diff --git a/.ipynb_checkpoints/raremodel-nb-checkpoint.ipynb b/.ipynb_checkpoints/raremodel-nb-checkpoint.ipynb
index 4688758..0be5ec8 100644
--- a/.ipynb_checkpoints/raremodel-nb-checkpoint.ipynb
+++ b/.ipynb_checkpoints/raremodel-nb-checkpoint.ipynb
@@ -294,19 +294,19 @@
     "        return tf.log(ztf.to_complex((1+tf.sqrt(q))/(1-tf.sqrt(q)))-tf.complex(ztf.constant(0), -1*ztf.constant(np.pi)))    \n",
     "    \n",
     "    def inner_right(q):\n",
-    "        return ztf.to_complex(2 * tf.atan(1/tf.sqrt(-q)))\n",
+    "        return ztf.to_complex(2 * tf.atan(1/tf.sqrt(tf.math.real(-q))))\n",
     "    \n",
-    "    big_bracket = tf.where(y > ztf.const(0.0), inner_rect_bracket(y), inner_right(y))\n",
+    "    big_bracket = tf.where(tf.math.real(y) > ztf.constant(0.0), inner_rect_bracket(y), inner_right(y))\n",
     "    \n",
     "    return ztf.to_complex(tf.sqrt(tf.abs(y))) * big_bracket\n",
     "\n",
     "def h_S(m, q):\n",
     "    \n",
-    "    return ztf.to_complex(2) - G(ztf.to_complex(1) - 4*tf.pow(m, 2) / ztf.to_complex(tf.pow(q, 2)))\n",
+    "    return ztf.to_complex(2) - G(ztf.to_complex(1) - ztf.to_complex(4*tf.pow(m, 2)) / ztf.to_complex(tf.pow(q, 2)))\n",
     "\n",
     "def h_P(m, q):\n",
     "    \n",
-    "    return ztf.to_complex(2/3) + (ztf.to_complex(1) - 4*tf.pow(m, 2) / ztf.to_complex(tf.pow(q, 2))) * h_S(m,q)\n",
+    "    return ztf.to_complex(2/3) + (ztf.to_complex(1) - ztf.to_complex(4*tf.pow(m, 2)) / ztf.to_complex(tf.pow(q, 2))) * h_S(m,q)\n",
     "\n",
     "def two_p_ccbar(mD, m_D_bar, m_D_star, q):\n",
     "    \n",
@@ -381,7 +381,9 @@
     "               'p4160_mass', 'p4160_scale', 'p4160_phase', 'p4160_width',\n",
     "               'p4415_mass', 'p4415_scale', 'p4415_phase', 'p4415_width',\n",
     "               'omega_mass', 'omega_scale', 'omega_phase', 'omega_width',\n",
-    "               'phi_mass', 'phi_scale', 'phi_phase', 'phi_width']  # the name of the parameters\n",
+    "               'phi_mass', 'phi_scale', 'phi_phase', 'phi_width',\n",
+    "               'DDstar_mass', 'DDstar_scale', 'DDstar_phase',\n",
+    "               'Dbar_mass', 'Dbar_scale', 'Dbar_phase'] # the name of the parameters\n",
     "\n",
     "    def _unnormalized_pdf(self, x):\n",
     "        \n",
@@ -422,9 +424,14 @@
     "        def p4415_res(q):\n",
     "            return resonance(q, _mass = self.params['p4415_mass'], scale = self.params['p4415_scale'],\n",
     "                             phase = self.params['p4415_phase'], width = self.params['p4415_width'])\n",
-    "               \n",
+    "        \n",
+    "        def P2_D(q):\n",
+    "            Dbar_contrib = ztf.to_complex(self.params['Dbar_scale'])*tf.exp(tf.complex(ztf.constant(0.0), self.params['Dbar_phase']))*ztf.to_complex(h_S(self.params['Dbar_mass'], q))\n",
+    "            DDstar_contrib = ztf.to_complex(self.params['DDstar_scale'])*tf.exp(tf.complex(ztf.constant(0.0), self.params['DDstar_phase']))*ztf.to_complex(h_P(self.params['DDstar_mass'], q))\n",
+    "            return Dbar_contrib + DDstar_contrib\n",
+    "    \n",
     "\n",
-    "        funcs = rho_res(x) + omega_res(x) + phi_res(x) + jpsi_res(x) + psi2s_res(x) + p3770_res(x) + p4040_res(x)+ p4160_res(x) + p4415_res(x)\n",
+    "        funcs = rho_res(x) + omega_res(x) + phi_res(x) + jpsi_res(x) + psi2s_res(x) + p3770_res(x) + p4040_res(x)+ p4160_res(x) + p4415_res(x) + P2_D(x)\n",
     "\n",
     "        vec_f = vec(x, funcs)\n",
     "\n",
@@ -575,13 +582,71 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Setup pdf"
+    "## Dynamic generation of 2 particle contribution"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 9,
    "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1.2615526703874353\n"
+     ]
+    }
+   ],
+   "source": [
+    "_0 = jpsi_scale*np.cos(jpsi_phase)*jpsi_width/jpsi_mass**3 + psi2s_scale*np.cos(psi2s_phase)*psi2s_width/psi2s_mass**3\n",
+    "_1 = p3770_scale*np.cos(p3770_phase)*p3770_width/p3770_mass**3 + p4040_scale*np.cos(p4040_phase)*p4040_width/p4040_mass**3\n",
+    "_2 = p4160_scale*np.cos(p4160_phase)*p4160_width/p4160_mass**3 + p4415_scale*np.cos(p4415_phase)*p4415_width/p4415_mass**3\n",
+    "\n",
+    "C_pert = np.random.uniform(0.03, 0.1)\n",
+    "m_c = 1300\n",
+    "\n",
+    "cDDstar_phase = 10\n",
+    "\n",
+    "while np.abs(cDDstar_phase) > 1:\n",
+    "    Dbar_eta = np.random.uniform(0.0, 5.0)\n",
+    "    DDstar_eta = np.random.uniform(0.0, 10.0)\n",
+    "    Dbar_phase = np.random.uniform(0.0, 2*np.pi)\n",
+    "    DDstar_mass = pdg['D0_M']\n",
+    "\n",
+    "    R = (C_pert/(m_c**2) - ((_0 + _1 + _2)))\n",
+    "    \n",
+    "    Dbar_mass = (pdg['D0_M']+pdg['Dst_M'])/2\n",
+    "\n",
+    "    R_ = R - Dbar_eta*np.cos(Dbar_phase)/(6*Dbar_mass**2)\n",
+    "\n",
+    "    cDDstar_phase = R_*10*DDstar_mass**2/DDstar_eta\n",
+    "#     print(\"hey\")\n",
+    "\n",
+    "DDstar_phase = np.arccos(cDDstar_phase)\n",
+    "\n",
+    "print(DDstar_phase)\n",
+    "\n",
+    "\n",
+    "Dbar_s = zfit.Parameter(\"Dbar_s\", ztf.constant(Dbar_eta), floating = False)\n",
+    "Dbar_m = zfit.Parameter(\"Dbar_m\", ztf.constant(Dbar_mass), floating = False)\n",
+    "Dbar_p = zfit.Parameter(\"Dbar_p\", ztf.constant(Dbar_phase), floating = False)\n",
+    "DDstar_s = zfit.Parameter(\"DDstar_s\", ztf.constant(DDstar_eta), floating = False)\n",
+    "DDstar_m = zfit.Parameter(\"DDstar_m\", ztf.constant(DDstar_mass), floating = False)\n",
+    "DDstar_p = zfit.Parameter(\"DDstar_p\", ztf.constant(DDstar_phase), floating = False)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Setup pdf"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "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",
@@ -592,8 +657,10 @@
     "                    p4415_mass = p4415_m, p4415_scale = p4415_s, p4415_phase = p4415_p, p4415_width = p4415_w,\n",
     "                    rho_mass = rho_m, rho_scale = rho_s, rho_phase = rho_p, rho_width = rho_w,\n",
     "                    omega_mass = omega_m, omega_scale = omega_s, omega_phase = omega_p, omega_width = omega_w,\n",
-    "                    phi_mass = phi_m, phi_scale = phi_s, phi_phase = phi_p, phi_width = phi_w) \n",
-    "    \n",
+    "                    phi_mass = phi_m, phi_scale = phi_s, phi_phase = phi_p, phi_width = phi_w,\n",
+    "                    DDstar_mass = DDstar_m, DDstar_scale = DDstar_s, DDstar_phase = DDstar_p,\n",
+    "                    Dbar_mass = Dbar_m, Dbar_scale = Dbar_s, Dbar_phase = Dbar_p)\n",
+    "                   \n",
     "# print(total_pdf.obs)\n",
     "\n",
     "# print(calcs_test)\n",
@@ -611,7 +678,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -654,12 +721,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": "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\n",
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZ8AAAD4CAYAAAAzZOvCAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjAsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+17YcXAAAgAElEQVR4nO29eXxkV3Xv+11VpXmeepS6pZ67PWK322OMMWCbsX3z7IshBkP8YnID792HP8nF/uSGBBPnE+fDxXn3BWL8MMEQoO0Aee4LBuNg4wQwbTeeu93tVs/qwZJaQ2uWqmq9P86uUqlUk9SSTkla38+nPzq1zz5r73NaOr9ae6+9tqgqhmEYhjGXBPzugGEYhrH4MPExDMMw5hwTH8MwDGPOMfExDMMw5hwTH8MwDGPOCfndAT+or6/X5uZmv7thGPOGQx0DAKxpKANgLKLsO32WldUl1JYV+tk1+kfCHO4cYE19GV2DowyORNi4rMLXPi1Ufve733WqasNM2FqU4tPc3Mzu3bv97oZhzBtufeg3hAIBvn/XFQB09I1w2f3/xhe3n8fHr2z2tW+/OtDJ7Y/s4tt/fCXf33WMF4508avPX+9rnxYqInJ0pmzZsJthGFlRBZHxz8GA9yES9X+dYNStVQwIIF5fjfzHxMcwjKwoSeLjPkTy4EUfcWojIgiSpbaRL5j4GIaRFVWd8GIPuDdHNA88n1iWlqAIIuOfjfxmUc75GIYxNSZ5PrFhtzx40Uej3s+AePLoV4/GxsZoa2tjeHjYpx7MHMXFxTQ2NlJQUDBrbZj4GIaRFW/OJ8Hzkfyb8xHBeT7+9KOtrY2Kigqam5snPKv5hqpy5swZ2traaGlpmbV2bNjNMIyseMNu48Q8n3wYdot1IeDmfNQn32d4eJi6urp5LTzgfcmoq6ubdQ/OxMcwjKykCzgI54H4xOZ4AgF/PR9g3gtPjLm4DxMfwzCyosoEzycQ8Cb3o3kw5xOJh1q7gAOf+2PkhomPYRhZUXTSt+GgSJ7M+Xg/vXkosXU+WfjlL3/JBz/4QQBGRkZ4z3vew8UXX8xjjz02p/2wgAPDMLKS7PmA5/3kQ7SbJiwy9fTR/z7NF15++WXGxsZ45ZVX5rztnDwfEblJRPaLSKuI3JPifJGIPObO7xKR5oRz97ry/SJyYzabItLibBxwNgtd+bUi8pKIhEXklhR9qBSREyLyD1N7BIZhZCM52g08zyc/Ag4Sht1Y3BkOjhw5wqZNm7jjjju48MILueWWWxgcHORnP/sZmzZt4pprruFHP/oRAO3t7dx+++288sorXHzxxRw8eHBO+5rV8xGRIPBV4L1AG/CiiOxU1b0J1e4EulV1nYjcBjwAfEREtgC3AecBK4B/E5EN7pp0Nh8AHlTVHSLykLP9j8Ax4JPAn6bp6peA53K/dcMwciWqSvIcdDAgRKL+9CeRCet88mTO54v/aw97T56dUZtbVlTylx86L2u9/fv388gjj3D11Vfzh3/4h3zlK1/h61//Os888wzr1q3jIx/5CABLlizhG9/4Bl/+8pf58Y9/PKN9zYVcPJ9tQKuqHlLVUWAHsD2pznbgUXf8A+Dd4n1N2g7sUNURVT0MtDp7KW26a653NnA2bwZQ1SOq+how6dddRC4FlgI/z/G+DcOYIpOG3fIk4GDCOh9k0Wc4aGpq4uqrrwbg9ttvZ/fu3bS0tLB+/XpEhNtvv93nHnrkMuezEjie8LkNuDxdHVUNi0gvUOfKf5t07Up3nMpmHdCjquEU9VMiIgHgfwAfB96dod5dwF0Aq1atymTSMIwkkhOLQszz8f9FHx92C+SP55OLhzJbJA+P9vb25mUIeC6eT6peJ///pqszU+WZ+BPgSVU9nqmSqj6sqltVdWtDw4xsR2EYiwZFJyXtDAYCebHOZzzajUU/5wNw7Ngxnn/+eQC+//3v8573vIfDhw/H53S+//3v+9m9OLmITxvQlPC5ETiZro6IhIAqoCvDtenKO4FqZyNdW8lcCXxWRI4AXwY+ISJ/m8N9GYaRI6k9n3zJcJCYWNSG3TZv3syjjz7KhRdeSFdXF5/73Od4+OGH+cAHPsA111zD6tWr/e4ikNuw24vAehFpAU7gBRB8LKnOTuAO4HngFuAZVVUR2Ql8T0S+ghdwsB54Ae8LyiSb7ppnnY0dzuYTmTqnqn8QOxaRTwJbVXVSRJ5hGNMnOcMBuHU+efCij+lfbGjJ/x75SyAQ4KGHHppQdtNNN7Fv375Jda+77jquu+66OerZRLJ6Pm7+5bPAU8CbwOOqukdE7hORD7tqjwB1ItIK3A3c467dAzwO7AV+BnxGVSPpbDpbnwfudrbqnG1E5DIRaQNuBb4uIrH6hmHMMqqTF5kGAvkRaj1pnY//XTJyIKdFpqr6JPBkUtkXEo6H8UQh1bX3A/fnYtOVH8KLhksufxFvGC5TP78FfCtTHcMwpk6qRabBPFlkGhPA8cSii5fm5mbeeOMNv7uRE5ZexzCMrHjDbvmfXsfvzeQWynzTXNyHiY9hGFlJ3lIB3LBbHrxs4+t8Avi6mVxxcTFnzpyZ9wIU28+nuLh4Vtux3G6GYWQlbcBBXng+ydto+9OPxsZG2tra6Ojo8KcDM0hsJ9PZxMTHMIyspJ3zyQvx8X4GYqHWPvk+BQUFs7rz50LDht0Mw8iKovGts2Pkj/gkptexRabzBRMfwzCyEo0yyfXxtlTwpTsT0ATPhzxJr2Nkx8THMIycmJReR/Ikw0E0YZ2Pqc+8wcTHMIysaNotFfx/00/eRtv/PhnZMfExDCMrqbL+BvIsvU4gYJvJzSdMfAzDyEq6LRXyYdhNVQm4vuXLlgpGdkx8DMPISuotFfLF8xmPxLPN5OYPJj6GYWRFFQJJb4t8mfOJKuPiY57PvMHExzCMrHgak6+53caDIWzOZ/5g4mMYRg5MjnYL5IvnE01YAJuH20UbqTHxMQwjKynT60i+JBb1hgBhvI8275P/mPgYhpGVlIlFg0I4HzyfxGE399O0J/8x8TEMIyvelgoT1acgIITzIL+OJgYcYFtpzxdMfAzDyIpCfC1NjGAgkB9zPgnrfAJxz8f/fhmZMfExDCMr0ahO2sm0ICiMRaI+9WicCet8JFbmY4eMnDDxMQwjK6ne5aE8mfOJRMe3+I79tPxu+U9O4iMiN4nIfhFpFZF7UpwvEpHH3PldItKccO5eV75fRG7MZlNEWpyNA85moSu/VkReEpGwiNySUP9iEXleRPaIyGsi8pHpPQrDMNKSIr1OKBDIC88nMb3OeJk/fTFyJ6v4iEgQ+CrwPmAL8FER2ZJU7U6gW1XXAQ8CD7hrtwC3AecBNwFfE5FgFpsPAA+q6nqg29kGOAZ8EvheUtuDwCdUNdbG34tIdW63bxhGLnhzPpOH3fIh4CAcVUKBicNuRv6Ti+ezDWhV1UOqOgrsALYn1dkOPOqOfwC8Wzz/dzuwQ1VHVPUw0OrspbTprrne2cDZvBlAVY+o6mvAhK9aqvqWqh5wxyeBdqAh5ydgGEZWoim8i1AwQDjqv+cTjSrBYFK0m/+aaGQhF/FZCRxP+NzmylLWUdUw0AvUZbg2XXkd0ONspGsrLSKyDSgEDqY4d5eI7BaR3R0dHbmaNAyDiZP6MQoCwlhEfY8sC0eVYFLAgc355D+5iE8qRzb5fzZdnZkqz4qILAe+A3xKVSd9HVPVh1V1q6pubWgwx8gwpkJUmRTtFgp6rw+/w60jUU2R4cC//hi5kYv4tAFNCZ8bgZPp6ohICKgCujJcm668E6h2NtK1NQkRqQR+Avx3Vf1tDvdkGMYUSLWTacgNdfkd8RaJKiGXcnvc8zHynVzE50VgvYtCK8QLINiZVGcncIc7vgV4Rj1ffCdwm4uGawHWAy+ks+muedbZwNl8IlPn3PX/CnxbVf8lh/sxDGOKeFkEJpbFJvn9Fp/wBM8nNufjb586+kY4embA1z7kO1nFx82/fBZ4CngTeFxV94jIfSLyYVftEaBORFqBu4F73LV7gMeBvcDPgM+oaiSdTWfr88Ddzlads42IXCYibcCtwNdFJFb/PwPXAp8UkVfcv4vP4ZkYhpFEqjmfmLcR9jncOhKNjotPnng+H39kFzc8+O8MjISzV16khLJXAVV9EngyqewLCcfDeKKQ6tr7gftzsenKD+FFwyWXv4g3DJdc/s/AP2e9CcMwpk2qOZ8CN+w25nO4dSQhq3UMPx2fzv4R9p3uA+A3B8/w3i1L/etMHmMZDgzDyEhsCCtVqDXge7h1JBpNWOfjv+vT1j0UP379RK9/HclzTHwMw8hIbEonOat1fM7HZ88nHFECydFuPqrP6d5x8dlj4pMWEx/DMDKSzvMpcJ6P3yl2ojo5w4Gfw24ne4YB+L319fHhN2MyJj6GYWQk5vkEAsnrfPIx2s3Dzx6dGRghGBCuXFvHiZ4heofGfOxN/mLiYxhGRmJbZadKLAr+ez4TFpmK/6HW/cNhygqDbF5WCcBbb5v3kwoTH8MwMhJ7j6dKLAr+z/lEUiQW9bNH/SMRKooL2LisAoB9p8762Jv8xcTHMIyMxD2fpPJgniwyzbf0OgMjYcqKgiyvKqayOGTzPmkw8TEMIyOx9/hkzyc/FpkmzvmQB5vJDYyGKSsKISJsWlbJfhOflJj4GIaRkfRzPvnh+USjSjCW2y1W6GOX+kfClBd56/c3La9g/+k+39P95CMmPoZhZCSWI35Sep08CbVOtZmcr3M+w2HKCj3x2bisgr6RMCd6hrJctfgw8TEMIyPRtOt88ifgICaM+bCZnDfn4zwfF3RgQ2+TMfExDCMj48NuaRKL+p5eJ5Xn42Oo9UiYimJPfDYsdRFvJj6TMPExDCMj4wEHE8vzJbFoeMI22h5+eT6qysBohLKiIAAVxQU01pSY+KTAxMcwjIyk9XzyKLHo5G20/WEkHCUS1fiwG3hDb/tP21qfZEx8DMPISLpFprGhLr89n0gebSbX7/bvKZ8gPpUc7BhgJBzxpU/5iomPYRgZSRdwEMqjgINQfJ2P98OvYbfY5nGxaDfwIt4iUeVgu+1smoiJj2EYGYlvqZAkPoV5FGqdnOHAL/qGnfgkeD6bl7uIt7dt6C0REx/DMDKiaeZ8igq8SXW/h5OimiqxqD99iXk+sWg3gOa6MgpDAfadsqCDREx8DMPISLo5n5jnMxr2z/NR1TRbKvijPgOjkz2fUDDAhqXltqtpEiY+hmFkJNMiUxF/xScSVVTH88z5vZlc/4jnBZa7UOsYlzXX8tKxbl+fVb6Rk/iIyE0isl9EWkXknhTni0TkMXd+l4g0J5y715XvF5Ebs9kUkRZn44CzWejKrxWRl0QkLCK3JLV/h6t/QETumPpjMAwjHdE0no+IUBgMMOLjCzWWV26S+PjUn3jAQYLnA3B5Sx3DY1Fea+vxo1t5SVbxEZEg8FXgfcAW4KMisiWp2p1At6quAx4EHnDXbgFuA84DbgK+JiLBLDYfAB5U1fVAt7MNcAz4JPC9pP7VAn8JXA5sA/5SRGpyfQCGYWQmXWJRgKKQv+Iz6oIdCoL5EWqdXnxqAfjtoTNz3qd8JRfPZxvQqqqHVHUU2AFsT6qzHXjUHf8AeLd4M3/bgR2qOqKqh4FWZy+lTXfN9c4GzubNAKp6RFVfA5J/028EnlbVLlXtBp7GEzrDMGaAdAEHAIWhoK/iM+baLgxN9Hz8SrTdnyLUGqCmrJBNyyrYdbjLj27lJbmIz0rgeMLnNleWso6qhoFeoC7DtenK64AeZyNdW9PpHyJyl4jsFpHdHR0dWUwahhFjPOBg8rmiUMDXeYzYAtfYsFss8MC3RabDYUoLg+P7CyVwxZo6XjzSxfCYLTaF3MQnVeh88v9sujozVZ6JnK5R1YdVdauqbm1oaMhi0jCMGOnmfCA27Obfy3QsPuzmvcpiffTL84ltJJeKazfUMzwW5cUj5v1AbuLTBjQlfG4ETqarIyIhoAroynBtuvJOoNrZSNfWdPpnGMY0SRftBt5wl5+eT/KcT6yPEZ/Up38kMiG1TiJXrqmnMBTg2X028gK5ic+LwHoXhVaIF0CwM6nOTiAWZXYL8Ix6fu9O4DYXDdcCrAdeSGfTXfOss4Gz+USW/j0F3CAiNS7Q4AZXZhjGDJAusSj4H3AQ83wKJ3k+/gUclCWFWccoKQxy5Zo6fvlW+xz3Kj/JKj5u/uWzeC/0N4HHVXWPiNwnIh921R4B6kSkFbgbuMdduwd4HNgL/Az4jKpG0tl0tj4P3O1s1TnbiMhlItIG3Ap8XUT2uDa6gC/hCdqLwH2uzDCMGSD2Hk81vu235xNOmvMJ+JzhIHEL7VRct7GBQx0DHDszOIe9yk/SP6UEVPVJ4Mmksi8kHA/jiUKqa+8H7s/Fpis/hBcNl1z+It6QWqo2vgl8M+NNGIYxLdJlOAAoCgUZHA1PKp8rYsNusSSnbn87Ij4GHKyoLk57/l0bl/DF/7WXX77VzieubJ67juUhluHAMIyMxOd8UrwtCkOBuAD4QTzUOl+G3UYzez7N9WU015Xy7D4bejPxMQwjI1nnfMbyINQ6lCQ+fgUcDKePdotx3cYlPH/ozKIPuTbxMQwjI5lCrX33fJJCrWPra/xcZFpenFl83rmxgeGxKC+kWXAajkT57q6jnOgZmo0u5g0mPoZhZCSe4SDFOb89n0npdeIZDuZefcYiUUbCUcoLM4vPFS11FIYCPPdW6pDrx3e38ef/+gZ//J3fzUY38wYTH8MwMhJ7jeez5zNpzscH1yeW1y2b51NSGOSKNXX8cn/qeZ/nXCj26yd6aW1fuHsAmfgYhpGR2Is8dXqdICM+zl0kh1r7OeyWahfTdLxzQwMHOwY43jU55Hr/6T4ubqoG4Lm3Ome2k3mEiY9hGBkZ30Y7/zyf+LBbPODAK/dj2C22kVxFDuJz3UYvxVfy0Juqcqp3mG0ttbTUl/GrAws3G4KJj2EYGdEM6XWKQgHGIupbOpuxSXM+3k8/1vn0T8HzWVNfRmNNySTx6R4cYyQcZVllMVesqeV3R7t9i9ybbUx8DMPISCbPp7jASyXjV9jwaNI6n2A8w4EP4pNmL59UiAjXbWzgN62dEzJEnHQRbiuqi3nHqhrODoc51Nk/Ox32GRMfwzAyoqT3fEoLPfEZHPVHfIZdpF1MBGMBB36MBPYOjQFQVVKQU/3fW9/AwGiEV46P7256qncYgOVVJVyyytsT86VjC3P3UxMfwzAyksnzKXVhxUO+iY/XblFszse90fyY8+keGAWgpjQ38YntbvrC4fHdTU/1ep7P8upi1tSXUVkc4uVj3TPc0/zAxMcwjIxk2lIh7vmM+ZPfbXgsQlEoEBfGgI/Dbt2DU/N8qksn7256qneYgqBQX1ZEICC8Y1UNL5vnYxjGYiQ24Z3K8ynxfdgtEh9yA3+H3XoGR6kqKSAUzP21uq3FCyoIuw6f6hliaWUxAaf0l6yqYf/bffH5pIWEiY9hGBmJRbKFUrg+pe7F79+wW5TigvHXWNDPYbfBsZyH3GJsa6llcDTCnpNnATjZO8yKqpL4+QubqlCFN070zmhf8wETH8MwMjI+7JZ+zsc3zyc80fMRH7Nadw+OUl1aOKVrtjV78z673LzPqd4hlidsyXDhyioAXm8z8TEMY5ERG8IKpvB8xofd/JvzKQ5NHnbzQ3w6+0epK5ua+CypLKalvoxdh7qIRpXTvcOsqB73fOrKi1hZXcJr5vkYhrHYiC3YTDWVEQs4yJtht3hut7nvS7LXkitXrKnlhcNdnD47zFhEJ4gPwIWNVbzWtvCCDkx8DMPIyHhut1TDbv4GHAyNJQ+7eT/nOsPB4GiYnsExlleVZK+cxFVr6+kbCfPUntMArKiaKGAXNFZx9MwgvS6abqFg4mMYRkZiAQeZht2GfMpwMJIc7RbwJ9T6ZI+3ODTTFtrpuGptHQDf23UMgMaa0gnnL2r0koy+vsCG3kx8DMPISCRDwEFhMEAwID7O+aQZdpvjKZ9DHV4KnNV1ZVO+tq68iM3LKznQ3k9hMMCahok2zl/hBR28usCG3nISHxG5SUT2i0iriNyT4nyRiDzmzu8SkeaEc/e68v0icmM2myLS4mwccDYLM7UhIgUi8qiIvC4ib4rIvdN9GIZhTCaawfMREUoLgnkT7Rbr4lwnOt1/2tt3Z+PSimld//7zlwFw/srK+PYQMapKC2iuK11wEW9ZxUdEgsBXgfcBW4CPisiWpGp3At2qug54EHjAXbsFuA04D7gJ+JqIBLPYfAB4UFXXA93Odto2gFuBIlW9ALgU+HSi+BmGcW6MBxyk2svUG3rzK+BgaDQp2m2Oht1Gw9H4wlCA3Ue7WdNQllNS0VTc+Xst/PE713Lf9vNTnr+wsXpRDrttA1pV9ZCqjgI7gO1JdbYDj7rjHwDvFi/gfjuwQ1VHVPUw0OrspbTprrne2cDZvDlLGwqUiUgIKAFGgbM5PwHDMDKSKeAAvKADvzyfgZHwhBd+YA6G3Z58/RQXffHnvONLT/NXO/fw69ZOnj94hndtXDJtm6WFIe553ybOd+t6krmwsYoTPUN09o9Mu418IxfxWQkcT/jc5spS1lHVMNAL1GW4Nl15HdDjbCS3la6NHwADwCngGPBlVR1PluQQkbtEZLeI7O7oWLgbNBnGTJMp4AC8baP9SP8SjSoDo5EJ21bP9rDb0GiEe374Gmsayrh+0xK+u+sof/CNXQQC8IkrV89KmwAXLMDFprn4iKl+45L/Z9PVSVeeSvQy1c/UxjYgAqwAaoD/EJF/U9VDEyqqPgw8DLB169aFuTuTYcwCbqfq+GR+MhVFBfQNz30YcKqdQwOB2V1k+vO9pzk7HOYvPriFK9bU8ecf2MxTe97m0lU10wo2yJXzV1YhAq+19fKuTdP3sPKJXDyfNqAp4XMjcDJdHTf8VQV0Zbg2XXknUO1sJLeVro2PAT9T1TFVbQd+DWzN4b4Mw8iB+LBbmrdFZUmIs0Nz7/nEvK2Jnk9szmd22vztoTNUlRTE0+IsqSjm41esZsuKytlp0FFWFGJdQzmvn1g4EW+5iM+LwHoXhVaIF0CwM6nOTuAOd3wL8Ix6M347gdtcpFoLsB54IZ1Nd82zzgbO5hNZ2jgGXC8eZcAVwL7cH4FhGJnIFnBQWeyT55Ni59D4sNssqc8rx3u5qKk67mHNJRc0VvFqW68v20XMBlnFx82vfBZ4CngTeFxV94jIfSLyYVftEaBORFqBu4F73LV7gMeBvcDPgM+oaiSdTWfr88Ddzlads522DbyouXLgDTxR+ydVfW1aT8MwjElEsgQcVJYUcHZ47j2fvuEUw26zmNtteCzCW2/3cVFj6qCA2eaixmo6+kZ4++zCCDrIKS5QVZ8Enkwq+0LC8TBeyHOqa+8H7s/Fpis/hDePk1yesg1V7U/XtmEY506mdT4AFS7gIByJTmkvm3NlYMSLsEs17BZNCDg43jXId3cd409v2HBO/WvrHiQSVdYtKZ+2jXPhgsbxxabLqpb50oeZxDIcGIaRkXBMfNJ5PsXeHjZzHfHWP+IN9ZUVjotPMDA51Prv/+0ADz13kDdOntsKjGNdgwA01ZZmqTk7bF5WSUBg7zneR75g4mMYRkaiqoiQdp6j0m0bPddBB/FhtxSh1onDbvvf9l7W7WeHz6m9Y2c88Vnlk/iUFAZZ21Ae33huvmPiYxhGRiJRTev1AFS6l//ZOQ46iIlPecKcj4ggMnGdT43b4O30OYrP0a5BygqDU96zZybZsqKSN0+Z+BiGsQiIqGaM7op7PnMsPj2DowRkvP0YBYEAY5Fx8YmJU0ffuU3UH+8apKm2NL5bqh9sWV7JiZ4hegZHfevDTGHiYxhGRqJZPJ/YsNdcD7t1uW2rkwMhQkGZkHctNmd1ruJz9Mwgq+v8GXKLEVtPtHcBeD8mPoZhZCQSTR/pBlDlPI/eobn9Nt49MEZNacGk8lBA4oIDXhJQODfxUVXauocm7bUz12xe7sRnAcz7mPgYhpGRqCqZ1lTWlRUBcGZgbsWna2CU2hTzLwXBAGMJns9I2AvJbj8H8ekeHGNoLMLK6qnvVDqT1JcXsbSyyDwfwzAWPpGoZvR8SgqDlBUG6eybY89ncDQeTJCIN+w2s57Pie4hAFbW+Cs+4M37mOdjGMaCJ6KZxQe83TjPDMztyvt0nk8oMNHzGXXHnf0jExafToUTPV6Ytd+eD3jzPq3t/Qz7tHX5TGHiYxhGRqJRTZtaJ0ZdeSFn+ufO81FVul3AQTKFoQBjCSIzMuaJTziq9AxNLyKvzXk+jXnh+VQRjiqt7f1+d+WcMPExDCMj2YbdwJuLmMuNzroHxxiLKEsriyadCwUmRruNRqIUBL3+t/dNb63PiZ4hygqD8eAKP4lHvM3zoTcTH8MwMhLR7J5PfXnhnAYcnOr1PJHlVcWTzoWCE9f5jIxF48Nl0533OdE9xIrqEl/X+MRYXVtKWWFw3gcdmPgYhpGRaA6eT11ZEV0Do9OeU5kqp3s9D2ZZ1eRhsIKgEI5O9HxiIdLTFZ8jZwZ8X+MTIxAQNi+vZM/J+b2rqYmPYRgZiWjmdT7gzflEot48zFxwyolPSs8nMDnaLeb5TCfcOhyJcrhzgLU+ZbNOxcZlFew/3Tev9/Yx8TEMIyNewEHmOksrPRGYq71mTvcOEwwI9eUp5nxSrPOpKSukpCA4Lc/nWNcgYxFlXUP+iM+GpRWcHQ6f09olvzHxMQwjI+FolFC6PbQdMc/iRM/QXHSJkz1DLK0oSumRecNunkcQjSpjEaUoFKCptoQjnQNTbiu2FcOmZbO7VfZU2LC0AoD9p/t87sn0MfExDCMj4YgSCmZ2fVY48Tk5R+JzqHOA5vqylOdCgUA82i22xqcwFPAWZ05jkv7Fw12UFQbZvLxi+h2eYTYs9bywt9428TEMY4EyFtWsO4DWlxdSGArMieejqhzq6GdNQ2rxKQhKPNptxGU3KAoFOG9FFad6hzkzhZDwsUiUp/ac5sq1dXO6S2s26sqLqC8vNPExDGPhEo5EKcgy6SMirKwumRPx6RoY5exwmJb61HMwoUAgHu02miA+l6yuAeBXrZ1Z2xDpmDwAACAASURBVBgNR9l9pIs/+5dXae8b4WOXr5qh3s8c65dU8Nbb83ehaSh7FcMwFjO5DLuBN+8Ty4E2mxx28zbpPJ/E3G6Jw24XN1VTX17IU3tOs/3ilSmvHR6L8D9/cYBvP3+U/pEwAYFPX7uG6zctnYU7OTc2LqvgX3YfR1XzYv3RVMnJ8xGRm0Rkv4i0isg9Kc4Xichj7vwuEWlOOHevK98vIjdmsykiLc7GAWezMIc2LhSR50Vkj4i8LiKT4y8Nw5gWY9EoBTkMOa2sLqGte3DW+xPbyTM26Z5MQTDAmPN8Rlz+s6JQkGBAuPnilTy1522OnpkceHC6d5j/9LXf8LVfHuS6jQ08dPslvPwXN3Dv+zfP0p2cG+uXljMwGpmzII+ZJutvlIgEga8C7wO2AB8VkS1J1e4EulV1HfAg8IC7dgtwG3AecBPwNREJZrH5APCgqq4Hup3tTG2EgH8G/lhVzwOuA+Z2S0XDWMCEI0ooW6w1nifS2T9K7+Ds/vm91tZLXVkhK1Ks8QE35xOe7PkA/NG1aygOBfizH7w2IQVPR98IH/vGbzneNcg/feoy/uFjl3DT+cupSrFfUL6w0YnvfJ33ycXz2Qa0quohVR0FdgDbk+psBx51xz8A3i2eH7gd2KGqI6p6GGh19lLadNdc72zgbN6cpY0bgNdU9VUAVT2jqvM73asPvH2O+9sbC5exSDSnyfb1LgKrtWN2X4avn+jlgsaqtENNxQXB+B4+sTmfQtf/pZXFfOnm83nhcBdf/vlbgDen9ZnvvsTJniH+6VOX8a6NS2a1/zPF+rj4zM95n1zEZyVwPOFzmytLWUdVw0AvUJfh2nTldUCPs5HcVro2NgAqIk+JyEsi8t9S3YSI3CUiu0Vkd0dHRw63vXh44pUTXP43v2D3kS6/u2LkIeGoxhNzZmJdg/cyPDCLL8PB0TAH2vu5YGVV2jrFBUGG3HBbLNot5vkA/P4ljXzs8lU89NxBfvr6Kb7y9Fu8cKSLv/39C7msuXbW+j7TVJUUsKyyeN56PrkEHKT6rUvO6ZCuTrryVKKXqX6mNkLANcBlwCDwCxH5nar+YkJF1YeBhwG2bt06f3NSzAK7Dnui8+bpPrbOoz8+Y24IR7IvMgVvo7WiUGBWU/2/cLiLSFTZ1pL+97S4IMjwWJRoVCdEuyXylx/awp6TZ/kv330JgFsvbeTmd6QOQshn1i8tn7fik4vn0wY0JXxuBE6mq+PmYKqArgzXpivvBKqdjeS2MrXxnKp2quog8CRwSQ73ZSQx/+JljLlgLMdot2BAWNtQzv5ZfBn+urWTwlAgo4dSUhAEPK9nNIXnA14Awp9ctzb++c8/kJ9BBdnYsLSC1vb+OUvoOpPkIj4vAutdFFohXgDBzqQ6O4E73PEtwDPqZbzbCdzmItVagPXAC+lsumuedTZwNp/I0sZTwIUiUupE6Z3A3twfgWEYmQhHoxTk4PkAXNhYxWttvbP2MvyPA51cuqqGYicwqSgp8Po6NBaJz/0UhSbXb3EZEtY0lKXclG4+sLahnOGxKCd751/EW9bfKDe/8lm8l/ybwOOqukdE7hORD7tqjwB1ItIK3A3c467dAzyOJwY/Az6jqpF0Np2tzwN3O1t1znamNrqBr+AJ2ivAS6r6k+k+kMXIPE6Ma8wBua7zAXjHqmp6h8Y4nCKU+Vw52NHPvtN9vHtz5oCAkkJPaDzxSe35AKxrKOcz71rLP995+Yz3da6IrXU61DHzz3u2yWmRqao+iTeclVj2hYTjYeDWNNfeD9yfi01XfggvGi65PFMb/4wXbm0YxgwzFsltnQ/AxU1eFoFXjvWwdoazQP/41VOIwAcvXJGxXswrGhqNTEivk0wgIPzZjZtmtI9zTewZH+zo59oNDT73ZmpYeh3DMDISjua2zgdg3ZJyKopC7D46s5GTkajyw5fauLyllmVp1vfEiM35DI9F0gYcLBTqywupKA7NS89nYf6PGFPEG3ebhxk6jDnAG3bL7VURDAhXravjuf0dM7rR2TP72jnWNcjHr2jOWrc4hfikGnZbCIgIaxrKOdQ5/9b6LMz/EWNaiMW7GSnw0uvk/rtx3cYlnOwdnrHFj6rKw/9+kBVVxdx4XvYca6nmfFIFHCwU1taXmedjzG900vItY7ETiSqq5LTOJ8Z1G725h6f3np6RPjy9921ePNLNn7xrXU4eWKkTn4GR8IL3fADWLinnVO8wAyPh7JXziIX7P2IYxjkT244612g3gOVVJWxrruWHL50456G3odEIf/Pkm6xtKOO2y5qyXwDxsOmewTFGwhFCAUm54+lCYY0LGT88jV1a/cTEx4hjw25GMrHtqHMNOIhx69ZGDncO8MLhcws8eOBn+zhyZpAvbT8/53mnGpcMtGdojJFwdEF7PQBrEiLe5hML+3/FMIxzIhz3fKb2qvjAhcupKS3gH587OO22d756km/95gifvKqZq9bV53xdSUGQwmCA7sFRBkbClBUt7G3LVteVIgIH59m8j4mPYRhpiW1JMNVQ5dLCEH907Rp+ub9jWt7P8wfP8Kf/8irbWmq59/1TW4sjIlSXFtA7OEb/SJjyBS4+xQVBmmpKOWSej2EYC4WRsemvk/nkVc001pTw+R++xuBo7pPhT+99mzv+6QVW15by9dsvnVakWnVpQYLns3Aj3WKsaZh/EW8mPoZhpCUeqpwhl1o6SgtD/N0tF3LkzAD/x/dejkeepW8rwt/+dB9/9O3dbFpWwWOfvpKasunlXKsvL6Kjb4SBkQhlhQvb8wFYU1/O4c6BeZVg1MTHiGOh1kYy44k5p/equGptPV/afj6/2NfOx/7f39LaPjnj9fBYhB/+ro33fOU5HnruIB/dtorHP30ltdMUHoCmmlKOdw8timE38DyfobEIp+fRppAL/3/FyAGLcjNSkyk3Wq7cfsVqqkoKuPdHr3PDg//O1tW1bF5egYhwrGuQFw930TcSZtOyCr5z5zZ+b/255yhbVVdKR98II2MRzl9Zec728p3mOi/c+uiZQVZUl/jcm9ww8TGYvDegYXjE5nzONVz5Qxet4Kq1dXz7+aM8s6+dH718AvC2tf7Ahcv58MUruKKljsAMrcdZVVsKwNnhMEsrM+eCWwisrvPu9+iZAa5cW+dzb3LDxMeIY+t8jGQy7YczVerKi/jcezfwufduOGdb2bhkdU38eL54AufC8qpiCoLC0a5Bv7uSMzbnYxhGWmZi2M0PVlaXxPe62ZogRAuVUDBAY00pR2dhH6XZwjwfwzDSEhOf4oL5JT4A3/rkNo52DbB+aYXfXZkTVteVcvTM/PF8THwMw0jLyNjMDbvNNavqSlnl5kIWA6trS9l9pBtVRebB/ijz7+uMYRhzxnwddluMrK4ro38kTNfAqN9dyQn7jTIMIy2LYT+chUIs4u3IPBl6M/Ex4tgiUyOZeLTbPJzzWWysdmt9jnXNj6CDnH6jROQmEdkvIq0ick+K80Ui8pg7v0tEmhPO3evK94vIjdlsikiLs3HA2SzM1oY7v0pE+kXkT6f6EIz8Hx82/CG+zmeKWa2NuaeptgQRONK5QDwfEQkCXwXeB2wBPioiW5Kq3Ql0q+o64EHgAXftFuA24DzgJuBrIhLMYvMB4EFVXQ90O9tp20jgQeCnud64YRjZGRqLUFwQmLHFn8bsURQKsqKqhGPzZK1PLl9ntgGtqnpIVUeBHcD2pDrbgUfd8Q+Ad4sXbrEd2KGqI6p6GGh19lLadNdc72zgbN6cpQ1E5GbgELAn91s3xrHhNiM1iyU32kJhVW0pR+bJWp9cxGclcDzhc5srS1lHVcNAL1CX4dp05XVAj7OR3FbKNkSkDPg88MVMNyEid4nIbhHZ3dHRkeWWFyeW4cBIZnAkTOkiyAq9UGiuL+XYAgo4SPVGSv6qnK7OTJVnauOLeMN0GXdSUtWHVXWrqm5taDj3xIWGsRgYGI1QWmiRbvOFVbVlnBkYpW94zO+uZCWXrzRtQFPC50bgZJo6bSISAqqArizXpirvBKpFJOS8m8T66dq4HLhFRP4OqAaiIjKsqv+Qw70ZhpGBwVEbdptPNMcTjA5y/soqn3uTmVw8nxeB9S4KrRAvgGBnUp2dwB3u+BbgGVVVV36bi1RrAdYDL6Sz6a551tnA2XwiUxuq+nuq2qyqzcDfA39jwmMYM0P/SIRSE595w6oE8cl3sv5WqWpYRD4LPAUEgW+q6h4RuQ/Yrao7gUeA74hIK543cpu7do+IPA7sBcLAZ1Q1ApDKpmvy88AOEflr4GVnm3RtGIYxewyOhFlRtfC3JFgoNNZ44tPWvQDEB0BVnwSeTCr7QsLxMHBrmmvvB+7PxaYrP4QXDZdcnraNhDp/lem8YRhTY3A0Qpl5PvOGqpICKotDtHUP+d2VrNjKMSOOZTgwkukfCVNmAQfziqbaUo7PA8/HxMfAMhwYqYhGlb7hMSqKC/zuijEFmmpKOT4PFpqa+BjYIlMjFX3DYaIK1aUmPvOJxpoS2rqH8OK38hcTHyOOLTI1EukZ8lLz15QW+twTYyo01ZYyEo7S0T/id1cyYuJjGEZKuge9hYrm+cwvGmtKAPI+6MDExzCMlHQPep5PtXk+84qmWi/cOt/nfUx8DMNISc9gbNjNPJ/5hHk+hmHMa7oGvGE3m/OZX5QWhqgrK8z7haYmPoZhpOR07xBFoYDN+cxDGmtLOd5lno8xT7BFpkYip3qHWV5VjNs2y5hHeOHW5vkY84SoaY+RwOneYZZZXrd5SVNNKSd6hojk8R+1iY8xTp4vSjPmFs/zKfG7G8Y0aKotYSyitPcN+92VtJj4GHFMeowYw2MRTvUO0VRj4jMfiWW3zud5HxMfI+7wmONjxDjcOUBUYf3SCr+7YkyD2JeGfF7rY+JjEHWqk++5oIy540C7tyv9uiXlPvfEmA4rqvN/rY+JjxEPNDDpMWK8caKXwmCAlvoyv7tiTIPigiBLK4vyemsFEx8jwfPxuSNG3rDrcBcXNVVRXGB7+cxXmmpK8zrc2sTHGJ/z8bcbRp7QNTDKGyd6ubylzu+uGOdAY02JBRwY+Y3N+RiJ/OT1U0SiyvsvWO53V4xzoKm2lFO9Q4xFon53JSUmPoYtLjXijEWifOM/DnHeiko2L7dIt/lMY00JUfUWC+cjOYmPiNwkIvtFpFVE7klxvkhEHnPnd4lIc8K5e135fhG5MZtNEWlxNg44m4WZ2hCR94rI70Tkdffz+uk+jMVKzOOJmuezqFFV7v/Jmxw9M8if3rDR0urMc5pq8ntrhaziIyJB4KvA+4AtwEdFZEtStTuBblVdBzwIPOCu3QLcBpwH3AR8TUSCWWw+ADyoquuBbmc7bRtAJ/AhVb0AuAP4ztQegWHrfBY3kajywuEu7nx0N9/6zRH+8OoW3rVpid/dMs6R+L4+eRp0EMqhzjagVVUPAYjIDmA7sDehznbgr9zxD4B/EO9r03Zgh6qOAIdFpNXZI5VNEXkTuB74mKvzqLP7j+naUNWXE/qxBygWkSLXppED8Tkfn/thzB6j4SgneoY41jXo/Tsz4I6HOHZmgIHRCBVFIf77BzZz5zUtfnfXmAGWVxUTDEjeBh3kIj4rgeMJn9uAy9PVUdWwiPQCda78t0nXrnTHqWzWAT2qGk5RP10bnQl2/jfg5VTCIyJ3AXcBrFq1KvMdLzIs1Hrh0DM4yp6TZ3njRC8HO/o51jXI8a4hTvYOTfj/LQoFWFVbyqraUi5vqeXS1TVct7GBimLbPmGhEAoGWFZZnLfh1rmIT6qB3+TXVLo66cpTDfdlqp+1HyJyHt5Q3A0p6qGqDwMPA2zdutVeswmMLzK1xzKfiIVEv36ilz0nvZ+J33IbKopY7cSlyQlNU20pq+tKaSgvIhCwOZ2FTlNtCcfzNMtBLuLTBjQlfG4ETqap0yYiIaAK6MpybaryTqBaRELO+0msn64NRKQR+FfgE6p6MId7MhJQ83zyno6+Ed440ZsgNmc50TP+UlldV8qFK6v52LbVnL+ykvNXVFFTZjuQLnaaakp57q0Ov7uRklzE50VgvYi0ACfwAgg+llRnJ95k//PALcAzqqoishP4noh8BVgBrAdewPNiJtl01zzrbOxwNp/I0kY18BPgXlX99XQewmLHQq3zB1WlvW+E19t6eePkuNi8fXZ8JHlNfRmXrq7hjqtWc/7KKs5bXkWV7TZqpKCptpT2vhGGxyJ5l60iq/i4+ZXPAk8BQeCbqrpHRO4DdqvqTuAR4DsuoKALT0xw9R7HC04IA59R1QhAKpuuyc8DO0Tkr4GXnW3StQF8FlgH/IWI/IUru0FV26f3SBYf4agtMvUDVeVk73Dco/GE5iyd/Z7QiMDahnKuWlvP+SurOH9FJVtWVNq8jJEzjS679YmeIdY25FeS2Fw8H1T1SeDJpLIvJBwPA7emufZ+4P5cbLryQ4xHxCWWp2xDVf8a+OusN2GkZTQcAWzYbTZRVdq6h+KezBsuKKBrYBSAYEBYv6Scd25o4IKVlZy/sootKyopLczpT9QwUhIPt+4anJ/iYyxsxiIWaj2TRKPKsa5BJzIxr+YsvUNjAIQCwoalFbx381JvfmZlFZuXV+bdsIgx/4kvNM3DoAMTHyOe+8kyHEydcCRKa0c/e06c5Y2TXiDA3pNn6R/xVgsUBgNsWl7B+y9YzgUrqzh/ZSUbl1VQFDKhMWafJRVFFAYDeRlubeJjMBr2xMe0JzPDYxH2n+6Li8yeE73sO93HiHt+JQVBNi+v4PcvWcn5K7xhsw1LKygMWQpFwx8CAWFlTQltebjQ1MRngXCwo5819WXTyscVF5+Z7tQ8pntglH2n+3jz1FlPaE72cqC9n4gLzqgsDnHeiio+caWLOFtRSUt9OUFbO2PkGY01JXmZYsfEZwHw6vEetn/119y3/Tw+cWXzlK+PfXNfjK7PSDjCwfYB9p0+y/7Tfbx5uo/9p89OCG2uLy/i/JWVvMfN0Zy3oorGmhJLvGnMC5pqS3nj9VN+d2MSJj4LgDdO9gKw9+TZaV3fN+xNhC9k6YmFNe8/fZY3T/Wxz4nMoY6BeKh5YTDAuiXlXL22nk3LK9i4rJLNyypYUlnsc+8NY/o01pTQPThG/0iY8qL8eeXnT0+MaTM44oVKl03jFysaVfrc5PhCcHyiUeVEzxCtHf0cbO/nYEc/re397DvdR99wOF5vZXUJm5ZV8N4tS+Mi01xfRkHQ5meMhUUs4q2te5BNyyp97s04Jj4LgMFRT3xKphGq2zccTthGe/6oz/BYhEMdAxNE5mDHAIc6+seHEYHaskLWNpSx/eIVcZHZsKyCSluoaSwSxtf6DJn4GDNL/4g3bDadue4zA+NzG/nm+agqXQOjHOwYoDUuMJ4nc6JnPEuziPftbm1DGdesq2NtQzlrl5SztqGcWstvZixymlyWg3wLtzbxWQC093kCMhye+l7txxJ2OfRLeyJRpa17cFxg2p1H09FPz+BYvF5xQYA19eW8Y1UNt17axNolZaxtKKelvswWaBpGGmrLCikpCObdvj4mPguAQx0DAAy54bep0NreHz+e7UWmg6NhDnUMOIHpj3s0h88MxMO9AerLC1nTUM77L1jueTENZaxbUs6KqhLbBsAwpoiIuK0VzPMxZpDB0TD7T/cB3jzIVPl1ayer60o53Ts8I66PqtLZPzppmOxQx8CELQACAqtqS1m3pJzrNjYkDJWVUV1qQ2WGMZM01ZRyvMvEx5hBvv/CcUZdepypDrvtPXmW597q4NPvXMs//frwlLQnGlVO9g5xoN3zYg683c+B9j5a2/s5mxBVVloYZG1DOZc113BbQxNrl5Szbkk5q+tKLcWMYcwRjTUlvHC4C1XNm