2019-12-14 11:43:02 +01:00
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{
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"cells": [
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{
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"cell_type": "code",
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2019-12-21 21:13:24 +01:00
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"execution_count": 1,
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2019-12-14 11:43:02 +01:00
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"metadata": {
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"autoscroll": false,
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"collapsed": false,
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"ein.hycell": false,
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"ein.tags": "worksheet-0",
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"slideshow": {
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"slide_type": "-"
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}
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},
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"outputs": [],
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"source": [
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"import numpy as np\n",
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"import matplotlib.pyplot as plt\n",
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2019-12-14 21:17:02 +01:00
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"import pandas as pd\n",
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2019-12-21 21:13:24 +01:00
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"import util\n",
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"from importlib import reload\n",
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"reload(util)\n",
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2019-12-15 17:36:13 +01:00
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"from util import *\n",
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2019-12-14 21:17:02 +01:00
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"from scipy.stats import binned_statistic"
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2019-12-14 11:43:02 +01:00
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"ein.tags": "worksheet-0",
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"slideshow": {
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"slide_type": "-"
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}
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},
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"source": [
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"# Kennlinien PM3"
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]
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},
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{
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"cell_type": "code",
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2019-12-21 21:13:24 +01:00
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"execution_count": 2,
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2019-12-14 11:43:02 +01:00
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"metadata": {
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"autoscroll": false,
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"collapsed": false,
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"ein.hycell": false,
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"ein.tags": "worksheet-0",
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"slideshow": {
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"slide_type": "-"
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}
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},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"131.75230566534913"
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]
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},
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2019-12-21 21:13:24 +01:00
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"execution_count": 2,
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2019-12-14 11:43:02 +01:00
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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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"eta = .03\n",
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"rt = 506/60\n",
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"T = 1/eta**2*1/rt\n",
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"T"
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]
