Adds absorption calculation from fluorescence

XAS.py

@@ -14,11 +14,15 @@ import matplotlib.gridspec as gridspec
 import matplotlib.pyplot as plt
 import re
 
-def absorption(T, Io):
-    """ Compute the absorption A = -ln(T/Io)
+def absorption(T, Io, fluorescence=False):
+    """ Compute the absorption A = -ln(T/Io) (or A = T/Io
+        for fluorescence)
+
         Inputs:
             T: 1-D transmission value array of length N
             Io: 1-D Io monitor value array of length N
+            fluorescence: boolean, if False, compute A as
+                negative log, if True, compute A as ratio
 
         Output:
             a structured array with:
@@ -61,14 +65,19 @@ def absorption(T, Io):
 
     p = np.corrcoef(T, Io)[0,1]
 
-    muA = -np.log(muT/muIo)
-    # from error propagation for correlated data
-    sigmaA = (np.sqrt((sigmaT/muT)**2 + (sigmaIo/muIo)**2
-        - 2*p*sigmaIo*sigmaT/(muIo*muT)))
+    if fluorescence:
+        muA = muT/muIo
+        sigmaA = np.abs(muA)*(np.sqrt((sigmaT/muT)**2 +
+            (sigmaIo/muIo)**2 - 2*p*sigmaIo*sigmaT/(muIo*muT)))
+    else:
+        muA = -np.log(muT/muIo)
+        # from error propagation for correlated data
+        sigmaA = (np.sqrt((sigmaT/muT)**2 + (sigmaIo/muIo)**2
+            - 2*p*sigmaIo*sigmaT/(muIo*muT)))
     
-    # direct calculation
-    #mask = (Io != 0)
-    #sigmaA = np.nanstd(-np.log(T[mask]/Io[mask]))
+        # direct calculation
+        #mask = (Io != 0)
+        #sigmaA = np.nanstd(-np.log(T[mask]/Io[mask]))
 
     return np.array([(muA, sigmaA, weights, muT, sigmaT, muIo, sigmaIo,
         p, counts)], dtype=fdtype)
@@ -108,7 +117,8 @@ def binning(x, data, func, bins=100, bin_length=None):
 
     return bins, res
 
-def xas(nrun, bins=None, Iokey='SCS_SA3', Itkey='MCP3apd', nrjkey='nrj', Iooffset=0, plot=False):
+def xas(nrun, bins=None, Iokey='SCS_SA3', Itkey='MCP3apd', nrjkey='nrj', Iooffset=0, plot=False,
+    fluorescence = False):
     """ Compute the XAS spectra from a xarray nrun.
 
         Inputs:
@@ -120,6 +130,8 @@ def xas(nrun, bins=None, Iokey='SCS_SA3', Itkey='MCP3apd', nrjkey='nrj', Iooffse
             NRJkey: string for the nrj fields, typically 'nrj'
             Iooffset: offset to apply on Io
             plot: boolean, displays a XAS spectrum if True
+            fluorescnce: boolean, if True, absorption is the ratio,
+                if False, absorption is negative log
 
         Outputs:
             a dictionnary containing:
@@ -157,9 +169,9 @@ def xas(nrun, bins=None, Iokey='SCS_SA3', Itkey='MCP3apd', nrjkey='nrj', Iooffse
 
         
         if len(data) == 0:
-            return absorption([], [])
+            return absorption([], [], fluorescence)
         else:
-            return absorption(It_sign*data['It'], Io_sign*data['Io'])
+            return absorption(It_sign*data['It'], Io_sign*data['Io'], fluorescence)
 
     if bins is None:
         num_bins = 80
@@ -183,7 +195,10 @@ def xas(nrun, bins=None, Iokey='SCS_SA3', Itkey='MCP3apd', nrjkey='nrj', Iooffse
         gs = gridspec.GridSpec(2,1,height_ratios=[4,1])
         ax1 = plt.subplot(gs[0])
         ax1.plot(bins_c, muA, color='C1', label=r'$\sigma$')
-        ax1.set_ylabel('XAS')
+        if fluorescence:
+            ax1.set_ylabel('XAS (fluorescence)')
+        else:
+            ax1.set_ylabel('XAS (-log)')
         ax1.set_xlabel('Energy (eV)')
         ax1.legend()
         ax1_twin = ax1.twinx()