Merge pull request #6573 from anntzer/cleanups

Some general cleanups

examples/misc/rec_join_demo.py

@@ -11,8 +11,7 @@
 r1 = r[-10:]
 
 # Create a new array
-r2 = np.empty(12, dtype=[('date', '|O4'), ('high', np.float),
-                         ('marker', np.float)])
+r2 = np.empty(12, dtype=[('date', '|O4'), ('high', float), ('marker', float)])
 r2 = r2.view(np.recarray)
 r2.date = r.date[-17:-5]
 r2.high = r.high[-17:-5]

examples/pylab_examples/demo_ribbon_box.py

@@ -121,7 +121,7 @@ def draw(self, renderer, *args, **kwargs):
                                interpolation="bicubic",
                                zorder=0.1,
                                )
-    gradient = np.zeros((2, 2, 4), dtype=np.float)
+    gradient = np.zeros((2, 2, 4), dtype=float)
     gradient[:, :, :3] = [1, 1, 0.]
     gradient[:, :, 3] = [[0.1, 0.3], [0.3, 0.5]]  # alpha channel
     patch_gradient.set_array(gradient)

examples/pylab_examples/dolphin.py

@@ -74,7 +74,7 @@
     vertices.extend([[float(x) for x in y.split(',')] for y in
                      parts[i + 1:i + npoints + 1]])
     i += npoints + 1
-vertices = np.array(vertices, np.float)
+vertices = np.array(vertices, float)
 vertices[:, 1] -= 160
 
 dolphin_path = Path(vertices, codes)

examples/units/units_scatter.py

@@ -15,7 +15,7 @@
 # create masked array
 
 
-xsecs = secs*np.ma.MaskedArray((1, 2, 3, 4, 5, 6, 7, 8), (1, 0, 1, 0, 0, 0, 1, 0), np.float)
+xsecs = secs*np.ma.MaskedArray((1, 2, 3, 4, 5, 6, 7, 8), (1, 0, 1, 0, 0, 0, 1, 0), float)
 #xsecs = secs*np.arange(1,10.)
 
 fig = figure()

lib/matplotlib/axes/_axes.py

@@ -2357,7 +2357,7 @@ def stem(self, *args, **kwargs):
 
         # Try a second one
         try:
-            second = np.asarray(args[0], dtype=np.float)
+            second = np.asarray(args[0], dtype=float)
             x, y = y, second
             args = args[1:]
         except (IndexError, ValueError):
@@ -4739,7 +4739,7 @@ def fill_between(self, x, y1, y2=0, where=None, interpolate=False,
                 continue
 
             N = len(xslice)
-            X = np.zeros((2 * N + 2, 2), np.float)
+            X = np.zeros((2 * N + 2, 2), float)
 
             if interpolate:
                 def get_interp_point(ind):
@@ -4889,7 +4889,7 @@ def fill_betweenx(self, y, x1, x2=0, where=None,
                 continue
 
             N = len(yslice)
-            Y = np.zeros((2 * N + 2, 2), np.float)
+            Y = np.zeros((2 * N + 2, 2), float)
 
             # the purpose of the next two lines is for when x2 is a
             # scalar like 0 and we want the fill to go all the way
@@ -5387,7 +5387,7 @@ def pcolor(self, *args, **kwargs):
 
         if t and any(t.contains_branch_seperately(self.transData)):
             trans_to_data = t - self.transData
-            pts = np.vstack([x, y]).T.astype(np.float)
+            pts = np.vstack([x, y]).T.astype(float)
             transformed_pts = trans_to_data.transform(pts)
             x = transformed_pts[..., 0]
             y = transformed_pts[..., 1]
@@ -5537,7 +5537,7 @@ def pcolormesh(self, *args, **kwargs):
 
         if t and any(t.contains_branch_seperately(self.transData)):
             trans_to_data = t - self.transData
-            pts = np.vstack([X, Y]).T.astype(np.float)
+            pts = np.vstack([X, Y]).T.astype(float)
             transformed_pts = trans_to_data.transform(pts)
             X = transformed_pts[..., 0]
             Y = transformed_pts[..., 1]
@@ -6203,7 +6203,7 @@ def _normalize_input(inp, ename='input'):
 
             for m, c in zip(n, color):
                 if bottom is None:
-                    bottom = np.zeros(len(m), np.float)
+                    bottom = np.zeros(len(m), float)
                 if stacked:
                     height = m - bottom
                 else:
@@ -6222,14 +6222,14 @@ def _normalize_input(inp, ename='input'):
 
         elif histtype.startswith('step'):
             # these define the perimeter of the polygon
-            x = np.zeros(4 * len(bins) - 3, np.float)
-            y = np.zeros(4 * len(bins) - 3, np.float)
+            x = np.zeros(4 * len(bins) - 3, float)
+            y = np.zeros(4 * len(bins) - 3, float)
 
             x[0:2*len(bins)-1:2], x[1:2*len(bins)-1:2] = bins, bins[:-1]
             x[2*len(bins)-1:] = x[1:2*len(bins)-1][::-1]
 
             if bottom is None:
-                bottom = np.zeros(len(bins)-1, np.float)
+                bottom = np.zeros(len(bins)-1, float)
 
             y[1:2*len(bins)-1:2], y[2:2*len(bins):2] = bottom, bottom
             y[2*len(bins)-1:] = y[1:2*len(bins)-1][::-1]

lib/matplotlib/axes/_base.py

@@ -12,7 +12,6 @@
 from operator import itemgetter
 
 import numpy as np
-from numpy import ma
 
 import matplotlib
 
@@ -1917,7 +1916,7 @@ def update_datalim(self, xys, updatex=True, updatey=True):
 
         if iterable(xys) and not len(xys):
             return
-        if not ma.isMaskedArray(xys):
+        if not isinstance(xys, np.ma.MaskedArray):
             xys = np.asarray(xys)
         self.dataLim.update_from_data_xy(xys, self.ignore_existing_data_limits,
                                          updatex=updatex, updatey=updatey)

lib/matplotlib/backend_bases.py

@@ -34,16 +34,17 @@
 
 from __future__ import (absolute_import, division, print_function,
                         unicode_literals)
-from contextlib import contextmanager
 
 import six
 from six.moves import xrange
 
+from contextlib import contextmanager
+import importlib
+import io
 import os
 import sys
-import warnings
 import time
-import io
+import warnings
 
 import numpy as np
 import matplotlib.cbook as cbook
@@ -65,14 +66,6 @@
 from matplotlib.cbook import mplDeprecation, warn_deprecated
 import matplotlib.backend_tools as tools
 
-try:
-    from importlib import import_module
-except:
-    # simple python 2.6 implementation (no relative imports)
-    def import_module(name):
-        __import__(name)
-        return sys.modules[name]
-
 try:
     from PIL import Image
     _has_pil = True
@@ -135,7 +128,7 @@ def get_registered_canvas_class(format):
         return None
     backend_class = _default_backends[format]
     if cbook.is_string_like(backend_class):
-        backend_class = import_module(backend_class).FigureCanvas
+        backend_class = importlib.import_module(backend_class).FigureCanvas
         _default_backends[format] = backend_class
     return backend_class
 
@@ -327,7 +320,7 @@ def draw_quad_mesh(self, gc, master_transform, meshWidth, meshHeight,
 
         if edgecolors is None:
             edgecolors = facecolors
-        linewidths = np.array([gc.get_linewidth()], np.float_)
+        linewidths = np.array([gc.get_linewidth()], float)
 
         return self.draw_path_collection(
             gc, master_transform, paths, [], offsets, offsetTrans, facecolors,

lib/matplotlib/backends/backend_ps.py

@@ -12,10 +12,7 @@
 def _fn_name(): return sys._getframe(1).f_code.co_name
 import io
 
