fix guess and return in adaptive fit

analysis/scripts/adaptive_fit.py

@@ -24,7 +24,7 @@ import matplotlib.pyplot as plt
 
 from pyhisto.histogram import Histogram
 from pyhisto.tools import GaussRealFit
-
+    
 
 def adaptive_fit(
     histo_file: str,
@@ -68,7 +68,11 @@ def adaptive_fit(
     # if no parasites,
     if parasites is None or len(parasites) == 0:
         # fall back to realfit
-        the_fit = GaussRealFit(the_histo)
+        the_fit = GaussRealFit(
+            the_histo,
+            _xguess=xpeak,
+            _stdevguess=guessstdev,
+        )
         if plot is not None:
             the_fit.plot(plot)
         return yaml.dump(
@@ -87,14 +91,19 @@ def adaptive_fit(
     variables_limits = []
     variables_parameters = []
     #   .. Background is either linear or constant
+    bg_guess = sum(the_histo)/(1.+ len(the_histo))
+    # print(f"# {bg_guess=}")
     if bg_type == "lin":
         functions_to_fit.append(lambda x, B, SL: B + SL * (x - xpeak))
-        variables_guess.extend([the_histo[0].count, 0.0])
+        variables_guess.extend([
+            bg_guess, 
+            0.0
+        ])
         variables_limits.extend([(0.0, np.inf), (-np.inf, np.inf)])
         variables_parameters.append(slice(0, 2))
     else:
         functions_to_fit.append(lambda x, B: B)
-        variables_guess.extend([the_histo[0].count])
+        variables_guess.extend([bg_guess])
         variables_limits.extend([(0.0, np.inf)])
         variables_parameters.append(slice(0, 1))
     #   .. Main peak
@@ -103,9 +112,19 @@ def adaptive_fit(
         / np.sqrt(2.0 * np.pi * R * R)
         * np.exp(-((x - M) ** 2.0) / 2.0 / R / R)
     )
+    sum_guess = max(
+        1,
+        (
+            sum(the_histo) - (
+                the_histo[0].count + 
+                the_histo[-1].count
+            )* len(the_histo) / 2.0
+        )
+    )
+    # print(f"# {sum_guess=}")
     variables_guess.extend(
         [
-            max(1, sum(the_histo) - the_histo[0].count * len(the_histo)),
+            sum_guess,
             xpeak,
             guessstdev,
         ]
@@ -129,7 +148,7 @@ def adaptive_fit(
         )
         variables_guess.extend(
             [
-                (max(1, sum(the_histo) - the_histo[0].count * len(the_histo)) / 2.0),
+                sum_guess / 2.0,
                 extra_peak,
                 guessstdev,
             ]
@@ -268,14 +287,31 @@ if __name__ == "__main__":
     try:
         print(adaptive_fit(**vars(the_arguments)))
     except:
+        # trying to get a guess of the integral
+        the_histo = Histogram(
+            fromfile=the_arguments.histo_file
+        ).slice(
+            the_arguments.xmin, 
+            the_arguments.xmax
+        )
+        sum_guess = max(
+            1,
+            (
+                sum(the_histo) - (
+                    the_histo[0].count + 
+                    the_histo[-1].count
+                ) * len(the_histo) / 2.0
+            )
+        )
+        # done 
         print(
-            """
-integral: 0.0
-integral_u: 0.0
-mean: 0.0
-mean_u: 0.0
-stdev: 0.0
-stdev_u: 0.0
+            f"""
+integral: {sum_guess}
+integral_u: {2.35 * (sum_guess ** 0.5)}
+mean: {the_arguments.xpeak}
+mean_u: 1.0e30
+stdev: {the_arguments.guessstdev}
+stdev_u: 1.0e30
 error: True
 """
         )