOXxRL_N@)<>}W;RPSGUIo_|1^iryxbjKg&^Xw5LUTWs-phMNA}rf~tb)!j z%eZpkFzjRQvT7YHS~}wCH8^%;*D7jXWd|#gAHWG`=0#9>;tZZ)_Y-dZ_D!^l()+g? z+w}|T&)IR239xwY&%=G-^zc3F$nu?o()z;o)!xHc+}_v71a2@Mev!r-obR=s!(mad zuTwm%2#LKl7S`}Ou{sWx4K0gz{;vm{9G?lNNB#+-`k;K}a%VnlY--QOFA|%1)0e=O zhNsP`J(eBt%MXRM|5}-l`H5q?uZ8{E+OhOJ$?dAmaM6Tf9qOON2fN+d3VRh8u=*yn zU0S*g&UMW=xgBxtelziQSb6xGKh-CW!624i=Gx5qC)LouJ!24mqiw?aC*@23Q#;_m zbnQB-U&pZw{&4-VDmGr0%7ybyVBzeS{!|_XC2RK&h0B|* zPE&mob5e~*!qMMC4d=l8w31e~u-6b%HebT`tQ0#qs#m=(&9{^*gHPDQ2C+IZUa-Pt z@~BC$+2)&zXTn_b_1aTm_3<)h^`JYSJz(XP+syLl9jP;5r(cHi>3HSty?y7xs?_@f zd|*-DzC-?S?4b~C5zOx*8W98sv~53>>c8a8$SQK~ri5^+AKVLqP%+GPa%Am|^0B(@ z8aV6ZEY(s|2 z$0tvHr^7sb`*5-%dn}J^7CEWRjI8Q>p7PWFDnDWha|4~T{K%KOZyZ9$AFW~6BltM) z^kA4XaqQPch-YueJ!b;T`j*u!h09Vubs}?1_BX79<%QXif1T3h?SInC=5}fS2UZ8)pU18*I?Sk31I8oET75F6=%Q|jpU+n>NLe`kH|4~r^Z=G4MXn$0zXD81{Q^mnk+;glRI z4}zx4Jzu~HeYbm!qxAh7-&MkSI@bg3VCfjsIrri6?G4AN{I;C0e%s1p(_s0*Rg>xZ zJUZNG^<7clIXfSgYuZ2ZMqK8zvG@{fZPRA?Tv!-+ybqaMVZquX;rj#34R;QAq53N; z4rS>TCtIaZ`=?mF!|w{x*Y_$ir}kC;d<(PJW?|$SnA3lT7M;)X^@}uWpH<)T9_7GV z^%iV?p>F@C;|aKB{vkbB!BMix;*MGl^V)^7`Gr!yAA256 zk*O^Imgg6`+-C*#pVHaqJKsZk;o9$Pey`dt@hGSKYo{KIL0oxr@tz7;y5lLEzlg`R zVei-aOwlar--O@nroKhoMcQK;jVI;ewxT9DFZeg>Z`B<8eZR?>d)WE$56~0;!WuX7 zHK~79)^>cwX+(Jo`Nq}{_*I`wHDKvZ%Mp7K7q~48YYQvt9`~d1vf6A&R2!IERcw<% z@&3^)zwo=a+8(9+lG9w9>3sh`ephw75~+p*%8S_gfPB-jxv$`&lr?O=%=>Bi;xS!M z!X36Apqkc$mFI-}Uk_YCe*BMXS@}||T~%8MOTA~FP$0dYNAL7fShd%9dLdkv(dptX zSeE$lC)sS;TD=Fb`1$}=UtC@fQ9XnO=U4Qh`r;L|v4-68KIBo>=N`fGg4w!vDL%dT z&nlS1?E~QzgLMT#%K(m5NJ5AHFW;(`<5%PZif z{5-ZkqHrl+@ea24@OP*Dq}6TL|ALD&eVO^^r@b}!j_a+>XXz#5SB8#+v$A8^dW+0k zwbmJKxgP1EEYsPrNXK^!ft6Q&ABwcmQVZ~(ga(c&XiL95D(uy zT(b%Nfz$o&WMPSuT~AnbIpGpn5pyWtkm6eJ*!)+%r}bDfSb9qTcP1V0Y`Jy>9GLFr zMAyrczTP+-<~qxD*!=gU;p1VHes_$I9C2=S)A{~zQ`MC3=V0Z-u_k?BKc9v#^n5r0 zU-`Y^H%IRNTYtN@pr#ir_-;Ic-mh$VV!wW{$*>z{^t=TXN7?o1WnE_Pk7K>q-w5%R z_odu6q`?H{*A4kVHzU)bFTHu>=&<2t1ue5}J< zxM}~T!W%HZZ@h3m%nkUURR*h)XUKeE4;K@|dob^1BS!?+w`$x;^+~Zy#*SCmUT%C0 z3nMLO%|kr*-UptexKvMzVeE{9X9-ZKkKlzk+y@M3BHQ^fhVcArm% zU3|9wtKW9~_xs@Lg&WH&DPH!aXC@q2xuO%*Z(gMLsVq2j_OZ14l>ZO=gtM?LD8x+( zb36^7oQG38uP&>v{DJR&pF~_e;_OPQ z&#EPU`%c4V&qrxrfEBl1y*vk-EPd>79#$uHG`a|D-g`cs>W|R%nd?h@jLrW<<=Q`d;Ph_q*?J{+Rl#c!?9}HuTVD|;Zr5G{ z%k3Aj^+Ha5OzH~QEdS8wDCEx%9~`|J_LJAm+z2ZUZwuZ5XI?M6umR?6n;5wdwy`(P zTMx^o46u~Likq(&P@FrWN19-`K7s3I&cyX4!n}7N$n5E!%)!^&igx+wLglmRp#B`?5*qB`nhuJ z-hQoN<@R6`*1o5o%>Ih|b9pz3^Mv)y09icl9*YA-lP@|u(|Z_nbJC9ukvZ(Ksp zWA~k8YA-~x{Er2&^xZfG^{4V;$AApj#C7;ns-N7w4lnn>;n{0SsQ>2m`_L~Q&M4DL ze?{p}9O)YdS6@6gy&jf*zu+EB$783>7xKg#TNc30%_ArMfQ93_IrD?Q-K`}5<>==eiV+4WgEdP|1G+&i92XChv`NUygArT1vK zUB5pXRuvoeAZr~QzStHPzSL&UK5@^}4ptwz8t#sGR{T(%NwD&)Q#0A8Y{wU}VqYw~ zo~-Y~&C&T=SdF*+My&hMSrQtXHcj9=HQSqmyz+9tZmtd z^F`N%ees3W$Jb5DLfoK|U$g)goEi1tEG%s>{UxIGS6kOn{nsiCWcjPoPOU73jrC%C z(fQ@|!K^(<=-#$s1}r{mvg0A*Dc4r!PosE;nYSOqS>HZ|O{MtO--6e0z1Ac4`~-G6 zcRs+1g&t`Rh^v#_w*7`HhspQR`w+D{$Le493JdcIl;4P_r8=mOPT@n@`;|`Dj_nVt zZzN2o_bsW5nosRV`ia4^v6Mc0Rrm35bie24M^ZfPXP*Ud?P%3iDnGKTAD69xi(G1$ zIY%-#Zh?7KeOdXE+}mi80*B{{SoxE+`~Bb~?6rJJ+F%^d&uY4O4we+)-z&hvB}thV z;DAkW$vl`7Vx4>$mf4tB^oDtjMN5le&4n-Pss3=A(o^ohfw~`9{o=Pd(We6T8nv%= zcgjzeKf4-^)zCS|C3~D4^aid>%|F!*7D&HrYl2hqlN+^Rkww7rZohCnjblISQ2KT` z4-8?|vr9{QQu-9vQIlXeb2wZ;8;$M9(>h&#-Y;0{p^<5x|NIC=y{r|G| zK;r%O%}v;EUiOzxi1XUJ1=qoWBf`_PU}fKmuFbIWrogT<O@b zM&W(n`sA)-+rnH|t5yTy=;ExPHZbS-;WNf?w$+Q~zi6+-D>Awp!5r^U|6j23u4L8# zSm_b#+(hwx+nWZ#xv{hUG{O=)eW$@N{y}jZwTG&af8)*IEXO%^YVwZ{A?C1Fl&bV2 z%*~BiFcNP17C7JoEPd&x;{=;=%0|A0IZ5jpSHYr$jKVrtG&thZURZTypcA#noaxUW z<-iK%AZA{)_nIJ-Gj*raZ`s?S+VYyjK(@Vrv zA9rRs!hDVKrY~TwAR)(#;{Nk0YG9?`p(DYtiTRpMWZ9>Ci`T<`8_wLJ<3&MhpKgO~ zCi}^1VR5$(Lu26R=}Q;ChPhpg%i>^rj|16sf0FYl;jyqm2kXc0V9t^RW@(bcA$lJC zg%dY#M?A2p^73a`sr@l78kSf0P=13&$G3mq1RL{bU!wOTi7}lY0hiyc{?r=pQ{C>8 zX(*gl_G+{U3^};f%1yqr@<;qA}q$th)aG#wu9S|LDQE_aM&`lm2g(Y*{ETZ{$!2kV%UCH??Z!N zK~NLdA5K_s^6vnc+k7*ZEH=2%i`qk8qwCQnu=JkbBx?_=O2!4l2J4j_^bwbr&Suw} zeeH=iwQurF{`g45Lv9{s?GM!Y9W8x%*}VLid6ku+YCC3Xr~4jWIMu%$hv@82&a98So+{ZJED7hCn&3@bW^ z6jAvXdAGZp1grN}vHO$m*chAz%U&)xPVK$AgMH*>Sk%FvS@b^Y<~>+ou!vbvj0qVX zKmRl9FF08r&b)*364p6Wd(RDD@cJ|C^-^r32TRL8j{FJ-{+ct3p10^pZqo<2Wqc+Z zy7kf<*n0oZF-C|B{#;)93eJvA5cMZ7I>(&L`7)H=r!cXIStIOKM|$6!#NoAcykwJ= z8(CH5;`Vl?R(SDsn*e>>}zfRo~2qHNuLH!|jGpe!h(ZJ92CV+07e9o58$$ z=@J9jx^aUUjrTBK9L$H`tSaq40v4G6X=e>bpSr~?`@xq_q2p(+?q`m;WaCx`AuL|r z-JRMCan!t(vtaGzYgm5bXESHbgPUqzw4?INebeN)05&^4>jIr$STk%(0BnLkl4A-> zOCJwi3~NkGHlXWQJ4_g`6fVu!xSow)kG8%|=Ee=*LjAvB@n(Y{xXi$PJdJl{!k0aQ zVL^<^O&SlYe-8h=8rEtGZR!Ja_Vo7IOgGkX4fZ@rVLaOl7W z_WT7Q16|VL%Cvf(3F5py?RW2oqi=Y#=PT$PUwQ=2^?uufu3s@y^Uq1RDQGi$zG~OK z_bs35%0{e;^wd zRZ0C87hz8Sz&m4LMSXC>S=edfj)~)7o>ukT6R_9I6Ks6P zvD;L+0~Vin_SO+`uDLOL9_iD!y>g=T;d#Xp#G?&I)=+!RS$;|o3fsiSvN-?PvISz8 zTU*19S1puXrsr+h`Nm{w&lK6)*!vK8&d(OWs!qoq()&!=GUq+J9y>LAzZrMS_Shh< z{A0-8f5z-!cKw_??=nS5pS8GO1>GOt$lS_c3RoEFKR-oY(kdDD_{G z!ly6H;M}qydh~oa$qROyQhtNFnUi@LFOtd1eidvyEu1)bETs=w=QGoVjvw-n9Uto3 zlg&S5D;hQqN4&x9j4qAO`M%NY{8@|IbeRq-`Mbmxh|9kmP4l4RSBu0}Fn^$pkrynD zoc?Dt+%zL>I)0aYCK)w(igv$`oV(0oBP~gzs3%K7Q?b*Vaa}Q zWz=ZaU&@Q#q^yAh@n?Kke>M3cpUQ*BiRB5)==ktJ*4>*oBzQsPX5zHuy~zs=Lpzx=x{b) z64?G}n*eK+>*&$_%fe3o$%Mlz!IfQ4b)fglw{V`P4?A@nR6_Gp!LpiDy}XP^Ju;+xw7P=1za2Qum{yoS!w7>8#usqZz4wRFAQ<2tPU=lZr^F&Gx~GBTn5Kxlf7o!=n*DH~7n z!+vE>MBL}5HEZvrK^dElyu*nLiz0@A^Kkcqu0hf2IV8_cRfA?4q zhc`IYQ2!zS^NP0(uHK{!q4rtq8lI2{*H5^9VgSswN$#}|R^FIuPW^>k(ogds99{d? zkNRJZllz(TaOm15)*q`s@ASR_H$`hdru4G5t99$)mj099#bs){HhBMvUuUU37he{8 z_Js4o?8>P<7wYN=&0u4l_Tg;&++o=BNpQ>cD_VaqSq^8|Zfni@+n*xWI9MKS$kt={ znVY@#QTj8Qw+A6VsgcvtldxsFoQ?0qB~iDpz_IIYu>0d|_m8*>M+Np`<3X`{z=#?+ zWBl36)E|kw!*_p%Ti&Pm`S|bJ8nyz}kEkGO54nx$>-n(Fb17Sok$=&RmN&Cyi; z1kc*Nn+6w6F8(nURyce7UzXWfbE{Ng!0hYa}J(JIP`kt(QbYTzG2a)T^s2#9I zPP^T+VWsWIy8ZB*d0b}Iky{%sko%jM&qZ8d8#3oUoKb%01l2cDb2pbqa6r)G0;=Df zxsrBuu+7MctUil0Z@>Hihop#E`z37%yZ;$(^1MHv<^w{@b2q-iv5h(Sk)T->+SQh32=98Nz&7)dK}A+pifk9M-zN zDW(Kgx*nM@8df^)`&vfnuS*AwhszHnR^5mBZVo&Tm=k$^LM1E;@wJ}^mwl+tc}&L# zIL7$GDLnsn)i5t6Cejb~i;W9=28$Q^4p|6CogH%EIk|ZWb4HJ?%W7a%z%t4*3eyB&H3&>iNK_EwI8tvhGg zl6me&ZO6j0imKplu=2*BN#1aHsn<6;Ua~0eOc8RVu;|0@L8oB8j%-ysm@^{q z)D1Wx-ct83UH{^Ous3kl@^7u^`Z=l9>h|pse=)zhiLQS^!%sap&}X#w4_N54^t=fi z;%Lh!tL{}RtzeaL`&-R0-)#-o71sXjarF-@i9hgQHY~j5&deRKcmE4(;reRVAZZ6e@?=FM-2?fqDOU>rLciTnTpD@;CmbC2e|U+ zydPA4WfRXIY^LSkMaaCEzb!Kqc+K9JQ zUwE%?2JqqL`akFH!Ggm9o|f>N@@~m@V8tvyX3gz;hTQy*Jzn3vc%6>tS??Q#^iBtk z+ZVy=I7#AY%0Fz+*?gGGPu91CCB-SVj_B)E;mrTl+1AwdY3ckyCu= zm(}ZFP6R*W6wFbDWNn14i`M97!iwenHpajW)6?!|z|!Z7LgQeb?uoMpVA-bcCGl{2 zS(EV|nCGuiO3u;=c$f-H##Wld!m_(|qGVWP=P@Q4cFK*3Pl5$i$KP#%qjsNl+zl(* z-{>0#zvv0uC{^g-_J{kf#o%qK3Kyk zGkUMw28(}oaUiGPJn&;HtQ`C}b}Xz+);qoh=BrOWu!YNPnmR_oSTB}4z;D(Y-&+r> z8g{N)1bdwso4pQZ<3Cu#QNq?|q{>Becf-noZS^)%e%(*p%!ZvRbE+vnd3kL3by(rz zxs_e7-mlg#;DG6;$I$(A++K`srnv5C2WsE>lO8no?TGR*zH}}sS{uvFk=z8TIcy7culxhTsb1un8y;Fote|MOl}C*bsP-NcvZ_{+!p zpN30!U6@PdMcF^`(@hG@zc(x#dN550t8OPm@?d3RtiKN|dMY>U4|A7UH~7M} z759<`z&snPdCOs^WiD-oz|x|p{dd7xw@fAvg(a&GJUj%e>{nQt!>Xjf)+b?!z)#;A z78{I9D}u$BwK~|qJj>!Sk71`@2AQ_7`h?rxpRh+x>7ntkNN>3}uQSfSS1^ApEN!lc zu!RkdzddOI^DPQ;ykMnk`<;APaI|Rbe3(0A7`q-(@0*InaQzWCcD;%m-KaHi^cCec z6Qoy*x$)6(_}FXb$ih_~(~@AHs&PZ<{!|Mc+hoE;Mo#Q_-aXH(%dq{HodE+8=Y1R( zS^}4qKR;vy^BwYbm%|3fYa4pOvgt1i)v#AZT`PM2{OOP4n&DVJ=T#3_?tWvIW*3Yf zXN(BpQu>H42m8WWNlRG&$hEM2X9+7W2Qu^e8XCI6E`9t8=>1B3MudmJO=~+|ru;<9 zI$T==XMf!LfX*lE;V>W?E_eGWqw7(m?bwzt>dCPjYko(PwaTmqI6fSoN-B`DFMqEtpAoL>(7Hr$A3IW?SX3W$E3w@%XmzE=396$EIQ1Y&hm@ewPOPuyP0?S8`3L( zd|RCit89bL()dNS@0H0h*h}4a4UIQAqqA#o!A{SOE$Dv5XMJ{jfom^b@nYk%++KZt zz{)9+U+H=A%|v~*xM+_|+mEF2ltOFkpV4sXnc=w|Vbuw*RwCHAj9=WI(rw4? zYbP7;aTi)2iiWkyqC2-jT=w=@MdZ_JXUE)<6G6;vCMhZ zFIK2v*`*EQqeyS&`|uczH^la?EWaY@oo^;H?>ZkUpiKbwV+v zclvTZ84lkjPrM9E^2Kda;nI^1e{<+~<4r8ROZ2FqGqC)^k%{{d&o&xx=M=1tytVNV z{AT?;ixaS-^}ephVaq4GuTp;{P`8_O2F~@B^gj%9tR`&Aq2v1$Bq@!_k_f?@)g)o>{O}7KKEs+*CpSh@d}@c9R^3n@yo=6K zf6L#f_r5!v=Mc9j5*DVOEHHpW1}^zR^DEKR^cWLZdtLLcP)dJnf8A6#;Hp&<&BvrZ zcZd1GweqG%D`3uv6SIThN~dXWmce54#w+V#m&f-HFQIt!*qC(K>GQ{FWKsWU*W>@O z;s)QbfR69h)@l*rs$nC~-iEb94=-8>^QNskb{EdvxPKq5rzxf;I^Bn>zXxP^!s>-X z`c%Wb$O)rpek-sU;!^|XrA6Fwg;mcasdcd4x<+g6!zt%h zVE#jYcXGhAJ|}0uqV5Mb{)CkpPB!i^&%G+N8I~WNyTBQihTJXq3tKuY60`Re-d2w+ zoxk(n`q{Ny-f!4tXgXV8mivBZ$9p{MXg%^j`L%o>P?YMrd`0@|MLm3bAuiaM!>@x? zdXemV5l^{)=S#R~p3U)X5tn^0 zpWC_zp2u-6+YcZbeN%q`Y%C@Vl;wMHow4(fbta>{W)>M0*{9{VxMV{pc z*RLBK%=SBdkDI$1Zuvef^R&Ak36~uXE~otgir04(Ti}MOyw+spjXSZsV7=9qYrez0 z0g{V*;LvxB*qdJGPRg-J?=(zqx-S^n>qt7>S0@}2Mbr+{OJyR9A3n} z*Qw^uo$LWCZOe0MeUN)WW3(6S*B~1_kesQ}Z!TOtqLFooDZv>mUs`N{08Z< z^IH#XVC$t^)4c&(5zk$7_c@gptao*z`_1r4**Xdqv~7R+6zmbx&U7@zr7xtnV3U`# zhg-wa?uENQ!^Xd4?0b-KLC)eX+O`7TV@I~$$Q?W58V@$G4`BPZlqXibHibnK&Dr-p zW$gKHzV)8xM!{Yyc2B4K;kox!2`K&l?;qnoimaJV{*iW`$}hjbb@Tx%&*G||^=sgk@+fQz$zZL$>`cdEbP;@+fV*F5Cl) z+INkh{VKez1rHCwxpl%?+7BUX=6{yMm4ntFr}8FF&gb8t_>Cocv>!##-(}`&IO~zK zoc3Qx+qGN#4HjElc6&qdCHu#5bkHB)o3oqtZ*ZEOyLW^Yc7Dt%*_IG}xM=_3fBQKu zZGCP68-#_h{Vbv#u3F}>^_Aj-w4Z}pahY!c#~LaOKEcB3{DULmfKBdJUtxJc==n)- zS>U}>4X`9*-`rVn;Nfy+b?S@uL9le9T@qct*1&+yw-2rC9`VEZM72O>rk!5MFg*#2t8!S^MlFy~Rw zPug$Ix%TKUxq5KwO4?tkY<_X_4qTtGo$a6I)d~(PVd-u+ImP88`JL~^9A{7OvHEd`<65IO(8iJ?s(nfbA!ejF_gch68&9 z_|W@SeK(%}6|SE%k?pq;oNkJ1hO+|)f2a4w^RQBD=~8=O#>_pV(Z&FdF52*mp0^?` z?9d2U&Y!^Ehdk&;TRXUUMO_rVKjGG!Vj(Pv>VJgx>nN|?nK=_qFRW(mok}_5h$pO2 z&iPFHsYHGyjb8AZ8FScv9!23H8y~pd_{l&H%DeE)sMtBM#)r;qe}!aZz$9srgH{pZFfm`_1GuU=n+`UigCoZxXV_o(z=`)J$gt2ta2JdL&YvbTO3!zn&; zJ=>ouJd)%y3>K#MnL+id#YIE+cc=O#NbTBrDB^mjSMR0kS8u*Gd;lC3)cz;kFL&&g zbNaA#x90xTzKc6YzU~4y6|Q0Fd86EnTfySv_cl{|uTHQ~e8=;bdFJJl@xdkY8*DxP zWeHiS&PX783>u)-468nTeMRq2ExNXq`Um;xwpZ!>iai%3u>H3EhZd8a>MPjulh{@+ z{tP!bZD9Aux$k`O3*6Gb$-L?`8(^36U4_)%NLrP+(e;>IEzxR6&+p7~uSVEw`b-~s zzQUL!Ii(Mue~Rso5g&81dHN*3Hx=?=X|ni39qiRg!Ypwe zRrd>4f1Tsf7jeb+3!UiwX-4%nHGt)FmTVgZhwSoa{iCe?aaO)$tyT`F^Gl@jOht$b zia%vidWqQb>2f&z_h0rsO>$vt(RMh+Whd)z_97itv9SyXf}yG zziyS${otmnhr81I5#H%g(-+Q;@&Bj=OJ81R@%n_~zf@mDZ+c1$5qC14%IXI%$vJ-@ z{ATR;`&1ufV<%;p!S=T{u>FxDquQRMVU^;D8TB{vNn4jsfiqSmrcrw@HL(=>!`2Zy z?os=}FYUfn0@oJXvHgG8D11l;3j*tUvHgx4P9C`q8%K_qLHiLU&$|qL0hb2MYDHF+ z+}hFrm#0jhu4F<=;fx~(K`lo(vy6%EY59uAF=b;=ldeKfe+9Sb;?q3?VvOo$e zLoTrUm*3wN8V9R}jbYE5KRa{RRyh4>!oT;iW_WWX%-4Lw_NOYxJYBef+`gbI?PpaF zI+C{pu20Nm7MXcIoCX`1h}n2TzEAjS3~Zwl%q(@de`6?|amqqL{k76Ps~z3Hc6^_w z)ZZ%xsiIrMf|u8BQTwPe$r9A#`DtkGW$mkS(Z~VMVfoZSzSQ1{4n4^%hZ~%aKB4wa znB+0*0W3JYt%>>v{_MOC_u+tUF08z&Pkozz57x@h5>fjlZ$09YlH$&Vtb8l|E3V#w zT{hUS?TEOlOQ-KwVUE1nlg%eqxbKz!=XjYt$M6U&8>_L9#%l^4yDrJFiMXEa|C9YJ z*NTF(oMKr2BAHWoX9H}R(`^{*e-yt2p>WIj__dPxbpWBvd``HZ2YN?{lP5y_=xRalsTJT^G00L$%5_o;x9bhG#@T) zIyI5nJJF>nuNT5an@p}4!m78W>zBZs+0`2dz{0px=hd*K(%YS@^8WrURGznqA`nqN!1hqLnnO?-><+aquT#lkMVIldmGmHV!Sty zuE*Cp`W>uzVrvsj`Oo8wX@e*{vOHKacRvZi6DYd|tp452RPOeSH2S+_E1+eZS-49=%aMDm$?KT|A8! z#v@_B#lGzM@z=joPayM)*z;9)*tNk4w)9)wNb5<$E{`V6gsa^;u>C)rw&f#&;Bvc2 z_WKx)(WT#^aM>;!wm(nVPIV*}<_xT7zn9@8xev{R`4jtCO{V(|p3|oQZhC&5t?#Jy z-mks~+vp8v``bhrl~>B)l%;pr{ykOHr9+S5*fUc!C(!k9Gv7Uh!<{qOdX(b9A&pAd z>+gc5F^G$nE{(noC-fNnZ@=Dv;&a#FtZ(nw?>l&sN3G7n(v(U*n;%CE{&WzIeP7M? zdy0B^UrmK8A8dCRjQpg1I=tEmhib($OBU<*+VY?DyrhE2jj;WiShjygs=k&U21ji- zVf&4gy`m1Ug@sEWvHhz2?B(s&z?ze{H;zF5oYJd3SHhzDO9HCD+`5oG0dTeJlX?qS zu;w;<{>E2Z2b1M{Y^J*-o_nkLFrU&}-I_KYRxEU5`;YiHezEuK)p3W?6mdQ`bA$!r zp~>wPL+JSaobi0PrM*!FIGz~{r;ptCZ@+IFtL}qfWo`9+8b8T??CReiP6^CApiRdc z9jNXHhpzp~)_Wu$qxR{;+B43x_Fb|$j+wJo&h}Gt#%yEO+A3n}HA?P{L<6KZIb0*4 z@if- z4lAAl+qb?hqTe$pPY<2A7?$^#eUtiMmE~tv9%ILat|m*=Gac!D2MX6RE4Ej-Uxyo} zTx0V$j^7JADqpf=yJpevD|oqiYrn&aIbYfDZzVUj#j1aR=lStEyWHW>u&z_s?@yxqgS}xJ*Uq-|`wGRkzjNlo z1}WZbKE{q(ONZ62(Lb4@;fr2d7gdipmCZW*s~wYO+)h9!&dE~4@xJTk7^CYXQk39BERpKBIJ z!akvMR+=L&|9a_37@W;9Vf9Pie-b-i!&XD~djVm?+Ih)G#P z>zBM+7n#*vr?L7heUrr;JK)$uT8|bDeVq`B^x=!n?W6vOSE92*0tYCn*nUI7rw-Q= z;E?g=%p#{93lqt9ui5@zX;7PmS6~BEK5I{er&9Cp!kNClM<*gb*_F{3YGLdAQSA3* z9GAaCK2!YvfBzt}T79csAGnJZYtMKUx669L(UaZZIMMNE%}PhWoU`$4|FQVJSj!yF zc(97~hy1Up{Bdw~55qq$NUxftdCwg-NS}C|EZkcXuml#Y60`Kmm>rx*_<#MYu}1P1 zIBIm5g9Fkl$8RW$gQbq9Y<*mO_xF~)u-|^kbh;nupzwpo;mVEKY&~6gwZn_6u=>&2 z7vmAdVtzD;g{W6mtkeU z`>g#FMvt3(0+t-GXX}NMk>Pn6aLS0*=Vm&xsswPpU&ay~dHX zm(ug+d$;LJ^Q&mq9?L_{2KIyn*53YBI9@z_>CZlJtmd^2bpNXU!U=|Oc%b!nErO(Os83uD(o3Z^hg4fU5(*0FVxmUIU zaan9o(z5@gR}a`RJ`!#k{bMubCv@-*jDefa+;ChCi+(y;C&M8dI-Xns^Fq}p4pDlk zcq?76Y|G1?$6&978SML=WPJY_7huJ(iEKYQ=a0zf5!~|qPBv}WfM&SiYVY#JbiQW& zn!f$ep1+;mkH&`>zjIyTqJuKsWw7L`_WEErcS1dfo(Fg4t?MapRLCk}2*srtyE5Q# z?RQ^S!6Fmy!{=b>SL^rmyadJF{}#g{CwsQPNHP6-t9qDo|A#9*59R!2E#LrI`Y1}~ z3yZ3@P9<}`SuNZI+w|#Fvx(9ho^Cu1bH^MQ7fI>$PQJPh=O&BU_ftWt*_LX!+1uSg zg1Dlnt@TIPr;|k*SuH3pFfc^kM^_P@cXVKTqF(xp!Pn_l9dV%fId-SH_2rfu&Uy zd5N%4e`nw{SopV!nYYSnp+8)@!@Mv7aq;!H`JpiH=ZF1tJbzTjmz&_s#!>8hv+O|7 z+Gsd-eSwD*alyW5y%;#F?d+m>@;H8K9Bi%Y`fvYP-O&r==J$!u>H36rdI7Ppv~hlv~Y-I3oNk-=&}wrY4dx+Mwst!H6;+%+cSgx{*SNnj1a-KhFXh55mzlR zIOqk}N35Pm<%Rb`={yVeQ)sWI^2GCUOZA4m+Ae1Mi-q=ow$6oH>Vw?Vj@^HeTY3ko z4~l$6S`gwHga5vy`Hey^SF#cg{Oi!2*=nCl1Z=Hp=uGn);rVG+C*VBOAI$u=X-&Cs z?YN?=)IP{wg+0rMvxkl;B+J92n2l8~Y=3l%t1~;Z`4;D`XnY>h*V}Jo?Ui&Fr{j58 zdDoq_Z-UI5ua3c)56k>~ke~E@O5AZ+Jlp%<@83`MeoEJqVD5j%^FQa;nijd0+Ec;e z2Pd8)&TE>@<~xFwI%}KZ()b%}zQbEmoU7d*&wu81W|{H40|K~ZzNyZCp5+Jg!*(5@ z`HkR_Kb%Kd-taoZgAEZ}tyGZTfB5ng)50&PRzvB$&wT*wGkGObu zyI<~bQ%0EunLoF0Te{!oIQdpGcb#>wEwE*709$XB7-^X$!Zy=(vE%t?W9l;CjCoJl z`iIDU@PTaD>D}cybi8W(u1A;Qmi1YcPw2rCSW*~l)E9A~d)|~M@c-6>br*)#!d}J6 zY(64u^0QaNg5x4hW29G)+R^?Gtlc_Ct3NE7S+lr{F^(UTwT|vrJ&iwY5L`d^Xns#v zyl?lpQE>HT0sB2WuWNRN9c=mO^mO`utv2^t=nVUuFSaMk`ejTfr$4(ou??)Wd^()c zYd!X3-$P_aZgisj6j~1M^nF1w=I67au(rN}t;g}F`0p@-Jr>)u?=jrh1zU!}^1PS- z*7H0xH}K(vh#~BIjwHgej|I%Jz0Cfehupwvwl$owI-gk_^C^2WtlH+imc9pZ`Z}gB zfHmIdIM8~odR*JYP`KrMytjX%cEEn+R&2dbeP(abQCOY3fc^fRD;|691UaIZeg6~X zFO;5x8ycqk`~LLDwIT-=TfKYup6>7OrJ&2O()up@ekIG?lbsI-YVXvc?_2V#$EFv; zW^u{?);mwEtSN!*`^;eL!*cFgmq&1Y+kI?)Bx+~GEeiu4?nF3i zfe!n7DT*%I{`PQqE^i@yf8?jNI_U^Uk8T@D_a}eVmRWm;1AAY>3^~g$)4oT0_Wf+~ z#kZr8zVcG{99sXBI_}mohkeT2D(L$^|NA%{K3tz-&A$J0H+Eh<6b_xRL5JpNTs>Xp zmi-aZ1^UcI-#qm?BQ7j@(q|ab3-|9G-4*5y*jr5Yuv#za4y$*)%A@=RGukS9z=EJW zzY(y_c0T+4yyD>z{y4bwM+N)dqS|tWUB8)UHd}AxET53!jJVgzJXT+noRz1%;e<7% zRaAfY$@^oL!CZGS`@N~i!TT@Sr+@3QwBJCbX>&$G@pv!xdsbfOJH~Oa){(ueeUYmk zciauXIcd%QzJ+)~`dKpHXd9b<%F8N_CBnwl3-;QEp*jWDduwOK zMO+dp?Q;;$@=s&WL*@_{Er(rlF23qQ*E_v^#ueB&<0Jc?CHlP9{s9~wafPiR z2<)s}z2KJlwlrUCDuM%|a)i`g@%C%XUj*xgR|=@T;`x4c2!dPghZAxuP7J3^Sx`ge zLv)_EG!%~YvSaJzildK{H^TqhU*l@09Si&I{aD-@=~Z{{yh(&DJ8v6KoLN8OU(=5P~#N#_cer7NoKEMr`;Xc_f$pd)S^Fdsm7&0 z?E9+iMxDQKW$ML7`hFn!6?CWTAhe%HS4-*ptT@s2mmaL)o4~#gsQvjR_}2=8XzT9m z`>`PDS7#HLlawUZ!u3ibk2jmb6293sDi5Lq9d`5KjCFzR_wUL-uc|EQ_*2)}-!qXK zB`q2Q*C!dU?}MBH^F?m3!Ga~Mf8jJfN}LI|^e@8rCfg;j$Fwo5|Kx-QKaYUzXW#uq z-zWJ`enlk0^>;6^_rpE8en=i{cCvLWeeYCkZID;Mnw3uM@2hZKUQ}qBU_6{v!2TYK zL|J!XB&@O=!~Wih(#vSj1~@ixI{O|Xo#k754i?wiu=YZI?nh=hTpM$St(Pf$g3|uL zE$e4OMQX3%gHgVoZ~6B8>k2SZH$2hMTKmC6}SJ;B^Tgwftsz)@vr^~ zdqHk?nd1Gd zdCvZxm7uV8c{e!fnSLylXTIs!azj|tzx@Z=4<@i$e?S1Ik6gh1K8(`H^jB8{_HH+dhfoOZ2y{Sc0~^Cv%-t57Ydyn z4qb&gUY`H*74f2q@kZqbEH3p%myZPLyqA^LFjK5rk|e;_~o^oJ3wG1rjo?~r(YiME0B&dg?U z)%l_3LO8p3ID5Y;Emh)5xNN(My+2WDrRp>tpHs;8TZp=AG~R>FY~Hc;VV-@OA#=-o8j#H5&t%! zy>L(R8h^O_qmC~5_Z+@omu&#-x+H>npPkxIU}lY_yf1R;{TXLtNIos-UkFU|9q*9< zhs+Z{Am1lh-u6kCV4u$mUC8%M+I0C@sjz8zn_~pbzQxi`hyVM2D%P0&?H2ss_g%I^ zZ}>CVK5~A3JnAKTOWQkP*Wg)O1;k#BhbPR#etO1Ag_!Zf|LsiJb@=o1$uR%d^4eu^ z>Q))ui!l93(4*%N{hl{OzF+gKj*P2>d0D@*Pm}th)hpXzoz?-$ zg1_hgD9%TJBZj=l_j}1`Jy{o6YIyE&A}q`>4cZTLYkaBq%_M7#y<$lHuTN&AKVro{ zoGWmuwQXe@%(T9_z5o{WZ`zd!v-eoo+=V^Y6wkT=OXsY5{s@juU&P6W`GF0?D&VH9 z=Y}P)q<5U;CEVvnpHn~q$7dh4eBB7i#3rkqP zaP~}MVNG9$C!DEaU-t@@)VDo63`?p*8Lwgf@qjJyu<+)vt&K4I?lGTCn4|WIdY^%H z@>O*a%$(;mzJ;{!Y1@4dW@Hb_eImAh=kyQ`4>i*0fTejGPL#t5?&0)rFlV`~xDu}Q zzCpcj$_Xlve+3JJm#i8>F8I9b+#9&uY(+D9e}m;FcjO%$bj>l7ygwtIxJX+9_gj1x zk@wN)YWG!IVfKi9)cZ+XbCsCyFg-+$dcR5XZo}#!m=W7ez5m41uBe+}g!6sziF!Xu zw7`7O2o70yaMWZRPq2kHdIencgP%>_M`JV2lsm)v44u(U0NdS8ffWaO)<0K;v-xvb|1e%Wk*2aH>|_4Q_YcghC?1#u zM~*%~y^ka6xqoOn9J6xsxSz=RpFWx!!b9D4rF}5RD*A{$Ts)zHvb1mKD-O(Guwn;Q zKihHSK{zwhFKv+2$2lLm0JCZ?eWa28d7Zye0GCbkq2B+anJ=*EgE@P;sPU#9H@h)w zA+EPQL+7+mFN{gnu!s8;=G17z+#8CzTv+7qY^_7QxnFt^=1*MWIRmDT9p)YdyFaL3 zF&CCJ3NBuS^|t!YFoH$qRUeDskk6HI<}fEN@zZBa)-Nzy>}j}~)JvIJ z|6oJ82YpU3J3>ED$prJ;+_T&TX8NpQOo2Pgm785jKEa-D4A(B4ZtVd}7U*WKfwgV%lmi*-Y~<$)$Rlw`EFm*K3K?gpKu;#{xw**AEt9J zox2R%Ecv1k086j_i@E~m&+B>_1dCtZza)g+qwUUy!h%=N#)@J3+zGOWVTRTT{x?`5 z>}4&ns6Xg~%p%Os0$D~lX>YLGo(?Ddy4@N9b4@?*m;{HXNVXFThxaU94C~z8{OuUb zj@#F73&)%u{qrcy;XB)Uz^%u8M(|+Kt+0kb*ynz7TnMS3A?*!^wdB@Z4u*w`4PVB< zor@hl`olbT@7N3Qf9Fs0_6QchI=u61_aJAg{T`74`)lN`@FML;Yzw~yhtIAv-vmou zwd*{E>DwJgk@=uI4b5$USr2weonfxu(+wYB-saw;Y?wErV8%DNzv#vm2Uw``aNb`y z|7YLRl`!+Hz4d~{cwX4~>8~}+&#CEN4-4+!{7Tl3aB0ciJXm%7*&|C~sla17a|z~u z?ZD1Oq<+bvnR{W+oSVK2VR~TUx@eejCi{>fOfwNpdI0B-mJ28EhtiA=UTlK<4~?YE z9bf#h2iDQMP1W-^#L>o?;(FZicrrPjG{1VwG&tzgAL{cYWpkS6z|u-b>itt*_(D%( z*mFvv9SimB*Ztk5u#Qr#59vSCvD}0O7i%Vz+QAIf!pT-}``%@_Wc_nRAy0_Qd)HX5 zg+;P?q(WFy81u)IUBmOv?pYdM*9?S?^(|!S# zhMny$fyJ8Zd$Qp2rdw(i#Dg)zt6_;+f6q&pWB6^v2RMA-xqbso_Zk}012>iMy5AD- z+~7ZK8J<5^e~OU6LbD9XG}tqF?2|TPgDsZUF#D&?=KHnQR70|+Zu-Lf@3ahlV1z7`p3QqgNO3Q%aEKiX2kGB z*m=^m>FZ$ehUE@9u+E=3RR3strQge7X;W?%`8?6YFSlyo{F@PN+lcLJ-oJ&bbefxX z!0hl>q3>avF>G5OnEq^3@kiL1KdyBzjQ>$H&<*G?+bv%#S0E~!-mYZ$Z(RI{c2G)!~PyO$L|EpT$Jyk4Rea?XYpZvNlo%X*it&L zBNk?Flq)rd4bu}3CzAHxhpg@3hSE_V1hDvRM{hXHTY4e$2F!{KeDMwT-?2$K9~O=| z^mDQ~j=%e`{~eh3IrW$cEYO#}62VND3#V=2nETPUpTe|ZPO>hr+#c13)v)-6Qs5TY zGp5A5fz+b=f#Y+V634(&w|W)gSbw>O@i6nuWG@;l3P`^*3FiDLVE+^Dvef5~kJCN@yB;&6`onO}-x~-I&AX~Y`Xl`?Z(s2ow=fh=zwUilY z(iIFi@=eMA`ZvS2W*lrHE-WR-XXjb0Qi97pXZz2A`Cp>aM#8ZjA5Q3z<8|&Gt^mt@ zaD}toc!}{kqN6@x@93C84nU!7XVCKD;sFP(EB<0t7HPuVkY(Qq<8qOR}GN6@}3(Iw3YX6l=D9EXLodXAgItcm5r zV`28I=1nU|y?p1LBA7p^+{6K0LN&owOk7e#xJ!PfNS@C({+crYu%{v)PH0? z$phw3$#XRz;~V;2eG@F*^Zm08tW|kaM9lhpOtKNyd98YCGt9hU-|GVl2B$H0z>MiB z)OtuQU#;m2i=V9WIEdWms6u8CO!pf%JOpkvyz3J}>aXZf_IVSNcnsz)`fPs)xfY9i zEt=$wXOKurQW8xgMt7 znt1soX@4@^y$SZb*V|AJGjq36dGSUaokkeXp8>CtOI4H&B{1jLb-ox5XRn#u2J_l# zY@fsWQ;fE?!z{7d)hgI^*ky&UFinR&Pzh^|8tc^!)61Kwde!r0=|4%m;?eq7$ZJ>J zpVALYo!EwNrwbc)ZZS{?2uwd791FQje*v{!CYDHfp5fd z66N_T@cnSn`ejAMIOc`g9kL!cfsxY%uzhVw|3_H5wZ6B6 zn4Yoz1I&(CUmgFf(3$205OX z8=f%A3j0z2kj5ui5MbCd1vYV5Hkb5={d#7LG0YynkLo{d^gHolnC3oMPR@@vTTHq9 z{j#r8nAt8fljNOU5^6jcUxRz+!@S_7P1N{ADV-vApVm*cXZcF!j)hI$6lRd|pg;Y& zY!LmkpL0@|jF$jEg+cn?K4LmGp5mbnulKO>gp9H-l4q<4ZiHPwt5DYm%_uwiHEei< zMO`0^5TEjLn9;a&%Mj{$#ak>yq+TYwpGM~Y$(Zd$a9PQ?q+u}rF42oTSno^#wLV1M zD;MR!No}8$ha;!?r#!w2(`D{N5z`0lZ4={r|lo6NBW=e_+AOQUYN4u?a6v^&z+Juo*ZB1(&5Lj z`awycDoi_NsZs@}ZW=v6K3}@`EH!?v%N|vdoGG^Y*Mhuts&o}GE8M8^I~;o>@*1gU zm;2`p!~8X|_0p;R!D7CaEZl$c19f~!{jh3PIJT*N7wHduF;iI+&b%U`_5-1@U6vMX zKX%b)ZRDKZ5Lsg3UbVTpu(X@LdjiZDzgc$%EQvb1kPZuUL(688`jE?xBViq`tI`~p zR=@V1Jghv?t=0hMWi;r>z@FYYmGfZM;MhL?}s)jHCkxDb|Jyzeas z>zrIxZ$jE1cuKWTI@6cA2o@dHiyy%G+icaIO-$PxG>wdhYTWhU#iae+6YI!$v2t@Q zmcoLpFCNc{m+IeN21_)SN0q{Y=~>1snDe*Hxfr&5r6gw#(@)c%-iB!xQ)XJgwCW34 znQ$hvedP*R$l26>0ruaz?}8P~`Z?$r2dmeO6J0?%BY+8arw|#(0`5 zt%Swp*Xp8?*IqoVZx7SoZx4-uvyF3iIl|JeWmLVOQ$xlDmYkTdg3yf@wvzVM)+L3oPPxg4?J&o+c=03HdHqwS z7fj#V7W5QOl0Vr%EDDO)Q3>apTV3BtJf-Vn9o(=>Zp|)O(02Q0D_mCQ#G`Vn3DkW4 zXYPsVSClY+L30mRdZV7h`j*Im@rO~PxiI7KQEESmw9vWX3-k7SEhhV$yGnhSA1sc~ zo@WBH4yI>?z@pd2<7dOFcg~j|g7JHSnYys>+}!Uxm>YGBTCd>&f4mRFLO=J%^Ah`$@Qa zeE2b#9b)BokR0!aS4lK1`gBc^2e&7;&f=4LMFIOLT%~K#e}=eSjoOcjdY&wfgE>+9 zx+jsx%B;1z1Pi|$Ha-E1yH_j{z#<*Z<56(=vVs?>FgrAjT5nAI2FnasQpcp)$JqPr z%7ht5epZp=ss1R6z78|j_)z;>h{mt%Y?wPcuqO*S%Y5b48?e-@|G@)TZ&Z3FF;CZx z+RyEcPby}?0&TCG)yR`Z?!1yla#erDK6t3+l-pI9t;?hCcRs^ak>DL33Fh8a* zM%SjmjJ20W&4C5_9h#)S(&kF){+4twWC1z8=t8)M8FEq7AF6+x_{)DRi2u3;-$X8W z{%I|7>?z%jTQFN&`=&X}kA9nT8>TzIr1JiRKRQJur^Qq41>H=25zMI-PFRL|W{HMv zDJ-f#AG8F{H|6gsgPA`fDGOucAN^mK`F*G#NJDjTe zVL}5;8_~CO8_ZStF+keWH~Aj(g(ENLzNvx5N2ZQDK-zOHjbDb(m_M;%{sTDZ(V?sY zQom|D7b28SszJtYmk0%L9`*AaleS(YC0;iMt<~P;!_rU(ex2g3jczxS; zgf-^dXk7_e-y+RHYHbXB>w-` z7xQUx^C7rgTP&MMKEEe&<0aU+I#MGRmI{M{@57v*$%?0lRdeUG!2H!kol!9V-0=KA zu)byC?+BQ-UUXL7W*JMGcEyeC2dobTBTQiR#_y5gFk?(@$U3++K~srXG-2MCAecF~ zY&fYGso6bChdm$qH4t;94_7~eSvm&SkHajP>i2J9`{u?p@_CF6u6q@1v7dz8r9Pjv zRoiziTwdj08HrrD+2%MKW;VvL$bQ7GW7T`Y&e|uZll@0{;>F=;xc&9|cyhjs_@6l$ zaMA%?g#?&BO5yqo*nfwc(*>COc=4hyu+6;As~2JEw`wo-mDmqcZ>~&&`3JpZUErX% zg$!bmp-oyCEVvL>PAtA!s{07${GR-s>~B)T(QiJ%O(wBBF2lTo&M``Mm_Lts)O?9A zILXY08-}umll_Gm)BD&CW>i${A^Qiz^yRu}nECtFLbAV!Kiwa96Ye{^K%VSxytEt4 zYM9G=K8x&Ug84s^x?oS=NmFxRzJXA4ls(SJyJ&G9%%BB@F=63y)2MuyHQ@Hb5*B@| zd_(Oo0Vjf7;m&uCGYgU9{_A)Vj=55OmFzbx#r&twVZmH;tGncQ0WB_{;WF7F3AsM- z|1(d@tU~>RcQYQs{A!w`3e1-nw?BsIij7(mVdq?>sboKqjE)MJ11D79qxK6CZQ)*X z*e70RQ6q9*pZk0^T-8V8y@Q48w_Ngo6sf_=`2o`x;TQkF z!tc*U_QSjshI!bg_^C^1#Z4AsPe&m^k+_c9qd_2tL-eTT@hh7iF z5;KHG&BXeHf*I!#RGZG!WJo zWxbY#xq_!#f?+x@^Vx7>#Rvbh{y|wX-qJI*A^xO(JhPk*GedQ#<1^0vHc^Gy4%>ga zA~y*+W;qdNXmqT#fGvI8_vsL?H|v=Kr#k1(n+dbN?Rz{PF7ggIZvcyC@yE%-L*t&= zEQIOf6#Tj{9}QnWd|pKA54@q~M`Arq(UdsyP0k18uIXZtIZSggHm!qWLpD^|!u)MN z-juRr?N!pUcn6L~dwirg<9XkGy>5Cd}Z;WS)m5i=R>J+ubE5F9{ZWS^u^kd1^q8-euyc zPwCBYgIC@6RG2fitMe<|uNG&L21`49!#iO5`|_h1FvCIcND8~hRxZCm@_X%fdSKPI zd+tJ*dVYkJ7w8lhlKKX1YX0)0*Ofki={gHCJ|b_GS?600bCk1&SCjfZ8l$R+4_I}G z;MA|yuGKKjqzLOAHI|-KMT@t-5&gS1|kHS6MP1`jx{z z)xtbwb!z_lR#}axhdDip_fwJclS~KR!2GQX)rg1I9i7+&OA^C&r;zreLT3_lWDf2p z&P-~(C4p%zmz2|Cp|h258!V1=T13_hXY}0D?J)mvhz04t&B$>di6vWiCAX1!r%88+ zSwc>h7S=ac{!zy#Qa?(ZWCgdohg7t}45z|xZZLDjtXC~Cd(7G<512FazQG5W`=X~} z3mnt=v$7H9?0ogh8#a_yU#Nj4i*HlcvwC&v&I*|3v@+o^a*5>6tx{Ne>h!=Fxc#~F zKrzhsK4g~;_g&ro?hY*edUkCgtX{Qh8JR!f^W}y$aO_?Bq}woS+Hsj~*h>`kJdd;= zm@q?SHLgFtt6e_KS!7h82lE;ed^X<2L!JM6`;%3$y=U!| zx5$Mpqwm(iO~sq`lKB&R4oJJ;)~An6-^1KmhmB*{`1gx#xl_q_v&Z>#&VucGo`-hA zyhWdVtzgd96HohL{`HLgKCtK2ZspYoJjO#Bl&V4WEHwyCgq;@s>3xWD{$xgM!Mb$9znN1VUr z7Nt3)UOvs94l^76{bs_<^wmpL;h^Hhqm7A|x>7bRe?5IM%-X)akJNi*u_~6pQj4j7 zM#G$65g8URKgK~U4;$i5ARAb;_@wzie17p^zmv8wi$1RR53Dymd6+%SUZhR+k5^v3 z$^jOHbU6(oPhwd=T@6e0ZVwW->So0{!nDD)cBFlubN)tWSoHEdb^HdSkz?Fp_G}*2 zzo2lM+$NY_G~>twM?C)oum?FXO+Gd<2^xbFl^H$4qDd|WmB7|i

9FvR6V*POVPtv@mTWp4v>$nG^WaBfj)N`zB+QA9 zW@W;>Ahl( zkKMOm;h~b$?Xb`HxWfgoAS*S>2UZA}ec>+5%6u?$FWkD7(^d@g77Q}>!ID=iOCH0V znf8B!VEx7s_En_*@7UQ#V4V>YI;vsjB;SX};iOsegSD`PrxeGBrBy~&^~C#>LgHZ0 z>?vCtVez}h%*(KTipcRj$&=Y-*>Io7)r1cuznQ}-gjM_fYCglf+0jF9NWC2+zJt`q zt@`mDHq~=b{tmN;(Z9<&FJtj5!q!Pi`#E1OtHG@Mau@nw&I{HRJveh%(!oKPJ8fcv zIqZ6M(yd_&(B8>p)fSl7i9aI*OXJdCg~F;=XIUt~^e&yQC^-ATfjOgK;mOp}SQu}Q zGw3k?`N@$9aMkflLp5T4i1ZR1GpXc>Cd|%Ao0|f&L?;$bhM9Ll_ou^JRnJGvgy{`U zN?CBWvZ-BtX#5D(-)Qq8=pRhna|&!^Mj=o?c2q0r+k!MFljFe7PY{F zwT?MFnEN`eqL;J}uWF5eX-gHVhOb5cFV5a`8s_i!daePxPhru{6L+pSG#j@0);UNl z_+3@44_7TLH&28mUWeTmz(xG;g%@GowxmHLn7`uKyChf?ygtPkHtcD;o($6s(=V97 zF*GZOD=deD}~>4%>8D%(w=Nv>#3}gVimjFU)}j zKO^i{!V2{32l8OPMSqI}%--p(S_rf1CmwNxhd%AXkH%np-L$oxVWIJS!%~>zZy<7k z^Cy3+egX?lbnaLOcUsQRu7;Thc!jQTZNC3=F{xKFq3V@m^v>3je9DQJYmm1-n~+lv z^DcXxTLou}iY~o{x$k=dS>$-5RlVQA((MXo7sIT8W22j4nq~quUarfE)_;J-4u1EU z$n&37^?!uvSInsKZ?Ml={`voy8&DV835z!R)z3n`dQV>ULE8WS@#p=`_UnhGTT4~Q`FW1HNtsjmVOlWE3e4k?dYZS$Ef#Lio4Nlx z%v8`g_Yk&snDDv-78U&ZSO@oiN}K@xyDu0x12ZiY9m#kwynQF+!sSCY z1!Vkqy_!um@DN|yj2d6NlK zEv#N3R#AaD!oSik*mdfXEgCTUcg0m1*JUh5TG-Kvu<$|h7dbd}Op^rz=6|F;Rfd)M zW9;-`>Uj@V)h%zB3p4%&4~>Hj`#Ih7|Bv-_xqKs7kQYGZvFiGhO-cJCR+J{{nF5h9 z3#Km-dFa65!xgnxklf~T3=0-HYUZtk86)biap0I@^}TGEcEfkiVc0Y$`Ia**{-#iV z4yOH@I@^WhY~$gHF#8{Ei95{s`Tp+>*wE)UV+$;rpm^#jtf#a>X$LIOT`H}I`OW2S zUNB#=;CK_V;GXEefNjSgdC9LyRDjNcGBWLE;z>J#;DPiPzq9(y>m^I4sI}c{heH_pW z_qi*ri6Hftj~&ohzl_B_qS+b)OZGjk}@oAbPK%zbGx@`On`MpuFH=n`HogEby$6ZvHe+Cvf)|IL|FJTc~>;d zzZz1c0sD^`L5&Yhw3jkHf0j}>a_K|~)n4>x#%eOYl3mZ9siB_x>gnSElH2WCFa|F6 zzBfeXljHvSqY6y>F?)R$IcX`mZ`mV4vmxPA!Mo85}orlCQ5|FNTHfw->I2xw>2b z8j$>Zp4kStYD8Bux&AqCRV?b(DzQJTs?ef54d%*+dK`osc2?~s`-fDH zJ1YW~6D*!Q1*ZMnOFs#h{geHp1vAq|wZ*|rnoHIa^Z%}}&VXs%ldchSXHQ<-3KtjM zuAT(5cKo!OxB=^ZO>n?um=lwxwhFGAmeEgg&eGMJ0^t5i$3BuX6W83m2$w~L{;&R0 z>zqtdACM(a_B*~bb?6N&h)Omg`yqcm)8{uFsmG?w=@ITDws%`4pYp}6U#<6+6fRM|za+^z{asxa3wEz%5TYs?ri2Id*1?XiYgIUaGNVA{0N zsSdEZ)hFZOq+TtUu^!es`io72nUhj1J>kx~S`O5He<1HwDBSPDS0eXg-bY2_Q*cb) zsny?L>Uk2@bIDa8_e=Wl!v!zl*0bV#a)0DhkA3t5mgBvkOl#e1GiD?9+nryW$o*fU z|02~E{_p-wubA;JfaG&zdddA-IzKn!5=`rEq3%aQC831~rZYNK$n%1zLGrVeyobu9hjoLS4s`Ur^eclhtgZE%c!ss31) z|GQ{i3mjfKdcp*l8>D}<0e0^$`rrEcs=nztEWUGmxi)f6VZn|{m}iocPu4eY;zsT> zxU(^$P#+fhXHe%qqkW4i!RvCC-vJOp1BDpXk8vl z?vJ#vYfjf-FB9bgW0-p}iE?s8lDa_9tfVDgR*}%UIk{i`t#YX~lE$HQ_#g&Cz7PW7(9C zngiGVUbo&CW{btW^I`g?7d2#m6lq+4V**=V4vr`HHyZuo`3*4Z(t5QZm~nHI@jlqz z_$Zar&wKn1gNt|t)BQ>N5cd}s;RNl2Z}-7mW81VM*ksCs1;l(j4ZMS6cJwEZ`!{Er ziDM6};Bqg}o8*rjUJt-tjA41?{?FMc_eF6N)`Lgu(rqxCxyDWt=9SC++6)Vvex&Nd zdedvO$bQVdnRv+z?tGKT*$9ifz6aaFL4yrLiI>2AG_WF=Z7eWS0Se_54yV>4hQCj3CG*A2&I{cOtJ?&XS;2hi zT$4jE#=X%DW>$xGq`>|SdSl3b&U+Xad>^LgJ*1wm@b5?OC2+&g_W7iq|2Z}5AFQCU zchCZ6oEXX;%RzgaabK-r+8awQ15Q{MOMO1weWl7YSm<`##U8oH*Kd^`T)T4w)qiPw zO6M$?w^%8Wjhy+H6FVD@T(NKa8kjq7vZ+3-KGyB%T9|&m(2~>(zhv%qgK5|9rpe0hqT&EubIg)3>1| zAqr;O^6s_6oqO{YPr=NDom!t@aZ`2|dA^}Nx1`2{7ZK8$)w>?>%ighO&; z4!?p~A*&V2;QTiO6B=Ot(A?q(;F}R?19C@ z?acXbYh%#wL72|Su09Hfzuwg>V}$d^?>#*X_p8{qjD%Ur8}3KIRjQZXD8ua29QP>L z*~Z~`Ld4syjX?47*&`b8AsKJmu#4(0SlXN zJH?>Bi4(JDA}qZp|Ku1PK99d}GE9Gxk3T(z{yQv~J{9KVL|+erO%54r=n@}IEAxf( zf7uqzfF)m_8c@R$=yqyHo|A^AJ!3@`b)ch15{4&vtw2wDFmx^3@*Jev! zm^19c%xkb@&A^8+Sh6aaa#_i<(UGvgunu^r}1j_b^?JJv6tz)bNd4D>G53kzMjiMD^YgYGdj^{{=N9(Bv|-D0Yv6FVpOwF0 z&ZddK>)?iQ7Nx&op}Lo}9!{9j{Pqtl**&`XEnH+V>$JQv+8@yJY=vXfiZc{pY5c)~ zc364c9-mRLD5YzLnM!Q*Vf)Cjr}`%~!BrhT->qQDsf3}Quwcso(++0rpJl4D1?PM37|$M- z8hm`F2FE(D)LTVzF)v&fW?iTabRZ_*Phh56==L=*-L{^_B<*`%E^vl9-V^)h!I_Sk zsq09-|NbWvJCD44HZFd+!Xv#+pHI}h{1j?IdIc_T8DlVG-D{l-atB#poA-sIHr4o>}8@mdzCU{un`v0L;ZVUdPs-156KVgX!!=S2LH|uh5N_dokY%;7d+PZ6ii!m zyoQX2v~GWH0$dgG^dK2O@vcwikBP6kXw}1vx~8L2IQykaKo!hZ`|V`34eK!|@Agwz zaHuPm1M6M6F#a*gQ|3QE35(T^2R?!&UxIkKuzuPor4pF4%H+D@cI^L4Wz6rv{D$H7 z^I?CVEl!0n^LVA92i&^-Ej(y11fnqRD8!uC}SSLAzPNu!5P-J#xKC zGgq-4U|yIg_bsWn8u#8A9`bf;Xo8vNj{2`BE>P;Fe3*O44bHw$POUG7iRkrCSgzEZqZ#Jq9{Caka}D)uB_!Xll^ODXa&h`V<5ifoYw*=OlB<`Gxex0{ zXE%}l&=Re4Yv3})ITuL(Sw?1&U2sgszYR4o&u-&c+D`Oe&-5V~Pg<#U&~W0J+kz{J zCoT6-hRe5pfBg)Wy6dH>zjjLxqF- zaLfXqwqlqYP<+=CrdtmNk@ZNQTKax9+#b0xDj#N*z1ivt`^*y7+<-X>YbSfb>M=bB zQ(<;a@daNv);+y05oQJs1qQ>WoUqh0Fu%5;BovnOy}95NO#fJ!dK9Ly_3y^OoUDz) z3^?ZZh1h6Vl=bJ-BbehTO(6R__x9z`CvZ@v%9;pRoUoo>2N$hOXgflTzw6opNA`;S z55c_C>v)~8v)LEV1F-b#-JzdwQ_#e|{jjJdJoOhW$(LR02eaaNlue$0Z05rFEwnd& zAP?H><44`Usy1xzfjc#3Z`y%evUc{KDi%DR=%K|U-054xxe$z z9HH80AM&YM2{RH$Dzu?qwRepXvGk9};72&9#_Ojo%udXi(@Kt)+;YYS=DB=bN$S1) zXNjzdtGuY=wU1r!+!E#-%iP$9oI5&n&;q7~O;MNIwTvZt-Y=81cvlGmJ*^tr< z({^qOAm=YwyELr_7TeA3BgXmvmGj2_Iy;?u{$QLQQWy{0c>7V$E8G=wVMZ|HMjiD$ zBV75zX$`D1)%`E|eC~9Uv+LlXrkB+C30muT8(?Si`g>$NL>i9T`(S&niYhT{^DWab zICF4X9yOkS$6vb)E3c;wkmo19-yZie*!?Ky=vr8!%$IL~E%nVRNPmS(pSCo^tirn% zZZLg*_RNp4&N7WLWd5bu2f3eM$&3X;514-~PUQ>y-}4g9QAzX}78yUhw~f@V36GM% ztvs1`WW8}_9i=|6>2tlC7tF}oJNN{-zr(mZk~8Ps)yjq?(?nEzdQNrQ8Q9+CW*a%4 z*rJ3wzowguj7U98(_`&6OFxGHIJ5lmK63wOjk`-(svV(B_D^AAD&>TNtNwRL`x7Q6cTg|%+}rQ~W?rNpDu(4M z3m=i^Nr{d5{fBVvl+x&@Fg>|F>oF|Z)=sN{IU@a(r*Qad&TevlpjT9%sf34C*-`gP z_5)7GJDA>ho_hY2_?jR60XL2OJCfXgM7rDJC-~s`RCNDK11z09muCSh_cT)XSK9M` zNn7Fa6>5Xz{>#VjX*dGcF5xA=h6U-@z0bq6O!GyJFcW_lJ_VM0c9FVYibg2+2;p|F zQ$!nbUi#AU#Ux+O?&*akw7SM7xV$0f!4H`J!1tL17W`cwI7E(jk7w9L>KP6j$@3?V zJJb3vT(nT&EC)*)W8MwhjqBsRcDw>CUQ~B?B<%d_+8bqJw@u!PFg@+(4<_U@%0AWDZe6+>hGS@ zFQ35fi`?DF`LKm&9=?N}4W?L)A?@9NS^tK^$K_sCAujkn=O1i1GK(57L1(})#XY!Q zZp(F$@#GKHg^h!ixBJzQ@n+ZhH|WBK)3+`l^T9T#WSGKzGe6!T-zTI_dJi38{f`m9 z2Qh!5IX#9RaC??b!*4R532Q62z>z0rFZu-wKFq!41;_loMa@6AqJP<5xYf~-S|2>w z?fwBUeHCr68}-s6(d*-|=vtTWuU1zRKc%{oBA?JQ=QhC2N;G%2k zKYL(NfUHA4?C-myj+_sp`!S~!ZsO%q=Obp{V>iM&bAqx+&Ntnt^c9Yg85$ttL5n!= zMB`#TjF+R%hjZ)dDm|DhYVsi8|7acgQ$xXfmIpSCpTF!|i5J*9%ve^ei2&4G=o2Bj=f<J}U$S7>HH>=}JNm)QS`rU}duyFI!GxBk0*XEDq=utf0z-0=P0eiqC>)F1a4 z<}UV{V+~8LUX<3udM6K5*%H51nBELWW=#upg1JV$^-?(Yjcmkvn6qMy^B^qZ9ecKs zroxSiB8pUwI@qfcj$AEdL#_B>Ua{pRhhd z^~^4q@h|I8H{3o&dm0xO@Bgx<9WI*cnBWUDyX<8@lU)AZ5`R+P@;I*zR<~Rc76h{< zc~E)e%}mu$n7z>F>KEiOmXRBHFg-ebfE-WwZvFY=u;9dE&sNy|IsFE)Fp0g7SaoC3 z+GzN{<8iu85>CUSSeq}M$XTcJ#+-rqz5FMAu+Kt|QD=!?Z3`N{Zy8HE`LPn^+krA8 zU@wQ@JEWeblAJyjrro|xwHG)a`(+IG@8Kt%LQXe+vV0YsEwDKl19OeHPxgZSb#7Dr zVf6iM2!y$TceX?#m;O$lA4T%zmOqced=iTUq)m>qET$_LbZ3k`lO9)dX`fjMM7FxqESsQF^Q%sh3B7%$-7 z)rT|dOjeThAmxndwu1jV9^0}~)&*uN_P3JH7dEQ$IB@@j_a@#j_e2^a0RFFkywtH% zPr#|2&Of&!=O{1Rbs5fnuwtDjsW;9#mJM6V{WIDE^L>s**1-6K@)tc|`mozceXt;_ za;_W9UUuQ#6hG{L4qcyJVDYPG-DWWNbm_KrFyqvIJ7?G?v|mWB54K_(*B7Qo-?k&y z2cxA$|2!Nt%EOu*U!Z9*HxuTB3cSemLbvTxE`_}umQ($sdFGVV!VPyS)5-NBlyke$ z0Jpo(r^cVIn9ThIN47p$y9c>M;B@o{tlV_zG`SwwXJXsr{g<&AUhM}C5T{HI83}V{ ztb0S&zo?lzRs~iKvZmIHm>+R#49r$DY&t=5t1j#DaDz>6z)6_*Dy~ioZr9cBBZ0LG2%>7znmJ18Zv>8`mq08b`uVGj1@`+br z!O=tKJK-SfZDuJjQ$E{f%zj)CHSh1H!Zf32_nC0)%5{e6uyBK85DR8n`@YJAB^K^S z9AW9NZ}!(oZmZn16;==VL75}_`xX!OF^M@u>IEwXuf)S428z-RR^-xP0%P z%Vhl7zY840VC4nuUSgWAxziaqH8Gl+A6n8B`4qUQYJ?)0KYGi!s4Q4ovT_fZZ^i?< zNdU?Zbs)0j}?er%5Cp$CvU>&TL^tC%2X6G6asT!6P zwV&VfUeBMO>;A5_*Is+Az4uJX?sdf6M`^nwVgJj@k)*vu(cIt~tkCzdj~q`x)4oy% zbN0TdC*v1Pa34241kc+~mJBjKv^VKXcEkVP&xN8f9minr&5!iR^FUnosp}TZV+Vbr zo;MGC)x>b8a^pYy?N(gPTiABpw7n-u|KX>ye!(JDx#ASkzfHv|l~Am=j6c+VVmEgE zF@Q}&$5Q)=!&yO_2}hRi&?5Us#7@{?2dj_!vzhE4k=%hP&am;#?TTc*OSeRw@`7W$ zR;;7;)77!P+u?$}85=H=oPTrrQP@*4Ya3br;)C~NZ^A*TTd4KT-lw>$3}zZFtRU;1 zsU?bf1N)}G?jid_5^b&03wt;2{AWA{E(b@2;r&ldTaAo|yTf74R9L)Dm$IO2*;N~u zCy9GPj>n$)tli^3rq5F4v|2*HfNELadDtP#OQGv98x4vS?z?0N#HI4@DYN$R*SoH97gRo(19OnV_ux#Fgr>wjI_M^h_A&h6;WCiUEk<-dzz!QHW6pTph{&-InU zOwZA^RWQ?}bjwrH-?{pw64+j#x4r_VFDntZ!RAp4$B4y?y6b=b$BeBf%$34%KU_Y= zwj4RPFZ;;^;-Z|Vq<@CsUMmwe4$9Se1dD<@JGQ_f_6gbfF#m|_+XOh$)qhtGEV1@l zkqr-yzg{nd=}T5_tAZV;8Kscxtzb4p>ZtS9w8OV!IrJC^x&$o;V^6Xl@SRrYgOw%?-SLWzK3vG zaqg60iw1XEba7V*patq8JTa+ zBL9z9VTEsWA98)TXO2;M(At&M`!j#9seC%}M89iusr#qlO#L%(ZA0{dMAQpLt*t!; zN6ULq&tpdZj)~E*+tAt6{+BpBOxX|HD@UeXLp{4^x&sf^Jv@Cq+3!r2?$teTfznyg zUDDt7Q6s|P>M3PC4`J?2u|*`zYHwas1haza8sz*E*{;<7rZ>AO#lfau-YGms&U-p< zb~5ql`hk})qtuNWpEz%s#~b+H`DpyTMC&c_^&d+wq24zCxaE78=B3h+3487@I`{$R z2JNBd$Nb^Z)>c>=7~qqQoH_MuXcx>$d-Ul6tg!8dwiITcOQx5?;ysD}#L{B@XEku= z_nGIuzzpvbew}bg`j?z`SnSdLKy4rHm##TmNdFwm_bSG)aE@yDCs@*cGR6*;B$%v| zkoxa84+p@J?meU4k@};X#YwQP)5|jrupstb!#&tPp_Nw)i*DK*EAnuCl4Z<@C8?9W z4B?8TiVv@0#`+WNWw7Xo-HB>g*xPR51FOF@FCgaCj{Fb?v(2++)(~H53OND$UX`W# zqgz>VuEAD2{hu@<=X|w!{gl-2=qPM~`5Iw$vios=8^-+92Gf4rGZ+KYdekh)`SZ#L zmd}FIPN-1k)W)g1!rJ>QHOPGMOr*RJxTrKzuNjsUrg$a5T#gzwKOE7cop)fDn}ZEx zek9v|Ppl{TP~DT{`Z9KX8T?LcV|J5Vf06w2(OL&^ef9~d^I|+xYSu6#W6`-vn0IgBf*UMsdH2tH)90Y``S zUZ&RH-?C$oaN8Gk7h?90>7xX&MAs?n1i0^RBNa=oa3252u{quiZk^F0AC#f$rqVh7v%B9r) zx+j=?0PU;I>QlRs^Ve%MlKwqMKK$?%7N}pei-0)?b#C^NykMfqHdwpHblDG><55hF zzu)ti&QDl0@G{#9dETOn_Xc44eCwZNJ{+Uf*Zn5#OI*~9;HJ|{ul$7tra>)ZVMC95 z3d1aMJ)~12m0^XC+BFKW_}%JxiZCbA!C(Z;)~>uH4|`v{@L?n@dGSN^ms}su8Pil@ z=@0p^L0InC`RY+HjeGMGxxPY=i)S=o;i(A5XP6f}YJnEav#pud0$0B~*rW}M9F>kX z!vf<;lF_hOdb*C}G4{8n>X7`%*o^luGkw+-U6^}odZp*iF9U`dl?!Am&J@NNeK z<{YY%RKh83&yS8L?WazBSO?Q5jyo^`=9e~z+u+V+n!Ai){GqX7vN*r~1r~43NWCD` zZz7y&{y={^EOnTj>89fh{ehab8N3Jyf?caqkUumX6Gt3iq zl-ZH`p(EzYN6u$SKh}LE7Die0OoID!&ZjPdd1n@tE`n2XhVG*B_x8DKVQnoV17i9U znWz46V{jYQA1`FB=^ofW`8CxaYoTT}F~?#_(qh!}?W@*D!u{DH9~?+~y)~l_!n{_C z2q&0J%Xz#Xt~k4~XF1F{I&&uvb}TJ;=nS)4E{@*^=RJ5F7siIcE|ouDt%gMt z)6VXOGr5wFo-p&v+%p^Dihb)n*O9#QO!=zMB^cpc5JcEU{e5PdRV!bx0>5aJ#l zoHi6>EgH??h75K5(`V$pSN*_Sp}to ze3z+@U7yLD=*0?CNxwS3U8*;vx8t!OZh8JtMwg zENrh|HIEu^hSf_`n3oo>dj>i4(cl9n929VPWh%_slDyOcPD~2ONrojU>L2IArbm`q zpM=@6At5Z7x$=h#nJ?PGl)HAYK=fLZ%r9f_;zu^HON!%xXqbEFm??1qt!_NA^zR8R z;?C>G$C2y9G8xfFY}j?a_7L&7H!7q*+vP@HkuZ&~KXfsiS$bj-xxO5a?+H$Dv{}l^ zJtU9(p}iVTQ>M#Bz+#_=UTff(i@yJ?kL9|q9~)y$csaS8aloa#mA0r$fUm1QztAnUen4BdRCF!jikIO3C`??cqq$ zVEb7))cTj^&d$n&>G?l0$@oPH5udNaT;H2E!KDA0AN23QeTJp4$@zKS1 z`I68#)fd5zLA`1RkkcNAhnB)Ltw)h$zWF1D-FyMly;Qr2>7{8eU&1bvO?8gK;(1rc zHIUrLfZ8u?_JXb|n7cV4>^O4XpM74>VYhx)4YEG)AIkeagK48Rg()z{YJN-&oM_N$ zb_!+(80Ei**`F^fk@dxlUl-8@YtP(Wmch5| z>K>E*`LAg@0S(t+q2Bfnj(ke?BirTY9Wyx5BfFUFSLVyIK_=X9rkQXXX51Yu znh&c_pJq+Wo40Y?a#(mEG~)(L-~D9#2G~9>aV_be7J8cP2g|vxvLgGLR&`?5cG%Hc z_MiL3FQW;&;1r`Qr93ISoeY0g%$^DA4 zVs=L+oOSzoaRSWxSX`F{OXbp{;z)k%5<>__SiPY3E8BgW4XMxhYW&ajPL4gE1eYmu zb{;{!psl+g2KK%_yNB}ed26=7+yhIi50gA(U^^SOt@fBp)(=B{&bur zYGO%5(Q&f=c=>-AR`9?3S-AA>q&aYee%q3L$R#T~_s@nsf9-r128*A4Iz0nUl&$LC z4Rh>%Qg++)fE@(WPG>!wj6BUdirUZgW?N$;xag+zJr_Ci_DALf(qBe*X8@^RaHp2o zzF6>$>~ALHZLl%1_C*=89|Z;uD&%^{Xe^uQ1B>3bP}jTDx$^!dSn{)vIzBJyD7C-o z(F)Y{Z}YQAUr*ZqZ$8?XAp_+3$F7yG_d$PhdclooN!hgZP`mdp4tFkcY3aV^YRa_kmapVF7JuX14X8(P$U=cO;= zZ-yg=3SX1`P2YL*cqp9pGW$4ro(beKH4ni2xd$I+!lFwLPalQbxDFa*Kl0UwCB(qA z=4Gk(V3yvM{d|}+Kd-Y0rhQ-BZ~?ZeeYE{KX@79pvwYZFY-s%wW{$KP^#E2s^6A2B z;tK+^r*Nj>64SS^aOZ)aui=zUldV3%toB~@PcVDWo*B)sSUmON zcUZlswznN-J`mK&9+}VL-FsKi3Ddn&@~6R(?1!>am|?=cuoezlV{*9%7PnbH+5=Y% z9=t|!ZsD=X=iq)n{(fSCT351&v{$dW@Rj7V4nBAXM{n0E>VxSeG9h1JR^dCN0a(oK zo}qda^Os$2`v;bOt=~Es))o8DkeQ9~zc1I^1T%M!m6wGDxxU%grh&{K*GyTX1g;G!UEc)LwyF+1gNxE0UwlXU)4N{R0;haqZLBBlQ_{_q zj^TcG=+A;$SafpiEPdGOtmTawSi0!y3L}^yo>o~6vrN9UGU2=<+h%o zQw?(cSu^9cIIx8ND#^OWDw#iYNs z$b@&Wl|z+m70gf8FzrWhWId&+VqvekaWEk=C2D>`a;_Kz14pON8~0_>xtju_xUNTWNJ$%>q&fM|FvS` z$USF!Nq?s&28v**Vx!+Tm^*yirpGXCPP2JG%#z&jEP`D$axRkfCRKa5`8Bb*&FSAT z-6Ex03LB4+KSRt&XLb=6@mSx;dJ)8B%S^=iMF_w55VI!@H6o7uxk%<0%rjqJx(F_~ zd4A|mSd=ci$_Xy2iJSKwra2pXt%DhN#ngUa4`WvEf%9DU`;c7FRj8H(=QIkb@rmVK z+s=`E_SX_J9_f?5nsiv#t~Z~oZ| z>lVT4v(KHJO5BRyaDgoy6J$+bmf(z$DJ&V>TxTu>2n zOb-^AUZiqnd(>kcSSTD`Yl6I5ahoQY4`%z8x_K~degaibD^!@lhNZo~48|iDGlV;Q z;k@1RZ6=WZ-W2IXz|5h6aYiIxT{|Hb9^8JC8XsRy(fb4(;x%>BWKyrEEqjvGcf9K& z^UJ;d`6_XW?V?v?e(B1?`;thWQvc6;Qm=>R6e+X7T9(H><$=NzoE}eW$>H-VK6|_bn zZ<6s}ybfkmsZrxid7EXp8J5IN?!AUQI`l?WAgSLysk{s}UF+cz0`n3sSP#IQvr}IY z(;H+w6yk7yiBg*wN?a0Rv!Mc5ide_2y7rCYf zuy^nBOb?iLU^S-1Nj>pG9h7yPo` z3Y)IE{+qPtC!gDP3O2st^Ku2uJ>uwl1MV-X-a(E>KRE8t3s`-3hpH>grt2=~furZ= zA8;o&d2nw5&gb!5K<-yL@4Oxl3Ut?7iRNpZOo( zd8ix?IWQ)eTpxjtyHYdUsX~h)*Ow#Hw)8itSG(&L4Aaw3%o%cgK8xOzZx9C4wybfY z!{PaGv7A#v?HA(}#p8IBoTZA;(DWmEKwi>-LFO z5sT+3$-aaO_LXgjCiUKl58lDt&w4&lF!y+!{5P1vymFW1k{x4~jW~h+hxH3$VQFmC zay?=r4)^6o{LG0W<~3Yax+U&%F?>XGXHvH0F(jMRQ=feE^=l=5HOttn8d9Y;H(lzODgmdkm`=tG* zeNm5K!CR}WhS^^ox2h#zJ|2x|e@p5&jyZ1z zH!WDMD1n7{8m5@T>}GaJGt7T|%E$qh+?xHV4Hg_~QrQ68?q9L%3rs8O+Y$vcDyOXM zfw`%HA5Op#Gd+g&!JJ^j+h^dQhuMF=!J=x~PXTNwt8wxdEPPWt;VP-mvdovCgYhK% zcDMuU4ot~Xfa#68?YS^(uX67QVw=-?kKjhPOWMk$e&?%!5}3a3#7sKOc;YelJ)BW5 z;*El(%k7^iCu03&tPrcgT$RIoQ`qoxqrL_#{(4s45v~|fw3C=KyE%^wbAE@|X~K*i zx1vN?$z6Ye7A&%zSb7g;w4P|vhUwlDtm@&c)pI1HVVX{-c{kkewcv^_EV%d7Z}>@k z9`L>Hs0Z`fqxWjVO4(fj3|RVxt;vM@TDh$Tr2bE>*c$e{5Vzb2W*VNX+5`){7k@T^ z*=oAoJ7B}gjH^>&(X>G4gRr;juBMqV_r&A$t1$0*MS}&&BO}Dwa3-yXJqM=MUg>)X zN4Snnvx4c<&({~htaIlz=EKZ2v&ze0@ze{IHZcApuRkwg>HP6a?O;Z)=J!h2xX&eN zF)S%x-BktqZyav843^$oT~!Awj90s|8Wv0r{?P<$|M_ywi?olb-`Nf8Hbob0gxM?p z-s*#GqeajBNP9EBpda?%TQxBVX6+A6_B9=CiXRdEzse=eXSMBl3bQ-7PO+@fq3MWIi&Die0N< z`r^npqmr<`YCcrFh9$22&8Bd)Wl(b+%(uJNVGc_b-`sA1g|ZIymT%nv4xTFuV55t5%p-vQ{GomTW0q{28WCnmj!V?%SW^-33c>@?4+7mU&Mr zzQXu}DYKeko@$KGcUXMfKI$jzeM;?1A1rMzxiB<&K8q%MYQGf5FG9Z@2^S4~CHW z6v}fx%cbD^kuQg~llhj;Suk-VTphGLr;_9z*&SN2XS4o4*XQ76D^pnVs;&9#u2=e1{o8*DG1nI|UwdD^|%0!M4P z4e{f*oo8X=BLz!{B`;N%--LA)f8Klri|%e1^#E3%?M@w!R-u27ShV&So2)0U!VdOh zIAZ;XH{~$9LvQL^*xPW0JTWu+O>{R*GYH5a^)xGW`CqW{d&LB5{k2wv3_XSZ65q&@ z`Jg%O+%E@rCWTV-$qi84`~&p`yZ;!yL(Xt7q57jAT`1oI^S{o~??cY?yiKhip5O7) zf8e~*7Qb)E#Q|4sN1?yW;XUU4FwfUd%il&0O~w79 z;T4UTb0N8DBrMMQdw{C1)z2Rd3m?%A5(}nGpFa-fHp@_^)$P?WffGqx%|o5 z1UTAFqkkOnr^C9p;H>(mhbEEs3`>m?xN(a8eI{wYR4(BY%xlPup8g*T+G!3JFqggR zXftw4br;rbSomW2^cL9BhbBLdEZeIY4H78ZJ!GpmkD;!`^n-*n-L%O<4 zVP2K{B$Cr69Dn2pOI2&B_Jf{7O`T!x_q7@Ck?X3AeYO&2?9%5|!=_z+k5|D0$+8!Z z;p$Tr@vgAs=S}y!aLTV(3pZGrWnDzhHzrl4VGS&tcWl-*m}{^;(}UEL_b=E!b!31S z%p7$)@)Ybl;5TPIESTZ2B?eaLKU}wwc*4fqFxbt?X^9Uk4!vp<2?Z)8&4;WC%kF@B`|`-Z5cu(z*LZ2-($D`iiECFQcuxG<}SXQvF)!pCgg z2@4tdAKI~AvjPNJ!7w9I-|RMQ`ZD=k7-?VXZW04~Dv$fP7iLP!7t+7eUa3@cgmy*Uc=S8Pmig!^Sf#>A8Qn0RLgxK?fI z`xCHmy@rfEJZM;1p8zv7RVlj!{nkDOb1hf>B=tEj;td6`WOiZvGT6{nhIJhleWk4= z$5(h#>3(fW=DKH?g?9#cMMR+bDtl&3+CNePPG?&x1jcyZ*yEZ;vW|Bum?41dxbOR;ys}%R)!$&gj`B`bICFbZX+O;Q*wv)~=j>go^$Qk7 zG%Onmdl$XlJOE3!$|}ghjpGjL$ynk1d?z>j#QBKTt2E?b;er0&J>>jG8!8Wl`N5w< z+F?WM8Rtg8l8N6gRKO8&UT^6z@B7oAd9Z>$=aeQ)+rQ==*`I?B%kGXQ?PG$HGhy8( z`zyLIvqZqY2(#=i?$#rD`Jr1E;HDmVTL#PuW!@9OiT?Yu#>3p|=ME{b+r6qBVn*7G znZz-N4YDS{!oG*pe&=VVPcS0wO-19=kVjY@S!e=_eT#;Z{{4+~6)j+q$2_Wk`>Vq~ zS;O>csyb(pJ3iy2+mc*zpK9Oec~pBb%zO4H{50|=vq{62l6=S_{KY9;-8Yt5z`laNkndMIb?zJf1F{Nwt2;WoN_mgPb|8;jH+)8Sv+|)%q(6&x%O5w z+m+N$6I1v9NR=2zH&}9V_vPd0-|&?}$r@OYhC2W}c=%k0JIvd4Ei4+&c@wX&4raE~ z4R*tp$vv;Vhy&w~ZijiprDr$5jN>Qdx4^p3mL_?_g8s4Fyx}r>P4gz0HGPTkdbrQ# zm%A_Vf+Y(*VDo3aPJXadZ|KrBuy67Ly8u|Cx^KW0=Dyv0Z`*&&5O)slglTtGsCsYK zQ=4FzF4p}_`V$;yk8Bd~K&v z8R?J1+bAIQEh9Y3;h5NXnRJ+CQZM@yPMfB`B7?NAId_fNH`gKZGHIV>zMSkYZea7B ztFUB7#MkGrzu)Pw8!)FWn6mA)Yol+$LSNS$ay)OlOG-Jg^!nL}KVwauU*aPDnGa$9j7GI=z6R|qY<@Vq*X`% zR>6WJXHgHS3#TTHu}c50SH6wuQC9oE<-@ z`$wC>B!zC6-{o8O89BGyj@t)wO%J=u;r!PS6r1M{!fnrIWsD>FgoewsG|b=bBd#W- z{lVNXvM@WK*l#KUfFWuyosmMfC3$6avKHJFlwP+8mX=J|r4NhFBv&}VeEO}c6Jh>l+Qwxt z&nlU33M=^CHD6BJ{~0YYgR5)aUU7!GzI%30hmCg^<-5WxTTzY$T+p3az7`g($WEOH zTg{22uZP9{g|k_3$hmrwa8+y_HqN+#r}8XePFW`&P(>g~C#&4e4`X^GAUd2VuI*!y*P8 z(ztl&QCJW)a7z_tT+Ve%ggG`Z{<$8nlYLWRewudp4_vQ^cW0-afrY#0RegtDR@)pTJVZhJg|| z&vfSc3YgxWZkZ3aO?k7hiqxN7S8xM1w|qCEhU8s~$7T_8SBBJ+{Q9~YvK|$NEWYqx zYd4O&_zAh-sWX)`kCZI$f~7uM-ZxQi%!#`69p-d%N*=+M&tDDDyxC%AIIMIxeZU45gtul!!5J~-rj9VX zqH%8qtUj&p{4&yh+Q;_Wu+`n(t=Q!hkRU?l&9ey9wqb^Q4Dh^`8g7Y=n8H^og;s zq)&8oElgMU%*upqFFj3mhZ#Ta)fd6W7jAYCvy5h#zkwBUqVQ*Iu|FGQlUm`T?uZFw z{Jii-_rAi_u7>L5c+yN&i{G#`AqRif2>!PJ`H-`i&uc>s{b2UWb?WkP)&}dzJ7Mmp z&oaYd%jO^WXR@fD(oI?Sq}i(fn%NxxE(|#@Q%;uDhm=44x{vgy>GVqx7RT<%JqR;g zMi{BXf-9fh55XcejVxW5t`U6oFf1G&WNZZ6ekeL04NG3B{$|3ChJuDTn3K_4W(8-a zq`Wu|^UFteErzw_Cg>%=QrFiU7ufsMi_wX&c-A}j2)Jyk!jw~_{=-wvb8yqow3Vqa z{lkNgSK$n|d7-CaUSgL?A?&tobFKhp#w^*>2B%E@^D+aL?zCSie-8Wqz}3V|n6)Ha zQwMXc#WZ-2opWGM6t8HKx#~w2>Ua{r3t@bco<2H3ZoCli)%VD7)e>6FN)0EHw2p6e*+k2hlid#Q~!5IeMDGL_s)W^dLuWDYAoWWto=8)VyG2;fQ zKa$R>gFSb&oF@HA>ryno!;wFly~*_u1x;M7avtCBy-avT#wXZ&@%b3yd6)hYvsT7$ z*MS@Fr%WQ}!~g3tT9>$Q)^c)vd3OqKGT{GT4|@h1(1x6 zu7LB#$*gz>bBCK8Uk@8H4NE03E#JrXFx+px>)}UOWDz(f9WHoLeXI$__gTf|aP6yZ z3u5k;WVw&9^z>4zPb81aHtzaQd!h3Mm0nnVHh(MWPdNY2$e|bTJm_CNiqwmj`2Em> zO@}nPC_o^d^ z#p5PzG=|dxFEL3yf5)R5bJ+aA+wcKcIIY0Q0k%(ln(~L#E1$F70&Cy3u%cOGex0_o z9RH7*a;kGGVBI~ZH244`x;dCKA&$Hat;&U&>PU067MbCe9s z-sdJa8#X<8cm^?TWbH$HSa56qR??o?;Ni6n_Ez(MHVCudY;y+(O9$(Ja(5Z^Bx1k9m!!Su%d6tc@W1CP+B~wp z95%MlT_cO}&?WLSyNS8?_R7I5)wfdBix^)Jmn{$T_Ddzku=&?g7m|y7KOAs|C4(mh ziJ6P`wgkb^Yi9RT^^!k5DX?R2O7Sp~PwOka0}BlNZ4^lROPfADhPAy!a};4lS^SPh z*rmuKX9O%h#4ziG2iHWrR)+ajGi(0B1vW`RbeQ*1oXWU_=Y^w;_9&P&&FPXY%yV8j zQXS@w$sM&BHZ0c2C6+vFVI6?Ioh&yJ3&qaUvtX;Z?f2D)&rf{%1ZInh9+Cd&y6ugx zV5XPvS8_Z?`)K|Tk{5P`jD#g_4978-F&@SKWyJh%6IV}$&Fvi*sgnA_jvY&3q4|T` z8pI7T1wk;w$?n@|SbQp?E)fp#9xNFHGxyBuOoywbv-OEN+Lc~HQlF5dLh9L1zxUjM zO+CK-)*|)GMnv9+1Wq1j8gzo>XIxn5#Hs9_~+_1kRRP#8bY(W!!6v^Ry3>&qVQ zEp3Fe(nCj+_O#NztDP_{$$J**pL_a7`jE`|EP8RkUvmApJwq<4!}vw0lVpA+p;x!e zgc$+{8FD`Kf~+-;uu{NtTOF9uD=2a&`7c(RF3kFK!(k(wwWaOTSXjLIl*TqV<Xr7vj%J(I>LV9UnO-S>1)tv@_GU3)(WZ%3>ae-@@HNEz>gOjX!gSf|$Mj%^@|5J+ zq+Ye3IzN8!akKfb@zukcomcE($8X*LoZpAm_+_x+@1xXs1<(Av-C(N~N8HK! z5LeUmePOPFK`gcYV_6?}!A0sbsr`WOJDnn7L9o=EtXF&=g}-5l@wnDIlk??nPpvvf z{Bb39zLFh7w`Ra8&I8o?73s7JFT;I}BR7-l!LK*)JXoZCVG1!l;kUXCEWwYl3t?R+p6g<<*8Z4ZVpvjcet#>>|G)}-3!C>3o9##HWkaTQz!_&(G6P`o<;z~fu3|j%gX9!~6~LbYeAhST@LqOFspGmwkzSGn#dzC1^93fx%IZ5a&< zWv|^ch5LmWzmLHJwvO{OSQL1aKV94ixV)@ z+N$&o+P;3gkHW0k`r4ha0Op)EJopRd zi>6;A=gUgp6*dU7?kIBh!L)C)6lAYqK8yyFB4F`^z-C3*v+7uH7|dKWdz3n?O}~1M znDHn+P7hX+`SnkI{Gellfv!yV>gk>;CbUJqt5xI#z9hwVl4} zT!3l3$$vxO#Ny%iGhq6H^J5OfZM}__7h&#t|Hbie$gddlOR(5>qWf7`Y5rLE%dm90 z`TI+7g^|V2D==$ykNN<&>rHOM zjK7uHLga2|GSzco*2a*68*s?Ly-Ni!+u}q`CY*`QE?XX0*&SCB(U&#!-{O!?SM;72h6^)E&3iTq8)wPOWL2Ybt!=}X54QY zARc!5ZVk+P(J^ia3ue4M(+zuSY&PQ-Tev$Bb2ir5!63RbgQSXTAz77LBnO16y_un==w-zf>N}fKA`I zPE&<>H)a-$gFOoit4EPM`*5})oMpY-P#qSAy8kkQ`}g;a(uTQeN8U_@6>j}*7(?oZ z*XLNkMbY+S`q`C6*3JsyPPRF8f?a%wKAtlmIL3TM<8w*uL?I z5LRdR?3oCQ?_B-S1PdOegc`xTiO+PTu*+G;rN%I4;JE!yxSAI5gXE&)4Mnmyus+nP zDoJ~OsYbgB925PAPU^*pfA)`t%l3L15VI|3DUX4z)+bQ?3#aLwX22n|S5$kk<~d^{ zI7sUYH6DqxMTjw+wsdaR1k&Fxtw)pLg1@H^8^TPBt*e-D?X8A)<6+MD(YEHWDEenP zG1K3qgE&ZA%Yv9ux#YlX*sWphCUX9wp;fDG;6!;%D(7943tt8c26*+Pf4ar*zpLSY z&sSK#PsbbX95UZ`GRe1>CkMg>s~92EVD9-|)&bbEJLZig%>U7UJPEFN`y*f;%zl?F zpAM%~r+6-a#WzDMZ@_8O>i5`?`jQJHb4b2Fak(uldQ|I_4=Y#?ezk*{T_#VTz_!O` z&9{dcOMW%Jf@7|&$zB2r3Y#9+!fxr~J(j}4hOg@zV9#UkzAYo|_kUUZ0oJDNP+19! zS^X95u)@NKH>+ULxJ3?quzv$<-)fk(;>r7dnDubvZ(`=T8M}YOgEJ~#xRUy#<3EhJ ziSu9cf-=|0q(c`r>^-sD4Y}my@86S&eWW(-Ft0Iq^lUioyMDYUEGQZ1S_pH>#~$#4 zIoc1x9bo^+@;w|=B)-!fsx zZer7F(RGqntPqC7w9T}ztFXA#HfcXB^$E32hlK}UBp!qLCS^h_)+{11Q?HNm(Mv^WZ!DI7Os9_Wt;)?VjffHH&`5Ebp@t-aU&Na=ax3T zx(+jY1+Meq$oG}qLRk89D>a{V8#3tlxKHEF5&}drA>33hmF*f~^8--A}{O$|PIGjb*v!j=!_Q68S zsnq^3A9=;+J1qDxQneKAs}~)-@e`)6`Sra5&Uq&KIRLXP$0fal8yh!#8zk0BX%@pu z0cFRAEWr3=8s-tJ8xBbr4*$E~GB3G-bIMs%ZIyT>$E9p;^P z)K|s)1T8oasR?r;e^UFQV8e^s8nC4EQ@I85f=NuOUbywxJX@HSv-7?ha@OkbOtRjE zIy|bqls$HjCoFe=bD}PC#-Z8w*TZ>hr^_>7;h$xcS#B%C#=*3OV>fOTHEc|?&VByNC)cqsv`OWC%Fw1=J ztwzk3ZibnRGx0kUVL8kjqUhiPbG8pMgmBRyXZRYJ@mgkS0^EP=go_8sT|M3$hGR?x zW7omL-a?r@u=tBx-g=mBG;PXG*d@{BtPd;-^EXplCkS~#ltyzD%-;I;sU;T!;@5z)bmHTQ|Wj9V6~%ko;FivnMPtyLBZKrX6nKxWID9 zVx`w$j+Mt`C({0*VUHbMlCf&uwXg`Rx3+`7m2zxVQz5 zX_)SMpR^A+=JpYGoKqE10E@rgSA7qQ;%pcXVbPF+$#pO*H?8XtX-{LHtcEk#>raYc zVd}i^m9Qjiv`-9VfQ<868RPz_+AGjH&lDo!1OHvpAFzPr;+<>VaD#h+2dh3%Obl* zn0YJah#@SkGhRi^FnG)(?(3S|D1ik_!>IPY&ozF2g6T(pq>}d`^XsW|+hFO{mjh#A ziAm~@E?CSjq{gGpJgnXWb7LE+oM9-Q*$a!rSJU-SpZA#e>pLuT8#0HChgN9t{Rhl4 z8c{ca^vBa{{{_3${3V!s482>8WzzUAs)HiO(Lac`wA)@(k z?K3Oop)hkG_sk+VktLp>2uu8G3!ULkopVXbq(0m7?K+tCBVd>+%niEH?+3>OaE;W6 z*Lm=gMw9yeWnanu&uphPjv@K#c>ObQ z&?;l+v9Q$UYQ{y_GW*;E16aJnH#Z07%Tx?ZgxP_)XP?6{C7)6!k$UOP;pF}!9WVM| z3e$F}Q}=J7{V8K6%(B*}-fwK5Tp=2zrZXxn>J*h$$ z&Swcq`z8j#^j9bAbztps&Vvw`zj_#bJZ!(FWaS=M@ZgZ032b|Ou4q3jTA>j>4Ne?c zKJp;Rw+E@tg2gQn)rVn85`X3b*m9rtlcTV(&H2e0T(=aP+i9H*x;L6TF2h)d-l@hbRpD|5`IY|eHIKk0t z`o>>``Nngp{`xj+p1KS(hT2ibXDs1aU4d!uZ&ur&p4Rs3&Q+Lod9c|U&REEsnnk=` zVZ8-hpqL$Y0~WPC)HZ#D8n(C?cjw*V66~w6vl}Y~4 zde;3qn7jPUHf30QUF^tuk{@u=Qh{y5PS!TUl8EjOEtorwRsELKr))9MhjsV0ynF}K zLVvHGNZKoM93;f=*gIyxRxX0AA7Rm*?q_yzT5R2&CYXKaW3n^cmL%ER3Ja#`?OFpz zCk%}J3^NUG7rDcEpC89{!Q95+!>i!Nm2U#BAOm-X!b5`t1(?q?o{Dnc1vn>=(%fdksRy%*AKhYd_ zYX4*;bi(sX5OoZdGD20kn!+LSH)Js{M061A4 z@NO_wQ2LLpWN4uyVVX>L4*7m#@Yagi>aci&ka}J>xzFHf!IJVRC&nRXde3nf1G5WK zRmk%<$Vm4L@xRYsPJzizV&S3!>iKGGd;6z0%wbeh&)0}(@of!Q>@;ycd0%XMCA)AG zEXWF^zMrV>%~?Vx^_S_=0OY(Fo69ONS4We2KMlDT{#O|m_1IfRBKM9q9w0d)=NQ3~nBA1sC&u|OVcNPps(?YCKgoKrvlmK7{=9k@f*v((RbqBTsF3yy_WAdz_lJUm8(uGl zc~u3}`$NQsx&@9f^K&!x{Yu2e9?3G|O+N3M(Vv@*zdIYIRo#5u3OANCm$<;}ypOlZ z=S6mz^V&5qt8Bs4U$AHO&EB=JSh_Gr2KSGORL_s=VMd(7!r|~>&-j&_{*S49kBjMh z-^V`)rD9U4NJfez45B1Vi4KyHN)$#y6io?9G^Lbu7?h4O9T0`-fDlcJlG3E2Cp1emoxK1+ym}qAdO~#Ay%AI$uY<&v=bkn9haix>=pGQC~h$>gxkD zl+sVkg|pY#%-c`=eINDps=Grv&7b(ZJGEX)hOd2a2o?o9Y;Y(2CeM}}hItQTsLwx| zqC9&)w@YL9aL9n5w zTKH+0(P|SM50`fgW}boh^LHM)3x__{Se68H=MU(%!Om`N1*tH8dS7M`Hr*(&6TtKz zBE3;~-pEMad1sLR*V8vC!F*%yl1$Qn=%}MIoO&eA@B+-%ai^YF{5L&!ufQUi6(O3) znf6@$d{X~^&*M<}yORrHskDpBK<;&D(EJAR`cBFns_`!P4{A7nH~#lLWX)if!@?)R zgZRNFR`vGA_9~cPmeEDtXSmN-In=@eQB{QkTPe`|~>2HA9^4iq< zOjdc9{4>)3u$_7zsm!daC#FT43A=IpIw`(k&q=-eZR4Nhe0;MU)&z^)E4mbdf5xJzxM(`4D$PV`)+mosUU22BQ^EDqlg&7wY9f?#BG+#&I)voxF#bxf_zc`~=y1qqn7+T7 z`n->)_iFVQSZZQOy-!H=4(|I(>SOjekoSu~p<>JrnBB$uNj@J;Rodm!Me_gqdZ4Le zBBK|kebcTapHDUzT{9klc{L}f&nE@3`>cosUDp>}Amcf?8~uUV-(t0MU}w)g^@A|o z`&}D3Ug3(x7Q-x%?;mWag+oIPwv2#<`_C18f*G4qYevC>qqnY%#r5{T`n_BM=GU~9 z8p1k3-O*!Uu|dt@b+CuDpE7S_!ti5ogURfPq@JO0hkYNWd(6?7BmF#1;d{8cH!yW5 z%&hA$oSujE>hAN~{bYZ4@4M~=8-6TW{u`EP7I>V5%jIhIe!;>qC1p3@h+lJub;FFz zvb(R~&^vpn^Cies?d*fO85_gM`J<<|o>j=l{muB?KM`y< zgc>>N0p=}SLTztgm7WCyX54xHpd2}eaY9KOW|t{y z5gWSd70-ZadyA&thfS5Yl+A-#Z@mgDVVMoZM~z`|+_#Tp`#fIP6BCj%TB-dDT=uSz z1@o5wc6fzcWw+wbYHETa>b%I)}-F#NWcIb#qS?#OX{O@(nc0we$8A}#)j#~ z`@$HoiqW?`2UybfN3tAt-ZQJ-31$ytFLQ!fQSkz2Sc+d;-UjE6UXr>V=6;nfCT9SQJ>e^(d+L%amro(iLx-!eDOk{n+!czpT4nILw?FYIFfk+$rjgApMzp zC>ylZ+&e+?i^Ds}c!J5=H=<#2+C&y{oA=?Pv9PEwni{{QC$}^X=EUSt;~O*Yu1zNS zs}bQBQ6G1w?nf$2eg7MFcPknrfN5Q2v$A2or5{2w{!=fs54oHP3r!AC`=c_{Wcfu{ ztapyu9)0keTn_Oz;i$7@ydu*fS75HHi*gEFbXwk52uoJ1@k@l+bBh-g!o1Bt1JA&P zf9JEI?#J~CQa|%ufe^X6-Y(TzSor1@ z<-|QtcQnHs3k~XeSa4>4m%yU8m#Oii#0ti3FugnW!%6g4uNyI=9hQC!-f|okp5T1` z4D&i~T{{4abzNS4g$25CCeEH9f?xWw$$B)H4bq;53K8}pvJ-wm5Z zwd%x^y)|`_7fwkXrwI#BrTXc>O`dnIYyHQ5xAU?m!(v`JHNIe~-!fg8A<0Y3qk=w}WY2CR<6rSo7Sbt*~Crjvr)u{H7ar z0dRxuEe0{C?~76dEW?~qPPWh0ahVwp7j8Pag9)?WENLXJ96I?X8Bfap*m@dfI3`fr z6X%sKA=fuzTHhZ-lArt76Ae2r+mXHi7Wq5KMZ!_R-`|X7wuAYob|u`r?F9I;2m;|yzMZ``N(%?l(i0V>z)P^UIVg zsF%rbuOw!L4&6&Hyf1W9F{wsr)F1RpI&}j_QdeVhh{8l9{gx^RCYwxf$-TU`4Bt@lI(w?}ej^ zhEh3a%-w_&u*w!ICpF}PH6`I0uxn!Y0c!k(6N9o~|NUETllzGkHLm{x?2x;@gP2~d zt#uLB%imjU zpGz@@rMpZD$@t7IyRKTo1A$q`W{~j?tr*RQi|2$?lKY4Dds3+r?B6S;t|uqt?U&7P zb;5HCG9Sc>V#BSl?7NO4>ur#>wmM7eBuTO`}XWG0@=RGh_ChULr!_1hO{-poio|CJP3*v9> zi-Bb#{z~j%_HTQeMA+|VB$Ewu172%ozzy9inuvKrJ7lt9q4UtoYhcdvt8?;Tg;R^V z*TSNngPU%{+^=6lHo$ZPt4EGc4|=#g)Rc4^#@>V47H$%5C@X89V-CjSfWVyW>xhJD-SN1S|C?Isz&P@wM*Gx|4`5IcW=>~WY__QU8~ zFjs%Ag8Sga)^he z56y$~;fUg?>@zTPd5>NJ%xdtrOoV9-kHo~iDfHb*u%OkG+8_6I>ta$!zb#|cJ>>ks zA(5#t!`Ia35%B|)oHUY4-c`MX^`y}m0$9?S=JpeIAEsZK4zo&~U1V<{pKd^zQ}e)l zGMtz&bnQ7(zh|O_Ax!(dhT5JW^Mue5=G%`*Ap66gzB6zO?Cx`SUjnJWFs%3>ssF#@ zlWe~-=`39NR3Yy)aw%(@b{Wi4irjJv7GF7?*Fy4yRdOU37~l8%L@b~4EDjc(t$5uF z(@I+=lkw?(eHkNe;`5l;5|3n<`uTZc$KK=Q`1x}BH}&9%ZP)C`@iJ1hP3OR-flX28 ziHD~97{kspzs<;oX=!~PCa{9s`un*g|DYke7|wq8z5NPI{rm}>dUX4VYovb5M>`g5 zD@MtxDC5xVOt;(;rwObM9vhY-REylUO`&NHKBN-oy2Sq@HFs z5CwOxD{7Zn@&AuUGaC7N1T4A|WO53*$VGymn8y4mSx21@W`);2c~Wn4+CKrgq0R@E z0;%7($%LGr(2PYNM#GZHVgEZntTkauFyo00b$*NkmKBaAeo*?CoG-6qFB^!ZvlOV~ zPrNl~tqOAnW2o_M>(-d5!T*lO_{R8;#O(PCejGx-v)X|h8ZcAp(&z%GD)-Eu0Q1IK zc`1<|^*fzXbC<-czRc+RUtlxeHsUe){S^{2#EM z#;pch)N`iT7|trf{ET>TnOLfoG{F+iHXEB^N5+2@@8tqBZtI$@foap+w06TPmmO}b zCH1G~tlST$Y7FE#z~U^qQwQPdOIxhg!@P4j+mFI6`YX1%!W=)}(@}6y^bn(sFx@Wl zW&&*N9wgiZ^G)qnUxV4{#vz+wcAD>c5$xK&v1bcRdm6m84(=E>_@VKxGij)fZ4M|LqhQPU`1Aqw3k?`gWaw z882+8@diHpnHK|#XNKMTMaBy>cjW&kw;0j;?KCWqc|i61TZGI_ggLv8XAPjfML#km z38vSF3;w|L%W-wdr2n!g&I`Q8OWI_qF|X@cz^H@y_^ZN zvMuX|!$sH19-oH=sRrtDu=|e4?U!J-<|?Prumks(Tn;Q%_vog>%%>6B*I?SmR(*B2 zH+t93d{S?HDoqDwXY~f(fJNi|b}`|W-wV&&ff=)kEREs7;%FxkERCY+vS7x}uT$>B zlBsXjSi`xc2ltl2?7R9;*21>7&K1PWqu(WNa8yG8l}lJ5DZa45fiwd$o*-DgI~4Zc zS~C46%oz9a!Wp<}w)68`STb?!<8;`IR@jjZ3zfHKUxLebc~@M3`5n_6^WiSLp*rMz z@D7i>Qv@?gj0)08z4Zx~Cotba|5F+)Ffcax2>V@+FFy-2g|Q=LZ{zy#XHmJRAry8xa&w|B+MY@Y%X1kJ*H_?> zbFUe08&Uec7^Z&T1Lj^EF|q`vwRAB)!A(1Lyvt#(?~=1!u*&Iz@Jdp@Z(P+M*j>r& z`6F1k(mGoH4!)k)c4#)Sbj!8`O}O~%5z0K5MTMp?_q5#=lJoE1`nU|vYS5lh1q-Gd zPg)6!lv)Oed6SM0*uy$Gf~?2H&cEhtfOB^Z^{pZOPKS4Ffd#JiJ8NN%)?JI8#4gi? zPhkP?Q^+1TuqWhV1F09)Kk^~DSy|;XnDt>`GBMv~wpSDB7flY}!WIY5j(Gu#4<()O zf?L)unEw)Htl8a3#*g~sxrW9$0*^;%yt;=CY302Xo@h)H`9TTk|UZkosM+ z%gOOISQ{Q4W{L5`_N|b`@fWsV9gu;Ar4=m-u!^~vtOCr~-ZN@CY{*Wkq?7t_U1nr{ z8{G6>p#*dF-&6B}gU8~`L`^)ryDC;}%ugO5X4@PxeF5`DS{3Ac@t>)l>4Xzc zeb_SrmV8{lw+j~BIKE30W)=)@r``RJPrpbU3r9@rwjlLpKPDr^kNhbHnq_XCHu#|e>yT0R)5|T zPfXWcnNIAmPjNmu9!{Uu&=9yfe~SS*UQVbWpY&%5&QRx@{&+1ley(u>{wFZ@Kg3Wo z5ILvWY&HXC9n&~`5a#vN29fzH?fI@pT;%TbL>uPmIyd^mwih1wkon7edweEw;v(@+s8*TD_?FI2#t+ST+@Z zn1m&C<-TXaoM0Db8Z5K3^nZCngu^9RuTZ~y7IOaSddFh8E%0*&8Ba8vL$8MUk+frE zzO&{myx$6k);dIz`-xwAfc6ef-8^>ZR9M)o(AWw8+kdIN(CRmAp_oeDpM1l-nj!Zv zAH)H<i&IJVLuA4uC?qX=SS3F7oh|Trkx!i=TH2m@%dP|p-cG+ zIlqD;)mjxeaU!RKoPX9kvps6CCiCHaGJgg061Hi;|K=}e{DNlP|Jq3N{2W-+y4srD zFTBQ><(4qVW7b(>=0d&kYhc?_hp`i3{$j7zO~mu_Uu(f!*T;Le!vFS{eMfnXC;b1N z58w8~elV-9AckB&QFo{20XRZ>#bzc<4+-CY95#M8FM19c@3j5#M3OgZT{eVy`|KUF z;I^mUaSLI7lfi`|So-?cI}@1w&9Ut+Y&vLf(;Qk4S5NJ8SwY-=Uh^}|#ow*9 zBKyj)58J+ij!gSmHj7c5{=)Z35)utYUXv;-EX8g{hilMTu)%KToh(?Gwa#NHEKm;2zW{Tpc+~#!9giKl^dDCqQH{Go z`t6TX$J5s3b>bS#u5tcC=6kB;5&e8pZ??&o4Rah9J-7}_58k8ZcgG*zhnq0Xz;NGw zinEJc|c4a4TBIiTAV$TM5m>wiMG#jQLQBn1SX@B}=5OaKHW<|rj zHyUr8hoznqr=NjkKk8BEm+`GZEg2TB==IJZ{pU@urok;{7ySjWXnA{T2JD~f_#hEx zwlFoWz^79%9KimBw1weeakKKFr|f$G(Pn z8_RR!Nc|eUoKBLT)Y_c{^V@x<^}wb#D|N{B>1yBZ_QR}mM@uqEZnjhL53G4r>G5S) zvh|A0AWXl&H`4L;kg~P`6{rz&Jf8O0` zM~QhoUlm}k%BGV?;OhD1o9V>N_k9Q9@)RE(6_{QxTeu(g+U4M>2GiKXsQZN}JmxzI zW>n@L@kSn}xb>Dc%*v>Hwg=|d1vu-%qLOmT!dW}NrK?Ou8^n&FmpzDEjgZ0^{Ej? zFpb&tFbp>JG@51tOM)2G`Qbm!u3AJqZ;aD9S!#cLnFU(+;9h;g@ z;*)Cn%V4hk+wb3ydmOFqCl;I-a-bJxT|Vb!Nj&tm-Y607-zTln*06X%|MoGkz)7*% z4wmYMglNJoGfVmQFlX9V^~rGMDr?DF;t}p1+N6H(&x8#yE935F9av`69+}Os(0VR? z3hZAuws;#X9w(;yvn|JZ?tum8Gel&(@?zGdy)gIk&zq#);rR52`(c{b{*k({RZ@g` zF!AhdU-V(G%7tZ-FmKb_qqAXW{$%GQm{XM}G={k&4z5mth2uwNo4`eRs!Ptn0`ZHkA zoZ$wW($2lCgc&WGlpD_ZuYdHPJTN@*$zzxv_NQYT>N%r(QfgopTYH)(ES{E~@C4=y z4ruL$t(w=nK82-<1#bf2h|krP&xogXSDuDDtuBpu0dqCBEKh@V_HKDY%&R)kCV;uJ z@~tmn1~c+#CLDPD+wWH}=hV&8Y`FT=ulsLciBZADt1xe!#gz}RaGy`ZP1v?y67z-R z!@_#+!VV1&kvaU;Utx3-`kbk1&et>>qW>rXIZ`JfjPhD_gsTh z&p2G}gK4^ff0N;&(KS;BVETxO9%tZewS^UbNWVh+^pkLIx#4Y^HIAos!nVD(fNSMAf^6XhSs%yeA1(=q)UYH9@*YsA8fqA#;Cw-_Mzc@uvNN_yedrZlijRPis$p` z%d0eCp1Mn^CfxAy*^$Yx@M4XnG2Hp8(nkm8KQwq{3aj@X8`LND97XvRaFfP3`MEIL z`FTFE!iLh?`7l>^-yI7$*QsTXG0dbFDHCVSq2D$k^{tMY=CG+)bYuxEy%O2D1a?UL z`G^IJoZCK_z=3b)eKsd~tBSw~R)3{iX9aUCH5z8XLNm3mD`DC_r;FMoU%hav9nAH6 z>@xvo%XnU86X&&@P=!5C57w_C{RvMx$HLib6FMDX$s$j#EL?PF$;%3)yIDhTePy5QkVCKOxYQ7n^Jsou% zX02+?ia=hzNm73T7M|GP7!Kg7&K6$KX_}4E=bLCvWf$hB=N46jEW{-UqzH zu*BUyCk^J-&Yu$qySq+*p9PC1%lzCATg7hZ%!T=K-qdh7w5h1yy! zn_=erMX#%2R`fhGvOZa@W4vvIIUYCr{a~GKVTKY|Jh=HqEIe?+`TRRr^x7~g9adlL zZSet?GL6pU!{y664t{~Thm)!GfjuVNrHkY#og3@q)+`k+Fk;WntB zs4==1ZfJL*$-zA5yp6x%#BTKz1(^Tx1~q@GOShjI{hz$>bu`r(gR2fqza*jNm-L8|&vckpB+(%2E6Zmf+5i?tsMcqZ@my^E=aBmB zU}}9bFi~tVf|;iI+&JVOo<+yZV1}3JBC_6jWXPCU6CX0A=2xIvB7F_a+B>@jUs16H zmDXy+oUpSM#0DGoMy-V<3zI0blkQeHkp8=-+sOQDI``)#G4sPFYCif6ryp7e)AwAb z<}15JZ~O*WRA)zBUuHn&X&3n4{#%^l3pbIx<(pgy#><}J=)4&g$Y0fb0!#HLDQ<*m z;kDHLs$+OWYdtK9cXJwz^&sx2ks;Z>aKpyDd1U>tRLmz9i%O5!!=VG2&mCdD){-nP z>^|l^WzJxU$4R(r_MO}I$T^aqfdy~`O|9Av=3QHD^At|(>)J}r7x#C-rY@M~YH@Bk zOw(%mD|a8`Suapz!OYf%)mkvqWqYbA%m_8D)`LSM-Rc*T{{6pt=fHmL{igF__L2q9 z7Q(CvSC`L&#Q`S8OJR$3XD%>dp?N@(C2ZXERDLGR*izlU4rY7sKk37wGY;Y{@W9$B z4pU)v)=aw~SmnK3Cz(%7fu>3UEVPRa(uBDMKU3boi7U@WX}~P&vKXy0?B7Mj#bmxR zG&XDC5Am{WX9TFJ!JOm1a*?oed1lf$lDB`Il?}TqD%+^S0^2zMI=JhF4uhCe@oQ&4 zsXsB?kC>6ecF`@z`(9ma8R@47{h71^_FKHXT7~r6ZJ)dzZjyStD3N~4*crRvj=dK? zDU$w`DxM)Q)79apJj}hQ)0YCT~q7$}T6QYvI;r5ZHaN)S|%H;eq zokkyI!K{npYsvjDeK_mvN;vSQ*DY;WP+X&L5BpWAQujN%{r4jW*xAJS)C@A7^E7SZ zfAc}e*%+`EmUw;LI~O_U2AvvDoi*EHF)S30J#B)VahAJnDJ;n;O)-XD7afx^hgq#R z2j;_32kDk8V7B+&>vQ4ktq2;rg}J_kU2|bZX{c8)OpBSNZvr>`FkKl$`gds^vV*gdZ6{OKQncIv^Mh0$5i`#8 z7;xZ$PwDEUpD7$(?FN?{TB?M=blc5k?yy7jQ)+vX_M+R{;YvqA!!hLi9nHO-aN*U5 z*2iJ|ywEXU*kHJ`cO=ZK&(_@sw^cnEOO6LWuhn-17I|1x$15C>(|;0n-tc#0402&r z@3L%Iok!b5OgkH{T?Y4ZgW5%=L-2M`1sy=-va8drzLd2NoY{+*nEa*F|k`hb5D?U3o+tch+wc zoZY%(e>E&Ro;t+_)|}g<@r1aj;>29oaMc*!8d&hOJ!Jx1S+$+27iKN{-i7mLVUhXd zF>Y|)=3wFQ!Z9yMeVaf$4fb+=aJ2>IuUO+FjsB(7Lu!MXFC~^d`C9b-?=s{eF04S zFq-Cv+^=~O-vp-9CZ5|5$FaVIOwZT%SQA;_nfN^!O9WK5%mNO@G!~BQqf9!zimz>IFalO3m1drPbvmVc+ zp11r*&Cd>y{@<@G$n%nSW}`+REPc%lb%#Z#T2CD&{hyD>?S>^c*9->3w2~Q5qG6$> zCN~UbE0`|30#{mnkwn1Up8@d=uyn+U+9;Tr^HtCbJFi-~i%;sMm3P(2{bagk>KT}~ zI`7~jm~l`oG8N_*e4^HSWBgx$G+5xrvD%HiNv6V10E@2#)sXeNX!YVdS)~58s$vYR zp#RP{2WG^s73ITv0+U@=U^ZP6R}9;3YI4ql>5mhs^}0AZ(DFJgtSBgbfZV{aa>-3t zy4+*uL%8AU;Sv!{`_xlZ0T<~j>pg<`X`gqKoVzF7f|%!YjH)kM`&sER%yiLJBK3tC zf%>&1pFcqLd;IjWdwM))=*wUGWd0UT4fV__p@x>v1#0&FqlDP^vg_qu%8HcyS} zm(b6JJ%J^&6T_aPpZ{4^;S(%;e$j=PZTq}{xMlRx2V{R4I{Ov>z*#vRHDZ|lxb^tR z$|WqpeVG7a!MsXGWq9EJxpQ@}^xCj_6X42O6)Fud>-Cd^TEsiHnKZ(J**ZQ9*y7@g zf@d(-aG!@Z%q@Ex*bMXRj*irYRW7~pCdbFIXlOHlHETjWTVYY$^p;t0qQ|nuuVC?V z)}cAD${VK^3CX9$^elqQ$2Z#Z zb2yQn8S)tx!mlQ-bL;Hoy4_ z+io-ar2_LJ7U}(fyPm&jQ-?WoG`+iti&t7ONdLREtO1zUXZceP7Tmp2Ir7mGmUvOQ z9upQGF=$hP2l%_x3}J3Yi_ZkuuO{AcAuPVYm_HqEFpIG=CHZoJ+&oyOUNdnqEJ$9_ zy8vdZ-alprvkokn-#dR)svF;;19cUzgxE6Q3Uz7|4dwZ0xo*;J>dZ9U+UtO53?SPRSY2gNz1j$Vbh0|b%#kl>{4MR?02Y7lLvFW z1e7J0)8s>8iP_X`707MVvl@=Uyj5mYe}na>?g*F}*ERJ%@?v>e&l51C{K)s)u;AeI zQ86$p&(i859H^FcG?w^}b6^TAS@bsRBrMT5w=)W+J!?sbgSoZaC+~;FW-^Kiu=GzT zb^IQhXXhru%w^RTTakN63hR<#)}_zM8(_WRG3sf=QNwG=`PBRL_rQ75KiV^P0W1pJ zHslJ-3!gl5Dl9n_CwravqU|9Sn4P1!@)pc-vZ1aY``yW#cVU)dwYL=4*YC&JIVCVx zcs2Je>=l>oB!cO`Uj#M7((~^R+=pq-{QTRnh2pvi<*-0d9F_-1-O?;07JhR0mJ56E z=et$F>>J0nT!8he^&P87f2Mh88f^74=gDK3nf5&@73S8{Q)^((rrYt!u*KGw3U#D^ ziuQ^mShhU1keHioKNwHygWfgP!@SYHPorR(zRkf#STK-u;y7HiRqIm|sqfl4FA}z> zfAGE;=GUK~>Lrtln_6JTlSAvscw#G~A?+krbl#N(>uk(Q_)L7Qn7SW|8aIFKg6Xdp zE)XK`7+ZQ^02TzzZOn&ded^_guEO#BwxsO0qxq=}%7Y`=6*%s) zYq=81KhO=%!R14A=BvW=(c97zVbR~_-|DbHkLME$XRnP8(1w}&*wYWfSs7ZVr@}lL z=I}kRS5ir^KFseDM{I@T`2F#-VewI+yECaTk=Z<#n0%fFcg%i!em?2HayZ=v)_kx0 z-3aEWfAw4j7mnG{u@GjBeK&In9Q9J;wh2t@)m~}>^A5#qG=;gflW)&~bFbz|re z_v@>}4Z9BpEG7LtZ-ZrE{yuX9lC#}D7JSC~m(}t~pO_hPB$>Et@(TxI?#=Dg`Yn5H zRWH?F>vHT7^29?6RF{!@ui*LD;cA}t+!e5Rp@Hv3SX$+_)(RFx#+*G1vkTu;Tfhl9uabY&+Yp)$Fl%Bi359W=Y>1hSq zR*lgPAo&aW%w@2+^n(kJc&hdmBiL})j?=*~W2WHtJlJZ`WNiqHKL}1e&vU07^b3Ur z@5U}B&wmxQ`9n{@v@PSGPJ}~$X-$fOIm`BiXu=-)2Vb8e`8+u)=Wkf6kwp4Ub3~Jo z4@5k%6TtKn;a~LOuB}6pGhj(<6ZL-4Rp05G19LZqW-D|5LGOESMrKM9NEeqAVr*{1Fy za(#I}K1AIo{k%@<`VXl6(yD}M*Zxp0{`6DnF)Y+7;l`o9TI0`)8kkpna&9c#_Gb1} zG0aGPIQ$sQd{nrv4(5D~tKh*5-tq@eNxpTx_z28aYEWq){p9;Mu--Ubg=Zum_iqE2JG-^aP(`K_iJMndEcV{PBCjG^^O-V zUWIji_c$id4fDnX=EBP|I*x)4{+h&taW5O_7k%ce_)Tf z^lu6<&DDx}{)#_rH5x(s&1#Mr<9R$Vv0pF*rl(&wcY)*5QY8a8K7rZKmtk^ z#1nNcW&OvTajOch!+~xJmA%M?9WPIc;i&ZjogSFE?a{qv*gs&+OzM25PjGxi^2sxb z$oUqWny{!1ZW5cMd?R){$^8s_*_}G~6_%da{P#Ot`J%Sx3oKTey}TQCb)GiRN%Ge- zgu@=={#fgyPfQu2xH=pTuuI*BWJAC(+kohb57HKdV7Tz`UBlA@_tYz^i*!@sy z7`eX$!yRSG{taAm^C9;qCv#fWAM8J)-uh`R%s#F$qaU_dXuhffW}Mw++5>lmEInBY zGncQEIRfosH z(r!If6Sz2sy^9a?mKNHW!7Q8g2cltipI4J5+?!S$aUABaN*=I>E$&ZW7y;A%eplnb zwv0ICaMHi{iM}_ivTNZOlJgEE-#-S6FRZ&ma?buIqt3z(w?Eh&g$48MPZq&NuUAbc z&kxSmQIkqw+XM2dhe^No?UA2huHeT$@;s8PRtW8e8RFwl{bBADja~h);J5tx1F)#} zmC^t#GimR<{jl^#U{<$!7!3)Yf%K_dpxH6^Fkc`KA zklD2wF3)>C%^Q}4UODIt`^WemAkSZ3&k7G$xZz=l9NAxv@Ysx9aNPdJN91^=y1gq7 zz){yV_Z))7!AoWxh9kz=><)xkzL(Y?gGFb5gb{N*xn|)cSDjoKL~J&E+Ie`O^{Wm! z-;9#n~8b7HFwd45aE_@*ylX+g1P zCd^mY@A(Lq7b$K%4>K-n2mXMK6MKTPi4XNCkE+4vJ8LejxkS9=!|yRLVXQ(u|7nX1wH;t}nTp&zVD)^HP6X|WrW{nk?}ZdWAz`x1ChD`1*HDo znkg^g)aD7hZ@~P)_P{TsKHe>cykAHf$FurL?v>|%7v|PVT_-#t>tS5H2NJG2 zryAZN>xING=!+$6@t`f}0nAs`Z&?XvT`^%*!P4Lni>+auozpeyVd13azsq35Pl^l3 z`+;z*{WWvA_f>Sp3z&B)d$SpwSS?>d%(@o8mAEKI;zpk5Y~w>CNqr})(wD5K;)U&J zSHPm>8oS8zR8z|7t6+)R@keBR7j3IKY72MWQP+^dY^xWPiw{nC zOP&wh1w*O*{rCJ3i@#ChI~;#)OrB2+<<0LFpkI=;e$OyF9G~JI#qqFh(eh#B`6n0{ z!*0X*(@d;dPM)7kh1xIm@W8qXV-4bO9g{0z@$mFnx-i@2)bFFj2mkg@g}DRPnt`xr zMaYsFu&B~xtS8)*ZWC_^OKx3UWdp~}O%Ykb%!ngftjznGuQBbNSl zFk_RF6|uo^yFX5_;E)VuEB*UBU0`YdJGtM;{ewK5H^bufHK9_ts9@ugZ7^%Yi=sAI z_JWm*2h89)wUPV7ea^=S-@1#n;H%z}DT0!o|sMKFCeMr8>J@XnI_uJ^t zURZiT?cqf@aJ7n204x-2FiwYMLN2QZ!<;!6E+@n6f%3h_VdnN=uXvc*wV<943lx88 z#=)8@CEBN8#*B}Y-3{L+#KGJ>H_J&*KcJnP1oIxwmyq#IeX{$LNx#$mCuF^mM)dti zh3VfGY`YArh^0*#Fl$~uwSLID{-U-C`MZBHwVCRv{KGM(6i5c4iJ2)$ck@0!aEiprCu)PHPf^=B0GT`uVxM|MI zg#wrsUAsXMZd-c7JdJpFmck@B_4I?}v#>ytx^W&Hq1V7AW)BOV$NrDS$AvTfVOzUr zRL)k{%{-j=u2dc(T$f>Q{K7RHp^M6Rj6`(P1_zXxI=f>ZN{o+^fg zlI}(K;l%yp=aj&-VJ7p+;RfZAY7a?a} z8^(qAH^7|E!#0UptDBr6)c+cWnT;&HFk2+8<=Apdn5{$HD=Ce zhxuDpheyH;+SdIaVY;B&=r|lVq*mc8EV|K6^_wOr&yvFY==r<($W>C^b$`I(j2|lG z{2BUaiMnCN;YY>9?t4Y)129+NQsOb#OFX3PFU(~0T?&B>icQ;StFb@l^|uDWT}ybw zhr#S2DauD+KfBbd;qbrpfZ@F4v>YtSyjLB7JoVQ6V+zD)j1C`!*^|vzkAXR*c~oBR zcYL7=%sXvQttSfR+36av_`(@#y|GfCcZC6Sr^mXVLci@kzbD!xANbgJ2DY%BuWJC) zrpawTPjcbTNk%Yx`6d5CI8huGw-9E&8OkNs$19}y$P$vD6KuK-xA?!dG=u+LzoON> zAC|%R|0v6@Ah*gsx?njhmbjfvft&oi)>^^z6^(1iddP^6t+avpt)IdUz=7+{qwPq2 zW9b$zxO`gVRW{71p0w8k=D&|RuomXd*ca*!S2NS(*2At zUlrQu4hup{*15ob7jCwA!P0`rv`uia(bso=u*iJl3evBh`{CIEnC_QO)eByq_c;V} zP8`>8LSF6`T66@K1fHSpN7?!aMjX;CoCZ2|Ru zApRq>{UprR*i}gGcTs`YKs?O(opXo0U(je5EfZm8!Vzk{Xe%u*PKLRApHS<4V5eGP zD#^nq4e77O`AhN-PJ;#J3Do)%F=6uIbFlQ)pT%F07e&roOUw;la-k7s#=g@LkUV_l zi$}2Z%Tb$jn6K}B`XQ{|A(xy1O9sum%HZB#3Fj{nA1|lY!!CsriMhn@?nG814^>_i zcLnC0UqX#no!u6D6=uJmHn|3QwPgK3KFROvP}^^+4{$GnC6fKJACQaU6Ps_tG*Q~c z4mhi7!rEe(z4ArcCs;?dC#i(w3no(UXVrVwZYYPPeW6r+E^kJ91tP_uX4ok#pQ7_?+$)!3kU{OSGXA^81AvfkV{O|c@JA-@v9V{L?f!crB z8HbHN!km?s)cK$Z-AsOv`u}_0Wy?zff59xj9a`jh*D;dws|V&=FABB845MOTCO>5+PS zi>L2k;exz^nJ~-X(aK8Lxw^YyHq18^10jx76K@c(1#-s58U{y%^(g`&$w zDTx+EC>EiT+H?_$ElDM87u^(#5W=buN-If7tqRd?Bb6juLWs76C~UeYC5t3o_`S~j zz8;UypU=nZea_6BIWs%6O|y)fnHScOykdJ47p5yuuycZgzt^aRkb2JhH*RpH#oyKY zVet}u=S^_pg0sp%nwW@P}dkdy*owL#t^_B5w zx{6?4#(YCRxY^3nu@n}#+r0>Y#VW0F4@mnDR%UzP;+|~NhcM%t*5OdNA=76`9jV`u zPTgMyN8HkXM{?`qK6jB@HRsn$VBxyQi^=)UDRf#?Gs#zTHL75~=Hi*3NIvJyyat#j zPAlzzrQY?_^|OL-LHtixaP#v$OPsGwS&906uqeZc`urHUIOg0Rm?vMoc|Y>RS7Ryv zyM8ElHy=-Ou|VNx33ATDI@Vv(AOFR(A-KQLC>NFxbD!FU=)rNH+vJBjV0;`V&{=Su zk=1J%n7g`7ZVk-H-0Vs$Oxb1|1eZT^%p#U-`Pmi*t0vuH4};m)p68u}<63vB$-~Tp z-InQa;Q6LQim+6nf8ISf3V%scBGznd5yNaZNidz5Yxt=Nwi**_qXKg_jhpoerj>Or z9s`SS{&bVVth<(f)M4uXl}Wzk-tKX*z<2KJJ~;73^;rB*vxv86#G@ZhzW1xIuhys3FYkZ)51diL-ufF@hPv<`-wdtopU4CL}-fuyZzC zt~)P$F>%go$_-yOJvWE>r{+@iw87S;7BJnn(B1&`y`FIj*0A87C6yO{jQ+lywAZMj z+M9N-9<~A&M}PZ7`e$8wIGhdtTYh=RCHvJdJ9Oty(qG`Lj~*_hzUcd9eOT}4U4}0# z8tdge6ApH2hzx}J%Rk&wgN>%Z0`p+v9$8c z5g5PEL)H6j^4}f(pZ4X`wM7a^Esq3t%2&`*}Hx zr^{C31-D^o!1a$4VcM_*Ywy6^r3UX^;k+#wDR*HO-fnpe4*k}3{~pO#9XoLa)-#k> zDTBp|{uge+e&IJa+$T9>!;})Z`_k^Jha?YFUhoW7NVR1>foZWb-Rj_4-6)SrnEHQL zSYuMtq-vN|S3T<^%)OR%_%+NoI+D@_x1Ao`+5mIkmC62vB_ajo4=`^8lc6BS`ir9@ zB`|%CdOwagBf<8j5f3+9|_y!itb zde~^lS7Us7Ccpd%iyUqKD8UNsgJ=3+*7+;rv|yJBNrQh$ePQoDeK=wB?!F^586{q@b(V_~s(bI4xUF3jbICM>AY=Z3-!_aCj%f~A%U z8v9|!ywIk}Ft1s*;2&h*W-L-Sjf5*R zP8@@|tT{4vFn7*Q`v}<1U$vl|ZfIDVfEi=7S+Y=r~cZx3-Mo}tm`3Cr4r zZE}Z&XCgD!!KzEX$Zdc*K6kxcNWQ`~b0aKHGf`U$hYpO~<^|Kn`UN?|de4+hd|;Mc z&Vx0u{#X7@-~YJRtxzujrWgGxChd#;cHR$yxt2xk95}epbXqXXuH5=@C(M5*JRJhl zBahZZ!2=1u^7g`_Y~|=gIPzPVY$(i)oR)hPZnpfre?KhTs<5iY5)M!S&hw?_S}s&{#HNG|aVGaQHAxdo*Z1AC9{k z>v$CAbaz1OCP9mI^G&P6xCmuPs zuMpNa(`Mu7#x&m3JH_xuTNJ2bku6wSfm)sVwkphUv>~ZOHOF zSMS=@4m08;Ib{8!<_U-P!K`{gr$5}QF#MYAYHXkUJ;nw&Zba2fMVN6#*UkwxRho88 zjksxn&q|o_tgmc5aRHlW3o|FIx0?d9PIslS;JVz9JUv*@z2*4=xG?h6>Y1>_W-)&b z%q`ngJ_}|K=DN**CHNg+Lzp@H*4Oc{$n4Uk1+erU%~K85@B36{1Pk6ii%^6Ij@_7N z3e#uutJ|=Dz2#N9S+MB$@Q3BF-p>_kmc$Af<+-rYxq5FaSTgPK_#`qPj`J5-!`x20 zFNa{+QtQDC#z#`mKQrRPLRc_YK;=x^U;Y~Kzy4U4=he{QK>RM71?usB zI|ex(BCo8OOxB0B^3axduw+@zRC8ERG9>RAthMNIjTtP}>h-)0%We;PPI9T~6zce6 z9hRrIhhO#S%Q@t=4@1r_K`yQ@ok5OYR*%*Z8<-xicYGHd8aG#W8T{{j-K}6#z5n9?bjAF*`&VG)YXu*7yYqX1ONNH(3`E1LvAtGz zPI|zSnDU^Hu(0^Kfj3MmN_t-b8;-ah>twiV{nT@EjS(@)1x?b%NAJQl)E zf_>SfKd}?*%uG0MwD|Rj^faLrOR-IP7(@5{~#4QV~J&JH7EwVEyR3?MH~iPI%mh1zKMW zkHNGRb=&X3btCUyjv{v1SSEsHZ~9(}hWV*pl;x&9*b)P?PF~R@xtJRez=yft?WpUe zLg|y#YPT6YcH_2s~`hOFWt890>3bQ{>+pz+6mnn0&M(Ry& zmf68Bj;jN*VEXVnnj@^gHq9f4w11=^aDpq?J0IMFr6ua+8{xucw~vJ|{(pL5Al#O< zA*lrBKL}(*SMvWTrV?qhEx4ZO&sJHuu;Iwy<+6tJ|EMRR8>Ml{#->(Xdy(Baz$g4{d)3bInpqdc?S1e(S<)gn1KN9yG#wQ6qmg z!~f2weq&_YKEd?;edEaegsH<1*$!Ab{@m#(xI0xg?F%t^KL@P(1SD3uG`1pC&@2xehh^b{-j#;!6Jo|D~P4ap^<~I;OE*maxh(caL8Ynb!=bA zD7c~F?~@@;SfBWz736-6Gp$}_7>xHzP|NQgyVYYj@zj`nI`YuNUs6Ys`qX3UN^o~^ z6K51GJX!fb5f(0dd6N$R>z~`kHW*FX?@paVmLI9pXgwAdr^Zw3S3Lch=QxR-E;Q^Ci)&-F&IsVgqy%$b_d8f}x8(_}C{8_s2zw^KQNM`y} zSfnxk?+xU2#UrOqgE@{K)cM-5*ZYSajNemhx{SQMO#h@lEJ+;hoD4g~sAbHC1?6rP z39yy*ehWjGyEX4k9LXPgY*+}3>-i?Ju z1q)j}4V_?)*@m@b`{-4L-7ciw&Pu)v{~1V5!3Ie6i$KTjvY`X^^yu}O+&c+7VJs)s)ofLy6p}yci4hsU*V8%mV1fWEAM9g zgcT|tjAz5FtmV}Gz5CKoYCL%pHc;oI?i2H6$ao8S8_Q=Ky$K99P;58N?K-h$-m+Pj>P*B)XoB>P|V zfkEAmY7`Z!n8V!mG8uCJnzqSy%VJ`~qADL)wPtYFBGTUV@jY_?F74NxLQJE-r|z$5 zw?7GprF#sYUP7*6v(AdN7kFiCE`ZZl>X#9-H`udEVUBk49byUZusgBjX&cp^`9v$| z2^^U>r;5y1ntJ_Ba{W9IXLXsfT>ZHhaHXyhm2;g79+LZ0+f7-vrl{v+hsU5D7L{HB|v zc^bg7ekS+G{^R-fyeIbu49BnGYhcM{lN4*1hu^^@^ZVcZt%iP|+B%r^X`T8ekJs_5Dg6Z^Dae%Ti#g+B6k+sGL27)(ZP6>c;7;ZW#r3F{zE%zB zC+(~Ht2v~9orj?#Yt2~f<_zl~STZNMZyMYgpnf@+^dFo(aUR_E-KJ>|$;)NN&W9OW z-^%TS>CcR}7{aQyU#j=RjIR4sy||e7`4B8-|KaN+r;lnV2#0ygEo7#`TJKUfM!*6i z|KiD{{Xyfyhhb6WxtMWqv2WN-Dt|uOZ8R*sc980iarB1SFj($E{u0tZ?^XJX9&C@^ zja!u2wr6rW;J67(V#AON^S05+c%+TVnos(ZhL%iffjfKYGDk@MM}BWJTv0VWB@$+8 z4elYfu3;4ii%S@k<#tG}o`;#P zN^$Rz)8)?2zXWrP7EpQRt%@C2h@YOP_K$c)deJqQal^Q*5B0X}4N=!&-j#{+f8fvq za^@MtV*?e2V*9;^?3|eea|2t(E5f#sFM6|K@oLL4%CNrj+=oJ#zU$~n6*$vXQ85n| z{BJz1A~>@7u%xMK$Qa~KH~pU#!i;bwYP=&XtGw=#_8$&V?Gt&C^Gjg1+Cxt|>O^9la<{ZQgk-R+-A&bqQT1bLuWtXd~5 zj13;Q1CAPulKTpa-xxY?f@$M69wDaR(xtxdsiaSzB_*C*y4(W!z@$AT-(bF9@CfpK zPQ&oHYGR3TKK1>Xa8ygh58~LB<+{lA=T1=VfqC<{@1G2N-;g{a=0+zEBet4#zK57y zxw~}|Z0Iaf{soIR-+oD4s5bdsFU)^4KqbYwk9ZYNi}Wuidw!VA8nllqqP|b! zRMpz=D7;6O5448g)W7VERjTo+;_i%;%ye zELM<4GYPp=S=sdj@<=<^a0aQL zcdzvnoSyw&o-Cgx?7M#sb{{uiX(FkA?&_8V%kGxFKOSbyR(gINHf(pRRfoB2jyaL} zWjJN>O*PWqQ-=Ej)+lfFCFAk$cx}+FuAsxBq@T^-NxfO_Y9*N6;j&Z)^SwCg@5zxc zUui&~23M-A6_D{}c65B34jX1#4UvUeYOab4;mq7AQA3E+@9J3*haC2y!D2Voh7~YF z{(Zn->`#WrV21mmU8x^GIe< z_)4N+Zg90R4^EiYH-+@i*;ids z3^xZ7bk;Pmpn!;lN^ zxHS_y{V@3?4~t&Q_t7HMqi#$ z`-gg-2bSiJ|Ns3-p64O?*@HZ?zj>cEezn7eh8wBn4Vk!S8O-u|yIcq623dElAni}qSLws)?R8`9 zVgBqRB}}-p#U*+rEM4W$VGQd;Y|(ds>DBM;yf+`>f#*04fhMdun)Pu@oa z*I6x_ydGvh-Oh1^%k|pn9x(HBbP7fj!DX|V@faW^^17v`9Uv=HarFC88P z(>`8E+5qdX{^qb1<}t>tBJCOavg5bI4DtQ7>tGGnF-5_!WXhi$H#kDot$x>k%zSRq zy9Z`XYofa#m))S?zaM5F82ZEsuDzi?mj~0*W=jx3+cXPTaZX@4eDW}ZGOe;WR`e)?;Z)6bBc zJP(L^z3~N06G(gV{s}lef9JS!Fw-K!?GW6?-$7Zb7sgD2<$RrkNG?e_J>wy4YvDwh zQ4*Wn0@n)r{mzs82iZvLw>6h(`hSXxUf{Q#e$ggisyI$t`NDkq``ve*}IOxfsGoxbXa=J zR(t_=c~H0|6K3f&{k#n~r{7(e1B<>f-qpaWCU!@KFpY2htQ`(5?hea^S-E!=hQ2mq zaV%G}Z^E3cr@QHJ;pz0Hw_t(t?^|kc^TW#G0$8e+Z>a@~zKU{+VD1)+(-UAL1GTHg zut;;nqKR<&#SJ%z#e(nih%^0JEu}E$?u>3t*ff2U{Ucb=wPlz(9Jf3^;W5ninx{mT z*O0n)-ZNM_Z0#czxU+^cPzCeT_wtlr>36vnL-=)`NM+)_rJ)#rqpi%!TFdYVQ34^Q%(E8N-6-eqXwXZ{5sV z1V^4u_(9B^f2)wVjWt{02l2fH{wA>9^7l~#upn%1xFMYB5O`v^GnV(bh3Y>-t0#3N z%rhC&JO{bc#KDbp;&m6}m~hmnG<9`Yx>??OHZ0h{ZqS6O=S$!=%e-q^FkPHuZvb~6 zdoxiRX7fXwXTl+2Z&W4|Tgr*1!W@J8w>qT#TG#5yaNHH02h&M=>DvqA;oe-`LVcKa zy>;vuSZk$)!EBi0S(_mXi{`Nyb79_{i3j^%Vf-3j87+XR_bI`gqKt#aF#T4}r60sU zmR&Z1@%+Nsk8oPDvi%~MwVpk*6c*&q{7rJs1%tsfxBNqdK(?P16@yaOIv!xGg$L-)fK6C7J@VWFh4d^a2@ zxAk6-itJ6hXE$QOH^pD-4 zUtsr54@X77tm1JmJ7MpP4&D)18nVKGn8gXZaugPNojlwD2ZkQsdK{J%8U}xbwYbKc zPrzLB>icAQcB@mBqG3_*1ZsUEzZGW1z{V{_KLbr+Sd2dVUUh-l)r2nJ{C`QEL9`m>2V}!JOLjb2E{LT-&{s zERPd;>`wt4)$l_<6{eo2g0;5!zPkjA6;8fyfxWL)6_D)_+qmr-gu548`X$4n)xlg{ z9FOVg=K{(0v&K44UI+({jCe-IN4zqB%5u0OzEqc3QlqK69>OW8N%YH|LU_+Dtv|wCuoF}!IJT;TZ3?DyW+H`FumS%_~<$_ zmT>A;VGYc+`TJ)oEE`>L{xxZ@?UHN?SBASBtA}}vf+6;>+&YDoA4vU_m|4!S`1lIX zW|-5{!1sY!J+E_HNIm&`F5EUY-maZk!r8=y1H+!I{sv3W8piH{8+0p`f5DRGG`$0G zf=B7lK3LfPQ=13ZZX5IE56nNjN<9pgwx95!t;P242~&!IncEJ#%D^<~jjSVZw`I89 zaG2g@+!PHPEzz+Z0Sk&P!}u`mfv>+jEd9`0p8^XD$8H)4GwRr$B{0*ur;wOlsH-Z5 znB4La-{4zt!#QvBsjyJ*!R7*(?U*)kI?Px)iFF4an745* zv1I>dH{!I4z2|4ayk!?F^GIHv@^k?#(EM=wGMrbsX)+5ICkg4Xu;0b1LMvGG+=N=6 zkP!*fZDD$$%EiOT+234w4lp-~K6f{4_oelO3(3z{Zt;g{ZgF=4U{1r8-Ozp|2TXRmY6HA>4&Aa z+cu}d;$r#yURYGC@0<<`NBtPv0}n)LcM#LPcUJy{71V32GD-X4Dcap|_u-DrTv+r} zUi=--{AGCjHq5zmk;>^>J2LLT((7}!lJ>GoJt|6w8Nw3Mf1AYwuQHh3f5hh(obHvm zwlv?-Pzpo1^jQmCT{;Y z=QS*Oy{ljp#>c2XS*8wVB`yD>1XtMqb$mnGml`iqf!S)=DQ{t+Tz1?TxGpuiK?2ii zT;<687GAxj*$NBh|Dfi#%i_L8Z7@4MirRkzXACT*r2R$?wSVdhcP9L`^LtOBAA-=LL2Ci@xN{7O<3hp>Q+%RnV05MnBSee*Q(;z?==3IYDwRf3v zPH{LaIq0xA8hPFn!+9fMMvb;G^0?`ysx^V@d0>H<13edH1jT$hr3SOu0E zK9X4n7Z$~*jE2Q~CMS~PySX}jr7Fy+{&2t%W`$1G8VgJA(M(suZMVJJ)nSocF?D_@ zJl8f;3zpu9rj8eH_7TM?urO16auMojb(&MB!GaSu?@eL3Eq#GAVA|RVftIjNR;`Fw z#CSEv3QjbYRWN|LUM-QG^&WrK_rqL0zP9J%5BGqQcWkym2dkT*P7wQV{q=zX@Lh1fXD zY$i;-KMIb^=ovo`mYOU+^%Bs_PZxLMmoWCXZ5yH9W-F;D@4tc@ z;uL=e!;bTF+TZ_v>@%z}TbOzPW|qhL4SQ>b=N)}_ z9)vkdm#>wFZG%i&!(eIgo$X^`Mr+&kBQS25ZjXZ-+RQ^DVZr4W!trq3wuG(6VH&Gc zh2-qd8A{PGV^;eUHP~h8m)S8eJ!nUc9IS6=HtiJ5QP5uBi1p!L)7%mVGo6wqknt~W zFl&m3*>ioV&z}Ud>i+XEUvioH{E6h%mtTZwk(&E&qQ058>vIw;tao~O1J0Yaenbi^ z=(|aMzKK4{$)v*4#)kuzim+tP_6UiOJe{deDkS;W)MQS--e$hu88VTOv#Uosvn z70+|!Fun4SCZ#}4s11__3Jw*}Q5yo)+sr6b3SHAwv`H)_5`wcPwUj^zJ4p6gur^Txyc zOY$E|&|hHqtgXbnql(MO@n0A<-)JH%$WB>!A1-dypQr=V8@g|j2Kkhu*8coGA7;(DkW!62@yMStBbfaycinS1%GAeo zG0eFYNS#kO76oi`n9(UZoQpi}+||9r)ZfqHVr}t&87x`#C?^h9H4+{*g$2eLm&4(} zl+8XSFi*GWRv^sGbtod1SWTs_FC_uSugUVnzSH~NkxLZ?K}%rjd30EW-?_Dd`MtqG z&M@0X_;)GE$Jsw$P3rd_w^{KY7q{o$v4^SW2as!>wXJo4S?9Ff-C)zpf;eZGrv9kP z2M(!n@mUM=S5{Ehhu&Uh;cl?dXT9YP;1k5;Dk#R4>rJzq1T5Wf*Vw> zD0srss5^^Ly<5rmNf2TDYxmNz+r(BqN ze;zFRW%{FS@W1Qn^cuaQU|6cyYfI)s<<*Z)A*B8EP1OF5+ACSQ4`%%GxJAy-PLUTs z9w0fla83s-*t=Md2XnP&_I`m4l?QT&S+krOUtwN@rp6IiFg$Ot2bPp`>yE>`AYRh| z>@xI0RTRt|k{0$CPP4Qa76;RcnyeMyV!m|r#-4|{76$1P;NI1Xt|r5L|4F~e`JjBB zs67>C7RQ`h3ODpU-ggcDcYYDKS1rte=^Ig=M4H z<-@#V4ng@ahnK4M5N^+&~r2Tc0Prsf!7H+sV zvNj!N@2)*L5f)ytvq^#ZpFSO!4@-+wbOo^JZ|G`sI5ODbcp@zHe{pLS92HW2;0!F? zQhp*3Zl35@!iNQ;o}Sqc2b&+b6b(x@^xlqydmjc2JwfW-4}Oh-P2aJ-B4Lrvy~?w& zL?bCF0%n9=7;^#E-&JF92xd;5>6Hk}`Rxq~g=vEUZb`7ljnS`nlltsKzXhbeWhzDE$(52lw$c4ok) zef^bW{xZ#jmfwP-o<>jKMB0D*daxK4%k;>26Pt0ymcpTl5gaoAC7XWAK7id9>7C}l z;>+(>RKST2O4RYg32yuR1ge9|oWk*EaPOQwqsj3{i;Xs^gvFnXzB$3%l;2c+ zvD*Xt)i5*K)RMGsP7m=~3A5eaQvK`n4z%0BJWqPsBjmK~;)>rw1K%fJZgEoO^>8Jex94 zk=Z^IwtKO!gS4lerX8|`gJYMRCjH~@6NzkCxV&T>$+;dEp9I2m(T4}5f9Cq6AxGg_ z#wF@}De9l_iVw&ANa9$N_CxEv&cdo|;#b?kEKRP_C0L<2u$owCRCYcW9x&e6Ps|XF z8Z3o7+g=os?d88;zp(~39Le}Z)`$B0AzaB_XHG2K@g?pJ%=`ZAE497zevSJGr!8Li zglw-UNZj%T7C5b@wpX;+X8cchAZR9)3oiSm_rtVdhLowl-@)94lePnobT)(~GWx83m>#gMmYBWLJ5uF6J|B)BSVr1&YATND zz`cDvw-&+De!XprVYyp-ddd3m43BNcF~sUz??$aJulH4v4;=b6Jdey*hLWL080;4j zdz_eaVCkH5aNgCwCS*Q}9EZ%vhPC8ZQS(V$Q)=@ZPTah8GMO(N+j*Y#aI^Y+YCh2% zU$Q^I?phre$ov!?i!^V7?R3{t%NN=0)@_IVUIo1;%cK5Z8J3JGdr9UOcch%87cPuG zM;$+S|L3M5AF%&(FHI-M3)4mSm;#))S^X{9A7b^{N@_4WEP*=ysK1B7mHoBU@g(t_ z`j!Fne4VKEm$b=T(T7cc?V3%lf0#!e&zlFQRZcM?#mx^ z6?f0*lkt~6+!$>QGqqcMnJ`=C#+2o7ZRM7(S+F4Z)Kg-~k9E5GFirb?x+5IvGq_$VOP+{C8*oA!~SDVy-*^#3Qi>m0`b%?zk zJQl)2`u9UKU`f`5G0R}S`>Cts`W#>Ij9dYi^A;~A`-^QBINAYDFkr^dh6Q%37COPU zXG}NFh3VI49#{(p$CPE0>v_S@f@dx;jd4G~7{(7Y7Q4e5SqqfN{9)~6Q!e+JIMWQ4 z&IwZ`IU|3$KRJI38ucf*!QHcd8&IE*mwxJ!{t{ld4kza$=H<6X))3$2X_NCMBVY-& zKB4;OkGc|f-~Z%@JVf{1T5p)MFg9j2oVWD>GYA&CzM|TvpTB9(g<0cWUpXTe?EI9l z9j3LYP}@_x=lTG-o@E%!q1LA|YIWX0STx;^T7T(iUwF zN9#$1t;|^ULgY+dMEnidFX-F4B3Qg>t>a75-g&=oDJ*$=$G8g)who{22$^Wk7iU&`VkyZ4*J zf;}%sl6rw4{nB!{JH6V1^e1G+DY(LUnGO|XdA!&)=QqO4*8?M7!<=PWr-Na=Q?Fxd zVP?~EZYa#HJeE$jhvT_nZ#1c|4W+hUJp5$(HP|~vE4mE1gxP;TA1=Q{g8tj4TT3fzff@zk+a-?YFUZZH z+S9kW_nX6c8ErpE|E%wARm)(pP@g*fB)0udj^8|%0*Xd$o&|@Wma!AY^W&t|Mk%dWu?KA`}-@% z{^!qp9$5slCeo?+2i0aX z7VS&&iAtFNiZ!^xyjpBwzEA$~$dsTHeD_lSdmjpuS?8NgX-f@qRU`KmDq) z+ASnMQg_-Bxj0X4bQ{ds@LFj(>A&M)au>-HCNO8if?GdSzrlDOZTWQADsT3JA26+; z>ZT6Nd6Sy*3ue4Lv``ba?P<&(fcb%EH!H!ceUEMrbHn(U4ACd!C9avy9tqQgHGXX{ zt0Om73Fex3*w@3&&o_+GfO*RewC}+>yK^6E!>o&PYT2-AVeBhi(!QqYRXWTZv(8{P zOjrIXO@Sj{H5)B}#glD@Cd1Db8xL)RI)Y9biQS92G%iKDPspq_sa`T z!s&xKVtbfXTuY0B{XRY3%7z*Gwxc6q+rw7v4kYJyX&!;Cmbv5cbsT?|2O?mAffA^vX&oThH)u^txX9;O_1^kmpZ(mwP$ z)xKAL;Q=%x?05?y+N9Dq>FBS!`;nwV5 zq+UWF;!_5T9vLt7f<@m~eyoIP^)CaxVg87}Yu>M= zK3KeL=>)3%(QReK%rzyoWc_&S^6txHe}`NhTSF|!pZ$`UD}DFGg|z?mV3rnKtQu@c zjaT2rb-J*X%BS&Ue5Gum!~mwL`QmkzSiWcEv^g-x;jfDtdFzBnN7r&6*sARu}pd}tWO`CLH4(xPQUUXY}@yQ z+P_luwK378zC1gF>|cgn#H$!s+;sejX5ludL90Hz`oP7?Kh@}E*`xadL zb`dTb^{}ezfYI!ANsfM=}$>} z^=@kXS)+sen_#11C(SmI`kI&Q0oXgs$ixTciB~x)x0tcm!#C#m!7P>5j7e}|Qs2cu znE&Vt!vf}=xe&#LrIQY~c))qeO3dvf*Z$iY3=4Bp*X)29rz$7Jz@o;NuSw3FT(~6_ zW>2x+7Yx(uf6d8-HG)3x+6mLDs;ortzzneL-!IFkOi{M1>QsF*Q-}A(J zDcl)UvF8BHD;YUu6@adEU|;?rpXJ+nyc*^f>&Wa! zPWLO1ehUjWEQ&k~*NHDZk`OP>eSHLWx;xsi1!k>to)!t~1>EO$!=l6DUB}?KbG{pU z|6^v$81=ufuBK6r5wdS=`#Ul+1}8* zK-!0TXE9)YK#%rCI7F^^`b=1O>V(oI*n3o*vmwljZy1&e*Xb@EZv@jK+o}3A^JAta zu=w}O{!_>+|Ey{zmRN4t6b+}}Y?U`9^(qQmPQYw>^svRSaP2*6`!nf!GtG$qHy)y| zyC$<>mft%n*T5T?mJpj%Qts~dG`ECl7osN~M}K+aOpn+QZMQonU%Ol~)&>*EGR; z4a_l#z5Wf>pS0a{Ei7cMGU|o>!jt#9!&3SF4B1ckyqs`8Xd}#+%;zb=wD>FqPgu~r zZ7zfO`Q$x5FyAd)-xgNY&b|`>a~Cs5dBGYZ{GGRu_O^4}cfr@R$1-i3|LRI=)pc1zjtO>1iNI|s_lnG5&8S>!g<$=4jqDpKC-=!VDIIo1(7g6 z-|o&!*gZZxjt|rSWWM?cvrd`Mj)S?OW1cs|nWCl%38da&M$jixKYUBjIapwlTigbx zZJaH4k>q>N%XGqihAa0c!;*QQ`o6#{w_d|5F!g-_oY|LiD+lIH8p`V-?G;^v@?qu< zm6Cp#qZCtE2y^p4Y#M+W_eRbwf?4X96bE5RQ&Y%Ym|anj`4=wN@cmu_)ADQP$hBeq zDlO#RgZcfp!?a-A8EIomVY;Pe(sbBJGV1yRn9F+_ZwAMum27zo3qSRbUkZ0esm^{* z+RwE}aDb`nNtpBHx2_B9lu`2LHOz7ekg{Prudm_tB%hitvWNZt1SAnl^pdX;7l!tJ zX@CXmRynMMn-gdSA7D{=uV58%Ty$A0%(WW#!3pklu^%Ue=|P&_>*0i5r6IpycEj^; zUNG~=*ZaRo`#o#pOY&57}_Xid~H`%fU=GICQhq`0=2H`7q5pQ%G zD+`O>pZgpF2j6xWkSBQ>XYeSTI4^pfBFx&YZW;}%CQVyFhxro<_Qb*!PXifhFn-^J z8wblp$r_I%?MrPQpN1v63MG?Z>E6?#Gcap|7JC}Zoh-MF)CZ4Qd0{#%;T)bs>Vscz zGSP==X_u(}gCjf)W|Q{ckH3vU-f${BWe&`lBX{B?tZ>rI#hADxyp^n9oXoR>#MJYG zuIHcWY!#Fid~V zdUKf6-;rxL46BZrZW0a)-nf~?!=YJYFYsXLE=FY{oHygeo&&@w!*8U(;(%92$okV} zDkr4Fh5p0FlI@dz8Qh-@^YUIiA=}S8{o5-K7AE*i3x*{|mVJeA)Y0;pJ7Bh*@w76S z7SJ2Moj5mYNfq2Jem;ccT=`KO8sGr~`V`WiRPN98Hdxlke&Jr2v-NKMZ&*-L7*EE7 zG4th9dI!#L8p6<{FjKcsOBdEUb$|SEm_L2s!(6yH`h8IpvCYQ}GuUq8hh4F-cv0{$ z2bj(Bke((vFWb@?HqsgL;VkjAu_6v!*Xop<2s6*;WP8KiR=#smV2Pf1IC0|J497I$ zO^u8VaN&uvZ&zS}j@jw8un@0_zYYtFJ~5ZVlG-<}nJ_c6d$|QHz8(533+BbI4POY? z{(ZD72Nu6Jany%Z2bM~Oq&~;^*%Wvnz{4dUW^GcsJp|4RySb_mmT;c@{DJYwtO=TQ zn|SyOGPhfWASj7t1>5#7GbC}y}cd(Sy-#&k&2Id(LJwWC|@T2cGuVIODO8gmEu=V1S zI+$U&(S;9(EMlv^fu->)G*7^7F`|OEFsHeqIUH6<(g^tg^JgBLvUl?7nRX?HeqKk}ryc2L|L6yI|HS z`~C4SJ?-OJvcIGSFBe{c_3n+6ekS?$L#GO1=7fkR?JzyHaEk~Iy|sJDCzy6qz2gZi z`tqT^h2&}K)nZ~i*WL_s<6P!7!qW9>n?J(*3uP~QU>e_VYa{VRUV?fjwvRiO%IUY9 z+h@Z6>IETZ2kl{<R36~MBeH?AS;%MJUhT?DgMy?)*X3+|_6iD17w=lnZi-pjq+_hF%6Z~++~#t&_) z2XJWo&OkDr^w9_3K87Py=_?0dmdypc(HH%x*--OWd{0O@SZ3_Vq1b<-Q^}N>vUY38 z{FF{vJLwtf8H$C}{NxzkuYL-1k88z{`N&oCUi1j2J+}Cz4DH$Iu+VqzU@7W39bT*SVQJ=`t#@EsxfHKC zuqb5xoP0QM>zxu~(*E}9%q*DRU$x$pc=8I$-2tNynh~E^LRsJS<5vrqvrx_{8}+KP z&4pI5(A92*5U$-LIBpHImPyX%!jklBt8HOXqZ73}g=3HJ*u&ycWp-q|ovw8<*~A*l zsO{q&Q#iK?f8KA8=kr|Gb*^)rwi+86 zH81kqXwDp{4fIAmf7+!{m+!e!?@#)lDvZv*arZR!0GOlH$$o|L{M6rd5i`H(`v_s3 z1$wQ4Fso7D@f~dSQoAk)7CNaIH^HLJlA&N&e95@x1MKmt$2tV&c-++%k^0+lCSfr3 z@ry^FVa9w!Zb;hcHD{)EH4PwkClyokM$7b0QG-4e3~n0h87;w1dPGEkHoluORaGmrF1w^z-QTnVv8$OC|MX z64ckoJ7(VN&xV<7nbGSh&sBYX1E#0#G$7X_JaYF=9;u)3YV`d5Vt-I>llu0yw(rPu zrgrockbKe7=AUG|BU}6nVaBX#!)~}<;A>J03&J*r{(|jQ8s6U{{VSR)dtg`L@wX+g z`1wKg-*D8Swd+b@{<-;2h|BZ#{CYs@e^*`{foV=W^GE+NMXYxBF)Rov9L??fwKz{< z{6`F<$1_fqd|nRoj|Yujk4JLU9v(TT_*z)2vF_%}D2B&wwE3JhoAF8`A!y0o93f{pq ztu9;QuHzd<>S1mXp?Autl`U#_{nwnm!5cpz$M@lc zrK8?Bx?*h$ES{O1mIwD;IMMY5W;Cab?oa97r|x`(1%tmw*BdjyWz#p9rF~=}xnCY( z%{iSgZ!k)C?02lU8E@VZ(`Cl2P=dRZ){fS*(}yP0VZHpbqvHt|NlbEuJM{f!$#|l5 zF9SnhQN^dx@z`rLJmO*JFZufTOb0$6v}1NYEY%dAMJ!6Yu=fRQZ)?;-%)M0o{6Egf%O_jJJ|yZ z9__Z1hr3GOyZ6Gv++AN4VDa#&rhhPH#WCk;FvaM624yqG%U8G5BK=2$?u>;gyMFG` zhf9^rnB!r_EamuxaKW9|`{iJ&Tw{YNY-o`5eiEs_bs)tJcHaEZQ5ojlQ$4eZ)NkGx zsPZ2hFRcGQ8D?d;fAK(0t#R^JCHX7$pBrJFpwER<;s1@-I%_js9TunkT;qy7YhF%0 zF)v3rxB=F9wy$~`%s#lELTp7X5NpExGxM{EOO z<^gQT`7?>+{Fyg@yn>|$UXIo?$6Q(70(;bdSxRz&miTH9Z2!sWBN<U{coSQr^~R}c0}{?zLVb1d&oB<>wId58tm=m+ML@x+yL zlD3n4X04?L95q+7Xb0)nn7>{XHe@zC?S%Q};|t08)h_z?+>dy3{0?Q<)hA$9Ak4U{ zIhx00P3sDR>4y}O)sdUcTDA2MEco2#JqxZfe3^Cx=C(>TuY#$QWQxLJ=FEWvUsxI6 z;}`)`1E*LVhKK0famQhHR$FyAoOM8L1O9*@V0=j%ih(sMPY#@b1)I)%j)fD7bZF5q zZ|t7g7vPRP%=4#+*-uYhgAFs!7M_N=mP$Vg;i8lAVO*FWeCA9Q?0luNA{G|%j(rrt z6zAmaaj;-y=a_c5Lv29iJk0D{5!46g9K5vj0!$T(gyY5V_rEd;uxMG7{xmpn(PgPb zSh%SjO9J<|@ae%MQa>xs*a()nbyPV87PQXzX%4e)z8Ow~x!WqY*}>ePclWYk)`xz% z&9FxM;lk@M<3xkgR(Pmy{l8q8a&}CRAIxhuScGR%Ao`lQ=O~=@?RMlHm}>tvHxgD> z+pxbFW-G~TxCEPSJD^+&(h!&E+RBE88lC4~rA3-Cn@> z{QSZ@aJ;O|vzM^2W!kP%xUcbgTs6s84BUDMdtN9XtR?-|Ml@c*?81YS-@&wNg8bJo z$NJ-?I+(IUHuW8BSg1asp5zHd)x@Q=f=>-FXI4yi9ZVa)z`Y6PZPQut9xjjQXl#QS ztIfhbz)@;%3x2><8`<5TN&gq^95LyC)#Fc0k=9M=`H$%|sl0xe?x>v8j$Eqdpwl4f z|K^|e6|OzV+A~b@EVknzeJav83H83q^ z+jd=;aemQv2RO7_=ugZMo$DYrF7}C=3A5*h*sg}<{XUrJ!BpQ&NgG%vvg@rrsi#{@ zEQI+}Ca<0Y)9&cTsKAEY+27{E;ymNRK3q?PYOjL{%rrCeYK5&jyBUjNx=Q8&5nR=# zShy7C1P+~Of(3Qka?MFzZX#-gN|;uXH)_?+YbXC}VQ14@ zYm%S7GMY!6eLX_Vt4|$uM`y%oN0@ogVf6kwi+pR>!hE{c=>4i!q!hXm_wMffhvU)y zE~(fA(?W|}6mWgp?hF1<-Um-EOxm3U>{6vc`<5N z_lFA({KsPZKkw2W6p?{UKQR!sj}< zpZx2cdqZHVZD@fT+?6CfomiM3Hv1rKe1Gj|J(JRMDjN1#t6xWsCvbdN6bmO$zj}h4 z4>#%X$ay$DRBqdTn8Mw*Dv?;OubNyBV=>DTW7fHWF$>@AgnFmMv zb^b|xBlAVE{FN7v+^_BSFK<}*ef1+Q%vHFw&I@K5zSw&frhZ|J&c9G%jrJKbp6mSk zERuh8Z9ECf$Jp}7{S$6%&j^RvxA=!VV1ecumt(Nuikby(FlE)E`9ZLbit;rlQqNfB zu@R0*XxqqysnagJS_jt(9Tu*EIc4L+oZuoE^>6m1e)4+;d488ee1INB*E{AEnB!3t9f6oI`QBQ9NTqK<& zO`Z>m_oL7IVNu!E(dUKP<8BrOdrma(CF6-x9z9Nj|95;=W54(z9Pue<^!#{nhv@aB z|M{nL4yYHLYAx)6*{+`**1>Ff&keG_u%24aDQ+OS633bbH!W~GK<1Ob>4v)nEONeW zLY@bFp6I?4OiP;}LBZ#A2@DCG#qtqPHP~{Jd!=>H0)uOHoD%ZhK*}tVeb8|(e=v?j@fn< z4!s(~3PU~R_}C*iVL#Vtd=5;@N%Ff5d;VGeDGC;xSDa7|o9+3L5ku;~E}vHems3>L z&%(?|>4)#(WbWrvu`n;@vAqZmowC9$9;Qz(dEEgQt?1W3Pb_Kd^Br!zkTNw9rYZa! zk?O&I9`%!&MC|jWZ9FW>iIq==sgs@?D!>T=vi!?1t3pYK3I|goW~adXaF)6v>F<|J zm|&4V!KTR7GAo(_YJ9k-uPAvZk;t< zr52_gOfS=gttMSQ^A_gcH_V?6`^mgdZGhQJYiF9k33(gaK9Ks@FE^IK!H?z}ekS#2 zBv!A0OYh&#Xn}?E&t0-0`Ek3-R+zH%`7e?u#8+BaIbvj&{<|65Em7)9KZO zudsLyBaHz^;d@BG{ii-&WAIof%yzvu+F!qJN&0t~ckX?V9qJ7yoJjgf>PM89y27q^ z=3NxSG-Ga~KdiTT=S))1*W|1UCG`q?=PpZg$Yd(Z^+G7G6VM>UsYAwuJVwm|JW~`a?s2whSzjLIX)K9kBIS9+# z7;>*8`DT~5vb}gc5q;=0nQzXXJqc4_QAJbFYglNPk*WtXENvRe{SyT$K3E91-d$i; z2~(-L^DN+knHgO#NbcOe#~NlzdRLPB&)(h9upSl|um3&jG0z0BOlSO?n+Dg@c!dvOUigc%#LPt=>q=qD&6Tq< zV45iYa}g|X5AwPWXQeVa3t{H8fr1-wLe5+3J22}}v{WwfzD3#vFz370k^j}5vGbCM@KEG7NvlZLa1-%o#E2%BDGOA`O@dSp~S-@Zor9nR$^ zATPL?Sbh~2$JUR|uQ*z8^a}jH`E`~UGrE7UH5^itQ6I6mu8{060`2U5d2snT_X1*0 z056SL)JC6A_8YqImAGfHRH;%VvGC=E3K7g)d;kB&7b%E`VZRp(2g!aURGL08@i+Dx zOUtrUm}@%c+f2B$=X^va8E@<~trakXpF24hX5E@R$rYvz&KR9fF(*uM7c3r>h`dMo z53QejiuC{g{fNw(p?