black style

bhepop2/functions.py

@@ -309,6 +309,7 @@ def build_cross_table(pop: pd.DataFrame, names_attribute: list):
 
     return table_percentage
 
+
 # check functions
 
 

tests/conftest.py

@@ -90,9 +90,7 @@ def filosofi_distributions_nantes(test_insee_code):
 
 @pytest.fixture(scope="session")
 def filosofi_global_distribution_nantes(filosofi_distributions_nantes):
-    return filosofi_distributions_nantes[
-            filosofi_distributions_nantes["modality"] == "all"
-        ]
+    return filosofi_distributions_nantes[filosofi_distributions_nantes["modality"] == "all"]
 
 
 @pytest.fixture(scope="function")

tests/sources/test_base.py

@@ -1,4 +1,7 @@
-from bhepop2.sources.global_distribution import QuantitativeGlobalDistribution,QuantitativeAttributes
+from bhepop2.sources.global_distribution import (
+    QuantitativeGlobalDistribution,
+    QuantitativeAttributes,
+)
 
 import pytest
 
@@ -9,8 +12,13 @@ def test_init(self, filosofi_global_distribution_nantes, mocker):
         test_name = "test_name"
 
         # mock feature values evaluation and validation
-        mocker.patch("bhepop2.sources.global_distribution.QuantitativeGlobalDistribution._validate_data")
-        mocker.patch("bhepop2.sources.global_distribution.QuantitativeGlobalDistribution._evaluate_feature_values", return_value=[1, 2, 3])
+        mocker.patch(
+            "bhepop2.sources.global_distribution.QuantitativeGlobalDistribution._validate_data"
+        )
+        mocker.patch(
+            "bhepop2.sources.global_distribution.QuantitativeGlobalDistribution._evaluate_feature_values",
+            return_value=[1, 2, 3],
+        )
 
         source = QuantitativeGlobalDistribution(filosofi_global_distribution_nantes, name=test_name)
 
@@ -43,4 +51,3 @@ def test_delta_min_check(self):
         with pytest.raises(AssertionError):
             QuantitativeAttributes(delta_min=None)
             QuantitativeAttributes(delta_min=-1)
-

tests/sources/test_global_distribution.py

@@ -12,7 +12,10 @@ def test_init(self, filosofi_global_distribution_nantes, test_parameters):
         relative_max = 1.5
 
         global_distribution = QuantitativeGlobalDistribution(
-            filosofi_global_distribution_nantes, name=test_name, abs_minimum=abs_min, relative_maximum=relative_max
+            filosofi_global_distribution_nantes,
+            name=test_name,
+            abs_minimum=abs_min,
+            relative_maximum=relative_max,
         )
 
         # check class attributes
@@ -29,22 +32,18 @@ def test_validate_data(self, filosofi_global_distribution_nantes):
 
         # DataFrame with missing decile should raise an error
         with pytest.raises(AssertionError):
-            QuantitativeGlobalDistribution(
-                filosofi_global_distribution_nantes.drop(["D5"], axis=1)
-            )
+            QuantitativeGlobalDistribution(filosofi_global_distribution_nantes.drop(["D5"], axis=1))
 
         # empty DataFrame should raise an error
         with pytest.raises(AssertionError):
             QuantitativeGlobalDistribution(
-                filosofi_global_distribution_nantes.drop([filosofi_global_distribution_nantes.index[0]], axis=0)
+                filosofi_global_distribution_nantes.drop(
+                    [filosofi_global_distribution_nantes.index[0]], axis=0
+                )
             )
 
     def test_get_value_for_feature(self, filosofi_global_distribution_nantes, test_seed, mocker):
         rng = default_rng(test_seed)
-        global_distribution = QuantitativeGlobalDistribution(
-            filosofi_global_distribution_nantes
-        )
+        global_distribution = QuantitativeGlobalDistribution(filosofi_global_distribution_nantes)
 
         assert round(global_distribution.get_value_for_feature(5, rng), 2) == 23526.75
-
-

tests/sources/test_marginal_distributions.py

@@ -1,6 +1,9 @@
 import pandas as pd
 
-from bhepop2.sources.marginal_distributions import QuantitativeMarginalDistributions, QualitativeMarginalDistributions
+from bhepop2.sources.marginal_distributions import (
+    QuantitativeMarginalDistributions,
+    QualitativeMarginalDistributions,
+)
 from bhepop2.analysis import QuantitativeAnalysis, QualitativeAnalysis
 from bhepop2.functions import build_cross_table
 
@@ -15,18 +18,15 @@ def test_init(self, filosofi_distributions_nantes, test_modalities):
         attribute_selection = list(test_modalities.keys())
 
         source = QuantitativeMarginalDistributions(
-            filosofi_distributions_nantes,
-            attribute_selection=attribute_selection
+            filosofi_distributions_nantes, attribute_selection=attribute_selection
         )
 
         # check class attributes
         assert source.attribute_selection == attribute_selection
         assert source.modalities == test_modalities
 
     def test_get_modality_distribution(self, filosofi_distributions_nantes):
-        source = QuantitativeMarginalDistributions(
-            filosofi_distributions_nantes
-        )
+        source = QuantitativeMarginalDistributions(filosofi_distributions_nantes)
 
         modality_distribution = source.get_modality_distribution("ownership", "Tenant")
 
@@ -39,19 +39,25 @@ class TestQuantitativeMarginalDistributions:
 
     @pytest.fixture(scope="class")
     def quantitative_marginal_distribution(self, filosofi_distributions_nantes):
-        return QuantitativeMarginalDistributions(
-            filosofi_distributions_nantes,
-            delta_min=1000
-        )
+        return QuantitativeMarginalDistributions(filosofi_distributions_nantes, delta_min=1000)
 
     def test_feature_values(self, quantitative_marginal_distribution):
 
         assert len(quantitative_marginal_distribution.feature_values) == 50
-        assert round(sum(quantitative_marginal_distribution.feature_values)/len(quantitative_marginal_distribution.feature_values), 2) == 40296.2
+        assert (
+            round(
+                sum(quantitative_marginal_distribution.feature_values)
+                / len(quantitative_marginal_distribution.feature_values),
+                2,
+            )
+            == 40296.2
+        )
 
     def test_compute_feature_prob(self, quantitative_marginal_distribution):
 
-        feature_prob = quantitative_marginal_distribution.compute_feature_prob("ownership", "Tenant")
+        feature_prob = quantitative_marginal_distribution.compute_feature_prob(
+            "ownership", "Tenant"
+        )
 
         assert len(feature_prob) == quantitative_marginal_distribution.nb_feature_values
         assert set(feature_prob.columns) == {"feature", "prob"}
@@ -64,16 +70,15 @@ def test_get_value_for_feature(self, quantitative_marginal_distribution, test_se
 
         assert round(value, 2) == 10589.92
 
-    def test_compare_with_populations(self, quantitative_marginal_distribution, test_feature_name, mocker):
+    def test_compare_with_populations(
+        self, quantitative_marginal_distribution, test_feature_name, mocker
+    ):
         # avoid the analysis table evaluation, we just want the class instance
         mocker.patch("bhepop2.analysis.PopulationAnalysis._evaluate_analysis_table")
-        enriched = pd.DataFrame({
-            test_feature_name: [10, 20]
-        })
+        enriched = pd.DataFrame({test_feature_name: [10, 20]})
 
         analysis = quantitative_marginal_distribution.compare_with_populations(
-            {"pop": enriched},
-            feature_name=test_feature_name
+            {"pop": enriched}, feature_name=test_feature_name
         )
 
         assert isinstance(analysis, QuantitativeAnalysis)
@@ -105,7 +110,12 @@ def qualitative_marginal_distribution(self, car_distributions):
         )
 
     def test_feature_values(self, qualitative_marginal_distribution):
-        assert qualitative_marginal_distribution.feature_values == ['0voit', '1voit', '2voit', '3voit']
+        assert qualitative_marginal_distribution.feature_values == [
+            "0voit",
+            "1voit",
+            "2voit",
+            "3voit",
+        ]
 
     def test_compute_feature_prob(self, qualitative_marginal_distribution):
 
@@ -120,22 +130,20 @@ def test_get_value_for_feature(self, qualitative_marginal_distribution, test_see
 
         assert value == qualitative_marginal_distribution.feature_values[2]
 
-    def test_compare_with_populations(self, qualitative_marginal_distribution, test_feature_name, mocker):
+    def test_compare_with_populations(
+        self, qualitative_marginal_distribution, test_feature_name, mocker
+    ):
         # avoid the analysis table evaluation, we just want the class instance
         mocker.patch("bhepop2.analysis.PopulationAnalysis._evaluate_analysis_table")
-        enriched = pd.DataFrame({
-            test_feature_name: [10, 20]
-        })
+        enriched = pd.DataFrame({test_feature_name: [10, 20]})
 
         analysis = qualitative_marginal_distribution.compare_with_populations(
-            {"pop": enriched},
-            feature_name=test_feature_name
+            {"pop": enriched}, feature_name=test_feature_name
         )
 
         assert isinstance(analysis, QualitativeAnalysis)
 
 
-
 # def test_qualitative_enrich(
 #     pop_synt_men_nantes,
 #     distributions,