cetin/tracking-parametrisation-tuner
1
1
Fork 0
Code Issues Pull Requests repo.project_board Releases Wiki Activity
0c953e30a7
tracking-parametrisation-tuner/thesis/TMVA_stuff.ipynb

468 lines
355 KiB
Plaintext
Raw Normal View History

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
2024-02-13 09:34:22 +01:00
"execution_count": 1,
saved delta p dist True - EndVelo Momentum saved TMVA nice plots
2024-02-12 15:57:23 +01:00
"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
2024-02-19 15:41:09 +01:00
"execution_count": 2,
saved delta p dist True - EndVelo Momentum saved TMVA nice plots
2024-02-12 15:57:23 +01:00
"metadata": {},
"outputs": [],
"source": [
logs and gitignore
2024-02-21 08:34:33 +01:00
"train_tree = uproot.open(\n",
" {\n",
correct insanity cuts, and GEC Filter to eff options files trained network with correct parameterisation sample4 new effs with sample4 NN weights
2024-02-25 12:06:14 +01:00
" \"/work/cetin/LHCb/reco_tuner/nn_electron_training/result_e_filterT_sample4/matching_ghost_mlp_training.root\": \"MatchNNDataSet/TrainTree\"\n",
logs and gitignore
2024-02-21 08:34:33 +01:00
" }\n",
")\n",
"test_tree = uproot.open(\n",
" {\n",
correct insanity cuts, and GEC Filter to eff options files trained network with correct parameterisation sample4 new effs with sample4 NN weights
2024-02-25 12:06:14 +01:00
" \"/work/cetin/LHCb/reco_tuner/nn_electron_training/result_e_filterT_sample4/matching_ghost_mlp_training.root\": \"MatchNNDataSet/TestTree\"\n",
logs and gitignore
2024-02-21 08:34:33 +01:00
" }\n",
")\n",
saved delta p dist True - EndVelo Momentum saved TMVA nice plots
2024-02-12 15:57:23 +01:00
"train_array = train_tree.arrays()\n",
"test_array = test_tree.arrays()"
]
},
{
"cell_type": "code",
trained e def and e filter
2024-02-19 15:41:09 +01:00
"execution_count": 3,
saved delta p dist True - EndVelo Momentum saved TMVA nice plots
2024-02-12 15:57:23 +01:00
"metadata": {},
"outputs": [
{
"data": {
correct insanity cuts, and GEC Filter to eff options files trained network with correct parameterisation sample4 new effs with sample4 NN weights
2024-02-25 12:06:14 +01:00
"image/png": "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
saved delta p dist True - EndVelo Momentum saved TMVA nice plots
2024-02-12 15:57:23 +01:00
"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
2024-02-19 15:41:09 +01:00
"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
2024-02-12 15:57:23 +01:00
"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",
correct insanity cuts, and GEC Filter to eff options files trained network with correct parameterisation sample4 new effs with sample4 NN weights
2024-02-25 12:06:14 +01:00
"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
2024-02-12 15:57:23 +01:00
]
},
{
"cell_type": "code",
trained e def and e filter
2024-02-19 15:41:09 +01:00
"execution_count": 4,
saved delta p dist True - EndVelo Momentum saved TMVA nice plots
2024-02-12 15:57:23 +01:00
"metadata": {},
"outputs": [
{
"data": {
correct insanity cuts, and GEC Filter to eff options files trained network with correct parameterisation sample4 new effs with sample4 NN weights
2024-02-25 12:06:14 +01:00
"image/png": "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
saved delta p dist True - EndVelo Momentum saved TMVA nice plots
2024-02-12 15:57:23 +01:00
"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
2024-02-19 15:41:09 +01:00
"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
2024-02-12 15:57:23 +01:00
"axes[0, 0].set_xlabel(r\"$\\chi^{2}_{\\mathrm{match}}$\")\n",
trained e def and e filter
2024-02-19 15:41:09 +01:00
"axes[0, 0].legend(prop={\"size\": 20})\n",
saved delta p dist True - EndVelo Momentum saved TMVA nice plots
2024-02-12 15:57:23 +01:00
"# 1,0\n",
trained e def and e filter
2024-02-19 15:41:09 +01:00
"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",
correct insanity cuts, and GEC Filter to eff options files trained network with correct parameterisation sample4 new effs with sample4 NN weights
2024-02-25 12:06:14 +01:00
"axes[1, 0].set_ylabel(\"Number of tracks (normalised)\",\n",
" va=\"bottom\",\n",
" 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",
correct insanity cuts, and GEC Filter to eff options files trained network with correct parameterisation sample4 new effs with sample4 NN weights
2024-02-25 12:06:14 +01:00
"plt.savefig(\n",
" \"/work/cetin/LHCb/reco_tuner/thesis/filtered_NN_elec_variables.pdf\",\n",
" format=\"PDF\")\n",
"# plt.show()"
logs and gitignore
2024-02-21 08:34:33 +01:00
]
},
{
"cell_type": "code",
correct insanity cuts, and GEC Filter to eff options files trained network with correct parameterisation sample4 new effs with sample4 NN weights
2024-02-25 12:06:14 +01:00
"execution_count": 5,
logs and gitignore
2024-02-21 08:34:33 +01:00
"metadata": {},
"outputs": [
{
"data": {
correct insanity cuts, and GEC Filter to eff options files trained network with correct parameterisation sample4 new effs with sample4 NN weights
2024-02-25 12:06:14 +01:00
"image/png": "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
logs and gitignore
2024-02-21 08:34:33 +01:00
"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",
correct insanity cuts, and GEC Filter to eff options files trained network with correct parameterisation sample4 new effs with sample4 NN weights
2024-02-25 12:06:14 +01:00
"axes[0, 0].set_ylabel(\"Number of tracks (normalised)\",\n",
" va=\"bottom\",\n",
" ha=\"center\")\n",
logs and gitignore
2024-02-21 08:34:33 +01:00
"# 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",
correct insanity cuts, and GEC Filter to eff options files trained network with correct parameterisation sample4 new effs with sample4 NN weights
2024-02-25 12:06:14 +01:00
"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