preprocess measurements according to fit requirements

epsfits/measurements.py

@@ -2,6 +2,10 @@ import numpy
 import scipy.linalg
 
 
+tauBzero = 1.520e-12 * 1. / 6.582119e-16 / 1e-9
+tauBplus = 1.638e-12 * 1. / 6.582119e-16 / 1e-9
+
+
 class BaBarBToRho:
     """
     Reference:
@@ -19,8 +23,8 @@ class BaBarBToRho:
         babar_covariance = (babar_covariance + babar_covariance.T - numpy.diag(babar_covariance.diagonal())) * 1e-12
 
         self.bins = babar_bins
-        self.central = babar_central
-        self.covariance = babar_covariance
+        self.central = babar_central / (tauBplus / 2)
+        self.covariance = babar_covariance / (tauBplus / 2) ** 2
 
 
 class BelleBToRho:
@@ -83,6 +87,12 @@ class BelleBToRho:
         belle_charged_covariance_statistical = numpy.diag(belle_charged_error) @ belle_charged_correlation @ numpy.diag(
             belle_charged_error)
 
+        belle_charged_central = belle_charged_central / (tauBplus / 2)
+        belle_neutral_central = belle_neutral_central / (tauBplus / 2)
+
+        belle_charged_covariance_statistical = belle_charged_covariance_statistical / (tauBplus / 2) ** 2
+        belle_neutral_covariance_statistical = belle_neutral_covariance_statistical / (tauBplus / 2) ** 2
+
         belle_central = numpy.concatenate((belle_charged_central, belle_neutral_central))
         belle_covariance_statistical = scipy.linalg.block_diag(belle_charged_covariance_statistical,
                                                                belle_neutral_covariance_statistical)
@@ -160,9 +170,11 @@ class BaBarBToOmega:
         babar_bins = [[0, 4], [4, 8], [8, 10], [10, 12], [12, 21]]
         babar_central = numpy.array([0.214, 0.200, 0.147, 0.169, 0.482]) * 1e-4
         babar_central = numpy.array([x / (b[1] - b[0]) for x, b in zip(babar_central, babar_bins)])
+        babar_central = babar_central / (tauBplus / 2)
 
         babar_error = numpy.array([0.060, 0.037, 0.029, 0.031, 0.093]) * 1e-4
         babar_error = numpy.array([x / (b[1] - b[0]) for x, b in zip(babar_error, babar_bins)])
+        babar_error = babar_error / (tauBplus / 2)
 
         babar_correlation = numpy.array([
             [1.000, 0.121, 0.061, 0.001, -0.011],
@@ -237,9 +249,11 @@ class BelleBToOmega:
         belle_bins = [[0, 7], [7, 14], [14, 21]]
         belle_central = numpy.array([22.8, 56.5, 33.9]) * 1e-6
         belle_central = numpy.array([x / (b[1] - b[0]) for x, b in zip(belle_central, belle_bins)])
+        belle_central = belle_central / (tauBplus / 2)
 
         belle_error = numpy.array([6.2, 10.6, 11.5]) * 1e-6
         belle_error = numpy.array([x / (b[1] - b[0]) for x, b in zip(belle_error, belle_bins)])
+        belle_error = belle_error / (tauBplus / 2)
 
         belle_correlation = numpy.array([
             [100, 1.5, 10.3],