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Minimizer.py
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from Parameters import ParaManager_Unp, ParaManager_Pol
from Observables import GPDobserv
from DVCS_xsec import dsigma_TOT, dsigma_DVCS_HERA, M
from DVMP_xsec import dsigma_Jpsi_dt,dsigma_phi_dt,dsigma_rho_dt, M_jpsi
from multiprocessing import Pool
from functools import partial
from iminuit import Minuit
import numpy as np
import pandas as pd
import time
import csv
import os
dir_path = os.path.dirname(os.path.realpath(__file__))
Minuit_Counter = 0
Time_Counter = 1
Q_threshold = 1.9
xB_Cut = 0.5
xB_small_Cut = 0.0001
PDF_data = pd.read_csv(os.path.join(dir_path,'GUMPDATA/PDFdata.csv'), header = None, names = ['x', 't', 'Q', 'f', 'delta f', 'spe', 'flv'], dtype = {'x': float, 't': float, 'Q': float, 'f': float, 'delta f': float,'spe': int, 'flv': str})
PDF_data_H = PDF_data[PDF_data['spe'] == 0]
PDF_data_H_g = PDF_data_H[PDF_data_H['flv'] == 'g']
PDF_data_H_g = PDF_data_H_g[PDF_data_H_g['x'] < xB_Cut]
PDF_data_E = PDF_data[PDF_data['spe'] == 1]
PDF_data_Ht = PDF_data[PDF_data['spe'] == 2]
PDF_data_Et = PDF_data[PDF_data['spe'] == 3]
PDF_smallx_data = pd.read_csv(os.path.join(dir_path,'GUMPDATA/PDFg_smallx.csv'), header = None, names = ['x', 't', 'Q', 'f', 'delta f', 'spe', 'flv'], dtype = {'x': float, 't': float, 'Q': float, 'f': float, 'delta f': float,'spe': int, 'flv': str})
PDF_H_smallx_data = PDF_smallx_data[PDF_smallx_data['spe']==0]
PDFg_smallx_data = PDF_H_smallx_data[PDF_H_smallx_data['flv']=='g']
PDFg_smallx_data = PDF_H_smallx_data[PDF_H_smallx_data['x'] > xB_small_Cut]
tPDF_data = pd.read_csv(os.path.join(dir_path,'GUMPDATA/tPDFdata.csv'), header = None, names = ['x', 't', 'Q', 'f', 'delta f', 'spe', 'flv'], dtype = {'x': float, 't': float, 'Q': float, 'f': float, 'delta f': float,'spe': int, 'flv': str})
tPDF_data_H = tPDF_data[tPDF_data['spe'] == 0]
tPDF_data_H_g = tPDF_data_H[tPDF_data_H['flv'] == 'g']
tPDF_data_H_g = tPDF_data_H[tPDF_data_H['x'] < xB_small_Cut]
tPDF_data_E = tPDF_data[tPDF_data['spe'] == 1]
tPDF_data_Ht = tPDF_data[tPDF_data['spe'] == 2]
tPDF_data_Et = tPDF_data[tPDF_data['spe'] == 3]
GFF_data = pd.read_csv(os.path.join(dir_path,'GUMPDATA/GFFdata_Quark.csv'), header = None, names = ['j', 't', 'Q', 'f', 'delta f', 'spe', 'flv'], dtype = {'j': int, 't': float, 'Q': float, 'f': float, 'delta f': float,'spe': int, 'flv': str})
GFF_data_H = GFF_data[GFF_data['spe'] == 0]
GFF_data_E = GFF_data[GFF_data['spe'] == 1]
GFF_data_Ht = GFF_data[GFF_data['spe'] == 2]
GFF_data_Et = GFF_data[GFF_data['spe'] == 3]
GFF_Gluon_data = pd.read_csv(os.path.join(dir_path,'GUMPDATA/GFFdata_Gluon.csv'), header = None, names = ['j', 't', 'Q', 'f', 'delta f', 'spe', 'flv'], dtype = {'j': int, 't': float, 'Q': float, 'f': float, 'delta f': float,'spe': int, 'flv': str})
GFF_Gluon_data_H = GFF_Gluon_data[GFF_Gluon_data['spe'] == 0]
GFF_Gluon_data_E = GFF_Gluon_data[GFF_Gluon_data['spe'] == 1]
GFF_Gluon_data_Ht = GFF_Gluon_data[GFF_Gluon_data['spe'] == 2]
GFF_Gluon_data_Et = GFF_Gluon_data[GFF_Gluon_data['spe'] == 3]
DVCSxsec_data = pd.read_csv(os.path.join(dir_path,'GUMPDATA/DVCSxsec.csv'), header = None, names = ['y', 'xB', 't', 'Q', 'phi', 'f', 'delta f', 'pol'] , dtype = {'y': float, 'xB': float, 't': float, 'Q': float, 'phi': float, 'f': float, 'delta f': float, 'pol': str})
DVCSxsec_data_invalid = DVCSxsec_data[DVCSxsec_data['t']*(DVCSxsec_data['xB']-1) - M ** 2 * DVCSxsec_data['xB'] ** 2 < 0]
DVCSxsec_data = DVCSxsec_data[(DVCSxsec_data['Q'] > Q_threshold) & (DVCSxsec_data['xB'] < xB_Cut) & (DVCSxsec_data['t']*(DVCSxsec_data['xB']-1) - M ** 2 * DVCSxsec_data['xB'] ** 2 > 0)]
xBtQlst = DVCSxsec_data.drop_duplicates(subset = ['xB', 't', 'Q'], keep = 'first')[['xB','t','Q']].values.tolist()
DVCSxsec_group_data = list(map(lambda set: DVCSxsec_data[(DVCSxsec_data['xB'] == set[0]) & (DVCSxsec_data['t'] == set[1]) & ((DVCSxsec_data['Q'] == set[2]))], xBtQlst))
DVCSxsec_HERA_data = pd.read_csv(os.path.join(dir_path,'GUMPDATA/DVCSxsec_HERA.csv'), header = None, names = ['y', 'xB', 't', 'Q', 'f', 'delta f', 'pol'] , dtype = {'y': float, 'xB': float, 't': float, 'Q': float, 'f': float, 'delta f': float, 'pol': str})
DVCSxsec_HERA_data_invalid = DVCSxsec_HERA_data[DVCSxsec_HERA_data['t']*(DVCSxsec_HERA_data['xB']-1) - M ** 2 * DVCSxsec_HERA_data['xB'] ** 2 < 0]
DVCSxsec_HERA_data = DVCSxsec_HERA_data[(DVCSxsec_HERA_data['Q'] > Q_threshold) & (DVCSxsec_HERA_data['xB'] < xB_Cut) & (DVCSxsec_HERA_data['t']*(DVCSxsec_HERA_data['xB']-1) - M ** 2 * DVCSxsec_HERA_data['xB'] ** 2 > 0)]
xBtQlst_HERA = DVCSxsec_HERA_data.drop_duplicates(subset = ['xB', 't', 'Q'], keep = 'first')[['xB','t','Q']].values.tolist()
DVCSxsec_HERA_group_data = list(map(lambda set: DVCSxsec_HERA_data[(DVCSxsec_HERA_data['xB'] == set[0]) & (DVCSxsec_HERA_data['t'] == set[1]) & ((DVCSxsec_HERA_data['Q'] == set[2]))], xBtQlst_HERA))
# rho and phi data from HERA, R = sigma_L / sigma_T is currently handled on the theory side for these
DVrhoPZEUSxsec_data = pd.read_csv(os.path.join(dir_path,'GUMPDATA/DVMP_HERA/DVrhoPZEUSdt.csv'), header = None, names = ['y', 'xB', 't', 'Q', 'f', 'delta f'] , dtype = {'y': float, 'xB': float, 't': float, 'Q': float, 'f': float, 'delta f': float})
DVrhoPZEUSxsec_data['Q'] = np.sqrt(DVrhoPZEUSxsec_data['Q'])
DVrhoPZEUSxsec_data['t'] = -1 * DVrhoPZEUSxsec_data['t']
DVrhoPZEUSxsec_data = DVrhoPZEUSxsec_data[(DVrhoPZEUSxsec_data['Q']>Q_threshold)]
xBtQlst_rhoZ = DVrhoPZEUSxsec_data.drop_duplicates(subset = ['xB', 't', 'Q'], keep = 'first')[['xB','t','Q']].values.tolist()
DVrhoPZEUSxsec_group_data = list(map(lambda set: DVrhoPZEUSxsec_data[(DVrhoPZEUSxsec_data['xB'] == set[0]) & (DVrhoPZEUSxsec_data['t'] == set[1]) & ((DVrhoPZEUSxsec_data['Q'] == set[2]))], xBtQlst_rhoZ))
DVrhoPH1xsec_data = pd.read_csv(os.path.join(dir_path,'GUMPDATA/DVMP_HERA/DVrhoPH1dt.csv'), header = None, names = ['y', 'xB', 't', 'Q', 'f', 'delta f'] , dtype = {'y': float, 'xB': float, 't': float, 'Q': float, 'f': float, 'delta f': float})
DVrhoPH1xsec_data['Q'] = np.sqrt(DVrhoPH1xsec_data['Q'])
DVrhoPH1xsec_data['t'] = -1 * DVrhoPH1xsec_data['t']
DVrhoPH1xsec_data = DVrhoPH1xsec_data[(DVrhoPH1xsec_data['Q']>Q_threshold)]
xBtQlst_rhoH = DVrhoPH1xsec_data.drop_duplicates(subset = ['xB', 't', 'Q'], keep = 'first')[['xB','t','Q']].values.tolist()
DVrhoPH1xsec_group_data = list(map(lambda set: DVrhoPH1xsec_data[(DVrhoPH1xsec_data['xB'] == set[0]) & (DVrhoPH1xsec_data['t'] == set[1]) & ((DVrhoPH1xsec_data['Q'] == set[2]))], xBtQlst_rhoH))
DVphiPZEUSxsec_data = pd.read_csv(os.path.join(dir_path,'GUMPDATA/DVMP_HERA/DVphiPZEUSdt.csv'), header = None, names = ['y', 'xB', 't', 'Q', 'f', 'delta f'] , dtype = {'y': float, 'xB': float, 't': float, 'Q': float, 'f': float, 'delta f': float})
DVphiPZEUSxsec_data['Q'] = np.sqrt(DVphiPZEUSxsec_data['Q'])
DVphiPZEUSxsec_data['t'] = -1 * DVphiPZEUSxsec_data['t']
DVphiPZEUSxsec_data = DVphiPZEUSxsec_data[(DVphiPZEUSxsec_data['Q']>Q_threshold)]
xBtQlst_phiZ = DVphiPZEUSxsec_data.drop_duplicates(subset = ['xB', 't', 'Q'], keep = 'first')[['xB','t','Q']].values.tolist()
DVphiPZEUSxsec_group_data = list(map(lambda set: DVphiPZEUSxsec_data[(DVphiPZEUSxsec_data['xB'] == set[0]) & (DVphiPZEUSxsec_data['t'] == set[1]) & ((DVphiPZEUSxsec_data['Q'] == set[2]))], xBtQlst_phiZ))
DVphiPH1xsec_data = pd.read_csv(os.path.join(dir_path,'GUMPDATA/DVMP_HERA/DVphiPH1dt.csv'), header = None, names = ['y', 'xB', 't', 'Q', 'f', 'delta f'] , dtype = {'y': float, 'xB': float, 't': float, 'Q': float, 'f': float, 'delta f': float})
DVphiPH1xsec_data['Q'] = np.sqrt(DVphiPH1xsec_data['Q'])
DVphiPH1xsec_data['t'] = -1 * DVphiPH1xsec_data['t']
DVphiPH1xsec_data = DVphiPH1xsec_data[(DVphiPH1xsec_data['Q']>Q_threshold)]
xBtQlst_phiH = DVphiPH1xsec_data.drop_duplicates(subset = ['xB', 't', 'Q'], keep = 'first')[['xB','t','Q']].values.tolist()
DVphiPH1xsec_group_data = list(map(lambda set: DVphiPH1xsec_data[(DVphiPH1xsec_data['xB'] == set[0]) & (DVphiPH1xsec_data['t'] == set[1]) & ((DVphiPH1xsec_data['Q'] == set[2]))], xBtQlst_phiH))
# Jpsi data from HERA as well as R = sigma_L / sigma_T values hardcoded and used to convert data xsec simga_tot to sigma_L
DVJpsiPH1xsec_data = pd.read_csv(os.path.join(dir_path,'GUMPDATA/DVMP_HERA/DVJpsiPH1dt_w_mass.csv'), header = None, names = ['y', 'xB', 't', 'Q', 'f', 'delta f'] , dtype = {'y': float, 'xB': float, 't': float, 'Q': float, 'f': float, 'delta f': float})
DVJpsiPH1xsec_data['Q'] = np.sqrt(DVJpsiPH1xsec_data['Q'])
DVJpsiPH1xsec_data['t'] = -1 * DVJpsiPH1xsec_data['t']
DVJpsiPH1xsec_data = DVJpsiPH1xsec_data[(DVJpsiPH1xsec_data['Q']>Q_threshold)]
xBtQlst_JpsiH = DVJpsiPH1xsec_data.drop_duplicates(subset = ['xB', 't', 'Q'], keep = 'first')[['xB','t','Q']].values.tolist()
DVJpsiPH1xsec_L_data = DVJpsiPH1xsec_data.copy()
R_H1 = DVJpsiPH1xsec_L_data['f'].copy()
R_H1[(DVJpsiPH1xsec_L_data['Q']**2 > 1) & (DVJpsiPH1xsec_L_data['Q']**2 < 5)] = 0.052
R_H1[(DVJpsiPH1xsec_L_data['Q']**2 > 5) & (DVJpsiPH1xsec_L_data['Q']**2 < 10)] = 0.23
R_H1[(DVJpsiPH1xsec_L_data['Q']**2 > 10)] = 0.62
R_H1_err = DVJpsiPH1xsec_L_data['f'].copy()
R_H1_err[(DVJpsiPH1xsec_L_data['Q']**2 > 2) & (DVJpsiPH1xsec_L_data['Q']**2 < 5)] = 0.113
R_H1_err[(DVJpsiPH1xsec_L_data['Q']**2 > 5) & (DVJpsiPH1xsec_L_data['Q']**2 < 10)] = 0.27
R_H1_err[(DVJpsiPH1xsec_L_data['Q']**2 > 10)] = 0.61
DVJpsiPH1xsec_L_data['f'] = DVJpsiPH1xsec_L_data['f'] / ((1 - DVJpsiPH1xsec_L_data['y']) / (1 - DVJpsiPH1xsec_L_data['y'] - DVJpsiPH1xsec_L_data['y']**2 / 2) + (1 / R_H1))
DVJpsiPH1xsec_L_data['delta f'] = np.sqrt((DVJpsiPH1xsec_L_data['delta f'] / ((1 - DVJpsiPH1xsec_L_data['y']) / (1 - DVJpsiPH1xsec_L_data['y'] - DVJpsiPH1xsec_L_data['y']**2 / 2) + (1 / R_H1)))**2 + (R_H1_err * DVJpsiPH1xsec_L_data['f'] / (1 + (1 - DVJpsiPH1xsec_L_data['y']) / (1 - DVJpsiPH1xsec_L_data['y'] - DVJpsiPH1xsec_L_data['y']**2 / 2) * R_H1)**2)**2)
DVJpsiPH1xsec_group_data = list(map(lambda set: DVJpsiPH1xsec_data[(DVJpsiPH1xsec_data['xB'] == set[0]) & (DVJpsiPH1xsec_data['t'] == set[1]) & ((DVJpsiPH1xsec_data['Q'] == set[2]))], xBtQlst_JpsiH))
DVJpsiPH1xsec_L_group_data = list(map(lambda set: DVJpsiPH1xsec_L_data[(DVJpsiPH1xsec_data['xB'] == set[0]) & (DVJpsiPH1xsec_L_data['t'] == set[1]) & ((DVJpsiPH1xsec_L_data['Q'] == set[2]))], xBtQlst_JpsiH))
DVJpsiPZEUSxsec_data = pd.read_csv(os.path.join(dir_path,'GUMPDATA/DVMP_HERA/DVJpsiPZEUSdt_w_mass.csv'), header = None, names = ['y', 'xB', 't', 'Q', 'f', 'delta f'] , dtype = {'y': float, 'xB': float, 't': float, 'Q': float, 'f': float, 'delta f': float})
DVJpsiPZEUSxsec_data['Q'] = np.sqrt(DVJpsiPZEUSxsec_data['Q'])
DVJpsiPZEUSxsec_data['t'] = -1 * DVJpsiPZEUSxsec_data['t']
DVJpsiPZEUSxsec_data = DVJpsiPZEUSxsec_data[(DVJpsiPZEUSxsec_data['Q']>Q_threshold)]
xBtQlst_JpsiZ = DVJpsiPZEUSxsec_data.drop_duplicates(subset = ['xB', 't', 'Q'], keep = 'first')[['xB','t','Q']].values.tolist()
DVJpsiPZEUSxsec_L_data = DVJpsiPZEUSxsec_data.copy()
R_ZEUS = DVJpsiPZEUSxsec_L_data['f'].copy()
R_ZEUS[(DVJpsiPZEUSxsec_L_data['Q']**2 > 2) & (DVJpsiPZEUSxsec_L_data['Q']**2 < 5)] = 0.13
R_ZEUS[(DVJpsiPZEUSxsec_L_data['Q']**2 > 5) & (DVJpsiPZEUSxsec_L_data['Q']**2 < 10)] = 0.33
R_ZEUS[(DVJpsiPZEUSxsec_L_data['Q']**2 > 10)] = 1.19
R_ZEUS_err = DVJpsiPZEUSxsec_L_data['f'].copy()
R_ZEUS_err[(DVJpsiPZEUSxsec_L_data['Q']**2 > 2) & (DVJpsiPZEUSxsec_L_data['Q']**2 < 5)] = 0.19
R_ZEUS_err[(DVJpsiPZEUSxsec_L_data['Q']**2 > 5) & (DVJpsiPZEUSxsec_L_data['Q']**2 < 10)] = 0.25
R_ZEUS_err[(DVJpsiPZEUSxsec_L_data['Q']**2 > 10)] = 0.58
DVJpsiPZEUSxsec_L_data['f'] = DVJpsiPZEUSxsec_L_data['f'] / ((1 - DVJpsiPZEUSxsec_L_data['y']) / (1 - DVJpsiPZEUSxsec_L_data['y'] - DVJpsiPZEUSxsec_L_data['y']**2 / 2) + (1 / R_ZEUS))
DVJpsiPZEUSxsec_L_data['delta f'] = np.sqrt((DVJpsiPZEUSxsec_L_data['delta f'] / ((1 - DVJpsiPZEUSxsec_L_data['y']) / (1 - DVJpsiPZEUSxsec_L_data['y'] - DVJpsiPZEUSxsec_L_data['y']**2 / 2) + (1 / R_ZEUS)))**2 + (R_ZEUS_err * DVJpsiPZEUSxsec_L_data['f'] / (1 + (1 - DVJpsiPZEUSxsec_L_data['y']) / (1 - DVJpsiPZEUSxsec_L_data['y'] - DVJpsiPZEUSxsec_L_data['y']**2 / 2) * R_ZEUS)**2)**2)
DVJpsiPZEUSxsec_group_data = list(map(lambda set: DVJpsiPZEUSxsec_data[(DVJpsiPZEUSxsec_data['xB'] == set[0]) & (DVJpsiPZEUSxsec_data['t'] == set[1]) & ((DVJpsiPZEUSxsec_data['Q'] == set[2]))], xBtQlst_JpsiZ))
DVJpsiPZEUSxsec_L_group_data = list(map(lambda set: DVJpsiPZEUSxsec_L_data[(DVJpsiPZEUSxsec_data['xB'] == set[0]) & (DVJpsiPZEUSxsec_L_data['t'] == set[1]) & ((DVJpsiPZEUSxsec_L_data['Q'] == set[2]))], xBtQlst_JpsiZ))
'''
JpsiphotoH1xsec_data = pd.read_csv(os.path.join(dir_path,'GUMPDATA/DVMP_HERA/DVJpsiPZEUSdt_w_mass.csv'), header = None, names = ['y', 'xB', 't', 'Q', 'f', 'delta f'] , dtype = {'y': float, 'xB': float, 't': float, 'Q': float, 'f': float, 'delta f': float})
JpsiphotoH1xsec_data['Q'] = np.sqrt(JpsiphotoH1xsec_data['Q'])
JpsiphotoH1xsec_data['t'] = -1 * JpsiphotoH1xsec_data['t']
JpsiphotoH1xsec_data = JpsiphotoH1xsec_data[(JpsiphotoH1xsec_data['Q']>Q_threshold)]
xBtQlst_JpsiphotoH1 = JpsiphotoH1xsec_data.drop_duplicates(subset = ['xB', 't', 'Q'], keep = 'first')[['xB','t','Q']].values.tolist()
'''
def PDF_theo(PDF_input: pd.DataFrame, Para: np.array, p_order = 1):
# [x, t, Q, f, delta_f, spe, flv] = PDF_input
xs = PDF_input['x'].to_numpy()
ts = PDF_input['t'].to_numpy()
Qs = PDF_input['Q'].to_numpy()
flvs = PDF_input['flv'].to_numpy()
spes = PDF_input['spe'].to_numpy()
xis = np.zeros_like(xs)
ps = np.where(spes<=1, 1, -1)
spes = np.where(spes<=1, spes, spes-2)
Para_spe = Para[spes] # fancy indexing. Output (N, 3, 5, 1, 5)
'''
if(spe == 0 or spe == 1):
spe, p = spe, 1
if(spe == 2 or spe == 3):
spe, p = spe - 2 , -1
'''
# Para: (4, 2, 5, 1, 4)
def PDF_theo_scalar(x, xi, t, Q, p, flv, Para):
_PDF_theo = GPDobserv(x, xi, t, Q, p)
return _PDF_theo.tPDF(flv, Para, p_order)
result = np.array([PDF_theo_scalar(x_i, xi_i, t_i, Q_i, p_i, flv_i, Para_i) for x_i, xi_i, t_i, Q_i, p_i, flv_i, Para_i in zip(xs, xis, ts, Qs, ps, flvs, Para_spe)])
return result
tPDF_theo = PDF_theo
def GFF_theo(GFF_input: np.array, Para):
# [j, t, Q, f, delta_f, spe, flv] = GFF_input
js = GFF_input['j'].to_numpy()
ts = GFF_input['t'].to_numpy()
Qs = GFF_input['Q'].to_numpy()
#fs = GFF_input['f'].to_numpy()
#delta_fs = GFF_input['delta f'].to_numpy()
flvs = GFF_input['flv'].to_numpy()
spes = GFF_input['spe'].to_numpy()
x = 0
xi = 0
'''
if(spe == 0 or spe == 1):
spe, p = spe, 1
if(spe == 2 or spe == 3):
spe, p = spe - 2 , -1
'''
ps = np.where(spes<=1, 1, -1)
spes = np.where(spes<=1, spes, spes-2)
Para_spe = Para[spes] # fancy indexing. Output (N, 3, 5, 1, 5)
GFF_theo = GPDobserv(x, xi, ts, Qs, ps)
return GFF_theo.GFFj0(js, flvs, Para_spe) # (N)
def CFF_theo(xB, t, Q, Para_Unp, Para_Pol):
x = 0
xi = (1/(2 - xB) - (2*t*(-1 + xB))/(Q**2*(-2 + xB)**2))*xB
H_E = GPDobserv(x, xi, t, Q, 1)
Ht_Et = GPDobserv(x, xi, t, Q, -1)
HCFF = H_E.CFF(Para_Unp[..., 0, :, :, :, :])
ECFF = H_E.CFF(Para_Unp[..., 1, :, :, :, :])
HtCFF = Ht_Et.CFF(Para_Pol[..., 0, :, :, :, :])
EtCFF = Ht_Et.CFF(Para_Pol[..., 1, :, :, :, :])
return [ HCFF, ECFF, HtCFF, EtCFF ] # this can be a list of arrays of shape (N)
# return np.stack([HCFF, ECFF, HtCFF, EtCFF], axis=-1)
def CFF_quad_theo(xB, t, Q, Para_Unp, Para_Pol):
x = 0
xi = (1/(2 - xB) - (2*t*(-1 + xB))/(Q**2*(-2 + xB)**2))*xB
H_E = GPDobserv(x, xi, t, Q, 1)
Ht_Et = GPDobserv(x, xi, t, Q, -1)
HCFF = H_E.CFF_quad(Para_Unp[..., 0, :, :, :, :])
ECFF = H_E.CFF_quad(Para_Unp[..., 1, :, :, :, :])
HtCFF = Ht_Et.CFF_quad(Para_Pol[..., 0, :, :, :, :])
EtCFF = Ht_Et.CFF_quad(Para_Pol[..., 1, :, :, :, :])
return [ HCFF, ECFF, HtCFF, EtCFF ]
'''
def TFF_theo_rho(xB, t, Q, Para_Unp):
x = 0
xi = (1/(2 - xB) - (2*t*(-1 + xB))/(Q**2*(-2 + xB)**2))*xB
H_E = GPDobserv(x, xi, t, Q, 1)
HTFF_rho = H_E.TFFLO(Para_Unp[..., 0, :, :, :, :],1)
ETFF_rho = H_E.TFFLO(Para_Unp[..., 1, :, :, :, :],1)
return [ HTFF_rho, ETFF_rho ]
def TFF_theo_rho_NLO(xB, t, Q, Para_Unp):
x = 0
xi = (1/(2 - xB) - (2*t*(-1 + xB))/((Q**2 )*(-2 + xB)**2))*xB
H_E = GPDobserv(x, xi, t, Q, 1)
HTFF_jpsi = H_E.TFF(Para_Unp[..., 0, :, :, :, :],1,2)
ETFF_jpsi = H_E.TFF(Para_Unp[..., 1, :, :, :, :],1,2)
return [ HTFF_jpsi, ETFF_jpsi ]
def TFF_theo_phi(xB, t, Q, Para_Unp):
x = 0
xi = (1/(2 - xB) - (2*t*(-1 + xB))/(Q**2*(-2 + xB)**2))*xB
H_E = GPDobserv(x, xi, t, Q, 1)
HTFF_phi = H_E.TFFLO(Para_Unp[..., 0, :, :, :, :],2)
ETFF_phi = H_E.TFFLO(Para_Unp[..., 1, :, :, :, :],2)
return [ HTFF_phi, ETFF_phi ]
'''
def TFF_theo_jpsi(xB, t, Q, Para_Unp, p_order = 1, muset = 1, flv = 'All'):
x = 0
xi = (1/(2 - xB) - (2*t*(-1 + xB))/((Q**2 + M_jpsi**2)*(-2 + xB)**2))*xB
H_E = GPDobserv(x, xi, t, Q, 1)
HTFF_jpsi = H_E.TFF(Para_Unp[..., 0, :, :, :, :], muset * Q, 3, p_order, flv)
ETFF_jpsi = H_E.TFF(Para_Unp[..., 1, :, :, :, :], muset * Q, 3, p_order, flv)
return [ HTFF_jpsi, ETFF_jpsi ]
def DVCSxsec_theo(DVCSxsec_input: pd.DataFrame, CFF_input: np.array):
# CFF_input is a list of np.arrays
# [y, xB, t, Q, phi, f, delta_f, pol] = DVCSxsec_input
y = DVCSxsec_input['y'].to_numpy()
xB = DVCSxsec_input['xB'].to_numpy()
t = DVCSxsec_input['t'].to_numpy()
Q = DVCSxsec_input['Q'].to_numpy()
phi = DVCSxsec_input['phi'].to_numpy()
#f = DVCSxsec_input['f'].to_numpy()
pol = DVCSxsec_input['pol'].to_numpy()
[HCFF, ECFF, HtCFF, EtCFF] = CFF_input # each of them have shape (N); scalar is also OK if we use
return dsigma_TOT(y, xB, t, Q, phi, pol, HCFF, ECFF, HtCFF, EtCFF)
def DVCSxsec_cost_xBtQ(DVCSxsec_data_xBtQ: pd.DataFrame, Para_Unp, Para_Pol):
[xB, t, Q] = [DVCSxsec_data_xBtQ['xB'].iat[0], DVCSxsec_data_xBtQ['t'].iat[0], DVCSxsec_data_xBtQ['Q'].iat[0]]
[HCFF, ECFF, HtCFF, EtCFF] = CFF_theo(xB, t, Q, Para_Unp, Para_Pol) # scalar for each of them
# DVCS_pred_xBtQ = np.array(list(map(partial(DVCSxsec_theo, CFF_input = [HCFF, ECFF, HtCFF, EtCFF]), np.array(DVCSxsec_data_xBtQ))))
DVCS_pred_xBtQ = DVCSxsec_theo(DVCSxsec_data_xBtQ, CFF_input = [HCFF, ECFF, HtCFF, EtCFF])
return np.sum(((DVCS_pred_xBtQ - DVCSxsec_data_xBtQ['f'])/ DVCSxsec_data_xBtQ['delta f']) ** 2 )
def DVCSxsec_HERA_theo(DVCSxsec_HERA_input: pd.DataFrame, CFF_input: np.array):
#[y, xB, t, Q, f, delta_f, pol] = DVCSxsec_data_HERA
y = DVCSxsec_HERA_input['y'].to_numpy()
xB = DVCSxsec_HERA_input['xB'].to_numpy()
t = DVCSxsec_HERA_input['t'].to_numpy()
Q = DVCSxsec_HERA_input['Q'].to_numpy()
#f = DVCSxsec_data_HERA['f'].to_numpy()
#delta_f = DVCSxsec_data_HERA['delta f'].to_numpy()
pol = DVCSxsec_HERA_input['pol'].to_numpy()
[HCFF, ECFF, HtCFF, EtCFF] = CFF_input
return dsigma_DVCS_HERA(y, xB, t, Q, pol, HCFF, ECFF, HtCFF, EtCFF)
def DVCSxsec_HERA_cost_xBtQ(DVCSxsec_HERA_data_xBtQ: pd.DataFrame, Para_Unp, Para_Pol):
[xB, t, Q] = [DVCSxsec_HERA_data_xBtQ['xB'].iat[0], DVCSxsec_HERA_data_xBtQ['t'].iat[0], DVCSxsec_HERA_data_xBtQ['Q'].iat[0]]
[HCFF, ECFF, HtCFF, EtCFF] = CFF_theo(xB, t, Q, Para_Unp, Para_Pol) # scalar for each of them
# DVCS_pred_xBtQ = np.array(list(map(partial(DVCSxsec_theo, CFF_input = [HCFF, ECFF, HtCFF, EtCFF]), np.array(DVCSxsec_data_xBtQ))))
DVCS_HERA_pred_xBtQ = DVCSxsec_HERA_theo(DVCSxsec_HERA_data_xBtQ, CFF_input = [HCFF, ECFF, HtCFF, EtCFF])
return np.sum(((DVCS_HERA_pred_xBtQ - DVCSxsec_HERA_data_xBtQ['f'])/ DVCSxsec_HERA_data_xBtQ['delta f']) ** 2 )
'''
def DVrhoPxsec_theo(DVrhoPxsec_input: pd.DataFrame, TFF_rho_input: np.array):
y = DVrhoPxsec_input['y'].to_numpy()
xB = DVrhoPxsec_input['xB'].to_numpy()
t = DVrhoPxsec_input['t'].to_numpy()
Q = DVrhoPxsec_input['Q'].to_numpy()
[HTFF_rho, ETFF_rho] = TFF_rho_input
return 2*np.pi*dsigma_rho_dt(y, xB, t, Q, 0, HTFF_rho, ETFF_rho)
def DVphiPxsec_theo(DVphiPxsec_input: pd.DataFrame, TFF_phi_input: np.array):
y = DVphiPxsec_input['y'].to_numpy()
xB = DVphiPxsec_input['xB'].to_numpy()
t = DVphiPxsec_input['t'].to_numpy()
Q = DVphiPxsec_input['Q'].to_numpy()
[HTFF_phi, ETFF_phi] = TFF_phi_input
return 2*np.pi*dsigma_phi_dt(y, xB, t, Q, 0, HTFF_phi, ETFF_phi)
'''
def DVjpsiPxsec_theo(DVjpsiPxsec_input: pd.DataFrame, TFF_jpsi_input: np.array):
y = DVjpsiPxsec_input['y'].to_numpy()
xB = DVjpsiPxsec_input['xB'].to_numpy()
t = DVjpsiPxsec_input['t'].to_numpy()
Q = DVjpsiPxsec_input['Q'].to_numpy()
[HTFF_jpsi, ETFF_jpsi] = TFF_jpsi_input
return dsigma_Jpsi_dt(y, xB, t, Q, 0, HTFF_jpsi, ETFF_jpsi)
'''
def DVrhoPxsec_cost_xBtQ(DVrhoPxsec_data_xBtQ: pd.DataFrame, Para_Unp, xsec_norm):
[xB, t, Q] = [DVrhoPxsec_data_xBtQ['xB'].iat[0], DVrhoPxsec_data_xBtQ['t'].iat[0], DVrhoPxsec_data_xBtQ['Q'].iat[0]]
[HTFF_rho, ETFF_rho] = TFF_theo_rho(xB, t, Q, Para_Unp) # scalar for each of them
DVrhoP_pred_xBtQ = DVrhoPxsec_theo(DVrhoPxsec_data_xBtQ, TFF_rho_input = [HTFF_rho, ETFF_rho]) * xsec_norm
return np.sum(((DVrhoP_pred_xBtQ - DVrhoPxsec_data_xBtQ['f'])/ DVrhoPxsec_data_xBtQ['delta f']) ** 2 )
def DVrhoPxsec_NLO_cost_xBtQ(DVrhoPxsec_data_xBtQ: pd.DataFrame, Para_Unp, xsec_norm):
[xB, t, Q] = [DVrhoPxsec_data_xBtQ['xB'].iat[0], DVrhoPxsec_data_xBtQ['t'].iat[0], DVrhoPxsec_data_xBtQ['Q'].iat[0]]
[HTFF_rho, ETFF_rho] = TFF_theo_rho_NLO(xB, t, Q, Para_Unp) # scalar for each of them
DVrhoP_pred_xBtQ = DVrhoPxsec_theo(DVrhoPxsec_data_xBtQ, TFF_rho_input = [HTFF_rho, ETFF_rho]) * xsec_norm
return np.sum(((DVrhoP_pred_xBtQ - DVrhoPxsec_data_xBtQ['f'])/ DVrhoPxsec_data_xBtQ['delta f']) ** 2 )
'''
def DVjpsiPxsec_cost_xBtQ(DVjpsiPxsec_data_xBtQ: pd.DataFrame, Para_Unp, xsec_norm, p_order = 2):
[xB, t, Q] = [DVjpsiPxsec_data_xBtQ['xB'].iat[0], DVjpsiPxsec_data_xBtQ['t'].iat[0], DVjpsiPxsec_data_xBtQ['Q'].iat[0]]
[HTFF_jpsi, ETFF_jpsi] = TFF_theo_jpsi(xB, t, Q, Para_Unp, p_order, muset = 1)
DVjpsiP_pred_xBtQ = DVjpsiPxsec_theo(DVjpsiPxsec_data_xBtQ, TFF_jpsi_input = [HTFF_jpsi, ETFF_jpsi]) * xsec_norm**2
return np.sum(((DVjpsiP_pred_xBtQ - DVjpsiPxsec_data_xBtQ['f'])/ DVjpsiPxsec_data_xBtQ['delta f']) ** 2 )
def cost_forward_H(Norm_HuV, alpha_HuV, beta_HuV, alphap_HuV,
Norm_Hubar, alpha_Hubar, beta_Hubar, alphap_Hqbar,
Norm_HdV, alpha_HdV, beta_HdV, alphap_HdV,
Norm_Hdbar, alpha_Hdbar, beta_Hdbar,
Norm_Hg, alpha_Hg, beta_Hg, alphap_Hg,
Norm_EuV, alpha_EuV, beta_EuV, alphap_EuV,
Norm_EdV, R_E_Sea, R_Hu_xi2, R_Hd_xi2, R_Hg_xi2,
R_Eu_xi2, R_Ed_xi2, R_Eg_xi2,
R_Hu_xi4, R_Hd_xi4, R_Hg_xi4,
R_Eu_xi4, R_Ed_xi4, R_Eg_xi4, bexp_HSea):
Paralst = [Norm_HuV, alpha_HuV, beta_HuV, alphap_HuV,
Norm_Hubar, alpha_Hubar, beta_Hubar, alphap_Hqbar,
Norm_HdV, alpha_HdV, beta_HdV, alphap_HdV,
Norm_Hdbar, alpha_Hdbar, beta_Hdbar,
Norm_Hg, alpha_Hg, beta_Hg, alphap_Hg,
Norm_EuV, alpha_EuV, beta_EuV, alphap_EuV,
Norm_EdV, R_E_Sea, R_Hu_xi2, R_Hd_xi2, R_Hg_xi2,
R_Eu_xi2, R_Ed_xi2, R_Eg_xi2,
R_Hu_xi4, R_Hd_xi4, R_Hg_xi4,
R_Eu_xi4, R_Ed_xi4, R_Eg_xi4, bexp_HSea]
Para_all = ParaManager_Unp(Paralst)
# PDF_H_pred = np.array(list(pool.map(partial(PDF_theo, Para = Para_all), np.array(PDF_data_H))))
PDF_H_pred = PDF_theo(PDF_data_H, Para=Para_all)
cost_PDF_H = np.sum(((PDF_H_pred - PDF_data_H['f'])/ PDF_data_H['delta f']) ** 2 )
# tPDF_H_pred = np.array(list(pool.map(partial(tPDF_theo, Para = Para_all), np.array(tPDF_data_H))))
tPDF_H_pred = tPDF_theo(tPDF_data_H, Para=Para_all)
cost_tPDF_H = np.sum(((tPDF_H_pred - tPDF_data_H['f'])/ tPDF_data_H['delta f']) ** 2 )
# GFF_H_pred = np.array(list(pool.map(partial(GFF_theo, Para = Para_all), np.array(GFF_data_H))))
GFF_H_pred = GFF_theo(GFF_data_H, Para=Para_all)
cost_GFF_H = np.sum(((GFF_H_pred - GFF_data_H['f'])/ GFF_data_H['delta f']) ** 2 )
return cost_PDF_H + cost_tPDF_H + cost_GFF_H
def cost_forward_E(Norm_HuV, alpha_HuV, beta_HuV, alphap_HuV,
Norm_Hubar, alpha_Hubar, beta_Hubar, alphap_Hqbar,
Norm_HdV, alpha_HdV, beta_HdV, alphap_HdV,
Norm_Hdbar, alpha_Hdbar, beta_Hdbar,
Norm_Hg, alpha_Hg, beta_Hg, alphap_Hg,
Norm_EuV, alpha_EuV, beta_EuV, alphap_EuV,
Norm_EdV, R_E_Sea, R_Hu_xi2, R_Hd_xi2, R_Hg_xi2,
R_Eu_xi2, R_Ed_xi2, R_Eg_xi2,
R_Hu_xi4, R_Hd_xi4, R_Hg_xi4,
R_Eu_xi4, R_Ed_xi4, R_Eg_xi4, bexp_HSea):
Paralst = [Norm_HuV, alpha_HuV, beta_HuV, alphap_HuV,
Norm_Hubar, alpha_Hubar, beta_Hubar, alphap_Hqbar,
Norm_HdV, alpha_HdV, beta_HdV, alphap_HdV,
Norm_Hdbar, alpha_Hdbar, beta_Hdbar,
Norm_Hg, alpha_Hg, beta_Hg, alphap_Hg,
Norm_EuV, alpha_EuV, beta_EuV, alphap_EuV,
Norm_EdV, R_E_Sea, R_Hu_xi2, R_Hd_xi2, R_Hg_xi2,
R_Eu_xi2, R_Ed_xi2, R_Eg_xi2,
R_Hu_xi4, R_Hd_xi4, R_Hg_xi4,
R_Eu_xi4, R_Ed_xi4, R_Eg_xi4, bexp_HSea]
Para_all = ParaManager_Unp(Paralst)
# PDF_E_pred = np.array(list(pool.map(partial(PDF_theo, Para = Para_all), np.array(PDF_data_E))))
PDF_E_pred = PDF_theo(PDF_data_E, Para=Para_all)
cost_PDF_E = np.sum(((PDF_E_pred - PDF_data_E['f'])/ PDF_data_E['delta f']) ** 2 )
# tPDF_E_pred = np.array(list(pool.map(partial(tPDF_theo, Para = Para_all), np.array(tPDF_data_E))))
tPDF_E_pred = tPDF_theo(tPDF_data_E, Para=Para_all)
cost_tPDF_E = np.sum(((tPDF_E_pred - tPDF_data_E['f'])/ tPDF_data_E['delta f']) ** 2 )
# GFF_E_pred = np.array(list(pool.map(partial(GFF_theo, Para = Para_all), np.array(GFF_data_E))))
GFF_E_pred = GFF_theo(GFF_data_E, Para=Para_all)
cost_GFF_E = np.sum(((GFF_E_pred - GFF_data_E['f'])/ GFF_data_E['delta f']) ** 2 )
return cost_PDF_E + cost_tPDF_E + cost_GFF_E
def forward_H_fit(Paralst_Unp):
[Norm_HuV_Init, alpha_HuV_Init, beta_HuV_Init, alphap_HuV_Init,
Norm_Hubar_Init, alpha_Hubar_Init, beta_Hubar_Init, alphap_Hqbar_Init,
Norm_HdV_Init, alpha_HdV_Init, beta_HdV_Init, alphap_HdV_Init,
Norm_Hdbar_Init, alpha_Hdbar_Init, beta_Hdbar_Init,
Norm_Hg_Init, alpha_Hg_Init, beta_Hg_Init, alphap_Hg_Init,
Norm_EuV_Init, alpha_EuV_Init, beta_EuV_Init, alphap_EuV_Init,
Norm_EdV_Init, R_E_Sea_Init, R_Hu_xi2_Init, R_Hd_xi2_Init, R_Hg_xi2_Init,
R_Eu_xi2_Init, R_Ed_xi2_Init, R_Eg_xi2_Init,
R_Hu_xi4_Init, R_Hd_xi4_Init, R_Hg_xi4_Init,
R_Eu_xi4_Init, R_Ed_xi4_Init, R_Eg_xi4_Init, bexp_HSea_Init] = Paralst_Unp
fit_forw_H = Minuit(cost_forward_H, Norm_HuV = Norm_HuV_Init, alpha_HuV = alpha_HuV_Init, beta_HuV = beta_HuV_Init, alphap_HuV = alphap_HuV_Init,
Norm_Hubar = Norm_Hubar_Init, alpha_Hubar = alpha_Hubar_Init, beta_Hubar = beta_Hubar_Init, alphap_Hqbar = alphap_Hqbar_Init,
Norm_HdV = Norm_HdV_Init, alpha_HdV = alpha_HdV_Init, beta_HdV = beta_HdV_Init, alphap_HdV = alphap_HdV_Init,
Norm_Hdbar = Norm_Hdbar_Init, alpha_Hdbar = alpha_Hdbar_Init, beta_Hdbar = beta_Hdbar_Init,
Norm_Hg = Norm_Hg_Init, alpha_Hg = alpha_Hg_Init, beta_Hg = beta_Hg_Init, alphap_Hg = alphap_Hg_Init,
Norm_EuV = Norm_EuV_Init, alpha_EuV = alpha_EuV_Init, beta_EuV = beta_EuV_Init, alphap_EuV = alphap_EuV_Init,
Norm_EdV = Norm_EdV_Init, R_E_Sea = R_E_Sea_Init, R_Hu_xi2 = R_Hu_xi2_Init, R_Hd_xi2 = R_Hd_xi2_Init, R_Hg_xi2 = R_Hg_xi2_Init,
R_Eu_xi2 = R_Eu_xi2_Init, R_Ed_xi2 = R_Ed_xi2_Init, R_Eg_xi2 = R_Eg_xi2_Init,
R_Hu_xi4 = R_Hu_xi4_Init, R_Hd_xi4 = R_Hd_xi4_Init, R_Hg_xi4 = R_Hg_xi4_Init,
R_Eu_xi4 = R_Eu_xi4_Init, R_Ed_xi4 = R_Ed_xi4_Init, R_Eg_xi4 = R_Eg_xi4_Init, bexp_HSea = bexp_HSea_Init)
fit_forw_H.errordef = 1
fit_forw_H.limits['alpha_HuV'] = (-2, 1.2)
fit_forw_H.limits['alpha_Hubar'] = (-2, 1.2)
fit_forw_H.limits['alpha_HdV'] = (-2, 1.2)
fit_forw_H.limits['alpha_Hdbar'] = (-2, 1.2)
fit_forw_H.limits['alpha_Hg'] = (-2, 1.2)
fit_forw_H.limits['alpha_EuV'] = (-2, 1.2)
fit_forw_H.limits['beta_HuV'] = (0, 15)
fit_forw_H.limits['beta_Hubar'] = (0, 15)
fit_forw_H.limits['beta_HdV'] = (0, 15)
fit_forw_H.limits['beta_Hdbar'] = (0, 15)
fit_forw_H.limits['beta_Hg'] = (0, 15)
fit_forw_H.limits['beta_EuV'] = (0, 15)
fit_forw_H.fixed['alphap_Hqbar'] = True
fit_forw_H.fixed['alphap_Hg'] = True
fit_forw_H.fixed['Norm_EuV'] = True
fit_forw_H.fixed['alpha_EuV'] = True
fit_forw_H.fixed['beta_EuV'] = True
fit_forw_H.fixed['alphap_EuV'] = True
fit_forw_H.fixed['Norm_EdV'] = True
fit_forw_H.fixed['R_E_Sea'] = True
fit_forw_H.fixed['R_Hu_xi2'] = True
fit_forw_H.fixed['R_Hd_xi2'] = True
fit_forw_H.fixed['R_Hg_xi2'] = True
fit_forw_H.fixed['R_Eu_xi2'] = True
fit_forw_H.fixed['R_Ed_xi2'] = True
fit_forw_H.fixed['R_Eg_xi2'] = True
fit_forw_H.fixed['R_Hu_xi4'] = True
fit_forw_H.fixed['R_Hd_xi4'] = True
fit_forw_H.fixed['R_Hg_xi4'] = True
fit_forw_H.fixed['R_Eu_xi4'] = True
fit_forw_H.fixed['R_Ed_xi4'] = True
fit_forw_H.fixed['R_Eg_xi4'] = True
fit_forw_H.fixed['bexp_HSea'] = True
global time_start
time_start = time.time()
fit_forw_H.migrad()
fit_forw_H.hesse()
ndof_H = len(PDF_data_H.index) + len(tPDF_data_H.index) + len(GFF_data_H.index) - fit_forw_H.nfit
time_end = time.time() -time_start
with open(os.path.join(dir_path,'GUMP_Output/H_forward_fit.txt'), 'w') as f:
print('Total running time: %.1f minutes. Total call of cost function: %3d.\n' % ( time_end/60, fit_forw_H.nfcn), file=f)
print('The chi squared/d.o.f. is: %.2f / %3d ( = %.2f ).\n' % (fit_forw_H.fval, ndof_H, fit_forw_H.fval/ndof_H), file = f)
print('Below are the final output parameters from iMinuit:', file = f)
print(*fit_forw_H.values, sep=", ", file = f)
print(*fit_forw_H.errors, sep=", ", file = f)
print(fit_forw_H.params, file = f)
with open(os.path.join(dir_path,"GUMP_Output/H_forward_cov.csv"),"w",newline='') as my_csv:
csvWriter = csv.writer(my_csv,delimiter=',')
csvWriter.writerows([*fit_forw_H.covariance])
print("H fit finished...")
return fit_forw_H
def forward_E_fit(Paralst_Unp):
[Norm_HuV_Init, alpha_HuV_Init, beta_HuV_Init, alphap_HuV_Init,
Norm_Hubar_Init, alpha_Hubar_Init, beta_Hubar_Init, alphap_Hqbar_Init,
Norm_HdV_Init, alpha_HdV_Init, beta_HdV_Init, alphap_HdV_Init,
Norm_Hdbar_Init, alpha_Hdbar_Init, beta_Hdbar_Init,
Norm_Hg_Init, alpha_Hg_Init, beta_Hg_Init, alphap_Hg_Init,
Norm_EuV_Init, alpha_EuV_Init, beta_EuV_Init, alphap_EuV_Init,
Norm_EdV_Init, R_E_Sea_Init, R_Hu_xi2_Init, R_Hd_xi2_Init, R_Hg_xi2_Init,
R_Eu_xi2_Init, R_Ed_xi2_Init, R_Eg_xi2_Init,
R_Hu_xi4_Init, R_Hd_xi4_Init, R_Hg_xi4_Init,
R_Eu_xi4_Init, R_Ed_xi4_Init, R_Eg_xi4_Init, bexp_HSea_Init] = Paralst_Unp
fit_forw_E = Minuit(cost_forward_E, Norm_HuV = Norm_HuV_Init, alpha_HuV = alpha_HuV_Init, beta_HuV = beta_HuV_Init, alphap_HuV = alphap_HuV_Init,
Norm_Hubar = Norm_Hubar_Init, alpha_Hubar = alpha_Hubar_Init, beta_Hubar = beta_Hubar_Init, alphap_Hqbar = alphap_Hqbar_Init,
Norm_HdV = Norm_HdV_Init, alpha_HdV = alpha_HdV_Init, beta_HdV = beta_HdV_Init, alphap_HdV = alphap_HdV_Init,
Norm_Hdbar = Norm_Hdbar_Init, alpha_Hdbar = alpha_Hdbar_Init, beta_Hdbar = beta_Hdbar_Init,
Norm_Hg = Norm_Hg_Init, alpha_Hg = alpha_Hg_Init, beta_Hg = beta_Hg_Init, alphap_Hg = alphap_Hg_Init,
Norm_EuV = Norm_EuV_Init, alpha_EuV = alpha_EuV_Init, beta_EuV = beta_EuV_Init, alphap_EuV = alphap_EuV_Init,
Norm_EdV = Norm_EdV_Init, R_E_Sea = R_E_Sea_Init, R_Hu_xi2 = R_Hu_xi2_Init, R_Hd_xi2 = R_Hd_xi2_Init, R_Hg_xi2 = R_Hg_xi2_Init,
R_Eu_xi2 = R_Eu_xi2_Init, R_Ed_xi2 = R_Ed_xi2_Init, R_Eg_xi2 = R_Eg_xi2_Init,
R_Hu_xi4 = R_Hu_xi4_Init, R_Hd_xi4 = R_Hd_xi4_Init, R_Hg_xi4 = R_Hg_xi4_Init,
R_Eu_xi4 = R_Eu_xi4_Init, R_Ed_xi4 = R_Ed_xi4_Init, R_Eg_xi4 = R_Eg_xi4_Init, bexp_HSea = bexp_HSea_Init)
fit_forw_E.errordef = 1
fit_forw_E.limits['alpha_HuV'] = (-2, 1.2)
fit_forw_E.limits['alpha_Hubar'] = (-2, 1.2)
fit_forw_E.limits['alpha_HdV'] = (-2, 1.2)
fit_forw_E.limits['alpha_Hdbar'] = (-2, 1.2)
fit_forw_E.limits['alpha_Hg'] = (-2, 1.2)
fit_forw_E.limits['alpha_EuV'] = (-2, 1.2)
fit_forw_E.limits['beta_HuV'] = (0, 15)
fit_forw_E.limits['beta_Hubar'] = (0, 15)
fit_forw_E.limits['beta_HdV'] = (0, 15)
fit_forw_E.limits['beta_Hdbar'] = (0, 15)
fit_forw_E.limits['beta_Hg'] = (0, 15)
fit_forw_E.limits['beta_EuV'] = (0, 15)
fit_forw_E.fixed['Norm_HuV'] = True
fit_forw_E.fixed['alpha_HuV'] = True
fit_forw_E.fixed['beta_HuV'] = True
fit_forw_E.fixed['alphap_HuV'] = True
fit_forw_E.fixed['Norm_Hubar'] = True
fit_forw_E.fixed['alpha_Hubar'] = True
fit_forw_E.fixed['beta_Hubar'] = True
fit_forw_E.fixed['alphap_Hqbar'] = True
fit_forw_E.fixed['Norm_HdV'] = True
fit_forw_E.fixed['alpha_HdV'] = True
fit_forw_E.fixed['beta_HdV'] = True
fit_forw_E.fixed['alphap_HdV'] = True
fit_forw_E.fixed['Norm_Hdbar'] = True
fit_forw_E.fixed['alpha_Hdbar'] = True
fit_forw_E.fixed['beta_Hdbar'] = True
fit_forw_E.fixed['Norm_Hg'] = True
fit_forw_E.fixed['alpha_Hg'] = True
fit_forw_E.fixed['beta_Hg'] = True
fit_forw_E.fixed['alphap_Hg'] = True
fit_forw_E.fixed['R_Hu_xi2'] = True
fit_forw_E.fixed['R_Hd_xi2'] = True
fit_forw_E.fixed['R_Hg_xi2'] = True
fit_forw_E.fixed['R_Eu_xi2'] = True
fit_forw_E.fixed['R_Ed_xi2'] = True
fit_forw_E.fixed['R_Eg_xi2'] = True
fit_forw_E.fixed['R_Hu_xi4'] = True
fit_forw_E.fixed['R_Hd_xi4'] = True
fit_forw_E.fixed['R_Hg_xi4'] = True
fit_forw_E.fixed['R_Eu_xi4'] = True
fit_forw_E.fixed['R_Ed_xi4'] = True
fit_forw_E.fixed['R_Eg_xi4'] = True
fit_forw_E.fixed['bexp_HSea'] = True
global time_start
time_start = time.time()
fit_forw_E.migrad()
fit_forw_E.hesse()
ndof_E = len(PDF_data_E.index) + len(tPDF_data_E.index) + len(GFF_data_E.index) - fit_forw_E.nfit
time_end = time.time() -time_start
with open(os.path.join(dir_path,'GUMP_Output/E_forward_fit.txt'), 'w') as f:
print('Total running time: %.1f minutes. Total call of cost function: %3d.\n' % ( time_end/60, fit_forw_E.nfcn), file=f)
print('The chi squared/d.o.f. is: %.2f / %3d ( = %.2f ).\n' % (fit_forw_E.fval, ndof_E, fit_forw_E.fval/ndof_E), file = f)
print('Below are the final output parameters from iMinuit:', file = f)
print(*fit_forw_E.values, sep=", ", file = f)
print(*fit_forw_E.errors, sep=", ", file = f)
print(fit_forw_E.params, file = f)
with open(os.path.join(dir_path,"GUMP_Output/E_forward_cov.csv"),"w",newline='') as my_csv:
csvWriter = csv.writer(my_csv,delimiter=',')
csvWriter.writerows([*fit_forw_E.covariance])
print("E fit finished...")
