{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Import"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\zfit\\util\\execution.py:57: UserWarning: Not running on Linux. Determining available cpus for thread can failand be overestimated. Workaround (only if too many cpus are used):`zfit.run.set_n_cpu(your_cpu_number)`\n",
" warnings.warn(\"Not running on Linux. Determining available cpus for thread can fail\"\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"WARNING: The TensorFlow contrib module will not be included in TensorFlow 2.0.\n",
"For more information, please see:\n",
" * https://github.com/tensorflow/community/blob/master/rfcs/20180907-contrib-sunset.md\n",
" * https://github.com/tensorflow/addons\n",
"If you depend on functionality not listed there, please file an issue.\n",
"\n"
]
}
],
"source": [
"import os\n",
"\n",
"# os.environ[\"CUDA_VISIBLE_DEVICES\"] = \"-1\"\n",
"\n",
"import numpy as np\n",
"from pdg_const import pdg\n",
"import matplotlib\n",
"import matplotlib.pyplot as plt\n",
"import pickle as pkl\n",
"import sys\n",
"import time\n",
"from helperfunctions import display_time, prepare_plot\n",
"import cmath as c\n",
"import scipy.integrate as integrate\n",
"from scipy.optimize import fminbound\n",
"from array import array as arr\n",
"import collections\n",
"from itertools import compress\n",
"import tensorflow as tf\n",
"import zfit\n",
"from zfit import ztf\n",
"# from IPython.display import clear_output\n",
"import os\n",
"import tensorflow_probability as tfp\n",
"tfd = tfp.distributions"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"# chunksize = 10000\n",
"# zfit.run.chunking.active = True\n",
"# zfit.run.chunking.max_n_points = chunksize"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Build model and graphs\n",
"## Create graphs"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"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\n",
" #check if subscript is viable\n",
"\n",
" if subscript != \"0\" and subscript != \"+\" and subscript != \"T\":\n",
" raise ValueError('Wrong subscript entered, choose either 0, + or T')\n",
"\n",
" #get constants\n",
"\n",
" mK = ztf.constant(pdg['Ks_M'])\n",
" mbstar0 = ztf.constant(pdg[\"mbstar0\"])\n",
" mbstar = ztf.constant(pdg[\"mbstar\"])\n",
"\n",
"\n",
" mmu = ztf.constant(pdg['muon_M'])\n",
" mb = ztf.constant(pdg['bquark_M'])\n",
" ms = ztf.constant(pdg['squark_M'])\n",
" mB = ztf.constant(pdg['Bplus_M'])\n",
"\n",
" #N comes from derivation in paper\n",
"\n",
" N = 3\n",
"\n",
" #some helperfunctions\n",
"\n",
" tpos = (mB - mK)**2\n",
" tzero = (mB + mK)*(ztf.sqrt(mB)-ztf.sqrt(mK))**2\n",
"\n",
" z_oben = ztf.sqrt(tpos - q2) - ztf.sqrt(tpos - tzero)\n",
" z_unten = ztf.sqrt(tpos - q2) + ztf.sqrt(tpos - tzero)\n",
" z = tf.divide(z_oben, z_unten)\n",
"\n",
" #calculate f0\n",
"\n",
" if subscript == \"0\":\n",
" prefactor = 1/(1 - q2/(mbstar0**2))\n",
" _sum = 0\n",
" b0 = [b0_0, b0_1, b0_2]\n",
"\n",
" for i in range(N):\n",
" _sum += b0[i]*(tf.pow(z,i))\n",
"\n",
" return ztf.to_complex(prefactor * _sum)\n",
"\n",
" #calculate f+ or fT\n",
"\n",
" else:\n",
" prefactor = 1/(1 - q2/(mbstar**2))\n",
" _sum = 0\n",
"\n",
" if subscript == \"T\":\n",
" bT = [bT_0, bT_1, bT_2]\n",
" for i in range(N):\n",
" _sum += bT[i] * (tf.pow(z, i) - ((-1)**(i-N)) * (i/N) * tf.pow(z, N))\n",
" else:\n",
" bplus = [bplus_0, bplus_1, bplus_2]\n",
" for i in range(N):\n",
" _sum += bplus[i] * (tf.pow(z, i) - ((-1)**(i-N)) * (i/N) * tf.pow(z, N))\n",
"\n",
" return ztf.to_complex(prefactor * _sum)\n",
"\n",
"def resonance(q, _mass, width, phase, scale):\n",
"\n",
" q2 = tf.pow(q, 2)\n",
"\n",
" mmu = ztf.constant(pdg['muon_M'])\n",
"\n",
" p = 0.5 * ztf.sqrt(q2 - 4*(mmu**2))\n",
"\n",
" p0 = 0.5 * ztf.sqrt(_mass**2 - 4*mmu**2)\n",
"\n",
" gamma_j = tf.divide(p, q) * _mass * width / p0\n",
"\n",
" #Calculate the resonance\n",
"\n",
" _top = tf.complex(_mass * width, ztf.constant(0.0))\n",
"\n",
" _bottom = tf.complex(_mass**2 - q2, -_mass*gamma_j)\n",
"\n",
" com = _top/_bottom\n",
"\n",
" #Rotate by the phase\n",
"\n",
" r = ztf.to_complex(scale*tf.abs(com))\n",
"\n",
" _phase = tf.angle(com)\n",
"\n",
" _phase += phase\n",
"\n",
" com = r * tf.exp(tf.complex(ztf.constant(0.0), _phase))\n",
"\n",
" return com\n",
"\n",
"\n",
"def axiv_nonres(q, b0_0, b0_1, b0_2, bplus_0, bplus_1, bplus_2, bT_0, bT_1, bT_2):\n",
"\n",
" GF = ztf.constant(pdg['GF'])\n",
" alpha_ew = ztf.constant(pdg['alpha_ew'])\n",
" Vtb = ztf.constant(pdg['Vtb'])\n",
" Vts = ztf.constant(pdg['Vts'])\n",
" C10eff = ztf.constant(pdg['C10eff'])\n",
"\n",
" mmu = ztf.constant(pdg['muon_M'])\n",
" mb = ztf.constant(pdg['bquark_M'])\n",
" ms = ztf.constant(pdg['squark_M'])\n",
" mK = ztf.constant(pdg['Ks_M'])\n",
" mB = ztf.constant(pdg['Bplus_M'])\n",
"\n",
" q2 = tf.pow(q, 2)\n",
"\n",
" #Some helperfunctions\n",
"\n",
" beta = 1. - 4. * mmu**2. / q2\n",
"\n",
" 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.)\n",
"\n",
" #prefactor in front of whole bracket\n",
"\n",
" prefactor1 = GF**2. *alpha_ew**2. * (tf.abs(Vtb*Vts))**2. * kabs * beta / (128. * np.pi**5.)\n",
"\n",
" #left term in bracket\n",
"\n",
" 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)\n",
"\n",
" #middle term in bracket\n",
"\n",
" _top = 4. * mmu**2. * (mB**2. - mK**2.) * (mB**2. - mK**2.)\n",
"\n",
" _under = q2 * mB**2.\n",
"\n",
" 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)\n",
" \n",
" #Note sqrt(q2) comes from derivation as we use q2 and plot q\n",
"\n",
" return prefactor1 * (bracket_left + bracket_middle) * 2 * q\n",
"\n",
"def vec(q, funcs, b0_0, b0_1, b0_2, bplus_0, bplus_1, bplus_2, bT_0, bT_1, bT_2):\n",
" \n",
" q2 = tf.pow(q, 2)\n",
"\n",
" GF = ztf.constant(pdg['GF'])\n",
" alpha_ew = ztf.constant(pdg['alpha_ew'])\n",
" Vtb = ztf.constant(pdg['Vtb'])\n",
" Vts = ztf.constant(pdg['Vts'])\n",
" C7eff = ztf.constant(pdg['C7eff'])\n",
"\n",
" mmu = ztf.constant(pdg['muon_M'])\n",
" mb = ztf.constant(pdg['bquark_M'])\n",
" ms = ztf.constant(pdg['squark_M'])\n",
" mK = ztf.constant(pdg['Ks_M'])\n",
" mB = ztf.constant(pdg['Bplus_M'])\n",
"\n",
" #Some helperfunctions\n",
"\n",
" beta = 1. - 4. * mmu**2. / q2\n",
"\n",
" 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)\n",
" \n",
" #prefactor in front of whole bracket\n",
"\n",
" prefactor1 = GF**2. *alpha_ew**2. * (tf.abs(Vtb*Vts))**2 * kabs * beta / (128. * np.pi**5.)\n",
"\n",
" #right term in bracket\n",
"\n",
" prefactor2 = tf.pow(kabs,2) * (1. - 1./3. * beta)\n",
"\n",
" 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)\n",
"\n",
" bracket_right = prefactor2 * abs_bracket\n",
"\n",
" #Note sqrt(q2) comes from derivation as we use q2 and plot q\n",
"\n",
" return prefactor1 * bracket_right * 2 * q\n",
"\n",
"def c9eff(q, funcs):\n",
"\n",
" C9eff_nr = ztf.to_complex(ztf.constant(pdg['C9eff']))\n",
"\n",
" c9 = C9eff_nr + funcs\n",
"\n",
" return c9"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"def G(y):\n",
" \n",
" def inner_rect_bracket(q):\n",
" return tf.log(ztf.to_complex((1+tf.sqrt(q))/(1-tf.sqrt(q)))-tf.complex(ztf.constant(0), -1*ztf.constant(np.pi))) \n",
" \n",
" def inner_right(q):\n",
" return ztf.to_complex(2 * tf.atan(1/tf.sqrt(tf.math.real(-q))))\n",
" \n",
" big_bracket = tf.where(tf.math.real(y) > ztf.constant(0.0), inner_rect_bracket(y), inner_right(y))\n",
" \n",
" return ztf.to_complex(tf.sqrt(tf.abs(y))) * big_bracket\n",
"\n",
"def h_S(m, q):\n",
" \n",
" 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)))\n",
"\n",
"def h_P(m, q):\n",
" \n",
" 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)\n",
"\n",
"def two_p_ccbar(mD, m_D_bar, m_D_star, q):\n",
" \n",
" \n",
" #Load constants\n",
" nu_D_bar = ztf.to_complex(pdg[\"nu_D_bar\"])\n",
" nu_D = ztf.to_complex(pdg[\"nu_D\"])\n",
" nu_D_star = ztf.to_complex(pdg[\"nu_D_star\"])\n",
" \n",
" phase_D_bar = ztf.to_complex(pdg[\"phase_D_bar\"])\n",
" phase_D = ztf.to_complex(pdg[\"phase_D\"])\n",
" phase_D_star = ztf.to_complex(pdg[\"phase_D_star\"])\n",
" \n",
" #Calculation\n",
" left_part = nu_D_bar * tf.exp(tf.complex(ztf.constant(0.0), phase_D_bar)) * h_S(m_D_bar, q) \n",
" \n",
" right_part_D = nu_D * tf.exp(tf.complex(ztf.constant(0.0), phase_D)) * h_P(m_D, q) \n",
" \n",
" right_part_D_star = nu_D_star * tf.exp(tf.complex(ztf.constant(0.0), phase_D_star)) * h_P(m_D_star, q) \n",
"\n",
" return left_part + right_part_D + right_part_D_star"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Build pdf"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"class total_pdf_cut(zfit.pdf.ZPDF):\n",
" _N_OBS = 1 # dimension, can be omitted\n",
" _PARAMS = ['b0_0', 'b0_1', 'b0_2', \n",
" 'bplus_0', 'bplus_1', 'bplus_2', \n",
" 'bT_0', 'bT_1', 'bT_2', \n",
" 'rho_mass', 'rho_scale', 'rho_phase', 'rho_width',\n",
" 'jpsi_mass', 'jpsi_scale', 'jpsi_phase', 'jpsi_width',\n",
" 'psi2s_mass', 'psi2s_scale', 'psi2s_phase', 'psi2s_width',\n",
" 'p3770_mass', 'p3770_scale', 'p3770_phase', 'p3770_width',\n",
" 'p4040_mass', 'p4040_scale', 'p4040_phase', 'p4040_width',\n",
" 'p4160_mass', 'p4160_scale', 'p4160_phase', 'p4160_width',\n",
" 'p4415_mass', 'p4415_scale', 'p4415_phase', 'p4415_width',\n",
" 'omega_mass', 'omega_scale', 'omega_phase', 'omega_width',\n",
" 'phi_mass', 'phi_scale', 'phi_phase', 'phi_width',\n",
" 'Dbar_mass', 'Dbar_scale', 'Dbar_phase',\n",
" 'Dstar_mass', 'DDstar_scale', 'DDstar_phase', 'D_mass',\n",
" 'tau_mass', 'C_tt']\n",
"# the name of the parameters\n",
"\n",
" def _unnormalized_pdf(self, x):\n",
" \n",
" x = x.unstack_x()\n",
" \n",
" b0 = [self.params['b0_0'], self.params['b0_1'], self.params['b0_2']]\n",
" bplus = [self.params['bplus_0'], self.params['bplus_1'], self.params['bplus_2']]\n",
" bT = [self.params['bT_0'], self.params['bT_1'], self.params['bT_2']]\n",
" \n",
" def rho_res(q):\n",
" return resonance(q, _mass = self.params['rho_mass'], scale = self.params['rho_scale'],\n",
" phase = self.params['rho_phase'], width = self.params['rho_width'])\n",
" \n",
" def omega_res(q):\n",
" return resonance(q, _mass = self.params['omega_mass'], scale = self.params['omega_scale'],\n",
" phase = self.params['omega_phase'], width = self.params['omega_width'])\n",
" \n",
" def phi_res(q):\n",
" return resonance(q, _mass = self.params['phi_mass'], scale = self.params['phi_scale'],\n",
" phase = self.params['phi_phase'], width = self.params['phi_width'])\n",
"\n",
" def jpsi_res(q):\n",
" return ztf.to_complex(tf.pow(q, 2) / tf.pow(self.params['jpsi_mass'], 2)) * resonance(q, _mass = self.params['jpsi_mass'], \n",
" scale = self.params['jpsi_scale'],\n",
" phase = self.params['jpsi_phase'], \n",
" width = self.params['jpsi_width'])\n",
" def psi2s_res(q):\n",
" return ztf.to_complex(tf.pow(q, 2) / tf.pow(self.params['psi2s_mass'], 2)) * resonance(q, _mass = self.params['psi2s_mass'], \n",
" scale = self.params['psi2s_scale'],\n",
" phase = self.params['psi2s_phase'], \n",
" width = self.params['psi2s_width'])\n",
" def p3770_res(q):\n",
" return ztf.to_complex(tf.pow(q, 2) / tf.pow(self.params['p3770_mass'], 2)) * resonance(q, _mass = self.params['p3770_mass'], \n",
" scale = self.params['p3770_scale'],\n",
" phase = self.params['p3770_phase'], \n",
" width = self.params['p3770_width'])\n",
" \n",
" def p4040_res(q):\n",
" return ztf.to_complex(tf.pow(q, 2) / tf.pow(self.params['p4040_mass'], 2)) * resonance(q, _mass = self.params['p4040_mass'], \n",
" scale = self.params['p4040_scale'],\n",
" phase = self.params['p4040_phase'], \n",
" width = self.params['p4040_width'])\n",
" \n",
" def p4160_res(q):\n",
" return ztf.to_complex(tf.pow(q, 2) / tf.pow(self.params['p4160_mass'], 2)) * resonance(q, _mass = self.params['p4160_mass'], \n",
" scale = self.params['p4160_scale'],\n",
" phase = self.params['p4160_phase'], \n",
" width = self.params['p4160_width'])\n",
" \n",
" def p4415_res(q):\n",
" return ztf.to_complex(tf.pow(q, 2) / tf.pow(self.params['p4415_mass'], 2)) * resonance(q, _mass = self.params['p4415_mass'], \n",
" scale = self.params['p4415_scale'],\n",
" phase = self.params['p4415_phase'], \n",
" width = self.params['p4415_width'])\n",
" \n",
" def P2_D(q):\n",
" 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))\n",
" 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)))\n",
" return Dbar_contrib + DDstar_contrib\n",
" \n",
" def ttau_cusp(q):\n",
" 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)))\n",
" \n",
"\n",
" 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)\n",
"\n",
" vec_f = vec(x, funcs, b0_0, b0_1, b0_2, bplus_0, bplus_1, bplus_2, bT_0, bT_1, bT_2)\n",
"\n",
" axiv_nr = axiv_nonres(x, b0_0, b0_1, b0_2, bplus_0, bplus_1, bplus_2, bT_0, bT_1, bT_2)\n",
"\n",
" tot = vec_f + axiv_nr\n",
" \n",
" #Cut out jpsi and psi2s\n",
" \n",
" tot = tf.where(tf.math.logical_or(x < ztf.constant(jpsi_mass-60.), x > ztf.constant(jpsi_mass+70.)), tot, 0.0*tot)\n",
" \n",
" tot = tf.where(tf.math.logical_or(x < ztf.constant(psi2s_mass-50.), x > ztf.constant(psi2s_mass+50.)), tot, 0.0*tot)\n",
" \n",
" return tot\n",
" \n",
"class total_pdf_full(zfit.pdf.ZPDF):\n",
" _N_OBS = 1 # dimension, can be omitted\n",
" _PARAMS = ['b0_0', 'b0_1', 'b0_2', \n",
" 'bplus_0', 'bplus_1', 'bplus_2', \n",
" 'bT_0', 'bT_1', 'bT_2', \n",
" 'rho_mass', 'rho_scale', 'rho_phase', 'rho_width',\n",
" 'jpsi_mass', 'jpsi_scale', 'jpsi_phase', 'jpsi_width',\n",
" 'psi2s_mass', 'psi2s_scale', 'psi2s_phase', 'psi2s_width',\n",
" 'p3770_mass', 'p3770_scale', 'p3770_phase', 'p3770_width',\n",
" 'p4040_mass', 'p4040_scale', 'p4040_phase', 'p4040_width',\n",
" 'p4160_mass', 'p4160_scale', 'p4160_phase', 'p4160_width',\n",
" 'p4415_mass', 'p4415_scale', 'p4415_phase', 'p4415_width',\n",
" 'omega_mass', 'omega_scale', 'omega_phase', 'omega_width',\n",
" 'phi_mass', 'phi_scale', 'phi_phase', 'phi_width',\n",
" 'Dbar_mass', 'Dbar_scale', 'Dbar_phase',\n",
" 'Dstar_mass', 'DDstar_scale', 'DDstar_phase', 'D_mass',\n",
" 'tau_mass', 'C_tt']\n",
"# the name of the parameters\n",
"\n",
" def _unnormalized_pdf(self, x):\n",
" \n",
" x = x.unstack_x()\n",
" \n",
" b0 = [self.params['b0_0'], self.params['b0_1'], self.params['b0_2']]\n",
" bplus = [self.params['bplus_0'], self.params['bplus_1'], self.params['bplus_2']]\n",
" bT = [self.params['bT_0'], self.params['bT_1'], self.params['bT_2']]\n",
" \n",
" def rho_res(q):\n",
" return resonance(q, _mass = self.params['rho_mass'], scale = self.params['rho_scale'],\n",
" phase = self.params['rho_phase'], width = self.params['rho_width'])\n",
" \n",
" def omega_res(q):\n",
" return resonance(q, _mass = self.params['omega_mass'], scale = self.params['omega_scale'],\n",
" phase = self.params['omega_phase'], width = self.params['omega_width'])\n",
" \n",
" def phi_res(q):\n",
" return resonance(q, _mass = self.params['phi_mass'], scale = self.params['phi_scale'],\n",
" phase = self.params['phi_phase'], width = self.params['phi_width'])\n",
"\n",
" def jpsi_res(q):\n",
" return ztf.to_complex(tf.pow(q, 2) / tf.pow(self.params['jpsi_mass'], 2)) * resonance(q, _mass = self.params['jpsi_mass'], \n",
" scale = self.params['jpsi_scale'],\n",
" phase = self.params['jpsi_phase'], \n",
" width = self.params['jpsi_width'])\n",
" def psi2s_res(q):\n",
" return ztf.to_complex(tf.pow(q, 2) / tf.pow(self.params['psi2s_mass'], 2)) * resonance(q, _mass = self.params['psi2s_mass'], \n",
" scale = self.params['psi2s_scale'],\n",
" phase = self.params['psi2s_phase'], \n",
" width = self.params['psi2s_width'])\n",
" def p3770_res(q):\n",
" return ztf.to_complex(tf.pow(q, 2) / tf.pow(self.params['p3770_mass'], 2)) * resonance(q, _mass = self.params['p3770_mass'], \n",
" scale = self.params['p3770_scale'],\n",
" phase = self.params['p3770_phase'], \n",
" width = self.params['p3770_width'])\n",
" \n",
" def p4040_res(q):\n",
" return ztf.to_complex(tf.pow(q, 2) / tf.pow(self.params['p4040_mass'], 2)) * resonance(q, _mass = self.params['p4040_mass'], \n",
" scale = self.params['p4040_scale'],\n",
" phase = self.params['p4040_phase'], \n",
" width = self.params['p4040_width'])\n",
" \n",
" def p4160_res(q):\n",
" return ztf.to_complex(tf.pow(q, 2) / tf.pow(self.params['p4160_mass'], 2)) * resonance(q, _mass = self.params['p4160_mass'], \n",
" scale = self.params['p4160_scale'],\n",
" phase = self.params['p4160_phase'], \n",
" width = self.params['p4160_width'])\n",
" \n",
" def p4415_res(q):\n",
" return ztf.to_complex(tf.pow(q, 2) / tf.pow(self.params['p4415_mass'], 2)) * resonance(q, _mass = self.params['p4415_mass'], \n",
" scale = self.params['p4415_scale'],\n",
" phase = self.params['p4415_phase'], \n",
" width = self.params['p4415_width'])\n",
" \n",
" def P2_D(q):\n",
" 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))\n",
" 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)))\n",
" return Dbar_contrib + DDstar_contrib\n",
" \n",
" def ttau_cusp(q):\n",
" 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)))\n",
" \n",
"\n",
" 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)\n",
"\n",
" vec_f = vec(x, funcs, b0_0, b0_1, b0_2, bplus_0, bplus_1, bplus_2, bT_0, bT_1, bT_2)\n",
"\n",
" axiv_nr = axiv_nonres(x, b0_0, b0_1, b0_2, bplus_0, bplus_1, bplus_2, bT_0, bT_1, bT_2)\n",
"\n",
" tot = vec_f + axiv_nr\n",
" \n",
" #Cut out jpsi and psi2s\n",
" \n",
"# tot = tf.where(tf.math.logical_or(x < ztf.constant(jpsi_mass-60.), x > ztf.constant(jpsi_mass+70.)), tot, 0.0*tot)\n",
" \n",
"# tot = tf.where(tf.math.logical_or(x < ztf.constant(psi2s_mass-50.), x > ztf.constant(psi2s_mass+50.)), tot, 0.0*tot)\n",
" \n",
" return tot"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Setup parameters"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"WARNING:tensorflow:From C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\tensorflow\\python\\ops\\resource_variable_ops.py:435: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.\n",
"Instructions for updating:\n",
"Colocations handled automatically by placer.\n"
]
}
],
"source": [
"# formfactors\n",
"\n",
"b0_0 = zfit.Parameter(\"b0_0\", ztf.constant(0.292), floating = False) #, lower_limit = -2.0, upper_limit= 2.0)\n",
"b0_1 = zfit.Parameter(\"b0_1\", ztf.constant(0.281), floating = False) #, lower_limit = -2.0, upper_limit= 2.0)\n",
"b0_2 = zfit.Parameter(\"b0_2\", ztf.constant(0.150), floating = False) #, lower_limit = -2.0, upper_limit= 2.0)\n",
"\n",
"bplus_0 = zfit.Parameter(\"bplus_0\", ztf.constant(0.466), floating = False)#, lower_limit = -2.0, upper_limit= 2.0)\n",
"bplus_1 = zfit.Parameter(\"bplus_1\", ztf.constant(-0.885), floating = False)#, lower_limit = -2.0, upper_limit= 2.0)\n",
"bplus_2 = zfit.Parameter(\"bplus_2\", ztf.constant(-0.213), floating = False)#, lower_limit = -2.0, upper_limit= 2.0)\n",
"\n",
"bT_0 = zfit.Parameter(\"bT_0\", ztf.constant(0.460), floating = False) #, lower_limit = -2.0, upper_limit= 2.0)\n",
"bT_1 = zfit.Parameter(\"bT_1\", ztf.constant(-1.089), floating = False) #, lower_limit = -2.0, upper_limit= 2.0)\n",
"bT_2 = zfit.Parameter(\"bT_2\", ztf.constant(-1.114), floating = False) #, lower_limit = -2.0, upper_limit= 2.0)\n",
"\n",
"\n",
"#rho\n",
"\n",
"rho_mass, rho_width, rho_phase, rho_scale = pdg[\"rho\"]\n",
"\n",
"rho_m = zfit.Parameter(\"rho_m\", ztf.constant(rho_mass), floating = False) #lower_limit = rho_mass - rho_width, upper_limit = rho_mass + rho_width)\n",
"rho_w = zfit.Parameter(\"rho_w\", ztf.constant(rho_width), floating = False)\n",
"rho_p = zfit.Parameter(\"rho_p\", ztf.constant(rho_phase), lower_limit=-2*np.pi, upper_limit=2*np.pi)\n",
"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))\n",
"\n",
"#omega\n",
"\n",
"omega_mass, omega_width, omega_phase, omega_scale = pdg[\"omega\"]\n",
"\n",
"omega_m = zfit.Parameter(\"omega_m\", ztf.constant(omega_mass), floating = False)\n",
"omega_w = zfit.Parameter(\"omega_w\", ztf.constant(omega_width), floating = False)\n",
"omega_p = zfit.Parameter(\"omega_p\", ztf.constant(omega_phase), lower_limit=-2*np.pi, upper_limit=2*np.pi)\n",
"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))\n",
"\n",
"\n",
"#phi\n",
"\n",
"phi_mass, phi_width, phi_phase, phi_scale = pdg[\"phi\"]\n",
"\n",
