Merge pull request #99 from erinwild-rubikloud/erinwild/cosmetic_cleanup

Delete .pyc and .directory files + linting of whitespace and imports

.gitignore

@@ -1 +1,124 @@
-
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+pip-wheel-metadata/
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+.hypothesis/
+.pytest_cache/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# IPython
+profile_default/
+ipython_config.py
+
+# pyenv
+.python-version
+
+# celery beat schedule file
+celerybeat-schedule
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# VS Code project settings
+.vscode/
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+.dmypy.json
+dmypy.json
+
+# Pyre type checker
+.pyre/
+
+# macOS
+.DS_Store
+
+.directory

README.md

@@ -30,9 +30,9 @@ python setup.py install
 
 
 Many thanks to @sebastiandev, the library is now standardized in a pythonic tradition. Below you can see some basic examples, showing how to use the library.
-But I recommend you to go through the codes. There are several functionalities already implemented, but not documented. I would be very happy to add your new examples here. 
+But I recommend you to go through the codes. There are several functionalities already implemented, but not documented. I would be very happy to add your new examples here.
 
-[Basice Example](https://gist.github.com/sevamoo/035c56e7428318dd3065013625f12a11)
+[Basic Example](https://gist.github.com/sevamoo/035c56e7428318dd3065013625f12a11)
 
 ### Citation
 
@@ -42,7 +42,7 @@ There is no published paper about this library. However if possible, please cite
 Main Contributers:
 Vahid Moosavi @sevamoo
 Sebastian Packmann @sebastiandev
-Iván Vallés @ivallesp 
+Iván Vallés @ivallesp
 ```
 
 
@@ -51,5 +51,5 @@ For more information, you can contact me via [email protected] or [email protected].
 
 
 
-Thanks a lot. 
+Thanks a lot.
 Best Vahid Moosavi

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
 
 
@@ -13,13 +12,14 @@ class InvalidNodeIndexError(Exception):
 class InvalidMapsizeError(Exception):
     pass
 
+
 def generate_hex_lattice(n_rows, n_columns):
     x_coord = []
     y_coord = []
     for i in range(n_rows):
         for j in range(n_columns):
-            x_coord.append(i*1.5)
-            y_coord.append(np.sqrt(2/3)*(2*j+(1+i)%2))
+            x_coord.append(i * 1.5)
+            y_coord.append(np.sqrt(2 / 3) * (2 * j + (1 + i) % 2))
     coordinates = np.column_stack([x_coord, y_coord])
     return coordinates
 
@@ -35,13 +35,13 @@ def __init__(self, mapsize, lattice='rect'):
         elif 1 == len(mapsize):
             _size = [1, mapsize[0]]
             print('input was considered as the numbers of nodes')
-            print('map size is [{dlen},{dlen}]'.format(dlen=int(mapsize[0]/2)))
+            print('map size is [{dlen},{dlen}]'.format(dlen=int(mapsize[0] / 2)))
         else:
             raise InvalidMapsizeError(
                 "Mapsize is expected to be a 2 element list or a single int")
 
         self.mapsize = _size
-        self.nnodes = mapsize[0]*mapsize[1]
+        self.nnodes = mapsize[0] * mapsize[1]
         self.matrix = np.asarray(self.mapsize)
         self.initialized = False
 
@@ -59,7 +59,7 @@ def random_initialization(self, data):
         """
         mn = np.tile(np.min(data, axis=0), (self.nnodes, 1))
         mx = np.tile(np.max(data, axis=0), (self.nnodes, 1))
-        self.matrix = mn + (mx-mn)*(np.random.rand(self.nnodes, data.shape[1]))
+        self.matrix = mn + (mx - mn) * (np.random.rand(self.nnodes, data.shape[1]))
         self.initialized = True
 
     @timeit()
@@ -108,8 +108,8 @@ def pca_linear_initialization(self, data):
 
         mx = np.max(coord, axis=0)
         mn = np.min(coord, axis=0)
-        coord = (coord - mn)/(mx-mn)
-        coord = (coord - .5)*2
+        coord = (coord - mn) / (mx - mn)
+        coord = (coord - .5) * 2
         me = np.mean(data, 0)
         data = (data - me)
         tmp_matrix = np.tile(me, (self.nnodes, 1))
@@ -122,11 +122,11 @@ def pca_linear_initialization(self, data):
         eigvec = pca.components_
         eigval = pca.explained_variance_
         norms = np.sqrt(np.einsum('ij,ij->i', eigvec, eigvec))
-        eigvec = ((eigvec.T/norms)*eigval).T
+        eigvec = ((eigvec.T / norms) * eigval).T
 
         for j in range(self.nnodes):
             for i in range(eigvec.shape[0]):
-                tmp_matrix[j, :] = tmp_matrix[j, :] + coord[j, i]*eigvec[i, :]
+                tmp_matrix[j, :] = tmp_matrix[j, :] + coord[j, i] * eigvec[i, :]
 
         self.matrix = np.around(tmp_matrix, decimals=6)
         self.initialized = True
@@ -169,7 +169,7 @@ def _rect_dist(self, node_ind):
         dist = None
 
         # bmu should be an integer between 0 to no_nodes
-        if 0 <= node_ind <= (rows*cols):
+        if 0 <= node_ind <= (rows * cols):
             node_col = int(node_ind % cols)
             node_row = int(node_ind / cols)
         else:
@@ -179,7 +179,7 @@ def _rect_dist(self, node_ind):
         if rows > 0 and cols > 0:
             r = np.arange(0, rows, 1)[:, np.newaxis]
             c = np.arange(0, cols, 1)
-            dist2 = (r-node_row)**2 + (c-node_col)**2
+            dist2 = (r - node_row)**2 + (c - node_col)**2
 
             dist = dist2.ravel()
         else:

sompy/neighborhood.py

@@ -1,7 +1,8 @@
-import numpy as np
 import inspect
 import sys
 
+import numpy as np
+
 small = .000000000001
 
 
@@ -24,7 +25,7 @@ class GaussianNeighborhood(object):
 
     @staticmethod
     def calculate(distance_matrix, radius, dim):
-        return np.exp(-1.0*distance_matrix/(2.0*radius**2)).reshape(dim, dim)
+        return np.exp(-1.0 * distance_matrix / (2.0 * radius**2)).reshape(dim, dim)
 
     def __call__(self, *args, **kwargs):
         return self.calculate(*args)
@@ -38,7 +39,7 @@ class BubbleNeighborhood(object):
     def calculate(distance_matrix, radius, dim):
         def l(a, b):
             c = np.zeros(b.shape)
-            c[a-b >= 0] = 1
+            c[a - b >= 0] = 1
             return c
 
         return l(radius,

sompy/normalization.py

@@ -1,6 +1,7 @@
-import numpy as np
-import sys
 import inspect
+import sys
+
+import numpy as np
 
 
 class NormalizerFactory(object):
@@ -37,12 +38,12 @@ def _mean_and_standard_dev(self, data):
     def normalize(self, data):
         me, st = self._mean_and_standard_dev(data)
         st[st == 0] = 1  # prevent: when sd = 0, normalized result = NaN
-        return (data-me)/st
+        return (data - me) / st
 
     def normalize_by(self, raw_data, data):
         me, st = self._mean_and_standard_dev(raw_data)
         st[st == 0] = 1  # prevent: when sd = 0, normalized result = NaN
-        return (data-me)/st
+        return (data - me) / st
 
     def denormalize_by(self, data_by, n_vect):
         me, st = self._mean_and_standard_dev(data_by)