Merge branch 'pr/515' into merge-515

Conflicts:
	scipy/io/wavfile.py
	scipy/lib/blas/setup.py
	scipy/optimize/tests/test_optimize.py
	scipy/sparse/compressed.py
	scipy/sparse/tests/test_base.py
	scipy/stats/tests/test_fit.py

scipy/__init__.py

@@ -84,7 +84,7 @@
 majver, minver = [float(i) for i in _num.version.version.split('.')[:2]]
 if majver < 1 or (majver == 1 and minver < 5):
     import warnings
-    warnings.warn("Numpy 1.5.0 or above is recommended for this version of " \
+    warnings.warn("Numpy 1.5.0 or above is recommended for this version of "
                   "scipy (detected version %s)" % _num.version.version,
                   UserWarning)
 

scipy/_build_utils/_fortran.py

@@ -1,6 +1,7 @@
 import re
 import sys
 
+
 def _uses_veclib(info):
     r_accelerate = re.compile("Accelerate|vecLib")
 
@@ -11,6 +12,7 @@ def _uses_veclib(info):
 
     return False
 
+
 def _uses_mkl(info):
     r_mkl = re.compile("mkl_core")
 
@@ -21,6 +23,7 @@ def _uses_mkl(info):
 
     return False
 
+
 def needs_g77_abi_wrapper(info):
     """Returns true if g77 ABI wrapper must be used."""
     if _uses_veclib(info):
@@ -30,4 +33,3 @@ def needs_g77_abi_wrapper(info):
         return True
     else:
         return False
-

scipy/cluster/doc/ex1.py

@@ -3,6 +3,7 @@
 from scipy import *
 from scipy.cluster import vq
 
+
 def cluster_data(data,cluster_cnt,iter=20,thresh=1e-5):
     """ Group data into a number of common clusters
 

scipy/cluster/hierarchy.py

@@ -631,7 +631,7 @@ def linkage(y, method='single', metric='euclidean'):
         [y] = _copy_arrays_if_base_present([y])
 
         Z = np.zeros((d - 1, 4))
-        _hierarchy_wrap.linkage_wrap(y, Z, int(d), \
+        _hierarchy_wrap.linkage_wrap(y, Z, int(d),
                                    int(_cpy_non_euclid_methods[method]))
     elif len(s) == 2:
         X = y
@@ -642,7 +642,7 @@ def linkage(y, method='single', metric='euclidean'):
         if method in _cpy_non_euclid_methods:
             dm = distance.pdist(X, metric)
             Z = np.zeros((n - 1, 4))
-            _hierarchy_wrap.linkage_wrap(dm, Z, n, \
+            _hierarchy_wrap.linkage_wrap(dm, Z, n,
                                        int(_cpy_non_euclid_methods[method]))
         elif method in _cpy_euclid_methods:
             if metric != 'euclidean':
@@ -1548,7 +1548,7 @@ def fcluster(Z, t, criterion='inconsistent', depth=2, R=None, monocrit=None):
     return T
 
 
-def fclusterdata(X, t, criterion='inconsistent', \
+def fclusterdata(X, t, criterion='inconsistent',
                  metric='euclidean', depth=2, method='single', R=None):
     """
     Cluster observation data using a given metric.
@@ -1657,6 +1657,7 @@ def leaves_list(Z):
 _drotationsortedkeys = list(_drotation.keys())
 _drotationsortedkeys.sort()
 
+
 def _remove_dups(L):
     """
     Removes duplicates AND preserves the original order of the elements.
@@ -1670,16 +1671,19 @@ def _remove_dups(L):
             L2.append(i)
     return L2
 
+
 def _get_tick_text_size(p):
     for k in _dtextsortedkeys:
         if p <= k:
             return _dtextsizes[k]
 
+
 def _get_tick_rotation(p):
     for k in _drotationsortedkeys:
         if p <= k:
             return _drotation[k]
 
+
 def _plot_dendrogram(icoords, dcoords, ivl, p, n, mh, orientation,
                      no_labels, color_list, leaf_font_size=None,
                      leaf_rotation=None, contraction_marks=None):
@@ -2209,15 +2213,15 @@ def _append_contraction_marks_sub(Z, iv, i, n, contraction_marks):
         _append_contraction_marks_sub(Z, iv, Z[i - n, 1], n, contraction_marks)
 
 
-def _dendrogram_calculate_info(Z, p, truncate_mode, \
-                               color_threshold=np.inf, get_leaves=True, \
-                               orientation='top', labels=None, \
-                               count_sort=False, distance_sort=False, \
-                               show_leaf_counts=False, i=-1, iv=0.0, \
-                               ivl=[], n=0, icoord_list=[], dcoord_list=[], \
-                               lvs=None, mhr=False, \
-                               current_color=[], color_list=[], \
-                               currently_below_threshold=[], \
+def _dendrogram_calculate_info(Z, p, truncate_mode,
+                               color_threshold=np.inf, get_leaves=True,
+                               orientation='top', labels=None,
+                               count_sort=False, distance_sort=False,
+                               show_leaf_counts=False, i=-1, iv=0.0,
+                               ivl=[], n=0, icoord_list=[], dcoord_list=[],
+                               lvs=None, mhr=False,
+                               current_color=[], color_list=[],
+                               currently_below_threshold=[],
                                leaf_label_func=None, level=0,
                                contraction_marks=None,
                                link_color_func=None):
@@ -2683,7 +2687,7 @@ def _leader_identify(tr, T):
         right = tr.get_right()
         lfid = _leader_identify(left, T)
         rfid = _leader_identify(right, T)
-        print('ndid: %d lid: %d lfid: %d rid: %d rfid: %d' \
+        print('ndid: %d lid: %d lfid: %d rid: %d rfid: %d'
               % (tr.get_id(), left.get_id(), lfid, right.get_id(), rfid))
         if lfid != rfid:
             if lfid != -1:

scipy/cluster/setup.py

@@ -10,31 +10,32 @@
 else:
     DEFINE_MACROS = []
 
-def configuration(parent_package = '', top_path = None):
+
+def configuration(parent_package='', top_path=None):
     from numpy.distutils.misc_util import Configuration, get_numpy_include_dirs
     config = Configuration('cluster', parent_package, top_path)
 
     config.add_data_dir('tests')
 
     config.add_extension('_vq',
         sources=[join('src', 'vq_module.c'), join('src', 'vq.c')],
-        include_dirs = [get_numpy_include_dirs()],
+        include_dirs=[get_numpy_include_dirs()],
         define_macros=DEFINE_MACROS)
 
     config.add_extension('_hierarchy_wrap',
         sources=[join('src', 'hierarchy_wrap.c'), join('src', 'hierarchy.c')],
-        include_dirs = [get_numpy_include_dirs()],
+        include_dirs=[get_numpy_include_dirs()],
         define_macros=DEFINE_MACROS)
 
     return config
 
 if __name__ == '__main__':
     from numpy.distutils.core import setup
-    setup(maintainer = "SciPy Developers",
-          author = "Eric Jones",
-          maintainer_email = "[email protected]",
-          description = "Clustering Algorithms (Information Theory)",
-          url = "http://www.scipy.org",
-          license = "SciPy License (BSD Style)",
+    setup(maintainer="SciPy Developers",
+          author="Eric Jones",
+          maintainer_email="[email protected]",
+          description="Clustering Algorithms (Information Theory)",
+          url="http://www.scipy.org",
+          license="SciPy License (BSD Style)",
           **configuration(top_path='').todict()
           )

scipy/cluster/setupscons.py

@@ -3,7 +3,8 @@
 
 from os.path import join
 
-def configuration(parent_package = '', top_path = None):
+
+def configuration(parent_package='', top_path=None):
     from numpy.distutils.misc_util import Configuration, get_numpy_include_dirs
     config = Configuration('cluster', parent_package, top_path)
 
@@ -18,11 +19,11 @@ def configuration(parent_package = '', top_path = None):
 
 if __name__ == '__main__':
     from numpy.distutils.core import setup
-    setup(maintainer = "SciPy Developers",
-          author = "Eric Jones",
-          maintainer_email = "[email protected]",
-          description = "Clustering Algorithms (Information Theory)",
-          url = "http://www.scipy.org",
-          license = "SciPy License (BSD Style)",
+    setup(maintainer="SciPy Developers",
+          author="Eric Jones",
+          maintainer_email="[email protected]",
+          description="Clustering Algorithms (Information Theory)",
+          url="http://www.scipy.org",
+          license="SciPy License (BSD Style)",
           **configuration(top_path='').todict()
           )

scipy/cluster/tests/test_hierarchy.py

@@ -425,6 +425,7 @@ def test_fcluster_maxclusts_4(self):
         T = fcluster(Z, criterion='maxclust', t=4)
         self.assertTrue(is_isomorphic(T, expectedT))
 
