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488 lines
201 KiB
488 lines
201 KiB
{
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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 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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"import awkward as ak\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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"execution_count": 3,
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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_bestprecuts_NozMagCut/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_bestprecuts_NozMagCut/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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"execution_count": 4,
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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 1200x900 with 1 Axes>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"train_bkg = train_array[train_array.classID == 1]\n",
|
|
"train_sig = train_array[train_array.classID == 0]\n",
|
|
"test_bkg = test_array[test_array.classID == 1]\n",
|
|
"test_sig = test_array[test_array.classID == 0]\n",
|
|
"plt.hist(\n",
|
|
" train_sig.matching_mlp,\n",
|
|
" bins=50,\n",
|
|
" alpha=0.5,\n",
|
|
" density=True,\n",
|
|
" color=\"#107E7D\",\n",
|
|
" label=\"training sample, true pairs\",\n",
|
|
")\n",
|
|
"plt.hist(\n",
|
|
" train_bkg.matching_mlp,\n",
|
|
" bins=50,\n",
|
|
" alpha=0.5,\n",
|
|
" density=True,\n",
|
|
" color=\"#F05342\",\n",
|
|
" label=\"training sample, wrong pairs\",\n",
|
|
")\n",
|
|
"mplhep.histplot(\n",
|
|
" np.histogram(np.array(test_sig.matching_mlp), 50),\n",
|
|
" histtype=\"errorbar\",\n",
|
|
" density=True,\n",
|
|
" yerr=True,\n",
|
|
" color=\"#107E7D\",\n",
|
|
" marker=\"^\",\n",
|
|
" markersize=7,\n",
|
|
" label=\"test sample, true pairs\",\n",
|
|
")\n",
|
|
"mplhep.histplot(\n",
|
|
" np.histogram(np.array(test_bkg.matching_mlp), 50),\n",
|
|
" histtype=\"errorbar\",\n",
|
|
" density=True,\n",
|
|
" yerr=True,\n",
|
|
" color=\"#F05342\",\n",
|
|
" label=\"test sample, wrong pairs\",\n",
|
|
")\n",
|
|
"plt.xlabel(\"neural network response\")\n",
|
|
"plt.ylabel(\"Number of Tracks (normalised)\")\n",
|
|
"mplhep.lhcb.text(\"Simulation\", loc=0)\n",
|
|
"plt.legend(loc=\"upper center\")\n",
|
|
"# plt.savefig(\n",
|
|
"# \"/work/cetin/LHCb/reco_tuner/thesis/new_electron_NN_response.pdf\",\n",
|
|
"# format=\"PDF\",\n",
|
|
"# )\n",
|
|
"plt.show()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 10,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"The cut is set to 0.5, thereby rejecting 0.901 of fake tracks, while keeping 0.935 of the true matches.\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"response = 0.5\n",
|
|
"\n",
|
|
"n_sig_keep = ak.num(train_sig[train_sig.matching_mlp >= response], axis=0)\n",
|
|
