{
"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": [],
"source": [
"def formfactor( q2, subscript): #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",
" b0 = ztf.constant(pdg[\"b0\"])\n",
" bplus = ztf.constant(pdg[\"bplus\"])\n",
" bT = ztf.constant(pdg[\"bT\"])\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",
"\n",
" for i in range(N):\n",
" _sum += b0[i]*(tf.pow(z,i))\n",
"\n",
" return tf.complex(prefactor * _sum, ztf.constant(0.0))\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",
" b = bT\n",
" else:\n",
" b = bplus\n",
"\n",
" for i in range(N):\n",
" _sum += b[i] * (tf.pow(z, i) - ((-1)**(i-N)) * (i/N) * tf.pow(z, N))\n",
"\n",
" return tf.complex(prefactor * _sum, ztf.constant(0.0))\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",
"def bifur_gauss(q, mean, sigma_L, sigma_R, scale):\n",
"\n",
" _exp = tf.where(q < mean, ztf.exp(- tf.pow((q-mean),2) / (2 * sigma_L**2)), ztf.exp(- tf.pow((q-mean),2) / (2 * sigma_R**2)))\n",
"\n",
" #Scale so the total area under curve is 1 and the top of the cusp is continuous\n",
"\n",
" dgamma = scale*_exp/(ztf.sqrt(2*np.pi))*2*(sigma_L*sigma_R)/(sigma_L+sigma_R)\n",
"\n",
" com = ztf.complex(dgamma, ztf.constant(0.0))\n",
"\n",
" return com\n",
"\n",
"def axiv_nonres(q):\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, \"+\"))**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\")), 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):\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, \"+\") + tf.complex(2.0 * C7eff * (mb + ms)/(mB + mK), ztf.constant(0.0)) * formfactor(q2, \"T\"))**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": 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(-q)))\n",
" \n",
" big_bracket = tf.where(y > ztf.const(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) - 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) - 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": [
"## C_q,qbar constraint"
]
},
{
"cell_type": "code",
"execution_count": 46,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"False\n"
]
}
],
"source": [
"# r = rho_scale * rho_width/rho_mass * np.cos(rho_phase)*(1-np.tan(rho_phase)*rho_width/rho_mass)\n",
"# o = omega_scale*np.cos(omega_phase)*omega_width/omega_mass\n",
"# p = phi_scale*np.cos(phi_phase)*phi_width/phi_mass\n",
"\n",
"# # phi_s = np.linspace(-500, 5000, 100000)\n",
"\n",
"# # p_ = phi_s*np.cos(phi_phase)*phi_width/phi_mass\n",
"\n",
"# # p_y = r+o+p_\n",
"\n",
"# # plt.plot(phi_s, p_y)\n",
"\n",
"# print(r + o + p)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Build pdf"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"class total_pdf(zfit.pdf.ZPDF):\n",
" _N_OBS = 1 # dimension, can be omitted\n",
" _PARAMS = ['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'] # the name of the parameters\n",
"\n",
" def _unnormalized_pdf(self, x):\n",
" \n",
" x = x.unstack_x()\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",
"\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)\n",
"\n",
" vec_f = vec(x, funcs)\n",
"\n",
" axiv_nr = axiv_nonres(x)\n",
"\n",
" tot = vec_f + axiv_nr\n",
"\n",
" return tot"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Load data"
]
},
{
"cell_type": "code",
"execution_count": 7,
"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": 8,
"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": [
"#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,\n",
"# upper_limit = rho_mass + rho_width)\n",
"rho_w = zfit.Parameter(\"rho_w\", ztf.constant(rho_width), floating = False)\n",
"rho_p = zfit.Parameter(\"rho_p\", ztf.constant(rho_phase), lower_limit=-2*np.pi, upper_limit=2*np.pi)\n",
"rho_s = zfit.Parameter(\"rho_s\", ztf.constant(rho_scale), floating = False)\n",
"\n",
"#omega\n",
"\n",
"omega_mass, omega_width, omega_phase, omega_scale = pdg[\"omega\"]\n",
"\n",
"omega_m = zfit.Parameter(\"omega_m\", ztf.constant(omega_mass), floating = False)\n",
"omega_w = zfit.Parameter(\"omega_w\", ztf.constant(omega_width), floating = False)\n",
