Added fit_elastic method to hRIXS class that allows one to automatically fit the images in each run

added elastic line fitting.

Made the centroid algorithm selection a class attribute, and also made the "manual threshold" default

src/toolbox_scs/detectors/hrixs.py

@@ -234,12 +234,16 @@ class hRIXS:
         self.Y_RANGE = np.s_[:]
 
         # centroid
+        self.C_METHOD='manual'
         self.THRESHOLD = None  # pixel counts above which a hit candidate is assumed
         self.STD_THRESHOLD = 3.5  # same as THRESHOLD, in standard deviations
-        self.DBL_THRESHOLD = 0.1  # factor used for double hits in centroid_one
+        self.DBL_THRESHOLD = 5  # factor used for double hits in centroid_one
+
+
         self.CURVE_A = 0  # curvature parameters as determined elsewhere
         self.CURVE_B = 0
 
+
         # integral
         self.BINS = 100
 
@@ -442,7 +446,7 @@ class hRIXS:
                     dres.append((mx, my))
         return res, dres
 
-    def centroid(self, data, bins=None, method='auto'):
+    def centroid(self, data, bins=None, method=None):
         """calculate a spectrum by finding the centroid of individual photons
 
         This takes the `xarray.Dataset` `data` and returns a copy of it, with
@@ -462,6 +466,8 @@ class hRIXS:
         """
         if bins is None:
             bins = self.BINS
+        if method is None:
+            method = self.C_METHOD
         ret = np.zeros((len(data["hRIXS_det"]), bins))
         retd = np.zeros((len(data["hRIXS_det"]), bins))
         total_hits = np.zeros((len(data["hRIXS_det"])))
@@ -553,6 +559,55 @@ class hRIXS:
         data['spectrum'] = (("trainId", "energy"), ret)
         return data
 
+    def fit_elastic(self, data, fit_hw=10, plot=False):
+        """Fits elastic line in each spectum found in data. Returns fit params
+        and integrated area of RIXS spectrum.
+
+        data - xarray containing hRIXS data that has already been centroided
+               or integrated
+        fit_hw - half width of the fitting window (in bins) around the elastic
+               line 
+        plot - plot each individual fit 
+        
+        Example
+        -------
+
+            data = h.centroid(data)
+            p, int = h.fit_elastic(data)
+
+        """
+        #if data["spectrum"] == None:
+        #    print("No spectrum found in data structure. "+ 
+        #           "Centroid or integrate images first.")
+        #    return
+
+        if self.ENERGY_SLOPE == None:
+            energy = np.arange(self.Y_RANGE.start, self.Y_RANGE.stop)
+        else:
+            energy = (np.arange(self.Y_RANGE.start, self.Y_RANGE.stop) *
+                      self.ENERGY_SLOPE+self.ENERGY_INTERCEPT)
+
+        
+
+        fit_param = []
+        data_int = []
+        for spec in data["spectrum"].data:
+            x0 = np.argmax(spec)
+            fit_start = x0-fit_hw
+            fit_end = x0+fit_hw
+            fit_result = gaussian_fit(energy[fit_start:fit_end], 
+                                      spec[fit_start:fit_end])
+            fit_param.append(fit_result)
+            data_int.append(np.trapz(spec))
+            if plot:
+                plt.figure()
+                plt.plot(energy, spec)
+                plt.plot(energy, gauss1d(energy, *fit_result))
+
+
+        return np.array(fit_param), data_int
+
+
     aggregators = dict(
         hRIXS_det=lambda x, dim: x.sum(dim=dim),
         Delay=lambda x, dim: x.mean(dim=dim),