{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"pygments_lexer":"ipython3","nbconvert_exporter":"python","version":"3.6.4","file_extension":".py","codemirror_mode":{"name":"ipython","version":3},"name":"python","mimetype":"text/x-python"}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"markdown","source":"Hello everyone!\n\nI have generated a lot of data for this competition and want to share it.\nIt was generated by [this notebook](https://www.kaggle.com/code/kirillka95/gen-grav-wave-sign) which was copied from [Mark's one](https://www.kaggle.com/code/markwijkhuizen/generating-continuous-gravitational-wave-sign-pub) and contains compressed images with height 360 and width from 256 (most common) to 1024.\n\nEach dataset contains 2 folders with noise and signal files to use it with tf.keras.preprocessing.image.ImageDataGenerator or tf.keras.utils.image_dataset_from_directory\n\nTrain tadasets:\n\n256\n\n* [g2n-gen-2](https://www.kaggle.com/datasets/kirillka95/g2n-gen-2): 114k files, snr 30 to 100\n* [g2n-gen-3](https://www.kaggle.com/datasets/kirillka95/g2n-gen-3): 139k files, snr 17 to 125\n* [g2n-256-tr35-85](https://www.kaggle.com/datasets/kirillka95/g2n-256-tr35-85): 126k files, snr 35 to 85\n* [g2n-hard](https://www.kaggle.com/datasets/kirillka95/g2n-hard): 90k files, snr 17 to 70\n* [g2n-super-hard](https://www.kaggle.com/datasets/kirillka95/g2n-super-hard): 60k files, snr 5 to 50\n\n512\n\n* [g2n-512-hard](https://www.kaggle.com/datasets/kirillka95/g2n-512-hard): 240k files, snr 5 to 85 + cluster val data\n\n1024\n\n* [g2n-1024-data](https://www.kaggle.com/datasets/kirillka95/g2n-1024-data): 60k files, snr 35 to 85 + cluster val data\n\n\nCluster validation:\n\n* [g2n-cluster-val](https://www.kaggle.com/datasets/kirillka95/g2n-cluster-val)\n\nTest data (can be used for submission) [notebook](https://www.kaggle.com/kirillka95/g2n-test-data-gen):\n\n* [g2n_512_test_data](https://www.kaggle.com/datasets/kirillka95/g2n-512-test-data)\n* [g2n-test-data](https://www.kaggle.com/datasets/kirillka95/g2n-test-data) (width = 256)\n\n\nThere is detailed description below.                                                                                                           \n\nI hope this data would be helpful for someone.