diff --git a/.ipynb_checkpoints/raremodel-nb-checkpoint.ipynb b/.ipynb_checkpoints/raremodel-nb-checkpoint.ipynb
index 529e151..4ebcc77 100644
--- a/.ipynb_checkpoints/raremodel-nb-checkpoint.ipynb
+++ b/.ipynb_checkpoints/raremodel-nb-checkpoint.ipynb
@@ -9,9 +9,31 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\zfit\\util\\execution.py:57: UserWarning: Not running on Linux. Determining available cpus for thread can failand be overestimated. Workaround (only if too many cpus are used):`zfit.run.set_n_cpu(your_cpu_number)`\n",
+      "  warnings.warn(\"Not running on Linux. Determining available cpus for thread can fail\"\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "WARNING: The TensorFlow contrib module will not be included in TensorFlow 2.0.\n",
+      "For more information, please see:\n",
+      "  * https://github.com/tensorflow/community/blob/master/rfcs/20180907-contrib-sunset.md\n",
+      "  * https://github.com/tensorflow/addons\n",
+      "If you depend on functionality not listed there, please file an issue.\n",
+      "\n"
+     ]
+    }
+   ],
    "source": [
     "import os\n",
     "\n",
@@ -42,7 +64,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -68,7 +90,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -258,7 +280,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -313,7 +335,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -409,7 +431,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -437,9 +459,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 7,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "WARNING:tensorflow:From C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\tensorflow\\python\\ops\\resource_variable_ops.py:435: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.\n",
+      "Instructions for updating:\n",
+      "Colocations handled automatically by placer.\n"
+     ]
+    }
+   ],
    "source": [
     "# formfactors\n",
     "\n",
@@ -548,7 +580,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -560,10 +592,10 @@
     "Dbar_mass = pdg['D0_M']\n",
     "D_mass = pdg['D0_M']\n",
     "\n",
-    "Dbar_s = zfit.Parameter(\"Dbar_s\", ztf.constant(0.0), lower_limit=-1.464, upper_limit=1.464)\n",
+    "Dbar_s = zfit.Parameter(\"Dbar_s\", ztf.constant(0.0), lower_limit=-0.3, upper_limit=0.3)\n",
     "Dbar_m = zfit.Parameter(\"Dbar_m\", ztf.constant(Dbar_mass), floating = False)\n",
     "Dbar_p = zfit.Parameter(\"Dbar_p\", ztf.constant(Dbar_phase), lower_limit=-2*np.pi, upper_limit=2*np.pi)#, floating = False)\n",
-    "DDstar_s = zfit.Parameter(\"DDstar_s\", ztf.constant(0.0), lower_limit=-2.0, upper_limit=2.0)#, floating = False)\n",
+    "DDstar_s = zfit.Parameter(\"DDstar_s\", ztf.constant(0.0), lower_limit=-0.3, upper_limit=0.3)#, floating = False)\n",
     "Dstar_m = zfit.Parameter(\"Dstar_m\", ztf.constant(Dstar_mass), floating = False)\n",
     "D_m = zfit.Parameter(\"D_m\", ztf.constant(D_mass), floating = False)\n",
     "DDstar_p = zfit.Parameter(\"DDstar_p\", ztf.constant(DDstar_phase), lower_limit=-2*np.pi, upper_limit=2*np.pi)#, floating = False)"
@@ -578,7 +610,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -595,7 +627,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -632,7 +664,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -678,9 +710,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 12,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "plt.clf()\n",
     "# plt.plot(x_part, calcs, '.')\n",
@@ -699,7 +744,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -715,7 +760,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -731,7 +776,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -744,7 +789,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -801,7 +846,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -885,7 +930,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -894,7 +939,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -903,7 +948,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -912,7 +957,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 21,
    "metadata": {
     "scrolled": false
    },