/VpJj7zlLfPDvPIrw7zyK8O884NDbx9djhnz0dVeWZfO/f86HXqyov4369p4dHfHCGaZmOfgZEwe0+djSfIjInMYMIwX315IeuWlPOhi1awzgnM2oZyllUW21CZYfhMU20pfSNheofG8mZkwcRnHvH22WGe29/Bz/ee5tn9HUSiyn/e2sgXPnQet39jV0bxGQlH2HPyLM+82c5P3zjFwY4B1tSX8dDHL6WuvIjSwiCDY7GtFZRdh7t4Zl87/3Ggk32nz8Yjy+rLi9i8vIKPXNbE+iUVcaGxqDLDyF8aa8bDrU18jIxEokprez+vHu/hlbYeXjrazT43t7O8qpi7rl3DR7Y20VxfBniRYCNjsQSh3mr+l4918/KxHl461s2eE2cZjUQJBoTLW2r59DvX8p/esTK+qLKsKMTASJin9pzmb3+6j8OdAxQGA1y6uob/+u71XLCyigtWVtlqf8OYh6xya32Odg1wQWOVz73xMPHxkWhUebtvmMOdAxw9M8iRzgGOnHHHZwYYdmJSURzi4qZqtl+8kndtamDj0opJ47YlBUGOdw9x/0/28uPXTnGqdxiAolCACxur+OTVzVyyqpptLXUpvZTyohBPvHKSJ145ycalFTz4kYu48bxltpmZYSwA1jSUERA48HZ/9spzhL1ZZploVDl9dtgJiycqR5zYHO0aFxjwcoutqiulua6Ua9bVs2VFJRc3VdNcV5Z13qQwFHBRZf28d8tSPn3tGi5ZXcPm5ZU5pYyJrfjffvEKvnzrRZZmxjAWEMUFQVbVlnKgvc/vrsQx8ZkhzvSPcKjTS+9yqGOAQ51OZLoGJ6xhKQwFWF1byuq6Mq7dUM/qujJa6stYXVfK8qqSaafkv6y5lmf2tfP/fPQSbjp/2ZSvv/uGDTyzr53PvWeDCY9hLEDWL60wz2ch0DUwylN7TvOrA528fKybk26YCzwPZnVdKc31Zbxr0xLvuK6M5voyls9S9Ned17Rw+xWrp73S/7LmWi5rrp3hXhmGkS+sX1LOs/vaGQ1H82KDw5zER0RuAv5vIAh8Q1X/Nul8EfBt4FLgDPARVT3izt0L3AlEgP9TVZ/KZFNEWoAdQC3wEvBxVR2dThuzwWg4ypd/vp9v/eYIo+EoK6qKuWR1DZ9qrGbd0nLW1pezsmb6Hsx0ERFLMWMYRlo2LK0gHFUOdw6wcVmF393JLj4iEgS+CrwXaANeFJGdqro3odqdQLeqrhOR24AHgI+IyBbgNuA8YAXwbyKywV2TzuYDwIOqukNEHnK2/3Gqbajq1Jf758A9P3qNH710glsubeRTVzezZXll3izaMgzDSMdFTdUAvHD4TF6ITy6+1zagVVUPqeoonleyPanOduBRd/wD4N3ivZG3AztUdURVDwOtzl5Km+6a650NnM2bp9nGjNM/Eub/e/kEn7yqmS/fehHnragy4TEMY17QXFdKY00JT7xyMu2C8rkkl2G3lcDxhM9twOXp6qhqWER6gTpX/tuka1e641Q264AeVQ2nqD+dNuKIyF3AXe5jv4jsB+qBznQ3no4vun8LiGk9hwWKPQsPew7jLLhnEfyTaV+6cab6kIv4pPpqnyyb6eqkK0/lcWWqP502JhaoPgw8nFgmIrtVdWuK6xcV9hzGsWfhYc9hHHsW44jI7pmylcuwWxvQlPC5ETiZro6IhIAqoCvDtenKO4FqZyO5ram2YRiGYeQpuYjPi8B6EWkRkUK8yf2dSXV2Ane441uAZ1RVXfltIlLkotjWAy+ks+muedbZwNl8YpptGIZhGHlK1mE3N7/yWeApvLDob6rqHhG5D9itqjuBR4DviEgrnjdym7t2j4g8DuwFwsBnYlFoqWy6Jj8P7BCRvwZedraZThs58HD2KosCew7j2LPwsOcwjj2LcWbsWYjO8u6VhmEYhpGM/8tcDcMwjEWHiY9hGIYx5yxK8RGRm0Rkv4i0isg9fvdnNhCRb4pIu4i8kVBWKyJPi8gB97PGlYuI/E/3PF4TkUsSrrnD1T8gInekaiufEZEmEXlWRN4UkT0i8l9d+WJ8FsUi8oKIvOqexRddeYuI7HL39ZgLAsIF8TzmnsUuEWlOsHWvK98vIjf6c0fnhogEReRlEfmx+7xYn8MREXldRF6JhVLPyd+Hqi6qf3gBDgeBNUAh8Cqwxe9+zcJ9XgtcAryRUPZ3wD3u+B7gAXf8fuCneGumrgB2ufJa4JD7WeOOa/y+tyk+h+XAJe64AngL2LJIn4UA5e64ANjl7vFx4DZX/hDwX9zxnwAPuePbgMfc8Rb3d1MEtLi/p6Df9zeN53E38D3gx+7zYn0OR4D6pLJZ//tYjJ5PLumC5j2q+u94UYGJJKYoSk5d9G31+C3eWqvlwI3A06rapardwNPATbPf+5lDVU+p6kvuuA94Ey8DxmJ8FqqqsZz6Be6fkscprWYLEWkEPgB8w33O69RePjDrfx+LUXxSpQualI5ngbJUVU+B91IGlrjydM9kQT0rN1zyDrxv/IvyWbihpleAdrwXxEFyTGkFJKa0mu/P4u+B/wbENtvKObUXC+s5gPcF5Oci8jvx0pDBHPx9LMb9fHJKx7PIOKfURfMBESkHfgj8X6p6VtInhF3Qz0K9NXAXi0g18K/A5lTV3M8F+SxE5INAu6r+TkSuixWnqLqgn0MCV6vqSRFZAjwtIvsy1J2xZ7EYPZ/FnI7nbeci4362u/KppkGaV4hIAZ7wfFdVf+SKF+WziKGqPcAv8cbtF1tKq6uBD4vIEbxh9+vxPKHF9hwAUNWT7mc73heSbczB38diFJ9c0gUtVBJTFCWnLvqEi2S5Auh1rvZTwA0iUuOiXW5wZfMGNzb/CPCmqn4l4dRifBYNzuNBREqA9+DNgS2qlFaqeq+qNqpqM97f/zOq+gcssucAICJlIlIRO8b7vX6Dufj78DvSwo9/eBEbb+GNd/+53/2ZpXv8PnAKGMP7VnIn3jj1L4AD7metqyt4m/sdBF4HtibY+UO8idRW4FN+39c0nsM1eO7/a8Ar7t/7F+mzuBAvZdVr7gXzBVe+Bu+l2Qr8OvmLHgAAAHJJREFUC1Dkyovd51Z3fk2CrT93z2g/8D6/7+0cnsl1jEe7Lbrn4O75VfdvT+x9OBd/H5ZexzAMw5hzFuOwm2EYhuEzJj6GYRjGnGPiYxiGYcw5Jj6GYRjGnGPiYxiGYcw5Jj6GYRjGnGPiYxiGYcw5/z8itycNeSvgBQAAAABJRU5ErkJggg==\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -679,7 +746,7 @@
     "# plt.plot(test_q, fplus_y, label = '+')\n",
     "# plt.plot(test_q, res_y, label = 'res')\n",
     "plt.legend()\n",
-    "plt.ylim(0.0, 6e-6)\n",
+    "plt.ylim(0.0, 1.5e-5)\n",
     "# plt.yscale('log')\n",
     "# plt.xlim(770, 785)\n",
     "plt.savefig('test.png')\n",
@@ -688,7 +755,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -704,7 +771,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -720,7 +787,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -733,7 +800,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -787,7 +854,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -871,7 +938,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -880,7 +947,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -889,7 +956,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -898,7 +965,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 21,
    "metadata": {
     "scrolled": false
    },
@@ -908,7 +975,7 @@
      "output_type": "stream",
      "text": [
       "6/6 of Toy 1/1\n",
-      "Time taken: 1 min, 22 s\n",
+      "Time taken: 1 min, 55 s\n",
       "Projected time left: \n"
      ]
     }
@@ -956,7 +1023,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -973,14 +1040,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 23,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Time to generate full toy: 82 s\n",
+      "Time to generate full toy: 115 s\n",
       "(5404696,)\n"
      ]
     }
@@ -1006,7 +1073,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 24,
    "metadata": {},
    "outputs": [
     {
@@ -1018,7 +1085,7 @@
     },
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYMAAAD8CAYAAACVZ8iyAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjAsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+17YcXAAAXBklEQVR4nO3df7BcZZ3n8fd3IZoVMkOIFzZFGG6gApIZJLBXpGShVFB+WUF0Zgw1pdGhyMpg1TiO7kaxZtmyqGJ01V1qdrHCQgFVCDj8iHFgdkyxOjilIDcSQjBGEiYzXkkld8KAoRCXH9/9o8+FJvS9t2/36e7T3e9XVVef8/TTp5/z3NvPp8+PPh2ZiSRpuP2bXjdAktR7hoEkyTCQJBkGkiQMA0kShoEkiSbCICKOjojvRcS2iHg8Iv60KD88IjZGxBPF/cKiPCLi2ojYERFbIuLUTq+EJKk9zWwZvAT8eWaeCJwOXBERy4G1wP2ZuQy4v5gHOB9YVtzWANeV3mpJUqlmDYPM3J2ZPymm9wPbgKOAi4Cbi2o3Ax8spi8CbsmaB4HDImJx6S2XJJXm4LlUjohR4BTgIeDIzNwNtcCIiCOKakcBv6h72kRRtvuAZa2htuXAIYcc8u/f9ra3tdB8abg89stnZ61z0lG/3YWWTK9RG3vZpsd++WzP+6RTNm3a9C+ZOVLGspoOg4g4FLgL+HRm/ioipq3aoOwN17zIzHXAOoCxsbEcHx9vtinS0Bpde++sdcavubALLZleozb2sk2ja+/teZ90SkT8U1nLaupsooiYRy0Ibs3Mu4viPVO7f4r7vUX5BHB03dOXAE+V01xJUic0czZRADcA2zLza3UPbQBWF9OrgW/XlX+sOKvodODZqd1JkqRqamY30RnAR4HHImJzUfYF4BrgWxFxKfDPwB8Uj90HXADsAJ4HPlFqiyVJpZs1DDLzH2h8HADg7Ab1E7iizXbx4osvMjExwQsvvNDuoipt/vz5LFmyhHnz5vW6KZKG2JzOJuqmiYkJFixYwOjoKDMcrO5rmcm+ffuYmJhg6dKlvW6OpCFW2ctRvPDCCyxatGhggwAgIli0aNHAb/1Iqr7KhgEw0EEwZRjWUVL1VToMJEndUdljBgdq5ss2c7GrhS+hXHXVVRx66KF89rOfbfj4+vXrOf7441m+fHm7zZOkrnLLoETr16/npz/9aa+bIUlzZhjM4uqrr+aEE07gnHPOYfv27QBcf/31vOMd7+Dkk0/mwx/+MM8//zw//OEP2bBhA5/73OdYsWIFO3fubFhPkqrIMJjBpk2buP3223nkkUe4++67efjhhwH40Ic+xMMPP8yjjz7KiSeeyA033MC73vUuVq5cyVe+8hU2b97Mcccd17CeJFVR3xwz6IUf/OAHXHzxxbzlLW8BYOXKlQBs3bqVL37xizzzzDM899xznHvuuQ2f32w9Seo1w2AWjU79/PjHP8769es5+eSTuemmm/j+97/f8LnN1pOkXnM30QzOOuss7rnnHn7961+zf/9+vvOd7wCwf/9+Fi9ezIsvvsitt976av0FCxawf//+V+enqydJVdM3WwatnArarlNPPZWPfOQjrFixgmOOOYYzzzwTgC996Uu8853v5JhjjuGkk056NQBWrVrFZZddxrXXXsudd945bT1Jqpq+CYNeufLKK7nyyivfUH755Ze/oeyMM8543amll19+ecN6klQ17iaSJBkGkqSKh0HtpxEG2zCso6Tqq2wYzJ8/n3379g30YDn1ewbz58/vdVMkDbnKHkBesmQJExMTTE5O9ropHTX1S2eS1EuzhkFE3Ah8ANibmb9XlN0BnFBUOQx4JjNXRMQosA3YXjz2YGZ+spWGzZs3z1//kgbE6Np7e3J6uJrXzJbBTcBfAbdMFWTmR6amI+KrwLN19Xdm5oqyGihJ6rxZwyAzHyg+8b9B1K7V8IfAe8ttliSpm9o9gHwmsCczn6grWxoRj0TE30fEmW0uX5LUBe2GwSXAbXXzu4HfycxTgM8A34yI32r0xIhYExHjETE+6AeJJfVW2b+UOIhaDoOIOBj4EHDHVFlm/iYz9xXTm4CdwPGNnp+Z6zJzLDPHRkZGWm2GJKkE7WwZnAP8LDMnpgoiYiQiDiqmjwWWAU+210RJUqfNGgYRcRvwI+CEiJiIiEuLh1bx+l1EAGcBWyLiUeBO4JOZ+XSZDZYkla+Zs4kumab84w3K7gLuar9ZkqRuquzlKCRJ3WMYSJIMA6kfjK69t+nTIz2NUq0wDCRJhoEkyTCQJGEYSJIwDCRJGAaSJAwDSRKGgSQJw0CShGEgqQf8lnT1GAaSJMNAkmQYSJIwDCRJGAaSJAwDSRJNhEFE3BgReyNia13ZVRHxy4jYXNwuqHvs8xGxIyK2R8S5nWq4JKk8zWwZ3ASc16D865m5orjdBxARy4FVwO8Wz/lfEXFQWY2VJHXGrGGQmQ8ATze5vIuA2zPzN5n5j8AO4LQ22idJ6oJ2jhl8KiK2FLuRFhZlRwG/qKszUZS9QUSsiYjxiBifnJxsoxmSpHa1GgbXAccBK4DdwFeL8mhQNxstIDPXZeZYZo6NjIy02AxJUhlaCoPM3JOZL2fmK8D1vLYraAI4uq7qEuCp9pooSeq0lsIgIhbXzV4MTJ1ptAFYFRFvjoilwDLgx+01UZLUaQfPViEibgPeDbw1IiaA/wK8OyJWUNsFtAv4jwCZ+XhEfAv4KfAScEVmvtyZpkuqGq9G2r9mDYPMvKRB8Q0z1L8auLqdRklSGQyn5vkNZEl9bboB3yCYG8NAkmQYSJIMA0kShoEkCcNAkoRhIGlIeHbRzAwDSV3loFxNhoEkyTCQNDzcKpmeYSBJMgwkSYaBJAnDQJKEYSBJwjCQNGQ8o6gxw0CSZBhIVecnWXXDrGEQETdGxN6I2FpX9pWI+FlEbImIeyLisKJ8NCJ+HRGbi9s3Otl4SVI5mtkyuAk474CyjcDvZebbgZ8Dn697bGdmrihunyynmZKkTpo1DDLzAeDpA8q+m5kvFbMPAks60DZJUpeUcczgj4G/rZtfGhGPRMTfR8SZ0z0pItZExHhEjE9OTpbQDGnweLxgZvZPedoKg4i4EngJuLUo2g38TmaeAnwG+GZE/Faj52bmuswcy8yxkZGRdpohSWpTy2EQEauBDwB/lJkJkJm/ycx9xfQmYCdwfBkNlSR1TkthEBHnAf8ZWJmZz9eVj0TEQcX0scAy4MkyGipJ6pxmTi29DfgRcEJETETEpcBfAQuAjQecQnoWsCUiHgXuBD6ZmU83XLAk4X7/qjh4tgqZeUmD4humqXsXcFe7jZI0+EbX3suuay7sdTNU8BvIkoaOWyNvZBhIkgwDSZJhIFVWO7syhm03SP36Dtu6l8UwkCQZBpJ6z0/zvWcYSCqFA3p/MwwkSYaBJMkwkFQh7mrqHcNAkmQYSJIMA2lg9dsul35r76AxDKQB5gDbvmHpQ8NAkmQYSKq2uXwyH5ZP8Z1gGEgaOIbC3BkGFeU/swZNp/+nvcpre5oKg4i4MSL2RsTWurLDI2JjRDxR3C8syiMiro2IHRGxJSJO7VTjJbWuCgNgFdqgmma3DG4CzjugbC1wf2YuA+4v5gHOB5YVtzXAde03U5LKZRC9XlNhkJkPAE8fUHwRcHMxfTPwwbryW7LmQeCwiFhcRmMlVU/Zg+rU8hysu6udYwZHZuZugOL+iKL8KOAXdfUmirLXiYg1ETEeEeOTk5NtNEPSsDAgOqcTB5CjQVm+oSBzXWaOZebYyMhIB5oh9Yd+HOD6sc2NjK69t+l1GZR1nk47YbBnavdPcb+3KJ8Ajq6rtwR4qo3XkYbKXAaoVpffK2W+9qAPzt3WThhsAFYX06uBb9eVf6w4q+h04Nmp3UmSesdPwJpJs6eW3gb8CDghIiYi4lLgGuB9EfEE8L5iHuA+4ElgB3A98Celt1pS6VoJgV4Fh4FVvoObqZSZl0zz0NkN6iZwRTuNkiR1l99AlnrET7fTs2+6zzCQ1BcMiM4yDKSKqOJgZ5uGh2Eg9UC3B7R2X2/YB+BhWH/DQOqCqp7bX/VBrurtGySGgaSmOTgPbh8YBlIFDOoA02+avUjeIP69DANJorkBfhBDYIphIGlG0w2AgzwwDiPDQFLlGTydZxhImpNhGZiHZT2nGAaS1KJBCgzDQJJkGEh6zSB90m3VsPaBYSCpbcM6gA4Sw0CSg7kMA2kYOfjrQIaBJKm5n71sJCJOAO6oKzoW+AvgMOAyYLIo/0Jm3tdyCyV1VLNbCW5NDLaWwyAztwMrACLiIOCXwD3AJ4CvZ+Z/K6WFkqSOazkMDnA2sDMz/ykiSlqkNHyq8um7Ku1Q95R1zGAVcFvd/KciYktE3BgRCxs9ISLWRMR4RIxPTk42qiL1naoPolVvn3qn7TCIiDcBK4G/LoquA46jtgtpN/DVRs/LzHWZOZaZYyMjI+02Q6qcAwdeB+LB0uxvH/SLMrYMzgd+kpl7ADJzT2a+nJmvANcDp5XwGpKkDiojDC6hbhdRRCyue+xiYGsJryFJ6qC2wiAi3gK8D7i7rvjLEfFYRGwB3gP8WTuvIZWhm5vyc9k9NCi7GNT/2gqDzHw+Mxdl5rN1ZR/NzJMy8+2ZuTIzd7ffTKm6HNB1oH78n/AbyFKX9eNAoZkNwt/UMJAkGQaSJMNA6qhB2H2g4WAYaCDNdRAeXXtvS8/RYBumv7FhIDVpmAYGDR/DQJojQ0GDyDCQJBkGklSGft9iNAykktQPBv0+MGj4GAaSJMNAg6edT+V+otewMgykBmb74RJ/uEaDpqzfQJZ6ruwBuZnl+UU1DQq3DKQmOIhr0BkGGnoO9JJhoCFmCKgT+vX/yjDQQGj1Ddivb1xVUz//P7V9ADkidgH7gZeBlzJzLCIOB+4ARoFdwB9m5r+2+1oaXqNr72XXNReWtixJr1fWlsF7MnNFZo4V82uB+zNzGXB/MS+9joOyVB2d2k10EXBzMX0z8MEOvY4kqQRlhEEC342ITRGxpig7MjN3AxT3Rxz4pIhYExHjETE+OTlZQjM06NrdknBLRJpeGV86OyMzn4qII4CNEfGzZp6UmeuAdQBjY2NZQjskSS1qe8sgM58q7vcC9wCnAXsiYjFAcb+33deRWuUWhTS7tsIgIg6JiAVT08D7ga3ABmB1UW018O12Xkf9r5XfGJ5pWfX3ktrX7m6iI4F7ImJqWd/MzP8TEQ8D34qIS4F/Bv6gzdeRKsEA0qBqa8sgM5/MzJOL2+9m5tVF+b7MPDszlxX3T5fTXKl5DtzqtX76H/QbyOor/fTmkvqJYaCWdWJgbnQ8YLbXmelxf3dAvdJv/2uGgbpqLgO3pO4xDNRRDvBSfzAMVIpBGvQHaV2kZhkGFeRgJKnbDANVggEo9ZZhoJaU/W3iuT5ueEjlMgwkSYaByjOXawb5yV6qFsNATSnzQnONlt2L50rd0C//o4aBGpppX73f6pUGj2EwZDo9cJexi8hLVGvQ9MP/smEwhOZy3R9Jw8Ew6BAHWftA6ieGgWY9l99BXRp8hoHewMFfGj6GgSSp9d9AjoijgVuAfwe8AqzLzP8REVcBlwGTRdUvZOZ97Ta0n/TDJ+tutrEf+kMadu1sGbwE/HlmngicDlwREcuLx76emSuK28AFQT8MbvXfB+iH9krqrZa3DDJzN7C7mN4fEduAo8pqmCSpe0o5ZhARo8ApwENF0aciYktE3BgRC8t4jWHRyqf4mbYAerVV4NaI1F/aDoOIOBS4C/h0Zv4KuA44DlhBbcvhq9M8b01EjEfE+OTkZKMqqoB2foxeUv9oKwwiYh61ILg1M+8GyMw9mflyZr4CXA+c1ui5mbkuM8cyc2xkZKSdZlRKK4Nn2QNqq1sXnbwYnaRqazkMIiKAG4Btmfm1uvLFddUuBra23rz+5sAqqV+0s2VwBvBR4L0Rsbm4XQB8OSIei4gtwHuAPyujoVVRlQG+Ku2QNBjaOZvoH4Bo8NBAnkq665oLm6rXyrIlqdeG9hvIVRiEywiPZr9LUIX1lVRdQxsG3eSBWUlVZxj0wGxXCZ2tXivLlqSZGAYzmGlgbXbQ7cTg7IAvqWyGQZ1Wd+d0MhjKvL6QISL1VpXfg4ZBRbhLSFIvGQaSpOEIg3Y+QfvpW9IwGIowmE67A71BIWlQDHUYzKTTF5QzSKThVNXvHQ1VGDR7fn8rdTr5x/UXy6T+V/X378CGwUwDf/1jjQbaufzRqv4HlqRmDGwYgAO1JDVroMOgnsEgSdMbqDCo0m8AS9JMqjY2DVQYSJJa0/KP21RV1dJWkvqBWwaSJMNAktTBMIiI8yJie0TsiIi1nXodcNeQJLWrI2EQEQcB/xM4H1gOXBIRyzvxWpKk9nVqy+A0YEdmPpmZ/w+4HbioQ68FuHUgSe3o1NlERwG/qJufAN5ZXyEi1gBritnnImIf8C8dak+/eSv2xRT7osZ+eM3A9EX8ZVtPfytwTDkt6VwYRIOyfN1M5jpg3atPiBjPzLEOtaev2BevsS9q7IfX2Bc1RT+MlrW8Tu0mmgCOrptfAjzVodeSJLWpU2HwMLAsIpZGxJuAVcCGDr2WJKlNHdlNlJkvRcSngL8DDgJuzMzHZ3naulkeHyb2xWvsixr74TX2RU2p/RCZOXstSdJA8xvIkiTDQJJUkTDo5qUreiUiboyIvRGxta7s8IjYGBFPFPcLi/KIiGuL/tgSEafWPWd1Uf+JiFjdi3VpR0QcHRHfi4htEfF4RPxpUT5UfRER8yPixxHxaNEP/7UoXxoRDxXrdEdxAgYR8eZifkfx+Gjdsj5flG+PiHN7s0bti4iDIuKRiPibYn4o+yIidkXEYxGxOSLGi7LOvz8ys6c3ageYdwLHAm8CHgWW97pdHVjPs4BTga11ZV8G1hbTa4G/LKYvAP6W2vc1TgceKsoPB54s7hcW0wt7vW5z7IfFwKnF9ALg59QuWTJUfVGsz6HF9DzgoWL9vgWsKsq/AVxeTP8J8I1iehVwRzG9vHjPvBlYWryXDur1+rXYJ58Bvgn8TTE/lH0B7ALeekBZx98fVdgy6PqlK3ohMx8Anj6g+CLg5mL6ZuCDdeW3ZM2DwGERsRg4F9iYmU9n5r8CG4HzOt/68mTm7sz8STG9H9hG7RvrQ9UXxfo8V8zOK24JvBe4syg/sB+m+udO4OyIiKL89sz8TWb+I7CD2nuqr0TEEuBC4H8X88GQ9sU0Ov7+qEIYNLp0xVE9aku3HZmZu6E2SAJHFOXT9clA9VWxeX8KtU/FQ9cXxW6RzcBeam/WncAzmflSUaV+nV5d3+LxZ4FFDEA/FP478J+AV4r5RQxvXyTw3YjYFLXL9kAX3h9V+KWzWS9dMYSm65OB6auIOBS4C/h0Zv6q9sGucdUGZQPRF5n5MrAiIg4D7gFObFStuB/YfoiIDwB7M3NTRLx7qrhB1YHvi8IZmflURBwBbIyIn81Qt7S+qMKWwTBfumJPsUlHcb+3KJ+uTwairyJiHrUguDUz7y6Kh7IvADLzGeD71Pb5HhYRUx/S6tfp1fUtHv9tarsdB6EfzgBWRsQuaruJ30ttS2EY+4LMfKq430vtQ8JpdOH9UYUwGOZLV2wApo7yrwa+XVf+seJMgdOBZ4tNw78D3h8RC4uzCd5flPWNYt/uDcC2zPxa3UND1RcRMVJsERAR/xY4h9rxk+8Bv19UO7Afpvrn94H/m7UjhRuAVcUZNkuBZcCPu7MW5cjMz2fmkqxddG0VtXX7I4awLyLikIhYMDVN7f96K914f/T6yHndEfGfU9tnemWv29OhdbwN2A28SC21L6W2n/N+4Ini/vCiblD7caCdwGPAWN1y/pjagbEdwCd6vV4t9MN/oLa5ugXYXNwuGLa+AN4OPFL0w1bgL4ryY6kNYDuAvwbeXJTPL+Z3FI8fW7esK4v+2Q6c3+t1a7Nf3s1rZxMNXV8U6/xocXt8ajzsxvvDy1FIkiqxm0iS1GOGgSTJMJAkGQaSJAwDSRKGgSQJw0CSBPx/Vnu3Jq1jkDcAAAAASUVORK5CYII=\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -1053,7 +1120,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 25,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1076,7 +1143,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 26,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1085,7 +1152,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 27,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1101,25 +1168,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 28,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "0.6628998341322463\n",
-      "6.258685216704922\n",
-      "2.249780125960397\n",
-      "-4.790281653148225\n",
-      "-0.546187997552245\n",
-      "-4.053408742543857\n",
-      "3.3921906479017068\n",
-      "1.380997849250071\n",
-      "0.5628459148842957\n",
       "------------------------------------------------------------------\n",
-      "| FCN = -7.175E+05              |     Ncalls=253 (253 total)     |\n",
-      "| EDM = 2.21E-05 (Goal: 5E-06)  |            up = 0.5            |\n",
+      "| FCN = -3.432E+05              |     Ncalls=386 (386 total)     |\n",
+      "| EDM = 2.21E-06 (Goal: 5E-06)  |            up = 0.5            |\n",
       "------------------------------------------------------------------\n",
       "|  Valid Min.   | Valid Param.  | Above EDM | Reached call limit |\n",
       "------------------------------------------------------------------\n",
@@ -1129,34 +1187,34 @@
       "------------------------------------------------------------------\n",
       "|     False     |     True      |   True    |   True    | False  |\n",
       "------------------------------------------------------------------\n",
-      "Function minimum: -717521.3025826928\n",
+      "Function minimum: -343194.2940834424\n",
       "---------------------------------------------------------------------------------------------\n",
       "|   | Name    |   Value   | Hesse Err | Minos Err- | Minos Err+ | Limit-  | Limit+  | Fixed |\n",
       "---------------------------------------------------------------------------------------------\n",
-      "| 0 | jpsi_p  |   1.486   |   0.016   |            |            |-6.28319 | 6.28319 |       |\n",
-      "| 1 | p3770_p |   2.55    |   0.09    |            |            |-6.28319 | 6.28319 |       |\n",
-      "| 2 | psi2s_p |   1.344   |   0.027   |            |            |-6.28319 | 6.28319 |       |\n",
-      "| 3 | omega_p |   -5.80   |    0.22   |            |            |-6.28319 | 6.28319 |       |\n",
-      "| 4 | p4040_p |   -3.37   |    0.16   |            |            |-6.28319 | 6.28319 |       |\n",
-      "| 5 | rho_p   |   -0.19   |    0.30   |            |            |-6.28319 | 6.28319 |       |\n",
-      "| 6 | phi_p   |   -5.59   |    0.15   |            |            |-6.28319 | 6.28319 |       |\n",
-      "| 7 | p4160_p |   4.04    |   0.08    |            |            |-6.28319 | 6.28319 |       |\n",
-      "| 8 | p4415_p |   -3.13   |    0.13   |            |            |-6.28319 | 6.28319 |       |\n",
+      "| 0 | rho_p   |   6.22    |   0.23    |            |            |-6.28319 | 6.28319 |       |\n",
+      "| 1 | phi_p   |   0.74    |   0.13    |            |            |-6.28319 | 6.28319 |       |\n",
+      "| 2 | p4160_p |   -2.08   |    0.05   |            |            |-6.28319 | 6.28319 |       |\n",
+      "| 3 | p4415_p |   -2.74   |    0.09   |            |            |-6.28319 | 6.28319 |       |\n",
+      "| 4 | jpsi_p  |   4.493   |   0.014   |            |            |-6.28319 | 6.28319 |       |\n",
+      "| 5 | p4040_p |   3.77    |   0.09    |            |            |-6.28319 | 6.28319 |       |\n",
+      "| 6 | p3770_p |   -1.85   |    0.08   |            |            |-6.28319 | 6.28319 |       |\n",
+      "| 7 | psi2s_p |   5.862   |   0.014   |            |            |-6.28319 | 6.28319 |       |\n",
+      "| 8 | omega_p |   0.29    |   0.21    |            |            |-6.28319 | 6.28319 |       |\n",
       "---------------------------------------------------------------------------------------------\n",
       "-------------------------------------------------------------------------------------\n",
-      "|         |  jpsi_p p3770_p psi2s_p omega_p p4040_p   rho_p   phi_p p4160_p p4415_p |\n",
+      "|         |   rho_p   phi_p p4160_p p4415_p  jpsi_p p4040_p p3770_p psi2s_p omega_p |\n",
       "-------------------------------------------------------------------------------------\n",
-      "|  jpsi_p |   1.000  -0.133   0.219   0.015  -0.176  -0.110   0.023  -0.065  -0.143 |\n",
-      "| p3770_p |  -0.133   1.000  -0.527  -0.007   0.014   0.005  -0.011   0.001   0.014 |\n",
-      "| psi2s_p |   0.219  -0.527   1.000   0.016  -0.312  -0.060   0.025  -0.006  -0.140 |\n",
-      "| omega_p |   0.015  -0.007   0.016   1.000  -0.007  -0.255   0.034  -0.011  -0.012 |\n",
-      "| p4040_p |  -0.176   0.014  -0.312  -0.007   1.000   0.001  -0.011  -0.381  -0.104 |\n",
-      "|   rho_p |  -0.110   0.005  -0.060  -0.255   0.001   1.000  -0.246   0.026   0.017 |\n",
-      "|   phi_p |   0.023  -0.011   0.025   0.034  -0.011  -0.246   1.000  -0.018  -0.018 |\n",
-      "| p4160_p |  -0.065   0.001  -0.006  -0.011  -0.381   0.026  -0.018   1.000   0.164 |\n",
-      "| p4415_p |  -0.143   0.014  -0.140  -0.012  -0.104   0.017  -0.018   0.164   1.000 |\n",
+      "|   rho_p |   1.000  -0.123   0.015   0.007  -0.009   0.008   0.013  -0.004  -0.152 |\n",
+      "|   phi_p |  -0.123   1.000  -0.006  -0.005  -0.023  -0.005  -0.007  -0.010  -0.014 |\n",
+      "| p4160_p |   0.015  -0.006   1.000   0.075   0.027  -0.288  -0.006   0.016   0.000 |\n",
+      "| p4415_p |   0.007  -0.005   0.075   1.000  -0.030  -0.008   0.023  -0.031  -0.001 |\n",
+      "|  jpsi_p |  -0.009  -0.023   0.027  -0.030   1.000  -0.012   0.043   0.171  -0.014 |\n",
+      "| p4040_p |   0.008  -0.005  -0.288  -0.008  -0.012   1.000   0.041  -0.013  -0.001 |\n",
+      "| p3770_p |   0.013  -0.007  -0.006   0.023   0.043   0.041   1.000   0.004  -0.001 |\n",
+      "| psi2s_p |  -0.004  -0.010   0.016  -0.031   0.171  -0.013   0.004   1.000  -0.005 |\n",
+      "| omega_p |  -0.152  -0.014   0.000  -0.001  -0.014  -0.001  -0.001  -0.005   1.000 |\n",
       "-------------------------------------------------------------------------------------\n",
-      "Hesse errors: OrderedDict([(<zfit.Parameter 'jpsi_p' floating=True>, {'error': 0.015523017204917444}), (<zfit.Parameter 'p3770_p' floating=True>, {'error': 0.0949040578205631}), (<zfit.Parameter 'psi2s_p' floating=True>, {'error': 0.027368429790692872}), (<zfit.Parameter 'omega_p' floating=True>, {'error': 0.21984151021717713}), (<zfit.Parameter 'p4040_p' floating=True>, {'error': 0.1583791551448439}), (<zfit.Parameter 'rho_p' floating=True>, {'error': 0.2962129131334841}), (<zfit.Parameter 'phi_p' floating=True>, {'error': 0.15237884170408078}), (<zfit.Parameter 'p4160_p' floating=True>, {'error': 0.08086187469444539}), (<zfit.Parameter 'p4415_p' floating=True>, {'error': 0.12776706213127875})])\n"
+      "Hesse errors: OrderedDict([(<zfit.Parameter 'rho_p' floating=True>, {'error': 0.22722319040312122}), (<zfit.Parameter 'phi_p' floating=True>, {'error': 0.13174329420879438}), (<zfit.Parameter 'p4160_p' floating=True>, {'error': 0.05174968879202346}), (<zfit.Parameter 'p4415_p' floating=True>, {'error': 0.09075842158618563}), (<zfit.Parameter 'jpsi_p' floating=True>, {'error': 0.014043622288271607}), (<zfit.Parameter 'p4040_p' floating=True>, {'error': 0.09035710897513649}), (<zfit.Parameter 'p3770_p' floating=True>, {'error': 0.0768543526788319}), (<zfit.Parameter 'psi2s_p' floating=True>, {'error': 0.014018435747616742}), (<zfit.Parameter 'omega_p' floating=True>, {'error': 0.20679579074364973})])\n"
      ]
     }
    ],
@@ -1187,14 +1245,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 29,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Time taken for fitting: 2 min, 6 s\n"
+      "Time taken for fitting: 5 min, 6 s\n"
      ]
     }
    ],
@@ -1209,12 +1267,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 60,
+   "execution_count": 30,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": "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\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -1240,7 +1298,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": 31,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1254,7 +1312,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
+   "execution_count": 32,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1275,7 +1333,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
+   "execution_count": 33,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1285,7 +1343,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
+   "execution_count": 34,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1316,29 +1374,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 59,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "2.404870995999285"
-      ]
-     },
-     "execution_count": 59,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "_0 = jpsi_scale*np.cos(jpsi_phase)*jpsi_width/jpsi_mass**3 + psi2s_scale*np.cos(psi2s_phase)*psi2s_width/psi2s_mass**3\n",
-    "_1 = p3770_scale*np.cos(p3770_phase)*p3770_width/p3770_mass**3 + p4040_scale*np.cos(p4040_phase)*p4040_width/p4040_mass**3\n",
-    "_2 = p4160_scale*np.cos(p4160_phase)*p4160_width/p4160_mass**3 + p4415_scale*np.cos(p4415_phase)*p4415_width/p4415_mass**3\n",
-    "\n",
-    "R = (0.1/(1300**2) - ((_0 + _1 + _2)))\n",
-    "\n",
-    "R*10*2010**2"
-   ]
+   "outputs": [],
+   "source": []
   },
   {
    "cell_type": "code",
diff --git a/__pycache__/pdg_const.cpython-37.pyc b/__pycache__/pdg_const.cpython-37.pyc
index e91f5c6..5b51a60 100644
--- a/__pycache__/pdg_const.cpython-37.pyc
+++ b/__pycache__/pdg_const.cpython-37.pyc
Binary files differ
diff --git a/data/zfit_toys/toy_0/0.pkl b/data/zfit_toys/toy_0/0.pkl
index b6df2c8..d772366 100644
--- a/data/zfit_toys/toy_0/0.pkl
+++ b/data/zfit_toys/toy_0/0.pkl
Binary files differ
diff --git a/data/zfit_toys/toy_0/1.pkl b/data/zfit_toys/toy_0/1.pkl
index 9968cdd..ffabd1f 100644
--- a/data/zfit_toys/toy_0/1.pkl
+++ b/data/zfit_toys/toy_0/1.pkl
Binary files differ
diff --git a/data/zfit_toys/toy_0/2.pkl b/data/zfit_toys/toy_0/2.pkl
index c020748..383f7c3 100644
--- a/data/zfit_toys/toy_0/2.pkl
+++ b/data/zfit_toys/toy_0/2.pkl
Binary files differ
diff --git a/data/zfit_toys/toy_0/3.pkl b/data/zfit_toys/toy_0/3.pkl
index 4b4828d..bd6c12f 100644
--- a/data/zfit_toys/toy_0/3.pkl
+++ b/data/zfit_toys/toy_0/3.pkl
Binary files differ
diff --git a/data/zfit_toys/toy_0/4.pkl b/data/zfit_toys/toy_0/4.pkl
index 967cb5a..375672c 100644
--- a/data/zfit_toys/toy_0/4.pkl
+++ b/data/zfit_toys/toy_0/4.pkl
Binary files differ
diff --git a/data/zfit_toys/toy_0/5.pkl b/data/zfit_toys/toy_0/5.pkl
index 8c05273..d9fcb6f 100644
--- a/data/zfit_toys/toy_0/5.pkl
+++ b/data/zfit_toys/toy_0/5.pkl
Binary files differ
diff --git a/pdg_const.py b/pdg_const.py
index 52f9764..975b31e 100644
--- a/pdg_const.py
+++ b/pdg_const.py
@@ -100,10 +100,10 @@
     
     "phi": (1019.46, 4.25, 0.5, 19.2),
     
-    "jpsi": (3096.0, 0.09, -1.5, 9897.0),
+    "jpsi": (3096.0, 0.09, -1.66, 9897.0),
     "jpsi_auc": 0.2126825758464027,
     
-    "psi2s": (3686.0, 0.3, -1.5, 1396.0),
+    "psi2s": (3686.0, 0.3, 2.0, 1396.0),
     "psi2s_auc": 0.0151332263,
     
     "p3770": (3773.0, 27.2, -2.13, 2.5),
diff --git a/raremodel-nb.ipynb b/raremodel-nb.ipynb
index 4688758..0be5ec8 100644
--- a/raremodel-nb.ipynb
+++ b/raremodel-nb.ipynb
@@ -294,19 +294,19 @@
     "        return tf.log(ztf.to_complex((1+tf.sqrt(q))/(1-tf.sqrt(q)))-tf.complex(ztf.constant(0), -1*ztf.constant(np.pi)))    \n",
     "    \n",
     "    def inner_right(q):\n",
-    "        return ztf.to_complex(2 * tf.atan(1/tf.sqrt(-q)))\n",
+    "        return ztf.to_complex(2 * tf.atan(1/tf.sqrt(tf.math.real(-q))))\n",
     "    \n",
-    "    big_bracket = tf.where(y > ztf.const(0.0), inner_rect_bracket(y), inner_right(y))\n",
+    "    big_bracket = tf.where(tf.math.real(y) > ztf.constant(0.0), inner_rect_bracket(y), inner_right(y))\n",
     "    \n",
     "    return ztf.to_complex(tf.sqrt(tf.abs(y))) * big_bracket\n",
     "\n",
     "def h_S(m, q):\n",
     "    \n",
-    "    return ztf.to_complex(2) - G(ztf.to_complex(1) - 4*tf.pow(m, 2) / ztf.to_complex(tf.pow(q, 2)))\n",
+    "    return ztf.to_complex(2) - G(ztf.to_complex(1) - ztf.to_complex(4*tf.pow(m, 2)) / ztf.to_complex(tf.pow(q, 2)))\n",
     "\n",
     "def h_P(m, q):\n",
     "    \n",
-    "    return ztf.to_complex(2/3) + (ztf.to_complex(1) - 4*tf.pow(m, 2) / ztf.to_complex(tf.pow(q, 2))) * h_S(m,q)\n",
+    "    return ztf.to_complex(2/3) + (ztf.to_complex(1) - ztf.to_complex(4*tf.pow(m, 2)) / ztf.to_complex(tf.pow(q, 2))) * h_S(m,q)\n",
     "\n",
     "def two_p_ccbar(mD, m_D_bar, m_D_star, q):\n",
     "    \n",
@@ -381,7 +381,9 @@
     "               'p4160_mass', 'p4160_scale', 'p4160_phase', 'p4160_width',\n",
     "               'p4415_mass', 'p4415_scale', 'p4415_phase', 'p4415_width',\n",
     "               'omega_mass', 'omega_scale', 'omega_phase', 'omega_width',\n",
-    "               'phi_mass', 'phi_scale', 'phi_phase', 'phi_width']  # the name of the parameters\n",
+    "               'phi_mass', 'phi_scale', 'phi_phase', 'phi_width',\n",
+    "               'DDstar_mass', 'DDstar_scale', 'DDstar_phase',\n",
+    "               'Dbar_mass', 'Dbar_scale', 'Dbar_phase'] # the name of the parameters\n",
     "\n",
     "    def _unnormalized_pdf(self, x):\n",
     "        \n",
@@ -422,9 +424,14 @@
     "        def p4415_res(q):\n",
     "            return resonance(q, _mass = self.params['p4415_mass'], scale = self.params['p4415_scale'],\n",
     "                             phase = self.params['p4415_phase'], width = self.params['p4415_width'])\n",
-    "               \n",
+    "        \n",
+    "        def P2_D(q):\n",
+    "            Dbar_contrib = ztf.to_complex(self.params['Dbar_scale'])*tf.exp(tf.complex(ztf.constant(0.0), self.params['Dbar_phase']))*ztf.to_complex(h_S(self.params['Dbar_mass'], q))\n",
+    "            DDstar_contrib = ztf.to_complex(self.params['DDstar_scale'])*tf.exp(tf.complex(ztf.constant(0.0), self.params['DDstar_phase']))*ztf.to_complex(h_P(self.params['DDstar_mass'], q))\n",
+    "            return Dbar_contrib + DDstar_contrib\n",
+    "    \n",
     "\n",
-    "        funcs = rho_res(x) + omega_res(x) + phi_res(x) + jpsi_res(x) + psi2s_res(x) + p3770_res(x) + p4040_res(x)+ p4160_res(x) + p4415_res(x)\n",
+    "        funcs = rho_res(x) + omega_res(x) + phi_res(x) + jpsi_res(x) + psi2s_res(x) + p3770_res(x) + p4040_res(x)+ p4160_res(x) + p4415_res(x) + P2_D(x)\n",
     "\n",
     "        vec_f = vec(x, funcs)\n",
     "\n",
@@ -575,13 +582,71 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Setup pdf"
+    "## Dynamic generation of 2 particle contribution"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 9,
    "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1.2615526703874353\n"
+     ]
+    }
+   ],
+   "source": [
+    "_0 = jpsi_scale*np.cos(jpsi_phase)*jpsi_width/jpsi_mass**3 + psi2s_scale*np.cos(psi2s_phase)*psi2s_width/psi2s_mass**3\n",
+    "_1 = p3770_scale*np.cos(p3770_phase)*p3770_width/p3770_mass**3 + p4040_scale*np.cos(p4040_phase)*p4040_width/p4040_mass**3\n",
+    "_2 = p4160_scale*np.cos(p4160_phase)*p4160_width/p4160_mass**3 + p4415_scale*np.cos(p4415_phase)*p4415_width/p4415_mass**3\n",
+    "\n",
+    "C_pert = np.random.uniform(0.03, 0.1)\n",
+    "m_c = 1300\n",
+    "\n",
+    "cDDstar_phase = 10\n",
+    "\n",
+    "while np.abs(cDDstar_phase) > 1:\n",
+    "    Dbar_eta = np.random.uniform(0.0, 5.0)\n",
+    "    DDstar_eta = np.random.uniform(0.0, 10.0)\n",
+    "    Dbar_phase = np.random.uniform(0.0, 2*np.pi)\n",
+    "    DDstar_mass = pdg['D0_M']\n",
+    "\n",
+    "    R = (C_pert/(m_c**2) - ((_0 + _1 + _2)))\n",
+    "    \n",
+    "    Dbar_mass = (pdg['D0_M']+pdg['Dst_M'])/2\n",
+    "\n",
+    "    R_ = R - Dbar_eta*np.cos(Dbar_phase)/(6*Dbar_mass**2)\n",
+    "\n",
+    "    cDDstar_phase = R_*10*DDstar_mass**2/DDstar_eta\n",
+    "#     print(\"hey\")\n",
+    "\n",
+    "DDstar_phase = np.arccos(cDDstar_phase)\n",
+    "\n",
+    "print(DDstar_phase)\n",
+    "\n",
+    "\n",
+    "Dbar_s = zfit.Parameter(\"Dbar_s\", ztf.constant(Dbar_eta), floating = False)\n",
+    "Dbar_m = zfit.Parameter(\"Dbar_m\", ztf.constant(Dbar_mass), floating = False)\n",
+    "Dbar_p = zfit.Parameter(\"Dbar_p\", ztf.constant(Dbar_phase), floating = False)\n",
+    "DDstar_s = zfit.Parameter(\"DDstar_s\", ztf.constant(DDstar_eta), floating = False)\n",
+    "DDstar_m = zfit.Parameter(\"DDstar_m\", ztf.constant(DDstar_mass), floating = False)\n",
+    "DDstar_p = zfit.Parameter(\"DDstar_p\", ztf.constant(DDstar_phase), floating = False)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Setup pdf"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "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",
@@ -592,8 +657,10 @@
     "                    p4415_mass = p4415_m, p4415_scale = p4415_s, p4415_phase = p4415_p, p4415_width = p4415_w,\n",
     "                    rho_mass = rho_m, rho_scale = rho_s, rho_phase = rho_p, rho_width = rho_w,\n",
     "                    omega_mass = omega_m, omega_scale = omega_s, omega_phase = omega_p, omega_width = omega_w,\n",
-    "                    phi_mass = phi_m, phi_scale = phi_s, phi_phase = phi_p, phi_width = phi_w) \n",
-    "    \n",
+    "                    phi_mass = phi_m, phi_scale = phi_s, phi_phase = phi_p, phi_width = phi_w,\n",
+    "                    DDstar_mass = DDstar_m, DDstar_scale = DDstar_s, DDstar_phase = DDstar_p,\n",
+    "                    Dbar_mass = Dbar_m, Dbar_scale = Dbar_s, Dbar_phase = Dbar_p)\n",
+    "                   \n",
     "# print(total_pdf.obs)\n",
     "\n",
     "# print(calcs_test)\n",
@@ -611,7 +678,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -654,12 +721,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": "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\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -679,7 +746,7 @@
     "# plt.plot(test_q, fplus_y, label = '+')\n",
     "# plt.plot(test_q, res_y, label = 'res')\n",
     "plt.legend()\n",
-    "plt.ylim(0.0, 6e-6)\n",
+    "plt.ylim(0.0, 1.5e-5)\n",
     "# plt.yscale('log')\n",
     "# plt.xlim(770, 785)\n",
     "plt.savefig('test.png')\n",
@@ -688,7 +755,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -704,7 +771,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -720,7 +787,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -733,7 +800,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -787,7 +854,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -871,7 +938,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -880,7 +947,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -889,7 +956,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -898,7 +965,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 21,
    "metadata": {
     "scrolled": false
    },
@@ -908,7 +975,7 @@
      "output_type": "stream",
      "text": [
       "6/6 of Toy 1/1\n",
-      "Time taken: 1 min, 22 s\n",
+      "Time taken: 1 min, 55 s\n",
       "Projected time left: \n"
      ]
     }
@@ -956,7 +1023,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -973,14 +1040,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 23,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Time to generate full toy: 82 s\n",
+      "Time to generate full toy: 115 s\n",
       "(5404696,)\n"
      ]
     }
@@ -1006,7 +1073,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 24,
    "metadata": {},
    "outputs": [
     {
@@ -1018,7 +1085,7 @@
     },
     {
      "data": {
-      "image/png": "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\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -1053,7 +1120,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 25,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1076,7 +1143,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 26,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1085,7 +1152,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 27,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1101,25 +1168,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 28,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "0.6628998341322463\n",
-      "6.258685216704922\n",
-      "2.249780125960397\n",
-      "-4.790281653148225\n",
-      "-0.546187997552245\n",
-      "-4.053408742543857\n",
-      "3.3921906479017068\n",
-      "1.380997849250071\n",
-      "0.5628459148842957\n",
       "------------------------------------------------------------------\n",
-      "| FCN = -7.175E+05              |     Ncalls=253 (253 total)     |\n",
-      "| EDM = 2.21E-05 (Goal: 5E-06)  |            up = 0.5            |\n",
+      "| FCN = -3.432E+05              |     Ncalls=386 (386 total)     |\n",
+      "| EDM = 2.21E-06 (Goal: 5E-06)  |            up = 0.5            |\n",
       "------------------------------------------------------------------\n",
       "|  Valid Min.   | Valid Param.  | Above EDM | Reached call limit |\n",
       "------------------------------------------------------------------\n",
@@ -1129,34 +1187,34 @@
       "------------------------------------------------------------------\n",
       "|     False     |     True      |   True    |   True    | False  |\n",
       "------------------------------------------------------------------\n",
-      "Function minimum: -717521.3025826928\n",
+      "Function minimum: -343194.2940834424\n",
       "---------------------------------------------------------------------------------------------\n",
       "|   | Name    |   Value   | Hesse Err | Minos Err- | Minos Err+ | Limit-  | Limit+  | Fixed |\n",
       "---------------------------------------------------------------------------------------------\n",
-      "| 0 | jpsi_p  |   1.486   |   0.016   |            |            |-6.28319 | 6.28319 |       |\n",
-      "| 1 | p3770_p |   2.55    |   0.09    |            |            |-6.28319 | 6.28319 |       |\n",
-      "| 2 | psi2s_p |   1.344   |   0.027   |            |            |-6.28319 | 6.28319 |       |\n",
-      "| 3 | omega_p |   -5.80   |    0.22   |            |            |-6.28319 | 6.28319 |       |\n",
-      "| 4 | p4040_p |   -3.37   |    0.16   |            |            |-6.28319 | 6.28319 |       |\n",
-      "| 5 | rho_p   |   -0.19   |    0.30   |            |            |-6.28319 | 6.28319 |       |\n",
-      "| 6 | phi_p   |   -5.59   |    0.15   |            |            |-6.28319 | 6.28319 |       |\n",
-      "| 7 | p4160_p |   4.04    |   0.08    |            |            |-6.28319 | 6.28319 |       |\n",
-      "| 8 | p4415_p |   -3.13   |    0.13   |            |            |-6.28319 | 6.28319 |       |\n",
+      "| 0 | rho_p   |   6.22    |   0.23    |            |            |-6.28319 | 6.28319 |       |\n",
+      "| 1 | phi_p   |   0.74    |   0.13    |            |            |-6.28319 | 6.28319 |       |\n",
+      "| 2 | p4160_p |   -2.08   |    0.05   |            |            |-6.28319 | 6.28319 |       |\n",
+      "| 3 | p4415_p |   -2.74   |    0.09   |            |            |-6.28319 | 6.28319 |       |\n",
+      "| 4 | jpsi_p  |   4.493   |   0.014   |            |            |-6.28319 | 6.28319 |       |\n",
+      "| 5 | p4040_p |   3.77    |   0.09    |            |            |-6.28319 | 6.28319 |       |\n",
+      "| 6 | p3770_p |   -1.85   |    0.08   |            |            |-6.28319 | 6.28319 |       |\n",
+      "| 7 | psi2s_p |   5.862   |   0.014   |            |            |-6.28319 | 6.28319 |       |\n",
+      "| 8 | omega_p |   0.29    |   0.21    |            |            |-6.28319 | 6.28319 |       |\n",
       "---------------------------------------------------------------------------------------------\n",
       "-------------------------------------------------------------------------------------\n",
-      "|         |  jpsi_p p3770_p psi2s_p omega_p p4040_p   rho_p   phi_p p4160_p p4415_p |\n",
+      "|         |   rho_p   phi_p p4160_p p4415_p  jpsi_p p4040_p p3770_p psi2s_p omega_p |\n",
       "-------------------------------------------------------------------------------------\n",
-      "|  jpsi_p |   1.000  -0.133   0.219   0.015  -0.176  -0.110   0.023  -0.065  -0.143 |\n",
-      "| p3770_p |  -0.133   1.000  -0.527  -0.007   0.014   0.005  -0.011   0.001   0.014 |\n",
-      "| psi2s_p |   0.219  -0.527   1.000   0.016  -0.312  -0.060   0.025  -0.006  -0.140 |\n",
-      "| omega_p |   0.015  -0.007   0.016   1.000  -0.007  -0.255   0.034  -0.011  -0.012 |\n",
-      "| p4040_p |  -0.176   0.014  -0.312  -0.007   1.000   0.001  -0.011  -0.381  -0.104 |\n",
-      "|   rho_p |  -0.110   0.005  -0.060  -0.255   0.001   1.000  -0.246   0.026   0.017 |\n",
-      "|   phi_p |   0.023  -0.011   0.025   0.034  -0.011  -0.246   1.000  -0.018  -0.018 |\n",
-      "| p4160_p |  -0.065   0.001  -0.006  -0.011  -0.381   0.026  -0.018   1.000   0.164 |\n",
-      "| p4415_p |  -0.143   0.014  -0.140  -0.012  -0.104   0.017  -0.018   0.164   1.000 |\n",
+      "|   rho_p |   1.000  -0.123   0.015   0.007  -0.009   0.008   0.013  -0.004  -0.152 |\n",
+      "|   phi_p |  -0.123   1.000  -0.006  -0.005  -0.023  -0.005  -0.007  -0.010  -0.014 |\n",
+      "| p4160_p |   0.015  -0.006   1.000   0.075   0.027  -0.288  -0.006   0.016   0.000 |\n",
+      "| p4415_p |   0.007  -0.005   0.075   1.000  -0.030  -0.008   0.023  -0.031  -0.001 |\n",
+      "|  jpsi_p |  -0.009  -0.023   0.027  -0.030   1.000  -0.012   0.043   0.171  -0.014 |\n",
+      "| p4040_p |   0.008  -0.005  -0.288  -0.008  -0.012   1.000   0.041  -0.013  -0.001 |\n",
+      "| p3770_p |   0.013  -0.007  -0.006   0.023   0.043   0.041   1.000   0.004  -0.001 |\n",
+      "| psi2s_p |  -0.004  -0.010   0.016  -0.031   0.171  -0.013   0.004   1.000  -0.005 |\n",
+      "| omega_p |  -0.152  -0.014   0.000  -0.001  -0.014  -0.001  -0.001  -0.005   1.000 |\n",
       "-------------------------------------------------------------------------------------\n",
-      "Hesse errors: OrderedDict([(<zfit.Parameter 'jpsi_p' floating=True>, {'error': 0.015523017204917444}), (<zfit.Parameter 'p3770_p' floating=True>, {'error': 0.0949040578205631}), (<zfit.Parameter 'psi2s_p' floating=True>, {'error': 0.027368429790692872}), (<zfit.Parameter 'omega_p' floating=True>, {'error': 0.21984151021717713}), (<zfit.Parameter 'p4040_p' floating=True>, {'error': 0.1583791551448439}), (<zfit.Parameter 'rho_p' floating=True>, {'error': 0.2962129131334841}), (<zfit.Parameter 'phi_p' floating=True>, {'error': 0.15237884170408078}), (<zfit.Parameter 'p4160_p' floating=True>, {'error': 0.08086187469444539}), (<zfit.Parameter 'p4415_p' floating=True>, {'error': 0.12776706213127875})])\n"
+      "Hesse errors: OrderedDict([(<zfit.Parameter 'rho_p' floating=True>, {'error': 0.22722319040312122}), (<zfit.Parameter 'phi_p' floating=True>, {'error': 0.13174329420879438}), (<zfit.Parameter 'p4160_p' floating=True>, {'error': 0.05174968879202346}), (<zfit.Parameter 'p4415_p' floating=True>, {'error': 0.09075842158618563}), (<zfit.Parameter 'jpsi_p' floating=True>, {'error': 0.014043622288271607}), (<zfit.Parameter 'p4040_p' floating=True>, {'error': 0.09035710897513649}), (<zfit.Parameter 'p3770_p' floating=True>, {'error': 0.0768543526788319}), (<zfit.Parameter 'psi2s_p' floating=True>, {'error': 0.014018435747616742}), (<zfit.Parameter 'omega_p' floating=True>, {'error': 0.20679579074364973})])\n"
      ]
     }
    ],
@@ -1187,14 +1245,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 29,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Time taken for fitting: 2 min, 6 s\n"
+      "Time taken for fitting: 5 min, 6 s\n"
      ]
     }
    ],
@@ -1209,12 +1267,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 60,
+   "execution_count": 30,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": "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\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -1240,7 +1298,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": 31,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1254,7 +1312,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
+   "execution_count": 32,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1275,7 +1333,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
+   "execution_count": 33,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1285,7 +1343,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
+   "execution_count": 34,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1316,29 +1374,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 59,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "2.404870995999285"
-      ]
-     },
-     "execution_count": 59,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "_0 = jpsi_scale*np.cos(jpsi_phase)*jpsi_width/jpsi_mass**3 + psi2s_scale*np.cos(psi2s_phase)*psi2s_width/psi2s_mass**3\n",
-    "_1 = p3770_scale*np.cos(p3770_phase)*p3770_width/p3770_mass**3 + p4040_scale*np.cos(p4040_phase)*p4040_width/p4040_mass**3\n",
-    "_2 = p4160_scale*np.cos(p4160_phase)*p4160_width/p4160_mass**3 + p4415_scale*np.cos(p4415_phase)*p4415_width/p4415_mass**3\n",
-    "\n",
-    "R = (0.1/(1300**2) - ((_0 + _1 + _2)))\n",
-    "\n",
-    "R*10*2010**2"
-   ]
+   "outputs": [],
+   "source": []
   },
   {
    "cell_type": "code",
diff --git a/test.png b/test.png
index b85c7d2..b16875b 100644
--- a/test.png
+++ b/test.png
Binary files differ
diff --git a/test2.png b/test2.png
index ef7d7d5..e2aa409 100644
--- a/test2.png
+++ b/test2.png
Binary files differ
diff --git a/test3.png b/test3.png
index 43cb0f4..2d85781 100644
--- a/test3.png
+++ b/test3.png
Binary files differ