2024-02-12 15:57:23 +01:00
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{
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"cells": [
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{
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"cell_type": "code",
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2024-02-13 09:34:22 +01:00
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"execution_count": 1,
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2024-02-12 15:57:23 +01:00
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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 matplotlib.pyplot as plt\n",
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"import numpy as np\n",
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"import mplhep\n",
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"\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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2024-02-13 09:34:22 +01:00
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"execution_count": 3,
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2024-02-12 15:57:23 +01:00
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"metadata": {},
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"outputs": [],
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"source": [
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"train_tree = uproot.open({\n",
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" \"/work/cetin/LHCb/reco_tuner/nn_electron_training/result_e_sig_def_bak/matching_ghost_mlp_training.root\":\n",
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" \"MatchNNDataSet/TrainTree\"\n",
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"})\n",
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"test_tree = uproot.open({\n",
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" \"/work/cetin/LHCb/reco_tuner/nn_electron_training/result_e_sig_def_bak/matching_ghost_mlp_training.root\":\n",
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" \"MatchNNDataSet/TestTree\"\n",
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"})\n",
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"train_array = train_tree.arrays()\n",
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"test_array = test_tree.arrays()"
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]
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},
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{
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"cell_type": "code",
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2024-02-13 09:34:22 +01:00
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"execution_count": 4,
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2024-02-12 15:57:23 +01:00
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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 1200x900 with 1 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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"train_bkg = train_array[train_array.classID == 1]\n",
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"train_sig = train_array[train_array.classID == 0]\n",
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"test_bkg = test_array[test_array.classID == 1]\n",
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"test_sig = test_array[test_array.classID == 0]\n",
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"plt.hist(train_sig.matching_mlp,\n",
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" bins=50,\n",
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" alpha=0.5,\n",
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" density=True,\n",
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" color=\"#107E7D\",\n",
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" label=\"training sample, true pairs\")\n",
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"plt.hist(train_bkg.matching_mlp,\n",
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" bins=50,\n",
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" alpha=0.5,\n",
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" density=True,\n",
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" color=\"#F05342\",\n",
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" label=\"training sample, wrong pairs\")\n",
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"mplhep.histplot(np.histogram(np.array(test_sig.matching_mlp), 50),\n",
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" histtype=\"errorbar\",\n",
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" density=True,\n",
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" yerr=True,\n",
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" color=\"#107E7D\",\n",
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" marker=\"^\",\n",
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" markersize=7,\n",
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" label=\"test sample, true pairs\")\n",
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"mplhep.histplot(np.histogram(np.array(test_bkg.matching_mlp), 50),\n",
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" histtype=\"errorbar\",\n",
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" density=True,\n",
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" yerr=True,\n",
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" color=\"#F05342\",\n",
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" label=\"test sample, wrong pairs\")\n",
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"plt.xlabel(\"neural network response\")\n",
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"plt.ylabel(\"Number of tracks (normalised)\")\n",
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"mplhep.lhcb.text(\"Simulation\", loc=0)\n",
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"plt.legend(loc=\"upper center\")\n",
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"plt.savefig(\"/work/cetin/LHCb/reco_tuner/thesis/default_NN_elec_response.pdf\",\n",
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" format=\"PDF\")\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": 34,
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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 1400x1500 with 6 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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"fig, axes = plt.subplots(3, 2, figsize=(14, 15), sharey=False)\n",
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"# 0,0\n",
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"axes[0, 0].hist(train_sig.chi2,\n",
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" bins=50,\n",
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" alpha=0.5,\n",
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" density=True,\n",
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" log=False,\n",
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" color=\"#107E7D\",\n",
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" label=\"training sample, true pairs\")\n",
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"axes[0, 0].hist(train_bkg.chi2,\n",
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" bins=50,\n",
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" alpha=0.5,\n",
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" density=True,\n",
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" log=False,\n",
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" color=\"#F05342\",\n",
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" label=\"training sample, wrong pairs\")\n",
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"axes[0, 0].set_xlabel(r\"$\\chi^{2}_{\\mathrm{match}}$\")\n",
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"axes[0, 0].legend(prop={'size': 20})\n",
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"# 1,0\n",
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"axes[1, 0].hist(train_sig.distX,\n",
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" bins=50,\n",
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" range=(0, 100),\n",
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" alpha=0.5,\n",
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" density=True,\n",
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" log=False,\n",
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" color=\"#107E7D\",\n",
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" label=\"training sample, true pairs\")\n",
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"axes[1, 0].hist(train_bkg.distX,\n",
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" bins=50,\n",
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" range=(0, 100),\n",
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" alpha=0.5,\n",
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" density=True,\n",
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" log=False,\n",
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" color=\"#F05342\",\n",
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|
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" label=\"training sample, wrong pairs\")\n",
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"axes[1, 0].set_xlabel(r\"$D_{x}$ [mm]\")\n",
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"axes[1, 0].set_ylabel(\"Number of tracks (normalised)\",\n",
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" va=\"bottom\",\n",
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" ha=\"center\")\n",
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"# 0,1\n",
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"axes[0, 1].hist(train_sig.teta2,\n",
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" bins=50,\n",
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" range=(0.0, 0.02),\n",
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" alpha=0.5,\n",
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" density=True,\n",
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" log=False,\n",
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" color=\"#107E7D\",\n",
|
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|
|
|
" label=\"training sample, true pairs\")\n",
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"axes[0, 1].hist(train_bkg.teta2,\n",
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" bins=50,\n",
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|
" range=(0.0, 0.02),\n",
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" alpha=0.5,\n",
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" density=True,\n",
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" log=False,\n",
|
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|
" color=\"#F05342\",\n",
|
|
|
|
|
" label=\"training sample, wrong pairs\")\n",
|
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|
"axes[0, 1].set_xlabel(r\"$t_{x}^{2}+t_{y}^{2}$\")\n",
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|
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"# 1,1\n",
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"axes[1, 1].hist(train_sig.distY,\n",
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" bins=50,\n",
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" range=(0, 100),\n",
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" alpha=0.5,\n",
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" density=True,\n",
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|
|
" log=False,\n",
|
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|
|
|
" color=\"#107E7D\",\n",
|
|
|
|
|
" label=\"training sample, true pairs\")\n",
|
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|
|
"axes[1, 1].hist(train_bkg.distY,\n",
|
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|
|
" bins=50,\n",
|
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|
|
|
" range=(0, 100),\n",
|
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|
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" alpha=0.5,\n",
|
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|
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" density=True,\n",
|
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|
|
" log=False,\n",
|
|
|
|
|
" color=\"#F05342\",\n",
|
|
|
|
|
" label=\"training sample, wrong pairs\")\n",
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|
"axes[1, 1].set_xlabel(r\"$D_{y}$ [mm]\")\n",
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|
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"# 2,0\n",
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|
"axes[2, 0].hist(train_sig.dSlope,\n",
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" bins=50,\n",
|
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|
" alpha=0.5,\n",
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" density=True,\n",
|
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|
|
|
" log=False,\n",
|
|
|
|
|
" color=\"#107E7D\",\n",
|
|
|
|
|
" label=\"training sample, true pairs\")\n",
|
|
|
|
|
"axes[2, 0].hist(train_bkg.dSlope,\n",
|
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|
|
|
" bins=50,\n",
|
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|
|
|
" alpha=0.5,\n",
|
|
|
|
|
" density=True,\n",
|
|
|
|
|
" log=False,\n",
|
|
|
|
|
" color=\"#F05342\",\n",
|
|
|
|
|
" label=\"training sample, wrong pairs\")\n",
|
|
|
|
|
"axes[2, 0].set_xlabel(r\"$|\\Delta t_{x}^{\\mathrm{match}}|$\")\n",
|
|
|
|
|
"# 2,1\n",
|
|
|
|
|
"axes[2, 1].hist(train_sig.dSlopeY,\n",
|
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|
|
|
" bins=50,\n",
|
|
|
|
|
" range=(0, 0.02),\n",
|
|
|
|
|
" alpha=0.5,\n",
|
|
|
|
|
" density=True,\n",
|
|
|
|
|
" log=False,\n",
|
|
|
|
|
" color=\"#107E7D\",\n",
|
|
|
|
|
" label=\"training sample, true pairs\")\n",
|
|
|
|
|
"axes[2, 1].hist(train_bkg.dSlopeY,\n",
|
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|
|
|
" bins=50,\n",
|
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|
|
|
" range=(0, 0.02),\n",
|
|
|
|
|
" alpha=0.5,\n",
|
|
|
|
|
" density=True,\n",
|
|
|
|
|
" log=False,\n",
|
|
|
|
|
" color=\"#F05342\",\n",
|
|
|
|
|
" label=\"training sample, wrong pairs\")\n",
|
|
|
|
|
"axes[2, 1].set_xlabel(r\"$|\\Delta t_{y}^{\\mathrm{match}}|$\")\n",
|
|
|
|
|
"plt.savefig(\"/work/cetin/LHCb/reco_tuner/thesis/default_NN_elec_variables.pdf\",\n",
|
|
|
|
|
" format=\"PDF\")\n",
|
|
|
|
|
"# plt.show()"
|
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|
|
|
]
|
|
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|
|
},
|
|
|
|
|
{
|
|
|
|
|
"cell_type": "code",
|
|
|
|
|
"execution_count": null,
|
|
|
|
|
"metadata": {},
|
|
|
|
|
"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,
|
|
|
|
|
"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",
|
|
|
|
|
"execution_count": null,
|
|
|
|
|
"metadata": {},
|
|
|
|
|
"outputs": [],
|
|
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|
|
"source": []
|
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|
|
},
|
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{
|
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|
|
"cell_type": "code",
|
|
|
|
|
"execution_count": null,
|
|
|
|
|
"metadata": {},
|
|
|
|
|
"outputs": [],
|
|
|
|
|
"source": []
|
|
|
|
|
}
|
|
|
|
|
],
|
|
|
|
|
"metadata": {
|
|
|
|
|
"kernelspec": {
|
|
|
|
|
"display_name": "tuner",
|
|
|
|
|
"language": "python",
|
|
|
|
|
"name": "python3"
|
|
|
|
|
},
|
|
|
|
|
"language_info": {
|
|
|
|
|
"codemirror_mode": {
|
|
|
|
|
"name": "ipython",
|
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|
|
|
"version": 3
|
|
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|
|
},
|
|
|
|
|
"file_extension": ".py",
|
|
|
|
|
"mimetype": "text/x-python",
|
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|
|
|
"name": "python",
|
|
|
|
|
"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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|
}
|
