mirror of
https://github.com/c-sooyoung/lemon-sandbox.git
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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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"id": "090d6ad2",
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"metadata": {},
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"outputs": [],
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"source": [
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"import os\n",
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"import numpy as np\n",
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"from matplotlib import pyplot as plt"
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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": 7,
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"id": "e8c7045b",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"image/png": 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truncated
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"text/plain": [
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"<Figure size 700x300 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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"train_x = np.load(\"./train_X_8keV.npy\")[1:].T\n",
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"train_y = np.load(\"./train_Y_8keV.npy\")[1:].T\n",
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"\n",
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"xparams = [\n",
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" 'defocus [A]',\n",
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" 'layers',\n",
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" 'thickness [A]'\n",
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"]\n",
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"\n",
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"sort_index = np.argsort(train_y)\n",
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"\n",
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"x = train_x[:,sort_index]\n",
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"y = train_y[sort_index]\n",
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"\n",
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"EPSILON = 1e-5 # choose best according to plot\n",
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"\n",
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"y_scaled = np.log(-y + y[-1] + EPSILON)\n",
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"\n",
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"fig, ax = plt.subplots(1, 2, figsize=(7,3))\n",
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"ax[0].plot(y, 'k')\n",
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"ax[1].plot(y_scaled, 'k')\n",
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"plt.show()"
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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": 8,
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"id": "adeb047c",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"image/png": "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 truncated
|
||||||
|
"text/plain": [
|
||||||
|
"<Figure size 950x350 with 3 Axes>"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
"metadata": {},
|
||||||
|
"output_type": "display_data"
|
||||||
|
}
|
||||||
|
],
|
||||||
|
"source": [
|
||||||
|
"# Projection plots\n",
|
||||||
|
"\n",
|
||||||
|
"cut = 200 # set to -1 to get all points\n",
|
||||||
|
"\n",
|
||||||
|
"fig, axs = plt.subplots(1, len(x), figsize=(3*len(x)+0.5, 3.5), sharey=True)\n",
|
||||||
|
"\n",
|
||||||
|
"for i, axi in enumerate(axs):\n",
|
||||||
|
" axi.scatter(x[i][cut:], -y[cut:], c=y_scaled[cut:], cmap='coolwarm')\n",
|
||||||
|
" axi.set_xlabel(xparams[i])\n",
|
||||||
|
"\n",
|
||||||
|
"axs[0].set_ylabel('$- \\\\log (\\ \\\\mathtt{fourier\\_error}\\ )$')\n",
|
||||||
|
"\n",
|
||||||
|
"plt.tight_layout()\n",
|
||||||
|
"plt.show()"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "code",
|
||||||
|
"execution_count": 9,
|
||||||
|
"id": "02c25124",
|
||||||
|
"metadata": {},
|
||||||
|
"outputs": [
|
||||||
|
{
|
||||||
|
"data": {
|
||||||
|
"image/png": "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 truncated
|
||||||
|
"text/plain": [
|
||||||
|
"<Figure size 950x950 with 9 Axes>"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
"metadata": {},
|
||||||
|
"output_type": "display_data"
|
||||||
|
}
|
||||||
|
],
|
||||||
|
"source": [
|
||||||
|
"# Cross-correlation plots\n",
|
||||||
|
"\n",
|
||||||
|
"fig, axs = plt.subplots(len(x), len(x), figsize=(3*len(x)+0.5, 3*len(x)+0.5))\n",
|
||||||
|
"\n",
|
||||||
|
"for i, axi in enumerate(axs):\n",
|
||||||
|
" for j, axij in enumerate(axi):\n",
|
||||||
|
" axij.scatter(\n",
|
||||||
|
" x[i], x[j],\n",
|
||||||
|
" c = y_scaled, # color\n",
|
||||||
|
" cmap = 'coolwarm',\n",
|
||||||
|
" s = 20, # size\n",
|
||||||
|
" alpha = 0 if i == j else 1 # make diagonal transparent\n",
|
||||||
|
" )\n",
|
||||||
|
" if i == len(x)-1:\n",
|
||||||
|
" axij.set_xlabel(xparams[j])\n",
|
||||||
|
" else:\n",
|
||||||
|
" axij.set_xticks([])\n",
|
||||||
|
" if j == 0:\n",
|
||||||
|
" axij.set_ylabel(xparams[i])\n",
|
||||||
|
" else:\n",
|
||||||
|
" axij.set_yticks([])\n",
|
||||||
|
"\n",
|
||||||
|
"plt.tight_layout()\n",
|
||||||
|
"plt.show()\n",
|
||||||
|
"\n"
|
||||||
|
]
|
||||||
|
}
|
||||||
|
],
|
||||||
|
"metadata": {
|
||||||
|
"kernelspec": {
|
||||||
|
"display_name": "lemon",
|
||||||
|
"language": "python",
|
||||||
|
"name": "python3"
|
||||||
|
},
|
||||||
|
"language_info": {
|
||||||
|
"codemirror_mode": {
|
||||||
|
"name": "ipython",
|
||||||
|
"version": 3
|
||||||
|
},
|
||||||
|
"file_extension": ".py",
|
||||||
|
"mimetype": "text/x-python",
|
||||||
|
"name": "python",
|
||||||
|
"nbconvert_exporter": "python",
|
||||||
|
"pygments_lexer": "ipython3",
|
||||||
|
"version": "3.12.12"
|
||||||
|
}
|
||||||
|
},
|
||||||
|
"nbformat": 4,
|
||||||
|
"nbformat_minor": 5
|
||||||
|
}
|
||||||
Reference in new issue
Block a user