411 lines
42 KiB
Plaintext
411 lines
42 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 7,
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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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"<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": 7,
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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": null,
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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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" }\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>1255.000000</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>3.394741</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>3.877424</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>0.960000</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>1.879998</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>4.120002</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>19.880012</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 1255.000000\n",
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"mean 3.394741\n",
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"std 3.877424\n",
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"min 0.319997\n",
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"25% 0.960000\n",
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"50% 1.879998\n",
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"75% 4.120002\n",
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"max 19.880012"
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]
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},
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"execution_count": 8,
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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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"cropped_data = data[data['Decay Times in Microseconds'] >= 0.3]\n",
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"cropped_data = cropped_data[cropped_data['Decay Times in Microseconds'] <= 20]\n",
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"cropped_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": 79,
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"metadata": {},
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"outputs": [
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{
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"data": {
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" 14, 17, 6, 5, 5, 9, 9, 8, 9, 3, 6, 3, 5,\n",
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" 9, 9, 2, 2, 4, 6, 4, 5, 2, 1, 5, 8, 2,\n",
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" 2, 0])"
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]
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},
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"execution_count": 79,
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"metadata": {},
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"output_type": "execute_result"
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{
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"data": {
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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",
|
|
"\n",
|
|
"values = np.histogram(cropped_data[\"Decay Times in Microseconds\"], range=data_range, bins=bins)[0]\n",
|
|
"bincenters = 0.5*(bins[1:]+bins[:-1])\n",
|
|
"\n",
|
|
"hist[0][0].errorbar(bincenters, values, yerr=np.sqrt(values), fmt='none', color='red')\n",
|
|
"\n",
|
|
"hist[0][0].set_yscale('log')\n",
|
|
"values"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 87,
|
|
"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": 88,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"array([-0.47310124716106117+/-0.0245690118244238,\n",
|
|
" 5.625239459215871+/-0.1002975555031522], dtype=object)"
|
|
]
|
|
},
|
|
"execution_count": 88,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"line, cov = np.polyfit(bins, times, deg=1, cov=True)\n",
|
|
"\n",
|
|
"coeff = unumpy.uarray(line, np.sqrt(np.diag(cov)))\n",
|
|
"coeff"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 70,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"2.1137124579584188+/-0.10976894836916962"
|
|
]
|
|
},
|
|
"execution_count": 70,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"gamma = coeff[0]\n",
|
|
"tau = -1 / gamma\n",
|
|
"tau"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/html": [
|
|
"<div>\n",
|
|
"<style scoped>\n",
|
|
" .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",
|
|
" <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>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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},
|
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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": "3.11.2"
|
|
}
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 2
|
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}
|