{
 "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": [
    "## Build pdf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "class total_pdf(zfit.pdf.ZPDF):\n",
    "    _N_OBS = 1  # dimension, can be omitted\n",
    "    _PARAMS = ['jpsi_mass', 'jpsi_scale', 'jpsi_phase', 'jpsi_width',\n",
    "                'psi2s_mass', 'psi2s_scale', 'psi2s_phase', 'psi2s_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'], 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'], phase = self.params['psi2s_phase'], width = self.params['psi2s_width'])\n",
    "\n",
    "        def cusp(q):\n",
    "            return bifur_gauss(q, mean = self.params['cusp_mass'], sigma_L = self.params['sigma_L'], sigma_R = self.params['sigma_R'], scale = self.params['cusp_scale'])\n",
    "\n",
    "        funcs = jpsi_res(x) + psi2s_res(x) #+ cusp(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": 6,
   "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": 7,
   "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",
    "#cusp\n",
    "\n",
    "# cusp_mass, sigma_R, sigma_L, cusp_scale = 3550, 3e-7, 200, 0\n",
    "\n",
    "# cusp_m = zfit.Parameter(\"cusp_m\", ztf.constant(cusp_mass), floating = False)\n",
    "# sig_L = zfit.Parameter(\"sig_L\", ztf.constant(sigma_L), floating = False)\n",
    "# sig_R = zfit.Parameter(\"sig_R\", ztf.constant(sigma_R), floating = False)\n",
    "# cusp_s = zfit.Parameter(\"cusp_s\", ztf.constant(cusp_scale), floating = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Setup pdf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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",
    "            #cusp_mass = cusp_m, sigma_L = sig_L, sigma_R = sig_R, cusp_scale = cusp_s) \n",
    "    \n",
    "# print(total_pdf.obs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Test if graphs actually work and compute values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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": 10,
   "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(3080, 3110)\n",
    "plt.savefig('test.png')\n",
    "# print(jpsi_width)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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": 12,
   "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": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# total_f.update_integration_options(draws_per_dim=20000000, mc_sampler=None)\n",
    "# inte = total_f.integrate(limits = (3080, 3112), 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": 14,
   "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",
    "\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": 15,
   "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": 16,
   "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": 17,
   "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": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# print(36000*(1+ pdg[\"jpsi_BR\"]/pdg[\"NR_BR\"] + pdg[\"psi2s_BR\"]/pdg[\"NR_BR\"]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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": 20,
   "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": 21,
   "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": 22,
   "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": 23,
   "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": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "total_f._sample_and_weights = UniformSampleAndWeights"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.001309082138940001"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "0.00133/(0.00133+0.213+0.015)*(x_max-3750)/(x_max-x_min)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "# zfit.settings.set_verbosity(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6/6 of Toy 1/1\n",
      "Time taken: 1 min, 6 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": 28,
   "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": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Time to generate full toy: 66 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": 30,
   "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": 31,
   "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": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "# jpsi_width"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "# plt.hist(sample, weights=1 / prob(sample))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Fitting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<hr>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<table>\n",
       "    <tr>\n",
       "        <td title=\"Minimum value of function\">FCN = -861523.9193443996</td>\n",
       "        <td title=\"Total number of call to FCN so far\">TOTAL NCALL = 31</td>\n",
       "        <td title=\"Number of call in last migrad\">NCALLS = 31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td title=\"Estimated distance to minimum\">EDM = 1.78687712418138e-05</td>\n",
       "        <td title=\"Maximum EDM definition of convergence\">GOAL EDM = 5e-06</td>\n",
       "        <td title=\"Error def. Amount of increase in FCN to be defined as 1 standard deviation\">\n",
       "        UP = 0.5</td>\n",
       "    </tr>\n",
       "</table>\n",
       "<table>\n",
       "    <tr>\n",
       "        <td align=\"center\" title=\"Validity of the migrad call\">Valid</td>\n",
       "        <td align=\"center\" title=\"Validity of parameters\">Valid Param</td>\n",
       "        <td align=\"center\" title=\"Is Covariance matrix accurate?\">Accurate Covar</td>\n",
       "        <td align=\"center\" title=\"Positive definiteness of covariance matrix\">PosDef</td>\n",
       "        <td align=\"center\" title=\"Was covariance matrix made posdef by adding diagonal element\">Made PosDef</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td align=\"center\" style=\"background-color:#92CCA6\">True</td>\n",
       "        <td align=\"center\" style=\"background-color:#92CCA6\">True</td>\n",
       "        <td align=\"center\" style=\"background-color:#92CCA6\">True</td>\n",
       "        <td align=\"center\" style=\"background-color:#92CCA6\">True</td>\n",
       "        <td align=\"center\" style=\"background-color:#92CCA6\">False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td align=\"center\" title=\"Was last hesse call fail?\">Hesse Fail</td>\n",
       "        <td align=\"center\" title=\"Validity of covariance\">HasCov</td>\n",