%%v38i0Yk1){EdUtL-hEPG4e6))T+I(E1B( zruA%eJ@Ocz_e=i4?{j(4L)FN6V#7`9u=2_^7i(Z)O2s@~*yrY_QnG)EnV*ES;N)f2 z>&Wv*UFwxF4>nx+*OBaBqFjE92^^DJbG{j-eQ&sC1yh6dT*&^#eH5SK278|JTR`?3 zykFdZ01lpVcN*DGSiwIm&cjR%8I^9BdZf$#8q79`KJyC}rOcofz%2i`4ZmU9oM~0> zNPT&O<6oHjy=MClSR*^iu8-sq)pG~n(p^pC|G{iQ0bBPk_N%*5kH@gE-os5KoZ#M| z5{fJ=@C>r`fitiPsH#?kD~3V=|Rs%Cx>H z4qSD3$xbbpA0W=)!3nalUku>U_q(w29L}G4uyBs8;$yf-nYYUr#``16GuS7;uYDOz zYf!jHav6^=0&AFJJHz)TEaoS!SPOF}bS{a7xzkdLJz!SvpE1EOEBntvZ&F`r|uUS>7J9sCn=*2{c4m{Z?0YK=mZ<4q)AQ9J7Rx;NTiValT8KP*rm|1R6N3uc(L zOkE6{T5G=(!}JjUVFOquN$SfV;)*8)lCa=tt?fTpoI>1#eg|8K0fy=pzE-?GWow7+V z*YNI4Hte}7Y@rHF6bhkkO`NmT;>>)`klFvFJNw+ zO}iz`X?m~O3k$iWCM#i@l-jmF*hYV2#E*`I25y1;C| znC&a!su@w8TVT3k$(tCsLuhcpm(*A9{rna7Dd+Zg;NT4GUg4FBpae zg1hoz#9x+-$PQqA{n>vq8Ro4FZ<+)*o&EeZ4dx6#PSJ!T4jr>BgvD{c@>jsl9-D9B z1Hb}$uD4DyY-kg-;2SJR-7BFwxQszrqv8v>NpP;9u$tB1UJ22d2S)h4E79^ zhebzLM3}@nO3CU;hT;e%vNr><^YR=HZWA-`i*HT*20``Vj~T>Yp-|4 z2AFAfyHp$Q_%P1WljM8nY%+!IH<{b{!3;zEwHOY5G19sR7U@ZxTmjR$yVnN7)Om|O zGho{IACV!j!1n!Uy^NKjcMQpM--zZQ=Ur>4h=qA!9M2iB&XrqIi7-WQ;G-HG{B@^e z1}vH&@nIa?_ji-=4Vc+B;yOa+Geza`Em-VsZukebYB{AjO;jTlGo-#}m|CZ*74&vm|XN!(DI2 zS;=h2_1}I{Xa|=TKRo0C^P@_01#s0XZQp3$4wi-`_oUpHxyy!grZwaIQ`?l08N-WHSx+FxG%-r zQXdxF99%k;vYeqBqjYi(sqeU#Vg`p^=zOvOrmS20@E9!Wc|Lv-%uic&K8575!PQG( z_B@BaPYh{rl&`J+NpiRFy4tv|LA3-X755}S(kuiyjl0!r7E?uW2hL{Y#2n6bnE z=5N@uAT}oy7V6vxQI=fJs2Xsx=D_@Mt{dmVs@x%qqcBs^?S>`nlk6~>^PD~lx5B0? zBj1ho=g!PJ2a9YrX9SY^s3+kAaI(X=wmoG0m*!y$$1KO^DSoZ=hiU4Lz0NS(*mi?2 z$$f^c&%(m&onwgUbqW5tFu!!y|5@DN{0xqYn*MgQexNa<0StcXAob>Qt6ZfW;5D`3PZCu@~hMseipBuLd@jQq4^w`I}Dnx5Nd9Q<6#l zA_vEJaMWw8=-FV}Wrwd}OFFfF^Pwj0jso^-nnX5e4# z4UpV<|I+U;J0RF*7#5W|9TUTX_|oMRY0STV#b7t__@!qhV4LyZw*Q624cnB*6I(__ z_rZ+#JUMx|Z)nf8L6~avp+gByxE5(D;f3}4%V3^9Ty@4P z_ot|PwgF6k+3R!!rY>HpH3t^;gibgEx0=VO&V?z}l=924PQcM&W0-Rz{7F6BA=J__ zAvwc&|2NXV!S32Zn6>qYl+-xfk0Rr73t(!*wgwv9bRx832F%THx||BftgLI)gayi% z-d4i}%e=NtCwBi{+5ro$h#zPW3tJuaWH5f4Oe(n^)(uI|*RZQAU1!vXO*Z$#q8Qa+ zVt%_}ijFMiS3UQ#8mTu((q+Ix+0PwhJeHnI+9sHNpq)kLgZEG6G<7_lkNe;1$@vN% zT7E5s^(xmG;2&%V1P_E0x5;7tvfa~cVYR ztOCZrrE_f$%-J&a)_RztU$b)`%;?za zd=5@H+a`N}SS2$#ljIYvDh|QCJg&rTuvl8L(U#jhQe-wzy*_%!!|>o&)o=@2xusw;J4MvN0gZ` zJ?V~XF05xesc#m{e|tu!7*;-|{zxBYw0Nh!gM&N%jx!?pjtR70m{K0yw1`+(%8HwW z``>g$-x3xaeHgV`5$ng>{ir=G%pJBD!O1@7svKdl0;^^aZZ&!Tok{8)4sTOYLjCcb zW7osQ!`0%{0cvAn+hSCAkj_D^|h8aECrYb5}kM^Fa86>|k&1x}R z?&LY4faEMUi9N91$ZNk6;xW?_BVndd`1dlHt(G921)F6_&Zr{&e^0oS!hWVc#X{nq zmc1|FQmdakT3~*JR(C(ltI!GQAf6H_KW;LfuSKk7e_+NvGqyggY$6$zu+~g86yjG?>ZWx#S{jdgEUNljOXOQhiqI0`AC`u{DEP*ECA4 z;3%g*S}S4pjs^Qyz@9q$l$>D7w2fz%lKxoRAr{Qoc2!~_Y%`Ab%Ma$R4t_Eh*4SF5 z6$T5L?|$gQtP4L6CXxDA&EZpE_R`#?`7q64ZQcafFxETxF--MX`(hkSl_<6N4O8m7 zp7-K<&7Rqhm-Iz_%YgkqSYy{c4|$k>d%GhQ^~x3@`6|S#W>w9BOYM3)G)Vnk<7ewh zUh}|y2F!WqcP|4rz0p@`0@GK|qYKn9|5aB9ZAg9B0KG&V*Sq(w^hTJ<{@`_81M}gP z{b3ua&z@90bsFvuC+dd}EZSEZz7`f1zp7-z+&S59yJ4O4m(2oUu|r^F5G?v}wIB?p z#yO^4ghMwNo5sSzN$U?5z^!v8=3j#;gAdx?!k!d0(>$1I&F*W38TS3be@QM}y~=tz z_S3|zCe)ocpSi}8J7K{w&GMNrZ|>@2Ct=qo#a8ApL+yIYb(q_^-oX`S-aGGD49hz> zFx+9W;av0kFz?ws=2n>c_(4D!TzY;(pdZY3^IrQAjyLF%3WY_pq>^Mbv0f_PC?A7a zYV0XWaNm>r$#JCK=}GH6n5nGlkOxz0e_nKfho*Prz98Pe#A`p?m9i1PBF6bP-q;ZZ zS5;&*{Dj4eN`GI4#iHq7ly+f0_eh?tf?HW>mz`l&xxjlO4X@8PbXjhJDS^*VslohB z3OSyno?p3_2AjI)Ne7Yo^WAF3FeTE-FA^5`-cYiIhkl!_JPV5+&7SK5)AnawOeDU4 z+-^JUT(j*{2F%5~`zyhZX^oOSPECZ*&_30(Vp?%*Go^YZeuPq5j2ivp@2?$-~QH_}>Iuj}fp zXfW^b;|evn>z1ar9iBN8S1^6vA{iNNtiNxx zKSQK`taybDTok6ZRn8ytIdxX2JIs;Xd~5>Di~lok3*5AK-!BE2GEsS$1=p`VDNuyP zm+~Ka!J>BGag$-@ZUvW}a8=^wY%St?&!R&xZSl(DS+Iz*!#o_W6&#J84^#i`%Q^=e zUOwVy57W**a4&>ilXBfQlKzJ#cgo?evbu{~NS;NptcJ^P%eQQWxyfzOZ(xq}^kbf| zU|6xd4%Vxwo45-Wo(pPeg`HVFJNLmN%kQm!;a-c}#yFTZWw+3F2JZiI)o*Dqr#`%5 zFYHse@jwAgkE@nUfs3ZP>E4H#mlmfLz!5$vCtkv$3+-LaaEwXslD9Blaq@+3IQe1k z*JhaI5}`3c2ha10>p`tBx9v}=9?Z0!cdr9xx9WE;gO&ZpOLUR``wJV_!^UH!dw&o& z9+C}!Iq&>0ieXO5WXTh-(0TAnH|d{RCAtbneX8;N1@nRnlFMNEu>2xomjB|%pJ6`l zl6()$U9e#D0$uE9rG6)g#RFdBtYPtw-|u^2<_e5kPuY#}8{#gu!&W(+yT`!n{Ior+_s?5UvdQWyc8y2pW+G++DY>4hjCiytszXfo4OHK1tSd_Z=-ecHj zq1~MVnEAJ}`Y+s7y0p3y<{stAn(ARac-)t&f!Y6F;XgXYdg;*~`vs=9QeGc{r5;$P z4v~7bklPPo>gY{z23AD=52c%DGyU*f?~hJRugA9E0DZb z_h>h)ciz=U2^Jif-ZcWVqJQ;i!_0kE3un*9>&HK_7v{p$JCC|o!@cpA?h9awk?L+2 z*lhe`nN={yIw$-T95FK>dJW88=dGRwOMPZ-bA@R}AFe)tJp+9jw!@-R8=p79S? z|5^nbe!SXuf#imjUv1#B6+1mH!<_c;pfxZpUsX9B7SDJ$!5J3Z96yi^3%@g@UEr?S zSI6HV`FTY*;?_Pp^&FV$_dAE!u-R!DF=M6H4dS9tEk#A7-^FyapY>vK-78q&Wva!a5@v_xHX z3M`r$&sd0DRaP}z7Z#2jzd9c_q&0mpfccJlBr!Cmjllems9X1$nUDZR0aA$M%S!63koi!zK>qL3{Dz#sFFRq8KUrUb9%v+u}nYiv8b2=RJz(+J4rdA!es09~! zebb%+bGLMS(T3R(b9?5%%yeyCJvjMCin=K*{+_#WF5K#AeaxEV5r5w;hFK#`msZ2H zcSW8IShRBG_w_JOB6hM9tSV~1xCLgf$$RJpXH|S$yo>a|@Vvr-JN9(kIzZ}qf1X?+ z{S)L`j=-Gtv8lx{NA_QN1WeJZ5x#|OC|B-A!6MG^a5r&o;`LK7GvLY-MFTu{i>O>q7EC#@WKK8SRGX=JgLtk4{v&qeN7EE< zl6vOB)jBXeXH4=Pn5*^Ukqg`C39^8jX3 zJUhiO{h+=poSxp@7$(Q!GkXy)PORdCi%4;5n841-y0*lOB<^-oyX z!&n*%`=zfA?}QoD+`~e+DwE#Y0dqn%qI+Q}-S3aTkp5zUg9;tL5BnPXyA`IZlw~b} zo4U&#h`H-($FN}T`TIemxk$n6G;9^P*Opu#)i&` zdrX6=!*9l?!tA}bKhA)eH~uQ;!%<&E(`Li`B-5ZLFn{f#k-0E?2lHqxOl#;;GlwZ^ z^QU&fB9o=BtYAjGMN>b_-p?|zgXvEVWXCMPet2Z!-E}Z;MCz>qoa}Owx&vl@4cV^) zE5CY`5(ZO8POe@7Q}e3EpMm+Te(ANaRRZNz63m)@Qow>^c*-a4z`V6F7Y@T+5r3T? z!2-=_i5R%PsaN4S%zPuaC=O1y(TI3K{6ubABCI1Nx9JT`J4Aby15PFaYy4=rUW3vHowJ)1vOf@gzHv%ix@4|2lPHX6U4&1`Dy@ zv}i_|!NMxTob#~ExXt63Fnhz%GBF&rGe>s^Os{ggwcZ5RXPk845X_%6HM0n=`mpH9 zSy&LdCS&R%ygysjWtm9oC7h32!?a6{ZRs#qb8+kuxOUm_(Gr--cD~;M*KezvDTEm_ zu5YwA#dx3oKK?@bGfKYX!x4kLwn5VWVp-GV#dv+>Hl|%65c}sA*1tV)R;^5(E==hN zecl9jT==VI4l@$nE?FJ7Mm8?<(q2yndXlb20+v=>N?; z2wU~g{I9^`&{LP2V5uqN(u-iG26wiV8SalV^UiCS{>p%p4mWKG+1y6zbsjGJ4j0YY zWz`9@W}RFlX^!VVx_G_Re)K=pp5sOQ*uz*8=FL9qTL}vrgPxee!s<1xNOhqA(+>%b^HoUT@>E1azD1HRm0_+zf}xTHo`<9f3Nv8Qg_@*|aF;>$&)G0r&A|5{EammHY935aKOJ}( zw(5L1))Z!CAF;a!C$wH3vLyMswO4v!pP`F4>|t@v?2nq3*dK0Z_HTfNla;z$U=81u zjhjh-d&8PTFn>(=4PTh8@%2(6oE$&>G#eI;o#5CAvl?i%jbGQA2^}d>P1jC&8 zYaOaqczqtM(0LT*w)bz{3sZ$IB{9U?&Nel{qMNs^;$gQ^H1m-m%-&U`*Yc_uw+%h z3)nO;F61W3UmaF&hWm6T{>~#-|8eURtmC?1=WUqI+tS_v8?$LA?vVaJd;Pj$zi+Yn zcZrKxPJiLxYkmo@NWaJ3_=T%*KXy#AtcN+eF0~G@;g0EVn_;oaMU&02svpC=1s3Gm z+xo*@>~qF#F!QL}+2I0RwGVb}d1iJFW_8Fs=E8lGjm{>)Lh9x2hj5YU{Y$AZJ?6oVkHpiA z2C_;0cg^nuaBEqf*-e-h?8uj~!+crptGfjY)L&Psz`eYJzI>Q+>bRQ*T;6=h;~p%s ze5#=jGp5Z9tA;rmlcHC{q23Cv^(3Ful(Z4%tG>MX3Fdxi8|w?Z@_h#Xz?5cr^;2-x z710cdV4Ux;<%a~gD#x@{8m380_@%&6`WB{RVUc@GF>(CdP-z)hEbFxWD(P3K9Fm3k zW|DHLu$85|%p{l>9dPv;obX9?iyBPJ^cuB%OT_Q#Fm+!0+6?5SN1OM~hUuCUNBiSP zwyk2qZ0Em}bI8T>>-1T$=xShPG#Rhm)$%AT+{s~u!13(Do@AJNwYF4+~hV z+3GY%a{1fxD_~>u`!Ny+asAI{PdA6ntOs~wiRoISW-Hkw$iSjR)lqAViAbTsjBoxb z%TaIBk~VHKEVStNWWXHx1vEAIf5)S(cw(Rh|L=U1)BX(_z|5kZ90zhdo6V(kn5Fsk zjx$Vk$jhG(bDJa6U14^V>``NwzW2JNJFF3VV~;7!?}(qhmDHEAYnH(RMah+9zD)HN z?6x32$T_qh4%VEq#1W?6ljX(1K6>HJYlxL4A`^*^9eT2!c&GD;95|=jXsrj#+j;Fy zIUE=m5U?5M^DixV4V%q1pm@RJ3F8e1VXHAG`MzZ1r+SZvpB5HM5`Xfah_CsZj|`J>xtj9gcF7aC}bcKUwdY;)wmRMm@V4 zrZ}DH*aKVTOFw%F#C$C{Kxhdi;^b6tgL5qKO^_Fobc2L7L<%1y?*B%D*ele zRU~_UlJQ1L?*+oNv6ky8IKQr!#k%3J@Y;7vDRMpeDr$)&&%OS7JnTHUV)a#6JiKem zBv>!UD>R$bi;5f7;Zj|@m^_%Ov}uhVZ2xP1_HCGTY*MokTrmB#$30kd7|#9$7mW>f>m>QV>5XI7;`c|2QxeC7 zV!rmCn&%1E_qqBiz%<(m-*d$DS8o+z>c%LW9C*m``Or+_eEIbooXG2;S6$|?_(A@D zoprdst9D2!$0)4F&!_5-Jz=E2F6W6aJoNj$T0BhC8w^r$!Ow@3f6G!}uGFOIL2zmI z+u#gXIHe;a0}kv@Zn^=}i{GsMNa{=Ot-T4e&n(_FW&?gc|G67NEJ$riR)_f)Z;zU9 zI<;*cTwvXoolEj5wMOo6nVWiMI?VbU`@A1ce-f8<31&1Z^AlZhf8V^e8a=D-(>n6>?-Fy=ulTs*{AT%ld=2xLNiQ1bf%~y}O;H0Z8fUeB3G5oRVZ#6{97q4~ z6YlL1Usvg$4eLOvfI<^=EqbB)~ke_N0l# zLCtzRxc0Y#ks{0*S@19&cCL!aRDu~vrwgya1qGkIRbgsXS=>#y@0-!Vc`)UkVrn03 zJbmxZ^TcCxN*uS~^`J)X*DRRz_HW!dxM=Mz?`On+&g=b!MK+lsmBh3$R#Ggy{xr<7 zdIhsj96cxp4>8$Cx?zD^$50?F2o0hbgkgTqeRkE_isxm}EBicgf&S7>+i<@x%WO@C zDUSB7Y`AwM|HC_2^rd)_=638K54wIe!mRA1uXGr{i1qqK@>$_(`(XA(sr`L0{j*Wv zaag8>Z#V)|H*cDj2s6)wmrdp%XZw6_hD#$wMTRif!t5E%6VHd8^fzNzI8Z9R81~zA z^XgKV6JmDF5zcv`+Pe~Fyl`8y4K9$Y3|s?K_bWcx4I7_P*V_QozfQ3Uf<+haPj-iC zDPbQ%;HWDq72Alreg<-2&dilEJ}_nYc}X~2wId-S0A{(TOP+u!bM8Do1dHdN4UUG3 znqM9{3^S8X+n$8Wi?WYK!Xn>`9Wij}=WOeHFe7B!U=qw6UsU=YrWMolGT?%fRzG`5 zUS^b=3wz|oPg#5v_p3kK;RzhSi+St{@oE1`5zOt6uxN(qR>uN+VZ)NPUwyEsJfnXI zj<9$fMXSnS*7x&Pi(!h^ zl=NS)p!@weYuIDj^0x!9NGOqM3l{|}Tp%Bg`AweL<_P;`kA!c8IfuRHc)?a7ckAL| znuE|NnB*^2tqO@F%{{|l(`^0NN2K2@V;~&%c_F&>4Hh!j@8-fqwoeT)BEH{AVdw&> z=gtX}jllV=o0F6X%Uc}sRe*)8f^V1L`2CBe6k*CopV%bW>fWVGlS%HsbV)L-`j<7V z0gEP8Mqh^YYP{}g!yHkq9uL;Jc-T!B7Q|c<68CmKiC7QQ7w&G!gSk@4Cf+csNA68N z>?t$yE)k}36`RUnO1X?jDom@qZtx5)H;SK;2Mg%;E6QPE*}k2(VdjNu-%7a5vtvR5 zsh3jS`V!WuziWLDrcX-Yzk;(uPX<0A{TG)~gs`hgZch))o>g-1Bg}lzG_N0~>b!IN z3>Q@nUmJvJTXtu+z`d!rca4zxqqli&uus}=n{mf6pZ;6Veud-hf=b2X{oRKCg@PRO=f;s{d(za#8Pep0&J85UAvK5Qoa z@!#TDFnhb(;1RetX2~Bm%=tW$R|pTyu*o|KGbCI8sd{7mTicB1!@RAr-_zhCFAJUL zu<*?Ihl6mzrL4O{q+jFTqH-Us=QOSFVi^Vlr@~gQC+_@!h1?x!da$T0X3hZd z{TrRjU~$j+KSMBQSMzKu*yh#Jn`2I3z1<2Qc7TUkJ-cLJj-OeKGwjjz>zg9X9pPne zg#$O;G}eTLPOmEc;QAe#?&!f3Nk85Jm~uhEWIoJV_P`_rHuZlzun-o{af%FuTk|9* zE`^y#G7letOFa{f&0s;&rBNrxmu%Y))A|pT9VPusJd2ZHYU&2L<8VRZ@|Gf)eX-u>|kRsYFjc^+J)@yC2DD)56=70JKGtP`1AP!x( z@p>mrN#FfDn&jbO2mivdE+L#Cvu5FZdCt$wsee(vAf694! zne?yx^iIkj>v7Ymku+Ga*8SHc*k<#jd-*WF-EQ*&Snu(`zY5}gPnT_l=>@-XYGB^m zHk$yLrqr_HGfbPF@$4*29Z3t3jz<0`ytxiGdm?vI1!jod2L8fa!&+B;;wi`Cw0EO_ zzI&1pEKbT$HGo@#PN$l{R1vLa3G6pUZ{tRocX;ZzV=&W{AG06kKbp0s7*41(f5?R? zw+G$DaKv|wB^fZcg#KG?51#+)5lf3mze7mYa#$+!r)@dRs%y^Q4l}kZ@2-LQ=b|SZ zfbC!Sertx=g$e&+U~%qpomOJof2x;Az0JwUUYPkaq4^%nK6bTs1QtATdRGne3oqQ0 zK8fplvNE+1PPk-}Eeo?78xFL=_BT~>6k%Ru?T#Thu=$yx1}xNmWj2+KpEqkCYHGrC z6Gb&Gxb>=5_Dq;*6v{J$lYO2&SO$yV7DlAO9(&tw*pvFd`yRBw5q5U19;AMuSD?{e zJa4K$#|FR@o5ulXVb_0Uj9{48JFEXXETTs=hQQp-N8C$bT8X4z7IA_}XjA~!&w<3D zyRdNi>!Y=>i~;9o9n7pr%JbZZ<1Z_#`Uvxsbl)C>d*}Bb6T|!mxAq8NdY9juZkUld zWpf$qcSByg2d2LHZC?)C9JyUJMEX_=8cvsGdKg~|Vx zz&dAk+o{1kH|Z99c_f4OBr!yrIJ35KJ*+2fKV$%l9S+y-gcH`=-Y|w)#mirY!KEqD zRVFa!p`F}uxG3ME^ren0l}W0UM|z}!`$DRej{GSsUGX8BBMG>7SG z8TV^pc3g|AI~;s=#;$f)Y`k>iZdm4}rkWULb}5_)g6$6^MfMU4PfQDi^&%+mhe`iE zxnIX&+IVHh=`on!)!!7Z!L1MJMss1F+B#}6OnrFz^g37+`B=Rf=1TwhxCs`Hs3x?- ze##M@o}_+lgYFMlu(Hl!Kk>Z0x)FG&Rc*@|nEl1BQ0)Nj_as(kIxKM6d~F_VI;Va2 zEtq2$;kz78P`$7845kR3B{sm+Gm$}6#0jg6Sg^qB@YH%(lyQj5hE?C3o$(bG4re@w zft^pEI`bE%E}z?a9rmy)J3j)my4L+Dfn^+bM2tC&`7Vg7d;-@RRR7b0g}W{tRR}^o zr`=T_rd(Cg)`6KVcAw{wdjG`7i(#J|hvyg*Kj&sGhs&=2Of-c#V@q~9z!8k5fE6%r z_4z1&IA{GV`86uJv)T^`I;z2fi>7L*@Mz5}y%Pf6c&5Z7Blgdc7onExYa24 zU;`}vQd}W<2>X+prDH4U|712W59Thdw*NtL)$lkEn657Sei-IPc7MD92QM{rlsJR? zRoC;i6z=%w@?;Dwba(BnhU3Ly8PYJT>F<(<1Xr?!&$HrRC8u{YvAKAF%RramGzpF!f2x{-el! zmzI^mte3iXUcy7Mcd9!`?%Vdj=@`y`M3MU)=8e6g>ILg?OU4Yq!ef5CIJjt5UV_Y7 zjDK})ZUr1sq5Vh^rd9l+{(uF_a+lOeZe&=d7>?gp$RF<4hN-)RQ48RLO)_S)NPSH% zZ8e;5ZrhLnEKu%_@q?|ln#<28{mMaOQeX}LqfKTo<$P(KMg;a-JI|!musAL2P!$}y zuB65t7VZxpbL=?Q$CthfzA$fre=;o+&x=I;wLLIj=o%IX+n+r%>mbZ7RhZrhdv2zBrIJ_b`gZ6%%HD7%xfCt!hBhgJg28hRHRLtMOm z%zLM>^t=wn8`72i-KbwpD)aV zd5<*w?~**wIwT*a`4=wz3aiE#CltceXyF>Ulh|*5Y<*V(3yd{(FM|c=w#Pn#DYw4; z_Jhst2IxG8Mf}6OIM~m+{$3@_E07f3fO`wW3aelicjfe3uuid7LJiDy)orMOBd!Ly z*2A1n4R1PN)$$GQpGZF^=dHpitZ(%tyV_xjanm$ixXN)?z)zTI6Sm15_7KIlcf+FX z{jpZCNYUU$A1rR)6u$xH-dsF>92esqe?mD3bJRY?%feJ;8O9OVwEN(6d6;`%`*1ky z8vEIsSYYD#=M1ds;u0{L>zzp8!s!A1EGlw-a?G?unDKAIW*wNle#@zsaF#{Jas!yd zu8I-C1yL0Rb7025eQND6$4k0&AUQ?N|-eclZR! zDfKr6aIhC=@pYK(nJzDX8ow`~^g7>#xgH8}vtZRDuAL>YKrY979!zzy7<@$f-7Ol; z;aX1H*4Hqrn9bY-yB<<{{tc#lSCl?a`d9xJk2{C)?!>0Nf$OIj9ae>f7BTL>VdnIu zhYVp}=ZL-L85}=O>cf1P{ZW@X2hKSo2sR=0cf-FM!%}M~wku$6iOd`aSoCdkzXweJ zdHquaT;yBV><3eZ&97y^_8J?N0%7(<+2R72zd5t`2hUG#U`9s$jc&N8DfHiMm_=(0F*%F-Rpf7e2Ns5~<(I;|jMJ+h!2DT%cR9dAY4Ta` zVW#y3ykWxgz4pxdE|@k)z48X!8@Ai)7tFF;^tKe{Bu{ew19M;7^*@I-bguagz>Kqv z`E@XhHuK9MOfPwq*bFE44;YTX%#+i+hhR^J%l9#{=zq6WXFeC>DXeo($ z-~D?!%<8(cHSQedx4FDiAEudQSR=CJs{`1bK}Sl=(5 zw=rOmb--*}xQcSH&I0Ca{(nr}eO$}m|3C1l=n_LE6&6JjhDs={3xr}Qm6A!QB%>%K zlT;LjBos?Ux?n1m3QJL_j7p)HMCpR5kd(fU^ZNYWzxVsk`|bHW7q4^9>%7j^wq}mr z19QH8+_4nqchpF(!TEtVU)YoUBXpluxH;|R5;iPSS72(#;Q71XHrR>eTd8i}3F{C3 zn(Gd8K0g_GA5QK#?XnT3C$E262^$%lRPcg%uS#}VokM;6T^fEc!)DI?(=hMYxRO9v zw5Gjo5YA|f?G1s&>z5w2i^cx`+}Z6YOrI5_A4%M~?ZXLJ=zgZR0%n(%Y>0!|XU~pQ zJCFWky8TIpB_Fl;&D-iCCN|0DcxD4a7f?Cl+xlX+rZC2V(3xuuB2cO7v53)AJc zT`z{k-CNIT#9=*25^p|%B~Aicb6BHb?~e*t;Hnqt2#X?J%*4coeG_-U;`nOMH!yQ< zo5>y6^WpKEpJ8Ej!Tw-Bo4%6#b!$s!!2!!XlDlA@;l8!Y;P`!G^!j0m;P$4o z#O`5XvS-nsI@7>%m|Ok8VFWB)p0{Y^MVvQHBJ)*Yj`7>?b72j8J8dnP*;i}l0yn=3 zRi6U$9B4%XICbueCo_mMMupbELaSRgRxm$ax!L&=)<<4r=u((I>h{uLIC^Ebsy!^; zIAg5il8fDGR9nuwE+C6X9fb*DtDn z86F+z5g>8%h@(f|J)Qo4g+uJ{=s0hEx5oehh)RT0g39z!@=rl8(Tl#N8ocxPJW| z-4mqzjhXf`0$l$glgZ~{w)xRLOW^uvl{+(F=B=l*p2DSz?NhR0smH~2e_-eEv#0Wj zH#*j6B;tC*G#@>H`Rk9bT?TvV%d6MG+)bLxGGOVQhpT?UV$-4aE?05AZa1DP$6)`9 zx?oiV^WKCX(}ZcVdADVga6Y*9z~2m(?p!o612#Lp+>rzGhOf|cO~(D!%U!h{<_yWL zA19cj>c-C|`MI&NT8KZhd+F45rU(_`U;HBu=JC%hVE$y;MXEQjzGu46T>?uobt`AU z8GD;DmcoKkd*!(>?ct@LYhh{A91S0sb#=2k2c|z0S0uvDSCy-_!n~cdKlk98Qz_ls zV0LkFWGNh0JImS|=IFETKZ8}TYH)mE+JT$yAK;w27rI=Sdz^Ot7l}XkJ3R;%mDwzx zn}+wxjb`{9gz3?9c6q`LQ?fG-!@~E2{>NeFi9fO7r2Kl1Eje(qe|_{B;z?e6b<=Ub z9Ut=SJj`3hAGr#SANA+nMOd6?V7vo%{-e4f0Ty1Y8xskaUsfNN1k*02FSrZSQ>t;` zMtyFK^WR~^HG!+MVP^3KdAkgZkL;z;LRbHg`H}*!s5Xr0{Lu=->~admtfWq zsfh}#D?7R|omlHM<{^t}V!hId^4sR={D_)&*jiol_qY3kQGL(q0dXOAN{~U?Wq8 zek;uUAly|AOS#Y9eC)y)eI6PllF5?*FT&Y3C6)cSs)tt8RI?YZS~r z|85}*W;hivG-1|)tJO}h!wbVwEm)X5)W;1D@cBESLp;K*!WZtK=}ejdGqR)lB4Jjy zvIP?s&%1T#G%S58^I;*(-q>s>go7uaWSYa=oZ|~~;8fY0aaJ&0u>aaaSQzIyWf{!( zaC5GK#d9`}vxCJi=%3$^@?ZM<*1)WXt8Lq1)~s1Ft}rdc+f?Qb#%smyjnmp1VfLCoR-v%Isa-`gEX^t3coo(dDjx9#W~_c_ z`~Y_QqBQv{EOwfBRRT9VSQz|-d3zR5pOlB^)i0$>zhQQ1vB?s+xL@aNd8`5p-rFx{!$!j| zj2a2^zDh>yg56m6!bidMQxj4z!Ct<>sv59Z{?fi~I6hIxo=IVYtVFHU{=RORAwPzj;UH}Uh^$E_x%^OPj z7BFjIo?jO1pecL62Ih?vo)yFKI$w&H!s1s=X3cPNSA=i{EZtUhq8HAO9ZSQ1)Wh%P z*p&>zJO|Aut4Mjl%u~vRnBPS{*$%Lv_Qot7*yg;wb#B#SkigE=`NgKjkh~Vd~Je#Iqc=%=5r1f-728Jf;r)5C&k0ei8@~! zVeN21P9m|^LB)1B;MI@xBv_dJ{mM7EXq#bnI*k7=wM@1M`_UtXzMC+ssO*&@?3t?R zmJ1wWsR@w4!efmdyWp^_4QHBR`W65BeQ?gp zJ9ws|KWUCvkCFTjsZ+XOt{ltZ66`$c2&WfjhPISlhxNb9C-uXuH5&$Q!lHGWR)esx zNOU$EwmV&MNBIKwzu%tDHE@q!>-bTypx}-F2iSCy*ZI+~I4H>D6I}61dDS?W_F%%i zZ?L4=``Kg?S22^w+{6Ajqk8;ISbG2V2Q^sr-OrUKB!AYJ(CM%=XzGcDu)zCrf+?&& z)1}@V=0^_9HHV8FJz}g$elth76wY7$?aFeJKd?z_9h|eR>5M&07tR^t24{4Xom&S> z?Aq7+!J>|x_8VZH>Dx!aaI$}u=0;drQm(>-9p2nF*h2EN66&L1`e>tRJ7M>CZ3N?0)otSW;bjmqDX2us81FI3^y^u6>Hl3%;vrZybCXxGm)SY)oZU!UYZ&U}1_ z#7lmRV!<`p-;(aZ%vkFHHf&Q7__-LS%Zy2MfrWitT2El^&Xu0qU~aQY;WLsyo_Ez3 z_Ikh?Rs}PvTTFMsf{d1XHL&Q&_oPFxhP6`DdzhaX7J3mjTkUbR85TblTuXuTD}Lzx zg6U(A*;c_0P4?ZrFz?pDyO(ezquzJ`W{x?Z-2l_)D=-x+`)mH zbsA<_!Qz=dWxL^uTQgqS5iiZS83p4@`bQmM;c)-ri?D*9f3OqGZI~&%43}P;VB}8h zp%R=1ix-c6 z$xr|EtpFBVy=mJ5%W+x0k6=dopG<#}|H9^bCCphN)ZxLYkEWTvfF*X?`C+hw^|YQ> zF!ONR;wac?;ia|jV4A*QcMRO|c)3Os%oC|9UVwcKE|~O^{Jhb>Z^5?F(^vJwoIS#p zY&auU_L$5iv|qMsPXS!|eA!kdSS%jnc^~dCl8zq*3!B>0ieW+H&3sju_3}{PLzsO& zwstJcITCx}5o}ZJI)4Hz(NbnUg*8&E9j23f=M9gmVD7KMGfY^#^t+lE_ELT+YXtL0 zrQIWLIJEnwIZSJlzf=z!n(XnhCi!2T&%A+KwY1gjVP?!#{r9k)-m`P-U=DBf*pD## z(8B|6Ft41O`w7-vD&yh_vnz`;Ti^=ONyaW%G*8c_12$bY#4H4+hp*Dro@u?izS_duOf5UEtVmu$uf6KlG3vS&~7!C)|2+R_ac*4^Y zYOsy}jl20U<85TY1epDnomxclm4>aJ4Ck26`1Sy%y?A|o3f!SrbL}zAZXUm9ChV2+ zX2MIDw=QkgA~=|rtMe8X8#Z>WfQ4&+eg6atUyO5fgIgM#5Z~X?0n!t;#am7?^$Iooo@T zFt%u;CQNJW99{|c>};yhhI#Fh?w4@Jhv`|mutfh%M++=B^3`5_Sp0DJxbLt)`McV5 znEhhQ;7?eg(ZGO7@*Cf4%00w>8z22^Hq5)rRaAiE#_v)!Ci#bjpGOie} z9D?ghl{F8;+~~BPN|<$MT4*>dyl?OI1~wbUls^Rv_NZ?91{)R}sL6zxcXnkiD#3gh z>z3Rm?*1@s87y+Ro172R<0eGA!1>!e+#bMu`+Z-%VU?=1C8aR$@pGR5IKW;}{s}DA zncETqi|^;%e@@Crn65bi=iK#osV3$BPBJ|UOV8K}UcsU#OZUdXVTZF#-jcYMqTCf& zAxl5DiTLJ@i5aj)>e>0tF!#fA^_#Fz`RKY9m?nIEM+oO99i7+)GoH3MWW$_+5AVOi z;_748d9dWAiJ%)6j`2TU05{n9xBq}ywLe}L!U3Ax%|jB*tn{{sv8$P%std@1eCN!`iy`IdfoX zV0i0k*lnl#5>uG=F;ylSmfJ@&HX}ZETRR*MTj}_N1xvbhsPW`Ymuz1LGqiN4yCELL zlC0#C{Dg*jBUm!WfAW4<6fUZoOzN|I_A!JwcliZvxM6y3cNi=gdCqqXEY(-#o`SjS z7bwZXy=x9f#lzx*%kO?A>uKKNmMgGOVshXM%w*|XCz0}(`pueQ-!9cF*I*jQvF|+` zefw444VbmKE$=O?I$l{O3+7&!ZS$OX@9UX`uu$u`_#rGTKKi*Brmy<_hpgY2#4Je# z$zOe_F9lA%W2jh1tdMOR0~dLSvn4RID|XQlSY@?sLmMo2U;bHx*J{rPYndY5-aG31oJ#%1s za_ZytaE9h{z6HtG+Y~UDI9j>ElK7eT$r*4^@0`^(u;5V!Z7NK6{gbmC7LV(R)`X*% z`s%EL`Dqq|V_@FrJY@%%cIwd+6*#PF_cbS&eRT3$1=wM7$j#{yvz`zkH_+=cj-x$rhoo@S=S^IbSgXR$n^}OU;>TwXkZf$Gt1Cc;$`X zWw6Fu!^U*t@@F%O;NYR#%PNVd9_A##%wvBn8)1=#@ajp}Ss=LILE^<#O^4uszgg@q zShBX}QZO9-cfG=In7O@ZLm(WZ5U2eYrm3s%^oR4K_HG_>1@TKu^tmM7sSrFAX1Q+o z;0p`u?y(eM$pG)7Hym8$K5IBE81*uJ3#__hWDlMA(poo9QvRG*@o1R;t3+ip92f6; zQ43~DwDooeV13^w4z@5edD__!*k)bGI6GLn+V#Z|m>p_JhW$t@n#?ae- zEwIfq3z;C|FVlv0z`iE0KZd|kPv@J1a8b7T{v)v9q9jxi>%-=Awd-*bm)EAw%PQVu z4@Se1dq*0y5El$FdUhVBx%~{D4GXvTFT4cvBA(B*h9xVvzK@4lr+U|{Byo-2nU`Va z8Sx<}IDe9{brMW3K6z#4|c+g*Y9(0!J-y*`@JO2 z+w?md{^z{h`}de%F3elIPxl()!sGMj7Qpn+V&w-gOKyU-2$mN9yhzTQ!s`iENV8mF$wN5Szz7@ zGdFe?&W7WfcCGG&=|(xN3t;J-GTUz??z*gwoDXA?EberZ@{io_*}!&JC!g#mHpr@9 z4F@Mb+9@kQd!1*GZ-&`TInl#lNk+m*Uzq+$dA$P6%eDBs6EKibJeC|0?BvXG`|F<6}}ee!_2we?jK;G&OPBwn9*g< z>4Ec?=G9rjQuo0NM%eGgTl`el!0ez88@*tK@=I%+h{MZ9?T4j*Emk_iB9}?2Auvy_ zjPC}E15Vu_=e3}u(FZocyj8=PnQ%_-O@Rk2ppAci3ziez@!kTnoMutyHSv`N8Qw5c ze3W_~s>T=SZHM`>E@K}fzhd*66~3_a_#*cwaF333J{OkwM!%_s`Aw68_Q0&CUOIKK zbe-1rAXs$e>x)`A|FwJcewf>>Z$R8I|BT=e%vK!|^%Bl0aQhVrGllCzYhbSp`d-nn zz;hjC{T}na)38ML9Mzuf`?iQUV%9b4e3{Q2B~5@C%Np#+c{9kd%_I?Kh6Th|!J-8# z<&t1p@FME@U+(^WkXTf>XWr7kd!~HUw01HefhKL9*OUA8=3{X zMd;5I!EB+5MHeh`iTYDa;vL@)jW5OPjT6gbAHlTwtNAVm3F!e6N?ER>F*+ z1sgw+{Qe(dtKqQtAYKc}H@`62ndFyhdwhi%3b(br;Zn=Pyqc`?ZjN^sEmNEQd?_N{w46^?6k+~EyNE6&qwVC|gh&pxnVht;>G zu!H&#JuXaJ-9N+$&e$?z^B$OC$Sc|kmq$H~+zX30&rSD%y;d!i34)mh@?AS%(^(ZZ z!7!Jb`8W^`Gp!zeh?HOcs4bMle`m@@!Tk1Hu|;se`mD&aBp$hJMGxG+*D>%s%u+HA z9Z`n;_T;#?2{0%1`XpUg?yh{V0G2LF`Z^2lZK`m+PU4JalO?dthL+r0r2L4^=CXG=}?8<=sidZij%Zqims zEDWEdZ3J^Gol;-J{Bw6C4sgu-NgL`&+$iGXewfKAjekkJeZ=ouaMA2jU8Ft9^~nW~ z;ehPIu}@+8kQaHqa7N1C;K#72F3Cmb3EJx)Z&E_a$7M`(B;J)=R1DLO@eHHkoHLG( z$n^*s>}O@crR){`MI^sx&&?({rg!~I$~hB94||IDYmUsHau0El%ZSpku!8-wJ@;WL zD201lRahggqW!CL9r8(h`_Oc4m>F?*!fg__c#t%k#Ervx_dmmUY+8BC6y^k~ zY%GPfHGWT8Oe`~G-GU0tcR+@41t~wI+%^!7q^}$63iDq{lH%cLZ(SQtn6}EGBMWx( zky*NftDT#pvaZiTozIcU*?K#-SVM!bVSyhF^tozr`$oV-DOKnhT5TKhGe9o#UU*fSJ5G zanE6AEf20K%=XFkeGT_qJ`!UM^XGa-0B@5oQBFhBZp2cP7tv)*^Zj3;d`V_~|bW0rg} z#(P!y<*Tr>%G$W$#Pfa+(8PHEs8!|iM3^;EPGK~h5zv*X2lEckUNRQuUYf)(g9WUa z=cmIxPyHS*f!V_!Y&V7@w`9bwCgp$rSg8in9-1HB4b$&V7?O?l%42u*|v&YZAz>=50)dS$T!w)0A z!Q6)@w(fz;mp``tLE`%M8WFG??QBjjOiO!Bjfd`x6HByGP=DwEb-m3y_l%wj3%l>p zrlP+J-D_P8V5VDY#T>ZxW}}l4EFGg=zYezJjrE-mi}Kr>Pr!`(!;YDe{E(EAR9NcS znPLI+d$+yHgS~#m*RF=?h6%>%FL1uUbZp92Ql54^&liq=@@?e_m^CXVK>a1obI&(T z5y0Y&->+?ld9>#lg{1tPG0aRjzWw&cTA2O2{zwZfd}IHH)uIFoH>-b+Ch^;Lr<7q%*{y#hkKYEc&;#Ipf>|x%t1${SRf!F76!LZ!Uh5JM>LuuEl7qElv)KFrnG zAGR~s!Tmc9KfMbJ`|+Q0;Q-C^AIf0f1Yz}QIBfgW#!sX?Q~&oZxc*y+T{kQ>H1WI- ztNifs{z=Nq?sXNxHM(gB|G@P2>NO8x+XK7o=-1KS=TItMO5dJ0f%wAcM}>%s7Y>Qm zhnecbr`(03E$ht}z~bd5aanNDlnW}WNdC^08#iGU^#xxYVd3ON%DUqWKRCmDmF@HH zAl{MhShW?VTZQL-g5#a*Wc^@9UR%Onm~&ZCc`wX9`P5(QHTqi}I4+!&S76Lu4F`?V z=ADCu5syDb!YtSR#uAwM(n)3J8{E&l^o$=c=Y*ouJ2*9Z&U%GZ)R+EYmd;y@XV-q4 z;V}MwKiddy_PDoYB+Rih9I$|s?ds%4!}J85YBxA;Z@iZV%sBcWA_g{f8qCxqrssqx zHDJD?T>K1RiQlPV^I^e#4aJ$T_@w=yJsiJy;z<)&I!-%rFI;2$EnptZe(PkmALf5L zJKGdy<$aiW5a#yim|Ku|WbWe_Sfh_UZ#gWCT$geS*7qEjY7dM0T@CVK=~?HZH84L_ z^>QhkQ#1wrE8E-nX1M0n_F%Z@9qPoT_)`P25T&sN|`=MJtp=Yo)7nDQWj5I zx4Hsup8fqc$>%$+=Sg)`#J+z-I~@Dtl_z=prFef?nehtd(B;D)cYZeAq+a;v}A2kg&oQ)a9q78GbF z!749Com@-uP1=}`;2@_NA!I)26?RqnP3Z5G{9q?4{_5_2m_KNAfXoNIV{=LcT<#II zdoxTsY#H?uraj6RlJ;n)rkXdyZacEI0!jH>M^#ln;`%tc-N%UO`xmc+8x}{3qG0;{ z^dqh?d&#qev&6lDTK=#^kf}k&hxTb}Sq2<%O5;`r%o?k-@GhK@f4z*1FKy!7jiqp^ z?!*icEH1mo{t8Dn4c0aiXKBxw)r|VQ6L~GLV0f{C4cw|DYuNz{8%;PX;NBg-PO9F( zc>Y~m$c2LiCF5qn^iHj{k+3vuWr8Kl7$WF=1UqOwQCvdeKLTmBaH&ti;A&W8`Q-aw zSeR;5;|dGzuN#>33Hv|WCXJZRvAp31GhgPeUk|fKn!5df8+II@zYrGB)APyxjP^&> zr5nM#A-js+!2Cm_N6jYrZfbYF!EvREs^ej)&CodO7K}&F&`t#sA7dPS7S5N+{V@y{ zIvn@D1oPf5-6#W#7;Xi(V95i$-95N|#ztSYTG&>&Nvj>^emRl+6?RMa@@;~7WnZG? zTk(3g=DONzm|g!vQwJ7k&o8Nh`B!P*&EcFr7uy1u`ElBhy)Z-nx^g2X->Ge7DZ++!m%EEuK*R4pEaja*JI+)Cmpo=3-iLHj+PnoD6Ot$&#* z9OVD0&Wgla$3&XLLhEZK<}i2dG(T(Dd1=TQW0*0+^2|!uFlkBD43a<0CD#Sc;jEsa z4NG$js`tTRHSSS#m?l176Ajy%EbACfd^Wt`3>;bi;*cWo(mmmqi8u9B{v!K*keyXA zEP5xT_Io}%ZQ>JH!ZMlLgE*~lcy1+J-g(SV3iD+AUc7;m@7*$LgC!OdpMHbQ)+<)M zfd!%dt&`di_j2fe4GY(N8E*hLw_l0Ng&F3b_9Vcfx6+KuqT^T8F)QR(h%#Q7gU`az& z4i7e6e~6k-fotvY1egii{ULCV zf0pzcv0Kc6TQHMX)ldhsf2KZ+{f71KH1;jA$as1+v9rZ!*?JP+Jg+SY_8ceP^A?tv zn01v(vH!e@q~?>xIW>A%7oH!5bhTu}1%9@M0kC+uMeh+<{APL6eV9|&Hr@-Szdg8} z){XTrxb_4&k1&1&p3R4S$Cr0(gqd;%KH?tqPq2i03KoXAyv&DdM#fG)5A%|Z_TPmw z9<49DN&IBI+7Va~zb@bt$=BDkm7>0)x$=e`Fl*EPWwMCV)Tab0lkthw_g@e5CQFl4 zVX+2nuE7u3mE$ysd@g<22>GAb&(pR$HNldqap88b=fLRA6|nRMvpp0JxIC%q9?Y5W zYUMRpcU|^_Oqgz8kSp^G>(%{vcOon{c%ETtUoDh12x`Xz6*!{ zMtMEolkbtwl>bCIxuIn@nJ?kij#0{pv$muSko!-(S8skf%Eu`v7EXdW>+k+N2G>`m zIC5a#m9t~g;MV9@U&;Q$k**v+3+0}A(h53mi6|TdA4(^%lgIEtu3#$aMM3(lk8?Ilo zYeNEDF3@irMPvQ1zc_Dmo*k$e1B*tD{CWznzZjbfCaS|?hZ3`Fy#7?^^w3v@gBI9r zcZC@T2E#_f`38n%o;d^)vt1{SA=WoEr<}iE`T7mK ze?$-4PW3Ni-Gr#+ct5z;&~E2gn4MNX?>sXVV>9PE{MZgeO9 zUaBE9$kl|4l4wjTn7zeq$avTx@ve*-9H(C3KpbbKd!Y)S$B=Fc6ikHigZKU9^FOp< z@=^v|Kjdt0Ej~Y{@U{53E}U$vbmbcC?7QlX9?V#CYSc1Vd?rFqANKup?T{kO9lv!S zalU1i<9|)d`yu#>&nNbJ`A~7^-7cv`ux-n)@@dHTJkWKN4Kwc3y$s=uB?S+y{$sOQ zuJg&~Ul~&ssJM0;Pg@Jt=&%({N4{LT@x9mhe6E7+U9JJ_dur-Zby#$%o2uU}_*I<( z9JKG&i>Zjm6*g|UgwMxIXQWf6dE36Zitk^Piyj;#ab~s5WM#Nj;k`DgPdm;apM0Mv zf1#HzY0s!VcGDp^RZ)%VPwU|3LMQV5owp4mXCTh^J9;Vu-yfq*IJs*U%xJjzVJ&QW zEjf(|OIu&bj>Y%s6!Mdn&4q0}yA9{#`=IPO-(rkm!_Nzzi}C$odjEAZ6F7f)R3Q!K z>^F{{2e)4M{wM+8-xgP;%$g6IIXYHuC*S7|WKUlJD;Tn+^om0G5)Trf>*-q;csRsz|QR< zWo%eFzk2$)2<&&#=l9pZ+z}7Pu7jP2>@HXf(;8*Rg&$oniagM~!4ZzEoPF^_7@ij~ z8%H?6@!_@BOJS+nOR9X(6TJ-bdw{J9D|W6%Jf?E!&*mfRMfIMA6G?ksnilpf*vmJj z#-79*MymY`#d`d6f^tCVT1Q3Lw(y|w3dHHON+X%WSWi3mQ8t?2vST<8^EW!-E4luj z>YY8xaM0xz>U!g1BiDR9gz+i;LOFTo73=@HB0fQ%m>FA7#dQ}H4I#g`DGsI`CF7-{ zE}A+NW}jHxNZL1xd^Nrx1nmzCrP|kC3*=_QtMjc4BmiO(SF>I zA=(+%aJgq@dN>@n_Nto|%z5;Y5$J?!`_J(wpmR&x=FYhCl$ z6NvrhM$}WXo|9jmjCY4kwa!ph*m_KNE!T-#RhQx zhSi6NJHjq}c)thhp*MiKzvyyLUlhUODVaye{nzm3NoF7%xxeMDDeRT_@w)@u`b{B{ zn5j9x{z(9y2cx7^TpA#?351=`AEfS2W`zE#m9Uszz$f>+*ZhkYfBWP9Z_lG#J|~*` ze|EJ&dxinmAi+7sg|L z;&-y&1Q)&VPvD~du9o>EKcmrF*s~M;|GMV_al@tQ57xm_c@8N3&N9((b&Q5~)zGKTvJh9$>f3zU>>QNdo45sC(#p%G#Gxv?yvl-X>(87ke z;pOHoj`g--!F)Y7OTw$+0&#C$vG#3q5hFP97vPgZY zSDW_Ly5oL&p)rhe~<^wYxl~LsbY#FM=`J7nFD$^WKdcpj_iGN9c!}7!q2K-O^ zrk(*2@^EUpI#r*M#SU)623+r==~R2Z7Y&^~;ojg>4{}~fE-HDq7>=tb8$nz&LPovA z4L+@~jht_U#|;(ZV6QJ5wv+RZ-08V_{jTUw!M8GUzO$XHtjmR^5A_(t<>kjlDZ%<4 zo#*96%B{!_qI~jcNj@owXWlJog=Q-JmiWB;$HE#C$~DFzn{8RDZ@0w zo{rsXG5#ay{Yr4X<+Yg?*1*+l2V&bvcjbundFGTkBX}pauSR`SqcRi`x0`w~{2Lqd zt0}iZ0TyVQ#>K$pF7JfI3eB}2hr;~Tsm>&y$9VjpaTV_Gj~h41!}`ICmX*W3?dR?e zgX85E3iiXy$7d-gJLg$+ti<^34H!=48<*~_fThA$qey=6g*(RgVg0)*K0{%{y3{?3 z?NDFnWvaY$=0?3`E3p1E3b&Aa(TJRJ8<*jJxodTY)W^y*hzqpEdQ|kF`ctuUN2~u* ztfwP2a-_dKQ6-s2mf(32s0Osr+~u^BJ>X@piAJWd6&Ibt2j?A&S^Tx1z9 zTWEpuUhAI^frIA1^ch@)`sZ{^kcI7vbeGS8O;y?`N4sgUJIzu4-R;LDp8RF)>0($x zE{Af>t@vTbi7ypVmb_XviS?g&Fn^`qNVt4jE)};MEYR*T``2v2e$UTvr04epQh%h% z8c`w4O6sE8=j1URtp8)9?+bUSz`TP}DjrGx0rEnWSN%;nhyF~~6XskW{!9k-^-CM9 zs}^8Ct6e;qI67hCIX{>m5O#oEzfHN{D+N>J%Q{iK2iN!1Lj@cPnlD4?DBGBtnVduRC(W#_vH&_|659}4iB`Pj5Qa_zM4eRBDZJ-SDZI{Iv9h-{z zZwsO7Pj-_=zMX>hik49ELkCN-X1s~=BK=;8iYK8~tSZo2DxXPE7!LOJqu zP~lHq^v~q3rX1-XJ~n+7=F#&g=Zk{Wg8t*y$0ynY|6`4FnNdR~V?WIP^1t==EO!$z z=S2f$@uIIP@{_QBl@|w*>y2~UTCj$J{(m;3`e*l{>G&{Mqs@uxzy2ckAi+e8j|QK* zKK8Q0R}46RejqhpG5ewuQzl?PJ>F>Maj z9`D(*eKxQ_UYELlJLaOkpIWG|Xd-34d;SG4nB`DQ{5uOI^|UhkRtB6AGx$1zD%`BbYcZ(_+c56b1kM;fNV zUd2WMWc``l44a%n$MqYiZTgG%g95DSNj9)3opW~pu2|rvOea46N?{OAUE(uG9%hal zp!PGBE3;M#Mxws4a}CM<)-XoruiXgj=khPS`;l*UrC{RQ;kdrGM#`#BcC@^KY0H=S zka&-)Nt`LnKDmQxuX$q>U99qN`~A+ens8Wb9cN#9PSq{8|r#9@Q)+RV8-9MJTiaL zCzPK~fNg8kYl!vNX?FaT!G6@ILD|`D)M{$O8otF#AIS<#@-xTYvs_5lQ}fQ}qk`4h%1Y^YtUB`c)@H7iGc! z^v|@e?)btVE~3ah7pi}yYpUGoT`nSxTD>w^T%Vb#U6-=dMI`9@?nmv<9ak0%g@b-s zZ6)U`_U7D4^B~*u_mZ}+E+X39N7Q+Xv-)_h0A?9fP?nmc+aBw1!S9bc z4JGHTd__~{iZ+zrBc|d)3zgOHT3tkOm3jrFeua$}?FB6^BHRA6RQ=+l&O$}_pM3hg zv)b;TTtqz1m=WZB*MA^)RznlYPun?~ln<&)6{NtDS|h5yup&#Fy3m5T}YhaId400q_;C=yQoWeCEz0B3i_$@bw&5k!S*`D zXI`MnCs&2m@1BuVf9n~h)!r~YLU#!1zly@e&TlU;zY}IqHhNp!mj%~nOrhLr(G(r>%thoi z#f-A&rxD_dPcdG0izsv5ophC8-WGML{*IfS6|LpyuiCFvQorr_-@d2H(7(AQY-Kp| z`|HA>$C$4p8n)!TX{2L#d(NYO&G`{!Jh>G4DTCDcwY==Bj2_&3#=Wal3Fi}97iHR8 z&6_&PIPd)UG zFmxs6WQiJgsNwa!wcb%;uME-k*&2BNbLrc4q&#iky@iLy;r!b@^fieqjOaP@OB?Gw z?GM!-0WYmDZ6fxc(0U%prw5Ika$k?d3cFS8Ox$`Rd}!Si7ONs5*nyb$^kR1pT||YlxCQ-cQt$q53bdowC_wI$nS9C*LCNac8Fc9h}8tu{vi`{j2wAuv=n;>(!e> znX%@^%VoxB&)xWcPKdp*$OPlRLyPJ!+s)&InFZS0^&*Y5FBxxO6N>7R!(LKuz49?a zYK8kl?Qso>r_SE>ak&lNukxnNAm(@4$Yd|Z_1n*+`eXWBQ%7ql>c8HUHwkf`>_$nP zE#j*DV`TmmcCM{(S&r93J{zg=X3KxgOJ9lC@B0>0{gWJ+z3D0&uisNOsOx1;d|Xzw z2CuKiC{gW6#?hv**8XeW78iYD(=Q#=_?o89I5@-^#p=~lrrKlK*OgVR$NPO%cl4(qzgO*SXO=r& zFQ%?sITdzJ^vHMM;Pv>2)s*!QxAZx~4d?7BE8L8e$=-zZZCFT^ueX#@$?(A6TZVh< zlJa8H8x2-lv7U-ODGQhb4_duZULku8X)jXFpzyp8`hV>gb$uE`roJ=v#rtuAy43iv zTaQ?z>|n9n<``4`FCQBfvfU5kC)`3^FZbaeyXiYwteOEk%K1}Y9U<;mJ&7vM)%@{b zC>PJC)bG@Mr#kliD%p?mv{rK>^GoOQBJ6^({+X(jxg9QPQ$uk5`~_5d{5>=8%sm7z z+DO$Obhx&b#>0BbDx&(&dOT8f80?iBqcZ{Z#f@4iNjQS_p|wr6y#)Kuv(aBWV)1(E z+S`(su<)$>^6D#?-^6d%p2PWlf2@rK7%!>+j4HU-tyfn!5%b}mw>=luXy2@>l7`p! zM~*OZVAIvM`gh@+CC*JZVZo2Ae3?w_zuY;uufbB4bz|<{LVeTfq=_*1hWX{(+t}}P z7i|uKnYcoZUnQgU)-Jg2J6+ex|RX+CthZkf56`x z(s^pyaQ&my*v=-z&srZ-hB;3=zwy6detO*Y4TZVCa}(^^@p^ix_f$Dj{?SgQc35&? z*&7*{_S%UV(Sh}p@{BVCE?+#kp$``Dy2kx|=z`A|{!y~~iuuZ*P49F`-bP= zx+{~28TxvbD`EP~=266vx6=LB{>S_;ssY6D8!x#0M%;Gfw9@HPT)&RTh+deJl5p+} z%pVuf{1bLoUQYi8+x}+n?17ml+siL}$9x^_PV9nt3wUF~x=?;nLYfqoT$x!hvm5JY zyj}KJSZwvK;}1-)RzA}KD{w19Z}#ANEe~3?!_p19vW7oUe}hj_D;#+yg)Q?F*YB$z z`WY6CIJh(m7VYTZG{Y>-ZAo#zu%1;Pw|{^&=1#C%-;4F)xqP<-=FYKv{~2aYadm8j z>EhAKc750{hnh;>z(JG4FWCRV_;)TZse|(`GjoFb|22Ko@OKwr@reFswTQExJ=54U zfd1}Rzx5K`oGTTuVCvSr zvM^`v(WGZ^s!``7Iap;8dj*NJmKxg1DOs|@qy|rj#evtl6-HU&?^S1FNqw|WoVKoU zmiT_=sHar?@Fw$73`>?>rZJVzJl$rf3Nu9}l-Y^Oe#FvGrvLM{N*=M%4bAbDs87Sf z!>fDhzn03g4=pg7-IiKZ3d3(MOAIEm?AVr^c1SL90F0 z7Oz5o>zCRPGr|{Hrm-zqv=i2pMNQ&IoYj`N{})rHe;ym)x5kp?*=|c&|NH6ug0+?` z)j9ok4^f_ZBVYZDlO;=VJR-gX7Re1}h+&)3Y)27XKGS1e+D1!O?@Xoj58&R;c|~tI zmMq`p4Ryq<5T386FE9^sgf;l*$CCP_k3zz)z{$75 zsCfDCP3M>KEb)EVk_!chGcV5jGT|`B|IXj8#99OIF;~0m_ljmNQ$!F#k)s zv`D_FTvyb8)RNUuZ?l}_YphzUdnyY3?VU~ahv8%R=~J{N%WbdjWfJF0^_On zmnzRzHGgWr$NF*Urp&vO{Ee7C#&hdvbE>OC1d?u+~AoFOAQ2lCsQn0<*C;Bl)W5R z^j)`PwVt!0%&3_gv-pN3OPH*$LdtU^ZfEJFW4z8M7G=Tu93OewO-q*kge_EkqG!8( zly70a%S^}JMm)~HqT4Rdk|ma}TX74Hj7uC_QHcBPx?1v0nC9iNss5fN%T#^Reqx5A z85ywi#<_O0iY;07R}_X4Gvh<%6qjQD&(DiUhfQ-T z<7>+Puc@<-hC1KlxFW`y^3;Qpl3U{;qB3DoKa;kpBwERC?NXrKD?~ilp@L*yx zBc2w3rA=SjesdrDXJ7j)9~`tOJKw6_gJ~aR8xbbIkF5ROgAvxaNMv9ui(B1Q4cNbk zIgg3IJr~UL3Y#%6!N%0y59Yc^ zMxfg^Cto0XbnfDzx7h#N7uTnQBmI5siq+WvGC>_-=hk;|X75qIiz*LdU)H*Af&Uoh z{i>iM6D+s88$Rg+?00#&ka&E3r%Q%9Jchq%jNZrmQ0gOv36EJASfb2>-g2ecrpJbd zzc)I`l(2L2(_417Jf{8Li~Up&EbUlf$796j{`pt^cjOywSi)n{*Dn^6{y3DIS$o*? z7{42_R2M(oH(<7u$FL8pok)M{qPFe773&^Er=E zCX7;h=DK`djw_F;N?LDE=jn+gm(B1P{6DX2ek7hw$!5Y{20tG9z8dxEEvCHv&Gi{t zRq$_HN;zg$qQBQI%%^;H@n^7CTudvwUWxbEeY8Li9ALK4skj36cPgp9TgNXV@A6M9 zNi2weTn7KSDr*THoPSkV`;jrbe?Jg-{wCce+YL>5=U7rRawr>+TeA`uyK! z`Vy}7G+==}oo?FKT*vp`wErWC&kp8#9t5)n8~?hGATf>&&MN4my39bt{-y-`kZie? z*!$T%kCa|R{E{K6_x;hDvIs0YQbg;cb}veq4`Nq8;l*j zf_`)vP`!Qsk?_>ZsApg~Jue&4?ml1iNtbNN+PSn4^YeO(Ch=EOZ290!9_nLSL+w?8 zS4wv0B7frms*8U%atyzOe0E0DdIveiKJNJ$@xw0D_+|c*Lk-!;FZz$`y71?-`dv8} zEcAUyx#;)$Zx|zGxTF`Ue~mwg>-&{DgBS zNPZ2B@ke8ChBZU1ix;`RSjsROP$v@-oe681@@ zr1p+04QuBl!hiY^+JA}di@3&fVD;TS#9pNwzLExZcp1HsutBI%z9Tp$L_VEx@S|Cy zVW;sP+YC}x>Mv*}%#`<2j?WYsIh?{gt*)p3oWIUc*n?#n4=M9ca2hR6V*d7CqVZ*y z1AX%2Fh8AbR3HD-=5W}*@Sfdwh|os7J+BN4Vq?+I@7_}XmHpb!lZEKVkGJXlj*I^G zOaxX59@F!l&n>cl0AC&ARFm^XWt(cl!TcFq>d$0FZ;B?E$-HSs_Ms+p!86ehxbGOJ zNfTVQB4i4eqKHqWKVdNjRi*ityeO>84aDVQpw#m?$rtGx>u+O$Llw~_C z_4L4X`X?z%XZITGf(>F5D6?mNm-Z?K`JB3AL(a3;aN@oNv)oTTomd4<4olJkGZ$In zkKmqZ_a=%C;(jkGq~l;wlk?fc2;8sjqiT$B`qUW9FwAq~<d(nKUz=|0^lw@TyQ9w++}AJ)u9*_l!@RtzL)oU+%>}$K^3| zUp8B5frG5QXnl{bN*J8PNB=e~-%RSC&Nm;b_yYc>8m}S!D*g4@3&Q+1Tgp~kt)#!N z!TmSv%^>~TUOi>7*z=P|Mpd{y6*S;ue~nU~dy>B! z_ie|pJKlTtxpgF-azOhsVKuLqvLa^hOdB`M-_?1k#Gk>aXr{^q_dhejd;quCHQ&DB zg!kfUNFSNshNA&hri9-W(DUJsYCQ+Im_NgE1+nib8JU;~X4RHbwpyvQD09TmPa3mm zzN!SndApaRUv1qIVsEm}=70y7KR!m|NvhgiG{8cwWi%g#+iu8T2EKog*2|4Or@w0{ z-rLzpwB9-U(pT&Q|1Td=@sjxS4%pW;E${d6?@;->e7-&AEvadQu%gg(>%k?TxZ&FJ zUxV!6ue*)vj$`g(!U1J1lx0TQZLy0{@46Y(UruMcEE!j-esfEFC&#=svGc2tV*c`p7aK2PX&Y N+EGM}7`Qop000VB5`h2! diff --git a/examples/shapes/US/Shape.cpg b/examples/shapes/US/Shape.cpg deleted file mode 100644 index 3ad133c..0000000 --- a/examples/shapes/US/Shape.cpg +++ /dev/null @@ -1 +0,0 @@ -UTF-8 \ No newline at end of file diff --git a/examples/shapes/US/Shape.dbf b/examples/shapes/US/Shape.dbf deleted file mode 100644 index e26408546ce0a1456d392706969715e1cab3150c..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 830 zcmcJLO-sW-5QZD=Nl@>e<|piY?Z zAL^!#d3H|AZ@PY{aTBZG3spXR7)aN|LwqUsZT~O72Q*}IUTeha^3j}6Z=VfJ7wZ88 z1FQ3_Dw=*$LmJo^WCt|p5N+R&*VCjj07(afa0rf^kTL*zA;)B$A^{IN%=P7~IZwBk xc5>1olHeU$>)D2+!(zNxG%5~c&=EOIy_zJHbVS*bK_||70${Z29R(5%Pd{jzYLfr} diff --git a/examples/shapes/US/Shape.prj b/examples/shapes/US/Shape.prj deleted file mode 100644 index 747df58..0000000 --- a/examples/shapes/US/Shape.prj +++ /dev/null @@ -1 +0,0 @@ -GEOGCS["GCS_North_American_1983",DATUM["D_North_American_1983",SPHEROID["GRS_1980",6378137,298.257222101]],PRIMEM["Greenwich",0],UNIT["Degree",0.017453292519943295]] \ No newline at end of file diff --git a/examples/shapes/US/Shape.shp b/examples/shapes/US/Shape.shp deleted file mode 100644 index 548ba7148df448d3e021dc35dd2a744f63072e83..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 112648 zcmZs@2{e^o*fy?=2_YIJCDBMJNru|xOjL5tagL!$(xgO`u_UFGN@${pCPhL>hO`rv zR1_5|V-g~oRq|br-uL^j|8ITPI%{>c_p_fp-1oij@sW_2Broy*{CgwyK~h3O8f}jQ z439^zTTdq#E>x^eWX`>R$JVx^ z{>$64YkVw8)Q5FyWd*i$-jGW(_T_Pj4Em_zz04o;Z)NM@#zHN zd?`^6n~W}&_4zZGPp`-9%zW~NP2x%Q6tOX%y1pNL>cK}g5lTL3ePhC>H&yc%H@#<* zYGut++syg&>7A9|JKE9ypYf6&KbdXC z6EiNpNo0|EY_3;tFrN;sxEZ@6o<%Nr4bL{(kN3r!Pp<+#J8N!z*bzRh$xsP{|Q^2ag9YZMk?)dy@vG$UHa0P!XnDP z-=@yW;8VA^GTHT)S>&`qaeG7-pYk>ya z+w*vVX(ONNJE}M8MPvUy@C^F#{Ly23%=4pI=mJwx*g3tK7CT66q`iM(b@ZE zh#htKyK{NE5}O=T&A*u=XGc4+9frf%#9e4^kSuLS*9>eJn>-)!>gSR{c63sn!Jh^- zHnHm;AEo_UNN3L(Qg=*)O-!E*?o|3Nq-LVR4HLE4r1{|Zr}w&r^x1;DC5I-kNlp6Y z8seD27vWO{cBA3iblX)wocNV>WlwiarZGnn+w^a+NyXS;o*=B6Qom+H%!gC=N zt+(#*SjZ-^6KLDY$3ogqa^36MY$7NRv8{V3q&4kc)`Z6;0~Hf%y~Jin+`EToUy$8J3!U=vkKKDO=Y@C|ITZ{3y; z>vM$Et+vFdaub`}|L#-~cuPndPeU?z;Q9F$H`nC}sYd9;%z$ldvh;`P)fvFo8YleN z?u-4nfA`Ybd?9`KOw+N~pG{V|4h}kUTS&*=zj)_V0GpWIS0Bl}E2KZ~=uEZS4f)pn z)!LB?X~LB)4#ESFj{Cc`ia}{Nk0y< ziQht&TP@bVv9x(pZv>mjuKN zMRQJ-#R=&a6PsXv;QI&DeAG?|X;5*R!#&`F2|w$t4+^RJ)tNVy)7iv-^`gR@-PoVK zVLOYiv&r%-3HKD>_J@&ADR9>PgyD;}3+ak6Q}6etu*rff?~{4nLK-{9&a?}-C+6e1 zBu^o&e(`Qf3h?jai=LYnGk#hrH%`{nSU$8s(3(zSv5@ocg-ph$I?vyg5(rra_# zflVGNx|*vv2 zA$?yqG(7_2-@X6s)2{_MPrY81JLlPC_x`z?yUlTa7l#kI3_LRDnMBIM?jH6}^lE|i90MVpSJYKG2lJPYUv#}{nvnJ`4*xYVl}&C~x5ccQ zETp@hc-4H!!ud(bZVjF!q-uMlY#(N`iTY*SXxc*B{d?{Vn*ugDapuaUTn!;T;}ziOZLF+kwU6>V)Y&FJ*;2;rO6p!hV5*CTk8#e?@$v`{|8e{qwa$K1iM;C4inNb zXU1MFM0@oOgK`S|pO zyiNZE{`7uf&ea5eZInzO1btw{n_+&=SkFm^(MNWEXOqcQmD~muA)T~z?|J_}X#Zx_ zl|EcZFHNiFm`id<^XBsDLq-W{iD2UDAyORDmtY{6jPWVd1Dhe7fBOU6rIVxgR)Ir4 z&8^*bZv^Q5$G8|J6%P60Ik<9@x{%)Ge{R=Q=MeR6rln%ccm0NEX6hOoGG$`S3p1>* z*Lu9J&lnDwe9M8qS6)a5?Y+1xMT!DhsEqp|ptehX*u9<2AyH{k0+(+B zs@L#}r)tb0o5t)uf8&dQx~3*ptup73%qK_MhV%$%_tMG@vWqz6*>MosI{|gHJ{fIm z#~~3TpEqj17SPdygV&ZjbBNZmvSBM;2&nUz9@n`WIOOcb-nC8@0{VKUe8>ZL4talV z**v{c0UcSnz$xQp^1h!D&^?k) zc_F|SSrO`gjtZ#Ly31LA@%)0yG>^M`1azN*T*_6n2M@imuGd#UU4mJ|CkJ!LhN;Cm zJsSmd*>atbZNLlTR~CK~321m>h|yZi@2Atw3Y#SY%AK&#;}URi-9)(tYXNQZlVGI* zySALVJIYc(H%!`l;T`aWfW|_91A3ANjQO{$70_;JnX{@v^Oansxn|R|4=| zjSZD54Sc$$XruTh@NaQPaZ5d)&gpcCZ42X&=VQPLZ}^m9_b*2{M2B~&`dKZX>KI5I zd42*o^hWo$8b1AcV14|G7!FDPY@wrq{=1&Oy?!d5Lllj7XsN$~zB*a{>%sHbub58_ zlb-YGj;K_Fq|2bEc@yd{mGkM?ci)@^XrHm!WlvfepH4kw@Aw+e&klXM`Qt-AjnUh( zVDvc-*?jYJRR4WG9X%$mX52*%S@p7b@ahsiZM~N%H9Cny>_#n@^D5%gC~ucxoxn|A z1%sbaK9$`WG)ns#hrGM(wyvmvPp9V|D)C6>keI3p8RtAcoiSqn!?sHtav- zZD}T-y2PKzYQp<%76uu6K_9MhT0Jo<1@b`8BRB@_L-uFqcGJym%lH?YgbSTD7uV zlAxCQW+?2#zG3O&P6aOc{4`NP6v3z5quXUTs$Al7exZDP6zD^L!lBZUT+)rVIO6>k zx|#vupfP6S*YLS|)fZ?7jVCx_j5?amk}C$E-NOS^|Y*8>ixZ zj2?H0Ps3-jbBzqR}p)lwem-NMTJ{z+F^!@F*+Mon3QMA=?Q?r46_CNOe8qX(V`>go1cY68W z{9Nou;F-T4IedEV(WYgkIb8B|i~Wbai@=|$kbF0}Bz1;rt(PU}se6;ns&p>V6G~r* zLi?9-&plS3^yv&wpP)owD_?|NE*sKLVc-ol8m8VXk z{k+5_R{J>V+s5)~{XN|WacD0w**#o-6rXN>aec@pv^&;`hMflfBTQ!1T;-DDi7OuJ zjO5dcmp=RKOXZR}$NbJkD%dZWQ(Rzh;U!f?KAl!j#@?C2B`Q}pjjSBPr$sfX>tH1{Xu^7~^k3`k`D07t(;N-P#BfR3zSZrm zKW*ulS9dyQM{tQm@~v&*pKU4mdU1(c7?=3gYN#9rKEPKtFI>kZBZEBW?*JZ<>r1(W z5wA+X)iCpmbhzZ$g?TqV_t?^NW_SN~NO4Jd&EQ2Hz>FHG{*y!U75dv`x^3ySuszpz zeS>^UsM+E1#gK}3H|0xXTT<4-kgs=ih-3E^jr9K+R|GeT(5ouUUz7ooN0$G z{ic*-c@OgBu5nZ8)(^JS&LqU{P&tPjr|&h*n`|j_jT=M$+#Wq=weDM6dQ$4j)pbue zWLAl$)Y#XsN|MZ$nU``%_2)<5Zr0k;Ncpux7g7#+82IARzB*f)qP;ER(j5+2J)zFD zADC*}U9Exq^VgL5Vvpx>%c#Ccn(G9W=q3*V(oXF=8$sNesjeqwsdroc5?R#4v}d2RL6g0OD#3jO=^yFh|z{! zj|?B!Qv0&F80VwV-^|oK%S&wOtosLQ%5Xo2`q*R=;L|m&hL84g$k1ono)e2~sUl-= z`f-RXW55>L(q&^)j>kh^8yhkD%Hcb<)HLJ3_lzyjAOFW6w@>-ljKqx`a`J}4wrh87 z>E{m|2Rj!IVZ>-=u`PWs+YGxWOwlvD|_PtJH4uO?^H39UrF+;7?em?F;+fBuf+id9o zUs~cmnnCBAf*#$Cf^!G~y{z0f!E9izafh9`oGYG!aq!SOs-&7pkid?d3?4$lj!Vgh++ z*PzSCFPsE^npychO^HvJFZWwDbpnSR)|Z+$8U4$@Bpqqjgngk`Hzw!Pmr7HXT zeAsuhH%}SgiTlHey?f((^f!h(_YM5VIWjk`)0MIR$G?=+!9QFzWaES_Y8;YezBcJ4 z+LwzL)&Io$K32;WcbM~O$)K;l|H6K|>Z)MnxDfiHL}+W!SPprjeKnN1zb*&N>i+;d zAUAQp_4b?^@l74_^z{0?S1jnW&4icC0*yl#KQGyd3Q}Zcfn} zDGmLym$SVb?Pabe@#R=AxL&3U?Kb84840joOCx@&Z{|UNgE`m@JZzOgZ*4=Z+2)IpZ4sOsX7DvrFn*aJ0Ihp^B8)v8~&i+GbQ16 ze7Zsto&ea_fq8jrGsJw_Vf0+!kN$;=j`TP>@Tu)&Nu$!A@Sg>2O`PV)r}yH0es2B+ ze}E|N#o*=8M_+ht(1X2gSKrGX0)2rwp3Pt3kBV8;I>#CINWFWVNeBEBu18x|uH{qH zvgwO5fKM4~o|IX~r;<9*^xN?MO@}0sHu33?K|FWHUq5L25sR=b(4VHNXt%Vm$@ff$ zL+{-AbTQ0nci?4DT4XFe`Sk2kuEgAW__KKGB`Mx~y0&9Oi5+5hOyN&6jqI&;#- z=zBH)A=f&6?pc z0icIfxpl_P@Fz~}%J>?@r;{huigZtMBy z@J~9_(|6DZpZ@MQS@W1pr20a&41qry*b1BCHH*O5l$$TV}R?8h1y!Dpf9EQ?k)f}_Bq&|dYVtuA}7k(7qUr^2`}t3 zo`Mz50i)babo^m*`4#w9FOI+x6+-J2&4DS|(9uX>Wx&{Wu47XA)b;1AutYn+m5 z8voyYw1@rA-cLvSsV(N!JK03rXyve*;4iN4>sgfzdrB@PV_&w2O&GCx zE`?8xT-Kj^UeshOUTPD4jKXwZESx4IXD6nfuTCKuloX^Z@YHfIbPP?g{a02}ICwiu56+r&K zcr|O{L^e^>_%-Pk<~wbR%&qa0*hJU2nzbJ59Xw27{7yYKNjJW4_y=K3``FeAXf0t2VlfxfxCi(%7A3DNhx-y%b0UN!6{XH#|7;KZrc$cwLI8RrZ zsm=A$@PA*L>b?#A_rEtik|hEE^yz{@>p{;;*B7LI{ly}CCX6ur2znTZ2SMmcaM~7JO_Ei_&1wbWS3<081`8{y>xcQ_f-unl9?s+&V_uq z|4T-1ZXJs>?0se69?7S*hbxsuZ&*ZAr$(m=@=3s1A)!&vB6H_d?0gD)^+eb8OzlP% zapJUFoPa&bsM!zSu}Hv(eV^w;e&(s!pV)=>i>zG7xdJ!Dw=Nrl`5JLLuDC&d%H+ZV z?qU7Yzkz=mzQ3-E^~>%_Joz42<5}_RIP8x(qi}|z{~6PWLxb3)!G*1O`5@%~(9W~# zB_U5O%Fh`?z8(&i=N=mjc@Su|HVg7|zj)Gy$#RIdFbWjfM~#0bbqMDZ7xHJzyD<1S zcpIGG4TZd+TEnO8<{{U{8;lkd)Ti&myBH2s8_VApee^R3Cx!c+d@p z1TZ6}HhpIi#Vd0!ybr{F*}c^{gY`0Qj*Y+~!K8$P)hzPn<%sxG!08WI)|urja{lQX z%c5QUfAO;WEHb?3Wp57lr(LJ}z_}9EzgRr5{c+V05&EC#w5;<29*MBrcP9R~*gpLT z=6m~(P4sBsm2Weu^>E*$4cT;LVI_+UvbH?32@+Q7kx*@~UjgxaI@}o%!v`#KJiol>8RYw% z>@BG?FyG9fGOyl%zD+i;HhC1W$i8-i+Gn8W-l_GeNq1RfOU0?Wzu+H}FDdf~;+f~k zxrfoU}8 zqG#t}KWq7CFF^jikS=(fj(BL^?|T-07=I1#T4@;e>$tVwqBO|M0eu#3>_c4n&99us<7Wc#ArfXrbk65HP@p)Jb=D#=Lf=D0x)#|s}rWx41L$*p4 zICCB+!T|DVUFnhP=28|(v&fuCfFI4Cao{cR!kkk^9IRJjn|;~@%-`VXqoq}lkI+7P zt+0P9zi#PSau@t&(qF`(EW(6u*+saY)UT8@VEy3?LzjSml|Bc|M1PB0W}9;!V7+_NKZ{L8g}@*0JY9MQ`1sq$AGpsUf7(8}dSblKZ5jHKRnXV3xgUCf^W5YK zOXDSQtASTAj+@u;=bhPFqYVtN z+2e_Cp$`mNGTH>^A6%tp(^Uuhi=BA#cp8iNe!a6IyqQlo9X#>uA+Yk}XU#uap|2|b z{4*2z5i*Ry^^s4F-|r~aM!tgUkMOcS0C3RTgRPRk$+u z2kfuMi_DUeknh0wcYp`kd}``L{sM25%=Fx!=r0o=I1J-`X@bJt$EOP3A;$AC-Xfp6 z`|IJKzoh(7_bB=g=p!mh-{I883{Z2tZCFa2ck9PON zaowuJ1oWUTI1=roE-6wMP(@bwv17ogg?UqL5g+(DyTEZT zaBWJ&fx=M&O4J^o4oQSOihF+d*JuHKbUr=&JlZ#v%vGDOC7`G7&nSpG5B|?jTV{-S z!=_te+g-?a2v+*iB8_;&KpYBq%8pVKj-G(35yKm<=sz%zDTog-_9gI9owQF4z!#77 z%1Z!;Fpl%-0-6`%UL1XnMY3nZgECV<*_Ab=4e=}jYhTZHmVjC*AtHl2@#HWVun*Rv-B;$;f3x+Kg zP?n8${{^&PRIj)FiFlA_#j0KAcwW+Hj{hTD0fqN}jC=%(_$>IE&f*Jbs&5AC(@_>_ zIT#!vEfmo9=Gyebz#97_!lod;1h2278t}O>u{SF11vF84&;i>cEOK2b_*RNoK#yum zn0!Bk`;m87_|O6LF_c``egN;&cBE3v1#}{-H)OR z)*v1TGwjuS zR>~Hk+K&NOFv`%-i$%#%OWp! zT)8~aQ9z3Wm8Nq8kWchDN@5Q7w>qy%t`E;YD|=BkavA9L#E?x&n19mPzp>YB1a#c@ zMT1^@Lw|v}bkGX#yJD#Q9{rawfmdLWYK^QT#yg$Z!&2trJnI^LHtu8*N7aPOCl?`} zdf-CJ_COYiWXJl+&K1zD2lkg-0q%;vAi>uc&{@}SnM~cqBIR+;{W}o<8;DEqM*h-~ zfH2N@0fo2WNq8uWM9f}V9i$0*tXf$k9gcjo6tBe3;OBw7Hqb+U_uem8!S4(A_+>N$^tsp zv3K1}(Az*93D3V6hYX)E&`VWmLx) z$S-o|Q+)xRAIMjc7SO4ULvH^)jQc)ER(rgRfSM)8j&Dc*M;^~g1(E`KGOeup9O$bv zMt6`KCZYzlX9GXGD;9)13$BbZtd{J7Lrg z$R9)%KfGMSBI}nd)t=fwAr8CcNJ`Mi5y75|H9B`rkqjy!?1ynlY2kVX%#`|^Z;;fwlIuI9T!#+5k z6DtS;eMih6@rVog&cq*gqyL2{(?RGzKDzyzI0W(J)g3?AV!ZG(9b>ivk9*RjO<{F4)`hc<-xMUn1A+fQSd_OpKv>-9)rA^dinV> z!Xj6~)gBcAGvT{HGo1Iif-Gmm-%~E$DRG|%{lRF`j9Et@Z-%i(Y%+m-9R0Ua@sNNf zKT8>8jrM{2eLwCG{m7wD(LN?vF-#frF_e;c5I=`SUT?WpY`#Z85gpCto3n_U3V%r* z*0*wh|8_|W7WtkLQokSTFB%@uXJrKWe%pA=G~g-rey75xvq;AN@Hdsfix>y74)np) z&|KwU$ajzQh>|g|Px`EOc47Qig~6+C4a5Fwym@Yp{YgJ|AW&No_iG~*mwk|DOg?I# zCCN%%AJ+rSq)D9lYDuJL{E~kjDxl1^tb1=s3QGo0cE)-X+hWu1HCmE!wGZTjfVCqU zIFA}EN$x1g4ioIByz1+>k`Rn61VgePw1944iNrv?dTOJTC zpze&pUtmer^qsr6^OS&A#`hZFR*B)4bR zCQk$&zjBMylweE3glAvmg8!NP#BfWpmW3Pa7VZnALAgZXea!ix{la6J7}t}QB=18~ zTO{&LzIu(6i3jd{@xZeF4(OY)V@_L=zt84w(zqv}vFUxThq2z_(=J4omk6lI5BrGU zv6h7YrbqA2L)@Q^GkdsL|M}y}*ODIzDD9ZNVb@tpk|Gs;FXjpEU$_Yq6D`Sr-?v;q zpP0cgy=X}k>+D+=JqJA@oVVeUB{}}>%)z!Q=sWL|{f{PFl36?KeeS*#&>t<2eL}BU zl2z6#XC18((B;Q##z&`Hk{vUe8Z&AIRF*8Me2`)JPv3hZpp3gE2a&aPSdtUBj&3Ufwx58Tx7d;xw>HF7 zG(evT-_rWK)RL50M+++OJejj9&+4%yG4wnA{Yf+KGuz*(aiGtOSK}WqKs&hn)vM>0 z#JlFh*>iY)ARpwFCCRT?krY`cpp2V2^tC1NVjRVopJb=f6qzPV()Y#4`54wiLXk-V z{&@0!+o;FreBrz! zWb(_b64DIa5+4RzFbNRA$XXe|$j0z7+$N-2%toO7IwSb^2`R(o{=g$EXB-sm7gCqv z8zl@*&W5K2`J2~9K~n|(7195@8~B!zX?|BKZwc@MMsOkDxVw(Ob`h{b`XbqcU&znf9~HsuC!+ASZ=~#~ z=FT;BkpT^s^Pvp>GX-o-+u2i<45T<7BLkqd&uC65mP)T&4#|l zii&3N%}g6}4cu1)el_5MxBwsbO}JUjBH&+B_s0I*!Xm4upAKj0Cu%c~R#-s4`ER~C zN+bqLNKE!&YAs$DeGz0xI#JKSkYbvrFD+i2lXA-y;TYpea{6LPXX4qqaY3LXKSZ~av% z0ow@ktC)-(cNWos-0a7QsWVQpwIaIs+F{Q8Qp6-pRQhT+i0EYYv>$`ZIHYm4)R7px ze`93l&IL6b^56Mvow4cg%PmOVvlnOH!^#_V7G`aFga%` zMh?O`h8cH>s56FTMIfe(IC*H0h|Y6C^d30_x{^eFZm5VZ{4&osKZrwaOhy*rK@s(; z6?)kObI5<+hpHd_vV76MoUv@gY~k|XnJuDc8RHx|6uR@A?V~eA^l|paLA6=P0XXq# z^uZhvU1iW@W|+z$g||0XoXQo^1B|op5^_EhMmf&9EuuU69m15*{@?ML5XMv+G4;9I z5Nr08OD+|N7x^w0(;I7bl+%82$>UguhG`4MbY}jfVMF`5q^+XOf3CThsw7u^+%uR* z26AuaiD{E3ocK~avh#+^EmI>g4SxCO=VDnNc}gFa#Ttm|iQv=whAQ&NfNy@Xm|D45 z<^ERXktK67PY%`*)3odIRW2iVWV~}~?VK@U8p1gFHF#vrVV;^3unFTFROgX}u^Agp z(ZAyXlDsiqRJz9`6|0|o{i_IX&2KLG zwieYjnD5Qq>n>S+;r{cnEE3Zz_ZQDS*vTbfjB#d-@tGKH8<(K+VrTmjF^v|Q-K+Y@ zCCIEX71@eu>#zBm6T7+Ozx(&<;%n914Dr7l+`Y&FvidY;b()yEYi5i%w~tHgE}!0; za8*o;Tak>spGyvvj&Xi}K}->`c~yXRO($2g`txF1L8hOX7s4f*OxA5JOu%}WB+Ec9 z@nVwS62&xAdf&@?zFcyAKdKw>d~Ty}rTz}&xYggjaQ}*!K3p(6Y}j@#@pl|PbZ3f~ z_I|&Za00mUD3fmh4T`_vD4ux3QQ%wJ zfX{_Uzi$A3)6g)b@&@RK$w>gN-VJT(3753{uGxGOc=P1!kEXYR=I$4vWrtKe9t zKbXJ2Z?W@o01%tkwW}F(pttMwZ89ZW(PW(r-moz?3d#}YK$`{WB`~@CiZF;84 zkoPag7WWq8s$8&uMZ4`%Tv@pTx^sj=)%Ld>dOW&wm zYILAuuIY~Z0365Kw_By!fu=}~?_%b=T*>OepF0lJOh^7mKk&QYl}qDu9B7Sha*j+e zkBqUnKDlBi+IMR=Dg&4Q5H?@h=0K;%4J%g#KAd0}eZ|J%pHC3@q#vm{ZstILEqdtB zjOXOC&gk>#{|^tuivQaG-*(S3uHg9#dy3P&`4aszUp-Osud=6g%I3Dr{;)%?u76o- zPnqlb4RGq>BNgSqaj&X%lTYD1>k}te0RI}YA?IWgj~EZy-@>&2uO2dam-f4Yd-fEW zNY{)~c|@NHe%-OBOz7JrjYn2I>UH^4U{BrNoT;A)d}7h~Qzp0U>BI59DwmUa(%a|G{SPIbCH{V+XKuH-O} z+{{@XB?-(-gV_&MSpFV--JV{%6r9CiJ7(f3_S7h-@x;V!Sns)rSxvwt_1~oqZsrjU zra1DNJsr0%c;}FHJhFkQjY_ho$ed)mtO7j^MOHw9JvF_~@ww#4BhQZYOg?eep7!4e z{G9Fp`Xis+!p_*!75d2rE@)rz;8s)9X?q%UKU7kCIggBf!22#2Wlv8qNu^FaGS_h8 zq~XV~Kl+2$?y~0*({xR3Nnke{REOE|$gDoqo|mEa)PLT`!nFe4zntbkd+K66@mZNI zkNjP$Z7StsPf>{=c?`JhESz56_H^%0dbFPh%-pBj?P>TSlQ>sP9$BBiXx#M8_OxrT zuJnjSxKGtup8s5HPe1m?w%kDb=d_j+9V_kWnx*Ub)ojq~v-c)Bz#FBIwYr!`vfW2X z-?g`=oAv6qcrV5NpH+KrA-1QxL&DYP;QgyB77gYswWp1i2Tz(~zCWVwZyal7Pj%<} zhV(7t5xrr?7FXH!bnMY$FEjKX_Gww^G9jL9D$0H^$OWcjd+SBAuf6hzI;gL|L=uFF=4xE!d zkIZ0--8Ag!qY+m5&vbF$Pxup_Fw&j|>VLRxsL3N?3Mqa%YW7qf>`)5%$EcYfRqbi3 zF{~RM9+`MEU-koV&LrLz-zlKC7{yto%Jww-{F7~4G5+U{1J%y**iWRk?Ka?%tnJdT z!zAq~G9EL2q5a%dP=*BfEpyzI33z@W*Y>X%HIUvtIpd(8?81rnis^ta7w;b@&d!ke zBBr-b?s_YRK4ZreJ@km_?1KS!lSV^+^?q@`+bO2|Dnmx@0-l)@n7izwm=44yhw+Gu zYpG^gvzW>-#m9;`k1b^1ttK%=#=y>JGCZ>1j$iQTt(Y#=U)~We2YR~T9dzi0m=5?p zfRC%+doid&Oqmp^d-6PD(_wt2uT)H#(9CZYoZqWG-kc&a-RR3RB%^rb!Yc9op zWt&}r2ly@z>tPfltS9%tzE6GG(C3Dp*sYKC-eiggGR0JvQIMd&shNkaxC(tDr+n({ zOPIfn+}Q0ypl?Jm$$1Jq@@S6qEVGMZii$4D8)$z$t6{osyqNyp_PZq->#MF*+P(C+ zn8IT>!V35--R_hVhVh*~`#C9LK3s3tmOWxRP#ZiH^z!{8bTjSxvvuWp&i}i&{XZML|!m@W(&s(_!R-5VI z!(uw%+iFG*{h(TLCe}ZlDRODxk_mGWU5ypfdzI7HjjrR8$!jimu7-XntNS>suZBw+ z}nIGL($t8>nV>I+l&ipjL_Hr&^?H3h9=7_0jRM~Fc11^C_{%ycrF^&I_ z*S$9%H3L8Q7i|DN^e*{cUWuB4v`p>($Do%73U>GJT;P(`zIk7YtHqR2uX|6Sh5@05 z`JlJ;p%dPgM{-G%Nw-_>J28c}us$T5OSUt~#%*Gn&5u)Zh5mcy&xp9`pTv|Y4Su={ zIr`0}RTZDb)M+huNU9%~Ak)C>0{CP0EMx;ipMsEkCix5d9pWji=EWsUiT!Kv+xm6C zkL9>=iT$e23wpti1HOv&SkJBxA-~Xmk&i4|Cob9H(0DSU5BzX$tmjI5=ue(IB#M8E zsrR&fFC0*l!I*D>U&Zu?MSGe(@Kh!l2J>C5J1&3dDlU0|)aScc&p-^%nM*<^4mPj? z|26vBeEAA&GQ(zPc7vFHNLXyb;PYz?+ns8~lqsE3MU8+AQ(s3R zy)T4UY!hlM82t$Q3+qR7+-5HM_5Ag@@F!xLlyQ5+*R5QF3WVO=M>t=mhH49!?Auw9 z>G)7g8CTm14=(wzuVUe;5;1+Z)^|^oC-N|Cwxm*caus?jvKt&!_K>ikyt| zc|W0WcqZrxl_2dtIFEtYKHBFn$pBc-o|D&|$AG??-rLQRaKZln?Yy!2mY9yqozr%c z*-z`onNb*joyU~ca7WbSAmuO;@}xi2^#R9@OO7(hg_zIN-YG{VmvM>QnD|vwZi}h< zyA3+87K4AIoW2>Oz4GV8@>#%&Q0sQ(g8qKITa#$bB^e{32|^y_GD)5`T(WuE_fv0g zK%V%hs{F-xk}_83#OY$nq?+amVBbxNWX-^Oo+1_WEY9Dz>a?gd!vM%Pld4Ze*gyUBQLj(?p?$>0Z@<=XNpPog@%$Z_Z-V=r4$#*? zE#6i!)jv6**Bbo6r2cDc5>tVE-tCJxA8<>)udA4%Vsv=`+Rri$4ahq)+a&Y6&0Ml| zwt7>flbA9o3$ma$b0*mX{s0?i)700nAFtz>##@Q$M|D*H27%sw828n{AMj$i}u~4*t7mlK@C%ZEt4m&S{KSCGhKJ2w3>)% zY0H9(^@mZzv9@A#6qhicn0PQB@U*>!Gf3hC>6lTDlX7p@4 ze}pL-xxppS_SRbd5zzvfU2UCchgV>*Gw?u6HyQTf{oB2YgFr9V?NUZbpr3UIpU#pI z({0TozIZ2cNlg@@M+#zk#DDCzhuEK~>s4n|0hc&Z!LbcduQA;vF=##vs34q z8IT{mr0Y9RYQg_;kW4`TdIBjI0=d0ukagG*A(M!dd=_bL9lvcC`g2V#yx#1t89 zsris6L4C?&rb~(GhRI!i(eO_oHn^>3FwRf>rGG21lA7P7bG;%O3bE!1dXo^GG0E)_ z(Go_HgnVP&xf%2e`;E+o*@d82O=)~J1^YWtyYGtgDLlVC?VX5bE=N}X3dkd=k_f{F z5tUEeYtRUOX0Ju{ZncOGbBQ5Ad&M`!_gKN;2k*F;oeE~*nD9}XPa_E+x;&ij7v8Cw>YJhVX8 z-~|y)3!vt%^SR`%rbUoPoQOu(pO3!8K@A=gGdL`wmRE0w-4t@k^TWvQfq(K=^2huM zVlJ5xD*NO*{E-z3O_FB78+vEvihM=%;bR}aM%;fftlJHhJ4AHm^xcK}kaxpf46fF$ z5>d}+{<=|UAIK%M6;USDcB?a&(M4 zhd&P`7`{L4=V!(TRRm+!R2zwGFXvQI`asM&2hIkst>H2m3Gvsx_w zaLC-idpAW`-<9*9Horsr%)N#a_{uncCP(xa=byeaN<`C{13*pi;jia??r4eV+Ss;L zllo8t*g5$rf1-#&+AOib`-jRdx)D5CL}#4uJPqF$$zHO*xz_;n&giXQIi&K9{F*uX zqJK5YA35YwymVs0ED>Eg!|-Gq;t6LNN1&;Q>ZUz49FO;LnU7Ww5uI>vYTZK#E@9kh zc~+p0p%W(u$#4m`G{swiFQT^_=X-6IgM49f{}4Y&aQo_pG-cAUt==upMMR|yE3#(* z<4a49A&5sD)w+|@hWV>?*!9LEUNC&O+K%^FZ-{Ulug+UU6|^qz@<#hqwVIAi;J57G zyusF3kL~@RGXn$QkALB)pN{>9wOM#}pNJ-BIypyR{~lV_zkL)bqRqdX1Shcne|~8j zJDddnR^X#Oh%YE%xzm_8&ES@Ecxj2c@Z`HGsp1) zYP<((x06J4;GQf;4SN6l=AtXWHQImIlptO*$l&40R1xji0*~-h#BRT1qso$G!d@tlEJdxnVa%g^+Ajd)HH^KmA`A0))6!hXXc?&4k7Z{8Ns zF?(Uzp{Df9u_@z1ibXV5hW$kX?LB+O_De&a+HTYI%dX;(7dIa5GpPXo_Y9rsb|3rG z5Vtenxrl1q)<`g-sF6g8*7lb;KcwEzL_CA}L{7! zf0{#LcijI`g8O!$w*D|`TAA9|4>;eEtFs;;Uejl=<%t-0;9DHK@J)(917Y+>$gfug z$(cJjM8)x%N_jW#o32P7vq04JQ%Om=uOgZspgwwR0Pw2~Pk((A(QRJv==$Ir3zd7W zB>{&i+dr-I;QY&x{w|{XO|N;DxpRoP`?Yd2?yt(*Htl5_QIon=y_n!WAJAi*@r?&> zc%=S9-ZA&-3h>t;?fKRHB08>HC*!u5Lk9c^5@M<~+jexn9cun|@BQA6cI<{;-%`*+ zA}nm&mrs+9ima_TWS`5ljZWy#xM8K)I3JYaZovDPPv1O7jip*f*qQ^F&&m1Q7w$s; z`O9Tx1AmC9b~Ar!BgR8zp2=CTCq zB6Ovi#d@5_Z8a?;oR`1kMw4xW$t&rRa~&%Ne&s%n;wYh+wmwyipBcirucRr^6k%N|CR*A14mAt zl73G_MSJ0iLHul>cJQu4JAntjp%%#@Z=A2(lLx=p%e;`8g!u-pt4r>9D5Akb-sGLZ zdZI^3iIpCUXizw!+y3CEDH8W*KM~PsHrXTAV?Ur(`6oZa{lt9p$qn-l4w&s$1^LJn z>AG-;uk1}lIp{kIFRToFabA*gsM^H-4agl^e6yzUhWr`q@9G7`GD8|YOIs9-{8PU*IK;s_*!d{>-@j9S%To)81kB`z4#q8si!j z`B_9;x~(RuK19u8?2lk-G4_~rzF9x2r*KP0cJNRZ)EFyv6|L-Tg3ahi( zkZu{-fUb)fBP$q=^FQ*FcORJDAtM+4)VCExy4E9lL zz2eR$=RB-kzd>FJYV1RW=CTT`V zp(4!#vW?5$NP{7Jn;MiP8dQozNdu)sl!}s+qzQ#IXjmvI4GNVsD;14Irt;rwJKz6* z*Sh|5UCuf8^}hR^*YrI1eLs3}+usd`22MJ!#Qt}ODH#8OrcKHf0+!B$p*b@d{qILO z_J6k!0j}}TWTyRou>#l2q!Mw^fVGER?(GD8>gfA5YQVoYkGpGv-zPuLzfn3E8e1KC zYh7StCYk~+7_;_a9Qvd5RW)&&;fbfYw0lJW-hXEFh=0qV@v1(H$OrJU-0nA)0&DL! zn7;|Lt3)iKRMFiDI*T|`wfmVy&v!scwhV7H`nW-VHpKBKX7t}`U96b zaE7DdW&{j}j}!X8z?uI!$;B7=bl;Zvpda8=GcG8wlq2-jf~4fc)Tg39EZF(8@zEz} zK8#h*?FCko&8Xf5>~&{;ivjSXmhtzzs{iBUWBhyf=;xAN?=8r`g>SF?LHi44CTwq@ zAzA>IC2*m=ZT4C4MWk^A;JFV|E}wY;eh3TDzD9p$)5m|YkF>Alcj zfl;JlQVNb{+s&GyJoHz-r)SK?@vRpuy`KgCLPp!KF*%jcl&p=LFySihZ^&00g;&rR z>AJVx&32^vQ7#d7@1W^{m6+Q(^fzpRS_2JT>F5^GNt~Z)%o;36>-pEG1|~UDX*Am0TKIT)>aRQn`Zak@q+Dn3=<<%H`PgSoQ&f@!hEC*KU9?S<|`83qhl5D&c`r~+%BlVgz{`o$8%Z_|E z@HAtnhtWRbk68YYL}I?S;B(((cW5Ak-(GM5F6e#ct?44m4o?NP{qQb{iZ3+PK@dOS z_rbA!l{as)?C=1JcBK2`yhf(&vLt`@2lfcZ^%$B3r)%x8B)Xv<2TWqI9^3SCQ#1OH zyU|cvAHw{Lc~w9I%q$o#Cpgl{0;oMM!BcNuuJ8<=#~tm=S^n3cF=k?yL`T|u-6o_l z5B;5t#JB1g#wS<`H5TLf74|nzIPOTdVw$$~2{hU7y7WDb=euRUC}KqgG?A>K;P~l_ zejUz7fBMEIx$X5~@Nc}uidCQCK`0+`GY+^mA~YwV79N!vg$4a4)7uFt1n{ey7acf5a{+Nu3ZO{_@qw>_?7 zfLphpIqWQiCzr&odKlgx9PVTytTF#(-WB-0G;if#MPyro+pDp^)|A&yL9SM0@X4Gq z^Ie#)YxkR_Hy`U8d%USId~b?askmW287ESc2VUT`(>cil9(6hp{a}6YaebHL+Dom7 z)SGl0;zJsdJrexj!P2t+bM7WbI`vhisBJCyRG{1+-QY-B7*H1TS*iEiI?VTKeWnat zkK=(pQ0EK&5KF8CWB%Ar@%rsmj+9wbMQz9PW16Vt7;jnNAp-LQMj-Kaq$v%TI={ty z$#>PJ0t*lH?{)HV&6poax!#K~9*jEF^*rWJdZ5J^Eynt=ljg1cVVG}C5c==H{`~_c zz4qUR`A=4cv9&weAEcOPaedN$+70Wq2g6EVfd90!d#YW+T*Pnub7M5NV*ZY>{yeP5 zf|P&U`G(^`kx;VU8PChQ{|}i+@SntKVXoK?CgCqF%%@JB`E5BKc>T{!%CWe<9&CYw z{Uc!6cjmYiNpcu@@3n(t2S@T0*26a!+&?f2_cN3tq0!w*J zBCOw;rVv=L>6Gpw6Gtjt2cHKY?JV3C3=xll)x%n>Pgb)FHO2UHW7^+_9C%jPcFjx_ zI@0Xu&5u7`0^cQXP}^S|AC8A7Zso&c=gz|0r>9{&W+JR2c$QerU;YE%Q8sMUrEkO$o_pW@9kBjmRLqT2(Z8|}0PDZlqmov?NqBx0=_Or1 zT9M%MyL-JFkM>AMI(({vC!fjJFgf5&Qzp$12Ud39wPxvXN4inLDPdU!z6TpjsMRo@ zGwyFGJiu5yY%tbuUC$R~lw&^aH&;`6Ansprkyj7kAKM>Th4cb{E^E8fs1mIAy?*|Q zP{4Ws)*ca9Z?0ydx^9ki;8gU;;O_(+yJ9)S#!FnU2Gn(EPg({ITRv3cejc1&EVCJ&L7>MeVS8D*T7(bUBTFa7K9x|jHOrs) zVE%6PxYMw0IR6;0AG>o_V?8iKH`@ew(c&TEYOIH(`GiuO|A1h=k~`-62b|L{;ri;* z5g^2R%U1ZMrv~mw;i-golC|k~?cEz>gpp;wiBoTkC!qxuJ>!1Go@k(JM_fd@+T^i$a$z$V`LcJ6O5{@L9XTtIy8)d=}5 z`#)Neo*STI!S~TGbWD={FWf)li@uQ1cO`R;uVK8L=h!&E?-L0f``C3!-zH1a$4LD7 zT8V_(FFzEJfbkO1fU09f66&+(_3B~mmPBf;moK4=rYk3BMbb6He>Nvh%tMwcD zc;(zZ0^Qlj&k4t;16~KGkO6hot$jF!ore7 zv}T_O1O989+v~isC8^XOx;+cHam?ZLs{%{%cLp+iz>WhlGIM|xhV>O|;{DdPu8!_A z4)fO~8+`6u!}(9gSM(WcN&49&g1nuBj>Pj8S=)<% zLpvFENf?3q)s%Jhx+9^Y7t@+z)G$AtSvYAgzPEcP=e?V+WZB_~2fSlKe@pA0mgGXR z@2NM%=zk3qsx#!#{%#5f1>KiWjWx^P8Fz-K3M{nVJ&;iJZlibp180AvT)7$gug-Uk zc1&)9W*JiDSLmOlTB$~8`o@3g`x5<=v_JX@4J$LbMgP=N(xqYCHw!Z2Yvptc^iNh@ zY;(3?eKaBObng*pPq)|RY%X~XO+G9td%nW^GvB!I4%QbLU$^Gzp?xJAsucxceIxBB zalQ|Vhl}o4VEqMa!P18k3We=8fAHrmPM0cdyodO_-QF3@caA@0Jco*v>=DBqd|bW416^0tP!eurqMYeUlLz(f(L^ z`)LmJ0KsbpsH1)TodqF4CNz7@GX>v&L&L@gYcAk;y^ugVD4{DuEiDtUfAl}sI?DqR zTJq65`aJOR(GmIMW6;09Nhuq73F}$b@kU6fITPJogJ-_Nv8$1x61rXO^`k>LfBuAq z`ja8>#D5zyrULg@)??#=)?nll95;k+qIe!(UpD#!FMl{kqaU#SE~oEZHcIFoCK|bc zngd^YRVVvN=m=l+(~EJu!8gMEtJdOrb?z+o1=d`1&vBx!gsL{RXneu`%O>_Jn6^qn zTMc1$1dLuGFVb5=tykJy9f#*NV#lw2Rtqp*n2cA`e{4bA#wx^=&c}GG0TmC{pX(yv z;^czw3zh=gu-^U5;*SmzidDl2w!UN~7w*rLP-H2OUw|h{_-442iX@a-p{}dN`uRfa z_>n|H(`D<@FJOIMXoC?_AfXD8+FK2*R;tO=Mt|ZsE9IyVH5ZaX<8!nm zlt~81LUa0a-+_x-_&t5B&=@-z8hb`;0lrgO*U5PjG@|Rfu78649oa%u3z}GqCS`LS zv}dF6BRc99Wbfovo9>Q5J}fmr_op&yR>*n(YaEOI{^S8YHE4pM%#@ulPC`{O0v)~j zLql8DSgbt(njog7>tR7g38R`!fpxYtkC@pR8g@fpfg5n4^0!~YHfZi%iv`~%V!Yl) z?;UG~MzH?O!)18?ZeF5)@eLxfVgEI~EM2shhCdY#zKRI5Af&*|LVXo*#)HBc_kmfn z+Xc;C=(u&Q=cYo_R1rJo8Z>ZvCt4;1>R~+Wyu!B+G*25U*1Z@E%q%$7pyBIfml5lU z{goyoLR=;yjLyBp82xd?ESm8^M5a5rOk6CK&~;{ha&Mq1^bGfy|I$K2ndP%FG_1e; zm)GY{!|!`fEZYxFqS33s_X2B-7YJ{JT@jH4-Cnz@(H@E?Adz=TL|*&BOx*6j`7kt( z4|kzwc9Kv*p+y`n&Aa9(3CP- z)wtCX8ar<9x@2fbXD(lVd?fJj!W|>W0^gbsm+n;(DxG&iL%XHUU_obWcj#07!5-Mu z(Eno=`m2n4VfSwC5)qra;hS!+kVA9OnDXV|c2Fe9TV$ zkdT1(^f%EZRbNC(8iv&D#e8m;j%ly{deBtDLib&=ggzIwy5vm}5h+gw^E;@5p8myl z>&mA7`!Qa28gYp9H4%~SNmD*-J|m&?zw|C`BchIc&{+u$3u@`sW{&fZ3VQ32j`<7o zM8@?p`nO(|q$3_`4Gp8~db^*%&Xb3Fi>8Z+I+o)bFrSlpq2l=<>gbtz3G=~v*)0#9 zpn26)PRIjZ9Q;(O*|KKerzn4bg9q?u&! zq&t$18XS(Ij{dPoLJ{Rts-KPT?YzuMg<=Ukom{nSv7LxqXuhm3d4%~h^JcJzhP>Y| z%bF6j-?g6WgKR}ayRpR7>4}80bXC?YXsi$T#j^Ej@<-D9B7Xnu1eyfjLvP2ab_$K!e53-4k5t{MH!*3`NB zi=cV-7vEdnivBKCYv)K05rG47)d}!3Hd}ppa&xhWcv+bZw(W#?F4CIUvH#TGicxOB zeUzImZUGafrAEBN?b(Fqy*%+eS%-l52%d_!h8MoKAU*-0*y~ojC90(}yR1GZk0!M!WcCO6nLCCjOotXFo{S~vszNqR* zD`IMlSbx^xd4hNpGjE9!5rgN4RrVqtB-8r2Z2umNcfGZ4-x&)2#r4;Je_=cEh6_0O zH&WdI`o}SGQ5WifCs_=+(jR!Ae9VJo>c~HZY)O{~KFt!Hh&O!^DVJT^Eh4G0&t@<_ z&yJ;UUM|=PP5-eeHjlKCbxsP?3IcmHy4%p8aOozS(ZB^v!N zmNNIk+Wleq}63E`*O6{F1l+k~mt@#*m$O8FsmN!QKl8w??OQwT=!#pF<9!}Z6n%6cH z{3NEkaY29oYEjA;#6PuvdR@1~cBu!x9lozSh$zhykvSeE-H**iergU(SaG~hljgNe zcSJtbI6F`gSgO+l&VF}Z_b2+NXUkB0#})ByCQ!ik%tPtgSLZo)%(v10q~1!vPwbPU z7o)uvEP!b#;=Sot70YAMzqsXSIk5QeR=o+{Ju#kq>vdyd{2at**+R<_-@CoTz)YSvRT1r< z)o|K?`0cJLXw*dHQG@^RFB=`Hb<*poFS5|!{~KW%unGK>yS}M@GVtWES4=1iL3>%& zdBfX(Lc+oinp?r=U<;QvAz8vqg0_R-)MAl10>3}v^>wB`@>55wCzjur!|yM$89n0P zEa9UdB0CD6&^@u>Q$R-ix}S(_ZueRE2>FzWi*FR!_r?7&esUc0S)Kp=tnZESXy^I# zn}?APaaP@}-BU!CU4P?!2bft)s{+S0C7+s-=-8o0=prHmMa#e$#`}Ffs`LP`biRxH z)ceA5)t@?xh!6(uAp|}b7EF%%~#`Cq+<)O-<=Hdqno7b1T=d3Ub)dm)j&ry{iXL^B`BCm~T(Y}DFb>`1{9 z8JAfrBz}xAQUbm)^ESf$*)M35w|$KK&%|#pdg1u3`#sY9OC4!`nNRy%+%MRF$wuY) z-fy}eH^=kSUUFUK-V5|!%wxGpNUruCyEf+~?#E!R-A>#O*xPpMZ@}kc0!&=L8zaC~ zInu+?PgVSIf9~Q>vwu}1Us1a=y(iv}RPXiqKaBUm(n?2bkw1@^bUq4r!=?(;lyAtd zDLD1${!2(!phT4Yckox(c#Z3eST$;6PCfMf3k5=XTz@TOLeKtyUm}j3S%vdYWab<# z;M=i|1=nv7r;{RRMLxfqsAMG0FZBR#gWtiCU%|e39!ElQzdmaQ9|N?6bDxDIjD1+x zzu4+i+klTkGK-nH{=@T74yo+_K}b%BolT3uzn0eCs}_=$hZAO$bb@{hpLMS{Lh|-1 zBAwl!Pj_sngL?$AEYI*W?cqcl=bhBbD;1KDSN>gEhwZ^rGq=Uu6_TR4>6=ynOV_u+ zQV$pKp*52`?LA*0B&T<^`kDay+C4n9g9^!*3C%~_aK3bpdA`0kgd{u>3{@O|#Yn${ zw{nGqEeqwrXGf)uGvBY^`7;wkCFu39mal!1h46gnc;PDF^|;f^udS2J}aYyyEJRkz)^0jxdem(nm`sm_5oQK|PN z_V>cHBS+*!nLJ4+PS{a{%*u(fG(%6EKcuPoN8Su4I-GT+JB4JI+mlbHf&Fy6zZe3K zWyLG3ohY-OnXy$!m?iiYVD|IuV4R=v#Bn^Sel18yZhEb_9S>}p^Ze_#^+Gb25mTo@ zzjUcKMQ4qWNO`BwV;o!+?6PWwkg#b?3-EaTv;lISLNc^S{ZuRPXN_;hDT{?f>NSY# zv0kdbr(hv4t06VZi9)S8{OVlvkF4kqw(phex$vGuNIw1A_1pyh58WD@ecZ%CGUZk6 zi5u7-PCga`?S(|@-2{Ee29}7iM*C$&XJ+I0f7bS>n=T~GZsLvv-y5q9XNUIZdGcW$ z@QV3m>t6wrS@~hcF7RWRc2blI%&bCHp+BiWDS(z~Xm9hO1b2g;ig`xi_*#0|vdIKI>~eKGV8u*7_8AtZ2+EZmCiQm<_@Az@a5F78frm!Daxx{;7fKV)|DBDOP` z^KCsLVf4i_3-SC>qQ+;Ukf^YRgZ+5^&l}ooIhntgaXOzw$Y8VK_c3@vx@{#U*0`i>ccM@&Y83!? zIp8^L(_ZM&6kKmd0!uyn4?6u{-8)be-k!A2li~;qCcp}iH6h{mR|yvdN)=$Q5ls-NymT_4fJ*?zJMO}Or-5` zV6_CFclTdHf7`vpGXq%4d;fy|lFh4u%fnMob**)xDkhUod;-?nZ|Sbw?nLEASsl0! z?4oBiZqpGoi&xGI^L2Q1a^={eJZtSB?^imi#)Pw6{T>QHOQFzW_- zTRT%^g<`J&e|x{|*&PRGD!dch$d2a|c4PKy(ZSH9dxE^c13-t1O7e&0&@5X^Z{+`D=;c-P1^!$+=q;a^=G@^S z4BW{Wj9d6ENNa`wPuhIs`9|tYkM8&}=O^&;DXr79Z^19&ppM}WU?z_{QRYmgJaFLl z!v<@VUO3Zfla*H10Q)XnW3uX-U*Iq7kAR~YR zGa5!`br%}N0+hhzRj)OZHC*UyzxK2;U}nqncY+Ib*?sltYv8MWl?M7+xzLS^j*NZ- z>=$rC=r`SkPKp$Z*>>r?+Qo%Bf`0e{_{)k1{SVG}p)Y#K1Xch``MU7eII-HK$1~uX zDVtSX*SgTDzemh12bSuswz|;ym6hY}0vGvxDj2=Pg%+!;9V`T1)VM8U>H!xjF52jK zo#Bf68mECbpp*{7npZzuuRQKTC5u;{cm#Y%gJo7-D3iHKpU=pg@13a&{V8<%!=CrQ z1^yqbi(EQt90HeYIuWk$!i7qGJ%DW-GM7(&DYT08Z*9UkH5ez z8$tZ5aiL5;A-@^x1yR{A$NOIG;!3-2I3F|- zIDA{^s0G7asosX9fhxdjpM-gak8-7B4>_Ih0xYdHsP9TI2lQ%rg7Z0l*V?hw*p=4K zbBtuqBmS1xWn$q^ARtJ3U0@3~PRU)KYQ*PxhrXIG=y{&y_rhlIZt5@{_l;8`fq8-Vq5baCeNO5nV^g;@tQ zL?o%-_(+BuuDxBb1nc(;J5eNg%(%D)93U;SzONq8Lp_)zLG7ufPy*suh6 zs9P6ko^t`-5UcuBg8cU)y7w%@6)5TF47{Lz@q9Pn(zw|Z4U6D8%k;FsU@!mJ{SY3( zt5t&Cf!A-digZH${|HM&18-*nz+dqAo$c$#_HU}Fv@t+U5IB8*Spdw!rb=>_9W^$A znKk@B@QaxoVF@r?%bxr*vm~>QnLK9a_kEN;*VYy_-lNXO%m*&KvA8M^e37XrvB&mT z|KT{MKlm-7TOG3307so`o2-%oy@6SFeF*T`{Z_qouH*Pm-rw8;d{EQ1yAMT8ZO9PA zfTs!KY&PFP4H%>eBZ1HR{>?T0W=S4oxc-a)-g@|Fonk-m@g`2X!|;y~gS$$~sCiXo zvx(u+NWZravmzIs#choSmLIT2Uuin{c}x%i%))5F;Fs}!UotW>on&Nmqr2dLBAIya z)Et^IY|*x`5Cigk7%Jcuz#pE~S(=U9EB1+!$G`9o`;QX-hTu;Ve@qV8ASb3Wtk@&s zT4s!Zgxbp}lv9+6xX;sF`ggAUgBNjFxe7PW>o$&5?Tt7M ze>}GAs9TG?XhDu;e2f8^1Ub?Gb~rb<}|LeBeKLDUK)A z6Q3|3tR$A2J9skfRiWLnou!WkEkWL?D|uOd(13Jh#xuBHRMz$Avd4g+qVfS5BicEekkL~^}fWApW1+WWDjWr;KXS+1U1t8D*)4M8sKaPI^)qaDJ z=lt(oHtw5bdVdS9=P!)%@NW6z9bkq|$RnDtVx0R7I=qy3N~qNT0@sg9U3(&sw_G~= z=m$kyzm%VXyd_wLd&0sE$W`lOV>d>CC&GyTI}8YayzF(AVb!T~C@V$r?~55xWAi|K zYwKFf=bvaYBV+PytoCEO)RSI@klZ1i4iAr;L&s)aK0XRK&oPla-#v%kvpW-P2>dZ) zasKNN^b5v)vk7n`=#Jf1!>{w3#dK@nAHFBMS5BWpH!m1Iy$^7|Vaw(f8qPuO=Mw)AfDguwS*g<1t=TKIvQU>v1B{WLq$7_8 zeBxzr)SiBXF#Dn5z=_O13Crk1Y#|IRtu-Q1YfXu-CbO_JSas zuYz2CUf@dOa}PxKP9UV(C-BG-;AOiHL|wW-$lT7Go8|-8xoO6AyNl~#0T$r&K29Fp zfyc5yGO*GzoBF6PgzVFI{8N;VaSf$Ubb6Q*yW3)V7kKCQb#r|c&B+(z`??Q-_YGP# z=DLg`I~8_fLd>>N8eE74rb*^=)TPl=@A)dJUMB zCOEyooERy@8!?<55h>OPGAA2*))wXh4=5g&wtcTTiEK_LSAkFGuF3R0Xil{ARG*vz z&N}Fmn)b+?EU7(xAO-mDnbN$J*XCrPz~W^BaK${mM}4~@u4E|lJ{@?LL;NB|BJ8N0 zO=jGAdyq%xd^YeawUBIs!_;m%p z?%>xY{JMo-*YN8eeqF?`oA`AVzwYAKW&FC0U)S;LK7L)uuN(PwCBN?E*QNZrm0#EL z>t23c%&(jIbv3{4=GW!?x}9Iw^Xq;-F2KhP__zWeci`g^eB6SMYw&RoJ}$z?P58J9 zA9vy7GJM>IkL&PpA3iR`$Bp>75+8Ts<5GOwijQmYaW6hD#>dV0xEddKVdy z@o_&sF386X`M4qugT{<`MfBfH|6uH zeBPDM%kp_!KCjE?efhjFpEu_7%6#6L&r9=pYd){d=e_y7IG;D?^Xh!wozKhjd3!#u z&*%MlUI5P<;CTf+?||ne@Vo_{*TC~0cwPk0o8WmBJnw?%W$?TWp4Y+iK6qXT&l}-+ zB|Pth=cVwx6`t3^^IoJp#s9^N;dwJWuZHK{@Vp$Jx5M*#c-{}s3*vc0Jg$ffjn=J=N0n2L!Ot&^A>qtBhP!}d67JClIKc6nYe z&->+h!8~u6=M_tRhrrwYAKo#~OXhjYJg=GOJ@dS1o;S_&s(Icu&&%d{+dQwE=Y8|M zaGp2L^U8VNInPVydFwo{o#(ytym+2B&-3be-aXID=Xv`)ub=1r^SS_DH^A!(c-;Z7 zOW<`2ysm-QJ@C2+UN^z(DtO%mugl!Ntwl+<_qf9a}t-4(CP;&ofRu8Y@w@wzZxH^%GAc-UiB9ugl|gd%UiX*ZuLjKwdY<>k4_@A+Jm1b&I^Nk=H%)x=3C($?Gb4-6gNfxy~ZF|SMJb<4c2nb$q@x@cZE&FiXp-8HYv=5^bA?L)pcB40a^udT?}UgT>t^0gcJ z+KznfN4_>BUptboEy>rOwy*B<3-lk&Ao`P!y@?Nh!sDqlO5udT}0Ugc}E^0iy}+OB-8zkF@rfc_gxf1|eU zHCH%xUxzMrYURr~)Z&#n1trvF5n-iKJy1Is6^HXuE{KT4=;hjZsQtU#?q~m{;F%qG zxWlVEYWYfQQ=ftE7v(`UQR|nLiF|hqIy~m{joQY&yT55wjYDl=a=iNhY`>UnFij1- zHMnpcnuqODpWGcHGJ+MbDtDl|M~)~&ZAI;1&oL8|N>NMsaMHI~fv8>Wh{|2S{;R<> zST7<>UbqhUkF2%Nd*~wV(siOM9y)aBvY>Nhc4OmEyE&c(&OJrs4P+p&MC)hXjZA+FC?wbxs1)Y859=k(~i4jsO*L-0J*QPjHFftJgG_}2%z zzwJ)iUiTen*(M0VpnJ>ACDXT{mh^zwrUrbCqj4ZR5G_c!3_ z|0<7qkN16q8ON7_2g)X7pB!lRy;)2DJ`fVg%Hs#Bt5FNw@u~jpJ3^9gdtq6hS_kTO zNRxiK4*r3Qtlk0CS|05*%&G)BAZg8TU@89*I-oBb*B?;-?m(xippt5akYpx6!TQ62 zPF6!E;|z2nBZR}BqxN#^zNVMmPvUy6NBpTm?d4O9cz#q!qx2Bz)KK}vM9*dcG zSNKf)y>{9f$4hBy3jK=j(`?8Hi{@6mUsm7^x=U6j)DF+5P{rlWFX%{FStlFpUlW5O z+k>Ff?y4i{+T=i;tn^k5!}q__;+W?vU~Brht{S>jIMy|oJXRgK_FF!**VbB-h&Qx_3&fTywoFse2z{EPER>wSU0w>8S?;t5<&{M9kJ z3xThxy~}>v9XjU^3b)ldiRrs1lRgcC4m9KbTz@rr)ZPxh-?cN^pR^8W7csqP^Xu{{ z73i2@&vp*AzYjdBGrp__ABu?n3%>Od(<@DtiRx(2yP~fi3GRdKtiB@JJ1B)RGQjT+ z8MQ1JkNzfL{O;d<#T`E3@QKK%RsOntAilpq|0r4bP#iQYs?EdpuG+^J%(WDe{K{>8 zqWg*IYvuzE--nno;oT3|-bn*oSnz;P_t|r+7`4V1utLVbQvHd7nBG2(l?<@7E=mtE z^_M%;O%+%WckFu=@G0FZL%KS^M?g#EpiUJY>$~HAe;3k!G2IcFZ6aJKHposUg(|qB_65wH%7MQx?e&FW+CI#cg z4m-n`g}DAj9&Xcyti<7ADXaN)KV5x(U98r`WSCkFarSmUV6OE z_#RyctecJd2~>wuXc%hMXPL}v#QA#Pyd9Og4Z8QI5DwygVnJ(k!+7Q+3x_daY5R&0 zk$^B$mqI*$laLpuZ1;%B;!a-{mEw8q4N&wvwHLkt3)H(sWBcvFWO?~6@Jk(x zj91`$Vck;fjr~)XY@P5ESa!hhnbkPnnN6rz0jwtba;#f-G5CAJ*zH%72DtC84`tN}Wd{?SoYQ4n$HEaX3 z)&S4T1ch|*d`{hd>;7UA=5x${7VQa9u6C30Xphrs?j~US>LISEkc#nTbxo(cz;k}v zc6o{Zb=E)akUwZ|sMe7CeWZv~MKrG0!1iZ58m!VVUP$9V_&$Cfhw-@*+G9+%YB0W^ zweBaBFZPA62gChv$0del$~Y<#;^WOzeau6MStxw zqop@=A-=m8@3EhX@d$RFD>UIl)HS`Xvk~4ersWlQ9*GOv{a2cbDYNnOAB*;`YWZ|9 z5mTwZuD*!GE`bB0xtKzoy1APG?GQglO2Jje#^5(WX-lpFBe-e9a<9=W&@m4x3~Q+jt8~H zJzHRDog@b_Ek0(MQ$G#ue?ZqnEiwA5kDK%+S&7JMgC!SbC1N_vD0++;5fP;uH=d>d z+c!U}yJv*svwHQw(t2}~;G^VtI(IUT_b_DXD!FlZzZfQ4a6j$)QL%9}e51;;BbLq( z)A_dD!>8l<`9?lk+-@nRn?q*oABFv+nP6EYrl{s_Gzi~kUfaOQ&Ul_KEYXSIoBwP= z#d0w{|L>9e4K0jU52JmG&9MJ-s+Kz(K09BxI4uNTss?9!V5i<-Qk(oYU%>ojqUYPq z2Y~yt!i)G`61&^mG-GFDZ0F}0wj$?&u@jdDVucQ82Vyf^t%t#sEYZ&`5@x8L{tGXL}uXbF~ z%RqbXov+#4s|(r(^HoRty|d`BL|0BkZnMHS`2M9l-M>O2^-CXs_r-ia{yXr1y z@eMWnN`46mW^ZOn7~gCU`wl*i^@Y?o6XRbWjr4@;nBQiw0;~AFU$-IMb)l0=bljfx z7`SeGUaT7k$U>|~()-_6*n#a~Dr?meZ$bBla-o~?d#QfsCg!(y^;dVr_5GfWh6$em z+k>}nRpa{K&OpKS^U#4Y14iuM8W3E4E)~~z0V4tWgSI@n_6ODj()#Dr_(pv`oT@%W0AI9<0*b7Ae_Lcb$<9IigdFD^R@v=m11qY79 z*C9S8cNn&Nv>kN$f%Q%5z7_rg9Pho0@}oo9gltU__Nr4`mI!LXle!1FoU z87ujNSZ}d}KklEEhr1dhBs*%o4(8+jYs$lG65vC?${ozZ{kZN7bb7K&NV+IpHXe=b zKX*NRS>ZD9zt?Ffr~JeA$!!(8bVHz9cbdGW6~C7oT$!x}AAlVv zlxAoF8?r*y8-!%s$l~dvFyCTjf&~FW=!hCs#h7ml9wRgN#X2DoYRD-b!~B`adu+is zj|q#e^No*nN3yzu^kbG!aJP`HO1FIfUz^<9VBzby_O zbq!tc{?%dFhV{uTpVQl}LFdn=Ex}k%^kWlIE36;SpRPaE;6T^*fpTRkd@p*PbZ^4? zXL9;wgV7`L{QGa4;sH{`*G4FUE`2JS)NgL?1 z82W?*olMH`{h|u7%G$Z+MDFm~pRB*gSIP}`nu+^k0VB*GnElQ&d*~YuVTOtRX5l)! zp-N8XWGpMNI9^Pdz4|y;bK*K_o|+8$Gua*XDNCHqiPn1AM}`=0f&-G}(`K8KpUX#d z&&T-icCqT^PQcEew|TC>c<^yQ3I*VNdOfp^PM|-B%h>ry3v(hl8MiHIKKkqBKg5p4 z=ETFP#I|`M`s*vbjM}D}6I56CJmC$jSpTuhcyn?=W#p7IJ{bR|zj>@R(wvN`+;Q#W zYRuQ~D310WW=_npDK6v5qu*gXfs+Qx-h$^h zS>G?~7xXvN;P@OardRvG0s1#SFI2xd7bB)a6RbhS?aK49OF>db(QGq?E;UzJZmKS(I z$dVbgtGZvq{WIVH$AoO0`ay91rkKvX|EQM@^fIVordyha_E_`D#_|>+vEGPS-p2Zi z^()s2@mho{EkgevQ1|6R79lLm=W$O=558GCx#~0_JN^uDi7FOTvHjZxcFbe+r?>vdxb1;{X$o5M6EXc3jEaO&g#29!N91xb z-M)L;>Cgy5sP;U8Qx#@y`&eL31Q`~2UX&ImucC4-$F>J z&b^dhuf;Tmb*JBJ@kF`wRY#^&P7p z>qW@$=a>J@|1GA{xYi=bG+CXnKVpiicHzC<2?`e@oqy%cL^P;9~0km1|fE7$oMJ1 zSCQ3ghb;6ME4ahZ>*JG!sJJeLweV z-C+2@&ujCsGbZH23;RhA)!+;IV%J|!GeV5U{!%a;itSE&7eZ6pQMY**d=!T^3T2H5 zk@{6(|GIf^c5KrnWV6Sk#9~zm9pEA(mY+ySsw)`s*v{y)T$HT{QY*4D4CLu~ojvXn5&#%Z-QYtV;}saA8}n+njcIeY%tHWhly@MjG@dk`Wi*kpIk0KVb|PjCBl zCM1(>fDbOIT=sNOCqh_QaTb36)PcNcZ!;qpWv0$H#QuzE{mYDe%zP{`GnP=_+qY+5 zfnIR)sJKa82z*?7A~IEJMs8Z|Ri7t>ujK854T>dZMCvbRf#<;jr}<{&%BqXD^Q_@> zI^eFe+!Zr&SAKc!Dd6me>w7+3G$U*}Ukl8_spGQDNE&{!p)JMxtPI zp9?*zv|fk08Odb&GvLv;qY97pGb5;;*z*f~eA%?$skIWnq-cXFsTTy~y5aij zB|Ba!IGd8^(KGHe;CXZ!*+q#GoSzli#q(pf$ykj?JR^*+EWnZJFJ2|4lik!t|n=dYKkY8huu$U{36yqp1F_+rbA+EFIN zx&Ok>2dyO3eG&@Z_?QsKmzQt1;d?y%_VCF^wk8D8<uhlSstKNqg-+hEWYJARGwX^M?jB;) zF~9B#A8?6kLU=zTB6qQT#OyA3->H!`eocm?UPh_8K~6&d1%-9_Q)x(YE#T{(raL27dA3=XBt`=T~X>O*14fRj*vL z0?zQ7yhIpk9gbXW*7fCv68qa%RlYkAb-U$3_EJKQc2UUD_T9f6GI^y(nGv zy|N)OJU>LE6Yf`KZnrh|N`_?nVU6xa!1ubIO`qG@kX-&}>o*VBde_}AN1FvCDL32F z4Op0bXjKnjR3X0}0i1F0WVh8d*zO!TMFPBO+@RgXZv@0?j+c@H@U%S^6&}FScrfta zqXioS9}CD3k<99^z>fniWm*&n$hW$*-nDo?Crbh&=bsS}R4F>>&=Yk6aN7$r1jL{p zRt$=Gen>}z8eB_MwcPfwWHA9XU$7VF3!5fJG*8sCS(cqr|53CR5u z4)q4WH?7V*bqyDg7?ya$_jF#O{Bv@EfQ$&4`12)x&%)sa9s(lupTc-N&!ErVE-M5b zb>wh8&EpJRmxT$)-0rVN`u2b?J;ImyYX!tH>Ey;YxV}}V)F;ev5)k>)i5o5Odue@5 zv4Aie>#s`i87@OtqAeiaMkl}D!}xqu^-yMMJM`^INdwZ*{>*#5$-nx_fT-qLG(1In z4)LzOt9Q|Wgzde1`Uu+p!*?kBde(qc$8L@boen+x9_1(Ruzg`wOF*y?^N}vi6-BoV z$aS&oA7hMn%9g!1D!hOn0jpo54WDM?q!ssM1*ECqQAlsJ=Oe6;ql%y--mZ@EMswF) z)lmX+WK{Q^*|@$lLCr_BreHg(7p{r%H@3HVxvqeykS6=%xZXXt^-p_G6p%b|(f%Ne z|L0D@`4;zg^IP|Rl@lcNtJ0PWH%1DGHa*|gJO%Ij&PI|nK>(ldZo;p)zU~iSHu;Vd zkVz))JG)~1-X)8QBVz?b>Sv4oYvBh^o3SGV1fv5vg7Ns@hom7g0S7+spr>8jslW= zUp{^@FsdT|aqUsiz0PC}y_&FGwXL5H6M`uFb2b=_8;6A-tC z&4EQum=C-^J#BC{&cFV#bF>TEr>3oTMWKMK?KXbfG>kuz*a@%V9|(v<00(O~^iQnb z3ch~>_5&{owOweKR`~(zd z{8`?*Z{Ku{t7$yKQ3BjNSNJ@AQ<1VqvhrCI|g-Z1S#r$?imtBD^QD- zP-c^P6a6h#X#bK9qCZ)8dH&=&L()5QY+O@ zkPM>s%maTSTpUPit!a?T4%W$k)PfPNgGaMz8sf${+5Rk`Lz5%vDG=$ zNiir5{kF=8JbE_vU}T1b_ME?yH0&@Ue!;^O`=w)kSU6GJ8#q2E#4`oI@4*I~Fe4&e zXQ!a<2lFMy_>?zJe#;{G%>Ouhy_a!{5s}vaPJ*AuTI*%UvW*BU10Iuz_Vd>2p3)T~ za#jNo=fm(RpS?Kk*exS+TTt`)GVV`j|IbjjM@A$u;b8qN?0;u=@(i2DMkL`zVutHc z2}M=61qYrO5%i`NYjM5-)g}*hD~yQupMkMvCnVG<@SAY(QzKIKx^4STTwesz9sTk9 zs#h@=Uf_8wn_T6Sa>s~BeJ4&ysOkRF#J#yj1a5(!@^OEx-1@XEJg*%`+lQuMzWDNo z{eUEl&u;OTYVbUU%umU!O)w%;Bb0lJ@Vs{Dj$WOaW<&xt)Q08*&xH-NK8{zTp3!w* zCVcdlg>E`|&xoYicx)J#gZ_mXjJz-+@7G+DdwdZ->b-0#zWgvEMOo?Yoq(789z))A zHYV#PE;=d$EY*#6F(%9FV$1#T`)R&WhK7TUNvK}Fj?5*@FA|oW=|9$(l;yY&=nPz_ zv~@@<@UdSOeLkJX{jmCLrp83L|By)uuHX7*u$qC`n4H_$<;HQme~k~<+V?CsCS%V{ zZ_@+*uBjaIW2G@Uk^fv6eFpElU*)S#8;wZ@$`yA7*7h3peSL&6x!Ty}cRnz)8F4yl zOtjB_9(^C!Zouct>lciPch`B&40}#NWG)le@9{PCAf9J+yW_N|%f_U{5+ez)wEoO} zV}fc_j@NK~pLU>NLWMEuSJBl~3D?8QCX|0NCX3f8T^D2f_+3MrRDK(i)@^1Eb8&y6 zTZ?}i%a{;{((xzbfz84f7hRAwA&B<;O$0{tfBTzWCWM8LatLzLD@*`rV;|pNctw?~G4VY0e zzCZRNT};RXX83#mzq)UWO~_=|mm!NEVf=M@LqDv<^(_h0eDwr%CPo)rR$XmEq;Z~7 z%wO1hTyH|geHqm?s{(brW(;4_7-mBL>7Zf^aAZ)pRX`kme>hlS%L~-e(jGy!oiQQ% zwfn5w1&nNn*(uD|Cmu$o`8DqUUFVafnI;6)d9I+*#e{gn4)91T z*263w-oun^XfW)wwVlncKmH%G-UKeDFMb~n2~iQ*vLLWb)m!w3mwT^v>IU6b&fkjjZ_ReS=wkw% zS9$B}b?-!U@zI<78q)yQg*)tDqWvy+++qP7Q~so8!hh($AFO{LFavKj4+ojXc&#B_}lI%vSt zABs1I_XX>ER6;n~cjVlHTj(D{vO6~UFdMwzwiaX4`iW^r?~c70c;Ba+MrW0{im43p z?e)O029)@_p+BAJQ*#+Qvx07%ub7k_pbQ)h-E6o?zF|L{vb_Azv_$Z<-*~ciuUbr) zCFg`Bp+E3_kaMU9E|d$#h$fKAA=_zved05sIULj;eDS6u;2q*;;(y% zX%8nHhK{%Ys^X88j$(qEk^jUR^iQ~vXW5C#t9s24EAW0h*56GPi^LSk1!%x4z93@p zgWdSO5O?<_fMriD-&;Ql_6L>Aj9~DLpH5qN%@WVYWyzQS0#E!1r=>$@iRo;wl=kPr z8{TZu!Kt|sVj5ejmwFoY@6h`>L1&Yg9{Z>4jmG(#YbF`}*(N4IzX9j(@flvz{(zV= z73!FET2fYR4W{d3s4r=5{txhc!&({JVLyJ4?WValc&nGy_$I}RsnzTiLv5Q%S*9a0 zDK>~HZ#x)jb-=4VA7pEhVrmOn-(29q6W{H6ror=SzUiNBE@idPFmdB~ygA`OD=E|N z9A4o7yplU8TZ6ZGNAdRkXkVy_jQiYH%Hr(@U!H;YwZ*B*MAAXZ9;Yn`9y1>Hm-QR< zJv&L6*=Q)3qny*6JAg-Ah`&Pr`F;u+jNtv|Zd)C>msZ_M4ug#l6A1Trqb88v}dN=2c7V&84jK_r>FOC~*Dc=Q zxuma@aoV%r_`O$-=F6=CQg%r-_VOQ@m;xU(Djq%?nXGLp(2!c9XJsm!Bos3H$-h@;9@=YpvlY4iXc*dJDIJcRK7q_hPsH z=#Nc5Xkj!9Jmco@ zoVLVxShiw=(L5;&;=S)4?mu{aUbxdCdB0@E$zU+bSP6&np+v?WFF@CQSk_^cC->MrEP~9!uP> zp!fY;L?fu}*xXR?oZo*>C3Pa=&9*Z{%0yhi7vI0#zWTf7pgj`8MmA115I5*71mJol z;jeoG3%rbI-&sCbS$9DD?p$bFIYP>Wyd4uUbg+;Sh4&Npc*8_{GxTqZB|TpO+w;J- z)?(`YYUR#bzEbx3E(m(-VXx!%a{PWDkDtVRtjGD*Mxyt0?``Y#RUI_7lN-0~&6D!k1 zRO$Al!~neF)t#1_Sfz@nQ*O#`U*Ij4-B%piBO>wfmzM~<;x}E=BAVm;-tPv~M4|mQ zy>H>;kb?CPWEV})em9%;kFnVxqBPSFGv)yQD0p*d;Zl5$_LX1M$M4U52rqD)E~3e8 z{3TD(pD?L=j)Q+no;KX}!-`9p&B)J{|7;GbyE&q?U;<3%L+S^dDv4p}vkR76RT zosZEuLMsiBm#J{P3=@&1 zWBT{^BxOI{=lqvrB_gO#&318>GDGi>=C&R1JPa9G_`Q%9Vkn{}i{MG~f(`(W^KK@h zc~hsKH3jDGjwgQ{8g#wz{?r-T8*-b9D7xutr$pSZurdg!tJc;9^TiVk`ZyM zNJOiZ?L^fRF+VH}6sfVkbpw6w!f8@w$Avb1MKqXynC42^)S=RGJN-q(!wT0g08e_P z(s$>u0V0 zWtUb<+45uC^o`Npow{oVJXs6gakudwKbfNa2S0Kg87*Zk`3KBMMBaC8VyAyF^WvPoxY!opg$#DPdz?G zMCWaTGejlO73ehjc>YXLLp=H;DJzpBf?*lP+vJ+bk!4cm5N4wsiTy~mU%Pj0%hCSa z!G`jY-IqK5evbKIa`xc1Q6hp0=CZICQufxG8(#4IqY$|T%-yl(*sok1&_7_(8+>2! z*^I?}!D&T@yvO{>*IB@|qZZt*e2evh{;JSMnYh20y|yPSpj)x``oOA-7;m*h%m;ju zvdARGnUYH)TE{nlU!`p6)IT-5u%F4g`Tk_Wckt)*H{S6Tzi+{uO@{oy{33fi_3)*qV^T`#P{_=h{Hs!<^0VV_A`vEFaSu3c;#$Sj9% zxO6{OL;^p*b|9NJ$;Py5n~01$U}dfw$b`7;I1yD`2f=#lK(_2hbmz9~;eY7_2E=xO z%%HM%Kx?#bo8QxW+jkCRA4a~IZN5=NJe*`?w?GC}wnlqnFusQCpI4X$GJ!V+*ZZCM zG%wOTkm)~{Z@r2A!U9v(&Z0hn%(&HD+4Cff*8x8Y{f7s#gm2?4%#Vs_@qmjHV;lon z@4yqazGzRAHC^-fxq%lRwAA@%@1zey?vC;aY*=sNdtuRAJEH*k{@=ZynzJY*y$V1MIve_q>&>49wC-k6_}=dqtHUhv**HlDB7Di7nc z*q{IH>c3}hAcMNo#GA*l{|fL5?GE01$>q)y?i~@4puY;auK;ZinPJC*8GgeUs>irren)Tn3M)K#9usdpy!%8$?Ru=tnu06vYz)GD2fv( zz6LKo7iueTpvhc7`a@uYF4H*&+WjNHwN_0a+rtOmAqQGlxw3Cd@aCW7@n-Q3bTguv zWI*_o=Gy*R?_eX_0(1F*cFKs)@*om9>1{IBWpgvwZKhyyuI(YZRu?_^* z)4hRRWUPZ;rC1tag#OZ{1n;~m#=8zC1V0l)meMu%cvAz+|N;~ zx}&h4mK)C46)C3VRax$VbVTkoP#%o{E)^nyw6x zF@YDr-=5q&nig40Wo+C}8D@Vk-!f>M#h9Z@k~3a>iooT09^QQiuiWJjEaHc295r~dTm<2%BRURkxjWa%wq-;W(0VrgVYm&WOJ$j0;cJTmCr zD)`%w4OHs~9Lyb%wYClMmpGrhHNJj!E+u(oV| zs~{QcY_hBX_-=T&wU>dr&S|msDRA?`#%41jWGvM@ zY?OYTp*(V5(;(X!y>tw7o zSc1J!-|^Y^G$|WpY&jP$0`A$>`bXDIGG^7J^n44PFZe5CWDHR+m(Cd45u$h-hsMg- zrOKTSeY@Dv!uxd_vw=l=Ycf9p-+DPP;ZlN(QO!QdWD8vX-pUoDQej<$yld=H8Y1f*iobCMSE)Z%a|%Qq3s|s+Q;BjW0wQ}(_hH0 zVSJ^_n2y!U@(DPf(@5tWmN9#4C?~ks(TW*O+f|$ZzIwJRzL%9RVty@#*)njxZj6=8+|}=|0c#i=M6ase>;!2xP|dH`QfK^*?1quYOI_l=kKSqhzw;`*t5O%Pw;fByR8G+;h`X1OBXK#h#{av6K> zh`>}Ids@}%)uY$XF+U(IMLxow9Itt77>x3fT2~9UPO_(@X2%PT0SkGjv+b!os!IL< zSbc5T@*a`)bYKo5*YW#Loon`=U1m>M-(S%VJS4tp{g zmaA9$7UPw#+f(fc-sb)tKgyU8CwRb~O0&U0SB?4To!*Zvn145at#=&pRmPfk_dfLs z@fLg=*ZRARB^KY-QNurk=)1N3Yh{A@K=+;I`@?Tj4AO0VE`tfS)o{~m#mVIea=~+VuLX`XF zVr@CAeB$L=+uecgT{7Kf*%CSy0Z)9Z5ihcWhBeyPO3qG1-Pt?M+JPP-{HI47IV&Fw z!FO^X_1ssU6AYotp(mNv&KKvyt+BkFoCS~Wk(n9nAjmLmH9~!my^~LMpoC||&9-)s zH|Ve}!uRs1p3%#Wa)w>2LGx7(w10|Od3Yx|>!|Yj5*+J5J05pRJJSU^9!+<>d9}lV z43Z0OHthlabLY1<^U@t?`uAqnrvi`MdNeWOtOIT3g#IRSR(5=PaP_;+~N>_kgB6XGnomh*<__Y{$O>-BcC z2g#ZJUaX97dB|Mh?esFW}FhB_4sSB&max{)@3;1tVL93_;9;v7um{~#o!N0_m7F<+EUKGbi2dZTg#i=R%9+5+iuzm+z;m`<&fIvw z#~l$ByU(@T94%*>*4-a|et`Wh*K?1RGrsIN_Dn?AkBsvVi-Qh}&fFHB<=C%c`5qdN z{!p-`h?R;cmlM)%mox8M-6l4@3;Rnan~>g#(B*-=@NYbCZkW-~COhSfr-7v6`M7)R zzuj^s@T3AKcM-pL-79B4FAhf~G5Fu)VAwkVzJ16@L}7no3-^3r2Kf2kJ0Gq``xj?H z0raq(@if>0*grf>SvkS^n4EFC{-L|Df3g_YCijG#&4asoUNqM0+|iOLZ}2DJ{X6kQ z=hJd#c>`(MaGf0=b<)t!MNaz?sc_9Z!+vh??UH%r)Gmdp2eUV%OU z!d=vzMRcaNoz}}dIUBlh#k0>CPZr#7k}qfHIkC77>joIyp<(YTF> zcA$A;pF@uUGG;%!!T!Pb9k1mKQCl1h;E{2u~mDm!xFH@mNG!^5gK;e?E zuVDN6hI}#lFLw~PRWQChwOa#w#p=Q_mpdq!z{|HmL`ToRTK1)jg7N8bU6P2j4rR^U zV4`4L?z1iWhxF47`yr;#zd#z7!&U6hU_RPuu3#hh13pB3zV{|H>!WDc|Gvk5Jb&cN zr@(XHu6rB)2mP1Z>G}b4Lmd2Fk=1)#efmPrgX^gpi;0K(`~nv8qlSv9ykq0Y@dFgB z{`Sq-Db8XN?C=8>EW)W}i;l3r@8tm_Llg|rJf$Vvem_DL+IX0Py|1z}{sRBSppVIQ z8Nj0!+FzM87yhYv@L&#Cu$Z(()_ zUe&5LBM-Pmv)1>1?uNgN$5D`iA=_Eg|FD?2jME<%vx43+0(PuH;`1-8s@OTB&+Jp#L#OIn?jTzP9 zl%6qY;n<)%I1%rg8)7>-5>GdoI90*E{()fu@xV6|Kk6=-sbEz%wR5wr97)g>nWbP; zK3i(#4|b#_JkM~xf-PJ5z2c=8>f;~U2-Mf%^Ns|6M|%EWwMK6Vbc=WZy3&za_RLv! zb(w-u!0shp<8eJ()6;&D3U)?sZ;u6`j@Oy(6>Q_9tpRN`jx@eX;Vj;uV2vJ2 zGmGXpQtiv{H_T%ctmh`(l<<{~#Q(c?3;G||{f%)Xs5P5Rja4uqPkW~$Z8@IRZ%e#_ z<^Gc1bbV|T+5yxX3A8|C*(bDROqbCk?9DQ-v;Bdj=ha&>V z85~<%YH4NS$iZda!>&f(G}AS7jc+b~ zdr`4@c5d1%4IS^g@u7KRZxS?7<^V^(T|boT~An z|0ZT^Ex`F}FPTj#{^Uj8$%ht)&eV{=b@IiFzHWvS2G^?%@7ksEn-}!Q|L^O&#B>x5 z8?B*wf;ae{7bQdbyH|jQCN^Djc-jLma@0!qO;Kv-*9W8arT=*e8u5-A4GFm^7rf}~ zsm_M;Cu!*Q*>|Dor@g4B!{g!OrfP_XcDn&XxqMp)uJ2`uD3haJRKyBybe^c8{2vJ7 z&hVo9^OyQJ9;+cJ+TYxk?L}ANWXZ$zgk0oYFG17wc94d63WU!!FUp@fB=W3G)4-9D z??pHDKN|$${)dGJ#XTzWqJN)fH^e)OM`&o)Af1w#^;dWNTlP91@Y_j51$w>}8Kn+d!FKn`5b8p&>SeZm?4ap8%|FO}~o2GJ-ry&~p%)iO4y$Q;5 znmsle+A;2akDEwF;!EcxgEchwFj$8$9`3%_ICcbnpQo}=+iQ8#p@#_%wxHZ^!{yvl zcz--q&K_8ZN&W6ck)K<2{xv{D3EWu=+(jS@&=8lJ>W%w<2B+wA+&?@&pJw6x*0

AhLKHN-<@)>nJc zzwOnZf27WZ(F7jc9_CG4``P?T7^UyL>G(_SO0<$5P!YH(-opvA%@A75{$vEKA={aQ_&B3m5|qYgZ0&%vAGa?N)X zoCu@khYv_YM2L*2ds9F8R2WT8{m-M5i#OR+{>aP83Zs}P%Pp=>-VM3QXTs=;k>9}s z!@NmvRCT*AXTwO)FtqljJBmARh6Agzz<$uroA$k&9QpwDx$d=(+5z0Du-hIDuGhPV z*>h){&nX5r9}6QWZ=dczz?lVzsQ@q zaZ;+dFp?WRH`^PD0(meO-k%pl?b_jbQ`cW}J00px+qt;Ot}t57#k^4dcRrk{DPhDV zCzs;=Jbbiy*&Ez%I;7&8p&S_-OQ$A;k-%w(>+w`46W}<{)b0K=yora9&)A0c`mEki zHpiP%ONM;P+8RcGzUj8Cnv3(XH18P`MpwD0&rEOnx4m}FMFXcP*+cbn(_80=9`!zS zR%e$Ic`P>`gk2;;L}W76L~f*6S$x3`C2JEWLvD(2g;8T1>S5v z-C&=RMMPHJNIXmQ@BJM5^gC4>_t$>UL)|3gP6qZy_I9e0C32DWB}5*aJU2zjgcy3{ zMhZUfcqQXfWJ4zr4OullUX-k4Le7W=xw2&oU)bXL4!KNQV~pI%9E5HS+plB-r}TUx zPQ_u6j_XaF?>T4%(ZAP6hTb^6*Gl%gyp7SJy+qYq1Mr!WZQ>+g2Z;J*Hoq!;qHM?| zO+{{D-{!BE07C^bcE}E*O%`A~FHFN(H@XS{PaS}0%bECpP~HU`jfP$d+zZ7+ZP!;yCUA-a$EWSv z*yE{^{b{l+_&soFFjP!Sl?)LR+p2(19dqy;`9R48A1&~1p7ajfL>DVDEhiGV;~y#6 zh%?LW{r(c&HSKV7XgSJT{4Lr9tdAvY#7ia1-j6I+V2h<2v!_%jnH?wbt0$WC!A4|J zh4$n*k-vzn-+$U){YA-;iRjQE4xUGA%^RWe!OwW`KVoao=@ zUo#ic-5pfyi|PBMLOg%L;``TE8K_t?LT>!gKCQJ2Zl7qbVuBA3zyE#$S({B%tbg=% z-(uuaFXiO@^-8v2aqGrkaJ``l{Te@Dfn%f=xzV7Q>xc6nZ(gQpjofPM{I!p6G*dO? zI)6c~GEBB}TcW<1ua*~l|DRmdRw{Pv)W+h)c)r#cIe)y<*i&ea(S=%mA)a(UF0^a(F%^qKi2M05 zp2Vp;Ofpr>Av=vUzMgcDlmDMkv8y)X*gqbgRPy|tRT(f(}&RT2ZN$Gnr!JktxZVB`o?n#nTtElByRIG9> zW*9q9+MR#lr(U6odDeATA0bbg^fKyn?M)R!rccj}KA!Y%dyKh0rT3ivA<#;Mk!Fx5 z-Q`X>%Mf<WS+ecr$CIP6%7jY{A_t#-8+s=f*Y(VPG5=kW|pehyB)e&Zw)+Y z&x+iZCdI0TSaW?(610W!zBQ8#&-X-mTP~i1_TVYJt8x8YtEUAHc>nI(hpnB0a&E$M zVJi0Wxu{uuOHcZG?bOgsc;Co)QtW8uNh1ezKH-_8Vhhq5f6znyPB{-xjXJGj(*}VB zyp1QZ*!KN1j;ome1)JjmhMoi0tF9E|tX0`sW2Ms9yb^K|Xit&`YA5AV@LJv;Hd z0%w#9`u8)dkv)L*>O?0-pnvASyfx|ve!nd=&?0e^CtVG+Uq2hyYsMQC<8x{Hu3<(; zRZNJ{!FUwVr`ql8@9}o6r3bWu43mt zb?jCLEZAZ^Rm|r78Q1yn``T)u|M1vh)aUlFy2cyduW);;#8KW2)I4(vluY3C1(p*OBfWO6w<*H!zaKgn` z6~M1}4B(<*w*Ih9++*OUNBZ88hX=DWSv(66{bRs6a|2m0+rvdufYpbmt7mxxGm+V- zq+7rX*IV3Iw+dzz;1hWQ%w4~OTLiNhn_eqRf&ctGqfY-G#JGej1D+9_@2mGLh}l}+ zT$}=&_}6uc+npfxV_}}{e&B?rb5e|yp#N=eeeZ-xb?bweyp7GBw2kgZngAl9Ym_hfyv$JX5m1!F>j*zMWh z3iv^63 zpj?RUKA>PJ{$B&W02}vB+4nPA!Nz~O+TkPc?AZM+`>s_m$)zRZ-vJ+Pe7d8X263gfRAav&>V-Yv&?hG zIsSZtmNIOqg@0dtdI~&==f=WTEO0CW--}9I(yfVt;jPy4=i5AJe!rf;Le5IKCvAE9 zysRzof03^K=Kgs889hu}0`K5lKaQT%dSCLQ`o;>@-rFv@7~jYCoNONW>hqh&-gLu! zFkpN5UEsIFQ)WbBzV%rs7jQ*Lsu{;uH-Uu(<%0bI^L@qqu~W_fBjU5eoi`qo!-H0V z1Kq6OP@Wnym!)r6x^u;JSapJx#W-s{qVRqhU33c)_a_QZEi;tl>rN} z=9@hTOtcmiz~OOVX`Al>a+kYl9QW_`bhbLkgBEI6#=iw#y|?02mr))xG$k|OCGdT> z9)m<~xPMS&y#SVeIdP7hJgB6;==(ch&-Dez*IRl}K!#og$GNXF2bcBoAm`JU*Ks`G zyV-}oweGZLU3uGkz`t`kCcdh6r?KrH+uj1kYAWk!l{uR4LKodhhyer^e8U;;GjU;EeE0vA|DxhC3mGLoyu5_2j^|=KWexu4Q+X7FURu9 zZM-u3x>Bg)hW#81oYs9@N#MKz?ww&$8mfi*xbp@$_rZgl;&(0-@qVb`U|@a6tr?37 z!QHAD9%~I;-}7SooC0t)W_GtU0X}1}@Wy>`xlU=)Pudi?WbUS~x?5bxndNnZPpM_|^MY;LLNiU8s7v^WigR4^D_y(420NXCO1_wRJ*?bK6nwxT2)KhRz?>gM^ zD6kL%SL#gLZs`Vcyx`O4d3hffm82dB3}hA!u^P50!E}#U{`S0{i{{}ua^10TIc_2q5rFu{;!t$ zzgp{&iJzXCv;U*T{;yX1zgq79YQ6uf1^=&B{J&cA|7y+ut405>R{g(P_Wx?#zjwGf zr05EKzudWQ=0qdG16ByUu4-P;OnoPc9UAbX09gOaqJfsW;CkcYe7SOP>G^(*eor(y zIN-Yy_$Xh`7ZL?&N7i!uMP-uE9Qe)Dg*$ctPu>_Gef9+G7mq0CA#mb3M17_Z8MeD! zZh-MR;qd&wxr>Ov;BRUUENBeFUX;W+`hiE@dadK)1Foi)Cw}z;7JP!v;NmKry`($v zzS1SpF^0IG1t!Zl&gBN=mbf0ztpNrD=g-z!M8Z0@Sk45ES>TKJ@=v2YUk=zw4>u~< z^^u2`*a4sG(j>W8f`awU%u2NY?sLW0eH+$S&n6)14!A2%5^9JrgO0Vu7a6ZZ^%dnUNo$I zJDn%+<-<;!2rT5R>jnKU?!mdWft$}jl-V*zY7Vmj$79nzhR6dP8+_CKf|%bZbt4II ze;%Yd4CP2MH3Sy4g^?ta^?x#zV1d< zVO|i+m@|Ke9&ooWou)=S!1}m(vFGJ*M~dNMf*pd{ZX>8LUvQ+6y<~HO1Ai-$!A;i;EZ^7<2|3-tM5@olz4TZx6V^3y)Ccp))v{oA9MoLn1JFO_S;>Ri zfbq3HlM{;ZfzY=)>_2~W%^mMJ3GKTG(UqfM-{jn*Q!qZHE!wX|`7%zXjrpM~&#l7m zzkNo~Iq*ZhC+)+46Yu^CYCH}0Lr05Gp}-Tq78bMs7C5zlo8A6YH*7Yz^FBnJn~eIN ziv9nTMG$3Fo-?ThzI!NsP8_%emlo!)>^DZq3Yt!+{;>gEcPRtM4hdAU$HNeOyN#%r zC*cJv8Fz1}V1E{355_Cmg+JL73yu<9H+uTo1pDn%f!z(3VE!{IkFPNYj!fI%CJI>K zj+>-pf~Hm$xEl5QdK64mGOa%smwnA4>c+(xrYYHU{-L=5uETEc{L6ql)=VBb7yg1u zq^29sfjz(ZA@^hX;2Pw?(Q}n-3GE&{3G?qRxB&weE17#!Sd?!OaqmzI*aHMEsC)1) zfa>=6Y9#~n^S;StL8B$JkFb1?YQObr~^k+mn_{Mw#_WzvY^&nmTiiSfX-H&8x0P<-R- z8_dUvf5}j+X z*BtD8p>r#6quQziV~dn*Q8?5`z(vZ#17_cbzeW$N{>JbJ_!{VX-%+x&&A@`&1Kgwm zF0D=ho9G7S{s%nLBQ_)SW0d$qF@?VHv1^pIXECU6?LInm%w(QgO$hCR|adz!b0 z6LELVLvz^MKnY#p?nGVq2g_2$IOSi6w-dFy(eKC_GZnkTllf&%#NB9eoR7%e^0i7Q z;wiXB7Ap3+CDdG0PSpNK_e6b^Pte`*;32qmuSHgVtFlrtPU)Wm?%g1Nmber3zZm?` z@ot0@37m-deZt>J(nrNs?$3$b z9_vK6wrbg&hW#+T9kPq#@jRf?8b1*J4}|Wmi*urP9gw{Xd!xV+nuPwr8{SsMxb#5I zF0`jfZMpYw6)TQ(x_e;16DhvF9MEqV?6I6f=cp6u|GN8R7s`XDC#SbQ>qJAjc!NmA zxEZTmjuTBSJk)#`!QQ%H`-WOz&!1kWM|!}%>F}dl|3W94{k>>nhPR6C;va}R;6CPo zEB-2GG}Eq7dLR9pC-aX{vDr1X7kr<<|Aka`_W%`(<-wP)oJjl9jh;iLum=~((+gfZ zk+7~*;C zObZw7@9PqwV*94w9bzE{*K~_V3yqhm*pi~MquafmDTiyuELJf@2ie*BL+dB=rb)Z` zD&}-bddhZ$Gl2=bcMn{TyH6$rLMw>J1kQqezw5NMYsNd1k<{woL100{ccL?u20Sfl zg7f9mH%}=Fccxz{=ad6bU%fK|hrrE!mWyF6QZXUMIKr7U`w{7b`&;%%v`_{t;pBVE zRcz~xWuBK7qJ3uFs~E9L#nh40!VMOoe0|Kw0@%L=O)lWGZi#x?AN4TlYiX@UX5_0xxiqeh|Y*pu@!%hgkFnwCS9&s z6%T&{kN?@~Oj$Djm6H-xOyHo{?o3hVUsqg9g8$+e*xtdFzWZJ9z;7tGa&*Y|JLpWp zcMmu&C$vki3}+Jj$T;8o`0$jU;BJRkLlu^yV%!XN^DMNaO7- zPn5I^{+kPF&6-|vrazJk)7tD&F;y#g_^vopQJ1X>3*e$Z_g`KtMEmpPMV#MT@nwOc z$eCPyx{c&m=ob&bMLz3}am(H4Pt(`5-}l&=>h8<01_6Ic>fY8I`{DQQvN3tUa~6Az zKlQ?yx>WkMD%^qk*Zdy5@(sA}5r(uF&$C_X@}k>YXF}wA&-4WNE888OITT#_H5&|T zIsh;2w6V=P)Ys)x^v3&WKV|BoDe5vjKhI6wu42eYnYH=}w4+u|t^9)XJC}FZehByD z$0v5w-=IMU?suNkjPnI6S^CtR}z;XE!Y#0S{><%D51{=tpmdlWt74BTW_eQu+GAbXffgul za;;CNNnbEt#;&TK2rO_7)i~3W@WXiyXs-@;=1Iz+wZ)~zh9#>Qm=NcV{^d;GW(Vf2 zL3>&MeV07sk27I(zDou!Xnt^pb|V)mSiNOgHR_8TW^O+Kc#M5+aXju%;JyL2N&I}~ zF7Ds?_MJoVIxe)Q(SY?1TQHvvsh8(8!}+tJ?v4AseHN;pdM@Oylf7Xe-q)17Q`ZF> zx{%qG4Yf0Y8yl?b+Yflk(Lb>+=zo9GuN8gA`F&PX|CzvoJqWlERHi?GPp^xu>k6%t zPMa@0{e%7wh9=F5zs@9RucN%u`-bO*I%k4{L0%8w7jMQ`Ykha7#cO8VJc;v@YqYnJTLgb#h*Q#uiKu_v0|c-gSFypMqPg?~?;k0g&Efx^ z#7TzG-pF+BaQBRgA@a4L7qlvz`XZY5yo#+Cor*ES^YIk6VEF6xLH*VY&sR9-UCnuh z@ycJ27PM?Q*USZc?|EDS>{Xq493JqgnR$u`XvdV@`8%uMB^4WU&1(MyXti`Q&Uw@w z_$`*rKVbiw(8P1oVAMA~Z%oD`XwmTSJnk=!bQUeqh8Bnkj}gvQvC^6ej-!Cr@`Vlj z%VB)pg?302WnJS%S5*vAoY}u&U*i&QY1dWkkkydZHymJ}n+VG<{MDg68PW;%wSB(# zMfcHvoq8V~@99D-curvnzCYY>4eb?==s7y>r79*a>^gJ{?0L>Twh#aEOvP+NDlgQk zpdB+QX#UO@_+Hwzjd335Lj8~7%l-=Ed*vSeJ{lML%i~Jlsjx^np4TKC+Bcx$v#Y@S ze|xFfvFXrSkKs}HH%)@H7JaD1X@$X$a zd{Z%jbFmb^e=&XWitnf&Df5vP7%!j%>+nm(O!Bs6&aZNz-y`d*y8eN`m2>8NhxU@8 z)%TP}A&A{R6l(>0><3I6lExv7Q=6V@JedaXKT|CrsY3y zysoY*?Pm5rYnq2JL1RbXwIP?N72+Y(hZ0Y>cBQgrJx;Gfe8hvn2$bsJN* z1XvJscO^5s8-1NSgs|jI+Y%=9guVUAgl&oKLs)T(i6-Gzt`xW(mT{E-ewyZ3H^7xT z^PHH@A;>L)rWfqZ1HX3torib~cm}Vi&%Wzen-Ai4`PueQ58SejHsqR>tHLkRGHdJ3)gfQ$bbu2c(e$R6k zbwe1G&pNX$uGHt^p6W;Vy`T}3;7Z{Ui0nXnfGKg{y?w3?S_Eh>LmqdLfy^}sv&Z&-KhuWLhmR+|u}yVLn-aZ@*1(;aF}?Lyc|ucdj% zn#2Fn>EOm?cpnpE5xCjfjlT3+dbKm&hoE`c!Hv?arq0}i_UCc-ZM(WrI>K~(@qVnM zw^X+2;YPkZ*&BEQe*klwkL9m}aR?JQ5Bs^%(=lL&1m+ZNfy3P>rM}W*eJ8Xx=ah4E zBQTZjJqUabsVR#kZVg;ks4srrdr?}CrMk{O=p*;jH(^NNV z#FOAr4uz>V8EI}LwYd8y1??&L*E8HGS*p7{*f4~(;{o?)-5TaIyua}O`nK?c|ExM@ ziEdNiUvG4q3@&n`$1&};jK%v0vqQc5u^V;WGN6ReKd;O^9U5KbM#xC{xdZ*>#kGWU z@ilIQDAHy3(Z2*PIX!n0I9-9~?_5ySzMDH4_AV{)2d+D8JzCMroxXOzJ3Rz=)mXMQ z!_=MXjv=`l{k3T7l?&E3?lje|ah4pIQ(u^pJMCQR*TE9~dop*t`MJ}H4!RHZfk&Cy zoNAbNx1Ac&C_x>Au3I5RdV>haI zVSW?17WTR~#3P|RRC~-T|Rd%sE&e1RNUsJ1V592ko?+qZ*IjZ{riBp$FYSs@fjR*G~_9@bm42c&U#| zjh>;tyVGh0b?@duVoutM`G0WgsbRJ4J!l>0*1-3|4k_YE9X;q~=2mG$wTgXP{%VJd zu?K-^+59`ce~50rJ`m-|>`wQ=_c5zSQ2a5Jcd7n4cGYVYyVNQ_%C#%}?L0{n@o79f z(W)o>`|q+kKf?LS+*X^+O+4t-CaAdJd)c|;LU~(r5883-)z5ACURsCgM0of1pm8M| z_N3tYJC<+RZUcXM6{Od;<9o@&q$Q>v)SHWX)~Z-;B-mc?JiD5Jr3KH^-#*VVygT9t zz9n^QhxQQi7w~&6PNs|ZDQJ+P{&vSsp0PxK6*RDMKXSeSMgQyDYRkO`@aOZC)HKBB zJ?Gp8jvn+v>eOlu$~`;k^v(A3phzANh5B{+K@Gs)gFKPq)*kOe;P#Vx5D(9oi0e(; z|3EVy@dd3+z#fb1fl)NBoeK4_w2DE92c%sEvHQY28o0iJ!Gs)}JHdmbyE2YE#P`RE z#}Ul-pzY~kOT_m|(8!tRK~0SUF5SiW?opK6LbA+*9?yH+xednu22L&z=RtKn5a|!R zk|E zpnC_~_Z6ArexsTto~ibrpxcYbH@663*u7sp_}PO}d6Hls%r9^1D`wVtkm0YyMP