+
 class TestLeaders(TestCase):
     def test_leaders_single(self):
         "Tests leaders using a flat clustering generated by single linkage."
@@ -828,7 +829,7 @@ def test_is_monotonic_empty(self):
 
     def test_is_monotonic_1x4(self):
         "Tests is_monotonic(Z) on 1x4 linkage. Expecting True."
-        Z = np.asarray([[0, 1, 0.3, 2]], dtype=np.double);
+        Z = np.asarray([[0, 1, 0.3, 2]], dtype=np.double)
         self.assertTrue(is_monotonic(Z) == True)
 
     def test_is_monotonic_2x4_T(self):
@@ -879,7 +880,7 @@ def test_is_monotonic_tdist_linkage1(self):
     def test_is_monotonic_tdist_linkage2(self):
         "Tests is_monotonic(Z) on clustering generated by single linkage on tdist data set. Perturbing. Expecting False."
         Z = linkage(_ytdist, 'single')
-        Z[2,2]=0.0
+        Z[2,2] = 0.0
         self.assertTrue(is_monotonic(Z) == False)
 
     def test_is_monotonic_iris_linkage(self):

scipy/cluster/tests/test_vq.py

@@ -15,10 +15,10 @@
 from scipy.cluster.vq import kmeans, kmeans2, py_vq, py_vq2, vq, ClusterError
 try:
     from scipy.cluster import _vq
-    TESTC=True
+    TESTC = True
 except ImportError:
     print("== Error while importing _vq, not testing C imp of vq ==")
-    TESTC=False
+    TESTC = False
 
 #Optional:
 # import modules that are located in the same directory as this file.
@@ -39,6 +39,7 @@
 
 LABEL1  = np.array([0, 1, 2, 2, 2, 2, 1, 2, 1, 1, 1])
 
+
 class TestVq(TestCase):
     def test_py_vq(self):
         initc = np.concatenate(([[X[0]], [X[1]], [X[2]]]))
@@ -91,6 +92,7 @@ def test__vq_sametype(self):
             b = a.astype(float)
             assert_raises(ValueError, _vq.vq, a, b)
 
+
 class TestKMean(TestCase):
     def test_large_features(self):
         # Generate a data set with large values, and run kmeans on it to
@@ -111,13 +113,13 @@ def test_large_features(self):
     def test_kmeans_simple(self):
         initc = np.concatenate(([[X[0]], [X[1]], [X[2]]]))
         code = initc.copy()
-        code1 = kmeans(X, code, iter = 1)[0]
+        code1 = kmeans(X, code, iter=1)[0]
 
         assert_array_almost_equal(code1, CODET2)
 
     def test_kmeans_lost_cluster(self):
         """This will cause kmean to have a cluster with no points."""
-        data = np.fromfile(DATAFILE1, sep = ", ")
+        data = np.fromfile(DATAFILE1, sep=", ")
         data = data.reshape((200, 2))
         initk = np.array([[-1.8127404, -0.67128041],
                          [ 2.04621601, 0.07401111],
@@ -139,31 +141,31 @@ def test_kmeans2_simple(self):
         """Testing simple call to kmeans2 and its results."""
         initc = np.concatenate(([[X[0]], [X[1]], [X[2]]]))
         code = initc.copy()
-        code1 = kmeans2(X, code, iter = 1)[0]
-        code2 = kmeans2(X, code, iter = 2)[0]
+        code1 = kmeans2(X, code, iter=1)[0]
+        code2 = kmeans2(X, code, iter=2)[0]
 
         assert_array_almost_equal(code1, CODET1)
         assert_array_almost_equal(code2, CODET2)
 
     def test_kmeans2_rank1(self):
         """Testing simple call to kmeans2 with rank 1 data."""
-        data = np.fromfile(DATAFILE1, sep = ", ")
+        data = np.fromfile(DATAFILE1, sep=", ")
         data = data.reshape((200, 2))
         data1 = data[:, 0]
         data2 = data[:, 1]
 
         initc = data1[:3]
         code = initc.copy()
-        code1 = kmeans2(data1, code, iter = 1)[0]
-        code2 = kmeans2(data1, code, iter = 2)[0]
+        code1 = kmeans2(data1, code, iter=1)[0]
+        code2 = kmeans2(data1, code, iter=2)[0]
 
     def test_kmeans2_rank1_2(self):
         """Testing simple call to kmeans2 with rank 1 data."""
-        data = np.fromfile(DATAFILE1, sep = ", ")
+        data = np.fromfile(DATAFILE1, sep=", ")
         data = data.reshape((200, 2))
         data1 = data[:, 0]
 
-        code1 = kmeans2(data1, 2, iter = 1)
+        code1 = kmeans2(data1, 2, iter=1)
 
     def test_kmeans2_init(self):
         """Testing that kmeans2 init methods work."""

scipy/cluster/tests/vq_test.py

@@ -3,6 +3,7 @@
 import numpy as np
 from scipy.cluster import vq
 
+
 def python_vq(all_data,code_book):
     import time
     t1 = time.time()
@@ -26,6 +27,7 @@ def python_vq(all_data,code_book):
 
     return codes1,dist1
 
+
 def read_data(name):
     f = open(name,'r')
     data = []
@@ -34,6 +36,7 @@ def read_data(name):
     f.close()
     return array(data)
 
+
 def main():
     np.random.seed((1000,1000))
     Ncodes = 40