"omega_p = zfit.Parameter(\"omega_p\", ztf.constant(omega_phase), lower_limit=-2*np.pi, upper_limit=2*np.pi)\n",
"omega_s = zfit.Parameter(\"omega_s\", ztf.constant(omega_scale), floating = False)\n",
"\n",
"\n",
"#phi\n",
"\n",
"phi_mass, phi_width, phi_phase, phi_scale = pdg[\"phi\"]\n",
"\n",
"phi_m = zfit.Parameter(\"phi_m\", ztf.constant(phi_mass), floating = False)\n",
"phi_w = zfit.Parameter(\"phi_w\", ztf.constant(phi_width), floating = False)\n",
"phi_p = zfit.Parameter(\"phi_p\", ztf.constant(phi_phase), lower_limit=-2*np.pi, upper_limit=2*np.pi)\n",
"phi_s = zfit.Parameter(\"phi_s\", ztf.constant(phi_scale), floating = False)\n",
"\n",
"#jpsi\n",
"\n",
"jpsi_mass, jpsi_width, jpsi_phase, jpsi_scale = pdg[\"jpsi\"]\n",
"# jpsi_scale *= pdg[\"factor_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)\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)\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), floating = False)\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), floating = False)\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), floating = False)\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), floating = False)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Setup pdf"
]
},
{
"cell_type": "code",
"execution_count": 9,
"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",
" \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": 10,
"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",
"f0_y = zfit.run(formfactor(test_q,\"0\"))\n",
"fplus_y = zfit.run(formfactor(test_q,\"+\"))\n",
"fT_y = zfit.run(formfactor(test_q,\"T\"))"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
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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": 12,
"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": 13,
"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": 14,
"metadata": {},
"outputs": [],
"source": [
"total_f.update_integration_options(draws_per_dim=200000, mc_sampler=None)\n",
"# inte = total_f.integrate(limits = (1000, 1040), 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": 15,
"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() - _))))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Tensorflow scaling"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [],
"source": [
"# def scaling_func(x):\n",
"\n",
"# funcs = resonance(x, _mass = ztf.constant(jpsi_mass), scale = ztf.constant(jpsi_scale), phase = ztf.constant(jpsi_phase), width = ztf.constant(jpsi_width)) + resonance(x, _mass = ztf.constant(psi2s_mass), scale = ztf.constant(psi2s_scale), phase = ztf.constant(psi2s_phase), width = ztf.constant(psi2s_width))\n",
"\n",
"# vec_f = vec(x, funcs)\n",
"\n",
"# axiv_nr = axiv_nonres(x)\n",
"\n",
"# tot = vec_f + axiv_nr\n",
"\n",
"# return tot\n",
"\n",
"\n",
"# def s_func(x):\n",
" \n",
"# q = ztf.constant(x)\n",
" \n",
"# return zfit.run(scaling_func(q))\n",
" \n",
"\n",
"# print(integrate.quad(s_func, x_min, x_max, limit = 50))"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [],
"source": [
"# factor_jpsi = pdg[\"NR_auc\"]*pdg[\"jpsi_BR\"]/(pdg[\"NR_BR\"]*pdg[\"jpsi_auc\"])\n",
"# factor_jpsi = pdg[\"NR_auc\"]*pdg[\"jpsi_BR\"]/(pdg[\"NR_BR\"]*inte_fl)\n",
"# print(np.sqrt(factor_jpsi)*jpsi_scale)\n",
"# print(np.sqrt(factor_jpsi))\n",
"# # print(psi2s_scale)\n",
"# factor_psi2s = pdg[\"NR_auc\"]*pdg[\"psi2s_BR\"]/(pdg[\"NR_BR\"]*pdg[\"psi2s_auc\"])\n",
"# factor_psi2s = pdg[\"NR_auc\"]*pdg[\"psi2s_BR\"]/(pdg[\"NR_BR\"]*inte_fl)\n",
"# print(np.sqrt(factor_psi2s)*psi2s_scale)\n",
"# print(np.sqrt(factor_psi2s))"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [],
"source": [
"# def _t_f(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",
"# funcs = psi2s_res(xq) + jpsi_res(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 t_f(x):\n",
"# _ = np.array(x)\n",
"# probs = zfit.run(_t_f(_))\n",
"# return probs"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [],
"source": [
"# print(36000*(1+ pdg[\"jpsi_BR\"]/pdg[\"NR_BR\"] + pdg[\"psi2s_BR\"]/pdg[\"NR_BR\"]))"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [],
