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{ "cells": [ { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "import uproot\t\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "import pylab\n", "from mpl_toolkits import mplot3d\n", "import awkward as ak\n", "from scipy.optimize import curve_fit\n", "from methods.fit_linear_regression_model import fit_linear_regression_model\n", "import sklearn\n", "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "40402 10099\n", "50501\n" ] } ], "source": [ "file = uproot.open(\n", " \"tracking_losses_ntuple_B_default_radlength_beginVelo.root:PrDebugTrackingLosses.PrDebugTrackingTool/Tuple;1\"\n", ")\n", "\n", "# selektiere nur elektronen von B->K*ee\n", "allcolumns = file.arrays()\n", "found = allcolumns[\n", " (allcolumns.isElectron) & (~allcolumns.lost) & (allcolumns.fromB)\n", "] # B: 9056\n", "lost = allcolumns[\n", " (allcolumns.isElectron) & (allcolumns.lost) & (allcolumns.fromB)\n", "] # B: 1466\n", "\n", "electrons = allcolumns[(allcolumns.isElectron) & (allcolumns.fromB)]\n", "\n", "print(ak.num(found, axis=0), ak.num(lost, axis=0))\n", "print(ak.num(electrons, axis=0))\n", "# ak.count(found, axis=None)" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "stretch factor: 0.24996287312509283\n" ] } ], "source": [ "rad_length_found = ak.to_numpy(found[\"rad_length_frac\"])\n", "eta_found = ak.to_numpy(found[\"eta\"])\n", "phi_found = ak.to_numpy(found[\"phi\"])\n", "\n", "rad_length_lost = ak.to_numpy(lost[\"rad_length_frac\"])\n", "eta_lost = ak.to_numpy(lost[\"eta\"])\n", "phi_lost = ak.to_numpy(lost[\"phi\"])\n", "\n", "stretch_factor = ak.num(eta_lost, axis=0) / ak.num(eta_found, axis=0)\n", "print(\"stretch factor: \", stretch_factor)" ] }, { "cell_type": "code", "execution_count": 12, "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", " 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 beginVelo\")\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 = 60\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 beginVelo\")\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': 0.4397780582886272, 'x': -0.00033503537608265674, 'y': -4.789321464958182e-05, 'tx': 0.29235639572920724, 'ty': 0.04582618208302177, 'qop': -6.415064049320476, 'x^2': -1.7148780985188313e-05, 'x y': 6.213092137729638e-05, 'x tx': 0.0357792373581695, 'x ty': -0.03383507316198001, 'x qop': 5.411983741236269, 'y^2': -0.0001816058133305051, 'y tx': -0.060157737422332554, 'y ty': 0.3005860193729231, 'y qop': -0.5258168180545822, 'tx^2': -20.581151289333324, 'tx ty': 33.044369657406456, 'tx qop': -4029.9409136973454, 'ty^2': -125.62371943789807, 'ty qop': 353.08874965548245, 'qop^2': -3.455196781164501, 'x^3': 1.5371780097645036e-07, 'x^2 y': -1.2426481494632311e-06, 'x^2 tx': -0.0003102207951011151, 'x^2 ty': 0.0008294673378711963, 'x^2 qop': -0.0013130710648701907, 'x y^2': 1.3249247615395102e-07, 'x y tx': 0.0018733641082853674, 'x y ty': 0.0011459295349262105, 'x y qop': 0.0019703515773188207, 'x tx^2': 0.20678893428535178, 'x tx ty': -1.2036256002683805, 'x tx qop': 0.5976253324202965, 'x ty^2': -1.0782570211818505, 'x ty qop': 102.9459912574048, 'x qop^2': -399.16918894718305, 'y^3': 6.233244675968308e-07, 'y^2 tx': -0.0013994133271424403, 'y^2 ty': -0.001494031769823323, 'y^2 qop': 0.000739172959467016, 'y tx^2': -0.7290175680182052, 'y tx ty': 1.243439836769458, 'y tx qop': -105.55527881418587, 'y ty^2': 1.1837378224511292, 'y ty qop': -0.7657500120637686, 'y qop^2': 337.9089334089693, 'tx^3': -46.002810282557306, 'tx^2 ty': 453.57769058127064, 'tx^2 qop': -38.01772137810428, 'tx ty^2': -37.626648454968965, 'tx ty qop': 1.8221207378503694, 'tx qop^2': -0.40208255096589585, 'ty^3': -309.9803925733283, 'ty^2 qop': 15.736234283455065, 'ty qop^2': 0.6119680938280082, 'qop^3': -0.0011848472786331894, 'x^4': 1.4258219493967772e-08, 