Rename to more general case

eFFORT/BToPiLNu.py → eFFORT/BToPLNu.py

@@ -7,7 +7,7 @@ import uncertainties
 from eFFORT.BRhoLepNuRateExp import getDiffRatedq2
 
 
-class BToPiLNu:
+class BToPLNu:
 
     def __init__(self, m_B: float, m_P: float, m_L: float, V_ub: float, eta_EW: float = 1.0066) -> None:
         self.m_B = m_B
@@ -71,66 +71,4 @@ class BToPiLNu:
     def Gamma(self):
         return scipy.integrate.quad(lambda x: self.dGamma_dq2(x).nominal_value, self.q2min, self.q2max, epsabs=1e-20)
 
-    # def cosTheta(self, q2, El):
-    #     return ((self.m_B**2 - self.m_P**2 + q2) * (q2 + self.m_L**2) - (4*q2*self.m_B*El)) / \
-    #            (2*self.m_B*self.pion_momentum(q2) * (q2 - self.m_L**2))
-    #
-    # def dcosTheta_dEl(self, q2):
-    #     return 2 * q2 / (self.pion_momentum(q2) * (q2 - self.m_L**2))
-    #
-    # def ddGamma_dq2dEl(self, q2, El):
-    #     # This term already has approximations
-    #     return self.N0(q2) * self.pion_momentum(q2) * (2 * (1 - self.cosTheta(q2, El)**2) * self.H0(q2)**2) * self.dcosTheta_dEl(q2)
-
-
-class BToPiLNuBCL(BToPiLNu):
-
-    def __init__(self, m_B: float, m_V: float, m_L: float, V_ub: float, eta_EW: float = 1.0066):
-        super(BToPiLNuBCL, self).__init__(m_B, m_V, m_L, V_ub, eta_EW)
-        self._coefficients = None
-
-        # correlation_matrix = np.array([
-        #     [1, -0.870, -0.400, 0.453, 0.428, -0.175, -0.201, -0.119, -0.009],
-        #     [0, 1, 0.14, -0.455, -0.342, 0.224, 0.174, 0.047, -0.033],
-        #     [0, 0, 1, -0.789, -0.874, -0.068, 0.142, 0.025, -0.007],
-        #     [0, 0, 0, 1, 0.879, -0.051, -0.253, 0.098, 0.234],
-        #     [0, 0, 0, 0, 1, 0.076, 0.038, 0.018, -0.200],
-        #     [0, 0, 0, 0, 0, 1, -0.043, -0.604, -0.388],
-        #     [0, 0, 0, 0, 0, 0, 1, -0.408, -0.758],
-        #     [0, 0, 0, 0, 0, 0, 0, 1, 0.457],
-        #     [0, 0, 0, 0, 0, 0, 0, 0, 1],
-        # ])
-        # correlation_matrix = correlation_matrix + correlation_matrix.T - np.diag(correlation_matrix.diagonal())
-        # (v_ub, fp0, fp1, fp2, fp3, f00, f01, f02, f03) = uncertainties.correlated_values_norm([
-        #     (3.72e-3, 0.16e-3),
-        #     (0.419, 0.013),
-        #     (-0.495, 0.054),
-        #     (-0.43, 0.13),
-        #     (0.22, 0.31),
-        #     (0.510, 0.019),
-        #     (-1.700, 0.082),
-        #     (1.53, 0.19),
-        #     (4.52, 0.83),
-        # ], correlation_matrix)
-        # self.fplus_par = [fp0, fp1, fp2, fp3]
-        # self.fzero_par = [f00, f01, f02, f03]
 
-    @property
-    def coefficients(self):
-        return self._coefficients
-
-    @coefficients.setter
-    def coefficients(self, coefficients):
-        self._coefficients = coefficients
-        self.gamma = None  # Clear cache of normalization integral when updating coefficients
-
-    def fzero(self, q2):
-        N = 4
-        return sum([b * self.z(q2) ** n for n, b in enumerate(self.coefficients[N:])])
-
-    def fplus(self, q2):
-        m_Bstar = 5.325
-        N = 4
-        return 1 / (1 - q2 / m_Bstar ** 2) * sum(
-            [b * (self.z(q2) ** n - (-1) ** (n - N) * n / N * self.z(q2) ** N) for n, b in
-             enumerate(self.coefficients[:N])])