"source": [
"# start = time.time()\n",
"\n",
"# result, err = integrate.quad(lambda x: t_f(x), x_min, x_max, limit = 5)\n",
"# print(result, \"{0:.2f} %\".format(err/result))\n",
"# print(\"Time:\", time.time()-start)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Sampling\n",
"## One sample\n",
"! total_f.sample() always returns the same set !"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [],
"source": [
"# nevents = int(pdg[\"number_of_decays\"])\n",
"# event_stack = 5000\n",
"\n",
"# calls = int(nevents/event_stack + 1)\n",
"\n",
"# total_samp = []\n",
"\n",
"# start = time.time()\n",
"\n",
"# samp = total_f.sample(n=event_stack)\n",
"# s = samp.unstack_x()\n",
"\n",
"# for call in range(calls):\n",
"\n",
"# sam = zfit.run(s)\n",
"# clear_output(wait=True)\n",
" \n",
"# # if call != 0:\n",
"# # print(np.sum(_last_sam-sam))\n",
" \n",
"# # _last_sam = sam\n",
" \n",
"# c = call + 1 \n",
"# print(\"{0}/{1}\".format(c, calls))\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-c)))))\n",
" \n",
"# with open(\"data/zfit_toys/toy_1/{}.pkl\".format(call), \"wb\") as f:\n",
"# pkl.dump(sam, f, pkl.HIGHEST_PROTOCOL)"
]
},
{
"cell_type": "code",
"execution_count": 22,
"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_1/{}.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",
"# print(total_samp[:nevents].shape)"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [],
"source": [
"# bins = int((x_max-x_min)/7)\n",
"\n",
"# # calcs = zfit.run(total_test_tf(samp))\n",
"\n",
"# plt.hist(total_samp[:event_stack], bins = bins, range = (x_min,x_max))\n",
"\n",
"# # plt.plot(sam, calcs, '.')\n",
"# # plt.plot(test_q, calcs_test)\n",
"# plt.ylim(0, 20)\n",
"# # plt.xlim(3000, 3750)\n",
"\n",
"# plt.savefig('test2.png')\n",
"# 1-(0.21+0.62)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Toys"
]
},
{
"cell_type": "code",
"execution_count": 24,
"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(0.05, 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(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(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": 25,
"metadata": {},
"outputs": [],
"source": [
"total_f._sample_and_weights = UniformSampleAndWeights"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [],
"source": [
"# 0.00133/(0.00133+0.213+0.015)*(x_max-3750)/(x_max-x_min)"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [],
"source": [
"# zfit.settings.set_verbosity(10)"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {
"scrolled": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"6/6 of Toy 1/1\n",
"Time taken: 1 min, 21 s\n",
"Projected time left: \n"
]
}
],
"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": 29,
"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": 30,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Time to generate full toy: 81 s\n",
"(5404696,)\n"
]
}
],
"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": 31,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(5404696,)\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",
"\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": 32,
"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": 33,
"metadata": {},
"outputs": [],
"source": [
"# jpsi_width"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {},
"outputs": [],
"source": [
"# plt.hist(sample, weights=1 / prob(sample))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Fitting"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"-0.6837632761492838\n",
"3.7285129659499887\n",
"4.5760200973853085\n",
"4.0873765340620665\n",
"5.696265762936989\n",
"-2.5717909121593525\n",
"-4.32139458348885\n",
"-4.6490244502769835\n",
"-2.4543520459301043\n",
"------------------------------------------------------------------\n",
"| FCN = -7.131E+05 | Ncalls=359 (359 total) |\n",
"| EDM = 6.99E-05 (Goal: 5E-06) | up = 0.5 |\n",
"------------------------------------------------------------------\n",
"| Valid Min. | Valid Param. | Above EDM | Reached call limit |\n",
