8000 Random test cleanups by timhoffm · Pull Request #18577 · matplotlib/matplotlib · GitHub
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Sep 27, 2020
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Random test cleanups #18577

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4 changes: 2 additions & 2 deletions lib/matplotlib/tests/test_afm.py
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Original file line number Diff line number Diff line change
Expand Up @@ -107,7 +107,7 @@ def test_font_manager_weight_normalization():
def test_bad_afm(afm_data):
fh = BytesIO(afm_data)
with pytest.raises(RuntimeError):
header = afm._parse_header(fh)
afm._parse_header(fh)


@pytest.mark.parametrize(
Expand All @@ -132,6 +132,6 @@ def test_bad_afm(afm_data):
def test_malformed_header(afm_data, caplog):
fh = BytesIO(afm_data)
with caplog.at_level(logging.ERROR):
header = afm._parse_header(fh)
afm._parse_header(fh)

assert len(caplog.records) == 1
20 changes: 10 additions & 10 deletions lib/matplotlib/tests/test_agg.py
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8000
Original file line number Diff line number Diff line change
Expand Up @@ -161,30 +161,30 @@ def process_image(self, padded_src, dpi):
fig, ax = plt.subplots()

# draw lines
l1, = ax.plot([0.1, 0.5, 0.9], [0.1, 0.9, 0.5], "bo-",
mec="b", mfc="w", lw=5, mew=3, ms=10, label="Line 1")
l2, = ax.plot([0.1, 0.5, 0.9], [0.5, 0.2, 0.7], "ro-",
mec="r", mfc="w", lw=5, mew=3, ms=10, label="Line 1")
line1, = ax.plot([0.1, 0.5, 0.9], [0.1, 0.9, 0.5], "bo-",
mec="b", mfc="w", lw=5, mew=3, ms=10, label="Line 1")
line2, = ax.plot([0.1, 0.5, 0.9], [0.5, 0.2, 0.7], "ro-",
mec="r", mfc="w", lw=5, mew=3, ms=10, label="Line 1")

gauss = DropShadowFilter(4)

for l in [l1, l2]:
for line in [line1, line2]:

# draw shadows with same lines with slight offset.
xx = l.get_xdata()
yy = l.get_ydata()
xx = line.get_xdata()
yy = line.get_ydata()
shadow, = ax.plot(xx, yy)
shadow.update_from(l)
shadow.update_from(line)

# offset transform
ot = mtransforms.offset_copy(l.get_transform(), ax.figure,
ot = mtransforms.offset_copy(line.get_transform(), ax.figure,
x=4.0, y=-6.0, units='points')

shadow.set_transform(ot)

# adjust zorder of the shadow lines so that it is drawn below the
# original lines
shadow.set_zorder(l.get_zorder() - 0.5)
shadow.set_zorder(line.get_zorder() - 0.5)
shadow.set_agg_filter(gauss)
shadow.set_rasterized(True) # to support mixed-mode renderers

Expand Down
85 changes: 41 additions & 44 deletions lib/matplotlib/tests/test_axes.py
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< 8000 td class="blob-num blob-num-deletion empty-cell"> F438
Original file line number Diff line number Diff line change
Expand Up @@ -2961,15 +2961,14 @@ def test_boxplot_mod_artist_after_plotting():
def test_vert_violinplot_baseline():
# First 9 digits of frac(sqrt(2))
np.random.seed(414213562)
data = [np.random.normal(size=100) for i in range(4)]
data = [np.random.normal(size=100) for _ in range(4)]
ax = plt.axes()
ax.violinplot(data, positions=range(4), showmeans=0, showextrema=0,
showmedians=0)

# Reuse testcase from above for a labeled data test
data = {"d": data}
fig, ax = plt.subplots()
ax = plt.axes()
ax.violinplot("d", positions=range(4), showmeans=0, showextrema=0,
showmedians=0, data=data)

Expand All @@ -2979,7 +2978,7 @@ def test_vert_violinplot_showmeans():
ax = plt.axes()
# First 9 digits of frac(sqrt(3))
np.random.seed(732050807)
data = [np.random.normal(size=100) for i in range(4)]
data = [np.random.normal(size=100) for _ in range(4)]
ax.violinplot(data, positions=range(4), showmeans=1, showextrema=0,
showmedians=0)