"n_bkg_rej = ak.num(train_bkg[train_bkg.matching_mlp < response], axis=0)\n",
|
|
"nevents = 115e3\n",
|
|
"\n",
|
|
"print(\n",
|
|
" f\"The cut is set to {response}, thereby rejecting {np.round(n_bkg_rej/nevents,3)} of fake tracks, while keeping {np.round(n_sig_keep/nevents,3)} of the true matches.\"\n",
|
|
")"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": []
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 17,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"image/png": 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",
|
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"text/plain": [
|
|
"<Figure size 1400x1500 with 6 Axes>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"fig, axes = plt.subplots(3, 2, figsize=(14, 15), sharey=False)\n",
|
|
"# 0,0\n",
|
|
"axes[0, 0].hist(\n",
|
|
" train_sig.chi2,\n",
|
|
" bins=50,\n",
|
|
" alpha=0.5,\n",
|
|
" density=True,\n",
|
|
" log=False,\n",
|
|
" color=\"#107E7D\",\n",
|
|
" label=\"training sample, true pairs\",\n",
|
|
")\n",
|
|
"axes[0, 0].hist(\n",
|
|
" train_bkg.chi2,\n",
|
|
" bins=50,\n",
|
|
" alpha=0.5,\n",
|
|
" density=True,\n",
|
|
" log=False,\n",
|
|
" color=\"#F05342\",\n",
|
|
" label=\"training sample, wrong pairs\",\n",
|
|
")\n",
|
|
"axes[0, 0].set_xlabel(r\"$\\chi^{2}_{\\mathrm{match}}$\")\n",
|
|
"axes[0, 0].legend(prop={\"size\": 20})\n",
|
|
"# 1,0\n",
|
|
"axes[1, 0].hist(\n",
|
|
" train_sig.distX,\n",
|
|
" bins=50,\n",
|
|
" range=(0, 150),\n",
|
|
" alpha=0.5,\n",
|
|
" density=True,\n",
|
|
" log=False,\n",
|
|
" color=\"#107E7D\",\n",
|
|
" label=\"training sample, true pairs\",\n",
|
|
")\n",
|
|
"axes[1, 0].hist(\n",
|
|
" train_bkg.distX,\n",
|
|
" bins=50,\n",
|
|
" range=(0, 150),\n",
|
|
" alpha=0.5,\n",
|
|
" density=True,\n",
|
|
" log=False,\n",
|
|
" color=\"#F05342\",\n",
|
|
" label=\"training sample, wrong pairs\",\n",
|
|
")\n",
|
|
"axes[1, 0].set_xlabel(r\"$D_{x}$ [mm]\")\n",
|
|
"axes[1, 0].set_ylabel(\"Number of Tracks (normalised)\",\n",
|
|
" va=\"bottom\",\n",
|
|
" ha=\"center\")\n",
|
|
"# 0,1\n",
|
|
"axes[0, 1].hist(\n",
|
|
" train_sig.teta2,\n",
|
|
" bins=50,\n",
|
|
" range=(0.0, 0.02),\n",
|
|
" alpha=0.5,\n",
|
|
" density=True,\n",
|
|
" log=False,\n",
|
|
" color=\"#107E7D\",\n",
|
|
" label=\"training sample, true pairs\",\n",
|
|
")\n",
|
|
"axes[0, 1].hist(\n",
|
|
" train_bkg.teta2,\n",
|
|
" bins=50,\n",
|
|
" range=(0.0, 0.02),\n",
|
|
" alpha=0.5,\n",
|
|
" density=True,\n",
|
|
" log=False,\n",
|
|
" color=\"#F05342\",\n",
|
|
" label=\"training sample, wrong pairs\",\n",
|
|
")\n",
|
|
"axes[0, 1].set_xlabel(r\"$t_{x}^{2}+t_{y}^{2}$\")\n",
|
|
"# 1,1\n",
|
|
"axes[1, 1].hist(\n",
|
|
" train_sig.distY,\n",
|
|
" bins=50,\n",
|
|
" range=(0, 150),\n",
|
|
" alpha=0.5,\n",
|
|
" density=True,\n",
|
|
" log=False,\n",
|
|
" color=\"#107E7D\",\n",
|
|
" label=\"training sample, true pairs\",\n",
|
|
")\n",
|
|
"axes[1, 1].hist(\n",
|
|
