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476 lines
190 KiB
476 lines
190 KiB
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 43,
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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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"%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": 44,
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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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"40402 10099\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_default_radlength_endVelo.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[(allcolumns.isElectron) & (~allcolumns.lost) &\n",
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" (allcolumns.fromB)] # B: 9056\n",
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"lost = allcolumns[(allcolumns.isElectron) & (allcolumns.lost) &\n",
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" (allcolumns.fromB)] # 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": 45,
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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: 0.24996287312509283\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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"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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"\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": 46,
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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": 47,
|
|
"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": 50,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"Parameterisation for rad_length_frac:\n",
|
|
"intercept= 0.0\n",
|
|
"coef= {'1': 0.19436659767031483, 'x': -0.0002934072350452474, 'y': -0.00011050606423805956, 'tx': 0.22076931190684765, 'ty': 0.07813480395105185, 'qop': -11.837351954632934, 'x^2': -2.0111718729828063e-06, 'x y': 9.09756297060689e-06, 'x tx': -0.0071441555126283435, 'x ty': 0.01601317688273909, 'x qop': 0.6215416537184049, 'y^2': -6.877429438893236e-06, 'y tx': -0.03246512737106646, 'y ty': 0.009236443466565325, 'y qop': -0.7656982001003853, 'tx^2': 3.2721253340872036, 'tx ty': 6.990115604991107, 'tx qop': -728.51712427984, 'ty^2': -4.166569974702085, 'ty qop': 587.2224646668662, 'qop^2': 6.145749766573158, 'x^3': -1.6562683153707752e-07, 'x^2 y': -1.2001632657575304e-06, 'x^2 tx': 0.00040230376049067056, 'x^2 ty': -0.0003226579491818191, 'x^2 qop': 0.002985359658961018, 'x y^2': -5.185010866064232e-07, 'x y tx': 0.0029804615695217043, 'x y ty': 0.0012164498083769432, 'x y qop': 0.0029776397974184527, 'x tx^2': -0.3154073863149152, 'x tx ty': -0.4010820725541255, 'x tx qop': -2.1214929005307828, 'x ty^2': -0.6824284368708862, 'x ty qop': -14.944686049485084, 'x qop^2': -110.18157556408426, 'y^3': 2.4713398261155817e-07, 'y^2 tx': -8.884404196152218e-05, 'y^2 ty': -0.0005372353581591938, 'y^2 qop': -0.0036932151114263956, 'y tx^2': -1.5419692025080922, 'y tx ty': -0.11860718642094348, 'y tx qop': 11.982547200064268, 'y ty^2': 0.38784142586108383, 'y ty qop': 3.211190671760084, 'y qop^2': 46.063719746472096, 'tx^3': 79.89361864801454, 'tx^2 ty': 468.84573407547805, 'tx^2 qop': -100.98490043430814, 'tx ty^2': 186.00390862227553, 'tx ty qop': 7.2472113332685115, 'tx qop^2': 0.3801287848125358, 'ty^3': -92.81882058287101, 'ty^2 qop': 31.69467333269877, 'ty qop^2': 0.2896764307992765, 'qop^3': -0.004367926967413577, 'x^4': 4.0132150047611503e-08, 'x^3 y': -2.8422402209571374e-09, 'x^3 tx': -0.00012178298386800179, 'x^3 ty': -3.6970947007830546e-05, 'x^3 qop': -0.00037942687285408283, 'x^2 y^2': 7.386681488696922e-09, 'x^2 y tx': 4.123194972294186e-05, 'x^2 y ty': -5.2067299577629456e-05, 'x^2 y qop': 8.539712169408631e-06, 'x^2 tx^2': 0.1375456025515689, 'x^2 tx ty': 0.07276853278925399, 'x^2 tx qop': 0.5913446466084463, 'x^2 ty^2': 0.05481754041796982, 