92 lines
15 KiB
Plaintext
92 lines
15 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": 3,
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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, 'T for source 1')"
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]
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},
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"execution_count": 3,
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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 432x288 with 2 Axes>"
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]
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},
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"metadata": {
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"needs_background": "light"
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},
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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_T = './OUTPUT_FILES/out_data_sim.h5'\n",
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"\n",
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"import h5py\n",
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"\n",
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"# source id to be retrieved\n",
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"i_src = 0\n",
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"# dataset name\n",
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"dsetname = 'T_res_merged_inv_0000'\n",
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"\n",
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"# read vel, xi, eta dataset \n",
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"with h5py.File(fpath_T, 'r') as f:\n",
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" T = f['src_{}'.format(i_src)][dsetname][:]\n",
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"\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 T\n",
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"plt.figure()\n",
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"plt.imshow(T[5,:,:], origin='lower', aspect='auto')\n",
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"plt.colorbar()\n",
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"plt.title('T for source {}'.format(i_src))\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",
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"pygments_lexer": "ipython3",
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"version": "3.9.1"
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},
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"vscode": {
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"interpreter": {
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"hash": "02f83e1f4cd9619657a6845405e2dd67c4de23753ba48bca5dce2ebf57b3813a"
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}
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}
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