{
"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(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",
" 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), floating = False) #, lower_limit=-2*np.pi, upper_limit=2*np.pi)\n",
"rho_s = zfit.Parameter(\"rho_s\", ztf.constant(rho_scale), floating = False) #, 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), floating = False) #, lower_limit=-2*np.pi, upper_limit=2*np.pi)\n",
"omega_s = zfit.Parameter(\"omega_s\", ztf.constant(omega_scale), floating = False) #, 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), floating = False) #, lower_limit=-2*np.pi, upper_limit=2*np.pi)\n",
"phi_s = zfit.Parameter(\"phi_s\", ztf.constant(phi_scale), floating = False) #, 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), floating = False) #, 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), floating = False) #, 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=-0.5, upper_limit=0.5)"
]
},
{
"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",
"epsilon = 0.3\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 + epsilon, jpsi_mass - 50. - epsilon))\n",
"obs2 = zfit.Space('q', limits = (jpsi_mass + 50. + epsilon, psi2s_mass - 50. - epsilon))\n",
"obs3 = zfit.Space('q', limits = (psi2s_mass + 50. + epsilon, x_max - epsilon))\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(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(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": "markdown",
"metadata": {},
"source": [
"## Test if graphs actually work and compute values"
]
},
{
"cell_type": "code",
"execution_count": 11,
"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.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": 12,
"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": 13,
"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": 14,
"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": 15,
"metadata": {},
"outputs": [],
"source": [
"total_f.update_integration_options(draws_per_dim=200000, mc_sampler=None)\n",
"# inte = total_f.integrate(limits = (x_min, x_max), 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": 16,
"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][3]*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": 17,
"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": 18,
"metadata": {},
"outputs": [],
"source": [
"total_f._sample_and_weights = UniformSampleAndWeights"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [],
"source": [
"# 0.00133/(0.00133+0.213+0.015)*(x_max-3750)/(x_max-x_min)"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [],
"source": [
"# zfit.settings.set_verbosity(10)"
]
},
{
"cell_type": "code",
"execution_count": 21,
"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": 22,
"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": 23,
"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": 24,
"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": 25,
"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": 26,
"metadata": {},
"outputs": [],
"source": [
"# jpsi_width"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [],
"source": [
"# plt.hist(sample, weights=1 / prob(sample))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Fitting"
]
},
{
"cell_type": "code",
"execution_count": 28,
"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": 29,
"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": 30,
"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": 31,
"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": 32,
"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": 33,
"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": 34,
"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": 35,
