matplotlib 3.5.0b1 breaks test suite #2663
Comments
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Does something like this work? diff --git a/seaborn/tests/test_categorical.py b/seaborn/tests/test_categorical.py
index d4e09b7..bb5bc51 100644
--- a/seaborn/tests/test_categorical.py
+++ b/seaborn/tests/test_categorical.py
@@ -1,5 +1,6 @@
import itertools
from functools import partial
+from unittest.mock import patch
import numpy as np
import pandas as pd
@@ -852,15 +853,17 @@ class TestBoxPlotter(CategoricalFixture):
p.establish_variables("h", "y", "g", data=self.df)
npt.assert_array_almost_equal(p.hue_offsets, [-.2, 0, .2])
- def test_axes_data(self):
+ @patch('matplotlib.axes.Axes.boxplot')
+ def test_axes_data(self, boxplot):
- ax = cat.boxplot(x="g", y="y", data=self.df)
- assert len(ax.artists) == 3
+ cat.boxplot(x="g", y="y", data=self.df)
+ assert boxplot.call_count == 3
plt.close("all")
+ boxplot.reset_mock()
- ax = cat.boxplot(x="g", y="y", hue="h", data=self.df)
- assert len(ax.artists) == 6
+ cat.boxplot(x="g", y="y", hue="h", data=self.df)
+ assert boxplot.call_count == 6
plt.close("all")
I think this preserves the intent of this test, but I have not looked at all of them, and it may not work for all. |
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Hm I've never really thought about patching matplotlib functions and checking the call count. I actually think this would fail here because in the first case I think the way forward here is to have a |
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No, it does passes. The seaborn |
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Ah, I just realized that some tests that |
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Ah, seems you're right. Those functions aren't so consistent ... That zip issue is a recurring frustration! |
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So the problem with switching to I experimented a bit with catching the results with a mock patch, but it looks like you have to do a few steps to capture return values. I pulled that together into a context manager, which looks like the following: diff --git a/seaborn/tests/test_categorical.py b/seaborn/tests/test_categorical.py
index d4e09b7..fcea099 100644
--- a/seaborn/tests/test_categorical.py
+++ b/seaborn/tests/test_categorical.py
@@ -1,5 +1,8 @@
+from contextlib import contextmanager
import itertools
+import functools
from functools import partial
+from unittest import mock
import numpy as np
import pandas as pd
@@ -503,6 +506,21 @@ class TestCategoricalPlotter(CategoricalFixture):
assert p.colors == [(.25, .25, .75), (.75, .25, .25), (1, 1, 1)]
+@contextmanager
+def return_value_collector(obj, method_name):
+ func = getattr(obj, method_name)
+ collection = []
+
+ @functools.wraps(func)
+ def wrapper(*args, **kwargs):
+ result = func(*args, **kwargs)
+ collection.append(result)
+ return result
+
+ with mock.patch.object(obj, method_name, side_effect=wrapper):
+ yield collection
+
+
class TestCategoricalStatPlotter(CategoricalFixture):
def test_no_bootstrappig(self):
@@ -854,53 +872,67 @@ class TestBoxPlotter(CategoricalFixture):
def test_axes_data(self):
- ax = cat.boxplot(x="g", y="y", data=self.df)
- assert len(ax.artists) == 3
+ fig, ax = plt.subplots()
+ with return_value_collector(ax, 'boxplot') as bxp_artists:
+ cat.boxplot(x="g", y="y", data=self.df, ax=ax)
+ assert len(bxp_artists) == 3
plt.close("all")
- ax = cat.boxplot(x="g", y="y", hue="h", data=self.df)
- assert len(ax.artists) == 6
+ fig, ax = plt.subplots()
+ with return_value_collector(ax, 'boxplot') as bxp_artists:
+ cat.boxplot(x="g", y="y", hue="h", data=self.df, ax=ax)
+ assert len(bxp_artists) == 6
plt.close("all")
def test_box_colors(self):
- ax = cat.boxplot(x="g", y="y", data=self.df, saturation=1)
- pal = palettes.color_palette(n_colors=3)
- for patch, color in zip(ax.artists, pal):
- assert patch.get_facecolor()[:3] == color
+ fig, ax = plt.subplots()
+ with return_value_collector(ax, 'boxplot') as bxp_artists:
+ cat.boxplot(x="g", y="y", data=self.df, saturation=1, ax=ax)
+
+ pal = palettes.color_palette(n_colors=3)
+ for bxp, color in itertools.zip_longest(bxp_artists, pal):
+ patch, = bxp['boxes'] # There must be 1 only.
