Don't check iterable() before len().

... because the former does not imply the latter anyways, e.g.
generators are iterables but unsized.

Now `plot(zip([1, 2], [3, 4]))` (py3) raises `RuntimeError: matplotlib
does not support generators` (from `cbook.safe_first_element`) which is
probably the intended exception, rather than `TypeError: object of type
'zip' has no len()`.  Perhaps this exception should be changed to a
`TypeError`, by the way...

lib/matplotlib/axes/_axes.py

@@ -4,6 +4,7 @@
 import six
 from six.moves import reduce, xrange, zip, zip_longest
 
+from collections import Sized
 import itertools
 import math
 import warnings
@@ -2938,8 +2939,11 @@ def extract_err(err, data):
             data : iterable
                 x or y from errorbar
             '''
-            if (iterable(err) and len(err) == 2):
+            try:
                 a, b = err
+            except (TypeError, ValueError):
+                pass
+            else:
                 if iterable(a) and iterable(b):
                     # using list comps rather than arrays to preserve units
                     low = [thisx - thiserr for (thisx, thiserr)
@@ -2953,8 +2957,8 @@ def extract_err(err, data):
             # special case for empty lists
             if len(err) > 1:
                 fe = safe_first_element(err)
-                if not ((len(err) == len(data) and not (iterable(fe) and
-                                                        len(fe) > 1))):
+                if (len(err) != len(data)
+                        or isinstance(fe, Sized) and len(fe) > 1):
                     raise ValueError("err must be [ scalar | N, Nx1 "
                                      "or 2xN array-like ]")
             # using list comps rather than arrays to preserve units

lib/matplotlib/axes/_base.py

@@ -1923,11 +1923,9 @@ def update_datalim(self, xys, updatex=True, updatey=True):
         # limits and set the bound to be the bounds of the xydata.
         # Otherwise, it will compute the bounds of it's current data
         # and the data in xydata
-
-        if iterable(xys) and not len(xys):
+        xys = np.asarray(xys)
+        if not len(xys):
             return
-        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)
         self.ignore_existing_data_limits = False

lib/matplotlib/cm.py

@@ -305,10 +305,11 @@ def set_clim(self, vmin=None, vmax=None):
 
         ACCEPTS: a length 2 sequence of floats
         """
-        if (vmin is not None and vmax is None and
-                cbook.iterable(vmin) and len(vmin) == 2):
-            vmin, vmax = vmin
-
+        if vmax is None:
+            try:
+                vmin, vmax = vmin
+            except (TypeError, ValueError):
+                pass
         if vmin is not None:
             self.norm.vmin = vmin
         if vmax is not None:

lib/matplotlib/collections.py

@@ -156,31 +156,6 @@ def __init__(self,
         self.update(kwargs)
         self._paths = None
 
-    @staticmethod
-    def _get_value(val):
-        try:
-            return (float(val), )
-        except TypeError:
-            if cbook.iterable(val) and len(val):
-                try:
-                    float(cbook.safe_first_element(val))
-                except (TypeError, ValueError):
-                    pass  # raise below
-                else:
-                    return val
-
-        raise TypeError('val must be a float or nonzero sequence of floats')
-
-    @staticmethod
-    def _get_bool(val):
-        if not cbook.iterable(val):
-            val = (val,)
-        try:
-            bool(cbook.safe_first_element(val))
-        except (TypeError, IndexError):
-            raise TypeError('val must be a bool or nonzero sequence of them')
-        return val
-
     def get_paths(self):
         return self._paths
 
@@ -486,7 +461,7 @@ def set_linewidth(self, lw):
             if lw is None:
                 lw = mpl.rcParams['lines.linewidth']
         # get the un-scaled/broadcast lw
-        self._us_lw = self._get_value(lw)
+        self._us_lw = np.atleast_1d(np.asarray(lw))
 
