{
"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",
"\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",
"\n",
"Ctt.set_value(0.5)\n",
"\n",
"probs1 = total_f_fit.pdf(test_q, norm_range=False)\n",
"\n",
"calcs_test1 = 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:13: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
" del 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 (Ctt = 0.0)')\n",
"plt.plot(test_q, calcs_test1, label = 'pdf (Ctt = 0.5)')\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": 47,
"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": 48,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
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"297846.39122335566\n",
"297839.99318561575\n",
"297840.56383983896\n",
"297692.9585121496\n",
"297688.5535658027\n",
"297818.77499847737\n",
"297818.98953200254\n",
"empty in 10\n",
"empty in 11\n",
"empty in 12\n",
"empty in 13\n",
"empty in 14\n",
"empty in 15\n",
"empty in 16\n",
"empty in 17\n",
"empty in 18\n",
"empty in 19\n",
"empty in 20\n",
"empty in 21\n",
"(22,)\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",
"# print(_Nll_list)\n",
" \n",
" if bo5: \n",
" for s in range(2*ste):\n",
" if _Nll_list[s]: \n",
" Nll_list[s].append(np.min(_Nll_list[s]))\n",
" print(np.min(_Nll_list[s]))\n",
" else:\n",
" print('empty in {}'.format(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": 49,
"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 and load: \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[2*i])):\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": 50,
"metadata": {},
"outputs": [],
"source": [
"# print(CLs_values)\n",
"# print(Nll_list)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Plot"
]
},
{
"cell_type": "code",
"execution_count": 51,
"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": 54,
"metadata": {
"scrolled": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"BR: 0.0000\n",
"0.696078431372549\n",
"\n",
"BR: 0.0001\n",
"0.7156862745098039\n",
"\n",
"BR: 0.0002\n",
"0.6274509803921569\n",
"\n",
"BR: 0.0003\n",
"0.6764705882352942\n",
"\n",
"BR: 0.0004\n",
"0.7549019607843137\n",
"\n",
"BR: 0.0005\n",
"0.7093596059113301\n",
"\n",
"BR: 0.0006\n",
"0.6108374384236454\n",
"\n",
"BR: 0.0007\n",
"0.6108374384236454\n",
"\n",
"BR: 0.0008\n",
"0.6896551724137931\n",
"\n",
"BR: 0.0009\n",
"0.6305418719211823\n",
"\n",
"BR: 0.0010\n",
"0.7586206896551724\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[s]))\n",
" print()"
]
},
{
"cell_type": "code",
"execution_count": 53,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"297818.356392785\n",
"297819.7667433609\n",
"297840.3929684116\n",
"297842.76270291663\n",
"297824.18234748516\n",
"297824.15246830357\n",
"297805.96860094374\n",
"297805.6297383122\n",
"297815.3593473281\n",
"297816.9535084667\n",
"297813.1327635662\n",
"297813.25842232205\n",
"297822.5320305655\n",
"297823.22816647816\n",
"297826.72528264904\n",
"297826.3537664936\n",
"297836.0488055982\n",
"297836.00021670037\n",
"297815.56969196635\n",
"297815.8245172473\n",
"297814.9244276687\n",
"297816.06427965104\n"
]
}
],
"source": [
"for i in range(len(Nll_list)):\n",
" print(np.mean(np.array(Nll_list[i])))"
]
},
{
"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
}
