Fixed some linting errors

src/caf/distribute/cost_functions.py

@@ -15,7 +15,6 @@
 
 # Third Party
 import numpy as np
-from scipy.stats import lognorm
 
 
 # Local Imports

src/caf/distribute/gravity_model/core.py

@@ -3,22 +3,21 @@
 # Built-Ins
 import abc
 import dataclasses
-import functools
 import logging
 import os
 import warnings
-from typing import Any, Optional, Union
+from typing import Any, Optional
 
 # Third Party
 import numpy as np
 import pandas as pd
-from caf.toolkit import cost_utils, io, timing
 from scipy import optimize
 from matplotlib import pyplot as plt
-
+from caf.toolkit import cost_utils, io, timing
+from caf.distribute import cost_functions
 # Local Imports
 # pylint: disable=import-error,wrong-import-position
-from caf.distribute import cost_functions
+
 
 # pylint: enable=import-error,wrong-import-position
 
@@ -96,6 +95,9 @@ class GravityModelCalibrateResults(GravityModelResults):
     cost_params: dict[str, Any]
 
     def plot_distributions(self):
+        """
+        Plot a comparison of the achieved and target distributions.
+        """
         fig, ax = plt.subplots(figsize=(10, 6))
         df_1 = self.cost_distribution.df
         df_1["normalised"] = (
@@ -464,11 +466,6 @@ def _calculate_perceived_factors(
     def _apply_perceived_factors(self, cost_matrix: np.ndarray) -> np.ndarray:
         return cost_matrix * self._perceived_factors
 
-    def _calculate_initial_parameters(self) -> dict[str, Any]:
-        ...
-        # TODO(MB) Make an abstract method for calculating initial parameters
-        # This already exists in SingleArea class
-
     def _guess_init_params(
         self,
         cost_args: list[float],

src/caf/distribute/gravity_model/multi_area.py

@@ -7,13 +7,13 @@
 import os
 from typing import Optional
 from pathlib import Path
+import functools
 
 # Third Party
 import numpy as np
 import pandas as pd
 from scipy import optimize
 from caf.distribute.gravity_model.core import GravityModelCalibrateResults
-import functools
 
 # Local Imports
 from caf.toolkit import cost_utils, timing, BaseConfig
@@ -197,6 +197,7 @@ def __init__(
             self.furness_jac = params.furness_jac
 
     def process_tlds(self):
+        """Get distributions in the right format for a multi-area gravity model"""
         dists = []
         for cat in self.tlds.index.unique():
             tld = self.tlds.loc[cat]
@@ -217,8 +218,8 @@ def process_tlds(self):
 
         return dists
 
-    def _calculate_perceived_factors(self) -> None:
-        # TODO this
+    def _calculate_perceived_factors(self, target_cost_distribution: cost_utils.CostDistribution,
+        achieved_band_shares: np.ndarray) -> None:
         raise NotImplementedError("WIP")
 
     @property
@@ -450,7 +451,6 @@ def _jacobian_function(
         jac_width = len(cost_distributions) * params_len
         jacobian = np.zeros((jac_length, jac_width))
         # Build seed matrix
-        # TODO pull this out into a function/method
         base_mat = self._create_seed_matrix(cost_distributions, init_params, params_len)
         # Calculate net effect of furnessing (saves a lot of time on furnessing here)
         furness_factor = np.divide(
@@ -509,7 +509,6 @@ def _gravity_function(
         del diff_step
 
         base_mat = self._create_seed_matrix(cost_distributions, init_params, params_len)
-        # TODO add furness tolerance as parameter rather than hard coding
         matrix, iters, rmse = furness.doubly_constrained_furness(
             seed_vals=base_mat,
             row_targets=self.row_targets,
@@ -535,7 +534,7 @@ def _gravity_function(
 
         for i, dist in enumerate(cost_distributions):
             j = 0
-            for name, val in dist.function_params.items():
+            for name in dist.function_params.keys():
                 log_costs[f"{name}_{i}"] = init_params[params_len * i + j]
                 j += 1
             log_costs[f"convergence_{i}"] = convergences[dist.name]

src/caf/distribute/gravity_model/single_area.py

@@ -8,18 +8,19 @@
 
 # Third Party
 import numpy as np
-from caf.toolkit import cost_utils, timing, toolbox
 from scipy import optimize
-
-# Local Imports
-# pylint: disable=import-error,wrong-import-position
+from caf.toolkit import cost_utils, timing, toolbox
 from caf.distribute import cost_functions, furness
 from caf.distribute.gravity_model import (
     core,
     GravityModelCalibrateResults,
     GravityModelRunResults,
 )
 
+# Local Imports
+# pylint: disable=import-error,wrong-import-position
+
+
 # pylint: enable=import-error,wrong-import-position
 
 # # # CONSTANTS # # #

src/caf/distribute/utils.py

@@ -46,7 +46,6 @@ def infill_cost_matrix(
     -------
     np.ndarray: The input matrix with values infilled.
     """
-    # TODO add multiple factors by area-type
     min_row = np.min(np.ma.masked_where(cost_matrix <= 0, cost_matrix), axis=1) * diag_factor
     np.fill_diagonal(cost_matrix, min_row)
     cost_matrix[cost_matrix > 1e10] = zeros_infill