'x^2 ty qop': 0.12778897954363583, 'x^2 qop^2': 7.599038189537376, 'x y^3': 4.017000065914544e-09, 'x y^2 tx': 3.555973574975724e-05, 'x y^2 ty': 2.5015913422521407e-05, 'x y^2 qop': 0.0006923060020653793, 'x y tx^2': -0.0728178744632082, 'x y tx ty': -0.030849602066386872, 'x y tx qop': -0.053150965744121384, 'x y ty^2': -0.054271488256181, 'x y ty qop': -0.3845284787500334, 'x y qop^2': 3.5382762706451607, 'x tx^3': -68.2763499577661, 'x tx^2 ty': -35.09421296048657, 'x tx^2 qop': -209.5850008849592, 'x tx ty^2': -25.837301721685865, 'x tx ty qop': -72.99376890218326, 'x tx qop^2': -20.075729532656002, 'x ty^3': 24.168201409024896, 'x ty^2 qop': 77.70073016158322, 'x ty qop^2': -33.972956067372266, 'x qop^3': 4.163350823159558, 'y^4': 2.935763632194721e-09, 'y^3 tx': -3.516699649708244e-05, 'y^3 ty': -8.2068742131014e-06, 'y^3 qop': -1.523748721155016e-05, 'y^2 tx^2': -0.009411124024499173, 'y^2 tx ty': 0.06338416840865246, 'y^2 tx qop': -0.6891109275638628, 'y^2 ty^2': 0.008253872692153719, 'y^2 ty qop': 0.02346960416570875, 'y^2 qop^2': -0.4386626212085398, 'y tx^3': 32.94713780622537, 'y tx^2 ty': 19.368744361202825, 'y tx^2 qop': 17.426884247940652, 'y tx ty^2': -27.528264177777427, 'y tx ty qop': 341.8447125490805, 'y tx qop^2': -32.944204297917075, 'y ty^3': -3.4360959680136522, 'y ty^2 qop': -10.352300738708175, 'y ty qop^2': 79.19546293155958, 'y qop^3': 0.22012908047276727, 'tx^4': 12563.778412336536, 'tx^3 ty': 749.5755851917718, 'tx^3 qop': -9.94600150329609, 'tx^2 ty^2': 1299.398342139433, 'tx^2 ty qop': 0.1819109935589588, 'tx^2 qop^2': -0.05652058959156786, 'tx ty^3': 402.1468237499123, 'tx ty^2 qop': 0.5288025377553879, 'tx ty qop^2': -0.09503849406227534, 'tx qop^3': 0.005526136730138975, 'ty^4': 490.8007853758815, 'ty^3 qop': -0.032786661645227415, 'ty^2 qop^2': 0.21306953809390777, 'ty qop^3': 0.00041305791547802015, 'qop^4': 5.0463795291232325e-05}\n",
|
|
"r2 score= 0.052596793429776634\n",
|
|
"RMSE = 0.12333913500444761\n",
|
|
"\n"
|
|
]
|
|
},
|
|
{
|
|
"data": {
|
|
"image/png": 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",
|
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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 = 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",
|
|
" {\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",
|
|
" 4,\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,\n",
|
|
" range=[[-0.1, 0.6], [-0.1, 0.6]],\n",
|
|
")\n",
|
|
"plt.plot([-0.1, 0.6], [-0.1, 0.6], 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",
|
|
"# ax1.set(xlim=(0,4000), ylim=(-1000,1000))\n",
|
|
"\n",
|
|
"plt.colorbar(a0[3])\n",
|
|
"plt.show()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 51,
|
|
"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, 1],\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, 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": 49,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"Parameterisation for rad_length_frac:\n",
|
|
"intercept= 0.0\n",
|
|
"coef= {'1': 0.24562140630345797, 'x': -0.0006147519300623754, 'y': 0.00013149294035773212, 'tx': 0.40993139584024857, 'ty': -0.17613130993997955, 'qop': -2.431621847221756, 'x^2': -2.537929422256502e-05, 'x y': 1.6503609236837168e-05, 'x tx': 0.014114349319941343, 'x ty': -0.015914217992780068, 'x qop': -0.3138750804691517, 'y^2': 8.004082295422125e-06, 'y tx': -0.0007103518977325451, 'y ty': -0.012102735170731998, 'y qop': -0.01613366131755978, 'tx^2': -1.1805199392607937, 'tx ty': 2.630285174738731, 'tx qop': 0.2259863956524285, 'ty^2': -0.9000770143303645, 'ty qop': 0.035317808178541486, 'qop^2': 0.506257028947111, 'x^3': -1.054613771280784e-06, 'x^2 y': -7.542919321773425e-07, 'x^2 tx': 0.0016046817739745617, 'x^2 ty': 0.0041420285105903745, 'x^2 qop': 0.021289920901612722, 'x y^2': 1.6630958165414549e-06, 'x y tx': -0.0029322410866066016, 'x y ty': 0.00026646565396820643, 'x y qop': 0.008117926325135527, 'x tx^2': -0.6007987905052633, 