" train_bkg.distY,\n",
|
|
" bins=50,\n",
|
|
" range=(0, 150),\n",
|
|
" alpha=0.5,\n",
|
|
" density=True,\n",
|
|
" log=False,\n",
|
|
" color=\"#F05342\",\n",
|
|
" label=\"training sample, wrong pairs\",\n",
|
|
")\n",
|
|
"axes[1, 1].set_xlabel(r\"$D_{y}$ [mm]\")\n",
|
|
"# 2,0\n",
|
|
"axes[2, 0].hist(\n",
|
|
" train_sig.dSlope,\n",
|
|
" bins=50,\n",
|
|
" alpha=0.5,\n",
|
|
" density=True,\n",
|
|
" log=False,\n",
|
|
" color=\"#107E7D\",\n",
|
|
" label=\"training sample, true pairs\",\n",
|
|
")\n",
|
|
"axes[2, 0].hist(\n",
|
|
" train_bkg.dSlope,\n",
|
|
" bins=50,\n",
|
|
" alpha=0.5,\n",
|
|
" density=True,\n",
|
|
" log=False,\n",
|
|
" color=\"#F05342\",\n",
|
|
" label=\"training sample, wrong pairs\",\n",
|
|
")\n",
|
|
"axes[2, 0].set_xlabel(r\"$|\\Delta t_{x}^{\\mathrm{match}}|$\")\n",
|
|
"# 2,1\n",
|
|
"axes[2, 1].hist(\n",
|
|
" train_sig.dSlopeY,\n",
|
|
" bins=50,\n",
|
|
" range=(0, 0.03),\n",
|
|
" alpha=0.5,\n",
|
|
" density=True,\n",
|
|
" log=False,\n",
|
|
" color=\"#107E7D\",\n",
|
|
" label=\"training sample, true pairs\",\n",
|
|
")\n",
|
|
"axes[2, 1].hist(\n",
|
|
" train_bkg.dSlopeY,\n",
|
|
" bins=50,\n",
|
|
" range=(0, 0.03),\n",
|
|
" alpha=0.5,\n",
|
|
" density=True,\n",
|
|
" log=False,\n",
|
|
" color=\"#F05342\",\n",
|
|
" label=\"training sample, wrong pairs\",\n",
|
|
")\n",
|
|
"axes[2, 1].set_xlabel(r\"$|\\Delta t_{y}^{\\mathrm{match}}|$\")\n",
|
|
"plt.savefig(\n",
|
|
" \"/work/cetin/LHCb/reco_tuner/thesis/new_electron_NN_variables.pdf\",\n",
|
|
" format=\"PDF\",\n",
|
|
")\n",
|
|
"# plt.show()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"fig, axes = plt.subplots(2, 3, figsize=(25, 13), sharey=False)\n",
|
|
"# 0,0\n",
|
|
"axes[0, 0].hist(\n",
|
|
" train_sig.chi2,\n",
|
|
" bins=50,\n",
|
|
" alpha=0.5,\n",
|
|
" density=True,\n",
|
|
" log=False,\n",
|
|
" color=\"#107E7D\",\n",
|
|
" label=\"training sample, true pairs\",\n",
|
|
")\n",
|
|
"axes[0, 0].hist(\n",
|
|
" train_bkg.chi2,\n",
|
|
" bins=50,\n",
|
|
" alpha=0.5,\n",
|
|
" density=True,\n",
|
|
" log=False,\n",
|
|
" color=\"#F05342\",\n",
|
|
" label=\"training sample, wrong pairs\",\n",
|
|
")\n",
|
|
"axes[0, 0].set_xlabel(r\"$\\chi^{2}_{\\mathrm{match}}$\")\n",
|
|
"axes[0, 0].legend(prop={\"size\": 20})\n",
|
|
"\n",
|
|
"# 0,1\n",
|
|
"axes[0, 1].hist(\n",
|
|
" train_sig.teta2,\n",
|
|
" bins=50,\n",
|
|
" range=(0.0, 0.02),\n",
|
|
" alpha=0.5,\n",
|
|
" density=True,\n",
|
|
" log=False,\n",
|
|
" color=\"#107E7D\",\n",
|
|
" label=\"training sample, true pairs\",\n",
|
|
")\n",
|
|
"axes[0, 1].hist(\n",
|
|
" train_bkg.teta2,\n",
|
|
" bins=50,\n",
|
|
" range=(0.0, 0.02),\n",
|
|
" alpha=0.5,\n",
|
|
" density=True,\n",
|
|
" log=False,\n",
|
|
" color=\"#F05342\",\n",
|
|
" label=\"training sample, wrong pairs\",\n",
|
|
")\n",
|
|
"axes[0, 1].set_xlabel(r\"$t_{x}^{2}+t_{y}^{2}$\")\n",
|
|
"# 0,2\n",
|
|
"axes[0, 2].hist(\n",
|
|
" train_sig.distX,\n",
|
|
" bins=50,\n",
|
|
" range=(0, 100),\n",
|
|
" alpha=0.5,\n",
|
|
" density=True,\n",
|
|
" log=False,\n",
|
|
" color=\"#107E7D\",\n",
|
|
" label=\"training sample, true pairs\",\n",
|
|
")\n",
|
|
"axes[0, 2].hist(\n",
|
|
" train_bkg.distX,\n",
|
|
" bins=50,\n",
|
|
" range=(0, 100),\n",
|
|
" alpha=0.5,\n",
|
|
" density=True,\n",
|
|
" log=False,\n",
|
|
" color=\"#F05342\",\n",
|
|
" label=\"training sample, wrong pairs\",\n",
|
|
")\n",
|
|
"axes[0, 2].set_xlabel(r\"$D_{x}$ [mm]\")\n",
|
|
"axes[0, 0].set_ylabel(\"Number of tracks (normalised)\", va=\"bottom\", ha=\"center\")\n",
|
|
"# 1,0\n",
|
|
"axes[1, 0].hist(\n",
|
|
" train_sig.distY,\n",
|
|
" bins=50,\n",
|
|
" range=(0, 100),\n",
|
|
" alpha=0.5,\n",
|
|
" density=True,\n",
|
|
" log=False,\n",
|
|
" color=\"#107E7D\",\n",
|
|
" label=\"training sample, true pairs\",\n",
|
|
")\n",
|
|
"axes[1, 0].hist(\n",
|
|
" train_bkg.distY,\n",
|
|
" bins=50,\n",
|
|
" range=(0, 100),\n",
|
|
" alpha=0.5,\n",
|
|
" density=True,\n",
|
|
" log=False,\n",
|
|
" color=\"#F05342\",\n",
|
|
" label=\"training sample, wrong pairs\",\n",
|
|
")\n",
|
|
"axes[1, 0].set_xlabel(r\"$D_{y}$ [mm]\")\n",
|
|
"# 2,0\n",
|
|
"axes[1, 1].hist(\n",
|
|
" train_sig.dSlope,\n",
|
|
" bins=50,\n",
|
|
" alpha=0.5,\n",
|
|
" density=True,\n",
|
|
" log=False,\n",
|
|
" color=\"#107E7D\",\n",
|
|
" label=\"training sample, true pairs\",\n",
|
|
")\n",
|
|
"axes[1, 1].hist(\n",
|
|
" train_bkg.dSlope,\n",
|
|
" bins=50,\n",
|
|
" alpha=0.5,\n",
|
|
" density=True,\n",
|
|
" log=False,\n",
|
|
" color=\"#F05342\",\n",
|
|
" label=\"training sample, wrong pairs\",\n",
|
|
")\n",
|
|
"axes[1, 1].set_xlabel(r\"$|\\Delta t_{x}^{\\mathrm{match}}|$\")\n",
|
|
"# 2,1\n",
|
|
"axes[1, 2].hist(\n",
|
|
" train_sig.dSlopeY,\n",
|
|
" 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",
|
|
")\n",
|
|
"axes[1, 2].hist(\n",
|
|
" train_bkg.dSlopeY,\n",
|
|
" bins=50,\n",
|
|
" range=(0, 0.02),\n",
|
|
" alpha=0.5,\n",
|
|
" density=True,\n",
|
|
" log=False,\n",
|
|
" color=\"#F05342\",\n",
|
|
" label=\"training sample, wrong pairs\",\n",
|
|
")\n",
|
|
"axes[1, 2].set_xlabel(r\"$|\\Delta t_{y}^{\\mathrm{match}}|$\")\n",
|
|
"plt.savefig(\n",
|
|
" \"/work/cetin/LHCb/reco_tuner/thesis/newparams_filtered_NN_elec_variables_landscape.pdf\",\n",
|
|
" format=\"PDF\",\n",
|
|
")\n",
|
|
"# plt.show()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": []
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": []
|
|
},
|
|
{
|
|
"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"
|
|
},
|
|
"language_info": {
|
|
"codemirror_mode": {
|
|
"name": "ipython",
|
|
"version": 3
|
|
},
|
|
"file_extension": ".py",
|
|
"mimetype": "text/x-python",
|
|
"name": "python",
|
|
"nbconvert_exporter": "python",
|
|
"pygments_lexer": "ipython3",
|
|
"version": "3.10.12"
|
|
}
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 2
|
|
}
|