return fit_forw_E
def cost_forward_Ht(Norm_HtuV, alpha_HtuV, beta_HtuV, alphap_HtuV,
Norm_Htubar, alpha_Htubar, beta_Htubar, alphap_Htqbar,
Norm_HtdV, alpha_HtdV, beta_HtdV, alphap_HtdV,
Norm_Htdbar, alpha_Htdbar, beta_Htdbar,
Norm_Htg, alpha_Htg, beta_Htg, alphap_Htg,
Norm_EtuV, alpha_EtuV, beta_EtuV, alphap_EtuV,
Norm_EtdV, R_Et_Sea, R_Htu_xi2, R_Htd_xi2, R_Htg_xi2,
R_Etu_xi2, R_Etd_xi2, R_Etg_xi2,
R_Htu_xi4, R_Htd_xi4, R_Htg_xi4,
R_Etu_xi4, R_Etd_xi4, R_Etg_xi4, bexp_HtSea):
Paralst = [Norm_HtuV, alpha_HtuV, beta_HtuV, alphap_HtuV,
Norm_Htubar, alpha_Htubar, beta_Htubar, alphap_Htqbar,
Norm_HtdV, alpha_HtdV, beta_HtdV, alphap_HtdV,
Norm_Htdbar, alpha_Htdbar, beta_Htdbar,
Norm_Htg, alpha_Htg, beta_Htg, alphap_Htg,
Norm_EtuV, alpha_EtuV, beta_EtuV, alphap_EtuV,
Norm_EtdV, R_Et_Sea, R_Htu_xi2, R_Htd_xi2, R_Htg_xi2,
R_Etu_xi2, R_Etd_xi2, R_Etg_xi2,
R_Htu_xi4, R_Htd_xi4, R_Htg_xi4,
R_Etu_xi4, R_Etd_xi4, R_Etg_xi4, bexp_HtSea]
Para_all = ParaManager_Pol(Paralst)
# PDF_Ht_pred = np.array(list(pool.map(partial(PDF_theo, Para = Para_all), np.array(PDF_data_Ht))))
PDF_Ht_pred = PDF_theo(PDF_data_Ht, Para=Para_all)
cost_PDF_Ht = np.sum(((PDF_Ht_pred - PDF_data_Ht['f'])/ PDF_data_Ht['delta f']) ** 2 )
# tPDF_Ht_pred = np.array(list(pool.map(partial(tPDF_theo, Para = Para_all), np.array(tPDF_data_Ht))))
tPDF_Ht_pred = tPDF_theo(tPDF_data_Ht, Para=Para_all)
cost_tPDF_Ht = np.sum(((tPDF_Ht_pred - tPDF_data_Ht['f'])/ tPDF_data_Ht['delta f']) ** 2 )
# GFF_Ht_pred = np.array(list(pool.map(partial(GFF_theo, Para = Para_all), np.array(GFF_data_Ht))))
GFF_Ht_pred = GFF_theo(GFF_data_Ht, Para=Para_all)
cost_GFF_Ht = np.sum(((GFF_Ht_pred - GFF_data_Ht['f'])/ GFF_data_Ht['delta f']) ** 2 )
return cost_PDF_Ht + cost_tPDF_Ht + cost_GFF_Ht
def cost_forward_Et(Norm_HtuV, alpha_HtuV, beta_HtuV, alphap_HtuV,
Norm_Htubar, alpha_Htubar, beta_Htubar, alphap_Htqbar,
Norm_HtdV, alpha_HtdV, beta_HtdV, alphap_HtdV,
Norm_Htdbar, alpha_Htdbar, beta_Htdbar,
Norm_Htg, alpha_Htg, beta_Htg, alphap_Htg,
Norm_EtuV, alpha_EtuV, beta_EtuV, alphap_EtuV,
Norm_EtdV, R_Et_Sea, R_Htu_xi2, R_Htd_xi2, R_Htg_xi2,
R_Etu_xi2, R_Etd_xi2, R_Etg_xi2,
R_Htu_xi4, R_Htd_xi4, R_Htg_xi4,
R_Etu_xi4, R_Etd_xi4, R_Etg_xi4, bexp_HtSea):
Paralst = [Norm_HtuV, alpha_HtuV, beta_HtuV, alphap_HtuV,
Norm_Htubar, alpha_Htubar, beta_Htubar, alphap_Htqbar,
Norm_HtdV, alpha_HtdV, beta_HtdV, alphap_HtdV,
Norm_Htdbar, alpha_Htdbar, beta_Htdbar,
Norm_Htg, alpha_Htg, beta_Htg, alphap_Htg,
Norm_EtuV, alpha_EtuV, beta_EtuV, alphap_EtuV,
Norm_EtdV, R_Et_Sea, R_Htu_xi2, R_Htd_xi2, R_Htg_xi2,
R_Etu_xi2, R_Etd_xi2, R_Etg_xi2,
R_Htu_xi4, R_Htd_xi4, R_Htg_xi4,
R_Etu_xi4, R_Etd_xi4, R_Etg_xi4, bexp_HtSea]
Para_all = ParaManager_Pol(Paralst)
# PDF_Et_pred = np.array(list(pool.map(partial(PDF_theo, Para = Para_all), np.array(PDF_data_Et))))
PDF_Et_pred = PDF_theo(PDF_data_Et, Para=Para_all)
cost_PDF_Et = np.sum(((PDF_Et_pred - PDF_data_Et['f'])/ PDF_data_Et['delta f']) ** 2 )
# tPDF_Et_pred = np.array(list(pool.map(partial(tPDF_theo, Para = Para_all), np.array(tPDF_data_Et))))
tPDF_Et_pred = tPDF_theo(tPDF_data_Et, Para=Para_all)
cost_tPDF_Et = np.sum(((tPDF_Et_pred - tPDF_data_Et['f'])/ tPDF_data_Et['delta f']) ** 2 )
# GFF_Et_pred = np.array(list(pool.map(partial(GFF_theo, Para = Para_all), np.array(GFF_data_Et))))
GFF_Et_pred = GFF_theo(GFF_data_Et, Para=Para_all)
cost_GFF_Et = np.sum(((GFF_Et_pred - GFF_data_Et['f'])/ GFF_data_Et['delta f']) ** 2 )
return cost_PDF_Et + cost_tPDF_Et + cost_GFF_Et
def forward_Ht_fit(Paralst_Pol):
[Norm_HtuV_Init, alpha_HtuV_Init, beta_HtuV_Init, alphap_HtuV_Init,
Norm_Htubar_Init, alpha_Htubar_Init, beta_Htubar_Init, alphap_Htqbar_Init,
Norm_HtdV_Init, alpha_HtdV_Init, beta_HtdV_Init, alphap_HtdV_Init,
Norm_Htdbar_Init, alpha_Htdbar_Init, beta_Htdbar_Init,
Norm_Htg_Init, alpha_Htg_Init, beta_Htg_Init, alphap_Htg_Init,
Norm_EtuV_Init, alpha_EtuV_Init, beta_EtuV_Init, alphap_EtuV_Init,
Norm_EtdV_Init, R_Et_Sea_Init, R_Htu_xi2_Init, R_Htd_xi2_Init, R_Htg_xi2_Init,
R_Etu_xi2_Init, R_Etd_xi2_Init, R_Etg_xi2_Init,
R_Htu_xi4_Init, R_Htd_xi4_Init, R_Htg_xi4_Init,
R_Etu_xi4_Init, R_Etd_xi4_Init, R_Etg_xi4_Init, bexp_HtSea_Init] = Paralst_Pol
fit_forw_Ht = Minuit(cost_forward_Ht, Norm_HtuV = Norm_HtuV_Init, alpha_HtuV = alpha_HtuV_Init, beta_HtuV = beta_HtuV_Init, alphap_HtuV = alphap_HtuV_Init,
Norm_Htubar = Norm_Htubar_Init, alpha_Htubar = alpha_Htubar_Init, beta_Htubar = beta_Htubar_Init, alphap_Htqbar = alphap_Htqbar_Init,
Norm_HtdV = Norm_HtdV_Init, alpha_HtdV = alpha_HtdV_Init, beta_HtdV = beta_HtdV_Init, alphap_HtdV = alphap_HtdV_Init,
Norm_Htdbar = Norm_Htdbar_Init, alpha_Htdbar = alpha_Htdbar_Init, beta_Htdbar = beta_Htdbar_Init,
Norm_Htg = Norm_Htg_Init, alpha_Htg = alpha_Htg_Init, beta_Htg = beta_Htg_Init, alphap_Htg = alphap_Htg_Init,
Norm_EtuV = Norm_EtuV_Init, alpha_EtuV = alpha_EtuV_Init, beta_EtuV = beta_EtuV_Init, alphap_EtuV = alphap_EtuV_Init,
Norm_EtdV = Norm_EtdV_Init, R_Et_Sea = R_Et_Sea_Init, R_Htu_xi2 = R_Htu_xi2_Init, R_Htd_xi2 = R_Htd_xi2_Init, R_Htg_xi2 = R_Htg_xi2_Init,
R_Etu_xi2 = R_Etu_xi2_Init, R_Etd_xi2 = R_Etd_xi2_Init, R_Etg_xi2 = R_Etg_xi2_Init,
R_Htu_xi4 = R_Htu_xi4_Init, R_Htd_xi4 = R_Htd_xi4_Init, R_Htg_xi4 = R_Htg_xi4_Init,
R_Etu_xi4 = R_Etu_xi4_Init, R_Etd_xi4 = R_Etd_xi4_Init, R_Etg_xi4 = R_Etg_xi4_Init, bexp_HtSea = bexp_HtSea_Init)
fit_forw_Ht.errordef = 1
fit_forw_Ht.limits['alpha_HtuV'] = (-2, 1.2)
fit_forw_Ht.limits['alpha_Htubar'] = (-2, 1.2)
fit_forw_Ht.limits['alpha_HtdV'] = (-2, 1.2)
fit_forw_Ht.limits['alpha_Htdbar'] = (-2, 1.2)
fit_forw_Ht.limits['alpha_Htg'] = (-2, 1.2)
fit_forw_Ht.limits['alpha_EtuV'] = (-2, 1.2)
fit_forw_Ht.limits['beta_HtuV'] = (0, 15)
fit_forw_Ht.limits['beta_Htubar'] = (0, 15)
fit_forw_Ht.limits['beta_HtdV'] = (0, 15)
fit_forw_Ht.limits['beta_Htdbar'] = (0, 15)
fit_forw_Ht.limits['beta_Htg'] = (0, 15)
fit_forw_Ht.limits['beta_EtuV'] = (0, 15)
fit_forw_Ht.fixed['alphap_Htqbar'] = True
fit_forw_Ht.fixed['alphap_Htg'] = True
fit_forw_Ht.fixed['Norm_EtuV'] = True
fit_forw_Ht.fixed['alpha_EtuV'] = True
fit_forw_Ht.fixed['beta_EtuV'] = True
fit_forw_Ht.fixed['alphap_EtuV'] = True
fit_forw_Ht.fixed['Norm_EtdV'] = True
fit_forw_Ht.fixed['R_Et_Sea'] = True
fit_forw_Ht.fixed['R_Htu_xi2'] = True
fit_forw_Ht.fixed['R_Htd_xi2'] = True
fit_forw_Ht.fixed['R_Htg_xi2'] = True
fit_forw_Ht.fixed['R_Etu_xi2'] = True
fit_forw_Ht.fixed['R_Etd_xi2'] = True
fit_forw_Ht.fixed['R_Etg_xi2'] = True
fit_forw_Ht.fixed['R_Htu_xi4'] = True
fit_forw_Ht.fixed['R_Htd_xi4'] = True
fit_forw_Ht.fixed['R_Htg_xi4'] = True
fit_forw_Ht.fixed['R_Etu_xi4'] = True
fit_forw_Ht.fixed['R_Etd_xi4'] = True
fit_forw_Ht.fixed['R_Etg_xi4'] = True
fit_forw_Ht.fixed['bexp_HtSea'] = True
global time_start
time_start = time.time()
fit_forw_Ht.migrad()
fit_forw_Ht.hesse()
ndof_Ht = len(PDF_data_Ht.index) + len(tPDF_data_Ht.index) + len(GFF_data_Ht.index) - fit_forw_Ht.nfit
time_end = time.time() -time_start
with open(os.path.join(dir_path,'GUMP_Output/Ht_forward_fit.txt'), 'w') as f:
print('Total running time: %.1f minutes. Total call of cost function: %3d.\n' % ( time_end/60, fit_forw_Ht.nfcn), file=f)
print('The chi squared/d.o.f. is: %.2f / %3d ( = %.2f ).\n' % (fit_forw_Ht.fval, ndof_Ht, fit_forw_Ht.fval/ndof_Ht), file = f)
print('Below are the final output parameters from iMinuit:', file = f)
print(*fit_forw_Ht.values, sep=", ", file = f)
print(*fit_forw_Ht.errors, sep=", ", file = f)
print(fit_forw_Ht.params, file = f)
with open(os.path.join(dir_path,"GUMP_Output/Ht_forward_cov.csv"),"w",newline='') as my_csv:
csvWriter = csv.writer(my_csv,delimiter=',')
csvWriter.writerows([*fit_forw_Ht.covariance])
print("Ht fit finished...")
return fit_forw_Ht
def forward_Et_fit(Paralst_Pol):
[Norm_HtuV_Init, alpha_HtuV_Init, beta_HtuV_Init, alphap_HtuV_Init,
Norm_Htubar_Init, alpha_Htubar_Init, beta_Htubar_Init, alphap_Htqbar_Init,
Norm_HtdV_Init, alpha_HtdV_Init, beta_HtdV_Init, alphap_HtdV_Init,
Norm_Htdbar_Init, alpha_Htdbar_Init, beta_Htdbar_Init,
Norm_Htg_Init, alpha_Htg_Init, beta_Htg_Init, alphap_Htg_Init,
Norm_EtuV_Init, alpha_EtuV_Init, beta_EtuV_Init, alphap_EtuV_Init,
Norm_EtdV_Init, R_Et_Sea_Init, R_Htu_xi2_Init, R_Htd_xi2_Init, R_Htg_xi2_Init,
R_Etu_xi2_Init, R_Etd_xi2_Init, R_Etg_xi2_Init,
R_Htu_xi4_Init, R_Htd_xi4_Init, R_Htg_xi4_Init,
R_Etu_xi4_Init, R_Etd_xi4_Init, R_Etg_xi4_Init, bexp_HtSea_Init] = Paralst_Pol
fit_forw_Et = Minuit(cost_forward_Et, Norm_HtuV = Norm_HtuV_Init, alpha_HtuV = alpha_HtuV_Init, beta_HtuV = beta_HtuV_Init, alphap_HtuV = alphap_HtuV_Init,
Norm_Htubar = Norm_Htubar_Init, alpha_Htubar = alpha_Htubar_Init, beta_Htubar = beta_Htubar_Init, alphap_Htqbar = alphap_Htqbar_Init,
Norm_HtdV = Norm_HtdV_Init, alpha_HtdV = alpha_HtdV_Init, beta_HtdV = beta_HtdV_Init, alphap_HtdV = alphap_HtdV_Init,
Norm_Htdbar = Norm_Htdbar_Init, alpha_Htdbar = alpha_Htdbar_Init, beta_Htdbar = beta_Htdbar_Init,
Norm_Htg = Norm_Htg_Init, alpha_Htg = alpha_Htg_Init, beta_Htg = beta_Htg_Init, alphap_Htg = alphap_Htg_Init,
Norm_EtuV = Norm_EtuV_Init, alpha_EtuV = alpha_EtuV_Init, beta_EtuV = beta_EtuV_Init, alphap_EtuV = alphap_EtuV_Init,
Norm_EtdV = Norm_EtdV_Init, R_Et_Sea = R_Et_Sea_Init, R_Htu_xi2 = R_Htu_xi2_Init, R_Htd_xi2 = R_Htd_xi2_Init, R_Htg_xi2 = R_Htg_xi2_Init,
R_Etu_xi2 = R_Etu_xi2_Init, R_Etd_xi2 = R_Etd_xi2_Init, R_Etg_xi2 = R_Etg_xi2_Init,
R_Htu_xi4 = R_Htu_xi4_Init, R_Htd_xi4 = R_Htd_xi4_Init, R_Htg_xi4 = R_Htg_xi4_Init,
R_Etu_xi4 = R_Etu_xi4_Init, R_Etd_xi4 = R_Etd_xi4_Init, R_Etg_xi4 = R_Etg_xi4_Init, bexp_HtSea = bexp_HtSea_Init)
fit_forw_Et.errordef = 1
fit_forw_Et.limits['alpha_HtuV'] = (-2, 1.2)
fit_forw_Et.limits['alpha_Htubar'] = (-2, 1.2)
fit_forw_Et.limits['alpha_HtdV'] = (-2, 1.2)
fit_forw_Et.limits['alpha_Htdbar'] = (-2, 1.2)
fit_forw_Et.limits['alpha_Htg'] = (-2, 1.2)
fit_forw_Et.limits['alpha_EtuV'] = (-2, 0.8)
fit_forw_Et.limits['beta_HtuV'] = (0, 15)
fit_forw_Et.limits['beta_Htubar'] = (0, 15)
fit_forw_Et.limits['beta_HtdV'] = (0, 15)
fit_forw_Et.limits['beta_Htdbar'] = (0, 15)
fit_forw_Et.limits['beta_Htg'] = (0, 15)
fit_forw_Et.limits['beta_EtuV'] = (0, 15)
fit_forw_Et.fixed['Norm_HtuV'] = True
fit_forw_Et.fixed['alpha_HtuV'] = True
fit_forw_Et.fixed['beta_HtuV'] = True
fit_forw_Et.fixed['alphap_HtuV'] = True
fit_forw_Et.fixed['Norm_Htubar'] = True
fit_forw_Et.fixed['alpha_Htubar'] = True
fit_forw_Et.fixed['beta_Htubar'] = True
fit_forw_Et.fixed['alphap_Htqbar'] = True
fit_forw_Et.fixed['Norm_HtdV'] = True
fit_forw_Et.fixed['alpha_HtdV'] = True
fit_forw_Et.fixed['beta_HtdV'] = True
fit_forw_Et.fixed['alphap_HtdV'] = True
fit_forw_Et.fixed['Norm_Htdbar'] = True
fit_forw_Et.fixed['alpha_Htdbar'] = True
fit_forw_Et.fixed['beta_Htdbar'] = True
fit_forw_Et.fixed['Norm_Htg'] = True
fit_forw_Et.fixed['alpha_Htg'] = True
fit_forw_Et.fixed['beta_Htg'] = True
fit_forw_Et.fixed['alphap_Htg'] = True
fit_forw_Et.fixed['R_Htu_xi2'] = True
fit_forw_Et.fixed['R_Htd_xi2'] = True
fit_forw_Et.fixed['R_Htg_xi2'] = True
fit_forw_Et.fixed['R_Etu_xi2'] = True
fit_forw_Et.fixed['R_Etd_xi2'] = True
fit_forw_Et.fixed['R_Etg_xi2'] = True
fit_forw_Et.fixed['R_Htu_xi4'] = True
fit_forw_Et.fixed['R_Htd_xi4'] = True
fit_forw_Et.fixed['R_Htg_xi4'] = True
fit_forw_Et.fixed['R_Etu_xi4'] = True
fit_forw_Et.fixed['R_Etd_xi4'] = True
fit_forw_Et.fixed['R_Etg_xi4'] = True
fit_forw_Et.fixed['bexp_HtSea'] = True
global time_start
time_start = time.time()
fit_forw_Et.migrad()
fit_forw_Et.hesse()
ndof_Et = len(PDF_data_Et.index) + len(tPDF_data_Et.index) + len(GFF_data_Et.index) - fit_forw_Et.nfit
time_end = time.time() -time_start
with open(os.path.join(dir_path,'GUMP_Output/Et_forward_fit.txt'), 'w') as f:
print('Total running time: %.1f minutes. Total call of cost function: %3d.\n' % ( time_end/60, fit_forw_Et.nfcn), file=f)
print('The chi squared/d.o.f. is: %.2f / %3d ( = %.2f ).\n' % (fit_forw_Et.fval, ndof_Et, fit_forw_Et.fval/ndof_Et), file = f)
print('Below are the final output parameters from iMinuit:', file = f)
print(*fit_forw_Et.values, sep=", ", file = f)
print(*fit_forw_Et.errors, sep=", ", file = f)
print(fit_forw_Et.params, file = f)
with open(os.path.join(dir_path,"GUMP_Output/Et_forward_cov.csv"),"w",newline='') as my_csv:
csvWriter = csv.writer(my_csv,delimiter=',')
csvWriter.writerows([*fit_forw_Et.covariance])
print("Et fit finished...")