-try:
-    from hashlib import md5
-except ImportError:
-    from md5 import md5 #Deprecated in 2.5
+from hashlib import md5
 
 from tempfile import mkstemp
 from matplotlib import verbose, __version__, rcParams, checkdep_ghostscript
@@ -44,10 +41,6 @@ def _fn_name(): return sys._getframe(1).f_code.co_name
 import numpy as np
 import binascii
 import re
-try:
-    set
-except NameError:
-    from sets import Set as set
 
 if sys.platform.startswith('win'): cmd_split = '&'
 else: cmd_split = ';'

lib/matplotlib/cbook.py

@@ -31,7 +31,6 @@
 from weakref import ref, WeakKeyDictionary
 
 import numpy as np
-import numpy.ma as ma
 
 
 class MatplotlibDeprecationWarning(UserWarning):
@@ -677,7 +676,7 @@ def is_string_like(obj):
     if isinstance(obj, six.string_types):
         return True
     # numpy strings are subclass of str, ma strings are not
-    if ma.isMaskedArray(obj):
+    if isinstance(obj, np.ma.MaskedArray):
         if obj.ndim == 0 and obj.dtype.kind in 'SU':
             return True
         else:
@@ -1728,7 +1727,7 @@ def delete_masked_points(*args):
     for i, x in enumerate(args):
         if (not is_string_like(x)) and iterable(x) and len(x) == nrecs:
             seqlist[i] = True
-            if ma.isMA(x):
+            if isinstance(x, np.ma.MaskedArray):
                 if x.ndim > 1:
                     raise ValueError("Masked arrays must be 1-D")
             else:
@@ -1739,8 +1738,8 @@ def delete_masked_points(*args):
         if seqlist[i]:
             if x.ndim > 1:
                 continue  # Don't try to get nan locations unless 1-D.
-            if ma.isMA(x):
-                masks.append(~ma.getmaskarray(x))  # invert the mask
+            if isinstance(x, np.ma.MaskedArray):
+                masks.append(~np.ma.getmaskarray(x))  # invert the mask
                 xd = x.data
             else:
                 xd = x
@@ -1758,7 +1757,7 @@ def delete_masked_points(*args):
                 if seqlist[i]:
                     margs[i] = x.take(igood, axis=0)
     for i, x in enumerate(margs):
-        if seqlist[i] and ma.isMA(x):
+        if seqlist[i] and isinstance(x, np.ma.MaskedArray):
             margs[i] = x.filled()
     return margs
 
@@ -2304,7 +2303,7 @@ def pts_to_prestep(x, *args):
     # do normalization
     vertices = _step_validation(x, *args)
     # create the output array
-    steps = np.zeros((vertices.shape[0], 2 * len(x) - 1), np.float)
+    steps = np.zeros((vertices.shape[0], 2 * len(x) - 1), float)
     # do the to step conversion logic
     steps[0, 0::2], steps[0, 1::2] = vertices[0, :], vertices[0, :-1]
     steps[1:, 0::2], steps[1:, 1:-1:2] = vertices[1:, :], vertices[1:, 1:]
@@ -2344,7 +2343,7 @@ def pts_to_poststep(x, *args):
     # do normalization
     vertices = _step_validation(x, *args)
     # create the output array
-    steps = ma.zeros((vertices.shape[0], 2 * len(x) - 1), np.float)
+    steps = np.zeros((vertices.shape[0], 2 * len(x) - 1), float)
     # do the to step conversion logic
     steps[0, ::2], steps[0, 1:-1:2] = vertices[0, :], vertices[0, 1:]
     steps[1:, 0::2], steps[1:, 1::2] = vertices[1:, :], vertices[1:, :-1]
@@ -2385,7 +2384,7 @@ def pts_to_midstep(x, *args):
     # do normalization
     vertices = _step_validation(x, *args)
     # create the output array
-    steps = ma.zeros((vertices.shape[0], 2 * len(x)), np.float)
+    steps = np.zeros((vertices.shape[0], 2 * len(x)), float)
     steps[0, 1:-1:2] = 0.5 * (vertices[0, :-1] + vertices[0, 1:])
     steps[0, 2::2] = 0.5 * (vertices[0, :-1] + vertices[0, 1:])
     steps[0, 0] = vertices[0, 0]