add reader for dnest4

README.rst

@@ -2,7 +2,7 @@
 anesthetic: nested sampling post-processing
 ===========================================
 :Authors: Will Handley and Lukas Hergt
-:Version: 2.8.14
+:Version: 2.9.0
 :Homepage: https://github.com/handley-lab/anesthetic
 :Documentation: http://anesthetic.readthedocs.io/
 

anesthetic/__init__.py

@@ -44,6 +44,7 @@ def wrapper(backend=None):
 Samples = anesthetic.samples.Samples
 MCMCSamples = anesthetic.samples.MCMCSamples
 NestedSamples = anesthetic.samples.NestedSamples
+DiffusiveNestedSamples = anesthetic.samples.DiffusiveNestedSamples
 make_2d_axes = anesthetic.plot.make_2d_axes
 make_1d_axes = anesthetic.plot.make_1d_axes
 

anesthetic/_version.py

@@ -1 +1 @@
-__version__ = '2.8.14'
+__version__ = '2.9.0'

anesthetic/gui/plot.py

@@ -155,10 +155,14 @@ class RunPlotter(object):
     param_choice : :class:`anesthetic.gui.widgets.CheckButtons`
         Checkbox that selects which parameters to plot.
 
+    plot_color : str
+        color for plots and color maps
+
     """
 
-    def __init__(self, samples, params=None):
+    def __init__(self, samples, params=None, color='k'):
         self.samples = samples
+        self.color = color
 
         if params:
             self.params = np.array(params)
@@ -199,7 +203,8 @@ def _set_up(self):
         gs0 = sGS(1, 2, width_ratios=[19, 1], subplot_spec=gs[0])
         gs1 = sGS(1, 3, width_ratios=[4, 1, 1], subplot_spec=gs[1])
         gs10 = sGS(2, 1, height_ratios=[1, 4], subplot_spec=gs1[0])
-        gs11 = sGS(3, 1, height_ratios=[1, 1, 2], subplot_spec=gs1[1])
+        gs11 = sGS(3, 1, height_ratios=[1, 1, len(self.samples.plot_types())],
+                   subplot_spec=gs1[1])
 
         self.triangle = TrianglePlot(self.fig, gs0[0])
         beta = np.logspace(-10, 10, 101)
@@ -213,14 +218,15 @@ def _set_up(self):
         self.reload = Button(self.fig, gs11[1],
                              self.reload_file, 'Reload File')
         self.type = RadioButtons(self.fig, gs11[2],
-                                 ('live', 'posterior'), self.update)
+                                 self.samples.plot_types(), self.update)
         self.param_choice = CheckButtons(self.fig, gs1[2],
                                          self.params, self.redraw)
 
     def redraw(self, _):
         """Redraw the triangle plot upon parameter updating."""
         self.triangle.draw(self.param_choice(),
-                           self.samples.get_labels_map())
+                           self.samples.get_labels_map(),
+                           self.color)
         self.update(None)
         self.reset_range(None)
         self.fig.tight_layout()
@@ -238,25 +244,27 @@ def points(self, label):
         -------
         array-like:
             sample 'label'-coordinates.
+        array-like:
+            colors to use for plotting
 
         """
-        if self.type() == 'posterior':
-            beta = self.beta()
-            return self.samples.posterior_points(beta)[label]
-        else:
-            i = self.evolution()
-            logL = self.samples.logL.iloc[i]
-            return self.samples.live_points(logL)[label]
+        return self.samples.points_to_plot(
+            plot_type=self.type(),
+            label=label,
+            evolution=self.evolution(),
+            beta=self.beta(),
+            base_color=self.color
+        )
 
     def update(self, _):
         """Update all the plots upon slider changes."""
         logX = np.log(self.samples.nlive / (self.samples.nlive+1)).cumsum()
         beta = self.beta()
-        LX = self.samples.logL*beta + logX
+        LX = self.samples.LX(beta, logX)
         LX = np.exp(LX-LX.max())
         i = self.evolution()
         logL = self.samples.logL.iloc[i]
-        n = self.samples.nlive.iloc[i]
+        n = self.samples.n_live(i)
 
         self.triangle.update(self.points)
 

anesthetic/gui/widgets.py

@@ -226,7 +226,7 @@ def __init__(self, fig, gridspec):
         self.fig.delaxes(self.ax)
         _, self.ax = make_2d_axes([], fig=self.fig, subplot_spec=self.gridspec)
 
-    def draw(self, params, labels={}):
+    def draw(self, params, labels={}, color='k'):
         """Draw a new triangular grid for list of parameters.
 
         Parameters
@@ -252,9 +252,9 @@ def draw(self, params, labels={}):
             for x, ax in row.items():
                 if ax is not None:
                     if x == y:
-                        ax.twin.plot([None], [None], 'k-')
+                        ax.twin.plot([None], [None], '-', color=color)
                     else:
-                        ax.plot([None], [None], 'k.')
+                        ax.plot([None], [None], '.', color=color)
 
