Returns bin center instead of energy mean in xas()

XAS.py

@@ -138,7 +138,7 @@ def xas(nrun, bins=None, Iokey='SCS_SA3', Itkey='MCP3apd', nrjkey='nrj',
 
         Outputs:
             a dictionnary containing:
-                nrj: the mean of photon energy in each bin
+                nrj: the bin centers
                 muA: the absorption
                 sigmaA: standard deviation on the absorption
                 sterrA: standard error on the absorption
@@ -193,12 +193,11 @@ def xas(nrun, bins=None, Iokey='SCS_SA3', Itkey='MCP3apd', nrjkey='nrj',
     
     bins_c = 0.5*(bins[1:] + bins[:-1])
     bin_idx = np.digitize(rundata['nrj'], bins)
-    nrj = np.array([ np.mean(rundata['nrj'][idx+1==bin_idx]) for idx in range(len(bins)-1)])    
     if plot:
         f = plt.figure(figsize=(6.5,6))
         gs = gridspec.GridSpec(2,1,height_ratios=[4,1])
         ax1 = plt.subplot(gs[0])
-        ax1.plot(nrj, muA, color='C1', label=r'$\sigma$')
+        ax1.plot(bins_c, muA, color='C1', label=r'$\sigma$')
         if fluorescence:
             ax1.set_ylabel('XAS (fluorescence)')
         else:
@@ -206,7 +205,7 @@ def xas(nrun, bins=None, Iokey='SCS_SA3', Itkey='MCP3apd', nrjkey='nrj',
         ax1.set_xlabel('Energy (eV)')
         ax1.legend()
         ax1_twin = ax1.twinx()
-        ax1_twin.bar(nrj, nosample['muIo'], width=0.80*(bins_c[1]-bins_c[0]),
+        ax1_twin.bar(bins_c, nosample['muIo'], width=0.80*(bins_c[1]-bins_c[0]),
                 color='C1', alpha=0.2)
         ax1_twin.set_ylabel('Io')
         try:
@@ -217,13 +216,13 @@ def xas(nrun, bins=None, Iokey='SCS_SA3', Itkey='MCP3apd', nrjkey='nrj',
             f.suptitle(nrun.attrs['plot_title'])
         
         ax2 = plt.subplot(gs[1])
-        ax2.bar(nrj, nosample['counts'], width=0.80*(bins_c[1]-bins_c[0]),
+        ax2.bar(bins_c, nosample['counts'], width=0.80*(bins_c[1]-bins_c[0]),
                 color='C0', alpha=0.2)
         ax2.set_xlabel('Energy (eV)')
         ax2.set_ylabel('counts')
         plt.tight_layout()
     
-    return {'nrj':nrj, 'muA':muA, 'sterrA':sterrA, 'sigmaA':nosample['sigmaA'],
+    return {'nrj':bins_c, 'muA':muA, 'sterrA':sterrA, 'sigmaA':nosample['sigmaA'],
         'muIo':nosample['muIo'], 'counts':nosample['counts']}
 
 def xasxmcd(dataP, dataN):