Jan 16, 2018 · Jan 16, 2018 · Jan 16, 2018 · Jan 16, 2018 · Jan 16, 2018
diff --git a/examples/statistics/hist.py b/examples/statistics/hist.py
    thispatch.set_facecolor(color)

 # We can also normalize our inputs by the total number of counts
 axs[1].hist(x, bins=n_bins,normed=True)
 axs[1].hist(x, bins=n_bins,density=True)

 # Now we format the y-axis to display percentage
 axs[1].yaxis.set_major_formatter(PercentFormatter(xmax=1))
diff --git a/examples/statistics/histogram_features.py b/examples/statistics/histogram_features.py
 fig, ax = plt.subplots()

 # the histogram of the data
 n, bins, patches = ax.hist(x, num_bins,normed=1)
 n, bins, patches = ax.hist(x, num_bins,density=True)

 # add a 'best fit' line
 y = mlab.normpdf(bins, mu, sigma)
diff --git a/lib/matplotlib/tests/test_axes.py b/lib/matplotlib/tests/test_axes.py
    d2 = np.linspace(0, 10, 50)
    fig = plt.figure()
    ax = fig.add_subplot(111)
    ax.hist((d1, d2), stacked=True,normed=True)
    ax.hist((d1, d2), stacked=True,density=True)


 @image_comparison(baseline_images=['hist_stacked_normed'], extensions=['png'])
diff --git a/tutorials/introductory/pyplot.py b/tutorials/introductory/pyplot.py
 x = mu + sigma * np.random.randn(10000)

 # the histogram of the data
 n, bins, patches = plt.hist(x, 50, normed=1, facecolor='g', alpha=0.75)

 n, bins, patches = plt.hist(x, 50, density=True, facecolor='g', alpha=0.75)

 plt.xlabel('Smarts')
 plt.ylabel('Probability')
Original file line number	Diff line number	Diff line change
Expand Up		@@ -63,7 +63,7 @@
		thispatch.set_facecolor(color)

		# We can also normalize our inputs by the total number of counts
		axs[1].hist(x, bins=n_bins,normed=True)
		axs[1].hist(x, bins=n_bins,density=True)

		# Now we format the y-axis to display percentage
		axs[1].yaxis.set_major_formatter(PercentFormatter(xmax=1))
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Original file line number	Diff line number	Diff line change
Expand Up		@@ -35,7 +35,7 @@
		fig, ax = plt.subplots()

		# the histogram of the data
		n, bins, patches = ax.hist(x, num_bins,normed=1)
		n, bins, patches = ax.hist(x, num_bins,density=True)

		# add a 'best fit' line
		y = mlab.normpdf(bins, mu, sigma)
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Original file line number	Diff line number	Diff line change
Expand Up		@@ -2932,7 +2932,7 @@ def test_hist_stacked_normed():
		d2 = np.linspace(0, 10, 50)
		fig = plt.figure()
		ax = fig.add_subplot(111)
		ax.hist((d1, d2), stacked=True,normed=True)
Copy link Member dstansbyJan 16, 2018 Choose a reason for hiding this comment The reason will be displayed to describe this comment to others.Learn more. I think this should stay as it is, because we should still be testing that`normed` works until it is removed. Copy link Member efiringJan 16, 2018 Choose a reason for hiding this comment The reason will be displayed to describe this comment to others.Learn more. In that case, shouldn't the test have two forms, one for 'normed' and the other for 'density'? At a more general level, we never have tested everything, and never will--there are too many possible combinations. We need to concentrate on the most useful tests, putting our resources, and those of the CI services, where they will be most effective in improving mpl. Testing that a deprecated kwarg still works seems very low priority, doesn't it? The time of deprecation seems like a perfectly reasonable time to switch a test to the non-deprecated alternative. Copy link Member dstansbyJan 16, 2018 Choose a reason for hiding this comment The reason will be displayed to describe this comment to others.Learn more. There is a test directly below this one for`density`. Sure we've never tested everything, but we shouldaim to test everything. This test definitely isn't a CI bottleneck. In my opinion, something being deprecated means it is still supported, but there are plans for it to be removed. If it's supported, it should be tested. I'm not going to block on removing the`normed` test, but I am -2 on it. Copy link Member efiringJan 16, 2018 Choose a reason for hiding this comment The reason will be displayed to describe this comment to others.Learn more. Aiming to test everything doesn't make sense to me, for the reason stated above.
		ax.hist((d1, d2), stacked=True,density=True)


		@image_comparison(baseline_images=['hist_stacked_normed'], extensions=['png'])
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Original file line number	Diff line number	Diff line change
Expand Up		@@ -333,8 +333,7 @@ def f(t):
		x = mu + sigma * np.random.randn(10000)

		# the histogram of the data
		n, bins, patches = plt.hist(x, 50, normed=1, facecolor='g', alpha=0.75)

		n, bins, patches = plt.hist(x, 50, density=True, facecolor='g', alpha=0.75)

		plt.xlabel('Smarts')
		plt.ylabel('Probability')
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