1d Confidence Regions

README.rst

@@ -3,7 +3,7 @@ anesthetic: nested sampling visualisation
 =========================================
 :anesthetic: nested sampling visualisation
 :Author: Will Handley and Lukas Hergt
-:Version: 2.0.0-beta.6
+:Version: 2.0.0-beta.7
 :Homepage: https://github.com/williamjameshandley/anesthetic
 :Documentation: http://anesthetic.readthedocs.io/
 
@@ -64,9 +64,9 @@ Features
 - Both samples and plotting axes are stored as a ``pandas.DataFrame``, with parameter names as indices, which makes for easy access and modification.
 - Sensible color scheme for plotting nearly flat distributions.
 - For easy extension/modification, uses the standard python libraries:
-  `numpy <https://www.numpy.org/>`__, 
-  `scipy <https://www.scipy.org/>`__, 
-  `matplotlib <https://matplotlib.org/>`__ 
+  `numpy <https://www.numpy.org/>`__,
+  `scipy <https://www.scipy.org/>`__,
+  `matplotlib <https://matplotlib.org/>`__
   and `pandas <https://pandas.pydata.org/>`__.
 
 Installation
@@ -82,7 +82,7 @@ or via the setup.py
 
 .. code:: bash
 
-    git clone https://github.com/williamjameshandley/anesthetic 
+    git clone https://github.com/williamjameshandley/anesthetic
     cd anesthetic
     python setup.py install --user
 
@@ -97,7 +97,7 @@ You can check that things are working by running the test suite:
 
 
 Dependencies
-~~~~~~~~~~~~ 
+~~~~~~~~~~~~
 
 Basic requirements:
 
@@ -173,10 +173,10 @@ Another posterior plotting tool?
 
 There are several excellent tools for plotting marginalised posteriors:
 
-- `getdist <http://getdist.readthedocs.io/en/latest/intro.html>`__ 
+- `getdist <http://getdist.readthedocs.io/en/latest/intro.html>`__
 - `corner <https://corner.readthedocs.io>`__
 - `pygtc <https://pygtc.readthedocs.io>`__
-- `dynesty <https://dynesty.readthedocs.io>`__ 
+- `dynesty <https://dynesty.readthedocs.io>`__
 - `MontePython <http://baudren.github.io/montepython.html>`__
 
 Why create another one? In general, any dedicated user of software will find that there is some functionality that in their use case is lacking, and the designs of previous codes make such extensions challenging. In my case this was:
@@ -192,7 +192,7 @@ Why create another one? In general, any dedicated user of software will find tha
     samples['omegab'] = samples.omegabh2/(samples.H0/100)**2      # Define omegab
     samples.tex['omegab'] = '$\Omega_b$'                          # Label omegab
     samples.plot_1d('omegab')                                     # Simple 1D plot
-    
+
 3. Many KDE plotting tools have conventions that don't play well with uniformly distributed parameters, which presents a problem if you are trying to plot priors along with your posteriors. ``anesthetic`` has a sensible mechanism, by defining the contours by the amount of iso-probability mass they contain, but colouring the fill in relation to the probability density of the contour.
 
 What's in a name?

anesthetic/plot.py

@@ -251,24 +251,50 @@ def fastkde_plot_1d(ax, data, *args, **kwargs):
         lower/upper prior bound
         optional, default None
 
+    levels: list
+        values at which to draw iso-probability lines.
+        optional, default [0.68, 0.95]
+
+    facecolor: bool or string
+        If set to True then the 1d plot will be shaded with the value of the
+        ``color`` kwarg. Set to a string such as 'blue', 'k', 'r', 'C1' ect.
+        to define the color of the shading directly.
+        optional, default False
+
     Returns
     -------
     lines: matplotlib.lines.Line2D
         A list of line objects representing the plotted data (same as
         matplotlib matplotlib.axes.Axes.plot command)
 
     """
+    kwargs = normalize_kwargs(
+        kwargs,
+        dict(linewidth=['lw'], linestyle=['ls'], color=['c'],
+             facecolor=['fc'], edgecolor=['ec']))
+
     if len(data) == 0:
         return np.zeros(0), np.zeros(0)
 
     if data.max()-data.min() <= 0:
         return
 
+    levels = kwargs.pop('levels', [0.68, 0.95])
+
     xmin = kwargs.pop('xmin', None)
     xmax = kwargs.pop('xmax', None)
     cmap = kwargs.pop('cmap', None)
     color = kwargs.pop('color', (next(ax._get_lines.prop_cycler)['color']
                                  if cmap is None else cmap(0.68)))
+    facecolor = kwargs.pop('facecolor', False)
+
+    if 'edgecolor' in kwargs:
+        edgecolor = kwargs.pop('edgecolor')
+        if edgecolor:
+            color = edgecolor
+    else:
+        edgecolor = color
+
     q = kwargs.pop('q', '5sigma')
     q = quantile_plot_interval(q=q)
 