    UdJmCJX2N1is{q2zkUC^6=$>h{v41vtn~H;vJ#f5ip={2)ko;1Z_wJ%04fhgG9h_A+GNvG6IbS8|+TT3Xa=E;NQC|~;+*{xk9Ls%8T?*-Cc_`Q+9gcHg6B$@5!2 zOXwyceNbF}-l=AsiuyF}XBMWX2`TCZE}Hfd`eh06?IbnhlHL+M3FSFC#~nlY%-?>K z@>)r#yz9EamC0&m_{O^TWgQ8vt~|f#7S4x~SnsZA4=7a4y&jA9+E6CWsPUmmhs?hk z#HbtM+g|xlFf^5JMXQ;RQ(xjk{|)M~ShP{ieuuxxvApj?JjXb7otmA?ocCmPz7KJV ztk_jJpNrS!_)yU>MEh~=o=rZ)B}tZ}oKwojtpv_^HGV#@kYhT_hj=RI zfu-sOF77ZNY7WxB-zd)z2L_!``VgW8j(tUa$S#Rtavz%f`$`|L)oLbiEXaIFj5s;p zwP@d_WvaWuK7#hyV?3V_ZxZAqXxU|NP%}|_i_j0?A|3bT#`hc*2tF$!P!R`t2VMshOaup5!BF z&wM_tW&$S&$}{F?b?tmi%>+%!T|NXQ+tj+FYR09M^!NGD%z#JpuVVb{gyhMZ13pwb z009-5c>gu+4L;%boU^VC#v7DcoeOsOQ2d>Ow?=2w?EbB*<=@kMNYISM`2Dc%XiLYV zJ`}ojOXfh}K`=kMpuY{_gN)*{$)ON>$rc(bd>wH zp4&b0l@IOyU1mQS7|J{;13&msoOSx1P3P3?OXijjX_Y<=THlzjuGOsS-L1-p{+++P zQElu^HRDnTyQfLW=+BRVbg@1C1|x=g_QrTp3VKips8 zaQBlQ;S%B$RS~#9m-4dfhKnT)xzMPOQ+b|WDWR{Bfbm3qy&{vZZdfOwURV}RK9BKr zywkH48zsceUFm4Q6Z<_zi%|Y&%{m>+6Kb}gU(fC}t0YvvbWqSq^cO+nI8s8frBD9$ z0dBEy^!`BL^q*EgW$3>Gwl6z2>}RI@$F zf0oZ0FQL}2TPg~+tC=bJ_6rJWP*TT2}VHC>HNi*lz;+ z@c_;%YIa6n|GQZ$^atOpz&E*S*8d>bNbtRIhaqba@cFM)^^qO;dq5lppzdW)F`ah?P|5>PJO|;=5d5igZ^Pcg|ZlHdi zkAm_&AZz>!45i_F55N1+7tW!I^94;l;MR~-UV!>naFJGR^apTpj70sN_hJTXBOx)5 z@4)$|2e$l}(nCW1AfYx5cxX(nx*p$OVgGjx^SuMa$I-rr%)s`5-@iY5tjSxne?83L z0jQswdum-I)aCS*nhQ9en~jYfehEtKiWc-;{W67z2jBuq|7oivXT*^ z&MDg^BRZpV%FH?|A(0V6AuHouMnXg~64@gax~L>0Dny8kb}1ud^n1SUKELnx`_O-{ z?)%*Lbzl2>yi|~6jqtCunaSG=o_LVWOghNGxU#K{x-|BUjv8i&$ z;%zxtUx|y~LdM>m>|WLncD{*LV%lED7PstdGW!|c&t5$}-BKpt=T>K{IHh_2XeDDq z+={N>$-sUkqb5{~>v5~%U4rpFH;xT(cQTbR-*dZbY*8OV9=e%~soFYJbc4O4;iDld zVGCRysVd%m8rbie%h)kEhTg;W+qLxJ2V)u28hdHyV?NqB20)GmGPYzL)DqC28d(C# zRV!s)B#xYf@-{$(@SBu1h*TzgMgMIIDx#@{7>`xz>Wi>%yL4blIZ|fPe%*@K@z|f- z`m@$3L&^k>aEym@_cq>3x{Ljm`?EjqF@I?r$G-n@L&_E&Y*}yvwy++alQO@h-TL&# z?+ZSRv3eVg{U44WMfm+&53oT*N!j^Mn)QZwU%i0;EJwmVmDM_D1-@rt^EyzU1KF(B4I*I=H z#B#-`c5P&g_GK~XuWqyf>Lp|U_Fpi!mKmCW1^?K ze>yc)b5l^2uWKq}KcXjWx!6d}R}-0i3mKa;?crc;V>OT81jJQm^e^wHrY1jJb*^V-1d#dsKicc!ukS2bNk834^CuA34Jre-jGUiO&pzpDNH~_~Y z%HL&)hr%1*-*fLBo9p<#iyJ!|9e=IjKr=7Wuf}>z+6r$~ytN!^L{(DevY>5(8uyo1 zmdW%!NSOnX@54Su8^?U~&lQi>dE{b#qXQS0vSn}!c)=!$peXd;U!={4`p?@2Rn#Ns z52JH79>#e8(C>E3kE^BZ`L{;PMxi~yglQpLB4y;R9Q;(p3EohP^)U`Ni$sEKnE<$XS*(r$y$Kl%c`!OY>oLZfyAb8y}l9H zj0RzTBd#mhtwen<*!Gb!>zYw^F0i4DdbdGWDZ7DgSO(jg0-)wtzlcKx&l_2#eK4aH z=4X(nEI@xNI|jCuc2Y)?IA!Q>f(9YZKkqvDE`5dX*AfYI4=D@C-(%^5`qf?zK4sNU z$|m@L4g8IY6UEC<2Pu=$z{|&Z=iTX!sVMKaZ>t}EMtjZamLuyWL-~F_pAq>HOTs}5ITe}?e<8856ql1XC*R$~1jam)o6KgZ)+Iif!qQNLU)Ww~%i_Q3DG zJ0P=s1@<$5;k)4Z6Jnt57bs=xAVLz2>rmeA{VNFj7vfm_tl}FjFRk4CpOlRjC9I$F zMa4;)^7Te38?msrtNJT`FKBN`Stz8>y=qh((WL$R0&rfew%Vloh5auiiWjYsvJde$ zn;F3t_FY@0jQp0peyBJ#V_pdMAJ+>ec7#pvMUOpF_Nh~&%p-W-wwCv9=fEc1+e`f3 z)$#nPnFpop;N}+lGH{(JzS{}!1x}e|MFH4>&`CeGwN@43)K|NUt$ukzD>AmJ z-9}5hzFIEi$-^d!x6amDP85jh^D;KK0}wcUVDD~gFzJ+x-62t;{#t$u;cl}Stnc7P z3K*c}1n-)Q>;Jwl=T&Cy=P6nq)BkGo4`-!}DCBJ>X*p5Sna4|6)pam{7) zU9oe1DrLb0Y#5>CZSQSa*6IoT8Sxb{scekcdYN5TvzSvrR9AHc=Q+Z<=x?TFQUG`dX+}k zVm+AD4#pYo5z_s-#-J+ypd z{Vr2E_G2xwH%{@l(sGh^jA|!i+aLwv*iFli*`{_dzcF=OZ)IDw__LI}ev1hL3MD2WBg!Y{mcyd)6d=DMS2g}%ovF8{0 zx770TIFKq1l`#wIkgc>lKP=?fu`x0RCe=xCEwtQ%L@#8ry7}5v%RAD2E;5#JdhqFu zO||^f*0*b{VYdTi`_x8SzJL-Al`>{mlj(EMM9bX|87=pe$QY$ddN$T_GRqxh%+art z(--`H${*8B`om;w0O0Afn`^ni6Jal7@%9t8e`%)W^)h$c#@JxLa6BOINfRya04l=e z*dLP{%LC6NDsqEu_3NVGBDx46}|H?So` zBj-CCVf+C0*ss5gRjkyJ`wpvb7YuWW>0zA$F?SJ(!cG~E_ zKd`-Rj_U$n0Qz^O*})o){z8)CZNFeW8Xh`lJnRm}qUTRAU$$~9?$H7FuQ0(*0Q0FZ z{_(z9%Mg&n`0DBgHf{7LlEU5d4gSS`i(PV1zF}ki3O-`IMUV(N+RxeV%!Ue#uT38x z#chOrXyvh6-ap|_0B_eU^q1Q6a3J9K;r{TT;rma%*?8s!-nYwlV8Xoy+B&Vrk5VR@ z>*gDR_C)k}#mYBQW}hDNqZRrSvO%`idyVx5L{KX94}r@T^NWBl#q$L&(oFbAZ03Ep zHq>$<59kr*hlonE4zLBS@%uRcFFRHvM|)INA<->aD&&(-!|#b&`CTH`hmXb^I^w#( z&vXIn<%eg-==!UydE;K4ma^kWZ?HvsL20FScO3k$Ap0I^rsXFpBVYJp{!G_T3?GW| z6*x(Cx8%H(-U8`iI#mOr`FIrKaB zgTV;5^taG*+77>u#Q6t#)GF9eHuzez7v%?+u{GucWFeTh-GlPC0l)~h839dqV1JU5 z{-@_|G`(H{?i@oP+ttxaEZ)HTD}syR@aJmM@w#TJ{zDi7*n`>8<5{Q%;H> z`eS_u3HT?>kH|K+k#ChUWNqx)iTShtp^>9%VYfa1p?8?AmQz}+(ohKJP$=vIz#Y~0S}thPd&6Hreu05n4#m!7 zr+HE~1QK_7gHeB=KN^hlb&BJexp_lT9uZCi*sqZrw=LF_1wl|_nI&arB%;o=oTQD1 zOu_yyWJ_qlXv{CVqUR$gOPTpn$D+=#kC=j}YbM$UbV7HrUUet(!TD15x%fig`p#O8 z-Cv8nzNnvyl-Vy8N#mjC>@V;3jMkzylqx%lCM69p=och)5mv)TO z)Mfr!K6k;!^X=Z-vB8&S90^&C{sRe*s0urVY&`3^t5Clr_VmM!bq4Ls;gwn*;s!SM zUv^A4*?sIh*oK?ij6T*tROipd{Uo`o*IZP`^^51#Y3qpC(ogxj-ox&vkCSy@5gXF> zg3DUi+m9TaGkK(l{qE4IuN=QuN|sn}7$ahvV*2{-@Y8bXe6Tv>`q@aR+%DJhZIm!N zNW`EdlfMP^vyp(114PW_!o23QmtuW@q-wt&A_nh%>uzX|eJw7h=XVmZ?@N8t-l6{e zyMo=hi-aeI#&cFYO^QjzL^;~n<+b%y;tW@&kR>hD*N&)c!gZet(y zg{`~)+1EVEjtM*+Gqs$KLpkAS55l)j({jE1QCq^c+p&x-!!9?&cuXVm*Ufh9x>3>1 z4zR&6zP$HZJ9dqTD5v9o3e+sHW7Un8CEMe98;e&pxuv!f_>4}XJOYo$1Um+lS=nKH zZ{9t#2oGmFw$}JnLVMJQrumdXmq*(%A+K__mJ`hCo+IA>YSiuJs6QHBwqxzs#tU#r z%)|WKXU)yN%#Q7NZRot!3+pc=KURxTAK*1ui~4sfR93CAvty4aFL!~KXNG`4!3y;; zzS$^iv=>o{%evXI`$PnS_H<}la{jg{>Z85Kp8|}Z@@AngtMySI(;6H+j`kOUT73Qw zTNX(~b?6`FV8I{cBth80K*QbiH=R@1y@gxqR~O`gUwM zEiCA-1SeM;+p*~cP{sHmSc=-*j*SG>wGC`R6Ra=lkEq^p|7g$sV|t=Kg!mwA&A}w& zjZ)MHw4!`q2a+RdsvRSkP8qZ)ErHbqls*4X3~}1UoXNP=5Y^9lJ^*P_Us~gB`OSLloSr9P>ljfSpO!!; zlSa*~{3v3=y1!k^NxFDRiHKc5`0B(~j9)3f^s`9NPDtL2{`2{wRqIL-gQ9EmA)B;y z`Sc$|tS^Wnhi}mGbQ05kC8}HR@O?tw*dq};1vpFxe1EewvS%mni`ZrT=WBydKUVgg zayH!*3FqBrxK0uemoJJ~0{?hnHh#aMk)MIXDG{S|)Sh^L{_2t`$0J3|f^Z#p|Ax=Q zg9~7T1vAln3(mt!4SX)`6R`jge73^(gmt=huSKYcy;?W;W9EO@A5L&7=(JzNC@f+d zjQXZTp=c3%&;yYb*z1#KJqWulVj1nQfWiL3gKO*E!uy{Dc=bSi5&Uk!JrO(H(Pzd0 z)F-8t#yk+Qm8Z{+67R%#t+GlQlZNL*IwT3UzzdfyVjq4sKIssu=0_5rLi+|!+2*2;@9RL~9cbSj>&<58hKpES z_Ol=r-Z!@NYPn9t z3=nr&f%1@ai#$lg{(b*#Z|{7+y;?44Mj69D1Cr#I`2ASNX&J{64+Rsu?5_&@Nx`2-Zi^?#pF(()gUXzRGai$yH< zPei#gQp<^M(x5`b2sWd425Y8U`aU6*D+C*r%sRF7{H|px= z2i~`*>XThR^pES)kU?}9=NZs^9l0Z7pSu9q1{>&#kq5IyY$C|}r=q-@NQ^gE#PkNK zZx_+`ltVP}9s1ip--m3$-+3poJ`>MlPcb8&lRwc|52ns36xoQ`p~k_xCgXbTUlYczPhkCTSyJ3>pqQoC-#Mn;G3>XtT#O5~6EjNBb;A2l%X8oK7qdjY5BW<` zo}Xq-FR5_7dhD70{wVL`v1bps+Kbsq{q@Hl;roRB9PIP;R>%3_`=35=HyLLwX3MVB zZ$AsRcX-sV(Xi+2n&Mi4`YY~^0AFu0TlsF#G>-NZ@-_R4SzKB3+Vkj752uJCBJe#J zeCJQ%x@jIf1|7s~*nx?MPoq7KP~NnunC${w&lL4PodS`K#cVi<45K`|rrt;{Z!Tst zQkH9;q5P|I^UtNX7BlSfQ$nNgeKjic_U*)ML;UOe{>M=s&`%9)BWA)ngZjekvUL&e z-_Yyc@v0cqzZN|R_y1epV~uMX4a0RX+J9ey{cVLRW%xQgPtdxQO6&4J@xDJ2*Zmr2 z_;XRw4yeENkw+)?MSQqx&=;RT)HnI*{=_kFt_lyqE>rsX7Ew{#EM8^4Rk|BH?-e!N*tLYW@Q#NVf|ree2B81I91F# zeVsqaA9gI|AI%aon*~SpucxB?bpAkn3492!1CfqtzCg@s0_I&`ivA(IceR+|c)>kj z3)<$J#HT7hKXDuq+Cwp({^?J7_i@tuiT%NW->oB>+cT@+q0L`>$9!X+HmCeE#41G02|D zNo=4B;~nAXTLbOcb_&c_;(S3`@gjRh(g+csv>Z`r>8-)`Y)gjiw#VgK-t9==LRZ}1 ze1%FczYO0;8m~j_1?{ArrPzNG52L+3ivn-$-(oFaO~hFJ>;=9S73@iIE9EOY*)yWU zv?)RRcmX-o%$|K*HMZ+z*!nK+*J4fVnZOePo1_t+){2=o0U=@U0eoQX7coPYV7Cc) zzrd4MEM{Oyn>Mrp?N0~zJTVjUwDCP&bntqI`Ekf=KMlTLn6E#G8POWbQ9t>mUNh#w z#_sOjy|0+h2v7P6^CA55Cs3b)R${f76&OBu>hVL%?^l_OcBv7w3HGrueb7FcU3@Gb z8`!gd+pGH&FrOLe`0_Q#uy~B;&6so64Yq=aWZz@{g(!=tfsWrF4-wunF`MLn_NvA} z#|gdv;t7WTQPI@z(_;+z+xEFANAW7QcQadb^H^gA|5>#vn;Pm7tHWIvuFdF zC1w=24}%RhasS{n%qMi-$M<7avg~J#Pw%yD≀57f=2362`h)xVt4$0Q*g%# z?~vct2?OUZ-j|`kjx9vOC=FbT@?0=QG_M;c|97~bEMzHqIHCV3zQ@R%>VU16(=(gmWwvHbb`HCc=5{v$` zk?<&y5_XXQ4Hyqu+j9T(-iLfRn#a)}1-|Ev5{9h#=W7dao?2VF)!bLYN@f5tUWD_^ zmaw6R7D!m{g3d)n=>L!6Uw+GiEh(6iHxd0+FM4@ZzzRJ7aH^{%-bWG?Ymg5q`s|nL zhW^;i8X4R8{!7qEY>oaWd`}R{e@EhAjPf@l$0hC;@+LlPX4`{>M^cHs1eleFJ6UGq=5thqU@_pK?-CH$R!{IHmuq&f)#UL(odcp)5aW z)g=k*Id1;)kmfpWM}FJ$$mg57L37$fCxk1?<0NbWfP|CKo|pB!PftyR|L1YV4(i`v zxp{g%Uc$<@f>|E6pb>}P=a@mI)>y~iO@Syb@&RAg6ptvy^8~FG)W4u%-c-k-Y!~ng z?Mci3JG@^w@8kNR(Hqw6Y^LMahelXCqy5IygpTWrUlbiYyhFki5V5IgspBMtv_2H~ zXS6!Lp^c6co#@Qn5|$1Lp>geWeEsa48?Dg4NLsQIHdggE*62_F){l^v-Brf}w#mcX z5g#EQyGLDg{O(@Gt^BFTX8=8lM@L;goeYaL%*)=%Mj5c!D+8wU5*agwNQ1zXy_siZ!xud{^u80uq(4(c@hblk)< zE-W46;os*04VW*t*VXamV*Cj4+WtDOH}TcA)F=r%kBx5m0F=LA@{+ZvulX6b?7HCo zGe0)n_C&tvzrWv9yR^RHG#wwb%JcHXkrIZiY+K z9rA5A4UYAZ>iFl*!^SkVk$|eQ^T;Z>ju%ODC%m@C`)U8{hWS;z5{t8h4Tcz>+Z0`$ zwmNKqzXIi7ft zYl?hOBHTQtjqb5h3mM$ z)7?SB1i$P)9p6sGwy+l@_iSi^>pz>cXyV#Z!cxi~pWk-~<2io*(hMUBn;D;HpB;tz z=)6bcWQ_6`4E|3PqvQAHA+gK=-)jiu?*$!yt3xJuedJ&N^F1QMk=(-ksDdZ44aQSU zg45NTI=-I>pYVR5e+S=9)Nw&Wu08U{^{_L%tmF2Rp%Q3?@g25vrOP=T$Lz4;VRs30 zU1jR+6pQu`9=56<}$H{$vwi)Kbf6Cu;Q*Ba;Pcs&x^YAPW)N!yzNR|EMEb^3N{R@vU|1AkO%pD+S0#EUM9T)j8WBNx2IeQ*Y4pJQ#GaQ&kz#!kC9*KEh(eX*)p@9p!%h~UfoBmkDVgEq{v=+!mP#=wbgX=5ZcA98W zUqn}6h3i@?rOl6)at4&x!il&Zq~1KBxS5=d1rX`NRUL0UP?Em5p`5+VK6rf``UAm( z0_(|HG!n}ip}l*z1)`}&#)LTeMa-Ap?Z=M%iumlvm0J(u_uZYdTKT z*r+cuM&Ug#^fzQvj&S=V6ZlsaCh7P`e>j3*yZ2t`bnQ0APY4*SN@Z-bsJG8W*zGCN z<$YZ|zRevSCwFm!w=yQ+R_|iI3oE?!x=_X*6E6ztJCPic_Iky;30-`237&tDfGW~K^}ISQD4X|Z}1nkkhj=e&i?JM0>A!G9lx|2MBAsF z>$Fj;b=)DZqxtCz&J4To(WRfU-syYxntIWh#ZqEQ1?IcY1wLm$kTnBhiBC(h-=aLz zcxOTTwya3UUGx#LyW!02Z12u6D8TxN?MBOFX9ia3h{+!?zdsvzAoz|mvv)Yrqt8bj zZ+@ngckn~!IzAta7ob~ej53|sKseT<_+DvprRjjD&g^S)!+d4Fj+ZWaGcO|7xenj+ z7V8^mo-e+2W;LJdm371KGdoQE=}~~^QKCNH2gTT;=f%#f>~OUHdf0;hvjX}P6d%I# zh)(1859no(NYpDGZ%q!uzo0-Ue>5gJN5@+d0hT`S2HJ;rBcJHFeC3-Txv!067RXa1vTm1bH`v;f8t**Gc1)ivy0co&Pv9f)@_Ncropzadq}yVC+zi|ctG;^v_Lfz`OM-EU{s z;@(n;6|VaPz4(3myE6nYR#eime!TQ`1>fe#hjATU0 zzw5X!5!d5-0mAqreqsKJ$z2c_?Ocba{(r_}_j4HoIvaR!WS3s~K3BmKTK8qsYIXdX zIU-mK6wKS%Xu+KcZkloOQd751vw6*#EeKCAR#y z^EB+V;?T*qutm*OJ7=xL_mM*uHpLU%mgqRq48$E(uw*v~+IpFWA%2hg^6=Qw)<30uJ4jSA(umugc-3s>RP2`OlJfGlpM|LO}rA3DO zW4#*{e8w|a!Qw_GYbL%y<_)f0wo$=`{@7B#$V4vurGYO-x=%G znuK-raJ^D&kmfNO>jCJz8sPVrDv%&KSjP|MrT$v9PQfrrY%>SqJWd*~c;7O&dtS%w zbai<2Z3=eL<7dM50Xohh-RmBrU^AaoOkQWBdQNUv9iKQ485N=E zFSBi4%JF-Fe_^jeh|kQmMSD>~`+fx@soR0}Iv%A)CM)ckwan9Xn2ul61%I3oreH&U zi>DqPg8kU4UPI?ZDC)F2Vds~CNG?*rNaIsIO2@NFZ8}=P1pe1Cn18fAt{cRneL;sg zK%(QdI6eoSRj}S5(rTp8aj?BvZ#<`9txZ!uG?}2|r%60L4)ss`AgAS0#P0=MN)mn#I_;nC7%%Um7cGQ+BW22o zSt_i@l$d`@!T3}N7|hjiW70BAR@B9d7h`{cb8B&`0tAXJdMrnO)1$vM^lzi?4=$ts z)3$x#Lj`LIn1{t`^dAD;<|+n6vquP%6pkNb^teavVq~qVG!qHKT`s~)Y ze(RmsAAasW!KXsOZcE^iJgVb}&W$_%3+27p^q}2o9S2P9e6+rj-4#!ioX37RC&0bI zpn6I+J@uV~;Z^is;!QGCGXFvlo+V;GMq=wmO195z{8!r~?8m%Rrwxpitm6EZ2d}Wc zJS=ib?$uPu6vuuIGQ5ZV{{3q)>&%o);91Sae2jgwdn+YF)c@)8T=WOxe}paQuwcG! zyu9D`ujWd&?A_w)f%tuOR!e<(2W4HnE*<3yf=G84B|CfYhMhas2a&=%_8S~clQ!*nfhpl~Z{fV`Q`8x+C6Le2*V7 zD%l#FjYcw!jxT5vUMgCsWCfl4cb~xgv1KGO|CTBlr5`#>#rG{4<+f~ zpKd>!ze&kHy$7Mqc$`1{J2f@?Pg$q6ITY*H@o_iJ_9)r&F`4xb+oHX^?I&q=D_QEG zlA?jV;Gf$IW|m#3za4#4_1aVB}-?pEiz2 zJ|I4$WX4-A|Ca>+j`N88vS$~Stby*_@--ik4@H6IYf82!;P#6%@8Msj1jWlr*5aD4 zgUd_AgA;mvyn_2{;_e5`$b$d(Pqpt`*iibsdF?*@nGK*t&)Li}e71&p$kEVun?wJqY4|GysR>ruDA|J{N3>ro(0aXp*>30n|vf%NH-$4Uk@ zquuI23gZ#Vh@7ty)DA~n1gNN+**K$E$@~TqsKY1-j{^7!_ z@dHqQ1S41BedCf4*ueFv2$OAJi2R1VU}iymt^@F7KFTNP=Ui8^8HzWy(a6`Jbn8gG zKYEDEP|rEY*NlXMW2%zfX*(sYN{#$2S25x>J!h^Vx~} zJ72cADQ&Ig2X;JbyA-y-bJPU!L~>lA{_X`eV%>}oKRASlB+551{>_n|4G{mML^1T| z+--A)ST#ca#DdvhkDkN$0KM24*ptRwOFtQ_WX%cq-VFYE5)3%4WG2XS%*XR5uJ0eW zDq6`75-`p9fANVEO7>&>=s6qeAzs%OJ^8qjoq4_N$ROnZ(zd11F(vyotzM-|wT90l zz$eBl@dT{-uHh7J9~6o4b;0aQENs`T&C<>gcx`9&pN}Neb>KT8YwEn!0?82qi;0%idluG@QQs z1Z=3OOn#QB;Xuz1%RZ!J-2IA|<3kOPGtoqP;d=AJe1j9|8cuN1JK;(J@2$SA;ojs& zI;dnw2mh0psNsSx7|JiSbDV}Zo0)hy|1jG7hu6hBXEprJ>(1`hM=)M3kr8@a!!MUC zxzY;Pp{`?_5TW6V7Q?fN@-{@`@RU6oF7UpEKm=|98h$kg2z%WB4l-6pS7~@# zq=%*Mg1+xaP2#E*8cyl6romXR=%DVc;Rgtqvt7xOk#@M;Q^P+Kjv4c{5pa^$9vVI~ zuHp7?TW~)Ji{oZ#xc)KwMT=p-*gJpdl<68yd2a>Vlq{@sw@62Yh70R5>@S5c1G|sb zaFXcmvsKBi&db>#f=UF~bf)|cQnH>s$5<~Pg7(ZjcjhpzXT>5T*G|J{)89snSI}IP z^wsdy#>mVJ#CnJWQh0ZKUlR!7twH^g|D}V51N{_~;ICwkkl>zSj{59BuI6$8)@T1a z8`rneaBbEbNj|RkiD>p{S5plKON-A)Ja6;>zxodvYPi*(8!au?D_K)BFm(J;bFixH z@Z79qrbL`ssphKt);TXx-pLU0K2WZ%<2%6hyA}1i8y2hi}z-*;cNpkfWNWWSgJ&Mh(Dn6ftvSt$vd80sbu}fTAG|mQ}ezh zyI-mOlx!q`!fUyj3%aYmn6E&voP8B|ovis5n-?qD+xYK3>1TisA_BVkN>&&>u<7*^ zYHk4cSGpI*Bl)Exf!ASkPb%l)`Lb&&vWLjK*?#P#WG*18Y6+WY)02G_?7pR&U@uzx z$}ArCNsl4_HOKWNMVH5MU*t@C)~H7_*aJ`Xf7;bnUdPW3+lCGhJ>L#wM)xbwiZ{Pb)t&HUZN4b2Gny>2P zUla&`)2qtF;;;#7F7Up5l(DVvCO*6Bs^%oyr+|OUuFbWflk8C6kd5qcSjO6dKKHAQ zn$HRC_eT-{e9-56Q?mN2xp&0Xu~U}@W@%HF1NIQ<-DP!~ZH%?p9PR)xp4rwzs6!_1G^2`IkQxQ#J`#mz& zIKydjir%^|W6i8m(|WuEpN1Pw=vQO{Ow1M66Z*mv1H6{aiblZ| zus3zwzhP)B@TXbg-Jf6!d`7Tm67dP}qfn!p{{ATNUw4a3{eb_3ifEg7v@gNg8Ss{I ze(#U<-;4HUKNMYoCoc1NIoE54jAbFub2jkC0xli4L8tYtdH|n1DYH|!-v${|Iyap3 z6n5}L=SN>x!$0ebj5y%KEByXmR<4q<&$D8R$(}c{W2_@=K@Su56@~4fE57)C5=sPK zoMh~tcmwYQdWH`P;2+R|;dF+KwY)U5vBy>5!S^8p1$ZKXM>tkh7Y_m+CjjXmDQ8ii zZ;=2v0R3<9uKTs8z_&s1@UG}TAHXtH)$< z)c^4AwaBku`D;n$0pQmYBK$v=N|`#`PNtNQP|( zp8aWq9#QMSw^4uii0!~TwD|F@PsVx`?|l95L&Ad!dDY;*5zajerA!T;qK~UoT)-`P z0v`~b?(lLo@ZSiRda970gl%p7O7J}p@C*6;Unbw3zJD?JX;3d~kk8*ntLm7xK*jxI z-^O(TemDK&h-0%nR2=McAGCv{tTgGgVVVYb;Bza^wif|!drZ;#CTtsc;f|)s^2@Qct}B`QXk|GZXlu{*g|{+Ht`;g z9|im{37R#P*69f_@F%4-Xw(Dwg94u_Y{Bmieu>glh5WK3_;%K2C$0cKAb1Eerto`# zhaY?rJJ*j?&L6Gf*;_5&pUOvk4YWuPVbk%^6nrg!=e*p7`_bDM%W@=a?QAe$!7h?4 zT+{C^_-sUtE+^vo>BOIYQ^JPaY1PJQq>9gNlP&#z6?_&|mw$7Vr)oTySWY88OoS&R zQ2s*2rc;PNk8K!Y-xSv|yXd_>B4NXN?QZ9e=i}5fu|MMfZThm8^TwclNr)MIMo=$^ zY>oQ(Qm^c&8{+XH2ur<2{pFo(b>b890kAz@_(`GScd5ZWC2aGn#ty0pDo$aCV_FI8 z1sJF~?3DZ2@li8@Z+Kod?%PBaU)&ly_-PVmv(@6qKC~y)v3o65A)bkRps8qYCkoK% zBn)tgq4lQ$58VmOh8hX;)k7v6YzG4F!M>|Xjd?Z$^Vye>o8$3*N6@t?W~%tqV_TN5 zT_jS-}P$gzR? zJTeCg66o*qoG&(YLcCbePnnJS9uqe+8}-}Wur+T98*F+PHsO6^jV#q~JXO3>^5=R? z0OFkmgSwK9?CyFRAJ$VcKQ?kT;vlAAo_`AXAIH(VLZ-XT&PV(k9u=SCaTL61d_J;Bv zerWIY5B7(`UgRool<1H8qw_9oA-=m7d_da?tdE)4<-Ky#=l+keT@e7 zg{VZ-Ox*v50)+Tp-{#44KKP=4WE}Gyj{2iz?DKN8{{**PzQ||r=|03h5c7$E>p^`9 z{LPqu?nJh5G&(L}TQNM%>Ar*w%o=`AyGh00Z>wI>B~!wnZma6E8GHzUZ)U$hJZ630 zWveisj;Sav3C4I8{K=Rf`_l#y<5|#q4#av5nv!~z;Nzl$GM?XZ=*HpHn$F8yzYMepr||Y zr2!NA5{dSw{EeP<{3XMpRCRpxy`?PLFRj;L*j>HfyG-ZX{A>i+WWYQ6J3YNHt_Ayxj9B0e&rov}HG``zyD zS$Q7!Q{4AD@_D=d-WCaa!CbLrMY5DRO>8mx^(FAjAsxNlJt^z=->enKuYe!#)j{V5 zkEP70VL?&VW$*`$9NEkLCGe>eO>EX+f7505wMFWWzy~4#M>fGcJz!gj#`o!!sN&?d zyAHgq*$bnoVYk3P0{4C;@VYn+4qtm4^Vv=3;mfd}F-f>_>_Q6qduY_2Rpzofy^}N* z*F3pWJsfs(P=*^|zZP%{h-=`*m)Q<<&db1fAiz^c@bQd}dTjdy>rHZiOKc|@BR7r# z_&4g&feiTOraAMP{Q|$neofQBVBo=5C0o|7$p?Q&j|Ek!gJkSYP{q{A@4<%#`nPqk zlf&;t^?wKck#U}?4>s7}VL!AJ_v18D6V^k<3iWPv7>4^12I;;Od_@5WGd{xo@CN-k zfbtg9uNN`C5c@5p3A=*7NXWkf|HeEDkfD4}S0-gPEeC&HS`S?z@a(l=zMW=QsJQ84 z)3DjFUmZ~om{$dU#16xYNB0JP9(<+uzvBCA3sSd$kEY%C0te}L+#iD#8{gLxfbLm; zF#ZVe(I0qkB0vTIgkSpu9b;g3>Nlt&w7$BoKjZo3@lshJ_$8_zWv?CC6L|Ep{#JjQ z;QU4oHY*uBMS|j{YW`gJ-^w6dzX)PlUz~4#E&BCwcQ+Zcyx8nwAK0yK&+DVc_f%iq zZ&C&OVP_~ww?}=M%q8JkDy>LB`Ar47@+X&fn+ZQV+kMw%TOvBAllVkRJ-q z&%Aeo_vr|}8`42XeG7QKPB^cEE-KDWCd60QbX9ZZy4~Jy@x5u+-n~27L(QS4&`}@l zNpRVj);M2X^6SuG1oqFwS8A)SYn^KXQgAFF+HTL7=&w3;Vf6v7=hZ;;k ze%gLGWCyG3_!Vc$Sfm`83B%Q#^H6ZJT;sz zW1)EtH<#i5Sv|ga%v}V${>1URPL66mqeWJ`^($qJWDNQ`skwv5Bf1mLANG)qUm{WS zs?qRN1<6X-N#ioqX1ew7t!E;|B+l@b}dd*NH-d^~>{3E|*;*>c**>|KHIZ>-7j z%)oj5#6BPmHlqLNy*C%-;rGN(vsum4qtmW7ehz*H%1i%G&C8x40saNrbFvYezF*DB z{WB8h*JHpLZHiEHBMO+|d4U~k4eK3ObD`aeaGrcTt7vDenop&J@h8~(pFG}lPR&2* zC)jva%h;55Ka@=_!3LbM(=YT7*54^4?*H0Qk$&I*2F~OAY<-x%oLN?GK3scC&B?9Q zs6O(YVqI6Bc&O&d$U|#uEN6FD&inQh{voII(@PpRma~K66|-8s1|QVdet9AAZ)}d9 ze9!hB_*-bdi2RrDMNPi%ECQd?EHG;u$r;Jq{rrOV9*dopft)RbKzaS2>NqY<36hof7r?VX;YAYLp0Qz z>udO^6Hr<~`MNAS@%X2qh8w?hx$~nb@})NHlk{$+;XA%PDLIYuLVb45^(LCS{2Mbl zi|XOwnry1!4~j4Dd)`{kUU?){d~K=WdL%4}{4m0Q+~^3t7b+0)$(ZuR`4bizPPAn2 zEahzLpoW8UyTZQ*y1Z)SXSosqjirXSA>wJ|W0_RXDzxgU;rHxpHot~{Nh3vMwvUE8 zoAg=R3;rVn3Lx zHf?+5HbcV~1TG98j^B@MJY;)1{I5gok(q|y7vA~0;kZu2+Y_Lrm7FPVOJ9zdqv2=B zuiHe<05@OMXugK~4W1loR!`2F$DZ2zW*)xxN>EQL^xqL6gjlfv{!-%KuaPmr`7~aL z`hu)Yv#&BnGT&SA`w4l$KJJysrv+j7XdkpM=@6E}Uov^e?AWCmPP_|eF@8KNy5y68 znPfWh;cuh7^~S3;94M*bY56iH;K2RSzLYro0r|ME{oW0Ny|ZLUQcpZDy2Rqeboi@# ze6}nO$N1|5eWwWcv#ma?KlBjg`_FOL)M4v2JiK^jk8eNWzbRbk*d<8A`|Q{HP5zGc zzVoQGrkgczih>Tp}Zy$T< zqW@j|zHL!%8$~GM36xm>O~y{Q8T0ur>{!=*f9x^-DNUz&n1*L0&8RN@A!Eo&SbA*# z|1A$~L)IP8aAfz#y5fF8HzY#C=Nlno59J@VJ?DDwBN|>C`?G29S!=V}$-?m)Fy0$gi+w_u#Uq3tRe0$8NI}ZOh-Qm2350%Zne&(Hw zk*>&l*r0)|@WcE}$6J%j@VA%m@2-#emD0ZEBfdeyYdNmJ{epxw*zopf4#HnZ{`>C< z8h#SfR0`%>TDIrkKs>@+zf~~iQx5KpkYnrMXI+thaXQw^J@NzVlIZ`EIu{aG3T?*8W*_6Kuo z%GPG1fB(I5eenhC2gzaeRKt4}ZoAPYTE@D~b*O2ZgYhAm(q4NU>ni~#axwqC?!Jil zqQH86U;G;7vkYgZxPN)|9;3O4KOvjWP7)qWegqgl zi2XzZD+iMgnmYVuIQS?XHhYN??|Qu#glo9|XU;&6iV_VMcx_;l?qsu1h?n)k3W@bx z;P)uk@R&`14o%sK^=X*O!uFe{4$r<##>A8t`wRCIU}zWivo>H<{)2c{NS;q8l!wn$ z-}0};_fyA#ztrDPzdG};hW8x-MF;p(-9Y>@^EcvGDfi}lfIs#`*qLvs-!*l3VYFA; zlNm2IR%$rOFxN}Q_w0z9?vL`eFb4BfI_85b++ueD+H0R@_cLgJI?t@d_X+w5=r07z zm{y_T2(ymUH-K>;c_(6@A3}4qY&)<3o`xKWDvm-w2HFFbtdNdNmN zzrfpv_2Kgs&oBen76d>WA!l~@y97JH_Ri)PPI6(r72)?iNqAH#XYh7^?Tq)e&K>ly z-ZVKo`)!QlOI%-kAge$0{#3wot$OF==4sa ze-R8?I) zPs0n7FBzDxM}D^_JcxJD|Hgl6KWmAc9Y|C6Uz4n<W0Ev{E(sp{z@Al7 z`0zU7rQm^d!~4j8tV~3ICjQ%1a+coRY2Fvi52mBF!9CX^pLvS7;g_pe-(wf57Na~9 zbKkW)jQJva6AHQo`xhKgdSQNOPC|=2k>CCPXsY>b4X5MQq}_71dTduwAm)eX*-h8T zb|Bx{Ft?*?nuf;{0dgpwFH(56gx#i_MOOSite1p~!uROC@y;7~1o!X9ALa*mLq{w) zEN5K@_Iq!I_-|)&NJim%2sn-L-o#FHs3B}&e~R&)KcnBfArW#G1o&Q4j9;QnD-D;k zMcKyft>0k2F{+e(!25`nb8|lC`_7h)_MtrRDinNpkNPA2)U8!uhRYpz4oDgOBWm} zX^!zw+IroEn`kd3CBA@9%i~r`r!nXct_KkDNB>`y10i2eIU^eCjp+ZzM0kPzr8JG+ zT?U(YCM(?KOweJ%_&!yYCDTktf7DbRaMTBWKfZs~wh3~Uq;i-)%>edTAk3WQjC4e1 zHU$40C6tcFe(hH3)^HQ>@py$VKR6Woowtp4#~?o-tfD>C*-{|?2z4&4QzOhG#?mvM&8MO4yox#UPgbY}3 zJ2yzE-UyqSRhL`h{6I7+Cy=jEKNL)ZSntLbd6s;$)N-&-Z@ZN(V~c{JT#WpVik%N9 ze!=`l`nLJKfRCmCChU1Zy?>h{A4K4f$9hWmx@iNz=RkThbU)xQmtgO1m#a7b0oKDw zh`8ATZ%Pv%*3UxZ~@wNr~lZdzrFYx|m2kt&F#QB!ed*=+&@<`E>o|d@Y zy|Y!mBfjTJ32J4Ibm_2uz&B3cditI_;;9>&xST_KNu_Nx-zI~v*Kkv81={P} zl;klLO2i)rep{zQ{X3Au8vX>L4;YU6cRKB!rIe$*7a%Z#@5i=3`>B(ReFaa%9oR?u ztoA=M0{e+Mldd*H{R{kguqQ$;$`SP^^v{8)zeC7aMtcDTII^KF&iknoJ!av0aQeHy z-TH$6nxD0DL3^E%1Mvxe3dtOwKzoU&A;Q!Sy5EXo_@4m)=`vT#1s%71_-no2Hl2X{Fro|n5B^4?5fmeT zOyF0<^#&jeTeAfGA3Gqh{2u(!v357l!KS*Z$d^M6c zYMCizjtxGSEr#uMmmQjM=YR1ze{G%qdkXxSz1w-MS%-YF>Hn2E!#_&%&fK-&56w_I zrGd}x&FzG$ia_v3%`;U>uYvEY)iLu%n~{%aZ0B7IoBUpTwrTmV#j_I~FM*#EZvB+4 z$QPsq`!wnc2c4g=AEs}Ua`9r)?g5Kx>N zg?vfSZppn6f5l_o9@lbf(y7!)8HEWXG5G$cMNjn*-yQ!fzo_>ajK9M9`bV^gH^b5Q z`#ks$K&Q3}{_^?d{-;bXqJDP&cxCJk{=1zc+O9YY{smK?h7(;8KY%0s{W&cs8P#ez z`28zqH<@_}_~KhNBR0E$U%1%MA?ym~gB*#Q0j?8YMZGvJACh`%zmZOHJjw@I zwigq@SD|iuUUviY72!SceBz;bmIywOQ0zqE{~xdtN~GX}n0nMADsyBV-~J<97veC8 zFVJ(_CSg2)$I%dcaDEM%{ArN_K8tyQCx7Ak++_9Kw`ssTSFZmOgLnh+rgTUL-$Y_V zw@}lbVUrx&U_7tU192Q8}Q3@pBI;De6F9lwh^tJFj!QX@UTgy&|cXa%P{9NLH#{Hd< zfcE>(|M&o5Te-j!sRuj=>4AO(eig!TdHTq&M|e^F3V3x8&Gs<>9)Q{caf1LPx9By4t{@Qe)DbS%x~5=Qa| zXPX1>K@;9}=>1H&v#Fvb@Ezc7J9rCtU;|gp@rbYI ztXpyi`Pv2d0mQ)XjeqACjsPBmWc~tS6Tkaz;4zSv88iMe;+>Ek>D*Jtp^~K+dZekA0d>(a({p7}_^ zw2)P7E(Tr>@}AezB&>fxyYI7s2YEq+2>AZw#OW#DaXprRjVS-Ber~(p0^cI&xSRss z8$8^Rz=H^J4&b++^uW$;u#P{a1gtQ`lW8Hw^Mw8leEe6r;j1Nhe?~MiivE+ZKkLVB zItV;WM~mA(<_1aFo(KpE;(q&?3F8}YMf-u5!hI0%fmSCPUD$+p^@z9Y^np(qz7sp* z?Gm=VxrO(^;lN`gUX7a;EMZm6rmeg@68PEGsCvgvePeHbE|2}x1|FrWH z+KdH0WH1n4c%R+zZ*c}rz{6fb!YcADHrPl{Y$cl#c2^-k#0G(9*e$}KjN~I>^!ZJY z-%rQ)STDrmBTt0&1-@m3_k`&BuqmzbGVnuS4-Ab!{GakcKEcLm=1A2t@H^1NMff5j z#7F#I;Qe+%eP#5&ava}B>3zZr@t%C zt&}kKYiHMs?!a>%L53)vPkaDFX8_L~b$(ma|JT)dhDDLIU07DZ95Ls_nlmOqDN)oJ zlpa(B1QSk=0re9wvMORusHljp2?fkKXG_+sASM(svW9g{uK~08-PP_`34mNd~Y5`05U-=P(#Z{V~~na}V2^Zg9GK>dTE8y<=t6ms)U zL#)zo_vh{dna`;neWh0j^|S>byaey@)1Uh5q12N-pY(SKc#~>J;!ix$sXt0kim>P( zRl9!2nf^C@%ahm&yh)0ELe@@QHQ_&7S5y4I#1L&3%qd!rZg_G;Gr;Z~XB znYP^2i}O_B3&X7PgFH9jjK=;_d7cTp&#GYDhxJkdn^DwrS3Gq#o9kuoKdIS%wpA|5 z2>9S_qJCTz{?4Ud1{%nU9npV6cGNV_DicCs40Z)Spm3B6s4r0j4K2Ndg5geHN`25q zw<9H+gAYIkCN+-zes0p%F|=X(bBkUEq*ZG#QEBSm&0nvgUPjfsxqFGP*E^4K zp`(|ArnK>tTF{W2qPU zeZ-d60qn00D_)OT%>Gag)le^S_T-gAqi93Z8uMou<5|)p?Nzi@7F9e3~%IliL zeDlnWaSG@90>TyxW>PPs3J276#rr&s`Wh9eWdEo-khDmvH1gQsOV&dJx{i7#m&dk4 zcHm+1WbNp8W<2xHioRTH7W*Z7x8wa$)T1>*NR8_`QDK=UMo?d)^t0H1^_64A{vcX2 z>H5|0UOU$zjw@(RT{K|ljr$6X^9N6!k9nU88+TbPBu{fLhuP4i48rsAB z+g`a{j`!tqEQ*Rpy+kyLh26ZLpchLGO7RjZo#(*&Tx~EG`W^TbFiY=qKFrs@I%Pdz zeLJTnui^Z_+N8d3-!i|2Wd|22WzE$;dJW#m*0qc$-!CJc z^2SR*FW|e5@0-F^4aR=hw{_7w$CA{Wg9CiPcxE3=J$aA+e{SwG{RZ}mzBzkxLh=9R z55M1$#@`=)=lf~!iC63=V#+6`NbS3B@N{@EP`s zGYp3Pz;DFct-@r59efk}R?RD%a z*nM2i(pF_Gd2V7q?W{dv2fwfBivlS2m!ZQ3I7Puiv$?)qF1opWbT^SXeB0?`v=zJ9 zJna4cua_j`xeC9N0-P>FPeK7OF7o@2yUG)?-}8kBR7>prEK@k7=_34QFtfAiQ~#`#S*LDfW`a@hOfiUo36Q5Y{tVKNYGxXdt2IcDSF@8h0kn8Imt~Z^?`2U*v&2Az4yR}L@ z1%QuGdUkj4$6}R7M*d9IDW{{?G5oh8mv}yNp*atyzks`uRu^Z+1L0y*-j{e}?Cpq$ zsIZ~~{sYxqA@y2;FIm{5pvL=V;K7N|`PCq~f9UX?_Jg{M>_JPu7kCSQS}@}ecnx8b3-}jz zlmjuA@lgi&7XPA*;%gj5y+WO0@%_L2r`JGyN%1X}#Qs-#-|O7p@JBg6p5Nd@$^G3D z%U8e3`7*xF+NznXpW;6cV@mcvjN%0ltN>Z>f{26ajvr=!iV63cz1N_{uAi zc#*1u=6bM7ln_5t=l@L3%Zkn)v{6yuH7F>sma|GFC;WU2>kZFJAME*CpB`1O!TrM? z-VA$I2|feTzoCr(?1PW_3RdU)hOBim>k--{W|3VDtK6_{&-Kad@2=6ior19U4|aUw zbd>oiJ=uzNuvZ@S&lk=71}u#~nO2|rd6jQtf8A*7ar++apjpq7*7Lp~KlAf>1NcN# ze4G7f@HJo`8+E(v-<|j$5Q^hzH@hjpLr|aBTtoZ;JV`3dMK=N|MZDzdwIT~ zVNXt`9y>q@0QH3L30TbyclaI@U!Xtv+--?v`eU!Ju0zI&`=@UDQhx&dPrJ8kgb(xk zCv$4j5&B=G3N9zJe-s}+_P0^bH;?!v0E(yd$F@i|5$~L+{2IQWWRoAvjt9R}Yuep2 zT%V)_Vzx3~74L9`&nYwhW8gvJy``o}GyTJM_UIMyT1A)e2j9~vs=(+3@5j#3?T;~@ zV6TN6BF^(Z`V0y9r2b#__Mn>A!9V2`Y}}jgb33JTO@6JTUroy-?tlMj^pfP4xp=le z_`Vlb^~J=$4S%c?d~kk?-EMs&KUsKhnm6{q1jXYEz9gjU-9gW>7uLoUh#o+^ov2aP zC~xc=RZj$-=cgDi!h;TLCitNn62UmV586n-Qv`dDD*I(g>|F?I?)iByqw{7hGAiye#hE|#PiF_vh!cpw` z4pWL$aRr}1wRy{#*xRUM->uL541U#-*b^}zW}Xp`oDx^z0QTGU4;gm1z#|xSsgvN# zc(QNf@3ZnX2cy|)kwmybPa=r59AeEDtEqD70~XW<-~4L{Ib!s*wRSab2N z+uR%%Z*hNX@82iJTP5ftFF*1zixr_e_IC?yN4-TIy+sATpvtlI zcWjBaaD*rISbql1w@a|f3OUz{mL{Kb(Pv8C@*Cg-Q28SAnLj=~+<$4)fBXZqr%gO%e-nkt*%6KNlq%8{%@D{Lqto3F53cl&v-&<_j^flId9eh5D$c%hw z(y`tipSYgx{HBd(2tO%( zg8rte!tQg7m+$cRlf18yQ|FaA z<1Mo6L5?QFzjN{PrwS*%g`%0eaM~)Byx`tb>;#~9!oOF#yZ?9#;H?WPov=!W+g cnKa&)yp#e zDk`A#oab5B^0O$B^6{;zQGykDU02Y2YDi9X9`j7|O1DmhJV9Prb`F>pM} z-_2e*=efrKO^}srZ@&uvN7a)L)`ZHd=i~QSw`=x>bARYfUg7_$aNVt`nxOjBVHD>@ z?E#}FgfGwp@-+|ZEa3e5C%ewu6`D}=`}<~co+|>Bcuf@Uq+P5%75}ayNU3#NE`P~H zt4zBJp~WgqbVU9obR7PDj{$bwR%)XBj$%^|g~LA>Jf)N4V*cNEj>sMb|B?cT#%N;y zcj;So7<_#{^Mu@-t_daYdt@N-kZQ#zjhm&3w_SV&kK{Zy@VpB(5frmL-XW0lqCw^? z&KQxNCuv>hK2}*(725r&3D6~*8*siau7btL{ilajOEC8&o_C<{!H5J+{F~}{C4=un zad6wyH)*2i94a}xvz{rH7x&qx3H7=5&~p7{{NLqksK`yz#EqzJ8`Ha4B~h>cHNUEfYo}eMV^{bIPtC9Ae?}9=x#_`q zb`N0IDNXEp68mC=JNz~9WK}<`2}Ogtf%u=|A(?Sh6Un3%CN$#v6aTr{Q^tclnqAf3 z;Un~Wy8XxrOV(U{@>jk|J8(| z*Ga1kA0zO$3qLinQwgc!AFpvh!6YC2Eu)?#pQo*GaaD?0#IR{pPUBC*vAX1Y8H@Ol zsh=EKm-#3Hy9)5BlZd3T9!agTDRCF<1kE)W83C%2GMc`t8ei#S{E)mslM z{IP@~Msa_I=lnbvezFE1PBpJ$5pj!(N3bHXaD%Z$6;o}(Wb%kfH4d2g( zls3^3ynpFG+g_&Khhwx+B>#`plT7S~#4yMG`=+y=Z4a!e!F8n=FvQ%m=Te zn#&R5>sNy7EoT1v01n2UW4{tU4se3csOs@Y?Nh9|c*T0mcixN|VORLxs)gtID><8< zwDbL6*M#3%SVY*2afb`=dqaP{l|_tk0hvsDi%Jx+zh)|3nQO#%D^MTZj`a-+4egZ< zKakR?cZWagffgNi3qG8%q2H4`SVZ}>!s8m>!+yFuJ)Q z|Jg78@QZ!jb-(Tl@(bk{cmU&RI?%bn8~CdRU(tv4u?W~Rhd<5cd{Fi2{dpgrmC@RL zBc7`OmBHNa#(;`5?R1H1%bVd=_{)CbYrDMfM}r3SbXi{oG_3I3DST)RU5-%(U6@%I zdA+K-T&D^Y#+k)}z{^*nE9trUsHtW#ceMM)>J@bP_nhjff6ahj?|;Wno}z8!#}>l3 zSiNS;5#@C`d6RRnEzT^?-g%I*rj(xRUnIh32mwm_V!C|RBgpsTx?H}$(M5F$3u2+)%Ba>oZG8> zV1e^y(G}Q3rys{FWems1W2F^1Fd$~;X{*znb(_{O+{f+sxq%w;66tlM5A5l=3 zw`A{vWmlVp(&_ukcnhuZ*l{BkKENP$0P{2an+5!T;`n9p%pbPZqfWETV&du>aY>H4 z+%XxZn<%sRSO`m?te&es_J>*U7HN%G&tmT)n${a_7FqC=x$->j9$C53h4%7|L=LL! za@OZrjoJ-^Z_+7id$^M>_e3EiJ;*FzvluX-t}a&%&pR%DfLZ+d{0+TR7hNh|j*33S zKQp}p6I$qUUu7~dcif1@X=H+gcBs(O8!uum6cWjDEy6*Rq+3&Gp=P zc_;LB+jw?)*HV{Fi=aSBeDv4;$3AH0Hr7*@M3tM3Xa6a?=)H%Y8{Zsb7Aj17OXzZr z3TTao&(RvtG}Kj>l^Qg=VrIT>$jAYym33bgJ<)WMS={J?5*_1Tno)hp{MlyF!}D$Z z7p+;poQ_{#%`*$7lby_X44s1MW)WRDzejW03N~vB-+wHC+wn8mO2E%s%wLuhwh#yxVsCstCwxa`S`JtkeE_Iz~Q8tTy%pM57? zcHFw8MQQLMMxLH_4Y z%W5nb6Y68m)rawAKYnzVeFE68%VFU3(WRmXFX(3$-*yzpIoLy&sDV7M>tz;HLD;?L z_lkB)H^WbQ>Ry0rfG+Rk#K!*a$@^IUbgj4azuk1uDXfcG{CYo7#9ns8bV(UdsRr(5 zQNed+XzL-mRPsKXI?`XMV#hsgoL7p-Meu*SMtwUB)}>MR(%PJhZyd_=QaTN7;k*2$ zzb~f!+PMhEH@j$A@AKHN*du-u^j!b&iY8PU(k|Ks9jOq=)I?)sd@qdC<=((c);5nd zLqBhAxGt5P<|Nv8+9A*trppco8upLl_lEvUDDSHR-rmGNxaL#rDdUM(f&lcd{QeRB zbg6hMzHom-k0wx;i5vQMYjH^v&pT0Y+*6mR%a*B}uI1{TXu6!Vvqqc78JbuY*r#Yg zFJ0=h2lc&s8+&zb>*fo)GT&lRwW9caX7h&)lXzc#Ov&$10{b^t-;lP#Q>HTBv3DxQ zbkOBO?R;t`_VY+|Mmq5R)K-bhW14t4?N;Lvyg!ES#UV{BErTF1`@LxQjOY0eYC@U! zHF$qll}tK*YB%FYHz}3(Tfz2g?a;*BK>KECH5pIj?Q02JnLj|@pH(=222w%2MH7o5 z)PGqujT^_y;p%V1%rw%L{%mOyf_u*nzO)`%-;HvDItv zPwZw{l5F(<_Eq0$Czf%Yu;hR`x>VuoTC{(ZdYk^GkuFvGYYzTXCnW^mjP*@=H+K3Q zO?)}tJ#sAL@1ZvMG{%S9+=#Z3kD7#i|0%~|gtIQc6q}rtXS^m3wd)Y<%K4$>B_2m; z!iYn))up0)i<+T{n&&}sx#^PX#u4+QHPKWR#<0Fil>@kx@j*!UiRW>kF$B$Vn%Hm{ z&xz-?yll4VHRCn(gn2&~{Ih0E^cqc!iYWKy9Pg)*SH&OtIhGF=@6(@sKlQG?OB1yT zP;3ukKC@9;*stZ*V+HHEc&!7Ph~lxg=Y81Rtj5#Ddo*FY=U%oZ`)}jYTjqawK1Tj) zgf0s!LV%5$z^g9tbd)X?{ZZUzO-vZGz%F2nE?vKTxV>zG5ojCOeW3YyjFzZ_4)oqtbgGV&H1c=$+h?%c}}eJ zbdjE0Z&KYXMyoperMm2<2n?&4#jp2g*NhvqUYBG$S~Lg${2+0t#d)Jf86y{7BB;!asD&{-*PFNtADBPE&8;a{k4s#94YSp8@q> z8}`(FS#vh~5jb@T>IbHd!JuQjh7cX~XZymMG;P56e)a(%SKnLM4gHyS+bUi5nR4Lf zwzl2|-l7oWGjQX?*HoRvo&?Tc#a9g;``7V1D?-obx=gt=@Kcri@C%*n4?KmTi)( zOVX-+KQ=Rqh$^AyXP(sMP2AOu{NLaaIHk+fM1+56P1?$aeI?I8zB!Iw;3NA@mVD!Eo0EXCM_H1+hN}6g@l7{%M5e!t+7CJh}a4Jy)Nq5d6ytetAo$=<-lrfRuLd zUBkbb!tj*lDmmhC+NjReSy;*@`xUiR9L9JQ&B)@CHo30;hHKhalQ{Nb`1zBx zzgQtQ`Ct-GHDV5g^84v)Qnp-qVG>oFKk1lO)F!KJsdvHUwMo<@aQC^OO@gYwzm4&z zdc8&VHo0*(h`9fm#K2Z|Gj-aEe%1erNqE01(le9x*B52V-u-D3x9g!$?_iUo6flSV zv7%`63+MCMa`_U9n?+lI<`?a3GHUFs(s_%)cO77A&_0h%HY{|sZUnzK3cbGTQppz$ zpue!`MIL8-;rS|lrW}*l)O`NY4%yf<3LndLMMpL4HQ(RF#98ZjKJ`+bhAw}_c{C!Y z|CsM4p~_arf6(RFIsa~t`eYI>e66De{V81XwrrCiYvg)0hx1YaYF?Ve_%3IMz5mJg zr^)(P%Xwa(Tm8r=&iuNKEFBP%WfHJV7wE$JTrX|s{pP+&R0u~QrkG8lLNhw=J(Fm9 zYrXvt)|+hV+W*Ti30r*goFApx4~mfThDk{O2l6q`&;IWF??bPcM1~Tk=6QQw&$#d~ z)nw>L7iRy|?daPr>5@ql-shUQyqZm>j{y0T!g~C&zxHjntqyHzlMj!kRI2m9Bu4MK z5plAKO)7ZG3dWn-rl^m7eVatB;>xPOO(K}rsCylo>|H1-D2w^sL53#ycbi1r^uSd1 z2hpU@mz`{q(`C=$&!*h(c`e3U#`okk*00#`*a4w6nE%vrlkDG{gi0gT<@&M8SlaC8 zv`KCE_NA?OI34e@o(iu^|MOQ?9MYckXxMnc#gw`>`LY`Y>%1RIwy|adn?$9rcO9+= z?v0I`$oL-)+LhId>z1tWI=7u|GF%ZcTsH|Tv~wF;*`#v+^W8CtU+1s*tGzqhBy2YA zpE5qj$kJn;da(Z{rrz7Z^Zr~mJ$hVwo0KjKs@13cz4wdLx|>bba|c1o^Y@3g->r>J z`ZVbFAcXdnqr2mGwcz<3edQ7Uiu>omvSK_2K92S4edtN8dg}M!bYHUGO7{8>7n>wn zyWT02{c^5Iud}W;>Ho07k?eGnP<2k_+ws1tgve#~@2%{@V>{TSq6tb$GYN-=hv%N@ z$oshx#WQ{%$yN>K*lm9LPcz)I-e(hhB zD_WsA_cQ!2b^au{y?^I#lUYhnl=U9F^;zOL`cq}<{>$Z*Z!bmT)ITJ$=X3EvnH5w^|t!kWHfP - - - Feature Catalog for the 2018 United States 1:20,000,000 Cartographic Boundary File - - - The Region at a scale of 1:20,000,000 - - - cb_2018_region_20m - - - 2019-05 - - - eng - - - utf8 - - - - - - - cb_2018_us_region_20m.shp - - - Current Region (national) - - - false - - - - - - REGIONCE - - - Current Census region code - - - - - - - 1 - - - Northeast - - - - - - - - 2 - - - Midwest - - - - - - - - 3 - - - South - - - - - - - - 4 - - - West - - - - - - - - - - AFFGEOID - - - American FactFinder summary level code + geovariant code + '00US' + GEOID - - - - - - - American FactFinder geographic identifier - - - - - - - - - - GEOID - - - Region code identifier, Census Region code - - - - - - - - Region code code found in REGIONCE - - - - - - - - - NAME - - - Current region name - - - - - - - Census geographic region names - - - - - - - - - - LSAD - - - Current legal/statistical area description code for region - - - - - - - 68 - - - Region (suffix) - - - - - - - - - - ALAND - - - Current land area (square meters) - - - - - - - - - - - - - - Range Domain Minimum: 0 - Range Domain Maximum: 9,999,999,999,999 - - - - - - - - - AWATER - - - Current water area (square meters) - - - - - - - - - - - - - - Range Domain Minimum: 0 - Range Domain Maximum: 9,999,999,999,999 - - - - - - - - \ No newline at end of file diff --git a/examples/shapes/US/Shape.shx b/examples/shapes/US/Shape.shx deleted file mode 100644 index 9a8dbd4376bec4046a916d0d40b0561c14f9c650..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 132 zcmZQzQ0HR64xC;vGcd3M<<3+YPMMsVc0kF{k@x;9BZseF1b-Z^NOKT1V~&|{EzkkG jb`((~1_p^uK>UJ%L8FX;!7+(}!DIphL+LjLhBPh!epeN_ diff --git a/examples/shapes/US/Shape.xml b/examples/shapes/US/Shape.xml deleted file mode 100644 index fda6aef..0000000 --- a/examples/shapes/US/Shape.xml +++ /dev/null @@ -1,531 +0,0 @@ - - - - cb_2018_us_region_20m.shp.iso.xml - - - eng - - - UTF-8 - - - -dataset - - - - - 2019-05 - - - ISO 19115 Geographic Information - Metadata - - - 2009-02-15 - - - https://www2.census.gov/geo/tiger/GENZ2018/shp/cb_2018_us_region_20m.zip - - - - - - - - - complex - - - 4 - - - - - - - - - - - - - INCITS (formerly FIPS) codes - - - - - - - - - - - - - 2018 Cartographic Boundary File, Region for United States, 1:20,000,000 - - - - - - 2019-05 - - - publication - - - - - - - - - - - - The 2018 cartographic boundary shapefiles are simplified representations of selected geographic areas from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). These boundary files are specifically designed for small-scale thematic mapping. When possible, generalization is performed with the intent to maintain the hierarchical relationships among geographies and to maintain the alignment of geographies within a file set for a given year. Geographic areas may not align with the same areas from another year. Some geographies are available as nation-based files while others are available only as state-based files. - -Regions are four groupings of states (Northeast, South, Midwest, and West) established by the Census Bureau in 1942 for the presentation of census data. - - - These files were specifically created to support small-scale thematic mapping. To improve the appearance of shapes at small scales, areas are represented with fewer vertices than detailed TIGER/Line Shapefiles. Cartographic boundary files take up less disk space than their ungeneralized counterparts. Cartographic boundary files take less time to render on screen than TIGER/Line Shapefiles. You can join this file with table data downloaded from American FactFinder by using the AFFGEOID field in the cartographic boundary file. If detailed boundaries are required, please use the TIGER/Line Shapefiles instead of the generalized cartographic boundary files. - - - - completed - - - - - - - - notPlanned - - - - - - - - Boundaries - - - theme - - - - - ISO 19115 Topic Categories - - - - - - - - - - 2018 - - - SHP - - - Cartographic Boundary - - - Generalized - - - Region - - - theme - - - - - None - - - - - - - - - - United States - - - US - - - place - - - - - ISO 3166 Codes for the representation of names of countries and their subdivisions - - - - - - - - - - otherRestrictions - - - - - - Access Constraints: None - - - Use Constraints:The intended display scale for this file is 1:20,000,000. This file should not be displayed at scales larger than 1:20,000,000. - -These products are free to use in a product or publication, however acknowledgement must be given to the U.S. Census Bureau as the source. The boundary information is for visual display at appropriate small scales only. Cartographic boundary files should not be used for geographic analysis including area or perimeter calculation. Files should not be used for geocoding addresses. Files should not be used for determining precise geographic area relationships. - - - - - - - vector - - - - - - - 20000000 - - - - - - - eng - - - - - - boundaries - - - The cartographic boundary files contain geographic data only and do not include display mapping software or statistical data. For information on how to use cartographic boundary file data with specific software package users shall contact the company that produced the software. - - - - - - - -179.174265 - - - 179.773922 - - - 17.913769 - - - 71.352561 - - - - - - - - publication date - 2019-05 - 2019-05 - - - - - - - - - - - - - true - - - - - Feature Catalog for the 2018 Region 1:20,000,000 Cartographic Boundary File - - - - - - - - - - - https://meta.geo.census.gov/data/existing/decennial/GEO/CPMB/boundary/2018cb/region_20m/2018_region_20m.ea.iso.xml - - - - - - - - - - - SHP - - - - PK-ZIP, version 1.93A or higher - - - - - - - HTML - - - - - - - - - - - The online cartographic boundary files may be downloaded without charge. - - - To obtain more information about ordering Cartographic Boundary Files visit https://www.census.gov/geo/www/tiger. - - - - - - - - - - - https://www2.census.gov/geo/tiger/GENZ2018/shp/cb_2018_us_region_20m.zip - - - Shapefile Zip File - - - - - - - - - - - https://www.census.gov/geo/maps-data/data/tiger-cart-boundary.html - - - Cartographic Boundary Shapefiles - - - Simplified representations of selected geographic areas from the Census Bureau's MAF/TIGER geographic database - - - - - - - - - - - - - dataset - - - - - - - Horizontal Positional Accuracy - - - - - - Data are not accurate. Data are generalized representations of geographic boundaries at 1:20,000,000. - - - - - - meters - - - - - Missing - - - - - - - - - The cartographic boundary files are generalized representations of extracts taken from the MAF/TIGER Database. Generalized boundary files are clipped to a simplified version of the U.S. outline. As a result, some off-shore areas may be excluded from the generalized files. Some small geographic areas, holes, or discontiguous parts of areas may not be included in generalized files if they are not visible at the target scale. - - - - - - - - The cartographic boundary files are generalized representations of extracts taken from the MAF/TIGER Database. Generalized boundary files are clipped to a simplified version of the U.S. outline. As a result, some off-shore areas may be excluded from the generalized files. Some small geographic areas, holes, or discontiguous parts of areas may not be included in generalized files if they are not visible at the target scale. - - - - - - - - The Census Bureau performed automated tests to ensure logical consistency of the source database. Segments making up the outer and inner boundaries of a polygon tie end-to-end to completely enclose the area. All polygons were tested for closure. The Census Bureau uses its internally developed geographic update system to enhance and modify spatial and attribute data in the Census MAF/TIGER database. Standard geographic codes, such as INCITS (formerly FIPS) codes for states, counties, municipalities, county subdivisions, places, American Indian/Alaska Native/Native Hawaiian areas, and congressional districts are used when encoding spatial entities. The Census Bureau performed spatial data tests for logical consistency of the codes during the compilation of the original Census MAF/TIGER database files. Feature attribute information has been examined but has not been fully tested for consistency. - -For the cartographic boundary files, the Point and Vector Object Count for the G-polygon SDTS Point and Vector Object Type reflects the number of records in the file's data table. For multi-polygon features, only one attribute record exists for each multi-polygon rather than one attribute record per individual G-polygon component of the multi-polygon feature. Cartographic Boundary File multi-polygons are an exception to the G-polygon object type classification. Therefore, when multi-polygons exist in a file, the object count will be less than the actual number of G-polygons. - - - - - - - - - - Spatial data were extracted from the MAF/TIGER database and processed through a U.S. Census Bureau batch generalization system. - - - 2019-05-01T00:00:00 - - - - - Geo-spatial Relational Database - - - - - Census MAF/TIGER database - - - MAF/TIGER - - - - - - U.S. Department of Commerce, U.S. Census Bureau, Geography Division, Geographic Customer Services Branch - - - originator - - - - - - Source Contribution: All spatial and feature data - - - - - - - - - - 201706 - 201805 - - - - - - - - - - - - - - - - - - - notPlanned - - - - This was transformed from the Census Metadata Import Format - - - - - \ No newline at end of file diff --git a/examples/steady_state/02_scopf_analysis.ipynb b/examples/steady_state/02_scopf_analysis.ipynb index 06c33bc..60747a8 100644 --- a/examples/steady_state/02_scopf_analysis.ipynb +++ b/examples/steady_state/02_scopf_analysis.ipynb @@ -127,7 +127,7 @@ "delta = post_opf_online['GenMW'].values - pre_opf_online['GenMW'].values\n", "colors = ['#55A868' if d >= 0 else '#C44E52' for d in delta]\n", "axes[1].bar(range(len(delta)), delta, color=colors)\n", - "from map import format_plot\n", + "from plot_helpers import format_plot\n", "format_plot(axes[1], title='Redispatch Delta',\n", " xlabel='Generator Index', ylabel='\\u0394 MW',\n", " plotarea='white', titlesize=11, labelsize=9, ticksize=8)\n", diff --git a/examples/steady_state/05_ptdf_lodf_analysis.ipynb b/examples/steady_state/05_ptdf_lodf_analysis.ipynb index beddeef..3d884cc 100644 --- a/examples/steady_state/05_ptdf_lodf_analysis.ipynb +++ b/examples/steady_state/05_ptdf_lodf_analysis.ipynb @@ -66,9 +66,7 @@ "from plot_helpers import (\n", " plot_sensitivity_map, plot_sensitivity_dual, plot_sensitivity_triple,\n", " plot_flow_map, plot_bus_markers,\n", - ")\n", - "\n", - "SHAPE = 'Texas'" + ")\n" ] }, { @@ -98,7 +96,6 @@ "\n", "plot_flow_map(\n", " lines_geo, flows['LinePercent'].values,\n", - " shape=SHAPE,\n", " title='Base Case Branch Loading',\n", " figsize=(8,8)\n", ")" @@ -135,7 +132,6 @@ "\n", "ax = plot_sensitivity_map(\n", " lines_geo, ptdf_df['LinePTDF'].values,\n", - " shape=SHAPE,\n", " title=f'PTDF: Bus {seller_bus} \\u2192 Bus {buyer_bus}',\n", " clabel='PTDF',\n", " figsize=(8,8)\n", @@ -172,7 +168,6 @@ "# Show base loading with the outaged branch highlighted\n", "plot_flow_map(\n", " lines_geo, flows['LinePercent'].values,\n", - " shape=SHAPE,\n", " title=f'Outaged Branch: {branch_key[0]}-{branch_key[1]} '\n", " f'({flows.loc[most_loaded_idx, \"LineMW\"]:.0f} MW)',\n", " highlight_idx=most_loaded_idx,\n", @@ -191,7 +186,6 @@ "\n", "plot_sensitivity_map(\n", " lines_geo, lodf_df['LineLODF'].values,\n", - " shape=SHAPE,\n", " title=f'LODF: Outage of {branch_key[0]}-{branch_key[1]}',\n", " clabel='LODF',\n", ")" @@ -219,7 +213,6 @@ " lines_geo,\n", " ptdf_df['LinePTDF'].values,\n", " lodf_df['LineLODF'].values,\n", - " shape=SHAPE,\n", " titles=(\n", " f'PTDF: {seller_bus}\\u2192{buyer_bus}',\n", " f'LODF: Outage {branch_key[0]}-{branch_key[1]}',\n", @@ -262,7 +255,6 @@ "plot_sensitivity_triple(\n", " lines_geo,\n", " [flows['LinePercent'].values, delta_mw, post_loading],\n", - " shape=SHAPE,\n", " titles=('Pre-Outage Loading', '\\u0394 MW (LODF)', 'Est. Post-Outage Loading'),\n", " clabels=('Loading (%)', '\\u0394 MW', 'Loading (%)'),\n", " cmaps=('YlOrRd', 'RdBu_r', 'YlOrRd'),\n", @@ -305,7 +297,6 @@ "\n", "plot_sensitivity_triple(\n", " lines_geo, ptdf_sets,\n", - " shape=SHAPE,\n", " titles=pair_labels,\n", " clabels=('PTDF', 'PTDF', 'PTDF'),\n", ")" @@ -346,7 +337,6 @@ "\n", "plot_sensitivity_triple(\n", " lines_geo, lodf_sets,\n", - " shape=SHAPE,\n", " titles=[f'Outage {l}' for l in outage_labels],\n", " clabels=('LODF', 'LODF', 'LODF'),\n", ")" @@ -375,7 +365,6 @@ "\n", "plot_sensitivity_map(\n", " lines_geo, max_lodf,\n", - " shape=SHAPE,\n", " title='Worst-Case |LODF| Across Top 3 Outages',\n", " clabel='max |LODF|',\n", " cmap='inferno',\n", diff --git a/examples/steady_state/07_state_chains_and_stress.ipynb b/examples/steady_state/07_state_chains_and_stress.ipynb index 3b041e1..52068f5 100644 --- a/examples/steady_state/07_state_chains_and_stress.ipynb +++ b/examples/steady_state/07_state_chains_and_stress.ipynb @@ -73,9 +73,7 @@ " plot_state_chain, plot_snapshot_comparison,\n", " plot_sensitivity_map, plot_voltage_profile,\n", " plot_pv_curve, plot_histograms,\n", - ")\n", - "\n", - "SHAPE = 'US'" + ")\n" ] }, { @@ -329,7 +327,6 @@ "\n", "plot_sensitivity_map(\n", " lines_geo, branch_swing,\n", - " shape=SHAPE,\n", " title='Voltage Swing Under Random Load (Monte-Carlo)',\n", " clabel='V swing (pu)',\n", " cmap='YlOrRd',\n", From 716ad871715c3da217ec0a6876f95d66af31660d Mon Sep 17 00:00:00 2001 From: Wyatt Lowery Date: Mon, 31 Aug 2026 16:11:38 -0500 Subject: [PATCH 06/10] cleanup/modernize --- docs/api/saw.rst | 5 +-- esapp/__init__.py | 1 - esapp/indexable.py | 4 --- esapp/saw/__init__.py | 2 -- esapp/saw/_enums.py | 10 ------ esapp/saw/_exceptions.py | 16 --------- esapp/saw/atc.py | 22 ------------ esapp/saw/base.py | 46 ++++-------------------- esapp/saw/data.py | 26 +++----------- examples/plot_helpers.py | 37 ------------------- examples/statics.py | 8 ----- tests/test_helpers_unit.py | 19 ++-------- tests/test_indexing.py | 10 ------ tests/test_integration_saw_operations.py | 5 --- 14 files changed, 15 insertions(+), 196 deletions(-) diff --git a/docs/api/saw.rst b/docs/api/saw.rst index a01ef73..2877267 100644 --- a/docs/api/saw.rst +++ b/docs/api/saw.rst @@ -356,8 +356,6 @@ Exception classes for handling PowerWorld and COM errors. - Raised when a command requires an unlicensed PowerWorld add-on (e.g., TransLineCalc) * - ``COMError`` - Raised when COM communication fails (SimAuto crash, unresponsive, or invalid function call) - * - ``CommandNotRespectedError`` - - Raised when PowerWorld silently ignores a command (e.g., setting a value outside allowed limits) * - ``GridObjDNE`` - Raised when a grid object data query fails (object does not exist in the case) * - ``FieldDataException`` @@ -394,8 +392,7 @@ Exception classes for handling PowerWorld and COM errors. └── PowerWorldError ├── SimAutoFeatureError ├── PowerWorldPrerequisiteError - ├── PowerWorldAddonError - └── CommandNotRespectedError + └── PowerWorldAddonError .. code-block:: python diff --git a/esapp/__init__.py b/esapp/__init__.py index c9ccb9f..80a29e1 100644 --- a/esapp/__init__.py +++ b/esapp/__init__.py @@ -15,7 +15,6 @@ SAW, PowerWorldError, COMError, - CommandNotRespectedError, Error, SimAutoFeatureError, PowerWorldPrerequisiteError, diff --git a/esapp/indexable.py b/esapp/indexable.py index 8d67bf4..0b51fd3 100644 --- a/esapp/indexable.py +++ b/esapp/indexable.py @@ -5,10 +5,6 @@ from os import path -# Helper Function to parse Python Syntax/Field Syntax outliers -# Example: fexcept('ThreeWindingTransformer') -> '3WindingTransformer -fexcept = lambda t: "3" + t[5:] if t[:5] == "Three" else t - # Power World Read/Write class Indexable: """ diff --git a/esapp/saw/__init__.py b/esapp/saw/__init__.py index 6766ca8..3f33dc4 100644 --- a/esapp/saw/__init__.py +++ b/esapp/saw/__init__.py @@ -17,7 +17,6 @@ class built from numerous mixins. Each mixin corresponds to a specific from ._exceptions import ( PowerWorldError, COMError, - CommandNotRespectedError, Error, SimAutoFeatureError, PowerWorldPrerequisiteError, @@ -86,7 +85,6 @@ class built from numerous mixins. Each mixin corresponds to a specific "SAW", "PowerWorldError", "COMError", - "CommandNotRespectedError", "Error", "SimAutoFeatureError", "PowerWorldPrerequisiteError", diff --git a/esapp/saw/_enums.py b/esapp/saw/_enums.py index 1aaa53a..511369d 100644 --- a/esapp/saw/_enums.py +++ b/esapp/saw/_enums.py @@ -349,16 +349,6 @@ def base_columns(cls) -> list: cls.DISPLAY_NAME.value, ] - @classmethod - def old_columns(cls) -> list: - """Returns the legacy 4-column format (older Simulator versions).""" - return [ - cls.KEY_FIELD.value, - cls.INTERNAL_FIELD_NAME.value, - cls.FIELD_DATA_TYPE.value, - cls.DESCRIPTION.value, - ] - @classmethod def new_columns(cls) -> list: """Returns the extended 6-column format (newer Simulator versions).""" diff --git a/esapp/saw/_exceptions.py b/esapp/saw/_exceptions.py index f51d545..04a7c66 100644 --- a/esapp/saw/_exceptions.py +++ b/esapp/saw/_exceptions.py @@ -132,22 +132,6 @@ class COMError(Error): pass -class CommandNotRespectedError(PowerWorldError): - """ - Raised if a command sent into PowerWorld is not respected, but - PowerWorld itself does not raise an error. This exception should - be used with helpers that double-check commands. - - Nuance: - SimAuto may return "success" even if a parameter change was ignored due to - internal logic (e.g., setting a generator MW above its PMax when limits are - enforced). This error is raised by wrapper methods that verify the state - change actually occurred. - """ - - pass - - # ============================================================================= # Application-level exceptions (consolidated from utils/exceptions.py) # ============================================================================= diff --git a/esapp/saw/atc.py b/esapp/saw/atc.py index 901747b..2624029 100644 --- a/esapp/saw/atc.py +++ b/esapp/saw/atc.py @@ -267,28 +267,6 @@ def ATCDataWriteOptionsAndResults(self, filename: str, append: bool = True, key_ app = YesNo.from_bool(append) return self._run_script("ATCDataWriteOptionsAndResults", f'"{filename}"', app, key_field) - def ATCWriteAllOptions(self, filename: str, append: bool = True, key_field: Union[KeyFieldType, str] = KeyFieldType.PRIMARY): - """Writes out all information related to ATC analysis (deprecated name). - - .. deprecated:: - Use `ATCDataWriteOptionsAndResults` instead. This method was renamed - in the December 9, 2021 patch of Simulator 22. - - Parameters - ---------- - filename : str - Name of the auxiliary file to save. - append : bool, optional - If True, appends results to existing file. Defaults to True. - key_field : str, optional - Identifier to use for the data. Defaults to "PRIMARY". - - Returns - ------- - None - """ - return self.ATCDataWriteOptionsAndResults(filename, append, key_field) - def ATCWriteResultsAndOptions(self, filename: str, append: bool = True): """Writes out all information related to ATC analysis to an auxiliary file. diff --git a/esapp/saw/base.py b/esapp/saw/base.py index 92a95d3..a654718 100644 --- a/esapp/saw/base.py +++ b/esapp/saw/base.py @@ -38,7 +38,6 @@ def __init__( UIVisible=False, CreateIfNotFound: bool = False, UseDefinedNamesInVariables: bool = False, - pw_order=False, ) -> None: """Initializes the SimAuto Wrapper (SAW) and establishes a COM connection to PowerWorld Simulator. @@ -55,9 +54,6 @@ def __init__( Sets the SimAuto property to create new objects during `ChangeParameters` calls. Defaults to False. UseDefinedNamesInVariables : bool, optional If True, configures the case to use defined names instead of internal IDs. Defaults to False. - pw_order : bool, optional - If True, disables automatic sorting of DataFrames to match PowerWorld's internal memory order. - Defaults to False. Raises ------ @@ -88,7 +84,6 @@ def __init__( self.pwb_file_path = None self.set_simauto_property("CreateIfNotFound", CreateIfNotFound) self.set_simauto_property("UIVisible", UIVisible) - self.pw_order = pw_order # Initialize temporary file for UI updates self.empty_aux = get_temp_filepath(".axd") @@ -141,9 +136,6 @@ def set_simauto_property(self, property_name: str, property_value: Union[str, bo ValueError If the `property_name` is unsupported, the `property_value` has an incorrect type, or if `CurrentDir` is set to an invalid path. - AttributeError - If the property does not exist on the current SimAuto version (e.g., `UIVisible` - on older versions of Simulator). """ if property_name not in self.SIMAUTO_PROPERTIES: raise ValueError( @@ -161,16 +153,7 @@ def set_simauto_property(self, property_name: str, property_value: Union[str, bo if property_name == "CurrentDir" and not os.path.isdir(property_value): raise ValueError(f"The given path for CurrentDir, {property_value}, is not a valid path!") - try: - self._set_simauto_property(property_name=property_name, property_value=property_value) - except AttributeError as e: - if property_name == "UIVisible": - self.log.warning( - "UIVisible attribute could not be set. Note this SimAuto property was not introduced " - "until Simulator version 20. Check your version with the get_simulator_version method." - ) - else: - raise e from None + self._set_simauto_property(property_name=property_name, property_value=property_value) def _set_simauto_property(self, property_name, property_value): """Internal helper to directly set a SimAuto COM property.""" @@ -303,30 +286,15 @@ def RequestBuildDate(self) -> int: @property def UIVisible(self) -> bool: - try: - return self._pwcom.UIVisible - except AttributeError: - self.log.warning( - "UIVisible attribute could not be accessed. Note this SimAuto property was not introduced " - "until Simulator version 20. Check your version with the get_simulator_version method." - ) - return False + return self._pwcom.UIVisible @property - def ProgramInformation(self) -> Union[tuple, bool]: + def ProgramInformation(self) -> tuple: """Tuple property: Detailed information about the Simulator version and license.""" - try: - result = self._pwcom.ProgramInformation - result = [list(x) for x in result] - result[0][2] = datetime.datetime.fromtimestamp(result[0][2].timestamp(), tz=result[0][2].tzinfo) - result = tuple(tuple(x) for x in result) - return result - except AttributeError: # pragma: no cover - self.log.warning( - "ProgramInformation attribute could not be accessed. Note this SimAuto property was not " - "introduced until Simulator version 21. Check your version with the get_simulator_version method." - ) - return False + result = self._pwcom.ProgramInformation + result = [list(x) for x in result] + result[0][2] = datetime.datetime.fromtimestamp(result[0][2].timestamp(), tz=result[0][2].tzinfo) + return tuple(tuple(x) for x in result) def _com_call(self, func: str, *args): """Internal helper to execute SimAuto COM methods and handle error codes. diff --git a/esapp/saw/data.py b/esapp/saw/data.py index 4dab9ad..dd23d6c 100644 --- a/esapp/saw/data.py +++ b/esapp/saw/data.py @@ -1,5 +1,4 @@ """Data retrieval and modification functions (SimAuto data access layer).""" -import re from typing import List, Tuple, Union import numpy as np @@ -216,27 +215,12 @@ def GetFieldList(self, ObjectType: str, copy=False) -> pd.DataFrame: result = self._com_call("GetFieldList", ObjectType) result_arr = np.array(result) - # Try standard 5-column format first, fall back to old/new formats - base_cols = FieldListColumn.base_columns() - old_cols = FieldListColumn.old_columns() - new_cols = FieldListColumn.new_columns() - + # Standard 5-column format, with the extended 6-column format + # from newer Simulator versions as the fallback. try: - output = pd.DataFrame(result_arr, columns=base_cols) - except ValueError as e: - exp_base = r"\([0-9]+,\s" - exp_end = r"{}\)" - r1 = re.search(exp_base + exp_end.format(len(old_cols)), e.args[0]) - r2 = re.search(exp_base + exp_end.format(len(base_cols)), e.args[0]) - r3 = re.search(exp_base + exp_end.format(len(new_cols)), e.args[0]) - - if (r1 is None) or (r2 is None): - if r3 is None: - raise e - else: - output = pd.DataFrame(result_arr, columns=new_cols) - else: - output = pd.DataFrame(result_arr, columns=old_cols) + output = pd.DataFrame(result_arr, columns=FieldListColumn.base_columns()) + except ValueError: + output = pd.DataFrame(result_arr, columns=FieldListColumn.new_columns()) output.sort_values(by=[FieldListColumn.INTERNAL_FIELD_NAME.value], inplace=True) self._object_fields[object_type] = output diff --git a/examples/plot_helpers.py b/examples/plot_helpers.py index 9d9bdb6..69e9c2d 100644 --- a/examples/plot_helpers.py +++ b/examples/plot_helpers.py @@ -31,24 +31,16 @@ def format_plot(ax, title='', xlabel='', ylabel='', grid=True, **_ignored): if grid: ax.grid(alpha=0.3, linewidth=0.5) -# --------------------------------------------------------------------------- -# Standard figure dimensions (inches) for 6.5" LaTeX text width -# --------------------------------------------------------------------------- _W1 = 4.5 # single panel width _H1 = 3.2 # single panel height _W2 = 6.5 # two-panel row width _H2 = 2.8 # two-panel row height -_W3 = 6.5 # three-panel row width -_H3 = 2.5 # three-panel row height _WFULL = 6.5 # full page width # Font sizes for multi-panel (3+) plots to avoid title crowding _FS3 = dict(titlesize=10, labelsize=9, ticksize=8) _FS2 = dict(titlesize=11, labelsize=9, ticksize=8) -# --------------------------------------------------------------------------- -# Professional color palette -# --------------------------------------------------------------------------- _C1 = '#4C72B0' # primary blue _C2 = '#DD8452' # secondary orange _C3 = '#55A868' # tertiary green @@ -509,25 +501,6 @@ def plot_histograms(datasets, titles, xlabels, colors=None, bins=25, figsize=Non plt.show() -# --------------------------------------------------------------------------- -# Direction sensitivity (GIC) -# --------------------------------------------------------------------------- - - -# --------------------------------------------------------------------------- -# GIC matrix / sensitivity -# --------------------------------------------------------------------------- - - -# Keep backward compatibility alias -plot_gic_bar_hist = plot_gic_distribution - - -# --------------------------------------------------------------------------- -# Geographic / E-field -# --------------------------------------------------------------------------- - - # --------------------------------------------------------------------------- # Dynamics # --------------------------------------------------------------------------- @@ -617,13 +590,3 @@ def plot_comparative_dynamics(ctg_names, all_results, figsize=None): plt.show() -# --------------------------------------------------------------------------- -# Discrete calculus / Grid2D utilities -# --------------------------------------------------------------------------- - - -# --------------------------------------------------------------------------- -# Spectral analysis utilities -# --------------------------------------------------------------------------- - - diff --git a/examples/statics.py b/examples/statics.py index 92e0066..350c84e 100644 --- a/examples/statics.py +++ b/examples/statics.py @@ -329,14 +329,6 @@ def pushstate(self, verbose: bool = False) -> None: if self.stateidx >= self.maxstates: self.pw.esa.DeleteState(f'GWBState{self.stateidx - self.maxstates}') - def istore(self, n: int = 0, verbose: bool = False) -> None: - """Update the nth state in the chain with current state.""" - if n > self.maxstates or n > self.stateidx: - raise Exception("State index out of range") - if verbose: - print(f'Store -> {self.stateidx - n}') - self.pw.esa.StoreState(f'GWBState{self.stateidx - n}') - def irestore(self, n: int = 1, verbose: bool = False) -> None: """Restore the nth previous state from the chain.""" if n > self.maxstates or n > self.stateidx: diff --git a/tests/test_helpers_unit.py b/tests/test_helpers_unit.py index a42534b..6a863ee 100644 --- a/tests/test_helpers_unit.py +++ b/tests/test_helpers_unit.py @@ -492,21 +492,6 @@ def test_exit_cleanup(self): assert saw._pwcom is None mock_pythoncom.CoUninitialize.assert_called_once() - def test_set_simauto_property_uivisible_attribute_error(self, saw_obj): - """set_simauto_property logs warning for UIVisible on old versions.""" - saw_obj._pwcom.UIVisible = PropertyMock(side_effect=AttributeError) - with patch.object(saw_obj, '_set_simauto_property', side_effect=AttributeError): - saw_obj.set_simauto_property("UIVisible", True) - - def test_uivisible_property_attribute_error(self, saw_obj): - """UIVisible property returns False on AttributeError.""" - original = saw_obj._pwcom - mock_pwcom = MagicMock(spec=[]) # spec=[] means no attributes allowed - saw_obj._pwcom = mock_pwcom - result = saw_obj.UIVisible - assert result is False - saw_obj._pwcom = original - def test_request_build_date(self, saw_obj): """RequestBuildDate property accesses COM.""" saw_obj._pwcom.RequestBuildDate = 20230101 @@ -704,8 +689,8 @@ def test_list_of_devices_decimal_delimiter(self, saw_obj): assert result is not None saw_obj.decimal_delimiter = "." - def test_set_simauto_property_non_uivisible_attribute_error(self, saw_obj): - """set_simauto_property re-raises AttributeError for non-UIVisible properties.""" + def test_set_simauto_property_attribute_error_propagates(self, saw_obj): + """set_simauto_property propagates AttributeError from the COM property.""" with patch.object(saw_obj, '_set_simauto_property', side_effect=AttributeError("oops")): with pytest.raises(AttributeError, match="oops"): saw_obj.set_simauto_property("CreateIfNotFound", True) diff --git a/tests/test_indexing.py b/tests/test_indexing.py index 346bd87..901ade1 100644 --- a/tests/test_indexing.py +++ b/tests/test_indexing.py @@ -350,16 +350,6 @@ def test_open_absolute_path(): mock_saw_class.assert_called_once_with('/absolute/path/case.pwb', CreateIfNotFound=True, early_bind=True) -def test_fexcept_helper(): - """fexcept converts 'Three' prefix back to '3'.""" - from esapp.indexable import fexcept - - assert fexcept("ThreeWindingTransformer") == "3WindingTransformer" - assert fexcept("ThreePhase") == "3Phase" - assert fexcept("NormalName") == "NormalName" - assert fexcept("Bus") == "Bus" - assert fexcept("") == "" - def test_getitem_with_gobject_enum_field(indexable_instance: Indexable): """idx[GObject, GObject.Field] retrieves field using enum member.""" diff --git a/tests/test_integration_saw_operations.py b/tests/test_integration_saw_operations.py index 972bfee..411e09c 100644 --- a/tests/test_integration_saw_operations.py +++ b/tests/test_integration_saw_operations.py @@ -133,11 +133,6 @@ def test_atc_data_write_options(self, saw_instance, temp_file): tmp_aux = temp_file(".aux") saw_instance.ATCDataWriteOptionsAndResults(tmp_aux, append=False, key_field="PRIMARY") - @pytest.mark.order(4041) - def test_atc_write_all_options_deprecated(self, saw_instance, temp_file): - tmp_aux = temp_file(".aux") - saw_instance.ATCWriteAllOptions(tmp_aux, append=True, key_field="PRIMARY") - @pytest.mark.order(4042) def test_atc_write_results_and_options(self, saw_instance, temp_file): tmp_aux = temp_file(".aux") From 153929e555244077f932c9531b5f405fb1bbeed3 Mon Sep 17 00:00:00 2001 From: Wyatt Lowery Date: Mon, 31 Aug 2026 16:53:37 -0500 Subject: [PATCH 07/10] misc clenaup --- CLAUDE.md | 4 +++- docs/dev/tests.rst | 12 +++++++++--- esapp/utils/network.py | 2 +- esapp/workbench.py | 2 +- tests/README.md | 13 +++++++++++-- tests/config_test.example.py | 16 ++++++++-------- tests/conftest.py | 24 ++++++++++++++++++------ 7 files changed, 51 insertions(+), 22 deletions(-) diff --git a/CLAUDE.md b/CLAUDE.md index 98b1b8b..2063f58 100644 --- a/CLAUDE.md +++ b/CLAUDE.md @@ -52,9 +52,11 @@ cd docs && sphinx-build -b html . _build ## Test Configuration Integration tests require a PowerWorld case file path, configured via: -1. Environment variable `SAW_TEST_CASE`, or +1. Environment variables `SAW_TEST_CASE` and optionally `SAW_GIC_TEST_CASES` (`;`-separated list), or 2. `tests/config_test.py` (user-created, not committed) +On this machine the env vars point at `C:\Users\wyattluke.lowery\Documents\GitHub\cases` (Hawaii40.SimpleDynamics as the main case). Integration tests never modify the case file. + Tests without PowerWorld access should use `-m unit`. The `--maxfail=5` default stops early on failures. ## Architecture diff --git a/docs/dev/tests.rst b/docs/dev/tests.rst index 93ada1c..dcbb639 100644 --- a/docs/dev/tests.rst +++ b/docs/dev/tests.rst @@ -62,8 +62,14 @@ Test Coverage Configuration ------------- -1. Copy ``tests/config_test.example.py`` to ``tests/config_test.py`` -2. Set ``SAW_TEST_CASE = r"C:\Path\To\Your\Case.pwb"`` +Preferred — environment variables (keeps machine-specific paths out of the repo): + +1. Set ``SAW_TEST_CASE`` to your PowerWorld case path +2. Optionally set ``SAW_GIC_TEST_CASES`` to a ``;``-separated list of case + paths for the parametrized GIC tests + +Alternative — copy ``tests/config_test.example.py`` to ``tests/config_test.py`` +and set the same names there (the file is gitignored). Running Tests ------------- @@ -78,6 +84,6 @@ Running Tests Troubleshooting --------------- -- **PowerWorld not found**: Ensure ``tests/config_test.py`` exists with valid case path +- **PowerWorld not found**: Ensure ``SAW_TEST_CASE`` is set (or ``tests/config_test.py`` exists) with a valid case path - **Integration tests slow**: Use ``pytest -m "not integration"`` for unit-only runs - **Import errors**: Install in editable mode with ``pip install -e .`` diff --git a/esapp/utils/network.py b/esapp/utils/network.py index 9b41f52..9b0cf9f 100644 --- a/esapp/utils/network.py +++ b/esapp/utils/network.py @@ -224,7 +224,7 @@ def lengths( def zmag(self) -> Series: """ - Get branch impedance magnitudes |Z|. + Get branch impedance magnitudes ``|Z|``. Returns ------- diff --git a/esapp/workbench.py b/esapp/workbench.py index b7e1d5c..53c4246 100644 --- a/esapp/workbench.py +++ b/esapp/workbench.py @@ -112,7 +112,7 @@ def voltage( Returns ------- pd.Series or tuple of pd.Series - If ``complex=True``, a complex-valued Series V = |V| * exp(j*theta). + If ``complex=True``, a complex-valued Series ``V = |V| * exp(j*theta)``. If ``complex=False``, a tuple ``(magnitude, angle_rad)``. """ fields = ["BusPUVolt", "BusAngle"] if pu else ["BusKVVolt", "BusAngle"] diff --git a/tests/README.md b/tests/README.md index 1108df9..b6d1f74 100644 --- a/tests/README.md +++ b/tests/README.md @@ -8,7 +8,9 @@ pytest -k "not integration" # Unit tests only (no PowerWorld) pytest -m integration # Integration tests only ``` -**PowerWorld Setup**: Copy `config_test.example.py` to `config_test.py` and set `SAW_TEST_CASE` path. +**PowerWorld Setup**: Set the `SAW_TEST_CASE` environment variable to a case path +(and optionally `SAW_GIC_TEST_CASES`, a `;`-separated list for the parametrized +GIC tests). Alternatively, copy `config_test.example.py` to `config_test.py`. ## Test Categories @@ -26,7 +28,14 @@ pytest --cov=esapp --cov-report=html ## Configuration -Create `config_test.py` from the example template: +Preferred: environment variables (keep machine-specific paths out of the repo): + +```powershell +setx SAW_TEST_CASE "C:\path\to\test_case.pwb" +setx SAW_GIC_TEST_CASES "C:\path\case1.pwb;C:\path\case2.pwb" +``` + +Alternative: create `config_test.py` from the example template: ```python SAW_TEST_CASE = r"C:\path\to\test_case.pwb" diff --git a/tests/config_test.example.py b/tests/config_test.example.py index efb6881..a20c57f 100644 --- a/tests/config_test.example.py +++ b/tests/config_test.example.py @@ -1,18 +1,18 @@ """ Test configuration template for ESA++ tests. -Copy this file to 'config_test.py' and update with your local settings. -The config_test.py file is gitignored so you can safely store your paths. +Preferred: set the SAW_TEST_CASE environment variable (and optionally +SAW_GIC_TEST_CASES, a ';'-separated list of case paths) so machine-specific +paths never live in the repository. Environment variables take priority +over this file. -Usage: - 1. Copy this file: cp config_test.example.py config_test.py - 2. Edit config_test.py with your PowerWorld case path - 3. Run tests normally with pytest or VS Code test extension +Alternative: copy this file to 'config_test.py' and update with your local +settings. The config_test.py file is gitignored. """ # Path to PowerWorld case file for integration tests # Set to None to skip online tests SAW_TEST_CASE = r"C:\Path\To\Your\Case.pwb" -# Alternative: Use None to always skip online tests -# SAW_TEST_CASE = None +# Optional: additional cases for the parametrized GIC tests +# GIC_TEST_CASES = [SAW_TEST_CASE, r"C:\Path\To\Another\Case.pwb"] diff --git a/tests/conftest.py b/tests/conftest.py index b997aa0..f9fb216 100644 --- a/tests/conftest.py +++ b/tests/conftest.py @@ -49,18 +49,30 @@ def _get_gic_test_cases(): """ Get additional GIC test case paths from configuration. + Priority order: + 1. Environment variable SAW_GIC_TEST_CASES (os.pathsep-separated paths) + 2. config_test.py file + 3. Empty list (GIC parametrized tests skip) + Returns a list of (path, label) tuples for parametrization. Only includes paths that exist on disk. """ + def _existing(paths): + cases = [] + for path in paths: + if os.path.exists(path): + label = os.path.splitext(os.path.basename(path))[0] + cases.append((path, label)) + return cases + + env_paths = os.environ.get("SAW_GIC_TEST_CASES") + if env_paths: + return _existing(p.strip() for p in env_paths.split(os.pathsep) if p.strip()) + try: import config_test if hasattr(config_test, 'GIC_TEST_CASES'): - cases = [] - for path in config_test.GIC_TEST_CASES: - if os.path.exists(path): - label = os.path.splitext(os.path.basename(path))[0] - cases.append((path, label)) - return cases + return _existing(config_test.GIC_TEST_CASES) except ImportError: pass return [] From a66a675d6d6a06dcc003378d78597c6d094b16c3 Mon Sep 17 00:00:00 2001 From: Wyatt Lowery Date: Mon, 31 Aug 2026 17:09:11 -0500 Subject: [PATCH 08/10] cleanup warnings and one imprvment speed --- esapp/indexable.py | 37 +++++++++++++++++++++++++++ esapp/saw/_enums.py | 57 +++++++++--------------------------------- esapp/utils/buscat.py | 5 ---- esapp/utils/network.py | 15 +++-------- tests/test_indexing.py | 57 ++++++++++++++++++++++++++++++++---------- 5 files changed, 96 insertions(+), 75 deletions(-) diff --git a/esapp/indexable.py b/esapp/indexable.py index 0b51fd3..f7c2e8d 100644 --- a/esapp/indexable.py +++ b/esapp/indexable.py @@ -1,6 +1,8 @@ from .saw import SAW, PowerWorldPrerequisiteError from .components import GObject from typing import Type, Optional +from numbers import Real +from math import isfinite from pandas import DataFrame from os import path @@ -246,10 +248,31 @@ def _bulk_update_from_df(self, gtype: Type[GObject], df: DataFrame): else: raise + @staticmethod + def _as_scalar_broadcast(fields: list[str], value) -> Optional[list]: + """Return one finite numeric value per field if `value` is a scalar + broadcast (a single number, or one number per field), else None.""" + def ok(v): + return isinstance(v, Real) and not isinstance(v, bool) and isfinite(float(v)) + + if ok(value): + return [value] * len(fields) + if ( + isinstance(value, (list, tuple)) + and len(fields) > 1 + and len(value) == len(fields) + and all(ok(v) for v in value) + ): + return list(value) + return None + def _broadcast_update_to_fields(self, gtype: Type[GObject], fields: list[str], value): """Modifies specific fields for existing objects by broadcasting a value. This corresponds to the use case: `pw[ObjectType, 'FieldName'] = value`. + Numeric scalar broadcasts are dispatched as a single ``SetData`` script + command (no key read); arrays and non-numeric values are written via + ``ChangeParametersMultipleElementRect`` after reading the primary keys. Parameters ---------- @@ -273,6 +296,20 @@ def _broadcast_update_to_fields(self, gtype: Type[GObject], fields: list[str], v raise ValueError( f"Cannot set read-only field(s) on {gtype.TYPE()}: {non_settable}" ) + + # Fast path: numeric scalar broadcasts need no key read — a single + # SetData script call updates every object of the type in place. + # Per-object arrays and non-numeric values use the Rect path below. + if gtype.keys(): + per_field = self._as_scalar_broadcast(fields, value) + if per_field is not None: + field_list = ", ".join(fields) + value_list = ", ".join(str(v) for v in per_field) + self.esa.RunScriptCommand( + f"SetData({gtype.TYPE()}, [{field_list}], [{value_list}], ALL);" + ) + return + # For objects without keys (e.g., Sim_Solution_Options), we construct # the change DataFrame directly without reading from PowerWorld first. if not gtype.keys(): diff --git a/esapp/saw/_enums.py b/esapp/saw/_enums.py index 511369d..be239be 100644 --- a/esapp/saw/_enums.py +++ b/esapp/saw/_enums.py @@ -534,19 +534,10 @@ class BusType(_StrEnum): The three fundamental bus types that determine which equations a bus contributes to the power flow Jacobian. - - Attributes - ---------- - SLACK : str - Reference bus — fixes voltage magnitude and angle. - PV : str - Generator bus — specifies P injection and V magnitude. - PQ : str - Load bus — specifies P and Q injection. """ - SLACK = "Slack" - PV = "PV" - PQ = "PQ" + SLACK = "Slack" #: Reference bus — fixes voltage magnitude and angle. + PV = "PV" #: Generator bus — specifies P injection and V magnitude. + PQ = "PQ" #: Load bus — specifies P and Q injection. class BusCtrl(IntFlag): @@ -555,25 +546,12 @@ class BusCtrl(IntFlag): Bitwise-combinable flags describing how a bus participates in voltage regulation. A remotely regulated bus with droop control would have ``BusCtrl.REMOTE | BusCtrl.DROOP``. - - Attributes - ---------- - NONE : int - No special control. - REMOTE : int - Remote voltage regulation (controls voltage at another bus). - DROOP : int - Voltage droop control with deadband. - LDC : int - Line drop compensation. - TOL : int - Voltage setpoint tolerance band (PVTol mode). """ - NONE = 0 - REMOTE = auto() - DROOP = auto() - LDC = auto() - TOL = auto() + NONE = 0 #: No special control. + REMOTE = auto() #: Remote voltage regulation (controls voltage at another bus). + DROOP = auto() #: Voltage droop control with deadband. + LDC = auto() #: Line drop compensation. + TOL = auto() #: Voltage setpoint tolerance band (PVTol mode). class Role(_StrEnum): @@ -581,19 +559,8 @@ class Role(_StrEnum): When multiple generators coordinate to regulate voltage at a remote bus, each participating bus takes on a distinct role. - - Attributes - ---------- - NONE : str - Not part of a regulation group (local control only). - PRIMARY : str - Enforces the voltage equation at the regulated bus. - SECONDARY : str - Shares reactive power proportionally with the primary. - TARGET : str - The bus whose voltage is being regulated remotely. """ - NONE = "None" - PRIMARY = "Primary" - SECONDARY = "Secondary" - TARGET = "Target" + NONE = "None" #: Not part of a regulation group (local control only). + PRIMARY = "Primary" #: Enforces the voltage equation at the regulated bus. + SECONDARY = "Secondary" #: Shares reactive power proportionally with the primary. + TARGET = "Target" #: The bus whose voltage is being regulated remotely. diff --git a/esapp/utils/buscat.py b/esapp/utils/buscat.py index 90f4f52..fe5930c 100644 --- a/esapp/utils/buscat.py +++ b/esapp/utils/buscat.py @@ -134,11 +134,6 @@ class BusCat: ``LimHigh``, ``Type``, ``Ctrl``, ``Role``, ``Lim``, ``SVC``, ``Eff``, ``Reg``. - Attributes - ---------- - df : DataFrame - Parsed classification data. Raises ``RuntimeError`` if - accessed before :meth:`refresh` is called. """ _COL_MAP = { diff --git a/esapp/utils/network.py b/esapp/utils/network.py index 9b0cf9f..93e6274 100644 --- a/esapp/utils/network.py +++ b/esapp/utils/network.py @@ -44,19 +44,10 @@ class BranchType(Enum): """ Branch weighting schemes for Laplacian construction. - - Attributes - ---------- - LENGTH : int - Weight by inverse squared physical length (km^-2). - RES_DIST : int - Weight by inverse impedance magnitude (resistance distance). - DELAY : int - Weight by inverse squared propagation delay (s^-2). """ - LENGTH = 1 - RES_DIST = 2 - DELAY = 3 + LENGTH = 1 #: Weight by inverse squared physical length (km^-2). + RES_DIST = 2 #: Weight by inverse impedance magnitude (resistance distance). + DELAY = 3 #: Weight by inverse squared propagation delay (s^-2). class Network: diff --git a/tests/test_indexing.py b/tests/test_indexing.py index 901ade1..26ccab1 100644 --- a/tests/test_indexing.py +++ b/tests/test_indexing.py @@ -142,18 +142,21 @@ def test_setitem_broadcast(indexable_instance: Indexable, g_object: Type[grid.GO unique_keys = sorted(list(set(g_object.keys()))) if not unique_keys: + # Keyless objects build the change DataFrame directly. indexable_instance[g_object, field] = 1.234 expected_df = pd.DataFrame({field: [1.234]}) + mock_esa.ChangeParametersMultipleElementRect.assert_called_once() + sent_df = mock_esa.ChangeParametersMultipleElementRect.call_args[0][2] + assert_frame_equal(sent_df, expected_df) else: - mock_key_df = pd.DataFrame({k: [101, 102] for k in unique_keys}) - mock_esa.GetParamsRectTyped.return_value = mock_key_df + # Keyed objects take the SetData fast path for numeric scalars: + # no key read, no Rect write. indexable_instance[g_object, field] = 1.234 - expected_df = mock_key_df.copy() - expected_df[field] = 1.234 - - mock_esa.ChangeParametersMultipleElementRect.assert_called_once() - sent_df = mock_esa.ChangeParametersMultipleElementRect.call_args[0][2] - assert_frame_equal(sent_df, expected_df) + mock_esa.RunScriptCommand.assert_called_once_with( + f"SetData({g_object.TYPE()}, [{field}], [1.234], ALL);" + ) + mock_esa.GetParamsRectTyped.assert_not_called() + mock_esa.ChangeParametersMultipleElementRect.assert_not_called() def test_setitem_bulk_update_from_df(indexable_instance: Indexable, g_object: Type[grid.GObject]): @@ -190,14 +193,42 @@ def test_setitem_broadcast_multiple_fields(indexable_instance: Indexable, g_obje assert sent_df.iloc[0][fields[0]] == values[0] return - mock_key_df = pd.DataFrame({k: [101, 102] for k in unique_keys}) - mock_esa.GetParamsRectTyped.return_value = mock_key_df + # One numeric value per field takes the SetData fast path. indexable_instance[g_object, fields] = values + mock_esa.RunScriptCommand.assert_called_once_with( + f"SetData({g_object.TYPE()}, [{fields[0]}, {fields[1]}], [1.1, 2.2], ALL);" + ) + mock_esa.ChangeParametersMultipleElementRect.assert_not_called() + - expected_df = mock_key_df.copy() - expected_df[fields] = values +def test_setitem_broadcast_array_uses_rect_path(indexable_instance: Indexable): + """Per-object array broadcasts read keys and use the Rect write path.""" + mock_esa = indexable_instance.esa + keys = sorted(set(grid.Bus.keys())) + field = [f for f in grid.Bus.editable() if f not in grid.Bus.keys()][0] + mock_key_df = pd.DataFrame({k: [1, 2, 3] for k in keys}) + mock_esa.GetParamsRectTyped.return_value = mock_key_df + + indexable_instance[grid.Bus, field] = [1.0, 2.0, 3.0] + + mock_esa.RunScriptCommand.assert_not_called() + mock_esa.ChangeParametersMultipleElementRect.assert_called_once() sent_df = mock_esa.ChangeParametersMultipleElementRect.call_args[0][2] - assert_frame_equal(sent_df, expected_df) + assert list(sent_df[field]) == [1.0, 2.0, 3.0] + + +def test_setitem_broadcast_string_uses_rect_path(indexable_instance: Indexable): + """Non-numeric scalar broadcasts read keys and use the Rect write path.""" + mock_esa = indexable_instance.esa + keys = sorted(set(grid.Bus.keys())) + field = [f for f in grid.Bus.editable() if f not in grid.Bus.keys()][0] + mock_key_df = pd.DataFrame({k: [1, 2] for k in keys}) + mock_esa.GetParamsRectTyped.return_value = mock_key_df + + indexable_instance[grid.Bus, field] = "Connected" + + mock_esa.RunScriptCommand.assert_not_called() + mock_esa.ChangeParametersMultipleElementRect.assert_called_once() # ============================================================================= From 5498b76ae2dbb922408b494579dc15bd51bd023d Mon Sep 17 00:00:00 2001 From: Wyatt Lowery Date: Mon, 31 Aug 2026 17:16:22 -0500 Subject: [PATCH 09/10] dependency cleanup --- .readthedocs.yaml | 5 +++-- docs/conf.py | 8 ++------ docs/requirements.txt | 8 -------- pyproject.toml | 9 +-------- pytest.ini | 8 +------- 5 files changed, 7 insertions(+), 31 deletions(-) delete mode 100644 docs/requirements.txt diff --git a/.readthedocs.yaml b/.readthedocs.yaml index fde023e..3a7e878 100644 --- a/.readthedocs.yaml +++ b/.readthedocs.yaml @@ -25,6 +25,7 @@ sphinx: # See https://docs.readthedocs.io/en/stable/guides/reproducible-builds.html python: install: - - requirements: docs/requirements.txt - method: pip - path: . \ No newline at end of file + path: . + extra_requirements: + - docs \ No newline at end of file diff --git a/docs/conf.py b/docs/conf.py index 1c9e90d..6de59a8 100644 --- a/docs/conf.py +++ b/docs/conf.py @@ -124,13 +124,9 @@ def setup(app): html_css_files = ["custom.css", "custom_tables.css"] autodoc_mock_imports = [ - "win32com", - "win32com.client", + "win32com", + "win32com.client", "pythoncom", - "geopandas", - "shapely", - "fiona", - "pyproj", ] latex_documents = [ diff --git a/docs/requirements.txt b/docs/requirements.txt deleted file mode 100644 index a72235d..0000000 --- a/docs/requirements.txt +++ /dev/null @@ -1,8 +0,0 @@ -sphinx -sphinx-rtd-theme -sphinx-copybutton -nbsphinx -nbconvert -numpy<2.0 -ipykernel -matplotlib \ No newline at end of file diff --git a/pyproject.toml b/pyproject.toml index 0c3f1e8..340c65e 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -60,14 +60,10 @@ dependencies = [ test = [ "pytest>=7.0", "pytest-cov>=4.0", - "pytest-xdist>=3.0", - "pytest-timeout>=2.1", - "pytest-mock>=3.10", "pytest-order>=1.2" ] dev = [ "matplotlib", - "geopandas", "pytest>=7.0", "pytest-cov>=4.0", "pytest-order>=1.2" @@ -76,10 +72,7 @@ docs = [ "sphinx", "sphinx-rtd-theme", "sphinx-copybutton", - "nbsphinx", - "ipykernel", - "matplotlib", - "geopandas" + "nbsphinx" ] [project.urls] diff --git a/pytest.ini b/pytest.ini index 9687125..dc0a234 100644 --- a/pytest.ini +++ b/pytest.ini @@ -41,10 +41,4 @@ markers = integration: marks tests requiring PowerWorld connection unit: marks unit tests with mocked dependencies requires_case: marks tests requiring valid PowerWorld case file - order: marks test execution order (via pytest-order) -# */test_*.py -# */__pycache__/* - -# Timeout for tests (if pytest-timeout is installed) -# timeout = 300 -# timeout_method = thread \ No newline at end of file + order: marks test execution order (via pytest-order) \ No newline at end of file From cb547f2c1b1fb1bf4249212297196bffd011725c Mon Sep 17 00:00:00 2001 From: Wyatt Lowery Date: Mon, 31 Aug 2026 17:22:34 -0500 Subject: [PATCH 10/10] v0.2.0 Release --- CHANGELOG.md | 19 +++++++++++++++++++ VERSION | 2 +- examples/plot_helpers.py | 2 -- 3 files changed, 20 insertions(+), 3 deletions(-) diff --git a/CHANGELOG.md b/CHANGELOG.md index 61b23c7..d778bca 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -1,3 +1,22 @@ +[0.2.0] - 2026-08-31 +-------------------- + +**Added** +- Added a PEP 561 `py.typed` marker for downstream type checking +- Added a faster single-command path for numeric scalar broadcasts +- Added environment-variable configuration for PowerWorld integration test cases + +**Changed** +- Streamlined package discovery, optional dependencies, and Read the Docs installation +- Consolidated the maintained examples and moved GIC formulations into the main documentation +- Reduced SimAuto logging overhead by avoiding expensive debug formatting unless enabled +- Modernized supported Simulator field metadata and property behavior + +**Removed** +- Removed deprecated or unused APIs: `ATCWriteAllOptions`, `CommandNotRespectedError`, `esapp.utils.timing`, and the `pw_order` constructor argument +- Removed legacy four-column field metadata handling and compatibility fallbacks for older Simulator properties +- Removed obsolete reference files, unrelated examples, bundled shapefiles, and machine-specific case paths + [0.1.5] - 2026-08-05 -------------------- diff --git a/VERSION b/VERSION index 9faa1b7..0ea3a94 100644 --- a/VERSION +++ b/VERSION @@ -1 +1 @@ -0.1.5 +0.2.0 diff --git a/examples/plot_helpers.py b/examples/plot_helpers.py index 69e9c2d..8ed1186 100644 --- a/examples/plot_helpers.py +++ b/examples/plot_helpers.py @@ -588,5 +588,3 @@ def plot_comparative_dynamics(ctg_names, all_results, figsize=None): axes_flat[j].set_visible(False) plt.tight_layout() plt.show() - -