'x^3 y': -2.866883619390137e-09, 'x^3 tx': -4.622450717306492e-05, 'x^3 ty': -9.82760022623097e-05, 'x^3 qop': -0.0003703064328215433, 'x^2 y^2': -2.5736142106325133e-09, 'x^2 y tx': 0.00010255319807583874, 'x^2 y ty': 4.727919866809316e-05, 'x^2 y qop': -0.0006108544453127251, 'x^2 tx^2': 0.0554175462089006, 'x^2 tx ty': 0.16472559466997527, 'x^2 tx qop': 0.4783758545936343, 'x^2 ty^2': -0.029491297111002623, 'x^2 ty qop': 0.4072459238308874, 'x^2 qop^2': 1.274082272606741, 'x y^3': 1.019856199491187e-08, 'x y^2 tx': -4.0220230914655986e-05, 'x y^2 ty': -7.807169754414645e-05, 'x y^2 qop': 0.0009660222713137046, 'x y tx^2': -0.1655492341343554, 'x y tx ty': -0.026431127204681104, 'x y tx qop': 0.6220634937641649, 'x y ty^2': 0.09539033507193473, 'x y ty qop': -1.5617368820840163, 'x y qop^2': 9.235370076473936, 'x tx^3': -29.04572443637665, 'x tx^2 ty': -68.28508620169197, 'x tx^2 qop': -138.17259913877646, 'x tx ty^2': 35.45859347871301, 'x tx ty qop': -273.5835489656032, 'x tx qop^2': -13.43661305575325, 'x ty^3': -32.63704432223729, 'x ty^2 qop': 266.4268072225656, 'x ty qop^2': -19.06231959583929, 'x qop^3': 1.7675392248918753, 'y^4': -5.9619367220875574e-09, 'y^3 tx': 5.3697276143793715e-05, 'y^3 ty': 1.6576491020714457e-05, 'y^3 qop': -0.00017261869555795784, 'y^2 tx^2': 0.05014539344681879, 'y^2 tx ty': -0.07584181301500559, 'y^2 tx qop': -0.11650839062004614, 'y^2 ty^2': -0.015097852966895253, 'y^2 ty qop': 0.30517451156427916, 'y^2 qop^2': -6.650919392954738, 'y tx^3': 67.30430825382783, 'y tx^2 ty': -34.801544500318485, 'y tx^2 qop': -142.686910608299, 'y tx ty^2': 27.185653317937266, 'y tx ty qop': 439.1032304393983, 'y tx qop^2': -18.23933135674488, 'y ty^3': 4.265555264813383, 'y ty^2 qop': -131.08781810964697, 'y ty qop^2': 34.20534645433437, 'y qop^3': 0.11852127731033578, 'tx^4': 5649.699838454551, 'tx^3 ty': 329.9615549901753, 'tx^3 qop': -5.54031835759426, 'tx^2 ty^2': 584.460443200635, 'tx^2 ty qop': -0.7411483064667538, 'tx^2 qop^2': -0.03588219456135393, 'tx ty^3': 173.96226240131907, 'tx ty^2 qop': 1.0493642752682577, 'tx ty qop^2': -0.052660249222163585, 'tx qop^3': 0.001973073444663188, 'ty^4': 215.62061929608907, 'ty^3 qop': -0.5416245412488983, 'ty^2 qop^2': 0.09234573370962393, 'ty qop^3': 0.0002451739001199065, 'qop^4': 2.303474287996047e-05}\n", "r2 score= 0.07032743800675567\n", "RMSE = 0.1530771991719649\n", "\n" ] }, { "data": { "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", " 4,\n", " include_bias=True,\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=[[-0.2, 1.0], [-0.2, 1.0]],\n", ")\n", "plt.plot([-0.2, 1], [-0.2, 1], 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": 17, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Parameterisation for rad_length_frac:\n", "intercept= 0.0\n", "coef= {'1': 0.4479234646536249, 'x': -0.0004084072172278079, 'y': -0.0004337281522268355, 'tx': 0.36162738714391346, 'ty': 0.3074416852772706, 'qop': 8.6879246998874, 'x^2': 6.870638463563449e-05, 'x y': -0.00012148333505099224, 'x tx': -0.08753632946361847, 'x ty': -0.008841630356866574, 'x qop': 2.3636302084612413, 'y^2': -0.00010231235440107948, 'y tx': 0.18497450621593262, 'y ty': 0.1675559125761048, 'y qop': -4.153708925054445, 'tx^2': 23.782917176875543, 'tx ty': -66.05577891519296, 'tx qop': -1451.879997584788, 'ty^2': -72.70407315466386, 'ty qop': 2811.440565472475, 'qop^2': 25.48376520289995, 'x^3': 3.312211575601438e-06, 'x^2 y': 4.1376174463449524e-06, 'x^2 tx': -0.007073302591271084, 'x^2 ty': -0.007343778680981201, 'x^2 qop': -0.010800896868279859, 'x y^2': -1.888730589918227e-06, 'x y tx': -0.0015500723141160817, 'x y ty': 0.003351080332733322, 'x y qop': -0.01735357445380728, 'x tx^2': 4.990549905339206, 'x tx ty': 7.558985701868668, 'x tx qop': 4.476707229937508, 'x ty^2': -1.104445169997775, 'x ty qop': -9.151537967253478, 