{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Decay Times in Microseconds</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1257.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.421448</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.931721</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.319997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.960001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.880002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>4.120004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>20.319996</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Decay Times in Microseconds\n",
       "count                  1257.000000\n",
       "mean                      3.421448\n",
       "std                       3.931721\n",
       "min                       0.319997\n",
       "25%                       0.960001\n",
       "50%                       1.880002\n",
       "75%                       4.120004\n",
       "max                      20.319996"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas\n",
    "\n",
    "data = pandas.read_csv('20250212_EC_decays.txt')\n",
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Decay Times in Microseconds</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1257.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.421448</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.931721</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.319997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.960001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.880002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>4.120004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>20.319996</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Decay Times in Microseconds\n",
       "count                  1257.000000\n",
       "mean                      3.421448\n",
       "std                       3.931721\n",
       "min                       0.319997\n",
       "25%                       0.960001\n",
       "50%                       1.880002\n",
       "75%                       4.120004\n",
       "max                      20.319996"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cropped_data = data[data['Decay Times in Microseconds'] >= 0.3]\n",
    "cropped_data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data_range = (0.3, max(data['Decay Times in Microseconds']))\n",
    "bins = np.arange(0.3, max(data['Decay Times in Microseconds']) + 0.5, 0.5)\n",
    "hist = cropped_data.hist(range=data_range, bins=bins)\n",
    "hist[0][0].set_yscale('log')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cropped_data_2 = cropped_data[cropped_data['Decay Times in Microseconds'] <= 7]\n",
    "data_range = (0.3, max(cropped_data_2['Decay Times in Microseconds']))\n",
    "bins = np.arange(0.3, max(cropped_data_2['Decay Times in Microseconds']) + 0.5, 0.5)\n",
    "hist = cropped_data_2.hist(range=data_range, bins=bins)\n",
    "hist[0][0].set_yscale('log')\n",
    "\n",
    "times, bins = np.histogram(cropped_data_2, bins=bins, range=data_range)\n",
    "times = np.log(times)\n",
    "bins = bins[:-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([11.89013872])"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "line = np.polynomial.polynomial.Polynomial.fit(bins, times, deg=1)\n",
    "line.convert()\n",
    "line.roots()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": ".venv",
   "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": "3.12.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}