Dec 30, 2015 · Nov 24, 2015 · Nov 24, 2015 · Dec 13, 2015 · Dec 5, 2015 · Dec 22, 2015
diff --git a/lib/matplotlib/axes/_axes.py b/lib/matplotlib/axes/_axes.py
        """
        # process the unit information
        if len(xranges):
            xdata =six.next(iter(xranges))
            xdata =cbook.safe_first_element(xranges)
        else:
            xdata = None
        if len(yrange):
            ydata =six.next(iter(yrange))
            ydata =cbook.safe_first_element(yrange)
        else:
            ydata = None
        self._process_unit_info(xdata=xdata,

        if ecolor is None:
            if l0 is None and 'color' in self._get_lines._prop_keys:
                ecolor =six.next(self._get_lines.prop_cycler)['color']
                ecolor = next(self._get_lines.prop_cycler)['color']
            else:
                ecolor = l0.get_color()

        .. plot:: mpl_examples/statistics/histogram_demo_features.py

        """
        def _normalize_input(inp, ename='input'):
            """Normalize 1 or 2d input into list of np.ndarray or
            a single 2D np.ndarray.

            Parameters
            ----------
            inp : iterable
            ename : str, optional
                Name to use in ValueError if `inp` can not be normalized

            """
            if (isinstance(x, np.ndarray) or
                    not iterable(cbook.safe_first_element(inp))):
                # TODO: support masked arrays;
                inp = np.asarray(inp)
                if inp.ndim == 2:
                    # 2-D input with columns as datasets; switch to rows
                    inp = inp.T
                elif inp.ndim == 1:
                    # new view, single row
                    inp = inp.reshape(1, inp.shape[0])
                else:
                    raise ValueError(
                        "{ename} must be 1D or 2D".format(ename=ename))
                if inp.shape[1] < inp.shape[0]:
                    warnings.warn(
                        '2D hist input should be nsamples x nvariables;\n '
                        'this looks transposed '
                        '(shape is %d x %d)' % inp.shape[::-1])
            else:
                # multiple hist with data of different length
                inp = [np.asarray(xi) for xi in inp]

            return inp

        if not self._hold:
            self.cla()

        input_empty = len(flat) == 0

        # Massage 'x' for processing.
        # NOTE: Be sure any changes here is also done below to 'weights'
        if input_empty:
            x = np.array([[]])
        elif isinstance(x, np.ndarray) or not iterable(x[0]):
            # TODO: support masked arrays;
            x = np.asarray(x)
            if x.ndim == 2:
                x = x.T  # 2-D input with columns as datasets; switch to rows
            elif x.ndim == 1:
                x = x.reshape(1, x.shape[0])  # new view, single row
            else:
                raise ValueError("x must be 1D or 2D")
            if x.shape[1] < x.shape[0]:
                warnings.warn(
                    '2D hist input should be nsamples x nvariables;\n '
                    'this looks transposed (shape is %d x %d)' % x.shape[::-1])
        else:
            # multiple hist with data of different length
            x = [np.asarray(xi) for xi in x]

            x = _normalize_input(x, 'x')
        nx = len(x)  # number of datasets

        # We need to do to 'weights' what was done to 'x'
        if weights is not None:
            w = _normalize_input(weights, 'weights')
        else:
            w = [None]*nx

        if len(w) != nx:
            raise ValueError('weights should have the same shape as x')

        for xi, wi in zip(x, w):
            if wi is not None and len(wi) != len(xi):
                raise ValueError(
                    'weights should have the same shape as x')

        if color is None and 'color' in self._get_lines._prop_keys:
            color = [six.next(self._get_lines.prop_cycler)['color']
            color = [next(self._get_lines.prop_cycler)['color']
                     for i in xrange(nx)]
        else:
            color = mcolors.colorConverter.to_rgba_array(color)
            if len(color) != nx:
                raise ValueError("color kwarg must have one color per dataset")

        # We need to do to 'weights' what was done to 'x'
        if weights is not None:
            if isinstance(weights, np.ndarray) or not iterable(weights[0]):
                w = np.array(weights)
                if w.ndim == 2:
                    w = w.T
                elif w.ndim == 1:
                    w.shape = (1, w.shape[0])
                else:
                    raise ValueError("weights must be 1D or 2D")
            else:
                w = [np.asarray(wi) for wi in weights]

            if len(w) != nx:
                raise ValueError('weights should have the same shape as x')
            for i in xrange(nx):
                if len(w[i]) != len(x[i]):
                    raise ValueError(
                        'weights should have the same shape as x')
        else:
            w = [None]*nx

        # Save the datalimits for the same reason:
        _saved_bounds = self.dataLim.bounds

                    xmax = max(xmax, xi.max())
            bin_range = (xmin, xmax)

        #hist_kwargs = dict(range=range, normed=bool(normed))
        #hist_kwargs = dict(range=range, normed=bool(normed))
        # We will handle the normed kwarg within mpl until we
        # get to the point of requiring numpy >= 1.5.
        hist_kwargs = dict(range=bin_range)
diff --git a/lib/matplotlib/axes/_base.py b/lib/matplotlib/axes/_base.py
 import matplotlib.image as mimage
 from matplotlib.offsetbox import OffsetBox
 from matplotlib.artist import allow_rasterization
 from matplotlib.cbook import iterable, index_of

 from matplotlib.rcsetup import cycler

 rcParams = matplotlib.rcParams
diff --git a/lib/matplotlib/backends/backend_pdf.py b/lib/matplotlib/backends/backend_pdf.py
        fontsize = prop.get_size_in_points()
        dvifile = texmanager.make_dvi(s, fontsize)
        dvi = dviread.Dvi(dvifile, 72)
        page =six.next(iter(dvi))
        page = next(iter(dvi))
        dvi.close()

        # Gather font information and do some setup for combining
diff --git a/lib/matplotlib/cbook.py b/lib/matplotlib/cbook.py
 from matplotlib.externals import six
 from matplotlib.externals.six.moves import xrange, zip
 from itertools import repeat
 import collections

 import datetime
 import errno
        return np.arange(y.shape[0], dtype=float), y


 def safe_first_element(obj):
    if isinstance(obj, collections.Iterator):
        raise RuntimeError("matplotlib does not support generators "
                           "as input")
    return next(iter(obj))


 def get_label(y, default_name):
    try:
        return y.name
diff --git a/lib/matplotlib/dates.py b/lib/matplotlib/dates.py
            x = x.ravel()

        try:
            x =x[0]
        except (TypeError,IndexError):
            x =cbook.safe_first_element(x)
        except (TypeError,StopIteration):
            pass

        try:
diff --git a/lib/matplotlib/tests/test_axes.py b/lib/matplotlib/tests/test_axes.py
 import io

 from nose.tools import assert_equal, assert_raises, assert_false, assert_true
 from nose.plugins.skip import SkipTest

 import datetime

    ax.broken_barh([], (.1, .5))


 @cleanup
 def test_pandas_indexing_dates():
    try:
        import pandas as pd
    except ImportError:
        raise SkipTest("Pandas not installed")

    dates = np.arange('2005-02', '2005-03', dtype='datetime64[D]')
    values = np.sin(np.array(range(len(dates))))
    df = pd.DataFrame({'dates': dates, 'values': values})

    ax = plt.gca()

    without_zero_index = df[np.array(df.index) % 2 == 1].copy()
    ax.plot('dates', 'values', data=without_zero_index)


 @cleanup
 def test_pandas_indexing_hist():
    try:
        import pandas as pd
    except ImportError:
        raise SkipTest("Pandas not installed")

    ser_1 = pd.Series(data=[1, 2, 2, 3, 3, 4, 4, 4, 4, 5])
    ser_2 = ser_1.iloc[1:]
    fig, axes = plt.subplots()
    axes.hist(ser_2)


 if __name__ == '__main__':
    import nose
    import sys
Original file line number	Diff line number	Diff line change
Expand Up		@@ -2331,11 +2331,11 @@ def broken_barh(self, xranges, yrange, **kwargs):
		"""
		# process the unit information
		if len(xranges):
		xdata =six.next(iter(xranges))
		xdata =cbook.safe_first_element(xranges)
		else:
		xdata = None
		if len(yrange):
		ydata =six.next(iter(yrange))
		ydata =cbook.safe_first_element(yrange)
		else:
		ydata = None
		self._process_unit_info(xdata=xdata,
Expand DownExpand Up		@@ -3016,7 +3016,7 @@ def xywhere(xs, ys, mask):

		if ecolor is None:
		if l0 is None and 'color' in self._get_lines._prop_keys:
		ecolor =six.next(self._get_lines.prop_cycler)['color']
		ecolor = next(self._get_lines.prop_cycler)['color']
		else:
		ecolor = l0.get_color()

