suspend isort switch to black

cities/modeling/model_interactions.py

@@ -7,8 +7,10 @@
 import torch
 
 import pyro
-from cities.modeling.modeling_utils import (prep_wide_data_for_inference,
-                                            train_interactions_model)
+from cities.modeling.modeling_utils import (
+    prep_wide_data_for_inference,
+    train_interactions_model,
+)
 from cities.utils.data_grabber import DataGrabber, find_repo_root
 
 

cities/modeling/modeling_utils.py

@@ -9,9 +9,11 @@
 from scipy.stats import spearmanr
 
 import pyro
-from cities.utils.data_grabber import (DataGrabber, list_available_features,
-                                       list_tensed_features)
-
+from cities.utils.data_grabber import (
+    DataGrabber,
+    list_available_features,
+    list_tensed_features,
+)
 
 
 def drop_high_correlation(df, threshold=0.85):

cities/modeling/tau_caching_pipeline.py

@@ -3,8 +3,12 @@
 import time
 
 from cities.queries.causal_insight import CausalInsight
-from cities.utils.data_grabber import (DataGrabber, find_repo_root,
-                                       list_interventions, list_outcomes)
+from cities.utils.data_grabber import (
+    DataGrabber,
+    find_repo_root,
+    list_interventions,
+    list_outcomes,
+)
 
 root = find_repo_root()
 log_dir = os.path.join(root, "data", "tau_samples")

cities/modeling/training_pipeline.py

@@ -4,8 +4,7 @@
 import time
 
 from cities.modeling.model_interactions import InteractionsModel
-from cities.utils.data_grabber import (find_repo_root, list_interventions,
-                                       list_outcomes)
+from cities.utils.data_grabber import find_repo_root, list_interventions, list_outcomes
 
 if __name__ != "__main__":
     sys.exit()

cities/queries/causal_insight.py

@@ -11,9 +11,11 @@
 import pyro
 from cities.modeling.model_interactions import model_cities_interaction
 from cities.modeling.modeling_utils import prep_wide_data_for_inference
-from cities.utils.cleaning_utils import (revert_prediction_df,
-                                         revert_standardize_and_scale_scaler,
-                                         sigmoid)
+from cities.utils.cleaning_utils import (
+    revert_prediction_df,
+    revert_standardize_and_scale_scaler,
+    sigmoid,
+)
 from cities.utils.data_grabber import DataGrabber, find_repo_root
 from cities.utils.percentiles import transformed_intervention_from_percentile
 

cities/queries/causal_insight_slim.py

@@ -6,9 +6,11 @@
 import plotly.graph_objects as go
 from sklearn.preprocessing import StandardScaler
 
-from cities.utils.cleaning_utils import (revert_prediction_df,
-                                         revert_standardize_and_scale_scaler,
-                                         sigmoid)
+from cities.utils.cleaning_utils import (
+    revert_prediction_df,
+    revert_standardize_and_scale_scaler,
+    sigmoid,
+)
 from cities.utils.data_grabber import DataGrabber, find_repo_root
 from cities.utils.percentiles import transformed_intervention_from_percentile
 

cities/queries/fips_query.py

@@ -2,12 +2,18 @@
 import pandas as pd
 import plotly.graph_objects as go
 
-from cities.utils.data_grabber import (DataGrabber, MSADataGrabber,
-                                       check_if_tensed,
-                                       list_available_features)
-from cities.utils.similarity_utils import (compute_weight_array,
-                                           generalized_euclidean_distance,
-                                           plot_weights, slice_with_lag)
+from cities.utils.data_grabber import (
+    DataGrabber,
+    MSADataGrabber,
+    check_if_tensed,
+    list_available_features,
+)
+from cities.utils.similarity_utils import (
+    compute_weight_array,
+    generalized_euclidean_distance,
+    plot_weights,
+    slice_with_lag,
+)
 
 # from scipy.spatial import distance
 

cities/utils/cleaning_pipeline.py

@@ -1,34 +1,36 @@
-from cities.utils.cleaning_scripts.clean_age_composition import \
-    clean_age_composition
+from cities.utils.cleaning_scripts.clean_age_composition import clean_age_composition
 from cities.utils.cleaning_scripts.clean_burdens import clean_burdens
-from cities.utils.cleaning_scripts.clean_ethnic_composition import \
-    clean_ethnic_composition
-from cities.utils.cleaning_scripts.clean_ethnic_composition_ma import \
-    clean_ethnic_composition_ma
+from cities.utils.cleaning_scripts.clean_ethnic_composition import (
+    clean_ethnic_composition,
+)
+from cities.utils.cleaning_scripts.clean_ethnic_composition_ma import (
+    clean_ethnic_composition_ma,
+)
 from cities.utils.cleaning_scripts.clean_gdp import clean_gdp
 from cities.utils.cleaning_scripts.clean_gdp_ma import clean_gdp_ma
 from cities.utils.cleaning_scripts.clean_hazard import clean_hazard
-from cities.utils.cleaning_scripts.clean_homeownership import \
-    clean_homeownership
-from cities.utils.cleaning_scripts.clean_income_distribution import \
-    clean_income_distribution
+from cities.utils.cleaning_scripts.clean_homeownership import clean_homeownership
+from cities.utils.cleaning_scripts.clean_income_distribution import (
+    clean_income_distribution,
+)
 from cities.utils.cleaning_scripts.clean_industry import clean_industry
 from cities.utils.cleaning_scripts.clean_industry_ma import clean_industry_ma
 from cities.utils.cleaning_scripts.clean_industry_ts import clean_industry_ts
 from cities.utils.cleaning_scripts.clean_population import clean_population
-from cities.utils.cleaning_scripts.clean_population_density import \
-    clean_population_density
-from cities.utils.cleaning_scripts.clean_population_ma import \
-    clean_population_ma
-from cities.utils.cleaning_scripts.clean_spending_commerce import \
-    clean_spending_commerce
+from cities.utils.cleaning_scripts.clean_population_density import (
+    clean_population_density,
+)
+from cities.utils.cleaning_scripts.clean_population_ma import clean_population_ma
+from cities.utils.cleaning_scripts.clean_spending_commerce import (
+    clean_spending_commerce,
+)
 from cities.utils.cleaning_scripts.clean_spending_HHS import clean_spending_HHS
-from cities.utils.cleaning_scripts.clean_spending_transportation import \
-    clean_spending_transportation
+from cities.utils.cleaning_scripts.clean_spending_transportation import (
+    clean_spending_transportation,
+)
 from cities.utils.cleaning_scripts.clean_transport import clean_transport
 from cities.utils.cleaning_scripts.clean_unemployment import clean_unemployment
-from cities.utils.cleaning_scripts.clean_urbanicity_ma import \
-    clean_urbanicity_ma
+from cities.utils.cleaning_scripts.clean_urbanicity_ma import clean_urbanicity_ma
 from cities.utils.cleaning_scripts.clean_urbanization import clean_urbanization
 from cities.utils.cleaning_scripts.cleaning_poverty import clean_poverty
 

cities/utils/cleaning_scripts/clean_burdens.py

@@ -1,8 +1,7 @@
 import numpy as np
 import pandas as pd
 
-from cities.utils.clean_variable import (VariableCleaner,
-                                         communities_tracts_to_counties)
+from cities.utils.clean_variable import VariableCleaner, communities_tracts_to_counties
 from cities.utils.data_grabber import DataGrabber, find_repo_root
 
 root = find_repo_root()

cities/utils/cleaning_scripts/clean_hazard.py

@@ -1,8 +1,7 @@
 import numpy as np
 import pandas as pd
 
-from cities.utils.clean_variable import (VariableCleaner,
-                                         communities_tracts_to_counties)
+from cities.utils.clean_variable import VariableCleaner, communities_tracts_to_counties
 from cities.utils.data_grabber import DataGrabber, find_repo_root
 
 root = find_repo_root()

cities/utils/cleaning_scripts/clean_health.py

@@ -1,8 +1,7 @@
 import numpy as np
 import pandas as pd
 
-from cities.utils.clean_variable import (VariableCleaner,
-                                         communities_tracts_to_counties)
+from cities.utils.clean_variable import VariableCleaner, communities_tracts_to_counties
 from cities.utils.data_grabber import DataGrabber, find_repo_root
 
 root = find_repo_root()

cities/utils/percentiles.py

@@ -3,8 +3,7 @@
 import dill as dill
 import numpy as np
 
-from cities.utils.data_grabber import (DataGrabber, find_repo_root,
-                                       list_interventions)
+from cities.utils.data_grabber import DataGrabber, find_repo_root, list_interventions
 
 
 def export_sorted_interventions():

cities/utils/years_available_pipeline.py

@@ -3,8 +3,7 @@
 import dill
 
 from cities.modeling.modeling_utils import prep_wide_data_for_inference
-from cities.utils.data_grabber import (find_repo_root, list_interventions,
-                                       list_outcomes)
+from cities.utils.data_grabber import find_repo_root, list_interventions, list_outcomes
 
 root = find_repo_root()
 interventions = list_interventions()

docs/guides/counterfactual-explained.ipynb

@@ -21,10 +21,14 @@
     "import seaborn as sns\n",
     "\n",
     "from cities.queries.causal_insight_slim import CausalInsightSlim\n",
-    "from cities.utils.data_grabber import (DataGrabber, find_repo_root,\n",
-    "                                       list_available_features,\n",
-    "                                       list_interventions, list_outcomes,\n",
-    "                                       list_tensed_features)\n",
+    "from cities.utils.data_grabber import (\n",
+    "    DataGrabber,\n",
+    "    find_repo_root,\n",
+    "    list_available_features,\n",
+    "    list_interventions,\n",
+    "    list_outcomes,\n",
+    "    list_tensed_features,\n",
+    ")\n",
     "\n",
     "root = find_repo_root()"
    ]