     def update(self, f):
         """Update the points in the triangle plot using f function.
@@ -269,13 +269,32 @@ def update(self, f):
         for y, row in self.ax.iterrows():
             for x, ax in row.items():
                 if ax is not None:
-                    if x == y:
-                        datx, daty = histogram(f(x), bins='auto')
-                        ax.twin.lines[0].set_xdata(datx)
-                        ax.twin.lines[0].set_ydata(daty)
+                    x_values, colors = f(x)
+                    if x == y and len(ax.twin.lines) == len(colors):
+                        # if there are as many lines as colors,
+                        # the lines can be updated efficiently
+                        for i, color in enumerate(colors):
+                            datx, daty = histogram(x_values[i], bins='auto')
+                            ax.twin.lines[i].set_xdata(datx)
+                            ax.twin.lines[i].set_ydata(daty)
+                            ax.twin.lines[i].set_color(color)
+                    elif x == y and len(ax.twin.lines) != len(colors):
+                        # if there are NOT as many lines as colors,
+                        # the lines need to be replaced
+                        ax.twin.clear()
+                        for i, color in enumerate(colors):
+                            ax.twin.hist(x_values[i], color=color)
+                    elif len(ax.lines) == len(colors):
+                        y_values, _ = f(y)
+                        for i, color in enumerate(colors):
+                            ax.lines[i].set_xdata(x_values[i])
+                            ax.lines[i].set_ydata(y_values[i])
+                            ax.lines[i].set_color(color)
                     else:
-                        ax.lines[0].set_xdata(f(x))
-                        ax.lines[0].set_ydata(f(y))
+                        ax.clear()
+                        y_values, _ = f(y)
+                        for i, color in enumerate(colors):
+                            ax.plot(x_values[i], y_values[i], 'o', color=color)
 
     def reset_range(self):
         """Reset the range of each grid."""

anesthetic/read/chain.py

@@ -5,6 +5,7 @@
 from anesthetic.read.multinest import read_multinest
 from anesthetic.read.ultranest import read_ultranest
 from anesthetic.read.nestedfit import read_nestedfit
+from anesthetic.read.dnest4 import read_dnest4
 from anesthetic.read.csv import read_csv
 
 
@@ -19,6 +20,7 @@ def read_chains(root, *args, **kwargs):
         * `Nested_fit <https://github.com/martinit18/Nested_Fit>`_,
         * `CosmoMC <https://github.com/cmbant/CosmoMC>`_,
         * `Cobaya <https://github.com/CobayaSampler/cobaya>`_,
+        * `DNest4 <https://github.com/eggplantbren/DNest4/>`_,
         * anything `GetDist <https://github.com/cmbant/getdist>`_ compatible,
         * files produced using ``DataFrame.to_csv()`` from anesthetic.
 
@@ -54,7 +56,7 @@ def read_chains(root, *args, **kwargs):
     errors = []
     readers = [
         read_polychord, read_multinest, read_cobaya, read_ultranest,
-        read_nestedfit, read_getdist, read_csv
+        read_dnest4, read_nestedfit, read_getdist, read_csv
     ]
     for read in readers:
         try:

anesthetic/read/dnest4.py

@@ -0,0 +1,80 @@
+"""Read NestedSamples from dnest4 output files."""
+import os
+import numpy as np
+from anesthetic.samples import DiffusiveNestedSamples
+
+
+def _determine_columns(n_params, header, delim=' '):
+    """
+    Determine column names from DNest4 output.
+
+    If none are given by DNest4, parameters are named x_i.
+    ' ' is the default delimiter in DNest4.
+    """
+    dnest4_column_descriptions = header[1:].lstrip().split(delim)
+    if len(dnest4_column_descriptions) != n_params:
+        # header can not have contained column names
+        columns = [f'x_{i}' for i in range(n_params)]
+    else:
+        columns = [d.strip() for d in dnest4_column_descriptions]
+    return columns
+
+
+def read_dnest4(root,
+                levels_file='levels.txt',
+                sample_file='sample.txt',
+                sample_info_file='sample_info.txt',
+                *args,
+                **kwargs):
+    """
+    Read dnest4 output files.
+
+    Parameters
+    ----------
+    root : str
+        root specify the directory only, no specific roots,
+        The files read files are levels_file, sample_file and sample_info.
+    levels_file: str
+        output name from DNest4
+    sample_file: str
+        output name from DNest4
+    sample_info_file: str
+        output name from DNest4
+    """
+    levels = np.loadtxt(os.path.join(root, levels_file),
+                        dtype=float,
+                        delimiter=' ',
+                        comments='#')
+    samples = np.genfromtxt(os.path.join(root, sample_file),
+                            dtype=float,
+                            delimiter=' ',
+                            comments='#')
+    sample_info = np.loadtxt(os.path.join(root, sample_info_file),
+                             dtype=float,
+                             delimiter=' ',
+                             comments='#')
+
+    with open(os.path.join(root, sample_file), 'r') as f:
+        header = f.readline()
+
+    n_params = samples.shape[1]
+
+    sample_level = sample_info[:, 0].astype(int)
+    logL = sample_info[:, 1]
+    logL_birth = levels[sample_level, 1]
+
+    kwargs['label'] = kwargs.get('label', os.path.basename(root))
+    columns_ = _determine_columns(n_params, header)
+    columns = kwargs.pop('columns', columns_)
+    labels_ = {c: '$' + c + '$' for c in columns}
+    labels = kwargs.pop('labels', labels_)
+
+    return DiffusiveNestedSamples(sample_info=sample_info,
+                                  levels=levels,
+                                  samples=samples,
+                                  logL=logL,
+                                  logL_birth=logL_birth,
+                                  columns=columns,
+                                  labels=labels,
+                                  *args,
+                                  **kwargs)