63 lines
1.6 KiB
Plaintext
63 lines
1.6 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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"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, (ax1, ax2) = plt.subplots(1, 2, figsize=(14, 5))\n",
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"\n",
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"# Histogram of fidelity\n",
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"ax1.hist(data['Entanglement Fidelity'], bins=15, color='coral', alpha=0.7, edgecolor='black')\n",
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"ax1.set_xlabel('Entanglement Fidelity', fontsize=12)\n",
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"ax1.set_ylabel('Frequency', fontsize=12)\n",
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"ax1.set_title('Distribution of Entanglement Fidelity', fontsize=13)\n",
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"ax1.grid(True, alpha=0.3)\n",
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"\n",
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"# Category pie chart\n",
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"category_counts = data['Fidelity Category'].value_counts()\n",
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"colors = ['#2ecc71', '#3498db', '#f39c12', '#e74c3c']\n",
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"ax2.pie(category_counts.values, labels=category_counts.index, autopct='%1.1f%%',\n",
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" colors=colors, startangle=90)\n",
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"ax2.set_title('Fidelity Categories', fontsize=13)\n",
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"\n",
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"plt.tight_layout()\n",
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"plt.savefig(here() / 'output/plots/fidelity_dist.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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