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qu-en/notebooks/tab_laboratories.ipynb
Jalmari Tuominen 60e6abc831 Initial
2025-11-27 08:59:35 +02:00

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{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"id": "imports",
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"from pyprojroot import here"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "load",
"metadata": {},
"outputs": [],
"source": [
"data = pd.read_csv(here() / 'data/base/data.csv')"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "create-table",
"metadata": {},
"outputs": [],
"source": [
"# Group by detector type and calculate statistics\n",
"detector_stats = data.groupby('Detector Type').agg({\n",
" 'Study ID': 'count',\n",
" 'Bell Parameter (S)': ['mean', 'std'],\n",
" 'Entanglement Fidelity': ['mean', 'std'],\n",
" 'Detection Efficiency A (%)': 'mean'\n",
"}).round(3)\n",
"\n",
"detector_stats.columns = ['N', 'Bell S (mean)', 'Bell S (std)', \n",
" 'Fidelity (mean)', 'Fidelity (std)', 'Efficiency (%)']"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "save-latex",
"metadata": {},
"outputs": [],
"source": [
"# Convert to LaTeX\n",
"latex_table = r'''\\begin{table}[h]\n",
"\\centering\n",
"\\caption{Performance Metrics by Detector Type}\n",
"\\label{tab:laboratories}\n",
"\\begin{tabular}{lcccccc}\n",
"\\toprule\n",
"\\textbf{Detector} & \\textbf{N} & \\textbf{Bell S} & \\textbf{$\\sigma$} & \\textbf{Fidelity} & \\textbf{$\\sigma$} & \\textbf{Eff (\\%)} \\\\\n",
"\\midrule\n",
"'''\n",
"\n",
"for idx, row in detector_stats.iterrows():\n",
" latex_table += f\"{idx} & {int(row['N'])} & {row['Bell S (mean)']:.3f} & {row['Bell S (std)']:.3f} & \"\n",
" latex_table += f\"{row['Fidelity (mean)']:.4f} & {row['Fidelity (std)']:.4f} & {row['Efficiency (%)']:.1f} \\\\\\\\\\n\"\n",
"\n",
"latex_table += r'''\\bottomrule\n",
"\\end{tabular}\n",
"\\end{table}\n",
"'''\n",
"\n",
"# Save to file\n",
"with open(here() / 'output/tables/laboratories.tex', 'w') as f:\n",
" f.write(latex_table)\n",
"\n",
"print(\"Laboratories table saved\")"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
}
},
"nbformat": 4,
"nbformat_minor": 5
}