remove pylint remains

ms2deepscore/MS2DeepScoreMonteCarlo.py

@@ -76,7 +76,6 @@ def __init__(self, model,
             Set to True to monitor the embedding creating with a progress bar.
             Default is False.
         """
-        # pylint: disable=too-many-arguments
         self.model = model
         if self.model.encoder.dropout.p == 0:
             raise TypeError("Monte Carlo Dropout is not supposed to be used with a model where dropout-rate=0.")

ms2deepscore/benchmarking/plot_ridgeline.py

@@ -30,8 +30,6 @@ def create_combined_ridgeline_plot(reference_scores,
     compare_score_name
         Label string. The default is "MS2DeepScore".
     """
-    # pylint: disable=too-many-arguments
-
     histograms, used_bins, _, _ = calculate_histograms(reference_scores, comparison_scores,
                                                        n_bins, min_resolution, max_resolution)
 
@@ -114,8 +112,8 @@ def score_histogram(scores, n_bins, ax=None, ylabel="scores"):
 
 
 def calculate_histograms(reference_scores, comparison_scores, n_bins=10, min_resolution=20, max_resolution=100):
-    """Calcualte a series of histograms, one for every bin."""
-    # pylint: disable=too-many-locals
+    """Calcualte a series of histograms, one for every bin.
+    """
     def get_hist_bins(resolution):
         hist_bins = np.linspace(0, 1, resolution)
         hist_bins = np.concatenate((hist_bins, np.array([2.0])))

ms2deepscore/tensorize_spectra.py

@@ -33,7 +33,6 @@ def tensorize_spectra(
 @numba.jit(nopython=True)
 def vectorize_spectrum(mz_array, intensities_array, min_mz, max_mz, mz_bin_width, intensity_scaling):
     """Fast function to convert mz and intensity arrays into dense spectrum vector."""
-    # pylint: disable=too-many-arguments
     num_bins = int((max_mz - min_mz) / mz_bin_width)
     vector = np.zeros((num_bins))
     for mz, intensity in zip(mz_array, intensities_array):