diff --git a/.ipynb_checkpoints/Untitled-checkpoint.ipynb b/.ipynb_checkpoints/Untitled-checkpoint.ipynb
new file mode 100644
index 0000000..2fd6442
--- /dev/null
+++ b/.ipynb_checkpoints/Untitled-checkpoint.ipynb
@@ -0,0 +1,6 @@
+{
+ "cells": [],
+ "metadata": {},
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/.ipynb_checkpoints/numpy_backup_for_scaling-checkpoint.ipynb b/.ipynb_checkpoints/numpy_backup_for_scaling-checkpoint.ipynb
new file mode 100644
index 0000000..76cad46
--- /dev/null
+++ b/.ipynb_checkpoints/numpy_backup_for_scaling-checkpoint.ipynb
@@ -0,0 +1,401 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Numpy backup for scaling in the beginning"
+   ]
+  },
+  {
+   "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:53: 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 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"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "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 = pdg['Ks_M']\n",
+    "    mbstar0 = pdg[\"mbstar0\"]\n",
+    "    mbstar = pdg[\"mbstar\"]\n",
+    "    b0 = pdg[\"b0\"]\n",
+    "    bplus = pdg[\"bplus\"]\n",
+    "    bT = pdg[\"bT\"]\n",
+    "\n",
+    "    mmu = pdg['muon_M']\n",
+    "    mb = pdg['bquark_M']\n",
+    "    ms = pdg['squark_M']\n",
+    "    mB = 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)*(np.sqrt(mB)-np.sqrt(mK))**2\n",
+    "\n",
+    "    z_oben = np.sqrt(tpos - q2) - np.sqrt(tpos - tzero)\n",
+    "    z_unten = np.sqrt(tpos - q2) + np.sqrt(tpos - tzero)\n",
+    "    z = 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]*(np.power(z,i))\n",
+    "\n",
+    "        return prefactor * _sum\n",
+    "\n",
+    "    #calculate f+ or fT\n",
+    "\n",
+    "    else:\n",
+    "        prefactor = 1/(1 - q2/(mbstar**2))\n",
+    "        _sum = 0\n",
+    "\n",
+    "        if subscript == \"T\":\n",
+    "            b = bT\n",
+    "        else:\n",
+    "            b = bplus\n",
+    "\n",
+    "        for i in range(N):\n",
+    "            _sum += b[i] * (np.power(z, i) - ((-1)**(i-N)) * (i/N) * np.power(z, N))\n",
+    "\n",
+    "        return prefactor * _sum\n",
+    "\n",
+    "def resonance(q, _mass, width, phase, scale):\n",
+    "\n",
+    "    q2 = np.power(q, 2)\n",
+    "\n",
+    "    mmu = pdg['muon_M']\n",
+    "\n",
+    "    p = 0.5 * np.sqrt(q2 - 4*(mmu**2))\n",
+    "\n",
+    "    p0 =  0.5 * np.sqrt(_mass**2 - 4*mmu**2)\n",
+    "\n",
+    "    gamma_j = p/ q2 * _mass * width / p0\n",
+    "\n",
+    "    #Calculate the resonance\n",
+    "\n",
+    "    _top = _mass * width\n",
+    "\n",
+    "    _bottom = np.vectorize(complex)(_mass**2 - q2, -_mass*gamma_j)\n",
+    "\n",
+    "    com = _top/_bottom\n",
+    "\n",
+    "    #Rotate by the phase\n",
+    "\n",
+    "    r = scale*np.abs(com)\n",
+    "\n",
+    "    _phase = np.angle(com)\n",
+    "\n",
+    "    _phase += phase\n",
+    "\n",
+    "    com = r * np.exp(np.vectorize(complex)(0.0 , _phase))\n",
+    "\n",
+    "    return com\n",
+    "\n",
+    "\n",
+    "def axiv_nonres(q):\n",
+    "\n",
+    "    GF = pdg['GF']\n",
+    "    alpha_ew = pdg['alpha_ew']\n",
+    "    Vtb = pdg['Vtb']\n",
+    "    Vts = pdg['Vts']\n",
+    "    C10eff = pdg['C10eff']\n",
+    "\n",
+    "    mmu = pdg['muon_M']\n",
+    "    mb = pdg['bquark_M']\n",
+    "    ms = pdg['squark_M']\n",
+    "    mK = pdg['Ks_M']\n",
+    "    mB = pdg['Bplus_M']\n",
+    "\n",
+    "    q2 = np.power(q, 2)\n",
+    "\n",
+    "    #Some helperfunctions\n",
+    "\n",
+    "    beta = np.sqrt(np.abs(1. - 4. * mmu**2. / q2))\n",
+    "\n",
+    "    kabs = np.sqrt(mB**2. +np.power(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. * (np.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. *np.abs(np.vectorize(complex)(C10eff, 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 *np.power(np.abs(np.vectorize(complex)(C10eff, 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 *np.sqrt(q2)\n",
+    "\n",
+    "def vec(q, funcs):\n",
+    "    \n",
+    "    q2 = np.power(q, 2)\n",
+    "\n",
+    "    GF = pdg['GF']\n",
+    "    alpha_ew = pdg['alpha_ew']\n",
+    "    Vtb = pdg['Vtb']\n",
+    "    Vts = pdg['Vts']\n",
+    "    C7eff = pdg['C7eff']\n",
+    "\n",
+    "    mmu = pdg['muon_M']\n",
+    "    mb = pdg['bquark_M']\n",
+    "    ms = pdg['squark_M']\n",
+    "    mK = pdg['Ks_M']\n",
+    "    mB = pdg['Bplus_M']\n",
+    "\n",
+    "    #Some helperfunctions\n",
+    "\n",
+    "    beta = np.sqrt(np.abs(1. - 4. * mmu**2. / q2))\n",
+    "\n",
+    "    kabs = np.sqrt(mB**2. + np.power(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. * (np.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 = np.abs(c9eff(q, funcs) * formfactor(q2, \"+\") + np.vectorize(complex)(2.0 * C7eff * (mb + ms)/(mB + mK), 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 * np.sqrt(q2)\n",
+    "\n",
+    "def c9eff(q, funcs):\n",
+    "\n",
+    "    C9eff_nr = np.vectorize(complex)(pdg['C9eff'], 0.0)\n",
+    "\n",
+    "    c9 = C9eff_nr\n",
+    "\n",
+    "    c9 = c9 + funcs\n",
+    "\n",
+    "    return c9"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "jpsi_mass, jpsi_width, jpsi_phase, jpsi_scale = pdg[\"jpsi\"]\n",
+    "psi2s_mass, psi2s_width, psi2s_phase, psi2s_scale = pdg[\"psi2s\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def t_f(x):\n",
+    "        \n",
+    "    def jpsi_res(q):\n",
+    "        return resonance(q, _mass = jpsi_mass, scale = jpsi_scale, phase = jpsi_phase, width = jpsi_width)\n",
+    "\n",
+    "    def psi2s_res(q):\n",
+    "        return resonance(q, _mass = psi2s_mass, scale = psi2s_scale, phase = psi2s_phase, width = psi2s_width)\n",
+    "\n",
+    "    funcs = jpsi_res(x) + psi2s_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": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Area: 7.126305680132978e-06, rel. err.: 0.1894614654746863%\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\ipykernel_launcher.py:4: IntegrationWarning: The integral is probably divergent, or slowly convergent.\n",
+      "  after removing the cwd from sys.path.\n"
+     ]
+    }
+   ],
+   "source": [
+    "x_min = 2*pdg['muon_M']\n",
+    "x_max = (pdg[\"Bplus_M\"]-pdg[\"Ks_M\"]-0.1)\n",
+    "\n",
+    "result, err = integrate.quad(lambda x: t_f(x), 3600, 3800, limit = 100000000)\n",
+    "print(\"Area: {0}, rel. err.: {1}%\".format(result, err/result))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "tot:  Area: 0.0013270139056058355, rel. err.: 0.001434977783938156%\n",
+      "jpsi: Area: 6.527491388105805e-06, rel. err.: 0.0012105326520570792% (before scaling)\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"tot:  Area: 0.0013270139056058355, rel. err.: 0.001434977783938156%\")\n",
+    "print(\"jpsi: Area: 6.527491388105805e-06, rel. err.: 0.0012105326520570792% (before scaling)\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "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": [
+    "test_q = np.linspace(x_min, x_max, 2000000)\n",
+    "calcs_test = t_f(test_q)\n",
+    "plt.clf()\n",
+    "# plt.plot(x_part, calcs, '.')\n",
+    "plt.plot(test_q, calcs_test, label = 'pdf')\n",
+    "# plt.plot(test_q, res_y, label = 'res')\n",
+    "plt.legend()\n",
+    "plt.ylim(0.0, 1e-8)\n",
+    "# plt.yscale('log')\n",
+    "# plt.xlim(3080, 3110)\n",
+    "plt.savefig('np-test.png')\n",
+    "# print(jpsi_width)"
+   ]
+  },
+  {
+   "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
+}
diff --git a/.ipynb_checkpoints/raremodel-nb-checkpoint.ipynb b/.ipynb_checkpoints/raremodel-nb-checkpoint.ipynb
index 196fc5f..6bee98e 100644
--- a/.ipynb_checkpoints/raremodel-nb-checkpoint.ipynb
+++ b/.ipynb_checkpoints/raremodel-nb-checkpoint.ipynb
@@ -9,7 +9,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [
     {
@@ -52,11 +52,22 @@
     "import tensorflow as tf\n",
     "import zfit\n",
     "from zfit import ztf\n",
-    "from IPython.display import clear_output\n",
+    "# from IPython.display import clear_output\n",
     "import os"
    ]
   },
   {
+   "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": [
@@ -66,7 +77,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -267,7 +278,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -322,7 +333,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -366,7 +377,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -394,7 +405,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
@@ -446,7 +457,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -466,7 +477,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -506,12 +517,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -544,7 +555,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -557,10 +568,88 @@
   },
   {
    "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "211.4\n",
+      "4781.599999999999\n",
+      "807.8926831881124\n",
+      "Time taken: 2 s\n"
+     ]
+    }
+   ],
+   "source": [
+    "# 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": 13,
    "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": 14,
+   "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",
@@ -574,7 +663,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 33,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -586,10 +675,7 @@
     "#     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 = psi2s_res(xq) + jpsi_res(xq) + cusp(xq)\n",
+    "#     funcs = psi2s_res(xq) + jpsi_res(xq)\n",
     "\n",
     "#     vec_f = vec(xq, funcs)\n",
     "\n",
@@ -600,13 +686,14 @@
     "#     return tot\n",
     "\n",
     "# def t_f(x):\n",
-    "#     probs = zfit.run(_t_f(ztf.constant(x)))\n",
+    "#     _ = np.array(x)\n",
+    "#     probs = zfit.run(_t_f(_))\n",
     "#     return probs"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 34,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -615,13 +702,35 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 35,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "nan nan %\n",
+      "Time: 1000.0239598751068\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\ipykernel_launcher.py:3: IntegrationWarning: The maximum number of subdivisions (5) has been achieved.\n",
+      "  If increasing the limit yields no improvement it is advised to analyze \n",
+      "  the integrand in order to determine the difficulties.  If the position of a \n",
+      "  local difficulty can be determined (singularity, discontinuity) one will \n",
+      "  probably gain from splitting up the interval and calling the integrator \n",
+      "  on the subranges.  Perhaps a special-purpose integrator should be used.\n",
+      "  This is separate from the ipykernel package so we can avoid doing imports until\n"
+     ]
