8000 Merge pull request #21634 from greglucas/future-deps · matplotlib/matplotlib@0407b9f · GitHub
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Merge pull request #21634 from greglucas/future-deps
TST: Add future dependency tests as a weekly CI job
2 parents 4359e3a + 4cf54f4 commit 0407b9f

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‎.github/workflows/tests.yml

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@@ -8,6 +8,9 @@ on:
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pull_request:
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branches-ignore:
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- v[0-9]+.[0-9]+.[0-9x]+-doc
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schedule:
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# 3:47 UTC on Saturdays
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- cron: "47 3 * * 6"
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env:
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NO_AT_BRIDGE: 1 # Necessary for GTK3 interactive test.
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echo 'wxPython is available' ||
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echo 'wxPython is not available'
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- name: Install the nightly dependencies
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# Only install the nightly dependencies during the scheduled event
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if: ${{ github.event_name == 'schedule' && matrix.name-suffix != '(Minimum Versions)' }}
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run: |
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python -m pip install -i https://pypi.anaconda.org/scipy-wheels-nightly/simple --upgrade numpy pandas
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# Turn all warnings to errors, except ignore the distutils deprecations and the find_spec warning
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cat >> pytest.ini << EOF
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filterwarnings =
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error
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ignore:.*distutils:DeprecationWarning
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ignore:DynamicImporter.find_spec\(\) not found; falling back to find_module\(\):ImportWarning
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EOF
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- name: Install Matplotlib
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run: |
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ccache -s
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with:
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name: "${{ matrix.python-version }} ${{ matrix.os }} ${{ matrix.name-suffix }} result images"
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path: ./result_images
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- name: Create issue on failure
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uses: imjohnbo/issue-bot@v3
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if: ${{ failure() && github.event_name == 'schedule' }}
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with:
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title: "[TST] Upcoming dependency test failures"
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body: |
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The weekly build with nightly wheels from numpy and pandas
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has failed. Check the logs for any updates that need to be
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made in matplotlib.
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pinned: false
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close-previous: false
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env:
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GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}

‎lib/matplotlib/tests/test_axes.py

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ax.errorbar(x, y, yerr=[yerr_lower, 2*yerr], xerr=xerr,
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fmt='o', ecolor='g', capthick=2)
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ax.set_title('Mixed sym., log y')
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# Force limits due to floating point slop potentially expanding the range
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ax.set_ylim(1e-2, 1e1)
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fig.suptitle('Variable errorbars')
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‎lib/matplotlib/tests/test_colors.py

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@@ -568,14 +568,15 @@ def test_Normalize():
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# Don't lose precision on longdoubles (float128 on Linux):
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# for array inputs...
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vals = np.array([1.2345678901, 9.8765432109], dtype=np.longdouble)
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norm = mcolors.Normalize(vals.min(), vals.max())
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assert_array_equal(np.asarray(norm(vals)), [0, 1])
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norm = mcolors.Normalize(vals[0], vals[1])
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assert norm(vals).dtype == np.longdouble
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assert_array_equal(norm(vals), [0, 1])
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# and for scalar ones.
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eps = np.finfo(np.longdouble).resolution
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norm = plt.Normalize(1, 1 + 100 * eps)
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# This returns exactly 0.5 when longdouble is extended precision (80-bit),
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# but only a value close to it when it is quadruple precision (128-bit).
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assert 0 < norm(1 + 50 * eps) < 1
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np.testing.assert_array_almost_equal_nulp(norm(1 + 50 * eps), 0.5)
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def test_FuncNorm():

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