From 4ee3b15e45d912814c9a69d2f56a49d2fad4b4ca Mon Sep 17 00:00:00 2001
From: Jürgen Knödlseder <jurgen.knodlseder@irap.omp.eu>
Date: Thu, 24 Jun 2021 20:35:02 +0200
Subject: [PATCH] Use matplotlib in comobsres if it is available

---
 modules/comscripts/comobsres.py | 130 ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 130 insertions(+), 0 deletions(-)

diff --git a/modules/comscripts/comobsres.py b/modules/comscripts/comobsres.py
index 06099c1..6e062e5 100755
--- a/modules/comscripts/comobsres.py
+++ b/modules/comscripts/comobsres.py
@@ -498,6 +498,128 @@ class comobsres(ctools.csobservation):
         # Return
         return sig
 
+    def _plot_sighist(self, sighist, nbins=80, sigma_min=-6.0, sigma_max=6.0, fontsize=11, title=None):
+        """
+        Plot the significance distribution
+
+        Parameters
+        ----------
+        sighist : list of floats
+            Significance values
+        nbins : int, optional
+            Number of histogram bins
+        sigma_min : float, optional
+            Lower limit of the x axis in the plot
+        sigma_max : float, optional
+            Upper limit of the x axis in the plot
+        fontsize : int, optional
+              Font size for axes
+        title : str, optional
+            Title string
+        """
+        # Plot only if matplotlib is available
+        try:
+
+            # Import matplotlib
+            import matplotlib.pyplot as plt
+
+            # Plot histogram
+            self._plot_histogram(sighist, nbins=nbins, sigma_min=sigma_min,
+                                 sigma_max=sigma_max, fontsize=fontsize,
+                                 title=title)
+
+        except (ImportError, RuntimeError):
+
+            # Inform about impossibility to plot
+            self._log_string(gammalib.NORMAL, 'matplotlib not available, '+
+                             'no significance histogram plotted.')
+
+        # Return
+        return
+
+    def _plot_histogram(self, sighist, nbins=80, sigma_min=-6.0, sigma_max=6.0, fontsize=11, title=None):
+        """
+        Plot the significance distribution
+
+        Parameters
+        ----------
+        sighist : list of floats
+            Significance values
+        nbins : int, optional
+            Number of histogram bins
+        sigma_min : float, optional
+            Lower limit of the x axis in the plot
+        sigma_max : float, optional
+            Upper limit of the x axis in the plot
+        fontsize : int, optional
+            Font size for axes
+        title : str, optional
+            Title string
+        """
+        # Compute bin edges, hence use nbins+1
+        binwidth  = (sigma_max - sigma_min) / float(nbins)
+        bin_edges = [sigma_min + binwidth*i for i in range(nbins+1)]
+
+        # Draw significance histogram
+        f, ax   = plt.subplots()
+        y, _, _ = plt.hist(sighist, bins=bin_edges, histtype='step', color='k')
+
+        # Set initial Gaussian parameters
+        y_max = float(y.max())
+        par1  = gammalib.GOptimizerPar('Norm',  y_max)
+        par2  = gammalib.GOptimizerPar('Mean',  0.0)
+        par3  = gammalib.GOptimizerPar('Sigma', 1.0)
+        pars  = gammalib.GOptimizerPars()
+        pars.append(par1)
+        pars.append(par2)
+        pars.append(par3)
+
+        # Set fit function
+        x = [0.5*(bin_edges[i]+bin_edges[i+1]) for i in range(nbins)]
+        fct = gaussian(x, y)
+
+        # Optimize function and compute errors
+        opt = gammalib.GOptimizerLM()
+        opt.optimize(fct, pars)
+        opt.errors(fct, pars)
+
+        # Recover parameters and errors
+        norm    = pars[0].value()
+        e_norm  = pars[0].error()
+        mean    = pars[1].value()
+        e_mean  = pars[1].error()
+        sigma   = pars[2].value()
+        e_sigma = pars[2].error()
+
+        # Draw text box
+        msg = 'mean: %.3f +/- %.3f \n width: %.3f +/- %.3f' % \
+              (mean, e_mean, sigma, e_sigma)
+        ax.text(0.97, 0.95, msg, ha='right', va='top',
+                bbox=dict(edgecolor='red', facecolor='white'),
+                transform=ax.transAxes, fontsize=8)
+
+        # Plot the normal
+        yvals = [norm * math.exp(-0.5*(x-mean)**2/(sigma**2)) for x in bin_edges]
+        ax.plot(bin_edges, yvals, 'r-')
+
+        # Configure the plot
+        ax.set_ylim(0.5, ax.get_ylim()[1]*2.5)
+        ax.set_xlim(sigma_min, sigma_max)
+        ax.set_xlabel('Significance', fontsize=fontsize)
+        ax.set_ylabel('Entries', fontsize=fontsize)
+        ax.grid()
+        ax.set_yscale('log')
+
+        # Add title
+        if title != None:
+            ax.set_title(title)
+
+        # Save figure
+        plt.savefig(title+'.png')
+
+        # Return
+        return
+
 
     # Public methods
     def run(self):
@@ -582,6 +704,10 @@ class comobsres(ctools.csobservation):
             # Fill residual map
             sig = self._fill_map(dre, drm, imap)
 
+            # Generate significance histogram
+            if self['png'].boolean():
+                self._plot_sighist(sig, title='%s_map' % (fname))
+
             # Build DRI residuals
             if self['dri'].boolean():
 
@@ -591,6 +717,10 @@ class comobsres(ctools.csobservation):
                 # Generate residual data space
                 sig = self._compute_residual_dri(dre, drm, imap, '%s' % fname, group)
 
+                # Generate significance histogram
+                if self['png'].boolean():
+                    self._plot_sighist(sig, title='%s_dri' % fname)
+
         # Return
         return
 
--
libgit2 0.21.4