\n\nv2 -> try to make images work","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19"}},{"cell_type":"markdown","source":"# Config","metadata":{}},{"cell_type":"code","source":"import os\nimport cv2\nimport numpy as np\nimport pandas as pd\nimport tensorflow as tf\nfrom tqdm.notebook import tqdm\n\nclass conf:\n    colored = False\n    target_height = 360\n    target_width = 256\n    shape=[target_height, target_width, 3 if colored else 1]\n    color_mode = \"rgb\" if colored else \"grayscale\"\n    batch_size = 16\n    is_interactive = os.environ['KAGGLE_KERNEL_RUN_TYPE'] == 'Interactive'\n    verbose = 1 if is_interactive else 2\n\n\ndef get_gen(path:str):\n    print(\"Load... \" + path)\n    return flow(tf.keras.preprocessing.image.ImageDataGenerator(rescale=1./255), path)\ndef get_gens(path:str, split=0.1):\n    print(\"Load... \" + path)\n    gen = tf.keras.preprocessing.image.ImageDataGenerator(rescale=1./255, validation_split=split)\n    return flow(gen, path, split, False), flow(gen, path, split, True)\ndef flow(gen, path:str, split=0.0, val=False):\n    return gen.flow_from_directory(\n        path,\n        batch_size=conf.batch_size,\n        target_size=(conf.target_height, conf.target_width),\n        class_mode='binary',\n        color_mode=conf.color_mode,\n        subset=(\"training\" if not val else \"validation\") if split > 0 else None,\n    )","metadata":{"execution":{"iopub.status.busy":"2022-12-26T18:52:54.380114Z","iopub.execute_input":"2022-12-26T18:52:54.380674Z","iopub.status.idle":"2022-12-26T18:52:54.394297Z","shell.execute_reply.started":"2022-12-26T18:52:54.380627Z","shell.execute_reply":"2022-12-26T18:52:54.393Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# 256","metadata":{}},{"cell_type":"markdown","source":"[g2n-gen-2](https://www.kaggle.com/datasets/kirillka95/g2n-gen-2) \nContain 2 datasets with mean snr of 56 and 72 and some val data\n\n72 snr hist\n![72.png](attachment:76518911-92fc-4e88-b2ab-e230831653f6.png)\n\n56 snr hist\n![56.png](attachment:444151fc-1a62-4417-92dc-3b25f0a2d217.png)","metadata":{},"attachments":{"444151fc-1a62-4417-92dc-3b25f0a2d217.png":{"image/png":"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"}}},{"cell_type":"code","source":"get_gen(\"/kaggle/input/g2n-gen-2/train_56\")","metadata":{"execution":{"iopub.status.busy":"2022-12-26T18:52:54.396561Z","iopub.execute_input":"2022-12-26T18:52:54.396992Z","iopub.status.idle":"2022-12-26T18:53:09.393951Z","shell.execute_reply.started":"2022-12-26T18:52:54.396957Z","shell.execute_reply":"2022-12-26T18:53:09.392684Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"[g2n-gen-3](https://www.kaggle.com/datasets/kirillka95/g2n-gen-3)\nOne dataset with 130k train data and 9k val data, mean snr = 52\n\nsnr 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.execute_reply":"2022-12-26T18:53:58.647625Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"[g2n-256-tr35-85](https://www.kaggle.com/datasets/kirillka95/g2n-256-tr35-85)\n120k train data and 6k val data, mean snr = 58\n\nsnr 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90k files train only, mean snr = 43\n\nsnr 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2 datasets\n\nI tried to give nn only low snr.