2023-12-19 13:00:59 +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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2024-02-27 15:59:53 +01:00
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"execution_count": 83,
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2023-12-19 13:00:59 +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 awkward as ak\n",
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2024-02-27 15:59:53 +01:00
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"import matplotlib.pyplot as plt\n",
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"import seaborn as sns\n",
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2023-12-19 13:00:59 +01:00
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"import numpy as np\n",
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2024-02-27 15:59:53 +01:00
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"import mplhep\n",
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"\n",
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"from sklearn.preprocessing import PolynomialFeatures\n",
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"from sklearn.linear_model import LinearRegression, Lasso, Ridge, ElasticNet\n",
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"from sklearn.model_selection import train_test_split\n",
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"from sklearn.pipeline import Pipeline\n",
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"from sklearn.metrics import mean_squared_error\n",
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"\n",
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"mplhep.style.use([\"LHCbTex2\"])\n",
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"input_tree = uproot.open(\n",
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" {\n",
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" \"/work/cetin/LHCb/reco_tuner/data/tracking_losses_ntuple_B_BJpsi_def_selected.root\": \"Selected\"\n",
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" }\n",
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")\n",
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2023-12-19 13:00:59 +01:00
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"array = input_tree.arrays()\n",
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2024-02-27 15:59:53 +01:00
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"\n",
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"array[\"yStraightOut\"] = array[\"ideal_state_770_y\"] + array[\"ideal_state_770_ty\"] * (\n",
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" array[\"ideal_state_10000_z\"] - array[\"ideal_state_770_z\"]\n",
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")\n",
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"array[\"yDiffOut\"] = array[\"ideal_state_10000_y\"] - array[\"yStraightOut\"]\n",
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"array[\"yStraightEndT\"] = array[\"ideal_state_770_y\"] + array[\"ideal_state_770_ty\"] * (\n",
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" 9410.0 - array[\"ideal_state_770_z\"]\n",
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")\n",
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"array[\"yDiffEndT\"] = array[\"ideal_state_9410_y\"] - array[\"yStraightEndT\"]\n",
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"\n",
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"array[\"dSlope_xEndT\"] = array[\"ideal_state_9410_tx\"] - array[\"ideal_state_770_tx\"]\n",
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"array[\"dSlope_yEndT\"] = array[\"ideal_state_9410_ty\"] - array[\"ideal_state_770_ty\"]\n",
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2023-12-19 13:00:59 +01:00
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"array[\"dSlope_xEndT_abs\"] = abs(array[\"dSlope_xEndT\"])\n",
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"array[\"dSlope_yEndT_abs\"] = abs(array[\"dSlope_yEndT\"])\n",
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2024-02-27 15:59:53 +01:00
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"\n",
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2023-12-19 13:00:59 +01:00
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"\n",
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"def format_array(name, coef):\n",
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2024-02-27 15:59:53 +01:00
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" coef = [str(c) + \"f\" for c in coef if c != 0.0]\n",
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2023-12-19 13:00:59 +01:00
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" code = f\"constexpr std::array {name}\"\n",
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2024-02-27 15:59:53 +01:00