Expand All @@ -2989,7 +2988,7 @@ def test_vert_violinplot_showextrema():
ax = plt.axes()
# First 9 digits of frac(sqrt(5))
np.random.seed(236067977)
data = [np.random.normal(size=100) for i in range(4)]
data = [np.random.normal(size=100) for _ in range(4)]
ax.violinplot(data, positions=range(4), showmeans=0, showextrema=1,
showmedians=0)

Expand All @@ -2999,7 +2998,7 @@ def test_vert_violinplot_showmedians():
ax = plt.axes()
# First 9 digits of frac(sqrt(7))
np.random.seed(645751311)
data = [np.random.normal(size=100) for i in range(4)]
data = [np.random.normal(size=100) for _ in range(4)]
ax.violinplot(data, positions=range(4), showmeans=0, showextrema=0,
showmedians=1)

Expand All @@ -3009,7 +3008,7 @@ def test_vert_violinplot_showall():
ax = plt.axes()
# First 9 digits of frac(sqrt(11))
np.random.seed(316624790)
data = [np.random.normal(size=100) for i in range(4)]
data = [np.random.normal(size=100) for _ in range(4)]
ax.violinplot(data, positions=range(4), showmeans=1, showextrema=1,
showmedians=1,
quantiles=[[0.1, 0.9], [0.2, 0.8], [0.3, 0.7], [0.4, 0.6]])
Expand All @@ -3020,7 +3019,7 @@ def test_vert_violinplot_custompoints_10():
ax = plt.axes()
# First 9 digits of frac(sqrt(13))
np.random.seed(605551275)
data = [np.random.normal(size=100) for i in range(4)]
data = [np.random.normal(size=100) for _ in range(4)]
ax.violinplot(data, positions=range(4), showmeans=0, showextrema=0,
showmedians=0, points=10)

Expand All @@ -3030,7 +3029,7 @@ def test_vert_violinplot_custompoints_200():
ax = plt.axes()
# First 9 digits of frac(sqrt(17))
np.random.seed(123105625)
data = [np.random.normal(size=100) for i in range(4)]
data = [np.random.normal(size=100) for _ in range(4)]
ax.violinplot(data, positions=range(4), showmeans=0, showextrema=0,
showmedians=0, points=200)

Expand All @@ -3040,7 +3039,7 @@ def test_horiz_violinplot_baseline():
ax = plt.axes()
# First 9 digits of frac(sqrt(19))
np.random.seed(358898943)
data = [np.random.normal(size=100) for i in range(4)]
data = [np.random.normal(size=100) for _ in range(4)]
ax.violinplot(data, positions=range(4), vert=False, showmeans=0,
showextrema=0, showmedians=0)

Expand All @@ -3050,7 +3049,7 @@ def test_horiz_violinplot_showmedians():
ax = plt.axes()
# First 9 digits of frac(sqrt(23))
np.random.seed(795831523)
data = [np.random.normal(size=100) for i in range(4)]
data = [np.random.normal(size=100) for _ in range(4)]
ax.violinplot(data, positions=range(4), vert=False, showmeans=0,
showextrema=0, showmedians=1)

Expand All @@ -3060,7 +3059,7 @@ def test_horiz_violinplot_showmeans():
ax = plt.axes()
# First 9 digits of frac(sqrt(29))
np.random.seed(385164807)
data = [np.random.normal(size=100) for i in range(4)]
data = [np.random.normal(size=100) for _ in range(4)]
ax.violinplot(data, positions=range(4), vert=False, showmeans=1,
showextrema=0, showmedians=0)

Expand All @@ -3070,7 +3069,7 @@ def test_horiz_violinplot_showextrema():
ax = plt.axes()
# First 9 digits of frac(sqrt(31))
np.random.seed(567764362)
data = [np.random.normal(size=100) for i in range(4)]
data = [np.random.normal(size=100) for _ in range(4)]
ax.violinplot(data, positions=range(4), vert=False, showmeans=0,
showextrema=1, showmedians=0)

Expand All @@ -3080,7 +3079,7 @@ def test_horiz_violinplot_showall():
ax = plt.axes()
# First 9 digits of frac(sqrt(37))
np.random.seed(82762530)
data = [np.random.normal(size=100) for i in range(4)]
data = [np.random.normal(size=100) for _ in range(4)]
ax.violinplot(data, positions=range(4), vert=False, showmeans=1,
showextrema=1, showmedians=1,
quantiles=[[0.1, 0.9], [0.2, 0.8], [0.3, 0.7], [0.4, 0.6]])
Expand All @@ -3091,7 +3090,7 @@ def test_horiz_violinplot_custompoints_10():
ax = plt.axes()
# First 9 digits of frac(sqrt(41))
np.random.seed(403124237)
data = [np.random.normal(size=100) for i in range(4)]
data = [np.random.normal(size=100) for _ in range(4)]
ax.violinplot(data, positions=range(4), vert=False, showmeans=0,
showextrema=0, showmedians=0, points=10)