'x tx ty': -2.98887506065023, 'x tx qop': -13.962516223136573, 'x ty^2': -0.8388571705095013, 'x ty qop': -3.036359977877559, 'x qop^2': 0.055324587199241985, 'y^3': -8.030979246897996e-08, 'y^2 tx': -0.002767856037518542, 'y^2 ty': 0.00010106006074174414, 'y^2 qop': 0.0087584836303898, 'y tx^2': 2.52086358169213, 'y tx ty': 1.771954217213756, 'y tx qop': -3.4960101511969945, 'y ty^2': -0.02778479572722497, 'y ty qop': -8.306825049361231, 'y qop^2': 0.011633837171348742, 'tx^3': -0.1376005395953692, 'tx^2 ty': 0.08788148993360365, 'tx^2 qop': -0.03627295634145932, 'tx ty^2': 0.03876102551307681, 'tx ty qop': -0.008986008881728739, 'tx qop^2': 9.888031370307247e-05, 'ty^3': -0.3359335900376418, 'ty^2 qop': -0.02333508540052829, 'ty qop^2': 9.573184641700597e-06, 'qop^3': -4.597612275351735e-06, 'x^4': -6.108581640518196e-09, 'x^3 y': -6.564809273967853e-08, 'x^3 tx': 1.5150502404637223e-05, 'x^3 ty': 3.104184179880676e-05, 'x^3 qop': 8.671096332828199e-05, 'x^2 y^2': -7.650740829062697e-09, 'x^2 y tx': 0.00011735105575993998, 'x^2 y ty': -3.2175113096499786e-05, 'x^2 y qop': 6.774919445144169e-05, 'x^2 tx^2': -0.011724689654149013, 'x^2 tx ty': -0.043137944250594654, 'x^2 tx qop': -0.0234816944437721, 'x^2 ty^2': -0.015910369617829878, 'x^2 ty qop': 0.47749147066068204, 'x^2 qop^2': 10.59413029512916, 'x y^3': 3.234245482541809e-08, 'x y^2 tx': 5.3043264426122505e-05, 'x y^2 ty': -5.8168920071072217e-05, 'x y^2 qop': -0.00018640913107006996, 'x y tx^2': -0.06797789037420698, 'x y tx ty': 0.044976962291121, 'x y tx qop': -0.5074481714386293, 'x y ty^2': 0.03709689469083238, 'x y ty qop': 0.42061549746065124, 'x y qop^2': 5.878761197856953, 'x tx^3': 2.928172660629093, 'x tx^2 ty': 15.518053547545511, 'x tx^2 qop': -0.4179045108625407, 'x tx ty^2': 2.863496001226073, 'x tx ty qop': 0.03316794782068423, 'x tx qop^2': 0.011365298564370092, 'x ty^3': -8.111988620415804, 'x ty^2 qop': -0.21644263264392571, 'x ty qop^2': 0.00843273298050687, 'x qop^3': -0.00016877332507871679, 'y^4': 6.023639254060242e-09, 'y^3 tx': -1.594750695611824e-05, 'y^3 ty': -1.437326358022517e-05, 'y^3 qop': -2.1442523502287515e-05, 'y^2 tx^2': -0.04753636763140216, 'y^2 tx ty': 0.018902432682699272, 'y^2 tx qop': -0.31014755455483073, 'y^2 ty^2': 0.011363729381750311, 'y^2 ty qop': 0.01681916414935703, 'y^2 qop^2': -5.242995516210874, 'y tx^3': 12.07113658017565, 'y tx^2 ty': 2.5063182953688687, 'y tx^2 qop': 0.05008258889078114, 'y tx ty^2': -5.840846904263012, 'y tx ty qop': -0.2222888950764648, 'y tx qop^2': 0.008403354774191822, 'y ty^3': -2.8576703684113554, 'y ty^2 qop': 0.156828601288708, 'y ty qop^2': -0.010489395507591974, 'y qop^3': 1.6753570524717795e-05, 'tx^4': -0.01910867380555672, 'tx^3 ty': 0.0837123623190646, 'tx^3 qop': -0.0014826028602247271, 'tx^2 ty^2': 0.0015368291104171875, 'tx^2 ty qop': 8.478961198550636e-05, 'tx^2 qop^2': 1.2316906385746789e-05, 'tx ty^3': -0.0271004544137963, 'tx ty^2 qop': -0.0008124467158542836, 'tx ty qop^2': 1.1953538675306295e-05, 'tx qop^3': -1.6561432689863052e-07, 'ty^4': -0.051346978654440316, 'ty^3 qop': 0.0005598437003601672, 'ty^2 qop^2': -1.8064704330828996e-05, 'ty qop^3': 3.8598127279103275e-08, 'qop^4': 3.326704284195817e-08, 'x^5': 6.821222753305989e-11, 'x^4 y': 2.526661102564276e-10, 'x^4 tx': -1.251389993850438e-07, 'x^4 ty': 3.4387986458384034e-08, 'x^4 qop': 8.496537125812775e-06, 'x^3 y^2': 5.454374729652045e-10, 'x^3 y tx': -5.823518507419578e-07, 'x^3 y ty': 1.573480655436832e-06, 'x^3 y qop': 1.1149119954367848e-05, 'x^3 tx^2': 7.080646439604904e-05, 'x^3 tx ty': -0.00019832304528768704, 'x^3 tx qop': -0.013548718492619474, 'x^3 ty^2': -0.0011831441371068785, 'x^3 ty qop': -0.007475115795384159, 'x^3 qop^2': -0.05297421345055594, 'x^2 y^3': -3.0988589472258354e-10, 'x^2 y^2 tx': -2.827730114507432e-06, 'x^2 y^2 ty': 3.25980575599516e-06, 'x^2 y^2 qop': -1.1472914959842484e-05, 'x^2 y tx^2': 0.0005880138985921626, 'x^2 y tx ty': -0.00028281256289024864, 