93 lines
3.2 KiB
Plaintext
93 lines
3.2 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": null,
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"id": "imports",
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"metadata": {},
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"outputs": [],
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"source": [
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"import pandas as pd\n",
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"import matplotlib.pyplot as plt\n",
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"import numpy as np\n",
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"from pyprojroot import here"
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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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"id": "load",
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"metadata": {},
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"outputs": [],
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"source": [
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"data = pd.read_csv(here() / 'data/base/data.csv')"
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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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"id": "plot",
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"metadata": {},
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"outputs": [],
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"source": [
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"fig, axes = plt.subplots(2, 2, figsize=(14, 10))\n",
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"\n",
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"# Group by detector type\n",
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"detector_groups = data.groupby('Detector Type')\n",
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"\n",
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"# Bell parameter by detector\n",
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"ax1 = axes[0, 0]\n",
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"detector_bell = detector_groups['Bell Parameter (S)'].mean().sort_values(ascending=False)\n",
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"ax1.bar(range(len(detector_bell)), detector_bell.values, color='steelblue', edgecolor='black')\n",
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"ax1.set_xticks(range(len(detector_bell)))\n",
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"ax1.set_xticklabels(detector_bell.index, rotation=45)\n",
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"ax1.set_ylabel('Mean Bell Parameter (S)', fontsize=11)\n",
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"ax1.set_title('Bell Parameter by Detector Type', fontsize=12)\n",
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"ax1.grid(True, alpha=0.3, axis='y')\n",
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"\n",
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"# Fidelity by detector\n",
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"ax2 = axes[0, 1]\n",
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"detector_fid = detector_groups['Entanglement Fidelity'].mean().sort_values(ascending=False)\n",
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"ax2.bar(range(len(detector_fid)), detector_fid.values, color='coral', edgecolor='black')\n",
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"ax2.set_xticks(range(len(detector_fid)))\n",
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"ax2.set_xticklabels(detector_fid.index, rotation=45)\n",
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"ax2.set_ylabel('Mean Entanglement Fidelity', fontsize=11)\n",
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"ax2.set_title('Fidelity by Detector Type', fontsize=12)\n",
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"ax2.grid(True, alpha=0.3, axis='y')\n",
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"\n",
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"# Detection efficiency by detector\n",
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"ax3 = axes[1, 0]\n",
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"detector_eff = detector_groups['Detection Efficiency A (%)'].mean().sort_values(ascending=False)\n",
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"ax3.bar(range(len(detector_eff)), detector_eff.values, color='mediumseagreen', edgecolor='black')\n",
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"ax3.set_xticks(range(len(detector_eff)))\n",
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"ax3.set_xticklabels(detector_eff.index, rotation=45)\n",
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"ax3.set_ylabel('Mean Detection Efficiency (%)', fontsize=11)\n",
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"ax3.set_title('Detection Efficiency by Detector Type', fontsize=12)\n",
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"ax3.grid(True, alpha=0.3, axis='y')\n",
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"\n",
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"# Sample counts\n",
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"ax4 = axes[1, 1]\n",
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"detector_counts = data['Detector Type'].value_counts()\n",
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"ax4.bar(range(len(detector_counts)), detector_counts.values, color='mediumpurple', edgecolor='black')\n",
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"ax4.set_xticks(range(len(detector_counts)))\n",
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"ax4.set_xticklabels(detector_counts.index, rotation=45)\n",
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"ax4.set_ylabel('Number of Experiments', fontsize=11)\n",
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"ax4.set_title('Sample Size by Detector Type', fontsize=12)\n",
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"ax4.grid(True, alpha=0.3, axis='y')\n",
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"\n",
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"plt.tight_layout()\n",
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"plt.savefig(here() / 'output/plots/detector_comparison.png', dpi=300, bbox_inches='tight')"
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]
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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",
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"language": "python",
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"name": "python3"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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