       "        <td align=\"center\" title=\"Is EDM above goal EDM?\">Above EDM</td>\n",
       "        <td align=\"center\"></td>\n",
       "        <td align=\"center\" title=\"Did last migrad call reach max call limit?\">Reach calllim</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td align=\"center\" style=\"background-color:#92CCA6\">False</td>\n",
       "        <td align=\"center\" style=\"background-color:#92CCA6\">True</td>\n",
       "        <td align=\"center\" style=\"background-color:#92CCA6\">False</td>\n",
       "        <td align=\"center\"></td>\n",
       "        <td align=\"center\" style=\"background-color:#92CCA6\">False</td>\n",
       "    </tr>\n",
       "</table>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<table>\n",
       "    <tr>\n",
       "        <td><a href=\"#\" onclick=\"$('#UcOlWKvXls').toggle()\">+</a></td>\n",
       "        <td title=\"Variable name\">Name</td>\n",
       "        <td title=\"Value of parameter\">Value</td>\n",
       "        <td title=\"Hesse error\">Hesse Error</td>\n",
       "        <td title=\"Minos lower error\">Minos Error-</td>\n",
       "        <td title=\"Minos upper error\">Minos Error+</td>\n",
       "        <td title=\"Lower limit of the parameter\">Limit-</td>\n",
       "        <td title=\"Upper limit of the parameter\">Limit+</td>\n",
       "        <td title=\"Is the parameter fixed in the fit\">Fixed?</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>0</td>\n",
       "        <td>jpsi_s</td>\n",
       "        <td>10204.3</td>\n",
       "        <td>28.6702</td>\n",
       "        <td></td>\n",
       "        <td></td>\n",
       "        <td></td>\n",
       "        <td></td>\n",
       "        <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>1</td>\n",
       "        <td>psi2s_s</td>\n",
       "        <td>1239.48</td>\n",
       "        <td>3.62611</td>\n",
       "        <td></td>\n",
       "        <td></td>\n",
       "        <td></td>\n",
       "        <td></td>\n",
       "        <td>No</td>\n",
       "    </tr>\n",
       "</table>\n",
       "<pre id=\"UcOlWKvXls\" style=\"display:none;\">\n",
       "<textarea rows=\"10\" cols=\"50\" onclick=\"this.select()\" readonly>\n",
       "\\begin{tabular}{|c|r|r|r|r|r|r|r|c|}\n",
       "\\hline\n",
       " & Name & Value & Hesse Error & Minos Error- & Minos Error+ & Limit- & Limit+ & Fixed?\\\\\n",
       "\\hline\n",
       "0 & $jpsi_{s}$ & 10204.3 & 28.6702 &  &  &  &  & No\\\\\n",
       "\\hline\n",
       "1 & $psi2s_{s}$ & 1239.48 & 3.62611 &  &  &  &  & No\\\\\n",
       "\\hline\n",
       "\\end{tabular}\n",
       "</textarea>\n",
       "</pre>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<hr>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<span>Minos status for jpsi_s: <span style=\"background-color:#92CCA6\">VALID</span></span>\n",
       "<table>\n",
       "    <tr>\n",
       "        <td title=\"lower and upper minos error of the parameter\">Error</td>\n",
       "        <td>-28.784729404343782</td>\n",
       "        <td>28.58795673661853</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td title=\"Validity of minos error\">Valid</td>\n",
       "        <td style=\"background-color:#92CCA6\">True</td>\n",
       "        <td style=\"background-color:#92CCA6\">True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td title=\"Did minos error search hit limit of any parameter?\">At Limit</td>\n",
       "        <td style=\"background-color:#92CCA6\">False</td>\n",
       "        <td style=\"background-color:#92CCA6\">False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td title=\"I don't really know what this one means... Post it in issue if you know\">Max FCN</td>\n",
       "        <td style=\"background-color:#92CCA6\">False</td>\n",
       "        <td style=\"background-color:#92CCA6\">False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td title=\"New minimum found when doing minos scan.\">New Min</td>\n",
       "        <td style=\"background-color:#92CCA6\">False</td>\n",
       "        <td style=\"background-color:#92CCA6\">False</td>\n",
       "    </tr>\n",
       "</table>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<span>Minos status for psi2s_s: <span style=\"background-color:#92CCA6\">VALID</span></span>\n",
       "<table>\n",
       "    <tr>\n",
       "        <td title=\"lower and upper minos error of the parameter\">Error</td>\n",
       "        <td>-3.6382958457829684</td>\n",
       "        <td>3.6140145472909015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td title=\"Validity of minos error\">Valid</td>\n",
       "        <td style=\"background-color:#92CCA6\">True</td>\n",
       "        <td style=\"background-color:#92CCA6\">True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td title=\"Did minos error search hit limit of any parameter?\">At Limit</td>\n",
       "        <td style=\"background-color:#92CCA6\">False</td>\n",
       "        <td style=\"background-color:#92CCA6\">False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td title=\"I don't really know what this one means... Post it in issue if you know\">Max FCN</td>\n",
       "        <td style=\"background-color:#92CCA6\">False</td>\n",
       "        <td style=\"background-color:#92CCA6\">False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td title=\"New minimum found when doing minos scan.\">New Min</td>\n",
       "        <td style=\"background-color:#92CCA6\">False</td>\n",
       "        <td style=\"background-color:#92CCA6\">False</td>\n",
       "    </tr>\n",
       "</table>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "jpsi_s: ^{+28.58795673661853}_{-28.784729404343782}\n",
      "psi2s_s: ^{+3.6140145472909015}_{-3.6382958457829684}\n",
      "Function minimum: -861523.9193443996\n"
     ]
    }
   ],
   "source": [
    "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": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 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": 36,
   "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, 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": 37,
   "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": 38,
   "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": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.522535170724314\n",
      "1.522535170724314\n"
     ]
    }
   ],
   "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
}