return fit_forw_Et
def cost_off_forward(Norm_HuV, alpha_HuV, beta_HuV, alphap_HuV,
Norm_Hubar, alpha_Hubar, beta_Hubar, alphap_Hqbar,
Norm_HdV, alpha_HdV, beta_HdV, alphap_HdV,
Norm_Hdbar, alpha_Hdbar, beta_Hdbar,
Norm_Hg, alpha_Hg, beta_Hg, alphap_Hg,
Norm_EuV, alpha_EuV, beta_EuV, alphap_EuV,
Norm_EdV, R_E_Sea, R_Hu_xi2, R_Hd_xi2, R_Hg_xi2,
R_Eu_xi2, R_Ed_xi2, R_Eg_xi2,
R_Hu_xi4, R_Hd_xi4, R_Hg_xi4,
R_Eu_xi4, R_Ed_xi4, R_Eg_xi4, bexp_HSea, bexp_Hg, norm,
Norm_HtuV, alpha_HtuV, beta_HtuV, alphap_HtuV,
Norm_Htubar, alpha_Htubar, beta_Htubar, alphap_Htqbar,
Norm_HtdV, alpha_HtdV, beta_HtdV, alphap_HtdV,
Norm_Htdbar, alpha_Htdbar, beta_Htdbar,
Norm_Htg, alpha_Htg, beta_Htg, alphap_Htg,
Norm_EtuV, alpha_EtuV, beta_EtuV, alphap_EtuV,
Norm_EtdV, R_Et_Sea, R_Htu_xi2, R_Htd_xi2, R_Htg_xi2,
R_Etu_xi2, R_Etd_xi2, R_Etg_xi2,
R_Htu_xi4, R_Htd_xi4, R_Htg_xi4,
R_Etu_xi4, R_Etd_xi4, R_Etg_xi4, bexp_HtSea):
global Minuit_Counter, Time_Counter
time_now = time.time() - time_start
if(time_now > Time_Counter * 600):
print('Runing Time:',round(time_now/60),'minutes. Cost function called total', Minuit_Counter, 'times.')
Time_Counter = Time_Counter + 1
Minuit_Counter = Minuit_Counter + 1
Para_Unp_lst = [Norm_HuV, alpha_HuV, beta_HuV, alphap_HuV,
Norm_Hubar, alpha_Hubar, beta_Hubar, alphap_Hqbar,
Norm_HdV, alpha_HdV, beta_HdV, alphap_HdV,
Norm_Hdbar, alpha_Hdbar, beta_Hdbar,
Norm_Hg, alpha_Hg, beta_Hg, alphap_Hg,
Norm_EuV, alpha_EuV, beta_EuV, alphap_EuV,
Norm_EdV, R_E_Sea, R_Hu_xi2, R_Hd_xi2, R_Hg_xi2,
R_Eu_xi2, R_Ed_xi2, R_Eg_xi2,
R_Hu_xi4, R_Hd_xi4, R_Hg_xi4,
R_Eu_xi4, R_Ed_xi4, R_Eg_xi4, bexp_HSea, bexp_Hg, norm]
Para_Pol_lst = [Norm_HtuV, alpha_HtuV, beta_HtuV, alphap_HtuV,
Norm_Htubar, alpha_Htubar, beta_Htubar, alphap_Htqbar,
Norm_HtdV, alpha_HtdV, beta_HtdV, alphap_HtdV,
Norm_Htdbar, alpha_Htdbar, beta_Htdbar,
Norm_Htg, alpha_Htg, beta_Htg, alphap_Htg,
Norm_EtuV, alpha_EtuV, beta_EtuV, alphap_EtuV,
Norm_EtdV, R_Et_Sea, R_Htu_xi2, R_Htd_xi2, R_Htg_xi2,
R_Etu_xi2, R_Etd_xi2, R_Etg_xi2,
R_Htu_xi4, R_Htd_xi4, R_Htg_xi4,
R_Etu_xi4, R_Etd_xi4, R_Etg_xi4, bexp_HtSea]
Para_Unp_all = ParaManager_Unp(Para_Unp_lst)
Para_Pol_all = ParaManager_Pol(Para_Pol_lst)
cost_DVCS_xBtQ = np.array(list(pool.map(partial(DVCSxsec_cost_xBtQ, Para_Unp = Para_Unp_all, Para_Pol = Para_Pol_all), DVCSxsec_group_data)))
cost_DVCSxsec = np.sum(cost_DVCS_xBtQ)
cost_DVCS_HERA_xBtQ = np.array(list(pool.map(partial(DVCSxsec_HERA_cost_xBtQ, Para_Unp = Para_Unp_all, Para_Pol = Para_Pol_all), DVCSxsec_HERA_group_data)))
cost_DVCSxsec_HERA = np.sum(cost_DVCS_HERA_xBtQ)
# DVCS_HERA_pred = np.array(list(pool.map(partial(DVCSxsec_HERA_theo, Para_Unp = Para_Unp_all, Para_Pol = Para_Pol_all), np.array(DVCS_HERA_data))))
#DVCS_HERA_pred = DVCSxsec_HERA_theo(DVCS_HERA_data, Para_Unp=Para_Unp_all, Para_Pol=Para_Pol_all)
#cost_DVCS_HERA = np.sum(((DVCS_HERA_pred - DVCS_HERA_data['f'])/ DVCS_HERA_data['delta f']) ** 2 )
return cost_DVCSxsec + cost_DVCSxsec_HERA
def off_forward_fit(Paralst_Unp, Paralst_Pol):
[Norm_HuV_Init, alpha_HuV_Init, beta_HuV_Init, alphap_HuV_Init,
Norm_Hubar_Init, alpha_Hubar_Init, beta_Hubar_Init, alphap_Hqbar_Init,
Norm_HdV_Init, alpha_HdV_Init, beta_HdV_Init, alphap_HdV_Init,
Norm_Hdbar_Init, alpha_Hdbar_Init, beta_Hdbar_Init,
Norm_Hg_Init, alpha_Hg_Init, beta_Hg_Init, alphap_Hg_Init,
Norm_EuV_Init, alpha_EuV_Init, beta_EuV_Init, alphap_EuV_Init,
Norm_EdV_Init, R_E_Sea_Init, R_Hu_xi2_Init, R_Hd_xi2_Init, R_Hg_xi2_Init,
R_Eu_xi2_Init, R_Ed_xi2_Init, R_Eg_xi2_Init,
R_Hu_xi4_Init, R_Hd_xi4_Init, R_Hg_xi4_Init,
R_Eu_xi4_Init, R_Ed_xi4_Init, R_Eg_xi4_Init, bexp_HSea_Init, bexp_Hg_Init, norm_Init] = Paralst_Unp
[Norm_HtuV_Init, alpha_HtuV_Init, beta_HtuV_Init, alphap_HtuV_Init,
Norm_Htubar_Init, alpha_Htubar_Init, beta_Htubar_Init, alphap_Htqbar_Init,
Norm_HtdV_Init, alpha_HtdV_Init, beta_HtdV_Init, alphap_HtdV_Init,
Norm_Htdbar_Init, alpha_Htdbar_Init, beta_Htdbar_Init,
Norm_Htg_Init, alpha_Htg_Init, beta_Htg_Init, alphap_Htg_Init,
Norm_EtuV_Init, alpha_EtuV_Init, beta_EtuV_Init, alphap_EtuV_Init,
Norm_EtdV_Init, R_Et_Sea_Init, R_Htu_xi2_Init, R_Htd_xi2_Init, R_Htg_xi2_Init,
R_Etu_xi2_Init, R_Etd_xi2_Init, R_Etg_xi2_Init,
R_Htu_xi4_Init, R_Htd_xi4_Init, R_Htg_xi4_Init,
R_Etu_xi4_Init, R_Etd_xi4_Init, R_Etg_xi4_Init, bexp_HtSea_Init] = Paralst_Pol
fit_off_forward = Minuit(cost_off_forward, Norm_HuV = Norm_HuV_Init, alpha_HuV = alpha_HuV_Init, beta_HuV = beta_HuV_Init, alphap_HuV = alphap_HuV_Init,
Norm_Hubar = Norm_Hubar_Init, alpha_Hubar = alpha_Hubar_Init, beta_Hubar = beta_Hubar_Init, alphap_Hqbar = alphap_Hqbar_Init,
Norm_HdV = Norm_HdV_Init, alpha_HdV = alpha_HdV_Init, beta_HdV = beta_HdV_Init, alphap_HdV = alphap_HdV_Init,
Norm_Hdbar = Norm_Hdbar_Init, alpha_Hdbar = alpha_Hdbar_Init, beta_Hdbar = beta_Hdbar_Init,
Norm_Hg = Norm_Hg_Init, alpha_Hg = alpha_Hg_Init, beta_Hg = beta_Hg_Init, alphap_Hg = alphap_Hg_Init,
Norm_EuV = Norm_EuV_Init, alpha_EuV = alpha_EuV_Init, beta_EuV = beta_EuV_Init, alphap_EuV = alphap_EuV_Init,
Norm_EdV = Norm_EdV_Init, R_E_Sea = R_E_Sea_Init, R_Hu_xi2 = R_Hu_xi2_Init, R_Hd_xi2 = R_Hd_xi2_Init, R_Hg_xi2 = R_Hg_xi2_Init,
R_Eu_xi2 = R_Eu_xi2_Init, R_Ed_xi2 = R_Ed_xi2_Init, R_Eg_xi2 = R_Eg_xi2_Init,
R_Hu_xi4 = R_Hu_xi4_Init, R_Hd_xi4 = R_Hd_xi4_Init, R_Hg_xi4 = R_Hg_xi4_Init,
R_Eu_xi4 = R_Eu_xi4_Init, R_Ed_xi4 = R_Ed_xi4_Init, R_Eg_xi4 = R_Eg_xi4_Init, bexp_HSea = bexp_HSea_Init, bexp_Hg = bexp_Hg_Init, norm = norm_Init,
Norm_HtuV = Norm_HtuV_Init, alpha_HtuV = alpha_HtuV_Init, beta_HtuV = beta_HtuV_Init, alphap_HtuV = alphap_HtuV_Init,
Norm_Htubar = Norm_Htubar_Init, alpha_Htubar = alpha_Htubar_Init, beta_Htubar = beta_Htubar_Init, alphap_Htqbar = alphap_Htqbar_Init,
Norm_HtdV = Norm_HtdV_Init, alpha_HtdV = alpha_HtdV_Init, beta_HtdV = beta_HtdV_Init, alphap_HtdV = alphap_HtdV_Init,
Norm_Htdbar = Norm_Htdbar_Init, alpha_Htdbar = alpha_Htdbar_Init, beta_Htdbar = beta_Htdbar_Init,
Norm_Htg = Norm_Htg_Init, alpha_Htg = alpha_Htg_Init, beta_Htg = beta_Htg_Init, alphap_Htg = alphap_Htg_Init,
Norm_EtuV = Norm_EtuV_Init, alpha_EtuV = alpha_EtuV_Init, beta_EtuV = beta_EtuV_Init, alphap_EtuV = alphap_EtuV_Init,
Norm_EtdV = Norm_EtdV_Init, R_Et_Sea = R_Et_Sea_Init, R_Htu_xi2 = R_Htu_xi2_Init, R_Htd_xi2 = R_Htd_xi2_Init, R_Htg_xi2 = R_Htg_xi2_Init,