{
"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 = 1000000\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": 5,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[<matplotlib.lines.Line2D at 0x2ab162cceb8>]"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"rho_mass, rho_width, rho_phase, rho_scale = pdg[\"rho\"]\n",
"omega_mass, omega_width, omega_phase, omega_scale = pdg[\"omega\"]\n",
"phi_mass, phi_width, phi_phase, phi_scale = pdg[\"phi\"]\n",
"\n",
"\n",
"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 = ['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",
" ] # the name of the parameters\n",
"\n",
" def _unnormalized_pdf(self, x):\n",
" \n",
" x = x.unstack_x()\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 = 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": [
"#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), floating = False)\n",
"jpsi_s = zfit.Parameter(\"jpsi_s\", ztf.constant(jpsi_scale))\n",
"\n",
"#psi2s\n",
"\n",
"psi2s_mass, psi2s_width, psi2s_phase, psi2s_scale = pdg[\"psi2s\"]\n",
"\n",
"psi2s_m = zfit.Parameter(\"psi2s_m\", ztf.constant(psi2s_mass), floating = False)\n",
"psi2s_w = zfit.Parameter(\"psi2s_w\", ztf.constant(psi2s_width), floating = False)\n",
"psi2s_p = zfit.Parameter(\"psi2s_p\", ztf.constant(psi2s_phase), floating = False)\n",
"psi2s_s = zfit.Parameter(\"psi2s_s\", ztf.constant(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), floating = False)\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), floating = False)\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), floating = False)\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), floating = False)\n",
"p4415_s = zfit.Parameter(\"p4415_s\", ztf.constant(p4415_scale))"
]
},
{
"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",
" \n",
"# print(total_pdf.obs)\n",
"\n",
"# print(calcs_test)"
]
},
{
"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": 16,
"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(3080, 3110)\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=2000000, mc_sampler=None)\n",
"# inte = total_f.integrate(limits = (4000, 4400), 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 = \"p4160\"\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": null,
"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": null,
"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": null,
"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": null,
"metadata": {},
"outputs": [],
"source": [
"# print(36000*(1+ pdg[\"jpsi_BR\"]/pdg[\"NR_BR\"] + pdg[\"psi2s_BR\"]/pdg[\"NR_BR\"]))"
]
},
{
"cell_type": "code",
"execution_count": null,
"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": null,
"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": null,
"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": null,
"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": null,
"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": null,
"metadata": {},
"outputs": [],
"source": [
"total_f._sample_and_weights = UniformSampleAndWeights"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"0.00133/(0.00133+0.213+0.015)*(x_max-3750)/(x_max-x_min)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# zfit.settings.set_verbosity(10)"
]
},
{
"cell_type": "code",
"execution_count": null,
"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": null,
"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": null,
"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": null,
"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": null,
"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": null,
"metadata": {},
"outputs": [],
"source": [
"# jpsi_width"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# plt.hist(sample, weights=1 / prob(sample))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Fitting"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"start = time.time()\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)"
]
},
{
"cell_type": "code",
"execution_count": null,
"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": null,
"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, 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": null,
"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": null,
"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": null,
"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": []
}
],
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