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463 lines
352 KiB
463 lines
352 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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"\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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"train_tree = uproot.open(\n",
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" {\n",
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" \"/work/cetin/LHCb/reco_tuner/nn_electron_training/result_e_sig_filter_bkg/matching_ghost_mlp_training.root\": \"MatchNNDataSet/TrainTree\"\n",
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" }\n",
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")\n",
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"test_tree = uproot.open(\n",
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" {\n",
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" \"/work/cetin/LHCb/reco_tuner/nn_electron_training/result_e_sig_filter_bkg/matching_ghost_mlp_training.root\": \"MatchNNDataSet/TestTree\"\n",
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" }\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": 3,
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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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",
|
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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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"source": [
|
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"train_bkg = train_array[train_array.classID == 1]\n",
|
|
"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",
|
|
"plt.hist(\n",
|
|
" 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",
|
|
" color=\"#107E7D\",\n",
|
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" label=\"training sample, true pairs\",\n",
|
|
")\n",
|
|
"plt.hist(\n",
|
|
" train_bkg.matching_mlp,\n",
|
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" bins=50,\n",
|
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" 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(\"/work/cetin/LHCb/reco_tuner/thesis/filtered_NN_elec_response.pdf\",\n",
|
|
"# format=\"PDF\")\n",
|
|
"plt.show()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 4,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
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"image/png": 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",
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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",
|
|
"# 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, 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",
|
|
"axes[1, 0].set_xlabel(r\"$D_{x}$ [mm]\")\n",
|
|
"axes[1, 0].set_ylabel(\"Number of tracks (normalised)\", va=\"bottom\", 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, 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",
|
|
"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, 0.02),\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",
|
|
"axes[2, 1].set_xlabel(r\"$|\\Delta t_{y}^{\\mathrm{match}}|$\")\n",
|
|
"# 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": {
|
|
"image/png": 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|
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"text/plain": [
|
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"<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()"
|
|
]
|
|
},
|
|
{
|
|
"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"
|
|
}
|
|
},
|
|
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}
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