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" code += \"{\" + \", \".join(list(coef)) + \"};\"\n",
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2023-12-19 13:00:59 +01:00
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" return code"
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]
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},
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{
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"cell_type": "code",
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2024-02-27 15:59:53 +01:00
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"execution_count": 53,
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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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},
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{
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"cell_type": "code",
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"execution_count": 54,
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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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|
},
|
|
|
|
|
"metadata": {},
|
|
|
|
|
"output_type": "display_data"
|
|
|
|
|
}
|
|
|
|
|
],
|
|
|
|
|
"source": [
|
|
|
|
|
"bins = np.linspace(-200, 200, 50)\n",
|
|
|
|
|
"sns.regplot(\n",
|
|
|
|
|
" x=ak.to_numpy(array[\"ideal_state_770_y\"]),\n",
|
|
|
|
|
" y=ak.to_numpy(array[\"yDiffOut\"]),\n",
|
|
|
|
|
" x_bins=bins,\n",
|
|
|
|
|
" fit_reg=None,\n",
|
|
|
|
|
" x_estimator=np.mean,\n",
|
|
|
|
|
")\n",
|
|
|
|
|
"plt.xlabel(\"y (VELO)\")\n",
|
|
|
|
|
"plt.ylabel(\"$y_{corr}$ [mm]\")\n",
|
|
|
|
|
"mplhep.lhcb.text(\"Simulation\")\n",
|
|
|
|
|
"plt.show()"
|
|
|
|
|
]
|
|
|
|
|
},
|
|
|
|
|
{
|
|
|
|
|
"cell_type": "code",
|
|
|
|
|
"execution_count": 55,
|
|
|
|
|
"metadata": {},
|
|
|
|
|
"outputs": [
|
|
|
|
|
{
|
|
|
|
|
"data": {
|
|
|
|
|
"image/png": "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
|
|
|
|
|
"text/plain": [
|
|
|
|
|
"<Figure size 1200x900 with 1 Axes>"
|
|
|
|
|
]
|
|
|
|
|
},
|
|
|
|
|
"metadata": {},
|
|
|
|
|
"output_type": "display_data"
|
|
|
|
|
}
|
|
|
|
|
],
|
|
|
|
|
"source": [
|
|
|
|
|
"bins = np.linspace(-200, 200, 50)\n",
|
|
|
|
|
"sns.regplot(\n",
|
|
|
|
|
" x=ak.to_numpy(array[\"ideal_state_9410_y\"]),\n",
|
|
|
|
|
" y=ak.to_numpy(array[\"yDiffOut\"]),\n",
|
|
|
|
|
" x_bins=bins,\n",
|
|
|
|
|
" fit_reg=None,\n",
|
|
|
|
|
" x_estimator=np.mean,\n",
|
|
|
|
|
")\n",
|
|
|
|
|
"plt.xlabel(\"y (T)\")\n",
|
|
|
|
|
"plt.ylabel(\"$y_{corr}$ [mm]\")\n",
|
|
|
|
|
"mplhep.lhcb.text(\"Simulation\")\n",
|
|
|
|
|
"plt.show()"
|
|
|
|
|
]
|
|
|
|
|
},
|
|
|
|
|
{
|
|
|
|
|
"cell_type": "code",
|
|
|
|
|
"execution_count": 56,
|
|
|
|
|
"metadata": {},
|
|
|
|
|
"outputs": [
|
|
|
|
|
{
|
|
|
|
|
"data": {
|
|
|
|
|
"image/png": "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
|
|
|
|
|
"text/plain": [
|
|
|
|
|
"<Figure size 1200x900 with 1 Axes>"
|
|
|
|
|
]
|
|
|
|
|
},
|
|
|
|
|
"metadata": {},
|
|
|
|
|
"output_type": "display_data"
|
|
|
|
|
}
|
|
|
|
|
],
|
|
|
|
|
"source": [
|
|
|
|
|
"bins = np.linspace(-1, 1, 50)\n",
|
|
|
|
|
"sns.regplot(\n",
|
|
|
|
|
" x=ak.to_numpy(array[\"ideal_state_9410_ty\"]),\n",
|
|
|
|
|
" y=ak.to_numpy(array[\"yDiffOut\"]),\n",
|
|
|
|
|
" x_bins=bins,\n",
|
|
|
|
|
" fit_reg=None,\n",
|
|
|
|
|
" x_estimator=np.mean,\n",
|
|
|
|
|
")\n",
|
|
|
|
|
"plt.xlabel(\"ty (T)\")\n",
|
|
|
|
|
"plt.ylabel(\"$y_{corr}$ [mm]\")\n",
|
|
|
|
|
"mplhep.lhcb.text(\"Simulation\")\n",
|
|
|
|
|
"plt.show()"
|
|
|
|
|
]
|
|
|
|
|
},
|
|
|
|
|
{
|
|
|
|
|
"cell_type": "code",
|
|
|
|
|
"execution_count": 76,
|
|
|
|
|
"metadata": {},
|
|
|
|
|
"outputs": [
|
|
|
|
|
{
|
|
|
|
|
"data": {
|
|
|
|
|
"image/png": "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
|
|
|
|
|
"text/plain": [
|
|
|
|
|
"<Figure size 1200x900 with 1 Axes>"
|
|
|
|
|
]
|
|
|
|
|
},
|
|
|
|
|
"metadata": {},
|
|
|
|
|
"output_type": "display_data"
|
|
|
|
|
}
|
|
|
|
|
],
|
|
|
|
|
"source": [
|
|
|
|
|
"bins = np.linspace(-1, 1, 50)\n",
|
|
|
|
|
"sns.regplot(\n",
|
|
|
|
|
" x=ak.to_numpy(array[\"ideal_state_770_ty\"]),\n",
|
|
|
|
|
" y=ak.to_numpy(array[\"yDiffOut\"]),\n",
|
|
|
|
|
" x_bins=bins,\n",
|
|
|
|
|
" fit_reg=None,\n",
|
|
|
|
|
" x_estimator=np.mean,\n",
|
|
|
|
|
")\n",
|
|
|
|
|
"plt.xlabel(\"ty (VELO)\")\n",
|
|
|
|
|
"plt.ylabel(\"$y_{corr}$ [mm]\")\n",
|
|
|
|
|
"mplhep.lhcb.text(\"Simulation\")\n",
|
|
|
|
|
"plt.show()"
|
|
|
|
|
]
|
|
|
|
|
},
|
|
|
|
|
{
|
|
|
|
|
"cell_type": "code",
|
|
|
|
|
"execution_count": 74,
|
|
|
|
|
"metadata": {},
|
|
|
|
|
"outputs": [
|
|
|
|
|
{
|
|
|
|
|
"data": {
|
|
|
|
|