"phi_m = zfit.Parameter(\"phi_m\", ztf.constant(phi_mass), floating = False)\n",
"phi_w = zfit.Parameter(\"phi_w\", ztf.constant(phi_width), floating = False)\n",
"phi_p = zfit.Parameter(\"phi_p\", ztf.constant(phi_phase), lower_limit=-2*np.pi, upper_limit=2*np.pi)\n",
"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))\n",
"\n",
"#jpsi\n",
"\n",
"jpsi_mass, jpsi_width, jpsi_phase, jpsi_scale = pdg[\"jpsi\"]\n",
"\n",
"jpsi_m = zfit.Parameter(\"jpsi_m\", ztf.constant(jpsi_mass), floating = False)\n",
"jpsi_w = zfit.Parameter(\"jpsi_w\", ztf.constant(jpsi_width), floating = False)\n",
"jpsi_p = zfit.Parameter(\"jpsi_p\", ztf.constant(jpsi_phase), lower_limit=-2*np.pi, upper_limit=2*np.pi)\n",
"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))\n",
"\n",
"#psi2s\n",
"\n",
"psi2s_mass, psi2s_width, psi2s_phase, psi2s_scale = pdg[\"psi2s\"]\n",
"\n",
"psi2s_m = zfit.Parameter(\"psi2s_m\", ztf.constant(psi2s_mass), floating = False)\n",
"psi2s_w = zfit.Parameter(\"psi2s_w\", ztf.constant(psi2s_width), floating = False)\n",
"psi2s_p = zfit.Parameter(\"psi2s_p\", ztf.constant(psi2s_phase), lower_limit=-2*np.pi, upper_limit=2*np.pi)\n",
"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))\n",
"\n",
"#psi(3770)\n",
"\n",
"p3770_mass, p3770_width, p3770_phase, p3770_scale = pdg[\"p3770\"]\n",
"\n",
"p3770_m = zfit.Parameter(\"p3770_m\", ztf.constant(p3770_mass), floating = False)\n",
"p3770_w = zfit.Parameter(\"p3770_w\", ztf.constant(p3770_width), floating = False)\n",
"p3770_p = zfit.Parameter(\"p3770_p\", ztf.constant(p3770_phase), lower_limit=-2*np.pi, upper_limit=2*np.pi)\n",
"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))\n",
"\n",
"#psi(4040)\n",
"\n",
"p4040_mass, p4040_width, p4040_phase, p4040_scale = pdg[\"p4040\"]\n",
"\n",
"p4040_m = zfit.Parameter(\"p4040_m\", ztf.constant(p4040_mass), floating = False)\n",
"p4040_w = zfit.Parameter(\"p4040_w\", ztf.constant(p4040_width), floating = False)\n",
"p4040_p = zfit.Parameter(\"p4040_p\", ztf.constant(p4040_phase), lower_limit=-2*np.pi, upper_limit=2*np.pi)\n",
"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))\n",
"\n",
"#psi(4160)\n",
"\n",
"p4160_mass, p4160_width, p4160_phase, p4160_scale = pdg[\"p4160\"]\n",
"\n",
"p4160_m = zfit.Parameter(\"p4160_m\", ztf.constant(p4160_mass), floating = False)\n",
"p4160_w = zfit.Parameter(\"p4160_w\", ztf.constant(p4160_width), floating = False)\n",
"p4160_p = zfit.Parameter(\"p4160_p\", ztf.constant(p4160_phase), lower_limit=-2*np.pi, upper_limit=2*np.pi)\n",
"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))\n",
"\n",
"#psi(4415)\n",
"\n",
"p4415_mass, p4415_width, p4415_phase, p4415_scale = pdg[\"p4415\"]\n",
"\n",
"p4415_m = zfit.Parameter(\"p4415_m\", ztf.constant(p4415_mass), floating = False)\n",
"p4415_w = zfit.Parameter(\"p4415_w\", ztf.constant(p4415_width), floating = False)\n",
"p4415_p = zfit.Parameter(\"p4415_p\", ztf.constant(p4415_phase), lower_limit=-2*np.pi, upper_limit=2*np.pi)\n",
"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))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Dynamic generation of 2 particle contribution"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"m_c = 1300\n",
"\n",
"Dbar_phase = 0.0\n",
"DDstar_phase = 0.0\n",
"Dstar_mass = pdg['Dst_M']\n",
"Dbar_mass = pdg['D0_M']\n",
"D_mass = pdg['D0_M']\n",
"\n",
"Dbar_s = zfit.Parameter(\"Dbar_s\", ztf.constant(0.0), lower_limit=-0.3, upper_limit=0.3)\n",
"Dbar_m = zfit.Parameter(\"Dbar_m\", ztf.constant(Dbar_mass), floating = False)\n",
"Dbar_p = zfit.Parameter(\"Dbar_p\", ztf.constant(Dbar_phase), lower_limit=-2*np.pi, upper_limit=2*np.pi)#, floating = False)\n",
"DDstar_s = zfit.Parameter(\"DDstar_s\", ztf.constant(0.0), lower_limit=-0.3, upper_limit=0.3)#, floating = False)\n",
"Dstar_m = zfit.Parameter(\"Dstar_m\", ztf.constant(Dstar_mass), floating = False)\n",
"D_m = zfit.Parameter(\"D_m\", ztf.constant(D_mass), floating = False)\n",
"DDstar_p = zfit.Parameter(\"DDstar_p\", ztf.constant(DDstar_phase), lower_limit=-2*np.pi, upper_limit=2*np.pi)#, floating = False)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Tau parameters"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"tau_m = zfit.Parameter(\"tau_m\", ztf.constant(pdg['tau_M']), floating = False)\n",
"Ctt = zfit.Parameter(\"Ctt\", ztf.constant(0.0), lower_limit=-1.5, upper_limit=1.5)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Load data"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"x_min = 2*pdg['muon_M']\n",
"x_max = (pdg[\"Bplus_M\"]-pdg[\"Ks_M\"]-0.1)\n",
"\n",
"# # Full spectrum\n",
"\n",
"obs_toy = zfit.Space('q', limits = (x_min, x_max))\n",
"\n",
"# Jpsi and Psi2s cut out\n",
"\n",
"obs1 = zfit.Space('q', limits = (x_min, jpsi_mass - 60.))\n",
"obs2 = zfit.Space('q', limits = (jpsi_mass + 70., psi2s_mass - 50.))\n",
"obs3 = zfit.Space('q', limits = (psi2s_mass + 50., x_max))\n",
"\n",
"obs_fit = obs1 + obs2 + obs3\n",
"\n",
"# 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:\n",
"# part_set = pkl.load(input_file)\n",
"\n",
"# x_part = part_set['x_part']\n",
"\n",
"# x_part = x_part.astype('float64')\n",
"\n",
"# data = zfit.data.Data.from_numpy(array=x_part, obs=obs)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Setup pdf"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
"total_f = total_pdf_cut(obs=obs_toy, jpsi_mass = jpsi_m, jpsi_scale = jpsi_s, jpsi_phase = jpsi_p, jpsi_width = jpsi_w,\n",
" psi2s_mass = psi2s_m, psi2s_scale = psi2s_s, psi2s_phase = psi2s_p, psi2s_width = psi2s_w,\n",
" p3770_mass = p3770_m, p3770_scale = p3770_s, p3770_phase = p3770_p, p3770_width = p3770_w,\n",
" p4040_mass = p4040_m, p4040_scale = p4040_s, p4040_phase = p4040_p, p4040_width = p4040_w,\n",
" p4160_mass = p4160_m, p4160_scale = p4160_s, p4160_phase = p4160_p, p4160_width = p4160_w,\n",
" p4415_mass = p4415_m, p4415_scale = p4415_s, p4415_phase = p4415_p, p4415_width = p4415_w,\n",
" rho_mass = rho_m, rho_scale = rho_s, rho_phase = rho_p, rho_width = rho_w,\n",
" omega_mass = omega_m, omega_scale = omega_s, omega_phase = omega_p, omega_width = omega_w,\n",
" phi_mass = phi_m, phi_scale = phi_s, phi_phase = phi_p, phi_width = phi_w,\n",
" Dstar_mass = Dstar_m, DDstar_scale = DDstar_s, DDstar_phase = DDstar_p, D_mass = D_m,\n",
" Dbar_mass = Dbar_m, Dbar_scale = Dbar_s, Dbar_phase = Dbar_p,\n",
" tau_mass = tau_m, C_tt = Ctt, b0_0 = b0_0, b0_1 = b0_1, b0_2 = b0_2,\n",
" bplus_0 = bplus_0, bplus_1 = bplus_1, bplus_2 = bplus_2,\n",
" bT_0 = bT_0, bT_1 = bT_1, bT_2 = bT_2)\n",
"\n",
"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,\n",
" psi2s_mass = psi2s_m, psi2s_scale = psi2s_s, psi2s_phase = psi2s_p, psi2s_width = psi2s_w,\n",
" p3770_mass = p3770_m, p3770_scale = p3770_s, p3770_phase = p3770_p, p3770_width = p3770_w,\n",
" p4040_mass = p4040_m, p4040_scale = p4040_s, p4040_phase = p4040_p, p4040_width = p4040_w,\n",
" p4160_mass = p4160_m, p4160_scale = p4160_s, p4160_phase = p4160_p, p4160_width = p4160_w,\n",
" p4415_mass = p4415_m, p4415_scale = p4415_s, p4415_phase = p4415_p, p4415_width = p4415_w,\n",
" rho_mass = rho_m, rho_scale = rho_s, rho_phase = rho_p, rho_width = rho_w,\n",
" omega_mass = omega_m, omega_scale = omega_s, omega_phase = omega_p, omega_width = omega_w,\n",
" phi_mass = phi_m, phi_scale = phi_s, phi_phase = phi_p, phi_width = phi_w,\n",
" Dstar_mass = Dstar_m, DDstar_scale = DDstar_s, DDstar_phase = DDstar_p, D_mass = D_m,\n",
" Dbar_mass = Dbar_m, Dbar_scale = Dbar_s, Dbar_phase = Dbar_p,\n",
" tau_mass = tau_m, C_tt = Ctt, b0_0 = b0_0, b0_1 = b0_1, b0_2 = b0_2,\n",
" bplus_0 = bplus_0, bplus_1 = bplus_1, bplus_2 = bplus_2,\n",
" bT_0 = bT_0, bT_1 = bT_1, bT_2 = bT_2)\n",
" \n",
"# print(total_pdf.obs)\n",
"\n",
"# print(calcs_test)\n",
"\n",
"# for param in total_f.get_dependents():\n",
"# print(zfit.run(param))"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<tf.Tensor 'normalization/hook_integrate/hook_numeric_integrate/mul_1:0' shape=() dtype=float64>"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"total_f_fit.normalization(obs_toy)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Test if graphs actually work and compute values"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [],
"source": [
"# def total_test_tf(xq):\n",
"\n",
"# def jpsi_res(q):\n",
"# return resonance(q, jpsi_m, jpsi_s, jpsi_p, jpsi_w)\n",
"\n",
"# def psi2s_res(q):\n",
"# return resonance(q, psi2s_m, psi2s_s, psi2s_p, psi2s_w)\n",
"\n",
"# def cusp(q):\n",
"# return bifur_gauss(q, cusp_m, sig_L, sig_R, cusp_s)\n",
"\n",
"# funcs = jpsi_res(xq) + psi2s_res(xq) + cusp(xq)\n",
"\n",
"# vec_f = vec(xq, funcs)\n",
"\n",
"# axiv_nr = axiv_nonres(xq)\n",
"\n",
"# tot = vec_f + axiv_nr\n",
" \n",
"# return tot\n",
"\n",
"# def jpsi_res(q):\n",
"# return resonance(q, jpsi_m, jpsi_s, jpsi_p, jpsi_w)\n",
"\n",
"# calcs = zfit.run(total_test_tf(x_part))\n",
"\n",
"test_q = np.linspace(x_min, x_max, int(2e6))\n",
"\n",
"probs = total_f_fit.pdf(test_q, norm_range=False)\n",
"\n",
"calcs_test = zfit.run(probs)\n",
"# res_y = zfit.run(jpsi_res(test_q))\n",
"# b0 = [b0_0, b0_1, b0_2]\n",
"# bplus = [bplus_0, bplus_1, bplus_2]\n",
"# bT = [bT_0, bT_1, bT_2]\n",
"# f0_y = zfit.run(tf.math.real(formfactor(test_q,\"0\", b0, bplus, bT)))\n",
"# fplus_y = zfit.run(tf.math.real(formfactor(test_q,\"+\", b0, bplus, bT)))\n",
"# fT_y = zfit.run(tf.math.real(formfactor(test_q,\"T\", b0, bplus, bT)))"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\ipykernel_launcher.py:12: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
" if sys.path[0] == '':\n",
"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\IPython\\core\\pylabtools.py:128: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
" fig.canvas.print_figure(bytes_io, **kw)\n"
]
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plt.clf()\n",
"# plt.plot(x_part, calcs, '.')\n",
"plt.plot(test_q, calcs_test, label = 'pdf')\n",
"# plt.plot(test_q, f0_y, label = '0')\n",
"# plt.plot(test_q, fT_y, label = 'T')\n",
"# plt.plot(test_q, fplus_y, label = '+')\n",
"# plt.plot(test_q, res_y, label = 'res')\n",
"plt.legend()\n",
"plt.ylim(0.0, 1.5e-6)\n",
"# plt.yscale('log')\n",
"# plt.xlim(770, 785)\n",
"plt.savefig('test.png')\n",
"# print(jpsi_width)"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [],
"source": [
"\n",
"\n",
"# probs = mixture.prob(test_q)\n",
"# probs_np = zfit.run(probs)\n",
"# probs_np *= np.max(calcs_test) / np.max(probs_np)\n",
"# plt.figure()\n",
"# plt.semilogy(test_q, probs_np,label=\"importance sampling\")\n",
"# plt.semilogy(test_q, calcs_test, label = 'pdf')\n"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [],
"source": [
"# 0.213/(0.00133+0.213+0.015)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Adjust scaling of different parts"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [],
"source": [
"total_f.update_integration_options(draws_per_dim=2000000, mc_sampler=None)\n",
"# inte = total_f.integrate(limits = (950., 1050.), norm_range=False)\n",
"# inte_fl = zfit.run(inte)\n",
"# print(inte_fl/4500)\n",
"# print(pdg[\"jpsi_BR\"]/pdg[\"NR_BR\"], inte_fl*pdg[\"psi2s_auc\"]/pdg[\"NR_auc\"])"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [],
"source": [
"# # print(\"jpsi:\", inte_fl)\n",
"# # print(\"Increase am by factor:\", np.sqrt(pdg[\"jpsi_BR\"]/pdg[\"NR_BR\"]*pdg[\"NR_auc\"]/inte_fl))\n",
"# # print(\"New amp:\", pdg[\"jpsi\"][3]*np.sqrt(pdg[\"jpsi_BR\"]/pdg[\"NR_BR\"]*pdg[\"NR_auc\"]/inte_fl))\n",
"\n",
"# # print(\"psi2s:\", inte_fl)\n",
"# # print(\"Increase am by factor:\", np.sqrt(pdg[\"psi2s_BR\"]/pdg[\"NR_BR\"]*pdg[\"NR_auc\"]/inte_fl))\n",
"# # print(\"New amp:\", pdg[\"psi2s\"][3]*np.sqrt(pdg[\"psi2s_BR\"]/pdg[\"NR_BR\"]*pdg[\"NR_auc\"]/inte_fl))\n",
"\n",
"# name = \"phi\"\n",
"\n",
"# print(name+\":\", inte_fl)\n",
"# print(\"Increase am by factor:\", np.sqrt(pdg[name+\"_BR\"]/pdg[\"NR_BR\"]*pdg[\"NR_auc\"]/inte_fl))\n",
"# print(\"New amp:\", pdg[name][0]*np.sqrt(pdg[name+\"_BR\"]/pdg[\"NR_BR\"]*pdg[\"NR_auc\"]/inte_fl))\n",
"\n",
"\n",
"# print(x_min)\n",
"# print(x_max)\n",
"# # total_f.update_integration_options(draws_per_dim=2000000, mc_sampler=None)\n",
"# total_f.update_integration_options(mc_sampler=lambda dim, num_results,\n",
"# dtype: tf.random_uniform(maxval=1., shape=(num_results, dim), dtype=dtype),\n",
"# draws_per_dim=1000000)\n",
"# # _ = []\n",
"\n",
"# # for i in range(10):\n",
"\n",
"# # inte = total_f.integrate(limits = (x_min, x_max))\n",
"# # inte_fl = zfit.run(inte)\n",
"# # print(inte_fl)\n",
"# # _.append(inte_fl)\n",
"\n",
"# # print(\"mean:\", np.mean(_))\n",
"\n",
"# _ = time.time()\n",
"\n",
"# inte = total_f.integrate(limits = (x_min, x_max))\n",
"# inte_fl = zfit.run(inte)\n",
"# print(inte_fl)\n",
"# print(\"Time taken: {}\".format(display_time(int(time.time() - _))))\n",
"\n",
"# print(pdg['NR_BR']/pdg['NR_auc']*inte_fl)\n",
"# print(0.25**2*4.2/1000)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Sampling\n",
"## Mixture distribution for sampling"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [],
"source": [
"\n",
" \n",
"# print(list_of_borders[:9])\n",
"# print(list_of_borders[-9:])\n",
"\n",
"\n",
"class UniformSampleAndWeights(zfit.util.execution.SessionHolderMixin):\n",
" def __call__(self, limits, dtype, n_to_produce):\n",
" # n_to_produce = tf.cast(n_to_produce, dtype=tf.int32)\n",
" low, high = limits.limit1d\n",
" low = tf.cast(low, dtype=dtype)\n",
" high = tf.cast(high, dtype=dtype)\n",
"# uniform = tfd.Uniform(low=low, high=high)\n",
"# uniformjpsi = tfd.Uniform(low=tf.constant(3080, dtype=dtype), high=tf.constant(3112, dtype=dtype))\n",
"# uniformpsi2s = tfd.Uniform(low=tf.constant(3670, dtype=dtype), high=tf.constant(3702, dtype=dtype))\n",
"\n",
"# list_of_borders = []\n",
"# _p = []\n",
"# splits = 10\n",
"\n",
"# _ = np.linspace(x_min, x_max, splits)\n",
"\n",
"# for i in range(splits):\n",
"# list_of_borders.append(tf.constant(_[i], dtype=dtype))\n",
"# _p.append(tf.constant(1/splits, dtype=dtype))\n",
" \n",
"# mixture = tfd.MixtureSameFamily(mixture_distribution=tfd.Categorical(probs=_p[:(splits-1)]),\n",
"# components_distribution=tfd.Uniform(low=list_of_borders[:(splits-1)], \n",
"# high=list_of_borders[-(splits-1):]))\n",
" mixture = tfd.MixtureSameFamily(mixture_distribution=tfd.Categorical(probs=[tf.constant(0.05, dtype=dtype),\n",
" tf.constant(0.93, dtype=dtype),\n",
" tf.constant(0.05, dtype=dtype),\n",
" tf.constant(0.065, dtype=dtype),\n",
" tf.constant(0.04, dtype=dtype),\n",
" tf.constant(0.05, dtype=dtype)]),\n",
" components_distribution=tfd.Uniform(low=[tf.constant(x_min, dtype=dtype), \n",
" tf.constant(3090, dtype=dtype),\n",
" tf.constant(3681, dtype=dtype), \n",
" tf.constant(3070, dtype=dtype),\n",
" tf.constant(1000, dtype=dtype),\n",
" tf.constant(3660, dtype=dtype)], \n",
" high=[tf.constant(x_max, dtype=dtype),\n",
" tf.constant(3102, dtype=dtype), \n",
" tf.constant(3691, dtype=dtype),\n",
" tf.constant(3110, dtype=dtype),\n",
" tf.constant(1040, dtype=dtype),\n",
" tf.constant(3710, dtype=dtype)]))\n",
"# dtype = tf.float64\n",
"# mixture = tfd.MixtureSameFamily(mixture_distribution=tfd.Categorical(probs=[tf.constant(0.04, dtype=dtype),\n",
"# tf.constant(0.90, dtype=dtype),\n",
"# tf.constant(0.02, dtype=dtype),\n",
"# tf.constant(0.07, dtype=dtype),\n",
"# tf.constant(0.02, dtype=dtype)]),\n",
"# components_distribution=tfd.Uniform(low=[tf.constant(x_min, dtype=dtype), \n",
"# tf.constant(3089, dtype=dtype),\n",
"# tf.constant(3103, dtype=dtype), \n",
"# tf.constant(3681, dtype=dtype),\n",
"# tf.constant(3691, dtype=dtype)], \n",
"# high=[tf.constant(3089, dtype=dtype),\n",
"# tf.constant(3103, dtype=dtype), \n",
"# tf.constant(3681, dtype=dtype),\n",
"# tf.constant(3691, dtype=dtype), \n",
"# tf.constant(x_max, dtype=dtype)]))\n",
"# mixture = tfd.Uniform(tf.constant(x_min, dtype=dtype), tf.constant(x_max, dtype=dtype))\n",
"# sample = tf.random.uniform((n_to_produce, 1), dtype=dtype)\n",
" sample = mixture.sample((n_to_produce, 1))\n",
"# sample = tf.random.uniform((n_to_produce, 1), dtype=dtype)\n",
" weights = mixture.prob(sample)[:,0]\n",
"# weights = tf.broadcast_to(tf.constant(1., dtype=dtype), shape=(n_to_produce,))\n",
" # sample = tf.expand_dims(sample, axis=-1)\n",
"# print(sample, weights)\n",
" \n",
"# weights = tf.ones(shape=(n_to_produce,), dtype=dtype)\n",
" weights_max = None\n",
" thresholds = tf.random_uniform(shape=(n_to_produce,), dtype=dtype)\n",
" return sample, thresholds, weights, weights_max, n_to_produce"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [],
"source": [
"# total_f._sample_and_weights = UniformSampleAndWeights"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [],
"source": [
"# 0.00133/(0.00133+0.213+0.015)*(x_max-3750)/(x_max-x_min)"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [],
"source": [
"# zfit.settings.set_verbosity(10)"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"scrolled": false
},
"outputs": [],
"source": [
"# # zfit.run.numeric_checks = False \n",
"\n",
"# nr_of_toys = 1\n",
"# nevents = int(pdg[\"number_of_decays\"])\n",
"# nevents = pdg[\"number_of_decays\"]\n",
"# event_stack = 1000000\n",
"# # zfit.settings.set_verbosity(10)\n",
"# calls = int(nevents/event_stack + 1)\n",
"\n",
"# total_samp = []\n",
"\n",
"# start = time.time()\n",
"\n",
"# sampler = total_f.create_sampler(n=event_stack)\n",
"\n",
"# for toy in range(nr_of_toys):\n",
" \n",
"# dirName = 'data/zfit_toys/toy_{0}'.format(toy)\n",
" \n",
"# if not os.path.exists(dirName):\n",
"# os.mkdir(dirName)\n",
"# print(\"Directory \" , dirName , \" Created \")\n",
"\n",
"# for call in range(calls):\n",
"\n",
"# sampler.resample(n=event_stack)\n",
"# s = sampler.unstack_x()\n",
"# sam = zfit.run(s)\n",
"# # clear_output(wait=True)\n",
"\n",
"# c = call + 1\n",
" \n",
"# print(\"{0}/{1} of Toy {2}/{3}\".format(c, calls, toy+1, nr_of_toys))\n",
"# print(\"Time taken: {}\".format(display_time(int(time.time() - start))))\n",
"# print(\"Projected time left: {}\".format(display_time(int((time.time() - start)/(c+calls*(toy))*((nr_of_toys-toy)*calls-c)))))\n",
"\n",
"# with open(\"data/zfit_toys/toy_{0}/{1}.pkl\".format(toy, call), \"wb\") as f:\n",
"# pkl.dump(sam, f, pkl.HIGHEST_PROTOCOL)"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [],
"source": [
"# with open(r\"data/zfit_toys/toy_0/0.pkl\", \"rb\") as input_file:\n",
"# sam = pkl.load(input_file)\n",
"# print(sam[:10])\n",
"\n",
"# with open(r\"data/zfit_toys/toy_0/1.pkl\", \"rb\") as input_file:\n",
"# sam2 = pkl.load(input_file)\n",
"# print(sam2[:10])\n",
"\n",
"# print(np.sum(sam-sam2))"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [],
"source": [
"# print(\"Time to generate full toy: {} s\".format(int(time.time()-start)))\n",
"\n",
"# total_samp = []\n",
"\n",
"# for call in range(calls):\n",
"# with open(r\"data/zfit_toys/toy_0/{}.pkl\".format(call), \"rb\") as input_file:\n",
"# sam = pkl.load(input_file)\n",
"# total_samp = np.append(total_samp, sam)\n",
"\n",
"# total_samp = total_samp.astype('float64')\n",
"\n",
"# data2 = zfit.data.Data.from_numpy(array=total_samp[:int(nevents)], obs=obs)\n",
"\n",
"# data3 = zfit.data.Data.from_numpy(array=total_samp, obs=obs)\n",
"\n",
"# print(total_samp[:nevents].shape)"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [],
"source": [
"# plt.clf()\n",
"\n",
"# bins = int((x_max-x_min)/7)\n",
"\n",
"# # calcs = zfit.run(total_test_tf(samp))\n",
"# print(total_samp[:nevents].shape)\n",
"\n",
"# plt.hist(total_samp[:nevents], bins = bins, range = (x_min,x_max), label = 'data')\n",
"# # plt.plot(test_q, calcs_test*nevents , label = 'pdf')\n",
"\n",
"# # plt.plot(sam, calcs, '.')\n",
"# # plt.plot(test_q, calcs_test)\n",
"# # plt.yscale('log')\n",
"# plt.ylim(0, 200)\n",
"# # plt.xlim(3080, 3110)\n",
"\n",
"# plt.legend()\n",
"\n",
"# plt.savefig('test2.png')"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [],
"source": [
"# sampler = total_f.create_sampler(n=nevents)\n",
"# nll = zfit.loss.UnbinnedNLL(model=total_f, data=sampler, fit_range = (x_min, x_max))\n",
"\n",
"# # for param in pdf.get_dependents():\n",
"# # param.set_value(initial_value)\n",
"\n",
"# sampler.resample(n=nevents)\n",
"\n",
"# # Randomise initial values\n",
"# # for param in pdf.get_dependents():\n",
"# # param.set_value(random value here)\n",
"\n",
"# # Minimise the NLL\n",
"# minimizer = zfit.minimize.MinuitMinimizer(verbosity = 10)\n",
"# minimum = minimizer.minimize(nll)"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [],
"source": [
"# jpsi_width"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [],
"source": [
"# plt.hist(sample, weights=1 / prob(sample))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Fitting"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {},
"outputs": [],
"source": [
"# start = time.time()\n",
"\n",
"# for param in total_f.get_dependents():\n",
"# param.randomize()\n",
" \n",
"# # for param in total_f.get_dependents():\n",
"# # print(zfit.run(param))\n",
" \n",
"# nll = zfit.loss.UnbinnedNLL(model=total_f, data=data2, fit_range = (x_min, x_max))\n",
"\n",
"# minimizer = zfit.minimize.MinuitMinimizer(verbosity = 5)\n",
"# # minimizer._use_tfgrad = False\n",
"# result = minimizer.minimize(nll)\n",
"\n",
"# # param_errors = result.error()\n",
"\n",
"# # for var, errors in param_errors.items():\n",
"# # print('{}: ^{{+{}}}_{{{}}}'.format(var.name, errors['upper'], errors['lower']))\n",
"\n",
"# print(\"Function minimum:\", result.fmin)\n",
"# # print(\"Results:\", result.params)\n",
"# print(\"Hesse errors:\", result.hesse())"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {},
"outputs": [],
"source": [
"# print(\"Time taken for fitting: {}\".format(display_time(int(time.time()-start))))\n",
"\n",
"# # probs = total_f.pdf(test_q)\n",
"\n",
"# calcs_test = zfit.run(probs)\n",
"# res_y = zfit.run(jpsi_res(test_q))"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {},
"outputs": [],
"source": [
"# plt.clf()\n",
"# # plt.plot(x_part, calcs, '.')\n",
"# plt.plot(test_q, calcs_test, label = 'pdf')\n",
"# # plt.plot(test_q, res_y, label = 'res')\n",
"# plt.legend()\n",
"# plt.ylim(0.0, 10e-6)\n",
"# # plt.yscale('log')\n",
"# # plt.xlim(3080, 3110)\n",
"# plt.savefig('test3.png')\n",
"# # print(jpsi_width)"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {},
"outputs": [],
"source": [
"# _tot = 4.37e-7+6.02e-5+4.97e-6\n",
"# _probs = []\n",
"# _probs.append(6.02e-5/_tot)\n",
"# _probs.append(4.97e-6/_tot)\n",
"# _probs.append(4.37e-7/_tot)\n",
"# print(_probs)"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {},
"outputs": [],
"source": [
"# dtype = 'float64'\n",
"# # mixture = tfd.Uniform(tf.constant(x_min, dtype=dtype), tf.constant(x_max, dtype=dtype))\n",
"# mixture = tfd.MixtureSameFamily(mixture_distribution=tfd.Categorical(probs=[tf.constant(0.007, dtype=dtype),\n",
"# tf.constant(0.917, dtype=dtype),\n",
"# tf.constant(0.076, dtype=dtype)]),\n",
"# components_distribution=tfd.Uniform(low=[tf.constant(x_min, dtype=dtype), \n",
"# tf.constant(3080, dtype=dtype),\n",
"# tf.constant(3670, dtype=dtype)], \n",
"# high=[tf.constant(x_max, dtype=dtype),\n",
"# tf.constant(3112, dtype=dtype), \n",
"# tf.constant(3702, dtype=dtype)]))\n",
"# # for i in range(10):\n",
"# # print(zfit.run(mixture.prob(mixture.sample((10, 1)))))"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {},
"outputs": [],
"source": [
"# print((zfit.run(jpsi_p)%(2*np.pi))/np.pi)\n",
"# print((zfit.run(psi2s_p)%(2*np.pi))/np.pi)"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {},
"outputs": [],
"source": [
"# def jpsi_res(q):\n",
"# return resonance(q, _mass = jpsi_mass, scale = jpsi_scale,\n",
"# phase = jpsi_phase, width = jpsi_width)\n",
"\n",
"# def psi2s_res(q):\n",
"# return resonance(q, _mass = psi2s_mass, scale = psi2s_scale,\n",
"# phase = psi2s_phase, width = psi2s_width)\n",
" \n",
"# def p3770_res(q):\n",
"# return resonance(q, _mass = p3770_mass, scale = p3770_scale,\n",
"# phase = p3770_phase, width = p3770_width)\n",
" \n",
"# def p4040_res(q):\n",
"# return resonance(q, _mass = p4040_mass, scale = p4040_scale,\n",
"# phase = p4040_phase, width = p4040_width)\n",
" \n",
"# def p4160_res(q):\n",
"# return resonance(q, _mass = p4160_mass, scale = p4160_scale,\n",
"# phase = p4160_phase, width = p4160_width)\n",
" \n",
"# def p4415_res(q):\n",
"# return resonance(q, _mass = p4415_mass, scale = p4415_scale,\n",
"# phase = p4415_phase, width = p4415_width)"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {},
"outputs": [],
"source": [
"# 0.15**2*4.2/1000\n",
"# result.hesse()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Constraints"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {},
"outputs": [],
"source": [
"# 1. Constraint - Real part of sum of Psi contrib and D contribs\n",
"\n",
"sum_list = []\n",
"\n",
"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))))\n",
"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))))\n",
"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))))\n",
"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))))\n",
"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))))\n",
"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))))\n",
"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)))))\n",
"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))))\n",
"\n",
"sum_ru_1 = ztf.to_complex(ztf.constant(0.0))\n",
"\n",
"for part in sum_list:\n",
" sum_ru_1 += part\n",
"\n",
"sum_1 = tf.math.real(sum_ru_1)\n",
"# constraint1 = zfit.constraint.GaussianConstraint(params = sum_1, mu = ztf.constant(1.7*10**-8), \n",
"# sigma = ztf.constant(2.2*10**-8))\n",
"\n",
"constraint1 = tf.pow((sum_1-ztf.constant(1.7*10**-8))/ztf.constant(2.2*10**-8),2)/ztf.constant(2.)\n",
"\n",
"# 2. Constraint - Abs. of sum of Psi contribs and D contribs\n",
"\n",
"sum_2 = tf.abs(sum_ru_1)\n",
"constraint2 = tf.cond(tf.greater_equal(sum_2, 5.0e-8), lambda: 100000., lambda: 0.)\n",
"\n",
"# 3. Constraint - Maximum eta of D contribs\n",
"\n",
"constraint3_0 = tf.cond(tf.greater_equal(tf.abs(Dbar_s), 0.2), lambda: 100000., lambda: 0.)\n",
"\n",
"constraint3_1 = tf.cond(tf.greater_equal(tf.abs(DDstar_s), 0.2), lambda: 100000., lambda: 0.)\n",
"\n",
"# 4. Constraint - Formfactor multivariant gaussian covariance fplus\n",
"\n",
"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)],\n",
" [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)],\n",
" [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)],\n",
" [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)],\n",
" [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)],\n",
" [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)],\n",
" [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)],\n",
" [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)],\n",
" [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.)]]\n",
"\n",
"def triGauss(val1,val2,val3,m = Cov_matrix):\n",
"\n",
" mean1 = ztf.constant(0.466)\n",
" mean2 = ztf.constant(-0.885)\n",
" mean3 = ztf.constant(-0.213)\n",
" sigma1 = ztf.constant(0.014)\n",
" sigma2 = ztf.constant(0.128)\n",
" sigma3 = ztf.constant(0.548)\n",
" x1 = (val1-mean1)/sigma1\n",
" x2 = (val2-mean2)/sigma2\n",
" x3 = (val3-mean3)/sigma3\n",
" rho12 = m[0][1]\n",
" rho13 = m[0][2]\n",
" rho23 = m[1][2]\n",
" 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))\n",
" d = 2*(rho12*rho12+rho13*rho13+rho23*rho23-2*rho12*rho13*rho23-1)\n",
" \n",
" fcn = -w/d\n",
" chisq = -2*fcn\n",
" return chisq\n",
"\n",
"constraint4 = triGauss(bplus_0, bplus_1, bplus_2)\n",
"\n",
"# mean1 = ztf.constant(0.466)\n",
"# mean2 = ztf.constant(-0.885)\n",
"# mean3 = ztf.constant(-0.213)\n",
"# sigma1 = ztf.constant(0.014)\n",
"# sigma2 = ztf.constant(0.128)\n",
"# sigma3 = ztf.constant(0.548)\n",
"# constraint4_0 = tf.pow((bplus_0-mean1)/sigma1,2)/ztf.constant(2.)\n",
"# constraint4_1 = tf.pow((bplus_1-mean2)/sigma2,2)/ztf.constant(2.)\n",
"# constraint4_2 = tf.pow((bplus_2-mean3)/sigma3,2)/ztf.constant(2.)\n",
"\n",
"# 5. Constraint - Abs. of sum of light contribs\n",
"\n",
"sum_list_5 = []\n",
"\n",
"sum_list_5.append(rho_s*rho_w/rho_m)\n",
"sum_list_5.append(omega_s*omega_w/omega_m)\n",
"sum_list_5.append(phi_s*phi_w/phi_m)\n",
"\n",
"\n",
"sum_ru_5 = ztf.constant(0.0)\n",
"\n",
"for part in sum_list_5:\n",
" sum_ru_5 += part\n",
"\n",
"constraint5 = tf.cond(tf.greater_equal(tf.abs(sum_ru_5), ztf.constant(0.02)), lambda: 100000., lambda: 0.)\n",
"\n",
"# 6. Constraint on phases of Jpsi and Psi2s for cut out fit\n",
"\n",
"\n",
"# constraint6_0 = zfit.constraint.GaussianConstraint(params = jpsi_p, mu = ztf.constant(pdg[\"jpsi_phase_unc\"]),\n",
"# sigma = ztf.constant(jpsi_phase))\n",
"# constraint6_1 = zfit.constraint.GaussianConstraint(params = psi2s_p, mu = ztf.constant(pdg[\"psi2s_phase_unc\"]),\n",
"# sigma = ztf.constant(psi2s_phase))\n",
"\n",
"constraint6_0 = tf.pow((jpsi_p-ztf.constant(jpsi_phase))/ztf.constant(pdg[\"jpsi_phase_unc\"]),2)/ztf.constant(2.)\n",
"constraint6_1 = tf.pow((psi2s_p-ztf.constant(psi2s_phase))/ztf.constant(pdg[\"psi2s_phase_unc\"]),2)/ztf.constant(2.)\n",
"\n",
"# 7. Constraint on Ctt with higher limits\n",
"\n",
"constraint7 = tf.cond(tf.greater_equal(Ctt*Ctt, 0.25), lambda: 100000., lambda: 0.)\n",
"\n",
"constraint7dtype = tf.float64\n",
"\n",
"# zfit.run(constraint6_0)\n",
"\n",
"# ztf.convert_to_tensor(constraint6_0)\n",
"\n",
"#List of all constraints\n",
"\n",
"constraints = [constraint1, constraint2, constraint3_0, constraint3_1,# constraint4, #constraint4_0, constraint4_1, constraint4_2,\n",
" constraint6_0, constraint6_1]#, constraint7]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Reset params"
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {},
"outputs": [],
"source": [
"def reset_param_values(): \n",
" jpsi_m.set_value(jpsi_mass)\n",
" jpsi_s.set_value(jpsi_scale)\n",
" jpsi_p.set_value(jpsi_phase)\n",
" jpsi_w.set_value(jpsi_width)\n",
" psi2s_m.set_value(psi2s_mass)\n",
" psi2s_s.set_value(psi2s_scale)\n",
" psi2s_p.set_value(psi2s_phase)\n",
" psi2s_w.set_value(psi2s_width)\n",
" p3770_m.set_value(p3770_mass)\n",
" p3770_s.set_value(p3770_scale)\n",
" p3770_p.set_value(p3770_phase)\n",
" p3770_w.set_value(p3770_width)\n",
" p4040_m.set_value(p4040_mass)\n",
" p4040_s.set_value(p4040_scale)\n",
" p4040_p.set_value(p4040_phase)\n",
" p4040_w.set_value(p4040_width)\n",
" p4160_m.set_value(p4160_mass)\n",
" p4160_s.set_value(p4160_scale)\n",
" p4160_p.set_value(p4160_phase)\n",
" p4160_w.set_value(p4160_width)\n",
" p4415_m.set_value(p4415_mass)\n",
" p4415_s.set_value(p4415_scale)\n",
" p4415_p.set_value(p4415_phase)\n",
" p4415_w.set_value(p4415_width)\n",
" rho_m.set_value(rho_mass)\n",
" rho_s.set_value(rho_scale)\n",
" rho_p.set_value(rho_phase)\n",
" rho_w.set_value(rho_width)\n",
" omega_m.set_value(omega_mass)\n",
" omega_s.set_value(omega_scale)\n",
" omega_p.set_value(omega_phase)\n",
" omega_w.set_value(omega_width)\n",
" phi_m.set_value(phi_mass)\n",
" phi_s.set_value(phi_scale)\n",
" phi_p.set_value(phi_phase)\n",
" phi_w.set_value(phi_width)\n",
" Dstar_m.set_value(Dstar_mass)\n",
" DDstar_s.set_value(0.0)\n",
" DDstar_p.set_value(0.0)\n",
" D_m.set_value(D_mass)\n",
" Dbar_m.set_value(Dbar_mass)\n",
" Dbar_s.set_value(0.0)\n",
" Dbar_p.set_value(0.0)\n",
" tau_m.set_value(pdg['tau_M'])\n",
" Ctt.set_value(0.0)\n",
" b0_0.set_value(0.292)\n",
" b0_1.set_value(0.281)\n",
" b0_2.set_value(0.150)\n",
" bplus_0.set_value(0.466)\n",
" bplus_1.set_value(-0.885)\n",
" bplus_2.set_value(-0.213)\n",
" bT_0.set_value(0.460)\n",
" bT_1.set_value(-1.089)\n",
" bT_2.set_value(-1.114)\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Analysis"
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {
"scrolled": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"WARNING:tensorflow:From C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\zfit\\core\\sample.py:163: to_int32 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\n",
"Instructions for updating:\n",
"Use tf.cast instead.\n",
"Toy 0: Generating data...\n",
"Toy 0: Data generation finished\n",
"Toy 0: Loading data...\n",
"Toy 0: Loading data finished\n",
"Toy 0: Fitting pdf...\n",
"------------------------------------------------------------------\n",
"| FCN = 2.977E+05 | Ncalls=1160 (1160 total) |\n",
"| EDM = 7.03E-05 (Goal: 5E-06) | up = 0.5 |\n",
"------------------------------------------------------------------\n",
"| Valid Min. | Valid Param. | Above EDM | Reached call limit |\n",
"------------------------------------------------------------------\n",
"| True | True | False | False |\n",
"------------------------------------------------------------------\n",
"| Hesse failed | Has cov. | Accurate | Pos. def. | Forced |\n",
"------------------------------------------------------------------\n",
"| False | True | True | True | False |\n",
"------------------------------------------------------------------\n",
"Function minimum: 297698.8532113871\n",
"----------------------------------------------------------------------------------------------\n",
"| | Name | Value | Hesse Err | Minos Err- | Minos Err+ | Limit- | Limit+ | Fixed |\n",
"----------------------------------------------------------------------------------------------\n",
"| 0 | rho_p | -0.4 | 0.5 | | |-6.28319 | 6.28319 | |\n",
"| 1 | omega_s | 8.1 | 1.8 | | | 4.19232 | 9.40768 | |\n",
"| 2 | omega_p | 0.7 | 0.5 | | |-6.28319 | 6.28319 | |\n",
"| 3 | rho_s | 0.7 | 0.3 | | |0.0253049| 2.0747 | |\n",
"| 4 | phi_s | 18.7 | 1.5 | | | 14.8182 | 23.5818 | |\n",
"| 5 | p3770_s | 1.70 | 0.26 | | |0.918861 | 4.08114 | |\n",
"| 6 | jpsi_p | 1.711 | 0.023 | | |-6.28319 | 6.28319 | |\n",
"| 7 | phi_p | -5.7 | 0.3 | | |-6.28319 | 6.28319 | |\n",
"| 8 | Ctt | 0.52 | 0.14 | | | -1 | 1 | |\n",
"| 9 | p3770_p | -3.20 | 0.15 | | |-6.28319 | 6.28319 | |\n",
"| 10| p4415_p | -2.68 | 0.15 | | |-6.28319 | 6.28319 | |\n",
"| 11| p4415_s | 1.39 | 0.19 | | |0.126447 | 2.35355 | |\n",
"| 12| DDstar_s | -0.300 | 0.022 | | | -0.3 | 0.3 | |\n",
"| 13| p4040_p | 3.28 | 0.15 | | |-6.28319 | 6.28319 | |\n",
"| 14| DDstar_p | -1.40 | 0.23 | | |-6.28319 | 6.28319 | |\n",
"| 15| psi2s_p | 1.94 | 0.03 | | |-6.28319 | 6.28319 | |\n",
"| 16| Dbar_s | -0.300 | 0.017 | | | -0.3 | 0.3 | |\n",
"| 17| p4160_s | 2.13 | 0.17 | | | 0.71676 | 3.68324 | |\n",
"| 18| p4160_p | 4.02 | 0.10 | | |-6.28319 | 6.28319 | |\n",
"| 19| p4040_s | 1.16 | 0.19 | | |0.00501244| 2.01499 | |\n",
"| 20| Dbar_p | -1.19 | 0.27 | | |-6.28319 | 6.28319 | |\n",
"----------------------------------------------------------------------------------------------\n",
"-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\n",
"| | rho_p omega_s omega_p rho_s phi_s p3770_s jpsi_p phi_p Ctt p3770_p p4415_p p4415_s DDstar_s p4040_p DDstar_p psi2s_p Dbar_s p4160_s p4160_p p4040_s Dbar_p |\n",
"-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\n",
"| rho_p | 1.000 0.085 0.030 0.181 -0.178 0.033 0.061 -0.248 -0.133 -0.032 0.009 -0.016 -0.001 0.013 0.128 -0.009 -0.004 -0.002 0.007 -0.006 0.030 |\n",
"| omega_s | 0.085 1.000 0.881 0.036 -0.017 -0.003 -0.033 -0.071 0.005 -0.006 -0.004 0.001 -0.000 -0.006 -0.001 -0.005 -0.000 -0.000 -0.007 0.002 0.000 |\n",
"| omega_p | 0.030 0.881 1.000 0.299 0.028 -0.004 -0.033 -0.006 0.007 -0.006 -0.005 0.001 -0.000 -0.007 -0.002 -0.005 -0.000 -0.002 -0.007 0.002 0.000 |\n",
"| rho_s | 0.181 0.036 0.299 1.000 -0.008 -0.004 -0.011 0.017 -0.003 -0.009 -0.004 -0.006 0.001 -0.007 0.010 0.001 0.000 -0.008 -0.002 -0.002 0.009 |\n",
"| phi_s | -0.178 -0.017 0.028 -0.008 1.000 -0.021 -0.076 0.857 0.075 0.008 -0.010 0.009 0.000 -0.015 -0.067 -0.002 0.002 -0.001 -0.013 0.005 -0.015 |\n",
"| p3770_s | 0.033 -0.003 -0.004 -0.004 -0.021 1.000 0.048 -0.016 0.035 -0.383 -0.036 0.054 -0.011 -0.093 0.035 -0.253 -0.008 0.048 -0.137 0.186 0.166 |\n",
"| jpsi_p | 0.061 -0.033 -0.033 -0.011 -0.076 0.048 1.000 -0.059 -0.399 0.012 -0.023 -0.096 -0.053 -0.036 0.224 -0.005 -0.046 -0.111 0.070 -0.047 -0.145 |\n",
"| phi_p | -0.248 -0.071 -0.006 0.017 0.857 -0.016 -0.059 1.000 0.057 0.007 -0.008 0.008 -0.000 -0.011 -0.052 -0.003 0.001 0.001 -0.010 0.005 -0.014 |\n",
"| Ctt | -0.133 0.005 0.007 -0.003 0.075 0.035 -0.399 0.057 1.000 -0.297 -0.071 0.336 0.006 -0.124 -0.137 0.305 -0.004 0.291 -0.437 0.418 0.439 |\n",
"| p3770_p | -0.032 -0.006 -0.006 -0.009 0.008 -0.383 0.012 0.007 -0.297 1.000 0.043 -0.127 -0.024 0.103 -0.061 -0.213 -0.035 -0.095 0.195 -0.150 -0.165 |\n",
"| p4415_p | 0.009 -0.004 -0.005 -0.004 -0.010 -0.036 -0.023 -0.008 -0.071 0.043 1.000 -0.196 -0.034 0.067 0.067 -0.098 -0.016 -0.106 0.262 -0.204 0.071 |\n",
"| p4415_s | -0.016 0.001 0.001 -0.006 0.009 0.054 -0.096 0.008 0.336 -0.127 -0.196 1.000 0.014 0.082 0.252 0.076 0.006 0.372 -0.145 0.189 0.004 |\n",
"| DDstar_s | -0.001 -0.000 -0.000 0.001 0.000 -0.011 -0.053 -0.000 0.006 -0.024 -0.034 0.014 1.000 -0.034 0.022 -0.023 -0.002 0.003 -0.053 0.013 -0.011 |\n",
"| p4040_p | 0.013 -0.006 -0.007 -0.007 -0.015 -0.093 -0.036 -0.011 -0.124 0.103 0.067 0.082 -0.034 1.000 0.011 -0.226 -0.024 0.328 -0.045 -0.219 0.111 |\n",
"| DDstar_p | 0.128 -0.001 -0.002 0.010 -0.067 0.035 0.224 -0.052 -0.137 -0.061 0.067 0.252 0.022 0.011 1.000 0.072 -0.011 0.237 -0.210 0.238 -0.665 |\n",
"| psi2s_p | -0.009 -0.005 -0.005 0.001 -0.002 -0.253 -0.005 -0.003 0.305 -0.213 -0.098 0.076 -0.023 -0.226 0.072 1.000 -0.024 -0.030 -0.184 0.145 0.054 |\n",
"| Dbar_s | -0.004 -0.000 -0.000 0.000 0.002 -0.008 -0.046 0.001 -0.004 -0.035 -0.016 0.006 -0.002 -0.024 -0.011 -0.024 1.000 0.005 -0.032 0.012 0.015 |\n",
"| p4160_s | -0.002 -0.000 -0.002 -0.008 -0.001 0.048 -0.111 0.001 0.291 -0.095 -0.106 0.372 0.003 0.328 0.237 -0.030 0.005 1.000 -0.245 -0.049 0.121 |\n",
"| p4160_p | 0.007 -0.007 -0.007 -0.002 -0.013 -0.137 0.070 -0.010 -0.437 0.195 0.262 -0.145 -0.053 -0.045 -0.210 -0.184 -0.032 -0.245 1.000 -0.530 0.000 |\n",
"| p4040_s | -0.006 0.002 0.002 -0.002 0.005 0.186 -0.047 0.005 0.418 -0.150 -0.204 0.189 0.013 -0.219 0.238 0.145 0.012 -0.049 -0.530 1.000 0.029 |\n",
"| Dbar_p | 0.030 0.000 0.000 0.009 -0.015 0.166 -0.145 -0.014 0.439 -0.165 0.071 0.004 -0.011 0.111 -0.665 0.054 0.015 0.121 0.000 0.029 1.000 |\n",
"-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\n",
"Hesse errors: OrderedDict([(<zfit.Parameter 'rho_p' floating=True>, {'error': 0.4837472298559913}), (<zfit.Parameter 'omega_s' floating=True>, {'error': 1.8230423175976291}), (<zfit.Parameter 'omega_p' floating=True>, {'error': 0.452729301427385}), (<zfit.Parameter 'rho_s' floating=True>, {'error': 0.31705121493663646}), (<zfit.Parameter 'phi_s' floating=True>, {'error': 1.4871082055111255}), (<zfit.Parameter 'p3770_s' floating=True>, {'error': 0.2631626205737271}), (<zfit.Parameter 'jpsi_p' floating=True>, {'error': 0.023395072427270236}), (<zfit.Parameter 'phi_p' floating=True>, {'error': 0.34291663689305096}), (<zfit.Parameter 'Ctt' floating=True>, {'error': 0.1382524227983245}), (<zfit.Parameter 'p3770_p' floating=True>, {'error': 0.15454495067808272}), (<zfit.Parameter 'p4415_p' floating=True>, {'error': 0.14765622133701495}), (<zfit.Parameter 'p4415_s' floating=True>, {'error': 0.19125316798637082}), (<zfit.Parameter 'DDstar_s' floating=True>, {'error': 0.02194924057582351}), (<zfit.Parameter 'p4040_p' floating=True>, {'error': 0.14881866482336825}), (<zfit.Parameter 'DDstar_p' floating=True>, {'error': 0.2284167183553456}), (<zfit.Parameter 'psi2s_p' floating=True>, {'error': 0.0322565248156339}), (<zfit.Parameter 'Dbar_s' floating=True>, {'error': 0.017151566032725857}), (<zfit.Parameter 'p4160_s' floating=True>, {'error': 0.17088081601709282}), (<zfit.Parameter 'p4160_p' floating=True>, {'error': 0.10376333863688103}), (<zfit.Parameter 'p4040_s' floating=True>, {'error': 0.18592794223223952}), (<zfit.Parameter 'Dbar_p' floating=True>, {'error': 0.2744720989913345})])\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\ipykernel_launcher.py:196: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Toy 1/10\n",
"Time taken: 1 min, 56 s\n",
"Projected time left: 17 min, 24 s\n",
"Toy 1: Generating data...\n",
"Toy 1: Data generation finished\n",
"Toy 1: Loading data...\n",
"Toy 1: Loading data finished\n",
"Toy 1: Fitting pdf...\n",
"------------------------------------------------------------------\n",
"| FCN = 2.978E+05 | Ncalls=1005 (1005 total) |\n",
"| EDM = 6.22E-05 (Goal: 5E-06) | up = 0.5 |\n",
"------------------------------------------------------------------\n",
"| Valid Min. | Valid Param. | Above EDM | Reached call limit |\n",
"------------------------------------------------------------------\n",
"| True | True | False | False |\n",
"------------------------------------------------------------------\n",
"| Hesse failed | Has cov. | Accurate | Pos. def. | Forced |\n",
"------------------------------------------------------------------\n",
"| False | True | True | True | False |\n",
"------------------------------------------------------------------\n",
"Function minimum: 297771.1911008895\n",
"----------------------------------------------------------------------------------------------\n",
"| | Name | Value | Hesse Err | Minos Err- | Minos Err+ | Limit- | Limit+ | Fixed |\n",
"----------------------------------------------------------------------------------------------\n",
"| 0 | rho_p | -6.28 | 0.13 | | |-6.28319 | 6.28319 | |\n",
"| 1 | omega_s | 9.1 | 0.9 | | | 4.19232 | 9.40768 | |\n",
"| 2 | omega_p | 1.19 | 0.25 | | |-6.28319 | 6.28319 | |\n",
"| 3 | rho_s | 1.5 | 0.4 | | |0.0253049| 2.0747 | |\n",
"| 4 | phi_s | 16.6 | 1.1 | | | 14.8182 | 23.5818 | |\n",
"| 5 | p3770_s | 2.06 | 0.26 | | |0.918861 | 4.08114 | |\n",
"| 6 | jpsi_p | -4.579 | 0.024 | | |-6.28319 | 6.28319 | |\n",
"| 7 | phi_p | -0.29 | 0.25 | | |-6.28319 | 6.28319 | |\n",
"| 8 | Ctt | 0.64 | 0.13 | | | -1 | 1 | |\n",
"| 9 | p3770_p | -3.41 | 0.12 | | |-6.28319 | 6.28319 | |\n",
"| 10| p4415_p | 3.53 | 0.15 | | |-6.28319 | 6.28319 | |\n",
"| 11| p4415_s | 1.30 | 0.19 | | |0.126447 | 2.35355 | |\n",
"| 12| DDstar_s | 0.300 | 0.027 | | | -0.3 | 0.3 | |\n",
"| 13| p4040_p | -3.20 | 0.12 | | |-6.28319 | 6.28319 | |\n",
"| 14| DDstar_p | -4.49 | 0.24 | | |-6.28319 | 6.28319 | |\n",
"| 15| psi2s_p | 1.97 | 0.03 | | |-6.28319 | 6.28319 | |\n",
"| 16| Dbar_s | 0.300 | 0.023 | | | -0.3 | 0.3 | |\n",
"| 17| p4160_s | 2.26 | 0.17 | | | 0.71676 | 3.68324 | |\n",
"| 18| p4160_p | 3.94 | 0.10 | | |-6.28319 | 6.28319 | |\n",
"| 19| p4040_s | 1.46 | 0.18 | | |0.00501244| 2.01499 | |\n",
"| 20| Dbar_p | 2.05 | 0.30 | | |-6.28319 | 6.28319 | |\n",
"----------------------------------------------------------------------------------------------\n",
"-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\n",
"| | rho_p omega_s omega_p rho_s phi_s p3770_s jpsi_p phi_p Ctt p3770_p p4415_p p4415_s DDstar_s p4040_p DDstar_p psi2s_p Dbar_s p4160_s p4160_p p4040_s Dbar_p |\n",
"-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\n",
"| rho_p | 1.000 -0.014 -0.044 0.036 0.035 0.005 0.008 0.025 -0.025 -0.006 0.001 -0.002 -0.000 0.001 0.024 -0.002 -0.001 0.001 -0.001 -0.001 0.003 |\n",
"| omega_s | -0.014 1.000 0.444 0.092 -0.002 -0.007 -0.028 -0.024 0.028 0.001 -0.004 0.002 0.000 -0.006 -0.024 -0.001 0.001 -0.002 -0.003 0.002 -0.003 |\n",
"| omega_p | -0.044 0.444 1.000 0.668 0.035 -0.002 -0.016 0.040 0.002 -0.007 -0.003 -0.002 -0.000 -0.006 0.003 -0.001 -0.000 -0.004 -0.004 0.000 0.002 |\n",
"| rho_s | 0.036 0.092 0.668 1.000 0.046 -0.004 -0.019 0.031 0.000 -0.013 -0.005 -0.004 0.001 -0.010 0.007 0.000 0.001 -0.007 -0.005 -0.001 0.009 |\n",
"| phi_s | 0.035 -0.002 0.035 0.046 1.000 -0.012 -0.050 0.692 0.043 -0.003 -0.007 0.001 0.001 -0.012 -0.034 -0.001 0.002 -0.005 -0.007 0.003 -0.001 |\n",
"| p3770_s | 0.005 -0.007 -0.002 -0.004 -0.012 1.000 0.033 -0.001 0.067 -0.354 -0.036 0.059 -0.010 -0.094 0.018 -0.185 -0.006 0.055 -0.149 0.180 0.157 |\n",
"| jpsi_p | 0.008 -0.028 -0.016 -0.019 -0.050 0.033 1.000 -0.020 -0.416 0.042 -0.036 -0.084 -0.060 -0.043 0.254 0.001 -0.058 -0.115 0.063 -0.024 -0.206 |\n",
"| phi_p | 0.025 -0.024 0.040 0.031 0.692 -0.001 -0.020 1.000 -0.004 -0.009 -0.003 -0.001 -0.001 -0.006 0.009 -0.003 -0.001 -0.002 -0.005 0.001 0.002 |\n",
"| Ctt | -0.025 0.028 0.002 0.000 0.043 0.067 -0.416 -0.004 1.000 -0.321 -0.042 0.320 0.004 -0.089 -0.159 0.286 -0.011 0.305 -0.429 0.381 0.447 |\n",
"| p3770_p | -0.006 0.001 -0.007 -0.013 -0.003 -0.354 0.042 -0.009 -0.321 1.000 0.017 -0.117 -0.034 0.068 -0.002 -0.255 -0.053 -0.107 0.171 -0.098 -0.191 |\n",
"| p4415_p | 0.001 -0.004 -0.003 -0.005 -0.007 -0.036 -0.036 -0.003 -0.042 0.017 1.000 -0.171 -0.038 0.031 0.085 -0.093 -0.021 -0.098 0.235 -0.186 0.059 |\n",
"| p4415_s | -0.002 0.002 -0.002 -0.004 0.001 0.059 -0.084 -0.001 0.320 -0.117 -0.171 1.000 0.015 0.099 0.252 0.073 0.007 0.376 -0.142 0.153 -0.016 |\n",
"| DDstar_s | -0.000 0.000 -0.000 0.001 0.001 -0.010 -0.060 -0.001 0.004 -0.034 -0.038 0.015 1.000 -0.046 0.032 -0.027 -0.002 0.003 -0.064 0.015 -0.014 |\n",
"| p4040_p | 0.001 -0.006 -0.006 -0.010 -0.012 -0.094 -0.043 -0.006 -0.089 0.068 0.031 0.099 -0.046 1.000 0.045 -0.218 -0.036 0.311 -0.111 -0.235 0.081 |\n",
"| DDstar_p | 0.024 -0.024 0.003 0.007 -0.034 0.018 0.254 0.009 -0.159 -0.002 0.085 0.252 0.032 0.045 1.000 0.089 -0.009 0.236 -0.193 0.210 -0.720 |\n",
"| psi2s_p | -0.002 -0.001 -0.001 0.000 -0.001 -0.185 0.001 -0.003 0.286 -0.255 -0.093 0.073 -0.027 -0.218 0.089 1.000 -0.032 -0.030 -0.182 0.166 0.015 |\n",
"| Dbar_s | -0.001 0.001 -0.000 0.001 0.002 -0.006 -0.058 -0.001 -0.011 -0.053 -0.021 0.007 -0.002 -0.036 -0.009 -0.032 1.000 0.005 -0.044 0.016 0.022 |\n",
"| p4160_s | 0.001 -0.002 -0.004 -0.007 -0.005 0.055 -0.115 -0.002 0.305 -0.107 -0.098 0.376 0.003 0.311 0.236 -0.030 0.005 1.000 -0.273 -0.090 0.110 |\n",
"| p4160_p | -0.001 -0.003 -0.004 -0.005 -0.007 -0.149 0.063 -0.005 -0.429 0.171 0.235 -0.142 -0.064 -0.111 -0.193 -0.182 -0.044 -0.273 1.000 -0.489 -0.010 |\n",
"| p4040_s | -0.001 0.002 0.000 -0.001 0.003 0.180 -0.024 0.001 0.381 -0.098 -0.186 0.153 0.015 -0.235 0.210 0.166 0.016 -0.090 -0.489 1.000 -0.010 |\n",
"| Dbar_p | 0.003 -0.003 0.002 0.009 -0.001 0.157 -0.206 0.002 0.447 -0.191 0.059 -0.016 -0.014 0.081 -0.720 0.015 0.022 0.110 -0.010 -0.010 1.000 |\n",
"-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\n",
"Hesse errors: OrderedDict([(<zfit.Parameter 'rho_p' floating=True>, {'error': 0.12714020334238896}), (<zfit.Parameter 'omega_s' floating=True>, {'error': 0.9110103885391752}), (<zfit.Parameter 'omega_p' floating=True>, {'error': 0.2460069060649248}), (<zfit.Parameter 'rho_s' floating=True>, {'error': 0.3563319128397502}), (<zfit.Parameter 'phi_s' floating=True>, {'error': 1.0772374995228464}), (<zfit.Parameter 'p3770_s' floating=True>, {'error': 0.25935040803259213}), (<zfit.Parameter 'jpsi_p' floating=True>, {'error': 0.02373819154920831}), (<zfit.Parameter 'phi_p' floating=True>, {'error': 0.24601393087014545}), (<zfit.Parameter 'Ctt' floating=True>, {'error': 0.1346308082213049}), (<zfit.Parameter 'p3770_p' floating=True>, {'error': 0.1232910506439493}), (<zfit.Parameter 'p4415_p' floating=True>, {'error': 0.15494883031381956}), (<zfit.Parameter 'p4415_s' floating=True>, {'error': 0.19229245139061413}), (<zfit.Parameter 'DDstar_s' floating=True>, {'error': 0.026888783503803526}), (<zfit.Parameter 'p4040_p' floating=True>, {'error': 0.11733959684499085}), (<zfit.Parameter 'DDstar_p' floating=True>, {'error': 0.24494151750819793}), (<zfit.Parameter 'psi2s_p' floating=True>, {'error': 0.03223014448246175}), (<zfit.Parameter 'Dbar_s' floating=True>, {'error': 0.02301682578992814}), (<zfit.Parameter 'p4160_s' floating=True>, {'error': 0.17218414806610172}), (<zfit.Parameter 'p4160_p' floating=True>, {'error': 0.0965882723114424}), (<zfit.Parameter 'p4040_s' floating=True>, {'error': 0.18228913672392133}), (<zfit.Parameter 'Dbar_p' floating=True>, {'error': 0.3036868434218727})])\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Toy 2/10\n",
"Time taken: 3 min, 31 s\n",
"Projected time left: 14 min\n",
"Toy 2: Generating data...\n",
"Toy 2: Data generation finished\n",
"Toy 2: Loading data...\n",
"Toy 2: Loading data finished\n",
"Toy 2: Fitting pdf...\n",
"------------------------------------------------------------------\n",
"| FCN = 2.979E+05 | Ncalls=1132 (1132 total) |\n",
"| EDM = 7.59E-06 (Goal: 5E-06) | up = 0.5 |\n",
"------------------------------------------------------------------\n",
"| Valid Min. | Valid Param. | Above EDM | Reached call limit |\n",
"------------------------------------------------------------------\n",
"| True | True | False | False |\n",
"------------------------------------------------------------------\n",
"| Hesse failed | Has cov. | Accurate | Pos. def. | Forced |\n",
"------------------------------------------------------------------\n",
"| False | True | True | True | False |\n",
"------------------------------------------------------------------\n",
"Function minimum: 297868.2298033267\n",
"----------------------------------------------------------------------------------------------\n",
"| | Name | Value | Hesse Err | Minos Err- | Minos Err+ | Limit- | Limit+ | Fixed |\n",
"----------------------------------------------------------------------------------------------\n",
"| 0 | rho_p | -0.6 | 0.3 | | |-6.28319 | 6.28319 | |\n",
"| 1 | omega_s | 6.9 | 1.1 | | | 4.19232 | 9.40768 | |\n",
"| 2 | omega_p | 0.15 | 0.28 | | |-6.28319 | 6.28319 | |\n",
"| 3 | rho_s | 1.15 | 0.29 | | |0.0253049| 2.0747 | |\n",
"| 4 | phi_s | 19.7 | 1.0 | | | 14.8182 | 23.5818 | |\n",
"| 5 | p3770_s | 1.97 | 0.27 | | |0.918861 | 4.08114 | |\n",
"| 6 | jpsi_p | 1.670 | 0.024 | | |-6.28319 | 6.28319 | |\n",
"| 7 | phi_p | 0.77 | 0.18 | | |-6.28319 | 6.28319 | |\n",
"| 8 | Ctt | 0.66 | 0.14 | | | -1 | 1 | |\n",
"| 9 | p3770_p | -3.47 | 0.13 | | |-6.28319 | 6.28319 | |\n",
"| 10| p4415_p | -2.43 | 0.16 | | |-6.28319 | 6.28319 | |\n",
"| 11| p4415_s | 1.28 | 0.19 | | |0.126447 | 2.35355 | |\n",
"| 12| DDstar_s | -0.30 | 0.04 | | | -0.3 | 0.3 | |\n",
"| 13| p4040_p | 3.22 | 0.15 | | |-6.28319 | 6.28319 | |\n",
"| 14| DDstar_p | 4.91 | 0.25 | | |-6.28319 | 6.28319 | |\n",
"| 15| psi2s_p | 2.00 | 0.03 | | |-6.28319 | 6.28319 | |\n",
"| 16| Dbar_s | 0.300 | 0.027 | | | -0.3 | 0.3 | |\n",
"| 17| p4160_s | 2.18 | 0.18 | | | 0.71676 | 3.68324 | |\n",
"| 18| p4160_p | -2.46 | 0.10 | | |-6.28319 | 6.28319 | |\n",
"| 19| p4040_s | 1.18 | 0.18 | | |0.00501244| 2.01499 | |\n",
"| 20| Dbar_p | 1.99 | 0.31 | | |-6.28319 | 6.28319 | |\n",
"----------------------------------------------------------------------------------------------\n",
"-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\n",
"| | rho_p omega_s omega_p rho_s phi_s p3770_s jpsi_p phi_p Ctt p3770_p p4415_p p4415_s DDstar_s p4040_p DDstar_p psi2s_p Dbar_s p4160_s p4160_p p4040_s Dbar_p |\n",
"-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\n",
"| rho_p | 1.000 0.140 -0.046 0.153 -0.118 0.028 0.053 -0.244 -0.125 -0.029 0.010 -0.010 -0.001 0.012 0.120 -0.007 -0.005 -0.000 0.001 -0.004 0.030 |\n",
"| omega_s | 0.140 1.000 0.600 -0.369 -0.028 0.006 -0.000 -0.124 -0.027 -0.008 0.001 -0.001 -0.001 0.001 0.026 -0.004 -0.001 0.001 -0.002 0.000 0.005 |\n",
"| omega_p | -0.046 0.600 1.000 0.006 0.006 0.003 -0.002 -0.036 -0.015 -0.005 0.000 -0.001 -0.001 0.000 0.015 -0.003 -0.001 0.000 -0.002 0.000 0.002 |\n",
"| rho_s | 0.153 -0.369 0.006 1.000 0.004 -0.009 -0.023 0.093 0.029 -0.003 -0.005 -0.004 0.001 -0.010 -0.022 0.004 0.002 -0.008 -0.003 -0.001 0.003 |\n",
"| phi_s | -0.118 -0.028 0.006 0.004 1.000 -0.018 -0.062 0.583 0.073 0.006 -0.010 0.003 0.001 -0.015 -0.065 0.000 0.003 -0.003 -0.008 0.003 -0.012 |\n",
"| p3770_s | 0.028 0.006 0.003 -0.009 -0.018 1.000 0.032 -0.012 0.103 -0.353 -0.052 0.064 -0.011 -0.103 0.016 -0.142 -0.002 0.068 -0.155 0.186 0.168 |\n",
"| jpsi_p | 0.053 -0.000 -0.002 -0.023 -0.062 0.032 1.000 -0.037 -0.404 0.036 -0.006 -0.104 -0.088 -0.045 0.240 -0.007 -0.067 -0.109 0.051 -0.035 -0.180 |\n",
"| phi_p | -0.244 -0.124 -0.036 0.093 0.583 -0.012 -0.037 1.000 0.050 0.009 -0.006 0.004 -0.001 -0.008 -0.047 -0.000 0.001 -0.001 -0.004 0.002 -0.014 |\n",
"| Ctt | -0.125 -0.027 -0.015 0.029 0.073 0.103 -0.404 0.050 1.000 -0.308 -0.118 0.327 0.008 -0.090 -0.191 0.301 -0.006 0.318 -0.388 0.390 0.462 |\n",
"| p3770_p | -0.029 -0.008 -0.005 -0.003 0.006 -0.353 0.036 0.009 -0.308 1.000 0.050 -0.122 -0.052 0.064 -0.022 -0.276 -0.062 -0.104 0.156 -0.097 -0.150 |\n",
"| p4415_p | 0.010 0.001 0.000 -0.005 -0.010 -0.052 -0.006 -0.006 -0.118 0.050 1.000 -0.140 -0.059 0.037 0.000 -0.103 -0.024 -0.207 0.258 -0.224 0.076 |\n",
"| p4415_s | -0.010 -0.001 -0.001 -0.004 0.003 0.064 -0.104 0.004 0.327 -0.122 -0.140 1.000 0.021 0.101 0.276 0.057 0.008 0.333 -0.054 0.137 0.008 |\n",
"| DDstar_s | -0.001 -0.001 -0.001 0.001 0.001 -0.011 -0.088 -0.001 0.008 -0.052 -0.059 0.021 1.000 -0.058 0.042 -0.038 -0.004 0.007 -0.093 0.022 -0.019 |\n",
"| p4040_p | 0.012 0.001 0.000 -0.010 -0.015 -0.103 -0.045 -0.008 -0.090 0.064 0.037 0.101 -0.058 1.000 0.004 -0.218 -0.034 0.348 -0.032 -0.220 0.126 |\n",
"| DDstar_p | 0.120 0.026 0.015 -0.022 -0.065 0.016 0.240 -0.047 -0.191 -0.022 0.000 0.276 0.042 0.004 1.000 0.067 -0.016 0.235 -0.187 0.228 -0.724 |\n",
"| psi2s_p | -0.007 -0.004 -0.003 0.004 0.000 -0.142 -0.007 -0.000 0.301 -0.276 -0.103 0.057 -0.038 -0.218 0.067 1.000 -0.034 -0.007 -0.188 0.155 0.045 |\n",
"| Dbar_s | -0.005 -0.001 -0.001 0.002 0.003 -0.002 -0.067 0.001 -0.006 -0.062 -0.024 0.008 -0.004 -0.034 -0.016 -0.034 1.000 0.008 -0.049 0.019 0.027 |\n",
"| p4160_s | -0.000 0.001 0.000 -0.008 -0.003 0.068 -0.109 -0.001 0.318 -0.104 -0.207 0.333 0.007 0.348 0.235 -0.007 0.008 1.000 -0.272 -0.004 0.099 |\n",
"| p4160_p | 0.001 -0.002 -0.002 -0.003 -0.008 -0.155 0.051 -0.004 -0.388 0.156 0.258 -0.054 -0.093 -0.032 -0.187 -0.188 -0.049 -0.272 1.000 -0.533 0.031 |\n",
"| p4040_s | -0.004 0.000 0.000 -0.001 0.003 0.186 -0.035 0.002 0.390 -0.097 -0.224 0.137 0.022 -0.220 0.228 0.155 0.019 -0.004 -0.533 1.000 -0.006 |\n",
"| Dbar_p | 0.030 0.005 0.002 0.003 -0.012 0.168 -0.180 -0.014 0.462 -0.150 0.076 0.008 -0.019 0.126 -0.724 0.045 0.027 0.099 0.031 -0.006 1.000 |\n",
"-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\n",
"Hesse errors: OrderedDict([(<zfit.Parameter 'rho_p' floating=True>, {'error': 0.3192089338950037}), (<zfit.Parameter 'omega_s' floating=True>, {'error': 1.137685506959543}), (<zfit.Parameter 'omega_p' floating=True>, {'error': 0.2846998997007937}), (<zfit.Parameter 'rho_s' floating=True>, {'error': 0.29214974737534866}), (<zfit.Parameter 'phi_s' floating=True>, {'error': 0.9819225623644812}), (<zfit.Parameter 'p3770_s' floating=True>, {'error': 0.2690738235920809}), (<zfit.Parameter 'jpsi_p' floating=True>, {'error': 0.023671555100508446}), (<zfit.Parameter 'phi_p' floating=True>, {'error': 0.17813011578819848}), (<zfit.Parameter 'Ctt' floating=True>, {'error': 0.13852949875331488}), (<zfit.Parameter 'p3770_p' floating=True>, {'error': 0.12504677307460277}), (<zfit.Parameter 'p4415_p' floating=True>, {'error': 0.16104954289529805}), (<zfit.Parameter 'p4415_s' floating=True>, {'error': 0.18758340988883737}), (<zfit.Parameter 'DDstar_s' floating=True>, {'error': 0.0380684391866056}), (<zfit.Parameter 'p4040_p' floating=True>, {'error': 0.14737125993810363}), (<zfit.Parameter 'DDstar_p' floating=True>, {'error': 0.25487217509592686}), (<zfit.Parameter 'psi2s_p' floating=True>, {'error': 0.03241349374817748}), (<zfit.Parameter 'Dbar_s' floating=True>, {'error': 0.026694730912351528}), (<zfit.Parameter 'p4160_s' floating=True>, {'error': 0.1757761529731341}), (<zfit.Parameter 'p4160_p' floating=True>, {'error': 0.09900060329444083}), (<zfit.Parameter 'p4040_s' floating=True>, {'error': 0.18453556087558376}), (<zfit.Parameter 'Dbar_p' floating=True>, {'error': 0.31499935605380003})])\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Toy 3/10\n",
"Time taken: 5 min, 14 s\n",
"Projected time left: 12 min, 8 s\n",
"Toy 3: Generating data...\n",
"Toy 3: Data generation finished\n",
"Toy 3: Loading data...\n",
"Toy 3: Loading data finished\n",
"Toy 3: Fitting pdf...\n",
"------------------------------------------------------------------\n",
"| FCN = 2.978E+05 | Ncalls=1108 (1108 total) |\n",
"| EDM = 5.81E-05 (Goal: 5E-06) | up = 0.5 |\n",
"------------------------------------------------------------------\n",
"| Valid Min. | Valid Param. | Above EDM | Reached call limit |\n",
"------------------------------------------------------------------\n",
"| True | True | False | False |\n",
"------------------------------------------------------------------\n",
"| Hesse failed | Has cov. | Accurate | Pos. def. | Forced |\n",
"------------------------------------------------------------------\n",
"| False | True | True | True | False |\n",
"------------------------------------------------------------------\n",
"Function minimum: 297821.0954135446\n",
"----------------------------------------------------------------------------------------------\n",
"| | Name | Value | Hesse Err | Minos Err- | Minos Err+ | Limit- | Limit+ | Fixed |\n",
"----------------------------------------------------------------------------------------------\n",
"| 0 | rho_p | 6.3 | 0.5 | | |-6.28319 | 6.28319 | |\n",
"| 1 | omega_s | 6.1 | 1.1 | | | 4.19232 | 9.40768 | |\n",
"| 2 | omega_p | -0.14 | 0.30 | | |-6.28319 | 6.28319 | |\n",
"| 3 | rho_s | 0.6 | 0.3 | | |0.0253049| 2.0747 | |\n",
"| 4 | phi_s | 16.0 | 1.1 | | | 14.8182 | 23.5818 | |\n",
"| 5 | p3770_s | 2.06 | 0.27 | | |0.918861 | 4.08114 | |\n",
"| 6 | jpsi_p | -4.565 | 0.024 | | |-6.28319 | 6.28319 | |\n",
"| 7 | phi_p | 6.08 | 0.25 | | |-6.28319 | 6.28319 | |\n",
"| 8 | Ctt | 0.44 | 0.14 | | | -1 | 1 | |\n",
"| 9 | p3770_p | -3.50 | 0.12 | | |-6.28319 | 6.28319 | |\n",
"| 10| p4415_p | -2.49 | 0.18 | | |-6.28319 | 6.28319 | |\n",
"| 11| p4415_s | 1.12 | 0.19 | | |0.126447 | 2.35355 | |\n",
"| 12| DDstar_s | 0.300 | 0.028 | | | -0.3 | 0.3 | |\n",
"| 13| p4040_p | -2.74 | 0.14 | | |-6.28319 | 6.28319 | |\n",
"| 14| DDstar_p | -4.52 | 0.25 | | |-6.28319 | 6.28319 | |\n",
"| 15| psi2s_p | 1.97 | 0.03 | | |-6.28319 | 6.28319 | |\n",
"| 16| Dbar_s | -0.300 | 0.023 | | | -0.3 | 0.3 | |\n",
"| 17| p4160_s | 2.00 | 0.17 | | | 0.71676 | 3.68324 | |\n",
"| 18| p4160_p | -2.35 | 0.11 | | |-6.28319 | 6.28319 | |\n",
"| 19| p4040_s | 1.29 | 0.18 | | |0.00501244| 2.01499 | |\n",
"| 20| Dbar_p | -1.06 | 0.31 | | |-6.28319 | 6.28319 | |\n",
"----------------------------------------------------------------------------------------------\n",
"-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\n",
"| | rho_p omega_s omega_p rho_s phi_s p3770_s jpsi_p phi_p Ctt p3770_p p4415_p p4415_s DDstar_s p4040_p DDstar_p psi2s_p Dbar_s p4160_s p4160_p p4040_s Dbar_p |\n",
"-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\n",
"| rho_p | 1.000 -0.095 0.016 -0.203 -0.063 -0.011 -0.022 -0.044 0.054 0.012 -0.004 0.004 0.000 -0.004 -0.051 0.003 0.002 0.000 -0.002 -0.000 -0.006 |\n",
"| omega_s | -0.095 1.000 0.622 -0.321 -0.000 -0.005 -0.023 -0.029 0.022 0.001 -0.003 0.002 -0.000 -0.005 -0.019 -0.001 0.001 0.000 -0.004 0.001 -0.002 |\n",
"| omega_p | 0.016 0.622 1.000 -0.135 0.040 0.002 -0.003 0.023 -0.013 -0.005 0.000 -0.001 -0.000 -0.001 0.014 -0.002 -0.001 0.000 -0.001 0.001 -0.000 |\n",
"| rho_s | -0.203 -0.321 -0.135 1.000 0.069 0.008 0.018 0.060 -0.047 -0.018 0.002 -0.009 0.001 -0.001 0.050 0.001 -0.001 -0.006 0.002 -0.003 0.014 |\n",
"| phi_s | -0.063 -0.000 0.040 0.069 1.000 -0.009 -0.047 0.686 0.038 -0.005 -0.007 0.001 0.000 -0.011 -0.029 -0.002 0.001 -0.004 -0.008 -0.000 0.002 |\n",
"| p3770_s | -0.011 -0.005 0.002 0.008 -0.009 1.000 0.026 0.001 0.098 -0.381 -0.054 0.070 -0.008 -0.168 0.031 -0.140 -0.003 0.065 -0.165 0.179 0.155 |\n",
"| jpsi_p | -0.022 -0.023 -0.003 0.018 -0.047 0.026 1.000 -0.016 -0.433 0.066 -0.006 -0.104 -0.061 -0.005 0.283 -0.003 -0.058 -0.118 0.075 -0.073 -0.244 |\n",
"| phi_p | -0.044 -0.029 0.023 0.060 0.686 0.001 -0.016 1.000 -0.009 -0.009 -0.002 -0.002 -0.000 -0.004 0.013 -0.003 -0.001 -0.002 -0.004 0.000 0.002 |\n",
"| Ctt | 0.054 0.022 -0.013 -0.047 0.038 0.098 -0.433 -0.009 1.000 -0.345 -0.124 0.348 0.003 -0.272 -0.147 0.292 -0.012 0.318 -0.448 0.437 0.440 |\n",
"| p3770_p | 0.012 0.001 -0.005 -0.018 -0.005 -0.381 0.066 -0.009 -0.345 1.000 0.044 -0.134 -0.037 0.108 0.018 -0.267 -0.056 -0.120 0.174 -0.122 -0.219 |\n",
