592 lines
283 KiB
Plaintext
592 lines
283 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 24,
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"metadata": {},
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"outputs": [],
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"source": [
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"import uproot\t\n",
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"import numpy as np\n",
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"import matplotlib.pyplot as plt\n",
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"from mpl_toolkits import mplot3d\n",
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"import awkward as ak\n",
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"from scipy.optimize import curve_fit\n",
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"from methods.fit_linear_regression_model import fit_linear_regression_model\n",
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"import sklearn\n",
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"import seaborn as sns\n",
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"import pandas as pd\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": 10,
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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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"41978 8523\n",
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"50501\n"
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]
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}
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],
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"source": [
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"file = uproot.open(\n",
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" \"tracking_losses_ntuple_B_EndVeloP.root:PrDebugTrackingLosses.PrDebugTrackingTool/Tuple;1\"\n",
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")\n",
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"\n",
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"# selektiere nur elektronen von B->K*ee\n",
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"allcolumns = file.arrays()\n",
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"found = allcolumns[\n",
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" (allcolumns.isElectron) & (~allcolumns.lost) & (allcolumns.fromB)\n",
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"] # B: 9056\n",
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"lost = allcolumns[\n",
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" (allcolumns.isElectron) & (allcolumns.lost) & (allcolumns.fromB)\n",
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"] # B: 1466\n",
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"\n",
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"electrons = allcolumns[(allcolumns.isElectron) & (allcolumns.fromB)]\n",
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"\n",
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"print(ak.num(found, axis=0), ak.num(lost, axis=0))\n",
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"print(ak.num(electrons, axis=0))\n",
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"# ak.count(found, axis=None)"
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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": 11,
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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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"stretch factor: 0.20303492305493354\n"
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]
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}
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],
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"source": [
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"rad_length_found = ak.to_numpy(found[\"rad_length_frac\"])\n",
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"eta_found = ak.to_numpy(found[\"eta\"])\n",
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"phi_found = ak.to_numpy(found[\"phi\"])\n",
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"rad_length_lost = ak.to_numpy(lost[\"rad_length_frac\"])\n",
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"eta_lost = ak.to_numpy(lost[\"eta\"])\n",
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"phi_lost = ak.to_numpy(lost[\"phi\"])\n",
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"\n",
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"stretch_factor = ak.num(eta_lost, axis=0) / ak.num(eta_found, axis=0)\n",
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"print(\"stretch factor: \", stretch_factor)"
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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": 12,
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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 640x480 with 1 Axes>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"plt.hist(\n",
|
|
" rad_length_lost,\n",
|
|
" bins=100,\n",
|
|
" density=True,\n",
|
|
" alpha=0.5,\n",
|
|
" color=\"darkorange\",\n",
|
|
" histtype=\"bar\",\n",
|
|
" label=\"lost\",\n",
|
|
" range=[0, 1],\n",
|
|
")\n",
|
|
"plt.hist(\n",
|
|
" rad_length_found,\n",
|
|
" bins=100,\n",
|
|
" density=True,\n",
|
|
" alpha=0.5,\n",
|
|
" color=\"blue\",\n",
|
|
" histtype=\"bar\",\n",
|
|
" label=\"found\",\n",
|
|
" range=[0, 1],\n",
|
|
")\n",
|
|
"plt.xlim(0, 1)\n",
|
|
"# plt.yscale(\"log\")\n",
|
|
"plt.title(\"radiation length fraction endVelo\")\n",
|
|
"plt.xlabel(f\"$x/X_0$\")\n",
|
|
"plt.ylabel(\"a.u.\")\n",
|
|
"\n",
|
|
"plt.legend()\n",
|
|
"plt.show()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 13,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"image/png": 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",
|
|
"text/plain": [
|
|
"<Figure size 2000x800 with 3 Axes>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"nbins = 100\n",
|
|
"vmax = 80\n",
|
|
"\n",
|
|
"fig, ((ax0, ax1)) = plt.subplots(nrows=1, ncols=2, figsize=(20, 8))\n",
|
|
"\n",
|
|
"a0 = ax0.hist2d(\n",
|
|
" rad_length_found,\n",
|
|
" eta_found,\n",
|
|
" density=False,\n",
|
|
" bins=nbins,\n",
|
|
" cmap=plt.cm.jet,\n",
|
|
" cmin=1,\n",
|
|
" vmax=vmax,\n",
|
|
" range=[[0, 0.6], [2, 5]],\n",
|
|
")\n",
|
|
"ax0.set_xlabel(f\"$x/X_0$\")\n",
|
|
"ax0.set_ylabel(f\"$\\eta$\")\n",
|
|
"ax0.set_title(f\"found $\\eta$ rad_length_frac\")\n",
|
|
"\n",
|
|
"a1 = ax1.hist2d(\n",
|
|
" rad_length_lost,\n",
|
|
" eta_lost,\n",
|
|
" density=False,\n",
|
|
" bins=nbins,\n",
|
|
" cmap=plt.cm.jet,\n",
|
|
" cmin=1,\n",
|
|
" vmax=vmax * stretch_factor,\n",
|
|
" range=[[0, 0.6], [2, 5]],\n",
|
|
")\n",
|
|
"ax1.set_xlabel(f\"$x/X_0$\")\n",
|
|
"ax1.set_ylabel(f\"$\\eta$\")\n",
|
|
"ax1.set_title(f\"lost $\\eta$ rad_length_frac\")\n",
|
|
"# ax1.set(xlim=(0,4000), ylim=(-1000,1000))\n",
|
|
"\n",
|
|
"plt.suptitle(\"radiation length fraction and eta endVelo\")\n",
|
|
"plt.colorbar(a0[3], ax=ax1)\n",
|
|
"\n",
|
|
"plt.show()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 14,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"Parameterisation for rad_length_frac:\n",
|
|
"intercept= 0.0\n",
|
|
"coef= {'1': 0.19830920321074946, 'x': -4.49175976974402e-05, 'y': 0.00039490060416272056, 'tx': 0.00015102371088508598, 'ty': -0.3004315695136339, 'qop': -15.314945266490128, 'x^2': -1.8619394568578818e-05, 'x y': -4.953907513838906e-06, 'x tx': 0.021617503882699386, 'x ty': 0.03829244150062255, 'x qop': -0.41798007270055415, 'y^2': -2.4410328131494868e-05, 'y tx': -0.03443063985633742, 'y ty': 0.024201355785359608, 'y qop': 0.069823295273139, 'tx^2': -9.507076220830514, 'tx ty': -0.3980701633198789, 'tx qop': -0.04742639222342226, 'ty^2': -5.342167619183405, 'ty qop': 0.04842038611881145, 'qop^2': 0.2070268831284635, 'x^3': 1.5823479402461545e-07, 'x^2 y': -5.806838940825474e-07, 'x^2 tx': -0.00023418353598118923, 'x^2 ty': 0.0037081774556846224, 'x^2 qop': 0.01641641113222204, 'x y^2': 6.398758958085149e-08, 'x y tx': -0.002932641224303519, 'x y ty': -0.001396824762733282, 'x y qop': -0.020888196868450136, 'x tx^2': 0.09096908124129072, 'x tx ty': -2.939755357349759, 'x tx qop': -8.73057282483271, 'x ty^2': -0.15340975596199197, 'x ty qop': 9.249941815315987, 'x qop^2': 0.030205199863621846, 'y^3': 1.6478595155078324e-07, 'y^2 tx': 0.0013152209574444013, 'y^2 ty': -0.000257931039205234, 'y^2 qop': -0.0057816482028933735, 'y tx^2': 2.685350530706497, 'y tx ty': 0.17814134491255038, 'y tx qop': 9.050929476915277, 'y ty^2': 0.10064678584510746, 'y ty qop': 4.6142369495362185, 'y qop^2': -0.00046589334175238057, 'tx^3': -0.6242025517665986, 'tx^2 ty': -0.017658603327465147, 'tx^2 qop': -0.022216794668845363, 'tx ty^2': -0.01024816705930792, 'tx ty qop': 0.024042119917448937, 'tx qop^2': 6.093129132646114e-05, 'ty^3': 0.09834545208780196, 'ty^2 qop': 0.011664187426493774, 'ty qop^2': -2.1825340747940462e-05, 'qop^3': -1.559907925017188e-06, 'x^4': -2.9483981922595603e-09, 'x^3 y': -6.13444928188045e-09, 'x^3 tx': 7.101384723817716e-06, 'x^3 ty': 7.16725431293419e-06, 'x^3 qop': 4.00953960828232e-05, 'x^2 y^2': 1.0679747086683733e-08, 'x^2 y tx': 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0.0020744456340701305, 'y^3 ty^2': 4.4960392427497754e-06, 'y^3 ty qop': 0.001141710321238258, 'y^3 qop^2': 0.002696814911126153, 'y^2 tx^3': -2.1363036642104674, 'y^2 tx^2 ty': 0.0687773207312733, 'y^2 tx^2 qop': 0.9871972836921945, 'y^2 tx ty^2': -0.2701567032221813, 'y^2 tx ty qop': -0.848252914690415, 'y^2 tx qop^2': 0.03660886322324573, 'y^2 ty^3': -0.005452852127128314, 'y^2 ty^2 qop': -0.7021206166732931, 'y^2 ty qop^2': -0.08018559047526934, 'y^2 qop^3': -0.00011753193330693663, 'y tx^4': -0.6680420541418445, 'y tx^3 ty': -3.384167006412971, 'y tx^3 qop': 0.006637451265825805, 'y tx^2 ty^2': -0.2331985780185122, 'y tx^2 ty qop': 0.006353552479363033, 'y tx^2 qop^2': 5.615722587673721e-06, 'y tx ty^3': -0.7572903037298312, 'y tx ty^2 qop': 0.0005634713263895614, 'y tx ty qop^2': 0.00010068765868206922, 'y tx qop^3': 2.012241389483668e-06, 'y ty^4': 1.5039421870145853, 'y ty^3 qop': -0.004362654564735129, 'y ty^2 qop^2': -0.00021869646501481262, 'y ty qop^3': 2.17774577667171e-07, 'y qop^4': -5.1794510300353154e-09, 'tx^5': -0.3290348306030112, 'tx^4 ty': -0.004713501192249353, 'tx^4 qop': 0.00039080511691431735, 'tx^3 ty^2': -0.02293656439194589, 'tx^3 ty qop': 1.795982117437259e-05, 'tx^3 qop^2': 1.3703545654537173e-06, 'tx^2 ty^3': -0.0016697750693905097, 'tx^2 ty^2 qop': 2.67609154249412e-05, 'tx^2 ty qop^2': -3.12791956432394e-08, 'tx^2 qop^3': -1.1594178303086993e-08, 'tx ty^4': -0.005076176679563355, 'tx ty^3 qop': 1.1834454126493736e-05, 'tx ty^2 qop^2': 1.9879319916637808e-07, 'tx ty qop^3': 4.055187311820455e-09, 'tx qop^4': 4.214995181248244e-11, 'ty^5': 0.010165548951092954, 'ty^4 qop': -1.6822916965291464e-05, 'ty^3 qop^2': -4.4801213131046415e-07, 'ty^2 qop^3': 7.260260169082666e-10, 'ty qop^4': -1.2734042962503632e-11, 'qop^5': -3.472724885502462e-13}\n",
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"r2 score= -0.008270330873300091\n",
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"RMSE = 0.10823208615961777\n",
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"\n"
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]
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},
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{
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"data": {
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"image/png": 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",
|
|
"text/plain": [
|
|
"<Figure size 640x480 with 2 Axes>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"rad_length_frac = found[\"rad_length_frac\"]\n",
|
|
"# @ z = 9400.mm or 770.mm\n",
|
|
"state = 1\n",
|
|
"\n",
|
|
"if state == 1:\n",
|
|
" slopex = found[\"ideal_state_770_tx\"]\n",
|
|
" slopey = found[\"ideal_state_770_ty\"]\n",
|
|
" x = found[\"ideal_state_770_x\"]\n",
|
|
" y = found[\"ideal_state_770_y\"]\n",
|
|
" qop = found[\"ideal_state_770_qop\"]\n",
|
|
"elif state == 2:\n",
|
|
" slopex = found[\"ideal_state_9410_tx\"]\n",
|
|
" slopey = found[\"ideal_state_9410_ty\"]\n",
|
|
" x = found[\"ideal_state_9410_x\"]\n",
|
|
" y = found[\"ideal_state_9410_y\"]\n",
|
|
" qop = found[\"ideal_state_9410_qop\"]\n",
|
|
"\n",
|
|
"data = ak.zip({\n",
|
|
" \"rad_length_frac\": rad_length_frac,\n",
|
|
" \"x\": x,\n",
|
|
" \"y\": y,\n",
|
|
" \"tx\": slopex,\n",
|
|
" \"ty\": slopey,\n",
|
|
" \"qop\": qop,\n",
|
|
"})\n",
|
|
"lin_reg, features, xx0_test, xx0_predicted = fit_linear_regression_model(\n",
|
|
" data,\n",
|
|
" \"rad_length_frac\",\n",
|
|
" [\"x\", \"y\", \"tx\", \"ty\", \"qop\"],\n",
|
|
" 5,\n",
|
|
" include_bias=True,\n",
|
|
")\n",
|
|
"\n",
|
|
"nbins = 100\n",
|
|
"vmax = 100\n",
|
|
"\n",
|
|
"a0 = plt.hist2d(\n",
|
|
" xx0_test,\n",
|
|
" xx0_predicted,\n",
|
|
" density=False,\n",
|
|
" bins=nbins,\n",
|
|
" cmap=plt.cm.jet,\n",
|
|
" cmin=1,\n",
|
|
" vmax=vmax,\n",
|
|
" range=[[0, 0.5], [0, 0.5]],\n",
|
|
")\n",
|
|
"plt.plot([0, 0.5], [0, 0.5], marker=\"\", alpha=0.8)\n",
|
|
"plt.xlabel(f\"True $x/X_0$\")\n",
|
|
"plt.ylabel(f\"Predicted $x/X_0$\")\n",
|
|
"plt.title(f\"found rad_length_frac\")\n",
|
|
"\n",
|
|
"plt.colorbar(a0[3])\n",
|
|
"plt.show()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 15,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"image/png": 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",
|
|
"text/plain": [
|
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"<Figure size 1500x600 with 3 Axes>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"nbins = 100\n",
|
|
"vmax = 80\n",
|
|
"\n",
|
|
"fig, ((ax0, ax1)) = plt.subplots(nrows=1, ncols=2, figsize=(15, 6))\n",
|
|
"\n",
|
|
"# ax0.set_aspect(\"equal\")\n",
|
|
"\n",
|
|
"a0 = ax0.hist2d(\n",
|
|
" xx0_test,\n",
|
|
" xx0_predicted,\n",
|
|
" density=False,\n",
|
|
" bins=nbins,\n",
|
|
" cmap=plt.cm.jet,\n",
|
|
" cmin=1,\n",
|
|
" vmax=vmax,\n",
|
|
" range=[[0, 0.5], [0, 0.5]],\n",
|
|
")\n",
|
|
"ax0.plot([0, 0.5], [0, 0.5], marker=\"\", alpha=0.8)\n",
|
|
"ax0.set_box_aspect(1)\n",
|
|
"ax0.set_xlabel(f\"True $x/X_0$\")\n",
|
|
"ax0.set_ylabel(f\"Predicted $x/X_0$\")\n",
|
|
"ax0.set_title(f\"found rad_length_frac\")\n",
|
|
"plt.colorbar(a0[3], ax=ax0)\n",
|
|
"\n",
|
|
"ax1.hist(\n",
|
|
" xx0_test,\n",
|
|
" bins=100,\n",
|
|
" density=True,\n",
|
|
" alpha=0.5,\n",
|
|
" color=\"darkorange\",\n",
|
|
" histtype=\"bar\",\n",
|
|
" label=\"test\",\n",
|
|
" range=[0, 0.5],\n",
|
|
")\n",
|
|
"ax1.hist(\n",
|
|
" xx0_predicted,\n",
|
|
" bins=100,\n",
|
|
" density=True,\n",
|
|
" alpha=0.5,\n",
|
|
" color=\"blue\",\n",
|
|
" histtype=\"bar\",\n",
|
|
" label=\"predicted\",\n",
|
|
" range=[0, 0.5],\n",
|
|
")\n",
|
|
"ax1.set_xlim(0, 0.5)\n",
|
|
"ax1.set_title(\"radiation length fraction endVelo\")\n",
|
|
"ax1.set_xlabel(f\"$x/X_0$\")\n",
|
|
"ax1.set_ylabel(\"a.u.\")\n",
|
|
"ax1.set_box_aspect(1)\n",
|
|
"\n",
|
|
"ax1.legend()\n",
|
|
"\n",
|
|
