{
"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), lower_limit = -2.0, upper_limit= 2.0)\n",
"bplus_1 = zfit.Parameter(\"bplus_1\", ztf.constant(-0.885), lower_limit = -2.0, upper_limit= 2.0)\n",
"bplus_2 = zfit.Parameter(\"bplus_2\", ztf.constant(-0.213), 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": [],
"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 = 1\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": [],
"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": [],
"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": [
"# Ctt.floating = False"
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {},
"outputs": [],
"source": [
"# zfit.run(nll.value())"
]
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {},
"outputs": [],
"source": [
"# result.fmin"
]
},
{
"cell_type": "code",
"execution_count": 46,
"metadata": {},
"outputs": [],
"source": [
"# BR_steps = np.linspace(0.0, 1e-3, 11)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# CLS Code"
]
},
{
"cell_type": "code",
"execution_count": 48,
"metadata": {
"scrolled": true
},
"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"
]
}
],
"source": [
"# zfit.run.numeric_checks = False \n",
"\n",
"load = True\n",
"\n",
"bo5 = True\n",
"\n",
"bo5_set = 5\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 = 1\n",
"nevents = int(pdg[\"number_of_decays\"]*cut_BR)\n",
"# nevents = pdg[\"number_of_decays\"]\n",
"event_stack = 1000000\n",
"# nevents *= 41\n",
"# zfit.settings.set_verbosity(10)\n",
"\n",
"mi = 0.0\n",
"ma = 1e-3\n",
"ste = 11\n",
"\n",
"BR_steps = np.linspace(mi, ma, ste)\n",
"\n",
"Ctt_steps = np.sqrt(BR_steps/4.2*1000)\n",
"\n",
"total_samp = []\n",
"\n",
"start = time.time()\n",
"\n",
"Nll_list = []\n",
"\n",
"sampler = total_f.create_sampler(n=nevents)\n",
"\n",
"__ = 0\n",
"\n",
"#-----------------------------------------------------\n",
"\n",
"if not load:\n",
"\n",
" for Ctt_step in Ctt_steps:\n",
" \n",
" __ += 1\n",
" \n",
" newset = True\n",
" \n",
" for floaty in [True, False]:\n",
"\n",
" Ctt.floating = floaty\n",
"\n",
" Nll_list.append([])\n",
" \n",
" if bo5:\n",
" \n",
" if __ < 6:\n",
" \n",
" while len(Nll_list[-1])/bo5_set < nr_of_toys:\n",
"\n",
" print('Step: {0}/{1}'.format(__, ste))\n",
"\n",
" print('Current Ctt: {0}'.format(Ctt_step))\n",
" print('Ctt floating: {0}'.format(floaty))\n",
"\n",
" print('Toy {0}/{1} - Fit {2}/{3}'.format(int(len(Nll_list[-1])/bo5_set), nr_of_toys, len(Nll_list[-1]), bo5_set))\n",
"\n",
" reset_param_values()\n",
"\n",
" if floaty:\n",
" Ctt.set_value(Ctt_step)\n",
" else:\n",
" Ctt.set_value(0.0)\n",
"\n",
" if newset:\n",
" sampler.resample(n=nevents)\n",
" s = sampler.unstack_x()\n",
" total_samp = zfit.run(s)\n",
" calls = 0\n",
" c = 1\n",
" newset = False\n",
"\n",
"\n",
" data = zfit.data.Data.from_numpy(array=total_samp[:int(nevents)], obs=obs_fit)\n",
"\n",
" ### Fit data\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",
" Nll_list[-1].append(result.fmin)\n",
"\n",
" else:\n",
"\n",
" while len(Nll_list[-1]) < nr_of_toys:\n",
"\n",
" print('Step: {0}/{1}'.format(__, ste))\n",
"\n",
" print('Current Ctt: {0}'.format(Ctt_step))\n",
" print('Ctt floating: {0}'.format(floaty))\n",
"\n",
" print('Toy {0}/{1}'.format(len(Nll_list[-1]), nr_of_toys))\n",
"\n",
" reset_param_values()\n",
"\n",
" if floaty:\n",
" Ctt.set_value(Ctt_step)\n",
" else:\n",
" Ctt.set_value(0.0)\n",
"\n",
" if floaty:\n",
" sampler.resample(n=nevents)\n",
" s = sampler.unstack_x()\n",
" total_samp = zfit.run(s)\n",
" calls = 0\n",
" c = 1\n",
"\n",
"\n",
" data = zfit.data.Data.from_numpy(array=total_samp[:int(nevents)], obs=obs_fit)\n",
"\n",
" ### Fit data\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",
" Nll_list[-1].append(result.fmin)\n",
"\n",
" _t = int(time.time()-start)\n",
"\n",
" print('Time Taken: {}'.format(display_time(int(_t))))\n",
"\n",
" print('Predicted time left: {}'.format(display_time(int((_t/(__+1)*(ste-__-1))))))\n"
]
},
{
"cell_type": "code",
"execution_count": 49,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(22, 1)\n",
"(22, 2)\n",
"(22, 3)\n",
"(22, 4)\n",
"bo1\n"
]
}
],
"source": [
"if load:\n",
" Nll_list = []\n",
" CLs_values = []\n",
"\n",
" _dir = 'data/CLs/finished/f1d1'\n",
" \n",
" jobs = os.listdir(_dir)\n",
" \n",
" for s in range(ste):\n",
" CLs_values.append([])\n",
" \n",
" for s in range(2*ste):\n",
" Nll_list.append([])\n",
" \n",
" for job in jobs:\n",
" if not os.path.exists(\"{}/{}/data/CLs/{}-{}_{}s--CLs_Nll_list.pkl\".format(_dir, job, mi,ma,ste)):\n",
" print(job)\n",
" continue\n",
" \n",
" with open(r\"{}/{}/data/CLs/{}-{}_{}s--CLs_Nll_list.pkl\".format(_dir, job, mi,ma,ste), \"rb\") as input_file:\n",
" _Nll_list = pkl.load(input_file)\n",
" \n",
" if bo5: \n",
" for s in range(2*ste):\n",
" Nll_list[s].append(np.min(_Nll_list[s]))\n",
" else:\n",
" for s in range(2*ste):\n",
" Nll_list[s].extend(_Nll_list[s])\n",
" \n",
" with open(r\"{}/{}/data/CLs/{}-{}_{}s--CLs_list.pkl\".format(_dir, job, mi,ma,ste), \"rb\") as input_file:\n",
" _CLs_values = pkl.load(input_file)\n",
" \n",
" for s in range(ste):\n",
" CLs_values[s].extend(_CLs_values[s])\n",
" \n",
" print(np.shape(Nll_list))"
]
},
{
"cell_type": "code",
"execution_count": 50,
"metadata": {},
"outputs": [],
"source": [
"dirName = 'data/CLs'\n",
"\n",
"# if bo5 and not load:\n",
"# for s in range(2*ste):\n",
"# Nll_list[s] = [np.min(Nll_list[s])]\n",
"\n",
"if bo5: \n",
" CLs_values= []\n",
" for i in range(int(len(Nll_list)/2)):\n",
" CLs_values.append([])\n",
" for j in range(len(Nll_list[0])):\n",
" CLs_values[i].append(Nll_list[2*i][j]-Nll_list[2*i+1][j])\n",
"\n",
"\n",
"if not load:\n",
" \n",
" if not os.path.exists(dirName):\n",
" os.mkdir(dirName)\n",
" print(\"Directory \" , dirName , \" Created \")\n",
"\n",
" with open(\"{}/{}-{}_{}s--CLs_Nll_list.pkl\".format(dirName, mi,ma,ste), \"wb\") as f:\n",
" pkl.dump(Nll_list, f, pkl.HIGHEST_PROTOCOL)\n",
" \n",
" CLs_values = []\n",
" \n",
" for i in range(int(len(Nll_list)/2)):\n",
" CLs_values.append([])\n",
" for j in range(nr_of_toys):\n",
" CLs_values[i].append(Nll_list[2*i][j]-Nll_list[2*i+1][j])\n",
"\n",
" with open(\"{}/{}-{}_{}s--CLs_list.pkl\".format(dirName, mi,ma,ste), \"wb\") as f:\n",
" pkl.dump(CLs_values, f, pkl.HIGHEST_PROTOCOL)"
]
},
{
"cell_type": "code",
"execution_count": 51,
"metadata": {},
"outputs": [],
"source": [
"# print(CLs_values)\n",
"# print(Nll_list)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Plot"
]
},
{
"cell_type": "code",
"execution_count": 56,
"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"
}
],
"source": [
"l = []\n",
"\n",
"if not os.path.exists('data/CLs/plots'):\n",
" os.mkdir('data/CLs/plots')\n",
" print(\"Directory \" , 'data/CLs/plots' , \" Created \")\n",
"\n",
"for i in range(len(CLs_values)):\n",
" plt.clf()\n",
" plt.title('Ctt value: {:.2f}'.format(Ctt_steps[i]))\n",
" plt.hist(CLs_values[0], bins = 100, range = (-25, 25), label = 'Ctt fixed to 0')\n",
" plt.hist(CLs_values[i], bins = 100, range = (-25, 25), label = 'Ctt floating')\n",
" plt.axvline(x=np.mean(CLs_values[0]),color='red', linewidth=1.0, linestyle = 'dotted')\n",
" plt.legend()\n",
" plt.savefig('data/CLs/plots/CLs-BR({:.1E}).png'.format(BR_steps[i]))\n",
" \n",
" l.append(len(np.where(np.array(CLs_values[i]) < np.mean(CLs_values[0]))[0]))\n"
]
},
{
"cell_type": "code",
"execution_count": 57,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"BR: 0.0000\n",
"1.0\n",
"\n",
"BR: 0.0001\n",
"1.0\n",
"\n",
"BR: 0.0002\n",
"0.0\n",
"\n",
"BR: 0.0003\n",
"1.0\n",
"\n",
"BR: 0.0004\n",
"1.0\n",
"\n",
"BR: 0.0005\n",
"1.5\n",
"\n",
"BR: 0.0006\n",
"0.5\n",
"\n",
"BR: 0.0007\n",
"0.0\n",
"\n",
"BR: 0.0008\n",
"0.5\n",
"\n",
"BR: 0.0009\n",
"0.5\n",
"\n",
"BR: 0.0010\n",
"0.5\n",
"\n"
]
}
],
"source": [
"for s in range(len(l)):\n",
" print('BR: {:.4f}'.format(BR_steps[s]))\n",
" print(2*l[s]/len(CLs_values[0]))\n",
" print()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# print(np.array(Nll_list[0][:10])-np.array(Nll_list[1][:10]))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"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
}
