Sort and remove unused mports

sompy/__init__.py

@@ -1,7 +1,6 @@
-
 from logging.config import dictConfig
-import matplotlib
 
+import matplotlib
 #matplotlib.use('Agg')  # Use whatever backend is available
 
 dictConfig({
@@ -25,7 +24,5 @@
     }
 })
 
-
-
 from .sompy import SOMFactory
 from .visualization import *

sompy/codebook.py

@@ -1,8 +1,7 @@
 import numpy as np
 import scipy as sp
-
 from sklearn.decomposition import PCA
-#from sklearn.decomposition import RandomizedPCA# (randomizedpca is deprecated)
+
 from .decorators import timeit
 
 
@@ -13,6 +12,7 @@ class InvalidNodeIndexError(Exception):
 class InvalidMapsizeError(Exception):
     pass
 
+
 def generate_hex_lattice(n_rows, n_columns):
     x_coord = []
     y_coord = []

sompy/neighborhood.py

@@ -1,7 +1,8 @@
-import numpy as np
 import inspect
 import sys
 
+import numpy as np
+
 small = .000000000001
 
 

sompy/normalization.py

@@ -1,6 +1,7 @@
-import numpy as np
-import sys
 import inspect
+import sys
+
+import numpy as np
 
 
 class NormalizerFactory(object):

sompy/sompy.py

@@ -8,23 +8,21 @@
 # Contributor: Sebastian Packmann ([email protected])
 
 
-import tempfile
-import os
 import itertools
 import logging
+import os
+import tempfile
+from multiprocessing import cpu_count
+from multiprocessing.dummy import Pool
+from time import time
 
 import numpy as np
-
-from time import time
-from multiprocessing.dummy import Pool
-from multiprocessing import cpu_count
 from scipy.sparse import csr_matrix
 from sklearn import neighbors
-from sklearn.externals.joblib import Parallel, delayed, load, dump
-import sys
+from sklearn.externals.joblib import dump, load
 
-from .decorators import timeit
 from .codebook import Codebook
+from .decorators import timeit
 from .neighborhood import NeighborhoodFactory
 from .normalization import NormalizerFactory
 

sompy/visualization/bmuhits.py

@@ -5,9 +5,7 @@
 from matplotlib import pyplot as plt
 
 from .mapview import MapView
-
-from sompy.visualization.plot_tools import plot_hex_map
-
+from .plot_tools import plot_hex_map
 
 
 class BmuHitsView(MapView):

sompy/visualization/dotmap.py

@@ -1,6 +1,7 @@
-from .view import MatplotView
-from matplotlib import pyplot as plt
 import numpy as np
+from matplotlib import pyplot as plt
+
+from .view import MatplotView
 
 
 class DotMapView(MatplotView):

sompy/visualization/histogram.py

@@ -1,8 +1,8 @@
-from .view import MatplotView
-from matplotlib import pyplot as plt
-from matplotlib import cm
-from matplotlib.colors import LogNorm
 import numpy as np
+from matplotlib import cm, pyplot as plt
+from matplotlib.colors import LogNorm
+
+from .view import MatplotView
 
 
 class Hist2d(MatplotView):

sompy/visualization/hitmap.py

@@ -1,10 +1,8 @@
-from .view import MatplotView
-from sompy.visualization.plot_tools import plot_hex_map
-from matplotlib import pyplot as plt
 import numpy as np
+from matplotlib import pyplot as plt
 
 from .mapview import MapView
-
+from .plot_tools import plot_hex_map
 
 
 class HitMapView(MapView):

sompy/visualization/mapview.py

@@ -1,10 +1,9 @@
-from matplotlib import colors
 import matplotlib
+import numpy as np
+from matplotlib import pyplot as plt
 
-from sompy.visualization.plot_tools import plot_hex_map
+from .plot_tools import plot_hex_map
 from .view import MatplotView
-from matplotlib import pyplot as plt
-import numpy as np
 
 
 class MapView(MatplotView):
@@ -188,5 +187,3 @@ def show(self, som, what='codebook', which_dim='all', cmap=None,
             plt.plot(mp, '-k', linewidth=0.8)
 
         #plt.show()
-
-

sompy/visualization/umatrix.py

@@ -1,9 +1,11 @@
-from .view import MatplotView
-from matplotlib import pyplot as plt
-from pylab import imshow, contour
 from math import sqrt
+
 import numpy as np
+import pylab
 import scipy
+from matplotlib import pyplot as plt
+
+from .view import MatplotView
 
 
 class UMatrixView(MatplotView):
@@ -36,16 +38,16 @@ def show(self, som, distance2=1, row_normalized=False, show_data=True,
         coord = som.bmu_ind_to_xy(proj)
 
         self._fig, ax = plt.subplots(1, 1)
-        imshow(umat, cmap=plt.cm.get_cmap('RdYlBu_r'), alpha=1)
+        pylab.imshow(umat, cmap=plt.cm.get_cmap('RdYlBu_r'), alpha=1)
 
         if contooor:
             mn = np.min(umat.flatten())
             mx = np.max(umat.flatten())
             std = np.std(umat.flatten())
             md = np.median(umat.flatten())
             mx = md + 0 * std
-            contour(umat, np.linspace(mn, mx, 15), linewidths=0.7,
-                    cmap=plt.cm.get_cmap('Blues'))
+            pylab.contour(umat, np.linspace(mn, mx, 15), linewidths=0.7,
+                          cmap=plt.cm.get_cmap('Blues'))
 
         if show_data:
             plt.scatter(coord[:, 1], coord[:, 0], s=2, alpha=1., c='Gray',
@@ -76,7 +78,7 @@ def show(self, som, distance2=1, row_normalized=False, show_data=True,
             # 'Laplacian of Gaussian'
             blobs = blob_log(image, max_sigma=5, num_sigma=4, threshold=.152)
             blobs[:, 2] = blobs[:, 2] * sqrt(2)
-            imshow(umat, cmap=plt.cm.get_cmap('RdYlBu_r'), alpha=1)
+            pylab.imshow(umat, cmap=plt.cm.get_cmap('RdYlBu_r'), alpha=1)
             sel_points = list()
 
             for blob in blobs: