#!/usr/bin/env python
# coding: utf-8
# # Import
# In[ ]:
import os
# os.environ["CUDA_VISIBLE_DEVICES"] = "-1"
import random
import numpy as np
from pdg_const import pdg
import matplotlib
import matplotlib.pyplot as plt
import pickle as pkl
import sys
import time
from helperfunctions import display_time, prepare_plot
import cmath as c
import scipy.integrate as integrate
from scipy.optimize import fminbound
from array import array as arr
import collections
from itertools import compress
import tensorflow as tf
import zfit
from zfit import ztf
# from IPython.display import clear_output
import os
import tensorflow_probability as tfp
tfd = tfp.distributions
# In[ ]:
# chunksize = 10000
# zfit.run.chunking.active = True
# zfit.run.chunking.max_n_points = chunksize
# # Build model and graphs
# ## Create graphs
# In[ ]:
# In[ ]:
def formfactor(q2, subscript, b0_0, b0_1, b0_2, bplus_0, bplus_1, bplus_2, bT_0, bT_1, bT_2): #returns real value
#check if subscript is viable
if subscript != "0" and subscript != "+" and subscript != "T":
raise ValueError('Wrong subscript entered, choose either 0, + or T')
#get constants
mK = ztf.constant(pdg['Ks_M'])
mbstar0 = ztf.constant(pdg["mbstar0"])
mbstar = ztf.constant(pdg["mbstar"])
mmu = ztf.constant(pdg['muon_M'])
mb = ztf.constant(pdg['bquark_M'])
ms = ztf.constant(pdg['squark_M'])
mB = ztf.constant(pdg['Bplus_M'])
#N comes from derivation in paper
N = 3
#some helperfunctions
tpos = (mB - mK)**2
tzero = (mB + mK)*(ztf.sqrt(mB)-ztf.sqrt(mK))**2
z_oben = ztf.sqrt(tpos - q2) - ztf.sqrt(tpos - tzero)
z_unten = ztf.sqrt(tpos - q2) + ztf.sqrt(tpos - tzero)
z = tf.divide(z_oben, z_unten)
#calculate f0
if subscript == "0":
prefactor = 1/(1 - q2/(mbstar0**2))
_sum = 0
b0 = [b0_0, b0_1, b0_2]
for i in range(N):
_sum += b0[i]*(tf.pow(z,i))
return ztf.to_complex(prefactor * _sum)
#calculate f+ or fT
else:
prefactor = 1/(1 - q2/(mbstar**2))
_sum = 0
if subscript == "T":
bT = [bT_0, bT_1, bT_2]
for i in range(N):
_sum += bT[i] * (tf.pow(z, i) - ((-1)**(i-N)) * (i/N) * tf.pow(z, N))
else:
bplus = [bplus_0, bplus_1, bplus_2]
for i in range(N):
_sum += bplus[i] * (tf.pow(z, i) - ((-1)**(i-N)) * (i/N) * tf.pow(z, N))
return ztf.to_complex(prefactor * _sum)
def resonance(q, _mass, width, phase, scale):
q2 = tf.pow(q, 2)
mmu = ztf.constant(pdg['muon_M'])
p = 0.5 * ztf.sqrt(q2 - 4*(mmu**2))
p0 = 0.5 * ztf.sqrt(_mass**2 - 4*mmu**2)
gamma_j = tf.divide(p, q) * _mass * width / p0
#Calculate the resonance
_top = tf.complex(_mass * width, ztf.constant(0.0))
_bottom = tf.complex(_mass**2 - q2, -_mass*gamma_j)
com = _top/_bottom
#Rotate by the phase
r = ztf.to_complex(scale*tf.abs(com))
_phase = tf.angle(com)
_phase += phase
com = r * tf.exp(tf.complex(ztf.constant(0.0), _phase))
return com
def axiv_nonres(q, b0_0, b0_1, b0_2, bplus_0, bplus_1, bplus_2, bT_0, bT_1, bT_2):
GF = ztf.constant(pdg['GF'])
alpha_ew = ztf.constant(pdg['alpha_ew'])
Vtb = ztf.constant(pdg['Vtb'])
Vts = ztf.constant(pdg['Vts'])
C10eff = ztf.constant(pdg['C10eff'])
mmu = ztf.constant(pdg['muon_M'])
mb = ztf.constant(pdg['bquark_M'])
ms = ztf.constant(pdg['squark_M'])
mK = ztf.constant(pdg['Ks_M'])
mB = ztf.constant(pdg['Bplus_M'])
q2 = tf.pow(q, 2)
#Some helperfunctions
beta = 1. - 4. * mmu**2. / q2
kabs = ztf.sqrt(mB**2. + tf.pow(q2, 2)/mB**2. + mK**4./mB**2. - 2. * (mB**2. * mK**2. + mK**2. * q2 + mB**2. * q2) / mB**2.)
#prefactor in front of whole bracket
prefactor1 = GF**2. *alpha_ew**2. * (tf.abs(Vtb*Vts))**2. * kabs * beta / (128. * np.pi**5.)
#left term in bracket
bracket_left = 2./3. * tf.pow(kabs,2) * tf.pow(beta,2) * tf.pow(tf.abs(ztf.to_complex(C10eff)*formfactor(q2, "+", b0_0, b0_1, b0_2, bplus_0, bplus_1, bplus_2, bT_0, bT_1, bT_2)),2)
#middle term in bracket
_top = 4. * mmu**2. * (mB**2. - mK**2.) * (mB**2. - mK**2.)
_under = q2 * mB**2.
bracket_middle = _top/_under *tf.pow(tf.abs(ztf.to_complex(C10eff) * formfactor(q2, "0", b0_0, b0_1, b0_2, bplus_0, bplus_1, bplus_2, bT_0, bT_1, bT_2)), 2)
#Note sqrt(q2) comes from derivation as we use q2 and plot q
return prefactor1 * (bracket_left + bracket_middle) * 2 * q
def vec(q, funcs, b0_0, b0_1, b0_2, bplus_0, bplus_1, bplus_2, bT_0, bT_1, bT_2):
q2 = tf.pow(q, 2)
GF = ztf.constant(pdg['GF'])
alpha_ew = ztf.constant(pdg['alpha_ew'])
Vtb = ztf.constant(pdg['Vtb'])
Vts = ztf.constant(pdg['Vts'])
C7eff = ztf.constant(pdg['C7eff'])
mmu = ztf.constant(pdg['muon_M'])
mb = ztf.constant(pdg['bquark_M'])
ms = ztf.constant(pdg['squark_M'])
mK = ztf.constant(pdg['Ks_M'])
mB = ztf.constant(pdg['Bplus_M'])
#Some helperfunctions
beta = 1. - 4. * mmu**2. / q2
kabs = ztf.sqrt(mB**2. + tf.pow(q2, 2)/mB**2. + mK**4./mB**2. - 2 * (mB**2 * mK**2 + mK**2 * q2 + mB**2 * q2) / mB**2)
#prefactor in front of whole bracket
prefactor1 = GF**2. *alpha_ew**2. * (tf.abs(Vtb*Vts))**2 * kabs * beta / (128. * np.pi**5.)
#right term in bracket
prefactor2 = tf.pow(kabs,2) * (1. - 1./3. * beta)
abs_bracket = tf.pow(tf.abs(c9eff(q, funcs) * formfactor(q2, "+", b0_0, b0_1, b0_2, bplus_0, bplus_1, bplus_2, bT_0, bT_1, bT_2) + ztf.to_complex(2.0 * C7eff * (mb + ms)/(mB + mK)) * formfactor(q2, "T", b0_0, b0_1, b0_2, bplus_0, bplus_1, bplus_2, bT_0, bT_1, bT_2)),2)
bracket_right = prefactor2 * abs_bracket
#Note sqrt(q2) comes from derivation as we use q2 and plot q
return prefactor1 * bracket_right * 2 * q
def c9eff(q, funcs):
C9eff_nr = ztf.to_complex(ztf.constant(pdg['C9eff']))
c9 = C9eff_nr + funcs
return c9
# In[ ]:
def G(y):
def inner_rect_bracket(q):
return tf.log(ztf.to_complex((1+tf.sqrt(q))/(1-tf.sqrt(q)))-tf.complex(ztf.constant(0), -1*ztf.constant(np.pi)))
def inner_right(q):
return ztf.to_complex(2 * tf.atan(1/tf.sqrt(tf.math.real(-q))))
big_bracket = tf.where(tf.math.real(y) > ztf.constant(0.0), inner_rect_bracket(y), inner_right(y))
return ztf.to_complex(tf.sqrt(tf.abs(y))) * big_bracket
def h_S(m, q):
return ztf.to_complex(2) - G(ztf.to_complex(1) - ztf.to_complex(4*tf.pow(m, 2)) / ztf.to_complex(tf.pow(q, 2)))
def h_P(m, q):
return ztf.to_complex(2/3) + (ztf.to_complex(1) - ztf.to_complex(4*tf.pow(m, 2)) / ztf.to_complex(tf.pow(q, 2))) * h_S(m,q)
def two_p_ccbar(mD, m_D_bar, m_D_star, q):
#Load constants
nu_D_bar = ztf.to_complex(pdg["nu_D_bar"])
nu_D = ztf.to_complex(pdg["nu_D"])
nu_D_star = ztf.to_complex(pdg["nu_D_star"])
phase_D_bar = ztf.to_complex(pdg["phase_D_bar"])
phase_D = ztf.to_complex(pdg["phase_D"])
phase_D_star = ztf.to_complex(pdg["phase_D_star"])
#Calculation
left_part = nu_D_bar * tf.exp(tf.complex(ztf.constant(0.0), phase_D_bar)) * h_S(m_D_bar, q)
right_part_D = nu_D * tf.exp(tf.complex(ztf.constant(0.0), phase_D)) * h_P(m_D, q)
right_part_D_star = nu_D_star * tf.exp(tf.complex(ztf.constant(0.0), phase_D_star)) * h_P(m_D_star, q)
return left_part + right_part_D + right_part_D_star
# ## Build pdf
# In[ ]:
class total_pdf_cut(zfit.pdf.ZPDF):
_N_OBS = 1 # dimension, can be omitted
_PARAMS = ['b0_0', 'b0_1', 'b0_2',
'bplus_0', 'bplus_1', 'bplus_2',
'bT_0', 'bT_1', 'bT_2',
'rho_mass', 'rho_scale', 'rho_phase', 'rho_width',
'jpsi_mass', 'jpsi_scale', 'jpsi_phase', 'jpsi_width',
'psi2s_mass', 'psi2s_scale', 'psi2s_phase', 'psi2s_width',
'p3770_mass', 'p3770_scale', 'p3770_phase', 'p3770_width',
'p4040_mass', 'p4040_scale', 'p4040_phase', 'p4040_width',
'p4160_mass', 'p4160_scale', 'p4160_phase', 'p4160_width',
'p4415_mass', 'p4415_scale', 'p4415_phase', 'p4415_width',
'omega_mass', 'omega_scale', 'omega_phase', 'omega_width',
'phi_mass', 'phi_scale', 'phi_phase', 'phi_width',
'Dbar_mass', 'Dbar_scale', 'Dbar_phase',
'Dstar_mass', 'DDstar_scale', 'DDstar_phase', 'D_mass',
'tau_mass', 'C_tt']
# the name of the parameters
def _unnormalized_pdf(self, x):
x = x.unstack_x()
b0 = [self.params['b0_0'], self.params['b0_1'], self.params['b0_2']]
bplus = [self.params['bplus_0'], self.params['bplus_1'], self.params['bplus_2']]
bT = [self.params['bT_0'], self.params['bT_1'], self.params['bT_2']]
def rho_res(q):
return resonance(q, _mass = self.params['rho_mass'], scale = self.params['rho_scale'],
phase = self.params['rho_phase'], width = self.params['rho_width'])
def omega_res(q):
return resonance(q, _mass = self.params['omega_mass'], scale = self.params['omega_scale'],
phase = self.params['omega_phase'], width = self.params['omega_width'])
def phi_res(q):
return resonance(q, _mass = self.params['phi_mass'], scale = self.params['phi_scale'],
phase = self.params['phi_phase'], width = self.params['phi_width'])
def jpsi_res(q):
return ztf.to_complex(tf.pow(q, 2) / tf.pow(self.params['jpsi_mass'], 2)) * resonance(q, _mass = self.params['jpsi_mass'],
scale = self.params['jpsi_scale'],
phase = self.params['jpsi_phase'],
width = self.params['jpsi_width'])
def psi2s_res(q):
return ztf.to_complex(tf.pow(q, 2) / tf.pow(self.params['psi2s_mass'], 2)) * resonance(q, _mass = self.params['psi2s_mass'],
scale = self.params['psi2s_scale'],
phase = self.params['psi2s_phase'],
width = self.params['psi2s_width'])
def p3770_res(q):
return ztf.to_complex(tf.pow(q, 2) / tf.pow(self.params['p3770_mass'], 2)) * resonance(q, _mass = self.params['p3770_mass'],
scale = self.params['p3770_scale'],
phase = self.params['p3770_phase'],
width = self.params['p3770_width'])
def p4040_res(q):
return ztf.to_complex(tf.pow(q, 2) / tf.pow(self.params['p4040_mass'], 2)) * resonance(q, _mass = self.params['p4040_mass'],
scale = self.params['p4040_scale'],
phase = self.params['p4040_phase'],
width = self.params['p4040_width'])
def p4160_res(q):
return ztf.to_complex(tf.pow(q, 2) / tf.pow(self.params['p4160_mass'], 2)) * resonance(q, _mass = self.params['p4160_mass'],
scale = self.params['p4160_scale'],
phase = self.params['p4160_phase'],
width = self.params['p4160_width'])
def p4415_res(q):
return ztf.to_complex(tf.pow(q, 2) / tf.pow(self.params['p4415_mass'], 2)) * resonance(q, _mass = self.params['p4415_mass'],
scale = self.params['p4415_scale'],
phase = self.params['p4415_phase'],
width = self.params['p4415_width'])
def P2_D(q):
Dbar_contrib = ztf.to_complex(self.params['Dbar_scale'])*tf.exp(tf.complex(ztf.constant(0.0), self.params['Dbar_phase']))*ztf.to_complex(h_S(self.params['Dbar_mass'], q))
DDstar_contrib = ztf.to_complex(self.params['DDstar_scale'])*tf.exp(tf.complex(ztf.constant(0.0), self.params['DDstar_phase']))*(ztf.to_complex(h_P(self.params['Dstar_mass'], q)) + ztf.to_complex(h_P(self.params['D_mass'], q)))
return Dbar_contrib + DDstar_contrib
def ttau_cusp(q):
return ztf.to_complex(self.params['C_tt'])*(ztf.to_complex((h_S(self.params['tau_mass'], q))) - ztf.to_complex(h_P(self.params['tau_mass'], q)))
funcs = rho_res(x) + omega_res(x) + phi_res(x) + jpsi_res(x) + psi2s_res(x) + p3770_res(x) + p4040_res(x)+ p4160_res(x) + p4415_res(x) + P2_D(x) + ttau_cusp(x)
vec_f = vec(x, funcs, b0_0, b0_1, b0_2, bplus_0, bplus_1, bplus_2, bT_0, bT_1, bT_2)
axiv_nr = axiv_nonres(x, b0_0, b0_1, b0_2, bplus_0, bplus_1, bplus_2, bT_0, bT_1, bT_2)
tot = vec_f + axiv_nr
#Cut out jpsi and psi2s
tot = tf.where(tf.math.logical_or(x < ztf.constant(jpsi_mass-60.), x > ztf.constant(jpsi_mass+70.)), tot, 0.0*tot)
tot = tf.where(tf.math.logical_or(x < ztf.constant(psi2s_mass-50.), x > ztf.constant(psi2s_mass+50.)), tot, 0.0*tot)
return tot
class total_pdf_full(zfit.pdf.ZPDF):
_N_OBS = 1 # dimension, can be omitted
_PARAMS = ['b0_0', 'b0_1', 'b0_2',
'bplus_0', 'bplus_1', 'bplus_2',
'bT_0', 'bT_1', 'bT_2',
'rho_mass', 'rho_scale', 'rho_phase', 'rho_width',
'jpsi_mass', 'jpsi_scale', 'jpsi_phase', 'jpsi_width',
'psi2s_mass', 'psi2s_scale', 'psi2s_phase', 'psi2s_width',
'p3770_mass', 'p3770_scale', 'p3770_phase', 'p3770_width',
'p4040_mass', 'p4040_scale', 'p4040_phase', 'p4040_width',
'p4160_mass', 'p4160_scale', 'p4160_phase', 'p4160_width',
'p4415_mass', 'p4415_scale', 'p4415_phase', 'p4415_width',
'omega_mass', 'omega_scale', 'omega_phase', 'omega_width',
'phi_mass', 'phi_scale', 'phi_phase', 'phi_width',
'Dbar_mass', 'Dbar_scale', 'Dbar_phase',
'Dstar_mass', 'DDstar_scale', 'DDstar_phase', 'D_mass',
'tau_mass', 'C_tt']
# the name of the parameters
def _unnormalized_pdf(self, x):
x = x.unstack_x()
b0 = [self.params['b0_0'], self.params['b0_1'], self.params['b0_2']]
bplus = [self.params['bplus_0'], self.params['bplus_1'], self.params['bplus_2']]
bT = [self.params['bT_0'], self.params['bT_1'], self.params['bT_2']]
def rho_res(q):
return resonance(q, _mass = self.params['rho_mass'], scale = self.params['rho_scale'],
phase = self.params['rho_phase'], width = self.params['rho_width'])
def omega_res(q):
return resonance(q, _mass = self.params['omega_mass'], scale = self.params['omega_scale'],
phase = self.params['omega_phase'], width = self.params['omega_width'])
def phi_res(q):
return resonance(q, _mass = self.params['phi_mass'], scale = self.params['phi_scale'],
phase = self.params['phi_phase'], width = self.params['phi_width'])
def jpsi_res(q):
return ztf.to_complex(tf.pow(q, 2) / tf.pow(self.params['jpsi_mass'], 2)) * resonance(q, _mass = self.params['jpsi_mass'],
scale = self.params['jpsi_scale'],
phase = self.params['jpsi_phase'],
width = self.params['jpsi_width'])
def psi2s_res(q):
return ztf.to_complex(tf.pow(q, 2) / tf.pow(self.params['psi2s_mass'], 2)) * resonance(q, _mass = self.params['psi2s_mass'],
scale = self.params['psi2s_scale'],
phase = self.params['psi2s_phase'],
width = self.params['psi2s_width'])
def p3770_res(q):
return ztf.to_complex(tf.pow(q, 2) / tf.pow(self.params['p3770_mass'], 2)) * resonance(q, _mass = self.params['p3770_mass'],
scale = self.params['p3770_scale'],
phase = self.params['p3770_phase'],
width = self.params['p3770_width'])
def p4040_res(q):
return ztf.to_complex(tf.pow(q, 2) / tf.pow(self.params['p4040_mass'], 2)) * resonance(q, _mass = self.params['p4040_mass'],
scale = self.params['p4040_scale'],
phase = self.params['p4040_phase'],
width = self.params['p4040_width'])
def p4160_res(q):
return ztf.to_complex(tf.pow(q, 2) / tf.pow(self.params['p4160_mass'], 2)) * resonance(q, _mass = self.params['p4160_mass'],
scale = self.params['p4160_scale'],
phase = self.params['p4160_phase'],
width = self.params['p4160_width'])
def p4415_res(q):
return ztf.to_complex(tf.pow(q, 2) / tf.pow(self.params['p4415_mass'], 2)) * resonance(q, _mass = self.params['p4415_mass'],
scale = self.params['p4415_scale'],
phase = self.params['p4415_phase'],
width = self.params['p4415_width'])
def P2_D(q):
Dbar_contrib = ztf.to_complex(self.params['Dbar_scale'])*tf.exp(tf.complex(ztf.constant(0.0), self.params['Dbar_phase']))*ztf.to_complex(h_S(self.params['Dbar_mass'], q))
DDstar_contrib = ztf.to_complex(self.params['DDstar_scale'])*tf.exp(tf.complex(ztf.constant(0.0), self.params['DDstar_phase']))*(ztf.to_complex(h_P(self.params['Dstar_mass'], q)) + ztf.to_complex(h_P(self.params['D_mass'], q)))
return Dbar_contrib + DDstar_contrib
def ttau_cusp(q):
return ztf.to_complex(self.params['C_tt'])*(ztf.to_complex((h_S(self.params['tau_mass'], q))) - ztf.to_complex(h_P(self.params['tau_mass'], q)))
