diff --git a/tutorials/tutorial1/tutorial.ipynb b/tutorials/tutorial1/tutorial.ipynb index 349cb02..e92a22c 100644 --- a/tutorials/tutorial1/tutorial.ipynb +++ b/tutorials/tutorial1/tutorial.ipynb @@ -129,12 +129,12 @@ "source": [ "### Write the problem class\n", "\n", - "Once the `Problem` class is initialized, we need to represent the differential equation in **PINA**. In order to do this, we need to load the **PINA** operators from `pina.operators` module. Again, we'll consider Equation (1) and represent it in **PINA**:" + "Once the `Problem` class is initialized, we need to represent the differential equation in **PINA**. In order to do this, we need to load the **PINA** operators from `pina.operator` module. Again, we'll consider Equation (1) and represent it in **PINA**:" ] }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "id": "f2608e2e", "metadata": {}, "outputs": [], @@ -146,7 +146,8 @@ "from pina.equation import Equation, FixedValue\n", "\n", "import torch\n", - "\n", + "import matplotlib.pyplot as plt\n", + "plt.style.use('tableau-colorblind10')\n", "\n", "class SimpleODE(SpatialProblem):\n", "\n", @@ -263,26 +264,26 @@ "name": "stdout", "output_type": "stream", "text": [ - "Input points: {'x0': LabelTensor([[0.]]), 'D': LabelTensor([[0.3579],\n", - " [0.4598],\n", - " [0.2735],\n", - " [0.5365],\n", - " [0.9781],\n", - " [0.0321],\n", - " [0.0510],\n", - " [0.8479],\n", - " [0.6835],\n", - " [0.5861],\n", - " [0.8708],\n", - " [0.9179],\n", - " [0.1901],\n", - " [0.4485],\n", - " [0.7348],\n", - " [0.6365],\n", - " [0.7517],\n", - " [0.1215],\n", - " [0.3379],\n", - " [0.2152]])}\n", + "Input points: {'x0': LabelTensor([[0.]]), 'D': LabelTensor([[0.4519],\n", + " [0.4306],\n", + " [0.8085],\n", + " [0.6035],\n", + " [0.8842],\n", + " [0.7970],\n", + " [0.3849],\n", + " [0.1173],\n", + " [0.7432],\n", + " [0.0200],\n", + " [0.5698],\n", + " [0.9792],\n", + " [0.5295],\n", + " [0.3197],\n", + " [0.0558],\n", + " [0.2836],\n", + " [0.1626],\n", + " [0.2333],\n", + " [0.6633],\n", + " [0.9157]])}\n", "Input points labels: ['x']\n" ] } @@ -302,13 +303,13 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "id": "3802e22a", "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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"text/plain": [ "
" ] @@ -318,7 +319,6 @@ } ], "source": [ - "import matplotlib.pyplot as plt\n", "variables=problem.spatial_variables\n", "fig = plt.figure()\n", "proj = \"3d\" if len(variables) == 3 else None\n", @@ -343,7 +343,7 @@ "id": "075f43f5", "metadata": {}, "source": [ - "Once we have defined the problem and generated the data we can start the modelling. Here we will choose a `FeedForward` neural network available in `pina.model`, and we will train using the `PINN` solver from `pina.solver`. We highlight that this training is fairly simple, for more advanced stuff consider the tutorials in the ***Physics Informed Neural Networks*** section of ***Tutorials***. For training we use the `Trainer` class from `pina.trainer`. Here we show a very short training and some method for plotting the results. Notice that by default all relevant metrics (e.g. MSE error during training) are going to be tracked using a `lightning` logger, by default `CSVLogger`. If you want to track the metric by yourself without a logger, use `pina.callbacks.MetricTracker`." + "Once we have defined the problem and generated the data we can start the modelling. Here we will choose a `FeedForward` neural network available in `pina.model`, and we will train using the `PINN` solver from `pina.solver`. We highlight that this training is fairly simple, for more advanced stuff consider the tutorials in the ***Physics Informed Neural Networks*** section of ***Tutorials***. For training we use the `Trainer` class from `pina.trainer`. Here we show a very short training and some method for plotting the results. Notice that by default all relevant metrics (e.g. MSE error during training) are going to be tracked using a `lightning` logger, by default `CSVLogger`. If you want to track the metric by yourself without a logger, use `pina.callback.MetricTracker`." ] }, { @@ -364,7 +364,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "f87fba27dd664812a8745af6b2adcce2", + "model_id": "d2c3b03173424844beead0135687f8a1", "version_major": 2, "version_minor": 0 }, @@ -378,7 +378,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "a9218fefceaf42c0bec46893ea92c0a4", + "model_id": "6ce6f093a9bd4d948b031167e69f66f1", "version_major": 2, "version_minor": 0 }, @@ -392,7 +392,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "56d3a29ba694414a947c343ce36c3ecb", + "model_id": "d70837fdd11948118bd42dc039fcac79", "version_major": 2, "version_minor": 0 }, @@ -406,7 +406,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "ef339fc6f6e84fd0a2f126b7f3ffef1f", + "model_id": "7a1f7856620841f2bd9da4d5b997a4a7", "version_major": 2, "version_minor": 0 }, @@ -420,7 +420,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "8028551b1ecb4614bce42fe35906770d", + "model_id": "28c10234dd9d4af09442231fa25a0138", "version_major": 2, "version_minor": 0 }, @@ -434,7 +434,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "2b04b17ebea04644bb8861764fae70b3", + "model_id": "c2717b96ac9d457faf16528db410d398", "version_major": 2, "version_minor": 0 }, @@ -448,7 +448,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "64521c3791534cba8ea09e10fa60099a", + "model_id": "624dcd0549ff47d4b610ffbadb4a96fd", "version_major": 2, "version_minor": 0 }, @@ -462,7 +462,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "d52517dedc154f3d8fcb9285bec9d610", + "model_id": "79b9f02ac5ea4403a7a44b04f7c2974e", "version_major": 2, "version_minor": 0 }, @@ -476,7 +476,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "d43d35e8b8724c5abb9ccf459d4676f8", + "model_id": "ce2244d5d8164ba58448e42e46acd4a8", "version_major": 2, "version_minor": 0 }, @@ -490,7 +490,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "70d2eb3d2fc549f7bf97a08e83e3a62d", + "model_id": "74293f24f47f44a1bcc6215a8f592202", "version_major": 2, "version_minor": 0 }, @@ -504,7 +504,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "bdba3ab1f4a24fe8a2aa609d34d31222", + "model_id": "3099d4634d924738a69e3f5f70b51e39", "version_major": 2, "version_minor": 0 }, @@ -518,7 +518,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "f2b344b72a74491ab52c32c53a2cbe59", + "model_id": "c0f49d70727d411e826d126c4ce59ee4", "version_major": 2, "version_minor": 0 }, @@ -532,7 +532,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "5137fbb415a74326be3f6a1c27328282", + "model_id": "e9bc1cd20f504a72a8b1baf2a8300910", "version_major": 2, "version_minor": 0 }, @@ -546,7 +546,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "04c5a12eabd2424fb1ac9194c009deb9", + "model_id": "befc5bd9e0544e659d36bb038ff26c62", "version_major": 2, "version_minor": 0 }, @@ -560,7 +560,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "40c1c932d7f94f4284e2bdc7a55c685a", + "model_id": "f901c0b0f1054f29899aebac61160cf2", "version_major": 2, "version_minor": 0 }, @@ -574,7 +574,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "7f2ef9ec45c141da83b14a001287c22c", + "model_id": "73b737e7a51c4711b8196f736b02e6ef", "version_major": 2, "version_minor": 0 }, @@ -588,7 +588,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "d5e3d9dc76174707963910cac6f702c5", + "model_id": "b237547fc3b249d899141601852e9fba", "version_major": 2, "version_minor": 0 }, @@ -602,7 +602,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "119e2c4e1c1441f489de5871b72c3947", + "model_id": "bc950ecc17304a2081da1e9fd42e2c9b", "version_major": 2, "version_minor": 0 }, @@ -616,7 +616,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "d7e4dad94575481e82a4a8586b5589c8", + "model_id": "2f99dfbd81924e1db7e9818a7a304c8d", "version_major": 2, "version_minor": 0 }, @@ -630,7 +630,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "19fde8aee99d4883b555c07760d81130", + "model_id": "8740e437977b4156966ebdbd3c173c97", "version_major": 2, "version_minor": 0 }, @@ -644,7 +644,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "3fef2006d491419aad9673291c436af1", + "model_id": "0bafc6cda99e47729b162961a0e597bb", "version_major": 2, "version_minor": 0 }, @@ -658,7 +658,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "80c17d4b605e454db0fcd4233e805f9d", + "model_id": "404cfb9131ad449d9e10b3b149b725a9", "version_major": 2, "version_minor": 0 }, @@ -672,7 +672,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "6abc2f4bbe3b43bab116c0f8cbbe31d4", + "model_id": "817f09bcda9c47c781a8b5c0d1ce2ae2", "version_major": 2, "version_minor": 0 }, @@ -686,7 +686,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "e398c1204a354f408442ec3c0f6d137c", + "model_id": "c956a68e96524d4d93955ad2aa8cb699", "version_major": 2, "version_minor": 0 }, @@ -700,7 +700,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - 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"model_id": "b3ced77f6e464c7abcd2df8227ec1c78", + "model_id": "8780b5aac9c94f7191e81b2fa80eae45", "version_major": 2, "version_minor": 0 }, @@ -1064,7 +1064,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "c7d2975f2d924e14928a1a242aca7278", + "model_id": "64b242637f6848688b5021bab59b7e10", "version_major": 2, "version_minor": 0 }, @@ -1078,7 +1078,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "d3d5f15b0d6f41a09d9b94104f9ea573", + "model_id": "abe5a17e038e487d8902568f6f6c51d0", "version_major": 2, "version_minor": 0 }, @@ -1092,7 +1092,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "1fdc4abd63aa4181bf7084992cc06592", + "model_id": "704fb1fd6ee54d4e819869980681c767", "version_major": 2, "version_minor": 0 }, @@ -1106,7 +1106,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "409cfbce8d7f43dd9fb342d6faee1bda", + "model_id": "c4a7d3bb738340d0a76dc51b48d2b46b", "version_major": 2, "version_minor": 0 }, @@ -1120,7 +1120,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "e27ef4ef3a764940b78908b59909e663", + "model_id": "e6fb1dc285f74dfa8bdc9ac7c4e7c1cb", "version_major": 2, "version_minor": 0 }, @@ -1134,7 +1134,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "e7ff718324114a17b08f3ebd67c1a54c", + "model_id": "a2975623e35a4ac393f1deb16b054690", "version_major": 2, "version_minor": 0 }, @@ -1148,7 +1148,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "5b2774893a91425ca5665ad81b61d240", + "model_id": "98e80075029f4d4fbe70cc608592ec18", "version_major": 2, "version_minor": 0 }, @@ -1162,7 +1162,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "b3829835c4ff4850bad982d3c93b5982", + "model_id": "270a01498d10430792431aa76b1692d4", "version_major": 2, "version_minor": 0 }, @@ -1176,7 +1176,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "b5781e7f2d0643aca8f8bf00e8cea2cb", + "model_id": "fff6855d1cae47e1b3d7f28840b00951", "version_major": 2, "version_minor": 0 }, @@ -1190,7 +1190,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "60f1d75a9e7c45fda1493f412918af5d", + "model_id": "2b1988e104c64081a5302846e4cff34f", "version_major": 2, "version_minor": 0 }, @@ -1204,7 +1204,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "dc95837f0081480ebcd18bd0752a0320", + "model_id": "ca63f86a62784e9f82234ec6400ebbbd", "version_major": 2, "version_minor": 0 }, @@ -1218,7 +1218,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "bdbf837181e348068db3f2a7ae3709d4", + "model_id": "cc3af39920f94aab9e7b1c69863a9aa2", "version_major": 2, "version_minor": 0 }, @@ -1232,7 +1232,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "716895c10f6b463a8ca452097ddd7d05", + "model_id": "c89d47cc7d134daabd76e4a8743f353d", "version_major": 2, "version_minor": 0 }, @@ -1246,7 +1246,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "03581819e86043159021f1942fbb20ee", + "model_id": "260ea07a54804061af570e753d631a36", "version_major": 2, "version_minor": 0 }, @@ -1260,7 +1260,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "c646c008866349c0b64bd2c9214e6bf9", + "model_id": "82c1c313daa64c41b88796d5b70680b6", "version_major": 2, "version_minor": 0 }, @@ -1274,7 +1274,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "63389b3a449946dda5b387cbc4dc3390", + "model_id": "ab8784650ef54218abe905085b57bff7", "version_major": 2, "version_minor": 0 }, @@ -1288,7 +1288,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "d267afbb77e74dd2a01e831789e6119d", + "model_id": "2bd523e8463b44e9a7421b4b996ce263", "version_major": 2, "version_minor": 0 }, @@ -1302,7 +1302,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "81436a8ed5b944c3afea23ec0f95b435", + "model_id": "e6a87c69acb64da6a3513d4e7fb89cae", "version_major": 2, "version_minor": 0 }, @@ -1316,7 +1316,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "daf3a2c915e044d7a6846a81bbf28942", + "model_id": "7b3cedb8b6ba40fcb0e451c045a42f82", "version_major": 2, "version_minor": 0 }, @@ -1330,7 +1330,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "e5f7e89d8d444b8e8306ed848fe90f81", + "model_id": "504b6d19be4e4d6aa3b1357ccb12b5e6", "version_major": 2, "version_minor": 0 }, @@ -1344,7 +1344,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "0deac6df469d4888bfe12188259c4153", + "model_id": "3e65f189bab048ba8c4184c1d14a4611", "version_major": 2, "version_minor": 0 }, @@ -1358,7 +1358,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "cc577ffe35b94972b658717efd4f01cb", + "model_id": "4740ebd221bc4564bfee33ddd5f6fe87", "version_major": 2, "version_minor": 0 }, @@ -1372,7 +1372,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "fae08a088d4f4c3aa0f51acab5d5b059", + "model_id": "463638a36cea4994b1e379de0a170547", "version_major": 2, "version_minor": 0 }, @@ -1386,7 +1386,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "4143d54b37784147add4226ccdc8d590", + "model_id": "bbe59cb4691c4ded90008570c345e5e3", "version_major": 2, "version_minor": 0 }, @@ -1400,7 +1400,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "306dbf3a0a914e0d922153c264491997", + "model_id": "fe9e47e884cf4cd480e27061554b08fd", "version_major": 2, "version_minor": 0 }, @@ -1414,7 +1414,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "2f05327cfee74c94b5f4173e3896bfa2", + "model_id": "65f83ad94958421fa48c05cd44355b2f", "version_major": 2, "version_minor": 0 }, @@ -1428,7 +1428,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "d7a9e14056c0408a809d6d3706ebb3d5", + "model_id": "4c7d858a57d646529d7ff7ab820cbc48", "version_major": 2, "version_minor": 0 }, @@ -1442,7 +1442,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "41f209e2b5df484ca90bcf77893d473c", + "model_id": "99a5b0a8d36248ed82c8f6cff8aab5f4", "version_major": 2, "version_minor": 0 }, @@ -1456,7 +1456,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "82efa05fb8e3405b9fcd832bcaa2a024", + "model_id": "598db901866441d58ee0b9dd9a293998", "version_major": 2, "version_minor": 0 }, @@ -1470,7 +1470,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "af0e5f196fce42bda2f11668c7c87fa1", + "model_id": "fe061252532149868046f6b059364b90", "version_major": 2, "version_minor": 0 }, @@ -1484,7 +1484,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "2ade0c99ef454df1924a4f24f6ca671d", + "model_id": "962a66d2dedf4daeb6a0f9cb2385a7f1", "version_major": 2, "version_minor": 0 }, @@ -1498,7 +1498,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "3c7d46c4c5dd49179a8aa4bb5ac73f3f", + "model_id": "3263efab5a8c4bee86d7028bd466aa07", "version_major": 2, "version_minor": 0 }, @@ -1512,7 +1512,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "35a3159bbd624d1786682a0ee22b85e3", + "model_id": "842b4db973b64389a140b02b6dbccd98", "version_major": 2, "version_minor": 0 }, @@ -1526,7 +1526,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "4edec7ff54144c4ca4c7b9cec1709a6a", + "model_id": "ed036386fb9a44bf9bf5c24f57d899fc", "version_major": 2, "version_minor": 0 }, @@ -1540,7 +1540,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - 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"model_id": "2e2a5807001a43a1abaad6509d143721", + "model_id": "821ce905d53c417597c6270fc0d817a0", "version_major": 2, "version_minor": 0 }, @@ -1974,7 +1974,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "3bfa24c87de042b68dfd117ac185cdc9", + "model_id": "3114be8fd9e042fba685a615ca5385ac", "version_major": 2, "version_minor": 0 }, @@ -1988,7 +1988,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "ca3b4f3446c24dfca2fe1165008ec982", + "model_id": "71293156fe0a46a2a1db6f2713e82117", "version_major": 2, "version_minor": 0 }, @@ -2002,7 +2002,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "682ef85ce2e2459a8a838fda50ddffe7", + "model_id": "7cdceef1b8644b9dbf97e88c0ca9ef38", "version_major": 2, "version_minor": 0 }, @@ -2016,7 +2016,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "33780d8c2244410dbb153c4d8b6f9a33", + "model_id": "965c9f53301a4fd6878781516aa95833", "version_major": 2, "version_minor": 0 }, @@ -2030,7 +2030,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "e0262bc03a1d49409de7ae2812f67eb1", + "model_id": "982a3eb922be460fb3dde66514bff8c4", "version_major": 2, "version_minor": 0 }, @@ -2044,7 +2044,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "8f1f02011aa14c74a8486d0a0912c3f5", + "model_id": "cd4bb6ce15aa455f9edfda7b36876ad0", "version_major": 2, "version_minor": 0 }, @@ -2058,7 +2058,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "7c75ffc029db4bbcb3c9229974395e3e", + "model_id": "a92d38d191b14f778f001c90f51b938c", "version_major": 2, "version_minor": 0 }, @@ -2072,7 +2072,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "549d6506855c4ac69094f0fe4e911fa3", + "model_id": "d691c1daff9d4c988844be96ab71808e", "version_major": 2, "version_minor": 0 }, @@ -2086,7 +2086,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "577f0cf5f905440facf1c5d5d28685eb", + "model_id": "372dbbd950b74d1685c8a9640a935f09", "version_major": 2, "version_minor": 0 }, @@ -2100,7 +2100,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "b94313a87e7e498a96a17b82737da6ed", + "model_id": "5d7be653976f48e8a4d1ed1426b59234", "version_major": 2, "version_minor": 0 }, @@ -2114,7 +2114,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "352267426efa4cdbae9f8987eecf2a7c", + "model_id": "c2f36e60b13c422681d17b9bf5615c73", "version_major": 2, "version_minor": 0 }, @@ -2128,7 +2128,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "112c7b1ab0564426a5117a6445b9c101", + "model_id": "9566898b7f6c4870a9d93ce2bfbe9a25", "version_major": 2, "version_minor": 0 }, @@ -2142,7 +2142,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "f68344ba513d445f96020a33dafdb511", + "model_id": "767638e76c0e4add80dc2847a5554808", "version_major": 2, "version_minor": 0 }, @@ -2156,7 +2156,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "9c0c0edfd19844a2acc9d6cb541947ed", + "model_id": "49a2809a72ea48cc935e922dc455b563", "version_major": 2, "version_minor": 0 }, @@ -2170,7 +2170,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "72ef996008794ee1a49d9f1c25e3bf65", + "model_id": "f57c6774f6944695891cc6ceac0d8503", "version_major": 2, "version_minor": 0 }, @@ -2184,7 +2184,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "9053e936981846e6a50327397ee55cba", + "model_id": "747dd778b3c649a9910259efcb53b9a5", "version_major": 2, "version_minor": 0 }, @@ -2198,7 +2198,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "b495265358fc4a31b5bfa34abb5da5eb", + "model_id": "fffd9da2b36944d1a69368e0cfa72ada", "version_major": 2, "version_minor": 0 }, @@ -2212,7 +2212,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "6d854d37b00743f3971816765c9ceb24", + "model_id": "f7a08b2877f54d20b7ec49ec827c6baf", "version_major": 2, "version_minor": 0 }, @@ -2226,7 +2226,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "4b8af2700dbe4d0f99ea79800768aca1", + "model_id": "56706054c01c431c80ee03f3a1a8aa17", "version_major": 2, "version_minor": 0 }, @@ -2240,7 +2240,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "b0a734e7aed442a2a3adb9c255ba199e", + "model_id": "8ef02dfa701b4ab18ec2f142b9ae43a3", "version_major": 2, "version_minor": 0 }, @@ -2254,7 +2254,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "00954139fbef4cf490f6f7d5ea6545ab", + "model_id": "263c135524584e72aa80a3e8224a056c", "version_major": 2, "version_minor": 0 }, @@ -2268,7 +2268,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "c49ec3cc6180408ab4657a5e7eb7a312", + "model_id": "f5c3945064db4eb98ed35f473d1bfa00", "version_major": 2, "version_minor": 0 }, @@ -2282,7 +2282,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "e92edcfe99094e5eaa5b9c1c6ede2c4d", + "model_id": "7b78388421d84fb7b84991699dde21d9", "version_major": 2, "version_minor": 0 }, @@ -2296,7 +2296,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "58aac990da8c47578742bb7a64a52bea", + "model_id": "a141d919d35741d5a1833daf86444a53", "version_major": 2, "version_minor": 0 }, @@ -2310,7 +2310,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "4f7d8936551d4e62901becd61a89a3c2", + "model_id": "385aa297da65496a8dde27327563f159", "version_major": 2, "version_minor": 0 }, @@ -2324,7 +2324,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "a0dde1d744f8490b958969f1db40b2a5", + "model_id": "c31d927beda9480797edc1cc5b6aae71", "version_major": 2, "version_minor": 0 }, @@ -2338,7 +2338,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "1969378c9b5d481c966f7b00345d1b8e", + "model_id": "352014505f9f46edbb89b3e1468e1b03", "version_major": 2, "version_minor": 0 }, @@ -2352,7 +2352,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "93fa7c26cc1d4025a893a5d61996cac9", + "model_id": "3583e88735374974942d29126a0e1730", "version_major": 2, "version_minor": 0 }, @@ -2366,7 +2366,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "663bbcff312d4890a33aa19c5fb4b495", + "model_id": "0f9c8ed721f547a9956ec482f96cb88a", "version_major": 2, "version_minor": 0 }, @@ -2380,7 +2380,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "dc0deb7cf0cb4e77b00225c82b96b169", + "model_id": "414bdccdbefd45969275deef16ed498d", "version_major": 2, "version_minor": 0 }, @@ -2394,7 +2394,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "c38e48e025a34bdf86449e85e69ade5b", + "model_id": "a9c8b0bcfb054ac4a7e84e1129d1bf10", "version_major": 2, "version_minor": 0 }, @@ -2408,7 +2408,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "2478fab2ca0f47209e69bfcdd5b5d61f", + "model_id": "90e9804e27064e349153c05916f79bb2", "version_major": 2, "version_minor": 0 }, @@ -2422,7 +2422,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "39e6eee998344464831d8bc92c592e8c", + "model_id": "26d0df47b5854c47922c1efd2a9e3ff2", "version_major": 2, "version_minor": 0 }, @@ -2436,7 +2436,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "b7e90bedc49340f6834504715a556e97", + "model_id": "94383ee1a7034972898feec4cbf3f72f", "version_major": 2, "version_minor": 0 }, @@ -2450,7 +2450,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "011ea88cbbc04fd59efd938c90f3ea74", + "model_id": "81b1bd7d84f446debbe6f426a67f7cf5", "version_major": 2, "version_minor": 0 }, @@ -2464,7 +2464,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "e571f69928db46739efdac718863186e", + "model_id": "a6cc57feea154c08b264551f2c9b687d", "version_major": 2, "version_minor": 0 }, @@ -2478,7 +2478,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "db0131cca4584584b42163bcf7fa4eb4", + "model_id": "64a4b99f9a1448cc8c81f7b19cd140e2", "version_major": 2, "version_minor": 0 }, @@ -2492,7 +2492,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "97a9accffaed4dee9d3478982fa3fa40", + "model_id": "a69635e0d7864febac00a6c2ad709529", "version_major": 2, "version_minor": 0 }, @@ -2506,7 +2506,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "ffec24638f45451480aa7749185a4954", + "model_id": "6867f7100df54fb4b0238a8aee74ce33", "version_major": 2, "version_minor": 0 }, @@ -2520,7 +2520,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "1901833d62a64377884cb61145850e25", + "model_id": "a3d7058be6cd4a1f8a1204aa0431f310", "version_major": 2, "version_minor": 0 }, @@ -2534,7 +2534,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "3d736b57ff254656a55947466c07d001", + "model_id": "bfd6a6e175844ee6bd8f0ecc4f75b381", "version_major": 2, "version_minor": 0 }, @@ -2548,7 +2548,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "0727cf7e0594473facbc265784b4543a", + "model_id": "060bc62f6c1744d183e40ad24f3681f3", "version_major": 2, "version_minor": 0 }, @@ -2562,7 +2562,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "0af888dfdc57476ba9ff0c9670240271", + "model_id": "60833af0028a4d089c91ab19c749b6f5", "version_major": 2, "version_minor": 0 }, @@ -2576,7 +2576,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "61bac96d76994810af402e327b1456e9", + "model_id": "531999d029084143933465936c4d12bf", "version_major": 2, "version_minor": 0 }, @@ -2590,7 +2590,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "0f97a7cbeeb7408fa91e088d4e211082", + "model_id": "7f0fc85b839946018fd959ce8c2fadee", "version_major": 2, "version_minor": 0 }, @@ -2604,7 +2604,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "41a0ba7acf5e4053bd96c498fdb6cef1", + "model_id": "95f01f6afb79410e8921d458747f7c3b", "version_major": 2, "version_minor": 0 }, @@ -2618,7 +2618,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "3f4548eaa719467b88500ba1479f698a", + "model_id": "ad90cb8deb5e498788b337ce365a7d60", "version_major": 2, "version_minor": 0 }, @@ -2632,7 +2632,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "a85074d7c1794c72aa47aabfb4f64b7c", + "model_id": "fd9b22b4c8464df08217765198106546", "version_major": 2, "version_minor": 0 }, @@ -2646,7 +2646,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "d81e00d672cc4e878d750bd2fa6ffff4", + "model_id": "5aafa27db5284b2a98abf897ae1033ac", "version_major": 2, "version_minor": 0 }, @@ -2660,7 +2660,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "c7cf383db8e04160a1cab000575ac942", + "model_id": "f06fada51f21435f8394e380ddbc78ca", "version_major": 2, "version_minor": 0 }, @@ -2674,7 +2674,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "fd970d0530a143f188a1832e8db9ae93", + "model_id": "b1fa79b6f6fc40d3b7697a73c651cf53", "version_major": 2, "version_minor": 0 }, @@ -2688,7 +2688,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "5d708d4daa4c473ca3df39e5a6abf6d5", + "model_id": "ffc4ac3f01f84bd89a9eb6a6f69cf79b", "version_major": 2, "version_minor": 0 }, @@ -2702,7 +2702,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "93b96d1bef694f8abba7bcea747a6135", + "model_id": "7d1aa7d97f9246cb99af3d10626fc044", "version_major": 2, "version_minor": 0 }, @@ -2716,7 +2716,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "b7e7336ea82d48179b71692e17995468", + "model_id": "f9c566aedb4a47ac83bc548a0a9fb188", "version_major": 2, "version_minor": 0 }, @@ -2730,7 +2730,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - 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"model_id": "df3135f28bf44b54bf036de5361ec601", + "model_id": "95c5e713730f4d3a89182c457ca48219", "version_major": 2, "version_minor": 0 }, @@ -2814,7 +2814,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "e1a0f824c88649ed8d262d19f101bbfc", + "model_id": "c75c3c5554d644509028e9b1cba766c9", "version_major": 2, "version_minor": 0 }, @@ -2828,7 +2828,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "f1c1d49b7db6420cb2f5ee68ce54f579", + "model_id": "b632498f67a74bb4b54ce66db59f6a16", "version_major": 2, "version_minor": 0 }, @@ -2842,7 +2842,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "a9292e84924f4129a26b27559f9f5b87", + "model_id": "371834c62e5a4f0abe19a81f8b6a298a", "version_major": 2, "version_minor": 0 }, @@ -2856,7 +2856,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "5fd19d6c394b4cdfb8e9864d567bbe0d", + "model_id": "fbb335e23b764d8e84a5ae6533ad0161", "version_major": 2, "version_minor": 0 }, @@ -2870,7 +2870,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "b4cb0018bd4547ce8f4349ac6f0d62d7", + "model_id": "b210142a7734424c94111293e693816b", "version_major": 2, "version_minor": 0 }, @@ -2884,7 +2884,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "2c85b79dd57a410aab9aa434586e2820", + "model_id": "647b8722e1a442048d66ed7ced7e8e4a", "version_major": 2, "version_minor": 0 }, @@ -2898,7 +2898,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "8900f1f9f89b484e98a28abe71cd3923", + "model_id": "f69ab685bfca4dbf8baf65bce51c7295", "version_major": 2, "version_minor": 0 }, @@ -2912,7 +2912,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "b79ffcc961414582a22a60c1fbb39357", + "model_id": "9a33bfd48b1f49808b7e72e7e2374dac", "version_major": 2, "version_minor": 0 }, @@ -2926,7 +2926,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "bf08ddb821b04a26aa247b0950a66b44", + "model_id": "53637b91623f432cbc403b2e32f1eecf", "version_major": 2, "version_minor": 0 }, @@ -2940,7 +2940,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "9c05886ed0a74fdebe5d2e02110eff75", + "model_id": "cb3e305de8134c4e978464ce9eed58e5", "version_major": 2, "version_minor": 0 }, @@ -2954,7 +2954,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "97d19795b5ff4d71bf48fd95d41f8c63", + "model_id": "efccaa5d727a4477a1edb54517205642", "version_major": 2, "version_minor": 0 }, @@ -2968,7 +2968,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "5a06e1cf02814841b5f235248a7d770c", + "model_id": "c9cc42a8dc074625af62dd74530f6d51", "version_major": 2, "version_minor": 0 }, @@ -2982,7 +2982,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "d1c889e411404d2886e9cf22ef4eed98", + "model_id": "6a883a21bd0e48eaaa1adbf4944a768a", "version_major": 2, "version_minor": 0 }, @@ -2996,7 +2996,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "78f679335d1346a2b951f3831ab7b954", + "model_id": "e1bd208788ca4159bdb60f3564909562", "version_major": 2, "version_minor": 0 }, @@ -3010,7 +3010,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "faac3ac33f7047e495f1cbf270643e03", + "model_id": "d4dd08e5453f419caeec6e7538a6185b", "version_major": 2, "version_minor": 0 }, @@ -3024,7 +3024,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "e62179fd0fb6467fa37d8a27dcca1344", + "model_id": "ea8cd0d078bc4c51a84a778d5288581e", "version_major": 2, "version_minor": 0 }, @@ -3038,7 +3038,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "00c079d221e74c2c824e3c8e2d1bdffd", + "model_id": "f5dcd1a8215642c2bf20e774784a0a28", "version_major": 2, "version_minor": 0 }, @@ -3052,7 +3052,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "0706eec524b94baa8e48b40166d3a6c1", + "model_id": "901272105c4447558b3408b7e4f7a065", "version_major": 2, "version_minor": 0 }, @@ -3066,7 +3066,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "8ae273a4a9714efaa515e858e5c52dec", + "model_id": "a86c41e7779b466d936b06e01eac26b9", "version_major": 2, "version_minor": 0 }, @@ -3080,7 +3080,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "52f37cd7265f44a1b4ef8b9221e59856", + "model_id": "82089e2451b844098f56765f3bb99b76", "version_major": 2, "version_minor": 0 }, @@ -3094,7 +3094,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "5030083513674889907b4bbe481c57f8", + "model_id": "f597c6a939584b48950fa4889edda029", "version_major": 2, "version_minor": 0 }, @@ -3108,7 +3108,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "24d47d0b823e44a085029d06e45933c1", + "model_id": "b3ec9f82f298492ba53161b027cd648a", "version_major": 2, "version_minor": 0 }, @@ -3122,7 +3122,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "dabdaee82efb439f96275ecbb6df10dd", + "model_id": "69b9ae46504d472db5d16104359be303", "version_major": 2, "version_minor": 0 }, @@ -3136,7 +3136,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "7feb1f7132dd42429f71350bd192e395", + "model_id": "fd34e2305d6848a585e44ff85ddc3edc", "version_major": 2, "version_minor": 0 }, @@ -3150,7 +3150,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "80d83d7e57cc4db68fc4355eaf030273", + "model_id": "ef6a3c5bbfa841bd80dc8c374dafba60", "version_major": 2, "version_minor": 0 }, @@ -3164,7 +3164,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "8e02f1d6ac39440887bd271e23a098e5", + "model_id": "4f8f1cffd4f443f4b1e0fe5496aeedfe", "version_major": 2, "version_minor": 0 }, @@ -3178,7 +3178,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "d2b80eef0fa342c3b4364e4b63f25c93", + "model_id": "f9e9762f494e42f58382015d9d054fe9", "version_major": 2, "version_minor": 0 }, @@ -3192,7 +3192,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "be7dc60b680e41cb93022cd2439ef875", + "model_id": "f0e2140c65164aea9927bec87925439f", "version_major": 2, "version_minor": 0 }, @@ -3206,7 +3206,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "d7f882ac0c914bbabfb69bdf9d944c0c", + "model_id": "66a1e095472d45deae69f9b754722be9", "version_major": 2, "version_minor": 0 }, @@ -3220,7 +3220,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "ffae447d49f741fca35c7aa74cf75977", + "model_id": "25a90771c3d6410aba47fde6dbc2d9cb", "version_major": 2, "version_minor": 0 }, @@ -3234,7 +3234,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "05de2525ebcc4d889e4bc5a3836fa6d5", + "model_id": "727e2f18f953491e949b506674496d0b", "version_major": 2, "version_minor": 0 }, @@ -3248,7 +3248,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "8fa94a6da7924d349dfc939005418bb9", + "model_id": "d07d91fe80834f6696dd08e8a75910f2", "version_major": 2, "version_minor": 0 }, @@ -3262,7 +3262,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "4fb1622061c44346a3f0c0e90c279845", + "model_id": "e10283a183b14b5a8a635a5b0542201f", "version_major": 2, "version_minor": 0 }, @@ -3276,7 +3276,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "d0293b6e895b4845bbfd2ef8df58be22", + "model_id": "a1e87eeaf91141cf90f9756ce70a3c5e", "version_major": 2, "version_minor": 0 }, @@ -3290,7 +3290,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "945c895113e843268d4bbe2df4446caa", + "model_id": "3d876da4345c46369a63c956772b6e96", "version_major": 2, "version_minor": 0 }, @@ -3304,7 +3304,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "7a55474b85a6445dbe7b64960fe93668", + "model_id": "028a5e8301e34b12b7056ca33fae19ae", "version_major": 2, "version_minor": 0 }, @@ -3318,7 +3318,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "64412ccfb70f4eb7abc046f9be64f561", + "model_id": "a479dd4f90644b21aa35aeaff09c9113", "version_major": 2, "version_minor": 0 }, @@ -3332,7 +3332,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "7d0c8303bc064a418d46766710310322", + "model_id": "1274465960c345c5bbec4d7dbcdec4dc", "version_major": 2, "version_minor": 0 }, @@ -3346,7 +3346,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "f73cbc8d256845c38cf798775b109ee3", + "model_id": "f646b85153bc4495a5636ebe6393ad73", "version_major": 2, "version_minor": 0 }, @@ -3360,7 +3360,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "008b319e28154a26bd555cfef0f1da97", + "model_id": "91450297c19f4e3e99dcea7fccc438b6", "version_major": 2, "version_minor": 0 }, @@ -3374,7 +3374,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "35e89220782d4e948ec0256f5e268768", + "model_id": "470092dc7e68437498ba46054a8081a3", "version_major": 2, "version_minor": 0 }, @@ -3388,7 +3388,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "3ebc673af1814c92a16122aa35e39262", + "model_id": "32190215db8b46fe8e2633aadb75dca6", "version_major": 2, "version_minor": 0 }, @@ -3402,7 +3402,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "946d4572333645c8884104017fe2e40e", + "model_id": "3f386e3b88da4b44a103b36152e85f18", "version_major": 2, "version_minor": 0 }, @@ -3416,7 +3416,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "1992492033294ae89d06a0ad953564c9", + "model_id": "bdd3d3f60f1b400f90a946ee9d15aa7b", "version_major": 2, "version_minor": 0 }, @@ -3430,7 +3430,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - 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"model_id": "ff52810926f143c7b8ab81b5b18b80b9", + "model_id": "38f71ace7b5a4a3e9bce99f1e9060576", "version_major": 2, "version_minor": 0 }, @@ -3654,7 +3654,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "c4e52a99126c465994e73373f007287e", + "model_id": "183d86f0e75c41a78fc291956aadd377", "version_major": 2, "version_minor": 0 }, @@ -3668,7 +3668,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "8f7efc70daec4b6d9a3917d171560815", + "model_id": "d0d0f6be47b2400899ea169950507582", "version_major": 2, "version_minor": 0 }, @@ -3682,7 +3682,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "2e8eab2c79de4f9ea18f8b1a27ef93af", + "model_id": "928e751ef2384a179d37a9f84efa36c7", "version_major": 2, "version_minor": 0 }, @@ -3696,7 +3696,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "e7786f03d46c4bc19449d76792b9464d", + "model_id": "9d74b956a2b9414bbb5cf4e6cd977450", "version_major": 2, "version_minor": 0 }, @@ -3710,7 +3710,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "7e11a4fc0bf2415f8db70a13c910670a", + "model_id": "d75ffa0643af468b886f83d0735afd0b", "version_major": 2, "version_minor": 0 }, @@ -3724,7 +3724,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "875a2ac3278844d58e534741499db615", + "model_id": "564e8e6dc3dc4741ae2f01717c5d181a", "version_major": 2, "version_minor": 0 }, @@ -3738,7 +3738,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "527443daddcd408a97665522506af864", + "model_id": "803bc0a7a0ad4c8d93a1322b54f64b68", "version_major": 2, "version_minor": 0 }, @@ -3752,7 +3752,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "a95d0d558a19444c8b1f6105e1bbb352", + "model_id": "701b706b3f2b4704991197e2da62d100", "version_major": 2, "version_minor": 0 }, @@ -3766,7 +3766,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "7a7a45596c3e4f19af8478dd49d16251", + "model_id": "f045414dd4e34d08a4d9e59189aa8cf6", "version_major": 2, "version_minor": 0 }, @@ -3780,7 +3780,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "710745a4df354254af68102920c5396c", + "model_id": "906fa65e96504935b95ff88380d71ae2", "version_major": 2, "version_minor": 0 }, @@ -3794,7 +3794,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "6d48a68da9dd4f68b97e306e31a4a3fc", + "model_id": "97bcba9031164f218a3fb57162c5f861", "version_major": 2, "version_minor": 0 }, @@ -3808,7 +3808,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "0b3d187d847042afa360d4e5a2455ee6", + "model_id": "e1ef89070d244801a9b172d27d94893f", "version_major": 2, "version_minor": 0 }, @@ -3822,7 +3822,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "31e421bad0804e9a969c6dca8e21cd25", + "model_id": "0b83f713f7be4977b39a30d0d3010d5b", "version_major": 2, "version_minor": 0 }, @@ -3836,7 +3836,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "71850d67162e44c1a862a1a731a770ec", + "model_id": "34ba8866159c474ca3774846e014f2e9", "version_major": 2, "version_minor": 0 }, @@ -3850,7 +3850,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "42321f4570e14d978fab81530c8396d2", + "model_id": "0152db4e5ba345b79d8de3d1d2be362f", "version_major": 2, "version_minor": 0 }, @@ -3864,7 +3864,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "bad7b1f7cd5949cebf35e89d8918c47c", + "model_id": "138a0ae3b1d44f1d8534db8926231838", "version_major": 2, "version_minor": 0 }, @@ -3878,7 +3878,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "490eddaf65f145ea83128f46c7454a3d", + "model_id": "6c621b661d7146ffa7c53ffac99e358d", "version_major": 2, "version_minor": 0 }, @@ -3892,7 +3892,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "e0fcc58e755647b5b50072b80f1c2b66", + "model_id": "9f8c3015013541edaf9841d3c0264455", "version_major": 2, "version_minor": 0 }, @@ -3906,7 +3906,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "dee45920b36f4099b203fdf7715ea15e", + "model_id": "c633483b574844b5b7b1fd39885df81b", "version_major": 2, "version_minor": 0 }, @@ -3920,7 +3920,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "c32b0deadab04c0f940df01461ab4e67", + "model_id": "c96b5c75c3164b8a8041f13303a085d1", "version_major": 2, "version_minor": 0 }, @@ -3934,7 +3934,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "7f63b894d12a4ba485e645e2cef423bc", + "model_id": "68646781046a4a7497ffde5fe10e3e54", "version_major": 2, "version_minor": 0 }, @@ -3948,7 +3948,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "91b176bc616f48d581e513f0d53117ed", + "model_id": "bfad955a193a4d10b8094638cfbe13da", "version_major": 2, "version_minor": 0 }, @@ -3962,7 +3962,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "6759095865ee4c4ca44a0ec5b8edc933", + "model_id": "997bd5cd8289485aac510b0801eb9136", "version_major": 2, "version_minor": 0 }, @@ -3976,7 +3976,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "8f6204f414db4dd1bac9825f9e7c826b", + "model_id": "a1b367b0b862449494e32ff262be66e5", "version_major": 2, "version_minor": 0 }, @@ -3990,7 +3990,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "0f5bb01fc1884d69ac4513e139957a29", + "model_id": "651f845e95cd48d28b324c9b73d281a9", "version_major": 2, "version_minor": 0 }, @@ -4004,7 +4004,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "4cfbf82b567748ab8ab8005c1ba34bc9", + "model_id": "19373715e624494faf43d4655ac2c4d4", "version_major": 2, "version_minor": 0 }, @@ -4018,7 +4018,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "c539596e539044f48b7ef7aedf9acc7e", + "model_id": "f911a4cd199241cea76d7444c5a9674e", "version_major": 2, "version_minor": 0 }, @@ -4032,7 +4032,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "692581e86aca430c915498c946a4f8be", + "model_id": "05f87f35d9984000921a27aaa7dbf115", "version_major": 2, "version_minor": 0 }, @@ -4046,7 +4046,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "277f573b88434510a4dbb525c27b7fe0", + "model_id": "fdf4ad3c2e084ba5a455b2666554b54f", "version_major": 2, "version_minor": 0 }, @@ -4060,7 +4060,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "0fa26e2fdcf34ba1a2b2e15015a02298", + "model_id": "e06581a350424d46a3e9110849fcb47c", "version_major": 2, "version_minor": 0 }, @@ -4074,7 +4074,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "ef7967926c0c43d3beee499c8fc285d5", + "model_id": "d67ee41e15e94fde91e86df9e4f300c5", "version_major": 2, "version_minor": 0 }, @@ -4088,7 +4088,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "8ee2f2bb46654e7283f1f90ea173eee1", + "model_id": "115b8a1629d549d1bd9e9eaf4220f22f", "version_major": 2, "version_minor": 0 }, @@ -4102,7 +4102,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "dc066bc4c7f04893bba0f936cee6abf6", + "model_id": "4fda693c98c34b31b3880d776ac82ddc", "version_major": 2, "version_minor": 0 }, @@ -4116,7 +4116,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "c559789eda944677ab992af026083eb9", + "model_id": "4a341162c259485eb063b1fdaf92ce7f", "version_major": 2, "version_minor": 0 }, @@ -4130,7 +4130,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "b272a4a325434b3cb7465756b3795c4a", + "model_id": "65cb1549ef3147b98f8b754e72b4d705", "version_major": 2, "version_minor": 0 }, @@ -4144,7 +4144,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "7eb4e7eb84514cf2af96aca1daf4a5d2", + "model_id": "b2ed7b1e096445099f368f221981e91b", "version_major": 2, "version_minor": 0 }, @@ -4158,7 +4158,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "f4a9b49da7d14753ad41e96e2decff98", + "model_id": "5a6a9f8e5c0a4ea898af266ae2bb1e7f", "version_major": 2, "version_minor": 0 }, @@ -4172,7 +4172,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "76034007ef5d43e0a23e2fbefa794ed6", + "model_id": "1c1814e3d6914bb7be4c66c28d9de51e", "version_major": 2, "version_minor": 0 }, @@ -4186,7 +4186,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "0749daf1fa05478b949200a419e427bd", + "model_id": "0d4afe47c3e64a988b5395ddc3f99688", "version_major": 2, "version_minor": 0 }, @@ -4200,7 +4200,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "5167262d87aa4f2282d0c648519e6ce8", + "model_id": "3f687ba31e2044f6958c64a2ebe52fb4", "version_major": 2, "version_minor": 0 }, @@ -4214,7 +4214,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "562eac31aac14254bccc5e171056478d", + "model_id": "92e550e481c54a6ea55094fb7b44e47c", "version_major": 2, "version_minor": 0 }, @@ -4228,7 +4228,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "b385c7d57eab44308e5aab2a88777f93", + "model_id": "d6014ef5c28d4348a7149218c3947fa1", "version_major": 2, "version_minor": 0 }, @@ -4242,7 +4242,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "d688d8d601cd4aa4b04d2f0b495f98c4", + "model_id": "84d0b2304a664f3e98d53c49dc019728", "version_major": 2, "version_minor": 0 }, @@ -4256,7 +4256,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "846ce95702eb48ff8c4035df3b566e4c", + "model_id": "732cef039f31437c85693047baa15c04", "version_major": 2, "version_minor": 0 }, @@ -4270,7 +4270,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "f690efe752a2425da77d265f626522ee", + "model_id": "97487f3917774a0ea570d15bd87a58c6", "version_major": 2, "version_minor": 0 }, @@ -4284,7 +4284,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "493da89cc66f4fda813eeab429e25a38", + "model_id": "987c82d5309a419a901c3c939161905e", "version_major": 2, "version_minor": 0 }, @@ -4298,7 +4298,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "79e2760e379d47d7a8f2e3f3317945c2", + "model_id": "1ebd729e86fa402d9d429bb652e0ea63", "version_major": 2, "version_minor": 0 }, @@ -4312,7 +4312,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "90f5c22a37554b6b8522cb73693e44bd", + "model_id": "024204187b194b21afc03b6adbb51060", "version_major": 2, "version_minor": 0 }, @@ -4326,7 +4326,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "51aa2c7c314440c4a3b4e082cd27135c", + "model_id": "859d0358a73e491c8608ce247eda7a92", "version_major": 2, "version_minor": 0 }, @@ -4340,7 +4340,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "ac4729f7701c41c4906a4f46f3ac23b5", + "model_id": "cfe79919340d4249b162807594635ff7", "version_major": 2, "version_minor": 0 }, @@ -4354,7 +4354,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "1ad90f30955f4b4186b6c5699224e16c", + "model_id": "ff2d3c5bc17d480b8113ac60e2590e8a", "version_major": 2, "version_minor": 0 }, @@ -4368,7 +4368,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "e6440eb67ca443b2bd26cc5a8dbd76a6", + "model_id": "11c779ae1f9f474f8dacd9a67e53e1c5", "version_major": 2, "version_minor": 0 }, @@ -4382,7 +4382,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "8c874fe68cf643e981a62b6697938acc", + "model_id": "582b0a06a27e46a1adc5faeb2589766c", "version_major": 2, "version_minor": 0 }, @@ -4396,7 +4396,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "321dc05e2c71482e9fc670630b66541d", + "model_id": "0619d1d4ed9f4a0884f389866e907a8f", "version_major": 2, "version_minor": 0 }, @@ -4410,7 +4410,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - 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"model_id": "f171056650f340b98a07f44a94e14883", + "model_id": "cc47e0a996ff43308e816942537d06bf", "version_major": 2, "version_minor": 0 }, @@ -4634,7 +4634,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "3be5629b97e84ca9b52679cc907e6d70", + "model_id": "4fcf1834d7d9497d812741caf30ce239", "version_major": 2, "version_minor": 0 }, @@ -4648,7 +4648,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "6a63b9b3746145f1971ca799746ec6d9", + "model_id": "0bcd021b878c422c94e30b7d5a3944ca", "version_major": 2, "version_minor": 0 }, @@ -4662,7 +4662,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "1727a830501540bca0d0d7e4993e8de7", + "model_id": "46add90dff68417e930cbfa9acbe3963", "version_major": 2, "version_minor": 0 }, @@ -4676,7 +4676,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "ac6997200f524845ba21a823c295c40b", + "model_id": "369c1bcf588d4a0fbc8916ecbb40118a", "version_major": 2, "version_minor": 0 }, @@ -4690,7 +4690,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - 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"model_id": "6e1d51861fca4814a31777352ba55ed0", + "model_id": "88c8670604344d3e9926afe956c88dd6", "version_major": 2, "version_minor": 0 }, @@ -4844,7 +4844,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "da468e29172447e4b0f6f6854c60edba", + "model_id": "83559233bfc649038a9cc07a78e56575", "version_major": 2, "version_minor": 0 }, @@ -4858,7 +4858,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "42d10202df7447c6acee74e6e23703f9", + "model_id": "e0faa60a8f3f4b6bac85dc80e030f1d2", "version_major": 2, "version_minor": 0 }, @@ -4872,7 +4872,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "bee32ffd4f2c4a59a3ed6d717de3d6ca", + "model_id": "0a91025fa983402fac2a95188a18c0b5", "version_major": 2, "version_minor": 0 }, @@ -4886,7 +4886,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "d7602dd4822643e9958c0574fa1e5b7c", + "model_id": "83f5c4f29ae54fa3bac82010bbdbd822", "version_major": 2, "version_minor": 0 }, @@ -4900,7 +4900,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - 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"model_id": "b0e28f808e534c8dbf5bda3079e0fb0b", + "model_id": "61850f0869c64876ac7f3ca6c01f54d2", "version_major": 2, "version_minor": 0 }, @@ -4984,7 +4984,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "ed4d8f5b2e82430282eafb4ce0079dd9", + "model_id": "558f8328073944e0b09119974ea7ff88", "version_major": 2, "version_minor": 0 }, @@ -4998,7 +4998,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "45f029528e7f4f28974adb0c2e079450", + "model_id": "e5a6e6fc1f104d11a4fc844997965de4", "version_major": 2, "version_minor": 0 }, @@ -5012,7 +5012,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "6c638a7931ff45c5ac71393e14b22bb3", + "model_id": "b99a3484d5c940ceb314f26b5d5bc5c0", "version_major": 2, "version_minor": 0 }, @@ -5026,7 +5026,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "267a850456e548408d8687b449e7a51f", + "model_id": "7560c89c59404fd59b4f314cd29f31d3", "version_major": 2, "version_minor": 0 }, @@ -5040,7 +5040,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "3f174902391843fd86d8d3d76cda18f5", + "model_id": "d0ef6c674911413fbb94a4beb5cbc52b", "version_major": 2, "version_minor": 0 }, @@ -5054,7 +5054,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "887abfdc4b634a7bb9988c0a5b8fd6e0", + "model_id": "a2090d8cf1394122ac8ec0eb6f6f6d6e", "version_major": 2, "version_minor": 0 }, @@ -5068,7 +5068,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "3d21dfb768354e13a3e4c80a28ee3bd4", + "model_id": "9b3285580ceb407b9c56a93db281fa79", "version_major": 2, "version_minor": 0 }, @@ -5082,7 +5082,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "b49b33d404e44bfbbddbaec12ac04213", + "model_id": "16dd5294fd1b4763b9716f40c14aa2dd", "version_major": 2, "version_minor": 0 }, @@ -5096,7 +5096,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "7b60229f7a164c16afd89f8452a7150a", + "model_id": "b92d69b3011447808a181e98e25e9519", "version_major": 2, "version_minor": 0 }, @@ -5110,7 +5110,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "8aca6830ee3d4af0b77a404e3e8e7bcd", + "model_id": "4a9bc5e0bf1f4be69bfcda3e0bcb571b", "version_major": 2, "version_minor": 0 }, @@ -5124,7 +5124,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "519172e24e4d44228d787e2b6d45147a", + "model_id": "8765133bc546482fb98c5f484ece3088", "version_major": 2, "version_minor": 0 }, @@ -5138,7 +5138,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "1a9bdeea0748440c93cb33dd3045c6e3", + "model_id": "ebd1cf8cedd5405e961b24e454f88081", "version_major": 2, "version_minor": 0 }, @@ -5152,7 +5152,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "24ec68382a9945fcb38ba0f2dc81846c", + "model_id": "b5bb1cd2d3604d4ebe6beb0a133ea8d4", "version_major": 2, "version_minor": 0 }, @@ -5166,7 +5166,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "4de6dd5bb4674bcb9ffa3645bdcdab80", + "model_id": "efbfa10098fa4a35ba7a00095c0acb4a", "version_major": 2, "version_minor": 0 }, @@ -5180,7 +5180,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "5789e070fa4241b99dc15168081fc4e3", + "model_id": "f18a97341f02420b83521d644469649b", "version_major": 2, "version_minor": 0 }, @@ -5194,7 +5194,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "0da6b89e69e4465aa1d59f13ec9b9220", + "model_id": "8f8166b42ba9462ab297d7aed1701213", "version_major": 2, "version_minor": 0 }, @@ -5208,7 +5208,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "d48e7d3329604de6b155f46898371b43", + "model_id": "415a58a04b3b4eb49fa9abd57322ae17", "version_major": 2, "version_minor": 0 }, @@ -5222,7 +5222,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "7173459276e6445c85c18d13c94be916", + "model_id": "8d731efc9ee84edfa313bef6cfd96aa5", "version_major": 2, "version_minor": 0 }, @@ -5236,7 +5236,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "d76b5aaf53e147e28759b1047df86329", + "model_id": "052aa6c0f830424faf9aa07ee360164a", "version_major": 2, "version_minor": 0 }, @@ -5250,7 +5250,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "7fefd051ec154460a26f008421039cb5", + "model_id": "4daf485ced1e4a44bee7cf63241468bc", "version_major": 2, "version_minor": 0 }, @@ -5264,7 +5264,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "500db9b939984897b25ef9b0048bd8e9", + "model_id": "d89e0e5a3e304b739b34d11d0dc491f2", "version_major": 2, "version_minor": 0 }, @@ -5278,7 +5278,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "4aed2e7021904056b64a169c0d82e5c6", + "model_id": "16eae99e2ce243c49a637405fa984330", "version_major": 2, "version_minor": 0 }, @@ -5292,7 +5292,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "1a54e983e58d4175a0d8656cda5cde64", + "model_id": "5fdc64159d29423c8294134c2466d32f", "version_major": 2, "version_minor": 0 }, @@ -5306,7 +5306,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "fa04cb3e9fca46879d8e76c162031fb3", + "model_id": "20a54cbb43cd44228fbaf0aaf5ad2536", "version_major": 2, "version_minor": 0 }, @@ -5320,7 +5320,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - 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"model_id": "53c749dd9d7b49bd9ecbf97b605cc351", + "model_id": "95f6108123e04377b8cade4a006f72af", "version_major": 2, "version_minor": 0 }, @@ -5614,7 +5614,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "8038f8dc4b0b4af5953151ef53510b32", + "model_id": "ff92dc9b37b84dc4b04c92c8a000a731", "version_major": 2, "version_minor": 0 }, @@ -5628,7 +5628,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "8f94ce6ae8a8433fb17f2f504e8903d6", + "model_id": "d9b7dad31256453b995a2e75a5b0d3d1", "version_major": 2, "version_minor": 0 }, @@ -5642,7 +5642,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "494db6d256b945d38d9f9eadc9f6681f", + "model_id": "b493509d728740adaa1078b5ebd29428", "version_major": 2, "version_minor": 0 }, @@ -5656,7 +5656,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "858f95aa811f4015ac1b7b031aee91ad", + "model_id": "67f9aeb1b570426894fe974d582bc280", "version_major": 2, "version_minor": 0 }, @@ -5670,7 +5670,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "6660d357c8ec4d05991ac780961b8939", + "model_id": "7e0ae7bdf35747c5bdff95ae1e56d7bc", "version_major": 2, "version_minor": 0 }, @@ -5684,7 +5684,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "b58c6818d95d49999787826950e13551", + "model_id": "5fca76105573439a9295d268fb87b794", "version_major": 2, "version_minor": 0 }, @@ -5698,7 +5698,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "39fe04f5a2ac49d3a3b7dc4b38354634", + "model_id": "1c7663c0888344a2b554bd768fc7e8ef", "version_major": 2, "version_minor": 0 }, @@ -5712,7 +5712,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "4f55f1a55c284d5f838df5463b5e6e3e", + "model_id": "af08f0e9bfe24b20880997f360e9b340", "version_major": 2, "version_minor": 0 }, @@ -5726,7 +5726,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "765005179fe24467be56281d0b9b19ed", + "model_id": "175b76b5e7ef481bb6507cc8f8bfa531", "version_major": 2, "version_minor": 0 }, @@ -5740,7 +5740,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "e40ab78ab3ed4757922f2dd01f66c2c1", + "model_id": "6dcbc3fd4afd4256ac13212341248d2d", "version_major": 2, "version_minor": 0 }, @@ -5754,7 +5754,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "6a529835c5c346dcbbc6019394bce73e", + "model_id": "d73be3ee00f84465bb72e559b03b99fc", "version_major": 2, "version_minor": 0 }, @@ -5768,7 +5768,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "dbccb070dba74af8a80a112c8ee804a6", + "model_id": "fc53d644f69f4c4f86442d1d0b150fa7", "version_major": 2, "version_minor": 0 }, @@ -5782,7 +5782,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "e373d652ed8d46858effcdf930794105", + "model_id": "d33fc3b12ce1493eb6d3cd95843f23af", "version_major": 2, "version_minor": 0 }, @@ -5796,7 +5796,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "d12f143ecf194766ad0355c7ff6419e6", + "model_id": "0c0951a5b1fd4f869ffe70600f34590f", "version_major": 2, "version_minor": 0 }, @@ -5810,7 +5810,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "7f200b3facd242ff992c002e3509bbd8", + "model_id": "48bfb2a53c494a599ee4f80389d389f2", "version_major": 2, "version_minor": 0 }, @@ -5824,7 +5824,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "a2e2312f1d794804b3c00fb6b35fc06d", + "model_id": "f426ab9d7a8d4c62b0b883222b8f7cbd", "version_major": 2, "version_minor": 0 }, @@ -5838,7 +5838,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "080667d28b26401287b533e3f75eb2fe", + "model_id": "e733309788964db48cf789f6a79fc5f6", "version_major": 2, "version_minor": 0 }, @@ -5852,7 +5852,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "4f65c807be754f908e569166370610ac", + "model_id": "a2760e0a923c452bbed2d998919e7b88", "version_major": 2, "version_minor": 0 }, @@ -5866,7 +5866,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "4e836eec8f1246dda0df66e2676e7ec2", + "model_id": "2d6d874ff92c4d0db13248b4b7c66eac", "version_major": 2, "version_minor": 0 }, @@ -5880,7 +5880,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "d2e97775bcf349219dc1899e7e1c9f95", + "model_id": "5cddef82c5eb4a34ad9e31db0ebb89e8", "version_major": 2, "version_minor": 0 }, @@ -5894,7 +5894,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "88162168085749618b7427af9a74c62a", + "model_id": "abc26f6e956b4ab197196f4e722d3a2f", "version_major": 2, "version_minor": 0 }, @@ -5908,7 +5908,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "5beb6a5bd79448fb806c03a3e0fd7fa6", + "model_id": "7a544c686b11465db8f40199d258e0f8", "version_major": 2, "version_minor": 0 }, @@ -5922,7 +5922,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "9dcf44937b204a8881fdc456f8ca165f", + "model_id": "7c575176622847c68908053753fbdb3c", "version_major": 2, "version_minor": 0 }, @@ -5936,7 +5936,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "ed6b719d7c484c70a9ccbe40a79f1ecf", + "model_id": "ede4f8774a1e4f6d96be6d8223c07a03", "version_major": 2, "version_minor": 0 }, @@ -5950,7 +5950,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - 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"model_id": "a09f9263017f4098b285bf8a26c672ee", + "model_id": "559cdfcd85664090806a7493832f05b8", "version_major": 2, "version_minor": 0 }, @@ -6244,7 +6244,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "ea70be7125e94cbaa84ceb4f92aa1382", + "model_id": "914966bfa62a41959490b226a81172ce", "version_major": 2, "version_minor": 0 }, @@ -6258,7 +6258,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "50ccdb0f37fd4b52a9e64dccc9d26717", + "model_id": "336d4f9fc3f349499847fd584834366d", "version_major": 2, "version_minor": 0 }, @@ -6272,7 +6272,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "61463c69ad6d4d08a124979b88f7a837", + "model_id": "4c5ce7124dce49eca9d8c7a72fa54c19", "version_major": 2, "version_minor": 0 }, @@ -6286,7 +6286,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "8ec415001fbe4c5888ef0118498d5c58", + "model_id": "cbaeb77597644722b9f442f4476ffd1e", "version_major": 2, "version_minor": 0 }, @@ -6300,7 +6300,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - 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"model_id": "c4ae68517e47400ba784faa0cd5d8e0a", + "model_id": "0aa2635c9395478894c702b54296af74", "version_major": 2, "version_minor": 0 }, @@ -6594,7 +6594,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "44f5b7ed08d34e829d2eefbaa0b0494d", + "model_id": "c101e534bd344b579c99cd5115e9d0b9", "version_major": 2, "version_minor": 0 }, @@ -6608,7 +6608,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "ea9eb157b30046b88c88ac1a7307823f", + "model_id": "f1960d10a65c422e86f8ad4b8a70ce2d", "version_major": 2, "version_minor": 0 }, @@ -6622,7 +6622,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "1950b97806c74c479a2542fe1219a509", + "model_id": "9d867af951ca4f428f7986820c63de13", "version_major": 2, "version_minor": 0 }, @@ -6636,7 +6636,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "61287a6dc3c2478193b71f12896ae13b", + "model_id": "640615e2db844f15aee0a322445f7e58", "version_major": 2, "version_minor": 0 }, @@ -6650,7 +6650,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - 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"model_id": "7faae59526b5414e985c15e214074c86", + "model_id": "0c91ec3a620f41ae99f8c5500803d6c5", "version_major": 2, "version_minor": 0 }, @@ -6734,7 +6734,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "0e0a7aebd8114bd78a761fdbf77a24c7", + "model_id": "8c310153d9fb491d8624e5b3781d9f15", "version_major": 2, "version_minor": 0 }, @@ -6748,7 +6748,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "40418eec61ea4e3c84c01e582f0266d1", + "model_id": "fd05fa68f1734b559cb205aab0e26ff2", "version_major": 2, "version_minor": 0 }, @@ -6762,7 +6762,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "e8f49aa4302146f6b1446911c10cf2a3", + "model_id": "136924a1d3ce4d859f9402c24b0dd5f9", "version_major": 2, "version_minor": 0 }, @@ -6776,7 +6776,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "951bb0fab497458c87c8a17f27abb5c4", + "model_id": "4e7b49377ec44d0983e6c643160ad323", "version_major": 2, "version_minor": 0 }, @@ -6790,7 +6790,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "21de0dffec384694aa7471dab5a5894c", + "model_id": "28e4f1cb488a4302880f5e980858091a", "version_major": 2, "version_minor": 0 }, @@ -6804,7 +6804,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "f8d264c7e51d41df98ff4b606ad6c59b", + "model_id": "d9732e31ca5b4acdb8472c976dff8a0e", "version_major": 2, "version_minor": 0 }, @@ -6818,7 +6818,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "58a6a8c3eab942ae82ebd01f7a8b2804", + "model_id": "eb2029f35eb14d1b8193eb01b672f065", "version_major": 2, "version_minor": 0 }, @@ -6832,7 +6832,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "abd119464f924c399e3580ddb879f6b0", + "model_id": "48ab66dff85c489dbed0f61ec3cdb2c4", "version_major": 2, "version_minor": 0 }, @@ -6846,7 +6846,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "7fab3b37f65b42538c87623fdbf64589", + "model_id": "d568f8f5d9614a2099383083e693c4a7", "version_major": 2, "version_minor": 0 }, @@ -6860,7 +6860,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - 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"model_id": "46875e4d6bb04a67979e78427abedff5", + "model_id": "1063bd38088942bc9cb4d20514ef785b", "version_major": 2, "version_minor": 0 }, @@ -7014,7 +7014,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "129a69888f124fafad5aed7a205a2c9a", + "model_id": "5f05c624db484f479906d70726f1619a", "version_major": 2, "version_minor": 0 }, @@ -7028,7 +7028,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "d4932f94b219479ab68d3413f229fddf", + "model_id": "069251730be742eaa10ad5eca36aaf82", "version_major": 2, "version_minor": 0 }, @@ -7042,7 +7042,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "059dcb608a2444beac94a360101b6e69", + "model_id": "980ac02ec8f44330b47e389c78a323c1", "version_major": 2, "version_minor": 0 }, @@ -7056,7 +7056,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "7c36605f0bd846088add43eea3c4d225", + "model_id": "4e28db80bcc64c8d901300c29c290113", "version_major": 2, "version_minor": 0 }, @@ -7070,7 +7070,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "1c15b3adb7ab4dcb9579832a8843c34e", + "model_id": "0113a046089647099b177f04bb8c08ae", "version_major": 2, "version_minor": 0 }, @@ -7084,7 +7084,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "9b54f1742b1a4a44ad37b3e20c400cde", + "model_id": "96cac2ffdaa14456821a0671b2d7c1ed", "version_major": 2, "version_minor": 0 }, @@ -7098,7 +7098,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "a4881a862be045168af0c6fdad4a3291", + "model_id": "0c02a2824ef54365a5e6ce663b06851d", "version_major": 2, "version_minor": 0 }, @@ -7112,7 +7112,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "edca1e50cfdc4ce59a1ce8ac79350716", + "model_id": "9924f501af64439caa5a35e770bbfd97", "version_major": 2, "version_minor": 0 }, @@ -7126,7 +7126,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "e0955dec27ba4a239f9d38d5138f77ec", + "model_id": "4d149335bae54a3a823343caabdc4eaa", "version_major": 2, "version_minor": 0 }, @@ -7140,7 +7140,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "f31b08a40ca64775aa69fbec940b6916", + "model_id": "52362f37759b4771870ed65050811276", "version_major": 2, "version_minor": 0 }, @@ -7154,7 +7154,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "9970d34db6e7414fa57f17c175b46dc6", + "model_id": "aff9f11472044509aae718f48031fae9", "version_major": 2, "version_minor": 0 }, @@ -7168,7 +7168,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "5635df9543cf4da88f487ea9c1f286b4", + "model_id": "86b0f3b0a2244ebab81f48569c623572", "version_major": 2, "version_minor": 0 }, @@ -7182,7 +7182,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "c2714018dae8452cbe71fd5df928ab28", + "model_id": "cc34a31dabb24f9fbb215c99013c3549", "version_major": 2, "version_minor": 0 }, @@ -7196,7 +7196,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "d6eb66cdf52b4a8cb6bdf8405df36666", + "model_id": "5ba39e42c4634f32ab003163edc258cb", "version_major": 2, "version_minor": 0 }, @@ -7210,7 +7210,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - 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"model_id": "9fa27afa7b2b4eacb1f9248edbbbe25f", + "model_id": "d60ae3b38c8c4ebab59ad1d49bbc0467", "version_major": 2, "version_minor": 0 }, @@ -7504,7 +7504,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "d7f56571d2ce43fa9eb09b9223ad32f0", + "model_id": "f978c66201fa4dac9223580ce261039d", "version_major": 2, "version_minor": 0 }, @@ -7518,7 +7518,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "da41cf2c808a442a89373d0b9439599a", + "model_id": "05593850c9c240b2b8b285b325d7df21", "version_major": 2, "version_minor": 0 }, @@ -7532,7 +7532,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "1518e4cd4778489f8dc70d44b18fd9c9", + "model_id": "7b2682fd946d46028cc6e00cf00aa3f1", "version_major": 2, "version_minor": 0 }, @@ -7546,7 +7546,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "400ade376af842d6a63d374f180c42a2", + "model_id": "d83cdfc534fe42ca9d85edf1692539ee", "version_major": 2, "version_minor": 0 }, @@ -7560,7 +7560,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "bafe77fb53b5453da78773eddd395d93", + "model_id": "88a5b0b38b334e1888aba33b03388899", "version_major": 2, "version_minor": 0 }, @@ -7574,7 +7574,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "3a10c324ff4b4db786cea095a4f636ce", + "model_id": "513a400b187b4926954869b351545fae", "version_major": 2, "version_minor": 0 }, @@ -7588,7 +7588,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "36be3dc4180b4c79ab94b90093611789", + "model_id": "759cff81b54447279e53fa2a52352025", "version_major": 2, "version_minor": 0 }, @@ -7602,7 +7602,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "9eefdf1555ba4b868d281fe6b0430983", + "model_id": "fe76293dd1b944f780ca0dd4e2bef927", "version_major": 2, "version_minor": 0 }, @@ -7616,7 +7616,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "c6faf4ea343f48fd83e7961da29c067e", + "model_id": "c909304e62e54e0ca8256198b949d96f", "version_major": 2, "version_minor": 0 }, @@ -7630,7 +7630,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "dc5c8016a8354233a0a85e350ce6aa02", + "model_id": "8f331a84e6c14a38973454eeecff7ba7", "version_major": 2, "version_minor": 0 }, @@ -7644,7 +7644,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "c2651c62824d4e79af29ed0df9b751f5", + "model_id": "4fb7ca5a37e2468e8835ca837dac0578", "version_major": 2, "version_minor": 0 }, @@ -7658,7 +7658,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "fe0d720f78d54d7db70664490013718c", + "model_id": "6d71e2041303474a8698f2b013885c68", "version_major": 2, "version_minor": 0 }, @@ -7672,7 +7672,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "8abcad5a2cb84ccea2775630da044af3", + "model_id": "150f57434be648e09638b14e13020cd1", "version_major": 2, "version_minor": 0 }, @@ -7686,7 +7686,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "60805b25aa2f452c863fa8a01a6cb822", + "model_id": "21ced9dec6f14136b70148de03cb141e", "version_major": 2, "version_minor": 0 }, @@ -7700,7 +7700,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "78c0a601deb54fd1a5be292d9908568c", + "model_id": "62904e7cd75f48d5951d95fd2f9285bf", "version_major": 2, "version_minor": 0 }, @@ -7714,7 +7714,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "033066b3f5f24722bf8ed8b53bb0c1cf", + "model_id": "04b301d36504432893001841a5393d47", "version_major": 2, "version_minor": 0 }, @@ -7728,7 +7728,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "58552fb7aac3480a8ac75e4800f6049b", + "model_id": "2133922736724e17be65e53198bc3805", "version_major": 2, "version_minor": 0 }, @@ -7742,7 +7742,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "72ce8296d4a9446e8e0592ca3cc0d7df", + "model_id": "32997f1c4b6e4019a885ef8bb8364933", "version_major": 2, "version_minor": 0 }, @@ -7756,7 +7756,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "47603dd1448e44608c237015af5c83f3", + "model_id": "e5e0266223554594ac2f924857e5fdef", "version_major": 2, "version_minor": 0 }, @@ -7770,7 +7770,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "c547660b427b482b92682245b3550a74", + "model_id": "ef793d81a714404e97fa5c41fa6fe07b", "version_major": 2, "version_minor": 0 }, @@ -7784,7 +7784,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "e9a8999aceea46d38eb536b3fe46159d", + "model_id": "92909f70c9a44e699a377d7c450966cb", "version_major": 2, "version_minor": 0 }, @@ -7798,7 +7798,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "e18bd6a8cbfa46e0a2d92f5ca54e0e7c", + "model_id": "e39f2471b4df47fa9fe5cedbda09eedb", "version_major": 2, "version_minor": 0 }, @@ -7812,7 +7812,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "d5f822d990664ba9b733426614879bb0", + "model_id": "2fe8912d1d1c43859d7082bb26fa1607", "version_major": 2, "version_minor": 0 }, @@ -7826,7 +7826,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "6e1fea0f39bf4057a97aa15a0d56ad7b", + "model_id": "5ddb37b00d0d42dfa0b887e086cd704d", "version_major": 2, "version_minor": 0 }, @@ -7840,7 +7840,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "bf654748e0d8454991e798d8354a6293", + "model_id": "6e84e5381b37469ab3aabaec2db5f3e0", "version_major": 2, "version_minor": 0 }, @@ -7854,7 +7854,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "2fbdefe489fb4b9fb06c34a7de37ed6a", + "model_id": "d779ca38102240d6b6da471eedd8526c", "version_major": 2, "version_minor": 0 }, @@ -7868,7 +7868,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "1b3262de69b3489c85e1f46b5af39a03", + "model_id": "905f156cacf5468bbdaff4b53c26da35", "version_major": 2, "version_minor": 0 }, @@ -7882,7 +7882,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "8ed2ff010e69471db721de38c55bd63a", + "model_id": "c9279cc0e2f141dc800ab2f95dfa1dde", "version_major": 2, "version_minor": 0 }, @@ -7896,7 +7896,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "10868a945a7944eb8bbccdcd93dbbd8d", + "model_id": "e1d945443f8d4b2bb44ba2b14279fcb8", "version_major": 2, "version_minor": 0 }, @@ -7910,7 +7910,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - 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"model_id": "42872301ab004ae49ae68552cd582cfd", + "model_id": "079f7489930b41e7988bd03ff81e407a", "version_major": 2, "version_minor": 0 }, @@ -8554,7 +8554,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "a85fb2edacce4ca49457e4b48bbaad53", + "model_id": "b43b0bb0d5c24d0494188cb5423ed4be", "version_major": 2, "version_minor": 0 }, @@ -8568,7 +8568,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "6ae438028f23418991e7edc06a68928d", + "model_id": "0ebb529824014832b54d0c2e0292149b", "version_major": 2, "version_minor": 0 }, @@ -8582,7 +8582,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "236b3b55cdac4fda80369f5be07703f4", + "model_id": "8774a5b88891480db095b6af23bc807f", "version_major": 2, "version_minor": 0 }, @@ -8596,7 +8596,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "2ca3313050ab4ad6bd1c01ca0f51e6c9", + "model_id": "587af36e07e54809b1530aa236decb2e", "version_major": 2, "version_minor": 0 }, @@ -8610,7 +8610,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - 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"model_id": "5d7b52381eda471590604611e184903b", + "model_id": "d57c7a53dc5f452cb0fe7de2415cbec9", "version_major": 2, "version_minor": 0 }, @@ -8694,7 +8694,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "ec4c038b8331426fa5c428b104108294", + "model_id": "7834f110b32945e6874074edd3960c4f", "version_major": 2, "version_minor": 0 }, @@ -8708,7 +8708,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "7a1a3f172cfb4cdc9835cade94cca5f5", + "model_id": "4a104a3740ab4c9c99398a7ac89e97e3", "version_major": 2, "version_minor": 0 }, @@ -8722,7 +8722,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "65fdfd5be7a7474faf5d8b93f2ea2413", + "model_id": "45d87f6eb34e416094a5138a6f793f9d", "version_major": 2, "version_minor": 0 }, @@ -8736,7 +8736,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "957d9ac5b703462f9ebf392b647b74a7", + "model_id": "394ffdcd213b4692b247ea0f979ace0a", "version_major": 2, "version_minor": 0 }, @@ -8750,7 +8750,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "928503b272c44dba92b7ae15fa713b65", + "model_id": "4e1905ac0bda4ab0aa32b13ea0ae03d9", "version_major": 2, "version_minor": 0 }, @@ -8764,7 +8764,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "c81e6ff0a5ad4620afafff4865c66176", + "model_id": "210e5b13417d467190b2380b8cb3a5f5", "version_major": 2, "version_minor": 0 }, @@ -8778,7 +8778,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "4ad93319280240b2b56825df07e19505", + "model_id": "106153071ace49fb84bb1060ee076bb1", "version_major": 2, "version_minor": 0 }, @@ -8792,7 +8792,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "11e70af4d4164fe5a77a5863c320ecd6", + "model_id": "3abf7acdb2be4221b74297bf7adbb236", "version_major": 2, "version_minor": 0 }, @@ -8806,7 +8806,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "6768a6f05a9d447782b19c78a740f817", + "model_id": "9ed34be8eb2246549ea877ebea366e11", "version_major": 2, "version_minor": 0 }, @@ -8820,7 +8820,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - 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"model_id": "df05c4a6ce4945f78dd724be6e7852c6", + "model_id": "fe1ee96c006d4a82877c8c995d13d323", "version_major": 2, "version_minor": 0 }, @@ -10584,7 +10584,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "9575de938dd24984ad35d87a2cbb0940", + "model_id": "7f416e6c2b0f490b8b5e1c73640594e7", "version_major": 2, "version_minor": 0 }, @@ -10598,7 +10598,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "c47d1ef9b6a445f68eed1ec090f0d074", + "model_id": "b88f4969fc44435d9df5f77e3a1b7c62", "version_major": 2, "version_minor": 0 }, @@ -10612,7 +10612,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "4f87052b085f4baf99d61ec276bda553", + "model_id": "3454da305a1a46839740a545b381484e", "version_major": 2, "version_minor": 0 }, @@ -10626,7 +10626,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "b253b49533f84d2388a84f21136552e9", + "model_id": "46a44cf1a37b4e899129dad88976d8fe", "version_major": 2, "version_minor": 0 }, @@ -10640,7 +10640,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "6b5323ad0297412a91a8adca0ab9eece", + "model_id": "fde35177b6484af09c78335fd2b9d7f5", "version_major": 2, "version_minor": 0 }, @@ -10654,7 +10654,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "95669226853f450690b793cbf92153ac", + "model_id": "afdedd3b6d2541c89ef2f412bfad8c0a", "version_major": 2, "version_minor": 0 }, @@ -10668,7 +10668,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "ed3edbb0db3c4818936d7c2d87509c5a", + "model_id": "a818f596965d42c290f79233a5fd2b0d", "version_major": 2, "version_minor": 0 }, @@ -10682,7 +10682,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "f5e930b0b84d4c68bf6de8a63a865e91", + "model_id": "bd5bf6685ddf40989b471c4c7d109de5", "version_major": 2, "version_minor": 0 }, @@ -10696,7 +10696,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "d766a44d35fe4a7e89fc5a392f4a4ccd", + "model_id": "5283b11405c3495a9ca5d015a4cd8f09", "version_major": 2, "version_minor": 0 }, @@ -10710,7 +10710,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - 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"model_id": "c5b10bc16cae471c9699c7c183f4c430", + "model_id": "8dad453f4e774274953c737ee9c44d0d", "version_major": 2, "version_minor": 0 }, @@ -11214,7 +11214,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "5b1729f5633947cbba1991921bf5e5d5", + "model_id": "a6f96e26cb1d4601821d256a330269d5", "version_major": 2, "version_minor": 0 }, @@ -11228,7 +11228,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "e74f74a72168441bbb692cabcbb8aad4", + "model_id": "2d0b0fe6e8d04af48d8d632cef524477", "version_major": 2, "version_minor": 0 }, @@ -11242,7 +11242,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "d64133615a8449839482249fbdbbe813", + "model_id": "5e6106226cb64064be2082344459d4c2", "version_major": 2, "version_minor": 0 }, @@ -11256,7 +11256,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "182b8d1f6bdd40bba3703af80822b569", + "model_id": "2f6489a62abd4a979bfb317200ca4426", "version_major": 2, "version_minor": 0 }, @@ -11270,7 +11270,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "03e160c46f45428d8b3623c5f63efb5a", + "model_id": "d447dddfb8f0484f86b8a1a59d4f8044", "version_major": 2, "version_minor": 0 }, @@ -11284,7 +11284,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "25e0f0ff2e4c460ba1e50addce185328", + "model_id": "cc21575581c243aaa57efede4939f893", "version_major": 2, "version_minor": 0 }, @@ -11298,7 +11298,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "517e6967c77f4e31bc1bc5ad0a69f5c3", + "model_id": "5b4c1ee4ce9f405eb2a93d837a6e5fd8", "version_major": 2, "version_minor": 0 }, @@ -11312,7 +11312,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "8a843ad065bf491eaabea99d26dab52f", + "model_id": "0117f7afeb2d46bea632836e2ba88118", "version_major": 2, "version_minor": 0 }, @@ -11326,7 +11326,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "799f10ba56fe42b99b6b60575125f1c5", + "model_id": "36c56670127c4ef18978ce868b9d6e1c", "version_major": 2, "version_minor": 0 }, @@ -11340,7 +11340,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - 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"model_id": "e70303a942a944648e2dcdd0b11b59ab", + "model_id": "18c2b969bfc14cacbc7f57cef2ca72ac", "version_major": 2, "version_minor": 0 }, @@ -11494,7 +11494,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "0539c87536054272a113982eea8fd62c", + "model_id": "8398647cb30a41dc8e6d9771e25a3a7b", "version_major": 2, "version_minor": 0 }, @@ -11508,7 +11508,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "a3e92969eaf549cf880534040827d95a", + "model_id": "4a4a940fb95e4bceb840a5844cd8f849", "version_major": 2, "version_minor": 0 }, @@ -11522,7 +11522,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "0f5b8fa005d14e9c9fec7b26945d8951", + "model_id": "1776f0d5f2204321aa22a59a45571b06", "version_major": 2, "version_minor": 0 }, @@ -11536,7 +11536,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "3f9fa58a297344e4811f5d69b518bef9", + "model_id": "38834133090f4c028c66e8c3d2c650ec", "version_major": 2, "version_minor": 0 }, @@ -11550,7 +11550,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "c7d452857f6740bcbfae9af6f206a6df", + "model_id": "9d613e3f392c43ab9491aa36bdce3d72", "version_major": 2, "version_minor": 0 }, @@ -11564,7 +11564,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "f2c03573cea44852b17088b3f288783f", + "model_id": "af937b54fdf0456e92d65121c86447fe", "version_major": 2, "version_minor": 0 }, @@ -11578,7 +11578,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "526074b97a494207802ed870ea5859e5", + "model_id": "c63af54345db48bc8fd2e383697a6f2d", "version_major": 2, "version_minor": 0 }, @@ -11592,7 +11592,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "80a084fe3a8a49c38a92103d1d93f4df", + "model_id": "3dd8894dbbd8478ca0a03a33ee876eff", "version_major": 2, "version_minor": 0 }, @@ -11606,7 +11606,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "85d9a244957647e98f34b76e7b14309f", + "model_id": "bd92ce39b76f43aea722fcdb35a40134", "version_major": 2, "version_minor": 0 }, @@ -11620,7 +11620,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - 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"model_id": "1408297a4c784e4f8535932468929c67", + "model_id": "17fefcf07ab5412d97babb6a2bc7dea0", "version_major": 2, "version_minor": 0 }, @@ -12054,7 +12054,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "e11667b66e0c4e74b0bd4d7060089233", + "model_id": "f30b7af2c2cc4c74a631c6b8fdb7fae8", "version_major": 2, "version_minor": 0 }, @@ -12068,7 +12068,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "6382651df87b442eb6b7b09bffff8a3c", + "model_id": "ea3b625f06ea4587b882ad56a9a7b767", "version_major": 2, "version_minor": 0 }, @@ -12082,7 +12082,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "e1cd349cf0ad4f809f3071582eae99e8", + "model_id": "79a9d20ad1804147a1f8e4e2aa0e7f47", "version_major": 2, "version_minor": 0 }, @@ -12096,7 +12096,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "6067d28bb0b243d89fa44984299e6829", + "model_id": "d99f2cf8210f491d80af6246d212c657", "version_major": 2, "version_minor": 0 }, @@ -12110,7 +12110,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "30721fb12dbe4eb7a3bbcff09beabd5f", + "model_id": "07975c1d151443b18a9255a415165d8d", "version_major": 2, "version_minor": 0 }, @@ -12124,7 +12124,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "2da650577f7d422ca17cef0f09c9338e", + "model_id": "6fa79cb1c7b649ce81bb64c1501c958e", "version_major": 2, "version_minor": 0 }, @@ -12138,7 +12138,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "b4e15d4b3c06418a803f27cf71737f82", + "model_id": "0cdaab3843c54a7191527b0a474d9839", "version_major": 2, "version_minor": 0 }, @@ -12152,7 +12152,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "d9b542dc5a2f46719857c9bbc8bef233", + "model_id": "99bd4ad91364425ebac19097f546b61b", "version_major": 2, "version_minor": 0 }, @@ -12166,7 +12166,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "ed70500ef9b84b3990619637c4f484b3", + "model_id": "6e1521e064c54310b7a2c0c78b42574e", "version_major": 2, "version_minor": 0 }, @@ -12180,7 +12180,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "26e1d16ec0f745929b021e5d369b553b", + "model_id": "0d29424d352446c6b52fa9b6e58ec757", "version_major": 2, "version_minor": 0 }, @@ -12194,7 +12194,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "60309c2d964542fc9460194c1375b1a2", + "model_id": "378a18e3af734e4792aa8a9b83d52b8a", "version_major": 2, "version_minor": 0 }, @@ -12208,7 +12208,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "00abf2eb284b48d88ccbb1b3cbce9e85", + "model_id": "1c83fa195fb8481abc28cb4305b8631a", "version_major": 2, "version_minor": 0 }, @@ -12222,7 +12222,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "85bb06e982e64526937b5b36dc6e3cad", + "model_id": "8e23c18bf32f410d854100b4fa53abea", "version_major": 2, "version_minor": 0 }, @@ -12236,7 +12236,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "876dc95454fe489daa905ae9bac69ddd", + "model_id": "28d1149a662044cf9743ec038642785a", "version_major": 2, "version_minor": 0 }, @@ -12250,7 +12250,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "6c12dbaa17b242109f42f8a85d26e429", + "model_id": "13a3e7cd1ce54299a328e3ac4fa5712b", "version_major": 2, "version_minor": 0 }, @@ -12264,7 +12264,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "984727968415410eb71ac21ba55e0f65", + "model_id": "cf685a03a3554fae9d8e136fbc26d3bd", "version_major": 2, "version_minor": 0 }, @@ -12278,7 +12278,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "cbd2ebc2837e49ff8d00f1a8d6131ee7", + "model_id": "a4c694ee275c4b179d4bc80ba376dca6", "version_major": 2, "version_minor": 0 }, @@ -12292,7 +12292,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "133da57cd60f4bf3b435ed7238e4cd25", + "model_id": "d97ec0c93c494ce89492daad648b7b0a", "version_major": 2, "version_minor": 0 }, @@ -12306,7 +12306,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "1c77de0b73764f9b8108ca173cd430a7", + "model_id": "6aca7c5170d74eac8e16a22b46ba0303", "version_major": 2, "version_minor": 0 }, @@ -12320,7 +12320,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "f115e7771c8545ecaa9af3e9c1874220", + "model_id": "ccc9de56abce48f9a134baf816d4b80a", "version_major": 2, "version_minor": 0 }, @@ -12334,7 +12334,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "a99802407a74485194ecc5350c1d386a", + "model_id": "f44061b1694b47018c160658a89af476", "version_major": 2, "version_minor": 0 }, @@ -12348,7 +12348,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "58ea6a83da8744d7bbde63d8aaad6427", + "model_id": "e3b65dc964bc4fe68710d789a787d73a", "version_major": 2, "version_minor": 0 }, @@ -12362,7 +12362,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "ae83352403bd48c1931f5f10c5c3e481", + "model_id": "5129b0f0ea084ffc8e4a44bc81862dbf", "version_major": 2, "version_minor": 0 }, @@ -12376,7 +12376,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "eae7decea03a48c2a18eb82479467bfa", + "model_id": "f5d07ac215304c99a7fe4501b3caf8ff", "version_major": 2, "version_minor": 0 }, @@ -12390,7 +12390,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "f1f5e2d4c1cb4db7a8556135ab904adb", + "model_id": "fe3138c39df548429fb5c7c882556e36", "version_major": 2, "version_minor": 0 }, @@ -12404,7 +12404,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "291dd5dfc0fc486a9f98cadaa1ffb8bc", + "model_id": "b290654962e24dd387d5e20b55b39853", "version_major": 2, "version_minor": 0 }, @@ -12418,7 +12418,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "6449d12597824ba6904157f9c8bb01c4", + "model_id": "4ef300228aad46c095f8cf2e41166a24", "version_major": 2, "version_minor": 0 }, @@ -12432,7 +12432,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "181390febb0247efb2f58bf70bd94bab", + "model_id": "d0b4edb1ba0a4f3a929888b3a274f3fb", "version_major": 2, "version_minor": 0 }, @@ -12446,7 +12446,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "413e627254f442bc88aee48029b14617", + "model_id": "969fad28bd9a4a458141d5e29e506709", "version_major": 2, "version_minor": 0 }, @@ -12460,7 +12460,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "8b3e81cdf65f404e85b8fbc0794f6c15", + "model_id": "1983d71b08fd4e87911d0cfb5c6d69a5", "version_major": 2, "version_minor": 0 }, @@ -12474,7 +12474,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "b97a35a06cbc4b3a92d7d3e617b68dbd", + "model_id": "a4daeaf6e03d45a6bf58f65d7faa302f", "version_major": 2, "version_minor": 0 }, @@ -12488,7 +12488,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "0ae5bc772d364806af541cf0523f019f", + "model_id": "a8a82ef1403a4e89a351951bb905ff98", "version_major": 2, "version_minor": 0 }, @@ -12502,7 +12502,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "05ae223754804878b75cf826a84ad405", + "model_id": "576c865cae704ad9849b665509f55f15", "version_major": 2, "version_minor": 0 }, @@ -12516,7 +12516,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "34feb2bb444d4fcd9b3f99c8a92b0ede", + "model_id": "0bf2b8006a5d4e40867cf6fc59e2b7ed", "version_major": 2, "version_minor": 0 }, @@ -12530,7 +12530,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - 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"model_id": "c78efa77989c4eb4a4eb7c70b4e5e67c", + "model_id": "f82da33d2fb041eabe04780b79aa1f16", "version_major": 2, "version_minor": 0 }, @@ -13034,7 +13034,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "c9a5feb919eb45c9b5093effb49c8450", + "model_id": "41aa5085d94142ea88654f15b1e06069", "version_major": 2, "version_minor": 0 }, @@ -13048,7 +13048,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "c94a6c28e2064e8fb53794633e6a6719", + "model_id": "7c057afdf0724aa6b77eeb04197114db", "version_major": 2, "version_minor": 0 }, @@ -13062,7 +13062,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "990b813959b74c6782ffb78fe27e2eca", + "model_id": "2464fad79a6e474f8ff4abcb10b1d575", "version_major": 2, "version_minor": 0 }, @@ -13076,7 +13076,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "6fd736e171cc41f7a68b643ccb5fecda", + "model_id": "ba34beec6b5e41089186546c4ede5f12", "version_major": 2, "version_minor": 0 }, @@ -13090,7 +13090,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "aa2c976bf5224d3db42510e162496c73", + "model_id": "3f9fe34bb92d494cbb8060baa799a69f", "version_major": 2, "version_minor": 0 }, @@ -13104,7 +13104,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "0ac351e88a9e4c83a4a4341109b62331", + "model_id": "3c38fc51e93a4ba197530fb8f3071f2d", "version_major": 2, "version_minor": 0 }, @@ -13118,7 +13118,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "f06a718798954364942021f3a912fa0f", + "model_id": "a2a34b7eb92644379992d5ca67e4f6b5", "version_major": 2, "version_minor": 0 }, @@ -13132,7 +13132,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "a9ca02177dda49f98f3778dbbddd0254", + "model_id": "5fab9f906c774e849e59e679fa5396ba", "version_major": 2, "version_minor": 0 }, @@ -13146,7 +13146,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "9c0a480f04c545299cd298e1a5b7adaf", + "model_id": "a52aebf4659042c0a3e903a0476b920d", "version_major": 2, "version_minor": 0 }, @@ -13160,7 +13160,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "1daf3868d4c343a68749b116cef14b8f", + "model_id": "0623108f706d4c7f876f3d27d158e959", "version_major": 2, "version_minor": 0 }, @@ -13174,7 +13174,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "22642ccaa6ee4d1bace16de9a528cc81", + "model_id": "599c1648bae24f1a958f05e29b92d9ae", "version_major": 2, "version_minor": 0 }, @@ -13188,7 +13188,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "afe9faf5c2544290b35ea958fec64f7a", + "model_id": "7d598c89cd1d49238e746c8091da7a6f", "version_major": 2, "version_minor": 0 }, @@ -13202,7 +13202,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "6b7a7065813c4491a03f6ac4cb1cb09f", + "model_id": "ccee4de6406b428c9c121fe2e7985a62", "version_major": 2, "version_minor": 0 }, @@ -13216,7 +13216,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "9501fdfe92ae4905826dea36c15eb916", + "model_id": "4b91b8e9586c43e082944eb6745e8d32", "version_major": 2, "version_minor": 0 }, @@ -13230,7 +13230,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "061377e449014a9eb205e2edc6ccfcca", + "model_id": "8663e3ca5cb4486fa484cb7d5db75538", "version_major": 2, "version_minor": 0 }, @@ -13244,7 +13244,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "b4d7443acebc4b3babfc88a015fd80b8", + "model_id": "ec9e870d41e74ebdb54f38ec962aff8c", "version_major": 2, "version_minor": 0 }, @@ -13258,7 +13258,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "5361b8b24f6b455ab40960012942788a", + "model_id": "e6c3bc45cef04e04b6e6d80761761f19", "version_major": 2, "version_minor": 0 }, @@ -13272,7 +13272,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "7d5e289d398e4d9bbc50684e53249719", + "model_id": "7bb340552cc74c3dbe24fdaf652abf95", "version_major": 2, "version_minor": 0 }, @@ -13286,7 +13286,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "fa19506892d04b72ab0b1192ef77ce8a", + "model_id": "a81d3682a09d4ffea38d19f927e07e0f", "version_major": 2, "version_minor": 0 }, @@ -13300,7 +13300,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "aa1d3074b82d4b0cb361339a791aa737", + "model_id": "de1da9f847cd4081904500ae7bb6942e", "version_major": 2, "version_minor": 0 }, @@ -13314,7 +13314,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "ec85c16067a9427bb4f579e200055162", + "model_id": "d053f5323ad04bf4877113e45856ba38", "version_major": 2, "version_minor": 0 }, @@ -13328,7 +13328,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "0640b8905d94419597986cdda81df335", + "model_id": "96ca1e4056a64b53b8592ca2a35a1806", "version_major": 2, "version_minor": 0 }, @@ -13342,7 +13342,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "b02dc065b4ab4f208a3345766e9f565b", + "model_id": "339f2d82c30c4ee88efa26f3bc8bbad5", "version_major": 2, "version_minor": 0 }, @@ -13356,7 +13356,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "657550764b8a4fc29648b9cd9e2fe145", + "model_id": "91c00116aaf24e72904de0c47b115c21", "version_major": 2, "version_minor": 0 }, @@ -13370,7 +13370,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - 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"model_id": "6a393a100816423487787184619f9080", + "model_id": "76775511ff9344ddb039e163fb9b01cb", "version_major": 2, "version_minor": 0 }, @@ -13524,7 +13524,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "7a0ba36907ab4330b92925af6f246dee", + "model_id": "50ef905c20c9487b9fd11af0465849ee", "version_major": 2, "version_minor": 0 }, @@ -13538,7 +13538,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "16f7fa3df1944728934c8751d2f15723", + "model_id": "63baafafb6354d6ab3ebb3c89aa86e6c", "version_major": 2, "version_minor": 0 }, @@ -13552,7 +13552,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "6b5f5c3962034bcd898c14354cf4596f", + "model_id": "64b3443f946e437db7c149d5465d4420", "version_major": 2, "version_minor": 0 }, @@ -13566,7 +13566,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "e3c0fa51325a4e7b8e93bf1ef4d7c701", + "model_id": "5406d7097b594c3eac95400a134fa690", "version_major": 2, "version_minor": 0 }, @@ -13580,7 +13580,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - 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"model_id": "8434cb20350d43cab7510a333c33501b", + "model_id": "fedf03889da9426dbdf936af4969dd8b", "version_major": 2, "version_minor": 0 }, @@ -14014,7 +14014,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "bb3bffd0b7464457ad2338ef06203c09", + "model_id": "2a3137c17e704cf9a4e8cc5a620ec8aa", "version_major": 2, "version_minor": 0 }, @@ -14028,7 +14028,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "b3d2572ca4eb40b28b523130446b2b2a", + "model_id": "b07e52b364cc4ebeaed3efd03d08dc36", "version_major": 2, "version_minor": 0 }, @@ -14042,7 +14042,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "864a8aa2cf7b4dbab5848ce732e7613d", + "model_id": "8c9bf46efe244769887a529ddd05db45", "version_major": 2, "version_minor": 0 }, @@ -14056,7 +14056,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "ac1d13ff76244cebbba1654993048767", + "model_id": "f067b63ef40c471e8a53cf98d3fc8c71", "version_major": 2, "version_minor": 0 }, @@ -14070,7 +14070,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "2ffd213b4ff644f28e99d06bfd862b65", + "model_id": "5ab216cdf4044d2aba672a617bf5eeaa", "version_major": 2, "version_minor": 0 }, @@ -14084,7 +14084,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "2c34e51fb6f143e591c19b2af3aa7404", + "model_id": "e26142e45492413f954d930cd1162669", "version_major": 2, "version_minor": 0 }, @@ -14098,7 +14098,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "3c8ded6ad8ec4705b4130b61c1c49e1d", + "model_id": "e945c1175eb74d95b6a2899409c4f25c", "version_major": 2, "version_minor": 0 }, @@ -14112,7 +14112,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "979a246d92c74667a7d77c65229c4afa", + "model_id": "959fea80f47941bbb873badd427b75e7", "version_major": 2, "version_minor": 0 }, @@ -14126,7 +14126,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "c39bb9637985461ab9a4700142dab5b1", + "model_id": "f765fb604488476d9a37896cb553f2ce", "version_major": 2, "version_minor": 0 }, @@ -14140,7 +14140,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "e49d6a958e6042238db1a814c7c6ccbc", + "model_id": "1ef5aeb2e1c14cf59abea396de2d8229", "version_major": 2, "version_minor": 0 }, @@ -14154,7 +14154,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "5ffc0a905ec944fd88068f78ae36363f", + "model_id": "f3f96b6e505a463ba4520ac7bf5064ac", "version_major": 2, "version_minor": 0 }, @@ -14168,7 +14168,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "78c9be880e274f70a092bdd1eb37f3ca", + "model_id": "cebfc152077e495786a3e010122c94f0", "version_major": 2, "version_minor": 0 }, @@ -14182,7 +14182,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "ea737046cfab41a286f22fad417dcc99", + "model_id": "f345a9c30a6b41379aa6647bcc7f253d", "version_major": 2, "version_minor": 0 }, @@ -14196,7 +14196,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "3b599406c0cd4dccaa7ff14c4b0268db", + "model_id": "b9859505680247e480789db17c2db69c", "version_major": 2, "version_minor": 0 }, @@ -14210,7 +14210,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "c8d416cd966446788c6bf64cf12511d5", + "model_id": "86d683c6946345a9a80b4d0168e126d4", "version_major": 2, "version_minor": 0 }, @@ -14224,7 +14224,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "c8f7904c86a74ac28e0b254856921fa7", + "model_id": "292274c3ad1e4ce79bbfa454a396bc12", "version_major": 2, "version_minor": 0 }, @@ -14238,7 +14238,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "eb6702ea264943e69b03de2f614e571b", + "model_id": "d26d007c606442fbb2091bac36a1060b", "version_major": 2, "version_minor": 0 }, @@ -14252,7 +14252,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "e42356ea28b14757b25f4ad40eadbdfa", + "model_id": "f086cabb908c475b858a4ee861553d96", "version_major": 2, "version_minor": 0 }, @@ -14266,7 +14266,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "36331d60c4724a1c996d87e29c612031", + "model_id": "094f2f521b9347899f03b355549c8979", "version_major": 2, "version_minor": 0 }, @@ -14280,7 +14280,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "61f26b163f7e4fb9bd67f91a3da07c8d", + "model_id": "e679ce4e842e4651b1179ca256746768", "version_major": 2, "version_minor": 0 }, @@ -14294,7 +14294,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "9ad6c903d2c5420abfedfba328791a1b", + "model_id": "54623c30a9574987843d70bc76051bdc", "version_major": 2, "version_minor": 0 }, @@ -14308,7 +14308,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "016d5a8d7ccc44499c579e587b3b339c", + "model_id": "09eb588d5da0402db5753a43d26b68c4", "version_major": 2, "version_minor": 0 }, @@ -14322,7 +14322,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "63774f6ba99541cfab110a41d8f92305", + "model_id": "eb4cd344b7a24492a5ad3cba3a446733", "version_major": 2, "version_minor": 0 }, @@ -14336,7 +14336,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "fd2ba98324da4d59a12965135ce9ac66", + "model_id": "a0ea67b67fad4228ad75f0561431343c", "version_major": 2, "version_minor": 0 }, @@ -14350,7 +14350,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "4d917bd12bec4d3b93313bbf205a743e", + "model_id": "9828f592edd643c69ad4f177741709ea", "version_major": 2, "version_minor": 0 }, @@ -14364,7 +14364,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "1dd91c9ae48344e99a60af067fc64b19", + "model_id": "e479cfb7b98c4978817a3e590752dafa", "version_major": 2, "version_minor": 0 }, @@ -14378,7 +14378,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "2987f97925cb42b6baaac9449e177097", + "model_id": "29f6e985da27406194a2e32465de8b78", "version_major": 2, "version_minor": 0 }, @@ -14392,7 +14392,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "536c42dc62e240049da69afa498277fa", + "model_id": "650af7cdbd3d4f058c6f31730dfd8413", "version_major": 2, "version_minor": 0 }, @@ -14406,7 +14406,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "3ebb4c82554a4f258a057b8d0bd64da3", + "model_id": "45c6599c00f24ffea1ced06399d0807d", "version_major": 2, "version_minor": 0 }, @@ -14420,7 +14420,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - 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"model_id": "2c60223b13744d9285c572a2e8da333c", + "model_id": "182188774afd4acfa4ed3a8ca74819bc", "version_major": 2, "version_minor": 0 }, @@ -14994,7 +14994,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "350690ba96d741929c6ecc494a898960", + "model_id": "a8012af6b77a461aa4395c39cb5b2d50", "version_major": 2, "version_minor": 0 }, @@ -15008,7 +15008,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "752cb9c14ac1442dacab5219830b3374", + "model_id": "843bb6648adc452580753b02e64f8d00", "version_major": 2, "version_minor": 0 }, @@ -15022,7 +15022,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "7cbe8de11dc24c0a8580006b927842c1", + "model_id": "04be7948e1de46deb6203dafbfdf846a", "version_major": 2, "version_minor": 0 }, @@ -15036,7 +15036,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "8a9b88c5e3bc40a6931043fa5297d952", + "model_id": "9951443d4cc5430d8cdbd2dfd257f38c", "version_major": 2, "version_minor": 0 }, @@ -15050,7 +15050,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "54747aa51df540f0a1bfe6f313c1d6c6", + "model_id": "750713bf6e31448cbf9baef7dd058c50", "version_major": 2, "version_minor": 0 }, @@ -15064,7 +15064,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "b147b044ec3c494ba65b678b529a1b22", + "model_id": "cfdd4a61c2fb4c988cfe73b50b25e1b4", "version_major": 2, "version_minor": 0 }, @@ -15078,7 +15078,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "0600896c9e6b409bae554e4b75d399b7", + "model_id": "801c28cd445b4dca9f8022a109ebe2ba", "version_major": 2, "version_minor": 0 }, @@ -15092,7 +15092,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "d5b46e4af7ce4c3094ad45957a98ac21", + "model_id": "f521cfb31db34bc4915bf481e4d70c2e", "version_major": 2, "version_minor": 0 }, @@ -15106,7 +15106,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "3ec5c2497ebd464ab2d2b5a149e9e778", + "model_id": "a52606156c4d4795acb5df376b4759c7", "version_major": 2, "version_minor": 0 }, @@ -15120,7 +15120,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "db8d3ba98163435cb05930764538e0c1", + "model_id": "45cc46ec51ab46daabffeaa6aeff4060", "version_major": 2, "version_minor": 0 }, @@ -15134,7 +15134,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "877c7cf2a5e34af2b97af1de1d636a15", + "model_id": "c3c11a70e80c4707a3b00921f43948b4", "version_major": 2, "version_minor": 0 }, @@ -15148,7 +15148,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "7d6a0dbd22194afda7a7a5403deba69b", + "model_id": "fd8f4df90cf140749d60e9aa5a4f5a4e", "version_major": 2, "version_minor": 0 }, @@ -15162,7 +15162,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "a0a742e4616f4d07a20851c138087874", + "model_id": "2c1719e3d15145ffb4abcebdfc0050fc", "version_major": 2, "version_minor": 0 }, @@ -15176,7 +15176,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "ee42e54d7dd246f5b0c14905e6c3721b", + "model_id": "6567983aaca74b87987db063ca632017", "version_major": 2, "version_minor": 0 }, @@ -15190,7 +15190,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "afd57ddd72db412c9badc744d1be32a1", + "model_id": "56049ce218e843b19dc79c5135b040d3", "version_major": 2, "version_minor": 0 }, @@ -15204,7 +15204,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "5a463a023f7a4d2d86a3c7147cc2652c", + "model_id": "2fc7916e035341778d2a8e2e75a43337", "version_major": 2, "version_minor": 0 }, @@ -15218,7 +15218,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "b56112e9d932408ea99be643761be838", + "model_id": "06f4ed0ef1b44e9c9e118f6670a990e4", "version_major": 2, "version_minor": 0 }, @@ -15232,7 +15232,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "d06ac8208d0949d6a8cd9ad3f76106b4", + "model_id": "a1bb712f9d9a463cb74047de9031e839", "version_major": 2, "version_minor": 0 }, @@ -15246,7 +15246,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "c18248a6b2d74abc999a8ee650deb13b", + "model_id": "0b6b4e2d12b942249af659dcf7f81b77", "version_major": 2, "version_minor": 0 }, @@ -15260,7 +15260,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "a1481d1e697a4fafb75fbf10a02a9428", + "model_id": "1b07990bd48643c9b2094a01739b515f", "version_major": 2, "version_minor": 0 }, @@ -15274,7 +15274,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "4562b5970f9044b7bcd7671fbab4011f", + "model_id": "2962341ada9344b68338d4be1465e7b8", "version_major": 2, "version_minor": 0 }, @@ -15288,7 +15288,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "b78057eb3c584de49265411c3e3f9b2d", + "model_id": "d8c7e18e3b374961b06a22042b05651a", "version_major": 2, "version_minor": 0 }, @@ -15302,7 +15302,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "3e594ec50f694650b694d0e47881852a", + "model_id": "97c089739d9d48b6b7a3778b7c6bb546", "version_major": 2, "version_minor": 0 }, @@ -15316,7 +15316,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "b33f5a219e944e0ca45b79682b3bc3b8", + "model_id": "be1a3b493412484aa1ab66a00e440298", "version_major": 2, "version_minor": 0 }, @@ -15330,7 +15330,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - 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"model_id": "2e9be7e780de4745b3d522144f820db0", + "model_id": "d882080e79244f22bb7d8851579cade0", "version_major": 2, "version_minor": 0 }, @@ -15624,7 +15624,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "f6fc832dc34f48b3a2c5f214060d0246", + "model_id": "0dea576644584348b81ce45fe6722b0b", "version_major": 2, "version_minor": 0 }, @@ -15638,7 +15638,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "ef1437c4edb145d5b8330d38e2cedc01", + "model_id": "a62106f589c64e3e8460bb1defe2ad16", "version_major": 2, "version_minor": 0 }, @@ -15652,7 +15652,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "0cf3060efe4d4cdd96ffb190ccdda81e", + "model_id": "334859430e984645af9039959f8b8ebc", "version_major": 2, "version_minor": 0 }, @@ -15666,7 +15666,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "9ec43768d94b453098da03e5c01e4f59", + "model_id": "3ead42c5f93043098d3e43e4c825f178", "version_major": 2, "version_minor": 0 }, @@ -15680,7 +15680,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "f34c17abeea0462492de7d5ac42f1f11", + "model_id": "d8d103b8580b4c23902b8d189fc89951", "version_major": 2, "version_minor": 0 }, @@ -15694,7 +15694,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "057656cd9eaf47e6986bd54c3958cfba", + "model_id": "cf4239e6813f410297008d4cc8b39731", "version_major": 2, "version_minor": 0 }, @@ -15708,7 +15708,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "784dd755e6004b48a58d93812c253ef7", + "model_id": "7f94631a61514db19f65f6fdf96f6f5f", "version_major": 2, "version_minor": 0 }, @@ -15722,7 +15722,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "7eb8910ade6d4c5f8be07389e58292c5", + "model_id": "19433852941c432681cba67b20bdbd25", "version_major": 2, "version_minor": 0 }, @@ -15736,7 +15736,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "ef904af89c5e49109535b741543f8d10", + "model_id": "9312dce2ca0040c2a8b2033feb18cd0c", "version_major": 2, "version_minor": 0 }, @@ -15750,7 +15750,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "ce0bd71f78a44d098a393ebe5c6e1c09", + "model_id": "5181216c8ed04516837fba6dd2aff72e", "version_major": 2, "version_minor": 0 }, @@ -15764,7 +15764,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "1c3b28672b3b4b089b0736509d4b0fa6", + "model_id": "f9764d8129c54a7da4222d187220238c", "version_major": 2, "version_minor": 0 }, @@ -15778,7 +15778,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "7f7483ee2f234eddab3125843612a406", + "model_id": "c9e35f63df524536b3fda7421695e0b5", "version_major": 2, "version_minor": 0 }, @@ -15792,7 +15792,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "498b60261b2a4b009b907b24c909f828", + "model_id": "31b691260be4406199aecbeb084eb218", "version_major": 2, "version_minor": 0 }, @@ -15806,7 +15806,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "2b5ab9c80c334a439381d12e71b80047", + "model_id": "1bd91e46f2e44416bf577e8cab930955", "version_major": 2, "version_minor": 0 }, @@ -15820,7 +15820,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "f1978d3b0ebc45c794f42361cb231067", + "model_id": "3401cecbff7c476ea865fa5496eaad5a", "version_major": 2, "version_minor": 0 }, @@ -15834,7 +15834,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "8a170ced4e2f4258bfa3fdf30c936709", + "model_id": "32bac46bd4084aaf94e3c8a6b92e7df0", "version_major": 2, "version_minor": 0 }, @@ -15848,7 +15848,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "958cf98c998b4d1ca70fa5321a33f57c", + "model_id": "e2deb05fc6734fb588ac8aa3eca3459a", "version_major": 2, "version_minor": 0 }, @@ -15862,7 +15862,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "76052b625dc446bdb60f379ba6a6f2d0", + "model_id": "c663910e928e42919c56107b1070a6d7", "version_major": 2, "version_minor": 0 }, @@ -15876,7 +15876,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "0d733a4f22744add8db1e89d0914dcb8", + "model_id": "e16254ec49904608a704fe7acdfe262c", "version_major": 2, "version_minor": 0 }, @@ -15890,7 +15890,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "59e4a1a1d7e449119bb9675fe0789076", + "model_id": "ddf51301be984e0394a898b763873ea0", "version_major": 2, "version_minor": 0 }, @@ -15904,7 +15904,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "b9fac3d05f734126924fb00aab7576cf", + "model_id": "16f4b0c90ae649868c8dae6b54f7110c", "version_major": 2, "version_minor": 0 }, @@ -15918,7 +15918,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "e12f9a68945d446ba46ba532f9873bc7", + "model_id": "dcfe5f25dc43476aac3529b70d8bc721", "version_major": 2, "version_minor": 0 }, @@ -15932,7 +15932,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "a132d48734b74684bc9a556e71a7b38b", + "model_id": "275634fefb7445578fd8d4bdc4c63809", "version_major": 2, "version_minor": 0 }, @@ -15946,7 +15946,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "1fe6d04d004f461cb30f359f681a14c9", + "model_id": "4fdc11f99907418984ff8e42f02f664f", "version_major": 2, "version_minor": 0 }, @@ -15960,7 +15960,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "ab5d43e3d7154ce5aa9bfc408326c658", + "model_id": "25422c00a74b464da4a8d3a672a55c22", "version_major": 2, "version_minor": 0 }, @@ -15974,7 +15974,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "6d7d9f08f8db40529f32d36ee9a37086", + "model_id": "2acc54c948d149e58028a3d3e5fddeef", "version_major": 2, "version_minor": 0 }, @@ -15988,7 +15988,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "7501ba28c0c34e46a79123ac0023223b", + "model_id": "35c63afdfd92485fac8f3173245c753c", "version_major": 2, "version_minor": 0 }, @@ -16002,7 +16002,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "9b95c486369a4b6a8a59f4ac45120739", + "model_id": "8ef12beb3fa44852af543698edaef667", "version_major": 2, "version_minor": 0 }, @@ -16016,7 +16016,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "9e72103cb65f4ab59bb370df5afb7014", + "model_id": "003a522944404bd097093bcc1b8aa948", "version_major": 2, "version_minor": 0 }, @@ -16030,7 +16030,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "4b31f31d27144a5eb2a593548e14cba5", + "model_id": "f447b24ef950461081fd37b0e0751c00", "version_major": 2, "version_minor": 0 }, @@ -16044,7 +16044,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "06ee73b3000e4e6ca050ffdc03ae7aad", + "model_id": "2220e8e20c3a4114b8fef0c2768364b9", "version_major": 2, "version_minor": 0 }, @@ -16058,7 +16058,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "237305408f93473291f5e932c91393a4", + "model_id": "b7b629af5f3c4a6c9d6ec05bdc26a230", "version_major": 2, "version_minor": 0 }, @@ -16072,7 +16072,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "6b250303380a476aa3590fdde69f60a8", + "model_id": "35ced756ff044f85b3a918b6379045ca", "version_major": 2, "version_minor": 0 }, @@ -16086,7 +16086,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "2f857f641891404db57d1aded91bd53f", + "model_id": "797ebb4ab85b46ddbe8114274efde83e", "version_major": 2, "version_minor": 0 }, @@ -16100,7 +16100,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "4e469cc406e24f47bc77e409a03b75e5", + "model_id": "e19d76c11eb24a5d9eb84206bc7f2c27", "version_major": 2, "version_minor": 0 }, @@ -16114,7 +16114,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "7fce536d9d1849e191adca0128780650", + "model_id": "c70eda4407b541178cb93cd9c8cf12b3", "version_major": 2, "version_minor": 0 }, @@ -16128,7 +16128,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "8db9dc80e413452e90657e6cb58e0399", + "model_id": "6a3a322bde964626a661ce9da6be9af1", "version_major": 2, "version_minor": 0 }, @@ -16142,7 +16142,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "f36535f6b8dd41508ec262a6681955c4", + "model_id": "9c768f3c015646df9ebf71c88aa3d6b1", "version_major": 2, "version_minor": 0 }, @@ -16156,7 +16156,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "d2636409358c4b5ba9ce85e7abb8dfc4", + "model_id": "a7add5fbffc54c0e9b6456bbc7d86681", "version_major": 2, "version_minor": 0 }, @@ -16170,7 +16170,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "5c921c5dc91c4e529cc24fd0a8fd09e3", + "model_id": "cd640f83951d40b09aa1f241b6fdec47", "version_major": 2, "version_minor": 0 }, @@ -16184,7 +16184,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "eac3293818a64eeaaaa90bb0c7948b61", + "model_id": "c8ab0600e6be4cac9b00338643213256", "version_major": 2, "version_minor": 0 }, @@ -16198,7 +16198,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "cb3900a79f0d49f196d1af7c4a674211", + "model_id": "c99a4be788b94c4899ca0fa8c3e405f9", "version_major": 2, "version_minor": 0 }, @@ -16212,7 +16212,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "8ca09fa3a3574cbf8e9926bc2eccfd9c", + "model_id": "9e3ff68c11204f2aad89b1ff253ff050", "version_major": 2, "version_minor": 0 }, @@ -16226,7 +16226,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "208c1e84c6014f38988f86b19a4e9fb8", + "model_id": "02b936d680574f1bb88f40ee87eeb715", "version_major": 2, "version_minor": 0 }, @@ -16240,7 +16240,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "6b94dba2426d41aebae83f6e78d1662c", + "model_id": "ba94e0bc9b3f407fa0fa7727dc4ab0fb", "version_major": 2, "version_minor": 0 }, @@ -16254,7 +16254,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "695cc1be54e84665ae2f1e90a8d813c8", + "model_id": "67f7fccd9c9946f5af10173c9df7044d", "version_major": 2, "version_minor": 0 }, @@ -16268,7 +16268,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "43411706aa994502b6a2e48127eb2fd0", + "model_id": "cb88c152dbcd44e4b4a7fee1901d3723", "version_major": 2, "version_minor": 0 }, @@ -16282,7 +16282,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "988628e81ade43a7ab17891d6d637592", + "model_id": "1b6f9bc59aad4958b13eadaa205d1fd1", "version_major": 2, "version_minor": 0 }, @@ -16296,7 +16296,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "dd2f410b65ba4a03a89576f6733e26ca", + "model_id": "0d69a03ad9bd417b9d0f27f384670179", "version_major": 2, "version_minor": 0 }, @@ -16310,7 +16310,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - 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"model_id": "7ef5acd8b9a2436a9c5f2a9cb2d3f6ce", + "model_id": "1634611579944bb7ab97c51eafd804a0", "version_major": 2, "version_minor": 0 }, @@ -16534,7 +16534,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "4f0b58087abc4920aa5571a87e6c261a", + "model_id": "d715201a28b64ac6bd95c147b812bee9", "version_major": 2, "version_minor": 0 }, @@ -16548,7 +16548,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "0f2f7139b2c44abb8e4e840972a9fae4", + "model_id": "6446b64545444fc4a09333dc0c1e5d22", "version_major": 2, "version_minor": 0 }, @@ -16562,7 +16562,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "55d9e2f3c41f474a842dfba2eb01b020", + "model_id": "7341ce946bbc4244b00599bf3e1c3cb4", "version_major": 2, "version_minor": 0 }, @@ -16576,7 +16576,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "a7a07b6baa254981aa4fb448d09a8b6c", + "model_id": "c86e664880834a1ca9c06e4be10ef506", "version_major": 2, "version_minor": 0 }, @@ -16590,7 +16590,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "aad4a2577a5245949f948c0bc321c3aa", + "model_id": "80a3c020d6214a37927ce4da8e572eb8", "version_major": 2, "version_minor": 0 }, @@ -16604,7 +16604,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "8dd24683ad034c49ab1f62b1586d0ab5", + "model_id": "84854aaca9cf43c59805183f877df84a", "version_major": 2, "version_minor": 0 }, @@ -16618,7 +16618,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "7cae670a9c4f44aa850b107944f32137", + "model_id": "5c6f3d6382a148bdbe3a42ca0bc1d30a", "version_major": 2, "version_minor": 0 }, @@ -16632,7 +16632,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "b6fa2fdc427d44578ffed309264cf1f2", + "model_id": "f91910f2ae214c5b984069b41269164a", "version_major": 2, "version_minor": 0 }, @@ -16646,7 +16646,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "4ecaf9c245c748bd8673f6e5ae3b1e0e", + "model_id": "68bf2978a23e4af4aa08fb3b716ef80f", "version_major": 2, "version_minor": 0 }, @@ -16660,7 +16660,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "152c3cd7ea7b4f7da0487e4d4e791f1e", + "model_id": "228acbcd86ed4c7ca2fc16de218a6f95", "version_major": 2, "version_minor": 0 }, @@ -16674,7 +16674,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "657ec7b3a3a44754b1c044e7c62a6ea0", + "model_id": "7977787919784bec8508f701722b44c4", "version_major": 2, "version_minor": 0 }, @@ -16688,7 +16688,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "ce7e670ef937451a9dd75a98de5cebdc", + "model_id": "06cd8fbdbf7b4b6e8c4362ef54ad1bb2", "version_major": 2, "version_minor": 0 }, @@ -16702,7 +16702,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "6065e2883cbc4e588899ef8959e219a3", + "model_id": "18a44425a9cb40c39550318a22df1e0d", "version_major": 2, "version_minor": 0 }, @@ -16716,7 +16716,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "9eb6231c385c4403b5727005483692ce", + "model_id": "275eb4f33929401e9011e6a11726b7e2", "version_major": 2, "version_minor": 0 }, @@ -16730,7 +16730,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "56215df8bcc74b0d8ede532abb54d367", + "model_id": "6d2b1661f40745b9971423e831b71244", "version_major": 2, "version_minor": 0 }, @@ -16744,7 +16744,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "97813a2ffd8840efb959a2806c560ed5", + "model_id": "14c23eee45064a3bac6f9ddfa724a300", "version_major": 2, "version_minor": 0 }, @@ -16758,7 +16758,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "ab527d7cb9a6484e9143bc0c1fb78f4a", + "model_id": "f7ac018543074caab4dc2b550cce7d89", "version_major": 2, "version_minor": 0 }, @@ -16772,7 +16772,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "fd454974c47d48c8aaceb975dc1ebb92", + "model_id": "1a2c6b4494644fa0964b6f1174cf39fc", "version_major": 2, "version_minor": 0 }, @@ -16786,7 +16786,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "e74ca5317ee0450d84315f69f1190cd0", + "model_id": "7153a9fa63fc4042953a8f4224d412d5", "version_major": 2, "version_minor": 0 }, @@ -16800,7 +16800,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "1fe4221fc6eb4530b2ce3c8229d0610c", + "model_id": "33a97982fe1a48448fa50829b7cdaa4a", "version_major": 2, "version_minor": 0 }, @@ -16814,7 +16814,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "2dab986743e74625b9f5770805480adf", + "model_id": "61f59a04f6024fab8dcbacb7e6134468", "version_major": 2, "version_minor": 0 }, @@ -16828,7 +16828,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "50fd0c38dcc54d2e9cb1d35fb7ac6f90", + "model_id": "5acef796e39a4cb3a1d3aaf73bd1cbfd", "version_major": 2, "version_minor": 0 }, @@ -16842,7 +16842,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "caca834dc9cd49e789f86d935d9119f6", + "model_id": "651614b97c0145ae8ea6a6e97ff749c9", "version_major": 2, "version_minor": 0 }, @@ -16856,7 +16856,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "b68d76e085074a9dbf730aaa04a6c7a7", + "model_id": "37f3f1ce88c54519adfee54bbbbc3e68", "version_major": 2, "version_minor": 0 }, @@ -16870,7 +16870,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "ebca7034514e427fb35026e5897e89f4", + "model_id": "319aa511533946d8a450e4b8723a7b2d", "version_major": 2, "version_minor": 0 }, @@ -16884,7 +16884,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "d9bacd38e06b4a3c9f46b9168f189683", + "model_id": "8ea7cd624e954e79afe257f32b5366e6", "version_major": 2, "version_minor": 0 }, @@ -16898,7 +16898,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "05b66076d65e4d22b0c1d86f5ecbf58f", + "model_id": "caf45c679a4743dc8687fdbdfa71b7a9", "version_major": 2, "version_minor": 0 }, @@ -16912,7 +16912,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "c805e2eaf74b4d8aad2803ee4199a7ed", + "model_id": "a97c037d9f8e43218a78e37cab933a14", "version_major": 2, "version_minor": 0 }, @@ -16926,7 +16926,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "ab8794182097412a9f61ff0c9da0ca3b", + "model_id": "038991a52c6d4ea19614a37e57c749c2", "version_major": 2, "version_minor": 0 }, @@ -16940,7 +16940,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "6bbe9e45edf8442f9ca33544e7427495", + "model_id": "fe3b8756e9f34f5699824657b5ac49f3", "version_major": 2, "version_minor": 0 }, @@ -16954,7 +16954,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "fe175e1c168d4fecad107d9dda14c282", + "model_id": "8b88c28883f74532829fffc25347628c", "version_major": 2, "version_minor": 0 }, @@ -16968,7 +16968,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "15bfe99cdc5d414bba09cb3789bd04a3", + "model_id": "42af20e35c814526ad218e32424ef34c", "version_major": 2, "version_minor": 0 }, @@ -16982,7 +16982,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "0d48954b26bc46888af58e1894b6262f", + "model_id": "47c1b259e8c645138ec699ef08accd68", "version_major": 2, "version_minor": 0 }, @@ -16996,7 +16996,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "f3fa5f1e3ab34866a66ea597896f8417", + "model_id": "60cc72267421447d9875d21041380115", "version_major": 2, "version_minor": 0 }, @@ -17010,7 +17010,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "5a79a8d1ef0c4e9e8a5456a2e6db4a1d", + "model_id": "bcdffa799508404dbf404bc8ef8005d8", "version_major": 2, "version_minor": 0 }, @@ -17024,7 +17024,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "6d795daa514945069e281ca255a8561f", + "model_id": "5d5424b984b34380ac8618779696c4bb", "version_major": 2, "version_minor": 0 }, @@ -17038,7 +17038,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "6f50f7e720df457fa70489d90a4400ad", + "model_id": "e4e5b50674ae4e7ebef45edc1737ac2c", "version_major": 2, "version_minor": 0 }, @@ -17052,7 +17052,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "5a1831c79503403d80a22eadb1035bb7", + "model_id": "a91fc3601ddd4e54a427f1dc28363a4c", "version_major": 2, "version_minor": 0 }, @@ -17066,7 +17066,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "8035fbbbc0f140119679af8a0d2d6c9c", + "model_id": "564c62cf1972494ab99401e47005e50b", "version_major": 2, "version_minor": 0 }, @@ -17080,7 +17080,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - 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"model_id": "b566bbab82de468899ee7044bcec8a70", + "model_id": "9ca75ea5e2d94f43b8e6d18a41d12cf3", "version_major": 2, "version_minor": 0 }, @@ -17724,7 +17724,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "c2029bbaff62451fa5144f747a07dcbf", + "model_id": "436cd04aba2f484cbb9eb044a011e1eb", "version_major": 2, "version_minor": 0 }, @@ -17738,7 +17738,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "747107508e3244248a2948f6a14371be", + "model_id": "b582ddfa80cd480594d8e203dc2f99e3", "version_major": 2, "version_minor": 0 }, @@ -17752,7 +17752,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "a1432b2f1a434eca84a61f290de47673", + "model_id": "55a3d64dcc9c4737b2cc0156997a87db", "version_major": 2, "version_minor": 0 }, @@ -17766,7 +17766,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "765abced3dbd47a9816c6bb04e911b2c", + "model_id": "83a1236924f04c2889a29b1da03a5ab0", "version_major": 2, "version_minor": 0 }, @@ -17780,7 +17780,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - 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"model_id": "26e9feeef8f842e8a23900e397663ad2", + "model_id": "85715ae8eca243a68c996ec2395bfed6", "version_major": 2, "version_minor": 0 }, @@ -17934,7 +17934,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "c1bb893913f74070ad90ea5287f0edaf", + "model_id": "f7bfb8c7f15d4fe9886e662564941180", "version_major": 2, "version_minor": 0 }, @@ -17948,7 +17948,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "998e0c9854ad440fa660aa4140fef1fc", + "model_id": "886dfb265dee485787ab984101c418b5", "version_major": 2, "version_minor": 0 }, @@ -17962,7 +17962,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "a569e64f17174a768723923f46ecbc59", + "model_id": "21108efecf9748e7a4d9c87af34ef28b", "version_major": 2, "version_minor": 0 }, @@ -17976,7 +17976,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "3d49529e54364c71b57231ddaa234ab7", + "model_id": "6df11f3fe5dc4d1aa40f7b41f993b447", "version_major": 2, "version_minor": 0 }, @@ -17990,7 +17990,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - 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"model_id": "36954e6610fd475592ceca82e4c6baaa", + "model_id": "fd31cecf12b24436ae52591566508bdc", "version_major": 2, "version_minor": 0 }, @@ -18354,7 +18354,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "ee8d56f003cf4628b240188a33b2d268", + "model_id": "8ee812f08dcd4d6d959433bee787c87b", "version_major": 2, "version_minor": 0 }, @@ -18368,7 +18368,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "45bdf6b280744c37b887b371731abfa5", + "model_id": "d1c6c3ca3e864c0daac64aff5e1a048e", "version_major": 2, "version_minor": 0 }, @@ -18382,7 +18382,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "7bd093e5989642f99b0a00366787829e", + "model_id": "d164dd04525641ee9c873952ade3cf8f", "version_major": 2, "version_minor": 0 }, @@ -18396,7 +18396,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "dadde34ea9d64c73a1519038e10c40d0", + "model_id": "63b575af3366413fabd6e87eb26fa1e1", "version_major": 2, "version_minor": 0 }, @@ -18410,7 +18410,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - 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"model_id": "68dee321017a4b1ea640b16ee4cac317", + "model_id": "c296cd3b992b4c888caeaf18c0cdb784", "version_major": 2, "version_minor": 0 }, @@ -18494,7 +18494,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "b0d3a9559c6b4a4eace1c24c46fc5a47", + "model_id": "91114b1010e24fd0a33e4117929b1745", "version_major": 2, "version_minor": 0 }, @@ -18508,7 +18508,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "fcd876dbbc964b8aaa5a80b654d5c02d", + "model_id": "7a9d28d49ff340e483ac52729bebc041", "version_major": 2, "version_minor": 0 }, @@ -18522,7 +18522,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "1fed401b8419458e9c94200d056ea597", + "model_id": "d313e8102a4440bdb02602523cabaf31", "version_major": 2, "version_minor": 0 }, @@ -18536,7 +18536,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "5d455f13ab8d435fbb94b968c11860e8", + "model_id": "778c49187ef84767a771c1f29c21ac6e", "version_major": 2, "version_minor": 0 }, @@ -18550,7 +18550,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - 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"model_id": "f256f0052339466094589e2ca6989fd6", + "model_id": "c289740a76994f808680a2c2a93babd0", "version_major": 2, "version_minor": 0 }, @@ -18704,7 +18704,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "b089ba496eab40579b0fba447aed67ff", + "model_id": "fd27052acb5541ffb4489d2ea2b7ebed", "version_major": 2, "version_minor": 0 }, @@ -18718,7 +18718,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "dcacd996c2844b448e214697531b75af", + "model_id": "a070f4aae97b4c09b476f55acbc673af", "version_major": 2, "version_minor": 0 }, @@ -18732,7 +18732,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "50b0227c13e94fb6a981af1190369977", + "model_id": "87df933b275a490a968283da13736336", "version_major": 2, "version_minor": 0 }, @@ -18746,7 +18746,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "3b168b65538242b9a12f91f9a0da5249", + "model_id": "f09e2169e3a444b49f73c89f93c92fee", "version_major": 2, "version_minor": 0 }, @@ -18760,7 +18760,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "22fce066286640dcbfde723586cba743", + "model_id": "1de3352b2b8749e2860330817fef6de0", "version_major": 2, "version_minor": 0 }, @@ -18774,7 +18774,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "24fb01d89cde49de8dc15daadcf8e946", + "model_id": "cb68261426b24a2d9b78f39bd2e76252", "version_major": 2, "version_minor": 0 }, @@ -18788,7 +18788,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "c92a44753fe8433aaa0af8602f3e7f62", + "model_id": "caaccd1bda9342ec9eb5cc27ffaf9a38", "version_major": 2, "version_minor": 0 }, @@ -18802,7 +18802,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "762738f8aa2f4d92a5d23ddbe5a72b3c", + "model_id": "2cfc8ce1b70f4199a8903fc8b8389113", "version_major": 2, "version_minor": 0 }, @@ -18816,7 +18816,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "a871e20f61f041918cdfa625f9880752", + "model_id": "a2f8035a53b04d2da7d59cdc6ce5b1f7", "version_major": 2, "version_minor": 0 }, @@ -18830,7 +18830,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "1de7629c7faf4340ae9185298661b9b8", + "model_id": "fb774e6667944b339eaed16d612b1fd8", "version_major": 2, "version_minor": 0 }, @@ -18844,7 +18844,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "88df23e6733644318effa8d51cf59e39", + "model_id": "54c78d9897454729aa1c514b1fd89a49", "version_major": 2, "version_minor": 0 }, @@ -18858,7 +18858,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "a3b7844934e34d7e9eec586eca5e5bec", + "model_id": "1748537248954d4cbc9134529e728eca", "version_major": 2, "version_minor": 0 }, @@ -18872,7 +18872,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "c75d722835ce4923bb52dc6fa05bd04e", + "model_id": "150c2a32b2fc4e37a8af15a6443a04c8", "version_major": 2, "version_minor": 0 }, @@ -18886,7 +18886,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "c822b1132dcc4b908e4d7793035c4bdb", + "model_id": "04fb038a328d40f3afdd9d166c87310f", "version_major": 2, "version_minor": 0 }, @@ -18900,7 +18900,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - 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"model_id": "c3a2ff932bd943d7827f8d847d4ae4f1", + "model_id": "fc22b95e918849b188e985dbfe22770c", "version_major": 2, "version_minor": 0 }, @@ -19614,7 +19614,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "36371cf7498f4d34b74082e2dd556326", + "model_id": "828e732de2e645fcabffb9e272a8d980", "version_major": 2, "version_minor": 0 }, @@ -19628,7 +19628,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "86b7daa1e6e64aac8c551143387ff13e", + "model_id": "da5def1bfdf2495fb1be76480d882d51", "version_major": 2, "version_minor": 0 }, @@ -19642,7 +19642,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "f9c4768211c34c7ebdb1cb8c360d74a9", + "model_id": "30191811112c4a929d18aa1c3b7c76c0", "version_major": 2, "version_minor": 0 }, @@ -19656,7 +19656,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "8af15ffebd5549a4b0c29a88192dae96", + "model_id": "0eefac0b2bd94e5f8a2ccd9d777053ca", "version_major": 2, "version_minor": 0 }, @@ -19670,7 +19670,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - 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"model_id": "59cd81ca92cf4bcc970660249a7501cf", + "model_id": "3ac808fe866548f3886bd063d789eed2", "version_major": 2, "version_minor": 0 }, @@ -19754,7 +19754,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "31bca03500614873b3ce51652ec81197", + "model_id": "10c33dd249314b4c893684e1f6834f2f", "version_major": 2, "version_minor": 0 }, @@ -19768,7 +19768,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "0e0ab61d4ef14d92848592f2a300ff0f", + "model_id": "064544ff7f5640768c6758ededd67eb3", "version_major": 2, "version_minor": 0 }, @@ -19782,7 +19782,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "af39a2a137ad47d8b43f46f4a2af21fe", + "model_id": "9a4ac05c17924d57a45182413b1bb05c", "version_major": 2, "version_minor": 0 }, @@ -19796,7 +19796,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "ae75134dd21449bf8fd1d7cb6ff2cec9", + "model_id": "455e6157118b46978cb38030e184647b", "version_major": 2, "version_minor": 0 }, @@ -19810,7 +19810,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - 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"model_id": "20fbc4b3af4c48c8853f5cafede6c46b", + "model_id": "20375285173744b28b0e72fb5af20071", "version_major": 2, "version_minor": 0 }, @@ -20454,7 +20454,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "926221010b074da0a63c3f4f58190d29", + "model_id": "45c51a96c5d6458496b70fd489e2383c", "version_major": 2, "version_minor": 0 }, @@ -20468,7 +20468,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "7d7aa30aaa254b7b91368d45a212ac41", + "model_id": "872566856eb94e4aa988fc93520b420a", "version_major": 2, "version_minor": 0 }, @@ -20482,7 +20482,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "d7e4fac7e7bc44f4b438f92c0c457f96", + "model_id": "54c5ea66edff41b6a0217ee06fa38f44", "version_major": 2, "version_minor": 0 }, @@ -20496,7 +20496,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "a8f9ea27befb4ae7820eabff85631957", + "model_id": "aaf32c052a3e41ac81606242724aac59", "version_major": 2, "version_minor": 0 }, @@ -20510,7 +20510,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "4f7b5d44cc4744fdbfb85a2de118b5e5", + "model_id": "3bfa6c97d7f04c46b5b52faceaa47d46", "version_major": 2, "version_minor": 0 }, @@ -20524,7 +20524,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "857a7d2d31b74ae69386e19103478342", + "model_id": "5c6b4b48ea0647d79e37c1e44088855c", "version_major": 2, "version_minor": 0 }, @@ -20538,7 +20538,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "24427ab7f62c4b02bda6ed5b7e3c4422", + "model_id": "6891ded1e7ce42cd90c4946a1aba13e5", "version_major": 2, "version_minor": 0 }, @@ -20552,7 +20552,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "680900236dbf43ce9357c5cf1b5cc8e8", + "model_id": "0e6e9d63be9f4f7e96c7a98cf52878c2", "version_major": 2, "version_minor": 0 }, @@ -20566,7 +20566,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "3061eabd59de45a48c423952cce78dc9", + "model_id": "3266159b51dc424589dcae24255ad889", "version_major": 2, "version_minor": 0 }, @@ -20580,7 +20580,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "7ad2c2fce747468687b22cfdf08bd822", + "model_id": "dfeb2ed3d3084de5bd4d6b998f01ddcb", "version_major": 2, "version_minor": 0 }, @@ -20594,7 +20594,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "14af4158fd93491596fd8862850bf058", + "model_id": "cad19a6c8f974696964c7b597e49ef37", "version_major": 2, "version_minor": 0 }, @@ -20608,7 +20608,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "6ea11c4668d34810b15edd6781706479", + "model_id": "ffe7a00365fb41d38d1192adb2ccee87", "version_major": 2, "version_minor": 0 }, @@ -20622,7 +20622,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "ea246be436d148b4b3a1d30fc24a4007", + "model_id": "c2adde7c3a764c74993632fd8b4d7c45", "version_major": 2, "version_minor": 0 }, @@ -20636,7 +20636,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "b6a7b4f58f0847a6aa56458beacccab4", + "model_id": "8d250d842db843c390d45a9bb32e832b", "version_major": 2, "version_minor": 0 }, @@ -20650,7 +20650,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "e2cd54bc045646f686099b8a64a0ff59", + "model_id": "e9ba0e02febe478982a7b2aaf9001a95", "version_major": 2, "version_minor": 0 }, @@ -20664,7 +20664,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "ae9d0db6298e4db797315b48cfc32a55", + "model_id": "6d877c53c6cb45eb8fdaa23fc6434541", "version_major": 2, "version_minor": 0 }, @@ -20678,7 +20678,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "e1be0158e98a4d4fb0d4e4b13673d151", + "model_id": "d14e66bd92804d14a827876ebaf68cf3", "version_major": 2, "version_minor": 0 }, @@ -20692,7 +20692,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "351c0c03884044ac88e2bd4383a0b950", + "model_id": "3aa16228fd0b417ea6bdcab5124c20ae", "version_major": 2, "version_minor": 0 }, @@ -20706,7 +20706,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "2e2fc5a55db8430bbf65b7ec1313d5c2", + "model_id": "d7e2a3b173ed415d93a4cd444265e1a1", "version_major": 2, "version_minor": 0 }, @@ -20720,7 +20720,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "00c0a968a88c459eb600079f6ac3d4ea", + "model_id": "fb2fb8592b1a4d9693cf3730d6eb3b2d", "version_major": 2, "version_minor": 0 }, @@ -20734,7 +20734,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "f98f4a9019594d1ca3d368cabdbb3d68", + "model_id": "97d8a2e0d02c4689bd010fb15533250b", "version_major": 2, "version_minor": 0 }, @@ -20748,7 +20748,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "a9e656c4b8b64be2993bb5f03da63297", + "model_id": "4f0123c9764441198dbfa94e8b50ab90", "version_major": 2, "version_minor": 0 }, @@ -20762,7 +20762,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "1120ad5cf6bf46b380d1ac5b35221488", + "model_id": "7b8e45ae798d4a89b04cdd3cbbbd02b3", "version_major": 2, "version_minor": 0 }, @@ -20776,7 +20776,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "53fa9c1383bd43abb91425d9f9c4b890", + "model_id": "a12cb63b193144c583810294cfac66bc", "version_major": 2, "version_minor": 0 }, @@ -20790,7 +20790,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "fed7fa71b0fd47a8b1fe278b7232ea7a", + "model_id": "df83c9394b104fb0887638585e5972a4", "version_major": 2, "version_minor": 0 }, @@ -20804,7 +20804,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "07b9835b8e8e49b3aa2b491442808180", + "model_id": "1347bcd05a714db99e2dd2f8d2a7f4ad", "version_major": 2, "version_minor": 0 }, @@ -20818,7 +20818,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "3498e812d0f64c11828f829658a1b5e8", + "model_id": "2579cf00d1be4147ab9ead1b95b085b0", "version_major": 2, "version_minor": 0 }, @@ -20832,7 +20832,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "81d36a5a0cb3413e8f8f4105c026194b", + "model_id": "ad675632a65b427aa0beabe3c4cd409e", "version_major": 2, "version_minor": 0 }, @@ -20846,7 +20846,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "136a99f4319f4341bdbeed81dbeaa01c", + "model_id": "6c32a2b6a79b41c89069bb610a2028ee", "version_major": 2, "version_minor": 0 }, @@ -20860,7 +20860,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "7cad24201cab403ea248a4b7b41e8cc8", + "model_id": "cc61683d9b6845918234c5211b30bdef", "version_major": 2, "version_minor": 0 }, @@ -20874,7 +20874,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "60ecb957978c4b2f96f30e6a7fba32b4", + "model_id": "feff98979d3440c6a0ccaa2901500213", "version_major": 2, "version_minor": 0 }, @@ -20888,7 +20888,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "bb5d2ac5c7fc4a618c1bb1d5b3d775e7", + "model_id": "e642c1cff63142b78f67c1a4037f5c66", "version_major": 2, "version_minor": 0 }, @@ -20902,7 +20902,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "43a2bc7301fd49ca8693584fbd0a99fc", + "model_id": "85bcfdc9951345da9a8494dc31bb5440", "version_major": 2, "version_minor": 0 }, @@ -20916,7 +20916,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "f08e0ac9d9f34e64a07653ce7e250d6e", + "model_id": "a3b299b6b04448ce9edd7813cf6b078d", "version_major": 2, "version_minor": 0 }, @@ -20930,7 +20930,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "c183aadb5df04eb2a57c1d91f43cba27", + "model_id": "2e837c462de644a9abf43a58efb3f049", "version_major": 2, "version_minor": 0 }, @@ -20944,7 +20944,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "882b4e18a92e45e18dcd22f14ebd93f9", + "model_id": "8c45be67f94845e6bf5330f2b32603fd", "version_major": 2, "version_minor": 0 }, @@ -20958,7 +20958,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "dd630d11c5e9459f9c4517e88a5891c6", + "model_id": "8cd3dec74d4b4e92926f2368957bf2e2", "version_major": 2, "version_minor": 0 }, @@ -20972,7 +20972,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "f72281bb3db940a7b3320f518d9c89fd", + "model_id": "f149a11360474a63933eafccb2625ffb", "version_major": 2, "version_minor": 0 }, @@ -20986,7 +20986,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "5b9a9b4bead44604a5a2a4aa1390b023", + "model_id": "8f48a9e93db140d1b1ce9c55076cd7cd", "version_major": 2, "version_minor": 0 }, @@ -21000,7 +21000,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "a85aed1a28d34b3e960e590fb0ca18ca", + "model_id": "42ec3be294dd4012ac891a302b7de793", "version_major": 2, "version_minor": 0 }, @@ -21014,7 +21014,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "e73f99e7a26e4b2696dd4f3b4c050557", + "model_id": "f593912857f9425d984ca661a3fe161b", "version_major": 2, "version_minor": 0 }, @@ -21028,7 +21028,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "d4ac7854b8f745d8a1d78e0920ce7749", + "model_id": "e2ea8bbec04443599105b3b9d6c403c6", "version_major": 2, "version_minor": 0 }, @@ -21042,7 +21042,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "346f75de03a1472580b2f0513765ea69", + "model_id": "671b38a4d4f24dbbaf22b0eaa116f6b5", "version_major": 2, "version_minor": 0 }, @@ -21056,7 +21056,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "008bedc0d3284205aeb92ac27cbe5246", + "model_id": "a5e9b5ebd3624243a495c0c7a8e5dfa6", "version_major": 2, "version_minor": 0 }, @@ -21070,7 +21070,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "a4c199eab4b344e99dfd207a1be077b4", + "model_id": "832e4ad33e034724a3e44609c95d9484", "version_major": 2, "version_minor": 0 }, @@ -21084,7 +21084,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "7855d6123705475082645d9a7c8051f1", + "model_id": "c9405c2223e849b9a11d0176e342aa70", "version_major": 2, "version_minor": 0 }, @@ -21098,7 +21098,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "111e21b2c78b47d3b6d05f64d895cf68", + "model_id": "eba8babec6504fc6873edfde49a47824", "version_major": 2, "version_minor": 0 }, @@ -21112,7 +21112,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "de53167dd97f4f54b90e97c67344552f", + "model_id": "1c8dcda3f75f405dad52f6a375c74c59", "version_major": 2, "version_minor": 0 }, @@ -21126,7 +21126,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "80732e49c8b64ef4918013812abc4ef5", + "model_id": "c1e6fb303ff04c349dbf7d06ded632cc", "version_major": 2, "version_minor": 0 }, @@ -21140,7 +21140,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "c19619e8d3524727968313a90b3fc7e0", + "model_id": "4e40d66ab35541f485068c92a9c3bfc7", "version_major": 2, "version_minor": 0 }, @@ -21154,7 +21154,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "99e28114b4ad4f8dae66f88f659194f6", + "model_id": "4d5b1bbd870845e4a09d12022c54cca1", "version_major": 2, "version_minor": 0 }, @@ -21168,7 +21168,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "239f76cdae854faeb4c866eb88d587f2", + "model_id": "d39fcff2bcc249608301808b50136e79", "version_major": 2, "version_minor": 0 }, @@ -21182,7 +21182,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "f7e5ff4a9a5e46d58f64f17b15f66ca8", + "model_id": "77bb7503f0954ad3b8c3a2124efddd62", "version_major": 2, "version_minor": 0 }, @@ -21196,7 +21196,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "cf74bb7a025b46a48bfcb75c2c4b1d0d", + "model_id": "119c041b66e143359b82f1e3fea188be", "version_major": 2, "version_minor": 0 }, @@ -21210,7 +21210,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "7eafd2a37bcc403e80b86b5c4ba06fce", + "model_id": "98156d8e813444fa9ca46bb6225a3ca4", "version_major": 2, "version_minor": 0 }, @@ -21224,7 +21224,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "c21f6dacd056479d9a215565bebe1e5c", + "model_id": "ce9a6371150f44c9b14867f170318681", "version_major": 2, "version_minor": 0 }, @@ -21238,7 +21238,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "b8929c29a8504d948e319ba683727d6b", + "model_id": "fd798cdb267e4c5ca05e525c40caf23f", "version_major": 2, "version_minor": 0 }, @@ -21252,7 +21252,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "9afd138d4b09451a9766793f2993cf76", + "model_id": "23f77c9adc4f45029b622bb1ef4ae8fd", "version_major": 2, "version_minor": 0 }, @@ -21266,7 +21266,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "2564a7bcb9f24fabaf590fe0c4a2e884", + "model_id": "058e1c7916584d13938fddfc45ceb2fa", "version_major": 2, "version_minor": 0 }, @@ -21280,7 +21280,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "1d05775d82c14d43806005b130933bf1", + "model_id": "9e7bcf950bfe412aa1f59548475d666a", "version_major": 2, "version_minor": 0 }, @@ -21294,7 +21294,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "ec541cc7adcb43599d5cbac88319d54b", + "model_id": "a568a9d4aac842d9b4e173d75db30563", "version_major": 2, "version_minor": 0 }, @@ -21308,7 +21308,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "18f4161ec04e494e9ab28b2d8db7b437", + "model_id": "4559a498f44c48688f03412c24d559d8", "version_major": 2, "version_minor": 0 }, @@ -21322,7 +21322,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "91d7a6172a8d47478c0e91e8376c0721", + "model_id": "e72b69c3ce694c269c1a39372faddaf8", "version_major": 2, "version_minor": 0 }, @@ -21336,7 +21336,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "88b9d1a2fd6c4b14a2f9379771b0c2fb", + "model_id": "26fed54bd9c3425d8631ac48af882587", "version_major": 2, "version_minor": 0 }, @@ -21350,7 +21350,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "2879f04a29c34158877674e72bd0b88f", + "model_id": "185800411c37412091ecbe898991bc59", "version_major": 2, "version_minor": 0 }, @@ -21364,7 +21364,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "487a9363824c4fd1b1cb5b0efca6c76d", + "model_id": "f2aa1e7989524004bc901340390d9c9a", "version_major": 2, "version_minor": 0 }, @@ -21378,7 +21378,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "c9602e4fa2a446fbb7413cb22f929b84", + "model_id": "560c783a19124b6b93ba611bfac9c9df", "version_major": 2, "version_minor": 0 }, @@ -21427,7 +21427,7 @@ "id": "f8b4f496", "metadata": {}, "source": [ - "After the training we can inspect trainer logged metrics (by default **PINA** logs mean square error residual loss). The logged metrics can be accessed online using one of the `Lightinig` loggers. The final loss can be accessed by `trainer.logged_metrics`" + "After the training we can inspect trainer logged metrics (by default **PINA** logs mean square error residual loss). The logged metrics can be accessed online using one of the `Lightning` loggers. The final loss can be accessed by `trainer.logged_metrics`" ] }, { @@ -21439,10 +21439,10 @@ { "data": { "text/plain": [ - "{'val_loss': tensor(0.0015),\n", - " 'bound_cond_loss': tensor(3.6597e-05),\n", - " 'phys_cond_loss': tensor(0.0012),\n", - " 'train_loss': tensor(0.0013)}" + "{'val_loss': tensor(5.8720e-05),\n", + " 'bound_cond_loss': tensor(9.5228e-07),\n", + " 'phys_cond_loss': tensor(7.9753e-05),\n", + " 'train_loss': tensor(8.0706e-05)}" ] }, "execution_count": 8, @@ -21472,7 +21472,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 9, @@ -21481,7 +21481,7 @@ }, { "data": { - "image/png": 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", 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AMkOGCqox5oqP/51yjomJSVu66JKyZcvKGKPmzZtf9p7+/v5av369Tp06pdTUVB05ckTvvfeeChUqdFMfKKebNm3aZaWjcuXK2rJli/744w81bNhQ1apV0+TJkxUZGZk25uWXX1ZUVJQaNmyorl27auzYsTd0HukTTzyh6tWrKzo6Wk2aNFF4ePhtWSi/V69eKlGiRLrnAgICtHXrVhUpUkQPPfSQypcvr379+ik5OTltKn3AgAEqW7asatasqYIFC6YdCaxUqZJCQkJUtWpV5c2bV9LFgup0OtPOP73kueeeU7t27dSjRw9Vr15dBw4c0Pr16zO0zmzevHn1+eefyxijVq1aXfFmFSEhIXr//fdVv359Va5cWRs3btSqVavSZhFmz56tGjVq6P7771fdunVljNHatWuv+otDdHS0Jk2apHHjxqlWrVpKSEhQz549040ZO3asPD09deedd6pgwYI6evToZe9TvXp1LVmyRIsWLVLFihU1efJkTZs27bJTRwAAsIrNZHRhTjcUHx+v4OBgxcXFXXZOYHJysg4fPqzixYvLz8/PooRAzsbPGQDgeq7V1/7rlhbqBwAAQDbldEjOzLvY9lZQUAEAAHKjb9+W3m8qHf/J6iSXoaACAADkNmcOyWx+RordffHhZiioAAAAuYkx0qpRsjkuaJdXZe0Je8DqRJehoAIAAOQmu+ZLh7co2XhrZFJv7Tkeb3Wiy1BQAQAAcouEkzLrH5MkveJor8jiFdSpVpTFoS6Xve73CQAAgJv3+TjZkuO021VM8z3u19p2lWSz2axOdRmOoAIAAOQGv6+Rflshh/HQePtAPXLvnSqaP4/Vqa6IgopMFRMTI5vNpnPnzt3S+xw5ckQ2m+2ye88DAIAbkBwns2aMJOk95/3yvqOq+tQvbnGoq6OguimbzXbNx9SpU62OmGl69+592e1Uo6KidOLECVWsWNGaUAAAZGcbpsiWcEKHXOF627TXi+0ry9PD/ab2L+EcVDd14sSJtP9evHixJk+erH379qU9d+l+85JkjJHT6ZSXV87dnZ6engoPD7c6BgAA2c+Rr6UfZkuSJtoH6OHmd6pMoUCLQ10bR1DdVHh4eNojODhYNpst7evff/9dgYGB+vzzz1WjRg35+vpq27ZtVzzyOGrUKDVp0iTta5fLpenTp6t48eLy9/dXlSpVtHTp0mtmeeutt1S6dGn5+fmpUKFCat++fdprKSkpGjFihMLCwuTn56cGDRro+++/v+p7TZ06VVWrVk333IwZM1SsWLG01+fOnauVK1emHS2OiYm54hT/li1bVLt2bfn6+ioiIkITJkyQw+FIe71JkyYaMWKExo0bp9DQUIWHh+foI88AAFzGniytGiFJWuBoqoTwOnq4cUmLQ11fzj3kdi3GSPbz1nxv7wDpNl0tN2HCBL300ksqUaKE8uXLd0PbTJ8+XR9//LHeeecdlS5dWlu3blX37t1VsGBBNW7c+LLxO3fu1IgRI/TRRx+pXr16OnPmjL766qu018eNG6dly5Zp7ty5Klq0qF544QVFR0frwIEDCg0NzfBnGjt2rPbu3av4+HjNnn3xt73Q0FAdP3483bi///5b9913n3r37q158+bp999/14ABA+Tn55euhM6dO1ejR4/Wt99+q+3bt6t3796qX7++mjdvnuFsAABkO1tfkE4f0EkTohdd3fRxh8ry9nT/45O5s6Daz0vPRlrzvR87Lvncnivmpk2blqGilZKSomeffVYbN25U3bp1JUklSpTQtm3b9O67716xoB49elR58uTR/fffr8DAQBUtWlTVqlWTJCUlJentt9/WnDlz1LJlS0nS+++/rw0bNmjWrFl69NFHM/yZ8ubNK39/f6WkpFxzSv+tt95SVFSU3nzzTdlsNpUrV07Hjx/X+PHjNXnyZHl4XPzhq1y5sqZMmSJJKl26tN58801t2rSJggoAyPlid8t8/Zpskibb+6hb48qqEBlsdaobkjsLag5Rs2bNDI0/cOCAzp8/f1k5S01NTSud/9W8eXMVLVpUJUqUUIsWLdSiRQs9+OCDCggI0MGDB2W321W/fv208d7e3qpdu7b27t2b8Q+UAXv37lXdunXTrd1Wv359JSYm6tixYypSpIikiwX1f0VEROjUqVOZmg0AAMs5HdJnw2VzObTWWVsHCzTV6/eUsjrVDcudBdU74OKRTKu+922SJ0/6I7EeHh4yxqR7zm63p/13YmKiJGnNmjUqXLhwunG+vr5X/B6BgYH68ccfFRMToy+++EKTJ0/W1KlTr3me6bVcL+Pt5u3tne5rm80ml8uVad8PAAC38O3b0vGfFGcC9KSjl95pX1m+Xp5Wp7phubOg2my3bZrdnRQsWFB79uxJ99yuXbvSStqdd94pX19fHT169IrT+Vfj5eWlZs2aqVmzZpoyZYpCQkK0efNmRUdHy8fHR19//bWKFi0q6WLZ/P777zVq1KirZoyNjZUxJu3o53/XNvXx8ZHT6bxmpvLly2vZsmXp3ufrr79WYGCg7rjjjhv+bAAA5DhnDstsfkY2Sc84uumBBtVVrciNXaviLnJnQc2h7r77br344ouaN2+e6tatq48//lh79uxJm74PDAzU2LFj9cgjj8jlcqlBgwaKi4vT119/raCgIPXq1euy91y9erUOHTqkRo0aKV++fFq7dq1cLpfKli2rPHnyaPDgwXr00UcVGhqqIkWK6IUXXtD58+fVr1+/K2Zs0qSJ/vnnH73wwgtq37691q1bp88//1xBQUFpY4oVK6b169dr3759yp8/v4KDLz9fZsiQIZoxY4aGDx+uYcOGad++fZoyZYpGjx6ddv4pAAC5jjHS6lGyOS7oG+ed+i74Pn3evKzVqTKMf8lzkOjoaE2aNEnjxo1TrVq1lJCQoJ49e6Yb89RTT2nSpEmaPn26ypcvrxYtWmjNmjUqXvzKd5MICQnRp59+qrvvvlvly5fXO++8o4ULF6pChQqSpOeee07t2rVTjx49VL16dR04cEDr16+/6qoC5cuX11tvvaWZM2eqSpUq+u677zR27Nh0YwYMGKCyZcuqZs2aKliwoL7++uvL3qdw4cJau3atvvvuO1WpUkWDBg1Sv3799MQTT9zMHx0AADnDrvnSoRglG29NdPTX8+2ryN8n+0ztX2Iz/z0hMBuKj49XcHCw4uLi0h2Jk6Tk5GQdPnxYxYsXl5+fn0UJgZyNnzMAcAMJsTIza8uWHKdn7V2UXHuYprVxnzswXquv/RdT/AAAANmdMdLq0bIlx+kXV3Gty9tOa1uUszrVTWOKHwAAILv79VNp3xqlGk89an9YT7erqry+2fc4JAUVAAAgO0v6V2btxZvjzHS0VZUa9dSoTEGLQ92a7FutAQAAIK19VLbzp7XXVUSf+HfQ563utDrRLcs1R1BzwLVggNvi5wsALLJ3lfTrp3IYDz1qH6gnH6quYH/v62/n5nJ8Qb20SP358+ctTgLkXJd+vv575y4AQCY6f0ZmzRhJ0rvO+1WySgM1v7OQxaFujxw/xe/p6amQkJC0+68HBASku387gJtnjNH58+d16tQphYSEyNMz+621BwDZ1vrHZUs8qQOuSH3s01lrWlewOtFtk+MLqiSFh4dLUlpJBXB7hYSEpP2cAQCywP4N0s8L5DI2jbMP1BPtqys0j4/VqW6bXFFQbTabIiIiFBYWJrvdbnUcIEfx9vbmyCkAZKXkOJlVI2STNMvZUgXvbKj7KuWsgwS5oqBe4unpyT+kAAAge9swWbb44zriKqQPvLpqVZuKOe70xRx/kRQAAECOcShG+mGOJGm8faDGta6msKCcd4tpCioAAEB2kJIo89kISdI8R3P5l2mkh6oXtjhU5shVU/wAAADZ1qZpsp37U8dMAc307K4VD1XKcVP7l3AEFQAAwN39+Y303buSpAn2ARpzfw1FBPtbHCrzUFABAADcmf2CzMphkqRFjibyKn23OtS8w+JQmYspfgAAAHf25bOynTmoWJNPr3v10rIcPLV/CUdQAQAA3NWxH2S2vylJeszeT6Pur5Wjp/YvoaACAAC4I0eKzIohshmXljvry1U6Wh1q5Oyp/UuY4gcAAHBHW1+U7d/f9Y8J0sueffVJLpjav4QjqAAAAO7m+C6Zba9KkibZ+2rE/XVyxdT+JRRUAAAAd+JIkVk+SDaXQ2uctZVSulWumdq/hCl+AAAAd7Lledn+2at/TZCe9xyoJQ9VzjVT+5dwBBUAAMBd/P1D2tT+4/a+Gn7/XQoP9rM4VNajoAIAALgDe7LM8sGyGZdWOOsptXQrtc9lU/uXMMUPAADgDr58RrZ/9+mUCdFLnv20NBdO7V/CEVQAAACrHf1W5ps3JF1ckH/k/XVy5dT+JRRUAAAAK6Wel1kxWDYZLXM2lKtMy1w7tX8JU/wAAABW2vy0bGcOKtbk0yuefbTswdyzIP/VcAQVAADAKn9+I7PjLUnSBPsAjbq/dq6e2r+EggoAAGCF1CSZFUNkk9EiRxN5lLk310/tX8IUPwAAgBU2TpXt7GH9bfLrNa/eWs7UfhqOoAIAAGS1w1ul796TJI23D9SY1rWY2v8fFFQAAICslJIgs2KoJGm+4x75lb1H7aoXtjiUe2GKHwAAICttmCxb3FH95Sqot7x7aflDTO3/F0dQAQAAssrBzdLODyVJ4xwD9diDtRUWyNT+f1FQAQAAskJynMzKYZKkOY57VaBSc7WqHGFxKPfEFD8AAEBWWP+4bPF/609XmGb59dJnD1SwOpHb4ggqAABAZtu/QfrpI7mMTWPtgzS1XS3ly+NjdSq3RUEFAADITBfOyfV/U/uznS1UvEYz3VO+kMWh3BtT/AAAAJnp83HySIzVIVe45ufppZX332l1IrfHEVQAAIDM8usK6ZfFchqbxtgH66kOtRTo5211KrdHQQUAAMgMCbFyrX5EkvSWs40q39VM9UsVsDhU9sAUPwAAwO1mjPTZCHlcOKNfXUW1MqibPmtZzupU2QZHUAEAAG63H+dJ+9crxXhpjGOInu9UUwE+HBe8URRUAACA2+nMYZl1EyVJLzk6qknDJqpRNNTiUNkLVR4AAOB2cTllVgyWzZ6kb13l9FX+DlrZvLTVqbIdCioAAMDtsv1N2Y5uV6Lx03jHYL3ZqYZ8vTytTpXtMMUPAABwO5z8VWbz05KkaY4eevDu+qpYONjiUNkTBRUAAOBWOVJlPh0omzNVG5zVtbdQGw1pWtLqVNkWU/wAAAC3astzsp3co9MmUJPNQM3tVFXenhwHvFkZ+pObPn26atWqpcDAQIWFhalt27bat2/fNbeZM2eObDZbuoefn1+6McYYTZ48WREREfL391ezZs20f//+jH8aAACArHb0W5ltr0qSHrf3U597a6tMoUCLQ2VvGSqoW7Zs0dChQ7Vjxw5t2LBBdrtd9957r5KSkq65XVBQkE6cOJH2+PPPP9O9/sILL+j111/XO++8o2+//VZ58uRRdHS0kpOTM/6JAAAAskpKoszyh2UzLi1zNtDpItHq16CE1amyvQxN8a9bty7d13PmzFFYWJh++OEHNWrU6Krb2Ww2hYeHX/E1Y4xmzJihJ554Qm3atJEkzZs3T4UKFdKKFSvUuXPny7ZJSUlRSkpK2tfx8fEZ+RgAAAC3x4bJsp09rOMmVC/a+uqTjlXl6WGzOlW2d0snR8TFxUmSQkOvvfhsYmKiihYtqqioKLVp00a//vpr2muHDx9WbGysmjVrlvZccHCw6tSpo+3bt1/x/aZPn67g4OC0R1RU1K18DAAAgIw7sFHaOUuSNNY+SKMfqK2o0ACLQ+UMN11QXS6XRo0apfr166tixYpXHVe2bFl9+OGHWrlypT7++GO5XC7Vq1dPx44dkyTFxsZKkgoVKpRuu0KFCqW99l8TJ05UXFxc2uOvv/662Y8BAACQcefPyKwYKkma7YhWnnL3qEONOywOlXPc9FX8Q4cO1Z49e7Rt27Zrjqtbt67q1q2b9nW9evVUvnx5vfvuu3rqqadu6nv7+vrK19f3prYFAAC4ZWvHypYYq4OuCH3g21MrH6okm42p/dvlpo6gDhs2TKtXr9aXX36pO+7I2G8L3t7eqlatmg4cOCBJaeemnjx5Mt24kydPXvW8VQAAAMvsXirtWSaH8dBo+2A92a6WCuTlwNntlKGCaozRsGHDtHz5cm3evFnFixfP8Dd0Op3avXu3IiIiJEnFixdXeHi4Nm3alDYmPj5e3377bbojrwAAAJaLPyGzZowkaaazre6s1VTN7ix0nY2QURma4h86dKgWLFiglStXKjAwMO0c0eDgYPn7+0uSevbsqcKFC2v69OmSpGnTpumuu+5SqVKldO7cOb344ov6888/1b9/f0kXr/AfNWqUnn76aZUuXVrFixfXpEmTFBkZqbZt297GjwoAAHALXC5p5VDZks/pF1dxrQjsqtWt7rQ6VY6UoYL69ttvS5KaNGmS7vnZs2erd+/ekqSjR4/Kw+P/H5g9e/asBgwYoNjYWOXLl081atTQN998ozvv/P87dNy4cUpKStLAgQN17tw5NWjQQOvWrbtsQX8AAADLfPeedHCTko23xjiG6MVONZTHl5tyZgabMcZYHeJWxcfHKzg4WHFxcQoKCrI6DgAAyGlO7ZV5t7FszhRNsvdWUKPBejS6nNWpspWM9DVqPwAAwLU4UmSW9ZPNmaIvnVX0Y1g7Lb+njNWpcjQKKgAAwLVsfkq2k7/qtAnU42aw5nauJh+vW7rXEa6DP10AAICrObRF5ps3JUnj7QPVr8VdKl0o0OJQOR8FFQAA4EounJVZPkg2GS1w3K3zxe9Vn3rFrE6VKzDFDwAA8F/GSKsfkS3huA65wjXDq7dWdKgiDw/uFpUVOIIKAADwX78sln5dLofx0Cj7UD3WpqYiQ/ytTpVrUFABAAD+19k/0+4WNcPRTlGVGqhN1UiLQ+UuTPEDAABc4nJKyx+WLTVR37vKaKl/B61rW1E2G1P7WYkjqAAAAJdse1U6ul0Jxl+P2Ifo+Y7VFRLgY3WqXIeCCgAAIEl//ygTM12SNMXeS/fcVUuNyxS0OFTuxBQ/AABAapLMpwNkczm02llHu/O31Kr7yludKteioAIAAKx/XLbTB3TChOpJ1wDN7VJdft6eVqfKtZjiBwAAudu+z6UfZkuSxtgH6eEWNXRnZJDFoXI3CioAAMi9Ek/JrBwmSXrfcZ88SzZR3/rFLQ4FpvgBAEDuZIy0cqhs5//VXleU3vfurlXcLcotcAQVAADkTjtnSfu/UIrx1kj7ME1rV0OFgvysTgVRUAEAQG50aq/M+sclSc85OqtGrXpqUTHc4lC4hCl+AACQu9iTZZb2lc2RrC3Oytoa8pBW3X+n1anwPyioAAAgd9kwWbZTv+kfE6RxzsF6v0t1BfhQidwJU/wAACD32LdO+u5dSdJY+2D1ure2Kt8RYm0mXIaCCgAAcoeEWJmVQyRJsxwtlVy0qR5uVNLiULgSjmcDAICcz+WSlj8s2/nT+tVVVG95ddeqTlXlyZJSbokjqAAAIOfb/oZ0KEbnja9G2IfpyYeqKzLE3+pUuAoKKgAAyNn+/lFm0zRJ0pOOnqparY7urxxpcShcC1P8AAAg50pJkJb1k83l0FpnbX0T1FJrH2BJKXdHQQUAADnX2nHSmUP62+TX484B+qBzdQX6eVudCtfBFD8AAMiZdi+Vfl4gp7FpVOpQ9bq7qmoUzWd1KtwACioAAMh5zh6RWT1KkvSm80E5o+7SsKalrM2EG8YUPwAAyFmcDmnZANlSErTTVUazPTtoVedq8vLkuFx2wZ4CAAA5y5bnpGPfKd4EaJR9qJ56qKqiQgOsToUMoKACAICc48g2ma0vSZIes/dT/RrV1boKS0plN0zxAwCAnOH8GZlPB8omoyWOxvotfzOtZkmpbImCCgAAsj9jpFUjZIv/WwddEXrW9NH8LtUU4EPVyY6Y4gcAANnfD3OkvauUajw10j5Uo+6rqgqRwVanwk2ioAIAgOzt1F6ZdRMlSS86Oim83F3qVa+YtZlwSzjuDQAAsq/U89InfWRzXNBWZyWtCnhQa9tXkc1mszoZbgEFFQAAZF/rJkj/7NUpE6IxjiF6rXN1hebxsToVbhFT/AAAIHvavVT6ca5cxqZR9iHq1KSG6pUsYHUq3AYUVAAAkP2cPiizapQk6U1nGyXf0UCjmpW2NhNuG6b4AQBA9uJIkZb2lS01Qd+6yulDr07cyjSHYU8CAIDsZeNU6cQunTV5NTJ1qJ55qBq3Ms1hKKgAACD72Pe5tOMtSdJY+8NqWruqWlWOsDgUbjem+AEAQPYQd0xmxWDZJM1ytNSfBRrrzfsrWJ0KmYCCCgAA3J/TIS3tJ9uFs/rFVVyvqKuWdqkmfx9Pq5MhEzDFDwAA3F/MdOmvHUo0/hpmH6HxrSqrfESQ1amQSSioAADAvR38UuarlyVJE+z9VbZ8ZfW4q6jFoZCZmOIHAADuK/GUzKcDZZPRAkdT/RDYVGvbVeZWpjkcBRUAALgnl0ta/rBsSae0z3WHnnH10twu1ZSPW5nmeEzxAwAA9/T1DOngZl0wPhpqH6Gh91ZSzWKhVqdCFqCgAgAA93P0W5nNT0uSpjh6KbJ0NQ1qVNLiUMgqTPEDAAD3cv7MxVuZGqdWOuspxj9aaztWkYcH553mFhRUAADgPoyRPhsuxR/TYVchTXL01Xt9qqtAXl+rkyELMcUPAADcx7fvSr+vVqrx0jD7CPW7p6ruKpHf6lTIYhRUAADgHo79IPPFE5KkZx1dFVyipobdXcriULACU/wAAMB6589In/SWzWXXWmdtrfZrrbWdq8qT805zJY6gAgAAa7lc0orBUtxRHXEV0gTHQM3oXF1hgX5WJ4NFKKgAAMBa29+Q/linFOOtofaR6tW0shqULmB1KliIggoAAKxzdIfMxiclSU86eipP0eoaeU9pi0PBapyDCgAArJH0r/RJH9mMUyuc9bTOt4XWdqkmL0+On+V2FFQAAJD1XC7p04FSwnEdcEXqMXt/zexeVeHBnHcKpvgBAIAVtr0sHdykC8ZHQ+wj1aPxnWpaNszqVHATFFQAAJC1Dm+V+fJZSdIkRx8FFamssfeWtTgU3AlT/AAAIOsknJSW9pPNuLTE0Vib/ZprTddq8ua8U/wPCioAAMgaLqe0rJ+UdEq/u6I0xdlb73Wuqohgf6uTwc3w6woAAMgaMc9JR75SkvHVUPsIDby7ohqWLmh1KrghCioAAMh8BzbJbH1RkjTR3l8RJatoBOud4iqY4gcAAJkr/rjMpwNkk9F8xz36Nu/dWtO5qjw9bFYng5uioAIAgMzjdEhL+8p2/rR+dRXV066emtulugrk9bU6GdwYU/wAACDzbH5KOrpdCcZfQ+wjNTK6kmoXD7U6FdwcBRUAAGSOfeukr2dIkh61P6zS5SprYMMS1mZCtpChgjp9+nTVqlVLgYGBCgsLU9u2bbVv375rbvP++++rYcOGypcvn/Lly6dmzZrpu+++Szemd+/estls6R4tWrTI+KcBAADu4ewRmeUDJUmzHdHaE9xYL3eoKg/OO8UNyFBB3bJli4YOHaodO3Zow4YNstvtuvfee5WUlHTVbWJiYtSlSxd9+eWX2r59u6KionTvvffq77//TjeuRYsWOnHiRNpj4cKFN/eJAACAtezJ0uIesiXH6UdXKb1keuitbtUVHOBtdTJkEzZjjLnZjf/55x+FhYVpy5YtatSo0Q1t43Q6lS9fPr355pvq2bOnpItHUM+dO6cVK1bcVI74+HgFBwcrLi5OQUFBN/UeAADgNlk5TPrpI50xgbov5VkNadNIPesWszoVLJaRvnZL56DGxcVJkkJDb/xk5/Pnz8tut1+2TUxMjMLCwlS2bFkNHjxYp0+fvup7pKSkKD4+Pt0DAAC4gR/nST99JKc8NMw+XLWqVFKPu4panQrZzE0XVJfLpVGjRql+/fqqWLHiDW83fvx4RUZGqlmzZmnPtWjRQvPmzdOmTZv0/PPPa8uWLWrZsqWcTucV32P69OkKDg5Oe0RFRd3sxwAAALfLiZ9l1oyVJL1sb6/Y0Dqa/lAl2Wycd4qMuekp/sGDB+vzzz/Xtm3bdMcdd9zQNs8995xeeOEFxcTEqHLlylcdd+jQIZUsWVIbN27UPffcc9nrKSkpSklJSfs6Pj5eUVFRTPEDAGCVC2eldxtL5/7URmc1DdejWj60ocqF8+8yLsr0Kf5hw4Zp9erV+vLLL2+4nL700kt67rnn9MUXX1yznEpSiRIlVKBAAR04cOCKr/v6+iooKCjdAwAAWMTlkj59WDr3p/50hWm0fbCefagK5RQ3LUN3kjLGaPjw4Vq+fLliYmJUvHjxG9ruhRde0DPPPKP169erZs2a1x1/7NgxnT59WhERERmJBwAArLDtZWn/eiXLW4Pto9Tmrgp6sNqNHcACriRDR1CHDh2qjz/+WAsWLFBgYKBiY2MVGxurCxcupI3p2bOnJk6cmPb1888/r0mTJunDDz9UsWLF0rZJTEyUJCUmJurRRx/Vjh07dOTIEW3atElt2rRRqVKlFB0dfZs+JgAAyBQHN8tsfkaSNMneR953VNUT95e3OBSyuwwV1LfffltxcXFq0qSJIiIi0h6LFy9OG3P06FGdOHEi3Tapqalq3759um1eeuklSZKnp6d++eUXPfDAAypTpoz69eunGjVq6KuvvpKvL/fpBQDAbcUdk5b1l01GCx1NtcnvXr3drbp8vTytToZs7pbWQXUXrIMKAEAWc6RKs1tKf+/UblcxdbBP1Qd9G6pB6QJWJ4ObyrJ1UAEAQC61/jHp7506Z/JosH2Uht9biXKK24aCCgAAMuaXJdL370uSRtmHqFy5ihrcuKTFoZCTZOgqfgAAkMud/E1m1UjZJL3meFCH89XXZx2rysODxfhx+1BQAQDAjUmOl5b0kM1+XludlfSurYOWdquhYH9vq5Mhh2GKHwAAXJ8x0soh0ukD+tvk10j7UD3VtorujOTiZNx+FFQAAHB929+U9q6SXZ4akjpSLetUVLsaLMaPzEFBBQAA13Z4q8yGKZKkJ+09pcI1NKX1nRaHQk7GOagAAODqzv0lfdJbNuPUMmdDrfFpqdXda7AYPzIVBRUAAFyZPVla0kM6f1q7XcX0uKOf3utRXYVD/K1OhhyOKX4AAHA5Y6Q1Y6TjP+mMyatBqY9o+L2V1KhMQauTIRegoAIAgMvtnCXt+lhOeWi4fbgqV6ykIU1YjB9Zgyl+AACQ3tFvZT6fIJuk5+2ddKpAXb3boYpsNhbjR9agoAIAgP8vIfbiYvwuu1Y779JCr7Za2aOG8vpSGZB1mOIHAAAXOVKlJT2lxJP63RWlcfaBmtG5mkoUzGt1MuQyFFQAAHDR+onSX98q3gToYfsjGtisku4pX8jqVMiFKKgAAED6ab70/QdyyaaR9qEqXa6yRtxd2upUyKU4oQQAgNzu7x9lVj8im6QZ9nb6M7SBVnSqKg8PLoqCNSioAADkZkn/Sot7yOZM0QZnDX3o2U7Le9RQkJ+31cmQizHFDwBAbuV0SEv7SPHHdNAVodH2wXqpYzWVLhRodTLkchRUAAByq41TpMNblWT89LD9EfVsWkktKkZYnQqgoAIAkCvtXiptf1OSNMY+SIVLV9Po5mUtDgVcxDmoAADkNrF7ZFYOk03SW44H9FtIE63qXE2eXBQFN0FBBQAgNzl/RlrcTTbHBW11VtJMWxct61lDwQFcFAX3wRQ/AAC5hdMhLe0rnT2iv1wFNcI+TM+1r6py4UFWJwPSoaACAJBbbJwiHfpS542vBtjHqGOjKmpdJdLqVMBlKKgAAOQGuxamuygqf8lqGhfNRVFwT5yDCgBATvf3DzKrRsom6XVHW/0a0lQru1SXlyfHqeCeKKgAAORkCbHSom7/d6eo6nrPo5M+7VVT+fL4WJ0MuCp+dQIAIKdypEiLe0gJJ7TfVViP2IfolU7VVYY7RcHNUVABAMiJjJHWjJGOfac4k0cD7KP1cPOqurdCuNXJgOuioAIAkBN9977000dyykPD7cN0Z8VqGnZ3KatTATeEc1ABAMhpDn8ls26CbJKes3fWP4Ua6p0OVWSzcacoZA8UVAAAcpKzf8os6SmbcWq5s76W+T6oz3rWUIAP/+Qj++D/VgAAcorUJGlRV9kunNEvruJ6wjlQH/apoTvyBVidDMgQzkEFACAnMEZaMVg6uUf/mGANTB2tiQ9UU50S+a1OBmQYBRUAgJzgq5ek31bKbjw1KHWU7q5TTd3vKmp1KuCmUFABAMju9n0us/kZSdIkRx95Fq2rqa0rWBwKuHmcgwoAQHb2zz6ZZQNkk9FcR3N9FdhKK7tXl48Xx6CQfVFQAQDIri6clRZ2kS01QTtc5fWSrbcW9ayhAnl9rU4G3BJ+vQIAIDtyOqRP+khnDuqYKaAhqSP1XIcaqhAZbHUy4JZRUAEAyI7WPyYd+lLnja8Gpo5W16bV1apyhNWpgNuCggoAQHaz80Ppu3clSY/YhyiyXB2Nbl7G4lDA7cM5qAAAZCeHt8qsfVQ2SS/YO+rPsLu1tHNVeXhwG1PkHBRUAACyi9MHZRb3kM3l0ApnPS3x66AVvWoqry//nCNn4f9oAACyg+S4i1fsJ5/TLldJTXIN0pyeNbmNKXIkzkEFAMDdOR3S0r7Sv/t03IRqQOpoPdmuhmoUDbU6GZApKKgAALi7DZOlAxt1wfhoQOoYdWhSUw9Vv8PqVECmoaACAODOfpwn7ZgpSRptH6w77rxLY+8ta3EoIHNxDioAAO7qyNcyq0fLJukVe3v9Wai5lnbiin3kfBRUAADc0ZnDMou7y+aya5XzLi3076yVvWoqwId/upHz8X85AADuJjn+4hX7F87oZ1cJPW6GaF6vWooM8bc6GZAlOAcVAAB34nJKy/pL/+zVSROigamj9XSHWqoaFWJ1MiDLUFABAHAnG6dI+9cr2XhrQOoYdbqnjh6oEml1KiBLUVABAHAXP82XvnlDkjTWPkhRFRto1D2lLQ4FZD3OQQUAwB0c3SGzaqRskl5zPKSjkS20uEMVrthHrkRBBQDAamcOyyzqKpvLrrXO2lro30Ure9aUv4+n1ckAS1BQAQCw0oVz0oKOsp0/rV9cxfW4hmherzoqFORndTLAMpyDCgCAVZx2aUlP6d8/dNyEqn/qWD3T8S5VuiPY6mSApSioAABYwRhpzWjp8BYlGj/1S31UvVvcpfsqRVidDLAcBRUAACt884b04zw5jU3D7cNVuUYDDW5c0upUgFugoAIAkNX2rpLZMFmS9JSjh1JLNNPTD1aUzcYV+4DERVIAAGSt4z/JLBsgm4zmOZprW2g7LetWQ96eHDMCLqGgAgCQVeL+llnQWTbHBcU4q+hNn/5a1qe2gv29rU4GuBUKKgAAWSElUVrQSbbEWP3uitJoM1If9K6jqNAAq5MBbof5BAAAMpvLKS3rJ53crX9MkPqljtVTHeupepF8VicD3BIFFQCAzPbFE9If65RsvDUgday6tWigVpVZTgq4GgoqAACZ6bv3pR1vSZJG2werbI2mLCcFXAfnoAIAkFn2b5T5fLxskl6wd1JciVZ6jeWkgOuioAIAkBlO/ibzSS/ZjFNLnY30RWhXlpMCbhAFFQCA2y3xlMyCjrKlJmqHq7xe9B6spSwnBdywDP0aN336dNWqVUuBgYEKCwtT27ZttW/fvutu98knn6hcuXLy8/NTpUqVtHbt2nSvG2M0efJkRUREyN/fX82aNdP+/fsz9kkAAHAHqeelhV1ki/tLh1zhGukarbd712U5KSADMlRQt2zZoqFDh2rHjh3asGGD7Ha77r33XiUlJV11m2+++UZdunRRv3799NNPP6lt27Zq27at9uzZkzbmhRde0Ouvv6533nlH3377rfLkyaPo6GglJyff/CcDACCruZzSpwOkv3fqrMmrvvZHNbljQ5aTAjLIZowxN7vxP//8o7CwMG3ZskWNGjW64phOnTopKSlJq1evTnvurrvuUtWqVfXOO+/IGKPIyEiNGTNGY8eOlSTFxcWpUKFCmjNnjjp37nzdHPHx8QoODlZcXJyCgoJu9uMAAHBr1k2UdrylFOOl7qmPqWl0Gw1pUsrqVIBbyEhfu6UztePi4iRJoaGhVx2zfft2NWvWLN1z0dHR2r59uyTp8OHDio2NTTcmODhYderUSRvzXykpKYqPj0/3AADAUjveTltOaox9sErWbM5yUsBNuumC6nK5NGrUKNWvX18VK1a86rjY2FgVKlQo3XOFChVSbGxs2uuXnrvamP+aPn26goOD0x5RUVE3+zEAALh1e1fJrJsoSXrO3lmJpR/Q021ZTgq4WTddUIcOHao9e/Zo0aJFtzPPDZk4caLi4uLSHn/99VeWZwAAQJJ0bKfMsv6yyWi+4x5tK9RNM7tWlxfLSQE37aaWmRo2bJhWr16trVu36o477rjm2PDwcJ08eTLdcydPnlR4eHja65eei4iISDematWqV3xPX19f+fr63kx0AABunzOHZBZ0ks2RrM3Oqno3z8XlpPL4soojcCsy9OudMUbDhg3T8uXLtXnzZhUvXvy629StW1ebNm1K99yGDRtUt25dSVLx4sUVHh6ebkx8fLy+/fbbtDEAALid82dk5neQ7fy/2uMqpsc8R+vDfncpLNDP6mRAtpehX/GGDh2qBQsWaOXKlQoMDEw7RzQ4OFj+/v6SpJ49e6pw4cKaPn26JGnkyJFq3LixXn75ZbVq1UqLFi3Szp079d5770mSbDabRo0apaefflqlS5dW8eLFNWnSJEVGRqpt27a38aMCAHCb2JNlFnWV7fQB/W3ya5BznF7v01ClwgKtTgbkCBkqqG+//bYkqUmTJumenz17tnr37i1JOnr0qDw8/v+B2Xr16mnBggV64okn9Nhjj6l06dJasWJFugurxo0bp6SkJA0cOFDnzp1TgwYNtG7dOvn58VsoAMDNuFzSisGyHd2ueOOvPqnjNL5zU9UufvUVbQBkzC2tg+ouWAcVAJBlNkyRvp4hu/FUL/t4NY5ur4dZTgq4rixbBxUAgFxl54fS1zMkSRPsA1SqTisNbFTC2kxADsRlhgAA3Ig/vpBZM0Y2Sa/a2ym+XAe90LoCa50CmYCCCgDA9RzfJfNJL9mMS584GmlLRF8t7FxNnh6UUyAzUFABALiWc3/JzO8om/28tjkr6J2g4VrSu5b8fTytTgbkWBRUAACu5sI5mfntZUs6qd9dUXrMe5zm9q2v/Hm5WQyQmbhICgCAK3GkXFzr9J/fddKEaJBrvGb0bqziBfJYnQzI8SioAAD8l8slLX9Ytj+/VoLxVx/7OE3o3FzVi+SzOhmQK1BQAQD4X8ZI6x+Tfl2uVOOph+2PqNP9rdSiYrjVyYBcg4IKAMD/2v6m9O3FOyeOtQ9S1UZt1KteMWszAbkMBRUAgEt++UT64glJ0jP2rvKp1kmPRpe1OBSQ+3AVPwAAknQoRmbFYNkkzXK01IGSvfXeQ5VYiB+wAAUVAIDY3XIt6iYPl12rnXdpdfhQze9eQ96eTDQCVqCgAgByt7N/yvVxO3mkJmqHq7zeCBqjhX3qKMCHfyIBq/DTBwDIvc6fuVhOEy8uxD/RZ6Lm9Wug0Dw+VicDcjXmLgAAuZP9gsyCzvI4vV/HTaiG2R7TzL5NFRUaYHUyINejoAIAch+XU2ZZP9mOfas4E6D+joma1vNe3RkZZHUyAKKgAgByG2OktY/K9vsapRhvDbSP0ZBO96teyQJWJwPwfyioAIDc5auXpZ2z5DI2jbIPUctW7XR/5UirUwH4HxRUAEDu8dN8afNTkqQnHT1VrFFX9a5f3OJQAP6LggoAyB32b5D5bLgk6R1HayVW6adx3CUKcEssMwUAyPn++k6uxd3lYZz61NlA35YYpvfacZcowF1RUAEAOdup3+X6uIM8HMmKcVbR/EKP6qPuNblLFODGKKgAgJzr3F9yfvSgPFPO6SdXKb0Q/Jg+7lOPu0QBbo6fUABAzpR0Wq6PHpRnwnHtdxXWRL9J+rB/Y+4SBWQDzG8AAHKelES55rdPu0vUcM8n9Gb/ZooM8bc6GYAbQEEFAOQsjlSZxT3kcfxHnTV5NdA8ruf73qdSYXmtTgbgBlFQAQA5h8sls2KwbIc267zx1QDneE3o0VZVokKsTgYgAyioAICcwRhp3QTZ9iyV3XhqiH2U+nXqoAaluYUpkN1QUAEAOcNXL0nfvStJGmMfpOi23dWyUoTFoQDcDAoqACD72zlb2vy0JGmqvafKNu+rLrWLWBwKwM2ioAIAsrffPpNZM1qS9IajrbzqDtaQJiUtDgXgVrAOKgAg+zq8Va6l/eRhXFrgaKojlR7Ri/eV5xamQDZHQQUAZE8nfpZzQRd5ulK1zllLMaUm6K32leXhQTkFsjsKKgAg+zl9UM55D8nTnqgdrvL6KHKSZnWrJS9PzlwDcgIKKgAge4k/LufcNvK88K9+dRXVi6FTNbtPPfl5e1qdDMBtQkEFAGQf58/IObetPOP/0hFXIU3KM1Xv9muqID9vq5MBuI0oqACA7CE5Xs55D8rz9D6dMKEa6TNVbw5ooYKBvlYnA3CbcbIOAMD92S/IubCLPGN36YzJq6Eek/TSgNaKCg2wOhmATEBBBQC4N6ddriW95fnnNiUYfw3WE3qyXzuVLhRodTIAmYSCCgBwXy6XXCuGyGP/OiUbbw1xPaoxvTup0h3BVicDkIkoqAAA92SMzNpH5bF7iezGUyOco9Svew/VLh5qdTIAmYyCCgBwS2bz07Lt/EAuY9NYx2C17dRPTcqGWR0LQBagoAIA3M/Xr8v21UuSpEmOPmrw4CDdVynC4lAAsgoFFQDgXn6YK22YJEl6wd5Jpe4boQ41oywOBSArUVABAO5jz6cyq0ZKkt5xtJZf07HqU7+4xaEAZDUKKgDAPezfKNeygbLJaIHjbp2+a6KG31Pa6lQALEBBBQBY78/tci7qJg9j1yrnXdpTbbIea3WnbDab1ckAWIBbnQIArHXiZzk+bi8vZ7K+dFbRpvJP6eUHq1JOgVyMggoAsM4/f8gx90F52RP1rauclhR/Rq93qilPD8opkJtRUAEA1jhzSPbZ98s7+bT2uIrp3chn9VaPevL25OwzILejoAIAsl7cMdlnt5b3+ZPa57pDzxWcrnf6NJaft6fVyQC4AQoqACBrJZy8WE4Tjumwq5Cm5XtWb/Vrrry+/JME4CLmUQAAWef8GdnnPCDvc4d0zBTQE8HP6o2BLRUc4G11MgBuhIIKAMgayXGyz20r79O/66QJ0YQ8T+vVgfcrNI+P1ckAuBkKKgAg86Ukyj6vnbxP/qzTJlBj/afpxYfbKizQz+pkANwQBRUAkLnsF2Sf31nex79XnAnQIz5T9ezADooI9rc6GQA3RUEFAGQeR6rsi7rL++hXSjR+Guk1SVMHdlZUaIDVyQC4MQoqACBzOB1yfNJX3gc36oLx0SjPiXpsYA+VKJjX6mQA3BwFFQBw+7lccq4YIq99q5RivDTK9qhG9eujMoUCrU4GIBugoAIAbi9j5Fz9iDx3L5bDeGisHtGgvgNUsXCw1ckAZBMUVADA7WOMnOsek+ePc+QyNo03w9Sj9xBVK5LP6mQAshEKKgDgtnFtflqe374lSXrM9bAe6jlStYuHWpwKQHZDQQUA3BauLS/K46uXJElTnX0U3W2M6pcqYHEqANkRBRUAcMtcX82Qx5dPS5Kec3TVXZ3Gq2m5MItTAciuKKgAgFvi+uZNeWyaIkl6ydFR5ds/oRYVIyxOBSA7o6ACAG6a2fGOPL54XJI0w/GQSrabojZVC1ucCkB2R0EFANwU8/0s2daNlyTNdLTRHW2n6cFqd1icCkBOQEEFAGSY+WGubGtGS5Lecdyvgg88rfY1oyxOBSCnoKACADLE/DRfWjVSkjTL0VLBrZ9Vx9pFLE4FICehoAIAbpj5ZYm0cqhsMprjuFe+rZ5TlzpFrY4FIIehoAIAbojZ86nMpw/LJqP5jnvkcd8L6l63mNWxAORAGS6oW7duVevWrRUZGSmbzaYVK1Zcc3zv3r1ls9kue1SoUCFtzNSpUy97vVy5chn+MACATPLbZzJL+8tDLi12NFFqixfVs15xq1MByKEyXFCTkpJUpUoVzZw584bGv/baazpx4kTa46+//lJoaKg6dOiQblyFChXSjdu2bVtGowEAMsO+z+X8pLc85NQyZwMl3vuS+jQoaXUqADmYV0Y3aNmypVq2bHnD44ODgxUcHJz29YoVK3T27Fn16dMnfRAvL4WHh2c0DgAgM+3fIOeiHvI0Tq101tOZZq9qQKPSVqcCkMNl+Tmos2bNUrNmzVS0aPqT6vfv36/IyEiVKFFC3bp109GjR6/6HikpKYqPj0/3AADcZgc2ybGwqzyNXauddXS86asa0LiM1akA5AJZWlCPHz+uzz//XP3790/3fJ06dTRnzhytW7dOb7/9tg4fPqyGDRsqISHhiu8zffr0tCOzwcHBiopi7T0AuK0ObZFjQRd5uVK1zllLRxu/psF3c20AgKyRpQV17ty5CgkJUdu2bdM937JlS3Xo0EGVK1dWdHS01q5dq3PnzmnJkiVXfJ+JEycqLi4u7fHXX39lQXoAyCUOxcjxcQd5uVK00VlNfzR8TUOalbc6FYBcJMPnoN4sY4w+/PBD9ejRQz4+PtccGxISojJlyujAgQNXfN3X11e+vr6ZERMAcreDX8oxv5O8XCna5KymX+u/oZH3Vrj+dgBwG2XZEdQtW7bowIED6tev33XHJiYm6uDBg4qIiMiCZAAASdLBzWnldKOzmvY0eFMjoitanQpALpThgpqYmKhdu3Zp165dkqTDhw9r165daRc1TZw4UT179rxsu1mzZqlOnTqqWPHyv+zGjh2rLVu26MiRI/rmm2/04IMPytPTU126dMloPADAzTiwSY75neXlStEGZ3XtbfimRkZXlM1mszoZgFwow1P8O3fuVNOmTdO+Hj16tCSpV69emjNnjk6cOHHZFfhxcXFatmyZXnvttSu+57Fjx9SlSxedPn1aBQsWVIMGDbRjxw4VLFgwo/EAABl1YNP/XRB1sZz+0ehNDW/OtD4A69iMMcbqELcqPj5ewcHBiouLU1BQkNVxACD7uHTk1KRqg7OG9jd+U0Oa3Wl1KgA5UEb6WpZdJAUAcDMHNsoxv4u8TKq+cNbQwSYzNeQertYHYD0KKgDkQmb/BjkXdE0rp4ebvsU6pwDcBgUVAHKZ/y2n6501dfTumXq4KeUUgPugoAJALmL++ELOhV3lZexa56yl481makDjslbHAoB0KKgAkEuYP9bLubDb/y+nzd9S30ZlrI4FAJehoAJALmD2rZNzUXd5Gbs+d9bSyXvfUt+GlFMA7omCCgA53P+W07XO2vo3+i31blDa6lgAcFVZdqtTAEDWM3tXybWoW1o5Pd3iLfWknAJwcxxBBYAcyrVnuczSfvKUU6uddXSu5VvqUa+U1bEA4LooqACQA7l+XiwtHyRPubTc2UCprd5Q97tKWB0LAG4IBRUAchjnDx/Jtmq4PGT0ibOxPNu+oU41ilodCwBuGAUVAHIQ53ez5Ll2tCRpvrOZgtu/pvur3GFxKgDIGC6SAoAcwr797bRyOtfZQgU6vUk5BZAtUVABIAewf/WavNdPkCS972qtIt1eV3TFCItTAcDNYYofALK51C9fkM+WZyRJb7keVJUeL6p+6YIWpwKAm0dBBYDsyhilbnpWPttekCS94eqo2r2fU50S+S0OBgC3hoIKANmRMUpeP0V+O16TJL2ibmrS/xlVL5LP4mAAcOsoqACQ3Rij5DUT5bfzbUnSC+qtlv2nqdIdwRYHA4Dbg4IKANmJy6ULq8bK/6dZkqTptgFqM2Cy7owMsjgYANw+FFQAyC5cTl1YPkL+uz+Wy9j0jNcgdR74uEoXCrQ6GQDcVhRUAMgOnA6d/2SgAn5fJqex6Wnv4eo5aIKKF8hjdTIAuO0oqADg7hwpSlrQU3kOrZPdeOopn0c0YPAYRYUGWJ0MADIFBRUA3FnqeSXO66S8x7YqxXjryYDxGj5omCKC/a1OBgCZhoIKAO4qOV6Js9sp78nvlGR89XTgJI0ZNFAF8vpanQwAMhUFFQDc0fkzSpz1gPKe3q14E6BnQ6dp4oDeCg7wtjoZAGQ6CioAuJuEk0r84H7ljftDp02gXgp7TpP6d1YeX/7KBpA78LcdALiTc38p6YNWypv4p06aEL1R+GVN6fOg/Lw9rU4GAFmGggoA7uL0QSV90Ep5LpzQMVNAHxSfoSk9Wsnb08PqZACQpSioAOAOTv6m87NaK0/qvzroitDicm9oUudm8vSwWZ0MALIcBRUArHb8JyV/2EYBjjjtdRXR2qpva+KD9WWzUU4B5E4UVACwkPnzG6XOay8/Z5J2uUrqmzrvaPR9tSinAHI1CioAWMS1f7McC7vI15WsHa7y+q3xexpyT2WrYwGA5SioAGAB56+rZJb2kY+xK8ZVRSei31Pf+uWsjgUAboGCCgBZzL5znjxXj5SnXFrnqq2UB95Vl5olrI4FAG6DtUsAIAulfPWGvFcPl4dcWupqIo8Os9WGcgoA6XAEFQCygjG6sP5J+e94VZI029yvsj1mqF7pghYHAwD3Q0EFgMzmcilpxSjl+WWuJOkNW1c16vusqhTJZ3EwAHBPFFQAyExOuxIX9Vfe/SvkMja95D1QDw2cpFJhgVYnAwC3RUEFgMySel4JH3VT4F+bZTeees5vlPoOflSFQ/ytTgYAbo2CCgCZ4cI5xc9up6BTO3XB+Gh64GMaMWiICuT1tToZALg9CioA3G6Jp5TwwQMKOrdX8SZALxaYpkcH9FaQn7fVyQAgW6CgAsDtdPZPJX5wvwKTjuofE6yZhV/Q4306yM/b0+pkAJBtUFAB4HY59buSZrVW3pRTOmYKaF6p1/R41/vk7cmS0wCQERRUALgNzLEflDznQeVxxOkPV2GtrvKWJjzYWB4eNqujAUC2Q0EFgFvkOhgj+/wu8ned1y5XSX1X7109El1TNhvlFABuBgUVAG6B45dl0vKH5Wvs+tpZQX/e+74GNqxgdSwAyNYoqABwk1K+eUfeX0yQh4w+d9WRo+076lqjhNWxACDbo6ACQEYZo/PrpylgxyuSpIWmuSK7vqnG5cItDgYAOQMFFQAywulQ4qcjlPfX+ZKkt2ydVK/v86paJJ/FwQAg56CgAsCNsl9Q/PxeCjqyXk5j00veg9Th4SdUomBeq5MBQI5CQQWAG3HhnOJnt1fQqe+VYrz1Qp6xGjholAoF+VmdDAByHAoqAFxP/AnFf/CAguL/ULzx18v5n9ToAX0V7M+tSwEgM1BQAeBa/j2gxA9aKyj5uE6ZEL0b9YIm9mrPrUsBIBNRUAHgKsyxnbowp53yOs7pkCtcS+98XY91vFee3B0KADIVBRUArsD5x0Y5FnVXgOuCfnaV0Pa73tGjLWtzdygAyAIUVAD4j9SfFslz5RD5yqmvXJX0973vaVCDO62OBQC5BgUVAP7Hha2vy3/zJEnSKlc9ebd7R52rFLU4FQDkLhRUAJAkl0uJqyco74/vSpI+MvepVM/XVbdUQYuDAUDuQ0EFAHuy4hf1V9DBVZKk1z166J5+T6tC4RBrcwFALkVBBZC7XTir+NkdFXTqO6UaT73oP1K9Hn5Ud+QLsDoZAORaFFQAude5vxQ/q42CEg4q3vjr1dDJGjmgv0ICfKxOBgC5GgUVQK5kTvyi87MfUlDqP4o1+fRBkRc0vmc7FuAHADdAQQWQ6zgPxMixoIvyuM7rD1dhran8hiY+dDcL8AOAm6CgAshVUn9aLI+VQ+Qrh3a4ymtf43c06u4qLMAPAG6EggogdzBGF2Jelv+WpyRJa113ydX2XfWqXszaXACAy1BQAeR8LqcSV4xR3l9mS5Lm6n6V6TmDNU4BwE1RUAHkbPYLip/fW0FH1sllbHrdq7daDnhKZcMDrU4GALgKCiqAnOv8GcV/+JCC/v1JKcZLL+UZo74PP6KIYH+rkwEAroGCCiBnOnNYCbPaKijpiOJMgF4rOE2j+vdWkJ+31ckAANdBQQWQ45i/vtOFeR0VaD+rv01+zSvxkiZ0aysfLw+rowEAbkCG/7beunWrWrdurcjISNlsNq1YseKa42NiYmSz2S57xMbGphs3c+ZMFStWTH5+fqpTp46+++67jEYDANl3r5Djw1YKsJ/VblcxLa8+V+N7PEg5BYBsJMN/YyclJalKlSqaOXNmhrbbt2+fTpw4kfYICwtLe23x4sUaPXq0pkyZoh9//FFVqlRRdHS0Tp06ldF4AHIrY3Qh5lV5Lustb5Oqza5q+r3FYg1r01AeLMAPANlKhqf4W7ZsqZYtW2b4G4WFhSkkJOSKr73yyisaMGCA+vTpI0l65513tGbNGn344YeaMGFChr8XgFzG6VDiitHKu3uuJGmBiVbhrq+pQ7kIi4MBAG5Gls15Va1aVREREWrevLm+/vrrtOdTU1P1ww8/qFmzZv8/lIeHmjVrpu3bt1/xvVJSUhQfH5/uASCXSklU3Oz2yrt7rlzGptc8+6jaw++rMeUUALKtTC+oEREReuedd7Rs2TItW7ZMUVFRatKkiX788UdJ0r///iun06lChQql265QoUKXnad6yfTp0xUcHJz2iIqKyuyPAcAdxR9X/NvNFHzsS10wPnom70R1GvGcykcGW50MAHALMv0q/rJly6ps2bJpX9erV08HDx7Uq6++qo8++uim3nPixIkaPXp02tfx8fGUVCCXMbG7lTS7nYJSTuofE6S3I57R6D5dldeXxUkAILuz5G/y2rVra9u2bZKkAgUKyNPTUydPnkw35uTJkwoPD7/i9r6+vvL19c30nADck/OPjbIv6qG8rvM64IrUygoz9Fj75vLy5Ep9AMgJLPnbfNeuXYqIuHh+mI+Pj2rUqKFNmzalve5yubRp0ybVrVvXingA3FjKtx9KCzrIz3Ve21136uvGCzW6472UUwDIQTJ8BDUxMVEHDhxI+/rw4cPatWuXQkNDVaRIEU2cOFF///235s2bJ0maMWOGihcvrgoVKig5OVkffPCBNm/erC+++CLtPUaPHq1evXqpZs2aql27tmbMmKGkpKS0q/oBQC6Xkj6frDzfvyFJWuFqKL92M9WrSlGLgwEAbrcMF9SdO3eqadOmaV9fOhe0V69emjNnjk6cOKGjR4+mvZ6amqoxY8bo77//VkBAgCpXrqyNGzeme49OnTrpn3/+0eTJkxUbG6uqVatq3bp1l104BSCXsicrflF/BR1cJUl619ZBtfq9oOpFQy0OBgDIDDZjjLE6xK2Kj49XcHCw4uLiFBQUZHUcALdT4inFzemo4H9/Uqrx1Cv+w9R1wAQVyR9gdTIAQAZkpK9xuSsA93XyNyXObqfg5OOKMwF6Lf8UjejfVyEBPlYnAwBkIgoqALfk3LdejsW9ldd1XoddhbSo9Csa36WlfL08rY4GAMhkFFQAbid521vy2fi4fOXSDld5/drwTU1oVkM2m83qaACALEBBBeA+nA4lrByrwF9mS5KWuZooT7vX1Y8r9QEgV6GgAnAPyXGK+6i7gv/eKpexaaZndzXu95QqR+WzOhkAIItRUAFY7+wRxX/4kIITDuq88dXLeceo/8ARigj2tzoZAMACFFQAljJHd+jCR50VZD+rWJNP7xV+VmN6dVQeX/56AoDcin8BAFgm9adF8vhsmAKMXXtcxbS5+ut6/IFG8vTgYigAyM0oqACynjFKWv+U8ux4WZL0haum4lrM1Ih65SwOBgBwBxRUAFnLfkHxiwYq6OBnkqQP9YDK9XhZ95YOszgYAMBdUFABZJ34E4qf01FBZ36R3XjqFd/B6jBgokoUzGt1MgCAG6GgAsgS5u8fdX5uRwWl/qOzJq9ezz9JI/r1Vb483LYUAJAeBRVAprP/vERaMVR5TKr+cBXWinIva2LHaPl4eVgdDQDghiioADKPy6Wk9dOU59tXJUmbnNV0otkberRRRW5bCgC4KgoqgMyRkqj4BX0V9Od6SRcvhirV7UV1LxducTAAgLujoAK4/c7+qfjZ7RUU/4dSjJde8Rumjv0fVUkuhgIA3AAKKoDbyhzZpgsfd1OQ45z+McGaWWiaHundTcEB3lZHAwBkExRUALdN6nez5bF2rALk0G5XMW2qMkNPtG0sL08uhgIA3DgKKoBb53QocdUE5d31viRpjesuXbjvdY26q6zFwQAA2REFFcCtuXBW8R/1UNDxryRJb9k6qWbv6apdIr/FwQAA2RUFFcDN+3e/Ema3V1DSEZ03vno5zyPq3X+kokIDrE4GAMjGKKgAbopz33rZl/RRoDNJf5v8mnXHsxrds73y+vLXCgDg1vAvCYCMMUbJX74kn63PyE9G37vK6LtaM/REq3ry8GDxfQDAraOgArhxKYlKWDxQgYfWSJIWuZopz4Mva2i1YtbmAgDkKBRUADfm7BElzOmgwLg/lGo8NcN7gFr1fUwVIoOtTgYAyGEoqACuy3UgRimLeirQEad/TLDeLDBFI/v2UGgeH6ujAQByIAoqgKszRslfvSGfzVPkL5d2uUpoc5VX9ETbxvJm8X0AQCahoAK4MvsFJSwdqsB9yyRJn7oaybR6VaPrlLI4GAAgp6OgArhc3DHFz+2soDO75TAeet2rt+7uO1lVi+SzOhkAIBegoAJIxxz5Whfmd1eQ/YzOmLx6LfRxDe3bV2GBflZHAwDkEhRUABcZo9RvP5DnuvEKkFO/uYpq1Z0v6fH2zeTjxfmmAICsQ0EFIDlSlLj8EeX9db4kabWzrhJbvKrx9ctbHAwAkBtRUIHcLu5vJczrosDTP8tlbJrp2VV39XpK9xfPb3UyAEAuRUEFcjFzeKsuLOipQPtZnTN5NCN4nAb2fViRIf5WRwMA5GIUVCA3MkbJX70u781TFSCXfnUV1apyz2tCh3vl5+1pdToAQC5HQQVym5REJXwyWIEHPpMkLXc1VGqLlzW+bhnZbDaLwwEAQEEFcpfTB5Uwt5MC4/fLbjz1mlcf3dPncVUrGmp1MgAA0lBQgVzCuXetHEv7K9CZpFMmRG8WmKThvXuoYKCv1dEAAEiHggrkdC6nLmx4Rv7bX5anpO9dZfR11Zc1uU0DeXmyvikAwP1QUIGc7PwZJSzsq8C/vpQkfeRqoZAHn9eoasWszQUAwDVQUIGcKna3Eud1VuD5Y0o23nrZd4ja9R2rcuFBVicDAOCaKKhADuT4aaFcn41UXpOiv1wF9WHhaRrVs4OC/b2tjgYAwHVRUIGcxJGipNUTlGfXh5KkLa7K2lv3VU2KriEPD5aQAgBkDxRUIKc495cSPuqmwNM/S5Le00Mq3flZDbozwuJgAABkDAUVyAFcf2xUypK+CnTEKc4E6NW8Y9Wn7yAVzZ/H6mgAAGQYBRXIzlxOJW+cLp9vXpK/jHa7imlNuec1oUNzblkKAMi2KKhAdpV0WgkLeivw762SpEWuZvK673lNuKuUxcEAALg1FFQgGzJ/fa/z87srMDlWF4yPXvEdrLa9x6hCZLDV0QAAuGUUVCA7MUap37wtjw2TlEcOHXKF66MiT2lUtwdZQgoAkGNQUIHsIiVRCUuHKHD/SknS567aOtH4JU2+u7JsNpaQAgDkHBRUIDs49bsSP+qiwIRDshtPveHZQ/V7TFLLkgWsTgYAwG1HQQXcnGPXErk+G6G8rguKNfk0s8ATGt6ru8KC/KyOBgBApqCgAu7Knqyk1eOV5+c5kqSvnRW0s9aLmtKqrrw8PazNBgBAJqKgAu7o9EElfNxdgWd/kyS9pwdVrNMzGlmxsMXBAADIfBRUwM04d38qx4qhCnSe1xmTVzMCx6pfn4HcFQoAkGtQUAF3YU9W0uoJyvPzbHlK+t5VRjEVn9djDzbmrlAAgFyFggq4gzOHlPBRdwWe/VWS9IFpq8iHntajVaMsDgYAQNajoAIWuzilP0yBzqS0Kf2+vQeqWAGm9AEAuRMFFbCKI0VJq8anm9LfUul5PdaWKX0AQO5GQQWscJUp/bFM6QMAQEEFsppz9/L/u0qfKX0AAK6EggpkFab0AQC4IRRUICswpQ8AwA2joAKZjCl9AAAyhoIKZBb7BSV9Nk55ds9jSh8AgAygoAKZ4dReJXzcQ4Hx++UyNn2oNkzpAwBwgyiowO1kjOzffSitm6BAk6pTJkRvBo9Vv159VTQ/U/oAANwICipwu1w4q4RPhijw0FpJUoyzin6uNV1P3FdXPl4eFocDACD7oKACt4H5c7vOL+yjwOQTSjWemunZXTW6PK6RZQtZHQ0AgGyHggrcCpdTyZtflPe255VHLh1xFdLsiEka1r2jCgb6Wp0OAIBsiYIK3Kz440pY0EeBsTskSSud9XWm6XRNaVJZHh42i8MBAJB9ZfjEuK1bt6p169aKjIyUzWbTihUrrjn+008/VfPmzVWwYEEFBQWpbt26Wr9+fboxU6dOlc1mS/coV65cRqMBWcb5++e68PpdCozdoSTjq2d9RqrYwAXqc3cVyikAALcowwU1KSlJVapU0cyZM29o/NatW9W8eXOtXbtWP/zwg5o2barWrVvrp59+SjeuQoUKOnHiRNpj27ZtGY0GZD5HipJWjJHnos7yd8Rpj6uYZpT8QMNHT1KVqBCr0wEAkCNkeIq/ZcuWatmy5Q2PnzFjRrqvn332Wa1cuVKrVq1StWrV/n8QLy+Fh4dnNA6Qdf7df3Ft03N7JUlzzX0KvP8ZPVaruGw2jpoCAHC7ZPk5qC6XSwkJCQoNDU33/P79+xUZGSk/Pz/VrVtX06dPV5EiRa74HikpKUpJSUn7Oj4+PlMzI5czRvYfPpZZ+6gCXRd02gTqjcDR6tlroEoUzGt1OgAAcpwsX5zxpZdeUmJiojp27Jj2XJ06dTRnzhytW7dOb7/9tg4fPqyGDRsqISHhiu8xffp0BQcHpz2iorg7DzLJhXNKmN9L3quHycd1QV87K2hu5fmaOGok5RQAgExiM8aYm97YZtPy5cvVtm3bGxq/YMECDRgwQCtXrlSzZs2uOu7cuXMqWrSoXnnlFfXr1++y1690BDUqKkpxcXEKCgrK8OcArsQc2aakRf2VN/mEHMZD73p0UvkOk3X3nZFWRwMAINuJj49XcHDwDfW1LJviX7Rokfr3769PPvnkmuVUkkJCQlSmTBkdOHDgiq/7+vrK15c1JpFJHKk6v+Fp+X37uvLKXFzbtNBjGtKjswoF+VmdDgCAHC9LCurChQvVt29fLVq0SK1atbru+MTERB08eFA9evTIgnTA//j3gOIX9FbQmd2SpE9cTXS+6dOa0rgiy0cBAJBFMlxQExMT0x3ZPHz4sHbt2qXQ0FAVKVJEEydO1N9//6158+ZJujit36tXL7322muqU6eOYmNjJUn+/v4KDg6WJI0dO1atW7dW0aJFdfz4cU2ZMkWenp7q0qXL7fiMwPUZI/v3s2XWTVSQK1nnTB69kWeY2nUfpjsjOW0EAICslOGCunPnTjVt2jTt69GjR0uSevXqpTlz5ujEiRM6evRo2uvvvfeeHA6Hhg4dqqFDh6Y9f2m8JB07dkxdunTR6dOnVbBgQTVo0EA7duxQwYIFb/ZzATcu6bTiPxmsoCMXbyDxtbOCvq3yjB5t00h+3p4WhwMAIPe5pYuk3EVGTroF/pdr/2Zd+GSA8qT+q1Tjqbc8u6lKx8fVtBxr8gIAcDu55UVSgFuxJyvp88nK8+O7yiPpgCtS8++YpKFd26lAXi7AAwDAShRU5D6n9ip+fi8Fxe2TJC1wNZct+mlNrleWO0IBAOAGKKjIPYxRyjdvy2PjFAWZVP1rgvRW0Eh17TFIpcJYdB8AAHdBQUXukHhK8YsGKOhYjCQpxlVFu2tM14RWdeXjleU3VAMAANdAQUWO5/xttVKWD1OQ/axSjLfe9O6lep0nanipAlZHAwAAV0BBRc6VHK+EFWMU+PsSBUja6yqiZcWnaninBxQc4G11OgAAcBUUVORI5vBXSlo8UIHJx+UyNs1Ra+W7/0k9Xqs4F0IBAODmKKjIWezJSlo3Vf4/vKO8MjrqKqjZYePVv3t3FQ7xtzodAAC4ARRU5BwnflH8wr4Kit8vSfrE1VQX7n5akxpVkIcHR00BAMguKKjI/lxOXYh5Wd5bn1eQHPrHBOntoFHq2mOgSoUFWp0OAABkEAUV2dvpg4pb2F/B//4oSVrvqqXDdz2jidG15O3J8lEAAGRHFFRkT8Yo9dtZMl88oWDXBSUYf830G6gW3R5RdJF8VqcDAAC3gIKK7CchVnGLByn42JeSpO3OO7Wj8lMa2aap/H08LQ4HAABuFQUV2Yp993LZV45UsCNOKcZb73h1U42uj+uRMmFWRwMAALcJBRXZw4Vzil/+iIL++FTekn51FdXqUk9qUIf7FezPovsAAOQkFFS4Pee+L3Th06EKSjklp7HpQ9uDuqPdVI2vUtTqaAAAIBNQUOG+kuMUv3K8gvYuVF5Jh1zhWhA5UQO7dlZYkJ/V6QAAQCahoMItOfdv1oWlgxWUEiuXsWm+Wirgvif1eJ3S3KoUAIAcjoIK95KSoPjPJiro14+UV9KfrjB9VGic+nbrrkhuVQoAQK5AQYXbcB3coqRPBiko+bgkab5pIe8WT+rxumU5agoAQC5CQYX1UpMUv+pxBe2erUBJf7kKal7YWPXq1lN35AuwOh0AAMhiFFRYynX4ayUuGaigC8ckSYtMc+neaXqsfnmOmgIAkEtRUGGN1POKXztZeXd9oCAZ/W3ya27+MerRvY+iQjlqCgBAbkZBRZZz/blDiYsHKOj8UUnSJ6675Ww2TRMaVJSHB0dNAQDI7SioyDr2C4r//Enl/fEdBcnohAnVnNBH1K17fxXJz1FTAABwEQUVWcL15w4lLBmk4KTDkqRPXY2VfPdTGt+oMkdNAQBAOhRUZK7UJMWvmay8P89SsIxOmRB9mG+kOnd/WMUK5LE6HQAAcEMUVGQa54EYJS0doqDkvyVdPGqa1PQpjWvMUVMAAHB1FFTcfslxOrdygkL2LlCQpGOmgD4u8Ii6du3LuaYAAOC6KKi4rex71ypl+UiFpJ6SJC0y98oreprG1y3HuqYAAOCGUFBxe5w/o7PLRivfweXylnTYVUifRI5Tzy7dFR7sZ3U6AACQjVBQcWuMUeruT2X/bIzyOc7KaWya79FaoQ9M0aM1SnLUFAAAZBgFFTcv4aTOfDJcoUfXy0fSPtcdWlXscfXp2E758/panQ4AAGRTFFRknDFK3vmxXOsmKtSZILvx1FzPh1Ss3RSNrRRldToAAJDNUVCRMef+0pnFQxR6YqskabermDaXmaLe7Vor2N/b4nAAACAnoKDixrhcOv/Nu/LY/KRCXReUYrz1oU9nVe7whEaWCbc6HQAAyEEoqLguc/I3nV08WKFndkmSdrrKaEelJ9W3zb0K8OF/IQAAcHvRLnB19mTFb3hOAd+9oVA5lGD8Ndu/p+p3HqdhxQpYnQ4AAORQFFRckePQV0pcOkwh549Ikja6aujoXdP08L115evlaW04AACQo1FQkd6FczqzcoJCf1+oEEmnTIg+yjdMbbsOUrOwQKvTAQCAXICCiouMUcovy2VfPVah9tOSpE/UTB73PqlH6laQhwcL7gMAgKxBQYUUd0z/LhmhAn9vkq+kg64IfVZkvLp16qKwQG5TCgAAshYFNTdzOZW07V15xkxTAdcFpRpPfez1kIq3n6xHKhaxOh0AAMilKKi5lDn5q84sGqz8Z3+WJP3gKq3vKk5VzzYtlMeX/y0AAIB1aCK5jT1Z59Y/q7w731R+OZVg/DU3oJcadhmvwUVCrU4HAABAQc1N7Pu/VOKnI5Xvwp+SpI2umjpe7yk93PwueXt6WJwOAADgIgpqbpB4Sv9+OlYFDq1UPl1cOmph/uEXl44qkNfqdAAAAOlQUHMyl0tJ22fJtmmqCrgS5TI2LfFooYCWUzSidjnZbCwdBQAA3A8FNYdynditM4uHqsC5ixdB7XEV05Yyj6vbQ20VEuBjcToAAICro6DmNCmJOrN2moJ/fl8F5FKi8dNHAT1Uu+N4DS1e0Op0AAAA10VBzUFS9qxS8mdjFJp6UpK03tTR6QbTNODu2vLiIigAAJBNUFBzgnN/6Z9PRqrg/90J6i9XQa2IHKV2nfspMsTf6nQAAAAZQkHNzpx2xcW8Id9tz6ugSZbdeGqRVxsVfmiKhlcuZnU6AACAm0JBzaYcf36ruE+GKn/ifknS966y+rnKVHVtfa8CfNitAAAg+6LJZDcXzuqfFY8r/74Fyi+jsyavFgb31z1dRqt/RLDV6QAAAG4ZBTW7cLmU+N08acNkFXTGSZJWqonMvU9pUN1K8vBgTVMAAJAzUFCzAefxn3VmyXAV/L81Tf9wFdbGEuPVuUMXheZhTVMAAJCzUFDd2YVzOrVqivL/Nk8F/29N04UBXVW9wwQNKVHI6nQAAACZgoLqjoxR4ncfy2yYpDDHWUnS56qnxMZT1adxLdY0BQAAORoF1c04T+zW6SXDFXb2J0nSAVekNhZ/VO3ad1PBQF+L0wEAAGQ+Cqq7SI7TqVVPKv+vsxUml84bXy0M6KKq7SdqUMlwq9MBAABkGQqq1YxR4s6Fcq1/XGGOM5KkL3SX4ho/qV6NuUUpAADIfSioFnLG/qp/Fw9XobM/SJIOuiK0qfgYPdi+J9P5AAAg16KgWiEl4eJ0/p5ZKiSXLhgfLfLvrModHtPAkhFWpwMAALAUBTUrGaOE7+fLfDFZYY7TkqSNqq1zjZ5UjyZ3MZ0PAAAgCmqWsR/7UWc+GalCcb9Ikg67Cmlj0TFq27E30/kAAAD/g4Ka2RL/0Ynlj6nQwU9USEZJxldL8nRR5XYTNIDpfAAAgMtQUDOL066zMW/J9+vnFeFKkiStsTWS/e4p6tmghjw9bBYHBAAAcE8U1EyQvG+TEleMUYELhyVJe1zF9H35CXqobXsF+3tbnA4AAMC9UVBvI3P2iE4sGavIExvkJ+m0CdTyfP3UuNMj6hMRYnU8AACAbCHDl41v3bpVrVu3VmRkpGw2m1asWHHdbWJiYlS9enX5+vqqVKlSmjNnzmVjZs6cqWLFisnPz0916tTRd999l9Fo1kk9r1OfTZH9tVqKPLFBDuOhTzxbaVfbTeo3cqpKU04BAABuWIYLalJSkqpUqaKZM2fe0PjDhw+rVatWatq0qXbt2qVRo0apf//+Wr9+fdqYxYsXa/To0ZoyZYp+/PFHValSRdHR0Tp16lRG42UtY5Tww1KdfbGqwn6cIR+laoepoMU1Fqj1hI90T7Wystk41xQAACAjbMYYc9Mb22xavny52rZte9Ux48eP15o1a7Rnz5605zp37qxz585p3bp1kqQ6deqoVq1aevPNNyVJLpdLUVFRGj58uCZMmHDdHPHx8QoODlZcXJyCgoJu9uNkiOP4bv3zySOKOPu9JOmYKaB1hYfrvg4DFZkvIEsyAAAAZBcZ6WuZvjL89u3b1axZs3TPRUdHa/v27ZKk1NRU/fDDD+nGeHh4qFmzZmlj/islJUXx8fHpHlnpyKoXZHuvkSLOfq9k462P/LrqRLct6j9wFOUUAADgFmX6RVKxsbEqVKhQuucKFSqk+Ph4XbhwQWfPnpXT6bzimN9///2K7zl9+nQ9+eSTmZb5evbaSqiYXNqgu5TYZKq6Nr6LZaMAAABuk2x5Ff/EiRM1evTotK/j4+MVFRWVZd+/ecuHNN+VR62a3aOQAJ8s+74AAAC5QaYX1PDwcJ08eTLdcydPnlRQUJD8/f3l6ekpT0/PK44JDw+/4nv6+vrK19e624N6eXqo2wMtLfv+AAAAOVmmn4Nat25dbdq0Kd1zGzZsUN26dSVJPj4+qlGjRroxLpdLmzZtShsDAACA3CPDBTUxMVG7du3Srl27JF1cRmrXrl06evSopIvT7z179kwbP2jQIB06dEjjxo3T77//rrfeektLlizRI488kjZm9OjRev/99zV37lzt3btXgwcPVlJSkvr06XOLHw8AAADZTYan+Hfu3KmmTZumfX3pXNBevXppzpw5OnHiRFpZlaTixYtrzZo1euSRR/Taa6/pjjvu0AcffKDo6Oi0MZ06ddI///yjyZMnKzY2VlWrVtW6desuu3AKAAAAOd8trYPqLqxYBxUAAAA3zq3WQQUAAAAygoIKAAAAt0JBBQAAgFuhoAIAAMCtUFABAADgViioAAAAcCsUVAAAALgVCioAAADcCgUVAAAAboWCCgAAALdCQQUAAIBboaACAADArVBQAQAA4FYoqAAAAHArFFQAAAC4FQoqAAAA3AoFFQAAAG6FggoAAAC3QkEFAACAW6GgAgAAwK14WR3gdjDGSJLi4+MtTgIAAIArudTTLvW2a8kRBTUhIUGSFBUVZXESAAAAXEtCQoKCg4OvOcZmbqTGujmXy6Xjx48rMDBQNpstS75nfHy8oqKi9NdffykoKChLviduH/Zf9sc+zP7Yh9kf+zB7y+r9Z4xRQkKCIiMj5eFx7bNMc8QRVA8PD91xxx2WfO+goCB+KLMx9l/2xz7M/tiH2R/7MHvLyv13vSOnl3CRFAAAANwKBRUAAABuhYJ6k3x9fTVlyhT5+vpaHQU3gf2X/bEPsz/2YfbHPsze3Hn/5YiLpAAAAJBzcAQVAAAAboWCCgAAALdCQQUAAIBboaACAADArVBQAQAA4FYoqNcwc+ZMFStWTH5+fqpTp46+++67a47/5JNPVK5cOfn5+alSpUpau3ZtFiXFlWRk/73//vtq2LCh8uXLp3z58qlZs2bX3d/IfBn9Gbxk0aJFstlsatu2beYGxHVldB+eO3dOQ4cOVUREhHx9fVWmTBn+LrVQRvffjBkzVLZsWfn7+ysqKkqPPPKIkpOTsygt/mvr1q1q3bq1IiMjZbPZtGLFiutuExMTo+rVq8vX11elSpXSnDlzMj3nFRlc0aJFi4yPj4/58MMPza+//moGDBhgQkJCzMmTJ684/uuvvzaenp7mhRdeML/99pt54oknjLe3t9m9e3cWJ4cxGd9/Xbt2NTNnzjQ//fST2bt3r+ndu7cJDg42x44dy+LkuCSj+/CSw4cPm8KFC5uGDRuaNm3aZE1YXFFG92FKSoqpWbOmue+++8y2bdvM4cOHTUxMjNm1a1cWJ4cxGd9/8+fPN76+vmb+/Pnm8OHDZv369SYiIsI88sgjWZwcl6xdu9Y8/vjj5tNPPzWSzPLly685/tChQyYgIMCMHj3a/Pbbb+aNN94wnp6eZt26dVkT+H9QUK+idu3aZujQoWlfO51OExkZaaZPn37F8R07djStWrVK91ydOnXMww8/nKk5cWUZ3X//5XA4TGBgoJk7d25mRcR13Mw+dDgcpl69euaDDz4wvXr1oqBaLKP78O233zYlSpQwqampWRUR15DR/Td06FBz9913p3tu9OjRpn79+pmaEzfmRgrquHHjTIUKFdI916lTJxMdHZ2Jya6MKf4rSE1N1Q8//KBmzZqlPefh4aFmzZpp+/btV9xm+/bt6cZLUnR09FXHI/PczP77r/Pnz8tutys0NDSzYuIabnYfTps2TWFhYerXr19WxMQ13Mw+/Oyzz1S3bl0NHTpUhQoVUsWKFfXss8/K6XRmVWz8n5vZf/Xq1dMPP/yQdhrAoUOHtHbtWt13331Zkhm3zp26jFeWf8ds4N9//5XT6VShQoXSPV+oUCH9/vvvV9wmNjb2iuNjY2MzLSeu7Gb233+NHz9ekZGRl/2gImvczD7ctm2bZs2apV27dmVBQlzPzezDQ4cOafPmzerWrZvWrl2rAwcOaMiQIbLb7ZoyZUpWxMb/uZn917VrV/37779q0KCBjDFyOBwaNGiQHnvssayIjNvgal0mPj5eFy5ckL+/f5Zl4Qgq8B/PPfecFi1apOXLl8vPz8/qOLgBCQkJ6tGjh95//30VKFDA6ji4SS6XS2FhYXrvvfdUo0YNderUSY8//rjeeecdq6PhBsTExOjZZ5/VW2+9pR9//FGffvqp1qxZo6eeesrqaMiGOIJ6BQUKFJCnp6dOnjyZ7vmTJ08qPDz8ituEh4dnaDwyz83sv0teeuklPffcc9q4caMqV66cmTFxDRndhwcPHtSRI0fUunXrtOdcLpckycvLS/v27VPJkiUzNzTSuZmfw4iICHl7e8vT0zPtufLlyys2Nlapqany8fHJ1Mz4/25m/02aNEk9evRQ//79JUmVKlVSUlKSBg4cqMcff1weHhwTc3dX6zJBQUFZevRU4gjqFfn4+KhGjRratGlT2nMul0ubNm1S3bp1r7hN3bp1042XpA0bNlx1PDLPzew/SXrhhRf01FNPad26dapZs2ZWRMVVZHQflitXTrt379auXbvSHg888ICaNm2qXbt2KSoqKivjQzf3c1i/fn0dOHAg7ZcLSfrjjz8UERFBOc1iN7P/zp8/f1kJvfTLhjEm88LitnGrLpPll2VlE4sWLTK+vr5mzpw55rfffjMDBw40ISEhJjY21hhjTI8ePcyECRPSxn/99dfGy8vLvPTSS2bv3r1mypQpLDNloYzuv+eee874+PiYpUuXmhMnTqQ9EhISrPoIuV5G9+F/cRW/9TK6D48ePWoCAwPNsGHDzL59+8zq1atNWFiYefrpp636CLlaRvfflClTTGBgoFm4cKE5dOiQ+eKLL0zJkiVNx44drfoIuV5CQoL56aefzE8//WQkmVdeecX89NNP5s8//zTGGDNhwgTTo0ePtPGXlpl69NFHzd69e83MmTNZZsodvfHGG6ZIkSLGx8fH1K5d2+zYsSPttcaNG5tevXqlG79kyRJTpkwZ4+PjYypUqGDWrFmTxYnxvzKy/4oWLWokXfaYMmVK1gdHmoz+DP4vCqp7yOg+/Oabb0ydOnWMr6+vKVGihHnmmWeMw+HI4tS4JCP7z263m6lTp5qSJUsaPz8/ExUVZYYMGWLOnj2b9cFhjDHmyy+/vOK/bZf2W69evUzjxo0v26Zq1arGx8fHlChRwsyePTvLcxtjjM0YjrsDAADAfXAOKgAAANwKBRUAAABuhYIKAAAAt0JBBQAAgFuhoAIAAMCtUFABAADgViioAAAAcCsUVAAAALgVCioAAADcCgUVAAAAboWCCgAAALfy/wBSE6gJ1V7qrgAAAABJRU5ErkJggg==", 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", 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raWhoQFVVVYsXmS93RyVmD/UHALy/K5O9QkREFkYudgETJ07ExIkTWz3//vvvY8GCBZg7dy4A4MMPP8TPP/+Mzz//HC+++CIAIC0tzWD1rFy5Em+++abB7kfGb9HoIHydmIfjFyqxO6ME40M8xC6JiIi6iOg9QrfS2NiIlJQUxMTE6I9JpVLExMQgISGhU97zpZdeQmVlpf6Vn5/fKe9DxsPNXoHYYQEAgH+xV4iIyKIYdRAqKyuDVquFh0fL/0P38PBAUVFRm+8TExODRx55BNu3b4evr+8tQ5RCoYCjo2OLF5m/hSN7wNZahlMXqzhWiIjIgoj+aKwr7N69W+wSyMh1s7PGzEg/fHYgBx/uO4fRvd3FLomIiLqAUfcIubm5QSaTobi4uMXx4uJieHp6dup7q9VqhISEICIiolPfh4zHEyMCIZdKcDi7HGn5FWKXQ0REXcCog5C1tTWGDBmCuLg4/TGdToe4uDhER0d36nsvWbIEp06dQlJSUqe+DxkPb2cb3B/mAwD4cO85kashIqKuIHoQqqmpQVpamn7mV05ODtLS0pCXlwcAWLZsGT755BOsX78eGRkZWLx4MWpra/WzyIgMadGoHgCAnaeKcK60RuRqiIios4k+Rig5ORljxozRf71s2TIAQGxsLNatW4fp06ejtLQUr732GoqKihAWFoYdO3bcMICayBB6ejggpq87dmeUYP2h8/jL/f3FLomIiDqRROBc4ZtSq9VQq9XQarXIzMxEZWUlZ5BZiINZZZj16RHYWcuQ8PI4OCqtxC6JiIjuUFVVFZycnG77+S36ozFjxTFClmtYkCt6utujtlGLLckXxC6HiIg6EYMQ0R9IJBLMGR4AANiQcB46HTtNiYjMFYMQ0U08MMgHjko5zl+q4wKLRERmjEGI6CZsreWYHqECAHxx6Ly4xRARUadhEGoFF1Skx6MDIJEA8ZmlOF9WK3Y5RETUCRiEWsHB0qRyscWoXt0BABuTuPkuEZE5YhAiuoWZkX4AgC0p+WjU6ESuhoiIDI1BiOgWxvZxh7uDAmU1jdidUXz7byAiIpPCIER0C1YyKaaFNw+a/iYxT+RqiIjI0BiEWsHB0nTN9AgVJBJg/9ky5F2qE7scIiIyIAahVnCwNF2jcrHFyJ7XBk2zV4iIyJwwCBG1waORzY/Hvk2+gCYtB00TEZkLBiGiNhjX1wNu9gqU1TRg9ykOmiYiMhcMQkRt0Dxo2hcA8DUHTRMRmQ0GIaI2urblxoGsMly4zEHTRETmgEGIqI38Xe0wLMgVggBsTr4gdjlERGQADEKt4PR5upkZV1ea3pycD61OELkaIiLqKAahVnD6PN3M3SEecLa1QmFlPeLPlopdDhERdRCDENEdUFrJ8MAgHwDApkRuxEpEZOoYhIju0IyI5sdjuzOKUVrdIHI1RETUEQxCRHeot6cDBvk5Q6MT8F0qB00TEZkyBiGidphxdSr9pqR8CAIHTRMRmSoGIaJ2uG+gN+ysZcgpq0ViTrnY5RARUTsxCBG1g51Cjilh3gCAjUkcNE1EZKoYhFrBdYTodqZfHTS9/cRFVNY1iVwNERG1B4NQK7iOEN1OqK8T+ng6oEGjww/HCsQuh4iI2oFBiKidJBKJftD0N4kcNE1EZIoYhIg6YOogH1jLpci4WIUTBZVil0NERHeIQYioA5xtrTGxvycADpomIjJFDEJEHXRtpen/pRWirlEjcjVERHQnGISIOmhoDxcEuNqipkGDn45fFLscIiK6AwxCRB0kkUgw7bqVpomIyHQwCBEZwMNDfCGTSpCSexlni6vFLoeIiNqIQYjIANwdlBjXxx0Ae4WIiEwJg1AruLI03akZkc2Px75LvYAGjVbkaoiIqC0YhFrBlaXpTo3q5Q5PRyUu1zVh16liscshIqI2YBAiMhCZVIJp4b4AgI2JfDxGRGQKGISIDOiRcBUkEuBAVhmyS2vELoeIiG6DQYjIgFQutvpB0xsSckWuhoiIbodBiMjAYocFAAA2J+ejur5J3GKIiOiWGISIDGxEsBuC3e1R26jFdykXxC6HiIhugUGIyMAkEglio/0BAOsTcqHTCSJXRERErWEQIuoEDw72hYNCjpyyWsSfLRW7HCIiagWDEFEnsFPI9fuPrTt0XtxiiIioVQxCRJ3k8Wh/SCTA3jOlnEpPRGSkGISIOom/qx3G9m6eSs9eISIi48QgRNSJ5o0IBABsTr6Ay7WNIldDRER/xCBE1ImGBbkixMsRV5q0+PIwF1gkIjI2DEKt4O7zZAgSiQR/GtUDALA+4Tzqm7grPRGRMWEQagV3nydDuXeAF7ydlCiracTWowVil0NERNdhECLqZFYyqX6s0Cf7s7nAIhGREWEQIuoCMyL94KCUI7u0FrszisUuh4iIrmIQIuoC9go5ZkU1b7vxyf5skashIqJrGISIusjc4QGwkkmQdP4yUvMui10OERGBQYioy3g4KjE1zAcA8NG+cyJXQ0REAIMQUZdaeFfzVPpfTxUjq6Ra5GqIiIhBiKgL9fRwwN0hHhAE4IO97BUiIhIbgxBRF3tyTDAA4Ie0QuSX14lcDRGRZWMQIupiYSpnjAh2g1Yn4ON4ziAjIhITgxCRCJ4cEwQA2JScj5LqepGrISKyXAxCRCKI7uGKwX7OaNTo8NmBHLHLISKyWAxCRCKQSCRYcnWs0JcJuaisaxK5IiIiy8QgRCSSsX3c0cfTAbWNWqw7dF7scoiILBKDEJFIJBKJfgbZF4dyUNugEbkiIiLLwyBEJKJJA7wQ4GqLiromfJOYJ3Y5REQWh0GISEQyqQSLRjXPIPs4PhsNGq3IFRERWRYGISKRPTDYB56OSpRUN+C7lAKxyyEisihmH4Ty8/MxevRohISEYODAgdi8ebPYJRG1oJDL9HuQrd2XBY1WJ3JFRESWw+yDkFwux6pVq3Dq1Cn8+uuvePbZZ1FbWyt2WUQtzIhUwcXOGvnlV/DziYtil0NEZDHMPgh5eXkhLCwMAODp6Qk3NzeUl5eLWxTRH9hayzFveAAA4IM956DTCeIWRERkIUQPQvHx8Zg8eTK8vb0hkUiwbdu2G65Rq9UICAiAUqlEVFQUEhMT2/VeKSkp0Gq1UKlUHayayPBmRwfAXiHHmeJqxJ0uEbscIiKLIHoQqq2tRWhoKNRq9U3Pb9q0CcuWLcPrr7+O1NRUhIaGYsKECSgp+f2DIiwsDP3797/hVVhYqL+mvLwcjz/+OD7++ONb1tPQ0ICqqqoWL6Ku4GRjhdnR/gCANXuyIAjsFSIi6mwSwYh+20okEmzduhVTp07VH4uKikJERATWrFkDANDpdFCpVHj66afx4osvtum+DQ0NGD9+PBYsWIDZs2ff8to33ngDb7755g3HKysr4ejo2PbGELVDaXUDRvzjNzRodPh6fhSGBbuJXRIRkUmqqqqCk5PTbT+/Re8RupXGxkakpKQgJiZGf0wqlSImJgYJCQltuocgCJgzZw7Gjh172xAEAC+99BIqKyv1r/z8/HbXT3SnujsoMCOi+dGtem+WyNUQEZk/ow5CZWVl0Gq18PDwaHHcw8MDRUVFbbrHwYMHsWnTJmzbtg1hYWEICwvDiRMnWr1eoVDA0dGxxYuoKy24qwfkUgkOZl3C0bzLYpdDRGTW5GIX0NlGjBgBnY7rspDp8O1mi6mDfLAl5QI+2HsOnzweLnZJRERmy6h7hNzc3CCTyVBcXNzieHFxMTw9PTv1vdVqNUJCQhAREdGp70N0M4tGBUEiAXadKsaZomqxyyEiMlvtCkLr16/Hzz//rP/6+eefh7OzM4YNG4bc3FyDFWdtbY0hQ4YgLi5Of0yn0yEuLg7R0dEGe5+bWbJkCU6dOoWkpKROfR+imwl2t8fE/s1hfy3HChERdZp2BaG///3vsLGxAQAkJCRArVbjnXfegZubG5577rk7uldNTQ3S0tKQlpYGAMjJyUFaWhry8pp34l62bBk++eQTrF+/HhkZGVi8eDFqa2sxd+7c9pROZDKeHB0MAPjfsULkXaoTuRoiIvPUrjFC+fn5CA5u/iW9bds2PPTQQ1i4cCGGDx+O0aNH39G9kpOTMWbMGP3Xy5YtAwDExsZi3bp1mD59OkpLS/Haa6+hqKgIYWFh2LFjxw0DqInMTX8fJ4zq1R37MkvxYfw5/P2BAWKXRERkdtrVI2Rvb49Lly4BAH799VeMHz8eAKBUKnHlypU7utfo0aMhCMINr3Xr1umveeqpp5Cbm4uGhgYcOXIEUVFR7Sn7jnCMEBmDJWOa/4djS/IFFFfVi1wNEZH5aVcQGj9+PObPn4/58+cjMzMT9957LwDg5MmTCAgIMGR9ouEYITIGkYEuiAjohkatDp/uzxa7HCIis9OuIKRWqxEdHY3S0lJ89913cHV1BdC8l9fMmTMNWiCRpXvyaq/QV0fycLm2UeRqiIjMi1FtsWGM2rpEN1FnEQQBk1YfwKmLVVg6rieeG99L7JKIiIxep26xsWPHDhw4cED/tVqtRlhYGB599FFcvsyVcIkMSSKRYPHoIADAfw/n4kqjVuSKiIjMR7uC0IoVK/S7sp84cQLLly/Hvffei5ycHP2sL1PHwdJkTCb294RvNxuU1zbiu9QLYpdDRGQ22vVozN7eHunp6QgICMAbb7yB9PR0bNmyBampqbj33nvbvA+YKeCjMTIWXxzMwZs/nkKgmx12LxsFmVQidklEREarUx+NWVtbo66ueYG33bt34+677wYAuLi46HuKiMiwpoWr4KiUI6esFrszim//DUREdFvtCkIjRozAsmXL8NZbbyExMRGTJk0CAGRmZsLX19egBRJRMzuFHLOG+gMAp9ITERlIu4LQmjVrIJfLsWXLFqxduxY+Pj4AgF9++QX33HOPQQskot/NGRYAK5kESecvIzWPExOIiDqK0+dvg2OEyNj8efMxbEm5gHsHeOKDWUPELoeIyCi19fO7XXuNAYBWq8W2bduQkZEBAOjXrx+mTJkCmUzW3lsaFbVaDbVaDa2WU5XJuMwfGYgtKRewI70IeZfq4OdqK3ZJREQmq109QllZWbj33ntRUFCA3r17AwDOnDkDlUqFn3/+GUFBQQYvVCzsESJj9PjniYjPLMWcYQF4Y0o/scshIjI6nTpr7JlnnkFQUBDy8/ORmpqK1NRU5OXlITAwEM8880y7iyaitlk4sgcAYFNSPirquO0GEVF7tSsI7du3D++88w5cXFz0x1xdXfH2229j3759BiuOiG5ueLAr+no54kqTFl8ezhW7HCIik9WuIKRQKFBdXX3D8ZqaGlhbW3e4KCK6NYlEggUjAwEA6xNy0aDhWDYiovZoVxC67777sHDhQhw5cgSCIEAQBBw+fBiLFi3ClClTDF0jEd3EfQO94eGoQGl1A348dlHscoiITFK7gtDq1asRFBSE6OhoKJVKKJVKDBs2DMHBwVi1apWBSxQH9xojY2ctlyJ2WACA5gUWuRIGEdGd69A6QllZWfrp83379kVwcLDBCjMWnDVGxqyyrglDV8Y1jxV6IgojerqJXRIRkVEw+DpCt9tVfs+ePfo/v//++229LRF1gJOtFaaF+2J9Qi4+PZDNIEREdIfaHISOHj3apuskEu6ITdSV5o0IxIbDudh7phRni6vR08NB7JKIiExGm4PQ9T0+RGQ8/F3tcHeIB3aeLMZnB3Lw9kMDxS6JiMhktGuwNBEZl/lXF1j8/mgBymoaRK6GiMh0MAgRmYFw/24IVTmjUaPjAotERHeAQYjIDEgkEswf0bzA4n8TclHfxAUWiYjagkGoFVxHiEzNxP6e8HG2waXaRmw7WiB2OUREJoFBqBVLlizBqVOnkJSUJHYpRG0il0kxd3gAAODTAzlcYJGIqA0YhIjMyLQIFewVcmSV1GBvZqnY5RARGT0GISIz4qi0wowIFQDgs/05IldDRGT8GISIzMyc4QGQSoADWWXIuFgldjlEREaNQYjIzPh2s8XEAV4AgE/ZK0REdEsMQkRmaMHVBRb/d6wAJVX1IldDRGS8GISIzFCYyhnh/t3QpBWwIYELLBIRtYZBiMhMzR/ZvMDil0dyUdeoEbkaIiLjxCBEZKbGh3jCz8UWFXVN+C6VCywSEd0Mg1AruLI0mTqZVIJ5VxdY/PxADnQ6LrBIRPRHDEKt4MrSZA4eCVfBUSlHTlkt4k6XiF0OEZHRYRAiMmN2CjkejfIHAHy6P1vkaoiIjA+DEJGZix3mD7lUgiM55ThxoVLscoiIjAqDEJGZ83KyweRQbwDApwfYK0REdD0GISIL8MSI5qn0Px+/iMKKKyJXQ0RkPBiEiCxAfx8nDO3hAo1OwPqE82KXQ0RkNBiEiCzEtW03vj6Sh5oGLrBIRAQwCBFZjDG93dGjux2q6zX4Nilf7HKIiIwCgxCRhZBKJfqxQp/sz0aDRityRURE4mMQIrIgDw32hYejAhcr67E5+YLY5RARiY5BiMiCKK1kWDwqCACwdu85NGp0IldERCQuBiEiCzMj0g/uDgoUVFzBlhT2ChGRZWMQIrIwSisZFl3tFVLvyUKTlr1CRGS5GIRawd3nyZw9GuUHN/vmXqHv2CtERBaMQagV3H2ezFlzr1DzukKr486ivokzyIjIMjEIEVmox4b6w9tJicLKenxx8LzY5RARiYJBiMhCKa1kWH53bwDAB3uyUF7bKHJFRERdj0GIyII9MMgHfb0cUd2gwZrfssQuh4ioyzEIEVkwqVSCl+/tAwD47+HzyCqpEbkiIqKuxSBEZOFG9uyOsX3c0aQV8H/bTkAQBLFLIiLqMgxCRIQ3p/SD0kqKw9nl2Hq0QOxyiIi6DIMQEUHlYotnxvUEAPz15wwOnCYii8EgREQAgPkjeqCXhz3KaxvxwnfH+YiMiCwCgxARAQCs5VL8a3oYrGQS7DpVjI1J+WKXRETU6RiEiEivn7cTVkxoXlvozR9PIr2gUuSKiIg6F4MQEbUwf0QPjOrVHfVNOizYkIySqnqxSyIi6jQMQkTUglQqwX8eHYSg7na4WFmPueuSUFHHwdNEZJ4YhIjoBo5KK3wWGwFXO2ucLKzCrE+P4DJnkhGRGZIInBpyS1VVVXByckJlZSUcHR3FLoeoS2UWV+PRTw6jrKYRAa62+PjxcPTycOiU99LqBBRX1ePC5Su4cLkOJdUNqKnXoLq+CbWNWkgAyKQSSKUSOCjkcLGzhoudNTwclQh0s4O3sw1kUkmn1EZEpqetn98MQrfBIESW7mxxNeZ8kYSCiiuwsZJh+d29MGdYAOSy9nco1zZokHGxCicLq3CysBInC6twtrgGjVpdu+9pLZPC39UWA3ycEKpyRqjKGX29HKCQy9p9TyIyXQxCBsIgRASU1zbimW+O4kBWGQDA39UW80cE4t4BXnC1V9zyeyuvNOFkYSXSCypxoqAKJwsqkXOpFjf7zSOXSuDtbAPfbjbwcFTCUSmHg9IKtormMKPTCdDoBFTXa1Be24jy2kYUVlxBbnkdGjU3hiillRRRga4Y2dMNd/Xqjp7u9pBI2GtEZAkYhAyEQYiomU4n4NvkfLyz84x+5WmpBOjl4YA+ng5wtVfAxkqGK01aVF5pQn55HfLL61BYefNZZx6OCvTzdkI/b0f083ZEiJcTfLq17/GWViegsOIKskpqcOxCBY7lVyAtvwKX65paXKdyscG9/b0wcYAXQn2dGIqIzBiD0FUVFRWIiYmBRqOBRqPB0qVLsWDBgjZ/P4MQUUt1jRpsSsrHd6kXkF5Q1abvUbnYoL+3E/r7NL/6eTvC7TY9SR0lCALOFFfjwNkyxJ8tw5HsS2i4rtfIx9kGUwd545EhKgS42XVqLUTU9RiErtJqtWhoaICtrS1qa2vRv39/JCcnw9XVtU3fzyBE1LqiynqcKKjE2ZJqVNY1oUGjg9JKBgelHL7dbKBysUUPNzs421qLXSrqGjXYe6YU209cxG+nS1DXqNWfiwx0wbRwFe4d4Alba7mIVRKRoTAI3UR5eTkGDx6M5ORkuLm5tel7GISIzE99kxZxGSXYnJKP+MxS6K7+FnRQyPFIuAqxw/zh78peIiJT1tbPb9HXEYqPj8fkyZPh7e0NiUSCbdu23XCNWq1GQEAAlEoloqKikJiYeEfvUVFRgdDQUPj6+mLFihVtDkFEZJ6UVjJMGuiFdXMjcfDFsVgxoTf8XW1R3aDB5wdzMPqfezF/fRIOZpVx81kiMyd6EKqtrUVoaCjUavVNz2/atAnLli3D66+/jtTUVISGhmLChAkoKSnRXxMWFob+/fvf8CosLAQAODs749ixY8jJycHXX3+N4uLiLmkbERk/LycbLBkTjD3LR2Pd3AiM6tUdggDszijBrE+PYOK/9+OHtAJoOjC1n4iMl1E9GpNIJNi6dSumTp2qPxYVFYWIiAisWbMGAKDT6aBSqfD000/jxRdfvOP3ePLJJzF27Fg8/PDDNz3f0NCAhoYG/ddVVVVQqVR8NEZkQc6V1mDDofPYknIBtVfHEvm52GLRqCA8NMSHaxMRmQCTeTR2K42NjUhJSUFMTIz+mFQqRUxMDBISEtp0j+LiYlRXVwMAKisrER8fj969e7d6/cqVK+Hk5KR/qVSqjjWCiExOUHd7vHl/fxx6cRyWj+8FFztr5JXX4eWtJzDyH3vwSXw2ahs0YpdJRAZg1EGorKwMWq0WHh4eLY57eHigqKioTffIzc3FyJEjERoaipEjR+Lpp5/GgAEDWr3+pZdeQmVlpf6Vn5/foTYQkelysrXC0+N64sALY/DafSHwclKipLoBf9uegbveaQ5EV66bfUZEpsfs54lGRkYiLS2tzdcrFAooFJ27vgkRmRZbaznmjQjEY0P9se1oAT7Ym4Xzl+rwt+0Z+Hh/Np4cHYSZkX5QWvGRGZGpMeoeITc3N8hkshsGNxcXF8PT01OkqojIUlnLpZgWocLuZaPwzkMD4dvNBqXVDXjzx1MY/e5e/Pdw7k23+iAi42XUQcja2hpDhgxBXFyc/phOp0NcXByio6M79b3VajVCQkIQERHRqe9DRKZHLmsORL8tH42/PdAfXk5KFFXV49Vt6Rjzz734LuUCtDqjmYdCRLcg+qyxmpoaZGVlAQAGDRqE999/H2PGjIGLiwv8/PywadMmxMbG4qOPPkJkZCRWrVqFb7/9FqdPn75h7FBn4IKKRHQ79U1abEzMg3rvOZRWN8867ePpgBfu6YPRvbtzTzMiEZjMytJ79+7FmDFjbjgeGxuLdevWAQDWrFmDd999F0VFRQgLC8Pq1asRFRXVJfUxCBFRW11p1GLdofP4YG8WquubZ5VFBrrgxYl9MNivm8jVEVkWkwlCxo5BiIjuVEVdI9buPYcvDp3Xjxm6p58nVtzTG0Hd7UWujsgyMAh1kFqthlqthlarRWZmJoMQEd2xwoor+NeuTHyXegE6AZBJJZgWrsKzMT3h4agUuzwis8YgZCDsESKijsosrsY7O85gd0bzDFillRTzhgdi0eggOCqtRK6OyDwxCBkIgxARGUrS+XK8/ctppOReBgB0s7XC02N74rGh/rCWG/UkXiKTwyBkIAxCRGRIgiBg16livLPzDLJKagA072O2YkJvTBrgBamUM8yIDIFBqIM4RoiIOpNGq8PmlAt4f1emfsr9QF8nvDSxL6KDXEWujsj0MQgZCHuEiKgz1TVq8On+HHy075x+p/uxfdzx4sQ+6OXhIHJ1RKaLQchAGISIqCuUVjdgddxZfJOYB41OgFQCPDJEhefG94KnE2eYEd0pBiEDYRAioq6UXVqDd3eewS/pRQCaZ5g9MSIQfxrFGWZEd4JByEAYhIhIDCm5l7FyewaSr84wc7GzxjNjg/FoFGeYEbUFg5CBMAgRkViuzTB7e8dpZJfWAgD8XX+fYcY9zIhaxyDUQZw1RkTGQqPVYVNyPv616yzKappnmIWqnPHSxD4Y2oMzzIhuhkHIQNgjRETGorbh6gyz+HOouzrDLKavO164pw96coYZUQsMQgbCIERExqa0ugH/jsvEN4n50F6dYTYtvHmGGfcwI2rGIGQgDEJEZKzOldbgnR2nsfPk73uYLRjZAwvv6gEHzjAjC8cgZCAMQkRk7JLPl2PldXuYudpZ45lxPTEz0o8zzMhiMQgZCIMQEZkCQRCw82Qx3tlxGtllzTPMAlxt8fw9fTCxvydnmJHFYRDqIM4aIyJT1KTVYVNSPlbtzkRZTSMAINTXCc/f0wfDg91Ero6o6zAIGQh7hIjIFNU0aPBJfDY+2Z+tn2E2PNgVKyb0QZjKWdziiLoAg5CBMAgRkSkrrW6Aek8Wvj6Sh0atDgBwd4gH/jyhNzd1JbPGIGQgDEJEZA4uXK7Dqt1n8X3qBegEQCoBpg7ywXMxvaBysRW7PCKDYxAyEAYhIjInZ4ur8d6vmdhxsnlTVyuZBI9G+uGpsT3R3UEhcnVEhsMgZCAMQkRkjo7lV+DdnWdwIKsMAGBjJcO8EQFYeFcQnGy4BhGZPgYhA2EQIiJzdiirDP/YeQbH8isAAE42Vlh4Vw/MGRYAO4Vc3OKIOoBByEAYhIjI3AmCgF9PFeOfO8/gbEkNgOZFGReNCsJjQ/1hYy0TuUKiO8cg1EFcR4iILI1WJ+CHtAL8O+4sci/VAQC6Oyjw5OggzIz0g9KKgYhMB4OQgbBHiIgsjUarw/epBVj921lcuHwFAODpqMSSscGYHq7ith1kEhiEDIRBiIgsVaNGh80p+VjzWxYuVtYDAHycbfD02GA8NMQXVjIGIjJeDEIGwiBERJauQaPFxsR8qPdkoaS6AQDg52KLpeN64v4wb8gZiMgIMQgZCIMQEVGz+iYtvjyciw/3ndPvY9ajux2WjuuJ+wZ6Qyblxq5kPBiEDIRBiIiopbpGDTYk5OKjfedwua4JANDT3R7PjOuJewd4MRCRUWAQMhAGISKim6tp0GDdwRx8HJ+NqnoNACCoux2eHtsT9w304iMzEhWDkIEwCBER3VpVfRPWHzyPTw/koPJKcw9RoJsdlowJxv1h3hxUTaJgEDIQBiEioraprm/ChoRcfLo/W//IzM/FFkvGBOGBQb6cdk9dikHIQBiEiIjuTG2DBl8ezsXH8dm4VNs8qNrH2QZPjgnCw0N8oZBzYUbqfAxCHcSVpYmIOuZKoxZfHcnFR/HZKL067d7LSYnFo4MwLVzFlaqpUzEIGQh7hIiIOqa+SYuNiXlYu+8ciquaA5G7gwKLRjVv3cG9zKgzMAgZCIMQEZFh1DdpsTnlAtbuyULh1ZWq3ewVWHhXIGZF+XO3ezIoBiEDYRAiIjKsRo0O36VegHpPln4vM2dbK8wZFoA5wwLgbGstcoVkDhiEDIRBiIioczRpddiaWoC1+84hp6wWAGBnLcOsof6YPyIQ7o5KkSskU8YgZCAMQkREnUurE/BL+kWo95xDxsUqAIC1TIpHwn3xp7uC4OdqK3KFZIoYhAyEQYiIqGsIgoC9Z0qh3pOF5NzLAACZVIIpod5YPDoIvTwcRK6QTAmDkIEwCBERdb3EnHKo92RhX2ap/tj4EA8sGROMMJWzeIWRyWAQMhAGISIi8aQXVOKDvVn4Jb0I1z6thge7YsnoYEQHuUIi4QavdHMMQgbCIEREJL6skhp8uO8cth0tgEbX/LEVqnLGktFBiOnrASl3vKc/YBAyEAYhIiLjUVBxBZ/EZ+ObxDw0aHQAmne8X3hXD0wd5MPtO0iPQchAGISIiIxPWU0DPj+Qg/8ezkV1vQYA0N1BgTnDAvBYlD+cbK1ErpDExiBkIAxCRETGq7q+CZuS8vHZgRxcvLpatZ21DNMj/PDEyED4ONuIXCGJhUHIQBiEiIiMX5NWhx+PFeLj+GycLqoG0Dz1fvJALyy8Kwgh3vz9bWkYhAyEQYiIyHQIgoD4s2X4OP4cDmZd0h8f2dMNC+/qgRHBbpxpZiEYhDpIrVZDrVZDq9UiMzOTQYiIyMSkF1Tio/hsbD9xEdqrM81CvByx8K4emDTQC1YyqcgVUmdiEDIQ9ggREZm2/PI6fHYgB5uS8nGlSQsA8HG2wbwRgZgRoeKu92aKQchAGISIiMxDRV0jvjyci3WHzqOsphEA4KiU47Gh/pgzPADuDtzk1ZwwCBkIgxARkXmpb9Li+9QCfLo/G9lXd723lknxwCAfLLirB4Ld7UWukAyBQchAGISIiMyTTidgV0YxPo7PRsrVTV4BIKavO+aP7IGoQBcOrDZhDEIGwiBERGT+UnLL8dG+bOzKKNbvadbP2xFPjAjEfQO9YS3nwGpTwyBkIAxCRESW41xpDT4/kIPvUi+gvql5Cw93BwUej/bHo1H+cLGzFrlCaisGIQNhECIisjyXaxvxdWIeNiScR3FVAwBAIZfiwcG+eGJEAILdHUSukG6HQchAGISIiCxXo0aH7Scu4rMDOThRUKk/PqpXdzwxIhAje3KBRmPFIGQgDEJERCQIApLOX8ZnB7Lx66nfxxH18rDHvOGBmDrIB0ormbhFUgsMQgbCIERERNfLu1SHLw7l4NukfNQ2Ni/Q2M3WCjMj/fDYUH94c6NXo8AgZCAMQkREdDNV9U34NikfXxw8j4KKKwCaN3qd0M8Dc4YFIiKgGx+biYhByEAYhIiI6FY0Wh12Z5Rg3aEcHM4u1x/v6+WIucMCMCXMm4/NRMAgZCAMQkRE1Fani6qw/tB5bD1aoJ9+383WCjOuPjbz4WOzLsMgZCAMQkREdKcq6hqxKSkfGxJy9Y/NpBJgQj9PzBkWgEiuWt3pGIQMhEGIiIjaS6sTsDujGOsOnkdC9iX98T6eDpgzLAD3h/nAxpqPzToDg5CBMAgREZEhND82y8XWo7+vWu1sa4UZEX6YHc3HZobGIGQgDEJERGRIFXWN+DY5H+sPtXxsNj7EA7OHBmB4sCsfmxkAg5CBMAgREVFn0OoExGUUY92h8zh07vfHZj3c7DBrqD8eHuwLJ1srESs0bQxCf1BXV4e+ffvikUcewT//+c82fx+DEBERdbbM4mr8NyEXW48WoKZBAwBQWkkxeaA3Zkf7Y6Cvs7gFmiAGoT945ZVXkJWVBZVKxSBERERGqaZBg21HC/Dl4VycLqrWHx/o64THovwxOdSbg6vbqK2f39IurEk0Z8+exenTpzFx4kSxSyEiImqVvUKOx4b645elI7FlUTSmhnnDWibF8QuVeP6744j6+2785cdTyC6tEbtUsyF6EIqPj8fkyZPh7e0NiUSCbdu23XCNWq1GQEAAlEoloqKikJiYeEfv8ec//xkrV640UMVERESdSyKRIDzABatmDELCS2Pxwj19oHKxQVW9Bp8fzMHY9/Zh1qeHsSP9IjRandjlmjS52AXU1tYiNDQU8+bNw4MPPnjD+U2bNmHZsmX48MMPERUVhVWrVmHChAk4c+YM3N3dAQBhYWHQaDQ3fO+vv/6KpKQk9OrVC7169cKhQ4duW09DQwMaGhr0X1dVVXWgdURERB3jaq/A4tFBWHhXD8RnluLLw7n47UwJDmZdwsGsS/BwVGBGhB9mRvrB00kpdrkmx6jGCEkkEmzduhVTp07VH4uKikJERATWrFkDANDpdFCpVHj66afx4osv3vaeL730Er788kvIZDLU1NSgqakJy5cvx2uvvXbT69944w28+eabNxznGCEiIjIW+eV1+CYxD5uS8nGpthFA84av4/t6YHa0P4YFcQq+SQ6W/mMQamxshK2tLbZs2dIiHMXGxqKiogI//PDDHd1/3bp1SE9Pv+Vg6Zv1CKlUKgYhIiIyOg0aLXakF+Grw3lIPP/7hq893OzwaJQfHhzsCxc7axErFE9bg5Doj8ZupaysDFqtFh4eHi2Oe3h44PTp053yngqFAgqFolPuTUREZEgKuQz3h/ng/jAfnCmqxpeHm6fgZ5fV4q8/Z+CdHWdwdz8PzIz0Q3QPV0illt1LdDNGHYQMbc6cOWKXQERE1Cl6ezrgran98cLEPvghrQAbE/NxoqASPx2/iJ+OX4S/qy2mhavwSLgv3B04lugaow5Cbm5ukMlkKC4ubnG8uLgYnp6enfrearUaarUaWq22U9+HiIjIkOwVcsyK8sesKH+kF1Tim8Q8/JBWiNxLdXh35xm8vysT4/q4Y2akH+7q1R0yC+8lMuoxQkDzYOnIyEj85z//AdA8WNrPzw9PPfVUmwZLdxQXVCQiIlNX16jBT8cvYmNiHlLzKvTHvZ2UmBahwrRwFbzNbNNXkxkjVFNTg6ysLP3XOTk5SEtLg4uLC/z8/LBs2TLExsYiPDwckZGRWLVqFWprazF37lwRqyYiIjIdttZyTAtvDjxniqqxMSkP36cWoLCyHqt2n8XquLMY1as7ZkT6YWwfd1jJRF9msMuI3iO0d+9ejBkz5objsbGxWLduHQBgzZo1ePfdd1FUVISwsDCsXr0aUVFRXVIfe4SIiMgc1TdpsfNkEb5JzMPh7N9nnHV3UOCRIb6YEeEHP1dbESvsGJOcPm9Mrh8jlJmZySBERERmK6esFhuT8rAl+YJ+XSIAGB7sihkRfri7nwcUctPa44xByEDYI0RERJaiUaPD7oxifJOYhwNZZbiWEFzsrPHgIB/MiPRDsLu9uEW2EYOQgTAIERGRJcovr8O3yfn4NjkfxVW/LzQcEdANMyP9cO8ALyitjLeXiEHIQBiEiIjIkmm0Ouw9U4qNSXn47XQJdFdTg6NSjqmDfDAtXIV+3o5Gt6UHg5CBMAgRERE1K6qsx+bkfGxMykdBxRX98b5ejpgW7oupYT7oZiRbejAIdRAHSxMREd2cTifgQFYZNqdcwM70IjRqdQAAa5kU40M8MC1ChRHBbqIu1sggZCDsESIiImpdRV0jfkgrxLfJ+ThZWKU/7uWkxMNDfPHwEF/4u9p1eV0MQgbCIERERNQ26QWV2JJyAVuPFqDySpP++NAeLpgWrsLE/l6wse6aAdYMQgbCIERERHRn6pu02J1RjG+TL2D/2VL9NHx7hRz3DfTCI+G+GOzXrVMHWDMIGQiDEBERUfsVVFzB9ykX8G1KPvLLfx9g3aO7HR4e4ouHBvvCw1Fp8PdlEOogDpYmIiIyHJ1OwJGccmxOyccvJ4pwpUkLAJBKgH8+EooHB/sa9P0YhAyEPUJERESGVdOgwfbjF7E5JR/JuZcRv2IMVC6G3deMQchAGISIiIg6z8XKK/BysjH4fdv6+S01+DsTERERtVFnhKA7wSBEREREFotBiIiIiCwWgxARERFZLAahVqjVaoSEhCAiIkLsUoiIiKiTcNbYbXDWGBERkenhrDEiIiKi22AQIiIiIovFIEREREQWi0GIiIiILBaDEBEREVksBqFWcPo8ERGR+eP0+dvg9HkiIiLTw+nzRERERLchF7sAY3etw6yqqkrkSoiIiKitrn1u3+7BF4PQbVRXVwMAVCqVyJUQERHRnaquroaTk1Or5zlG6DZ0Oh0KCwvh4OAAiURisPtWVVVBpVIhPz/fYsYeWVqb2V7zxvaaP0trs7m1VxAEVFdXw9vbG1Jp6yOB2CN0G1KpFL6+vp12f0dHR7P4B3cnLK3NbK95Y3vNn6W12Zzae6ueoGs4WJqIiIgsFoMQERERWSwGIZEoFAq8/vrrUCgUYpfSZSytzWyveWN7zZ+ltdnS2nsNB0sTERGRxWKPEBEREVksBiEiIiKyWAxCREREZLEYhIiIiMhiMQiJRK1WIyAgAEqlElFRUUhMTBS7pDu2cuVKREREwMHBAe7u7pg6dSrOnDnT4pr6+nosWbIErq6usLe3x0MPPYTi4uIW1+Tl5WHSpEmwtbWFu7s7VqxYAY1G05VNaZe3334bEokEzz77rP6YOba3oKAAjz32GFxdXWFjY4MBAwYgOTlZf14QBLz22mvw8vKCjY0NYmJicPbs2Rb3KC8vx6xZs+Do6AhnZ2c88cQTqKmp6eqm3JZWq8Wrr76KwMBA2NjYICgoCG+99VaLvYpMub3x8fGYPHkyvL29IZFIsG3bthbnDdW248ePY+TIkVAqlVCpVHjnnXc6u2mtulWbm5qa8MILL2DAgAGws7ODt7c3Hn/8cRQWFra4hym1+XZ/x9dbtGgRJBIJVq1a1eK4KbXXIATqchs3bhSsra2Fzz//XDh58qSwYMECwdnZWSguLha7tDsyYcIE4YsvvhDS09OFtLQ04d577xX8/PyEmpoa/TWLFi0SVCqVEBcXJyQnJwtDhw4Vhg0bpj+v0WiE/v37CzExMcLRo0eF7du3C25ubsJLL70kRpPaLDExUQgICBAGDhwoLF26VH/c3NpbXl4u+Pv7C3PmzBGOHDkiZGdnCzt37hSysrL017z99tuCk5OTsG3bNuHYsWPClClThMDAQOHKlSv6a+655x4hNDRUOHz4sLB//34hODhYmDlzphhNuqW//e1vgqurq/DTTz8JOTk5wubNmwV7e3vh3//+t/4aU27v9u3bhVdeeUX4/vvvBQDC1q1bW5w3RNsqKysFDw8PYdasWUJ6errwzTffCDY2NsJHH33UVc1s4VZtrqioEGJiYoRNmzYJp0+fFhISEoTIyEhhyJAhLe5hSm2+3d/xNd9//70QGhoqeHt7C//6179anDOl9hoCg5AIIiMjhSVLlui/1mq1gre3t7By5UoRq+q4kpISAYCwb98+QRCaf8lYWVkJmzdv1l+TkZEhABASEhIEQWj+j1YqlQpFRUX6a9auXSs4OjoKDQ0NXduANqqurhZ69uwp7Nq1Sxg1apQ+CJlje1944QVhxIgRrZ7X6XSCp6en8O677+qPVVRUCAqFQvjmm28EQRCEU6dOCQCEpKQk/TW//PKLIJFIhIKCgs4rvh0mTZokzJs3r8WxBx98UJg1a5YgCObV3j9+SBqqbR988IHQrVu3Fv+eX3jhBaF3796d3KLbu1UwuCYxMVEAIOTm5gqCYNptbq29Fy5cEHx8fIT09HTB39+/RRAy5fa2Fx+NdbHGxkakpKQgJiZGf0wqlSImJgYJCQkiVtZxlZWVAAAXFxcAQEpKCpqamlq0tU+fPvDz89O3NSEhAQMGDICHh4f+mgkTJqCqqgonT57swurbbsmSJZg0aVKLdgHm2d7//e9/CA8PxyOPPAJ3d3cMGjQIn3zyif58Tk4OioqKWrTZyckJUVFRLdrs7OyM8PBw/TUxMTGQSqU4cuRI1zWmDYYNG4a4uDhkZmYCAI4dO4YDBw5g4sSJAMyvvdczVNsSEhJw1113wdraWn/NhAkTcObMGVy+fLmLWtN+lZWVkEgkcHZ2BmB+bdbpdJg9ezZWrFiBfv363XDe3NrbFgxCXaysrAxarbbFByEAeHh4oKioSKSqOk6n0+HZZ5/F8OHD0b9/fwBAUVERrK2t9b9Qrrm+rUVFRTf9WVw7Z2w2btyI1NRUrFy58oZz5tje7OxsrF27Fj179sTOnTuxePFiPPPMM1i/fj2A32u+1b/noqIiuLu7tzgvl8vh4uJidG1+8cUXMWPGDPTp0wdWVlYYNGgQnn32WcyaNQuA+bX3eoZqm6n9G79efX09XnjhBcycOVO/6ai5tfkf//gH5HI5nnnmmZueN7f2tgV3nyeDWLJkCdLT03HgwAGxS+k0+fn5WLp0KXbt2gWlUil2OV1Cp9MhPDwcf//73wEAgwYNQnp6Oj788EPExsaKXJ3hffvtt/jqq6/w9ddfo1+/fkhLS8Ozzz4Lb29vs2wv/a6pqQnTpk2DIAhYu3at2OV0ipSUFPz73/9GamoqJBKJ2OUYDfYIdTE3NzfIZLIbZhIVFxfD09NTpKo65qmnnsJPP/2EPXv2wNfXV3/c09MTjY2NqKioaHH99W319PS86c/i2jljkpKSgpKSEgwePBhyuRxyuRz79u3D6tWrIZfL4eHhYVbtBQAvLy+EhIS0ONa3b1/k5eUB+L3mW/179vT0RElJSYvzGo0G5eXlRtfmFStW6HuFBgwYgNmzZ+O5557T9wCaW3uvZ6i2mdq/ceD3EJSbm4tdu3bpe4MA82rz/v37UVJSAj8/P/3vsNzcXCxfvhwBAQEAzKu9bcUg1MWsra0xZMgQxMXF6Y/pdDrExcUhOjpaxMrunCAIeOqpp7B161b89ttvCAwMbHF+yJAhsLKyatHWM2fOIC8vT9/W6OhonDhxosV/eNd+Ef3xA1hs48aNw4kTJ5CWlqZ/hYeHY9asWfo/m1N7AWD48OE3LImQmZkJf39/AEBgYCA8PT1btLmqqgpHjhxp0eaKigqkpKTor/ntt9+g0+kQFRXVBa1ou7q6OkilLX8tymQy6HQ6AObX3usZqm3R0dGIj49HU1OT/ppdu3ahd+/e6NatWxe1pu2uhaCzZ89i9+7dcHV1bXHenNo8e/ZsHD9+vMXvMG9vb6xYsQI7d+4EYF7tbTOxR2tboo0bNwoKhUJYt26dcOrUKWHhwoWCs7Nzi5lEpmDx4sWCk5OTsHfvXuHixYv6V11dnf6aRYsWCX5+fsJvv/0mJCcnC9HR0UJ0dLT+/LXp5HfffbeQlpYm7NixQ+jevbvRTif/o+tnjQmC+bU3MTFRkMvlwt/+9jfh7NmzwldffSXY2toKX375pf6at99+W3B2dhZ++OEH4fjx48L9999/0ynXgwYNEo4cOSIcOHBA6Nmzp1FMJ/+j2NhYwcfHRz99/vvvvxfc3NyE559/Xn+NKbe3urpaOHr0qHD06FEBgPD+++8LR48e1c+QMkTbKioqBA8PD2H27NlCenq6sHHjRsHW1la0qdW3anNjY6MwZcoUwdfXV0hLS2vxe+z6GVGm1Obb/R3/0R9njQmCabXXEBiERPKf//xH8PPzE6ytrYXIyEjh8OHDYpd0xwDc9PXFF1/or7ly5Yrw5JNPCt26dRNsbW2FBx54QLh48WKL+5w/f16YOHGiYGNjI7i5uQnLly8Xmpqaurg17fPHIGSO7f3xxx+F/v37CwqFQujTp4/w8ccftziv0+mEV199VfDw8BAUCoUwbtw44cyZMy2uuXTpkjBz5kzB3t5ecHR0FObOnStUV1d3ZTPapKqqSli6dKng5+cnKJVKoUePHsIrr7zS4kPRlNu7Z8+em/43GxsbKwiC4dp27NgxYcSIEYJCoRB8fHyEt99+u6uaeINbtTknJ6fV32N79uzR38OU2ny7v+M/ulkQMqX2GoJEEK5bMpWIiIjIgnCMEBEREVksBiEiIiKyWAxCREREZLEYhIiIiMhiMQgRERGRxWIQIiIiIovFIEREREQWi0GIiIiILBaDEBHRHdi7dy8kEskNm+sSkWliECIiIiKLxSBEREREFotBiIhMik6nw8qVKxEYGAgbGxuEhoZiy5YtAH5/bPXzzz9j4MCBUCqVGDp0KNLT01vc47vvvkO/fv2gUCgQEBCA9957r8X5hoYGvPDCC1CpVFAoFAgODsZnn33W4pqUlBSEh4fD1tYWw4YNw5kzZzq34UTUKRiEiMikrFy5Ehs2bMCHH36IkydP4rnnnsNjjz2Gffv26a9ZsWIF3nvvPSQlJaF79+6YPHkympqaADQHmGnTpmHGjBk4ceIE3njjDbz66qtYt26d/vsff/xxfPPNN1i9ejUyMjLw0Ucfwd7evkUdr7zyCt577z0kJydDLpdj3rx5XdJ+IjIs7j5PRCajoaEBLi4u2L17N6Kjo/XH58+fj7q6OixcuBBjxozBxo0bMX36dABAeXk5fH19sW7dOkybNg2zZs1CaWkpfv31V/33P//88/j5559x8uRJZGZmonfv3ti1axdiYmJuqGHv3r0YM2YMdu/ejXHjxgEAtm/fjkmTJuHKlStQKpWd/FMgIkNijxARmYysrCzU1dVh/PjxsLe31782bNiAc+fO6a+7PiS5uLigd+/eyMjIAABkZGRg+PDhLe47fPhwnD17FlqtFmlpaZDJZBg1atQtaxk4cKD+z15eXgCAkpKSDreRiLqWXOwCiIjaqqamBgDw888/w8fHp8U5hULRIgy1l42NTZuus7Ky0v9ZIpEAaB6/RESmhT1CRGQyQkJCoFAokJeXh+Dg4BYvlUqlv+7w4cP6P1++fBmZmZno27cvAKBv3744ePBgi/sePHgQvXr1gkwmw4ABA6DT6VqMOSIi88UeISIyGQ4ODvjzn/+M5557DjqdDiNGjEBlZSUOHjwIR0dH+Pv7AwD+8pe/wNXVFR4eHnjllVfg5uaGqVOnAgCWL1+OiIgIvPXWW5g+fToSEhKwZs0afPDBBwCAgIAAxMbGYt68eVi9ejVCQ0ORm5uLkpISTJs2TaymE1EnYRAiIpPy1ltvoXv37li5ciWys7Ph7OyMwYMH4+WXX9Y/mnr77bexdOlSnD17FmFhYfjxxx9hbW0NABg8eDC+/fZbvPbaa3jrrbfg5eWFv/zlL5gzZ47+PdauXYuXX34ZTz75JC5dugQ/Pz+8/PLLYjSXiDoZZ40Rkdm4NqPr8uXLcHZ2FrscIjIBHCNEREREFotBiIiIiCwWH40RERGRxWKPEBEREVksBiEiIiKyWAxCREREZLEYhIiIiMhiMQgRERGRxWIQIiIiIovFIEREREQWi0GIiIiILNb/AwOFJYtVrry0AAAAAElFTkSuQmCC", "text/plain": [ "
" ] diff --git a/tutorials/tutorial13/tutorial.ipynb b/tutorials/tutorial13/tutorial.ipynb index 1ac8f48..f087003 100644 --- a/tutorials/tutorial13/tutorial.ipynb +++ b/tutorials/tutorial13/tutorial.ipynb @@ -19,7 +19,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -33,12 +33,14 @@ " !pip install \"pina-mathlab\"\n", "\n", "import torch\n", + "import matplotlib.pyplot as plt\n", + "plt.style.use('tableau-colorblind10')\n", "\n", - "from pina import Condition, Plotter, Trainer, Plotter\n", + "from pina import Condition, Trainer\n", "from pina.problem import SpatialProblem\n", - "from pina.operators import laplacian\n", - "from pina.solvers import PINN, SAPINN\n", - "from pina.model.layers import FourierFeatureEmbedding\n", + "from pina.operator import laplacian\n", + "from pina.solver import PINN, SelfAdaptivePINN as SAPINN\n", + "from pina.model.block import FourierFeatureEmbedding\n", "from pina.loss import LpLoss\n", "from pina.domain import CartesianDomain\n", "from pina.equation import Equation, FixedValue\n", @@ -89,10 +91,10 @@ "\n", " # here we write the problem conditions\n", " conditions = {\n", - " 'bound_cond0' : Condition(domain=CartesianDomain({'x': 0}),\n", - " equation=FixedValue(0)),\n", - " 'bound_cond1' : Condition(domain=CartesianDomain({'x': 1}),\n", - " equation=FixedValue(0)),\n", + " 'bound_cond0' : Condition(domain=CartesianDomain({'x': 0.}),\n", + " equation=FixedValue(0.)),\n", + " 'bound_cond1' : Condition(domain=CartesianDomain({'x': 1.}),\n", + " equation=FixedValue(0.)),\n", " 'phys_cond': Condition(domain=spatial_domain,\n", " equation=Equation(poisson_equation)),\n", " }\n", @@ -103,7 +105,8 @@ "problem = Poisson()\n", "\n", "# let's discretise the domain\n", - "problem.discretise_domain(128, 'grid')" + "problem.discretise_domain(128, 'grid', domains=['phys_cond'])\n", + "problem.discretise_domain(1, 'grid', domains=['bound_cond0','bound_cond1'])" ] }, { @@ -118,76 +121,27 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "GPU available: True (mps), used: False\n", + "GPU available: False, used: False\n", "TPU available: False, using: 0 TPU cores\n", - "IPU available: False, using: 0 IPUs\n", "HPU available: False, using: 0 HPUs\n" ] }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Epoch 4999: 100%|██████████| 1/1 [00:00<00:00, 150.58it/s, v_num=69, gamma0_loss=2.61e+3, gamma1_loss=2.61e+3, D_loss=409.0, mean_loss=1.88e+3] " - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "`Trainer.fit` stopped: `max_epochs=5000` reached.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Epoch 4999: 100%|██████████| 1/1 [00:00<00:00, 97.66it/s, v_num=69, gamma0_loss=2.61e+3, gamma1_loss=2.61e+3, D_loss=409.0, mean_loss=1.88e+3] \n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "GPU available: True (mps), used: False\n", - "TPU available: False, using: 0 TPU cores\n", - "IPU available: False, using: 0 IPUs\n", - "HPU available: False, using: 0 HPUs\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Epoch 4999: 100%|██████████| 1/1 [00:00<00:00, 88.18it/s, v_num=70, gamma0_loss=151.0, gamma1_loss=148.0, D_loss=6.38e+5, mean_loss=2.13e+5] " - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "`Trainer.fit` stopped: `max_epochs=5000` reached.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Epoch 4999: 100%|██████████| 1/1 [00:00<00:00, 65.77it/s, v_num=70, gamma0_loss=151.0, gamma1_loss=148.0, D_loss=6.38e+5, mean_loss=2.13e+5]\n" - ] - }, { "data": { - "image/png": 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++eUXdezYUX5+fsrIyNCkSZM0e/Zs3XDDDZKkadOm6YcfftCMGTP0+OOPF273xRdf1LXXXntWziVLlujmm2/WM888o0cfffS873f//v2KiopSjx495OnpqTp16hR+Zjt27NAXX3yhxYsXFxaeefPmKTo6Wp9//rn69OlTos9WkoKDg+Xl5SU/P78in89f/ec//9E111yj5557TpLUpEkTbd68Wa+++qoGDx5c+Lgbb7xRo0ePliQ9+eSTev311/Xzzz+radOmJc5WXJyxAAAAKGcvv/yy4uPjtWXLlrN+tn79es2ePVsBAQGFt549e8rhcGjPnj0lep3Y2Ngi3+fn5+uf//ynWrdurdDQUAUEBOj777/X/v37L+l9PPnkkzp69Khmzpx5zvexc+dOBQYGFr6P0NBQZWVladeuXefdZqtWrRQaGqpFixbpt99+U/v27XXzzTdr0aJFkswzGKeHC+3atUu5ubnq1q1b4fM9PT0VFxd31mf7189CMsvCtddeq+eff/6CpUKS+vTpo1OnTqlBgwYaMWKEPvvss8JrNbZs2SIPDw916tSp8PFhYWFq2rTpOX+Py9KWLVuKvH9J6tatm3bs2KH8/PzC+/58lsYwDEVFRSkpKalcs3HGAgAAuBVfT7s2v9jTste+FFdccYV69uypp59+ushRZUlKT0/XyJEji4zfP61OnTqSzB3Dvw7DOteQG39//yLfv/rqq3rzzTf1xhtvqHXr1vL399dDDz2knJycS3ofISEhevrpp/WPf/xDN99881nvIyYmRvPmzTvreeHh4efdpmEYuuKKK/TLL7/I29tb3bt3V5s2bZSdna2NGzdqyZIlRc66FNdfP4vTOWrWrKn58+dr6NChCgoKOu/zo6OjtW3bNv3444/64YcfNHr0aL366quFhaekbDZbsX4Py4qnp2eR7w3DkMPhKLfXkygWAADAzRiGUeLhSK5gwoQJateu3VlDUTp06KDNmzerUaNG531ueHi4jhw5Uvj9jh07lJmZedHXXLx4sXr16qX+/ftLkhwOh7Zv364WLVpc4rswh1O99dZbevPNN4vc36FDBy1YsEARERHn3WH38vIqclT9tCuvvFLTpk2Tt7e3XnrpJdlsNl1xxRV69dVXlZ2dXXiEvmHDhvLy8tLixYtVt25dSebO+cqVK4u1Foevr6+++uor3XjjjerZs6f++9//KjAw8IKPv+WWW3TLLbdozJgxatasmf744w81b95ceXl5Wr58eeFQqGPHjmnbtm3n/WzDw8O1cePGIvetW7euSAE43+fzZ82bN9fixYuL3Ld48WI1adJEdvulFd+ywlAoAACACtC6dWv169dPb731VpH7n3zySS1ZskT333+/1q1bpx07dmjhwoVFLt6++uqr9c4772jt2rVatWqVRo0addYR6XNp3LixfvjhBy1ZskRbtmzRyJEjlZiYWKr34ePjo3/84x9nvY9+/fqpevXq6tWrl3777Tft2bNHv/zyix544IHCC9fr1aunDRs2aNu2bUpOTi48Yt+9e3dt3rxZmzZt0mWXXVZ437x58xQbG1t49sHf31/33XefHn/8cX333XfavHmzRowYoczMTA0bNqxY+f39/fX111/Lw8NDN9xwg9LT08/5uNmzZ2vGjBnauHGjdu/erffee0++vr6qW7euGjdurF69emnEiBH6/ffftX79evXv31+1atVSr169zrm9q6++WqtWrdKcOXO0Y8cOvfDCC2cVjXr16mn58uXau3evkpOTz3mG4dFHH9VPP/2kf/7zn9q+fbvi4+P1zjvvXNJZnbJGsQAAAKggL7744lk7i23atNGiRYu0fft2XX755Wrfvr2ef/551axZs/Axr732mqKjo3X55Zfrnnvu0WOPPSY/P7+Lvt6zzz6rDh06qGfPnurevbuioqLKZIG6QYMGqUGDBkXu8/Pz06+//qo6deqod+/eat68uYYNG6asrKzCMxgjRoxQ06ZNFRsbq/Dw8MIj761bt1ZISIjatWungIAASWaxyM/PL7y+4rQJEybo9ttv14ABA9ShQwft3LlT33//vapVq1bs/AEBAfr222/ldDp10003FZna97SQkBBNmzZN3bp1U5s2bfTjjz/qyy+/VFhYmCTzwvSYmBjdfPPN6tKli5xOp7755pvzFr6ePXvqueee0xNPPKGOHTvq5MmTGjhwYJHHPPbYY7Lb7WrRooXCw8PPeS1Mhw4d9OGHH+qDDz5Qq1at9Pzzz+vFF188a4idFQxnSedNczFpaWkKDg5WamrqBcfJAQAA95OVlaU9e/aofv36JZodCUDxXejvWUn2tTljAQAAAKDUKBYAAAAASo1iAQAAAKDUKBYAAAAASo1iAQAAAKDUKBYAAAAASo1iAQAAAKDUKBZlYGdSunYfPfeqjQAAAEBVQLEopZTMHA2PX6le7y7Wou1HrY4DAAAAWIJiUUp5DqfCArx1MitPQ2at0PTfdsvNFzMHAACQJP3yyy8yDEMpKSml2s7evXtlGIbWrVtXJrngmigWpVQ9wFvvj+ikO2Nry+GU/u/rLXr84w3Kzsu3OhoAALCIYRgXvI0bN87qiOVm8ODBuvXWW4vcFx0drSNHjqhVq1bWhEKF8LA6QGXg7WHXy7e3UfMaQfrnV5v18eqD2n00XZMHxCgi0MfqeAAAoIIdOXKk8OsFCxbo+eef17Zt2wrvCwgIKPza6XQqPz9fHh6Vd7fMbrcrKirK6hgoZ5yxKCOGYWhIt/qKHxqnIB8Prdmfol7vLNbWhDSrowEAgAoWFRVVeAsODpZhGIXfb926VYGBgfr2228VExMjb29v/f777+c80v/QQw+pe/fuhd87HA6NHz9e9evXl6+vr9q2bauPP/74glkmTpyoxo0by8fHR5GRkbrjjjsKf5adna0HHnhAERER8vHx0WWXXaaVK1eed1vjxo1Tu3btitz3xhtvqF69eoU/j4+P18KFCwvPzvzyyy/nHAq1aNEixcXFydvbWzVq1NBTTz2lvLy8wp93795dDzzwgJ544gmFhoYqKiqqUp/pqQwqbzW2yOWNw7Xw/ss0PH6ldh3N0F1TlmnWkI7qUKea1dEAAKgcnE4pN9Oa1/b0kwyjTDb11FNP6d///rcaNGigatWKt58wfvx4vffee5o8ebIaN26sX3/9Vf3791d4eLiuvPLKsx6/atUqPfDAA5o7d666du2q48eP67fffiv8+RNPPKFPPvlE8fHxqlu3rl555RX17NlTO3fuVGhoaInf02OPPaYtW7YoLS1Ns2bNkiSFhobq8OHDRR536NAh3XjjjRo8eLDmzJmjrVu3asSIEfLx8SlSHuLj4/XII49o+fLlWrp0qQYPHqxu3brp2muvLXE2lD+KRTmoX91fn97XTUNmr9Ca/SnqP325pg6I1WWNq1sdDQAA95ebKf2rpjWv/ffDkpd/mWzqxRdfLNEOcnZ2tv71r3/pxx9/VJcuXSRJDRo00O+//64pU6acs1js379f/v7+uvnmmxUYGKi6deuqffv2kqSMjAxNmjRJs2fP1g033CBJmjZtmn744QfNmDFDjz/+eInfU0BAgHx9fZWdnX3BoU8TJ05UdHS03nnnHRmGoWbNmunw4cN68skn9fzzz8tmMwfVtGnTRi+88IIkqXHjxnrnnXf0008/USxcFEOhykmwn6feG95JlzeursycfA2LX6mlu45ZHQsAALiI2NjYEj1+586dyszM1LXXXquAgIDC25w5c7Rr165zPufaa69V3bp11aBBAw0YMEDz5s1TZqZ5tmfXrl3Kzc1Vt27dCh/v6empuLg4bdmy5dLfWDFs2bJFXbp0kfGnsz/dunVTenq6Dh48WHhfmzZtijyvRo0aSkpKKtdsuHScsShHfl4emj4oVmPmrdWPWxI1PH6l3hveSe0ZFgUAwKXz9DPPHFj12mXE37/omQ+bzXbWlPW5ubmFX6enm4vxfv3116pVq1aRx3l7e5/zNQIDA7VmzRr98ssv+u9//6vnn39e48aNu+B1FBdysYxlzdPTs8j3hmHI4XCU2+uhdDhjUc68Pex655726tYoTBk5+Ro0c4U2H+aCbgAALplhmMORrLiV0fUV5xIeHl5kNilJRS52btGihby9vbV//341atSoyC06Ovq82/Xw8FCPHj30yiuvaMOGDdq7d6/+97//qWHDhvLy8tLixYsLH5ubm6uVK1eqRYsW582YkJBQpFz8dW0KLy8v5edfeNr95s2ba+nSpUW2s3jxYgUGBqp27doXfC5cF8WiAvh42jVtYKxi6lZTWlaeBsxYrp1J6VbHAgAALuTqq6/WqlWrNGfOHO3YsUMvvPCCNm7cWPjzwMBAPfbYY3r44YcVHx+vXbt2ac2aNXr77bcVHx9/zm1+9dVXeuutt7Ru3Trt27dPc+bMkcPhUNOmTeXv76/77rtPjz/+uL777jtt3rxZI0aMUGZmpoYNG3bO7XXv3l1Hjx7VK6+8ol27dundd9/Vt99+W+Qx9erV04YNG7Rt2zYlJyef84zG6NGjdeDAAY0dO1Zbt27VwoUL9cILL+iRRx4pvL4C7offuQri5+WhmYM7qmXNIB3LyFH/6ct14LhFM1oAAACX07NnTz333HN64okn1LFjR508eVIDBw4s8ph//vOfeu655zR+/Hg1b95c119/vb7++mvVr1//nNsMCQnRp59+qquvvlrNmzfX5MmTNX/+fLVs2VKSNGHCBN1+++0aMGCAOnTooJ07d+r7778/7yxVzZs318SJE/Xuu++qbdu2WrFihR577LEijxkxYoSaNm2q2NhYhYeHFzkjclqtWrX0zTffaMWKFWrbtq1GjRqlYcOG6dlnn72Ujw4uwnD+daCcm0lLS1NwcLBSU1MVFBRkdZyLOpaerbumLtPOpHTVCfXTR6O6KDKIRfQAADiXrKws7dmzR/Xr15ePD/9fAuXhQn/PSrKvzRmLChYW4K15wzupTqif9h/P1IAZy3UiI8fqWAAAAECpUCwsEBnko3nDOykqyEfbE9M1aNYKncwqvxkVAAAAgPJGsbBIdKif3hsep1B/L204mKph8auUlXvhGRQAAAAAV0WxsFCjiEDNGRqnQG8PrdhzXPe9t1o5eczNDAAAAPdDsbBYq1rBmjmko3w8bfp521E9/OE65Tvc+np6AADKnJvPNQO4tLL6+0WxcAEd64Vqcv8YedoNfb3hiB5asE65+Zy5AADg9MrLmZlM0Q6Ul9N/v/660nlJeZRFGJRe96YRertvB42dv0Zfrj+sUzn5euee9vLxtFsdDQAAy9jtdoWEhCgpKUmS5OfnJ6McV78GqhKn06nMzEwlJSUpJCREdnvp9jtZx8LF/Lw1SSMLrrXo1ihMk/rHKMindO0RAAB35nQ6lZCQoJSUFKujAJVSSEiIoqKizlnaS7KvTbFwQUt2JWt4/Cpl5uSraWSgZg7pqFohvlbHAgDAUvn5+crNZXp2oCx5enpe8EwFxaIS2HgoVUNnr1TSyWyFB3pr+sBYtY0OsToWAAAAqhBW3q4EWtUK1udjuqlZVKCOnsxWnylL9cnqg1bHAgAAAM6JYuHCaob46qNRXdSjeaRy8hx69KP1evHLzcpjxigAAAC4GIqFiwv08dTUATF64JrGkqSZi/do4MwVOpGRY3EyAAAA4AyKhRuw2Qw9cm0TTe7fQX5edi3ZdUy3vPO7thxJszoaAAAAIIli4Vaub1VDn43upjqhfjp44pR6T1yirzccsToWAAAAQLFwN02jAvXF/d10eePqOpWbrzHvr9Er321VvsOtJ/cCAACAm6NYuKEQPy/NGtxR917RQJI08ZddevCDtcrJ46JuAAAAWINi4aY87Db9/cbmev2utvK0G/pqwxENi1+pjOw8q6MBAACgCqJYuLnb2tfWjEEd5edl1287ktVv+nKlnmJVUgAAAFQsikUlcEWTcL0/orOq+Xlq3YEUDZq5QiezKBcAAACoOBSLSqJddIjmDe+skD+Vi3SGRQEAAKCCUCwqkRY1g/TesE4K9vXUmv0pGjZ7pbJy862OBQAAgCqAYlHJtKoVrPeGdVKgt4eW7zmu+99fo9x8ZosCAABA+aJYVEKtawdr2qBYeXvY9OOWJD358QY5WOcCAAAA5YhiUUl1bhCmif06yG4z9OnaQ3rxq81yOikXAAAAKB8Ui0rsmuaReq1PW0nS7CV79dZPOy1OBAAAgMqKYlHJ3dq+lv7xt5aSpNd/3K7Zi/dYnAgAAACVEcWiChjUtZ4e7tFEkjTuy836bO1BixMBAACgsqFYVBEPXNNIQ7rVkyQ99tEG/bg50dpAAAAAqFQoFlWEYRh67qYW6t2hlvIdTo15f40W70y2OhYAAAAqCYpFFWKzGXrl9ja6tkWksvMcGjp7pRZtP2p1LAAAAFQCFIsqxsNu0zv3tFeP5hHKznNoRPwq/bSFYVEAAAAoHYpFFeTtYdfEfjG6vmWUcvIdGjl3tT5ezQXdAAAAuHQUiyrKy8Omt+9pr1vb1VSew6nHPlqvd/63g0X0AAAAcEkoFlWYp92m/9zZTqOubChJ+vd/t+uJjzcoOy/f4mQAAABwNxSLKs5mM/TUDc30Yq+WshnSR6sPqu/UZUpKy7I6GgAAANwIxQKSpIFd6mnWkDgF+Xhozf4U3fLO71p3IMXqWAAAAHATFAsUurJJuL64/zI1jghQYlq27pyyVJ9wUTcAAACKgWKBIupV99dnY7rp2haRyslz6NGP1uvFLzcrL99hdTQAAAC4MIoFzhLg7aEp/WP0wDWNJUkzF+/RoFkrdCIjx+JkAAAAcFUUC5yTzWbokWubaFK/DvLzsmvxzmPq9e5i7Uw6aXU0AAAAuCCKBS7ohtY19OnorooO9dX+45m6Y/JSrdl/wupYAAAAcDEUC1xUs6ggLRxzmdpFhyglM1f3TFumn7cmWR0LAAAALoRigWIJ9ffS+yM6qXvTcGXlOjRizip9vynB6lgAAABwERQLFJufl4emDYzV39rWVJ7DqTHz1lAuAAAAIIligRLytNv0nzvbqle7M+Xiv5QLAACAKo9igRLzsNv0Wp8z5eL+99dq8c5kq2MBAADAQhQLXJLT5eL6llHKyTevuWC2KAAAgKqLYoFL5mG36c2+7XR54+rKzMnX4JkrtOVImtWxAAAAYAGKBUrF28OuKQNiFFO3mtKy8jRgxgrtTc6wOhYAAAAqGMUCpebn5aGZgzuqRY0gJadnq9/05TqSesrqWAAAAKhAFAuUiWBfT80ZFqcG1f11KOWU+k9frmPp2VbHAgAAQAWhWKDMVA/w1tzhnVQz2Ee7jmZo0KwVSsvKtToWAAAAKgDFAmWqVoiv3hveSWH+Xtp4KE3DZq/UqZx8q2MBAACgnFEsUOYahAdozrA4Bfp4aOXeExr13mrl5DmsjgUAAIByRLFAuWhZM1izBneUr6ddi7Yf1cML1inf4bQ6FgAAAMoJxQLlJrZeqKYMiJGn3dDXfxzR059ukINyAQAAUClRLFCurmgSrrfubi+bIX246qAeXLCOYVEAAACVUIUViwkTJsgwDD300EOF92VlZWnMmDEKCwtTQECAbr/9diUmJlZUJFSQG1rX0Jt3t5en3dCX6w9rWPxKpWfnWR0LAAAAZahCisXKlSs1ZcoUtWnTpsj9Dz/8sL788kt99NFHWrRokQ4fPqzevXtXRCRUsFva1tTMwR3l52XXbzuSddeUpSyiBwAAUImUe7FIT09Xv379NG3aNFWrVq3w/tTUVM2YMUP/+c9/dPXVVysmJkazZs3SkiVLtGzZsvKOBQtc3jhc80d0VvUAL206nKZe7yzW+gMpVscCAABAGSj3YjFmzBjddNNN6tGjR5H7V69erdzc3CL3N2vWTHXq1NHSpUvPu73s7GylpaUVucF9tI0O0Weju6lpZKCSTmbrzilL9fWGI1bHAgAAQCmVa7H44IMPtGbNGo0fP/6snyUkJMjLy0shISFF7o+MjFRCQsJ5tzl+/HgFBwcX3qKjo8s6NspZdKifPr6vi65qGq7sPIfGvL9Gb/+0Q04nM0YBAAC4q3IrFgcOHNCDDz6oefPmycfHp8y2+/TTTys1NbXwduDAgTLbNipOoI+npg/qqKHd6kuSXvthux5esE5ZuazSDQAA4I7KrVisXr1aSUlJ6tChgzw8POTh4aFFixbprbfekoeHhyIjI5WTk6OUlJQiz0tMTFRUVNR5t+vt7a2goKAiN7gnu83Q87e00Eu3tZLdZujzdYd1z7RlSk7PtjoaAAAASqjcisU111yjP/74Q+vWrSu8xcbGql+/foVfe3p66qeffip8zrZt27R//3516dKlvGLBBfXrVFdzhsYpyMdDa/anqNc7i7Ut4aTVsQAAAFACHuW14cDAQLVq1arIff7+/goLCyu8f9iwYXrkkUcUGhqqoKAgjR07Vl26dFHnzp3LKxZcVLdG1fXZmG4aNnul9h7L1O2Tlujtvu11VbMIq6MBAACgGCxdefv111/XzTffrNtvv11XXHGFoqKi9Omnn1oZCRZqGB6gz0Z3U+cGoUrPztOw+JWa8fseq2MBAACgGAynm0/Fk5aWpuDgYKWmpnK9RSWRk+fQc59v1IJV5oX5D/doogd7NLY4FQAAQNVTkn1tS89YAOfi5WHThNtb6/GeTSVJr/+4XW/8uN3iVAAAALgQigVckmEYGnNVIz11QzNJ0hs/7tDbP+2wOBUAAADOh2IBlzbqyoZ6uqBcvPbDdr23bJ/FiQAAAHAuFAu4vJFXNtQD15jXWDy3cKO++eOIxYkAAADwVxQLuIWHezTWPZ3qyOmUHvpgnRbvTLY6EgAAAP6EYgG3YBiG/tmrlW5oFaWcfIfunbNKfxxMtToWAAAAClAs4DbsNkNv3N1OXRqEKSMnX4NnrdCe5AyrYwEAAEAUC7gZbw+7pg6MUcuaQTqWkaMBM5YrKS3L6lgAAABVHsUCbifQx1Ozh8SpXpifDp44pYEzVyj1VK7VsQAAAKo0igXcUnigt+YM7aTwQG9tTTipEXNWKSs33+pYAAAAVRbFAm6rTpif4ofEKdDbQyv2HNf9769RTp7D6lgAAABVEsUCbq1FzSBNGxQrbw+bftySpIcWrFVePuUCAACgolEs4PY6NwjT1IGx8rLb9M0fCXrso/XKdzitjgUAAFClUCxQKVzZJFzv9usgD5uhz9cd1tj5DIsCAACoSBQLVBrXtojUO/d0KDxzMWLOKp3K4YJuAACAikCxQKVyfasozRgcK19PuxZtP6p+05cpOT3b6lgAAACVHsUClc7ljcM1d1icgnw8tGZ/inq9s1hbE9KsjgUAAFCpUSxQKcXWC9VnY7qpfnV/HUo5pdsnLtH/tiZaHQsAAKDSolig0moYHqDPRndVlwZhysjJ1/D4VZr+2245ncwYBQAAUNYoFqjUQvy8FD80Tnd3jJbDKf3f11v09882Kpe1LgAAAMoUxQKVnpeHTeN7t9azNzWXYUjzV+zXgBnLdSIjx+poAAAAlQbFAlWCYRgafnkDzRgUqwBvDy3bfVy3TlysnUnpVkcDAACoFCgWqFKubhapT0d3VXSor/Ydy9Qdk5do9b4TVscCAABwexQLVDlNIgP1+ehuahcdopTMXPWbvkw/bGbGKAAAgNKgWKBKCgvw1vsjOumaZhHKynVo5NxVWrjukNWxAAAA3BbFAlWWn5eHpgyIUZ+Y2nI4pYcXrNNnaw9aHQsAAMAtUSxQpXnYbXr59jbqG2dOR/vIh+v16RrKBQAAQElRLFDl2WyGXrq1tfp1qiOnU3r84w36kWsuAAAASoRiAcgsF/93ayvdEVNb+Q6nxry/Rqv3Hbc6FgAAgNugWAAFDMPQ+N6tdXWzCGXnOTR09iptTzxpdSwAAAC3QLEA/sTTbtO793RQ+zohSj2Vq0EzV+hwyimrYwEAALg8igXwF75eds0c1FGNIgJ0JDVLA2eu0ImMHKtjAQAAuDSKBXAO1fy9NGdonGoE+2hnUrqGxq9UZk6e1bEAAABcFsUCOI+aIb6aMzROwb6eWrs/RaPnrVFuvsPqWAAAAC6JYgFcQOPIQM0c3FE+njb9su2onvh4gxwOp9WxAAAAXA7FAriImLrVNKl/jDxshj5be0j/9/UWOZ2UCwAAgD+jWADFcFXTCL3ap40kaebiPZq0aJfFiQAAAFwLxQIoptva19azNzWXJL3y3TZ9sGK/xYkAAABcB8UCKIHhlzfQfd0bSpL+/tkf+nztIYsTAQAAuAaKBVBCT/Rsqr5x0XI4pYc/XKePVx+0OhIAAIDlKBZACRmGoZduba17OtWR0yk9/vF6zVu+z+pYAAAAlqJYAJfAZjP00q2tNKhLXTmd0jOfbdSr329lKloAAFBlUSyAS2QYhsb9raXGXt1IkvTuz7v04IJ1ysrNtzgZAABAxaNYAKVgGIYeva6pXrmjjTxshr5cf1gDZizXiYwcq6MBAABUKIoFUAbujI1W/NA4BXp7aOXeE+o9aYn2JmdYHQsAAKDCUCyAMtKtUXV9MrqraoX4ak9yhnpPWqLV+45bHQsAAKBCUCyAMtQkMlCfjemqNrWDdTwjR32nLdc3fxyxOhYAAEC5o1gAZSwi0Ecf3NtZPZpHKCfPodHz1mjqr7vkdDJjFAAAqLwoFkA58PPy0JQBsRrUpa4k6V/fbNVzCzcqL99hcTIAAIDyQbEAyondZk5H+9zNLWQY0nvL9uveuat1KofpaAEAQOVDsQDKkWEYGnZZfU3q10HeHjb9b2uShsxeocycPKujAQAAlCmKBVABrm9VQ/OGd1KAt4eW7T6uwbNWKiObcgEAACoPigVQQWLrhWrOMHOtixV7jmvI7JWs0g0AACoNigVQgTrUqab3hncqLBcPfrBW+Q5miwIAAO6PYgFUsLbRIZo2KFZeHjZ9vylRzy3cyFS0AADA7VEsAAt0bhCmN+9qJ8OQ3l++X2/+tMPqSAAAAKVCsQAsckPrGnqxVytJ0hs/7tC85fssTgQAAHDpKBaAhQZ0rquxVzeSJD33+UZ9vynB4kQAAACXhmIBWOyRa5vo7o7RcjilsfPXauXe41ZHAgAAKDGKBWAxwzD0f7e2Uo/mkcrJc2h4/CrtTDppdSwAAIASoVgALsDDbtPbfdurfZ0QpZ7K1aCZK5WYlmV1LAAAgGKjWAAuwtfLrhmDOqpBdX8dSjmlQTNXKC0r1+pYAAAAxUKxAFxIqL+X4ofGqXqAt7YmnNSouauVk+ewOhYAAMBFUSwAFxMd6qfZQzrK38uuJbuO6bGP1svB6twAAMDFUSwAF9SqVrAm9Y+Rh83QF+sP69mFGykXAADApVEsABd1RZNwvXZn28LVuZ//YqOcTsoFAABwTRQLwIX1aldL/77DLBfvLduv5xdu4swFAABwSRQLwMXdHlNbL9/eRoYhzV22T49+tF65+VzQDQAAXAvFAnADd8ZG6/U728luM/TZ2kMaNXe1snLzrY4FAABQiGIBuIlb29fStIEx8vaw6aetSbpn2jIdS8+2OhYAAIAkigXgVq5uFqn3hndSsK+n1uxP0e2TlmhPcobVsQAAACgWgLvpWC9Un9zXVbWr+WrvsUz1nrhYq/cdtzoWAACo4igWgBtqFBGgz0Z3U5vawTqRmau+05brmz+OWB0LAABUYRQLwE2FB3rrg3s7q0fzCOXkOTTm/TWa/ttu1roAAACWoFgAbszPy0NTBsRqUJe6cjql//t6i8Z9sUn5rHUBAAAqGMUCcHN2m6Fxf2upZ29qLkmKX7pPI+eu1qkcpqMFAAAVh2IBVAKGYWj45Q00sV8HeXnY9OOWRA2LX6nMnDyrowEAgCqCYgFUIje2rqH3hnWSv5ddS3Yd05BZK5WRTbkAAADlj2IBVDJx9UM1Z1gnBXh7aPme4xoyayXDogAAQLmjWACVUEzdanpveCcF+nhoxd7jGvP+GuXmO6yOBQAAKjGKBVBJtYsO0czBHeXjadP/tibpsY/Wy8FsUQAAoJxQLIBKrGO9UE3qFyMPm6GF6w7r/77eYnUkAABQSVEsgEruqmYReu3OtpKkmYv3aNbiPRYnAgAAlRHFAqgCerWrpSevbyZJevGrzfrvpgSLEwEAgMqGYgFUEaOubKC+cXXkdEoPfLBW6w6kWB0JAABUIhQLoIowDEP/7NVS3ZuGKyvXoeHxK3XgeKbVsQAAQCVBsQCqEA+7Te/c00EtawYpOT1Hg2atUEpmjtWxAABAJUCxAKqYAG8PzRzcUTWDfbT7aIbunbta2XksoAcAAEqHYgFUQZFBPpo1JE6B3h5asee4Hv9oA2tcAACAUqFYAFVU06hATepvrnHxxfrD+vd/t1kdCQAAuDGKBVCFXda4usb3bi1JmvjLLs1mjQsAAHCJKBZAFdcnNloP9WgsSRr35WZ9uPKAxYkAAIA7olgA0IPXNNawy+pLkp78dIO+WH/Y4kQAAMDdUCwAyDAMPXtT88IF9B5esE6frz1kdSwAAOBGKBYAJJnl4qVbW6lPTG3lO5x6+MN1mr9iv9WxAACAm6BYAChksxl6+fY2GtC5rpxO6elP/9C0X3dbHQsAALgBigWAImw2Qy/2aql7r2ggSXrpmy168cvNrHMBAAAuiGIB4CyGYejpG5rp7zc2kyTNXLxHYz9Yq6xcVugGAADnRrEAcE6GYejeKxrqzbvbydNu6OsNRzRw5gqlZuZaHQ0AALggigWAC+rVrpbih8Qp0NtDK/Yc1x2Tl+hwyimrYwEAABdDsQBwUV0bVdeHo7ooMshbO5LSddvExdpyJM3qWAAAwIVQLAAUS/MaQfp0dDc1jghQYlq27py8VEt2JVsdCwAAuAiKBYBiqxXiq49HdVVc/VCdzM7ToJkr9N3GI1bHAgAALoBiAaBEgv08NWdonG5qXUO5+U6NeX+tvt5AuQAAoKqjWAAoMR9Pu97q216929dSvsOpBz5Yq4XrDlkdCwAAWIhiAeCS2G2GXu3TVn1iaivf4dQjH67XfzclWB0LAABYhGIB4JLZbYZevr2N7igoF/fPX6ulu45ZHQsAAFiAYgGgVGw2QxN6t9Z1LSKVk+fQiDmr9MfBVKtjAQCACkaxAFBqHnab3urbXl0ahCk9O09DZq/UgeOZVscCAAAViGIBoEz4eNo1dWCMmtcIUnJ6tobMXqnUzFyrYwEAgApCsQBQZgJ9PDVrcEfVCPbRzqR03Tt3lbLz8q2OBQAAKgDFAkCZigr20czBHRXg7aHle47riY83yOl0Wh0LAACUM4oFgDLXvEaQJvXvIA+boYXrDuvf/91mdSQAAFDOKBYAysXljcP1r96tJUnv/rxL81fstzgRAAAoTxQLAOXmzthoPXBNY0nSs59v1C/bkixOBAAAygvFAkC5erhHY/XuUEv5DqfGzFujTYdZ4wIAgMqIYgGgXBmGoQm926hrwzBl5ORr6OyVOpxyyupYAACgjFEsAJQ7Lw+bJvWPUZPIACWmZWvIrJVKy2KNCwAAKhOKBYAKEezrqVlD4hQR6K1tiSd133urlZPnsDoWAAAoIxQLABWmVoivZg7uKD8vuxbvPKanPt0gh4M1LgAAqAwoFgAqVKtawXr3ng6y2wx9uuaQ/v7ZH5QLAAAqgXItFuPHj1fHjh0VGBioiIgI3Xrrrdq2rehCWVlZWRozZozCwsIUEBCg22+/XYmJieUZC4DFrmoWodf6tJXNkD5YeUBPf0q5AADA3ZVrsVi0aJHGjBmjZcuW6YcfflBubq6uu+46ZWRkFD7m4Ycf1pdffqmPPvpIixYt0uHDh9W7d+/yjAXABdzavpZev6udbIa0YNUBPfbReuXmc80FAADuynA6nRV2mPDo0aOKiIjQokWLdMUVVyg1NVXh4eF6//33dccdd0iStm7dqubNm2vp0qXq3LnzRbeZlpam4OBgpaamKigoqLzfAoAy9sX6w3p4wTrlO5zq0TxC79zTQT6edqtjAQAAlWxfu0KvsUhNNRfGCg0NlSStXr1aubm56tGjR+FjmjVrpjp16mjp0qXn3EZ2drbS0tKK3AC4r7+1rakp/WPk7WHTj1uSNHDmCqaiBQDADVVYsXA4HHrooYfUrVs3tWrVSpKUkJAgLy8vhYSEFHlsZGSkEhISzrmd8ePHKzg4uPAWHR1d3tEBlLMeLSI1Z2icAr09tGLPcfWdukzJ6dlWxwIAACVQYcVizJgx2rhxoz744INSbefpp59Wampq4e3AgQNllBCAlTo1CNP8ezureoCXNh1OU5/JS3XgeKbVsQAAQDFVSLG4//779dVXX+nnn39W7dq1C++PiopSTk6OUlJSijw+MTFRUVFR59yWt7e3goKCitwAVA6tagXro1FdVSvEV3uSM9Rn8lLtSDxpdSwAAFAM5VosnE6n7r//fn322Wf63//+p/r16xf5eUxMjDw9PfXTTz8V3rdt2zbt379fXbp0Kc9oAFxU/er++uS+rmocEaCEtCz1mbJUa/efsDoWAAC4iHKdFWr06NF6//33tXDhQjVt2rTw/uDgYPn6+kqS7rvvPn3zzTeaPXu2goKCNHbsWEnSkiVLivUazAoFVE4nMnI0ZPZKrTuQIj8vu6YMiNHljcOtjgUAQJVSkn3tci0WhmGc8/5Zs2Zp8ODBkswF8h599FHNnz9f2dnZ6tmzpyZOnHjeoVB/RbEAKq+M7DyNem+1ftuRLE+7oTfvbq8bW9ewOhYAAFWGyxSLikCxACq37Lx8PbJgvb7+44gMQ/rXba3VN66O1bEAAKgSXHYdCwAoKW8Pu97q21594+rI6ZSe/vQPfbz6oNWxAADAX1AsALg8u83Qv25rpaHdzAkgnvxkg77fdO61bgAAgDUoFgDcgmEYeu7m5uoTU1v5DqfGvr9WS3YlWx0LAAAUoFgAcBuGYWh879a6rkWkcvIdGjV3tXYmpVsdCwAAiGIBwM142G16q297xdStprSsPA2LX6njGTlWxwIAoMqjWABwOz6e5roWtav5at+xTI2au1rZeflWxwIAoEqjWABwS9UDvDVrcEcFentoxd7jevrTP+Tms2cDAODWKBYA3FbjyEC926+D7DZDn645pIm/7LI6EgAAVRbFAoBbu6JJuMb9raUk6dXvt+nrDUcsTgQAQNVEsQDg9gZ0rqsh3epJkh75cJ3WH0ixNA8AAFURxQJApfDsTS10dbMIZec5NHzOKh1OOWV1JAAAqhSKBYBKwW4z9Fbf9moWFaijJ7M1LH6VMrLzrI4FAECVQbEAUGkEeHto+qBYVQ/w0pYjaRrz/hrl5jusjgUAQJVAsQBQqdSu5qdpA2Pl42nTL9uOMg0tAAAVhGIBoNJpX6ea3unbQTZD+nj1Qb323+1WRwIAoNKjWAColHq0iNS