diff --git a/.ipynb_checkpoints/raremodel-nb-checkpoint.ipynb b/.ipynb_checkpoints/raremodel-nb-checkpoint.ipynb
index d62d7a9..c1d32bf 100644
--- a/.ipynb_checkpoints/raremodel-nb-checkpoint.ipynb
+++ b/.ipynb_checkpoints/raremodel-nb-checkpoint.ipynb
@@ -16,7 +16,7 @@
      "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",
+      "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"
      ]
     },
@@ -327,7 +327,7 @@
     "        def cusp(q):\n",
     "            return bifur_gauss(q, mean = self.params['cusp_mass'], sigma_L = self.params['sigma_L'], sigma_R = self.params['sigma_R'], scale = self.params['cusp_scale'])\n",
     "\n",
-    "        funcs = jpsi_res(x)# + psi2s_res(x) + cusp(x)\n",
+    "        funcs = jpsi_res(x) + psi2s_res(x) + cusp(x)\n",
     "\n",
     "        vec_f = vec(x, funcs)\n",
     "\n",
@@ -382,7 +382,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "WARNING:tensorflow:From C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\tensorflow\\python\\ops\\resource_variable_ops.py:435: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.\n",
+      "WARNING:tensorflow:From c:\\users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\tensorflow\\python\\ops\\resource_variable_ops.py:435: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.\n",
       "Instructions for updating:\n",
       "Colocations handled automatically by placer.\n"
      ]
@@ -392,6 +392,7 @@
     "#jpsi\n",
     "\n",
     "jpsi_mass, jpsi_width, jpsi_phase, jpsi_scale = pdg[\"jpsi\"]\n",
+    "jpsi_scale *= pdg[\"factor_jpsi\"]\n",
     "\n",
     "jpsi_m = zfit.Parameter(\"jpsi_m\", ztf.constant(jpsi_mass), floating = False)\n",
     "jpsi_w = zfit.Parameter(\"jpsi_w\", ztf.constant(jpsi_width), floating = False)\n",
@@ -401,6 +402,7 @@
     "#psi2s\n",
     "\n",
     "psi2s_mass, psi2s_width, psi2s_phase, psi2s_scale = pdg[\"psi2s\"]\n",
+    "psi2s_scale *= pdg[\"factor_psi2s\"]\n",
     "\n",
     "psi2s_m = zfit.Parameter(\"psi2s_m\", ztf.constant(psi2s_mass), floating = False)\n",
     "psi2s_w = zfit.Parameter(\"psi2s_w\", ztf.constant(psi2s_width), floating = False)\n",
@@ -421,7 +423,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Test if graphs actually work and compute values"
+    "## Setup pdf"
    ]
   },
   {
@@ -430,6 +432,26 @@
    "metadata": {},
    "outputs": [],
    "source": [
+    "total_f = total_pdf(obs=obs, jpsi_mass = jpsi_m, jpsi_scale = jpsi_s, jpsi_phase = jpsi_p, jpsi_width = jpsi_w,\n",
+    "            psi2s_mass = psi2s_m, psi2s_scale = psi2s_s, psi2s_phase = psi2s_p, psi2s_width = psi2s_w,\n",
+    "            cusp_mass = cusp_m, sigma_L = sig_L, sigma_R = sig_R, cusp_scale = cusp_s)\n",
+    "\n",
+    "# print(total_pdf.obs)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Test if graphs actually work and compute values"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
     "def total_test_tf(xq):\n",
     "\n",
     "    def jpsi_res(q):\n",
@@ -456,26 +478,20 @@
     "\n",
     "# calcs = zfit.run(total_test_tf(x_part))\n",
     "\n",
-    "test_q = np.linspace(2950, 3150, 200000)\n",
+    "test_q = np.linspace(x_min, x_max, 2000000)\n",
     "\n",
-    "calcs_test = zfit.run(total_test_tf(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))"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\numpy\\core\\numeric.py:538: ComplexWarning: Casting complex values to real discards the imaginary part\n",
-      "  return array(a, dtype, copy=False, order=order)\n"
-     ]
-    },
-    {
      "name": "stdout",
      "output_type": "stream",
      "text": [
@@ -484,7 +500,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -496,13 +512,14 @@
     }
    ],
    "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.plot(test_q, res_y, label = 'res')\n",
     "plt.legend()\n",
-    "# plt.ylim(0.0, 5.5e-7)\n",
-    "plt.yscale('log')\n",
-    "plt.xlim(3050, 3150)\n",
+    "plt.ylim(0.0, 4e-4)\n",
+    "# plt.yscale('log')\n",
+    "# plt.xlim(3080, 3110)\n",
     "plt.savefig('test.png')\n",
     "print(jpsi_width)"
    ]
@@ -511,26 +528,6 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Setup pdf"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "total_f = total_pdf(obs=obs, jpsi_mass = jpsi_m, jpsi_scale = jpsi_s, jpsi_phase = jpsi_p, jpsi_width = jpsi_w,\n",
-    "            psi2s_mass = psi2s_m, psi2s_scale = psi2s_s, psi2s_phase = psi2s_p, psi2s_width = psi2s_w,\n",
-    "            cusp_mass = cusp_m, sigma_L = sig_L, sigma_R = sig_R, cusp_scale = cusp_s)\n",
-    "\n",
-    "# print(total_pdf.obs)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
     "## Adjust scaling of different parts"
    ]
   },
@@ -540,56 +537,84 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "total_f.update_integration_options(draws_per_dim=3000000, mc_sampler=None)\n",
-    "inte = total_f.integrate(limits = (x_min, x_max), norm_range=False)\n",
-    "print(zfit.run(inte))"
+    "# total_f.update_integration_options(draws_per_dim=10000000, mc_sampler=None)\n",
+    "# inte = total_f.integrate(limits = (3000, 3200), norm_range=False)\n",
+    "# print(zfit.run(inte))\n",
+    "# print(pdg[\"jpsi_BR\"]/pdg[\"NR_BR\"], zfit.run(inte)/pdg[\"NR_auc\"])"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# factor_jpsi = pdg[\"NR_auc\"]*pdg[\"jpsi_BR\"]/(pdg[\"NR_BR\"]*pdg[\"jpsi_auc\"])\n",
+    "# print(np.sqrt(factor_jpsi))\n",
+    "# factor_psi2s = pdg[\"NR_auc\"]*pdg[\"psi2s_BR\"]/(pdg[\"NR_BR\"]*pdg[\"psi2s_auc\"])\n",
+    "# print(np.sqrt(factor_psi2s))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# def _t_f(xq):\n",
+    "\n",
+    "#     def jpsi_res(q):\n",
+    "#         return resonance(q, jpsi_m, jpsi_s, jpsi_p, jpsi_w)\n",
+    "\n",
+    "#     def psi2s_res(q):\n",
+    "#         return resonance(q, psi2s_m, psi2s_s, psi2s_p, psi2s_w)\n",
+    "\n",
+    "#     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",
+    "\n",
+    "#     vec_f = vec(xq, funcs)\n",
+    "\n",
+    "#     axiv_nr = axiv_nonres(xq)\n",
+    "\n",
+    "#     tot = vec_f + axiv_nr\n",
+    "    \n",
+    "#     return tot\n",
+    "\n",
+    "# def t_f(x):\n",
+    "#     probs = zfit.run(_t_f(ztf.constant(x)))\n",
+    "#     return probs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "1.0 0.00 %\n",
-      "Time: 32.94996476173401\n"
+      "5404695.652173913\n"
      ]
     }
    ],
    "source": [
-    "def _t_f(xq):\n",
+    "print(36000*(1+ pdg[\"jpsi_BR\"]/pdg[\"NR_BR\"] + pdg[\"psi2s_BR\"]/pdg[\"NR_BR\"]))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# start = time.time()\n",
     "\n",
-    "    def jpsi_res(q):\n",
-    "        return resonance(q, jpsi_m, jpsi_s, jpsi_p, jpsi_w)\n",
-    "\n",
-    "    def psi2s_res(q):\n",
-    "        return resonance(q, psi2s_m, psi2s_s, psi2s_p, psi2s_w)\n",
-    "\n",
-    "    def cusp(q):\n",
-    "        return bifur_gauss(q, cusp_m, sig_L, sig_R, cusp_s)\n",
-    "\n",
-    "    funcs = 0.0 # + psi2s_res(xq) + jpsi_res(xq) + cusp(xq)\n",
-    "\n",
-    "    vec_f = vec(xq, funcs)\n",
-    "\n",
-    "    axiv_nr = axiv_nonres(xq)\n",
-    "\n",
-    "    tot = vec_f + axiv_nr\n",
-    "    \n",
-    "    return tot\n",
-    "\n",
-    "def t_f(x):\n",
-    "    probs = zfit.run(_t_f(ztf.constant(x)))\n",
-    "    return probs\n",
-    "\n",
-    "start = time.time()\n",
-    "\n",
-    "result, err = integrate.quad(lambda x: t_f(x), x_min, x_max, limit = 1000000)\n",
-    "print(result, \"{0:.2f} %\".format(err/result))\n",
-    "print(\"Time:\", time.time()-start)"
+    "# result, err = integrate.quad(lambda x: t_f(x), x_min, x_max, limit = 50)\n",
+    "# print(result, \"{0:.2f} %\".format(err/result))\n",
+    "# print(\"Time:\", time.time()-start)"
    ]
   },
   {
@@ -604,9 +629,19 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "WARNING:tensorflow:From c:\\users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\zfit\\core\\sample.py:98: to_int64 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\n",
+      "Instructions for updating:\n",
+      "Use tf.cast instead.\n"
+     ]
+    }
+   ],
    "source": [
-    "nevents = 440000\n",
+    "nevents = 5404696\n",
     "\n",
     "samp = total_f.sample(n=nevents)"
    ]
diff --git a/__pycache__/pdg_const.cpython-37.pyc b/__pycache__/pdg_const.cpython-37.pyc
index 684e9de..b9aaa10 100644
--- a/__pycache__/pdg_const.cpython-37.pyc
+++ b/__pycache__/pdg_const.cpython-37.pyc
Binary files differ
diff --git a/pdg_const.py b/pdg_const.py
index d122d06..47c0f94 100644
--- a/pdg_const.py
+++ b/pdg_const.py
@@ -63,13 +63,18 @@
 "bT" : [0.460, -1.089, -1.114],
 
 "NR_BR": 4.37e-7,
+"NR_auc": 0.00133,
 
 #Resonances format(mass, width, phase, scale)
 "jpsi": (3096.0, 0.09, -1.5, 2e-2),
+# "jpsi": (3096.0, 0.09, -1.5, 0.0),
 "jpsi_BR": 6.02e-5,
-"jpsi_auc": 9.98349284137246e-16,
+"jpsi_auc": 2.336e-9,
+"factor_jpsi": 8856.2,
 
 "psi2s": (3686.0, 0.3, -1.5, 3.14e-3),
+# "psi2s": (3686.0, 0.3, -1.5, 0.0),
 "psi2s_BR": 4.97e-6,
-"psi2s_auc": 3.6013262555097607e-16
+"psi2s_auc": 2.853e-10,
+"factor_psi2s": 7281.4
 }
diff --git a/raremodel-nb.ipynb b/raremodel-nb.ipynb
index d62d7a9..c1d32bf 100644
--- a/raremodel-nb.ipynb
+++ b/raremodel-nb.ipynb
@@ -16,7 +16,7 @@
      "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",
+      "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"
      ]
     },
@@ -327,7 +327,7 @@
     "        def cusp(q):\n",
     "            return bifur_gauss(q, mean = self.params['cusp_mass'], sigma_L = self.params['sigma_L'], sigma_R = self.params['sigma_R'], scale = self.params['cusp_scale'])\n",
     "\n",
-    "        funcs = jpsi_res(x)# + psi2s_res(x) + cusp(x)\n",
+    "        funcs = jpsi_res(x) + psi2s_res(x) + cusp(x)\n",
     "\n",
     "        vec_f = vec(x, funcs)\n",
     "\n",
@@ -382,7 +382,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "WARNING:tensorflow:From C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\tensorflow\\python\\ops\\resource_variable_ops.py:435: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.\n",
+      "WARNING:tensorflow:From c:\\users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\tensorflow\\python\\ops\\resource_variable_ops.py:435: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.\n",
       "Instructions for updating:\n",
       "Colocations handled automatically by placer.\n"
      ]
@@ -392,6 +392,7 @@
     "#jpsi\n",
     "\n",
     "jpsi_mass, jpsi_width, jpsi_phase, jpsi_scale = pdg[\"jpsi\"]\n",
+    "jpsi_scale *= pdg[\"factor_jpsi\"]\n",
     "\n",
     "jpsi_m = zfit.Parameter(\"jpsi_m\", ztf.constant(jpsi_mass), floating = False)\n",
     "jpsi_w = zfit.Parameter(\"jpsi_w\", ztf.constant(jpsi_width), floating = False)\n",
@@ -401,6 +402,7 @@
     "#psi2s\n",
     "\n",
     "psi2s_mass, psi2s_width, psi2s_phase, psi2s_scale = pdg[\"psi2s\"]\n",
+    "psi2s_scale *= pdg[\"factor_psi2s\"]\n",
     "\n",
     "psi2s_m = zfit.Parameter(\"psi2s_m\", ztf.constant(psi2s_mass), floating = False)\n",
     "psi2s_w = zfit.Parameter(\"psi2s_w\", ztf.constant(psi2s_width), floating = False)\n",
@@ -421,7 +423,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Test if graphs actually work and compute values"
+    "## Setup pdf"
    ]
   },
   {
@@ -430,6 +432,26 @@
    "metadata": {},
    "outputs": [],
    "source": [
+    "total_f = total_pdf(obs=obs, jpsi_mass = jpsi_m, jpsi_scale = jpsi_s, jpsi_phase = jpsi_p, jpsi_width = jpsi_w,\n",
+    "            psi2s_mass = psi2s_m, psi2s_scale = psi2s_s, psi2s_phase = psi2s_p, psi2s_width = psi2s_w,\n",
+    "            cusp_mass = cusp_m, sigma_L = sig_L, sigma_R = sig_R, cusp_scale = cusp_s)\n",
+    "\n",
+    "# print(total_pdf.obs)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Test if graphs actually work and compute values"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
     "def total_test_tf(xq):\n",
     "\n",
     "    def jpsi_res(q):\n",
@@ -456,26 +478,20 @@
     "\n",
     "# calcs = zfit.run(total_test_tf(x_part))\n",
     "\n",
-    "test_q = np.linspace(2950, 3150, 200000)\n",
+    "test_q = np.linspace(x_min, x_max, 2000000)\n",
     "\n",
-    "calcs_test = zfit.run(total_test_tf(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))"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\numpy\\core\\numeric.py:538: ComplexWarning: Casting complex values to real discards the imaginary part\n",
-      "  return array(a, dtype, copy=False, order=order)\n"
-     ]
-    },
-    {
      "name": "stdout",
      "output_type": "stream",
      "text": [
@@ -484,7 +500,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -496,13 +512,14 @@
     }
    ],
    "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.plot(test_q, res_y, label = 'res')\n",
     "plt.legend()\n",
-    "# plt.ylim(0.0, 5.5e-7)\n",
-    "plt.yscale('log')\n",
-    "plt.xlim(3050, 3150)\n",
+    "plt.ylim(0.0, 4e-4)\n",
+    "# plt.yscale('log')\n",
+    "# plt.xlim(3080, 3110)\n",
     "plt.savefig('test.png')\n",
     "print(jpsi_width)"
    ]
@@ -511,26 +528,6 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Setup pdf"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "total_f = total_pdf(obs=obs, jpsi_mass = jpsi_m, jpsi_scale = jpsi_s, jpsi_phase = jpsi_p, jpsi_width = jpsi_w,\n",
-    "            psi2s_mass = psi2s_m, psi2s_scale = psi2s_s, psi2s_phase = psi2s_p, psi2s_width = psi2s_w,\n",
-    "            cusp_mass = cusp_m, sigma_L = sig_L, sigma_R = sig_R, cusp_scale = cusp_s)\n",
-    "\n",
-    "# print(total_pdf.obs)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
     "## Adjust scaling of different parts"
    ]
   },
@@ -540,56 +537,84 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "total_f.update_integration_options(draws_per_dim=3000000, mc_sampler=None)\n",
-    "inte = total_f.integrate(limits = (x_min, x_max), norm_range=False)\n",
-    "print(zfit.run(inte))"
+    "# total_f.update_integration_options(draws_per_dim=10000000, mc_sampler=None)\n",
+    "# inte = total_f.integrate(limits = (3000, 3200), norm_range=False)\n",
+    "# print(zfit.run(inte))\n",
+    "# print(pdg[\"jpsi_BR\"]/pdg[\"NR_BR\"], zfit.run(inte)/pdg[\"NR_auc\"])"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# factor_jpsi = pdg[\"NR_auc\"]*pdg[\"jpsi_BR\"]/(pdg[\"NR_BR\"]*pdg[\"jpsi_auc\"])\n",
+    "# print(np.sqrt(factor_jpsi))\n",
+    "# factor_psi2s = pdg[\"NR_auc\"]*pdg[\"psi2s_BR\"]/(pdg[\"NR_BR\"]*pdg[\"psi2s_auc\"])\n",
+    "# print(np.sqrt(factor_psi2s))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# def _t_f(xq):\n",
+    "\n",
+    "#     def jpsi_res(q):\n",
+    "#         return resonance(q, jpsi_m, jpsi_s, jpsi_p, jpsi_w)\n",
+    "\n",
+    "#     def psi2s_res(q):\n",
+    "#         return resonance(q, psi2s_m, psi2s_s, psi2s_p, psi2s_w)\n",
+    "\n",
+    "#     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",
+    "\n",
+    "#     vec_f = vec(xq, funcs)\n",
+    "\n",
+    "#     axiv_nr = axiv_nonres(xq)\n",
+    "\n",
+    "#     tot = vec_f + axiv_nr\n",
+    "    \n",
+    "#     return tot\n",
+    "\n",
+    "# def t_f(x):\n",
+    "#     probs = zfit.run(_t_f(ztf.constant(x)))\n",
+    "#     return probs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "1.0 0.00 %\n",
-      "Time: 32.94996476173401\n"
+      "5404695.652173913\n"
      ]
     }
    ],
    "source": [
-    "def _t_f(xq):\n",
+    "print(36000*(1+ pdg[\"jpsi_BR\"]/pdg[\"NR_BR\"] + pdg[\"psi2s_BR\"]/pdg[\"NR_BR\"]))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# start = time.time()\n",
     "\n",
-    "    def jpsi_res(q):\n",
-    "        return resonance(q, jpsi_m, jpsi_s, jpsi_p, jpsi_w)\n",
-    "\n",
-    "    def psi2s_res(q):\n",
-    "        return resonance(q, psi2s_m, psi2s_s, psi2s_p, psi2s_w)\n",
-    "\n",
-    "    def cusp(q):\n",
-    "        return bifur_gauss(q, cusp_m, sig_L, sig_R, cusp_s)\n",
-    "\n",
-    "    funcs = 0.0 # + psi2s_res(xq) + jpsi_res(xq) + cusp(xq)\n",
-    "\n",
-    "    vec_f = vec(xq, funcs)\n",
-    "\n",
-    "    axiv_nr = axiv_nonres(xq)\n",
-    "\n",
-    "    tot = vec_f + axiv_nr\n",
-    "    \n",
-    "    return tot\n",
-    "\n",
-    "def t_f(x):\n",
-    "    probs = zfit.run(_t_f(ztf.constant(x)))\n",
-    "    return probs\n",
-    "\n",
-    "start = time.time()\n",
-    "\n",
-    "result, err = integrate.quad(lambda x: t_f(x), x_min, x_max, limit = 1000000)\n",
-    "print(result, \"{0:.2f} %\".format(err/result))\n",
-    "print(\"Time:\", time.time()-start)"
+    "# result, err = integrate.quad(lambda x: t_f(x), x_min, x_max, limit = 50)\n",
+    "# print(result, \"{0:.2f} %\".format(err/result))\n",
+    "# print(\"Time:\", time.time()-start)"
    ]
   },
   {
@@ -604,9 +629,19 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "WARNING:tensorflow:From c:\\users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\zfit\\core\\sample.py:98: to_int64 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\n",
+      "Instructions for updating:\n",
+      "Use tf.cast instead.\n"
+     ]
+    }
+   ],
    "source": [
-    "nevents = 440000\n",
+    "nevents = 5404696\n",
     "\n",
     "samp = total_f.sample(n=nevents)"
    ]
diff --git a/test.png b/test.png
index 51292dc..6356b16 100644
--- a/test.png
+++ b/test.png
Binary files differ