"metadata": {},
"outputs": [],
"source": [
"# 0.15**2*4.2/1000\n",
"# result.hesse()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Constraints"
]
},
{
"cell_type": "code",
"execution_count": 36,
"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",
"# 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*10000.\n",
"\n",
"constraint4 = triGauss(bplus_0, bplus_1, bplus_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), 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",
"# 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]#, ztf.convert_to_tensor(constraint6_0)]#, ztf.convert_to_tensor(constraint6_1)]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Reset params"
]
},
{
"cell_type": "code",
"execution_count": 39,
"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": 40,
"metadata": {
"scrolled": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Toy 0: Generating data...\n",
"Toy 0: Data generation finished\n",
"Toy 0: Loading data...\n",
"Toy 0: Loading data finished\n",
"Toy 0: Fitting pdf...\n",
"------------------------------------------------------------------\n",
"| FCN = 241.5 | Ncalls=33 (33 total) |\n",
"| EDM = 2.47E-06 (Goal: 5E-06) | up = 0.5 |\n",
"------------------------------------------------------------------\n",
"| Valid Min. | Valid Param. | Above EDM | Reached call limit |\n",
"------------------------------------------------------------------\n",
"| True | True | False | False |\n",
"------------------------------------------------------------------\n",
"| Hesse failed | Has cov. | Accurate | Pos. def. | Forced |\n",
"------------------------------------------------------------------\n",
"| False | True | True | True | False |\n",
"------------------------------------------------------------------\n",
"Function minimum: 241.45590864280427\n",
"----------------------------------------------------------------------------------------------\n",
"| | Name | Value | Hesse Err | Minos Err- | Minos Err+ | Limit- | Limit+ | Fixed |\n",
"----------------------------------------------------------------------------------------------\n",
"| 0 | p4040_s | 1.1 | 0.5 | | |0.00501244| 2.01499 | |\n",
"| 1 | p4160_s | 2.7 | 0.6 | | | 0.71676 | 3.68324 | |\n",
"| 2 | p4415_p | 4.2 | 2.2 | | |-6.28319 | 6.28319 | |\n",
"| 3 | bplus_1 | -0.885 | 0.000 | | | -2 | 2 | |\n",
"| 4 | Ctt | 0.16 | 0.21 | | | -0.5 | 0.5 | |\n",
"| 5 | DDstar_p | -4.8 | 1.9 | | |-6.28319 | 6.28319 | |\n",
"| 6 | Dbar_p | 0.11 | 9.47 | | |-6.28319 | 6.28319 | |\n",
"| 7 | p4415_s | 0.18 | 0.16 | | |0.126447 | 2.35355 | |\n",
"| 8 | p3770_p | 2.7 | 2.7 | | |-6.28319 | 6.28319 | |\n",
"| 9 | DDstar_s | 0.020 | 0.138 | | | -0.3 | 0.3 | |\n",
"| 10| bplus_2 | -2.130E-1 | 0.019E-1 | | | -2 | 2 | |\n",
"| 11| p3770_s | 1.6 | 0.6 | | |0.918861 | 4.08114 | |\n",
"| 12| Dbar_s | 0.07 | 0.13 | | | -0.3 | 0.3 | |\n",
"| 13| bplus_0 | 0.466 | 0.000 | | | -2 | 2 | |\n",
"| 14| p4160_p | 6.0 | 0.6 | | |-6.28319 | 6.28319 | |\n",
"| 15| p4040_p | -0.11 | 3.88 | | |-6.28319 | 6.28319 | |\n",
"----------------------------------------------------------------------------------------------\n",
"--------------------------------------------------------------------------------------------------------------------------------------------------------------\n",
"| | p4040_s p4160_s p4415_p bplus_1 Ctt DDstar_p Dbar_p p4415_s p3770_p DDstar_s bplus_2 p3770_s Dbar_s bplus_0 p4160_p p4040_p |\n",
"--------------------------------------------------------------------------------------------------------------------------------------------------------------\n",
"| p4040_s | 1.000 0.045 0.009 -0.001 -0.002 -0.003 -0.082 -0.005 -0.013 -0.009 -0.001 0.011 -0.032 -0.000 -0.065 0.621 |\n",