@@ -960,7 +1005,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -977,7 +1022,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 23,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1001,7 +1046,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 24,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1028,7 +1073,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 25,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1051,7 +1096,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 26,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1060,7 +1105,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 27,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1076,7 +1121,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 28,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1106,7 +1151,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 29,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1120,7 +1165,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 30,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1138,7 +1183,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 31,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1152,7 +1197,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 32,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1173,7 +1218,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 33,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1183,7 +1228,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 34,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1214,7 +1259,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 35,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1231,7 +1276,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 36,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1260,13 +1305,13 @@
     "# 2. Constraint - Abs. of sum of Psi contribs and D contribs\n",
     "\n",
     "sum_2 = tf.abs(sum_ru_1)\n",
-    "constraint2 = tf.cond(tf.greater_equal(sum_2, 5.0e-8), lambda: 10000., lambda: 0.)\n",
+    "constraint2 = tf.cond(tf.greater_equal(sum_2, 5.0e-8), lambda: 100000., lambda: 0.)\n",
     "\n",
     "# 3. Constraint - Maximum eta of D contribs\n",
     "\n",
-    "constraint3_0 = tf.cond(tf.greater_equal(tf.abs(Dbar_s), 0.2), lambda: 10000., lambda: 0.)\n",
+    "constraint3_0 = tf.cond(tf.greater_equal(tf.abs(Dbar_s), 0.2), lambda: 100000., lambda: 0.)\n",
     "\n",
-    "constraint3_1 = tf.cond(tf.greater_equal(tf.abs(DDstar_s), 0.2), lambda: 10000., lambda: 0.)\n",
+    "constraint3_1 = tf.cond(tf.greater_equal(tf.abs(DDstar_s), 0.2), lambda: 100000., lambda: 0.)\n",
     "\n",
     "# 4. Constraint - Formfactor multivariant gaussian covariance fplus\n",
     "\n",
@@ -1317,7 +1362,7 @@
     "for part in sum_list_5:\n",
     "    sum_ru_5 += part\n",
     "\n",
-    "constraint5 = tf.cond(tf.greater_equal(tf.abs(sum_ru_5), 0.02), lambda: 10000., lambda: 0.)\n",
+    "constraint5 = tf.cond(tf.greater_equal(tf.abs(sum_ru_5), 0.02), lambda: 100000., lambda: 0.)\n",
     "\n",
     "#List of all constraints\n",
     "\n",
@@ -1337,7 +1382,21 @@
    "metadata": {
     "scrolled": false
    },
-   "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:163: to_int32 (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",
+      "WARNING:tensorflow:From C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\tensorflow_probability\\python\\distributions\\categorical.py:263: multinomial (from tensorflow.python.ops.random_ops) is deprecated and will be removed in a future version.\n",
+      "Instructions for updating:\n",
+      "Use tf.random.categorical instead.\n",
+      "Toy 0: Generating data...\n"
+     ]
+    }
+   ],
    "source": [
     "# zfit.run.numeric_checks = False   \n",
     "\n",
@@ -1419,6 +1478,21 @@
     "    params = result.params\n",
     "    Ctt_list.append(params[Ctt]['value'])\n",
     "    Ctt_error_list.append(params[Ctt]['minuit_hesse']['error'])\n",
+    "    \n",
+    "    #plotting the result\n",
+    "    \n",
+    "    plotdirName = 'data/plots'.format(toy)\n",
+    "    \n",
+    "    if not os.path.exists(plotdirName):\n",
+    "        os.mkdir(plotdirName)\n",
+    "#         print(\"Directory \" , dirName ,  \" Created \")\n",
+    "    calcs_test = zfit.run(probs)\n",
+    "    res_y = zfit.run(jpsi_res(test_q))\n",
+    "    plt.clf()\n",
+    "    plt.plot(test_q, calcs_test, label = 'pdf')\n",
+    "    plt.legend()\n",
+    "    plt.ylim(0.0, 6e-6)\n",
+    "    plt.savefig(plotdirName + '/toy_fit{}.png'.format(toy))\n",
     "\n",
     "    print(\"Toy {0}/{1}\".format(toy+1, nr_of_toys))\n",
     "    print(\"Time taken: {}\".format(display_time(int(time.time() - start))))\n",
@@ -1447,22 +1521,7 @@
    "execution_count": null,
    "metadata": {},
    "outputs": [],
-   "source": [
-    "calcs_test = zfit.run(probs)\n",
-    "res_y = zfit.run(jpsi_res(test_q))\n",
-    "plt.clf()\n",
-    "# plt.plot(x_part, calcs, '.')\n",
-    "plt.plot(test_q, calcs_test, label = 'pdf')\n",
-    "# plt.plot(test_q, f0_y, label = '0')\n",
-    "# plt.plot(test_q, fT_y, label = 'T')\n",
-    "# plt.plot(test_q, fplus_y, label = '+')\n",
-    "# plt.plot(test_q, res_y, label = 'res')\n",
-    "plt.legend()\n",
-    "plt.ylim(0.0, 6e-6)\n",
-    "# plt.yscale('log')\n",
-    "# plt.xlim(770, 785)\n",
-    "plt.savefig('test2.png')"
-   ]
+   "source": []
   },
   {
    "cell_type": "code",
diff --git a/raremodel-nb.ipynb b/raremodel-nb.ipynb
index 529e151..4ebcc77 100644
--- a/raremodel-nb.ipynb
+++ b/raremodel-nb.ipynb
@@ -9,9 +9,31 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\zfit\\util\\execution.py:57: UserWarning: Not running on Linux. Determining available cpus for thread can failand be overestimated. Workaround (only if too many cpus are used):`zfit.run.set_n_cpu(your_cpu_number)`\n",
+      "  warnings.warn(\"Not running on Linux. Determining available cpus for thread can fail\"\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "WARNING: The TensorFlow contrib module will not be included in TensorFlow 2.0.\n",
+      "For more information, please see:\n",
+      "  * https://github.com/tensorflow/community/blob/master/rfcs/20180907-contrib-sunset.md\n",
+      "  * https://github.com/tensorflow/addons\n",
+      "If you depend on functionality not listed there, please file an issue.\n",
+      "\n"
+     ]
+    }
+   ],
    "source": [
     "import os\n",
     "\n",
@@ -42,7 +64,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -68,7 +90,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -258,7 +280,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -313,7 +335,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -409,7 +431,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -437,9 +459,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 7,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "WARNING:tensorflow:From C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\tensorflow\\python\\ops\\resource_variable_ops.py:435: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.\n",
+      "Instructions for updating:\n",
+      "Colocations handled automatically by placer.\n"
+     ]
+    }
+   ],
    "source": [
     "# formfactors\n",
     "\n",
@@ -548,7 +580,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -560,10 +592,10 @@
     "Dbar_mass = pdg['D0_M']\n",
     "D_mass = pdg['D0_M']\n",
     "\n",
-    "Dbar_s = zfit.Parameter(\"Dbar_s\", ztf.constant(0.0), lower_limit=-1.464, upper_limit=1.464)\n",
+    "Dbar_s = zfit.Parameter(\"Dbar_s\", ztf.constant(0.0), lower_limit=-0.3, upper_limit=0.3)\n",
     "Dbar_m = zfit.Parameter(\"Dbar_m\", ztf.constant(Dbar_mass), floating = False)\n",
     "Dbar_p = zfit.Parameter(\"Dbar_p\", ztf.constant(Dbar_phase), lower_limit=-2*np.pi, upper_limit=2*np.pi)#, floating = False)\n",
-    "DDstar_s = zfit.Parameter(\"DDstar_s\", ztf.constant(0.0), lower_limit=-2.0, upper_limit=2.0)#, floating = False)\n",
+    "DDstar_s = zfit.Parameter(\"DDstar_s\", ztf.constant(0.0), lower_limit=-0.3, upper_limit=0.3)#, floating = False)\n",
     "Dstar_m = zfit.Parameter(\"Dstar_m\", ztf.constant(Dstar_mass), floating = False)\n",
     "D_m = zfit.Parameter(\"D_m\", ztf.constant(D_mass), floating = False)\n",
     "DDstar_p = zfit.Parameter(\"DDstar_p\", ztf.constant(DDstar_phase), lower_limit=-2*np.pi, upper_limit=2*np.pi)#, floating = False)"
@@ -578,7 +610,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -595,7 +627,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -632,7 +664,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -678,9 +710,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 12,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "plt.clf()\n",
     "# plt.plot(x_part, calcs, '.')\n",
@@ -699,7 +744,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -715,7 +760,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -731,7 +776,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -744,7 +789,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -801,7 +846,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -885,7 +930,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -894,7 +939,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -903,7 +948,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -912,7 +957,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 21,
    "metadata": {
     "scrolled": false
    },
@@ -960,7 +1005,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -977,7 +1022,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 23,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1001,7 +1046,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 24,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1028,7 +1073,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 25,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1051,7 +1096,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 26,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1060,7 +1105,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 27,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1076,7 +1121,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 28,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1106,7 +1151,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 29,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1120,7 +1165,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 30,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1138,7 +1183,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 31,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1152,7 +1197,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 32,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1173,7 +1218,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 33,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1183,7 +1228,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 34,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1214,7 +1259,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 35,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1231,7 +1276,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 36,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1260,13 +1305,13 @@
     "# 2. Constraint - Abs. of sum of Psi contribs and D contribs\n",
     "\n",
     "sum_2 = tf.abs(sum_ru_1)\n",
-    "constraint2 = tf.cond(tf.greater_equal(sum_2, 5.0e-8), lambda: 10000., lambda: 0.)\n",
+    "constraint2 = tf.cond(tf.greater_equal(sum_2, 5.0e-8), lambda: 100000., lambda: 0.)\n",
     "\n",
     "# 3. Constraint - Maximum eta of D contribs\n",
     "\n",
-    "constraint3_0 = tf.cond(tf.greater_equal(tf.abs(Dbar_s), 0.2), lambda: 10000., lambda: 0.)\n",
+    "constraint3_0 = tf.cond(tf.greater_equal(tf.abs(Dbar_s), 0.2), lambda: 100000., lambda: 0.)\n",
     "\n",
-    "constraint3_1 = tf.cond(tf.greater_equal(tf.abs(DDstar_s), 0.2), lambda: 10000., lambda: 0.)\n",
+    "constraint3_1 = tf.cond(tf.greater_equal(tf.abs(DDstar_s), 0.2), lambda: 100000., lambda: 0.)\n",
     "\n",
     "# 4. Constraint - Formfactor multivariant gaussian covariance fplus\n",
     "\n",
@@ -1317,7 +1362,7 @@
     "for part in sum_list_5:\n",
     "    sum_ru_5 += part\n",
     "\n",
-    "constraint5 = tf.cond(tf.greater_equal(tf.abs(sum_ru_5), 0.02), lambda: 10000., lambda: 0.)\n",
+    "constraint5 = tf.cond(tf.greater_equal(tf.abs(sum_ru_5), 0.02), lambda: 100000., lambda: 0.)\n",
     "\n",
     "#List of all constraints\n",
     "\n",
@@ -1337,7 +1382,21 @@
    "metadata": {
     "scrolled": false
    },
-   "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:163: to_int32 (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",
+      "WARNING:tensorflow:From C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\tensorflow_probability\\python\\distributions\\categorical.py:263: multinomial (from tensorflow.python.ops.random_ops) is deprecated and will be removed in a future version.\n",
+      "Instructions for updating:\n",
+      "Use tf.random.categorical instead.\n",
+      "Toy 0: Generating data...\n"
+     ]
+    }
+   ],
    "source": [
     "# zfit.run.numeric_checks = False   \n",
     "\n",
@@ -1419,6 +1478,21 @@
     "    params = result.params\n",
     "    Ctt_list.append(params[Ctt]['value'])\n",
     "    Ctt_error_list.append(params[Ctt]['minuit_hesse']['error'])\n",
+    "    \n",
+    "    #plotting the result\n",
+    "    \n",
+    "    plotdirName = 'data/plots'.format(toy)\n",
+    "    \n",
+    "    if not os.path.exists(plotdirName):\n",
+    "        os.mkdir(plotdirName)\n",
+    "#         print(\"Directory \" , dirName ,  \" Created \")\n",
+    "    calcs_test = zfit.run(probs)\n",
+    "    res_y = zfit.run(jpsi_res(test_q))\n",
+    "    plt.clf()\n",
+    "    plt.plot(test_q, calcs_test, label = 'pdf')\n",
+    "    plt.legend()\n",
+    "    plt.ylim(0.0, 6e-6)\n",
+    "    plt.savefig(plotdirName + '/toy_fit{}.png'.format(toy))\n",
     "\n",
     "    print(\"Toy {0}/{1}\".format(toy+1, nr_of_toys))\n",
     "    print(\"Time taken: {}\".format(display_time(int(time.time() - start))))\n",
@@ -1447,22 +1521,7 @@
    "execution_count": null,
    "metadata": {},
    "outputs": [],
-   "source": [
-    "calcs_test = zfit.run(probs)\n",
-    "res_y = zfit.run(jpsi_res(test_q))\n",
-    "plt.clf()\n",
-    "# plt.plot(x_part, calcs, '.')\n",
-    "plt.plot(test_q, calcs_test, label = 'pdf')\n",
-    "# plt.plot(test_q, f0_y, label = '0')\n",
-    "# plt.plot(test_q, fT_y, label = 'T')\n",
-    "# plt.plot(test_q, fplus_y, label = '+')\n",
-    "# plt.plot(test_q, res_y, label = 'res')\n",
-    "plt.legend()\n",
-    "plt.ylim(0.0, 6e-6)\n",
-    "# plt.yscale('log')\n",
-    "# plt.xlim(770, 785)\n",
-    "plt.savefig('test2.png')"
-   ]
+   "source": []
   },
   {
    "cell_type": "code",
diff --git a/raremodel-nb.py b/raremodel-nb.py
index fab7c0c..ab43271 100644
--- a/raremodel-nb.py
+++ b/raremodel-nb.py
@@ -284,7 +284,7 @@
 
 # ## Build pdf
 
-# In[11]:
+# In[5]:
 
 
 class total_pdf(zfit.pdf.ZPDF):
@@ -372,7 +372,7 @@
 
 # ## Load data
 
-# In[12]:
+# In[6]:
 
 
 x_min = 2*pdg['muon_M']
@@ -392,7 +392,7 @@
 
 # ## Setup parameters
 
-# In[13]:
+# In[7]:
 
 
 # formfactors
@@ -495,7 +495,7 @@
 
 # ## Dynamic generation of 2 particle contribution
 
-# In[14]:
+# In[8]:
 
 
 m_c = 1300
@@ -506,10 +506,10 @@
 Dbar_mass = pdg['D0_M']
 D_mass = pdg['D0_M']
 
-Dbar_s = zfit.Parameter("Dbar_s", ztf.constant(0.0), lower_limit=-1.464, upper_limit=1.464)
+Dbar_s = zfit.Parameter("Dbar_s", ztf.constant(0.0), lower_limit=-0.3, upper_limit=0.3)
 Dbar_m = zfit.Parameter("Dbar_m", ztf.constant(Dbar_mass), floating = False)
 Dbar_p = zfit.Parameter("Dbar_p", ztf.constant(Dbar_phase), lower_limit=-2*np.pi, upper_limit=2*np.pi)#, floating = False)
-DDstar_s = zfit.Parameter("DDstar_s", ztf.constant(0.0), lower_limit=-2.0, upper_limit=2.0)#, floating = False)
+DDstar_s = zfit.Parameter("DDstar_s", ztf.constant(0.0), lower_limit=-0.3, upper_limit=0.3)#, floating = False)
 Dstar_m = zfit.Parameter("Dstar_m", ztf.constant(Dstar_mass), floating = False)
 D_m = zfit.Parameter("D_m", ztf.constant(D_mass), floating = False)
 DDstar_p = zfit.Parameter("DDstar_p", ztf.constant(DDstar_phase), lower_limit=-2*np.pi, upper_limit=2*np.pi)#, floating = False)
@@ -517,7 +517,7 @@
 
 # ## Tau parameters
 
-# In[15]:
+# In[9]:
 
 
 tau_m = zfit.Parameter("tau_m", ztf.constant(pdg['tau_M']), floating = False)
@@ -526,7 +526,7 @@
 
 # ## Setup pdf
 
-# In[16]:
+# In[10]:
 
 
 total_f = total_pdf(obs=obs, jpsi_mass = jpsi_m, jpsi_scale = jpsi_s, jpsi_phase = jpsi_p, jpsi_width = jpsi_w,
@@ -555,7 +555,7 @@
 