"| p4415_p | -0.004 -0.003 0.000 0.002 -0.007 -0.054 -0.006 -0.002 -0.124 0.044 1.000 -0.166 -0.038 0.100 0.034 -0.105 -0.020 -0.183 0.261 -0.213 0.047 |\n",
"| p4415_s | 0.004 0.002 -0.001 -0.009 0.001 0.070 -0.104 -0.002 0.348 -0.134 -0.166 1.000 0.017 0.012 0.278 0.073 0.007 0.362 -0.101 0.222 0.005 |\n",
"| DDstar_s | 0.000 -0.000 -0.000 0.001 0.000 -0.008 -0.061 -0.000 0.003 -0.037 -0.038 0.017 1.000 -0.047 0.033 -0.027 -0.003 0.006 -0.063 0.013 -0.019 |\n",
"| p4040_p | -0.004 -0.005 -0.001 -0.001 -0.011 -0.168 -0.005 -0.004 -0.272 0.108 0.100 0.012 -0.047 1.000 -0.042 -0.255 -0.038 0.278 0.133 -0.285 0.047 |\n",
"| DDstar_p | -0.051 -0.019 0.014 0.050 -0.029 0.031 0.283 0.013 -0.147 0.018 0.034 0.278 0.033 -0.042 1.000 0.104 -0.006 0.243 -0.171 0.244 -0.721 |\n",
"| psi2s_p | 0.003 -0.001 -0.002 0.001 -0.002 -0.140 -0.003 -0.003 0.292 -0.267 -0.105 0.073 -0.027 -0.255 0.104 1.000 -0.033 -0.009 -0.196 0.111 0.016 |\n",
"| Dbar_s | 0.002 0.001 -0.001 -0.001 0.001 -0.003 -0.058 -0.001 -0.012 -0.056 -0.020 0.007 -0.003 -0.038 -0.006 -0.033 1.000 0.006 -0.042 0.015 0.021 |\n",
"| p4160_s | 0.000 0.000 0.000 -0.006 -0.004 0.065 -0.118 -0.002 0.318 -0.120 -0.183 0.362 0.006 0.278 0.243 -0.009 0.006 1.000 -0.279 0.112 0.101 |\n",
"| p4160_p | -0.002 -0.004 -0.001 0.002 -0.008 -0.165 0.075 -0.004 -0.448 0.174 0.261 -0.101 -0.063 0.133 -0.171 -0.196 -0.042 -0.279 1.000 -0.579 -0.022 |\n",
"| p4040_s | -0.000 0.001 0.001 -0.003 -0.000 0.179 -0.073 0.000 0.437 -0.122 -0.213 0.222 0.013 -0.285 0.244 0.111 0.015 0.112 -0.579 1.000 0.066 |\n",
"| Dbar_p | -0.006 -0.002 -0.000 0.014 0.002 0.155 -0.244 0.002 0.440 -0.219 0.047 0.005 -0.019 0.047 -0.721 0.016 0.021 0.101 -0.022 0.066 1.000 |\n",
"-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\n",
"Hesse errors: OrderedDict([(<zfit.Parameter 'rho_p' floating=True>, {'error': 0.5253488076410386}), (<zfit.Parameter 'omega_s' floating=True>, {'error': 1.0743625512789028}), (<zfit.Parameter 'omega_p' floating=True>, {'error': 0.29737019163662826}), (<zfit.Parameter 'rho_s' floating=True>, {'error': 0.32988167051897904}), (<zfit.Parameter 'phi_s' floating=True>, {'error': 1.054141274608022}), (<zfit.Parameter 'p3770_s' floating=True>, {'error': 0.2699310999673744}), (<zfit.Parameter 'jpsi_p' floating=True>, {'error': 0.024080721050292464}), (<zfit.Parameter 'phi_p' floating=True>, {'error': 0.2515079933112867}), (<zfit.Parameter 'Ctt' floating=True>, {'error': 0.14067675303626526}), (<zfit.Parameter 'p3770_p' floating=True>, {'error': 0.12281932100448989}), (<zfit.Parameter 'p4415_p' floating=True>, {'error': 0.1812502673680072}), (<zfit.Parameter 'p4415_s' floating=True>, {'error': 0.19350066864116627}), (<zfit.Parameter 'DDstar_s' floating=True>, {'error': 0.027745812242241708}), (<zfit.Parameter 'p4040_p' floating=True>, {'error': 0.1400363699842424}), (<zfit.Parameter 'DDstar_p' floating=True>, {'error': 0.24980753440385461}), (<zfit.Parameter 'psi2s_p' floating=True>, {'error': 0.03262849021570613}), (<zfit.Parameter 'Dbar_s' floating=True>, {'error': 0.022641162303397938}), (<zfit.Parameter 'p4160_s' floating=True>, {'error': 0.1743659152126219}), (<zfit.Parameter 'p4160_p' floating=True>, {'error': 0.10974552439331475}), (<zfit.Parameter 'p4040_s' floating=True>, {'error': 0.18158799997476216}), (<zfit.Parameter 'Dbar_p' floating=True>, {'error': 0.30785393602108835})])\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Toy 4/10\n",
"Time taken: 7 min, 3 s\n",
"Projected time left: 10 min, 30 s\n",
"Toy 4: Generating data...\n",
"Toy 4: Data generation finished\n",
"Toy 4: Loading data...\n",
"Toy 4: Loading data finished\n",
"Toy 4: Fitting pdf...\n",
"------------------------------------------------------------------\n",
"| FCN = 2.978E+05 | Ncalls=651 (651 total) |\n",
"| EDM = 0.000236 (Goal: 5E-06) | up = 0.5 |\n",
"------------------------------------------------------------------\n",
"| Valid Min. | Valid Param. | Above EDM | Reached call limit |\n",
"------------------------------------------------------------------\n",
"| True | True | False | False |\n",
"------------------------------------------------------------------\n",
"| Hesse failed | Has cov. | Accurate | Pos. def. | Forced |\n",
"------------------------------------------------------------------\n",
"| False | True | True | True | False |\n",
"------------------------------------------------------------------\n",
"Function minimum: 297789.2074864934\n",
"----------------------------------------------------------------------------------------------\n",
"| | Name | Value | Hesse Err | Minos Err- | Minos Err+ | Limit- | Limit+ | Fixed |\n",
"----------------------------------------------------------------------------------------------\n",
"| 0 | rho_p | -6.28 | 0.08 | | |-6.28319 | 6.28319 | |\n",
"| 1 | omega_s | 6.9 | 1.0 | | | 4.19232 | 9.40768 | |\n",
"| 2 | omega_p | -0.007 | 0.272 | | |-6.28319 | 6.28319 | |\n",
"| 3 | rho_s | 1.5 | 0.3 | | |0.0253049| 2.0747 | |\n",
"| 4 | phi_s | 18.6 | 2.7 | | | 14.8182 | 23.5818 | |\n",
"| 5 | p3770_s | 2.10 | 0.26 | | |0.918861 | 4.08114 | |\n",
"| 6 | jpsi_p | 1.697 | 0.024 | | |-6.28319 | 6.28319 | |\n",
"| 7 | phi_p | 0.29 | 0.75 | | |-6.28319 | 6.28319 | |\n",
"| 8 | Ctt | 0.46 | 0.14 | | | -1 | 1 | |\n",
"| 9 | p3770_p | -3.24 | 0.13 | | |-6.28319 | 6.28319 | |\n",
"| 10| p4415_p | 3.51 | 0.13 | | |-6.28319 | 6.28319 | |\n",
"| 11| p4415_s | 1.64 | 0.19 | | |0.126447 | 2.35355 | |\n",
"| 12| DDstar_s | 0.300 | 0.023 | | | -0.3 | 0.3 | |\n",
"| 13| p4040_p | -3.13 | 0.12 | | |-6.28319 | 6.28319 | |\n",
"| 14| DDstar_p | 1.75 | 0.22 | | |-6.28319 | 6.28319 | |\n",
"| 15| psi2s_p | 1.94 | 0.03 | | |-6.28319 | 6.28319 | |\n",
"| 16| Dbar_s | 0.300 | 0.017 | | | -0.3 | 0.3 | |\n",
"| 17| p4160_s | 2.09 | 0.17 | | | 0.71676 | 3.68324 | |\n",
"| 18| p4160_p | 3.87 | 0.10 | | |-6.28319 | 6.28319 | |\n",
"| 19| p4040_s | 1.48 | 0.18 | | |0.00501244| 2.01499 | |\n",
"| 20| Dbar_p | 1.94 | 0.27 | | |-6.28319 | 6.28319 | |\n",
"----------------------------------------------------------------------------------------------\n",
"-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\n",
"| | rho_p omega_s omega_p rho_s phi_s p3770_s jpsi_p phi_p Ctt p3770_p p4415_p p4415_s DDstar_s p4040_p DDstar_p psi2s_p Dbar_s p4160_s p4160_p p4040_s Dbar_p |\n",
"-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\n",
"| rho_p | 1.000 0.079 0.021 0.006 0.003 0.004 0.007 -0.001 -0.018 -0.005 0.001 -0.002 -0.000 0.001 0.017 -0.001 -0.000 0.000 0.001 -0.001 0.003 |\n",
"| omega_s | 0.079 1.000 0.538 -0.369 -0.059 -0.006 -0.020 -0.071 0.025 0.005 -0.002 0.004 0.000 -0.003 -0.023 -0.001 0.001 0.001 -0.003 0.002 -0.005 |\n",
"| omega_p | 0.021 0.538 1.000 -0.050 0.109 0.000 -0.017 0.102 -0.006 -0.007 -0.003 -0.000 -0.000 -0.004 0.008 -0.004 -0.001 -0.000 -0.004 0.001 0.001 |\n",
"| rho_s | 0.006 -0.369 -0.050 1.000 0.026 -0.003 -0.010 0.035 -0.007 -0.011 -0.005 -0.006 0.001 -0.009 0.015 0.002 0.000 -0.008 -0.003 -0.002 0.010 |\n",
"| phi_s | 0.003 -0.059 0.109 0.026 1.000 -0.019 -0.115 0.963 0.061 -0.006 -0.015 0.008 -0.000 -0.023 -0.046 -0.012 0.000 -0.000 -0.022 0.009 -0.010 |\n",
"| p3770_s | 0.004 -0.006 0.000 -0.003 -0.019 1.000 0.054 -0.015 0.030 -0.430 -0.036 0.051 -0.010 -0.093 0.031 -0.234 -0.007 0.058 -0.130 0.182 0.161 |\n",
"| jpsi_p | 0.007 -0.020 -0.017 -0.010 -0.115 0.054 1.000 -0.105 -0.393 0.007 -0.029 -0.089 -0.050 -0.037 0.218 -0.003 -0.040 -0.108 0.054 -0.032 -0.134 |\n",
"| phi_p | -0.001 -0.071 0.102 0.035 0.963 -0.015 -0.105 1.000 0.045 -0.009 -0.014 0.007 -0.001 -0.020 -0.032 -0.013 -0.000 0.000 -0.020 0.008 -0.008 |\n",
"| Ctt | -0.018 0.025 -0.006 -0.007 0.061 0.030 -0.393 0.045 1.000 -0.289 -0.057 0.327 0.004 -0.113 -0.135 0.305 -0.005 0.319 -0.405 0.398 0.443 |\n",
"| p3770_p | -0.005 0.005 -0.007 -0.011 -0.006 -0.430 0.007 -0.009 -0.289 1.000 0.034 -0.122 -0.025 0.090 -0.052 -0.190 -0.034 -0.114 0.178 -0.141 -0.177 |\n",
"| p4415_p | 0.001 -0.002 -0.003 -0.005 -0.015 -0.036 -0.029 -0.014 -0.057 0.034 1.000 -0.206 -0.036 0.056 0.083 -0.097 -0.017 -0.119 0.255 -0.203 0.070 |\n",
"| p4415_s | -0.002 0.004 -0.000 -0.006 0.008 0.051 -0.089 0.007 0.327 -0.122 -0.206 1.000 0.012 0.083 0.242 0.078 0.005 0.367 -0.109 0.166 0.001 |\n",
"| DDstar_s | -0.000 0.000 -0.000 0.001 -0.000 -0.010 -0.050 -0.001 0.004 -0.025 -0.036 0.012 1.000 -0.037 0.021 -0.021 -0.001 0.004 -0.049 0.012 -0.010 |\n",
"| p4040_p | 0.001 -0.003 -0.004 -0.009 -0.023 -0.093 -0.037 -0.020 -0.113 0.090 0.056 0.083 -0.037 1.000 0.027 -0.220 -0.025 0.316 -0.055 -0.249 0.099 |\n",
"| DDstar_p | 0.017 -0.023 0.008 0.015 -0.046 0.031 0.218 -0.032 -0.135 -0.052 0.083 0.242 0.021 0.027 1.000 0.070 -0.010 0.255 -0.184 0.220 -0.670 |\n",
"| psi2s_p | -0.001 -0.001 -0.004 0.002 -0.012 -0.234 -0.003 -0.013 0.305 -0.190 -0.097 0.078 -0.021 -0.220 0.070 1.000 -0.021 -0.010 -0.187 0.163 0.064 |\n",
"| Dbar_s | -0.000 0.001 -0.001 0.000 0.000 -0.007 -0.040 -0.000 -0.005 -0.034 -0.017 0.005 -0.001 -0.025 -0.010 -0.021 1.000 0.005 -0.028 0.011 0.013 |\n",
"| p4160_s | 0.000 0.001 -0.000 -0.008 -0.000 0.058 -0.108 0.000 0.319 -0.114 -0.119 0.367 0.004 0.316 0.255 -0.010 0.005 1.000 -0.264 -0.033 0.111 |\n",
"| p4160_p | 0.001 -0.003 -0.004 -0.003 -0.022 -0.130 0.054 -0.020 -0.405 0.178 0.255 -0.109 -0.049 -0.055 -0.184 -0.187 -0.028 -0.264 1.000 -0.515 0.014 |\n",
"| p4040_s | -0.001 0.002 0.001 -0.002 0.009 0.182 -0.032 0.008 0.398 -0.141 -0.203 0.166 0.012 -0.249 0.220 0.163 0.011 -0.033 -0.515 1.000 0.012 |\n",
"| Dbar_p | 0.003 -0.005 0.001 0.010 -0.010 0.161 -0.134 -0.008 0.443 -0.177 0.070 0.001 -0.010 0.099 -0.670 0.064 0.013 0.111 0.014 0.012 1.000 |\n",
"-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\n",
"Hesse errors: OrderedDict([(<zfit.Parameter 'rho_p' floating=True>, {'error': 0.08357517721798491}), (<zfit.Parameter 'omega_s' floating=True>, {'error': 1.0049914177996428}), (<zfit.Parameter 'omega_p' floating=True>, {'error': 0.272458798760685}), (<zfit.Parameter 'rho_s' floating=True>, {'error': 0.3221960515757538}), (<zfit.Parameter 'phi_s' floating=True>, {'error': 2.6581437103506875}), (<zfit.Parameter 'p3770_s' floating=True>, {'error': 0.26397137630208367}), (<zfit.Parameter 'jpsi_p' floating=True>, {'error': 0.02352616682248776}), (<zfit.Parameter 'phi_p' floating=True>, {'error': 0.7542794669888098}), (<zfit.Parameter 'Ctt' floating=True>, {'error': 0.13617660096433504}), (<zfit.Parameter 'p3770_p' floating=True>, {'error': 0.12818736712297785}), (<zfit.Parameter 'p4415_p' floating=True>, {'error': 0.12518448663435056}), (<zfit.Parameter 'p4415_s' floating=True>, {'error': 0.18745411898244746}), (<zfit.Parameter 'DDstar_s' floating=True>, {'error': 0.022828856062053482}), (<zfit.Parameter 'p4040_p' floating=True>, {'error': 0.1178426450685306}), (<zfit.Parameter 'DDstar_p' floating=True>, {'error': 0.223088729599501}), (<zfit.Parameter 'psi2s_p' floating=True>, {'error': 0.032541087430189464}), (<zfit.Parameter 'Dbar_s' floating=True>, {'error': 0.01663222847958673}), (<zfit.Parameter 'p4160_s' floating=True>, {'error': 0.17373213442903246}), (<zfit.Parameter 'p4160_p' floating=True>, {'error': 0.1021550034529426}), (<zfit.Parameter 'p4040_s' floating=True>, {'error': 0.18281533830420027}), (<zfit.Parameter 'Dbar_p' floating=True>, {'error': 0.2690546277375332})])\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Toy 5/10\n",
"Time taken: 8 min, 38 s\n",
"Projected time left: 8 min, 35 s\n",
"Toy 5: Generating data...\n",
"Toy 5: Data generation finished\n",
"Toy 5: Loading data...\n",
"Toy 5: Loading data finished\n",
"Toy 5: Fitting pdf...\n",
"------------------------------------------------------------------\n",
"| FCN = 2.98E+05 | Ncalls=1050 (1050 total) |\n",
"| EDM = 1.38E-05 (Goal: 5E-06) | up = 0.5 |\n",
"------------------------------------------------------------------\n",
"| Valid Min. | Valid Param. | Above EDM | Reached call limit |\n",
"------------------------------------------------------------------\n",
"| True | True | False | False |\n",
"------------------------------------------------------------------\n",
"| Hesse failed | Has cov. | Accurate | Pos. def. | Forced |\n",
"------------------------------------------------------------------\n",
"| False | True | True | True | False |\n",
"------------------------------------------------------------------\n",
"Function minimum: 297999.1346405293\n",
"----------------------------------------------------------------------------------------------\n",
"| | Name | Value | Hesse Err | Minos Err- | Minos Err+ | Limit- | Limit+ | Fixed |\n",
"----------------------------------------------------------------------------------------------\n",
"| 0 | rho_p | -6.3 | 0.8 | | |-6.28319 | 6.28319 | |\n",
"| 1 | omega_s | 6.8 | 1.1 | | | 4.19232 | 9.40768 | |\n",
"| 2 | omega_p | -0.11 | 0.29 | | |-6.28319 | 6.28319 | |\n",
"| 3 | rho_s | 0.7 | 0.3 | | |0.0253049| 2.0747 | |\n",
"| 4 | phi_s | 18.2 | 1.5 | | | 14.8182 | 23.5818 | |\n",
"| 5 | p3770_s | 1.89 | 0.26 | | |0.918861 | 4.08114 | |\n",
"| 6 | jpsi_p | -4.598 | 0.023 | | |-6.28319 | 6.28319 | |\n",
"| 7 | phi_p | -0.05 | 0.39 | | |-6.28319 | 6.28319 | |\n",
"| 8 | Ctt | 0.62 | 0.14 | | | -1 | 1 | |\n",
"| 9 | p3770_p | -3.54 | 0.13 | | |-6.28319 | 6.28319 | |\n",
"| 10| p4415_p | 3.76 | 0.15 | | |-6.28319 | 6.28319 | |\n",
"| 11| p4415_s | 1.41 | 0.19 | | |0.126447 | 2.35355 | |\n",
"| 12| DDstar_s | 0.300 | 0.030 | | | -0.3 | 0.3 | |\n",
"| 13| p4040_p | 3.30 | 0.14 | | |-6.28319 | 6.28319 | |\n",
"| 14| DDstar_p | 2.04 | 0.26 | | |-6.28319 | 6.28319 | |\n",
"| 15| psi2s_p | 1.99 | 0.03 | | |-6.28319 | 6.28319 | |\n",
"| 16| Dbar_s | 0.300 | 0.027 | | | -0.3 | 0.3 | |\n",
"| 17| p4160_s | 2.12 | 0.17 | | | 0.71676 | 3.68324 | |\n",
"| 18| p4160_p | 3.94 | 0.11 | | |-6.28319 | 6.28319 | |\n",
"| 19| p4040_s | 1.23 | 0.18 | | |0.00501244| 2.01499 | |\n",
"| 20| Dbar_p | 2.01 | 0.31 | | |-6.28319 | 6.28319 | |\n",
"----------------------------------------------------------------------------------------------\n",
"-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\n",
"| | rho_p omega_s omega_p rho_s phi_s p3770_s jpsi_p phi_p Ctt p3770_p p4415_p p4415_s DDstar_s p4040_p DDstar_p psi2s_p Dbar_s p4160_s p4160_p p4040_s Dbar_p |\n",
"-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\n",
"| rho_p | 1.000 0.349 0.070 0.003 0.124 0.019 0.020 0.096 -0.082 -0.021 -0.001 -0.006 -0.004 0.002 0.069 -0.013 -0.004 0.002 -0.006 -0.002 0.024 |\n",
"| omega_s | 0.349 1.000 0.623 -0.325 0.023 0.002 -0.013 -0.006 -0.007 -0.005 -0.002 0.000 -0.001 -0.002 0.006 -0.004 -0.001 0.001 -0.003 0.001 0.002 |\n",
"| omega_p | 0.070 0.623 1.000 -0.118 0.079 0.004 -0.007 0.065 -0.019 -0.008 -0.002 -0.002 -0.001 -0.002 0.017 -0.005 -0.001 -0.000 -0.004 0.000 0.006 |\n",
"| rho_s | 0.003 -0.325 -0.118 1.000 0.063 0.004 -0.000 0.065 -0.027 -0.017 -0.004 -0.008 -0.000 -0.006 0.030 -0.001 -0.000 -0.007 -0.004 -0.003 0.018 |\n",
"| phi_s | 0.124 0.023 0.079 0.063 1.000 -0.004 -0.052 0.860 0.015 -0.013 -0.007 0.000 0.000 -0.011 -0.005 -0.005 0.000 -0.003 -0.010 0.002 0.002 |\n",
"| p3770_s | 0.019 0.002 0.004 0.004 -0.004 1.000 0.024 0.002 0.114 -0.328 -0.056 0.064 -0.008 -0.130 0.004 -0.125 -0.000 0.055 -0.156 0.180 0.181 |\n",
"| jpsi_p | 0.020 -0.013 -0.007 -0.000 -0.052 0.024 1.000 -0.036 -0.397 0.043 -0.024 -0.147 -0.081 -0.043 0.100 -0.021 -0.070 -0.156 0.083 -0.080 -0.097 |\n",
"| phi_p | 0.096 -0.006 0.065 0.065 0.860 0.002 -0.036 1.000 -0.014 -0.016 -0.006 -0.002 -0.001 -0.008 0.017 -0.009 -0.001 -0.002 -0.009 0.001 0.008 |\n",
"| Ctt | -0.082 -0.007 -0.019 -0.027 0.015 0.114 -0.397 -0.014 1.000 -0.309 -0.100 0.346 0.013 -0.140 -0.142 0.304 -0.006 0.313 -0.405 0.413 0.452 |\n",
"| p3770_p | -0.021 -0.005 -0.008 -0.017 -0.013 -0.328 0.043 -0.016 -0.309 1.000 0.051 -0.143 -0.044 0.080 -0.087 -0.296 -0.064 -0.115 0.176 -0.105 -0.110 |\n",
"| p4415_p | -0.001 -0.002 -0.002 -0.004 -0.007 -0.056 -0.024 -0.006 -0.100 0.051 1.000 -0.198 -0.052 0.078 -0.078 -0.110 -0.026 -0.182 0.288 -0.245 0.133 |\n",
"| p4415_s | -0.006 0.000 -0.002 -0.008 0.000 0.064 -0.147 -0.002 0.346 -0.143 -0.198 1.000 0.011 0.045 0.279 0.047 0.008 0.360 -0.134 0.173 0.016 |\n",
"| DDstar_s | -0.004 -0.001 -0.001 -0.000 0.000 -0.008 -0.081 -0.001 0.013 -0.044 -0.052 0.011 1.000 -0.051 0.050 -0.034 -0.003 -0.002 -0.068 0.011 0.005 |\n",
"| p4040_p | 0.002 -0.002 -0.002 -0.006 -0.011 -0.130 -0.043 -0.008 -0.140 0.080 0.078 0.045 -0.051 1.000 -0.111 -0.237 -0.038 0.307 0.037 -0.286 0.168 |\n",
"| DDstar_p | 0.069 0.006 0.017 0.030 -0.005 0.004 0.100 0.017 -0.142 -0.087 -0.078 0.279 0.050 -0.111 1.000 0.020 -0.018 0.208 -0.305 0.239 -0.703 |\n",
"| psi2s_p | -0.013 -0.004 -0.005 -0.001 -0.005 -0.125 -0.021 -0.009 0.304 -0.296 -0.110 0.047 -0.034 -0.237 0.020 1.000 -0.036 -0.044 -0.172 0.129 0.075 |\n",
"| Dbar_s | -0.004 -0.001 -0.001 -0.000 0.000 -0.000 -0.070 -0.001 -0.006 -0.064 -0.026 0.008 -0.003 -0.038 -0.018 -0.036 1.000 0.007 -0.047 0.018 0.031 |\n",
"| p4160_s | 0.002 0.001 -0.000 -0.007 -0.003 0.055 -0.156 -0.002 0.313 -0.115 -0.182 0.360 -0.002 0.307 0.208 -0.044 0.007 1.000 -0.270 -0.027 0.143 |\n",
"| p4160_p | -0.006 -0.003 -0.004 -0.004 -0.010 -0.156 0.083 -0.009 -0.405 0.176 0.288 -0.134 -0.068 0.037 -0.305 -0.172 -0.047 -0.270 1.000 -0.549 0.072 |\n",
"| p4040_s | -0.002 0.001 0.000 -0.003 0.002 0.180 -0.080 0.001 0.413 -0.105 -0.245 0.173 0.011 -0.286 0.239 0.129 0.018 -0.027 -0.549 1.000 0.024 |\n",
"| Dbar_p | 0.024 0.002 0.006 0.018 0.002 0.181 -0.097 0.008 0.452 -0.110 0.133 0.016 0.005 0.168 -0.703 0.075 0.031 0.143 0.072 0.024 1.000 |\n",
"-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\n",
"Hesse errors: OrderedDict([(<zfit.Parameter 'rho_p' floating=True>, {'error': 0.7893690770980153}), (<zfit.Parameter 'omega_s' floating=True>, {'error': 1.1073110371129964}), (<zfit.Parameter 'omega_p' floating=True>, {'error': 0.290718274952539}), (<zfit.Parameter 'rho_s' floating=True>, {'error': 0.321432655811662}), (<zfit.Parameter 'phi_s' floating=True>, {'error': 1.485231427003166}), (<zfit.Parameter 'p3770_s' floating=True>, {'error': 0.2644285099582999}), (<zfit.Parameter 'jpsi_p' floating=True>, {'error': 0.023233029264642546}), (<zfit.Parameter 'phi_p' floating=True>, {'error': 0.38829348564946553}), (<zfit.Parameter 'Ctt' floating=True>, {'error': 0.13948410995367289}), (<zfit.Parameter 'p3770_p' floating=True>, {'error': 0.12958098981086663}), (<zfit.Parameter 'p4415_p' floating=True>, {'error': 0.14774882746787377}), (<zfit.Parameter 'p4415_s' floating=True>, {'error': 0.19040016665362336}), (<zfit.Parameter 'DDstar_s' floating=True>, {'error': 0.030256002147912242}), (<zfit.Parameter 'p4040_p' floating=True>, {'error': 0.1439617180047632}), (<zfit.Parameter 'DDstar_p' floating=True>, {'error': 0.25651487234416503}), (<zfit.Parameter 'psi2s_p' floating=True>, {'error': 0.03218187655946547}), (<zfit.Parameter 'Dbar_s' floating=True>, {'error': 0.026782207558354476}), (<zfit.Parameter 'p4160_s' floating=True>, {'error': 0.1732156419373394}), (<zfit.Parameter 'p4160_p' floating=True>, {'error': 0.10677934543795686}), (<zfit.Parameter 'p4040_s' floating=True>, {'error': 0.1843266721357142}), (<zfit.Parameter 'Dbar_p' floating=True>, {'error': 0.3074504844036339})])\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Toy 6/10\n",
"Time taken: 10 min, 45 s\n",
"Projected time left: 7 min, 8 s\n",
"Toy 6: Generating data...\n",
"Toy 6: Data generation finished\n",
"Toy 6: Loading data...\n",
"Toy 6: Loading data finished\n",
"Toy 6: Fitting pdf...\n",
"------------------------------------------------------------------\n",
"| FCN = 2.977E+05 | Ncalls=1180 (1180 total) |\n",
"| EDM = 0.00808 (Goal: 5E-06) | up = 0.5 |\n",
"------------------------------------------------------------------\n",
"| Valid Min. | Valid Param. | Above EDM | Reached call limit |\n",
"------------------------------------------------------------------\n",
"| False | True | True | False |\n",
"------------------------------------------------------------------\n",
"| Hesse failed | Has cov. | Accurate | Pos. def. | Forced |\n",