         # scale all of the dash patterns.
         self._linewidths, self._linestyles = self._bcast_lwls(
@@ -608,7 +583,7 @@ def set_antialiased(self, aa):
         """
         if aa is None:
             aa = mpl.rcParams['patch.antialiased']
-        self._antialiaseds = self._get_bool(aa)
+        self._antialiaseds = np.atleast_1d(np.asarray(aa, bool))
         self.stale = True
 
     def set_antialiaseds(self, aa):

lib/matplotlib/colors.py

@@ -58,9 +58,11 @@
 
 from __future__ import (absolute_import, division, print_function,
                         unicode_literals)
-import re
 import six
 from six.moves import zip
+
+from collections import Sized
+import re
 import warnings
 
 import numpy as np
@@ -685,12 +687,12 @@ def from_list(name, colors, N=256, gamma=1.0):
         if not cbook.iterable(colors):
             raise ValueError('colors must be iterable')
 
-        if cbook.iterable(colors[0]) and len(colors[0]) == 2 and \
-                not cbook.is_string_like(colors[0]):
+        if (isinstance(colors[0], Sized) and len(colors[0]) == 2
+                and not cbook.is_string_like(colors[0])):
             # List of value, color pairs
-            vals, colors = list(zip(*colors))
+            vals, colors = zip(*colors)
         else:
-            vals = np.linspace(0., 1., len(colors))
+            vals = np.linspace(0, 1, len(colors))
 
         cdict = dict(red=[], green=[], blue=[], alpha=[])
         for val, color in zip(vals, colors):

lib/matplotlib/legend.py

@@ -33,7 +33,7 @@
 
 from matplotlib import rcParams
 from matplotlib.artist import Artist, allow_rasterization
-from matplotlib.cbook import is_string_like, iterable, silent_list, is_hashable
+from matplotlib.cbook import is_string_like, silent_list, is_hashable
 from matplotlib.font_manager import FontProperties
 from matplotlib.lines import Line2D
 from matplotlib.patches import Patch, Rectangle, Shadow, FancyBboxPatch
@@ -408,17 +408,6 @@ def _set_loc(self, loc):
         # find_offset function will be provided to _legend_box and
         # _legend_box will draw itself at the location of the return
         # value of the find_offset.
-        self._loc_real = loc
-        if loc == 0:
-            _findoffset = self._findoffset_best
-        else:
-            _findoffset = self._findoffset_loc
-
-#       def findoffset(width, height, xdescent, ydescent):
-#           return _findoffset(width, height, xdescent, ydescent, renderer)
-
-        self._legend_box.set_offset(_findoffset)
-
         self._loc_real = loc
         self.stale = True
 
@@ -427,24 +416,20 @@ def _get_loc(self):
 
     _loc = property(_get_loc, _set_loc)
 
-    def _findoffset_best(self, width, height, xdescent, ydescent, renderer):
-        "Helper function to locate the legend at its best position"
-        ox, oy = self._find_best_position(width, height, renderer)
-        return ox + xdescent, oy + ydescent
-
-    def _findoffset_loc(self, width, height, xdescent, ydescent, renderer):
-        "Helper function to locate the legend using the location code"
+    def _findoffset(self, width, height, xdescent, ydescent, renderer):
+        "Helper function to locate the legend"
 
-        if iterable(self._loc) and len(self._loc) == 2:
-            # when loc is a tuple of axes(or figure) coordinates.
-            fx, fy = self._loc
-            bbox = self.get_bbox_to_anchor()
-            x, y = bbox.x0 + bbox.width * fx, bbox.y0 + bbox.height * fy
-        else:
+        if self._loc == 0:  # "best".
+            x, y = self._find_best_position(width, height, renderer)
+        elif self._loc in Legend.codes.values():  # Fixed location.
             bbox = Bbox.from_bounds(0, 0, width, height)
             x, y = self._get_anchored_bbox(self._loc, bbox,
                                            self.get_bbox_to_anchor(),
                                            renderer)
+        else:  # Axes or figure coordinates.
+            fx, fy = self._loc
+            bbox = self.get_bbox_to_anchor()
+            x, y = bbox.x0 + bbox.width * fx, bbox.y0 + bbox.height * fy
 