"image/png": "iVBORw0KGgoAAAANSUhEUgAABMEAAAOWCAYAAADm+IXMAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjguMCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy81sbWrAAAACXBIWXMAAA9hAAAPYQGoP6dpAAB7DklEQVR4nOz9X2xj+XkneD9UV4z2Gy+Lqg4CCORFikovPFc7RVXvxbxXO0VNFlgbcaal6mA8doB3UpITDGYBLSKlnYudvXC3pUV0s0DSUjsvsHZyUS11JkE8wMaiPdeLktjeO2MnZHcACQQG6xJbceCG4ybfi3rF6C9FSfx7+PkAAkTq4e88R9Qp9vn27/xOqtlsNgMAAAAAEmxi0A0AAAAAQK8JwQAAAABIPCEYAAAAAIknBAMAAAAg8YRgAAAAACSeEAwAAACAxBOCAQAAAJB4QjAAAAAAEk8IBgAAAEDiCcEAAAAASDwhGAAAAACJJwQDAAAAIPGEYAAAAAAknhAMAAAAgMQTggEAAACQeEIwAAAAABJPCAYAAABA4gnBAAAAAEg8IRgAAAAAiScEAwAAACDxhGAAAAAAJJ4QDAAAAIDEE4IBAAAAkHhCMAAAAAASTwgGAAAAQOIJwQAAAABIPCEYAAAAAIknBAMAAAAg8e4MugG4rV/8xV+MTz75JF566aX45V/+5UG3AwAAAEPtv/yX/xKffvppvPzyy/H3f//3g26nb1LNZrM56CbgNl566aVoNBqDbgMAAABGysTERHz66aeDbqNvzARj5B2HYBMTEzE1NRUREbVa7dxzo0T/gzfq+zDq/UeM/j6Mev8Ro78Po95/xOjvg/4Hb9T3YdT7jxj9fRj1/iNGfx9Gvf+I0d+HJPZ//NxLL7004O76SwjGyPvlX/7lODg4iKmpqdjf34+IiFwud+65UaL/wRv1fRj1/iNGfx9Gvf+I0d+HUe8/YvT3Qf+DN+r7MOr9R4z+Pox6/xGjvw+j3n/E6O9DEvs/fm7clhQaaAj26quvDnLzp6RSqfi//+//e9BtAAAAANADAw3BKpVKpFKpGOSyZMfbT6VSA+sBAAAAgN6aGHQDg+a+AAAAAADJN/A1wZrNZszNzUU+nx/I9uv1emxubg5k2wAAAAD0x8BDsM3Nzfjt3/7tgfYwNzcXv/ZrvzbQHgAAAADonYFfDlksFgfdQrz22muDbgEAAACAHhp4CHbv3r1BtwAAAABAwg30ckh3ZISLLS0txdHRUaTT6UG3ciOj3n/E6O/DqPefBEl4D0Z9H0a9/4hk7MMoS8Lvf9T3YdT7j0jGPoy6UX8PRr3/iNHfh1Hvn3+Uag7w9ogTExNRr9cH/of08ccfx+TkZDQajYH2wc3kcrk4ODiIbDYb+/v7lz4HjBbHMYw+xzGMPscxjD7nzP9ooJdDrq6uDjwAi4i4e/durK6uDroNAAAAAHpkoCHY7/3e7/V1ex999NGlP+t3L3RfrVaLXC4XuVwuarXauedyuVysr68PuEsAAADonfX19VPnwRedHx8/N24GuiZYvy0uLsZf//VfD7oNeqTRaMTBwUHb546OjvrdFgAAAPTN0dHRuXPjiIvPmcfNWIVgu7u7g26BHpqYmIipqamIeJFwNxqNU89FxFBcfgsAAAC9kk6nI5vNth5fdH58/Ny4GckQ7KOPPop6vd5xfbVajY2NjWu9htEzNTV1bpG/k88BAABA0i0tLcXS0lLr8UXnx8fPjZuRCME++uijWF1djVKpFNVq9UZjNJvNSKVSXe4MAAAAgFEw9CHYm2++GWtraxHxIsgCAAAAgOsa6hDs/fffj9XV1YiISKVSkUqlBGF0ZGlpKY6OjqwBBiPMcQyjz3EMo89xDKPPcfyPUs0hTpUePnwY5XK5FX7l8/koFAqRz+cjIuKVV165cowf//jHUa/X47333ouPP/44Pv300163TZ8dX8uczWat/wUAAABXGNfz6KGeCXYcgEVE7OzsxKNHj2481vLycrz66qvdag0AAACAETIx6AbayWQyEfEiwLpNABYRkc/n4/79+13oCgAAAIBRM9Qh2PFlj6+99lpXxtvY2OjKOAAAAACMlqEOwY5nfz1//ryr4wEAAAAwXoY6BPv6178ezWYzyuVyV8b71re+1ZVxAAAAABgtQx2C3b17N775zW/G06dP4+/+7u9uPZ7LIQEAAADG01CHYBEvFsUvFAoxPz9/q3E+/PDDrs0oAwAAAGC03Bl0A53Y2dmJhw8fxquvvhorKytx7969jl73/PnzqNfrUalU4r333utxlwAAAAAMq5EIwX7wgx9ERESlUonFxcUbjdFsNiOVSnWzLQAAAABGxNCHYL/zO78Tm5ubERGRSqWi2WxeewzhFwAAANAtzWZE44p4YiIVIY4YLkMdgr377rutxexvGoBFxI1fBwAAAHBWoxlx5w/a1/z8GxEvCcGGylCHYGcDsGKxGLOzs5HJZK69Ltg777wTH330UQ+7BQAAAGBYDXUIVi6XI5VKRSaTid3d3bh///6Nx3ry5Em88sorXewOAAAAgFExMegG2slkMhER8eabb94qADse68GDB13oCgAAAIBRM9Qh2MzMTERE5PP5roz37rvvdmUcAAAAAEbLUIdgi4uL0Ww2o1qtdmU8M8GAbqnX64NuAQAAgGsY6hBsbm4uHjx4EE+fPu3KeH/+53/elXGA8VQul2NlZSUmJyfjyZMng24HAACAaxjqhfEjIra2tuJXf/VX4z/8h/8Qv/Ebv3Grsd5+++34l//yX3apM2CYlMvlePr0aZTL5ahWq607w2Yymcjn83Hv3r0oFAoxOzsbxWLx3Ovn5+ejWq3G3t5e67l6vR6lUimePn0apVLJ7C8AAIARNvQhWD6fj3feeSd++7d/+1Yh2McffxzlcrmLnUF3bW5uxuLi4rVfVygUTgU3N5VKpa79mkwmE4eHhxf+bH5+Pra3t6895s7OzoUh1UXq9Xq8/fbbsba21rbm+NgvlUqt2kKhEMViMaanp2NnZye2t7ejUCiceu2TJ0+iWq36twMAACABhjoEO7588Zd+6ZdicnIyXn311Zibm7v2OPV6Pd57771ut8eQqdVqkcvl2tYsLS3F0tJSnzq6noWFhXj8+HFUq9V4++232wZIxWIxVlZW4uHDh627qN7W8fp7pVIpVlZWLp31lM/nY2VlJYrFYtubVmxtbUW9Xo/d3d1YXV2NUql0ae3y8nK88cYbkc/nO96flZWVC8Ovubm51ljH41Wr1ahWq7G1tRXvvfdeKxg7G26d3eetra3W99PT011bnxAAAKBX1tfXY319vW1NrVbrUzfDJdVsNpuDbuIyDx8+jA8++KD1uNls3mi2ysnXfvrpp91qjyGRy+Xi4OCgo9r/+X/+n+Pf//t/39uGumRxcTE2NzfPPT83N3cqnOmFarUa09PTF/7s8PDwRsHbzMzMhTOqNjY2YmFh4Vq9zc7OngukFhYWYmNjo6Mx1tbWYmVl5dzz+Xw+KpXKha85+370430AAACG06eNiDt/0L7m59+IeGkAK7H/+3//7+N/+V/+l45qs9ls7O/v97ij4THUC+M/fvw4ms1mHOd0Nw3AGA8TExORzWbbfqXT6UG32bHLLo28ySWT15XP589dGhjxIvi56cyzy/q+TgBWKpXOzcjKZDKxt7fXcQAW8WLmWaVSObcvz58/v/Q13ZpxBwAAjIc/++Dqml5Ip9NXnhtPTAx1HNQzQ3055OLiYvz+7/9+pFKpGOIJawyJqampRCXYl11q2O4SxG5v/+zMrdts+6LXXidYKpfLMTs7e+713//+9y8M7Drp58MPP4z79++3LoO08D0AANCJTgKu39p+MRPsyw96389JnSwDdJ0rqpJkqEOwu3fvRqFQiA8++CBWV1ejUCjEvXv3rj1OtVqNt956K374wx92v0nokcsCopscA6OuWq3Go0ePzj2/tbV1owDs2PEssssu/RxVx4v89yswHTWlUqnjmy8AAMBZH38S8bt/2Vnt1/4i4ov/JCL9ck9bokNDHYJFRLzxxhsxPT0dv/d7v3fjMR48eBC
|
|
|
|
|
"text/plain": [
|
|
|
|
|
"<Figure size 1200x900 with 1 Axes>"
|
|
|
|
|
]
|
|
|
|
|
},
|
|
|
|
|
"metadata": {},
|
|
|
|
|
"output_type": "display_data"
|
|
|
|
|
}
|
|
|
|
|
],
|
|
|
|
|
"source": [
|
|
|
|
|
"bins = np.linspace(-0.5, 0.5, 50)\n",
|
|
|
|
|
"sns.regplot(\n",
|
|
|
|
|
" x=ak.to_numpy(array[\"dSlope_yEndT\"]),\n",
|
|
|
|
|
" y=ak.to_numpy(array[\"yDiffOut\"]),\n",
|
|
|
|
|
" x_bins=bins,\n",
|
|
|
|
|
" fit_reg=None,\n",
|
|
|
|
|
" x_estimator=np.mean,\n",
|
|
|
|
|
")\n",
|
|
|
|
|
"plt.xlabel(\"dSlope y\")\n",
|
|
|
|
|
"plt.ylabel(\"$y_{corr}$ [mm]\")\n",
|
|
|
|
|
"mplhep.lhcb.text(\"Simulation\")\n",
|
|
|
|
|
"plt.show()"
|
|
|
|
