From bdcc3e6072b801040d77d23f48cc5bd2d691c4cd Mon Sep 17 00:00:00 2001
From: Isaac Wheeler <ickaser@gmail.com>
Date: Mon, 3 Aug 2026 13:29:02 -0400
Subject: [PATCH 1/4] Add a Google-style docstring to all 'dry' functions

---
 lyopronto/calc_knownRp.py   | 10 +++++-----
 lyopronto/calc_unknownRp.py | 20 +++++++++++++++++---
 lyopronto/design_space.py   | 17 ++++++++---------
 lyopronto/freezing.py       | 12 ++++++++++++
 lyopronto/high_level.py     | 13 ++++++++++---
 lyopronto/opt_Pch.py        | 20 ++++++++++++++++++++
 lyopronto/opt_Pch_Tsh.py    | 23 +++++++++++++++++++++--
 lyopronto/opt_Tsh.py        | 20 ++++++++++++++++++++
 8 files changed, 113 insertions(+), 22 deletions(-)

diff --git a/lyopronto/calc_knownRp.py b/lyopronto/calc_knownRp.py
index c2a1f959..f1dec3ed 100644
--- a/lyopronto/calc_knownRp.py
+++ b/lyopronto/calc_knownRp.py
@@ -28,11 +28,11 @@ def dry(vial,product,ht,Pchamber,Tshelf,dt):
     """Simulate the primary drying process for known condiditions and parameters.
 
     Args:
-        vial (dict): see master simulation inputs
-        product (dict): see master simulation inputs
-        ht (dict): see master simulation inputs
-        Pchamber (dict): see master simulation inputs
-        Tshelf (dict): see master simulation inputs
+        vial (dict): Vial properties, including 'Vfill' and 'Ap'.
+        product (dict): Product properties, including 'cSolid' and Rp parameters 'R0', 'A1', and 'A2'.
+        ht (dict): Heat transfer properties, including 'KC', 'KP', and 'KD'.
+        Pchamber (dict): Chamber pressure control data.
+        Tshelf (dict): Shelf temperature control data.
         dt (float): Fixed time step for output [hours]
 
     Returns:
diff --git a/lyopronto/calc_unknownRp.py b/lyopronto/calc_unknownRp.py
index c0f92dc9..3ac2aebe 100644
--- a/lyopronto/calc_unknownRp.py
+++ b/lyopronto/calc_unknownRp.py
@@ -22,9 +22,23 @@
 
 ################# Primary drying at fixed set points ###############
 
-def dry(vial,product,ht,Pchamber,Tshelf,time,Tbot_exp):
-
-    ##################  Initialization ################
+def dry(vial, product, ht, Pchamber, Tshelf, time, Tbot_exp):
+    """Simulates the primary drying process for a vial.
+
+    Args:
+        vial (dict): Vial properties, including 'Vfill' and 'Ap'.
+        product (dict): Product properties, including 'cSolid' and Rp parameters 'R0', 'A1', and 'A2'.
+        ht (dict): Heat transfer properties, including 'KC', 'KP', and 'KD'.
+        Pchamber (dict): Chamber pressure control data.
+        Tshelf (dict): Shelf temperature control data.
+        time (np.ndarray): Array of time points for the simulation.
+        Tbot_exp (np.ndarray): Array of expected bottom temperatures at each time step.
+
+    Returns:
+        tuple[np.ndarray, np.ndarray]: A tuple containing:
+            output_saved (np.ndarray): Saved output data for each time step, including time, sublimation temperature, bottom temperature, shelf temperature, chamber pressure, sublimation rate, and percent dried.
+            product_res (np.ndarray): Calculated product resistance data, including time, cake length, and product resistance.
+    """
 
     # Initial fill height
     Lpr0 = functions.Lpr0_FUN(vial['Vfill'],vial['Ap'],product['cSolid'])   # [cm]
diff --git a/lyopronto/design_space.py b/lyopronto/design_space.py
index dc4c6385..3abd1ace 100644
--- a/lyopronto/design_space.py
+++ b/lyopronto/design_space.py
@@ -22,19 +22,18 @@
 
 ################# Primary drying at fixed set points ###############
 
-# TODO: document this properly
 def dry(vial,product,ht,Pchamber,Tshelf,dt,eq_cap,nVial):
     """Compute quantities necessary for constructing a graphical design space. 
 
     Args:
-        vial (dict): _description_
-        product (dict): _description_
-        ht (dict): _description_
-        Pchamber (dict): _description_
-        Tshelf (dict): _description_
-        dt (float): _description_
-        eq_cap (dict): _description_
-        nVial (int): _description_
+        vial (dict): Vial properties, including 'Vfill' and 'Ap'.
+        product (dict): Product properties, including 'cSolid', 'T_pr_crit', and Rp parameters 'R0', 'A1', and 'A2'.
+        ht (dict): Heat transfer properties, including 'KC', 'KP', and 'KD'.
+        Pchamber (dict): Chamber pressure control data.
+        Tshelf (dict): Shelf temperature control data.
+        dt (float): Fixed time step for output [hours]
+        eq_cap (dict): Equipment capability line, with 'a' slope and 'b' intercept
+        nVial (int): Number of vials in the load, used for equipment capability calculation
 
     Returns:
         ndarray: table of results for shelf isotherms
diff --git a/lyopronto/freezing.py b/lyopronto/freezing.py
index 5cafeced..ce0de3ec 100644
--- a/lyopronto/freezing.py
+++ b/lyopronto/freezing.py
@@ -23,6 +23,18 @@
 ################# Freezing ###############
 
 def freeze(vial,product,h_freezing,Tshelf,dt):
+    """Simulates the primary drying process for a vial.
+
+    Args:
+        vial (dict): Vial properties, including 'Vfill' and 'Ap'.
+        product (dict): Product properties, including 'cSolid' and Rp parameters 'R0', 'A1', and 'A2'.
+        h_freezing (float): Heat transfer coefficient during freezing [W/m²/K].
+        Tshelf (dict): Shelf temperature control data.
+        dt (float): Time step for the simulation [hr]. Used only as a sampling rate for output.
+
+    Returns:
+        np.ndarray: An array containing the time, shelf temperature, and product temperature at each time step.
+    """
 
     ##################  Initialization ################
 
diff --git a/lyopronto/high_level.py b/lyopronto/high_level.py
index 84984653..37fc376a 100644
--- a/lyopronto/high_level.py
+++ b/lyopronto/high_level.py
@@ -21,9 +21,16 @@
 
 
 def execute_simulation(inputs):
-    """
-    Run the selected simulation tool with the provided inputs.
-    Returns output data based on the chosen simulation mode.
+    """Runs the selected simulation tool with the provided inputs.
+
+    Args:
+        inputs: A dictionary containing simulation parameters and tool selection.
+
+    Returns:
+        The output data from the selected simulation tool.
+
+    Raises:
+        ValueError: If an invalid simulation tool is selected or required parameters are missing.
     """
     sim_type = inputs["sim"]["tool"]
     output_data = None
diff --git a/lyopronto/opt_Pch.py b/lyopronto/opt_Pch.py
index 6a9be783..adafeb08 100644
--- a/lyopronto/opt_Pch.py
+++ b/lyopronto/opt_Pch.py
@@ -26,6 +26,26 @@
 ################# Primary drying at fixed set points ###############
 
 def dry(vial,product,ht,Pchamber,Tshelf,dt,eq_cap,nVial):
+    """Find optimal chamber pressures for a given lyophilization process, with fixed shelf temperatures.
+
+    Args:
+        vial (dict): Vial properties, including 'Vfill' and 'Ap'.
+        product (dict): Product properties, including 'cSolid', 'T_pr_crit', and Rp parameters 'R0', 'A1', and 'A2'.
+        ht (dict): Heat transfer properties, including 'KC', 'KP', and 'KD'.
+        Pchamber (dict): Chamber pressure control data.
+        Tshelf (dict): Shelf temperature control data.
+        dt (float): Fixed time step for output [hours]
+
+    Returns:
+        output_table (ndarray): Simulation output table with columns for:
+            0. Time [hr],
+            1. Sublimation front temperature [°C],
+            2. Vial bottom temperature [°C],
+            3. Shelf temperature [°C],
+            4. Chamber pressure [mTorr],
+            5. Sublimation flux [kg/hr/m²],
+            6. Drying percent [%]
+    """
 
     ##################  Initialization ################
 
diff --git a/lyopronto/opt_Pch_Tsh.py b/lyopronto/opt_Pch_Tsh.py
index 214edf81..b301e16c 100644
--- a/lyopronto/opt_Pch_Tsh.py
+++ b/lyopronto/opt_Pch_Tsh.py
@@ -20,9 +20,28 @@
 from . import functions
 
 
-################# Primary drying at fixed set points ###############
-
 def dry(vial,product,ht,Pchamber,Tshelf,dt,eq_cap,nVial):
+    """Find optimal shelf temperatures and chamber pressures for a given lyophilization process.
+
+    Args:
+        vial (dict): Vial properties, including 'Vfill' and 'Ap'.
+        product (dict): Product properties, including 'cSolid', 'T_pr_crit', and Rp parameters 'R0', 'A1', and 'A2'.
+        ht (dict): Heat transfer properties, including 'KC', 'KP', and 'KD'.
+        Pchamber (dict): Chamber pressure control data.
+        Tshelf (dict): Shelf temperature control data.
+        dt (float): Fixed time step for output [hours]
+
+    Returns:
+        output_table (ndarray): Simulation output table with columns for:
+            0. Time [hr],
+            1. Sublimation front temperature [°C],
+            2. Vial bottom temperature [°C],
+            3. Shelf temperature [°C],
+            4. Chamber pressure [mTorr],
+            5. Sublimation flux [kg/hr/m²],
+            6. Drying percent [%]
+    """
+
 
     ##################  Initialization ################
 
diff --git a/lyopronto/opt_Tsh.py b/lyopronto/opt_Tsh.py
index 20f6b3e9..fde922a3 100644
--- a/lyopronto/opt_Tsh.py
+++ b/lyopronto/opt_Tsh.py
@@ -24,6 +24,26 @@
 ################# Primary drying at fixed set points ###############
 
 def dry(vial,product,ht,Pchamber,Tshelf,dt,eq_cap,nVial):
+    """Find optimal shelf temperatures for a given lyophilization process, with fixed chamber pressures.
+
+    Args:
+        vial (dict): Vial properties, including 'Vfill' and 'Ap'.
+        product (dict): Product properties, including 'cSolid', 'T_pr_crit', and Rp parameters 'R0', 'A1', and 'A2'.
+        ht (dict): Heat transfer properties, including 'KC', 'KP', and 'KD'.
+        Pchamber (dict): Chamber pressure control data.
+        Tshelf (dict): Shelf temperature control data.
+        dt (float): Fixed time step for output [hours]
+
+    Returns:
+        output_table (ndarray): Simulation output table with columns for:
+            0. Time [hr],
+            1. Sublimation front temperature [°C],
+            2. Vial bottom temperature [°C],
+            3. Shelf temperature [°C],
+            4. Chamber pressure [mTorr],
+            5. Sublimation flux [kg/hr/m²],
+            6. Drying percent [%]
+    """
 
     ##################  Initialization ################
 

From 32dbaa07c504386e7330e0bd8265b7abf0f35ecc Mon Sep 17 00:00:00 2001
From: Isaac Wheeler <ickaser@gmail.com>
Date: Mon, 3 Aug 2026 15:58:33 -0400
Subject: [PATCH 2/4] Clarify readme

---
 README.md | 20 +++++++++++---------
 main.py   |  9 ---------
 2 files changed, 11 insertions(+), 18 deletions(-)

diff --git a/README.md b/README.md
index 0dd8e68b..1ef2f110 100644
--- a/README.md
+++ b/README.md
@@ -9,15 +9,7 @@ Maintained and updated by Isaac S. Wheeler.
 A web-based GUI is available for this software at http://lyopronto.geddes.rcac.purdue.edu.
 