@@ -281,6 +307,19 @@ def fastkde_plot_1d(ax, data, *args, **kwargs):
 
     ans = ax.plot(x[i], p[i], color=color, *args, **kwargs)
     ax.set_xlim(*check_bounds(x[i], xmin, xmax), auto=True)
+
+    if facecolor and facecolor not in [None, 'None', 'none']:
+        if facecolor is True:
+            facecolor = color
+        c = iso_probability_contours(p[i], contours=levels)
+        cmap = basic_cmap(facecolor)
+        fill = []
+        for j in range(len(c)-1):
+            fill.append(ax.fill_between(x[i], p[i], where=p[i] >= c[j],
+                        color=cmap(c[j]), edgecolor=edgecolor))
+
+        return ans, fill
+
     return ans
 
 
@@ -309,6 +348,16 @@ def kde_plot_1d(ax, data, *args, **kwargs):
         lower/upper prior bound.
         optional, default None
 
+    levels: list
+        values at which to draw iso-probability lines.
+        optional, default [0.68, 0.95]
+
+    facecolor: bool or string
+        If set to True then the 1d plot will be shaded with the value of the
+        ``color`` kwarg. Set to a string such as 'blue', 'k', 'r', 'C1' ect.
+        to define the color of the shading directly.
+        optional, default False
+
     Returns
     -------
     lines: matplotlib.lines.Line2D
@@ -324,15 +373,27 @@ def kde_plot_1d(ax, data, *args, **kwargs):
 
     kwargs = normalize_kwargs(
         kwargs,
-        dict(linewidth=['lw'], linestyle=['ls'], color=['c']),
-        drop=['fc', 'ec'])
+        dict(linewidth=['lw'], linestyle=['ls'], color=['c'],
+             facecolor=['fc'], edgecolor=['ec']))
+
+    levels = kwargs.pop('levels', [0.68, 0.95])
+
     xmin = kwargs.pop('xmin', None)
     xmax = kwargs.pop('xmax', None)
     weights = kwargs.pop('weights', None)
     ncompress = kwargs.pop('ncompress', 1000)
     cmap = kwargs.pop('cmap', None)
     color = kwargs.pop('color', (next(ax._get_lines.prop_cycler)['color']
                                  if cmap is None else cmap(0.68)))
+    facecolor = kwargs.pop('facecolor', False)
+
+    if 'edgecolor' in kwargs:
+        edgecolor = kwargs.pop('edgecolor')
+        if edgecolor:
+            color = edgecolor
+    else:
+        edgecolor = color
+
     q = kwargs.pop('q', '5sigma')
     q = quantile_plot_interval(q=q)
 
@@ -354,6 +415,20 @@ def kde_plot_1d(ax, data, *args, **kwargs):
     pp /= pp.max()
     ans = ax.plot(x[i], pp, color=color, *args, **kwargs)
     ax.set_xlim(*check_bounds(x[i], xmin, xmax), auto=True)
+
+    if facecolor and facecolor not in [None, 'None', 'none']:
+        if facecolor is True:
+            facecolor = color
+        c = iso_probability_contours_from_samples(pp, contours=levels,
+                                                  weights=w)
+        cmap = basic_cmap(facecolor)
+        fill = []
+        for j in range(len(c)-1):
+            fill.append(ax.fill_between(x[i], pp, where=pp >= c[j],
+                        color=cmap(c[j]), edgecolor=edgecolor))
+
+        return ans, fill
+
     return ans
 
 

tests/test_plot.py

@@ -15,7 +15,7 @@
 from matplotlib.tri import TriContourSet
 from matplotlib.lines import Line2D
 from matplotlib.patches import Patch, Polygon
-from matplotlib.colors import ColorConverter
+from matplotlib.colors import ColorConverter, to_rgba
 from matplotlib.figure import Figure
 from pandas.core.series import Series
 from pandas.core.frame import DataFrame
@@ -230,6 +230,16 @@ def test_kde_plot_1d(plot_1d):
         plot_1d(ax, data, q=0.9)
         plot_1d(ax, data, q=(0.1, 0.9))
 
+        # Check iso-probability code
+        line, fill = plot_1d(ax, data, facecolor=True)
+        plot_1d(ax, data, facecolor=True, levels=[0.2, 0.6, 0.8])
+        line, fill = plot_1d(ax, data, fc='blue', color='k', ec='r')
+        assert(np.all(fill[0].get_edgecolor()[0] == to_rgba('r')))
+        assert (to_rgba(line[0].get_color()) == to_rgba('r'))
+        line, fill = plot_1d(ax, data, fc=True, color='k', ec=None)
+        assert(len(fill[0].get_edgecolor()) == 0)
+        assert (to_rgba(line[0].get_color()) == to_rgba('k'))
+
     except ImportError:
         if 'fastkde' not in sys.modules:
             pass

tests/test_samples.py

@@ -218,6 +218,9 @@ def test_plot_2d_types():
     fig, axes = ns.plot_2d(params, types={'lower': 'kde', 'diagonal': 'kde'})
     assert((~axes.isnull()).sum().sum() == 6)
 
+    fig, axes = ns.plot_2d(params, types={'lower': 'kde', 'diagonal': 'kde'})
+    assert((~axes.isnull()).sum().sum() == 6)
+
     fig, axes = ns.plot_2d(params, types={'lower': 'kde', 'diagonal': 'kde',
                                           'upper': 'scatter'})
     assert((~axes.isnull()).sum().sum() == 12)