\n\ntrain_hard25: 30k files, mean snr = 13, min = 5, max = 25\n\nsnr hist\n![d25.png](attachment:519b06e0-58a9-43ff-82f9-e67d115a35fb.png)\n\ntrain_hard50: 30k files, mean snr = 35, min = 25, max = 50\n\nsnr hist\n![d50.png](attachment:39e13674-9d79-482e-9be1-3e255566f496.png)","metadata":{},"attachments":{"519b06e0-58a9-43ff-82f9-e67d115a35fb.png":{"image/png":"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512","metadata":{}},{"cell_type":"markdown","source":"[g2n-512-hard](https://www.kaggle.com/datasets/kirillka95/g2n-512-hard) 3 datasets + clust val data\n\ntrain: 90k files, mean snr = 58, min = 35, max = 85\n\nsnr hist\n![35-85.png](attachment:ae17c958-0416-4129-9159-21801f32b706.png)\n\n\ntrain2: 60k files, mean snr = 56, min = 30, max = 85\n\nsnr hist\n![30-85.png](attachment:d0c58932-c2a7-4b77-bee7-a1e208cb47e6.png)\n\nhard: 60k files, mean snr = 31, min = 12, max = 50\n\nsnr hist\n![5h.png](attachment:0ebcad98-5075-4ac4-8cdb-4cc8d3f824c2.png)\n\nval data contains one dataset called 58 (mean snr of it) to validate train data, and cluster datasets where digit means max snr in 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1024","metadata":{}},{"cell_type":"markdown","source":"[g2n-1024-data](https://www.kaggle.com/datasets/kirillka95/g2n-1024-data) 30k files train data + cluster validation\n\ntrain mean snr = 58, min = 35, max = 85\n\nsnr hist\n![35-85.png](attachment:477461c7-e033-42ec-ae8c-f059ba643177.png)\n\nval data also contains one dataset called 58 (mean snr of it) to validate train data, and cluster datasets where digit means max snr in 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5W9du2a9fu8jsBzd3e3/vz7+eefM5VduXKlUaFCBWv5Hj16ZNvmvF6jI/0wPz8vHKlz5MgRw9fX11ruzjvvNA4fPmxXJjU11Zg+fbr1c0iS8cILL2R7Xtvr9/T0NCpXrmz89NNPmcpt2rTJ7jmdOHGiQ9cFAKUFAR4AIF/uvffeTNNaLBaLERQUZDz++OPGBx98YOzcudNISUlx+JiO/PJgO6WxWbNmRmJiYrbHe/HFF+3a50iAJ92Y8hkbG5vtcR988EFr2WHDhjl8fdnZsWOH9Xg5TUk2S4CXMcD94Ycfsj3esWPH7KYRFsQvXNkFeGvXrrVu9/DwMI4fP56priMB3pEjR+x+8Zw5c2a2bbl06ZJRt25da9knn3wyy3K5BXirVq2y7n/00UdzeQayl5qaatx6663WY3333Xc5lo+OjjaqVq1qDSSioqLyfW7DyFuAt3HjRmtoKcl47bXXnDp3uoULF1qP+eqrr2ZbrrACvLvvvttapk2bNkZSUlK2x7P9jKpQoYIRExPjUDuyUxAB3rx58+yOcfLkyUxlcvssGjlypHX/7Nmz89WOdM4EeJIMb2/vXKewGkbeA7z0gDOnad+2n0mSjPXr1xfINRZngBcSEmItU79+/Ryn37/33nt2z1V2gXrGAG/v3r3ZHnPmzJnWsg0bNnTougCgtGARCwBAvixYsED9+vWz22YYhg4ePKivv/5aL730klq1aiV/f389/PDD2rBhg9PnvHz5spYvX259/M477+Q4JWfy5Mk5Tq3KzrRp0+Tr65vt/sGDB1u/t51imV+tWrWyTtVbv36908crbOnT0SSpb9++6t27d7Zl69atq9dee836+NNPP7VO6yxoXbt2td58Pjk5WW+++Wa+jjN79mzrDfXvuOMOPffcc9mWrVixot566y3r4wULFujq1at5PmdMTIz1+8qVK+e5frpVq1bp8OHDkm5MC834Hs2oWrVq1hv5p6SkaPHixfk+tyMSExP1119/aezYserRo4euX78uSWrXrp1dP3HGgw8+aH3/rlu3rkCO6aj9+/dr8+bN1sczZ86Uh4dHtuWnTJmigIAASTf6wIIFCwq9jbnJOCXx8uXLeT5GQfXngvD888/naaX0vHj00UfVpk2bbPd37dpVDzzwgPXx7NmzC6UdReXKlStatGiR9fHbb7+d4xTWl156SY0aNZIkpaWl2d0iIDtDhw5V06ZNs90fEhJiXS354MGDdn0NAEo7AjwAQL74+vrqu+++U3h4uLp165btvYTi4+O1aNEide7cWffdd1++fhlMt337diUnJ0u6ETx06dIlx/Lly5fXfffdl6dzlCtXTvfee2+OZZo3b2793nb1w5wcOnRICxYs0KRJk/TKK6/ohRde0PPPP2/9Sg99Ll68aF1B16xsw1jbMDM7Tz75pLV/REdHO3QPt/yaPHmy9fv58+dbw6y8sF2ZNX1Vxpz069dPlSpVkiQlJSVlu3JnTmzvjfXdd9/p33//zfMxJNndk3HQoEEO1encubP1+61bt+brvFnZtGlTplUvvb291bRpU02ePFlJSUny8PDQ4MGD9dNPP8nHx8fhY//555+aP3++Jk6cqJEjR9q9l1566SXra/bXX3/ZrW5a2GzfG3fccYfdZ0VWfHx89Mgjj2RZv7hk/ONFbGxsno9h259nz55tvVdacXj44YcL7dghISG5lnniiSes35vh9XXG9u3bravqBgQE5Pqz0sXFxe5nhCPXP2DAgBz3ly9f3nofXsMwdOLEiVyPCQClBYtYAACc0qtXL/Xq1Uvnz5/Xxo0btX37dv3++++KiIhQXFycXdmVK1eqffv2+vXXX1W+fPk8n+uPP/6wft+yZUuHbkDeqlUrff311w6fIygoSO7u7jmWuemmm6zf5/bX//DwcI0dO1YREREOt+HChQtFcrPz/Dh9+rRduJTT6JN0lStXVoMGDaw3VN+zZ0+WN1QvCG3btlXPnj31008/KTU1VePHj8/TqCbDMOz6mSPX5+7urjvvvFOrV6+WdOP67rnnnjy1u3Xr1goMDFRUVJROnjypRo0a6cknn9S9996rVq1a5TiKy5ZteLhs2TJt2rQp1zq2IwaLOjwePHiwZsyY4dDN7aUboeyUKVN06NAhh8qnpKTo6tWrqlixojPNdJjt+9yRviPd6LMfffSRpBt9p7hlDOzyM4r5wQcf1IQJE5SWlqbw8HA1btxYgwcPVs+ePdWoUaMiW6zD3d1dTZo0KZRjWywWtWrVKtdyd911l/X7c+fOKTo62qkFdoqTbf++8847rSPhcmK7cFFERIQMw8jx9Xfk9crLz2AAKE0I8AAABaJy5coaMGCA9a/n169f144dOzR37lx99dVX1qlyf//9t15//XV9+OGHeT7H+fPnrd87GnDVqlUrT+dwZEU724Av/bqyMmHCBE2cODFP55fyN+KlqNi+Bl5eXg5Pj6tbt641wLtw4UKhtC3d5MmTtXr1ahmGoUWLFiksLMzhX+KvXr2qlJQU6+M6deo4VK9u3brW7/Nzfe7u7vr666/Vp08fxcXF6cKFC3rnnXes08SDg4N19913q1evXmrTpk22vwDbrupqO9XNUc6MkM2oRo0adlN4k5OTderUKe3atcv6HH366ac6fPiwVq1aJS8vr2yPZRiGhgwZorlz5+a5HbGxsUUW4Nm+P4qq7xS0jFPA00eX5sVtt92mt99+W6NGjZJhGDpw4IBeffVVvfrqq6pYsaLatGmjDh066L777iu06a3SjSnujoRM+T22I3+Iqly5ssqVK6dr165JutFHSmqA52z/Tk5OVmxsbI6hcF5/Btt+XgNAaccUWgBAoXBzc1O7du00Z84cbdq0yW5a1uzZs5WYmJjnY9qO6PP29naoTk73sstKQY0MWbt2rV14d9ddd+nzzz9XRESELly4oGvXrsm4sZiUDMNQhw4drGWLcspfXtm+BnmZ8mhbtrADyhYtWliDo7S0NI0dO9bhuhlHjTp6jQVxfR06dNDevXsVEhJiF2Zdu3ZNW7du1ZQpU9SuXTs1bNhQK1asyPIY+bn/nq2cAum8uvXWWzVz5kzr1+eff64ff/xRJ0+e1LRp06wjaNevX68RI0bkeKzZs2fbhXf33HOP5s+fr7/++kuXL19WUlKS3fvJNlwoyvdTft4fRfnecER60C5Jrq6u1nv05dXIkSO1YcMGdenSxe5z9fLlywoPD9err76qoKAgde3aVX/99ZfT7c5KTqGwsxz9GSSZ7zXOL2f7t5T79RfV6EwAKIkI8AAAha5NmzZ2N6i/du2adu3alefj2IZxCQkJDtWJj4/P83kKwjvvvGP9fvDgwdq2bZuefvpp3XHHHbrpppvk6elpV76k/FJn+xrk5bm1LZuf6dN59eabb1oDou+//97h/pYx8HX0Ggvq+m655RbNnz9f58+f1+rVq/XGG2+oU6dOdkHEoUOH1K9fP7333nuZ6tv+srxnzx67UMuRL0