Tests passing

src/caf/distribute/furness.py

@@ -162,7 +162,7 @@ def doubly_constrained_furness(
 
 
 @dataclass
-class props_input:
+class PropsInput:
     """
     props: np.ndarray
         This is essentially a cost matrix, but costs are replaced by the percentage
@@ -205,7 +205,7 @@ def cost_to_prop(costs: np.ndarray, bands: pd.DataFrame, val_col: str):
 
 
 def triply_constrained_furness(
-    props: list[props_input],
+    props: list[PropsInput],
     row_targets,
     col_targets,
     max_iters,
@@ -224,7 +224,7 @@ def triply_constrained_furness(
 
     Parameters
     ----------
-    props: list[props_input]
+    props: list[PropsInput]
         A list of info about cost bins. This is produced by cost_to_props
     row_targets: np.ndarray
         The targets for the rows (origins) in the matrix

src/caf/distribute/gravity_model/core.py

@@ -14,8 +14,9 @@
 import pandas as pd
 from matplotlib import pyplot as plt, figure
 from scipy import optimize
-from caf.toolkit import cost_utils, io, timing, BaseConfig
 import seaborn as sns
+from caf.toolkit import cost_utils, io, timing, BaseConfig
+
 
 # Local Imports
 from caf.distribute import cost_functions
@@ -60,14 +61,17 @@ class GravityModelResults:
 
     @property
     def achieved_rows(self):
+        """Return the achieved row totals."""
         return self.value_distribution.sum(axis=1)
 
     @property
     def achieved_cols(self):
+        """Return the achieved column totals."""
         return self.value_distribution.sum(axis=0)
 
     @property
     def matrix_total(self):
+        """Return the total trips in the matrix."""
         return self.value_distribution.sum()
 
 
@@ -105,13 +109,15 @@ class GravityModelCalibrateResults(GravityModelResults):
     cost_function: cost_functions.CostFunction
     cost_params: dict[str, Any]
 
-    class output_yaml(BaseConfig):
+    class OutputYaml(BaseConfig):
+        """Class for outputting some data from this class."""
         cost_params: dict[str, Any]
         cost_function: str
         matrix_total: float
         cost_convergence: float
 
     def save(self, out_dir: Path):
+        """Save method for class"""
         out_dir.mkdir(parents=False, exist_ok=True)
         achieved = self.cost_distribution.df.copy()
         achieved["achieved_normalised_demand"] = (
@@ -126,7 +132,7 @@ def save(self, out_dir: Path):
         target.index = achieved.index
         dists_out = achieved.join(target["target_normalised_demand"])
         dists_out.to_csv(out_dir / "dist_comparison.csv", index=False)
-        yaml_output = self.output_yaml(
+        yaml_output = self.OutputYaml(
             cost_params=self.cost_params,
             cost_function=self.cost_function.name,
             matrix_total=self.matrix_total,
@@ -136,6 +142,7 @@ def save(self, out_dir: Path):
 
     @property
     def target_mean_trip_length(self):
+        """Return the mean trip length of the target distribution."""
         temp = self.target_cost_distribution.df.copy()
         temp["weighted"] = (
             temp[self.target_cost_distribution.avg_col]
@@ -145,6 +152,7 @@ def target_mean_trip_length(self):
 
     @property
     def achieved_mean_trip_length(self):
+        """Return the mean trip length of the achieved distribution."""
         temp = self.cost_distribution.df.copy()
         temp["weighted"] = (
             temp[self.cost_distribution.avg_col] * temp[self.cost_distribution.trips_col]

src/caf/distribute/gravity_model/multi_area.py

@@ -91,6 +91,7 @@ class MultiDistInput(BaseConfig):
 
     @property
     def log_path(self):
+        """Create path to a log file from out_path."""
         return self.out_path / "log.csv"
 
 
@@ -344,8 +345,8 @@ def _calibrate(
 
     def calibrate(
         self,
-        update_params: bool = False,
         *args,
+        update_params: bool = False,
         **kwargs,
     ) -> GravityModelCalibrateResults:
         """Find the optimal parameters for self.cost_function.
@@ -637,7 +638,7 @@ def run(self, triply_constrain: bool = False, xamax: int = 2):
                     ],
                     val_col="normalised",
                 )
-                props = furness.props_input(prop_cost, dist.zones, band_vals)
+                props = furness.PropsInput(prop_cost, dist.zones, band_vals)
                 props_list.append(props)
             # triply contrained furness on seed matrix
             # tol is higher as it is more difficult to converge when triply contrained
@@ -654,7 +655,7 @@ def run(self, triply_constrain: bool = False, xamax: int = 2):
             for i, dist in enumerate(self.dists):
                 (
                     single_cost_distribution,
-                    single_achieved_residuals,
+                    _,
                     single_convergence,
                 ) = core.cost_distribution_stats(
                     achieved_trip_distribution=new_mat[dist.zones],

tests/gravity_model/test_multi_area.py

@@ -120,7 +120,7 @@ def fixture_conf(data_dir, mock_dir):
         lookup_cat_col="cat",
         lookup_zone_col="zone",
         init_params={"mu": 1, "sigma": 2},
-        log_path=mock_dir / "log.csv",
+        out_path=mock_dir,
         furness_tolerance=0.1,
         furness_jac=False,
     )
@@ -140,7 +140,7 @@ def fixture_jac_furn(data_dir, mock_dir):
         lookup_cat_col="cat",
         lookup_zone_col="zone",
         init_params={"mu": 1, "sigma": 2},
-        log_path=mock_dir / "log.csv",
+        out_path=mock_dir,
         furness_tolerance=0.1,
         furness_jac=True,
     )
@@ -158,7 +158,7 @@ def fixture_cal_no_furness(data_dir, infilled, no_furness_jac_conf, trip_ends, m
         cost_function=cost_functions.BuiltInCostFunction.LOG_NORMAL.get_cost_function(),
         params=no_furness_jac_conf,
     )
-    results = model.calibrate(running_log_path=mock_dir / "temp_log.csv")
+    results = model.calibrate()
     return results
 
 
@@ -173,7 +173,7 @@ def fixture_cal_furness(data_dir, infilled, furness_jac_conf, trip_ends, mock_di
         cost_function=cost_functions.BuiltInCostFunction.LOG_NORMAL.get_cost_function(),
         params=furness_jac_conf,
     )
-    results = model.calibrate(running_log_path=mock_dir / "temp_log.csv")
+    results = model.calibrate()
     return results