         return x + xdescent, y + ydescent
 
@@ -701,6 +686,7 @@ def _init_legend_box(self, handles, labels, markerfirst=True):
                                    children=[self._legend_title_box,
                                              self._legend_handle_box])
         self._legend_box.set_figure(self.figure)
+        self._legend_box.set_offset(self._findoffset)
         self.texts = text_list
         self.legendHandles = handle_list
 

lib/matplotlib/markers.py

@@ -89,10 +89,12 @@
 import six
 from six.moves import xrange
 
+from collections import Sized
+
 import numpy as np
 
-from .cbook import is_math_text, is_string_like, is_numlike, iterable
-from matplotlib import rcParams
+from . import rcParams
+from .cbook import is_math_text, is_string_like, is_numlike
 from .path import Path
 from .transforms import IdentityTransform, Affine2D
 
@@ -247,7 +249,7 @@ def get_marker(self):
         return self._marker
 
     def set_marker(self, marker):
-        if (iterable(marker) and len(marker) in (2, 3) and
+        if (isinstance(marker, Sized) and len(marker) in (2, 3) and
                 marker[1] in (0, 1, 2, 3)):
             self._marker_function = self._set_tuple_marker
         elif isinstance(marker, np.ndarray):

lib/matplotlib/rcsetup.py

@@ -18,18 +18,13 @@
 
 import six
 
+from collections import Iterable, Mapping
 from functools import reduce
 import operator
 import os
 import warnings
 import re
 
-try:
-    import collections.abc as abc
-except ImportError:
-    # python 2
-    import collections as abc
-
 from matplotlib.cbook import mplDeprecation
 from matplotlib.fontconfig_pattern import parse_fontconfig_pattern
 from matplotlib.colors import is_color_like
@@ -92,7 +87,7 @@ def f(s):
         # Numpy ndarrays, and pandas data structures.  However, unordered
         # sequences, such as sets, should be allowed but discouraged unless the
         # user desires pseudorandom behavior.
-        elif isinstance(s, abc.Iterable) and not isinstance(s, abc.Mapping):
+        elif isinstance(s, Iterable) and not isinstance(s, Mapping):
             # The condition on this list comprehension will preserve the
             # behavior of filtering out any empty strings (behavior was
             # from the original validate_stringlist()), while allowing

lib/matplotlib/tests/test_collections.py

@@ -8,9 +8,8 @@
 
 import io
 
-from nose.tools import assert_equal
 import numpy as np
-from numpy.testing import assert_array_equal, assert_array_almost_equal
+from numpy.testing import assert_array_equal, assert_array_almost_equal, assert_equal
 from nose.plugins.skip import SkipTest
 
 import matplotlib.pyplot as plt
@@ -646,12 +645,12 @@ def test_lslw_bcast():
     col.set_linestyles(['-', '-'])
     col.set_linewidths([1, 2, 3])
 
-    assert col.get_linestyles() == [(None, None)] * 6
-    assert col.get_linewidths() == [1, 2, 3] * 2
+    assert_equal(col.get_linestyles(), [(None, None)] * 6)
+    assert_equal(col.get_linewidths(), [1, 2, 3] * 2)
 
     col.set_linestyles(['-', '-', '-'])
-    assert col.get_linestyles() == [(None, None)] * 3
-    assert col.get_linewidths() == [1, 2, 3]
+    assert_equal(col.get_linestyles(), [(None, None)] * 3)
+    assert_equal(col.get_linewidths(), [1, 2, 3])
 
 
 if __name__ == '__main__':

lib/matplotlib/units.py

@@ -157,11 +157,15 @@ def get_converter(self, x):
                     converter = self.get_converter(next_item)
                 return converter
 
-        if converter is None and iterable(x) and (len(x) > 0):
-            thisx = safe_first_element(x)
-            if classx and classx != getattr(thisx, '__class__', None):
-                converter = self.get_converter(thisx)
-                return converter
+        if converter is None:
+            try:
+                thisx = safe_first_element(x)
+            except (TypeError, StopIteration):
+                pass
+            else:
+                if classx and classx != getattr(thisx, '__class__', None):
+                    converter = self.get_converter(thisx)
+                    return converter
 
         # DISABLED self._cached[idx] = converter
         return converter