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},
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{
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"cell_type": "code",
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2019-12-21 21:13:24 +01:00
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"execution_count": 3,
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2019-12-14 11:43:02 +01:00
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"metadata": {
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"autoscroll": false,
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"collapsed": false,
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"ein.hycell": false,
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"ein.tags": "worksheet-0",
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"slideshow": {
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"slide_type": "-"
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}
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},
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2019-12-14 21:17:02 +01:00
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"outputs": [
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{
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"data": {
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"text/plain": [
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"0.029514066805047763"
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]
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},
|
2019-12-21 21:13:24 +01:00
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"execution_count": 3,
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2019-12-14 21:17:02 +01:00
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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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2019-12-14 11:43:02 +01:00
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"source": [
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"N=1148\n",
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"c=N/T\n",
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"dc=np.sqrt(N)/T\n",
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"dc/c"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"ein.tags": "worksheet-0",
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"slideshow": {
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"slide_type": "-"
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}
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},
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"source": [
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"## Plot"
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]
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},
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{
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"cell_type": "code",
|
2019-12-21 21:13:24 +01:00
|
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|
"execution_count": 4,
|
2019-12-14 11:43:02 +01:00
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"metadata": {
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"autoscroll": false,
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"collapsed": false,
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"ein.hycell": false,
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"ein.tags": "worksheet-0",
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"slideshow": {
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"slide_type": "-"
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}
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},
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"outputs": [
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{
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"data": {
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2019-12-21 18:28:41 +01:00
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"image/png": [
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2019-12-21 21:13:24 +01:00
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"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
|
2019-12-21 18:28:41 +01:00
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],
|
2019-12-14 11:43:02 +01:00
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"text/plain": [
|
2019-12-14 21:17:02 +01:00
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"<Figure size 360x288 with 1 Axes>"
|
2019-12-14 11:43:02 +01:00
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]
|
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},
|
|
|
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|
"metadata": {},
|
