diff --git a/.ipynb_checkpoints/raremodel-nb-checkpoint.ipynb b/.ipynb_checkpoints/raremodel-nb-checkpoint.ipynb
index 7de93aa..b3e49f8 100644
--- a/.ipynb_checkpoints/raremodel-nb-checkpoint.ipynb
+++ b/.ipynb_checkpoints/raremodel-nb-checkpoint.ipynb
@@ -339,17 +339,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 46,
+   "execution_count": 5,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "False\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# r = rho_scale * rho_width/rho_mass * np.cos(rho_phase)*(1-np.tan(rho_phase)*rho_width/rho_mass)\n",
     "# o = omega_scale*np.cos(omega_phase)*omega_width/omega_mass\n",
@@ -1025,16 +1017,19 @@
     "                                                                                    tf.constant(0.93, dtype=dtype),\n",
     "                                                                                    tf.constant(0.05, dtype=dtype),\n",
     "                                                                                    tf.constant(0.065, dtype=dtype),\n",
+    "                                                                                    tf.constant(0.04, dtype=dtype),\n",
     "                                                                                    tf.constant(0.05, dtype=dtype)]),\n",
     "                                        components_distribution=tfd.Uniform(low=[tf.constant(x_min, dtype=dtype), \n",
     "                                                                                 tf.constant(3090, dtype=dtype),\n",
     "                                                                                 tf.constant(3681, dtype=dtype), \n",
     "                                                                                 tf.constant(3070, dtype=dtype),\n",
+    "                                                                                 tf.constant(1000, dtype=dtype),\n",
     "                                                                                 tf.constant(3660, dtype=dtype)], \n",
     "                                                                            high=[tf.constant(x_max, dtype=dtype),\n",
     "                                                                                  tf.constant(3102, dtype=dtype), \n",
     "                                                                                  tf.constant(3691, dtype=dtype),\n",
-    "                                                                                  tf.constant(3110, dtype=dtype), \n",
+    "                                                                                  tf.constant(3110, dtype=dtype),\n",
+    "                                                                                  tf.constant(1040, dtype=dtype),\n",
     "                                                                                  tf.constant(3710, dtype=dtype)]))\n",
     "#         dtype = tf.float64\n",
     "#         mixture = tfd.MixtureSameFamily(mixture_distribution=tfd.Categorical(probs=[tf.constant(0.04, dtype=dtype),\n",
@@ -1096,7 +1091,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": null,
    "metadata": {
     "scrolled": false
    },
@@ -1105,9 +1100,9 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "6/6 of Toy 1/1\n",
-      "Time taken: 1 min, 21 s\n",
-      "Projected time left: \n"
+      "3/6 of Toy 1/1\n",
+      "Time taken: 3 min, 27 s\n",
+      "Projected time left: 3 min, 27 s\n"
      ]
     }
    ],
@@ -1154,7 +1149,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1171,18 +1166,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Time to generate full toy: 81 s\n",
-      "(5404696,)\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "print(\"Time to generate full toy: {} s\".format(int(time.time()-start)))\n",
     "\n",
@@ -1204,29 +1190,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "(5404696,)\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "plt.clf()\n",
     "\n",
@@ -1251,7 +1217,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1274,7 +1240,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1283,7 +1249,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1299,65 +1265,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "-0.6837632761492838\n",
-      "3.7285129659499887\n",
-      "4.5760200973853085\n",
-      "4.0873765340620665\n",
-      "5.696265762936989\n",
-      "-2.5717909121593525\n",
-      "-4.32139458348885\n",
-      "-4.6490244502769835\n",
-      "-2.4543520459301043\n",
-      "------------------------------------------------------------------\n",
-      "| FCN = -7.131E+05              |     Ncalls=359 (359 total)     |\n",
-      "| EDM = 6.99E-05 (Goal: 5E-06)  |            up = 0.5            |\n",
-      "------------------------------------------------------------------\n",
-      "|  Valid Min.   | Valid Param.  | Above EDM | Reached call limit |\n",
-      "------------------------------------------------------------------\n",
-      "|     True      |     True      |   False   |       False        |\n",
