Merge branch 'master' into tension

.github/workflows/CI.yaml

@@ -72,6 +72,9 @@ jobs:
         uses: actions/setup-python@v5
         with:
           python-version: ${{ matrix.python-version }}
+      - name: Install hdf5 for macOS
+        if: ${{ matrix.os == 'macos-latest' }}
+        run: brew install hdf5 c-blosc
 
       - name: Install dependencies
         run: |
@@ -107,9 +110,12 @@ jobs:
     steps:
       - uses: actions/checkout@v4
       - name: Set up Python ${{ matrix.python-version }}
-        uses: conda-incubator/setup-miniconda@v2
+        uses: conda-incubator/setup-miniconda@v3
         with:
           python-version: ${{ matrix.python-version }}
+      - name: Install hdf5 for macOS
+        if: ${{ matrix.os == 'macos-latest' }}
+        run: brew install hdf5 c-blosc
 
       - name: Install dependencies
         shell: bash -l {0}

anesthetic/plot.py

@@ -394,8 +394,12 @@ def _set_logticks(self):
                 if ax is not None:
                     if x in self._logx:
                         ax.xaxis.set_major_locator(LogLocator(numticks=3))
+                        if x != y:
+                            ax.set_xlim(ax.dataLim.intervalx)
                     if y in self._logy:
                         ax.yaxis.set_major_locator(LogLocator(numticks=3))
+                        if y != x:
+                            ax.set_ylim(ax.dataLim.intervaly)
 
     @staticmethod
     def _set_labels(axes, labels, **kwargs):

anesthetic/plotting/_matplotlib/core.py

@@ -100,6 +100,8 @@ def _make_plot(self, fig):
         ax = self._get_ax(0)  # another one of these hard-coded 0s
 
         kwds = self.kwds.copy()
+        if self.color is not None:
+            kwds["color"] = self.color
         label = pprint_thing(self.label)
         kwds["label"] = label
 

anesthetic/samples.py

@@ -356,7 +356,7 @@ def plot_2d(self, axes=None, *args, **kwargs):
                             if np.isinf(self[x]).any():
                                 warnings.warn(f"column {y} has inf values.")
                             selfxy = self[[x, y]]
-                            selfxy = self.replace([-np.inf, np.inf], np.nan)
+                            selfxy = selfxy.replace([-np.inf, np.inf], np.nan)
                             selfxy = selfxy.dropna(axis=0)
                             selfxy.plot(x, y, ax=ax, xlabel=xlabel,
                                         logx=x in logx, logy=y in logy,

pyproject.toml

@@ -32,10 +32,10 @@ readme = "README.rst"
 license = {file = "LICENSE"}
 requires-python = ">=3.8"
 dependencies = [
-    "scipy",
-    "numpy",
+    "scipy<2.0.0",
+    "numpy<2.0.0",
     "pandas~=2.2.0",
-    "matplotlib>=3.6.1,<3.9.0",
+    "matplotlib>=3.6.1,<3.10.0",
 ]
 classifiers = [
     "Programming Language :: Python :: 3",
@@ -68,7 +68,7 @@ astropy = ["astropy"]
 fastkde = ["fastkde"]
 getdist = ["getdist"]
 hdf5 = ["tables==3.8.0"]
-all = ["h5py", "astropy", "fastkde", "getdist", "tables==3.8.0"]
+all = ["h5py", "astropy", "fastkde", "getdist", "tables"]
 
 [project.scripts]
 anesthetic = "anesthetic.scripts:gui"

tests/test_samples.py

@@ -308,31 +308,29 @@ def test_plot_2d_colours(kind):
     kinds = {'diagonal': kind + '_1d',
              'lower': kind + '_2d',
              'upper': 'scatter_2d'}
-    gd.plot_2d(axes, kind=kinds, label="gd")
-    pc.plot_2d(axes, kind=kinds, label="pc")
-    mn.plot_2d(axes, kind=kinds, label="mn")
-    gd_colors = []
-    pc_colors = []
-    mn_colors = []
+    gd.plot_2d(axes, kind=kinds, label="A")
+    pc.plot_2d(axes, kind=kinds, label="B")
+    mn.plot_2d(axes, kind=kinds, label="C")
+    gd.plot_2d(axes, kind=kinds, label="D", color='C7')
+    pc.plot_2d(axes, kind=kinds, label="E", color='C6')
+    mn.plot_2d(axes, kind=kinds, label="F", color='C5')
+
+    from collections import defaultdict
+    d = defaultdict(set)
+
     for y, rows in axes.iterrows():
         for x, ax in rows.items():
             handles, labels = ax.get_legend_handles_labels()
             for handle, label in zip(handles, labels):
                 if isinstance(handle, Rectangle):
-                    color = to_hex(handle.get_facecolor())
+                    color = handle.get_facecolor()
                 else:
                     color = handle.get_color()
+                color = to_hex(color)
+                d[label].add(color)
 
-                if label == 'gd':
-                    gd_colors.append(color)
-                elif label == 'pc':
-                    pc_colors.append(color)
-                elif label == 'mn':
-                    mn_colors.append(color)
-
-    assert len(set(gd_colors)) == 1
-    assert len(set(mn_colors)) == 1
-    assert len(set(pc_colors)) == 1
+    for v in d.values():
+        assert len(v) == 1
 
 
 @pytest.mark.parametrize('kwargs', [dict(color='r', alpha=0.5, ls=':', lw=1),
@@ -526,20 +524,31 @@ def test_plot_logscale_2d(kind):
 def test_logscale_ticks():
     np.random.seed(42)
     ndim = 5
-    data = np.exp(10 * np.random.randn(200, ndim))
+    data1 = np.exp(10 * np.random.randn(200, ndim))
+    data2 = np.exp(10 * np.random.randn(200, ndim) - 50)
     params = [f'a{i}' for i in range(ndim)]
     fig, axes = make_2d_axes(params, logx=params, logy=params, upper=False)
-    samples = Samples(data, columns=params)
-    samples.plot_2d(axes)
-    for _, col in axes.iterrows():
-        for _, ax in col.items():
+    samples1 = Samples(data1, columns=params)
+    samples2 = Samples(data2, columns=params)
+    samples1.plot_2d(axes)
+    samples2.plot_2d(axes)
+    for y, col in axes.iterrows():
+        for x, ax in col.items():
             if ax is not None:
                 xlims = ax.get_xlim()
                 xticks = ax.get_xticks()
                 assert np.sum((xticks > xlims[0]) & (xticks < xlims[1])) > 1
                 ylims = ax.get_ylim()
                 yticks = ax.get_yticks()
                 assert np.sum((yticks > ylims[0]) & (yticks < ylims[1])) > 1
+                if x == y:
+                    data_min = ax.twin.dataLim.intervalx[0]
+                    data_max = ax.twin.dataLim.intervalx[1]
+                    assert xlims[0] == pytest.approx(data_min, rel=1e-14)
+                    assert xlims[1] == pytest.approx(data_max, rel=1e-14)
+                else:
+                    assert_array_equal(xlims, ax.dataLim.intervalx)
+                    assert_array_equal(ylims, ax.dataLim.intervaly)
 
 
 @pytest.mark.parametrize('k', ['hist_1d', 'hist'])