459 residuals plotting options

examples/examples_fast/Outputs/ST_live_1000_eff_0.3_seed0_v2post_equal_weights.dat

@@ -0,0 +1 @@
+    0.140117499828338632E+01    0.116140460968017578E+02    0.908264352013451459E+00    0.720717012882232666E+00   -0.188007950782775879E-01    0.145097667350371728E+01    0.931132949382279529E+00    0.658978956937789917E+01   -0.554647675200076410E+05

examples/examples_fast/Outputs/ST_live_1000_eff_0.3_seed0_v2summary.txt

@@ -0,0 +1,2 @@
+    0.140117499828338632E+01    0.116140460968017578E+02    0.908264352013451459E+00    0.720717012882232666E+00   -0.188007950782775879E-01    0.145097667350371728E+01    0.931132949382279529E+00    0.658978956937789917E+01    0.514165644197529786E-08    0.000000000000000000E+00                         NaN    0.000000000000000000E+00    0.000000000000000000E+00    0.117746097693800428E-07    0.603268879392227826E-08                         NaN    0.140117499828338632E+01    0.116140460968017578E+02    0.908264352013451459E+00    0.720717012882232666E+00   -0.188007950782775879E-01    0.145097667350371728E+01    0.931132949382279529E+00    0.658978956937789917E+01    0.140117499828338632E+01    0.116140460968017578E+02    0.908264352013451459E+00    0.720717012882232666E+00   -0.188007950782775879E-01    0.145097667350371728E+01    0.931132949382279529E+00    0.658978956937789917E+01   -0.146886492504450813E+06   -0.554647675200076410E+05
+    0.140117499828338632E+01    0.116140460968017578E+02    0.908264352013451459E+00    0.720717012882232666E+00   -0.188007950782775879E-01    0.145097667350371728E+01    0.931132949382279529E+00    0.658978956937789917E+01    0.514165644197529786E-08    0.000000000000000000E+00                         NaN    0.000000000000000000E+00    0.000000000000000000E+00    0.117746097693800428E-07    0.603268879392227826E-08                         NaN    0.140117499828338632E+01    0.116140460968017578E+02    0.908264352013451459E+00    0.720717012882232666E+00   -0.188007950782775879E-01    0.145097667350371728E+01    0.931132949382279529E+00    0.658978956937789917E+01    0.140117499828338632E+01    0.116140460968017578E+02    0.908264352013451459E+00    0.720717012882232666E+00   -0.188007950782775879E-01    0.145097667350371728E+01    0.931132949382279529E+00    0.658978956937789917E+01   -0.554706595430130692E+05   -0.554647675200076410E+05