 # ## Test if graphs actually work and compute values
 
-# In[17]:
+# In[11]:
 
 
 # def total_test_tf(xq):
@@ -598,7 +598,7 @@
 # fT_y = zfit.run(tf.math.real(formfactor(test_q,"T", b0, bplus, bT)))
 
 
-# In[18]:
+# In[12]:
 
 
 plt.clf()
@@ -616,7 +616,7 @@
 # print(jpsi_width)
 
 
-# In[19]:
+# In[13]:
 
 
 
@@ -629,7 +629,7 @@
 # plt.semilogy(test_q, calcs_test, label = 'pdf')
 
 
-# In[20]:
+# In[14]:
 
 
 # 0.213/(0.00133+0.213+0.015)
@@ -637,7 +637,7 @@
 
 # ## Adjust scaling of different parts
 
-# In[21]:
+# In[15]:
 
 
 total_f.update_integration_options(draws_per_dim=200000, mc_sampler=None)
@@ -647,7 +647,7 @@
 # print(pdg["jpsi_BR"]/pdg["NR_BR"], inte_fl*pdg["psi2s_auc"]/pdg["NR_auc"])
 
 
-# In[22]:
+# In[16]:
 
 
 # # print("jpsi:", inte_fl)
@@ -696,7 +696,7 @@
 # # Sampling
 # ## Mixture distribution for sampling
 
-# In[23]:
+# In[17]:
 
 
 
@@ -777,25 +777,25 @@
         return sample, thresholds, weights, weights_max, n_to_produce
 
 
-# In[24]:
+# In[18]:
 
 
 total_f._sample_and_weights = UniformSampleAndWeights
 
 
-# In[25]:
+# In[19]:
 
 
 # 0.00133/(0.00133+0.213+0.015)*(x_max-3750)/(x_max-x_min)
 
 
-# In[26]:
+# In[20]:
 
 
 # zfit.settings.set_verbosity(10)
 
 
-# In[27]:
+# In[21]:
 
 
 # # zfit.run.numeric_checks = False   
@@ -838,7 +838,7 @@
 #             pkl.dump(sam, f, pkl.HIGHEST_PROTOCOL)
 
 
-# In[28]:
+# In[22]:
 
 
 # with open(r"data/zfit_toys/toy_0/0.pkl", "rb") as input_file:
@@ -852,7 +852,7 @@
 # print(np.sum(sam-sam2))
 
 
-# In[29]:
+# In[23]:
 
 
 # print("Time to generate full toy: {} s".format(int(time.time()-start)))
@@ -873,7 +873,7 @@
 # print(total_samp[:nevents].shape)
 
 
-# In[30]:
+# In[24]:
 
 
 # plt.clf()
@@ -897,7 +897,7 @@
 # plt.savefig('test2.png')
 
 
-# In[31]:
+# In[25]:
 
 
 # sampler = total_f.create_sampler(n=nevents)
@@ -917,13 +917,13 @@
 # minimum = minimizer.minimize(nll)
 
 
-# In[32]:
+# In[26]:
 
 
 # jpsi_width
 
 
-# In[33]:
+# In[27]:
 
 
 # plt.hist(sample, weights=1 / prob(sample))
@@ -931,7 +931,7 @@
 
 # # Fitting
 
-# In[34]:
+# In[28]:
 
 
 # start = time.time()
@@ -958,7 +958,7 @@
 # print("Hesse errors:", result.hesse())
 
 
-# In[35]:
+# In[29]:
 
 
 # print("Time taken for fitting: {}".format(display_time(int(time.time()-start))))
@@ -969,7 +969,7 @@
 # res_y = zfit.run(jpsi_res(test_q))
 
 
-# In[36]:
+# In[30]:
 
 
 # plt.clf()
@@ -984,7 +984,7 @@
 # # print(jpsi_width)
 
 
-# In[37]:
+# In[31]:
 
 
 # _tot = 4.37e-7+6.02e-5+4.97e-6
@@ -995,7 +995,7 @@
 # print(_probs)
 
 
-# In[38]:
+# In[32]:
 