|
]
|
|
|
|
|
},
|
|
|
|
|
{
|
|
|
|
|
"cell_type": "code",
|
|
|
|
|
"execution_count": 75,
|
|
|
|
|
"metadata": {},
|
|
|
|
|
"outputs": [
|
|
|
|
|
{
|
|
|
|
|
"data": {
|
|
|
|
|
"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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"bins = np.linspace(-1, 1, 50)\n",
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"sns.regplot(\n",
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" x=ak.to_numpy(array[\"dSlope_xEndT\"]),\n",
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" y=ak.to_numpy(array[\"yDiffOut\"]),\n",
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" x_bins=bins,\n",
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" fit_reg=None,\n",
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" x_estimator=np.mean,\n",
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")\n",
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"plt.xlabel(\"dSlope x \")\n",
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"plt.ylabel(\"$y_{corr}$ [mm]\")\n",
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"mplhep.lhcb.text(\"Simulation\")\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": 151,
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2023-12-19 13:00:59 +01:00
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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2024-02-27 15:59:53 +01:00
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"intercept= 0.16257747434812642\n",
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"coef= {'dSlope_yEndT': 4379.8079670973475, 'dSlope_xEndT dSlope_yEndT': 37.76975769672208, 'ideal_state_770_ty dSlope_xEndT^2': 1224.0422108322969, 'ideal_state_770_ty dSlope_yEndT^2': -18042.13200642719}\n",
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"r2 score= 0.8441034202616307\n",
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"RMSE = 10.185808113150346\n"
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2023-12-19 13:00:59 +01:00
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]
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}
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],
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"source": [
|
2024-02-27 15:59:53 +01:00
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"features = [\n",
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" \"ideal_state_770_ty\",\n",
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" # \"ideal_state_9410_ty\",\n",
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" # \"ideal_state_770_y\",\n",
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" # \"ideal_state_9410_y\",\n",
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" \"dSlope_xEndT\",\n",
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" \"dSlope_yEndT\",\n",
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"]\n",
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"target_feat = \"yDiffOut\"\n",
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"order = 3\n",
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"\n",
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"data = np.column_stack([ak.to_numpy(array[feat]) for feat in features])\n",
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"target = ak.to_numpy(array[target_feat])\n",
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"X_train, X_test, y_train, y_test = train_test_split(data,\n",
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" target,\n",
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" test_size=0.2,\n",
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" random_state=42)\n",
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"\n",
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"poly = PolynomialFeatures(degree=order, include_bias=False)\n",
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"X_train_model = poly.fit_transform(X_train)\n",
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"X_test_model = poly.fit_transform(X_test)\n",
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"\n",
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"poly_features = poly.get_feature_names_out(input_features=features)\n",
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"\n",
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"# keep = [\n",
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"# # \"ideal_state_770_ty\", #\n",
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"# # \"dSlope_xEndT\", #\n",
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|
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"# \"dSlope_yEndT\", # keep\n",
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"# # \"ideal_state_770_ty^2\", #\n",
|
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"# # \"ideal_state_770_ty dSlope_xEndT\", #\n",
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"# # \"ideal_state_770_ty dSlope_yEndT\", #\n",
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"# # \"dSlope_xEndT^2\", # do not keep\n",
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"# \"dSlope_xEndT dSlope_yEndT\", # keep\n",
|
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"# # \"dSlope_yEndT^2\", #\n",
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"# # \"ideal_state_770_ty^3\",\n",
|
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"# # \"ideal_state_770_ty^2 dSlope_xEndT\", #\n",
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"# # \"ideal_state_770_ty^2 dSlope_yEndT\",\n",
|
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"# \"ideal_state_770_ty dSlope_xEndT^2\", # keep\n",
|
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"# # \"ideal_state_770_ty dSlope_xEndT dSlope_yEndT\", #\n",
|
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"# \"ideal_state_770_ty dSlope_yEndT^2\", # keep\n",
|
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|
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"# # \"dSlope_xEndT^3\", # do not keep\n",
|
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"# # \"dSlope_xEndT^2 dSlope_yEndT\", #\n",
|
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"# # \"dSlope_xEndT dSlope_yEndT^2\", #\n",
|
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"# # \"dSlope_yEndT^3\",\n",
|
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"# # \"ideal_state_770_ty^4\",\n",
|
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|
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"# # \"ideal_state_770_ty^3 dSlope_xEndT\",\n",
|
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|
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"# # \"ideal_state_770_ty^3 dSlope_yEndT\",\n",
|
|
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|
|
"# # \"ideal_state_770_ty^2 dSlope_xEndT^2\", #\n",
|
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|
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"# # \"ideal_state_770_ty^2 dSlope_xEndT dSlope_yEndT\",\n",
|
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|
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"# # \"ideal_state_770_ty^2 dSlope_yEndT^2\",\n",
|
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"# # \"ideal_state_770_ty dSlope_xEndT^3\", # do not keep\n",
|
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"# # \"ideal_state_770_ty dSlope_xEndT^2 dSlope_yEndT\", #\n",
|
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|
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"# # \"ideal_state_770_ty dSlope_xEndT dSlope_yEndT^2\",\n",
|
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|
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"# # \"ideal_state_770_ty dSlope_yEndT^3\",\n",
|
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|
|
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"# # \"dSlope_xEndT^4\", #\n",
|
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|
|
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"# \"dSlope_xEndT^3 dSlope_yEndT\", # keep\n",
|
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|
|
"# # \"dSlope_xEndT^2 dSlope_yEndT^2\", #\n",
|
|
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|
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"# # \"dSlope_xEndT dSlope_yEndT^3\",\n",
|
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|
|
"# # \"dSlope_yEndT^4\",\n",
|
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|
|
"# # \"ideal_state_770_ty^5\",\n",
|
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"# # \"ideal_state_770_ty^4 dSlope_xEndT\",\n",
|
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|
|
"# # \"ideal_state_770_ty^4 dSlope_yEndT\",\n",
|
|
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|
|
"# # \"ideal_state_770_ty^3 dSlope_xEndT^2\",\n",
|
|
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|
|
"# # \"ideal_state_770_ty^3 dSlope_xEndT dSlope_yEndT\",\n",
|
|
|
|
|
"# # \"ideal_state_770_ty^3 dSlope_yEndT^2\",\n",
|
|
|
|
|
"# # \"ideal_state_770_ty^2 dSlope_xEndT^3\", #\n",
|
|
|
|
|
"# # \"ideal_state_770_ty^2 dSlope_xEndT^2 dSlope_yEndT\",\n",
|
|
|
|
|
"# # \"ideal_state_770_ty^2 dSlope_xEndT dSlope_yEndT^2\",\n",
|
|
|
|
|
"# # \"ideal_state_770_ty^2 dSlope_yEndT^3\",\n",
|
|
|
|
|
"# \"ideal_state_770_ty dSlope_xEndT^4\", # keep\n",
|
|
|
|
|
"# # \"ideal_state_770_ty dSlope_xEndT^3 dSlope_yEndT\", #\n",
|
|
|
|
|
"# # \"ideal_state_770_ty dSlope_xEndT^2 dSlope_yEndT^2\",\n",
|
|
|
|
|
"# # \"ideal_state_770_ty dSlope_xEndT dSlope_yEndT^3\",\n",
|
|
|
|
|
"# # \"ideal_state_770_ty dSlope_yEndT^4\",\n",
|
|
|
|
|
"# # \"dSlope_xEndT^5\", #\n",
|
|
|
|
|
"# \"dSlope_xEndT^4 dSlope_yEndT\", # keep\n",
|
|
|
|
|
"# # \"dSlope_xEndT^3 dSlope_yEndT^2\", #\n",
|
|
|
|
|
"# # \"dSlope_xEndT^2 dSlope_yEndT^3\",\n",
|
|
|
|
|
"# # \"dSlope_xEndT dSlope_yEndT^4\",\n",
|
|
|
|
|
"# # \"dSlope_yEndT^5\",\n",
|
|
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|
"# ]\n",
|
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|
"\n",
|
|
|
|
|
"keep = [\n",
|
|
|
|
|
" \"dSlope_yEndT\", # keep\n",
|
|
|
|
|
" \"dSlope_xEndT dSlope_yEndT\", # keep\n",
|
|
|
|
|
" \"ideal_state_770_ty dSlope_xEndT^2\", # keep\n",
|
|
|
|
|
" \"ideal_state_770_ty dSlope_yEndT^2\", # keep\n",
|
|
|
|
|
" # \"dSlope_xEndT^3 dSlope_yEndT\", # do not keep\n",
|
|
|
|
|
" # \"ideal_state_770_ty dSlope_xEndT^4\", # keep\n",
|
|
|
|
|
" # \"dSlope_xEndT^4 dSlope_yEndT\", # keep\n",
|
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|
"]\n",
|
|
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|
"\n",
|
|
|
|
|
"# keep = [\n",
|
|
|
|
|
"# \"ideal_state_770_ty dSlope_yEndT^2\",\n",
|
|
|
|
|
"# \"ideal_state_770_ty dSlope_xEndT^2\",\n",
|
|
|
|
|
"# ]\n",
|
|
|
|
|
"remove = [i for i, f in enumerate(poly_features) if f not in keep]\n",
|
|
|
|
|
"\n",
|
|
|
|
|
"X_train_model = np.delete(X_train_model, remove, axis=1)\n",
|
|
|
|
|
"X_test_model = np.delete(X_test_model, remove, axis=1)\n",
|
|
|
|
|
"poly_features = np.delete(poly_features, remove)\n",
|
|
|
|
|
"# print(poly_features)\n",
|
|
|
|
|
"lin_reg = LinearRegression()\n",
|
|
|
|
|
"# lin_reg = Lasso(fit_intercept=False, alpha=0.000001)\n",
|
|
|
|
|
"# lin_reg = Lasso(alpha=0.1)\n",
|
|
|
|
|
"# lin_reg = LassoCV(max_iter=2000)\n",
|
|
|
|
|
"# lin_reg = ElasticNet(alpha=0.1)\n",
|
|
|
|
|
"# lin_reg = Ridge(alpha=0)\n",
|
|
|
|
|
"lin_reg.fit(X_train_model, y_train)\n",
|
|
|
|
|
"y_pred_test = lin_reg.predict(X_test_model)\n",
|
|
|
|
|
"print(\"intercept=\", lin_reg.intercept_)\n",
|
|
|
|
|
"print(\"coef=\", dict(zip(poly_features, lin_reg.coef_)))\n",
|
|
|
|
|
"print(\"r2 score=\", lin_reg.score(X_test_model, y_test))\n",
|
|
|
|
|
"print(\"RMSE =\", mean_squared_error(y_test, y_pred_test, squared=False))"
|
|
|
|
|
]
|
|
|
|
|
},
|
|
|
|
|
{
|
|
|
|
|
"cell_type": "code",
|
|
|
|
|
"execution_count": 148,
|
|
|
|
|
"metadata": {},
|
|
|
|
|
"outputs": [
|
|
|
|
|
{
|
|
|
|
|
"data": {
|
|
|
|
|
"text/plain": [
|
|
|
|
|
"[]"
|
|
|
|
|
]
|
|
|
|
|
},
|
|
|
|
|
"execution_count": 148,
|
|
|
|
|
"metadata": {},
|
|
|
|
|
"output_type": "execute_result"
|
|
|
|
|
}
|
|
|
|
|
],
|
|
|
|
|
"source": [
|
|
|
|
|
"koeffs = dict(zip(poly_features, lin_reg.coef_))\n",
|
2023-12-19 13:00:59 +01:00
|
|
|
"\n",
|
2024-02-27 15:59:53 +01:00
|
|
|
"remove = []\n",
|
|
|
|
|
"for itr in koeffs.items():\n",
|
|
|
|
|
" if abs(itr[1]) < 1.0:\n",
|
|
|
|
|
" remove.append(itr[0])\n",
|
|
|
|
|
"remove"
|
|
|
|
|
]
|
|
|
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|
},
|
|
|
|
|
{
|
|
|
|
|
"cell_type": "code",
|
|
|
|
|
"execution_count": 147,
|
|
|
|
|
"metadata": {},
|
|
|
|
|
"outputs": [],
|
|
|
|
|
"source": [
|
|
|
|
|
"ls_koeffs = []\n",
|
|
|
|
|
"for itr in koeffs.items():\n",
|
|
|
|
|
" ls_koeffs.append(itr[0])\n",
|
|
|
|
|
"# ls_koeffs"
|
|
|
|
|
]
|
|
|
|
|
},
|
|
|
|
|
{
|
|
|
|
|
"cell_type": "code",
|
|
|
|
|
"execution_count": 65,
|
|
|
|
|
"metadata": {},
|
|
|
|
|
"outputs": [
|
|
|
|
|
{
|
|
|
|
|
"data": {
|
|
|
|
|
"text/plain": [
|
|
|
|
|
"['1', 'dSlope_xEndT^2', 'dSlope_xEndT^3', 'dSlope_xEndT^6']"
|
|
|
|
|
]
|
|
|
|
|
},
|
|
|
|
|
"execution_count": 65,
|
|
|
|
|
"metadata": {},
|
|
|
|
|
"output_type": "execute_result"
|
|
|
|
|
}
|
|
|
|
|
],
|
|
|
|
|
"source": []
|
|
|
|
|
},
|
|
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|
|
{
|
|
|
|
|
"cell_type": "code",
|
|
|
|
|
"execution_count": 149,
|
|
|
|
|
"metadata": {},
|
|
|
|
|
"outputs": [
|
|
|
|
|
{
|
|
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|
"data": {
|
|
|
|
|
"image/png": "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
|
|
|
|
|
"text/plain": [
|
|
|
|
|
"<Figure size 1200x900 with 2 Axes>"
|
|
|
|
|
]
|
|
|
|
|
},
|
|
|
|
|
"metadata": {},
|
|
|
|
|
"output_type": "display_data"
|
|
|
|
|
}
|
|
|
|
|
],
|
|
|
|
|
"source": [
|
|
|
|
|
"bins = np.linspace(-40, 40, 50)\n",
|
|
|
|
|
"ax = sns.regplot(\n",
|
|
|
|
|
" x=y_test,\n",
|
|
|
|
|
" y=abs(y_test - y_pred_test),\n",
|
|
|
|
|
" x_bins=bins,\n",
|
|
|
|
|
" fit_reg=None,\n",
|
|
|
|
|
" x_estimator=np.mean,\n",
|
|
|
|
|
" label=\"bla\",\n",
|
|
|
|
|
")\n",
|
|
|
|
|
"ax2 = ax.twinx()\n",
|
|
|
|
|
"ax2.hist(y_test,\n",
|
|
|
|
|
" bins=20,\n",
|
|
|
|
|
" range=[-40, 40],\n",
|
|
|
|
|
" color=\"#2A9D8F\",\n",
|
|
|
|
|
" alpha=0.5,\n",
|
|
|
|
|
" align=\"left\")\n",
|
|
|
|
|
"ax.set_xlabel(r\"y$_{corr}$ [mm]\")\n",
|
|
|
|
|
"ax.set_ylabel(\"Mean Deviation [mm]\")\n",
|
|
|
|
|
"ax2.set_ylabel(\"Number of Tracks\")\n",
|
|
|
|
|
"mplhep.lhcb.text(\"Simulation\", loc=0)\n",
|
|
|
|
|
"plt.show()"
|
|
|
|
|
]
|
|
|
|
|
},
|
|
|
|
|
{
|
|
|
|
|
"cell_type": "code",
|
|
|
|
|
"execution_count": null,
|
|
|
|
|
"metadata": {},
|
|
|
|
|
"outputs": [],
|
|
|
|
|
"source": []
|
|
|
|
|
},
|
|
|
|
|
{
|
|
|
|
|
"cell_type": "code",
|
|
|
|
|
"execution_count": null,
|
|
|
|
|
"metadata": {},
|
|
|
|
|
"outputs": [],
|
|
|
|
|
"source": [
|
2023-12-19 13:00:59 +01:00
|
|
|
"features = [\n",
|
2024-02-27 15:59:53 +01:00
|
|
|
" \"ideal_state_770_ty\",\n",
|
|
|
|
|
" \"ideal_state_770_tx\",\n",
|
2023-12-19 13:00:59 +01:00
|
|
|
" \"dSlope_xEndT\",\n",
|
|
|
|
|
" \"dSlope_yEndT\",\n",
|
|
|
|
|
" \"dSlope_xEndT_abs\",\n",
|
|
|
|
|
" \"dSlope_yEndT_abs\",\n",
|
|
|
|
|
"]\n",
|
|
|
|
|
"target_feat = \"yDiffEndT\"\n",
|
|
|
|
|
"\n",
|
|
|
|
|
"data = np.column_stack([ak.to_numpy(array[feat]) for feat in features])\n",
|
|
|
|
|
"target = ak.to_numpy(array[target_feat])\n",
|
2024-02-27 15:59:53 +01:00
|
|
|
"X_train, X_test, y_train, y_test = train_test_split(\n",
|
|
|
|
|
" data, target, test_size=0.2, random_state=42\n",
|
|
|
|
|
")\n",
|
2023-12-19 13:00:59 +01:00
|
|
|
"\n",
|
|
|
|
|
"poly = PolynomialFeatures(degree=6, include_bias=False)\n",
|
2024-02-27 15:59:53 +01:00
|
|
|
"X_train_model = poly.fit_transform(X_train)\n",
|
|
|
|
|
"X_test_model = poly.fit_transform(X_test)\n",
|
2023-12-19 13:00:59 +01:00
|
|
|
"poly_features = poly.get_feature_names_out(input_features=features)\n",
|
|
|
|
|
"keep = [\n",
|
|
|
|
|
" #'dSlope_xEndT',\n",
|
2024-02-27 15:59:53 +01:00
|
|
|
" \"dSlope_yEndT\", # keep\n",
|
2023-12-19 13:00:59 +01:00
|
|
|
" #'dSlope_yEndT_abs',\n",
|
|
|
|
|
" #'ty dSlope_xEndT',\n",
|
|
|
|
|
" #'ty dSlope_yEndT',\n",
|
2024-02-27 15:59:53 +01:00
|
|
|
" \"ideal_state_770_ty dSlope_xEndT_abs\", # keep\n",
|
|
|
|
|
" \"ideal_state_770_ty dSlope_yEndT_abs\", # keep\n",
|
|
|
|
|
" \"ideal_state_770_ty dSlope_yEndT^2\", # keep\n",
|
|
|
|
|
" \"ideal_state_770_ty dSlope_xEndT^2\", # keep\n",
|
2023-12-19 13:00:59 +01:00
|
|
|
" #'tx dSlope_xEndT',\n",
|
|
|
|
|
" #'tx dSlope_xEndT_abs',\n",
|
|
|
|
|
" #'tx dSlope_yEndT',\n",
|
2024-02-27 15:59:53 +01:00
|
|
|
" \"ideal_state_770_ty ideal_state_770_tx dSlope_xEndT\", # keep\n",
|
|
|
|
|
" \"ideal_state_770_tx^2 dSlope_yEndT\", # keep\n",
|
2023-12-19 13:00:59 +01:00
|
|
|
" #'ty^2 dSlope_xEndT',\n",
|
2024-02-27 15:59:53 +01:00
|
|
|
" #'ty^2 dSlope_yEndT',\n",
|
2023-12-19 13:00:59 +01:00
|
|
|
" #'ty^2 dSlope_xEndT_abs',\n",
|
|
|
|
|
" #'ty^2 tx dSlope_xEndT',\n",
|
|
|
|
|
" #'ty tx^2 dSlope_yEndT',\n",
|
2024-02-27 15:59:53 +01:00
|
|
|
" \"ideal_state_770_ty ideal_state_770_tx^2 dSlope_xEndT_abs\", # keep\n",
|
|
|
|
|
" \"ideal_state_770_ty^3 ideal_state_770_tx dSlope_xEndT\", # keep\n",
|
2023-12-19 13:00:59 +01:00
|
|
|
" #'ty tx^3 dSlope_xEndT',\n",
|
|
|
|
|
" #'ty^3 dSlope_yEndT_abs',\n",
|
|
|
|
|
"]\n",
|
|
|
|
|
"do_not_keep = [\n",
|
2024-02-27 15:59:53 +01:00
|
|
|
" \"dSlope_xEndT\",\n",
|
|
|
|
|
" \"dSlope_yEndT_abs\",\n",
|
|
|
|
|
" \"ideal_state_770_ty dSlope_xEndT\",\n",
|
|
|
|
|
" \"ideal_state_770_tx dSlope_xEndT\",\n",
|
|
|
|
|
" \"ideal_state_770_tx dSlope_xEndT_abs\",\n",
|
|
|
|
|
" \"ideal_state_770_tx dSlope_yEndT\",\n",
|
|
|
|
|
" \"ideal_state_770_ty^2 dSlope_xEndT\",\n",
|
|
|
|
|
" \"ideal_state_770_ty^3 dSlope_yEndT_abs\",\n",
|
|
|
|
|
" \"ideal_state_770_ty ideal_state_770_tx dSlope_yEndT\",\n",
|
|
|
|
|
" \"ideal_state_770_ty ideal_state_770_tx^3 dSlope_xEndT\",\n",
|
|
|
|
|
" \"ideal_state_770_ty ideal_state_770_tx^2 dSlope_yEndT\",\n",
|
2023-12-19 13:00:59 +01:00
|
|
|
"]\n",
|
|
|
|
|
"reduce = True\n",
|
|
|
|
|
"if reduce:\n",
|
2024-02-27 15:59:53 +01:00
|
|
|
" remove = [i for i, f in enumerate(poly_features) if (keep and f not in keep)]\n",
|
|
|
|
|
" X_train_model = np.delete(X_train_model, remove, axis=1)\n",
|
|
|
|
|
" X_test_model = np.delete(X_test_model, remove, axis=1)\n",
|
|
|
|
|
" poly_features = np.delete(poly_features, remove)\n",
|
2023-12-19 13:00:59 +01:00
|
|
|
" print(poly_features)\n",
|
|
|
|
|
"if not reduce:\n",
|
2024-02-27 15:59:53 +01:00
|
|
|
" remove = [\n",
|
|
|
|
|
" i\n",
|
|
|
|
|
" for i, f in enumerate(poly_features)\n",
|
|
|
|
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" if (\"dSlope_\" not in f)\n",
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" or (\"EndT^\" in f)\n",
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" or (\"abs^\" in f)\n",
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" or (\"EndT dSlope\" in f)\n",
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" or (\"abs dSlope\" in f)\n",
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" ]\n",
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" X_train_model = np.delete(X_train_model, remove, axis=1)\n",
|
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|
|
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" X_test_model = np.delete(X_test_model, remove, axis=1)\n",
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|
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" poly_features = np.delete(poly_features, remove)\n",
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" # print(poly_features)\n",
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2023-12-19 13:00:59 +01:00
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" lin_reg = Lasso(fit_intercept=False, alpha=0.000001)\n",
|
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"else:\n",
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" lin_reg = LinearRegression(fit_intercept=False)\n",
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2024-02-27 15:59:53 +01:00
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"lin_reg.fit(X_train_model, y_train)\n",
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"y_pred_test = lin_reg.predict(X_test_model)\n",
|
2023-12-19 13:00:59 +01:00
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"print(\"intercept=\", lin_reg.intercept_)\n",
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2024-02-27 15:59:53 +01:00
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"print(\n",
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" \"coef=\",\n",
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" {\n",
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" k: v\n",
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" for k, v in zip(poly_features, lin_reg.coef_)\n",
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" if abs(v) > 1.0 and k not in keep and k not in do_not_keep\n",
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" },\n",
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")\n",
|
2023-12-19 13:00:59 +01:00
|
|
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"print(\"r2 score=\", lin_reg.score(X_test_model, y_test))\n",
|
|
|
|
|
"print(\"RMSE =\", mean_squared_error(y_test, y_pred_test, squared=False))\n",
|
2024-02-27 15:59:53 +01:00
|
|
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"print(\n",
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" \"straight RMSE =\",\n",
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" mean_squared_error(\n",
|
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|
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" array[\"ideal_state_9410_y\"],\n",
|
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|
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" array[\"ideal_state_770_y\"]\n",
|
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" + array[\"ideal_state_770_ty\"]\n",
|
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|
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" * (array[\"ideal_state_9410_z\"] - array[\"ideal_state_770_z\"]),\n",
|
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|
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" squared=False,\n",
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" ),\n",
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")\n",
|
2023-12-19 13:00:59 +01:00
|
|
|
"print(format_array(\"y_xEndT_diff\", lin_reg.coef_))"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3.10.6 (conda)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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|
"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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|
"mimetype": "text/x-python",
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|
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|
"name": "python",
|
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"nbconvert_exporter": "python",
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|
"pygments_lexer": "ipython3",
|
2024-02-27 15:59:53 +01:00
|
|
|
"version": "3.10.12"
|
2023-12-19 13:00:59 +01:00
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},
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"orig_nbformat": 4,
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"vscode": {
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"interpreter": {
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|
"hash": "a2eff8b4da8b8eebf5ee2e5f811f31a557e0a202b4d2f04f849b065340a6eda6"
|
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}
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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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}
|