M+PGgwCW*>eZ?G1C}c{YFX`zJ7S zxu_Sco)dSi39cOSp3xIl<;m`o-$#v!RU;i>86E@r1&(%@eX|>^;IAHA2gi4+?`aP= zcH~T{h08AZJ9UKx?=PIJf#v&OSP-*o1xtt<-987~!N#pG7k-32-85}F!xH(@hgGnP z=Y&NaVXnFK75RM^I^$53EopC8zx@sDKF5yT0oFZyRQn1}-+E_o8{#%q*3V$ota4T> z*f8I+@G&e?diAw}8-24~9+KQiG2a5#Oo*btPxWS(8~)Z{`@ZjEc?)?{P={N8V66j_ zbrX(z)SlY}D{Y(W4z)}DyaTRQXZIi$*)R{0`mO0l4~jm(f+S8y+<*i3%+2g0{aqkBgZGMM|*?)tTOWYit8O~ zNWIwVkvo%#1@aRIh zQd*Bz<~Ml19u~REqUInk$bEe8J}msSI&K!+TTnQlnDqY~`f>*Glg)2$!wPQU=O9>G zT-k5~PK%u(3V?a%ohA^g8I5_9VgC=YlZ#;4ZJ!IIeyVt`ef)J;QgAtZ63ltgbH`Pf zp)2d@Px`lx*?k4(a7H+dheJ=ijk^f*eGc&aV7=W%D-EpeHowLP*7lkdmJh2}Jw7lR zj=bC=_B6~~bMX%!&KnqXE*I9+WzY74)5Cj5oFMIUGa`q>Hk@-ekHNCQ3#)i=Q{#!> zM@at?=Gq}}*z{k#Owzsww=Z!+{*Sl=uqwfcTKS@{%|5u1agO$Pzt2cbg$rUyJlNq zW&0p8u|Ipuhj`eyaV9mZ>;B^P|K#$Hwh3$g$%~b;rK?~@;gAB-pXpK~S^;zRcA@oT zo67hpOG!RBn%0{&?d<9h1qbTVgASLDWlLoa=%ZN7!LjAH_;nz+E8#f z4A!0C(0aDE9b(tdCH-$Vj3wK{CZ87?3Twt67(=#Kx#OeTGhojBF{8%7#p_4MPKO0q zoZZCkL8c8Nl3O?bKx`lQCwVF?96pfdd`(tbz(4&9mV9YB0p=}xtnfv9)iLW|zOZ(X z$UycNe(k57qeyNHpmr&9avcuqnzLy=UY7weE8SsPiXF9V53|$_=C2E(?aR!H&v0RO zCC{DI?-d(IOI%=Wc1pi-aLBju*LwY9$pJ_I9x$WpOIK3=SG(lA$^jNn9ZCB)nco>g zth{<=7+GF*q`KOUv^R94uOD6D^rk(mfBK&G5B=`-&<2*wy-CNTC4Db#XTYrZ+^1yu zo~M^)S-{GSRqM$5gdTpI)>MuCZ}m!Q?IO2U-(c~GpR_)4!4(fyJuGT<^bYyHiK-us ztAj&dE}`|$ZHm>8Kf}g@b=M{$=k)&JXM`JT<1ESd+p_$bstT43Ur)^{m>gXBPafxf z8P5>K>#Gmb`pE+B&+=!)s~lUCO~2@T4(6wO=aBWW+3xXP4a;IM6$oaI%)Xxkrzs|mC-t56)>~YU z!_q=E9j_O@=|5Kms}{}h3Pvs{y?W{>ES@2bBercBdmsxoO)(@*hl|ykW*MaaiAD7I z$J)D~)gf4`#Xm;S_1nY;;&l@Gv_6*S_YSI z)_5)^W^RjI0T;iWd_)X00`t$WgjMM?v~x-SK6%GuVcFrwk~uKja~wTisjmwM%!IWk z-x}8;&l`r(QrIy2=avnyJm|dtG}suq{^>^8U{+b*v zK7R7n1k%4{7K5D6eQw{8jU)ZHF3e1ZJsbP&^o9SsKCrzV+inCbu#f7xA9>s%;iDnM zSqFoau+0{oVh}8x)q~!@kWAria)BEc7Sj6k>Azj@NC&LXuxo2hpnYKHhmMZ0s{0&T zZ(pVy{gw@jr%$8TQw2LW<54Qu{{}|=$wPZ(H&II#tO#kO_b)^^SWmd#}3R zZ&(v~lwM!5Jfm9vf*b3V4yDNBIJ~QkFv}3L;3@30yhZdkIP{N{-v8kF{61a}m-U?O z_7-`A;K;|%Fpsh5Jh`7C+3M4+hU6p1{2})*8nrPwAKFw)dY~&tR69eWnGj|0RWwr#yvC%|m3ZV6JZM&Jwtc;ZA>Vh{p_H zs)s{Yo~b3jM^q_SzuzbMsEmrPr2nQpH*bxyMlPpl*DV`}MmW8}QjfooyG`rmZC zShwb}*J_v%@wweq^tbG0n;Hv?jH~BpVTrP8yc9NVxG?Z8S^oD!ePdwlxF2mE!7|4Y zGoy*KObunQy)LxVQdqF*#D>>!;KQpkQEw!^$|wCBuXw`dDTZytL|3`_mf2H3$(msgLQ56iZXJJl0bOsV}j z4>o-2^UV>CtlQatE-X6psL~l`Jvo>;8&<~FHg|!;-cPlgN$eZwGywLQFr&|OSQqrI zrz>gyXsH!3seehFe!f$45zG>3FT26LW3RWK2AjEr=6JwGcUmr=0?SskA2tH6T$j>f zA}r5%cxyDttM+yn4>R{Yyyy#8M}C^<2RE)+dTbnQ92fq03~Z<^_4kJ@i&`>#;LuL3 z(oV21;PVP*XNj*+l^Vf+Ml zSnAQ$Y$@Du$3f->s~=gti-C=o)3^g+=C|IPR+0R!V5loxW>Mj=26nF)u+If%_pEF$ zgNyqZrF~)1(~h$?z`B)pEjX}j_gVkVFmGvBh7&Adh73=Dz%j5{&wB{ zd%z~Cuhkyd#M;=04eMX_oIxD&w8O^kuqf3tc+GhT3uMj>0kpvsDMu|GL2PI2`z4yRHo^v^u`;BpmX% zAgC40>bP+58RBuHUs=OOJB>>ota7V+X9ep_`n&mX^}tK}o5N;DnsvSavv2O|V*!^P z4`_1e@u|sZTs^z$TBNvAqFt|uL%+Vy{sFUw@}J)%zPxPcS2%R1`HDNRSkf%h z1T(Kiv5Miq9cNE`Ci%$U3-7`HKh9Kqg4z5*XCJ`&lGxM_u!1-J&qFxQZh-PVtU7u6 z>mxXkdA+F$Hv8M6<}oZbAGNU(HhH`8%3xt)`!TO!f$mIb1uR&fv+^bJijx6Vuy*sd zW-nl28fVW3xNKnA`iHQz=g)IrVR_u2wK}+wrSkX-C$_vk=q9YOd$r!Y0iV~p+>}C? znf--r39}Ox9lrwef2Or%z>QUdGV@{8NN!yln0fo__S3L-?#34#U`FFKWiHGZ$LQCQ z^nYGka)R{#J2a*US7j;^2D+^}bS{2{`8}lBWRKfj6(jBhsG4$g)*i@TYJDB9cF!d^!dnbchZ~S5- zg@wxa(H_WE^KEZN!t}EM!eIhj#+=$n-cS0`w=Uyg z$@ZKkH}ZOuL@x!f(dt7WaiaA3JU`O@|38mn-Ih#mn6*zilZ#wkAe=t}{_pcFWxQ?V z!OG?0_T=-m*?j1!2h5#*zEgiVPd>3`AZ%*A=5AkD=gN_~!u;25ILIKqiNIA*y8ej|B;L@J>cq9^9-F~cB{2?fAE|? zqj!5)Eq^?YKEC+w0jD7HG_F` z5B%*8bMIPkn?B(4xo3a53#_jluvT}_7&Cy9JeF;tEX$T_4TCv48u|u zY=1xdY#rQKq^5bpzV%+UFr#EhI9Xn~^Ua0|m>XLW))j8BksDsZX1R;nSqWziSqy6;%xVUk?**#biXrOtt``O{QVXHlmN5M zAMaJdM&0UZt_vD%qVL|@lqUNwV zl&+izF=smJFnOPfcm8@j!UI3!$aiXvt%dqs}F?l(^#4uhGOhio=O zfBQdcgXY5WrGuyoUd++VhRgEI>GDe#Ii${n_1%8-C$CquBCmc1EHZ0BbIBaPnL%*q z@Eeaw9yese4D`w5xfe&1MF`95I{c|R#ZQ~!>EW$je-{droMF??XteAYb+_(CNH)jd?ytLVjj>M{z%@X2L|B6Fiu;f--E%|&+H#U}d!qUA@7n1E!&AsR20qc|A zwjkR#tSBmeAZ++{_9Sr*PkXgL{NMh-)-PV;472^Xf5`UtWb>%S8V~Bkub%hWq`k#( z>h!!BSGvIe<$S9f*_~khUxz7Ve^j5E+qOMy7Q6FnYq-g&&qym+>TqrlIbU&3r!_T$ zdF5BB1E)uxHiM141+KQp<>!uc`fbGf+flccoX?8+9sV@JW~VOq=|uVm*UP`bWpO)V ziNlUstRog(+#5q&^sAG1J^bJIi?yJHT}N`=Zra}cTk_w}uzGz3J)VTrc`<5W-l3E9 zc;Yknc*Q3;bj1#OJmPq^zWfpX@B3V=ZtP}+rFo0!_gk2tEvX{?+kK+PgX(q0wAZk4 zsy{vc*&J^?^9vX(JA5vp5Z2_Jr037FuNPKo zVg8bVBgy$tVoYsz1=i2Jv7HC=gRIXMz#<31tD&&_xDE3z!LqM?-G;#mjoRZ3tZJ=o z;R83?nJqgBGn-{k@Ph^EX89^ud7@&7KOB;3Wu=7qEaRyl*mQBB<6hY8k#fW=*p@xy z{VrH>cE;T>xYAPeGzn&nYVW@UmKXbtkrNA=JFbM|p7nXT0ah2bJRb))Z6Cj99jwY5 z_;o$Z$XK!~2Ijw8c_R@{ZyMb_8kQ|ys!4{qExe{Kg&TJrv)Bo%9vxjCLHciMk+c`i zY964S3yVG+nv)4fcHTH?Hfis%c>gh2S^4AdOjz2G`|bp6@0jKm0-LRJus;i{^iIjs zVfleq=>>2#LlGVf^BUdy6v3E?yI2IXGCC~21#2v_{HMVpi%}0A>Wa_Y&^QLyyo!>RQ!U${SHBrFTrZTbegkG5^?1)E(-eES2go~a(`33EFP zD)|lTd7h7k!0LAkm=<4gy}c!&jXTU!Wc{>)g$o8wbcGF?wNC9|@rE6<`@yV%heUR8 zUc^YTGc3-1KDY;*e%Pj)6C8Tf_bLZAJdghFNb+R!=`L{5flH3Wj9V|1u5f(D@SI+- z;^>|M9ruFt7L*as5Yum9&9fQ}D^#~p{ z24<|8`lt(>*1~o5csQ~l)tp#pHUH)W*njl;xt(D#|3v0gI5Eg~R7Y5quq$pBoG#{X zY6}~DHOg>UnPfApHQX3_b7Ul3lwZHEC9K$HH)ILSF6lPM5|*94%vwR(7xrEK=RNk< zYq4jgaH+`UY9lNOcRaNUE{kctzlJ!ga#}3R3euSyU};RwA7bH|W(&({3`g>Y=9$4(qA#DuH$9 zIqn-^K@QJP52vlaxN#$_+xN@yKFn*zWI5}Vwhv%-P{7JAGhzMBkf(nER$fK z+9Lis%$s$*G?nBUmvIF!|3%l|88G+w(6D^cev|n5G1z@*pAt2!?BXC+!#1nsZ*yRE zX~yw9xObn&b2Ew8jqi5_E*4`!e<_EZCOS`yk8>SQlOh>pNWZN`p0q!5@p^ z%Jp9}Q(&{6XBJzyS>?!^o{i|V9Tt3= zxyt~1TJd{rBmK{7?2RyY!ezr|Sax~E@y~GY88-&5g&B`#g#U)+ZA{u&Sa^7UK(lXH zUu{ag6i(ap>Yyd8-@tE*fpwWyA6vi;J3kIs0jtiv=C*|MeARE4!7`V{%iF;As=wW% zVCAx5%Fb}~_~2ekh$~}0*}($V;FF7BUBZLt9R6Ru2mf_)wbN6&|iy)sS` zr%BIB!(d_Q`eq#1{#CcoP*@@JJ5J1V7}sYyX}|mB4`(>*lE^v;mhWs#bAg4;Y}!wS z1@~*Vxxyjyv`#{p-{e?10B*{kq7%S+^V~jzV9_jxYkauul56-7*fP81wim3B&bj6R z>(w1E6AM>F-XU(ZNIEwZPW#?FWEkArb3)lbm~Vb-oEMxIGVjs=;*-}$jDpo_#d;1b z`a3~31}N3nJP(fSb#IB?KQ3sPb+Cf-Mjf&6`mja| zSlchRdI4fn0yaviVDx*EUbF80KZIZT13I%y;g%6t*cG{H6@n^txrS0*-b(`S>BMUQ*>G zgMD@jM&5(9%wEmqqU3bm4!etHtL+-$gh{{9TU}bN8i4ImR`0KSD)-&e0 z-hveeGKVF@jLi;hZot~kon9&6#M$q!Ux(FI*Prcxvud+4w6NKswhwo~y2*VxS7C9& zsp1rvIjoF(8I}$hX}QQ z^6@vTlW^(YMGKC?>>;0js$tJ4R#Z03>onra8MtiR^9h--Dm!w-IXER_)|-Q{HnVI4?Z+6RuwU_w_cI@vbb+>N}3Vc?&Nlz}k0h zbk=a%R@*{3EPL=bw=K*WoaL|uE(?@Bv4h3&oFkiHednkG2iRr0q%OnlfDK!% z25yAYN}R^^ftwVOtaY%sYSZ|ku+6oD`_{s$ZRg`g!IIpClj30Ym%(QQuvD1*c@->c zKmOPxxHs40!b+GEme^w&>{B^!R}8Fs;>es%@+&tNuYl!|?LW+gnXFFf(Xi@H@6}>h zGX2%n<-`_+%5XSdwM0oQ{hfP^lJ#y4?;AArLcayG=x!@U->?+ z>-}}Reg3@Pp4WADcFuLqxz2UYd0sAXapT3AWPNputA@J~7q<2!^$juAS^Z(%XO0aq zTfM_^G|bgpSxnZSpAjEF0WQ7RWyE39ii`EbzC883oh^Eazb zOJRTCcZ24_%xc5JWpHTP>g%&%wctG?6qfb9^Kb^t$~{=M7S?vQ^O^=*s^`0GfD;XnUG!!Gtc=8lI2(T@l3fKyzbN_=5qvj4_dn00r&>lj#-Q+0PYTv~kh^e9-- zd+f?Rux9J79V21GRvX*>aGYzuE5l*Sy`HBJ!i>8om-)a9!})LV#EPcA!(g+qL&9OW z!1v1~Z&-~_df6TR$2fMraj#}hOD99cyi-}B&gV)2Zu-Q@qL zC%(%h^+nr0RBwk_YgSozhYd?yeTccWh4lCIeHO*Xz;0!2>w6-u|CC&}4K}F#oA-h_ z=9Bfr!M)b#I54xBh?Wnpa>?xt^VUu@k^0sbo0Yl4fe}k-+|ssUW-ua@AJ{l#*i5qjSZD3~kpC~3aJun`Azj}TY%s#SsZD*Jr z*MhYX=60^}X$NzxUzCNzHB$;pnXu;0g=4GXy72wG9AL?&aN$Z=WkT=EwA6nqh_0yVS9;DO57z6D(dn@~j_hA3dzT4pto* zl|zrG&SzejU_(xB_z1+!?A{A&VZj2=9>Zb1*Yu7xur{lHC=V8YJP>V!)nEFa_k?4v z@@|&HiZLNF4qVZr^Q>pE?W8MzyTEm4!^Mwbc5de`ELd)6eM}Dv>>dql1B<3RJ$?Y2 zhH6i?f>kePFo-Rl{*5hR{Vh|_eb~0mhY%aMp!x8H_h8;YagU~OCiC(2BG|Ba%E3Q4 z-{8fs^Len|^|(_5@sQRbH(>=oeF!-}`PdceGD-P`^%wMTpks@kmtfP7DG%<#Zg!np zT!7Wh=G5lH!Re`qXGr}K!WlPU(Qf~W6j-n5(*6p}|2*~WNm$h6)BS8%-&$OJ0@hiV zx@W?sWttI6Se~5r@d6x})33#T*pk>a{VXhTn3T5%)~0)0ISm^}1*gWsszJ|hrNYIZ zo49O)1CwV)pM(q2n~9=e?z60CWUE#3s{R~@N24jZPwezy@;D>D_uy1y+}{$E>0 z*`%yTT-@sdjR!N7j0o7!qRNzr@}?zLrZr@Fzvs~FhpFG5O)KG|9Tnpf5a*Y2uZdyv z#r(5JV9Q92G#F-S|!!gpQ?I*zUs^781OpVRyv9NNqoL)~$$6f5| z2OFd%Bgyqu$u`XOg*m#{@8z()wf9Os%yhi#zXN7C+`Bu1#0_p!x5Js4w*7~}=6RRT zZ-Xt(pPRj5WvhAFTi}Yl^VfU9ftDl(Vy_<^W)6Tg&$L%%u&lG@W z4_MSWG=DQ}e|~F=&M@cHkFHU0;M;iyXIMF5m3|XkY_oBHYnXj_EwwYhY6TMx^uEL) zaanBJU-rcL;V-2yckAHy%}M#U5!BA_cMN2}(mVThk$BJ(FQ3L5>>s~c+W+9XXZ-j9 z8`eu`d1>FM3*TYOr0Jd;P(E{i=gP0J&Tc++!>f098(@`NrGdmHuKa{AaNr3wT_2wr zUy{wFK4)hnSwF$z`|mY99kdNWH79r zGc{r*f_32G|=b{Hx=KKa#kYGT#@Dlxp=Yx7O($M(;JR& z{^jg5*f_?6-mi!kNW_mx`Q%AVHsTgLMxh=yqz+iq8O}X@=D|bQ-0a9Y7nnUe4hYt<)<8 z*6UN5s3C_L2I-5B*2Rzgz~)Q0{>Ri&Hv~@iXqu=_RqS_%UjTql-xaV;$8ZMHDDH`DL294)t;@pu7>foAVJ*pyM#Y5GrD%k8bC74(> zyxAWz{u;UGMMf0N3LC!2Kzv(TM(P`sBdO~=oAs8#=J7M>_^j}rs(lP>%uA2@59PVy zPW#FF1{ScK-@#t7$L)5)+`G$KlHV8JnOQ;1{(f@tdst;Zwz(XZh2}dFr+n=_hnUy< zYY3_TFD~iCd+-J>via(;195|GpJA17js1}DtuSZ#qZ`j)so3E!`Tz3&c2quugLmDu zA?q)BAq~0>7tar6lK)$DwVIAsOGCHROJU85vvhpBP9E7O0#@{R8FdcjMa%Xvh&4J! zAL5{Kt#*dP>d4=N)Nn)i*(+;dY29JfDcCx6M7uSx@QC?R67fKlc{MD{S+V*UZ1M4) zvWPiEwj=R4u+ zy17h-StBoep@$nr`W^kZSgtmmgX!PeYP(}`K% zUTavei_YNb3#&Z0dpW?CbxP)FQhtY(y*2E-F(Y>rEZ7x$_b;A@xxIOiGy>*nN(AKj zS!Db7fx}>P{+R>+!KNp31`dTeL0?-}!0ErDvWCFog6!L6aK&q;6AxBC9I^i~9GBo~ z8VoZ@67x>jp>eO((AVbY=i740Cl#hGo?zV}DpRCcs_+o2Q(3+Yc5`&bu51>zMC9xWlr} zm;0@Qy-tq#(iawuccsr~CH$=uh}mu3mx>YRwYw+n1MB?SlrDk=t>!=Fz?PTur_UzK zoAleJ7i{ZfeQzpUw6-YM4K8}=P%{x`MR&N>9cDed5i$yP3y(Y24OVWuL!VFQx}O?H zEZ)-NJb9nYS^s;YD;#*Yp@KX=&zw`9M9ggb_Km!M6i}^PL&~#EO|8lMM!t2{ZOQLx zYr32waZzy8-X5?ZgG1kcvc73$OV*Dm8A$EcSTUDaf5@9!f9^zA@_%dtH!tdj`ZYmO zTZkFOr|A1n?B3B)#CrMphpvd{e%jxUSm%3|zVBo>`Di;?p8Ve6pw5W<59kxy6V_T; z{`Y=E{DeWo{8z2%`#Xv2J7m&0uN{5A!#MO8-F|_EW9j=TqT`)toICuE;X^&n51+o# zxc=--`hH?eSR*wK{J!Ogo9y#1lHWHC{xe4pD?3~)Bi3+Adl$iygF9x?|M&SvRRJ8? zHlD7(#^9WE9@fkhpCfVML!Tw&eba)~o9X}8@psf5htpTa(E66+PPqqR!;96lyyXe^ zPYldhsHDp?{l4=t91i{&MB9hn`$o`e*m=Wt+MXn*vTGK@MOBv%k@jd3Td$b~+or|S z_Q`u&J%GGlZ0s5u;(@s3>ehT;IPSCZf9>}H|BVmK%P(9(>pwfNWFRaUV-6)&_pg0=ZF3e4dSkV&Byg10< z2}|6fwl{+#Z@9RV<4etW@Vyc5iw7s%Mksc8x&;^tG)IGiwjB%cXCaMY3J za8c7}ntvr|-uB;CuwhdBAte7xcx3*zvUWeSkHYb)bz|Pby0!%?NInjsrJJIX)VGrD zCHXX5qRUb%V0Hhk$4EX6X)B(r9F})6(tH|Reh1bwn3WmXVkhDz)0t2AN&R`tX?~{o zEj<5wuwWyjG7@pwt4CRdF#mub&0nF`_G-LA%3s#ed=*@qh#}Wt(UUdyVJM#|nch|d z>*rqBO!84=w%=|_g9E#6qxq~#+pS1Tg;jHN?vQ*Fx$oAEPk}Xg6~4rQ0lzAeNcq7< zsf%GrjrQ7cSiGzg{XWDx%lW}kSUdDQ{l3Kf@TC1A*w#Vx-+Yu;om=9sfYq`-^m~-Z zoUpK+Fyr#LsM#bwqjJtR*s$ac%}>D{KARN{>vkNent`~q+4BzTV8O92BLm>l`1$80 zaM8KNG+)K@*p+R6ZI<@?WIe$xA1|ENv!TzVomAs8O)qwTkA?@M=1(%TU;K zWXP+TC~ut}dwvicc263TDRgH7@Qb}X1bvzLz; zwq>7ZwS?tCHFsCRzAp>>|5jpu%2xg*`A8&&uo-_~Q*%$6uf#2CKkp|j+*Ovm9`S%N zZJB0R@#GfGr=l*XOZWgwB9rq-zO9<1HUaNo!w!Gd{?1I zF80lbHTQTlUr#zC;pla^Xy&$x9F(u9asPG=mbv_lz5?@j#hk0Ksca$5Z{#95`Z5O& z^ewzXzMoO9lZvxo<^3D!d9d}bgEQSVVueo}l2zH+^Zq->>R_q({ z7}jpt@Fxwn*h*5L!-~l>qWJoPte9Tkd=X*S)Ohq=Ffi&K+uqQ$sEu zBXN$$i8^BcqgxNa>SYD=e3{-jJWByfjZ=pVCLB1GID;6UvsZDu{@!a zet*M0^INqWRyLV_*#Ys=q_5BAFz*eg&KXWOdT@ztJ6s;tjx0~Venku%_<0aLzx!D4 zo4f@U-AYe$LtNMHD@O*4i;|-Ik@8BbztON_;E;u0aOtrq%~^+d25X7@a>EDer~ zol~+B=DwZ%JsqyQzUK0Bn0bTkM!xT8kTh{w3d?t`+Hejnr?;S*aJKmoPvyTqmRR@=D`?JU&Rz6*H=QEr;esA$O z*u@S@(}{uxiPtZZP+L z?4I{D~DB4q0B= zgB46z`hKlS2iGmw--g8b4SP$;_cW|*(KTBBm*)r#>_47Km&bGeyE6%v#n#x7-z&PR zW*vYVQnTg6x_1MrV@W)JE7uv8r^GpIfmvCDt=f|E4k~XcTpC%@s~xPpxpc`&n5kGX zsXeSJb33#M_KE$R$bu~kln-XW`VUoCI>FMkUrWcry8iYG7g+i7k6{!nobn>Q3oI@< zK-;%r{4ZBmQvT`3vf{|Z|R`Z$vPX}Vt1mwbPxTpQKg z6IK`uY42dxv1d;Pz|2pVX#dB{|HE2uSkO9}o{u6ur}9aA5nW4O@d)KLe@+Y@0;@#p zhLZk|Wv1w{4;&a|NBcYM$%$`9!9{T=Y5zs#)=@MD)*e|v`!DhBme2BoSxY-CCgp$Tx&>Aub6u&MV2uSqcH zQ0K6%aIwbTY6`3?|NbNzj!Woyaw=@xE28gqLZ`8 z^{mFVXyRO0kzIa~u$8BHr#wikMDow8_pz+3PD^c zKSh@(O-}M6=Lg~Bj=v(XJpUE8_gBF(&lviBsmqO9^!ycQdz|L4l(^a1N0E3yBh61~ zu`WEi4d&PRxRd|GD-6246Q<)~aFFBiC%a(Q=RMs?{xX*z*=yrq=A7-+$~hO7?1MSo zD|^Wh4}B+nMy?NmT@>8SaORkyYYxNwk)vpS%{aksu@aVVxOa$r?`q$*Mb%MQ{Fj@s z0d~7^+=g6lbT7x#{$5HsJt9B0ba+;q^H+a{?%Ot)* zM8D^)sc)vuhI#pUY?5!x5H_XNJy;PPkT4P!`gGp>0M`5(X*(2-`8@e<39Nq`a%&JQ ztGnEZ^dAhLC((RP!oU}b3RuuZ^(cfP>_ zo2gTp!gYgOTUn#O#eAh0+YDy3lUO&06}=Bp+qyn`WCKfkY)l|=|E}vF*ut8mI=cM2 zeV>N3BK3t2_LJY!PpNZf!nRims0)g-j<on=Fa&4ritq?Eu*`^@<-M|5tXue+Sdneup!fgFEp9XyIF1D}R zPx{NI1Kl#};F`28XG37-_o8d>U`BQ8{1q_c;?@EaY$*>o7y;AwM`3#p!-*)^*k%Fi zEvdg}y!Td^qjS>{8=Zw$V_|;5$WUU9N-Vbj?V`g^ipIa?mVsv!%hxf^nZKZlFb{ORurvc}CM z_g}iV8!`%9>iY3Uvl5U#w#H- zzd#CS(hz#T=?a}jzs9lyvPUTI)8yVECTu%m$gUE&IMCg_ zEgYC+r1_#HBl>n_!Hl@X{v;nm=`ow7onUrkySW)~!OPbDyTIIip04NNT&>?mazC!~ zX=6@-!&}bkK*p~We=jXP4m(Gd(D5G2$?Y1FpTe16>qf@M3~x8_NWQ0-u8&5M`+tjI z3(ddeEDfUL57KE*X#NiFi68$PfAIc5^Nai&f8h5lp!rQW&GcSmJVKOwk>+R7zV9-G zj8~YR>uA20f8!;J^uaVA4tvt7a57$JwsLJs@*Tz0&ZXlo0{?BUB;SzzptDRe9#&NM zQq>=}@ciS+_yX@8UUY@6WeIs5VA=ZK6I#HGCnrkU!J3P2Lz}=FW5(q+u%aDn;4kEB z*B=@_mTZ5{0^jCeU?DP(*unCRtBT*lr4EB{k?{uJOZABVV6Wqcjx~d2VVmkne)tq$ zJAZ3fuq~Ehgf+VZHk>Wj77_ z02c+O%+G<-``axe#{Svw%zsz>1Xi|7UZsMWtIlZ2`9aeWALzn$dpDgSR^AMCAy#&oyNp=y zZFhHK=fLbjGX5e=Jx1#XC_IPWg9A?njwQ>lTQJpf7v_fvmJ`?9n0f6sZ20fzBC zwh@ zy)TjfYnyAoAQ!fs{vbUSc1etJB;z~gU2D{2`$#$mC1{C*Q|b1rQ1zRV4U2cb9B~eD zw(5J+OxQ9bwd?|{i+T}!36}Qj+@xS&xS4+o_H0Sro$?;&$$rkQIoHy(K+A=tP zb5yfEuz09k*CP)zrGnU9(k>km-Vl^FKlI_+7zJ`6RMsv24`cnu0Ho`*Y7cL!d zYWMVeC0rDe?zjbUxm^rBUMn1SBy5J&#--z`5!bmrdb$zjmMtCn1};8rvt&K2{9E|4 z7A`G*LdU0crzK@3I5+TNd??~YO~g0Jd=RSDqjxQVB`xhPkn@LqY5CuIu*p8c_dCoT za_&|jELG`-{eWZow|Ei&^O|g}CQiJ0`_eR6Z*EJ^N4mq0Z%l#tc6U5}BVMsc+Bg9g zPHWY(5tf9m^%lU4Et~HCh2#Eim@*nxzjQ5R;QS`@`))r1HuVl-HiZptL)Mbb=VP7WKxiUIF#K7Ry{v7tpPULj=0hrTwXKjyAnO~iIak_d2%v^41_YAgPI${#JKQ|e-(|lQ$n%UgRu*zi)aG<58 zsuJZ5WfRYl@d9~%3)fm$Q{{Ms4@)|{nezc=n0E#Ez&hX6$4UOI#4bWQexdw!ZRa=G ztP;j}A};N+ljhF~y8Pb;|4ZD*kvPKYg{C%ch)3q`o7EDg-*>?cw^miz!jf6&5x|x|GsiTC4VgX) z514<>FQqAocbr1=6^D9j`dGn51;xJyAf9{K?-B#%-E_X^1*cEV+x^!FFTTIV8+IGi zerY2tAD-NS+|LHy`(*wF^G}xq^I+e=mLI>s+{o2KM#HvSWmWHC$@}g!zqBUp(i#)2 zc>aUthgQDY-17}wG&PjwgJ#yuyHpJuxYyKEP+w6x%Bd38+LY4g5k*rQ&97k1#J>l~ z^N^sqmR=PwKVn+!Ot@~7L(4Ll*X~X~^1PvRbWy)YFzdtX;yJMO;r5?z!T+Au@RA~| zZosxzhR$4scrO3$gdCWDzXfLw&iZr#<|d~6AkTl)V^%y&hxP0H{#X9)du1wYc&fiF zLcCz{h|-fJp3u4_F?ZdQ;m2X&OFezwQ4{*L-w~L_uc6OJeEbI=JP2!xomJ#{jl25v zm;=Of40OJhn*7lIdtmjJ&NLr5yHBe{yJ2og7|my0K{|=abXD6(1 zTu#Ru(m5yZ?|{uqrqliH%xjym71qQon#n?|!t-HSzR(Sn2+vh#X%PEiT5+g^g`5(fN23!$OwLhUID9XOR0x)rzC< z17U${WGT5HNfsneMN5}%(# z^9^Q}mD0HGKoFfTNf$4p_38JyrDx8J#lAVpQB%h-7e#UL$f9LlKzaw;gjN{JH z{FmzC3lEa@>q>khBjor zFvZ+oQ;D_HFLWaJFTdN{OeE$WO`!ehAXc(uA^ zi(&nP9`1>VYw9JH!LaS_;UWcWNgA|k38}CB^d}lNk1D=E+K*`K-Yx6kz+G8Zqsrpx1i)?yxdvkGCeOCSiv92B5DUK?;!l7}?@sq7 zzo!`xcWo5Rm)Gnk>&I!*_26Jw)MQ5i`9E5>=u2EU=EOa^Js57|mwLdmtT!jf|L3_! z-{=Q>JzO}2Y)?_$s;+%u{jbI?Wc%y-wBhuIYuXKZLH3WfbDW_U%wPVL?oZntJ-vIv zvdsy!{b;-&4R?bZdT&(-QJ&R3|6CVX`%2O{0S??9BXWU5$KSCcrul71-16+du`uI^ z^i~I$8GJ2j6sezKc-$6N-0iVsI4M81^0gDpo`1Bh56ms$)wG7y>9s=4k%0VR9-kdx zjoq}qgJI*VRpW_+P8oN4!-hw-KkQ+5T|~`5SU!Eu*_JR@lIuh)+j78)*xzT-G!Ixm z&22#oIP^rV!5wCQi&|_8i>imc>Y_m(RQS z_Nx=jAAGjA6@HJ?F#Kn0nC0_}Z4L7xChCc$b>-p2>kKVjDTt3@R4 zGQHlt9=52S(|FyW_lwQ2()^7s&;Rz?@9*J%|IelH$HU71V;u%GDqveXwF}vQYT;}* z11#z;lhFO?y2ydpxWR5UaU`!`%0m)2>FDnp@85Wy2TOY$qxFCLitKK|s=OOCuE;mu z$%WYt`P8N>Bg(We%}<2o@!!uqkOkBHMX<7|Pv;CcQ0`5)zx{=uwijT{NhCl)Jf(kO zlQj6>_RQqp^i#n~!zH?X>vr^Ca~P)il~CUH_4?|4FwKuc>NDkbJ7J7};z{+yRl zq`cx9ogY%;QdzqOHcp=1tv%xY6Hh-0g=xMkIC4d|{bE=r@?6#tW<32HIuBN^x+vx6y%e?P?7!QjVeOToVSDgBeTw!Rxm6{gheC6WM(li*R`C;HdpFtNq zVfGilM?SDfIeuPeSYBgD9SfVs`Co8`8BvpbCc?pe&)j6f%89eeC&B3+Z^SmRdi*Qq z47hmk@x0%!us!#VJUts`1r2n!!1~Uszs!YoPsBx^Vb*%?o&~VaV!UK!* zUckj|#;d|e{rWj{Jkq&fi_c1!cg~~g4dS`ep2RGLY5o;h)MZfZY*?^-+U}olinm{r z09bxv+!`{zAZjA)G#NIIoYJ&8xt`nHyYCOv{3>uwIe(V`7U?#7Il#W(mrof@%JVNQ zX$Svy;tm@QtNc6cCHM38%ANj0V8-_)D>!hH=fs3-e-HyWAoRgaSFzeoiCggt6ID2cH4pyD@qVr3dSETOBfz<~-(fK94 ze#I1SVn-R@d z!2h;~@K;@vP}sD5zu#uWwI!0ND`Dk=fm6k>R91J7)YmB($7aDU{U#|TFjKYyiQIAg z^=V4iM^bh)zZdKiK4x?vTp0S9RCi@njfJIkY_E-TZ)}|*U!Q9 zePB4#)MfX1SeCltbXPb$+N*O0{O@`z^4BgP?Tr`W)j;O^W%Uj9)WXWeJtIHC1^GYs zWRvodQ?DCgp7Cn|sV{G9y!HUL1pa=cfhD)o-si!hE$3^=@9TO^Uaf@{%QASUV3S1D zB?Zp3ab=VL&zROko&*PtSvB_1q~bU%A9JPkKE(M`@B0uZzB`ae+BegQHHyst?0jgdd?&0ev$=Q}_Wihg1!?c{ zn1#9nuv>scx`mWa?k7G7H{?}vNc(4ne|3z9r4zr8Chf;IYGKA9*kwtc^*UHlw1m1~ zvR55xk0QKYNtWkR^{g*x&+J~?H|&Ge*S>KU!x9X??13xV&lwj)yj8h_%wMg0`q#l9 z=2p-4AaVD2#fk~AKp0Brw~l+{bIuQz@5mTH%A3|3-|=Doix088;kdf{6Gya1^ zuwFPVdkf5vPU3fj1^Y^t%3yJu;5p8)COC5SX4orn*cXTYW1l>(s0D0UJk~7|aZYum z*ajBb;Cn{cwp8?~8LXPDDcuOi?Aorjf<=u3w{L(=cQhtry{warfEjmzZ9K(uANqO@9BH{%(>+DNl zse0B#;u>Sn%3_%Nd;h7muyI=_|9h~0;-x9XrNvdgd9Wgl9jM{%;=mGu%VHEg*bhTcaNhmvup%)$`A`P9u^M2u`CpE+n%=y_rr{z zpKh&y)t8DM?}a6X%C;n45VguF7S_g`zD}(ArH$D}>Mx4zvK;2FI(tn9tDDtyTL!bz z)=M_S(%al#Vwg9#iS3u(h2_ka(h}U{JY#5&6N6qRT=LIvj9HkET zD0b`v%YQySE<$%GHKB`Si@L$ zCLd-@+nw(Vr{7`(XTzF>w>YC<+tjS<=U~-!&$5xQN&7lN4f7cHyhp&!V>W$I!ICfb z$A-dji*ySTV40gJX$V{=xY@89E^0M+p%-kpems90DL-y;M^D&v>C&*Zu(2d5t`FRh zcP=^HDRyeEo#Yvtio@Dw}Sk{(*4c>9EkIPcR$KT_7wG!sbq~ z;;y8A?pvNeEWOjAurn--DN7p*%X_7|y1=>|^R6+l;a+2pPB3GBU27gJ9#-;#1qW~W zt?N$8f7xJe0~fC>T;~dNMjuTimakov1kLn{>Qzqa6ljT$`i0|XT+ukShg^KLINy4pY)_2_Sy2Q&t6!X zGLZWP*3HR(y&E=km{IW=4(^sQX$Q=`e!*ylSsl`2wvqaUH9TUD;a+$Y%(PdI`Uu;P zt14d)^NwrMKETdxs)C923mzXORwg_w42L;S>-~sL%LizJ{*NPPha8{uf9&7!qGf`97wlRwr3cJ9rJ}A&k#Fk&|NDKe)0_Y7VY7GE2eN$MvActs!JPMR==$n@U2JPb z%6HrCMgCvl$64Rsm!UmQa$Zlimu;HKR1V8SeqATqJ7Cs?5s%@(r(@{;(TftV7r{l= zrc;fGTQjZS-heH$_a3mq{tn8~S7yV88DVdT!$00Uc#c@TzMkxV>l1l3$*{Inz&Hlt z1}W6{zr$W&0*Djx2!N&>pYI#0;1cCBFyr3nnaF|_|_rJ(XjE;J$iik?3lJ@ zB&@q#y@4Em=?$FUKCsNHEVK(Os+&+Z2)1xqYsvBbyJKsEC(KL|UnIxBFyy*tFIXi= zJK75t3>UWR2=nc%_VG{e(J}#>Y{&#*8gbn=q5?1VfRmw;C;#*s|FJN&mUh!y{|0*)%Im~EZ z+=-k|z0SLY-GVhw{)CNz)emA%tgE;T>@B~udFQ8fo|K?79e*iXw zsv*dcF zT6#ET23&O1`5V37<-BS!4HmKu^m-_G-SA@~%ufIOhhyq{OwEFva5$XpXJJ@Ux zEM2ubgZy6no@dJkz_xW&IdfpQ3w4wF!?J_3#N_%|dNaI74_K)AK(CLgnG5f`!2D0! zI|m>xGqKKegxPGl$8=aTt4H+zwdMR$ZwJJUez7#}zfyY83f9%Gr`Jox#J4+tJx6;O z_uxFaKGuEXtgC~iUN`Cfa~n3!<1MUiGKlWq(k(v+)xv_QGo)mHHw=ooR|VUypG9ro zacuTWVz=pZe`bzY@Am>WoH5=Z`=1d!NceaPftk z%&1bm8wPWHytiv%<@(_PgJFf^?nS3y=GEV}-mveyUkQg{>Fu6boR+PhTC9k&5yM?AVt%&>{xx(*iCKBta3^K;t@ zSX-SJLVEAin#W37=u+@pi+>`M|t> z$e=n{c-ViF2OPs>msgYWHzqO2_EibHn~bphnwa)qBu8f_5v#rze<1xCmyvI7zk>O> zC+Pl>e5^#{j7V9~6+!^E0JWB+>&bHVw}Ss| zuTZuo(;8+>eo3z{YNz@RO<-PAM|yn`@BY!V@hP^~^k8~@agOAS_ymh~d}t)wpIa)? zm`I!}Dna_`D@hkZ9WmT6&Q2CF+c{`^CR zi?d+!fe_lCHhOD0q-#%eJxxQ$-TBn|dZEHtO zoe1maz3HC{7q$5Nklepj46ryQ!^V>@E!$z`{q8yCJ9=4*biLo`4xc z2hit5by*(C#6{j`|8v9mQOc-SOUe3Z_C9Sxo*$};XBkhC`Xa?nGG6Yk2|AMob7sAI zvj*0kxI_QHAW=)luX$~+29fQf`h0;tA2s{?%q81XAM%#I-zTYk6qXB15;qSfe>tr2Tjv@EH+-FZD1^kjhEe--4%sY(*_<Z<(z zK7!>xo9`v_M{=u2t}G_y8=~oal+5vd8}GuZls*9gi0hq&@%b?O@ZeRG$@1sKhUdUi z>!oylRG-b;wU=P?p;|hht#-$S&F4t{_H!u8@x`TdnKH1{MX%iAi{AXA- zd7q$E_~*bun5$q;>JLi`P4x;AUvhm$KR7&P-@rK7(y!Wqyzi&(wDn6AY<|zB&l{XQ z_-!LdeNQQ)BjTzhYg5+3iu+6G`w6nHsq2@);*b02^NYHaBNvvE__RU8?NQ$M`AfD4 zmNaxfN5-2aJ@yZp3$yo@>zcqWUL}tMV166>jpThz)79;!DR5EU6Z$?UZ{I@M1lT-u z=l|XhJy}sWl-Rg${%4d|?z=Un3vBXzL*HMm`#x9Y0<+I7QIq#u(_@R`+Y-ADT29`N zE#_~kYz3>&OpJL7OXjW)Y6i=?ZVbK)n_8Uo`SApvp-jCB2gfv@{1sLtaUHYa@aR@a zb+9yVAbnrE=3w2J8dzO^jlLgk7B}{+gjv0Z3^|JO>{lg=pOE_EGe2aoBs_HWby!f} zkUR&LYKLA)hZ&ApPlv(&(f)2HV4D9FF0RRLnE(gQwzHb%zb0sb2u!Yl3aNSj(pssyBEya^)TTA9Pp*tH4oTw>%B+~E7q&Gc7gRnLh1Og zVoqMR6U_X+t#%jUjJ;(?9bu*WAv*r5FKj&A95!pRugVbjZ+`r)HC&YBd@~y6jmo^z z_!#@M=vUV$SRH;p@fXZcPiRl#rK>ll{vh#JADu}3;6v_%>R{#b5$hDNUe{}9H5|CI z@8qL!qQGWmIjpL9U7iGs$8;KEfaTkD-A=(3(&;BkVPT&g*VABwLfP~YY|adBeHvyu z%&2+@^LnnJ4tTua(|uT1`+@#{x9nLx@4~WZ_w=(U&++q(&xg6`f|r+JS&xgCb79f@ zUxo}g@Je0#D==SZaLb^m&2T^eaQ-Vd;V<^K%h* z=@>RI4h}4Md5M^r*|~BHiC=EE;tH&3zOyI_7CS_ABGn6P%Z9B7JXHt4f|6r*$o`L4R@Vt&%U<`zS7G1B&07zLZKEDEyAG#M3#{w|8<&+G z$%8ZRPYvn@t5P4v7Q#VK61%p6c~PC$-6PJgrk1+?P_Fbj(>00(n2Q-5vzpPpnBF_3?XZ(rf>og~8LWc?MdZ?)1P?o~f(h7s1xxk|t9l?Dr_buLHecB{wnQD3pK67j%Q z&UKSvpFf+I{RhkMh&?yJ6`qdk>R@)+wv0nCd)ef}A7R0QOCK-5Md>+<$@*~TPo>|p z+wRPwR)#*O^8txkKJWbwaiKUuG7|ZF^cmp?$?};t>i7w8#Vxl)vc87c>djMN)1|_$ zWdATng}ak{NM4H?uRVsv=Y@L~z%iYV_aN<+ef$3ZpARQxTOO=fI;m{{;@bFqH8){l z+!Sg?P>nljpXTyax27YW*^(SyQZ;a5iI z43t-Mojx9hb4ShaxCm=M?`)9*D~|n6ISuQ#hRxB!LDnhW39zlt^xsBU({_LDURb!` z8=XH!ku~qhF4!`rcpsTB#^1hb>2{cz{K}Q&$147HWJV}#lC(BBB7c^Bo71b8!b;oq zo5=hy_93O-B3K`A>2@19a9Z((Ik3ESbr)i{ldZZ>g>AcW);qy^)|i_UU{T=~THn^! zZGr$czu8CUo3Z_{=8YSy^*7~z!0(q1S=hN7%wK$q=HpV2EPud;CB6NYmm?ltDDKl0 zwspOkO!95{H&5Q$1r};fY|e*^8iE(Pz^2d7rI+9uOLQw@*46WQD%iGU5sllfv0b(o z<|O~xK$dU*^R<2zY>w#oiTu8B%7|Yi|CQ`>FKrK);cKMxCn>MYY07~uGh^udh%VBD z?fb#}yHRxhIL#HSYaVdXoz~H0{;SL>RZRxKw%?b~`M1IcpY<39a~FU4^b74ba95jf zvOR?TMp@Uu{Fldv&44xH!*qU3_QJ~#$o4Ioa^G?j@i@iI-1#swF6~YZT=RPC&&9Cv z>$X!QUqo*4-neD3_JWMgKU(u^WUDY(biqjHKaI0e>NdjSUMXFcqr7ID+o2fP>>BGi z5*7>AMw9lTi%^W>!1fJi4wCkx9GOq&$1O^>-mQQYwVkioBVP1k!}oZY`FQZ}KRDhk z?aO6}uy|OZ_&+#6_}(cQmaS`?@eFoeS1>yj=Dl6~J0C9S|Fl1`V!-W=*I~ERZ{;bl zIkw3!4a|NfU9EySkA_7Zgu@^F9dZ;_J^YbNzUTHmYpvV^>r0<-ePMOW0NTFI*KAvp z_tV+Yw_8U2Kc?#qhZ>%k$;cXUHwW?s?9HIRhuCpTOE) zgBE$h`fu`mk6_b@&g&+_++PjU_02}s8uBe^;Dgo>lMc99U47P(!wV zY2kaSzIj3quuER4Q0f%$SuhikcY=D-PVEhj~iM; z+P_=*-m6uO?Pb|z>Ux*3*kY^iFzfvX>iU~1zr4j?h!+i}u9q=4AK&jItg}8rT~EWk z+Eu58jciv8FUIofK8CQK!y0vVpF6N@UiVw J@Z%HS$rR`DHJAp6&T4~`Wa8<7i3 zLsFW^{T&I@#QU$pVa4B?AHWt+NqEMJ$d1G2XY+wA^xufR8QWWiWgOeQ3G*5%I5yOX&`xgX@H$P&-bk;ZO zc@fqL1Luu|)xk@xk0KYfiN`wHh0GqdY2HL@_-I_CZVdj0y1^r23uxS8gmJ&qLF!?3>XO)=%r)JQS8@T$tVkPW`pi*BiDBEN|}w3*HK!hQii8vtG1T zVtc=O_ewk*nByAx3+B|HUU~%P82{>PfcbKF=N#e=40Q`E^ct{O4X65#{`DEw@64v^ z^M#H1((fJdy;|z|g>}7+^lxE74JYU&#!vpTA?t~?jhQuVupd4D7HO~f80yy_`xoZB zu*2_RPDmp4JW8M8OE;6{OE$fuk>_JJE+4OKf@PksH=KZZHlo*hSYB8>Boh|uzjDa? zWv4lGc?N4;<<#es1m{2c4OBWhh?RaME2 z-*9roTK6%qEITpV4$tc?fB3uiFxZe#wtNPxRSjyeC0?`t%QBdAa7L;%%>Fi(dOmci zwm7*DtnPnwK>~7%`{Ad0!$$Iyc~Ur6^>|Y+SYOMbo+q#Q>6h3M7BGyi6(SG6CqHHk zD<*X~UjmC4zg|t&55wwT+EZA4tZ+P8f803_bKk(Uu&qPL`c~b|JE4PPy`Q@_!1Msu z`bOBkHDNOOepu>#RZVbu<3ayduyTg)@*i;6oYNi}KS+H6N#3a@ zHLStIb}V4kt?6#}iR-*aOV!#RgT6W!nxlu-wNPQBY>t!pczT`)HcI(cz6lnMy0COBtXbbB zoYc2)QjIO#0*5_3Qm_S9{dm%}8D<}G=t}BgDB454g(QD1&LZ_PXpetih=vtEZT^vZ z8fwX$x9ef<6`2Ye_cp@6HAu|iHl%EMeUYTuqLqY?fGzO%FWxUaQK-ln|UxT zz%Jx0T)1IRz7MSJ8p*u?o4URF=Ls`7!LC=}gkFix9RA?S@5ov=8P=HW4$Xt> z%omLt4;y8i!2^%*em62s54zOUv$2pH+Jk)eE12)pGNvMW}8?$&eurTsuWi9OD|F6~(7FFFl z^q%DZ)EVZm`gsDQ5%wAoYSI;Ertw0*!i@`aCv}FkJ3G((3G0+qzf54>EBgG-zp;Hp z+~FC+Lbqq@d&BhCbMx9C;rsl?F{8t(n9dLXz}n@d(6)#^mG6!uX$B;!9Dr9yc4`9(PW!m8hOFt1|%7X|Ec;B~$R7XFwqsRB0W zA6nJG(s`lEk1&1p+F4bw-eQ4GD_l4)U-%T(##|P4{e$mk;bVJZN!l2<0kCB3&Y#43 zOcqAK=8vbTNPp#dXXCjrPobiguS;0bYbWfHxUqLF>Ho5H?>U%r>R<_ZefC%X`Eppk z*v`5d7Wev_c^j6}q8B}e6(wFhp2Gb886PT0|4hF$4O|}lY~usi$mX)e2RKmkePt=k z>>0&shD)>VJCwj$hf@*1V7ahjXAaC?pgPo{72~C@WSorn(>8_~oP7EB(=^iG;8tJ` zSIyoLdJ1MdjqKC|4osbP>=3LBniD|G8)|(i0oHRAe=T6zoq?@eV53Xo^F3ij3M*tS zOpE2t?gh6s{c{L|1wV4A_JP;!Uk8)Ci)9DWU$aXyApmB2b5!L0NR4U6elV+ZpRJu? z*;%V_PuQzJYnm}!cl*$=DX=2(y>~l4Ut5l;%>>vmOV4YAy@sDj9u4a<_-3szfBF9D z4zTLvyacj-+9q;`3?b$puKNbd1 z+y=OO{f~*}Fhjn3-CLNs@X|43SjGq#`Wlwfvvh_Ee809$2kKz!n?}yfu+a7I#Tqzw z1LtTH@owYmYsz80-KwBlaMDI|eHpBwEiNvGjRv(O7sJ{S4jT$#S>wWAS7BP^%Okn4 z`u@+D3$Qx;>Y{9zwtVWJQ?N3^(M|>{^UrrZ0ZSUpYR|(dt-icNFbZuar@{6$Q_GTI zdOh#QY1nYq#&-`a{c>tHv9Mv3Fb>wom@Yj*@}2CR+hE=eS*R3FvD{O!3D#bBe|#7g zZR=3E7EZlcy8i&otpDR42DA1uQuo4=@@^xR!%Xv_j$+uu%Ei$i7Ii4-n*c|Zom?;% zmbmiw#llHrjiS6@ixp0DH^KJ7WlN{QoF8u&ZG^??h0iCz^cQUw>*3V1-9ksgimvar zg~O%`I3t*FSeEwHD%d)6=?8mQ9luW(3fD>RC6qwKU`IM?gO z_C7GbNc(#Q%uDE0(39l**DqWS3!Kc_yAuB{P%VZ{*WIo+gS{dy4D*L$C7rn@u=wOf z*M+c=;m$=vIkvB{%Y1y{@Z%8^KEqPG`8T~`k5$P=?};}qy5|K;%2Wql!@Bx!gJ+Q3 zC(Gp-Y>>q5n+h}Yhq_k67Ei9scZW3t>Yf(E^wHzx6JVJ^>zo5Ci&sUvz)qryLvmP9 z`)=qMICt7vzsoSICG7waM&6K)%lIPnzB+2Xx)C|qu~E$ReJf10{t2%Ie2{PiHrTsBcT7K>=&}RmP0Zb64O=X>DcuG$-aS0l4^}Msc_7CH^qX6Ve^~Ovk!MWW_p^>(Nb<76+;;3g0%J!> zxUiAWm$hwh+mS!M-Z0~wxx;TbS*)q^f|(}L_+PN*bn(RLu%_hxi=VKR-ekgoWxeYu z%UiR*PJnsp?k*(9)kW{dz|z~k$D85U=be;HSnR14=wMvUG};apG*rBN12YUB(blBB zfRpkP7C%(C^@nNnJfC`4b#cnK-o)|$ifdr=wjMd%Va|jdu}@&#VWqh#tm^mjf(jP7 z+?i$!3x+(QmBGO^@~f>6u|5O3MBIk8^Tv7pf;F?_ZEnE{8Ta3Rg_X6#9ZE?5&}FG_ zV2kljFXh5@H43L1Sm5_pkOdbCGft>Uf918!nQ&k^+wDFqdXdp`4i5Glov(yb7adxD z8g6mAw4o3-{P`Sm3T_SMTj0dAv>N3OMjmbcP5P z*sNN!9M)ZUy>L76+lzOW!ItSa=rORx<>StQu-6s3V>HZ^-7+QfFCnknvJJ2z`@{_{ z?7m~if^b-RbGw5#9Bbe8V<;@n=-AGI!$n(;hrmYfcVtf{?SnU{R}iyz6_WX0(xtsQ zd}5Qpy)LAG=OF_FVd>NBDJ+=LdC#u^SRysFjDq!_;zsgd&9U1TN5I^QZk1e^^)`9} z12#-QQa=q&Rhl_jz&_c(B_m6>K(ecl&Qax ze6VG$7!J%%-b>7lJR%}aXmX2df-Ne~joAg$pAQ}LmbC9Qk~$tz$L;s6gT+4|?I-mj zW33|@Rj@9td+Z9B{V#mPW0@1DGL&VgkwF6m#?wF2lU) zPi>>&@J(CtWU#cu7c&-VZ?_^K6*hYQIl%$edGS;yU|Re2@k2;|an8VlFk9?M9skCT z4Ih&P^W&dU_57^sI=qg9b&5|^{Z7lH^=r1n(g{ID{n4MNyF4NW)`Tv7UhO(<8Ll2VS3?qs=l9MiSpxen7jLD7CByL+QhU6!6I9IL1)-yhmgAfmb@qx zbR_++6zBQE+J-(GP2oU$f2Ua_A9;Uk2bi_B_M<1Ps_0x}47+FaJ?suEuDiMYBj+0` z^~+pf!QcX_KB;x}@o6@2*nZPB-;vV~H{7;_Y2ghI^{`CeUD=i7uW3SJ%LgNiI>3yv z)6`#Kn#Q&JpZnOJoR>s4!`#rmpPFEOXu0h-SpKWbwE<@Gil};o>~5QU-oRdtOQ`c5 z3FlYGzk)U1;oZsk53b_QLk%oDcZ+Ioy0n9HHR+$*iF$wbsUv?qg6S5U29VdQs)%6T zgi{x9q0BrxOPLF+S6tmdUZ44L^q_07>flG}^ThJUtjzqM_J;4(=@*EPPPjvs=d`<4 zmj-hVFQDGvyEXr^PQr%q$@j?el!J=*9fz5M4%GTE8k{a94y*qhPu5Rnmp{V}!vcOe z_5R;EkG4u6?K^j++Up-C%oD-%>g&||lg9Eax4_EqfmUSw^Is>)qhKTRKx%yoFZYZP z!2i8|Qn;^n6|7i#lzKmwYsVEXfu*~1sO6OxeH%3g7VeZ%d6l^ApC_z8yO*-*ffrT8 zf~EtM6P7<)H4av6df`OQM}=Mc9PbRXoN6hvKUu^vVeY*@nlH#*-n2B?!Qw7@$`(~A zBW+;W`QAH8-q>%wOK+Gnen~9ZKG*}Z>&@W*mM1uwywMbv%xR&%56fW+?*&<-L363^yKcSR`*Ju;%yRs~6$aZglGR$!b39aURyqNPA3v zpN!JmE@xore!u7B{GGUQ!>kjqa*hY}`?OB$HFFnC7cFZc=lg=gLJx{yRzhqlu`V}x z)>fF8FQd)}w$KlsjDr9Bd_KK{(^ir8|L^+?kN7!fDJ<=GpE@7eW^z~&K>AngpZfvJ zQ=QjD%z=%j#>n52*GqUO_JSoPkE!#S>Wwo$yAeAMaHvDBbqRYh7S?%seyN6OJDSJ2 zz-+79nN@I9pj(pzoLYG*u?#LR`&m8&rmfyf)f4rqv^{JKGX#c?Mab=UzFIdBRtGH4 zFM!QQkGawhwg_-qL+XphULW$VFU<4*X?q=(Bq)++;?b6~&G`gKOI;Zo=%IqYT9Ap3g{zenLqsvaqKW$C6LFg<3`4N`xUb!hm~ zZ!mZEa_woje5~;CCs<&UUU2|64G4*U1uNvL;iNub`Itc&wXkf+I;x(YdHi{&8q(fq z!3YlWx|Q2b{w&uKYn7<({g4Cb0Z#MI;gv0Wux08C| z{Eis{HEeicI8gULxv-7Zs{}4Q_*O;QD^-JNr2cth)a>xbu$pyv_GvhFtIa3k)F1J# z5?IGx*|iE5G&WK7-OLO6AAb(Z1jl1YJ^I|auEtteIkp&`GKGcpzr=mED4Snl5j8|lyW`HTM>(oJ?e`Uk5!&N$u-`y4O(O1@8;VfVi# zn6tNULkD8-{ZxH4=8=qFCdB*BhJQp(>mESs2z#9hr0Smq8hH*O+lPgbph1f~_23pK z3;4hPJA#8Z+xCVfafg^skelDK9bie^V_|(I9Q8iPoos)sr5kq>vyMK?vx1rXu2A*T z%oDmB_lLtQFH}87ZuwwLv^B{y-`iHfe)W|n$@WgCEmV``8+Nl+QNM?<*G{CpZnCs} zFmmZ`Vb4<1pP>jH1`DV7ufGLbxLyu$f(=PQ|7Tgl`H`^1Gs=eKfmfR%oJsrPA=`^# z&9DyL$?sEpcfPg&PPwy;`u+0H3^;NFF0?O}jYDqn;nAt4Lt{Dk@gE7 zjo`pE{RrQ)uxZmOYY$l8%enkG?9o}h%ojGW^%4=R;9LzR~Rrrpm6WyAecG! z(8W+#Y}>7A8Ei3^`z#Q4seLM52}>0t@_gagUqAnb!gT9R{pY~CFKuP3Vc~kd*b5fX zxy9kI#{Te}sc>WU^<@#TUU6s8csS{}%Zo@@+ilA4k+9Rp`MZdPCaX%Aupl6SGHuuZ zE~%fUJTvnu$rbZ-N!{So&6#`2^0d>_3p>EEqrz8{*JHA_Z2OD<+gM-9wuHglT%YQX zFk|!p5qZC2+3U<#aLb)>D}rIZi;1WPerGG|y@KR>TeVN&@VBwmWPMm1KM;H$=9mPn z<-uWQ7ME_oNuRq!FN7H;^R07W*>!myS-;|fe*Wj-(!!ShWW46Ca~7q*joU|Fp8*?A z{3|5aZ*b##(>;kfoomH#lHEK3`My*^r^f7pg{i)7}-C@D-LG&2d zrB*tW4QsOpBu2s3`VpsCuymZ=l{Ii+o?$H6-W2}tJwjmnJ2wADzhyLuH(bZXTSxoi~b%_pFsShjL--EoX!1#Ez}h z^+((uTXbZ6=Z|)-or7Fy)^ffltQ|b*n-}b4eNANsdj-3Mb6{pY z_BZTR&58kNpSm~t64~D?t_xqO`OlayIAr^rNu6{~5|2 zw;Qbg*A_*s?T3)d;*V0f`1bQX64+~Xv4Hf?^@%p#4RfSFRi?0LsIz?>EH?W~x$W%r z>#_fnTR1LrTMtwJZ=!w5G`-_G*l4opq|UJJ6{EiZ_VR2lAWnXm6Tb%5*Dazxk9c;! zv%xU8J&L;CsAcfi6-#05p^_in(7t?nzi|s;;p*<%Ua(PJ|1Dft@vZYya=lXca@RGp zVE(W-)b(>N#-l$>fiT==#uxS@*WX2@ zwvRS~m8++$BG>Dwzn@`|gLeKVWs3in=~eXgKqw z85TeJ@i7Q_V@R}64;zMtP}c_wD?Sec#0I*DBJtkU}mEI&NQ_6*G8yEdPIy#$j4 zWc>EqCz){&<|Qbo`j$>d{l+B0EZ2_lw~_mJzbxAYE7Ifdmcf=E<>DQ%w#UE=6|i-W zjGwWv-mT813J#=Y?2RD(H?EKS2fG>%wg#)GuY|V^kY0&X)mYhg{EfC(#<7#?x?Q=kthG0aKZytU&%{h z!KwH=WD?Ab_z~^~yD+_PxxxBtqb_scwwlSS#=-2nvMV#;mOdIkV(!yFwZw976Xr;m zV|{P;Vz{oQrfxXQynBJFry7;jcPbOsU6JMoBTsHlnq~t_mYf~50T%Ch@7JH?N3Z1w zVUbA2=|l4T8ArCms#V)NSiqVgZFY&oMVTrySf1cvv>#SZ4YX^&i}#z~KwbaRHg_bm z1*Xo&A~!8x_p$+I#ILcG!$z0sHg92_gL8Hc=^t3ceGLmf=KRZrnWIPK`G4}@ z(yD#E>R@%0ikeSJJ`|;CV2i^KBsY;;cdkmPhUuKL_##-HbYoZrtTbQ5y9Y;Uy&v3# zwe#$Es$tngOC9fG+4Yjb26Fl>L2juor2jtd%b-PvttBfPrycIb$i~xmL=9Z4#8o26B6ITjeeQ_ zVwh*M{yDMgI4?s4%N*}e^^a3W9PPRl7WrwZ{+jjmTchFs=GQ)#LKlR?j9E0_I`n5n z)Vhbk@}$%&&*79VA8c0=_YeNR&*!~K83aqdKiWX%%d9`s%I3jR7rR3hq`hZqcON*F z=AKE`mq9vt$}Cv_tb#`B6U#21UOJ8B#xtLi`LANSTa6nmzShHpd|&Q^oIG4%*>q0L zC78D0rH>2A16Cin0CP?ZDH#D9))z9RaA3&8e@s}j{X2F26lcn!L-sI-ebaLrSzhwu zGlO83J!fDPoSff)Z0n>!HgTd4^D@x(o_$uVCs4T z*m_U&(>}0z&OmB@DmBR&PHc4G&f<>9oxH-k^oE5CR|GWU_bp7{r0oUsz6E`K3zz;| z=0fG%6;wT5x|8|uzR2Z0D#j_1w^cPx?hk7^zIc`e>$W%11`-=h*g(cFMb+=ep}m>c$#ZA#DuI5q0I)tW;oWU?AvhozwtThOwT$N%+6X$jraDC zwr?LzT$cWR3EHPRjTko`W?pkFBKsTj#jFh;FLuFr(5<~5c}n#U z?jBfWdqva)=bmDwB*D6F)#E?I=^4qB_mlp-b4@zfY3_5r1XgF?rt05GGV?DSh7~$R zA1!ht@dz~;AGF3({yZVeOVAI!01JC$4Y?0{y^EN86=uwhSWpZLGiT1b33GiS)p@X~ z>dvlPFmIm8j4YU$`nMAq?~E#|B&T3@Y==9~VZr|W8B$pCaHO#o7HNiBB*Wn=T@!V% zsy@MO59~Keuul(%wYXl}0hgb%VE=*XQ?^m}Lj+zt7V{6*+(?)djof43xK$l6-YZ-) zeyxYyQ?(2-{%gK)yG6hZhsjM{U`eO)qHtI=GjLxw*ziJFwi+%USCwZ43tEfV!LVM+ zith*WUS}U)2K)Uqe_#!B7Ocz;gsrEVePhA=fDLOrVab+H-^atsYgyjoVO8J!MI4wh z{o>ORux?YYAyeV7F1b{_-go_Ev!=n?p1EHfkmJPFM^8AlzPPg;tXLS4H4~QdXTP$7 z>6|YP-Z1UxP7h01^}EVr4spKxPzP8W>EzCZ)hx4Kzj1vl_l07wFD!bJVetWG_X^rI z57y?-A4BR}GPP#$^I>kM!2&gGXpJ7a05*~-BgpmV;Z`%#iM{G&!I`j=>6LAMu=s|d z<~*z`UmHZ0M~i+=)sy4h`_P}fAMWMbd5Oq><`{#V}q3W5*6AeQ0`7J8?E+x+g z*MEKU!5ya0t}vSohdEz*LgovKW8T#Byjw<1avuX5ISe08p3iRT@I>kiYtH$+BhPa; zZ5p$ItbY-m{eRc9Z`_&XMEXl7Egg#7sN(rGGQaThcvL{17oVQ|fH4%7n}{FV!tB$f ziw48mf3D95!iB=Ty8~f%(y7nndSrH}Fns_l9(s+cFP0l2yh?{tJJ0Vdk{ulvZOvR=F*+dpf_Me6$U!Yk*K4Y0Pfg}UB7 z<+*t*na^14dvNar+P5?}c4~$h3dipfSU#$2&No=zipm=j+#Z#F3HIBM}*QaBJsuEUp zzenAVEz3N9>mDp*C+k^QeqmD2^<}WpyTR1`xw<>D7i7GUUG7~#?&mf&&D0gc65IP~ zd)Vovv9kgeDP~diKU{eB)O<&_ZU$99#NA_FiwrsE*=6c}TX7N{#)Umr0BiK4bQ6%{Koxi!P)W+J79kJ z*7GfJtZ6niKJ$M5TkswBx!anx4Y@w6PX}V{^=IQ_VR@AmWu*yC*4-bq2G%bv@wSF#dmm8qC)(4;N;i@x z@zg7ji-w){S`9N8N5_)=lSY01+yU7B(aqOOVfM%!pANx-Zw`l+z!sO}hcn>x;?q;f zd`mjE&sp-kPp7z9-iu))d2KGa-z_X~ohc9I_gp^jE3DkR)@=bSS#hVBZ10lR&gaSg zRrMjk)(hsv9WkB@Yu?Nkhy=sM0J{S9Z*g30#3G;P34Sk^bQ(@E0)dd^g`|EFE; z;Y8|XDz*E1vSFc{WiTOYgxk|#m0d@cC9FK&c!KPoJXV}r-U}98xH$G2%&RQ& z>;W6)rwmZQ_R;B7P9K&nsf9ByCFPU$MzyR-pWwn#@9uRccFa(U5dI9VA63x@=HtbrTN z5?CRyv~`k{m~m!j6dyKNS~U_2)n`Wp!Qw^PUaR4-=i~M)hK(M7PF)4N?>u;N5zLG7 zJWuBPUS{9ka$(NDFIPg~!cMt$Ua&Id=gk!`yHIn)6J~p?S+g8Y_qP~58J3tIv|kFl z$iuqfurP4#5wgD2_nM9{Vch@+>i9dboiStx ztd#G&PuBN4@kp8-oH}pII2YLIQjF^$SU$5yGWotFfmho4!-{|UNC%i@+&s>Tw4Wa_ zgB<@$uC;nt!Uk`fhrM8?p}wv=%=}S6-G4&AU)9?j79O8k#yTDSj~0U|OI~|W=SRE(R;=iT+-p8B{Q~K)`AfYXcSC&h5t#k-#w+rE zIMuuQ?SolaUmVH%6~~{U&S%@FPPDUz8GfxE@yN}8=+BbR&pFktB?1#z)V=5)cyEO*fKNN zoP7Ub?ThH-e6#8HYY}VU)RCFT$oV|wwi2Ut#6v=TT!?G;jfjCc_EkYFSmZNk`Zieb zZB}=3zS!{hNf)xca;_cgH4+YQpOi+nXX^e6Siq1j+Y1ZRpKo%6U2Z(uD1n)u_ff|y zZP}5*$*^FERsB%p%A0TQoP_maK6U&O?31fchrPBJ&$mUcSZb)qg!w&BtoYs;;o+gpbiWIHl9yke;xpZV6S-+&+7%m>fSFTF+Vf zBX@tWnL55^hg^5#!sVY1+#&l1`a&9)4RfZ>I@=AFu59uo^Y_$g-Q&pe!&aWI=?BZ| z#+eWswNBvlfnCJ+mYTtG^E*~u;Y>Osmx@}78{Td}rqv}cfq&uwZh+KTj zFQFOx$5KI9jWKLkRJ?Qu2G|cFGZpsZ)zlUrKgXGWpATzK)KK;A^{cbG6vB*??epxBx4i7CDT4nye$u4P z>qM-7GR$l&@|6Ag)c%RvYedvESn?xj3F*)DxtKB=<~^)RDu8*V_o;f~tZw7}Z~ryI z)GU(pw<+(OgIwFwkviU#Xfh6w&(Geza-#%!@TwDq#JZ__sN+{r#{B(P;V|E=NvFy3 z8BtLMU+7ja`7 z(O!3DtnF-A)i#=%Khf=8o}CKocl}!X3b}{>MsqSAs}>D(c?Q#_Kl|WL`p>ya&A(i} zW}YPDH}A`hQ&q@I^?Nv@VTSXO&E))q#ax|uILz8I;%zCc|C%{*7|ed5n0yCT^!NU3 zL;734??~2v?2@7Pdc(pU_0;#H`Ze`B*&kUf*lT(TdG613)c#DdXhQ$NP9v-oT=kbDhcg2mZ#T z{;yzF@zlM(Fx~oe@eBCB?VrDf=|!w4Y+E@Bx!3qg4zb0vqYfkCs&`9HJ%bH9R&VPM z%iMF*G%zdTW|!`;^zrRY#L5o>*3K04YPxqZtuP%{NB+rn78Zs=}K6-WQ(f=7Oz>`S^=wCffeR&bW8#}NEb zrYnYxIt}tTk38^?*`J#*f0#3Md|tQ2n@KD$F_G;@E;NpOoewLb-p7;U^T4`4QP)WS zOBK}dd0_fZS{CfJWHWVquJONg>k_P`zhN#x`?guu`R8Df)7$@7zfsofEUek+`Oy=( z%Ygc{)5IMYTabE@9&`Mco*?bJ1WKLZ@Z-0~ABX8~(>ghkJS`~v7%bD-R}Y4h`ea== z3>!{t`%aEe8ObA89)RUH_Vu@fIVpu5#jvP*vg2PI?}iQTJ|zJbRL`dBFSHzK-xLR{ zmgiE>x5`v$d&I)J@D*_%(Vlzox9bMdo;>dYE_V|$GjZd2-9~IQT4SLRyE-t;neC2R6Q)8U0vJW z!>ld=RQ;cpNIyqno|{Ad4?IsueSBTbJJ>7c>Ioez_$b*ytULN^7kPe`bwQCSF+b4L zvI6E*h;O`xMc#bsc~2>}?W5nq`mHmm=TH5&d`8t9s@{P0r~yyN>oNU~|5}g!nY%Iu zHNcvj1nT+f9Lc6L#HzXhq0^Cz=KFsq=1UG{y27#Ndf(N-a*v7B^BWb(Bi0d%V!HMq z&qFpn?K0srY;dr2HHG~owu_ozJv)SY-nL=9>&NdfeNN`t&v>45U0bXbG4J@Uz4fqa z;7A5ppY*wBu2sP)s$I@sU~b1!oEXFF*IoTY)~Bj`h4~ej{(Us{`bM>%YtF;MQTe|< zAm`6Z`+W*dF4;h>Pj0`()<sVLaPPAYHuO7Ea|veL_qL0G zeR^7_or7ugvGHqQC->!@QemEb;;z-O-|oicr(t=AGh>J?t^F4rgY{(>IiaxZ#52po zuw+%m?-eiyf4uF66S&D426~LoaW*aIxj1^{||$yFC*&FFYh&1B>?EN$`Mq3$n#4 zVZLYj`zf%cjUaRd%y#MUi37XWm5CO^`V=eKB-qK%l}{`hQ+}FQ_m2Wu;Cw7bEj z)kgmFi8tKt=L)-Q-Doxs_L>=K=K@>4jp;iVW{G;+jD=}>|E!rX{rYvPJwLf_jVE!e zVI9f!U$6e03d_W!C7ReK9onhvfvlEBG>TRM?#8nNt z@78c?qiX0FSot}J)dLnfJ-bVmFWHzI*9oRKxfG3qJzUs6?YFSKh2J(G4~ra|&bPq) z9eLE}i`BUe`3(Q}`DMDq7cXHW^^WmV(Z12IAfXy&zy4b{4NgAjy!jEV>k%&UgcZem zACu(1KxGh|xS3v zdXxUQyynYct+CCv4zPYebb^B9N4) z`!?^pq<#47+ULZ{AM78&ja!F4QNgL3kICO^@G3a&5+rWqVvPi=k)eAg^lJ2Ry>7ygKjyM+`mT9-u+gug_H3B7_}=J?u+z<7CojU% zG++DUu-BhIvJ7J9P;UvGFr;owI?UC7>a!bWsM;G-VRn~`p4(vIg(>q+!8+d5aS?Fp z4fD-}l=`^5_m(POyb~qD>Mkd2>DA5tiFs=qQHi4|yX7!+2WlrR^|p6?`0K*TRZtim(oFxM`~Y8j`E}t~Dn4zc&wqVe!oQPyRMyeskY* zBo8)->MwqWy;}5Z=acrjDFt8Q@XS(oZgc+3*%6{&nGu>dtt_y2O z?i1GgnFB2D8`GQg7lw|lv4OqLZ=kGICG;Es|F`_cm4n2+VWW(TOTM9f`FdVhXINPN zntDIxdqQ^@!_43rvwt9${*7DTT8#bauwwmBxNa`jkAL7zrtZbTlfAU z?OPpZ=wPFNbEwZ7HriqD8(5QlvXELovYH3=uwsmS4h`$ar+VVGXRtoN=Dj(byrQM* zG0Z8k45Gtc>J;w>Fze9r5+-b&pJ#dx*1e*ok?+&c@5F&p*f4!pjVoy%Lk}o|RUzRH z-f(b%2je>IHRbRmf0B0@8J7#EzB!>@0z18vEY5*NH*zAE!K|P?HJ4z`V`+{MmX03x zN(OURcbl~pPBIzUHvs(=gMo0&Z}}-KUlY@;LHY?&GI|kAJ$L%I8p$!6!DL3 z;e^zN^{Zj-or4RRaO3@3QcCG8bH_q>pEzdY8@hPnMcZj$+><(+=E#L9@= zpxH1>6_hX&wwTm1gUm+@qxzokfaUFTCzJV&TvRTc3bPV>Q}bD)d+!!chDD}nt_zXt zqjx+R2Q!QdtI7PA@yYlU3r^KXQ}dzNYYq!Xz~aWmkIDRqb!p~w2Ur?o7(v=Q{hdAE z9>%fT=tZ#nTe_@2thn|{HW$|2-aoq+%vv>K0GSW_rDV-ABkhB4Q}a9XX7Qp$%{O&x$Mop{3#JQB4M%^bmosJV{*WOISkqzZXJh2t*Q-Ylgi~*= zT}v!Hm)X%0_IbZ?8s)D=<7sfxTIp9KqfqllGGi!y#Z;#Hs56ho~UL{rs#?)zNkJ@(Em=lv&31fEtTrCJZ5_;dWfI;*`p^CsvI5q>&Aj{(rt#0NjBmLq-MiPr)brpl9>%U;=w1giPL@*Rr}enDi080))iy>Wa>eYeKQyqx%*gN> z_IaZ}Ukgj-IX%?C={}hW)v(agbJs1HEo6?Zf@L-pzBl2p!2`n|!`i?>pD)3&`@0_{ zmhCaCN`YCwgHNboY4m~1hu~zJ@~BE!{d7RQ7^Y1)-Av4j>9Tnf92mAvK-vqw`H7ap zeov(QB@^n_Wvm~$T?S3f;pJOXChI8!z< z4|zQVt~>bsC&}dln-l|JzpgAQm$o%>EMU`pXWPrk>lN7!GKH&Lu2oZ>J~guWGuD6B z?)equ^*43c`30uk-@|_ZvuFBdJb|@GLO$PvwIloMZ^6mq7xz)Y^yr+bTv%_!qa0S! z=lc~{`F7}Gl3TP_+nxKL{>sk>Jx{{YB@PenB2RU%pdW(8H;=`X5|4d8c{iLgWOq7o z*y=pv1lZK2>d770$og4T3~XI`@aipC!&%UO4P5SNx?BmX3iWgzoV)dL-(pyLDr}7> zoZd^@SOg0+wddHRy_6P7EPm;6+ZhfnJD*kn^G03x;s9Ijc*Rl>mlRss!fhTymtKeE zXB@`R;qyy4=C7GM<|XWrvLHMLwy1rie+Gvc4;i@; zmY4s`e+ny>mtS2?`cL>eNd>FBRQ?EtB}qj_ci!x^w{<=E*taPIxkiixn-i-pbCV9vxHMdL{OEWh`8#JrwMSg>4Ld?XhZ$%;=6 z|DXQave9P-!R(7BO=Nj4KS$Qsz^q=aUx@1r^`HB}OwO49d%X=W?W|z>dEFGUJjJJ- z!~4Ke|96zNR$5V4ST}d-d^vK9b-!28U`c!9;w;!`T0g%Iu;KXg!pm^_;OY9-LcD&` zKsy;sZ(8}(02}qXye$I`AE)~E4OSPLO3uP<58ck{Vbv#hT^ii7v+sx|SXrMZO@*~< zhgp1v4V8w!DX{YEhv<4(p5wLkFf4H}kE(*1(nm9rVDn-_(?giQ=2=8MY~Odvvb!*A zr11R~II6?)C50p>=eywatnG91VXr<{?nc7l;tA`rU>W_;(6w-8^K9vPSP>KXBN$dC zZd;QA3yyEI4unn1@75oIxp%)zSO6zKnaqlZIZX*po^Xn?#CbEU9DQ`YI~-*#c3cN5 z^lzVxh65uC%2vaY-rX~X!SdjKnh;q0c<~ShEY$71wvx2Zw{jZ*n{WNSJQ$WaR&e{m z%7(e2D`1t)Zc$e_@U||AA%{lP!98y<#)Eh2ItO>mtjVi^(EV3M)S`-GT8EB(ZvK<+H6vi0;ga8 zBTpjzccz^?0Q+3q`umWbM(6n{@{p9L2#9{jrm zIVYp8VKm%gvB2XttX_5X;s`h@`H{Q~7McAWI|wd4RlWH>EL$Y^wuU+EHHRu-&4W>W z`@n(&qfH*eyh3+Y57_?Kr1Gc4KJylJf@#6yu04Yp6_aNF(4oD}g8r{y;qRMg-oW*@wW>m9X^Iim*?xxa+gGC9vP^<2j8mhx6j^4fx$X#!vEl zHFW)8l?w-UJ{s8!^D~|bufi$)M<4kPd$D5UvS9&l(6yhi^~uYX zr%3-J2P#Z3eyEOiS+yUoOTQf631+3nqqa4^&-~e8onf)4X~|YNczC2Gv9dnKbp!0x zlsA>M=Nlrfu7>4%zr1Y6@)h=sPnIF-|SemZPlUw?{>KitH*J}$6nY@GTl ztg?D+#v)d%a{fZ{7v~>3!tdT>AN>d`ONJjE4AUolYJCs0M{n%YAEt@Z*S&=q(yOvQ zu-ZSnK}+)CJKTH1<(Ceve+dhx9$eTJwy(<`{sNY-dtYq~r;J(Mg?wL3+om={1GdlG z-%gS7%&_k42^}0|dSiDb%rfUBX<&8v%cp^znsj$tIHQP!3WIn$g;||+- zJ7kf1NguQ?b37~>rtlAg#k9KvS;VnC(;%4IsPP^KGg~}Y1i<`cCtrIwVaJ0GUs&@n z`;Q&WP90(61#`}Zbg_lg6XWM{VBw{M3x>c&3)WRmfMq==Mlj%DQQL)4#4F?T2flUtfKDbgW-yZxM+Kr_bl6l^rs%&5oZfijRsyK+t2Ul>^2A%ZJTd+J#~6*nGH;F znqzQC`q4&9n7OwlQCFMKG?Vj*#%t-(Ky-pGfOz*W) zVeyha2HWf#d)@?AaAVz>$kR?7jWdSjGe6f4hh;T!Z@Z9u*^SsyuuXEtaSG{w=T@(= zaDwNbcfW7rc)4&FIm7Cf1rwWLWj#|q0hT`M8=`|n-`{(^l>y!cO)*pAA1vgJv zzVr+nXcrStocZRP@o|_j{Nd)=a3gK%t^>qw%19neIX=;Q2TU*hY8c;<^MB0W4l`HA z{#l4z;_G^N3rss>+ZqOkNP~Ap!xC%0Ke654D=$}&_Vleyi(qCpqi`wAf0)URfVJiC zvlhX+;>?lDVEx*=6T@JwW4ggMJFlIe2a9gT$CA9Z$U1p8+^n--y%MfqB)SE|{LY%? zk+A&RivnL*5%}^;G|XT0Fv^Xz4>MwHgNyv{zM4pKWpOF7T~NB)1Xy`6Y0Y+6pV(VC zg4n41mI$txc(-j3%m~lulmJ_H8CODwwVL^9yJ6wcY^MRR;=$5inQ-K|D{qFv^g}-r zn&61(=~YZvXKyvw2=$K}_9%(u+|Xwn8k|0?W$F-EY87{E2%Ix6J%9l#S3K)G3J&>r z&(8{u!WVSs!m{;UxAukUy*@2k4y%n)%SkQ`$}`>$2e+><8VEB-t}ZTt8MR*x+m|U% z)ac-lEnOSzkkhyyTzzA9N&_X|M#9u1*YZZd+?d>XV_^E6=V4P| zv2I|d3#>jrQo0hR9f){185WwZ|FadAw{DE&z=HU)jl1B+Zx0>3NiJMbcpMg;KX!r( z8~&dF=})n5ng(lkzgv6-7Ej(%KOLs6<7mnJiW+dEt3ON~HLdP3Z0Xr)SRgFSJ?N%| z&DqE2kz7|)fTEe-n>)YPL?3QtROks}o-q@Eg{n@S^zwmyeH#ME8 zg1OfMlpkRhbt3H%%-Ul5?hQE}mlqGa3yTNN`Bet%ct3XBA?=TZ7?r}ots@vW;O66@ zhWRa5D4CK2Yx|t1XCu#95}$by7Iq&z^9*U9@X`7>EZ$W=gq&}Oq{}9UN&hLA!i2D& zCqH;UtYiv0mcgnWe|IIq+SuKBWd0curd+ZY79HC=8F-Hv*l}HS)ae<7RZat$}cQ~Y1J2-TgiBSH1WV824Oy2KMXPup|)HZ;m#Xz--~V`~EP4H+fn-+>$F< zO{^LmJA5M?apkR!xndOxMusYygCfUA_x%7!E%qo;L%!4&OmMnFI z{i4tB41xcfZ`m|R`>%u<@iTW(kVkg}G!knpPUwH5p5#<@xv~bPy$7`=u;RS)s9adK=Sj#KxRJ5l>L$#r zRvGR;^fG4eTQECxT{F4=d98ig<2I}rux6(lOzG5GO2#XF*4(X*aI?2>MINjkf8H>^ zVa;p!Msock?L%wis$YlmNPqsSOIF=rcJ6Cd{T(ZLFDuc8oG zETNA20@LPiy)pwfw~Rmf9p-(UpX33z_}U-(0gKxG+gxFzEu$B7f(yhfv*9u3aNw8)Pl>svXYaMY$MMp*H@Cs+&WDS1uqG|}1qJn#W33CV zf$5(=o$n0G-iPgZ3`c+7`LGL2NuFU;3OlED^63gI)}7dK2TnKHf7qC~?e3FHu!b9M zN$NARFib6ltE4s!hI)O@^X6W-Xw*nU{gyl$(O(Sbt3Mti+hYcl%9g_|megSK`jom! z4fEir{a%@!U}55+SYNn#q#cFi-1gBQy+Fe5l*(7BOhd^B&E+W4y;*W)>* zH5p&hhtuOe!gRN~Ze)Cl@!0bvEK3^cL&kH?V4JCGIC78RT@kEWFnjDhxMjrckp(bs zXu--{SlrfkGPxf0rEl!6kp4yM+{yJS={L*sB24evE1Fzy8o!Svr(ow0mf`vqcfZ@^ zAYAd-!uT55-nXEVM3`9@d63*+)Z3Pxj)VPjg{MgUP+v=UyABrI%r_$U8}Za3rAy#U zui=g7U`5!)OFUS1eonCk4nrsK#FqT}6hvmMdS!QtKg(Y3Kz|Fo1>%YCj>vzu0q;ZB`gdY#~%#S#fbw~!0ieZr9UhmFkHL@7EHN4!3yU1KKjOoY0OcB zdc(p^>c63IT3&T_S6F?~ZA37fe>cgr?IvE&r&i_%*H@kS-2#jF+b{USnpp+4-{9to zu~)gU{Ql^qW|$u>5BGu7{VVT&fmwgX-Svi}3nGVqf|bdYOm|qD($7gp`iD%bafAJW zF79uDb$jE+5WCu*E`A5+eD*Npb_-T-c>@RX9VbmjuKv+p{TkMGD>&>z+Lu>KU%-kP z4EY4OB5Y0PYFNA?&uBc%m31Fi1v76w%NYkp8s$!|gq!8_yF0_qR^4|!fR*&08%M*M zjNQBM!L$Nd(=eD)^``a)tZTh@8~(pI%pAN-lm@eA zu3OlHco1#&9+<{&%V~Ry_rHGq(w#6nE32dxPOM6t6AO!6Tv^}Y5OvviA*}3lG+PHN zf6WZoMDl@iA{t;n!4RVja3Ez=zj`?T%jA~Tu-H3#UmYwz`EtcFnEsOXQv=f|BQqAm zvRuuDYMAA9`usdtKg49)Ls(q)***}K&_{cg!t5D2)3`AA?kag9+#LVX#tmj}mTkQS z^F0nZO@jF>yNP+QP4x)vXp)=eow))>+ISW^z?A9q+{K$-Y(XtG4n0Yx|vmMs^pY$+>l`Q{R8)3h5%S$`MG%M}U zb#Tt;^j#E~A-}hF4V*d6Y<0T=<0EU|*cEWIbbreqnB~1tw+wDyT_$OPrEQg0`EaAj z^aV{Y<>`}AJXpZ}nDiOe4Zgj9Hk`Xkp7RbC1-Go81`7|je0~ixd~L>g!ZwHJmAr!G zKQA3{hbz*Lglk|{*q&A6VQ2RY?`Lq}-us+UaAM}(y2r5O*@@wUU|nhM*)o!!{d3&{ zE)o^oDTMXo?tV9dr8jPMDu5X^s@<(`u)U3kEpuSio~5!nIKuAPt_!ec3jSjqoUn9+ zv5d6Od;7f{HjjB>eH><2dKi_$LS3@mQMmcjo!nyLa?vRXtdngJl6*vp2)SF5B=!IMge5+pp2EYWR}a1lUhi zV-y8za(Z;xLfZGqwhD*2E+;-jl019qs8E<)!!n2Zhkv!$|B8MVePLnZpxa;J{9P?+yfXkhbdv%_enh| zDjqEQ0nKEnt(k*Xb&ci5!gH!F7*24M)D-4#dSN5)i1zlb@pF;m=MI^tRv|r%-lWb4L zn<}~q>smgW9fKvm+}mun5S*Z5J7vMLYiphzgwsXloibp4?3S;IFv~o0P8uv} zneGrv+V77%c9`U~d2=_wjS(Ip2VknxiY=?)#I1#Ili|QI&ZEik^7@qcayLvltn9rI zc1@aoYbUJiQOZ~V%cs}&O@KM;XuIaZ-0O=j#*+SP*z^2hJMHkqOgy>V8phPL1N( zK-jhaoAyXpdwTz%9j0QinY^L^rcJ9i)Pwxn7fkOF+ppN#3wiMT`HzZVR-NSmQa^;J19Qpv(;k;D z=n6+OsP2geJ~tolxuOiFRxjCe6_$N@K_k|W zsh)U+w72*CTnbBj-aM8C%PrO03t7W)+Kqq;O*F+KFU*G8&7Y?1we2-Ev61%DL`n77sId*T$0ZE*&}k;6_;Jko@2z zthwZx83pGa9Q`*9ma8hMYv8EM4-D4}ujj5_t6f{SY2vRKsut-?g!v}WOIL0i7xqky!dc*A-I!##!(=S|%afiJo&)GE>mOa}z z#TE7oAL23_=J4J&+!6N_@j!BKNf?0jI&>%UauOwIUT zlVS0qwKvDY%BPdL<6v68|5ish)hXx`3uZO=4H^!s=M`8ulKlFDGehBsuk&Mu!@Qbr z-3G%7S5~q$tT?-Tf)%V^?vzG{Wuq)OePHvY;Th(zz&QN^4W^g#e|3SGm05*ma1qUt z(izr%8Xj#5Ydn@2bt3(rMOIMZ^sdHRf9GMmmCB}chWSHU27QCsnIXX)ugLd>m)AAI zyl0-*|G<*$nkVmJdB(F|zhV0NtQ0Lw_xHX33)X9{$?9NAYJoEb*7HLxQ5Vo57Z z4Rc;uP4W|CZGXatS(;sks?e@CIFf-IP;XABeTfd?Lmc9J7`x~rTSl_0C zB~I$ZW>`|b_+}BwBWw?Rg_(E5OYXtC#ZBH#F#VG5#0^-RvWeCJs}vogT$on=URe)o zy4*dR2~*#Wuzv%mPgt9I5mql##J`4}Q>IRk!}0|G*L85>c$v$2lHbZtu7!i2PV+hk z3sOTHU%-vQmXlAw!ZClspTZ$l^!=%@tozzg6)j*Hx+B?Vu8{t>!#;+?w3LOHvSH4T1Hv#^;bEG48FqM?z90lv{jC#Rg41$~ zc(Y*sjTTE{SKEf8UZnq{0kd-8s@kFdIHdnqqw~bH!{t+4Va`UCKY6{VryI9VfMwG6 zhW(LwKTIAA=WKF*PxhDn&-~|T(*Np@#5`CuIKFlyOliI6d;^X+$1ffLGv^Bo?=OMc zvui(CYufm~?S1L*K!Z6~cDIt((>U*&YYYqfm>BklQ+fZ#zw4++a%;o>viDA%^$8XX zTchaAAyKqzm z%+VPgBgfnF@r}vXVd07KhWA%7Id1%A(%z1xA?J(h^Yr>zIIwPN0mV-r?#zay=%x z#NFx0#p|V~%zO@uU%92V!935(3)QeJ_(%%L1(DUwr{G2xn|FU<_Jjq2d*Pz%KluM( zY4CILHke*t@`_lMwD(3TGA^XQZk#wXV{NMhHMp$?113QPf8Mdb_qJ~?*ROI zgVV>)$Sj3ryNe2{;hb(^$wja_*k#Q#xIJH4e~aY*QbkW-h4{hWT$p~K@05qIOewu~ z73L%sPkjIjgRAM;aMb;=zsg{}_dBNxuqI?MuN2O$c06|m)~9m}x#Gp?qsL)|cHE~5 zlTU$Pg@*}rQNsoyQ?xj!T%FA03y0@u2&U2quY?p3(- z#r;*9YZQG9mW@#!c7e6tE?dd_7w~%puZ1JJp5UgF_6G*nY=&iLuYSmYsg*aJ65)2o z(p}{E(N(&W#Zd|`ef9yXtTB7}nREP8#p%R#uR;zm(dGQRJdtv>~~7yi(g!Mx^!hV@3( z#}krXFheyawE#J5zHPEK%*_5hum;vTd43}GK=rsOycrg}2n!kx^Me`;&u1zoxE^(Y zIY&Qwn_)eqt!rE{3g+D(J7o}@(6`xOS@ki4so^dE%X^&|JP~=js$6Gi|8l$52j*m0 z*o{Dc1#82Ud2sH8)HkF)NuR7qTS2^6`i0aVrt{>RdtvcK;jRI&%x3M23$WLu<8J+7 zk$!~sHq2XP)zJr51mWLS!`fdnj`oCee(g3qUulo}`k*_kJX&m{L#{4cwTIL%jYAjh zPnfcCpP^pxyG6Ej#`;;7Xl|%y>cjIqjNzgYTRxC_rI@ViOM~k(mdw_}tWO=I$oiYM z;?%cqu-vmJdo-*c)w8AvmI)^m{7}4aNvp~-=~oFJ3mAH-n^4MyQ@?au{ThyXoEAC*=CVz%*1*h$;^RTE;yksLj3?g2 z{`63|cDrSlCotpHYs2%}ihypD9>Ls21-};~uUNiza5*e5IL0B*cN^cibT5YWA8mJ# z=N;n0C@mSUf)!iL*Tb42CEsqqIXaV&XxO#tv7sKZ*F5M#o}Z+@KK8%sNpt)Xu_So2 zRT|o}fBWT<{*4nH*B*eGTlVyef}7X(t=tdGe-s;@KT`)zl8RxK&E?8SDzQzD6XO>zr{ zyXEII&eK^nhgp z9~M-@)Cm(VafrK2Tv7z5ZGLRx2J5Y&g091nW`$#jsas|Yz6=X4$I&LkQ9*8*Nie;2 z_VGzDeYO3B)o^`o+yG+6wE>3by&{Qc`vh1@jcX#$Q>46&J|w5QtXMb_ZsGM>#Mfz{qYv{fM)tH$enoO$Hb?w*N(NV_%+6NYAMFReFg^)jCuWsj{`?`>?tl zra5^g?}hU%7TNzL`HQDZMQ{RR;Jr?G{T%b>ZRCEW&z@m!1UI{k+$w-=3eHtfVdkmn zWpiM5y~lS`n47fTus-K(88WduES&Xg-ZbQCMm+}ghUEvQ{~+sWLDV?I`<2~$cE<_k z46V4?7dd}YZTV<8^1%M!{xF4QbbzdnRb?j|2En4}J_-gLIPiu$6PEZFa(lz(4gSl9 z!@Bg$yza1{^(td>eAzCsE4#r=uSLHdiOW0~Wc?d+sWOcP^Y=K#8N)d_1vkgS-22S0 z6j-%+&?a)d=#L!Ce^2i3FHJSmV5PEV3R#c+H(tcU;~tawF@5(@XEJ^ix!(WCdMF`u zjbZ$;GlKeEgrnnAeoRD8buL+W47OPl(3gw{`qNyWM3~pu?dNDXa3!Ub%rB}=TlFIQ z&+}Pdy#-F}`J`zWai@zB>qxFYpFM>5z}w?+sW~g?$g!Gh5hah{AmG;ldtvl zh3P-k9Wz#k@In0k< z+}0W9aSB@>!GX(aFLi?TjR0QYTDegz+v-$S-_bFjjp%eQj zY^1S$lLsq5ZMyLp_B$C6bq!|tW<=>=X1(1;IV@~2Pb2fi{NW}&&cc$(4zpjvQ4ET= z3})E{cOq8$Xg?l-4c}*hbJ91@N`a-}dea)%HPAHY0IW>4%hQnl@6B5F!Tif5hWT;( zyOW{2VdmIiYcfA>USbp@g0(c7-BZ}k-|XQgm^NnLn@2EZbD&c+$;I0DWWMasrBNOU z|2JRG-9P5u5jP`EiZ+Jnr8Is2)D7r@fm{)YLsdZKjOJXn_X*08r_}p zd)Jc+i_6y)kn!x*e_Zn4tEdMro=lnz$JnOI{=i&reb!PL6cd(Rxb9*GrouJj%!h!on_E`%v9V*vX!PGOs zvo^v~`{`rUB;QkWW)ocGbuO|3R@P0Mx*3jQv8zi-KK|Jua{UH=bZ@=~bE~IKjDf2r zNJbWr_DeP=Y=@;y=Yy`p`qGU1J4k+h!}QCrJkZ>4AKcCzwfY>K^ZtI#0a%mewDk-u z$$u$Kg?0M!`V%m{`B-l$oDd!;JPK=;24o$FIk8g)93Xl3xYuW4UO2@x5oWDSS$hF? zwl=ff1vkI(F(=P&>LW~+iC|g$_dn!$Omn}F9vfh7#S6ppf#$+6sXt5?o9kX8&zw_{ z=mQJfOjgyyIS#9Y#1h}`am2JkD{Du>!sJt@{=tnFdHzF5`vrfPCaAY*hh{1mu%Do|wJV>%Q4^Z&YhgzE zqnZcsf1hteU*R4pCHI-OLotI8}o(@Ay#F*+qxJQjLo{Pgy|03 zZ*PTV&2zilh1nZ|?(Bx`+E=F*z`~yFM|)w*)Y5aeUolE;$Vv)yM6tZEru%Y`-9wT(+*S~cT3`94DWl>vMBaE|)p8}j`HX}qN}6qXE# zQ~h5W-L64s&3^@M76=xa#0Y?l_p6(O~$# zL_#eyaWv`AJ{cZ?JY<6E!3bEMqA4YxPuA~pQ`*8&V=nDk0NYU4r1Xb#xVO&Eh9y(f zS9-%*&*ho3NdLcfO+8`Ohx<7}aPB#JL=RYRb-a?iAF)`umOI>Tbt(8tr^OzNPLwj7LSy|0)YpDM}GTP?6U_%@rYpX>X4d(aFAKKdn@ z3g@2oOnw8)M$I_m0mpO=SoaEMZ;SLNb~V#qsUhvBs)F3%{59c6pTXje>9ol(ZG>i8 z6)Y)vWca)_a_Y#ka`?aXLgt6PXUkx^)8AXe(O$9Qo>K|&#YZvZ{0lDyJuQIQp%eBq zV3u?IwX3l9>Zcq!Y?MS1eA#iV`2#i^A2 zFms_#!ZVn$-sJT@xOsLjhibC@Ic4R$VBL;shTJl)Cu;{R&Wn#ILmoNo+M4Y!K zY&e}|5w`^vOsLI10Q17j!-TLr{)Ti1EJ-gtyn*-^+pzwr?>2GLCRjRfyJ7v4Is4T( z(q7`z=ea-H2XAiZMl4vL@Y4ro9*z-ggn6n?4=2Ma#)5oe&Sv~YUO49M!j-YGrg@NI z{X_p!uulZ5HyS&bA*b1Yl@Rkyej5`{=6jy@Q^K5rq#g!^1LpyA!N)om=W5uyBub0OpLh>3yywzc?y=S-#PFeEKQgsNP*M7 z4Wg)spHJB!ffX+>N7;!wZK?7j$?4;ocf&E%$8)~Gvi|j5cfhhb z@waa;dBR_71BL*Ma_y^AEU0$^X4jy6Lza8dQ zXhp=)``_Uam#eU=O|cd(l7fk zY43IE_EysWZC`@}U$n2@0c-2pIv+vqcl%Gx0a$q0!jRXO9olpfW@RUZ9YUTv@HFQ% z%zdL>OtxRKc1G88u;l7Gs-V9l2qhV?(U@y26vznA?DNh9Z1`sz&M9avGYes%(!Rv&3zPWu1<=h6Bk z3F{fG>Ne9o9Jyj!^w)QApzQ#LCv0iDXMY16RlReJ8?2Mg`u7o5FLU&n1lR68+xi*i zY5p2MPq$n1%Iq7=tu14ZKu+5@+~Egt@UcI3u*}0^S}QD(Y$&sVHKT7*I$-5JkKX2R zlxOqwe=x;i&ZI7IdXGMPjWGXX`P;70W4;-|N4x<{jP-rTS#Z>(W_PllsA`p*ngdgA9aXG@*=0W>d9Zl!&>^egM8S)g zg|PHp(u@eWc0g|IGMG9xbnhHkdCZcv5@!0jO`i$3f3jUzq~uh(y-|;N3Q7n1x}mtdW{#%FVEcj7S6fs zxqmXOuUTnWza|*ppFR=RWDi|Zgq%NpwR8-uzF)TZ3LI0^^TBA+KK^D=7OdEoYev=| z%*Y$B01#!lt{g@?*ad7Uu>dbaBUK^$gHo_dn&kJPya$;3ZYe@g(d8$TO+SJ;0DV%2X zH2*!!n!Ug(9M-SZ7}g7H>Ss|XY%X4{dW@X*BK&3`-2NbSz5=G}T@HD}`BK%WY?wbQ z;EV@c^m0`JsSh-Zr;#pjZqbGL$6?*C&qLX8uvKplGN0xg_Z~5UcqNBQ>J3fta@RQ6 zXhc!v9+>O@EX4^9+^t`<3sxoud5wXoA`5F$ubStTwTy;2#q*M4Nd9`xO#EwTPV@00 zchCn9gYFy%1)1`Z8`X9QAM9=J_xq zbYa6FSia!sky)_(;F=#cFsov@q23AxFE_Y8(4OswT)2PhDUxS?(f^(bQ(nXxT-(xJ z{eRv3z@+xw%v^2**<6aSBEkLos&`L!V1K~D1g&TPZ{nr8m=tTn7Li5bd3 zt~XKqBCeV^Y#kjAj3h4g%b?-Z+^h(>E!)3ulu#P7&+5t zrQvumQjd2c^IKWOLW9j?vg&Wa+Gi04Q^XO43Yhn`k70WWeCKR3-;i#ZX*k~0$x}~} z`G`n8ieZoKb8dCsehKD0S-g@73my$RN9MD-e2491{IDXwFFgVCf6vW#h2{TJGo)}% zm@LB`PVjR7A%Q7vz3=f9gYupyc3q!?+uTD zGlQ?kY=xCi?sgNx(#hZGWWGspI{thIT(#2X^=eq(`+nPgSU`~^uYzemXg?${V>Ros z0A`r%vpxJQj5rsIwi%xw24{{x%%`yOKl=foSYYl9VG=XMT(srLMhB#$z#|1=N| z%y%->OZ*+@D|A><|Bgep$JUzt7V$g57wTxJe8KfCkf5*)L;(4Pv6VlqpQ!3vIG ze-~K#Ej=U=HnRBhsT0h0FQO9bhI!ln&BpQea;uJl1MfTSZiBg*Tht3-yEh+~{D$fG z2LuPhHg~43(8Hpsbi;UYuF2Ti0!tVrL1a8Jc0TF&0kds42*`M&%~3gjgQ;ppBdJfh z<<|~}7m_ zT+h_+;Wwx-PpXqGhsABVEzGAj_Wc1@n7Yx(`cyP_=%W**Ya+s1mwCupE=dAqQ+waSwD+|zCA02tM<;TCiROyFV(FOj;;wPBlV3l-hFlf z+|K<~MAnN!l|D3|IPTD=gRojM(CQ{Es)!nM7^bjSa$|iY{5J@AurzWyn@jwuz3bd zIpZQ9(DQUkescs4!;#WH~8I}i6s&Rv(Dnpug!-ns}!jbO%=92Y4 zV@}2zFPQ(b@WmRK-#OCC8&*dQH(m;B4jd2nfkoK?UC8+gZ0Ke@4HoB_j}3x3yF?d! zVV%QVsXysY>3?oIoElsi?gP_HBwVt;?Ns|q9Fnh_W7vN?oBsXC_+hPJNd1vF`s5^! zg?Tgfx&*>4?FUve;T-qFFM?qGqzc3M6;9;G&w?X*7smHPP8;6xBpBwU{`)}cq3}}L z!`ZOlwbHBy%ujaBC&!P{G-^D#URchZ=Z3s5CZ2vEp2Ya^+$_4>b(dy z;?f<+^%_V`VMoApkJiE__`mbhtbetD)E~jMK8sc($KMgyS4X_??wd_;&iO7Et6}Q( zCrd?Sd*7C*NIl_qdG=WhGv_Y!EG6x46y8XM(xE`+0YPi#64%Ql|9u7sQCuNrw8 z)@@j|q5xKIxY8#Bwwsx1dmHBcY5RT|7B1}ZCJ)x`>8|hJaFR?9DNm! zw_u9#pJH-7WX3(M$$~Xi4l!S0_RHOGGhzCa87UobB4d;JWjJa*{VW65U(VUtvq*nN zukAfO;M~NChV3yoUaTbbBJ)Sjp;t(M-?>MkVCI@t|FVe{Upr#q3Y%X$$?H)(e+l-( zs22#`m`Bb1)|)eA#taKVEKo5*F#&m8EcWmyhCOuuL)iTrAg2j(Q+ zUbdC=Ke8>mEAA(eFOQ6053@VJ@iv1g>t_#H4GU{u8JrpuFmELsWoC8K6uG|cWa=_l z$4&L8!hUu)Hbs#3C7**#VBRWw=3+O z_s@nojZKFB^w}Se2f-?@&1cB=gIl>9XTpIGTz$#w3lF{S>kCuItvJ*T<}(0@iGh9mn_+6%4gW`^z1mvv36^O#8t$h#9yx9AVMd{3R5o&lyW^AJzP!1beCJ{iMGQIPjDnENmKZ#P&fxD)jNmJE`vC+Cy1GICc5 zOttg(?g>-#Cb<;BmMhlZ>P7lzZ@YX4W>ogS&<9pHneV&?CuW?~_k{&DzMryT_R!K* z{a|VMo%5Gq!F30tfiTrPq5Lc?I=;G!NxW^*wL@@>?MVwqI50KpVKU6{3b{N6Hawq% zSq(?`PK22^8!bpZjl44>U^1++i&T^QC$DdGksD0Ay`)YEM^lFH@r1dT9T*tPfl#vQOuJo+xVzZ!XH zd=lZnE7~d!99{0a_$aK2zJAyR)nFIb7z0gybo*ESh5`8`hl$i73tq&z~teu;={s!6)-#0_@q6Y zX)(h_4L9d-T8F}d`=@Hje3*H2qd#%*EEmK4mV5rB$q+cPsZQ`7d5&4n;sJ01WjCV{ zRi5-I7!PUSNIQ=lau8u!uLLy#wZiPJQi6>@nK34Hix- zs+$6<+<)KE!`$bqDm-AppxS}0upo1oVLeAnEsgyFE5b+ElJ%Z$x?I}~2b-KTtRHnV z{seu36HohGU4mRXWG$@$4y+%&oy{gUmnKlI7ADFym6pbTYpT%q+W54GZc@j*|JC=-SHumGFP-ZLRy#-o)u=X6+>N zOP)#YsbZMpy=h7cZ1{ZyEbCdYnauZC*C_Wc!Zh-K0AbCmK~aZc_4IFs&r?)yKV^zZ z|8AEu$oiYIb-Y6?9DVbnVg0QzN-kalw_gvTkoTv~y1H*UtWSBdi_8z{-a9$V;O29) zE5cw}K>d%IFk_~#VSbo1V`f)>Skz@(*&O8ZZl@BwV4AdfGg*I&5BVf{z_O|3y~z4o zR@T_X1+Kca?vOVeRq8v$84mfLZb!}+Z|08?PH?W>XTy3!ku$j75iZ(eae6dz<+CQK z16+UD_3HrA-zdbD{^ zxK#KVmcAS#vw{^lR$X7ix}b`_G&tD0WF}efsY~JxTV@>bDyw{nl+G|ihD|;`! z3rjw{EpCHV;?;)vnqmD9N8CQ|dlfk+W2vMCHX1tW6`5aA^ERCL2Fn+nt;~RR2Zvm1 zgr%A@hWU?j*q$fv;r!2s4@gP-Ept<}aM9vrvr=GvLZ1sSVQSvEKKo%|qs-zt-1xBa zKB+ep>$_7Qz)@?4cH0KCeS4SOgPHq2wUYWIr-&uDV1{>b+ydO_&|Grm|1B=en|hN$@QFh#XN z9|+4FzPLHT>DJVv(_mVc{k(p#?Eb_^4r$-`XIGmB^>Izoc{1M-Z&_XO8P3)Ji(tcy zSve&&usO5+_&AvPu=3ACm{qcT2f5#ao?)lz2U$emaaQtuZ)Z*onigI_dBCuqu?`3 zTX4Tt&vn_m9+tneG0cAi=jV?nuIRxy_z*dZYV(RX?dY_$dvNpaK1G}17**%iJXp+I z`)mX3u*AqG3zkJcX;=$KtV!&3p0pPY`5Z<1n+>Tr3R8r6K5Jm$!)@3I=e!%&btSBh z>sh%LZkElM7zR^IM(tY-3)*JRng`n*xY%nIO!w#-G8g9S*LN%<_L}=B5Dt!?l^6k2 zum5)RhlR^D){96!bdY2^OuyycB^0JaQ@?TH_7L8PU|5&=ccCZD2^sQwCM-;uvDgEq zr3|$7gE<@Q=p0ye!9zX)=B9M~aE2LH-1}o-{Uu@gSh(WjiH=dQ;Mdu9Cpf2Q_<`Xt zWs>W>F>rAIx@&`Bnbn|Mj&OT<&ITJe=iKeSqhR%sL`r{H{`ALc2Uxl%QQQOOZ*i(( z!i|+*GE87;hWis&xN2&8erGuF)Pb=xU>apwd&gz$@4DBALgBy_A+|r^z@?5+){HD*ZWFX5%GZA3P(-!F*v95m(71+`4=IJv}aG2HJRc1 zqxL%dm~2lf@v0dDr`~Q~M_!NaIKP@)zs!!6eP5D%oB60va=lJccCUja@_(JTz}k+} zi-@CcJ)gD>mfrt5{Vm*Vx>t4KRe+?RMgySo$+>k^?{nnX9^frg}fwTNby{A_#d)y0Fow0l| z0M7Y)tE4xqXmA_KfQ6%;3?}E7d-VSNVX*n@5hv|os!xmI{-ZdgiY4QPIkx=uXyoF@ zUWV~Sx$$c4IJnuecFz>#fhQN=8xKqT^3?7yFKXoS39yj--&C0HIXq+%Y-ykCI183n zub<@#%bF-B`LN_xZx0Tv%gZ!eKm4$oT2DClQHF8@a?!Ya7P;Tr@jMR2!iK;10E=Tp zd2w*yStI6DSl)N*g#=h?+09^arICC;Y2VS--4A)xmwsK2z`DsE`aoEzHDVltMgLBG z4T4+dZz?BnB)4TbfeZ-rbS{nM}gUJN(x zH8#{Ms&drmm2lP0`){rwXZQ*GMZpo7XXLV56Z7?gKZ}(zY@wC_HB$#GDIm$J>*m=^-L=8-NzNhsHoaQyWgscw@zh{Ki zt+eWTm|+uPm~Z{JUK1Sbw&e}-^z+UpO~|=pjWyrlme2`>-(lYO;c>>8pSo_J&;CjB zGaWJu*g;>jimcD{C(?eg;oy;f-;(v2@?U-@^1O_3WGIiU&(wbDVGCg8d+X_B{j7ZC zHjF&~b6!8f(gfCA9ldiiOw$+(h-Gz^hUZ7L6Fy3^p3{$FzT1mjUS4llf3qaB2TO($47e&h3n53gkr zYp%xK{|eItNB$D~EF2X>j-UFy062F??J;V6y}QGiWSI9lZ}4kUe)I$H4*a`+kKA3 zVG^Icje0&fR|h>m0E^#TeHezg@bD*U{26fVPSg_OdHbmGqeht(zW~LaaW`-nF^Q|<`cGmW=;U1To zzYU0fH^CAvckV!q|7_<}OeFnF-`c@8Ihza2)#N4z8EZ%Sb1kMvoeVH3MW{34je zbytz`Pn^Ef!n?5HUWr9LT(19BoDXv@oqkTnJ04+6n{!|Ry;plOzA#S^edIJER89I){_Z7*!>WI>H*RC8Td?Sf@>KL#lfXMbqF zG#Xat57(cDIVW!J*aRzXr{^3eZqj>gfVp;^i^%xLXR+;twXk&PGjS4}a3pTe8d$>g z8Fm=vZCc$k7`FK_VqXGWao=IeVptrwc<~=;_3yJfVt%-$|*XEw&!P@Bo&0<)6 z;r%a9ShO_%$7WLgnz@<_%RR(q8%g=)HUq|x`ax&jlJQi<{OXwwq`bIgEomRQ`Ki$Z zVU@qucQ(w~eCCc7%#3K?&l}boEy9?v;@iT3JXn{vKdURO=kD1u8jf3g?reLQcHEdT z47T|3c4})j&X3CUA?d#w?%sU#3pQp=ckU0fd+$qVftiWhd-i~1Rqv9%!J1ypcg;!p zxlg_uU{-aXC^I;{SHzT$FgJeCa3kq&Tej|g2dfe!)cB;RV-Ld{STQwuV+G<8n+tHk{jfB zRXglo1i(g*2kGQ|&`bCJ^n;@grhg{qOEltl?i?8J-FMT-`L2%+n+cmuu^(hY&hKgK zY91VW?S5%DSecW4Wg@H!+C8`rOs&U&E6zJVw1GL3XL*c*txZZV+QZs+p>0lZ@V5HS zY?xo1;YS=^SaXluA2tcD86)6)>onmwIM}VtXC$oj3@(}ku4`>`2!Vw!2X1pkT%5ogydGu*B#t2Ync&s! z_%K-M|Bo79@rMr)Zh^(VgBFwhGc4^KLabHfQ2Wb~IMzhL0@*t1cyuqMr+2_Mr9Y_s z75uTC6c0;B7Kf1W97~!Jegvk4e9hv*N{i8MXJEenYHIwKd+y@{1+4if8$ix?++bOJ zHmQGd2^G(uC|__1mLyc|^hUgz!N|J?YYROuO@b?AlfK`CmF3X~N5jmAF0byu!D0R4 zZAkgk^QiVq_{Y7vJ1pHOd0L3L(Z&$c4wkL47)jbQo4L2H8Ia#ve3n|%N~+>Z!VmD+t%Cw({IcDJz(o$(u@|^(3I9R24>n# zTw{c_7+#R^MELEFZN$=}YYz;C%?7Er|AK{~FI@WbPaa)tWz>K%IRsCR-sh*W&JfZrOV`T-iYO6QZ!gO=*offb~{Wfa+AQ=6&!5o%q zUUjKJT(2DawL2W;^^-auIs6lJ zZyMR2b+-hz3YMkUQ`?vHNS#jl6Ps1RZaom!?d|M*5oSN;)DeddHayROh1^wCoc6v* zCWraPfz@4x*ms_<|l{Rwx?PCWzlHQGG(HdtEdK^-6S!eNaV<{h09(Fbu> z!L@s&KM`&^w2qt)p~+uY@_gym9THl@^0Sv>m%;2UwSnaP`Hr|<>jz8wyRGgE2b|Ns z^?_M`29%KPDH1Q8_JWxU4sNl81qH=cV_}2r>;9gwPg%xgs(t9*i$fgS|2+LP;NlF0sx+rs{k{zEZ|O`T7`Jfn@L>J zua@c`7)1_CJHxh*1N$|j{nL!hvhD~s^bOeC088I(c+ws&^C}eSVS~#0B@K27JvoWA z$BLzkCbd=J{?&i*EQQ$*IQ^R8mWXi&i(qwfl4B!W?)YonUE(2@uNz=DvEQ|uu+Z*a z^9MM+y3sM0)IYj3;2n(L`CwdzwbQz+tbrLBBfK_InJIZ0Krf{o*%ULt}q`>l7cFC2n#Aco8QCP9DdFC@XK-GWQewev_ zZP&*zQ|mW*J1lu=WAPBK8@hf}B+L#tTvQ5c-}3S!V8(8xh}ck|4%q}7E@R{yBL<-)K}hzt6RSf^@F7u-)oCt*?~#2SuopA{`oGVh@|$pVA>V5}tZsb- zRa-~Ej8po0CG6<`Wv(q8e4}S}7EIT=-s=xDt-AKjfU`$fobCxrqPAAaVc)oSehipj zQU2-_j6Xo>?NW|Q(W@~HW$zz)Z) zC&9`)7oW~Xd91_w;0kNjyp;-JpK*-LVX*jS#gb^aVqU=NzOa6?ck>b0x8eBwt}tC{ ze0>3CnQd+C2q&b4^|}gkN|WFG%fj^t+n;d*ZjiP#{ecrOBd&&Hr?~hTVgCAk{fpt0 zp+C6aV8Lk1&20>BdP|xp@QidAXPGUDyfJZoHx9&r(Wvz81sM zDJ_`~Q6B3Y5>~+Cwfkjc{Utd?D|cXloBxbML`6a{VP1PX6XFZ&aQ9j_IDz$A<_%kKf0bzp zi)zh7xUeRDOcMcTIuF|&%gKU4PFjQs$!Ck?qp z`a5NQ%C}mWDYhEkn|K$W`wC|C85z8Z7hd}C>^dx2&Y$}f*2E@N=aKlkxV%TO=xqAOT$nwmf@;s@ z2LzfNnBysbqCuSB81gR>W)AkRBHK&iPPlUbRwQVx-oon5PaXEctfFJNwXpQA{OxX1 z{$U3J**}lDg|eNn_Tjj+_i#h|WB%JTtRuJxyx}loi_812jHht|!MMzsXoY^8478OK-=*0>@EXXTbUMBi9hKIh$t8hjkeu4`RAw_QR!cnRYjq zSoi$leIaZ^-?@1Q%-nk;K>|xWI{hV|&oU1+IRF_oPrZdc5B42{@mZG z8F0w=GN(16|EUH;M<}q9r_;JAmST=Itj1pK)ySkVSmxr&qRS3(Hn(sQo zHeJVsk@syXcIWNAVJzerkp;Ug`TNU^cy`u}lW>aJlnDF*%>H1lmhn153cA6%ltE! zrNQMss}p*_3UjHu490uHr{sLg=|=(&!qp`m!k94k$UO)0e$G}l_+@_*-&$0#1rGRS zx6}@n?HaO?JTKXT9ILUgp!MMiGTxGO`PYG5Z!PcMc`{zIR_r#PLfrjVFH^YHV*7V5 zm}8hf!iQNiZ?u0xJY@U2c`%PQ zgvvML3ggGlhgD-1MUwoH{-`@^1uS~=nNRW!v&v34cEJK^!P)b0Qsj&G@v!h-`5ltK z8E=a;M_{F7!|>B^#l)Vg&cN!!`>6aV>B%<#6Qq7s?wnmq6-y432ze()t|NLy`M-HR2-syQX^B;KoV_ zhNVaC>}p_UoXJultXkT%{W_eIx@O!`*e2}4nagng+|pi4Nd1pqR#`A_U)vdfSpPY* z;23NZx#;Xdm@hi*lnCp4cT_Hbsrf!Q<$KL|KUiv4ERBXmGhHY7!rX@@jgfHrx}4V8 zu&U&)lK>9i$6GiHRx<4>rop}^y(u%FG%z@D+}J{@JrQ0h|1}IYo~+Iw*HaO_A$lOp zIr`Bd1UA00Y}XsE@cVWt9G2alQPC9+k$9dZ{ek-W@9NGlpY@kIzv`tOes+L;dyUIY zK%DEdA*-VV*O#ugY zo7=AvR@Kh`cL|oyO+N4lu3KlLzYVKK>0`@bab)Q9$FOGF@(ZNC&GSB<tR_+(P;zXoS<=BBV5k8y}29qpU!J&>4|)UIT`;J3^!~mrt%$8 z&F8g?VHfj?56*}yrkm|tOMEkv%9rF1er#F~YeX9p-4N&OpSw;3^M*0m-mv=le9Efa zirpk1qh0o;>{vFM;SUGjQTN|~@+QNhzAS@v|J)y~gB_1el&*yT`#kcUOctqs{lFeJtXFlrbOko9{g6TKA6?nofh2#G>z;P9 zC-q0Ox{&-fVVQZz5V+2E*Wp5#*F`?n3N{m*qZh-<^aJUZuw!%D9}THLul@3#aPH9v z)>D|{=n+7J8;ohINWQJRO;2vd^etLE3wm z{@Eq!DO`Q9bMPlv8U0ID1ap^7jcS7RwQD1@;lR2&*aQe?%dTM;2omwt~ z-In~K#tWLO&+ka#xZq(_dBwj0`4Mo`KS4iIU-IN=%W9bUs_$>IeQ~W`g1>;|$~qR0 z`%L0T*Z!VNoRo969%fy+{J|M+c;mkP6}=9j$wV64FB?^l1m3rnv$g*U(!+fpOcFzuGpj*oE2;lI`wVdf6~fERG! z-aGVjFrB@)x&oHX=UhJvvxl42KPB~_-S$a?8HwZ1m%+5<9XcF?X~Xk3-GfE8roQp8 zINyEr9XRPv&t>F((yz#QR{&@4FP}v2w}e^NNw;Ce538IQSa)vZ>6K9Y}_A4=rsABA(OZ>|t0Ew^0`eE4KB@O@OVZ+l?pBpHgRt zk;42pe~T8t+E=Ic?}p<#>t_4G{M*mo?Sg5;hTfe8mj_fEVqoPxyI!8K{NPG%EL`-f zbnir16ZV)Y&py_{jsq*Y4|}x-aZTLthEcFUJl}2~Y!QAwhK!ej(|c3Sf`c{$Or;l$X}T3DAE?!OI=TJD!h+F#k(tPHaK{Enud@5BEc|9|Z@W8%XH`w`bR zOenpHxH9c+Fxh|JMavb}V8w8A>iU%JW$(KH^KR*$r6A7T^DQO=W?gabrlYGjSZ5)?9>Tda=#_nRlYa`iyc{JC9tEjA(7-G?3W9tKZeu)vW@#-WxOBd z|HcFIil?TfhzE?l=)M(ktySMGMKEVdejpj|Fpt|%_p@R12Un60s7g%RUq?J;y8FhJ zFx$h!Uj@?#^%_I+0e0tIIpqHK*;w;~^!E(zu!|&K?Y}UWpEr!z zvR@6$9^Ll$AaVOD3k__M>ix$JmLA3e9XM8YSH*$LD+X`Y!mMr=sr-;TX?M5RFxz>{ zqtT@NvrhaP*xJ`OVFaA;@)zq9tj*F6aex(VKE0aYy6czgZDD!i#yKr;lx+Llfv~Xc zeA-V^zu4cIGJ!B)=8E+WwAM7RgeR@dGzW|EH!Eh4DTK{j;H6?)VaB|2py86lN@*zM&E}#7mzsVB@05zfWN5`>Ak1Oy|Uhu)441 zuMx1klGg1WRli5Ua#+t}9V~*i_s$#+fm6frFom-mi?MlCt+jE%& zhvD)!{D-7JYtXj$jfZtFcO~3_l{EvTF|ek2vHUgMVzp=a4wzcM2}cE2o{WZNFDh?- zf=z-}n?}Nn!q$dnSUY{$#0Z!-)M2<0u2z&XH^G`ax_f^}{Q{p;8(}RsUHA`H`p$Z{ z4i+~pT4;g(yF#*kdk`!wE()@RrJKdq0%7jHk1|Kt_|B5%b#)_`?a;UR?@@V?!?<6~L_Xj8(f}sej@p zV(x>oMHgYsIm^%mFf(|QS1~La(Eh0}tekJT=M8MqwD=t#)s7%Jf!coVzJHah8ds#XYbM^_w!qNrZ zW{!kqOO1`=i3iGb4kUg%$aV^xzxB`GA+Y?Eplv#AadC2Je^~3gk4MHc+46y{ePPLE zw#O{kXIuNlOqg5cOgUU2$mjt}-%h5?{+QOMD=eOq@nJT~%L*c2bcE@TTR)TfyvLU& zwue>x4b*svSyDLY-&uTKyWJD`h#NeL1g)@?-STD*%<J%8 zhgYV&fO%H+MP$5}ME-{kroN8|OYh`Oegdcx9(iewd?&{;O zC&G+;*@-wp3?)?TaF&Q9#*`#SZ)eC&OXr^0&{Bi?CJX#-(N8xnKj&nVUZw)Z+gN@VL?VSos+x>pF9j^Y+_`{vlKl0LbHLT1n*)ajuoa-@^ zjE7j$hU^|s>O0KZHy`#jby@8S|NrsOopV-WVOHXDs(ta$I>=pM-n01Ti6}33{I-82 zOjkYGF#r~w2y`1k$`?JgWWcmHtujZL_Hh<9UJ1zWJ&jm4YU{|KXb)R1etSF&mV7(> z`7NyL5-4H8%(BY*hcJDBpm8v)7B(bZhh5H=oF4$QPS=`Wh8<_*Otpdy$7cGG@riKe zfG9Iqv;1l9eiE;!IAjVd8_P!Sgj1$gRJMZ^*?liX!mZmE1khmVpAHPtpA^m4Ol{4? z{qt%))gPrBuU`HEi!-WLtw3D6#;mXzmhY6O`N52jU;V$pinhB|-f&CyvMC>7WAd;L zTv&5BEw>idYp+n_nUJJWyf-AiiN9nx;!*SX94#za^0i_JoS#2F^C_$-`Z&8EtXLgi z^9VLrs(M($N%#I9dI+nQ?Jg$cAL9f34;QTTKm4T&%uz(k)iBdJk;(@Qd;EvrhJ~X7 z`qL0^`RDLO1#_Qy@BfYbK{sLT(M!btn~(U0mE|g6+Ud+bjfnFk6&>ZUq&{zAJxo`- zeK`TMdCkfFTK%t2iY(v&rvbz@nqu*1v>9$nPk_0{gyH{>U`#y?_gA4n@Bw=Z8&x7XfB6 zx{mz-(;okH9s~35xl!Xy&2Dz}a9BI~X5kma&3Y9_*}|-)9-W%umYxId^@oEGk82?L zrMQ#NvOX|FY;N}#b~|11p*PI_^ChGmuBT}I44DO~Kgmkf0d9y0U(y|x<=jao_d^Kz z9xNjX2a&W4fuM{-K!(3XMEBkONq>A1p`zhGVS^a2*lH}9Hcgbn|m zba#NcvyO;A!^{KCgMDDPmXnF~uv(wdJrEWz3fiWF@o$e0Ho`V$6S%L4ZAWAufNAI7 zguQ@mxJLdNm|8}7Pe-AG*2clO9paCouOKy81_gFrIB7aN;tUxhgRR|3_3 zi9)V-setiEB5V)DQ$~KvdKXf@UYA1H!yusVE#b#M~vGeYv03~=t1M%;q(s+Z5m)|Ju2)$`XyNQFu{-H zN9h9}#~DcZs1Xh%A1S*2VEZ@NFuCBX5RTJadPdB-X7N4(4sI@^;#`+{(IFB~7$4Mx zxVEhP(HYn+b@p`fdCK8d)OrL1J#!YhANVYn85a<5_}~*y_D^;HZqFOAF@H(uJ6QKK z#YYWWKOSaR0~;<6JV@44`1Y`0OpedE_6+sDj)On4BiCC@Ul>w`xJ7aAQZ1})y0(J6 z@2hxlWDYq$(swJ-z@Y!(`vg5Dc47V|?+2wt?V`!`qdPIbko6-L(&rO3urYOOLKUe` zdp(0(58)u&I4x{wH0gL37VT>pP1d8-{dt#?53A`h)cZ%lj{tuaEIV;@Bzb?x&&_&q z5#|Xr)cO^Dt?1r4m^))Khip&mx4bnI7Fe95*0Xq|r)8Xk4IZ&sPY^fhpOteQR@*J2 z_TSd)Mp+VUlY56+pAypDymvgDVAs_^){}()S-E%*%(!i1OV)=tuI_X)1~yt4){*xI zxp}_YZLl(xXL=M?A2=8;hHWNW&)EeB_U{t0p45*qQtu0RidU|yVePzQoIu2B{HgPo zllnu=LrMNfKe(xR5iE7~oI~=pqQe867r?>xoHCMsXAdxZn*%c~J(sz_+WAV$>9B13 z5cgql%O?L7Q(&8qG^+t{?20BckL?(6Kv2QGF|e`IJ}SSm{^PrGILtG8vYT=L zD@&EJ_Ap~<%B;`CWiQ-qiF2DDlJ^OWCm$9JfYq5(f}X+_6{8OKg{l7^g3UG?LYXjY z$`C4_N^kLs=tIf}RR*6&oJ-b;k@7tj@=3mAn`mD`tO=u4kbKF_-@iB69yf8CCwYHR zRGe2yK2Pall@JVTJ}{0Bff@6w%1Hi{lDt^Lg4q=o)Oagk=jMUrctphusQKiSE}=a~ zz>4JMJ; zZ@pth>PO}_o4~;(2h%O#|JqYcr(VN*!`!`+B0bvImUeeX_k#6>r>OS}<-^~;?Fq|g z8tn=Y=eJ+H&;n+UO+J|ibDug?5o;G)1e5l>j{NQ%tm*#hSO(nM^`95HKB8XL1CPU! z64!wKFn3kLt-Y{_wBsiQ(O1U;J$bMF7nu|s)%m72=eq}_T^*Hhegp$m^} zPgY0WU!22BCr^WE-)m2h{bgIEWRm&mhTcik`J&dx!mKD4>Ufo`AymHSQ_~^Y9Obq8 zo`LRUd+r7Q+QEhen`6epE%(2l_>+$I);i|kXqfKmf1?Fv2`{`H1ILO7Wj4Y5cl!s8 zgY&;CZYkQ7_nFwTcj$OhfBos)T3B4~M14Nv!ZUs~O!v*6=7D(l@q4$Q z!vi7b_46=9#v)@FoEXh@}mcYL4KO|j;jZ<|S zg|P8Jo8~7_Oyd=$Zx0j3t`21`%?+9X7ANVKbY1UbuWK51n8eNw-4fobgqP~|=9BE@uAb!#@YJwy4J6Es+8(qRet{JJ;p4L{Sc zKTM}SWPc4^?w5apMQL9>55ej!%f`QlnZMFklk4M~dPne<#Pf$y?~kM*%kI8}4MR<3 zYY`Xz+q9mT8`JIT9N2o3GxrtD69p8{gjI#C(Qjb>r)|{xB*SH6S`8fh_a@f!m zmN*cm2R}SW-Vd=XRU7PKW=dEmG9PYnf2Uw9DIe!uc?ssv7|n2pc~^gb%7S(3^x@NC z?a33%(qNywtQH@b;r{aa8Mr#=Rh~aA$PH(ofR&@xnUeccTXbD|9M=1{4kY)l?tojp z3~qT*aAO6`Y`%Hq7+j~h7qJ?am+qW?1P*+DFf9!J?|nqT<-*2rSfsb2-cMLF0xcq7 zUQgF~o09f7;p@`x zUU0hBmujyWv##x!0JpNTF8@HB$Nlx*6_$2hIjIe%ZJxPtIP810KHLQDzua{?y+3U9 zPY{y+*l=a*#;?!NUPa7&;szUEI`QtnWe#7ZQ{mto$3hisbBSlR)J)%7-n2&=0(9g-}Ddx zOxqxtxdT>qs&bwSYd?(WxeM0T&TN|w>+g1<@=;^stVB1M!`*N`5phn!+ki2!o=jb5H^%9d`iYs28%(ZH(`&_rJG3p&P+Wl&4WXFeSTd6+pN9u zKmogXhC7v!^1UryrNh<&pYX?UuynEi2{>TGqWA}}y#C{;LvY-?4m}HC>A$nfcEbYa zX*aLJik0%}XjpXm9F_0Ok2su-fb$QOJkLO!pCL5c2nSE;aUdDy$zJ4#!byvgL*roG z__j^!VO`_r$y;F2`?#kea6mfaPXrunanFHR+$vZng85@2Mz4W+qx~+gf%RYF-mZpI zmOkkqfHl>h-UY)A>%zB?`$07B5ET!e$f}q|>NB#ok?r|j&SiVROnSK8I@p6<-fJ?f zAPpyM_UKII1X#Il_q`3U!8&g2I9N4l);bYvYcq&73{H6A*DQwl1E*5?nA)T1*>;%8 z=-Wc_W#O=U-N^ZBNSE~N0c)!qj~#&7v3=E@Nc|IYG)Lf;_yrNa@jPe)+a{cW^%WJP z$@60KD|%ZdobDJSX@pr5*#9oU;fyU*ej`YD{^bfBaAHOzc^+8;OX~WkXKh;x!&t?oX$mgKg?o+fIQwt>L#aVaD+%H#}jT>s{j+IQZD$j2SRP z;C_wdM_RXO@A$C#rFJ0I{*5?8J+GSLPM407@;TT3E=D|@bHU~a%qf08PzZB=_fI@b zoH$|fa#&ZpN}UKZj>T~TVV~1Ktq;KoB{QA{!Hk2k$~f3Y(B3x`Hay?LA?>wd?Ai_6 zU~Z_oW;4vadDUk(ELvFXyaCpS&Kq$6Rt;>vvzB;|=b!{Qwr{5ll27rnR;UwUlX8b8 zD`ECL57VQtbUJMYxv3CWmw%hRB3YIz- z?|DJuU4pvxfpsJMguQ`zpQg%4|1Mu^wz38mlK%mRmBLxG-^1y37O9mGh!J_oh>}HaW;Ok#ZIR8@I+D2IObKL5_u-S>&OM2Mo zqgyZl&W`SIo8&{1mq)esu;Z+}K2KnUq-ohmxS{x#?IV~sWyZo$aQQwbvl3V}J}iAS ztdSUg7QyQ4VHXe6OMz|DYaM38a-ZNJ87v#!GlKNL ztwD)SiLj))Io}tqSU=|20hrFnSs;K7?c6W#frFhN{~-Oj$vI26T`<#bTk|5AYo51$ zC(M!eX$*iHetT=T!HVmekBedb3qzkRFsq-sS_rFFPJOiork{*yCHaKl>vVk(Y;^m1 zYCT+T9&|boE}x8*`>-^yBu5ByLmPI5!;CFkG>c$SySdhpa6`JqmZcjCO$y%Tom;zp|agilBU_W)X4g zv-l(@Qa{_4T3=HgC%b45>uM9J^)*S>H-l_pk?<3>J&$W)(F5UxVV>&_pniaQR(wBL zJ%HA_mwf&y?@4`OS#>4;SR2nHM|*+^E1s>U_TT62Wjjk)(!0!q9G{zexOaDAV?CA6 zh4WgfJHwpnF~)U>+n($8umh}q_FPJ?kDh$54c5Oep{}=DooDv%4DLry{E9K+;gZ0F z7T7rc#LdaD^-s~puds|=Nabt!-TPf>fW=qDm)VG0Z233+157hraeOcwH9LD=J&8wV zTUn8K`OL#QSkq@^9vyZRb`5(2bJuCse8clgi$49R8s>XN{Hcd4DtYnju$^P`a)Ie*&o#UFaZ9v-72$@x}tojRGrj;tp^rZDyU88GA0$T8&l z@J?fa6e+(o+OaFFG0~@yd@f+6^H*{`W$iuh{)A~>x0}fIXASDOr3Q|3C{&UA#pZsu zq*t)|ZA*s%u(av+x#zHGN&iS2STe>f=qX%&^H;$TSixggmcg6@qbpdjywf&c(*N7m zvlB+Z>VhrRBp-}>(YtCC@$7^SB%e@ctavmU7H#hO=qwx+@0&Xg=4(7pr^2i!Nt%hU z>`%3A5-h}PwaKt>iD6zF(!Qgrv6VAUg0>^3+sE@3ozp4pevseCeD?et_JOkZ1dDI9U@+Ch0sVOHB!Mi{K^-zRH1 z%xUqa@-ao$vA3&XZigWw*CH<8AsD+6R`TV)R=~E;?B;KW^%oqUEQIU4Zcy!={PV`K z^WnHOSE_yFt3Q3419MM3nU#d{?5ox5y2balh{*}R! zk@-QkaMF-Ko5wIaVuNTfteRcAvKm$=KaTGQ2X?*^L&pF5g#}Ga*hFw{Q!T9NA?I7d z>6$^cWWK6$IPmGPdLfG=jQLEeo2b93!D&GN#s>!diL<{$H4D z@$W|m*kfNzVte$z2K}boe^1bUXs}`*Rtp#d{=jjXTx$Hz+Hq&}54iezgrOJWHay?I zjWExDL@3D*v{Cb!A7P7PzxQN3uSjkP{{R=MUa`o03#ZGiVfAqEi{wHwzUSxan%BJS+Ep%P9~C4`ar9L0EF)>F9kUd{)SpQy{6_dbTL7q6h^gE$SF|7hTlR_{wB zzoET9+`AAiIyqyxa36tm7AST?Dh4z8g-!Zu^Y$7sKUolgeeV z#OE?KA4aDY96tneUhLgK^1bq1K0gnV@_)L1Tn;OfBb0HlT%aGh3QlMz+r0tScI&&9 zaIrUEk9sWd4D-d-ioc%$;{RfaIUj&L{kwV3RKlhfp{;{>;yQaJhhUY9lQA z826BPku7nk$BmW5HdfQa5uN`OF54BdCC|u%{5kCLs9lFGu(+Gy zVG+z|w}0+dnC*GA>@pl{y||oM;h9Fugk7rEWfBVxDz=`4B?bL1MZ!XZ9Two@`sm8? zNd8QB9=C5J+|YUUL^403-X`h122SaFkvbkR{lyu7xZ=sEz9gR(Y%pEz3wxCA7_bJW z`G;D~gJtsF)cNF&y|dp37WBO~hFlM(c*sc}+`@LvC)bN!ad@T|ssF&?+k9BLbK?AI zFn7$!0W)DKO=vy^PTKa*&6AYRyRc&d%pNr4#}pFxyB5!Z9WUHtO@_sn?IyXxvY*C0 zcUTjnIxz-TTy1O|56iPFy@*vC>xPp2nl||7Hy7CByd^bXsm<*wCl)qL**XGo!9Bil zH0*mPJCi&gq8epqHk{OW@yZ|)$8z@(a7%Ok!+x-0bC#tOERPF+X9-JI(fc{VvCfs# z$b2OC{)4;3;k9A;{^M9&MqkPEz zMIC7isUz(}YYVmialY1z-w?azQpcNqeu5>*uX&xLrjA8<>x;+pHE?kHsU~v#4Vt?JV*GqmXB>V`hQgrD$6}GisSV{6@X;_NC2P~>O6dDKfKJvel>+d_c z%f=X(DSYEf?iY`j``x#~n$;3JK5V_Ac~Lk_8<-P17nW-6C8Yn6W(PMfg45Ze=0&ia zbER4c(?eISn@8%8Vr*OqCmo97ll;}V``WVAaCznN&ZNI%^|3atgPGGmdyxK7qjsk5 z|G@P-E|UIJq`SX%3*xz^si~y@Wp+Lz+X*Yi{<}o-S5=P6VJ}==QZ=3f>qPUd4!{-_ zV|_^eD)DoicNn&PztDR)tZrtGONOP-X5=ah5d;6Eoe=8^c^ou347cmRKI4lHcP z>$4noJ3C;a5>Dtha>+`VDLYwo5!RVcXbXbNmCQ{SV152r!y4G;CtHvO(+}N$5(=wH zKL#7#r*##>ju%H?ONE(hV&-mz&G>_ClVRGd!IQVcf!(`2NPzXD?vC6E>n{cL*awSU zy~FmvEkBD~6j)j!%xV_>y=Ct5tL8P!)D1q<%-Umt;KlNzE!Vd;jHNtv)x z`ti>$jhi?dh(xjD(s0E~Zz* z*~@b|4zT9b!oxba!mZ1WAuxMmE|sqok?-BWXiS@cHnEE~ytoF0rau#OXUTGxtnRn$B(jZ=Rf$uGl)&vKjwvn;Hs`$1XX zZ>T%0xUh5QEyS}qHqPT=0n)lFuwnK{9~W3$YGz1>86B^`9SH~LCEh;{i=zH?BNmYF zf5DE|9&dAo`BM)}Nra_Kznvl5x3TSbggj3!b0g~~!}QO$^EMOD;Zys=V3kwPC->N< z(_W-}Snz~(h|}_C>8HcWuRU_e^DZA2Z8Z;O?w3>Tg&-e2P}YT zd#fURV8-O?Gybsft-8(=uC`fLzXaxl+jro?W|xb%lJhJ1d?%6v>w7Lt3W8+=gA8mq z_SycYA+R=dOwMo;X9ru6>%&f)yOsr)GmiAy3JW{~Xno=AFnh=CurXrldJ9-Fc2emM zxLlR7odFkp@LLlLvo3WwWCn}Jwpft+hh{!$TN~PE&63g9M`6i4entx%cwv}%GR!TT zpYs(KR5ga3g4qsMl^@}{z>IGSIC$YYUmYwmH#T9@c$2-%Du*SXbW-YAj z_HIG}%=EI}bBEaMM$BE9KW=HmEm*#1d1x_AecuN5br@0mfYg5<)axqTpm~$|5Vkqf zDOw5BbJpZkz@iQpsr*-X@8ZE~Sn_(&AsOPL(^qEH!VF(^emvYdqi@qYm>C%t8V3jT z8@+=(?>4Vib4h+|`}}iv18lroLFEIi+nMLS!R02d(GiHV`{_%*!;E#tQQ@$~8Bf_y zSX5^BSp*kFD@U}F`nCVA2gAPIFMn!>_J_Ijv;7JXIK$4 z!!rO5JSx?7gIVvB&I@4r)+K!{;Q#U=b)vF|B`Lph2WJ_JdX76WwOOMyIGv z*04S+yTKbyD03Wd1FN?@&6x^^ta2$}!L0t7t*rtM( zW<%;vsc{|ym-o#*KLFO+PoudKo7_?LgI%sHJVn}b*_p#zEa9jnKMuH)`Ypr1c83io zrtc!{zfqcKPKU*VyE}1V_MkWMe;=d2I34oE1D4M8ulWq~YRSPOeh7C;Gk$=6POuEV*%?tGmJ)9Jc;C0xd@$s(VpcQ%|( zhB*~EODB^0U7D;9!A76!Ars*8>O$i#5?^uZA&11d-Im0_hOK?Kk>h3Zt=DdcW#4A^ z9R>5!SNsfv6+_bouwmmTOIj%0pfvB~NW8_-J{V?BI3HyTYyXXYFdr6W9<&$;i+2w6 zm<5;D_B_!arnNK~XTUx=Cnu2WD;mC~d@>v`wr!_5%$?ld*A*^%9qQ5@R&76SG8S$b zK9stD^vza}N5F3KO}jcGF5kIwG7Gluv^B9kY%IIKbP%j?zcA<@?oU}(mU;lprY&@6 zgXwbzhW3Y5g-#9Reirw5v6~6gm9suJ!outR_<<|5ua;hc?_rxgc|ZR?!u>pUu@`xs zI0tMkjj+qk?{pn(oMIf<46|;DU0;*9#rLlbuwmg~?kiZ5bwBeHT<&u5*>hO_`CCXG z%q|)e^c0pZJs0y97AiD_<*?}Eoc*t1#rclw?!&AtC)=LGfhN^q`LIOvr~5OQcJ|+J z(%*_xM+ZHHE3E3ul`uQ+VxK2)$};EEsieM5CgrGiO{RxnX5-vmq`cYTW`Pve)U-1v z+iN|kT)&gVPt5962|L~%PPMP}^c&~M=Ql8iOLEWxsmNT>`@EgfN5p{ zq<R&+y<>L>91wO>IO!`NpB=cPB3l5)^@RQ(cVp^4zMvN(`Pp+ZyNlaSX$X@(NS1d zQ}TEi%-ZhonIV8?@%9}#s|Jy(0PIfs! ztlZO(y%zC=k*l>Hu<#kzIug!~Pai_AH}h-2=smDN=Gk)wEFF4AdK@+@`D@^j_!U9t zbl4@`cO2QCV#Qu+zDQbLzMtev!k6c+lkr{t%BR%%Q!1;Y$oMVf$ZAIx;^N!MeHvk# zH|}{uiC;hQ`v$j!rB@Ds`B^#1WIjt(5kJ>DFmNq(SdH@TuKteoS)t|al$SMH{8xz}83e5(3Ve7+sbm>~5!hj`IlZTw$3+J^_D zv?pLzcb>LAR#K34vl#5dc#BIl#RJEms~%#3>SZ9N=*#Wb^t#5ab;E`!w! zw=oSc{c9vOe`V`hrujhP6O2np{}^Xa4|@YA9KUeL7S^Vo(LaaFH>IBL277$0-24ny z##HWT2j?&ES^gB(tcdvBiu*%)v`AYH2VWo7{X1M2KQ*Kb*7cSg*TW(4w1Y)3clJ)V zDmZF-<sj({{F7l0h0E-4#Ub_iXzvlqIT3 zQLv(Dgl!bro}4#hBZ-%pzKDdYcX$d!Fz>JbYcU+s=iQyPBtFpWeFWU{-P3j{%-w!$ z$|gA2v%7;Y%p%)?bwd^(nhpmCv@9k4tuRO8N34DSXC85Y-y|0vESY*{HtC;fbqP_E zVO7TC1Dj#V^WxbYnEN80wH4MmDsMT%(i2{V+hA#?YuQjz-dRo^zZq}e|6}U@<63(E z|A7x-NETr#3}F#QVNwjqq%sOav=T%~_jS8{{=DCw*L8Mw&ULQqI@fibb8Py;qMqx+_9LDmR{gMpg&}WRAA+^cy*Jy! zie5}#(!UTk-_S5&&5_LhS7^DT9@g$$*7+A~UJ!Mx64rR`e)tU*Rot;8{VV;a#GapE2D5+kXSiY9k1q|d zap3bF-(ZK$-A})R6}cW%{U@<81-^yNce_yaACI(iFe@-}3F+TCWp?aU539-oN7%s1 zLFa8=6Q6W>F%V|{;p`#lCFP-+WW3evzVZ!;GqM&^K+1^2e z#lCPj%$3aBD1hrQy)zP))t|iJ4f9ry{NMQfxU7chF!#GC#trefyy!R&SX_7IrzfmA z?e94e*7t~}>g$vP%N^Zd?S|*n{6qx%?zGV`&o%tzTBN71+A+u(76mV%+Q<9}`RY;d zf9*x|^&jOfFs-sf*L0*;uC-b*7FIqRd70c_miW!vH4bLnn^AfbPVj1$x|93~HCrCT zmZxu?n*z&jvzC+bO-|u7r>P`=`N@>;uxQ5PO`fodA3F9ITqAkinfza7lgyWn_E8s_ zrk)8mo3>o)0atQ%Y3IP|7x(N2!(sa!mjuG5^n-YT1dgv|wsINFo))>3%r^<@_p8H* zkK0xSz=q+gsq@EZ`rdU39R0e}{q=}*P1i<|`Kves<0P^9uSpUCSH%2l9|1F8&bC<( z+r*R?t%1d&x+P>hB%spSDHs;|lpc$Lbvre~gs>XZ%iCbp=5%WRIf9b@+u@|=x$VgL z(k#A6C-Y}X*N<-<5A%L|&QQQoFTwROFq5XF#$&|U9nXw}<;__((pN-X8_*f1{ky$O4J&)Kt){`!GtM>|SeCtZ z`j=Fc-x+hKYT*VEJR=4sR)kop@nPhK+~nC>QN{;(7qq_WPvL zBR}KCa2w*f73!13_I)qViCIZ@Cx{L6NB2_@|Nrg9WsF+73l_bqr?%&0CvFu7Yqn3o zr!rCg7cJ#(g-v@R{ zswRfP|D7N9_0ZrDm^HEOq)Uj~ytEGuhI!(Kv;x?8S2i^W)^rJ)O~$J;%ahoPVNULf zz;m$P=5OmoF!lW(*kE7(!55}SrBL(9vedaLKCtNQUqL$JO3VGNykP2i1>(+^3q4`H zx2_-+&fgyJ(E~Qvgj*43zL~h+4Q8Ykt|Q}-9=j4O#=y#Rlc@4+dGtW_Xp;Z`k54ia zw;LQ_=HIDQyrOGZq&-ZZyvFV*@=JPnc-X*(zndu20|wPtk^J!!i%DFyv)HOLtPFIg zZ2Hs9s{<@uh!;L1&K@Q%|91@kzcz^)A9Y(Y{KgO1WLx8O2yy-t>rLNb`3=8_WLQmG zw)hLo>fCq#0a)U#U;Y_ZcAEN`%$G;FFez)axu1yDduLaCK%Bp`BQ?KZzCG;dE4c3d zn1^wQ(@lvv&tdVwPSkj-@?P7OPhrt`o4gGq|INcUt6=JVDX@2&Bav8=dlF_kbYvCl_RpDCDkf*-=_y)LiF_^f~L^`jra^zM#LtKo*wu!Hwu z(VWxN{5`i^r0%Ttm>vD`CV@{ zko7Dj_JY&rMN z)bof)Z|^HU9ZAxU>X{e~(~hs*vlcdP*)d=%Y^5FaeMZ{rbpKI=fiqe1&gfLZq7|s&xEyfcl%_Rv0s?v1*;rCQR|~Zc0OM` z1(r*n#P37g@UZKri7+&LR09X}{ir z`RA8V<%u)t*sfBN{_n{7q`XzO51CjD^KPp<_`n`{#?RMb?U|Y&Z#XmRulg!XJCoJJ z3l7oloqiG4d2KLyz;Ua3)t)2y-L`+70Bgkh*BV&yKzNd@_oC(1>CeDo@|-ToKiFH7 zP4biPcfu`oySrz>hSELuWc^lM#)By*U`DC%eqWgNZSJiMSh#b^GkaJl>6V=abK1op z>IE|+4vbL2+@Z6m`8H+O6t`nAuWl4IABNFQrlL;v<2}B@_Wi^ z{q2WvFpH!-+Wo~tm7w7!=jt= z@F2wHedHy%uwm$o<8$E%bFV0Jex?1IZjk=1F!5&uu|XU_t*>hCX{63SBgpySRK&x6 zT&K#1K(WKd4Q}*tI#0?E^}QOnri&N*0!