325 lines
56 KiB
Plaintext
325 lines
56 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 11,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/html": [
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"<div>\n",
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"<style scoped>\n",
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" .dataframe tbody tr th:only-of-type {\n",
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" vertical-align: middle;\n",
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" }\n",
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"\n",
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" .dataframe tbody tr th {\n",
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" vertical-align: top;\n",
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" }\n",
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"\n",
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" .dataframe thead th {\n",
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" text-align: right;\n",
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" }\n",
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"</style>\n",
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"<table border=\"1\" class=\"dataframe\">\n",
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" <thead>\n",
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" <tr style=\"text-align: right;\">\n",
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" <th></th>\n",
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" <th>Decay Times in Microseconds</th>\n",
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" </tr>\n",
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" </thead>\n",
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" <tbody>\n",
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" <tr>\n",
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" <th>count</th>\n",
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" <td>1257.000000</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>mean</th>\n",
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" <td>3.421448</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>std</th>\n",
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" <td>3.931721</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>min</th>\n",
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" <td>0.319997</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>25%</th>\n",
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" <td>0.960001</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>50%</th>\n",
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" <td>1.880002</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>75%</th>\n",
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" <td>4.120004</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>max</th>\n",
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" <td>20.319996</td>\n",
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" </tr>\n",
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" </tbody>\n",
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"</table>\n",
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"</div>"
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],
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"text/plain": [
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" Decay Times in Microseconds\n",
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"count 1257.000000\n",
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"mean 3.421448\n",
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"std 3.931721\n",
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"min 0.319997\n",
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"25% 0.960001\n",
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"50% 1.880002\n",
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"75% 4.120004\n",
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"max 20.319996"
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]
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},
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"execution_count": 11,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"import matplotlib.pyplot as plt\n",
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"import numpy as np\n",
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"import pandas\n",
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"\n",
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"data = pandas.read_csv('20250212_EC_decays.txt')\n",
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"data.describe()"
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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": 20,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"image/png": 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urWeffVavvvqqoqKiqrzeT37yE8XFxWnWrFkVrjCpytGDAQMGqLS0VK+++qrL+CuvvCIfHx/1799fkipc2SXJGS7KH7u7775b//3vf/U///M/FeaeOnVKRUVFkqQhQ4bI19dX06ZNq3A+S3nNiYmJCggI0OzZs13244033tDx48c1cODAS+5bZfu6f/9+LVq0yDl28uRJ/f73v3eZV1BQ4PydKdehQwf5+vp6/PcE9uMICrzS0qVLtWPHDp09e1YHDx7U6tWrlZubq9jYWH3yyScuH8CVnZ2tW265RR06dNC4cePUokULHTx4UBs2bNC+ffv0r3/9S5L02GOPadGiRfrZz36mBx54QF27dtXRo0f1ySefaO7cuerUqZPuuOMOffDBBxo6dKgGDhyovLw8zZ07V+3atVNhYWGFOgcOHKiGDRtq4cKF6t+/vyIiIq7YY1Qdjz32mD755BPdcccdGjNmjLp27aqioiJ99dVXWrRokb799ls1atRIv/jFL3T06FH16dNHTZo00Xfffac5c+aoc+fOznMuPMXX11fPPPNMtdZ7/fXXNWjQIHXu3Fn333+/oqOjtWPHDm3btq3S0HauQYMGqXfv3nr66af17bffqlOnTlqxYoU+/vhjTZgwwXmEZtq0aVq/fr0GDhyo2NhYHTp0SK+99pqaNGmiW265RdKPwfb999/Xr371K61Zs0Y9e/ZUaWmpduzYoffff1/Lly9Xt27d1KpVKz399NN69tlndeutt2rYsGFyOBz68ssvFRMTo6ysLDVu3FiTJk1SZmam+vXrp8GDB2vnzp167bXXdNNNN1Xrkvdx48bp1Vdf1ahRo7R582ZFR0fr7bffVlBQkMu81atXKy0tTT/72c/UunVrnT17Vm+//bb8/PyUkpLi9v3iGuOhq4eAaim/zLj8JyAgwERFRZm+ffua3/72t6agoKDS9fbs2WNGjRploqKijL+/v7nuuuvMHXfcYRYtWuQy78iRIyYtLc1cd911JiAgwDRp0sSMHj3aHD582Bjz46Wbzz//vImNjTUOh8N06dLFLF682IwePdrExsZWet8PPfSQkWRycnLc3t/qXGZcfsntuW6//XZz4403VhiPjY01AwcOdBk7ceKEmTRpkmnVqpUJCAgwjRo1Mj/96U/NzJkzzZkzZ4wxxixatMgkJSWZiIgIExAQYJo1a2Z++ctfmgMHDlxyn87fn/LLjM+/7La813l5eRfd3oX2+VxVucy43Oeff2769u1r6tevb4KDg03Hjh3NnDlzqnR/J06cMBMnTjQxMTHG39/fXH/99ebFF190uUx51apV5s477zQxMTEmICDAxMTEmHvvvbfCpd1nzpwx06dPNzfeeKNxOBymQYMGpmvXriYzM9McP37cZe6bb75punTp4px3++23m9zcXJc5r776qmnbtq3x9/c3kZGRZvz48RUup77Q70llv9/fffedGTx4sAkKCjKNGjUyjzzyiFm2bJnLY/rNN9+YBx54wLRs2dIEBgaa8PBw07t3b7Ny5cpKHz/gXD7GVPPMJwBVMnHiRL3xxhvKz8+v8BcmAKBynIMC1KLTp0/rnXfeUUpKCuEEANzAOShALTh06JBWrlypRYsW6ciRI3rkkUc8XRIAeBUCClALtm/frpEjRyoiIkKzZ8++4CWgAIDKcQ4KAACwDuegAAAA6xBQAACAdbzyHJSysjLt379f9evXr/VvhQUAADXDGKMTJ04oJibmkt9n5ZUBZf/+/bX6ZVkAAKD2/Oc//7nkl0V6ZUAp/yKu//znPwoJCXF7/ZKSEq1YsUJJSUny9/ev6fJQS+ibd6Jv3om+eSfb+1ZQUKCmTZu6fKHmhbgVULKysvTBBx9ox44dqlu3rn76059q+vTpLl8f36tXL61bt85lvV/+8peaO3eu8/bevXs1fvx4rVmzRvXq1dPo0aOVlZWlOnWqVk752zohISHVDihBQUEKCQmxsoGoHH3zTvTNO9E37+QtfavK6RluBZR169YpNTVVN910k86ePaunnnpKSUlJ2r59u4KDg53zxo0bp2nTpjlvn/sJmqWlpRo4cKCioqL017/+VQcOHNCoUaPk7++v559/3p1yAADAVcqtgLJs2TKX2/Pnz1dERIQ2b96s2267zTkeFBR0wa85X7FihbZv366VK1cqMjJSnTt31rPPPqsnnnhCGRkZCggIqMZuAACAq8llnYNy/PhxSVJ4eLjL+IIFC/TOO+8oKipKgwYN0uTJk51HUTZs2KAOHTooMjLSOT85OVnjx4/Xtm3b1KVLlwr3U1xcrOLiYuftgoICST8eyiopKXG77vJ1qrMuPIe+eSf65p3om3eyvW/u1FXtgFJWVqYJEyaoZ8+eat++vXN8xIgRio2NVUxMjP7973/riSee0M6dO/XBBx9IkvLz813CiSTn7fz8/ErvKysrS5mZmRXGV6xYcVlfwJabm1vtdeE59M070TfvRN+8k619O3nyZJXnVjugpKamauvWrfr8889dxh988EHnvzt06KDo6GglJCRoz549atmyZbXua9KkSUpPT3feLj8LOCkpqdonyebm5qpv375Wn0QEV/TNO9E370TfvJPtfSt/B6QqqhVQ0tLStHjxYq1fv/6S1zH36NFDkrR79261bNlSUVFR+tvf/uYy5+DBg5J0wfNWHA6HHA5HhXF/f//LasDlrg/PoG/eib55J/rmnWztmzs1ufVR98YYpaWl6cMPP9Tq1asVFxd3yXW2bNkiSYqOjpYkxcfH66uvvtKhQ4ecc3JzcxUSEqJ27dq5Uw4AALhKuXUEJTU1VTk5Ofr4449Vv3595zkjoaGhqlu3rvbs2aOcnBwNGDBADRs21L///W9NnDhRt912mzp27ChJSkpKUrt27XTfffdpxowZys/P1zPPPKPU1NRKj5IAAIBrj1tHUF5//XUdP35cvXr1UnR0tPPnvffekyQFBARo5cqVSkpKUtu2bfWb3/xGKSkp+vTTT53b8PPz0+LFi+Xn56f4+Hj9/Oc/16hRo1w+NwUAAFzb3DqCYoy56PKmTZtW+BTZysTGxuqzzz5z564BAMA1xK0jKAAAAFcCAQUAAFiHgAIAAKxDQAEAANa5rO/iuVo1f3JJled++8LAWqwEAIBrE0dQAACAdQgoAADAOgQUAABgHQIKAACwDgEFAABYh4ACAACsQ0ABAADWIaAAAADrEFAAAIB1CCgAAMA6BBQAAGAdAgoAALAOAQUAAFiHgAIAAKxDQAEAANYhoAAAAOsQUAAAgHUIKAAAwDoEFAAAYB0CCgAAsA4BBQAAWIeAAgAArENAAQAA1iGgAAAA6xBQAACAdQgoAADAOgQUAABgHQIKAACwDgEFAABYh4ACAACsQ0ABAADWIaAAAADrEFAAAIB1CCgAAMA6BBQAAGAdAgoAALAOAQUAAFiHgAIAAKxDQAEAANYhoAAAAOsQUAAAgHUIKAAAwDoEFAAAYB0CCgAAsA4BBQAAWIeAAgAArENAAQAA1iGgAAAA6xBQAACAdQgoAADAOgQUAABgHQIKAACwDgEFAABYh4ACAACsQ0ABAADWIaAAAADrEFAAAIB13AooWVlZuummm1S/fn1FRERoyJAh2rlzp8uc06dPKzU1VQ0bNlS9evWUkpKigwcPuszZu3evBg4cqKCgIEVEROixxx7T2bNnL39vAADAVcGtgLJu3TqlpqZq48aNys3NVUlJiZKSklRUVOScM3HiRH366adauHCh1q1bp/3792vYsGHO5aWlpRo4cKDOnDmjv/71r3rrrbc0f/58TZkypeb2CgAAeLU67kxetmyZy+358+crIiJCmzdv1m233abjx4/rjTfeUE5Ojvr06SNJmjdvnm644QZt3LhRN998s1asWKHt27dr5cqVioyMVOfOnfXss8/qiSeeUEZGhgICAircb3FxsYqLi523CwoKJEklJSUqKSlxe6fL17nQug4/4/a2UPsu1TfYib55J/rmnWzvmzt1+Rhjqv5qfJ7du3fr+uuv11dffaX27dtr9erVSkhI0A8//KCwsDDnvNjYWE2YMEETJ07UlClT9Mknn2jLli3O5Xl5eWrRooX+8Y9/qEuXLhXuJyMjQ5mZmRXGc3JyFBQUVN3yAQDAFXTy5EmNGDFCx48fV0hIyEXnunUE5VxlZWWaMGGCevbsqfbt20uS8vPzFRAQ4BJOJCkyMlL5+fnOOZGRkRWWly+rzKRJk5Senu68XVBQoKZNmyopKemSO1iZkpIS5ebmqm/fvvL396+wvH3G8ipva2tGstv3j+q5VN9gJ/rmneibd7K9b+XvgFRFtQNKamqqtm7dqs8//7y6m6gyh8Mhh8NRYdzf3/+yGnCh9YtLfdzaBq6sy+07PIO+eSf65p1s7Zs7NVXrMuO0tDQtXrxYa9asUZMmTZzjUVFROnPmjI4dO+Yy/+DBg4qKinLOOf+qnvLb5XMAAMC1za2AYoxRWlqaPvzwQ61evVpxcXEuy7t27Sp/f3+tWrXKObZz507t3btX8fHxkqT4+Hh99dVXOnTokHNObm6uQkJC1K5du8vZFwAAcJVw6y2e1NRU5eTk6OOPP1b9+vWd54yEhoaqbt26Cg0N1dixY5Wenq7w8HCFhITo4YcfVnx8vG6++WZJUlJSktq1a6f77rtPM2bMUH5+vp555hmlpqZW+jYOAAC49rgVUF5//XVJUq9evVzG582bpzFjxkiSXnnlFfn6+iolJUXFxcVKTk7Wa6+95pzr5+enxYsXa/z48YqPj1dwcLBGjx6tadOmXd6eAACAq4ZbAaUqVyQHBgYqOztb2dnZF5wTGxurzz77zJ27BgAA15BqX8WDHzV/ckmV5377wsBarAQAgKsHXxYIAACsQ0ABAADWIaAAAADrEFAAAIB1CCgAAMA6BBQAAGAdAgoAALAOAQUAAFiHgAIAAKxDQAEAANYhoAAAAOsQUAAAgHUIKAAAwDoEFAAAYB0CCgAAsA4BBQAAWIeAAgAArENAAQAA1iGgAAAA6xBQAACAdQgoAADAOgQUAABgHQIKAACwDgEFAABYh4ACAACsQ0ABAADWIaAAAADrEFAAAIB1CCgAAMA6BBQAAGAdAgoAALAOAQUAAFiHgAIAAKxDQAEAANYhoAAAAOsQUAAAgHUIKAAAwDoEFAAAYB0CCgAAsE4dTxdwLWn+5JIqz/32hYG1WAkAAHbjCAoAALAOAQUAAFiHgAIAAKxDQAEAANYhoAAAAOsQUAAAgHUIKAAAwDoEFAAAYB0CCgAAsA4BBQAAWIeAAgAArENAAQAA1iGgAAAA6xBQAACAdQgoAADAOgQUAABgHQIKAACwDgEFAABYh4ACAACsQ0ABAADWcTugrF+/XoMGDVJMTIx8fHz00UcfuSwfM2aMfHx8XH769evnMufo0aMaOXKkQkJCFBYWprFjx6qwsPCydgQAAFw93A4oRUVF6tSpk7Kzsy84p1+/fjpw4IDz591333VZPnLkSG3btk25ublavHix1q9frwcffND96gEAwFWpjrsr9O/fX/3797/oHIfDoaioqEqXff3111q2bJm+/PJLdevWTZI0Z84cDRgwQDNnzlRMTIy7JQEAgKuM2wGlKtauXauIiAg1aNBAffr00XPPPaeGDRtKkjZs2KCwsDBnOJGkxMRE+fr6atOmTRo6dGiF7RUXF6u4uNh5u6CgQJJUUlKikpISt+srX+dC6zr8jNvbrGnV2a+r3aX6BjvRN+9E37yT7X1zp64aDyj9+vXTsGHDFBcXpz179uipp55S//79tWHDBvn5+Sk/P18RERGuRdSpo/DwcOXn51e6zaysLGVmZlYYX7FihYKCgqpda25ubqXjM7pXe5M15rPPPvN0Cda6UN9gN/rmneibd7K1bydPnqzy3BoPKMOHD3f+u0OHDurYsaNatmyptWvXKiEhoVrbnDRpktLT0523CwoK1LRpUyUlJSkkJMTt7ZWUlCg3N1d9+/aVv79/heXtM5ZXq86atDUj2dMlWOdSfYOd6Jt3om/eyfa+lb8DUhW18hbPuVq0aKFGjRpp9+7dSkhIUFRUlA4dOuQy5+zZszp69OgFz1txOBxyOBwVxv39/S+rARdav7jUp9rbrCk2/mLZ4nL7Ds+gb96JvnknW/vmTk21/jko+/bt05EjRxQdHS1Jio+P17Fjx7R582bnnNWrV6usrEw9evSo7XIAAIAXcPsISmFhoXbv3u28nZeXpy1btig8PFzh4eHKzMxUSkqKoqKitGfPHj3++ONq1aqVkpN/fMvihhtuUL9+/TRu3DjNnTtXJSUlSktL0/Dhw7mCBwAASKrGEZS///3v6tKli7p06SJJSk9PV5cuXTRlyhT5+fnp3//+twYPHqzWrVtr7Nix6tq1q/7yl7+4vEWzYMECtW3bVgkJCRowYIBuueUW/f73v6+5vQIAAF7N7SMovXr1kjEXvgx3+fJLn2AaHh6unJwcd+8aAABcI/guHgAAYB0CCgAAsA4BBQAAWIeAAgAArENAAQAA1iGgAAAA6xBQAACAdQgoAADAOgQUAABgHQIKAACwDgEFAABYh4ACAACsQ0ABAADWIaAAAADrEFAAAIB1CCgAAMA6BBQAAGAdAgoAALAOAQUAAFiHgAIAAKxDQAEAANYhoAAAAOsQUAAAgHUIKAAAwDoEFAAAYB0CCgAAsA4BBQAAWIeAAgAArENAAQAA1iGgAAAA6xBQAACAdQgoAADAOgQUAABgHQIKAACwDgEFAABYh4ACAACsQ0ABAADWIaAAAADrEFAAAIB1CCgAAMA6BBQAAGAdAgoAALAOAQUAAFiHgAIAAKxDQAEAANYhoAAAAOsQUAAAgHUIKAAAwDoEFAAAYB0CCgAAsA4BBQAAWIeAAgAArENAAQAA1iGgAAAA6xBQAACAdQgoAADAOgQUAABgHQIKAACwDgEFAABYh4ACAACsQ0ABAADWIaAAAADruB1Q1q9fr0GDBikmJkY+Pj766KOPXJYbYzRlyhRFR0erbt26SkxM1K5du1zmHD16VCNHjlRISIjCwsI0duxYFRYWXtaOAACAq4fbAaWoqEidOnVSdnZ2pctnzJih2bNna+7cudq0aZOCg4OVnJys06dPO+eMHDlS27ZtU25urhYvXqz169frwQcfrP5eAACAq0odd1fo37+/+vfvX+kyY4xmzZqlZ555Rnfeeack6Y9//KMiIyP10Ucfafjw4fr666+1bNkyffnll+rWrZskac6cORowYIBmzpypmJiYy9idq0fzJ5dUee63LwysxUoAALjy3A4oF5OXl6f8/HwlJiY6x0JDQ9WjRw9t2LBBw4cP14YNGxQWFuYMJ5KUmJgoX19fbdq0SUOHDq2w3eLiYhUXFztvFxQUSJJKSkpUUlLidp3l61xoXYefcXubnlSdx8AbXapvsBN98070zTvZ3jd36qrRgJKfny9JioyMdBmPjIx0LsvPz1dERIRrEXXqKDw83DnnfFlZWcrMzKwwvmLFCgUFBVW73tzc3ErHZ3Sv9iY94rPPPvN0CVfUhfoGu9E370TfvJOtfTt58mSV59ZoQKktkyZNUnp6uvN2QUGBmjZtqqSkJIWEhLi9vZKSEuXm5qpv377y9/evsLx9xvLLqvdK25qR7OkSrohL9Q12om/eib55J9v7Vv4OSFXUaECJioqSJB08eFDR0dHO8YMHD6pz587OOYcOHXJZ7+zZszp69Khz/fM5HA45HI4K4/7+/pfVgAutX1zqU+1teoKNv4S16XL7Ds+gb96JvnknW/vmTk01+jkocXFxioqK0qpVq5xjBQUF2rRpk+Lj4yVJ8fHxOnbsmDZv3uycs3r1apWVlalHjx41WQ4AAPBSbh9BKSws1O7du5238/LytGXLFoWHh6tZs2aaMGGCnnvuOV1//fWKi4vT5MmTFRMToyFDhkiSbrjhBvXr10/jxo3T3LlzVVJSorS0NA0fPpwreAAAgKRqBJS///3v6t27t/N2+bkho0eP1vz58/X444+rqKhIDz74oI4dO6ZbbrlFy5YtU2BgoHOdBQsWKC0tTQkJCfL19VVKSopmz55dA7sDAACuBm4HlF69esmYC1+G6+Pjo2nTpmnatGkXnBMeHq6cnBx37xoAAFwj+C4eAABgHQIKAACwDgEFAABYh4ACAACsQ0ABAADWIaAAAADrEFAAAIB1CCgAAMA6BBQAAGAdAgoAALAOAQUAAFiHgAIAAKxDQAEAANYhoAAAAOsQUAAAgHUIKAAAwDoEFAAAYB0CCgAAsA4BBQAAWIeAAgAArFPH0wXg8jV/ckmV5377wsBarAQAgJrBERQAAGAdAgoAALAOAQUAAFiHgAIAAKxDQAEAANYhoAAAAOsQUAAAgHUIKAAAwDoEFAAAYB0CCgAAsA4BBQAAWIeAAgAArENAAQAA1iGgAAAA6xBQAACAdQgoAADAOgQUAABgHQIKAACwTh1PF4Arq/mTS6o899sXBtZiJQAAXBhHUAAAgHUIKAAAwDoEFAAAYB0CCgAAsA4BBQAAWIeAAgAArENAAQAA1iGgAAAA6xBQAACAdQgoAADAOgQUAABgHQIKAACwDgEFAABYh4ACAACsQ0ABAADWIaAAAADrEFAAAIB1CCgAAMA6BBQAAGAdAgoAALAOAQUAAFinjqcLgL2aP7mkynO/fWFgLVYCALjW1PgRlIyMDPn4+Lj8tG3b1rn89OnTSk1NVcOGDVWvXj2lpKTo4MGDNV0GAADwYrVyBOXGG2/UypUr//9O6vz/3UycOFFLlizRwoULFRoaqrS0NA0bNkxffPFFbZSCK4SjLQCAmlQrAaVOnTqKioqqMH78+HG98cYbysnJUZ8+fSRJ8+bN0w033KCNGzfq5ptvro1yAACAl6mVgLJr1y7FxMQoMDBQ8fHxysrKUrNmzbR582aVlJQoMTHRObdt27Zq1qyZNmzYcMGAUlxcrOLiYuftgoICSVJJSYlKSkrcrq98nQut6/Azbm8TVVednp27XnXXh2fQN+9E37yT7X1zpy4fY0yNvhovXbpUhYWFatOmjQ4cOKDMzEz997//1datW/Xpp5/q/vvvdwkbktS9e3f17t1b06dPr3SbGRkZyszMrDCek5OjoKCgmiwfAADUkpMnT2rEiBE6fvy4QkJCLjq3xgPK+Y4dO6bY2Fi9/PLLqlu3brUCSmVHUJo2barDhw9fcgcrU1JSotzcXPXt21f+/v4VlrfPWO72NlF1WzOSq7XepfoGO9E370TfvJPtfSsoKFCjRo2qFFBq/TLjsLAwtW7dWrt371bfvn115swZHTt2TGFhYc45Bw8erPSclXIOh0MOh6PCuL+//2U14ELrF5f6VHubuLTLfdJcbt/hGfTNO9E372Rr39ypqdY/qK2wsFB79uxRdHS0unbtKn9/f61atcq5fOfOndq7d6/i4+NruxQAAOAlavwIyqOPPqpBgwYpNjZW+/fv19SpU+Xn56d7771XoaGhGjt2rNLT0xUeHq6QkBA9/PDDio+P5woeAADgVOMBZd++fbr33nt15MgRNW7cWLfccos2btyoxo0bS5JeeeUV+fr6KiUlRcXFxUpOTtZrr71W02UAAAAvVuMB5U9/+tNFlwcGBio7O1vZ2dk1fdcAAOAqwZcFAgAA6xBQAACAdQgoAADAOgQUAABgHQIKAACwDgEFAABYp9Y/6h44X/Mnl1R57rcvDKzFSgAAtuIICgAAsA4BBQAAWIeAAgAArENAAQAA1iGgAAAA6xBQAACAdQgoAADAOgQUAABgHQIKAACwDgEFAABYh4+6h9XO/Vh8h5/RjO5S+4zlKi71qTCXj8UHgKsHR1AAAIB1CCgAAMA6BBQAAGAdAgoAALAOAQUAAFiHgAIAAKxDQAEAANYhoAAAAOsQUAAAgHUIKAAAwDoEFAAAYB2+iwdXjXO/t+dS+N4eALAbR1AAAIB1CCgAAMA6BBQAAGAdAgoAALAOAQUAAFiHgAIAAKxDQAEAANYhoAAAAOvwQW24JvGhbgBgNwIKcAmEGQC48niLBwAAWIcjKICHcGQGAC6MIygAAMA6BBQAAGAdAgoAALAO56AANcid80oAABfGERQAAGAdAgoAALAOAQUAAFiHgAIAAKxDQAEAANYhoAAAAOtwmTGAKqnuJdQOP6MZ3aX2GctVXOpTYbk7H+N/NX89QG1dou5tjwNQjiMoAADAOhxBAeBRfLgdrhVX8xHA2sARFAAAYB0CCgAAsA5v8QBegEPDtYvHt3bx+KI6CCjAVYZzOuxBL2qXDcGHHtceAgoAwG219cLMCz7KcQ4KAACwjkePoGRnZ+vFF19Ufn6+OnXqpDlz5qh79+6eLAnAVYK/8AHv5rGA8t577yk9PV1z585Vjx49NGvWLCUnJ2vnzp2KiIjwVFkAcFWprU8A9jY2nK/iDhs+udnTPPYWz8svv6xx48bp/vvvV7t27TR37lwFBQXpzTff9FRJAADAEh45gnLmzBlt3rxZkyZNco75+voqMTFRGzZsqDC/uLhYxcXFztvHjx+XJB09elQlJSVu339JSYlOnjypI0eOyN/fv8LyOmeL3N4mal+dMqOTJ8tUp8RXpWXe/xfdtYK+eadruW+tHn2/ynNtu9LkUn07cuSIB6r6fydOnJAkGWMuOdcjj+3hw4dVWlqqyMhIl/HIyEjt2LGjwvysrCxlZmZWGI+Li6u1GmGnEZ4uANVC37wTffNOF+tbo5euWBkXdeLECYWGhl50jm3hr1KTJk1Senq683ZZWZmOHj2qhg0bysfH/WRfUFCgpk2b6j//+Y9CQkJqslTUIvrmneibd6Jv3sn2vhljdOLECcXExFxyrkcCSqNGjeTn56eDBw+6jB88eFBRUVEV5jscDjkcDpexsLCwy64jJCTEygbi4uibd6Jv3om+eSeb+3apIyflPHKSbEBAgLp27apVq1Y5x8rKyrRq1SrFx8d7oiQAAGARj73Fk56ertGjR6tbt27q3r27Zs2apaKiIt1///2eKgkAAFjCYwHlnnvu0ffff68pU6YoPz9fnTt31rJlyyqcOFsbHA6Hpk6dWuFtI9iNvnkn+uad6Jt3upr65mOqcq0PAADAFcR38QAAAOsQUAAAgHUIKAAAwDoEFAAAYB0CCgAAsM41GVCys7PVvHlzBQYGqkePHvrb3/7m6ZJwERkZGfLx8XH5adu2rafLwnnWr1+vQYMGKSYmRj4+Pvroo49clhtjNGXKFEVHR6tu3bpKTEzUrl27PFMsnC7VtzFjxlR4/vXr188zxULSj99Pd9NNN6l+/fqKiIjQkCFDtHPnTpc5p0+fVmpqqho2bKh69eopJSWlwqe32+6aCyjvvfee0tPTNXXqVP3jH/9Qp06dlJycrEOHDnm6NFzEjTfeqAMHDjh/Pv/8c0+XhPMUFRWpU6dOys7OrnT5jBkzNHv2bM2dO1ebNm1ScHCwkpOTdfr06StcKc51qb5JUr9+/Vyef+++++4VrBDnW7dunVJTU7Vx40bl5uaqpKRESUlJKioqcs6ZOHGiPv30Uy1cuFDr1q3T/v37NWzYMA9WXQ3mGtO9e3eTmprqvF1aWmpiYmJMVlaWB6vCxUydOtV06tTJ02XADZLMhx9+6LxdVlZmoqKizIsvvugcO3bsmHE4HObdd9/1QIWozPl9M8aY0aNHmzvvvNMj9aBqDh06ZCSZdevWGWN+fG75+/ubhQsXOud8/fXXRpLZsGGDp8p02zV1BOXMmTPavHmzEhMTnWO+vr5KTEzUhg0bPFgZLmXXrl2KiYlRixYtNHLkSO3du9fTJcENeXl5ys/Pd3nuhYaGqkePHjz3vMDatWsVERGhNm3aaPz48Tpy5IinS8I5jh8/LkkKDw+XJG3evFklJSUuz7e2bduqWbNmXvV8u6YCyuHDh1VaWlrh4/QjIyOVn5/voapwKT169ND8+fO1bNkyvf7668rLy9Ott96qEydOeLo0VFH584vnnvfp16+f/vjHP2rVqlWaPn261q1bp/79+6u0tNTTpUE/ftHuhAkT1LNnT7Vv317Sj8+3gIAAhYWFucz1tuebx76LB6iq/v37O//dsWNH9ejRQ7GxsXr//fc1duxYD1YGXP2GDx/u/HeHDh3UsWNHtWzZUmvXrlVCQoIHK4MkpaamauvWrVfleXnX1BGURo0ayc/Pr8KZzAcPHlRUVJSHqoK7wsLC1Lp1a+3evdvTpaCKyp9fPPe8X4sWLdSoUSOefxZIS0vT4sWLtWbNGjVp0sQ5HhUVpTNnzujYsWMu873t+XZNBZSAgAB17dpVq1atco6VlZVp1apVio+P92BlcEdhYaH27Nmj6OhoT5eCKoqLi1NUVJTLc6+goECbNm3iuedl9u3bpyNHjvD88yBjjNLS0vThhx9q9erViouLc1netWtX+fv7uzzfdu7cqb1793rV8+2ae4snPT1do0ePVrdu3dS9e3fNmjVLRUVFuv/++z1dGi7g0Ucf1aBBgxQbG6v9+/dr6tSp8vPz07333uvp0nCOwsJCl7+q8/LytGXLFoWHh6tZs2aaMGGCnnvuOV1//fWKi4vT5MmTFRMToyFDhniuaFy0b+Hh4crMzFRKSoqioqK0Z88ePf7442rVqpWSk5M9WPW1LTU1VTk5Ofr4449Vv35953kloaGhqlu3rkJDQzV27Filp6crPDxcISEhevjhhxUfH6+bb77Zw9W7wdOXEXnCnDlzTLNmzUxAQIDp3r272bhxo6dLwkXcc889Jjo62gQEBJjrrrvO3HPPPWb37t2eLgvnWbNmjZFU4Wf06NHGmB8vNZ48ebKJjIw0DofDJCQkmJ07d3q2aFy0bydPnjRJSUmmcePGxt/f38TGxppx48aZ/Px8T5d9TausX5LMvHnznHNOnTplHnroIdOgQQMTFBRkhg4dag4cOOC5oqvBxxhjrnwsAgAAuLBr6hwUAADgHQgoAADAOgQUAABgHQIKAACwDgEFAABYh4ACAACsQ0ABAADWIaAAAADrEFAAAIB1CCgAAMA6BBQAAGCd/wNWzG8d/mtgHAAAAABJRU5ErkJggg==",
|
|
"text/plain": [
|
|
"<Figure size 640x480 with 1 Axes>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"cropped_data = data[data['Decay Times in Microseconds'] >= 0.3]\n",
|
|
"cropped_data = cropped_data[cropped_data['Decay Times in Microseconds'] <= 20]\n",
|
|
"cropped_data.describe()\n",
|
|
"hist = cropped_data.hist(range=data_range, bins=bins)\n"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 15,
|
|
"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": 4,
|
|
"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": null,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/latex": [
|
|
"$x \\mapsto \\text{5.62523946} - \\text{0.47310125}\\,x$"
|
|
],
|
|
"text/plain": [
|
|
"Polynomial([ 5.62523946, -0.47310125], domain=[-1., 1.], window=[-1., 1.], symbol='x')"
|
|
]
|
|
},
|
|
"execution_count": 7,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"line = np.polynomial.polynomial.Polynomial.fit(bins, times, deg=1)\n",
|
|
"line=line.convert()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 30,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"np.float64(2.113712457958419)"
|
|
]
|
|
},
|
|
"execution_count": 30,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"gamma=line.convert().coef[1]\n",
|
|
"tau = -1/gamma\n",
|
|
"tau"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 22,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/html": [
|
|
"<div>\n",
|
|
"<style scoped>\n",
|
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" .dataframe tbody tr th:only-of-type {\n",
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" vertical-align: middle;\n",
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" }\n",
