69 lines
2.0 KiB
Plaintext
69 lines
2.0 KiB
Plaintext
{
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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, (ax1, ax2) = plt.subplots(1, 2, figsize=(14, 6))\n",
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"\n",
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"# Scatter plot: Singles vs Coincidence\n",
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"geometric_mean_singles = np.sqrt(data['Singles Rate A (Hz)'] * data['Singles Rate B (Hz)'])\n",
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"ax1.scatter(geometric_mean_singles, data['Coincidence Rate (Hz)'],\n",
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" alpha=0.6, s=80, c=data['Visibility (%)'], cmap='viridis',\n",
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" edgecolors='black', linewidth=0.5)\n",
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"ax1.set_xlabel('Geometric Mean of Singles Rates (Hz)', fontsize=11)\n",
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"ax1.set_ylabel('Coincidence Rate (Hz)', fontsize=11)\n",
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"ax1.set_title('Coincidence vs Singles Rates', fontsize=12)\n",
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"ax1.grid(True, alpha=0.3)\n",
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"cbar1 = plt.colorbar(ax1.collections[0], ax=ax1)\n",
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"cbar1.set_label('Visibility (%)', fontsize=10)\n",
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"\n",
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"# CAR distribution\n",
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"ax2.hist(data['CAR'], bins=20, color='coral', alpha=0.7, edgecolor='black')\n",
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"ax2.set_xlabel('Coincidence-to-Accidental Ratio (CAR)', fontsize=11)\n",
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"ax2.set_ylabel('Frequency', fontsize=11)\n",
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"ax2.set_title('Distribution of CAR Values', fontsize=12)\n",
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"ax2.grid(True, alpha=0.3)\n",
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"\n",
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"plt.tight_layout()\n",
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"plt.savefig(here() / 'output/plots/coincidence_rates.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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