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>"
|
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|
|
|
]
|
|
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|
|
},
|
|
|
|
|
"metadata": {},
|
|
|
|
|
"output_type": "display_data"
|
|
|
|
|
}
|
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|
|
],
|
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": "-"
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|
|
|
|
}
|
|
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|
|
},
|
|
|
|
|
"source": [
|
|
|
|
|
"# Statistische Auswertung der Langzeitmessung"
|
|
|
|
|
]
|
|
|
|
|
},
|
2019-12-14 21:17:02 +01:00
|
|
|
{
|
|
|
|
|
"cell_type": "code",
|
2019-12-22 19:24:15 +01:00
|
|
|
"execution_count": 44,
|
2019-12-14 21:17:02 +01:00
|
|
|
"metadata": {
|
|
|
|
|
"autoscroll": false,
|
|
|
|
|
"collapsed": false,
|
|
|
|
|
"ein.hycell": false,
|
|
|
|
|
"ein.tags": "worksheet-0",
|
|
|
|
|
"slideshow": {
|
|
|
|
|
"slide_type": "-"
|
|
|
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|
}
|
|
|
|
|
},
|
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",
|
2019-12-22 19:24:15 +01:00
|
|
|
"interval = (22, 150)"
|
2019-12-16 23:04:02 +01:00
|
|
|
]
|
|
|
|
|
},
|
|
|
|
|
{
|
|
|
|
|
"cell_type": "code",
|
2019-12-22 19:24:15 +01:00
|
|
|
"execution_count": 45,
|
|
|
|
|
"metadata": {
|
|
|
|
|
"autoscroll": false,
|
|
|
|
|
"collapsed": false,
|
|
|
|
|
"ein.hycell": false,
|
|
|
|
|
"ein.tags": "worksheet-0",
|
|
|
|
|
"slideshow": {
|
|
|
|
|
"slide_type": "-"
|
|
|
|
|
}
|
|
|
|
|
},
|
|
|
|
|
"outputs": [
|
|
|
|
|
{
|
|
|
|
|
"data": {
|
|
|
|
|
"image/png": [
|
|
|
|
|
"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
|
|
|
|
|
],
|
|
|
|
|
"text/plain": [
|
|
|
|
|
"<Figure size 360x288 with 1 Axes>"
|
|
|
|
|
]
|
|
|
|
|
},
|
|
|
|
|
"metadata": {},
|
|
|
|
|
"output_type": "display_data"
|
|
|
|
|
}
|
|
|
|
|
],
|
|
|
|
|
"source": [
|
|
|
|
|
"fig, ax = plot_spectrum(counts)\n",
|
|
|
|
|
"ax.axvline(interval[0], color='grey', linestyle='--')\n",
|
|
|
|
|
"ax.axvline(interval[1], color='grey', linestyle='--')\n",
|
|
|
|
|
"save_fig(fig, 'spectrum_overview', 'haupt')"
|
|
|
|
|
]
|
|
|
|
|
},
|
|
|
|
|
{
|
|
|
|
|
"cell_type": "code",
|
|
|
|
|
"execution_count": 72,
|
|
|
|
|
"metadata": {
|
|
|
|
|
"autoscroll": false,
|
|
|
|
|
"collapsed": false,
|
|
|
|
|
"ein.hycell": false,
|
|
|
|
|
"ein.tags": "worksheet-0",
|
|
|
|
|
"slideshow": {
|
|
|
|
|
"slide_type": "-"
|
|
|
|
|
}
|
|
|
|
|
},
|
|
|
|
|
"outputs": [
|
|
|
|
|
{
|
|
|
|
|
"data": {
|
|
|
|
|
"image/png": [
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"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
|
|
|
|
|
],
|
|
|
|
|
"text/plain": [
|
|
|
|
|
"<Figure size 360x288 with 1 Axes>"
|
|
|
|
|
]
|
|
|
|
|
},
|
|
|
|
|
"metadata": {},
|
|
|
|
|
"output_type": "display_data"
|
|
|
|
|
}
|
|
|
|
|
],
|
|
|
|
|
"source": [
|
|
|
|
|
"fig, ax = plot_spectrum(counts[interval[0]:interval[1]], offset=25, errorevery=5, zorder=-100)\n",
|
|
|
|
|
"save_fig(fig, 'spectrum_detail', 'haupt')\n"
|
|
|
|
|
]
|
|
|
|
|
},
|
|
|
|
|
{
|
|
|
|
|
"cell_type": "code",
|
|
|
|
|
"execution_count": 51,
|
|
|
|
|
"metadata": {
|
|
|
|
|
"autoscroll": false,
|
|
|
|
|
"collapsed": false,
|
|
|
|
|
"ein.hycell": false,
|
|
|
|
|
"ein.tags": "worksheet-0",
|
|
|
|
|
"slideshow": {
|
|
|
|
|
"slide_type": "-"
|
|
|
|
|
}
|
|
|
|
|
},
|
|
|
|
|
"outputs": [],
|
|
|
|
|
"source": [
|
|
|
|
|
"tau, d_tau, N, T, taus = continous(counts, interval)"
|
|
|
|
|
]
|
|
|
|
|
},
|
|
|
|
|
{
|
|
|
|
|
"cell_type": "code",
|
|
|
|
|
"execution_count": 74,
|
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": [
|
2019-12-22 19:24:15 +01:00
|
|
|
"(15565, 2234.844617545439, 17.535875533982885, 5292.09)"
|
2019-12-21 21:13:24 +01:00
|
|
|
]
|
|
|
|
|
},
|
2019-12-22 19:24:15 +01:00
|
|
|
"execution_count": 74,
|
2019-12-21 21:13:24 +01:00
|
|
|
"metadata": {},
|
|
|
|
|
"output_type": "execute_result"
|
2019-12-16 23:04:02 +01:00
|
|
|
}
|
|
|
|
|
],
|
|
|
|
|
"source": [
|
2019-12-22 19:24:15 +01:00
|
|
|
"N, tau, d_tau, T"
|
|
|
|
|
]
|
|
|
|
|
},
|
|
|
|
|
{
|
|
|
|
|
"cell_type": "code",
|
|
|
|
|
"execution_count": 64,
|
|
|
|
|
"metadata": {
|
|
|
|
|
"autoscroll": false,
|
|
|
|
|
"collapsed": false,
|
|
|
|
|
"ein.hycell": false,
|
|
|
|
|
"ein.tags": "worksheet-0",
|
|
|
|
|
"slideshow": {
|
|
|
|
|
"slide_type": "-"
|
|
|
|
|
}
|
|
|
|
|
},
|
|
|
|
|
"outputs": [
|
|
|
|
|
{
|
|
|
|
|
"data": {
|
|
|
|
|
"image/png": [
|
|
|
|
|
"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
|
|
|
|
|
],
|
|
|
|
|
"text/plain": [
|
|
|
|
|
"<Figure size 360x288 with 1 Axes>"
|
|
|
|
|
]
|
|
|
|
|
},
|
|
|
|
|
"metadata": {},
|
|
|
|
|
"output_type": "display_data"
|
|
|
|
|
}
|
|
|
|
|
],
|
|
|
|
|
"source": [
|
|
|
|
|
"fig, ax = set_up_plot()\n",
|
|
|
|
|
"ax.plot(taus, linestyle='None', marker='x', label='Iterationswert')\n",
|
|
|
|
|
"ax.set_xlabel('Iteration')\n",
|
|
|
|
|
"ax.axhline(tau, color='gray', linestyle='--', label='Letzter Iterationswert')\n",
|
|
|
|
|
"ax.set_ylabel(r'$\\tau$ [ns]')\n",
|
|
|
|
|
"ax.legend()\n",
|
|
|
|
|
"#ax.set_xlim(0)\n",
|
|
|
|
|
"#ax.set_ylim(0)\n",
|
|
|
|
|
"save_fig(fig, 'continous', 'haupt')"
|
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,
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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": "-"
|
|
|
|
|
}
|
|
|
|
|
},
|
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"
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},
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{
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"data": {
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"image/png": [
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|
|
|
|
|
],
|
|
|
|
|
"text/plain": [
|
|
|
|
|
"<Figure size 360x288 with 1 Axes>"
|
|
|
|
|
]
|
|
|
|
|
},
|
|
|
|
|
"metadata": {},
|
|
|
|
|
"output_type": "display_data"
|
|
|
|
|
}
|
|
|
|
|
],
|
|
|
|
|
"source": [
|
|
|
|
|
"maximize_and_plot_likelihood(poisson, (1000, 3000), save=('poisson', 'haupt'))"
|
|
|
|
|
]
|
2019-12-16 23:04:02 +01:00
|
|
|
},
|
|
|
|
|
{
|
|
|
|
|
"cell_type": "code",
|
2019-12-21 21:13:24 +01:00
|
|
|
"execution_count": 14,
|
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 0x7f3b301ed9e8>),\n 2269.835038293664,\n (30.152362502227334, 30.977313295185468),\n 206.42320956201473)"
|
|
|
|
|
]
|
|
|
|
|
},
|
|
|
|
|
"execution_count": 14,
|
|
|
|
|
"metadata": {},
|
|
|
|
|
"output_type": "execute_result"
|
|
|
|
|
},
|
|
|
|
|
{
|
|
|
|
|
"data": {
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|
|
|
|
"image/png": [
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],
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|
|
|
|
"text/plain": [
|
|
|
|
|
"<Figure size 360x288 with 1 Axes>"
|
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|
|
|
]
|
|
|
|
|
},
|
|
|
|
|
"metadata": {},
|
|
|
|
|
"output_type": "display_data"
|
|
|
|
|
}
|
|
|
|
|
],
|
|
|
|
|
"source": [
|
|
|
|
|
"maximize_and_plot_likelihood(gauss, (1000, 3000), save=('poisson', 'haupt'))"
|
|
|
|
|
]
|
2019-12-16 23:04:02 +01:00
|
|
|
},
|
|
|
|
|
{
|
|
|
|
|
"cell_type": "code",
|
|
|
|
|
"execution_count": null,
|
|
|
|
|
"metadata": {
|
|
|
|
|
"autoscroll": false,
|
|
|
|
|
"collapsed": false,
|
|
|
|
|
"ein.hycell": false,
|
|
|
|
|
"ein.tags": "worksheet-0",
|
|
|
|
|
"slideshow": {
|
|
|
|
|
"slide_type": "-"
|
|
|
|
|
}
|
|
|
|
|
},
|
|
|
|
|
"outputs": [],
|
|
|
|
|
"source": []
|
2019-12-14 11:43:02 +01:00
|
|
|
}
|
|
|
|
|
],
|
|
|
|
|
"metadata": {
|
|
|
|
|
"kernelspec": {
|
|
|
|
|
"argv": [
|
2019-12-16 19:41:50 +01:00
|
|
|
"python",
|
2019-12-14 11:43:02 +01:00
|
|
|
"-m",
|
|
|
|
|
"ipykernel_launcher",
|
|
|
|
|
"-f",
|
|
|
|
|
"{connection_file}"
|
|
|
|
|
],
|
|
|
|
|
"display_name": "Python 3",
|
|
|
|
|
"env": null,
|
|
|
|
|
"interrupt_mode": "signal",
|
|
|
|
|
"language": "python",
|
|
|
|
|
"metadata": null,
|
|
|
|
|
"name": "python3"
|
|
|
|
|
},
|
|
|
|
|
"name": "Untitled.ipynb"
|
|
|
|
|
},
|
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|
|
"nbformat": 4,
|
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"nbformat_minor": 2
|
|
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|
|
}
|