'x qop^2': -910.3882764082207, 'y^3': -1.052533550094381e-06, 'y^2 tx': 0.0005507301794551722, 'y^2 ty': 0.002357501338451584, 'y^2 qop': 0.007420585610647734, 'y tx^2': -1.250712090046683, 'y tx ty': -1.5531355410763075, 'y tx qop': 19.84686939382025, 'y ty^2': -1.7462277992951187, 'y ty qop': -5.356037569819987, 'y qop^2': -279.7638120237527, 'tx^3': -1165.212129591651, 'tx^2 ty': -1478.3661751151212, 'tx^2 qop': 11.51188378893173, 'tx ty^2': 595.4188893174843, 'tx ty qop': 230.63755941369075, 'tx qop^2': -0.10882234534500138, 'ty^3': 429.2917082507941, 'ty^2 qop': -82.0662171571117, 'ty qop^2': -1.9459565902791414, 'qop^3': 0.024415724018080082, 'x^4': -1.475717681387323e-08, 'x^3 y': -8.159629061310625e-08, 'x^3 tx': 1.8736542919839394e-05, 'x^3 ty': 0.00021665972144546686, 'x^3 qop': 0.0006989376250565726, 'x^2 y^2': 2.718623193231906e-08, 'x^2 y tx': -2.4239682488769176e-05, 'x^2 y ty': -8.084108489470054e-05, 'x^2 y qop': -0.003167100608976625, 'x^2 tx^2': 0.008320473005383633, 'x^2 tx ty': -0.31099673106925607, 'x^2 tx qop': -0.9590579633902261, 'x^2 ty^2': -0.06873651193829033, 'x^2 ty qop': 5.972868443095723, 'x^2 qop^2': 8.034990024240143, 'x y^3': 4.6192553782020696e-08, 'x y^2 tx': 1.2378456318629105e-05, 'x y^2 ty': -5.5412774827345856e-05, 'x y^2 qop': -0.0013019927555433242, 'x y tx^2': 0.15448353292862294, 'x y tx ty': 0.2778324639753933, 'x y tx qop': -1.204014224641027, 'x y ty^2': 0.016434784323337226, 'x y ty qop': 2.818541810627377, 'x y qop^2': 8.034350363325663, 'x tx^3': -18.77883071123092, 'x tx^2 ty': 117.5404832475735, 'x tx^2 qop': 350.73626782625695, 'x tx ty^2': -43.39398761296841, 'x tx ty qop': -3601.6230380405145, 'x tx qop^2': -33.190862612375746, 'x ty^3': 0.3797357610145699, 'x ty^2 qop': -1234.3747950404918, 'x ty qop^2': 83.83558397109758, 'x qop^3': -5.702215445849627, 'y^4': -2.336150828341488e-09, 'y^3 tx': -4.0570866303824005e-05, 'y^3 ty': -2.348613449498771e-06, 'y^3 qop': 0.001158537918854563, 'y^2 tx^2': -0.1482288223321575, 'y^2 tx ty': 0.04527719555944287, 'y^2 tx qop': -1.0009453937475368, 'y^2 ty^2': 0.013540534251312408, 'y^2 ty qop': -1.6075747378736662, 'y^2 qop^2': -9.84256818846896, 'y tx^3': -69.10849756704471, 'y tx^2 ty': 21.121054597115727, 'y tx^2 qop': 1848.9215712994694, 'y tx ty^2': -9.234237076693203, 'y tx ty qop': 564.3519892221238, 'y tx qop^2': 68.38095505038116, 'y ty^3': -12.464249025545804, 'y ty^2 qop': 551.51219619504, 'y ty qop^2': 39.825100090236965, 'y qop^3': -1.0176409285192534, 'tx^4': 6221.962679980016, 'tx^3 ty': -3592.5750251228064, 'tx^3 qop': -27.664174751180994, 'tx^2 ty^2': 2731.878418172401, 'tx^2 ty qop': 13.736715296965578, 'tx^2 qop^2': -0.04199811775300766, 'tx ty^3': -2185.0268615440154, 'tx ty^2 qop': -15.049641135727613, 'tx ty qop^2': 0.13949642224427028, 'tx qop^3': -0.008320294336365015, 'ty^4': 3532.7413364811573, 'ty^3 qop': 24.266869324033923, 'ty^2 qop^2': 0.13092733573127732, 'ty qop^3': -0.0016184008383619774, 'qop^4': -0.00044028977350282306}\n", "r2 score= 0.005485148161997189\n", "RMSE = 0.27783626532276307\n", "\n" ] }, { "data": { "image/png": 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"text/plain": [ "<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", " 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 * stretch_factor,\n", " range=[[-0.1, 1.0], [-0.1, 1.0]],\n", ")\n", "plt.plot([-0.1, 1], [-0.1, 1], 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", "plt.colorbar(a0[3])\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "test = ak.zip({\n", " \"tx\": slopex,\n", " \"ty\": slopey,\n", " \"x\": x,\n", " \"y\": y,\n", " \"qop\": qop,\n", "})\n", "test = np.column_stack([ak.to_numpy(test[feat]) for feat in features])" ] } ], "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}
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