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"\n",
|
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" .dataframe tbody tr th {\n",
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" vertical-align: top;\n",
|
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" }\n",
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"\n",
|
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" .dataframe thead th {\n",
|
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" text-align: right;\n",
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" }\n",
|
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"</style>\n",
|
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"<table border=\"1\" class=\"dataframe\">\n",
|
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" <thead>\n",
|
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" <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>1102.000000</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>mean</th>\n",
|
|
" <td>2.161488</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>std</th>\n",
|
|
" <td>1.617221</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>min</th>\n",
|
|
" <td>0.319997</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>25%</th>\n",
|
|
" <td>0.879998</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>50%</th>\n",
|
|
" <td>1.640000</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>75%</th>\n",
|
|
" <td>3.040010</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>max</th>\n",
|
|
" <td>6.960006</td>\n",
|
|
" </tr>\n",
|
|
" </tbody>\n",
|
|
"</table>\n",
|
|
"</div>"
|
|
],
|
|
"text/plain": [
|
|
" Decay Times in Microseconds\n",
|
|
"count 1102.000000\n",
|
|
"mean 2.161488\n",
|
|
"std 1.617221\n",
|
|
"min 0.319997\n",
|
|
"25% 0.879998\n",
|
|
"50% 1.640000\n",
|
|
"75% 3.040010\n",
|
|
"max 6.960006"
|
|
]
|
|
},
|
|
"execution_count": 22,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"cropped_data_2.describe()"
|
|
]
|
|
}
|
|
],
|
|
"metadata": {
|
|
"kernelspec": {
|
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"display_name": ".venv",
|
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"language": "python",
|
|
"name": "python3"
|
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},
|
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"language_info": {
|
|
"codemirror_mode": {
|
|
"name": "ipython",
|
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"version": 3
|
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},
|
|
"file_extension": ".py",
|
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"mimetype": "text/x-python",
|
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.12.7"
|
|
}
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 2
|
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}
|