Oct 26, 2017 · Oct 19, 2017
diff --git a/lib/matplotlib/axes/_axes.py b/lib/matplotlib/axes/_axes.py
        --------
        hist2d : 2D histograms

        Notes
        -----
        Until numpy release 1.5, the underlying numpy histogram function was
        incorrect with ``normed=True`` if bin sizes were unequal.  MPL
        inherited that error. It is now corrected within MPL when using
        earlier numpy versions.

        """
        # Avoid shadowing the builtin.
        bin_range = range
        else:
            hist_kwargs = dict(range=bin_range)

        n = []
        # List to store all the top coordinates of the histograms
        tops = []
        mlast = None
        # Loop through datasets
        for i in xrange(nx):
            # this will automatically overwrite bins,
            # so that each histogram uses the same bins
            m, bins = np.histogram(x[i], bins, weights=w[i], **hist_kwargs)
            m = m.astype(float)  # causes problems later if it's an int
            if mlast is None:
                mlast = np.zeros(len(bins)-1, m.dtype)
            if density and not stacked:
                db = np.diff(bins)
                m = (m.astype(float) / db) / m.sum()
            if stacked:
                if mlast is None:
                    mlast = np.zeros(len(bins)-1, m.dtype)
                m += mlast
                mlast[:] = m
 n.append(m)
 tops.append(m)

        # If a stacked density plot, normalize so the area of all the stacked
        # histograms together is 1
        if stacked and density:
            db = np.diff(bins)
            for m inn:
                m[:] = (m.astype(float) / db) /n[-1].sum()
            for m intops:
                m[:] = (m.astype(float) / db) /tops[-1].sum()
        if cumulative:
            slc = slice(None)
            if cbook.is_numlike(cumulative) and cumulative < 0:
                slc = slice(None, None, -1)

            if density:
 n = [(m * np.diff(bins))[slc].cumsum()[slc] for m inn]
 tops = [(m * np.diff(bins))[slc].cumsum()[slc] for m intops]
            else:
 n = [m[slc].cumsum()[slc] for m inn]
 tops = [m[slc].cumsum()[slc] for m intops]

        patches = []


            if rwidth is not None:
                dr = np.clip(rwidth, 0, 1)
            elif (len(n) > 1 and
            elif (len(tops) > 1 and
                  ((not stacked) or rcParams['_internal.classic_mode'])):
                dr = 0.8
            else:
                _barfunc = self.bar
                bottom_kwarg = 'bottom'

            for m, c in zip(n, color):
            for m, c in zip(tops, color):
                if bottom is None:
                    bottom = np.zeros(len(m))
                if stacked:
                    # For data that is normed to form a probability density,
                    # set to minimum data value / logbase
                    # (gives 1 full tick-label unit for the lowest filled bin)
                    ndata = np.array(n)
                    ndata = np.array(tops)
                    minimum = (np.min(ndata[ndata > 0])) / logbase
                else:
                    # For non-normed (density = False) data,
            fill = (histtype == 'stepfilled')

            xvals, yvals = [], []
            for m inn:
            for m intops:
                if stacked:
                    # starting point for drawing polygon
                    y[0] = y[1]
                    p.set_label('_nolegend_')

        if nx == 1:
            returnn[0], bins, cbook.silent_list('Patch', patches[0])
            returntops[0], bins, cbook.silent_list('Patch', patches[0])
        else:
            returnn, bins, cbook.silent_list('Lists of Patches', patches)
            returntops, bins, cbook.silent_list('Lists of Patches', patches)

    @_preprocess_data(replace_names=["x", "y", "weights"], label_namer=None)
    def hist2d(self, x, y, bins=10, range=None, normed=False, weights=None,
diff --git a/lib/matplotlib/tests/baseline_images/test_axes/hist_density.png b/lib/matplotlib/tests/baseline_images/test_axes/hist_density.png
diff --git a/lib/matplotlib/tests/test_axes.py b/lib/matplotlib/tests/test_axes.py
    assert all([p.get_facecolor() == p.get_edgecolor() for p in patches])


 @image_comparison(baseline_images=['hist_density'], extensions=['png'])
 def test_hist_density():
    np.random.seed(19680801)
    data = np.random.standard_normal(2000)
    fig, ax = plt.subplots()
    ax.hist(data, density=True)


 @image_comparison(baseline_images=['hist_step_log_bottom'],
                  remove_text=True, extensions=['png'])
 def test_hist_step_log_bottom():
Original file line number	Diff line number	Diff line change
Expand Up		@@ -6105,13 +6105,6 @@ def hist(self, x, bins=None, range=None, density=None, weights=None,
		--------
		hist2d : 2D histograms

		Notes
		-----
		Until numpy release 1.5, the underlying numpy histogram function was
		incorrect with ``normed=True`` if bin sizes were unequal. MPL
		inherited that error. It is now corrected within MPL when using
		earlier numpy versions.

		"""
		# Avoid shadowing the builtin.
		bin_range = range
Expand DownExpand Up		@@ -6204,38 +6197,37 @@ def hist(self, x, bins=None, range=None, density=None, weights=None,
		else:
		hist_kwargs = dict(range=bin_range)

		n = []
		# List to store all the top coordinates of the histograms
		tops = []
		mlast = None
		# Loop through datasets
		for i in xrange(nx):
		# this will automatically overwrite bins,
		# so that each histogram uses the same bins
		m, bins = np.histogram(x[i], bins, weights=w[i], **hist_kwargs)
		m = m.astype(float) # causes problems later if it's an int
		if mlast is None:
		mlast = np.zeros(len(bins)-1, m.dtype)
		if density and not stacked:
		db = np.diff(bins)
		m = (m.astype(float) / db) / m.sum()
		if stacked:
		if mlast is None:
		mlast = np.zeros(len(bins)-1, m.dtype)
		m += mlast
		mlast[:] = m
		n.append(m)
		tops.append(m)

		# If a stacked density plot, normalize so the area of all the stacked
		# histograms together is 1
		if stacked and density:
		db = np.diff(bins)
		for m inn:
		m[:] = (m.astype(float) / db) /n[-1].sum()
		for m intops:
		m[:] = (m.astype(float) / db) /tops[-1].sum()
		if cumulative:
		slc = slice(None)
		if cbook.is_numlike(cumulative) and cumulative < 0:
		slc = slice(None, None, -1)

		if density:
		n = [(m * np.diff(bins))[slc].cumsum()[slc] for m inn]
		tops = [(m * np.diff(bins))[slc].cumsum()[slc] for m intops]
		else:
		n = [m[slc].cumsum()[slc] for m inn]
		tops = [m[slc].cumsum()[slc] for m intops]

		patches = []

Expand All		@@ -6253,7 +6245,7 @@ def hist(self, x, bins=None, range=None, density=None, weights=None,

		if rwidth is not None:
		dr = np.clip(rwidth, 0, 1)
		elif (len(n) > 1 and
		elif (len(tops) > 1 and
		((not stacked) or rcParams['_internal.classic_mode'])):
		dr = 0.8
		else:
Expand All		@@ -6279,7 +6271,7 @@ def hist(self, x, bins=None, range=None, density=None, weights=None,
		_barfunc = self.bar
		bottom_kwarg = 'bottom'

		for m, c in zip(n, color):
		for m, c in zip(tops, color):
		if bottom is None:
		bottom = np.zeros(len(m))
		if stacked:
Expand DownExpand Up		@@ -6323,7 +6315,7 @@ def hist(self, x, bins=None, range=None, density=None, weights=None,
		# For data that is normed to form a probability density,
		# set to minimum data value / logbase
		# (gives 1 full tick-label unit for the lowest filled bin)
		ndata = np.array(n)
		ndata = np.array(tops)
		minimum = (np.min(ndata[ndata > 0])) / logbase
		else:
		# For non-normed (density = False) data,
Expand All		@@ -6346,7 +6338,7 @@ def hist(self, x, bins=None, range=None, density=None, weights=None,
		fill = (histtype == 'stepfilled')

		xvals, yvals = [], []
		for m inn:
		for m intops:
		if stacked:
		# starting point for drawing polygon
		y[0] = y[1]
Expand DownExpand Up		@@ -6409,9 +6401,9 @@ def hist(self, x, bins=None, range=None, density=None, weights=None,
		p.set_label('_nolegend_')

		if nx == 1:
		returnn[0], bins, cbook.silent_list('Patch', patches[0])
		returntops[0], bins, cbook.silent_list('Patch', patches[0])
		else:
		returnn, bins, cbook.silent_list('Lists of Patches', patches)
		returntops, bins, cbook.silent_list('Lists of Patches', patches)

		@_preprocess_data(replace_names=["x", "y", "weights"], label_namer=None)
		def hist2d(self, x, y, bins=10, range=None, normed=False, weights=None,
Expand Down
Original file line number	Diff line number	Diff line change
Expand Up		@@ -1493,6 +1493,14 @@ def test_hist_step_filled():
		assert all([p.get_facecolor() == p.get_edgecolor() for p in patches])


		@image_comparison(baseline_images=['hist_density'], extensions=['png'])
		def test_hist_density():
		np.random.seed(19680801)
		data = np.random.standard_normal(2000)
		fig, ax = plt.subplots()
		ax.hist(data, density=True)


		@image_comparison(baseline_images=['hist_step_log_bottom'],
		remove_text=True, extensions=['png'])
		def test_hist_step_log_bottom():
Expand Down