+    }
+   ],
    "source": [
     "# start = time.time()\n",
     "\n",
-    "# result, err = integrate.quad(lambda x: t_f(x), x_min, x_max, limit = 50)\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)"
    ]
@@ -631,12 +740,13 @@
    "metadata": {},
    "source": [
     "# Sampling\n",
-    "## One sample"
+    "## One sample\n",
+    "! total_f.sample() always returns the same set !"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -673,7 +783,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -695,7 +805,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -722,37 +832,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 21,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "2/1081\n",
-      "Time taken: 44 s\n",
-      "Projected time left: 6 h, 38 min\n"
-     ]
-    },
-    {
-     "ename": "KeyboardInterrupt",
-     "evalue": "",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
-      "\u001b[1;32m<ipython-input-19-5cddfaa6bdd9>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m     21\u001b[0m     \u001b[1;32mfor\u001b[0m \u001b[0mcall\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcalls\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     22\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 23\u001b[1;33m         \u001b[0msampler\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mresample\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mn\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mevent_stack\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     24\u001b[0m         \u001b[0ms\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0msampler\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0munstack_x\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     25\u001b[0m         \u001b[0msam\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mzfit\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrun\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0ms\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;32mc:\\users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\zfit\\core\\data.py\u001b[0m in \u001b[0;36mresample\u001b[1;34m(self, param_values, n)\u001b[0m\n\u001b[0;32m    624\u001b[0m                     \u001b[1;32mraise\u001b[0m \u001b[0mRuntimeError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"Cannot set a new `n` if not a Tensor-like object was given\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    625\u001b[0m                 \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mn_samples\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mload\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mn\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msession\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msess\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 626\u001b[1;33m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msess\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrun\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msample_holder\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0minitializer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    627\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_initial_resampled\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mTrue\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    628\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;32mc:\\users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\tensorflow\\python\\client\\session.py\u001b[0m in \u001b[0;36mrun\u001b[1;34m(self, fetches, feed_dict, options, run_metadata)\u001b[0m\n\u001b[0;32m    927\u001b[0m     \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    928\u001b[0m       result = self._run(None, fetches, feed_dict, options_ptr,\n\u001b[1;32m--> 929\u001b[1;33m                          run_metadata_ptr)\n\u001b[0m\u001b[0;32m    930\u001b[0m       \u001b[1;32mif\u001b[0m \u001b[0mrun_metadata\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    931\u001b[0m         \u001b[0mproto_data\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtf_session\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mTF_GetBuffer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrun_metadata_ptr\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;32mc:\\users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\tensorflow\\python\\client\\session.py\u001b[0m in \u001b[0;36m_run\u001b[1;34m(self, handle, fetches, feed_dict, options, run_metadata)\u001b[0m\n\u001b[0;32m   1150\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mfinal_fetches\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0mfinal_targets\u001b[0m \u001b[1;32mor\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mhandle\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0mfeed_dict_tensor\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1151\u001b[0m       results = self._do_run(handle, final_targets, final_fetches,\n\u001b[1;32m-> 1152\u001b[1;33m                              feed_dict_tensor, options, run_metadata)\n\u001b[0m\u001b[0;32m   1153\u001b[0m     \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1154\u001b[0m       \u001b[0mresults\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;32mc:\\users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\tensorflow\\python\\client\\session.py\u001b[0m in \u001b[0;36m_do_run\u001b[1;34m(self, handle, target_list, fetch_list, feed_dict, options, run_metadata)\u001b[0m\n\u001b[0;32m   1326\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mhandle\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1327\u001b[0m       return self._do_call(_run_fn, feeds, fetches, targets, options,\n\u001b[1;32m-> 1328\u001b[1;33m                            run_metadata)\n\u001b[0m\u001b[0;32m   1329\u001b[0m     \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1330\u001b[0m       \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_do_call\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0m_prun_fn\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mhandle\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfeeds\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfetches\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;32mc:\\users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\tensorflow\\python\\client\\session.py\u001b[0m in \u001b[0;36m_do_call\u001b[1;34m(self, fn, *args)\u001b[0m\n\u001b[0;32m   1332\u001b[0m   \u001b[1;32mdef\u001b[0m \u001b[0m_do_call\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfn\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1333\u001b[0m     \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1334\u001b[1;33m       \u001b[1;32mreturn\u001b[0m \u001b[0mfn\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1335\u001b[0m     \u001b[1;32mexcept\u001b[0m \u001b[0merrors\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mOpError\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1336\u001b[0m       \u001b[0mmessage\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcompat\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mas_text\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0me\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmessage\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;32mc:\\users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\tensorflow\\python\\client\\session.py\u001b[0m in \u001b[0;36m_run_fn\u001b[1;34m(feed_dict, fetch_list, target_list, options, run_metadata)\u001b[0m\n\u001b[0;32m   1317\u001b[0m       \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_extend_graph\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1318\u001b[0m       return self._call_tf_sessionrun(\n\u001b[1;32m-> 1319\u001b[1;33m           options, feed_dict, fetch_list, target_list, run_metadata)\n\u001b[0m\u001b[0;32m   1320\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1321\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m_prun_fn\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mhandle\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfeed_dict\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfetch_list\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;32mc:\\users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\tensorflow\\python\\client\\session.py\u001b[0m in \u001b[0;36m_call_tf_sessionrun\u001b[1;34m(self, options, feed_dict, fetch_list, target_list, run_metadata)\u001b[0m\n\u001b[0;32m   1405\u001b[0m     return tf_session.TF_SessionRun_wrapper(\n\u001b[0;32m   1406\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_session\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0moptions\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfeed_dict\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfetch_list\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtarget_list\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1407\u001b[1;33m         run_metadata)\n\u001b[0m\u001b[0;32m   1408\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1409\u001b[0m   \u001b[1;32mdef\u001b[0m \u001b[0m_call_tf_sessionprun\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mhandle\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfeed_dict\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfetch_list\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "nr_of_toys = 1\n",
     "nevents = int(pdg[\"number_of_decays\"])\n",
@@ -779,7 +861,7 @@
     "        sampler.resample(n=event_stack)\n",
     "        s = sampler.unstack_x()\n",
     "        sam = zfit.run(s)\n",
-    "        clear_output(wait=True)\n",
+    "#         clear_output(wait=True)\n",
     "\n",
     "        c = call + 1    \n",
     "        print(\"{0}/{1}\".format(c, calls))\n",
@@ -792,7 +874,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -809,9 +891,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 23,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Time to generate full toy: 0 s\n",
+      "(5404696,)\n"
+     ]
+    }
+   ],
    "source": [
     "print(\"Time to generate full toy: {} s\".format(int(time.time()-start)))\n",
     "\n",
@@ -826,32 +917,51 @@
     "\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,
+   "execution_count": 24,
    "metadata": {},
-   "outputs": [],
+   "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": [
     "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",
+    "plt.hist(total_samp[:nevents], bins = bins, range = (x_min,x_max), label = 'data')\n",
+    "plt.plot(test_q, calcs_test*nevents*4.5 , label = 'pdf')\n",
     "\n",
     "# plt.plot(sam, calcs, '.')\n",
     "# plt.plot(test_q, calcs_test)\n",
-    "plt.ylim(0, 20)\n",
-    "# plt.xlim(3000, 3750)\n",
+    "# plt.yscale('log')\n",