-      "------------------------------------------------------------------\n",
-      "| Hesse failed  |   Has cov.    | Accurate  | Pos. def. | Forced |\n",
-      "------------------------------------------------------------------\n",
-      "|     False     |     True      |   True    |   True    | False  |\n",
-      "------------------------------------------------------------------\n",
-      "Function minimum: -713057.7941560786\n",
-      "---------------------------------------------------------------------------------------------\n",
-      "|   | Name    |   Value   | Hesse Err | Minos Err- | Minos Err+ | Limit-  | Limit+  | Fixed |\n",
-      "---------------------------------------------------------------------------------------------\n",
-      "| 0 | p4415_p |   -2.93   |    0.12   |            |            |-6.28319 | 6.28319 |       |\n",
-      "| 1 | p4160_p |   3.77    |   0.08    |            |            |-6.28319 | 6.28319 |       |\n",
-      "| 2 | p3770_p |   2.45    |   0.09    |            |            |-6.28319 | 6.28319 |       |\n",
-      "| 3 | phi_p   |   6.28    |   0.04    |            |            |-6.28319 | 6.28319 |       |\n",
-      "| 4 | omega_p |   6.283   |   0.027   |            |            |-6.28319 | 6.28319 |       |\n",
-      "| 5 | p4040_p |   -3.07   |    0.17   |            |            |-6.28319 | 6.28319 |       |\n",
-      "| 6 | jpsi_p  |  -4.810   |   0.016   |            |            |-6.28319 | 6.28319 |       |\n",
-      "| 7 | psi2s_p |  -4.946   |   0.027   |            |            |-6.28319 | 6.28319 |       |\n",
-      "| 8 | rho_p   |   6.28    |   0.04    |            |            |-6.28319 | 6.28319 |       |\n",
-      "---------------------------------------------------------------------------------------------\n",
-      "-------------------------------------------------------------------------------------\n",
-      "|         | p4415_p p4160_p p3770_p   phi_p omega_p p4040_p  jpsi_p psi2s_p   rho_p |\n",
-      "-------------------------------------------------------------------------------------\n",
-      "| p4415_p |   1.000   0.054   0.008   0.000  -0.000   0.001  -0.150  -0.158  -0.001 |\n",
-      "| p4160_p |   0.054   1.000   0.010   0.000  -0.000  -0.278  -0.111  -0.097  -0.001 |\n",
-      "| p3770_p |   0.008   0.010   1.000   0.000   0.000  -0.042  -0.116  -0.511   0.000 |\n",
-      "|   phi_p |   0.000   0.000   0.000   1.000   0.000   0.000   0.004   0.002  -0.000 |\n",
-      "| omega_p |  -0.000  -0.000   0.000   0.000   1.000   0.000   0.002   0.001  -0.003 |\n",
-      "| p4040_p |   0.001  -0.278  -0.042   0.000   0.000   1.000  -0.193  -0.278  -0.001 |\n",
-      "|  jpsi_p |  -0.150  -0.111  -0.116   0.004   0.002  -0.193   1.000   0.221   0.008 |\n",
-      "| psi2s_p |  -0.158  -0.097  -0.511   0.002   0.001  -0.278   0.221   1.000   0.004 |\n",
-      "|   rho_p |  -0.001  -0.001   0.000  -0.000  -0.003  -0.001   0.008   0.004   1.000 |\n",
-      "-------------------------------------------------------------------------------------\n",
-      "Hesse errors: OrderedDict([(<zfit.Parameter 'p4415_p' floating=True>, {'error': 0.12052654582200373}), (<zfit.Parameter 'p4160_p' floating=True>, {'error': 0.07990595220555008}), (<zfit.Parameter 'p3770_p' floating=True>, {'error': 0.09399014188036858}), (<zfit.Parameter 'phi_p' floating=True>, {'error': 0.03841947108985533}), (<zfit.Parameter 'omega_p' floating=True>, {'error': 0.027374388569219477}), (<zfit.Parameter 'p4040_p' floating=True>, {'error': 0.17137144953528938}), (<zfit.Parameter 'jpsi_p' floating=True>, {'error': 0.015550554940964911}), (<zfit.Parameter 'psi2s_p' floating=True>, {'error': 0.02746038930647643}), (<zfit.Parameter 'rho_p' floating=True>, {'error': 0.03774225139990062})])\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "start = time.time()\n",
     "\n",
@@ -1385,17 +1295,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Time taken for fitting: 2 min, 33 s\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "print(\"Time taken for fitting: {}\".format(display_time(int(time.time()-start))))\n",
     "\n",
@@ -1407,22 +1309,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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2biWTRiXHbktqzCecSDpCFpl6g263+IpPoP9Bt1uOZruNglY4UIIYDfooQK/TQ3FeTN9LAb97zulORsxnlGy3UMvHm5iEg16nP75TEOJ26xrw6N9VBFR8FEUZQq/LQ+GYxMfOgCfx+9aMFvPxWz4Bt1sg5hPf4H/ArTeYau3A6zP0JiDLLh1R8VEUZQj+bQGiZ7oFKMixJySNeTijxXwcVrabz2eCFQ4S7XYL1HfTpIPwqPgoQdQ5oIDffVQ0BssnL8ce3AMokQxaPiNjUwG3mydkQzmnxxeM/8SDgGVVkGMlHGiJnVFR8VE01VoZQq/LG8zYioV8hy0p20UHLJ+8CBUOPD4TFJuSfP/9xDPjLWD9FeUNVjgAtXwioeKjqMWjDMGfcDAGt1uuPSni4x4t5mOz4fH6gvGegPjEM+4Tye2mlk94VHwUrWygDKFvrAkHjuSIz6gxH8vyCWS6BayQeMamgm633GFuN91WISwqPkoQFSEFoMfpGVPCQX6OLSHbFQwnUFUhvOUzNOYzaPnEU3wst5smHMSEio+iKEG8PsOA2zemhAO/2y3xCQfOqItMQ2I+eX4hSIT4BOJlxZbgqdstPCo+iqIE6bVcR0VjSDjIS5bbzTNKwoGVah2wjoIJB3F2uzlsEnQD2m1Cab6DE32uuI2Zzqj4KEGMph5kPX3OQMbWWC2fxItPcN+hMC5Ch7WlQsDyCcR84plwEChLFEplcR5tvSo+4VDxUVR0lCA9VomYojFku+U77LhDPugTRcCKKQiz9YN/MzkfXiuQGSgXFM/YVJ9r5PqoiuJc2npUfMKh4qMoSpBAfbKxuN0CWy8k2vrpd3tx2GTUhIOAIAaC/wFxjQe9Lu+IrccrivJo63XGbcx0RsVHGUQNoKwnsO1zwE0VC4EP3ERnvA24fWGtHvBnwHl8JhjzKS/KBYjr5m79Lu8I0VbLJzIqPoqiBAmkBZeNQXzyHX4BSIblEy7eA4NrfwZCin3m2m1xTXvudXpGxHwqivNo73Ml3CWZDqj4KLq+RwkSsAxKC8ZS2y05brcBtzfibquBDLheK4Eix26jtMARV/Hpc4VLOMjFGOjQjLcRqPgoKj5KkPFYPgHXV6LX+vS7vBHdbgHxCbgCbTahtCC+m7uFqwxRUZQHoK63MKj4KEFUg5SuATc5don4oR6OZMV8+t2RxSfgdgtkxDlsQllBTtwtn+GVISqK/bGmth5NOhiOio+iKEE6+92U5ufEvIU2DK4JimcmWTj63V7yo4hPoOqA3RKfQEJFPPDHfIZaPpWW+LTqWp8RqPgoihKks989JpcbDK6h6UlwAU2ne2Rqc4Bc+1BrzB5ny8cYEzbmE3C7He9Wy2c4Kj5KEI39KF39bkrGKT69ybB8HNHcbv45+UvdxE98Btw+PD5DSf7QZzepMIc8h43mroG4jJvOqPgoihKkaxyWTzLdbpEsn2C227CYT1e/G18c0p4jJWqICNPK8jlyon/Cx0x3VHwURQnSNeAZv9st0eLjih7zCSQc2ESYXJSLz8Rni4PBxbkjU9SnluVztFMtn+Go+ChBtMab4k84iH2ND/hdWgU59oTHfLoHPBHnOiLbzS5UlfjjLy1xiL8EUrhL80cKd01ZAcdUfEag4qMoCgA+n6Gz382kwrFZPuDfuyaRlo/L48Pp8QWtruEE9vjpcw+63QLiE4/g/2jro6ZNyudY14BWORiGio+C0UwDBf8HqNdnqCzOG/O1JXmJFZ/AWCURLJ+8YQkHNhGqSvMBaOmeeCtktJp408oK8PqMZrwNQ8VHUWebAhCsvhwowjkWihIsPt3Wh31xGDcXjFzn47DZmBJHt1tnX8DtNlIMp5X5Re9IpyYdhKLiowRRAyi7abVKwIzH8inOcyQ01brbii9FcrvlWSnYwUWmdqE4z0Fhrv2kLJBNBzrC7kzaGayJF97yATh6QuM+oaj4KIoCQLu1Cj9QEmYsFOU5goKQCAJWVrSEg0CxU7tVsaGqJG/cls9ru1r43M/f5rqH1o9wVbf1OikryAm7t9DMcr/4HGjvHde4mYqKj6IowGD9sfG43UryHfTGcYvq4QQtnyjiE1peB/xWyOGOvnGN+cftzQBsPdzFtiNdQ95r6XIGExqGU5KfQ2VxLgdaxzdupqLiowRRr1t202ZZPuWFYxef4jxHXDdqG06P0x9jGV5RIEAw2y1kkSnA7IpCDraPTwQ2HzzB4mmliMDLO1qGvHe8xxmMKYWjtqKIfW1q+YSi4qNorEcB/GX/Jxfm4AjjOorG5EJ/0c5EpRMHLJ+ivEg7mfrFJpjtZonPrIpCWntcY06OMMawr7WHj82rYPnMSby8s3nI+8e7o4hPZRH7W1V8QlHxURQF8MctxuNyA5hU6N80LZ5bFoRywsoum1QQfr4iQp7DNmSdD8Ds8iIADraNzfpp73Ux4PYxY3IBFy2q4sOmzmDKtjGGlu6BiG43gDmVRbR0OxNe/y6VUfFRguh6n+ymtdtFxTgy3WAwTpSoHTvbe12U5DmCsZ1w5OfYg1a9PcTtBnBgjC6wpg5/mvT0SQV8YlEVAK/tOg74U7cH3D5mlRdGvL62wi96+9X1FiQm8RGRVSKyS0QaROTWMO/nicjj1vvrRaQ25L3brPO7ROTSaH2KyByrjz1Wn7mjjSEiK0Vki/XzgYh8drwPQ1GymWNdA8E1KWMlUBUhXBpyPGjrdVEeJSsvdHsDm5XtVlvpF4G9x3vGNN5hqzDo9MkF1E8rZWppPq9YcZ99ljtttiUw4QiI3j51vQWJKj4iYgfuAS4D6oFrRKR+WLOvAB3GmPnA3cBd1rX1wBpgCbAK+JmI2KP0eRdwtzGmDuiw+o44BrAVWGGMWW6NcZ+IjK04VZajNd0UYwzHOgeYOk7xmWwlKbT3Jsbt1tHriuoiDBWfQAyoOM/B7IpCth/tinRZWA5bls+MSYWICJ9YVMWbe47j8viCQjanMrL4zJtSjE1gd/PYRC+TicXyWQk0GGMajTEuYC2welib1cAj1vGTwEXi3wpxNbDWGOM0xuwDGqz+wvZpXXOh1QdWn1eONoYxps8YE3Ck5qNJW2NGvW1Ke68Ll9fHtNKTE59Eud3ael1Rs/ICu4rm2m1Ddmatn1Y6IlU6GodP9FOc5whWrb5wURW9Li9/2tvKloMnmFyYw4zJBRGvL8i1U1tRxM4xil4mE4v4TAcOhbxuss6FbWMJQSdQMcq1kc5XACdCxCR0rEhjICJnicg24CPg6yHXBxGRG0Vko4hsPH78eAy3nX2oBmUvgZL/47Z8ihLrdmuPITkiYPkMjwvVTyvlQFtfsERPLASswoCInVdXSWVxHg+80cibe1o5s7Y86tbji6aVsKu5O+YxM51YxCfcEx3+ORWpzUSdH3Uexpj1xpglwJnAbSIy4i/IGHO/MWaFMWbFlClTwnSlKNnLsaD4RP72PhrFeQ4cNqGjL/5uN2MMHb3umGM+AZdbgKXTywD4qKkz5jGbuweYGmIV5ufY+fr5c3l7bxvHuga46owZUftYNNUveqNlvHl9hr974gPWbjgY89zSlVhiI03AzJDXM4AjEdo0WfGWMqA9yrXhzrcCk0TEYVkvoe0jjRHEGLNDRHqBpcDGGO5NURT8yQbAuBMOxNqsraM3/pZPZ78bl9fHlCiZeYVW3bfhJW/OqJ2MTeDdxjY+Nr8ypjGbOwc4e17FkHM3nDMHmwi5DhuX1FdH7WPR1BIAdjV3c/qsyWHbbD/SxVPvN/HU+01cfebMqNZUOhOL5fMeUGdloeXiTyBYN6zNOuB66/gq4BXjz9tdB6yxMtXmAHXAhkh9Wte8avWB1eczo41h9eEAEJHZwEJgf8xPQBlE/W5Zy7HOAew2GVdR0QCVxeOvmzYWjlgFOmsmjW6lFeaEd7uV5udwyvQy3mlsi2k8n8/Q0u2kelg8zGYTbjh3DtedPTsmkVg8rRSAXcciu94+PHwieBxI785UooqPZYF8E3gB2AE8YYzZJiJ3iMhnrGYPARUi0gDcDNxqXbsNeALYDjwP3GSM8Ubq0+rrFuBmq68Kq++IYwDnAh+IyBbgd8A3jDGt43sc2YlqjtLU0cfU0vzgepjxMLU0LyE7dh61tiaIZqUV5Q0mHAzn7HkVbDl0IqZKB+19Ljw+M8TtNh6mTyqgJM/B1sOR3X2hz2/bkdjdgulITCnJxpjngOeGnbs95HgA+HyEa+8E7oylT+t8I/5suOHnw45hjHkUeDTqTSiKEpED7X3BtSjjZWpZAR+OIY4yXo50xmb5FERIOAC4cGEV973eyCs7W/jMqTWj9tNsuSSrS8dvFYLfUlo2s4wPmk5EbNPa46Qw106fy0tjhq8J0goHShBd75O9HGibAPEpzaet14XT452gWYXn6Il+HDG4CItyw9d9A1hRW05VSR7PfXg06niD4nNylg/AqTMmsfNod3Crh+Ec73Yxq7yQyuK8jK+CreKjKFlO14Cb9l7XqCv0Y2FqmbVTaFd84z6HT/QztSy6izCwzifcOja7Tbhs6VRe3dUS3IU0Es3W/UyE+CyfOQmPz0R0qbX1OqkszmNOZWHGV8FW8VF0kWmWEyiyOXuU2mSxEPhwDlgK8WLv8R7mTimO2i6wKV4kS+wLZ87E6fHxm02Hwr4f4GjnADZh1KrVsbJ85iTAvz1DOFp7nFQW5zK7omjM9efSDRUfBU05yG4OBMTnpC0fv/gcjWPSgc9n2NvSy7wp0edaVeKfT68rvPgsqSnjzNrJ/PKdA6NuBXGovY9pZQVhdykdK1Wl+dSU5fNBhNhYa7eLyuI8aisKae5y0pfADfoSjYqPEkQtoOwkUGl51knGfGZM9l8/3s3aYuFY1wD9bi/zq6JbPlVWgsBoFt0N58zhYHsfz2w5HLHNgbbeUStWj5Xlsyax+WDHiPN9Lg/9bi8VxXnBAqgHxrj1Qzqh4qMoWc7u5m5qyvIpzju5erzFeQ6qSvLiumnabqs8zdzK6OJTW1HEV8+dwx2rl0Zsc+mSqSypKeVHL+7G5fGFbXOwvf+kkzFCOWN2OU0d/cGU8QCt3f4FupXFuYNbMGRwxpuKj6JkObuOdbPQWn1/stRWFsV1z5oPmzoRgSXTS6O2tduE715RT31N5LY2m/APqxbR1NHPQ2/tG/F+94Cb1h4nMyfQ8jlrTjkAG/YNKdDC8R5/YkNlcV5Q7Par5aNkA+p2yz7cXv+WAAsmSHzmVhbFdc+aDw6dYN6UYkrzcyasz/MXTOHSJdX8+KXdI+a+3ap+XT8tutjFyuJppZTkO3i3caj4tIWIT0l+DpXFuWr5KJmNik72sq+1F7fXBOuOnSy1lUW09rjoGkPF6FgxxrDl0IlgxthEcsfqpeTabXz7Nx/g9g6637Za4hOLpRUrdptwZm056/cNLe/TZtXFC2Tp1VbE14pMNio+ipLF7LTqjC2snpgP1zorEWD3KPXLxsvOY9209bpYabmtJpLq0ny+99mlbDzQwb/9YWfw/NsNrcwsLwhmzk0UZ80pp/F4Ly3dg5mBrVZdvID4zFbxUbIFNYCyj21HOsmxC/OqTi7NOsAp1nYF8Siz8+ou/7bVFyyIz5Yoq5dP50sfq+Wht/bx89f20tI9wJsNrVy0KHrF6rESEND39g1mvbX1uijJd5Dn8FdmmFOZ2enWKj6KksVsPniC+pqy4AfeyVJVmk9VSd6oxTPHy4vbm6mfVkrVBFQaiMR3Ll/Mp0+t4a7nd3L+918DA9d/rHbCx1k6vYzCXPsQ15t/gengQtbAuqtMTbc+udxKJSNQiyc78Xh9fNh0gjVnzprQfpfNGL145nhoaOlh88ET3HbZogntdzg5dhs/vno5Z80pZ/2+dj5/xgzmVE6MVTh8nBW15bzVMFiAP1DdIEBtUHx6g9sxZBJq+SgYzTjISnYe62bA7eO0WRMbwD999mT2DotnnCxrNxzEbhM+e/r0CeszEnabcN3Zs/npNafx8Ti5+MCfZdd4vJdD1qLcth4XFUUhlk+lP916X4YWGFXxUYKoCGUXmw/5rZPTZobfVXO8fLzO/4H91p6J2VarrcfJr9Yf5Ipl0yY88J9MLljof06v7T6OMYajnQNDtm0ozc+hoig3Y2u8qYtc50oAACAASURBVPgoSpby7t42qkvzmFk++r44Y6V+WikVRbm8vvv4hPT3k5f3MODx8r8unD8h/aUKcyuLmFlewOu7WjjR56bH6RmxmLU2zuumkomKj6JkIT6f4U97Wzl3/pSYtoAeCzabcPHial7a3nzSmVrrG9t49N0DfOljtcyvmpi1SKmCiHDBgir+1NBGw/EegBE15GorVHyULECdbtnD9qNdnOhzc25dRVz6/9wZM+h1eXnuo2Pj7uPwiX5u+vX7zC4v5O8/uXACZ5c6XLpkKv1uL3e/uBuAuuqhArugupiWbicn+lzJmF5cUfFRlCzkTSsec878yrj0f2btZOZNKeKBNxrxjbJdQSQOtfdx7QPv4nT7ePD6FRSdZNHTVOVj8yqYVV7I23vbKC/KpXZYAdNA2aPdzT3JmF5cUfFR1OLJQl7a0cziaaVxC+CLCN+6eAG7mrt58v2mMV379t5WPvfzt+nodfFfN6zMOHdbKDabcNtli8jPsfFX580d4QJdaFlCu5onvmJEssnMrxPKmNAkt+yiuWuATQc6+LtLFsR1nCtOmcaj7+znjv/ZzumzJkUVkY5eFz9+aTe/fPcAcyqLePQrZ01Yte1U5rJTpnFJfTWOMJvVTSvLpyTPEZdyRclGLR8liIpQdvDCNn8c5rJTpsZ1HJtNuPvq5eTn2Flz//qIqdeNx3u489ntfPwHr/Louwf4i7Nn8/v/dW5WCE+AcMIDfguyrrpYLR9FUdKfZz88yvyq4oS4s2ZMLmTtjWdx4y83cd1D61k6vZTTZk6mINdOc9cAHzZ1sq+1F4dNuHTpVL51UR0LqrNHdGJh4dQSnt96DGPMhGcmJhMVH0XJIva39rJ+XzvfvjRx2WPzq0p47lvn8diGgzz74VGe3nIYl8dHZXEeC6eW8KWP1bJq6VSq41izLZ1ZUF3CYxsOcbzHmVGLbFV8lBDU75bpPLHxEHabcNUZMxI6bn6OnS+fM4cvnzMnoeNmAsGkg2PdGSU+GvNRtKxOluD2+vjNpiY+sbBKrYw0IhD72nk0s+I+Kj6KkiWs23KE491Ovnj2xFaxVuJLRXEe08ry2XZk4repSCYqPkoQNYAyF5/PcN8be1k0tSRum7Ep8WNJTWlwS+9MQcVHUbKAl3e2sLu5h6+dP3Iho5L6LKkpo/F4T0btaqrio2iaQYbj9Rl++MIuZpUXcsWymmRPRxkHS2pK8RnYkUFxHxUfRd1tGc5Tm5rY1dzNP6xaSE6ExYxKarN0ehlARsV99DdRCWa7qQZlHj1OD//+4i6Wz5zE5adMS/Z0lHEyrSyfyYU5bDucOXEfFR9FRSeD+cHzO2npdnL7p+s11pPGiAhLp5exVS0fJZPwqd8tI9l0oJ1fvnuA6/+sltNnTexW2UriWVJTxu7mblweX7KnMiGo+CjBmI9qUObQ4/Tw7d98SE1ZQUJL6SjxY0lNKW6vYXeGFBmNSXxEZJWI7BKRBhG5Ncz7eSLyuPX+ehGpDXnvNuv8LhG5NFqfIjLH6mOP1WfuaGOIyCUisklEPrL+vXC8DyNbUc3JLIwxfPd3H7G/rZd//8KpGbsRW7YRSDrYniHrfaKKj4jYgXuAy4B64BoRqR/W7CtAhzFmPnA3cJd1bT2wBlgCrAJ+JiL2KH3eBdxtjKkDOqy+I44BtAKfNsacAlwPPDq2R6Co+mQWT2w8xNNbjvA3Fy/g7Lnx2SZbSTyzywspznPw0eHMiPvEYvmsBBqMMY3GGBewFlg9rM1q4BHr+EngIvFHN1cDa40xTmPMPqDB6i9sn9Y1F1p9YPV55WhjGGM2G2OOWOe3AfkikhfrA1AGYz5GVSjt2bCvnX98ehvnzK/gpk/MT/Z0lAnEZhPqa0ozJt06FvGZDhwKed1knQvbxhjjATqBilGujXS+Ajhh9TF8rEhjhPI5YLMxxjn8JkTkRhHZKCIbjx8/HuWWswuVnMxgX2svX3t0IzMmF3DPtadjt2l2W6axtKaM7Ue78PrS/682FvEJ9xs8/M4jtZmo81HnISJL8LvivhamHcaY+40xK4wxK6ZM0dpWoWhV6/TneLeTG/7rPQB+8eUzmVSYm+QZKfFg6fRSBtw+Go/3JHsqJ00s4tMEzAx5PQM4EqmNiDiAMqB9lGsjnW8FJll9DB8r0hiIyAzgd8BfGmP2xnBPSggZ8CUqq2nrcfLFB9/lWOcAD16/gtkVRcmekhInAkkHmbDeJxbxeQ+os7LQcvEnEKwb1mYd/mA/wFXAK8b/dXodsMbKVJsD1AEbIvVpXfOq1QdWn8+MNoaITAKeBW4zxvxpLDevDEUNoPTjRJ+L6x7awIG2Ph66fgVnzC5P9pSUODK3soj8HBsfNaV/xltU8bHiK98EXgB2AE8YY7aJyB0i8hmr2UNAhYg0ADcDt1rXbgOeALYDzwM3GWO8kfq0+roFuNnqq8LqO+IYVj/zgX8UkS3WT9U4n4eipA0t3QNc88B69rb0cP9fruBj8yuTPSUlzjjsNhZPK80IyyemBQDGmOeA54aduz3keAD4fIRr7wTujKVP63wj/my44efDjmGM+R7wvag3oSgZxIG2Xv7ioQ209jh54PoVnK979GQNp0wv47fvH8bnM9jSOKlEKxwoQdTrlh5sO9LJ537+Dl0Dbn711bNUeLKMpTVl9Dg9HGjvS/ZUTgoVH0VJI57feozP3/sOOXbhya//GadpzbasY8n0UgC2pvliUxUfRUkDjDH8x8t7+Pp/b6KuuoRnbjqH+VUlyZ6WkgTqqkrItdvSPu6jRZ+UILreJzXpdXr4h6c+5NkPj/Lnp03nX//8FPJz7MmelpIkch02Fk4tSfu9fVR8FCWF2XWsm2/8ahONrb3cdtkibvz4XN2XR2Hp9FKe++gYxpi0/X1Qt5uipCDGGB5/7yCr73mLzn4P//2Vs/ja+fPS9oNGmViWTi+js99NU0d/sqcybtTyUZQUo9fp4btPb+V3mw9zzvwK7r56OVUl+cmelpJCLK3xVzrYdqSTmeWFSZ7N+FDxUZQUYuP+dv7uNx9wqL2Pmy9ZwE2fmK8FQpURLJxagt0mbD3cxaql05I9nXGh4qMoKYDT4+XuF/dw/xt7qZlUwGN/dTZn6V48SgTyc+zUVRWndcabio+iJJntR7q4+Ykt7DzWzTUrZ/Kdy+sp1t1HlSgsnV7Ga7ta0jbpQH/DlSCaaZ1Y3F4f972+l5+8vIeyglwe/tIKLlxUnexpKWnC0ppSntzUREu3k+rS9IsJqvgoShLYfLCDW5/6iF3N3Vy+bBr/snop5UW6B48SO4HtFT5q6qS6XsVHUZRR6HF6+OELu3jknf1Ul+TzwF+u4JJ6tXaUsVNfU4qIf2+fi9Pwd0jFRwlitLRoXHl5RzP/+PRWjnYN8Bdnz+bbly6kJD8n2dNS0pTCXAfzphSzNU0rHaj4KEqcOXKinzuf3cGzHx1lQXUxT177Mc6YrQVBlZNnaU0p6/e1J3sa40LFJwMwxrDtSFfQB6ykBk6Plwff3Md/vtKAzxj+7pIFfO38eeQ6tLCIMjEsnV7G01uO0NrjpLI4L9nTGRP6V5ABPL3lMFf89C2e33o02VNRLF7b1cKqH7/JD17YxccXVPLSzefzvy6qU+FRJpQlwUoH6ed6U8snA9jT3ANAQ0vPSfWjqdYnz6H2Pu74/XZe3N7MnMoiHrlhpW72psSN0L190u33TMUnAwisLxuPeOg2ChNDn8vDfa83cu/re7HbhFtWLeKGc2vJc+jWB0r8KM3PobaikG1pWOlAxScDEPzqMx4Z8an2nBRen+Gp95v49z/uornLyeXLpvHdyxczrawg2VNTsoQlNWV8lIa7mqr4ZAA+y3oZT/1Jj88XPFYjaGy83dDK957dwfajXZw6cxL3XHs6K2rLkz0tJctYNLWEZz86Sq/TQ1EalWVKn5kqEfEGxGcc6hOiPUqMNLT08G9/2MFLO1qYPqmAn6xZzqeX1Yzr+SvKybJwqn879d3N3Zw2K31S+FV8MgCf5Tuzj6O4oEfVJ2baepz85OU9/Gr9QQpy7NyyahFfPqdWt7RWksqiqf6kg13HVHyUBOO19GM8+754Q4I+6nULT6/Tw8Nv7eP+Nxrpc3u5ZuVM/ubiBWm3rkLJTGZMLqAo187OY93JnsqYUPHJAAIxn/GIj0czDiLi9Hh5bP1B/vPVBlp7XHyyvppvX7qQuuqSZE9NUYLYbMKCqSXsPJZea31UfDKAgPUyHvHxqfiMwOszPL35MD96cTeHT/Rz9txy7v/LRZyeRi4NJbtYNLWE57ceS6u9fVR8MoBgwsG4Yj4qPgGMMby4vZkf/nEXu5t7WDq9lP/356dwXl1l2vxBK9nJoqmlPLbhUFrt7aPikwH4TsLyGRLzyeJc63f2tvH9F3ay+eAJ5lYWcc+1p3PZ0qmawaakBYGMt53HulV8lMThOalst+wVHIAN+9q5+8XdvNPYxtTSfP7tz0/hqjNm4LBrDTYlfVgUEJ+jXWlTZkfFJwMIWD7j2Y/Hm6Wp1u/t94vO23vbmFKSx+1X1HPtWbM0bVpJSyYV5jK1NJ9daZTxpuKTAQRiPuMxYjxZlmq9cX87P35pD281tFJZnMd3L1/MdWfPVtFR0p6FU0vSKt1axScDCMRtfOOI2Tjd2WH5bDrQwY9f2s2be1qpLM7lu5cv5otnzaYgV0VHyQwWTSvhnb1tuL0+ctLAbazikwH4TsLycXoyW3zeP9jB3S/6RaeiKJfvfMpv6ajoKJnGoqkluLw+9rf2psVaNBWfDCBg+YwnW83p8U70dJKOMYbXdx/n3tf38m5jOxVFufyfTy3iurNnU5irv/JKZrKw2l9mZ+ex7rQQn5hsMxFZJSK7RKRBRG4N836eiDxuvb9eRGpD3rvNOr9LRC6N1qeIzLH62GP1mTvaGCJSISKvikiPiPzneB9EOhMorzOeBaND3G5pHvTxeH08s+Uwn/qPt/jSL95jf2sf3718MW/e8glu/Pg8FR4lo5lXVYTdJmmTdBD1r1FE7MA9wCVAE/CeiKwzxmwPafYVoMMYM19E1gB3AVeLSD2wBlgC1AAvicgC65pIfd4F3G2MWSsi91p9/zzSGMAA8I/AUusn63Bb6pOtbrd+l5ffbDrEA282cqi9n/lVxfzgqmWsXj5dt61WsoY8h53ZFYUnvaNxoojlq+BKoMEY0wggImuB1UCo+KwG/sk6fhL4T/EvCV8NrDXGOIF9ItJg9Ue4PkVkB3AhcK3V5hGr359HGsMY0wu8JSLzx3DfGUVAfMZjuKSz2+1En4tfvnOA/3p7P+29Ls6YPZnbr1jCRYuqdHGokpXUVRWzpyVDLB9gOnAo5HUTcFakNsYYj4h0AhXW+XeHXTvdOg7XZwVwwhjjCdM+0hitMdwDInIjcCPArFmzYrkkbfB4TybmE7KZXJr43Y6c6OfBN/ex9r2D9Lm8XLioir++YB5n6kZuSpZTV1XCSztacHl8KW/1xyI+4b5CDv+UitQm0vlwT2W09rHOIyLGmPuB+wFWrFiRHp+yMeIKut3GfluuNHK77W7u5t7X97JuyxEAPnNqDV87f16wtIiiZDt11cV4fYb9bb0sSPGkg1jEpwmYGfJ6BnAkQpsmEXEAZUB7lGvDnW8FJomIw7J+QttHGiPrOZmYT6/LE71Rktm4v517X9/LSztaKMix8xd/NpuvnjeX6ZMKkj01RUkp5lcVA7CnuScjxOc9oE5E5gCH8ScQXDuszTrgeuAd4CrgFWOMEZF1wK9F5Ef4Ew7qgA34rZgRfVrXvGr1sdbq85nRxhjfbWcW7pOwfLoHBsUnlZ6mz2d4ZWcL976+l40HOphcmMPfXFzH9X9Wy+Si3GRPT1FSknlTihHBivtMS/Z0RiWq+FjxlW8CLwB24GFjzDYRuQPYaIxZBzwEPGolFLTjFxOsdk/gT07wADcZY7wA4fq0hrwFWCsi3wM2W30TaQyrr/1AKZArIlcCnxyWjZfRuIMxn7Ff2z3gDh6nQo1Rl8fHug+OcP8be9nd3MP0SQX806fr+cKZMzVVWlGikJ9jZ1Z5IXvSIOMtpr9mY8xzwHPDzt0ecjwAfD7CtXcCd8bSp3W+kcGMuNDzo41RO+oNZDiBuM141vl09XsozLXT5/KOy3KaKHqdHh7bcJCH3trH0c4BFk0t4e6rT+WKZTVpUSpEUVKFuqpiGpozRHyU1Kbf7U+XHo/l0j3gpqwghz6XNyn7+bT1OHnk7f088s4BOvvdrJxTzr9+9hQuWDhFN3BTlHEwv6qE13cfx+P1pfTWICo+aY4xJug6G4/l0tnvZlJhLkc7B4ZsLBdvDrX38cCbjTyx8RADbh+frK/m6xfM062qFeUkqasqxu01HGjvY96U4mRPJyIqPmmO0+MLifmMXTxaup0sqC5hx9GuhMR8th/p4t7X9/LsR0exCVy5fDpfO38u86tSOzNHUdKFuurBjDcVHyVudIUkDIxVO4wxtHQ5gzsfxivmY4zhncY27n29kTd2H6co184N59Ryw7lzmFam6dKKMpEEBKehpRuYmtzJjIKKT5rT2TcoPmPdErujz43L66PGWi8z0eLj9Rle3H6Mn7+2lw+aOqkszuXbly7kurNmU1aYM6FjKYripyjPwfRJBSmf8abik+Yc7RwIHo81221/Wy8As8oLgcHq2CeLy+Pjd5ubuO/1Rhpbe5lVXsi/XLmUz58xQ3cMVZQEML+qmD0pnvGm4pPmHO3sDx6P1fLZbZVeXzzNH285Wcunz+XhsQ2HeOCNRo51DbCkppSfXnMaly2dmtJZN4qSadRVFfNuYxten8GeokV2VXzSnP1tfeTYhTyHfczZatuPdlGYa2emZfmMN9W6s8/NI+/s5xd/2kdHn5uz5pRz11XL+HhdpaZLK0oSqKsuxunxcbijn1kVhcmeTlhUfNKcrYc7WVBdQnPXQLDMTqy8vbeNFbXlOGx+q2Ssbrf2Xhf3vbGX/37nAL0uLxctquIbn5jHGbO1urSiJJNA9uielm4VH2Xi6XN5eG9/O19YMZMXth0bk+VzsK2PhpYevrBiBgGrPFa3W2e/mwffbOTht/bR7/ZyxbIa/vqCeSyeVjqe21AUZYIJFBjd3dzDRYurkzyb8Kj4pDHPbDnCgNvHZUun8fKOljHFfB7feBCbwKdPrUFEEIkuPk6Pl4ff2s/PX2uga8DD5adM428vqdM1OoqSYpQV5FBdmpfSu5qq+KQpx7ud/OjF3SyfOYmz55Zjt0nMlo+/pM0BLqmvDq6zsYuMKj4v72jmjt9v50BbHxcuquLvPrmAJTVlE3IviqJMPHVVJSm9q6mKTxrS6/TwjV9tonvAzf/785WICA6bxGT5GGO4fd02+t1evn3pouB5m0jYmM+JPhff+d1Wnv3oKPOmFPHoV1ZyXt2UibwdRVHiwPyqYp7YeAifz6TktvIqPmlGj9PDDb94j00HOvjpNacH4yx+yyd6xsDPX9/Lsx8e5ZZVi4J+YQCbbWS22+aDHXz9vzfR3uvi25cu5K/Om5vyW/MqiuKnrrqYPpeXI539zJicekkHKj5pRFNHH199ZCN7Wnr4j2tO4/Jlg5tF2W2CxxvZ8jHG8MCbjXz/+V18+tQavvbxuUPetw1zu724vZlv/vp9qkvz+d03zmHpdHWxKUo6URfMeOtR8VHGzzt72/jmr9/H5fXx0PUruGBh1ZD3HfbIMR+Xx8f/XbeNxzYc5PJl07j7C6eOMMND3W7vNrZx06/eZ3FNKQ9fv4KK4ry43JOiKPGjzvJsNDT38IlhnxepgIpPijPg9vLvf9zFg2/tY05FEQ9cvyJspVq7zRY25tPU0cffPr6F9/Z38NcXzOPvP7kw7Ipnm5Xt1tHr4pu/3szM8gIe+fKZTCrULasVJR2ZXJRLZXFuyiYdqPikMH9qaOWf1m1jT0sPXzxrFv/nU4spygv/X+YYlu1mjOGp9w/zz+u2YYCfrFnO6uXTI45ls/ndbj95eQ8n+lz88oaVKjyKkubMrypO2QKjKj4pyKH2Pv71uR38YesxZpYX8IsvnxnVbLbbJFjhoLXHyXd+9xEvbGtm5Zxy/v3zpwZL6ES8XoQBt5cXtjXzqVOmUV+jC0YVJd2pqyrh6c2HMcakXKkrFZ8U4lB7Hz97rYHfbGwix27j7z+5gK+eNzemStB5DhtdAx7WN7bx1796n54BD7ddtoivnjc3psKCOXYbL2xrprPfzWdPj2whKYqSPtRVF9Pt9NDc5WRqWX6ypzMEFZ8UYO/xHh54o5EnNzVhE+Has2bxjQvmj+mXpbwolzf3tHLjo5uoKM5l7Y1ns6A69soDJfkOjnUNUFmcx3nzK8dzG4qipBiB5RR7WrpVfBQ/Pp/hjT3H+cWf9vP67uPkOmx88axZfP2CeePa3bO8yB+fsQn815dWjrmYYCBetHp5jW5/oCgZQuAL6J7mnpRbHK7ik2B6nR5++34Tv3h7P43He5lSksfNlyzgmpWzmFIy/pTmixZV88dtzdx99fJxVbHNsQTns6epy01RMoWKolwmF+akZNKBik+C2Hakk1+vP8jTmw/T6/Jy6owyfnz1cj51yrQJqRpwbl0lf7r1wnFff/un61m/r10XkypKBiEi1FWV0JCC6dYqPnGk3+Xlfz48wq/XH2TLoRPkOWxcsayGL549i9NmTkqp7JNz5ldyjsZ6FCXjmF9dzLMfHk25jDcVnziw93gPj75zgKfeb6J7wMO8KUXcfkU9nzt9BmWFOcmenqIoWURdVTGd/W5ae1wn5dqfaFR8JpD39rdz3+uNvLSjmRy7cNnSaXzxrFmsnFOeUt84FEXJHupCdjVV8ckwth3p5N/+sJM397QyuTCH/33hfP7iz2pT6j9aUZTspK7aqvHW0sPH5qWOa13F5yRweXz86MXd3PfGXsoKcvjHK+q5duUsCnKjLwpVFEVJBFUleZTkO9jTnFoZbyo+46RrwM1XH9nIhn3trDlzJrd9ajFlBRrPURQltfBnvBWzuzm1Mt5UfMaB2+vjq/+1kfcPdvDjq5dzpa6NURQlhamrKuGlHc3JnsYQdCn7OPiPl/ewYX87P/z8qSo8iqKkPHXVxbT1umjtcSZ7KkFUfMbIsc4B7n+jkdXLa1R4FEVJCwKLxz9sOpHkmQyi4jNGnnq/CafHx82XLEj2VBRFUWLilOll2AS2HOpM9lSCqPiMkT9uO8YZsyczu6Io2VNRFEWJiaI8BwuqS/jgUJpZPiKySkR2iUiDiNwa5v08EXncen+9iNSGvHebdX6XiFwarU8RmWP1scfqM3e8Y0w0To+X7Ue7WFE7OV5DKIqixIXlMyfxQdMJjDHRGyeAqOIjInbgHuAyoB64RkTqhzX7CtBhjJkP3A3cZV1bD6wBlgCrgJ+JiD1Kn3cBdxtj6oAOq+8xjzHWBxELLV1O3F7DvMrieHSvKIoSN86sLedEn5sPmlLD9RaL5bMSaDDGNBpjXMBaYPWwNquBR6zjJ4GLxF9PZjWw1hjjNMbsAxqs/sL2aV1zodUHVp9XjnOMCaez3w1Aqa7nURQlzbh4cTU5duHht/YleypAbOt8pgOHQl43AWdFamOM8YhIJ1BhnX932LWBFLFwfVYAJ4wxnjDtxzNGEBG5EbjRetkjIm1Aa8S7HoXL7hrPVSlNJeN8FhmIPgs/+hwGyahn8VPgp9eO69JKYPZEzSMW8QlXEXO40zBSm0jnw1lco7UfzxhDTxhzP3B/4LWIbDTGrAhzbdahz2IQfRZ+9DkMos/Cj/Ucaieqv1jcbk3AzJDXM4AjkdqIiAMoA9pHuTbS+VZgktXH8LHGOoaiKIqSosQiPu8BdVYWWi7+4P66YW3WAddbx1cBrxh/SsU6YI2VqTYHqAM2ROrTuuZVqw+sPp8Z5xiKoihKihLV7WbFV74JvADYgYeNMdtE5A5gozFmHfAQ8KiINOC3RtZY124TkSeA7YAHuMkY4wUI16c15C3AWhH5HrDZ6pvxjBGF+6M3yRr0WQyiz8KPPodB9Fn4mdDnIKmS860oiqJkD1rhQFEURUk4Kj6KoihKwslK8YlWLigTEJGHRaRFRLaGnCsXkRet0kUvishk67yIyH9Yz+NDETk95JrrrfZ7ROT6cGOlMiIyU0ReFZEdIrJNRL5lnc+qZyEi+SKyQUQ+sJ7DP1vnU7acVbyxqq1sFpHfW6+z8lmIyH4R+UhEtojIRutc/P8+jDFZ9YM/wWEvMBfIBT4A6pM9rzjc58eB04GtIee+D9xqHd8K3GUdfwr4A/41U2cD663z5UCj9e9k63hysu9tjM9hGnC6dVwC7MZf0imrnoV1P8XWcQ6w3rq/J4A11vl7gb+2jr8B3GsdrwEet47rrb+ZPGCO9bdkT/b9jfOZ3Az8Gvi99TornwWwH6gcdi7ufx/ZaPnEUi4o7THGvIE/KzCU0BJFw0sX/dL4eRf/WqtpwKXAi8aYdmNMB/Ai/vp5aYMx5qgx5n3ruBvYgb8CRlY9C+t+eqyXOdaPIYXLWcUTEZkBXA48aL1O6dJeSSDufx/ZKD7hygVly65w1caYo+D/UAaqrPORnklGPSvLXXIa/m/9WfcsLDfTFqAF/4fDXmIsZwWElrNK6+dg8WPgHwCf9Trm0l5k3rMwwB9FZJP4y5BBAv4+Yimvk2nEVI4nyzip0kXpgIgUA08Bf2OM6fJ/cQ3fNMy5jHgWxr/+bbmITAJ+BywO18z6N2Ofg4hcAbQYYzaJyAWB02GaZvyzsDjHGHNERKqAF0Vk5yhtJ+xZZKPlk83leJotExnr3xbr/FjLIKUVIpKDX3h+ZYz5rXU6K58FgDHmBPAafp99NpazOgf4jIjsx+92vxC/JZSNzwJjzBHr3xb8X0pWkoC/j2wUn1jKBWUqoSWKhpcu+ksrk+VsoNMytV8APikik61sl09a59IGyzf/ELDDHkY8KAAAAQ1JREFUGPOjkLey6lmIyBTL4kFECoCL8ce/sq6clTHmNmPMDOMvkrkG/719kSx8FiJSJCIlgWP8v9dbScTfR7IzLZLxgz9jYzd+n/d3kj2fON3jY8BRwI3/W8lX8PupXwb2WP+WW20F/+Z+e4GPgBUh/dyAP5DaAHw52fc1judwLn7z/0Ngi/XzqWx7FsAy/OWqPrQ+XG63zs/F/4HZAPwGyLPO51uvG6z354b09R3r+ewCLkv2vZ3kc7mAwWy3rHsW1j1/YP1sC3weJuLvQ8vrKIqiKAknG91uiqIoSpJR8VEURVESjoqPoiiKknBUfBRFUZSEo+KjKIqiJBwVH0VRFCXhqPgoiqIoCef/AyGH8+1vrJPuAAAAAElFTkSuQmCC\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "plt.clf()\n",
     "# plt.plot(x_part, calcs, '.')\n",
@@ -1438,7 +1327,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1452,7 +1341,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1473,7 +1362,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
diff --git a/__pycache__/pdg_const.cpython-37.pyc b/__pycache__/pdg_const.cpython-37.pyc
index 0bc95ea..6b27d3c 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 d6b5aa5..7b1e788 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 e8234af..1376396 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 45bb5d8..c787017 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 b4586d0..15f1a4d 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 9818c56..f5ef0a4 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 363bf41..f027626 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 494c5b9..c14d561 100644
--- a/pdg_const.py
+++ b/pdg_const.py
@@ -129,7 +129,7 @@
 
     # 2P contributions format(mass, amp, phase)
     
-    "D_bar": (
+#     "D_bar": (
     
     #general
     
diff --git a/raremodel-nb.ipynb b/raremodel-nb.ipynb
index 7de93aa..7cf7c7e 100644
--- a/raremodel-nb.ipynb
+++ b/raremodel-nb.ipynb
@@ -9,31 +9,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\zfit\\util\\execution.py:57: UserWarning: Not running on Linux. Determining available cpus for thread can failand be overestimated. Workaround (only if too many cpus are used):`zfit.run.set_n_cpu(your_cpu_number)`\n",
-      "  warnings.warn(\"Not running on Linux. Determining available cpus for thread can fail\"\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "WARNING: The TensorFlow contrib module will not be included in TensorFlow 2.0.\n",
-      "For more information, please see:\n",
-      "  * https://github.com/tensorflow/community/blob/master/rfcs/20180907-contrib-sunset.md\n",
-      "  * https://github.com/tensorflow/addons\n",
-      "If you depend on functionality not listed there, please file an issue.\n",
-      "\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "import os\n",
     "\n",
@@ -64,7 +42,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -83,7 +61,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -284,7 +262,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -339,17 +317,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 46,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "False\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# r = rho_scale * rho_width/rho_mass * np.cos(rho_phase)*(1-np.tan(rho_phase)*rho_width/rho_mass)\n",
     "# o = omega_scale*np.cos(omega_phase)*omega_width/omega_mass\n",
@@ -375,7 +345,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -452,7 +422,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -480,19 +450,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "WARNING:tensorflow:From C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\tensorflow\\python\\ops\\resource_variable_ops.py:435: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.\n",
-      "Instructions for updating:\n",
-      "Colocations handled automatically by placer.\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "#rho\n",
     "\n",
@@ -588,7 +548,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -619,7 +579,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -662,22 +622,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "plt.clf()\n",
     "# plt.plot(x_part, calcs, '.')\n",
@@ -696,7 +643,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -712,7 +659,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -728,7 +675,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -741,7 +688,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -794,7 +741,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -823,7 +770,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -840,7 +787,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -870,7 +817,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -879,7 +826,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -901,7 +848,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -938,7 +885,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -960,7 +907,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -988,7 +935,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1025,16 +972,19 @@
     "                                                                                    tf.constant(0.93, dtype=dtype),\n",
     "                                                                                    tf.constant(0.05, dtype=dtype),\n",
     "                                                                                    tf.constant(0.065, dtype=dtype),\n",
+    "                                                                                    tf.constant(0.04, dtype=dtype),\n",
     "                                                                                    tf.constant(0.05, dtype=dtype)]),\n",
     "                                        components_distribution=tfd.Uniform(low=[tf.constant(x_min, dtype=dtype), \n",
     "                                                                                 tf.constant(3090, dtype=dtype),\n",
     "                                                                                 tf.constant(3681, dtype=dtype), \n",
     "                                                                                 tf.constant(3070, dtype=dtype),\n",
+    "                                                                                 tf.constant(1000, dtype=dtype),\n",
     "                                                                                 tf.constant(3660, dtype=dtype)], \n",
     "                                                                            high=[tf.constant(x_max, dtype=dtype),\n",
     "                                                                                  tf.constant(3102, dtype=dtype), \n",
     "                                                                                  tf.constant(3691, dtype=dtype),\n",
-    "                                                                                  tf.constant(3110, dtype=dtype), \n",
+    "                                                                                  tf.constant(3110, dtype=dtype),\n",
+    "                                                                                  tf.constant(1040, dtype=dtype),\n",
     "                                                                                  tf.constant(3710, dtype=dtype)]))\n",
     "#         dtype = tf.float64\n",
     "#         mixture = tfd.MixtureSameFamily(mixture_distribution=tfd.Categorical(probs=[tf.constant(0.04, dtype=dtype),\n",
@@ -1069,7 +1019,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1078,7 +1028,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1087,7 +1037,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1096,21 +1046,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": null,
    "metadata": {
     "scrolled": false
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "6/6 of Toy 1/1\n",
-      "Time taken: 1 min, 21 s\n",
-      "Projected time left: \n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# zfit.run.numeric_checks = False   \n",
     "\n",
@@ -1154,7 +1094,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1171,18 +1111,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Time to generate full toy: 81 s\n",
-      "(5404696,)\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "print(\"Time to generate full toy: {} s\".format(int(time.time()-start)))\n",
     "\n",
@@ -1204,29 +1135,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "(5404696,)\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "plt.clf()\n",
     "\n",
@@ -1251,7 +1162,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1274,7 +1185,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1283,7 +1194,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1299,65 +1210,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "-0.6837632761492838\n",
-      "3.7285129659499887\n",
-      "4.5760200973853085\n",
-      "4.0873765340620665\n",
-      "5.696265762936989\n",
-      "-2.5717909121593525\n",
-      "-4.32139458348885\n",
-      "-4.6490244502769835\n",
-      "-2.4543520459301043\n",
-      "------------------------------------------------------------------\n",
-      "| FCN = -7.131E+05              |     Ncalls=359 (359 total)     |\n",
-      "| EDM = 6.99E-05 (Goal: 5E-06)  |            up = 0.5            |\n",
