[pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

notebooks/boston_housing_hmc.py

@@ -2,7 +2,6 @@
 NN performance on the simple Boston housing dataset.
 """
 
-
 # %%
 import tensorflow as tf
 import tensorflow_probability as tfp

notebooks/data/feature_manifolds.py

@@ -2,7 +2,6 @@
 several dimensional reduction algorithms (t-SNE, UMAP, PCA) to check for clustering.
 """
 
-
 # %%
 import matplotlib.pyplot as plt
 import pandas as pd

notebooks/data/gaultois_stats/gaultois_stats.py

@@ -3,7 +3,6 @@
 the four target columns in the Gaultois database: rho, seebeck, kappa, zT.
 """
 
-
 # %%
 import matplotlib.pyplot as plt
 import numpy as np

notebooks/dropout.py

@@ -4,7 +4,6 @@
 figure of merit (zT).
 """
 
-
 # %%
 import matplotlib.pyplot as plt
 

notebooks/hmc.py

@@ -3,7 +3,6 @@
 Hamiltonian Monte Carlo (HMC).
 """
 
-
 # %%
 from functools import partial
 

notebooks/leaderboard/cv.py

@@ -1,6 +1,5 @@
 """Cross-validated benchmarks."""
 
-
 # %%
 import pickle
 from functools import partial

notebooks/leaderboard/mnf_vs_rf/mnf_vs_rf.py

@@ -5,7 +5,6 @@
 automatminer-generated subselection of magpie features.
 """
 
-
 # %%
 import os
 

notebooks/random_forest.py

@@ -4,7 +4,6 @@
 of merit (zT).
 """
 
-
 # %%
 import numpy as np
 

notebooks/relaxation_time.py

@@ -2,7 +2,6 @@
 to experimentally non-measured temperatures.
 """
 
-
 # %%
 import matplotlib.pyplot as plt
 import numpy as np

notebooks/screen/dft.py

@@ -3,7 +3,6 @@
 risk management. See src/notsbooks/screen/random_forest.py for details.
 """
 
-
 # %%
 import pandas as pd
 from matplotlib import pyplot as plt

notebooks/screen/mnf_magpie/mnf_magpie_screen.py

@@ -2,7 +2,6 @@
 for viable thermoelectrics.
 """
 
-
 # %%
 import os
 

notebooks/screen/random_forest_magpie/random_forest.py

@@ -3,7 +3,6 @@
 regression.
 """
 
-
 # %%
 import os
 import pickle

thermo/data/transform.py

@@ -7,7 +7,6 @@
 - thermoelectric figure of merit (zT): dimensionless
 """
 
-
 import numpy as np
 import pandas as pd
 

thermo/rf.py

@@ -1,6 +1,7 @@
 """If enough time, compare our implementation
 with https://github.com/CitrineInformatics/lolo.
 """
+
 from collections.abc import Sequence
 from typing import Any
 

thermo/utils/__init__.py

@@ -43,10 +43,12 @@ def predict_multiple_targets(pred_func, X_train, y_train, X_test, y_test=None):
 
     processed = [
         # convert lists and arrays to dataframes, restoring former label names and index
-        pd.DataFrame(np.array(x).T, columns=col_names, index=idx)
-        if isinstance(x[0], np.ndarray)
-        # convert single-entry results (e.g. trained models) to dicts named by label
-        else dict(zip(col_names, x))
+        (
+            pd.DataFrame(np.array(x).T, columns=col_names, index=idx)
+            if isinstance(x[0], np.ndarray)
+            # convert single-entry results (e.g. trained models) to dicts named by label
+            else dict(zip(col_names, x))
+        )
         # transpose results so first dim is different result types (y_pred, y_var, etc.)
         # where before first dim was different targets
         for x in zip(*results)

thermo/utils/decorators.py

@@ -12,8 +12,12 @@ def wrapped_function(*args, **kwargs):
             try:
                 return func(*args, **kwargs)
             except KeyboardInterrupt:
-                handler() if handler else print(
-                    f"\nDetected KeyboardInterrupt: Aborting call to {func.__name__}"
+                (
+                    handler()
+                    if handler
+                    else print(
+                        f"\nDetected KeyboardInterrupt: Aborting call to {func.__name__}"
+                    )
                 )
 
         return wrapped_function