+    "plt.ylim(0, 12000)\n",
+    "# plt.xlim(3080, 3110)\n",
+    "\n",
+    "plt.legend()\n",
     "\n",
     "plt.savefig('test2.png')"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 25,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -881,14 +991,182 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 26,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<hr>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "    <tr>\n",
+       "        <td title=\"Minimum value of function\">FCN = 23448660.25141733</td>\n",
+       "        <td title=\"Total number of call to FCN so far\">TOTAL NCALL = 73</td>\n",
+       "        <td title=\"Number of call in last migrad\">NCALLS = 73</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "        <td title=\"Estimated distance to minimum\">EDM = nan</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:#FF7878\">False</td>\n",
+       "        <td align=\"center\" style=\"background-color:#FF7878\">False</td>\n",
+       "        <td align=\"center\" style=\"background-color:#FF7878\">True</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=\"$('#mfLVoBAVNv').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_p</td>\n",
+       "        <td>-1.5</td>\n",
+       "        <td>nan</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_p</td>\n",
+       "        <td>-1.5</td>\n",
+       "        <td>nan</td>\n",
+       "        <td></td>\n",
+       "        <td></td>\n",
+       "        <td></td>\n",
+       "        <td></td>\n",
+       "        <td>No</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "        <td>2</td>\n",
+       "        <td>psi2s_s</td>\n",
+       "        <td>23.07</td>\n",
+       "        <td>nan</td>\n",
+       "        <td></td>\n",
+       "        <td></td>\n",
+       "        <td></td>\n",
+       "        <td></td>\n",
+       "        <td>No</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "        <td>3</td>\n",
+       "        <td>jpsi_s</td>\n",
+       "        <td>184.39</td>\n",
+       "        <td>nan</td>\n",
+       "        <td></td>\n",
+       "        <td></td>\n",
+       "        <td></td>\n",
+       "        <td></td>\n",
+       "        <td>No</td>\n",
+       "    </tr>\n",
+       "</table>\n",
+       "<pre id=\"mfLVoBAVNv\" style=\"display:none;\">\n",
+       "<textarea rows=\"14\" 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 & $jpsi_{p}$ & -1.5 & nan &  &  &  &  & No\\\\\n",
+       "\\hline\n",
+       "1 & $psi2s_{p}$ & -1.5 & nan &  &  &  &  & No\\\\\n",
+       "\\hline\n",
+       "2 & $psi2s_{s}$ & 23.07 & nan &  &  &  &  & No\\\\\n",
+       "\\hline\n",
+       "3 & $jpsi_{s}$ & 184.39 & nan &  &  &  &  & No\\\\\n",
+       "\\hline\n",
+       "\\end{tabular}\n",
+       "</textarea>\n",
+       "</pre>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<hr>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "ename": "RuntimeError",
+     "evalue": "exception was raised in user function\nUser function arguments:\n     jpsi_p = -nan(ind)\n    psi2s_p = +nan\n    psi2s_s = +nan\n     jpsi_s = +nan\nOriginal python exception in user function:\nKeyboardInterrupt: \n  File \"c:\\users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\zfit\\minimizers\\minimizer_minuit.py\", line 48, in func\n    loss_evaluated = self.sess.run(loss_val)\n  File \"c:\\users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\tensorflow\\python\\client\\session.py\", line 929, in run\n    run_metadata_ptr)\n  File \"c:\\users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\tensorflow\\python\\client\\session.py\", line 1152, in _run\n    feed_dict_tensor, options, run_metadata)\n  File \"c:\\users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\tensorflow\\python\\client\\session.py\", line 1328, in _do_run\n    run_metadata)\n  File \"c:\\users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\tensorflow\\python\\client\\session.py\", line 1334, in _do_call\n    return fn(*args)\n  File \"c:\\users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\tensorflow\\python\\client\\session.py\", line 1319, in _run_fn\n    options, feed_dict, fetch_list, target_list, run_metadata)\n  File \"c:\\users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\tensorflow\\python\\client\\session.py\", line 1407, in _call_tf_sessionrun\n    run_metadata)\n",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mRuntimeError\u001b[0m                              Traceback (most recent call last)",
+      "\u001b[1;32m<ipython-input-26-17821450db47>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mminimizer\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mminimize\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnll\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      6\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 7\u001b[1;33m \u001b[0mparam_errors\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mresult\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0merror\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      8\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      9\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mvar\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0merrors\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mparam_errors\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32mc:\\users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\zfit\\minimizers\\fitresult.py\u001b[0m in \u001b[0;36merror\u001b[1;34m(self, params, method, error_name, sigma)\u001b[0m\n\u001b[0;32m    227\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    228\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0muncached_params\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 229\u001b[1;33m             \u001b[0merror_dict\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_error\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mparams\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0muncached_params\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mmethod\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msigma\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0msigma\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    230\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_cache_errors\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0merror_name\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0merror_name\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0merror_dict\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    231\u001b[0m         \u001b[0mall_errors\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mOrderedDict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mp\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mparams\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mp\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0merror_name\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mp\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mparams\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32mc:\\users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\zfit\\minimizers\\fitresult.py\u001b[0m in \u001b[0;36m_error\u001b[1;34m(self, params, method, sigma)\u001b[0m\n\u001b[0;32m    238\u001b[0m             \u001b[1;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    239\u001b[0m                 \u001b[1;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"The following method is not a valid, implemented method: {}\"\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmethod\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 240\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mmethod\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mresult\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mparams\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mparams\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msigma\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0msigma\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    241\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    242\u001b[0m \u001b[1;31m# def set_error_method(self, method):\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32mc:\\users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\zfit\\minimizers\\fitresult.py\u001b[0m in \u001b[0;36m_minos_minuit\u001b[1;34m(result, params, sigma)\u001b[0m\n\u001b[0;32m     46\u001b[0m                         \"`MinuitMinimizer`.\")\n\u001b[0;32m     47\u001b[0m     result = [minimizer._minuit_minimizer.minos(var=p.name, sigma=sigma)\n\u001b[1;32m---> 48\u001b[1;33m               for p in params][-1]  # returns every var\n\u001b[0m\u001b[0;32m     49\u001b[0m     \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mOrderedDict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mp\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mresult\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mp\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mparams\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     50\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0mresult\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32mc:\\users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\zfit\\minimizers\\fitresult.py\u001b[0m in \u001b[0;36m<listcomp>\u001b[1;34m(.0)\u001b[0m\n\u001b[0;32m     46\u001b[0m                         \"`MinuitMinimizer`.\")\n\u001b[0;32m     47\u001b[0m     result = [minimizer._minuit_minimizer.minos(var=p.name, sigma=sigma)\n\u001b[1;32m---> 48\u001b[1;33m               for p in params][-1]  # returns every var\n\u001b[0m\u001b[0;32m     49\u001b[0m     \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mOrderedDict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mp\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mresult\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mp\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mparams\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     50\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0mresult\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32miminuit\\_libiminuit.pyx\u001b[0m in \u001b[0;36miminuit._libiminuit.Minuit.minos\u001b[1;34m()\u001b[0m\n",
+      "\u001b[1;31mRuntimeError\u001b[0m: exception was raised in user function\nUser function arguments:\n     jpsi_p = -nan(ind)\n    psi2s_p = +nan\n    psi2s_s = +nan\n     jpsi_s = +nan\nOriginal python exception in user function:\nKeyboardInterrupt: \n  File \"c:\\users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\zfit\\minimizers\\minimizer_minuit.py\", line 48, in func\n    loss_evaluated = self.sess.run(loss_val)\n  File \"c:\\users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\tensorflow\\python\\client\\session.py\", line 929, in run\n    run_metadata_ptr)\n  File \"c:\\users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\tensorflow\\python\\client\\session.py\", line 1152, in _run\n    feed_dict_tensor, options, run_metadata)\n  File \"c:\\users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\tensorflow\\python\\client\\session.py\", line 1328, in _do_run\n    run_metadata)\n  File \"c:\\users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\tensorflow\\python\\client\\session.py\", line 1334, in _do_call\n    return fn(*args)\n  File \"c:\\users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\tensorflow\\python\\client\\session.py\", line 1319, in _run_fn\n    options, feed_dict, fetch_list, target_list, run_metadata)\n  File \"c:\\users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\tensorflow\\python\\client\\session.py\", line 1407, in _call_tf_sessionrun\n    run_metadata)\n"
+     ]
+    }
+   ],
    "source": [
-    "nll = zfit.loss.UnbinnedNLL(model=total_f, data=data2, fit_range = (x_min, x_max))\n",
+    "nll = zfit.loss.UnbinnedNLL(model=total_f, data=data3, fit_range = (x_min, x_max))\n",
     "\n",
     "minimizer = zfit.minimize.MinuitMinimizer()\n",
-    "# minimizer._use_tfgrad = False\n",
+    "minimizer._use_tfgrad = False\n",
     "result = minimizer.minimize(nll)\n",
     "\n",
     "param_errors = result.error()\n",
@@ -905,7 +1183,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "(-3.14+2*np.pi)/np.pi"
+    "(-7.95933+2*np.pi)/np.pi+np.pi"
    ]
   },
   {
@@ -939,7 +1217,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "probs = total_f.pdf(test_q)\n",
+    "# probs = total_f.pdf(test_q)\n",
     "\n",
     "calcs_test = zfit.run(probs)\n",
     "res_y = zfit.run(jpsi_res(test_q))"
@@ -956,7 +1234,7 @@
     "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, 4e-4)\n",
+    "plt.ylim(0.0, 5e-4)\n",
     "# plt.yscale('log')\n",
     "# plt.xlim(3080, 3110)\n",
     "plt.savefig('test3.png')\n",
@@ -969,6 +1247,13 @@
    "metadata": {},
    "outputs": [],
    "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
diff --git a/Untitled.ipynb b/Untitled.ipynb
new file mode 100644
index 0000000..720b3d9
--- /dev/null
+++ b/Untitled.ipynb
@@ -0,0 +1,282 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Numpy backup for scaling in the beginning"
+   ]
+  },
+  {
+   "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:53: 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 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"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "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 = pdg['Ks_M']\n",
+    "    mbstar0 = pdg[\"mbstar0\"]\n",
+    "    mbstar = pdg[\"mbstar\"]\n",
+    "    b0 = pdg[\"b0\"]\n",
+    "    bplus = pdg[\"bplus\"]\n",
+    "    bT = pdg[\"bT\"]\n",
+    "\n",
+    "    mmu = pdg['muon_M']\n",
+    "    mb = pdg['bquark_M']\n",
+    "    ms = pdg['squark_M']\n",
+    "    mB = 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, q2) * _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"
+   ]
+  }
+ ],
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+   "display_name": "Python 3",
+   "language": "python",
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diff --git a/np-test.png b/np-test.png
new file mode 100644
index 0000000..2523832
--- /dev/null
+++ b/np-test.png
Binary files differ
diff --git a/numpy_backup_for_scaling.ipynb b/numpy_backup_for_scaling.ipynb
new file mode 100644
index 0000000..76cad46
--- /dev/null
+++ b/numpy_backup_for_scaling.ipynb
@@ -0,0 +1,401 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Numpy backup for scaling in the beginning"
+   ]
+  },
+  {
+   "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:53: 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 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"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "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 = pdg['Ks_M']\n",
+    "    mbstar0 = pdg[\"mbstar0\"]\n",
+    "    mbstar = pdg[\"mbstar\"]\n",
+    "    b0 = pdg[\"b0\"]\n",
+    "    bplus = pdg[\"bplus\"]\n",
+    "    bT = pdg[\"bT\"]\n",
+    "\n",
+    "    mmu = pdg['muon_M']\n",
+    "    mb = pdg['bquark_M']\n",
+    "    ms = pdg['squark_M']\n",
+    "    mB = 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)*(np.sqrt(mB)-np.sqrt(mK))**2\n",
+    "\n",
+    "    z_oben = np.sqrt(tpos - q2) - np.sqrt(tpos - tzero)\n",
+    "    z_unten = np.sqrt(tpos - q2) + np.sqrt(tpos - tzero)\n",
+    "    z = 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]*(np.power(z,i))\n",
+    "\n",
+    "        return prefactor * _sum\n",
+    "\n",
+    "    #calculate f+ or fT\n",
+    "\n",
+    "    else:\n",
+    "        prefactor = 1/(1 - q2/(mbstar**2))\n",
+    "        _sum = 0\n",
+    "\n",
+    "        if subscript == \"T\":\n",
+    "            b = bT\n",
+    "        else:\n",
+    "            b = bplus\n",
+    "\n",
+    "        for i in range(N):\n",
+    "            _sum += b[i] * (np.power(z, i) - ((-1)**(i-N)) * (i/N) * np.power(z, N))\n",
+    "\n",
+    "        return prefactor * _sum\n",
+    "\n",
+    "def resonance(q, _mass, width, phase, scale):\n",
+    "\n",
+    "    q2 = np.power(q, 2)\n",
+    "\n",
+    "    mmu = pdg['muon_M']\n",
+    "\n",
+    "    p = 0.5 * np.sqrt(q2 - 4*(mmu**2))\n",
+    "\n",
+    "    p0 =  0.5 * np.sqrt(_mass**2 - 4*mmu**2)\n",
+    "\n",
+    "    gamma_j = p/ q2 * _mass * width / p0\n",
+    "\n",
+    "    #Calculate the resonance\n",
+    "\n",
+    "    _top = _mass * width\n",
+    "\n",
+    "    _bottom = np.vectorize(complex)(_mass**2 - q2, -_mass*gamma_j)\n",
+    "\n",
+    "    com = _top/_bottom\n",
+    "\n",
+    "    #Rotate by the phase\n",
+    "\n",
+    "    r = scale*np.abs(com)\n",
+    "\n",
+    "    _phase = np.angle(com)\n",
+    "\n",
+    "    _phase += phase\n",
+    "\n",
+    "    com = r * np.exp(np.vectorize(complex)(0.0 , _phase))\n",
+    "\n",
+    "    return com\n",
+    "\n",
+    "\n",
+    "def axiv_nonres(q):\n",
+    "\n",
+    "    GF = pdg['GF']\n",
+    "    alpha_ew = pdg['alpha_ew']\n",
+    "    Vtb = pdg['Vtb']\n",
+    "    Vts = pdg['Vts']\n",
+    "    C10eff = pdg['C10eff']\n",
+    "\n",
+    "    mmu = pdg['muon_M']\n",
+    "    mb = pdg['bquark_M']\n",
+    "    ms = pdg['squark_M']\n",
+    "    mK = pdg['Ks_M']\n",
+    "    mB = pdg['Bplus_M']\n",
+    "\n",
+    "    q2 = np.power(q, 2)\n",
+    "\n",
+    "    #Some helperfunctions\n",
+    "\n",
+    "    beta = np.sqrt(np.abs(1. - 4. * mmu**2. / q2))\n",
+    "\n",
+    "    kabs = np.sqrt(mB**2. +np.power(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. * (np.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. *np.abs(np.vectorize(complex)(C10eff, 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 *np.power(np.abs(np.vectorize(complex)(C10eff, 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 *np.sqrt(q2)\n",
+    "\n",
+    "def vec(q, funcs):\n",
+    "    \n",
+    "    q2 = np.power(q, 2)\n",
+    "\n",
+    "    GF = pdg['GF']\n",
+    "    alpha_ew = pdg['alpha_ew']\n",
+    "    Vtb = pdg['Vtb']\n",
+    "    Vts = pdg['Vts']\n",
+    "    C7eff = pdg['C7eff']\n",
+    "\n",
+    "    mmu = pdg['muon_M']\n",
+    "    mb = pdg['bquark_M']\n",
+    "    ms = pdg['squark_M']\n",
+    "    mK = pdg['Ks_M']\n",
+    "    mB = pdg['Bplus_M']\n",
+    "\n",
+    "    #Some helperfunctions\n",
+    "\n",
+    "    beta = np.sqrt(np.abs(1. - 4. * mmu**2. / q2))\n",
+    "\n",
+    "    kabs = np.sqrt(mB**2. + np.power(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. * (np.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 = np.abs(c9eff(q, funcs) * formfactor(q2, \"+\") + np.vectorize(complex)(2.0 * C7eff * (mb + ms)/(mB + mK), 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 * np.sqrt(q2)\n",
+    "\n",
+    "def c9eff(q, funcs):\n",
+    "\n",
+    "    C9eff_nr = np.vectorize(complex)(pdg['C9eff'], 0.0)\n",
+    "\n",
+    "    c9 = C9eff_nr\n",
+    "\n",
+    "    c9 = c9 + funcs\n",
+    "\n",
+    "    return c9"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "jpsi_mass, jpsi_width, jpsi_phase, jpsi_scale = pdg[\"jpsi\"]\n",
+    "psi2s_mass, psi2s_width, psi2s_phase, psi2s_scale = pdg[\"psi2s\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def t_f(x):\n",
+    "        \n",
+    "    def jpsi_res(q):\n",
+    "        return resonance(q, _mass = jpsi_mass, scale = jpsi_scale, phase = jpsi_phase, width = jpsi_width)\n",
+    "\n",
+    "    def psi2s_res(q):\n",
+    "        return resonance(q, _mass = psi2s_mass, scale = psi2s_scale, phase = psi2s_phase, width = psi2s_width)\n",
+    "\n",
+    "    funcs = jpsi_res(x) + psi2s_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": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Area: 7.126305680132978e-06, rel. err.: 0.1894614654746863%\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\ipykernel_launcher.py:4: IntegrationWarning: The integral is probably divergent, or slowly convergent.\n",
+      "  after removing the cwd from sys.path.\n"
+     ]
+    }
+   ],
+   "source": [
+    "x_min = 2*pdg['muon_M']\n",
+    "x_max = (pdg[\"Bplus_M\"]-pdg[\"Ks_M\"]-0.1)\n",
+    "\n",
+    "result, err = integrate.quad(lambda x: t_f(x), 3600, 3800, limit = 100000000)\n",
+    "print(\"Area: {0}, rel. err.: {1}%\".format(result, err/result))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "tot:  Area: 0.0013270139056058355, rel. err.: 0.001434977783938156%\n",
+      "jpsi: Area: 6.527491388105805e-06, rel. err.: 0.0012105326520570792% (before scaling)\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"tot:  Area: 0.0013270139056058355, rel. err.: 0.001434977783938156%\")\n",
+    "print(\"jpsi: Area: 6.527491388105805e-06, rel. err.: 0.0012105326520570792% (before scaling)\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "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": [
+    "test_q = np.linspace(x_min, x_max, 2000000)\n",
+    "calcs_test = t_f(test_q)\n",
+    "plt.clf()\n",
+    "# plt.plot(x_part, calcs, '.')\n",
+    "plt.plot(test_q, calcs_test, label = 'pdf')\n",
+    "# plt.plot(test_q, res_y, label = 'res')\n",
+    "plt.legend()\n",
+    "plt.ylim(0.0, 1e-8)\n",
+    "# plt.yscale('log')\n",
+    "# plt.xlim(3080, 3110)\n",
+    "plt.savefig('np-test.png')\n",
+    "# print(jpsi_width)"
+   ]
+  },
+  {
+   "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
+}
diff --git a/pdg_const.py b/pdg_const.py
index f831ad0..910d7b4 100644
--- a/pdg_const.py
+++ b/pdg_const.py
@@ -36,13 +36,13 @@
 "C3" : -0.005,
 "C4" : -0.078,
 