 # How to Use This Code Directly
-Download this repository, then in your preferred command line navigate to the containing directory (so that `LyoPronto` is a subdirectory).
-Execute:
-```
-python3 LyoPronto.main -m
-```
-This will execute the file `main.py` in an appropriate scope. Parameters can be changed in `main.py`. Files listing the inputs and outputs will be generated in the current directory, along with some plots of temperature, pressure, and drying progress vs. time.
-A video tutorial by the authors illustrating this process can be found [on LyoHUB's YouTube channel](https://youtu.be/DI-Gz0pBI0w).
-
-Alternatively, construct a YAML file with all the necessary inputs (see YAML files under `test_data` of this repository for examples), then run a Python script like the following:
+Construct a YAML file with all the necessary inputs (see YAML files under `test_data` of this repository for examples), then run a Python script like the following:
 ```python
 import time
 
@@ -42,6 +34,16 @@ This will generate a record of both inputs and outputs each time you execute the
 
 See also documentation examples online [here](https://lyohub.github.io/LyoPRONTO/dev/examples/knownRp_PD/) and [here](https://lyohub.github.io/LyoPRONTO/dev/examples/unknownRp_PD/)
 
+The original method for running this code, as illustrated in a video tutorial
+[on LyoHUB's YouTube channel](https://youtu.be/DI-Gz0pBI0w),
+is to download this repository, edit the script `main.py` in its root directory, then run that script from the command line (in the directory containing the repo directory, so that `LyoPronto` is a module).
+```
+python3 LyoPronto.main -m
+```
+This will execute the file `main.py` in an appropriate scope.
+Files listing the inputs and outputs will be generated in the current directory, along with some plots of temperature, pressure, and drying progress vs. time.
+
+
 # Citation
 G. Shivkumar, P. S. Kazarin, A. D. Strongrich, & A. A. Alexeenko, "LyoPRONTO: An Open-Source Lyophilization PRocess OptimizatioN TOol",  AAPS PharmSciTech (2019) 20: 328. 
 
diff --git a/main.py b/main.py
index efb0ac74..b5a16fa4 100644
--- a/main.py
+++ b/main.py
@@ -20,15 +20,6 @@
 
 
 from lyopronto import *
-# from . import constant
-# from . import freezing
-# from . import calc_knownRp
-# from . import calc_unknownRp
-# from . import design_space
-# from . import opt_Pch_Tsh
-# from . import opt_Pch
-# from . import opt_Tsh
-# from . import functions
 
 import time
 

From 284e379fc74b8821db963a2113b08712b738f9b9 Mon Sep 17 00:00:00 2001
From: Isaac Wheeler <ickaser@gmail.com>
Date: Fri, 7 Aug 2026 20:44:49 -0400
Subject: [PATCH 3/4] Check all details (with Copilot's help)

---
 lyopronto/calc_knownRp.py   | 6 +++---
 lyopronto/calc_unknownRp.py | 8 ++++----
 lyopronto/design_space.py   | 6 +++---
 lyopronto/freezing.py       | 6 +++---
 lyopronto/high_level.py     | 2 +-
 lyopronto/opt_Pch.py        | 8 +++++---
 lyopronto/opt_Pch_Tsh.py    | 8 +++++---
 lyopronto/opt_Tsh.py        | 8 +++++---
 8 files changed, 29 insertions(+), 23 deletions(-)

diff --git a/lyopronto/calc_knownRp.py b/lyopronto/calc_knownRp.py
index f1dec3ed..845d65fd 100644
--- a/lyopronto/calc_knownRp.py
+++ b/lyopronto/calc_knownRp.py
@@ -28,11 +28,11 @@ def dry(vial,product,ht,Pchamber,Tshelf,dt):
     """Simulate the primary drying process for known condiditions and parameters.
 
     Args:
-        vial (dict): Vial properties, including 'Vfill' and 'Ap'.
+        vial (dict): Vial properties, including 'Vfill', 'Ap', and 'Av'..
         product (dict): Product properties, including 'cSolid' and Rp parameters 'R0', 'A1', and 'A2'.
         ht (dict): Heat transfer properties, including 'KC', 'KP', and 'KD'.
-        Pchamber (dict): Chamber pressure control data.
-        Tshelf (dict): Shelf temperature control data.
+        Pchamber (dict): Chamber pressure set points and time (see docs).
+        Tshelf (dict): Shelf temperature set points and time (see docs).
         dt (float): Fixed time step for output [hours]
 
     Returns:
diff --git a/lyopronto/calc_unknownRp.py b/lyopronto/calc_unknownRp.py
index 3ac2aebe..45d5b63d 100644
--- a/lyopronto/calc_unknownRp.py
+++ b/lyopronto/calc_unknownRp.py
@@ -26,11 +26,11 @@ def dry(vial, product, ht, Pchamber, Tshelf, time, Tbot_exp):
     """Simulates the primary drying process for a vial.
 
     Args:
-        vial (dict): Vial properties, including 'Vfill' and 'Ap'.
-        product (dict): Product properties, including 'cSolid' and Rp parameters 'R0', 'A1', and 'A2'.
+        vial (dict): Vial properties, including 'Vfill', 'Ap', and 'Av'..
+        product (dict): Product properties, including 'cSolid'. 
         ht (dict): Heat transfer properties, including 'KC', 'KP', and 'KD'.
-        Pchamber (dict): Chamber pressure control data.
-        Tshelf (dict): Shelf temperature control data.
+        Pchamber (dict): Chamber pressure set points and time (see docs).
+        Tshelf (dict): Shelf temperature set points and time (see docs).
         time (np.ndarray): Array of time points for the simulation.
         Tbot_exp (np.ndarray): Array of expected bottom temperatures at each time step.
 
diff --git a/lyopronto/design_space.py b/lyopronto/design_space.py
index 3abd1ace..fe7e13d3 100644
--- a/lyopronto/design_space.py
+++ b/lyopronto/design_space.py
@@ -26,11 +26,11 @@ def dry(vial,product,ht,Pchamber,Tshelf,dt,eq_cap,nVial):
     """Compute quantities necessary for constructing a graphical design space. 
 
     Args:
-        vial (dict): Vial properties, including 'Vfill' and 'Ap'.
+        vial (dict): Vial properties, including 'Vfill', 'Ap', and 'Av'..
         product (dict): Product properties, including 'cSolid', 'T_pr_crit', and Rp parameters 'R0', 'A1', and 'A2'.
         ht (dict): Heat transfer properties, including 'KC', 'KP', and 'KD'.
-        Pchamber (dict): Chamber pressure control data.
-        Tshelf (dict): Shelf temperature control data.
+        Pchamber (dict): Chamber pressure set points and time (see docs).
+        Tshelf (dict): Shelf temperature set points and time (see docs).
         dt (float): Fixed time step for output [hours]
         eq_cap (dict): Equipment capability line, with 'a' slope and 'b' intercept
         nVial (int): Number of vials in the load, used for equipment capability calculation
diff --git a/lyopronto/freezing.py b/lyopronto/freezing.py
index ce0de3ec..94d981d3 100644
--- a/lyopronto/freezing.py
+++ b/lyopronto/freezing.py
@@ -26,10 +26,10 @@ def freeze(vial,product,h_freezing,Tshelf,dt):
     """Simulates the primary drying process for a vial.
 
     Args:
-        vial (dict): Vial properties, including 'Vfill' and 'Ap'.
-        product (dict): Product properties, including 'cSolid' and Rp parameters 'R0', 'A1', and 'A2'.
+        vial (dict): Vial properties, including 'Vfill', 'Ap', and 'Av'..
+        product (dict): Product properties, including 'cSolid', initial temperature 'Tpr0', freezing temperature 'Tf', and nucleation temperature 'Tn'.
         h_freezing (float): Heat transfer coefficient during freezing [W/m²/K].
-        Tshelf (dict): Shelf temperature control data.
+        Tshelf (dict): Shelf temperature set points and time (see docs).
         dt (float): Time step for the simulation [hr]. Used only as a sampling rate for output.
 
     Returns:
diff --git a/lyopronto/high_level.py b/lyopronto/high_level.py
index 37fc376a..78066776 100644
--- a/lyopronto/high_level.py
+++ b/lyopronto/high_level.py
@@ -30,7 +30,7 @@ def execute_simulation(inputs):
         The output data from the selected simulation tool.
 
     Raises:
-        ValueError: If an invalid simulation tool is selected or required parameters are missing.
+        ValueError: If an invalid simulation tool is selected.
     """
     sim_type = inputs["sim"]["tool"]
     output_data = None
diff --git a/lyopronto/opt_Pch.py b/lyopronto/opt_Pch.py
index adafeb08..b6587ba0 100644
--- a/lyopronto/opt_Pch.py
+++ b/lyopronto/opt_Pch.py
@@ -29,12 +29,14 @@ def dry(vial,product,ht,Pchamber,Tshelf,dt,eq_cap,nVial):
     """Find optimal chamber pressures for a given lyophilization process, with fixed shelf temperatures.
 
     Args:
-        vial (dict): Vial properties, including 'Vfill' and 'Ap'.
+        vial (dict): Vial properties, including 'Vfill', 'Ap', and 'Av'..
         product (dict): Product properties, including 'cSolid', 'T_pr_crit', and Rp parameters 'R0', 'A1', and 'A2'.
         ht (dict): Heat transfer properties, including 'KC', 'KP', and 'KD'.
-        Pchamber (dict): Chamber pressure control data.
-        Tshelf (dict): Shelf temperature control data.
+        Pchamber (dict): Chamber pressure bounds between which optimizer will search.
+        Tshelf (dict): Shelf temperature set points and time (see docs).
         dt (float): Fixed time step for output [hours]
+        eq_cap (dict): Equipment capability line, with 'a' slope and 'b' intercept.
+        nVial (int): Number of vials in the load, used for equipment capability calculation.
 
     Returns:
         output_table (ndarray): Simulation output table with columns for:
diff --git a/lyopronto/opt_Pch_Tsh.py b/lyopronto/opt_Pch_Tsh.py
index b301e16c..3fd51013 100644
--- a/lyopronto/opt_Pch_Tsh.py
+++ b/lyopronto/opt_Pch_Tsh.py
@@ -24,12 +24,14 @@ def dry(vial,product,ht,Pchamber,Tshelf,dt,eq_cap,nVial):
     """Find optimal shelf temperatures and chamber pressures for a given lyophilization process.
 
     Args:
-        vial (dict): Vial properties, including 'Vfill' and 'Ap'.
+        vial (dict): Vial properties, including 'Vfill', 'Ap', and 'Av'.
         product (dict): Product properties, including 'cSolid', 'T_pr_crit', and Rp parameters 'R0', 'A1', and 'A2'.
         ht (dict): Heat transfer properties, including 'KC', 'KP', and 'KD'.
-        Pchamber (dict): Chamber pressure control data.
-        Tshelf (dict): Shelf temperature control data.
+        Pchamber (dict): Chamber pressure bounds between which optimizer will search.
+        Tshelf (dict): Shelf temperature limits, between which optimizer will search.
         dt (float): Fixed time step for output [hours]
+        eq_cap (dict): Equipment capability line, with 'a' slope and 'b' intercept.
+        nVial (int): Number of vials in the load, used for equipment capability calculation.
 
     Returns:
         output_table (ndarray): Simulation output table with columns for:
diff --git a/lyopronto/opt_Tsh.py b/lyopronto/opt_Tsh.py
index fde922a3..47489792 100644
--- a/lyopronto/opt_Tsh.py
+++ b/lyopronto/opt_Tsh.py
@@ -27,12 +27,14 @@ def dry(vial,product,ht,Pchamber,Tshelf,dt,eq_cap,nVial):
     """Find optimal shelf temperatures for a given lyophilization process, with fixed chamber pressures.
 
     Args:
-        vial (dict): Vial properties, including 'Vfill' and 'Ap'.
+        vial (dict): Vial properties, including 'Vfill', 'Ap', and 'Av'..
         product (dict): Product properties, including 'cSolid', 'T_pr_crit', and Rp parameters 'R0', 'A1', and 'A2'.
         ht (dict): Heat transfer properties, including 'KC', 'KP', and 'KD'.
-        Pchamber (dict): Chamber pressure control data.
-        Tshelf (dict): Shelf temperature control data.
+        Pchamber (dict): Chamber pressure set points and time (see docs).
+        Tshelf (dict): Shelf temperature limits, between which optimizer will search for optimal values.
         dt (float): Fixed time step for output [hours]
+        eq_cap (dict): Equipment capability line, with 'a' slope and 'b' intercept.
+        nVial (int): Number of vials in the load, used for equipment capability calculation.
 
     Returns:
         output_table (ndarray): Simulation output table with columns for:

From 1231c1adfd3d800639bd41fc82d0f93fb555628a Mon Sep 17 00:00:00 2001
From: Isaac Wheeler <ickaser@gmail.com>
Date: Fri, 7 Aug 2026 21:47:03 -0400
Subject: [PATCH 4/4] Major docs additions, including two new notebooks

---
 README.md                      |  11 +-
 docs/dev.md                    |   6 +-
 docs/examples/freezing.ipynb   | 257 ++++++++++++++++++++++++
 docs/examples/knownRp_PD.ipynb |  67 +------
 docs/examples/opt_Pch_PD.ipynb | 356 +++++++++++++++++++++++++++++++++
 docs/how-to-guides.md          |   4 +-
 docs/reference.md              |   4 +-
 docs/tutorials.md              |  33 ++-
 docs/yaml_inputs.md            | 229 +++++++++++++++++++++
 mkdocs.yml                     |   7 +-
 10 files changed, 901 insertions(+), 73 deletions(-)
 create mode 100644 docs/examples/freezing.ipynb
 create mode 100644 docs/examples/opt_Pch_PD.ipynb
 create mode 100644 docs/yaml_inputs.md

diff --git a/README.md b/README.md
index 1ef2f110..00864b69 100644
--- a/README.md
+++ b/README.md
@@ -26,9 +26,9 @@ inputs = lp.read_inputs(yaml_fname)
 output = lp.execute_simulation(inputs)
 
 # Record the simulation inputs, outputs, and figures
-save_inputs(inputs, current_time)
-save_csv(output, inputs, current_time)
-generate_visualizations(output, inputs, current_time)
+lp.save_inputs(inputs, current_time)
+lp.save_csv(output, inputs, current_time)
+lp.generate_visualizations(output, inputs, current_time)
 ```
 This will generate a record of both inputs and outputs each time you execute the file, so you can edit the original YAML and rerun the script without worrying about losing prior values of the inputs.
 