2019-12-14 21:17:02 +01:00
|
|
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"output_type": "display_data"
|
2019-12-14 11:43:02 +01:00
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}
|
|
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|
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],
|
|
|
|
|
"source": [
|
2019-12-14 21:17:02 +01:00
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"%matplotlib inline\n",
|
2019-12-16 00:35:31 +01:00
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"\n",
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"T = 140\n",
|
2019-12-14 21:17:02 +01:00
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"fig, ax = set_up_plot()\n",
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2019-12-16 00:35:31 +01:00
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"calib = pd.read_excel('../messungen/vorversuch_kennlinnien.xlsx',\n",
|
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" sheet_name='Kennl')\n",
|
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|
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"ax.set_xlabel('Spannung $U_{3,HV}$ [V]')\n",
|
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|
|
"ax.set_ylabel('Zaehlrate PM3 [$s^{-1}$]')\n",
|
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|
|
"ax.errorbar(calib['U/V'], calib[\"N123\"]/T, yerr=np.sqrt(calib[\"N123\"])/T,\n",
|
|
|
|
|
" marker='.', label='Koinzidenzrate 123')\n",
|
2019-12-14 21:17:02 +01:00
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|
|
"ax.axvline(2300, linestyle='dotted', color='gray', label='Gewaehlte Spannung')\n",
|
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|
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"ax.legend()\n",
|
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|
|
"ax.set_xlim([calib['U/V'].min(), calib['U/V'].max()])\n",
|
2019-12-21 18:28:41 +01:00
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|
"ax.set_ylim(0)\n",
|
2019-12-16 00:35:31 +01:00
|
|
|
"save_fig(fig, 'kennlinie_123', 'vorversuch')\n"
|
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|
|
]
|
|
|
|
|
},
|
|
|
|
|
{
|
|
|
|
|
"cell_type": "code",
|
2019-12-21 21:13:24 +01:00
|
|
|
"execution_count": 5,
|
2019-12-16 00:35:31 +01:00
|
|
|
"metadata": {
|
|
|
|
|
"autoscroll": false,
|
|
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|
"collapsed": false,
|
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|
|
"ein.hycell": false,
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|
|
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|
"ein.tags": "worksheet-0",
|
|
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|
"slideshow": {
|
|
|
|
|
"slide_type": "-"
|
|
|
|
|
}
|
|
|
|
|
},
|
|
|
|
|
"outputs": [
|
|
|
|
|
{
|
|
|
|
|
"data": {
|
2019-12-21 18:28:41 +01:00
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"image/png": [
|
2019-12-21 21:13:24 +01:00
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|
|
"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
|
2019-12-21 18:28:41 +01:00
|
|
|
],
|
2019-12-16 00:35:31 +01:00
|
|
|
"text/plain": [
|
|
|
|
|
"<Figure size 360x288 with 1 Axes>"
|
|
|
|
|
]
|
|
|
|
|
},
|
|
|
|
|
"metadata": {},
|
|
|
|
|
"output_type": "display_data"
|
|
|
|
|
}
|
|
|
|
|
],
|
|
|
|
|
"source": [
|
|
|
|
|
"fig, ax = set_up_plot()\n",
|
|
|
|
|
"calib = pd.read_excel('../messungen/vorversuch_kennlinnien.xlsx',\n",
|
|
|
|
|
" sheet_name='Kennl')\n",
|
|
|
|
|
"ax.set_xlabel('Spannung $U_{3,HV}$ [V]')\n",
|
|
|
|
|
"ax.set_ylabel('Zaehlrate PM3 [$s^{-1}$]')\n",
|
|
|
|
|
"ax.errorbar(calib['U/V'], calib[\"N3\"]/T, yerr=np.sqrt(calib[\"N3\"])/T,\n",
|
|
|
|
|
" marker='.', label='Kennlinie PM3')\n",
|
|
|
|
|
"ax.legend()\n",
|
|
|
|
|
"ax.set_xlim([calib['U/V'].min(), calib['U/V'].max()])\n",
|
2019-12-21 18:28:41 +01:00
|
|
|
"ax.set_ylim(0)\n",
|
2019-12-14 21:17:02 +01:00
|
|
|
"save_fig(fig, 'kennlinie_pm3', 'vorversuch')"
|
|
|
|
|
]
|
|
|
|
|
},
|
|
|
|
|
{
|
|
|
|
|
"cell_type": "markdown",
|
|
|
|
|
"metadata": {
|
|
|
|
|
"ein.tags": "worksheet-0",
|
|
|
|
|
"slideshow": {
|
|
|
|
|
"slide_type": "-"
|
|
|
|
|
}
|
|
|
|
|
},
|
|
|
|
|
"source": [
|
|
|
|
|
"# Peakhoehen der Photomultiplier"
|
|
|
|
|
]
|
|
|
|
|
},
|
|
|
|
|
{
|
|
|
|
|
"cell_type": "code",
|
2019-12-21 21:13:24 +01:00
|
|
|
"execution_count": 5,
|
2019-12-16 23:04:02 +01:00
|
|
|
"metadata": {
|
|
|
|
|
"autoscroll": false,
|
|
|
|
|
"collapsed": false,
|
|
|
|
|
"ein.hycell": false,
|
|
|
|
|
"ein.tags": "worksheet-0",
|
|
|
|
|
"slideshow": {
|
|
|
|
|
"slide_type": "-"
|
|
|
|
|
}
|
|
|
|
|
},
|
|
|
|
|
"outputs": [],
|
|
|
|
|
"source": []
|
|
|
|
|
},
|
|
|
|
|
{
|
|
|
|
|
"cell_type": "code",
|
2019-12-21 21:13:24 +01:00
|
|
|
"execution_count": 6,
|
2019-12-14 21:17:02 +01:00
|
|
|
"metadata": {
|
|
|
|
|
"autoscroll": false,
|
|
|
|
|
"collapsed": false,
|
|
|
|
|
"ein.hycell": false,
|
|
|
|
|
"ein.tags": "worksheet-0",
|
|
|
|
|
"slideshow": {
|
|
|
|
|
"slide_type": "-"
|
|
|
|
|
}
|
|
|
|
|
},
|
|
|
|
|
"outputs": [],
|
|
|
|
|
"source": [
|
|
|
|
|
"peaks = pd.read_excel('../messungen/vorversuch_kennlinnien.xlsx')\n",
|
|
|
|
|
"peak_labels = ['P1', 'P2', 'P3']\n",
|
2019-12-16 23:04:02 +01:00
|
|
|
"bin_offsets = [8, 15, 40]\n",
|
|
|
|
|
"scale_factors = [100, 10, 1]"
|
2019-12-14 21:17:02 +01:00
|
|
|
]
|
|
|
|
|
},
|
|
|
|
|
{
|
|
|
|
|
"cell_type": "code",
|
2019-12-21 21:13:24 +01:00
|
|
|
"execution_count": 7,
|
2019-12-14 21:17:02 +01:00
|
|
|
"metadata": {
|
|
|
|
|
"autoscroll": false,
|
|
|
|
|
"collapsed": false,
|
|
|
|
|
"ein.hycell": false,
|
|
|
|
|
"ein.tags": "worksheet-0",
|
|
|
|
|
"slideshow": {
|
|
|
|
|
"slide_type": "-"
|
|
|
|
|
}
|
|
|
|
|
},
|
2019-12-16 23:04:02 +01:00
|
|
|
"outputs": [
|
|
|
|
|
{
|
|
|
|
|
"name": "stdout",
|
|
|
|
|
"output_type": "stream",
|
|
|
|
|
"text": [
|
2019-12-21 18:28:41 +01:00
|
|
|
"P1 & 0.20 & 3.00 & 0.72 & 0.60 & 1.31 \\\\\n",
|
|
|
|
|
"P2 & 0.20 & 11.40 & 4.44 & 3.60 & 1.89 \\\\\n",
|
|
|
|
|
"P3 & 3.60 & 51.20 & 9.30 & 7.00 & 1.06 \\\\\n"
|
2019-12-16 23:04:02 +01:00
|
|
|
]
|
|
|
|
|
}
|
|
|
|
|
],
|
2019-12-14 21:17:02 +01:00
|
|
|
"source": [
|
2019-12-16 23:04:02 +01:00
|
|
|
"for peak in peak_labels: # nice and dirty :{}\n",
|
|
|
|
|
" cur = peaks[peak]\n",
|
2019-12-21 18:28:41 +01:00
|
|
|
" print(f\"{peak} & {cur.min():.2f} & {cur.max():.2f} & {cur.mean():.2f} & {cur.median():.2f} & {cur.mean()/cur.std():.2f} \\\\\\\\\")\n",
|
2019-12-14 21:17:02 +01:00
|
|
|
"peaks['dP1'] = calculate_peak_uncertainty(peaks[\"P1\"])\n",
|
|
|
|
|
"peaks['dP2'] = calculate_peak_uncertainty(peaks[\"P2\"])\n",
|
|
|
|
|
"peaks['dP3'] = calculate_peak_uncertainty(peaks[\"P3\"])\n"
|
|
|
|
|
]
|
|
|
|
|
},
|
|
|
|
|
{
|
|
|
|
|
"cell_type": "code",
|
2019-12-21 21:13:24 +01:00
|
|
|
"execution_count": 8,
|
2019-12-14 21:17:02 +01:00
|
|
|
"metadata": {
|
|
|
|
|
"autoscroll": false,
|
|
|
|
|
"collapsed": false,
|
|
|
|
|
"ein.hycell": false,
|
|
|
|
|
"ein.tags": "worksheet-0",
|
|
|
|
|
"slideshow": {
|
|
|
|
|
"slide_type": "-"
|
|
|
|
|
}
|
|
|
|
|
},
|
2019-12-15 17:36:13 +01:00
|
|
|
"outputs": [
|
|
|
|
|
{
|
|
|
|
|
"data": {
|
|
|
|
|
"image/png": [
|
2019-12-21 21:13:24 +01:00
|
|
|
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2019-12-15 17:36:13 +01:00
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],
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"text/plain": [
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"<Figure size 360x288 with 1 Axes>"
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]
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},
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"metadata": {},
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},
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{
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2019-12-21 21:13:24 +01:00
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2019-12-15 17:36:13 +01:00
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],
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"text/plain": [
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"<Figure size 360x288 with 1 Axes>"
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]
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},
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{
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2019-12-21 21:13:24 +01:00
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"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
|
2019-12-15 17:36:13 +01:00
|
|
|
],
|
|
|
|
|
"text/plain": [
|
|
|
|
|
"<Figure size 360x288 with 1 Axes>"
|
|
|
|
|
]
|
|
|
|
|
},
|
|
|
|
|
"metadata": {},
|
|
|
|
|
"output_type": "display_data"
|
|
|
|
|
}
|
|
|
|
|
],
|
2019-12-14 21:17:02 +01:00
|
|
|
"source": [
|
|
|
|
|
"for index, peak in enumerate(peak_labels):\n",
|
2019-12-16 23:04:02 +01:00
|
|
|
" plot_hist(peaks[peak], calculate_bins(peaks[peak]) + bin_offsets[index],\n",
|
|
|
|
|
" scale_factors[index],\n",
|
|
|
|
|
" save=(f'muon_{peak}_spec', 'vorversuch'))"
|
2019-12-14 21:17:02 +01:00
|
|
|
]
|
|
|
|
|
},
|
2019-12-21 21:13:24 +01:00
|
|
|
{
|
|
|
|
|
"cell_type": "markdown",
|
|
|
|
|
"metadata": {
|
|
|
|
|
"ein.tags": "worksheet-0",
|
|
|
|
|
"slideshow": {
|
|
|
|
|
"slide_type": "-"
|
|
|
|
|
}
|
|
|
|
|
},
|
|
|
|
|
"source": [
|
|
|
|
|
"# Statistische Auswertung der Langzeitmessung"
|
|
|
|
|
]
|
|
|
|
|
},
|
2019-12-14 21:17:02 +01:00
|
|
|
{
|
|
|
|
|
"cell_type": "code",
|
2019-12-21 21:13:24 +01:00
|
|
|
"execution_count": 10,
|
2019-12-14 21:17:02 +01:00
|
|
|
"metadata": {
|
|
|
|
|
"autoscroll": false,
|
|
|
|
|
"collapsed": false,
|
|
|
|
|
"ein.hycell": false,
|
|
|
|
|
"ein.tags": "worksheet-0",
|
|
|
|
|
"slideshow": {
|
|
|
|
|
"slide_type": "-"
|
|
|
|
|
}
|
|
|
|
|
},
|
2019-12-21 21:13:24 +01:00
|
|
|
"outputs": [],
|
2019-12-16 23:04:02 +01:00
|
|
|
"source": [
|
2019-12-21 21:13:24 +01:00
|
|
|
"counts = load_counts('../messungen/LM_2019_12_20.txt')\n",
|
|
|
|
|
"interval = (24, 150)"
|
2019-12-16 23:04:02 +01:00
|
|
|
]
|
|
|
|
|
},
|
|
|
|
|
{
|
|
|
|
|
"cell_type": "code",
|
2019-12-21 21:13:24 +01:00
|
|
|
"execution_count": 16,
|
2019-12-16 23:04:02 +01:00
|
|
|
"metadata": {
|
|
|
|
|
"autoscroll": false,
|
|
|
|
|
"collapsed": false,
|
|
|
|
|
"ein.hycell": false,
|
|
|
|
|
"ein.tags": "worksheet-0",
|
|
|
|
|
"slideshow": {
|
|
|
|
|
"slide_type": "-"
|
|
|
|
|
}
|
|
|
|
|
},
|
|
|
|
|
"outputs": [
|
|
|
|
|
{
|
2019-12-21 21:13:24 +01:00
|
|
|
"data": {
|
|
|
|
|
"text/plain": [
|
|
|
|
|
"(2284.5670041107596,\n 17.920165966087293,\n 14788,\n 5208.75,\n [1691.0727468217476,\n 1941.9569883386273,\n 2073.5778898432864,\n 2150.8399584168337,\n 2198.4854603882904,\n 2228.649422367671,\n 2248.0403530470658,\n 2260.622878770272,\n 2268.835615801081,\n 2274.216322707795,\n 2277.750151601415,\n 2280.0746994393876,\n 2281.6053663448415,\n 2282.6139632173627,\n 2283.2788508733183,\n 2283.717287097231,\n 2284.006453996329,\n 2284.197195887302,\n 2284.3230246968747,\n 2284.4060361778124,\n 2284.4608023152377,\n 2284.4969346923817,\n 2284.5207736832017,\n 2284.536502054614,\n 2284.546879313538,\n 2284.553726048772,\n 2284.5582434194002,\n 2284.5612239170055,\n 2284.5631904099864,\n 2284.5644878772227,\n 2284.56534393021,\n 2284.565908743696,\n 2284.5662814009484,\n 2284.566527275832,\n 2284.5666895012137,\n 2284.5667965356365,\n 2284.5668671557096,\n 2284.566913750017,\n 2284.5669444924033,\n 2284.566964775875,\n 2284.5669781586757,\n 2284.5669869884937,\n 2284.5669928143056,\n 2284.5669966581095,\n 2284.566999194207,\n 2284.5670008674947,\n 2284.5670019715108,\n 2284.567002699928,\n 2284.567003180529,\n 2284.567003497624,\n 2284.5670037068394,\n 2284.5670038448775,\n 2284.5670039359534,\n 2284.567003996044,\n 2284.5670040356918,\n 2284.5670040618506,\n 2284.56700407911,\n 2284.5670040904974,\n 2284.567004098011,\n 2284.567004102968,\n 2284.5670041062385,\n 2284.5670041083968,\n 2284.5670041098206,\n 2284.5670041107596])"
|
|
|
|
|
]
|
|
|
|
|
},
|
|
|
|
|
"execution_count": 16,
|
|
|
|
|
"metadata": {},
|
|
|
|
|
"output_type": "execute_result"
|
2019-12-16 23:04:02 +01:00
|
|
|
}
|
|
|
|
|
],
|
|
|
|
|
"source": [
|
2019-12-21 21:13:24 +01:00
|
|
|
"continous(counts, interval)"
|
2019-12-16 23:04:02 +01:00
|
|
|
]
|
2019-12-14 21:17:02 +01:00
|
|
|
},
|
|
|
|
|
{
|
|
|
|
|
"cell_type": "code",
|
2019-12-21 21:13:24 +01:00
|
|
|
"execution_count": 12,
|
2019-12-14 21:17:02 +01:00
|
|
|
"metadata": {
|
|
|
|
|
"autoscroll": false,
|
|
|
|
|
"collapsed": false,
|
|
|
|
|
"ein.hycell": false,
|
|
|
|
|
"ein.tags": "worksheet-0",
|
|
|
|
|
"slideshow": {
|
|
|
|
|
"slide_type": "-"
|
|
|
|
|
}
|
|
|
|
|
},
|
2019-12-21 18:28:41 +01:00
|
|
|
"outputs": [],
|
2019-12-14 21:17:02 +01:00
|
|
|
"source": [
|
2019-12-21 21:13:24 +01:00
|
|
|
"poisson, gauss, N = binned_likelihood(counts, interval)"
|
2019-12-14 11:43:02 +01:00
|
|
|
]
|
2019-12-16 23:04:02 +01:00
|
|
|
},
|
|
|
|
|
{
|
|
|
|
|
"cell_type": "code",
|
2019-12-21 21:13:24 +01:00
|
|
|
"execution_count": 13,
|
2019-12-16 23:04:02 +01:00
|
|
|
"metadata": {
|
|
|
|
|
"autoscroll": false,
|
|
|
|
|
"collapsed": false,
|
|
|
|
|
"ein.hycell": false,
|
|
|
|
|
"ein.tags": "worksheet-0",
|
|
|
|
|
"slideshow": {
|
|
|
|
|
"slide_type": "-"
|
|
|
|
|
}
|
|
|
|
|
},
|
2019-12-21 21:13:24 +01:00
|
|
|
"outputs": [
|
|
|
|
|
{
|
|
|
|
|
"data": {
|
|
|
|
|
"text/plain": [
|
|
|
|
|
"((<Figure size 360x288 with 1 Axes>,\n <matplotlib.axes._subplots.AxesSubplot at 0x7f3b30ae5278>),\n 2263.6033071565366,\n (31.093969139121782, 31.90186452553553),\n -146787.68145272764)"
|
|
|
|
|
]
|
|
|
|
|
},
|
|
|
|
|
"execution_count": 13,
|
|
|
|
|
"metadata": {},
|
|
|
|
|
"output_type": "execute_result"
|
|
|
|
|
},
|
|
|
|
|
{
|
|
|
|
|
"data": {
|
|
|
|
|
"image/png": [
|
|
|
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],
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"text/plain": [
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"<Figure size 360x288 with 1 Axes>"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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}
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],
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|
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"source": [
|
|
|
|
|
"maximize_and_plot_likelihood(poisson, (1000, 3000), save=('poisson', 'haupt'))"
|
|
|
|
|
]
|
2019-12-16 23:04:02 +01:00
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},
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{
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"cell_type": "code",
|
2019-12-21 21:13:24 +01:00
|
|
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"execution_count": 14,
|
2019-12-16 23:04:02 +01:00
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"metadata": {
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"autoscroll": false,
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"collapsed": false,
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"ein.hycell": false,
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"slideshow": {
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"slide_type": "-"
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}
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|
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},
|
2019-12-21 21:13:24 +01:00
|
|
|
"outputs": [
|
|
|
|
|
{
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"data": {
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"text/plain": [
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"((<Figure size 360x288 with 1 Axes>,\n <matplotlib.axes._subplots.AxesSubplot at 0x7f3b301ed9e8>),\n 2269.835038293664,\n (30.152362502227334, 30.977313295185468),\n 206.42320956201473)"
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]
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},
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"execution_count": 14,
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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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"data": {
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],
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"text/plain": [
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"<Figure size 360x288 with 1 Axes>"
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]
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|
},
|
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|
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|
"metadata": {},
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|
"output_type": "display_data"
|
|
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|
|
}
|
|
|
|
|
],
|
|
|
|
|
"source": [
|
|
|
|
|
"maximize_and_plot_likelihood(gauss, (1000, 3000), save=('poisson', 'haupt'))"
|
|
|
|
|
]
|
2019-12-16 23:04:02 +01:00
|
|
|
},
|
|
|
|
|
{
|
|
|
|
|
"cell_type": "code",
|
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|
|
|
"execution_count": null,
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"metadata": {
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"autoscroll": false,
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"collapsed": false,
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"ein.tags": "worksheet-0",
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"slideshow": {
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"slide_type": "-"
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}
|
|
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},
|
|
|
|
|
"outputs": [],
|
|
|
|
|
"source": []
|
2019-12-14 11:43:02 +01:00
|
|
|
}
|
|
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],
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"metadata": {
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"kernelspec": {
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"argv": [
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2019-12-16 19:41:50 +01:00
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"python",
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2019-12-14 11:43:02 +01:00
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"-m",
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"ipykernel_launcher",
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"-f",
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"{connection_file}"
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],
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"display_name": "Python 3",
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"env": null,
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},
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"name": "Untitled.ipynb"
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},
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}
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