installed ruff and formatted all the docs

pip installed ruff and re-ran the format to include the 3 I was missing

examples/example_df_dataset.py

@@ -1,84 +1,76 @@
-import pandas as pd
 import numpy as np
+import pandas as pd
 
-groups_to_split_into=['age_group_0','age_group_1','age_group_2','age_group_3']
-baseline_patterns=pd.DataFrame.from_dict({
-    0:np.array([0.4,0.2,0.3,0.75]),
-    1:np.array([0.1,0.15,0.2,0.7])
-},orient='index')
-baseline_patterns.index.name='pattern_id'
-baseline_patterns.columns=groups_to_split_into
+groups_to_split_into = [
+    "age_group_0",
+    "age_group_1",
+    "age_group_2",
+    "age_group_3",
+]
+baseline_patterns = pd.DataFrame.from_dict(
+    {0: np.array([0.4, 0.2, 0.3, 0.75]), 1: np.array([0.1, 0.15, 0.2, 0.7])},
+    orient="index",
+)
+baseline_patterns.index.name = "pattern_id"
+baseline_patterns.columns = groups_to_split_into
 
 
-#Population ids correspond to the overall aggregated population we are splitting
-#For example, this would be the id for a country
+# Population ids correspond to the overall aggregated population we are splitting
+# For example, this would be the id for a country
 
-pop_group_sizes={
-    0:[10,10,20,20],
-    1:[10,20,20,13],
-    2:[20,20,10,10],
-    3:[5,40,30,20]
+pop_group_sizes = {
+    0: [10, 10, 20, 20],
+    1: [10, 20, 20, 13],
+    2: [20, 20, 10, 10],
+    3: [5, 40, 30, 20],
 }
-population_sizes=pd.DataFrame.from_dict(pop_group_sizes,orient='index',columns=groups_to_split_into)
-population_sizes.index.name='demographic_id'
-
-pop_df=pd.DataFrame(
-    {
-        'demographic_id':[0,1,2,3],
-        'pattern_id':[0,0,1,1]
-    }
+population_sizes = pd.DataFrame.from_dict(
+    pop_group_sizes, orient="index", columns=groups_to_split_into
 )
+population_sizes.index.name = "demographic_id"
 
-group_partitions={
-    0:[('age_group_0','age_group_1'),('age_group_2','age_group_3')],
-    1:[('age_group_0','age_group_1','age_group_2'),('age_group_3',)],
-    2:[('age_group_0','age_group_1','age_group_2','age_group_3')],
-    3:[('age_group_0'),('age_group_1'),('age_group_2','age_group_3')]
+pop_df = pd.DataFrame(
+    {"demographic_id": [0, 1, 2, 3], "pattern_id": [0, 0, 1, 1]}
+)
+
+group_partitions = {
+    0: [("age_group_0", "age_group_1"), ("age_group_2", "age_group_3")],
+    1: [("age_group_0", "age_group_1", "age_group_2"), ("age_group_3",)],
+    2: [("age_group_0", "age_group_1", "age_group_2", "age_group_3")],
+    3: [("age_group_0"), ("age_group_1"), ("age_group_2", "age_group_3")],
 }
 
-def get_dummies(partition,splitting_groups):
-   dummies=[
-      [int(group in data_partition) for group in splitting_groups]
-      for data_partition in partition
-   ]
-   return dummies
-
-def build_dummy_df(dummies,id,groups_to_split_into):
-   df=pd.DataFrame(
-        dummies,
-        columns=groups_to_split_into
-   )
-   df['demographic_id']=id
-   return df
-
-splitting_df=pd.concat([
-    build_dummy_df(get_dummies(partition,groups_to_split_into),id,groups_to_split_into)
-         for id,partition in group_partitions.items()
-])
-
-observations=[
-    7,
-    20,
-    10,
-    11,
-    10,
-    0.8,
-    8,
-    22
-]
 
-SE_vals=[
-    1,
-    2,
-    3,
-    1,
-    1.5,
-    0.1,
-    1,
-    3
-]
+def get_dummies(partition, splitting_groups):
+    dummies = [
+        [int(group in data_partition) for group in splitting_groups]
+        for data_partition in partition
+    ]
+    return dummies
+
+
+def build_dummy_df(dummies, id, groups_to_split_into):
+    df = pd.DataFrame(dummies, columns=groups_to_split_into)
+    df["demographic_id"] = id
+    return df
+
+
+splitting_df = pd.concat(
+    [
+        build_dummy_df(
+            get_dummies(partition, groups_to_split_into),
+            id,
+            groups_to_split_into,
+        )
+        for id, partition in group_partitions.items()
+    ]
+)
+
+observations = [7, 20, 10, 11, 10, 0.8, 8, 22]
+
+SE_vals = [1, 2, 3, 1, 1.5, 0.1, 1, 3]
 
-data_df=pop_df.merge(splitting_df,on='demographic_id')
+data_df = pop_df.merge(splitting_df, on="demographic_id")
 
-data_df['obs']=observations
-data_df['obs_se']=SE_vals
+data_df["obs"] = observations
+data_df["obs_se"] = SE_vals