@@ -36,11 +36,10 @@ See also documentation examples online [here](https://lyohub.github.io/LyoPRONTO
 
 The original method for running this code, as illustrated in a video tutorial
 [on LyoHUB's YouTube channel](https://youtu.be/DI-Gz0pBI0w),
-is to download this repository, edit the script `main.py` in its root directory, then run that script from the command line (in the directory containing the repo directory, so that `LyoPronto` is a module).
+is to download this repository, edit the script `main.py` in its root directory, then run that script from the command line (in the same directory):
 ```
-python3 LyoPronto.main -m
+python main.py
 ```
-This will execute the file `main.py` in an appropriate scope.
 Files listing the inputs and outputs will be generated in the current directory, along with some plots of temperature, pressure, and drying progress vs. time.
 
 
diff --git a/docs/dev.md b/docs/dev.md
index ccb29def..2bc24236 100644
--- a/docs/dev.md
+++ b/docs/dev.md
@@ -28,11 +28,11 @@ Run
 ```
 mike deploy [name]
 ```
-to deploy a docs version with ID `[name]`, which could be e.g. `v1.1.0` or `pr-10`, etc. Preview locally by navigating to `LyoPRONTO_folder/site`, then running
+to deploy a docs version with ID `[name]`, which could be e.g. `v1.1.0` or `pr-10`, etc. Preview locally by running
 ```
-python -m http.server --bind localhost
+mkdocs serve
 ```
-to spin up a local HTTP server on your own machine. 
+which will spin up a local HTTP server showing the docs, which will hot-reload as you edit the files in `docs` or the configuration in `mkdocs.yml` 
 