"------------------------------------------------------------------\n",
"| False | True | False | False | True |\n",
"------------------------------------------------------------------\n",
"Function minimum: 297738.4083025212\n",
"----------------------------------------------------------------------------------------------\n",
"| | Name | Value | Hesse Err | Minos Err- | Minos Err+ | Limit- | Limit+ | Fixed |\n",
"----------------------------------------------------------------------------------------------\n",
"| 0 | rho_p | 6.16 | 0.28 | | |-6.28319 | 6.28319 | |\n",
"| 1 | omega_s | 8.3 | 1.4 | | | 4.19232 | 9.40768 | |\n",
"| 2 | omega_p | 0.5 | 0.4 | | |-6.28319 | 6.28319 | |\n",
"| 3 | rho_s | 1.20 | 0.30 | | |0.0253049| 2.0747 | |\n",
"| 4 | phi_s | 19.7 | 0.9 | | | 14.8182 | 23.5818 | |\n",
"| 5 | p3770_s | 2.4 | 0.5 | | |0.918861 | 4.08114 | |\n",
"| 6 | jpsi_p | 4.81 | 0.04 | | |-6.28319 | 6.28319 | |\n",
"| 7 | phi_p | -5.53 | 0.16 | | |-6.28319 | 6.28319 | |\n",
"| 8 | Ctt | 0.19 | 0.43 | | | -1 | 1 | |\n",
"| 9 | p3770_p | 3.51 | 0.16 | | |-6.28319 | 6.28319 | |\n",
"| 10| p4415_p | 4.11 | 0.22 | | |-6.28319 | 6.28319 | |\n",
"| 11| p4415_s | 1.23 | 0.18 | | |0.126447 | 2.35355 | |\n",
"| 12| DDstar_s | 0.21 | 0.51 | | | -0.3 | 0.3 | |\n",
"| 13| p4040_p | -2.76 | 0.26 | | |-6.28319 | 6.28319 | |\n",
"| 14| DDstar_p | -3.9 | 0.7 | | |-6.28319 | 6.28319 | |\n",
"| 15| psi2s_p | 2.08 | 0.06 | | |-6.28319 | 6.28319 | |\n",
"| 16| Dbar_s | -0.11 | 0.39 | | | -0.3 | 0.3 | |\n",
"| 17| p4160_s | 2.15 | 0.18 | | | 0.71676 | 3.68324 | |\n",
"| 18| p4160_p | -2.28 | 0.17 | | |-6.28319 | 6.28319 | |\n",
"| 19| p4040_s | 1.40 | 0.17 | | |0.00501244| 2.01499 | |\n",
"| 20| Dbar_p | 4 | 4 | | |-6.28319 | 6.28319 | |\n",
"----------------------------------------------------------------------------------------------\n",
"-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\n",
"| | rho_p omega_s omega_p rho_s phi_s p3770_s jpsi_p phi_p Ctt p3770_p p4415_p p4415_s DDstar_s p4040_p DDstar_p psi2s_p Dbar_s p4160_s p4160_p p4040_s Dbar_p |\n",
"-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\n",
"| rho_p | 1.000 0.183 0.112 0.249 -0.137 0.306 -0.029 -0.230 0.307 0.203 0.198 0.093 -0.317 0.287 -0.060 0.286 0.349 0.167 0.222 -0.063 0.351 |\n",
"| omega_s | 0.183 1.000 0.809 -0.118 -0.013 0.037 -0.011 -0.119 0.039 0.026 0.027 0.011 -0.035 0.036 -0.017 0.035 0.042 0.020 0.030 -0.008 0.043 |\n",
"| omega_p | 0.112 0.809 1.000 0.148 -0.015 0.070 -0.009 -0.090 0.072 0.049 0.048 0.020 -0.068 0.068 -0.024 0.066 0.080 0.038 0.055 -0.015 0.080 |\n",
"| rho_s | 0.249 -0.118 0.148 1.000 -0.063 0.081 -0.001 -0.035 0.090 0.049 0.049 0.025 -0.096 0.074 -0.006 0.079 0.100 0.043 0.054 -0.018 0.099 |\n",
"| phi_s | -0.137 -0.013 -0.015 -0.063 1.000 -0.189 0.015 0.517 -0.186 -0.124 -0.120 -0.060 0.198 -0.176 0.027 -0.176 -0.213 -0.105 -0.134 0.037 -0.215 |\n",
"| p3770_s | 0.306 0.037 0.070 0.081 -0.189 1.000 0.226 -0.091 0.800 0.473 0.552 0.215 -0.683 0.742 -0.397 0.661 0.850 0.419 0.626 -0.092 0.840 |\n",
"| jpsi_p | -0.029 -0.011 -0.009 -0.001 0.015 0.226 1.000 0.019 0.154 0.349 0.365 -0.123 0.197 0.336 -0.657 0.294 0.127 -0.007 0.476 -0.110 0.122 |\n",
"| phi_p | -0.230 -0.119 -0.090 -0.035 0.517 -0.091 0.019 1.000 -0.094 -0.054 -0.052 -0.033 0.109 -0.082 -0.003 -0.084 -0.108 -0.053 -0.056 0.017 -0.109 |\n",
"| Ctt | 0.307 0.039 0.072 0.090 -0.186 0.800 0.154 -0.094 1.000 0.539 0.527 0.357 -0.846 0.760 -0.323 0.811 0.951 0.538 0.565 -0.049 0.944 |\n",
"| p3770_p | 0.203 0.026 0.049 0.049 -0.124 0.473 0.349 -0.054 0.539 1.000 0.508 0.045 -0.432 0.653 -0.430 0.562 0.586 0.220 0.634 -0.227 0.630 |\n",
"| p4415_p | 0.198 0.027 0.048 0.049 -0.120 0.552 0.365 -0.052 0.527 0.508 1.000 0.014 -0.318 0.623 -0.524 0.550 0.595 0.136 0.672 -0.265 0.549 |\n",
"| p4415_s | 0.093 0.011 0.020 0.025 -0.060 0.215 -0.123 -0.033 0.357 0.045 0.014 1.000 -0.319 0.197 0.162 0.186 0.271 0.359 0.095 0.063 0.291 |\n",
"| DDstar_s | -0.317 -0.035 -0.068 -0.096 0.198 -0.683 0.197 0.109 -0.846 -0.432 -0.318 -0.319 1.000 -0.578 -0.085 -0.610 -0.838 -0.432 -0.343 0.113 -0.907 |\n",
"| p4040_p | 0.287 0.036 0.068 0.074 -0.176 0.742 0.336 -0.082 0.760 0.653 0.623 0.197 -0.578 1.000 -0.530 0.713 0.825 0.501 0.717 -0.270 0.803 |\n",
"| DDstar_p | -0.060 -0.017 -0.024 -0.006 0.027 -0.397 -0.657 -0.003 -0.323 -0.430 -0.524 0.162 -0.085 -0.530 1.000 -0.449 -0.427 -0.042 -0.673 0.206 -0.248 |\n",
"| psi2s_p | 0.286 0.035 0.066 0.079 -0.176 0.661 0.294 -0.084 0.811 0.562 0.550 0.186 -0.610 0.713 -0.449 1.000 0.808 0.364 0.638 -0.125 0.811 |\n",
"| Dbar_s | 0.349 0.042 0.080 0.100 -0.213 0.850 0.127 -0.108 0.951 0.586 0.595 0.271 -0.838 0.825 -0.427 0.808 1.000 0.488 0.666 -0.155 0.948 |\n",
"| p4160_s | 0.167 0.020 0.038 0.043 -0.105 0.419 -0.007 -0.053 0.538 0.220 0.136 0.359 -0.432 0.501 -0.042 0.364 0.488 1.000 0.185 -0.074 0.479 |\n",
"| p4160_p | 0.222 0.030 0.055 0.054 -0.134 0.626 0.476 -0.056 0.565 0.634 0.672 0.095 -0.343 0.717 -0.673 0.638 0.666 0.185 1.000 -0.417 0.625 |\n",
"| p4040_s | -0.063 -0.008 -0.015 -0.018 0.037 -0.092 -0.110 0.017 -0.049 -0.227 -0.265 0.063 0.113 -0.270 0.206 -0.125 -0.155 -0.074 -0.417 1.000 -0.158 |\n",
"| Dbar_p | 0.351 0.043 0.080 0.099 -0.215 0.840 0.122 -0.109 0.944 0.630 0.549 0.291 -0.907 0.803 -0.248 0.811 0.948 0.479 0.625 -0.158 1.000 |\n",
"-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\n",
"Hesse errors: OrderedDict([(<zfit.Parameter 'rho_p' floating=True>, {'error': 0.27771994822257895}), (<zfit.Parameter 'omega_s' floating=True>, {'error': 1.3959468507042243}), (<zfit.Parameter 'omega_p' floating=True>, {'error': 0.3691553864967809}), (<zfit.Parameter 'rho_s' floating=True>, {'error': 0.3006300727134063}), (<zfit.Parameter 'phi_s' floating=True>, {'error': 0.9091835835554605}), (<zfit.Parameter 'p3770_s' floating=True>, {'error': 0.46126289546140464}), (<zfit.Parameter 'jpsi_p' floating=True>, {'error': 0.03629362081781995}), (<zfit.Parameter 'phi_p' floating=True>, {'error': 0.16053174956105432}), (<zfit.Parameter 'Ctt' floating=True>, {'error': 0.4254592501236948}), (<zfit.Parameter 'p3770_p' floating=True>, {'error': 0.16063717375062758}), (<zfit.Parameter 'p4415_p' floating=True>, {'error': 0.22220018497165217}), (<zfit.Parameter 'p4415_s' floating=True>, {'error': 0.18442248665248195}), (<zfit.Parameter 'DDstar_s' floating=True>, {'error': 0.5121605805414826}), (<zfit.Parameter 'p4040_p' floating=True>, {'error': 0.2576226743388321}), (<zfit.Parameter 'DDstar_p' floating=True>, {'error': 0.6602293730335576}), (<zfit.Parameter 'psi2s_p' floating=True>, {'error': 0.059082172929771914}), (<zfit.Parameter 'Dbar_s' floating=True>, {'error': 0.39460513178279527}), (<zfit.Parameter 'p4160_s' floating=True>, {'error': 0.1812535589055404}), (<zfit.Parameter 'p4160_p' floating=True>, {'error': 0.16685474673584277}), (<zfit.Parameter 'p4040_s' floating=True>, {'error': 0.1655797281434641}), (<zfit.Parameter 'Dbar_p' floating=True>, {'error': 4.083121106609253})])\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Toy 7/10\n",
"Time taken: 13 min, 8 s\n",
"Projected time left: 5 min, 36 s\n",
"Toy 7: Generating data...\n",
"Toy 7: Data generation finished\n",
"Toy 7: Loading data...\n",
"Toy 7: Loading data finished\n",
"Toy 7: Fitting pdf...\n",
"------------------------------------------------------------------\n",
"| FCN = 2.98E+05 | Ncalls=1207 (1207 total) |\n",
"| EDM = 4.17E-06 (Goal: 5E-06) | up = 0.5 |\n",
"------------------------------------------------------------------\n",
"| Valid Min. | Valid Param. | Above EDM | Reached call limit |\n",
"------------------------------------------------------------------\n",
"| True | True | False | False |\n",
"------------------------------------------------------------------\n",
"| Hesse failed | Has cov. | Accurate | Pos. def. | Forced |\n",
"------------------------------------------------------------------\n",
"| False | True | True | True | False |\n",
"------------------------------------------------------------------\n",
"Function minimum: 298029.8179642496\n",
"----------------------------------------------------------------------------------------------\n",
"| | Name | Value | Hesse Err | Minos Err- | Minos Err+ | Limit- | Limit+ | Fixed |\n",
"----------------------------------------------------------------------------------------------\n",
"| 0 | rho_p | -0.3 | 0.4 | | |-6.28319 | 6.28319 | |\n",
"| 1 | omega_s | 6.9 | 1.0 | | | 4.19232 | 9.40768 | |\n",
"| 2 | omega_p | -6.28 | 0.12 | | |-6.28319 | 6.28319 | |\n",
"| 3 | rho_s | 0.96 | 0.30 | | |0.0253049| 2.0747 | |\n",
"| 4 | phi_s | 16.6 | 0.8 | | | 14.8182 | 23.5818 | |\n",
"| 5 | p3770_s | 1.94 | 0.27 | | |0.918861 | 4.08114 | |\n",
"| 6 | jpsi_p | -4.566 | 0.024 | | |-6.28319 | 6.28319 | |\n",
"| 7 | phi_p | 6.28 | 0.09 | | |-6.28319 | 6.28319 | |\n",
"| 8 | Ctt | 0.28 | 0.14 | | | -1 | 1 | |\n",
"| 9 | p3770_p | 2.91 | 0.14 | | |-6.28319 | 6.28319 | |\n",
"| 10| p4415_p | -2.75 | 0.14 | | |-6.28319 | 6.28319 | |\n",
"| 11| p4415_s | 1.47 | 0.19 | | |0.126447 | 2.35355 | |\n",
"| 12| DDstar_s | -0.300 | 0.020 | | | -0.3 | 0.3 | |\n",
"| 13| p4040_p | -2.69 | 0.20 | | |-6.28319 | 6.28319 | |\n",
"| 14| DDstar_p | -1.34 | 0.22 | | |-6.28319 | 6.28319 | |\n",
"| 15| psi2s_p | 1.93 | 0.03 | | |-6.28319 | 6.28319 | |\n",
"| 16| Dbar_s | -0.300 | 0.016 | | | -0.3 | 0.3 | |\n",
"| 17| p4160_s | 2.26 | 0.17 | | | 0.71676 | 3.68324 | |\n",
"| 18| p4160_p | -2.36 | 0.10 | | |-6.28319 | 6.28319 | |\n",
"| 19| p4040_s | 0.92 | 0.19 | | |0.00501244| 2.01499 | |\n",
"| 20| Dbar_p | -1.13 | 0.26 | | |-6.28319 | 6.28319 | |\n",
"----------------------------------------------------------------------------------------------\n",
"-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\n",
"| | rho_p omega_s omega_p rho_s phi_s p3770_s jpsi_p phi_p Ctt p3770_p p4415_p p4415_s DDstar_s p4040_p DDstar_p psi2s_p Dbar_s p4160_s p4160_p p4040_s Dbar_p |\n",
"-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\n",
"| rho_p | 1.000 0.391 0.013 0.127 0.115 0.033 0.057 -0.002 -0.144 -0.033 0.005 -0.020 -0.002 0.016 0.132 -0.009 -0.004 -0.007 0.010 -0.005 0.027 |\n",
"| omega_s | 0.391 1.000 0.126 -0.388 0.065 0.008 0.002 0.007 -0.032 -0.009 0.000 -0.004 -0.000 0.003 0.030 -0.005 -0.001 -0.000 -0.000 0.000 0.005 |\n",
"| omega_p | 0.013 0.126 1.000 -0.019 0.009 0.001 0.001 0.000 -0.006 -0.002 0.000 -0.001 -0.000 0.000 0.005 -0.001 -0.000 -0.000 0.000 -0.000 0.001 |\n",
"| rho_s | 0.127 -0.388 -0.019 1.000 -0.009 -0.003 -0.006 -0.004 -0.002 -0.009 -0.004 -0.005 0.001 -0.005 0.008 0.003 0.001 -0.008 -0.001 -0.003 0.009 |\n",
"| phi_s | 0.115 0.065 0.009 -0.009 1.000 -0.013 -0.044 -0.141 0.047 0.001 -0.006 0.005 0.001 -0.010 -0.038 0.000 0.002 -0.001 -0.009 0.001 -0.003 |\n",
"| p3770_s | 0.033 0.008 0.001 -0.003 -0.013 1.000 0.041 0.000 0.059 -0.411 -0.047 0.063 -0.007 -0.153 0.051 -0.185 -0.005 0.058 -0.161 0.179 0.158 |\n",
"| jpsi_p | 0.057 0.002 0.001 -0.006 -0.044 0.041 1.000 0.003 -0.417 0.034 -0.035 -0.108 -0.048 -0.013 0.216 -0.008 -0.042 -0.129 0.083 -0.089 -0.162 |\n",
"| phi_p | -0.002 0.007 0.000 -0.004 -0.141 0.000 0.003 1.000 0.000 0.001 0.001 0.000 0.000 0.001 -0.001 0.001 0.000 0.000 0.001 -0.000 -0.000 |\n",
"| Ctt | -0.144 -0.032 -0.006 -0.002 0.047 0.059 -0.417 0.000 1.000 -0.319 -0.068 0.347 0.006 -0.265 -0.106 0.295 -0.006 0.317 -0.472 0.451 0.415 |\n",
"| p3770_p | -0.033 -0.009 -0.002 -0.009 0.001 -0.411 0.034 0.001 -0.319 1.000 0.032 -0.135 -0.026 0.119 -0.051 -0.243 -0.037 -0.121 0.203 -0.153 -0.194 |\n",
"| p4415_p | 0.005 0.000 0.000 -0.004 -0.006 -0.047 -0.035 0.001 -0.068 0.032 1.000 -0.185 -0.032 0.135 0.080 -0.106 -0.017 -0.113 0.265 -0.193 0.067 |\n",
"| p4415_s | -0.020 -0.004 -0.001 -0.005 0.005 0.063 -0.108 0.000 0.347 -0.135 -0.185 1.000 0.012 0.003 0.242 0.081 0.005 0.366 -0.153 0.246 0.013 |\n",
"| DDstar_s | -0.002 -0.000 -0.000 0.001 0.001 -0.007 -0.048 0.000 0.006 -0.026 -0.032 0.012 1.000 -0.029 0.022 -0.021 -0.001 0.003 -0.049 0.010 -0.009 |\n",
"| p4040_p | 0.016 0.003 0.000 -0.005 -0.010 -0.153 -0.013 0.001 -0.265 0.119 0.135 0.003 -0.029 1.000 -0.062 -0.257 -0.023 0.300 0.163 -0.277 0.079 |\n",
"| DDstar_p | 0.132 0.030 0.005 0.008 -0.038 0.051 0.216 -0.001 -0.106 -0.051 0.080 0.242 0.022 -0.062 1.000 0.086 -0.007 0.244 -0.205 0.258 -0.646 |\n",
"| psi2s_p | -0.009 -0.005 -0.001 0.003 0.000 -0.185 -0.008 0.001 0.295 -0.243 -0.106 0.081 -0.021 -0.257 0.086 1.000 -0.023 -0.015 -0.204 0.106 0.048 |\n",
"| Dbar_s | -0.004 -0.001 -0.000 0.001 0.002 -0.005 -0.042 0.000 -0.006 -0.037 -0.017 0.005 -0.001 -0.023 -0.007 -0.023 1.000 0.004 -0.032 0.011 0.013 |\n",
"| p4160_s | -0.007 -0.000 -0.000 -0.008 -0.001 0.058 -0.129 0.000 0.317 -0.121 -0.113 0.366 0.003 0.300 0.244 -0.015 0.004 1.000 -0.291 0.113 0.123 |\n",
"| p4160_p | 0.010 -0.000 0.000 -0.001 -0.009 -0.161 0.083 0.001 -0.472 0.203 0.265 -0.153 -0.049 0.163 -0.205 -0.204 -0.032 -0.291 1.000 -0.596 -0.020 |\n",
"| p4040_s | -0.005 0.000 -0.000 -0.003 0.001 0.179 -0.089 -0.000 0.451 -0.153 -0.193 0.246 0.010 -0.277 0.258 0.106 0.011 0.113 -0.596 1.000 0.091 |\n",
"| Dbar_p | 0.027 0.005 0.001 0.009 -0.003 0.158 -0.162 -0.000 0.415 -0.194 0.067 0.013 -0.009 0.079 -0.646 0.048 0.013 0.123 -0.020 0.091 1.000 |\n",
"-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\n",
"Hesse errors: OrderedDict([(<zfit.Parameter 'rho_p' floating=True>, {'error': 0.36398836101189147}), (<zfit.Parameter 'omega_s' floating=True>, {'error': 0.9573655938922179}), (<zfit.Parameter 'omega_p' floating=True>, {'error': 0.1161196521304051}), (<zfit.Parameter 'rho_s' floating=True>, {'error': 0.30100333318176625}), (<zfit.Parameter 'phi_s' floating=True>, {'error': 0.7880287454443637}), (<zfit.Parameter 'p3770_s' floating=True>, {'error': 0.268531326789599}), (<zfit.Parameter 'jpsi_p' floating=True>, {'error': 0.02377233463435191}), (<zfit.Parameter 'phi_p' floating=True>, {'error': 0.09280934619497128}), (<zfit.Parameter 'Ctt' floating=True>, {'error': 0.1426871546019346}), (<zfit.Parameter 'p3770_p' floating=True>, {'error': 0.13564354111121535}), (<zfit.Parameter 'p4415_p' floating=True>, {'error': 0.1406477225445224}), (<zfit.Parameter 'p4415_s' floating=True>, {'error': 0.1890333499607768}), (<zfit.Parameter 'DDstar_s' floating=True>, {'error': 0.019919995842848176}), (<zfit.Parameter 'p4040_p' floating=True>, {'error': 0.19843285731605675}), (<zfit.Parameter 'DDstar_p' floating=True>, {'error': 0.22233334722540876}), (<zfit.Parameter 'psi2s_p' floating=True>, {'error': 0.03243402725807609}), (<zfit.Parameter 'Dbar_s' floating=True>, {'error': 0.015898744089318018}), (<zfit.Parameter 'p4160_s' floating=True>, {'error': 0.17238201397747432}), (<zfit.Parameter 'p4160_p' floating=True>, {'error': 0.1024453058239232}), (<zfit.Parameter 'p4040_s' floating=True>, {'error': 0.18940950203249995}), (<zfit.Parameter 'Dbar_p' floating=True>, {'error': 0.26492991575374436})])\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Toy 8/10\n",
"Time taken: 15 min, 32 s\n",
"Projected time left: 3 min, 52 s\n",
"Toy 8: Generating data...\n",
"Toy 8: Data generation finished\n",
"Toy 8: Loading data...\n",
"Toy 8: Loading data finished\n",
"Toy 8: Fitting pdf...\n",
"------------------------------------------------------------------\n",
"| FCN = 2.979E+05 | Ncalls=1817 (1817 total) |\n",
"| EDM = 1.18E-05 (Goal: 5E-06) | up = 0.5 |\n",
"------------------------------------------------------------------\n",
"| Valid Min. | Valid Param. | Above EDM | Reached call limit |\n",
"------------------------------------------------------------------\n",
"| True | True | False | False |\n",
"------------------------------------------------------------------\n",
"| Hesse failed | Has cov. | Accurate | Pos. def. | Forced |\n",
"------------------------------------------------------------------\n",
"| False | True | False | False | True |\n",
"------------------------------------------------------------------\n",
"Function minimum: 297909.4708085202\n",
"----------------------------------------------------------------------------------------------\n",
"| | Name | Value | Hesse Err | Minos Err- | Minos Err+ | Limit- | Limit+ | Fixed |\n",
"----------------------------------------------------------------------------------------------\n",
"| 0 | rho_p | -0.60 | 0.27 | | |-6.28319 | 6.28319 | |\n",
"| 1 | omega_s | 8.2 | 0.9 | | | 4.19232 | 9.40768 | |\n",
"| 2 | omega_p | -5.71 | 0.20 | | |-6.28319 | 6.28319 | |\n",
"| 3 | rho_s | 1.48 | 0.22 | | |0.0253049| 2.0747 | |\n",
"| 4 | phi_s | 18.0 | 0.8 | | | 14.8182 | 23.5818 | |\n",
"| 5 | p3770_s | 2.5 | 0.4 | | |0.918861 | 4.08114 | |\n",
"| 6 | jpsi_p | -1.48 | 0.05 | | |-6.28319 | 6.28319 | |\n",
"| 7 | phi_p | 0.24 | 0.19 | | |-6.28319 | 6.28319 | |\n",
"| 8 | Ctt | 0.27 | 0.09 | | | -1 | 1 | |\n",
"| 9 | p3770_p | 3.33 | 0.13 | | |-6.28319 | 6.28319 | |\n",
"| 10| p4415_p | -2.5 | 0.4 | | |-6.28319 | 6.28319 | |\n",
"| 11| p4415_s | 1.17 | 0.22 | | |0.126447 | 2.35355 | |\n",
"| 12| DDstar_s | -0.30 | 0.58 | | | -0.3 | 0.3 | |\n",
"| 13| p4040_p | -2.78 | 0.31 | | |-6.28319 | 6.28319 | |\n",
"| 14| DDstar_p | -4.0 | 0.7 | | |-6.28319 | 6.28319 | |\n",
"| 15| psi2s_p | 2.08 | 0.08 | | |-6.28319 | 6.28319 | |\n",
"| 16| Dbar_s | 0.28 | 0.47 | | | -0.3 | 0.3 | |\n",
"| 17| p4160_s | 1.91 | 0.29 | | | 0.71676 | 3.68324 | |\n",
"| 18| p4160_p | -2.23 | 0.19 | | |-6.28319 | 6.28319 | |\n",
"| 19| p4040_s | 1.22 | 0.29 | | |0.00501244| 2.01499 | |\n",
"| 20| Dbar_p | 2.80 | 0.23 | | |-6.28319 | 6.28319 | |\n",
"----------------------------------------------------------------------------------------------\n",
"-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\n",
"| | rho_p omega_s omega_p rho_s phi_s p3770_s jpsi_p phi_p Ctt p3770_p p4415_p p4415_s DDstar_s p4040_p DDstar_p psi2s_p Dbar_s p4160_s p4160_p p4040_s Dbar_p |\n",
"-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\n",
"| rho_p | 1.000 0.092 0.120 -0.064 -0.090 0.636 0.633 0.078 -0.230 0.526 0.660 0.542 0.703 0.660 0.673 0.673 -0.663 0.638 0.644 0.640 0.220 |\n",
"| omega_s | 0.092 1.000 0.591 -0.166 -0.006 0.069 0.067 -0.024 -0.020 0.058 0.072 0.060 0.077 0.072 0.075 0.073 -0.073 0.070 0.070 0.070 0.019 |\n",
"| omega_p | 0.120 0.591 1.000 0.161 -0.007 0.139 0.137 0.029 -0.046 0.116 0.144 0.119 0.154 0.144 0.148 0.147 -0.145 0.140 0.141 0.140 0.044 |\n",
"| rho_s | -0.064 -0.166 0.161 1.000 0.029 -0.199 -0.189 -0.006 0.075 -0.165 -0.205 -0.169 -0.217 -0.205 -0.208 -0.207 0.204 -0.199 -0.199 -0.199 -0.061 |\n",
"| phi_s | -0.090 -0.006 -0.007 0.029 1.000 -0.157 -0.156 0.556 0.066 -0.129 -0.162 -0.133 -0.172 -0.162 -0.163 -0.165 0.161 -0.157 -0.158 -0.157 -0.059 |\n",
"| p3770_s | 0.636 0.069 0.139 -0.199 -0.157 1.000 0.845 0.122 -0.252 0.633 0.851 0.705 0.905 0.844 0.881 0.841 -0.849 0.826 0.827 0.840 0.235 |\n",
"| jpsi_p | 0.633 0.067 0.137 -0.189 -0.156 0.845 1.000 0.119 -0.161 0.730 0.873 0.714 0.925 0.874 0.917 0.887 -0.880 0.838 0.859 0.841 0.136 |\n",
"| phi_p | 0.078 -0.024 0.029 -0.006 0.556 0.122 0.119 1.000 -0.036 0.102 0.127 0.105 0.135 0.127 0.131 0.129 -0.128 0.123 0.124 0.123 0.033 |\n",
"| Ctt | -0.230 -0.020 -0.046 0.075 0.066 -0.252 -0.161 -0.036 1.000 -0.268 -0.219 -0.057 -0.229 -0.267 -0.217 -0.190 0.266 -0.147 -0.297 -0.128 0.040 |\n",
"| p3770_p | 0.526 0.058 0.116 -0.165 -0.129 0.633 0.730 0.102 -0.268 1.000 0.722 0.572 0.761 0.736 0.750 0.722 -0.711 0.684 0.728 0.689 0.046 |\n",
"| p4415_p | 0.660 0.072 0.144 -0.205 -0.162 0.851 0.873 0.127 -0.219 0.722 1.000 0.714 0.943 0.892 0.916 0.896 -0.894 0.845 0.884 0.845 0.270 |\n",
"| p4415_s | 0.542 0.060 0.119 -0.169 -0.133 0.705 0.714 0.105 -0.057 0.572 0.714 1.000 0.780 0.742 0.741 0.742 -0.746 0.754 0.727 0.724 0.231 |\n",
"| DDstar_s | 0.703 0.077 0.154 -0.217 -0.172 0.905 0.925 0.135 -0.229 0.761 0.943 0.780 1.000 0.941 0.977 0.956 -0.959 0.914 0.918 0.914 0.287 |\n",
"| p4040_p | 0.660 0.072 0.144 -0.205 -0.162 0.844 0.874 0.127 -0.267 0.736 0.892 0.742 0.941 1.000 0.920 0.889 -0.886 0.887 0.862 0.841 0.243 |\n",
