Merge branch 'master' into logL_birth_inf

anesthetic/plot.py

@@ -18,7 +18,7 @@
 from matplotlib.axes import Axes
 import matplotlib.cbook as cbook
 import matplotlib.lines as mlines
-from matplotlib.ticker import MaxNLocator, AutoMinorLocator
+from matplotlib.ticker import MaxNLocator, AutoMinorLocator, LogLocator
 from matplotlib.colors import LinearSegmentedColormap
 from matplotlib.transforms import Affine2D
 from anesthetic.utils import nest_level
@@ -388,6 +388,15 @@ def _set_scale(self):
                     if y in self._logy:
                         ax.set_yscale('log')
 
+    def _set_logticks(self):
+        for y, rows in self.iterrows():
+            for x, ax in rows.items():
+                if ax is not None:
+                    if x in self._logx:
+                        ax.xaxis.set_major_locator(LogLocator(numticks=3))
+                    if y in self._logy:
+                        ax.yaxis.set_major_locator(LogLocator(numticks=3))
+
     @staticmethod
     def _set_labels(axes, labels, **kwargs):
         all_params = list(axes.columns) + list(axes.index)

anesthetic/samples.py

@@ -369,6 +369,8 @@ def plot_2d(self, axes=None, *args, **kwargs):
                         else:
                             ax.plot([], [])
 
+        axes._set_logticks()
+
         return axes
 
     plot_2d_default_kinds = {

tests/test_samples.py

@@ -523,6 +523,25 @@ def test_plot_logscale_2d(kind):
                     assert ax.twin.get_yscale() == 'linear'
 
 
+def test_logscale_ticks():
+    np.random.seed(42)
+    ndim = 5
+    data = np.exp(10 * np.random.randn(200, ndim))
+    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():
+            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
+
+
 @pytest.mark.parametrize('k', ['hist_1d', 'hist'])
 @pytest.mark.parametrize('b', ['scott', 10, np.logspace(-3, 0, 20)])
 @pytest.mark.parametrize('r', [None, (1e-5, 1)])