2019-12-14 11:43:02 +01:00
{
"cells": [
{
"cell_type": "code",
2019-12-16 19:41:50 +01:00
"execution_count": 9,
2019-12-14 11:43:02 +01:00
"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",
2019-12-14 21:17:02 +01:00
"import pandas as pd\n",
2019-12-15 17:36:13 +01:00
"from util import *\n",
2019-12-14 21:17:02 +01:00
"from scipy.stats import binned_statistic"
2019-12-14 11:43:02 +01:00
]
},
{
"cell_type": "markdown",
"metadata": {
"ein.tags": "worksheet-0",
"slideshow": {
"slide_type": "-"
}
},
"source": [
"# Kennlinien PM3"
]
},
{
"cell_type": "code",
2019-12-16 19:41:50 +01:00
"execution_count": 10,
2019-12-14 11:43:02 +01:00
"metadata": {
"autoscroll": false,
"collapsed": false,
"ein.hycell": false,
"ein.tags": "worksheet-0",
"slideshow": {
"slide_type": "-"
}
},
"outputs": [
{
"data": {
"text/plain": [
"131.75230566534913"
]
},
2019-12-16 19:41:50 +01:00
"execution_count": 10,
2019-12-14 11:43:02 +01:00
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"eta = .03\n",
"rt = 506/60\n",
"T = 1/eta**2*1/rt\n",
"T"
]
},
{
"cell_type": "code",
2019-12-16 19:41:50 +01:00
"execution_count": 11,
2019-12-14 11:43:02 +01:00
"metadata": {
"autoscroll": false,
"collapsed": false,
"ein.hycell": false,
"ein.tags": "worksheet-0",
"slideshow": {
"slide_type": "-"
}
},
2019-12-14 21:17:02 +01:00
"outputs": [
{
"data": {
"text/plain": [
"0.029514066805047763"
]
},
2019-12-16 19:41:50 +01:00
"execution_count": 11,
2019-12-14 21:17:02 +01:00
"metadata": {},
"output_type": "execute_result"
}
],
2019-12-14 11:43:02 +01:00
"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",
2019-12-16 19:41:50 +01:00
"execution_count": 12,
2019-12-14 11:43:02 +01:00
"metadata": {
"autoscroll": false,
"collapsed": false,
"ein.hycell": false,
"ein.tags": "worksheet-0",
"slideshow": {
"slide_type": "-"
}
},
"outputs": [
{
"data": {
2019-12-16 23:04:02 +01:00
"image/png": "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
2019-12-14 11:43:02 +01:00
"text/plain": [
2019-12-14 21:17:02 +01:00
"<Figure size 360x288 with 1 Axes>"
2019-12-14 11:43:02 +01:00
]
},
"metadata": {},
2019-12-14 21:17:02 +01:00
"output_type": "display_data"
2019-12-14 11:43:02 +01:00
}
],
"source": [
2019-12-14 21:17:02 +01:00
"%matplotlib inline\n",
2019-12-16 00:35:31 +01:00
"\n",
"T = 140\n",
2019-12-14 21:17:02 +01:00
"fig, ax = set_up_plot()\n",
2019-12-16 00:35:31 +01:00
"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",
2019-12-14 21:17:02 +01:00
"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",
2019-12-16 00:35:31 +01:00
"save_fig(fig, 'kennlinie_123', 'vorversuch')\n"
]
},
{
"cell_type": "code",
2019-12-16 19:41:50 +01:00
"execution_count": 13,
2019-12-16 00:35:31 +01:00
"metadata": {
"autoscroll": false,
"collapsed": false,
"ein.hycell": false,
"ein.tags": "worksheet-0",
"slideshow": {
"slide_type": "-"
}
},
"outputs": [
{
"data": {
2019-12-16 23:04:02 +01:00
"image/png": "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
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",
2019-12-14 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",
2019-12-16 23:04:02 +01:00
"execution_count": null,
"metadata": {
"autoscroll": false,
"collapsed": false,
"ein.hycell": false,
"ein.tags": "worksheet-0",
"slideshow": {
"slide_type": "-"
}
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 60,
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",
2019-12-16 23:04:02 +01:00
"execution_count": 77,
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": [
"P1 & 0.20 & 3.00 & 0.72 & 0.60 & 0.55\n",
"P2 & 0.20 & 11.40 & 4.44 & 3.60 & 2.35\n",
"P3 & 3.60 & 51.20 & 9.30 & 7.00 & 8.81\n"
]
}
],
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",
" print(f\"{peak} & {cur.min():.2f} & {cur.max():.2f} & {cur.mean():.2f} & {cur.median():.2f} & {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",
2019-12-16 19:41:50 +01:00
"execution_count": 16,
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": [
2019-12-16 23:04:02 +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"
},
{
"data": {
"image/png": [
2019-12-16 23:04:02 +01:00
"iVBORw0KGgoAAAANSUhEUgAAAWAAAAEYCAYAAABiECzgAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8QZhcZAAAgAElEQVR4nOydeXxU1fn/389MJnsIhCCGRVlFFgFJWGSxoqCICipWXMClKlKK1dZWtH7barWtWlr3n0irda9WqxUUN3ArKLKoKOACYVEEJSGQlSSzPL8/5maYTGaSmXCHDMn5vF55zTn3Oefcz9xMbj7zec49R1QVAwMDA4NDD0dLEzAwMDBoqzA3YAMDA4MWgrkBGxgYGLQQzA3YwMDAoIVgbsAGBgYGLYSkliZwKJCbm6s9evQAwOPxkJSU1KAcWt+/fz9paWlNtos21twxEpFHPMZPRB6NxaLlEW+OdvCwi2Okz2pr4hEtx7Vr1xaraieagqq2+p/8/HytQ1FRUdhyaH3NmjVRtYs21twxEpFHPMZPRB6NxaLlEW+OdvCwi2Okz2pr4hEtR2CNRnFvatUKWETOAs7q2bMnxcXFAJSWlgbiweXQek1NTdg+sYzRnHaHA494jJ+IPBqLRcsj3hzt4GEXx0if1dbEIwaO2SKyEFisqouJgFZ9A7be+OKCgoKrcnNzA8cjlYPr27dvj6pdLLHmjJGoPOweP1F5RIrFwiOeHO3iYQfHxj6rrYlHlBxLVXUWTSCuSTgRmSQiX4nIZhG5MUxcROQ+K/6ZiAyzjqeKyCoRWSciG0Tk1pB+11jjbhCRu+L5HgwMDAziBdE4PYosIk7ga2AisANYDVyoqhuD2kwGrgEmAyOBe1V1pIgIkKGqFSLiApYD16rqShEZD9wMnKGqNSJyhKrujsChzoK4atWqVYD/a0J2dnaDcmj966+/5phjjmmyXbSx5o4RLx5er5d9+/ZRW1uL/3KDz+fD4TjwPzm4HpqICI5FKjc3lig8GotFyyPeHO3gYRfHYC6tlUdoXUTIzc3F6XTW+zvr1KnTZuAdmrAg4pb4Ak4A3giq3wTcFNLmYfw35br6V0BeSJt04GNgpFX/NzAhFi4mCdewvGXLFi0qKtKamppArLa2tt4YwfWKioqIsUjl5sYShUdjsWh5xJujHTzs4hjMpbXyCK77fD79/vvvdcuWLaravCRcPBXwecAkVb3Sqs+0bqJzg9q8Atyhqsut+jJgnqqusRT0WqAP8KCqzrPafAq8DEwCqoFfqerqMOefBcwCyMvLy1+82P9PqKamhpSUlAbl0HpJSQk5OTlNtos21twx4sXD6XTSp0+fumsF+P8Z15VD616vF6fTGTYWqdzcWKLwaCwWLY94c7SDh10cg7m0Vh6hdZ/PR2FhIV6vt97fWUFBwXbgQHYUFqrqQkIRzV26OT/Aj4F/BNVnAveHtHkVGBtUXwbkh7Rpj1/KD7Lq64H7AAFGAFuxrJRIP0YBNyxv3LhRVRNTeSYKj8ZiRgEbBVxXrvtbao4CjmcSbgfQPajeDdgZaxtV3Qe8i1/x1vV50XqfqwAfUD9FaWBgYHAYIJ7T0FYDfUWkJ/AdcAFwUUibRcBcEXkWfxKuVFV3iUgnwK2q+0QkDZgA3Gn1+S9wMvCuiBwDJFNf6gdg5gFHLnu9XtxuN16vNxALLofWVRW32x02Fu0Y0cYShUdjsWh5xJujHTzs4hjMpbXyCBfzer0UFxcn1jxgVfWIyFzgDcAJPKqqG0RkthVfACzBPwNiM1AFXG51zwMet3xgB/BvVX3Fij0KPCoi64Fa4FJL8ofjYOYBRygXFRXhcrkAAq8AX/xQGSinOKHG+qx1TIE9+zwNYoO7tW8wRnC5ubFI7Wpraw96DDtisfCIJ0e7eNjBMZRLa+URWnc6nYG/r1jnAcf1QQxVXYL/Jht8bEFQWYGfhen3GXB8hDFrgRnRnN8o4MjlSAo45UA+B1eQQeUQwsZiUdHhYh06dGDv3r22KuA//OEPpKen86tf/SpqHrHGIvHYt28fzzzzDHPmzGlyjHHjxvG///0v5mvQkgq4srKSiRMnUlNTg8fj4dxzz+X3v/89AG+++Sbz5s3D5/Nx6aWXcuONN4Ydo0+fPmRlZeF0OklKSmLlypUx8zhYBdy3b18yMzMDHFasWBF2fK/Xy6hRo+jatSv//e9/w46ZkAo4EWAUcOwKuKb+ZzlQ9yWFj4UbIxYlEXzMLgXsdDpxOByHVAGrKk6nk8rKShYuXMi1117b5Bj/+9//Grz3RFfA6enpvPPOO2RmZlJVVcX48eM588wzGT58ONdffz1Lly6lW7duFBQUMG3aNAYMGNBgDBHh3XffJTc3F7fb3WIK+K233iIvLw8gIo/777+fY489loqKiojjHIwCNstRGiQcpk2bRn5+PgMHDmThQv/MnW3bttG/f3/mzp3LwIEDOfXUU9m/f3+gzx//+EcGDhzIhAkT+OqrrwLHzz77bPLz8xkyZEi9sQYNGhRo87e//Y1bbrmFefPmsWBB4Asat9xyC3fffTcATz31FKNHj2bo0KFcffXVeL3eAKc5c+YwYsQIvv32W2688Ua2bNnC0KFD+fWvfx22Xx06dOhQ732H8po/fz633HJLvfd/1VVXMWTIkHrv/7bbbuPYY49l4sSJzJgxg/nz5ze4pieddFLguuzZs4ehQ4fG8Bs5ABEhMzMT8N+03G43IsKqVavo1asXvXr1Ijk5mfPPP5+XX365WeeIhDvvvJNZs2Zx9tlnc8wxx4R9n3Zix44dvPrqq/zkJz+J2zlatQI2FkTkcrAF4XjzZuSHz3Eq9PH4Am1FoM5ddwp0CHLa62I+lwOngs+aJllX1s7H4Tv1j1F9jQy1MRYsWECnTp3Yv38/J5xwAmeccQZut5tNmzbxyCOPsHDhQi688EKee+45LrjgAj7++GP+9a9/sXLlSlSVkSNHMnjwYNxuNw8//DA5OTlUVFQwduxYpkyZgs/nq/c11efz4fV6mTZtGtdffz2zZ88G4LnnnuPll1/ms88+41//+hdvv/02qampXHPNNTz77LOMHz+er776ir///e/87W9/w+l0ctttt/H555+zevVqvvjiC2666aZ6/Z5++mkuueSSwHut4+B2u+vxqvtqW/d78vl8bNq0iSeeeIL777+fGTNm8Nxzz9GrVy9eeOEFVq1ahcfjYcSIEQwbNqzBNd28eTO9evXC7Xbz8ccfM3DgwAbWxfjx4ykvLycUd955J6ecckqgndfrZeTIkRQWFjJ79myGDRvGf/7zH7p27RoYs0uXLqxZs6YBjzpMnDgREeGKK65g1qwDQrExC2LdunU4nU6effZZNm/ezIUXXsi1114baFfHX0Pm7dbxDx5PRJg8eTIiwlVXXcXll19OMLxeLz//+c/505/+RGlpaaO2hrEgIsBYENFZEE6HA8SBDx++oJusAwJ1pxA25rD6OcR6bLOu7HDgjOJrdfCxuteHHnqIRYsWAX4VsnXrVrp160bPnj0ZMmQILpeL4cOHs2PHDpxOJx9++CHnnnsuWVlZuFwupkyZErAgHnroIV566SVUlR07drBt2zY6duyIiATO53A4cDqdjBgxgqKiosBPTk4OPXv25OGHH+aTTz5h7NixiAj79+8nJycHl8vF0UcfzdixYwNfYYPfy/vvv9+gX6dOncJeD5fLhcPhqMfL6XTidDoDsZ49ezJ8+HDcbnfg/e/evZuzzz6bdu3aAXDmmWcG+tSNu337drp27Rrgt3HjRgYPHtzgd7F8+XKg/tfxSF/N161bR1FREeeffz5fffUVTqezHncRacCjDu+99x5HH300u3fvZsKECRx33HGceOKJYT8jweX169fz0ksvkZqaisvlomPHjvXGr+NfWVlJRkZGWP515RUrVtCpUyf27t3LxIkT6devHyeffHKg3auvvsqRRx7JyJEjWbp0ab33FsorYZNwLQ2jgCOX6yXhJtwWOPbd3upAW5cD3JYgbueCsgOCKRDrlZuB13vgyaPgMlEm6IJV0nvvvceyZct4//33SU9PZ8K
2019-12-15 17:36:13 +01:00
],
"text/plain": [
"<Figure size 360x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": [
2019-12-16 23:04:02 +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"
}
],
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
]
},
{
"cell_type": "code",
2019-12-16 23:04:02 +01:00
"execution_count": 46,
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": [
{
"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)",
"\u001b[0;32m<ipython-input-46-71496d78a884>\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",
"\u001b[0;31mNameError\u001b[0m: name 'ROOT' is not defined"
]
}
],
"source": [
"vec = ROOT.std.vector(\"float\")(2)"
]
},
{
"cell_type": "code",
"execution_count": 55,
"metadata": {
"autoscroll": false,
"collapsed": false,
"ein.hycell": false,
"ein.tags": "worksheet-0",
"slideshow": {
"slide_type": "-"
}
},
"outputs": [
{
"ename": "TypeError",
"evalue": "double TMath::Landau(double x, double mpv = 0, double sigma = 1, bool norm = kFALSE) =>\n could not convert argument 1 (must be real number, not vector<float>)",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-55-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;31mTypeError\u001b[0m: double TMath::Landau(double x, double mpv = 0, double sigma = 1, bool norm = kFALSE) =>\n could not convert argument 1 (must be real number, not vector<float>)"
]
}
],
"source": [
"ROOT.TMath.Landau(vec)"
]
2019-12-14 21:17:02 +01:00
},
{
"cell_type": "code",
2019-12-16 19:41:50 +01:00
"execution_count": 18,
2019-12-14 21:17:02 +01:00
"metadata": {
"autoscroll": false,
"collapsed": false,
"ein.hycell": false,
"ein.tags": "worksheet-0",
"slideshow": {
"slide_type": "-"
}
},
"outputs": [
{
2019-12-16 19:41:50 +01:00
"ename": "ValueError",
"evalue": "Buffer dtype mismatch, expected 'double_t' but got 'long'",
"output_type": "error",
"traceback": [
"\u001b[0;31m\u001b[0m",
"\u001b[0;31mValueError\u001b[0mTraceback (most recent call last)",
"\u001b[0;32m<ipython-input-18-aa065eebfcba>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mpylandau\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlandau\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;32mpyLandau\\cpp\\pylandau.pyx\u001b[0m in \u001b[0;36mpylandau.landau (pyLandau/cpp/pylandau.cpp:3788)\u001b[0;34m()\u001b[0m\n",
"\u001b[0;31mValueError\u001b[0m: Buffer dtype mismatch, expected 'double_t' but got 'long'"
]
2019-12-14 21:17:02 +01:00
}
],
"source": [
2019-12-16 19:41:50 +01:00
"x = np.arange(0, 100, 0.01)\n",
"\n",
"y_landau = pylandau.landau(x)"
2019-12-14 11:43:02 +01:00
]
2019-12-16 23:04:02 +01:00
},
{
"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": []
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"
},
"nbformat": 4,
"nbformat_minor": 2
}