Expand DownExpand Up		@@ -5875,6 +5875,41 @@ def hist(self, x, bins=10, range=None, normed=False, weights=None,
		.. plot:: mpl_examples/statistics/histogram_demo_features.py

		"""
		def _normalize_input(inp, ename='input'):
		"""Normalize 1 or 2d input into list of np.ndarray or
		a single 2D np.ndarray.
Copy link Member WeatherGodDec 28, 2015 Choose a reason for hiding this comment The reason will be displayed to describe this comment to others.Learn more. So, which does it do? A list of ndarrays or a 2D array? This isn't very clear. Copy link MemberAuthor tacaswellDec 28, 2015 Choose a reason for hiding this comment The reason will be displayed to describe this comment to others.Learn more. it really does return either/or 👿 Note that this is a`_`'d private to this method function. The main reason for this existing is to remove copy-paste code so the questionable API is OK (imo) as it preserves the behavior of the block of code the function call is replacing. Copy link Member WeatherGodDec 28, 2015 Choose a reason for hiding this comment The reason will be displayed to describe this comment to others.Learn more. Right, the API is awkward, but I am fine with it as it is private and internal, and it does clean up the code (and fixes a possible mutation side-effect in weights processing). I was more aiming for a clearer docstring for future devs. The if-statements are quite opaque. Copy link Member WeatherGodDec 29, 2015 Choose a reason for hiding this comment The reason will be displayed to describe this comment to others.Learn more. I was waiting for a clearer docstring here because the if-statements are opaque.

		Parameters
		----------
		inp : iterable
		ename : str, optional
		Name to use in ValueError if `inp` can not be normalized

		"""
		if (isinstance(x, np.ndarray) or
		not iterable(cbook.safe_first_element(inp))):
		# TODO: support masked arrays;
		inp = np.asarray(inp)
		if inp.ndim == 2:
		# 2-D input with columns as datasets; switch to rows
		inp = inp.T
		elif inp.ndim == 1:
		# new view, single row
		inp = inp.reshape(1, inp.shape[0])
		else:
		raise ValueError(
		"{ename} must be 1D or 2D".format(ename=ename))
		if inp.shape[1] < inp.shape[0]:
		warnings.warn(
		'2D hist input should be nsamples x nvariables;\n '
		'this looks transposed '
		'(shape is %d x %d)' % inp.shape[::-1])
		else:
		# multiple hist with data of different length
		inp = [np.asarray(xi) for xi in inp]

		return inp

		if not self._hold:
		self.cla()

Expand DownExpand Up		@@ -5918,58 +5953,34 @@ def hist(self, x, bins=10, range=None, normed=False, weights=None,
		input_empty = len(flat) == 0

		# Massage 'x' for processing.
		# NOTE: Be sure any changes here is also done below to 'weights'
		if input_empty:
		x = np.array([[]])
		elif isinstance(x, np.ndarray) or not iterable(x[0]):
		# TODO: support masked arrays;
		x = np.asarray(x)
		if x.ndim == 2:
		x = x.T # 2-D input with columns as datasets; switch to rows
		elif x.ndim == 1:
		x = x.reshape(1, x.shape[0]) # new view, single row
		else:
		raise ValueError("x must be 1D or 2D")
		if x.shape[1] < x.shape[0]:
		warnings.warn(
		'2D hist input should be nsamples x nvariables;\n '
		'this looks transposed (shape is %d x %d)' % x.shape[::-1])
		else:
		# multiple hist with data of different length
		x = [np.asarray(xi) for xi in x]

		x = _normalize_input(x, 'x')
		nx = len(x) # number of datasets

		# We need to do to 'weights' what was done to 'x'
		if weights is not None:
		w = _normalize_input(weights, 'weights')
		else:
		w = [None]*nx

		if len(w) != nx:
		raise ValueError('weights should have the same shape as x')

		for xi, wi in zip(x, w):
		if wi is not None and len(wi) != len(xi):
		raise ValueError(
		'weights should have the same shape as x')

		if color is None and 'color' in self._get_lines._prop_keys:
		color = [six.next(self._get_lines.prop_cycler)['color']
		color = [next(self._get_lines.prop_cycler)['color']
		for i in xrange(nx)]
		else:
		color = mcolors.colorConverter.to_rgba_array(color)
		if len(color) != nx:
		raise ValueError("color kwarg must have one color per dataset")

		# We need to do to 'weights' what was done to 'x'
		if weights is not None:
		if isinstance(weights, np.ndarray) or not iterable(weights[0]):
		w = np.array(weights)
		if w.ndim == 2:
		w = w.T
		elif w.ndim == 1:
		w.shape = (1, w.shape[0])
		else:
		raise ValueError("weights must be 1D or 2D")
		else:
		w = [np.asarray(wi) for wi in weights]

		if len(w) != nx:
		raise ValueError('weights should have the same shape as x')
		for i in xrange(nx):
		if len(w[i]) != len(x[i]):
		raise ValueError(
		'weights should have the same shape as x')
		else:
		w = [None]*nx

		# Save the datalimits for the same reason:
		_saved_bounds = self.dataLim.bounds

Expand All		@@ -5985,7 +5996,7 @@ def hist(self, x, bins=10, range=None, normed=False, weights=None,
		xmax = max(xmax, xi.max())
		bin_range = (xmin, xmax)

		#hist_kwargs = dict(range=range, normed=bool(normed))
		#hist_kwargs = dict(range=range, normed=bool(normed))
		# We will handle the normed kwarg within mpl until we
		# get to the point of requiring numpy >= 1.5.
		hist_kwargs = dict(range=bin_range)
Expand Down
Original file line number	Diff line number	Diff line change
Expand Up		@@ -32,7 +32,7 @@
		import matplotlib.image as mimage
		from matplotlib.offsetbox import OffsetBox
		from matplotlib.artist import allow_rasterization
		from matplotlib.cbook import iterable, index_of

		from matplotlib.rcsetup import cycler

		rcParams = matplotlib.rcParams
Expand Down
Original file line number	Diff line number	Diff line change
Expand Up		@@ -1846,7 +1846,7 @@ def draw_tex(self, gc, x, y, s, prop, angle, ismath='TeX!', mtext=None):
		fontsize = prop.get_size_in_points()
		dvifile = texmanager.make_dvi(s, fontsize)
		dvi = dviread.Dvi(dvifile, 72)
		page =six.next(iter(dvi))
		page = next(iter(dvi))
		dvi.close()

		# Gather font information and do some setup for combining
Expand Down
Original file line number	Diff line number	Diff line change
Expand Up		@@ -12,6 +12,7 @@
		from matplotlib.externals import six
		from matplotlib.externals.six.moves import xrange, zip
		from itertools import repeat
		import collections

		import datetime
		import errno
Expand DownExpand Up		@@ -2536,6 +2537,13 @@ def index_of(y):
		return np.arange(y.shape[0], dtype=float), y


		def safe_first_element(obj):
		if isinstance(obj, collections.Iterator):
		raise RuntimeError("matplotlib does not support generators "
		"as input")
		return next(iter(obj))


		def get_label(y, default_name):
		try:
		return y.name
Expand Down
Original file line number	Diff line number	Diff line change
Expand Up		@@ -1561,8 +1561,8 @@ def default_units(x, axis):
		x = x.ravel()

		try:
		x =x[0]
		except (TypeError,IndexError):
		x =cbook.safe_first_element(x)
		except (TypeError,StopIteration):
		pass

		try:
Expand Down
Original file line number	Diff line number	Diff line change
Expand Up		@@ -7,6 +7,7 @@
		import io

		from nose.tools import assert_equal, assert_raises, assert_false, assert_true
		from nose.plugins.skip import SkipTest

		import datetime

Expand DownExpand Up		@@ -4183,6 +4184,36 @@ def test_broken_barh_empty():
		ax.broken_barh([], (.1, .5))


		@cleanup
		def test_pandas_indexing_dates():
		try:
		import pandas as pd
		except ImportError:
		raise SkipTest("Pandas not installed")

		dates = np.arange('2005-02', '2005-03', dtype='datetime64[D]')
		values = np.sin(np.array(range(len(dates))))
		df = pd.DataFrame({'dates': dates, 'values': values})

		ax = plt.gca()

		without_zero_index = df[np.array(df.index) % 2 == 1].copy()
		ax.plot('dates', 'values', data=without_zero_index)


		@cleanup
		def test_pandas_indexing_hist():
		try:
		import pandas as pd
		except ImportError:
		raise SkipTest("Pandas not installed")

		ser_1 = pd.Series(data=[1, 2, 2, 3, 3, 4, 4, 4, 4, 5])
		ser_2 = ser_1.iloc[1:]
		fig, axes = plt.subplots()
		axes.hist(ser_2)


		if __name__ == '__main__':
		import nose
		import sys
Expand Down