'x^2 y tx qop': -0.011299091385256742, 'x^2 y ty^2': -0.0028883860783337767, 'x^2 y ty qop': 0.015857147862434813, 'x^2 y qop^2': -0.13581771520502617, 'x^2 tx^3': -0.011192107550488224, 'x^2 tx^2 ty': 0.12486137523256921, 'x^2 tx^2 qop': 5.225482032478654, 'x^2 tx ty^2': 0.7550762571921301, 'x^2 tx ty qop': 3.755310333424372, 'x^2 tx qop^2': 0.03529628798503831, 'x^2 ty^3': 0.0680249197813556, 'x^2 ty^2 qop': -2.149779516970109, 'x^2 ty qop^2': -0.10043851798514322, 'x^2 qop^3': -0.008357891977863841, 'x y^4': -3.010369731271112e-10, 'x y^3 tx': -2.5632317350865463e-06, 'x y^3 ty': -1.5046913748317792e-06, 'x y^3 qop': -7.480400594950254e-06, 'x y^2 tx^2': 0.0024216018281754056, 'x y^2 tx ty': 0.0007084178707292381, 'x y^2 tx qop': 0.00044348353393648763, 'x y^2 ty^2': 0.0026684932410411567, 'x y^2 ty qop': 0.004142697700575095, 'x y^2 qop^2': 0.04019255202302588, 'x y tx^3': -0.21609228057090962, 'x y tx^2 ty': -0.39898880238968853, 'x y tx^2 qop': 3.716497181247638, 'x y tx ty^2': 1.7005468399990162, 'x y tx ty qop': -1.6246665305727963, 'x y tx qop^2': -0.10861921254973068, 'x y ty^3': -1.0206195922719212, 'x y ty^2 qop': -2.2538410252773176, 'x y ty qop^2': 0.12471546683983396, 'x y qop^3': 0.0010355202027286007, 'x tx^4': -0.38054051948182677, 'x tx^3 ty': 0.02811376405424165, 'x tx^3 qop': 0.024914607795988558, 'x tx^2 ty^2': -0.2390526017685849, 'x tx^2 ty qop': 0.016195442265039908, 'x tx^2 qop^2': -7.88453042343562e-06, 'x tx ty^3': 0.15152568889899992, 'x tx ty^2 qop': -0.008761905989985623, 'x tx ty qop^2': -0.00028476668103488885, 'x tx qop^3': -1.3992733945114136e-05, 'x ty^4': -0.17984996125743222, 'x ty^3 qop': -0.010938588150706838, 'x ty^2 qop^2': 0.00028736668746421283, 'x ty qop^3': 1.0017755372740274e-06, 'x qop^4': 5.647205119564429e-08, 'y^5': 2.2355894913062002e-11, 'y^4 tx': 2.1532131875279248e-06, 'y^4 ty': -4.644247597607176e-08, 'y^4 qop': -6.709499208279457e-06, 'y^3 tx^2': 0.0016621305979105314, 'y^3 tx ty': -0.003130496631703698, 'y^3 tx qop': 0.0073473064465471, 'y^3 ty^2': 3.1438102391929956e-05, 'y^3 ty qop': 0.009790096490369568, 'y^3 qop^2': 0.007755101347045138, 'y^2 tx^3': -0.5967738984072392, 'y^2 tx^2 ty': -1.6414346613041646, 'y^2 tx^2 qop': -1.9221000353689144, 'y^2 tx ty^2': 1.1296103381428921, 'y^2 tx ty qop': -2.0138360070431838, 'y^2 tx qop^2': 0.10014966880153775, 'y^2 ty^3': -0.006765532531283773, 'y^2 ty^2 qop': -3.489466677603648, 'y^2 ty qop^2': -0.15818840693723135, 'y^2 qop^3': -0.006154290860857408, 'y tx^4': 0.06713941420974948, 'y tx^3 ty': -0.18765968488942172, 'y tx^3 qop': 0.01494671954085084, 'y tx^2 ty^2': 0.02688242692225791, 'y tx^2 ty qop': -0.008806488292543392, 'y tx^2 qop^2': -0.0002890003079734742, 'y tx ty^3': 0.0560088747156591, 'y tx ty^2 qop': -0.0091269069301046, 'y tx ty qop^2': 0.0002557631704771736, 'y tx qop^3': 9.13083085488923e-07, 'y ty^4': -0.11308027221966196, 'y ty^3 qop': -0.015522487055330275, 'y ty^2 qop^2': -0.0004358549211642052, 'y ty qop^3': -9.99916544547057e-06, 'y qop^4': -1.583944618976334e-08, 'tx^5': -0.0039057973828551464, 'tx^4 ty': 0.0011021677966443968, 'tx^4 qop': 8.364509298284483e-05, 'tx^3 ty^2': -0.0019793041595461795, 'tx^3 ty qop': 4.1168080154112314e-05, 'tx^3 qop^2': -2.463097953215033e-07, 'tx^2 ty^3': 0.0007208243773861416, 'tx^2 ty^2 qop': -2.5859488653297416e-05, 'tx^2 ty qop^2': -5.822330221226121e-07, 'tx^2 qop^3': -2.1963163726583706e-08, 'tx ty^4': 0.00020328501105408336, 'tx ty^3 qop': -2.7986469931730596e-05, 'tx ty^2 qop^2': 4.5313369723243955e-07, 'tx ty qop^3': 6.464504187809654e-10, 'tx qop^4': 9.133899373812518e-11, 'ty^5': -0.0025485559385795883, 'ty^4 qop': -5.08771174897897e-05, 'ty^3 qop^2': -9.412302186542877e-07, 'ty^2 qop^3': -1.55479697478695e-08, 'ty qop^4': -1.861631536431087e-11, 'qop^5': -1.2879922620235152e-12}\n",
|
|
"r2 score= 0.013501590753704495\n",
|
|
"RMSE = 0.2590497099709809\n",
|
|
"\n"
|
|
]
|
|
},
|
|
{
|
|
"data": {
|
|
