Fix misleading docstrings in fsp, CFM and rupture_propagation

source_modelling/community_fault_model.py

@@ -1,7 +1,7 @@
 """This module provides classes and functions for the New Zealand Community Fault Model.
 
 The New Zealand Community Fault Model (CFM) is a collection of fault data for New
-Zealand. The CFM is stored in a GeoPackage file and can be accessed using the
+Zealand. The CFM is stored in a shapefile and can be accessed using the
 functions in this module. The function `most_likely_nodal_plane` can be used to
 find the most likely nodal plane for a given centroid and two nodal planes, by
 querying nearby faults in the CFM.
@@ -249,13 +249,13 @@ class CommunityFault:
     slip_rate_timeframe_unit: int | None
     """The unit of the slip rate timeframe."""
     down_dip_depth90: float
-    """Not sure!"""
+    """The D90 down-dip depth estimate of the fault (km)."""
     down_dip_depth_dfc: float
-    """Not sure!"""
+    """The Dfc down-dip depth estimate of the fault (km)."""
     down_dip_depth90_method: DipMethod
-    """Not sure!"""
+    """The method used to derive the D90 down-dip depth."""
     down_dip_dfc_method: DipMethod
-    """Not sure!"""
+    """The method used to derive the Dfc down-dip depth."""
     up_dip_depth: Range
     """The up dip depth range."""
     quality_code: int
@@ -405,7 +405,7 @@ def community_fault_model_as_geodataframe() -> "gpd.GeoDataFrame":
 
     model = get_community_fault_model()
 
-    # Transform the trace to WGS84
+    # Swap traces from (northing, easting) to (x=easting, y=northing) as required by EPSG:2193
     for fault in model:
         fault.trace = shapely.transform(fault.trace, lambda coord: coord[:, ::-1])
     return gpd.GeoDataFrame(

source_modelling/fsp.py

@@ -180,9 +180,9 @@ class FSPFile:
         Average speed of rupture front.
     slip_velocity_function : str
         Description of the slip velocity function used
-    nx : float or None
+    nx : int or None
         The number of subfaults along strike.
-    nz : float or None
+    nz : int or None
         The number of subfaults along dip.
     dx : float
         The length of each subfault.
@@ -193,13 +193,14 @@ class FSPFile:
     fmax : float or None
         The maximum frequency in the inversion.
     time_window_count : float or None
-        The slip (or rake) time windows for each subfault.
+        The number of slip (or rake) time windows for each subfault.
     time_window_length : float or None
         The time window length for each subfault.
     time_shift : float or None
         The time shift for each time window.
     segment_count : int
-        Number of segments (subfaults).
+        Number of fault segments. This is not the number of subfaults
+        (rows of `data`), which are counted across all segments.
     data : pd.DataFrame
         The data frame of info on each subfault. Columns vary for this
         dataframe, but each comes with 'segment', 'length', and 'width' which
@@ -251,7 +252,7 @@ class FSPFile:
     time_window_length: float | None
     time_shift: float | None
 
-    # Number of segments (== len(data))
+    # Number of fault segments (not subfaults, so != len(data))
     segment_count: int
     data: pd.DataFrame
 

source_modelling/rupture_propagation.py

@@ -31,7 +31,7 @@
 
 def spanning_tree_with_probabilities(
     graph: nx.Graph,
-) -> tuple[list[nx.DiGraph], list[float]]:
+) -> tuple[list[nx.Graph], list[float]]:
     r"""
     Compute all spanning trees of a graph and their unnormalised weights.
 
@@ -51,7 +51,7 @@ def spanning_tree_with_probabilities(
 
     Returns
     -------
-    list[nx.DiGraph]
+    list[nx.Graph]
         A list of spanning trees of the graph.
     list[float]
         The unnormalised weight P(T) of each spanning tree, in the same order