*9P>A4H+QrXg=CENQMLd~ZcWs3?6Vd{G` zh{yFve_8}#T1a{2HP&JUBl`Q!Q*Y^Dy~Tib#9^Z@jv?h=z2~;o5V$6` zHkGl2vOs{0-mEc^L=%`;f2 zRnD-7?Uz2}e1zr8o?Ph;yZy~v`HiH{4!0%qc@YDiHm%jEt_ zP%`o?X)lC_EF8)Bq+-U<%vEsR+KdH9VZrr1LyBQ`;4!MbR`1MzMb_KYe@v##SrV-z^O-f}-{zzsJ@4SXlp;9d zheA%aCr>&&s}S}WI%OYe-&xr|=UjrdW<_tR{T@Dc$|+d(E_U-y#AUxGEh6K&#%J2c z)c(&s*Es_&adq55+IP-9t*wgq=Iu-IaPwT{yF;*{a?_1i*f60@_dRf?(|ziADE+gj z^-bnK!2`A;{@;2)WnZr-Vy5L=+hs`K@+HTE8~sbqd?Nr2OzBbNyb!!bJ<6N%_K4uGTN$lHK?7=E0o4+fLnw z4WIAPNdJTW`K07F?0ur~(hQhk6ETpC&kA1D*iMJV(N^2Y_*~BQY2zlthUc3nlJPml zfI|_a|Do91+I$_RZD~Ky9oEq}eBzRmexh-(`A$~zHJDo(C2)rE6wV(q{>PfhqWUW= zi|*8TAnn3H>UuNj`cgK=N4$1GdghBG%LQLZ5;|= z#v89@a=kOmM`Zc1f63*BF0e`8c0U=PJ0xkUdi%4dYSZJlwQ)wd5E+3^1Oec zzF~iFf1Hdrv+Z7OCiRhSU0_BIEc^L;2(e+b{%$&~O6vRM2h41Jsbn9VRJHEucbKuP z3pIXR^Xpm%V!Fd3YCM?PP<5dRmRQfE#(TL{M#{9>4&MWjza^Jd--x*Gx_A6UShCl- zkn)~&H#u44?fSp)zRR4}QkTvK9 z+;H&*rz7Hul){uMxP-H&i5yRblcCrEv*+aXV8A->8Cxshl>Ke2y28ecm9FKmo2`SN~DT_=vgHNV-uz1YVS)Q;e@}E!+ zYuPtflk#majJp~KE7!R_^?_w8zdYPR;!4f5*)X48a&RM@xzuIXTv#$U>U1Qmd385* z5vpRRu2XAO7$;0K4uzqF5ll_$fe5Vvf|sv(~hZxU)+D<3&PEzWAiBaB; z?Fn;y-e!~fisiUJ&I1;d22%B@!q9H?6j-8c9#86H;p(dI?r(^-z$?w@eEfsOuHeEzNShwc%t8`eCK7LehILEbUWG*b+ z`@_FG+%T$~x?k0v>Sxyl4qKcq)+5e2I6i;|TZ$I1u7(+f7t_plP#=$XUGoZ77!(o{ zY(X>LYJ&M^KK6JF({?A}@4OqBBb+{v}s2{xLDqQ*E zJeQ15=q3i&7ZTU)+QNWECvP?tz!CojE6MnVsPCbRXW^W~wWm)`X3fw#*T|>rexMf2b?yz}L zHJw;KXluW5u*g1iDcQdK%N#QYZu#NlF%p*LWrPog|Jy(H>@ya!mnxcP6(+pBQPFvb812F~7F1nOK-})*Im+J&(z5LfzL`E>`*?3{FO3Gtcyx`%M(aIR+ntckd~ zwhHE_j-}cQ!I~=T8d$`5^NRGZ3QuPzz(NZuO#XJ`+bvkXj@;{zzh6Yhv3P?M$-S&UtC3h1&ch(Z;<{U=k&LRM=-0~pM&jSx*>ej zO;}~oQA2}epYt=X!IC{4ss5B?$;!e)*x}dCtK|Mv%s$_ z#!DoR4|gZOXZCoRn~b<&#%1dFWJ{a(9e~x5Mk;-@wk1^o>o^8&f&o(#7@6ii_lOthszf@}bx`Pw@ts@RtME$?Yy}d83g6XY3QpY1l6vhpK zc|L=M z`0xsM8ce_N)Oa3_C_QTD0ZZ3TiM{|c{Yp;ZC8s*&;$FpCSX5^7#tr6)FP|-fGiPtT z!-bp8tnno<->*xM3v6f!>2wdSQGRJ24r{rmRy`;2Q`>`wz^2zt_ddfe`wveY3@aRp zKmUdes}?#8gjpZC%yuZxEhEC(_lL#WF@HP3mV;>_ELe2c@rxBqZ+9}o4rad1OzR9A zdOjJ&B=I+^=aTe|3H41qVabntHgWwA-&Z||nKu@6gpK;Cwl=W(BJF$!xb8wqaA(+1 z_qJPmIC|a8qSnMh?W0z(u*$#u-%*tB>LJ@{Fmw0M-+y4%5U)|hyrZk)O|ZG+jku5a zKP*Kct&Z4XegL`tlm{p5u7x$mKGrW_wcWKjkBK)A9{LoPOm*?P2aCI`J5~wPn#LTx z3DcY(UcLi2x7|6o5N0NLye)=X?r+b_g;l$Z0&=}JOSRihz~+T3&s>2u2mCK&z=rr8 z^ND3`KKD5e%i90yK+d1K)?>L6=6!Vwz6=Lcx~Cq5jgtbbh(qMxb&0TI{Bc=6T;o4> z&@R{@PD?xsGu+*yw!$o-B~_jUb)Skiz-r;ww8MyNqyJ>Bhxxy|FHDA+mt?~tVVcdp zP6y$Z0sT$OVZnsUALMXYpQ9(1z-+4*XSczPy^!_@j)*mB%n z)*ZWy~u99I`#c*$1YV zozjekjl)|XEBJxbI60cQtf_alp?yZ3h7JM_`@hu@2o}VL)m{65Qgdzh?>8)#^HLhjSQW&$cjccI>=J zxbo1Yt*u~LR>tlXFm2(t-oKCF{G^;6CWKWHSD!b*^3{&ZX22niXMD^sV|ViD@vuWr z*B2jPP4gaS4s8Fxec&6IxjtFnkEEZv^yF)j{>Ou1ePCls-|eqpRYLfoUL^l7zwUJ; z{hFqru5iVeTOF!k>PZ+ra$uf@77iMC=*+qa4?>)4WsnVT0|4>0e-*-u>Pd!_@P} zaF}l6ha0f8*GBqHSe^gu!BvGH%pTY4^DEfNC44)vaMH|a#5pxxDYM^XdlH*U3Y*1< z>nf+;djjiNv0p?m?eWs@cVS6-Q(7RbD9ZnJhr~^t;{#xBSYVq9*nFq_$UKsMQl3~3 zi$7X*o(1!G8+zX)aSRa5faxRtZYY5nOFgo^V1deFBXP;0fs>|^^ao;w6vOoO$)hL0 zy0~2)S76be^}6veclp(nBG_S5^$}NCS%VKP!#w|Ci^srni#xPJI6ru{kXX4g0DC!dRqBZO^4X z;_zUY`rZt-C!=?2GXU1NENG{N>o3_bSTL>azQq^ek_A;IyQGtknW*u2>55V2{e+W_L`$<+bBVdcsEo+N+fQ}Lyr@PFHL2zY^a z$)i4PQSr$BsK-4#KrF6yUrwCU=Do)cSZkecMfP9*<$Plk++2R2`hVuKodsrCIpo&1EEoUck7`-W+K#m`)R6-*11Q|G(o=&Hd_V16gx26FxtVddP1 zB>jg6!*9UeYt~Bd!rHeV?~w9iO6_vG0@gJ~Q02{ndA9ZztRC8{R~h0-#XQ$?SamzG zh?GwUVgCCv*gW0N@)j%^5>6{7=`;E64`ANmI?Z+1lr)a>2v(((T3>?&|59#N!8LBf z23&?|s%aN$VeY))94)MS>ZY!POL|pv3t)q(_{R%4WK~#IE^MxSw(~8_`g?JP8Wztf z5B~_W2gK2`U>RA50jJ!V+@KcJnF|F}TB2Uhm;VYESgL7(0J><$v2`sY{&*z%>laWl*|Np5z78)7?pZXoGP z7;0Nmzii1VS_89Ym$6BGl6x8t7d3!4vQOLAdO!}dWv;Q!he&5xghEn!W^TZ^hl{_4=opGwq! zZB~vT?G>{^di5J@>}#u`p*_n*XgFRl$OoDh3yJIpSJY z35%E5y_gK=7Z>?Fg!Q@~gFRthOV8vAn5VP$o&_hExCe?!{N%NBBG|mKWAJ5|wfSr0 zYFO^#Rdx}UWN*^0g)6QVs@b2WxY_6(_^G^_D3+V0qqG{&85H*Sh02nBS4L z@g%IuOmUOJf||Ge)UeiNf!$`9YpjSQPCA#F7zK+Z%%)VBzlJT8ko-et=nuk9X;mA= zu>5Us%TAJi=f*E0m@$iI+zfN2?uNy%`rDFyo8T6kx5faNUbNFo3bU=R9hnE~*QBip zhZ6(|0kdIR|50;;VW*k{D`vuV%O8g>f{SM6&Yw>5H#KzjhD#Rju=614ll`xcfmP`p z^CrTYUv(!OVHfU6=6LwO{!-Y5B|o?@FD!E<191!c<1>kcW4>)}MtO@<4t(Vd8w7`T zy@XZPW!*=?#*faep2FgQgSSS&oO#z*-GGzkc|05j3l7&!IRRVj8uf>m*5h1a63iJ@ z+dLfBL@TKNjy5f>k>oehg2%5yJZ$QQJ{(w*vfqyMZ#?36ULn7yNxr;dCLGa&+1Ul= zKY#1y35&ufY#ReN-)Xu(7Ph>gzdsf>1;!N*hQ+*Ei7SZ@m>AR(u8SEdc7uiVhS)A7 zzkl8fcUYOnD5b#`rGtGZ!^SZ7&qmZ|OlDmu$f7k#URxM2y!t6R5s(p~&R30Pc!*I8M zE@{6j*NrF$flcl^D$c^0e)-L$Jc$-nOgIgjy01DA2AjW%)mgAGV^csltTS#6${?0E zTwV`T<8`p^ZqD3|aPu$UeM;CeWbW)uFfIA;!9y^8Vc~j< zd<87=Z8PBjTsN!F(;cvzY)R!ISh{FjQXE_p^!Y&=Ec^ASV+?G+pk>}En0kH{c9|S_ zDjyatux?%jYm8g(UW0Y_<9aNI`Tgp>Zo!6qNAE3xnePU$AHc%0oEj0VIQvEU2-A!i|G>bRq<)iCtGpJ%>{$k?K4fWQPYYoig<{cnl0UB}eICs0 zSNXjSsc#q7<`Hx5Mx}Lzwdll>^ajQ4psuiPa_~Q5W%HJocCg{i7vBYN*y{RFQlBgC zy>ub_$FkPc41(3NpAUm!CpXWt9GJ@LA+T(qGu4jD;Iy@#>kdIb>YgR@gq|Gu7UyPB&z4fty>cES-(GCSBHU zGc3+Za-Bn*kl1Y#ti9hmWj+64}GzdMn2RFl%FnE4HvUKdOTCKiJhty?VhKcSCV9iGQuy+#N3Q zPa1p-=5B~>-3j(svZHeb-0ao=z@IXd2PbpJNtp5D&}}m;J$|kB49tE~_24PooclCd zL-G$O$u+DZd&29ZQlm40bR7kN0OjDh> zln(Qc&z;}~(-T`-k^Y%!=nji9u(qLrdl;5?Sh>_0PVkuQPWo%c>fhH#!YvKYv&iw2 zFV@vNlKj3&MO1%njD4E{aMCaD2Rjkhrr2z@g;`lO!{xA8O@H1K&VPTf^LALWP|5EG z7cJQF-Gm8MBVTyz#p4 zEV8|ij6;bq|GYv=>JK4Hb|V({Xn%Ecci8ayj&Bs4bJ@Dg3RX|;;3kG;)%p`u|L;+s zC4sQE?I})2#5G+xG4qLqx$f;@`nMxTXTt$^4yRhcQXX^J3|J@;#@C@0PY^!~74oZhS?Y6*+d9HOv@uILb`w z&(*z0cYwK!&)43;n$Hv0|0>1y6-`yIU{#O!CNmsX{d%{Nq~AOKKpkwFchG2n&1bv{ zpTHGAw|U-%<=@vQ-i1~FTE3UV(w^+(3OLU9&Ac+0nb7S=IjmRZ_qqYoY2}us#3Ryn z6v1pq&8i!)Lzh<#T38s^r_XgbXVB}2Gq9#1Zp0N>akgNLn#5B^a4*9N{*m3YVA`C% z3B>Y~8~T&}BkgJr%3@`+nrhEHs-24vFWNJ=@EGDum({Ukdm2WOF&S14a2$FSP6|9R zE(ta!jiAh2+sTHsmyBaesr39ljuwfqDTDq|i}Vc6`~y2-e2q4(05;zBw<7H=<6&$` zF6_~RerXjfm{TK2huPIpsueJu*Ec&2<|rd|@F6#MrS5KI(>9Fk-EOFc(+!@wbYGO{q9@Cfo zY7a|}W>aqYoEc~VGX}};leo?B9q(I_{EON~o+0VKy`cX-gyXq)4|P1E|EzfU4Hos7 z9hQf<>G4j(dlGlsb@43RvN^%#HB9rxI!(B~!{W!!VX>hy_dM*+yVhug6))DG%7^XK zv*$d9)${IB$Di+$n(&aM7g|u~r(||a&-<{fgB?{KbfbJ2<*+h_WnF^&9+k(1H%MI8 z?0yHZh<% z#9T)!Bhrg^*|%ho^if$?$@Ubg1t*Td#y33n16VVso_iRkz5L8Ez*PGO>knRdP4*}A ztMSJ!lHRs6b^UM(&RmLzB_me-)+26?AF7waCfz43`9Jx4rnQ8@EYUFP|1ny_^|6bH*DULFaUe;*;X)I+K61R; zeCiL^IZT~>0TxYaabUsC1p}z_ZD})GWDoyWUU=U#A=!3o?O5w}SCi|gx&SzGRuUWZwl z^;CY*x{+tDz+C@!)bAxty5HjxT=yaL)^?=VOb@(x7UoUoQ~M{X_{BN{n`giOmxy>} z>wt+_uu*+;ToOz_$mHpKplONL$czj}}cv)rdeE8&_=a|(~ajBmMJkHG;iuB9D@ z|EpgDMy-mCg?Zj>gk^}ETbCzofoUh6^&sU@9(N!y3O4voWxj*`Wd_GJu;j9rq7fFo zh?^J&>z7RZ-5Tvh+_#yHD`0J}GyB@Z5l@4kEQ8gPX05Y=C50V&2E&RoELBf9di}H8 zfw0bf`xtxJt-$mALRhjgeYpc{p zHB(@xt8w+7u&zT%)jZhEqix|OAN#KN<=GShp`Ph@2+hFaJlb>8+S$gPC(w?)p zv%V7ZOMPrpVgC=$#<|11ITKRP!MrUcb9u0-Af8qXYX*G2JsDPC&2_j7oA=J|;{#Lg zCxKh0*NA4pb&+oVe_)YakI1>OY_I#ZPUt_`JS{uB6jr7F#7GN{$AUp?SHi5`Pv%dD z>)1VJtb-Y%E(e3*%Cex*QdqIuP&e;c-W+S-Pg-}mR5XRyh6>-{}2_u>wxPq6aBx2=gJ-ltt~ z6Rgd!8@~${@Ylt(M*q4dW(`$-IQ|!XI>Um67LjCs>waiBgJJEnM^t&ytM)|=fn}Sv zj0#6wmR5Gd3D!(%(~s1`MB>pb$?t{e31p4!*s!CJnoPdC`aN~2}Lf<3_noneW;=x8>~`@GtwHOb$lm!k$|Z{$60If(jc zZtECgmZZ&%AF$|Ru>%ray!U+V zam15$G;Gf#`5)H(Isofi%U`9#B7VifeXyuY#hesa@lZkCuX3#XhV6&V_bcNg5I0wE zr|pE9ZPKax{Q!r3kL9rBMd2oL|7hlVZPg()>Fy$JN5Y&35#uZ z=_kOiwm#qM+-Tb+GW2Vdo(}lA1ZH>ln)wD+Jp9IA3ezLw z-JZa4e(jq`dUoZmXQgnygd0Un&1b{Rc;JeOZHAnT?n(cGCHq@8-24w7s0~mZ{{w7l{Z4WEhawrW7jO0!z=Dd;N$xWj22EVdl?2`7SVPJMWPYmK$0|IKk#=1^fMB;o%Xzd&45LU&~CG z=N|rt0n0e^I{3g+#zqG^oNzu-IUN?9sGCgg|MJHb*bIg=Mc z?&k!Jea20OxjP(u$o-q{)1mL~ur6L(@DPr;8?kjfEc<)4nB32~{dPXfA^GJ#YEr+7 ze{@IQW5_qeb%bf2-B+E0BNV^( z|4Bl5=sw;#6Be#Iscwd;@m!L>tLW@^;^hysQ()y4f73U(IXkr7QCJf)eBBq~>zdF* zuv7NLBPN)=c9LKhENr%0Q3p%jJ*C0Z05b*l|{%>11<7U{huXM{dShsvdk2sh=v|q&sn5EjF-U-`$x;D8U zR&=bLum_gWHZQFq`S)AK9Dt2B7iQmqd3I~5^5*b&w~e0owO?sA;;h8A$VID_I z^6zmA(!tCjZEs(OBPuWTDT1}ifD1ZU*E~~x5jH+^d|6Ih&1;hf(+ba|RKfJa>HBhE z-NB=OU&Hp_SbK;Cw!sW?y_&B+3_JsKrn3f+@tlZ7HDRY<$pLx`)qlC<-8&m*+VwStapvQ&@KbyW882!X&%1Yw2)s$+9A$1 zExL9HW`0{ijc@VV9PCNVo}T=6CgSz3;fs&J9M0mkD`Cn0`;DovZ2cyyt#E$X=L2c5 zse8xAi7-E=$l?SnzL({847T*MaL$C;%f3bxz@~cx8&t4ni|zOlxIVXn+8?grTG~T6 z!ehlj^7|%z7mIpWwcq~qewaUIM%fqG;l;`~d*J`Z7unfO8+O66ltBT#(SFF}W1nw_ zrEfiCq&<@K311xxOX~mqA?;aFhvnyDh)bQjlKx7~DBo_IU`E6Ts(&IEsuxDUf~w{2 zN)q3Zxql5TyuYA@^e0$L8}6)v>DQN1^Yxb7k39}07Ragf3{K+*uUrnBGrE4gNAkxx z(0pyWInd;>V;P` zU~b-<|E(_>v0XVGmTc_wZYtu6f+r8B!iFP{yOa4E`%fK~OokcfJ@(Is!+yuICXoE+ zlc@D9bjN;vZX|#A8xzA27j;NjGa5E$@Bb15r`+J2<-l@zW&Unha3!T?5Zt_lZq9_0 zEc-1P2#c7R&&d3$@qzc(ez3BkvseQs>@yVE!n(43{dKVZ+V5>Puz5YEQ{jgFi@#dH z9B$LrdN|4O=lHMvINp=aCVhk5rge<{0UJHJPssdh#5f<*H&`~*%K9(tvf;wL&#*GY z|5IDcpHb}r{NMVcmgnQ2y@W*$xi9SyFY%CUtbrND?E(kEVOQ?ndjQiG9_&Tt>yj3_ zUAzlxtetBe!85d9N%ibsayk7}huSpYID>dW>GLgZZEOQ}eCS=Q_2z3XA9? zKlmXowF@FnxU^3L|2MzLShm$O7v|sn@rTSeI&@xm z|1`{0?(_+PHI~fA3|K?H(*euw6|78w4SkMcKL&WzhHA17Wl7{JV{?V*7*{_AqTCvp<=? zj)&J4ia`MU7oy2$5F17Lp9s$Y{}#>RAoBdlEbhs{H^gU~&*Hm~`&xUDR9Ot!znbqQ%b6}%xx41PNHmUmSTv$3fs{9}7M~hyL zwF^mnw`lAySbD=Cze{R&ve5LcOCr(rX$guy~-#rt~LyKJf^9A@c{zaH`W%Uk+V?qdHz0&BZH8gLkK)twb9$$YJgYv=Hz#G2St zGT+Ldb^k#cEb}o?_vg)DHeEjfQ{T@(T&n-+dJ>lRQpS<{{|dg^QVknUl|LZ!bCtG| zd}5|+c5X+Q*Y)@qV%odjVeR00YYS_VpJkZsP42%UvZAQ%%RHCUU%}G3S3YMVu9_`A zRtdY^HJXXpWn&i+d%xB6BKyM%D5SUC4MxPTpf`|Jhyl(z9WuVaL1L~>!JCo1Z zE>SS+t(GZ7ykVAu1*tD(dC%_q!y(zvUXuFKaCyXIKiGdUvwZ+;9&6{}3+vP~l7+DL z(y|mEI7vTrxF0N78^b4du{+p(CafQ{fwIsedc8NS@ZLnZp~sM4lVI_HB&z?#6D2Um zll&DC!^!WpOg{h21s3LyD4z@a_ivgq5~hVzQ~OsrBkI-&SX$d-2-)ApamQnw{!f0% zX3Z&5KZwYC|4IJ0|Gtv?Rh!mtEOF!7_B30PegXHD0QMf$Yq1rqTDFtgA7Q(VxoxD`3s< z%fW|X>5e-`B49<(_3&hv&6e(z!sgUfdHZ1M{gQBmg)uo1mKoMk;|GoDbs4*0k^heE z35Zu1wHxDM=}14jqa;5gVwMbURt0HD`FDA=d2tN!j7}rZ!x}*iH=20R#OMpKo6k&^ z6qZ+Ar{=%KKf@SnVfMEh)b-_*yKr+DtaEL7ZbW+NgD-EFll<2*L@#0c#XlQEVA`Ao zpG;)?>E|8>lX%*#xQ}G}@(x`BVd0L)9lpc)b~(Na;JUb|C3MsWb?JY0&V^-FW7qeD zD|Rtf`N8Hw>D6Iy^Qg;We^|PS{+INx9n#At5cBCfoqb69S!<`whJ|mR44DagZ)&_F zfEg>io|3qvSMUXISlPqr6v>|ya(nL#SbcVVt_Q4IyGc16=5%aLZQsB4ehME}{e9yx z9dUcMYL+K#wjSDl7F=VryFCrojvYq1q>Jt*NiY5I^)XowA+g=Pnru(?ChfU5tS`EJ ziTs|7acws-*V<{353C6Cx+{P)`$p|1`)3N6?im0JWQtAX_Y9hc)Ow7BGyCOJNcw_P z*EW*)(Z2Q*VUbs#ySw1}&`ra}!_@nHVb;-mIU`}tpwP|5uyoS81}9i`FnrS!VwSBF z8)nD!e)$I0C9PcUK&<($Y9jvdLTn3jP1C9M;e1z4W)E1qIc$Glw5Q^f3q!iYCaVY3 zdQN{vD31X%f7DRzyCLDr!%nc~NTBazq^G^gV>cD(;7C;(H1U&1xE^| z{Z`=oZQU0Z2^)472Q|SGn|mW-N&2x`A`?CnUee_a2X7-Vd9Z<*@Ag%jZq7wCs^<2`nzk{%V2# zSlG6z%~xT`>3PenVc`|?YAsCnJUGT4HuX3WRRHr=wYkHBD`yog*T9^+Hub|{#oVPQ zPs4J*gO=l94;!8KB*|}VH<<@}k4so}g5>`fVI=*<6wh`;)8OVE)j?#vpWs})DFrsY zcc=RMh8hlA35&WoQ2l+UYejkmtb8&4=}M%}iFmO!5!P<(+(JLue;RqKHNNUb---c7?01L!^RWqR?H-RF88P=+xxME zJqZ@oI;J+jEW3;OE-;&Z$D#!ecocWW31)bPRJFzPHPSebe)h0bH>kk|rY%v&c7Zt~ zr!iP?#FmUgOIYC-lGh*BdqU_r#aLQlBao}cp^)?b?F;R|baTc3CWbN)RZNS-HBhkPoz z3G*C1CI!QarQtV=U~Nm(mgO+xWz&2u%=i-9XC+)S!piF$%vqJQD zcEJpBkYN)X_P2A?R@fAFt}F&-zi@Hi1j~!=uOnu-?AyNvX1Y2`qv52W?VVS^%>`Z~ zH^7oT9~(np)<^A&C|GTHKQtJY&heYDp2SyQ`4~W~o;PD1EbYIZF$Xr9d~TBe>tJ)Y zZZdI6M8COk&9*~Tt|WbVWVRQq)ZWM$2`eK%yl{v0M)#G&Nc=>K#Td9bKCYVsjIZSu z^@Vx5&XWGHM09j7c^)gSInt{iEFbbd<1Z;culVH*52=7`r|9PUvblCKF z@#uT7N4C=-0W97;!lDdLS@rzAA8cUuURnb4d9KgN`IV_Kg13g35!mMc@98bdz zVYvm-u&Ffuj0)zs`<#%$g7Z;!N8y$?=d%vL?9DA3<6(Q{t*ObdG-5%*R#@QEqEN!J z#mf(GCh>x*Td8oJQ$OnYDcT_W1{Ey4n!Y_8aj9dr#R=kzd1scv7M|YUNck7IPKXbH zjaC<^>w~^#zcqOtpy9~Bb>#XnN+ze1_NjjDyIAV_IyCF?7&wGK!Q%?z^wvlDe{NgWYZ=-*3ZL48J&^Nn}uxew^pjxB~C%lFgK8|tZc@k|PNBRL4WKS=>33Gp@JoyUK@}Cu-hZ8z?T>S&4-d_MGJyQq# zhS{#I6A#0Zk7LBGN&PlxHIJF8ta!Kha7U7U z&S7;J;tsTrXRTm~f=;dHWiAnn?*Y^1^&d#qf0}KM4YY%~TVijM`C(pr`Q$#ZVO}XU zKc~#rCbHnVk?ZD?`8k)YhfV`XywtC-5&gxQUdffjGz+!gYgonpe@xwdT#MiT2k=n{ zVG)K@h=yb+CectSg;5A$2#YWzLzqPCMlGTtT1ir2bYm12A&M3uiiR+RA&SE9b&hX8 zpFf|E_c`Y}*SW6iTzBU>S1{WOX3ZLqdJnFfH7%9cvfq?9}UJSKU1Rpgp-kZo8b|+HVVOn_%IAYZJ!6`F>^3e!@KR z-y7IFJuB!NtTpOzbr_s-eu&*?Skimd4GtW|KG>0*Ph`{7w}-;Q;}>t%!lI)m2M>Xn zx8Kit3k!bss2U76If}F1ko^0{N{MUU7JqyRvz8q^Xbop351CH(x3r-x@AZT$9v5D` z2m2h}5z`HhOWt)*12Z>`m7BusKRch^h6TH#>GS=Dktd%Nz{**cKTHrebsDwhBHUno zZYFtuDD2XuOAag@;7Z3k82ZfgWU#a)AuPhGiRqwgEeunVlw{2s>^X}SGb|(039FVx#>vqSQ5|9zmbo4 zV4!CP>2IZXMr|bTZ)E$v*N=dCfk9R0N&X{xThf20x%YdO17`}Z>j%R{>)N?y!AjSG z0yDTGW&UI`9wpeftlW> znWWc`YYeG}#oK*ReweuCHZSz>G&BRzgqcWIPKBvgJgZ|s&fbIf_?UH z^d|3njr;WA?tm-4uby-R_TKNmek-g$vFmjaEIM?mViU~RJSCdE-{K@~JQN5Ud3GIn z8+Q9~#dtMbl=<)}aYc`ZpH{%aQ8~kk;fA{lcP@i{KA07i!U5;<;}*kdPhT&-4;S6- zWI6|C#E+Q!05+^t38uq(&%=81{w&4Tqs3%cx*=i13)tq1#WiPG=bY0<2m7t5N}2$( zV?WJ!2Wx|>PCF5AO8-Fiw}4wmp4q|LjnVXdxn0VRivvk~&i2GVh$}@`Ee61PuHg#< z?Ej|sOy>24d3QqT`*+U3VWt+a+mDIoIwD@wt5c#GEDk?O$7lOp9`4fv=8yS4oQ1fZ z?VSQsSQDD2Hisq8ryXnu>t`IN{e@~ke0xJ!9o5Hs6ynO64Q34364M`&@#cnu%Cmp& z!SPeEfsRM72$`|&JFF{6sPjZT&Gv!gCs;kU_=o@w&9pe9g9X)%bbL}kP3@A`u;sXw zwEvX3=l6dOOD3Fg3P<{U=ashAuqLg3AnAWYXC1y&3A5{eo<0CqdH%Tj2zFyN4M>KS z&li5sz>Jq&=<|y5_M$e$un&J)of>h^#Lkm$!1{PhX9k-cp7!zziBD;_j*M@Tu6MP* z2y4UgU%i8cQ}12Of!WoAJCgBD@`CBRP80W<%p%Vtwf4#CbeJ1bM8`KJ{9c@x1`9je z)|eo!>fNDM0V^vX&o+kzceSSu!}R|Yu=?J|oBLt4yT>wHIBmWoPFlf|&~?Iz<{uvwew1{+|@ zf}-zpVY|)jZp&f8heuym!$yaUPA`J>PkPYjy;X@J3j`#8t5ro}#N8ZY$IpjZu36`{ zlJfR<`r`vzD$?oqB(k&ZM$UpOoa5>E9)0$w175`c8!sg;`E2e6D<*my%aC7u`o%#f zSUbYy&tAB}G{}Jq%VmYhWIR<}yU_ziz^Zl&j_!w9y$vflFzap5Wa6U4L}wdV)bBC< zJ}KbBiq_V|x*jLU_#;`deV!F;dEA4RFD~9=U=H(6F3TXx3-If7w;L>Jv|F_YX1t7v z>lx>Oh*zhAMLQ58(wP!l+2B+?tc%J6O=>7QKU(D887 zTF$A58)ro;uyHk1+db>A)hmDSu(!J6O~8h$I`fc_JH2Y_z4*_ym|Wvc*RoEcQtrPM!~z z|1Kzb4YMLEJCp03Cgz?UHE{FuMo;rLPFh&?vp;>FZS>}5?`JUY%ME(G8~0OgtAzC% zBJm9u`akbGqblI$>z~3_kDMRC98q@1Nr+eNkL!1zxZ5T6csT0V$<`WJIU)QVxgJvO zSU2%1EH_L}wSq(2uaCO`H{_NFkp4@gx>1?~i!YaNBI5_jQ({81;G&pi4|~9F1rd2l znE(8@XJk+@+u6>We%=m6}{DN?f?M?+r|G(=K z$=mmfjuQ8{vZfvKr!`fV%3(p*lxxKDIPUW}Sfad1<2>WlTciJzpY!cx4=Jp<*M2!! zzUJGgQ#)aHzeNj3|1ICp_;v$qy2Wq>H$G|*(kGmW zvJk-BdD*o8wyWM!I2+cP%;;c;c*(fJiBsXU7r*w9@n7mIjFXdL#`p0nU0~1Svuj*o z@$gkAC&RM5u`bTAURyq#KCkXu_R0~q9AhS3ig?APURl_LyC!gAN!@1?LbuIJJbFmJG;e+(Sl)FXid3(Wmp$n$8kqy>*` zV4rK-re(rBgPQq+N%|`HH7eM~YX7odr=7mi4MNdEdn8u<0TxkN^D+eIMoha$d(luzXlYiwg_!{{^Eq&l*;o@&e>dKb*P~!ZQD`GsU*^h$DU)FKJA+|PFyDXu7xI2A>V8N% z4^~!9uvtvfzgcw39k!G%9T5fx+h5x30jsAjrr&4k!-tqpA?Z(64?B&xH^RB)O%ZKxiGk2ea>3oy0 z@9y=*xv=*0qgQueNnCMc9xU-&TTb4utGyiFlm1A!J$l(AINK`gJ=L$4L47UYCc!e8GA;KA=WA zm`~2n>~D{4;t>~|G8yOyvr-jw`_**of7%&tV9%n@V-lXYS9`+3duC@gA-(a%5lz!z zrm?#)2*#=9DXC{DDIBaD7v_J|6GsnEnfVF}-@AksRPafYq2kUM=vLO4fU&oD0$@Qn$%aM-9P~F)R zd(#JPEPhsB@QXT2d_FsMBlZ`9xww~(0IxyPenaFfB4JtV!4LkG4i z%yXPT%k%lwNaqufbto@!JVECx;6I&C z>(A=6{12HAs%Gu=oevP_4BT*(%x}PGW5K)yvz2G*d{S{96(7meLelJ^Hj50~A)fH?E)sIU}RkX!bdv@d7z)B$AvhN??tzt6xr z>+843d<~+T#rCIQR{No=(qZk$;!P)D#gdEp|5bH@66*;fkI1bbg4E#vyCT{vvy{=3Xk|bs^g`r7-vE9W{kGfMt{VK!- z=Kb@=!%{;hI{!oYnN;yu*yy{5$QN<>`Ga5WV2x%d9sgByG$?W?@!5L1{SxZy+y=wE zOi}S9q;GgEdD{=>yLX`Pm-vFZKHXu3*=IVxhp{SYbtjm!FnkpmkEWCAKalI;itit3 zT)$z`$Uo?hn$yRX^y^#)H^+TK#*BUs>kNVf4cadF7B7^LSJ^czaXYc-#5m@{kg z$@{R-_iYA;BF=Da-xvfJ1qF5{=PT_k3;I01M6knRE3BSkXAy#U#p+?~Ein6aDV-0^ zty^jZIsZvU_NU|9m|4@FZ-k3tUeoREeE;^*^(6gA8@GC&uBg@@3W2Z3y$);L9Y)jWla-W;QfYa_E_#D#Pw0j zr<=gS^uz8GVMg7n-7HvJbiO6Io^iYKGQb=bd|f5Ys1@Twf`|m$q|*Q;w8fCf8#{#?I3{V4pK{4w%CX)3Ae!V80)q z|MY+>PDRuAS-x2%5#3>W|ADw5x7&>_u;uM>iR66{V@rO639RLP9ZlXJnbpl7%!J+S zD$u0vT#2v zX?ix=09N}HyB~tBxHcR9M54Y9`F)SUwe5S0-xIf(vyjY3Y4`2x=-05)@5_kCa7D)} zjar!dWXZJGa2(T|K`a||!Bq$A6OZq!gqaZwZhnTn_r$k&Nb(C65r1IEm8&#&VD_3* zVO=o)qxhiwz)e{1*Z)#aV)ib>t8m)Vc}BhAT4%|aD==2fuNN#`*L(FPSeEVVPtv;y z(py}BHN{>#O<|e5bJclRyybUfXE^Zm{O;Ls(bN}o{>lWS!^@Sh53A=f1H@GhU(aWf z{IOy0n^3<{wZt?7=H0x){{j1bWab=$xkqN$*OGY3wH65^{i|(seps{qOUK8<`uFb# zR3XkdyD&c*W**m_*1##_q#lv5Tl`?tJ21y!_l*!(q1{90k2M;gJh%zw=NRe>5ci## zsR@F$!+q=2u;gnG)jC*~eV5KJo0;q79RLds9H;YB%1VyL2w{%dh+H!NWbNreoJFv- z#%B$ge^hMIShD~YpHy3r`DXd-E1Eg5WllDopR&-{a}7rEZpv8Najm5+Z)r<9o9C)_@0ED>f@|kVNr_31v1~PVQUwTGwil+E1i#3 zZy0!m2XnJ+>3o-JgT@0RVg1}ygOiZnG+_T%JL1n9sC|YPgbag)lJv;b#Q;pY2)wW8jAK z*E)QIiw=vPM8OQZHibG^^i${-2D8FjHNJu+pPN$1@u-~OB6tC7!b<1tBlmfGsU6rr(3Lhu&Fih3k0FlW)OB2M)5y`{>XG8YY?xxe(FU8%u8NR-=E9WJv#1yxeL~;Bk!9% zSvTiyg572slnP<}3eJQLB>m1Y*2JtGtKY9A9v&X$2QxxG)h>d?y~0u#z`TCbZVF(1 z+_m5HVae|elKHTu^_DiiaG;9cejY67aXG*TF1&ubXckFN?$2Ry$3!o0xS~g~!V^|= zOvmuy24}-S(%%N##T872xs2QPghTYGFsdTxH{q!~zW8L@TGTv&Z%D;ssk9g*H{|V+Wv$GeypD^ljslXJb|0_kj zT=3v*N0{L?^k)t!|8mMQV_1JWF`c}xkJ@@|R%=+Yy)ln`PazC1+xjaU+rxX^CNkft z4Rf$-J*+$x8Fmt`iTyc|m@{tsZepWOomPH?RpC3SqZaI5_y!j32&7g{Yw*&-|34le zPn%i^E5hpPN%<_E-LHpm+UE4a)3BY3c=3H$;_PTj>XQ;%vc42n*$ruv1=nVcpH~bw zua8Y^_<@@+{l6LFoH?e0uEFAsUGHCpS+UQbsbP-T!@K|%jPJSSA}kN-=R?LbG&F`B z%z|0ot*l6U7G3Vc%!Jbf+nq@Jd%pirmI}MgbEEg`dih45&A>6bqp5I ze>X)BOQRFb6JbkJDV@(Rbl8Gl2{3z9%XN*2b8gqpISdOgH~RgC4g0O^9uKQpuUy{| z`$PUXj=^476Iivw9BvwM=~x7;ZQ$)B_j9bG4?#O%pG^1lZm`?i*bkdvU7tL9{ahG- z?anG##`*nHh`8Ne$4GzJa^$W7Rvr#4$O`oMDJ(Rh6CG9fpv%9*lk66T`$>69;|nJ zNx!eK(qHqigH^m!r4qz_n*!uEu%hGyEiZe=`R@H-dBnF{hbJ*B)|B=SXMv8)iT**vON8g`pIPcpJ7qyPYaU1 zGPa-z+pF;G+k<^!Eyty%4vxDsYpfMqRJrlUXSi$baVQ!|+d-9vwTpj{jPRxq0 zMqH8|#N@(^4Y@Ytc~jIX%|R!a)yIa8r|~gawZ#<{UD`{ZX9c%f=sgWKnsbfLC+WE4 zq1p>pA87Y5os`#(k+%R=hREpnn!x936Cuo+8^e|(9yrsynr?sHjHmly>8kck0f@^z z5+;!67i6HxYS{8!3Vl9jWRzsO7WV0Wf)0`LJE<2N&fqPm(vm2WAI^Y$wMLCvuaI7aXPRBUh00S%X$jg$r$RE+xSY zxt&VMc+!gYhB@STWb;CzCc)m9ayFeH`7=^d$@eL`3`=@^^Y6%QN5X2W((*GT{-m_y zAXx5^>_Ykj;Tan`Un#ef#g%Nh=$<3}o}||4bWIK{H)=od9iGpo?9(|CTjFi_V>oW{ z<r_Tda$!e*Z6oPlbiwa@j>8F_`)88% zWrdXGN5gh^7fy|Xg-=F&BlEj8?A*8^60R6%Fr0jEui`FyN!nW?ZA>KZ|LQL6&kcrk zA1?i!Z|`Mr_sy_oxsQ3tRf$3an$cF zBS`;%wWa9=8CP;Ako-fo(fRiRpYJ;305fAY(C6vGkA9QvVa=lTbpAiHwT!bISorM{ zoiEUMLj4$Ab^T_eYt+J={5&Eti_=of-+P(K5llF2O zIjb8J7Or_0B85xJ7xe1@8$BCO-ycSuJS}Yt>qK>2G9Jn=`Y1I&z;-mTba`?2)`(Yx zrL9)L+1rM1{vk#G(sv}C-_g6BL)drX@vCbuA{%v#aXjErY<-nOaY0L=K*_Hrfcy`h8sepu}3HCF}eBct>7 z!TPD^=zJPcUv70FmPfDS97bGyg;y907v0+UU;}KLv%!p5)MqRGekp1>{}8c{ZseYY zh^K^veUZTpEeF&2AuXG(O(vF1-f@<^?+NWQxPJ`HoZe_a-sdwd4m{WcbLKb6JHY|_ zwGI)m8>1h6|0H7``xHhz%5Lciyl)CMFjMS?@${hmewcBAH)kizzZY1)4mR@I7q=Zw zd){uFH{2BeM!pT!>n`?khCPojnzb2b&mZ!=KU{7z_54QI=g9pQWIUCff7#d|n7@A6 z>y~i9sZZb6!AkSg`(H7Bq>%64xE7XNH8y@j%qovw1vktKJXcP}mqdl92w{Cw<ciNJOJK!-lXSdJLeZ@$3t?e*$y)OLc~tha{xe}!@!*zZegutEzVURp;+ci$0Gw}K z`e_QxePLL(5BAOelQW5=C*N&(d3v}Z zEdOOo=i@6WI_lIAhW%k~e_Hr~@A3dh+$J!`SK7%Xr&Loog2Af*USoav#rqAzk z4_4e7ue}c&{kpuJY#*PdzT&&EtZFx%UnBHqulypodAyC~!>5OCz~YpXbbO87*7N-f z;fmtrbbMjLv(}5$Fms#twQbn`ff+B}UxKyex##0xzKO2kBCL3_$3GQjt-m)%1&c!K zR$PERw-}u}PvS*xcW%MX<~L97BzP(q#lIl|iB%ZKh^{`l2G{&6HM;mx!9k9%#g;6Zh zn^p>T1;S#t<21kMdxXbol0HX9%hy|ujP!>E&JomB7t^o#!p!-tXnuX4frn?nl2vO} zF({AywKH!r%-EL8B=+sz;>$R=f>}(@zot(xu@NkPS#*XhU&@?QIuPb+U()d^c6$c( z=mG1O)h|s(-0XzTts|WFsC&dInAz4`@Mky9hs*`^d(NUa2TJe3irgQAA0l26UiwuH zt3tmYsDZ7{M0dLYOHDSGyoXadwCW)O8fsp?UKq6lAl>V zn2G*Z*Ve;P0&`DA(Ed4lR=}xku#7)#SvSN@OXgW@g*o;Y4ST?~?y0dsupl6;I~%TB zbNG)4E>fA?Bj4+47EN>tfECAk`;CR8CXV;?C-EhDGsyK6M`d$r8LaUL`ru3~InP@R zGhH$VPln~k&3i3^#ZmjW6W1~gvgQ%r^rqkc*e!SVp9Zrl-hP~oxSRDX|KYIDH;X~! zeW6`~9d{7i@c0{jUndL;E$j`e)061+XDDl>FAEmGKJbWqA5^|ky~-3872NK-8?IS* z^KyGwJ8``^xqfv#-eFiPlD<`G*KnBa7196qF0}WQt{oy_Gwb5`dbsG|G@m_iNz;ms zdRSt7^EwO3KF7xHJS;PRN8fKZMK0GU;fAI68%UggZ+q4$SX_I6zOO9_O9@Da)pB3A6vP9b zbns4v8K-;D`4O1@3lkDyc}2jRRK#o7d5({VnZktOxG;%kre{iWR1983X`lD^IB;T0cU-(S!+GWds(kImIj1j^?YB>!Ild-F(**IRd)JMN4UBE*$EzN?P1C0xigax zXDE)?~fvcXn_^bMQ5V&B1>h}~{0;DFmbC$03;%QT3YE^La{vP0HCdr1~p4QX(G7OnlOETf2yM}c9gfu=SVHc^N4J5{Zz6(vV|5;dc!iCrFlwN znm+b78&+D*j=KO065i4KYvbPAuNS~vslleXi1VvQ39iG!pD`oI{k%MLR?%%(#TKpz zg;NIiTymGBzp-^Wc|UBJG^P`AgP3njp8u6U?0Bdcb{llQ=OtM6AtbsGW}ZFvxENNN z)g-E6{hEzk?!m0(-G^R+g_+6IAHhtW*WdbuJxn0)*Q6hYTbw89s~6Rh@5gFNFJCzW zYscK9aU17OjTx|yQJ0qFd8K2FrgsX7o7ZaYz}#O;ib?ya`Ja|vfh%kSzsO*ntt$B} zER0+?G7@GlFz%ZMYhTX1yNh_v$~>}uX%}zg?0_vBBy_%tl6_yDx53)6r|I($Nkr|I zEwJR;d-}X1^hSA=7{=oh`hO+Io}KK8Rk?jmO+|Tu&No|cf;rm?=>L^M9fqfGhSi5` zKMzLSS5UQxEKg>%D(EM+Z$K~U{1DjfPAGjoAvFr4F7i9{o;;sObBkE99dTi&ls#m; zNr}aQjlnSIPSTO{aNG(%8&ZDr@hnV9&k(^ryX@(Bjq-$+ZvtRe_rUw4zt10#@o5FD zJeNZM*P(dV^U*R`IH28JGG583OG)h_ST_67v0iY3=<8b_Sl#kwA2L5f+KvxHykLE= zY4rX_pwAuPK|E%&H<{l?a-4V36}Ft&a`b&zY@J%+1hY5a{Xp)gqUQEJFa~D$+tB%O zN}|+!TbOsi_D>1o@@@r5{oyp*+cbSu-LM1Zuy(Ke(Ne?}uZNj;fu%MrvTwtj(tC03 z;iA@mvWVH?r!3mSJ|Cv2Zowr{yFM~twP)~J^8HP~y4MBuJ8?YS^q_HeV-~fxbS||z zc-zHKh_kxzsaX!=Qs4c@g*$o*Uy=M#QA|?4Ps6r-m9W&=mHuC#V)N}ix8Sr!J^g=; z*6nTlC75sDTXz}hd5ygmoQFAAuG9bFs7ohg9)}qZgn{P}R|od(a10h4i5_wWPKi9w zA_3;@mC*kWI9~MqZ~#_DdsLA7FZ~&tmGQ8?QcTCU8Qz*Oo>+4H`AYIVvb3A<8cCn_ z@N|wptjfBijD;ERtA~^Ce++$wo*{NyIE;P|UU4Tw4Hu@`M& zj@PsEF|dLc<7ELe8b9nIW_t5>Ghp_PZcJijw-`EJmfJsdp$xWs=yr_E-^Z!%{+zg| zymQMMSZ^?Q7+F4lVIKWnJ@8uIZ?Zgf+}G=5yts_9L9-7QRi@spg4G6{wj6 z!kKs6LP-0G*C*5Y|4h}Dv(CWUBgdCrMBFF&n=Tus|4WDYIY$+FBz;Eby-L_Ir0>fs zux35i;}q<>x?$ion4>k#Oo!zznICV!inRym^}OiBxHiSGXfpeL65<-K%w_lBqOI}2 zryHM8&Ws=w3G$xPf)FkXSgiPm9Sg@A{H}5?D0b-ggGf7o4W)tG-|Bw~)j+ zr|9<4f9<32hlSe&^m_p#&N(>g0h&Q7~r@Gm&iH=JT^` zbIT5CaGZVM{6xgr%*3E$ur}JBo_`hH6L*sKb+#^hor*YbdNG~Ptm(aZ895)RM9ec} z`NQ-&`DH!(mJG{>)kb4ZPeoi)wrT`%nzoGz2{*%yMQlIItUFFHLrzY5Lb>GxvgCby*LU?0f`dqc!S=iQ*!M}iaW z=Qra1m~X%16FI*!%PQ#iS*-hBXG!}QIrgQ`Gx9qo)gC70Ikj0;gY+o}*PSNUGaT14 z+iJLcn{8n%thtyUQw2+vuaje7o!gw+Cvd1^_=7zpy<5K|G9M-L=9RT!uplpaWf|<< z^L#Bi|Ee41^m`))yQGwye+5VL>HWR#+>zKInC%#|h|IU=_%kik7gpUfuS|iB6pMzs z!%T;{5B9=Bk0dh(Sn;8>=T4Zlg}aFh`xHm^+X2UQtW=GF1rvQ=k>?v`lSZ}}0*gbX z85`g<&BU64B>$;3#_NeY%>3OC=4>nOy#}_L`jyic)*D}^7MI@6vw)SuZ~pb>H9I@O z4L)anEJ6CXLleRcVfoZYSIB&c+1CcXYXPf6T|~Zc=!inEUpU^2R?ndSA5hF#t@s8D zJ?)>(LR@s!i1rU$|6+%kB>k{eY;t~*8TWVcCh;DU_HSVMv)b3hvb}xAk@J^o3ZP0T&^8BdAzs>hJSlZ&}{q1lPsQ)VHV&}t$T0^}|CE(_`_ZsvuSPnbB*SC)VSCtUMNMKP z;!R5n#@NF0%;2VQ*m-HBelV<$*iYx9RJrL|uwiD5(woE``zmw$lk{B!Y5J-++mbC| z^(FI$2*mA9o((aD*}R)yN%>}WJDoehMYoSnih{)}95Y(LI^Hi@AJ5|%FY7{Z{B=yH z^>t2&m)62`zBZ)KUU2yFOPJxpqR$7j-;FhX0XMIoS?3!{z;-0B~W4?2K&w83( zeD*n=|6gctOXF$t3meG)8!EbvA4$^tT;)IA4|9ez(DbtFukMoh<(tm!@+(A~Bl;u? zgzb{IMH5TJd(Ef7QJN2fNPT3}w>%mT`|Z*VBi1GFPo)lPOY5g~V()T7oTVQ}m&dxs z^cxF1FZ3{`^?hF3;%g!H=ilLUdAza@sdr$-dnvU~ocoJRShqm=`Wnio--p4WZ&s;D zedzlW*vIKwK8fobO?NxMRo8dZ{F=MT8wbI@>=R6qUKg}tPW?4p-+ekptvc>9;~s1_ z^&~a>^D^#Hxb~as@A6`7+J(S&{#qd^pWkxd8fVz}<#`&{&dGV#0#^2}qH&IG)U6j+ z(Lc|srdG7n{7QgR+`o<^`HQ@Y_HTyW4v$!M6=uX{&9#OZY~%ODEV0g^7aXv*TLH0j z$h*r41-M^Q`F$#Y(;S{H*$7)*=l39S%X!^i&w-m-MXV)p`n|mx*Q*uNX`H$Fn~Me( z3)6d(I6I>nUqIn_iq51i3Y*i?1s48NeN`i_i~W7BEgW}z30)q~cImS}S8%>^y>^Sl z>Gy|l)U$;&z2wb|um`a8-TA-ye-CjXww(UegQVv*XezScv`4u#uKpF+A{4G0ag~lMD8Gd3lT!!(#ydYcdK(VheuL)Md)+@W5iTsuq084QM=lr)H?I%E+;~7ON*aQ>GMbpO*&emeLu+StdL@TF zJvPzprFz`I)iAjEcoocWZ}^e*FV4HT;%*h(yuYL+&1ew{+bq~j^A|Op^z?*PaXab$ zl6E&kU2_iWvv@z<|HLB$Ra}^JaP&mde<_7`$4;mkn z=v#1hMj|cGQj=kL6>cy~{d@d0CM6`op^p1#{qzImc3a`v2eaw=$}JnBCcuWDqiI}v zBzWMfQ#f8OD`M5pSAaXJ*3a2^V&Y{ky$uU(ad{`z5WR?Zdk6GD@9^`XyYT>#O|McSIN*yrP)y z&*B?CpX}i#o;xjH>e}g0Uzop}OZP90UOfMM#=q^M>Ez;G4%e;TLHAG2)^=`Tux5TW z-5)=7A$tzoloC+YE+ z_Cxdi)=Bhl!-{D7hQ{h4>9A2!IW13lJ$lJTxaRg}y1)9EhZ)U)o0q4r-EH3oPQeX1 z+5U>}=N8^h`;4y>or`YJN}>ibCeTT!UAVoTE0LwyTuT=xxTVFmh)P{ z3FbC5&R$YI^g#;xW6wYJq&}MNeTJNY>wfm5_2Ic^jM)VT$_?o9@T16MtKh2CsRKxQ z@$LhR;c)0o4XtmH*;jq}v47=@ddQ1|VeennwEvcdfBLZi&Tl`ME?+ix>YZM&p-NA; zN80lpQMt)j-n(Zsy<4x-E?IEgu9kH96{A=EN`M_twxjJ?v8L$uFjfa~qWX~eoLoUy8qE)&Fw3${Qh|!+`K;+{qTIy z0#?^`r1fc7{Y-v1@!$HY$1l4%5pLd}MGbpuJ{?7WapNIvZ>h_lt*LO+D>iLU@saaO zJz$RYFS@>5n=wiHBj~SNG*VmcjkxOp%M#50?r%RPXx=5D|G$5g9#2Ap<1Po_;9)~( zd0MZ}ce)YpH2T|K@m2F)9!C4G_Mq*fsu@uh4QCDypyi7?nOqXU&F!sYxgH^|Y8Xh% zV@&UtY6Y|ITT^p7d1&q&!u9^C{?u&E)3Vh6n64k(+@5Niq^$LDb9ubmyjq+8q?a!n z=2dhM=Qs5LS{^e$B5phET{xcBH%;xRWWfU0Y4muixO_3_s2s=BfW`O7_T#ksF<~TJ z*JTm4oOxp|j%wZ)m2kX|(* zE;R_QdvcXpabtL3A6VQegF%;P;r8d|eq6siJVCAL_2KavIIhbLn!X}((Z}8}zj8jU zk9t(tc>}m9GlK4KX&3Hy&WS^No;^~}Gfo=*f{-1?-!&Bw1oW`2ALY&K~ZJs)v=A5Y`MMXYl)y*|ssp%=`&zLB0k z-1eUo{)+t+^LoEl<|f`C1mN@64s=Ll!Sv zu>%g(y3=@CDS!NY*!vobUav44T^~EZ&aFLZTrh8!v^EC!Pa)3KMt5?OGvVg^-1p^M zw!qEH)8!p`!T(Qs^<+ctAXst7jplc|AU0}@{#PGa{pk8a*zBNyuAeY0{GAuv+#alR z?xTjn&E<<*4qWkY&%g52GqtyN!&W_}(DLQ8gEo7>&OX+(Jro^0mJNiP>npN)6jmOE z`{#!}==y5b`z( zbNQ+frW|Y7am~cP?Uno3q%IQM`)yBJe#6E11*NdU!s_q#BHIn-?(a32T>lCOWqi$q zoAX{jm;aC7}cF3N4oVJ2e~-M;eJHcETAdHoc%pO0I?&D&SxlseHA zHrzIlE?@b=%(glL$79B#zwK||(C;qXTwnEy3k8?p=JijL+&UBm%g*M}^%GgI+eqwI z++<7Y&pz9x%_g{cc}8Cx^-JM~#$I&&)eDpFjDVZ#hnM+m3zC0)`*@O{HOFO9XSlh3 zYWpi6ioe_vVqI{kEZ!`yytJtN&5e7A1z}?Wk=Z8cLH5MWrXYP3hBS~VQ&z$zYPmq2GIJj zd|iT-|A`wdGPo59H?N=g!%GV>+`NA=t5_ZUVa9$JTE1oOjU)45%|=sNf6etTGAme? zJ)G8$yKqO02`v1!lJ+lB|DZE3!*G85U{B+EhYgGJ;O65;H>HOl4c75QG(CHp;8Pf! zFp@`?r&%&^^hUV3JXzAj@*|P_6N~u4s52NU@YZUzd<*|*Nm%)WA_R#wY z**lGFJe=RpiPlFq-8gb6+`NA(cA50+@SpsO{Jw#2cKxd_`;rmA3bs44h1O3u$@@nR z?AvGO-})}p2*cs#`UqC_69vK*54O>`eDpBSp)m8NoL;Z%+WNWnhQ)&Uf7fT{F~QSN z9Ph85A0qv?;C1L2;^z7bj})9e4;#8f{=Gl?=Km%XZmy4P-rGt69JOiv-|aK#tq}*- zu1=%pleEm=bK1eOffwlYj662hr+z2S2W%T!KasQ>>nrRXv6-5Au~#?Z=K3f8^W`L4WU_O4D;EJGx}S z+}#W5`fGljjW`DLlMc}KVLw?tBM5F@f353ti+TSsgYoRdG`P7vM0RIViJQw;jA$pf zf$JV0q~)M$M|qyf_}#>vR6z zuN~}eX$YenYPYMedKGWS@x5^q-5%=V* z@;F^SYxLK+`Ec|8BI{RBs<%cZuwW9 zFyh6f3vhFJT>syLH^a^CQ{=UUZvv}#ocz1|(?c77ZN~N~Jo>jiTD$B{ft%M?^)1+b z7Tnw(4Oa8?pEsfZ`;|n~D+W!Imcp#}V`%@v9not_?tjdj5r1npYZa+!qwtL3H%pQSs|Fm3uF7QPV_Q%%isrh#v#74ucb4O_V2!55E6~NikH`2JK zvu4&9xcPj+I@+SDE!h$|8Gl55m0kmh}2S*>(j(i z*la-K0dl_Qo$6*#vmX7a-ScHH!n}MVZ@Y$a8&D#v^-Wu z-Mgu(>2SzC3Q|z9iVPQ#CDLY*HCE@jvFhHR#>A z_TTbkOYNnlaKOrUYEnN%^ypGKT;=hLmd}55uU-gycdeswS=)dg<6%L33%dS3QypGe zz$tTI(&ZVw-rwz`=-={L1tZVjg8d|~XqWBU3+7p^y&E7j=>jLyYMnmcT-N5O-v~C{y$Lpi16*i^Ai7T?}XnplI3)`Oe z!TxDBQ$mh^H|C=NnJ4c5tPVoc?sJpby$(LE9C?Yy*eRL9p?**b5h7-G|nxJ>l#w4{3R#PUl+qSfYK+ zlS0V-u6)7N93!}f(`G(dp4_S1MgutEp;=pE#>Dd4-o3GYAGkSW`NANxA;Ws2zkg{o zg2Xlbt{N=sf&DG8$(NYjHoVUu7S>PMljct=bZs}fGq&&2Zq!B&!dx!g99IuB_ioR` z@mW+x>&K63?AjWZ#oVQ?SkyJ}X(z0Y^KELk!c@6Kd$j-5pLBUXj*(^C+u?fqNDo@R zZcgfqhBmk#?pjadx+w!92Eja&@sXtd`jOthrLEDwEm+opn7e(2Z9BNgIFhy(x5HP- z69XKtCrjUu_SA8Bm(Agld<``>`r3efEwO)@7MPIs_{cayAUtiI$^!^_wo=}+-qJY)@J5}9b8Qt#BQH{JMp5lTk=z3Sp58kX*kSivH6K9Y}NlKcOxuwPd(Khb{;(Jv@b0Bqxd)yj(XhLVj|4nWwO*C zE*~<&YZT0k_||$a%pO;5FMh)1&W|0cVC^K& zZ*9>&p)U+vG_b7x`>!^ze&^{2S7A~69KWYXU)O!q=W{T}_rR=uu(Ioi*~dx#hl&2S zuy>E^1CGF&?#$I+e_{Cx{GP|a%5eY00+`u&X|sgHyWqz>FmGg0MIbCw78{1bGEoh zKN8u)ytaiW7a-0a=Ik>JR*3FC?g4ZA`)DjkJWe%2)#SvpD+xc=4d%bx;v|H*M(Yes zVM*|#>@Kh|dqzS>SaT$8>Cr|fo^NXP`%bWB=7a8zuv<$$jVoJE3hM+Lo*s0DLCQN* z=Un#Fi6@);vanHt^rmym3Sno~xQF$yDB;rKOEAOE;OZAxaBlR?EZFy2#LSPd;^o$g zGqC4+cFAj4k{P}#iFi!b!WXdS-dgtq#J3agRKc9q&jX`i8~=%nN?5)5>yU7m&zshv z0%rK#{|}`9NQ^#IEj}etve6PCRkVxf-^TZ?#zOD9*eVj!#+o5mSmFr zr6bz4gAISL&QB%rVFmtmKhPhH>+mTBW+a6S{sdRe8I^Ms<`0Q@@&qpOeawx6nI9NA z`LGcqa#s{AIK6*E3e4PEUl&Z`P6;12z-G@f+KFLha6sfzxQ^>Fc|ELGdmo+(r{ulP zS`AyqT%I=)uB{sWVihdvbhP&{m_PIAEq@ZVors>ft4M*5hHlLB@<* zaP82hsIer!O^+cNu;{MgAqQCSH0Hxz*r+L~giG?rK6|ng=51P#F_NV3Z}ws}%=o%z zEeBSwzB+CMTp`S#Zbjk`A1A-~j`rWTJk|oXWF(s>wqP1U}?tgq`NS0+CqK>tW32} zFM(xOs`nSc4PI95Z^M%ErUj?re4g}G5pm&>7sud=3j`gZ1v1+dt?v9U{&J$ z^DuK=+YdZAd*+m7ImAJy%0|F(c{A3ZBQCpa(H9m@Su^VlEJz=@v@_gv`^MbUu%xf{ zZU?w#PlxbRFyC*2wk=%yLH;TO<}J%hZwWUvtv!(nt7rKyuKkMhSNHYV$4LG|&g1@% zse2ELsr&!OKT(9#bU={|iX==#QEDV9g`uQKMx_H`R8$m0Nr*<0qB4;VBqK!>O%$Cl zIw*-IDj^x9GrrHY-@p6%eg1r0&(~}3wf9*?Dw0_6IY;h2%?~wD%T8}0m&JmYUwb|`0lIuF#GeteW`H9 zCr%@i)YG-%6XB2-SM2mi{fMTDP`GyS@m6h^c4$VmH_WRs8<+wMH-!Z`!h)7(*R^2& zp_Pl)!yeOgH&2G?mx6z-hO<70TpbTHb{pO>f=ixzSu4Wqr&BbwVD(co4M)MER(9(+{raD?1Z^}Lz^GNYK5taUtw{~m%<`ADzeT06D+}h+{=UQ z_SvjyhXp4#3=_h_i$dKtm@`wiQ=6Gjy5bW{KbZi6Z?^HDLfrFZJAHIgg?8kwd zVg4S!#9ElG^SRUp&gxKoSxvk`P0biqdnBqC!>r<4Dmt)Bk8aLOSn}@ozj3g@y4Syg z?b#lf^Sb5|CV-0aa8Uzl_9_VXf=SDE2&&R{$(;)p_6n0@2e zBGT{UG`4_vpzyvPtbH&t@g6K%aKUm6?7J*?b}r0uKf?Unh50nT>2{mc=f?&-h52b= z-?Cw;-(Zaprl~IO$%45~s~yk5yxYr$Ux)eo1c5=Y=-Ra}*I-^ZuWu#na>T3rD$KH_ z)y#q&v}a_bklcOP9|gECUU}4ISo*InwCNkJUrDR;5==Ye?VS$u-~SRMz~Zm~+v9NS zj5#CXNc~?oqwTQ#lBofFm~&}Sz9DSwZMWnkEDAsHQl7ZiI3t|&FIijA-ihA>m1?ZR zFyn|@TRAMY5+;U{{A1$AC$Qmy;B^OKsp4zB2e5VH`lcY5li;Cr6OMgMYugV?l%z3N zV9hn0CA(l=$k+G-utWBu)mvfy`mfbiaJr>W5^^A^nvbo?e1Qb}lSkn9VF$9}Rm)W**drnU`iyj)W66kB*oI3*7Y%9fMEu-VEmfBRKylBeEr*rEsv4&VMn`dvcCnJU0EFQw+Au=e|ZiKAfpk;&9}q7EqX zM!?d+&Jr@-rv8#hS(qP9_c#UTe_5_HoaEM9HlKzqztH9nhGISywRb1N4tuZXNnvi2 z>b1LY$m`(1uQ2Bbd*m}%)9pxj2h1Dx?yVT^v!7M+5f%pc#7W?)e>cuF!8Fw~5xsEP z`}HsCVR6X(gb|oO#<8BUHL$eBNOl~VU&UV63YasQJKq52SSVUNgLy>}_ZGvV!w-%; zhS_(r4y=Uj#+o?i!^}8mXE!*=`ZNB=AhJFYn&VPPEDVpTT(_A9qkP&I_5itJZLE~XqawU;Nxmc&3m=l|MfQMWVcrhgij*@L2 z6-xTa_aLkpv$e$!77y<1^MJEv4z1b^Gp0GzI>R*XLg5b5f8R%b4eW5);FA|D-5_3I z371q`S9rjZpT9rMgPpFXzU2@v&B#@SgU(jPZh^TjUcP@YKUq50rfh~;Gdn9k!PaGE z4I5#p!`T$N(E znJ{z4Tk|AXLCpIxmH4G@Q!wn|-}svW3q=ci{9!eNg(D}!?6B}Zu5cf3<q9Hb}wt54I2&%>>f+<8Piy^VEOjlv&X;^PxS%@?2#SY zIuho5pHnvx4k$XKI}E19tUEIS7PYRn9SA}HElbMUTkg;Afkh`KEm9}_CVDf!!~C~R zPqg69b;EnwNuDM%OdFPL9kcR1>DRaT)+AGJeU!1F{BJ;D|J$CGTprS zDNJ*pF@xm3f%Q5?Fu&fIx*qw3ClBYs^zs$`I^_5{wLfmcoE%N+dh+Mg>E3`@G9xs} zc!G8d8||kHh@s`?e{~P7wqv{l{=YO>1oghIO;;TtqLq&xA0Ll+?nUSS{{~; zkS;5OxqRn>VKBGe@5U3D^=!kxp@W!@rYBcQVY@mx`9YXJC1%xASZedbyAPH$yr=rv zt6!J@hIw`Uk_zN5)0I8H!`xHb0$;+u3k}`dVSzC8QX|Z+pZZDyGmdWcYKNP`8r$E( z62{q6KS=&lxc3dr?*2Np2M)>+RKJF~IVb1?Fg^Wpa}6vQ$hb8GYpXtZEQWb=#-#s+ zHD8Uict-l8?5X?9s9hKO1ZHq2R*u5`bb1>5p@`)9i+3o%#`&hx3Sidm6dGAC^xZ?W z`!K!fbFT`?SI*++!P0NVW@P=b8Y_d#lX8g8s_GlB)ql}7iF z=RRQA8p8Nz@vjTvCiz;n4VXVJ`Uy#oJf*`GxlgdRfM_EEjN7sgyRYG zJV(Op;2m0ZFyCn8mEkb2%VhI2xbtLMtqjbx{2p2acki!t|9b%Ai>vU@hH1ybeTQJF z+28alu*dbUR)a7-C$-`t9DC-_)qawz&euBz^ELW3NiK}lVg$f;6X$yWfdyOi^0vd_ zq2XKqk$NkcKn~oekb9k+U)Wk>ycuRbZu?EfC-hl=#Sxb1mdlNXIp4;Yu7Q0Ijh?6k zOZMqETf?39TN_njzJ13hE4Y@M)TK`PRsRW=!wqM9sQW9HnYoDt>$+`SKLt7W_A56t zSloNnpUj6i_wExDn7?6?gv_7x_WQW`a7gyD4Z6gdGPm_%M*F11*)W}-UZV}mn_DQ) zg+(FmJ({rkZresCEQo2IstWsZ7WFQG*%_ICMv^>DWVMjw1A#@uVD7^}^+hCi%M1F~ zZqDi$s8lj0`IxJ}dSH?Nfm#!odCvKU1Wv3|_+}1E@2@E+hf^obce5b5uYt;4ShMwC z^$M8vy1V=`ToR`zvWFRWxk=MtX`p}02AF|=Mwl&A_h=+6FxdRp3Fd9k zv>j~2`MPadU17m3UPL1t(pf+~uM*kVihHorn`d?8`4&gd`XMvOZV^!b44Us{>)y{!7n&VEMRt^@m7(c7gIrn0f2l|MsU5 z=||_lwBh1_Q^+L+yO&IWvubLVlKoA1bZwp@9Cf|3iR^!z5o!%1Nd1)4$1lRnz?5}g zTk-qq{xm@{EXuK}lfX{RRU5AnSMQzv5~lZRtxbm+AzPE5!d2-P%Q9f$J=tl6aFd%= zNhZwI+>w0?He9xqOU!f9^h|&=;`*vczqob!xMQ&H8Z-G+SQPL$U=N&Cr{|LbOXVg_ z+Cu8T?Ri1=cmA4{UG{K*v02z9nCtOGV--A9Z0i{h)5`Zdn!+C9xY!FYFL`K$3CyS+ zPrVN$qu+QL6W5v7lJ|jxxAER$_`ml7t?)ZlpOJGQ^aOILj+Kus@`h5>faRsV=u)bl^Bl14rTwi4q3^(01)}`JD zzn4+hUy|o_&yVCMrwpD%9{zCC{2efT(0oc9tox{=bUVx+x{-Gg)^uD}zYS)Vo1RRC zz3=SvB9xNld z%oB+$thUx>1$q9s%d%$5!?8h?l}llUSKZ-}#FZ}_mJo}pDBJldni;{&mfkyqIDYEM zt}^m|VIRFkoj-MbunXBQL{4kJHzIGHBN#Ua<_?*0ieZPu{Q^B$FhS!Uxqj~U>QwT+ z5fAfOo&xhMr&9Y7ZPhH^IXLxNs1NCv2H#l~1(zMix>e z{MznK9A`e8>_7aV+)>-$vP(~Q*^v5kNA#Ux-Fk+JElj^?ORe`1vzYhf{muBXBb|+0 z;wJt_=8HXRc7-)8PHik(2Q!u9ew)D_FXvG6%Zc!sZvZncl(v!Y0nRabF@yBie5Ae) z==;6v$H4l&^;ADMUPJrSN355_o-V6NeagWNd9Y=ZhhQZvnP|89G|9JZtS9p?Y_Qq6 zAI{h~@d=r4QT{2vZE)>XGL%Xd7p`#L%02a%e1qd$@@>p zzS#2t7PVAQk%f6FLsQ?twU7NzkoPI~>b}lbFnhR4A$i}@trD)ifV;~Zxa55-jXgS| ziqxli$o3L1ImLJi56!-7{tFgfvMVFjHapTSg?V~YxX*|getSA#(St47q<`r3rjZ{> z{aTaQYBGso|4EwG0`nI*+rA_HhTE35!zIrQUN^#woz?*~TyO57$2v{0;PFTW zby)gw@nup^KX1Ln5cV)Sa^eHbzBi@G1=c<|m_g3RK3w(8oAkSlu4;$5GrZJJ!t^e! zou6TW$vw~8u(#Z6(=Q~SYU)=9i+?2l_yh}YzOE$mFF%BiwXn47k^Oi$T)cYpOIYwgT1x7};~SDHVD`bM!-m27 zmyX{lCHcyGeNuA#mDjEo!PK8`!G>&~=m)U0r+8W|Y#r8Scb8bjCh8^0H6}OZzG&FpYp_^{x9}sJ_|=A%0<$ly_Wc8^gH{+-f#LV<*g! zwU~Dp{SvL$%I+{r-jKQ;f%t&sHkc-Rh3$bn$UR!hfkn|@sQ05M!LkCkc3)U{ z>FhkRf2nO&{ICla*Hvf^VSWsI49-w?{kG!^EIA|kLiM-Bbv47W$L>${LA`X|Jg2v? zf`-dBG9F3DWh%EEbNMM5KhHGNzXQ2%rb4z0EXW$I_7`TZY~HhxA{2@t}y-dp(}!Y3ZF*$ zH%`{s4YRLq4bX(eXT;R^jA+(DkMXc*dUk&xa__ZuSxUrbGgt@VR>!*g3NY`4Uk7pg zV~0R_m><_~6bj37qEv^$5?3qgd#LuspC>d}{NOHiym)4Z_TONv_s*&VwT4mS=UAxc4iLMhQTHp~`Q@=e_`mmM)35R%a=icML*%Y-k(`fb zDK~W?j>j&%@L2|?W$~%`6UU5mA5O+2*=aW$dB-N}Y&lpI{B68896}#AWfaW39zISP zZv7D0IU45tuv|YJ){kBqI|k-CN1JzJy-2_RH6izd=~hkcum9Bx`O<%6{e{@vu_XP1 z{U11QV0MbU4LQElmqqQ*tQ$$!M#6k%*IO}i&HiuOiJ7xAxWvA@vLj_-(ZefAm2m3x zQK!lHxtCni%3&At7u5ChKZa8KA1%g+L+%%=>U`KMWk|8jsYkL%-kx@LF4(%Ux|Yr?S{Q>%}#C}!SY^8M}fS5f{w=~ul%%||CI z-J=O+HLZS6)>}tgMClutdubK5pEOu7=GDO*GsS1*dslF}>ti)6N$ZQb0;?@_k9`FT zx7}uB!fg7<)h}Si=Yga5Vbi`uW+lujIkdDKc2KR2tblosRNudcJ9BQ1AUSVcQ2$q0 zXg9XD3g%>K{-lxb)$5-pieaI`tLcn?KN_dYiY;^KiRD_tDbCpdkhA`PgZ5X%y2cmE3i}FsEAaUJ@oKg z60AE;@Zk!~yFMg$0XF?T;am!-Pc;wX!@0#yeMzuzSjYZQn0Y2)%SD*3vG=tXoON$f zYdp+2%+GX%Q;nT$&Xazg%QkCRXeGb?9L!qlZ)gsSmL_JOg=sQ28;xN3uG&?nh@~fk zG+_^mq(u?1WSu(od$r0(=Uh0ehu>6 z9ci%#U{TvR>i4NgYwJBfn03^#kG!vVHLG=Y!Qvg?hGxM|f?2{Hq`$ZO26^A){wn+6 z4GXgu&g~=5OF3PcSWx^%zYWgyUhCrpOQ(pb=hJSg=Qv{aWwuEHa@i#>|9edRU> z=H7UfpalnUEm~b+>E-E*2Jt<~(cI-p%wG|;w-Gjuv!rs-v>w|+xG?>Q9625>*!yQD z>{O7?Cgb6(ml$0n^&{pzB;#kfJ8PbXTf^oCllvo7^t*isW^DejbT7=P-c=a{^M1Ho z-v^5am&osggEo%d6AX*~+^KelIsQ{B$o$X-=AN^KyK~R%3WJ5E9Jv*+?|n;iGN0m> zgh$I@{6WlzV=%iWt-%E53wib@V3vx(!@02C?YqfjeQ>4y{S3H);d(KO)W;uuuL?`I zOz=GobA~7PjfGiFsRd_X{CQ`{aM}%IN{2DCTIj{XB+A9&pV1DH)NAmnLylgGq;H;2szi*R#&D}jtaCc((4r;&X zyZU7<92uqlAP+g6(dS|VyL_~i%ZJ&9j#n1LC3)%9_hDYe?PxtX*6Pv_F=M&+oQbgY z?H@E^@%iP`N5MfZ?M8QD{^A9(-z8YD9}`!T^NZS5QXAmd+_~4u_?UZBA}ir|>G&~Z z{Opde(Rbkx-PR(qKS@U1SSf%bi^p0J^UC|zoPvked0J({^aa9d9voD8g}Oh?6Wm$5 zVCzpxRK3K;%5y7hw|SRR7V1TtidL(1L|Ey{ZCqCNwh};i);C5$SQvdDE zHZp$pa>un3VD{^WdBoh%Xj6H(X{^-p4#{J(vWblxAJ)TV^}nv9!;(?5 z%bvrUtTX?}e6mdjQYv8mzhR56!Zd|>o1ek_@?Uz1q<(CMjtK6YTsWSrAI5L{=fwZ* zkCM*G`-)+)(E%&6z6FQMY${=OBg;YRdHAu-_$6HXPqy(ma*lfMj2bvYt1zEDKf=7i z+&6I4q>9p`q9`oZO`Z<)froB8%=FPbss;;`=WK6)JvMg!B**6)G9_fbM44{B8l?Z+u^(hUz0K24 zPl7pvbZUKc8m05JU}@M4YW|yoBKg`dUB0jKChBv4EZU$;`XgJ56XE>rvD5Tm-unx! zv9Pr4i-tbwpLhQp4=!}fCm?tu;R)&S~-nvs@c5U>>v2eV2A$5Q0+uv>&32UB6eMRn{r~!X< z?>+AS>D9{0utep+=Qg<8`4?LO=3g_L(E!V?JAQLC@rSjnYS_4KtFb)sU7t-=u-c_5 z6Gp&{Sbx=WxPj}nLXOyOM9DMQ$vsJZC!@}K%=sL#F%53LGjj5KnD=)5@AI&Febug?3Z0jbGbWYf&x5NrIldOaw8xK{IdEB) zfgU+Oqg~PNC_I#7MI*-lPSEij%nJ64PJ)?QZW<{t(|&&~8IRyCCz{M>U+WLM1ehJ; zm7EECtB+#F!NT;{=WoCnSNBbbfjOceD$nn1ea45yUf&UuRjAbo>_2G;eh2n zo1fZri-0*pderzD3jYj-!~EViu4j?wPuG8Z2o}qj zygCV&WrWonAf}sq4TqhU_J#$++*$8_k?ZA^?hW#TnX1K~_rfltevjD=b0TAAc)(JX z?-n~@p0r7I1FU|Lb;t`Ashd5pfV)o?$$G+c#ic%TVPTZksO>Oq^|RGeVdKW-r#xV$ zcS`D5IMLYVf6mQmkRkQHdCA4V>ryT8$ZBLfwy!(q<-tILML+W)S6 zA@jpsySn*D6V5l_dW_5;>zzpZ1Ki1b_`mtB&bj;sjw|vj zkzpllxAx|2vYzSMEl2Oek($dxh}jdbvGZZ$)0Wiw6kY!^GZwCLo-;X>9Ph_=>5~x;w!$9kvJ`K_tkDx&Env~=TJ!s)fAF0BcsOc~^U;T- z{%zeR8JPCKEQahilH-HZKfT5N=EW74!F0#%tuNrtM@Gx4V0Qn?sl{+?^Y6DWVNPO@ zVh&t}fAHfCOuNxwa~6)Akuv53>ECU5j0+26+|$}%>8$$|9C(OxBVw-EqP*E~?g6c3#H@uW1C!vYG?{AoCH)#Ks^jD2%6MLVvcv=gyi=t`Wu+uV$DcQdnaWAR*c>hDaMX-3$ zfp&7fL^J!R_elNMeUw{suF6pR^+)qF1ITsF7hlOj&f3>YjYqa{)8r&Qa!}ULx}DdDhS{zC=NWG=SwS&YNpU$Yj|LuR=>fB0Mcxa^8>oDXD z^-+F5-~9jggK;d?u??=(@2DmBOJWc>zY(_EVM;xp?3;3bs^P-AvPox=i$fzlU%+LK zH;rRqdaLt^r^L~=uIGt;8h#eTt#Vg4P|xqW-@ER^4q08)`+ixuVdlvR&t(izHbym5y71lhu=pcFjF>mR7HiaFY zIvA4k3yzh_Xu}<${?vZL?!4+mhlTb9E6DhT!J~I5z^PmJYLop(w1586;qZU!gTE{3 z=&uIsXY12@k0EF44|I3J9x1aAk^PPvrjh*_4l(%baunw8UFOveOEMiS$@_-q|G=Ob zPOT|7ChLQ9V$qg5xM8krGMR7A)0KfQVgBo#g}yNR$BTgyIONS(mK)3$8mHvIri(T% zVZ+iXo5EvY&ZQYct6>OWUO8STD zUT%W3-Ubwzz>;RxtN{ALKow4w*SY{NmmMt;UJxfR6l*?+cWcE=C|9DrKsnR zuFseS(_Uj8tBK4J44jQn+ z!uQI=d;!mjSm>MYWCOEhEV_tOKOZT#BlR!NQhC4~RgE<;BkVL~&DvLY*1=4})V4{e zFBz{=<_HVbA}Y!GvmzWmJHhm!kB_IrwfSqWxx(zIX)%p;)W^Ck=+-~s$a{c1b+pduB2iBQO z)cM6Pf~fbYbjj}Y@yLBMmA%ORK>w$^Vg&r(ej)g_Wqxlx=3|TB|K7i`I^uS?ti)sv zIX*MU+4l)7nY6i`tQT5VUFA(!z>u9m#w(flN{4qpUIHV(#~gG*A_&3drtly#Y9yOd1(_r@NZB#BeJ@=mm z{NM2=E>f?leBBx5j2OuL40F~zJ!1zCWlVN&hXsOI_DWdOHNB?=mY4-tErOYH zM~*bZ(w5W(Ght)*ubbb)v|lg1Cc)}u&u$TO5+8n0fwjZ@Z;*O^i|hskSlV@w@c|aZ z#y%Pe^H=oFZ-W_wijV)*Vf?BAUc~%+7Zwx$Hy+mCRTh6>R%*cXucSY>NTCPrE_gce z8_XW`8`lm0cYUJ8($b%B6aB!Y9^}HH1J~PN*;uC!{V>NeOS2gcSdvQJPqwY@+_$jy z6(Kc${19LBdYBXJ;WZ5FgSR56whY!c@?y%tf>7gL5iIIlkRlJW*zE`I!Xb)JvXo$k zW$?Kq*tGXf%sA3NHpV3kE~($m&?LUw+_x2O_`Z4R6qsEdRI?23C>zXS!1TMHBXnW4 z4K;U(rNT9q#DY&cZKR%dmT#*H2YnY!(1Jy>JwIr$ox;Wda(vO=v#)>EV!!g`dWfvVXdqn#GoM=plJRpEJ^dqu6HnFGkmp1AS4HV2tovk?J9$2&+Jj$G z;3l3&0C_&>J*EvYuwbD*bw7ExdbD@L`V)_Mko(K6?!Ua9^cy76iP_iX)~OhzoL?YW z`|~lJnC3~1hjl%ROZ?ySNSmGDc^eLz9yy0RKf-Y7u0+_wa`7T^e&MW(IuY=H<7bxI zo9+5PW}l5|-2^vm9g|Dy1q+wGSp>UG*>!{*kGbBW>R&a!ho;C=&oA>_Z$&4p`O|m_ z8L!}ipV}K(opJSlNV~oX0O_uOs-GjdzbD5 z3mmhk^(f&C2CajIhU0gV{em_7QO626-bTlsn4V`LNBrOYWL*0gxdfKyMeHT(pAq?| za4sA);>tED%q#Hi&?dQV%OCRm^P(e$O@YTk4stcIn!<75Urc}(i#Dpr4j12T2qi(n4tyypwJ_4JWuVpc|P&2w0@ zQel2E@lDaN8*r1wmt(~2FRwJO!M^mN&BV+adD=0!qokKI!!=di7iOh=r|Nl+eV$sv z4I^h$=c6_MaW{njJ3sT`Q^5?<|No7TueGvD6LxxcIJp$Zr$yS*lwq~^d7sJkNzC=P zDZ)(0x$fmKz51h%0&#RzB)K27QkRKi;M9+%`c<$$=r>9;M}z?Wn{k< zO)#bpzzTxfMXfOBZCmhfIOD*oHnQI`haNWmg!#9-BxL`WPFGpo2@C5_jp%^+Q(l~S z4~HDS)I`Za@;Gov z!q8}PKlwX+12>cUmVer0Juw#VAMXl_7rHMZ>qU50#&avoIcr?}5T*}r8B6;6!p7Sb zz=9*5&J@DJ&W+jo z;0|tS(@j`9XV4@B7A;Njx&iYQqxXiv4bNSzGGT@hzvmPzA8a3*2D7HBRi1}C&m`wx zf$1J~W>;W!W4~PjSa5i4Kqjdl<&&BO3qxOL3gMFYQ(G>Q{$bY5_eieYXBh`GqEo2( zU@m&ne-0Mw*?5SMcQnat<&#{Fp->Df9Nc3SMRLW*10}HCVZn6DpO$wMXEEL_Ifh)c zWB!dNuy$U?f>4;bHh*6sJoM0aN)RkqV8+UWyLEgW_rcP^lk0B7eLsBO`;h+Ykt+po z{8l$lS7L7Atw@-c^Qgc9X7B!S*B@qzYLeH%th3z)J4t`SnPMB5?h<@<1DrT!V5t$z zosg9=3+AYe_nQqfnsh1@;j+(<56pu3@*1u3a8|qc_zY4XtGbm2dq`9Vrog=7(xLA! zF&|I3V>Dr*Tj-PyIR5sZV#C0*qH zcR%j`)In1QEE;#YYY-MF-rK4UGnKyo9Dr#miK1~Zzduf=AC`O>QLPMTu~`}YObXU}cVFEEeOX3+1B zyS$cc0WBdKGgF&OPsUI2RTzDw-Jx%kNk-;TA0Peq87c+x>p<_d3iRyOre% z^VVriPKCwJ^ZHi8vHgr~$*`cHaN~T~!#hSfiTLbVQysX|y~#Y0^z*`JjfeB&wnvkk z?!<49fm_Y!r$|5R&)4S8=NNxwhx}zyAAJ1%8<_5ANK1j)p6rEBVeb=PUlFrxKQ6cf z3$GhdIo&Pz*;zQeyz4zVp2$_Ea2H(Wu=(jlQg2lyZv)G&OrCuKX720d8o(}F58aK0 zg$iS@>A>=~$_zd%P|;%ln{^;9Rh3~VExt|Wh(w41N zAKh_^^?w;!qPOuYT|D1VY7^2;kIvEi{Ua=mn92e!J|SyF`OtdDrLf)6SW~N zu(jF919M^K&b+HC7{9k5iZ%xp?^U52!uiHLBR!Z_Hes7J%zHF8Ru|?f|FBvE7Yf<^ z+OQ7KL=l13xP1rJ{ zQ~YNiju)++rwGRdu{qhbDtRT{l8v#q{<1nkuO_UKRIQ<5JvSo68}_%4|J z;otQ?xIYfb>kHao{+ZJCe_@ulqedf4_plvKa$a6oKq*Z7Q}z5etjU;P{1E20tdH-4 z`;;4L_hC`H@JTBi8D4307iQmgUD*h8{AZcw!9w{B)O`0zd8}NRb4f$v5pwmG@`FN{ z?pn9+C@cyza=b-s=)GwltlO}qAseQCAHXGdrrgVd868g!Zi2ma$EaV2X)%xVtl*3d zV|CJD_V0U)g>Xam4Ea=;Rk=z|7q$yfxP1kdYV4;^gJW~j&QOjE37iW19zLoeKrY%$fGqgJb z^Uv_U-iJF+Jj*x?Gc8^=T!)>ko#YO|v}OzA^Du3PxihgaTRr*+oX@(Q(}g( zKDl9YV%TtqF#QI(KAQT@1QS?ax%kpim}hn3`8>GL-noxQ`gwT~`f#g$#PLvA zx+%eJDlA`7krx6p`-7b5aBOy;5wYa{n)Ab8hL&Ifsi(Ca_wFyldfCLJ=9^(+UDO5- zrQm-@h@7{2&XRX9m$z@$31U~L-LK$|_tRR)dSgcF+;{>D6W;D7*T*l2eSIG`9Tm2N z++T5L!>2r$5nOsIf%Fe(6=cD=Ud(Cae(~NJe;2?C`NydBPxss3bBg4iBYjekGneOo z41x7`K3qbc7hbcLl0VF$XBiVq>@AM^!6Ey;6_R?%o{B5$VB?%IRKKL-bihK`E+&jR zzO>KTN&{A#H-|buQ?`yV_zdrh$s68YL_b?aSGS$Gt(m$W#yK^uI@ofD5if=u|Kom} zN?0>1;p`chxx8xAGg5zMKJ~nD>%!bi;SkeR@5%EeUh_${1a`WQ|D86>5Irp*R$o}y zOy)x%$VjPxOPs&$4kq_-a?NCrr}g(a&Z zXZ6FPzt6^RA^kLI(I43EZ}{R3q~2k2t30mP#d`8pHmTR!@^&;_62qRqn$(|jpv-(1 zR%`?FmjC-m>h*aRf|al+*vgbxnsMgsa#*nb_G($!u%#!@0;Vlg_&W^F+Pn6_GFW)9 zXoL)Gt>*7<21~mp1P@?5y6usbOG*7TN1Hyl>1;(HG0$CZelHyUJwe}uVab-Ve+DrAp-k3u*mBj`+E@(@K>91B+=)sJxnBqh@b$h{qSuk&{>bDrU_Ti0GVt&F6$1||g zC?RFWJZsSjxUhSzfa*{Dm>L4hpX|Rh8}$NTPsJcuZN;?<eI7IWSZH zmH%$oJ8QhjT$rP=_=_(r8kfl-+LK^OO)%n4z7{-vn!Fl?w{3POvx4hMFJI(+Zo7aP7{zQDi=u z^@|2Jz{Y2OW~_ku@^d!Z!;!y~8_D{>1|Dh)CyLG2lJz56`|ZLCxX>U`b3IJAvdvru zo61j&vxkLe0z-`9{NHy99ANI<$}R&qS3&GW%#V$otqo@_kr$DA!F&DlQ%L<0$1ZX_ zX$3{xs7)&x277PzakhkouT(DeKE?X>nqo}WBfX59 z^$X^#(_di@GxaCr{eZpst}Wz#F`Lu0zQG=o^`2Af=U3+8FK|f7a3^xV=_hH-&oFld z>mivh@sWypV(T@snYu9l!zrr1d+`MoZCGTx@a0#M`<%0x3bV%MQ^&7;&3vT$7w%l}YTIa-9US2=OYUd&G5$!HQ%}Dp z4+s3Dtr-r}TXN1RzTDs>5R42f4qA9kp+1z^yOO zng4>hKSQbcivOpV_ygvw>2jQiJlA|~W*01F4pQcB{uTQfW?y$5N&3Cl-Q~5yf_Km7 zE0OwX*6%*RqRQW2$H0AyH}*D>`m>r08r-^Ny-7W(-!gXmcU+(5Tg8cWFn{=XYQFfd zh5Y9v&$BYBMb401e^vq03mkJQVg8$e%5s>gvVUh8%neHZ`wXTDt>-);$4h#7x&#(q zxZ`vm=Ka+w7s1k%JI~*M>Fse5g)rw=m|ha;ztz^4N9ubGmxse5pUs)MFuQ=ctO*K}{df|H(gYhjOidxK1vH^1Y)1)S(Wr>Db`=3tFEaO*x7vou)z z>YUdkxbW`zxNF2yZFs|A-Nj}xBvtpN*;QOECgcYXyc zWNaKD=NHYgTPuR~op&c>lX_L=id&?AT0T7o=AO|PB*6})OhaPU@$+93Vewb*!{qw- zH$$JDg|pPQ_}znPDZ{hEVekG%cXI#O(QR+>=$SGZw)3p9q2^?$8rp-eoxSrcKcVED~866MWVX?M?>r0aBm}Jz$>hTLCB&U9l!J@e-56FD; z^J2$dgbi2u8+ndT0={R2`OIm6ncZK#ZQ<}N>$#0EXUw#>mawa>Yn#F|4kn93z4G zi3g6%heL{fv{8=!Wncj3j*Oq%f}C;S!wn|fdcb((M_78tZTTEnvcY$K8}ZA}S-NmZ zX}13-n7(16rw**?-Ta6=Kazj`fm7km#(o9rd7QN@R}~JwJ;m$~a;D#|O&Yc{0sq z6G-mCkWGPW=jB+D{ZH(zk`M!@))#9MGe%9$JO~Th_EP9Q^W&B5}3M8Mv6?(#^?Hgsr!2ByW(!F82oRUNyrp-<0+6h}1__UJ!l)wGr>D_Rx zXFrSFU%ul-{{Wcx?|70dEL!+jB@pH$&lV3O{YD|q2VhZSwxbLzthn440^3EH-68ug z+x5wX!z3Sn;oe`E6*_HhI85`LM9nXMVn@MA*wo4OYCoB8HkTg@>*v0(?}KTN@{h;C z0f`|lJuufM?L`7i4>B|9hS`dWhvP|}bZs2jzv*t)^%r1Ytq%vj!>sgZo3rqcS>KjU zl4m=QiGr=^Cm(!)`Avc5JlO6SeHmGg%r6hA>kFv9m_*hqEp>v~F673B$NrN2oq2A| zEqmC~Vj;EOIS<>WEP-1`yj@H^4@>>;sKaIaFpFkV&&ppo_!#qd_ZVf`?G?A0VN>J2 zpYM=Mh6ThF!G@P#aLMz>GgZ3kEg3I&bp25`xWnyNN;}Lv5O28!Hg4hlpq|I_BiA;; zx?eh}_ZiQ9&j=^DVP0S>xj(|Fc}kA3+Q`||`;UEMRW`{LCjHU)iCj9ff;xWuk}u1N zc@tc#-H{uYmR%rb+&Hq=6BhLgzY>dcr*n3|4XY!3$b5;m$foatb*ohGllkQIcvhW) z)jBW9llc~CepsIl^WQ9-+Y9riJUA?bMLSAD$@&o9e15e6mYtjSikLOR`Bx##o5boR z>xp%w@kBA4^*8h4AS~)D+4Breta6MSf(4_K?pDA!IV23(ghE-iJLFxmjqyoC4Lr9GL0z_3T8_UwL@uZCH}MuZ4VHiDpXcZ^5zamaU%x z^OZIaPlJbC-Jg-~GXW#Q=px)utd%egmNxiP_fKfjGk!WuSG{>Z1i5zAnn@(5@vM{g zz#WIx#N>GNFJl~?VResmee!+BA8pxS0;?^4wq-KRQ<8h91M~cTZXy;0jN}uyj=49C z)YIZ8m@C6hGf(h{g;wrue~a;aF@H~Iz+#8HouA4?RFjx-!i9> z2}@5JDIbBW#11aRjJTYyZg6hiWa{?|E9{`fT3Aqb&3-;Pp5?m3%V5I^<9f;WH_z2~ zy)o>&?0K0XIi5zVyCLkMDWgVy|1l5dn9qWp5-P%%zyg&r|H*JH_mGVlj0dz=1s0zF zN&Oz>M9i5u2JSxVH;?>25Hltnl!puLW7JS(JD;_%|G4(*D?m)Gsu)?1DufjrV)NwCdzNt#J3$C;8s6bO&R} zJ250d;bPLI*9ASt5hL=+8*q?nQl(WoR9p7+|HpXd7i`MU0Vt$kX1@3q$6duAsr znBU8P3_EC*Fat}S2+bq~yRzioR9&gkQ(?}KT_XJwrw_WMT75Ae3XUwj;n zak`^+0CDNG)vKdmeM7dzL6~kl^guA&_rjE#kCS>2AGI4!RU9n{MjYe0&s$)+t@nTP z5ghm191=g9NzFI0)6RmVvE3HyIp!i6Oo>c?T0S=ewrSn%MlFERg)+}6=B ztCrUl4RikJJZdjO|65V;lav>Gp-C<9;1U<5c`&WOAn`fur!bS658)r`-g+Bmf4FN(&R1sl^ZFFHZ`iNS z8L&V&Q4$A7*8Zl>cb4qA>?5!szbJxSKO}Yho2y})0!Qll#~Um2%@o#D{YqWW#I7pO z)!=+D6A`%{3x5dh2kv8g5)RHL{W03~`S0Gqftx)ikn25nsMo+z51fw<#`hp zRUV0d3fH|pGx7#Zo4DMy0v5e0KYI;kh9_DVz~bSH){^lZE1`7XH8@o><-&Pb;9=$= zfNj>!)<_}ct>=D^Bl(pw)rrKt57!)r%Y1$wKL(3NnM~LN*Nu9v9tqR+v>JE7lF}%1 z9?a9YP`nY2yqfXT2Nq9RvcnwqO8KGW1WRYmS5+hVKbIxiz>=c6-85La*QQ|sETo&K z_1=Zg?!9gT^Ume8w8E0DE9cLF#m}|qjc~4N_Oh8UYsJ)y&tUyNg>?q7NZX;i5U%>^ zKX*Dz)6}lM0kcfMwCTgbhCZbXSaU(Rh8|3(4Zn8+mY#k)Q5R;v{q-aejvQ3mHx(Ae z-x}uv^EQ3$A!hxsG93d~74<`BH%WeRJnQ9a3IW8G8;+ZHUz)mcBO-hQflF znpN5`eLnxoM%ck~NZk~eGyLHuW4Py@IEDcW9Va~Q%}4*er8P(cmUvmNj)EC4Mw*U; zX_b$UxWmfbMGMA~xXreZGv4vp%RAV<`0C3;VS(4g#V6tX-!7IiFvG<^ z*%$Vj5?=LZH}+@nyL)Unv2!r!H_RBaeu6p7bTz9TgarkE{>~uz3ChkS&g&m?eLSon z9z~hq;+`i5XFppO@E36|!#=w^5BFE;)2}ovkA5=DtPSQbh&??7W;@#VH^DLEOC+)| zUurnF24+;pIt_y*bs1+Kz~T~1M+I2CQ+LB1IBQ?cuHnRsSqE>xqU+z!sKQeHM{-Bt zzVM4{)nV@Dk1PG*Lbn5_C&EI7tut1@0(J*={5X>Xqjh277NMIVsn6?HBn@WRu&MUN z%=&h;{x*((d3DfS#07_A&lSTuql1h|`{WwVm@R-MbBupk!OSgFFNDK|ZRy@iNdAFW zwOioSIabk2VafFem9B7BkkFjO>Bjsi*03~JU2QSR4=VgI50)Gq<+upuoiv-wg!M12 zJxJ>3+@goffaAZ%Dw5^19Zovv!b01e()lps=a+R;;24>vGqYg9)i%mieP^VkJqx)* zJV~54@?;@tFWmWe<+b6U{cnv%k@B%URDM?Fka^^I2u!w)oQ(LOs@H0A{KYkwHZx#V zk?>U)%shO$X&l`Cp1=JoiOarxrT{D7Nj>-(rk7gm>CMIcvt?dRJIs2gR@Dp#sCVSG z!lGwop;fTO>v8jlrJv5#=fe*1EmWN4Upny&>=tQMN6Is7xV}4KzFf)gPp~Aq)4&}b z4Bz&PERVB8E#3-NWk00i;@yw3_2K*#22^=TKbJWXj=98ZXhS}4${1%gIKZW5Tnj9= z9WYXYE2Q(S>q&h0bKl>$uzwcT7(aoz58kAGhaDc*94#jCX@dn{;J#_!_S}O-Ygm65slBVLB}R{jJI$W(mhz&wzcN%$&Uq=IGit&xFI2 zJo+}l>>NR$F|6F`u-Ow97oPH*PvSR^SFR!DZG$gb!s_?> z%(>-Gur0H{VIIsKvSI0F*dlJIxe?4L`aWbY9H?SCaw^RE9=|69HuhCk)qusmt0UuJ zaqV4>3dui`;++InTpoC#1WQV5FPww9YpUai!rYVZ7p22KsNCNi$3<{cvUr zocPf7X#>oUpPlaj3;mgH^)P4bq#Sb+zxZ3Zj@ZP0a2j0K6MX6w%vF%j9Rs)P)+W@# zqOvp8@%Agr{X#5B3i|sN$3I(S?EM-R#0|NYOPu-a&|8>2@3c%TER2m(e-ATs77I7S zstc@ck@XX0X`3&Fy&~Qm=!C_;){mCnMEjbi`0zVSTmRzuYq-Wc`P~ngV}2s(3T!-P z*3Vz0Jf;_;;K4;)rN6Lb$J`g2;Hpoq`ZCy`0t3a0m2iOdt#-1%r8!2*Ojv))mb=5q z{(d`IcRGnnbKVYz`E3ImHDSNXW?f~N_BMFwD42Iy!Fx0;ychDK^9HVe=JZBQn31LA zmkGOA2JN0qJh|HP7_3=z=B+L)6?i=Nfy3li@0|{dNA2Xfko?JI;|)oChPIqN%!nze zodxr64mmL$78M^%Hi4PS?q|lrZeK3e%_rppgm?Od7_a_5G-(0x?-%l)U{;XTT`O3$ zt}VG9wtW-w)*9xmcs1B8HGi#)c>5d9W1%C>{S7r zDEq-{8O+))65NE9-JBI1N&boria9WkF?l~57EK+ZeHzxkZP7%|CswF^(N4HQ)9#8J zi3bkc_k`Qe&Umzz#BC>)+reeDPl+BdvwXbnQaC2m@Gd!D1((yL3*kh$(XThaG!2F+ z6W07qf8qu6ZkTM-fjz<$?{9(m>94+yhx?}73-*OMU1sCyaFFW(%^k3q!-^jU3#&_q z2EdYaK3o1=$NkCt)SN>k-*@nHH{5eQT9*e)Cu_@pgEO}7whn~_Ve9s_k$4;JR2a;i z!kpI(t3UYAN-TMt@ks*n3bfjY#j9Sl6H8cn3K1~hajWthSp4(O>PVOuC_V8Ewk@re zCFWKhs(k<_#&6(|>!oz#so}R^{oDJf<+B)JA1}cU&L5{AA@QQ>ohfjSxsm%}SaPxW zMIxLrTaQn!r-BNVKe2G`!85D_FvCe-JqngKneGmRg^%Ce4kYI6`?U||*lFDffJJ|* zs>$`2b1b)O8!Y)!NttipH+B=uFlW2&L7dwrUE>UAgu75?Z*PxX3j4H;{X^p7PkY^2 zu%Yd1>Uu5;>C>JEd%W@fL6*nda`@XUlJ7Q+T3^=g%(qiu#?|>V$@b7Ws>?Ouf5(Gn zCF`RG^Qw%-lKm@MD?T<7PPAI*97XcwAIJ}d^`M^61FU<1s3e6 zrOcU}kVqVVt!8c#;$p3*n;K#LPV2YHFzfNhzGrZd);RYRnBALj<^i$m?Zc_Cz&$SJ z8l0=t_V^sk6x~0S2s329o=k`71F@1 zGp#dE5m%&+I3z(_GWlqc2F&~VJLv<--@15E8UEMb6Q`G*@4trmg}lv7(x2n^q$PfW zjV;Un>#y0}YIq0-ls%AkATFxwdLw{+tRgo^Vb+ezSHoblGpD9^lX!jIuU&Ba;NJPa zU|!FXyKCSY&A(Cou-G}Z(iQGA;K>fc4BxQhPO$AnUj`YU&<})NS_b!|o}$Jh^r*1n z#jxC|KW3!=OWUz`iZ$%AX-k6~%paZnb|KvNV?&7|%;SVwSi%*-1=RS9_cNMu)w}-n zWPB!7JwloB{3A7fqh&`>mUFYQ*Cg>_eI4c~Z)=$~j*L$@*Zt1Uhx0X08c!zmj~SC` zMm%9FHJ+5(3?F6+SIu!vB;!@K?YAv+V7Y|s6VqTu_tO0{;f#;xL&$iBQ=RZdA1-X& z-A%^Fm|s{o1rE$n8c)Ut!kQmylVEXK(kwE*7Ffj%jD{^{G%P0L35jNi(m*!)6V1DP z%}KuTz9FCCilbXjQ|`aA^#%MdAAcw~tOTwqUeRlYeCdfxJvU)nonNO+VUC5fUOMcR zJ0qBwe(Yzm02Z8Zp~hSM$s?-c;hxvW)5!XY_PDH#gi}SYo*I$z*LkW(;XwINYP>Eo zk1XZFUN>J+<7r96vZb5hhQveXCm^4l;C9*tHoGmJtO7GSJTF?pi9D8?5{Yw}gVW&D z9}iQ>cv4V(bc8NkHFnV&d1AXPD-AeTb?2ucFfHlaDLk2Giy9xOSh%pq~!ywJEy@W1gi!~1dK zN!WNn_YN{%X01ML8Ukl%HO5fmZ=BXiOt)Ngb~T*g_M(xrU)CNO z(F)kjH*CgqXFwP4?iZykM(*mU<|B2 zDeNd|U&8GwmxsapHR06wmF1tQA`3Gs|Li5>UFNkD?n7aY$MJ{OFzurI5RzZ@&ZLCQ z578yx*$S{gbLx9Co@Tt+vrrkP#c92;C*^hgmDFJm&DlF0;eYGP42am{01J<9=PoDZ zCk#$q2GbK0Kl;NJZ@+}w!qU8Bx=CF(hvF>8?90nk(*C zgK7H;FOc~$&e`Uq@i31*O_mGObCv2Q!V2f!hwg{jtvSmKVD?YJ#6vL6s5oyP9B(99 zcmx*OeC}BQ|C@i~+nIOZX2oKJ_9aIkF0eV2<_$Y+QkOjr)89Bc?Sr|yl7eDk<}okn ze&U_n(m0rRHtT6HY}RwD z%~4-CceRY{6;j_c`ni?Fdqzi+`A)8<+ih~b+8!!!AoHP|IWcp|{ud688*vZjS45V4 z!tqHg*I)aPZ(T<0kq@NO`LnA+KP%1!L9+ zn7!$F@_kr+X*l(KTXJ4QHWT*S;~w-8amm-JnkcxB@u`mFv)<;4*>L;qpVaea!Ir`! z7O-IW?EjvJkE>}M1GCE({rCP;H~(VmW%P&Uthm*T@`8;QU0%TLR>@6GFkN$f`Zd^1 z$n_)7%Ng(QHpCFi?%7YahgbM&C>J*L=r*k*<+V%V*syZcnu#^AxFthoF3jX?Hh2z; zt_c(L;kvK{>U|obln!R^CNwNdSJ^7Pvr*lg^Gi)4OV zG^kN-2j@09DxQWVyS=N7;fmt&yQKX~xyRECU{T)sujKp?=K36Dz;z>zWK!o(!T9)L zaOC5BlW4@b24ga$m(c&4>FyIn%Kx3$^$u<*Qd>&SM@dVSv=WY=?!PP?=8XBGBZ7lo zB|JS!;{Kfr?!qnHqeh`HcSptaL{c7qQbNvmf$U~yA6WfqyF(C6TTOfF09XBe+_D#D zoNheFgn7Ls69QoN{RN9BlDLNZ(H*c*ztCCYMmVf-h0O+7ve&!q1N?4d2^5iBgo-5Z$uHS|IfT~f^v{T1=U}Z#=npy@eK2h(aOjdjXG~10XG~>ct!d<{MSu-Ct!86!hQ`> zpKs#Me3gKk9jrIN!Fs3$F65lrtpdi{zbu z!2)CB6ei4@SK+OI{VQ4&v(^OW-kjL10_VGWR#E-m)fyG*aD~I;9i%@^A3kf#1UNP7 z#7h>;s#$fB0ZZwx%P0&{;X34YOY->&cS( z*m|p-VUDNG)jq6W_7Nt7oNs(d>f8>vMvnQ(4W`G>t9=Wby*`;o&QHOao!U=G{Dwip zT9`Re|LrZ97wYCoESU19I~lI~I>wUfuLN1nJOFFnoM=z#7u9K)tb_yP9|XC;+=aiQ z7r==|7aN_3x9jxi!Y)6(3s=Bmjn&_2a8^gMDzV`3x(O{A*gtQQD6_1xr&PmXD_mPi zoD=D{z8H@0{%At#6W1AKr^5fg{@LNTBH%!)Olo<|l3k_T|B3TXluRJDFjA!U7tMNo z!Ft%xER9+o=l4GRAuaY_<{oPQvYQpRn87Y@T<0z&%XfG-aSH5H@T`qoZ`iX%M<&5# zwyxCk7|DT-m1;20Yr+-_#Q9B6F3Z65qqFSi!3;z0(yj}L8)o*;g#`&|rzNmvPUoYR+R6Dm?DuP$C8?iX(tRQx_FDgX2x&hwTG@;+ zIHocwmAd{l?X27l3%1kdkp3(!GGhEj;uG#v|CCQ#aK{M_`u>G_{>pnLPO*U{_1lh; z>n~k)uRIIxDJYJ6Id9wHmaHNwU*P@O`Z;Vfcj6e*zWC;c^vmF$ z&9`@u`ZC)M$?{SK+X2AF1&P-KfHWVL`=D_2sCK-F>y{0vw6gs>t<#e=DFi9UhDhrtY`gz=op&IB|>` zbv`rfMlU=IryB40P43_P>VXBsX11BO9AdL>%2mI26>Nghf67Wkyzu6YGrlnA<=2<7 z#GiGk@d_<7Wpx@?Cp;<~<%oep~~ssBd$Pncg`O@{++R8sFBuzC}? zKLwb7^?pQI5Rx|O100sMeh*dt^~IT0F!L#2j`XM4du=WYVTD&NQZk<6F3~fNBbMHM zy&sllRL?#PN5+;?{VBmUm7<+6>q?}U+P;&CQQP2(zE?`5|HRaN?6Cp%n=M;T`cKj| z852EVdVkk!ZC&^#Zb7cjrzGno}0~YN{Qe6h?JP4AI^NG_lTgwh+ zm5QkIkCSXZ*BY(}KRt}Wq!Bp5q<%b73|5Fe{Qz`{gC$0xRFo%p>*DO!sOohYd|8QRf5O z5EHDh{x8#NE5t?Xp2RMNh3P@m`M}xhm#`TAcRompxs)B`)Hjp+rL;4c^1t(e@p{I> zCCKk+`(8uZKV5g6z#2BxY@^28%wzYntY8Pd9}71k&dPsAIdDFL|AN6{90C8b$F;#zl zrOx)luw={r8w-*DuYC&-J}EJW|MmZwa*_rUQvd6xRQsjPyLXNW2UsjVNyZNXn^&u6 zz!?XsYsmOraDL|$UD#%CF*QCBtrzBuhrKo?3`rnym-Ev`!;;8qha_0E=t=VkSm8$; zHU1IIEnFv0%AdLvo<`zRThwHTSr@4BxMYm7_|G}?XIj5;GZ5$H&5Rm=nL5@!nXr_t z^|=qOnwMgd1#`C3|MS(?JThKql-MsK@xD^ORk^UFHpRII4jgHldIuIC4odzB>#)~T z<3myGTWa||Y|H)%Qs2Ul!E~&TmrrV_nACUT;15l>{pP+WFJbogtT|I)oj;KtZ(yO- zpXoYq1#>3*7x-AUE1E!{3X(8onO17xM4)rGDk1*H4?>X6D z7MiT2c9_2P<^!@nj6>DNeS%r{f7$n9eL0(X)cat}QnmgjIDdFO^?ikK@2!OAuyN6! zeXYo+S!WyS5}3X_<=RO&MzGeS z7?!q;9!gxXbICFh%w5&HE)I4v^R^|&L$YS*n@G4L@fCIa#IMp{?|}`k7dBlY`TNWH zyWlKuMVEA#Uh;j@R+!DS`AymzH%r>L29B{*qvn?wTOXP@!g5O%J|@>+mf~Tz#jxn$ zwp4Px70w^KdNy3A>|{*c4_b;>O(OAxDZS);6FhctQHBL&QH|92RA$Q6zO(4R{NMS> zy!Wj1JuD2(dP>e;cHQKUWpMVWWwb3Yaj@LCP1(e}_BF~O za9`ue$y;HrSFhp@_+S0v)j^#bNck!5C;bo?-u1cR0gKjOi=wXg*?THo;g-u^uW(8I zrB^N4F#Anh5*cqxpFQk#fCCr1P}?V2R=nCCuKFV|AlC;meVT$D%$qT)n!5j$>rnnT zpC`UF>%aJ$CWR2>Gv!CFAoYn??{FjcFLCshbB?f$`&;UMNcVCzbb+%Dji(+ z&Irl%fj@Kve;=&8oTHiv3qA}V%Y*%zBdPZd1&*A3e7I1@=r=jvg|$BG&cHp(Tc6z~ z&V84i0N0HCK)uf>-W8OR0@L!m1OJ<^ zVxDXN`4Se{nW#!op1I+5;~SW5JR|Q5@kUeDXSi(OxqlZd9q`iXf?L*%puT^g3wr#z zVWz=e>U$T#iO9kpxMOpy>l-D8O zD~Nh`FRg{Es-@KTJnS9QsrDEYRkv<9;sTAh>9-Mg7#HF&0_H6o!MzBpCYMv+7fIBV zzof!h^-6B!dnL)r2j|1#f8T%pYoGbI+o<AbTl(Mk#!6dvre)^;aZ zl5a4@kC^$Ud58?`;z_5zKjsEbUNZz%wk+G?f;c_QV$7cu9M7){o5=U@>{p7leK6lR zC4CL?6`js5Snk&kE%LrMYh~ukk8on^+yOtB*L3uHJzP_IoBCd#Khn(c3EZ)Q_Jq7o z&#-S47r_cSDjwv0c5bWn;B69b@}qu_LZob1cLk2g5K+JHz)@&dJqri^Zgo0^e7a}K z+|#h}AI-C;Vb%<*2}fYLzx`?FV4>>FOFnReaOtrNFl~N5!x0v% z{aAhpmW(c|HiKz%evi8Xvt2!BYQwp;6_M9qLBe(wS=i^R2Jbp7n$+UhoQ(A?P5dQ< z8E@6R^WdPkE7ioTPh0)cVfLrLFG;?%{GrMTxZ*ZzUN&)b|ABpQLwjK_S-xPc`1vNd zqdA%SeG^IOtUxDtusi-mD#`!aGt(TdN`1P4Y@aB9|B0!vG$&ClfyB3Yw2p@Pf9{l! z-;-cD*DM}P!txF*YbSAbuiTukFk3rrcrx)O!8i$=IIK!54Hgz@4ZeWA+V@kx_rUl* zW}O(WlhJ-h_7}f4N$)Z2b4ok;GR%y(u`h<*LRXF=+r#C?7v6;jgJYRFFs*KqZXT@p z;qz~@{o-6B&5N)wNuo#g59ja8f_Reu;|mq%&Kq&*D9rW@Uqi}E-%t6m8xCS5SYCzc zfj_g?!CB3h7+J7nQIN7N+;eN*p-d9@ZMO1TdpNO8!$K+RM=gOUd?&QkHwv!s7e7)bCq}`i_;|hi%3-(a8R1>Dr%3hb#1! zQonb>2_9h_2XmZDso&?|npuS$fsKE6e@H|=vn%=99ysf`1GWD-q211#;EwWK>iCFM z|JbdB8EcL^#*^h2TpD5lyS=tEJV}=4ZENE~#mjTy3(yyVum&p+n;Zt~) zI9PW5Zp4}VwQ>Hi=KHzdcEAkZ>=JWWU;om%?IfNbl{21{Z@v>i%$^gN{PPUjn`u{- zKg|79@HPh)YJaE94Eo}K0QP$Hv6sYYr-FyL!>TpU8KgX`J$|t%oc|-As*nER!jAE< z!Xx|VsP>#jeY=i5SO<%ZLNuyi)nxf7SK=1ep;usaQGYKR z<~+%YjDdC9>23C;{JYJa8(`z@aW|}CQDE^!Gnlv9hx)w@LH~Z|(Qw^wrCak57Ydgz z?2Jc!hI9ANfq9QwUOa{AYZH&ng!!|wj@^Otml*U+hw0Xmt~A)Nvx54bjFqXEbR5?C z7)^bT#+f;2dI(OeX#el~HvSWpU2wyro7C?cFhX7%`oXp;t>?y}KIX1K;Re{OB$WDo zm^-W4bp_1QHZxEn%inr_|5BJAn)gE%W+aNPnZv>>K{Z2Q;ry1(W-z_tktVsHOK0C( zGy^s(OMCYN7V|Ui>%cW#-cO_?p5tnz0qf8t5gjD{+`Vu#oTaj4Bk4~F)=twJ1&eHz z*M1=GTVBR2Go1gd#lM!6FL$5*0%kCcss6i^p;b`~E2O7U;~D1e zFHwbXS)YTLjCWXR3(8aAima1Ve@9Zc?QkNjv%+*iDdNnL&H)jyg9`V*??Ze~H0*#S znIDebBjs=FE%AjjwAaPlfH|XLg6-j+#upYsSSbJbtN2eAzm}#3g4ky+(~_3WwhnsEO9e6p6A`NE|SFI{N8wp%9n6zCsx8bGx@v7 z{1q=R(yA2h2~9EI1he;iyj2Lt1n5!AV}*Tung<73U8Ux;XwlbC+<#)RM-hMaAOTMN!Wy73dyDpRdyWoz?FXFJd4j*`={?Bsh*I5E-vY+fI&Gf~9q{ zsORs3(Hz&`eC+SXW9O3N&34>1vkFcv|Js*G@{Nv9ISpsOYO^>+;zee8yJ1$A-!L+s z;O7Zvy1~+E@9&WD22T-gMGl7}nd27Q@-WG1UBLm;YI{rLarrcxzEx&{)mfLc&qJ2 zT&{Yomu*W{iQ(Hxg|-(k`9JmWT)T_@ep2K&{tm6G=(85uR& z%`h#~FSY@u8C0!)1$#vH8P&qv!SqV9Vzd-X^6 zA#mi;Qd$8l{dDL0Qs&A*!a?p zF7m#yu;bo|c(~5r^b&a=S>Sc+ax7eP^q?|%pPN70tTqg0lnc}j!0fnkpG|On@bo*o zU|QdWq7|@c$CZ2^Sp03IJPW2Poa^+4Sq%BjmT;JuU9t`q#ov8y1`h@WJs@VijISov z9LoqN@8{E3N%V-b9=P2xgQc$ST}(K2lK0pdu%y}9Umq6Te=(>J3v_4NX~DcJrmyv2 z*5j2j8n9Pb(hc(c0{?}JiY)BFESp0tn$8OSbPVU~AEi~KywIT~MGUj5%9}BhBa%LC zEWZWwFHBxP4;EaR@0SjXH}W5_VE(5vc08QT{Xt&_({6aJ2!c2fWzOMWC}M}?7zW*`TpYF>Tv3?wJM%4GxO{dIk-Z0PK_JM zcjHa|9);`Y$I79rVcwFg`meC+hAo>`!IGO{=3n64TVi`Bn4#Nvtp(=AY|M2aal=bH z-jjUAyZK~&*_n!rcd*UjjTTlgZ9=tv11x^-Fm(PUpsR!#nKD&gNdrU5LC~T;wFqtavlo8w=iT!tE-BdNi#W(WRKEhdd zuTbA7vG%)-eF3*0zU@B>aZczkvnO!hgq1DBVAk3(ZMWf!@88$ZV46Z}$rU&-b(tUe zK1q}~%OnGi?;HB^8_ai|x%fD2@xHH?d|$-er6%@=9hQwLAm0Zu=Uqq-!us<(unR*yifJfpp@mgdRCK=(~YlBFxu_llvBd z>&e7ZBjaIq`&WJw+;JdoWh~6wzhrJTth%Z#IRa*CnP%OFW3D}!9RiCqe6I^&^?*+^ z_mlkl1NZmB632{{J7LKOcBc(&JKulU4w!p+pY}Ys!tg@9H!S6S=pGJp+LV5I!n6%j z2YSM>J`#0?E6jiMHn9aZzOg0E0cLNl$a)GJmNvIoz+%hJ-g|Jz&S7&5U=B-f@fBD- zCSR-p^Q7;BQ(=qgy7_7({z}}O1Xrm%G8zR-YrGsz!>M;%8Gm;oK6LH1ld!nKKUxa& zdO|jzfa4iJk@W@Fqu*8$~w*;o;$>;H5g=^ia4`6A^PqkRs z!ecEfmw0&uFN)+J{Nj@iGvl9ig}@b-a#N4N+{bzCJ7L2SZ6ktUUcqX=EpV7ZQ+pbC+nyP&fKx5l$tz)Y?&dT0u-B~Qb=I)hB1mWh>+E*9V+PYt*zZ~h z$E3(VodYxS-3%te7+L zJIwr)b8RBboBd;W2Q0ZLktg=D+xn*kX6HHNX~GVv<<4(O`5OZdG+@@wnMa<(bc4}F z!~rK?{&)=2e*gV1KInhuB8d-#_e@4ypWU622#Y7q4bg{l`&~}(NZiIba}J#GxF>lV zES;ATY7UEIjOKd5LVC*qOSq#=)Zz~Ft(Q>SZ&PeG-5zGjvVSf`yv}`6wH3@Q3Yg#w zGpu|07NoqPU_Dxou4YSApy?O-p+7rKQCd}BYNwp6b&iAeQ zFyGsH>utnS?J5kkVZpT@H{Zd$j(00HVDZh?$N^Z;$X+^z_aNY6D$8|96 zJ5!6eW1z{X7Ur_e4!?i}TQ+ovVUE1j&4;k<`J>y3xf>5@T!(EopB?d%#5F3#N8tv( zRF0JAnU}m;2ea~4zbA2iv2x!c*f8zX!KX0${D@F(I9F=WRZje_X#NOTA^XYNVwmWo{l$&C%mJMPnPk@upCmpH%Ij(2*)oI5XvmW1JYdg)v_F=M22 z(*-!pJgnJ=lz*Qt69or+{}SjAOXI@a_rRQ=Bbs)>^d~k!{={Epo%WJ=bYH6vOwYMm zvL9wX@>}5rv)*g95_37FtBLD;bE1QY7f=349O-FHtv`=fuw*lw&v`{{za;GU@BmoY z)?my>K4bP2)uV9PLj%K;Fv}p5T0i3pl_Qd1iOnVd1jK^|Hg%@KLc2b%B-l&+yev7s zG_Hgd3s)uEQ^#AFY5X)8c3825vdHkrH?n-=!KC@;k{@QTN=zhrwSn{T^Y!ob%@vDu58KWNF{S%7(2Z{^H@#4IRKGO{=M7^zH~Jq+(&zQ zdlAgFd$aO9Y&Pze8VhD;R=~e@?VUF)1-zb=Uar~3%B!94? zF$8vTYO@~)vrJ_wca!|BafU-kexLQZZE(Y^EqkTgk^grD#~aqXGkk#rX6sB3Aopw}(!z|PJM)zUSrw<#W;Y5|eoXaqm)!TLocA5FvIUS~zoRG%B0*&T`6qrXF zkIhAmfNvApkp`}>mZu*;aWH@8MbDKa9%14f1q(Afy)9wB&(7wfusHFa>9rTLQvlpd52)c8Fr3eo)eF$I~oDgQVu@50q1`Dop%Bj z{|WAkC-H%b$LC0W>g@UraJ(dFDA|7T5!)nF*fwO+1G0ZuX6=Wj!IGzyd+w2ZyA2yP zVMFbZlMi6w^)ou-;EXiE^Ua!Zk@xHL9NGnOvz z561awy(yAvKMO>&zrq#T?N1cYo;VY8qu#(?4B2f;FkSEG*r%|ekw|V7X@4fGowDJc zecm)W$xnH;I1+Bq=7*3tr><>%2+Xqk=`oVzXVk6Q2@iS|+75?lIrZLaV2PgWKC(P< z_~L!8aK>1^B3U1ScEKwa?8ose8Uiy8-q$57T9{xf5B`Ay&F^D zmXZ84(%z-F8;#XrpHI9IQdqKp7CICzoc+Pz6EUy)(_0?S*BhHYk@JDU@p^X$_OL(x z-}$kP=Nbjq?MVGi<)?g5=D@j%PtD2xW<0NySpg@?N4@GIUiQt@8V<|PoJGz*u^`>u zoRrrH(i?{L=kQG~Tfl5qbb&I=>O2%~14}J3-N^o;Z*zRQ6c$cRSTL42Vos7XtWfbN zY8=duOq=NjNBZcMkn02Qo71&DFrAh0fCaJ`9HN5v%vt4s=Ezr)woJ0Z^4w!!8k%>LHNb0+mYS911&%|6%E5HkyO z*o$EOo~^T1lKL{Ur|ZDBT23cP{oKh7CzXl+T8&r=)0`LDeK?HcTQA!|u9tkdO`7$v z1!uXhIm~H}K5zpb6ueh5hQ*`H)M8r(C z(*&iivthG++s2XlL?6!0)`R6f#Kl;`obz_8Cc*LXUleRfeINHtl!y5;x2f$FR_?vk zdkFhq>G556#QE_dw(W5I!CR*`!VKEYGp}H|T?HQAFiUYm!85qb=FSQqSn}y~OgWr; ze?-+Tn7NiY{~Ro>GW~skc(lB35KMPCzv3t<@7X+h4ctR#T~2^GZ98vIhSe<&n`FU! z`M6kV5c)qK;~!mv>1Q)bT41vyL-cOLf^2V24cuU`u_q4}B_ALE9Ogfp;eD6nPw;~pQP z^)P)&k7qF~+Gw@%BP{KlqfiTT=bQDA`!`3g!s!oOqiDC{7x9;|$*S0%mMh*@$o-xh zJw#p`PAs{TE(?o~v@X+yxdC!93a}{TLEubS)8M@SD46xEgtETbwEJ|J$B3Ih9q|~& zm}4q1?ciw_9ay@>=lNKe+n4%jEZh(wWsv@c*2P&%s_bn7O_EI$7WRJyZS0 z!yNg_HIeY(N10oiuz1T0Yy8*{%dfnndJ@doS>rqnrVaDhJ(-k$-L1=j6&!=xwPEgo zSDREwT>thQU08TkW6wyq>QqCPKFs?X6s`#SHNQwPfcb7|0y#L){=l)Bu=s4N%AbSi zpVyi;nZcs5?+p9k#P@a87BIIv$)p#qa+`A33TDiAT+jones(LigPC6&E_K3%-Tl`| z|Ba5HfBFjN9~eUQ_vrgyPiZ4@decF7#96^34z|LBYr95}{vk{0guzGH_Uv}+&9Kz} z@!5B9z@gZyzA*cw+4XujOn!6vKA7<#xn2xwHblQTLdws-d+I6dBJ-T;pL6BkKYs!D zwPt=gjyNZK$?`^6-RNvJ86WW@o>1$jJgJQ8pL3Ik+@xWD=r0=AcNTHMh7hGOuA2hzvE5_yYhnXu%>wrl1vb5^${%>-^8jWYvNB%;t4CydK7na*Cmj~Rw2YQr)iB%r{kr+ErppVrT9|+J+9VUW zY{9OZZLrwzlASs!fAMZ=2P~PqOm#Gzs&72}JBcSPGE#zDzP}IpP0Ihab^C_x&&F$y zGMEn#`1tN>hkI@qz8^v653*#Ut6|==>Zha0{K1hSLm!d+oKBN*FvoU8N|YeV0|nybubj3@cr&Jj=G$T3w>WP5mt*1S^KEP{+i|j7LE{hrshQgdfuMP`gZm;D~d01dOP|aisVUq~<@YSpx#Qx{5jtzbV`>>4if5AM%VSgkrPg{DS2NoV0uFwS=H}Bs56=v;w zucLzPBgrfpyrv zFq`3&g=rdlZ^D79uOE^)zomG19IRgF%p&D^XSYuYgaeL#cFTvk&s0{q!)6mF``?FY z8S-mq!OROoxetktd5u#QHd_#(^#*49Bwu(5 z51yT*+z9gun`S_sq>$s zW-k7<567owLQXs4%!2}tudukT?CS@zJQtR^QrYNH;*H2cSbCvf4`snqdcaQ_-UfF;#h)bZzZtuD9$2gO{9 zyoP)(UD5Iq+|zQ5kp;7!Z1On^CuSdrCG#1Q%ZcXkux5nsU@9zd^&J;Q;*W;^H^1@l zo&Hf`*)D3nOQe47^AVW)#p7Nq@_98!B->%($>2e9Kj2SZSLFb^4f9`j2o^F&ncBki z&!@D>{e!2}J=ziun11Zsc38T4^nt0cfK{=S%r{9-mChOkOI&lP`3&0MkIRO`gO(p6 z$@~SkT(|w>UetGO>7^Ag|K{<`wwC0L=}={UK+ zlJQt9T%qw{#w?hX(w)d9E|2)ZfZ2;jd^Lp)Pj7pt0t;Gac`Lx;_QAtSB>(PJLE9ee zkEroMG9+&H^lUL4+4lBaKl(cY+oT;A;r3^;U)y2s)J?vAFl&3R^IMqq)Mlp*%<*VY zt|RgCr>XPdhO-y?NdHSxQ~y~X?y;5bdrX`>(s?x8ag#plAuR0s`BVY6X+7dm2=g5O zG=2}j`Z_O4%7K}Vo_}A#oObI*(tnh6T#kMMhtYwV#OgsY*GEJ6~{;Z&A*4SJlZ`d;I+X28__ z6%IRMy`35#ncA6Y!X6Rt1XB@bu)gRr;GX@32HLQ6f|2)B;wq6DgOuOAyJ;qDQ$N3V z0xWVY%dmvyvJ$BAEPcfFFkjfgO+1krzc#J4i-Gx(npXeeK0q%STlN}aw9CQ%{;FHD|?K!Ci4e^ zf$DzJ9s*~TEUSb0-)yha;hJAA zT@)jgN-~lZVUQG+i4a{3MNw)>Hzbonr5F^YFcO8-L==iiMNyfO6vad-Bon2JE)yXX zzt@`cIlu4u^Z9tK_ugx-wf5S3_BpUGhiSAdk9IioQdd_oOkK(hdj^{v^Ze)gl1%Es zLYT7p?#F!O>@hbVk@b@qB{TUpOkZ%h{s7F|_gEpD!yz!AX_n4zT{F znLBtSKN;*}0(Tc#NWL%82VQ@f2^+dU);x=xx8JWp8&01o$BBpeBb8lLi1#ZVC*ON0 z2Yc&B!+ZZLaQkFh-ZmvE^%z!etMALUMj!^fMfa)D`NR7=W8Dcbsv6S(P=q-{36!r5S5TeZ;=t z?=V+9M|CyKnY&lw!62z8EB})l-uw`-1ZE#^m(cKT~%#xTg!#B08#`BadFx<~0t+|Bs?}1Fq8>_c?17qTq~7!3 z4nvr$eJN@zoNN;4Fpsn^X8irS3;nx(kG=>N2Om863TE})=_cbP$Q%|4VC$C~-Yz5U zZF|;cz?=n<8mmbDWXzg_u+i$cdV7)=JMOW7gI)hxlKB$sOxKZt^|LCjY=o)W%&u2~ zcs}!EAm+yY=@Z&t8=o&p2|^UKy78S|wTkl;a^(C0MtmU$XvbBXz>$;Aq-|B(mOV1vwu_ zz~%N`Ze+a)|9Evv!I4g*PLuUSk9#^i%tpP2VT%b&>BVZsxM5nZP zy)(@Z=7iOFWFxQKSF_O-rue_vmjMrMd3oId#`~R9G8}SZf0sSXXnq;Xg`=aCSK7g} zyA~JYVC%J$JXVtWldEk*;8x$1_T?}$!*-7g@yt6%tYN{$3Ge7|<|9?d)iC!%341J@ z9y9B!Ey;&Wy+7~7_+Q;FyABrq-QQjVTWv4w-vIL>PV;kN*ZJLM8)5#5lGY4Z%lXk^ z(tp8-=Gr9K%Z!rbLHsHy<`AiW@R_|0rcAB!Tni75$*p6P+_3TLY*@VMS562loLy2u zf$5LEWDdie{?Mt90`NY&D*x^=n4hWnI1WyD-u3h(OyyZ|cfnd`)Cw=c+_5h!XTo0Y zpWChyUs!Z*Xb0X;-*u&Bk@_`f@3p|<=^kTpU{+ttih7t?|3fbyrWISftb_-PCM4g5 zMHJ0j#iYH}$~Q$YJ6h8zHQ+Q0MYbSX6kX;X2HuJGcBHj$@uofOY-9#ghG;K6!V~c9{3% z(z3BIpPs$m5e_eCI6?M*)}*Z=ws7T7hjMk8p~~B>2eXo7V#t2Xp%%Ry-iGmsKh`)2 zrfW98I@xddo{f!fw&MQP-+X)`%zZfORUZ8B^MUIzXm$+dzx#Td z^pBOesb~ev*yX#0v=`NdC@g@zF4#-1hr7LHrY78>vv``Mf7iEDm0&Y^{s(gXBJQ>; zfBf)1^J#25d0ugzoOSz6ER}jx9u|uCFA~Gl%moYnVtna#L)sr<{t?SHWc+Dsc6Yvo z%dM}6k@@4!|D9V0*TpYw?IP`8Y450on+(rPZihK5|ERox2L~6qll8)N#2>VWg;IZ4 zOZG#GjUuTx3fngCHFDwBr4q9`_sIR%bu=jvS-&({x$|$(UUAlS!^gz)dk=_U+wj@B zkBAw&pSQpq!v~24Fk{o{TmGR*j}@Q8lndpOdj)bKjYbiEnf z42Os5SdjZu-1SWK4$d2teZ_;>xiyyL`q>jE3@5^Lx|%}+oc=hYh1~zV1$xh(!OnKa zj~s$IyP}dF!_f~uChmttX9uo5gcJ4^*oMP`ws#7paJ5aS$zEceKHd9paIC4HKg=31 zU%`XBZ)BYIg!zlsvJb;%ih6D?Fm1J4Ng!;k`O0S_Ou1M4doaA~(e?1mXwp@Io z38ofXFxq_ZJ!Z9+<11J^tGciX&dZ4}DuLOf%TBn%^p!^ga!I|T-{+}t@~L~nw_ss- z)v0lCtL4{o*IYiU`X~FmFbU?~@#M9`-HngLXJA3jxNB{2 z$d;kFldx##t;9}PW#856V=$%QnWUa__tWl!B!4~f*H7fF&UGL6!StS04N^Ftv-zf* z!7$Y}K2ILjeV^*(2Xl^W=cvL1>CtMNNqZ-rsWRMsA^5%{$qj?gkA+Px>u$7y`Dx1~ z$MbuGfAtNf#A=-z!ub^ECZ!m`yd#bEy>Q&rXMr>oaQ zZ42&?cQf2Sz%*IAfW@$IT~3<_W~xn}-{Fb->*{B7Vh-P)6#+*U%XYno#kv>T#=*(2 zeSg1&`H`I~E_mR1ekpRmps zwXi60pKlZ#wsXWtj7+yI}}USh4K4DlCYaNhgk@*&m+>Q}#sb2vng{f(G)UX{&*lcK-vBkg(d-8N_7a+^8o8l*np+IT-WlQHEqmDHCp zOBpbu>~pLfEciBgj23KmfAIESj0dN7(z>6U@%`edtU$8fZ5T&-VEuP5g2{Skc@*V* zgpI;>e*OZB#{cRrgS&5?FX@CC(b^NP!i+M$0a=fn9&W>NSoonrv60kY2{eg?t@mt| ztRH$|*{Y+kYasjL6XblE#}cb(G~Ry*Q}$f#C%M@n&0Tk4q3x%R7&!gUpj{R$$bTns zR+rwpOp=#*NsgbD`^F#*7WoVujYE59-dWpoFi%CV;uNWmySs`D^ZRvd&cjL5OjTlF z=Hi>2G}tV{;#wG~pO8|U59cKxo)`=Z5AXO>0(*VpxCg-O38yZWlKQA+n~6C({wc%^ zucx)X#OKeNmBaiT>tq(GU+DVe32goQqP`U&z9>84i8Vsb?-wM$m3qP*=G|7` z`wZrWl~;Sg1DSiGpTM-cmAALSS~gBz4`9mFr1wE^aKyz|1u)Ny>rhR}1y>lu;Vb<>R`s996SrT1F%->cro7i<@ z%LFzo9IiX}12%b^vfq=mAMzXVm9*cmRne9>Dps;T@Yd_iv4VLG8^<>x_u9W}8w2L> z%m3MbhMc4q6Q5iZ{2Dp;&b;F$|MB3qBlUA(R$21)x5(Kh;;%Dc-rd=cdf=kXx74PR z+<3tS1?-=!Zi>4WOfTKyrVbl*xkaeL;&~ri=fj+_+B_wgksS8W9&TNuGI1nnpSn)$ z1luM=9Q)>n;}?DrPF@Gw6`~^ zZo}mf>Gy|WPI;e*%wO``hwtUkU-m6U$$Fz#jVz{;>$@X;KpJ^n_S^4jBp;yFh_QY% z6EE7)V9vPU-g?+^`;_1FVCIWElKa<@8NX=}$xSbfK7>5laeWDyAHGv*D--6r#lKh% z(?1kxPl7|1xrD8Nxj#Nl{NalJj{cgu5~lR#9IA#RFMoPP=9^l%J}nl`WW^1Udd3o^ zKWqMDPP5GhVqMi}NzR<+pQi^4CrQn-Lwmu(lE@z}IDej6`8t@E|45?~w(cq2w-FYl zTpQa6Hz|ic@+QasvMi4W_Q57w(0dn!Cs4!@S6PvmG$q+j~SA%-8ph3xHiO$ZxJA);#dW6|R(?S|GW< zn*`You$;P@d_Qt=k#xQV@guc-CG000xnKo#*!I{Oqbac9Ts40rOs$*odI8Kg{^H)h z3BP|Q_vfvIdAq}{TH)juKhxb{W~I}jm#|2GPw@em^)tIDAC_~TZb9~AE+gP)25ho4 z^4@7!__@v^31--<|GGiyS4Wsf!9s^rGd?WZqm#jbLptuI=fl)#9vWe=T(4z&A*s*L ziVcEAKH?k2B!9EjE&vX{oxG(SX1-t;Y=NEaYSOBRlY&p#5SPx_`4py@KlEA(v*N2Z zKZn`h!*eFVRvP*>xDt!< zJb2k~mfHi#^N|zw&0rtgT4o`6KcZH?Uu6f=+v_CnPvWp^2Sm=8FIlJ2YIK*X|=QHHggS^-K;W`ShqZ$?-A4s);wRF1GBp$9C zr2?mC-YOyHvBIBya>DpBH{Gj&X(k;hcj2V5N6d(s@`)`6;p7KTw~_X=FkKfPxO~}5 ziTSav5AFYB+Rno>7s5kEJ(BY=9|t5)BK034%E|b$4(!@50~ZC3en?D}_EjS0&nQli z^w0TId)o%w&+q1$kn7=T`%!9OnI$tM`>Tj+)~N=CVO#o5*zkniu=j?IwkAgEKibu zzn%7pw694mbAb8xl(hR{;k%H^nQ$b1SH>XBqBMEQ!J;{>$fq7v4Pl$RO}vkw?7&Vgx4o`Z_8cxpw-Rai^DZ-fd=9mrgD6z*6Y;y~UXS?>;v zK1Awm(jq0lZ^CZ(dcZPEyJqMkXI3VUF@h5~V^7Z_^|v`PQ(;#NWmh`Pb_qD33Dd?= z*UX1Sto^^n!L8agoyIVwPIr<5EF=6FZ3@%Hn_rEB2O8HHE{3_Tix$ejR`JPG&0&5> zkeCANZq1kcUK1YD8UND}&-)QJZAqRe4+}Rb+pZ+`_`K#btlv6v>uQ*8ySS+t z4$jf7T?30XlKftfylPM7T3E0-$m%iNVXn}!9_IY|bU6pkY}k6!3Fh5Oo1Ot%-IlI% zCN4>{OoPqR*FD`t+UK4wN`fQfl^zp|)z0e^C(PcNCb8ZM>S?$#nqx=IFyh=k3a3xk z<&fhEn*^F{Smy4H@#Oa^&DAn&6HE^+`pzVI)R?=ru*vwb^Xy=HXu^dha6;kjKx>$t z_RVq@oVOt5{ZiueMdf2*=IgGtW-!C@=G0#f`2GJ`v&95va=M&)Vd@Uf-?=br)Pm5L zaNXH&UuVD^2W9UZI9y4(f&AX3cC7JFh9l?yu+o7=&hOedu%P>bWPQ-@?+tZ_TOVee z(nL;~suR5cX2hmTOk3pqYc|X;j=xWG?$P@4=`hV;@@OrXwV$D&4u>1uqf6GC-1A-q zxN^JuPCPAY~6iYUb3E^>YCcZk;krOE=SJ#p!jeLBvBWJ1BPExP8-cS{e+?9G|4@_Bf z)qEUGpW(v@gT;4vDPv%**~Z34h+j;XQ-GCK%Kb$$EV}RZxf6~ST79?#)9N4WeMj2+Jh0Co zo>F_G7S4)II&+=WCui$b!B&>uqi({?E0?58;K&i$X*sa)rNH<$@$OCC{-NEU;<&aS!H1t#v;H8>!^_k?&Q^5kC5UaFR)XjDXbhl15v@T7&DH z%3$7?ir(okZ<&+C+#ivPN5FAg-d}x)oF7^6u$YP8KT?}$k6`xNJ3TzOx~uVA1*xA* zyBQ7Bt@A}yBv*_58UhzJ<|jXgSsM$B+~CM+MP>s`ZPJcd0o$s^#J`1w*Y?RTf*Xg7 ze|Euq-%kNUYp{N<&Fk)g>3@G;62sOiXAgZLR_3L>gL%{6xc`Dhn@pc%!%4T~Q~nZ% zj=U2~+`eD({g3@R)ZY^p#ZUYxi|>J=3Ac4jVXdoowdG-M_Wc$mxan!3#GE6itv}jh zeK}k`FbcW2ZT+nRIP*%dr4;e#(bE2~$>ZF2MDlCeHL>KVV>J3$@-#x z?lGM~tQQzd)*sceP2%W~^yz$_RKOoy8U7X!Uv#!9TL-q@LvdnAJ|BlWkO;=LyY%IARO2@1W@_eIS`I$Ebd0lwI zBl3I@_r8BN5oT9(N}dPYzLa(2V6A<0Ir6-w?`~Qm4OdT?w~_4kw9=lS@3wf~4Ar_v z_FLg89sGwy%&!(Bi0sEyyN=!-*eL0+0eN5GP8n<71gqcrF^PPiV#$a3y@0a{`W?yp z2Yu4h&}T505i0o}D(v|??RUw^F#Svm}Ll58|i=C}qP5`Beo2uwcyc z&NP^QDmGE_eaPGEQVMKT6ExU|oR0sG=p4+_4qVj^Gs1hMVqogl#1+jjE1)nn1RnJ3 zJJA61^l5)RVJlJ0Nb)^VqWc-xU#!A@5D=+`&7pr=1nNy;S94L3WGMl zjNK158pA`J!!T5a4{MCC#>gZqSd>SfBqY9)@nk8?`dfeR0X($jq7(z>tGa7m zhwFa4@gd*0M4aB32-1E?yV?Y1dnQlc0he=K=aS!(tl*Z1t6<85qd#WDG$ZGfnXsA6 z^R4A{Lvq*m z=X_v+on=!yEM}in3WBX0_lA(~XW|h9`W#rKHP`7qX}|IOgh*KBS=Rvh9>#C#Z@mO_ zR|iVIhY6WKw%&j{403xakc;l_PQ4A&ekaG5!1N!fo&q@Gip%~&m{Yb#TmdUS;)!pQ z_Wv9IrnmmHu8{Wb5x*LchtK(2a}j2jUN?CMyViCuPJ&q?`R6ULT=9V`r(y1{(uViM zt8^Mq!D7W*lJ<^QH~5}_>Fd5G(`KiI&Gpv+aIi=b1Yi^6vA>VV^0yYk1x#U!rd;}-{%wCsO27l zd5;Zi$@eRE;iTDNF!%Fx)mbD@q1xNR!HdT2A>RiX6@de$up>|H@N}4`>K8K;uDA>8n@|LP_W>?y#Nu<8PY4#|%+x%R;3i0E?oZnWs|2^j{A>Z5R zY9Ew;!ba6=1{Gme=DCSK;EtfXYNKGj%AW86xbCYzl zmNo}nSiHEp?JCSO{$e@7r<45TQ;8cF>(1tp`oKns72P}al3@OvJ^HiJUL~qI zA)e&Z8A9T^;nuX1uuy73$Q(GyQVw`4cC>f>OcTb+Gz9=Tv2w zb#d*J4WzyC!hN!^Xh8+b1*XcLjvE29Ppsv;!cA*EETv%H#y!0r@Zb~a;J?1O{`q!i zJz-wO=$Jn+Z=bS(7aac1&z_jkbM82C_3a(0-(dWuICZ}8P^c!o17;qN8r%i9GV0ce zNc{|(BcU)OAt+#~mmLtvU9>(@h=GUZz0ZkW5Gva1-Tjjd&p^E+nVwz&^;qK%{iVS%r| z#~qUYufMLf{R26$KgDaRT#UZn^kU+2GbN>n$N(L=YKC>B=r+D!=qsK;{2=&uwdw38wd6}?9hFd)T?gO z34%FqFSv1Gq2kfIUNHX%_snsaJL-vn8=U3CIuS+e?wRcXS995!2T1+cYSWdl;Nlua z7|ayeUtS6)d`q_qCGB^O%3{DB(vzik!))I{Jrh{G<>dr6EI9AGZZ=%L@~Qm}n9{RA z#emfBe;n@%^L7PEX~H~5={X*-xYd8$2v|mb!`@9WS6EfswG^%uDQtkL?J@^n5xZr5 zXTmJ!)Y-S-th$2NcCb)-qX!rEKeAcTmefa;ObLO-ic|848Kbv)EhQG3IIMvw^W1`G z!pRRy&N#qg?)VHvnC~;q&>0rmm>%o4#Ql)@MAMac-j7Q!VRhMeS){+hXT^OtVVT>_ zyS-q#%cGR@FmvFrmml%nOX<7d0bc9eZ7`FX8DRpd zRy84dp? zZ7)gxS@P=9&9MLFWsOcS=f@Juw{Y^&S4Lz!xt^Xb^|0W@lCkSy>io8HA!)z;*>*A> z{8qW7$FQr}qf2DG`QPSO--nBqTpwW%Q*5v9x(0I-|H_i_XRu@n&cXcei~bqkar;6J z!b268CG$ns&*=(-lkH6;=M!gM6m5lVdkd{3{SUmh+XZHh9Y2zcFTZSb+FIC9JB>=l zOE~7a?n;>RbLYYBFmt`|-7>iKM5$2_X@7?)zXbI$C%xY z$^XeI-}5E@H;-)b-cRy>-Q9ciYYNN}xZ6#@@%+l}&rOF}SJ&QDfuk20-M9j?ef)gK z!37~DJ=b7fivBTqSSG?i{w~ZvQKj^E3HFCGwt+>k==|l?{c!T`+FSw5Sg5e49d330 zIHro^I=;rw;W$e}^GcX{Y+dOsxUu0+|07~gsvsG5?eeNFC+%*b!E9QZ(#|3?&AL6 zb|;zahn!FvUXQW=W<^A2!xa0KGZ(_Hdw-3|f?2!nn9Ya%qOMQ44vV%t+%O9ka|V`^ z@erSLcAp8$=sy#Z{gI;+5IPN3v^yU~#*-mnET+Nw#;bU#u)u6sVLWUl>nE8HTH&kn z3b4P%{Q2jR3s0SN9SPUT?`$ITPxSk&TEY>yfQ}hyN7L%sszv z4@|3(aw>!ivO~rP!YupWoMhPRz?Cy>Sn&MnFAkg@Z+V~OVvVfA^|0AC?iEr`U+~p? zBFss9UJ(LQ{HR;LnPYrB<|dH+T721fX%l0 zc9Z>F$ULaL1LpY6P1_5zUynG%fGJPc+7VO2r!O{#!#R6K1;ZS@mL1w~rJwHDT`)5` zphgb%e>axB6Q;#SjQeDUdKcf|0MdTb!jSuLQ77vkw+~-WhMTODA4snEid7IN0U-T5`SI!*lJ{5;G>|M3CdP9G#>CXMT|`CG)}EWYaei<~bd$mfYXo zdY`KoWBsh!)p?v8@3W3;E-ce4WqJ~(eHPA2gxMwwDr1SKA8zBoCWl8vkojk4cxLQ_ z%Zm@JJOqoC)+)S(9L5?Sr zI@SyJ-!U!G2NwRXUNoWgkYv9T+x_uH?yR}YfIOc#k5py`!qqWLcdUX%J3Zt=VJq!1 zK9;cfi;tv#gH5OCi-_~rA3uoPa9FW-0Zg%Q3pxr9`DZxICHYpXjmKerYINUBm{ayV z^90=OJZsK$m=(sCkAoW-3}s!C`!$wD!+x)t_Dv@B|2ux&qCH~j12dYZ##qAx2UN2aNN(yFJ`QHS%zG~fQ(c_zyjz6v*>Q<6idg3A zx_dCcq3owL%vxO-ei1ftzqN@1bFwK@V_~jE|AQePj9+NKdKjE{&_eDzEIw*Q^Co$R z|Fb@rmosj&6RA&{(9i=5_TTbe4;wZoBy_@5MYX+aVcwB>e_BbsVQ+&ytkT@w^Z};- zGN%(;#k7nU!3^0)2iCxjFGC~V{--{8I3}?kX112fI3o|gIk>+T7KR&NaD_Y6Ms}6K z^j87H{&2{`i8T*M{RYd}AlT%|(b58#t)yBI4$Bxd||D5LHizGKxnUzd(yr`dpnR96cJlHH}?A$n*yTs?f?>qY|%rw(&w@;0#?ioO)8G)Fr+4SW?7sWTYOv$v`r28f{zA*!-{^0$ z9-qwQtBG@WCU@@_%cBj!%IC@%FhNQlY=IaO3%Fp}HMgH%4 z%NwacCFkQQTo>6Q?|!r0(;Q};He}LBdyVhumN0Ei&KW9fs5n7sCCuCw+dX88=ebo` zxgE?+QQ^FW(+!XHZ-9mO?w!bilbk}vY=RlQO{MX0baJ)wMwlH?elQF++caI5j6ZL4 z!50=>aG`fB6Q&-lOtU4q^dY`A%(d*s7Z)7g*GfeCCr&sbrvr;_)gLGQXRS%Ss{}W` zcXu{~1=Uw=|C(U@Zgman!py*B!U4E7F?Q!9Sf~+c@(#9*bkv;)bK3H+zk=nC=}j6B z)4ww)d{|hhaakD_?^(I}GR$qTNhPLLzihn(Tje$PlJ>l5p~-P@!eF}l1el>4=Hda< z*3xgOllGT;jyS=|TTYp4!d(141?ym=t&^(B`ROwf73^TY0QpXBn3t3iWd$eQ*tSa# z7G(`RF@mkW+Vm`fg~8W`Rbl`5_7!CO>F&0Be=WrRRy@yp3&~~jZ@-51S&_X*Nxf0l ziyN@0w&v+cm}}nWmJ0LkmE@d(#VWn8&%()fY>Ja%R{XbsXxQ+_sSI*I(_X9AxWm>) zHldeb;jydV=D@+1oz@W3wG&788zcXn)#Of$aSW{)wXve=1A+^J_=jC z8}sHha*^Ud=q8vOr(OIC7PS7=Ujd6N)0UC_nExr{s|75#(Ou^y$zN&gHG;!C@9MoE z_4W@%b6{5avU@e8{mF`x6Jf_%opqHkt6tMs9yS~wwWS>9uI}FTdp_pRy0}sRQ$I*$ z^uex2WM-4=Wu!9Ze}q-ic&z&{Gi|%wJ6PX3>@(Tl>3)^bV3&)WCU9f4|`2yH> z(wrJHzsx1-_m05>G}+|yFhAAwFdJ^%DIQ8B`GJ59zHr^q1)d2o-Qg9D1zR1uIhhM{ zDSn*QaDuj~%@J65(Bt|nm@-i65D8P%ixais%DuB^ll`8%#QmNcte=;YcaZq)G}$ln z@V$f8WK7K2+wD*TS6Xo84iT%K6NJHjRRONVwC2F-8L*Y%49W3W%b#Sm&c%E;%Vd$` zvmd*TWWxiW7D=8jf+-h1OTobn+TI-Gl$%XHQFJ`tI{Pa2!=lLyg_W>yMoA(ubuqPX z30(A`dIvGxqO9Wg99+LGV;rgH9GYNQ0=M3gJ0v;Zjr1E~aMsNi2IP9Fv9I?B!s&L} zDlxD)JuhJ%9GR5BIS#WezioRv8_z2zfwaV4-ixzfuRrIGA4ATvKGzTpTfLq(g^VX} z<_o?P%s4Q6F*(0@>F$sjaQN`}mtin9*o66M7WNbF#GYW7rdX+z2@mye(+`9>sRdKF z!bZQVOn1Qi&f*W|a8XIhZ}L3lmGpbjU@QE!5nd#pIJ-s;mNPcp#Uk~L>&ib3|8Kvi z25fC@g6WMK_GEwOAG=js16M!WRzsejjO~9EvtgC+sZk4HcATf58=U;~eCTYL_2hUL z1Ev(lwd<3-`{OzTSpDmxIt`faIY*;&CicIN8!O~t#+b5&nQ&hF1N~udT%QgnB?6{3 z1yppycN7nnpiuUpL86=-D`GGGS zzM?DUGRcRjG;dh{eaNRYn7%hyxCKs13txB+=B%{|@q#I`OMFj|JZjUS-Ed^IN9F;N zZ|^QW4l`3^40pqnT-$f&VXG{~2!B}EaYX7W%rP9F=tJ6{Em(2`R#|9#lm$}96aY2!}Mj^6%{Z~lhV5Y z=E{Fjd;+`1RDGHSb2fNheg?Az1xY%@mtUB^gbRjF`)a~0+g#2oxX5AjRw~RWIF$V! z=GU{2%ELU%20M>` z6Z;t!)iv#32xqo#@92bCbjEK>xST&uzYS)L@;T-L2hYzwB_iz)S+3Ym>XoxJ>S6A_ z;a~YMd%()}B`m&BIsE}_blo-R2~6L0;>=e#VdL2Kd8A$@iyw^j$$FohbDOl^KI79q z*!7SLmrwEl>G4r;vQp*VEaJ>(*OQ6QZM%LQ<{tX=ECXha4t|{h^Ny)b$%6%dO#QFG zg7S`%5;&_u%lbUbbo@Q_8{9ER_D%xK7+t2Nfcr67kJ57r=9{RTQ-QrqH!g^U*)P)? z4dJ-LyNUZ@>Or4UXW~QKTlbKBJ?-8OSnKLjX*SHM9^QTc7ABXQ?tuA9X+NXjtaXv< z+hI}TqlK|BZFs4S4@_D2Y(yfQeCA;P2AJ{N-XI??x;Qp|4QU^-GT|ZIp|pI8EzF8r z6jlYZllB!ahlQK9R@B4dnCONjFt@Kl^F8c$($~`*#-H=__y8wdxiZ=arv6gZ`T}c3 z$_Gp*`LpLLve@s$#cO&d!IX%b-VT5T;5*M4f94$a!ZE?cV^A(gqg<}AIbi!{@Zdz2Tc7K?sf~#O7l`^hZ%SD zs&2#KtH|0$f=!;UJ2dA{ua?=W4i*!niftyoJ(WB>OX zSa9MJ$wzV1C&DuBt#8i5;$d5LeOT+v^_TImKzTUU2v)qpu!Vhub`lisvSESNDaK@;XCJg|sF&Qz27s}IZ7 zZ^^hQO6U6BdOO_8WjzPXFa&)40FzOXA^haSbWn7=7%S(kAclxrfptN z^7xVTSXkt(W=c#+ZL%fq4yv$pfVs;;g&er((OSI~Fgw%cwjC_@nYzjX=5rs8Gl7}C zc3+HO@qxRCwBgJfO3ia%T9~YXB3u{rar$JKzWK!M*BF2O*&7=t!4$cUhxKssxI3L1 zFtcDSvkGSW=9P^kx%-i2@v!xT>j!^$Vtn&+`<&p8t>d?Tg+-AuE39D3!^nhAm^Eiv z=rmGKm3IFC3;jJFkB3|DcaC@i)8Dly%D}oh$BY|cuBc*%6inNFVp%;bTB3De825)_ zm%nli%vOFP{S|I(<$SAxnU01keej?j?`alH+ZZvr9~O=o{o@kJCrtb`0P6-%+j$D+ zo!-ze1RHvU-#rFXzAx||f%AE(4t$D%S?L4Hq(2;|HIT>6=3^bybe3 z60DziesCZ0^AjUAV6!aQxjRYy$GOYr!W4sp^St4I$0KB1SpNo?tJ8Pe8@cuO^DRu` z%xcN|pXW*Y`{}($ z;LJBmZRWt@&MA_5#fhdBdN5^TNV5y_ZpD_7Ixx?3`I@D$(ZRWv+Av#@@n;q+xSu_G z0?gnjOU6t5#cH=Q%spPvE5vx|YwJ;{F!kvxvj_0NHr0hnFwJOjd=hNkqqNl#dDyh0XNwvv zzD#v1h4qiOPu7B2<<_Fhu;J9lK9gbI;oC<}!EvJ}P1l8)RikDdfYraG)sgY#Hcsny zfE7ji87yx@ z&an8M*ncV<%o?rW0*fAgsG-67{1-bsVCq|Sh9;b3vOFdL=I)vuJO-9&TCqGBW?3j3 z%foWlFG$uO#VOB17PkJDb?hK=x_j5zQLxd3xRGbc@tQZ64UzX3&x?r{V2;+?UB6+i z;X{+NVdm5;M&IG4Pst^>V7}u8$@@%J#T2PL;@28~dy!||h-cj=b}*a&5thrEb)*QU zwp_A$4|6uZUoC*SAq62XVS2Ov(lVI-Vl1ZwPMW5CmRRUxck~+EeYKND+EdhP_eH_! zs<|2kFrRbg%Q9Hzr*Dv?e_BN>8Mtna*WCP!iE==ddT=vw||vsHNbwM zwR&A3%=#I$zY=bGn8V%%vz1znQsKI9uEm}(M``V`5ICgRcIIZ7@y0&S1x|Lkr@WE0 z|5@F)h}6flHLfFh{lb`jeXRFf=_q3H!`Dym!$x*HTSO&IgDam%*^vE;E44OO3wB;UR)y?mR7%^x1X#!! zcaH3TT*?=HMVNJYe?}V2D1GexXFB#YRI`1f0wW|o*Gh2t04jpm6-Zn<)l3aoXb{S$dU@JfC0N2l=n z<&}ENADCh`!$}`zry9&r!1IN3bhgAsyQb?X!E73T$`s^+s+fXtFvBZ$SQS=G%%n|# zh1DOlN&g!6%-S*$rq2By{sQ)&HT5{LxI3(@47U1N@1{lkOZMz?SgT#(x;D%lzgJb7 zv^V_ql34K0sm^JUqic`DYsTr{?eWY9uew-X?-=KN)7I z!lJ$@e`|@Q+bE-9uJq~P2srtbAc;Ibd284#7no}J(rlPBXm*hf z%lLKf-38NS6=rv8<9X((Alc6ZC3i<&f&DxE?(IR&bWavAV6W)B^nK)b;Z<>slkoeL z+WLV5i>Lgx*aO!!M4vwj3w#BO%wa~JemL1rX#ri6Ccz!nR;^_G1m{m4Rfns?Q__=Q zc98X|@$kRRCd zqV`%ETt4++3faFz8Y68tz?2IwJ<0pFNLu-}4b0s4(TMC<_=`LrErJV5FFz;m>pYDY zWh$_dyw6h_Or7O;P66gt3{*^k*_-MEDX^G7UQP#QO;w5grHS$X;IVBg%v`6q^9>xW zb}NbGocWrwZ^1*6QB%qBM7AAc!{P9ovXcFg&+j#Lht=24l^6IZW$0W-eGN{+{hI31M>M{a-qmh?~bv2x{2m_L1O0_l%KUN{z8rY)$Bbn@Fm1s6=3%(WV7}z{AhXH3AOV&+eM1~V&L3_x z^)j4k@lo>on)M^UFay^AnW=k>^rtdWa(zvCFKW-gl*bC&?jq0fI4vfo=C24Qj%&*} zbO9FX${F8=%LjzHmtmgS@XRz=(QfOR>oBd&JRt@a%`q8&17>F*yAlQKssv5vllD`F z5BS1aE9aHv!ea5fK@*sM+E`*fXR@3QEPj!4?G|$GIZ^&Eb#lGi+_GVz)ym&pu(icu zOL9K;@t|idaMs-kA1)Dh-7m+mOgt&e!sI^u8)j{8<#zwau62NYE8po`X1E;^81`2zv`S1oM%^5LB^LEygio% z7fJ2zWWyBZ)*t4uSpJ43u`trA&;({xE|z@HU@IKDY5@QHdtESRG;Iu=#~dZS8|^t8 zDD)o_Fn&LdT7|;&z?yY^u&sMqD(Mep=Jfq-uyeE5q9~YlvgXJ~*zt&$66rt1MW_7( zEVtZxbu7$W5G`&d?aQ)6Ct<<5eG+qH1D}%li39_NtL zi<>3K=U-kPm6#2bQhuLD0D+T>;bUUY;0aFxR;$>m}+$GP+d83YiaTbJpwvnA#XsdL7R4wp>|6+F#-(K86JbQYnvM z{-oQA?XXH!d-hY9y~CRG71m9E$gd;yM^}B8!um=6GyKo@E8gl2RJgm2zEF&ux8ZiT z3amSnQZqn~H?dk`k=97sUzl#fIwpf$&Ud+`6uz(V4R)me!ST6cV?D|DHrkcn0YBjK zHOA9rVbLyU$$A`E+bB1Nc*U%V#mHGNootk0?#hjl_0=TkXOD*|QsXUskymP3rjndD zMn%&YrZ4-Frvx*ms4tR(9WzV!$dP)@`!%01AJ%=oJrtP#dFWmP9NZhz^#lDC&MW>= z1-EucXMQF9_c5F*fD3MZ-blXh(fS`R&VX$T+PG~n#bdGEWtgL~>Q*z%-!sx6iL~Fy zoze)idX^5IfV+GAvg%2_RDx10ssEaKpqjL=xMz6)u1omxn9K*u;9G789ImME^#Er2 z9Z1>^GY#d23SqjgOtTAIz2kX1nSbgA*&sVu6f@^1Ss&D>d2biM1(SA2?r(Nqf_7j@bE4%w@*w0-wQZixIqvv~5U~ZTjmE8Y~iI*L^J@tYG9a73}F#Ymi{chON zdi5zsm?M_6IRg**@1w1Sh1c`aGhnSxS`KSSeq}>w4y?Pf)^9aT=~eyl3|3j|^T-ms8ir_?UfNk!B8yt}7ijh7~;v)|!!=U3X?V+~itqWD0YOmLU%SYB$k0aB&LbY)H`NyI$T;pam((ZU+h+rZoE)h-3l^UHV80Y*AL%dD zh1pr30#x8+Yw3&R`A7-8iGN!i>udBX$@5*@y=qAt?4`Y0Vxg3;+|&Q$6oredC$QDF zPBEz$+}UaN0Ol@QCwabdO5c6RB7XE#X&Q1~{AA%pxM-nsr4h{f{c(jWEV^er)&!CMPW)RN=-?MVh~EDp^z3Pg%E~9NJcwa21yjA1<^K9iNYWxEh9yeObSVuR3w$5 zB2>Q5bNjuo>+}2balOtt_uSjL&wZAA8Xm$L17?58^PIiyQTsJmJm6zb&bM&Zq&dNG z;Y#mer2mwb(8+kFiu{9)PHSP-Y?ZSDI6`?DWl`L^*`ct8rdM9-_AN6elj)j*t~uNz98??KOL+ORk? z-h`ZQ`eB(c9oSv{c+ymu)o{^!BAk82h33-2# ztXmME3s=;bTEB#uenxY(U`y+_tBYWEX#E;hSYu3}ijc(L+^iFFTuCKQ^X#@*&xH&A?&fxAE(!a@5-E3e6D{0(qQiB;ir+-a@nHZN*Ar8~H zYyu1KpRH7YIg7X4GlH{i4bKm5LVQN{7~(wF9={JT{ihFAo@YwM;Cq-Oyf9Wv~Coc}GA0c?nC$R#WkQUr|vFEGQ2yC;6ky-xpMnxS=n#eg7*7 z;s-Eufq$zK+5Ykgo+U8*(`Q$S3f>>Ol}`|}#;vu^fhA*KwUGRDf!KT_oa|in>Iuw? zU!}bQX2x6We+qMNuQ|IE&R93RLrl_N7+bgiRyzMUvW~jraLePNRhqDL;pC7M z*wZQ8i~)aXP9Y?2JA?KT(lzHYdTo-10X(EaL*-9NBe)ztgNw7qr>qIW%EMMB( zDKIlmc<&ZmmRV9`0&~xO97EiZ{HJv$@ru1=H{o2TppB-)_qdd4!!-|*{#~i_FnO7qF@=l58=btdUuH?|yx>zxLIR>G{z`_%qRS_fF9|Cd=&cFYfPZ`sN= zV)pG08!nu`A$O=d%zx+9z8+>D{o7Ca{{-omCRxFKW|yfr%f=_f2$pVBRHyR4Rv$kO z7LU-Q?l114|W}!%6z&{$<*?AD;wk-IcvI z8|KcRaCkDzt(0=8cu%Hk? zIana$eAzQ za{q~r|DAmUX1rKrOYR4WliJs-a7($?80vm{HhNqfoHww5PVP6F%KQ&OFwJuQrrWU8 zU|r67IAr6GN7nXbt&FTcbh5vHx`_R@hZ z9gLZ=uyFaDHyUu_6rWSaN&5DQmTIt}lVAZC7M-oORfQw&RjY1;B}aY?(BXQw4Zqfq z^iyBYlZB1nY_M`Aj@VP&N5}no?4g%EOdAq;sPx7L7JVJLSp+9cv`Dvt#TGGN zj>G=WPZrF9B?fA?hK-@{_eB ze_*%$DOlSIrsZa3R35@z2_EF{N+Hf{3RL$IgthnX*lzqg{| z!F<2-$6#^Plh{PW`_Z6`gSm%4GLv9IdDlV#%wnY#r^AwlruaNqI$_--@_Sk&IX6Wl zze7P#D%|K{O;_}9vIG8y!H2gCx8PZiA z1IvX?we5sO&aVuP!K$)%2T6U&=AM4%2fOKP4=44b_^0FP^{~j3wrM0REy%91gv;vJ zQ}rX`$CW^JI5>2_vJ&E=>=c>4k*MGERH*SWNvr0bCveQ^b@R#n!93?|q4^%WfT{^H&Ju;4+~(PyyIf!{I#FikE+?h)L-eNL(mEE=)x z;eEImSUPL+vl5u@)OU0{EIvqQ7Qub59y^fhPg0_5e+Oo?C(t&-Oyj}Jd9Yc{ zW64V5ZhpWOxc=IU-^*c9l*#*SxNb$lYbTiFSNtgpZkwre%n_y)?2%@`hW45ni(&DA zW7S#M&Y2gz5N6!7%02-z;y)zMheh2Z{_ca7r2B`>gXzy%v5R5jxBDow8UjBr`X7s~ zf4Hj#XUpDnwnTcCv5MZ`5%@hmj7tIMTcO4M$)}oaACl!-7zqI zZ(&3d%#XDla{^`@`;>nYW^6E_>QiPdL$nXp`kZx%-0!TLn;&g~6>NUIqUzhQtjmi? z{wmpfB)=q4H%*u1-z?B4*B8s|spIeAsNWTAsq2fIYdyIU*7~$OEF5uqg~5$NIB%@l zj(sG3Gi~@K*nLIJ20xge7I*MC++Py$kw-RShU@)0&#oYz zAiv8E&R*Rrrmh!{f`C;pk8`Pzsz252-#fzzSM$bOBfa=-<0X68|0wG!Isd{_MI-Fs z++hPTX0RY?&b`I3mtJY`ESUZ5^8;IwKklbI6Q;|RAGU`37CW$wU`EfD;j>}kUgNfj z#Pm*ICQSPy>!%BI%&+X9278t->Xn6=T%YxS6i~msIh6XFgX^VD|4%#2XOEcl3zldX z$bN>so=$z&3kzIMl{dqU^Xh&y!om@c$2=$T2{}%WVX5`?;rHRhDwVw@B>$6~hB4!>%nxqY_B^ z`6})EV8N|>JK{+C8~PW5V7F_7jnTwsKit~@v)X#>BVkUR;aeNHE}!jw5N0+OuQG(a zbEbU?hxy(++K0hWi6)de!{06aCXeUyRYn6zPfz=#@DOehTkSne@)z9Scmoz3tSl$x zvqGhjS#ZXpn%JW-r}v@UaX67%ay5p;56!a=g*k=yUI<{(&H1S|u)_WOYS&o4-@=!LM@@#Jb%Sa{zzel9E*J?@+aELNB$HiGNMw~Ww&IkxpKI_+V3v!sI zui^YL5sjq1!;=I%T!HIlW$!PA`Q1m;{NVoLRsPN}y>DLrTG)7b;Rw>6VegB#T?U8j zTjWIAGpy;!!3*JvFrgQz4+P_^zZ<~pGm_1|FqeLLt~y-*rLTz8C){III~8Ha^~+77 zNPfdKlVPxJ|2cKiK4hg%2$hFhcD7e0!0d%rsubWdEJ!#7bH+Hx4Tr1xRPxWm3{?_lX~pAL=MM0ZHVn;SX4fLYYA){Vnxk|61?uON`T!y4lN|_chYhVWnWlBth0{1 zpU_YLNm>DS+-v5R1KLB`&XFn`qfGV=aM ztG)TE2-cXQ_1hH|T#gyo21m4R)?5vX%jCb?!1_(DGUR{~1pSBeiGIYvr z4ncijr>5!+OV;UcKLJMt54;Z|UNl+Gjl^G$_6>!({HpXNFzw!VD>A=>@x$TtMA&iy zgBqU|@1@_>gdIhZDx|-MIdO2-7+6bnyWUxtzhu{<(XeXW$fxIF!KVE!%CK#U^3iOP zzF?RsN$+-RFf#{cWy#i#g$wQ3Ysh#lW7c}%1laZFhek3!%erOvX)c_-;&I0lnBU`j zc@eB|Vq(Elm~DQg%^v2ptq*-ey!%DcZn&=|;%YOD1$S4&VE;xrYQ6}kY}D*?u=jrU zv0lVE%HdB^;bf@+PX_&I;`ud8F2VIf&UFkW_WnS*N55>sDB>5dDF?gK%QRr&tOun6 zr2lU`l|Sy*L{gq@Uv4HDPo>>As!N7rG{ob{c&JF#)0o)Ne9kUolHa9^eiE)qXPA@z zQC{$lRpGGek%(cWKT!OtWZQl?WBjuL(myKwxThu%_Ov=uYE9zp9$UR&J2RX0r2mt~ znSEg`jDDj4GQKI)$qlt64sZHR`md#jO5Tv;Utc|vn*Yi9wyo#449b@|6SWF)&a}Ow zdSS6Qa|fA!Dt)n5^(h>zvu^cHSaf$}(02I0@k}AV;DIfi|6m?9KUWwyZl5NsaC;Ip zKUdnfXV_@iSp6U^oRsfRtC1)1TE)L)e2?CJKewNT`gffLHU7uYt)-lwGj(k&;{RD( zZvHEtq_4c${TJ!;XNQa@;|Joo{p~WOyiG&bpCfTuT_aL{VcPQ^~N@Rzi(u@h@JQ zeFN4I?mhXO}UvI87- zeO>zuSg6XrVw4b_$-&ixWzxsb$1 z#Z%?A1X@z__t~t|g_eltikh^X5T_kkT0ReE-fbGqCh41;-kZX+Jg^)NE;@4j z7;(zp#iwC~x!x&!SR!L`C<^vYuxp8j1#!W>2VicgYvO5Gw6V=C7>=1{^NLtFPH%4z z%;4VGmq_wI;(X!4^c#gXNihGLiJ=FqAFF7Q0t>oU{#gSX-nu#`mBcqZR$2wK&5nnr z!;D=VJ=^y z5eG|`)u#KxoWDJfLgC<4`_tt3vZB8(41hJJthhjqKW)!+*FCUd+mePYFz?Vq?H#bE zdh!}~5$gFvs%#zF9E$hL-$pm^I^O z?-aPBA)t<0pOT^Ir4RQnoIqV4?42DSG~tZYb3@7g(=^X~83$+oD#+MRY^j^90p~Ua zScjAHoZs1yc!!`RHIn#|d;ECV_{Hmp7?@|TdQcmd1ZDP~gt-$ow@ib3Dwt;Ed`XM% zwl09H7QF921536AHHAH*MMTd@Jw*mj}* z63o@I`@9+EH+owNi630svJ2J^t}VX<3!bzVdBgQ5`elp$$1%O{$2@?UA8D~3h)0B( zDVCG?@qh)SJPp&KAr&xVmH+vTFtg#7(_@l;qDszsI5|vG`UIxkZ}E16`@h%Us)pG; zE{V(FzJ>8ep1~{|t6vUq%zcf)77~A_Y%azAdd_=P+6MDA1_D07g{83uUt!V5t#c}2 zsl^+54~b`ZF1`uJsQJ(7g_&w;&$HmX#ml&VVEXrETMxrg4r$v4VBymJE`f03f)R>; zVP1~HO%5FFsz{k*f6;gmtf3JVL9H+IDH}Zru8JS0B!l&4?DPA#lk0=^YMC*y;LDQ? zHMlNDGer&-p6HL2hiOrjnL~(OnZLjOozG(H'c_M{88X5E5wE9bdW@t zys3_=?`W&#o}Gs)R-ZY(3vo`%4oNDU=u&624Hod3QE70poQ*Ph9!WAgrwd?e{1WD! zSD^AcGG~ZMeI}`?9h-$XyX@{4Tav#}%_kG?Gs|=&&qw~XliC?DwO#`jj+rx=Y@h4C z{-!BROBnk(8LkpeN}CR|7P_uG4VyOn-8C8JjauY<816e>t~Uv0{-ODY!klEfvp&rJ zY-q9%&WlShng|P=)Y=1Jck5?V`+(d1YPm1W-n#ZF193)q*wn2sGtr4!565V(?{S1h z#`jaSNdD+?>u1A)*pzR??ET}1(BXXxEk(petVz%eu@5v$px_BS!1XPO#9_^<~W>xX7ppSzXG+#9b4h-)U{N5grhq* z-W66DI`V=_$ZfYHApNPjV2~mPg3+n#0PYmr=zWIaWF?#<_I%_8E7B?!*5vC96zAgQY{oT`d zZ#gXSVUv;XW0M1*NVtgE?kDi@&g;<_TwKoLz4fvx{{2r5$|Joy5+5P9?jHUai z=NE&)sZEDzFHTgG_6x^3qa_9Q;=lMx)}xBP#4bvL3#Y{|&VVH+N@vEv9Y0SGA?sVY zi#|I>!p7a5rB_KjZmW7Y?0xg_n;WpS?{Iqn94u0rng_F|ggE%YEE~4tZIb^$k{lOS zx)yXlpTyS=Q}c%PgDxxGgC*4(&%9vOyLm52{X{EbMQwreU;A?J!wly=>)hcyL%E~n zu%LJ3duQ0~h3BeAB(85fW-;u|TJ^6!WaP=~!F~D>Lr9#b#kEz1&HNRp{G70X3xj<) zKYqJB$`I%Hq)zUE*-s`fEQR?m+f-X&;{%@IM*AmdS z4NkVU(j@DDX{`sf++kOp@mVKedgj>|Zm`ld%Z<@6b4uljm2k)1z15^WDN$Xs+y$;1 zv*%9)EcX4e$r0|6Ih%I?7Ua44*})vgDjRD3@Q{~Q3rKq9@nUlQvA(7zn82R1)_7Cx z%lFnA6JfiQnq8X^r*E0QaxCmWQhtEkA8eD5t;(?X1SVC#3X*DK<>8Q@Z#hd4=Q>Pl z9|qT#$d0##nR-4=3UFR_+7NPoOMkeI8xH$#o?Od$T>^iB@iw%c6fas=2|Yy^Mkz(O{V6z zvIKVTR>2)YE#_6knQMA~Si!1~)>89lnYU+bG=Te~yZ5Fb&Tm^_NY1xN-j zMo*#UJ99pss;hyG_w4-`f%J?~?TeqlbjP9lcrd%r@NOAwwj}Rh7|b1G=X)PE+Aej{E%RjfkofZ7MmaDuch(Eiem>csXhD;?nu=h{L#EbQ3&S`Us$ zd^b{o-6KPacj!R*Vaw=Tf8PEYp_A^Fdxtqp^DH+Nm7!L00!cNYGSnSIY* z$iVvZ9fy$qLUx1Y&yT(HSsIyoRR5uffBpV@Sb%kvc8Rz-fT7ncKkcdkE}Q0&$N864kuWQ zSV+z1yRUgq2{yghw4@Gk`o#9rGO*C9x|H;nqV&?AJ-D7c>XYuktelO{C2&lN9JPL# z@8PuVIc%z`+>nMiPqL)q8Qf^Tlj_gq^;ge$0=vd7qvrR~M|M(n4+~k!M|zrFEoBxn zJ(jHB;a)vz_!Q}*x~^06|AcL!3{qalm~l6w$@ZTdqinY=zm@ba({##%s}UC)d3GIv z`JL73HE`ox)t`r9seR6dI@qn#On4CHHtY1(!%=5f2|`GEb<0`rU=6>C>7+lKaewcG zX1MK6Wx6lSR(U7i2Dbz*XxIXCZant=0gLW+8f<|7J6@c7E2#c+q0Z1ba^!dxJ3e(l zTv%H1R1734 znFYJfK0~ed6?P^#n8UQ{Ni)d!fWRay&Jt!vB{j{0X+6%bt>LzPd%b6n__Gg>Y={@U zSxn|D3jKW57r`0^J{P9J{H(fBOJL^hsnq;MzLv`dJJ`!qc{~}fU~f8}>;${6EY+I? zb6esp*l_Ymhj21KiXMEsVFhftn@7!$VvX==Ch^?D0j)8Ji^m?N^3$vbIn;a+@9dTK zh-)aU-9pAoI60GsErlzNR*hAJ|J%OmCt9XF%<4Qs{ocHR15?TT6YhpvyKRxaKItnp z9>eD6%gur7y1I4#lJTBP<6@0qt*)ZFeweY&%98;rjNbkBH}R|b%_CvU#@6G%;Qy{~ zZj0teYCekUYU=tHud#|Fmev`MuEqIQa#&5xSD{;KoVy9T^A}U|y;$)X*+=2Liv#AQ zJl6KP$3tMr^6c4Ud(777(c9tR1?%Piz`T_%HEW2w+i` zV5y1R1aka&okxW+u-D9jE@Hlk#Bn>^@$BqERaoRZxnwi!{$;0#m@_-@p%YBkl%?`Z zhNq}n!uQil5|QocfP z*aJAG{WvwBlKCy&^xgSW+Z{A%q1n%=$aqv4#i+kGk@*CRo&F71{V7h-)K>_UgV7w2RA0@q^ zl6D2|d7aer1r~Ja2ZzA)m^Y5ZqNQzPy-9wq&m}Uyin%Xbbq$HnY}wjM@(cX+7s67- zB@0@JGc<Jb-+pKSRh#r4>e(NIUy zzuWk^3zqEY_bZ1vMLlmTVN3h@ugQEUW@7!_5?Eik@6TQ0GkaHGBl(w>3-7=*kM-j& z!!2XeEpCyxa&q(qn7uS#`vxpc|1^*c=lkgV62gMEbHkG0;jOWsvGz%QydV_9iKBDmSm_^30SH9N2`BzPyw8`{gA^wr00Ipii?NyqlcCk5?9W7cm%d>;j5DIWjdq1EgY8SCA=l` z>v#&Pi96wpCqKwUzm~&TgQV`sBe9yo&SY&9Tuo`9xKemwLN6)^pZ2_#+UZ;5pW=4JWF@}>Jd#U43 zPf;>cf$b*z;*<2uH=l?9=tO=C&W0aNXKoYjV7~4>oO= zz+TCo&D8b6i@*OK<||ANBi9%G$mrTPaN)IV>i5&U20LEB73UutlKo|RT5k4wKAES-^l)= zH{&K;HPFR-kGLe9W0p$ND;rdi`-x{W>ik*arAcNVVNQVZ+-TTsg?!^Dn6LJD%^|pA z#<#w9nAw|Jxg8eh82pTQOKflNmCt=>-yRU}AoFkLi$6%3h zbPstxh&(2%9ER)mmz9zFTQpmZa{iEsabsboe|+g7#0_a$rn)fAd;N|GIHIh&PakHT z&!FN_j8D}3XldK(CkGIB^MA}G?Fq)W;}eO!%Uy4p!t@6rl&g#)|34>BSoD^}`9Z-{ zoNFB-pxQfKw|)d6zw5(>U8H@)RS*0dK*~EY^6p}o{cIJ-4`#fp(<1X3IOD!P_JMO} zc2e`#x#6Qyw!or;%c=GkFJunaox~d)sqbIVlUBrA3xaIVord?$FTXwlv!gf6A+TF0|d#$G#tlFV2ChJW^iH%`1N&2|K4u6uq>ty;A zI6p*W6Gr^4ZsJH1PgtoD1xvHXW_|sF{@ml4`w~h1_b-nW!aXtSpORtv-DOX6;k@<} zF64U`oIgb>DX^K3f+!Of>SlGt!VzD*SCREI+${$xBVp!&>p{6N&2y~jLAWBJt@J8P zSIm~%wgdm`**5n)se3)I<{4PkcfSJ@(%VPM0b z2D`4`@FgGS4a;Y&gJVo|sP*|gX?0pO-2dg;tQ&|6C)u;oVd2S)^~93W^TE}ym!_-U zZCHFLXZ{e}uTi&_jU~r}mY6(k9Gv|i>jl{!$9vV}X|Uz|yi^g)>M*=z3cEhFD&6Uq_=qsuLb3)=K4o6Y}9>w?)Iw&{}m;(`f|hI)zo8dh%wFCCNGRIsK7C)Eit%uF5 zWT^H1;xbK}N3dwFo(_3m7bZ{segjrbi!UYfVYp@cJ#%4={<>!Je$RKyoqGY!FE-3D zBIV6WSrP*?ZjSsy=F14&#vKoVbL9;dQ|lvt_ea@~c!J_8GCxM3_HBsVc~JvWiUfrtT-Q*d>5LM`81O1J>ym3sLVII zo-i|{IQ(ThNgr3Z4VGFa6js35K@JB<|BPT@`>z6+UaY9K8x|F0AHE7lIazGm12dlU zmt2Bre>l`PobkAMQWg zEewTu^#xOo!ucyNdPTs(=ObzYVfXSMnlZ36)<4J-4!)K{txsp2d47H^oFHx7cmZ)n z)%b#Ga7BZ8;}uwRV*CAZu<&P)^adhrm$HR2zriDc?M@CjS0X9q0rPeRgtK%ak!~R!`j*1YMoSs=`4F{h| ze@o2CoOF2+%>NR&?mo;;_#NO3Yh3w7^#=;qndoeRRr~rMlq1eq_T<`jxTC#|S})Dt z7juia&3vEaF)3eIJCc|+Xq@>J{x84&;b>pd|0vWGeO`~aXyj5`G0gic@LUO3^f`?u zrmYm;uz@wKw+>Xn(g}@AX2Yu5Bdsc6ZcT8jKHL$U8$#wA(T=cEMv=JhJlRs19^u6u z3isiIYGi*HZU>M3{fxMIGPS?bA&p6YVAihBie&#;qN*S%oRCyV9UsnI_3$^a_^R7A zl3(IK`{`rYbfY;{zJy;mryS-@Kc7Xm&pcPW_$q99sBw^NU-0zYqfj{U<$h}WBHPw# zFIa9wylOGx!hzUN%VGM?B~!@xWDk5(m<==6>~1FKo0aL;IT;R_B13&&fS*(|I1$c} zTOCEN7hc1n1eAmfewsVCajc`y^^51BxH--EvF&&p)D z<)?Gf7E-=UbjcYwcYkpJx!?I!)#roYC~vF&%`jK`@UcL`chWQm^m+B(UqAQu7gd}S%|;l|)))c0dp zFRjxLz--2YQ{zba=b2}?uxpqJm(1s6^(r3U2>TbwQ}a8y!!p!NURpDPjUl|B-P|9*$V7K=nuC`M0^Z74@h0jVXqROIOwAH^CiE6RGDF?TpdNVpy1e z_9OXzfb?9H^-);hEurRT(jVSe;lYI$m#kflIBlroPhU7hUhO;S&*$&ha5?~H{Sg1; z!Q6JHeh5rIYoM_nHB1bhH7N>B$ z<6y0tGo6=UiRpIAG5%+%@4HAo^}RTbc*d3Yk#`Um9)5Br3Kr<-M%Ti;hMJ3eV12b` zif>`|RMXmRa9cu}8JX|MDe=tO09%R-_qW5W9mRLn!t^rs-LJ6tw1@60*sNztcP}iO z-uh)3ob1y%bSTE3Sj`8H&WAbY?oLvM#n#u0X2K!L!#|SwjG`o6pBb=t{g*avSl}|` z*)-UH-<*?1Fn4gBWD1;jb!q^aAHd%{u^-i$r0v~Gr8F$R@08^NAusvcoNnbX-KpAe6dOh-kMJfgPgUyJiiK+2* zR_L*eMwsePN1U$vX-6`gSa~}k4wf$R5XKM>F_t0oyTqL>3E{A+p+$T$ELdT$>j(4r zp%>F(c8%h|HaKK_^|maSWAQA{8I}m`>d5%Hz@_G$DO|riq4*lih>ZWK37eUZF}n_n z3JSN6h4rmx3&?o6a7Uzi*9Y8RD>u9&8`OtGXBg|H`tL5Gb3iW6SIbS zMMuK@cbYv5V8LD!mo>2D=`^Z5`cqRqbvPmB#tyQ5+WhI?>Y8x! zY$%(oF$3l|uG}{ZX72ZHm`d_nq^`Dr{YN<7X22r4Z@w8UeHo)U0p>lco-z||;cv?# z$P;`!+ERsQuCb|_4)Ou;l}j4Ge;rL*>G!VC|u!V z6+pJnDTrIX5|+MxxoRBD{dM%HE-XI9SxLs@S?s{Ojql*_a2ZK#CVm5J2xsM9_EfQ+|G9A^Hg zMm)J>BlW#;>Bn~s_hJ749jd)9z14dqpOp8$fojjwzF*vO9yZRV{rmoqef-NPSgX^O zM%s_Ukdntj4*(s;*P1_XV-u6Y6-?=XFdfAnC7`zn+IU&wd8=Ju2E- zW1Xomr!7ZB-X9nN!vfV|g)nJT2`nB#pWX2m_1UOZ!DTQj!TL@$@sget@;-o%d&fV3 z4UZ0Pt$>B9G1cc`>iZWk?bhjc(Qy8d=D=#=ytyI!;X;Kgr^)#cdS6%6h54avTU%k# zyP&v=Mx4(nE!6j(*mE+%@51zfu6I(zB~v{1?1oj>$S)!DC4~MJ&PK4MMs*i4)6rD# z_ZwU5~*J3+8< ztj>*3Fu!|w&vv-+b+;w?eVn;d#T#H%i*5ySz0lK_T{3|?PT!Lu?<<`35As2!4Cg;GjG~Socifg;aN)kLbJY16 zHc@{eOpQm9^8JbxX2Xe%yqmvai9=B3bh!T2E5E<6u*f4zAMSA+c0-QT4?TBgYQT)` zqxO^eH{z7cE2CllMpau@G7;uJtD@%LN*DX9 z(c!+>;F((x=Pk|k9DIfQDX3D{6J`mvh4sOzdUL4xx!hAliQnPGeuHo_e@wc)<)Z}l zWWGMK4i<8+X1{@LM_6uNOX6pJuD^!srz)eV5!aY1QXbD`Pk3o`NbBpzpj7{OVbxM!}PB7hX-M~ zj^c0Req)UDu@%5{pDOD9(;IF>}Ie z#Q8tBQ*LQ`J&8OI|BF-WNnz3H?9H`EpYieF7V7?A>_U~7U`(4x>T_}bquG^+XK!$s zOzMmO?xzUVF~i97kaPI$@-oB?*_;KWe&J0qUwsW`jVoM4JrC(-9pv}-U%W+`)3d?# zDBN+~{ycf!(0Ymkd|(C7W522Xp3tTT?j(KoJF35h_9NpI3$9XUQ~fmpxuG>WaE9Aa zs{R#S;?$3TV;=WV^&S1aUG*;0CVpE*k3z)GwJ_gBu@+309QEfr~1Q~E}JVI zVS1Fw52`%B;~EyQg7MfB9WzjCvD=_W2*R=kZNI%h!`o5@C z!{bsn?EkQ85qWzx4!cKBiSQ@oD~&Lp1owN5Y$g4-Tq~C&V_;Lg8&rP^vvWsl`wLueLr2fqPSU%2 zWR$?kFNf~h3iDFeO&7qPx^@5hkK0@dcrah<(3H(2er7|92OL~+C3h3d^uKq^8Fn9F zQs1Mck69}>hot9Aevl)B-4AfGQ?qt3EWHtK(Fp(7-^?#Q*HaI>AFZeQn;BoMH`Kui zoS2u?d_`%>(MnhpFLXG7{333sLpiK5r|I+|m>0BUqzI1jn|;hQz;S5GLNk2Sm?sS-z^ZOC8 z@G0koHr%pn0kyuAU$bHd9WJX~ESrfqdrR|%j#|uTIip#g2D7yJLA7wzk-wA3^~Wkp zjC~5*jypho50@2bIQ9W-w%LxFKg#@Qc{vYuoz_KNk96O;lXBsFpBCzPiK8=H<6)7) z+~?saPolDX`%&0^?m*E#nE6cdVHceJQ7JtL<|(rcH^2%TgQNUlF<*Sd4weflqWTY| zzvHcE!Xec`)cL1h`^r&=MX_tB{zi_)&(X55>Bq+pNPi=zY}|%#&vE}XG5%fe3v|0b zz;3_4Tp;%c^S#T{&4LY9%#aS@LuEDn+>dwv97+-f-0(kL;~MduQcI>V1vbGU;3!oIG1K>?PuKE@P|& z_CBVwggoyAuX4iPz;yv73mag5rT+8=n41$U|Bg6y{?a;_-CJHj+WS)B#Ih$aGx&OZ z7tHce6^LNNm_yX_oKrq)O%dGIK5YHW65~T$BtFY3s->oxY;jWd}h)wMhsr5=C z$)6gs{r~zec?xV>0W3=Uy@RwTxt^k)RG4`;XR9U55iv7UVB=@0YsmT~o>{}Fvv9_u z*uaG_yLlGl6rA8x_isJK+OWwyxQ-qyZ-+SF>X%0#95ORSfmpP$aRsqsL+(4WeuASn zb?qj&Fwu%y|HQpi_jVPWH)KN(DPOQEuhSZiu@zD4r-E4Q0u?y?j4+g^qh0W@5uWL zpJhLb39~a?ER100R=?=!ut1nWwWqm5Z!}MXD;^|L{WZMh_D)ma>{q2*$@&Xk?}}~u z@PF42Z>Hw~ZMd%E71dwFcyVN&CQQw@M0%-OyMsC`m?vFKu4jI0cl$WFO)qu1GKnt~ z&zJynb(H6hB;{v+5bMCIGiYOp1_=TZG#qIWI#8(^Mt^&#>;Au-S1SP3&meb)N|3-dLj zMR4x7&M%$ByYx%)V8>fKsP8L?gKC>{;p|&Osr{vYnd+Ja_sxoL_(sZ$8XTKWd{KIl z)OXx@dY{k0dD#Zkcm%`FtMCNOlDV<)C*rJ%Q`4egQI`Yty#^6G$L=7U@mXG$dS4oM z=dB-!pVK~1>PKEJefSRezw;^RYTvku#HHt{{!P}l^npdhU-veU_c@`%#A59goOrTP=NF*S)va7FnP3lGGF)?-#A!dmIS za!7vxcan=o0_@K5QuKk@*PY%)!wjEE)OttmoUk)du&7Y;tUuzM!`u1}!&QN)Z-QY- z$M;PK;1Kntm88F4YNMhV2D60W@5p*KiIH5$e%N!?hDnEE@nL`CeX#z@PG$`8)X>HL zu=mG_)OvqWYt;Rnu-I_VyHvz!0bVH_SR(7xbP;AgzGmbKJ7!I45fX>xueXEQF1oL7 z!UD0`Zd;gk&x9IJkv=@lW5LY66Gp{|vpfcJ=D^ve1=}9LQg@a%6XtkZZhZ>#HNUV7 zVfXWV_VfR-+?+#y8(_vM+5!?co!dXT73SS~vTFt$lQH+pcUT-2)MN#BluxDBr?Y-+ zdSnCV&v04%2XX1q!&?`_b>0QkdUnBU&4v}QvAtx$IIM4Hm3?arCh0p%zG)FpyQ~um zS8a1mXTZ|&a+}HVmgGda6#siPB9Uron=;*n9DC-HMY8Q<%{+iaOs>+w(V= z!{UR}FP|ahZ858#M||P#+S9OcdR(R@EDe2k;}k4cu|KpWSR>p`J-( z49sXhxx|jd^|RRIddS;a9A^*H+tPQ0!^RmZ#~ewVdnaNq+-LtQYdQSi`L)d`Sl|MS z%PT8Zk@B8iHC+WW(-w|e0_RSe7qJ=^bd=Ov!=`KX>WMjzF65b#^!qMPUIR1y1Nla< zf0O1D@_)Jv?TIJVVD{-7(seNFR$|N7D%^h`y-VFmTwoVh0Y|)w^dV-yKIwZ97Pado zkpJhU%NcFk0Jn&Swh^-igGV~To)ZnIxS&n*2=jl;Fqrp38&*=h1BnrDx7$685Z%UUZwUoOO$^M z&VH<`wF2pRH}lqSfjL~xfn}t;-~Q)T!+m#eja*2|bNI2t5N2}eDhps~>x>Y6*lnp* zx)scp%<>%rTP7L|A^$H-Gl=>>s_s23hWGy;_?99H+d-ma(MghxbP_f~Ixa#IEutf> zM4_^g6s3(&q*h8vheam|OX)yqk%Y=36~aam!tZ&{=j*!Ozds+>^S;qd!GNRFN5h7wXk4}@hKzZBIiwZ6|ku8 z=yp<%T-htL^WnY`n?4gWMyFgj4jUbOa*X_5v8a99?|pF0jkh|pVE&@=i;G~N#vA@- zFkR`_qRDVkOO+|9SMChOZzJKdgCc!vSRmST=2so|@57_&d9Y9@H?bFP4I7+8j#nJC z_)8aD^evaR2xf$4PH2bolKS71-&d9_lF5AtGZfzqbAkoUj#~HO(EM>LNPS9I8MWVq zrSEJLT}fX3FW%4fcz6YzS#^-AAMv03J7us&b+6n8lj@BSpe7yZv=7hLVPk@1=$shlr=R(}^vFW(V$742(#$LsN6nqc!=A)LJE z_v~oWetza$;<%$fpC-Z#Ii*hpFsuFcoMh7fdsJFJEd0i}n+~%Xi?T1l(vQ>ab7209 z5xy5-+ts4Li!g0`%(yJrXN%1{@_X9Cx|x&D!`;@mHs--xZ~2b1ut*`uG@rC@9eegP zTwghXSpZ8f=SIfEj4#rBVphbNkT6(ox6583%!yEFSP94Fn>P}R3)1f|fbFbJDAVOt zf|#(y;%e&my#>SUMcS}||9gWAqp4i!wssuupAau`tKMQguBX;1JRo1&c~blpSE!_aDM= zSp40eZUHAt&;2I9_bti`wl#uXjo)nyf?3OyYbU`28BLXeFgJJY84WlyeqJy+pM06U zu47;wqa~64Fgx8zoesBpE_<{Q7Fo2a{jS0BMRmRt*sq7|pSgKCe8`|vEPdebF zh3BYxqiJj({tOmZX8V)%lKbn__S>+VLsC5(=6$gERsu6lZlu-&d|}v!EI4ZaG&8dP zihiyxIs(h(lky2EYACr?gQ4e;JO8w!rk*p3HJ$if`zlrTy29{e{Q_WgOvg+ZUL(MTJ^A}BEMA=DG#!?-Cs6al8?I2F1k;b?KW{?LKRL(M1rFu7zj;LZ z7yneWB>k;RyB@;A$JZ(+!~DI8b4bpYzu~3^n|_?R;V~?FbMVAa*r;YMb$+DBbM+rp z;(nwYq4okf({1dSO4v@9eXt$om5i(^fQvL&Ig|aD_79nV0XCf$^pTt&`i!a#X|Uwi z&XP`;w!EhG1f27^-uDyCnqJlu0Vh?k^T>R$k3YS&6VAMH-G$62?U&l_AlSm`$Hc$H zKa15j!ewiF+pVz%} zBkenuB`A^hE1XZShK<&|&8EZbzp4vJp8A)Wt^$i{RL-x2-DuwL)nLZ`??YC=t^Q|v zwP3EH(-;?cph~&fi1c@!wO+#F^J=T}<6bi;fzQ@HAOScf&te>kaY zCam;wrS?M7f9UU=>9E#y_vdyn=USY~RM^#W(X@52I8WufCOn{RtIdHKR=X86VA~Dv zl)PZ(M};6&IL9wJek;tY=9LbC*%r$+gJEIj(!OtZu)XcSAMJrfOZL3#gj;5R4UQ)5 z4fY1q!1Y%u-p0b*;5fl`So-rtR2bW`Rrzt!ey#nr4A{2G?Z8P`5X7673Y#8z_vjQXoO!Av85aHIKS+ip z&f>Et;HV_&nbR(PKJySP45rHoU|vGA6v>0hqI2!+)i z_1K?>rF*743x-99I29LR4ktdw7p@MDqWepQ81X@0GxUPK~wqk<)ya zI}!`6z9znft6bx^mci_0g?p57z65342Z~|oG4ogM#QPpUBIEJChu7|amFn84@%dYA zRL;WnpI%aC?72QY7w-5h97o2Z-KFIg!OVm1W3IvcL7Ancr2mvBN6TTM`RU{`IB7!v zkQ=ag-!=2=utcZTUIa^~EIm{K_n2%teH&)mSX1V$jc=)jX{SD4B;zZEGHdR^JlTl@ z8F1z~cX=_)P~JCdKWx;zU=6YOu@!$YoSgKy_yNqkysDhkx9CEeUp*}Pkosa2OzWTA zOU4r%*A~gZtzGw+WP7}sH!MlLwiXyE-h~+|UzFNlX4~HwV!ZgzcmnGr@u~e2#mtaA zhHF{9S+%5noSMx;*yF}TYJ8Eg#m@%P|5;oT*}nLb^VNHBkMq(k4X|jgg<37_`mlEc z*V_(~=waNUMwokh&`dNhj`zSmP&Iv#!A4FD#n;4@g$-;tbwo_T8<>`GJi!6BejsP?7G~M6S+*Evm3HqXW?rb1 zSp@s+DxA^@^Dh=`TmY9{Pjez>)YXSs!&P(hsGQ!nf;JPT`FMRJx!~F!Q$3jdvsUIE z%-yynd;!Q6FEf4)V|n3Vcb0hTn~mVAJvJF{~X zVUFDV5GgEZJzS;?(<8^vQYHJZ@%Er9%v2c~r4NV7D3xjw|KEIa%o3OrNc-4b5>MoI z<7Z5q1Tze8=SRTFr;Zov!vC&sey;UaBba9^+k6K(_4{7L2|vy~fLl)Zxm&@4S+Tv1 zuv_Lj7K`*x45HRsarS#RTUg?=z3V0!e^Y(>64HK*Ew$e2Jl=7}4(5F-Xeva`Q;eGK z42$a0srAx!oK5XAm?5sKA?uyw>$8u`Nq_qvGdBFkA~#2KN4UjZ=)VFvGvK%Hba;?? z{1!1SpEhnB+%d+(+ZE<8s;T*u8?u%%SH0Kg0_IbpsLq*d;b3}D8z&l69;9^2&3Qs=YJdjtm_R1HcTk6hBY*?BHpR4(jShozOdCAx5G z1fyLA7ReQCR)zyUCLSgABr>&Nj)3Wg3I|5OjNnGQ-di}H`%adBP|y69r2aaXdFPV- zPnfes!MYsQa0*xY1~ZRDMdreeLt5y4q&>&&bvEp^U3Hg~ew376fS za0sR=_1SjA>`7;WcfbOHH+4O-7S4}%hpiWEq)acK=Ia8B;*pFEt*2)I)|PY&ZMBx*p`bqjb4(Fe`P<3u1m(&Y@8-=T1NE6D-)aTu~0r z6rCC*mQGvPHEsV)J1i&Y*)K z%(TpWl?TVIxa)5Qi(k~7z6gireUMm?_I}-8a$qHVZ4Rklq2c9U7hvh$Ji|pW?eNf% z=i%1%Q#g*W=(791R9MaIsSde*1io`5DKM>isgpZtpKG!<5$=lf+GRxGC!b-Ls z#v4e!y-N2mY**tq-5(YiYA@r#Q7U1#xG-yOvF2`=?{>~~2h8+Ws}F%$heo#q!;G4! zuwAg*?TBr=VYX8Jr$E>>YOCu$(tk#yuQ$oFs@0-k+K${s9XbEu3Fj~ z8%^4m^_H%LXLE$6w1WS|5*8_J z-FqD7jy<7d3ftY+h)RSd1-UE(nEE|rSS(`^tpR&jg=ZXw`2{03j)d`ro6QlVzdLi< z*9xo$59Dv|foV#vf8N1mzh%CKkoF_Gz2CwWJ0FzsJ)~^1ouyl`4{u7w> zbKWct%sR)8y$AohUkJ~wyKxhC-So_atl#YKbV&*9G4_}%sbBu-uUZ%3IPN9$1u$ok zlW`WTk<(T^4;FkqB1nVT#fA0MdYor*?I6rxJ!{rS&QkX{<_fpSicgG#C6#d^0P~7YQtJsXNWXk4tkhH6Po6IXdIw&Nf{mP=!v|nitNrnTa@2RzuD{=5$?S~N zZ(wHZNa}e+vZTvQ1k*=3aDO3}2B$O?!gl7*WB!unHo& z+cmZ@ujM_pp5i~?xtYM-K`rkOAgBFMb{Yu>sLJjk+heqSxA<@kpI_Y8)1y3e=$9_o zC{E!}I2qsUth5Q{Od4^Y9FO#G@wIX|=Y;!Ea{M&Lt6>-6uswUJ^+D_vbLJ2n%7~)- zAm`x+W!AxYMIL81!_551x7M&UWZq|D(S6ogJ-8ybW*#|T67{T`iZJ!}0Mh|a{{jSQ#(iYm#sU$dOs}1 z*U*ynPBJ;TVJloK@Hs-x2hBNO;03qtE8S1lN1>L^2^JjHx-Np8AF+dslOgQ&c|B!8 zdd^ST|KzOg-!;2SvH!=)sQ%24$HqT_b-uRCk@5H`cgGgOZlaaNWP1#MHOnNpYVr0g zQZIr9yy2VSQ2w@e>n=_4JyWlMhQjem@j^q(Ar_O6NIeva%z>c00)X&Y7LO+-m zJR$BeY;9~q%@60zyuhn4^?i=Wg^Qla9)mUNwo~J?7g_3r!2j;w!sN$i*OLAY`XSW$ zZMc5i9v=LaAq_=;@wBW_3rKsD+X;JN-pU{cQ&?Q3NYw}J=cvZ1u;@xRRc``?z!Ae? z)2q|GkCOJ;BcnbR<9VwoN}t@n`2Mw%+F%c*g(ayl=R@A#>u`s;_8>7m=g>%E&bvmD z02XC!^U8(=+`6mhN&i`|FP?&RzQ?W2gat0Xs>k6t{<0xiFfGzEm=6oLn69QA+@g~J zYpt_l7P0JSYAa$(8cp$dT{U)W@k2eWG9hI+s{-XW&>u=rk;t37G2I=7W_ zetXP3*mXcYssOq4Y1t1Q*immDmD38xo|J(H-_3|6?b*vuG&B{V{?B;E5R3MdtRrSl zp28&kx!JW^7vXN&Q<*}T(e(U9IIM8yVc%uazSqiRH5__%jd=;o+Azw?0Tr%cYNuH+9#HC$QJEPK92WQ?pXH3J!=hGMB>4ph7bd ztmOR2_Y*Anwqs2hEF5#^X*bMGE4nR!8Po2~ZX>yV$HD_}MZ?EW&tRG$K0g8${Z5$q z7-qfE%nl}g$a(V!<`1_uAubD^ct;F#kEi8N%Ykpf|;^ zxKOX30qZz+A1{Ih(y3WWaKJ$R9Fj|hA2Ff9wI*HBWIRdZ&6MuTxW9|`=8^gnT+2v! z3cHT!aVdvcZ@W+4fkQowwv+wgH*os%;LKy}PEvp3x)&u$ux)}rwf};K28U>vYxc3` zE^?tmlGj#P;^erjfwYg>_Sp&UzTY{s31)ozWI7LSxmHQdx7cdoi)nDHT1V4MyYuS5}o8NC725W6T*+9;}WEdy+oe=Z2sATp}SeW5H{{<}Q z>g17n6aD&fM+^^kMP8ulX;XwzJ={|y|4zmS^GnNoQ3^LDXFZjN`JEZui!k+j(y(yd z$B-o0c9Oam{WG&+6MM& z8Mr)&b_gv14$zWaKMxjc(Z1#btLZn$&xfVP{`b7#n7wl^vS41wsk3WgpK)cA7ZBH8 z(pUl4x~fPQ!Yqre&z<0+e2dMCVWHO7%*C+6m|O)r;+;nftYLoCGP$KNt>5px862nA zHPs&G>BMSJgX`DI*Ezz1qmIpLu$Hs*tqUwF(_8W@AM49%kJx1}_w$~|pJ0pCELB(1 zUyd=Q4Hn8Kon1w8ztX__Fx_x$!D?7?c$3|2I60{KrW?%WHffi_(mT^i++o4anfCdx zPtB`CYhh+^_0=3W>;iM&I%1DE#sb)Oqe?Xg<_Hb1ABR&z1?^tM$8A3!BDO9I_l9|A zzbf-!cHF*KTVdL&;u=r}&c0X9Bpd@oIEDG(I{of3=CN}b_{t^xI&25r4!Z~+#d!FCP}R43xOT%=5+;NwDDN`>}4Ymn7l#8JImu_wX_}PUT4WInrNcW~Kvd zJ-;RGJS=%+x6TgMm|+}{McSWPa&r+Z7vuUW8|H*{>)XH&eL z5f1Bk+gJ+=1lKsxaKOi9JMNM8&JH3j%#)wbZ-DtvWJ29xoy)s2AHm`g@3$_6J<=DC zX@^%7U}dQZGgOAI=MYyex~xa?2^)`l z!_@mJEdDTIzdtNEvPRKNe~4w1%1=mS8~=G1DeZb24(BO|f@Kd)hJD5)HVi<5$o; zSn7Rznj&2LGk;Y}q+5`}*{(&zErg_ex{P{?OL$HhBX#SuX!#3X8w@ z@Ec&sD6c0bFl)Br%j!SwVxe>0MIoEx$N9t_y!Z4L`R z_}_JclOM$lwSw6;6T26|b~4{DlH*~HKm5Z64t_oVJ~>|D3#FwC;kZF3YW|qPkuR6P z0``<{GJl*Y6N{F?Mn8OpFC_hkb^dgPlZpxr7r~Olxf96t3yPY$lcgmrntlOEM1kUa9B@dvIoq%z5c!~%R=3sQ%v~$Wd`SD36Ap}qX;y8Ofv}L)|3(RR zeOCE+Crn@e$dC@Rz2-WFz|z3n?W(YKzfAWYn0IY<_ITJXq*E>erX3l(Ll4eLe*XRd z%o=;8p3E2Z`2#ErKB{UBOO$n<#K6)$4(ArYtxHpE;$iw{ww?n_SMRb&fJF(v)Lcma zjTIM9PQG20%m0k-`{|lIh8@DVK2i3liRTPfa``d zSm(uvOLt&y0grZ$wAVhG+6W7($2`e}?e<)ddqVR2Tk7-RAm zkxT6*yFS3IiKF%$g!7!tZ9l>ssf+h6m{y$A^@-T6TW$+1xLg_C3-ixAQuEEK(RBSv z`k$Nizj`)~|K104XU0t-^(8enn9vWiy%NgQVfxCu3%`Oj34&L@a+A|ut2_F zULIzeM_DJ6_K)Z44}%3oM;^w)3I|=Qhr_fphqvs7yH6U897)<6y;1UoW85!BkAnFD z&kk{5?))I*(Xe!*d5Q;2AM!by4vSY$Rda=_Ox8K5kp43a5*%P#g%?$-Brn<=W(`;H zK5bHmSz+>fr^3l^F3uhcOAawi^@z7kDbygj$*95!aL>{&6SZLJ-p#3_VA{r)))Qgw z?$hUgTtvNBZeKYC7A}$VdIcM-(OjlO@)noo=P-ZE+-g0T&pKUsANKM;dcyz~-}&Nl z3)XpMpD-2X=}x~>28SNl`+OSA`DC#=7j9Z&oIZoJuWAWQgp&p?YM8(hhy7op;nq0A zwX)H#p9y>x(Ze3QTX_0@Fk8 zSZ;+mLn?Rr!aYteS^{B7>CON7N50Cg|2XV$!GpapKUmt}gZ37|wOb=#vFZ#xV#(U> z*F2c#?Mv-{t97SS!hiZFPbs;40%q45Qtbn-toBVM{i|;#Y(jr=B=^@jSoH4ph_$eR zlE*L22j}mVS$PA@D5%V7hI4Y$*^gk>@I#BM z;iAnBj*nr!PwVPCuz>C~rWqDhOz9|wg+;AiPhoEO4@M3g5Egv61*TW)_NBqRqz&0G zV4A$JB@t#9W+h5scDugze%SS)zT#_Gyy)toaM-KnSiu{ZQGIM_DBPquWcgc|Tj;B} z1Fj#rVrds?|7dsldN_1NTXHunZ1#6y!#&wf*6(5FJK6C|;g+NZdt$oPH+5p`(-vz! z!kieJAUl!=KaBYFA6xIcSk?ziXh*2=9e1jE^po5xXudbvhb3z1{(#xR53g^BQw3+! ze!~2%!+!39y$akd|G+#ITaQDqxb2U=tUp;#++QWYj`x$DhQLw|BbE;fKR0iahgo(J zykoGDOa4m*n5JTGa2yUcn>k|yEPC;|k(m9%z>0Fa)g5BDhqL!7A{Sm_KXIMo*Yl)m^9x|C>MYOm`0HFP>LoZG${XA>$|+pE=yv#~fDkzhgzr%St*v zop`3s7&^=kb#WLA$Bc>8Bl}BBIn?kY8|z>0e!rnG!|BbZb~rP}jhauX@SVmTn3ewQ z)*$AWYx#F(1#DfaO`Q+Enf}nL#Ck7|d_XQV)l57N%jGAY>x2cZsXL?L?n4u1lX??w zTKr=#Y~;MOs-28ya{gc-oU_8fr4<(GWtgso*&$3_V&1xbku%(M$CNTtc~QZ9SjXm0 za2s-IqDiJPTw4^SMaE+~ecwD4j&tsK_Y!90EB+b}2XHEwq`tXVeD#!J$2F^|dS)yv zP96cXni=J!UKz`augJk2Gs5?LhFSO4Yti6($LmK)eX`4^ zT_yv4Fu&u^F><~noteXX;eYkPyVAV(J!}_vFl{VyX8N(T*RWLX-mP)4ke^=G3U~C6 zE7gSQdl$^S4Oeh`O|@Xr$YFt3Vf;gwO(bVJ=-xO7n_f=v9uIS)E}uF8msNhVA>&CL zlv3^BfwNyJ3(Yn2Okm!~GjmCM=I6D>YH)3MXw(GKzu<0@44n6|JZmB>EqJ8RpM~S& z`8_d#>8sS3S7D1$N*|WOq9p$#Ua&@80CP3WH`Uo{0$V#egt)`BrKYk|U>_s@m!7cD zMngpj4qY3Zvys?hcJ}W~tdBKj3%9`ZKNpWk;pDzWuYF)?Z068zxZ}6uC4X2vCOz^c zO!phTIFRJ2cJfc)P~BDD+hLYrq54C(YSHf29kA%939lYbomHT}3l=QZd{qaFiZ>O9 z!NTm4OLyR&hJn~G6EfIm zVN+?4VJdO&b-z<^D2Kg09hS^-Z8`??z774G1v9(K#~pwNRuq-w!6Nl6`3-Pw^xUBZ zFmK+E5O+APA-VbrEFK(EVGf&aYU?T@{j-iWX~Qx1oR5?c`&U~JgS+cT%qk`AO|BWd zJ&*a$yu7&>W_Z>0-XmtZCSQg5q0gc#;Ii5!jY3#>(`rK+-0`>Fg6t2sVp7>*m}i`& zlMM?_zwZl!1rx`P6Oi^-Q%nM3rEis!(qKlQ@!eWPQ@7!P`E!WjC#6_Da$8fb49)OuHE9_Sx7wLs8 z*$+!I;xkslj+6W!@L)!m#|l@NmmcoA4`z+_8?pj!>V6p%N^;#!6qv~Ju%sFZ@%v@tQdL?ppSkT;wuufWpsw){UWYXdZaL(nC*)A}5 zh}La2n3m|G4H*$G>u9Q1NP&g|=+@C+VU zD$`E-Gdg7hYG9+P9g)tYKTUi7O*kxE6v~Eaiu*2?!nQQI)5~F2*MljSVFP?~4%uJ& zuZgqsVX>>$H*)=oN|(jw!A2`f$CKlg8ti*}2~N@<-@OhN=H8o2EUBn`vmR!8R#VQI zKTLiD%>5&GG8cKwvX>5CFn_TQHNM+c$IqK#xMS4LW<5%nzRxe~U}{ zUrc0n!!13_sr7>E`J=iAb~`-tI$1wNt4geX!1e7J>&f~dT={AgOn8|GBJT zu-NS$z91g$=dLV}hpR@fJwvV+PTV}Ip1K*oPm=42J$RY%7sr=5Y1Zq5FyC*O<2P8z z=W7_b9z_qoG(Um)`YY|p^(vfq^m;9vx2ooGJn3K56ec2W@qb6mpT#pH?$N6$Ng&qy z(MjsfXorj}x&FEPeNWwh18#dXlJ!$^?8%Y}m=&9vcLru#*$il*=&xenwgj`bU`t+LHB-dpYTE z797?r=SS90etJb&2COuLf2|l6_a5GJ78WgjEu_}ZnhBnKIBs5^TN!eWpN&R5oVUe~ zb`9pa`96$=2Y#sz5_7`es_%g-M*MUshv}7;H@snn?_bKoQI}L~;DEa$^hnN8I_5ha z7Mdwj=JvhvA?~OXeJ44)taPR-+;?l*nF^S-RkURotk9xSc8l~+{$cs&9NGsh?XQAq z+UEM7;VQrDE9+tYM7nANEDl+a)c}jeajuoayqPQZ5_9`2Jab@rP4zU=o~z!Se-gG{ zwlRZ@C*XVx@P~~iuX%PGW}mY8u^z5i5Lj^&rn`+bTL#G47K6 z<-N`tZwn`lc348p9yXa{3ft+Qok#j}*lWEf!pTEv6GgD_abbrF9QO77Y-096s{^r7 zSBeSQzG!tv-e_1(KUZ7{^R^bm$iW8MogKBXSTLdJrvS$%QfJD+KRkus=PIMi)zAzRFoEby$3Lv(ErbAJsEy1}wQKR{RMw z+Bd8=gGEam4*r70Eklf0uu-VS^g)<)c72XLth8*o^B>}r^2*h)fvoRGS^&mdw8PvB z9-QwsLJp?s-@dvHw(Xg4g>qzBN-%8EzV+fz=RGXi2qaXdH_j3v(-^HGJfb zQLZ|gFthfCn2g`?ZcF6^(qHj8wY}~Uq63p*vC;OCk;uCjuD>`1rY$pW+zSg7`yc4Q z{LXt8_L&8*;5v2QM1+3;d_ZqQq#p9rb zaE173DKY(ll_zme`YvT+5&PF7E122R{y`sRZdQCT4enU8EL9KYW$$5V!2v7J+#}l; z>$|@u_2KwE#)1LU>Q_U|0AeU20@|BZU4P1{Y4=lX|LNa~+|K+ZECxyOn4dUCx; zenmKt{VOW8uOOB_UbBjre$S!q8!S$~P3<4|{^*>q|LJf0zB;;>WnUqM4^Z4RvPePv-g%-EkyIhe&c_+K}LE8TrY+IwYDd9oijovaV^ zoyxnj(Z6ZYDYqvuS2pzUdDyh++f1^)NK`tkGT^e#wId%B-`$aU7S1eE7*2BL$`!n0 zaMbbx`$>Oxyz2SGaGZ{VAz6=v{DC1cFk`w_4zajv?2RZ`tL(%ivVETH+m~Umz}jL7 z**|I7n%teR!Y#>y=cK*n;s7qJ@s0bJm^a(tfIl2}VtoZ!&-hQ$Pi=wSYP%HMU}lni z`FdDn&mTq|zfI#?*Z-Kg>iJw^n(k357iR6ByaY}@Qs+tfOMcW9Sixc&9j{iHrV%!i z3G*Z49LWAMHc3{DA^lkssN8MW)PwM`|Nxc8326o#% zJBPZzMjJLJ!*L!5O{x2RqRQVDu=PycOXPiuzIT8_52~3k)mD%yH&4@NG1I@uAOlPj@m}sL)tU1-f&NZ z?G!jm$om}Uy|(6V*z4M^|2?1FuP^g~Ycsxio=48T@$At?xbNm~1M zx5;dedPIt9c%dB4Auz!>r0+j#9M_$GhpsptLb-O z?&zJ0T5$c_C(FtGP*8AnlP2up!PO`CcP4*>j3OM??>Amdyt?oH5I8R7p^(%cx9m># zz**GOmhy(DBoCQ5{tIlE(oWTn@Zl8G9#}g4XYNbnLife{pTm7FVLGp1_PE~6N3g}D z;j_v0#E%GI+=GkelrDNh{Pxx9+i+_@xkV?;P*`N11`8VYN{HG1acxInrf1el>iYEE zVHyf^^J!B)AQ$a_)#nKtSUnREbHjAJ*TA7)b(25Bw0$1MOJMPdRc}7SycU_QCa~?P zJG=XcXU}^w4rVy6Nhj+CP2b_ODy*R&|Nc8nFX#&z4%gSYttIOZZPV`yzf&=ur$q)? zuNaYqhF@VL@eKjFAM(b}n<|0%DvH$oN^cw2$6% z=?)zF#oR_4=9kUj7nAnfyZni;&|;lzA)NV`w~?&>;&Ffcb77A&8Znb#R%RUgEZpj% zMwzK>Fq;ntdj-dl_RMvbzEQB?@}F)pzS!+s)-JgF<1=c1#A~e#*1(z9Iv$espIKkN zbt$Z*X*zNw%vF|^EP!)1U!m4x#***D_28y6j3MOtgMI0&iViFlcu~(UQhC>~iLkkyu2q)B#KPIPOeeur{rXhST)3i49cF%7wSnZ4`7iRm zoW}XqyEUg6rmrm?@)CBP*|0GWW?pDAOof>`J2S}VDWWjF<56&JThXvgnBBCTw-X+? z!<|6Z7varsKi0r@ldFx-z>=9hO>^KJ&!BB5VVceL7kaSAD*r6Tx7M~+3k&q{}$+{}Y@q9(j0@4wPk z1N$AY!pH|YTS@z*T{L^xZc5QX@;=Tx-#lk3EapBNM$FW+9;CypWj)mACtR87NAU8;J%@F^F+)ONzMCUuj*b5vGQaF=BWW*?ch8A44gyQk?M!SVFusn zI~h;3!R+&Xm=k+o_G{R&#F={E7F~4S_yG={to-&Ua{e(5X)nq3ET<&F!fW~M-(W#} zZuJS6ebL7FJKXp6i|{lVuhOOA2k9RYK-Cj3^5ga2aBX{=NPwLFJXG;7%naFGNa|1Y zX8ccC%vY^*ZOTPhpdQ{U2h($6sr#*DnpKKC++wiz4OQP~y{TN_R!+UIGRG*dr6JE# z-SU-Of9&0*oBm+@9zEG{H(=pV?VUf#_J5D3-e0*RyUob{JC1gv-Y(|a?&}nj=PMSYT4oRI8aVDHF^y-i z#1Cea8B*_iv@c2c7mqmquU}B+i&wwXhO5pEqn^iv(`B+HC-FYDdL#9`#!O*;djLCb zZoNm|AEkD7D=xrI`Z15m^9n83KoAL6eKV!jGil_x%*C+Ly)x}aUmBWcaNJ61pA*!3 z0_)`gyJhcTp`OO$PjHos$u@F7GlRxe;rq=MkknE zd|HuguWTdxy)#U!jG*?XDrcwXGFY%bhB_XP4RMOZ!uOjsg(Pp~j?=_FY0Q0EG=J;_ef8`EKNvih6Fu=tm9tR*aso|n3iw9mdbxCo}dtDH>s zSA2Z;ho!JW+=H2E&0nO@7p5hfxI%(zjg+VJRfjEewf^b8PU4t^9`} z7*Y?Sc}6dqV2!YD>hl5aY{UB>NdJl1GUW3E+FoTX8m_09!oTw5{g7>@)iN2by|iy1 z$@yNI-qvtg(FdwO<9?iv3oHqWr9Ll^&gQpmgI$eZ=aB6)bT3(k!b#6hJSU$YNYgv# z?}ORw<$2`u14i5l;{&kVjG+-^|7pWE?~H~SGY6>SlgyqHa{^|5-AX+#34AmR$@=lH zKAGD;aav%5!t@0u=+B&OO|3ts%qUM%pPWe_PJKbHv3c0D*|0EAFQE_4@jI$Xoj=tq z>VBfOA*sU}xx~leDp^kl!ygThdKRYa=MTer7ILJ-`lu;e;R1`zzcI=E zs`ah0$#R&+Qxi{wH7c%IuYpA&?I8?UG)DdRI+*`fFK{xPw7iw$1#?%P=4rz|HDgTz zVR0sZ3>~JaR(0 zYQ5HAmuY2^Ts3G;IdU)Vi^K~sePVoH3F#j&q_O}ODlQ#c2oDUG<`lu=17CQ#aH!cA z)k>JX^Y7_Q*g8x{^FHwvdT|=8l$?089_Dsu-AILXI90)ivzFgIk&q0=yD*|R}1Kb&=L z-KXG|3F9|jh8bRysr8^~P;@R27VNqfd<1!n@oru&EaLf`IshvKU*)91%<8Xkb4NGMx&m_oFaNp;cRT#t zUJ8o^*&Y?JM(o;OH(}}b>M_?~JIUmLJ22CD)V4C%)_3owD$?J~wz?D+7|92c^TjwN zPvxv;O&O|w;#WC{kSBA7b&>PWP0=1(4~H@2o>TP^F~s%>?6cd@r4_kU*@wFS1>bV1 zYKQ;bPs8e)RVA=EJ2QI#?U@&SH@$-CUUm0}V1Amkj1*tPv|t0fQE>eM*;rz3`w{AS zA*mrd>J2RDXI#}nJ`f($Ld<*CkZ2AMZV52zfW?Dc>i$#Yoe|PW@^I-9PvogjJCeI$ zy04^vE6lGm<-do8$?c z9;G~d_Yaugt#?=im$j|p{(=SS7L=Kqm%M+&H2qAgTgd6E6KdqPV*HMC_Bn7zOe9+g z77o|uC&S5K3eJpzMVz@yPrxkIt!64P&z*PuDBSu{-a{3pZ#hHNcav-DGva^uN3DXh z!DC@sOJCL+w71>rrKLgId!|h#_urWB+4dx7Yb%JY$#|X%vd6%@`o#;(U}40MgVcCy z@>h`Ow_wSePC9bonbDh#;XWl{jv~yczpzOY9w={eC1$2?pq?ixTumpBg!yas+>_$` zDvgR9HyY+XeqZngjyfy!Qif@Vm8s{Uf7=uNcyzc0x!0t-{p!dCkLIQ}!O|LZVOJ}4n(wm%qW1ea+(TyIHoJqHgaoMh>?jyASbv)fbDK5G_rk%hVd^Z>zH^URH0*PIc@puzddrJ2?IvagGH0AY zZnW~sh6S+XlnGV;F_LQ9BACB@X-fggX~&Lu!W@Z|dVb=IGdjIt(W$ES_mJmBUOVVV z`tR!bO`fNW>g*G@!z@~%VKb~yy03KyOdkk+M9g-eO$;S|`e{-N%+*dY4}*DQ0;pVV z)sBdLFwOPd{wK&=QXkBVf?1;rZN+4~>84L&h~Lyw+h?mJXB>t(!Y$sl$di9)eK`V4 z4)%<#fu&6!HYN~H`$p}*@TR=%F_>#k|8N7jU`}Ju37FQ9=XwpU=y|K03=7%YsprYy z&=bvPV5#q;Az8==-Pm3kquwU3aMY67@4=1h=XDcYEj>L z%`S`_(w?6qa|)KkbkzPrsy*kf+h|x6v79L7Yz;rDCv6;U83cdT-|X3NJ=mv@NnIa;=RRxnVNux|YQCk>#xe%5g`MhKay}Rd zUQ`|$ZW5J9#{aBK?jH4*)R$!G1S${vQc+2+4+&j(nd~p?Zh1)_%x=1Lp8<>I zWzJoO#VS+7CX(^DSI#Mhr62RmX2ASZ^;xC=G3&Kpf7yR5E{ILP4zsQg9yKQ8*C@Zf zMeAmYmeSoWOsUK}6{jE1q7HZ5L@)D+XJiYl2IU}lW zxddk398@PZ9sS`zH!K{-IsO$6Zrq>R19R7(rnV=2CVTV;=`Z$HP{sb_q_IQ)z>HPj zr;zzdtsT=Uw+-9NFS%|G$1J>gPXQK~Xx^}g1M0rEkAzuY4p8;0boXtQDoneW>=c5W z7k_p3Skiu4V%b5s_N~KMZCLuHazhFk&$h>UBFt_$J^VCm`kc9b5@{dZMN5UnPbS;y z!;-q<9OAsg*L)3N{^xcockS}rJQZf;(bt?Ixp}e2G?;VRLM0jY2%n#41he&K#V5cz zJBPbYhXq=RX-D9qhNSbxr2p($*|9K>ru}sWEbVA3JOqawITc_6i-fng?}z(VY;H4! zY5$L@yN`?M`Txhi2t~0dqOcTFVG%-DDMGPDDvB*3NtQ083#}AISP3E7=*sn$BqWOv zUDy<&um~ZnL?Qeh&v|`(fA7D#-EYsCIp@ronKOINnbX|V6>DLW@<)=fu}v#PtF`(0L$`Xte?Z2Bc5IhVex78)_ZXNmvwDCV0BHG zf*Y`Iqt1}USmQJ_-9SSQiRpKA@p+3snp7(~6-gmDIg;lScPt1dPC1s*bFyHmdB`er& z&Z*33%J0`kI2<;d_d8_^tVp}IZZK?YoV;Tj<$v~bNiSGfIz4VXES{&+TNma=TyMJr zmZ#S|=>kiiUHrBS=0!Yl?f~12IMIC%tXR1(-m-8tS!Lh1ExCT{)81rW*HY7DxOng; z`vVkD$)6BI@h2|^Q~6x~O&$?&bl$P{`(UYK*3D%wz5!;L2y+6aoN$GO*%#^)VA;s` zr9!yiYTI69o>B9gS+Ko9bt)Yn|7~{k1lVU$&6#*u#f`jX4kza}?T&-xAG{6^fR%P- zhFf7#bw5RKI8E<3t8dQDCoj9f!ZYJEHX|-memm3!*6lXCUj!`vw)VxJ-ROV28$3c_ z{sB*?*RZsAU|lfG9pqkC3n%XU=}YIEpVU@eMe+2Wy~xrZQ3fUdWA1x?WiA|kGi5pD zSM>PPH50b!YU8z@(tmpK;SgN3=gn5yo;qzoULssvo<3p&`Naz5P8k0HWpEg*dZjwJ z6}C(J`I+`tI^95L6ReTWpD3ZYN4tAraCF19>2&VrS`=MbW1meC8hgX=={s$j5hU#^DpljNbQ5aYji6Y{@4DbALOcbu=t^wGvybL zpZHuYMSVz9S$h$TIlih6cJ1;*i?*-MU%j&ymWSm2xBn|HTHJ+0ZbTiHA}$N5^C^Vu zzaNm$@p5*pF3uv)Okw>=S!g>=24^{4!WZk%zD{~>ji5N+ERW6yr%&+Mm2mMs{*6Fb zVy@H13pUPK$ofCe!1|LDoVFs;dKKb=L0`DDVPR0>AR14U%_)fjIAqSXTpE7_QNuQj zhW)f|w$S(`&CF};1s4ok#Kt?p?2M&(MeXPWG-VOC;HbfKT8^aC6@ymlyJ1i!C)F+)#Lj1&w-oTy;)7;tNcsw ztSmVG+rcSbuwZMC++(oaDwl3cVU=RaY8hOorTK6BrNwB zWJlv4x2fKHILyClIPoydo4l!fD6G-?GUXU7o2}>6AFh5=cIG(D4;{Uw2ORH^U3iw_ zhwl1zfbG|9Ykv+F_TRPR&kkIVO5~yEVNT18xj$g5W#j+7f%0G8PyRmS!m5Uo$V4)}* z4x6tseZ=BRIy~qN`*d2#((}h}?9&OBYmKfeqw?hsoZrQxzYqVLR04BrJbPBae2ZsB zcVMYS$MQ?CPlMYD+F#MR&>wQR`eYBqby)0B9JUAMrYsnA73Ry1t&M{7yIXH5p!~V# zIt0QNyNY{L{RuuwJn&AlAgQB8n_O5~bAFa1%pX%RI)~y3rh2pBmV?9NvS4}K{AVLz z?eY`T(_nSNf=~Kz+Sd0cWt2Xw+@T$;j5?5;0?UGw(;By9e3~)kZ8D{=yw&&=wu=p! zLKbv>()KpYo78m~wHIOPW?3Fw^lr_lBb0u$-P0_%H6=A76&4LCzjg?AJz{n2D5ZCC zjZcKFvUT65!(9F0cecRVp%Ic3uz2&y+W~OmX)A*>utJc0%m+64e3G>{$(6cXZ#eq% z^g21EpYUAI3+DT;aijLfS@?T|JN&OcdC$tX*uhB^daS-xbxC&9;JO}{S$k4#OZA!r zr-`+^%8_4m%5nxDHjZqaQAOK7Q)t^4=DgS`s)qSTZA`gv{O^%-Yhkg|nY?x|UbbEO z0#@BSKl|G@)R)WH+izjP*PB08u;E*d!?W$W z3vT+dAbT(@KeKGbPFOmr-O6Dw|L00(Ra9fb|5|@xhRaCA6}7=EF7FypYypdmOvUlY z-+D7BdIBuAw;UP=bJGr8pF;71lM`ZKyAmxo8(0?IGJ6xuNeH=V2TRwlZH#2~>nmMh%m*Gk zfs1th=F#@08{5R(g5}@39dv?~Lw)Y%!O;#YQXDCL)wP-&_*7N?V+UAtw4YA~EGo(7 zQvIk}2S&+Yne(^FvtVJ`{!vF@KiQ41GbsNK|E2@5rVV%bR9I0pd}KUqw-Eou1m=0| zwT^|8%(H99Q~G&F-J{^r*0t<>%7zpKhr=?}%D;5JIlp&3+5lU6S+oqL^kqfTVAw<^ z_3Z!)Bi0-UgymjV0UEF}^Id%qT({~_H1!9yDjeg zA9zn;-lx{?Zm?Cp^&lD#WEsiLvtb)YzswrS-~4BwHLU9X_9U7A%eH1JT$JndlG3X? z9S$50H{DA#WaZ}`R`!8gSL}UF+ZS|uV%ZLM*E9c9N8Wb0)7w~_pLMJIv+*VTg6jj= zSi7vD9&u&n?3P2FdYbaa9({^m3)DF4^EH51^pQFk9)fn}=% zI;L>=#GTjZ`YDlkJnRZ5b?9t&fzr#u)LL-8?iqG{;U4wm)^5T4!A^AhG~%3zubx!F zexFm!>H4J9yPbCj=B)nCr}>af!7IKAr}-`FOxIh*8M{r_;Zv@02kCmt-!h~1B5bGl z$gZ~v*Kd($VT&&3jD_m=4E{I&7s;-x z_rl?SegEu0TzKQ=svWSsVVgzt`;ZrwQMwIoDml4y8?5MiH)bpBw`9(-7)pP8rBDJ( zI+zS+zXy{iWkzmN=&^deN-FA#jtE+OOz*Uu3mhO=Jz6w zO^t}s-;?Rn{9WyOkEO3#kvMlYEKlk%&>L}WOXrExVP$2?f7?4SC3zYw%sa_!(%#r& zGR1u}ne$DeoyPr7`uy{ACy#EUl-~W?)p@Yc;-FI>m_KCvhncYWozWXTSamRE_c*xTOEj6RaEffS zfW!S326czk8Mii?P&{vkMpsxk-}PB{IH~C9+s+gpdw+C849c5YB-4Z?K?UQB;Q9)~ z^o}s+p68d-aP-8ZmhC9NjcoG-FeQ_VZHH{Y1I7|3z)MHNj=^TUd2r%J)CfXusbt#XW^NahqO!hOOdVzE)HEs3PZg zaOvyLtq);gfx62Z^2CMz-4C_MJoNzPbz8FcHsZ3aQAdm5S|4q8zf~3Kw(%ldtbf9l z?$4@}tAEI0Mdj(_9CAaG*C|*yt9vm$&-iC)xIBuUw81zkp**Ufk|-G3H#o^i7P zPD%|QcK}w4-Mp{Dx-O||x?e3?ck|~BSmk)WGJ%|OQgsK8ep;ZS=Rcfy{CjdZzC&vq z-7goeb4`Cw`JHFojDnSKu`mrzllK?W{dO_8#Fm5lsy|&EwGo!ApBdH>E;4=3?%xZ( zsXz6A6Ui&$Vx(6bII1%rZdkapbT#>NzWyqhdsgGTAC>pVYghnW z>YvY^pNZYaxNe4J*x>33^GpZrIt&}`zxHY|EPD8UYzi!TW9#byE8QP3Yd0-)SV)do@-~^u zJ2R%*1s3x*%MQSn-qP9y@W1kvSu-!shXsYsUWte|oZi86ggGH+cWi@&U7s#;fcbqp z?+t>pHY81$4U5(~4Oj`cX0FO7b5p!klVC*$?PvC|;#K|@GuVEye;}2o=orh|Ys130 z9<)9Flg%ILd?X!tf7}%oz40*piu2WE`qyp|EOX0CZh&RShZNHB@n+x1qxM>4-l09! zhp2je$P2js7N=z?ta{k_Wi6Z-k@A?HUn-@&JgQ*HtbvAWVCjIR_sU?q$s6v`^Cp4v z3~P@n-;Z`7u*^em>jlKw`>^DZBT}SSXI0*1 z?TP2K!YC1zH?L;TJNfZ4ZVX&!Y26x$^rB&|qa)ygoZg|-9y#ia2|lpT?5u~ZJ!ZZw zm<78}9Rw(NPXvb9Ud zI=JY|?4F@WuYNyv^;g06sZ(9ydbdLb(XeE*eqRSz68-5M^>1}> zN*e*3Kc4rOo-gv8S4=*ZzS=RNVb#oNPj~l#*(2C>O!xnuziCW)UI}yK>?hIlICOOW3$Sa}Q~{kY!7SOu^RP|brgt=cNdiRI&%wNhLx<9M#S@2l=D|taKND#@ zQU5&7_DAF1&Lnr3-}jRN?JsAY!+IJ|R5SWqE`~#LKCu4En=1TK2KzNnIm5=6pUG-E zUJavy#WcPsU;KPq0joRRn>r7cyWbuD0Is%9{w{>M4~Mb((tZ@UX$~w{v^YtLxZz%3 zHlB#qC3dZXoGT!MeMz7ei>@+qeGO(cFx*RO{I|1=wqhE?0|O%=l}kFGxE!{Xy3_RoYhH5(U^ z=tbJPDHSKQ^;Rl%GSTQ`kFe&G;)crmQb`O3D( zeG!*g2wR*DNTK~vYCh|o4_EbX=tRdOXkGf}1biy#;5{qK|5U>#15Qru5J2@IeLX>u z4u^#F?>-fl&wNpQ3@$wydB~2^|JYuU3jaGF;uA;3X|VL&6LviiuGpea`Mqz{b1LC52; z*uO8kUP`LZvF)+%rBHlFL0S^x@+Vt!=zRd`Nq>`_aBF5ppAA%=&F{fsaGHh#8?R(~ zqg3nRe8UOsdAuxT{_y2+b;Ldn^&iO!(G3@vU;d$)#$(~A*8{C#Y5A7E)c;iB+Y}?= z+LX`i`MmPvd6Pb{VD)qM{9U4PrmRVV{ay9(JgJ;?D}F?to1u zq)aY^rF~}(mcX12j@+v-C-hdQ065ywnOWKSnZ*K%=LQu}deMx{`Lki6;+EnH+gw(k>e;Exe$T8A9YJ{i zHZ3Xd0*>G_&;%Tm-?EMt^%w*3c z@TrLD?ENc_ae$Tx*6@h^PV-My%{*TZSa8FHjprh}mwnt}$>T2DXuheM9vtlgb4m=- zXnrLA&|T93{x{yMTrcx1VAYj|Wa^*F{kH9gz^6LZvi>V^DR{04YwYiBPW@R}o%NtO z64!6VST>(j^=h1?g8$9mgunDNiebe&(-OK~h-{orpM&+!Jl(YzmVLL-kimg(J`SMy zt#pvcFBUfJc&dQr!}z_qe&W#M`~Zh)XJB#`weYx7M@wb7bo#xh{wQ z&EF)sM^<{lhIxNj{VUciGxLDCPMmEte~>x5#4m(nUyNYy)9@|5nOhXS|C=wQE@Cc@ zi;89a*&>qV_i_09Uwc?FqLRwv+#TkSi1JhyqVFz&1%C#z-!r0H>*dQ}_Pr#;MWM^X z*TNRlo7wwgvf4eDg5g}lpuP0|nD|9qb_lHA)X;_6zc}P0^ZYa|_P!Y3I>T)Pm8a;o zp85}8cQ5n5-&?X`DYM2jFVl;N%X^u3qVl+7Hz!bk6;#h{_Mz>)xRh~&;`cTv=D>y~ zVN>b%ouI<$lsO#z>YCRbSasH`wB{O6~~lt21B*9T_buOJ_J+G`%1 z>~i}pm9Lx}zIzoZ?*zH=4lwd-F@_{T_~0w@V#itEUPIDZtEhpi@n0Tkd=i%qJ$4mN+t+lL>R0r~ z+%g-sKX8nVN4)tC@+dgIe#i08 z-?zc?A^tj*aA{Wbf7hd(Zb|20{=VSt;fSlwM88ReTm3b^t%v!st!4>u;KZ2=17QV! zQer3^U($iSFUQ+)DP%Ql_qN2C#t)9!&s|gDqAOeTePNzz!O=0WYahFpKCoLOJSfiB#+b2!XrtwPAK4wQIthPDCe!nRqGp`+jU7fDXq5b8?wEwsp z7W_KAmyHMR8a{FGzxVru9T#tifm^mXu;b@no%|#U4xfH@2>sp@IevdBfpt$^W8<&d zy<2hw{O|XMKA-30)8L-N1PvJY*`=NWzX;`%B@petr zZ}Rq??D{C?-{f|KEh47flOrze`tfWJSn&G&m0Xzj$+?6Fv-P|%XaCsl#&Dof5Qo~k zqDCwn3bXIo!0L>>JBP#nuK&W-D<6!cc)|1ubp4k!)cM&7lsY|!^8yyuzJL53^(ne<%zH`kp+0G!Va~qP1Fv9Fu%-KBSXQ3KEF1Z! zZxyWWk$8jRsy+{%m&1z7$5>o>T=!`f?0s1?kjmr4;yogG{>{Tm+CHb>)(hKUar@M# zPhmypt_gmy)Hdz?6PP!j^dD?8g#CaG0DhcLV9$A!zIG3(taBb?EbegZKP2Fet!)d$wvfsztz9#h^ za8mTXRXZsC4Sri!SbDBGJPuZF-!{Vz{`bDB-Hf+HCkLBam)A%KryDp*zF1q;eIL(J-3w~WMq4>p#?EPH% z4>NfIT>P!DQvl-9T*p1hl%Bqq2#Yj-2!ddN|9zHU8JBj+4Yn~TPiFJ22`kM6@chx2 z*?f#=;G4&TWtMYy#vngm=Zjl?2-4r>vEvsW>*$aT8{S*X`a9QlG zXSoZuukyG@^{3kU({D5E`l7BMjra1gEne&X$D*%8FRz6|%3ra#Fw%JJN?5<+TW0lQ zH!Trt64%s3*9Wn&WYT;%QDU^7%*jgN&W1~cb^qO;Xuq@71P<@&!``>%Nv?QzgE^Bt zvLcb5r=`mIyB_T~VxAGzuX54ltWPjozYYuTUN?FJTjpP1MB~5WpzV$quqZ*F%@;+J zR!)BgxAb_$=7${LF7n54+QNP5Zb&bSlIK4npFWtg0OsUo^nO4dKk4;+@??{{l`wDV z)m8IgUgul)D`1nkAAin;MQ`|PruJEKll*bv%h|BN z_V3l}u#ZhB`+ch1VYcTYtlLZZ&<1f%yWjUt!hVIx?Dwt;t0yE*A7Dz9wTVLk}NAqV79Yy8y;!X$S z-vp!mP8r{#P36t2+*1KZ54*Kp6Bf3uh*QAfMse%g!OGXoU$S8Hktqu~upnjs!$YwB z!kEjnUP(^<7B<{Du86H?nm+h*6g(D1N&WAS<^sq7qjxAu=>P)wjN8Bsd~eOb+`1~KnE{97 zJvO+7xU_F&`BYdPIJxRJEHpkWwSsl+9yQSVIZ;dAiLtO{$@$T=9#ih3T`&r+{SZ)2 z>m|A2VP(T%NmA9JDwuzD{~%`NOifx(rxv`57>0QM74_BUuu{`#(HPh}=)yu;?)AN@iV;&_b^H9D?_kM>X^{dr>EomAA7Is;q+n~fwXmB^4J#Z!Yt!*q z4)odI2+QdI_`r!}rzX+)mt5~U(g~K?i>s-9s2zTL>wb?rmQy1oZC>B7?A znUZ<15!DtdJn6!AZ9-KCT&(=Fh z{EyD<2DATfg1At%)2su`30#SLE~xMB35w=G9B=r9LbCe#C;Jbu_Vkf#{gbSI> zb?(umDTpg0hI?OyWluj<()N|fKj$8X3;G!x90PM-Z?V|`hrF!`84Zg|Vjla#$&ZY4 zN5K5X(0B7-edj9H|GBc>5u@OiS)W;dR^*%1Xu*P67vJ?pdO@M(f;O<#;VW!CoT6dD zwr>Hr|Dhb8+Z}Pa*$e-An0=oc76$5AKZj2>#E;g3#r_`kr7%zV&Za#q9i_je0RDG= zRTVcz?}d$rcuuGOE!wtJKNhYHD`M*#d1cRc`@vS7j7Cxa7LGBE^!OhOoHK^^mIAlw(cnP+s1*Rjr7NXUx3S04vi(ZQEe~ z6i-UGRKbcjz9lq13F<|i?!t*@D?Mra;un7XQ~(>gsPE`dyjT3X9GKJU!PYmbA9|$DV8?Nei!;$UpVJ&$99IIEr z)(dhB@BwmIV$+wkUvZNDlOu5LJV?7C$pyIx89E&UY@o2%3zJ&|5u-6L*0EID_~g~khBP>^H?Jl|F^ zp&KljIPT7vv&C&*v{$5eHw36ZTfbh?TdBg=czsLemU9G{#mr0G*bgs z)UU2cg>z-L3#dIw<)_aahb=>OI{&5id;4Jn9dFCCP6K|zT(3oIFTllvZ}G^Yv`XgK zn%tdDFmGVk>l=t?&0lbh#z#)cB6F%wb#X>3tq+z(Jo7AvL$*7${|HOu0yia`sL?cz z##`me9m4yt%G2cbTbRGiHKGQNz1kf18di_nc3A~0e@NK+W#vq-wsmlbzu$OT4=r8Y zUHTrjU+~DAu7|>?>+PChW21AQ>3X5w`1I%x@{&VBx}KcZIIn8+?%&v+RapgFpD*h-bi_wk|EFc0oXT&y+=Gsn zGpfUZGcYIR_|8YLKxdfKNm%r9+NW!9UFFvf$6Vu} zDWAmH{*XQ6seI|)`Nl8cg6oc*vnc=PLFW&^Hg*j^X#eE8U4={G*kQukv>somyJoE& zY(@~p|W5fmT#^nNZZ$zRpe2v*!~Wb5$-%O*GWf<<$;Zln4VAO54uh3liH z54sBT@=L~P!jjONJ?Z-if+U?OU)P|0?@a4W-)m5r-Vb{Xhn$Rso!tSdg|I+$)q373)`(fdLw|i;47QWXji-G@N|LXtedLYbqYVJeV7m?P>{G~8& z(bB>W)E^ft+3XJgn}2ey@H6Ja7CifMHa|5HHCe-ni!#`HckbAx^_H+*KuZD_`BjtO z%^L>$d=P8X^-fZ8TG}7B3itkRJ^Yruq8_lce<2$`l*ve47F@Q>iiDd6)wB8&xvTH4hoh4o&!_Q$V>KwnpYpG|d!EJv(Lm$T zBAESu28vJdC)`P8wn{q9nhUbVSf?g+}C5y#G#e81OCGnoDV5i0Lz z^0+~8fjI9z+g?e9dH^hqHwvct9e1PW!oD!CJfSy@Ut;~am;1o2QA4ND{tJ~O4E5pI zBc;RW_*BbEV|u{K(GKi-&7XU0Mi*FaT@%(8^}`#Gz|2uh>E02RJ=nqQcUbc@)u(Xt zRhHlG&suiARN5A8`ys!iVQPdPrGFLhqdzPPkg@L%h}TU|<-w|%{fhJv7x;2#(*FOO z4+*=-k_N)1JzPs@KFRsN|M{oG=_#^}g)LwzoE# zJ?~V`=)?9$Q7&T7TNOpROAS!IA|ZsW?^l^zUez1cn5@Ulhx+1-NIjy^n8TJf3%1XtMgLB==ZVY`-!8YVeft0+4FR9bxXg= zaLeYhAa6>)zkcFkSibp5M_-tG>{w+GY~pX)=ue*a>C#R((Zr73&*VNaeRl%RMIWN) z`y7uOqpIMnkQ1-MV9DGOXP;5rUyr>%!i%}ExgNHP^`8)fxH`|_1_ycND_q(82ZD$J zoiyO2lvXFYpQCD5=++)KzcY8%c33p`I=P%4(!0l0e|<&sVJa^$-{c1D8sND02+VJzctri(_{w)b8LSXl?2Li)@lOR)VPSVy&v00^ zdfgl5#VhxUVey1T?b0cI!=c3sV1a=ayC25W)*L#QoDmh0iMYi6&#~!nozpAZ<1qjE zb(JM-|6u#n6R>jphFKQmE~e~xfZ%ta_i)Nzw42?pRhQ0U{Z%%=-{u_B%Qy7h-xYD6 z5oYXuo$C3giydK$9+&DbAg=11`LjKoWYMejBFsCK8`c(X*w^soCdIq-8K_>3`H-$- z>|I#h+4|cXxc0}~url(*(yPy5lRg3JdoX|J!A(!#rkI1v?o)j0)323q!L-A}AHX8N zrG=%iYTF*&8dxzne04r7&*0?OQd}>4;c>Xwe>7X)EP2y7;~-pjvZ3lZ;^ILO<~!il z`t%GH`GY($2#&7%%;Nl2lTGHs^;#}x>HaN8c`SDZ9R4`2BfFnlw0Fc5I63h2Il5me zh&=Pm6t=jyJc0H{?mefQHvI2-k<>{4@iRa4@577lR3pyaSzlBD*Y+Gc<1x%zHpBA( z%)TE?@w5utc$hcaY#8mY^6{-TYhdxDp0nuwt=Mn-DNoou=eXA!ijQqq?E>2ksg`|! zaZlfSHXJ)}gr^#o3|acz8djE-%x$9lr(W1ihO0h1-1!EJG@X7}!R|^;msXhjcuUTB zm>W=a`!B_Z)*hb-Cogy`ZHxCkd4l%!0=RyO%B?*tw~uT$2lg4exUvJR&~lvM1B(t@ zII{OeF9g|afNLH3%<|10dT)jm-XobMG2M3_g}og@BPl=6`jNC4&P`Z(oUF`g&aH&i zzSD3v*5+3w33l>V9rTQ4r$ z+40dinAhiBt|{V*T>-59Mc=)nX-4*cS8x&Ws)F1>!(nOm3H~kEJZba3kuY~f`#ld~ z8&Tei(Xe>d)%4eJ!QoDi$58&oUax<{tsjXKYg>L`1&+oP zIb_kjp-KB-pn zF~8I6wXosyl_qQ9fAb;UyxZT_!BVAHx*Ou|v#UaBy`x~_Yc^h%E_J^W2}>f&*!4cd zLbN3cR&{=GL>b_W{=bpUOdn4kpUIJ!bvChP$Fh|coHx6;JS;vu1uwAS*iz`o;cJB{2 zC~TRc zzy8=1G-O=_tn4+ef%>c9;FX#X%J0zsCG}^GtpnJ4UEcl+v%TS1(L1&tTUuw)(*x!V zpU2+MmoJL!YX^&Bma+B!+{Nt;$G|dC{7Nq>Z&1>kVX!>tN2)U{6F&Q>4|`igu=TSN zx5_&@u(Bdo(++XIbjX4>aO`0HJ~pthU3tilRk)rS2ebAlAN*{{Ygl;OqSr*k)vE>_ zd<(cgglv4gF?f>graXir_aR0RmpLWrcimeufLSQrGs9UPlaW7GpB8YYv+I1Py4HO z>Eav;`wghdodHY5a*q($rmGfb7OZINvpg7f_d3tk6U!YPZwJ8PzqYXc!t3C`9M~1B zwUC~3G@~#G@#u5)g|4u0jvveK{b#Wm^&iEmCCvP9e|yvV2ju}VvuxArp)39;uG^(# z{AzO50v6A|WY%#Fj2}|WiLd(qr2e7~*vqzO*z0*>5G?Alxj*fnaUhSi2dQj=$y!)2 zXdGLwp`Kt8=MQsQEbOQ~aT8`(uY%P@`P*oH2XEIJJukR^bS<;;)n8q5c-ED9tpBt> zUGEB4zxdAj4`)SKkpnC}Up8bb@^fAc?K&I&*FOZ3TH#bUIds^$?Nt7?=YP#%Kbx!U zdkv}*hm1aOZrC5AT@-hiq5SQO_Q=)wxf50oF+0@=8yf{w(RvoOsd)GUn0xOqTffhZ zsalx}=bPC$QF_(E&WEDl#EHLpCcxs+AGe5LcW$5fL|7DK;46S@7k<0HALf-EG&OvZ-dmZ$|LAPCguNpGN~TU%ItHF z*7vB}{yts@D_)%_IR)eYa6Whf*Hz#2$bkjge=nB7-1`HY^2qxnzNN7Dx=~jy!jhAd zx>Nkr6i2q+S6Q}6>mHokKlR!*#QAThFQ|lFxz3-+f_op2kac6~*?K3rsiWaDxXR!U zTW>75lGUymcAtA?G_7wIzSZlmf#cJy@oTvctNI7?deQOD+cA{ZpNU-xE>4A$+!Gt0 z!n}4jp4!9FkE~w2p!A&@+PK0d(TC5xg836J*e!y?C%sR94NG))s>!97*MECMj)-B7 zPv4#R4wi>l{BT3uqD|LFA7ORW-c|G9z~4KCpUE9{AIyP^={*lvsoMBv7Ob%=|K=B1 zb*;Z}IxOl_#@3Gsy7hcLj?xeL{P;WN@9nnM3^plTIsF$b;f+bthTU&CFZ}~6)~2!g zFdR^JuPv<~ym9}{L)4GLHM1wJA5^!uHopzm9eq2F*2_vZw%o~srS)sKYQZWOao0?^ zXanaFt)CU^Sl*Vxv9WJVJHx^$6WycW+~!6OS<)~7P@&n zSikP_dD@;J$6$7M*t}=gOC4d^oTy+;So?fVLwi`{rZ)WUgX{HP)5EmBQQ)uhrUovG z2qtCnnaJ_}nOu$@V9^^&F&v2X})iWQY7h#R0Kf|G2|WwHI+s~I{ScJ*1cx+~)R z!_U`@A;%sH>;(%W?RWQqlUqVA_km^6CknW5w2VJ<0Ic}cbmP|wdcI<2WlZs{pC^BX ztNQMJJ_uG>pXYpn>vXh-(fVG&&W_m)u=b`84~I~E&z-Q>aFco%ThFO*xqqh;R_EI8 zH%DB^=`pGRE=_eDIto@B{47X@lYK8;9Sid}`9(y-hNam#w0=~uF;N%>Tj^FfTEQ|q z?eMkaM_0#L!>YU9n#*A0%S#4NhdBeIuX(}>tp&Yj!n`e}cbC8x`z8*Z3k&OASo)%i z73xJW*Db_cjChFFf%#sr>csdnVRzXSEhAxung^4UC`Lv>ex8 z!(piuS1)P~eGjwmlTrF&Ro7bBym&x0ZBJ4(C;1*M9&f_TtJ0Zp70w@br!t808;h5n zhpn1TS$V3+P3Mlm^+EG`h9WK%xTyESCPGJMJa6r>6P9Mn*#65eq&7=n#l~K3sXXDA zGI<2d-p7KuV~)qKgDo$9&|Lv@q?ewEV2zK5`!0hepRY|^42yRC{ooC&5>l>tz^*@g z)rcs4;^M3GU_ZUNqsh|H(>I)8`;XG?OJMmaPO&3g>UjU+Vwm51SBMZcpSLfG)>m_m zEq?3(OSlW7X}z|*m(k1Fu>S7m8?=5}uyhEs@KEH=*_1y;hdI3BBU>NM8K#P}M|ydR z_h(vfE^%4N$}>ru7E0|=_{MzG9K^N1n%7K&McWFLbK&||Cq1eC%Em^q{jo|J(4E#( zt7cE((f;y2b_$^N)auXWYv_1%*NAtIqx1z!Y}{evi<-Zv|A_V+VfE8+xSDH9{u$hE zIpX5?eJ-+hW#yj4y?CHBc1Cd^`_pY-mtbRA4KlOjr9!RSkdrdl}}N<3@_dz~Uq4Z0g~L`WamF-CXQ29Q-2r6X-MU8?&_C|Xni|J=T1%*rElKWZlp3b^m+X&n2 z8k|k-jXOzkKNL>d)UA9emG^A@w{@`m`xNb!lwYLWv>Y~r%-%1DrSA*3PKLGh zKCg*~#pAQ;C&H4IOM2|2{Kr?G7z-=He_8H=)ygY|eE3wiPF>03dyZSo;J~+zlPJBY zORaA|*eBJzcstCsJf6vg?K+NnK-(8pb*=0G=iaRBqGG0fRf+l#*6 zz!~@S^I%xN??HC{CE816gJADZX7lKJz;8}(q4fMVF^8!C3T_pY7{HnrY*_zQOr5a75@kc_OCN`Jg#YzlvF}CkckF-e(MPYS{|Y5zk{e(Xn`3%p>BO@9=dfY+K-Rw% zsihLKGNW-9#l?l){grT$&K=gig^KN#ci?~Hg*;?K*(I2FKkU9O(kqYU$g*Ld3(t?t zfcbS<2FGFbpv5m~eBzY$m~jN2zj*??-br?K^*jIzCF+lCJiD;8G6Ck)xBmCN2qS@o z6h1XA^ap$E|R!q$bJ}anT( zhCc1mDgC)_eG6b$=a9W8U{$A)5A$K3eedWqu&~@*odXwSn;B)p-0u>vQ*b!9)Gh~B zw$3ungpF6PvpEOLC!c2f$Lp%;n+uCRM0QI;+|vHq#PhJaQ;Hd#udE9W+w)-IWE~f( zAM?rGMqhwA=^su9!lgU-&nd1PwdAWeT->{|8|4@D+_K04X8#WvmMkhOn-146{QHRZ zSJca?$2i#jVeZciik~ow?hA9uPLE83mCDYd?l5n}==`HFe^{JNdze4?_J8#=uK9iA zQp^_u8urPky!0dOp1?_4J~9g~6_j3u3-`uSu0_~O*ZH0_(cr|w7p^b z!}c35l7kJdc)_JN(`R2M>liavH=WTf{GWL4f~muCO7IAQwda0cSpf^K zUp*87D^jkMJ%UyKg9aXeEmmxwTtn$64cw6loAmu!^&FP=?*8mDEX8M@Uc#dMX>D)A z0=LZ{U%~2`R$K4ErETtXc>^ojhR=EmYw9htcuVPH0?&VdtV@hpAH|*K*q*7Wxmq zN974FWjP1It_gSUet>cRA!;LRZgi!IES_mlOXE#Jr!hISfBYN$R-J%F`GuCW|7zPi ztiR1K3hY$}3+!H&mm#i*Y9C0)&pCL^y$TLFBr$$L>Gy|NKY`_rH6|)pzT9HuGdQ~6 zg>z3~<&*JwZ(wDj$=b)Tcyi@7H7q`LUtR_COG5RVVS)7STRK1D^#TPsdRJodJy>|y zz^4UHizslW^DFFe@WKzuzhKT)I{ymY4s+;wW-;t)#tn))cCw)Bp^g087zM1F{WD)1 zHm|?_{tB$V`gf8ZJm3F=A6XUWJEj+0H0O^sSvJq;DcMKwX7eSoSnIeEtgn1fMiva{ zp*sq0iuU_T?U{G6c<@v>zoVSBXR$oypaZP%{=&A;IT_S{0ma)*;!%56+bpnO0-sv> zzOERS8|<6r3EL!2s=W(yzWaS32R01rQ1U-!|8JMP#GA$4_jYlh_N@|Ijar8EMK$IB z)!(JtEBxSwE??bQ{YFc=u7*YDC(nO~^or#>m?h%|-6snS;+TuYF+tU^`2X@7w;s@? z{vf~p`|2v>4_VqdzZOOYbdWz4!t?tlGb;@daLNFRNx=MQSnxG&z41Xs%nQ( zsbSR)SlDxmZVN1McFv53fU$5;VNy5y<|xngSoW7 zuA!H@w1WjDm2Cg5;uD-Zz)GVf)3+j?HeG(DGo|Ls!=z=U1zazCt&G=hbnTqdVd`yc&xnj+r(GezkKC!M}_cswl5n&dqjR_$)ho=owX-?mJH zxr;ddHZcE}{=x|`XOq)YM@lacm5zq_h3A&L!OABN!$!iQ=G0#+VAb7E^1*QZ>`M#8 zFh_FS!VuQ?x!ykj<~}NB;~jU}ci$jbG`8PgUBo#t9E%WGU2@5X3s*U;yc-V7vwC&? zgYnZueLyb~mi|hc{|Q!w&3qCC3y$wjDuL@vY|CO`alYI|26HPu7jJ<%f)!o2QT_*= zg2-yUpI^h_@Y~;Z$H9E#OTwveJ(uG{md_aH+yNF0RW(w2$sB*bCtheT7Y|oNQ`~&q z@lsgUH1{p-kKk{e`E@ucDBhm!Z-(1b1*P}4J`jkwI9l&bAuMtIQ@j!ub^4lp7gp7u z(er|ZX_e0^VbPPzBizYnN9fkTf_tY=%!36B_Df&GGRcJtLW=(!ta<|nrguvx%U-W~ zNKV__=Bqs{SzK0M2TR6ZtC$5V=bQah!KyPaBgoPz{!V1w3;GXdQhJStZJ)!nZQgF1 zLGkcNcDy#+Q5UDfqS_a1fB#vz@cf^2q|f>xA7zKQu-K#54$42?y?Hh)f8WL12QG-d zzm)cm+qnO@E9{!2b=HZ>yHs~+K5W0uEn_|`nW7iy2xnbiX}J&<+ci~*uvkUnc9Q0XXb%5 zaM4@+j`6V2>m;-J@W-jUVA06ye#;S;%{gGqDYs=}#1seTn7_U7u4V~$l(dlGwJ%zP-q{Pb>fHJuOn z!<-9O;oK!1*!hwA%G(6NnmZqxQ+jp0=NutiW#68~)n_aE4S+4K4_-w1Ws#XdI zZoynCuVm2QCri-ZG}H9wzYQgYv02bmKs9}R@(2X7riAF_IwxV zrJvvJ>k9vC@3Nsr(Hxk4Ka0w1j<>1z!1*7%i1i1Kq^;yOY;vzX>z{&&#Z#}t!UDTv zbo`=2*G^o4v(h*FQGIcZMz79?MbYCXQ+=wR^e8_M8`|Gv{YmB0WyCpH)iIlm5BPnO zbOzSH^70!Uzj)oywkODT_;pVCXPnK+g8j~xb*J{ho3{9J23-AYZ5!%e%3-NJ$ob2Z ztbK_#q%(V855Btuan%HqrJ0Dw+B$cm_Q?Hl)A1xMt9m!q8y2gKW}b#6ZbPD$Q~dqS zaoKS3hFQ%%uu|!Ll8(>hllA*musZ6=DJ5)Gw)@3u%5OaC%quu)w@t=cSmOGE)$e?3 z1-o9zH@f#VL;YIrTw)uDIH&hhleutWTUX2Vuq;X1EsWx4^V?JZ$9n>ocEa|(HwDvp zEO_eiGXrkffBeHnil5htJq;^ko{ilEOATYMX)A zL5E@XJ#1L6^Q~nMoHeh0^j=uiViU6!ZkX$HdLJx!KCRCNxO%M9BeF2(PlXp8yZjwX z&)?Q*(;T>d*c)bE&-Sghuw?HnTiQPFs9$MMxTycm02<#E&HX=jft#$Zexv;r=`OGR zi~2V{tM}jdIP=WcO4v{7Ic7WJg3oz6AE_&z25d7Q#xO7IwXt_K&T0g88$fJJa|rI{mBfG`KF@inR~PDDy{C zC_R1O4)Onwsk@Jh@p=D;zet5>BNSm(NK%Us6^m3z7NsI=H%O8#AtZ|s!XkvcEsCUA zDuraFloX4ilv*SqT7(d;*D=0bzt5k~$1!u>&dfRIoHwH$aQXUhxV5~haz<<$0t>h& zZ`1Wse4ytbvh?h56hYCY}Y#vf+g)vOUdex+l>QZ{Xy+mdnUgB z*`znD9Fp>p>Z72a`wTtG-%pXp>Zgv~6E583o~=#SJL&t$lR0qtFxwOhTK=!<-KV_K zU%jBi`h%j=B?ZB7g5RMXJrP&t`KS26*3BKp_JDc2FWs0Ax5TDR)Q8nCd++R&B52vSEe(4HxVjlMI z3@ZYjee6d0H(QP90LS$S(eDmRW|Y`!!Oi=YY8g=eh|t8guq&S%a_Yi9jV zS=vg6_J|ASbzg6axN29wN1b5anO$$pV42d3s|RN(tE;L1$*~;uZ+o=erdh%KhR2f) z5I6Oj`ExWZ9Pp)x2OB);wagY4Kj{!?0!zEC@U^G>RtK+|!gjTxLj^=H7s%DF6mal=}6{g7Ywsxm@;?&{cr zIf%<|d41~vJ6F8R@r1>NCRwyUvB9y|sJtlLciyGr>A$nhf_ZT3@k_cT*huZCYGkpp zc^=wh)5EK&z2t0&>HHp!_-hqU{S~gzDZdm}xV3#&+-y1(0NcsWv;L!?&$#iE;i{EptiQnZG3-4Sw)tyk zN$s&*;QEmd$9o&DqW-6Xv*_$lm^mZ7Imw8yg!diFMzDg$+WDSO8&Kpu$d)jO{ z!xC3It#u;oQt^oid9E5iaUMj>uD*LNHB$7LGvqnzM4tskuYlz;j@dM+)m z)bt3oM+%c$oOGDY&j1U8B#{r{fP4Adsr?h%X!Lsn=iL*r_D_B8p?ez~f9Y*=_rr*D z^T)CB#s23H^QyGMtP$7W5@UP>mgMD?IKnMU+>6MHu8qmAaI-`5;bX9H#uup@Eb7^L z(Q#OPD`ul7T&vsJQwDPu8$IxX4SM$aehL=UDp${illl~WJ`KyS&KX4U=3^cc6Jb?) zdscswUcA_M1{UUrsN50f_4nwMMCpgQPM8gszfSm?0xOTiSvtUW4@_2EfYo^q+t|X^ zAy;EB!s6*~dXI!fcNT6*qx2iU*Y<*A_ouGQfQ3;>zv*~5OwSRLxu!lf_i?;j&YKNM zhXsZ6qt3zBeKWRShDF_S6ZgRNOD^A|^~<)E^$}6}Jrftw_VL$VXq*HG%^&{k1T3j^ z-eL?V{k^uFj+Z3IH?s#Eef5KYu3xhM5^~zWv3vfS?1jZm9ftjyi}uxYbQ)bRIA?}x ze1@|umUkknjm;J{!lv?ZifBq-kXK&|tI~2Dx5LthD?2@hqkNqI-M?AQ-&P8{gw8lk z{dZA&bzlJ;9j~>DuE$dT;Bh&y+XHDH%fIf{*{iT*jV)WgXiwLx8L)VA{dMZEOS?@9 zy$qYh{g@jA%i~rKxd=BmXqeLWbD9h7&cj^a5x%jo(%|IrB-r4aV$^@I_8a?4Cds{TzZx4F=X>G*mJ5;$xWEE>HW?laNfORx_Pkd$nFD&;pWL*((++Z z;@tRya8+@TqyScV&0!XQzq#fvEPplN_kP6XlY39T2lFNkT_A&799DI9zp4!)-cP(4OU?k@8-9d^2=vy`Uk*v=2HraXnESY zao(`bNA7pF{kmh;+rfhP8EpTQ6EmI-guRwkeQrXEVC`V$(o&lVX4ifN@~9(-+GSsh2yjb=~4S4jM^17 z3l2N&F@xF@agD3-MA*~MjkO<=2aSoN;n>1^*IE0aac{|Rm_KvJ?z2cQn)i-p1&5#O z&)N%B*CD6%V3&T|nyLQt#twb+(G%xS*(Nj-R{3o5s)w^=?QO5Yf=(++YheDY%)d8a z&Wx$@QkXkf`JaN~Ck;X$z(V=meYauWx-DDo!40DqBvbhme75*-hy03POXY{F8Tls{ zZkbm8=pn4GuGhT@E4&UaFC%MxFT4)R5;{GrfR&ep&obeFkMgo-utci!Dg%z+IDSAC zEOP!ZDGfF&FA94O3rgSQB*UhSbv&~8viw&(Y;7~=VI`~#7;xtx`9gcWrwAK zDDqAV+n&|oB@$RNK(_q>;*#75W}69%R#JIaFa5M(BjUw+x1#RA;=Ui4!)`6xP4%_a z*}-!^-lF_}cfVPO^#1)U<=L=YA?iYQOn;PquD`#g08J+4W3w%;88l zyT`_dR6n_IDmd$4vq$;tdd9o{rB@hiaZSR?n|jf73o)Fvr5`JA{Mt69WVcS~?0O=8 zKDz5_*r?#(ecC@&$DMm5@}>=wf5J8)oYr3-jgkQ@%z9jYuYraVX=Qn-~Mn_#0PE@ z#kmLWkW=F3rhkR`mfM&$QVXX3gyo)`9wvy3Rvz2;o8tD{RJ~#01n1p2lMatuLhNT^8(JUEA(EEq|-a$-S_&KIncU%o*s9&s5Q0P}c6E{gpbH z9G?T{Ef`f=3oFYN`2%79i;-hq!1AcNJvy*zNc$41KjIBV|9zcZc?`SF0K}b6jBj7*<<%>R(Rz|C?}))$e!t2_~z5B#;M z_hA3AH&}l~sc0Yn0G7Y|+Ccpukw=?=N;sgzD2@6fqOdn?`#qmt(xvu~|I%v6Tg1gn zR(wo=dB%pJ4RAznGgkg3=PTL%v-!smS6z3S(L~Ez`|jWU@aH{ezrae1?zgG`DR!8@ z^drUleVIkk4S-YVKUuW?VQo9yef_Ut@0rxV&EoYmemJc_!^(m#`P?_{%Pw{P4>i`!9X_qY&wpF@feE;g}IZ zDLa3Mg2q0D^GZICrS_2*aZjBJ$8~z?@eG!BdtrJC_LL>Ase|R$GQMqvRZh7zRQ`Au zY%9azCT*Kv%@p@*cfc3sE}6>eizIzfjwc+pE1K00=^&jKLYTe(N%e)_=Eh{WCC6!s z1}uu``e_X8ZN2nsJ6P81bDbINvi8Ogsy|ZuuRc7u!Q*Z>sxRuM^XF3pSq=K4XhkCXyi+nGyG$}V7NZo@nbd2yVLiJKWw3& z#;#w&Kh@pm!}w0~-}P>|cJv(B?&t^^)hF?ZLpJWPBIU@sN3d+|b*USizBsJC5|*DW zX7;@Lyn)rPi*4V|M%?tn-mldE6NhYN`D0@>&d~Ygyg$ku_31e4Px7zaTs;@*T?)If z{+ei0ZTdpkEuz4X>KkuI=ZZjBeyEULe-t0_TWDA&oB!{AVsKUg?O(c1_+mM&??&a~ z5SY`vA~_l6w)J;k1-Cru@s93q6c$o;dvO$YtR!THdyKZ7#y) zdp@)0J3Kefpc`;N{h6cNX?dP@UFu+?7mC@`pOmC@(9=PAv#HZy^r|T9(@fu5X&?zV3e@CTgLixpoquKdxdi|uHmM^*6&dnEbdB5{s z-sCw&+a|&M8#`0U>N!uZjfV9jvTX&&P8bC=}<;kdrf*z)-0%GzFV z!bxtpJK~~Yy|eUSm*?}-X43L)i=K6W{g?ONKLh5Tc6VqGr)%15ooSX!zvtaj|g>Z|sTH+KDC(Z`KPW+Cq1?d=pJN*}%a=L|T3 zyFa2A%sVz~l@lEOu4#@TES>$?Yb>m@e8pq3`fg=g6F7Uw-BK3c+$YxX(EzC4&yx25xY*wZwGJx^BbpYyf`)(HG{-xP74>;0dVa8eKN_<^uISCv){ zM~xTmrQ^d-|4{M>&ini3>`++oxKn*GtW#^8JscKy&puZG8{PbqV+#wW9$uCUm#cOc z(fJWAR+`>~+5Cerf5FjWvPv_Wm0!-r(4jZr=s}J#a}nn~*;jWJ)_HT5l`pmA{r3!* z|IO?%)emlY$4i&t=JEI0^9;39WbOqxDcCMlgt$C&eAGG0e_$OOU*>yPX`O&IMl`bL z8Jw5*9QMK4dZ)D)A-(G1weYPl-UHl5R_q?1vkG?prrhZdi;}mVUkq!%vR>o|b6-@9 zo(q@vc(dFGmZxpMJ_D{j(&smApTNMjc>*kZw7(OTZ*hmD5&hv<1MXcHn0IBJZVxyw zWN-?VXXS0hOC8wTUu(1zELv5Y(GGUoKYp(x<*#hFS+^B;kj4--OM) zpB$#+BhK2$*2m^^r1kH57Fd9|si)s*Y7a%Vi`e$1q$Xr8ht-Mo9f}b5%yeYWi-bO+ z-gn?6>+^Qh-U|M2eL9-2h3jES+^xz~No3kddh(uaDJI`^w+$*O!i$b@bfZV_}Ku;FL&MBWEaUZ-wrn9ot}+ z;gPJp(~@_?VAYL8@bmczEg`l7|5)LtnXr@z|*bMIZOrS@9cnG?L0@*m!{g4$2%PUnrQ z;5=2bybGm2v&kkD)*i7ov;)OwR!#|q1#cg()`lfLCjAV8#U+a$Yr?#%i_@0EHi6tK z4OsRj>dk!E;$fXvdrE&GSw0E2i?^6d=AT-7k=!)fe7zRsuX?g&ENnVDwlA%Z`?uoH zFj#2te0(QZoZ))c92Tz~)44O{cRXrq3TKOQylH#ox6CyAz(IvY1N2}?ey?L)DDLRF zsw*t?-Se|~CXQ!q#xmwfU-pqjjyKrzBkB6aZ4Gei@;TDKy{lnQjZP0}`_zl}a~{HW zd#j{vVO4_B>)UWuvybT?df#fnYVI{yeZuzQFId!gYjYZ$^?rDtZ?JmK^o9$t#*`%~ zpJCzL;nP!Kvz2S^G{B0hV=kP7n+sg(*!!2&-&Q2Rg}I`;^gf!f)NPXtwwUi~PVcJ; zHEO0Efy0!mQp#Xu()tH`;HKe+JjsF!yQXe~d6fe8zMD8}?D9xhIAT~N$3so z?n`?23>GI=l1ffFC1oiI+WfQmO z$B%<~+q>5Q@2hh2t5);irs!Vm{a!^*zqf{PgSmVKy`L!C+3vhP z9Od3_$!l0VvvzaQT)ewX}R*_rJ`N5u4ci#nKM7LuwIkS=o*pF7`_ulb8;+0rg1EuD z81}w%>-JiI8Oq+z75PV-{y^Mr_Eq+Nv}Dxau%B>(;Joc4+P;P6KiXpdMW1H`)A?6d z{1~qZtNkY=()m#B^?A|}F4sG#qVviB+udIeHb`r_M(?{S{LR|Y@odNnUPkZls=ALq z)dQ|NeQyW7ugiPncc(w>;N{HTw^x})U9o{R9=XQR`})$ueG=_p!HQA&Ct%JN*|iYZ zMZ)iJ6c)O;SVoc;K6O4w%X_-+(=M3(KL=K4|82Pl>$i2x+6s%T-@VL$xj~EB_cQV# zXXoU?RiD1F?}5bq9cv3=QTr`E5lGLqa2oj>_TD!jb{#E`)5W(AmJaySH=LZ_GL-7G z_LVs%^u3L2&>^F5u)dA|kJYevdPpAGEPlv4vgomB`gho3_A&Ooo_MWCatkc^*2KQg z;nXah@)u_RPeyvl!?OGiXg{jTMqLkq`7^(@(}m+*?F)lp^`Ai#_2IC+t9fK`nhCS; z>H+pWq-se&=AauF!k7O~dU0>V*+G!vRk7{>|EN}mrZJ*ol-A;=re(eKu?Qbrp~AMN|G0xoV@y~&l*>-sEx2v zJ&>4_G{X;$h}+8Mr`}Atu~h4xW=@0MT)tS+_d)WIiN9@O{ZXCS_i&2$ z{=@h%o39RWPGS1#!7wK*;S`;Jp6)7DZ#e45IQBgd?}-*`@8XW%SA`-jIGvc*6>&G$ zsdK|%^;^$uE-f!7W<8Z(#mO;!y1;DyHHr@mWcA-H@4~C~uxwp|CA9|*tMWHTl3flk z$9|dhXd^7xcJmFj9}(u?S$*TLwTWvF`#Uc$H#B+qe@0 zbhg3VVDoNdPTlJ>J1G7CwZE~)F8AC`>20^4P$Mpyc*L5%N8*)#D5`^F-c91~gZakW ztY5+n@2cJR!{Q|+XDZ>s=|=}0fVr}xzYAdL!lEmCDSapN<;URSKidDk-`lWW>_4!o|0aqr?!MR#HfoIL(D4(EwUUg2P4$;%Q2QiWBRI;3W9)j} zqxMJ9?QH%)IR2+D`~H_ZWbgL=aFVv7<4VNkE3eNrfa^UR*!dUnKV0q!t0TCMD-c%( z2m1AZ{f~6oKgt2mubVTo<2hBQ*Lxw(Rr~G!0gHp~u=>q++T!>Pj{oMv>Z2@U z=dBO0gHbk{4_T5NVfPxYPws3t0r`au)ze>4`ezAo<6-spADY#${?E4^$G~!nvchum zs7K32!3y_Ro62DJKVew3Ir_a4_P?KhY6Ruq=d?oshlRR-wuU*)i@h`9sG)5>j->dH znt^gyP#v*kG{x`#5G2F3-90Xhh2;w-r=5m{lYhmJgGJ_R1{{Y~Q#aR4goU@88xO** z-$N?nB4x3#-P1hweY`5My_(_?3vym9Kzi}PBjXRl()Yu*i(vH}qrGu(?X1)0zA&$F z${UKujJvC8q`Fe)VH_ypDUHAk+>-9h8uhmNG z1V`Js4`uQByF)c#wOQYnr;wgEU}(sv={TM=pBOCok*3OnIpfnGQ+va`UGe-sxV8Ne z{5rxp3`gIQvGz*#bWY(?IQ#yS7;1k6T+@DY;MVp>*nh_Csc^uOMQ^FS5hjP6w1LAH z{P=e~ek>Vf4!5>9k|T?{_Jdp7PeJw-`|hx*!nTpx8{Qv-8G3L_OwV<6{97#+hwWnh zpCw!t|9|ICYh+KPPdG3piq0>0UcCOmn0zJhMR9@JfZ8IvR78G zeQ@#fum*2f;q=@0EF82nf?Yp3E_c2%%DQbU);vn2^MPF z@+rNM{D-FlEDn5OL+4vDe+#ScD!n;xb7698>8+g5v6vg4qC3hX`me4-tt-~5X0Z?p4b)}HY)`#Nq%T&@>8mD($b zX7|c)IDD*XD<78r^qS-YM+c4AX$7l)k4soU%iD3cZYagG-`=6}#m!ERG=oL9^FO=5 z{MY-~|3{>wbbe8JYYFVf{!hZGmYkmgm%i?|vOg_P`D30lta2=kHiBh6Q~e#`_$S{c z7{c6}{ku^K2Z+A!=OQkcK5~o@PXBh-sSC_|yN5puu9`5yxHBw@ zE{UEA$A4{p(h*j6$zrZj_LFMC@}_fdT@h#hV~1Pohs~PX?Ef^}DW~(Pd^NlVBit+hv>2`ja(v-%T| zF}*9TU+fy}*bQ-oMnz5GJkFO5DsLIbw=wb4))=3F}-+XnVLZQh;s zUpZ;{*FP?}-&xsyKo3gawrEK+EY+|&Y5?Posc(CNy zwBes%i)dkv2`m<+y=;VQx6c<2hPm7Y??1rauN1{}eUnXVx8fDd{>KjU^xG;`aLSsk_A2mK5R=#SNO!-x5*Dj^P{7~O0x?W1=R0k%*F@H`}(fWjKY$DFUoR=*h z=z1$0H|3BFPA~szO53k+&2T&chxhq;)fAT9-(H#sOO3~||7oay&+2^+*14iuK;==s zK>s#5At~oAl}ADOU}lHyMeX!qv7PeXS;Wn{#^kd%2xf=rf0niUkHf4h+kJxy}7mTU{ThxqF7j1*5x)?_BP}exxDs=?Sub!G8AtDaqCjEWhcS8G7_T zD&eUUR{Ic-kbb=UlG6A4K0*rH96ZeaXC-Rt!}iZ~Lvq)jh~;poy$M-vbi6;?Ahum3jnG^|Q~&+NUlKI=F+!-?e&*!L%i{@B0f;Ajb&}KnD5buiS6Wm0rE%{gVR_9nziDvN&sX!qVZniHR=%>bD%k(~ zq%Ix4_)|PL_|rd>Nvu)HkpkOf@-o%3+sZw$Ai_P*tSj z`E^+7C}384em`>(PI9eWN9j4T%vT3sq0MyPxv(H^&&_DqbG~6u`hTMI;_6LX;P7pn zaQYvm=yt*WjWGYjdNVgz*>vMe1RNb_!v1H<5AL6^9ya>g|EeS6!YhJFk+55x?IN-= z(2hCaPFnZLuprus+4|?AzLO}uAeSvKFY)WT39#Z$3UlrKs>*S&+$w?DX6EyL^nX`D zP*TNaEMJ;^D{};_c2o4E?GF(CHR8jn*mDbZ!QS($+5c#jFATr#g(bhH#Sf&obDa5M znAcKaWlZ@89K3h}&W>2Wq7TeH+dt|Ytc=iS*(NmIvFws%1uOdC}tishgGLiCVr=Qvh~gjuwC9m1G04dpup8Zhz_i5pmgnS*HSE)9Ss>4Ya(osgd*H^uPCO zU&C?-%dU2?na2(`AD`fD(ZOMGti`C}7l;e%J_ne=0fol0Le-?89u@3nexnC*^L*O6W=9&y|V zE)GAthAbE)sUtgQh*)~*>S1Sx!cj5$Y<@pw)`-Inu;i51`COz|9Ub8(gsme(*!-;O zhlB1ffc5XbNxetg^YrPU5LoQi;zIMIO7H!CNA*V_UdsOe&3&d;M#0?tu&ea{Z}}Bf z|08f!_Bfx%uwu;|`!jIZyUGI(VO|@Bbh*K`LuUNZYW!VK6fX%stN0-ZsPR34Ni z(hqdJ#503^F2VKfJnC{_LGG_-2Von%ho`Sm{;z?WG2~}a>dUa=%Jem%uqwBtFIjCH z$zK7t?4Ft^hvgHUKQD!4wxvTZQu?+F&o74Kmg+=ZAg2cE_`|$U&9&!YWtky6K2=TO z%jkdXqFpa~jYZr@dBT+b->&W&_s9fp5&Kspz?|TD;%;!U>d!>>zj(!{#cg5hiLR0H zi1V-B+}DirD;BSHI7InF4R3seEyj#BiG^ilv#sjkmb2#{?}r6P+8lWWCke*#>3{c} zhN?Y};P|{lT4d>GVRa51UOz>XEH2d?M>ef$X7dO0E0uq<;HI)yLuh&2i#A1Bd} zB3d6O!+q^>*x&cAE&VTF*};3qHn?SWWM}$cJ~zMrv`uil@JPxrSgdcL3WX)p+?`Iq ziY~pL_`qf_js(#E?*(aBygXo2{*X=d|9k0=yIaP>aZ$(E@#1HNof!_-8cyaVAuekU z>~9VytaLc@9om z-_$o87VR6z_E%k(v1tvg-n(O1HsYm)k?U8(yt@@=a$t3%1DpSW$M>wthrRPZ`Gz7c zPC0P31diDs&@F`Wn?#;?3}+eaJ1B-l1Ky1+hxJG7KeZgj_ZodZ!OGFLF9TrtM*YNo zIG+(60{qE}y8X`vk~3qtWI?;2&~dPxRUnIt_l(tF0`oSQv-x`E2949t{!f1HSi|QE z*kac+{Uyk+j#}V#5020I^xY3u+W%)%LB84Rk_eW#70j)MJ!jYl&W9DX?OFY5%F6xi zN%6@Af?C9Nj;Edy!d$JX(${c6L?fFYQtdwC(@WUsqVj|j;>yDZ+t$E^&e0h(Uzg~# z{CXuUd~}e_2PfHA9##%ZQ?uFpT9W&3S^X_d*`_&`(sNy8_YwEJ*`Tt4If`#d#W3%n zwJuqaJf;mfV4{h5B&FXOT$)SqL-YU5PqKUVNZNlhzMRdEC*NDl>U&n637bz&QQ^Nd z9dXZ7n%##Yzu4|XWh$&_H%B;_ESRK_!OpW1b`5}q^?3;g;PR4^SH>_WFl@kfIDV5X zmn_WjVC{ppU|7HYu&ht&(angvnbi#H2jju=(=b@c*Zycg>9;Fa&V-Zd3W{ldFUhZo zt|Q=5_tH~jzEAE%UD#&)Y&Jiez+LP7cPd|J@@^U;F4a3?S_NAKP8idhmN#clz*RWv z@#6(Xw7jaFtUmA!7hLEAi*^oP9*KBdTy`*RFORco#5g!_{lKoKW7<9K)Lb#^!`H_d5mfSb#fv-xAVoo+-`!v^Ajxpe-O>mnU1VAYVR zC+%UW!A$K^*vw^l4*{%B%rsQOyg?^@sC)^6HHH+!RfC?UIKW~~_J|@lc3u56DvvTn zlwBdLv&d$Y3oJGI+HeO}ujX!;0V{SJPbSANJ#=$6#dCja-h#t|-&}Er+4~K!_K~p{ zJShFZ1l^ky@BQi=)eqDmgBx({1jz$0SfyWFcpc6=BK|>^{_WDAtT25SIuGW~?Pi++ z%X0l2ykTMYik6G8TFb2m)j#Ppo;$g`!6=8Ue%<*>3e2_qWV-+sO!g~Cf*YhMofpD# zBj1TK*#Dq!4Ovn@S>qV&J-S)L2bOh-knVx=l-ViF<22`Ohjl*Hu=&B2kK5KQfi*m@ zu=&J!gL{}yhQ-laoEFjgDoPfQhGPo1x%tB4{&VMbgDq;bDyjY8_dZ^t14l2LJc2Av zjyTr_t`9bkrS0c_Ns4H8qVdtYcWD1PG3HL6V2z7IPtJu^&$=ajgqs`|@#*;TGW~kh z!?J24$5}8x@XMB0aGYN;YhMIiwmhzdxm)zQPC#6~b@|nIaMrICtUM{EZfCZBH{6lh z2eoO69>qB=zHB}|RnGyrw0xTkEmr@Pi@TWA!LizBov1u3ytcFDO9p$g_CTKTovlA1 zaZ(P=pQO;~X|F>1gkD7sRG$=w;yzTvinP7F*50t5 zh^wkIV_s1D)mzWge158vXF}h=c9|yCU18zmHRHa(Is*^x(WB*Mw)@c*$Fq53Z8n$E z`v{+Ogmp@)2@2-)KXV)A{8av%A9vEuN~+(b&kG}I{x0#S>#RMhc`P>`xCKWcjQa5?VF*WVZQ$6*DCU( zM&|Tm!H(6iEJc6YC&aTAV}H^7ah&RoZ$8rU^q&oV1`CsaRXvBL2GYM|QSg;@C9w6_ zHEg~)<=s+NziY24raq^*lonVJ$ zPucNTo_)&7SNYhEY(8A&%qWdPh(}vqW%*m%1B+AZC$af)1A4`^M_g!c9Kz;<^nGx$ z9UQy*?|zz3jWfe==y#O&Cexe#G=C&t_vOg1aDZUnOS1R|kF^h6vrBA#RrQ6Ttp1jJ z8V;cOlw0eoe>+Jbl_%-)@>;5&4Mr7g{x#u23y)`TsX@+zwkVIvDQ3*3M}k=SRI2#Q z#a}+L`S?^aC+<_Ex4GoS=Hrt*TY2{}oTXSbzXO%$k9p4?z%FfHv-x~QMo*Zviy{Wn z{Cv{g?M6_%>V8R|F0e!r@u~#YX#4r!{CsKeyX3)cPA^z}<`|7?Nr!`c4Z2hPRv2?~ zF2Xj4idcTpi)7VVIM!I3&F{&X=Hq`HF7&JV(i!Ph>)RiWgk@HH^J#s`|FpZVhNDkB ztfS>ggBJa_6fU)1|B~7Vv7jWt7k1e3p3Q$PU8ZU01zRZgviXR`CU4^Bz=cQ6&Qkjz z3|0P~4cBrd|55uT-n4ni3|Qu}Y9F-^yxQyvXE=LB;d<)tNZY&R*i!z%(^z{c(~CH7 z0h=vKWc?|n%Hh5#Y!IwxH3;cN9KZU$aOuc%Zf3A-a`IdQ*kPw(Gxayr9(&7lVSo9R zKGZ%7u1DYdHaZ|C;wEyQz-cL0m)qBU$%Kvr0HUfZLw>N1W3W#y*7=CfS_Huu!+}u~OLo ztyc?K@Kv|vAuNw<*X#(3ysl^{;i`o$hErgfPlNXZnDfe9MExm6{{BTJa7)m7-b|P) zTiW?P?67M?H#b=5VwFLb>-=`7{*pZC=L=eY@#$37f8j^Cn16&tyed!XpNL9at^dM> zS7jXPZ%E#FKW>BlZ5h!mf!)7E8T)9%>IH3XEvDuD4u9Sij*cGkcNxsfaAC(wetkw* zFsz=`?=l^~bgR?}V)C;=%%)L&UWdT4osX{dLOiDE`NmZ+Cwbh~Zm^sF7Iy!~ZL?q; z7mimro2)@xQto|K3yvMs?<84y|5{NyINU_zE192hb>UZRzxrcu7MI0jJ!^m?9#3KU z6^EAmu7#Cu{V#;k@=lhWenj!Lv+QYmR70Q7%7L9zH5^ya@~!6d$%J_y?f;|wVb6~# zf08TPKgr<(PtxG1Zi(S^e<}Mlrsfh{UearD5RAt>d*yK4Ce`z$uzd0(=Ttbw$8!(e zU-F8xnX_y6^r8NqVsGH(B*cyScV)+0eOT6!Tx$1pFZDk~bk?iJ%4(Ws}98#3t@ri1G9ejNdIZD?ACIpqlh=fw|`IN zOQCHU6$7ibG_v_1MU|CWn_-EhV>&BuVS$<};Hai0n_Q7z+-x~`3Cwxf<}CH+MZJe7 zEP%bwJ~yTHNi;lqcvAkXjjVjCdXF!k1(!b<^_j}6YSGgPu5ftq%zac|c>^ZY+rs`0 z6)M_(?x<_Umau5g3O3)QXlGiI30yek3fo_G-jBQ5u(;-@Gwa{mz7Brnfbw>#DxTSO z$8secR#?p}IH0V#3oC0522=lE71};c0qa~lz$_E^j!%ajMh#;9XHL%2(nPo=_pcru zf3D2*%nn%Nx-y*dOI;EQBH@PO*ni8@)7wlo>b?6ttzR%czd8cu23D~3^CLgJTn|U* zW?Roe91|Qc2kD(TNcTVTyPud9`!}%TCA_2aZ+REa)=foRJhrRWcI4+6@YsCsf_^sg zqp;bD5H_DYr)25J6R^?ikN@u9rJij28+zrk_Dyw?-}@Zm4Q@MFdx-IV-xN5jr`atF zET5~f!0Q5>v@$M)+I!*9zAUcOW!oca&xNYgduJ(s{^Ah>VNS>O+O&Qb+h}&bs{YuS zZJ)gJ`xMsR-@3k&_Fr{e_;3F0$e%sLu=D8d?`eI~kIT)1VCk!8?EY0!x^Tx*IDKeu zHeSGs>bQRiZ1W+4&4((emmggOiz{6|Q+qDo{NFEMSYw}|AB|5)0y_-yf~Cfj%ISVq z?Yj5VOxW&P5^JxO(obz&VbzJV!Q&~vyH}bE+K_0ZzE{ zl$CGA>4R--VP)?~cK^yNa}40Z`m=ASS^0T&WW}#ZIKMd;dQo}flx$e^4sO}GGm+}M z?8K0_H87{w8n%4Fj3=L-!*20wJZO8k9rtdmh2t_-w8CJYHbGi}MHlEL}M~d}bG&$HF`IjA$bbS&p+IprttiCU2^TG36OnVu_{MU!r z^-p-RNP8gMvTXVVI{wO>8_W)s?Z=IVc?W9OPrUj$_wj!Rbkl z`@+2WH`)C5yod|6y~*?i1iGbiD4 zPcH$DpK!8{2E@YwrqL6~f?(+qvi1J`yI6l=$>fluaCYc)R{nTtQ=rC-MY|YwRu5bl!8!StV zWbKPQ?%0ir)_y3~l zA^%yyc>^`=$m;w-cKzTm!x&axWV*b9zZ3sodEzxC?rVl+w??g_{*9nlRi6ep{zN+K z4=HNe&QihIyQ{9!@>Esk1D;acs%VfMED^irRlp84VSFlI;^LgJa*BUn%p*@?~XES&n-J_n9onwCiYZ=v2|?MyhHyPHqr5j@|* z5vg#K+4AeuUzg1;n|>D7o~K~r6M~>Ma}wcLO(7d^lytgsAps70JcRZCC1VPh#nV~> zxX3Tbk>9*v7s^tpRh&D~6WA(ceSZ{g_0{gB{-_aktVohzs(fjp%p>$k$*0jr%)w&)e*H z*H`?WK=Z4sVsDgDew_hJjDErLq%Lph_&HSG;?ez}pih6SxmZGyhjn(Oo$DW{Wk=_s$Pkd@&o2`Bls2yBD+^+MEU({FvcL z=R>@yz*Y_iH2AUeqaJyi+3iRsyPp@$zQQcMbCuoytKOSGN=JIps~NZ0{OcwbA(=2{ zsrffQJI{%G4bG}a{WpL6^2{?iuuyASdm7(R8~3xnP3dn9VB;U^h#n@iy|I1g_Irc4 zz%lnw8LVo2zn;cZFy6cJIb3*ocruN*2(@f3zl0U4z_x7sMf$V)J*>}NGKR)ycpGz9 zx5M$a>(pZ+jo%2S-(=^OT@JfdVFn3zFJx>wW4!iM) zmallXpckEQ_1z;nKYE#6c47x-l}yR0(&H{Q}Si#hM|ave7Q`ZJHY@Yqbz|GIvp3mY$0Ry|~Ko37)B zQGFB?r!gmmOmO^P^LuSyLhY?2|7}m&o``pi9gSdh;=SLaVE&$3YqH>4u>A~JX;s54 zf1|kS^*@%>?DUxnXXp3Gqvgx=HQS0|&d&Z1yTdA_`M~vX+$5h-w7u##UnU--^o9;Q z$^86NJriO6$R66%z6jl|G|$0dhBH}vFZWt`_X?~?eaN042`BE-qWY|Tzv*#bTK;tz zbIT2rDg9~r>oXNuh&PP=aFwo4+$-u-TAqeojyYY=_@$?Yoq)?V2C@FUAo|a&D7aSd z0_zV67VI0n22RKw%=$ly;aOW3!FGce{p%ksHFTT~x13zY#$zNeAF%Ul=g`E)Zx5~x%J{zq3Wu|Mt=D|@`#_ai`_^C5)6L!g-gISpQx;K5h9SxZ!p2s|iRinqXnG7tU@ocKamS zp7Dy4JK>1S8La=o-@nIiD_qz#x6v7KX}_;wn_yAMjp-izfTSTRmG*8^77m_PP}Wjh-;Qe1l8d-5#U;qNncKf#f{ z)SU*mu8&_kIDQ-)HY#)~ZI3ECe8os|=-l&kKOoBeT+WBNUw*x#`+w2=6E{rYBrR{Y z|GW~>#=dagqw-Zwh)X(q{Ok?~IpsZZARE6hC>xLSGwGZ;-5*HXn(!2G%KaO6DZRi# z?R^KH!i}0Q%BhGkz`vQ%Y^ef-)GOOBzq%%7r>m+ z^V#z?ey&AgAsjz7)||$t1ZsO`w`oPI{9)c}E4Kcyl!22L!@`zD`>!H>ZMFV#GJn~+ z35l?$t3G@F&M`DB+X6>zJ4)~Am zS47Xs)JAa2wklWZ|H{wYsp|yGZq_h!;`0xFABXe5Yd^cck@RkMcm;cQ{q$iZ(yOb# zEO-ukXY*fCe_FY7+{UM{j`^h5WR6k$wdDF;Wxs7 zK2$I6?W%)$1H;+=$@c!f`yN(h?`HLhx69>{2DVSqDQYN{N5P@FqdLP47q-5gP5IsZ zuXl&l4SDSRDxIG%(}#neEjUKwWvve9MX~oa6baLfx*?v@@K7rdafRiBVm(-xIf1=D zBAna3v<2(uBp1YoQ+~ZUb86tAs*F2iY3%hL1#s*5qcHM>&K0!-lt*z!TgjFWy>i4(Bx4^U|!_xeA+&a@!k#bu-%r!th|agF3URsXW3-GqVXh2 z!A`$Zl;5MAjSorqo0&D%WU=uf-py0jPa>Y~;>qeOUs=+45Z3UBX2(m>_`ZV__P<{= zkJ=;kU9Rt5IQH^V@iO7ONsisH{;H0j{*B)a*c&$%4#@C`qWfLps@lDF1Dqxm(%$Kb)*DX>>dSI;Le$rS01p+inW1(5(D03l_vq8aM%N9dA;N z)ZQ=@4lBrD<&*P1mtz7;I#;si14`$V^}S$+vttMPA-`hT#cz7B%_B3`-twxf?ApT3 zH~iT9QUcA6srA^u*80hx9J{X)cD|ea?|p|qg5+FS`{BiZ<6C~+V)nwZ#?EX!m|yg# zhZt7oDcN|kYU$L|esKB;Z&tolTOaBygquF9#?k(X@?3Ashg)2xvGEgWaa4D5(p(W6 z&*FA!duJXTv?Ya&UvWG`y+yG2X6(P`Hyw8T@`e4|cb!A+n=E8yK``uMU(=DE|8Q)a zx`e{94Ilrtmme-9tcI=aN6)7I3%BaY{FQJ-_}hS?l)tFcG=JFB@*JxloczucX2Zg` zyBzATDMpB8_HdkX80$}Q&F^0t11tKzR#SgZC=qxLqWsg(S5f^@`F#A^1CAaxj=g`R z()zBe2kS2t7xqM4+|g>mUt5&l#g*YbV1ZJ;x(*I+JCfbMN`L-;Ox=52%kTd`@Rg86 zlTwiyMF(M$3ZhNY7M)b`eO#B1 z-|hG3{q}rb*Is*_56|oMvaxywCwx=6GXin()18J7NPKF90So4iGIzQI|9f7@RNZ{$ zHvI2-9_Pc!j$81*?fqZA*MW!E$oZX^`}@#6#A})YX+4NM)V_rDpYm3X&QI<|wW0Yi z^M$59IbO)8O}joP<#dE)*CwZ)cK^n6QJ#-vs?;XWtF3)#|R z&QFlvJk3~_tj|)Hfxn93|93w%_-EC9xK(aN>+6_htM=r;RYlwZJmmA8i>73g_J=*` z@q|--vX0o9F<>!yej_PRq|>XkrO5r`P6;8+~3RFO4s)Kz@N!qIWRjT(e3# zza`$cRfzdrW$#)MZ|Lr_-ULe}=Z!nzf6tp3F~xqr;k4zACpRK4npg3qFP7)O=S>Xt z3+4mh;um{M$nQ(OHt`3;RSlN(c*9-g88ZSljuyTo$2+m~gsTahve}eAj}vaLpzD+9 z;K(0=h;zMy#yBGGa^iuEOb_?A|3DYwHis56z3lMXS@U7Dci#O%V4i2EC$U>i;_$67 ze}ejECzv^Z!sH#WsFwSY1B-ODxB{4yv*(@_?9usTR0Pbn9(C6c=38FL*a^#oI&}Ti z4l18O%)Glz=_~$!Y-P(U(x33QX2DYTFj}uFcWc@-6^{Bfd+tHR1;347 zkAe#Xwzh|1;edt3>adqkAvPYC7?<_=F%9P{>WgT7G0Wdi_a)5kYwLRsahb-{&QdsJ z^YF{5q`&HYf3n~(@7`WwY5D%Pi*V;qgDjFSOst=l04Mmx(0X0Ql=4$2;N}z2FHRvY z4v0E%68=~3Ed02*JP9s78~6Jd;-c19tYo-z@`8RxVVUXZt*7Avhn*)5!Hlux5vO25 zlA_6eSeU)xOB~DyNnf@Pmd+p38Vf7+lPQt;%ldp!{~#QrxVV`tU(vn6`}e{cg}tqz zu-s{L;%@lg`4jKN58v%DQ)F5egg9sSeZwuVv7|43UMiQyH}T=H(p`PX`H<{_Y3V9B zW9YzXYl%}2hx`X?bn99YGrNP95|`|2yGbnPgtcJ}`VY3nA zlF9OCw12G`3dbf`HOIli!(|06Q?WhV3C}qTOBTBie+oO7ZoHNP3v|Uk*|5dfxWD!G z^{(D&aPgYH#c3q}*nGEm*lX#%lj$&jrNQM$_}}^<%&J%5!{YM}L1z$Wt}tBZ0XJCb z(Ryup?x03jIE3|J(+R}ct!=Mnz&5!@ErhVtX7tr5u*d$v*<^j?s;7Bdz~Q{%#pM6+ z<7>1g!6{cu>&W`gZF_jk7?uxI+(5Pu(Ty8{BVp@-C3O2_l{u;of*Wc(&y(#7^>e!I z)|fw=&YdCKm%P1y)myl;?y*J`%)8Etds!F8tHTx^hZ(P9-%TL#WS2a${8)WAF4BY>h85EB18l|kF{-ft zi?IU|5$E1nn63bO6>niA!J+}I^*vU&A8@^G>uFf-zd5`UW}OQ8nN0EnJnP!vfc158 zB3Qi9ZpKgI^NaeB?VGFr*|7y~-YN~h0Q2&<#{Gax4yNUk>`TXU@Wx9! z74iGxNp@vo#N|uMcMOEt$1U@2lJ>nR!r`#Z{JTn7u(;(u{lBr_Ke^r`@u$yc+aaEr zx7$A#W{q#3&n5Y9$NZN^;yx!IdcnH4G;NDv{);WafiQa}&!`lZ_)Har!aNn<`U;ra zcgo%!Fr%OQ;~H3=`S$R3IC`q4Z!KwmV71*g*zneC!}l=UUo- zLt0S!Cs?rVqT_1BZHBY{j(=LYbF?SyVKetwC*o|g_%CGo`AR$Lx`=hHH%@^I))f~0 zA??RZ+ocD$PJSFo#&=1#&Cl<~{B)W7UlJMrB^g}9nMe+g_c=;V;^FhgAXY8l*_K5O__ zm{V<_vjPrSc<0F%Si0=Q0)MzCsPsw`EVtAd5C)4rWDWiRi$`YB>FexGI9U%1d0XW| z#4~lK(D7fAybBY^|7o(YPkV>B>`J^NG1J2GHpvVHSBUa zM}D7Q(7bUz;!V%|w8;9vT4Md)5w3}d+SCj)%PVD*;gI8N=e5D|HrKI+Fuyo9{5Q!r z8KkHK8y5ao*$>N$S+Bi=39D{UTQLY0>E$x|!7gi$(D8wc(dSq6fiqG*T_ocNg$taY z_FCfqEO|o56G~R^wC#j_)i@byh|40`55L0PjzvdQVa|9xn?~5$KX)xze^^7Ar{BX~ zH;wGc`or8PnEwvWDm7l%ALgxo<6Q?Ujax431M{bLhE>CY_1)@ZydlTz!OJQ*(|S`r z17=r0nN|T?t4|B-CI9zYWKRhkcI!()J1nXCGxI*2X1x9gSzox-R>I4qz2%4mvc53g z1ZOY8O7 zzBY-^_EGtWIP-hu#euNRuSX)XK696wR4TwVcYnQ+!}9&lY<^F{{^jJsn{@lGP3rA{ zqjcAG(dB*hAa(8fIBPOLS^snF?gv}Q_A0o}_h}>f1&`_W&Aip0*$RsvtskY1xUAS? zlpNNs^sUn(`F3%wA7K8~$qPon!qi(ub+FCwW%*=$s-VFxLkf#RtyD(A64kSda^cvu z`xFdd=8jPbSK$JemJ~yn^=dgI9!?pgwUAi;uz1`d5}!ZZo3xiImb?mseaE{_(uO5F zltY)mVay>Thr+@WzXR-H>+?H?koAof{bGhOoW&aNtptngv>vL!9w85UdoaH^>z5a| zSz!O1pnj0dKcUh0nXh2Rt+bEdVE&exbJeg$lVR#-V%>K+<#3k4CK=gZuvWzumcd@1 ztxwSX@$F|Li8b7_7LnzP>l@m>=En-U{R$&K7|9TK8=mh|hq&;Wdu{_9cBlVgvb{?b zgL^)~qQ@0-!C zSn|BG`YFkuQhVMTj{ULL;t4Fe{#d#O*3fOfM~+Y8OYG(>9^U(R5;8y3w z=L$%^{e>(^YSw-i6Re7KH4`AlCqr10~{(@K0@u}SLj;7mS?WRw&?jz1! zAf@>l)4$Q>01ytmN=}qP5O_p>$ee>KAlRBzijdHX{%u7hLcG*5SQ4Wnz;b3 z+P;g9=adx(Bus<(?%KmLN&l*8Cp2I)J@NEguxz+;`A^KRo~a|>lKxq1tS{xkZvJww zJ1{eEPNx{o*{oi1m$X0ora1II!>C?Ps!)FW=r1 zt3%p;H;%WI$oI{VL^XC zOHqeK_CE}XUQi<9zHP}X$@(oDl_8CS4JXCY{h_>|_C7{XaU{DA(DBo9whqSwX008N zNB5t00Zy}F-kOwq#KJ~9RpP8Gd31jzd*ESf4u^zhSd;!4&o0}Kgqu}d!|3t!&()WM ziQ`)6{*v*ja-K5G*LyRNY%c=emBSQZ-*NZPSHsexWvX&>EHCql5szT@@OXm)ILyGF zN!CxnNv+7MaP+&WPB}1B{TVA6W-Qx6m#5IRIWPtm>HX9u>nmTov(HXgdzl_RpA+|S z76!r0uC;V|@Kw|o1;FwjN}go?<3lE^{9)Dhb~+xKdnazkO1NaWl#XwfY&#b31!s*K zajTL{&k3J5TJBXYg$h^ZMr@?Y3-m%vtf5sK}NpOMs=fB4f z1I`jX*g0+$J>K(@!&|y0p}&XCOUd~RB2tLIwGMBDFr*0dUgB*IrdHf5*HVfw0B5Wy7VgoOz%2 z$DQCq&yShg{1yfwZum0G`Z3~SXTCfb7L1QDB>N*_3)dC5U4Seng3tno|>qVAJ-i5j3_v+OrC!#%1an5@am^ryr ziQG?+&QN~VANF{AjE@UpGg2P*uuQ&-y-Z%2TOvLiN@4uZ~kKuggcM<&WdXC|F`u;W8 z#a{hSG~)cgD~ry;W|y>7_mcLn_TNl~vzA=+kAm5|dsm%?|BV-x?$qfcf_eS6o9;rK zb#{3dX&)YEJzxjS`4oOK1-3XaWW_cTKR998C0KYP;qU#bgU9u*ka%B3#m$Jz!g>~G z!XnoxN6GyQ-jJZrSumS*^xOs#*Y}m?!r~+?dVfN)=uwdbZZ(Rc_1VlDBVOjfZod6# zeYRk!aDO&z^L*3Jm88Avx5PWJH1!#+*Jdy-RHwrt4Gw2H;(~XJH=l(6jVI&}{ZHi} z?A5!JKHp@;j~E{bYyUYypBIUyXNkk$f8zz&;k@lZuz$-oIzCY5rehlb$1b;}~tnGOW zShP584~YxBSI&Po0msXIYgmCK|EghP4lG>yO{Dj0q@C?$t}u7p8KW&EK7WSSY}n%SrWN5Z(|fJZ3J$25MepB8 zOHCJO!-H8SBG#q;$37-ImtLlypaKT>eiVMu@2?Mx57A0~KM_WI9w zoDXC?^k0Ma{QBV54{*(v4RrZS*)6$m;grkHba{)cvIVbT)rdA)pDucqPi;u0vv8PkJfif&)Nr=k@gYJ+e}FQ z<)4QQ;VAiCTA#w)F(q6RPReTDY>K#~erTlvY-V1*hYfQKL(f-W`n~cjX+1DMFnr}j zSV{MI?Nr1WSN!~U!Tbdgbi6ui;i^HNaGG!@{XbGy-;xP%(*D)6=Kx?q#^PWIG)n%06Zmc@*YXC!ZROIG?k= z?EtJg(~#B|GRF1%j)qeLAJY0m&WhRY#M~0$uzsXJO*?A2Ue|90Sjw$V-H*7yj`@z% zR|$s;=ac?&e$~BfhXtnJAIHMRyQEAq{*E(KTR9OgGCLQx7#YX;1j|m?Z;-4?!&Yhl$smpG&zPOy0^r-;N4oS#hU-{c;mv}gHqtv$=mI_wzcf+C~4sS?39CP{g_uJu|$s5&3Js{Jdajq|Hd@6b!`M=x~Z#?J2 zu}{pFl6p7JwxFfcVE?im-0vj+X=M}}4xj2s>l-;AuDsTT1HNCO^^lSv<*;Alu)dm% z{6^|08FoWoNMWvlvNm~M%-CLd=pM{V)ueG@(&M7NuxfKLtxx5}Z9TFS?yP)GmnX;n z;}RXX$9W~M4ehymriJ#JVtPC~9+UB(Or59eU%{Do#?k4Owi&4x!;%My_yoaWb6qO7x!2zkY*VV&Tki zOS(L9K4iQfuFYW5`bklMoiGS)b`7nmM|+X?(lacLq<)r}=#pv$ zvoHLl_0atG$NNoTX3JVS-jvU8zOM&!$N13tUG540K5B4OwIi)ZPyEr2pfHM zcf;BTp3(6JyuIN(A2{by4Bh{+1!l(PaDd4zI=(=vd@a5&Ya6PIO@K z8dq59@-8}Fh+#F;*8~m^mHd6)yX#fVudz6Pn6Lb1BKbd0W?X#(^Cz8}HUZ|87Gyky zE&ei9!IGtK>39pVURQ+ztnJ?4%mcsA z`&MT1Z48b-%MR1=U*ZFnOB!Iw#~NBM!LJ)KuM%!eQlaOIV(z;8C2;uY)MMoQLAua# z^KCe*D}vT9ar;)PUw}Ox7}0tP@q;f9kHQwA%Cx=k`I@i$;D6^EvP;t!?17^%DAD&p z@DDjtdz{s#@2}t+E~M7A`$Eq*1Q*?Dd$awKw7!awo);N|_TgdQMmb@6v;5Zf!~8Vc*Ydaie$jwBbvO#ufojMX$#fO; zV8fktjDavcAA$eX|1gi6zaz`r_sP3+q<&e<>3B@m7rw8>8?wKZOnWsv2L4yy&QA{h zegckt+=q^zXK{9LE|K^_HJG6IP4;*sNn(kjY-m}yCVf)}6nD>R$lZ(zI>=_LIt0!k=t7mJ%QJKs7lH;Y| znR~)Wn7Kk@#8X)IZB*e{Sh#4^ltR+KZph3@aL$>_NApR4hZcV#wh3KS@Q^s*vGrs) z({S0BEaJOU%dFs@=9=cKFl&2`-c-2iT*A`~nAv%ImnF#$?@ql83(kyVPl01jD|MVB z`KLYx7{OT)@r%#G>;<7OM#CS3B%+E@NI4r72+7Sh_?*!8IC(9(qGJ?eA&aEG?e#t&vj^7EhvP&12 z!wt=C729B;+3>sm&A9^g&DJ-CY!>73ti9Wz|vLc{}{nlrz1qOV8P)s8n;jAT0VnVX~DN~h}W0} z4z`7v%U{}#hXXDZs@uRaxATp}+`uVaR#LQryIcXwy!r_;P87# z8}wn}pCf0vu>ZM~B3+oVJfLYY+;cg(SBH4rPpc(xj$!IREm*X4-#&NPCE~p65SW>u zWKH5x2NYKfBKe6s6c@l2&dWCqfaNv)KXTw20gqaCHSy_WIP3TdYUch22E@htOEd=} zUshK*Sr-oJUsBG5#iKVAbs1oNs9%4R{65Ph{?aQrM;hud66PsxgV`&C zrewn<*7_EsV2M$}+G}vw9jCwlf9w}`5nRlV6^%h$JjZ5t9PDo~WH^~${O%#Chv7~} zmnE5>JST&fyGi>~M_ebsti;}DL9qRHjbr98Lwjh#e{fAsZKTunP zY#*|EmGUH*=WK8x2$uiW=AVJ>!*}>?f%)U(`lY}P*Dc49?N6fqxc)L+Gc5b>_WFKO zV=i1=KV$12(%<#O59P4jRe|mg7(ww?AK$I znoGgDW2F7iZ(9xF%xx*#5=efgwbN|4Cc0UxR2yF3gXHBm+QBi zI;3^*`3EpxARK%E@q!P(9^}D174-+vq`x$+zx((9wQn%lah+TbaVtl?MPRCcVQ=U{$fn~R&vxboP z|Lid_=|2saG5IuYA5fu4-!CO-)i1L`epBgCp$n<$^r|096=JrcwFsEHe zuV1*ERSU<$9=Z8+JTHso_hK~MTHdvZT(97M5JwO0nP3x3?r+Kamn-PNnGy3R&xXYl z$AqZE(ziZzyt?qEzCo`Z_6OE`>39OcTbqhcaLM^l`hE+3OGMx!*u&3Y!yMATQ>#WM zEchBi$0JA<>nf(gF;;`<`^==iPL?NOuZtJy{WInxP4&aX8_nr>Z?>mnV-)=FdP6Gu z^>sJwyNpNgH;JBCUE2X$T>0{Myj0vBmMgjGl7()WkSRnL2jhC9El+eYrsNatVZKNz;}45II^;w1aK zebUADb93b1{+C^p)x!4dkJrfWi*6oUm=F8Uu;Y{aHJqD2Hl@L$aUYBn@&844b={-k zv`guEeaZZ2d-!q(tWHC0+Y-1iGz;g}vRdOwlp^Wy^t?o2BC`#yt5JGeF^|Dr0bcNT6jUOEk~5%=D7A^opC zJ76j-9;{dMA1pJ}88HQJ=-fu%?(B%^L2!rGKQf+6|1*NKhS|~g*N9D{w&}wz zK4)ot9{)0*KNuENKcw$x6-}H~)HMRf>-&=Y81j1#xj*D^xKGR9dOFt)nx9~)_6%A- z%D>_+l))L_-BZc)8$oVFK@}|X7ad6;&a~^e1?TiBFFg$l;~xnkVPVNs`aFsof4pNR z9KP$n2bU0+ln-sp9gh9U&f~HSSh`H{OB`(Yx=P_1%nmYZ;=={i-$mDnrz=fyftA%J z(&z6ofye8qZ~)sN?JkMi9Q-i|c0P7Xav$bf_q*AqgZUFBs*}JX5m)p9_R?egeSdW7 z)u*+vY@%%md0r>>QSPXK8GT+fkmuLDM<3=tfSGS!(dX5i)q9H0!_ttVt>k$wE6$`V z3jS9w!5mw>bR+!l`J*K4ysJ0-?|HH~(aGNdmfU{P{gbqR`>tprTovB*x8CP;%x5Fm zOMKCX)WZnAebdx~dp33J|AB>Xj+`6{i=Iv}BK1bho3}qIz-fb3X#EokyA4~lv3-qI zVv_f%vly|JZ()w&Fj|iyJp3rB5Vl^he7XtZlI=%+UWd7PD!UBxB#=FZWAT_$<%UkJ;OTif-6&C0427sLENYYw$)VS7=TC?)U1=P$^e{S>Ym zx4oCd84KHw-i6&pMr$vCCF#pzHoy(nyNg|5p<~&X|6t=S{a(z0xk3K2$#7CbEzgOx zul`u22D2uQOPWpcCoQ$FABN>!@$4D-Kf+fRWDj6_FZJK#|8i#TvC4p3hZ$573vJGx zh=hBtxh$dnu{mM|$uCccv_o9_XszR5xHII?6=KP(z7E}*SRa25r~Pqr8LLa-oRfa7 zvk(`(P&^`r!<-V#ok{<>uhax^fb|3Ze9}JWSt1YaRPbIz<_GI|;7upE;NAOJ@;w25 zLdhFj*hMrVeK{;`F1%+>tlc$_e2+lVk;EYO4bgQW7N6Qi?fmlYGh+7dN$V$(_}ONi z6|gL$Hh3azzv7dsFU&ZlG~Nu(eCl$Ze1CyIYipe;i6=~vtcQ71ejGA^Yr1|LY=R}- zg;gZK$)x|+5R%{5Kc5Zf{MZ<|6&3`1=(K`;hku+G4zp(pr`f=rwh@}UV3w!CP7aA% z?0XYI^6T?2&4K-w$ddQMqJdf6%i*Yg9k%;nZg`FUTG%D8CMpL0H~*TRt4YcCFQirN zI!6&#&TAZYjKpiUFHMK5#t)+3&tQdl3iDw%ySZ2B_cq#E%U;8(!f8)VlKkKID-^Ii zmEJymPrlc|>7!6R4Q5^J9WR1;!7D?#u%Ydnkn^y7@>B1%urOfvs*A8_@T|rtm^rK3 zAOn_$n`WPdqf}C3ufdY6jU#WsIh`SWZxY{qwkwaguF;o#4@J0p!-H2aV}9Zf2`qn| zI_e8t`|%0;5zO&;*ZTwZnv-YnnE3lA>V(+JThCzb@vRG55!cn8Kf02{AJ))#O^j?B z`5p|X_mlQl#KW&7)9=T~Bd$(wgz5V<5oa3sdDp=q5~oA&V7W^9{z}-3eSm(ihP~6I zrv#QpbuDZp@xhw|AHkAGN+X(xdo_P#!_I-DBEP~s^&-R5aHg@{mme_WfF3WLuOcI18KV=YR>LyY3i>@C@lxdpH{qy+mu}xkyv}Tskoc5= z@H5Q4<*qSe`wnUXl2hPvS$Ed355f4;t7X z@FJewg82=PCp?A4!5``OWH?_Kj%Q$zL;vYy{gl0&GFb=P;rd8z11xsPh){u3s*sNUslpK$ElV9&x4p$kK zUK|f|rm3Hp1Q%RrI$#RR`F^9uz$}rlntXqReR_RHU)XTrpIklCe$cdYt?F3+LVRdF zD1U~~uo~7it$i~HamI%r(`zulsesmhvp6qSABH*gHGZUimpN?h+z412>Y}9p3wN~M z3@7=0+zfkhd=@DsTWx~ubT*&%bt)dL(`T|Lm_JxIP_P_qYCW;!d+yu<-#SRw3fdm+LQgts)@!fSTgf@hX9tmSyH+k4mgwYcYIgYwlT!oAtTn2dK}@O@e{U^{HH8B z9!=hM&~7uVdo_cO{}c|M;2aF6T+-jiLq4bJ#Nojj5vSi z*`*s{_Tq{3eQWHs4c7zV%)5E?{nU&DIqCthVT%iWejznFQsoCHZQ9kJJRcLKzf>m9 zh_$8`%)dj;&A!u*#JRm|seL~xmYWmnBz@x{UzRAP@8=NK^gXcvR;^e=$6KkpR23qNz1k-CjuPsyXKj~T-yrpomFxeULT-3D;lMH6~I zKx7!Dr%UpO4yNzh5g8lzQG=bkv+4Dctp01k0NAGVbs)KaA`4&+?PTKk8hD)E?-5>9 zj(P+02eRq&2xddk#vE9Z;?h|`u8-#3>`H<&ua6U!!rX}`KWu|j*iOMuNL<6LaShxw z=*ZPau%N=y!~qU3yh5KpaARvmj)K$n<%UQ|{(h@(EowO4&$Jjr?sv(j8*QzC%~tF+ z$%WZxPfooD%Qpt>AeIjm?TCk23f(0npP7|3dJ~+%KUYlpZ&2JuhDm6GfNbvMA%=Zf3^k*>EFUgkN5}27doB16s zsoJ@X{D0Z*;`60&z-X7u8kn87aO4fR!AEqN-2ai+n!U|{4V8a-llx=RhCGj>FmL@F zck(_W$qsvi1F+aIj=tZTAG+^EB+M<1980cW@j=Pd09fv(O|Msl)9R%wVK#T;-}hT} zRA&4K|9k(FX#C?B)8LRUZTfz2aZ6-~DeRK5`tSN>r(kCQS83eASF7z1#xYqt4RMkjl;DOZ@zkf z*55HoJaxyxzSm0W`(Q;wW^J&8b6DY4;mGG@l+pRe4#}tE*Z6DC)(0W(Y||bPN&35` z-q6_yjsWrk^VuoxVSe?EiSaE1!_{HEN#z-|=kh59^{ye8X8p!0L>`&&2ch>0)C4 z#b0#k6{*K%+n=)@2Dj$+r{j~vfx6LJuuI|#T5l~$&#cylwM%{I_yzVo7LEJw&7k8O z#GR*UdwFuL3#liTD$ZXv82MhJkbTXh|9<8<17VK4GOZ664$vA&_MhD9W;$MlH{-{f zKCu78v*%k$zWB{OvOlenJJ9iJa9Zg?vICLd=aBp?X^4NF3+W!Wc(3NGHxjY_W1A3 z7aGqCO1y*hM>uFI{oa{4f$gys&b+ikZvgV8nTA>mVE+Z1oQWkCzFp(t=$I@gVzK+_ z24Wkx=1y`yfuYuZu^(KVf1HkolYEPpzQFq4dS8c*cVheOSucW9n)iJq(<3m|`m+-b zYpJ2>x1>{VC_2@k>vc1mzA7-7uK+=eP{%8f(w#Q!Ud@h%E|d3_xf4>4!HUIpC1!p zNwnxo2weO~`IH0&#O>KD}ONt&3jLql)=?=*d)a zzk_xAD9>Ff=(cNo^8LG+2 zaL$~MQ5#{F^Fi;ua3(A5br|u;2Qq&+OykdC0r9qO=Y?>Lb9yeh-entVp4Ngh4sZE# z3KkpW2X+s{_I+km_gR=fVUp%2xOqKZv<~wQeQwNUmQ5c=+c4XWptB{|aV}vt) zyeE?O3s+nn39~G7ezM@0Iwt)dGh4LlvI^W(yPJN$ndy=mA|HVLiPyU`rpRYj`SYH^ zUc-FfPk=@3PkS!GDHjtzSrQx7MXiSE_m*JE>U-{UVEX=MSUggB5DPAOFfo@y@?US- z^j;bBYve^!2UzgwnEW7YSQ&hZd@oxxWoMZ=ERqa*I0qJv)jha%X3oJ=~f9HEYOz*C=g88u6 zux@P;9FldJnmf;n+F$NMzwgcTsT^?z@rH?c3%DeIhVu6l#OL*O7s0#}`yPkFVS3{m z-C+6tm#f`i)jmNM~O9x&!5DNP$S1cjlljj}DS{?`&=Y~H{hUK55CoX~mqVrc~z>ID;1xwgt(eJgH zux#qvMq}9QaH2*o%pY+mQXBT4`@s4!EU1u__k#^Pitd-gBJuvgzm)KMFFz=^z!FoP zghz13oC4QY5)as(o(G3)?c@6kmZnTExd#hZ9@)s?fN} zP-!<476=n!&cV8B^Ji$noC)g}oQCBtHp{eO-py0}6XB?gdy%7I*5Z%H55Zy2-6xpA z(*4s}5yaVYM>fgVuh}0Cvwv=KmA9 z2{Ky}cPu@*8qTQ8n`j5~YNF1qg4w4k4miQmor&^=u+8D0KU_$Ag`eRLu(R`)DGOm) z)AF?Gu&?>JE;pE;J9uA`6n!=uOj(IOWyY<@uB6q>q!2Z@rXYb<~v)7&%i3i0X^DyU)*Jl;j`Vgo063kyzR?w`7<+o{HqYM`F z6*c?8)_vGZzrx%Mjc-*7Sbw!ne{UiAG45>-;0$q~Nhd7i+!&S$SIL}L^}_NT&pCVH z=tzw!g&<6S$8pP0STgd?&Vj^{-TjY|Bot=+q8Lt&ZGor&78jS0_0 z6XwmTP*j5xGDC{BVD7mO@eDZjy6K@2u#|Bm?kfY!>tNz9eOT0!GsKpbqiav9eImN$0W9fE7WO@2l!jMo3PA9gvht8g4FzVT}8R8q%KY zHoXzHuzM`q1oJC0ifUlP@fYg1!-BG^#4@UPM?A`%zOTXqN5HChz?1ZG*AozuUfpI7v_tvNms%t-xY@{Zo>Ky{%)2R%naJSq#w-dktqEKmmHrqTm=@?tq5EM zbAk=62EmfltGgD!QoBKJOjwr5`!okO`+7b=gLvxOMGkO^N!cYGSkyVaa0blpr*dNy z@#)5JV^}w8K%fyUSs@y(4Hvr&vN9!3zHXroJ4dLUvxYh6`h02nZOcjXw@K%~V%^D$ z%VAlGM13hNGPE3h1a=F(b#V=G?6Cd5a9C8s)s4iSb6Z?U`{=st{V>-{Xs!gu7S5}V zBR=A@>unF_e{w$$5iC6wD=L6X20qNb05e-IHk^f}FEuw_g(b|%Zku7Y=7q+5;yl;T zU){DG-UvxZ9W1PW_G=BSYIOH-D`~G9Dtgz2`4RJUd@`|t4YTfsCv^Wp|Cf)utt9a_Pm@g8tk+J*59VmAZaD(`Hme<23v<7( zQxAY^=fp+_llC4-Z~DNzS+U!<5SROiKX&5xGtNAYfJIK#u3O*`#dmdkVa}2oV+UC1 z^W^psSmK)b_(O*+>KU^tkHO;EPVY*9Z`JFtNO98qSXdVLRV5it*>ZSz z94z%SR1Smr|Je+ShuIxvr`%!x`=4GV!jif6lCSOfe|D#>5dXm|@w zFqcR#!14iW|HQ!>EYJ3fFwb+G%O;rj?X1=%Sk`B+?rhTD=6AzolK*m#yBZw(^N__A zSkTdUxv3TZzi3F*Rg$llXjcU1jJ=V54Q6_tX*dfryjz}Jho!p9X8OU}Hy%0PfaP_z zH(cP%7@xqKFk_(H)drT@ZW(kNmWT^wtv_u!HWIx7cS!q^yJsH5_WkU4-i2A4cJ~~D zOQxAs5VJLJe%eV~m^v>TW)AxN!2@;+{1R}V#0w4_V8ICiG5)!*Y*3`80&M@--1h|!<}wG`%CC7*cSV#~=`u*_ac;!AozUxkYg zeDSJ+CB}{0*TCG#lXkvx2J6Dig(m5#u<^1D`6Eeu zfwx!)OEVuwj)r*&`NFxd*UxWXjEKJ}UTy!1{r~9YM@(SZ*)#J#!2Em?xp%%IQs6DG4o)-YTd$*U$A`iB?a?gSb-* z<;HlE8|u4FH1$aS&6;(`U{2hv?|+&Q_la^j2}>^=ZmWiyH-ws>fd%J>eZC1xg3j<$ zVAg}*A+fO1V&R-CFi)6q#~ZFGda9EJGnTx2GXj=hn-Gu(OSDc;mwm+k!$l$H87y`+ zeI5sAo>3oH4vS{m8|;BIW_WA9fMqTZe*3{@F2=Lo!orHRoJp|qOs?N&m~q5k?fVC8 zZ{=T{T1foAs=`88dAn|}!e&grwd<>3Slm{=b0EyHY*4g^o%`6Y8Ugb(Ke+sA#PZ`; z2ablh*E3F5!Mvq>)rqiZSLC%5u$$(&JUbE(S{E`4_Skqb%>kAkJ=bms7x-?;ng`2N z{EC%f&c*e^mc#r2oDno&ebDgjSqU?rYf0i@kGIj&{9#7^!p~b_;rCaD8%XVCT4{sbUiU z(|f=gcFUjHd=r*8G;h#`ef`pQXTj_ReTM#gXUh?E)eU|OGt>_norSf---xSV&KpYu ze>i%;5Azmc)$jH%WmulZN9MJW{K*Y&d2r3EXy*Z2@cY%r<|M=HA?eg&g=N$sXM7Du zAdVNFKNTRZeB`&8CCvL|Kiv(subSgA2bPY+9ecR5+>cs(+=W_}`QC9I;^G+B%8s{~ zzFPgSA*B8MG0e}fxY?Fk68cq*SW;NDBMNbulHais*f4gl-*K27>T~WEtPyaQn(<&b zb*xgOYBJ&y=d1aNh!M3QhXLXNFOqmQFjLLMObxc^ zdox=Ga~=+i?0$puRlDj_EyR0e(Uow*cb|)$BtQB2&OEqyXq2KtD3)*B`ZpP{OZoBT z17LxQ{*;rj(#@iw!(q;|sjVAe#^QlRqhPM?mRVk~)YgbvbZrWC=Yz|>6AgZ^I|Wa4d7(;XR~%bIIxen6>wH`Z1WZI7{vV*LDTw#gX=_Pp`L! z!`pdj_{0|W7kBQOllZx3{ZnA}H~rUIaG2Aa=9|R(_Z7ac#rAx)>ZSzdDI`bTfd$qJ z4px%(dfwfeVY%xSjn~B0DjLgS_DWX7Tay2}Cv5?oWH2f3E6j9Y?H&oIt#!AO!vbCX z-x{!S^M%hHF#F~{y{^|dKOO9ItsCZuueHcw*@@K_N@1Aak?Yc5!JW@84^f3BHx<%~ z;DBRwRXVUZ{lTj=n72Ufpef8+wbdvJW^PrPHW_B5gumDbXYI}~wt{)vw=Y}+XYQP8 zF$0#Y*!IB#<~S_>ynw`4?`&qn79+!`#Ve*zv%9-Y){y>o?|S>|75b0x`?w1h_ct9~ z04F&ft2zX8UWUz1hoj8)e@lS{3y&!7gd6sb33yB51E0?s1c$}XrWW!esfAwM2frXL zbsMp#`6c%6E3CA8VA;`=tq)+E`>t=)x8nCbrrn5u8-^RYn8K28Y9<^wGe+~BHO#f? za#kn#YnN|xgk`L+{tY$ApX%4M5@y!@PK$&vQF<$zV&S0mf2u{`y(p69{r%eOKX!PfO`YChAKTBaBI zxCU|V?JqH^h;wxmINxBo}-&!JOg2kwanr_}FnA66c$l9(s=L+mLl_5zJiKFWUk3@QWQ7NUYo~uc^ZS zxnDLX80Oz+E`11RO`4Xz4Q8cp&`F1NuP&Gn2}{~+%a6gWUsvrt1M?niwp$F_AKFeW zHZ-KRX?anTPWtQ5Z5WTZwaQ#^4$QOg^@NU_r|D&dzcy-^^!a`Y=Q3UFJnNEYyYBUN1}?VDW*}AGT7=zjr$> zFMvgTnhv}x!}0F!nrt^%U@$**4J`Y8^`H-FAL_ns|187rzTE;Wsc{JGaqArC^A=O!~A5>KC3R`70flX)6a&pE~Ph>5T|*My+HC?$CN&RCC#0Cge2ca zIQlkmCo4Dt?tHN$@+Qo<=+n53#7k1=T!DEGEvD;XYx~!Y7hw6UvF7e1Uw-A`DVUk7 zp=SYWU$9yd56dPhbQ!~LPR05{STrSI=}?$ukW#P@mZ&Tm*HMJ)0gI@HEhN9q>wX^W z+Z$WOCvpASIk#brm(ose(q5qAB!s1VstV@AVxO;H*1=(UbMHFByi4{km%%cQgZC&{ zcD&7D0Gv_O9jr_8=eq{|d4%mj`Ny}R#2dCf_zH(OH?18A3;me-HE{Dj57VF6KL}^+ zH#iM9Rd7;2!@P&0$$Med#(rm?!F;C+p0it@ZPdaSBbcRkOaX^#bF1V>FYxq`}^|NK5C;WdT-FsZi z-ya9?(M5UM}rxY?9ER<#(l~^g)tANMad8DfxTS+db!@0Qb?w z;s{lkDQnYq6}FqXvS1c0_%T~20Opt~w-d81N)F4x>Ek;eslY;etyxn^Jnih*c+k_H zjKBK`_g5X44a$g9^jDZ)gB{r6qY9+FxXCCGcGhvoC+8`9mZ04pt_og1cM2?Ue4s4{ z7e+M)Oopkx>mq-b;=b{V9x@4LwXP28g6a0NI>>kkpR7`BhNCE5Eu-Z8ekI*i0yEMa z=8DPr&A!+d4C|b6m-+&8eOkOv!k+)Set#lyg$`o}*p(i?;XN#l&E9E3$}cHNtA=^k zmhn|#KReIpa+qGq44Do$&Fw2Jf|=?`TI1mIJv#gIVA=)+_irUwU$0NPq`;yW**`B} zql6uAF2KS^7s_j3!HQQ+Q81;JJ+1`S=+(0ef$8@Wj^Bq1&1ty@N&SQi|1QA!yFcmJ zkoxjV{)WIb+s{TuFz4?%{D3*yr$zaZ`<0;3Ht--a+~FwEXPAAfO;Z|< ze>C#+1F>z_ItuKq+g0*{)L$W&(^ZUo53abM3o~*WJRZW#KM|kPVA_WI2{+-UVuSuG zqY4vP=; z7kw>4etPB)E`^zm?vysTWkBz}KFmuS7heKbJ?x@r!t7%4m`_3vT$UQPKztx8heF!||(E zFL?{oIP$;7!%_cs$`E6rtGz0~{o{eI12Ny;vGY2dt1+b83{%hE>pcwfN{>B$4RZyv zx=mrpj;7?X=evC^UkPgt1V4X?xHxq2k%e$^B%`Pf<{Vx4a^ybdZ{hdfRWLvFU;8Jx zyzWa-3CwolO}YZw!Uj`i9X&%>0~!)oGt z$VbDY|Ds{BQDA;MY-?O6cNP{_$0a_7H5Y!aIt{Z{d>y?3(@*<21jAInk0#-;x#Eh| z9GEsOy8R?PaGRE59Td&(c20SoMdYIlJf7QYK`F}NuQ#Vr2Lh@ zFAHGR(HgbmFjHp!qe*a7Xvuaqaj@BsfBCrI*{H2`g~bPGIbCqX51*3#FfUqvUJ)#- z?ryjf<_>qbCy;n!+_&v8Cux#e5IjJ8%CRBw_*XH9;12u|4{K7tK}*LJc2>K)#2jWP zxR%PnQ6cuLH^Ab=blHD*ao;+9?c_RA-z{-kBW&ulH;@6dN-n*72=_hKGgu2#Kg--t zg*(9t&WBe+R!6P7k_1l?GE~4=gH!%PUQ*r@@pr+uOq6(4EaEWngis z9%~y+zdR**5Z_0!vf66L!!m-2BaJY1uQ2%lyj1No{1(+5z(dG=yRY<+ncN_c1zlz1-Fr}x}=?Pry zwew{l%<|CPv<>d4n9}77^S9_HOoD|IY^LYH^uv=6^KRk1?A$(>35yz{uLr`0ewW(j z!_@k3`VAQvpE)l7h*`>+62zK6#k=S*&13HD3fQi*qca~CO9xh;MSXLBb^0?BPg|L` zfwVU$e)s|A&zz#%bQAlL@J%r>!}s}gV)KG8PsiemAGN)K?HFHA_9D)<%kLM!Mzdcm z`vY^YDa&WTnq7vsmC5|@W$$y~c(;S*CNMu!Ym*1e3q1Fa!0UUVI1$^47*!2Ze`-coTC_BP>d&V+^Zx&xswZLuJ#8m7kmxpoE) zwF`(H`+nw6*px8XsO#NLDRLfDuPR-DU3*Hh$$8Fww)1)lY^NV_{3a~EeSBsyEYjWa zUn5NWeqz!qxMQ)@1@e6a^IhA?0odqj#o$tM|Ir)PQbGR>wD(N(hsE7C7goVWw+-vb zeVnm!uFw~5aA~oY$Nio2c|&s~ELmC98$fLNd&doUXnOqir!Z@(>gfuYADzF4OFobB zEA9=z&PPn?w_u#_%2JrGTr0;@b+Ay`L}wwKUL>K?46_VM_UXai>B~=P_+h+Gu=)*P zUg_LZ#xQ$|;&@Y7Jf|h|5G?xsN6!it&YG_j3kzQP{cwYmiso;+26Jt!gFIo?g-;gr z!yM&;#m8aKy0j`Kf6UJ`%a;)_XUJo8GtAnyYcLLG*k>QfCh+s80Q7JBm-bJv(L0rxB3RUrcIGpj6#jJL#6ZNuls7(yP1iq<8b@w8Au>$)T5Ejovo-ewdY7wLSs1oS7`C!@>Kkcd6eG3)U2k znZ0%Gm`z)1LmWu_&wD#M;s$Hi{Pl%}-7l*ZVb>XpTPt9uRmR~$%m-(%akLqxC(~W7 z!|WbC3-2KGcU;SICfso^XvcM!y+8Fman&Wk6G|}p?{Tt26Ha<-Cp-tU>BdKd$S2P= z{wH6-JgadZ&cWt^JEcrR&>!cl+biKdp5Mb`F#T@P)$wrC*4#(gu&C^F!m}LQmzyJM z+hN{l`GQb5{qL!SaiQq{@&%noh`V=gIt_EY9QXHUV>}v`O{j#~x6WTofW6(4gCfGP z9{1F@rQX1NdCJ>}U|P57wH0vHAB9WAoPlGjh&89n+A5t!zW6Ng{+xyV{On)WWtidf zLPHJiV;!t}1oM73l@dxJ@tc~t*XW>aQa?JoD|$LBM@<_>e+lZxXR$?m|4ok zWA@%BZx)U?xCgKV;*J>`pe3@~rYv2|^;H+n`prO>FHy!&`!*pj6ESx>WY=kLV zlQbmGp#AOht25zRhADnv0q;L~p_LXqkkeXk5Azo9eya*|Zq0nN52hc>FqVS-JkO+g z!L-@6lX|aVd>&<11jAxCz2)t&@L&M_)>!%2-Irk*{*^Ivl6Q_-kPvxW`7GLdFkPeP zD)M<~xvmY&pIppMgbO>B7Nx;Lg&|jEIDK70&3%|zYuNuM4f9>wwEZb8?o{)A2eYnT zE+2+@-3K1V!p`g=`s_%&PozbGJ#4zMF@g)zT)#Qy@GyTFLu00VC>b*==W^Bq#D%`4 z1CEGm@-)N0!Ia2fv(#Y$e@5ajn77iTY$`1K)_#Xd6xOrKr=Is$uwNd1F>W_Zzrzc1 zhs&=iWY)r*G>sikE@QtEvBpf>o4cL3i}g~ilho&DgLF{@pf!i&q`oF#=Q7*$#hFaK06v9WyIm zf6OV8;d;Dt=>OYI)4P+AUuzDycfqV>gE4`yre@M{^%%6j_T|P|aA-iWi3QATv*5R0 z!u^w#l*fYk*Y++Ez$5R6Be<~8V8sC)uFdt+O?;PlCo zCYxcFy0Mfw>~z?3utnkn;KV6EDNHvkddT!2(vC#q$d&zh&<}r&#PSjFvb# zxbKMGse3Sek79gs0^a}SL~1+n_M*?5VMbAAxpExJhh9HF4%X=V^l$}CQ(=Csh{yYE zTj>xE^SbuRZ-jHF@LpesIf*q6(_m){;i5)TKV(B$Lmcj_6K`4>RtrW3x#45Y0uUFn61djSZ}!F>A_Cn6ucz zXfiCxe$*?MNZvQv?d>_Nw|j#VO<;k>#4iajHSB8FZkRc7MAjJ|cvzhEfSAJ(^+aQT z^KwovBIUP9pYMi;lqMIwglUJOZU|um#~rt%lF&c*_Ka*;bKwfAG|Z8SoK8HFc_(@Y z%xpGw)`xl1-uHed<&*n;*GFN$JNx#b?M399_Uq<{kyy`M2mdE9=VtIuNjT!vt8&UE zjIZ3Sh`VP|zD&M?SonxFW`oAZN9Mqc#I3GTh#Op4_w+o>EKyn3b_VyoiNPraFuiM; zQwD4rUTrBRaqnA4P2jG`&KJ6DXEc6>03SUlwE zC36}18&@ZN6ONbps;UI@Wis7j;S^!UDPx$ERqneU)-*Nhz6}dJ9b&JCV0|Y}l^Ayg z{f*T5J`2{+$3MWs)I8&Z`N7DqiFvHkFz-~H(Qa7t7_;FviI-4MD8f9i{byT=?@wRU z9EAOgeSK#a%ziuXZ3P@Z)R6QA7S_qos^MHWi8tS2ae&&RXRyv<$01_6PHPG=|9IWt zKjOpgn;yXw)8&^aJoNAO{gOhM$9lt%gsB?>J8r`CDXZ|C=`nueWAA3dmP4x1G+4ay z&aY6|uzK@?O~g6g0Xi^O#MtQr(=FG=f8pSK4xLZG3sa)@Wk$f7zdR;T((u0VT9z-lxBQcm|0V&Wv~tCmnozUJqukSKglj3vYiIv#{ySK4PlU@&_c& zPfn93F83%eB4%7kkRx_ty;`{qW-ir8c7e@b1n4KjqHjfS{{&!vU!%a4y^8+b=7sMi z7Ixp?Nz5)OZ}rFbUl0G3q`WRq&y#N;=IQHwB(9wsGE@XJrQ064!Q{T57pB|MJ(D*Zjui3oj85@7{*6!wb^hs@-0Q9W8P8ZSH}16kvS-T!fEh{ zE5>u*sM#RQNQ>+(ID+;$#@q2jzykb%O|zee@xHg5Og6*(rvq_9nDt+3gcD5vl+hUl z+ZN|Xc*6|2TQnw|uf%tLLgF#SoUJYx@2pK5YhX@NDR(t&sL-!~pESnr3thid6=r0l zDiRCT{?jG4{TLA=e-rgDDO;>@M*s7)UaW-~H$$Jq9>V%N+Qc=6Ijw&VMZkW)hVK(I zC+9y8hiy+@evtul8Gdfx4q$(yh89%ABL208tuXt6!~AyQrnP2GPH?l>Z2B!Y#Y{8X z5&3RroXCKwBGZd^_G7&`w68ED{?fDm9Nck^dWu-MWb@M~c)+ZnWfv@d)b?2prq5Z| zwIAj$4zFt2hkTlM?1LLDiZ43r2y6UYlXelNbG$~UvryhxcX<|xTW0*wa=`rfj+b%A zc!<4>6&Bm$eNw&7+rxs(%Qa@gvU`-~8^R3!(Wvn-UnRW83G+u=dh1t;9r8_za`Yg~ zZ;tuv2|K@(Q(leolt#%_?_jRo@sUF?lO48x6dp+X+De|!Z1&kXx(oMF89NcNSmHv# zKR7>nZ&oSkZ$gJWV>`}QDf;0Dq$Tx+*dd9Vd}4A#}>hCRonh_m^<5G=*2dC z{wdfTdVzRe>Bt4RSnB75C|Ibs@{KnvbFaMK7#1t`jvuzg`BqZUw-EhfcWCZLn0>ivrXMVrcSeo|a~a(eX|Pj*{?}D7)pkN` zGZXo1_DAUi%y>63d>t$vkXaKC(_L#O_gUlpV-7l%!=fR7n}e{!gt;nhFsuKb+zdEW z)#HeC9`+mh`Um-&v3^^RUL_VLaTUj`R>$1~Q@;&=ylsViIdQ8!1{MrXIWQR>DWcn5 zhq?Ry{(NGI{H%Ir^8{x5?XBJb+g+w9RKbixa$ah%?V^kRbueps%kOD$sAJx{23Tmh z@Ao*^(5G|aH<%taT$#HG`4DdDFigsa^lyoV2aIo~$=t#7c(S*eUF$XxVbZvA>>bu_hK5S>+IqFwT9Mdl%#LNqXl>*gHZ~eK{;XGh^CB*fL|z zB3GCnD6=HU4CC?JYQ2!yJR$DF2IT8X1IhQWu+(kxQP_3D1}S3R#tkyWn$uhwx?$$i z*UL7+hSM);48ok?KWf@=N9!%7d_MY{5w@jcJHwQ@hgdyV-FsHt9fiz5+H@Y~A#1E^twwNOSPTh+c zfca{N_w|_IzSIBLTJaw8voa?l6ILj-S$!223oJam)}g;Id)ubmM?U4a6$Tn(e82hM z(t&BezRp+-d)~AfUIB}&o~==UbL*n6tb@58&Uc0xcz#^RmQyhA&YNCu*iKfu&vp;GvNXg@us_%AGy?@FMB5rZ|TrnM{?T%3W39HTz zc&`NWz8BDy(B8<3z%jG_h-~M=O?%hxl|)=%+%R)HY7{?wYY~I_$9G+eTHGv9SFr6~@nv zEK!E3gA+Typnp#5(`;tKe5vZtR@ksH-ha$+@2h~*?T_>>hS}>f zXIy|K!(ud75FdTI@(j#=+8t>Ivs$N8_Q5SB{k9un{+WhMVpBojKt)%=exve9xQO2p#{V-e0XIcmBS5%NgEM_LJ z{|K8uKYz=C#H&Kv(_qfQR$Uudup*;82=<VyA*&3Gg8yYh`t!xu9>-)_%5@+~znG>gXKlI!V(@f3{7{RuDpZFs%WqtC739x~d zoV5qcwV1CvxEkk=a&Zn zFR2kOFm+AYtLw1&@o!DfU^@OhPq`t^BVO;}ucW>fHP|1f8t(pYNfFt9)Y|G+;69{$ zW0S!{tWWc`505Ovdb|>5cLWxm&pY?k0Qt7P+pQTEpIt_6T#ES*)*jF=#(vpXeMb=< z;n-PO!@Rtj@MlY~pU%!Jbb~1sS{H(0qoz-WT$s8iPjw-jg5ML9NgO^cvS%?q|L8pb zB2|L%bc^j9r6XVWe}7j1Q__z{xanhkTk`X?OObEtPX9URVLwcqY8?X$h8nkq=wg3c zy`=Oq%zH3Jdl$@dUb;OW7I|8Z%!N6(C?O>G2usTaGuVTRMD6EESGxrc+sJ;r>$ zlar2w)4$|&PK5>L-GxVB^HGZcYvRg-uNxMi{2QYy6)+>CyZ7dNjK8}>lduf)kxgAL zI}iE%sW74s78zvHr~vjfbdj)wnGLONGhxGK zr;A@;;ZVCl*i3w1vp0-4trGLMNbSN@xNy=;1tXYwW@#?ysIOuPJ9!k23PV zc8YrdEQp%mRu5Mt_9vc)IWPbCabcNbO=ZcjNXB2$os{33we$*1DP3{N33kq#$P>Wa zHL>aA;J&~4>K(AKdE<$zO2|*usdI_x+jn{rm*0OV)_#iT*C&eG74dmF^1`G4VD14a z>j!Xok&D?@n0fuzrZ`xfT%djw=8RUgY=U!x_SAa8!h-w9*1$<_JDsbDYj(01!5VR8 zO%7FXRm9V)Gcca1qka2euHnWrsc`6rhyEu3!Bf6ATC*>Jhy(#AHJSDm=9Y#QQRcRgZp`p>9PSXHklQ$)(Uj&I)xNBDZ~ z7Zb;*yo!>?{xzpES_18J)1H;DgIm~B8dYH7-bNW3-0##TjlSb_R9CmN>Qikr6tF25tw-V2MeQ#uyQ zVLj@5oSRjReCg4#%9x7%Y08{W#xQ5f;rL~+vbFNI^)U79S4&-(dbU}83oNRf*G`3_ zL?5s0BK2#AT>7VAe)p*_JxuD?eCQLvLrSh6ufmk+_uDmL1@G6qhcKH~t^HmW{aKo=2IFCB{LE!VaN(rM-Cs$3hR z(m3DV+FzSci~f!?@%4wBmfx{3foUo?vpz~;y}f9w2q*Dxwa-;x9p%TDZ^E2OGSUJ` ztUsN~r0*o2@jBs~1jaw`Wv@~leB$1RGjRORmaoM0{T9u{s(p7>=)*MOc1;VIzdZL( z94y*(<@4O}c;7D{obzF!R^#mIamfGrYiBxP7GEW1H5~6#wsGZiJYV8goCO8X^Ptw{ z!Xp1^SAYC7Vh9J9r#^+*d+hFIzz#RgGFxEk7q=}(U~_v%t?~6JFQny9fQzG|SC0A8 zs}N#K?IV2_h;#Bn4le#{#Gvo3$lVN6d4)D};r!@1CTy5Hf&TXX2;vQ=6MTtJQ+J+* z8Cwpf6Z84Hi-}dE&%PuUoZE0G3}y~*(T#=0XZ^g_!;(ukiU(ol{^e0ae~cJ*J3^KW z!4w;at;F=2c=|e_G>^5u;B+mwyB1 zIIg|%7#1G0k*S1P23Av!!OEWd|0Cuc$Sh#PmfdT|KLy;{o%7T=4mc`|6kFwOJHvW97u zmESn9QM-)Jewdf?YOgWutQ1~)4CbG8JwN;t?>9?0W}(^wF0p6V(3n}p$6|US!Muk?M# zgpE4QsnsyULUbu_z=%PMjK0wbGkx9^g%HOY9rytALT6Vyzzly~>Sto7_!K5A=q3#l+7yT^WU`pAQ}T{KJS5(eSce;wAd?bAenVObNSSZ3uHt zF7%UwYxmNMjbYmPKV7ZgG5;gBV-`7m>?AI4a33>gz<$gMf~9sgC@*-BvOWlLP3`%? z#7xHr`5btpsQ>$RSe)MX!V`Ad8BpX1(>KU(+yJv~D{M=Hg<0=^TYN*l`diLzB`&9p zP{c+Ih31AdKbSTgvo7JQ5u-`nepUd?JySOM6g(ttv4L2W7%M@{I3ykkg*lrS75l)F zdnWKd!n_d%10RSPDR0c45Ho*YZfb??wptARh8aITZ8-?DzEoeAeTC-_l&&#_m3R0Z zzl1ntS@!IhFUZfzdG5(Dty}6pPuS_jh38jcp24Is(-&@du88{V3nB4z)GyYaspg4v~PcZeCTfYZv z{@?aLUr7DiK6~w9zga$ZBd}K?FvBP) zV;?M-xzEuSrkZI*8o>@_;bW#pCZ8pCl~vs3fH-}{-}!UkzF(=uM`2D}(WtoBh|zIm zpcOm;0)=PY7@y{qc~&s>)yi}>tV$E7Z)(Kz@0Z!Td^Tc;nm@(|!aS#} zFRwmfz0=0UWWs{7Cg&Y+R+75lHgSsFk~J{bpQSVT4f4-D=Z$`c5kuzDp`}(Z?O4(( zyN_63riRa)VDY7wwew)+mPK>iVD{*{j}u^li*3IZC7AU;bS95n3?9`xd-mDm99PxbG7_gRqrvL zoxAx_uyDzO*txLn)zY6AVM_C%2W_pG|5YXnFTt#3?-hxS2F*({U_ti8s(Y~Lx|Vw( z%>AGx8wWEt1#f==3j?#S9E0)87i3qyMgKNc=}N*1{&Cayz@lb_y)Rpg7@@U?CLV_Q zUk|XK!-bb*bpl~=fqX|ZJYo`&6hh+4uOi>UwMlXNPQ#qc>q}c;`k|0jF~ro=F*9Cl z%87-k`VPGnh?^$0JtAgz{P)TYF(y>%a5vH)B1x zSs4tx!}Gc+np*G3`BgBr5w?|#urh;bM-Qd6z%n!ml@4Ml_L$=ZW!uO6?_B_ir|6G= zIkN@*$xAp;26yGA)mXyxAaC(i*mB(ue_NO`{=nz+@Q6x}8VhEIE~?rCC!K5EvmfRa zIBM)9@zIEH{xE}`p{ouT&#$z}AvP1#ziu{S@RhdQY=*gYS{D*vnLk^n>9wN04;=03 zuw|~@Az4xP-|QV2!`VR&!ytd~YNrv>>K*$iE?WmA`o81$aZd<$f0xZ^xo;w3(r*fneK4Ctac16yp#(m+#bLk~8uSbJh zUxo9#)l-I;y~iqw*mT;j6_zlE@x?n9PXB&tjT=ny{`$=d_LHt%bOvT_t{RyRx1lQ+3Z(Oai90AQhEV%XR;JL;bN<+Qg)w7{vR3KQ;B@iGXGT#vlTQxdsblnEy6y( zfyMYwbK78@x6CeLp64kW;(XsH;e#+OM)$WIEGu(3e`P1$x5Ia0SUKk3#-x^*Io0HS zD9kh+yzu~LN9j*WeS-5xf8Jb57vdTZw#vYoDtaFzV9K{M&J;Kz_UURxm{&1dwyF&K zZC7#%9cG*xRPu(Yg9d-Bh{Hq=O<_(mcbJ&E!}kHPs(gfkJ&9YKVJ?Ovb`45}z^o;g z-amPa^S9LKMk&lW9TBAoH+>$|UEPiHCPiM81-P#&p3bv^X)_O%e}07Z_h^sW0hoE) z(fTQzqT`gvhWY==eY{TM84I!lVRmd!Q#@?ABfN`PnCxLrY!@+eAP5$x{*H`+1scz1 zN0Iu*KgWB+>}8Ihl8B?FSGd9)dAFT~FlTL#r7o;+eBG_rupr^&E(w^oV9CNCFgw;_ z;p0-g@9<4+Vv41~Y~tLhx5R@m^}YM`Lb$NhQTi9mv|SZJEO|=sXF?CgBf$4w8Y#c+ zhKUR;=-IDI%-&}sya#hPF>(z`@V?>Me~Fou*4v0BD*~-Q!@LPsQr;A!KWfuh#3BjG zNn)L@iGpre+?JYD57VA~Tksp^zUsf51J@qXYo6VU=RbN-?MmVr!}s)IvG%=sQ@Clz zBmo1aim2bTVDqP@2c2R1nP(r`9%8(!c5U*6nerL;AHyjVBF9Xr*B!Ia&?MRqarOqU z@?6CG#=Uq(%nIDMl{mh5(Wib`I4|=+a}o0Qu* zVSas`Axy2$uU!qZTtimd!<3g+>g6sD%q3!2+rBZ#}SGz|-jG#BtrH zTVQjg(4qmDBkNlq07rzaH=Onb&x_p2+zf{rmu@E(PRaOZ1v@#nTvmcgq73bH(-ru~G%w*sbT|G3jrfb~#0QojM_wn+F1VecK+O>9Vf zs7E>vPMUgp{C-%l!od9^Ji=Z(?J99;r~(D|tg=_o%o;8HUqem_l!}JFa&8u6=DZ-?1=VGZ7}OnKwpT`)^5_uvR@w;*@hA(%IOHqHu` zbm?<(g}Do+DVe~GqE(}lLa92>l8E@j3i*gO%q29nhr(ot{ zP6-`mdT!hkOyc27MViDX)6OIiXJ6z0&c}T(BczL%@hdrZ2&VG4El(u%%i2|b!jei` z7T<+wdB0t=V4b49fu%6l^XKWSaIWtviPtdwiPSzfSeW^I%#66}dx;gwrk{}P$NR}1 zWBt90@rmtLmxqPF^nZPZyKI)p&w^>QHy(TqXGzQ4*M~*7PYvZ`qSho!3ziqn6fjw^>98MCd#FFuJ27E9a@MBF*x>D@8ESGr3);-9=&|`_<~+ZN`W?Dtk zG!nP`o&Ql0aat)ys}ioNtCUcJ*|PJ0R1niNJ? zK!;oQe03XvMIHfVQ(zg+K)K8h%;$`!?gKeEj||p7C8kM@9wuflsaKs#oaW*tfHk}_ zZFOKCGcm)o)k9wcECFonf(B zXwgELwtnxGBd}n$PmTthe$w%h4=G>j$XA3N;sf)-Vf;sIX9~OGfAq%FcwbT)FPlr1b*RIXq*9wcXiUL`1*G%!Xc34=?&DsiU-c)|y2~!3W zkF13iMz*SS!3<@eX~an@B{C-r!0Kz`r^58{SsldmD}7UlonLCZO@b+c}2o3?0;>uuSotxeRbaE)o_Yp(p&>r?0-Y|LnitgEH}Xv7IqdT z)WEJit>f%rQLM+=RG3wzF`k$`@a7D$vg64ybB@!;>{_ z%$rRaME=a?Z#@qyPhwdRQ#u{RT)0ENr+7Tfn(XNs2`5!=_L>HBU8N^F6Zd?$XbMxW zEtH)J3ro^_#yo$viwfM3o4~Ma2acGyL_Q!n6#Ilo`xYJ?BZxta#+U0d8ro{8lvJ3pGNum|RsZ0}wRH!TZNBNioUH4?b4F#9R3 z@(tYdx$mJF%wV*r7r{dXZTp>IvE#lJ4s1uIjU0oy(qY^DVFO9ELmXJ3{kMA$Y-u;p zF=k00(Qeo@ac&rc^dZwcZi=0j+TEYsoU!E<3h2mhN4X{I;S=}0#Zjz^^ z47=V?R^LT@A*Z~ZhxuNvbIl9peyiS44$D|wzm*L0j-3qjhsB$1=VlStOqDne(=I!n zAQrE<;^zTVA7t#yhbb|8=zC#RMK7bBntFf&CjC=q7+&|0ftqxZ*ipTfL# zdxwXK6}y#dVb&BcsVQi$FJAuKb66nAu$l)eP;%T~!yNn`o=Dg*Ep^}zsjn=(VqFO6yx#P=v-SO@_mM@yAJU~Hnj{korr&W zgM}tGX})m2*9)u5Fs)KK#0(aFnY_0FX0KVZ?9&CDXY)^gkf7M%=QlhYuELysrqA?Y z)?uwHu5j&R!<`|p;G4?4uL&6MXbHXhr2Ly@{ByACgVS_k)<8fGafjjW`PDFQY9{pv ztfQ~^-z(xWhTBd!-?00|_;Gduu|vs3S-8(}lD8Sm*}s3;xp>@vnleUfVR|ep>3$sY zOD8kZU_7klFE@@@@85khSTw{?U!UeGN#K1B&Aks?3Yjg!5%4pYVox-<`VpxVR75Rw=%N zX|C#%_Q1Kmf3K)Y+6nlzssV#B*gq5uWxZjF{j<$Bu+vKEFFYyq$A2PwTQuh9$6hNJ zY1A*+t<)Kb@tmVhZ-zx*DrU}yy?=2sv?ic@OPW^rS>(fk)pf40$nf$8IhaTPG#U%@ zt*1_Zb_V^q=(_d+i92q)5CB`ME#*Ih>6Kx3>|wjZzav{=rfg@_YPf6bYbOaAjQ6gQ z$_a4jd3p*luR(qTaf`|k+X*mJzBJ`u1lAv&zi|P~eyAOCA08SKBrPVsmA>>IEZenU z`VyG6MJFmB&I+fTC+7GLwB3bU3gTa`fCW2ZZ{@HO*V+m+aVrjkMQg5jr zMqKC?>}LvVL>vwBg?VR_pQ^z+El;k7!Q6$@N+-c=%8J8hV7kBOMG4r+FfM~w5UcG% zY@Rr9Hvwk%R+s+{M}K}?2m6MTwMaLBTg0VmLaP6=6{f69N!~#md?l-agZ;jw&v-wv(@x()n7u86 zeh_B#O8mPCYtE944uUBjQ?5n8k`MB4Ux3+n*vIW*jWr`Rt+3#O@!I!+SP!bFHKt6l z6Hvcu9lZ!sEgXf8u=t*TS#JQ|Kig0v2o@EsmU{{-q(3=(6XurY9`=R}&8DpW4Abks zS7^Z+3bk!YQ&HYb@xgb0^uP8)w!P~h{7+l<5!0=Z()B6L0LZ zAr9Jn?HDZ3mAOgGS~zbqF*oB-QX5P?tE{-d3-5DkUI{Tb%xcK+LV-nY? z^5wR`f_VwmKj6YW59t=uFdyAF)IJ@@{*oQt775e*W(1hSPQ$aZufx<^i(RDPp%NryRV=7~;hoFDx|zL_wkykq80xP^DAw*zMG@Y@>Yj{av} ztN8*8E`07j0#n;L(-ajjem)I?rlUp-e&FoW%VFwq+arfzjb-P*o4~Z{c~6aC**Rx5 zO<`6|dFyJ}d+DaUD45ZH++e&L?#Gn+A6%GS+B&tv75mS5f%bWr_wZBWM_6YcH<_4r zO70Lbr`|n*Is@^Y0n#2Y*Ql^(9!$|ltlt5P9ZG$6!mLJ{7lTLezU)uz3$Vz3tKc-u z86GE>3k&1+Tie3oXwxx^8MT|o+#pHeBhLLgqPhw3zWU*N&tU%f#!us6gce(Bvvx0onVMV5TU;=I#cm~QVS4M^!JbXbu?!zi95pSW-xC&E@g%3fM!Sb6+$H6GzHHv?jPZT7F6S%EyrJ!$dkE*z z`tkk$DWQMGAy57t#QF$w%SeSe*44ENaBi~89}X-`G<>JAAL}zQ*v(EEC-vJMp)%SJVO1Ln4 zUGXUrKM;8N-45($TW@XVkoqI+}$q;S=py z3Ws*=WD|2!4$BhfFJigH!aQr;=mgWKb#=T5 z7SBB&s|}BwR?4Hy#`|0{@9(!p|CDU%m%?K16{ZiYux9-!H<+b<{J)8?U5$zO1}rdI zXq>g#h{4IJ)%gKa-fTX)2F|iJwO%&|-O z)rG{s{L*^6Icj*G;)IF~uya|(H7}Ub)&DAM6Y?cuPcJbqro@<-GF`FLo0K=I_%8#_ zw@a0ef$7unYmH#fnO5Y5NISk`SxZ(0Ui?=XCsfprkhVfI&u^)lyx{rg*V9kQz{@HWU zztgYIO@eJ>`n2Z|H&2;HtjSAh)qwfD1@lHWpuTYy(*qWHb@uLrCC|%!ONS})Ci|q} zELoQoH!v6Wkau`+n5fucmNbUC>FGIr=Wq2R5G~9y5)9Zp?-=_&vUe zi^eBTWh1VstZ8@(=JlNObcgvK?wjtw{H(VF<6y}rAFYXnydOQp+!{CSLRh@@-^T$H zwC~EP8HH)pg-P3B$;wCRh8n1UqJr^Zoe_g)m$tJAW}crskZFwXPft(SO)OZx@+E{eb}rYrW%_1ErMAE%pA@6cwhc+#ViK$-)qgr12C^T-hC-dpZcRH z3}$sl8HoNvKCGyidJPskYJE?GRks|ptA(i>fBxM9XN}O6hDiC~fG?w;b)e_tCmx;n|~`wk$(?PgCa+(|+AHC1w;K ziTXo4x-xM-OrJIPJlQiFik6@~TkUk3Hsa^1 z*;;V=jvxm&nBqe9vR{n(^_-xR2y=s7m|b-2CudtHUWA$BDF1c9LQ5Up2e5FGWjhZx zh+X+=rVh%R+ii)`M|;LjykoFPm|Bsohw%#jA5-@q*V6z0kAEr@{TPZOwGu)xN%|!R zp@^nJk}Qf+G(<(R2t{ftilX&XshCRA4_1;WH3}hF2}LrbLh`*m_Iba)m*=0?%l-a1 z=W!lC&pB;tK{_$b=z1wJ)qg^2NqI{NF}pC78-_UJc6D(OtS_I!PKKG(0gV|jYe1LR z28&W!Beoji`Rdy|l{to}&%KEE$p)D3x{IG4fF%JV^6$;U{oA7{yE|aPl9x`rnU<8q zYGJa%Z1i8n2|Fz~_t8bqnJ}GyFtc(7j(0!g#3Gn>t&4XO9(>K+>;m(5WILF_Vk4Im zK`<*I`%tSs_8Z${RUst)Ds|6eSXJx2|3#Sk8hK+6+$p!!wgQ&CD;}W1j9qaGv^kiM z)=?AerenSRaV10>rYx!(e^0}Hn-lbQ4ossKzkUQOZZJEz5T*w#lSzZCLUjCXU~z1G z(@B`IH^+D<%vA59jN#%k?{g`{dd2qVr=tDW7Cf#e4t^7<3JaFWWPO5ZS%0na_0S%! zc6wu(Xzv9RK3c&}^gLHxSYR=K-M=Zgzjv4YWep20YR_lFl3Tg0n@C*qu-FGKKJQ<0 zl{nQX;EXQrH?k)>+=WFU1MZHnaew)q;d2pxT5#fk4#t1GWP%|~tNQIL4_ihUsw{!I ziCNVT7-)~eR;g)wPFE2pW% z2;+BDZB5T))c5t0OfOh6_Q6Lpn5t=OkApb}f9=iG#Qnlm+U!E&V>Q24!|9&UOS@pc z@eh^6Nf;k`rDOVG;hd=>55P84u8t!XjOrUk>^f^SUv3`SLnkxc6LW5p z{12^5rfT5+Wo*Q#3gXk@zm;L`vd%UMj33;77Oal(o_6j+KP*^udZ8yQJmj}-ficFr zT1?5w31q$T_uUGMrtPtA8;|i`JH|Pl*nXO}J)E#C+av*IjAAvdf;%2nr=$@VdBii| z$S<#Vl)zkdUv9G+uE%y}P$Mj9RyK}+vwo)U6vNDq^}N-voylNk2TVog)GdO=8=hvp zC*?J7ov?(3YZK?u=A*x|PUn75#dz9oT{#`*XwCFL1bc-$R1x!-3A>36*4~tw{C+Vp zKgmwU2yqr?Z_XB&Bbhzcl$8HimcJ4%YdN*Y6&9qZ%&s4Y_TAKXZ#yg=NVH6c^}ZO? z?uNM|2Lq15njiW1Lts(bi=|Gm*NUIj#Ei?I77@$c-`8>y<_U_t&0u!o!9NKw=WJ+v z?^v9#{;udZV0q|gD%>D*gH0<_>*)Wfq+VTfxDIID{9E1LF zx~)IP1oc}v;`larFsSTKCd{~_!BeMWydB)oaRufU2BfIMqDHTtVwjm8xcsXM#=q9n znp-f-?2Ifw>;rBur`we~>j994j z`q6Wkp(+#c85ZFOCOu(3H~OpY0+gQ|csO4P_vdm)XE?)R^$ls?6tVu?lu>ho>EX)E z7Py6xZ$Qj4D}VGF7CydzJDS8p#4ks}ra}1=Qb>IDc!!sxalfE>Wihd6vg`e3SUAPr zqlo0YzH2`V^N$&I{t z#Cfd?^hoR9q79UwS;}l$6=`zos z*o)UYD2MqjSZ_nj$?wS`mY*1Oh@$Lt=Gv81>gQnln^X230zzc5V@vyYh5efZ5ExS1XDdkk@hi{3Mszd^f8 z4Hob2lCOcAzGgf!gSoaNZ#cohVg0WiV4>O3w}Zp*eD3wsAPg2*?motX{l9X(V`0Wt z4+mwKGxe2XJ}mM!PB=xwew5FA^pTW*=$c;r&w`RSKWH!j^Yho734zs5cvpJTg{ys7B?*Mefk<&COaAHl*a zUF*ie#d)Ix+F{!59^>18EGSyN_nNVmxW3jmj&iU*W5i^4nDXH#=KMlj`M@I{%#<^E z(=%W}Wqr%8C*~Wje?;7EIHVB+3nN$ccfyg%?pu;ce%j*PTsSbr(f2mYPSR4G0tcI@ z6c9@^D`a%x&Yh=j&=#V;XNR&T^jqNlT1XSIz+7_-v5khkTwGWEsP~#mh|&!-7#uU&+Ay-BH7f$%2L3jWmcq2rm)E}hVL=(l1W)XM=`APN z7I5LCM{|jp?)wJK;jE({@-(b4A2W|QulsI6G5R(qPlIV+{=FCjr>~kgmk&#(mcDEK zYC*9+b?zCy2-mkvbKI;R)OS?-ZWCDGqWFUK1@(*k_;3@6`$P_1=(eD0_&415!IFbh zS6ISHS>?(3FfCJ~pyw0fLI?jcQvQCrZY9k6(Yfv(%(6HZn+10U%$rvW^Bj+tpN4sE zHp_^GhQrvz&B_;yTVZihg5L~SbT0Y!2bi7jUnU1jdf%4IP-w5bo%f@SXFob{%Npa;{w6^PK-zEu^732LzDR+7LKW}57`Zuw|I4btv55wo?uQeVRcC)=6ZVR4dw_6@iyd-|wI zl7G&sXCy4HU273X@_!4ief?lT<-TL3ohR`kmTw1ar^}U^-xuUV++A+_DT&0JUn)L; zvy9%zq`>qphi|vxj-tqtE97|Jora~sRWfrgRKmg+V*AOkwe5w^b+F**m7?$OEvUGu z1+QMhv`X0(8F1&EYSsr5KXM?)1!kKxuKo>k_s-D&)`9xwCYubwylLg^C$NpRw8f}j+ogdDxbBoer3@@?dA##BEbp^kYF0a4YSC}Q zM{kNSE^ zW1}NXDO<&Sgu`UNY43#TW3s&`;P|vt?D;QAKJ_>%o1CwC=xsZR(@l6~u=N(Jc4EoM zf$mb6RS-X%T%Typ>~-F->Gj}^#6r~y$MrDJ{OrI7Qohb2RYcCeP`%QE#E0{2w>DwE z`1SS@b1KIvxx>MBl%pky&v34EgDrVc77JnanljCeFiT$5j+kHILlJwOT~h52Q)!b% zKY?2o)~AsElALfqF2MOzjl7J$z|8G^L6LAb=Xk}4rKnHjuV5>J>UKA6E_ z-Y4_|VToGN(xgU=FUIoP(39idADZDP2V0A>_jkkm z(NkvLe}eIS$tu;(8rP>%wr{k^0^bK|x%LWXlpel!^D*Y*oZFh;iBGgVc7Qv@JKKJe z{L-p>i(zZ=1i|!W7>`5aOt;rzJ|1rGTLJT3)0^sRv3{&8%RdPV@5`6t#(<(vGF3@~ z8K=F=C&0X+ZTD(mk!bia=ZDz8RJY~-foVSc8S85>e%##4^p<0OMBf}6`~cTKtWs); zu!K%LczA7~9pdy+O&_wVF`o=Q3zA`KM@G!QD)f(vS!N3?)TYs|-9wx?wV)5CHOE!c zVOJ;h;PERkp18rRot4;+E|xOaz;wM5xp@_+U&W{UK`>*@kAMY2)IXwH_6Ui$XP9Wf zrha36!-$KF9{stC^(5xfE@ENNqY7en{O);YVSe-Tf91VbLbm!IN;{4T=6oSd#NP>ti|U_qT;f%y)?kA?8^`&n4!?K0ipD zYj5>d&IaY}M*p&bJI=QRkAZo`s^7cHus%Mw>@tGsAD3U!hxKB1CohF13+Fw4SBm~D z9(~^$rY?oIzJ;0PTnk&6**w#!8kSo-!;+YH--|`;5#iut4|6V8d`^Xv*o$Mhu;609 z_3{!N?*uMTGvlyn??l>_Ewk!E3HTUG=Fk-#S#drQ9&Kuk@EA$rD+x^$s z3bv?EcJ;8CaMczKKMh!Tv1dtJ5!y?+>X|+TKSo~J8m7-4d+XgzoUfvFHwWg6@7X*r#QmU}YkC08_({!6ffbE24U=HXH~Gf% z0<_P2b4C2iSnQvtv%K?BpWW~FzJn!QS#MQfR@*4YUXs7zbXC+1%)i6#Gxb&?f7OkN zTmjZ=w=L(4V6o%}Z7ggo{$sor79HO7e))CeZ&m7cCizwlx5vSvde?4GSTMq}w&|J$ zMQz$AwK!Ash?xF$iH|Shyit=~ZoB@d-esJOQgovZWmKND7V1w6dHXVamcVg#Tz`k2;)|`O(C&bGa zz|JMhMn%F*kx#G{9B8_@{tV2k-cUOq=3McRnsK)95wUz;tsRdXZ$;6u8L-o>KlP-3 zY=18u4VWFNluwSwu($iG1oKsyZp8FZ`Z7{7ZQB8Wv@*4o5g|5TgF`RFHtQ~$sn zmsU1L!2-KbxnHoyN#Pq}#+#L@#GJ4OZVHJvmK&+z_?^4@Ze+t;hq-Q}$@!fkpAysO zfA~6z_}yJK0W5xUqx>hz^Zq?(&4-!0)w@2!g~^fMDqvc3)`U0`PhB$a14dqC%0x`y`*_E-@;4_+ax;NWVgs= z5ElLLUOhdX+@Iu*aj`>tDC=BlPr?0!!g})nn7QI{BOPYnbILtPJp8Ww03Yi|thj<$ zQd@7=4~u`u?Y<5R*5nS%O2+!#MGqzxdTvf4Hdtroa1-XoAF><{^Cx^xe@ODzCL|rX zjPd4v$w)-gC&6Mn<-f%2pwiQcuq1z5 zkOUSO?7X-KZkcgm-yc{MG0u4&$zS<>9&I)1zi)%zNVrUAbfXN+e_meFkc9p!R_!O| z9*ULJ!;$^+BS*uWDvP2N*rsd45<1M_b-0GXdS8vM>%zQ>`jzhzu^z0nkJW?O;{=sY zVO~R5s6NabG+uuL7B=R-pAAbUM40%%Ws473n!>EzUPeZ+X5!nZCB)*{w_jdFeIqW$ zIl;8qKaRPY=e0LQ(r&7fc@#W%g@s!{`mIi7YUeeADEWWFtv1pxB&KG{#$by z7G~6HErY4YER{6k%k_=&FneY7ma8PcTWHl1kNS4#0(*&a5nhMg0fsZD7rnc{3jq&)Hwzd>->NtueU) zmi*gba~tMX%q}P93xhL?;krt}#wM6?JX<9h?$BzKny>RynYg*dzUC!~-x~PLg*C^2 zQ2h$i@2VI@oWp!*6;2p{@dNSuD4462dR%1<#zWhQWr8@g*MkM)@l()3_R=+bj9@1{ zov+hj1~Z^b7A`JH+++aLt>?2|#-e}Ej}0?{g(imr&ciHTLgNCG|K;oAXqcDvY>N#{ zJq+S9VCt8R4>5PtrN7#+S4@1-T39mSQr^uN)VF59$Q2e9>#XpFMXz7X_k`)I@A_F1 z-_>sMgE=YU%=WXme>{2p#ChVb)e#SPm~WcOb!jlG#P!Pon3XW+-3?f>!TXQ`?9625 z7Q*aB#x)($nD2AWbyUI3#8&G=uy6A&hZb10@ltDf6x#EXC!>{ky|&j)Sk$K3_#Woc z=dL*mJ9YTX{6PHbNOlw~nf*g*fl5#Yan-j&_7cQ-4!cHCaGl@yx&1J0qukP8XE0x! zRkLO6(SA3)7v{ls#m}y(!tBd+q2poonM*3-VJhuPVR<;l*V5R;0+Y?%z)Tb1Yj zh?IZ3ecq9i7+(S05pPNU1eM_z56)&V`OI` zvEcV2i_h@D$cIG}U|wi$*Iu{~U)wiU(P7Bv8|C}Kv;+RPs*mD+WOkVIL0EY5 z#3Of@W-f1C2UFkjkJKMQf8L~}zJUda%k5FQ{ z@3}8Nkn&NV(qF=2JL}9}F#YH>u`A54X<#wdqP@d%_ZS|w!0#>3c{~#q_SiN(495F| zL+^hO(;2quH8AJZy+X2sr!`|x1M$xXz(vd{y>PJXuPD`CE{c1+VDtiJ&hdpE(t zlFFAxu%bBq@=;R$NOR9X5ZZ6f#P|qUq_F2!AsiQ>Zj=a9#}Dk3hnv-UzPuvkzsy-a zF#zp1+B$5I#Ct52zVF9)xSN|z%$AFPFM*3+1n<^zzoBO7$!nxu>iKCoc>Ep3av82>fTV-LV|vt+-TJ$PPQ z*3FW2M1QqU_Ad3scuD2hE5QuoY5l9=0f%pI&0yy5(ABj&aXr63XKjQzw=*OCy)iy# z6*0qM{&E@FsvX#WjNBYA!R+5p%9^*M{KC{JS+MY@3nv0@Zds`!gsB;;ywW@oKlsDB z3+9;z&VRcNFWk$vL4vSFEn7+=}^FaQ>VcESy-se83&g1IBC3 zSHKj1SC1MS@r;triR*AZVduXtTaW$Wd4lG4n0NYE_*DmtpSkx==fRB0-=8SiBOZAn zms*edw*O*JUxE84x0J^hh`TJfRg2LdCr8R{-GJj4eQs=4$8Oklb1wD= zUv>UDnERxx)NT&e^Kx28E-Xk$-s)$7^~Zop`3IB#Z>7dS`|STzFv}VB(VhQ9X#(=s zdIX1;&Cgv4)c&~m0@Kl#9mSq=*hv`(0yubgMampLYA-Z=zR66vmeZfT72R~puh!Q)K9d4OZg2o6z0nEF!e(TXf zb82Ag?3ssPuHu@6J2%WJ?z-Y=S(w9AsMwQZPKn=*xzUO7$6fh!V9r%@N*Gc%GZyA= zu-xjIX-?^_`Z{hlEVvnQuQ0`&a^k!2=%pTUPPKx>dH;Mnq>W6Fs^qT7wx-NHHS{t=b%Re9!D^K zzFOUsgSoTof>7^bo*qmbgh&4Y8-|#D}9Q!AG+`cX5e&!TQ=W~1vOxG|t_sQ3s zvX-1m3x@gm(}UxD%qjU(*Okfr1>3n!)6pB_u|3&TdcRTE`ph0~-Z|af3UTJ^{A@Qb zxG!q1^nU21lKu@(jPK;GE#GjzB(StvY3^Z8byMA@Z(#PH&VOp#%&G92WnT+m(YM_( zr`VXEmX52lNZfrVV=`Rlx09Ix^L0x;v~0on&zuq;PvYL8+o!^k#mR<8VBx~P{-2x8 zDX(SI&v?U(>wnLjb~UFu{gS4vCGm3gte6#ziJi;9 zinW-pOGZ48hiU6tV`s3?KaOk*>GRQyGvCgxL4DrdHF72K1ldhj?J!=isWh0voL}3O zzS){nS%$pXQ((T%F}sU4<`kb%AKZ)QFTwWh%@)fr-gcaic?wgB-{O}o!TD7Gy}AbT zSH(xDQyA}U`%mY=Ed9i_0gEtxcl52xB=LPOC#%48`^GPPSlnn_5NTyjMJ}kzje)s8 z4wg<@i25ecY$IW6(XKb@mbhLsp5YN#q~2&f!UD&8wkBma@#66O4Q3c$REhNYQj};u zSHsktQcQ5ET}zI)b=~Ml6CA%H<*P2tsgqpOFv5Bhv6weRo@aON8#Zz_&R6%cdl2TV zAG}y@i1DZKZ@1LZdfr?Eb9^3`>Cl2WbuDuDh#BS-bwT%WBg}Gk-xNL_$6I^0q7G(% zoS!6`iuLBXO>Z77T>p6KXkGMox%%%^m@d>_b6*?lzwDp0mxvEK>g|Tr73)+k!-Dx4 zVof-_VL*et&k#*}TzG4;IaT#{iug1vZl9Im0*f{44Z>jdxX))>CSiWp+e^~^lHW7TV-74B@GdfjC7{zP&E$yW532njg(*XzQ+I#`#qno1oNIhxbSwYIb~eT z(@}sWHy^%>gMHH#cKpKoD4u)3j4`l5?qHh)=GIZ3!DGxR%k|X&Z-W=6q3-k1~e4I%B`r%SD9+&jk!bwg26ILS5e{KAk z*k4;a=Q(jqo30hii`8Skg+;6*W0t^yo81Y1nAW1X-boqz+u!Kq23QiIzeo|*XNEkK z`sA27l%hG6u#N_NN#!uR zB0%Sx9Ljs%*q;xJd){1`A&c>N)O*i)n6ccw@Zvu+Jg%fF@nD{U?I%OHyT-rr3@oUS z?Rzt1MhPS_r}x2}FOOVBgJzV+fk37YEE-)UUJI)i=j_`~@=X_MeEVZYu_s@Z-3qh+ zcI-J1_XcIHaD|zN_^w}mn^F3bJ*(Hk{QXn3X|VjGrs*tLq_t{$^e;1fpZLqD)v)ka zV*F-UFIbgp57Rw|=f4{;qwHiB$`CV;Us{&~3)oAfrtgvuO@X`HlcnbqRF4FHb!Ir@!5L*%KCNGTsKls>1#L#H?_8!yT}V)0%(edW7s} zo(%YZ$74N>DsJpIqj)u!&9{)_Jse?F42LQGuyTXB`ehy$VAa&^U&-~0N*7HHg?-O6 zc*H#Jv;%=~q|x0Un_&8Ev-vyWgdindVvcoXoD*z3NnUEntHAB1uz%XjdKbin-;{rk zhvjnrtRnfMRr}`m^_fw*+IOVK6YwTJY=Q?0cSz4qe>ra4DOgcq;jYcdr!8Zh^MyIv zH3~`luu|+OZCEom?f>~g&mpFeA@BV zjwP^lzhWynp2U8Ud1g9New4l&l~0zM9Y@aMMTSQtA5f zbgKR?IALc0iCV-tp?m6GV9wv9xzC74#cA5Yu4hzS$oePQe{bgkn18i1_C3r$reHN2 zR%hf(*JDnTkGCG|Km1N`FXGHQ_FKlm#*NyQ!^nQ{V6ETacl7`K{8z(azTHv%UvTe^ zcWq=ppa<<=@&gVV|3g<3X093^(+kThrKC-T#neshKXAC^7h5KYk8+P4iQ^ZajWs3v z5%Ui_NC^(lqqWS3MGtqX(@A_-$e4vNO>vj}bhxVba?lc(7hUyi8SItvZ@D!r{xh%5 z`#*7Zs9aDe9Q@+`B1^>Cg$C)LVZq&6-aMFIWzE=${?uIlk!uLE4E?fmVA}a#7pKDv z{g_)n;ka-0Q%U_L4WFq67@xvt?nkGP@&>w7FaO7sW<<*$;)|)TbP;F9TzozS^T%IK zBrVTMth8`}vu3KvlJkjI(!Pek#!u(^kiXAx&!|Y}qyFAZWzrs8k6|un5ZB*xc-;b+ zx?edg3buApaJM3UYP&WXrsqEDA=l5;nU^04a~u|0Er$ht#amCqz6;)-k=_sd-+YgJ z@{+p}aYjjD@NvWgZwC3TfyHSFd;Q_gP4~C6V0w$QbUhH-{=VS=vjgu6w<0c|Rw}*U zpbZV?Il|1wb0=I8=iE=qrC?3I#zQtK@9f@X1P3OnFZPA0_3Qk{!PbW^9pjMvD7E;} zFzYv4%O4iMj&YZRnTlqf0Wf2>`;>oZ&$!eA>HQLGyUy}{*emn3N+{wi#hL7HFmLST ztK|NQ_u#8eH|%8I|12Js=yO)L!_9ZkRwWWo>iR2&<96MXNg?HjmR@@c+l<&p%Z2$P z8sv#1i`%9V)4EQKAeLO2DZCAfS2Vo)0K48wV>iI~0;ROS?OL@WUXu9H-*1#rf3F8u z4ZwJ@!dMN5<)5kjP4cIg7p;X2R%Fkk;r^HT zV&vtWFzeCDgRM0GL-9_ir>zmkpgA53?(6ZPj6hX>?E{tT_DC=E*QEY@Txu zEV{eo^AuPhS1=%p{UNJ8b;oR&{WNus7CdmVI%6IzoD{642iJUdJGc;LQ_#UR}?cCdJq>|7G3rG1n*z`V6Z zX5@HDKTm{ig(dan^$ZesFjw)0Mc&2R#=?OQWcT?I4?iOKg8Fxa`HbHK(>KNRK7uXv z?@#2wvx5&a`@VZLom~4@4{ag z5A@Z+m1khKS&MXhnJ)WalMVC6FK@0S@wDN)3Q75Mw`Fg`!X+nDOJV*Vg#-C;^K}00 zhp=$Ts%>eo!C9~HR#N`JgUT~7@5a2FA7Sc?-1#ur)p_F6Pq4(bdczS|Ffb$W3oN>u zuW^u=nLP44%u(BJxfkwUGS`TH`XyOqS7uLW+rKj_iN%^pv*%WN~dr9h8m}aHx zVgg$~6e>@Ec^@nE=D@)_p66-6!unlx#I*byy_%%_#Jy6Bv!^)e!R)AjwWf%douBhz z1}vJ^{m~LujF>2E2s78;lAbT}sf_swm{lAw!548~P1ct+Fyqy=S3z+2h~fDTu+Yxt z;bAyo*RhWqVNpS~^!l2LYOjko4WisAJ#Jq>fHx3waOO>VD+LUp5*zT_F+)77|!ayAs+-wjCK`}`=5cl!m1FM zoE2ndH*3a^pf81#a&f7QUm{Qy!nUfYt_P3<2JiZ z$ah*ibxb`hkgS(F_+RC}C;zeh{hsc(uw=MuFUdEq-l6sh=B`zjx&apZVv4HeC@V5u((tB^Z;ziNFKj{l)u4` zx&Zqs`<*23pO|Z({AhsnE^66Xk@)}ZmsOh+?=ONy)rntxu-@=w+}TS>{-K!(F|gfr z@As=n{8NZrA>7d%D}CR^-=FuO0xpiY<+KrTRvx?PK0NTR`>89;W>2{M0Irfh^p%+3 zke)|uy}{Of2h7_Xy1bO+e}Caa%pUQ}j9A!dpRyYkCZ9CTg3CBZ-tHs$%_H8PgIO1| zCkMeIrLRq4aQb~C_Fw`JrCl%isZgZTt?*bQ&*RZ(GJbKXMOdyjHrJi8xCo)9WcIfBW3dDA@OI?3!j+ zXsRGxZ{s|AmC5@{ah;j>A;h_5-yXHYv=^PH_rZ04chNqQDPHxnK^>%yTTSL6>q4a*&b=l-_2U6bp+kGJX?a{_jq!RMVK`3@82pM;s; z<)=r(95|RSgPA*3SKWf?vX&1VU`5^? znL99RGM7RWY0uLSZI^paSCyd z#|+&~Fwc7Io)EaIM^)Y(=GT_??t?`dIW0S3wpRS7t+0*UpK3ptIYMUqM!0ysaKS!e zlSuJe*lD&`doWD#?Gr5EG95k6F__y|VKoExUDf9q4RbsKPRqgJ^K)j#lJc9{MZI{Q zWEJ7=eGRMQ=w9-w*B;Vo8_960oJ^w?b5T^a4FBZe5%{TtuBl*wo zv^K!{r!tcpV3x!n`4)*^>Q{aPGmN4I1@K_az1dx`fOf&Y2o|WHj`<36Ozo^nVCN6k zj6M>l57Y|btj5@AS@QQPHOuIFxGcZ@#Bf+(*cx94vr-Q{9|7|t*4Wg-;>@Nk$}sCq zIR6oBS+#EMB$)U9&cH)BUEzz7F3k2nD~-D@tw@{tpZqMkc=Rk-JYQ8pju$?1M*e)5 zn>bB+{@lu7&xNG?YnA6u5T{9|&0Gvil47Pd!X7?j`|Mzx|Op~}r z5(~FnVH0%FFu25&Etn6U}L|}F0Ww0-$m<*sb*nm zFNxoZTzwj@YKZ^&8|ISF5yIlp`pW-cmcH*#;&6ixSu*&0DgC?NrAS!si~7fru;ffg zTof$)H!VmB<}5iVHBg{Z-^wN523|4$6FU zAjezy@X!^wX|F%a1?HRjux`NN3o6aGz=CDprSmnoo2I@4#t*+*-6Z9^m;T%hb7?t- zw_#KI$2FlO{`t%CVmR#7u&dyG1a7grPwW5;X1gUdcmXs(1gLsd#2;hOO;JRib*`v(&SVA1g7FQ37To4)Qk zSkDGEzxB4mg3AYTH^bb@?pK{Kt^0%ydERMaU!D6Mmgt=vM&6IfeN(CFgN5aZ()}ZM z9;@XK@%-jf-H3b4`4l9NzwdLV*&VRL{=~f4IbRuOkLRYlz+QuD{^MZ! zQOl4aDL=rjJq@>1KA1EYW~*Kd3x}CA&F&h(ToqgC{X}4?U$r@jUtOvjh zpe-=7YvljiuNB;^w!!o;8LwWnSGwUaa}Ss=xFX%J?M`m~?FF;?{sg>1TycHpz+RYk zc=%-UJXjSKD+q$Qp+)nP;VhF?R$N$=w4{YRe^Q6WtRUt-53M9_8t;>NoLJ$Yv_BO? zVoQkmQ)+JqBOZR+WI_aq&&crefeZDQehG(JYjkdIf}QoV9-M^fcJhigaM@~=)Dti* zL4zXC)2i0H3Xa13A6;V@Fg@X7P%td=e`i77mpcU*Pbc-Ke6FQ7WBe)RZINCd`ThS= zIDJQr`c}lbmYd(F!UGoR0#{ff`}bWm>>N0K!6umVQ$f1kxL*44*%{`i*>H{_-khPC z?*Q|{UrX1!K{pNa6|mUi)Zyib8w5RiVM6lrjivi3XR*yo1DLx|bmuSjhv2zguDUR7 zN$3l*UsRo(Ic_}szx`Bwv=d#4c+Q5c_Yp7dG}$u>7DwgotAK5uy!a>sbKPC~$$nZ@ zHnClT`5>OOFEbTxlH7Iff<;$$>0E?6obF_cVTPyM-zd1yv`GAh#1~Gu8v)nxhVV=L zXm69Aiy^SbjQ9QZuy9N13l8jjRcCW8$)ES^+fKMiOwiJvbJb8PO;Re_l!$EgTOJWD6w-#46psLQ4r<{ll;q9Jald@Z>F7N_1@r2tcA zNo7xA>XgpX(XheU3xUlf?wKz=Ua<0zGO0gLwGs74xC=_ z5l#=D@Qj$U@3VOahedsz)DQD7cfVL{)Xw=W%S2QCr~jX|eA}{l%*<&zJ!Z{E0Vih4F`c zMFrS$@Kh>k4}o%9X|DwBv+vXo(tebS!J{C(KU6`Rs&GhjzjIy~)!3hUX|)=mi_=`!-aO<87(!^MIQ}Yof`1 z!+m?Tcni#?hNYA7%PpvSx)Ekv4x~52f4`2zDl6-%x7X5j|XKY}QmGv3J0vR&FRMB*cp75rh>bMaP+WWG)qIvxVsj7~o? zk<8EOn`I(Mys&BhED|rNn0y}Q$0e;>Ks+$_A|Gz$@mh*l6c|S~Bx>M@lwsd>4eoE%Qi@Axo@h&G-vYxZlt)%7uw_b}BR&Bn9 zI5TB^82*~?r`vRzXz?bC@1mS8dwsr(6pU+@iIFr68}(o@B=Bo$RKSd>=B(* z(FLU3Gt-<`cQqCH%v`B zJ|ay8XUXR!85T`mPZ4K5RoTLX>EA5=J%`itS{vrUY&mXAD>?q?p&wQ-|L@I-9q^z- z^uJ{=S0&2*Bi#MLU(*(5jdWJ%B=He9XAz57#lwkP^pCt>Me+lWOY>ca8Z%h1V4v~M z_lOT1_qyXm@?X5#`ws4CjheRs=53r-+YTFW?wfBR<-cD!_ZkkKR@3JJvu}sf8)4^) z0pp!8ePdHnHQYIDk+vT!yqo_cA69L5JLwOz_8-hiBKZ!M^#L#^*-7&lOqsNl9U{I^ zG;aeOiQD90n99v+UjoziKDizO(^m_pG2klhE!iV5d)>CCfgaqCP`Xm{wJQCHC13Bo zB>9pRmYFTEL9~n!F}LCS=4#kUZC2r7m~pM9J{MNEFqE2W=c0NE4*OM3C+8QBdYb15 zTWbrY7HLjc$%OTdDx{{DtxKIl$_J>ak@_?D^)2iFg6nm#WlN8rWwp5$=7xS*6H1OZ zRK6e&Hpm-u>jW&>FTW@OmcKaJHj>!F@(LHOQ}5Y-4(4cYQ}Kp9Y}y+$iMQO&Sq7Wh z3ZGwx#ib@ZQ@E-x)2)Jd@6Og~FtyO>cpc0iCN?GRW!}jp=FLemA@;WlfB6g+q@|S3 zhE0D_J6d2-^7Uy8VbzcO^IKt>+iJDNFlU}{mKdh89yu(Bc{aN;+F;49+_S4-eNEfM zFT{rHf9{66x2A9U4zskA2bV_~*wRzZVX3#TJK5 zVXl?`*3)qKG$G&d%apf$@2q8QWkFi#g&fm_Mo@ z(G|7~()ybNQ@=ijuZFwd4pYj9|2IA*{>2xIVcI2YdnLpr|K_Tez>K;3b>(2e(>Wu{ zVb;41L6UB??|u9SLztR>!c_#@#h69jhxz`K_uqnP!jJ=xV9D(#mZwNOsye=oI97fH z2e#3m#?+Jitw(;ZB6essX@I#dMg3D@W2tmhdi|2*03C+vT^uJ#Sg zo%!*8CoB=X-uw<0zju4l0W&ox_e)^ro{RM#VBe&TB}1@q%4E47cwkKJv(flI0W*qw z@jIMu7Hq2yi%um&1uNjxg(P_;M9Eec_yq&M>=9Jbjn23}Tx5!2np|#gdM9j-%3|L$IjYOxjK2%IPWYxcr_y=7N!aPiZ8%UCMA>46Dx_=T!y(R zj{lNiMvlYR0+`+X&?g0^46UZs!<`>KiPK<-uY3);o~FY1mUL2{@n+UUTyNl)i&FC@ zgwLj6-z)ofq#`b!HEXvs%t$GInFwLej(H06 z8|(q@;fvH(=L8qCx)9Yee3;4@(X|Uw!;jg zy$j^`{X?P2!a$I|=b!RfM0N0@oaL5bA2>izIv>qvgt*5^}T zgJQ4h4KO~>JLMnR$D^H*;0!Z{&bqb2oJ)1F8(~32_wN$8s=c(F7@r^VNrCGMk9oSl zlA)PrPQbzQAG^B3+)Tw-H@K{<+I|ZuFFbkD9M@Y zZ&;r+O}`vYfZ4`b)$d@5rPcn6uqgHV;@5CXoW)!|Ob_9&t%5nqjK!HG9(^Rb8F+_ zn_j_;=A3J)h--dWyX-B@y7$Lip5&itJo%2qRo%7^k^6&&zylv)o|BXG{z1`NL+2aJ z)SGGAjyV0*z>Z#+ineoTfjb?p7!1Iig{P~Y!A(4?VKVr>CAZOd>wVbr#ZcmKSa?0G z=?<*f^}Jo4xcgjBF>wRuk0Q*ve7oX0EF7_F2_5DgR4~qlS&j3yjDsmr+4>Z?sww&B z1Y+UlSLfllf^IuIql z*L8nCevkK`&&TV!uKRS|$KAVbg|>_m^8ZlY?XH#p*t*4di6P9K^6>i>Sj-ReG=dqC zx|jUm_>YHVX2bNCblJ^tmAPH=99Wtm=M$DiBc05fMlRP;vfFzda83oLC5JnIj~eJt{FhZ$K3)nxgj zuf3Bc{}<+6?0UH!7N{#N*$j&o&C(@S(O9h+2y>h&srALxXtmUz6yuPC4VKD99 zL8|@xL8E8yh5xOe;Dq8IQ84@TMr!_g{4h{1%paafEicP-fo41`CdVaA&pLB3`xq(z z{P5FNaD3=Phm)}MRPG#W*dfr_@(j$HnmlG9%wwl`orSq!&B;vI%*b)>IheOTM&1Bs zGpAikh9!GWWln$tn#`98V6jj6*`HsrK3pE;r@-v_2{CWrW*7FSOE5D@>r*~l?l@;2 z`M%r=mWHP?V9p)|J7T)p^#e56;HyyqSw7AlOSQ%poL~0q-zUq%-=O6D8unGr zucg{g)Yx7Ii=88bFCv%LjBS4c(`EW4WPW*jgxoB6;P}?zw@G^^4U%rdf)Go~`=tDH z!v%NYwozNB6#d6?qsP^k!Hj1OmUocfzkQls35zc$+TDV^#)UI#VdkRPTQ}fHeM&~Wlc%=1mpQ`*Eu--P~VGhu(0@n(GfUDf86vQ;_9d&L8N~3z|x;EZOo{K zH88jQq2xEIALl(`HY}Ha%D)fBKr(TvaNn``OT94fc(FAN))UHezQgR$$}yk6V0urs z&X+{*EttEIPpuEZyeTzTU~}~^5)pFxqAF=TEElD==MgMDpX(nDGsQn` z?~#1r&Z7}%&aM;MqoU=Fw3IUBqW7HOOKn89t+7VqJ~jA;{#=fJ^V%48#9<{mTNY&g;G zd=c4S#j8Kd>cJ((p&Nr?p`yQyDy*Ivnz9WRT`+$%_!;xpeuL=`bIE)2FnyrcZ3`?| z%@F6qCd=LD`N3jZYT;jpVvaj!ekGiAW2hh$W_D&@JO#6^FD?j& zxi42G1i|z}ojJQHG@BhX>aF6;l=09dwjIV2N#`cP^xKGAMU@`ZPdja=} zPG&_AC*_~H1E;=BJ5Sb^aPS##Gpr(m2NW=G*v8+sFwd@=nqOL-&gbcHj}eeh2|BxV3e0+cY3D0AbgTAuHIknVK3GS*%io7s`tZ?NVrS+~-Z+@Q{{5P2*sSvK zHFAEJJaMKa^@OOtI^q#r<7$5J2Q1X| zP7}fbhKc98NM6x1CLQj1_;PL=EIy%FmIiZ0KLv%QH{Mz~~5at}#Q6c7a zwY_zQBkmT=tAY6)50+cNtUmSM4WxYNnTb$dx~!i*_yyuJ@OzIY|PC(9$rxZd?0PO6E2-3fE`a&C6O(NSAfyJ3+<>ejDt6~jUA z7jfWBvk!3A)r01PFwlXv%Kdy#N7N&E^`sbO&?WuqTsYQ>+z+Ah- z?_a@%({|galKk6*FE8M%KMMp}FwIuex(tqJ`$6>|vH#Ds5;$R$7S-SQlE-(S!P19T z{mW2ZAgI{#6gK%r*JG3RoHNQqqPxRUD;rf*~3 zL0H|=X4!sN@WA!pJ~;ZxwPA52AB=MigA=?*C6ntB-nq^Lfv~tG;rwZsD^N1s0P|nR zL?yutmh;n9aQvxc208x=T?t8@s_`0ay}6T(+1?>me3;&1u!oyVnbIG z=4YQ#899IRucXfW3LA_l{8$QeehgdR2q)2=rQyHI6AI|ZdN8-m9@KpoFD0HtfG=&eh>Fm6U^`oe84B=uWD20d#?GH z-#n7nHqHEsoWHXmW)sX6{-)0V+zU;2tl)&$chvcqqw$2P0*hu^O2h7NpSL5xdHbeob93X68oM!7Aw? zon-q9ACHP?fU|b^Y&;H&WoN22!qLwr^^^0hsN_`HJ6KN9K%1POB>$`b?|ezK?QnWc z%8v`W>x5kT@Su4i%=5dj%bxg#O#2hqp<$o84NM<>y;lV1q#w;Qg@wV76Q06)v9}+S z<1@oCIlL6+gnnm``xo}Z=UFddmB3afJ(#Bybgcq5b2mPz1M}y;e?sP0uDO#czyH44`2H4>5I8$X(7$BvNcON{gs$HFGh7hjf={GefT(|cUsD_PXF!^}Sy z^Pa-em#rVZ!Gd!YDVO0ml^5ZkVOD0gK{VWK^pi!-Up&){b(>*zT9yGhe~GJeeb}(V z>CVUG{3HmT^=v+=A6u#Z1{U5Ml{gu0Yp$V8o2rl|57)?T3Lwu5MA2K?f4)O|I~Qft z!hH8trERdDQ9L1Kpn+MNf!K%g2ioyP&+<47i&{^#O!`kA&|^|P z8D<7(J&i-|>uv2v_BY`Ghk;&0w+aj!0A3Y`G3~eiik}Xz0R>8HGg_$c3l$%9Y{5 zYMqmdVa~Vuju9}|r0nPtk~htxk?otN7`c|5kD1w5#MM}T<~5tS7>7Utid865}r@cz(TV9v=*D=wUx&_F#;;9TjK z*ue>#ILmyH({dLbm=8BZ?SJeG)9a-pn6NOm>K?gY;9l{aI}P?#j^0J?KWGQWSB-a! zeYWtC)1m}{K*aY z{!u39A)o6!Bt`{_1k*9jeQuoWO!s!Q`V3XXV zce;?X9$qqC2e-UFP2F#^6D?EhVLIPr{QzlC;o-fNaL?1DlZIftHm1%ZbsIP#AfkIX z%x>w+HiOGwUY|7@W+e~lFo3D?s$r31N`)q@9+Td#0yB!9JosCO#Kba+^%e56k)0;ylux z&8Gb~kV_2%voc|6>TBccaNKcygR3yd_r=P~FmHmaMItPbe%PE0(_B@2j=`+GHeqLB z;npOoze}F&wBy57i}ifS{vlp6#&!>E);()SAS|*U=ob0^);toBgO`9P=0dW6_$Ej@34ZE#yqC(NBQHMN*z)HF)a^ ztUl#^12Ih@eL@P{*Y|?D-=%S_9`IqaEOYG*$OWsz;ts&B3)KAFU}3jHO&pw~bM6DV ze-<7ZLs?aLJIVv5+kcuIkDO~3_K6&Cn4g0kPQqRui)_gH5f;7+6u_$Cd{z)FnHcSs z3dg&IQ_n*f5qFcW!voE8spkXS5|2AsaPWGXYBZ^@&u`C#=^ql9aWG?LtVsdftTx8w z1k9Ybzw#-}{d8wObv&uR8ZUz5nrBka6Zs#ws~*FJ4GZ#alIic*ZFK`SS$@%h+z*KF z4V|70=jiP}p8*TQK3`6NqyMB(;~|O{=>;h||TT|G(v1uX8&^-_Q%lrMF^f`tr^Ghb`4zTFm2 zd`-$v8akr{4sL(5sDe1`(Z=hruVfWPyzK$E=5y9OqN}2S4Z4 z(7AAcx+!HrUsSFpES;H5O`qEvBLAiu{eR##jm(eGUu8fD`x>tuAm-&izJ3tSx+}3I z?eqJ$ZrTBtT(hO7&j}jT+5j^>{G8q*XWxFl*bp|?7)RYNupKHl=)!UW=ipk>UTJFL zM403GA(FbkP^#SY_brxxW2US^USR&3wvObyobJ> zq`iS1pNM_cY<>;GtSV&-d9>d*bn$s|ykZ+1U#|ieUVj%c24>y=B2|U0q)T6^!5r_w zV`{MU*Oy#PnE&$uHGd|3FOF!z%%MybGQVlRHHK@$qSHN8{kARrtEa-;Wfy;qN1m9j zeA5ud{}TP_a8_c=cP7j(Ql2^zP72@bHV3BHdd?jViwdtrlJPuwUtfJD^J~EC5X^%` zaiggDmuMY##!J{DCUbx1P~`lB$y%2GF~|4UjTJEaz@;=9M!uuEP(kZl7|j)!SqdrMBap(f9A{`4h!2l#wNp| zUk=Sufu-~HejI`YYL~Z8fh8{nDuQ4o+vTphq<;SJc7NhgbC(zrcb-Gn!W zL=1PwFn5@HBtd32%vL@#+Z7JJ-Tq`QEQ!7T%^A-6@FC3{mWmfUI>6;ko31T`h1a6& z*1^#eo=&lcxyv%Rn_#bVw+CEEzF_I|9k7+lzPTJ&eDdih9^BUP%*g|0ipNF7z&*AK zVO}t2YR;B}a6p{#LqC|#aI-iG$7wcid&P^e-nP0;J796^PMcIxf2DisE*O8d z-AmS&&Ol{;G|6X`GIL>v!rM=dz|6;oH$H>!n_YXrC;4K%Tg9+g%edeSslQ>a-E%l@ z*xh@nFeB!NLkX;ONMG|hOz&u?EMPHg{_B#!*QNJJ`Bz8to}zriDn`M5m~A|#m~79& zJdY2Vu++BtRX&_`+O8`HrfoU6_5rLqLfzmgEE?}>bORQql}n$Iy!&R#WthIsb9foa zM}`%ihXt$+H{TLp*7i6I7X~UBNMQcqBS)fOg+@{KXP9{=ZP{Kpz&x4O3JVr#{EUFJ zmZp}r!JLclFYJL;4Ql1PVChf44pKgq_ub<+Ec|;yne?ypqV~zO^~lHFQ76-vSa6;U zff*v38!0d|x6pYQ%z0Yypa?emz#l0G^Bk4@T4BZ{H>x~SE}1%hHous%>o3~p%f62p zf&Iy0er@C*nCmgkQyFgSR{it~rXAL8nFKc!j#mFk%3n><(1vR+XKo<#E0}phRu8rt zn|YBe53|W&o)N5LI-FV`Ld_dr$@w$OD#VGbFLB%Qt{E`X>v8ETm{)yh1#w&VyM5)b zs8MuD9}ew4ccl>icm6dfk9Nw28D|n+86%JTa$6$<=FB#-WWmnmuSVV?zOK}34_A#m z^XnQ+dpeTCfu$?Y4Z8v}=bW3wg?q+LJtrV}@I&f)AmD@d-y~9hu*U2%a+9oEhQv}n zlaWK(TM{2dEWC7n%x8Gu_9}H!pKj(Ro{8hH^PR7YQ($h8e={2nO=_Xcxj6dRuK!rL z{m-jQaKxCvl8Y$Ma+OzWgk2ZCy>lMsOyC@#;rc25Oy`M2lIQl@YQYMd!;O!V_A77D zT*&p6Ep6~1ELQuYN3QS8e3F=PFs-?vIUjCN{_}_A^e1NJHE>+%xkj?Tv5SP&1F#@u z(V=s&X!KXjvAF)GAA44Qfz)q5)1wY2ZgWmagC)TohqPesmtj8X|FJ`f>qN4Dv5GAB zO+l{H6d6Uf4_*H1NliE>lDRAwX7;|YBkcv;+R1zZbCl%r$H4}c9@PFsd$r~n4Za_u zmHLv@UpHX$gUp}hmCqHhIG+9XD@@z-e%2dUbZ4!76P%M(sZ#^f()11A!p+0wH9wMk z-A8JDD3zp+`~vfj%5DuuZr+^!_AAV5&$_q)?rHv@MfKNPM&S!#hN}FT4&=hg5xXY9 zg@v2Fq%e1B5Vbvo8b@nMf0ryU?|6glC9*qnsTZctXNG0M+}%T$k^Mt#VsZH_>=05Q zA^R_9t}39C~1@SQgtY8;S14rYdTZwY|oM_E6aOzIm>$=C-M zs$0+3hXvo(+TDVq!}G7sB>9|3&MR1bBG-R5EZM0L+YU>QR`tw-nUl)gm{=bsfA20> zNOF4Vs^xI`^r6NUFm2#w%qF70PUodlboH-? z?XDhvHlLJ#bZJcwsh=UZZVF4B6sZ0YZ?kZ$F)4pF`uRNc53cUm4`lv@rn1k1;ZXLP z!&)%6ZPw~cIB`+96OBEqgSL$+Ot=u>EQWJa7|R{G?-td;y)X{ z-?3R=2j-ru-?JE|@4m8c3QVhR^jr=LMo%=>h8YE(X7+GT>Cul2SahSV#s~KO#Mwla zM`+A{y&JX*$e&5-i&p)qI7;d-KYEc&PbAZJ;T$adrqM~}hr2W8$OSlO|Ku!cej{}w zF2VO-d7q-Tr-qB*7Cev?{fDey*5=u<8L*0epMY#%hHU(v2XMUK)=3Ls!Q$aF@00TG zJ3f-_^{>3ip&1Vr!6Nglx%ZIwBwSEg2Gf+HTyDVXMWL(gU`A>Fr)1dm!pc*tN%=8t zSqX4eTymrX%%*8wje!%_Z_i#&>W@^Qp05pft?1^!{MWBqRwMU4S)}JnYt?c)>B4HsF*_xxVZ2E>iwSsMa3j>`_moYhc0tw!*!zggNzy5v&?_>2xH?B}qzc z=ud?SM@7Vvxm(VZ!*=|d4pN@iP*FjiAIWhaghjy8W?gX%9Pj`4+HRPBrpRCgTvI$^ zFEL|}&PN8!`}+MgWv>tY^03Jet5PyQqM(me=E4!pjykb0t=dm%ILsKfFqByMa0_)m{?Cjuwdc>VzjGw>shm@3 zuT5d1>d%%KTvKeyvTRF!O70;4C=c z>@n+Ln3Ea#LIKWcyf%YuA7+#4lV+Shla}5xBioM=wm_fUKlJSCzOfk=+_)ei=U<7E zOcXI~&}@AioI0z%m~4OMP1P+yaKna^XZ>NmS>*1muz|sV`Bs?cJA9cBtfaFoiA-Or ztxa7I^%#zRAH!mP*5)cs?`i{QEpVg<(v7v$!in=*1>;hoZb&alVOT1k{=Qk|)o=9EKG2YDa_)F^V zIq5YOc^VnlcLS#1B5OqMkIToe%$0|kL!1w2!12L_)0JRO`L%oG`GnZ?>K6eh}RYsKM;B1(`XgF^+ zeE;h2XZVTSC4!tb|$1idK0c516?9%tlx`U6XYbRt*iz3xb*R?=G!_;}&*z zhr&V|*`wrsy8P49Ydo0k&QVdq{y9M!Pk6NI!De=IZQ@~3Na^V1aEogh{VdE- zcNXk`O)e~7CV=TK16~QRo~%s&BlzF>I@oG2^9jto=(}|o+25b+X)7k>yWWmeht2un zPH$nRLhO^Zu$jY&A2l#-UM_V#ByiipY$Tp|!hS3AxD1WEEu?<(k2O445^^o46Xsmb zki^0!ekW?XV5Y@L>Un9&-5$q3Fk{R>HF=(DzVL4PFjutq%SSd6R(d|`!$_EKqI4h_ zZeIMbT?M9(f289Dd;PicOatazU--!u9*B}&o(ePncYS7Xp)WuWW}kO5o{yZaGc(GN z)W4+FYXYkuPR}$U^~Z&8o(Y>2sjZwx>WeBv_2KA_H5yj1a8k_)9oX)MiPv&iFw)*~ z3f!_kRcs6M{R+Ct^>&(L*oZYSd#T)PUARx_N~8nKP`8>s9X1OaUEu~xK5wArr{-_? zEFYLQqq^M&xhqr3-wKPF2bR0SEKjY(oiJBWpFy6ln(W}I@L+bXgT4?>zjJd<49SZ$ zsOwo1=kGfXz!C%V#eK-R^sT>1&VJvON3NgsvZ_=gVb*EYNef_ucY+rYB;OybwhGQt zsdNp41?z_`rk>XqU7Z{Z3+tY3j7P5ie6?F3$-hXP?!yWp;gW4If0k0fTbS7&c0CB@ z92s5Q4+||#wvqW~+?)JR8RtWV&tBQPVA|x4k7}^*64m8es-`L$xoF9s%Gt0eIKksE@$N0d=fg^w^u!aS{)mNliQ_Is$(?~2>lRZMjttOS4|^qeUCoCjy}wS#!=mzFvm%(oi`(@Z%db~=`c^Tqv1(HiXH4`zJ_^wBV_847h0}qeG3bV%q?EPHKzw}*TMWpGanVgu1&SQ?_g0^ zM?(%QJ$==uiImq4yY>K9b!qSVPONHLcN%7R3|aLXX4bARJ^%;!D@^zU)Bny1j3d6h zXaA6m*#0Wr;}60n?(Wa!U|tBr@hD8QXjU2yi{j0x->0I-dBlz&Hb1Z|iX2`(OD^}qQtdIMJrFUsC%VY%gdmK%BkVU4?8T1IYMxH+M_>DeTbnpA* zKv-cD^W6`aN8e_31RhW`(IU$uIeaWT9j2!op4|=$r)m5ugi|$@q%AP}TGH7%*yMhs z;0r1LWc;fpxa!2=M`Zno!`>DDfJ0ACTt%(FQIBs8z;d^}$Gk*N+c%a$@*IAAHQ8SL zA5&|)VAqemc~4>9($VwD@39e6Z5KU)SzA5RzrsBa3k$Pg(P}Q`fZWTgGhwF18p?Vr zpDAR(!k+X|zfs=#p}XZhSbT7YDVhGi{v`~Yd1Vl04w^`BBc}%jY7NKrYJ=j6%A2qt zZDx!C%u~E`>juf^Kf1pG&Kf9jAr?1WP7%S*1>54&V76oX*SB!Z&MlP1(_TIJ0JE3e z{GN)OHT6fS9DdJszU(e9fEg!rTqeV5J11$Jhq>?H_#48sG|sUEQhw#0A`94hFlA{R z%vQYZwgt}lpi7S;9w(h~5Wer}wQ~V1iv!A8FjnC)+rX;0oKSl{=0H_S1VGa>IU#Q8o5AjdD>iQyOb zz}Cf%C&=+a>g_dWAKb9}m(2l~;qRz;5$2uUdi^L&w~!I%!>JAhuBTyfK#%?>*gTU% zEkC>BZ15<&4`bFjXpn-Ow!_L(8Sbm@q>c}q*O5xoV19_8nyhdB8l@&9*t~Mt|81Xu zNxa!`Vr5ewIeu{KJa(DDSz8;v-67Kx%*``}Rj#(}c>uG;84l*KT|!O==|5c6%I-z5 z`hv@fIk3=>f8GLaNSqWZf@wi+mMpl+rKSEE%p6o6x&US`bFnUi`G>>CllGaVR$5i? zzxlP6z5lQV78->-9)mnxx2uL6PXv#-bBB}mCeLhdBze$M?Jrm#4t46!$o|C0w5z@W z8>}rm)CRM!WmE5?&<{+tCHo_D>PzZ<5^msbMY6v#Jb!ImgYtSNrM={MBW#&#OWq#| zy=xf%7nUBL7&iwFV26c};|ra!VuJx}#>+ZHjxTr~_jU@L+N!2CinvTDP=}-EoH(Kk zGb+M8Ccp-=c4`x0!J^?~$HNIDbsRNeNlxw~V*2b6GxcD8;pg{Sa9pXQB9oMVQEJ14 zU5}q?F@-q~imCTqypsAK%!3&wFMIuvb98qaEr8iiSrIX?V8{;2(qFn6q(52740$>i zId_qz-E%nL&z&+8SXA@WsRMStUbm8181dPDJl3!3=|y_RFvCsvoB`QB?&l0VH3^o;{kHf78x(s!QiEw#jw9vpKB;QLmjSZ@|GAvwR6N6{d7QzhnL(0CMd+*p`12gF_`y63*v5qgL}ev!IH|BhvfZQE~|{O52lUMzM=*Tx1BdU2unQ*%ja_7oZ5|V-olL6$DNkK zR@My@>S2Mh!=Y&~GezdeJ5oN|uZDWxcD8|x1m>J*(ilq0fA<>y38r~5En3k(70g6$ zzrsR!hENO_rmyX6Cr&Tj{0MgLcygi>mJC%{a2F03df-PdELbqyJAwG#zOQ{SbJW9@ zgK)$15e9!?vHqVo;jo_j;cY|QFu!s;)%;WyYA>0`rNJTiwn#?mA!(z4_g(;v3G<8 zSF)}c!>R`Y{a3^Ek=5CbaKlvp&&yzb2%{y4lvkTsLFQL%QNCd>%zW~C%tDgq?s#DRFJyBG4KNnZ9MwgnE@y`Xah%#OFb)C)Je7i7zlJW$Fb`y;P(*~-79 zKb;C)K;D0Evs0BJ{e{-?W1S&v_SvNC2kAdAL)FOp_6lh??sUQY5?UO2Ki~J$I;wyE z`#%8BLsrm+oI7nzSU&QWH}l!wV0!({xvyaJh3VZS7gW1Y|F5tb9ekejZ}z5~!|M3| zg@}x62h+l=3}(aXb0)9tfw_|_bKKzOCH3t;N&U5}HU`6m@p>s_{%Pa)=10S-tB&i- zWBItZyBrU~zD|kV!(p2IUh4lOu0K9f{aZBBdUFhN^~S2FD#%4ub+h)uc0;bEPl5R- zY8G#Xd98JUvtSMjA4rA=tmAaeU{;;7f-cOz$v(UYmbC9YG#qxF5K1H4o2HUJ{tuS7 zwB7@*y(XBv#n0u^AW;2}RJ&MVNIi0;{oMDIW zxjj2zp@s)_e5bBQVNt3gZ87q`q+W$En9uDjSO7Qos_orPJUl4g1m-WAb}t^Low4d3 z4yP@8>30%l)f-U9XVywTc>&CwlKt}~`Wx4H?XkNs^Lcyg8Tfv~%l-#2_5Kvh`W4ig z4KvE**OLD)w5Zw3=fdn+wL3%Mtn0sfMKI@3(66m&6Ich2=kklP{&`P$X>1krfYAaj=$C+lXre6YdQJ9gqPv`(}plp z?&Ysva8K_1HOBD2_xbrsvzE??LrY>QrlOm>E_?T@NG`#f&4%$7A)S_#x+e zrY$pox!sd855v~Ge=nQ~v%mVEJPAinq`zarw2J+&6XBYyA8llL*&LgtSKxsb@wA07 z??I>Wb-3AithEIxPu|ajdsglJ#)A1NZ520Ry^`WLOJRESFUkhjx+K;l?{n(8iac(o zTZaw&ul=gM-Vg0yVf&qRDWts#rr+0)e2Lry^8X|8z0F3>uz3G7n=NpT;?dOguwa$# zbq82-#B%l~n7bfYYZ{z7a-r&GSaNvowjr>dv#R?RVl9c{cdTFMuUBvT!^~4=ds|_J zEz&DnNqyO)*ClYATx?zt%rYG@ybMn7FQ^ECdAwfthp^n}VZy!ezyJ5dEh$>D4;D2m zUxfBBs^Ft2UahqEyAvcuoE zFh{}e$749h_8g-Y7G5eJLe{s6enxo>DgQ)+y8bMe8SX{Q_o&iXkL_pGZ_-~4i}Le@ zq<==34gMzP#YR&7m4CTYhm;pBpdI{yyy5R}DwmA=6Jte=|C@MGWcren(D@OtiE4ce znLpu*wi#DR?s0uSnSaT6F7^M(bW=v(OPH@Zx@`#lkJ`4%NVyc|&9;=-!`w}$+Q|Bm ze13f>6t0=Oq_+qbo;_ew0P|B4e-*+4yBixn!s@Zlb`vv0Ml2nG1Iiv;A(l?B^Ebr* zYkiNsup;dV#`w2!;eXTPM=>4Rk+1VduTb#J)E)g9vuJnDYbpL zGRJibN&eQ2P3B(|Sl;>?PAqv`OO}_pszf*x{~xZZo4KqGW>py6Q-ynI<^J`gy+D>! z11>C4wD|~2ejA!syFTE$r3cIK>{7PMBVL=BENIVLkHigIVEfZOxdT!-MtX zWjN?hKlfdF3|psiD&%3#^M&iLkeqyn5Eg}Sog(41tgs z4+m7VoK=Cv^U6=S!95|5pJ~91==~`suvL%dMr~O7H-OE6({6SjoB{KcgScH--w_M) ze$Rw4-bkJV?g>@yV#318wM{j!b>sSmImG|ley*vOZ3|$|XrD*dkTa@qfI$6)J+QNd=SLZgtE$ktW*`$7fcFr<5(el6_ z2bf`@Z*NBOIol^W!_34dRfe#7i;KP+$@e%`P9pi?ABx_vba~zEp|JC1$5R2YXlTun z4)otd&-Pnf;>h12AK*S)%WXSh&hE^ND!5^z+JjJ%>!i*shvlBXTO9#&XSWU^|97q# zmYT5-=C3Stz5}cDFSI%VGkUL3|KAODo^<#WEG%C8f$TqC6=y|BuwdMKi630@sFRlh z(}#N%xx!YCF=wxk`gxOW*1&ztPNN$zUw6u$WpGdTyKzET!ipX~A7*Fq>oQ>Kc{yyJ zcW+5H%+lR|VFv8=pkrMg$;tC%!*$=MgitAV{WU!t_$6SBZ%!syN`u^nUSh=Sa2Y9^BCAUh@&?i zW_(DCQiOT$w4Y9dh0*2n72x~FMpSCTw4vz(GO$W0=cEoy*E04QB*)J@#!Nk!tD{=n z5397S2rwkMn-z6^Z})c6J0{F=xOBV+d1#mRRbyDxq;OdZt9xvBSpYMJPNe=Ho#S?- zZz1tbt$?@4X{XC4vS5jIkKt>WS+&m93g#Gw_mz_Ndcs~WgN0|;xfH{4)Ae^-le~*p z_6(NJdRanD+kBP!KeXP>2^lMi_ZzA`K`yzua&R?Fzq^QX!m5dZYhj-0bjpI^MOz&H zQ(mrX(hL`vU!KJ#)3<)>XyFDkSKp=n&z*WsLC%BJKU%-{6Y})S>h4>JONWJx#QG~4 zV)Q)_W|QA{;J)XxZ|#I>exd7E!NPSPg2G_#B%>OCm=?99p9k|dPI;3}>dVHe?}zDk zim34lxWTI?5({pZYJWx^y)p2|K3M4gWJ@a?Fa7!>3YI?EP&yp@yYx`CQzR@}v2?^7 zxKAc+VmQp%`z0>`W?vmLnM_|QxnCa->kO{b4uvJ+mf_c7>+|Q?yI>)cPo1Bn0VNe7 zFl_^~V>FJJ24{GwBo{o7UOx-w?FfBI%6>59wt#7?NACDYa=Rh) z<#5ekX6iSXRlB}@6>Jtf{Y(eUaL_j)-<1^K7=g^G043xUNwPW|^0?9E9U86z-b>OKJ@rZ^B-gno-2U<*N0>J*T?m zh&iPWt1DrJyZRfoVa9nsYJ8uDu#?@>V3GCABX$_yCw1Vh%5+#-Eg0(y_bloBIhD9* z!>+?{+tV3TeV*s&gqv_-*tZ1?y`gf=$2FVBuBn=L*<$)gx+oq}<&(ov`{^ z&nP-_np<_Q48}WBa-YZ<1@k*!Em4FA3_Gdy!+h6TJQj`~F=Xg4(w?IUU7w7Pl(pyA zAl5I>i4|i5m!FkLe#89wRBF7Ygy!3)`e1tfnKiy7fBXG+56oF>WV;9EAC~xb5`XC` zIR_`5L%$@Ukn?%aMIW^9NqHzMt) z)^|0LY!^TN@A)X(Up#+$P~ZrHd17Hl#Z9gAF5BW8mS%1p+$J7JwSpVj2T<*_hrh$P1i7QX$f&4>A?Bc`o~ z6RU4tEMKG$H8{0*wSl2M5FU0A&P zj;sgDD`ZBrW|N%J=Di(mE;7E83)5cGx%*)qm17b4Fz=lQH6E#8$n;AhSZHn=oP)f4 zp!!B0EO7RYdk^0?x#FG!i@#Kl`VA+pke=N#GE;SU0OL7ehc1k_gkv*c z{GWLT6Xw|lCEbR3bH5Z?!Kw+#fmevDXPxkbS+f6 z6YE`1!(x+^{YPMW{m6xfU{>}O&&ROz!C7$($x~0gEQJd>-Az$2ll9?vHEbsCni>HM zRt^_+!)X>>ejzYT`zbZPE$!ypAwe+5J@~E$##b~bP`SAo<}1FCCF7Hq)a12#z#>`G zLmW71zv_S+EYNZI6a#bXx6+73PoBk`ha2219+3V+^OK>*12uE6t8*p!*3Zw$c#+LU z?=QQ-!4@iGPWTknwm##NAiK*pcs`fYX#gqeON)cD|H``pRF zFfYk8ZVvMFO~D^`!O}J9Yi7a$E4)khz-+hLh%vB1TEm=uuwc4BHJ&MdXm$iyU%d6B zEnj1P1`ccrCH<4VU2dKT7G5}%c^H<~4*X1j(-yuJkp9Z6fAeuW++t?9=@cySkJ@Dq zTg@wpCEJIcJ-B!U$tM?wk^U>znMsZ3-nQ%1)l2Zd@rHY%Djt#k%egzc^)0ps&D`{g1}@ zJ~|3c<&UF3hK2VY*zJJjboo?&XRavYZHDhp9H0M`l#e&0`d_qYp#B*wC~~poAUCU7 zRapW{gEYH|`wD!wmyw*@&%x?vv`4;zSu+A!J>Y;()iXEOqC|Pq3I?!A^&J@}@{zVD`)rwpB2FprPO^ zEVSkKlJT19Q?loMgGJT*Y{_`}>^|28#Ns1Usr5|@fAx)&=fsSo#ux3oCT~S7x^$iz zuU{oyn`&Ro`#Jd;>L*l1u`$TKQcBiNCw6o_ehGH?SfOqJbKM6cuaf%Z zPi1DqtX~Gy_ec2NOpkf6cu5xZy%Mw7jsbI+X0$mf8|Cc|t}(VE|0NAfRoa@|PzO*bkfaF*T`4hLpFnlpubkAhzI=B5WsUw4f9 z9!4-n*L@4|e1*H@dl#YgyN2$BxgqP9x5Mg8Gu7z-xNVk2)j?QHw@zzC?wpY~{wU1r zkfrLI^tUcQ4GZ>}L^mSWk)1jFJjt&KOJBi)={|dsVadj+2_-N?tFn8)~>U$}L zA=CEXftee0sqg1l$9bmSg=sIwcKDz?ze&g90m&nOSgnOkCjO1df*Cyetp#vg)~8c> zB>%I^QyI2fv(!@rvvt={{UJ`eUgjw*daWp5hyKFeabZdk%=5;J4zSmn_DRLCaA@yi z@;!?dn?i>d#G#X}IKq-|F5xd>`uVc&yu#h!nu7U=f7^F#!Keu zZ2Z6SW4?P_M*o*^Bs9_<-}8NGC~R`|l|I!zw_@8`INd3#Q;b}4&DdxGTopT;TL%kL zA5WP8%$z>`vZ2Uw!>ex);{(w9U!=FjLj;_;~nV9{q53iaN|u)uP60=Ml(&ldOhM6M^jS%fUnGKSnh;dkQL0*{y`m|g>UoaZD70)LoHw5<0)@ez?|^ljZP>p z@vQf-C*_s(xjt}ouIZF@FeC8SzMXK+yfSMim=^V#T3^w<>BrWS{KuTTWIXVatfL=^ z`K)cfuE8}S(UY7>`3GaE?}sFf8JR-bqmMj6jR)?q+$?w{$(MhBG#=Y4;j4(6KejOL z>?}C=PpfDta_-c2-U>K+zyIMyFmF`rnGpW-qE{jt37s~K5dPHi#uy`9iq%D30T|Bg?3TN0&YdGS4z zd*u5(&At((9GJ!KHdTjv-nLhBN%`o4B5l~$v+_#_ESyv0H5D%T+20feOIMsyng;WC z^%fr>^^bbqCyr7X&5m&5*hZW8u;7?SFPWaS`uM01Fum1hv>lwVCBEki z%++{1pM2j&@gr-XoEeO@MhxQ>g7pUpIP$1}U#MIsOCk94l>SJ(xXwB=voqHhm?@ zJdy_+iQXa?ek$C`g2l(RDF>`~j`7Ii*|Pp;o(4>RO8nabh&Xm%+Z23Nxp-7!TYV2+L3 zk`*NXlwdmzHa|Mz*b8sv2OVZ;k~}}M`UspbRM%6DTHDe8>d=X0tZiRpVv$J zPjP*89c-YkFn0`G7 z^JBaRh|{!}EZ++=e793>{^LvA4U2VBsq!^1-AzMbUelX&(jM=z+In(45!ESvI|GM? z`PKTse1jLE7ht>g5Yr7XeS?XE6U(%~f)I(%y z?{AE4fSEE0w8gMA_uHBtxNxH%bv$J-Lr2Qt`-Or5r95-w^y)_H`-g(@JFb|K@}=LJ zjgdS1#_uK;-HvQ>f$2YEDAQ6ee<7yDC)ksm*6(P$2R1OVSUC^o?^%_37>4|N&P|1u3F@6jgCs>_|5h4@%aoJ zm=?Br!1PadXLXT0VS&?TnBnp;^Ed1~;BYSx=3LxJeJ|Tf_gicjOpS++?}54=>kQ$; z%;^0ls&L`Rse2Pi{rTUXtHZ1!fkzs#)9cfEBwzl0oDjzS(*I-X-s4(+|NnsxsZ=s_ zB$^~4Op+puk`P8ok&IGFw1}dEspz0IDurmMM5R$uBtukm7(ytmL?N}(Nh-g`_4@wa zx7+8>`|a_(UWe;-y{^}xwN=lCQ{>v@b70BQM$zd5^gGHi6wgmxDe z4_2S=CF4D2(yDxzndW@wGo0dE(?G6|oZLL>_cNO^DoUvSJTdaqE#!2Y4GLtw5SZGQ zCX@4_dxC!%OkcEOR0ilU)zok+?(gIl5h#5oXS6{V%Tm7KJ?9J)v4zT-zjb56>$I|yl z!(#uj)bj!BMws$N~n2KB!JGOB&`p;23WA`+&ysIf`bKvmX2To3h`I~}G_LKfy zT{B|_$=80Sem`CCYk2Z3n5Q`LqXNc5{@n#XX2aC)Ex>(dbL*He{hurK``)$Oh)ejw z8hpRF*eMJ-BW3doa(&`^Wl_HuP5-tdb~!9nwR3rcJmsQ+%t~0S8U365{(Nzx&JS|E z6TK*FCcn>{dstp>JzI5>>Hfevb?%r{@=Fhiv^=WAOB zGCiff6<+B4X!PSN0qf1Ep!H#23;Xa0M z$YPk0`sZT{%zXbUY!S@&T{LQB5AGivj#9bc^a#Caa5(2#3Te-N?VZVj-3Qfnlj}X_ znx%z3EdMz6vpLM2|HjY}mV8@sj`SCYe|6aka*6zhLf=1)4{?S z@WAWPmeVln!sPlXaN#xPo$>IE#JkMDW4Djw?5CE&ubsU^s_A|bx_ZV@F*&U zX;$n>)5-d39XQn_ANAbC2o_wqzUBigIjqbyhOM6L{#pz3P0XuIV4pqB(;HyclE%ZP zu*#Y>``SqU@G;e%l`P%X2{SlfJDA9;>y0gYVSIo6h}kf2yYcWrn09z4wSHRq*wr*w zwEuDN-BjfA;_Z#2iCxFMrNg$XH-u`!tdBnaLl|GY8D6cEVNPJGdpoRBzj(V2%zM|q z_#0emX{9#}X5W6J{)vpo(h~O>u*ADprX04=w8@waOAkCPybC+d-?@efa|SnFNry#C zcP*I@GgVh5B*3(TTc(=A!ux~4hhUj+rxq@SdDf~~_OMa9VHq*w`<3mR;q&K0^>A2)d*6OL%yVg4*N^j^U+G47fu&X+8o%LU-UfyTu}jDCHaO+%bhmvl z`>y2?DI5?u&i5dUe+Uzidb-D~i$O4V@w>P|xYe&9?F6Y`tnnagEHoNsnJ%3+ z6CN;~`{XRCC%<;C)+RVK?aboau;{?|u_JK&TfWO(%!h>+BM<4p^ak_nha}JI zrN*DZi>mv@q7J0dRBovEYRM6Qx(p9GNw!! z=BWIxnh*OG2=9%98TASuEnt^%?+sLmRp&?V`5#MSO`T7|oZX*HNj5 z>}>%@9sSAegjpt=C)mT1rnC)Gn5Nwo?E>4&v_Ae0GxyW|uE9yAJ(b^J{<^(_X4pMU zXCtv-bICfRKj@EXmtHi(qJgf#G+4Kd!G%K^0{E!r7h5>ad1lt3e;hSH!QM3$y#qQv1jCYJOn~d)~MaGK}=!k%)pN zus;7tvmDF{O;~D4>i>?T`d`w+oV^C-j@lbBp7d9wZMijU=B=$TiPX;zdAc6_d`M~00n_sDL^@oo1!!YfS*Q^ZKf*!#Qfu%hWiXULnjqa*QSfJO< z?1ee0&3E~vK0d}y^)HUE#e6|LES{b6(g-dORJnGU`1shYLvZ@U7%c(JB;Wf52QVb# zuflwTA@@Ys&&%5&4VF&2Ji7qqjof(f1}yQ_FZv1#2tc3gWrmXx4(<-x8T9NuTH#RZdv3d=$ySct||Nqoy#v4@1 zyJPwL{soyKXSeNmOD8U%=P==#DcW2WSnBLiYyj)4C`7Bn^i5|2XTX`}cJp$6Y+mOpQOW2re6Jf0?GO6qO!5!u zc71R-ZM7^H7QR$@GaSorN;mfof^mNnpa#boj9$kldH9iBb68+s{_z~ln0=#h2W%lD z(|!?Vt@vXU2D5C7L`g6|*#}vdmYSz+1KCQroq+aL+9tgQmc#dzpy=8&0E^1 z!yFI8U-hucKIZTVFe6luRRUWd`LKQ*%rdh7Q2^KK?#UqIQ#fF^_8#mPquxixuT(d` zBLx=uwH1tj`Rh;2Nq|FV*G(V7cxPDOUl0uY{2Ng3gCzsZo;`4;+~+6atb`p?w;v_v4=?v~#WdLOD<`al_~O?oF#G37r_H_-u$@^OYM~hmcv3n@e(p$3a>}Xxx#THuKr7cMeSxA zW8kD*dUGPob?{Ms2IqH;?;`Utz4GN_GXC6OsSpMiM=dfy{pYvYiF1X!$7RFVSo zT0E{_f&~`ck$2#MX zSHogaS9}q2>9FW<4cPkD=?9O<@?MYW91pwyToo;XIU5vg#=^N>>+PPx;*gcb9q2## zX48YuVM#_;R2^KJaJQ8jPpTR8H?Xx`8?A!0_u-i5!rVu$95No+Z&VM*!}j-e{=SE$ z_BPHZVY9Y?2W0#*dkq~9!L$KWI|(efy0*;~&J9hjZzj2b*S(SCk@IsqVDYZ10W-L% z(B;7&SQ2ymf&na;*Y{=+X8XwPo<{PI2U){$zDi=J9n*&8&z1Zh1=Cl!bZEfr3x;FJ z^+s@^oi%~fTi>;q46{Z@%$*Fkp8Z(BfEnXH>M>yJN~17xJz}mqaCi!w{_=!3nP0^E zBdGP!*NvpEPa^l$41MHgbJo9|ja;HRV(lzgzdzk+0n8h5^d}1rxR5{H1ZKA+$JxSh zaxdR3gr#%7tvUkN9O;iTBKa4e&SX;0dc52a7SA%`K7{$r7s}_tBHOobB=Eqz%54TP zN9UQsUzi(t`?nq}oq2J&DvqaFQbX`mnD_YH=qYgcf}sPGV8&R{EL+$hV#ic1m^HqO z<^)?Ow7ennB~7=N8c()`x#1cxTlVYRXyp0nflJh2;Q`rGWPIED9z3N=>N^D)58pf(C<+wiT`v$w`on?%N@qyJa+OS2e00sBU#PlhO^HgBI&gKVXd7Q6(TUNrtRb|sB z!fc&OTCT8(fA|w}d;}MaRQ+Lt)OC8(Nc+rx(<5Q4nBhP5VTq8f9YvgN*vTZ0E4#~w zi$_atkp88GbnK6Y#U;C17sE`|8@{LDruq$|$o+v}{nUn&aQV~r-W4!!g50&^u-1ye zRTeP+<0Qu*n9=UHXca8d-d*Vn*BsUVw+3dEZCdRGo6wKm*#y&`w4^)0i5^Nt4lvvM z_iHmabM!%5Cs=T_yKx#E7kKv&xn6QESt@J6!tUAaJ4t($(1vlaZqeTzSbWF$4*W+V1ccBkQ^*}aO_P6%%A+`&?q?f=82fw z#1rP8QG;naj6Xbt+3{zBS#Zt6puA@=Jwsr>9cGNLa1@jJBTKHHgsrU~bbN+I^8?Zz z!5a4pvYJS}tm27}aOeSA)i;w!(}D_BlPU_-6;V6P9L<%v8kjGEhjG zH~@3*8+@D&Ypo5OHGB{DcgeJs#;~MpoxLK=RAVk!2RkO&*Nul6-vbl2!#?dYSJh!| z`=V(saMQBk8yT=Lrl$TN>~1W4H3Jsx(pwP@2yB#0^@zJQwl84Z7^q%_S!!!Zo&dyrHv&l zl$O);U?uz4VOwDCSQD*}u+Z0Sj}t6Sm9P8>^9NFLIi&vhTBQ!SxU@547c9t{*VzLz zJ-gi8VbT8Uqh*FKV%g6;Yv&2`%ht+j!magAC0;PosZ!1Wj?2>6e3;a`rYyCDGZ#hm z`I7o6+smzC_P3`>fuz3j$tHK0k!^78IH^||B^wPJZHhHL2@7QNhOWTXs`Q*NSX^dj zbOWaAhiFH^v>D5!FX28V>k?w|s(JHzVW0Kmsd~xAgF~t#aDKTy>w z&LQg;ebn!r3AfDEJkNy%GrjMdz*_1(%)K!E_BP7=u*q{sfADv2p&U9Y)Os7tOb$7? z0QDBZkq-8xeH~5L2(})o7-J37q+c)3g8kf0kD0;JXMQ1*;rt+Ni7Cu@JUvPauJt-o zZAd(1lBNb5U43t905j`9j2Q=aP55j>#t%O?i#rBZ`d8XV#uvkH+eZbsb=@6@$uMtg zUgRh^>DJ5D8Zd3@-Ji0s{^9X7C6YHSTr3YKdfz=P1G85CX(VQj{$|&Q@y}nIM=f8V z)0Rulm;d@JQS+k%IlqL{&qvQi{eXkG`V%Z!92sZ{(-fB#zK2EYAM0&}U3%rb-@@ES z4QqD8<@Q@LU%|BCrvry!1KkqA3s|T46~AJHx|SE zOWT$|g}HC~V#?swfJK*G9iJ4o}&amMq z`P3XL*NGnVhOK$Qa%BEu8P4uG3D+b|>k*OqJ-Ld7u+b&a#3wL!<0I7yI5Z~1gv@V@ z$?;7?aJbp^S*5Ue9Zg}Jk0kz0kK_O=z!TO7&W(}~=dY@*iPN9IFm$n*F`3+YXDXJbcE>F2egI zSMeWMQgHmYIc%0S{0|Nj2}VHSYKE^LWLSnT;aIW$6;QyzPU1~ ze{Oy*jMRt9ej5u5PMxzl1H0^JoKS%o^UlsmfN56z4r+Y%+AmLoTLpQp6OoIj^C#be zHBRn7O2#*{a+l-*taWv6%ruxcTrRZ?7MzzYn*|H&B6oa%qdE`LV2)8(UA2ty5)E|U-Bdw=r61ylL zBlD@`%AHMDVcybQV={m7T!w9phEpD&4df9&JYN?K4Qu;`eTtPR}imCcTW8SQ(%8^YZCkqHSfyTVX!8r-!+Mka|kL~h&&Sip?5 z6u|tKv9k3TPXk?MiYc(TH<22D#Y-DyQ(?iCSXKsdndl|eX|UwG_JS~2e%Qq;>BMLE zQRA7t?L;>*J15n~61mXH!;aL`|Ah?o;ruXIH+unDzQlai+4nH}u*$hBFikG3B9Hja zK+k2;o;Gu5Jj^jts37Kj?z|KR_f30rIhlC$WLYlk*f+(Kw5P9g+iwpSYN~Cz4ol^t zH=4jkjbEPJglQx1>du9EKYyIcBJFwR?bBhVO|PvG7VVjETL-Qc_t5iTX2ePViEyav zV#9oxt$fc&1GYV1e7u0MqE`shXu9@96Cp~p&#ac z4UYW*x0rmMFw6t<&6&k=y|BDRYw{?V%lhz%hT~^Y^5UBk%-LabU?dzMew96jItF$lt|b@u@?vb>aW!$I{D_BWIEN z?+(MXkr$6K)S3s=7iIS`U<13(JR?{z!`w;-wqNjl_Y#upWhP98trMmiTfvf!7>CKQ zZc$I#T3DRdJBq9?G|k$LP0TuSet zdpWiLJXH%O7p6-?sQFzbMxS*UW?UP;z90Qn+^xtCfdA{i;;zPX$6)5KjCjiw~xQzsEV66PQ!eU9P4J-r0`+yd6@3EN?Z#w=c|9d2D1)JLq5TL1M?nYeqJ$; znEpI_a|X;9!=~!3^y^}UFz11)Umf!FV6WtRF#D2o7g>JSKt%r|lCRj5_J{Z{XZcfD znxg%b9G^^;+K$JtFgoBbIbUUdcU>ap?H_vN1`8_l0!Vw>-po;F;6mm55rwcg;_{k8 zn3eV9Tmi|;w&hDg@;+DD@HdwYX z{07WFZtH&l7Po#>PKW7#mQwRw={|Lz6j*q}s{AB!m6vNrTp>2gYL0~mhHK_uh6U*X zDwp6UTWi}y(muf=GZ(JCn%#c_7PlleHNf*YLp|Xzo2@~4c2En|Je)+ zdDq__gLx0#$CL5LcIpXFfx{b(O4q@XH*P7nVRona+chvt```K+*zBWS{wkQc`k#uD zJl=m5%r{?2ay4ct6E1wQNRwDXo_k?Y&g6Z>(v267oraUtJ}$K+_5U74B*RhKmW^b2 zB5t&(04}cfolVxq3-u1Dg$-ij<2S&9_w&{&kHY!iG5wYesn1byp99C;e;%`yn4glc z9gbr7*gC=D3*TGnl93m3?%zfCuE8zM zX@lOd$bZ6-%dm=Fv% zCbcaq!;y=7>vl8XxH&r>MZmo3MM}yreRG0B6lq^jq3{>uCp7(Y7ax}N$1JIZYeKSi z$H7AXfcY=sTILP=1XvnZ_o#^E4|HBr=ZmVMVLq(CNI2yJa!&k@-*;e>Jq16KVA>(= zk{hs}^iEweSzh0l?^j@n@j*W_KhYZRXC%Q&>ah}X{)*Et>czpKHbs+f!F>CXi+3CKm)zI~cR>W4VfV_=!EK4U#tIHzwK*?-<-3za#fUh`ry2j)M%dD9T4 z87#ZJ4o;7#>o$UUichAP!=X!kCK{7G?)~B=uzi5Di7Cu}XL)=!thL_cz%p2xWV%lg zrnAELEGK!b_(Ug;SGjb=mlZJo#qPLLm~AFcS(I31D})USW+ajJqULFeVX$>0eb5YM z)x8_#12Y!K+&6)_`{xRG!UN_uY4c%e-@`atIP=*vJF@+}iw)~G!UE;!4Krc-)WngC zN&T+Pi>JYYi|en=gdLM>9#4h2hYgQu!y3QGO(16cNmwQa`~1#YrVBHjxAjVK{b_mN zFPsi@beFonhojtU182bWyAdDB^8`yVz0ClY^tt3cga7MK&fg>%a((NQ+q;tVAAQNX z=j3_ImgWC`F--ga&tpQrQ&fL(CYDmq8|AqZgI6M#_PtU}M!op0&x$pqp8t_D|N80x zHq2FfMp<`j`Ry(6f6ps2sVgQs!i?E%t5eY4VqN?zXX4@>${KfuI=Qe=B5fzROXF`f zZ`=w{>PzK)4Sr~|MsU(DVQrD z&Jw-9iF*0%O@FS#0`0BT{tI`F@45p^rm5TBCCjrE`4qz9%H9k4u#3(!<`bCva@->F zJQ~`)ef4uvUmhfW1hcNXR#w6yll6n8aBb`N@*0@+Jyqi!On04suMTGH@k^_PIi>Ph z--#c+yxvZ7rt0BVQvZ!Z-CxwcxxM8#Eb@9gZU}k0?|RdISlBWvMjqoquqilnfV8ip zzf**}q{}{y^u+kStwD_+7x9cfd024ot&$${zMfHk$HL68rOKwTjE1gJ~&i=AMOHw_fa-OWN;?SoaK$)88CwOzQiCN43B;O8msd zFppu)8jka=cr9CR1uQMlHdTj3ukA9{koL|3rv-4ht@n5~EZWZhLeAef9`lMFEHTUf zzw24!_WErwca62IG4gZ^Y3~k_Kl>=21Ba#s(_LVJ*ovBOrAgr*TuH7nFKi^qKlcW> zllE!~2ftwZ^0mwRiFv!i2gvy?Zkhhijnv=OIUEn0tX_Ydn07tpHaWjtW;fLBhB<#; zlv=?mi+e=7NPR+?^Eg=d&rfQ5SQZ1ERvaJCw2$`Nk@Lr#?s@_9^HWEY?H8M?hu?rL zvM*cM!(4OU8>eBOyLqS$S>DMzY3?w~POQEG78-3_v+{|80^7N-FGhok0<0`5C zj5$7=4tK3y?O}$T7nC<)7;I+PdtedqgA${^xSy`!Jak$Bi_Ya+G{G{uz5R1wX7f?% z`P}SE&IbdSqr)@4fV}iX!!}~^C)1EaFu&^Lmf0}vQxw+~?lN$`%!C>Hof@oR-J*_P z^I*}|2{PpURQSY7BeFiR^yw8Nn7-acjclJdFtS<|&evMCeHkp+-ThAy_FK+KA^XF; zav@|GTzGr%$4Z#3vYdL}XqA7qjqJbh=dvjsc>ds>{kLf&%vi6}{~gZ%`fnFy(}zEa z<&%bMZ$U0DmqfP0O{Rf~RDZt)halBYQ+WD7>AFJ=vhYglr>%R)q52vYbfEk`= z)YD*A+=uE=Sa>REIkBL<>Mxm}-0Sntr^4LQihDJ%^lJ1r0W56S|7d~n=@YP-PL|K> zjHSl2lGpr;7h$?_d}AJR>)hc zVaV-Sw5=uoW2*wYcduZUsesB`etm1G`JeXrc^mxd{>OrI0S8)O>A3tt^1S1iuNc%0 z^TKD;Xu@$5ObfeUcEE;<(_!~Uzw-kyYeZBTxqlFOm=6x$hxYUBHXeXEZca%fVct8X zyYaBaJ6{<^SW@iP`v%U`GD%j3MZ=T|yWqq_PAX$z@l%QEINaX{EZ8Qe0^{?0uV%oZ zN4GrHgxS*?FI&N$3GweI!;CiGXAaDIHI_DmwEzFlLq5S`J452hYh7c=@=Cj38o{EH zSy^XcYpt43i(tBiTjT}OUh#7R3+C(jP)@AkJ6gc(FR6{^kyocqbYBZgO&ms&_Vf;e zel{%NFI<6d^uqVK?w@D_|Mxso>Yp>xp0v-LT;YPe{L+GB4y69I>j3&JQQZ-@e&(OD5nMxCE+V4QHQpG-;cG#BLjn~N+z!eWn|M~Efo>>}@x@mu1#ftZ{Am%6^$=Iy3(f#2uX4#@Le zEh|V)v(XXHheOX-{onQuexjb|2gZJRK(>c*(usPXD|uCaYcDKPSVCRz1HP*D?}J&E zjl3e9kCGL?S>7;nOq;3@*1dDR(if)hd(XcPi*D$NLP`6ulpJzDC0|jdnE(qrRe};> zYaI>I6`0>F`zHoYxl!Pe0gFxF-j9T3VxG0 z2+joj9OJLfJvRU46sH^vyR599`@p*nE1-E zG7elCJjL-X%+vJ!vK1C4@8Euf1zFegHj-SxY^Z}J5xx3rVcoHL_02HT*=zq|*ogHg z{RfOcOolHY_1&j5+hEa|=&N&KvpE*d{jgN$o7E&Z;Mbie!}eqQH_8N#g{|`@I>^Gp zb8FibV3oncnu;VhntYqgr$!0hO3E<1b!aSkKOJx{?l>LhHD^)R$HaA7ya_NrvABTb zHA<)Ir@;6-aGf%&73i)v4dxn}oS6@&?>v{TOWG?*V;x}IC)tUUVOGQHy%DfC#KXHDKDiO6vFyJQp&?llJLj9z8{UzTKM-V_>22)Gw8!{e_ORO0cBZSneZi zRk3g+**{TDId#8BGaEKEi2e|VIAoFMg}9*eTL(!0)%sp{h6`n$)c%Ee^bgeYDyw+v zO;Rs$jHI4d{g&*D8;<4CvVS@~MtyP6_p(tiyYj~y3G8=n@nh0|xL@`73ad0W`;CKz z@sk3(V8?@#ss0j7{A)$S`KFcF`+O2|NrowPf90s%QNVzu`J#8S$kSWL=;*=xxQ)*g z;C!{2L~`a2p}z^q?|wQo3bwa9sAdM!*k#o6#Ah$tE{EyY zTVh5aSE@TXdkt}3%Z)*7pWr)f^%hwCitUVxLb9Fu-JA&r9AdWA~RH7 z1PdJY{$Kz6Tsc8R+8?=-N}g9`#@od|g}G@nsP{Dn$26EFu%uMmW-M}+liTz%n0C~- zb3B|hrNdAR3oT2YPlEG#20^c3R`Mn_Z8)l0`E(5|7?`tN2iAAImR1Y%e21=0gGDEM zE`5e+;)vg5exmNVX9QYS1K+bS~^xnzp=h(1{K!;V^hYnacm;a*r@ zg)?RL)$CGD?*;1Ll$MafL11Cs5`LHBj%X?B8wPJqLfqBTb_FUFBmjXg_-ULbhaUH=~9_AhScwRLOnk?ez?#q2aC@- zlq^8*!d@XA3G+*gBABr4)KljN(4PYNA7PW=#6u3-dSU5=D(d=U%TT=A3bTSr`$iy_ zM)0j#U?y+*(*evMMq#G@-(b=9wE=%%`jLkF4J4oc%Ak+TFRQOo$D6Con@H--Udlat zi=1zhdGf)1(_5aDkw`MNA1ansN8s?&2Fx|i{su8B!Jy0O?0j+C1H6Po*P_*0&3znUr-v7H;YibdTb9&o?P#OE|)GU_g|wB}n4af-Nt zntvp*5^8y+4XN`4$k|FWIUUF|AHSZJ0W*jDFZl!OGk9Na!-Ae2p#!kWku>{U;t|bn z24Rg^=69aM{DSo!!?8a#eV$2jx&4d6VR_T%JVTh%G=_S9bKiR3)r8~$zFGq0O|L6P zE`~*AVbt?+%`?k9Q<$b!PVHaZi}r3KSP&~qJ+CPxFt^Pk^~-A_tk6G>?oaN`h4})8 z8yhyTzTaj53-?cY?F6UHRj1|~+U9KP^Ai>iCR6j1q|QYl3VEE(I14g=u^Q7W&ciK5 zJ)g+_vg>|O_mhd`ijOA1Ov^*Fz9P33pIS-gN8xFF917Y+Qt>I@W+$Cq<i9S^KU!T68`=Q#pNwkuhf|)-yWv3E*Q%ck zf~z&NKXYJ~b1wDwt%3K}6)vQGyh|Vtd9D2RP5WTR=Mb$BI8?uLz7H%}TR#2-%(VH{ z7yz^17f|o_i~UjzB4Exs6Y70_*Yfn1)3D&r;KoSQw@7}!j)jGM*-gZJ)9D{C!vDR` z9Z)TKa1~}uj5iKMUcTqX%XH!i#?=1W&$r6ThM8tjW}8Vq&hW(@l5dUTu7<1aw=BC0 z(>CCH^yi5@+POK=>cs#3ee2mmy9qGA&5F9eNZdPX;lYPekP(2jtZEQe;MYu#8H1g;Gf)Eehp?HjH5oUC3KMadL5Q{>r?ML4OR@_ou1y?yQ2P8$!LF!2F_#7vI9HipkmJ z`6%jsdRi^at3Ovw?q|Auo+~$!`nrcL6R^M4YwNR`V5a09bw6RhU90LFEL0kk#ztP_ z6@23>ES^{76ATNNg#D_AX*880a(~=qw()8mY40CGy^mIMxSjI>7Oi}|A_;lG=W9RS zk$Sfv>hIrCtK(JP!$PM|l_T)}qqQhcL~`D$0+u%{wp_Z3Y`=75WPTnT`m*0p4D>xr+-tApY=>n7ir2wE*HOq0B2-n)0zNANILV zYbVR+4ED81U^AhgF4=z8;l?rJ6!HAyJTH~(KTo=_VijC|Ztzt*OgrGh^Mn}_H!mNA zh4dL4F2gM4$va1RBj2CMDuo3#7glP*49oX^-Ee-*ia~9d^V<8hoD#NYOz>nKSR!%B z9}OodU3#bsi`^fr9|xyIE=-<9taWq3WZ2?S$xTC;d$2HB500w;p}hd+_p>PBu`!#yb@;I_e$4=-Oa0=tzqHbSpSJ|`sP()Hq7cu zk5`4IlYg6Uhb1(=>;SgEwZwmg8>ugfR{IKbY)sB`VS#3{%Ny9IJ~-qM%rWu|dkIUI zI&}HL{3AJkiec{K8`pU-J_*ZYSWvNz(c(n6}B(FbJmqDPrG->CP^$2Vl{TtDJkJ zUbuV{8xH+5Y)%33AvJq_*jm2jI5B6H?%;4(KB3|NEcHHLkMUKQ?P!>fob7mP;uE+} zbbaOha-->tFftz9U+R(dbB~=`?g`T-e_59fvyLntwG3{x?_Y8i zmY&c#It^Bt&pvq(7MU|5m0{1zMw3Z@F=ExUX>fUAx^@&yf3iHR1?Q7Ro!Z)9Soj$~ z-wfBDvl9lvyz$P))o@?(XaygbJI~pp6qY))#CgM#){p+ha82C5(7nV}UD^4tiTsO^ z_Asrk#Wof$7YJ`|fTfKiWsbo~?=#z0!*t7aH$91+H%zf2dAg~m8yx;#W}!LE8D2ki zBh0HP(OC*JzSQ5E2Uq{SARrc{&dJk)qn;W*C*zyZtxJ7=ru@OOwPbnx!s69`G2ax1 zKaD2io7cKtg}iUJNOcV$Ny|?tK4arS7RxPA{0#jlKixcir?^{*W=GyOq zIUde+ACSw-C~YJA%i6Ko{uRuz-MMKW%-t%pvkF#v6H($t>RCOH%i*SR7hDgJJVDjJ z3>J(R-zNPj^8EhkIc$~wQ}ZayR*Oh`1dHUJL zYzu)Ii$}JS&lhx=_irWVkHG4@S1K%7t@Sh-W-5!Wore>vuU5v9`t#cgd9aFSTSgMh z*EzM+85Rt*xsmZB?B&u|!3-7q2y%XiZd6}f1mog#GYzIMl%GNVUT9*WKTAmJt+Kwg zWBzBx7MSP4w9BHI6)<-~jxjkuB<*dZvtV}qY#|x%wEj!f^*pn~_3wRH#Oy3gMDCce zUZV)6zurb&-+2XTHBVr1S0;6Ts!^WnQTjjTjXN~59RBbAH7?L~?*BURosr{PQs@@mB0sCKi;{EqNn4R+O+%7o0$#+aY%=7wNO+H^Fj!_vJgauQ)-yMZhj=Zy# zJAm>1-fu1Wya~ObJ$no+JT99`KL3&Fwrz|$EYROSM`$KUK!$GfR8 z>%bqoBIGW?vw!N7_QJz*-t)G3(XRYWZ>{lmDLXqa8(ORcZ)^55oIm>Zy4MLz$cfAj$T zGRYskqwX&xWu`tUu*BQJ?;7&BhEsdfU}=|=dK%pFdRIz4GzR1h~uR@#a#P^DmFO-^zbq?M041@1gA|9pv1g@o&j^;c7lB zV8BVS`p@!VeBZ-Z;;!gFFS3X)Mbv7*DbDAYlkq27mPOr9nHY6%x(W-XCHIi~yHc%l zWfx$2W&~xEVuhpUVVZn@+F0b$(mxi&>=BB!#7bT+r{ZB@dJ45XNlByC8Ca_Dn%aK( z3%VnS1)g8D$^9HBIl?~%7B!50sRh@jMTAGebTx_nR5(g{viu~>IUH9$7q(WvL!Ga* zOsy-XFnhv)=ooUbi=)dbIP{=uN&qZfk-wG$Ga}E;^oJ$$qSZZNTKj<@Vu8(;69U-% zWjG!Nf#g-!;NnnGW|4hjFF1WM%9DOvdFH!NM$^J3_WUO8QtLR-i zdkJRnT6{OcHFb(#uao@ul$BiAW#5-(A7Za#T2;JO-_{@U)sNuQ;EfwKMy0vUvMaEYbwkf|Gw)5%&auInFf36 z(DcZ7;uoKLa1~ba``$s$XGYG;^jKK*JS?dN<~&NJwkLpV`K=4)akk3`ArCl{oc0Hn zp0U~E56iG??hnB%x9KnZ$ntWP?vBFwAt^Vdaz`-B0VcKoK`7JQpKWPV( zw0~!0tgMXvzt(MF2y@P__TCA%N^T7iGb6n+b770V-QxMM^ybHVH886%(90NR{dBJD zfdzY1!WWS|yll0`7_9G^fpi(niSx}`40DFwU$=y5H>>AwfeVdWOs!$&m*4q2;QaZQ zhc>{HT`IjUaMGNu=WR%?wX@9?7Ma~~u!rdzo_=+QRcaIWIKeEDbO~`a?SOD6%-b>d zoEywPG_q_rsqcRIZxT+!+EO}tB%!fHzi_dGqwuKjeCcy0VOXn!U`OnsPUV+60 z`vU%;f6D6?Mx?;h`vcfybggeX%v^YA>`&P9gaa;C3{=3< zO=TS^q6t9IjrV0^zxz7*pv?l=2Y7tA}6eEc2k*QVsz zN9^R=k^#G{n%~e5)5=y=N5DRZ&2A3D%r8$$cf%@+3a0*p#o@DLR>GOH_IDF=Zq8jg z7ETleyd;+JFY4D~elh!Vj56!F^kB;Wn09-9`8C*a+sI8rq+V6_78hn%?J*$hUIn#0)_?}}yek>la-kQwxIc4hI&ycW`hvg2 zK2=+i;LuIWT}Sw!Ki03h6a_O!)vr>38LXW@kHX~>e|;DO(?2xcJqWvWJ6fs`t94cF zgY`?dnyUYg^D7$sC;pGMk|**RuqZsb$Q$+fOUi9^VaB&TBM-spmL`4EV9uT?U4C%l zVCkyqFt=4N)*p__VSb(k3oaH@&(rBih2ITf)+`I^d7E>o(`W&V|2N}YK)oC5iID!vL1ESOu|cc=;uczdv71}bw8l; z-7w-(O3N zM^Rx+J28E(+jSo7GIs{mKa#Q6nNMJ|eHwqs@&siQ%)Y@YRX4|z^$WYc6^y|7qxGSg z+CHHhBTNTY5vB(%N6z}`Yr7l{{~mFC4J_T;+hqrvMQ!d}2eZ{IUOT}9=Snwjh6Uc; zLvC=3-r}P>NZzPA^&nilpzMSz%nb6Ha0u2loAdG@ESWrE)Dbv$WJTstn6Y{1PCwY< z_1&^S;^}P4_U8^U!ibCPDW}l+{xL9j(yaqNs5jG|rI`ea0!D;#VaZZa|5aG9d4Ieo zT)ke~H;ef1;cQn}zCLvH9hiCfg1a-EzG3i49xT3bcM1nKk_;{=B=w*9U$(-fa&n!I zVcG~G{L`Edq&-ii$PCusbvdt*BH_i##P^70nhA}jvs0I45+$M-l(9)%mQ40q|?2{8NmnYv`y ze(mVmNwB~rxF8OeI>!DX=4C8(B#vVL>0`iB+V`eNSS2(+Ru^V@Eu|kP_SlrEPwIyz zwH|`~4i1}b2n!{Vqqo7jGn17~VRrBa>i#fpRKzSZn084kY#DOT;Dyp*xuqd9EpbwV@?U-T(GiBt*k^5((TO;V!F!vRAArUAvGlNs9V~v|9TmQoimYy?B z`3W0%y)*D6dF3Lip1apBZ67QUe4^$Pqa)*&dy{%8GqMBqDr^JELE;>3ZZC0hMAjiv zZ~MaOFKqOEQ1d9vnzC*FNNoQ=NbIC3nyMmlj;y3H;UrJ!tI^R^)Rn?tfsNc{&J z=WB3Hz|QKYuqa~%`-5_?`X#l* z$rBFWfir7d=6;8zx$h_Cz^vzKN*%CB_53qpBLmx4T`)VpkUC$xRQGdxVZjNd_u0t% zj7yjU@PGHqQU8yrdyi}J{r>i4>?@5kfu`}6sDzps1u?%jv$zE1N#XQ{Le-*^As(^-f& zw`lelz#_l=n$I-#_VR63Gc3-%8rcvA?m^orZJ^6iV zZ>JP{!0hkE?^eKk)uBbJVCk5)6fanIk+s7g7DSF}Uk>L#{qbcz%&A`OLY%emw$CP5 za*a=|+CBQtHd4O0gO=}XVTVP)ybQ%uvOSNa`|$!2|8M)Lt*w!VNPKJ9lhuePG%6oY zg+=Fv(DyNm6V)mrn5p#VU=ZRJGh31~U`dn#{r*WNvxi?IaT6ImKC!1X62!3Bxa-$0 z1D`~H=_dBdZcBuPZhlsb5L|D4uk||uH;%W-P=JNx zf6U=*hab~aU^?F(b`w~-slor=H){+Y1TsnarYZ;W{;8_UH`Rru586K@!TEbP$&6r@ z)7R)CSo@D(^-x$;c(k<<&b!^{G7M%eE@7zP`6EwA8Z-jtKg@k`0nYYfPcnnWRR?Ch zfxGK(?;8no6f;XT@P5It?L9XN=Ixhz+rd`CwkO2=UDcNt!19WLJBj6)CSEDzeMGlm zA}KGt{CadHEZh>l#RO*MYn67u(wGCWWPj;+G$zhJs}}>bV3|>z)neE&=2XW(n6X6G zXEV&m@O`fev%l57h=Y@=M?WUVkFnA3{S{bSw4NScLDzzDUtue^3iVzbf7#>}Fkso54UF6dtEs)$BgdOh)-i>%jK5vb zA@O5|joEO8#oYA0f)>Maw0=6md1r*`?yyiR|_y+my<)) zYaT3lJmbd+m@&%U#}^hP>s4-rn|p_yS_8{M-beVus>h$Y2g2;f4KiJrJ=HCl4>K1X zJ0!>NYkynUDi~&n=M_DH{e6?Sg_8WZyp&v+7cyg9BrMGBSGfyLPcZb~3(Fo2pzq_s z!J-Q>FjG6}GY9dACvP7eggHt|BUvy<&mmR_W4%4YZk(@Ce?`mUVNvlakFT)m9M_LY zFfWal`Uq}6wcRU)ls~#yw*;1Mi|;xKGaK_}+=Pt=XiYr>3s=k>coi0^Mb>1J^0zIb z&%@#gKR6d*iDb~HOqeV9WO7Rjdq>5v-n*>Qn0xWf4W!@4-cz<<9f|MQfC*Iid<&ZcYe?LEpYK38A=a#D6)fxDcJl*{PkZ@!ES!RP z%8B`9u;9{*2R!0K0@ssp`kq@uNL=ic+&2c6ePHUWhIyCu==s)gzW0wm%om+HVS%__ z;TFFQuq+14Ns|1W2L@YViLVv?J2_r+hKM|!`lM5TyaOrPME*4 zkltVO%)AC4h5xBe3S zm^0>QpK@3{_oat2Tz-6UaRn^*n`ZGB&kNzCh@5(u*QaW93(T7|;r9m;x6q#W3=aI3 zJLNMh>T|G73JYdT+t~(7hbdpGfGhkhw0dA+`uYuduv(r&hQfA~Z(K>Am-1sp4~M}_ z=dVoi{vu8-vS!2l-F=3U_7~G<+av8^VQmp@kImZdl06G%*9qwRZqiLfg@v%dP|lfz z{MaE!X1T(go5g)6!gR|Vq`Z^^#Qdqu!r_W;IHDgvigqfBDejkRdPI`a4N8+1uZj0fD zUF-ZxN!;;7_+8lIDC<=bELa#ah1?%K4n1zVOX3>4B+p@0m(d+$|5-hhWP@|uGhv3g`{Kc{fBhc4(=g6w+Fn|*xUDyp$Sgf2Q80U#Xv9Fo1NRxq z$@w8!;Jm6A`{$DMb;oL0zzG>v1Izn){^7#^o*#~He4L$Nj@P3;hY*kOG8{dd5I6W(F&kntA8KJlA4cUu@Q7-Vu{{@jBI&h?hRnue|_^_C(VAbLoeyP;&p1jW$qy zhIsm)rxQf5a7A?RFS7k@@an+hvRv`G3WcH!QzbzialVi@kRGY z&a=~GdrO|GXTiaB;|k8f9J6MVhp_MA#6{UK#;;s`54U?*Tax{iW~2=G1v?g6zAJ!5 zx11yUqW)x`I5oHk7G5eH-v@4})GHw0U*uRxZSiwlKPk*ByE#IR@)F}^bDqHhd&RLY z;nGKLcD1ndV*q`AvM0Tbse_r{A2pUDUbC@4+5oeQFK^0#WqGn$Z%F*ZBl>+()BZHR zg@u)gr+A3-N~*^+!Q6{?|4xDnCsi0X!*u=zoH}REdvd?#TP>vZiS)QEkFT&`(V5~# z9M9;N{tR;e<{ee6k-_c#2ey5OMK4<4KY$rMW0!WqjAcpQmtj#<^7USrdF9E@qp(+| zd9gCiAK|*qtM|bcmghSL!rV(P)577Xxs#aWd=s)Vt^~vKE89(V$@%6qrzQ|)h3}9V zk+`#kbpWi`C%2tEALJ1xw0|XQ>eA&CV6opC+CS6Ho)K#G|Kbg!UJ1$d!uTPg{WXrV z{fgvy!JocEa|^atvn){C8D_Oz=7zu#Cp@pa6Za0J_0IWZ=J)7y z*ehNo^dQVhd-M1l%wEn{7n1TzE$*L(J?{0BC&Dc8jlmaTNBhAOGGM;OKy#8GyglSr zHq4Ywe|r}GcmHR2Os1b_TexChKH>uVyLA7!uXsHrB)_=(>VD+6?_Kbs0v0aQ{78Ne z4rATKXRvs*8$JGN(erP8Ao->pe$I#oHjV1j0&~_)t#^b?Z48XR!0f(~bO%_Pt6%>e zmaYwYJ06aDI76=!=9Qe$F@=MrzJk9n^UEfBKTZf(5yS|?`J}D6Rh?|_VurU8%&NSy ztPfnF*w?r}%)J zL^%f$i`QPdL@cnr-)ao=5A|V@{;G)IuMZA`cj~#XhF6tXnlDpTu=i)7UWM zif`dbIM81Az8%cc&R%s8F4PK|ISZ!qGfDZXF;ARf!D#DZ(w?ms)j543%se$Gd<$Hw z-(=tcOTE*-tcTScWQD6?8PjShX&+Z>j&ayP$}j!6Vht=^>lv`^|HUKDpV%1=^BLut z{)l&v`8;6{EIjji%w||%`q^|J%x?d(ZW}CO`S-JC&&Va^$mdU( zW%m zuD~YkA$-f!HL9@S{!4m1Oy0aN(8yu$XO|>)g_H2cjOH}3L{kXmTp-Ct#iP`2g61KJI?-&M4M;DEn4cAQ7 zdmc$#elK`89K7wj(H@x5D+gHxpZ$K(AsATZlpmC#cpO&aRwtCh zqEajRK5qW#(f2yy1OCs$5Rd+xIP^U%8`7G%3eG;K75fpEmTd@M0vo02FKK}VGP|_- zaQZBp+ift@{~P^2^8GI_{($)t`VSp~xX{^S#7`1u$_^{Q+9O!?-(hxMgVk#sAI3mS zT3%!weYqH}vCyL1=S{f6IRr=e^MoIekN&`wL2!kgVdh&{bgiw`751GFIHDF7IF4@_ z3+GQ~8%tr~Aq?)`F*(gSt_KzuY%tbl>xK< zE=+NR(_5bP%_QYJ*Z($z^>WV!k@5_FiB%WgcM`_j(9DJfRqN^am4w=>N;xp6?0IM{ z;vDy@MR~B?c$}aF&YrmVYyr#??=!ms%ZhJImcWwEtG~~|wevoPl);SXcshQDJtA&A z{r*kiv_FHda<1qJ$zRe&+oQSqk*d#N+0A3Lzt1RYM`=B=NgQ>j@#VfUm~VNJI;wB( zqj$uqXQ)FhLdSoC#ZLa3hw=Hg3sk1a;eYML=E6aWwe+(&%q{p5;{;2u*Ra?y zbN5Qx-`Y51=m2X{KJ1Y-c|Nr_?L9pi7DXhEBliRUOQV-@V7fjNY>_tUi8IV{uQ}HS z3l9IbS_}(CYV`gfW<^K2!i+Pa`}*Vjap_`Ra3lGyhw1y7+}eg-QGF;9h|3eWor#ASypS%2$!!_u5Bc7xBWEE zi9WOS8}WbdOVXI@CEX-{sM?+i4x;V(=@0spS-_&!ZS?)jpXt4K0?f8P`z#XMi`sjz#0KWNE?Kq} zuE@A-JDJ3{dC>Z0>$LM@?1@jmSxUx7B@A4Ba|XkH$tUxj$oXTuWcMmqII*Xc z)OX6+tF`@M#+Sjt`{CNrAxpNw{Nkmf$@m3}ik%7 zg+HO=ElL~s;uKiosT}tQ*Hhk7{Zpr4hVq7|q`gfj7#M$s#L0R@uzl7Yr5sZJzxzvJ zzw4b>VD{ildVi4@oK($+`74gl`$?ct;EX$@eDMJ~o-Z|i{kMCtXy4I<&DdT-hW7bN znEO}i=MOh}ag3#~+~zNfw7(_1iqUyW;&$(Lt%A9(vp*7Z#xAG*w}I}q_0M45kv>yM z|DepUP~#OTfB0CsC-MD5bKk?l!4ql!V0UOna})fpe=qdZE6Zk>S(`W08~J%xxT02A z#O56JfkO*RdVj#OQ|)wrl7_GlWF5V%uXwShV}TTP2*3Rg+~3%OAgZ&BJoQqwKj0$tW#}{u;|dshh%(A_U82(i(qD$kBro}1mTTjBQnafW9S;8+ zKj@P4{$d2lU#Ve8+QV#>4NP_rkJFM7m-i377zGPP&NViHJ5Rit9z)8X(OWYDZnb$M zKLRuMWYhL?|K(*5PQaq@;Y;ihC-db`!^|7Y_jtgZ@4L0m!R#%0ZQEh-PXFPTNdAR$ zv_GhDbIif3BtC07TOak2Y^6>yAg;Br&YE5fa&^5FtdKO zLMMrz*U~kB{V^=I3+BhS)M~@QLiG^ENbK*1s=R)%NB66<17Xg+wYI(3f3C*<<=U{U zH~w1}+&O;MFI`xCp|x)(iAQd>8bZ8NdB}ITVo}7+5wKiiMadhudqdQ2bC@x3f8ujE zYQZ$-cv$){D)TO!@GT?L5|(w?IOf2q3qIbrh9!F@+|7g=-O`uX!-9{KKE=S<&+pux zPRbh=(f+@PF%S1SlJfuc*EVRe=FBDWixm?rkk9k!igSUv6}TP4-J2)YEr9vD>z@6< z@9*(2Imx}hs6KfcyY3`fUhtGmOJ%lY*A66)ySvlM1MEst7{c*AM^ z3{O&ihI`LKSZ!kDloc@FY{yz#STgX_lhrU|)A`AyeIjY!@kBr3QvloiKaDN!s4anWCQ)4gc%^Ry|ueJ|5d8S{188hd^UV??D z-eFa6)a0LPH{gH$U0G)~1m1xK*EX`)IG)T)vocGGyNguj!ui+kynIBgqu90*PTg8i zRRzoLax2!sjx$`&*1|&5xe8G*ziR5NMwnGS(k%&&swfNlOycu2?b6`Rp##^*VTnzV z`5D+^wXM3M0LSY`lglO8E0@HDQTJ_uy5y#wF*nKFpHrS#l8; z)J3_(I9nr z5X_V$3;%0RRdsODI0%c|rzzzi zuH8|!=rAmsmQK%)W|=T631;ko?#MF>K_gpX=2=N%F~h=CD&-kBSIp znwQY}vVH&Z(6gkx?cwd!cwX4AY1Yjo<^O9ROaHq^`2x)Un0)p+;&wFpDK#qfqFJQ*r_**@QXI&o4ttRC^ zu6SXL-y_h4p;-k>oQmjp8KX=3^!u~E|9H9#amTI--4euw<1-#@B){h*Zdwt{JM<-M z8=QKYzw{2wO!!ROZv&q@>)(XAL#7|NfjCcSmVO;(*qFz?f>k+Bvh!e$ljv#}EH*cI znM?9h!#?TY{LKIJb~lO3G@bn{VcX$TxTHLH{koOQ;RufQfkIf^WnL$O< zW);K12Pa;>k2rgT(Rvxo-qM$TKT-A?`n-+E-8qLGZ;5Od?Jo%|Y-xE8^S+&;<}ZFY zfgC^PT=k|fHJf*%rIi9VbH~&zFxu2z{T4AeM zmWMQ9&f7yzE8zwUR|OrI{p%vF&xj|a{}~K3@7`dO@f{JPHlERi<;P8CoF>~-^|)(5 z;-S8CV_~Bayyzh?mp^61F1T~Qp{Fs-yYETgH){tj+Blr#`(*}fMw}ORd7L@%JW1Lb z*!+BH44atcO6yzNTb{j`0(0LK()Kdm$eemdShDRYJs(-WCf6;11$-T;CCUqvryO&G z`Ku2JCc+i`wq+hL`$l=~1lTPu-pLE*oejBfP4ZU{aa#?ueAu+U=aO1JAOIFjZ=wAM zY)3&dA7CgSN~gx8{M&+kU`=)x9Ry6 z80_%n0*N2_eEbvQg^roRY*=XXaYq}hJ-wJ!FLsR2ea3El{=xgU2jQfnO4k)( zZiY2|ezq$d%u|PD?qh?}b}(9?m%HvaRpV8u6UZ|K4T=9dyOeot-d>>dM{zsm63 z3z*se@4;atK6kcG5*+MT_tX>?`fK(f_cO6l+vbrlUEc$aJ-hC+IsET^fjwWaV;n3g z_%QoB?x&rPOVZddN1si{+cPRITU(RzXHe0A zqehxhGsyZ_u->|3wKk;uv>-Y^&*j28_esQN6Bd&3pn+;_4WvA4!u+#~>b-&UFQJ?J^M5Ek~PpC{wllUlxqFM+vV zEa>>|;OGrM-Qj=hcc@OaTk8q)fOtx3C83deRu25f`5Oph-aaJE;&q)=G0bA|s&*#F5UZUij4yGl0( zZtz~h+YO6tE*S^IT%RO|10?P*&Rqk`YBQAMU@8B@0Aky`;1D4!S-qlgHS8gpcjqX~ zo^)%mKOB+sXwos5;eG56G4I1B%S2c>=2Y@}IP@bYJsD=b9`<+(EYf+fJ`?87U#B91 zwa>n=IuFY~H@`d&C**0nUxY<`wUt-lEc;gH8?f-jkm+PS7rb!ri(#JT@z2WSc+D@B zKZ0f9GX3FjtMP#2&tb7^WRNxNVKq1HHO$Df{5Tyhd~jmX2bignQ{YU>r*Cs>gSjQu zZ`@(?SNlG+!@MF-9UnMK=XmHJnA{*&!fa0bNQFJv-%ra=tcA0#EkD=~mW`VAb{(8Q z)U;cT#BD0r>xmsKcj>^q*Blzx`}`nTpLmWfb?cXMV-r%o%aS_p?a-&AU`EHgPktyL z_3m%YM3{5q;`cRh`}pg9mcfG6o9X9A?CsF;fw`pjnyW<0vyjoufYEC^=a_ZYef<@HWB+}H<8&!l(t{A$bJ_Y8z?DE+UTg80g-GIgBYBgki7ymcN462&UFyX>ucJ11$J}1QL7JgAB~SY z3v(5{_6&zPud5FVVbw8BpeF)F5%X5Mm zNj0?{aB59&^kNdvOt3D5d4~>rd%-+r*^Ee-v9LI0C5hiz{oET)niDX4JlesU4qI-AC3R6_pTo>G zN85J7qT&K3S?{SeZvVSoBwrLo|G$WPwM-!jW;uKgCI2_nC}N+8hWX8l?i_;ox2vOL zNqO(#T>_Yu>@f5Q%+6d`6#|RSv6YU)lD?6B$@()=D;M4=n0wA}-%7Y4<>9eRlCPxH zhpg8f{C6Ek42u<#>3R){l~1Raz`Wd&6M9bmSjD=~hVfufzaJy*1 z;1*cwpZ07OY`bf)%`cc?ZL%#C&bL?6=!JRaQ|S8Qg^AtmihFTBr487VL-IA42`Vt_ zYeMmTSS%^>7yxsx2k&el@pi!vby%3a#%>^vcW8Lnhe0s!(b8~R@_YFD*lEI?U5n}c zm|-eW(I(|n@7~^qczeUDJzyB;h)d8g+z+^oX; zr3>>C3hDY;ib+GB>A*rO&-3MobL=wx$>;N%6Gv3T+FNx_lkX#4@|3Qx)qHVKQxEyR zm%VM@As)Q!M=m)Y;-0JH8{yVG*8VRrd)^IdUWjRJBP`8%Q1t=v8WXi04KRx_^j*+aiHr> zG_q$GMZ&_wOSFEFFm-u)1Sy{ve|;nB3xQ*AW`)Co`UP}8p1<4t=NIqN@4ODP zastORA)fGUx&BR9+SuOv6&B20_UsNUsWGSX<(mzTej?YG@X&o%(%)3E|KQ^X#DjPz z7ZYbS9F4iw8-^lpB}2IiTd6&I|oCUuKxn(ZTx)O6c#DX%pVQ2?luRGg~h*S zq_N@DvY+dwleo#KC>yvrcSF20EU}+7d@_mKR~x#*T#H9r>|p6NBV`YmH!!fl8n$md zK4k?gmHPb}4+~MbS_L!QlX5KJ=quOH`jYZv0-5CVYsNh334|re8hmZoJYN6PW>WqG zH>4Ne$HQy3PcSTd{_RgY+^}I!TLRbcHK>9!KDk*q+G-~ zkLR8p2y?c$Pnro!rw*a*A?YieG8|xb%86TV@q0_Xr+=CRi+ zf~}!eu*l6#B7b9IX?VacO3I{vlrhESK3m+D3QA^j^Ao3%Wrz}%in+P>c!x!!;S zi+3z&Sd6$@#+!YPr2JTA{aLVfj#0{75*Hlxv4(>mf5bproZsi7>HBy?Nrk#QELw7( zp8xhGQ^U#k=U8{=lKudzv@Q3%U}hiL)_$My zI(iM}4Zf1#21j2z<&_W1K3LNJ4BJinw->;CtvcGC!k;~5>Rl3def(|)@-32GUKNq@ z4UOHTzm5CAOt&27er_B*32waZ-~O1CkJO>#ZzOtMYBjKscb(qvqE@(9*22=cadf;U zGvaqa11uM1SFupOWi6?w%_2gjwgSa>#gdZT4yiqCXMV6OtDTNV7tCCEVm&NBYm+~RoZr4P z1)E{E!caON%Sh~|83NPw@(`~LX`K`b|LgB;UwOJa92O=Hr{lpI0|ttAz_N$mx=4SG zdGv?6J*2!r_!Kh#k)NLE83!}_=d7!QU6dHN5@3G+g-*?Iv)#>|Nw7%SJC}@S&+9Ch zlLAZr`n@3I*Yh%N*rma;*&k^CB*$`N!8w>~yYHkm;^~T6kF#Ncz22Z1a8~Q805OdE z-oiPsxTAd1RhXB;`#KknUACNi9cCnj$GO02d+trvgniR?tn9ZRzt@(@+U+<# zzRw%K1U%(*b5Z8~j#49@RUwif2w-=*{A zyporOZiIP#M$-AN7ON!cK`>|YiFVQ-Cmq;n5(*3bI%#_!??s|wB+L%dqV1dcD^~jN zg$3(2OG$s8;;XWr7+8M7ZvtuWciGsj6i?#!)#&)d!nMo(oFnBQ%S(46t~NCHtpsL` z%cT8jtSu`tOG*A>oqk@3i$gv%my`J5k}XT&{9VKUR>GX#kBwa7?>2|4VfpTC+F!{l zvGaHibGP@W&l8#HMCUqE-qd&WbmZH5cAkC*3;M6VI}ujh@}2vcSY0K31g!1b()$DE zU5}2|gDcvSOFLlM1YOrbu)V5@N;k}~zek@hHLr{#Rig2IdQ|2z5cksd=`n!m{}JK( zt`N;s9|p^KGiZA=&z%`*`v2nQb*>^an19dBvm5z=H5YheVDa4pNqw+Aq5U6a8ceeCw+dFM~)EBg_-q_B;@%j+;%T* zJ}mxGF~$upz1p+U1E%X2!W_+KZ#YMsoz;D$K6;L4G`IREv^wCl;||C^s2xVxr|>@Oqzu)+_-dF6}taA2mQcAq5Gt7B=cYGrpyTz66FTcgDV@7H4i(?}fWx4-5>1`LQ>LcEQp6UE?;w(m4|* zbik69^HF@5b#C7bvVEsz=J$h1Jfxtj4Q_a<_96^sSnj&r0vA5qX|o&Vk@cKm+i%x* zMZuD%6?0#c{M4_<#6Z3Z3#z7oZoA4 z2NpGkl&8b|Yq{-0IO}Rfcs9(%GOU~6n)ufDS73>s!fAKdDq_8NF3dI#UB3{P{SMH- z33H}AinW5BruFT*3-ibCnD-06C%gIG^b%Nl*Zg%2x&NKijw^#17xd};a{DuDqVALU zSZ6^D;;qND1?4a&2FEzhsG&bNTEaLHY=ea`E-w^d-F z>Duxlm^Zi1g3N!)GT#+U%(=KWwhRuH3g^*yO7NNlm{~l_sF-a3OaQ|NZdUJ?LoD8C zIF;N_{_Q`z{KxhgFqXcFzK6K{{TzCII++G}lKo@GoL%`5*DLpzU0=HY-@YuUg}v-~ zArBFkWQ}`5#%E^D^5ay(vb{-kJf~>c%lF*ttAqSNgF>J8uvC2&ov$v-2))({bDC#gC-W5= z+WS?>;eX@5*!@C$f5MWgt0E!dR^KM{c9Q)0`{;ZUafy9YpBQ)*laB8f2_^TGU_qEB zU0=^M`Zz-kX4EdH;|bdv&OA_u#p+@@o>1;*G?NLlhs_!F0`KRoZO*%NNcq{)GX*en zOCJp*nA22PLDsW~>iJ-70!y{e(e;LeW91)BVPUk|iU7ndzM7bghPl1h_uIi)Yi-Ib zh{wHaBkRE^exK(%9_Br}UZf1?&zQ1nA}rQ5qx~hariTkHVNP>T;&-%1lrAim5@WsZ zC!~GCZBx!4V%fqEw7tSZ>G1SPu%yFJ{T<@j>tBqqC-L(0U1a?tk1H#~9bnE~m4T08 z-&d@DjI3!G|$1Ds`_u6VBYPn<2kTkNloJxn0f!? zzFaun>7-&L$*=tLovg1T9XPJyAS_#E-Z79Izdw_Gj=+rOs5* z0W&9`r}NdB;rE2uux$P^I-iEySF$GumY#Ukt624iFu!zEHEDmTY545=f_T_kI=@%E;)R6_X0EfO>pNxzoj>^=#`-q2KaZtQ z*W3gPk{{CfHvV2G_Ix7wzka_yhV2OrY`1)YMV^=F_!Ha8RV%tk{^vDxeJ;6hzfj=- z^81gV(FReK8f$w0Lzu; zZXXYel`4Ob@$-5Y1cFJhEO)@#2XK4%-_4FBe%~vk3^pIvv}+zL+E_~STZ43C7Q@`O znRNSl4{nFJ!vfCiv|q?i8htHb87vIQrQ?a+)?V=Tf?3Nm*Xv;aRcC0o^I%!{ppeOM zp4Rkj#GDJoOWfet2aF{?Ff+S~jz5)u^&9UE^DC0+_!&`mgr+AfcYIIhkA*5t?{OnO zXfu+m?bvoagd-Zy)4J_+6iX4jjWzq@Xw^lGe=TshdBG{?v z!0R!v^jCbCCmc2E{Bd&Y~OuBWCXe^!^e6b>r17M-n zK~p;3Z>h?xt;A&gb;P?ftFppj{^nphU)bqW(D6uEHaUnsUz0R1`W=8-KW*uJrzpM3 z$A@9bxHWWs5!+w0^93;T$D}9 zDdajB0=d%@=5Usr>=>^4Bou=<(0PQGW@(QtI;_mZnxj zEr2Z)pZ{%wnWvh@u7aDzuXleX`Df(sv*FZOC;cBVS9o!b6y{Z*t?PhA!KH<5u*c*@ zX6V`v<2az)rWy*NlM03;dn%m*|{^K<5+-SfHyC z{T{CPq^-s#w))~q>bt%ZL!#|q(bE;}idwkd*ZHiR3G-$O^q6q|7lVrnVb;}=TTEb~ zR@o|dShjK5!(nh<;@R`dVD9iG)Cn63oxEU1b??)mh&PnEjb054zYqDMPs;nVQr5!a zFBLy^;m(%wL_ZQYw8$I`*J@g4`NOhW<^IH>wW|uLo5Oo_V190*%6i24?TI%uVS5j+ zGaF&%F#BQZu&IL2`pqQ%a8#-iY@uFi8w9iLK6^9Znt%YSV3>Q?P?x+9Ti-8|^o^wu9uCWf&I24UI>J?S>f%M$9zWe%S%-y|ARig#JE^ zAD`atgE@DLZ8sv$X)-T)zbN&?4`HURzzp6Xo zf9+T7U7arE_z0CU==*<^xP8x7SbTvIM(XFCM;9F<$Di4+!+acEk>~k={C?c#gG0#p z*;;PZSznm`Uli`owu|F;uYx(!)pR}-rzJ3oTF+3O%#W&V;6EppHO$kH5dT-6o7*z- z94uRIwRtUx|5u+ckFO0R=aX#AX*xe%aACCXI+$7iW|9#3ohDNi0%3moQo4SDe_wOf zCX(-2F_z44Dm*jk6WM>3NQ?FdnNF#GLB1b9ar6JyGsyfyzduhakghk7+VE-vIUcNY zk%naacl&+Uf#iJQ#kA1*lCrSP^mubdIMVr27AMx59z>iW>;6FI=c{gxDv5(--)iXR zwJ5+ne(zo8p86*D%U}<#AMC?#>1ZZQm#eH|Jtd*B;_Y7 zEZ2vHj6-use}hEXq{J8&D7hq*!{X`;rQsw$&;8Ctm}NfryeTY8NTTOca8_y92w1#g z2p!+(v9868n8)2=5r}+qmz(LtEX{f|(!S2`-05Nl%U|{m|AYFG+t|MMM#3DO*q)cL zY0A9YqhKlLsS;VwK>L;3J_}gf)zO>@XNM~}vtiMEvx8uVyC`zecEi*=WUL#tlqNs2kMv1Xe;qtSa7o+ zzaBPyu|eQQ@(*`!y$V|?hs<3HGmmWLko7VPFBb+ZgC#$5uBOA?izRIM?RrD&44l+4SUr@KH%)$X31(N>PZN;x zLt}ie!`+j8^LCT`Utt$-!WD6|OQT_F@qqu$$NhPxI|-KaU!}fB-2Z3Jp)?YgjG*;* z$BOc?r(w=%mDwGL^Lty*oQ0V`%B#uxS5c+UuAV2ZK1tWhN;heC%z=5FI;SBxevN&_ zz6HdxrL@1gQ$M)82$tF#IFR*v8s=9;-y`wMAA`tzL-VE27CnI3&Z0`Xen5iFODW79 zf8yk8#PftcYIU$Uq_vF9w~|iZdhsR9S~gaFGTeCNh1Gjl%DHnW0v0kE7n)#ZM;YzE z>fH2qOEb*be#P!O;>~7O%BS;7BK8eR9|&`sPSN>YYGpQRS}<$# z*i$tq@03!cYXCFM9?XxdUbgoE&@zRz0-lc_fMZ?DjqaTMTj> zvIpj!Eu{U|(YqcP#=-JK*~%G+yEOIBItep2sD8Z-=Y6*rB7!AXW>}QK_C703vq*mO zd>hh!*g4~k4*o(0{S}HQI$+7g!i5E}(15p#iTaF-!q5+QNqns$Zwy>+J}$Hj=4Br| z>ZLvm=39n^CBTLD+b#`wuE02zXrL*rWO@+PAjmou#*}=@3Oqi7- zSnB|D)-Z2n!!-^rFE}u7@W^F3u-hPk&TN>OIOljCoZ#p(V<9Q;*0eVtZoZIU;Rdse zxjSyb{MWAcm%_64!@-5H(^Zo(KBRoj4XwLy=^UfPK$8Ep>}nBgeqfz_2+U{pzfc16 zcGPPffQ25T=H7!F*zXlilk(|fsV$zHDN=7UqV_L(e>aoFk7xcaMZW6Kd74*X?usA% z$@aYxCYanH@&7(Qu*B~3O_<*}y!1BWrdG=}%3w}s@Uxq6joI5p4`5!M&a`WAc63lc zB`n6@tX+Yv=2@(MN6Oz^I$H!cPKjy#1hX8B)+NE>@_r`YV8Mrejfdf+EE}V4SYCIy zd^hnHy_fwD?{p{e>r~2KiiBESsjDs7myD`_nvS%-P&Eal&N#+Jvus(R5DeQJ_+xpEg zd(DlPL*SYNkH&0+g&CLJ$@^B#K95N|U|yWw?g6l)*3>zBV9w`0fBV9vp9&;Vut;Mw z?SBb;^3r-gEYWAt{+7`0&HazS!h>o9TgmmmEp|;ZES^$I`%h}8Y~6YamdbZllKvC^ znp3&yFh6IMoV>r8!9C9HCQG-M*Gi9=LU#1VBY7Ai^=_xT|U-X3l?t`zWoWS&QN+lEHd6~{u{Pcca`YC z@^Sn<#`ygt34{Fy6aV$;S`BkLY_y3PmK*5x<)rk{NE_yU7(e+H;{3redbGS=&_P9Q zTt9|et@RLRY52|=374 zIxvQm|5In~4>P9BUu;YAn+jL#hGX^aOqxydpS)w9fIZxnFLZ&$E-!=9;qKDv0xrzo zddTTAiR;d@UJNr{iav^A#yx*e4-$V>i6szmeO(A!>j}#zZpbZ!{L!>E)F znhr$CB#M&hq$n+-C=5v{OiH0xM5$;9p_odd;`h4t`F;1lW-`9QJ*L`n| ziD8b+EC25>YxRN?eIze?_k08v-&@8SfT`3gx#N^^Kk{K04Z`fx;Zu}h_tf3tL!|$O ze~dP)qhZWU0wyppO~6B4i@K4Ff@dv$Fs+bhnfE}=nLWM4k?2PFx_Te|5A8(@6;y~ zNxtr6n;V?)-1FUJm~$ic#2&bRak#}an0svTNgm8H+%tAMOo^CLdjZyAaW~H<{p8=b z!GnAq6FN-Wa69TPY`(rm+Zg7p=dbR8y8{i4tVn;;yq2k|xW41cBdkf@mS3y|C$*Rq zt%a!(k>^ZdHs_n^I#}{~EG#=Ccf^C#e_Yw_1oPJP_Itrhh0rKhSY3T&w-2fByZdVs zY}{#3vz647@1w%n8?XEchFRAtt;1o)q#RKgOj%(r6$iVYqQ*s#{`p^j#KVH=pME64 zd=r=Fb#Q)SM{^2H{c>Ph2b{Ur;At9+FHwy805_?r-#7`g&y-#qgyU8*Gm(?e!R-G^yuhX*}j_JHa@4NQ^uShkTkVCVf>m}hqI4GVU6P`>sUW^Y&+ zv0)vuYEc?1F0)~nf{4aWs|ydHviM| z@s+6QU2ssf*5MGCdh^4b5xDExN55c_8-+cZISuPm`o5EaFhzmAb}n4(_u_>g%nk4y zWe!Km_CDJP(`F0u9AVX6QxusnTNF6t4XZz&eSZnea~ix61{ysr)Ou<+%>U^gG94CH$FUEX%`7-7+%y_w0Qr~Il zsQnJ+uF&s1hTL?|k;89b$`_tw`?A-r9C`tZ-lWJMLar#-bnH1STr4GsgXzwG7aCyx zl!-udnR*vK;2D`(on@w_NU6PzLiHvz*EJ z@mFYMmcaZ|hPOAuI=Nxmg)qfm^SV78zP4CEt~aLR?{I6lFzjS#1}xGl7_@-%7kYg> z2(t{AL>s}3nE3K&l7BFfSZ-;1eeV6Z2R;ALEmPFve$(-N_p^6E8n0dA-OZdiG?!qVBf;v>Z8S^zbCIzR^UF)z`pH!J_j_ znA>3N#Dqmp(kg9X!NV1j>HT$kSad(8dz3oX-+3#-oyhiRZ!au@xxqb>^;UFD=baDSJvgex z9XZ8zzr!KerNsKTCoCNO)8issJ>!M-dRRcul5T{vS(Ea}@v*g?LdR%ee1w*C(l6Yy zrC~1YURKfL4AVc#*jmF@`a7h^dQYEXpSlsQUt#lX70kaO^=m)O+cGy`IV{>Af8qp8 z8)dX>DaqH=hUdcku8YQtVU|`$=0!OCbd{GOEDTn8D}dcgCZ3o_OdnhO0B#?>{+TZ6 zU;Wml0oMN>{geiCtdsrP;q0p%nkp=+V($40(^M7jQDH`Hn#FIpvmlrvNBWm+8tvPN4N1pl9GmQJYK)8Ot4QzZngGTQEloR(u!{O+} zH_Jz0u5&`fNw`yM4}BE+sm13*s^G$bU0Twx_|w}fC zMcuqBWc;XEqj$W3`@>Y!O<+c2RcZs=mD9e@92WmMCcFzXZlpR|z|8c$hPUC)&HdAt z!Ca}*Eba+$fGKUl>jAK^=gJuanB8k&5ef_BT_oe*!uGY=1B7v%5RtI8LL}5t!|ky#FQKGNWt@7nc0KCoJ2Q;+q8va`KAGVVAk4E;%rDWT5f} ztbQpfA{S;HyIgY}9`+v-egS5&*BV@byNp>Qd|3Qq^OLLaV75#*S)bU=4L+r$-tf+D za{p$SD6hB!`)E5lkoz@PZq1l_IQvfD;1ihYa)I^=)-HXtfUKu{V~cenn7_KefZVUC zL)4u2u=+=j(oe9kROjd)*k#_Jb3aT;DPBDu<1=Uzbe-IfS=V-jPl3y-c?;yR{)@jf zN6dz!#X(NfV8PRo>5E})n}?HWF!lZN;pMRYH2+cB#1>}nR>MBu3LEFc+{STf9~*gHxnI+J*G9vc2gm+#MJ~3E z*FFSG>&ljTk@~VF*OK7&cxI|M%s1BUNQdi2);H~dMc2(gpN9+O7fy?SX<9#aUV`h_ z>5Ynl#f$R3l*6)lQ(6ze+#~XdPvPuiG3*4G-gHv%67Dy=QgH+pzBa$o3j6qJJUR*E z?{|{xCDY{Vxnknk!}^2BscsvORKiq`;wIwGF`Lb5U~WEN;+DWkf9qh${~wRsf8nK$ zr^F`yuLsEX&z&f1fho~fB-dx!!V!np#OH3ylIzoDhRU}NnE#b6xjwU{oJN0uh4l(i zACa?fobLWi%+pI3!KU}U;(K70N!{Nzm>amRcL?Uz(#}1Es|#;NNu^>uc$t#%h&;FF znH-GYZ(mNvuXvrAt};yhnj^VB`-}Mr8ZeVd4cLjCR(C8@3+AnqpSlI6W|*gG!yJXa z*7YzaV)oTJFl*r8t2MByVsnHZ@vN)M7r@yWb99YhX45pu{0y4in`Hvi7upaj13=#J>ZtV)8nm3zw=pxC9qOOeqs_4Rg2{eAIL`ZHgAcK6_jPM{@aDQ5TVdKSPStdn;iEp)52l9R-Z+H&YxnTQ z2w#|UW6YP zU<^}FZFC(CTj!h0(P8|KbL1a9&vqN#oH(2Gw|1HRgssX)9aV=}Y8@rraGdO+Dbq+k z`%my^IKgUrxGKzb%5Cn1jo-{VG6m)@^Pk@cEAIQURDpOyR*>hpZ zzlS5+yHt3^5EguVKXW#0F2hSF$4`B|+(;9qC~csV@eyeKTtb7nfAyx?z+9bGlDz3n zpXoYMuYAQ{7kN^`wb(5%W9^e3LzqGR?i5V&9b>L8Bl#PX@DNxmx2bSBTs=1D`A$;r zPkmtnn@R;&9)!75Diyt9ad>IgQJ8jPitM{GGvwPsp=gT?EMGw#45UBiGGn9o6zHhNXT+!d<9 zD`D#oJO5OZd`yAiR#^ID@yc45qPLEc2{%RTDt!bq6Ka0kgLPhadDO#<$lo*Gko@Bj zYauMOykq_sHt#RK{fzV%*u0*C>(ieV;{Fomsaej~fvEust6O1?oOrec+~st!^CQWX zRFlGB?Pibc9^$~jqlaKt_^Q}GSo9@E@_bmhM~eD~Sh4$BKJrf0saJ+ceU-+K3vlHl z->gw-nBV00M`7N3dig|RrncmHu{&MBQG*4|!Iq84O}#t1X28_dnvKoyV0@W@7R=87 ztI|fc7bX9GF3eGtmfVkYM)dlQ?CBH`w+s%Jww43Csqpoa)#dpuLVqv~=r{w<3 z(%-0g5@zS>yWBv|uHUow3@kW!NAmpVb33o10Oo3hOP&|$6$=lPz#P#}lW{Y!{UAHd zn=rlY#DppE;PhScH85@avBp(!&?hTS9n5-j=H3Q4|CIgYC#3(Sob_%v?TGV}=P;F8 zUz80;iwtC2VP0rfT_w!ZUCr)*@#k-gcG&9qhS48Lzmj>YJ_EDf3EYjJ%b~VZUQ=Zab-nzhxL9l-1tMXYe=UJ9B z2d0+TPMrr!J}-ggmer)phsAym{m#JBZ>W2XV5-`(Z8u<@7k0-@VaCrYtr|E;c-GN^ z)Nfwq{Tj|+(0FVG%pcJkGgcGp>D|&?YnUr{_u*_<=TwE41I$dU$+d*lYdCjZU{M%5 zAq0*zG1%or{7JU#G;F0@QoacmT%JGg4%`&1%=97odWy{(nBGw>6A06^{FcaS;reQe z-xv-vLl4Ya4V$Jnx$Y$OCzk01!~KoDqll@)bM;bTaah8fT`+I@nWC2@-;i!h`YBQs zv;mm2UiWke%(CzEm(#|4s4tE1g=u#~zt4f|eMbKz`^&$$#nS}VH#~L55vG^BGR$G= zq{Z*-V4>aaj-{~m{cxLwupqD{!V*p>y?B?LFILZvnB{Qcf{c0OeA1(>X0L!b;e$&R zV9wpiPl)STQ_W>b{hJ*=t6{6&lfR{4;Sz@;8`yNQnb|)wpAzmYUkhtrQIY)xbH;AH z;RU;_o@YA%Q~sKGY=DE(^OJwVqI$F8jd16;ecyXwe%kI=TS>jw!a*^qKV{+<42#Cr zxO|6c^at7DaQ&Q}8+u?t>ZTnru)1Jd8kz5c2EB0^#J^>3eT5lCbwTBDSMIl$--wqb zeR&B>D`_b8!|eUZtvzt`QF;m4Kk61u>Q6XQiPbv<3tg?BkDi6=cc_Y2 z{siulCgT$=JUSV!@2=QBmWcl52lZ*N4@shGy!Ke$_|b25|Jd2M)$C|B2xrBe-yf*^9NLU+U7y zHL#9YIc+2H;UimJ;o`K<0zX)+m-lr8oTTz1iw$#Eo>>4!P8;l~Dx+XF(8K$LgobdqWRjrRoBe`m6cPA|9`prv++2&jKe1)yu^RhEw!7Dx4 zURbqrP&JR#Z+l~FIUDP5FZ0zom>E2EK(L?hRmc-p3}(K?uaHwwJS^-Z~Q zUy2Iz>`$fm!?arWmZ`80|8nsblCy&C&0w9fyg8mQ-{qi249xCxws3_xpMPGu2p7Jy z^OLw?yVVug?s0m$3)!C0{`Xg5`dg8Z35!SFnJ@tNk9{vO`#0lyfG*Z!d)Zaa$mwdu zcamYjg>}El>oF`Mg1^Cv2WJJ4^G_{|d^d3}uFuxT?qvUj-rQ%}aMA*w#oJ(!bX%?^ zT=y+OvR<+0o;b4&&a_OEtVevd#jY^e?$W)`-Kb~l4M^`H_0NRT`^fgrm8l$t=?{`p z4#ISUD614${KfZG0;#`S9(w|2E%8n`4pVL4Q5o3YS~ClBJc9ARPD~))cVC7_>h(5! zQGx~2<M^Zu&P5wrf^XF(>`A!Yn7i-Ln+NFM~NIMMZs>Uupi1)2(3Y zweyc_U|GW%$5z8U)4#M_SV!aM3mceU)jfL`%yby6vnTaVX6`Ft_5J%**TNj%FGps> zj0?*I#FTkQ4o`vQj`UP8VS0pe%y-QHrp)K^jxh7LQ-zQ)$L}tIWS+db458^pLS~R zL72Ov;cO|KbfVWL{(nrXTDjsdaci|CZ?B3vn)p9?;ZNz;Nie_Ix9=wU)elTLe+*_! zj+DG!q3`z_=`d?}Xlpfc_lBXEOjx*e(?}iMxnX$mX_&rb>Hs;Ou52Gp5zMjMb$trP zN7lsT)>V?fb`=`I-P^c(N@4b)mp>ih7JK@(TQGM=xqc8_>HF;ILs*<{uK5&J7x=UZ zNj_FArr>^^kmUP5X0j$2@d`CA-pCaq@7S`8YZ2lEy-g~uu6s|Av1G&HC_1Ox=?13l#w#2g%Cl|w&Rn;mkuz1hub%%-7D@QzFhKq9cZg_o%{GE-&_pWk+ z;5c1o2aD9x!&dmg;xS@@A56PpmE{aaYo=Zag!%J^Of2F2sZZ~-VV;rd$N6xi+Z|CD z%&|KDXeL~;&Cos)W@epe9S`@5=Q5&T`i`@Q2XMZni!R0PgT+!Wrd7eztsXHP(w~_4 zya=uqUXkRy;K6<_O!<5DSTu6{eQf>)*e)&3Y7fk&4!AR5^~aoqaF}N1W26apkJ?+m z9i}FxD36A-t?4gx$Y;#z!l+QX9RIRp6U@0i#Po#gZL%kloGDgyFovtIh9(j-nDgGxBK2de z$8CmbU*|-sz+FSvinqYxfE$wMqol$qdwfW)k-6m^uGh5d(_Ru&dv*w3!