Merge branch 'resolve-174' into 'master'

Add function to plot data with draws from the posterior and max like

See merge request Monash/tupak!176

tupak/core/result.py

@@ -6,6 +6,7 @@ import pandas as pd
 import corner
 import matplotlib
 import matplotlib.pyplot as plt
+from collections import OrderedDict
 
 from tupak.core import utils
 from tupak.core.utils import logger
@@ -235,7 +236,8 @@ class Result(dict):
         """
         return self.posterior_volume / self.prior_volume(priors)
 
-    def plot_corner(self, parameters=None, save=True, dpi=300, **kwargs):
+    def plot_corner(self, parameters=None, save=True, priors=None, dpi=300,
+                    **kwargs):
         """ Plot a corner-plot using corner
 
         See https://corner.readthedocs.io/en/latest/ for a detailed API.
@@ -342,6 +344,66 @@ class Result(dict):
         utils.check_directory_exists_and_if_not_mkdir(self.outdir)
         fig.savefig(filename)
 
+    def plot_with_data(self, model, x, y, ndraws=1000, npoints=1000,
+                       xlabel=None, ylabel=None, data_label='data',
+                       data_fmt='o', draws_label=None, filename=None,
+                       maxl_label='max likelihood', dpi=300):
+        """ Generate a figure showing the data and fits to the data
+
+        Parameters
+        ----------
+        model: function
+            A python function which when called as `model(x, **kwargs)` returns
+            the model prediction (here `kwargs` is a dictionary of key-value
+            pairs of the model parameters.
+        x, y: np.ndarray
+            The independent and dependent data to plot
+        ndraws: int
+            Number of draws from the posterior to plot
+        npoints: int
+            Number of points used to plot the smoothed fit to the data
+        xlabel, ylabel: str
+            Labels for the axes
+        data_label, draws_label, maxl_label: str
+            Label for the data, draws, and max likelihood legend
+        data_fmt: str
+            Matpltolib fmt code, defaults to `'-o'`
+        dpi: int
+            Passed to `plt.savefig`
+        filename: str
+            If given, the filename to use. Otherwise, the filename is generated
+            from the outdir and label attributes.
+
+        """
+        xsmooth = np.linspace(np.min(x), np.max(x), npoints)
+        fig, ax = plt.subplots()
+        logger.info('Plotting {} draws'.format(ndraws))
+        for _ in range(ndraws):
+            s = self.posterior.sample().to_dict('records')[0]
+            ax.plot(xsmooth, model(xsmooth, **s), alpha=0.25, lw=0.1, color='r',
+                    label=draws_label)
+        if all(~np.isnan(self.posterior.log_likelihood)):
+            logger.info('Plotting maximum likelihood')
+            s = self.posterior.ix[self.posterior.log_likelihood.idxmax()]
+            ax.plot(xsmooth, model(xsmooth, **s), lw=1, color='k',
+                    label=maxl_label)
+
+        ax.plot(x, y, data_fmt, markersize=2, label=data_label)
+
+        if xlabel is not None:
+            ax.set_xlabel(xlabel)
+        if ylabel is not None:
+            ax.set_ylabel(ylabel)
+
+        handles, labels = plt.gca().get_legend_handles_labels()
+        by_label = OrderedDict(zip(labels, handles))
+        plt.legend(by_label.values(), by_label.keys())
+        ax.legend(numpoints=3)
+        fig.tight_layout()
+        if filename is None:
+            filename = '{}/{}_plot_with_data'.format(self.outdir, self.label)
+        fig.savefig(filename, dpi=dpi)
+
     def samples_to_posterior(self, likelihood=None, priors=None,
                              conversion_function=None):
         """
@@ -362,6 +424,9 @@ class Result(dict):
                 self.samples, columns=self.search_parameter_keys)
             data_frame['log_likelihood'] = getattr(
                 self, 'log_likelihood_evaluations', np.nan)
+            for key in priors:
+                if getattr(priors[key], 'is_fixed', False):
+                    data_frame[key] = priors[key].peak
             # We save the samples in the posterior and remove the array of samples
             del self.samples
         else: