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{
"cells": [
{
"cell_type": "code",
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"execution_count": 12,
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"metadata": {
"autoscroll": false,
"collapsed": false,
"ein.hycell": false,
"ein.tags": "worksheet-0",
"slideshow": {
"slide_type": "-"
}
},
"outputs": [],
"source": [
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
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"import pandas as pd\n",
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"from util import *\n",
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"from scipy.stats import binned_statistic"
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]
},
{
"cell_type": "markdown",
"metadata": {
"ein.tags": "worksheet-0",
"slideshow": {
"slide_type": "-"
}
},
"source": [
"# Kennlinien PM3"
]
},
{
"cell_type": "code",
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"execution_count": 13,
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"metadata": {
"autoscroll": false,
"collapsed": false,
"ein.hycell": false,
"ein.tags": "worksheet-0",
"slideshow": {
"slide_type": "-"
}
},
"outputs": [
{
"data": {
"text/plain": [
"131.75230566534913"
]
},
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"execution_count": 13,
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"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"eta = .03\n",
"rt = 506/60\n",
"T = 1/eta**2*1/rt\n",
"T"
]
},
{
"cell_type": "code",
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"execution_count": 14,
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"metadata": {
"autoscroll": false,
"collapsed": false,
"ein.hycell": false,
"ein.tags": "worksheet-0",
"slideshow": {
"slide_type": "-"
}
},
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"outputs": [
{
"data": {
"text/plain": [
"0.029514066805047763"
]
},
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"execution_count": 14,
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"metadata": {},
"output_type": "execute_result"
}
],
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"source": [
"N=1148\n",
"c=N/T\n",
"dc=np.sqrt(N)/T\n",
"dc/c"
]
},
{
"cell_type": "markdown",
"metadata": {
"ein.tags": "worksheet-0",
"slideshow": {
"slide_type": "-"
}
},
"source": [
"## Plot"
]
},
{
"cell_type": "code",
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"execution_count": 33,
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"metadata": {
"autoscroll": false,
"collapsed": false,
"ein.hycell": false,
"ein.tags": "worksheet-0",
"slideshow": {
"slide_type": "-"
}
},
"outputs": [
{
"data": {
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"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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]
},
"metadata": {},
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"output_type": "display_data"
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}
],
"source": [
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"%matplotlib inline\n",
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"\n",
"T = 140\n",
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"fig, ax = set_up_plot()\n",
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"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[\"N123\"]/T, yerr=np.sqrt(calib[\"N123\"])/T,\n",
" marker='.', label='Koinzidenzrate 123')\n",
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"ax.axvline(2300, linestyle='dotted', color='gray', label='Gewaehlte Spannung')\n",
"ax.legend()\n",
"ax.set_xlim([calib['U/V'].min(), calib['U/V'].max()])\n",
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"ax.set_ylim(0)\n",
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"save_fig(fig, 'kennlinie_123', 'vorversuch')\n"
]
},
{
"cell_type": "code",
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"execution_count": 30,
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"metadata": {
"autoscroll": false,
"collapsed": false,
"ein.hycell": false,
"ein.tags": "worksheet-0",
"slideshow": {
"slide_type": "-"
}
},
"outputs": [
{
"data": {
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"image/png": [
"iVBORw0KGgoAAAANSUhEUgAAAWEAAAEZCAIAAADJ7AMsAAAAAXNSR0IB2cksfwAAAAlwSFlzAAALEgAACxIB0t1+/AAAIABJREFUeNrtnXlAE9f6/k82CEvYd1E2QUBkcVewKO61db22Vm1ra+2irbb+etVqbxXb2mo3bbUura3LtbVVilaoKwIKgmhAjJCwI1tIWBLIvkzm98e0uXyDhBASIPA+f53MTJ45OTMc3s+ZM+cl4TiOQCAQqAuRoQlAIBD0ESAQCPoIEAgEfQQIBII+AgQCQR8BAoEsRNSBUAkSiQRXAgTqAxkx14E8cKqO43hjY+MTC11t11/IyMjo6bfMZGuIf29s9ZzItLbagplsOzubydZU/oa3w0C4dS0yjti1a1diYiJCiMfjIYSEQiGxXafQ1Xb9BbVard/WuBMZYWuIf29s9ZzItLbagplsOzubydZU/oa3w0C4dYmYfefOnbt27bKkPmLXrl0kEsnT05PY0lWh2wM6FzgcjhHfMpNtt/69tO3qRCa3JQpmsn1ihc1kaxL/HrVDv9+6xrHGgBiPAIEMlFqtViqV7e3tCCG5XG54gUKhPHFXj0wMt+3liQy37cqfRCJhGDYYxiyBNYA1DGcNhUIhkUhoNJpAIEAIYRhmeMHDw+OJu3pkYrhtL09kuG1X/jiOSyQSuVxuZ2fXS9b4e7CwfwVjljBmaciYZVVVlVgsViqVxJYeFdrb25+4yzi3bm17eSLDbfX4y2QyDoej0Wg6jlka8ecJcQTEERYTR2g0GjKZrA2huyrIVNhNDj96uPMwJ3rHeFulUnU+uFs3/YWubHt5IsNt9fgTPUJjYyPBIDBmCWOWg3/MUiQSUalUhBCNRiO2PLHAF6vf/Z31xb8i/d2Ha3fJ5fKuvqXfTX9Bj21vTtQjWz3+VCrVzc2NTCb3ZswS5lmCQP9Tbm7uypUrP/3006amJoTQ5s2bExMTCc43XDdv3ly+fDlCqLGx8cCBA50P6Go7obt3765evXrfvn1JSUnffPNNZmYmQuj+/fsvv/zymTNntIfl5+fPmzfv5s2bCCEmk3nr1q0rV66Yo01ME0dcvXq1tbXVzs5u4cKF2o1nzpxZtWoVQujevXtyuRwhNG3aNGANYI3esIZardbOyu0q6hbLlQihNqlCpVL1lDXGjRtHo9EmT57s5ORUXV29YMGCp556CsOwriL/J9pOmzbtyJEjGIbZ2tqOGzeu84k6bu9sO3bsWAaDMWnSpKlTp6rV6uDg4Hv37sXExCxatOjAgQPLli0jDq6vr1er1dOmTbtz505aWtr27dsXLlzo7u4+evRorRuGYU1NTSKRCCHULFH1G2tcunTJy8tr7ty5S5cudXZ2JjqCzMzMs2fPrlq1isViPXjwYN26dQcOHHB3dw8NDQXWANboJWtQqFSRXK1SaWg0CkKoY0GlwVf/dA8htO9aWfgw5xB3G2KXWK7WUJH2YFsrCqWLEJ1EIlGp1Kqqqvr6+pkzZxK7WCyWQCAYOXKkn59fbm6uQCAICgqqqKiYMGFCUVGRSCQaNmzY48ePExISKBQKjUajUCgUCqWsrEwkEt2/f18mk3U8gNhOHJmfn9/U1BQRETFs2DAdTNDWh3gaamdnFx0dXVBQMGXKFB6P5+3tTSKRaDTa+PHjQ0JCKBQKQmjUqFGELWFCoVDc3d0pFArZ1mnF9xn9Nj+CTqdzuVyEUHh4OIvFmjZtGpfLdXJyIvYmJydPnToVIRQYGHjp0iWdPgIEMkJcoTx27039xyjVmpU/5Ha194t/RS6O8upqb2lp6ZkzZ0aNGjVjxgyE0JUrV1pbW19++eWNGzd+++23ERERcXFx58+fd3Z2Pnr06KZNm6ZNm/brr786Ozt/9913mzZt0vqEhYUlJiaePHkyISGh4wHE9lmzZl2+fJnP569cufLf//73/v37O9aByWTK5XI2m3306FFvb28iWlm7du3BgwenTJlSVVUVEBBAHGllZWVnZ7d3794NGzbY29sTR+oyVGWLWtN/c7Fnz55NBDZ5eXmnTp1CCD169CghIeHvy8nl2tjYIIQYDAbRlehQBiECulQqFYfD6Vzoarv+gkgk0m9r3ImMsDXEvze2ek5kWlttwUy2nSvc8QBXV1exWEyhUCgazbsJAU+8GwVS1Zm8erUGp5BJ8cEukcMctGN1HV8dDHSmyWQyuVyO4zgBwtoChmEkEunzzz+fO3fu9OnTQ0JCTpw4sWTJkvT0dEdHRz6fb2Nj4+7u7unpqVQqGxoacBx3c3Pz8fHRaDTV1dWErVqtlslkJBLJ2tqaQqHoHEBsl8lkx48fX7x48e3btxkMBp/Pp9Pp2soEBARMnDhxwoQJJBJJJBIRzzIDAgJKS0t5PB5xFgzDiF0kEmnjxo2vvPKKra3tpEmTtCYymezOnTsqlUqmoijVGOrw/mQPiMNUcxwOHjxYUFCA4/jt27fFYrFarX7mmWdwHH/nnXfS0tJwHL9y5cq///1vmB8B8yOMnh9RVlamVCq7nSlw7n6t39aUDWeYakxjxPyIlStXEnW4d+9ebGysWq1+++23mUwmjuNtbW0KhQLH8YULF+I4Xl1dvX79ehzHFy9erFQqq6ur3333XcJnxYoVHQtdHbBx40Ymk6lUKqVSqUKh0NZh1apVN27c0Kne5cuXcRw/dOjQ5s2bpVIpl8udNWsWjuPffPPNiRMnlErlhx9++PHHH3f8CtFijY2NKkwTs/taf86PSEtLmzdvXlBQEJPJRAjduHFDIpE0Njbm5eVFR0cTw04CgSAiIgLiZJC5NSXQFSEUH+JOIZM0PZyOnJubW15efvny5fDwcF9fX7FY/MYbb7z22mt//PGHRqOpqalZunRpdnZ2eXl5YWHhnTt3SktLz507V1ZW9vDhw7y8PA6Hw+Vy6+vrORzOo0eP5HJ5SUnJiRMnOh9QUlJSWlr6wQcfHDx4UKlU8ni8RYsWEZiQl5dXXl5+9erViIgIFxcXomJ5eXnff/+9jY3N888//9VXX5HJ5HPnzjU0NGRkZIwfP761tbW0tPTRo0c6wKLVrdKmVomy31gjIyPjnXfecXV1ValUu3fvnjNnjkajOXbsmFKp1Gg0y5cv37t3b2xsLIvF2rFjBzzXgOca5n6uoVaribJxzzUyMzOJ8T8Mw+7du0cUxo0bJxKJIiIiVCrVxIkTmUwmhUIJDw9fvXo1nU5fvHgxhmGRkZGvvfYahmHe3t55eXkYhlEolLt372IYtmbNGp0DiO0UCmXnzp1CoXDcuHHa2sbExFy/fp1Op3es3rhx45KSkogtxJ/MunXr3nzzTS0csdns06dPW1tbd/xF2ucaZ7JbHOlUNGDnYiuVSjabrVKpYC42sEYfsEaLWPHmqXs5Fc1DfC62ljVKq+uDd/yVeKnIuL/3vphDRaPRQkNDiadWIJC55WJn9e2KqMmBrtAUhK6VtCrVmn+N9e3POVRGC1gDWMPkrIF68gbEIH5fQ8safz7kjXSzcaXIELyvAXOoOp9oMM2hkkqlJBKp4xwhk7wBMVjf1yAoXkyxL2mWf/RMONGMlrfGDMQREEcYHkfQaLSGhgZ7e3uCW4nXQA0sKJVK4j+zzq4emRhu28sTGW7blT+xfgRC6NStUgoJTRlGI1oV4giIIwZzHOHp6SkUCpubm4lZeVKp1NbW1sBCQ0ODj49P5109MjHctpcnMty2K38SiaRWqwODRl47nx4X5BwW4Kt9RmFhcQQI1CM5OTkpFAqi1+DxeIYXysvLiRcidHb1yMRw216eyHBb/f5ZFS18keL96b69aXNgDWANWBcboUG6LjYxLWK0y99/X8AawBqwLjasi/2/jyIFdruyctVkPzcXZ1hjBgQC6eoap1XRi2kRwBrAGsAag5w1LrL+nhYBOXiANYA1gDV0/cv44pKm/02LANYAgUD/R0nMOiqZtDDap/dWwBrAGsAag401MA2exKydONwOkwh5Esj3CawBrAGs8X/9i4WkJrHy3zOGD4Z8nxBHQBwBcYTJb12daREQR0AcAXEExBH/83/itAgYswSBQH/LVNMigDWANYA1BidrdJ4WAaw
],
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"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",
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"ax.set_ylim(0)\n",