"| p4160_s | 0.045 1.000 0.003 -0.000 0.000 -0.001 -0.040 0.000 -0.009 -0.008 -0.000 0.002 -0.020 -0.000 0.093 0.075 |\n",
"| p4415_p | 0.009 0.003 1.000 0.000 -0.000 0.001 -0.008 -0.093 -0.002 -0.002 0.000 0.000 -0.008 0.000 0.008 0.013 |\n",
"| bplus_1 | -0.001 -0.000 0.000 1.000 0.000 -0.000 0.000 0.000 0.000 -0.000 0.677 -0.000 0.000 0.450 -0.000 -0.001 |\n",
"| Ctt | -0.002 0.000 -0.000 0.000 1.000 0.001 0.019 0.000 -0.001 0.002 0.000 -0.000 0.004 0.000 -0.002 -0.002 |\n",
"| DDstar_p | -0.003 -0.001 0.001 -0.000 0.001 1.000 -0.030 0.001 -0.003 0.298 -0.000 0.000 0.012 -0.000 -0.002 -0.004 |\n",
"| Dbar_p | -0.082 -0.040 -0.008 0.000 0.019 -0.030 1.000 0.011 0.059 -0.091 0.000 0.004 0.364 0.000 -0.008 -0.098 |\n",
"| p4415_s | -0.005 0.000 -0.093 0.000 0.000 0.001 0.011 1.000 0.002 0.002 0.000 -0.000 0.005 0.000 -0.003 -0.009 |\n",
"| p3770_p | -0.013 -0.009 -0.002 0.000 -0.001 -0.003 0.059 0.002 1.000 -0.000 0.000 -0.128 0.029 0.000 -0.003 -0.003 |\n",
"| DDstar_s | -0.009 -0.008 -0.002 -0.000 0.002 0.298 -0.091 0.002 -0.000 1.000 -0.000 0.001 -0.037 -0.000 -0.002 -0.008 |\n",
"| bplus_2 | -0.001 -0.000 0.000 0.677 0.000 -0.000 0.000 0.000 0.000 -0.000 1.000 -0.000 0.000 0.190 -0.000 -0.001 |\n",
"| p3770_s | 0.011 0.002 0.000 -0.000 -0.000 0.000 0.004 -0.000 -0.128 0.001 -0.000 1.000 0.002 -0.000 0.002 0.015 |\n",
"| Dbar_s | -0.032 -0.020 -0.008 0.000 0.004 0.012 0.364 0.005 0.029 -0.037 0.000 0.002 1.000 0.000 -0.001 -0.031 |\n",
"| bplus_0 | -0.000 -0.000 0.000 0.450 0.000 -0.000 0.000 0.000 0.000 -0.000 0.190 -0.000 0.000 1.000 -0.000 -0.001 |\n",
"| p4160_p | -0.065 0.093 0.008 -0.000 -0.002 -0.002 -0.008 -0.003 -0.003 -0.002 -0.000 0.002 -0.001 -0.000 1.000 -0.074 |\n",
"| p4040_p | 0.621 0.075 0.013 -0.001 -0.002 -0.004 -0.098 -0.009 -0.003 -0.008 -0.001 0.015 -0.031 -0.001 -0.074 1.000 |\n",
"--------------------------------------------------------------------------------------------------------------------------------------------------------------\n",
"Hesse errors: OrderedDict([(<zfit.Parameter 'p4040_s' floating=True>, {'error': 0.5475039449324027}), (<zfit.Parameter 'p4160_s' floating=True>, {'error': 0.6200845337212617}), (<zfit.Parameter 'p4415_p' floating=True>, {'error': 2.1784786478303917}), (<zfit.Parameter 'bplus_1' floating=True>, {'error': 0.00045254373012648674}), (<zfit.Parameter 'Ctt' floating=True>, {'error': 0.2132717560841134}), (<zfit.Parameter 'DDstar_p' floating=True>, {'error': 1.8581258189915308}), (<zfit.Parameter 'Dbar_p' floating=True>, {'error': 9.473191034944215}), (<zfit.Parameter 'p4415_s' floating=True>, {'error': 0.1559314560994186}), (<zfit.Parameter 'p3770_p' floating=True>, {'error': 2.6685592600641765}), (<zfit.Parameter 'DDstar_s' floating=True>, {'error': 0.13789484579224884}), (<zfit.Parameter 'bplus_2' floating=True>, {'error': 0.0019374513205349109}), (<zfit.Parameter 'p3770_s' floating=True>, {'error': 0.5908195162116194}), (<zfit.Parameter 'Dbar_s' floating=True>, {'error': 0.13394604673222416}), (<zfit.Parameter 'bplus_0' floating=True>, {'error': 4.94973454072678e-05}), (<zfit.Parameter 'p4160_p' floating=True>, {'error': 0.631080637656769}), (<zfit.Parameter 'p4040_p' floating=True>, {'error': 3.8768081157581706})])\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\ipykernel_launcher.py:160: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Toy 1/2\n",
"Time taken: 2 min, 59 s\n",
"Projected time left: 2 min, 59 s\n",
"Toy 1: Generating data...\n",
"Toy 1: Data generation finished\n",
"Toy 1: Loading data...\n",
"Toy 1: Loading data finished\n",
"Toy 1: Fitting pdf...\n",
"------------------------------------------------------------------\n",