"# plt.gca().set_aspect(\"equal\")\n",
|
|
"plt.show()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 16,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"image/png": 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",
|
|
"text/plain": [
|
|
"<Figure size 640x480 with 1 Axes>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"plt.hist(\n",
|
|
" xx0_test,\n",
|
|
" bins=100,\n",
|
|
" density=True,\n",
|
|
" alpha=0.5,\n",
|
|
" color=\"darkorange\",\n",
|
|
" histtype=\"bar\",\n",
|
|
" label=\"test\",\n",
|
|
" range=[0, 0.5],\n",
|
|
")\n",
|
|
"plt.hist(\n",
|
|
" xx0_predicted,\n",
|
|
" bins=100,\n",
|
|
" density=True,\n",
|
|
" alpha=0.5,\n",
|
|
" color=\"blue\",\n",
|
|
" histtype=\"bar\",\n",
|
|
" label=\"predicted\",\n",
|
|
" range=[0, 0.5],\n",
|
|
")\n",
|
|
"plt.xlim(0, 0.5)\n",
|
|
"# plt.yscale(\"log\")\n",
|
|
"plt.title(\"radiation length fraction endVelo\")\n",
|
|
"plt.xlabel(f\"$x/X_0$\")\n",
|
|
"plt.ylabel(\"a.u.\")\n",
|
|
"\n",
|
|
"plt.legend()\n",
|
|
"plt.show()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 17,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"Parameterisation for rad_length_frac:\n",
|
|
"intercept= 0.0\n",
|
|
"coef= {'1': 0.2484410418213911, 'x': -0.0007601095488043627, 'y': 0.0010569724392146917, 'tx': 0.6185505303064777, 'ty': -0.9394058560136732, 'qop': -9.741031889614183, 'x^2': -0.00016580416280366622, 'x y': 5.149038989659081e-05, 'x tx': 0.22996768886351043, 'x ty': -0.043161009059129354, 'x qop': -0.21658279194428842, 'y^2': 3.9826067539320166e-05, 'y tx': -0.033498957247677735, 'y ty': -0.08085122767618998, 'y qop': 0.06428923004582791, 'tx^2': -83.06687438225835, 'tx ty': 28.76266798578089, 'tx qop': -0.32072666519746007, 'ty^2': 32.80290436519906, 'ty qop': 0.29785759094660047, 'qop^2': 0.7177557091128425, 'x^3': -1.037888276319177e-06, 'x^2 y': 5.744977724613286e-07, 'x^2 tx': 0.0016261562680787358, 'x^2 ty': 0.00819223051446815, 'x^2 qop': 0.014940216048602184, 'x y^2': 1.55836456652794e-06, 'x y tx': -0.009042353485603404, 'x y ty': 0.002769481233443616, 'x y qop': 0.007035099510620806, 'x tx^2': -0.623629094925692, 'x tx ty': -0.5792857627094614, 'x tx qop': 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'y^4': 9.455609628616912e-09, 'y^3 tx': -2.99210479094425e-05, 'y^3 ty': -2.583154312318925e-05, 'y^3 qop': 5.980622324156491e-05, 'y^2 tx^2': -0.016833583405836638, 'y^2 tx ty': 0.02458057322196644, 'y^2 tx qop': -0.169419352054439, 'y^2 ty^2': 0.02331391517451325, 'y^2 ty qop': -0.04589466917231106, 'y^2 qop^2': -1.206224538163147, 'y tx^3': 15.19059646701743, 'y tx^2 ty': 5.490478536183617, 'y tx^2 qop': -0.25278933198832354, 'y tx ty^2': -3.577260288354769, 'y tx ty qop': 0.03161480393545275, 'y tx qop^2': 0.005952281392129449, 'y ty^3': -6.821531531863169, 'y ty^2 qop': 0.09776598757839021, 'y ty qop^2': 0.0023614626852912395, 'y qop^3': -1.973415862163393e-05, 'tx^4': -0.05310859403428924, 'tx^3 ty': 0.023505514315239354, 'tx^3 qop': 0.0021762610547612377, 'tx^2 ty^2': 0.04763359558524625, 'tx^2 ty qop': -0.0009428124122745564, 'tx^2 qop^2': 1.2893218326397415e-05, 'tx ty^3': -0.018754843662608302, 'tx ty^2 qop': 4.8170564904883127e-05, 'tx ty qop^2': 1.6878183944633644e-05, 'tx qop^3': -4.12730477319448e-08, 'ty^4': 0.013919613173737298, 'ty^3 qop': 0.00022052138073417403, 'ty^2 qop^2': 9.713683885787424e-06, 'ty qop^3': -5.4325325107576684e-08, 'qop^4': 1.3416430509507416e-09, 'x^5': 2.37521113888306e-11, 'x^4 y': 6.566414079145488e-11, 'x^4 tx': -3.448894636548516e-08, 'x^4 ty': 4.817853991312404e-07, 'x^4 qop': -2.777657812425005e-06, 'x^3 y^2': 6.322311563167204e-10, 'x^3 y tx': -6.301214341419836e-07, 'x^3 y ty': 5.610926190335874e-06, 'x^3 y qop': 1.114123449319493e-05, 'x^3 tx^2': 1.3140964452713899e-05, 'x^3 tx ty': -0.0006698763738434144, 'x^3 tx qop': 0.004955643949611535, 'x^3 ty^2': -0.00020626810552818679, 'x^3 ty qop': -0.008992879337357176, 'x^3 qop^2': -0.01489312550273759, 'x^2 y^3': -1.0491474355944774e-10, 'x^2 y^2 tx': -7.058621529054676e-06, 'x^2 y^2 ty': 1.8916574067162628e-06, 'x^2 y^2 qop': 1.7843819655416482e-05, 'x^2 y tx^2': 0.0007719987709393639, 'x^2 y tx ty': -0.008372751086851777, 'x^2 y tx qop': -0.0094662761279594, 'x^2 y ty^2': -0.003982741017285164, 'x^2 y ty qop': -0.014247815997652827, 'x^2 y qop^2': -0.0933449616547058, 'x^2 tx^3': -0.00211924986590092, 'x^2 tx^2 ty': 0.1354990398386329, 'x^2 tx^2 qop': -2.4034242614405197, 'x^2 tx ty^2': 1.709794803412642, 'x^2 tx ty qop': 2.929391659772189, 'x^2 tx qop^2': -0.12899963468243164, 'x^2 ty^3': 0.41237245103458525, 'x^2 ty^2 qop': 0.9459129147227289, 'x^2 ty qop^2': 0.6716348674023994, 'x^2 qop^3': 0.0065213742878363744, 'x y^4': -3.1660363219998544e-10, 'x y^3 tx': -1.6554844943783564e-06, 'x y^3 ty': -1.8566065946856725e-06, 'x y^3 qop': -9.75035823502779e-06, 'x y^2 tx^2': 0.009680843788897421, 'x y^2 tx ty': 0.004952813049497684, 'x y^2 tx qop': -0.016925260798516226, 'x y^2 ty^2': 0.003475670476866588, 'x y^2 ty qop': 0.01417176786308785, 'x y^2 qop^2': -0.01761212366224941, 'x y tx^3': -0.15192958327351774, 'x y tx^2 ty': -0.060788822811239596, 'x y tx^2 qop': 4.3726462974483224, 'x y tx ty^2': 0.253839828387595, 'x y tx ty qop': 2.515358567018165, 'x y tx qop^2': 0.7384529496261274, 'x y ty^3': -1.4590050438376516, 'x y ty^2 qop': -4.598361895076147, 'x y ty qop^2': 0.31170679839759735, 'x y qop^3': -0.01024453299524337, 'x tx^4': 1.0757508065772434, 'x tx^3 ty': -1.6800363456087655, 'x tx^3 qop': 0.025130628317134973, 'x tx^2 ty^2': 1.1790180726236092, 'x tx^2 ty qop': 0.013852458993079927, 'x tx^2 qop^2': -0.0004548226751025624, 'x tx ty^3': -0.0007529047666109905, 'x tx ty^2 qop': 0.007719696430873407, 'x tx ty qop^2': 0.0018986412331720088, 'x tx qop^3': 7.660654451074145e-06, 'x ty^4': 1.3495131383656807, 'x ty^3 qop': -0.027518299643244655, 'x ty^2 qop^2': 0.0007835380395695195, 'x ty qop^3': -1.7215942880021808e-05, 'x qop^4': -4.814887567180262e-08, 'y^5': 1.1069811733932511e-11, 'y^4 tx': 2.5401641039479728e-06, 'y^4 ty': -3.659565095404105e-08, 'y^4 qop': -8.233519552314217e-06, 'y^3 tx^2': -0.0011440038498780267, 'y^3 tx ty': -0.003958853981003943, 'y^3 tx qop': 0.0019569523522222627, 'y^3 ty^2': 4.620362246071652e-05, 'y^3 ty qop': 0.013501593121770603, 'y^3 qop^2': 0.00013943149170339843, 'y^2 tx^3': -1.930662753166049, 'y^2 tx^2 ty': -0.6244835760091016, 'y^2 tx^2 qop': 9.706592754727314, 'y^2 tx ty^2': 1.567415053011399, 'y^2 tx ty qop': -1.8692848199146312, 'y^2 tx qop^2': 0.2576730572143735, 'y^2 ty^3': -0.02555256934016916, 'y^2 ty^2 qop': -5.570585900979827, 'y^2 ty qop^2': 0.4524728269219666, 'y^2 qop^3': -0.0012165533583740602, 'y tx^4': -1.705633938705921, 'y tx^3 ty': 0.9697939146444974, 'y tx^3 qop': 0.01417529417479696, 'y tx^2 ty^2': 0.24892206316288593, 'y tx^2 ty qop': 0.02112066742630421, 'y tx^2 qop^2': 0.001983588330826661, 'y tx ty^3': 1.0027301725129028, 'y tx ty^2 qop': -0.019989975028401157, 'y tx ty qop^2': 0.000703255023993435, 'y tx qop^3': -1.7780249221490402e-05, 'y ty^4': 5.039018301955912, 'y ty^3 qop': -0.03181545636834986, 'y ty^2 qop^2': 0.0013016040850667482, 'y ty qop^3': -4.788925524502292e-06, 'y qop^4': 8.952440180633341e-08, 'tx^5': 0.009735740982675399, 'tx^4 ty': -0.013441638488465971, 'tx^4 qop': 0.00015663907591491764, 'tx^3 ty^2': 0.008836930257445317, 'tx^3 ty qop': 4.602358365785842e-05, 'tx^3 qop^2': -1.2528198912759552e-06, 'tx^2 ty^3': 8.384431822878086e-05, 'tx^2 ty^2 qop': 3.2449454369351206e-05, 'tx^2 ty qop^2': 3.870208943851117e-06, 'tx^2 qop^3': 1.1062889336368561e-08, 'tx ty^4': 0.008284216728547011, 'tx ty^3 qop': -8.101830871580909e-05, 'tx ty^2 qop^2': 1.3999083905485367e-06, 'tx ty qop^3': -2.8838718241492492e-08, 'tx qop^4': -7.302022445954643e-11, 'ty^5': 0.033986172363938666, 'ty^4 qop': -0.00011487779463878022, 'ty^3 qop^2': 2.8267877400850516e-06, 'ty^2 qop^3': -1.0303917259481338e-08, 'ty qop^4': 1.343104287668482e-10, 'qop^5': -5.690015320531571e-16}\n",
