{
"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": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0.46\n"
]
}
],
"source": []
},
{
"cell_type": "code",
"execution_count": 4,
"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 = ztf.sqrt(tf.abs(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. * kabs**2. * beta**2. *tf.abs(tf.complex(C10eff, ztf.constant(0.0))*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(tf.complex(C10eff, ztf.constant(0.0)) * 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 *ztf.sqrt(q2)\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 = ztf.sqrt(tf.abs(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 = kabs**2 * (1. - 1./3. * beta**2)\n",
"\n",
" abs_bracket = 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) + tf.complex(2.0 * C7eff * (mb + ms)/(mB + mK), ztf.constant(0.0)) * 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 * ztf.sqrt(q2)\n",
"\n",
"def c9eff(q, funcs):\n",
"\n",
" C9eff_nr = tf.complex(ztf.constant(pdg['C9eff']), ztf.constant(0.0))\n",
"\n",
" c9 = C9eff_nr\n",
"\n",
" c9 = c9 + funcs\n",
"\n",
" return c9"
]
},
{
"cell_type": "code",
"execution_count": 5,
"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": 7,
"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",
" 'DDstar_mass', 'DDstar_scale', 'DDstar_phase',\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 resonance(q, _mass = self.params['jpsi_mass'], scale = self.params['jpsi_scale'],\n",
" phase = self.params['jpsi_phase'], width = self.params['jpsi_width'])\n",
"\n",
" def psi2s_res(q):\n",
" return resonance(q, _mass = self.params['psi2s_mass'], scale = self.params['psi2s_scale'],\n",
" phase = self.params['psi2s_phase'], width = self.params['psi2s_width'])\n",
" \n",
" def p3770_res(q):\n",
" return resonance(q, _mass = self.params['p3770_mass'], scale = self.params['p3770_scale'],\n",
" phase = self.params['p3770_phase'], width = self.params['p3770_width'])\n",
" \n",
" def p4040_res(q):\n",
" return resonance(q, _mass = self.params['p4040_mass'], scale = self.params['p4040_scale'],\n",
" phase = self.params['p4040_phase'], width = self.params['p4040_width'])\n",
" \n",
" def p4160_res(q):\n",
" return resonance(q, _mass = self.params['p4160_mass'], scale = self.params['p4160_scale'],\n",
" phase = self.params['p4160_phase'], width = self.params['p4160_width'])\n",
" \n",
" def p4415_res(q):\n",
" return resonance(q, _mass = self.params['p4415_mass'], scale = self.params['p4415_scale'],\n",
" phase = self.params['p4415_phase'], 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['DDstar_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": [
"## Load data"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"x_min = 2*pdg['muon_M']\n",
"x_max = (pdg[\"Bplus_M\"]-pdg[\"Ks_M\"]-0.1)\n",
"\n",
"obs = zfit.Space('q', limits = (x_min, x_max))\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 parameters"
]
},
{
"cell_type": "code",
"execution_count": 9,
"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), 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": 11,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<zfit.util.temporary.TemporarilySet at 0x1d8e7681160>"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"m_c = 1300\n",
"\n",
"DDstar_eta = 0\n",
"Dbar_phase = np.random.uniform(0.0, 2*np.pi)\n",
"DDstar_phase = np.random.uniform(0.0, 2*np.pi)\n",
"DDstar_mass = pdg['D0_M']\n",
"\n",
"if Dbar_phase < np.pi:\n",
" Dbar_phase = 0.0\n",
"else:\n",
" Dbar_phase = np.pi\n",
" \n",
"Dbar_mass = (pdg['D0_M']+pdg['Dst_M'])/2\n",
"\n",
"Dbar_eta = R/np.cos(Dbar_phase)*(6*Dbar_mass**2)\n",
"\n",
"Dbar_s = zfit.Parameter(\"Dbar_s\", ztf.constant(Dbar_eta), lower_limit=-1.464, upper_limit=1.464)\n",
"Dbar_m = zfit.Parameter(\"Dbar_m\", ztf.constant(Dbar_mass), floating = False)\n",
"Dbar_p = zfit.Parameter(\"Dbar_p\", ztf.constant(Dbar_phase), floating = False)\n",
"DDstar_s = zfit.Parameter(\"DDstar_s\", ztf.constant(DDstar_eta), lower_limit=-2.0, upper_limit=2.0)#, floating = False)\n",
"DDstar_m = zfit.Parameter(\"DDstar_m\", ztf.constant(DDstar_mass), floating = False)\n",
"DDstar_p = zfit.Parameter(\"DDstar_p\", ztf.constant(DDstar_phase), floating = False)\n",
"\n",
"Dbar_s.set_value(0.0)\n",
"DDstar_s.set_value(0.0)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Tau parameters"
]
},
{
"cell_type": "code",
"execution_count": 12,
"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": [
"## Setup pdf"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [],
"source": [
"total_f = total_pdf(obs=obs, 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",
" DDstar_mass = DDstar_m, DDstar_scale = DDstar_s, DDstar_phase = DDstar_p,\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",
" \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": 14,
"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, 200000)\n",
"\n",
"probs = total_f.pdf(test_q)\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": 15,
"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": [
"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, 6e-6)\n",
"# plt.yscale('log')\n",
"# plt.xlim(770, 785)\n",
"plt.savefig('test.png')\n",
"# print(jpsi_width)"
]
},
{
"cell_type": "code",
"execution_count": 16,
"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": 17,
