201 lines
84 KiB
Plaintext
201 lines
84 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 30,
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"metadata": {},
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"outputs": [],
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"source": [
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"import uproot\t\n",
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"import numpy as np\n",
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"import matplotlib.pyplot as plt\n",
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"from mpl_toolkits import mplot3d\n",
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"import awkward as ak\n",
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"from scipy.optimize import curve_fit\n",
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"%matplotlib inline"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 31,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"40402 10099\n",
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"50501\n"
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]
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}
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],
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"source": [
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"file = uproot.open(\n",
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" \"tracking_losses_ntuple_B_default_radlength_endVelo.root:PrDebugTrackingLosses.PrDebugTrackingTool/Tuple;1\"\n",
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")\n",
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"\n",
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"# selektiere nur elektronen von B->K*ee\n",
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"allcolumns = file.arrays()\n",
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"found = allcolumns[\n",
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" (allcolumns.isElectron) & (~allcolumns.lost) & (allcolumns.fromB)\n",
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"] # B: 9056\n",
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"lost = allcolumns[\n",
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" (allcolumns.isElectron) & (allcolumns.lost) & (allcolumns.fromB)\n",
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"] # B: 1466\n",
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"\n",
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"electrons = allcolumns[(allcolumns.isElectron) & (allcolumns.fromB)]\n",
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"\n",
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"print(ak.num(found, axis=0), ak.num(lost, axis=0))\n",
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"print(ak.num(electrons,axis=0))\n",
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"# ak.count(found, axis=None)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 32,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"stretch factor: 0.24996287312509283\n"
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]
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}
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],
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"source": [
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"rad_length_found = ak.to_numpy(found[\"rad_length_frac\"])\n",
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"eta_found = ak.to_numpy(found[\"eta\"])\n",
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"rad_length_lost = ak.to_numpy(lost[\"rad_length_frac\"])\n",
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"eta_lost = ak.to_numpy(lost[\"eta\"])\n",
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"\n",
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"stretch_factor = ak.num(eta_lost,axis=0)/ak.num(eta_found,axis=0)\n",
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"print(\"stretch factor: \", stretch_factor)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 33,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"image/png": 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",
|
|
"text/plain": [
|
|
"<Figure size 640x480 with 1 Axes>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"\n",
|
|
"\n",
|
|
"plt.hist(rad_length_lost,bins=100,density=True,alpha=0.5,color=\"darkorange\",histtype=\"bar\",label=\"lost\",range=[0,1])\n",
|
|
"plt.hist(rad_length_found,bins=100,density=True,alpha=0.5,color=\"blue\",histtype=\"bar\",label=\"found\",range=[0,1])\n",
|
|
"plt.xlim(0,1)\n",
|
|
"#plt.yscale(\"log\")\n",
|
|
"plt.title(\"radiation length fraction\")\n",
|
|
"plt.xlabel(f\"$x/X_0$\")\n",
|
|
"plt.ylabel(\"a.u.\")\n",
|
|
"\n",
|
|
"plt.legend()\n",
|
|
"plt.show()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 35,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"image/png": 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",
|
|
"text/plain": [
|
|
"<Figure size 2000x800 with 3 Axes>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"nbins=100\n",
|
|
"vmax=80\n",
|
|
"\n",
|
|
"fig, ((ax0, ax1)) = plt.subplots(nrows=1, ncols=2, figsize=(20, 8))\n",
|
|
"\n",
|
|
"a0 = ax0.hist2d(\n",
|
|
" rad_length_found,\n",
|
|
" eta_found,\n",
|
|
" density=False,\n",
|
|
" bins=nbins,\n",
|
|
" cmap=plt.cm.jet,\n",
|
|
" cmin=1,\n",
|
|
" vmax=vmax,\n",
|
|
" range=[[0,0.5],[2,5]],\n",
|
|
")\n",
|
|
"ax0.set_xlabel(f\"$x/X_0$\")\n",
|
|
"ax0.set_ylabel(f\"$\\eta$\")\n",
|
|
"ax0.set_title(f\"found $\\eta$ rad_length_frac\")\n",
|
|
"\n",
|
|
"a1 = ax1.hist2d(\n",
|
|
" rad_length_lost,\n",
|
|
" eta_lost,\n",
|
|
" density=False,\n",
|
|
" bins=nbins,\n",
|
|
" cmap=plt.cm.jet,\n",
|
|
" cmin=1,\n",
|
|
" vmax=vmax * stretch_factor,\n",
|
|
" range=[[0,0.5],[2,5]],\n",
|
|
")\n",
|
|
"ax1.set_xlabel(f\"$x/X_0$\")\n",
|
|
"ax1.set_ylabel(f\"$\\eta$\")\n",
|
|
"ax1.set_title(f\"lost $\\eta$ rad_length_frac\")\n",
|
|
"# ax1.set(xlim=(0,4000), ylim=(-1000,1000))\n",
|
|
"\n",
|
|
"plt.suptitle(\"radiation length fraction and eta\")\n",
|
|
"plt.colorbar(a0[3], ax=ax1)\n",
|
|
"\n",
|
|
"plt.show()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": []
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": []
|
|
}
|
|
],
|
|
"metadata": {
|
|
"kernelspec": {
|
|
"display_name": "tuner",
|
|
"language": "python",
|
|
"name": "python3"
|
|
},
|
|
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"name": "ipython",
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},
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"mimetype": "text/x-python",
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"name": "python",
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"pygments_lexer": "ipython3",
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}
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