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"r2 score= 0.01281806793978646\n",
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"RMSE = 0.2644569540509028\n",
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"\n"
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]
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},
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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 640x480 with 2 Axes>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"rad_length_frac = lost[\"rad_length_frac\"]\n",
|
|
"# @ z = 9400.mm or 770.mm\n",
|
|
"state = 1\n",
|
|
"\n",
|
|
"if state == 1:\n",
|
|
" slopex = lost[\"ideal_state_770_tx\"]\n",
|
|
" slopey = lost[\"ideal_state_770_ty\"]\n",
|
|
" x = lost[\"ideal_state_770_x\"]\n",
|
|
" y = lost[\"ideal_state_770_y\"]\n",
|
|
" qop = lost[\"ideal_state_770_qop\"]\n",
|
|
"elif state == 2:\n",
|
|
" slopex = lost[\"ideal_state_9410_tx\"]\n",
|
|
" slopey = lost[\"ideal_state_9410_ty\"]\n",
|
|
" x = lost[\"ideal_state_9410_x\"]\n",
|
|
" y = lost[\"ideal_state_9410_y\"]\n",
|
|
" qop = lost[\"ideal_state_9410_qop\"]\n",
|
|
"\n",
|
|
"data = ak.zip(\n",
|
|
" {\n",
|
|
" \"rad_length_frac\": rad_length_frac,\n",
|
|
" \"x\": x,\n",
|
|
" \"y\": y,\n",
|
|
" \"tx\": slopex,\n",
|
|
" \"ty\": slopey,\n",
|
|
" \"qop\": qop,\n",
|
|
" }\n",
|
|
")\n",
|
|
"lin_reg, features, xx0_test, xx0_predicted = fit_linear_regression_model(\n",
|
|
" data,\n",
|
|
" \"rad_length_frac\",\n",
|
|
" [\"x\", \"y\", \"tx\", \"ty\", \"qop\"],\n",
|
|
" 5,\n",
|
|
" include_bias=True,\n",
|
|
")\n",
|
|
"\n",
|
|
"nbins = 100\n",
|
|
"vmax = 50\n",
|
|
"\n",
|
|
"a0 = plt.hist2d(\n",
|
|
" xx0_test,\n",
|
|
" xx0_predicted,\n",
|
|
" density=False,\n",
|
|
" bins=nbins,\n",
|
|
" cmap=plt.cm.jet,\n",
|
|
" cmin=1,\n",
|
|
" vmax=vmax * stretch_factor,\n",
|
|
" range=[[-0.1, 1.0], [-0.1, 1.0]],\n",
|
|
")\n",
|
|
"plt.plot([-0.1, 1.0], [-0.1, 1.0], marker=\"\", alpha=0.8)\n",
|
|
"plt.xlabel(f\"True $x/X_0$\")\n",
|
|
"plt.ylabel(f\"Predicted $x/X_0$\")\n",
|
|
"plt.title(f\"lost rad_length_frac\")\n",
|
|
"# ax1.set(xlim=(0,4000), ylim=(-1000,1000))\n",
|
|
"\n",
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"plt.colorbar(a0[3])\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": 25,
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"metadata": {},
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"outputs": [],
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"source": [
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"df = pd.DataFrame({\n",
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" \"phi\": phi_found,\n",
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" \"eta\": eta_found,\n",
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" \"rad_length_frac\": rad_length_found\n",
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"})"
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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": 27,
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"metadata": {},
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"outputs": [
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{
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"ename": "RuntimeError",
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"evalue": "latex was not able to process the following string:\nb'sale price $'\n\nHere is the full command invocation and its output:\n\nlatex -interaction=nonstopmode --halt-on-error --output-directory=tmpg0jlxahb 82002a6a534310632a0ec6d082310260.tex\n\nThis is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) (preloaded format=latex)\n restricted \\write18 enabled.\nentering extended mode\n(./82002a6a534310632a0ec6d082310260.tex\nLaTeX2e <2020-02-02> patch level 5\nL3 programming layer <2020-09-24>\n(/cvmfs/sft.cern.ch/lcg/external/texlive/2020/texmf-dist/tex/latex/base/article\n.cls\nDocument Class: article 2019/12/20 v1.4l Standard LaTeX document class\n\n(/cvmfs/sft.cern.ch/lcg/external/texlive/2020/texmf-dist/tex/latex/base/size10.\nclo))\n(/cvmfs/sft.cern.ch/lcg/external/texlive/2020/texmf-dist/tex/latex/psnfss/mathp\ntmx.sty)\n(/cvmfs/sft.cern.ch/lcg/external/texlive/2020/texmf-dist/tex/latex/type1cm/type\n1cm.sty)\n(/cvmfs/sft.cern.ch/lcg/external/texlive/2020/texmf-dist/tex/latex/cm-super/typ\ne1ec.sty\n(/cvmfs/sft.cern.ch/lcg/external/texlive/2020/texmf-dist/tex/latex/base/t1cmr.f\nd))\n(/cvmfs/sft.cern.ch/lcg/external/texlive/2020/texmf-dist/tex/latex/base/inputen\nc.sty)\n(/cvmfs/sft.cern.ch/lcg/external/texlive/2020/texmf-dist/tex/latex/geometry/geo\nmetry.sty\n(/cvmfs/sft.cern.ch/lcg/external/texlive/2020/texmf-dist/tex/latex/graphics/key\nval.sty)\n(/cvmfs/sft.cern.ch/lcg/external/texlive/2020/texmf-dist/tex/generic/iftex/ifvt\nex.sty\n(/cvmfs/sft.cern.ch/lcg/external/texlive/2020/texmf-dist/tex/generic/iftex/ifte\nx.sty)))\n(/cvmfs/sft.cern.ch/lcg/external/texlive/2020/texmf-dist/tex/latex/underscore/u\nnderscore.sty)\n(/cvmfs/sft.cern.ch/lcg/external/texlive/2020/texmf-dist/tex/latex/base/textcom\np.sty)\n(/cvmfs/sft.cern.ch/lcg/external/texlive/2020/texmf-dist/tex/latex/l3backend/l3\nbackend-dvips.def)\nNo file 82002a6a534310632a0ec6d082310260.aux.\n\n(/cvmfs/sft.cern.ch/lcg/external/texlive/2020/texmf-dist/tex/latex/psnfss/ot1pt\nm.fd)\n*geometry* driver: auto-detecting\n*geometry* detected driver: dvips\n\n(/cvmfs/sft.cern.ch/lcg/external/texlive/2020/texmf-dist/tex/latex/psnfss/ot1zt\nmcm.fd)\n(/cvmfs/sft.cern.ch/lcg/external/texlive/2020/texmf-dist/tex/latex/psnfss/omlzt\nmcm.fd)\n(/cvmfs/sft.cern.ch/lcg/external/texlive/2020/texmf-dist/tex/latex/psnfss/omszt\nmcm.fd)\n(/cvmfs/sft.cern.ch/lcg/external/texlive/2020/texmf-dist/tex/latex/psnfss/omxzt\nmcm.fd)\n! Extra }, or forgotten $.\n<recently read> }\n \nl.30 {\\rmfamily sale price $}\n %\nNo pages of output.\nTranscript written on tmpg0jlxahb/82002a6a534310632a0ec6d082310260.log.\n\n\n",
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"output_type": "error",
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"traceback": [
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"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
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"\u001b[0;31mRuntimeError\u001b[0m Traceback (most recent call last)",
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"File \u001b[0;32m/work/cetin/LHCb/reco_tuner/env/tuner_env/envs/tuner/lib/python3.10/site-packages/IPython/core/formatters.py:340\u001b[0m, in \u001b[0;36mBaseFormatter.__call__\u001b[0;34m(self, obj)\u001b[0m\n\u001b[1;32m 338\u001b[0m \u001b[38;5;28;01mpass\u001b[39;00m\n\u001b[1;32m 339\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 340\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mprinter\u001b[49m\u001b[43m(\u001b[49m\u001b[43mobj\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 341\u001b[0m \u001b[38;5;66;03m# Finally look for special method names\u001b[39;00m\n\u001b[1;32m 342\u001b[0m method \u001b[38;5;241m=\u001b[39m get_real_method(obj, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mprint_method)\n",