"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",
|
|
" \"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",
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")\n",
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"\n",
|
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"nbins = 100\n",
|
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"vmax = 50\n",
|
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"\n",
|
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"a0 = plt.hist2d(\n",
|
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" xx0_test,\n",
|
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" xx0_predicted,\n",
|
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" density=False,\n",
|
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" bins=nbins,\n",
|
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" cmap=plt.cm.jet,\n",
|
|
" cmin=1,\n",
|
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" vmax=vmax * stretch_factor,\n",
|
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" range=[[-0.1, 1.0], [-0.1, 1.0]],\n",
|
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")\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",
|
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"\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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{
|
|
"cell_type": "code",
|
|
"execution_count": 58,
|
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"metadata": {},
|
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"outputs": [
|
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{
|
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"data": {
|
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"text/html": [
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"<pre>[nan,\n",
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" nan,\n",
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" nan,\n",
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" nan,\n",
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" nan,\n",
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" nan,\n",
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" nan,\n",
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" nan,\n",
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" nan]\n",
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"-----------------\n",
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"type: 9 * float64</pre>"
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],
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"text/plain": [
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"<Array [nan, nan, nan, nan, nan, nan, nan, nan, nan] type='9 * float64'>"
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]
|
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},
|
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"execution_count": 58,
|
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"metadata": {},
|
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"output_type": "execute_result"
|
|
}
|
|
],
|
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"source": [
|
|
"endT_x = lost[np.isnan(lost.ideal_state_9410_x)][\"ideal_state_9410_x\"]\n",
|
|
"endT_x"
|
|
]
|
|
},
|
|
{
|
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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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"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": []
|
|
}
|
|
],
|
|
"metadata": {
|
|
"kernelspec": {
|
|
"display_name": "tuner",
|
|
"language": "python",
|
|
"name": "python3"
|
|
},
|
|
"language_info": {
|
|
"codemirror_mode": {
|
|
"name": "ipython",
|
|
"version": 3
|
|
},
|
|
"file_extension": ".py",
|
|
"mimetype": "text/x-python",
|
|
"name": "python",
|
|
"nbconvert_exporter": "python",
|
|
"pygments_lexer": "ipython3",
|
|
"version": "3.10.12"
|
|
}
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 2
|
|
}
|