"------------------------------------------------------------------\n",
"| True | True | False | False |\n",
"------------------------------------------------------------------\n",
"| Hesse failed | Has cov. | Accurate | Pos. def. | Forced |\n",
"------------------------------------------------------------------\n",
"| False | True | True | True | False |\n",
"------------------------------------------------------------------\n",
"Function minimum: -713057.7941560786\n",
"---------------------------------------------------------------------------------------------\n",
"| | Name | Value | Hesse Err | Minos Err- | Minos Err+ | Limit- | Limit+ | Fixed |\n",
"---------------------------------------------------------------------------------------------\n",
"| 0 | p4415_p | -2.93 | 0.12 | | |-6.28319 | 6.28319 | |\n",
"| 1 | p4160_p | 3.77 | 0.08 | | |-6.28319 | 6.28319 | |\n",
"| 2 | p3770_p | 2.45 | 0.09 | | |-6.28319 | 6.28319 | |\n",
"| 3 | phi_p | 6.28 | 0.04 | | |-6.28319 | 6.28319 | |\n",
"| 4 | omega_p | 6.283 | 0.027 | | |-6.28319 | 6.28319 | |\n",
"| 5 | p4040_p | -3.07 | 0.17 | | |-6.28319 | 6.28319 | |\n",
"| 6 | jpsi_p | -4.810 | 0.016 | | |-6.28319 | 6.28319 | |\n",
"| 7 | psi2s_p | -4.946 | 0.027 | | |-6.28319 | 6.28319 | |\n",
"| 8 | rho_p | 6.28 | 0.04 | | |-6.28319 | 6.28319 | |\n",
"---------------------------------------------------------------------------------------------\n",
"-------------------------------------------------------------------------------------\n",
"| | p4415_p p4160_p p3770_p phi_p omega_p p4040_p jpsi_p psi2s_p rho_p |\n",
"-------------------------------------------------------------------------------------\n",
"| p4415_p | 1.000 0.054 0.008 0.000 -0.000 0.001 -0.150 -0.158 -0.001 |\n",
"| p4160_p | 0.054 1.000 0.010 0.000 -0.000 -0.278 -0.111 -0.097 -0.001 |\n",
"| p3770_p | 0.008 0.010 1.000 0.000 0.000 -0.042 -0.116 -0.511 0.000 |\n",
"| phi_p | 0.000 0.000 0.000 1.000 0.000 0.000 0.004 0.002 -0.000 |\n",
"| omega_p | -0.000 -0.000 0.000 0.000 1.000 0.000 0.002 0.001 -0.003 |\n",
"| p4040_p | 0.001 -0.278 -0.042 0.000 0.000 1.000 -0.193 -0.278 -0.001 |\n",
"| jpsi_p | -0.150 -0.111 -0.116 0.004 0.002 -0.193 1.000 0.221 0.008 |\n",
"| psi2s_p | -0.158 -0.097 -0.511 0.002 0.001 -0.278 0.221 1.000 0.004 |\n",
"| rho_p | -0.001 -0.001 0.000 -0.000 -0.003 -0.001 0.008 0.004 1.000 |\n",
"-------------------------------------------------------------------------------------\n",
"Hesse errors: OrderedDict([(<zfit.Parameter 'p4415_p' floating=True>, {'error': 0.12052654582200373}), (<zfit.Parameter 'p4160_p' floating=True>, {'error': 0.07990595220555008}), (<zfit.Parameter 'p3770_p' floating=True>, {'error': 0.09399014188036858}), (<zfit.Parameter 'phi_p' floating=True>, {'error': 0.03841947108985533}), (<zfit.Parameter 'omega_p' floating=True>, {'error': 0.027374388569219477}), (<zfit.Parameter 'p4040_p' floating=True>, {'error': 0.17137144953528938}), (<zfit.Parameter 'jpsi_p' floating=True>, {'error': 0.015550554940964911}), (<zfit.Parameter 'psi2s_p' floating=True>, {'error': 0.02746038930647643}), (<zfit.Parameter 'rho_p' floating=True>, {'error': 0.03774225139990062})])\n"
]
}
],
"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": 36,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Time taken for fitting: 2 min, 33 s\n"
]
}
],
"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": 37,
"metadata": {},
"outputs": [
{
"data": {
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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, res_y, label = 'res')\n",
"plt.legend()\n",
"plt.ylim(0.0, 5e-6)\n",
"# plt.yscale('log')\n",
"# plt.xlim(3080, 3110)\n",
"plt.savefig('test3.png')\n",
"# print(jpsi_width)"
]
},
{
"cell_type": "code",
"execution_count": 38,
"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": 39,
"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": 40,
"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": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.3"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