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"File \u001b[0;32m/work/cetin/LHCb/reco_tuner/env/tuner_env/envs/tuner/lib/python3.10/site-packages/IPython/core/pylabtools.py:152\u001b[0m, in \u001b[0;36mprint_figure\u001b[0;34m(fig, fmt, bbox_inches, base64, **kwargs)\u001b[0m\n\u001b[1;32m 149\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mmatplotlib\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mbackend_bases\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m FigureCanvasBase\n\u001b[1;32m 150\u001b[0m FigureCanvasBase(fig)\n\u001b[0;32m--> 152\u001b[0m \u001b[43mfig\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcanvas\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mprint_figure\u001b[49m\u001b[43m(\u001b[49m\u001b[43mbytes_io\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkw\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 153\u001b[0m data \u001b[38;5;241m=\u001b[39m bytes_io\u001b[38;5;241m.\u001b[39mgetvalue()\n\u001b[1;32m 154\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m fmt \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124msvg\u001b[39m\u001b[38;5;124m'\u001b[39m:\n",
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"File \u001b[0;32m/work/cetin/LHCb/reco_tuner/env/tuner_env/envs/tuner/lib/python3.10/site-packages/matplotlib/backend_bases.py:2158\u001b[0m, in \u001b[0;36mFigureCanvasBase.print_figure\u001b[0;34m(self, filename, dpi, facecolor, edgecolor, orientation, format, bbox_inches, pad_inches, bbox_extra_artists, backend, **kwargs)\u001b[0m\n\u001b[1;32m 2155\u001b[0m \u001b[38;5;66;03m# we do this instead of `self.figure.draw_without_rendering`\u001b[39;00m\n\u001b[1;32m 2156\u001b[0m \u001b[38;5;66;03m# so that we can inject the orientation\u001b[39;00m\n\u001b[1;32m 2157\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mgetattr\u001b[39m(renderer, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m_draw_disabled\u001b[39m\u001b[38;5;124m\"\u001b[39m, nullcontext)():\n\u001b[0;32m-> 2158\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfigure\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdraw\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 2159\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m bbox_inches:\n\u001b[1;32m 2160\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m bbox_inches \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtight\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n",
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"File \u001b[0;32m/work/cetin/LHCb/reco_tuner/env/tuner_env/envs/tuner/lib/python3.10/site-packages/matplotlib/artist.py:95\u001b[0m, in \u001b[0;36m_finalize_rasterization.<locals>.draw_wrapper\u001b[0;34m(artist, renderer, *args, **kwargs)\u001b[0m\n\u001b[1;32m 93\u001b[0m \u001b[38;5;129m@wraps\u001b[39m(draw)\n\u001b[1;32m 94\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mdraw_wrapper\u001b[39m(artist, renderer, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[0;32m---> 95\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[43mdraw\u001b[49m\u001b[43m(\u001b[49m\u001b[43martist\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 96\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m renderer\u001b[38;5;241m.\u001b[39m_rasterizing:\n\u001b[1;32m 97\u001b[0m renderer\u001b[38;5;241m.\u001b[39mstop_rasterizing()\n",
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"File \u001b[0;32m/work/cetin/LHCb/reco_tuner/env/tuner_env/envs/tuner/lib/python3.10/site-packages/matplotlib/artist.py:72\u001b[0m, in \u001b[0;36mallow_rasterization.<locals>.draw_wrapper\u001b[0;34m(artist, renderer)\u001b[0m\n\u001b[1;32m 69\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m artist\u001b[38;5;241m.\u001b[39mget_agg_filter() \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 70\u001b[0m renderer\u001b[38;5;241m.\u001b[39mstart_filter()\n\u001b[0;32m---> 72\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mdraw\u001b[49m\u001b[43m(\u001b[49m\u001b[43martist\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 73\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[1;32m 74\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m artist\u001b[38;5;241m.\u001b[39mget_agg_filter() \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n",
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"File \u001b[0;32m/work/cetin/LHCb/reco_tuner/env/tuner_env/envs/tuner/lib/python3.10/site-packages/matplotlib/figure.py:3154\u001b[0m, in \u001b[0;36mFigure.draw\u001b[0;34m(self, renderer)\u001b[0m\n\u001b[1;32m 3151\u001b[0m \u001b[38;5;66;03m# ValueError can occur when resizing a window.\u001b[39;00m\n\u001b[1;32m 3153\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpatch\u001b[38;5;241m.\u001b[39mdraw(renderer)\n\u001b[0;32m-> 3154\u001b[0m \u001b[43mmimage\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_draw_list_compositing_images\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 3155\u001b[0m \u001b[43m \u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43martists\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msuppressComposite\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 3157\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m sfig \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msubfigs:\n\u001b[1;32m 3158\u001b[0m sfig\u001b[38;5;241m.\u001b[39mdraw(renderer)\n",
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"File \u001b[0;32m/work/cetin/LHCb/reco_tuner/env/tuner_env/envs/tuner/lib/python3.10/site-packages/matplotlib/image.py:132\u001b[0m, in \u001b[0;36m_draw_list_compositing_images\u001b[0;34m(renderer, parent, artists, suppress_composite)\u001b[0m\n\u001b[1;32m 130\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m not_composite \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m has_images:\n\u001b[1;32m 131\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m a \u001b[38;5;129;01min\u001b[39;00m artists:\n\u001b[0;32m--> 132\u001b[0m \u001b[43ma\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdraw\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 133\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 134\u001b[0m \u001b[38;5;66;03m# Composite any adjacent images together\u001b[39;00m\n\u001b[1;32m 135\u001b[0m image_group \u001b[38;5;241m=\u001b[39m []\n",
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"File \u001b[0;32m/work/cetin/LHCb/reco_tuner/env/tuner_env/envs/tuner/lib/python3.10/site-packages/matplotlib/artist.py:72\u001b[0m, in \u001b[0;36mallow_rasterization.<locals>.draw_wrapper\u001b[0;34m(artist, renderer)\u001b[0m\n\u001b[1;32m 69\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m artist\u001b[38;5;241m.\u001b[39mget_agg_filter() \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 70\u001b[0m renderer\u001b[38;5;241m.\u001b[39mstart_filter()\n\u001b[0;32m---> 72\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mdraw\u001b[49m\u001b[43m(\u001b[49m\u001b[43martist\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 73\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[1;32m 74\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m artist\u001b[38;5;241m.\u001b[39mget_agg_filter() \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n",
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"File \u001b[0;32m/work/cetin/LHCb/reco_tuner/env/tuner_env/envs/tuner/lib/python3.10/site-packages/matplotlib/axes/_base.py:3034\u001b[0m, in \u001b[0;36m_AxesBase.draw\u001b[0;34m(self, renderer)\u001b[0m\n\u001b[1;32m 3031\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m spine \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mspines\u001b[38;5;241m.\u001b[39mvalues():\n\u001b[1;32m 3032\u001b[0m artists\u001b[38;5;241m.\u001b[39mremove(spine)\n\u001b[0;32m-> 3034\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_update_title_position\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 3036\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39maxison:\n\u001b[1;32m 3037\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m _axis \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_axis_map\u001b[38;5;241m.\u001b[39mvalues():\n",
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"File \u001b[0;32m/work/cetin/LHCb/reco_tuner/env/tuner_env/envs/tuner/lib/python3.10/site-packages/matplotlib/axes/_base.py:2978\u001b[0m, in \u001b[0;36m_AxesBase._update_title_position\u001b[0;34m(self, renderer)\u001b[0m\n\u001b[1;32m 2976\u001b[0m top \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mmax\u001b[39m(top, bb\u001b[38;5;241m.\u001b[39mymax)\n\u001b[1;32m 2977\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m title\u001b[38;5;241m.\u001b[39mget_text():\n\u001b[0;32m-> 2978\u001b[0m \u001b[43max\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43myaxis\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_tightbbox\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m)\u001b[49m \u001b[38;5;66;03m# update offsetText\u001b[39;00m\n\u001b[1;32m 2979\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m ax\u001b[38;5;241m.\u001b[39myaxis\u001b[38;5;241m.\u001b[39moffsetText\u001b[38;5;241m.\u001b[39mget_text():\n\u001b[1;32m 2980\u001b[0m bb \u001b[38;5;241m=\u001b[39m ax\u001b[38;5;241m.\u001b[39myaxis\u001b[38;5;241m.\u001b[39moffsetText\u001b[38;5;241m.\u001b[39mget_tightbbox(renderer)\n",
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"File \u001b[0;32m/work/cetin/LHCb/reco_tuner/env/tuner_env/envs/tuner/lib/python3.10/site-packages/matplotlib/axis.py:1352\u001b[0m, in \u001b[0;36mAxis.get_tightbbox\u001b[0;34m(self, renderer, for_layout_only)\u001b[0m\n\u001b[1;32m 1350\u001b[0m \u001b[38;5;66;03m# take care of label\u001b[39;00m\n\u001b[1;32m 1351\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mlabel\u001b[38;5;241m.\u001b[39mget_visible():\n\u001b[0;32m-> 1352\u001b[0m bb \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlabel\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_window_extent\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1353\u001b[0m \u001b[38;5;66;03m# for constrained/tight_layout, we want to ignore the label's\u001b[39;00m\n\u001b[1;32m 1354\u001b[0m \u001b[38;5;66;03m# width/height because the adjustments they make can't be improved.\u001b[39;00m\n\u001b[1;32m 1355\u001b[0m \u001b[38;5;66;03m# this code collapses the relevant direction\u001b[39;00m\n\u001b[1;32m 1356\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m for_layout_only:\n",
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"File \u001b[0;32m/work/cetin/LHCb/reco_tuner/env/tuner_env/envs/tuner/lib/python3.10/site-packages/matplotlib/text.py:956\u001b[0m, in \u001b[0;36mText.get_window_extent\u001b[0;34m(self, renderer, dpi)\u001b[0m\n\u001b[1;32m 951\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mRuntimeError\u001b[39;00m(\n\u001b[1;32m 952\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCannot get window extent of text w/o renderer. You likely \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 953\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mwant to call \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mfigure.draw_without_rendering()\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m first.\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 955\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m cbook\u001b[38;5;241m.\u001b[39m_setattr_cm(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfigure, dpi\u001b[38;5;241m=\u001b[39mdpi):\n\u001b[0;32m--> 956\u001b[0m bbox, info, descent \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_get_layout\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_renderer\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 957\u001b[0m x, y \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mget_unitless_position()\n\u001b[1;32m 958\u001b[0m x, y \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mget_transform()\u001b[38;5;241m.\u001b[39mtransform((x, y))\n",
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"File \u001b[0;32m/work/cetin/LHCb/reco_tuner/env/tuner_env/envs/tuner/lib/python3.10/site-packages/matplotlib/text.py:381\u001b[0m, in \u001b[0;36mText._get_layout\u001b[0;34m(self, renderer)\u001b[0m\n\u001b[1;32m 379\u001b[0m clean_line, ismath \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_preprocess_math(line)\n\u001b[1;32m 380\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m clean_line:\n\u001b[0;32m--> 381\u001b[0m w, h, d \u001b[38;5;241m=\u001b[39m \u001b[43m_get_text_metrics_with_cache\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 382\u001b[0m \u001b[43m \u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mclean_line\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_fontproperties\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 383\u001b[0m \u001b[43m \u001b[49m\u001b[43mismath\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mismath\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdpi\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfigure\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdpi\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 384\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 385\u001b[0m w \u001b[38;5;241m=\u001b[39m h \u001b[38;5;241m=\u001b[39m d \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m0\u001b[39m\n",
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"File \u001b[0;32m/work/cetin/LHCb/reco_tuner/env/tuner_env/envs/tuner/lib/python3.10/site-packages/matplotlib/text.py:69\u001b[0m, in \u001b[0;36m_get_text_metrics_with_cache\u001b[0;34m(renderer, text, fontprop, ismath, dpi)\u001b[0m\n\u001b[1;32m 66\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"Call ``renderer.get_text_width_height_descent``, caching the results.\"\"\"\u001b[39;00m\n\u001b[1;32m 67\u001b[0m \u001b[38;5;66;03m# Cached based on a copy of fontprop so that later in-place mutations of\u001b[39;00m\n\u001b[1;32m 68\u001b[0m \u001b[38;5;66;03m# the passed-in argument do not mess up the cache.\u001b[39;00m\n\u001b[0;32m---> 69\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_get_text_metrics_with_cache_impl\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 70\u001b[0m \u001b[43m \u001b[49m\u001b[43mweakref\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mref\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtext\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfontprop\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcopy\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mismath\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdpi\u001b[49m\u001b[43m)\u001b[49m\n",
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"File \u001b[0;32m/work/cetin/LHCb/reco_tuner/env/tuner_env/envs/tuner/lib/python3.10/site-packages/matplotlib/text.py:77\u001b[0m, in \u001b[0;36m_get_text_metrics_with_cache_impl\u001b[0;34m(renderer_ref, text, fontprop, ismath, dpi)\u001b[0m\n\u001b[1;32m 73\u001b[0m \u001b[38;5;129m@functools\u001b[39m\u001b[38;5;241m.\u001b[39mlru_cache(\u001b[38;5;241m4096\u001b[39m)\n\u001b[1;32m 74\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_get_text_metrics_with_cache_impl\u001b[39m(\n\u001b[1;32m 75\u001b[0m renderer_ref, text, fontprop, ismath, dpi):\n\u001b[1;32m 76\u001b[0m \u001b[38;5;66;03m# dpi is unused, but participates in cache invalidation (via the renderer).\u001b[39;00m\n\u001b[0;32m---> 77\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mrenderer_ref\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_text_width_height_descent\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtext\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfontprop\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mismath\u001b[49m\u001b[43m)\u001b[49m\n",
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"File \u001b[0;32m/work/cetin/LHCb/reco_tuner/env/tuner_env/envs/tuner/lib/python3.10/site-packages/matplotlib/backends/backend_agg.py:213\u001b[0m, in \u001b[0;36mRendererAgg.get_text_width_height_descent\u001b[0;34m(self, s, prop, ismath)\u001b[0m\n\u001b[1;32m 211\u001b[0m _api\u001b[38;5;241m.\u001b[39mcheck_in_list([\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mTeX\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mTrue\u001b[39;00m, \u001b[38;5;28;01mFalse\u001b[39;00m], ismath\u001b[38;5;241m=\u001b[39mismath)\n\u001b[1;32m 212\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m ismath \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mTeX\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[0;32m--> 213\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43msuper\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_text_width_height_descent\u001b[49m\u001b[43m(\u001b[49m\u001b[43ms\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mprop\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mismath\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 215\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m ismath:\n\u001b[1;32m 216\u001b[0m ox, oy, width, height, descent, font_image \u001b[38;5;241m=\u001b[39m \\\n\u001b[1;32m 217\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mmathtext_parser\u001b[38;5;241m.\u001b[39mparse(s, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdpi, prop)\n",
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"File \u001b[0;32m/work/cetin/LHCb/reco_tuner/env/tuner_env/envs/tuner/lib/python3.10/site-packages/matplotlib/backend_bases.py:652\u001b[0m, in \u001b[0;36mRendererBase.get_text_width_height_descent\u001b[0;34m(self, s, prop, ismath)\u001b[0m\n\u001b[1;32m 648\u001b[0m fontsize \u001b[38;5;241m=\u001b[39m prop\u001b[38;5;241m.\u001b[39mget_size_in_points()\n\u001b[1;32m 650\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m ismath \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mTeX\u001b[39m\u001b[38;5;124m'\u001b[39m:\n\u001b[1;32m 651\u001b[0m \u001b[38;5;66;03m# todo: handle properties\u001b[39;00m\n\u001b[0;32m--> 652\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_texmanager\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_text_width_height_descent\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 653\u001b[0m \u001b[43m \u001b[49m\u001b[43ms\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfontsize\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrenderer\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 655\u001b[0m dpi \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpoints_to_pixels(\u001b[38;5;241m72\u001b[39m)\n\u001b[1;32m 656\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m ismath:\n",
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"File \u001b[0;32m/work/cetin/LHCb/reco_tuner/env/tuner_env/envs/tuner/lib/python3.10/site-packages/matplotlib/texmanager.py:363\u001b[0m, in \u001b[0;36mTexManager.get_text_width_height_descent\u001b[0;34m(cls, tex, fontsize, renderer)\u001b[0m\n\u001b[1;32m 361\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m tex\u001b[38;5;241m.\u001b[39mstrip() \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m'\u001b[39m:\n\u001b[1;32m 362\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;241m0\u001b[39m, \u001b[38;5;241m0\u001b[39m, \u001b[38;5;241m0\u001b[39m\n\u001b[0;32m--> 363\u001b[0m dvifile \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mcls\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmake_dvi\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtex\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfontsize\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 364\u001b[0m dpi_fraction \u001b[38;5;241m=\u001b[39m renderer\u001b[38;5;241m.\u001b[39mpoints_to_pixels(\u001b[38;5;241m1.\u001b[39m) \u001b[38;5;28;01mif\u001b[39;00m renderer \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;241m1\u001b[39m\n\u001b[1;32m 365\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m dviread\u001b[38;5;241m.\u001b[39mDvi(dvifile, \u001b[38;5;241m72\u001b[39m \u001b[38;5;241m*\u001b[39m dpi_fraction) \u001b[38;5;28;01mas\u001b[39;00m dvi:\n",
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"File \u001b[0;32m/work/cetin/LHCb/reco_tuner/env/tuner_env/envs/tuner/lib/python3.10/site-packages/matplotlib/texmanager.py:295\u001b[0m, in \u001b[0;36mTexManager.make_dvi\u001b[0;34m(cls, tex, fontsize)\u001b[0m\n\u001b[1;32m 293\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m TemporaryDirectory(\u001b[38;5;28mdir\u001b[39m\u001b[38;5;241m=\u001b[39mcwd) \u001b[38;5;28;01mas\u001b[39;00m tmpdir:\n\u001b[1;32m 294\u001b[0m tmppath \u001b[38;5;241m=\u001b[39m Path(tmpdir)\n\u001b[0;32m--> 295\u001b[0m \u001b[38;5;28;43mcls\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_run_checked_subprocess\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 296\u001b[0m \u001b[43m \u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mlatex\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43m-interaction=nonstopmode\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43m--halt-on-error\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m 297\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;124;43mf\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43m--output-directory=\u001b[39;49m\u001b[38;5;132;43;01m{\u001b[39;49;00m\u001b[43mtmppath\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mname\u001b[49m\u001b[38;5;132;43;01m}\u001b[39;49;00m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m 298\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;124;43mf\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;132;43;01m{\u001b[39;49;00m\u001b[43mtexfile\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mname\u001b[49m\u001b[38;5;132;43;01m}\u001b[39;49;00m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtex\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcwd\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcwd\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 299\u001b[0m (tmppath \u001b[38;5;241m/\u001b[39m Path(dvifile)\u001b[38;5;241m.\u001b[39mname)\u001b[38;5;241m.\u001b[39mreplace(dvifile)\n\u001b[1;32m 300\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m dvifile\n",
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"File \u001b[0;32m/work/cetin/LHCb/reco_tuner/env/tuner_env/envs/tuner/lib/python3.10/site-packages/matplotlib/texmanager.py:258\u001b[0m, in \u001b[0;36mTexManager._run_checked_subprocess\u001b[0;34m(cls, command, tex, cwd)\u001b[0m\n\u001b[1;32m 254\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mRuntimeError\u001b[39;00m(\n\u001b[1;32m 255\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mFailed to process string with tex because \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mcommand[\u001b[38;5;241m0\u001b[39m]\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m \u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[1;32m 256\u001b[0m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mcould not be found\u001b[39m\u001b[38;5;124m'\u001b[39m) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mexc\u001b[39;00m\n\u001b[1;32m 257\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m subprocess\u001b[38;5;241m.\u001b[39mCalledProcessError \u001b[38;5;28;01mas\u001b[39;00m exc:\n\u001b[0;32m--> 258\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mRuntimeError\u001b[39;00m(\n\u001b[1;32m 259\u001b[0m \u001b[38;5;124m'\u001b[39m\u001b[38;5;132;01m{prog}\u001b[39;00m\u001b[38;5;124m was not able to process the following string:\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m'\u001b[39m\n\u001b[1;32m 260\u001b[0m \u001b[38;5;124m'\u001b[39m\u001b[38;5;132;01m{tex!r}\u001b[39;00m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m'\u001b[39m\n\u001b[1;32m 261\u001b[0m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mHere is the full command invocation and its output:\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m'\u001b[39m\n\u001b[1;32m 262\u001b[0m \u001b[38;5;124m'\u001b[39m\u001b[38;5;132;01m{format_command}\u001b[39;00m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m'\u001b[39m\n\u001b[1;32m 263\u001b[0m \u001b[38;5;124m'\u001b[39m\u001b[38;5;132;01m{exc}\u001b[39;00m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m'\u001b[39m\u001b[38;5;241m.\u001b[39mformat(\n\u001b[1;32m 264\u001b[0m prog\u001b[38;5;241m=\u001b[39mcommand[\u001b[38;5;241m0\u001b[39m],\n\u001b[1;32m 265\u001b[0m format_command\u001b[38;5;241m=\u001b[39mcbook\u001b[38;5;241m.\u001b[39m_pformat_subprocess(command),\n\u001b[1;32m 266\u001b[0m tex\u001b[38;5;241m=\u001b[39mtex\u001b[38;5;241m.\u001b[39mencode(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124municode_escape\u001b[39m\u001b[38;5;124m'\u001b[39m),\n\u001b[1;32m 267\u001b[0m exc\u001b[38;5;241m=\u001b[39mexc\u001b[38;5;241m.\u001b[39moutput\u001b[38;5;241m.\u001b[39mdecode(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mutf-8\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mbackslashreplace\u001b[39m\u001b[38;5;124m'\u001b[39m))\n\u001b[1;32m 268\u001b[0m ) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m 269\u001b[0m _log\u001b[38;5;241m.\u001b[39mdebug(report)\n\u001b[1;32m 270\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m report\n",
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"\u001b[0;31mRuntimeError\u001b[0m: latex was not able to process the following string:\nb'sale price $'\n\nHere is the full command invocation and its output:\n\nlatex -interaction=nonstopmode --halt-on-error --output-directory=tmpg0jlxahb 82002a6a534310632a0ec6d082310260.tex\n\nThis is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) (preloaded format=latex)\n restricted \\write18 enabled.\nentering extended mode\n(./82002a6a534310632a0ec6d082310260.tex\nLaTeX2e <2020-02-02> patch level 5\nL3 programming layer <2020-09-24>\n(/cvmfs/sft.cern.ch/lcg/external/texlive/2020/texmf-dist/tex/latex/base/article\n.cls\nDocument Class: article 2019/12/20 v1.4l Standard LaTeX document class\n\n(/cvmfs/sft.cern.ch/lcg/external/texlive/2020/texmf-dist/tex/latex/base/size10.\nclo))\n(/cvmfs/sft.cern.ch/lcg/external/texlive/2020/texmf-dist/tex/latex/psnfss/mathp\ntmx.sty)\n(/cvmfs/sft.cern.ch/lcg/external/texlive/2020/texmf-dist/tex/latex/type1cm/type\n1cm.sty)\n(/cvmfs/sft.cern.ch/lcg/external/texlive/2020/texmf-dist/tex/latex/cm-super/typ\ne1ec.sty\n(/cvmfs/sft.cern.ch/lcg/external/texlive/2020/texmf-dist/tex/latex/base/t1cmr.f\nd))\n(/cvmfs/sft.cern.ch/lcg/external/texlive/2020/texmf-dist/tex/latex/base/inputen\nc.sty)\n(/cvmfs/sft.cern.ch/lcg/external/texlive/2020/texmf-dist/tex/latex/geometry/geo\nmetry.sty\n(/cvmfs/sft.cern.ch/lcg/external/texlive/2020/texmf-dist/tex/latex/graphics/key\nval.sty)\n(/cvmfs/sft.cern.ch/lcg/external/texlive/2020/texmf-dist/tex/generic/iftex/ifvt\nex.sty\n(/cvmfs/sft.cern.ch/lcg/external/texlive/2020/texmf-dist/tex/generic/iftex/ifte\nx.sty)))\n(/cvmfs/sft.cern.ch/lcg/external/texlive/2020/texmf-dist/tex/latex/underscore/u\nnderscore.sty)\n(/cvmfs/sft.cern.ch/lcg/external/texlive/2020/texmf-dist/tex/latex/base/textcom\np.sty)\n(/cvmfs/sft.cern.ch/lcg/external/texlive/2020/texmf-dist/tex/latex/l3backend/l3\nbackend-dvips.def)\nNo file 82002a6a534310632a0ec6d082310260.aux.\n\n(/cvmfs/sft.cern.ch/lcg/external/texlive/2020/texmf-dist/tex/latex/psnfss/ot1pt\nm.fd)\n*geometry* driver: auto-detecting\n*geometry* detected driver: dvips\n\n(/cvmfs/sft.cern.ch/lcg/external/texlive/2020/texmf-dist/tex/latex/psnfss/ot1zt\nmcm.fd)\n(/cvmfs/sft.cern.ch/lcg/external/texlive/2020/texmf-dist/tex/latex/psnfss/omlzt\nmcm.fd)\n(/cvmfs/sft.cern.ch/lcg/external/texlive/2020/texmf-dist/tex/latex/psnfss/omszt\nmcm.fd)\n(/cvmfs/sft.cern.ch/lcg/external/texlive/2020/texmf-dist/tex/latex/psnfss/omxzt\nmcm.fd)\n! Extra }, or forgotten $.\n<recently read> }\n \nl.30 {\\rmfamily sale price $}\n %\nNo pages of output.\nTranscript written on tmpg0jlxahb/82002a6a534310632a0ec6d082310260.log.\n\n\n"
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]
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},
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{
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"data": {
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"text/plain": [
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"<Figure size 900x600 with 2 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, ax = plt.subplots(figsize=(9, 6))\n",
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"\n",
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"contour = sns.kdeplot(\n",
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" data=df,\n",
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" x=\"phi\",\n",
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" y=\"eta\",\n",
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" cmap=\"Greens\",\n",
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" fill=True,\n",
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" cbar=True,\n",
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" # cbar_kws={\"label\": \"density of two variables (KDE weights)\", \"format\": \"{x:.1e}\"},\n",
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")\n",
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"\n",
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"ax.set_title(f\"Radiation Length Fraction $x/X_0$\", size=14)\n",
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"ax.set_xlabel(f\"$\\phi$ [°]\", size=10)\n",
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"ax.set_ylabel(f\"$\\eta$\", size=10)\n",
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"\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": null,
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"metadata": {},
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"outputs": [],
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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": null,
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "tuner",
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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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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.10.12"
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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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}
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