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tracking-parametrisation-tuner/thesis/TMVA_stuff.ipynb

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saved delta p dist True - EndVelo Momentum saved TMVA nice plots
2024-02-12 15:57:23 +01:00
{
"cells": [
{
"cell_type": "code",
git test
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"execution_count": 1,
saved delta p dist True - EndVelo Momentum saved TMVA nice plots
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"metadata": {},
"outputs": [],
"source": [
"import uproot\n",
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"import mplhep\n",
"\n",
"mplhep.style.use([\"LHCbTex2\"])\n",
"plt.rcParams[\"savefig.dpi\"] = 600\n",
"# %matplotlib inline"
]
},
{
"cell_type": "code",
trained e def and e filter
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"execution_count": 2,
saved delta p dist True - EndVelo Momentum saved TMVA nice plots
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"metadata": {},
"outputs": [],
"source": [
logs and gitignore
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"train_tree = uproot.open(\n",
" {\n",
" \"/work/cetin/LHCb/reco_tuner/nn_electron_training/result_e_sig_filter_bkg/matching_ghost_mlp_training.root\": \"MatchNNDataSet/TrainTree\"\n",
" }\n",
")\n",
"test_tree = uproot.open(\n",
" {\n",
" \"/work/cetin/LHCb/reco_tuner/nn_electron_training/result_e_sig_filter_bkg/matching_ghost_mlp_training.root\": \"MatchNNDataSet/TestTree\"\n",
" }\n",
")\n",
saved delta p dist True - EndVelo Momentum saved TMVA nice plots
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"train_array = train_tree.arrays()\n",
"test_array = test_tree.arrays()"
]
},
{
"cell_type": "code",
trained e def and e filter
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"execution_count": 3,
saved delta p dist True - EndVelo Momentum saved TMVA nice plots
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"metadata": {},
"outputs": [
{
"data": {
trained e def and e filter
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"image/png": "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
saved delta p dist True - EndVelo Momentum saved TMVA nice plots
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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",
trained e def and e filter
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"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",
saved delta p dist True - EndVelo Momentum saved TMVA nice plots
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"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",
logs and gitignore
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"# plt.savefig(\"/work/cetin/LHCb/reco_tuner/thesis/filtered_NN_elec_response.pdf\",\n",
"# format=\"PDF\")\n",
"plt.show()"
saved delta p dist True - EndVelo Momentum saved TMVA nice plots
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]
},
{
"cell_type": "code",
trained e def and e filter
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"execution_count": 4,
saved delta p dist True - EndVelo Momentum saved TMVA nice plots
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"metadata": {},
"outputs": [
{
"data": {
logs and gitignore
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"image/png": "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
saved delta p dist True - EndVelo Momentum saved TMVA nice plots
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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",
trained e def and e filter
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"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",
saved delta p dist True - EndVelo Momentum saved TMVA nice plots
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"axes[0, 0].set_xlabel(r\"$\\chi^{2}_{\\mathrm{match}}$\")\n",
trained e def and e filter
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"axes[0, 0].legend(prop={\"size\": 20})\n",
saved delta p dist True - EndVelo Momentum saved TMVA nice plots
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"# 1,0\n",
trained e def and e filter
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"axes[1, 0].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[1, 0].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",
saved delta p dist True - EndVelo Momentum saved TMVA nice plots
2024-02-12 15:57:23 +01:00
"axes[1, 0].set_xlabel(r\"$D_{x}$ [mm]\")\n",
logs and gitignore
2024-02-21 08:34:33 +01:00
"axes[1, 0].set_ylabel(\"Number of tracks (normalised)\", va=\"bottom\", ha=\"center\")\n",
saved delta p dist True - EndVelo Momentum saved TMVA nice plots
2024-02-12 15:57:23 +01:00
"# 0,1\n",
trained e def and e filter
2024-02-19 15:41:09 +01:00
"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",
saved delta p dist True - EndVelo Momentum saved TMVA nice plots
2024-02-12 15:57:23 +01:00
"axes[0, 1].set_xlabel(r\"$t_{x}^{2}+t_{y}^{2}$\")\n",
"# 1,1\n",
trained e def and e filter
2024-02-19 15:41:09 +01:00
"axes[1, 1].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, 1].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",
saved delta p dist True - EndVelo Momentum saved TMVA nice plots
2024-02-12 15:57:23 +01:00
"axes[1, 1].set_xlabel(r\"$D_{y}$ [mm]\")\n",
"# 2,0\n",
trained e def and e filter
2024-02-19 15:41:09 +01:00
"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",
saved delta p dist True - EndVelo Momentum saved TMVA nice plots
2024-02-12 15:57:23 +01:00
"axes[2, 0].set_xlabel(r\"$|\\Delta t_{x}^{\\mathrm{match}}|$\")\n",
"# 2,1\n",
trained e def and e filter
2024-02-19 15:41:09 +01:00
"axes[2, 1].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[2, 1].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",
saved delta p dist True - EndVelo Momentum saved TMVA nice plots
2024-02-12 15:57:23 +01:00
"axes[2, 1].set_xlabel(r\"$|\\Delta t_{y}^{\\mathrm{match}}|$\")\n",
logs and gitignore
2024-02-21 08:34:33 +01:00
"# plt.savefig(\n",
"# \"/work/cetin/LHCb/reco_tuner/thesis/filtered_NN_elec_variables.pdf\",\n",
"# format=\"PDF\")\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
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"text/plain": [
"<Figure size 2500x1300 with 6 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"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/filtered_NN_elec_variables_landscape.pdf\",\n",
"# format=\"PDF\",\n",
"# )\n",
"plt.show()"
saved delta p dist True - EndVelo Momentum saved TMVA nice plots
2024-02-12 15:57:23 +01:00
]
},
{
"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
}
Reference in New Issue Copy Permalink