+ assert patch.get_facecolor()[:3] == color
plt.close("all")
- ax = cat.boxplot(x="g", y="y", hue="h", data=self.df, saturation=1)
- pal = palettes.color_palette(n_colors=2)
- for patch, color in zip(ax.artists, pal * 2):
- assert patch.get_facecolor()[:3] == color
+ fig, ax = plt.subplots()
+ with return_value_collector(ax, 'boxplot') as bxp_artists:
+ cat.boxplot(x="g", y="y", hue="h", data=self.df, saturation=1, ax=ax)
+
+ pal = palettes.color_palette(n_colors=2)
+ for bxp, color in itertools.zip_longest(bxp_artists, pal * 2):
+ patch, = bxp['boxes'] # There must be 1 only.
+ assert patch.get_facecolor()[:3] == color
plt.close("all")
Note, this does require knowing that Matplotlb's |
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Alternately, could just change the tests to look for Or we could exclude the patches that get added for the legend by counting patches that don't have a label. |
As was noted in mwaskom#2663 (comment), the `test_box_colors` tests are broken because `zip` only iterates to the shortest input. For boxplot, there are in fact 3 copies of the palette not 2. For boxenplot, it doesn't add `Patch` at all but `PatchCollection`, as can be seen in the related tests.
As was noted in #2663 (comment), the `test_box_colors` tests are broken because `zip` only iterates to the shortest input. For boxplot, there are in fact 3 copies of the palette not 2. For boxenplot, it doesn't add `Patch` at all but `PatchCollection`, as can be seen in the related tests.
I'm pretty sure these failures are all related to things that happen as part of tests but not normal operations and so this isn't an urgent problem, but they should get addressed before the final release drops and breaks CI.
$ pytest seaborn -q ............ssssss..................................................................................... [ 8%] ...............................................................................F.FF.................... [ 17%] .............................................................................s......................... [ 25%] ............s................................F......................................................... [ 34%] ....................................................................................................... [ 43%] .........................sss........................................................................s.. [ 51%] ...........F..FFFFFFF..F............................................................................... [ 60%] ........................................................................................F.............. [ 68%] .....................................ss................................................................ [ 77%] ........................................................ss.ss.........ssss...................s......... [ 86%] ....................................................................................................... [ 94%] .......s........................................................ [100%] ================================================== FAILURES =================================================== ________________________________________ TestBoxPlotter.test_axes_data ________________________________________ self = <seaborn.tests.test_categorical.TestBoxPlotter object at 0x7f9dce7f2d60> def test_axes_data(self): ax = cat.boxplot(x="g", y="y", data=self.df) > assert len(ax.artists) == 3 E AssertionError: assert 0 == 3 E + where 0 = len(<Axes.ArtistList of 0 artists>) E + where <Axes.ArtistList of 0 artists> = <AxesSubplot:xlabel='g', ylabel='y'>.artists seaborn/tests/test_categorical.py:858: AssertionError ___________________________________ TestBoxPlotter.test_draw_missing_boxes ____________________________________ self = <seaborn.tests.test_categorical.TestBoxPlotter object at 0x7f9dc93d8eb0> def test_draw_missing_boxes(self): ax = cat.boxplot(x="g", y="y", data=self.df, order=["a", "b", "c", "d"]) > assert len(ax.artists) == 3 E AssertionError: assert 0 == 3 E + where 0 = len(<Axes.ArtistList of 0 artists>) E + where <Axes.ArtistList of 0 artists> = <AxesSubplot:xlabel='g', ylabel='y'>.artists seaborn/tests/test_categorical.py:887: AssertionError ______________________________________ TestBoxPlotter.test_missing_data _______________________________________ self = <seaborn.tests.test_categorical.TestBoxPlotter object at 0x7f9dceec4b50> def test_missing_data(self): x = ["a", "a", "b", "b", "c", "c", "d", "d"] h = ["x", "y", "x", "y", "x", "y", "x", "y"] y = self.rs.randn(8) y[-2:] = np.nan ax = cat.boxplot(x=x, y=y) > assert len(ax.artists) == 3 E assert 0 == 3 E + where 0 = len(<Axes.ArtistList of 0 artists>) E + where <Axes.ArtistList of 0 artists> = <AxesSubplot:>.artists seaborn/tests/test_categorical.py:897: AssertionError _______________________________________ TestCatPlot.test_plot_elements ________________________________________ self = <seaborn.tests.test_categorical.TestCatPlot object at 0x7f9daa710ee0> def test_plot_elements(self): g = cat.catplot(x="g", y="y", data=self.df, kind="point") assert len(g.ax.collections) == 1 want_lines = self.g.unique().size + 1 assert len(g.ax.lines) == want_lines g = cat.catplot(x="g", y="y", hue="h", data=self.df, kind="point") want_collections = self.h.unique().size assert len(g.ax.collections) == want_collections want_lines = (self.g.unique().size + 1) * self.h.unique().size assert len(g.ax.lines) == want_lines g = cat.catplot(x="g", y="y", data=self.df, kind="bar") want_elements = self.g.unique().size assert len(g.ax.patches) == want_elements assert len(g.ax.lines) == want_elements g = cat.catplot(x="g", y="y", hue="h", data=self.df, kind="bar") want_elements = self.g.unique().size * self.h.unique().size assert len(g.ax.patches) == want_elements assert len(g.ax.lines) == want_elements g = cat.catplot(x="g", data=self.df, kind="count") want_elements = self.g.unique().size assert len(g.ax.patches) == want_elements assert len(g.ax.lines) == 0 g = cat.catplot(x="g", hue="h", data=self.df, kind="count") want_elements = self.g.unique().size * self.h.unique().size assert len(g.ax.patches) == want_elements assert len(g.ax.lines) == 0 g = cat.catplot(x="g", y="y", data=self.df, kind="box") want_artists = self.g.unique().size > assert len(g.ax.artists) == want_artists E AssertionError: assert 0 == 3 E + where 0 = len(<Axes.ArtistList of 0 artists>) E + where <Axes.ArtistList of 0 artists> = <AxesSubplot:xlabel='g', ylabel='y'>.artists E + where <AxesSubplot:xlabel='g', ylabel='y'> = <seaborn.axisgrid.FacetGrid object at 0x7f9da8f82160>.ax seaborn/tests/test_categorical.py:2769: AssertionError ______________________________________ TestKDEPlotUnivariate.test_legend ______________________________________ self = <seaborn.tests.test_distributions.TestKDEPlotUnivariate object at 0x7f9d9d1cb130> long_df = x y z a b c d t s f a_cat s_cat s_str 0 12 0.449243 6.61188...8 99 15 0.073484 1.036343 c p 0 2005-01-01 2004-07-30 00:11:00 8 0.2 c 8 8 [100 rows x 13 columns] def test_legend(self, long_df): ax = kdeplot(data=long_df, x="x", hue="a") assert ax.legend_.get_title().get_text() == "a" legend_labels = ax.legend_.get_texts() order = categorical_order(long_df["a"]) for label, level in zip(legend_labels, order): assert label.get_text() == level legend_artists = ax.legend_.findobj(mpl.lines.Line2D)[::2] palette = color_palette() for artist, color in zip(legend_artists, palette): > assert_colors_equal(artist.get_color(), color) seaborn/tests/test_distributions.py:857: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ a = (0.3333333333333333, 0.6588235294117647, 0.40784313725490196, 1) b = (0.8666666666666667, 0.5176470588235295, 0.3215686274509804), check_alpha = True def assert_colors_equal(a, b, check_alpha=True): def handle_array(x): if isinstance(x, np.ndarray): if x.ndim > 1: x = np.unique(x, axis=0).squeeze() if x.ndim > 1: raise ValueError("Color arrays must be 1 dimensional") return x a = handle_array(a) b = handle_array(b) f = to_rgba if check_alpha else to_rgb > assert f(a) == f(b) E AssertionError seaborn/_testing.py:90: AssertionError ___________________________________ TestKDEPlotBivariate.test_fill_artists ____________________________________ self = <seaborn.tests.test_distributions.TestKDEPlotBivariate object at 0x7f9d9d6e9a00> long_df = x y z a b c d t s f a_cat s_cat s_str 0 12 0.449243 6.61188...8 99 15 0.073484 1.036343 c p 0 2005-01-01 2004-07-30 00:11:00 8 0.2 c 8 8 [100 rows x 13 columns] def test_fill_artists(self, long_df): for fill in [True, False]: f, ax = plt.subplots() kdeplot(data=long_df, x="x", y="y", hue="c", fill=fill) for c in ax.collections: if fill: assert isinstance(c, mpl.collections.PathCollection) else: > assert isinstance(c, mpl.collections.LineCollection) E AssertionError: assert False E + where False = isinstance(<matplotlib.collections.PathCollection object at 0x7f9d9d55f400>, <class 'matplotlib.collections.LineCollection'>) E + where <class 'matplotlib.collections.LineCollection'> = <module 'matplotlib.collections' from '/Users/mwaskom/miniconda3/envs/seaborn-release-test/lib/python3.9/site-packages/matplotlib/collections.py'>.LineCollection E + where <module 'matplotlib.collections' from '/Users/mwaskom/miniconda3/envs/seaborn-release-test/lib/python3.9/site-packages/matplotlib/collections.py'> = mpl.collections seaborn/tests/test_distributions.py:908: AssertionError ____________________________________ TestKDEPlotBivariate.test_common_norm ____________________________________ self = <seaborn.tests.test_distributions.TestKDEPlotBivariate object at 0x7f9d9d6f1430> rng = RandomState(MT19937) at 0x7F9DC56BE740 def test_common_norm(self, rng): hue = np.repeat(["a", "a", "a", "b"], 40) x, y = rng.multivariate_normal([0, 0], [(.2, .5), (.5, 2)], len(hue)).T x[hue == "a"] -= 2 x[hue == "b"] += 2 f, (ax1, ax2) = plt.subplots(ncols=2) kdeplot(x=x, y=y, hue=hue, common_norm=True, ax=ax1) kdeplot(x=x, y=y, hue=hue, common_norm=False, ax=ax2) > n_seg_1 = sum([len(c.get_segments()) > 0 for c in ax1.collections]) seaborn/tests/test_distributions.py:921: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ .0 = <generator object _AxesBase.ArtistList.__iter__ at 0x7f9d9d587f20> > n_seg_1 = sum([len(c.get_segments()) > 0 for c in ax1.collections]) E AttributeError: 'PathCollection' object has no attribute 'get_segments' seaborn/tests/test_distributions.py:921: AttributeError _____________________________________ TestKDEPlotBivariate.test_log_scale _____________________________________ self = <seaborn.tests.test_distributions.TestKDEPlotBivariate object at 0x7f9d9d5cddf0> rng = RandomState(MT19937) at 0x7F9DC56BE640 def test_log_scale(self, rng): x = rng.lognormal(0, 1, 100) y = rng.uniform(0, 1, 100) levels = .2, .5, 1 f, ax = plt.subplots() kdeplot(x=x, y=y, log_scale=True, levels=levels, ax=ax) assert ax.get_xscale() == "log" assert ax.get_yscale() == "log" f, (ax1, ax2) = plt.subplots(ncols=2) kdeplot(x=x, y=y, log_scale=(10, False), levels=levels, ax=ax1) assert ax1.get_xscale() == "log" assert ax1.get_yscale() == "linear" p = _DistributionPlotter() kde = KDE() density, (xx, yy) = kde(np.log10(x), y) levels = p._quantile_to_level(density, levels) ax2.contour(10 ** xx, yy, density, levels=levels) for c1, c2 in zip(ax1.collections, ax2.collections): > assert_array_equal(c1.get_segments(), c2.get_segments()) E