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chore(Linter): reformatted code with black (#211)

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* chore(Linter): reformatted code with black

* Create black.yaml
This commit is contained in:
Mark Aron Szulyovszky
2022-02-17 19:22:17 +01:00
committed by GitHub
parent f3fee4a4e1
commit 8dd2d88740
101 changed files with 2595 additions and 2319 deletions
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utils/helpers.py
+33 -11
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@@ -5,11 +5,19 @@ import string
import random
from itertools import dropwhile
def get_files_from_dir(path: str) -> list[str]:
return [f for f in os.listdir(path) if os.path.isfile(os.path.join(path,f)) and not f.startswith('.')]
return [
f
for f in os.listdir(path)
if os.path.isfile(os.path.join(path, f)) and not f.startswith(".")
]
def get_first_valid_return_index(series: pd.Series) -> int:
double_nested_results = np.where(np.logical_and(series != 0, np.logical_not(pd.isna(series))))
double_nested_results = np.where(
np.logical_and(series != 0, np.logical_not(pd.isna(series)))
)
if len(double_nested_results) == 0:
return 0
nested_result = double_nested_results[0]
@@ -17,42 +25,56 @@ def get_first_valid_return_index(series: pd.Series) -> int:
return 0
return nested_result[0]
def get_last_non_na_index(series: pd.Series, index: int) -> int:
return next(dropwhile(lambda x: pd.isna(x[1]), enumerate(reversed(series[:index+1]))))[0]
return next(
dropwhile(lambda x: pd.isna(x[1]), enumerate(reversed(series[: index + 1])))
)[0]
def flatten(list_of_lists: list) -> list:
return [item for sublist in list_of_lists for item in sublist]
def weighted_average(df: pd.DataFrame, weights_source: str) -> pd.Series:
if df.shape[1] == 0:
return df
mean_df = df.iloc[:,0]
mean_df = df.iloc[:, 0]
weights = df.loc[weights_source]
for i, row in df.iterrows():
if i == weights_source: continue
if i == weights_source:
continue
mean_df.loc[i] = (row * weights).sum() / df.loc[weights_source].sum()
return mean_df
def drop_columns_if_exist(df: pd.DataFrame, columns: list) -> pd.DataFrame:
for column in columns:
if column in df.columns:
df = df.drop(column, axis=1)
return df
def random_string(n: int) -> str:
return ''.join(random.choices(string.ascii_uppercase + string.digits, k=n))
return "".join(random.choices(string.ascii_uppercase + string.digits, k=n))
def equal_except_nan(row: pd.Series):
if np.isnan(row.iloc[0]) or np.isnan(row.iloc[1]):
return np.nan
if row.iloc[0] == row.iloc[1]:
return 1.
return 1.0
else:
return 0.
return 0.0
def drop_until_first_valid_index(df: pd.DataFrame, series: pd.Series) -> tuple[pd.DataFrame, pd.Series]:
first_valid_index = max(get_first_valid_return_index(df.iloc[:,0]), get_first_valid_return_index(series))
return df.iloc[first_valid_index:], series.iloc[first_valid_index:]
def drop_until_first_valid_index(
df: pd.DataFrame, series: pd.Series
) -> tuple[pd.DataFrame, pd.Series]:
first_valid_index = max(
get_first_valid_return_index(df.iloc[:, 0]),
get_first_valid_return_index(series),
)
return df.iloc[first_valid_index:], series.iloc[first_valid_index:]