funcs = rho_res(x) + omega_res(x) + phi_res(x) + jpsi_res(x) + psi2s_res(x) + p3770_res(x) + p4040_res(x)+ p4160_res(x) + p4415_res(x) + P2_D(x) + ttau_cusp(x)
vec_f = vec(x, funcs, b0_0, b0_1, b0_2, bplus_0, bplus_1, bplus_2, bT_0, bT_1, bT_2)
axiv_nr = axiv_nonres(x, b0_0, b0_1, b0_2, bplus_0, bplus_1, bplus_2, bT_0, bT_1, bT_2)
tot = vec_f + axiv_nr
#Cut out jpsi and psi2s
# tot = tf.where(tf.math.logical_or(x < ztf.constant(jpsi_mass-60.), x > ztf.constant(jpsi_mass+70.)), tot, 0.0*tot)
# tot = tf.where(tf.math.logical_or(x < ztf.constant(psi2s_mass-50.), x > ztf.constant(psi2s_mass+50.)), tot, 0.0*tot)
return tot
# ## Setup parameters
# In[ ]:
# formfactors
b0_0 = zfit.Parameter("b0_0", ztf.constant(0.292), floating = False) #, lower_limit = -2.0, upper_limit= 2.0)
b0_1 = zfit.Parameter("b0_1", ztf.constant(0.281), floating = False) #, lower_limit = -2.0, upper_limit= 2.0)
b0_2 = zfit.Parameter("b0_2", ztf.constant(0.150), floating = False) #, lower_limit = -2.0, upper_limit= 2.0)
bplus_0 = zfit.Parameter("bplus_0", ztf.constant(0.466), lower_limit = -2.0, upper_limit= 2.0)
bplus_1 = zfit.Parameter("bplus_1", ztf.constant(-0.885), lower_limit = -2.0, upper_limit= 2.0)
bplus_2 = zfit.Parameter("bplus_2", ztf.constant(-0.213), lower_limit = -2.0, upper_limit= 2.0)
bT_0 = zfit.Parameter("bT_0", ztf.constant(0.460), floating = False) #, lower_limit = -2.0, upper_limit= 2.0)
bT_1 = zfit.Parameter("bT_1", ztf.constant(-1.089), floating = False) #, lower_limit = -2.0, upper_limit= 2.0)
bT_2 = zfit.Parameter("bT_2", ztf.constant(-1.114), floating = False) #, lower_limit = -2.0, upper_limit= 2.0)
#rho
rho_mass, rho_width, rho_phase, rho_scale = pdg["rho"]
rho_m = zfit.Parameter("rho_m", ztf.constant(rho_mass), floating = False) #lower_limit = rho_mass - rho_width, upper_limit = rho_mass + rho_width)
rho_w = zfit.Parameter("rho_w", ztf.constant(rho_width), floating = False)
rho_p = zfit.Parameter("rho_p", ztf.constant(rho_phase), lower_limit=-2*np.pi, upper_limit=2*np.pi)
rho_s = zfit.Parameter("rho_s", ztf.constant(rho_scale), lower_limit=rho_scale-np.sqrt(rho_scale), upper_limit=rho_scale+np.sqrt(rho_scale))
#omega
omega_mass, omega_width, omega_phase, omega_scale = pdg["omega"]
omega_m = zfit.Parameter("omega_m", ztf.constant(omega_mass), floating = False)
omega_w = zfit.Parameter("omega_w", ztf.constant(omega_width), floating = False)
omega_p = zfit.Parameter("omega_p", ztf.constant(omega_phase), lower_limit=-2*np.pi, upper_limit=2*np.pi)
omega_s = zfit.Parameter("omega_s", ztf.constant(omega_scale), lower_limit=omega_scale-np.sqrt(omega_scale), upper_limit=omega_scale+np.sqrt(omega_scale))
#phi
phi_mass, phi_width, phi_phase, phi_scale = pdg["phi"]
phi_m = zfit.Parameter("phi_m", ztf.constant(phi_mass), floating = False)
phi_w = zfit.Parameter("phi_w", ztf.constant(phi_width), floating = False)
phi_p = zfit.Parameter("phi_p", ztf.constant(phi_phase), lower_limit=-2*np.pi, upper_limit=2*np.pi)
phi_s = zfit.Parameter("phi_s", ztf.constant(phi_scale), lower_limit=phi_scale-np.sqrt(phi_scale), upper_limit=phi_scale+np.sqrt(phi_scale))
#jpsi
jpsi_mass, jpsi_width, jpsi_phase, jpsi_scale = pdg["jpsi"]
jpsi_m = zfit.Parameter("jpsi_m", ztf.constant(jpsi_mass), floating = False)
jpsi_w = zfit.Parameter("jpsi_w", ztf.constant(jpsi_width), floating = False)
jpsi_p = zfit.Parameter("jpsi_p", ztf.constant(jpsi_phase), lower_limit=-2*np.pi, upper_limit=2*np.pi)
jpsi_s = zfit.Parameter("jpsi_s", ztf.constant(jpsi_scale), floating = False) #, lower_limit=jpsi_scale-np.sqrt(jpsi_scale), upper_limit=jpsi_scale+np.sqrt(jpsi_scale))
#psi2s
psi2s_mass, psi2s_width, psi2s_phase, psi2s_scale = pdg["psi2s"]
psi2s_m = zfit.Parameter("psi2s_m", ztf.constant(psi2s_mass), floating = False)
psi2s_w = zfit.Parameter("psi2s_w", ztf.constant(psi2s_width), floating = False)
psi2s_p = zfit.Parameter("psi2s_p", ztf.constant(psi2s_phase), lower_limit=-2*np.pi, upper_limit=2*np.pi)
psi2s_s = zfit.Parameter("psi2s_s", ztf.constant(psi2s_scale), floating = False) #, lower_limit=psi2s_scale-np.sqrt(psi2s_scale), upper_limit=psi2s_scale+np.sqrt(psi2s_scale))
#psi(3770)
p3770_mass, p3770_width, p3770_phase, p3770_scale = pdg["p3770"]
p3770_m = zfit.Parameter("p3770_m", ztf.constant(p3770_mass), floating = False)
p3770_w = zfit.Parameter("p3770_w", ztf.constant(p3770_width), floating = False)
p3770_p = zfit.Parameter("p3770_p", ztf.constant(p3770_phase), lower_limit=-2*np.pi, upper_limit=2*np.pi)
p3770_s = zfit.Parameter("p3770_s", ztf.constant(p3770_scale), lower_limit=p3770_scale-np.sqrt(p3770_scale), upper_limit=p3770_scale+np.sqrt(p3770_scale))
#psi(4040)
p4040_mass, p4040_width, p4040_phase, p4040_scale = pdg["p4040"]
p4040_m = zfit.Parameter("p4040_m", ztf.constant(p4040_mass), floating = False)
p4040_w = zfit.Parameter("p4040_w", ztf.constant(p4040_width), floating = False)
p4040_p = zfit.Parameter("p4040_p", ztf.constant(p4040_phase), lower_limit=-2*np.pi, upper_limit=2*np.pi)
p4040_s = zfit.Parameter("p4040_s", ztf.constant(p4040_scale), lower_limit=p4040_scale-np.sqrt(p4040_scale), upper_limit=p4040_scale+np.sqrt(p4040_scale))
#psi(4160)
p4160_mass, p4160_width, p4160_phase, p4160_scale = pdg["p4160"]
p4160_m = zfit.Parameter("p4160_m", ztf.constant(p4160_mass), floating = False)
p4160_w = zfit.Parameter("p4160_w", ztf.constant(p4160_width), floating = False)
p4160_p = zfit.Parameter("p4160_p", ztf.constant(p4160_phase), lower_limit=-2*np.pi, upper_limit=2*np.pi)
p4160_s = zfit.Parameter("p4160_s", ztf.constant(p4160_scale), lower_limit=p4160_scale-np.sqrt(p4160_scale), upper_limit=p4160_scale+np.sqrt(p4160_scale))
#psi(4415)
p4415_mass, p4415_width, p4415_phase, p4415_scale = pdg["p4415"]
p4415_m = zfit.Parameter("p4415_m", ztf.constant(p4415_mass), floating = False)
p4415_w = zfit.Parameter("p4415_w", ztf.constant(p4415_width), floating = False)
p4415_p = zfit.Parameter("p4415_p", ztf.constant(p4415_phase), lower_limit=-2*np.pi, upper_limit=2*np.pi)
p4415_s = zfit.Parameter("p4415_s", ztf.constant(p4415_scale), lower_limit=p4415_scale-np.sqrt(p4415_scale), upper_limit=p4415_scale+np.sqrt(p4415_scale))
# ## Dynamic generation of 2 particle contribution
# In[ ]:
m_c = 1300
Dbar_phase = 0.0
DDstar_phase = 0.0
Dstar_mass = pdg['Dst_M']
Dbar_mass = pdg['D0_M']
D_mass = pdg['D0_M']
Dbar_s = zfit.Parameter("Dbar_s", ztf.constant(0.0), lower_limit=-0.3, upper_limit=0.3)
Dbar_m = zfit.Parameter("Dbar_m", ztf.constant(Dbar_mass), floating = False)
Dbar_p = zfit.Parameter("Dbar_p", ztf.constant(Dbar_phase), lower_limit=-2*np.pi, upper_limit=2*np.pi)#, floating = False)
DDstar_s = zfit.Parameter("DDstar_s", ztf.constant(0.0), lower_limit=-0.3, upper_limit=0.3)#, floating = False)
Dstar_m = zfit.Parameter("Dstar_m", ztf.constant(Dstar_mass), floating = False)
D_m = zfit.Parameter("D_m", ztf.constant(D_mass), floating = False)
DDstar_p = zfit.Parameter("DDstar_p", ztf.constant(DDstar_phase), lower_limit=-2*np.pi, upper_limit=2*np.pi)#, floating = False)
# ## Tau parameters
# In[ ]:
tau_m = zfit.Parameter("tau_m", ztf.constant(pdg['tau_M']), floating = False)
Ctt = zfit.Parameter("Ctt", ztf.constant(0.0), lower_limit=-2.5, upper_limit=2.5)
# ## Load data
# In[ ]:
x_min = 2*pdg['muon_M']
x_max = (pdg["Bplus_M"]-pdg["Ks_M"]-0.1)
# # Full spectrum
obs_toy = zfit.Space('q', limits = (x_min, x_max))
# Jpsi and Psi2s cut out
obs1 = zfit.Space('q', limits = (x_min, jpsi_mass - 60.))
obs2 = zfit.Space('q', limits = (jpsi_mass + 70., psi2s_mass - 50.))