AttributeError: 'PathCollection' object has no attribute 'get_segments' seaborn/tests/test_distributions.py:949: AttributeError _____________________________________ TestKDEPlotBivariate.test_bandwidth _____________________________________ self = <seaborn.tests.test_distributions.TestKDEPlotBivariate object at 0x7f9d9e4a4f10> rng = RandomState(MT19937) at 0x7F9DC56BEB40 def test_bandwidth(self, rng): n = 100 x, y = rng.multivariate_normal([0, 0], [(.2, .5), (.5, 2)], n).T f, (ax1, ax2) = plt.subplots(ncols=2) kdeplot(x=x, y=y, ax=ax1) kdeplot(x=x, y=y, bw_adjust=2, ax=ax2) for c1, c2 in zip(ax1.collections, ax2.collections): > seg1, seg2 = c1.get_segments(), c2.get_segments() E AttributeError: 'PathCollection' object has no attribute 'get_segments' seaborn/tests/test_distributions.py:962: AttributeError ______________________________________ TestKDEPlotBivariate.test_weights ______________________________________ self = <seaborn.tests.test_distributions.TestKDEPlotBivariate object at 0x7f9d9f60e5b0> rng = RandomState(MT19937) at 0x7F9DC56BE840 def test_weights(self, rng): import warnings warnings.simplefilter("error", np.VisibleDeprecationWarning) n = 100 x, y = rng.multivariate_normal([1, 3], [(.2, .5), (.5, 2)], n).T hue = np.repeat([0, 1], n // 2) weights = rng.uniform(0, 1, n) f, (ax1, ax2) = plt.subplots(ncols=2) kdeplot(x=x, y=y, hue=hue, ax=ax1) kdeplot(x=x, y=y, hue=hue, weights=weights, ax=ax2) for c1, c2 in zip(ax1.collections, ax2.collections): > if c1.get_segments() and c2.get_segments(): E AttributeError: 'PathCollection' object has no attribute 'get_segments' seaborn/tests/test_distributions.py:983: AttributeError _________________________________ TestKDEPlotBivariate.test_hue_ignores_cmap __________________________________ self = <seaborn.tests.test_distributions.TestKDEPlotBivariate object at 0x7f9d9f166760> long_df = x y z a b c d t s f a_cat s_cat s_str 0 12 0.449243 6.61188...8 99 15 0.073484 1.036343 c p 0 2005-01-01 2004-07-30 00:11:00 8 0.2 c 8 8 [100 rows x 13 columns] def test_hue_ignores_cmap(self, long_df): with pytest.warns(UserWarning, match="cmap parameter ignored"): ax = kdeplot(data=long_df, x="x", y="y", hue="c", cmap="viridis") > assert_colors_equal(ax.collections[0].get_color(), "C0") E AttributeError: 'PathCollection' object has no attribute 'get_color' seaborn/tests/test_distributions.py:993: AttributeError ________________________________ TestKDEPlotBivariate.test_contour_line_colors ________________________________ self = <seaborn.tests.test_distributions.TestKDEPlotBivariate object at 0x7f9d9f2c5e50> long_df = x y z a b c d t s f a_cat s_cat s_str 0 12 0.449243 6.61188...8 99 15 0.073484 1.036343 c p 0 2005-01-01 2004-07-30 00:11:00 8 0.2 c 8 8 [100 rows x 13 columns] def test_contour_line_colors(self, long_df): color = (.2, .9, .8, 1) ax = kdeplot(data=long_df, x="x", y="y", color=color) for c in ax.collections: > assert_colors_equal(c.get_color(), color) E AttributeError: 'PathCollection' object has no attribute 'get_color' seaborn/tests/test_distributions.py:1001: AttributeError _________________________________ TestKDEPlotBivariate.test_levels_and_thresh _________________________________ self = <seaborn.tests.test_distributions.TestKDEPlotBivariate object at 0x7f9d9f821cd0> long_df = x y z a b c d t s f a_cat s_cat s_str 0 12 0.449243 6.61188...8 99 15 0.073484 1.036343 c p 0 2005-01-01 2004-07-30 00:11:00 8 0.2 c 8 8 [100 rows x 13 columns] def test_levels_and_thresh(self, long_df): f, (ax1, ax2) = plt.subplots(ncols=2) n = 8 thresh = .1 plot_kws = dict(data=long_df, x="x", y="y") kdeplot(**plot_kws, levels=n, thresh=thresh, ax=ax1) kdeplot(**plot_kws, levels=np.linspace(thresh, 1, n), ax=ax2) for c1, c2 in zip(ax1.collections, ax2.collections): > assert_array_equal(c1.get_segments(), c2.get_segments()) E