Remove unused imports and Cosmetics

invisible_cities/core/fit_functions.py

@@ -158,8 +158,8 @@ def fit(func, x, y, seed=(), fit_range=None, **kwargs):
     Returns
     -------
     fitted_fun : FitFunction
-    Extended function containing fit parameters (fitf, vals, errors, chi2, pval,
-    cov) and full_output from curve_fit and leastsq (infodict, mesg and ier).
+        Extended function containing fit parameters (fitf, vals, errors, chi2, pval,
+        cov) and full_output from curve_fit and leastsq (infodict, mesg and ier).
 
 
     Examples

invisible_cities/core/fit_functions_test.py

@@ -18,7 +18,6 @@
 from hypothesis.strategies import floats
 from . testing_utils       import float_arrays
 from . testing_utils       import random_length_float_arrays
-from .. evm.ic_containers  import FitFunction
 
 from .                import core_functions as core
 from .                import  fit_functions as fitf

invisible_cities/reco/icaro_components.py

@@ -1,15 +1,13 @@
 import numpy        as np
 import pandas       as pd
 
-from   typing              import Optional
 from ..types.symbols       import KrFitFunction
 from .. evm.ic_containers  import FitFunction
 from .. core.fit_functions import polynom, expo
 
 
 def lin_seed(x : np.array,
              y : np.array):
-
     '''
     Estimate the seed for a linear fit.
 
@@ -44,7 +42,6 @@ def lin_seed(x : np.array,
 
 def expo_seed(x   : np.array,
               y   : np.array):
-
     '''
     Estimate the seed for an exponential fit.
 
@@ -62,7 +59,6 @@ def expo_seed(x   : np.array,
     '''
 
     x, y = zip(*sorted(zip(x, y)))
-
     const = y[0]
 
     if const <= 0 or y[-1] <= 0:
@@ -76,16 +72,9 @@ def expo_seed(x   : np.array,
 
 def select_fit_variables(fittype : KrFitFunction,
                          dst     : pd.DataFrame):
-
     '''
     Select the data for fitting based on the specified fit type.
 
-    NOTES: Since x axis (DT) is never altered, maybe we can just
-    return the y values. However, when we implement the binned fit,
-    the profile could be done here (?) so it would make sense to
-    always provide both x and y. We could rename parameters and have
-    fittype (binned / unbinned) and fitfunction (lin, expo, log-lin...)
-
     Parameters
     ----------
     fittype : KrFitFunction
@@ -133,7 +122,6 @@ def get_function_and_seed_lt(fittype : KrFitFunction):
 
 
 def transform_parameters(fit_output : FitFunction):
-
     '''
     Transform the parameters obtained from the fitting output into EO and LT.
     When using log_lin fit, we need to convert the intermediate variables into

invisible_cities/reco/icaro_components_test.py

@@ -47,6 +47,7 @@ def sample_df():
         'S2e': [50, 45, 42, 41, 41]
     }
     return pd.DataFrame(data)
+
 def test_select_fit_variables(sample_df):
 
     x_linear,  y_linear  = icarcomp.select_fit_variables(KrFitFunction.linear,  sample_df)