fv6egMLy8vjR492m6RkU8//VQbN27Mts67775r/X7ixIn66aefFBISosaNG8vf3z/TFOPiej/l5/1R1O+N3OzcudP6ffPmzTN9VuVFhw4dtG7dOkVHR2vRokV68cUX1aJFC7vbH6xfv16tWrXStm3bnGp3UXP0Z5BU8K9xcf2Rx9n+LZmjjwNASUWABwAoEhnvCRYdHZ3nY9iOBDl16pRDdRwtV5BSU1Ot9x5zcXHR1KlTcx1VcPLkyaJomtNsp8wmJiY6POXPNhjK74ievGjUqJHdIg5vvPGGQ/X8/Pzspmc5+roU9PX5+PioR48emjRpkn755RddvHhRS5YssZsKHBYWptOnT9vVq1q1qvX7s2fPOt2OwjRmzBi1aNHC+nj06NFZlouKirIuRuLv76+wsLAcjxsTE1OgU4Hzwvb9UVx9xxnR0dF2Qdrdd99dIMetWrWqBg4cqA8++EC///67zp49q/fff996L7PExEQ988wzBXKuonL58uVMI3azkj7iOl1Wr7HtNF9HRsE6O9I2v5zt3x4eHgR4AOAEAjwAQJHIeJP6/IzquOOOO6zf79q1S4Zh5Frnt99+y/N5nHXhwgXrarlVqlRRlSpVciy/b98+U9z7yhE1a9a0u57t27fnWufChQt2iw7YhjaFacKECdZfjNesWaPNmzfnWsdisdj1M0eu7/r163Yj/Arj+ry8vPTggw9q48aN1pAuOTlZP//8s10525vqm31Ek6urq9566y3r499++y3LqcG29/Vr2LBhrovMbN261aHPhsKYqme76qwjfSdjuaJ6b2Tno48+srunWGGt4Fq5cmW99NJL+v77763b/v77b/3zzz+Fcr7CYBiG3WjF7NguLFO1alXVqFEjUxnbe8JdvHgxx+MlJyc7tIhLYffv3377zaHVhW37d/PmzZkiCwBOIMADABSJvXv32j2uXbt2no/Rpk0b61S56Oho/fLLLzmWj4uLy/ZeYYXJdnqYI/f6mzVrVmE2p8B16tTJ+v28efNyLT9v3jzrlK8aNWooKCiosJpmp169enryySetjx0dhde5c2fr9/Pnz881DFqxYoX1l+5y5crZrTpZ0CpVqmS3quO5c+fs9vfp08f6/Zdffmk38seMunbtanc9kyZNylTG9v3kyLRFR99Ptn9UKKgb4dv2nYiICP355585lk9ISNDChQuzrF/Utm/fbjf1v3v37mrZsmWhnrNt27Z2i2Rk7M9S4bxOBcWRFc6/+uor6/e2n522bBd6sF0FOysrV6506H1dGM9bmzZtrH98O3/+vMLDw3Msn5aWZnfvyuLs3wBQGhDgAQDy7L333tO6descLp+QkKApU6ZYH1etWtVulJOjKlWqpPvuu8/6+NVXX1VSUlK25ceNG1csU41uuukm60p6V69etU6nzcq2bdtKXIBnO9Vt+fLlmUaB2Tpx4oT+97//2dUtyhEYY8eOtf7CuWXLlhzbmu7pp5+2hkZ79uzR559/nm3ZK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Cluster validation","metadata":{}},{"cell_type":"markdown","source":"[g2n-cluster-val](https://www.kaggle.com/datasets/kirillka95/g2n-cluster-val)\n\nContains 5 datasets where each dataset have bounded snrs. \n* clust_25   min snr = 00,  max snr = 25\n* clust_50   min snr = 25,  max snr = 50\n* clust_75   min snr = 50,  max snr = 75\n* clust_99   min snr = 75,  max snr = 99\n* clust_100+ min snr = 100, max snr = 250\n\nresults for clust_99 and clust_100+ are very similar, so I rearly use clust_100+. It is too easy dataset.\n\nLets test it on a small simple model:","metadata":{}},{"cell_type":"code","source":"val_25 = get_gen(\"/kaggle/input/g2n-cluster-val/clust_25\")\nval_50 = get_gen(\"/kaggle/input/g2n-cluster-val/clust_50\")\nval_75 = get_gen(\"/kaggle/input/g2n-cluster-val/clust_75\")\nval_99 = get_gen(\"/kaggle/input/g2n-cluster-val/clust_99\")\nval_100p = get_gen(\"/kaggle/input/g2n-cluster-val/clust_100+\")\n\nmodel = tf.keras.models.load_model(\"/kaggle/input/g2n-simple-model/simple_model.h5\")\n\ne25 = model.evaluate(val_25, steps=len(val_25), verbose=conf.verbose)\ne50 = model.evaluate(val_50, steps=len(val_50), verbose=conf.verbose)\ne75 = model.evaluate(val_75, steps=len(val_75), verbose=conf.verbose)\ne99 = model.evaluate(val_99, steps=len(val_99), verbose=conf.verbose)\ne100p = model.evaluate(val_100p, steps=len(val_100p), verbose=conf.verbose)","metadata":{"execution":{"iopub.status.busy":"2022-12-26T18:55:53.922798Z","iopub.execute_input":"2022-12-26T18:55:53.923183Z","iopub.status.idle":"2022-12-26T19:02:38.938171Z","shell.execute_reply.started":"2022-12-26T18:55:53.923149Z","shell.execute_reply":"2022-12-26T19:02:38.936956Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"print(f\"        25      50      75      99      100+\")\nprint(f\"Loss  : {e25[0]:.4f}  {e50[0]:.4f}  {e75[0]:.4f}  {e99[0]:.4f}  {e100p[0]:.4f}\")\nprint(f\"Auc   : {e25[1]:.4f}  {e50[1]:.4f}  {e75[1]:.4f}  {e99[1]:.4f}  {e100p[1]:.4f}\")","metadata":{"execution":{"iopub.status.busy":"2022-12-26T19:02:38.939906Z","iopub.execute_input":"2022-12-26T19:02:38.940278Z","iopub.status.idle":"2022-12-26T19:02:38.946671Z","shell.execute_reply.started":"2022-12-26T19:02:38.940243Z","shell.execute_reply":"2022-12-26T19:02:38.945723Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# Test data","metadata":{}},{"cell_type":"markdown","source":"Test data for submission. Genererated by this [notebook](https://www.kaggle.com/kirillka95/g2n-test-data-gen)\n\n* [g2n_512_test_data](https://www.kaggle.com/datasets/kirillka95/g2n-512-test-data)\n* [g2n-test-data](https://www.kaggle.com/datasets/kirillka95/g2n-test-data) (width = 256)\n\nUse:","metadata":{}},{"cell_type":"code","source":"hs = []\nls = []\nfor i in tqdm(range(7975)):\n    h = cv2.imread(f'/kaggle/input/g2n-test-data/h1/H_{i}.png', (3 if conf.colored else -1)) / 255\n    h = np.expand_dims(h, axis=0)\n    ph = model.predict(h)\n    hs.append(ph)\n    l = cv2.imread(f'/kaggle/input/g2n-test-data/l1/L_{i}.png', (3 if conf.colored else -1)) / 255\n    l = np.expand_dims(l, axis=0)\n    lh = model.predict(l)\n    ls.append(lh)\n    \nhs = np.array(hs)\nls = np.array(ls)\npreds = (hs + ls) / 2\nsample_submission = pd.read_csv('/kaggle/input/g2net-detecting-continuous-gravitational-waves/sample_submission.csv')\nsample_submission.target = preds.reshape(-1)\nsample_submission.to_csv('submission.csv', index=False)\nsample_submission.head(15)","metadata":{"execution":{"iopub.status.busy":"2022-12-26T19:02:38.94949Z","iopub.execute_input":"2022-12-26T19:02:38.949859Z","iopub.status.idle":"2022-12-26T19:04:49.739907Z","shell.execute_reply.started":"2022-12-26T19:02:38.949826Z","shell.execute_reply":"2022-12-26T19:04:49.738095Z"},"trusted":true},"execution_count":null,"outputs":[]}]}