minor bug fixes (handley-lab#314)

* version bump to 2.0.1

* add test for `beta` kwarg in WeightedDataFrame method `neff`

* fix missing `beta` kwarg in `WeightedDataFrame.neff`

* remove the ylabel from `plot_1d`, otherwise pandas adds in the label "Frequency" messing with our plotting grid

* remove the 1d ylabel also from `plot_2d`, otherwise pandas again puts in a "Frequency" label messing with our plotting grid

* add `kind=fastkde` shortcut to `plot_2d`

* add test that checks that the 1d ylabels are indeed empty

* add `fig.align_labels()` to 2d plots, which aligns labels independent of the potentially different length of the tick labels (significantly improving appearance in my opinion)

* Neater way to correct for Frequency

* Removed blank lines

* Added a test to check for frequency

* Removed previous fix

* Fix for ranges

* Added tests to explictly check axes are adjusting correctly

* no support for horizontal

* Corrected ranges

* Bumped version

---------

Co-authored-by: Will Handley <[email protected]>

README.rst

@@ -2,7 +2,7 @@
 anesthetic: nested sampling post-processing
 ===========================================
 :Authors: Will Handley and Lukas Hergt
-:Version: 2.1.1
+:Version: 2.1.2
 :Homepage: https://github.com/handley-lab/anesthetic
 :Documentation: http://anesthetic.readthedocs.io/
 

anesthetic/_version.py

@@ -1 +1 @@
-__version__ = '2.1.1'
+__version__ = '2.1.2'

anesthetic/plot.py

@@ -699,6 +699,7 @@ def make_2d_axes(params, labels=None, lower=True, diagonal=True, upper=True,
                          ticks=ticks,
                          gridspec_kw=gridspec_kw,
                          subplot_spec=subplot_spec)
+    fig.align_labels()
     return fig, axes
 
 
@@ -966,17 +967,18 @@ def hist_plot_1d(ax, data, *args, **kwargs):
     q = quantile_plot_interval(q=q)
     xmin = quantile(data, q[0], weights)
     xmax = quantile(data, q[-1], weights)
+    range = kwargs.pop('range', (xmin, xmax))
 
     if type(bins) == str and bins in ['knuth', 'freedman', 'blocks']:
         try:
             h, edges, bars = hist(data, ax=ax, bins=bins,
-                                  range=(xmin, xmax), histtype=histtype,
+                                  range=range, histtype=histtype,
                                   color=color, *args, **kwargs)
         except NameError:
             raise ImportError("You need to install astropy to use astropyhist")
     else:
         h, edges, bars = ax.hist(data, weights=weights, bins=bins,
-                                 range=(xmin, xmax), histtype=histtype,
+                                 range=range, histtype=histtype,
                                  color=color, *args, **kwargs)
 
     if histtype == 'bar' and not density:
@@ -1285,7 +1287,8 @@ def hist_plot_2d(ax, data_x, data_y, *args, **kwargs):
             pdf[pdf < cmin] = np.ma.masked
         if cmax is not None:
             pdf[pdf > cmax] = np.ma.masked
-        image = ax.pcolormesh(x, y, pdf.T, cmap=cmap, vmin=vmin,
+        snap = kwargs.pop('snap', True)
+        image = ax.pcolormesh(x, y, pdf.T, cmap=cmap, vmin=vmin, snap=snap,
                               *args, **kwargs)
 
     ax.add_patch(plt.Rectangle((0, 0), 0, 0, fc=cmap(0.999), ec=cmap(0.32),

anesthetic/plotting/_matplotlib/hist.py

@@ -19,7 +19,9 @@
     kde_plot_1d,
     fastkde_plot_1d,
     hist_plot_1d,
+    quantile_plot_interval,
 )
+from anesthetic.utils import quantile
 
 
 class HistPlot(_WeightedMPLPlot, _HistPlot):
@@ -51,10 +53,9 @@ def _get_colors(self, num_colors=None, color_kwds='color'):
         return super()._get_colors(num_colors, color_kwds)
 
     def _post_plot_logic(self, ax, data):
-        super()._post_plot_logic(ax, data)
+        ax.set_xlabel(self.xlabel)
         ax.set_yticks([])
         ax.set_ylim(bottom=0)
-        ax.set_xlim(self.bins[0], self.bins[-1])
 
 
 class KdePlot(HistPlot, _KdePlot):
@@ -138,6 +139,20 @@ class Hist1dPlot(HistPlot):
     def _kind(self) -> Literal["hist_1d"]:
         return "hist_1d"
 
+    def _calculate_bins(self, data):
+        if "range" not in self.kwds:
+            q = self.kwds.get('q', 5)
+            q = quantile_plot_interval(q=q)
+            weights = self.kwds.get("weights", None)
+            xmin = quantile(data, q[0], weights)
+            xmax = quantile(data, q[-1], weights)
+            self.kwds["range"] = (xmin, xmax)
+            result = super()._calculate_bins(data)
+            self.kwds.pop("range")
+        else:
+            result = super()._calculate_bins(data)
+        return result
+
     @classmethod
     def _plot(
         cls,

anesthetic/samples.py

@@ -413,6 +413,7 @@ def plot_2d(self, axes=None, *args, **kwargs):
         'kde': {'diagonal': 'kde_1d', 'lower': 'kde_2d'},
         'kde_1d': {'diagonal': 'kde_1d'},
         'kde_2d': {'lower': 'kde_2d'},
+        'fastkde': {'diagonal': 'fastkde_1d', 'lower': 'fastkde_2d'},
         'hist': {'diagonal': 'hist_1d', 'lower': 'hist_2d'},
         'hist_1d': {'diagonal': 'hist_1d'},
         'hist_2d': {'lower': 'hist_2d'},

anesthetic/weighted_pandas.py

@@ -10,7 +10,7 @@
 from pandas.util._exceptions import find_stack_level
 from pandas.util import hash_pandas_object
 from numpy.ma import masked_array
-from anesthetic.utils import (compress_weights, neff as neff_, quantile,
+from anesthetic.utils import (compress_weights, neff, quantile,
                               temporary_seed, adjust_docstrings)
 from pandas.core.dtypes.missing import notna
 
@@ -222,10 +222,10 @@ def reset_index(self, level=None, drop=False, inplace=False,
         else:
             return answer.__finalize__(self, "reset_index")
 
-    def neff(self, axis=0):
+    def neff(self, axis=0, beta=1):
         """Effective number of samples."""
         if self.isweighted(axis):
-            return neff_(self.get_weights(axis))
+            return neff(self.get_weights(axis), beta=beta)
         else:
             return self.shape[axis]
 

tests/test_samples.py

@@ -990,20 +990,6 @@ def test_live_points():
     assert not live_points.isweighted()
 
 
-def test_hist_range_1d():
-    """Test to provide a solution to #89"""
-    np.random.seed(3)
-    ns = read_chains('./tests/example_data/pc')
-    ax = ns.plot_1d('x0', kind='hist_1d')
-    x1, x2 = ax['x0'].get_xlim()
-    assert x1 > -1
-    assert x2 < +1
-    ax = ns.plot_1d('x0', kind='hist_1d', bins=np.linspace(-1, 1, 11))
-    x1, x2 = ax['x0'].get_xlim()
-    assert x1 <= -1
-    assert x2 >= +1
-
-
 def test_contour_plot_2d_nan():
     """Contour plots with nans arising from issue #96"""
     np.random.seed(3)
@@ -1291,6 +1277,10 @@ def test_samples_dot_plot():
     axes = samples.x2.plot.hist_1d(ax=ax)
     assert len(axes.containers) == 1
 
+    fig, ax = plt.subplots()
+    axes = samples.x2.plot.hist_1d(ax=ax, range=[0, 0.2])
+    assert axes.get_xlim()[1] < 0.3
+
     axes = samples.drop_labels().plot.kde_2d('x0', 'x1')
     assert len(axes.collections) == 5
     assert axes.get_xlabel() == 'x0'
@@ -1380,6 +1370,23 @@ def test_samples_plot_labels():
         assert samples.get_label(col) == ax.get_xlabel()
 
 
+@pytest.mark.parametrize('kind', ['kde', 'hist', 'fastkde']
+                         if 'fastkde' in sys.modules else
+                         ['kde', 'hist'])
+def test_samples_empty_1d_ylabels(kind):
+    samples = read_chains('./tests/example_data/pc')
+    columns = ['x0', 'x1', 'x2', 'x3', 'x4']
+
+    axes = samples.plot_1d(columns, kind=kind+'_1d')
+    for col in columns:
+        assert axes[col].get_ylabel() == ''
+
+    axes = samples.plot_2d(columns, kind=kind)
+    for col in columns:
+        assert axes[col][col].get_ylabel() == samples.get_labels_map()[col]
+        assert axes[col][col].twin.get_ylabel() == ''
+
+
 def test_constructors():
     samples = read_chains('./tests/example_data/pc')
 
@@ -1693,3 +1700,83 @@ def test_groupby_plots():
             gb_colors = [p.get_facecolor() for p in gb_ax.patches]
             assert_allclose(mcmc_colors, gb_colors)
         plt.close('all')
+
+
+def test_hist_1d_no_Frequency():
+    np.random.seed(42)
+    pc = read_chains("./tests/example_data/pc")
+    axes = pc.plot_2d(['x0', 'x1', 'x2'], kind={'diagonal': 'hist_1d'})
+    for i in range(len(axes)):
+        assert axes.iloc[i, i].twin.get_ylabel() != 'Frequency'
+
+    axes = pc.plot_1d(['x0', 'x1', 'x2'], kind='hist_1d')
+    for ax in axes:
+        assert ax.get_ylabel() != 'Frequency'
+
+    fig, ax = plt.subplots()
+    ax = pc['x0'].plot(kind='hist_1d', ax=ax)
+    assert ax.get_ylabel() != 'Frequency'
+
+    fig, ax = plt.subplots()
+    ax = pc.x0.plot.hist_1d(ax=ax)
+    assert ax.get_ylabel() != 'Frequency'
+
+
+@pytest.mark.parametrize('kind', ['kde', 'hist'])
+def test_axes_limits_1d(kind):
+    np.random.seed(42)
+    pc = read_chains("./tests/example_data/pc")
+
+    axes = pc.plot_1d('x0', kind=f'{kind}_1d')
+    xmin, xmax = axes['x0'].get_xlim()
+    assert -0.9 < xmin < 0
+    assert 0 < xmax < 0.9
+
+    pc.x0 += 3
+    pc.plot_1d(axes, kind=f'{kind}_1d')
+    xmin, xmax = axes['x0'].get_xlim()
+    assert -0.9 < xmin < 0
+    assert 3 < xmax < 3.9
+
+    pc.x0 -= 6
+    pc.plot_1d(axes, kind=f'{kind}_1d')
+    xmin, xmax = axes['x0'].get_xlim()
+    assert -3.9 < xmin < -3
+    assert 3 < xmax < 3.9
+
+
+@pytest.mark.parametrize('kind, kwargs',
+                         [('kde', {}),
+                          ('hist', {'levels': [0.95, 0.68]}),
+                          ])
+def test_axes_limits_2d(kind, kwargs):
+    np.random.seed(42)
+    pc = read_chains("./tests/example_data/pc")
+
+    axes = pc.plot_2d(['x0', 'x1'], kind=f'{kind}_2d', **kwargs)
+    xmin, xmax = axes['x0']['x1'].get_xlim()
+    ymin, ymax = axes['x0']['x1'].get_ylim()
+    assert -0.9 < xmin < 0
+    assert 0 < xmax < 0.9
+    assert -0.9 < ymin < 0
+    assert 0 < ymax < 0.9
+
+    pc.x0 += 3
+    pc.x1 -= 3
+    pc.plot_2d(axes, kind=f'{kind}_2d', **kwargs)
+    xmin, xmax = axes['x0']['x1'].get_xlim()
+    ymin, ymax = axes['x0']['x1'].get_ylim()
+    assert -0.9 < xmin < 0
+    assert 3 < xmax < 3.9
+    assert -3.9 < ymin < -3
+    assert 0 < ymax < 0.9
+
+    pc.x0 -= 6
+    pc.x1 += 6
+    pc.plot_2d(axes, kind=f'{kind}_2d', **kwargs)
+    xmin, xmax = axes['x0']['x1'].get_xlim()
+    ymin, ymax = axes['x0']['x1'].get_ylim()
+    assert -3.9 < xmin < -3
+    assert 3 < xmax < 3.9
+    assert -3.9 < ymin < -3
+    assert 3 < ymax < 3.9

tests/test_weighted_pandas.py

@@ -379,14 +379,18 @@ def test_WeightedDataFrame_sample(frame):
 
 
 def test_WeightedDataFrame_neff(frame):
-    neff = frame.neff()
-    assert isinstance(neff, float)
-    assert neff < len(frame)
-    assert neff > len(frame) * np.exp(-0.25)
-
-    neff = frame.neff(1)
-    assert isinstance(neff, int)
-    assert neff == len(frame.T)
+    N_eff = frame.neff()
+    assert isinstance(N_eff, float)
+    assert N_eff < len(frame)
+    assert N_eff > len(frame) * np.exp(-0.25)
+
+    N_eff = frame.neff(1)
+    assert isinstance(N_eff, int)
+    assert N_eff == len(frame.T)
+
+    # beta kwarg
+    for beta in [0.5, 1, 2, np.inf, '0.5', 'equal', 'entropy', 'kish']:
+        assert frame.neff(beta=beta) == neff(frame.get_weights(), beta=beta)
 
 
 def test_WeightedDataFrame_compress(frame):