xpsi/PostProcessing/_1d_residual.py

@@ -0,0 +1,129 @@
+from ._global_imports import *
+from ._signalplot import SignalPlot
+from scipy.stats import norm
+
+class Residual1DPlot(SignalPlot):
+    """ Plot the count data, the posterior-expected count signal, and residuals.
+
+    The figure contain one panel :
+
+        * histogram of the standardised residuals between the
+          data and posterior-expected count signal over joint channel-phase
+          intervals.
+
+    The following example is (improved) from `Riley et al. 2019 <https://ui.adsabs.harvard.edu/abs/2019ApJ...887L..21R/abstract>`_:
+
+    .. image:: _static/_1d_residualplot.png
+
+    :param ... Todo
+
+    """
+
+    __figtype__ = 'signalplot_1dresiduals'
+
+    # do not change at runtime (see base class comments):
+    __caching_targets__ = ['expected_counts']
+
+    __rows__          = 1
+    __columns__       = 1
+    __ax_rows__       = 1
+    __ax_columns__    = 1
+    __height_ratios__ = [1]
+    __width_ratios__  = [1] # second column for colorbars
+    __wspace__        = 0.025
+    __hspace__        = 0.125
+
+    @make_verbose('Instantiating a 1D residual plotter for residual distribution checking',
+                  'Residual plotter instantiated')
+    def __init__(self,
+                 nbins=20,
+                 plot_fit=False,
+                 parameters_vector=None,
+                 **kwargs):
+        super(Residual1DPlot, self).__init__(**kwargs)
+
+        # Get the input parameters
+        self._nbins = int( nbins )
+        self._plot_fit = plot_fit
+        if not parameters_vector is None:
+            self.parameters_vector = parameters_vector
+
+         # Get on plotting
+        self._get_figure()
+        self._ax_1d_residuals = self._add_subplot(0,0)
+        self._ax_1d_residuals.set_xlabel(r'$(c_{ik}-d_{ik})/\sqrt{c_{ik}}$')
+        self._ax_1d_residuals.set_ylabel('Occurence')
+
+        plt.close()
+
+    @make_verbose('Residual1DPlot object iterating over samples',
+                  'Residual1DPlot object finished iterating')
+    def execute(self, thetas, wrapper):
+        """ Loop over posterior samples. """
+        self._num_samples = thetas.shape[0]
+
+        wrapped = wrapper(self, 'model_sum')
+        for i in range(self._num_samples):
+            wrapped(None, thetas[i,:])
+
+    def __next__(self):
+        """ Update posterior expected model given the updated signal.
+
+        .. note::
+
+            You cannot make an iterator from an instance of this class.
+
+        """
+        try:
+            self._model_sum
+        except AttributeError:
+            self._model_sum = self._signal.expected_counts.copy()
+        else:
+            self._model_sum += self._signal.expected_counts
+
+    @property
+    def model_sum(self):
+        """ Get the current posterior sum of the count numbers. """
+        return self._model_sum
+
+    @model_sum.deleter
+    def model_sum(self):
+        del self._model_sum
+
+    @property
+    def expected_counts(self):
+        """ Get the estimated posterior expectation of the count numbers. """
+        return self._model_sum / self._num_samples
+
+    @make_verbose('ResidualPlot object finalizing',
+                  'ResidualPlot object finalized')
+    def finalize(self):
+        self._add_1d_residuals()
+
+    def _add_1d_residuals(self):
+        """ Display the residuals in the third panel. """
+
+        # Get the residuals 
+        self._residuals = self.expected_counts - self._signal.data.counts
+        self._residuals /= _np.sqrt(self.expected_counts)
+        self._residuals = self._residuals.flatten()
+
+        # Plot them
+        self._ax_1d_residuals.hist(self._residuals, bins=self._nbins, density=True, alpha=0.5)
+
+        # Add the fit if needed
+        if self._plot_fit:
+
+            mean,std=norm.fit( self._residuals )
+            xmin, xmax = _np.min( self._residuals ) , _np.max( self._residuals )
+            x = _np.linspace(xmin, xmax, 100)
+            y = norm.pdf(x, mean, std)
+            self._ax_1d_residuals.plot( x , y , label=r'Fitted Normal $\sigma=$'+f'{std:.3f}', color='darkblue' )
+            self._ax_1d_residuals.axvline(mean, color='k', linestyle='dashed', linewidth=2, label=r'$\mu=$'+f'{mean:.5f}')
+            self._ax_1d_residuals.legend()
+
+
+        # Do the nice plotting
+        self._ax_1d_residuals.set_title('Residual histogram')
+        self._ax_1d_residuals.set_xlabel(r'$(c_{ik}-d_{ik})/\sqrt{c_{ik}}$')
+        self._ax_1d_residuals.set_ylabel('Density')

xpsi/PostProcessing/__init__.py

@@ -60,11 +60,13 @@
                "SignalPlotter",
                "PulsePlot",
                "SpectrumPlot",
-               "ResidualPlot"]
+               "ResidualPlot",
+               "Residual1DPlot"]
 
     from ._runs import Runs
     from ._signalplotter import SignalPlotter
     from ._residual import ResidualPlot
+    from ._1d_residual import Residual1DPlot
     from ._pulse import PulsePlot
     from ._spectrum import SpectrumPlot
     from ._backends import NestedBackend

xpsi/PostProcessing/_pulse.py

@@ -118,6 +118,7 @@ def __init__(self,
                  plot_truth=False,
                  truth_line_kwargs=None,
                  comp_truth_line_kwargs=None,
+                 parameters_vector=None,
                  **kwargs):
         super(PulsePlot, self).__init__(**kwargs)
 
@@ -201,6 +202,9 @@ def __init__(self,
         else:
             self._expectation_line_kwargs = expectation_line_kwargs
 
+        if not parameters_vector is None:
+            self._parameters_vector = parameters_vector
+
         if comp_expectation_line_kwargs is not None:
             self._comp_expectation_line_kwargs = comp_expectation_line_kwargs
         else: