move nD binning to parent class

XENONnT · May 7, 2021 · 08b3ced · 08b3ced
1 parent 4955fa4
commit 08b3ced
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Showing 2 changed files with 8 additions and 16 deletions.
diff --git a/GOFevaluation/evaluators_nd.py b/GOFevaluation/evaluators_nd.py
@@ -5,19 +5,7 @@
 from GOFevaluation import test_statistics
 
 
-class nd_test_statistics(test_statistics):
-    """
-        Override binning to work in arbitrary dimensions
-    """
-    def bin_data(self, bin_edges):
-        # function to bin nD data:
-        if len(self.data.shape)==1:
-            self.binned_data, _ = np.histogram(self.data, bins=bin_edges)
-        else:
-            self.binned_data, _ = np.histogramdd(self.data, bins=bin_edges)
-
-
-class binned_poisson_chi2_gof(nd_test_statistics):
+class binned_poisson_chi2_gof(test_statistics):
     """
         computes the binned poisson modified Chi2 from Baker+Cousins
         In the limit of large bin counts (10+) this is Chi2 distributed.
@@ -41,7 +29,7 @@ def from_binned(cls, data, expectations):
         In this case the bin-edges don't matter, so we bypass the usual init
         """
         self = cls(None, None, None, None)
-        nd_test_statistics.__init__(self=self,
+        test_statistics.__init__(self=self,
                                     data=data,
                                     pdf=expectations / np.sum(expectations),
                                     nevents_expected=np.sum(expectations))
@@ -56,7 +44,7 @@ def __init__(self, data, pdf, bin_edges, nevents_expected):
             # bypass init, using binned data
             return
         # initialise with the common call signature
-        nd_test_statistics.__init__(self=self,
+        test_statistics.__init__(self=self,
                                     data=data,
                                     pdf=pdf,
                                     nevents_expected=nevents_expected)

diff --git a/GOFevaluation/test_statistics.py b/GOFevaluation/test_statistics.py
@@ -13,7 +13,11 @@ def calculate_gof(self):
         raise NotImplementedError("Your goodnes of fit computation goes here!")
 
     def bin_data(self, bin_edges):
-        self.binned_data, _ = np.histogram(self.data, bins=bin_edges)
+        # function to bin nD data:
+        if len(self.data.shape)==1:
+            self.binned_data, _ = np.histogram(self.data, bins=bin_edges)
+        else:
+            self.binned_data, _ = np.histogramdd(self.data, bins=bin_edges)
 
     def get_result_as_dict(self):
         assert self._name is not None, str(self.__class__.__name__) + ": You need to define self._name for your goodnes of fit measure!"