 On pushing to master, GitHub actions will run
 ```
diff --git a/docs/examples/freezing.ipynb b/docs/examples/freezing.ipynb
new file mode 100644
index 00000000..bed34ced
--- /dev/null
+++ b/docs/examples/freezing.ipynb
@@ -0,0 +1,257 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "5d12411a",
+   "metadata": {},
+   "source": [
+    "# Simulate freezing"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "48ff1ce9",
+   "metadata": {},
+   "source": [
+    "We need a few imports:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "0830f8f9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "from matplotlib import rc as matplotlibrc\n",
+    "from ruamel.yaml import YAML\n",
+    "yaml = YAML()\n",
+    "\n",
+    "from lyopronto import freezing, plot_styling"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e5e07eea",
+   "metadata": {},
+   "source": [
+    "Then, we provide all the necessary simulation parameters."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "d12203bf",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Set up the simulation settings\n",
+    "# This needs to be a dict with string keys, which can be expressed in YAML as well\n",
+    "sim = yaml.load(\"\"\"\n",
+    "tool: Freezing Calculator\n",
+    "Kv_known: Y\n",
+    "Rp_known: Y\n",
+    "Variable_Pch: N\n",
+    "Variable_Tsh: N\n",
+    "\"\"\")\n",
+    "# Or, equivalently:\n",
+    "sim = {\n",
+    "    'tool': 'Freezing Calculator',\n",
+    "    'Kv_known': 'Y',\n",
+    "    'Rp_known': 'Y',\n",
+    "    'Variable_Pch': 'N',\n",
+    "    'Variable_Tsh': 'N'\n",
+    "}\n",
+    "\n",
+    "\n",
+    "# Vial and fill properties\n",
+    "vial = {\n",
+    "    # Av = Vial area in cm^2\n",
+    "    'Av': 3.80,\n",
+    "    # Ap = Product Area in cm^2\n",
+    "    'Ap': 3.14,\n",
+    "    # Vfill = Fill volume in mL\n",
+    "    'Vfill': 2.0\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "d69ec9e8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Product properties for freezing\n",
+    "product = {\n",
+    "    # cSolid = Fractional concentration of solute in the frozen solution\n",
+    "    'cSolid': 0.0,\n",
+    "    # Initial product temperature\n",
+    "    'Tpr0': 15.8,  # in deg C\n",
+    "    # Freezing temperature\n",
+    "    'Tf': -1.54,  # in deg C\n",
+    "    # Nucleation temperature\n",
+    "    'Tn': -5.84,  # in deg C\n",
+    "    # Critical product temperature\n",
+    "    # At least 2 to 3 deg C below collapse or glass transition temperature\n",
+    "    'T_pr_crit': -5  # in deg C\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "9a4f153e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Shelf temperature profile\n",
+    "Tshelf = {\n",
+    "    # init = Initial shelf temperature in C\n",
+    "    'init': 15.0,\n",
+    "    # setpt = Shelf temperature set points in C\n",
+    "    'setpt': [-40.0],\n",
+    "    # dt_setpt = Time for which shelf temperature set points are held in min\n",
+    "    'dt_setpt': [180.0],\n",
+    "    # ramp_rate = Shelf temperature ramping rate in C/min\n",
+    "    'ramp_rate': 1.0\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "86011cd0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Time step\n",
+    "dt = 0.01    # hr\n",
+    "\n",
+    "# Equipment capability parameters (not used for freezing, but included for completeness)\n",
+    "eq_cap = {\n",
+    "    'a': -0.182,  # kg/hr\n",
+    "    'b': 11.7     # kg/hr/Torr\n",
+    "}\n",
+    "\n",
+    "# Number of vials (not used for freezing, but included for completeness)\n",
+    "nVial = 398\n",
+    "\n",
+    "# Freezing heat transfer coefficient\n",
+    "h_freezing = 38.0  # W/m^2/K"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9a8a5f63",
+   "metadata": {},
+   "source": [
+    "Now, we are ready to actually run the simulation, which is `lyopronto.freezing.freeze`.\n",
+    "\n",
+    "??? info - documentation\n",
+    "    *Here is the docstring for `freezing.freeze`:*\n",
+    "    ::: lyopronto.freezing.freeze"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "9d486f41",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "output_table = freezing.freeze(vial, product, h_freezing, Tshelf, dt)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e1cf3066",
+   "metadata": {},
+   "source": [
+    "Now, let's plot the results."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ac9f58c3",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
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+      "text/plain": [
+       "<Figure size 3000x2000 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Figure dimensions\n",
+    "figwidth = 30\n",
+    "figheight = 20\n",
+    "# Line width\n",
+    "lineWidth = 5\n",
+    "# Font family\n",
+    "plt.rcParams['font.family'] = 'Arial'\n",
+    "\n",
+    "# Plot product and shelf temperature vs time\n",
+    "fig, ax = plt.subplots(figsize=(figwidth, figheight))\n",
+    "\n",
+    "ax.plot(\n",
+    "    output_table[:, 0],  # Time [hr]\n",
+    "    output_table[:, 2],  # Product Temperature [°C]\n",
+    "    linewidth=lineWidth,\n",
+    "    label='Product Temperature'\n",
+    ")\n",
+    "\n",
+    "ax.plot(\n",
+    "    output_table[:, 0],  # Time [hr]\n",
+    "    output_table[:, 1],  # Shelf Temperature [°C]\n",
+    "    linewidth=lineWidth,\n",
+    "    label='Shelf Temperature',\n",
+    "    linestyle='--'\n",
+    ")\n",
+    "\n",
+    "plot_styling.axis_style_temperature(ax)\n",
+    "\n",
+    "plt.legend(fontsize=40,loc='best')\n",
+    "ll,ul = ax.get_ylim()\n",
+    "ax.set_ylim([ll,ul+5.0])\n",
+    "plt.tight_layout()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4f77600e",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "lyopronto",
+   "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": "3.13.11"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/docs/examples/knownRp_PD.ipynb b/docs/examples/knownRp_PD.ipynb
index 8f5360eb..b2a53405 100644
--- a/docs/examples/knownRp_PD.ipynb
+++ b/docs/examples/knownRp_PD.ipynb
@@ -17,51 +17,6 @@
     "# Simulate primary drying with known Kv and Rp"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "id": "f6f064bf",
-   "metadata": {
-    "papermill": {
-     "duration": 0.002819,
-     "end_time": "2026-02-03T17:50:58.130404",
-     "exception": false,
-     "start_time": "2026-02-03T17:50:58.127585",
-     "status": "completed"
-    },
-    "tags": []
-   },
-   "source": [
-    "\n",
-    "Since this documentation example is a Jupyter notebook, inside the LyoPRONTO file structure, it needs to be directed to the LyoPRONTO code, which means adding `../../` to `sys.path`.\n",
-    "However, this step will in general not be necessary."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "63dabee9",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2026-02-03T17:50:58.138040Z",
-     "iopub.status.busy": "2026-02-03T17:50:58.137808Z",
-     "iopub.status.idle": "2026-02-03T17:50:58.141506Z",
-     "shell.execute_reply": "2026-02-03T17:50:58.140999Z"
-    },
-    "papermill": {
-     "duration": 0.008794,
-     "end_time": "2026-02-03T17:50:58.142674",
-     "exception": false,
-     "start_time": "2026-02-03T17:50:58.133880",
-     "status": "completed"
-    },
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "# import sys\n",
-    "# sys.path.append('../../')"
-   ]
-  },
   {
    "cell_type": "markdown",
    "id": "8075c1de",
@@ -440,7 +395,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 13,
    "id": "fd289577",
    "metadata": {
     "execution": {
@@ -461,7 +416,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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       "text/plain": [
        "<Figure size 3000x2000 with 2 Axes>"
       ]
@@ -475,7 +430,7 @@
     "fig = plt.figure(0,figsize=(figwidth,figheight))\n",
     "ax1 = fig.add_subplot(1,1,1)\n",
     "ax2 = ax1.twinx()\n",
-    "ax1.plot(output_table[:,0],output_table[:,4],'-o',color='b',markevery=5,linewidth=lineWidth, markersize=markerSize, label = \"Chamber Pressure\")\n",
+    "ax1.plot(output_table[:,0],output_table[:,4],'-',color='b',linewidth=lineWidth, markersize=markerSize, label = \"Chamber Pressure\")\n",