"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": 18,
"metadata": {},
"outputs": [],
"source": [
"total_f.update_integration_options(draws_per_dim=200000, mc_sampler=None)\n",
"# inte = total_f.integrate(limits = (2000, x_max), norm_range=False)\n",
"# inte_fl = zfit.run(inte)\n",
"# print(inte_fl)\n",
"# print(pdg[\"jpsi_BR\"]/pdg[\"NR_BR\"], inte_fl*pdg[\"psi2s_auc\"]/pdg[\"NR_auc\"])"
]
},
{
"cell_type": "code",
"execution_count": 19,
"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": 20,
"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": 21,
"metadata": {},
"outputs": [],
"source": [
"total_f._sample_and_weights = UniformSampleAndWeights"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [],
"source": [
"# 0.00133/(0.00133+0.213+0.015)*(x_max-3750)/(x_max-x_min)"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [],
"source": [
"# zfit.settings.set_verbosity(10)"
]
},
{
"cell_type": "code",
"execution_count": 24,
"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": 25,
"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": 26,
"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": 27,
"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": 28,
"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": 29,
"metadata": {},
"outputs": [],
"source": [
"# jpsi_width"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {},
"outputs": [],
"source": [
"# plt.hist(sample, weights=1 / prob(sample))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Fitting"
]
},
{
"cell_type": "code",
"execution_count": 31,
"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": 32,
"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": 33,
"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": 34,
"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": 35,
"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": 36,
"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": 37,
"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": 38,
"metadata": {},
"outputs": [],
"source": [
"# 0.15**2*4.2/1000\n",
"# result.hesse()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Constraints"
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {},
"outputs": [],
"source": [
"# 1. Constraint - Real part of sum of Psi contrib and D contribs\n",
"\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(DDstar_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"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Analysis"
]
},
{
"cell_type": "code",
"execution_count": 41,
"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"
]
},
{
"ename": "RuntimeError",
"evalue": "exception was raised in user function\nUser function arguments:\n p3770_s = +1.999020\n p4160_s = +2.376152\n p4040_p = -4.437832\n p4040_s = +1.423904\n Dbar_s = +0.342445\n p4160_p = +4.897009\n p4415_p = +4.364139\n p4415_s = +1.177263\n Ctt = -0.212905\n bplus_0 = -0.141110\n psi2s_p = -2.316177\n p3770_p = +0.511010\n bplus_2 = -1.674701\n jpsi_p = +4.878201\n bplus_1 = +1.591894\nOriginal python exception in user function:\nInternalError: Dst tensor is not initialized.\n\t [[node model_2/_call_unnormalized_pdf/Sqrt_27 (defined at <ipython-input-5-1272380e60a6>:4) ]]\n\t [[node gradients_1/AddN_11 (defined at C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\zfit\\core\\loss.py:220) ]]\n\nCaused by op 'model_2/_call_unnormalized_pdf/Sqrt_27', defined at:\n File \"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\runpy.py\", line 193, in _run_module_as_main\n \"__main__\", mod_spec)\n File \"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\runpy.py\", line 85, in _run_code\n exec(code, run_globals)\n File \"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\ipykernel_launcher.py\", line 16, in <module>\n app.launch_new_instance()\n File \"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\traitlets\\config\\application.py\", line 658, in launch_instance\n app.start()\n File \"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\ipykernel\\kernelapp.py\", line 505, in start\n self.io_loop.start()\n File \"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\tornado\\platform\\asyncio.py\", line 148, in start\n self.asyncio_loop.run_forever()\n File \"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\asyncio\\base_events.py\", line 539, in run_forever\n self._run_once()\n File \"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\asyncio\\base_events.py\", line 1775, in _run_once\n handle._run()\n File \"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\asyncio\\events.py\", line 88, in _run\n self._context.run(self._callback, *self._args)\n File \"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\tornado\\ioloop.py\", line 690, in <lambda>\n lambda f: self._run_callback(functools.partial(callback, future))\n File \"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\tornado\\ioloop.py\", line 743, in _run_callback\n ret = callback()\n File \"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\tornado\\gen.py\", line 781, in inner\n self.run()\n File \"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\tornado\\gen.py\", line 742, in run\n yielded = self.gen.send(value)\n File \"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\ipykernel\\kernelbase.py\", line 365, in process_one\n yield gen.maybe_future(dispatch(*args))\n File \"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\tornado\\gen.py\", line 209, in wrapper\n yielded = next(result)\n File \"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\ipykernel\\kernelbase.py\", line 272, in dispatch_shell\n yield gen.maybe_future(handler(stream, idents, msg))\n File \"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\tornado\\gen.py\", line 209, in wrapper\n yielded = next(result)\n File \"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\ipykernel\\kernelbase.py\", line 542, in execute_request\n user_expressions, allow_stdin,\n File \"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\tornado\\gen.py\", line 209, in wrapper\n yielded = next(result)\n File \"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\ipykernel\\ipkernel.py\", line 294, in do_execute\n res = shell.run_cell(code, store_history=store_history, silent=silent)\n File \"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\ipykernel\\zmqshell.py\", line 536, in run_cell\n return super(ZMQInteractiveShell, self).run_cell(*args, **kwargs)\n File \"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 2848, in run_cell\n raw_cell, store_history, silent, shell_futures)\n File \"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 2874, in _run_cell\n return runner(coro)\n File \"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\IPython\\core\\async_helpers.py\", line 67, in _pseudo_sync_runner\n coro.send(None)\n File \"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 3049, in run_cell_async\n interactivity=interactivity, compiler=compiler, result=result)\n File \"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 3214, in run_ast_nodes\n if (yield from self.run_code(code, result)):\n File \"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 3296, in run_code\n exec(code_obj, self.user_global_ns, self.user_ns)\n File \"<ipython-input-41-6edaa8476e31>\", line 71, in <module>\n result = minimizer.minimize(nll)\n File \"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\zfit\\minimizers\\baseminimizer.py\", line 207, in minimize\n return self._hook_minimize(loss=loss, params=params)\n File \"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\zfit\\minimizers\\baseminimizer.py\", line 216, in _hook_minimize\n return self._call_minimize(loss=loss, params=params)\n File \"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\zfit\\minimizers\\baseminimizer.py\", line 220, in _call_minimize\n return self._minimize(loss=loss, params=params)\n File \"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\zfit\\minimizers\\minimizer_minuit.py\", line 48, in _minimize\n gradients = loss.gradients(params)\n File \"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\zfit\\core\\loss.py\", line 158, in gradients\n return self._gradients(params=params)\n File \"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\zfit\\core\\loss.py\", line 251, in _gradients\n gradients = {(p, grad) for p, grad in zip(params_todo, super()._gradients(params_todo))}\n File \"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\zfit\\core\\loss.py\", line 220, in _gradients\n return tf.gradients(self.value(), params)\n File \"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\zfit\\core\\loss.py\", line 198, in value\n return self._value()\n File \"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\zfit\\core\\loss.py\", line 234, in _value\n loss = super()._value()\n File \"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\zfit\\core\\loss.py\", line 203, in _value\n constraints=self.constraints)\n File \"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\zfit\\core\\loss.py\", line 266, in _loss_func\n nll = _unbinned_nll_tf(model=model, data=data, fit_range=fit_range)\n File \"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\zfit\\core\\loss.py\", line 39, in _unbinned_nll_tf\n for p, d, r in zip(model, data, fit_range)]\n File \"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\zfit\\core\\loss.py\", line 39, in <listcomp>\n for p, d, r in zip(model, data, fit_range)]\n File \"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\zfit\\core\\loss.py\", line 43, in _unbinned_nll_tf\n probs = model.pdf(data, norm_range=fit_range)\n File \"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\zfit\\core\\basepdf.py\", line 314, in pdf\n value = self._single_hook_pdf(x=x, norm_range=norm_range, name=name)\n File \"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\zfit\\core\\basepdf.py\", line 324, in _single_hook_pdf\n return self._hook_pdf(x=x, norm_range=norm_range, name=name)\n File \"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\zfit\\core\\basepdf.py\", line 327, in _hook_pdf\n return self._norm_pdf(x=x, norm_range=norm_range, name=name)\n File \"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\zfit\\core\\basepdf.py\", line 330, in _norm_pdf\n return self._call_pdf(x=x, norm_range=norm_range, name=name)\n File \"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\zfit\\core\\basepdf.py\", line 338, in _call_pdf\n return self._fallback_pdf(x=x, norm_range=norm_range)\n File \"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\zfit\\core\\basepdf.py\", line 341, in _fallback_pdf\n pdf = self._call_unnormalized_pdf(x, name=\"_call_unnormalized_pdf\")\n File \"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\zfit\\core\\basepdf.py\", line 289, in _call_unnormalized_pdf\n return self._unnormalized_pdf(x)\n File \"<ipython-input-7-b8aee4565e4e>\", line 73, in _unnormalized_pdf\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 File \"<ipython-input-7-b8aee4565e4e>\", line 70, in ttau_cusp\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 File \"<ipython-input-5-1272380e60a6>\", line 15, in h_S\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 File \"<ipython-input-5-1272380e60a6>\", line 9, in G\n big_bracket = tf.where(tf.math.real(y) > ztf.constant(0.0), inner_rect_bracket(y), inner_right(y))\n File \"<ipython-input-5-1272380e60a6>\", line 4, in inner_rect_bracket\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 File \"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\tensorflow\\python\\ops\\gen_math_ops.py\", line 9251, in sqrt\n \"Sqrt\", x=x, name=name)\n File \"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\tensorflow\\python\\framework\\op_def_library.py\", line 788, in _apply_op_helper\n op_def=op_def)\n File \"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\tensorflow\\python\\util\\deprecation.py\", line 507, in new_func\n return func(*args, **kwargs)\n File \"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\tensorflow\\python\\framework\\ops.py\", line 3300, in create_op\n op_def=op_def)\n File \"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\tensorflow\\python\\framework\\ops.py\", line 1801, in __init__\n self._traceback = tf_stack.extract_stack()\n\nInternalError (see above for traceback): Dst tensor is not initialized.\n\t [[node model_2/_call_unnormalized_pdf/Sqrt_27 (defined at <ipython-input-5-1272380e60a6>:4) ]]\n\t [[node gradients_1/AddN_11 (defined at C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\zfit\\core\\loss.py:220) ]]\n\n File \"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\zfit\\minimizers\\minimizer_minuit.py\", line 116, in grad_func\n gradients_values = self.sess.run(gradients)\n File \"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\tensorflow\\python\\client\\session.py\", line 929, in run\n run_metadata_ptr)\n File \"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\tensorflow\\python\\client\\session.py\", line 1152, in _run\n feed_dict_tensor, options, run_metadata)\n File \"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\tensorflow\\python\\client\\session.py\", line 1328, in _do_run\n run_metadata)\n File \"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\tensorflow\\python\\client\\session.py\", line 1348, in _do_call\n raise type(e)(node_def, op, message)\n",
"output_type": "error",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[1;31mRuntimeError\u001b[0m Traceback (most recent call last)",
"\u001b[1;32m<ipython-input-41-6edaa8476e31>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m 69\u001b[0m \u001b[0mminimizer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mzfit\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mminimize\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mMinuitMinimizer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mverbosity\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m5\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 70\u001b[0m \u001b[1;31m# minimizer._use_tfgrad = False\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 71\u001b[1;33m \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mminimizer\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mminimize\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnll\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 72\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 73\u001b[0m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"Toy {}: Fitting finished\"\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtoy\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32m~\\.conda\\envs\\rmd\\lib\\site-packages\\zfit\\minimizers\\baseminimizer.py\u001b[0m in \u001b[0;36mminimize\u001b[1;34m(self, loss, params)\u001b[0m\n\u001b[0;32m 205\u001b[0m \u001b[0mtuple\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mstack\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0menter_context\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mparam\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mset_sess\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msess\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mparam\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mparams\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 206\u001b[0m \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 207\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_hook_minimize\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mloss\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mloss\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mparams\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mparams\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 208\u001b[0m \u001b[1;32mexcept\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mFailMinimizeNaN\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mRuntimeError\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0merror\u001b[0m\u001b[1;33m:\u001b[0m \u001b[1;31m# iminuit raises RuntimeError if user raises Error\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 209\u001b[0m \u001b[0mfail_result\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstrategy\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit_result\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32m~\\.conda\\envs\\rmd\\lib\\site-packages\\zfit\\minimizers\\baseminimizer.py\u001b[0m in \u001b[0;36m_hook_minimize\u001b[1;34m(self, loss, params)\u001b[0m\n\u001b[0;32m 214\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 215\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0m_hook_minimize\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mloss\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mparams\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 216\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_call_minimize\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mloss\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mloss\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mparams\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mparams\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 217\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 218\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0m_call_minimize\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mloss\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mparams\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32m~\\.conda\\envs\\rmd\\lib\\site-packages\\zfit\\minimizers\\baseminimizer.py\u001b[0m in \u001b[0;36m_call_minimize\u001b[1;34m(self, loss, params)\u001b[0m\n\u001b[0;32m 218\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0m_call_minimize\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mloss\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mparams\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 219\u001b[0m \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 220\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_minimize\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mloss\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mloss\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mparams\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mparams\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 221\u001b[0m \u001b[1;32mexcept\u001b[0m \u001b[0mNotImplementedError\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0merror\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 222\u001b[0m \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32m~\\.conda\\envs\\rmd\\lib\\site-packages\\zfit\\minimizers\\minimizer_minuit.py\u001b[0m in \u001b[0;36m_minimize\u001b[1;34m(self, loss, params)\u001b[0m\n\u001b[0;32m 136\u001b[0m minimizer_setter)\n\u001b[0;32m 137\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_minuit_minimizer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mminimizer\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 138\u001b[1;33m \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mminimizer\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmigrad\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m**\u001b[0m\u001b[0mminimize_options\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 139\u001b[0m \u001b[0mparams_result\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mp_dict\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mp_dict\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mresult\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 140\u001b[0m \u001b[0mresult_vals\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mres\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"value\"\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mres\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mparams_result\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32miminuit\\_libiminuit.pyx\u001b[0m in \u001b[0;36miminuit._libiminuit.Minuit.migrad\u001b[1;34m()\u001b[0m\n",
"\u001b[1;31mRuntimeError\u001b[0m: exception was raised in user function\nUser function arguments:\n p3770_s = +1.999020\n p4160_s = +2.376152\n p4040_p = -4.437832\n p4040_s = +1.423904\n Dbar_s = +0.342445\n p4160_p = +4.897009\n p4415_p = +4.364139\n p4415_s = +1.177263\n Ctt = -0.212905\n bplus_0 = -0.141110\n psi2s_p = -2.316177\n p3770_p = +0.511010\n bplus_2 = -1.674701\n jpsi_p = +4.878201\n bplus_1 = +1.591894\nOriginal python exception in user function:\nInternalError: Dst tensor is not initialized.\n\t [[node model_2/_call_unnormalized_pdf/Sqrt_27 (defined at <ipython-input-5-1272380e60a6>:4) ]]\n\t [[node gradients_1/AddN_11 (defined at C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\zfit\\core\\loss.py:220) ]]\n\nCaused by op 'model_2/_call_unnormalized_pdf/Sqrt_27', defined at:\n File \"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\runpy.py\", line 193, in _run_module_as_main\n \"__main__\", mod_spec)\n File \"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\runpy.py\", line 85, in _run_code\n exec(code, run_globals)\n File \"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\ipykernel_launcher.py\", line 16, in <module>\n app.launch_new_instance()\n File \"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\traitlets\\config\\application.py\", line 658, in launch_instance\n app.start()\n File \"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\ipykernel\\kernelapp.py\", line 505, in start\n self.io_loop.start()\n File \"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\tornado\\platform\\asyncio.py\", line 148, in start\n self.asyncio_loop.run_forever()\n File \"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\asyncio\\base_events.py\", line 539, in run_forever\n self._run_once()\n File \"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\asyncio\\base_events.py\", line 1775, in _run_once\n handle._run()\n File \"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\asyncio\\events.py\", line 88, in _run\n self._context.run(self._callback, *self._args)\n File \"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\tornado\\ioloop.py\", line 690, in <lambda>\n lambda f: self._run_callback(functools.partial(callback, future))\n File \"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\tornado\\ioloop.py\", line 743, in _run_callback\n ret = callback()\n File \"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\tornado\\gen.py\", line 781, in inner\n self.run()\n File \"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\tornado\\gen.py\", line 742, in run\n yielded = self.gen.send(value)\n File \"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\ipykernel\\kernelbase.py\", line 365, in process_one\n yield gen.maybe_future(dispatch(*args))\n File \"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\tornado\\gen.py\", line 209, in wrapper\n yielded = next(result)\n File \"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\ipykernel\\kernelbase.py\", line 272, in dispatch_shell\n yield gen.maybe_future(handler(stream, idents, msg))\n File \"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\tornado\\gen.py\", line 209, in wrapper\n yielded = next(result)\n File \"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\ipykernel\\kernelbase.py\", line 542, in execute_request\n user_expressions, allow_stdin,\n File \"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\tornado\\gen.py\", line 209, in wrapper\n yielded = next(result)\n File \"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\ipykernel\\ipkernel.py\", line 294, in do_execute\n res = shell.run_cell(code, store_history=store_history, silent=silent)\n File \"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\ipykernel\\zmqshell.py\", line 536, in run_cell\n return super(ZMQInteractiveShell, self).run_cell(*args, **kwargs)\n File \"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 2848, in run_cell\n raw_cell, store_history, silent, shell_futures)\n File \"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 2874, in _run_cell\n return runner(coro)\n File \"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\IPython\\core\\async_helpers.py\", line 67, in _pseudo_sync_runner\n coro.send(None)\n File \"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 3049, in run_cell_async\n interactivity=interactivity, compiler=compiler, result=result)\n File \"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 3214, in run_ast_nodes\n if (yield from self.run_code(code, result)):\n File \"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 3296, in run_code\n exec(code_obj, self.user_global_ns, self.user_ns)\n File \"<ipython-input-41-6edaa8476e31>\", line 71, in <module>\n result = minimizer.minimize(nll)\n File \"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\zfit\\minimizers\\baseminimizer.py\", line 207, in minimize\n return self._hook_minimize(loss=loss, params=params)\n File \"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\zfit\\minimizers\\baseminimizer.py\", line 216, in _hook_minimize\n return self._call_minimize(loss=loss, params=params)\n File \"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\zfit\\minimizers\\baseminimizer.py\", line 220, in _call_minimize\n return self._minimize(loss=loss, params=params)\n File \"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\zfit\\minimizers\\minimizer_minuit.py\", line 48, in _minimize\n gradients = loss.gradients(params)\n File \"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\zfit\\core\\loss.py\", line 158, in gradients\n return self._gradients(params=params)\n File \"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\zfit\\core\\loss.py\", line 251, in _gradients\n gradients = {(p, grad) for p, grad in zip(params_todo, super()._gradients(params_todo))}\n File \"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\zfit\\core\\loss.py\", line 220, in _gradients\n return tf.gradients(self.value(), params)\n File \"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\zfit\\core\\loss.py\", line 198, in value\n return self._value()\n File \"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\zfit\\core\\loss.py\", line 234, in _value\n loss = super()._value()\n File \"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\zfit\\core\\loss.py\", line 203, in _value\n constraints=self.constraints)\n File \"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\zfit\\core\\loss.py\", line 266, in _loss_func\n nll = _unbinned_nll_tf(model=model, data=data, fit_range=fit_range)\n File \"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\zfit\\core\\loss.py\", line 39, in _unbinned_nll_tf\n for p, d, r in zip(model, data, fit_range)]\n File \"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\zfit\\core\\loss.py\", line 39, in <listcomp>\n for p, d, r in zip(model, data, fit_range)]\n File \"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\zfit\\core\\loss.py\", line 43, in _unbinned_nll_tf\n probs = model.pdf(data, norm_range=fit_range)\n File \"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\zfit\\core\\basepdf.py\", line 314, in pdf\n value = self._single_hook_pdf(x=x, norm_range=norm_range, name=name)\n File \"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\zfit\\core\\basepdf.py\", line 324, in _single_hook_pdf\n return self._hook_pdf(x=x, norm_range=norm_range, name=name)\n File \"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\zfit\\core\\basepdf.py\", line 327, in _hook_pdf\n return self._norm_pdf(x=x, norm_range=norm_range, name=name)\n File \"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\zfit\\core\\basepdf.py\", line 330, in _norm_pdf\n return self._call_pdf(x=x, norm_range=norm_range, name=name)\n File \"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\zfit\\core\\basepdf.py\", line 338, in _call_pdf\n return self._fallback_pdf(x=x, norm_range=norm_range)\n File \"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\zfit\\core\\basepdf.py\", line 341, in _fallback_pdf\n pdf = self._call_unnormalized_pdf(x, name=\"_call_unnormalized_pdf\")\n File \"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\zfit\\core\\basepdf.py\", line 289, in _call_unnormalized_pdf\n return self._unnormalized_pdf(x)\n File \"<ipython-input-7-b8aee4565e4e>\", line 73, in _unnormalized_pdf\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 File \"<ipython-input-7-b8aee4565e4e>\", line 70, in ttau_cusp\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 File \"<ipython-input-5-1272380e60a6>\", line 15, in h_S\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 File \"<ipython-input-5-1272380e60a6>\", line 9, in G\n big_bracket = tf.where(tf.math.real(y) > ztf.constant(0.0), inner_rect_bracket(y), inner_right(y))\n File \"<ipython-input-5-1272380e60a6>\", line 4, in inner_rect_bracket\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 File \"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\tensorflow\\python\\ops\\gen_math_ops.py\", line 9251, in sqrt\n \"Sqrt\", x=x, name=name)\n File \"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\tensorflow\\python\\framework\\op_def_library.py\", line 788, in _apply_op_helper\n op_def=op_def)\n File \"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\tensorflow\\python\\util\\deprecation.py\", line 507, in new_func\n return func(*args, **kwargs)\n File \"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\tensorflow\\python\\framework\\ops.py\", line 3300, in create_op\n op_def=op_def)\n File \"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\tensorflow\\python\\framework\\ops.py\", line 1801, in __init__\n self._traceback = tf_stack.extract_stack()\n\nInternalError (see above for traceback): Dst tensor is not initialized.\n\t [[node model_2/_call_unnormalized_pdf/Sqrt_27 (defined at <ipython-input-5-1272380e60a6>:4) ]]\n\t [[node gradients_1/AddN_11 (defined at C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\zfit\\core\\loss.py:220) ]]\n\n File \"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\zfit\\minimizers\\minimizer_minuit.py\", line 116, in grad_func\n gradients_values = self.sess.run(gradients)\n File \"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\tensorflow\\python\\client\\session.py\", line 929, in run\n run_metadata_ptr)\n File \"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\tensorflow\\python\\client\\session.py\", line 1152, in _run\n feed_dict_tensor, options, run_metadata)\n File \"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\tensorflow\\python\\client\\session.py\", line 1328, in _do_run\n run_metadata)\n File \"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\tensorflow\\python\\client\\session.py\", line 1348, in _do_call\n raise type(e)(node_def, op, message)\n"
]
}
],
"source": [
"# zfit.run.numeric_checks = False \n",
"\n",
"Ctt_list = []\n",
"Ctt_error_list = []\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",
" ### Generate data\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",
" 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",
" 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",
" 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 = constraint1)\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",
" 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": null,
"metadata": {},
"outputs": [],
"source": [
"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": [
"calcs_test = zfit.run(probs)\n",
"res_y = zfit.run(jpsi_res(test_q))\n",
"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, 6e-6)\n",
"# plt.yscale('log')\n",
"# plt.xlim(770, 785)\n",
"plt.savefig('test2.png')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
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"display_name": "Python 3",
"language": "python",
"name": "python3"
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"file_extension": ".py",
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