-"C7eff" : -0.306,
-# "C7eff": 0.0,
+# "C7eff" : -0.306,
+"C7eff": 0.0,
 
-"C9eff" : 4.211,
-"C10eff" : -4.103,
-# "C9eff": 0.0,
-# "C10eff": 0.0, 
+# "C9eff" : 4.211,
+# "C10eff" : -4.103,
+"C9eff": 0.0,
+"C10eff": 0.0, 
 
 ###Other constants
 
@@ -67,8 +67,8 @@
 
 #Resonances format(mass, width, phase, scale)
 # "jpsi": (3096.0, 0.09, -1.5, 2e-2),    #---> prescaling
-"jpsi": (3096.0, 0.09, -1.5, 184.39),    #---> after scaling
-# "jpsi": (3096.0, 0.09, -1.5, 0.0),
+# "jpsi": (3096.0, 0.09, -1.5, 184.39),    #---> after scaling
+"jpsi": (3096.0, 0.09, -1.5, 0.0),
 "jpsi_BR": 6.02e-5,
 "jpsi_auc": 0.2126825758464027,
 
diff --git a/raremodel-nb.ipynb b/raremodel-nb.ipynb
index be1130c..5fdb27e 100644
--- a/raremodel-nb.ipynb
+++ b/raremodel-nb.ipynb
@@ -57,6 +57,17 @@
    ]
   },
   {
+   "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": [
@@ -66,7 +77,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -267,7 +278,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -322,7 +333,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -366,7 +377,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -394,7 +405,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
@@ -446,7 +457,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -466,7 +477,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -506,7 +517,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
@@ -544,7 +555,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -557,7 +568,74 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 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": 13,
+   "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": 14,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -574,7 +652,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -586,10 +664,7 @@
     "#     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 = psi2s_res(xq) + jpsi_res(xq) + cusp(xq)\n",
+    "#     funcs = psi2s_res(xq) + jpsi_res(xq)\n",
     "\n",
     "#     vec_f = vec(xq, funcs)\n",
     "\n",
@@ -600,13 +675,14 @@
     "#     return tot\n",
     "\n",
     "# def t_f(x):\n",
-    "#     probs = zfit.run(_t_f(ztf.constant(x)))\n",
+    "#     _ = np.array(x)\n",
+    "#     probs = zfit.run(_t_f(_))\n",
     "#     return probs"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -615,13 +691,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [],
    "source": [
     "# start = time.time()\n",
     "\n",
-    "# result, err = integrate.quad(lambda x: t_f(x), x_min, x_max, limit = 50)\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)"
    ]
@@ -631,12 +707,13 @@
    "metadata": {},
    "source": [
     "# Sampling\n",
-    "## One sample"
+    "## One sample\n",
+    "! total_f.sample() always returns the same set !"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -673,7 +750,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -695,7 +772,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -722,19 +799,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 21,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "55/55\n",
-      "Time taken: 6 h, 21 min\n",
-      "Projected time left: \n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "nr_of_toys = 1\n",
     "nevents = int(pdg[\"number_of_decays\"])\n",
@@ -746,35 +813,35 @@
     "\n",
     "start = time.time()\n",
     "\n",
-    "sampler = total_f.create_sampler(n=event_stack)\n",
+    "# sampler = total_f.create_sampler(n=event_stack)\n",
     "\n",
-    "for toy in range(nr_of_toys):\n",
+    "# for toy in range(nr_of_toys):\n",
     "    \n",
-    "    dirName = 'data/zfit_toys/toy_{0}'.format(toy)\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",
+    "#     if not os.path.exists(dirName):\n",
+    "#         os.mkdir(dirName)\n",
+    "#         print(\"Directory \" , dirName ,  \" Created \")\n",
     "\n",
-    "    for call in range(calls):\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",
+    "#         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",
-    "        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",
+    "#         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_{0}/{1}.pkl\".format(toy, call), \"wb\") as f:\n",
-    "            pkl.dump(sam, f, pkl.HIGHEST_PROTOCOL)"
+    "#         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": 19,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -791,14 +858,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 23,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Time to generate full toy: 22915 s\n",
+      "Time to generate full toy: 0 s\n",
       "(5404696,)\n"
      ]
     }
@@ -817,17 +884,19 @@
     "\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": 21,
+   "execution_count": 24,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -843,19 +912,23 @@
     "\n",
     "# calcs = zfit.run(total_test_tf(samp))\n",
     "\n",
-    "plt.hist(total_samp[:event_stack], bins = bins, range = (x_min,x_max))\n",
+    "plt.hist(total_samp[:nevents], bins = bins, range = (x_min,x_max), label = 'data')\n",
+    "plt.plot(test_q, calcs_test*nevents*4.5 , label = 'pdf')\n",
     "\n",
     "# plt.plot(sam, calcs, '.')\n",
     "# plt.plot(test_q, calcs_test)\n",
-    "plt.ylim(0, 20)\n",
-    "# plt.xlim(3000, 3750)\n",
+    "# plt.yscale('log')\n",
+    "plt.ylim(0, 12000)\n",
+    "# plt.xlim(3080, 3110)\n",
+    "\n",
+    "plt.legend()\n",
     "\n",
     "plt.savefig('test2.png')"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 25,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -885,7 +958,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 26,
    "metadata": {},
    "outputs": [
     {
@@ -902,12 +975,12 @@
       "text/html": [
        "<table>\n",
        "    <tr>\n",
-       "        <td title=\"Minimum value of function\">FCN = 18215610.730149463</td>\n",
-       "        <td title=\"Total number of call to FCN so far\">TOTAL NCALL = 76</td>\n",
-       "        <td title=\"Number of call in last migrad\">NCALLS = 76</td>\n",
+       "        <td title=\"Minimum value of function\">FCN = 18537900.293873098</td>\n",
+       "        <td title=\"Total number of call to FCN so far\">TOTAL NCALL = 194</td>\n",
+       "        <td title=\"Number of call in last migrad\">NCALLS = 194</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "        <td title=\"Estimated distance to minimum\">EDM = 6.769469250461445e-05</td>\n",
+       "        <td title=\"Estimated distance to minimum\">EDM = 0.03059806673262826</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",
@@ -922,7 +995,7 @@
        "        <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:#FF7878\">False</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",
@@ -938,7 +1011,7 @@
        "    <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\" style=\"background-color:#FF7878\">True</td>\n",
        "        <td align=\"center\"></td>\n",
        "        <td align=\"center\" style=\"background-color:#92CCA6\">False</td>\n",
        "    </tr>\n",
@@ -953,7 +1026,7 @@
       "text/html": [
        "<table>\n",
        "    <tr>\n",
-       "        <td><a href=\"#\" onclick=\"$('#JIwWuWnKke').toggle()\">+</a></td>\n",
+       "        <td><a href=\"#\" onclick=\"$('#kJHicCUbKd').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",
@@ -965,9 +1038,9 @@
        "    </tr>\n",
        "    <tr>\n",
        "        <td>0</td>\n",
-       "        <td>psi2s_p</td>\n",
-       "        <td>-69.4248</td>\n",
-       "        <td>0.023096</td>\n",
+       "        <td>jpsi_s</td>\n",
+       "        <td>464.524</td>\n",
+       "        <td>0.229484</td>\n",
        "        <td></td>\n",
        "        <td></td>\n",
        "        <td></td>\n",
@@ -976,9 +1049,9 @@
        "    </tr>\n",
        "    <tr>\n",
        "        <td>1</td>\n",
-       "        <td>jpsi_p</td>\n",
-       "        <td>-21.9928</td>\n",
-       "        <td>0.0112012</td>\n",
+       "        <td>psi2s_s</td>\n",
+       "        <td>76.5024</td>\n",
+       "        <td>0.0505517</td>\n",
        "        <td></td>\n",
        "        <td></td>\n",
        "        <td></td>\n",
@@ -987,9 +1060,9 @@
        "    </tr>\n",
        "    <tr>\n",
        "        <td>2</td>\n",
-       "        <td>jpsi_s</td>\n",
-       "        <td>464.531</td>\n",
-       "        <td>0.227734</td>\n",
+       "        <td>psi2s_p</td>\n",
+       "        <td>0.30538</td>\n",
+       "        <td>0.0245293</td>\n",
        "        <td></td>\n",
        "        <td></td>\n",
        "        <td></td>\n",
@@ -998,9 +1071,9 @@
        "    </tr>\n",
        "    <tr>\n",
        "        <td>3</td>\n",
-       "        <td>psi2s_s</td>\n",
-       "        <td>76.5102</td>\n",
-       "        <td>0.0504588</td>\n",
+       "        <td>jpsi_p</td>\n",
+       "        <td>-9.42323</td>\n",
+       "        <td>0.0114089</td>\n",
        "        <td></td>\n",
        "        <td></td>\n",
        "        <td></td>\n",
@@ -1008,19 +1081,19 @@
        "        <td>No</td>\n",
        "    </tr>\n",
        "</table>\n",
-       "<pre id=\"JIwWuWnKke\" style=\"display:none;\">\n",
+       "<pre id=\"kJHicCUbKd\" style=\"display:none;\">\n",
        "<textarea rows=\"14\" 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 & $psi2s_{p}$ & -69.4248 & 0.023096 &  &  &  &  & No\\\\\n",
+       "0 & $jpsi_{s}$ & 464.524 & 0.229484 &  &  &  &  & No\\\\\n",
        "\\hline\n",
-       "1 & $jpsi_{p}$ & -21.9928 & 0.0112012 &  &  &  &  & No\\\\\n",
+       "1 & $psi2s_{s}$ & 76.5024 & 0.0505517 &  &  &  &  & No\\\\\n",
        "\\hline\n",
-       "2 & $jpsi_{s}$ & 464.531 & 0.227734 &  &  &  &  & No\\\\\n",
+       "2 & $psi2s_{p}$ & 0.30538 & 0.0245293 &  &  &  &  & No\\\\\n",
        "\\hline\n",
-       "3 & $psi2s_{s}$ & 76.5102 & 0.0504588 &  &  &  &  & No\\\\\n",
+       "3 & $jpsi_{p}$ & -9.42323 & 0.0114089 &  &  &  &  & No\\\\\n",
        "\\hline\n",
        "\\end{tabular}\n",
        "</textarea>\n",
@@ -1040,166 +1113,27 @@
      "output_type": "display_data"
     },
     {
-     "data": {
-      "text/html": [
-       "<span>Minos status for psi2s_p: <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>-0.02330745637825202</td>\n",