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"save_fig(fig, 'kennlinie_pm3', 'vorversuch')"
]
},
{
"cell_type": "markdown",
"metadata": {
"ein.tags": "worksheet-0",
"slideshow": {
"slide_type": "-"
}
},
"source": [
"# Peakhoehen der Photomultiplier"
]
},
{
"cell_type": "code",
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"execution_count": 22,
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"metadata": {
"autoscroll": false,
"collapsed": false,
"ein.hycell": false,
"ein.tags": "worksheet-0",
"slideshow": {
"slide_type": "-"
}
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
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"execution_count": 23,
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"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",
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"bin_offsets = [8, 15, 40]\n",
"scale_factors = [100, 10, 1]"
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]
},
{
"cell_type": "code",
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"execution_count": 36,
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"metadata": {
"autoscroll": false,
"collapsed": false,
"ein.hycell": false,
"ein.tags": "worksheet-0",
"slideshow": {
"slide_type": "-"
}
},
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"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
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"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"
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]
}
],
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"source": [
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"for peak in peak_labels: # nice and dirty :{}\n",
" cur = peaks[peak]\n",
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" print(f\"{peak} & {cur.min():.2f} & {cur.max():.2f} & {cur.mean():.2f} & {cur.median():.2f} & {cur.mean()/cur.std():.2f} \\\\\\\\\")\n",
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"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",
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"execution_count": 44,
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"metadata": {
"autoscroll": false,
"collapsed": false,
"ein.hycell": false,
"ein.tags": "worksheet-0",
"slideshow": {
"slide_type": "-"
}
},
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"outputs": [
{
"data": {
"image/png": [
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"iVBORw0KGgoAAAANSUhEUgAAAWAAAAEYCAYAAABiECzgAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8QZhcZAAAgAElEQVR4nOydeXxU1fn/32cmk32BJOw7Yd8hcQEXUEFUhLq0av1RRbGIVSkqi6httWKLFtyx7kvRtn4tboiKiBsqUYhskT1AkD0hZN8mM8/vj1mYTCYzk2SSmQnn83rxyj33c55znntPODn3c5/7HCUiaGhoaGi0PAzBdkBDQ0PjdIWegDU0NDSCBD0Ba2hoaAQJegLW0NDQCBL0BKyhoaERJEQE24HmRmpqqvTs2ZOamhoiIk5drmvZnauoqCAmJsYj582uObj6fGlpv1z9aGlfwnF8AnWt4TA+jfUlHMfH3+vJysrKF5F2+IKItOp/6enpIiKSl5cnrnAtu3MbNmyol/Nm1xxcfb60tF+ufrS0L+E4Po1tMxzHp7G+hOP4eONcj4EN4sf8pCUIDQ0NjSCh1UoQSqnJwORevXqRn59PUVFRLd617M5VVVWRn5/vkfNm1xxcfb60tF+ufrS0L+E4Po1tMxzHp7G+hOP4eOPc6iUppV4EVojICupBq52A7Re9IiMj4/epqakAOH464Fp2Pc7Nza2X82bXHJw3X1rSL3c/WtqXcByfxrYZjuPTWF/CcXy8cS7HRSIyAx9Q0so/Rc7IyJANGzaQn59f60a5lt25rKws0tPTPXLe7JqDq88Xb20cO3aM8vJyKisrAbBYLBiNxjrHDeGqq6uJjIxssF0gOPd6rr40d98t7Us4jk9jfQnH8XEvGwwGevfujclkqvV/UCmVJSIZ+ECrXQGfzhKE4xehS5cuKKUC8otWVVVFVFRUg+2aY1Jy9SXYE3CgfQnH8WmsL+E4Pq5lEeHEiRPs3buXlJQULUG44nSWII4ePUr79u1RSjk5k8nk8dhfrrq6ulF2geJcj919CaZfzeFLOI5PY30Jx/FxLaekpFBYWIiHOcYvCUJHQbRSuE6+GhoazYOm/j9rtSvg01mCsFqtmM1mZ9lisXg8bggnIgFv01/OvZ6rL83dd0v7Eo7j01hfwnF8PHEWi8XTHKMlCE5jCSKYj5XNwWkJwjsX7PFprC/hOD7uZaPR2GgJotVOwMFeAd+1bB2Hy089nnSOFWfZ9bg+7u1bz/bqi14B6xVwqIxPY30Jx/HxxOkVsAcEewV8uFyRU1JbH3Ite+IMWOkqR8gp7VJvTKVeAesVsCcukONjsVg4//zzqaysxGKx8Otf/5qHHnoIgOeee46XXnoJq9XKjBkzmD17tkdf1qxZwx//+EcsFgs33XQT999/f8Cu1V/u5ptvZsWKFXTo0IHs7GzA9n/jhhtu4OjRoyiluPXWW/njH/9Yp41ffvmF6dOne6znXrcpK2D9Ei5kIPzD9AJv1MxlqnF1sJ1pMn788UfGjRvH+eefT2ZmZsDa/fbbbznnnHP46quvAtZmQ/Hkk0/6Ve/ss8/GYrHw1ltv0b59+2b2KnCIioriiy++ICsri02bNvHpp5+SmZlJdnY2L730Ej/++CNZWVl89NFH7N69u469xWLh9ttv55NPPmHbtm28/fbbbNu2rcWvY9q0abz//vu1zkVERLBkyRK2b9/Ot99+y9KlSz365m+9pqLVroCDLUF0jq39gYtr2RM3zbKcq61rOa7a8VfT6xRnplPd59KwlSBGjhzJeeedR0lJCenp6R4fAxvzWH7WWWcxePBgampqMJvNQZEgnnzySW6//XbA+yPut99+i9Vq5ZprruG+++5r1sdyEWHs2LEsXbqU/v37c/z4cS6++GI2bdrUqDajoqKwWCxUVVVRXV2NxWJh27ZtnHnmmZhMJiwWC+eeey7/+9//mDNnTq121q9fT1paGt26dQPgN7/5De+++y59+/b12fc//vEP9u7dy7Fjx8jOzmbmzJncfffdjbpHo0ePZteuXc4xslgspKamkpqaitlsJjY2lv79+5Obm0vv3r1rtdm+fXs6depUp56na9AShAeEkwRxqfUrfmd9j//WjONf0Tfw1+pFZKy+Gzr0JDcqtmkSxCf3YjyyGYOyPewYxeo8di974wxWC0aDPRi941AY/7DPR0Kj0YjBYKjFlZaWMnXqVM4//3y2b9/O1KlTGT9+PK+99hoLFizgrrvuYu/evWzbto2VK1eSmJgIwD333IPZbKZ3794cPnyYiIgIqqqquOaaaxg7diw7d+7k+uuvZ+zYsSxfvpyZM2eSl5fHxo0bmTFjBosXL+b5559n9+7dLFu2DBHhlltu4f777+eKK67gwQcfREQwGo3ExsayYMECp08zZszgyJEjbN68mdmzZ1NYWMgjjzzCgAEDmDx5Mg8//DA1NTUAtGnThnnz5vHhhx8ya9YsvvrqK3r27IlSCpPJxH//+1+Pvv3yyy8sWLCAWbNmkZuby/bt2/noo4+IiYnBZDIxa9Ysqqqq6NatG5988gl9+/ZlyZIlpKSkALbH/pycHAYNGoTBYGDbtm0MGzbMee8dP8877zyKi4ud4VMiglKKxYsXM378eGddi8VCRkYGOTk53H777ZxzzjkkJCTw0EMPUVxcTEREBKtWrSIjI6NOH0eOHKF79+7OcteuXcnKyqpTz9PvzbZt2zAajbzzzjv88ssvXH311cyfP79W3QsuuIDS0lKnjes1jB07tlabSinnvXfvb//+/WzevJlzzjkHo9FYr6zhWs9TO/olXBgjlkpmWV7nG8tQHqi5mR7KyPTqOWxu9w/4aDaMfiHYLgYUBoOBu+66i/Hjx3Ps2DEuv/xyxo8fz0033cTrr7/OiBEjmD9/PrfddhurV6/m6quv5uOPP2b37t188sknALz33nvOtmbNmsUll1xCQUEBEydO5Pvvv+e6667j3nvvBeDMM89kxIgRRERE8Pzzz5ORkUG/fv2oqqpi7NixXHHFFaxatYoff/yR1att0s/YsWO59NJLuemmm3jjjTfIyMhgypQpbNiwgeHDh3Pffffx4IMPAvDBBx+QmZnJZ599htlsZsKECVx88cVMmTKFJUuW1Ll+T74Bzr4c7d9+++2sXr2aKVOmsHLlSnbv3s2HH36IyWTi008/Zdq0ac4/TgAHDhygS5cuGAy2P5hbt25l2LBhdfpfu3YtZrPZOYG4HrvCaDSyYcMGysrKuPLKK8nOzmbgwIHMnz+fCRMmEBcXx/Dhw2vlyXXAU3oDf+Nlt2zZwnvvvYfRaMRoNJKcnFynzpdfflnLZ/fr8QelpaVce+21PPnkkyQmJtZr514v0Gi1E3C4SBBnWHcQaTHzUeQkekQb7VwcJcNvJuGLBZjytpKf37NBfdeSIMY/jMXSPJ+Xejp2LVssljpySE1NDWvWrOHbb7/FaDSSl5fn5EWEXr16YTabnV8Ymc1msrOzSUtLc9ZzJNivrq7mq6++IjMzE5PJRF5enke/rFYrNTU1xMfHM3HiRN566y0qKiq45pprMJvNbNy4kbKyMh555BEAunTpwpEjRxg8eDAiQr9+/TCbzQwfPtzZpsOXrVu3Om2tVmsdW7PZjNlsrlfCsVqtznskIs7rTE5OprCwEIvFwpYtW0hLS3PaOK7f9Vo3b97MkCFDnH1kZWU5r8+13gUXXEBJSQnuePTRR7nooovq3L+4uDjOO+88Vq5cSZ8+fbjhhhu44YYbsFgs/OUvf6Fr1651+ujcuTMHDhxw+nLw4EE6dOhQp577701lZSX5+fl0794di8XCTz/9xODBg+vYjRs3rtYK2PUaxo0bV+ucI++uaxtms5mrrrqKa6+9lsmTJ9crZXmq5+63liDqQbhIEL+J2I45wsiHZf2pRDm5hDP/H3zzEB2PfUVq6k0N6jtUoiDcJYitW7eyevVqjh07xquvvkp5eTmvvPKKk1dKERkZiclkQinlfCwcPHgwa9eurfVIGBERwRtvvMHRo0d5/fXXMZvNvPTSS06bhIQEysrKSElJ4eDBg0R
2019-12-15 17:36:13 +01:00
],
"text/plain": [
"<Figure size 360x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": [
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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"
},
{
"data": {
"image/png": [
2019-12-21 18:28:41 +01:00
"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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"
}
],
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"source": [
"for index, peak in enumerate(peak_labels):\n",
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" plot_hist(peaks[peak], calculate_bins(peaks[peak]) + bin_offsets[index],\n",
" scale_factors[index],\n",
" save=(f'muon_{peak}_spec', 'vorversuch'))"
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]
},
{
"cell_type": "code",
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"execution_count": 9,
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"metadata": {
"autoscroll": false,
"collapsed": false,
"ein.hycell": false,
"ein.tags": "worksheet-0",
"slideshow": {
"slide_type": "-"
}
},
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"outputs": [
{
"ename": "NameError",
"evalue": "name 'ROOT' is not defined",
"output_type": "error",
"traceback": [
"\u001b[0;31m\u001b[0m",
"\u001b[0;31mNameError\u001b[0mTraceback (most recent call last)",
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"\u001b[0;32m<ipython-input-9-71496d78a884>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mvec\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mROOT\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvector\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"float\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
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"\u001b[0;31mNameError\u001b[0m: name 'ROOT' is not defined"
]
}
],
"source": [
"vec = ROOT.std.vector(\"float\")(2)"
]
},
{
"cell_type": "code",
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"execution_count": 10,
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"metadata": {
"autoscroll": false,
"collapsed": false,
"ein.hycell": false,
"ein.tags": "worksheet-0",
"slideshow": {
"slide_type": "-"
}
},
"outputs": [
{
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"ename": "NameError",
"evalue": "name 'ROOT' is not defined",
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"output_type": "error",
"traceback": [
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"\u001b[0;31m\u001b[0m",
"\u001b[0;31mNameError\u001b[0mTraceback (most recent call last)",
"\u001b[0;32m<ipython-input-10-a52ea60adccc>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mROOT\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTMath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mLandau\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvec\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;31mNameError\u001b[0m: name 'ROOT' is not defined"
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]
}
],
"source": [
"ROOT.TMath.Landau(vec)"
]
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},
{
"cell_type": "code",
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"execution_count": 11,
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"metadata": {
"autoscroll": false,
"collapsed": false,
"ein.hycell": false,
"ein.tags": "worksheet-0",
"slideshow": {
"slide_type": "-"
}
},
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"outputs": [],
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"source": [
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"x = np.arange(0, 100, 0.01)\n",
"\n",
"y_landau = pylandau.landau(x)"
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]
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},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"autoscroll": false,
"collapsed": false,
"ein.hycell": false,
"ein.tags": "worksheet-0",
"slideshow": {
"slide_type": "-"
}
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"autoscroll": false,
"collapsed": false,
"ein.hycell": false,
"ein.tags": "worksheet-0",
"slideshow": {
"slide_type": "-"
}
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"autoscroll": false,
"collapsed": false,
"ein.hycell": false,
"ein.tags": "worksheet-0",
"slideshow": {
"slide_type": "-"
}
},
"outputs": [],
"source": []
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}
],
"metadata": {
"kernelspec": {
"argv": [
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"python",
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"-m",
"ipykernel_launcher",
"-f",
"{connection_file}"
],
"display_name": "Python 3",
"env": null,
"interrupt_mode": "signal",
"language": "python",
"metadata": null,
"name": "python3"
},
"name": "Untitled.ipynb"
},
"nbformat": 4,
"nbformat_minor": 2
}
