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Merge pull request #1 from deepskies/issue/jupyter_install
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Issue/jupyter install
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@beckynevin
beckynevin authored Oct 25, 2024
2 parents 1036423 + a8180aa commit 4485b7f
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190 changes: 190 additions & 0 deletions notebooks/fig1.ipynb
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{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"id": "09fcc173-1f36-4f32-8b22-d3f4c4307fc7",
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"import pandas as pd\n",
"import matplotlib.pyplot as plt\n",
"from data.data import MyDataLoader, DataPreparation"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "ab352b73-3515-42b7-9d92-f49a5e8485c8",
"metadata": {},
"outputs": [],
"source": [
"# lookup dict\n",
"noise_to_sigma = {\n",
" 'low': 0.01,\n",
" 'medium': 0.05,\n",
" 'high': 0.10,\n",
" 'vhigh': 1.00\n",
"}\n",
"inject_x_image = {'low': 0.01/32, 'medium': 0.05/32, 'high': 0.10/32}\n",
"noise = 'high'"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "4760fcec-b43c-477d-abaf-b8407ba257b4",
"metadata": {},
"outputs": [],
"source": [
"data = DataPreparation()\n",
"size_df = 1"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "76baf2a1-fb9c-43be-8deb-51e5e71d48ac",
"metadata": {},
"outputs": [],
"source": [
"fig = plt.figure(figsize=(10,4))\n",
"\n",
"data.params = np.array([[ 0.2, 0]])\n",
"\n",
"\n",
"ax1 = fig.add_subplot(141)\n",
"data.simulate_data(data.params,\n",
" noise_to_sigma[noise], \n",
" simulation_name = 'linear_homoskedastic',\n",
" x=np.linspace(0, 10, 100),\n",
" inject_type = 'predictive'\n",
" )\n",
"ax1.plot(np.array(data.input), np.array(data.output).flatten(), color = 'black')\n",
"ax1.scatter(np.array(data.input), np.array(data.output), color = 'black', s=0.25, zorder=-100)\n",
"ax1.set_title(r'$\\sigma_y = $'+str(noise_to_sigma[noise]))\n",
"ax1.set_xlabel(r'$x$', size=15)\n",
"ax1.set_ylabel(r'$y$', size=15, rotation=90)\n",
"ax1.set_aspect(5, adjustable='box')\n",
"\n",
"\n",
"ax0 = fig.add_subplot(142)\n",
"data.simulate_data(data.params,\n",
" noise, \n",
" simulation_name = 'linear_homoskedastic',\n",
" x=np.linspace(0, 10, 100),\n",
" inject_type = 'feature',\n",
" vary_sigma = True\n",
" )\n",
"ax0.plot(np.array(data.input).flatten(), np.array(data.output).flatten(), color = 'black')\n",
"ax0.scatter(np.array(data.input), np.array(data.output), color = 'black', s=0.25, zorder=-100)\n",
"#ax0.set_title('Input injection', size=15)\n",
"ax0.set_title(r'$\\sigma_x = $'+str(round(noise_to_sigma[noise] / data.params[0][0],3))+\n",
" r'$\\rightarrow \\sigma_y = $'+str(round(noise_to_sigma[noise],3)))\n",
"ax0.set_xlabel(r'$x$', size=15)\n",
"\n",
"ax0.set_aspect(5, adjustable='box')\n",
"#ax.set_aspect('equal', adjustable='box')\n",
"\n",
"# now doing 2D\n",
"data.params = np.array([[ 0.005, 5, 0.69598183]])\n",
"image_size = 32\n",
"\n",
"\n",
"\n",
"ax3 = fig.add_subplot(143)\n",
"\n",
"image_p, y_p = data.simulate_data_2d(\n",
" size_df,\n",
" data.params,\n",
" inject_type=\"predictive\",\n",
" sigma=noise_to_sigma[noise]\n",
" )\n",
"ax3.imshow(image_p[0])\n",
"ax3.annotate('y = ' + str(round(y_p[0], 2)),\n",
" xy=(0.2, 0.05),\n",
" xycoords='axes fraction',\n",
" color='white',\n",
" size=15)\n",
"ax3.set_title(r'$\\sigma_y = $' + str(noise_to_sigma[noise]))\n",
"\n",
"#ax3.set_title('Output injection', size=15)\n",
"\n",
"\n",
"ax2 = fig.add_subplot(144)\n",
"\n",
"image_f, y_f = data.simulate_data_2d(\n",
" size_df,\n",
" data.params,\n",
" inject_type=\"feature\",\n",
" sigma=inject_x_image[noise]\n",
" )\n",
"ax2.imshow(image_f[0])\n",
"ax2.annotate('y = ' + str(round(y_f[0], 2)),\n",
" xy=(0.2, 0.05),\n",
" xycoords='axes fraction',\n",
" color='white',\n",
" size=15)\n",
"ax2.set_title(r'$\\sigma_x = $' + str(round(noise_to_sigma[noise]/32, 3)) \n",
" + r'$\\rightarrow \\sigma_y = $' + str(noise_to_sigma[noise]))\n",
"ax1.annotate('(a)', xy=(0.02, 0.88), xycoords='axes fraction', size=15, color='black')\n",
"ax0.annotate('(b)', xy=(0.02, 0.88), xycoords='axes fraction', size=15, color='black')\n",
"ax2.annotate('(d)', xy=(0.02, 0.88), xycoords='axes fraction', size=15, color='white')\n",
"ax3.annotate('(c)', xy=(0.02, 0.88), xycoords='axes fraction', size=15, color='white')\n",
"\n",
"\n",
"# Add a shared title for the first two subplots\n",
"fig.text(0.31, 0.83, '0D Data', ha='center', fontsize=15)\n",
"\n",
"# Draw a bracket pointing to the first two subplots\n",
"x_bracket = [0.15, 0.15, 0.45, 0.45] # x-coordinates of the bracket\n",
"y_bracket = [0.77, 0.81, 0.81, 0.77] # y-coordinates of the bracket\n",
"plt.plot(x_bracket, y_bracket, color='black', lw=1.5, transform=fig.transFigure, clip_on=False)\n",
"\n",
"# Add a shared title for the first two subplots\n",
"fig.text(0.71, 0.83, '2D Data', ha='center', fontsize=15)\n",
"\n",
"# Draw a bracket pointing to the first two subplots\n",
"x_bracket = [0.56, 0.56, 0.86, 0.86] # x-coordinates of the bracket\n",
"y_bracket = [0.77, 0.81, 0.81, 0.77] # y-coordinates of the bracket\n",
"plt.plot(x_bracket, y_bracket, color='black', lw=1.5, transform=fig.transFigure, clip_on=False)\n",
"\n",
"\n",
"\n",
"\n",
"#plt.tight_layout()\n",
"plt.savefig('../../../Desktop/design_'+str(noise)+'.png', dpi=1000)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "8df66b56-e524-405a-a7bd-c81c4a391594",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"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": "3.10.12"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
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