     "ax2.plot(output_table[:,0],output_table[:,5],'-',color=[0,0.7,0.3],linewidth=lineWidth, label = \"Sublimation Flux\")\n",
     "\n",
     "plot_styling.axis_style_pressure(ax1)\n",
@@ -489,7 +444,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 14,
    "id": "1932f978",
    "metadata": {
     "execution": {
@@ -510,7 +465,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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       "text/plain": [
        "<Figure size 3000x2000 with 1 Axes>"
       ]
@@ -533,7 +488,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 16,
    "id": "db6d2df5",
    "metadata": {
     "execution": {
@@ -554,7 +509,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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D/RcuXCg/Pz+7+1epUkWffvqpQ3Ns2rTJof4AkBesWLFC8fHxdvWtUKGCvvnmG7vHfvfdd1WsWDG7++/evdvuvgAA+yQmJuqZZ55RQkKCu0sBgDxr//79dvetW7eu6woBAFj11VdfOdR/9uzZCgoKsqvv448/rocfftjusTdv3uxQLQAA5yG4DgBwC0ceiy1Jw4cPt6ufj4+PhgwZYve4ly5dUkhIiEO1AEBeFBISooEDB6pKlSqaPn26EhMT3V0SAEDSzZs3tW/fPrv7P/roo6pfv77D8/Tr108lS5a0u/+hQ4ccngMAcrs//vjD7r5jx45Vvnz57O7v5+enzp07293/+vXrunHjht39AQC2vffeewoNDXV3GQCQpzkSXH/ggQdcVwgAwODs2bNatWqV3f0ffPBB9ezZ06E5nnvuObv7bt++XSkpKQ6NDwBwDoLrAAC3cOQXkpo1a6pWrVp29+/Vq5dDtaxevdqh/gCQ1yxcuFDNmzfXrFmz7F4hEgCQNXbv3u3QE4Qc/Y/+O7y9vdWuXTu7+1+9elU3b97M0FwAkFvZ+zSKfP+vvfuOsrI+14d/DzMMvQ1FpKMgghKIKCioYFQsqKhoPC5LkIOxx5IjyTGcY0WjMVYSVywB2zEqigUNRBGNSoKgUqKGJr1JE6QOZd4/8otvCsp+9swzG2Y+n7VmxQX393tfwILAzLWfXa1anHXWWYnvT1q88ec0QNn58MMP44477sh1DIBKL9Piel5enieuA5Sz0aNHJyqK//jHP06844QTTvjW78/Ly4t99tknunXrFsccc0ysXLky8Q4ASq8g1wEAqHzWr1+f6Cnnu/vHxb9q165ddOzYMT777LOM5seNGxe33nproh0AlUlp3uK6oKAgtm/fXoZpAPhHSZ9snqR8/q86d+4czzzzTMbzX331VdSuXTvrfQAVzYQJE+KTTz6JTz/99J8+vvzyy3+aO+SQQ6JevXqJ769Tp06ieX9GA5SNrVu3xoUXXujzHwB7gEyL623btvX3YYBy9sorr2Q827Bhw8Q9kb+f69evX9SoUSNatGjx9Ufz5s2//t+qVasmvheAsqW4DkC5+/DDDxM9FbJ79+6Jdxx11FEZF9enTZsWxcXFUVhYmHgPAN/sRz/6UaxZsyaeeuqpXEcBqLBmz56d8WzdunWjZcuWWe8qKirK+iwA8fUXS//1C69Lly79pyL7AQcckNX9q1atyni2evXqUbdu3az2APDPfvazn8Wnn36627mioqJYs2ZNOSQCqJxWrFgRK1asyGg26bsVAVA6mzZtij/+8Y8Zz/ft2zcKCrKrNY4ZMyarcwCUH8V1AMrd5MmTE80ffPDBiXd06dIl49ni4uKYMWNGdOvWLfEeAP5dQUFBDB8+PC655JIYOHBgruMAVGh33XVXXHvttbFs2bJv/Vi5cmW0bt26VLu2bt2aaN6TywAy06xZs2jWrFkcd9xxpbrn448/znj20EMPjby8vFLtAyDi/fffj3vvvXe3c1WqVIn7778/LrjggnJIBVA5Jfn7cOfOnVNMAsC/mjJlSmzbti3j+T59+qQXBoCcU1wHoNx99NFHieb322+/xDvatWuXaP6jjz5SXAcoA/vss088/fTTceyxx+Y6CkClUK1atWjTpk20adPmW+d27NgR69evL9WuZcuWZTxbp06dqFOnTqn2AZC5devWxdixYzOeP/7441NMA1A5bNy4MQYOHBg7d+7c7ezVV18dRx55ZDmkAqi8pk6dmvGsJ64DlK9JkyYlmj/00ENTSgLAnkBxHYByN2vWrIxni4qKolatWol3tGzZMtF8kkwA7Nq5554bw4cPj6KiolxHAeBf5OfnR4MGDUp1x/vvv5/xbNIXkgJQOv/7v/8bGzZsyGg2Pz8/Bg0alHIigIpvyJAhMWfOnN3OtW/fPoYNGxYrVqwoh1QAlVc2xfWFCxfGyy+/HH/6059i+vTpsXz58li3bl0UFhZGvXr1om3btnHQQQdF796948QTT4yGDRumlB6gYpsyZUrGs/n5+XHwwQenmAaAXFNcB6DczZs3L+PZpk2bZrUj6bnPP/88qz0A/O3P3IceeihOP/30XEcBICWrV69O9FScww47LMU0APyjhx9+OB544IGM5y+++OJo0aJFiokAKr7x48fHQw89tNu5KlWqxIgRI6JGjRrlkAqgcsu0uF6tWrWYMWNGDB48ON59991dzmzfvj02bdoUy5Yti4kTJ8YjjzwSVatWjVNOOSV+8pOfRI8ePcowOUDFl+RBgm3bto3CwsIU0wCQa1VyHQCAymXNmjXx5ZdfZjzfqFGjrPY0aNAg8vPzM55XXAdIrl69enHLLbfErFmzlNYBKrhHH300iouLM57v06dPemEAiIiItWvXxsUXXxyXXHJJxmf23XffGDZsWIqpACq+9evXx6BBg6KkpGS3s9dcc0306tWrHFIBVG6bNm2K2bNnZzS7devWOOuss76xtP5Ntm3bFqNHj47DDz88zjzzzFiyZEk2UQEqpblz52Y8u99++/3btxUXF8fLL78cl19+efTo0SOaNGkS1apVi2rVqkXjxo3j0EMPjQsvvDAee+yxWL58eVlGByAFnrgOQLlaunRpovkGDRpkvatu3bqxdu3ajGZ9cgkgc7Vr146rrroqrr/++lL9OQ3A3mH9+vVx3333ZTxfvXr16NevX3qBACqhbdu2xbp162LZsmXx0UcfxZtvvhkvvPBCbN68OeM7qlWrFqNHj46ioqIUkwJUfFdffXUsXLhwt3MHHHBA3HbbbeWQCIDp06fHzp07y23f6NGjY/z48fHEE09E//79y20vwN7oiy++iK+++irj+ZYtW37935s2bYoHHnggfvnLX8aqVat2Ob9q1apYtWpVfPjhh/Hkk09Gfn5+nHTSSXHDDTfEEUccUer8AJQ9xXUAytXq1asTzdetWzfrXbVr1864uL527dooKSmJvLy8rPcBVHSHHXZYDB48OM4999yoU6dOruMAUE5uvvnmRE+pOfXUU0v193gA/llJSUnUr18/Nm3alPUdNWvWjFGjRkWPHj3KMBlA5TNmzJgYOXLkbueqVKkSI0eOjBo1aqQfCoCYOnVque9cv359nHHGGfHzn/88hgwZUu77AfYWy5YtSzTfvHnziIiYOHFiXHjhhYme1h4RsWPHjhgzZkyMGTMmzjrrrHjwwQejadOmie4AIF1Vch0AgMolaXG9Vq1aWe9Kcnb79u2xfv36rHcBVGTdu3eP6dOnxwcffBA//OEPldYBKpE33ngj0dPWIyKuu+66dMIAVFJLly4tVWm9WbNm8cYbb8RJJ51UhqkAKp/Vq1fHxRdfnNHsdddd5+mOAOUoF8X1iL+9yPQnP/lJ3HLLLTnZD7A3+KYnpX+TRo0axeOPPx59+vRJXFr/V6NGjYqDDz44xo8fX6p7AChbiusAlKs1a9Ykmq9evXrWu5KeTZoNoLLo1KlTdO7cOdcxAChnf/nLX+L73/9+orfaPvbYY+Pwww9PMRVA5TN79uysz/bv3z+mTZsWPXv2LMNEAJXT5ZdfntE7EXXo0CFuvfXWckgEwN/lqrj+dzfeeGOMGDEipxkA9lRJi+uvv/56XHTRRbFt27Yy2b969eo44YQT4vHHHy+T+wAoPcV1AMpV0ieEFRYWZr2ratWqieZL8/QyAACoSObNmxcnnnhifPnllxmfqVKlStx9993phQKopObMmZP4TMuWLePVV1+Nl156KRo1apRCKoDK5fnnn4/nnntut3P5+fkxcuTIUj2QBYBkdu7cGTNmzMh1jLjsssti+vTpuY4BsMdJ8jnmiIixY8dGSUlJmWbYsWNHXHTRRV5kBLCHUFwHoFwlfVVseRbXi4uLs94FAAAVxaJFi+LYY4+NJUuWJDp36aWXRteuXdMJBVCJZfPE9UWLFsX//M//xP/8z//EggULUkgFUHmsWLEiLrvssoxmf/zjH3sHIoByNmvWrD3i4VRbt26NCy64IHbs2JHrKAB7lC1btuQ6QkRElJSUxCWXXBLjx4/PdRSASk9xHYBylbQcXqVK9v9XlfSs4joAAJXdokWLok+fPjFv3rxE59q0aRN33nlnSqkAKrdsnrgeETF16tS47bbbol27djFo0KDEL0gC4G9++MMfxurVq3c717Fjx7j55pvLIREA/2jq1KlZnatXr16cc845cc8998SoUaPipZdeiocffjiuvvrq2G+//bK6c/r06fHoo49mdRagotq6dWuuI3xt27Ztcd5558XKlStzHQWgUlNcB6BcJX3iel5eXta78vPzE80rrgMAUJnNnz8/evfuHZ9//nmicwUFBfHkk09G7dq1U0oGULll88T1f7R9+/YYMWJEdOnSJV555ZUySgVQOYwcOTKjPzvz8/NjxIgRUb169XJIBcA/Slpcb9KkSQwfPjyWLl0av/vd7+Laa6+NAQMGRP/+/ePiiy+O++67L+bOnRu/+93vomXLlonz3HLLLYm/HgpQkZVFD6NBgwZxww03xAcffBCrV6+OjRs3xuzZs+Ohhx6K7373u4nuWrFiRVx99dWlzgRA9hTXAShXpXmCelI7d+5MNF+akjwAAOzNZs+eHUcffXTiJ61HRNx+++1x5JFHppAKgIiIuXPnlsk9q1evjv79+8evfvWrMrkPoKJbvHhxXHPNNRnN/td//Vf06NEj3UAA7FKS4vqAAQPis88+iyuuuCJq1qz5rbPnnHNOTJ48OXEhcunSpfHCCy8kOgNQkSXtbfyrfv36xcyZM2PYsGFx2GGHRVFRUdSsWTPatWsXl156aUyZMiXuvvvuRF2U3/3udzF58uRS5QIge4rrAJSrqlWrJpovzT9ikp4tLCzMehcAAOyt/vKXv8TRRx8dixYtSnz2vPPOi+uvvz6FVABERKxatSqOPPLIuPnmm+ONN96IWbNmxbp162LDhg3x2WefxX333RcdO3ZMdOdVV10Vjz/+eEqJASqGkpKSGDRoUKxbt263s506dYqbb765HFIBsCvXXXdd3HnnnTF48ODo06dPNG/efJcPq/rv//7veP7556OoqCjju/fZZ58YN25c7LPPPokyPfXUU4nmASqypB2Rf3TmmWfGSy+9FI0bN/7GmSpVqsSPf/zjGDFiRMb3lpSUxJ133pl1LgBKpyDXAQCoXBTXAQBgzzFp0qQ4+eSTY82aNYnPHnvssfHb3/42hVQA/F2jRo1i3Lhxu/y+Aw88MA488MC4/PLL4/bbb4+bbropoztLSkri0ksvjR49esSBBx5YhmkBKo6HHnoo3njjjd3O5efnx8iRI6NatWrlkAqAXenbt2/07dv3n75t8+bNMWfOnJg9e3bMmTMn6tSpE5dddllW9zdu3Dh+/etfx4ABAzI+M378+Ni4cWPUqlUrq50AFUm2PYxWrVrFb3/72ygoyKzeeOGFF8abb74ZTz75ZEbzL730UixdujSaNWuWVT4AsueJ6wCUq6T/KNm2bVvWu4qLixPNK64DAFCZjB8/Po477risSutHHXVUvPLKK/4ODbAHqFq1atx4443xm9/8JuMzW7ZsiYEDB5b67boBKqK5c+fGkCFDMpodMmRIHHbYYSknAiCpGjVqROfOnePMM8+MIUOGZF1a/7szzjgj9t9//4znt2zZEpMnTy7VToCKItvPIf/v//5v1KtXL9GZu+++O+OHKe7YsSNeeumlLJIBUFqK6wCUq9q1ayeaT1o+L83ZunXrZr0LAAD2Ji+99FL069cvNmzYkPjsUUcdFa+99lrUrFkzhWQAZOuHP/xhXHTRRRnPT5o0KcaMGZNiIoC9z86dO2PgwIGxcePG3c4edNBBceONN5ZDKgByLS8vLwYOHJjozAcffJBOGIC9TNKOSEREnTp14txzz018rkmTJnHyySdnPP/73/8+8Q4ASk9xHYBy1aBBg0TzmzdvznrXpk2bEs0XFRVlvQsAAPYWTz75ZJx11lmxdevWxGePO+64GDt2bNSpUyeFZACU1l133RU1atTIeP6+++5LLwzAXujee++N9957b7dz+fn5MXLkyKhWrVo5pAJgT3DEEUckmp8zZ05KSQD2Lg0bNkx8pmfPnlk/OOW0007LePbPf/5zVjsAKB3FdQDKVdLietLy+T/K5Kk4f1e9enVPjAQAoMJ75JFH4gc/+EHs2LEj8dkzzjgjxowZ4+/NAHuwRo0aJXoi2YQJE2LFihUpJgLYe3z22WcxdOjQjGZ/8pOfxKGHHppyIgD2JN26dUs0v3DhwpSSAOxdGjVqlPhMaf6u3bVr14xnV61aFcuWLct6FwDZUVwHoFwlfar5+vXrs961bt26jGez+ccSAADsTX71q1/FJZdcEiUlJYnPDh48OJ5//nlPlATYC/Tt2zfRfCZPFgaoDJ577rnYsmVLRrO333575OXlJfpo27ZtojzfdtfIkSOz+BECUBr169ePqlWrZjy/du3aFNMA7D0aN26c+Ezz5s2z3tepU6dE8wsWLMh6FwDZUVwHoFy1aNEi0fyXX36Z1Z7i4uLYvHlzxvOtWrXKag8AAOwNHnroobjyyiuzKq3fcMMN8cgjj0R+fn4KyQAoa927d080P2nSpJSSAOxdsvm7MgCVS7169TKeLc27SgNUJK1atYoqVZJVFOvXr5/1vurVq0f16tUznv/iiy+y3gVAdhTXAShX9evXj7p162Y8v2rVqqz2JP3HxX777ZfVHgAA2NM9+uijccUVVyQ+l5eXFw888EAMGzYshVQApKVJkyaJ5lesWJFSEgAAqFgKCwszns3Ly0sxCcDeo1q1aokfcFhQUFCqnUk6KRs3bizVLgCSU1wHoNy1bt0649nly5dntWPZsmWJ5hXXAQCoiJ599tm45JJLEj89srCwMJ555pm46qqrUkoGQFpq1aqV6F0yVq9enWIaAACoONatW5fxbK1atVJMArB3adeuXaL5JH/e7krVqlUznt2xY0epdgGQnOI6AOWuQ4cOGc+uXr06q1e4zp8/P9F8x44dE+8AAIA92bhx4+KCCy6InTt3JjpXs2bNeOWVV+Kcc85JKRkAu1JSUhLLli2LTZs2leqeLVu2JPqia3Fxcan2AQBAZbBjx45EX7Ns2rRpimkA9i5dunRJNL927dpS7fvqq68ynq1Zs2apdgGQXOneVwMAstC1a9cYNWpUxvOzZs2K7373u4l2zJkzJ9H8oYcemmgeAAD2ZB9//HGcddZZsW3btkTn6tWrF7///e/jiCOOSCkZQOVVXFwcixYtigULFsSCBQti4cKFX//3ggULYtGiRVFcXBzPPPNM/Md//EfWe5I+Qb127dpZ7wIAgD3Nzp07Y8WKFbF06dJYunRpLFmyJJYuXRr/+Z//mehdof/V7NmzE82XZhdARdOjR49E85988knWu0pKSmLDhg0Zz9erVy/rXQBkR3EdgHLXtWvXRPNTp05NXFyfNm1axrP169eP/fffP9H9AACwp1q2bFmccsopiT45HxHRsGHD+MMf/hCHHHJISskAKq9+/frF73//+ygpKdnt7NSpU0tVXE/yOZGIiH333TfrXQAAkCsrV66Mhx9++N8K6itWrIjt27f/23y7du3iwgsvzHrfRx99lGj+O9/5Tta7ACqapMX1yZMnZ73r888/T/QupC1btsx6FwDZqZLrAABUPt27d4+8vLyM5ydMmJB4x8SJEzOePfzwwxPlAQCAPdXWrVvjjDPOiKVLlyY6V79+/XjjjTeU1gFSUlRUlFFpPSJi7Nixpdr13nvvJZpv3759qfYBAEAu5OXlxdChQ+PXv/51vPTSSzF58uRYsmTJLkvrERHvvPNOqfYl/Xrl4YcfXqp9ABVJmzZtEj1M8LPPPou5c+dmtWvGjBkZzxYUFESrVq2y2gNA9hTXASh3jRs3TvSUgTFjxnzjJ5l2Zfr06bFo0aKM50866aSMZwEAYE/2X//1XzFp0qREZwoKCuKFF15I/C5HAGSuW7duGc9OmzYt67fE3r59ezz55JOJziR96hkAAOwJGjVqFM2bN894ftSoUbFp06asdm3cuDGeffbZjOdbtGgRnTp1ymoXQEV16qmnZjxbUlISjzzySFZ7/vCHP2Q827lz56hevXpWewDInuI6ADlx/PHHZzy7du3aePXVVzOef+aZZxJlOfnkkxPNAwDAnui1116L4cOHJz53zz33xPe+970UEgHwd3369Ek0f/PNN2e158knn4zFixdnPF+nTp049NBDs9oFAAC5dswxx2Q8u379+rj77ruz2jN8+PD46quvMp4/++yzs9oDUJGdfvrpieYfeuihWLhwYaIzxcXF8dxzz2U836tXr0T3A1A2FNcByIkBAwYkmv/FL36R0dy6devi4YcfzvjeLl26RLt27RJlAQCAPc369evjkksuSXzu+OOPj6uuuiqFRAD8o65du0abNm0ynh81alS8/PLLiXYsXLgwrr322kRnBgwYEFWrVk10BqCiuummm6KkpCS1j3nz5iXK8213DRw4MJ2fBIC9TL9+/RLN//znP48PP/ww0ZlPP/00brrppkRnLrrookTzAJXB0UcfHe3bt894fv369TFo0KDYsWNHxmduv/32WL16dcbz/fv3z3gWgLKjuA5AThx++OHRoUOHjOf/9Kc/xaOPPrrbuWuuuSbWrFmT8b2DBw/OeBYAAPZUd955ZyxZsiTxuTfeeCPy8vJS+Zg/f37Z/0AB9mLnnXdexrMlJSVx/vnnx7vvvpvR/OLFi+OEE06IdevWJcrk8yIAAOzNTjvttKhdu3bG85s3b45+/frFtGnTMpqfOXNmnHDCCbFly5aMd/Tt2zc6d+6c8TxAZZGXlxeXXnppojPjx4+P8847L6Py+rhx4+KOO+7I+O5999038TvkAVA2FNcByJmLL7440fzll18eI0aM2OX3bdmyJa644ooYOXJkxvfVrFkzzj///EQZAABgT7Ns2bK47777ch0DgN244oororCwMOP5DRs2xHHHHRd33XXXtxZlRo8eHYcffnj89a9/TZSnT58+3hIbAIC9Ws2aNeM///M/E51ZsWJFHHHEEfHAAw9EcXHxLmdKSkpixIgR0bNnz1i8eHHGd+fl5cXNN9+cKA9AZXLxxRdHkyZNEp159tlno3fv3jFr1qxdfv/OnTvjwQcfjP79+3/jn+u7ctlll0VBQUGiLACUjbySkpKSXIcAoHLauHFjtG3bNlauXJnoXLdu3eKcc86JAw44IIqLi+Pjjz+OJ554IvETJocMGRJ33nlnojMAJDNw4MB4/PHHM5rt3bt3vP322+kGAqiAfvSjH8WDDz6Y6xj/Zt68edGmTZtcxwDYo1x77bVZvdioadOm0b9//+jevXs0atQo1q1bF7Nnz44XX3wxPvnkk8T35efnxwcffBCHHHJI4rMAZGf+/PnRtm3bjOd9CRcgM4sXL44DDjggNm/enPhs8+bN4/TTT49DDz00ioqKYs2aNfHpp5/GqFGjYt68eYnvO//88+PJJ59MfA6gMvnVr34VV155ZeJzBQUFccIJJ8QJJ5wQLVu2jI0bN8ann34azz33XMyZMyfRXUVFRTFnzpxo0KBB4hwAlJ7iOgA5df/998c111xT7nvr1asXn3/+eRQVFZX7boDKRHEdIF0bNmyI5s2bx/r163Md5d8orgP8u7Vr10anTp1i+fLlOc1x/fXXx1133ZXTDACVjeI6QHpuvvnmuOmmm3KaoWnTpvGXv/wlGjZsmNMcAHu67du3R8+ePWPy5Mk5yzB8+PC44oorcrYfoLKrkusAAFRuV111VRx55JHlvvcXv/iF0joAAHu9p59+eo8srQOwaw0aNIgRI0ZEXl5ezjL06tUrbr/99pztBwCAsnbDDTdEt27dcra/oKAgnn76aaV1gAwUFBTEM888E3Xq1MnJ/uOPPz4uv/zynOwG4G8U1wHIqSpVqsQTTzwRjRs3Lred/fr1i4svvrjc9gEAQFq8/TTA3uf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       "text/plain": [
        "<Figure size 3000x2000 with 1 Axes>"
       ]
@@ -568,9 +523,9 @@
     "fig = plt.figure(0,figsize=(figwidth,figheight))\n",
     "ax = fig.add_subplot(1,1,1)\n",
     "plot_styling.axis_style_temperature(ax)\n",
-    "ax.plot(output_table[:,0],output_table[:,1],'-b',linewidth=lineWidth, label = \"Sublimation Front Temperature\")\n",
-    "ax.plot(output_table[:,0],output_table[:,2],'-r',linewidth=lineWidth, label = \"Maximum Product Temperature\")\n",
-    "ax.plot(output_table[:,0],output_table[:,3],'-o',color='k',markevery=5,linewidth=lineWidth, markersize=markerSize, label = \"Shelf Temperature\")\n",
+    "ax.plot(output_table[:,0],output_table[:,1],'-b', linewidth=lineWidth, label = \"Sublimation Front Temperature\")\n",
+    "ax.plot(output_table[:,0],output_table[:,2],'-r', linewidth=lineWidth, label = \"Maximum Product Temperature\")\n",
+    "ax.plot(output_table[:,0],output_table[:,3],'-k', linewidth=lineWidth, label = \"Shelf Temperature\")\n",
     "plt.legend(fontsize=40,loc='best')\n",
     "ll,ul = ax.get_ylim()\n",
     "ax.set_ylim([ll,ul+5.0])\n",
@@ -614,4 +569,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 5
-}
\ No newline at end of file
+}
diff --git a/docs/examples/opt_Pch_PD.ipynb b/docs/examples/opt_Pch_PD.ipynb
new file mode 100644
index 00000000..3d579b62
--- /dev/null
+++ b/docs/examples/opt_Pch_PD.ipynb
@@ -0,0 +1,356 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "3b5ff80f",
+   "metadata": {},
+   "source": [
+    "# Optimize time-varying chamber pressure during primary drying"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "78e95dbb",
+   "metadata": {},
+   "source": [
+    "We need a few imports:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "d8e3d597",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "from matplotlib import rc as matplotlibrc\n",
+    "from ruamel.yaml import YAML\n",
+    "yaml = YAML()\n",
+    "\n",
+    "from lyopronto import opt_Pch, plot_styling"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "058e9856",
+   "metadata": {},
+   "source": [
+    "Then, we provide all the necessary simulation parameters."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "c7d890c3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Set up the simulation settings\n",
+    "# This needs to be a dict with string keys, which can be expressed in YAML as well\n",
+    "sim = yaml.load(\"\"\"\n",
+    "tool: Optimizer\n",
+    "Kv_known: true\n",
+    "Rp_known: true\n",
+    "Variable_Pch: true\n",
+    "Variable_Tsh: false\n",
+    "\"\"\")\n",
+    "# Or, equivalently:\n",
+    "sim = {\n",
+    "    'tool': 'Optimizer',\n",
+    "    'Kv_known': True,\n",
+    "    'Rp_known': True,\n",
+    "    'Variable_Pch': True,\n",
+    "    'Variable_Tsh': False}\n",
+    "\n",
+    "\n",
+    "# Vial and fill properties\n",
+    "vial = {\n",
+    "    # Av = Vial area in cm^2\n",
+    "    'Av': 3.80,\n",
+    "    # Ap = Product Area in cm^2\n",
+    "    'Ap': 3.14,\n",
+    "    # Vfill = Fill volume in mL\n",
+    "    'Vfill': 2.0\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "2da0a9c5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Product properties\n",
+    "product = {\n",
+    "    # cSolid = Fractional concentration of solute in the frozen solution\n",
+    "    'cSolid': 0.05,\n",
+    "    # Product Resistance Parameters\n",
+    "    'R0': 1.4,  # cm^2-hr-Torr/g\n",
+    "    'A1': 16.0,  # cm-hr-Torr/g\n",
+    "    'A2': 0.0,  # 1/cm\n",
+    "    # Critical product temperature\n",
+    "    # At least 2 to 3 deg C below collapse or glass transition temperature\n",
+    "    'T_pr_crit': -5  # in deg C\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "a860b61a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Vial Heat Transfer Parameters\n",
+    "ht = {\n",
+    "    'KC': 2.75e-4,  # cal/s-cm^2-K\n",
+    "    'KP': 8.93e-4,  # cal/s-cm^2-K-Torr\n",
+    "    'KD': 0.46      # 1/Torr\n",
+    "}\n",
+    "\n",
+    "# Chamber Pressure bounds (optimizer will search within these)\n",
+    "Pchamber = {\n",
+    "    # min = Minimum chamber pressure in Torr\n",
+    "    'min': 0.05,\n",
+    "    # max = Maximum chamber pressure in Torr\n",
+    "    'max': 1000.0\n",
+    "}\n",
+    "\n",
+    "# Shelf Temperature\n",
+    "Tshelf = {\n",
+    "    # init = Initial shelf temperature in C\n",
+    "    'init': -40.0,\n",
+    "    # setpt = Shelf temperature set points in C\n",
+    "    'setpt': [10.0],\n",
+    "    # dt_setpt = Time for which shelf temperature set points are held in min\n",
+    "    'dt_setpt': [1800.0],\n",
+    "    # ramp_rate = Shelf temperature ramping rate in C/min\n",
+    "    'ramp_rate': 1.0\n",
+    "}\n",
+    "\n",
+    "# Equipment capability line\n",
+    "eq_cap = {\n",
+    "    'a': -0.182,  # slope\n",
+    "    'b': 11.7     # intercept\n",
+    "}\n",
+    "\n",
+    "# Number of vials (used for equipment capability calculation)\n",
+    "nVial = 398\n",
+    "\n",
+    "# Time step\n",
+    "dt = 0.01  # hr"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "dea7e3f7",
+   "metadata": {},
+   "source": [
+    "Now, we are ready to run the optimization, which is `lyopronto.opt_Pch.dry`.\n",
+    "\n",
+    "??? info \"documentation\"\n",
+    "    *Here is the docstring for `opt_Pch.dry`:*\n",
+    "    ::: lyopronto.opt_Pch.dry"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "ab9dd832",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "output_table = opt_Pch.dry(vial, product, ht, Pchamber, Tshelf, dt, eq_cap, nVial)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "23e6d595",
+   "metadata": {},
+   "source": [
+    "It's a good idea, particularly when you are exploring interactively, to write the simulation input and output to disk together so that as you do later analysis, you have a record of what you did. Uncommenting the following code will do so.\n",
+    "\n",
+    "Previously the inputs were recorded as a two-column CSV, which records info but isn't very readable (and frankly not very useful to a machine either). YAML provides a somewhat more sensible format, and fortunately a Python dictionary can be readily represented in YAML."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "7f0f6c30",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sim_setup = {\n",
+    "    'sim': sim,\n",
+    "    'vial': vial,\n",
+    "    'product': product,\n",
+    "    'ht': ht,\n",
+    "    'Pchamber': Pchamber,\n",
+    "    'Tshelf': Tshelf,\n",
+    "    'eq_cap': eq_cap,\n",
+    "    'nVial': nVial,\n",
+    "    'dt': dt,\n",
+    "}\n",
+    "\n",
+    "# # Write input data to disk as YAML\n",
+    "# import time\n",
+    "# current_time = time.strftime(\"%Y%m%d_%H%M%S\")\n",
+    "# save_inputs(sim_setup, current_time)\n",
+    "\n",
+    "# # Write simulation data to disk as CSV\n",
+    "# try:\n",
+    "#     csvfile = open('lyopronto_output_'+current_time+'.csv', 'w')\n",
+    "#     writer = csv.writer(csvfile)\n",
+    "#     writer.writerow(['Time [hr]','Sublimation Temperature [C]','Vial Bottom Temperature [C]', 'Shelf Temperature [C]','Chamber Pressure [mTorr]','Sublimation Flux [kg/hr/m^2]','Percent Dried'])\n",
+    "#     for i in range(0,len(output_table)):\n",
+    "#         writer.writerow(output_table[i])\n",
+    "# finally:\n",
+    "#     csvfile.close()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5743d06b",
+   "metadata": {},
+   "source": [
+    "Finally, it's a good idea to plot everything. Here, again, you could save plots to disk by uncommenting the lines below.\n",
+    "\n",
+    "Some default styling of the axes is carried out with `lyopronto.plot_styling.axis_style_*`, like turning on minor ticks and setting tick labels to be bold. However, this is entirely up to aesthetic taste and leaving it out does not affect the basic logic of the plots."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "eb4d2693",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "matplotlibrc('text.latex', preamble=r'\\usepackage{color}')\n",
+    "matplotlibrc('text', usetex=False)\n",
+    "plt.rcParams['font.family'] = 'Arial'\n",
+    "\n",
+    "figwidth = 30\n",
+    "figheight = 20\n",
+    "lineWidth = 5\n",
+    "markerSize = 20"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "37d421af",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
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+      "text/plain": [
+       "<Figure size 3000x2000 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig = plt.figure(0, figsize=(figwidth, figheight))\n",
+    "ax1 = fig.add_subplot(1, 1, 1)\n",
+    "ax2 = ax1.twinx()\n",
+    "ax1.plot(output_table[:, 0], output_table[:, 4], '-', color='b', linewidth=lineWidth, markersize=markerSize, label=\"Chamber Pressure\")\n",
+    "ax2.plot(output_table[:, 0], output_table[:, 5], '-', color=[0, 0.7, 0.3], linewidth=lineWidth, label=\"Sublimation Flux\")\n",
+    "\n",
+    "plot_styling.axis_style_pressure(ax1)\n",
+    "plot_styling.axis_style_subflux(ax2)\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "# figure_name = 'lyopronto_pressure_subflux_'+current_time+'.pdf'\n",
+    "# plt.savefig(figure_name)\n",
+    "# plt.close()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "e3efb8a6",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 3000x2000 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig = plt.figure(0, figsize=(figwidth, figheight))\n",
+    "ax = fig.add_subplot(1, 1, 1)\n",
+    "plot_styling.axis_style_percdried(ax)\n",
+    "ax.plot(output_table[:, 0], output_table[:, -1], '-k', linewidth=lineWidth, label=\"Percent Dried\")\n",
+    "plt.tight_layout()\n",
+    "# figure_name = 'lyopronto_percentdried_'+current_time+'.pdf'\n",
+    "# plt.savefig(figure_name)\n",
+    "# plt.close()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "623933f5",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
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+      "text/plain": [
+       "<Figure size 3000x2000 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig = plt.figure(0, figsize=(figwidth, figheight))\n",
+    "ax = fig.add_subplot(1, 1, 1)\n",
+    "plot_styling.axis_style_temperature(ax)\n",
+    "ax.plot(output_table[:, 0], output_table[:, 1], '-b', linewidth=lineWidth, label=\"Sublimation Front Temperature\")\n",
+    "ax.plot(output_table[:, 0], output_table[:, 2], '-r', linewidth=lineWidth, label=\"Maximum Product Temperature\")\n",
+    "ax.plot(output_table[:, 0], output_table[:, 3], '-k', linewidth=lineWidth, label=\"Shelf Temperature\")\n",
+    "plt.legend(fontsize=40, loc='best')\n",
+    "ll, ul = ax.get_ylim()\n",
+    "ax.set_ylim([ll, ul + 5.0])\n",
+    "plt.tight_layout()\n",
+    "# figure_name = 'lyopronto_temperatures_'+current_time+'.pdf'\n",
+    "# plt.savefig(figure_name)\n",
+    "# plt.close()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "lyopronto",
+   "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": "3.13.11"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/docs/how-to-guides.md b/docs/how-to-guides.md
index 489e81af..c9ff6264 100644
--- a/docs/how-to-guides.md
+++ b/docs/how-to-guides.md
@@ -1,3 +1 @@
-# How-to Guide
-
-Under construction, but do note that the examples (e.g. for [known Rp](examples/knownRp_PD.ipynb) and [unknown Rp](examples/unknownRp_PD.ipynb) ) are generated from Jupyter notebooks, which you can find in the full repo and modify.
\ No newline at end of file
+# How-to Guide
\ No newline at end of file
diff --git a/docs/reference.md b/docs/reference.md
index 3279b38c..bfa3bc8e 100644
--- a/docs/reference.md
+++ b/docs/reference.md
@@ -1,4 +1,6 @@
-# Full listing of all docstrings by module
+# Reference
+
+## Full listing of all docstrings by module
 
 ::: lyopronto
     options:
diff --git a/docs/tutorials.md b/docs/tutorials.md
index 65212555..68b6c70d 100644
--- a/docs/tutorials.md
+++ b/docs/tutorials.md
@@ -1,5 +1,34 @@
 # Tutorials
 
-Under construction. But for now, see the video tutorial at https://www.youtube.com/watch?v=DI-Gz0pBI0w.
+## Original approach
 
-The short version here is, edit `main.py` in LyoPRONTO then execute it with `python -m LyoPRONTO.main`.
+Edit `main.py` in the root folder of the repo, then execute it with `python main.py`.
+See also the video tutorial at https://www.youtube.com/watch?v=DI-Gz0pBI0w.
+
+## Newer approach: YAML Input Format
+
+LyoPRONTO accepts inputs as a YAML file, loaded via `read_inputs(filename)` and executed with `execute_simulation(inputs)`. 
+The expected keys, values, etc. are fully specified with all units and constraints in the [full reference](reference.md#input-yaml-specification). The keys are also exemplified in the example notebooks, with a set of dictionaries that are put together into a `sim_setup` dictionary for each example.
+
+With a YAML file for your inputs, the high-level workflow is as simple as this:
+
+```python
+import time
+
+import lyopronto as lp
+
+# get time for recording simulation results
+current_time = time.strftime("%y%m%d_%H%M", time.localtime())
+
+yaml_fname = "your_dir/cycle_setup.yaml" # Fill in with your filename and appropriate location
+
+# Read in simulation inputs
+inputs = lp.read_inputs(yaml_fname)
+# Execute the simulation
+output = lp.execute_simulation(inputs)
+
+# Record the simulation inputs, outputs, and figures
+lp.save_inputs(inputs, current_time)
+lp.save_csv(output, inputs, current_time)
+lp.generate_visualizations(output, inputs, current_time)
+```
diff --git a/docs/yaml_inputs.md b/docs/yaml_inputs.md
new file mode 100644
index 00000000..ebf4a867
--- /dev/null
