157 lines
28 KiB
Plaintext
157 lines
28 KiB
Plaintext
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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 18,
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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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"from methods.fit_linear_regression_model import fit_linear_regression_model\n",
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"import sklearn\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": 19,
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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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"298 72\n",
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"370\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_test_endVelo_momEff.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": 20,
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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: 0.24161073825503357\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": 22,
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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": [
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"<Figure size 640x480 with 2 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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"source": [
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"endVeloP_found = ak.to_numpy(found[\"ideal_state_770_p\"])\n",
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"trueP_found = ak.to_numpy(found[\"p\"])\n",
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"nbins = 50\n",
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"vmax = 5\n",
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"\n",
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"a0 = plt.hist2d(\n",
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" trueP_found,\n",
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" endVeloP_found,\n",
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" density=False,\n",
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" bins=nbins,\n",
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" cmap=plt.cm.jet,\n",
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" cmin=1,\n",
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" vmax=vmax,\n",
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" range=[[0, 30000], [0, 30000]],\n",
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")\n",
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"# plt.plot([-0.1, 1.0], [-0.1, 1.0], marker=\"\", alpha=0.8)\n",
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"plt.xlabel(f\"True $P$\")\n",
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"plt.ylabel(f\"endVelo $P$\")\n",
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"plt.title(f\"found P\")\n",
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"# ax1.set(xlim=(0,4000), ylim=(-1000,1000))\n",
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"\n",
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"plt.colorbar(a0[3])\n",
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"plt.show()"
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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": null,
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"metadata": {},
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"outputs": [],
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"source": []
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "tuner",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.10.12"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}
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