obs3 = zfit.Space('q', limits = (psi2s_mass + 50., x_max))
obs_fit = obs1 + obs2 + obs3
# with open(r"./data/slim_points/slim_points_toy_0_range({0}-{1}).pkl".format(int(x_min), int(x_max)), "rb") as input_file:
# part_set = pkl.load(input_file)
# x_part = part_set['x_part']
# x_part = x_part.astype('float64')
# data = zfit.data.Data.from_numpy(array=x_part, obs=obs)
# ## Setup pdf
# In[ ]:
total_f = total_pdf_cut(obs=obs_toy, jpsi_mass = jpsi_m, jpsi_scale = jpsi_s, jpsi_phase = jpsi_p, jpsi_width = jpsi_w,
psi2s_mass = psi2s_m, psi2s_scale = psi2s_s, psi2s_phase = psi2s_p, psi2s_width = psi2s_w,
p3770_mass = p3770_m, p3770_scale = p3770_s, p3770_phase = p3770_p, p3770_width = p3770_w,
p4040_mass = p4040_m, p4040_scale = p4040_s, p4040_phase = p4040_p, p4040_width = p4040_w,
p4160_mass = p4160_m, p4160_scale = p4160_s, p4160_phase = p4160_p, p4160_width = p4160_w,
p4415_mass = p4415_m, p4415_scale = p4415_s, p4415_phase = p4415_p, p4415_width = p4415_w,
rho_mass = rho_m, rho_scale = rho_s, rho_phase = rho_p, rho_width = rho_w,
omega_mass = omega_m, omega_scale = omega_s, omega_phase = omega_p, omega_width = omega_w,
phi_mass = phi_m, phi_scale = phi_s, phi_phase = phi_p, phi_width = phi_w,
Dstar_mass = Dstar_m, DDstar_scale = DDstar_s, DDstar_phase = DDstar_p, D_mass = D_m,
Dbar_mass = Dbar_m, Dbar_scale = Dbar_s, Dbar_phase = Dbar_p,
tau_mass = tau_m, C_tt = Ctt, b0_0 = b0_0, b0_1 = b0_1, b0_2 = b0_2,
bplus_0 = bplus_0, bplus_1 = bplus_1, bplus_2 = bplus_2,
bT_0 = bT_0, bT_1 = bT_1, bT_2 = bT_2)
total_f_fit = total_pdf_full(obs=obs_fit, jpsi_mass = jpsi_m, jpsi_scale = jpsi_s, jpsi_phase = jpsi_p, jpsi_width = jpsi_w,
psi2s_mass = psi2s_m, psi2s_scale = psi2s_s, psi2s_phase = psi2s_p, psi2s_width = psi2s_w,
p3770_mass = p3770_m, p3770_scale = p3770_s, p3770_phase = p3770_p, p3770_width = p3770_w,
p4040_mass = p4040_m, p4040_scale = p4040_s, p4040_phase = p4040_p, p4040_width = p4040_w,
p4160_mass = p4160_m, p4160_scale = p4160_s, p4160_phase = p4160_p, p4160_width = p4160_w,
p4415_mass = p4415_m, p4415_scale = p4415_s, p4415_phase = p4415_p, p4415_width = p4415_w,
rho_mass = rho_m, rho_scale = rho_s, rho_phase = rho_p, rho_width = rho_w,
omega_mass = omega_m, omega_scale = omega_s, omega_phase = omega_p, omega_width = omega_w,
phi_mass = phi_m, phi_scale = phi_s, phi_phase = phi_p, phi_width = phi_w,
Dstar_mass = Dstar_m, DDstar_scale = DDstar_s, DDstar_phase = DDstar_p, D_mass = D_m,
Dbar_mass = Dbar_m, Dbar_scale = Dbar_s, Dbar_phase = Dbar_p,
tau_mass = tau_m, C_tt = Ctt, b0_0 = b0_0, b0_1 = b0_1, b0_2 = b0_2,
bplus_0 = bplus_0, bplus_1 = bplus_1, bplus_2 = bplus_2,
bT_0 = bT_0, bT_1 = bT_1, bT_2 = bT_2)
# print(total_pdf.obs)
# print(calcs_test)
# for param in total_f.get_dependents():
# print(zfit.run(param))
# In[ ]:
# total_f_fit.normalization(obs_fit)
# ## Test if graphs actually work and compute values
# In[ ]:
# def total_test_tf(xq):
# def jpsi_res(q):
# return resonance(q, jpsi_m, jpsi_s, jpsi_p, jpsi_w)
# def psi2s_res(q):
# return resonance(q, psi2s_m, psi2s_s, psi2s_p, psi2s_w)
# def cusp(q):
# return bifur_gauss(q, cusp_m, sig_L, sig_R, cusp_s)
# funcs = jpsi_res(xq) + psi2s_res(xq) + cusp(xq)
# vec_f = vec(xq, funcs)
# axiv_nr = axiv_nonres(xq)
# tot = vec_f + axiv_nr
# return tot
# def jpsi_res(q):
# return resonance(q, jpsi_m, jpsi_s, jpsi_p, jpsi_w)
# calcs = zfit.run(total_test_tf(x_part))
test_q = np.linspace(x_min, x_max, int(2e6))
probs = total_f_fit.pdf(test_q, norm_range=False)
calcs_test = zfit.run(probs)
Ctt.set_value(0.5)
calcs_test1 = zfit.run(probs)
Ctt.set_value(0.0)
Dbar_s.set_value(0.3)
DDstar_s.set_value(0.3)
calcs_test2 = zfit.run(probs)
# res_y = zfit.run(jpsi_res(test_q))
# b0 = [b0_0, b0_1, b0_2]
# bplus = [bplus_0, bplus_1, bplus_2]
# bT = [bT_0, bT_1, bT_2]
# f0_y = zfit.run(tf.math.real(formfactor(test_q,"0", b0, bplus, bT)))
# fplus_y = zfit.run(tf.math.real(formfactor(test_q,"+", b0, bplus, bT)))
# fT_y = zfit.run(tf.math.real(formfactor(test_q,"T", b0, bplus, bT)))
# In[ ]:
plt.clf()
# plt.plot(x_part, calcs, '.')
plt.plot(test_q, calcs_test, label = 'pdf (Ctt = 0.0)')
plt.plot(test_q, calcs_test1, label = 'pdf (Ctt = 0.5)')
plt.plot(test_q, calcs_test2, label = 'pdf (D-contribs = 0.3)')
# plt.plot(test_q, f0_y, label = '0')
# plt.plot(test_q, fT_y, label = 'T')
# plt.plot(test_q, fplus_y, label = '+')
# plt.plot(test_q, res_y, label = 'res')
plt.legend()
plt.ylim(0.0, 1.5e-6)
# plt.yscale('log')
# plt.xlim(770, 785)
plt.savefig('test.png')
# print(jpsi_width)
# In[ ]:
# probs = mixture.prob(test_q)
# probs_np = zfit.run(probs)
# probs_np *= np.max(calcs_test) / np.max(probs_np)
# plt.figure()
# plt.semilogy(test_q, probs_np,label="importance sampling")
# plt.semilogy(test_q, calcs_test, label = 'pdf')
# In[ ]:
# 0.213/(0.00133+0.213+0.015)
# ## Adjust scaling of different parts
# In[ ]:
total_f.update_integration_options(draws_per_dim=2000000, mc_sampler=None)
# inte = total_f.integrate(limits = (950., 1050.), norm_range=False)
# inte_fl = zfit.run(inte)
# print(inte_fl/4500)
# print(pdg["jpsi_BR"]/pdg["NR_BR"], inte_fl*pdg["psi2s_auc"]/pdg["NR_auc"])
# In[ ]:
# # print("jpsi:", inte_fl)
# # print("Increase am by factor:", np.sqrt(pdg["jpsi_BR"]/pdg["NR_BR"]*pdg["NR_auc"]/inte_fl))
# # print("New amp:", pdg["jpsi"][3]*np.sqrt(pdg["jpsi_BR"]/pdg["NR_BR"]*pdg["NR_auc"]/inte_fl))
# # print("psi2s:", inte_fl)
# # print("Increase am by factor:", np.sqrt(pdg["psi2s_BR"]/pdg["NR_BR"]*pdg["NR_auc"]/inte_fl))
# # print("New amp:", pdg["psi2s"][3]*np.sqrt(pdg["psi2s_BR"]/pdg["NR_BR"]*pdg["NR_auc"]/inte_fl))
# name = "phi"
# print(name+":", inte_fl)
# print("Increase am by factor:", np.sqrt(pdg[name+"_BR"]/pdg["NR_BR"]*pdg["NR_auc"]/inte_fl))
# print("New amp:", pdg[name][0]*np.sqrt(pdg[name+"_BR"]/pdg["NR_BR"]*pdg["NR_auc"]/inte_fl))
# print(x_min)
# print(x_max)
# # total_f.update_integration_options(draws_per_dim=2000000, mc_sampler=None)
# total_f.update_integration_options(mc_sampler=lambda dim, num_results,
# dtype: tf.random_uniform(maxval=1., shape=(num_results, dim), dtype=dtype),
# draws_per_dim=1000000)
# # _ = []
# # for i in range(10):
# # inte = total_f.integrate(limits = (x_min, x_max))
# # inte_fl = zfit.run(inte)
# # print(inte_fl)
# # _.append(inte_fl)
# # print("mean:", np.mean(_))
# _ = time.time()
# inte = total_f.integrate(limits = (x_min, x_max))
# inte_fl = zfit.run(inte)
# print(inte_fl)
# print("Time taken: {}".format(display_time(int(time.time() - _))))
# print(pdg['NR_BR']/pdg['NR_auc']*inte_fl)
# print(0.25**2*4.2/1000)
# # Sampling
# ## Mixture distribution for sampling
# In[ ]:
# print(list_of_borders[:9])
# print(list_of_borders[-9:])
class UniformSampleAndWeights(zfit.util.execution.SessionHolderMixin):
def __call__(self, limits, dtype, n_to_produce):
# n_to_produce = tf.cast(n_to_produce, dtype=tf.int32)
low, high = limits.limit1d
low = tf.cast(low, dtype=dtype)
high = tf.cast(high, dtype=dtype)
# uniform = tfd.Uniform(low=low, high=high)
# uniformjpsi = tfd.Uniform(low=tf.constant(3080, dtype=dtype), high=tf.constant(3112, dtype=dtype))
# uniformpsi2s = tfd.Uniform(low=tf.constant(3670, dtype=dtype), high=tf.constant(3702, dtype=dtype))
# list_of_borders = []
# _p = []
# splits = 10
# _ = np.linspace(x_min, x_max, splits)
# for i in range(splits):
# list_of_borders.append(tf.constant(_[i], dtype=dtype))
# _p.append(tf.constant(1/splits, dtype=dtype))
# mixture = tfd.MixtureSameFamily(mixture_distribution=tfd.Categorical(probs=_p[:(splits-1)]),
# components_distribution=tfd.Uniform(low=list_of_borders[:(splits-1)],
# high=list_of_borders[-(splits-1):]))
mixture = tfd.MixtureSameFamily(mixture_distribution=tfd.Categorical(probs=[tf.constant(0.05, dtype=dtype),
tf.constant(0.93, dtype=dtype),
tf.constant(0.05, dtype=dtype),
tf.constant(0.065, dtype=dtype),
tf.constant(0.04, dtype=dtype),
tf.constant(0.05, dtype=dtype)]),
components_distribution=tfd.Uniform(low=[tf.constant(x_min, dtype=dtype),
tf.constant(3090, dtype=dtype),
tf.constant(3681, dtype=dtype),
tf.constant(3070, dtype=dtype),
tf.constant(1000, dtype=dtype),
tf.constant(3660, dtype=dtype)],
high=[tf.constant(x_max, dtype=dtype),
tf.constant(3102, dtype=dtype),
tf.constant(3691, dtype=dtype),
tf.constant(3110, dtype=dtype),
tf.constant(1040, dtype=dtype),
tf.constant(3710, dtype=dtype)]))
# dtype = tf.float64
# mixture = tfd.MixtureSameFamily(mixture_distribution=tfd.Categorical(probs=[tf.constant(0.04, dtype=dtype),
# tf.constant(0.90, dtype=dtype),
# tf.constant(0.02, dtype=dtype),
# tf.constant(0.07, dtype=dtype),
# tf.constant(0.02, dtype=dtype)]),
# components_distribution=tfd.Uniform(low=[tf.constant(x_min, dtype=dtype),
# tf.constant(3089, dtype=dtype),
# tf.constant(3103, dtype=dtype),
# tf.constant(3681, dtype=dtype),
# tf.constant(3691, dtype=dtype)],
# high=[tf.constant(3089, dtype=dtype),
# tf.constant(3103, dtype=dtype),
# tf.constant(3681, dtype=dtype),
# tf.constant(3691, dtype=dtype),
# tf.constant(x_max, dtype=dtype)]))
# mixture = tfd.Uniform(tf.constant(x_min, dtype=dtype), tf.constant(x_max, dtype=dtype))
# sample = tf.random.uniform((n_to_produce, 1), dtype=dtype)
sample = mixture.sample((n_to_produce, 1))
# sample = tf.random.uniform((n_to_produce, 1), dtype=dtype)
weights = mixture.prob(sample)[:,0]
# weights = tf.broadcast_to(tf.constant(1., dtype=dtype), shape=(n_to_produce,))
# sample = tf.expand_dims(sample, axis=-1)
# print(sample, weights)
# weights = tf.ones(shape=(n_to_produce,), dtype=dtype)
weights_max = None
thresholds = tf.random_uniform(shape=(n_to_produce,), dtype=dtype)
return sample, thresholds, weights, weights_max, n_to_produce
# In[ ]:
# total_f._sample_and_weights = UniformSampleAndWeights
# In[ ]:
# 0.00133/(0.00133+0.213+0.015)*(x_max-3750)/(x_max-x_min)
# In[ ]:
# zfit.settings.set_verbosity(10)
# In[ ]:
# # zfit.run.numeric_checks = False
# nr_of_toys = 1
# nevents = int(pdg["number_of_decays"])
# nevents = pdg["number_of_decays"]
# event_stack = 1000000
# # zfit.settings.set_verbosity(10)
# calls = int(nevents/event_stack + 1)
# total_samp = []
# start = time.time()
# sampler = total_f.create_sampler(n=event_stack)
# for toy in range(nr_of_toys):
# dirName = 'data/zfit_toys/toy_{0}'.format(toy)
# if not os.path.exists(dirName):
# os.mkdir(dirName)
# print("Directory " , dirName , " Created ")
# for call in range(calls):
# sampler.resample(n=event_stack)
# s = sampler.unstack_x()
# sam = zfit.run(s)
# # clear_output(wait=True)
# c = call + 1
# print("{0}/{1} of Toy {2}/{3}".format(c, calls, toy+1, nr_of_toys))
# print("Time taken: {}".format(display_time(int(time.time() - start))))
# print("Projected time left: {}".format(display_time(int((time.time() - start)/(c+calls*(toy))*((nr_of_toys-toy)*calls-c)))))
# with open("data/zfit_toys/toy_{0}/{1}.pkl".format(toy, call), "wb") as f:
# pkl.dump(sam, f, pkl.HIGHEST_PROTOCOL)
# In[ ]:
# with open(r"data/zfit_toys/toy_0/0.pkl", "rb") as input_file:
# sam = pkl.load(input_file)
# print(sam[:10])
# with open(r"data/zfit_toys/toy_0/1.pkl", "rb") as input_file:
# sam2 = pkl.load(input_file)
# print(sam2[:10])
# print(np.sum(sam-sam2))
# In[ ]:
# print("Time to generate full toy: {} s".format(int(time.time()-start)))
# total_samp = []
# for call in range(calls):
# with open(r"data/zfit_toys/toy_0/{}.pkl".format(call), "rb") as input_file:
# sam = pkl.load(input_file)
# total_samp = np.append(total_samp, sam)
# total_samp = total_samp.astype('float64')
# data2 = zfit.data.Data.from_numpy(array=total_samp[:int(nevents)], obs=obs)
# data3 = zfit.data.Data.from_numpy(array=total_samp, obs=obs)
# print(total_samp[:nevents].shape)
# In[ ]:
# plt.clf()
# bins = int((x_max-x_min)/7)
# # calcs = zfit.run(total_test_tf(samp))
# print(total_samp[:nevents].shape)
# plt.hist(total_samp[:nevents], bins = bins, range = (x_min,x_max), label = 'data')
# # plt.plot(test_q, calcs_test*nevents , label = 'pdf')
# # plt.plot(sam, calcs, '.')
# # plt.plot(test_q, calcs_test)
# # plt.yscale('log')
# plt.ylim(0, 200)
# # plt.xlim(3080, 3110)
# plt.legend()
# plt.savefig('test2.png')
# In[ ]:
# sampler = total_f.create_sampler(n=nevents)
# nll = zfit.loss.UnbinnedNLL(model=total_f, data=sampler, fit_range = (x_min, x_max))
# # for param in pdf.get_dependents():
# # param.set_value(initial_value)
# sampler.resample(n=nevents)
# # Randomise initial values
# # for param in pdf.get_dependents():
# # param.set_value(random value here)
# # Minimise the NLL
# minimizer = zfit.minimize.MinuitMinimizer(verbosity = 10)
# minimum = minimizer.minimize(nll)
# In[ ]:
# jpsi_width
# In[ ]:
# plt.hist(sample, weights=1 / prob(sample))
# # Fitting
# In[ ]:
# start = time.time()
# for param in total_f.get_dependents():
# param.randomize()
# # for param in total_f.get_dependents():
# # print(zfit.run(param))
# nll = zfit.loss.UnbinnedNLL(model=total_f, data=data2, fit_range = (x_min, x_max))
# minimizer = zfit.minimize.MinuitMinimizer(verbosity = 5)
# # minimizer._use_tfgrad = False
# result = minimizer.minimize(nll)
# # param_errors = result.error()
# # for var, errors in param_errors.items():
# # print('{}: ^{{+{}}}_{{{}}}'.format(var.name, errors['upper'], errors['lower']))
# print("Function minimum:", result.fmin)
# # print("Results:", result.params)
# print("Hesse errors:", result.hesse())
# In[ ]:
# print("Time taken for fitting: {}".format(display_time(int(time.time()-start))))
# # probs = total_f.pdf(test_q)
# calcs_test = zfit.run(probs)
# res_y = zfit.run(jpsi_res(test_q))
# In[ ]:
# plt.clf()
# # plt.plot(x_part, calcs, '.')
# plt.plot(test_q, calcs_test, label = 'pdf')
# # plt.plot(test_q, res_y, label = 'res')
# plt.legend()
# plt.ylim(0.0, 10e-6)
# # plt.yscale('log')
# # plt.xlim(3080, 3110)
# plt.savefig('test3.png')
# # print(jpsi_width)
# In[ ]:
# _tot = 4.37e-7+6.02e-5+4.97e-6
# _probs = []
# _probs.append(6.02e-5/_tot)
# _probs.append(4.97e-6/_tot)
# _probs.append(4.37e-7/_tot)
# print(_probs)
# In[ ]:
# dtype = 'float64'
# # mixture = tfd.Uniform(tf.constant(x_min, dtype=dtype), tf.constant(x_max, dtype=dtype))
# mixture = tfd.MixtureSameFamily(mixture_distribution=tfd.Categorical(probs=[tf.constant(0.007, dtype=dtype),
# tf.constant(0.917, dtype=dtype),
# tf.constant(0.076, dtype=dtype)]),
# components_distribution=tfd.Uniform(low=[tf.constant(x_min, dtype=dtype),
# tf.constant(3080, dtype=dtype),
# tf.constant(3670, dtype=dtype)],
# high=[tf.constant(x_max, dtype=dtype),
# tf.constant(3112, dtype=dtype),
# tf.constant(3702, dtype=dtype)]))
# # for i in range(10):
# # print(zfit.run(mixture.prob(mixture.sample((10, 1)))))
# In[ ]:
# print((zfit.run(jpsi_p)%(2*np.pi))/np.pi)
# print((zfit.run(psi2s_p)%(2*np.pi))/np.pi)
# In[ ]:
# def jpsi_res(q):
# return resonance(q, _mass = jpsi_mass, scale = jpsi_scale,
# phase = jpsi_phase, width = jpsi_width)
# def psi2s_res(q):
# return resonance(q, _mass = psi2s_mass, scale = psi2s_scale,
# phase = psi2s_phase, width = psi2s_width)
# def p3770_res(q):
# return resonance(q, _mass = p3770_mass, scale = p3770_scale,
# phase = p3770_phase, width = p3770_width)
# def p4040_res(q):
# return resonance(q, _mass = p4040_mass, scale = p4040_scale,
# phase = p4040_phase, width = p4040_width)
# def p4160_res(q):
# return resonance(q, _mass = p4160_mass, scale = p4160_scale,
# phase = p4160_phase, width = p4160_width)
# def p4415_res(q):
# return resonance(q, _mass = p4415_mass, scale = p4415_scale,
# phase = p4415_phase, width = p4415_width)
# In[ ]:
# 0.15**2*4.2/1000
# result.hesse()
# ## Constraints
# In[ ]:
# 1. Constraint - Real part of sum of Psi contrib and D contribs
sum_list = []
sum_list.append(ztf.to_complex(jpsi_s) * tf.exp(tf.complex(ztf.constant(0.0), jpsi_p)) * ztf.to_complex(jpsi_w / (tf.pow(jpsi_m,3))))
sum_list.append(ztf.to_complex(psi2s_s) * tf.exp(tf.complex(ztf.constant(0.0), psi2s_p)) * ztf.to_complex(psi2s_w / (tf.pow(psi2s_m,3))))
sum_list.append(ztf.to_complex(p3770_s) * tf.exp(tf.complex(ztf.constant(0.0), p3770_p)) * ztf.to_complex(p3770_w / (tf.pow(p3770_m,3))))
sum_list.append(ztf.to_complex(p4040_s) * tf.exp(tf.complex(ztf.constant(0.0), p4040_p)) * ztf.to_complex(p4040_w / (tf.pow(p4040_m,3))))
sum_list.append(ztf.to_complex(p4160_s) * tf.exp(tf.complex(ztf.constant(0.0), p4160_p)) * ztf.to_complex(p4160_w / (tf.pow(p4160_m,3))))
sum_list.append(ztf.to_complex(p4415_s) * tf.exp(tf.complex(ztf.constant(0.0), p4415_p)) * ztf.to_complex(p4415_w / (tf.pow(p4415_m,3))))
sum_list.append(ztf.to_complex(DDstar_s) * tf.exp(tf.complex(ztf.constant(0.0), DDstar_p)) * (ztf.to_complex(1.0 / (10.0*tf.pow(Dstar_m,2)) + 1.0 / (10.0*tf.pow(D_m,2)))))
sum_list.append(ztf.to_complex(Dbar_s) * tf.exp(tf.complex(ztf.constant(0.0), Dbar_p)) * ztf.to_complex(1.0 / (6.0*tf.pow(Dbar_m,2))))
sum_ru_1 = ztf.to_complex(ztf.constant(0.0))
for part in sum_list:
sum_ru_1 += part
sum_1 = tf.math.real(sum_ru_1)
# constraint1 = zfit.constraint.GaussianConstraint(params = sum_1, mu = ztf.constant(1.7*10**-8),
# sigma = ztf.constant(2.2*10**-8))
constraint1 = tf.pow((sum_1-ztf.constant(1.7*10**-8))/ztf.constant(2.2*10**-8),2)/ztf.constant(2.)
# 2. Constraint - Abs. of sum of Psi contribs and D contribs
sum_2 = tf.abs(sum_ru_1)
constraint2 = tf.cond(tf.greater_equal(sum_2, 5.0e-8), lambda: 100000., lambda: 0.)
# 3. Constraint - Maximum eta of D contribs
constraint3_0 = tf.cond(tf.greater_equal(tf.abs(Dbar_s), 0.2), lambda: 100000., lambda: 0.)
constraint3_1 = tf.cond(tf.greater_equal(tf.abs(DDstar_s), 0.2), lambda: 100000., lambda: 0.)
# 4. Constraint - Formfactor multivariant gaussian covariance fplus
Cov_matrix = [[ztf.constant( 1.), ztf.constant( 0.45), ztf.constant( 0.19), ztf.constant(0.857), ztf.constant(0.598), ztf.constant(0.531), ztf.constant(0.752), ztf.constant(0.229), ztf.constant(0,117)],
[ztf.constant( 0.45), ztf.constant( 1.), ztf.constant(0.677), ztf.constant(0.708), ztf.constant(0.958), ztf.constant(0.927), ztf.constant(0.227), ztf.constant(0.443), ztf.constant(0.287)],
[ztf.constant( 0.19), ztf.constant(0.677), ztf.constant( 1.), ztf.constant(0.595), ztf.constant(0.770), ztf.constant(0.819),ztf.constant(-0.023), ztf.constant( 0.07), ztf.constant(0.196)],
[ztf.constant(0.857), ztf.constant(0.708), ztf.constant(0.595), ztf.constant( 1.), ztf.constant( 0.83), ztf.constant(0.766), ztf.constant(0.582), ztf.constant(0.237), ztf.constant(0.192)],
[ztf.constant(0.598), ztf.constant(0.958), ztf.constant(0.770), ztf.constant( 0.83), ztf.constant( 1.), ztf.constant(0.973), ztf.constant(0.324), ztf.constant(0.372), ztf.constant(0.272)],
[ztf.constant(0.531), ztf.constant(0.927), ztf.constant(0.819), ztf.constant(0.766), ztf.constant(0.973), ztf.constant( 1.), ztf.constant(0.268), ztf.constant(0.332), ztf.constant(0.269)],
[ztf.constant(0.752), ztf.constant(0.227),ztf.constant(-0.023), ztf.constant(0.582), ztf.constant(0.324), ztf.constant(0.268), ztf.constant( 1.), ztf.constant( 0.59), ztf.constant(0.515)],
[ztf.constant(0.229), ztf.constant(0.443), ztf.constant( 0.07), ztf.constant(0.237), ztf.constant(0.372), ztf.constant(0.332), ztf.constant( 0.59), ztf.constant( 1.), ztf.constant(0.897)],
[ztf.constant(0.117), ztf.constant(0.287), ztf.constant(0.196), ztf.constant(0.192), ztf.constant(0.272), ztf.constant(0.269), ztf.constant(0.515), ztf.constant(0.897), ztf.constant( 1.)]]
def triGauss(val1,val2,val3,m = Cov_matrix):
mean1 = ztf.constant(0.466)
mean2 = ztf.constant(-0.885)
mean3 = ztf.constant(-0.213)
sigma1 = ztf.constant(0.014)
sigma2 = ztf.constant(0.128)
sigma3 = ztf.constant(0.548)
x1 = (val1-mean1)/sigma1
x2 = (val2-mean2)/sigma2
x3 = (val3-mean3)/sigma3
rho12 = m[0][1]
rho13 = m[0][2]
rho23 = m[1][2]
w = x1*x1*(rho23*rho23-1) + x2*x2*(rho13*rho13-1)+x3*x3*(rho12*rho12-1)+2*(x1*x2*(rho12-rho13*rho23)+x1*x3*(rho13-rho12*rho23)+x2*x3*(rho23-rho12*rho13))
d = 2*(rho12*rho12+rho13*rho13+rho23*rho23-2*rho12*rho13*rho23-1)
fcn = -w/d
chisq = -2*fcn
return chisq
constraint4 = triGauss(bplus_0, bplus_1, bplus_2)
# mean1 = ztf.constant(0.466)
# mean2 = ztf.constant(-0.885)
# mean3 = ztf.constant(-0.213)
# sigma1 = ztf.constant(0.014)
# sigma2 = ztf.constant(0.128)
# sigma3 = ztf.constant(0.548)
# constraint4_0 = tf.pow((bplus_0-mean1)/sigma1,2)/ztf.constant(2.)
# constraint4_1 = tf.pow((bplus_1-mean2)/sigma2,2)/ztf.constant(2.)
# constraint4_2 = tf.pow((bplus_2-mean3)/sigma3,2)/ztf.constant(2.)
# 5. Constraint - Abs. of sum of light contribs
sum_list_5 = []
sum_list_5.append(rho_s*rho_w/rho_m)
sum_list_5.append(omega_s*omega_w/omega_m)
sum_list_5.append(phi_s*phi_w/phi_m)
sum_ru_5 = ztf.constant(0.0)
for part in sum_list_5:
sum_ru_5 += part
constraint5 = tf.cond(tf.greater_equal(tf.abs(sum_ru_5), ztf.constant(0.02)), lambda: 100000., lambda: 0.)
# 6. Constraint on phases of Jpsi and Psi2s for cut out fit
# constraint6_0 = zfit.constraint.GaussianConstraint(params = jpsi_p, mu = ztf.constant(pdg["jpsi_phase_unc"]),
# sigma = ztf.constant(jpsi_phase))
# constraint6_1 = zfit.constraint.GaussianConstraint(params = psi2s_p, mu = ztf.constant(pdg["psi2s_phase_unc"]),
# sigma = ztf.constant(psi2s_phase))
constraint6_0 = tf.pow((jpsi_p-ztf.constant(jpsi_phase))/ztf.constant(pdg["jpsi_phase_unc"]),2)/ztf.constant(2.)
constraint6_1 = tf.pow((psi2s_p-ztf.constant(psi2s_phase))/ztf.constant(pdg["psi2s_phase_unc"]),2)/ztf.constant(2.)
# 7. Constraint on Ctt with higher limits
constraint7 = tf.cond(tf.greater_equal(Ctt*Ctt, 0.25), lambda: 100000., lambda: 0.)
constraint7dtype = tf.float64
# zfit.run(constraint6_0)
# ztf.convert_to_tensor(constraint6_0)
#List of all constraints
constraints = [constraint1, constraint2, constraint3_0, constraint3_1,# constraint4, #constraint4_0, constraint4_1, constraint4_2,
constraint6_0, constraint6_1]#, constraint7]
# ## Reset params
# In[ ]:
param_values_dic = {
'jpsi_m': jpsi_mass,
'jpsi_s': jpsi_scale,
'jpsi_p': jpsi_phase,
'jpsi_w': jpsi_width,
'psi2s_m': psi2s_mass,
'psi2s_s': psi2s_scale,
'psi2s_p': psi2s_phase,
'psi2s_w': psi2s_width,
'p3770_m': p3770_mass,
'p3770_s': p3770_scale,
'p3770_p': p3770_phase,
'p3770_w': p3770_width,
'p4040_m': p4040_mass,
'p4040_s': p4040_scale,
'p4040_p': p4040_phase,
'p4040_w': p4040_width,
'p4160_m': p4160_mass,
'p4160_s': p4160_scale,
'p4160_p': p4160_phase,
'p4160_w': p4160_width,
'p4415_m': p4415_mass,
'p4415_s': p4415_scale,
'p4415_p': p4415_phase,
'p4415_w': p4415_width,
'rho_m': rho_mass,
'rho_s': rho_scale,
'rho_p': rho_phase,
'rho_w': rho_width,
'omega_m': omega_mass,
'omega_s': omega_scale,
'omega_p': omega_phase,
'omega_w': omega_width,
'phi_m': phi_mass,
'phi_s': phi_scale,
'phi_p': phi_phase,
'phi_w': phi_width,
'Dstar_m': Dstar_mass,
'DDstar_s': 0.0,
'DDstar_p': 0.0,
'D_m': D_mass,
'Dbar_m': Dbar_mass,
'Dbar_s': 0.0,
'Dbar_p': 0.0,
'tau_m': pdg['tau_M'],
'Ctt': 0.0,
'b0_0': 0.292,
'b0_1': 0.281,
'b0_2': 0.150,
'bplus_0': 0.466,
'bplus_1': -0.885,
'bplus_2': -0.213,
'bT_0': 0.460,
'bT_1': -1.089,
'bT_2': -1.114}
def reset_param_values(variation = 0.05):
for param in total_f_fit.get_dependents():
if param.floating:
param.set_value(param_values_dic[param.name] + random.uniform(-1, 1) * param_values_dic[param.name]* variation)
# print(param.name)
# for param in totalf.get_dependents():
# param.set_value()
# # Analysis
# In[ ]:
# # zfit.run.numeric_checks = False
# fitting_range = 'cut'
# total_BR = 1.7e-10 + 4.9e-10 + 2.5e-9 + 6.02e-5 + 4.97e-6 + 1.38e-9 + 4.2e-10 + 2.6e-9 + 6.1e-10 + 4.37e-7
# cut_BR = 1.0 - (6.02e-5 + 4.97e-6)/total_BR
# Ctt_list = []
# Ctt_error_list = []
# nr_of_toys = 1
# if fitting_range == 'cut':
# nevents = int(pdg["number_of_decays"]*cut_BR)
# else:
# nevents = int(pdg["number_of_decays"])
# # nevents = pdg["number_of_decays"]
# event_stack = 1000000
# # nevents *= 41
# # zfit.settings.set_verbosity(10)
# calls = int(nevents/event_stack + 1)
# total_samp = []
# start = time.time()
# sampler = total_f.create_sampler(n=event_stack)
# for toy in range(nr_of_toys):
# ### Generate data
# # clear_output(wait=True)
# print("Toy {}: Generating data...".format(toy))
# dirName = 'data/zfit_toys/toy_{0}'.format(toy)
# if not os.path.exists(dirName):
# os.mkdir(dirName)
# print("Directory " , dirName , " Created ")
# reset_param_values()
# if fitting_range == 'cut':
# sampler.resample(n=nevents)
# s = sampler.unstack_x()
# sam = zfit.run(s)
# calls = 0
# c = 1
# else:
# for call in range(calls):
# sampler.resample(n=event_stack)
# s = sampler.unstack_x()
# sam = zfit.run(s)
# c = call + 1
# with open("data/zfit_toys/toy_{0}/{1}.pkl".format(toy, call), "wb") as f:
# pkl.dump(sam, f, pkl.HIGHEST_PROTOCOL)
# print("Toy {}: Data generation finished".format(toy))
# ### Load data
# print("Toy {}: Loading data...".format(toy))
# if fitting_range == 'cut':
# total_samp = sam
# else:
# for call in range(calls):
# with open(r"data/zfit_toys/toy_0/{}.pkl".format(call), "rb") as input_file:
# sam = pkl.load(input_file)
# total_samp = np.append(total_samp, sam)
# total_samp = total_samp.astype('float64')
# if fitting_range == 'full':
# data = zfit.data.Data.from_numpy(array=total_samp[:int(nevents)], obs=obs)
# print("Toy {}: Loading data finished".format(toy))
# ### Fit data
# print("Toy {}: Fitting pdf...".format(toy))
# for param in total_f.get_dependents():
# param.randomize()
# nll = zfit.loss.UnbinnedNLL(model=total_f, data=data, fit_range = (x_min, x_max), constraints = constraints)
# minimizer = zfit.minimize.MinuitMinimizer(verbosity = 5)
# # minimizer._use_tfgrad = False
# result = minimizer.minimize(nll)
# print("Toy {}: Fitting finished".format(toy))
# print("Function minimum:", result.fmin)
# print("Hesse errors:", result.hesse())
# params = result.params
# Ctt_list.append(params[Ctt]['value'])
# Ctt_error_list.append(params[Ctt]['minuit_hesse']['error'])
# #plotting the result
# plotdirName = 'data/plots'.format(toy)
# if not os.path.exists(plotdirName):
# os.mkdir(plotdirName)
# # print("Directory " , dirName , " Created ")
# probs = total_f.pdf(test_q, norm_range=False)
# calcs_test = zfit.run(probs)
# plt.clf()
# plt.plot(test_q, calcs_test, label = 'pdf')
# plt.legend()
# plt.ylim(0.0, 6e-6)
# plt.savefig(plotdirName + '/toy_fit_full_range{}.png'.format(toy))
# print("Toy {0}/{1}".format(toy+1, nr_of_toys))
# print("Time taken: {}".format(display_time(int(time.time() - start))))
# print("Projected time left: {}".format(display_time(int((time.time() - start)/(c+calls*(toy))*((nr_of_toys-toy)*calls-c)))))
# if fitting_range == 'cut':
# _1 = np.where((total_samp >= x_min) & (total_samp <= (jpsi_mass - 60.)))
# tot_sam_1 = total_samp[_1]
# _2 = np.where((total_samp >= (jpsi_mass + 70.)) & (total_samp <= (psi2s_mass - 50.)))
# tot_sam_2 = total_samp[_2]
# _3 = np.where((total_samp >= (psi2s_mass + 50.)) & (total_samp <= x_max))
# tot_sam_3 = total_samp[_3]
# tot_sam = np.append(tot_sam_1, tot_sam_2)
# tot_sam = np.append(tot_sam, tot_sam_3)
# data = zfit.data.Data.from_numpy(array=tot_sam[:int(nevents)], obs=obs_fit)
# print("Toy {}: Loading data finished".format(toy))
# ### Fit data
# print("Toy {}: Fitting pdf...".format(toy))
# for param in total_f_fit.get_dependents():
# param.randomize()
# nll = zfit.loss.UnbinnedNLL(model=total_f_fit, data=data, constraints = constraints)
# minimizer = zfit.minimize.MinuitMinimizer(verbosity = 5)
# # minimizer._use_tfgrad = False
# result = minimizer.minimize(nll)
# print("Function minimum:", result.fmin)
# print("Hesse errors:", result.hesse())
# params = result.params
# if result.converged:
# Ctt_list.append(params[Ctt]['value'])
# Ctt_error_list.append(params[Ctt]['minuit_hesse']['error'])
# #plotting the result
# plotdirName = 'data/plots'.format(toy)
# if not os.path.exists(plotdirName):
# os.mkdir(plotdirName)
# # print("Directory " , dirName , " Created ")
# plt.clf()
# plt.hist(tot_sam, bins = int((x_max-x_min)/7.), label = 'toy data')
# plt.savefig(plotdirName + '/toy_histo_cut_region{}.png'.format(toy))
# probs = total_f_fit.pdf(test_q, norm_range=False)
# calcs_test = zfit.run(probs)
# plt.clf()
# plt.plot(test_q, calcs_test, label = 'pdf')
# plt.axvline(x=jpsi_mass-60.,color='red', linewidth=0.7, linestyle = 'dotted')
# plt.axvline(x=jpsi_mass+70.,color='red', linewidth=0.7, linestyle = 'dotted')
# plt.axvline(x=psi2s_mass-50.,color='red', linewidth=0.7, linestyle = 'dotted')
# plt.axvline(x=psi2s_mass+50.,color='red', linewidth=0.7, linestyle = 'dotted')
# plt.legend()
# plt.ylim(0.0, 1.5e-6)
# plt.savefig(plotdirName + '/toy_fit_cut_region{}.png'.format(toy))
# print("Toy {0}/{1}".format(toy+1, nr_of_toys))
# print("Time taken: {}".format(display_time(int(time.time() - start))))
# print("Projected time left: {}".format(display_time(int((time.time() - start)/(toy+1))*((nr_of_toys-toy-1)))))
# In[ ]:
# with open("data/results/Ctt_list.pkl", "wb") as f:
# pkl.dump(Ctt_list, f, pkl.HIGHEST_PROTOCOL)
# with open("data/results/Ctt_error_list.pkl", "wb") as f:
# pkl.dump(Ctt_error_list, f, pkl.HIGHEST_PROTOCOL)
# In[ ]:
# print('{0}/{1} fits converged'.format(len(Ctt_list), nr_of_toys))
# print('Mean Ctt value = {}'.format(np.mean(Ctt_list)))
# print('Mean Ctt error = {}'.format(np.mean(Ctt_error_list)))
# print('95 Sensitivy = {}'.format(((2*np.mean(Ctt_error_list))**2)*4.2/1000))
# In[ ]:
# plt.hist(tot_sam, bins = int((x_max-x_min)/7.))
# plt.show()
# # _ = np.where((total_samp >= x_min) & (total_samp <= (jpsi_mass - 50.)))
# tot_sam.shape
# In[ ]:
# Ctt.floating = False
# In[ ]:
# zfit.run(nll.value())
# In[ ]:
# result.fmin
# In[ ]:
# BR_steps = np.linspace(0.0, 1e-3, 11)
pull_dic = {}
mi = 0.0
ma = 1e-2
ste = 11
for param in total_f_fit.get_dependents():
if param.floating:
pull_dic[param.name] = []
for step in range(2*ste):
pull_dic[param.name].append([])
def save_pulls(step):
for param in total_f_fit.get_dependents():
if param.floating:
pull_dic[param.name][step].append((params[param]['value'] - param_values_dic[param.name])/params[param]['minuit_hesse']['error'])
# for key in pull_dic.keys():
# print(np.shape(pull_dic[key]))
# save_pulls(New_step=True)
# params[Ctt]['value']
# In[ ]:
# for param in total_f_fit.get_dependents():
# if param.floating:
# print(param.name)
# print(params[Ctt])
# # CLS Code
# In[ ]:
# zfit.run.numeric_checks = False
load = False
bo = True
D_contribs = True
if not D_contribs:
Dbar_s.floating = False
Dbar_p.floating = False
DDstar_s.floating = False
DDstar_p.floating = False
bo_set = 1
fitting_range = 'cut'
total_BR = 1.7e-10 + 4.9e-10 + 2.5e-9 + 6.02e-5 + 4.97e-6 + 1.38e-9 + 4.2e-10 + 2.6e-9 + 6.1e-10 + 4.37e-7
cut_BR = 1.0 - (6.02e-5 + 4.97e-6)/total_BR
Ctt_list = []
Ctt_error_list = []
nr_of_toys = 1
nevents = int(pdg["number_of_decays"]*cut_BR)
# nevents = pdg["number_of_decays"]
event_stack = 1000000
# nevents *= 41
# zfit.settings.set_verbosity(10)
# mi = 0.0
# ma = 1e-3
# ste = 11
BR_steps = np.linspace(mi, ma, ste)
Ctt_steps = np.sqrt(BR_steps/4.2*1000)
print(Ctt_steps)
# total_samp = []
start = time.time()
Nll_list = []
sampler = total_f.create_sampler(n=nevents, fixed_params = False)
sampler.set_data_range(obs_fit)
__ = -1
#-----------------------------------------------------
if not load:
for Ctt_step in Ctt_steps:
__ += 1
for i in range(2):
Ctt_list.append([])
Ctt_error_list.append([])
Nll_list.append([])
for param in total_f_fit.get_dependents():
if param.floating:
pull_dic[param.name].append([])
for toy in range(nr_of_toys):
newset = True
while newset:
for floaty in [True, False]:
Ctt.floating = floaty
for bo_step in range(bo_set):
print('Step: {0}/{1}'.format(int(__), ste))
print('Current Ctt: {0}'.format(Ctt_step))
print('Ctt floating: {0}'.format(floaty))
reset_param_values(variation = 0.0)
if floaty:
print('Toy {0}/{1} - Fit {2}/{3}'.format(toy, nr_of_toys, bo_step, bo_set))
Ctt.set_value(Ctt_step)
else:
Ctt.set_value(0.0)
print('Toy {0}/{1} - Fit {2}/{3}'.format(toy, nr_of_toys, bo_step, bo_set))
if newset:
sampler.resample(n=nevents)
data = sampler
newset = False
### Fit data
if floaty:
# plt.clf()
# plt.title('Ctt value: {:.2f}'.format(Ctt_step))
# plt.hist(zfit.run(data), bins = int((x_max-x_min)/7), range = (x_min, x_max))
# plt.savefig('data/CLs/plots/set_histo{}.png'.format(__))
_step = 2*__
else:
_step = 2*__+1
save_pulls(step = _step)
nll = zfit.loss.UnbinnedNLL(model=total_f_fit, data=data, constraints = constraints)
minimizer = zfit.minimize.MinuitMinimizer(verbosity = 5)
# minimizer._use_tfgrad = False
result = minimizer.minimize(nll)
print("Function minimum:", result.fmin)
print("Hesse errors:", result.hesse())
params = result.params
if result.converged:
if floaty:
Nll_list[-2].append(result.fmin)
Ctt_list[-2].append(params[Ctt]['value'])
Ctt_error_list[-2].append(params[Ctt]['minuit_hesse']['error'])
else:
Nll_list[-1].append(result.fmin)
Ctt_list[-1].append(0.0)
Ctt_error_list[-1].append(0.0)
else:
for _ in [1,2]:
del Nll_list[-_][toy*bo_set:]
# print(np.shape(Nll_list[-_]))
del Ctt_list[-_][toy*bo_set:]
del Ctt_error_list[-_][toy*bo_set:]
for param in total_f_fit.get_dependents():
if param.floating:
del pull_dic[param.name][_step][toy*bo_set:]
newset = True
break
if not result.converged:
break
print()
print('Time taken: {}'.format(display_time(int(time.time()-start))))
print('Estimated time left: {}'.format(display_time(int((time.time()-start)/(__+(toy+1)/nr_of_toys)*(ste-__-(nr_of_toys-toy-1)/nr_of_toys)))))
# In[ ]:
if load:
phase_combi = '-+'
_dir = 'data/CLs/finished/f1d1/{}/'.format(phase_combi)
jobs = os.listdir(_dir)
First = True
# print(jobs)
for job in jobs:
dirName = _dir + str(job) + '/data/CLs'
if not os.path.exists("{}/{}-{}_{}s{}b{}t--CLs_Nll_list.pkl".format(dirName, mi,ma,ste,bo_set,nr_of_toys)):
# print(job)
continue
with open(r"{}/variab.pkl".format(dirName), "rb") as input_file:
variab = pkl.load(input_file)
# print(variab)
### sanity check:
if variab['mi'] != mi or variab['ma'] != ma or variab['ste'] != ste or bo_set != bo_set:
print('Fitting parameters of data dont equal the ones given -- Job {} skipped!'.format(job))
with open(r"{}/{}-{}_{}s{}b{}t--CLs_Nll_list.pkl".format(dirName, mi,ma,ste,bo_set,nr_of_toys), "rb") as input_file:
_Nll_list = pkl.load(input_file)
with open(r"{}/{}-{}_{}s{}b{}t--Ctt_list.pkl".format(dirName, mi,ma,ste,bo_set,nr_of_toys), "rb") as input_file:
_Ctt_list = pkl.load(input_file)
with open(r"{}/{}-{}_{}s{}b{}t--Ctt_error_list.pkl".format(dirName, mi,ma,ste,bo_set,nr_of_toys), "rb") as input_file:
_Ctt_error_list = pkl.load(input_file)
with open(r"{}/{}-{}_{}s{}b{}t--pull_dic.pkl".format(dirName, mi,ma,ste,bo_set,nr_of_toys), "rb") as input_file:
_pull_dic = pkl.load(input_file)
with open(r"{}/{}-{}_{}s--CLs_list.pkl".format(dirName, mi,ma,ste), "rb") as input_file:
_CLs_list = pkl.load(input_file)
if First:
Nll_list = _Nll_list
Ctt_list = _Ctt_list
Ctt_error_list = _Ctt_error_list
pull_dic = _pull_dic
# print(_pull_dic)
CLs_list = _CLs_list
First = False
else:
for step in range(2*ste):
# print(Nll_list[step], step)
Nll_list[step].extend(_Nll_list[step])
Ctt_list[step].extend(_Ctt_list[step])
Ctt_error_list[step].extend(_Ctt_error_list[step])
for key in pull_dic.keys():
# print(key, np.shape(pull_dic[key]))
pull_dic[key][step].extend(_pull_dic[key][step])
for step in range(ste):
CLs_list[step].extend(_CLs_list[step])
# print('----------------------')
# In[ ]:
dirName = 'data/CLs'
# if bo and not load:
# for s in range(2*ste):
# Nll_list[s] = [np.min(Nll_list[s])]
if not load:
if not os.path.exists(dirName):
os.mkdir(dirName)
print("Directory " , dirName , " Created ")
with open("{}/{}-{}_{}s{}b{}t--CLs_Nll_list.pkl".format(dirName, mi,ma,ste,bo_set,nr_of_toys), "wb") as f:
pkl.dump(Nll_list, f, pkl.HIGHEST_PROTOCOL)
with open("{}/{}-{}_{}s{}b{}t--Ctt_list.pkl".format(dirName, mi,ma,ste,bo_set,nr_of_toys), "wb") as f:
pkl.dump(Ctt_list, f, pkl.HIGHEST_PROTOCOL)
with open("{}/{}-{}_{}s{}b{}t--Ctt_error_list.pkl".format(dirName, mi,ma,ste,bo_set,nr_of_toys), "wb") as f:
pkl.dump(Ctt_error_list, f, pkl.HIGHEST_PROTOCOL)
with open("{}/{}-{}_{}s{}b{}t--pull_dic.pkl".format(dirName, mi,ma,ste,bo_set,nr_of_toys), "wb") as f:
pkl.dump(pull_dic, f, pkl.HIGHEST_PROTOCOL)
variab = {'mi': mi,
'ma': ma,
'ste': ste,
'bo_set': bo_set,
'nr_of_toys': nr_of_toys}
with open("{}/variab.pkl".format(dirName), "wb") as f:
pkl.dump(variab, f, pkl.HIGHEST_PROTOCOL)
CLs_values = []
toy_size = bo_set
print(np.shape(Nll_list))
print(Nll_list[0:1])
for step in range(ste):
CLs_values.append([])
for toy in range(nr_of_toys):
float_min = np.min(Nll_list[2*step][toy*bo_set:(toy+1)*bo_set])
fix_min = np.min(Nll_list[2*step+1][toy*bo_set:(toy+1)*bo_set])
CLs_values[step].append(float_min-fix_min)
print(np.shape(CLs_values))
with open("{}/{}-{}_{}s--CLs_list.pkl".format(dirName, mi,ma,ste), "wb") as f:
pkl.dump(CLs_values, f, pkl.HIGHEST_PROTOCOL)
# In[ ]:
# print(variab['mi'] != mi)
# ## Plot
# In[ ]:
# l = []
# if load:
# CLs_values = CLs_list
# if not os.path.exists('data/CLs/plots'):
# os.mkdir('data/CLs/plots')
# print("Directory " , 'data/CLs/plots' , " Created ")
# print(np.shape(CLs_values))
# for step in range(1,ste):
# plt.clf()
# plt.title('Ctt value: {:.2f}'.format(Ctt_steps[step]))
# plt.hist(CLs_values[0], bins = 100, range = (-25, 25), label = 'Ctt fixed to 0')
# plt.hist(CLs_values[step], bins = 100, range = (-25, 25), label = 'Ctt floating')
# plt.axvline(x=np.mean(CLs_values[0]),color='red', linewidth=1.0, linestyle = 'dotted')
# plt.legend()
# plt.savefig('data/CLs/plots/CLs-BR({:.1E}).png'.format(BR_steps[step]))
# l.append(len(np.where(np.array(CLs_values[step]) < np.mean(CLs_values[0]))[0]))
# for step in range(2*ste):
# if step%2 == 0:
# floaty = True
# else:
# floaty = False
# for key in pull_dic.keys():
# if not os.path.exists('data/CLs/plots/{}'.format(key)):
# os.mkdir('data/CLs/plots/{}'.format(key))
# plt.clf()
# plt.title('Pull {} - Ctt value {:.2f} - floating {}'.format(key, Ctt_steps[int(step/2)], floaty))
# plt.hist(pull_dic[key][step], bins = 50, range = (-5,5))
# plt.xlabel('Pull')
# plt.savefig('data/CLs/plots/{}/{:.2f}Ctt{}s{}f.png'.format(key, Ctt_steps[int(step/2)], step, floaty))
# In[ ]:
# for s in range(len(l)):
# print('BR: {:.4f}'.format(BR_steps[s]))
# print(2*l[s]/len(CLs_values[s]))
# print()
# In[ ]:
# for step in range(2*ste):
# for key in pull_dic.keys():
# print(pull_dic[key][step])
# In[ ]:
# for param in total_f_fit.get_dependents():
# if param.floating:
# print(params[param]['value'])
# In[ ]:
print(display_time(int(time.time()-start)))
# In[ ]:
# variab['mi'] =! mi
# In[ ]:
# In[ ]:
# In[ ]:
# In[ ]:
# In[ ]:
# In[ ]:
# In[ ]:
# In[ ]:
# In[ ]:
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