Expand All @@ -3101,7 +3100,7 @@ def test_horiz_violinplot_custompoints_200():
ax = plt.axes()
# First 9 digits of frac(sqrt(43))
np.random.seed(557438524)
data = [np.random.normal(size=100) for i in range(4)]
data = [np.random.normal(size=100) for _ in range(4)]
ax.violinplot(data, positions=range(4), vert=False, showmeans=0,
showextrema=0, showmedians=0, points=200)

Expand All @@ -3110,7 +3109,7 @@ def test_violinplot_bad_positions():
ax = plt.axes()
# First 9 digits of frac(sqrt(47))
np.random.seed(855654600)
data = [np.random.normal(size=100) for i in range(4)]
data = [np.random.normal(size=100) for _ in range(4)]
with pytest.raises(ValueError):
ax.violinplot(data, positions=range(5))

Expand All @@ -3119,7 +3118,7 @@ def test_violinplot_bad_widths():
ax = plt.axes()
# First 9 digits of frac(sqrt(53))
np.random.seed(280109889)
data = [np.random.normal(size=100) for i in range(4)]
data = [np.random.normal(size=100) for _ in range(4)]
with pytest.raises(ValueError):
ax.violinplot(data, positions=range(4), widths=[1, 2, 3])

Expand Down Expand Up @@ -3718,16 +3717,16 @@ def test_hist_emptydata():
def test_hist_labels():
# test singleton labels OK
fig, ax = plt.subplots()
l = ax.hist([0, 1], label=0)
assert l[2][0].get_label() == '0'
l = ax.hist([0, 1], label=[0])
assert l[2][0].get_label() == '0'
l = ax.hist([0, 1], label=None)
assert l[2][0].get_label() == '_nolegend_'
l = ax.hist([0, 1], label='0')
assert l[2][0].get_label() == '0'
l = ax.hist([0, 1], label='00')
assert l[2][0].get_label() == '00'
_, _, bars = ax.hist([0, 1], label=0)
assert bars[0].get_label() == '0'
_, _, bars = ax.hist([0, 1], label=[0])
assert bars[0].get_label() == '0'
_, _, bars = ax.hist([0, 1], label=None)
assert bars[0].get_label() == '_nolegend_'
_, _, bars = ax.hist([0, 1], label='0')
assert bars[0].get_label() == '0'
_, _, bars = ax.hist([0, 1], label='00')
assert bars[0].get_label() == '00'


@image_comparison(['transparent_markers'], remove_text=True)
Expand Down Expand Up @@ -3908,7 +3907,7 @@ def test_eventplot_defaults():
])
def test_eventplot_colors(colors):
"""Test the *colors* parameter of eventplot. Inspired by issue #8193."""
data = [[i] for i in range(4)] # 4 successive events of different nature
data = [[0], [1], [2], [3]] # 4 successive events of different nature

# Build the list of the expected colors
expected = [c if c is not None else 'C0' for c in colors]
Expand Down Expand Up @@ -4557,9 +4556,9 @@ def test_rcparam_grid_minor():
matplotlib.rcParams['axes.grid'] = True

values = (
(('both'), (True, True)),
(('major'), (True, False)),
(('minor'), (False, True))
('both', (True, True)),
('major', (True, False)),
('minor', (False, True))
)

for locator, result in values:
Expand All @@ -4576,7 +4575,6 @@ def test_vline_limit():
ax = fig.gca()
ax.axvline(0.5)
ax.plot([-0.1, 0, 0.2, 0.1])
(ymin, ymax) = ax.get_ylim()
assert_allclose(ax.get_ylim(), (-.1, .2))


Expand Down Expand Up @@ -4674,10 +4672,10 @@ def test_relim_visible_only():
ax.plot(x1, y1)
assert ax.get_xlim() == x1
assert ax.get_ylim() == y1
l = ax.plot(x2, y2)
line, = ax.plot(x2, y2)
assert ax.get_xlim() == x2
assert ax.get_ylim() == y2
l[0].set_visible(False)
line.set_visible(False)
assert ax.get_xlim() == x2
assert ax.get_ylim() == y2

Expand Down Expand Up @@ -5109,7 +5107,7 @@ def test_rc_spines():
'axes.spines.top': False,
'axes.spines.bottom': False}
with matplotlib.rc_context(rc_dict):
fig, ax = plt.subplots()
plt.subplots() # create a figure and axes with the spine properties


@image_comparison(['rc_grid.png'], savefig_kwarg={'dpi': 40})
Expand Down Expand Up @@ -5484,7 +5482,7 @@ def test_adjust_numtick_aspect():
@image_comparison(["auto_numticks.png"], style='default')
def test_auto_numticks():
# Make tiny, empty subplots, verify that there are only 3 ticks.
fig, axs = plt.subplots(4, 4)
plt.subplots(4, 4)


@image_comparison(["auto_numticks_log.png"], style='default')
Expand Down Expand Up @@ -6121,7 +6119,7 @@ class DummySubplot(matplotlib.axes.SubplotBase, Dummy):
assert DummySubplot is FactoryDummySubplot


def test_gettightbbox_ignoreNaN():
def test_gettightbbox_ignore_nan():
fig, ax = plt.subplots()
remove_ticks_and_titles(fig)
ax.text(np.NaN, 1, 'Boo')
Expand Down Expand Up @@ -6196,6 +6194,7 @@ def test_secondary_fail():
def test_secondary_resize():
fig, ax = plt.subplots(figsize=(10, 5))
ax.plot(np.arange(2, 11), np.arange(2, 11))

def invert(x):
with np.errstate(divide='ignore'):
return 1 / x
Expand All @@ -6209,6 +6208,7 @@ def invert(x):
def test_secondary_minorloc():
fig, ax = plt.subplots(figsize=(10, 5))
ax.plot(np.arange(2, 11), np.arange(2, 11))

def invert(x):
with np.errstate(divide='ignore'):
return 1 / x
Expand Down Expand Up @@ -6332,10 +6332,6 @@ def test_nodecorator():
bbaxis, bbspines, bbax, bbtb = color_boxes(fig, ax)

# test the axis bboxes
target = [
None,
None
]
for nn, b in enumerate(bbaxis):
assert b is None

Expand Down Expand Up @@ -6366,14 +6362,15 @@ def test_displaced_spine():
fig.canvas.draw()
bbaxis, bbspines, bbax, bbtb = color_boxes(fig, ax)

target = [
targets = [
[150., 24., 930., 11.111111],
[150.0, 1080.0, 930.0, 0.0],
[150.0, 119.9999, 11.111, 960.0],
[1068.8888, 119.9999, 11.111, 960.0]
]
for nn, b in enumerate(bbspines):
targetbb = mtransforms.Bbox.from_bounds(*target[nn])
for target, bbspine in zip(targets, bbspines):
targetbb = mtransforms.Bbox.from_bounds(*target)
assert_allclose(bbspine.bounds, targetbb.bounds, atol=1e-2)

target = [150.0, 119.99999999999997, 930.0, 960.0]
targetbb = mtransforms.Bbox.from_bounds(*target)
Expand Down
14 changes: 7 additions & 7 deletions lib/matplotlib/tests/test_bbox_tight.py
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Expand Up @@ -19,26 +19,26 @@ def test_bbox_inches_tight():
[78415, 81858, 150656, 193263, 69638],
[139361, 331509, 343164, 781380, 52269]]

colLabels = rowLabels = [''] * 5
col_labels = row_labels = [''] * 5

rows = len(data)
ind = np.arange(len(colLabels)) + 0.3 # the x locations for the groups
cellText = []
ind = np.arange(len(col_labels)) + 0.3 # the x locations for the groups
cell_text = []
width = 0.4 # the width of the bars
yoff = np.zeros(len(colLabels))
yoff = np.zeros(len(col_labels))
# the bottom values for stacked bar chart
fig, ax = plt.subplots(1, 1)
for row in range(rows):
ax.bar(ind, data[row], width, bottom=yoff, align='edge', color='b')
yoff = yoff + data[row]
cellText.append([''])
cell_text.append([''])
plt.xticks([])
plt.xlim(0, 5)
plt.legend([''] * 5, loc=(1.2, 0.2))
fig.legend([''] * 5, bbox_to_anchor=(0, 0.2), loc='lower left')
# Add a table at the bottom of the axes
cellText.reverse()
plt.table(cellText=cellText, rowLabels=rowLabels, colLabels=colLabels,
cell_text.reverse()
plt.table(cellText=cell_text, rowLabels=row_labels, colLabels=col_labels,
loc='bottom')


Expand Down
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