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TST: Add future dependency tests as a weekly CI job #21634

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Merged
merged 1 commit into from
Feb 12, 2022
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TST: Add future dependency tests as a weekly CI job #21634

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32 changes: 32 additions & 0 deletions .github/workflows/tests.yml
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Original file line number Diff line number Diff line change
Expand Up @@ -8,6 +8,9 @@ on:
pull_request:
branches-ignore:
- v[0-9]+.[0-9]+.[0-9x]+-doc
schedule:
# 3:47 UTC on Saturdays
- cron: "47 3 * * 6"

env:
NO_AT_BRIDGE: 1 # Necessary for GTK3 interactive test.
Expand Down Expand Up @@ -209,6 +212,20 @@ jobs:
echo 'wxPython is available' ||
echo 'wxPython is not available'

- name: Install the nightly dependencies
# Only install the nightly dependencies during the scheduled event
if: ${{ github.event_name == 'schedule' && matrix.name-suffix != '(Minimum Versions)' }}
run: |
python -m pip install -i https://pypi.anaconda.org/scipy-wheels-nightly/simple --upgrade numpy pandas

# Turn all warnings to errors, except ignore the distutils deprecations and the find_spec warning
cat >> pytest.ini << EOF
filterwarnings =
error
ignore:.*distutils:DeprecationWarning
ignore:DynamicImporter.find_spec\(\) not found; falling back to find_module\(\):ImportWarning
EOF

- name: Install Matplotlib
run: |
ccache -s
Expand Down Expand Up @@ -266,3 +283,18 @@ jobs:
with:
name: "${{ matrix.python-version }} ${{ matrix.os }} ${{ matrix.name-suffix }} result images"
path: ./result_images

- name: Create issue on failure
uses: imjohnbo/issue-bot@v3
if: ${{ failure() && github.event_name == 'schedule' }}
with:
title: "[TST] Upcoming dependency test failures"
body: |
The weekly build with nightly wheels from numpy and pandas
has failed. Check the logs for any updates that need to be
made in matplotlib.

pinned: false
close-previous: false
env:
GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
2 changes: 2 additions & 0 deletions lib/matplotlib/tests/test_axes.py
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Expand Up @@ -3438,6 +3438,8 @@ def test_errorbar():
ax.errorbar(x, y, yerr=[yerr_lower, 2*yerr], xerr=xerr,
fmt='o', ecolor='g', capthick=2)
ax.set_title('Mixed sym., log y')
# Force limits due to floating point slop potentially expanding the range
ax.set_ylim(1e-2, 1e1)

fig.suptitle('Variable errorbars')

Expand Down
7 changes: 4 additions & 3 deletions lib/matplotlib/tests/test_colors.py
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Expand Up @@ -568,14 +568,15 @@ def test_Normalize():
# Don't lose precision on longdoubles (float128 on Linux):
# for array inputs...
vals = np.array([1.2345678901, 9.8765432109], dtype=np.longdouble)
norm = mcolors.Normalize(vals.min(), vals.max())
assert_array_equal(np.asarray(norm(vals)), [0, 1])
norm = mcolors.Normalize(vals[0], vals[1])
assert norm(vals).dtype == np.longdouble
assert_array_equal(norm(vals), [0, 1])
# and for scalar ones.
eps = np.finfo(np.longdouble).resolution
norm = plt.Normalize(1, 1 + 100 * eps)
# This returns exactly 0.5 when longdouble is extended precision (80-bit),
# but only a value close to it when it is quadruple precision (128-bit).
assert 0 < norm(1 + 50 * eps) < 1
np.testing.assert_array_almost_equal_nulp(norm(1 + 50 * eps), 0.5)
Comment on lines 577 to +579
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This seems to be a bit too strict now, as it is failing on aarch64, ppc64le, and s390x, where the result is 0.50096339.

Also, on my 64-bit AMD system, which is apparently using np.float128 for np.longdouble (though I don't know if that means 80-bit internally), it seems to return 0.5 exactly, which seems the opposite of the comment.



def test_FuncNorm():
Expand Down