-       "        <td>0.025582271077546587</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 jpsi_p: <span style=\"background-color:#FF7878\">PROBLEM</span></span>\n",
-       "<table>\n",
-       "    <tr>\n",
-       "        <td title=\"lower and upper minos error of the parameter\">Error</td>\n",
-       "        <td>-0.011201240182803458</td>\n",
-       "        <td>0.011201240182803458</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "        <td title=\"Validity of minos error\">Valid</td>\n",
-       "        <td style=\"background-color:#FF7878\">False</td>\n",
-       "        <td style=\"background-color:#FF7878\">False</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 jpsi_s: <span style=\"background-color:#FF7878\">PROBLEM</span></span>\n",
-       "<table>\n",
-       "    <tr>\n",
-       "        <td title=\"lower and upper minos error of the parameter\">Error</td>\n",
-       "        <td>-0.22773449479366195</td>\n",
-       "        <td>0.22773449479366195</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "        <td title=\"Validity of minos error\">Valid</td>\n",
-       "        <td style=\"background-color:#FF7878\">False</td>\n",
-       "        <td style=\"background-color:#FF7878\">False</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:#FF7878\">PROBLEM</span></span>\n",
-       "<table>\n",
-       "    <tr>\n",
-       "        <td title=\"lower and upper minos error of the parameter\">Error</td>\n",
-       "        <td>-0.05045879620181111</td>\n",
-       "        <td>0.05045879620181111</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "        <td title=\"Validity of minos error\">Valid</td>\n",
-       "        <td style=\"background-color:#FF7878\">False</td>\n",
-       "        <td style=\"background-color:#FF7878\">False</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": [
-      "psi2s_p: ^{+0.025582271077546587}_{-0.02330745637825202}\n",
-      "jpsi_p: ^{+0.011201240182803458}_{-0.011201240182803458}\n",
-      "jpsi_s: ^{+0.22773449479366195}_{-0.22773449479366195}\n",
-      "psi2s_s: ^{+0.05045879620181111}_{-0.05045879620181111}\n",
-      "Function minimum: 18215610.730149463\n"
+     "ename": "RuntimeError",
+     "evalue": "Function mimimum is not valid. Make sure migrad converge first",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mRuntimeError\u001b[0m                              Traceback (most recent call last)",
+      "\u001b[1;32m<ipython-input-26-17821450db47>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mminimizer\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mminimize\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnll\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      6\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 7\u001b[1;33m \u001b[0mparam_errors\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mresult\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0merror\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      8\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      9\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mvar\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0merrors\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mparam_errors\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32mc:\\users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\zfit\\minimizers\\fitresult.py\u001b[0m in \u001b[0;36merror\u001b[1;34m(self, params, method, error_name, sigma)\u001b[0m\n\u001b[0;32m    227\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    228\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0muncached_params\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 229\u001b[1;33m             \u001b[0merror_dict\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_error\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mparams\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0muncached_params\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mmethod\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msigma\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0msigma\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    230\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_cache_errors\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0merror_name\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0merror_name\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0merror_dict\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    231\u001b[0m         \u001b[0mall_errors\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mOrderedDict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mp\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mparams\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mp\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0merror_name\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mp\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mparams\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32mc:\\users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\zfit\\minimizers\\fitresult.py\u001b[0m in \u001b[0;36m_error\u001b[1;34m(self, params, method, sigma)\u001b[0m\n\u001b[0;32m    238\u001b[0m             \u001b[1;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    239\u001b[0m                 \u001b[1;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"The following method is not a valid, implemented method: {}\"\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmethod\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 240\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mmethod\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mresult\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mparams\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mparams\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msigma\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0msigma\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    241\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    242\u001b[0m \u001b[1;31m# def set_error_method(self, method):\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32mc:\\users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\zfit\\minimizers\\fitresult.py\u001b[0m in \u001b[0;36m_minos_minuit\u001b[1;34m(result, params, sigma)\u001b[0m\n\u001b[0;32m     46\u001b[0m                         \"`MinuitMinimizer`.\")\n\u001b[0;32m     47\u001b[0m     result = [minimizer._minuit_minimizer.minos(var=p.name, sigma=sigma)\n\u001b[1;32m---> 48\u001b[1;33m               for p in params][-1]  # returns every var\n\u001b[0m\u001b[0;32m     49\u001b[0m     \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mOrderedDict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mp\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mresult\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mp\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mparams\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     50\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0mresult\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32mc:\\users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\zfit\\minimizers\\fitresult.py\u001b[0m in \u001b[0;36m<listcomp>\u001b[1;34m(.0)\u001b[0m\n\u001b[0;32m     46\u001b[0m                         \"`MinuitMinimizer`.\")\n\u001b[0;32m     47\u001b[0m     result = [minimizer._minuit_minimizer.minos(var=p.name, sigma=sigma)\n\u001b[1;32m---> 48\u001b[1;33m               for p in params][-1]  # returns every var\n\u001b[0m\u001b[0;32m     49\u001b[0m     \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mOrderedDict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mp\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mresult\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mp\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mparams\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     50\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0mresult\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32miminuit\\_libiminuit.pyx\u001b[0m in \u001b[0;36miminuit._libiminuit.Minuit.minos\u001b[1;34m()\u001b[0m\n",
+      "\u001b[1;31mRuntimeError\u001b[0m: Function mimimum is not valid. Make sure migrad converge first"
      ]
     }
    ],
    "source": [
-    "nll = zfit.loss.UnbinnedNLL(model=total_f, data=data2, fit_range = (x_min, x_max))\n",
+    "nll = zfit.loss.UnbinnedNLL(model=total_f, data=data3, fit_range = (x_min, x_max))\n",
     "\n",
     "minimizer = zfit.minimize.MinuitMinimizer()\n",
-    "# minimizer._use_tfgrad = False\n",
+    "minimizer._use_tfgrad = False\n",
     "result = minimizer.minimize(nll)\n",
     "\n",
     "param_errors = result.error()\n",
@@ -1212,22 +1146,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "1.0005069573828973"
-      ]
-     },
-     "execution_count": 24,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
-    "(-3.14+2*np.pi)/np.pi"
+    "(-7.95933+2*np.pi)/np.pi+np.pi"
    ]
   },
   {
@@ -1239,48 +1162,29 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'5 h, 55 min'"
-      ]
-     },
-     "execution_count": 25,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "display_time(int(395*pdg[\"number_of_decays\"]/100000))"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "6 h, 12 min\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "print(display_time(22376))"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
-    "probs = total_f.pdf(test_q)\n",
+    "# probs = total_f.pdf(test_q)\n",
     "\n",
     "calcs_test = zfit.run(probs)\n",
     "res_y = zfit.run(jpsi_res(test_q))"
@@ -1288,36 +1192,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0.09\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"
-    }
-   ],
+   "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, 4e-4)\n",
+    "plt.ylim(0.0, 5e-4)\n",
     "# plt.yscale('log')\n",
     "# plt.xlim(3080, 3110)\n",
     "plt.savefig('test3.png')\n",
@@ -1330,6 +1214,13 @@
    "metadata": {},
    "outputs": [],
    "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
diff --git a/raremodel-nb.py b/raremodel-nb.py
index 348f99b..9700fb7 100644
--- a/raremodel-nb.py
+++ b/raremodel-nb.py
@@ -3,7 +3,7 @@
 
