123 lines
51 KiB
Plaintext
123 lines
51 KiB
Plaintext
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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"Text(0.5, 1.0, 'vel_true')"
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]
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},
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"execution_count": 1,
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"metadata": {},
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"output_type": "execute_result"
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},
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{
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"data": {
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"image/png": "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"text/plain": [
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"<Figure size 640x480 with 2 Axes>"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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},
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{
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"data": {
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"image/png": "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"text/plain": [
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"<Figure size 640x480 with 2 Axes>"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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}
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],
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"source": [
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"# final model\n",
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"fpath_final_model = './OUTPUT_FILES/final_model.h5'\n",
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"fpath_true_model = \"./test_model_true.h5\"\n",
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"\n",
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"import h5py\n",
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"\n",
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"# read vel, xi, eta dataset \n",
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"with h5py.File(fpath_final_model, 'r') as f:\n",
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" vel = f['vel'][:]\n",
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" xi = f['xi'][:]\n",
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" eta = f['eta'][:]\n",
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" #zeta = f['zeta'][:]\n",
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"\n",
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"# read true model\n",
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"with h5py.File(fpath_true_model, 'r') as f:\n",
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" vel_true = f['vel'][:]\n",
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" xi_true = f['xi'][:]\n",
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" eta_true = f['eta'][:]\n",
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" #zeta_true = f['zeta'][:]\n",
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"# plot\n",
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"import matplotlib.pyplot as plt\n",
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"import numpy as np\n",
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"\n",
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"# plot vel\n",
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"plt.figure()\n",
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"plt.imshow(vel[5,:,:], origin='lower', aspect='auto')\n",
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"plt.colorbar()\n",
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"plt.title('vel')\n",
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"\n",
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"# plot xi\n",
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"#plt.figure()\n",
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"#plt.imshow(xi[:,:,10], origin='lower', aspect='auto')\n",
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"#plt.colorbar()\n",
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"#plt.title('xi')\n",
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"#\n",
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"## plot eta\n",
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"#plt.figure()\n",
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"#plt.imshow(eta[:,:,10], origin='lower', aspect='auto')\n",
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"#plt.colorbar()\n",
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"#plt.title('eta')\n",
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"\n",
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"# plot vel_true\n",
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"plt.figure()\n",
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"plt.imshow(vel_true[5,:,:], origin='lower', aspect='auto')\n",
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"plt.colorbar()\n",
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"plt.title('vel_true')\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3.9.1 64-bit ('3.9.1')",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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|
|
"nbconvert_exporter": "python",
|
||
|
|
"pygments_lexer": "ipython3",
|
||
|
|
"version": "3.10.8"
|
||
|
|
},
|
||
|
|
"vscode": {
|
||
|
|
"interpreter": {
|
||
|
|
"hash": "02f83e1f4cd9619657a6845405e2dd67c4de23753ba48bca5dce2ebf57b3813a"
|
||
|
|
}
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"nbformat": 4,
|
||
|
|
"nbformat_minor": 2
|
||
|
|
}
|