minor improvements.

Former-commit-id: 3728956d
Former-commit-id: cd3c924c

gms_preprocessing/algorithms/L2B_P.py

@@ -11,6 +11,7 @@ import scipy as sp
 import matplotlib.pyplot as plt
 from sklearn.cluster import KMeans
 from pandas import DataFrame
+from typing import Union  # noqa F401  # flake8 issue
 
 from sklearn.cluster import k_means_  # noqa F401  # flake8 issue
 from geoarray import GeoArray  # noqa F401  # flake8 issue
@@ -33,7 +34,7 @@ class L2B_object(L2A_object):
 
         self.proc_level = 'L2B'
 
-    def spectral_homogenization(self, subset=None, kind='linear'):
+    def spectral_homogenization(self, kind='linear'):
         src_cwls = self.meta_odict['wavelength']
         # FIXME exclude or include thermal bands; respect sorted CWLs in context of LayerBandsAssignment
         tgt_cwls = CFG.usecase.target_CWL
@@ -57,7 +58,7 @@ class L2B_object(L2A_object):
 
     @staticmethod
     def interpolate_cube_linear(arrcube, source_CWLs, target_CWLs):
-        # type: (np.ndarray,list,list) -> np.ndarray
+        # type: (Union[np.ndarray, GeoArray], list, list) -> np.ndarray
         assert arrcube is not None,\
             'L2B_obj.interpolate_cube_linear expects a numpy array as input. Got %s.' % type(arrcube)
         orig_CWLs, target_CWLs = np.array(source_CWLs), np.array(target_CWLs)
@@ -72,7 +73,7 @@ class SpectralResampler(object):
 
     def __init__(self, wvl_src, srf_tgt, wvl_unit='nanometers', logger=None):
         # type: (np.ndarray, SRF, str) -> None
-        """Get an instance of the SpectralResampler1D class.
+        """Get an instance of the SpectralResampler class.
 
         :param wvl_src:     center wavelength positions of the source spectrum
         :param srf_tgt:     spectral response of the target instrument as an instance of io.Input_reader.SRF.
@@ -261,7 +262,7 @@ class KMeansRSImage(object):
         """
         plt.figure(figsize=figsize)
         rows, cols = self.im_clust.shape[:2]
-        plt.imshow(self.im_clust, plt.cm.prism, interpolation='none', extent=(0, cols, rows, 0))
+        plt.imshow(self.im_clust, plt.get_cmap('prism'), interpolation='none', extent=(0, cols, rows, 0))
         plt.show()
 
     def get_random_spectra_from_each_cluster(self, samplesize=50):