 # # Import
 
-# In[3]:
+# In[1]:
 
 
 import numpy as np
@@ -23,14 +23,22 @@
 import tensorflow as tf
 import zfit
 from zfit import ztf
-from IPython.display import clear_output
+# from IPython.display import clear_output
 import os
 
 
+# In[2]:
+
+
+# chunksize = 1000000
+# zfit.run.chunking.active = True
+# zfit.run.chunking.max_n_points = chunksize
+
+
 # # Build model and graphs
 # ## Create graphs
 
-# In[4]:
+# In[3]:
 
 
 def formfactor( q2, subscript): #returns real value
@@ -228,7 +236,7 @@
     return c9
 
 
-# In[5]:
+# In[4]:
 
 
 def G(y):
@@ -275,7 +283,7 @@
 
 # ## Build pdf
 
-# In[6]:
+# In[5]:
 
 
 class total_pdf(zfit.pdf.ZPDF):
@@ -311,7 +319,7 @@
 
 # ## Load data
 
-# In[7]:
+# In[6]:
 
 
 x_min = 2*pdg['muon_M']
@@ -331,7 +339,7 @@
 
 # ## Setup parameters
 
-# In[8]:
+# In[7]:
 
 
 #jpsi
@@ -365,7 +373,7 @@
 
 # ## Setup pdf
 
-# In[9]:
+# In[8]:
 
 
 total_f = total_pdf(obs=obs, jpsi_mass = jpsi_m, jpsi_scale = jpsi_s, jpsi_phase = jpsi_p, jpsi_width = jpsi_w,
@@ -377,7 +385,7 @@
 
 # ## Test if graphs actually work and compute values
 
-# In[10]:
+# In[9]:
 
 
 def total_test_tf(xq):
@@ -414,7 +422,7 @@
 res_y = zfit.run(jpsi_res(test_q))
 
 
-# In[11]:
+# In[10]:
 
 
 plt.clf()
@@ -431,7 +439,7 @@
 
 # ## Adjust scaling of different parts
 
-# In[12]:
+# In[11]:
 