-      "------------------------------------------------------------------\n",
-      "|  Valid Min.   | Valid Param.  | Above EDM | Reached call limit |\n",
-      "------------------------------------------------------------------\n",
-      "|     True      |     True      |   False   |       False        |\n",
-      "------------------------------------------------------------------\n",
-      "| Hesse failed  |   Has cov.    | Accurate  | Pos. def. | Forced |\n",
-      "------------------------------------------------------------------\n",
-      "|     False     |     True      |   True    |   True    | False  |\n",
-      "------------------------------------------------------------------\n",
-      "Function minimum: -713057.7941560786\n",
-      "---------------------------------------------------------------------------------------------\n",
-      "|   | Name    |   Value   | Hesse Err | Minos Err- | Minos Err+ | Limit-  | Limit+  | Fixed |\n",
-      "---------------------------------------------------------------------------------------------\n",
-      "| 0 | p4415_p |   -2.93   |    0.12   |            |            |-6.28319 | 6.28319 |       |\n",
-      "| 1 | p4160_p |   3.77    |   0.08    |            |            |-6.28319 | 6.28319 |       |\n",
-      "| 2 | p3770_p |   2.45    |   0.09    |            |            |-6.28319 | 6.28319 |       |\n",
-      "| 3 | phi_p   |   6.28    |   0.04    |            |            |-6.28319 | 6.28319 |       |\n",
-      "| 4 | omega_p |   6.283   |   0.027   |            |            |-6.28319 | 6.28319 |       |\n",
-      "| 5 | p4040_p |   -3.07   |    0.17   |            |            |-6.28319 | 6.28319 |       |\n",
-      "| 6 | jpsi_p  |  -4.810   |   0.016   |            |            |-6.28319 | 6.28319 |       |\n",
-      "| 7 | psi2s_p |  -4.946   |   0.027   |            |            |-6.28319 | 6.28319 |       |\n",
-      "| 8 | rho_p   |   6.28    |   0.04    |            |            |-6.28319 | 6.28319 |       |\n",
-      "---------------------------------------------------------------------------------------------\n",
-      "-------------------------------------------------------------------------------------\n",
-      "|         | p4415_p p4160_p p3770_p   phi_p omega_p p4040_p  jpsi_p psi2s_p   rho_p |\n",
-      "-------------------------------------------------------------------------------------\n",
-      "| p4415_p |   1.000   0.054   0.008   0.000  -0.000   0.001  -0.150  -0.158  -0.001 |\n",
-      "| p4160_p |   0.054   1.000   0.010   0.000  -0.000  -0.278  -0.111  -0.097  -0.001 |\n",
-      "| p3770_p |   0.008   0.010   1.000   0.000   0.000  -0.042  -0.116  -0.511   0.000 |\n",
-      "|   phi_p |   0.000   0.000   0.000   1.000   0.000   0.000   0.004   0.002  -0.000 |\n",
-      "| omega_p |  -0.000  -0.000   0.000   0.000   1.000   0.000   0.002   0.001  -0.003 |\n",
-      "| p4040_p |   0.001  -0.278  -0.042   0.000   0.000   1.000  -0.193  -0.278  -0.001 |\n",
-      "|  jpsi_p |  -0.150  -0.111  -0.116   0.004   0.002  -0.193   1.000   0.221   0.008 |\n",
-      "| psi2s_p |  -0.158  -0.097  -0.511   0.002   0.001  -0.278   0.221   1.000   0.004 |\n",
-      "|   rho_p |  -0.001  -0.001   0.000  -0.000  -0.003  -0.001   0.008   0.004   1.000 |\n",
-      "-------------------------------------------------------------------------------------\n",
-      "Hesse errors: OrderedDict([(<zfit.Parameter 'p4415_p' floating=True>, {'error': 0.12052654582200373}), (<zfit.Parameter 'p4160_p' floating=True>, {'error': 0.07990595220555008}), (<zfit.Parameter 'p3770_p' floating=True>, {'error': 0.09399014188036858}), (<zfit.Parameter 'phi_p' floating=True>, {'error': 0.03841947108985533}), (<zfit.Parameter 'omega_p' floating=True>, {'error': 0.027374388569219477}), (<zfit.Parameter 'p4040_p' floating=True>, {'error': 0.17137144953528938}), (<zfit.Parameter 'jpsi_p' floating=True>, {'error': 0.015550554940964911}), (<zfit.Parameter 'psi2s_p' floating=True>, {'error': 0.02746038930647643}), (<zfit.Parameter 'rho_p' floating=True>, {'error': 0.03774225139990062})])\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "start = time.time()\n",
     "\n",
@@ -1385,17 +1240,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Time taken for fitting: 2 min, 33 s\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "print(\"Time taken for fitting: {}\".format(display_time(int(time.time()-start))))\n",
     "\n",
@@ -1407,22 +1254,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "plt.clf()\n",
     "# plt.plot(x_part, calcs, '.')\n",
@@ -1438,7 +1272,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1452,7 +1286,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1473,7 +1307,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
diff --git a/test2.png b/test2.png
index 0bcad3d..38efb57 100644
--- a/test2.png
+++ b/test2.png
Binary files differ
diff --git a/test3.png b/test3.png
index 5731d21..6a37a23 100644
--- a/test3.png
+++ b/test3.png
Binary files differ