185 lines
6.3 KiB
Plaintext
185 lines
6.3 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": 1,
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"metadata": {},
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"outputs": [],
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"source": [
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"import uproot\n",
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"import numpy as np\n",
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"import sys\n",
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"import os\n",
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"import matplotlib\n",
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"import matplotlib.pyplot as plt\n",
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"import mplhep\n",
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"from mpl_toolkits import mplot3d\n",
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"import itertools\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 utils.components import unique_name_ext_re\n",
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"mplhep.style.use([\"LHCbTex2\"])\n",
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"plt.rcParams[\"savefig.dpi\"] = 600\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": 2,
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"metadata": {},
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"outputs": [],
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"source": [
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"file = uproot.open(\n",
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" \"/work/cetin/LHCb/reco_tuner/data/resolutions_and_effs_B_thesis.root:Track/MatchTrackChecker_8319528f/Match;1\",\n",
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")\n",
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"\n",
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"P_recoed = file[\"01_long_P_reconstructed;1\"].to_numpy()\n",
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"P_recoable = file[\"01_long_P_reconstructible;1\"].to_numpy()\n",
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"\n",
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"Pt_recoed = file[\"01_long_Pt_reconstructed;1\"].to_numpy()\n",
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"Pt_recoable = file[\"01_long_Pt_reconstructible;1\"].to_numpy()"
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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": 3,
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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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"control eff: 0.8626619913200968\n",
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"new eff: 0.8626619913200968\n"
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]
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}
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],
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"source": [
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"P_Velo_recoed = file[\"01_long_EndVelo_P_reconstructed;1\"].to_numpy()\n",
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"P_Velo_recoable = file[\"01_long_EndVelo_P_reconstructible;1\"].to_numpy()\n",
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"\n",
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"print(\"control eff: \", np.sum(P_recoed[0]) / np.sum(P_recoable[0]))\n",
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"print(\"new eff: \", np.sum(P_Velo_recoed[0]) / np.sum(P_Velo_recoable[0]))"
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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": 4,
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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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"control eff: 0.8629752409817771\n",
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"new eff: 0.8629752409817771\n"
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]
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}
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],
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"source": [
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"Pt_Velo_recoed = file[\"01_long_EndVelo_Pt_reconstructed;1\"].to_numpy()\n",
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"Pt_Velo_recoable = file[\"01_long_EndVelo_Pt_reconstructible;1\"].to_numpy()\n",
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"\n",
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"print(\"control eff: \", np.sum(Pt_recoed[0]) / np.sum(Pt_recoable[0]))\n",
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"print(\"new eff: \", np.sum(Pt_Velo_recoed[0]) / np.sum(Pt_Velo_recoable[0]))"
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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": 27,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"(array([1.00000e+00, 8.01040e+04, 2.49215e+05, 2.50095e+05, 2.10569e+05,\n",
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" 1.75027e+05, 1.46219e+05, 1.23088e+05, 1.05255e+05, 9.01970e+04,\n",
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" 7.85600e+04, 6.79570e+04, 5.95880e+04, 5.19260e+04, 4.65560e+04,\n",
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" 4.07310e+04, 3.72930e+04, 3.35370e+04, 3.01050e+04, 2.72980e+04,\n",
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" 2.47460e+04, 2.24910e+04, 2.05840e+04, 1.88150e+04, 1.72910e+04,\n",
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" 1.57940e+04, 1.46220e+04, 1.34730e+04, 1.24900e+04, 1.14550e+04,\n",
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" 1.07880e+04, 9.95800e+03, 9.23400e+03, 8.56100e+03, 7.91100e+03,\n",
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" 7.36700e+03, 6.80700e+03, 6.42800e+03, 6.11500e+03, 5.63900e+03,\n",
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" 5.26200e+03, 4.91100e+03, 4.66800e+03, 4.39400e+03, 4.05300e+03,\n",
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" 3.75000e+03, 3.49800e+03, 3.30800e+03, 3.20800e+03, 3.03300e+03,\n",
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" 2.88100e+03, 2.72300e+03, 2.41900e+03, 2.42900e+03, 2.21800e+03,\n",
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" 2.17500e+03, 1.99500e+03, 1.91400e+03, 1.77100e+03, 1.66700e+03,\n",
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" 1.64800e+03, 1.49800e+03, 1.46400e+03, 1.40000e+03, 1.33000e+03,\n",
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" 1.23000e+03, 1.19300e+03, 1.11400e+03, 1.08300e+03, 1.02700e+03,\n",
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" 9.94000e+02, 9.44000e+02, 8.81000e+02, 8.85000e+02, 7.90000e+02,\n",
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" 7.94000e+02, 7.77000e+02, 7.05000e+02, 7.03000e+02, 7.09000e+02,\n",
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" 6.45000e+02, 5.87000e+02, 5.93000e+02, 5.35000e+02, 5.48000e+02,\n",
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" 4.68000e+02, 4.67000e+02, 4.58000e+02, 4.70000e+02, 4.26000e+02,\n",
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" 4.30000e+02, 4.23000e+02, 4.11000e+02, 3.76000e+02, 3.56000e+02,\n",
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" 3.49000e+02, 3.05000e+02, 3.24000e+02, 2.83000e+02, 3.18000e+02]),\n",
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" array([ 0., 1000., 2000., 3000., 4000., 5000., 6000.,\n",
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" 7000., 8000., 9000., 10000., 11000., 12000., 13000.,\n",
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" 14000., 15000., 16000., 17000., 18000., 19000., 20000.,\n",
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" 21000., 22000., 23000., 24000., 25000., 26000., 27000.,\n",
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" 28000., 29000., 30000., 31000., 32000., 33000., 34000.,\n",
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" 35000., 36000., 37000., 38000., 39000., 40000., 41000.,\n",
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" 42000., 43000., 44000., 45000., 46000., 47000., 48000.,\n",
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" 49000., 50000., 51000., 52000., 53000., 54000., 55000.,\n",
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" 56000., 57000., 58000., 59000., 60000., 61000., 62000.,\n",
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" 63000., 64000., 65000., 66000., 67000., 68000., 69000.,\n",
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" 70000., 71000., 72000., 73000., 74000., 75000., 76000.,\n",
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" 77000., 78000., 79000., 80000., 81000., 82000., 83000.,\n",
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" 84000., 85000., 86000., 87000., 88000., 89000., 90000.,\n",
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" 91000., 92000., 93000., 94000., 95000., 96000., 97000.,\n",
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" 98000., 99000., 100000.]))"
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]
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},
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"execution_count": 27,
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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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"P_recoable"
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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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"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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