"| FCN = 241.4 | Ncalls=39 (39 total) |\n",
"| EDM = 1.08E-06 (Goal: 5E-06) | up = 0.5 |\n",
"------------------------------------------------------------------\n",
"| Valid Min. | Valid Param. | Above EDM | Reached call limit |\n",
"------------------------------------------------------------------\n",
"| True | True | False | False |\n",
"------------------------------------------------------------------\n",
"| Hesse failed | Has cov. | Accurate | Pos. def. | Forced |\n",
"------------------------------------------------------------------\n",
"| False | True | True | True | False |\n",
"------------------------------------------------------------------\n",
"Function minimum: 241.3777412895305\n",
"----------------------------------------------------------------------------------------------\n",
"| | Name | Value | Hesse Err | Minos Err- | Minos Err+ | Limit- | Limit+ | Fixed |\n",
"----------------------------------------------------------------------------------------------\n",
"| 0 | p4040_s | 0.45 | 0.18 | | |0.00501244| 2.01499 | |\n",
"| 1 | p4160_s | 2.91 | 0.28 | | | 0.71676 | 3.68324 | |\n",
"| 2 | p4415_p | 3.1 | 1.4 | | |-6.28319 | 6.28319 | |\n",
"| 3 | bplus_1 | -0.885 | 0.000 | | | -2 | 2 | |\n",
"| 4 | Ctt | -0.022 | 0.107 | | | -0.5 | 0.5 | |\n",
"| 5 | DDstar_p | -1.8 | 1.3 | | |-6.28319 | 6.28319 | |\n",
"| 6 | Dbar_p | 4.3 | 1.0 | | |-6.28319 | 6.28319 | |\n",
"| 7 | p4415_s | 1.59 | 0.23 | | |0.126447 | 2.35355 | |\n",
"| 8 | p3770_p | -0.8 | 1.1 | | |-6.28319 | 6.28319 | |\n",
"| 9 | DDstar_s | -0.03 | 0.07 | | | -0.3 | 0.3 | |\n",
"| 10| bplus_2 | -2.130E-1 | 0.019E-1 | | | -2 | 2 | |\n",
"| 11| p3770_s | 3.59 | 0.25 | | |0.918861 | 4.08114 | |\n",
"| 12| Dbar_s | -0.08 | 0.06 | | | -0.3 | 0.3 | |\n",
"| 13| bplus_0 | 0.466 | 0.000 | | | -2 | 2 | |\n",
"| 14| p4160_p | -2.0 | 6.8 | | |-6.28319 | 6.28319 | |\n",
"| 15| p4040_p | -5.0 | 0.8 | | |-6.28319 | 6.28319 | |\n",
"----------------------------------------------------------------------------------------------\n",
"--------------------------------------------------------------------------------------------------------------------------------------------------------------\n",
"| | p4040_s p4160_s p4415_p bplus_1 Ctt DDstar_p Dbar_p p4415_s p3770_p DDstar_s bplus_2 p3770_s Dbar_s bplus_0 p4160_p p4040_p |\n",
"--------------------------------------------------------------------------------------------------------------------------------------------------------------\n",
"| p4040_s | 1.000 -0.001 0.131 0.000 -0.006 0.021 0.032 0.001 0.075 0.045 0.000 -0.003 0.041 -0.000 0.178 0.017 |\n",
"| p4160_s | -0.001 1.000 -0.007 0.000 0.001 -0.001 -0.002 0.001 -0.006 -0.003 0.000 0.000 -0.003 0.000 -0.011 -0.004 |\n",
"| p4415_p | 0.131 -0.007 1.000 0.001 -0.023 0.089 0.137 -0.025 0.305 0.180 0.001 -0.010 0.165 -0.000 0.738 0.079 |\n",
"| bplus_1 | 0.000 0.000 0.001 1.000 0.000 -0.000 -0.000 -0.000 -0.000 0.000 0.677 -0.000 0.000 0.450 0.001 0.000 |\n",
"| Ctt | -0.006 0.001 -0.023 0.000 1.000 -0.003 -0.004 -0.000 -0.012 -0.009 0.000 0.000 -0.008 0.000 -0.032 -0.003 |\n",
"| DDstar_p | 0.021 -0.001 0.089 -0.000 -0.003 1.000 0.005 0.000 0.052 -0.034 0.000 -0.001 0.022 -0.000 0.116 0.012 |\n",
"| Dbar_p | 0.032 -0.002 0.137 -0.000 -0.004 0.005 1.000 0.001 0.078 0.043 0.000 -0.002 -0.030 -0.000 0.180 0.019 |\n",
"| p4415_s | 0.001 0.001 -0.025 -0.000 -0.000 0.000 0.001 1.000 0.003 0.001 -0.000 -0.000 0.001 -0.000 0.009 0.001 |\n",
"| p3770_p | 0.075 -0.006 0.305 -0.000 -0.012 0.052 0.078 0.003 1.000 0.103 0.000 0.002 0.092 -0.000 0.420 0.039 |\n",
"| DDstar_s | 0.045 -0.003 0.180 0.000 -0.009 -0.034 0.043 0.001 0.103 1.000 0.000 -0.003 0.052 -0.000 0.252 0.026 |\n",