 
 # total_f.update_integration_options(draws_per_dim=2000000, mc_sampler=None)
@@ -441,9 +449,65 @@
 # print(pdg["jpsi_BR"]/pdg["NR_BR"], inte_fl/pdg["NR_auc"])
 
 
+# In[12]:
+
+
+# print(x_min)
+# print(x_max)
+# # total_f.update_integration_options(draws_per_dim=2000000, mc_sampler=None)
+# total_f.update_integration_options(mc_sampler=lambda dim, num_results,
+#                                     dtype: tf.random_uniform(maxval=1., shape=(num_results, dim), dtype=dtype),
+#                                    draws_per_dim=1000000)
+# # _ = []
+
+# # for i in range(10):
+
+# #     inte = total_f.integrate(limits = (x_min, x_max))
+# #     inte_fl = zfit.run(inte)
+# #     print(inte_fl)
+# #     _.append(inte_fl)
+
+# # print("mean:", np.mean(_))
+
+# _ = time.time()
+
+# inte = total_f.integrate(limits = (x_min, x_max))
+# inte_fl = zfit.run(inte)
+# print(inte_fl)
+# print("Time taken: {}".format(display_time(int(time.time() - _))))
+
+
+# ## Tensorflow scaling
+
 # In[13]:
 
 
+# def scaling_func(x):
+
+#     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))
+
+#     vec_f = vec(x, funcs)
+
+#     axiv_nr = axiv_nonres(x)
+
+#     tot = vec_f + axiv_nr
+
+#     return tot
+
+
+# def s_func(x):
+    
+#     q = ztf.constant(x)
+    
+#     return zfit.run(scaling_func(q))
+    
+
+# print(integrate.quad(s_func, x_min, x_max, limit = 50))
+
+
+# In[14]:
+
+
 # factor_jpsi = pdg["NR_auc"]*pdg["jpsi_BR"]/(pdg["NR_BR"]*pdg["jpsi_auc"])
 # factor_jpsi = pdg["NR_auc"]*pdg["jpsi_BR"]/(pdg["NR_BR"]*inte_fl)
 # print(np.sqrt(factor_jpsi)*jpsi_scale)
@@ -455,7 +519,7 @@
 # print(np.sqrt(factor_psi2s))
 
 
-# In[14]:
+# In[33]:
 
 
 # def _t_f(xq):
@@ -466,10 +530,7 @@
 #     def psi2s_res(q):
 #         return resonance(q, psi2s_m, psi2s_s, psi2s_p, psi2s_w)
 
-#     def cusp(q):
-#         return bifur_gauss(q, cusp_m, sig_L, sig_R, cusp_s)
-
-#     funcs = psi2s_res(xq) + jpsi_res(xq) + cusp(xq)
+#     funcs = psi2s_res(xq) + jpsi_res(xq)
 
 #     vec_f = vec(xq, funcs)
 
@@ -480,30 +541,32 @@
 #     return tot
 
 # def t_f(x):
-#     probs = zfit.run(_t_f(ztf.constant(x)))
+#     _ = np.array(x)
+#     probs = zfit.run(_t_f(_))
 #     return probs
 
 
-# In[15]:
+# In[34]:
 
 
 # print(36000*(1+ pdg["jpsi_BR"]/pdg["NR_BR"] + pdg["psi2s_BR"]/pdg["NR_BR"]))
 
 
-# In[ ]:
+# In[35]:
 
 
 # start = time.time()
 
-# result, err = integrate.quad(lambda x: t_f(x), x_min, x_max, limit = 50)
+# result, err = integrate.quad(lambda x: t_f(x), x_min, x_max, limit = 5)
 # print(result, "{0:.2f} %".format(err/result))
 # print("Time:", time.time()-start)
 
 
 # # Sampling
 # ## One sample
+# ! total_f.sample() always returns the same set !
 
-# In[ ]:
+# In[18]:
 
 
 # nevents = int(pdg["number_of_decays"])
@@ -537,7 +600,7 @@
 #         pkl.dump(sam, f, pkl.HIGHEST_PROTOCOL)
 
 
-# In[ ]:
+# In[19]:
 
 
 # print("Time to generate full toy: {} s".format(int(time.time()-start)))
@@ -556,7 +619,7 @@
 # print(total_samp[:nevents].shape)
 
 
-# In[ ]:
+# In[20]:
 
 
 # bins = int((x_max-x_min)/7)
@@ -575,12 +638,12 @@
 
 # ## Toys
 
-# In[19]:
+# In[21]:
 
 
 nr_of_toys = 1
 nevents = int(pdg["number_of_decays"])
-event_stack = 5000
+event_stack = 100000
 
 calls = int(nevents/event_stack + 1)
 
@@ -614,7 +677,7 @@
             pkl.dump(sam, f, pkl.HIGHEST_PROTOCOL)
 
 
-# In[ ]:
+# In[22]:
 
 
 # with open(r"data/zfit_toys/toy_0/0.pkl", "rb") as input_file:
@@ -628,7 +691,7 @@
 # print(np.sum(sam-sam2))
 
 
-# In[ ]:
+# In[23]:
 
 
 print("Time to generate full toy: {} s".format(int(time.time()-start)))
@@ -644,27 +707,33 @@
 
 data2 = zfit.data.Data.from_numpy(array=total_samp[:int(nevents)], obs=obs)
 
+data3 = zfit.data.Data.from_numpy(array=total_samp, obs=obs)
+
 print(total_samp[:nevents].shape)
 
 
-# In[ ]:
+# In[24]:
 
 
 bins = int((x_max-x_min)/7)
 
 # calcs = zfit.run(total_test_tf(samp))
 
-plt.hist(total_samp[:event_stack], bins = bins, range = (x_min,x_max))
+plt.hist(total_samp[:nevents], bins = bins, range = (x_min,x_max), label = 'data')
+plt.plot(test_q, calcs_test*nevents*4.5 , label = 'pdf')
 
 # plt.plot(sam, calcs, '.')
 # plt.plot(test_q, calcs_test)
-plt.ylim(0, 20)
-# plt.xlim(3000, 3750)
+# plt.yscale('log')
+plt.ylim(0, 12000)
+# plt.xlim(3080, 3110)
+
+plt.legend()
 
 plt.savefig('test2.png')
 
 
-# In[ ]:
+# In[25]:
 
 
 # sampler = total_f.create_sampler(n=nevents)
@@ -686,13 +755,13 @@
 
 # # Fitting
 
-# In[ ]:
+# In[26]:
 
 
-nll = zfit.loss.UnbinnedNLL(model=total_f, data=data2, fit_range = (x_min, x_max))
+nll = zfit.loss.UnbinnedNLL(model=total_f, data=data3, fit_range = (x_min, x_max))
 
 minimizer = zfit.minimize.MinuitMinimizer()
-# minimizer._use_tfgrad = False
+minimizer._use_tfgrad = False
 result = minimizer.minimize(nll)
 
 param_errors = result.error()
@@ -706,7 +775,7 @@
 # In[ ]:
 
 
-(-3.14+2*np.pi)/np.pi
+(-7.95933+2*np.pi)/np.pi+np.pi
 
 
 # In[ ]:
@@ -730,7 +799,7 @@
 # In[ ]:
 
 
-probs = total_f.pdf(test_q)
+# probs = total_f.pdf(test_q)
 
 calcs_test = zfit.run(probs)
 res_y = zfit.run(jpsi_res(test_q))
@@ -744,7 +813,7 @@
 plt.plot(test_q, calcs_test, label = 'pdf')
 # plt.plot(test_q, res_y, label = 'res')
 plt.legend()
-plt.ylim(0.0, 4e-4)
+plt.ylim(0.0, 5e-4)
 # plt.yscale('log')
 # plt.xlim(3080, 3110)
 plt.savefig('test3.png')
@@ -756,3 +825,9 @@
 
 
 
+
+# In[ ]:
+
+
+
+
diff --git a/test.png b/test.png
index 4d21890..17333a7 100644
--- a/test.png
+++ b/test.png
Binary files differ
diff --git a/test2.png b/test2.png
index 3394ae2..dd9354d 100644
--- a/test2.png
+++ b/test2.png
Binary files differ
diff --git a/test3.png b/test3.png
index 3e7c598..293bee4 100644
--- a/test3.png
+++ b/test3.png
Binary files differ