}>woK3|yI zxoGoK*vFIQvkhjisu7gK#n%Q`lJh|snkTuR7<(=C3xzqdRapthMdDb=_=x)MN9=`% zm45Y;^TT(ZT@ehs=pUT87Zz&!7jA^puE$Ww`JimjdAts$wwBt)!h%14k{n@mTDu?) z7CEY|a)67wt6#?xOJBUQ25#4#?RN~O1x7tv4hv>xKFEOSdMT3oStVPy<0Pryu`W(mxU zy(-DO)13M$VQ$o?SO)q_&d4VVNPmvro)s{&|Df-4nCi(qZ4dL_jU0UqbG#~zonZ5| zQe!{EG+SnzGaS8nXJ`+pFIg?IJ6k%Y|9^7-xR~>QVG6ZO(l31?$y7QMhCB?%&mA}5(`s<(k0syOinI12(v1`7?RgZ7-x`i z7^at$NM65~IV<`YOtFmLKM%S8$h=XRuwZI!qyb!Voqjt9rrt~%YXT?zn9<6E|9iYH zX{n=&U}o`jmNoL>633j&q~7d751DWMAI7!bhQ*V*qy1s)-}X0aVBYO$$@>+~$Nug0 zFirEm^aHv<;tuUtyjcdGoE&6Y6lsC>OKtSVwK zSHqkZ7wcuPV&JWZ)-cOi``8M&?%9JBTbS)wRJ{iFd9*Ul9Tv!3H`@jq?-~&J!T7#G z_fa^>sm3r6W)_N{rNZ>%DUQ2H{gmsE3*oZi2k!e|$`$)%H(+i=gvJ4wQFvhQEx2UR z+2;@}ww@n!2WC|Dt~?4;UCe(xfCpQq#ihW)l99|>m^Gt)dIrp-jLstV_Z^j%3G*~p zaUa5}?(r|PNxkTOOaq)mnfv=HOsg60BhL@f-wkfughj^`|BeuQAB`=C@p;I5DcsNb ze$0<|Ve0qcavI#-t>bndX4y_$vlymddHdr5$se76xEE$I*vso+@dCr}eAqnTXiqK7 zd#ypQC3$MNLk%nv1oRA({GfWW0H#$9w>Zqh^VE3xn%gkH_UO?=aEarFzDm+x=wkO2 zX3Ua%L2~Lg_sTw))->ijG4qn?w_mXFbr;G0^1K<|D)X^_Qu1YF`@+d@7R`itZu`8d zVET)RCkPSfHOAH>=RZ?h6$l&8U69!b(=?ou_rq4SoF&g;_AOQQ z1i1h1^c}4*7)3|~|@-Ty%650g|>(hgjV5Y{@ zs?V@`*?kW!nEv3mY!A%y3T`lfxmGO7ci5%K*wBPn>*ZQ89NFhAG=+txslSL-OAELb zusBR_buTRKqq~#|b6#0|?1$a2yo+B?`u8?093Xk?YZG6X9=!SBFPO^hiDSc*&}-4Z zVN>?Ro(Pz_&kR49iu0>zx+M}8cKit&f{Xu!zDFRZPSD9!?e7!sy48k z^gZPUm@?NxVH4>$IBN0=W{U5QJ`4}e>6L0DetWko8LswlI@tlUXDvB<8qW6@o%=|v zIY6s~?drxJ`UQ)t9{5b8WBk=Wr;otGMi1TjaMOaxO`}iY_>Z*uuY%>GUd@$*MYq;5 zcEYR&DdDOxFIKlM9~R9y;x`TE?)aO^hq<*Aax`Ji@yetcSkZ`)KNsd_T-x%ExGned zBAEL3@lI(&Jnw4Ot1*b*pADG`55AJtw1&CszOeOS-gep%dzdcM-?kX`|KVM<78Yq} zs4?KmCH+e_!Mw!53V+gnpd!T|raZh*vKN-#d42f~n3iyUObXn>I_N78cuk8aiJ>AZ&8y^giDGZ&LK5r!2)5<|tgw>VRbn=NUSa{=#*U ze@K1di!KkCH7X)h(+KsK?v2?%>bLHBO^3}peMfDC+3N4Y7Q)g>Po8WddAHjIb2#Do z*!MoLFokl)5f0a%=k5p7j@(w-2v;8b)*Aq`#x>3efeY#4ieOkITm5wp$>o<_4}rxu zudolmrWczYhmn50Q%BFj*1nwDaF{;Enfnk<3*oOJ=8V4FJ<1p!3H!T))UWB!oei6Z zZrVh)$63<%zzyandu9X?Z@p_30aH```CDP;;#=iuaPe@Dp+_Kg}hKv{Y3183$%N6YkT1)b-t9qjsV!oUI)+OW1@?82+0dC*EEZYiZo1Sc# z3p2D*J1s~(E%WSZIP=0-4MSMm^g#bG+#*m~qz4OWffMp!eu3KsZPM@a>iAvQrSO9} znNM72)}?ybKe0e(8tK3EYex@EFX{7EfjNh#O;9z#_|BtOD#QF^ftD*_u6XAcC74++ z-jWDc2YfzC>UodH#dX5nJ|VBke5T!MoHh*W9Gi1xDoi~(BSvl!&Zl&MEWZ zgvj%f*QfT=H!g-N_ol3zj$9m&cPkWbKWh*;gVYZvO^AnOZLeO^f;l^mH{OC>rl~p3 zf<-EeA3cT5tN)zPA>PzI$G1OInE3k?=~1;Y6@Np^dY3l>aH2#0wSqZS+> z^`UF_?SvEd-gZ7n>|13JLGn^5!=o_eplD49T>Lg`%n6tlE32^;HvVhge-fs@n)7lC z-1X}Gkz827xV~&7Y!@9=E}DpSOT+es%PAXMT(2B7{aRe?!?u?^d*{! zv*GGTtgy$#diN)(!U)<0Bi z|B1Nu_x2jIf0S`*AI;>f{SIw{}=;PeP4f8g6s37J7r;6k$rrL+2dWUwgzs1r`Pv z`)0wE9nbEn!Q#2L(=*_bT!yzg=|5EQBn_?}zu+h_bIDDK=^Z!PHDK1ZI*BR4n;y?3 z{fnLjpFn*Ob#we2n3vqyn+xZU$Sj)=^LN+klIyXgFZ7)O%$RXsUI2Hgy^S=6x$hjC z$njYPyY()FIo|`H$YMOyFLP~(*-ygj=&;CoYNHV>exy0s6E>$ROU(IUv5N~wYh4>4 z+vCqs-BSwlzyIqofmv7g9BhRXUK^X4!Q4mNEBj&UucO@zm}65VnNL>i=9epBX0ZDO z56q9we7|>guyDHkoLz7+qs!2NtFqs#E%y}-G*V0!RZ zXAZ2YRrjlc)UT^jih?=xFF|);zVs-``eJ$y<+i1c&)-B*%)rTORvV&!KI7O;Q7B>ShZ@KN4M6PV`Lc(w%=$Bb;9 z2G+BcYeW>269=U?vLti&HM>zTC4E0}30 z^{gLeT0~Hu!L+t*&t*=-j9alKFn8A;+sb;}RKjL6U4#xOr4D<%pyRaxj@28(N7AKL|Uw{A{a3Ud<^e}}^T zA=HW1Fg;LEzYQK7BX+ihnOj3%_`%s{B15KzHoPoMX49ec(e8UW>~eYaw;YPS$O1>ur7KKT9Y=DD)et4ArKjtcH?8_t`FDJ<>7tFFg^*_0lt?P>OF#m>c zfCu`m&)Mk|!-9xtt#xqrvw`j_FvE6qlMBq3dGz8AOo?5`vW9hZw}w52Sq5Eq7r>D& z-)}r6{Q+%#v*BWowr9^szfAx3>9E)>`pI*ct#_3^63*VD| zH!X$dWIeL~^zF0ttJkq{G@ z-S0n{m^MGaRu4|NT`lRSPoMCV2K#S3{P+{en{R%Sfpyl@YZ3E}K1rUBgo8WvKfr8{ zy$_r4ycB(W%we*B)b|@tmBCi+&n@1Pdj7bUi!il?+S&?pn(!ZDz~T89;)Sr_{mMO? z;7nnw)+3l1d0@W{Ogrv-u?}X(FZ@cL-=x(S1>Gn8!tK3uIPF7jekrL}rhof|=fC2e zhs>{%e8s}uEwE{X+Ie!mMEOtUZo=v#dHO}Lz|Qf<1z7a#v*$(99~2^a-i&rBaXv%( znN@aP$Op&0lFNnZQrDW7z|7BTp=5sWSwmr}aCDKLE15qmsifF%cpmQJv~0+LDN2S6 zK1^?`x=PIU?Mq68rMEZ#Cx2~I7XYU%`D{dT@mpD?RdBVtS}B>I_|1a?1vs*5^(eA^ z_Li#seR$r_ZmiprMI77n^evqE32z@@fy0{`A>5+gD%oF}qVvW3aQC4zd&%)|<=S7A z!%ZD^z4U&9T^W6!+l{sEH?A!lJTRCnje}0SMQwPf0MYtB#8?bKYkm2 z4;I~u&q;z!yW+fSV4nH&pNVjbx>86zj5nNr55sbe8lxoh+ciRBF=b4{Ym$5WcOFAN zd@El59n7C9+>-`#KeUeRgqg};r=Ebt(-ob_{1z?kKAj6kCRf;#`9~3tUU~**tZ5$e z85Yu>?mG|5StZ;d^PiqF_5MY;%zo~rZ!qUhZv11oWwEZrEbdl)HH`0|j^kD`|HaeQ z3>dK7i6qH<$M=nfH^OYgJ(Bs&e{Nm2AMRS4@qx@|Mm|;j94t&oa&9Nv&$m4Hk<@Pu zDS1V<7w%Cmi}_Ja=kEaFHH&~{f}6U) zKD(3YOJL55nWK~8%7^dgSip?4?=BF_o*2zt0Sk9@TOWtr75!$fhDA!znJKW&w$Sfu zV7iA5BNYyR=Y8Cj$h&9Cmju89w>OgQXEXP@1(AM#8_%Q2 zgMN93hrs`RKR6igLyv$t`xdAtAQ#Ko7{$P}rLUKAU~^^1VGc|^Y5zS2R#e-)_z=u9 z_~O0?b{X0>1){eh-EQ|EV7`3g3BWD-p=EA~JgCj05=fGz{9_jDDv(X7o z2ov;NB>vjgX9>5rbWFbvbB0}7OklQqvO^8&pZUgd8eDnO$l@VPiPD)h8D9TcmtGIE z2TH$>hpoo?TQ$PemtU(!!PL-W<~D|!&EsQUQe?*uMlP)kttMw`7O3V zd9YQ~{Ey18s6g>p7F>DLLtC9VYW{^(*wpE}ISuB0{T`4Ex0}@LoDKi?^M%S@hX^{% zRJvWjL0)pgXWw#|(|52l6lUMxWUPg`*Ryj%;5z9`O)fB{cdEm7n77)(-4AA)J7vVd zRxKYMhrwe1BNxxZI&zGpNRrFsNXElw#KV-BV==y23*+ySdb~CqX1;s%-V@GVk#}(i zsrRW-iy-H#Qm5PprtLdFliW`^7Vb%2Fi&Y==2+Z+g}$4=ko{v!wU1p1*Li8bv?296 zqE%aA|F9=Yt6)LYr8P-#wG%aJ1*snu^Y$!ktfW|P26OM#YQ2Lw6gjFfENof9mBRBy z<@`l2=aG8G=KNVOE$F?HF3efIH_aR_>yC5PhDGZ%jyl5ChiBH4@uzhvuL*~Z58hv) z1T!}s+MWW-CCAUF!u0x*ks>(K+j!9=n3@+g_XbSY|EWaIxA@AW?=N6!>n-IIVUcdN zth(3A0 zp46uwNsgDIAtiY~&wNi^WDVnZMv?bV+F@-4wytg8T4728iNItLVHrnXA z5oW%BIY>SqXg76}Cg-1Cq%8S7f%QjzBnqan?{f|#Hy8eUw;vY#Td^by4z~$CbO7e< znH-x9bB+EUKLU%!UTP!nSJa2YZl%H8iQ}z>u>Y8-11CuRgyVagVPT~Mp9c#Wa?^X@ zcCQsq#iTy;%!ViAZ9SX`9fb{%GD71vFH1!0|K6|m^Ld5#85rS3mk z1G8SAd!|h+@7`1gi|fraXOsTU)wiC&!qwxp=@LIIoB9&wgf5iThpS}@OYbJO55$LBUf#FszaRf7GkJdXZ@ z@qxiZ#SXCf=QOHsjzAJ)4PjcUe=r7J+Oe)J;ws(Shss`gRMG! z)0UI`ST@fK78bWv+QQ+6W7mj0K7_7L)H_Vj=o zFupHqmk#@@jEOHJ{jB_ZC2(e8>dSJ{|6C~f{D3*-ODoB_ZCR4}E>iDQD1`;nhpv$M z*EwUp{WX}X8CJoBrC;0>U4og9`$XH}Lfx>%XJKBu?R&zuX7YuG^%mEy3~L)xH`-a!Q5X3|RIw{c$Am-7)hPz)atR zQ$mTydMMh$BKz4#0$|bck7nE8&YA&RUzp`H$NwO#ou@Ho3(U{wA3Y1FS*P4!!GaL~ zoljx$Nm&DuGZS)j`(gdi%0ALh544e$H^=&!HzX4X3tjFNYQvPYzS1z5CLcU{5!@wZ z(h>oS$1b`^98P=nU^grX-aj{_UY@$jSz4xWQKZd>iTVe8HI`_Gg9p3$YBVA;FXJr_wX)SWJbhiB^SBJ+hy znYr`~+-S*Z$}`bMuz&F3$Qqb-_NyutZm|nXuY)-+hTi_f z`Q<9y+t)z)cMl#C!pti3-%ns>{8GEqh(V0QMxC!64wHS?!@goUxj-i~m6xH|na>2HquZUtwGEYE#~xfd|e z;dbWC?r+3NmId=+**E5^f58m8kt$hleO_O_J_HLaYEO-a+3Q0c{=t-q+&g5w6&$=U zef$|5e?^;*7}uj+>%HBRVQ$Zx-)~_#_GPDOuxP`kWzXOwy&OGlSllUlzZMqXQ_0kU zDF%7^_esBEvcg=LAK|i)tj`Hev*ykx^|hM6Dq#1Li$M!u#?{|t;@e?OtV6n*?v+bmxHTRqp=0kMto1bAYd)?&;_HfCK%UgGm{>$}aB48hl3WGf`^~LHW zF5DS&O>-~lzj*mX2`s);@pT`}d%|~ahSNOy{>8y`1!v81nC}VHxV491{%(tJimH2~6JG)6twON+f4O@%M1?Ne2ZJN6wq1&c>J9lA~GwM?YT;h>yPr|-cO zUp+w^Z2ENd!Uv>2Y6KC{d{ zVfNa%!RIjd^UvoyU~5XDZaYkSX_d!?8Na{${s?pE?Y>vx;;7W0pGZz)-?#=35B^H* zhJ_|seK%pM!iU1|FyDH|;tIIZ{ehPBS@Z`Kj?}@K-ftIAhDCh=bDzQ0a<>X~VY+Fi z@q1Xfh`HAYrtDm|xf_n2`||E0;`%vM#hh55{ePcm?q zXCciAW^7n3r3AAViOV;^v}~W0`NZ5iAN@(L@nfqg-10JJe-O-SOLjGfDZ4DwqF~O) z>{csSRr}M@gD{U$w0kw|{!ahHF<9i>lH&xk>$cxG2~#$G)bxURV}wi3kX*~*xd&Wr zn-f?Bb1ozbY+#=QyRKb^X}vd7m%~<`T*s?0H+a>?#W1zYjBx{IP$rzxhgrU-UzU@4 zZJii7xc&9jh)S5P@mS$2#)Ca$=F~f+UbS_}6PT4V#gdr%Ao6K3Oc6)Vy#`Z! zf&-IVu8`a}?sNdD-!i@T0?eNKJ9Y!?!k%rJNBma5(H1T&PuNV3kG?;0!!kIiKE||f6~WrAoVtau@MhSA ze7Ie13yTi()mROu;p~I{!Si7H;5Nzi*v*h>)`7Y5PSzR78HX9>vtZV(B99a}>0|ig znI!+$`#2Gf^KbpE28(_@Ea1Q{ew*)6Ngin6upMR_$&8YN1#+L*TVel~H67z%%E0Xf zn@Rt+hTl>oFHH?`hFiJ^2L9ms5@b3oUj%FC7^L=->v84&r3$dB+xY3dFnyK%z#ptX zB7G;jAF%Mx4cjKzWq;LolJh?J{w{{=f4@lnN$QyVe$DJcY|Q{4NVq* zNq(kL!wT+y5j%P`_JCiQO1pAk(?bIXn0}6kt zN&Pn2^@cESy!V8AaADTFbh19M;)~R8llqDsdF1?Z&vw2jht;1l^U3+=c@HI*!e#3| zRFd`LzxR{EjGQU1q+hA=>Sg52$~Ia;{De^$>FL$`_LRUAtla zs{~R{zTbkp*skk7xt^$(XY~2PLGMRgQ;B8Dc6z~O=6A&zut0uj^jcW3YFIiOX5U{U zUIxpyMX%<;j9JZV=faGg)8RwIqL87~h5GjUaQG4-a8{;9}AQ zyQk0dtS9-;@URE4sZrtc2AC3bP4c`mY%$H~1x&N+w=YDVt@$IU4QBF|w4NfKS$F*n z%wMc4`Fx>%=k_!2VBW=S$@?kAr(@$jllqCxlE3%X_t?7h!vepymIU+{#yh_nhB>*( zwMp=x`MmZq`551lrIPIl1}ABaBc5b3GZ#58#%j?dn4iBd=K^fE@SUm>%sG5cvcH|1 z7E)DUL2O%G74o!1k4SY`M01e*{g^f2W~>QwT`Q0OM$Yc8YSw~j#cDL6-&o#o|Bmld(uIXv#wmHj{q8hLE)G}xK+d1e?o+PX$f=|A zY=6Kd+IrW>>(RtsR}?WHtk1-j&V)Iy-Hxq*hnu_|$o_G~EN`_np7g7+!=(SVf*Z-{!*xA=GzqG9yIX>~(htsBFeQP%t9MXqrQxsfQ!Ti+j#`!RH{p&IA zuqfkZ&;pn>ut6ajZW;D5G=T+EIxEh=`q{S2%}M{UF@{yJ_Lcs@6)`})1}ywyg0F~3#P8}{NoDq9L}(Q!1;j+R$E~9@1Y$%aA$K=Mi4CQ_%pTx zPO~uSi6H&s>MR>!ap3%5LZSJp!ORC%i> z99{CZs10W9dveAOw*C|_)J{CBxpEb(m?-o5Jxsm%?V~PS89K-5BgxM!4by;qQa^Nm zhAGPVQzyV_jUfi#U||MV{txD3i|iGqnDlRYO(XZ8BzDErKA5BCx}X>yi3$x@`OH56u@*H&zMDSC1+P5YN*d+phwPbR{Y+y{QYN&kav zxhb&Nv>!9KoP9xGw`%2stAEZ+ZM^%|JDZSLWFu=}yDN?Taa z)V=>DOf6VkVh;-^yzTCU>kHWb*23JEg9AU|{BcsME->wfd$l5-=a>TCZ%>$CA1!%) zZ0XySwh^ZIjre;b*Qu^s#DYb!DtiyY`d*C^Q?ecy-iB#=$Nk-eoH0rDaW!0`v6n&m zxtB|~{DY03=SjBz-{)yTmSDgUIrX~0BLdIMNe!Wr{o`4czu61(Z&xg~Lr%FH^&<)9 zqQPS-M`H zf%Sf<)`H9z+83U^8QlG~=(H-#zPBLE4G#bGvtEVd29uOHFr{8=EwLawdh=6QoF%j& z^^`09hkD@R7d4XY2@a)-{=s&QH??LW7wy=oqP868SLJo47R-Gi->n7f1a)mD`$r3! zP-X!0T;)^e!|bRcz7?$cRraqr$q)GL+XoR-xI3$?zh?0~r*texGY|G^CJ zFp{hM^%29}&)Y|K!eXgQ&iybYGU)#?b?VDOpKHrKu=HqYx$2NeC;U6GlY{qf}JC-=BLP9?!?~ zx#zQkT?p~}S-HPpg{7)%4#1QdWk37j`VBu$gv0ExG^0N-KYlIq5G-;(pg~!M`D|cM zO*G7LO{tKAo1>2U9)Y<>&PyC5uNH9(X0*OlAvs6?xxnAEBEL7$3~bkQBUIZ%WHgJ z!?b4&NxxBFuyp3ePcX;BP?FPa9i00}Zun4=Q?>+^|0en0%;sU#(?--*NMFF`Mef=v zjrA3c;bzLi923R$W8i|S-5rWBW3rv?SXdl5zHuU{r!{5B!|a>cuT^1j@xXFLm^Hxp zI)l`|G+r_RR1~BEloH1FR z>XK-oA$tTiCED<5^idJg69A7y{Fl7hQV{>lGe<6$(cbtZ)**tXFh9*=IpT-{20E4Y605}68E^h*2GGMK)hz^4Z0^aVy~!`%T5 z=N`fIkfrBoa9!)eqe58tjU6%tjy8&{-J$0e)SFJ$2%-&gIz1b^MAw4l~cQ_VXbq`^ilaZK9BPs-h%rUxNVh#*$EM& zFTvq2_B~aEX$Oqv<-mefV_TI-{j$!rXW+WY1$C;hs9Z@l4Gsy7Z=L~DcRKCo!WJ{M z@63jocV`;K!;~>oc{(urY-P?7cu*xo%>Wjj%a@LU)lM(CWJ3D8_FX1s-CC4oMskk2 z(_z@q&*`ZZ%<0~!5Ctm?^c`LY^G$Nn_rWDb`e&R-{j{Agw!xk2SNOOPkHn1ifGxfx zs(8VorjGN5uz10<`93f`=G(LBaGvl?##WeCJk?MJrZni^-$wd#roDcH>xW!>M&fpo z*X6oY!T#}1^8#S%T(`J9I3zMCX%EckHvV%AF8`{nae(wsjv5tA@{En1k;MHO`a58c zXC>;JrNKDHd%7O)pRHXQ% ze$>Q-N3iqm!{@J&Jp0AQVVKpM97asdwcAP~`{&@9*sCz(MRbf49F%eD-es7-W?pwN z%t*bMQ$YHQj^8*17Yq!(&V!k+Dx*qZ@uE$oxx|t4|5d=$1}MnBO>^ zW{t04rnP(8YM8gm(f2hhP`Ejn347@JON(Io{;8LoVUzvucXh)2!wTGua0zSdxgO$E zI~BITK~#@TKVYt@t&<<==bVk|g=uN8a<;=Pd$sQa#ELFIcfdT>)|`JZYubsV0N8qm zT#|GFnLmm@`NO``HRz*Z#@qgt#7bqnEs8L0g{a&IE*Sb?KM5A7DNEKzsio+%GO0g5 zU$VaG`?emQ3bRxj>TOY9S1f*@4$~iNfUY75GwYU0Q^n#gTUE%F;U&FB(n@IoEQqxzkYk!@Y4{@7SWj*ntZCiX{ zTG6KVT39LXPlO*V3a{|H1JgeB8wJ8*#sSHENu8K6eIG1Hp_P#NDJ4~3{s3{wTB+Tz z$J7HlVKDFFiU>bgn>uaUL0C9y-nVtIKWBgyMe;*84=sQ>iaTXuV21gHIdU*{f$KtI zmg1MXa-5H%33CgHCBJU~%RT$*beQz}c1q57zwj9UIQW0h>tz~h{ExxxyE$(^;(oq5 zTqBv7CVy1&K0u&8(=@8DKSN=Lh}7USEbX4&k~YPQ4q*`xuV4{+LTJ zkCr5PUXaPV!nsWHu0g68Id`=C>LSvAbHSq?xO61dtpsK$+C_hYsT11_%3*%V{iWpj zgU3Hd?^=>mY9*%0*H%3FPd%euf5r<~^7~z6`A=!a&9ES5+4k=+-!9?!dzg|vAX&e` z*QeXQ!rYF7mIKJ;ZtCRzfoYznBp!BJ*Ccfj>G3Tq6NxY0 z`JoTX_#MzZ1#`C8?wkf^CgpBA1G84RP8&_~E){VGOuhK()hM_pIx#W}X0qH*jo|xv zpXgb3Hca2# z2Npj{e^~+-#+SQS!qn1D3(mppaW_}ik@^6&YpJlDzUbf+nDSUwHxYIY_PEgm^F1H= zabODPZ*DWp*xoghJda_7N2<5N0@Iz1VXz?Y)#oZ>%{z>eBz&*O)U`4wn3WC-T1J_jeUw%F%(^4KVxm zw6POlhHPP=D?B*&^xDZVH#lB}EH9*Tq3;x!W;kB*{K|h*=sGo+t@vKjUtqX!G7aV# zsu^we1XK)qbhI3!;IhJmki{>f;SpTFyG~g^KQK>y;S2 zhXpnHlyp+B_-$h+EQ((JJp&%TV5j|oW^REk7^3#&BRcrtXcDT>lNLu0Goj(|>Axnuhxc zH5lG5%RvibLA_GP?+h!lYGBOXRq!KgIRICYSG9Ax!XM>VSfI}Mai(xOWExp zEDD)6g*?yeTeeD4&+AH+e1D?W{i`L*7p=c!*@|4`+AAg&46PnF1bZ|tT0_i}Z;^aI zF1cT3Y`i7eUrf&i(>utUtbP}g?PWG>5BvmsWKNWeg?T|wI;WdsewlVVEQNUX z{)MYyzRWds9?XjFb@GAx9)8L!Cb{%hK@9A?;`YniFy%~<$q88Q@5%ZanEm48j?1uX zL%8!RSn|FW7F@izvI}O&y;f*~O>T0$WD7Cg{=~_Cf>n<;r7FXe+#R+(a8T>fE7M@% z==-C;z#IP&&kW3P0BWUP5QsuIIMzM>Dz~1!G71%jjdqH_Om5qef=l2daQxz zL+kH0!p%~tpY4fTA4|4}@^8-;Usyb2^x78Wf+r(Bfu#TDc!{|)MSFu_es{%BlGo>L z-4hA3ezM+?&mUYcq2)L%O7nQo0aMm5xqTL ztsxkM6O79>9>bFN9~PK@x*D$uVS)OS6)LdY_y>8^TeDV9vp>R&!zA zdWzpWm{#zuei2-%zO=XlW(QVIUJAF{DSrI~OMadauHWnJF#yx;UPrEk3mWr{{=vK> z8@f$k?wa>KnpbeV4<&31fP+>#+v>o)a~o2EVTEejTShQ*ZP(XWnCWN~y&C3N^}43P z)#o_@Hl#i<)+&S4|5dejfJHyd46&gyUvJ4K zu0Eb#1p7;EejGyTpM83A6E3X$-4PB`K3^=q16N=2FgQvaF~0c$tWc-VJ`VGr(>#T+ z#eAQu$*}Otjm53-@SHianJ|-cq~i;mw<9I~7A&&8v{`L6#z+1yrF$@!Z@z2}?C0k^ zAb>eX^&{uO^@_PIm85^?aJ4R+m&ek4M(SCKGtA(Ek%i1=;=mvw6V}~2Rjm`Ib#{I6 zByJ7<)CDvC_8w!wnGqr9zrf7lP{B6XzhUEzA25Bw_3dGBo+$dmZ&+MAdtWvzRzE&h z?ke{0&RE?MxXIz;NBqZG{6AmW+(nicAA8P6QDMIR?G4*tn)^5Y1XACX`so79eK+>p zSeUMGecfv~$iHo~3@r4beg8@7%@&`fz?^T{7p1I_+y6=(#OLz^ii;=1GKQ&&Wc_Ff zvMO4z-z|&BUy1v@s^-AuO1;HzV6I+^_ENa-p@!uHm@YPbXbpF+F0&|sIm}*@^|19h z1}l&BtH(_ACjGYBlHk2A{TCZ_D*rdm zCw}X?^wG#;xpt$z!=ktze=4l7gtw*}7K?Y}Oo5fIcKjpfv(W9AttreioR;|l#?P}; zd|+Y6_~(ru|w@-c`Dhb zs$lEu7ku8s{FQ(2j#-2212bg{!dCX!syeY)?2kAdIXBi8o$khBCg4v;3y-~#G z3k=7S{l!);Iu8qHT@O`+SvCuv^I>Y|epzj@e}jwD8sPdB;Soz=v5JG^J6KJ(@B9i_ zaCP(+G2Hj}G}8)}yx)OME*Y4ug;{~r&x3Hv@68cRl1u5u4Z*q|I`3Ry>V&f9VYqPm z+d_Akqr(axfkm_fkBOODAsk}v+MrNhn0>Lp=novyt0PCo51qefs1GhFV(JFNtVCnO zkFbo&X_YXT@@m_-H?WqmV)Q{+{Cmfd7qGU+R`VE`cVWfmN3d?hJ?S`@E*+3s0SjOJ zlqchz8};>90bHoAVR9N4?%1;aB&^rIfq9nnuU@k_7S`&j>dhwocicb6zyg0)1#&)# zZSp-L;clN4+C|bIaOu@PSUWv#bP>tbH!JzUF`8%3UxOKESH9c~w|`{rxB+tlPI_z2U0C$+=xaMT-F0^&nJ)#ZyG_@^RC!IO zYM3_RnY|Um)uU{NU%`}L0=@+t<9*++o#d-k3M^sU>A6L3Np4@7 zY6B;n+bfwrMa!bJ9AN5;B{83n(@rjRAnRLt=%m9JShVT8nHS8w&i9kdp9^V{@rR3_ z)<5LpA?AgAD%V_!L&nyK!+0aj}i|UpUCYwsIUSI-2(nKM=)mt*|JV z2(ulchr?j*-|zm)FtyU)@fEnauKM&0SUBIAHbnBe1|2O}VD%(*!CGv;;fDDOVb&tA zDZAljuJebbFxzsjUJ~4We&E4!m|?y2A|H14@!n|yb5?HucmsC5>*BBqrgR9p>);TR z%3NERb=k46hx9wmT;NQce)ZQs(jQ$j-4$laM9vy(i}4n4bk8Q3W67CBgG_HMtvvLXzhr1tAV^d*vr>a~u9Q2_$_zcVwKIlIJ>+bO}J_`#} zKc0$%ZPO{6vSI4NycNgbQYqhoT$pn{QIiYPA20UHhq==g_a?z5%8EsoV7`y;q!c)I zn%9LwnC;$wn^-sK&em%%)pep<8Z5XmPreuyl{Qk+VOLT3s2e0-cC>)F+ilatGMMWY z@jV0fkGpcK24>zdP0NI};_j?@3=0F7OWZT=?~W%htxaX{H0l4a_~tWMpk#h66=o-{ zoACnX{QN6fA5nmzaWkn`)t0Pp-;UHZyldkI)IOgRp2& zJ1Yp*d(p`lQ-tMP7H4dNIaMCpC&CQZFVZ%|`FRu6VDa4h8x7#nv5j^!VczA#5es3S z_tm3wNzU&m(1QCMPUp>sDThZYXTWrwz%L76?zJ4(QJ}xGv?48~zg5_do|1^X}A&IT6 zu=5FPl{X5|ZEN;_!-~qFv zTE2wBvESV%dBe7E3lv+v2p+8D{O+Boz!Zf@Ck-!!m6xfuW@S)a!9}aJ{EGHyjpr zPw};d^Ikr7=KRMop7Uo#k^Zd7l3dPZzVhM!n5*P7@7RCpg=d!faA9#rt-lSH$M~K* zIvHlRYDHSY>1Mx2PQt9RnvW}C=EXT{QejT4kJtB6q#*_qGWZ{95c41eZii&ufP1tW$jhh~K1c%xjy*f? z>L(aKf3+?i*1FF6{RO6eviW)(W}h3?_7mnUc{%G8tn0_N8h{0EJ13roE#5pZ{S6C8 z{;4sCXx2($F|t!jf~=8rK~hS~2ey@z0}jDtl} zVfyi<)5-a%PDmmYWV^)sVB(~OAvK2X_oHKVe{J;5w79KH8n>Z>mC=WTU zMbjK++V68;472m@x2_@m?(bLGz~bvA0(+8EG|q2?T?2>SIl!!G3oJZg zk9UjuU0~{Nvxi&YyyZW*8)3$Zs_kL$AkQew8>X#rQObd1r(0QUhK2pBK3BjN9dEY# z!0er8vO3@dm(e{WrfrRqDU$7FX@42h3M&bf@Ltre{1C@cv$&xHXUaA(sz7@4K4Uv=9Buy zkWD?XaOC@=IV9hG>-HB|=H!p@GyhZHr1sNWm3WHac^C5f$!}z+q(5M`)@tcL4CJ|^eDLd!v^3$XQ$zQRnHzF;6N4K4_1Q9J{)_L#~i!D_FY9a3Q0ndI~G3>5eIXWcMTkZ1xMfalKGn6JDxv{1P3LF_+&Sg5<uZG|2UeA)I?S7I}TxkLw#qy@Kw} zM7UiwaUK)qpL*w$3lE>lVOqf~l}$3YVOC~C4}w3)5UIB~W&7Ro;_g&~~ zUk(d9N9^9iDNBYwEhGK9UfaLJ-Cvv(SwJywMq&O{vyFKlrwP8+# zp{hMRY%1>2gqfw+D)+(Sd%BJ^Sg_z`>_ymlw}H1h%(GtecC0<-!`1~7v**trZ48I| z?b$mEIeYd$pChoUudf?fzL=sp!iO`jsC--qQbb=WHUq1N2 z;jypV$^Pa$3MU7^YPK6)++d;qo9nw^;q%lv7&B@Dye`osc;Q>rSlbcn(wYVp=oc(n*br$@Q0> zej>OB&a~A1NzQ)?Q#y+3i1RVde>Axs^Y6aVG=g1Qo}LiHEZ2XEA#kU4aQqKa&-pMv z9Ui`$Cz)Tk*{bt?llnEc{mAv0ahU$r+zIp3kv+@*z-&8e##XpbXm^NQp9Pl=CU9Y% zO0)1U$#dG@+=7R<@0TVyUuVh9kFe_W#GHRHJ#gD1hV%c;f1FL63OBgV{lOp7FN(Tk zw-@%)I<->@^P}MDI^9HArf^=&Xfj_~r3dA~3Y(1@$@QCA^S1Ue>2C<@oeXo=&yZ~( z_50q(k@=a&Q*LO7#j=q*HDK!JZ#Un={#%^eXTkI(<6XMo^l?`t^FM{>V)qf&%6i#K zuGh4-Z<*b&!sbk8xn7Iknx%Y(6E4a!EJ^>s zzR{g==dQrvXSRt#h#AIm(XdH* z|CfU>^ZwkwyJ1(y$j@=Gu%=ms>_4rXud-<{;}^A9ANGISZ+V{7XB?R|7B(zjyzUAt zh~_u+;dqpud~H+%bAD|oz7Kc0rQLZ7bL%GUybLqcXV$)e`I;}~GGL3~??2zbJnrtV z$4P(i65$7!HfDq!4F|2XX&oT_t}|%6U`||ZsNyxOPg}Ie1@3c5`9g&SQ%5DOhdt7r zk0`<7RrgOj!Fu+qUTMSBm&x+W;d+?~*19ldiFBDET-uO)j1G%9J<|dbp}`mpfX zi}0mz=Cyk<#-zS^+>0e}w@&L@b68NhZUJ$-LDp4Qn4K}^yDrRYbjlBa8RMNKIW@xC zB%IVIS8p~#PFI;090k)tXKgfwQ}*_kABOqH=P#PTw4iqmDI|AGmpEmgU3WT6N%+Gv zMeZy0RGkNlA9s9Q39}4@r!K?nteRpwc-Z>Q+-sy?{lgDOnD3sl`!1};4mO|JVp$=j9d3t;M6)qp`*7_;oe zO*r%1(ca0|vAxUec6Gv(Wi1z`!_*Gh4l$g5yJ6ydn7h0tR1V`sZtjf)Q&_-ImW(Hw zcO!odOu43=Yl~dhNv&}$Oj~&)i3w{hnCt5Ri-N!3aEDpa*LS%R8@7%#BZyOglUaSkH^EqPWsEDVCwtiv8k~0 zjw<^jq~0dt#W^_dlx^}cn10Atp`7#=*h!4*a|tm^%kuws^0N_gzH9GNvWi8$VB5SG z%ImTG2gx!qFk`@Yxh>pY9&<1ZW@=RFABE{-s2g^}9CzcHEwIS0%6k_q>Re?l?}G8~ zlJc6EHqqmj1MK|z=Ot24yAYXn5RRSZtakvW7WEaDzzVs6V~)duDd!8dH{gC-f0I%^ z%!vIm`w$#5ug0we<{h?-x=p+~%D;-_E9?wqT+#pL?474D-*C|*bC|u{(fS3<`p}CV zkNcn4j*8E)aE`e`KP)oJQSO5|>*oeJx?w&Tmh$@xGiODHSHp$BhsMemqu=cA9Pf=- z-bwdY<6wUFqmVzaU*L&Z<6*%xR__*foIhjTk0`;kIH!S09@xJN^`1HIM63gT=l zZ*`b0Y}pn7*Cn+soeT4yT}eC(XC{{#&`Eu+oyKEWaDV$PBbYh!)b5`!W$T(?Ym(3C zIBnpG{drAF;0%krcTza89{y5?J1hu0tds+b-)X$s3^R@{4XJ^L1+LcpPR#?J6sTDgCt)YZV`4+<;k*7G@me%&?8-6)^pPk^Oc! zc9GAEDwvzO=C3o{r)jPBfYk3_(7Fb$uL#*#Pn_neZ3z1xs-M;X)2yF$Yrx?i1?sJ& zU#Fib2Xp_8FL?v=rl&QHV0-H)U2b^`Q*OrS55aoLW6yNLjKg-Wz3}j4Eve72@W}z4 z&#*_fY2H_uC0w=ZJ*=R8c77jB@05yff(4JZXAHn1r)t}0u*F|(gCUY%NFM(PF7ZpK z{|AeoeBe~UjKhn{q)U*;6|Sm)J$7{-90Rki`>!pBt2gr$$HU^gM`doqu?3AvDlkvR zSn(Fjy*^>ybeN+u+4&Bv`r&ES4AMW{SJIy_v`)Mm=-yZVF9a!35p#_{TREwK`=dJ2Hg`DecbDm1KV!8Wb6ww zmlS4{!r}7{n+CvQx09vKaOuAZHbKNiA8vnvnP&y#LrDLYbxbj=r8GPJAS`T9kN6Fj zUn|T#0@KoeR1L!=HIYikU{1GX*$q6SibLwfrwcvxmy zM>BD{de&pOIooFD8QlD~x z84mkBa7`V7smXtaSHglLyRMA8@qgownb7}z0`Ws_y>1+z@V`-)G+^pIr*d-s+P)d{ zRTHL_sV#^j`Sk6iolHE`2A8j6%Wj5QueN&%VX>3PtSvCreIcg~E`6D8z76J9-u_etSIb^h*Z~XA zj`g?;YtR3GM}b<8tqMmg^k`@HzJoAWxOa8=G>be=LpwZ1{mbQ+}BSOtl@&rk45K6 z|Ea41tKsUWnL!s}+Luj|`MTZAwc-XW^m<^RgS_W`h4USlQxfW?30oZgo?TAr)k-JP zV6n8S@;#XT%qo03Y_d62r4eRsz1z@->w`km&dVZFA8$0J3--IcpZ*DEseWsI3$xcP ztoRPIxoNA~V5J(i4do{K569hYgbUI%k4}I^V~2jW!2a6@1@QX$tKNn9ZOfr;$ki+#>v)lRuI8o7aQVu+6MbQE zTu<^Vm=+^n8VGa$WN#Xa@mEdXd6*4zrv4jHgiTKL?AZ&mCs`Y+z)ah&JNsd3|2OMd zF#E&XE1@tilodG-9-NwO9SKu1bS^J}DGjgekHE~$Ue1=pr@nuSh1mmN9-c2^nzVSN&P+zQ-4%l2q5*RvsM?8{^!?5orDF45_N9CLYvHw>9Dq2vUe@X)Bdfw z2s3M(|2%|6M^2u;MDj=He>{e1OWjsphQ;^P=RSwoIf~D&!7{nZ+;+ z6`x`H0lV5_((f^4=b z{^!$G$6-$WB!M%@7roOz3j3M8sM!kh?mbz}hVztJ4FNDses7029MiOPToBAX8XDpN zGed+up)kuJB#;3QAKKFv35%I0S1uztjlLunW^6p=K;}0k-aOSr;-*`Nm0*#)cGF3i z-&B2D4yGGiQ%Q$u%4K>Km{Y8LB@?DTt1TM9_~eI)i_XKW1$VtVV6ky*-9=asP~6fA zTf8ZqTnG!-Ih((PgOY}#iD^!0sV_+X>3@1xVfK&x!;SDTZBY6e%raa5@(CQm40;iZ|{C*1a)n?4jg?R<+jh|p%`fW`v zOmP{r_bbeeyc}~J&KqTR^e4$*JX&##)H8+I{VVNaY;Gm9azs8b$M5l8<%zApITm|MooN!?e@sk+oX)x7%ugh+jK8stgMsjy$ z?_F@hOev$8FgI&Idnc(++ghm&^L8g4+X5R}AM2&Vj0YB1JYlVMh3*D0W!dOmuCTS{ z)_x-Lr`f&5_*mirEW}fH21g1aD@?*k0er)w(*kWV1<9cGzq!aVu@aX+J z-C$N^7jrgTt^7*I3uZXlO`ieFWae-2g++D8zA3>$$yXL`Bl*4Ve^hwbjcyeS)AI!! z5$0Pqe?U2l)NftpUJ2_()!XO7>?7;>V z@}_c9|1)XeES&l9j>lbCM4wQQ1?#m$7T1zo!{SsT9IItG(g-uh6bWKr&Slf6Hke^) zbbdd{51iERfH@_J!T>lgo9^C8@_9}zJ7JHTZl^!NqK_j(J}}+yLi9ISI3>2$11|ZZ zA^r(d&xjnEaIE$Rr{5%3wEVpS&Z`J;l)jDiiPqYo1#>Z`-ty-Un{@%<^Yc-6;+ zFm2zsMGdfM<)Y%{F!M=&_AS`g@byv)n4TW??;IR7`L=-_%$_qGas=i)F+Sh`Q>W=& z355$k{2uKJvu>VgX2X3+slVJ|#xbef?XYd#VcE?v<(bL5K$x*`>7y+${bR$85V*Vb zWZE`Zlp@_10cZXwf8kH^rUkd+VJ*)e?Lja@;ZV#axWKtiB@E_X7{z@G_oW8N9)u}t z7gv0Sol7@;iGf*ex7JO;_NO;Z^iPEO|9<{(gi|zgW0FbzO!Y(lFuUS;Wg0AO<|c6A zYLBF;=ZNoZj(Y?9-Tc~e9_A&Rrz&H7(J3ciT!7g--hU_8Yuc);vln4bj{aRsSj$&K z^)k!~akpi`oSUbT3Ss8C;i71mu_#kgPoZy?BG+rfYoqSwAZK`lNxp9mZV}JVg7JOU zcM;}G{}l z&~|euLlJXtNUg~JTV_EM9MYJP-M%*X0qCH1cjCEo|sidLB1gsHML zClzmuR|gfddoU+ttMfc~@E4=xK5_5I&kKmRI$J&G&}spfoAze6z@pxx53ON#?|M@aOk3psU^T2A5cq`r@ zabD5cQyQedy+V@v#oU#jMe^z$l77{0m$y1FPjI+b2laiw$`%{^r@u~B?J5K2r(`S6 zL0+dX6>3ZTEmcy_ubjVaBP>vrnP7xmZHx2{ADC8T7jF*Jp9h8Pg++D;i`Ef`j|4}M z`YydnPuO{{QG66kjs9cr3lFEh{d$b_-~DxBAF01%BAp0xFxGA%L?^T!{Y^3)XF5u0$UJP?Of(Dvl$|kR<3YhA0jw{W={8TOT z=?To6F;izH9Ba-tZid-26Qp*-a#L%D+F{zBL(RwGJm1~L9Wcc@e_KB3|Kpj^Lp-l7 z>>-@qy zw*7_G)U^N5V5Zr{A|)Rjk9&#S*)U!Cndw5fVEFyfxx^oGU#^CIDL&)pz`{dsv;*M( z^$T8J@~?oqw=+IVwx|8L{Ul$Uk0(}1Ox<&~!VRtuUnw!mcHh>Guv-4Py=41X;xtwi zJp3}&Y$5R!!S6!YV&HNK9TpwDR8tDath#Gy05hF^E1tlG``(oq!fey-q; z{Z*Z39bv()-Dihjk^GF!ESNDcSF-%(N0kaYNZz_mvOcztWk2sGju|SygZgxZD2)TK z@LNSt9-Lrc6@3th5fX`pB{o4QtbT0BzJa^i-oyw;~V^l|NV!;VR(VdnI4l!u2(s{?E@_29CTyAf(Z96VnO2-6B_I~bp2BoO!s(QAq5xC zFQRq8)G5+A?{WUAN?-7O4U5Vu9$kmqzmGL*hlO{Sow^KjQ|3z6hyS+iN-~`JI<@f? z$)_r3M8irQ`#fKe{^HH*TVd4?_tG0+p5SK3Y?v-ZyF|8^_5EsPJ?4+nk?qa(q<_?? zlUcBD>Y(0Zn0dGB_a0cQ{Ex)!E4__mJ`;HUl2{n5kU1AFyk+4d*`Ki%GE}(U!OVp$ zpZ-Cb{QtM{@m@nR7M$8%zt| zv2hfvJ+y7Y=|M&c|;M?dnU6^sz>dbkvJljaC#V~De zkxd@VHNWF-0JAc8NuGZ;PojTcM|?2uXex5mF;Tt5lmYuA#C{`llQzO^Galn49NsLQ z;sa9;)c-yK>-B3~-VF=q{9SemZnhEK<-mf-N7CnD?a#GOju3A)m#j~CY>XC{^!La< zxQd(^w|VYqn0`lU$z3?lcX4_qam0@)lIN3Kwso9^DO=0VKSZv;-JF~c3)37V&pZ1r z@Bdmv9CPJEA9DKm^Ex+R?yo2E6zu=<3AYajV2(Q{RvqT~n6G>SGZXjCw}hMTj~r_u zxz+mmKv;F5-|G%oB;UzB0aK!>_C^WUagt|Pf@{@z(ItJQG2E6iO#c4Qtb@*Z2f3od-L%y<#ZRoG<|4~v&8M=c@s z6I!Wda0%6uzZhoDezorhJm~$-fDSWMc8s1u&MyZi9kM)XdsUSaoc>fQWG2k7{Ppe# z>^E9lvVNj?J<1(;`0V&;ROBL^V&(5}LHr{VIpRlCs*EuoF!@80?WfAWptE601+{1t zS^nkg1vg;Z6FK83FlR;9@?UUx4Ym9)_8-;rnV}2juX^*dlAK19o*DyluJ%p%hn&0G zu~tatw~XHZ-zL#!^DtkB#A&;e<#VT;$@7MrDmPt_CZBiZVd){*FJ$4AF(kL=sGWy> z4WHj9>(BZAcX2B`xOvX)Nic6bRaFH0haO%vjnw~U{2PR2j-PDOfSFzIZ_91Ld{Z_` zvVRyEHcUlW7_C>OiJba%+?&a;?skT?F3hobyiW)2)ZKDG4;DS`ky#DLw$xY}z}zln za~rtnjoWkv%saxOZYTYViYBch{fky^@P`XV-AS{7g|b1@Lg8lFh#@8{V($EK7#8fi zr{+ZJb%&ech_C3yZ6Nte>+4CdNyM*4A6QWL>(T{S>5bQtoiNp4abO9oo5vmsApN`) zncJ|iO7?XS%o4XhsD)M6n{kYAMqd;xte^I<1!lQ;Rv(AiQ)D;&faxFR z>L-MWK z|M$-_71*|TG503SoRU6H73PWBobQnO5VvAtLvi-ys{c4B@rF$;%=v3B$+hlketYnr zoXu_zY=H5%vlmj)PYIf6)(%r9wna>YgEoHC>xS9KOeHzP;e=)n%&B=P$vv)y4EMpz zr5gJtqrQD%!GaN3P@zyx)>mt|_LNiwwoiAx5)amML>2STYT^S8#cxo71!NVu7JIsdJ`${G_z=Ac;78}6ycPbw_umUsT zo(U{&D=j+)D;1|v*22s=KcvsW94Grj&am*1Gv_i~w`a|YjWE|lar|{yZ}O-LA5uTM z_6D)qB9*EBuz**r!iN*SkMJU3eowVc9xSMM9DV{8e&tFf!XAp1yOLpXx7oM^*!En0 z!YP;_WJlAj3@uupTAYY>?bX@&)_~&Yxho=_4@5+az2&sPzn>n)TI<3HMrYG zuj3a?(M_!!4cFfZmKlVFaR*oY#rZ8ZyqzU`56l1K-~51_@0Wr;PK2q_>UUCzm#t2i z0}C=Ad3wOrD@(sDAi2@vI7b*S?p`m4Il=a?7%)xi;5R3jo$y#{8LY(2hzNigNos!# zVbP?0E+=7%hn|cLY?~Gokx%-sg-Y&U#i29*@?lYmUgv(~&Ci-5@4#HgnzKh?i(nTG zAxv%h;Xv;9^;m)hBAA!FNaHcgshG8>m*l7ZOd<1G;kn8%Il=$gX-ue{QPJU;)6sFudODFp$A&gb32lGc9jB4OI(W}J< zF#BLuQZ>vpm}+SZ3-%4=2w-7Jzl;q`?PNZ^2~(5S(mi4BhgivctWX=gX$wpruVa>l zJp51D%@CLo@~!^}tTttgXEd?9oK6@VyUK7wB1}6zPT)_L*K_&v1(?@7LE`%Kl3hhG zfAma=1ywl-*I~MrlEmqsY%OlXBGGD9(k}{)UV9&=K4i{{fK`=7OrDbdwCu8cxYPI4 z#AcGuP?zivL+%Xcc9<8Fxa}SC(yVSy7qQY+iUP)$U$9*JS6K3VANKdE3jP5zR*ze{ z366bfEdB|LW)4N~gHw_|e(8mU%Fk0{VA1o0)&ZDz$$aiLSSu}X;UKBcJ+S2_+*xL| zZwO}pnQ^KM&g7i(rrgK={D1Rl`yTf$a=Z*V_?|2U#hC^g#?+S$lj>_Xt!D_QkKRpC9y-&=`fjveU>L#)&I8y7M!+N^b%%n7U(U93nyh<7QwvrEmcl%`&QL=A7S>^F2?|P_(NgOH<;S` zI`(nXA{hRlRQlSdT&w z(=C>-#PT0U_~pR$w1rP6!2&RyKx zOg*o?tO@S^wk2a7%)k0*b~7CFQPI*BX0%xBYk^fWuZ?(-`hObgtuU*#V_-W>S#;@R zBb-t-rEEXU)RUY15H5|}8F&z8o8HpD54)zB(~@BEq&Lw;uy4nb8`&hkn|6~6XL^KZ z=fMKEzweH~dZMhVBA9vF%4Qd=T5-|145p}Ek>3E9SSV*y!s0aRu@-Qrb@Sgkn14uD za(q5R89kD|ztg4H&Rr**^39z19v$KSEwZXYa&%hY@XyH0%9 z@Ehi_YA|E=j^ZJVcjql@{?JIh^0;L`U}07NwYf0c-a_j=%$|}sZaFN{o_Fsu z+&!hCdnL>gZC{xPTNs3$Tnh^y=p}B4UH>fq>)QV2iHC9bqt2W%iQ0u+knUUKGq?dTCsRwI;ELj=|Ig zWi=UaXJEzEWRmZiFS)+!&Az`Q9cJhhF~gC^8Z8`^1=CHm^>>l_sb`yuhyx;Tk^7b5 z+Lh6_VPTiv`jv3dr_rsIq(8wXcM;tDYR;YKusE&pnk+2tD_QXYX6;cadW-ojJ#eLL zFYz{VEAiJrMVEcAP)d;{hztDE8m zi?;f$Er(0^6Q#UhzRjZKd$9l01ylWDreD)L0qpDed2`5r>=B)leH8xR`j?!hm&U`A zpMOF^?!nD3kW61dOZPI)l1Pi_{)A|Q{$hJ?7A^AU*>H9DqIrl~@oq$C@ZqV+-bWL7& zI?PG#z5N9)JY>hqCjP+6(8c*zQoF|EJd8g8Uw#-CXN}MxD%t`HEx7e7CSAOs*E&U%Pj=E*ySz)ze9^@YmuPW8!oCM`d@hIsvj{DnL4yyD{h!%n1r zT(3$nTpyZx&kd#~v+rlZv0}%hjj-@=YRo0L{bF{k2h1q3@~MGSTnZGo!1SOWwcT*u zj#&n~V7{>Uy!1{opI6jI!W5fJlzFh$mP_{%fh6Cf``R^__1SB}EjXd&x%O?CUC|o< z1EwhE>)t1EvLEb=^0zpil`t=bRbd5lgQPFl!d&ZPuRLL)e$e07Fq5XL9R?Twurh8U z`M>PH$HIeev!z>LN`n9aM@qQrgO=23 ze^^+ZZf5|CWAxe&ll%%Du?{S__JvPyma>QC1g(dNIVxZLh%K9rG?9Gab(-N#c%LGjkCvs}X~jX9 z|2Wzp5jIJbS{4Eej~}={VTo?YW+n1DgX0<5= zqkiK(7kyy%l?!v`!Ie21M?7I>yOXsBtYB%r)Pv+d4ydq#2fwYCSp1}`#v4{@v5}Y_ zl+X5oJ5o1D%5(N=>`sB5ZT<btY5GR zt@k=HC#7Aix*zYe=BIjWn0cF?>;jiBJ+j;%ram!S5CY37>sUzY&%b{9JRBv|keGjr z6?+RVbJsdf+RuwT$G`P&z0}Fe1uN;E=175#zfw?({bg2ihekUEUaU>qdG}nh| z%Oc!15f{B}afZw9mv1tM*>XQ#=92v2Wm`;Oitk*zHkfkfQ@Z5+#5-|s6z&LjIIe>@ z>v~+(l!JJ`{r&Ys17_}I-dBU`@3s4=z&t;@SvIiJ?v|g^VIgPXl@OTvT+L$=OmC@h zKM$)>t}GZ2(*lO`@?qa6b%ippC}sTRCvbWFKCMx_Z<#5l6^39|9i(@nyX`tm8s0osYQi&Ok{JoQ1y>Au%;;_BmJ(yXO`u&)s!NTm+jf zR}3Q0m+RAB@fwz|PRY`R>F&3i`(Wyqs(d4uV%p**ACC2G3teh8%+NNU?+w=k*QIZO z1r7H4&2VgKvClSGcsR|*@G#bg|CS_^@hceJ7@Pnzv>hbthtS8uc=8dUGQ7KCagf`tbdvvIiLG`7 zo=?t7$@DB*HQm>V zg}dly;0_!0vMiY0`Aa$tmKzu9!zb5^&_C5iyrce_02VcJ*O+q9UX#tSLYNy&JrfU$ ztiGwY!PM%hZBO8=6{i&5!*mty;2T)8A~~T47UWs{8G>sjeaM!QydUqcT^EV- zBC+fkMNc^R$y>?y1!YonN)Cz7`M^;@oaz+5s}yEQ`zI`fIU_lz-@v~2#V;1Yloo{? z5$yKre2gB<%71Zf08ZetboF6gRd?RFqqzS`b0^VByu9<$T$uS|{#z0k ze>QC{bwV_mK<7+-__S$)=}7Pjs`GcpyXKWO9k!6UcN=FEmUlQzVv#^C+1b*z#K z%rCPFSq7V!^ouoM@fwDKGt4_ud_tSVukQTq1*eVIm01i^-)Tp)Vb4SM8t!~p`#-D=FnNM-Q$y+bXf-CQ> zzOw}8k1#ycVcQ>$5mvBxiG`Ut+}dI{e={svFvo5a9DK$`+83tw3{MD#i-Sztf{7zs z7e>NLE}bF(SakVg|aQ4X5SbU#qObdTO z%Htoynh9sPHMZBov|lMD^I^f#(Xk>Dzme;p2K(+;bnS#i9wp5>uzX%`=2w`%;@2x( zSn0^AdGMHQbsHF&|{Lkj}E{@T?-9+*q=lK`I zA)QJy-oe6{CI3Wlqk5IYYnc7(n2*#cjOUcf^K~${FYzrE_T1&&R1H(k?!KxF3yo%e zsDe4?UYN{>%_e=jCV+*fPUJ3z`MtD}2QW`@#tCaUwKv4_4$OUbxF`W0*>FT+rr~g7 zHmv0EQTsNjPsYf6_G$Dl&vslfDPJV(x*Vq5yq4Ag^L19oI-kM)7Op7#M(WetwK6^# z?V&Zla6^Bw7p}U~cp2-xt73CIEP6TR{g*5FzFe6pdRd41UZ`bv-$4HljTyV`Io6NR zU-&x(xc=*a=l(D?+}AE?dzOjalxOOggf{>ke9d~8I97=IkF|e}ufuxqWT#^dO#8C& zO1%*A$d@sCuW-F}Q|5NO!~1~A)+~UjOPWSJoAG=sBF)4wyEf|dwH7=df%D=|uMxlg zWny9*+E@FS8~z5*_g9))2;BKFW3Ulsy;Sh_grhu57Fg7yKcq_M@!(`#3-7xyU(wGq zw;kgt#XDFej;AJ)9oK|?D5tkFqNUfnA(K;&-bj;=!aJs&^2M^ zwxdRr0X)x@U7-drXL{b_@37|CEnRBF%HgGjzp!4@ew&PksnWa;%D*u_#!M*qjrv(x zyYIQeY?|_gd}8&I;jggui-=klOx(9E^K=Q>)*CxG(nYIp7Qi#1@WX<@4@qe(msT!sZD4skF^Ya`6 zW}$tQcUkPZ5qzKRt6V39>6L#{R{ul%Xwu-cX3XE8i?(vcm@-n^uP*e08NQjPxMNKj zJ!{R)B4J93@7giari@CngTLcp>gJrh#jsHL+u$s5nc*)M+>y6vPa(`JR1folt*5?v z`VeNh4sLaUm9%O@p24)^woe`5WTo<_|H1tIktNn}+1t~*e!xk8eSH z0?O|Uqdl$jitkK=sV}TvzlSw7?;TZyg%QdFO|Y>1ZTD=L9pAYA3CvsPNK+;G^H=-j zz~%ZMwY6Y=-)YJHS*A037Q>XSaZwS7a|&ChE+HO2>-!;CF7I!Q9?Z48AHasWRf{9d zVCK2=dtFKXs=N{lSZKG*b~CIgzw*Qe;>H~p7;umAgq&@#INWK~9JsP;Yl;i8OxVfs zu+Eb22i%Dr=-0(krVNF5nbN*6>w!UY2TWjqak(-rupnEj@CdP;$nPyfy+c4oG%s4W7r4;U*70{gn^DaMqA%N|zdsK=@zVqISO4v5Vxug_k7loQX zCoY)v^a;#WXsPLhMaSx7-ja9=Wu+Y2J2=3XYlmr4af35qP5rLMF5;JWa?D`**N}-a ztth`bwD%<3`YS+P8Ri@Rb$ADxwM$uVgqhWz=hN_fh2y4H_>ugFQ|HZ+F=f=>dHXUP z=4|N=bAz2r4{Du;DJv2Js^Jjr!?(`C;w_yflgF7dm~Br}(_nh@;K?0u{hpJIOE9lI z)#NCgMfa+`01N*O#ovTQfgVz+Fje)H&TH7#Hs;?clK&^{&sbSJpZhP5M8o{)8HWsD z-+k8@9Fl+7bekPK@|mt4L>#o)`vJ_%ob}9`+CSGv` z9x%P8<9!I+ue|l7J1jc?=g1Sd(ewKWVusa&|A-kUbtL5(p5+Z@<4Jq9d8B@E{HM%F zIO=BW;sBW6e#UYTrn3%B2_f-{V%N|K7%$(3EhAuVPil1ooNV_vi`);ZbG8Y6BIb8i zVgtEf+6ig(6xb<0QPN+mzg-neVCbBI6pGdIu;h*zPzG<81n6v9)iS}g7zgJckBACIkx_THE?ru2q9;S@Q z{wjs*6Xsm%fO)q~C;x@*&n7Sa1k+!}z8^oulu^^wGP4^N&FU}Q1-p%O-Rvdh4^JPx z1eXia{C~m%mA~CmRIDFVM@B!)?-5vUgtL}v)&3xUXSC=v9MZaL`*)bDw%q3~?79D= z+83C%-a#maQ)!JmNP7epb@7y`=%3f&r#`?urKHmFFy$DN*#^^dx-YB3BgKEx8c6=$ z{43TlTVsuK9ZdDn$nz!TH7>bT!K@i?dZI}DWqQ^Fn6dj{Z8}`pzUN9QiN7&^Q3Xfw zCwSc;agQa*LvRmg#>BHQcX_z6;WSf5hLxjC6imy@61u|nIUlCk^f&u3owhW|mDc_zhd`uo_(r^CQgLCQQfpITv%o1m@g#^;LtTe0Ijnf~m19 zy#nF7kF=7>Bp%~_I2ksft`SJV!o_}#ci=Lqu%sc(e=6hC-*+&z<*CXynCG3bc8mhX zw``o<2eLj`)rhp=8l61bCRnsBH*F)#+7V<@4+}DxVP0@>mP1=D%nD9dNQU|EZaP0A z<%cZZw8C_!YaJ!9_~!J$EsCa$*cl0Nw@5r8`CvNCxoGS)XvDw#Q1SM{Ou6Tc0PJZ z2kxM%E#btUDi)Ss%BUak{E1SfrKi{schUE`;RENTJ zrI;B*aDC3O>QR_IbNh-#N{CxsdVYe$DW&@zVNITyPb#ss*8>jRD&Kt}o8o@CLWA5rL({>yi;ZiYq7)OsyA%{$Gj z6&9CbLkMv6uzguv*tn#oN#tZYJ)}I$}@*U5PNyH5UXFkJ?#Sb#3!4!}B{Bg4} z-i!A-lkq2b%xqAD)mn0w%_sT4`{JBoGlicsv`D_w+SX%m+4db?OJSaZeEUt9H$$+!!I+5kF!Nne z<$^i5zm!J{ZD9djZ@x2ZcH?9VnNNaY<;|Dj;I8;-q`bIj?$xX_(fSSgkV`{Sk)00Eqq*Tr~@KgGKJa z*}q_(#hD~e*jLbyA?fcD&Ng3IA>gKmn8XWSGNNJS(9@Fn#yTE*sEXvLuRi*i#8t!V zSo83{dhI7HfcYl(wI9HhmZf%CFzd~KilZ=R&VvIPuyDV zP$VK24W}m%Tb@w4S_pIfJh|6k-(ykxieXA)?ZPa$f6uDFWPb5Rx4bWft)CtL@_;x> zFYhs|yy|f&nQubo?F~(^=h`dPLYO*i81x&KULC0Y4rbS8cWJ4iKR22=iC|Xe0+ur@ z|8ufrzVR<>H-y2JYiMdch>JR2-He9C7oJR$Jnvfe+%vHCj&UD{5odb6jVpqqHh13| zhw;QuHvjw;E)Q9fK-O<&&ghGo>Uh2n;%%qHi~+5W%V3sstGp7)S21qe1=F_O z8zxVE2FsO8ebXfQ*B{(gT8QUm-QrE=AN%-wCwrK`q$*i5A9sI^I}2-OFS@Qy;OX^*gQ*-CRbxo_NrDKPJx>lX*u z>B(>TKe#{2V%6)HVFTA!|9ig-Pi-rMnfDT6+7TD;xq0smtm#F6L;6=(^yA2T*v<8` z&U2VIW{Tzj+_S>wv;Y>JycsF0iT;Xm5Rvth5$!QB9X9FFrrdy8&luAT;GSEm56Jjp zt!E{=!3Ha(E}n$B3(}t^!U{fKN8?~oo6Uj@xKXB0E*9pcjxETBYu$Kw@O-XWNq)aj3mj}N!YpHz8S6=X*ZS?gz+8J~stwFt z5h!w9jQn3V5{nPWeJ19wwv(80n(oepgIjL7+ajN~y>ml3i5DNswS%cI+uQ%Zme;u#rIX~`Qh!bNIpL0E-bou_8V!x@UQa1FEF)q|6g){w0rYEn=dhCZ^OMUcrQ6rbEAMD|Wl+$prSBouKKX~%bg>PW#pbv^yVgB9A-+sc%om6`=UIee7 z_^K^M|6X(4e-ox%7*aHWN4!UL_%L>-^dEn5-|f^D$;^ zaNX~4$@`oupZQ^>0mkS0(z_*yi`hqlkHEC=%|WC*d-4RMZ*Zr{O--_%v(hZ`*BD~H z&k4CsOlkjg_#iCZ{XlX(;k@1ZuE0?b6k^Lr{@3X5ui(lF3uoVl`AI!FvPSqG&>M<> z2s8W(KFo(%&z7W<--9C4E3?ets3yKr4a~jw&~Y`~ncXd!U(A@MU7KNr`1dUhq%8Y-o@T8?@9Sr*83*HjF_r!S!2A4!vbyk z81j7~ezNxYOIW<*r`sl&HI<|I8I~Jp33ep;wq+JlCiwj%+dXb4%v|5@s0Opd%{{we zM(>!-wy?>@VX8k&`M`5bg=;8>t@gvzJJTCmVDS>&>LW0#^!Zt}Wu}bqz7_xbeHiuZ z+eTO|Kxz&7y-4YuPPc<~Cg@1=1@g=9aAA`+Wli#XoTpW^p%axp5C-C@e6z4ZHVPw%-YJ4pTs^#*xUydN{m zYsmeIJ*Ks20 zfA;S@|Ld^WW4S9aqfP(NGnlDsEU|!|q%m^^`bRYNf6vRmW7Gci~u>uL~?-{`j=7 zQVd+r>cJIa_Hhf>DX>CUZ;xbrZK?jJ1~=aO)=tKkSh#D|3bT3Lcn0`bu zp4iUi)<$soMF+D;lAnB45C+>0XXl(E9yt4>0`9TCayb#^eCX>F!pSqEJ|)BAj*73J z;P4y2CZ)qnl|{Ri%+Wu0mrM2=gzI-7SPtio{?sTT^(CA*6a*X0_~Thg>a!`Iei>GC zYz=uv;xS_m+=Ba6)=YT`i(d$S*228q(F^Kfp}n5>C|tICaZ5AFUnT3OW`XO^3JUIo zMeDkhTwvzbWsToqZdN5PfyBAYg~KGj@x5X$tXbM98sCQJ@l5wy3+!xEFhI=Gwoq$@ z70O?oo<{sor>zfWK6@oQ52kOpD>c;;^GU~zrb^=OcJF4xO2bs;1u!Si|G-?BnQy*V z9p-Ktt(pgW?wnGq4GWYPNUOop?72JjNIc%>k2+k=-jcNxX3bi8Z6Pf5jrCa#i-OE6 zXmISHF?|ipEPUXn0&~tCn{7kN|KIwZ^-iR3hQ%kD&I=H?e6%of8_Y6N+ouobCdX~u z0aM?-*l7fZm@K64h6P5I8#a)*i_au)n0@@t0&h6@iOQ)Un7OmP@EAg^nJ{BsXGkgW!G%rvFi*islFv#pbSZ=R_0AT2 z#LJ9Uh$~>hyn);tIPIF@+s809z25&i?8IETqmKCK_p29RM&^%}23TD5!#Dv}I29lI z0j9~fWd*_`b6Qot!F2rdsypGb>U&=YVUG0tW(Lek*&q53rVPADTm%QJb}5hf@Be## zp3|v2#=*2N7nYFmkyiEFTOJk_|GqZ~RvRjjp~8IEP0z=};Us+{9QJZ^1s=YPB0~A z18p6w6d}{*40DHFo7TXZUC%eLV2)_hatoMhYMkT;i~5Xi;|0Q~yLF{6n3PZc{!Sle zCmqxYgT*C3HZOvOJ)$GgFspyc6(yKa=vJHn)BpQ=BRxY64rzWa<@ zAzb;%rJw>9_=h&6!sYAb4?KkVm&fuF;k2_yKUBfA15$J1VB5;-x@R!``TY85IQMgh z&vTd>EoFENR`^k#`39!gcz=q7%Nko}za{Z0Q4*IA8MZXT;x}JYIEbfN=B{lg@ki6` z4#UzXd_R1E*;Rx2hu{&dvbrxY|9e?k2(0`#Zrpd6$=>|%0L-`8)jL8gyF=n&Uj9|- zcGN%i04o&nG|tNj6Np3mCG`nrKl>{Wb18}c!VnLsetlFC=Ixy#X@8BJU70e>zH(yc zNyO{-J^!`<=I`o@K1cEc;(i*!;^(^(@4?}_i%CSIs;qSz^D~mdGW%vD#S%woZlXR%MZNxUJdi}UG>snk!^u_4NSkkdFEA^7g!`Q zrN=U`8V=v1|MUro$F8*QAo;Q9CG`nkME#MoLjMlE(0_zDO*w_F35S=(T9fv13o4^* zV3AMJJJNpfZ09}huw1EfA-O+Mu^HDNW<0JM<-@#d#s$?d+i1t2wlzLZ@}1Ad)WKyl z|E!9JdADihYM**1Hp`bFI;T!@lK#Ft>Ym_Ij8x{OW7~iJPgJY=-M9t~>d{ zqRH+a{&1^>#sYU32Q$>vI8@ZjPr(PaK`EcLIJ!;RbJ{ET6&e_CH*r|69=60WW!@deAqUV`hcwmMkBqOM%cd+^{omsD~;g8U%UXK;ea zQ(ZflqkCxT7nn-tc{;%Ocd6CK+hTlmR|vPmyqn?gwPEX1+Y5J*`~jN43g)UL-ti*w z<$qc|;L0^`CH>DW@L84)ch2VO?MGbL;Wwom9`TtI9164C<_~^>&HBFPMGznLSSY;# zj&h=q@xhYjO_~e49rD?6hWO^G2fFZJn{!AC%=lv4V+>~rKioYJQ@8f7v4PWKY&Knn z`OcdPePEp}OZ~6FJTITzB)C<6&IvNUDL0fevtTpt->=Ae!u+}}{|>BRe@C+bW`8U) zY=EU-n7EMnL)E$%(*<%EwFynZ6PIwdk2`6U_PIGpY-R z?7TLy6Q&m)lViYUL))bP!u&SpyPIG^*}YezFeP4f_h#7bQsP7z5&BC#e49O7bDfbe z8D<=F;yJ>e*LG-6g$0%^y-qO8OQ~-<%;(HJw+%KK^ynw1#@>{ed$FoOhvdsXYj8oF z+ZX6;M11+R#I%`XzO8}TpQ8VBMm%l1inKk zk^EfOo9|&!;o|JuBwwiP(+acQ`R(N}CDSPSEgT#z`dkS!HjU`KBl*Qq4K*;=<4!%X zIB(z1W|D8VN|LYn=0xNtSTM+LBjaJPtoh>rvCN>Pyt0Xv5#>GX-z%}gF}Lj#VQvsZ ztqJ)`vjeBA!<<9qlJUgfmGf2~=3fr|{gTwTo5^Y+n6(fFyqpsUA2f;u8S}@LTtHT(%#tF+PAT_jKA0N za@A+V?R_4{CBjtuz0$Ji&+zl>H(e(2DbF*M;gOS#c3CiuGFf&p@!v<91tflUyySU$ zPHQ|+4&y^T&=hgu>5j^35OHLn?FHYxqKfYpX_lUiZQ z$Ge}_!fyRz?cc*Z9jiHZFhzFS=g%;8ed=LXxOK+U>~5IRlyk)!uA3*H(hGAJvHpa^ z^7>hK#jwC7eA!XB(tAtbZgU1O#^@oMvBzu=B%tWRbGz)Q@qW>vfyx9JlfZZOws3qnZJ+~`_>8q|Nt%1eQwwIT| z)TGzRR-}A}+;jn4)}!mN9u}$?m%kz9ug?hF2-BCmEAE7KH1(Rdz$}N&rqWE*H@UXY z5vHCOJXC=zAD`Q^1E!n}TeSifmXy`9V1c;sLkP@mNu1^ei!Fi*vSHDu!_4|UxzAyC`2roiIxsqb<~K6Aye9L&=gJ@W!?ZJAs?mXuEzJK73Y#_s-2<{Mj_ z_WCny|7VNtUzjfYgxUkAeH>%_2c{fNaqNZFM8WeU^Yw(W)lb-@d2`7RQhu3N)F9j+ zB~ASTGj0@w4a31-&+*$~{yu)|Z#bDZ-?9a!%~U`63l=R6R(MLvYfX^*4qJ9sJ3k`% zU9TnA=am03D}gDRmjk*G@1(frUWb`JzO%aF+&(vhi!h_CHJ_BP4=veu4yM@~-4Mfr zzn+~vMdD$#DgCh1qva>a`YH%F))|6%ODBE^BJof22c@@Q{J7ja?FI8>zNt-ym2$+B zSupEZa_Ve2xoB3yHkey~_Nq3_7_Mt`glSVR#_GXpQ&+!c!VIe!T5C!EIp;?dyQc3vF&w7;n~hYBX$Ol8G+^G; zF&B$r`}$S#b4mW9fmbhJLDJBCC77OB+R+9}FRK$zfhqo5)qcU8LrJX@U{P3n%s6|j zkLusPje}`xjE1Mdj7gMNV`2X9vL& z&#gZT2V0*mI0jQyFPrAUzKL72!(e(wTwpbvaL=Q1Kgl;AnbrogTK6gX!7RTT?l5ff z?|kPTn7uzSiMAE(FZ*Ldz7NIz!`t`5zFVDn8%TWnVRjlU{uRB%hQzJc$Q8pipFS>K z2XoE{SKNb{S*Oj`!Yu7ih0U;V0>9IW#H)(@|H9I?Q641D%x`s3aKv~zHqLz$EFKIC zISQKv@ZvYaf{=~l;$Zw6LHsSGyv(np)3DB06~Q)`XL3T}4O~2ydDji5d^vsBzzOZm zn=Bmwb0=2)ONE^ZWs5>#@e}62AK25uf87ySX#3DdcN_NSK98hEz=ERJ>z!f#fx8C} z!>m6CcO}9Z_a=vj!;DUkNwu)rC$5yFf8$-g4Z?1|@dG5ERv$NJ&UUoNW&bqtJxqUD zTf7RcE41MfQ?y@Bafhvcc0MB4qgBj$90!+O*cn9HLm9ZsxB-i{Z>T5rvld#~*1{tT ze@;ILi&Xnk`eC(OmUclf{nFYki*{f>f15hhpX9r1OxXaN(SBxmlKkICe!0U6x3^XA zg84;fKZnD?KUz1iVA|}hsi$CiY+hlLroFyn{Ek673wBbZB`FQu$`#tqm~BS*y&rivGLRKXnk zJe}1fzUbsa>77{bUcLCVg7`$FqZMplo%qTGrpq$hgW>ugW>XD_tB1@}VW&{7Ks{Jk z8DyCQ51!5*TmrKbrDxrSQ|A=QEQVPHkET9@m8s)*EFgB(Xrj8{{WoO!TN$RWmZO-% zw88sklVHx1(VpwX9-F(RU~Xjh*k0IqytMHj@_X-}nyRWR+S8kT>?d(vT$d-Dyw*wh z73TS;f8@fHmuaCNh*j1tz6)~%H34jes zR_5o!%skWUF>rW^-rQ_hI54~*1Fn0V)0_!&l#l&a1ouyM74u-q{pR*3u)S`fWdDbE zDE&hZoGib{(_coC@+;QXc)-C~R+9Zh#=NlBSUB}(e!^kI#nO4h_u$ySmR(^m^?0h@TbPx2 zbYlq2+B@ly)NY)gbxpcV_8Tb7zZqL$A=}r7`zfI$~ioR>&Nh=N&6`@*FDqsV11ISY9?{IVgBvquxEtqcw&KD zUUURptWY4y7wWILSPYxgPkSxt520b<2rLYV`a{f{|07+;4fB1K^awFK!b5K{+{3k$ z^q+YC+!Q9P@Z|1PvVY1~-MKji9;{kreGI0r@TXlT-kTM{A@zA#bX39Ig1-}kVGeI+ z#(a0Ick6Dsll>abfL3Q9tgw`}f$WFzDx+^F!t$1ZO-?Z5>wJ4T4?KUpf4=rGd(102 z2F(3bIe#-uQyXrKf-Pqrrr>WS;Q!Y&`tkx6s|M`a0JG#>9;)p{dnTt#_LJ!j%$j4c zxM1hYwTLsmpL@~)8?1O%LiW=rg4{X!o>*V9rfaQ$nVJ=5Ct#-9zq4dNO(fA4nW#7E%O%+9{-)Q&6gP##mthmI zD8FpS9ax&;wb%?6%B0Kwgw1xJ^;r$mDrDMKy;1*b%@J~c)HltYH(>hv5?Rte;_(An zV}1Vb_Z-Y+&6ySiE7z@i=?3!->Qe*KD%iE57o%r~$5wXycZ^>@vf(nry5;jQuz7 z!W^#bqHyBc@2X_|U^oiaJ%Y<$_)Frft#_*!0eIhSh)*a*oHzb(*w^VT=2Wxc{;#*Xx?xex$>{<(ckbSrPMF&Bc;*o77I1||OkXzq-aQ2IGv79n z^CwPEdHq$mvP)jBgOvaM_d^eC+fc4YOq=n=#4Z&5bBLl(EEvT`5Udk5?D-z%weSlw zVcUe*@#OqVa62#mCoFwK&8!h-e>IHy3#+Br4%CylS$Er%L+C&KsKghrkXxy+2p%a* zkF0_Dg@4`|!@|ahS&vEi>YVkKaJkY2rU0hMjBB!im6`&Nlk+|HW7)l%;NsFFS4xRR z-}HCFJqi;;^GJT%#qbz7c1_>s8^i&w!`EQj8?9@}c_Xh<=kHV4o-yuO2Fx0#dZ!0g z7W~W4h8e3=&(99SdZB($D;K7&)}6c@&fQ{jgP8JTA}f?55Bp7&r*$QV0v9?57J zStxxN@8{Pt)eSKFi|lV}Sa4(eanj#BtqEUTNxWo(pNQoD9-4ayrtWIf5W~!rf1V{U zyR2V!48|8XG$(chZd9(+m_)`~_I|4N5voutFTr$o<=!UC&PgDYX4-#f|rCa@3AX2OFr)f~xsK)aPI zzYC^6cet8D@~1z@*#pxi8y8-Lg;TBGd&52IdXn=&eqMT`FKp|w`hV+RXSBp}KR!y< zC*h$Ll6>cRryR(7_y6+4#ea(*!pzdu)ofB<==r!>n3}llYZ%;V7Q62iOdEVhI|heq z|CD(PiyzD!jfd%)CC=@z=!`q-BFty+Zyq6Wm++i-Fy)PnP!8XR%$n)(eK6HA|J59r z&#{^%&%yXP+<%LFZ;B3XS}_ym+DxCR1&hsmBxX4NV-T}`c>K>@K1oYS{^acv%PAfG zZVJ=QpL$M0dHyHUaB~tr7k)wpF4LS@Vhz)}Z8lKg*xTp6+QBURIm98fN4Z%&ob2cG z+?!?+8yJ3nO@0puxYNG&!X^&$n%N}(bJe6yxaQ!AP7cYRb89TQzl{5*SP3xOm%8LR z?DX1iPYTT3C6!%C+`C2c`;}()IrZ z{Qu&bQ}$^+g*gePhwmY78Pk8}JxqUJZ(Iwfsugy1!=kR4mG@!J_pjnU5~oslMR4%L zWn0Mp0c(Gt$%Tlzl5^Ny?!ZduQNw$1dvA0i2GiNdb$thjVW%K2lG1e1CPPfl6;9P_spp}2~*Opn34K4XFoBy z0JG9#F3p0=93OqT3Uha=CCr8Sde1}2{=LZZsAT@dsz_C3!@|18S!#&O=?-M`VTPvJ zSzVaBcB4xfOnLlNa=)#61K!+)sf(;9ZbZEQYNI~cKcnAU>bx5kc1ycGgT?LUwxO`* zlQ;E}{r=J9TX7_R&{^#z;v5d=(=}MmZR18_?iik1J)9inrdJ0GP1B~hf*G+l| zi!yrml*8eRs*)bS^iVU|L70Bla??FnFxcC>AQI!ZCjN0TOuOB@_8i>rWoUl`re6MA zq;nMG%g9TEoM(s}`4t6l{Q}Sb`Akb`6U^tuiAetmrajVHdkp6juD5qf`jdG*=MtQp z<}ca*m$d#{L`ztEXe8`i^}6xPoKQvNG<{zW*=x1AqL z@}-A%jyaC{_B#1T5r>L<8L+|aiBS=-VBF!Y@h~f74J{NF%XDabfJ1IgoD~4`rK*N) zV=@1XlG4fe;(NVqJ_IL>oxt=b)UYMUUMzad$_lH>$^Ahy0%#Fu-|K^z~+3#djhAg*+2Nlxq z5VJQYxNL!`Ucr+3Ih8q&<6!IUr#_OnC^vk}*c0fFn|YG`QensLP-~bv$jWp>oYOex zOE}!YY~M}xPdU@qzR!ST6Lc;+6PtBf*1(icQ;f*|DKp&7s1G)~T)clXO!d!bTAYCK zl(=}|CgQ5CD>lKrO3U#ZVaoZjQh_i#=I)bqFl)n^+*Fw5Sn$^h7R00r?vnDsYqyjA zR>r!>z&cnuY1G65=DVHRJPaH3%v5H;++&<1^^@q2b2ma(!kh^e4_CwT@`FXoiJy6> zyTc=jC#lOw{>IvcAuwl8;Tf{N2pzu6I1P9HP5)^K;~#Xb;lW(hS4YYDAxlR6SrM!d z=e9r_W@l0jAHiiz`$TfyNY7ig?Kw>Uq!Lf|V;Lt>7@uM5F`SFDV2a^_3xlv)->77N zRyg;Y#y@zZk&!$Vaj~M~t=Xrr-lY!Tl!w`BHpw)Y;YnkY{aIEw^~MsIn)W(G8Wzc% zXf=W}syAv$)?3-%fu?ZQvsAZ#WIfi>HChLYS9YcUfw|Yj)=qH3d--i-|CSfzal{p_ zQP-YE&R0e2r^$Q3A92@; zL;YUE2FqneC2e3a&*e+M@oChsSqtl>E=m~Bm|Ap5_Zhx7fc z;GW7DrU0hSkymko-IP}^xliH_pSt(J)J0pT-GliZjsX!cV{(^df0$-_Ao>a%5+9y? z7jc1+ZEQ8%(RV-V0W1o6uPT#>@1X;0ZjkF!^zHZA!$KZoD`_v|>FL1~*tR<4Np;+4)O0oiL??{jVQZHddPGQ71M1RS66RGR;M!?Bx-_|RU@#Zckbs84>7GIqYGb`$! zUx3-R-*w4+;9m2XR0JnDj!5R0K=EhwBbaqd^Pd^we1pv{)i6cbkxu5X@Vw*G2AJ!# zNU|P?o$P0K!~O4KKRX~!eZDQd54OMTyqKKNFfwHp50UaG_l1%5NHEuK|CA)O$24$} zAIZPiRjdK4T}j-rALcDyoyLIc>ux_jMDh)C%(jyFZ^aEqNj&k8ojaWR;A2HJOn(~o zeJ{)&xjCP_4=85UI(~3@QG?{XiFb=NCjjP#uUwpjxah{&Z3keHA0_cTOdU$ya~$Rj zB;ULQ3)_v~CBclEGif}Ser}@lC0P8!?MyBypWip`F&w);rlb@WJdgg=2G>0BKP@2T zBmR6A!&Ku{`E?}!$;KDslkvThJ-O>O%zAX`wkllyO3nB`QvT{RD?3<_9@_m0WM#Fzi77%r|&3H=AtA2Rh6Q_$X;ifmb&M=~#buiXrLZh5_K z0y(cdDZ9fLmanuanGAE9C#0Q&L*5y!p9|A1?p>UC7VE8s!7w>rWjZ|ZVZzc=1v2D3 zmgQwL?i6f&XWAVDQa)nnZWY|qB$S-@vei=}KEvXeIxADeg>0Qglg{CJzPfR8CCpg! zSydITOV&G2&gYpW*VV1zkmh@x1Rj!N0vLC&9<+q^4WVawOiTw+%6yZeE# zASO~``kqyWnefO?L&3kk1MK7?JYE`vRWsknak?fA`z z^8*i$3xFGEJ=o$vuII#jlmu4>eySno{qzAjq_dcOg!2M`@37%ZG*+J za!!MA-g#Efc9_QbJ4NL@&fA`?w>@E&_Tu|au;hj%edPD^S(<%D0dNpA`!U==p| z65R4;_NxOh$N%-`8aQ?9yZAtu@@weqAY3y=x+xeI$t+kQpMw3_x8Yw1%r;M8&|o>u z&83H7fuHg?bGRg1CN3OiNLWwb2{USx)MAKx#)d_~y#5K2r(pilrO#90n6%>fL|Ev4 zD(D$ZJ$zXG0;%7SGrtY4TQspR9cEjHC``G4`#bENcLU}=l&;c%IXT7t*|2Cvp@lwd zrmbId59W_Fe=~>WlGVLSh#RInV8O#rQsQ63bkld|LSSmdmX7x@$28zz5*+jNn06B^ z=sTZs8IHS@y;}sc4pdCO0Vmh!$h5%XDpiNOaAMMT<8I<63F%^(7I>qmm-PS4JyZp| z8oh4&2lMi3Grz&ZQw??HzQAY4KbB9$`rAWWtPRr|A`7hH#_hT$OJI)O*fjxgkpGgh z6)@j1vG6F&uW#?PhZ*5HUXNfNdwrKDESh8^F$jAVu9+GNQ%=~vU6zLLmm`LsqDlTs zm9iR6wv~E!2IhXLpS1!mc(rfNMN(gpzIO&Jw~^PB4O8cp{;t9G>}S|)d<4^Cj;kJl zv#ze#^8{wTsIfGIg=b=JJcRjOa}SP(eK#fO7sIUAmfmmiym4=}mz2Ydn(fL@VQ#OZ zc{MDWX%KsjS(w`*ebxcaJeF~r3iFoEv9*I0?yXH#fGL?0cQ(LY zpZi$K#4F@R&3|DKp!y$kO67yJVG(DKU>)j>V>Oh8f{UNB0|d`m&x2Fn=?rw*Yz8LGAK%l3U*s-5~X;!lSof zx=(@2Wmwo`lb=KCznAML!!3Gu7!P6Qv!I-luvhg_zhY8PkvPYJWwt0rmBQ3|zsyj$ z=4SSmH!v@3yzYKDbnltST9}>G+u;Z6`c1ms08^e&u6x5e{mZ|JV9w&x`*y&z#N+Q; zVR4k4nG0MQMOE%3{Xx?m9pJt&8{Ho;^Ghtv7H%9LF#Rvg^^S@+hB>LLR!Oyz@o1^l zf=yXIffHf+l}#PwJmbyGNv6WIf5pk_FuN%E-c*>szjmVvoSxNhs0MRN&V8B%vz*`k zoeQ(pudiDKHy)N>qYKkblsuNhiP+|Jn0D0lvo&1i&7&@cd7KT|_OS4urUVwm=DNMH|(H80&ZgJpWf`Ws+M*7Y+MFlX12Y%7vK zlA^AM3){Spni1#kn7;*f_PxGG52pW+Y4e7C4Hj>r!L(O7VMk%(vq_nXFt;jf^nAB} z5+g?@Bsjtb7RxJ7aC-(b!{l`wC(?@jQ} z4w&!MtCj$pSzY!g=FJ)Y_Y{_teD$jxW(B_Y?Sqr8%u>F>T=&7tdvU(He!AdEu1CLj zbXG3xes|}jF4Di+H}5B07`K2!)~{fSR+`#HoWDHpiXNE#n7Y{W6`Gck^}%Y%pKT4hevQ2n1k<-(wh4yI zHXO2yggK|?Ij6yiHm!DvurTG=>ngZ$TKo$h%-ntSkSq`LTPsYu1#>kzHZF#(=Z3l5 zA^j)w4_m-_+@$;YFzbkWpc7nqur8{IU)Amd-LP<_q)P@Y++iNn1@rSdbEaRx{2XjbBRMnqz3px|nf-5y7#4eI zcO=5;??bJB!}MVjr$*R*siNwbHmr}Pw7wNrF<*H@y3#O3UppWIX5OOwl7&SrN0gt! z%KDZ{axfzp{{cE&DVu$p3UjyQdm5+X`x<{ReF~|s{b^|i3wMNmm`-x;rx}~!(2b{c zX2Z0!%_bhOP4njSIxy4ax7KdhzGLJ3b)-Kx+}{T-w9#1S^q>05AJNS&u&^ln)Gp-o z>n;;_!R%u5DK2n_;pgeTq<(t8@g~@2Q|-t;;{7KJtl@#fL!N;!eTTl;a#+DD>cC-` z^CNNn61Xv7<-~AO|4AopDV#d#YC{CfI`Mt!)F{w-aj(ta6Hzk^u8AkKeE!c8xlzegUT0?by8qPJI|Y?lQ?wc`#XUM-gk< zb&{JjP`1G>Yq^`UU}g`mh>V~6E0I(l%%IP|ZUak7Dc>oC`F@l^GTzFkTka9_7Ugvj zYl+h$-@sh6{fm`h_EpDm4WwQ^Pg@pdG*#=h5(n6n3}8MPLj{9km}5?l>V>m%Z)Xm| zqNb#~Utyi-=C*&No)K}N1+L4}ek0KiKOfWdiPX=2b5rzuE?ko4ssa~X_GwLR6y2Of!Wsw1~8rFdgeZCN&6nYfaLzZI+<|grwvSF zm~wB&)l0CX>?bc%SfEq3<|JJ6`fo9VGp|H$@EsS-rP-o#K513cJV!A!dtXkV` z4;ypjjyu6zNuw_-;naWT@!Mct>sy-@Fx^e?z?<~n`}1Zs%y#}R*atJr+D2{p+HP$y z%wBZ%gB9|;#h=a}f~ixrU#@{Gm+;3QhWXy6wsx?%a9469Ojkd{-$Ck6?>rGrOx{1R z^8u<{Jj^NjrF|AIFsHPhhsE(ft6#%GJ3JPplRRWtbpqzk?9>7$J}mlStm^<<7hKx^ z7#4D;q^H2fR~IZPC-py{J6FT*KJv@nlKPlQ?;7Dc-{80JVLIP&>StJ3t)1KevyO-O zG{arNclI>F%&3Nb5zMYKU-S{C1w1Jx)(u@-*aq{z3PYRV7@dNd-(lVXJ3Vs!%&79) zy~KatHhm!VRuV-6u*hew>>Id5#V+eFOrdOPEG6~KEZ?ymxSx@%mj!S^?-U7Xm}si{Nzm4BQURk?>e2-mrtl6&tI~{qf;Fg-?%+I8@5qdYp4lxX5UaD>$~r? zT>Yp8ROw?O3V?%)cr*Iv<6GcBhqye{M--kpAq6d!~{4yCoL- zuvTlRngUESR;|*2sUdl{sW5fTl4NPPF|Nf-p5#@L54v#P>U1hs$`adj?Jb5m+`3ra!-9ejN@qPQN;W@u8jx*7S!3QTuld!GdE?*BZi1`^crl{CtCj z!+0N&nx_wbY>e&O->0`fteyp!woRgNp^Jn{_lNc%&r+QAoaA&F*M|5OPHbr8G)VHR z2XA}Al(F{(eK75I*umW}PyS7C4=ks7JU2|5Q)v&vlJ{Uodug?$io% zKZ{45820&BJE??Jtsr>u?`=BSw*3WJ&Xk7(n*!j>cab8zBZal%YkoE18Hzj7b7 z`>kQ#<;33;F}^%a%O7l*Z?NUy#}*h zk0kDgHc9D1tu+X9&J_Qq}r?1#j156MNapgEN( z3wJ3620OxB>Ua4Su<-?<3Jc~`)ue2IgQiHR5;MjaTp$)Zn{*z8X^KaR4wC*j(;eZk zs9RMf7N(!tY;gu==Dr`g3Jcm=;uB$t(SYd<*i<;7I2ESWSV)z@+_v)#JectzVR=6+ z>7SW?6&9_!qGOEn!M$s0+$C7>NOxBXIWHz`{+A5%W5h+Ru=V1MXR*Y0lwGMf&w?nM z>_TCo`OZlTVR!Z02A(iSShn32Rw&HoxWQD5q~wFJY1NhQ&LsaW9*Tm6kKZ?}fw|4n zhw@=YxZb5DB>(FXGv*q;Zw-8%G7lEt__=d8%!_zbqebd3X{DZnvz%Xz&WG@a+;RaN z_nY0Rj+{O(IPWK1mUPF0SeQR$13d%x>mX}6nve7JAGOlpVltn?KeRj#_=!|a_5rMWOGB}nfnoOsjZuRbh#$9HLfb+6p}zLfM=X*(%iC+9)XVSSdjm~#8ZB;DHUSD=O1v%%(_$g=M$nXotZ@~O^ z|71Qac4N#gC;dkrJG8-C5BWwPNd5bz`+8y8{F3LNh#%!i55SxV$!*=FUTJp0FW9y8 z@r7ZSrf>V}4{Z6R`j_N4%}9n3!E2TsD){3&FidSZn;Ka1)S>88NPwoX!@v~m&WUDCf+`8 z)LnCji?_mbNn3gu>bYy;uep<4(dgDwIA-fkMK74q_dWM9to(gvwI9q}93)B6qMnAvC zfo-6MZ2!ToTD9kMKJHa!q#gr zzxLGV_zcUOsLg=6ZmoSC$ooY8ZCRwAyx(A^o#w?{m=-uAQ~n10+$pDk^qXkuDZ<7> zALc)Vd7mWxr^BIk2jri@?CX{rHDFPqAh!Y*2WhM7!IeR*D^fQ1kCykNj}B^wrfgxN7cyH>)^mX3G6!jz?dQr5x)QaAU0CwXKIhY5S_ zH+lL4rY;T_ZiKV^InIA!L2{tL3zqBHkRZ{8=VL3*2!RWy`VYy#f**6W;$X)3Ss_zk z-kQ9;0=VY)PUjiK4>CvkU@g}Z0(DsQVb|h?H?jZ6zR%ZysSdW6<6#|+ejyEJE||P! z>@7UMP_14Q7P_t3qXoxYtn-)$bLM}r(1*3UUvJif#b=k*FMz!)R4JyUpFV#H9quae z%UuC8PMF20!E`@?$7+&WuxsUD$(vE->tRm5`P317zE;(ci5;oW&T{?%TOYUW+zg8p zB@flWk*f|&+y&FhqWAFOy4TD0`@j_c+l!K5YQUN`ez5qnhu&$}-|xH0UYJ#UwKAIY z@7(->4byK;90-G1i`NJa!n{vw8n(iXT^so)VXnzl)=!L==t0=cWLP|QOx8nKK~xmY zBX;<+DiZefI{7UFX1p*-uz~H5RgBz%*Bf3Gy0ZN~Y#PblBmq+kj;|oLyd$L}Ph5I@ zGapXZ9GO9dnSbBhy#tE|W3`oFisi9~w_(?IO&euWzfo>H$?3_f!)UOubIIuS?Mt$Y z^k7~Ui(8IdXK`nODJ-`6Q}r3{YmD+=0khSbE_^5bA*oX>VA1`J3wmL*j0GL*VCt%p z0y6%-dri_e!b1E#BsG|)p_b=F>I1h7Xv3nt!ACd4oTI~S^WeJXv^F=APg&<_1jk68 z{YuPp8}uQjDQsqV!L))es#Y+|+E#lH%uljeYzGgoc%>8q(}fe#0$_Ke*lR~&>Q4(+ z1l+Zj+jbge_~jhC3>(K5iBd>^jqrRi+%f;u#)~kk%XvvX%<6L~x=hT^2>$`|PggZ% zlm6+3qvxUfN57Z?m_EC!gq(MAKTmtTgelhHo$KI&Be#m*z+9zo-`wEFTT^5|!BmrF zwtL~e_A44+VBWI4S;4SOfa3EOn6IBFa|9OexsmY==J>rimkc{k+CHZr=8nu<_ZZfx zaePVnj?X_*W%LymG?(3`!kiV;56;2<)Un#DK!f=XW%IVdbYs;RU6`u$QH$(v%g7^> zjfpSL*mM<+)cX@=3RCuI8ohx_0>3z#!;GvKrm8r9xK3W@t%%hrQf6=srDfb&m@Tt= z|5jLNYqM)3%vt_k{U%)ZY7)f(=7pBM>w_7dHP?x$YPm=Cvv8i3o!#IF3$m1J9N-{T zgDiWJ%g)`o7w%eAhaYiZ{UqLxIZ5iz@JhGCl=bEND@ncDRy7}(%dNON@iyjL)A7{- zSg?EHuZ3_{_q?G{n4b9Mu zi50?(<{t0acThj$YDEpqs&^4HV9D+&^BZB|3n$?om=a*&-Aw9P^It~5x?GLmc9I(; zmM6hn?bOz9Fz>$Fjk|E>g>uavn7X3WuNqcx>^SiY<}CYuv=4U8=J@@E=?c#@Rry%2 z1?-NMu$!d{i-V>tegRt^ym(8UctW84Te!?GpmjFP8F;So6Sm<^DAk396}dZ9 z@8W)Vw&}(&`^H#B3wYqgnWJVft5Y{8oZl_Mz7ScbizOER~JZ6`@1LiN< ztK3L(s|}ZT!A!T`)!$%xLC0xtm|F@!PtL*jiIFJ1gRsy=^2lu1 z#;Cdb5Xo0Oi8qF6#!tl&BR;-F?--Lqxyguda+hIA0cY-pQcJk!!U9jL9&#;o@r5?rm zVCNp2uhk^i*ccE2r_Q#hs)MQKllCOTiT-cf8%cfe-o3X-{gG9xTVUqR#lP#}%olN~ zzhU~)b!kiUuwL~hRR4oT=9#ivVM^51+;L*er;)?h{Ul%8lQtgaboZzsdzCgbBgm{)kbQypf%%)j&w=AN#zp~J$u)PyPbFdqR&elI5VqFrZA zVbeMO=dECBmg-@5I5TjcwJpr>|GPLEHY>t`ClF24##zq zPr#h5rt${d_#ita0T$TEo4+&7@6ju;|P`zk6_*;*+utVt&iwXRx)-pRb+7Sqz8Q zuvh)$*lw75|J|gIu*{>h=-)7H!lWBA5AptKKCU9wgU^?c`>+;v6`FFVz`V?q-GQ(r zBjm0EOe;~~9flQT9-UHw*|gaP$*{Bk>}GA4HM!tv3Cu6j@h~9y%%Qk1u)oDDn}smX zJYebw9Ai-~VFpt=ROJ*N;rra`nSYkSjPD=L&w#Dp`Chgr{kv~ytbrq!w9Hrwi)!Qw z*27x2T%&DCy@hp%3mg<)AhDU`OVyG?NdFSS24WtkeNiaPHE_+|0ke;e`+6D{_#__o zBmGJDTvOpZDL?;Un13d0Vm7Shv|BTr)E`lQ`52~|dY$4DhlI40!!6@Ij-G+(e_xmp zi%U9+&%wNZ7Yx}Ey6E2e#?tctZ8h)f)hG{JU^GaY*Rr9p- zaEHf(buVDP_`$&>SmsV$XBjN?+9`@Bz8w`*1=DYAc8P_p{nX;$!t6hDR3qU+!(jQ3 zu=v>hF@bPPO|;8bnDH(v$qSA=e6jmG=^wZ=nFT9nmd)>jS#-9sGiR{@D#{r^992 zzunY=sbh6-DZ(x5czbnVcF1X$X>d?L`E5Oz^=+)*WLRYjuiXe1b1v1&!Diw)ZOdT3 z+Wur|xWjMGm6b5{gzcatEa}|nu^Q&QthgZor;qTWHW6=Hzhx{;U2i|M4dxyCY%K*d z(r5bYB)Os)_7dvbye98~xnU0jCc)gnN=YY_>%7Gc@hdv&L?V~r%d;l|r-`AXk8KPkBBjP;I(e>qQ ze`Hz-sn5NjlZia_vrlssOsSDFu7q`IUfeq3q=vg+VY9uLSzlm2=WgDlLae`gvbo(b z+jO?T2(DC#>F9&0t;bhwfK$~@rw_p*w^Q%DU~5*!$k<-ozm!8>1k65D?mrG@o_O)~ zIGkLZo;?9(gbXQNhXJf9A8PQI4{RQe{tgrgh+Qu;dys3mAY#kB5-WsNLRSs0a!`04rZDFR1g5h5{QcBszj#$U4 zMdJzHhrZ9Ljxc>kx;6{0?6hM!!Mt*fv;?@&Q{~MTn3gc((8Q-$|F_LoZG~Ct3MPiI zm--hUH&{5yA=w-@ZfZ5&4pZVZX>2$sYWw9~F#kd3>_V7U7BuWf`X36S83N4TxDUqy zV7wnX?!t28+Lc0J#_0pgMqu`iVzKC8< z*9^18g@vbyKkPm@rwH$d$stZ>VBV2IDSJ5c_%_e;FlVpeQxa?zT>du=78pi!y@DxG zZlBU&iuS1(wPMUyhxW=_Fx4gX?G`vOp>F6d$*+X(N`dnZXPe)H`S(0D9>Xn@mc`#E z^$%~hl*6uu_4>sy`^nh26(xB8EVy~;1W zUlyD?(P`>inDPEj(JNTyAbU>(skiBulzWc%!<=aOk0kg1>Og}{4;fWA!~9kABF$i# z1iho3FvY9=aVYFB$h_YJvt$lPXTa&PS^Iy(v}Mf)N@3;VlL3@IjL*Mcem`K}i>_@F z#2+iv$GpJvJ~`qd1yesh`6dVV$;Nk2fCVwPt{cFaw1@L2!*sKTA6BsLMuQW|Fn4{% zSO?hH#rKjbEVkTva66nB$~vYAGn}t)@`jaPOqn_lW?9VFIRFc!=j~YlQ@7-n#lU7B z?`JQD1-~9tpNHL7-nO%V`S`gOAEq6-Vr2z$FTUQM3kM~%&00_D_xx&m1eej%gB@Xt zwsJ`^ocPXLVGAtYHD*F79O=Ema4XDzQItbG{89PrPMEtx<>pJc@#3@?kN?#3jx%p~ z!7P1>ZwYdhx8wKt!oqWsyaL#C(uF8Cv4r`o3vdBDA@~3+@{{}!32W8YoD71Q_Uw`k zaFBH7gkV^Fsd@Tbxbf#|=|iv}YJ9X3SSjp9se|n@XR91$&5uYmz?6Ww8H?daKhsV=$+e7yD)4aY zy4YNp>NP2AGA#1F>3EOSkBL7x9+oR&T0J23gPwLH*e|@X=M0{}q7%BOdtkFB1Ba(1 zKRsFe9k!lleYG5>^<3Og57TUpc+|nvo9ny;B(M25^&8B2<)V}Yb4+yY#4x+j#V`Yw zd=NTr0A{{fpOpqzp3Rq|^y7Z+$lkjEyKZy*E(LSCqel04VSZAy0?c6SaEV1OGdSh( zOqg%Azm@|t7g^Z)kO}^c24-aUXnc2WhDT(Rp z;4*&UzD=+=Q@eUK%$w_D;slE%&xNmoowfB(vPgZ_1S2cBFnwpD7tDQ8p|J*z>#M#K z0JDCN9bv*M8M?Y0;?f{vXIN)q&5TG=e@s!+73OpVS09JPTm32SaHP5Q$QhV#In|#H zyHivar@(@7v8TgenJqzs7h$G`#PdklO#6kHNBZ-$-J@Y@>byglF#Bo1=y-L-ZhL+c zrm<-D7m>Sutn$i&x%T5OT!t%;zR}8oSzQlr^I;XcC1tt9i|1S3gJY~2@%Kr+Bs2UW zEaPMpSO^OR{u$5V#&IUv&tZ{t*~yQvT+)TYm#|Qwfr)qFJpElE{V?UvHf9YR+PSJ|02X!o+!}-j-iz7?VeanL zo+{X{oYgU3hG6QmuB3IaX?NObVsXMA^Zjs8fM6-HaDK%7c-VeEX9l?*ZH?^q0$66N z=^*($=IpMpI=C`?$*9@?VwDwf-bB)U7yTjq2Q*^YaMzfOb(CMY{><86>98;LyrC2< zx}&T96&7#t50Zs>O=^cGmg48DRnqbDFm?4GyNz(offdQqVBx}F2X4ZZfA-YQfVtnx znm@vsTgUuRgBfx9wv;m5-;w?rEt1cg5MT)FFwH^?V9pgcvmlsfGJW4tk{i1FJ%r=N z`VU#aY}=+UU9kPJ{Fzp;D0!N$#7mq%(+b|Ng{cuAhm7IAT{E`Zk$S(g&K#KAogM5* z>~qI78&0fEcty->Q**0?eXm$ua)L#F&zE<@!(Z|&Hp8s_EA%IqAc;m{H-_`Wa^GYHjp} z#WQUljleaXuDkZYyp=|E>J|9;yn0O1L0G7pW@!#5{yY{NO#0=I%v%kMJr0M4!kkc} zg7q-dL)^iES^Fc;I>3y~p}P?M_s76gKkePyU{ixJi{ePV-R9Mv@NgMR z@)RssoWI-~c0M`vWjxH>SJ31G+sE>1l41HQom0DE_LBSq7hsMAyN%QTf_j&*|xrmjCba-<>h~1##-*^_01bkSr5Wj5g*&PX&K4)W_j7bky^8q&0*2b_&Xcmyu9daE13UtrxO!) zRW+|ArtW{Vm6$u6`)NH)k9wD}5f*1^tZ;z2N#>*Vc|Sr&Hj{oAmr>(;%)z~|s4`~s z`mRww686KiVB67L(*6c57#4?J8l4}CP4S{=Qg0;P>WJ%8Q)fRr1q-TJjn1&?mf|}J zu&}Xx{uY?ckvW+HQzlD}u3z0N=aMhO?9?9q4&;T`)f%qB4AXU)?yzyon(4P-TJDS= zWPNA0#Qn;Fxp`5?d|`1w@!y9e7g8P_fJ?s3i7tZKUo4YD;JjVe3tz&#KlQ%HV4eP2 z=}MR*pOYH{TN--Ogrr_~^4gQIjmGlz^)TN;&;BATx!mwpCrp{M>g8itccI_YA25AI zLs}_JS@Pt556ntD8dm{F?lw{!B>DY<4fU{sNpJ87Of#5N`30um(kPG|z<6;DYWv_C z$-_aiFn^}o`LUI#SDtZkJWN+DH;{(yFHMb?hiP(`{!D=zDq8tz~Z85 zFz=g3Fdg>VaZ`T=@ea%R7BI6Q%8LdId+y)e3McQ>bkc&^Iu|L zVS%)Zn=Y}Ra!U>@qjUJJKFodA+ExeWEm#v`05jEh$o+=t?UtivxLn^O{R-F9KXZfB zvz00nroaVG)989I?fSa)v*5~&y`^)AP0Ovf!kHHPZM9+H6!WSem@e_RhTNavo}?$2 zxZLfICQL6m{=1OWS1ntj0W+Hp>%WJys-pu`Nj@)p@7UK^|6|pwXA;j@)~ycH?zcou zhehYMPM#0jY%5r*L~_F;50}Gn6lFz4SnPhPWCQGOacIsIn14-6Z3iq`bI5oSOf9v& z9Re%F1^URr!f#vC65y;inw2}{FeP5w z^8nmc5x%h>=B@wceICy9(%sSv)6%);%V26@McnB6Z14H@1J<#L^&#t5bWGBRCdBja z6I*>J{pa?&u7xu-=@+|UzUjlc-mvBKd7~E9+*s)n~NwQnxPt;Ro zGe6hES)u>Rf065JFMZYr3ufI(CHE`VD*E*YHkBWV8itwU7Dem4!Tvct{Req|uFRbT z8<;cqjx-rR%DY>T0-4A^GEiP81KknS(o1#{ll`Klvl#fp9$feT)|Ng}37 z&zzVGyT5%bAZF&RP%a_)5(E6t7cNGl>Nl)+=Tsa;Ig_w8&c0uOKKOx{_3|K$n|;2&O;+G ze(w05>_>jl^J(%m*pEe0TZkzsPD|$#$9fr){ZB8{Z`uTFdEY#*L+U4;KF)*1xk01P z!)VxaT>v{TS}QTSUoF$#HNyN}(LypFtdK~#->`MtlhN_x?L4h0^A7jBaOG+>ay>@e z1u7h=(D}dp=^;H!7q-!!F}feA^=BHF!YUg2qw~v-E7r1uEmfMvkn@l6XZK-un0`=~ zPK7!DKF7wurrmE%$@&nMtO>~>^_fRT_b)5w_swQF?q$Uel5-Ee_mHf``FP)889866 z=lK6-!h$4=sl>cO-!T?&-`t-q#PnD$XFnWgZ`DZ71F=z<*;$x##OQzbcU{I*2&XT5 z(@xeO-(V!`2b}emKYAW>=0;DS{2uRKF$bnjdpDEJhfpU^?G0S07C4^Fm$0%{c@UNiGQBo>K2J+Z zQmjKBXtih|a$aFt&r(=OS=F`-=Juz(@rE0FJ#1IP455PaF<7w8B*ca|Y{uh9#08R> z4zM_<{X{oxY}J|XLi$_heN?E&`2C_dxxt+N*e&XCnURAni}ah&@(f}AM@OZdFkRdJ z^Lp5Sh4NEkp8dFIUa<3sLXHPa3te(58m^hW{;wA-wyb}32~N%As`(H+5sxBVm!lxvYoqzzjd@7?={Kd*KP}`*||uG|as1 zXju&>@6~fT2XofFY-uOOh|&(wi1*sQ${|KT2T3cDu(R!AQ1>V?^nv#zGY zz8xD|`eELXoyB!{K(fDj5T-1;R(A__?p}N5A1qwwU{e57$E6*o4C4A$CyqacnTr$H zQZOs$#rg`^w=;8+EG)`h&{75GS(x`sf|!;0vDH~X0B^q$Pt%+Q$4Rc*Ss8ZpW zhr`$A!Gf>d<}=~c@!Jd*kUSv7b1_^wq0Y$+W-U2bF1vuE*XF+A3_h2dXXHpO12T>m6-C` z5f*p)+@A%T(tf&ahZzTU%(sAT_Jv*E1v4u|t2V%K^e&1Y%$j1X?*&)RU-@hwOrd5Q zguu-1KM$f{{_fW5`!M^_fYw=9F!QvE2(D|TcwB|KjxwcEO?dvXD-LB6&v2Zi3X7%2 ze$0kBrWckihU2DF*?BPUh6`gGY`v*Pln;wq8Ap%9&OUGcK7?8K%=AiN(cJM<1TdqD zt)cJ{&vR&-Y!NJuE!=Dl$2_$)A{NYg^O^sP8s{n1^H5}$CMDok4S z1{N4tURQ#}Np_xZVUg!Ke-pSSVbLREK|n?ADpG$b=4my|=(=F<{@;2#dB;Gw@kdbO zJ5s;V{NhQNY5DcYdstYe#=HcV^`}@ikpBF(_WN+3&eI1?Fl|HX&~sR>r$k#LjDm*((*ge!%_*QYfRA-eD{y!usJCWRZH>SuZVBSdiGJPfTxoG{FS6{5;|A zCvv?Eg@lc;>p!0&^7+*Bx#!YgYjxF%dYJvNNjo2=(zPxV^PcQYDTaOfN7O#RVl}Oa zLfBq6eFo`g(tprC!Nx1Djhb$!-O>f;dCeK^r|!W&72S;eQ9gMrd47?TcgJkFE@bAU z8kj9*y?6<1-|sX!-du5oyd`nr19LLI^zT-aoM4-SiIt=CH4xnx02_a$&3#Qie`hT~4iDSukzeF{{mR$Fp&>ZjgNa z`X)B)&M%`}hdHwp#vg*U#Pp6UFr~z2!b!N&(<3q!rgyf*XTr)%g_9|;XwP`1yRh)8 zZR>ehVE1Ng9_*jq7)17`D8II>2(~tz@i89e>iq6{1?xsjtT+vet&66=hgmA)B4SB> z_rH*K*k+u$dIZd{Su*<%?7y4Z97gO->yi3``&}xn90K#ce3npzscTgJ1i}LAjy+m1 zyYA|#12AiL-BVLou|X5Y&Lxd zEOuD<^CZl=zA|MiERq}UyaKB%^4PQm<}oJp=fc!^pCmTJ%vU+#58=wN>8{Q&_rdmk zPhc6^l^>g6!M@M@T3GNxWKYgh-q5)l-(c&ac@Z06+8H^97^d`3n7I}ff1h+=S_|qI zraiJCmXxR1z8lqxMB0mobzqArH%QkTA^!2_~yE||cK z_Xi^G!J*B8uZ>~qk(en}Fem=;uo29@y}0KqOnI~-g1motW?6ybTCx5n9%?XzSt&Yc zn()BZf7|J>pw4u_8s==a<{H5Cv@`yjVAqIUW}{{}$@#!K7V%H?k#jfBQa=PcYir>j z#=v@Ock|@Jb*9Jr=fkwA&vba8f zhZtgVCRblD;!`!*15K;aLmI+GGx7RmRmf!4Oh;aw!wwu zMl}ioxKI?@x(#NgSSh}Rsc${U?u02rOQbqsR$Sg7S^v~&nbLpZWT`o69x&I}^O8ab z#_!NqOHY{d(j-R{X1;z>M9kWDU}!O1$n<>T1=G%iORj@u)bD)S4GXx#v!eD>~y7uB2uqZx$ZF>8R1b) zzHjghKXg8Y`!0M}AnzBZdjH-s(w~$+`hKFm41f6ncHc&;t3}T8uy$&O1;W>-o5=P0 zTtd3wNIlh+tuP}>iuntUF{Y|~hlTUUnhe8r)vxydhAH&K8#0~vKI$%cXauIKtW1`J zIY*EA$PM9s-YPzy2D_HUd{Kh&9|@jUhsEE$+GsFM=bwQV>F3Q^Hjm_-&~=8eBuk5~ zNBTWKSQx=JmfI{Bz|3iKM@``@-FuRYV79>w+EQ3}uz&Vqm}7Q%uNkRNl`L2ei*rvn zSioh<&-&Jr`t{pvonT+)g{2!|s!3p+D=cShUATqRAA7oCH|$7IbaH!YG$$Louw|%do;kYn*uRlzcl)e%VmppFB4T#g^47uV+B8h8;7dCWe|V9 zvPuM(ZTiK!L2NL!sRecoqg~2|sSXBX#IP2{UBHK#`K~Ma;f_^LIroVBD<6%(Ht82i z3t-;i(;YJ3;6|$PW0+I8e%cgR?!~;fPf7hXrK`%Yt7XBgXE5D%;Ok6S(70*xbC`0( zpi~o1b{Jb*26Ly)KVty5xXE6sfN9q?l}+JvYt7}aVSa{U%Su>fsAg<6%utCLVUm2x zxWA3CAT2K^0H(}6Gp(8UJ!kDnxXW|X)pnSj^IMn+Cl2qK&<%5MZg(nyO`9#PdSQ;1 zlF28yP(f1k3ud)`81oHw{*gLv02Z>#tiQu0%?2OF{K5EndMMGlu)a-d%f`X{-{&+8 z;Z*4l@iH)-72a(I(@K|a8xJ$+1G6k)t)vGP6Jeg;jhUO_Ec>6Irjq*NA02_PHM8aU zbXaWu>q#P9sl8*xOj7Tx{`WjAiQjgo!9uO8X?Nhd=MK%cesCyZz#{X{tJlKJyKk3Skp9RX9|u^{ zq0Ez5@GDGv7d#wJ5tzfQsJ|kAI7XkVLi%YPs^5;m)SjiYmy`a%j=za;s?MG~@_C|! z@z*n9VSkJJVwiGKS@$l?mhBEU}}rp-3PG$0+-DTV9wZWQ_Eqw zu9PSGB(E-2Xo78~r&Y{{1r{Bq9k5oF>&>~ud-aa~gNIgXAW+a!3M4@?!y=aO9Ho4p_scBLBX%!9@9gCkd9)Alw08{aD-WA4Lc zE8Z!R`=LZ^ZGQyUEo3}2fZ11d6rRJOPID&GVOI3zn=fI7HI1K$xzp~wu7N2^u?>c# z-v95dMp$?9r1?g~mIs=@kp9!DhsgX1-Iq`7hQ01+l`n*;8ao#Zzyl%j?WQorHuKQL zANcj4;_}h)=Uom6GloN3e&<*s7yoP7;|42)A9=D4rjOlvC>$1gZJ1>Tb2`31NP*qe zRL(fS?DxgTGGLy!-fS0``(o|oT-fxC(@qvF_~!Ba3Ea2kN6dDZdHbt^0A?LO6Sxbe zKYiv>4Cjd(w(lmsy5wgmoXk>-@Fo5GEaS`J4r6-BA(DH{t$PJCs3XRQVRnpPRTV7O z!5@W&Md#wRg>Y)b>phV$Lx~YctT1@4`Z&znlpy~O=1p7r>=Z0~S(No2&Z>GWafamc zBJJzozC4XLi7?eVaMaFGF0JQb{_Cmf^~hcKD00$ZuH)_{A4z}L)vAlIIOD0I2yXds zfN_Q7OTGv{!<9ahX5D~A-q}iRFg@F~EEg7({p#z6X(6KV1;i&>KlH-xIqJF(VP?x^?H@@V5pdrXuF=?1-3&7# zyu!U;*HifkZ6xQ=jSs;B?M+8JVUF(C>rt>xXyVx)u()~U_;YZmyk@Hy=9Rlxr^2az zj)VA*>;#;_=`Ocm_q@9ghKS3*|15%sOFJb0!tA~C9p1t^f8-90z?7HMq+4P3^=b{u zFs^@Lr*}WhHGicb0aMjB@Fw+OyfZ9MNy5CHCOw)kdrY~s6fB&tczYEbCs{Zo15@f> zZ9fbLy;-qL9_Dve{Cy3Ju3i~G2^M*z9QzBK8RXxX3{!9Zd9U>o=V!wHkSVZuU2nb} z%=evec`E5&p78i0%%sQ6m+2DOYFK!!0F$%E|Rbg9h=EeV9L{L>(38{hMl}2Pgk-+%XXruhYu3 zA$dCe;&_Tj*e0xK^!X`arzR%Bsl^{U$CCVI4?6>H`4>m|hw&8*Y}g@$MTH-Z z^uf%{i?*rt<9)_i>q+L1BWbu{ATlF2LUeKLs4hu6`PGtURKcwtj;Ni?$@?`xm zU1Vi4h>MqG*TUReJvUy%?kQCE8YnS@{sbA>mKV}H;2c->@i1}aU#B0F+*H62bDs2yE<>`;s3v|vpd%>wsik~KGX(wXJ-@(2G*4#(pA(|vb@x49vc|R}T*PqwR?S4Bu=W))?PCY%>_V03ka6bwgen{4X zXYl*#1UPN(^%SyRf|pc>NpO?q0fiixx~*}K4qR(v6>ydGk1CqcVZmtCXE$KV>)5qR zVSlIdG;;qGzF7U2xN6^u_jh2L+_V+TVUyx>9mS-dAnqZKzY;xk4`#k!(Pagvq~~lV z*OT!0^2hctvw0@#3CvO4BJT`GJIque_eZ*RoQ*4tKa3vROiYnVa)U#qPUW=1tk?Pr z-Qk|4ci+8&MSIepc*4Vz$}`@<%tWrltUc#Hko)C-Idxu?40*q>g!)sw$o9-OEG6$R z@#(8OHo>)-Su@G~QOwr9%Yv&6r)Cbo42QwZo8g2$vtjan<-d5J=MD4c)=BPvoY7N{ z`ok3MqIu+gN0T*~5d?dB)I1miGkvyg4TTFYYu+RGJ5ibVW*8j1>xc`vpD~V94TZxg z9Zr?xennMF&fE)!?m0l20<-GhrR;}?r=9ns!QA{YCI{e-iv_=D5brslcbMcg(|rwK z%0SoDXjpU0wDogfVRTV7G57X1>-l8kiExZwoU^i@jrD z0Y@;~0p=Z(zfW?d<2C(G|6{}5>1Hm(&%%^qk!wVhXRU=9!RQ#SKg1O>J z>4mUtK(c%n%$uaY_6E#)ets^o@PxAmv7l4*{tGPj92g-^k&R35Bl)Nn>)UW$WK+y9 znDYLwWdAgBJ~vB`V1JuL64OjGw<^G*?~M{yP1}>M0&~wCczzH4gi{uKwPE2h>wTp# z@7D+486=+-Bgym4-%XzlGb1Yu$UHr!Y{y)fRqP>|w^p04Oov7OZW7nd2r`=gKlA#j zzB&tGN>%8B67*f~1btZyixam>JZ!h)*-}_=Z`n(dGcFYGv4j6x&%yL+g;oF4@6gq4 zafbN=x{`U?$By7NFfA-jvcK`pT1VaC|Mp+FV14RFm}QQ?Z4m6*VGVL z@KLv-9IxL$_KS5i%ukPBR|Qij?|#I?yfyt_YhjapZpkOfyzH10PvBU8M&W5#RGGK` zDeOLe=CcHt`?YEEbJ+Ms=E@}Ex|KT`Vcp`MohdNO^z7zlSSc-jeHzJa>nN?TUBbE7 z*)X#ypIuqJ+Pp_;JFbjo2X**4Q4K?(Vho; zzV}`#CjH*=lNP`liA5v*F!e!T!D5&ikU91bOlhp2Z3)}$&paac7yY!o8LQyolbl!; znBy=p)Sb-7cgjwN#XmkD^@Tn6t(~I_3sYu(+5_kRy8A$%pbmV zz|==ZrW;_^iRWA2!3jgz=eEGKiZ=oMaDHH8i!aQ#YHyVu!SU+f>)!#3s(h{~!FEa; z-UY&xSAPyofNS5jda_~q{EO_#u%^AXZ5YhFl>1W$?tkZUWFJgz>3nVghpw;>kA!&< z?ZtGMm)h%d2&Nccf4>AyiMQ~Ig6RSEJ1k*GqcKm9!i=4+JbReB+vijqO!K_BXccVN zdbXYm^L8X^y21%>Qj^ZYtb(WOJ>YKJcbpWMZ#ri61~_fH)5LUGFeU%qS~6b|WS0X| zuEr%hzegy?$C@?F8!Z*`CW5I%84DB-xec}s1EyB1vB=!-nD|+ zMb(`TVR6t8+m&!n_9od{nD*m}d&76fQk#w(B*_ewean zFH9-hclHg;3ffy10rxmt%zY2jD@+}uV74v3&JCu{QQR93N0=@aieceHleG!3_nYeM z-!Svj-1KBvW5?{d(*JP$+ji{BfMW-!%j99&=a&VS;PB%E>&L*HPQk!sm>XB~L>XpI z|6^PLGy1N)R3qNrTT%pjc5LElz*LULU@1&FCJ2}e^CK1{m&4hsytYq)MSWWrRlcq-VkG_JYz^aQI}~E+d#<^vPd{0B$KdR(!AnQm1h@_+I3UeaMLdzef6WTp)B z%JVm_f(0F8S(9Lnxb*E>(tj9DF@jw=F>BV7zUt$MC2;E=uM;d{ha@!@ID5*UKwp@i zw$wBTwlgnL*a-_27g%#(ZpffP08EPy_KSlfmgndN!qlt6zo*FjwYu|c(m(22k^vjb z{h)@x41PsZJ}h+oB(t03Q7Vh?!=?W|*M-A8MUQj!Bp(Tu=D-vs_x3@!aBxm~6wJz^ zl#Ks}pEp`>zK((U`_6REgAD^3Y)`<#a{oUraFzAc`Db8(M#`)}IQ!Ju3rR4&ZB@k) zlBa+1IuFy*(-hKRYMF9VI_Vo~R$YZDlu7A4m_I%srUYjE3EG%LoJ$|o0(aNW`f?R! z+Nzj;C%%@t@)pdJeY0x}#hhX8`*Hbgn7cB@RvqTwTlfABnGamtJOwWO@U=uh^64RG zTwu<&vJa0)zr^y$N!U7nQf>px__ZRx9FERq`!>QN-=TfqVeZ$&j%Jv1*LTWjDRYM7 zmRUdBVCs&XhS@N?tMjl378beb_`vQDH}-aszR%IjB)Dhh$hmJYE%QiaFI*SA(rJLq zC#IhaAJOqsQJ)>^J6osr(8CZ0)!~7|neS@2*L~J?S zJOpd1s3%N-xiOE*X2_T`c-I}q8^FT9ZA0O(pT@d!8#145)+Z$kzuUZcHO!+t|K$WT zE4PKSNI!T+PAW`k2{85{xxcS_Ih-)!v^WT6o>urV3_FfrRk{b3+#g4oGva^xu8M?d z4+m4cV4ij6v{;f;FMD^w+%lT$DVR~P>f}N>bA~_dmj79pzQD#b2o`?t;iSN<4iCjb zSm2)+lt$(c@*VqN{hNj^StNfFQ@Cn0&flYVI@vJ)_Wf25m|MQ;VGd0F5%??+Zt{CP z{|d}GtoG&!?0sd)i>t7BagbF9Y<_*S+6|a_s^Z9Gd3>k9zoG8Cq`$&B*dI2YB7O5d z=}%JFaR{c2%i>kQf+OK!IdFEFOyC1p#2Plb4JZ8TihM-+qn|`hRKWhb#`)J1&;2pM z60UnbV^tGO|LLHf1?Qj9S>6JR*UU_R23tEU`T7FpG_PR}z{3|pRuIz_D@!LTV!e_s z)-=Oxo7@pgxc_v|PqICRR!+$_xO=cNgydo!gP}+`)Ne-+>9ZX93%iJ4i6!&m&0|ad z!G)H7lAIE7{ma}j<_z8SHG{86-(mRm0l4Gr`R#9DQA5J4JXqtB@{9K{H70NTN7!*> zPxD8Z%lM)E3l==AV}F8$qY6IBD48=nGq)vuhQ-4rV^!dYkMke=fO)sI^mf1r`BNH) zU`B-Az;&4ZNA=+_%zU=#R2wY%nk+9Rh50ZYKh3dNzq?Z$M#0qgX|v+sys0gka4buV|0~aT5+0P3EZ^M|GNszZ;Kyerh@HJclP@`gA z_1&b&FjbltXakFyHl3XUi9EiTAsRC73bgjNP`PZnQjoj z8P>gEv)v8m4=?BY!?gwx-F`6bl%Zulta;1EF$fl_2ZZ^Y0*HH_~U_YnPA?+}0bgW-B>}c?L-Y1yfHo;Z| z8-D(v+XIWAJ05ut7ivbz{eXGuo|nb2hVa!g8EL$}MJZQdJl;>dZ|BCqoD8K%q1b8fr&!L*6m)1qN|o63X$m}hgLFc#Ky)?BwA=7f3HB*U&Z>=QXK<*UuLblC5D zu4){aZ{Ixs65OqlA$t;L-^iSmNBYOQzny`pC+2J9!-dIF@fk4d*(RO)aNP=~IET!y z*p^!jvyKF4Ux#Vq2KGON<*(bWDuRVy#`4->akR8bDa;(Y()k)@7Y#HBU?~$#AMRNNyzrqPxHepX-T5`$jKA6AZ#fhgdd;HJR-*DHVDLY%p z{E4Ec3TjxNe=|$o!i?75i8MI$*ih>?n9tS8T>?|jci#8~)6WOzIl`qivm1wC>RsV7 z7nm9DnKW7k{gHX6!(k1T+$O+Wq1N*(IPdh1vr}O4rPYZyV2!oa zDtfTc@tenU*t|E%-Vhetu`Yc}=Fe7?&4oEuJ@%hq{h!4*=E3aH=G}d;i7=ti6c(2z z^sA`je&fGv%1W4KUpZ(8J8s$(~XV5Mbg(ktYwfju0;nJRKzUN@- zu=oFvE3^IO}J4qLB{vpPxU(?$s%!L^yEB&IAr`s6F=pS4y$MdpvMX&65lpBFlz ztkW?6#(v*ru>a9*kqIQXn>cP8+)ewFm;$pWpDT@nM#E)yw$?-CN zpMCHdrZ;>mC?NfblLJ+!;Paw|d*K!=j&_@;1vkxfU>A~puqO$IEaTVO$Jx9>H$e;~}M4W_#3uDA^ko1d?I z2@B78McsvEIlG77kp8_yn_8H6dPDPPn6tR5_a&S#VR`Ehm|agX>4Rg>D%$^o>Aptl zzhU-9YvwOBoADH>D_iYTE zR^RW*hPgj{3tz+Tma$=>B)9A;mzjq9ncBvJeXy`GsCPcxq;~c?!R8^d<|wxGep*~IJjuxEz*C-(cb{4ZT?|?59S>VcpL>&c3!++ zMZ7)Qz7Xyh)%fQTanhsHkKsWBTfYXF_Rg@s9nNDvD|!lZv<@DB4a=XYPH%=qT3XwG z!#x_hJ6mAttitJvG+gf+ybpH3LQ^euV>sL8sZJ-%eLv>eTA1?cU4;l{uQp||VeZ=< zvhPXXFZn|p?EUfHpAV!Tf9b_V*zf7?q|Y$@@w%r)utw!@>o=Hr%#~IL$1i?p{u8Ez zJQ2KsmCoHa8iYk*SzkZHp{}wnzhS|x&6E4#@Y>DOhGDA9%)H-laqQ3{$|&TDY6m82 zn=?{UpGC^R+?)Di=fPrg=|`hr$~Tso6Wo!gzf2w$ddeqmf)lLPM~sE(MHwC(n6j(+ zh8oQ1xt3iD%W6H#o(PL9_UFEZ^DItTOo93SYf`mz@P1Yb_nZc^zCJfy2piK@)@#Gm z9oGle!J)ZU0$o@zT5aoIn8&t_Fn}r7o#k%Bu09ig6Z5(jEqx02w-jp`!nCTtcYEN$ zap5(@B9~EDWv1i#!r*b)T++|>nK%Px{JQya9xQmjq0tt$en_`l1altUt_p_5if1}a zVZ6Wp#KCqYj^4}2{5Ge%sc?^+*5~Cg&(3r2Wmsss_lgb7v{=7g01w_@_uU@m9`f{P zfxW-}dF=$VDq2@c>Eix_FTP;H!nN|7ro*l~s+le@XGhOu8#sEE`!#2nO^to+15+0q zH6rFSRYx6x*}o4<%(%4fc^1rzQ!Gv4&;8p4Z$5i+m=fIKahbrZrK-r|$jltmYsX+N z)if;{?nykPnN0FI0-LL_-K?8V88BmKu!R7o6qHT^p&2ZI@1AVt(?x~PJZ{hxx) ze_^KXo<)n`LJP0AvT~Ted3^nHxZ6tGW(+JiwZzB)jt;-!G7hHPjMQ_2^A|ZYCcq5( z{08EtZIlWvm{naN<3{>hyp|fk?4ygdw!p(PZJp>a|L2!{KUm}Nm#nogO?42v3#!0g-iwza{6 zbyBY{!{RxkuJ^)K{HJ4Xk$%LR_CYws>#|fi%)2+{g4|3ze~nqctO2HERP)tg;h!hm zR+!2%tJ8s%6zk->V49ji%3PT4s$}qi%#RA6y8w>%+bj4?@@2`l%-~iJGu`hn{h!iP zJJ|cPnP>o}db#B@VNR1c`!_5On)71~Jp9V`mEvgZ-@rtP{qBA&Q-!(B7H8HX*9d+3 zQjPfS!#C^TgvK044Vac~KhFd9kA4(MgDKQWURz*ZRj8#d%;!IF^np9XQ5GgJYo68D z{Ura(Xg7lyCFi0zaJIXNU^z_Fc*}}~#m`qySqY00k2NKbJZxIL6U?)^H!c;fE%&mn_(;Ml*7(OXDAK!0v7JouTNxD}=wU5(6x z^X=3wdBcKf7WJ25rGYDDek8wQsCEtZ-;s4=CrmlyKc)cgxc~V)8|D{l{k{ngzl*5| zhS`(eWZr`H-M&;tllfDt?+ai`PoC0gm{lVp{Of4yIt%nWGZ#iYaG}&TS2t z&fJ&y1&-D>Wop7)=XR;JR$3%isZRO}m;TIZn@;ipos*PVxSoEW8qkB;88Xc> zaBHX0djpuIvED@)W=`2KoendmUbUmb!>sqa=D~u0MH0tP$hbqyneQhid4FipJ7dx> zuc+66DTkLWSOoJ{j#N#C1v}lJn~;2Pt_+RjcW-5w!i;YRMKfX9i9ZGAu;4KB%o4a? zYqR$Flfl_#7()hX1PSUlT!3*12|nrH)4w^r-;zznamUiPHF z=JToig@;X*zDxGGWnIyGnoJ z_^X^7Fzs5Ab|_3uQyjSmdk%f~-wm@{JwCdtuItceOQe_#4-94$NEm$w>&) z17xG4Vdj^!=1p+b^^loINk8q%%NMX(rkC|GnBU;!{0?qsPy6b_tr`0%Ahn5~s2a|*Vj9Ny9fQ-wK!=`bsd&+CK*oPxHi zu-GEp>@CSVSaP>v+L-jcA7T2FkJ^>6qwa>$-(b#(iP6vD@FyMieXw{qa&tQ@zfrEd z9~Mn{^{W#Otv#bWK=OrOlHS1CU8yaD#Di@SA7R&VqKXli5vIPr7gjQuU-AzY%vHced9!2AiY^^}+z zL(-pEcYhqr@vRtV1XGF>ClHG+P7%>bp6L{*3L6G*m0AEZf455J1$y*di(v7IVNZ4B ze4{jV6PV_weOnEdO&(ja1m<44ux&h?-SF|BIn4GtC^>%7uiYBUVdmn`ElS7*mS3Y+ zz+&fkks_?id-TK(7FmuutpIzoHg`F~H2>;zqv67Tw*9L~-aYoJ3@mz3Aib6JNw2beMtO+^zYj3j)w)=XC!7&0>7Lj9(!D3 z^SF=R-2bs`YJD*=qw@50QZjFa!+a36b)#8d4h zHcTvUIV3noi04(eFf6UKPyi)9e)?AN06ZT?4ah z*4YxXsP0ReV2;Z+$^LTNHvDXZ88`JpKOxsWrSJ3Vf1R&9{}XZVV#)qw_wA1ULHfoA zDX%cErubs)A6U?M-@OAaoZb?yposO@HOzkr%T5%Jo(|JePiMS_9p6+L8^Y|x>*kQ- zHxIkwVgystw@dbi-K+VN4pXn-FQy@P=M=wK4D+}D=8^k{e{zKBGWfsii|zCKk|oSp zMO#Vk7ygV2&hPW*!*-$!iUre5-3rWM(Vb!CZ7}6X>TFwB_F-hK4=k>V+O-mcP8AW^296< z=8Zm^53C|4|+{66WkH{CpN>zOG6=33tqNkV%1AuN;JlaC|`X>T@uCZ%$1z z96Rsp_wz6ihCT$lOSBm<@vZSCj5veUf0@NZY}^UmS3S+Mt${@vLyw|Z^+B{=%~ z>9ibJ*gpPnE?j)+!N*)O@3C+1W!OA&`R2e?{ zUVFkF*gZyX&TW|WM9#Pb_BS~3paiCGTc}zAGgF^--6Q=`qJLFzVbm(G`!Hi`sZA}+ z9(=RA0;c&Sggk?3Hy@p?g4qr=%r@BMR;p|*%-{TO{b$&Fy{2s)O#gVRPs$MI^Q_yM z2AEN;(mokB|LJ(-8O$v2GcbZR*1b+|hH2RvO_p%sjtQSyV9J%+Ic{*N^u(L(B%kdj zy@})(x6jswle54Ll6fA|_^Klj`i4Rh`uk$n$~Uny4PlH8o<{~4z5lTEIIg;S$% zeI@g98R8c(+kf9BG0f82_~{Fb@7!J94>L>8&6A#k=h>n-lL3-D9XUJ!cCVN-^Eb>b zS6!+Hmrhd*93lD6$Nc#)=ZF5ge=sF;^$~M;kRI7CH3rwWYRF|)ww$AqzJI#LDOldXZ_9XCba}&y6nJpwuGte{*5lTSi?H|C?*Wrx{;uEA zd{}r|^PMKlm=MXk4@Vn!uc5)h9o}Q=;Nb@oGjw43BC~VP;kpx-zfC8(YF*3=IJ>^& zgdWVbl-bq^$DX*unFWj8Y%MU#yShEhO`gFZ@)5L;aa-Gnkt0q&O9>s|hpwEgB>6j^HEZFHDChpwuy~L45>Hq@{IZoR%q*F^p9K#Z`NmjRi`1gMuwzETflV;2De812%su$&G%+v# zW)5*gM(`UJ%$1+MHUh2nET{Jopydx+=g zZ`nceW`QKnZ;LhZhuQVV%$%y*mgNP(36TeiZexVeZEi$^O(< zs+on5{^UPt2hoqtpW7Y=3#)AR#lXyg#^&85ci%bbIGnAx+T%5QiJql({`Fi;d%&U_-7Y)<;3^R*iU6;#cM_^9d@szu; ze8rd7F)%M#Kc)=!t2jF97))PzwX_<}pXcou53_=5L_)aGZM5A9n0Y8;W)rNo=g*w8 zu!wOvzXPUSlKqtk3o^bR>w=?Ct}jZ3>Ftl*#ISYGv*{VI@Pl>m2QF$);}(AUfbUv#DdsE4r^eBh3eTyFn`&nH|yYR)lk+Gn7ZFA+!KzS?wH#E z(|Z{s-Z1mhsahe-Flcl1A^nXsjps1yW$zb1*l+oD{YF@Lr{VZcI67g|k|vnCfoc#4 z4~E#UZGlD2ui3#cNAuL4HkkHQyjBBYJO% zBTP$TV4hy6)q9xn@^I2AnC0Mfyc-s*4v0vEOC7okKEW*K*eNM+#6oH39$38R7>@_* zp2=JJ6{erCvCD)t2i;V@!))r`ML8rtsc+Z^(-`9)U4rX2ZE5`h^R{KrxD0z1f1UXg zrfwMJ(sFD$DW_Y@*q+Qwe5G)tFZgr-dIM z8V56z0?}QSUlsK??<>}>e=9FFttT? z^k>-9@aQ^Sm^O7Nt`GM6IB(4im|hY4@(1kr_Ul)Dm@gi+YY?V8e`uTwvo`A`$(pN^&Mg2i?#D*|Aan^|=j%&{`H*$p#%$1ClHd2%oJN5a<54VNNd zzT;EMQ8=M~!$2e~9C@D+3u~OX|KK3WkC-ny0dtfzqoYY4`oJX>9v+Mr$HMI3PH`G6 zke#J)0;XS7Z^(vAPwstm8fLL>mFB@3HODq2!vb5?!YgnsCpbJ6X6%ZS$%j4v99W(X zGkIwe(q-%WeA#;C`zQyDMN?W#hvVIH9M}`7z8)8=OYmDpdA<3Jafv z-M$Zt<}i;uBl(Fz*>ckFGf8ZLDRZ-aRKgL-V=6^3?RCkSYS_@UWqub-?_s=s2&Z`e zbSD;!elH?c`xhEN0JHjcn-UjK5zLnwi|sFv(ju=uY@@ z`R^>^y2GDtkiNO?gE?e=;JW1XJi{}&MzG+|zk_#>mu|eKZbI^s^uW8YN$L9fW$=IN zV?HVE8v~}VXxmnbT=!U~iWSTXJSUmAOWAbH8fNJ2ds>9N$6Bz~2LA8$vXh_vw1c@v zo{DZEuX79Pa{Qn9uE*&*t6}~tr(yh2ApCQo+GJPKca%O}11H>{zHKcm7`!{u2sc?z zb@w2S)0*@KRtn5#Z-lwLr}1B5@4F{Md`O=cv{QZ_ejaQ0;03|-H-lU~IOUk9b2v;* zN(fpG59?_EA{Nx!daZ!F$6RUM57Q?8t6l@=89sO)0rTv4kM@B5O*tG6%ypZW!G<+f zIOrcD{VOdeLSf3RFB6WyRO_Kdv9Mh;zl>P0KYvC%thU#Q7Y_@ku64Ty3mNaLPrwYf z&lkGj@E5Z_o`RWj3EgAoV}H92>+vzOu2LK-Z_}rW;*K{T$<6mJ`H9(t}khWd+xoOo&gIS ze*6E0;}5P+=fPY`lEb70c>j4R2W7&d$2{#tFxOYzBpVj)&|16`7I{$Ia!G&Bl6hz0 zRtKizWio$I+PDO6ivAXS1?Kru6kou_$-NSbt~A^rb{F_BJ}mip4K^P7Aj#REvKiiR_Uz`9WS%#YNZ$|RpNq8@ zz%(Du@KKloSs(to5B(|& z|Nr%%^!ZSXVbN3#NzQJ}wOa|ZZ^mWkVV=?vdC(hnz0Mva<{3F0J`TGlyGquZZ=;rR z1x~0?nnu=}Guzaz46gf9)k`cm?_|;pbKWEbNY?jDY|S^AafT{cPf-=`%Mi@=T!uTJZHu}QDy0@Z9E(q)o4~09fs;<5VGv}Vp%Yw7} z{KpAkvBfFte%LkSjzT5ORyI62bum6~RvfrN%vnJjnhm$k*JybFQ#+=Yi8jjVgc}~ocn^nFE_79EOR}Hi3dY=WtwV^t^hh%>2(bKomvP_QVMAhVrL#y&jIm{coSp5TW<&mfsIO0ld^JiFaBR}#V9J+EOya(nszGI9v#qkEc zDDQ=-x2~2Mz%5&T;GT`_T(ddmZbx=0K3f4DpJLCoP4rD&DhWisE23Rn~=U=4_%(NP>vW?`+ zxi?nA@=3baePO!HcqvEN)!YB+c9^|ny@vxVUN-9I4pj?ocCBuKv3Ax{X z`T{oT$4&X@0>>Y}ksl0Gx&EDNV8LV0;4qk7R~G6Hr+hbh8xHfXei*+2j&6P`jQAhB zuR4&(fw{*$w|XMyezG4r3NtHJR0`=Pm|AxA@hGoA6`S9;a z)dlc>ucxV9^qCJ+ewIsKUv2S~SGQoMhlXVT^kWy&ieY|r|ILl)hn_b7bQk{bc&)3d zO-jl9Kbrq~18l|4oB02yxuTdTuJgNdChLH=$B#KLzu&BJ-He-PBC|V1dEa`^v{MBSxPaLp2 znF*)db17{g{b4%`az4FT6W2Y5nabKnUCBIeM;K(*e}pNPbnZ6T(MCD= z6U>kvlANC&g}Be3VG(!IcXEEZuhOVJFmJ!7u*vz`Q|S&ze=%w9rCoCbF+tF)=j?Td_){* zbo@j5|95_PCtM#6lfG2yB67Ve4T;qM!eUz|$@)d~e(0pAVE()5T|4AGR_hK+!|dHV zLsr0|g}HlWVg4pd$@`ghpXWcC`2X$)txbJf6=0#t@8J2EPqE!$GX|y}yZYP+W`|_y zk0pLmzF-dVOA}>fSa7Kmzuw3E{de{%r2isba=-Ea^W0|~%o)C&V1V4SZIUms_)D_H z)Vu}8YA}N-?wN(W`=eFk1ekv^AYC688)%K43RBOl{yPJ5p>F`QzUNAE_mkBjhA=ypFL4BQztjR^ ztrCef5B&&O3{y=uXwa~|s&?nO<}hcw%igK5yX-#y<*+bcOM4n@^770WYnbtPoWy?f z*GJg?Padwk$;1g}n=vGPdSkxMYU1jOS(@lmzKNCA5kI$+g{{vF4|cL%`~vjr>2`3Rjg%Pa&ICuRL4+v80yI35bK_wpr+>l4`#@4))I@2pTCbF{l9ZJ;ZJokc>6pT9)MZv zGbHQHy!^QJAWY3%Em?22r}Xbbr2l{Gnf)R(J&Md1o3>9zpKhahE*hq{jWg4L<86w{ zj=;=wv-;Iw?p?L`7?_e0bWIi3y^{Ab78Yt-D67Ec*HRneV8MkYK}s;oHnHL)@$}-> z(PaK{>su~Nce}o16x{K~p#2O?%}$nlKC?38a}!|kv15P!;q$x6w)H|1%uUS3*K^=` za^cv|DKIB?>b5^{-hFfZ3$UR5i{1dtxf&e8Bd+&u>4%xC-MLvX@77wAKG@uS-(Vih zWXE#9!_;o;?fEdZs3zF++dcSt2!)xym7&ORv|FI!A$uY-BJG9|~)teLd;DJ;Gn zBzYd848&+YgBe3hkI7*^O`+Vk5$0c8B5|nwQ@Ix~eak|L<kg zk>hWC>MOv}H+KJQBi`AvM-k?2Ig!u-bCYByj)l7q^A%o`?VX)=hn&AbJ%)z}roD_C zMb59e*TH>nVP=h~{hHEQQAsZ%vRqFLCM@$bUjEIG`+fzlu!+ z>pqk1`S?q&zwGf+3;SSU-hRpZrDOLx!+x?oIrpf!*q(nxXW$^|JHIPl2s@s z={SzZA3PsfIlUYveM8aGWw7ie`D-IEf8M&%_h;{#;WIKYBljYc zJntCiT{$TWGb{L#&s(NP#hlSFSFKa>Jkv_)OjIInsgr#E#2Ps+QHKBfd^GvAV67_5 z_STbp{uSE38LI&^3wEvv!S-Vv6db3*oEnOLBQ|sGK!tA#7eKTRMva3oG zT&#Mg!w?q!INFmA=l#ogY6SD1J~GIH1vSqn&xaXXzdp)=8RM>RUI>fdj+MU(%XS?; zZw7ORl|xG4s)WC4OJQnaltLNos&B7kLFUi6_dbBdq0=HQVal`Goegl0$s)}aFvra_ zvl;fgVSmpSW>qLObi!S~Zj4Qp!-ePqivm}fFsVLnXhxnW^V85bOOAo<}9WT<8;ksit6r*7N z*xc<|a74snrx=*t6&G<6)_7~*a}1_FY#S_vtHuVpo`S{a|HZt3b?s}lPs8}{U$Spt z!Sv0xXJDFgyyXYj@REYnS(x>sOzRu0p?z~+GI8VCXa8W{l*UWvh;R8tsV>9z`k1uy zq`zXk`xKb5gbo}ZBHWSz3$o?y=EK9xb0Zf?KAxXx4VxGQT3;f4`mrUR zF#gVp!WCF_IleXwZhgLCY5~l;(m8M%j_w#4a|>qg=G$I^OXcpoA{I$qIC&Yab$q${ z4w;uaz5X_=Y4I?q1ZG$`$CSbTJDhLcC;qW?&11N#@l8t^={GbiZ-9F&vUgR&w74JJ z+TcN@_TVa#zvCSg!TPHwzJ3S`?V@R)VEHW#S&w1H?iXo4V2Z5wqbH;vd;Q%2Y*Lb_ z);l&n zPC5J@rZTTCSqH~{2$NVGN;f5zZT#@<6WQLq7s{Jq-T6AEJus_K$;k)KK09enFD#7l zeYXSVO*?x@42uu!8oe9VuW7C6gPDSwu1J_QIy!y;rlt5S_5F*c1lTa& ze&%nOr7M+k5srR986p-IeCy1C^ABD4_?PszC4Vo39S@Wol~Tp=@K4?sz^$9hoMmB( z*?mp}?CR_uHVS4LU95QqC%ka+lZUy_iXGcw!!YSNN-&$UXVWJ*O>{D1EX=&T?otmt z{AlceGAtN>&-^>Av0pb)6=q23?iz$Ox4t$~hsEEP{h}~%{5s1=CcyNj;3j#vL$Ee& z63Mq|ULFUtP0jMPV1CXTo*FE9o`tCgZbWL5{Nthw9g-i>U#$llf9I^2Lvj`R-gPiV zS21xeEPB)}=LKgAcWj}<^jC&m+u%a&fox+~Y=3$B0hpgRJZmW|6lbk0fLU9*Y#1Xbsb(vT~YWM?blO6)+<#&)^ko5_|Hm4NU)YWS|>1w9r~*2Q$~mjvIgr zH&mKClD?94v&?cle@j~*WWwU4T`8)tyG77_H<)*V9jgzspOoaTCw-~yZRW7rjpEde z#Is-Y+rwRqs4<&iPJ76sHE{O#ztLM@(eGsgo@D-6cjh*j5iK>{2X?Qs*s=qr((fki zf<^1K(%3M4%rck5uzb#@`@3LDD<|v(EYQ$y+6{BISq@!-S@*s+6Eo9eWpm-!Z>kP^ zVR7QWvOBPMhfUIcn6@Z=??br%O?~S@n5}74(g|xi`QANB`W}su|49GqG&3&DIMcXs zjwPN~?-r<}62Ev;xfQOP*~z#_y!6M#)39jwMAd7s&>-`<0CvndV^c_+-k$LqHdb%; zy+eFgu=5M-zDfO9G0gq5wt57vV;$1E57Qsn3{SMe`}M)~jg@5HW5y&CIAv+6UNuZ@ zGnsA=yKhMhsD*h|`X|@Ht-}UK>tNBz1>T_~Z_N8wPjXkgnTKKV`#Yx_NxpK)yKFdZ z-}9kXm}Sa(RR{a6UuXUbW_&&T{Ts~hxikC*7Wm9gQ@6(bRX@MC3zqym0<+T;?tg&! zb+0BZfFs_{o&O0IHrn2`gP95YHNU|;iz}zYVcEsDC%%*ZcE!D?VOoCJlL6wa7u3se zzpgI#H%ys3s_70aHW{G&g$0vxPdVsI~!op?ebu$ad`bLA&gd7c9CnO zJSGracV2jPgyi-GR)-PS+)fBUr|4G8qC(Q8nS~6FPtpahDGt0 zPC3D>pfiVbVd27<7fe|9&e!F$V8NGf{%$0X4|k%&RLw~fw!@U+8j19jMrnhv_j2LagD}^=iY;w}>!Y$zF$xxJ_E@0=hu3|d77L5*y`87R z-d=Y~;z-}fK0*&pYqx(L53}vpO_>Ep%n&%7hUt+$GfZJMlZIDGFh9OHfC-!Y&^~QsI?GlB5`&kqnhW zF%*&xLm?_n2P#UFq9}|Cp%^KW%IJ(@D1_*FulYUqb$$PQUGLA@v-g^{*Is+geW$za z5=>jMa9%ykX6lCt;n*=5!k4h%R+Y;qc(6(OVk6AumtelNZD0<5_0emFTERrfNjmUzA+^*PMyGt(EtmQIiA$oa9h?EW== z1@4#PB7+*@F>gZD;po_X9S>l}+6ZT3m~z@RunMN>Wp%8AJDt;0$@L3g&=<4du=*do z`y>yiK8S~DM>6v6k@g#s4qb%RKJr8rFx_j-ll{PpFD z0ytM!>*@{SmY?@q;Leykt;H}!NOk%FCx2Iwe1Gi2kDjSG;PbwEP|5ennXi0PA5OaM zyoi`tz}UiogK2Kd3t?K-8WRsVs%_1|e3Bm!28Y1LdaEig6Kh`UyA7*qe3#0EMJZbw zyWt`KHqUgJs@Y&AyAt33#~b~r#6ia{&4p|K+8$1Z*{$K-F0h{ZuH~m;K^=1-4o)@M zelP*%R#O&ShHD%S1)hNU)9yArgX#It@{YnZT34?awl7jvIsyxu>C>h<;(WKopW(oa zy%C*rVcXS*W=6o=r3nA1qPzZYiPr|;Ye3qEyE3x=6r&L>@j zX(ftp*)Zc>UFB`K?0w$2U9jMTW_2B$`g72J2h2M6KK~b7qO#u252mUM3bj|EzPifF z3+B#qI_Lzq^k2zj!Hn98t0Liw5F^QaBUXxfc@a+aEh_XNdB<+mCor9Twc8D*G8$ft zz_K%UJRtKGGyInF+|{_Ba;%vyFz?X(scT^V)-uX^SU8du!-k#HMq4_;q7S#_GGN|< zCNY^0Ic|YX^|0bM|5dAC%4c1jVVIhE?SLc9A3PkdwFdpq@s#AO3g=0VaM;5>$$ZMc z`dF|Bj$OxMFiE}i+hdpErd~P8=TTIf-#>*JKjs#Z`IbA7vE>8&-}zDkKIM$S=GH;+ zn@Ichp7s-&xSwy1$szMEqxY7f0nAu=`~&GfdRK74O1Lcgt;h#vH;);+8P5MwwAmM? zHgRNk!~f0CqBRP2r{EfIQ7ZXv=i<}(Yu*sKBrvKk{=Jt3a6RwgPCTPnL4oWd!iN@PxR~3`E%gt%jt#0 ztgVK34Pnc>UoVsSUVMUXV-EX&ov2CXd;Fn8{DC6ezhO#}@k?o(rDO~1SAASh#_@y=$6(Qq&Gk-j)bTV8az9Xy?)l*kwRN*tx|{K z{4afiM3|Ri@Gcr=1WZvp3)6R;6Q#hZ*>f+ZkldnR@DAM4M2pIRxtBd}wZXyC!3VNP z{p?M;{jgf$)r4G__5Q8b=(V_>i;la=e8HHPO!a|zmouF+KtuSO4`q?ohq3>7qB<_!@Tv4 zJ=Mrr-QVJO!L=seCC?Ah>Q=V{u;s*e0d>eZhP$~(;Gs(DRr35`Exz>aG@Q>%sd@!- zh2GJx^VgJjyQ-_JE?^<19@f*KeBQSf!=a&yGyKT6Q zqJqy)_|~%x=2yC=kA{U=XJ#FN>B$9Kim7DVCRC3I`%O8 zj%ybmrW`cWC8o!&{Y)It#XQ7>1wMZ(+Tnb4J0BOAF}Y+BWj*fKQ+L}okb16ssXT0a z-?MHLOq;oUga()BxCu7HY^ex2BbeHwc+?XXXEw+$gKb$ZyZlJ|@OQsA!M(Qnc{^c> z?%;_KxZ&>AwqTgy9Z{GF3o=*Mg~7}j15bD`OF4zZf%&opt%b0^^D3zrnAYn1sv4%s zX5=Knl*myjU2s_CU8M^!NA{)BUzpF2=cK{(Z!2CZyI?#wm)*^T`Tfba=fVMJ6!%<# zsY+EVmcfpT_E(p{+zm$_dBVvXgE_ZJ{iH)`p|G(=&fR-3@8o=bJgmDZ`q=|m)O`EP zWmt8B*!v0b(1=zQtT(&$a2+gGdc3Fu4&T5ZAr=^Fv`yWB^>xpF_6wN3tMkxunD@pf zt^uYlS)jfLw$HV1cnz~kX9u5yo%?OMtuSrU=-~%2Jv!uBJ1qJz!tR1o2c)KUl6+_B zmJhI+XirQp%&@o6_(k$B@v}dXJSXxe#TETCXV$(xn3bDSI2xACc~VQv8*{g13>-W3 zTVm?VZLP#ZsWYsa&lWY{)?>oE*dV(71?z{#?sr2fI86j?28xU*?{_6W@0u3Rw<7H=?E zDy@p~F@5bJL%71M`0!Ym0n~$5qg5&J4Vpx-Ao4pWbyoh&w4?9O* zNU?y$$4&F6xZ!%wn%=jBSt94lHt=wktLakGeyWeuHh9R7-DpMpbh~dl%#_cd*}{~) z4xj5t`~3&~mc#TfF9*~&p}$%}+#Fz5<4~(BY`fF6(-CH?{F=KHu9$i=a1G3jZ#r}s z7JroXb%MnvqUOtR@4v=Z&ZK@~u4^^yoosFD4pX#SzkY_xt{zkIhG~b>nl;>UKjmJt z4S?yn4}zA#EuMb$dq{gHyYpVKYxU*_A+T@;<#ZTq&grc>1aq<$@vp*aPfS9O6EARE z@e1Z$5zEBGe5J*{KVW@|k$NJ@tD9^mc%XkC?f!8Ards4x8o(v7_eAM1^O23F70lDx zRCyVu*jH7!zybGvW);9R#yp!na1=B4@ikbazRWKIHXq~+5L3hVTsi`?^R6!`g1I$~ zV-w*DR$Fl~OnI1gD;uu4)V%8!OnWb1UJ6HDrk0n(qWue#?~%N9P0Jlpuf^1=frl=y zORj>&*JFL!;1=cS4{BlRknfRya6s(X#(J1-8rnL2Gv?EqwdQYN&g=VEtYMkdr*m6i zu9V@!HN^H8ecEB6O3tU9aCDf;gAXwCbe^xi*8w1_)w>9D(tUed1eAkSH2y+3{IUo zGBg=xG{lebf@8;xzM%y(Pq7w9!0xm6xN5_clu`PJ;fAt|tZBsGe@!?Jhu{0gE_T9D@c8Ckiit#{Nca-PB2ZJl(h)1INZO^4W@`^t=CG}yzhVD`#pk17&Z1kvW45AxhL-3!%>84)RUy_LoOu#vB%55&g=-3BAI6dP4f}_U z;i&UEK3w8}_RY)S)EPV1oFTq(jk*C=QyCj_7UpRy=WK#`1NTRz5j(W$?t`nPG^#RS zW>2R<3{15-JCqAkjyj}Vg-d1y7!{KG`TS{*VB-!qg)*40|E=K@%;E0~x&t$gm}km+ z;(UctRd-=_r;kVpuCaczppv+&+L{LI`+iP&1oJc)9VRfpex-XYEPCZV)(YmOKffp> z`J0$dds09Dm&!|6XfXe!Bdq3h*sB3%IL*>v!on81UK7k;{pIOaIB75U`x}_uPZI~g z$;ryQ-oo62idKhV*A?--Eih;3zIG~Hk|N{T1~V%>)QVuoMK@-)!z^73ehnD0~HYF>Cg9MIMK4hwmf_s!vw^6kpMVL|YRqZ{GqA^Kj*B>3my z=SN^Yy22f4n0++*-g!7&=f|5dFz3(?>NPk_JO83Q@hh$17jR2>bk{goG&bkZcapmd zIVr<}0&Ckz-srDY#=etD{gKoucCc~Lj7SZbb;?lW3)2qfJ(vn}-2(rehiiNuFPcW$ zb3R1ffTMgzj!q|eCe6MIrrwKb)P?!FiicWBecF=sP71Zc3pn zb%W_vFBBiau4`T%^?=1gwrZb9eU{DaEwFHw^xWUD|A1%XW|;H9|J)Qm^p9tQq@FhO z&IfClqg2&Rj>m8Oo$3eE>$HYkV9L5PEr;ORKOG%TuwYT*^;EbzoVH>$v4X!t2^_ud z6rEfjW&78xhcMsAy=ytlI%h+B0XOg;+#~%X9CPJw6PzTTmt#%pFKJ%<3=7-Yiljej zj|1eX{^+lxNe@VWGyL9NnE{Ii6Xi@{Dos0Z0j#J$H)uZ0ezIew1!`uyz zD<{HCCkx$!aF~qNzX`DL#$knXaIIgL^jMg2y>g@*PTIf3k=#FG!@pV@+ps?Px$ON{ za=&>7{W=2cl^=@uMD90s-}-Vms*w}Z3sY?l7{7v5?@p6?4~w^6-!*$X=HJxQlKFwL z_E#UAUv?S~n2@)nlDqW;mQwJ`7Pb-NOn6>LHugxP6&GJKeJb57r+0NkHR z(<};L;qCG9OJVz8;^7?9zM8pdGn}f|zu*$gbi8mUkkpU5(VI!yPdKBN1Sg-_pm!0b zeu=#D60X*&*E|RF&r46x+KKn$oc%M%^MJ);MJ$2C3kDk#VTzXJ!mY4ab5K1VW>-FW z90$|yg%_NFnf|`<*Wk_@A)Uuy!P&_>AH%H68<&yi6yEU@1_VfV$Q&om{fSqH1y8(o%+m&31X_rZLfr%%(# z@$N=Hh=*Md?S90AnNe$+(%>l8ZOM2OHJRGv!@SpWd1QR@ii}5|!7bBm2lGh%J^xARbKN8Tw_yfa}{QP<%@@4|EDZ>vYucEeSAG(58mIcv<%4oKrhpsG#U1G zX>KGIWgm2%0@GJ#M-&lXTAHE_E6%v1N$w}npVO&Eu&94VaT(0tw5Y`aE}Lh%wj5?Y zG3?$13(Qls$@+x0ZL7Q=?A+kI$ohmjTOlw8uC7~KPz4LOWjsoS z{fnJ=4`Ke&M=m$vsGBy4k73@DKNoA^&N-IH$ohrSyxg-HX1a-j$@+!zD9fuKHnuR8 ztWS6jf9Q%qIKR7vNo4)OnDyx}4X(IkwYLLi4?D%#!nLA%2z_A@WzN^3-Zr}$o%o*>dOy+BP4=3;!oU1)NPL1RR&Nqf(J=y8W z#Jv6GPe$NQqvQqZu;3`gMmiYh|2Awtng2Nt&!$dQ?3w0d)r$6QfMg4GgC zCF=!V=@-@&IO=Y5rVVn&qTZ)vq^6G z;?Y~oo8cz8q`oyUMaep=m*nyq(5VcXpW%UwzPKV0_M5X{$Sm^K?>cJ}U4 zdDwl=!Lm&-^-rXw3UT7=FcxW_`b^FUrkZAb@Phenim#clVw1U&4@~dmtlI>8r>Vvf z^Iv?3_Jjj|ufOI8^BfqXcfx&}R%L95IgC^7!6cubxpfyT+S#%B5NRLrVM!>=>ekOW z11JA5njH>P#@*O@2_AY^lo>(lr^e;x!l{#EKX72GnNGxYxF*1;DGCT`ACsY7sW4>{d-o_CO__Hs17?-q8aoya7?~fM4Kp-Gb&ZGZ zw^k?QlJ?;`U$kM%*z5UMVPW`lDMOgGHDGxmOpA<7Fo9bH)@9dW(dOrMi{Qa#=7t-v zATCa{0wNle^Erj+?dS)x+H6?B^HYVXW|9 z!n~I`x6|Qlz2UEoFe{#Af0?xZU_gIM+Q+R^y$-X+XQjS_1y3hfmBV^V+WDO@b)V_V zS~zy5`R)%eWkh9i6D*3$Q~d<9w}lq8koI#}g`Z)5Z%k=FoNe!!`IYzwd)o-i_2J$8 z0W&u)Ii?+o{xgwt8-j&?^8;qU!dV-ge#6vN-Ye$8yl`)gzc4%g)=U#P_33vbsmU0h zy?jLrxFk{UpfoJ}Gx)(8?z4A$JQ`+S2)^qGtM;D~j)Qp`J#zwK+lZgO$}oS(TKPCU zY_-g3GR*P&xZ^yWl(RvsPTId|ChAFJ}=J$Owm{aj2(FyjxtUhNk%$WKq%@YpW{)S@*v)=~l9)$h1 ze!g8zJkg@!46N_HHgz2=xaRJY2gf?*y1T;6l&eNHaMJtDA2$;(eWovloBo;U`oJ99 zd!xqfLw^~xC~t##D`#ivz~0XL%(uhb!BPDVd)P2*!K{5tVW#T}gS{}rv+Lw) zScaW4BMhb&uGd)yw@m9*iXin3yX`i^gF3f39GH2*eB(~oS@yx`6ENo&Z6X)0BH0;r}eyHnoC}iAsqax>$)dQ*O)DwPjcHsXUX?3 z$S+qnhiU8gHIwndn0UF%2Id{o)o_NzVM}kXf}2j(`8vYFoWHg1aP$QQX)^v;RiUH2 z;V>g_Rcn%O>Pz;AqpTLcvLyMOA7(q?RNWEDcxF6YW4IGGp6$mpMNZwLZW#zSO|qV4 z42w1fJ=+DV+S(e=huNosckY3!C%G|Yud&;Cm<2Vs8Ood&A1vtmyW|O+Z8f+un6%%pwecBT-SEdJkks#aEB}hLk5Q4Vk7!YswYuQmQHO%J zBBzK$96po$Qsz9eA0xCfYZ`?0UG{z<>ou{t_3#Kx8TIQH*>B-1yU`TFF`i#c<&gEC zK*!n!>3=DqLgaocy|-7y_desjT2F#%rEuI5Yb(X5w!>-w%GiSogQvuaggc~7OYyH>Q#!I^Rt@n1t-;wT5=PnC-2hsgDF{s|F<9H zmE;is>#KB0-gj9^5iud8-s0>h^1jT;4cs3KH^gY{A@9@N^MS0>@L*rMWPhH&L2=Dl zn5(gPE_ojp*ejZ(l6=eO>((cbO7FjvHu?4MBVep~#3Q@wI@Vo^_1{drmLAkN<}Hj?beFc+#kRfYRjr~8uq zA?A^H;WJ@NxrvjH!kk_Gb&KHMd3Kk{{*Q3t*@tUjT2P$Y0hk(c@W>um@r++{2rPb| zKOq(#dVcW9E|~jME;$FL^OoM+4pSzVyVt<618u$juxPb=a~B+a=%5%^^Shfbi?Be?0s_@`cY6mel z7ympO?B47mvlpgi_10;@^sIXq_QQ(6Q=9EmADu61#2Vk0a+TOP_BA_x-sb*?;46%nn<_=G-Zg z{Wf9XmPJcpz2*6z$o`@5wTr~VuhS=z{Ws?HP5+nI&yf5+#jQMldI{?NH)^|*{YToA z8SSLK`1mp|F{As)CI;+2U(TQGpYeXh{dI)(o4!P)!dxGZIWBNh`)z&l`xV_Y?6E7U zAN;v09i~JT#`?ky>SOH>dEu}?Bkls(KN5Dh)ENyG9&QDbqz=AUx2f1*|G`+diu*hz{ zRwDf0ei3cpM>R3~^`PYUE>T5cKoU%8J<#?P_1vDf3g=+iO_KyUW@fbfSN z&P`ZXVbPjCnBiUe=oTC>*Q5F~Ox-4XuN+P-uXG!L*$+EM-y`h}%UAs%`OUEP0+_G3 zI_)PcoV4gKu`RQ$a){KYy>hIC^?%*{zvn~4>rd6N|G9?Tf5<7!jYl5CQFnGf8-W>n z()K-tRsZaakj3*zG(YFxOE@6yVvRg3ZZuup2nXl9ZBZi6t5o?jO|a0lPMQkyzb0oA zE84|oko_u=M_lI{SU;yXko=y&ew`_CZ{n1PlVEB>-_B;_!YxGQuPTvd zFst}w(GaZb&|b)ZdHHhUzi`v}JeB1zb@I+*qYmTwUaA$e8m8wDm&?Ioc^{8;us|kS zK>?=pY*Q!uQ6k5jE8}2MROe|on0si$+VQY%#q<()nAX&up#(esidyUqv-;ClP~mW~ zz-b%Io>C;SzP91_?XakF#&6PI@yGI{oiOLi6EAr*1G`UyOFux`E*D+YU;(3G&%$8csh=JL`E9yqWWnIyd^{uc= zFJuhd+cU?M>`!rqog|+h#_EnuhZ!mdzLM)ty)sj>|0G!aouLeOS{@6$OmfHGnQCwV zdzsu7n7_*TjwYOR`{zn#o!