docs/guides/similarity_demo.ipynb

@@ -11,10 +11,15 @@
     "import os\n",
     "\n",
     "from cities.queries.fips_query import FipsQuery\n",
+    "\n",
     "# proper imports\n",
-    "from cities.utils.data_grabber import (DataGrabber, list_available_features,\n",
-    "                                       list_interventions, list_outcomes,\n",
-    "                                       list_tensed_features)\n",
+    "from cities.utils.data_grabber import (\n",
+    "    DataGrabber,\n",
+    "    list_available_features,\n",
+    "    list_interventions,\n",
+    "    list_outcomes,\n",
+    "    list_tensed_features,\n",
+    ")\n",
     "\n",
     "smoke_test = \"CI\" in os.environ\n",
     "smoke_test = True\n",

scripts/clean.sh

@@ -1,11 +1,11 @@
 #!/bin/bash
 set -euxo pipefail
 
-isort cities/ tests/
-#black cities/ tests/
+#isort cities/ tests/
+black cities/ tests/
 autoflake --remove-all-unused-imports --in-place --recursive ./cities ./tests
 
 nbqa autoflake --remove-all-unused-imports --recursive --in-place docs/guides/ 
-nbqa isort docs/guides/
-#nbqa black docs/guides/
+#nbqa isort docs/guides/
+nbqa black docs/guides/
 

scripts/lint.sh

@@ -2,12 +2,11 @@
 set -euxo pipefail
 
 mypy --ignore-missing-imports cities/
-isort --check --diff cities/ tests/
-#black --check cities/ tests/
+#isort --check --diff cities/ tests/
+black --check cities/ tests/
 flake8 cities/ tests/ --ignore=E203,W503 --max-line-length=127
 
 
-
 nbqa autoflake -v --recursive --check docs/guides/
-nbqa isort --check  docs/guides/
-#nbqa black --check docs/guides/
+#nbqa isort --check  docs/guides/
+nbqa black --check docs/guides/

tests/test_data_grabber.py

@@ -2,10 +2,14 @@
 
 import numpy as np
 
-from cities.utils.data_grabber import (DataGrabber, MSADataGrabber,
-                                       list_available_features,
-                                       list_interventions, list_outcomes,
-                                       list_tensed_features)
+from cities.utils.data_grabber import (
+    DataGrabber,
+    MSADataGrabber,
+    list_available_features,
+    list_interventions,
+    list_outcomes,
+    list_tensed_features,
+)
 
 features = list_available_features()
 features_msa = list_available_features("msa")

tests/test_data_grabber_sql.py

@@ -4,9 +4,14 @@
 import pytest
 from sqlalchemy import MetaData, create_engine
 
-from cities.utils.data_grabber import (DataGrabberCSV, DataGrabberDB,
-                                       MSADataGrabberCSV, find_repo_root,
-                                       list_available_features, list_csvs)
+from cities.utils.data_grabber import (
+    DataGrabberCSV,
+    DataGrabberDB,
+    MSADataGrabberCSV,
+    find_repo_root,
+    list_available_features,
+    list_csvs,
+)
 
 root = find_repo_root()
 

tests/test_grouped_predictions.py

@@ -2,8 +2,7 @@
 
 from pandas.testing import assert_frame_equal
 
-from cities.queries.causal_insight_slim import \
-    CausalInsightSlim as CausalInsight
+from cities.queries.causal_insight_slim import CausalInsightSlim as CausalInsight
 from cities.utils.data_grabber import list_interventions, list_outcomes
 
 

tests/test_inference.py

@@ -6,8 +6,7 @@
 
 from cities.modeling.model_interactions import InteractionsModel
 from cities.queries.causal_insight import CausalInsight
-from cities.utils.data_grabber import (find_repo_root, list_interventions,
-                                       list_outcomes)
+from cities.utils.data_grabber import find_repo_root, list_interventions, list_outcomes
 
 os.environ["CUDA_VISIBLE_DEVICES"] = ""
 device = torch.device("cpu")

tests/test_slim.py

@@ -1,9 +1,7 @@
 import random
 
-from cities.queries.causal_insight_slim import \
-    CausalInsightSlim as CausalInsight
-from cities.utils.data_grabber import (DataGrabber, list_interventions,
-                                       list_outcomes)
+from cities.queries.causal_insight_slim import CausalInsightSlim as CausalInsight
+from cities.utils.data_grabber import DataGrabber, list_interventions, list_outcomes
 
 data = DataGrabber()
 data.get_features_wide(["gdp"])

tests/test_sorted_interventions.py

@@ -3,8 +3,7 @@
 import dill
 import numpy as np
 
-from cities.utils.data_grabber import (DataGrabber, find_repo_root,
-                                       list_interventions)
+from cities.utils.data_grabber import DataGrabber, find_repo_root, list_interventions
 from cities.utils.percentiles import transformed_intervention_from_percentile