"| DDstar_p | 0.673 0.075 0.148 -0.208 -0.163 0.881 0.917 0.131 -0.217 0.750 0.916 0.741 0.977 0.920 1.000 0.931 -0.927 0.878 0.907 0.881 0.362 |\n",
"| psi2s_p | 0.673 0.073 0.147 -0.207 -0.165 0.841 0.887 0.129 -0.190 0.722 0.896 0.742 0.956 0.889 0.931 1.000 -0.904 0.864 0.875 0.872 0.227 |\n",
"| Dbar_s | -0.663 -0.073 -0.145 0.204 0.161 -0.849 -0.880 -0.128 0.266 -0.711 -0.894 -0.746 -0.959 -0.886 -0.927 -0.904 1.000 -0.868 -0.866 -0.871 -0.299 |\n",
"| p4160_s | 0.638 0.070 0.140 -0.199 -0.157 0.826 0.838 0.123 -0.147 0.684 0.845 0.754 0.914 0.887 0.878 0.864 -0.868 1.000 0.820 0.816 0.283 |\n",
"| p4160_p | 0.644 0.070 0.141 -0.199 -0.158 0.827 0.859 0.124 -0.297 0.728 0.884 0.727 0.918 0.862 0.907 0.875 -0.866 0.820 1.000 0.789 0.225 |\n",
"| p4040_s | 0.640 0.070 0.140 -0.199 -0.157 0.840 0.841 0.123 -0.128 0.689 0.845 0.724 0.914 0.841 0.881 0.872 -0.871 0.816 0.789 1.000 0.272 |\n",
"| Dbar_p | 0.220 0.019 0.044 -0.061 -0.059 0.235 0.136 0.033 0.040 0.046 0.270 0.231 0.287 0.243 0.362 0.227 -0.299 0.283 0.225 0.272 1.000 |\n",
"-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\n",
"Hesse errors: OrderedDict([(<zfit.Parameter 'rho_p' floating=True>, {'error': 0.27098401251561155}), (<zfit.Parameter 'omega_s' floating=True>, {'error': 0.8795791692660551}), (<zfit.Parameter 'omega_p' floating=True>, {'error': 0.19878574130391025}), (<zfit.Parameter 'rho_s' floating=True>, {'error': 0.21983936971915485}), (<zfit.Parameter 'phi_s' floating=True>, {'error': 0.8158249500568697}), (<zfit.Parameter 'p3770_s' floating=True>, {'error': 0.42953933743718586}), (<zfit.Parameter 'jpsi_p' floating=True>, {'error': 0.052411148720699074}), (<zfit.Parameter 'phi_p' floating=True>, {'error': 0.19152864374454648}), (<zfit.Parameter 'Ctt' floating=True>, {'error': 0.0904984667150246}), (<zfit.Parameter 'p3770_p' floating=True>, {'error': 0.1320625793194541}), (<zfit.Parameter 'p4415_p' floating=True>, {'error': 0.3996206195703933}), (<zfit.Parameter 'p4415_s' floating=True>, {'error': 0.21730905065700917}), (<zfit.Parameter 'DDstar_s' floating=True>, {'error': 0.579088304244729}), (<zfit.Parameter 'p4040_p' floating=True>, {'error': 0.3140623670807241}), (<zfit.Parameter 'DDstar_p' floating=True>, {'error': 0.7361126186488691}), (<zfit.Parameter 'psi2s_p' floating=True>, {'error': 0.08357871654532634}), (<zfit.Parameter 'Dbar_s' floating=True>, {'error': 0.4744964424913635}), (<zfit.Parameter 'p4160_s' floating=True>, {'error': 0.2853466413895025}), (<zfit.Parameter 'p4160_p' floating=True>, {'error': 0.19303201083625843}), (<zfit.Parameter 'p4040_s' floating=True>, {'error': 0.28818036516479095}), (<zfit.Parameter 'Dbar_p' floating=True>, {'error': 0.2313335191639263})])\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Toy 9/10\n",
"Time taken: 18 min, 33 s\n",
"Projected time left: 2 min, 3 s\n",
"Toy 9: Generating data...\n",
"Toy 9: Data generation finished\n",
"Toy 9: Loading data...\n",
"Toy 9: Loading data finished\n",
"Toy 9: Fitting pdf...\n",
"------------------------------------------------------------------\n",
"| FCN = 2.98E+05 | Ncalls=1130 (1130 total) |\n",
"| EDM = 5.08E-05 (Goal: 5E-06) | up = 0.5 |\n",
"------------------------------------------------------------------\n",
"| Valid Min. | Valid Param. | Above EDM | Reached call limit |\n",
"------------------------------------------------------------------\n",
"| True | True | False | False |\n",
"------------------------------------------------------------------\n",
"| Hesse failed | Has cov. | Accurate | Pos. def. | Forced |\n",
"------------------------------------------------------------------\n",
"| False | True | False | False | True |\n",
"------------------------------------------------------------------\n",
"Function minimum: 297973.98281601147\n",
"----------------------------------------------------------------------------------------------\n",
"| | Name | Value | Hesse Err | Minos Err- | Minos Err+ | Limit- | Limit+ | Fixed |\n",
"----------------------------------------------------------------------------------------------\n",
"| 0 | rho_p | 0.4 | 0.5 | | |-6.28319 | 6.28319 | |\n",
"| 1 | omega_s | 6.7 | 1.2 | | | 4.19232 | 9.40768 | |\n",
"| 2 | omega_p | 0.05 | 0.32 | | |-6.28319 | 6.28319 | |\n",
"| 3 | rho_s | 0.6 | 0.3 | | |0.0253049| 2.0747 | |\n",
"| 4 | phi_s | 19.3 | 1.0 | | | 14.8182 | 23.5818 | |\n",
"| 5 | p3770_s | 2.0 | 0.3 | | |0.918861 | 4.08114 | |\n",
"| 6 | jpsi_p | 4.75 | 0.07 | | |-6.28319 | 6.28319 | |\n",
"| 7 | phi_p | 0.54 | 0.21 | | |-6.28319 | 6.28319 | |\n",
"| 8 | Ctt | -0.03 | 0.18 | | | -1 | 1 | |\n",
"| 9 | p3770_p | -2.87 | 0.17 | | |-6.28319 | 6.28319 | |\n",
"| 10| p4415_p | 3.57 | 0.21 | | |-6.28319 | 6.28319 | |\n",
"| 11| p4415_s | 1.22 | 0.28 | | |0.126447 | 2.35355 | |\n",
"| 12| DDstar_s | -0.11 | 0.34 | | | -0.3 | 0.3 | |\n",
"| 13| p4040_p | 3.33 | 0.23 | | |-6.28319 | 6.28319 | |\n",
"| 14| DDstar_p | -1.6 | 2.4 | | |-6.28319 | 6.28319 | |\n",
"| 15| psi2s_p | 2.08 | 0.04 | | |-6.28319 | 6.28319 | |\n",
"| 16| Dbar_s | 0.30 | 0.52 | | | -0.3 | 0.3 | |\n",
"| 17| p4160_s | 1.98 | 0.19 | | | 0.71676 | 3.68324 | |\n",
"| 18| p4160_p | -2.32 | 0.28 | | |-6.28319 | 6.28319 | |\n",
"| 19| p4040_s | 1.50 | 0.21 | | |0.00501244| 2.01499 | |\n",
"| 20| Dbar_p | 5.2 | 1.2 | | |-6.28319 | 6.28319 | |\n",
"----------------------------------------------------------------------------------------------\n",
"-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\n",
"| | rho_p omega_s omega_p rho_s phi_s p3770_s jpsi_p phi_p Ctt p3770_p p4415_p p4415_s DDstar_s p4040_p DDstar_p psi2s_p Dbar_s p4160_s p4160_p p4040_s Dbar_p |\n",
"-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\n",
"| rho_p | 1.000 0.420 0.074 0.031 -0.088 -0.145 -0.250 -0.177 -0.216 -0.144 -0.156 0.185 0.239 -0.199 0.246 -0.146 0.204 0.150 -0.219 0.155 0.227 |\n",
"| omega_s | 0.420 1.000 0.670 -0.124 -0.019 -0.023 -0.045 -0.090 -0.035 -0.023 -0.025 0.029 0.037 -0.031 0.039 -0.024 0.034 0.024 -0.034 0.024 0.035 |\n",
"| omega_p | 0.074 0.670 1.000 -0.041 -0.000 -0.028 -0.052 -0.041 -0.044 -0.029 -0.031 0.036 0.047 -0.039 0.049 -0.029 0.042 0.029 -0.043 0.030 0.044 |\n",
"| rho_s | 0.031 -0.124 -0.041 1.000 -0.137 -0.188 -0.289 -0.146 -0.254 -0.187 -0.199 0.226 0.296 -0.252 0.302 -0.178 0.243 0.177 -0.274 0.189 0.277 |\n",
"| phi_s | -0.088 -0.019 -0.000 -0.137 1.000 0.141 0.222 0.689 0.199 0.139 0.151 -0.177 -0.230 0.192 -0.233 0.137 -0.188 -0.142 0.211 -0.148 -0.216 |\n",
"| p3770_s | -0.145 -0.023 -0.028 -0.188 0.141 1.000 0.635 0.100 0.434 0.289 0.482 -0.531 -0.678 0.600 -0.651 0.308 -0.469 -0.392 0.638 -0.382 -0.670 |\n",
"| jpsi_p | -0.250 -0.045 -0.052 -0.289 0.222 0.635 1.000 0.148 0.666 0.602 0.640 -0.726 -0.919 0.813 -0.884 0.614 -0.661 -0.574 0.875 -0.608 -0.848 |\n",
"| phi_p | -0.177 -0.090 -0.041 -0.146 0.689 0.100 0.148 1.000 0.129 0.097 0.105 -0.121 -0.157 0.134 -0.158 0.095 -0.125 -0.096 0.145 -0.101 -0.147 |\n",
"| Ctt | -0.216 -0.035 -0.044 -0.254 0.199 0.434 0.666 0.129 1.000 0.284 0.481 -0.429 -0.687 0.561 -0.711 0.538 -0.509 -0.272 0.572 -0.290 -0.743 |\n",
"| p3770_p | -0.144 -0.023 -0.029 -0.187 0.139 0.289 0.602 0.097 0.284 1.000 0.442 -0.514 -0.592 0.591 -0.570 0.375 -0.324 -0.413 0.619 -0.458 -0.522 |\n",
"| p4415_p | -0.156 -0.025 -0.031 -0.199 0.151 0.482 0.640 0.105 0.481 0.442 1.000 -0.598 -0.689 0.637 -0.638 0.434 -0.505 -0.460 0.705 -0.528 -0.668 |\n",
"| p4415_s | 0.185 0.029 0.036 0.226 -0.177 -0.531 -0.726 -0.121 -0.429 -0.514 -0.598 1.000 0.788 -0.671 0.794 -0.489 0.628 0.629 -0.748 0.581 0.764 |\n",
"| DDstar_s | 0.239 0.037 0.047 0.296 -0.230 -0.678 -0.919 -0.157 -0.687 -0.592 -0.689 0.788 1.000 -0.872 0.967 -0.636 0.821 0.608 -0.938 0.657 0.966 |\n",
"| p4040_p | -0.199 -0.031 -0.039 -0.252 0.192 0.600 0.813 0.134 0.561 0.591 0.637 -0.671 -0.872 1.000 -0.834 0.530 -0.627 -0.426 0.826 -0.651 -0.841 |\n",
"| DDstar_p | 0.246 0.039 0.049 0.302 -0.233 -0.651 -0.884 -0.158 -0.711 -0.570 -0.638 0.794 0.967 -0.834 1.000 -0.589 0.781 0.634 -0.912 0.674 0.980 |\n",
"| psi2s_p | -0.146 -0.024 -0.029 -0.178 0.137 0.308 0.614 0.095 0.538 0.375 0.434 -0.489 -0.636 0.530 -0.589 1.000 -0.381 -0.412 0.594 -0.383 -0.595 |\n",
"| Dbar_s | 0.204 0.034 0.042 0.243 -0.188 -0.469 -0.661 -0.125 -0.509 -0.324 -0.505 0.628 0.821 -0.627 0.781 -0.381 1.000 0.485 -0.706 0.502 0.808 |\n",
"| p4160_s | 0.150 0.024 0.029 0.177 -0.142 -0.392 -0.574 -0.096 -0.272 -0.413 -0.460 0.629 0.608 -0.426 0.634 -0.412 0.485 1.000 -0.623 0.373 0.571 |\n",
"| p4160_p | -0.219 -0.034 -0.043 -0.274 0.211 0.638 0.875 0.145 0.572 0.619 0.705 -0.748 -0.938 0.826 -0.912 0.594 -0.706 -0.623 1.000 -0.731 -0.900 |\n",
"| p4040_s | 0.155 0.024 0.030 0.189 -0.148 -0.382 -0.608 -0.101 -0.290 -0.458 -0.528 0.581 0.657 -0.651 0.674 -0.383 0.502 0.373 -0.731 1.000 0.636 |\n",
"| Dbar_p | 0.227 0.035 0.044 0.277 -0.216 -0.670 -0.848 -0.147 -0.743 -0.522 -0.668 0.764 0.966 -0.841 0.980 -0.595 0.808 0.571 -0.900 0.636 1.000 |\n",
"-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\n",
"Hesse errors: OrderedDict([(<zfit.Parameter 'rho_p' floating=True>, {'error': 0.47787650322626485}), (<zfit.Parameter 'omega_s' floating=True>, {'error': 1.1671499758739108}), (<zfit.Parameter 'omega_p' floating=True>, {'error': 0.31811570335196127}), (<zfit.Parameter 'rho_s' floating=True>, {'error': 0.33925542819480203}), (<zfit.Parameter 'phi_s' floating=True>, {'error': 1.049285588071493}), (<zfit.Parameter 'p3770_s' floating=True>, {'error': 0.3162497785112547}), (<zfit.Parameter 'jpsi_p' floating=True>, {'error': 0.06749011340636546}), (<zfit.Parameter 'phi_p' floating=True>, {'error': 0.21307465209402388}), (<zfit.Parameter 'Ctt' floating=True>, {'error': 0.18115444600402852}), (<zfit.Parameter 'p3770_p' floating=True>, {'error': 0.1657557614253573}), (<zfit.Parameter 'p4415_p' floating=True>, {'error': 0.21323294268173676}), (<zfit.Parameter 'p4415_s' floating=True>, {'error': 0.2815031802879736}), (<zfit.Parameter 'DDstar_s' floating=True>, {'error': 0.3353260528941947}), (<zfit.Parameter 'p4040_p' floating=True>, {'error': 0.23124270849511852}), (<zfit.Parameter 'DDstar_p' floating=True>, {'error': 2.396064403517207}), (<zfit.Parameter 'psi2s_p' floating=True>, {'error': 0.040594121775672676}), (<zfit.Parameter 'Dbar_s' floating=True>, {'error': 0.5200050109920695}), (<zfit.Parameter 'p4160_s' floating=True>, {'error': 0.19415791959034012}), (<zfit.Parameter 'p4160_p' floating=True>, {'error': 0.2822154575659217}), (<zfit.Parameter 'p4040_s' floating=True>, {'error': 0.20817892177171948}), (<zfit.Parameter 'Dbar_p' floating=True>, {'error': 1.166833707438908})])\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Toy 10/10\n",
"Time taken: 20 min, 56 s\n",
"Projected time left: \n"
]
},
{
"data": {
"image/png": 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cYY1VYI0d0QeAiOwCfgHcICKVIjJZREqA7+Kv7vtIRNaJyBcTf4sURQnF6/Px63mLImZKHdV33/teipV1cuj2bwLdp++8Pnt1KdFxxTYBY8wrwCsh1+4Ket0KXB3h2XuBe+MZ07q+A391Xuj1aD5KI0iP0MNYUZRjweMzfPW9xcBlYe939L77wQ9smyor+uVPIW1et+o7t826lOhoRwdFURLG4zM8P/OiiPfTnFY7n5tuimiTbGqu+Teg+z4lj826lOhoUFIUJWE8XsO5O9ZEvO8KtPN59dUUqurKoKVvAqEl4Q7/tKKNupToaFBSFCVhvD4f20eMi3jfFVhTmjYthaq60jTJ3yQmeN0rzSn+UnUbdSnR0aCkKErCuH2GgtbGiPfTnA7cPh/U1KRQVVechw8DXafvOk7EtVGXEh0NSoqiJIzXa8jweSPe79in1N6eQlUhuP2+g6fv/Edq2KxLiYoGJUVREsbjM1QNHR7xvsvp36Rqxo5NoaqutIzw788PbTPk8fqgtNQmVUosNCgpipIwHp+P6Xs3RbyfZq3jeJe/lypJ3cj96EMAgs8hdAbaDL1nny4lOhqUFEVJGI/P8O6McyLeT3P5P1raP/vZFCnqTtWCy4GubYbSAm2Grg675VHpA2hQUhQlYbxew5Xvh3Yb6yRwIq3jd79LlaRujHzSf9JN8PRdoNDBPPSQXbKUGGhQUiLy8vr9nHT367R5Ii9oK8cnHp+PxRd/IeL9NGvOrOlb30mVpG5s//IdQNc2Q4Gu5p7/p22G+ioalJSI/PDlzTS2eTh8VCuVlK54fIbr3ng84v3AibQZP+7WYSxllD70c6D7IX+ANmTtw2hQUhQlYbw+w18X/HvE+2lW9/D6r34jVZK6sf2LtwHdq+8AWr/5LVs0KbHRoKRERLSdrRIBt9fH5958MuL9QKaUc//PUyWpG2WP/gaA4NM1AkHJ9dOf2iFJiQMNSkpEop9WpRzPeH2GFaddHPF+YJqs4aprUiWpG3su+xwQfvqu+ZprbdGkxEaDkhITzZiUUDw+w7StkRuyBvYppf3z7RQp6k7BB8uB0I4OVlXgsqW2aFJio0FJUZSE8XgNh0aVRryfbu1Tah5XniJF3WksGw+EVt/5dbWOn8gHOw4z+a7XONjQaoc8JQIalBRFSRi318egtuaI9zNcTgB89Q2pktQNZ+MRoGumFAiW3vp6Hluxk+Z2L2t219miTwmPBiUlIgZdVFLC0+71keNpi3g/8OHvazyaKkndcDS3+P8bHJScnbpyMvwHbze1e1IvTomIBiUlJqKnyishtHt81I+M3Gw1wwpKDaNLU6SoO0cs38FrooFgeXTsuI5KPK9Pv3z1JTQoKTHRjEkJxe31MXFL5EKHjDT/R8tgq9jADopW+5uuBh/yFwhKWSvexWnVire5tWNJXyKuoCQiC0Rkq4hUiMi3w9zPEJGnrfsrRaQ06N6d1vWtInJRrDFFpMwaY5s1Zno0HyJSICLLROSoiPwmRNcsEdlgPfOAiNaRKUpv4PYaNp59WcT7gWmynRdemSpJ3dh5kd938PRdIIM7cMlnOrqHN2tQ6lPEDEoi4gQeBC4GJgPXisjkELObgDpjzATgfuA+69nJwCJgCrAAeEhEnDHGvA+43xhTDtRZY0f0AbQC3wPCbR3/LXAzUG7934JYv6/SHZ2+U0Jxe3yc/vriiPcz0vyFDmMWP5YqSd2Y8GygIWvntXSnX9ewPz3a8Xfd0q5BqS8RT6Y0B6gwxuwwxrQDi4GFITYLgSes188B51lZyUJgsTGmzRizE6iwxgs7pvXMfGsMrDGvjObDGNNkjFmOPzh1ICIjgMHGmPeNMQb4Y9BYiqIcA21eH+9fd0vE+4GMZO0N/5UqSd1Y/wV/myEJU3239Utfo93jA6BZg1KfIp6gNArYG/RzpXUtrI0xxgM0AAVRno10vQCot8YI9RXJRzTdlTF0AyAiN4vIahFZXV1dHWVIRVGMMbi9Ps5+6rcRbQJBacojv0qVrG5MffQBoOuaUmDzbNlDP+/ofq9BqW8RT1AKN3cTuvIdyaa3rserIx5N3S8a87AxZrYxZnZRUVGUIY8vtM2QEg6vz2AMrL32SxFtAhnJh//yxVTJ6sb6f7sZCJm+s3RtvO4/aff6M6UWLQnvU8QTlCqB0UE/lwD7I9mIiAvIA2qjPBvpeg0wxBoj1FckH9F0l8TQrShKggQ+zGe98HhEm0Chw9Rn/i8VksIy+Wm/73DTd+P+8oeO6bsWLXToU8QTlFYB5VZVXDr+woXQIyeXANdbr68CllrrOEuARVblXBn+YoMPI41pPbPMGgNrzJdi+AiLMeYA0Cgip1prVf8eNJaiKD3E7fH/s9t9/uURbUSEDJeDDWfYV1u0/dxLga6ZUoZV6LDtnEtp0zWlPknMoGSt39wKvA5sBp4xxmwUkXtE5ArL7FGgQEQqgDuAb1vPbgSeATYBrwG3GGO8kca0xvoWcIc1VoE1dkQfACKyC/gFcIOIVAZV8n0ZeAR/gcV24NVE3hxFUboTyJRKPlkd1S7d5WDEx6tSISksxZbvcPuUCtd+2BGUtPqub+GKbQLGmFeAV0Ku3RX0uhW4OsKz9wLdjp8MN6Z1fQf+6rzQ69F8lEa4vhqYGu6eEj+6eVYJJhCUWsdE7ugA/v53h4pLotokk4YRY2Bf+N53h4eVdAYlnb7rU2hHByUmWvCgBOO2PszT3e1R7TJcDkxz5KatycbR5u99F7xl3ukQf+bU0ty5pqSZUp9Cg5KiKAnhtjKl7KYjUe0y0hykN9rXJTz9iN+3I6SRS7rTgfNIA+1WSbhmSn0LDUpKTDRRUoLpmPaaOCmqXbrTwe7hZamQFJbqsRMAcIYGJZeDA6PGaaFDH0WDkqIoCRHIlIZ98G5Uu4w0JxPXf5AKSWEpWb0Ch4DD0TUopTkdjP3ovZSXhFc1tvIff1zN1oONKfHXX9GgpMQkSuW9chzi9vr/Hg5fdW1UuwyXg6Vz7SsJX3/eQlyO7h9xGS4H7595WUem1O7xpeT4ilc3HOQfmw7x6PIdSffVn9GgpMREY5ISTCBTKvnjH6LaZbgcXPCPp1MhKSwnv/jHLuXgATLTHMx7+S8dmRJAcwq6Omza71+D21VjX/FHf0CDkqIoCRH4MK++41tR7TJcDv544Q0pUBSet6/5SsdBfsFkp7t49rIbafN4GZKdBqRmCu9wk/+k3r11GpSioUFJUZSECOxTGvmrn0S1y0xz8i9/t6/N0JlPPojT2T0oZaU7ufj5h/EZGJJlBaUUFDvUN7sBOHiktaMZrNIdDUqKoiREIFNqvPWrUe1y0l08dvrnUiEpLP9ceH3YTCkn3ckT8/ydzIZkpwOpyZTqW/xByRioOtKWdH/9FQ1KSkx0TUkJJvABXvjHR6LaZaU7WbjixVRICsucVxaHXVPKTndx0T+fB+iYvktFWXh9s5uReZkAVDVqUIqEBiUlJtpmSAmmzQpK3oXRz8zMTnfy9/Gn2la9+fEp53bbowT+YPlS6VwgddN3xhgaWtqZMGwQANWNrTGeOH7RoKQoSkK0uv3Td1lrozdkzU53ctK+LR1rUKmm5NMNYdeUArogaPouyUGpud2L22uYWJwLaKYUDQ1KSkx0+k4JptXKlNJLozdkzU53cWBwkW295Wrzi8PuUwroAsizMqXmJK8pBdaTxhXl4hCo1qAUEQ1KSkw0JinBtHq8OB2CK8ZfRna6E6fPR5NNQcl4vRHWlPy6oHNNqTXJGuub/c1rh+akU5CboYUOUdCgpChKQrS6fWS6HHDoUFS7rHQnhU11th03nlN/OMI+Jb8uCC50SK7GBqscfEh2GsWDMqg+qkEpEhqUlJhomyElmFa3l8w0J8ycGdUuJ93FxmHjbWt4umv0xLCZUla6k43DxgMwJCtQEp7cda/A9N2Q7DSKBmVQpYUOEdGgpChKQrS6ff6g9Pe/R7XLTndy7vZVNLXZE5SmrlseYZ+Si3O3+0+lHZzlQoSkZ3MNVlDKy0pj2KBMnb6LggYlJSaaJynBtHq8ZKQ54D/+I6pdVrqTxdMvosVtz/Td0jOvCJspDc5ysXj6RYD/dNzsNGfS170C3RyGZKUzbHAGNUfb8NhUldjX0aCkxERn75RgWtu9ZLqccP/9Ue1yMlzctPol26bvLnz9qbDVd0NzMrhp9UsADMp0kZeV1pHJJIv6lnbSXQ4y0xwUD87EZ+BwU/STe49XNCgpipIQrR4vmWkOuOuuqHZZaU4emHctzTZN3z17yY1hM6WCnHQemOc/diM3w8XgFASlhmY3Q7LSEBGGDfZ3dTh0RNeVwhFXUBKRBSKyVUQqROTbYe5niMjT1v2VIlIadO9O6/pWEbko1pgiUmaNsc0aM/0YfHxNRDaKyCci8pSIZCb29ih+NFVSOulYU/r+96PaZac7uX35kyk5FiIcn/3bI7jCbJ4dmpPO7cufBCA308WQ7LSO6rhkUd/s7qj0GzY4A4BDuq4UlphBSUScwIPAxcBk4FoRmRxidhNQZ4yZANwP3Gc9OxlYBEwBFgAPiYgzxpj3AfcbY8qBOmvsnvgYBdwGzDbGTAWclp2iKMdAR/Xd178e1S4nw8Uf5nzGtn1KL8y/BkeYNkPZ6U7+MOczgH9NaUhWekqm7wKVfsOtTOmgZkphiSdTmgNUGGN2GGPagcXAwhCbhcAT1uvngPNERKzri40xbcaYnUCFNV7YMa1n5ltjYI15ZQ99ALiALBFxAdnA/jh+XyUEXVNSgml1e8lKc8Lvfx/VLsPl4PPrX+Nomz2Z0gXvvhS2+k5E+G7lO0wZORjwV8TVtyR3fae+2U2elSkV5GbgEKjSoBSWeILSKGBv0M+V1rWwNsYYD9AAFER5NtL1AqDeGiPUV0I+jDH7gJ8Be4ADQIMx5o1wv6CI3Cwiq0VkdXV1dcQ34nhFY5ISTKvb56++u/zyqHYiwodT5nEkyVlIJN6ffHrYNSWAz971nzzzpdMA/96h+iRP3zW0uDuavzodQtGgDF1TikA8QSnc/6qhn1ORbHrresI+RCQffxZVBowEckTkujC2GGMeNsbMNsbMLioqCmeiKIpFx/TdRx/FtJ15eGfSp8YiMW7v1rBrSgBZn6wnJ8MFwOCsNNo8vo6efsmgvtnd0WcPYNjgTF1TikA8QakSGB30cwndp8E6bKypsjygNsqzka7XAEOsMUJ9JerjfGCnMabaGOMG/gqcHsfvq4Sg03dKMEfbPORmuGD48Ji2bQVFHGm1Z/quOic/bEk40EV7oAAhWcGz1e2lxe0lPye945o/KGmmFI54gtIqoNyqikvHXyywJMRmCXC99foqYKnx96ZZAiyyKufKgHLgw0hjWs8ss8bAGvOlHvrYA5wqItnW2tN5wOb43hZFUcLh8fpo8/jISXdBpA/8ILIy02ybvms3QrorgsYg7fnW8RW1Sdo3VB/U9y7AsME6fRcJVywDY4xHRG4FXsdfwfZ/xpiNInIPsNoYswR4FPiTiFTgz14WWc9uFJFngE2AB7jFGOMFCDem5fJbwGIR+SGw1hqbHvhYKSLPAR9Z19cCD/f0jTqe0UP+lACBSrqcDCfsrYxpP+roYZa22hOU8mur8DkjBKXKTu2BEu2qxjZOHNH7OgJFFIHqO4BhgzKpa3Z3ToUqHcQMSgDGmFeAV0Ku3RX0uhW4OsKz9wL3xjOmdX0HndVzwdd74uNu4O5wzyjxo9N3SoAmq5IuJ8MFc7r9M+3G4ckzOFJnz/TdxyPKmR0pUwrSXjwouZtZ65r8QTk/KFMqGZoFwL76FsYX5SbFb39FOzooihI3XYLSCy/EtD/5o2UcaXHb0mn+7E0rSItQ6BCsvTiwmbUhOUEpcJZS4JRbgDFDswHYc7g5KT77MxqUlJhopqQECOw5ys1wwi23xLSvuPp62q11qFTzxMxLSIs0fRekPcPlJD87jUPHcJzEsi1V3Pj4KvbXt3S7V2etKeXndGZKowNBqVaDUigalBRFiZtAc9WcdBf87Gcx7ec+/xhAyosdfD7DjR/8NXKhQ4j2Yy3RvnvJRpZuqeI3yyq63Qu3plSUm0FWmpPdmil1Q4OSEhMtdFACHA2evovRkBVg91W4PawAACAASURBVK3fAJJXbh2Jdq+PB+ZdGzlTCtFefAwl2lWNrR0Zz6sbDuD1df33Unu0ncw0B1npnQUNIsKYodmaKYVBg5ISE52+UwJ0WVO6556Y9tP/7wEg9cc0tHt93LbiKTIiZUoh2kfnZ/U4QGw92AjA508dS12zm4/21HW5f6ixraMzeBefQ7PZU9vUI58DGQ1KiqLETWdQcsLtt8e0b7PWbmqOprZ7gdvj47HZV0TOlEK0lxbkUN/s7ihKSITAtN81p4zG5RCWbqnqer+hNWxQGlvgz5R8Pv3WF4wGJUVR4uaodTZSboYLHnsspv3IF54G4PDR1GdKV69/M/KaUoj20sIcAHbWJJ65BKb9xhflMrs0n2WhQamxtaMzeDATinNpdfuorOteHHE8o0FJiYlO3ykBmto8iPgP8OOii2LaZ112CQ6xI1My/LPs5MiZUoj2skJ/Ndyuw4kHperGNgZluMhKdzJ/UjFbDjZ2VOEZYzjY0NqxQTeYE4YPAmDrocaEfQ5kNCgpihI3DS3+xqIiAhs2xLR3bvyEoTkZKQ9K7V4vk6p3Rc6UQrSPHpqNQ2BndeJBqaqxlSIr6Jx7QjEAb2/1nzZQ3dhGm8dHSX52t+cmDrOC0sEjCfscyGhQUmKi1XdKgOAjGCgoiP1AQQGFuelUN6Z4+s5jqMsaRHqkzbMh2jNcTsoKc9h0IPGspepIG8WD/EFpQnEuJflZHetKFdVHO66HkpvhoiQ/i62HjibscyCjQUlRlLipbwk6giGj+5RUNzIyKMy1I1Py0e5Ki5wphdF+0qg8PtnXkLCvqsa2jlZFIsK5JxSzoqKGVreXiip/wBlXlBP22ROGDdJMKQQNSkpMdE1JCdDQ3E5eoF3Ozp2xH9i5k8Lc9JQHpTa3l9H1h0h3Rmh2Gkb71FF5HDzSSnVj/FqNMVQ1tnZkSgAXThlGi9vLa58cZPWuOooHZYQtdAA4ccRgtlc30WLTkfF9EQ1KSkw0JikBukzfzZsX+4F58xg2OJOqI20pLX1ucXtZXTK5y4bVUF2hTB2VB5BQttTY5qHV7evonwcwb3whE4pz+dkbW3lj00HmTSj0r8GFYcboIXh9hg09yNAGKhqUFEWJmy7Td88+G/uBZ59lVH4W7V5fSrOllnYvl25ZTnakoBRG+7SSPFwOYeXO2rj9VFl7lALTdwAOh/CdSyZRWddCu8fH508bG/H5GWOGALA2ZMPt8UxcR1coxzd2dHhW+h4+n/FnSoEjGOLYPMvttzNqpz8LqKxvoTjCNFZv09zu5bHZV7Ao0llFYbRnp7uYOWYI722vidtPldXENXj6DmD+pGEsudWfjU0rGRLx+cLcDMYWZLN2T33cPgc6mikpihIXjW0ejKEzU/rJT2I/9JOfdJRD70vhJtEWt5cvrXw+cqYUQfvp4wvZsK+Bhub4evUF1p+Kw+xDmlYyJGpACjBz9BA+2lOnX/4sNCgpMdF/KgrQ8UHdEZTiaMjKXXcxKt9/oF0qOxe0tHt5YN61kdeUImg/s7wQY+Ddiuq4/AS6ORQN6nkGOGtsPlWNbdqc1UKDkhIT/QKnABxu8mcFBblW9V0cDVm55x5yM1wMyU5jX33qPnSb273ctuIpf+eJCLrCMXNMPoW5Gbyy4UBcfqqOtJHhcjA4s+crIfMmFAKwvCL+acOBjAYlRVHiomOqKpAV/Nd/xX7IsinJz0rp2UEtbi9PnnIFrkhthiJodzqEi6cOZ+mWKprbYx/jXtXYRvHgjIjVdfFQVpjDyLxMlm+LHpSe+nAPN/9xNa3ugV0+rkFJiQNNlRSotqrnigKL+n/+c+yHLJsJRbkdG0lTQUu7h89tfjuyQRTtl04bQavbxxsbD8X049+jdGzFGyLCvAmFvLf9cLezmAJ4fYY7/7qBNzYd4o1NsXX1Z+IKSiKyQES2ikiFiHw7zP0MEXnaur9SREqD7t1pXd8qIhfFGlNEyqwxtlljph+DjyEi8pyIbBGRzSJyWmJvj6IoAaob2xCBoTnW9N38+bEfsmzKhw3iQEMrja2pOeyvxe1l/cRZMXWFY07pUMYWZPOXlbtj+qk60hZxY2winFFeSEOLO+IeqeBj1t8b4NN8MYOSiDiBB4GLgcnAtSIyOcTsJqDOGDMBuB+4z3p2MrAImAIsAB4SEWeMMe8D7jfGlAN11tgJ+7Ce+RXwmjFmEjAd2BzvG6N0omtKCvinqoZmp3d23t6yJfZDlk2g+ei2FGVLR9s8nFBfGdkginaHQ7hu7lhW7apjS4wWQIeOtIatvEuUM8uLcAi8tTl8FrS3zj/1mZnm4MNd8e+j6o/EkynNASqMMTuMMe3AYmBhiM1C4Anr9XPAeeKfZF0ILDbGtBljdgIV1nhhx7SemW+NgTXmlT3xISKDgbOARwGMMe3GGN0M0AM0Jingz5SKgvfjDBoU+yHLZuIwf0PSbSk6pqGhxY0MHhzZIIb2q2aVkOFy8NjyXRFtGlvdNLV7eyVTGpqTzimlQ3k9wpThXqsy78oZo9hR3RR3yXp/JJ6gNArYG/RzpXUtrI0xxgM0AAVRno10vQCot8YI9ZWoj3FANfCYiKwVkUdEJGxXRBG5WURWi8jq6ur4SkEV5XjjWILS6PxsstOdbNyfmuajDS1unMcQlPJz0ll0ymie/6iyIyCEEjhxdnhe72wIvnDKcLYeamRXmIMG99Q243QIC6YOB2D9voH7/TqeoBSurCT0y3Mkm9663hMfLuBk4LfGmJlAE9BtPQzAGPOwMWa2MWZ2UVFROJPjGp2+U8Caqgpe1N+6NfZDlo3DIcywNommgiMtHspqo0zfxaH9P88Zj0OE3/5ze9j7VUcC3Rx6KShNHgbAG5sOdru3t7aFkUMymTkmH4CP9x7fQakSGB30cwmwP5KNiLiAPKA2yrORrtcAQ6wxQn31xEelMWaldf05/EFKSRDdaa60e3wcPNJKibURFoBzz439YJDNyWPy2XygMa5S62OlocXNwZOj1DXFoX1EXhZXzy7h2dV72R3mRNpKq/hgRC9lSqOHZjN5xOCwU3h7apsZMzSbvKw0xhXlsG7vwG3gGk9QWgWUW1Vx6fiLCpaE2CwBrrdeXwUsNf5PsiXAIqtyrgwoBz6MNKb1zDJrDKwxX+qJD2PMQWCviJxgPXMesCmO31dRlBAONLRgDF2D0l/+EvvBIJuTx/o7Yn+c5A9Un89wpNXNnPdejUtXNG47r5w0p4Mfv9K9MGJXTRMuh3R9T46Ri6cOZ83uOirruk4ZVtY1M9pq1zSjZAjr9tYP2C+LMYOStX5zK/A6/uq1Z4wxG0XkHhG5wjJ7FCgQkQrgDqxpMmPMRuAZ/MHgNeAWY4w30pjWWN8C7rDGKrDGTtiH9cx/AX8RkfXADOBHib5BihY6KJ0tgroc6/3Nb8Z+MMhm1tihOB3C8jhb+PSUQI++rZ//z7h0RWPY4Ey+cs54Xtt4kPe3H+5yb2dNE2OGZkfeoNsDFs7wL6Ev+bhzMqqpzUPN0XZGD7WC0pgh1Bxt40BDa6/57UvE9W4aY14xxkw0xow3xtxrXbvLGLPEet1qjLnaGDPBGDPHGLMj6Nl7redOMMa8Gm1M6/oOa4wJ1phtx+BjnbVWNM0Yc6UxRvvDK0oPCCz2jx4alBX8+MexHwyyyctKY/bYfN7aXNXb8roQ6Dwx79k/xKUrFl88cxyjhmTx/b9txO31dVyvqDpKWWH4E2V7ypiCbGaNzefFtfs6MqFAOfgYKyhNt5q8DtR1Je3ooMRkgM4SKAlQWdeC0yFdy5/jbMgazHknFrPlYGO36aneJFCAUP+NO+PWFY3MNCd3XT6ZLQcbeWiZv+jhSKubiuqjTB8duwt4olw5YySfHjrK5gP+8vk9h7sGpUkjBpHudLCuUoOScpxidALvuGdnTRMl+Vldp6ribMgazIIpIwB4ce2+3pTXhUPWGUcnPHx/3LpicdGU4SycMZJfL93G+sp63qs4jDEwe2z+sUgNy6XTRuJyCC+u879Hge7hYwv8QSnD5eTEkYNZN0DPYNKgpChKTD491Eh5ccjenq98JfaDITZjCrI5bVwBT6/em7Tj0Q82+KfvMm67NW5d8fA/l09h2OBMbnhsFT95fQuFuRnMKRvaU5kRGZqTzrmTinl+TSWtbi87apoYnOnqPDIEmFGSx4Z9DRF75fVnNCgpsRl4f/dKArR7fOysaeroytDB00/HfjiMzbVzx7C3tiVpjUUr65rJy0oj58XnE9IVi/ycdP500xzys9PYV9fCPQun9GqRQzBfOL2Uw03tLPl4PxsqG5g6Kq9LJ/Lpo4fQ3O5NaZPbVKHHoSuKEpXdh5vw+ExH/7oOzjwz9sNhbC6ZOpxfFuZw/z8+5cLJw3A4en7sQzi2Vx9lfFEOnBFFXzzawzCuKJc37zgbt9eQ7kred/rTxhcwafggfvjyJo60eviv+RO63J8xurPY4YThcXTW6EdopqTERBOl45utVr+6CcUhmdL28J0OYtm4nA7uuHAiWw818th7u3pBYVd2VDcxvig3ur54tEdARJIakAI+vnfZZI60enA5pKNUPEBpQQ6DM10DsthBMyUlJlp9d3zz8d560l2O7plSVhybRiPYXHrSCF6YtI+fvLaFuWVDmToqrxeU+s83qmps82ttjqIvHu02M29CIX+79QxEun8hcDiE6aOHDMiycM2UFEWJyrq99UwdObh7dpAfR+VZBBsR4cefO4mCnHS+8PgqdlT3ztrIml3+rYizS/Oj64tHex/gpJK8iAF7eskQthxsHHAn0WpQUmKiJeHHL26vj/WVDcwYHeZD/JNPYg8QxaZ4UCaP3zgHr8/w2d++x7Ktx76p9p+fVpOd7mTKyLzo+uLR3seZPtrftmnj/oHVB0+DkhIRDUXKxv1HaPP4mDkmzCbRCy+MPUAMm4nDBvHiV+ZRPCiDLzy2ilv+8lHMg/Ui0dTm4ZUNB7hoynB/VhfNdzza+zjTR/szqLUDbL+SBiUlIoG1JF1TOn5559NqROD08QXdbz7+eOwB4rAZU5DN3/7rDG4/r5xlW6tY8Mt3+exDK3jk3R1sOXgk7v1MDy6r4Eirh8+fNja273i093GKB2UyZmg2H+wYWCfRaqGDEgUT9P+V45F/flrNSaPyKMgNc+T3d74Te4B4bPB3KfjaBRP5wrxSnvpwL3/7eD8//PtmAAZlujhpVB4TinOZUJxLWWEORYMyKMzNIDvdSW1TO396fze/f2cHV88q4WTrzKGovuPU1dc5o7yQJev24/b6Oo+p7+doUFIiohnS8U1dUzvr9tbz5bPHhze4997YPeTisQliSHY6Xz5nPF8+Zzx7a5v5cGctq3fXsenAEf760T6OtkU+i+lfZpdwz8Kp8flOUFdf5cwJhTy5cg8f761ndmnvd5ewAw1KSkQCMWmgntuiROflDQfw+gyXnDQivEEPGrImwuih2Ywems3nZpUA/r/DqsY2dtU0UXO0nZqjbTS3e8nJcHLGhELGFYXso4rmewAEJIDTxxfiEHh3W82ACUoDI99TkoIGo+ObF9fuY+KwXE4cEaFjQA8ash4LIsKwwZnMHVfApdNGcP3ppXz5nPH8+2ml3QNSLN+9qMtO8rLTOKlkCMsrauyW0mtoUFIiYkL+qxw/bD3YyJrddXxmZkmXnmtd+NKXYg8Uj02yiObbTl29zJkTClm3t54jrW67pfQKGpSUiBiNSsctf3h3B5lpDhadMjqy0QsvxB4oHptkEc23nbp6mTPKC/H6TLeTcfsrGpSUiOj03fHJ3tpmXlq3j6tnjSY/Jz2y4dy5sQeLxyZZRPNtp65e5uQx+WSnO1m+bWBM4WlQUiLSmShpcDqeuO+1LTgdwlfOjVB1F2Dv3tiDxWOTLKL5tlNXL5PucnDquALe3VZtt5ReQYOSEhndPHvcsXxbDS+vP8DNZ41nRF6MpqVOZ+wB47FJFtF826krCZxZXsiuw80dR6f3Z+IKSiKyQES2ikiFiHw7zP0MEXnaur9SREqD7t1pXd8qIhfFGlNEyqwxtlljpvfUh3XPKSJrReTl+N8WBXQp6XijodnNN579mHFFOZH3JgUzbFjv2CSLaL7t1JUEzppYBMA7AyBbihmURMQJPAhcDEwGrhWRySFmNwF1xpgJwP3Afdazk4FFwBRgAfCQFSSijXkfcL8xphyos8ZO2EeQttuBzfG9HUowgTUlzZQGPm6vj1uf+oiao2386pqZZKXHkUmsXds7Nskimm87dSWBcYU5jBqSxTufHgdBCZgDVBhjdhhj2oHFwMIQm4XAE9br54DzxF9HuhBYbIxpM8bsBCqs8cKOaT0z3xoDa8wre+gDESkBLgUeie/tUILRWHR84PMZvvfiJ7y7rYYfXjmVk0riPNvo0kt7xyZZRPNtp64kICKcNbGQ97cfxu312S3nmIgnKI0CglcFK61rYW2MMR6gASiI8myk6wVAvTVGqK9EfQD8EvhvIOr/SiJys4isFpHV1dX9/5tGb9HRkNVeGUoS8foMd/51A4tX7eWWc8ezaM6Y+B9+JI7vevHYJItovu3UlSTOKi+isc3Dun5+8F88QSnczrnQz6lINr11PWEfInIZUGWMWRPmfldjYx42xsw2xswuKiqKZX7cEKi609LwgUljq5sv/3kNT6/ey23nlfONC09IbIAktxk6Zo6DNkPBBFoO9fcpvHiCUiUQvIOuBNgfyUZEXEAeUBvl2UjXa4Ah1hihvhL1MQ+4QkR24Z8enC8if47j91UsNBYNXD491MjCB1fw1pYq7r58MndcMDFy54ZIfP/7vWOTLKL5tlNXksjLTmPG6CG808/3K8UTlFYB5VZVXDr+ooIlITZLgOut11cBS43/6/USYJFVOVcGlAMfRhrTemaZNQbWmC/1xIcx5k5jTIkxptQaf6kx5ro43xcFbTM0EHF7fTy4rILLHljOkRY3f/niXL4wr6xng919d+/YJItovu3UlUTOLC9ifWU9dU3tdkvpMTGDkrV+cyvwOv4qtmeMMRtF5B4RucIyexQoEJEK4A7g29azG4FngE3Aa8AtxhhvpDGtsb4F3GGNVWCNnbCPnr4hShAajQYUq3bVcuWDK/jp61u5YMowXvvqWZw6LszhffGS4oasCXMcNGQN5ayJRRgDK7b332xJdL2gK7NnzzarV6+2W0afYMJ3XsHjM/zuulksmDrcbjlKD9lV08T/vrqF1zYeZPjgTP7nisksmBrhOIpE2L8fRo48dptkEc23nbqSiMfrY+YP/sHFU4fzk6ump9S3iKwxxsw+1nG0o4MSB/rFpT+y53Azd/51Axfc/0/e2VbNHRdMZNk3zumdgATw97/3jk2yiObbTl1JxOV0cMaEQt7dVtNvC5T0kD8lIv3zT1qpqDrKQ29X8NK6/ThFuOaU0dw2v5ziwZm962jmzN6xSRbRfNupK8mcNbGIVz85SEXVUcqHRTgLqw+jQUmJiE87OvQbjDG8v+Mwj6/YxT82HyLD5eCG00u5+axxDOvtYBTg0KHesUkW0XzbqSvJnFleCMA722o0KCkDCw1GfZ+Wdi8vrtvH4yt2sfVQI/nZaXzlnPHcOK+MgtyM5Dr3xlFPFI9Nsojm205dSaYkP5txRTm882k1N53Rw8pKG9GgpMREY1PfY1dNE099uIfFq/bS0OLmxBGD+clV07hi+kgy01LUAXt0lAMAE7FJFtF826krBZxVXsTiVXtodXtT9/fQS2hQUmKiGVPfoNXt5bVPDrJ41R4+2FGLQ2DB1OHccHoZp5TmJ7759VhZuTL22kw8Nskimm87daWAsyYW8vh7u1i9q44zrOm8/oIGJUXp42zaf4SnV+3hhbX7ONLqYczQbL550Ql87uQShuclab0oHj7zmd6xSRbRfNupKwXMLSsgzSm8s6263wUlLQlXYqInz6aehmY3f/5gN1f8ZjmXPPAuT324l3NOKObJL87l7W+cwy3nTrA3IAH8/ve9Y5Msovm2U1cKyMlwMXvs0H7ZB08zJUXpI7R5vCzbUs0LaytZtqWadq+PScMHcfflk7lyxijyc9LtltgVbcjapzlzYiE/eW0rVUdae387QBLRTEmJia4pJQ9jDKt31fKdFzYw5963+M8/r2HN7jquO3Usf7v1DF69/Uy+MK+s7wUk0DZDfZyzyv0nHrzbzxq0aqakxERjUu+zo/ooL67dxwvr9rG3toXMNAcXTRnOZ2aO4owJhbic/eD74ne/2zs2ySKabzt1pYjJIwZTmJvOO9uq+dysErvlxE0/+MtXlIHBwYZWHl2+k4UPrmD+z//Jr5dVMHZoDj+/ejqr/98F/GrRTM45obh/BCSAH/2od2ySRTTfdupKEQ6HMG9CISsq+lfLIc2UlJj0pz/ovkZ1YxuvfnKAlz8+wKrdtRgDJ44YzHcumcQV00fZX6xwLNxwQ+/YJItovu3UlUJOG1fAS+v2s726iQnFuXbLiQsNSorSy9Q2tfPaJwd5ef1+PthxGJ+B8uJcvnreRC6bPoLxRf3jwyEmb7wBN9107DbJIppvO3WlkLnW0SQf7DisQUlRjicamt28vukgL68/wIqKGrw+Q1lhDrecO4HLpo3khOH9rwdZTKZO7R2bZBHNt526UkhpQTbFgzJYubOW604da7ecuNCgpMREZ+/CU9/czpubq3h1wwHe2VaN22sYPTSLm88ax6UnjWDKyMGp77KQSurqescmWUTzbaeuFCIizB1XwModhzHG9Iu/Rw1KSlh8Po1E4ag60srrmw7x+icHeX/HYbw+w8i8TG44vZTLpo1kWklev/iH3yu0tPSOTbKI5ttOXSlmbtlQ/vbxfnYdbqasMMduOTHRoKSExRMUlI73jg57a5t5feNBXv3kIB/tqcMYKCvM4eazxrFgyvDjKxAFM35879gki2i+7dSVYgJH3q/ccViDktJ/cXt9Ha+Pt+k7YwwVVUd57ZODvLbxIBv3HwH8+z6+dv5EFkwdTnlx7vEZiIJ5912YNu3YbZJFNN926kox44tyKMzN4IMdh1k0Z4zdcmKiQWmA8/M3tjK9ZAjnTx6W0HOt7oF73kw4fD7Dhn0NvL7RH4h2VDcBMGtsPt+95EQumjKcMQXZNqvsY1xzTe/YJItovu3UlWJEhLllQ1m5s7ZfrCvFtUtPRBaIyFYRqRCRb4e5nyEiT1v3V4pIadC9O63rW0XkolhjikiZNcY2a8z0nvgQkdEiskxENovIRhG5PfG3p//z66UVfPGPqxN+rtUz8DOlxlY3r244wDef/Zg5P3qLhQ+u4Pfv7GBkXhY/uHIqK79zHs9/+XT+46xxGpDC8dBDvWOTLKL5tlOXDcwdN5QDDa3sre37a2kxMyURcQIPAhcAlcAqEVlijNkUZHYTUGeMmSAii4D7gGtEZDKwCJgCjATeFJGJ1jORxrwPuN8Ys1hEfmeN/dse+PAAXzfGfCQig4A1IvKPEN1KBFraB2amtLOmiaVbqli65RAf7qzF7TUMznRx9gnFzJ9UxDkTi/tmn7m+iDZk7TcE1pU+2Hm4z3/BiidTmgNUGGN2GGPagcXAwhCbhcAT1uvngPPEnyMuBBYbY9qMMTuBCmu8sGNaz8y3xsAa88qe+DDGHDDGfARgjGkENgOj4ntbBgaeoHWhRAmevuvPiVK7x8d7FTX84OVNzP/Z25z7s7f5wcubqDrSxo1nlPH0zafy0fcu4NfXzuQzM0s0ICWCNmTtN5QX5zI0J52VO2rtlhKTeNaURgF7g36uBOZGsjHGeESkASiwrn8Q8mwgMIQbswCoN8Z4wtj3xAcA1lTfTGBluF9QRG4GbgYYM6bvLwTGS8sxrAt1CUr9bP6u5mgby7ZUsWxrFe98WsPRNg/pTgenjS/g+tNLmT+pmNFD+/a3xX7BnXf2jk2yiObbTl02ICKcPCafNbsHRlAKtyoW+ikVySbS9XAZWjT7nvjwPySSCzwPfNUYcySMLcaYh4GHAWbPnt2/PoGjcCxTcMcS0FKNMYaN+4+wdEsVb22pYn1lPcbAsMEZXD59BPMnDWPehAKy07Wup1f56U/hO985dptkEc23nbpsYnZpPm9uPkR1YxtFgzLslhOReP6VVgKjg34uAfZHsKkUEReQB9TGeDbc9RpgiIi4rGwp2D5hHyKShj8g/cUY89c4ftcBRfMxBKWGFnfH674YpZvaPKyoqGGplREdOtKGCEwvGcLXzp/I/EnFA7+jgt3827/1jk2yiObbTl02MXtsPgAf7anjoinDbVYTmXiC0iqgXETKgH34iwr+NcRmCXA98D5wFbDUGGNEZAnwpIj8An8RQjnwIf7sptuY1jPLrDEWW2O+1BMf1nrTo8BmY8wvEn1jBgLHEpTqmt2xjVLMnsPNLN1yiKVbq/lg+2HavT4GZbg4c2Ih8ycN45wTiijM7bvfAAccy5bF7rYdj02yiObbTl02MXVUHulOB2t29/OgZK3f3Aq8DjiB/zPGbBSRe4DVxpgl+D/8/yQiFfizl0XWsxtF5BlgE/5quFuMMV6AcGNaLr8FLBaRHwJrrbFJ1IeInAF8HtggIuusMb5jjHmlZ29V/6Op3RPbKAL1Te2dP9iUKrm9PtbsrmOZNS1XUXUUgHGFOfz7aWOZP6mY2aVDSXf1k/OHBhonnNA7Nskimm87ddlEZpqTk0ryWL2rb68rxTXJbn2QvxJy7a6g163A1RGevRe4N54xres78FfnhV5PyIcxZjnh15uOG4629Two1TZ3BqVUthmqbWrnn59W8dbmKt75tJojrR7SnMLcsgKunTOG+ZOK+0WrlOOCxsbesUkW0XzbqctGZo/N57EVu2h1e8lMc9otJyy68juAaW7zT985HYnH5r21LQzKdNHY2vPAFg/GGLYcbLT2DlV19JYrzM3goinDOe/EYs4oLyI3Q/9U+xwalPods8bm8/t3ustWSAAADThJREFUdrBhXwOnlA61W05Y9F/6AKbJypTSe3C89p7aJkoLctiwr6HXOzq0tHt5b3tNRyA60NAKwEmj8rhtfjnzJxVz0qg8HD0IpkoKmTSpd2ySRTTfduqykVlWscPqXXUalJTUc6TVX6yQmZZYUGpp97Kjuokrpo/0B6Ve0HKgoYU3N1exdPMh3tt+mDaPj+x0J2eWF/LV88s594Riigf346PBj0eWLoUpU47dJllE822nLhspyM1gXGGOtV+pb3ZK16A0gKk+2gaQ8P6cNbvr8PgMp5QN5a9r9/XIt89n+GR/A29uruKtzYc6Om2PGZrNtXPGcN6JxcwpG0qGq2/OaytxcN11vWOTLKL5tlOXzcwa69+v1Febs2pQGsBUH/EHJXeC7YZeWLuPnHQnc8v86X2803dur4/lFTW8sfEQS7cc4tCRNhwCJ4/J51sLJnHB5GLGF+mRDwOGX/86dg+5eGySRTTfduqymdml+Ty7ppLt1U1MKM61W043NCgNYCrr/B2BPQmcIrvl4BFeXLeP6+aOISeO4gKP18cHO2p5ef1+Xtt4kPpmNznpTs6aWMT5Jw7j3EnFDNV+cgMTbcjaL5k11v9lc83u2j4ZlHSDxwDFGMPmg/4ps3gzpb21zXzxidXkZ6dx+/kTCSQ03jCp0oGGFu7/x6fMu28p1z26kr99vJ9zJhbxyL/P5qO7LuC3183ic7NKNCANZLQha79kfFEO+dlprN5VZ7eUsGimNED5uLKBxlYPhbnpNLVF7+zg8xn+tn4//7NkI16f4U83zWVoTnpHq6H2oLOV9te38Oul23hmdSU+Yzh7YhH/c/lozp1U3Gf3PShJ4r//u3dskkU033bqshkRYdbYfNbs7ptBSTOlAYjXZ/jVm5+Sne7kwinD8fgiZ0ordxzmMw+t4PbF6xg5JIslt57B9NFDAMiwOiW0efxB7bk1lVzwi3/y/Br/9N473zyXx78wh4tPGqEB6XjkV7/qHZtkEc23nbr6ALPGDmVHTROHrWKovoRmSgMEYwzbq4/y1uYqXli7jy0HG/n+FVM4fLQNt9d0q7TZUX2U/311C29sOsTwwZn87OrpfGbmqC4bbQP7m9rcPh5+Zzs/emULp44byk+vmq5HPyhwddgGK4nbJItovu3U1QeYU2btV+qDffA0KPVj2jxePtxZy1ub/ZtQ99Q2AzB5xGB+868zuWzaSB54axvgL3ZIcwr1ze388s1t/PmD3WSmOfnmRSdw47wystK7ZzoOh5DudPDutmrW7q3n0pNG8KtFM3D1YDOuMgBZsQLGjTt2m2QRzbeduvoAJ40aQobLwYc7azUoKcdGdWMby7ZWsXRzFe9uq6ap3UuGy8EZEwr50tnjOPeEYkYOyeqwdzn9mY/Ha3hvezVff2YdtU3tLJozhjsumBizq3a6y8FHe+opyc/ip1dP04CkdFJW1js2ySKabzt19QHSXQ5mjhnCqj7YnFWDUh8n9AC7j/fWAzAiL5MrZ47ivBOLOW1cYdhMBzqn4J77qJK7X/qE8uJB/PHGuUweOTgu/4Gmrl86e7wekqd0pS2O9Yh4bJJFNN926uojzCkdym+WVXC0zdOnekv2HSVKBy3tXlZU1PDWliqWbani4JFWRGDG6CF848KJzJ80jBNHDIprE2qaFZS+9+InnDpuKP93wykJBZepowbzyb4jXHVySY9/H2WAcvhw79gki2i+7dTVR5hTVoBvaQUf7a7jrIlFdsvpQINSH2FffYu/QWlQb7jcDBdnHeMBdsMGdz7zwLUzE852Hvn3U2j3+CJmYspxzEkn9Y5Nsojm205dfYSZY4bgdAgf7qzVoKT49watq6znrc2HeGtzFVsO+lvpjxmazb/OHcN5k4Yxp+zYD7CbNXYoE4pz+co54ykelHjD0+F52iRVicDrr8OJJx67TbKI5ttOXX2EnAwXU0fl8eHOvrWuJKa3zyXo58yePdusXr06KWO3uv1HNryx8RBvbq6i5mgbTodwSmk+8ycVM3/SMMYX5WhvOKV/0NAAeXnHbpMsovm2U1cf4t6/b+KJ93ez/u4Lj3mvoYisMcbMPlZNWkqVZHw+w4qKGr729DpO/sE/uPHx1by8/gCnjhvKrxbN4KP/dwGLbz6Nm88az4RibVaq9CN082y/Z05ZAe0eH+srG+yW0oFmSiH0VqbU3O5h8Yd7eXT5TvbV+09xvWzaSBZMHc6p4/TIBkVR7Ke+uZ2ZP/gHt80v52sXTDymsTRT6qP4fIZnV+/lzPuWcc/Lmxg1JIvf/OtMVn33fH782ZM4e2KRBiRlYKANWfs9Q7LTmTUmn7e2HLJbSgdxBSURWSAiW0WkQkS+HeZ+hog8bd1fKSKlQffutK5vFZGLYo0pImXWGNusMdN720eyaGhxc9MTq/jmc+spLczh+S+fxjP/eRqXTRupveGUgcc3vtE7Nskimm87dfUxzp88jE/2HeFAQ4vdUoA4gpKIOIEHgYuBycC1IjI5xOwmoM4YMwG4H7jPenYysAiYAiwAHhIRZ4wx7wPuN8aUA3XW2L3to9epb27nX373Pu9uq+H7V0zh2S+d1nFuiaIMSB58sHdskkU033bq6mOcf+IwAF7++IDNSvzEkynNASqMMTuMMe3AYmBhiM1C4Anr9XPAeeJfsV8ILDbGtBljdgIV1nhhx7SemW+NgTXmlb3pI763JTHaPT5ueGwV/7+9swuxqori+O+f+REpOaMZMkoq+ZAPUVIpGSIRRhaB0MNA0GCB0AcUPZQhFEUE9RDRQ4ikQdGXUdEghA2l9KSm+TlZOpaQjDSYadlT1uphr+EexntnrtOZuefes35wOHuv2R9n/5l99tn77LPuz6f/4u01t9B12zwuuyw2LAQtzurV+aQZK4aru5HXVTCumzWVJfPbeXNHH/1nGz9bquc7pQ7gl0z8JLCkVhozuyDpHDDD7TuH5O3wcLUyZwBnzexClfR51XERktYCaz16XtJvwOlqaUdi+cujyVVYZjJKHVqQ0KJCaJFoOR06nh911pnAtXlcQz2DUrVH/qFb9mqlqWWvNkMbLn2edVxsNNsIbByMS9qTxy6SZid0qBBaVAgtEqFDBddiXh5l1bN8dxKYm4nPAfprpZF0OXAVcGaYvLXsp4HpXsbQuvKqIwiCICgo9QxK3wILfVfcJNKmgu4habqBLg/fD3xt6QOobqDTd87NBxYCu2uV6Xm2exl4mZ/nWUd9sgRBEASNYMTlO39/8ziwDZgAbDazXkkvAnvMrBvYBLwrqY80e+n0vL2StgDfAxeAx8zsH4BqZXqVzwAfSnoJ2Odlk3MdI7Fx5CSlIHSoEFpUCC0SoUOF3LQIjw5BEARBYQiPDkEQBEFhiEEpCIIgKAwxKGUYb7dEjUDSZkkDkg5nbO2Sety1U4+kNrdL0huux0FJizN5ujz9MUld1eoqMpLmStou6YikXklPuL2MWkyRtFvSAdfiBbfn5vKrmXCPMPskbfV4WXU4IemQpP2S9rht7PuHmcWR3qtNAI4DC4BJwAFgUaOvawzauRxYDBzO2F4F1nl4HfCKh1cBX5C+BVsK7HJ7O/CTn9s83Nbotl2iDrOBxR6eBhwluaMqoxYCpnp4IrDL27gF6HT7BuARDz8KbPBwJ/CRhxd5v5kMzPf+NKHR7RuFHk8B7wNbPV5WHU4AM4fYxrx/xEypwri5JWokZvYNafdilqwLp6Gund6xxE7SN2SzgbuAHjM7Y2a/Az0kv4NNg5mdMrPvPPwncITkCaSMWpiZnffoRD+M/Fx+NQ2S5gD3AG95PE/XZ63AmPePGJQqVHOn1FEjbatxjZmdgnSzBma5vZYmLaWVL7vcRJohlFILX7LaDwyQbhzHqdPlF5B1+dXsWrwOPA386/G6XZ/RWjpAejD5UtJeJVdsMA79ox43Q2WhHndKZeNSXTs1HZKmAp8AT5rZH6r9y78trYWlb/tulDQd+Ay4vloyP7ekFpLuBQbMbK+kFYPmKklbWocMy8ysX9IsoEfSD8OkzU2LmClVKLNbol99qo2fB9ze0i6cJE0kDUjvmdmnbi6lFoOY2VlgB+m9QF4uv5qFZcB9kk6Qlu/vIM2cyqYDAGbW7+cB0oPKrYxD/4hBqUKZ3RJlXTgNde30oO+sWQqc8yn7NmClpDbffbPSbU2Dr/1vAo6Y2WuZP5VRi6t9hoSkK4A7Se/Y8nL51RSY2bNmNseSY9FOUrseoGQ6AEi6UtK0wTDp//ow49E/Gr3Do0gHaQfJUdJ6+vpGX88YtfED4BTwN+kp5mHSOvhXwDE/t3takX4o8ThwCLg5U85DpBe4fcCaRrdrFDrcTlpGOAjs92NVSbW4geTS66DfeJ5z+wLSzbQP+BiY7PYpHu/zvy/IlLXeNfoRuLvRbfsfmqygsvuudDp4mw/40Tt4PxyP/hFuhoIgCILCEMt3QRAEQWGIQSkIgiAoDDEoBUEQBIUhBqUgCIKgMMSgFARBEBSGGJSCIAiCwhCDUhAEQVAY/gPmeVDqnLxQNAAAAABJRU5ErkJggg==\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# zfit.run.numeric_checks = False \n",
"\n",
"fitting_range = 'cut'\n",
"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\n",
"cut_BR = 1.0 - (6.02e-5 + 4.97e-6)/total_BR\n",
"\n",
"Ctt_list = []\n",
"Ctt_error_list = []\n",
"\n",
"nr_of_toys = 10\n",
"if fitting_range == 'cut':\n",
" nevents = int(pdg[\"number_of_decays\"]*cut_BR)\n",
"else:\n",
" nevents = int(pdg[\"number_of_decays\"])\n",
"# nevents = pdg[\"number_of_decays\"]\n",
"event_stack = 1000000\n",
"nevents *= 41\n",
"# zfit.settings.set_verbosity(10)\n",
"calls = int(nevents/event_stack + 1)\n",
"\n",
"total_samp = []\n",
"\n",
"start = time.time()\n",
"\n",
"sampler = total_f.create_sampler(n=event_stack)\n",
"\n",
"for toy in range(nr_of_toys):\n",
" \n",
" ### Generate data\n",
" \n",
"# clear_output(wait=True)\n",
" \n",
" print(\"Toy {}: Generating data...\".format(toy))\n",
" \n",
" dirName = 'data/zfit_toys/toy_{0}'.format(toy)\n",
" \n",
" if not os.path.exists(dirName):\n",
" os.mkdir(dirName)\n",
" print(\"Directory \" , dirName , \" Created \")\n",
" \n",
" reset_param_values()\n",
" \n",
" if fitting_range == 'cut':\n",
" \n",
" sampler.resample(n=nevents)\n",
" s = sampler.unstack_x()\n",
" sam = zfit.run(s)\n",
" calls = 0\n",
" c = 1\n",
" \n",
" else: \n",
" for call in range(calls):\n",
"\n",
" sampler.resample(n=event_stack)\n",
" s = sampler.unstack_x()\n",
" sam = zfit.run(s)\n",
"\n",
" c = call + 1\n",
"\n",
" with open(\"data/zfit_toys/toy_{0}/{1}.pkl\".format(toy, call), \"wb\") as f:\n",
" pkl.dump(sam, f, pkl.HIGHEST_PROTOCOL)\n",
" \n",
" print(\"Toy {}: Data generation finished\".format(toy))\n",
" \n",
" ### Load data\n",
" \n",
" print(\"Toy {}: Loading data...\".format(toy))\n",
" \n",
" if fitting_range == 'cut':\n",
" \n",
" total_samp = sam\n",
" \n",
" else:\n",
" \n",
" for call in range(calls):\n",
" with open(r\"data/zfit_toys/toy_0/{}.pkl\".format(call), \"rb\") as input_file:\n",
" sam = pkl.load(input_file)\n",
" total_samp = np.append(total_samp, sam)\n",
"\n",
" total_samp = total_samp.astype('float64')\n",
" \n",
" if fitting_range == 'full':\n",
"\n",
" data = zfit.data.Data.from_numpy(array=total_samp[:int(nevents)], obs=obs)\n",
" \n",
" print(\"Toy {}: Loading data finished\".format(toy))\n",
"\n",
" ### Fit data\n",
"\n",
" print(\"Toy {}: Fitting pdf...\".format(toy))\n",
"\n",
" for param in total_f.get_dependents():\n",
" param.randomize()\n",
"\n",
" nll = zfit.loss.UnbinnedNLL(model=total_f, data=data, fit_range = (x_min, x_max), constraints = constraints)\n",
"\n",
" minimizer = zfit.minimize.MinuitMinimizer(verbosity = 5)\n",
" # minimizer._use_tfgrad = False\n",
" result = minimizer.minimize(nll)\n",
"\n",
" print(\"Toy {}: Fitting finished\".format(toy))\n",
"\n",
" print(\"Function minimum:\", result.fmin)\n",
" print(\"Hesse errors:\", result.hesse())\n",
"\n",
" params = result.params\n",
" Ctt_list.append(params[Ctt]['value'])\n",
" Ctt_error_list.append(params[Ctt]['minuit_hesse']['error'])\n",
"\n",
" #plotting the result\n",
"\n",
" plotdirName = 'data/plots'.format(toy)\n",
"\n",
" if not os.path.exists(plotdirName):\n",
" os.mkdir(plotdirName)\n",
"# print(\"Directory \" , dirName , \" Created \")\n",
" \n",
" probs = total_f.pdf(test_q, norm_range=False)\n",
" calcs_test = zfit.run(probs)\n",
" plt.clf()\n",
" plt.plot(test_q, calcs_test, label = 'pdf')\n",
" plt.legend()\n",
" plt.ylim(0.0, 6e-6)\n",
" plt.savefig(plotdirName + '/toy_fit_full_range{}.png'.format(toy))\n",
"\n",
" print(\"Toy {0}/{1}\".format(toy+1, nr_of_toys))\n",
" print(\"Time taken: {}\".format(display_time(int(time.time() - start))))\n",
" print(\"Projected time left: {}\".format(display_time(int((time.time() - start)/(c+calls*(toy))*((nr_of_toys-toy)*calls-c)))))\n",
" \n",
" if fitting_range == 'cut':\n",
" \n",
" _1 = np.where((total_samp >= x_min) & (total_samp <= (jpsi_mass - 60.)))\n",
" \n",
" tot_sam_1 = total_samp[_1]\n",
" \n",
" _2 = np.where((total_samp >= (jpsi_mass + 70.)) & (total_samp <= (psi2s_mass - 50.)))\n",
" \n",
" tot_sam_2 = total_samp[_2]\n",
"\n",
" _3 = np.where((total_samp >= (psi2s_mass + 50.)) & (total_samp <= x_max))\n",
" \n",
" tot_sam_3 = total_samp[_3]\n",
"\n",
" tot_sam = np.append(tot_sam_1, tot_sam_2)\n",
" tot_sam = np.append(tot_sam, tot_sam_3)\n",
" \n",
" data = zfit.data.Data.from_numpy(array=tot_sam[:int(nevents)], obs=obs_fit)\n",
" \n",
" print(\"Toy {}: Loading data finished\".format(toy))\n",
" \n",
" ### Fit data\n",
"\n",
" print(\"Toy {}: Fitting pdf...\".format(toy))\n",
"\n",
" for param in total_f_fit.get_dependents():\n",
" param.randomize()\n",
"\n",
" nll = zfit.loss.UnbinnedNLL(model=total_f_fit, data=data, constraints = constraints)\n",
"\n",
" minimizer = zfit.minimize.MinuitMinimizer(verbosity = 5)\n",
" # minimizer._use_tfgrad = False\n",
" result = minimizer.minimize(nll)\n",
"\n",
" print(\"Function minimum:\", result.fmin)\n",
" print(\"Hesse errors:\", result.hesse())\n",
"\n",
" params = result.params\n",
" \n",
" if result.converged:\n",
" Ctt_list.append(params[Ctt]['value'])\n",
" Ctt_error_list.append(params[Ctt]['minuit_hesse']['error'])\n",
"\n",
" #plotting the result\n",
"\n",
" plotdirName = 'data/plots'.format(toy)\n",
"\n",
" if not os.path.exists(plotdirName):\n",
" os.mkdir(plotdirName)\n",
" # print(\"Directory \" , dirName , \" Created \")\n",
" \n",
" plt.clf()\n",
" plt.hist(tot_sam, bins = int((x_max-x_min)/7.), label = 'toy data')\n",
" plt.savefig(plotdirName + '/toy_histo_cut_region{}.png'.format(toy))\n",
"\n",
" \n",
" probs = total_f_fit.pdf(test_q, norm_range=False)\n",
" calcs_test = zfit.run(probs)\n",
" plt.clf()\n",
" plt.plot(test_q, calcs_test, label = 'pdf')\n",
" plt.axvline(x=jpsi_mass-60.,color='red', linewidth=0.7, linestyle = 'dotted')\n",
" plt.axvline(x=jpsi_mass+70.,color='red', linewidth=0.7, linestyle = 'dotted')\n",
" plt.axvline(x=psi2s_mass-50.,color='red', linewidth=0.7, linestyle = 'dotted')\n",
" plt.axvline(x=psi2s_mass+50.,color='red', linewidth=0.7, linestyle = 'dotted')\n",
" plt.legend()\n",
" plt.ylim(0.0, 1.5e-6)\n",
" plt.savefig(plotdirName + '/toy_fit_cut_region{}.png'.format(toy))\n",
" \n",
" print(\"Toy {0}/{1}\".format(toy+1, nr_of_toys))\n",
" print(\"Time taken: {}\".format(display_time(int(time.time() - start))))\n",
" print(\"Projected time left: {}\".format(display_time(int((time.time() - start)/(toy+1))*((nr_of_toys-toy-1)))))\n",
" "
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {},
"outputs": [],
"source": [
"with open(\"data/results/Ctt_list.pkl\", \"wb\") as f:\n",
" pkl.dump(Ctt_list, f, pkl.HIGHEST_PROTOCOL)\n",
"with open(\"data/results/Ctt_error_list.pkl\", \"wb\") as f:\n",
" pkl.dump(Ctt_error_list, f, pkl.HIGHEST_PROTOCOL)"
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"9/10 fits converged\n",
"Mean Ctt value = 0.4273973892545019\n",
"Mean Ctt error = 0.13801002900535617\n",
"95 Sensitivy = 0.0003199857041817954\n"
]
}
],
"source": [
"print('{0}/{1} fits converged'.format(len(Ctt_list), nr_of_toys))\n",
"print('Mean Ctt value = {}'.format(np.mean(Ctt_list)))\n",
"print('Mean Ctt error = {}'.format(np.mean(Ctt_error_list)))\n",
"print('95 Sensitivy = {}'.format(((2*np.mean(Ctt_error_list))**2)*4.2/1000))"
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"(36668,)"
]
},
"execution_count": 42,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"plt.hist(tot_sam, bins = int((x_max-x_min)/7.))\n",
"\n",
"plt.show()\n",
"\n",
"# _ = np.where((total_samp >= x_min) & (total_samp <= (jpsi_mass - 50.)))\n",
"\n",
"tot_sam.shape"
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {},
"outputs": [],
"source": [
"# sample from original values"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.3"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