+++ b/docs/yaml_inputs.md
@@ -0,0 +1,229 @@
+# Input YAML Specification
+
+LyoPRONTO reads simulation inputs from a YAML file via `read_inputs(filename)` and writes them back via `save_inputs(inputs, timestamp)`. The YAML uses `ruamel.yaml` (YAML 1.1, block style). Large arrays (`time_data`, `temp_data`) are stripped before writing; if your input references a temperature data file, the `product_temp_filename` key is preserved as a reminder, and you must load the data separately into `time_data` / `temp_data` before calling `execute_simulation()`.
+
+When read into Python, this YAML becomes a dictionary of dictionaries: you can construct that dictionary-of-dictionaries structure yourself in a script and pass it to simulations.
+
+## Top-Level Structure
+
+```yaml
+# always required:
+sim:           # simulation configuration (dict) — required
+vial:          # vial geometry (dict) — required
+product:       # product properties (dict) — required
+ht:            # heat transfer parameters (dict) — conditional
+Pchamber:      # chamber pressure profile (dict) — conditional shape
+Tshelf:        # shelf temperature profile (dict) — conditional shape
+# sometimes required:
+dt: 0.01       # time step (hr) — required
+eq_cap:        # equipment capability (dict) — design space / optimizer
+nVial: 398     # number of vials — design space / optimizer
+h_freezing:    # freezing heat transfer coefficient — freezing only
+t_dry_exp:     # experimental drying time — Kv unknown only
+Kv_range:      # Kv search bounds — Kv unknown only
+time_data:     # experimental time array — Rp unknown only
+temp_data:     # experimental temperature array — Rp unknown only
+product_temp_filename:  # path to temperature file — Rp unknown only
+```
+
+---
+
+## 1. `sim` — Simulation Configuration
+
+| Key | Type | Description |
+|---|---|---|
+| `tool` | `str` | One of: `"Freezing Calculator"`, `"Primary Drying Calculator"`, `"Design Space Generator"`, `"Optimizer"` |
+| `Kv_known` | `bool` | Whether the vial heat transfer coefficient is known (Drying calculator only)|
+| `Rp_known` | `bool` | Whether product resistance is known (Drying calculator only)|
+| `Variable_Pch` | `bool` | Chamber pressure is an optimization variable (Optimizer only) |
+| `Variable_Tsh` | `bool` | Shelf temperature is an optimization variable (Optimizer only) |
+
+**Constraints**:
+- For `"Optimizer"`, at least one of `Variable_Pch` or `Variable_Tsh` must be `true`.
+- For `"Primary Drying Calculator"`, `Kv_known` and `Rp_known` cannot both be `false`.
+
+---
+
+## 2. `vial` — Vial Geometry
+
+| Key | Type | Unit | Description |
+|---|---|---|---|
+| `Av` | `float` | cm² | Vial cross-sectional area |
+| `Ap` | `float` | cm² | Product surface area |
+| `Vfill` | `float` | mL | Fill volume |
+
+**Example**:
+```yaml
+vial:
+  Av: 3.8
+  Ap: 3.14
+  Vfill: 2.0
+```
+
+---
+
+## 3. `product` — Product Properties
+
+| Key | Type | Unit | Required When |
+|---|---|---|---|
+| `cSolid` | `float` | g/mL | Always |
+| `Tpr0` | `float` | °C | `Freezing Calculator` |
+| `Tf` | `float` | °C |  `Freezing Calculator` |
+| `Tn` | `float` | °C |  `Freezing Calculator` |
+| `R0` | `float` | cm²·hr·Torr/g | Always, unless `Rp_known` is `false` |
+| `A1` | `float` | cm·hr·Torr/g | Always, unless `Rp_known` is `false`  |
+| `A2` | `float` | 1/cm | Always, unless `Rp_known` is `false` |
+| `T_pr_crit` | `float` | °C | `Design Space Generator`, `Optimizer`, optional for `Primary Drying Calculator` |
+
+**Notes**:
+
+- `T_pr_crit` should be set 2–3 °C below the collapse or glass transition temperature, to allow a safety margin.
+- For `"Primary Drying Calculator"` with `Rp_known == false`, only `cSolid` and `T_pr_crit` are needed.
+
+**Examples**:
+
+*Freezing*:
+```yaml
+product:
+  cSolid: 0.0
+  Tpr0: 15.8
+  Tf: -1.54
+  Tn: -5.84
+```
+
+*Drying (Rp known)*:
+```yaml
+product:
+  cSolid: 0.05
+  R0: 1.4
+  A1: 16.0
+  A2: 0.0
+  T_pr_crit: -5
+```
+
+---
+
+## 4. `ht` — Heat Transfer Parameters
+
+Required for all tools except `Freezing Calculator` or `Primary Drying Calculator` with `Kv_known == false`.
+!!! info "Single value of Kv"
+    If you only have a single value of $K_v$ and you are simulating at the matching chamber pressure, you can put that value in as `KC` and set `KP`, `KD` both to zero.
+
+
+| Key | Type | Unit | Description |
+|---|---|---|---|
+| `KC` | `float` | cal/(s·K·cm²) | Conductive/radiative coefficient |
+| `KP` | `float` | cal/(s·K·cm²·Torr) | Pressure-dependent coefficient |
+| `KD` | `float` | 1/Torr | Pressure-dependent denominator |
+
+The effective vial coefficient is: `Kv = KC + KP × Pch / (1 + KD × Pch)`
+
+**Example**:
+```yaml
+ht:
+  KC: 0.000275
+  KP: 0.000893
+  KD: 0.46
+```
+
+---
+
+## 5. `Pchamber` — Chamber Pressure Profile
+
+The structure depends on the simulation mode:
+
+**Fixed setpoint** (Primary Drying Calculator, non-variable Optimizer):
+
+| Key | Type | Unit | Description |
+|---|---|---|---|
+| `setpt` | `list[float]` | Torr | Pressure set points |
+| `dt_setpt` | `list[float]` | min | Duration per set point (same length as `setpt`) |
+| `ramp_rate` | `float` | Torr/min | Ramp rate between set points |
+
+```yaml
+Pchamber:
+  setpt: [0.15]
+  dt_setpt: [1800.0]
+  ramp_rate: 0.5
+```
+
+**Sweep array** (Design Space Generator):
+
+```yaml
+Pchamber:
+  setpt: [0.02, 0.05, 0.1, 0.15]
+```
+
+**Bounds** (Optimizer with `Variable_Pch == true`):
+
+```yaml
+Pchamber:
+  min: 0.05
+  max: 1000
+```
+
+Not required for `Freezing Calculator`.
+
+---
+
+## 6. `Tshelf` — Shelf Temperature Profile
+
+**Fixed setpoint** (Primary Drying Calculator, non-variable Optimizer):
+
+| Key | Type | Unit | Description |
+|---|---|---|---|
+| `init` | `float` | °C | Initial shelf temperature |
+| `setpt` | `list[float]` | °C | Temperature set points |
+| `dt_setpt` | `list[float]` | min | Duration per set point (same length as `setpt`) |
+| `ramp_rate` | `float` | °C/min | Ramp rate between set points |
+
+```yaml
+Tshelf:
+  init: -35.0
+  setpt: [20.0]
+  dt_setpt: [1800.0]
+  ramp_rate: 1.0
+```
+
+**Sweep array** (Design Space Generator):
+
+```yaml
+Tshelf:
+  init: -5.0
+  setpt: [-15, 0, 30, 90]
+  ramp_rate: 1.0
+```
+
+**Bounds** (Optimizer with `Variable_Tsh == true`):
+
+```yaml
+Tshelf:
+  min: -45
+  max: 120
+```
+
+---
+
+## 7. Equipment and Scalar Keys
+
+| Key | Type | Unit | Required When | Description |
+|---|---|---|---|---|
+| `dt` | `float` | hr | Always | Simulation time step |
+| `eq_cap.a` | `float` | kg/hr | Design Space & Optimizer | Equipment capability intercept |
+| `eq_cap.b` | `float` | kg/hr/Torr | Design Space & Optimizer | Equipment capability slope |
+| `nVial` | `int` | — | Design Space & Optimizer | Number of vials in batch |
+
+Equipment capability models choked flow to the condenser: `dm/dt [kg/hr] = a + b × Pch [Torr]`
+
+---
+
+## 8. Other Top-level Keys by Mode
+
+| Key | Type | Required When | Description |
+|---|---|---|---|
+| `h_freezing` | `float` (W/m²/K) | `Freezing Calculator` | Heat transfer coefficient during freezing |
+| `t_dry_exp` | `float` (hr) | `Primary Drying Calculator` +  `Kv_known == false` | Experimental drying time for Kv optimization |
+| `Kv_range` | `list[float, float]` (cal/s/K/cm²) | `Primary Drying Calculator` + `Kv_known == false` | Lower and upper bounds for Kv root-finding |
+| `time_data` | `list[float]` (hr) | `Rp_known == false` | Experimental time measurements |
+| `temp_data` | `list[float]` (°C) | `Rp_known == false` | Experimental product temperature measurements |
+| `product_temp_filename` | `str` | `Rp_known == false` | Path to temperature data file (informational only) |
diff --git a/mkdocs.yml b/mkdocs.yml
index 4da4aec3..dc55ab74 100644
--- a/mkdocs.yml
+++ b/mkdocs.yml
@@ -45,11 +45,14 @@ nav:
     # List of web pages to show, in order
     - index.md
     - tutorials.md
-    - how-to-guides.md
+    # - how-to-guides.md
     - Examples:
         - examples/knownRp_PD.ipynb
         - examples/unknownRp_PD.ipynb
-    - explanation.md
+        - examples/freezing.ipynb
+        - examples/opt_Pch_PD.ipynb
+    # - explanation.md
+    - yaml_inputs.md
     - reference.md
     - dev.md