AttributeError: 'PathCollection' object has no attribute 'get_segments' seaborn/tests/test_distributions.py:1033: AttributeError _____________________________________ TestDisPlot.test_bivariate_kde_norm _____________________________________ self = <seaborn.tests.test_distributions.TestDisPlot object at 0x7f9d7d930b20> rng = RandomState(MT19937) at 0x7F9D8D67E840 def test_bivariate_kde_norm(self, rng): x, y = rng.normal(0, 1, (2, 100)) z = [0] * 80 + [1] * 20 g = displot(x=x, y=y, col=z, kind="kde", levels=10) > l1 = sum(bool(c.get_segments()) for c in g.axes.flat[0].collections) seaborn/tests/test_distributions.py:2325: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ .0 = <generator object _AxesBase.ArtistList.__iter__ at 0x7f9d7ee79510> > l1 = sum(bool(c.get_segments()) for c in g.axes.flat[0].collections) E AttributeError: 'PathCollection' object has no attribute 'get_segments' seaborn/tests/test_distributions.py:2325: AttributeError ============================================== warnings summary =============================================== seaborn/tests/test_axisgrid.py: 15 warnings seaborn/tests/test_distributions.py: 44 warnings /Users/mwaskom/code/seaborn/seaborn/distributions.py:832: MatplotlibDeprecationWarning: Auto-removal of grids by pcolor() and pcolormesh() is deprecated since 3.5 and will be removed two minor releases later; please call grid(False) first. mesh = ax.pcolormesh( seaborn/tests/test_categorical.py: 96 warnings seaborn/tests/test_relational.py: 1 warning /Users/mwaskom/code/seaborn/seaborn/utils.py:115: MatplotlibDeprecationWarning: Support for passing numbers through unit converters is deprecated since 3.5 and support will be removed two minor releases later; use Axis.convert_units instead. scout = method(scout_x, scout_y, **kws) seaborn/tests/test_categorical.py: 87 warnings /Users/mwaskom/code/seaborn/seaborn/categorical.py:281: MatplotlibDeprecationWarning: Support for passing numbers through unit converters is deprecated since 3.5 and support will be removed two minor releases later; use Axis.convert_units instead. ax.scatter([], [], s=60, color=mpl.colors.rgb2hex(color), label=level) seaborn/tests/test_categorical.py: 24 warnings /Users/mwaskom/code/seaborn/seaborn/categorical.py:266: MatplotlibDeprecationWarning: Support for passing numbers through unit converters is deprecated since 3.5 and support will be removed two minor releases later; use Axis.convert_units instead. points = ax.scatter(sub_data["x"], sub_data["y"], color=color, **plot_kws) seaborn/tests/test_categorical.py: 63 warnings /Users/mwaskom/code/seaborn/seaborn/categorical.py:371: MatplotlibDeprecationWarning: Support for passing numbers through unit converters is deprecated since 3.5 and support will be removed two minor releases later; use Axis.convert_units instead. ax.scatter([], [], s=60, color=mpl.colors.rgb2hex(color), label=level) seaborn/tests/test_categorical.py::TestSwarmPlot::test_order[str-order3] seaborn/tests/test_categorical.py::TestSwarmPlot::test_order[int-order7] seaborn/tests/test_categorical.py::TestSwarmPlot::test_hue_dodged[a] seaborn/tests/test_categorical.py::TestSwarmPlot::test_hue_dodged[a] seaborn/tests/test_categorical.py::TestSwarmPlot::test_hue_dodged[a] seaborn/tests/test_categorical.py::TestSwarmPlot::test_hue_dodged[a] seaborn/tests/test_categorical.py::TestSwarmPlot::test_hue_dodged[a] seaborn/tests/test_categorical.py::TestSwarmPlot::test_hue_dodged[a] /Users/mwaskom/code/seaborn/seaborn/categorical.py:319: MatplotlibDeprecationWarning: Support for passing numbers through unit converters is deprecated since 3.5 and support will be removed two minor releases later; use Axis.convert_units instead. points = ax.scatter(sub_data["x"], sub_data["y"], color=color, **plot_kws) seaborn/tests/test_distributions.py::TestKDEPlotBivariate::test_colorbar /Users/mwaskom/code/seaborn/seaborn/distributions.py:1176: MatplotlibDeprecationWarning: Auto-removal of grids by pcolor() and pcolormesh() is deprecated since 3.5 and will be removed two minor releases later; please call grid(False) first. ax.figure.colorbar(cset, cbar_ax, ax, **cbar_kws) seaborn/tests/test_distributions.py::TestHistPlotUnivariate::test_auto_linewidth[False] seaborn/tests/test_relational.py::TestLinePlotter::test_lineplot_vs_relplot[long_semantics2] /Users/mwaskom/code/seaborn/seaborn/utils.py:101: MatplotlibDeprecationWarning: Support for passing numbers through unit converters is deprecated since 3.5 and support will be removed two minor releases later; use Axis.convert_units instead. scout, = method([], [], **kws) seaborn/tests/test_distributions.py::TestHistPlotBivariate::test_colorbar seaborn/tests/test_distributions.py::TestHistPlotBivariate::test_colorbar /Users/mwaskom/code/seaborn/seaborn/distributions.py:850: MatplotlibDeprecationWarning: Auto-removal of grids by pcolor() and pcolormesh() is deprecated since 3.5 and will be removed two minor releases later; please call grid(False) first. ax.figure.colorbar(mesh, cbar_ax, ax, **cbar_kws) seaborn/tests/test_matrix.py: 77 warnings /Users/mwaskom/code/seaborn/seaborn/matrix.py:306: MatplotlibDeprecationWarning: Auto-removal of grids by pcolor() and pcolormesh() is deprecated since 3.5 and will be removed two minor releases later; please call grid(False) first. mesh = ax.pcolormesh(self.plot_data, cmap=self.cmap, **kws) seaborn/tests/test_matrix.py: 47 warnings /Users/mwaskom/code/seaborn/seaborn/matrix.py:316: MatplotlibDeprecationWarning: Auto-removal of grids by pcolor() and pcolormesh() is deprecated since 3.5 and will be removed two minor releases later; please call grid(False) first. cb = ax.figure.colorbar(mesh, cax, ax, **self.cbar_kws) -- Docs: https://docs.pytest.org/en/stable/warnings.html =========================================== short test summary info =========================================== FAILED seaborn/tests/test_categorical.py::TestBoxPlotter::test_axes_data - AssertionError: assert 0 == 3 FAILED seaborn/tests/test_categorical.py::TestBoxPlotter::test_draw_missing_boxes - AssertionError: assert 0... FAILED seaborn/tests/test_categorical.py::TestBoxPlotter::test_missing_data - assert 0 == 3 FAILED seaborn/tests/test_categorical.py::TestCatPlot::test_plot_elements - AssertionError: assert 0 == 3 FAILED seaborn/tests/test_distributions.py::TestKDEPlotUnivariate::test_legend - AssertionError FAILED seaborn/tests/test_distributions.py::TestKDEPlotBivariate::test_fill_artists - AssertionError: assert... FAILED seaborn/tests/test_distributions.py::TestKDEPlotBivariate::test_common_norm - AttributeError: 'PathCo... FAILED seaborn/tests/test_distributions.py::TestKDEPlotBivariate::test_log_scale - AttributeError: 'PathColl... FAILED seaborn/tests/test_distributions.py::TestKDEPlotBivariate::test_bandwidth - AttributeError: 'PathColl... FAILED seaborn/tests/test_distributions.py::TestKDEPlotBivariate::test_weights - AttributeError: 'PathCollec... FAILED seaborn/tests/test_distributions.py::TestKDEPlotBivariate::test_hue_ignores_cmap - AttributeError: 'P... FAILED seaborn/tests/test_distributions.py::TestKDEPlotBivariate::test_contour_line_colors - AttributeError:... FAILED seaborn/tests/test_distributions.py::TestKDEPlotBivariate::test_levels_and_thresh - AttributeError: '... FAILED seaborn/tests/test_distributions.py::TestDisPlot::test_bivariate_kde_norm - AttributeError: 'PathColl... 14 failed, 1159 passed, 24 skipped, 467 warnings in 459.94s (0:07:39)The text was updated successfully, but these errors were encountered: