Merge pull request #83 from StefRe/fix/pytest

TST: Update for pytest 7

Author: phobson

.github/workflows/check-test-coverage.yml

@@ -13,7 +13,7 @@ jobs:
     - name: Setup Python
       uses: actions/setup-python@v2
       with:
-        python-version: 3.8
+        python-version: "3.11"
     - name: Generate coverage report
       run: |
         python -m pip install --upgrade pip

.github/workflows/python-runlinter.yml

@@ -15,10 +15,10 @@ jobs:
     runs-on: ubuntu-latest
     steps:
     - uses: actions/checkout@v2
-    - name: Set up Python 3.8
+    - name: Set up Python
       uses: actions/setup-python@v2
       with:
-        python-version: 3.8
+        python-version: "3.11"
     - name: Install dependencies
       run: |
         python -m pip install --upgrade pip

.github/workflows/python-runtests-img-comp.yml .github/workflows/python-runtests-all.yml

@@ -13,12 +13,16 @@ jobs:
   build:
 
     runs-on: ubuntu-latest
+    strategy:
+        matrix:
+          python-version: ["3.8", "3.9", "3.10", "3.11"]
+
     steps:
     - uses: actions/checkout@v2
-    - name: Set up Python 3.8
+    - name: Set up Python ${{ matrix.python-version }}
       uses: actions/setup-python@v2
       with:
-        python-version: 3.8
+        python-version: ${{ matrix.python-version }}
     - name: Install dependencies
       run: |
         python -m pip install --upgrade pip

.github/workflows/python-runtests-basic.yml

@@ -1,4 +1,5 @@
 # mpl-probscale
+
 Real probability scales for matplotlib
 
 ![Code Style](https://github.com/matplotlib/mpl-probscale/workflows/black/badge.svg)
@@ -7,7 +8,6 @@ Real probability scales for matplotlib
 ![Basic Tests](https://github.com/matplotlib/mpl-probscale/workflows/Basic%20unit%20tests/badge.svg)
 ![Image Comparisons](https://github.com/matplotlib/mpl-probscale/workflows/Image%20comparison%20tests/badge.svg)
 
-
 [Sphinx Docs](http://matplotlib.org/mpl-probscale/)
 
 ## Installation
@@ -24,7 +24,7 @@ Official releases are available through the conda-forge channel or pip
 
 This is a pure-python package, so building from source is easy on all platforms:
 
-```
+```shell
 git clone git@github.com:matplotlib/mpl-probscale.git
 cd mpl-probscale
 pip install -e .
@@ -44,7 +44,6 @@ or
 
 In the next release, this depedency will be made optional.
 
-
 ## Quick start
 
 Simply importing `probscale` lets you use probability scales in your matplotlib figures:

README.md

@@ -3,4 +3,10 @@
 from .viz import *
 from .probscale import ProbScale
 
+
 scale.register_scale(ProbScale)
+
+
+__version__ = "0.2.6dev"
+__author__ = "Paul Hobson (Herrera Environmental Consultants)"
+__author_email__ = "phobson@herrerainc.com"

probscale/__init__.py

@@ -127,7 +127,7 @@ def _estimate_from_fit(xhat, slope, intercept, xlog=False, ylog=False):
     xhat = numpy.asarray(xhat)
     if ylog:
         if xlog:
-            yhat = numpy.exp(intercept) * xhat ** slope
+            yhat = numpy.exp(intercept) * xhat**slope
         else:
             yhat = numpy.exp(intercept) * numpy.exp(slope) ** xhat
 

probscale/algo.py

@@ -45,7 +45,6 @@ def _sig_figs(cls, x, n, expthresh=5, forceint=False):
 
         # check on the number provided
         elif x is not None and numpy.isfinite(x):
-
             # check on the _sig_figs
             if n < 1:
                 raise ValueError("number of sig figs (n) must be greater " "than zero")

probscale/formatters.py

@@ -29,7 +29,7 @@ def _approx_erf(cls, x):
 
         """
 
-        guts = -(x ** 2) * (4.0 / numpy.pi + cls._A * x ** 2) / (1.0 + cls._A * x ** 2)
+        guts = -(x**2) * (4.0 / numpy.pi + cls._A * x**2) / (1.0 + cls._A * x**2)
         with warnings.catch_warnings():
             warnings.filterwarnings("ignore", "invalid value encountered in sign")
             return numpy.sign(x) * numpy.sqrt(1.0 - numpy.exp(guts))
@@ -42,11 +42,11 @@ def _approx_inv_erf(cls, z):
 
         """
 
-        _b = (2 / numpy.pi / cls._A) + (0.5 * numpy.log(1 - z ** 2))
-        _c = numpy.log(1 - z ** 2) / cls._A
+        _b = (2 / numpy.pi / cls._A) + (0.5 * numpy.log(1 - z**2))
+        _c = numpy.log(1 - z**2) / cls._A
         with warnings.catch_warnings():
             warnings.filterwarnings("ignore", "invalid value encountered in sign")
-            return numpy.sign(z) * numpy.sqrt(numpy.sqrt(_b ** 2 - _c) - _b)
+            return numpy.sign(z) * numpy.sqrt(numpy.sqrt(_b**2 - _c) - _b)
 
     @classmethod
     def ppf(cls, q):

probscale/probscale.py

@@ -121,7 +121,7 @@ def test__bs_fit(plot_data, fitlogs, known_lo, known_hi):
 
 
 class Test__estimate_from_fit(object):
-    def setup(self):
+    def setup_method(self):
         self.x = numpy.arange(1, 11, 0.5)
         self.slope = 2
         self.intercept = 3.5

probscale/tests/test_algo.py

@@ -1,4 +1,5 @@
 import os
+import warnings
 
 import numpy
 import matplotlib.pyplot as plt
@@ -120,16 +121,16 @@ def test_minimal_norm_cdf(mn, mn_input):
 
 
 def test_sign_with_nan_no_warning(mn):
-    with pytest.warns(None) as record:
+    with warnings.catch_warnings():
+        warnings.simplefilter("error")
         res = mn._approx_erf(numpy.nan)
-    assert not record
     assert numpy.isnan(res)
 
 
 def test_sign_with_nan_no_warning_inv(mn):
-    with pytest.warns(None) as record:
+    with warnings.catch_warnings():
+        warnings.simplefilter("error")
         res = mn._approx_inv_erf(numpy.nan)
-    assert not record
     assert numpy.isnan(res)
 
 

probscale/tests/test_probscale.py

@@ -72,7 +72,7 @@ def plot_data():
 
 
 class Test_fit_line(object):
-    def setup(self):
+    def setup_method(self):
         self.data = numpy.array(
             [
                 2.00,
@@ -454,7 +454,7 @@ def test_custom_xhat(self):
 
 
 class Test_plot_pos(object):
-    def setup(self):
+    def setup_method(self):
         self.data = numpy.arange(16)
 
         self.known_type4 = numpy.array(
@@ -740,11 +740,11 @@ def test_probplot_prob(plot_data):
     fig, ax = plt.subplots()
     fig = viz.probplot(plot_data, ax=ax, problabel="Test xlabel", datascale="log")
     assert isinstance(fig, plt.Figure)
-    return fig
 
 
 @pytest.mark.mpl_image_compare(baseline_dir=BASELINE_DIR, tolerance=TIGHT_TOLERANCE)
 def test_probplot_qq(plot_data):
+    plt.close("all")
     fig, ax = plt.subplots()
     fig = viz.probplot(
         plot_data,
@@ -760,6 +760,7 @@ def test_probplot_qq(plot_data):
 @pytest.mark.mpl_image_compare(baseline_dir=BASELINE_DIR, tolerance=TIGHT_TOLERANCE)
 @pytest.mark.skipif(stats is None, reason="no scipy")
 def test_probplot_qq_dist(plot_data):
+    plt.close("all")
     fig, ax = plt.subplots()
     norm = stats.norm(*stats.norm.fit(plot_data))
     fig = viz.probplot(
@@ -770,6 +771,7 @@ def test_probplot_qq_dist(plot_data):
 
 @pytest.mark.mpl_image_compare(baseline_dir=BASELINE_DIR, tolerance=TIGHT_TOLERANCE)
 def test_probplot_pp(plot_data):
+    plt.close("all")
     fig, ax = plt.subplots()
     scatter_kws = dict(
         color="b", linestyle="--", markeredgecolor="g", markerfacecolor="none"
@@ -792,6 +794,7 @@ def test_probplot_pp(plot_data):
     remove_text=True,
 )
 def test_probplot_prob_bestfit(plot_data):
+    plt.close("all")
     fig, ax = plt.subplots()
     fig = viz.probplot(
         plot_data,
@@ -807,6 +810,7 @@ def test_probplot_prob_bestfit(plot_data):
 
 @pytest.mark.mpl_image_compare(baseline_dir=BASELINE_DIR, tolerance=TIGHT_TOLERANCE)
 def test_probplot_prob_bestfit_exceedance(plot_data):
+    plt.close("all")
     fig, ax = plt.subplots()
     fig = viz.probplot(
         plot_data,
@@ -823,6 +827,7 @@ def test_probplot_prob_bestfit_exceedance(plot_data):
 
 @pytest.mark.mpl_image_compare(baseline_dir=BASELINE_DIR, tolerance=TIGHT_TOLERANCE)
 def test_probplot_qq_bestfit(plot_data):
+    plt.close("all")
     fig, ax = plt.subplots()
     fig = viz.probplot(
         plot_data,
@@ -838,6 +843,7 @@ def test_probplot_qq_bestfit(plot_data):
 
 @pytest.mark.mpl_image_compare(baseline_dir=BASELINE_DIR, tolerance=TIGHT_TOLERANCE)
 def test_probplot_pp_bestfit(plot_data):
+    plt.close("all")
     fig, ax = plt.subplots()
     scatter_kws = {"marker": "s", "color": "red"}
     line_kws = {"linestyle": "--", "linewidth": 3}
@@ -858,6 +864,7 @@ def test_probplot_pp_bestfit(plot_data):
 
 @pytest.mark.mpl_image_compare(baseline_dir=BASELINE_DIR, tolerance=TIGHT_TOLERANCE)
 def test_probplot_prob_probax_y(plot_data):
+    plt.close("all")
     fig, ax = plt.subplots()
     fig = viz.probplot(
         plot_data, ax=ax, datalabel="Test xlabel", datascale="log", probax="y"
@@ -868,6 +875,7 @@ def test_probplot_prob_probax_y(plot_data):
 
 @pytest.mark.mpl_image_compare(baseline_dir=BASELINE_DIR, tolerance=TIGHT_TOLERANCE)
 def test_probplot_qq_probax_y(plot_data):
+    plt.close("all")
     fig, ax = plt.subplots()
     fig = viz.probplot(
         plot_data,
@@ -883,6 +891,7 @@ def test_probplot_qq_probax_y(plot_data):
 
 @pytest.mark.mpl_image_compare(baseline_dir=BASELINE_DIR, tolerance=TIGHT_TOLERANCE)
 def test_probplot_pp_probax_y(plot_data):
+    plt.close("all")
     fig, ax = plt.subplots()
     scatter_kws = dict(
         color="b", linestyle="--", markeredgecolor="g", markerfacecolor="none"
@@ -902,6 +911,7 @@ def test_probplot_pp_probax_y(plot_data):
 
 @pytest.mark.mpl_image_compare(baseline_dir=BASELINE_DIR, tolerance=TIGHT_TOLERANCE)
 def test_probplot_prob_bestfit_probax_y(plot_data):
+    plt.close("all")
     fig, ax = plt.subplots()
     fig = viz.probplot(
         plot_data,
@@ -918,6 +928,7 @@ def test_probplot_prob_bestfit_probax_y(plot_data):
 
 @pytest.mark.mpl_image_compare(baseline_dir=BASELINE_DIR, tolerance=TIGHT_TOLERANCE)
 def test_probplot_qq_bestfit_probax_y(plot_data):
+    plt.close("all")
     fig, ax = plt.subplots()
     fig = viz.probplot(
         plot_data,
@@ -934,6 +945,7 @@ def test_probplot_qq_bestfit_probax_y(plot_data):
 
 @pytest.mark.mpl_image_compare(baseline_dir=BASELINE_DIR, tolerance=TIGHT_TOLERANCE)
 def test_probplot_pp_bestfit_probax_y(plot_data):
+    plt.close("all")
     fig, ax = plt.subplots()
     scatter_kws = {"marker": "s", "color": "red"}
     line_kws = {"linestyle": "--", "linewidth": 3}
@@ -956,6 +968,7 @@ def test_probplot_pp_bestfit_probax_y(plot_data):
 @pytest.mark.mpl_image_compare(baseline_dir=BASELINE_DIR, tolerance=LOOSE_TOLERANCE)
 @pytest.mark.skipif(stats is None, reason="no scipy")
 def test_probplot_beta_dist_best_fit_y(plot_data):
+    plt.close("all")
     fig, (ax1, ax2) = plt.subplots(ncols=2)
     dist = stats.beta(3, 3)
     fig = viz.probplot(
@@ -991,6 +1004,7 @@ def test_probplot_beta_dist_best_fit_y(plot_data):
 )
 @pytest.mark.skipif(stats is None, reason="no scipy")
 def test_probplot_beta_dist_best_fit_x(plot_data):
+    plt.close("all")
     fig, (ax1, ax2) = plt.subplots(nrows=2)
     dist = stats.beta(3, 3)
     fig = viz.probplot(
@@ -1020,13 +1034,13 @@ def test_probplot_beta_dist_best_fit_x(plot_data):
 
 
 def test_probplot_test_results(plot_data):
+    plt.close("all")
     fig, ax = plt.subplots()
     fig, results = viz.probplot(plot_data, return_best_fit_results=True)
 
     assert isinstance(results, dict)
     known_keys = sorted(["q", "x", "y", "xhat", "yhat", "res"])
     assert sorted(list(results.keys())) == known_keys
-    return fig
 
 
 @pytest.mark.parametrize("probax", ["x", "y"])
@@ -1048,6 +1062,7 @@ def test__set_prob_limits_x(probax, N, minval, maxval):
 
 @pytest.mark.mpl_image_compare(baseline_dir=BASELINE_DIR, tolerance=TIGHT_TOLERANCE)
 def test_probplot_color_and_label(plot_data):
+    plt.close("all")
     fig, ax = plt.subplots()
     fig = viz.probplot(plot_data, ax=ax, color="pink", label="A Top-Level Label")
     ax.legend(loc="lower right")

probscale/tests/test_viz.py

@@ -3,12 +3,5 @@ description-file = readme.md
 license_file = LICENSE
 
 [tool:pytest]
-pep8ignore =
-    E501
-    probscale/tests/test_*.py E241 E131
-    docs/* ALL
-    check_probscale.py ALL
-
 markers =
     mpl_image_compare
-    pep8