"| bplus_2 | 0.000 0.000 0.001 0.677 0.000 0.000 0.000 -0.000 0.000 0.000 1.000 -0.000 0.000 0.190 0.002 0.000 |\n",
"| p3770_s | -0.003 0.000 -0.010 -0.000 0.000 -0.001 -0.002 -0.000 0.002 -0.003 -0.000 1.000 -0.003 -0.000 -0.014 -0.001 |\n",
"| Dbar_s | 0.041 -0.003 0.165 0.000 -0.008 0.022 -0.030 0.001 0.092 0.052 0.000 -0.003 1.000 -0.000 0.228 0.023 |\n",
"| bplus_0 | -0.000 0.000 -0.000 0.450 0.000 -0.000 -0.000 -0.000 -0.000 -0.000 0.190 -0.000 -0.000 1.000 -0.000 0.000 |\n",
"| p4160_p | 0.178 -0.011 0.738 0.001 -0.032 0.116 0.180 0.009 0.420 0.252 0.002 -0.014 0.228 -0.000 1.000 0.114 |\n",
"| p4040_p | 0.017 -0.004 0.079 0.000 -0.003 0.012 0.019 0.001 0.039 0.026 0.000 -0.001 0.023 0.000 0.114 1.000 |\n",
"--------------------------------------------------------------------------------------------------------------------------------------------------------------\n",
"Hesse errors: OrderedDict([(<zfit.Parameter 'p4040_s' floating=True>, {'error': 0.1817980073330014}), (<zfit.Parameter 'p4160_s' floating=True>, {'error': 0.28105296332603724}), (<zfit.Parameter 'p4415_p' floating=True>, {'error': 1.4460585352975572}), (<zfit.Parameter 'bplus_1' floating=True>, {'error': 0.00045254152319595953}), (<zfit.Parameter 'Ctt' floating=True>, {'error': 0.1070128591631859}), (<zfit.Parameter 'DDstar_p' floating=True>, {'error': 1.274894582621453}), (<zfit.Parameter 'Dbar_p' floating=True>, {'error': 0.9863367274899124}), (<zfit.Parameter 'p4415_s' floating=True>, {'error': 0.22635340269660909}), (<zfit.Parameter 'p3770_p' floating=True>, {'error': 1.0686131922004622}), (<zfit.Parameter 'DDstar_s' floating=True>, {'error': 0.06595112952400507}), (<zfit.Parameter 'bplus_2' floating=True>, {'error': 0.0019374367701227024}), (<zfit.Parameter 'p3770_s' floating=True>, {'error': 0.24635197699053668}), (<zfit.Parameter 'Dbar_s' floating=True>, {'error': 0.06369342157651345}), (<zfit.Parameter 'bplus_0' floating=True>, {'error': 4.949731518788525e-05}), (<zfit.Parameter 'p4160_p' floating=True>, {'error': 6.846065392563649}), (<zfit.Parameter 'p4040_p' floating=True>, {'error': 0.7735769115033309})])\n",
"Toy 2/2\n",
"Time taken: 5 min, 55 s\n",
"Projected time left: \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": [
"# zfit.run.numeric_checks = False \n",
"\n",
"fitting_range = 'cut'\n",
"\n",
"Ctt_list = []\n",
"Ctt_error_list = []\n",
"\n",
"nr_of_toys = 2\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",
" ### 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",
" 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",
" 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",
" tot_sam_1 = np.where((total_samp >= x_min) & (total_samp <= (jpsi_mass - 50.)))\n",
" \n",
" tot_sam_2 = np.where((total_samp >= (jpsi_mass + 50.)) & (total_samp <= (psi2s_mass - 50.)))\n",
"\n",
" tot_sam_3 = np.where((total_samp >= (psi2s_mass + 50.)) & (total_samp <= x_max))\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",
" 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.legend()\n",
" plt.ylim(0.0, 6e-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)/(c+calls*(toy))*((nr_of_toys-toy)*calls-c)))))\n",
" "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"2/2 fits converged\n",
"Mean Ctt value = 0.07076623985541536\n",
"Mean Ctt error = 0.16014230762364964\n"
]
}
],
"source": [
"print('{0}/{1} fits converged'.format(len(Ctt_list), nr_of_toys))\n",
"print('Mean Ctt value = {}'.format(np.mean(Ctt_list)))\n",
"print('Mean Ctt error = {}'.format(np.mean(Ctt_error_list)))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# sample from original values"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.3"
}
},
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