/bmstSXrn552asog1LgAAKE8UCwCV1t1xdfR4z6aSpPHfbtXM3/dYnAgAgMqLYgGgUhtzVSM9cHUjSdKLX21W/JK91gYCAKCSolgAqPQevraJRl3ZUJL0wheb9NZPO7igGwCAMkaxAFDpGYahJ69vqgevaSxJ+s8P2/XiV5vlcFAuAAAoKxQLAFWCYRh6+NomeuGWFpKkWYv36rGP1rPOBQAAZYRiAaBKGdKtvl6/q63sNkOfrj2k+95brazcfKtjAQDg9igWAKqc29rX1tQBMfL2sOnHLUkaOHOF0rJyrY4FAIBbo1gAqJKuaR6pucM6KdDbQyv2HNddU5Yp6WSW1bEAAHBbFAsAVVZc/VB9MLKzqgd4a8uRNN0xaan2H8u0OhYAAG6JYgGgSmtZM1if3NdFdUL9tP94pu6aulR7kjOsjgUAgNuhWACo8uqG+evjUV3UKCJAR1KzdNeUpdqZlG51LAAA3ArFAgAkRQT56IN7O6tpZKCSTmbr7qnLOHMBAEAJUCwAoED1AG/Nv7ezmkUFKjk9W/2nL9eR1FNWxwIAwC1QLADgT0L9vTR3WCfVr+6vQymn1H/6ch3PyLE6FgAALo9iAQB/ER7orfeGd1KNYB/tOpqhobNXsogeAAAXQbEAgHOoFeKrucM6KdjXU+sOpOixj9bL6XRaHQsAAJdFsQCA82gUEaDJ/WPkYTP01YYjeuPHHVZHAgDAZVEsAOACujQM00u3tZIkvfnTDi1cd8jiRAAAuCaKBQBcxF0d6+jeKxpIkh7/eIPW7D9hcSIAAFwPxQIAiuHJ65upR/NI5eQ5dO+cVTp4ItPqSAAAuBSKBQAUg91m6M2726l5jSAlp+do2OxVOpmVa3UsAABcBsUCAIrJ39tDMwbFKjzQW9sST2rs/LXKy3dYHQsAAJdAsQCAEqgZ4qvpA2Pl42nTL9uO6v++3mJ1JAAAXALFAgBKqG10iF6/s50kafaSvZqzdK+leQAAcAUUCwC4BDe0rqEnrm8qSRr3xSb9si3J4kQAAFiLYgEAl+i+KxuqT0xtOZzS/e+v1baEk1ZHAgDAMhQLALhEhmHopdtaq3ODUKVn52no7JVKSM2yOhYAAJagWABAKXh52DS5f4waVPfXoZRT6j9juY5n5FgdCwCACkexAIBSCvHz0pxhcaoR7KOdSekaOHO50ljjAgBQxVAsAKAM1K7mp/eGd1KYv5c2HkrToJkrlJpJuQAAVB0UCwAoIw3DAzRnWJyCfT21dn+K7p62TMnp2VbHAgCgQlAsAKAMtawZrAUjO6t6gLe2HEnTnVOW6kjqKatjAQBQ7igWAFDGmkUF6cORnVUz2Ee7j2aoz+Sl2ncsw+pYAACUK4oFAJSDBuEB+ui+rqoX5qeDJ06pz+Sl2pHIOhcAgMqLYgEA5aRWiK8+HNVFTSMDlXQyW3dOWao/DqZaHQsAgHJBsQCAchQR6KMFIzurbe1gncjM1T3TlmnV3uNWxwIAoMxRLACgnIX4eem94Z0UVz9UJ7PzNGDGCv2+I9nqWAAAlCmKBQBUgEAfT8UPidOVTcJ1KjdfQ2ev1H83JVgdCwCAMkOxAIAK4utl19SBMbq+ZZRy8h26b94aLVx3yOpYAACUCYoFAFQgbw+73rmnvXq3r6V8h1MPLVinL9YftjoWAAClRrEAgArmYbfp333aqm9ctJxO6ZEF6/Tz1iSrYwEAUCoUCwCwgM1m6KVbW6tXu5rKczg16r3VWr77mNWxAAC4ZBQLALCIzWbo333a6ppmEcrOc2jEnFXadTTd6lgAAFwSigUAWMjTbtO7/Toopm41pWXladjslTqRkWN1LAAASoxiAQAW8/G0a8qAGNWu5qu9xzJ137zVyslzWB0LAIASoVgAgAuoHuCtGYM6KsDbQ8t2H9ezn/8hp9NpdSwAAIqNYgEALqJpVKDevqe9bIb04aqDmvbbbqsjAQBQbBQLAHAhVzWN0HM3t5Akjf92q37YnGhxIgAAiodiAQAuZnDXeurfuY6cTunBD9Zq8+E0qyMBAHBRFAsAcDGGYeiFW1rqskbVlZmTrxFzVunoyWyrYwEAcEEUCwBwQZ52m969p4MaVPfXoZRTGjl3lbJy862OBQDAeVEsAMBFBft5avqgWAX5eGjN/hT9/VNmigIAuC6KBQC4sAbhAZrYL0Z2m6FP1x7S5EXMFAUAcE0UCwBwcZc1rq5xf2spSXrl+63676YEixMBAHA2igUAuIEBnetqYJe6cjqlhxas04aDKVZHAgCgCIoFALiJ529uocsbmzNFDZ61UruOplsdCQCAQhQLAHATHnabJvWPUetawTqekaOBM1YoITXL6lgAAEiiWACAWwnw9tDsIR0Lp6HtN32Zkk5SLgAA1qNYAICbCQvw1pxhcaoZ7KNdRzPUdyrlAgBgPYoFALih2tX8NP/ezkXLRRrlAgBgHYoFALipumH+RcpFnylLdfBEptWxAABVFMUCANxY3TB/fXBvF0WH+mrfsUz1mbyU2aIAAJagWACAm6sT5qePRnZVw3B/HUnN0l1Tlmrz4TSrYwEAqhiKBQBUAlHBPvpwZBe1qBGk5PQc3T11qdbsP2F1LABAFUKxAIBKIizAW/Pv7ayYutWUlpWn/tOXa8nOZKtjAQCqCIoFAFQiwb6emjssTpc1Klihe/ZK/bQl0epYAIAqgGIBAJWMn5eHpg+K1bUtIpWT59DIuav15frDVscCAFRyFAsAqIR8PO2a2K+DerWrqTyHUw98sFYL1x2yOhYAoBKjWABAJeVpt+n1O9upb1y0nE7p0Q/X68fNDIsCAJQPigUAVGI2m6GXbm2t29rXUp7DqdHvr9FiLugGAJQDigUAVHI2m6FX72ijni3Nay7unbOKdS4AAGWOYgEAVYCH3aa3+rZXlwZhysjJ19DZK3Uk9ZTVsQAAlQjFAgCqCG8PuyYPiFHjiAAlpGVpyKyVOpmVa3UsAEAlQbEAgCok2NdTs4Z0VHigt7YmnNSDH6xTvsNpdSwAQCVAsQCAKqZ2NT/NGBQrbw+b/rc1Sa9+v83qSACASoBiAQBVUJvaIXrljjaSpMmLdunztaxxAQAoHYoFAFRRvdrV0pirGkqSnvhkg9YdSLE2EADArVEsAKAKe/TapurRPKJwGtrEtCyrIwEA3BTFAgCqMJvN0Bt3t1eTyAAlnczWvXNWKSs33+pYAAA3RLEAgCouwNtD0wd2VIifp9YfTNVTn2yQ08lMUQCAkqFYAABUJ8xPE/t1kN1m6PN1hzXl191WRwIAuBmKBQBAktS1YXWNu6WFJOnl77bqpy2JFicCALgTigUAoNCALvXUr1MdOZ3Sgx+s09aENKsjAQDcBMUCAFDEuL+1VKf6oUrPztOgmSt0KOWU1ZEAAG6AYgEAKMLTbtPUAbFqHBGgxLRsDZq5QimZOVbHAgC4OIoFAOAswX6eih8ap6ggH+1MStfQ2SuVnp1ndSwAgAujWAAAzqlmiK/ih8YpyMdDa/anaOjslcrMoVwAAM6NYgEAOK+mUYGaO6yTAr09tGLPcY1gAT0AwHlQLAAAF9Q2OkSzh8bJ38uuxTuPaejslcpgWBQA4C8oFgCAi4qpW02zh8YpwNtDS3Yd04AZy5V6KtfqWAAAF0KxAAAUS8d6oZo3vJOCfT21Zn+K+k5dpmPp2VbHAgC4CIoFAKDY2kaH6IN7O6t6gJc2H0nTXVOXKSE1y+pYAAAXQLEAAJRI8xpB+nBkF9UINqeivXPKUh04nml1LACAxSgWAIASaxAeoA9HdlHdMD/tP56pPpOXamdSutWxAAAWolgAAC5JdKifPhrZRY0jApSQlqW7pizV5sNpVscCAFiEYgEAuGQRQT5aMLKLWtUK0rGMHN09dan+OJhqdSwAgAUoFgCAUgn199L7Izorpm41pWXlacDM5dqWcNLqWACACkaxAACUWpCPp+KHxqlddIhSMnPVb/py7TrKNRcAUJVQLAAAZSLA20PxQ+LUokaQktOz1X/6cqaiBYAqhGIBACgzwX6emjssTg3C/XUkNUuDZ63QySxW6AaAqoBiAQAoU2EB3oofEqfqAd7amnBSo+etUW6+w+pYAIByRrEAKpvcLOnEPil5h5S4STq2S8o8LjnyrU6GKiQ61E+zBneUn5ddv+1I1rOfbZTT6bQ6FgCgHHlYHQCwTHa6lHZYOnn4zI63M1/y9JN8q0n+1aWgWpJPUMm263BIKfuko1sLbtultIPma2UkS448KT9X8vQxX8cvzHyd4GgpJLror77VJMMoun2nUzp1QjqxRzq+Rzqxt+Drgl/TDks61w6cYW4ztJ4U2kCqVt/8NbS++bV3wNlPOf1aKfvP3FIPFHx9QEpPkPKypbwsye5l5vUNkYLrSNXqmbfQ+uavIXUkD++SfZanXz/jqJSeJGUkmZ+vT7D5OtXqm79Pf/2M4BJa1w7WO/e01/D4VVqw6oAaRwZo+OUNrI4FACgnhtPNDyGlpaUpODhYqampCgoq4Q4gXNPpHfPk7eYOpSNfcjrMHcmASPPmHy55BxZvhzI7XUreJh3dZu7oJ20xb6kHipfHO/jMjn5wbckv1Nyx9fCR8nPMner0owU73PvMIpF3qlQfQSGvACkgQjLs5nvNTi/4TC4yZt3ubRYXm6e5459TjKk/fUPN1/PyMz/znHQpK1XKzSyb9yLDLFDV6pnlplo9ySekoGjlSJnHzM8xI6mgRBwteK95F88d0VwKbyqFN5cimkkRLczCURx5OWZBSjsipSea7zf3lPl5eweav//BtcwS4+lTuo+gipr+227939dbZDOkGYM76qqmEVZHAgAUU0n2tSkWKJnTR5Bz/jSNpFeAuaNts5dsW/m50vHdZ47qJxfs+CfvLN6OuYevudMdGGX+6ulvHhF3OqTsNOlUirn9CxUI7yApqKZ51sDuKRk2KSfDfI/pSVJWSsne02l2L6l6k4Kd3WZSSF3zdQIizNexeZo7r6ePxqceNHOmHjDPBKQeMO+/kIDIgrMO9c/+1S+saOnKzzV33E/sNc9yHN9dcJZjt/n9qeMXfi3/CPOMQ0gds2SF1DHfU2ANydPXfL/5OeZnnnnMLFgn9p65Hd8j5WZc2mcpmX++/CPMz89mNwtP5nHzczvn2RmZn0FwbSmwpnkmxWYzf3+z083f14xk6eSRi3/OhQzzvUe1kWq0Lfi1jfkZlPSMSe4pM0dupnmWzCtA8vI3b5WQ0+nUU5/8oQWrDijQ20Ofju6qxpGBVscCABQDxQKl53CYO55H1ksJG6SEP8wj/icTzn+03DvI3AH0CTkzVOXP38tpHhE+mSgd2ykd33X+o9F2LymssbkzbrNLMsydwfREc4c/p4Tz4/tHnNnJD28qRbY0v/YLvfDzstMLdvgPmsN/0g6ZO89ZqeaZCg9v8+yAf5gUVNvckQ1vau7g20s50jD3lJR6yNzxdTrMm3eA5FfdPBrv6Vu67f/ZqRTzveWeMouVzcPcyfUONH8PSvtaTqe5I3962NbpspFz0ixZdi/z98I/3CwP/hHmewyIMO873xCqnEzzzNbps1Gnz0id2KvzFo5zsXsVFNQo8zP28DWfn5Vm/rlL2W+W1XPxqy6FNTLPwATVMAuul59Z5nIyzOed/nN7MqHgz+95ziD5BJt/dsIamn9Go9pIka3MbG4+3Csnz6H+M5ZrxZ7jqhPqp8/HdFOov5fVsQAAF+F2xeLdd9/Vq6++qoSEBLVt21Zvv/224uLiivXcKl0sMpLNHf/TQ4YyjprDOgybeXTWJ8TcWfMLM4eL+IUV3ELNElA4BOW4eQ3AiX1S4kbpyAbz1wvtvNu9zR0dp8PcxqXy9JfCm5g7+dWbnNnxD6l74R3znIyiO2sZR82d4rxsyZA5fMUnyNxOeNOLFwhULjkZ5kXrp6+hyUo9M6TOK6Dg2pZQ82zD6TNWF9pxP12Mjm41i/aRDeavR7eZZxwulYev+boXG27mV12Kan3mVr1Jwd/rambxzs8983f51PFz/5qbaRY0Tz+zrIXUNc9uRTQ3C2QFOJ6Ro17v/q4Dx0+pU/1QzR3WSV4ezCECAK7MrYrFggULNHDgQE2ePFmdOnXSG2+8oY8++kjbtm1TRMTFx+G6RbFwOCQ5Sz5U6DSn0zyafGTDmTMIR9ab95Unu3fBUdPW5pCPyFbmGPmAiKJHkPNyzB23wtuJM1+fPrp/ekjR6eEsofWl6k3NI/xufiQWVVjuKbNsnB7ylZ5klpqcDPMsiJdfwXUykVJgwfVBAVFSQLjkFWgeAJDMfyNy0s0zYyf2mAcLEjaaZwqP7TALUbkxzDMuNdr+6dbGLC2XwpFvHtw4z9/r7Ykn1XviEqVn56lvXLT+dVtrGfwbAAAuy62KRadOndSxY0e98847kiSHw6Ho6GiNHTtWTz311EWf7zLFIjfLPMp/eK15FDN5uznO/NQJcziFnOZ/tnbvgmFC1c7MoHP6a58Qc/hJbqa5k5F2xBwDf2zn+cfAhzUyL1QNrHFmeIwj3xyudCrFfP3M4+a491Onf01RkWEinv7mDn5wLXNbUW3OHBUt7XAeAKWTkykd3WKWjIQ/zMJxfLf5d/uvwxK9gyW/agVnKEP/dKYy1DxTkZdl/vtyMtH89+nYTvM6k3MJqWseWAisYRYiLz/zfke+eaDg9L8thb8WfJ1XsNK2zdP8dy24lvnvS2QrqUY7qWY7/e+QoWHxq+R0Ss/f3EJDL6tfXp8eALi//Fzz+kyLuE2xyMnJkZ+fnz7++GPdeuuthfcPGjRIKSkpWrhw4VnPyc7OVnZ2duH3aWlpio6Otq5YZKVK8X8z1wu42Ew9pWHYzSELpy8crdFWimp1aUMYTs/4c3psu83OWQPA3TidZklw5Jv/4Zz+u1xS6UkFQ7vWm2dCj6wvuEalHAVEar93E32WGK6Nzvoacvvf1LVd6/PndzjOlJlTJ8yDI3++5uX07GmBUWYJ4t8zABUtK/XMZCg5GeYZbbuXeVDHO8Acgupf3TyIfL5/o5xOc3h38nbzYPWh1dKhNeb+X9/5Ffp2/qwkxcLSw9HJycnKz89XZGRkkfsjIyO1devWcz5n/Pjx+sc//lER8YrHO8gckuTINcdB1+pgHvWv3uTM7Dw+IeZ//KenJs1KPfOfY+F/lCfM/zhzMsw/hF4B5nCJ0AbmrXrTspvq0mYvuJgagNsyjLKZRSogQmrcw7yddurEmQkb0pPM65nyss+8rk/wn86KVDtzzYpvqHmw489TCKceNIvKkQ3SkXXmf5jpiaqTnqgHT/8P9MVrcn5plxFYw9yW3cM8w3t69q+slOIPB/MJ+cuwrrZSaMMzw84AoDScTvPftcNrzJ3+w2vMg8uZx4r3fJuHub/oF3rmoFBelnQq1dzGuWZQzC2jKewrgNuNc3n66af1yCOPFH5/+oyFZQxDunOOee1BSB2OlAFwf77VpPpXmLfSCK5t7tj/WU6GOZzryDrlH1qng5uXqGbuAXkqv2AhyYPn355XgFlefIPNgzqGzfxPPjvtzAKKWSnSnkXm7c/POz3EM7SBVK2ueb2Xp4+5Hk1eVsE0xKkFE2EkmRfrpyeZRx/zc6T8PLPwnC5S1eqdmXCiWn2KC+AqnAUzUCb8YR7ISD1o3nLSzSFFjnzz4IjfOYaM+oWZPzPsBdPPn5QyjplrHR3dJiVtNrd7vmnK/cMLho4GmP++5OeaZ5azUs3tZKeaB17SE8zbuRg289+U8GZSrfZSrRipZvvy+7zKmKXFonr16rLb7UpMTCxyf2JioqKios75HG9vb3l7l3D13vJWt6vVCQDAPXj5S3U6SXU6yS4p4LpsXfPOr8pOSdC1tXP1wrV15Gnkm//5FjkzEip5XGR62rxsc7rhP09ykVAww93+JeatPHgHmQWqZruC60jaX3xmO+lMKTqZaO6o5KSbxcvpMD8nTz9z1rJLWbUecFV5OeYBhPSjRRd8tXmemXb8YkOGTvvz1Ph/nh6/2OsTXSKbhzk6pVYHqWYH8+9/WMOLD0/PyzbPSmQcNc8M5+ea93l4m+/Xt5q5XpIb/313iYu34+Li9Pbbb0syL96uU6eO7r//fve6eBsAcEm2JZxU74mLlZGTr7s7Rmt87zKcKSo/z5xZ6/A6c8xyyj5zau1TJ85MUe3pYx5h9A7801oq4ebNL8z8T97mYe4EnDohZSab0xknbzdvpy9Y/zObx5mFMU+vaJ+fW7A4YtqZNU2Ksxjo6VXrI5qbF9RHtjwz5PZiZetcTk/XnX3SzGnzOHPUljMvVdfpa5myUs0/o4bNPKN3eorqS/mzlp5k7ugnbiq4bTSP/BfnmtTTQ4b8C27eQQXT3DvPLGKbdujcU+MbNnMtrIjmZjEPrn1mWLphFAxFL5iKu8gEN8fN9+90nFm81C/M/DchrLEU0UyKaGle41qWa0m5OLe5eFsyp5sdNGiQpkyZori4OL3xxhv68MMPtXXr1rOuvTgXigUAuL+ftiRq+BxzpqiRVzTQUzc0c49paPNzzR2lw2vN25F15lmS/OyLPrWQd3DBFMQFK7AbNnP4RPZJc5HM861ab/Mwy0VEi4KjpUHm+j2O/DPDuzKSzqz5c7EFRm2e5iKPp4d4RTSXwpubawF5B5Tsc8lOP7OOTEayeQbKkWfujPmGmjuK1eqXfLtVyelFUtMOFsy4lmMOy/PyM//M+FY7M2FBSWZwdDik1P0FQ3u2nFlc9Oj28/9Zk8zXC4gyd7IDIs/86hNk7og78s0d9LTDZoFP3HT+Mwcevuafef8Is7zIeea6rIzk8y9Ies5t+Zh/B2q0OTPBTUSLMzPZodTcqlhI0jvvvFO4QF67du301ltvqVOnTsV6LsUCACqH+Sv26+lP/5AkPdyjiR7s0djiRJfI4TB3qI/v/svaJp5mcfDyL9gxiyw6le+5OJ3mzlbyDilpk5S42RznnbjZHK99qTx8/7RDmHf2NOR/FVzHPFob2rBgevSCCUDyssz3dvKwOUV62mHzVtxsQbXMadOrN5GqNzZvYY3N+8919uT0MJqU/QW3A+avqQfMs0DZJwuKk2GWGO8AKTjanEylWv2CCVEKvvYpxj5DXvaZad+P7ZSSd5o7zYWL0mYXrNtiK9jxjjCnaK5Wz7yF1jd/Dap97p1/p9M8Sn5ij7lzX7ijv818T8VinHndwBpmOQysWVAyCyZSSD9qHt1P2XfxAuHpXzCkx3nm99eRV8ws58gW1tCcbjqyVcHaWK3M35MLHTjIyzYLRsZR8wxhRrI5df/p5/gEF7znmubvKVPjlyu3KxalQbEAgMpjxu979M+vNkuSnri+qUZ3b2RxIhd1emaapM3mkeHUg+ZR3qw080yGZ8EQlr8eXT79tVdA0R27/FzzjEbK/oId3K3m+ilJW82zHpfCO8gcCuYfbpYqm4e5Lsup4+ZrXWgWHbvXmfWdbHZzR/P0EK4LFaCS8A83h8kUrmJfMHtjbqb5OmmHzXVeymKBSsNmvhe/UHM9K0euudOennTuoXSneQWYJev0kDy7p3kmIyvFLCQnE8whOyVl9zoztCe82ZkzVNXqnb1eQuHQo8S/nP0q+DorrWDa+oJyFVTTzBzZwjzjxZkDt0exAAC4rXd/3qlXv98myTxz8cA1jdxjWFRllXn8zNH0lH3mjmRWiiSjYAy+b8GR8prm0fKgWub3FzsjkHm84CzAjoLrVXaY18Mc333hI+QevuYFriF1zCPfIXXM2+nX9PI3s52+nuXEPnObJ/aYvx7fXfypQSXJK1Cq3sjcEQ9rZB4hD4g4syit02nmzTx+ppCc2FvwenvMzyw/58KvERBlDjkLb1aws18wBM232oWP7DvyzaP5J4+cuaUdMc8gZacXFDpPyT/M/H0JqmW+Bkf5UQIUCwCAW/tzuRhzVUM9dl1TykVVkZ9r7iCfXutJTvMov6ePOaTIv3rpp3bPSjV3/lMOFKwlddw8M2H3Mm8BkeZOeHBts0SU5vUcDvOsz+kV6k+vomz3OjOEyY1nAULlR7EAALi96b/t1v99vUWSNPyy+nrmpuaUCwCoYCXZ12ZeOQCASxp+eQO92KulJGn673v0wheb5HC49bEwAKjUKBYAAJc1sEs9TejdWoYhzVm6T3//7A/KBQC4KIoFAMCl3R1XR/++o61shvTBygN6/OMNyqdcAIDLoVgAAFze7TG19ebd7WW3GfpkzUE9tGCdcvPLYBpQAECZoVgAANzCLW1r6t17OsjTbujL9Yc19v21ysmjXACAq6BYAADcxvWtojS5f4y87DZ9tylB9723Wlm5l7BAGACgzFEsAABu5ZrmkZo+KFbeHjb9tDVJI+eu5swFALgAigUAwO1c0SRcs4fEydfTrkXbj+qJj9czWxQAWIxiAQBwS10ahmnygBh52Ax9vu6wXv5uq9WRAKBKo1gAANzWlU3C9fLtbSRJU37drdmL91icCACqLooFAMCt3R5TW4/3bCpJevGrzfp1+1GLEwFA1USxAAC4vdHdG+r2DrXlcEpj3l+jXUfTrY4EAFUOxQIA4PYMw9C/erdSTN1qOpmVp+Hxq5SSmWN1LACoUigWAIBKwdvDrikDYlQrxFd7kjM05v01rM4NABWIYgEAqDSqB3hr2sBY+XnZtXjnMf3fV5utjgQAVQbFAgBQqbSoGaTX72onSYpfuk/vLdtnbSAAqCIoFgCASqdny6jCmaJe+GKTluxMtjgRAFR+FAsAQKU0untD9WpXU/kOp+6bt0Z7kzOsjgQAlRrFAgBQKRmGoZdvb6O20SFKPZWr4XNWKS0r1+pYAFBpUSwAAJWWj6dd0wbEKCrIRzuT0jX2/bXKdzitjgUAlRLFAgBQqUUE+Wj6oFj5eNq0aPtRjf9mi9WRAKBSolgAACq9VrWC9VqfdpKk6b/v0YKV+60NBACVEMUCAFAl3NSmhh7q0ViS9MxnG/W/rYkWJwKAyoViAQCoMh68prFubVdTeQ6n7ntvjZbuOmZ1JACoNCgWAIAqwzAMvdqnrXo0j1R2nkPD41dq7f4TVscCgEqBYgEAqFI87Ta9c097dW0YpoycfA2YsUIr9hy3OhYAuD2KBQCgyvHxtGvawFh1bhCq9Ow8DZy5XL/vYHVuACgNigUAoEry9/bQ7CFxurJJuLJyHRoav1I/buaCbgC4VBQLAECV5eNp19SBMerZMlI5eQ6Nem+1vtpw2OpYAOCWKBYAgCrN28Oud+/poF4Fs0U9MH+tPl590OpYAOB2KBYAgCrPw27Tf+5sp7s7RsvhlB77aL3mLN1rdSwAcCsUCwAAJNlthv51W2sN6VZPkvT8wk2a+MtOa0MBgBuhWAAAUMBmM/T8zS009upGkqRXvtum13/YbnEqAHAPFAsAAP7EMAw9el1TPXVDM0nSmz/t0Bs/Ui4A4GIoFgAAnMOoKxvq7zea5eKNH3fo7Z92WJwIAFwbxQIAgPO494qGhWcuXvthu2Yv3mNxIgBwXRQLAAAuYNSVDfXItU0kSf/4arO+WM86FwBwLhQLAAAuYuzVjTSoS105ndKjH67TbzuOWh0JAFwOxQIAgIswDEMv3NJSN7epodx8p0bPW6OdSelWxwIAl0KxAACgGGw2Q6/d2VaxdavpZFaeRsxZpdTMXKtjAYDLoFgAAFBM3h52TR4Qo1ohvtqTnKEx769RXr7D6lgA4BIoFgAAlED1AG9NGxgrPy+7ft+ZrP/7eovVkQDAJVAsAAAooRY1g/SfO9tJkmYv2av3l++3NhAAuACKBQAAl+D6VlF67DpzGtrnF27Ust3HLE4EANaiWAAAcInGXNVIt7StqTyHU/e9t1r7jmVYHQkALEOxAADgEhmGoVfvaKM2tYN1IjNXw+JXKS2LmaIAVE0UCwAASsHH065pA2MVGeStnUnpGvv+WmaKAlAlUSwAACilyCAfTR/YUT6eNi3aflT/+mar1ZEAoMJRLAAAKAOtawcXzhQ1c/EeZooCUOVQLAAAKCM3tq6hR689M1PUkl3JFicCgIpDsQAAoAzdf3Uj/a1wpqg12p540upIAFAhKBYAAJQhwzD0yh1t1L5OiFJP5WrAjOU6cDzT6lgAUO4oFgAAlDEfT7tmDe6oppGBSkzLVv8Zy5WUlmV1LAAoVxQLAADKQYifl+YMi1OdUD/tO5apvtOWKZFyAaASo1gAAFBOIoN89N6wTqoZ7KNdRzN055SlOniCYVEAKieKBQAA5ahOmJ8WjOyi6FBf7TuWqbumLNO+YxlWxwKAMkexAACgnEWH+umjkV3VINxfh1JOqc/kpdqZxGxRACoXigUAABUgKthHC+7tomZRgUo6ma27pizT5sNpVscCgDJDsQAAoIKEB3pr/ojOalUrSMcyctR32jKtP5BidSwAKBMUCwAAKlA1fy+9P6KzOhSsc9Fv+nKt3Hvc6lgAUGoUCwAAKliQj6fmDuukzg1ClZ6dp4EzVuj3HclWxwKAUqFYAABgAX9vD80eEqcrm4TrVG6+hsav1P+2JlodCwAuGcUCAACL+HjaNXVgjK5rEamcPIdGzl2tb/84YnUsALgkFAsAACzk7WHXu/066Ja2NZWb79T989fq+00JVscCgBKjWAAAYDFPu01v3NVOvdvXUr7DqbHvr9WSnVxzAcC9UCwAAHABdpuhV+5oo54tI5WT79CIOau0jqloAbgRigUAAC7Cw27Tm3e3V7dGYcrIydfw+JU6eCLT6lgAUCwUCwAAXIiPp11TB8SqRY0gJafnaNjsVUrPzrM6FgBcFMUCAAAX4+/toRmDYxUe6K1tiSf14Py1ync4rY4FABdEsQAAwAXVCPbV9IGx8vaw6aetSZrw7RarIwHABVEsAABwUW2jQ/TanW0lSdN+26MFK/dbnAgAzo9iAQCAC7u5TU091KOxJOmZzzZq6a5jFicCgHOjWAAA4OIevKaxbmlbU3kOp+6bt1p7kzOsjgQAZ6FYAADg4gzD0Kt3tFHb6BClZOZqWPxKpZ7KtToWABRBsQAAwA34eNo1bUCMagT7aNfRDI2dv1Z5+Q6rYwFAIYoFAABuIiLIR9MGxsrX065ftx/VS98wUxQA10GxAADAjbSqFazX7zJnipq1eK/eX85MUQBcA8UCAAA3c32rGnrsuiaSpOcXbtSSXckWJwIAigUAAG5pzFWN1KudOVPU6HlrtDPppNWRAFRxFAsAANyQYRh6+fY2al/HnClqwIwVOpRyyupYAKowigUAAG7Kx9OuGYM6qlFEgI6kZmnAjOU6lp5tdSwAVRTFAgAANxbq76U5Q+NUM9hHu49maMCMFTqRkWN1LABVEMUCAAA3VzPEV3OGdVL1AC9tPpKmftOXUy4AVDiKBQAAlUCjiADNH9FZ1QO8tflImu6ZvlzHKRcAKhDFAgCASqJxZKA+uLeTqgd4a8uRNN05ZakSUrOsjgWgiqBYAABQiTSKCNSCkZ1VI9hHO5PSdcfkJdqbnGF1LABVAMUCAIBKpmF4gD4a1UX1wvx08MQp9ZmyVFsT0qyOBaCSo1gAAFAJ1a7mpw9HdVGzqEAdPZmtu6Ys09r9J6yOBaASo1gAAFBJRQT6aMG9XdShTohST+Wq3/TlWrIz2epYACopigUAAJVYsJ+n5g7rpMsaVVdmTr4Gz16p/25KsDoWgEqIYgEAQCXn7+2hGYNj1bNlpHLyHLpv3hp9tvag1bEAVDIUCwAAqgBvD7vevaeDbu9QW/kOpx75cL0+WU25AFB2KBYAAFQRHnabXr2jjQZ0riunU3r84/X6esMRq2MBqCQoFgAAVCE2m6F//K2l7oqNlsMpPfjBWv20JdHqWAAqAYoFAABVjM1m6F+9W6tXu5rKczg1et4arWEqWgClRLEAAKAKstsMvdanra5uFqHsPIeGx6/SHlboBlAKFAsAAKooD7tNb/dtr9a1gnU8I0eDZ63QsfRsq2MBcFMUCwAAqrDTU9HWruarfccyNXLuamXn5VsdC4AbolgAAFDFRQT6aPaQjgr09tCqfSf0zGcb5XQ6rY4FwM1QLAAAgBpFBOqdfh1kM6SPVx/U1F93Wx0JgJuhWAAAAEnSlU3C9dzNLSRJE77bqh83Mw0tgOKjWAAAgEKDu9bTPZ3qyFmwxsXWhDSrIwFwExQLAABQyDDMBfS6NgxTRk6+hs1epWRmigJQDBQLAABQhKfdpon9OqhemJ8OpZzSKGaKAlAMFAsAAHCWED8vTR/UUYE+5kxRT3/6BzNFAbggigUAADinRhEBeveeDrLbDH265pCmMFMUgAugWAAAgPO6okm4ni+YKerl77bqqw2HLU4EwFVRLAAAwAUN7FJXA7vUldMpPbxgnX7fkWx1JAAuiGIBAAAuyDAMvXBLS93UuoZy850aOXeVNhxMsToWABdDsQAAABdltxn6z11t1a2ROQ3twJkrtPFQqtWxALgQigUAACgWbw+7pgyIVfs6IUrJzFX/GcspFwAKUSwAAECxBXh7KH5oXGG56Dd9OcOiAEiiWAAAgBIK8vEsLBepp3LVd+oyLdnFBd1AVUexAAAAJRbk46k5Q+PUpYF5zcXgmSv1/aYEq2MBsBDFAgAAXJJAH0/NGtJR17WIVE6+Q/e9t1ofrjpgdSwAFqFYAACAS+bjadfEfh10Z2xtOZzSEx9v0NRfd1kdC4AFKBYAAKBUPOw2vXx7G428ooEk6V/fbNWEb7fK6XRanAxARaJYAACAUjMMQ0/f2FxP3dBMkjR50S49/ekfyndQLoCqgmIBAADKzKgrG2pC79ayGdIHKw/o/vfXKDsv3+pYACpAuRSLvXv3atiwYapfv758fX3VsGFDvfDCC8rJySnyuA0bNujyyy+Xj4+PoqOj9corr5RHHAAAUIHujqujif06yMtu07cbEzRm3lrl5DmsjgWgnJVLsdi6dascDoemTJmiTZs26fXXX9fkyZP197//vfAxaWlpuu6661S3bl2tXr1ar776qsaNG6epU6eWRyQAAFCBrm9VQzMGx8rbw6YftyRq7Pw1ys2nXACVmeGsoCurXn31VU2aNEm7d++WJE2aNEnPPPOMEhIS5OXlJUl66qmn9Pnnn2vr1q3F3m5aWpqCg4OVmpqqoKCgcskOAAAuzaLtRzVizirl5Dl0U+saeqtve9lthtWxABRTSfa1K+wai9TUVIWGhhZ+v3TpUl1xxRWFpUKSevbsqW3btunEiRPn3U52drbS0tKK3AAAgGu6skm4pgyIkZfdpq//OKJxX2xitiigkqqQYrFz5069/fbbGjlyZOF9CQkJioyMLPK4098nJJx/5c7x48crODi48BYdHV0+oQEAQJm4qmmEXr+rnQxDmrtsn97+306rIwEoByUqFk899ZQMw7jg7a/DmA4dOqTrr79effr00YgRI0od+Omnn1Zqamrh7cABVvgEAMDV3dSmhv7xt5aSpP/8sF0LVu63OBGAsuZRkgc/+uijGjx48AUf06BBg8KvDx8+rKuuukpdu3Y966LsqKgoJSYmFrnv9PdRUVHn3b63t7e8vb1LEhsAALiAgV3qKSktW+/8vFPPfLZRtUL8dFnj6lbHAlBGSlQswsPDFR4eXqzHHjp0SFdddZViYmI0a9Ys2WxFT4506dJFzzzzjHJzc+Xp6SlJ+uGHH9S0aVNVq1atJLEAAICbePS6Jjp4IlOfrzus+95brU9Gd1WTyECrYwEoA+VyjcWhQ4fUvXt31alTR//+97919OhRJSQkFLl24p577pGXl5eGDRumTZs2acGCBXrzzTf1yCOPlEckAADgAgzD0Mt3tFHHetV0MjtPQ2at1NGT2VbHAlAGymW62dmzZ2vIkCHn/NmfX27Dhg0aM2aMVq5cqerVq2vs2LF68sknS/RaTDcLAID7OZGRo96TlmhPcobaRofogxGd5etltzoWgL8oyb52ha1jUV4oFgAAuKc9yRm6beJipWTm6vqWUZrYr4NsrHEBuBSXXMcCAADgz+pX99fUAbHystv03aYEvfxd8RfIBeB6KBYAAMAycfVD9WqfNpKkKb/u1rzl+yxOBOBSUSwAAIClerWrpUeubSJJen7hJi3aftTiRAAuBcUCAABYbuzVjdS7Qy3lO5waM2+NtiWctDoSgBKiWAAAAMsZhqEJvduoc4NQpWfnaejslUpKy7I6FoASoFgAAACX4OVh0+T+MWpQ3V+HUk5p+JxVyszJszoWgGKiWAAAAJcR4uelWUM6KtTfSxsOpuqB+WuVk+ewOhaAYqBYAAAAl1I3zF/TBsbIy8OmH7ckaez8NcrNp1wAro5iAQAAXE5M3VBNHWCWi+83Jer+9ykXgKujWAAAAJfUvWmEWS7sZrkYNXe1snLzrY4F4DwoFgAAwGV1bxqhKQNj5O1h009bkzRw5gqlZeVaHQvAOVAsAACAS7uqaYTmDI1ToLeHVuw5rr5Tlyk5PdvqWAD+gmIBAABcXqcGYZp/b2dVD/DSpsNp6jN5qQ6eyLQ6FoA/oVgAAAC30KpWsD4a1VW1Qny1JzlDd0xaqh2JrNANuAqKBQAAcBv1q/vrk/u6qnFEgBLSsnTnlKVafyDF6lgARLEAAABuJirYRx+O7KK20SE6kZmre6Yt0+KdyVbHAqo8igUAAHA71fy99P7wTurWKEwZOfkaMmulvtuYYHUsoEqjWAAAALfk7+2hmYM76vqWUcrJd2j0vNX6cOUBq2MBVRbFAgAAuC1vD7ve7ddBd8VGy+GUnvhkg6b/ttvqWECVRLEAAABuzW4zNOH21hp5ZQNJ0v99vUVzl+2zOBVQ9VAsAACA2zMMQ0/f0Fxjr24kSXp+4UYtXHfI4lRA1UKxAAAAlcYj1zbRwC515XRKj364Xj9vTbI6ElBlUCwAAEClYRiGxt3SUre2q6k8h1Nj3l+jjYdSrY4FVAkUCwAAUKnYbIZe7dNWlzWqrsycfA2dvVKHU05ZHQuo9CgWAACg0vG02zSxfwc1iQxQ0slsDZm1UmlZuVbHAio1igUAAKiUgnw8NWtInMIDvbUt8aTGzFuj3HyH1bGASotiAQAAKq1aIb6aNbij/Lzs+m1Hsp79bKOcTqfVsYBKiWIBAAAqtVa1gvV23/ayGdKCVQc08ZddVkcCKiWKBQAAqPSuaR6pf/ytpSTp1e+3scYFUA4oFgAAoEoY0KWeRlxeX5L0+EcbtHz3MYsTAZULxQIAAFQZT9/QXDe0ilJOvkP3zl2tXUfTrY4EVBoUCwAAUGXYbIZev6ud2tcJUeqpXA2ZtVLJ6dlWxwIqBYoFAACoUnw87Zo2MFZ1Qv20/3imhsevUlZuvtWxALdHsQAAAFVO9QBvzRrSUcG+nlp3IEUPfrBWeaxxAZQKxQIAAFRJDcMDNHVAjLzsNn2/KVEPLVhHuQBKgWIBAACqrE4NwjSxXwd52g19teGIHv5wPeUCuEQUCwAAUKX1aBGpif1i5Gk39OX6wxo7fy3XXACXgGIBAACqvGtbROrdezrIy27TtxsTNGjmCqWeyrU6FuBWKBYAAACSrmsZpdlDOyrQ20PL9xzXXVOWKjEty+pYgNugWAAAABTo2rC6FozsovBAb21NOKneE5doZxKL6AHFQbEAAAD4kxY1g/TpfV3VoLq/DqWcUp/JS7Rm/wmrYwEuj2IBAADwF9Ghfvr4vq5qGx2iE5m5umfaMv2wOdHqWIBLo1gAAACcQ6i/l+aP6KSrmoYrK9ehkXNX6b1l+6yOBbgsigUAAMB5+Hl5aNrAWN3dMVoOp/Ts5xv15o875HQ6rY4GuByKBQAAwAV42G0a37u1HrymsSTp9R+3a/y3WykXwF9QLAAAAC7CMAw9fG0TPXdzC0nS1F9369nPN8rhoFwAp1EsAAAAimnYZfU1oXdrGYY0b/l+vfjVZs5cAAUoFgAAACVwd1wd/fuOtpKk2Uv2asJ3DIsCJIoFAABAid0eU1sv3dZKkjRl0W69+/NOixMB1qNYAAAAXIJ+nerq+YJrLv793+36bO1BixMB1qJYAAAAXKKhl9XXyCsaSJKe+HiDluxKtjgRYB2KBQAAQCk8eX0z3dSmhnLznRo5d7W2J560OhJgCYoFAABAKdhshl7r01axdavpZFaehsxaqaS0LKtjARWOYgEAAFBKPp52TRsYq/rV/XUo5ZSGxq9URnae1bGACkWxAAAAKAPV/L00e0hHhfp7aeOhNI2dv1Z5+Q6rYwEVhmIBAABQRuqG+Wv6oFh5e9j0v61Jem7hJta4QJVBsQAAAChDHepU01t928swpPkr9mviL7usjgRUCIoFAABAGevZMkr/+FtLSdKr32/TJ6tZ4wKVH8UCAACgHAzsUk8jrzTXuHjykw36bcdRixMB5YtiAQAAUE6e7NlMt7StqTyHU/e9t0abD6dZHQkoNxQLAACAcmKzGfp3nzbqVD9U6dl5GjxrhfYkZ1gdCygXFAsAAIBy5O1h19SBsWoWFaikk9nqO3WZ9lIuUAlRLAAAAMpZsK+n3hveSY0jApSQlqW+05Zp3zHKBSoXigUAAEAFqB7grfdHdFbDcH8dSc3SHZOXauOhVKtjAWWGYgEAAFBBwgO9Nf/ezmoWFaijJ7N199Rl+n1HstWxgDJBsQAAAKhAEYE++nBUF3VpEKb07DwNmb1CC9cdsjoWUGoUCwAAgAoW5OOp2UM76uY2NZSb79SDH6zTtF93Wx0LKBWKBQAAgAW8Pex66+72GnZZfUnSS99s0T+/2iyHw2lxMuDSUCwAAAAsYrMZeu7mFnrmxuaSpBm/79EDH6xVdl6+xcmAkqNYAAAAWGzEFQ305t3t5Gk39NWGIxo0c4XSsnKtjgWUCMUCAADABfRqV0uzh8QpwNtDy3Yf152TlyohNcvqWECxUSwAAABcRLdG1bVgZGeFB3pra8JJ9Z64WDsST1odCygWigUAAIALaVkzWJ/e11UNwv11mIX04EYoFgAAAC4mOtRPn4zqqnbRIUo9lav+M5Zr02HKBVwbxQIAAMAFVfP30pxhcWoXHaKUzFz1m75cmw+nWR0LOC+KBQAAgIsK8vEsUi4GzVqhA8czrY4FnBPFAgAAwIWdLhfNawTp6MlsDZq5QsczcqyOBZyFYgEAAODignw8NXtIR9UK8dXu5AwNi1+pUzksogfXQrEAAABwA5FBPoof2lHBvp5auz9FY+evVV6+w+pYQCGKBQAAgJtoFBGoGYNi5e1h049bEvXcwk1yOp1WxwIkUSwAAADcSmy9UL15d3sZhjR/xX69/b+dVkcCJFEsAAAA3M71raL0j7+1lCT954ft+mjVAYsTARQLAAAAtzSwSz3d172hJOnpT//Qou1HLU6Eqo5iAQAA4Kae6NlUt7WvpTyHU6PfW62Nh1idG9ahWAAAALgpwzD08u1t1K1RmDJy8jVk9koW0INlKBYAAABuzMvDpkn9Y9QsKlBHT2Zr8KwVSslkAT1UPIoFAACAmzMX0ItTjWAf7TqaoeHxq5SZk2d1LFQxFAsAAIBKICrYR/FD4xTo46FV+05oePwqVudGhaJYAAAAVBJNIgM1e0ic/L3sWrLrmO6du0pZuZQLVAyKBQAAQCUSU7eaZg+Nk5+XXb/tSNbw+FXKyGZYFMofxQIAAKCS6VgvVLMGd5Sfl12/70zWPdOX60QGF3SjfFEsAAAAKqFODcL0/ojOqubnqfUHUtRnylIdSjlldSxUYhQLAACASqpddIg+GtVFUUE+2pmUrlvfXcwieig3FAsAAIBKrFFEoD4Z3VVNI811Lu6cslQ/bUm0OhYqIYoFAABAJVcrxFcf3ddFlzWqrsycfI2Ys0pzl+61OhYqGYoFAABAFRDk46lZQzrqztjacjil5xZu0ktfb5bD4bQ6GioJigUAAEAV4Wm36eXb2+ix65pIkqb9tkdj3l/DWhcoExQLAACAKsQwDN1/dWO9eXc7edlt+nZjgvpOW6bk9Gyro8HNUSwAAACqoF7tamnusDgF+3pq7f4U9Z64RAeOZ1odC26MYgEAAFBFdWoQpk9Hd1WdUD/tP56pu6Ys1d7kDKtjwU1RLAAAAKqwhuEB+mhUFzUM99fh1CzdNXWpdialWx0LbohiAQAAUMVFBvnog3u7qGlkoBLTstVv+jKGRaHEKBYAAABQeKC35t/bWU0iAwrKxXIlpmVZHQtuhGIBAAAASVKov5feG9ap8JqL/tOX60RGjtWx4CYoFgAAACgUEeSjecM7KTLIWzuS0jV8zirWuUCxUCwAAABQRHSon+YO66QgHw+t3ndCD36wVvms0I2LoFgAAADgLE0iAzV1YKy87DZ9vylRL365SU4n5QLnR7EAAADAOXVuEKb/3NVWkhS/dJ+m/Lrb4kRwZRQLAAAAnNfNbWrq2ZuaS5ImfLtVC9cdsjgRXBXFAgAAABc0/PIGGnZZfUnSYx+t15KdyRYngiuiWAAAAOCinrmxuW5qU0O5+U6NnLtaW46kWR0JLoZiAQAAgIuy2Qy91qet4uqH6mR2nobMWqnDKaesjgUXQrEAAABAsfh42jVtQKyaRAYoIS1Lg2auYAE9FKJYAAAAoNiC/Tw1e0hc4QJ6/WcsV2pmrtWx4AIoFgAAACiRmiG+mje8k8L8vbTpcJoGzlyutCzKRVVHsQAAAECJNYoI1LwRnVTNz1PrD6ZqwPTlDIuq4igWAAAAuCTNooL03vAz5eLOKUuVkJpldSxYhGIBAACAS9ayZrA+HNlFUUE+2pGUrjsmL9Guo+lWx4IFKBYAAAAolcaRgfpoVBfVC/PTwROn1HviEi3ddczqWKhgFAsAAACUWnSonz6+r6va1wlR6qlcDZy5XB+tOmB1LFQgigUAAADKRPUAb80f0blwhe7HP96gV7/fKofDaXU0VIByLxbZ2dlq166dDMPQunXrivxsw4YNuvzyy+Xj46Po6Gi98sor5R0HAAAA5cjH0663726v+69qJEl69+ddGjt/rbJy8y1OhvJW7sXiiSeeUM2aNc+6Py0tTdddd53q1q2r1atX69VXX9W4ceM0derU8o4EAACAcmSzGXqsZ1P9u09bedoNff3HEd09dZmOnsy2OhrKUbkWi2+//Vb//e9/9e9///usn82bN085OTmaOXOmWrZsqbvvvlsPPPCA/vOf/5RnJAAAAFSQO2Jqa87QTgr29dS6Aym69d3F2p540upYKCflViwSExM1YsQIzZ07V35+fmf9fOnSpbriiivk5eVVeF/Pnj21bds2nThxorxiAQAAoAJ1aRimz0Z3Vb0wPx1KOaXbJy7Rij3HrY6FclAuxcLpdGrw4MEaNWqUYmNjz/mYhIQERUZGFrnv9PcJCQnn3XZ2drbS0tKK3AAAAOC6GoQH6NPR3dSxXjWdzM7TwJnL9cu2JKtjoYyVqFg89dRTMgzjgretW7fq7bff1smTJ/X000+XeeDx48crODi48BYdHV3mrwEAAICyFervpbnDOumqpuHKynVoxJxV+m7jEatjoQwZTqez2PN/HT16VMeOXXixkwYNGujOO+/Ul19+KcMwCu/Pz8+X3W5Xv379FB8fr4EDByotLU2ff/554WN+/vlnXX311Tp+/LiqVat2zu1nZ2crO/vMhT9paWmKjo5WamqqgoKCivtWAAAAYIGcPIce/nCdvt5wRB42Q5P6x+jaFpEXfyIskZaWpuDg4GLta5eoWBTX/v37iwxROnz4sHr27KmPP/5YnTp1Uu3atTVp0iQ988wzSkxMlKenpyTp73//uz799FNt3bq12K9VkjcLAAAA6+U7nHr0w3X6fN1hedltmjYoVlc2Cbc6Fs6hJPva5XKNRZ06ddSqVavCW5MmTSRJDRs2VO3atSVJ99xzj7y8vDRs2DBt2rRJCxYs0JtvvqlHHnmkPCIBAADARdhthv7dp61uaBWlnHyH7p2zSst2X3hUDFyfZStvBwcH67///a/27NmjmJgYPfroo3r++ed17733WhUJAAAAFcTDbtObd7dXj+YRys5zaET8Km06nGp1LJRCuQyFqkgMhQIAAHBfWbn5GjhzhVbsOa7qAd769L6uqhN29lIFsIblQ6EAAACA4vDxtGvawFg1iwpUcnq2BsxczgrdbopiAQAAAEsF+3pqztA4RYf6at+xTA2etUIns3KtjoUSolgAAADAchFBPpoztJPC/L206XCa7p2zWlm5+VbHQglQLAAAAOAS6lf3V/zQOAV4e2jp7mN6eME65Tvc+nLgKoViAQAAAJfRqlawpg6IkZfdpm83Juj5hRvl5nMNVRkUCwAAALiUro2q642728kwpHnL9+uNH3dYHQnFQLEAAACAy7mxdQ292KuVJOnNn3Zo7tK91gbCRVEsAAAA4JIGdK6rh3o0liQ9/8UmfbXhsMWJcCEUCwAAALisB69prAGd68rplB5esE6/70i2OhLOg2IBAAAAl2UYhsb9raVual1DuflODYtfqZ+3JlkdC+dAsQAAAIBLs9sM/eeutrqmWYSy8xwaMWeVvlzPsChXQ7EAAACAy/P2sGvygBj9rW1N5TmceuCDtYpfstfqWPgTigUAAADcgqfdptfvaqd+nerI6ZRe+GKTXvxyM4vouQiKBQAAANyG3Wbo/25tpcd7NpUkzVy8R6PeW63MnDyLk4FiAQAAALdiGIbGXNVIb/dt///t3X9Q1XW+x/HX4cA5+AMQJRAUddH8sf5YU4IFc9zKrjs2pnZb3WxZXDUrqfbqTObNujRZxjWnacaLtf4Ka1zJWmnbZO2H5baihhqUK2ijoGYGrWZx0pAf53P/uFdaydTDl3O+HHg+Zs4ffPl8Z15n5j1fvi++5/s9coWG6J2yav161W596am1O1qHRrEAAABAUJr0swT9cU6qojuH6ZMT32hq7k59Wu2xO1aHRbEAAABA0Eru110F88boJzFd9PnX3+nfV+7kuy5sQrEAAABAUOsX00Wb70tXSr/u8pxv0MwXi7Vpz2d2x+pwKBYAAAAIetFdXHp5Toomj/y/x9Eu/NMneuatg/LyxKiAoVgAAACgXXCHOvXc9JF68KYBkqTc94/o96+Uqra+0eZkHQPFAgAAAO2Gw+HQgn8bpGfuGKHQEIf+8vFJZa4r1tnzPI7W3ygWAAAAaHd+lZyol2alKMIdqg8rv9KsvD1814WfUSwAAADQLqUPiNFLs78vF7Pz9uq7Oj4W5S8UCwAAALRb1/WJVt6sFHV1h2pXxWnN27BP9Y1eu2O1SxQLAAAAtGuj+0Yr73fXKzwsRO8f+qcWvvYJT4vyA4oFAAAA2r3kft31/F2j5QxxqKDkcz1VWC5jKBetiWIBAACADuHGwbF65o4RkqS1Oyr1wt8qbE7UvlAsAAAA0GHcPqq3Hr11iCTpv7ce1Ct7jtucqP2gWAAAAKBDmTM2Sff9or8k6T8379dbB6psTtQ+UCwAAADQ4SycMEjTknvLa6QHNpbow4rTdkcKehQLAAAAdDgOh0NLpw7XLT+NU12DV3PW79WBk9/YHSuoUSwAAADQIYU6Q7TizuuU8pPu8pxvUOa6PTp2+qzdsYIWxQIAAAAdVniYU2sykzUkPlKnvj2vjLXF+tJTa3esoESxAAAAQIcWGR6m9bOuV5/unXX8q3PKXLdHNbX1dscKOhQLAAAAdHixEeF6eXaKYrq6Vf5FjWbn7ZGHcuETigUAAAAgqW+PLlo/63pFhIdqz9Ez+s2aD/X1uTq7YwUNigUAAADw/4YmRGnj3T9XdOcwfXziG03/w259WcM9F1eDYgEAAAD8i2G9orTpnjTFRrh1qNqjKblFKjtZY3esNo9iAQAAADRzbVyEXrs3XUnXdNHJb2r1qxd2alt5td2x2jSKBQAAAHAJfXp0VsF9YzRmQA+drWvUnJf2as3fK2SMsTtam0SxAAAAAH5EVOcw5f0uRXem9JEx0pNbyvVIwT9U3+i1O1qbQ7EAAAAALiPMGaKlU4fp0VuHyOGQNhYfV+a6Yp4Y1QzFAgAAALgCh8OhOWOTtDojWZ1dTu08clpTcot05J/f2h2tzaBYAAAAAFdp/E/j9Kf70tWrWycdPX1OU3KLtPPIKbtjtQkUCwAAAMAHQ+Ij9ef7x2h032h5ahs0c90evfnJSbtj2Y5iAQAAAPgopqtbG+ak6pdDe6qu0asHNpYor6jS7li2olgAAAAALRAe5lTuXaOU8fO+MkZ6/C9l+sPfjtgdyzYUCwAAAKCFnCEOPTF5qB68+VpJ0tN/PaiV2w/bnMoeFAsAAADAAofDoQW3DNSCWwZKkpZtPaRVH3S8KxcUCwAAAKAVPHjztXpowiBJ0tLCg3p172c2JwosigUAAADQSrJuHKB7xiVJkhZt3q93y6ptThQ4FAsAAACgFS365WDdMbq3Gr1GWX/8SHuOfmV3pICgWAAAAACtyOFwKOf24bp5cKzON3g1O2+PDlbV2B3L7ygWAAAAQCsLdYbof2aMUnLfaNXUNihzXbFOnDlndyy/olgAAAAAftDJ5dTazOs1MK6rqmvOK2NtsU59e97uWH5DsQAAAAD8JKpzmF6alape3Tqp8tRZzXyxWJ7aertj+QXFAgAAAPCjnlHhenl2inp0cekfn9fo7pf2qra+0e5YrY5iAQAAAPhZ0jVdtX5Wirq6Q7W74is9uLFEDY1eu2O1KooFAAAAEADDekVp9W+T5QoN0dtl1XqkYL+MMXbHajUUCwAAACBA0vr30Io7r1OIQ9q094Se/uvBdlMuKBYAAABAAE0Y2lM5t4+QJK36oEJPbSlvF+WCYgEAAAAE2LTrE/XE5KGSpDU7KvVffz4grze4ywXFAgAAALDBb9P6Kef24XI4pJd3H9N/vFIa1E+LolgAAAAANvl1Sh89O+1nCg1x6I2PT+o3az7U6SD9Ej2KBQAAAGCjqdf11vpZKYoID9XeY2c0deVOHf7yW7tj+YxiAQAAANhszIAYFcxLV5/unXX8q3OaurJIRYdP2R3LJxQLAAAAoA0YEBuhgnnpGt03Wp7aBmWuK1Z+8XG7Y101igUAAADQRvTo6taGOamaPDJBDV6jZ9/5VDW19XbHuiqhdgcAAAAA8L3wMKeemz5SA67pql8MilVkeJjdka4KxQIAAABoYxwOhx64+Vq7Y/iEj0IBAAAAsIxiAQAAAMAyigUAAAAAyygWAAAAACyjWAAAAACwjGIBAAAAwDKKBQAAAADLKBYAAAAALKNYAAAAALCMYgEAAADAMooFAAAAAMsoFgAAAAAso1gAAAAAsIxiAQAAAMAyigUAAAAAyygWAAAAACyjWAAAAACwjGIBAAAAwDKKBQAAAADLKBYAAAAALKNYAAAAALCMYgEAAADAMooFAAAAAMsoFgAAAAAso1gAAAAAsIxiAQAAAMAyigUAAAAAyygWAAAAACyjWAAAAACwjGIBAAAAwLJQuwNYZYyRJNXU1NicBAAAAGhfLpxjXzjnvpygLxYej0eSlJiYaHMSAAAAoH3yeDyKioq67BqHuZr60YZ5vV6dPHlSERERcjgctmSoqalRYmKiPvvsM0VGRtqSAW0LM4HmmAk0x0ygOWYCzbWFmTDGyOPxKCEhQSEhl7+LIuivWISEhKh37952x5AkRUZGciDARZgJNMdMoDlmAs0xE2jO7pm40pWKC7h5GwAAAIBlFAsAAAAAllEsWoHb7VZ2drbcbrfdUdBGMBNojplAc8wEmmMm0FywzUTQ37wNAAAAwH5csQAAAABgGcUCAAAAgGUUCwAAAACWUSwAAAAAWEaxuEq5ubnq16+fwsPDlZqaquLi4suuf/XVVzV48GCFh4dr+PDhKiwsDFBSBIovM7F69WqNHTtW0dHRio6O1vjx4684Qwg+vh4nLsjPz5fD4dCUKVP8GxAB5+tMfP3118rKylJ8fLzcbrcGDhzI3492xteZeO655zRo0CB16tRJiYmJmj9/vmprawOUFv70wQcfaNKkSUpISJDD4dDrr79+xX22b9+uUaNGye12a8CAAcrLy/N7Tp8YXFF+fr5xuVxm3bp15sCBA+buu+823bp1M9XV1ZdcX1RUZJxOp1m2bJkpKyszjz76qAkLCzP79+8PcHL4i68zMWPGDJObm2tKSkpMeXm5mTlzpomKijInTpwIcHL4i68zcUFlZaXp1auXGTt2rJk8eXJgwiIgfJ2J8+fPm+TkZDNx4kSzY8cOU1lZabZv325KS0sDnBz+4utMbNiwwbjdbrNhwwZTWVlp3nrrLRMfH2/mz58f4OTwh8LCQrN48WKzefNmI8kUFBRcdn1FRYXp3LmzWbBggSkrKzMrVqwwTqfTbN26NTCBrwLF4iqkpKSYrKyspp8bGxtNQkKCefrppy+5ftq0aebWW2+9aFtqaqq55557/JoTgePrTDTX0NBgIiIizPr16/0VEQHWkploaGgw6enpZs2aNSYzM5Ni0c74OhPPP/+8SUpKMnV1dYGKiADzdSaysrLMTTfddNG2BQsWmDFjxvg1JwLvaorFwoULzdChQy/aNn36dDNhwgQ/JvMNH4W6grq6Ou3bt0/jx49v2hYSEqLx48dr165dl9xn165dF62XpAkTJvzoegSXlsxEc+fOnVN9fb26d+/ur5gIoJbOxBNPPKHY2FjNnj07EDERQC2ZiTfeeENpaWnKyspSXFychg0bpqVLl6qxsTFQseFHLZmJ9PR07du3r+njUhUVFSosLNTEiRMDkhltSzCcX4baHaCtO3XqlBobGxUXF3fR9ri4OB08ePCS+1RVVV1yfVVVld9yInBaMhPNPfzww0pISPjBAQLBqSUzsWPHDq1du1alpaUBSIhAa8lMVFRU6L333tNdd92lwsJCHT58WPPmzVN9fb2ys7MDERt+1JKZmDFjhk6dOqUbbrhBxhg1NDTo3nvv1SOPPBKIyGhjfuz8sqamRt999506depkU7LvccUCCLCcnBzl5+eroKBA4eHhdseBDTwejzIyMrR69WrFxMTYHQdthNfrVWxsrFatWqXRo0dr+vTpWrx4sV544QW7o8Em27dv19KlS7Vy5Up99NFH2rx5s7Zs2aIlS5bYHQ24JK5YXEFMTIycTqeqq6sv2l5dXa2ePXtecp+ePXv6tB7BpSUzccHy5cuVk5Ojd999VyNGjPBnTASQrzNx5MgRHT16VJMmTWra5vV6JUmhoaE6dOiQ+vfv79/Q8KuWHCfi4+MVFhYmp9PZtG3IkCGqqqpSXV2dXC6XXzPDv1oyE4899pgyMjI0Z84cSdLw4cN19uxZzZ07V4sXL1ZICP8f7kh+7PwyMjKyTVytkLhicUUul0ujR4/Wtm3bmrZ5vV5t27ZNaWlpl9wnLS3tovWS9M477/zoegSXlsyEJC1btkxLlizR1q1blZycHIioCBBfZ2Lw4MHav3+/SktLm1633XabbrzxRpWWlioxMTGQ8eEHLTlOjBkzRocPH24qmZL06aefKj4+nlLRDrRkJs6dO/eD8nCheBpj/BcWbVJQnF/affd4MMjPzzdut9vk5eWZsrIyM3fuXNOtWzdTVVVljDEmIyPDLFq0qGl9UVGRCQ0NNcuXLzfl5eUmOzubx822M77ORE5OjnG5XOa1114zX3zxRdPL4/HY9RbQynydieZ4KlT74+tMHD9+3ERERJj777/fHDp0yLz55psmNjbWPPnkk3a9BbQyX2ciOzvbREREmI0bN5qKigrz9ttvm/79+5tp06bZ9RbQijwejykpKTElJSVGknn22WdNSUmJOXbsmDHGmEWLFpmMjIym9RceN/vQQw+Z8vJyk5uby+Nmg9WKFStMnz59jMvlMikpKWb37t1Nvxs3bpzJzMy8aP2mTZvMwIEDjcvlMkOHDjVbtmwJcGL4my8z0bdvXyPpB6/s7OzAB4ff+Hqc+FcUi/bJ15nYuXOnSU1NNW632yQlJZmnnnrKNDQ0BDg1/MmXmaivrzePP/646d+/vwkPDzeJiYlm3rx55syZM4EPjlb3/vvvX/Lc4MIMZGZmmnHjxv1gn5EjRxqXy2WSkpLMiy++GPDcl+MwhmtpAAAAAKzhHgsAAAAAllEsAAAAAFhGsQAAAABgGcUCAAAAgGUUCwAAAACWUSwAAAAAWEaxAAAAAGAZxQIAAACAZRQLAAAAAJZRLAAAAABYRrEAAAAAYBnFAgAAAIBl/wvd5GRm6rXHngAAAABJRU5ErkJggg==", + "application/vnd.jupyter.widget-view+json": { + "model_id": "c76b6408476946419a07fadff41994b1", + "version_major": 2, + "version_minor": 0 + }, "text/plain": [ - "
" + "Sanity Checking: | | 0/? [00:00" + "Training: | | 0/? [00:00" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, { "name": "stdout", "output_type": "stream", "text": [ - "Relative l2 error PINN with MultiscaleFourierNet 2.72%\n" + "Relative l2 error PINN with MultiscaleFourierNet 18071.59%\n" ] } ], "source": [ "# plot the solution\n", - "pl.plot(multiscale_pinn, title='Solution PINN with MultiscaleFourierNet')\n", + "#pl.plot(multiscale_pinn, title='Solution PINN with MultiscaleFourierNet')\n", "\n", "# sample new test points\n", "pts = pts = problem.spatial_domain.sample(100, 'grid')\n", @@ -467,7 +98477,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.12.3" + "version": "3.12.7" } }, "nbformat": 4, diff --git a/tutorials/tutorial2/tutorial.ipynb b/tutorials/tutorial2/tutorial.ipynb index df5245b..95ec79d 100644 --- a/tutorials/tutorial2/tutorial.ipynb +++ b/tutorials/tutorial2/tutorial.ipynb @@ -16,7 +16,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "id": "ad0b8dd7", "metadata": {}, "outputs": [], @@ -32,6 +32,8 @@ "\n", "import torch\n", "from torch.nn import Softplus\n", + "import matplotlib.pyplot as plt\n", + "plt.style.use('tableau-colorblind10')\n", "\n", "from pina.problem import SpatialProblem\n", "from pina.operator import laplacian\n", @@ -40,7 +42,7 @@ "from pina.trainer import Trainer\n", "from pina.domain import CartesianDomain\n", "from pina.equation import Equation, FixedValue\n", - "from pina import Condition, LabelTensor#,Plotter\n", + "from pina import Condition, LabelTensor\n", "from pina.callback import MetricTracker" ] }, @@ -512,14 +514,20 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": null, "id": "2855cea1", "metadata": {}, "outputs": [], "source": [ - "#plotter.plot_loss(trainer, logy=True, label='Standard')\n", - "#plotter.plot_loss(trainer_feat, logy=True,label='Static Features')\n", - "#plotter.plot_loss(trainer_learn, logy=True, label='Learnable Features')\n" + "trainer_metrics = trainer.callbacks[list_[0]].metrics\n", + "\n", + "loss = trainer_metrics['val_loss']\n", + "epochs = range(len(loss))\n", + "plt.plot(epochs, loss.cpu())\n", + "# plotting\n", + "plt.xlabel('epoch')\n", + "plt.ylabel('loss')\n", + "plt.yscale('log')\n" ] }, { @@ -557,7 +565,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.12.3" + "version": "3.12.7" } }, "nbformat": 4, diff --git a/tutorials/tutorial3/tutorial.ipynb b/tutorials/tutorial3/tutorial.ipynb index ddad985..ac10b41 100644 --- a/tutorials/tutorial3/tutorial.ipynb +++ b/tutorials/tutorial3/tutorial.ipynb @@ -16,7 +16,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "id": "d93daba0", "metadata": {}, "outputs": [], @@ -31,8 +31,9 @@ " !pip install \"pina-mathlab\"\n", " \n", "import torch\n", + "import matplotlib.pyplot as plt\n", + "plt.style.use('tableau-colorblind10')\n", "\n", - "import matplotlib.pylab as plt\n", "from pina.problem import SpatialProblem, TimeDependentProblem\n", "from pina.operator import laplacian, grad\n", "from pina.domain import CartesianDomain\n", diff --git a/tutorials/tutorial4/tutorial.ipynb b/tutorials/tutorial4/tutorial.ipynb index b070796..1d68d61 100644 --- a/tutorials/tutorial4/tutorial.ipynb +++ b/tutorials/tutorial4/tutorial.ipynb @@ -28,7 +28,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "id": "5ae7c0e8", "metadata": {}, "outputs": [], @@ -45,6 +45,7 @@ "import torch \n", "import matplotlib.pyplot as plt \n", "plt.style.use('tableau-colorblind10')\n", + "\n", "from pina.problem import AbstractProblem\n", "from pina.solver import SupervisedSolver\n", "from pina.trainer import Trainer\n",