 
 # dtype = 'float64'
@@ -1013,14 +1013,14 @@
 # #     print(zfit.run(mixture.prob(mixture.sample((10, 1)))))
 
 
-# In[39]:
+# In[33]:
 
 
 # print((zfit.run(jpsi_p)%(2*np.pi))/np.pi)
 # print((zfit.run(psi2s_p)%(2*np.pi))/np.pi)
 
 
-# In[40]:
+# In[34]:
 
 
 #         def jpsi_res(q):
@@ -1048,7 +1048,7 @@
 #                              phase = p4415_phase, width = p4415_width)
 
 
-# In[41]:
+# In[35]:
 
 
 # 0.15**2*4.2/1000
@@ -1057,7 +1057,7 @@
 
 # ## Constraints
 
-# In[62]:
+# In[36]:
 
 
 # 1. Constraint - Real part of sum of Psi contrib and D contribs
@@ -1085,13 +1085,13 @@
 # 2. Constraint - Abs. of sum of Psi contribs and D contribs
 
 sum_2 = tf.abs(sum_ru_1)
-constraint2 = tf.cond(tf.greater_equal(sum_2, 5.0e-8), lambda: 10000., lambda: 0.)
+constraint2 = tf.cond(tf.greater_equal(sum_2, 5.0e-8), lambda: 100000., lambda: 0.)
 
 # 3. Constraint - Maximum eta of D contribs
 
-constraint3_0 = tf.cond(tf.greater_equal(tf.abs(Dbar_s), 0.2), lambda: 10000., lambda: 0.)
+constraint3_0 = tf.cond(tf.greater_equal(tf.abs(Dbar_s), 0.2), lambda: 100000., lambda: 0.)
 
-constraint3_1 = tf.cond(tf.greater_equal(tf.abs(DDstar_s), 0.2), lambda: 10000., lambda: 0.)
+constraint3_1 = tf.cond(tf.greater_equal(tf.abs(DDstar_s), 0.2), lambda: 100000., lambda: 0.)
 
 # 4. Constraint - Formfactor multivariant gaussian covariance fplus
 
@@ -1142,7 +1142,7 @@
 for part in sum_list_5:
     sum_ru_5 += part
 
-constraint5 = tf.cond(tf.greater_equal(tf.abs(sum_ru_5), 0.02), lambda: 10000., lambda: 0.)
+constraint5 = tf.cond(tf.greater_equal(tf.abs(sum_ru_5), 0.02), lambda: 100000., lambda: 0.)
 
 #List of all constraints
 
@@ -1234,6 +1234,21 @@
     params = result.params
     Ctt_list.append(params[Ctt]['value'])
     Ctt_error_list.append(params[Ctt]['minuit_hesse']['error'])
+    
+    #plotting the result
+    
+    plotdirName = 'data/plots'.format(toy)
+    
+    if not os.path.exists(plotdirName):
+        os.mkdir(plotdirName)
+#         print("Directory " , dirName ,  " Created ")
+    calcs_test = zfit.run(probs)
+    res_y = zfit.run(jpsi_res(test_q))
+    plt.clf()
+    plt.plot(test_q, calcs_test, label = 'pdf')
+    plt.legend()
+    plt.ylim(0.0, 6e-6)
+    plt.savefig(plotdirName + '/toy_fit{}.png'.format(toy))
 
     print("Toy {0}/{1}".format(toy+1, nr_of_toys))
     print("Time taken: {}".format(display_time(int(time.time() - start))))
@@ -1256,20 +1271,7 @@
 # In[ ]:
 
 
-calcs_test = zfit.run(probs)
-res_y = zfit.run(jpsi_res(test_q))
-plt.clf()
-# plt.plot(x_part, calcs, '.')
-plt.plot(test_q, calcs_test, label = 'pdf')
-# plt.plot(test_q, f0_y, label = '0')
-# plt.plot(test_q, fT_y, label = 'T')
-# plt.plot(test_q, fplus_y, label = '+')
-# plt.plot(test_q, res_y, label = 'res')
-plt.legend()
-plt.ylim(0.0, 6e-6)
-# plt.yscale('log')
-# plt.xlim(770, 785)
-plt.savefig('test2.png')
+
 
 
 # In[ ]: