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{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "\n", "import uproot\t\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "from matplotlib import colormaps\n", "from mpl_toolkits import mplot3d\n", "import awkward as ak\n", "from scipy.optimize import curve_fit\n", "from scipy import stats\n", "from utils.fit_linear_regression_model import fit_linear_regression_model\n", "import sklearn\n", "import seaborn as sns\n", "import pandas as pd\n", "import mplhep\n", "mplhep.style.use([\"LHCbTex2\"])\n", "\n", "plt.rcParams[\"savefig.dpi\"] = 600\n", "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "def round(n, k):\n", " # function to round number 'n' up/down to nearest 'k'\n", " # use positive k to round up\n", " # use negative k to round down\n", "\n", " return n - n % k" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "40402 10099\n", "48290\n" ] } ], "source": [ "file = uproot.open(\n", " \"/work/cetin/Projektpraktikum/tracking_losses_ntuple_B_zmag.root:PrDebugTrackingLosses.PrDebugTrackingTool/Tuple;1\"\n", " # \"/work/cetin/LHCb/lb-stack/data/tracking_losses_ntuple_test_endVelo2endT.root:PrDebugTrackingLosses.PrDebugTrackingTool/Tuple;1\"\n", ")\n", "\n", "# selektiere nur elektronen von B->K*ee\n", "allcolumns = file.arrays()\n", "found = allcolumns[(allcolumns.isElectron) & (~allcolumns.lost) &\n", " (allcolumns.fromB)] # B: 9056\n", "lost = allcolumns[(allcolumns.isElectron) & (allcolumns.lost) &\n", " (allcolumns.fromB)] # B: 1466\n", "\n", "electrons = allcolumns[(allcolumns.isElectron)\n", " & (allcolumns.fromB)\n", " & (allcolumns.eta < 5.0)\n", " & (allcolumns.eta > 2.0)\n", " # & (np.abs(allcolumns.phi) < 3.142)\n", " ]\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": 4, "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", "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", "eta_a = ak.to_numpy(electrons[\"eta\"])\n", "phi_a = ak.to_numpy(electrons[\"phi\"])\n", "rad_length_frac_a = ak.to_numpy(electrons[\"rad_length_frac\"])\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": 5, "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<Figure size 1200x900 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=\"#F05342\",\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=\"#107E7D\",\n", " histtype=\"bar\",\n", " label=\"found\",\n", " range=[0, 1],\n", ")\n", "plt.xlim(-0.05, 1)\n", "# plt.yscale(\"log\")\n", "# plt.title(\"radiation length fraction endVelo2endUT\")\n", "plt.xlabel(f\"$x/X_0$\")\n", "plt.ylabel(\"Number of Tracks (normalised)\")\n", "plt.legend(loc=\"best\")\n", "mplhep.lhcb.text(\"Simulation\", loc=0)\n", "plt.show()\n", "# plt.savefig(\n", "# \"/work/cetin/Projektpraktikum/thesis/rad_length_frac_lost_found_endVelo2endUT.pdf\",\n", "# format=\"PDF\")" ] }, { "cell_type": "code", "execution_count": 6, "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": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Parameterisation for rad_length_frac:\n", "intercept= 0.0\n", "coef= {'1': 0.19432666451166924, 'x': -0.00010136532791187516, 'y': -0.00022209404990607013, 'tx': 0.058480207541052064, 'ty': 0.18599542917589618, 'qop': -17.657405901936546, 'x^2': 1.2271024867047469e-05, 'x y': 5.376824972059055e-08, 'x tx': -0.025941371835257743, 'x ty': 0.014328015524776564, 'x qop': -0.38410998543244235, 'y^2': -1.8261507968709735e-05, 'y tx': -0.014951077113227147, 'y ty': 0.02154311538810164, 'y qop': -0.008987663050567057, 'tx^2': 9.180854607357592, 'tx ty': 0.1873064867954838, 'tx qop': -0.47120712928737807, 'ty^2': -6.787398610853974, 'ty qop': -0.05031041352613623, 'qop^2': 0.24778641944197533, 'x^3': -5.992321693190818e-09, 'x^2 y': -2.649404482539432e-07, 'x^2 tx': 5.42176741002125e-06, 'x^2 ty': 0.0020253771536145383, 'x^2 qop': 0.003955288970107154, 'x y^2': 2.8906882704270233e-09, 'x y tx': -0.0016493773782229844, 'x y ty': 0.0005067592007986898, 'x y qop': -0.0031813438496783404, 'x tx^2': 0.0022653308811514867, 'x tx ty': -1.5366360302916284, 'x tx qop': -0.47946313010450653, 'x ty^2': -0.16776305178984158, 'x ty qop': 2.1889500302073115, 'x qop^2': 0.008745243979922932, 'y^3': 1.4363120470761714e-08, 'y^2 tx': -0.0005062978553841023, 'y^2 ty': 1.4165440283044409e-05, 'y^2 qop': -0.005787147937546765, 'y tx^2': 1.404047645962725, 'y tx ty': 0.1643910546131908, 'y tx qop': 2.0452379823825813, 'y ty^2': -0.02140660574730952, 'y ty qop': 4.596387657867597, 'y qop^2': -0.00994088354226498, 'tx^3': -0.1303976515212055, 'tx^2 ty': -0.01339610187978276, 'tx^2 qop': -0.0009383144690666937, 'tx ty^2': 0.010330807276347866, 'tx ty qop': 0.00564141621903312, 'tx qop^2': 1.1663926820802422e-05, 'ty^3': 0.00629773549670367, 'ty^2 qop': 0.012052455601588194, 'ty qop^2': -1.60166284721317e-05, 'qop^3': -1.5639205966369518e-06, 'x^4': 1.4244960766518489e-09, 'x^3 y': -3.1096076824610464e-09, 'x^3 tx': -3.587531603760352e-06, 'x^3 ty': 2.4713133050052738e-05, 'x^3 qop': 2.5845963699160437e-05, 'x^2 y^2': -2.0323240867980985e-09, 'x^2 y tx': -1.781414931473213e-05, 'x^2 y ty': -1.0125741814137612e-05, 'x^2 y qop': 0.00010504795400130865, 'x^2 tx^2': 0.00318540624554603, 'x^2 tx ty': -0.019979832566411563, 'x^2 tx qop': 0.006687084675785277, 'x^2 ty^2': 0.01525871952521068, 'x^2 ty qop': 0.5999059816243137, 'x^2 qop^2': 8.117293800523118, 'x y^3': 7.45386197209541e-10, 'x y^2 tx': 1.539536253053475e-05, 'x y^2 ty': -1.089334137566178e-05, 'x y^2 qop': -1.95550966909791e-05, 'x y tx^2': 0.015033712066153922, 'x y tx ty': -0.02448056876178019, 'x y tx qop': -0.6716914089728931, 'x y ty^2': 0.007182752502229531, 'x y ty qop': -0.18936713869537714, 'x y qop^2': 3.611377836849899, 'x tx^3': -0.9115227647899486, 'x tx^2 ty': 0.8829467327098821, 'x tx^2 qop': 0.2695607767961495, 'x tx ty^2': 0.6012451073716732, 'x tx ty qop': -0.00930712726835479, 'x tx qop^2': 0.009680202136377838, 'x ty^3': -0.06079594416933904, 'x ty^2 qop': 0.04029084967629493, 'x ty qop^2': 0.004774793771381167, 'x qop^3': -4.554461348017879e-05, 'y^4': 4.04032363121587e-12, 'y^3 tx': 8.925743423537913e-06, 'y^3 ty': -1.0182095510069544e-06, 'y^3 qop': -2.928516988854517e-05, 'y^2 tx^2': 0.004743417924369025, 'y^2 tx ty': -0.0055290127765494496, 'y^2 tx qop': 0.2133121554871387, 'y^2 ty^2': 0.0017685009285896116, 'y^2 ty qop': 0.020383665461783013, 'y^2 qop^2': -0.90657292007639, 'y tx^3': 0.27175992018246475, 'y tx^2 ty': 0.6761073293647273, 'y tx^2 qop': -0.01248506771745212, 'y tx ty^2': -0.39076013367696105, 'y tx ty qop': 0.04349449144257623, 'y tx qop^2': 0.004752914013263597, 'y ty^3': -0.729116504589348, 'y ty^2 qop': -0.015043560943011547, 'y ty qop^2': -0.001547809754472937, 'y qop^3': -1.316621299355546e-05, 'tx^4': -0.003228436324482351, 'tx^3 ty': 0.0044775047770867545, 'tx^3 qop': 0.001098230908109032, 'tx^2 ty^2': 0.0029303145639830285, 'tx^2 ty qop': -4.337425914757845e-05, 'tx^2 qop^2': 1.134299372765626e-05, 'tx ty^3': -0.0014086661449565522, 'tx ty^2 qop': 0.0001660768862220596, 'tx ty qop^2': 6.3521706353330705e-06, 'tx qop^3': -5.1281203369915226e-08, 'ty^4': -0.0071021220988862245, 'ty^3 qop': -6.7134108016768 "r2 score= 0.04993524171391073\n", "RMSE = 0.12351226258714283\n", "\n" ] }, { "data": { "image/png": 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"text/plain": [ "<Figure size 1200x900 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": 8, "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<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": 9, "metadata": {}, "outputs": [ { "data": { "image/png": "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 "text/plain": [ "<Figure size 1200x900 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": 10, "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': -1.806470433082 "r2 score= 0.013501590753704495\n", "RMSE = 0.2590497099709809\n", "\n" ] }, { "data": { "image/png": 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"text/plain": [ "<Figure size 1200x900 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", ")\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", "plt.colorbar(a0[3])\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>phi</th>\n", " <th>eta</th>\n", " <th>rad_length_frac</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>-87.0</td>\n", " <td>7.0</td>\n", " <td>0.1286</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>72.0</td>\n", " <td>5.6</td>\n", " <td>0.1478</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>55.0</td>\n", " <td>5.0</td>\n", " <td>0.1660</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>55.0</td>\n", " <td>5.0</td>\n", " <td>0.1660</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>11.0</td>\n", " <td>8.4</td>\n", " <td>0.1065</td>\n", " </tr>\n", " <tr>\n", " <th>...</th>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " </tr>\n", " <tr>\n", " <th>48285</th>\n", " <td>-81.0</td>\n", " <td>7.7</td>\n", " <td>0.1921</td>\n", " </tr>\n", " <tr>\n", " <th>48286</th>\n", " <td>35.0</td>\n", " <td>7.6</td>\n", " <td>0.2407</td>\n", " </tr>\n", " <tr>\n", " <th>48287</th>\n", " <td>63.0</td>\n", " <td>7.2</td>\n", " <td>0.2418</td>\n", " </tr>\n", " <tr>\n", " <th>48288</th>\n", " <td>13.0</td>\n", " <td>6.2</td>\n", " <td>0.1719</td>\n", " </tr>\n", " <tr>\n", " <th>48289</th>\n", " <td>0.0</td>\n", " <td>4.8</td>\n", " <td>0.1334</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "<p>48290 rows × 3 columns</p>\n", "</div>" ], "text/plain": [ " phi eta rad_length_frac\n", "0 -87.0 7.0 0.1286\n", "1 72.0 5.6 0.1478\n", "2 55.0 5.0 0.1660\n", "3 55.0 5.0 0.1660\n", "4 11.0 8.4 0.1065\n", "... ... ... ...\n", "48285 -81.0 7.7 0.1921\n", "48286 35.0 7.6 0.2407\n", "48287 63.0 7.2 0.2418\n", "48288 13.0 6.2 0.1719\n", "48289 0.0 4.8 0.1334\n", "\n", "[48290 rows x 3 columns]" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df = pd.DataFrame({\n", " \"phi\": phi_a * 90.0 / np.pi,\n", " \"eta\": eta_a * 2.0,\n", " \"rad_length_frac\": rad_length_frac_a,\n", "})\n", "df = df.round({\"phi\": 0, \"eta\": 1, \"rad_length_frac\": 4})\n", "df" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [], "source": [ "df_pivoted = df.pivot_table(\n", " index=\"eta\",\n", " columns=\"phi\",\n", " values=\"rad_length_frac\",\n", " margins=False,\n", " # fill_value=0,\n", ")" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th>phi</th>\n", " <th>-90.0</th>\n", " <th>-89.0</th>\n", " <th>-88.0</th>\n", " <th>-87.0</th>\n", " <th>-86.0</th>\n", " <th>-85.0</th>\n", " <th>-84.0</th>\n", " <th>-83.0</th>\n", " <th>-82.0</th>\n", " <th>-81.0</th>\n", " <th>...</th>\n", " <th>81.0</th>\n", " <th>82.0</th>\n", " <th>83.0</th>\n", " <th>84.0</th>\n", " <th>85.0</th>\n", " <th>86.0</th>\n", " <th>87.0</th>\n", " <th>88.0</th>\n", " <th>89.0</th>\n", " <th>90.0</th>\n", " </tr>\n", " <tr>\n", " <th>eta</th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>4.0</th>\n", " <td>NaN</td>\n", " <td>0.137133</td>\n", " <td>0.140800</td>\n", " <td>0.137350</td>\n", " <td>0.155200</td>\n", " <td>0.160000</td>\n", " <td>0.146800</td>\n", " <td>0.146500</td>\n", " <td>0.164300</td>\n", " <td>0.177060</td>\n", " <td>...</td>\n", " <td>0.18345</td>\n", " <td>0.176550</td>\n", " <td>0.149900</td>\n", " <td>0.152733</td>\n", " <td>0.161000</td>\n", " <td>0.160900</td>\n", " <td>0.13870</td>\n", " <td>0.137400</td>\n", " <td>0.137200</td>\n", " <td>0.13425</td>\n", " </tr>\n", " <tr>\n", " <th>4.1</th>\n", " <td>0.1510</td>\n", " <td>0.137575</td>\n", " <td>0.141467</td>\n", " <td>0.137575</td>\n", " <td>0.152850</td>\n", " <td>0.160033</td>\n", " <td>0.176100</td>\n", " <td>0.149350</td>\n", " <td>0.173250</td>\n", " <td>0.173386</td>\n", " <td>...</td>\n", " <td>0.16720</td>\n", " <td>0.169650</td>\n", " <td>0.163200</td>\n", " <td>0.153900</td>\n", " <td>0.157467</td>\n", " <td>0.151300</td>\n", " <td>NaN</td>\n", " <td>0.136850</td>\n", " <td>0.138880</td>\n", " <td>0.13610</td>\n", " </tr>\n", " <tr>\n", " <th>4.2</th>\n", " <td>0.1322</td>\n", " <td>0.137800</td>\n", " <td>NaN</td>\n", " <td>0.135900</td>\n", " <td>0.141100</td>\n", " <td>0.165500</td>\n", " <td>0.149975</td>\n", " <td>0.149429</td>\n", " <td>0.152883</td>\n", " <td>0.174633</td>\n", " <td>...</td>\n", " <td>0.16582</td>\n", " <td>0.155200</td>\n", " <td>0.153533</td>\n", " <td>0.147900</td>\n", " <td>0.164650</td>\n", " <td>0.140775</td>\n", " <td>0.14170</td>\n", " <td>0.137314</td>\n", " <td>0.137133</td>\n", " <td>0.13620</td>\n", " </tr>\n", " <tr>\n", " <th>4.3</th>\n", " <td>NaN</td>\n", " <td>0.135900</td>\n", " <td>0.136300</td>\n", " <td>0.135900</td>\n", " <td>0.137788</td>\n", " <td>NaN</td>\n", " <td>0.153367</td>\n", " <td>0.145900</td>\n", " <td>0.146350</td>\n", " <td>0.158700</td>\n", " <td>...</td>\n", " <td>0.16390</td>\n", " <td>0.148220</td>\n", " <td>0.146367</td>\n", " <td>0.147600</td>\n", " <td>0.156067</td>\n", " <td>0.141640</td>\n", " <td>0.14135</td>\n", " <td>0.136250</td>\n", " <td>0.135740</td>\n", " <td>0.13870</td>\n", " </tr>\n", " <tr>\n", " <th>4.4</th>\n", " <td>0.1356</td>\n", " <td>0.136075</td>\n", " <td>0.139525</td>\n", " <td>0.137800</td>\n", " <td>0.138975</td>\n", " <td>0.163250</td>\n", " <td>0.153900</td>\n", " <td>0.149725</td>\n", " <td>0.147800</td>\n", " <td>0.156467</td>\n", " <td>...</td>\n", " <td>0.15555</td>\n", " <td>0.149933</td>\n", " <td>0.150256</td>\n", " <td>0.158500</td>\n", " <td>0.157440</td>\n", " <td>0.137333</td>\n", " <td>0.13620</td>\n", " <td>0.136820</td>\n", " <td>0.136467</td>\n", " <td>0.14150</td>\n", " </tr>\n", " <tr>\n", " <th>...</th>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " </tr>\n", " <tr>\n", " <th>9.6</th>\n", " <td>NaN</td>\n", " <td>0.092700</td>\n", " <td>0.097750</td>\n", " <td>0.099525</td>\n", " <td>0.098250</td>\n", " <td>NaN</td>\n", " <td>0.092600</td>\n", " <td>0.097100</td>\n", " <td>NaN</td>\n", " <td>0.093400</td>\n", " <td>...</td>\n", " <td>NaN</td>\n", " <td>0.090000</td>\n", " <td>0.100500</td>\n", " <td>0.095100</td>\n", " <td>0.100500</td>\n", " <td>NaN</td>\n", " <td>0.09600</td>\n", " <td>0.091850</td>\n", " <td>0.094067</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>9.7</th>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>0.098700</td>\n", " <td>0.093400</td>\n", " <td>0.090750</td>\n", " <td>0.090000</td>\n", " <td>0.088100</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>...</td>\n", " <td>0.09120</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>0.093500</td>\n", " <td>0.096200</td>\n", " <td>0.097100</td>\n", " <td>NaN</td>\n", " <td>0.095400</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>9.8</th>\n", " <td>0.0922</td>\n", " <td>0.092300</td>\n", " <td>0.096600</td>\n", " <td>0.089067</td>\n", " <td>0.092600</td>\n", " <td>NaN</td>\n", " <td>0.080200</td>\n", " <td>0.091950</td>\n", " <td>0.090867</td>\n", " <td>NaN</td>\n", " <td>...</td>\n", " <td>NaN</td>\n", " <td>0.076700</td>\n", " <td>NaN</td>\n", " <td>0.087750</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>0.09200</td>\n", " <td>0.080300</td>\n", " <td>0.100300</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>9.9</th>\n", " <td>NaN</td>\n", " <td>0.090800</td>\n", " <td>0.084900</td>\n", " <td>0.076200</td>\n", " <td>NaN</td>\n", " <td>0.085500</td>\n", " <td>0.090000</td>\n", " <td>0.098400</td>\n", " <td>0.090000</td>\n", " <td>NaN</td>\n", " <td>...</td>\n", " <td>0.30180</td>\n", " <td>0.089000</td>\n", " <td>0.088900</td>\n", " <td>NaN</td>\n", " <td>0.090000</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>0.081600</td>\n", " <td>0.088767</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>10.0</th>\n", " <td>NaN</td>\n", " <td>0.191400</td>\n", " <td>NaN</td>\n", " <td>0.119100</td>\n", " <td>NaN</td>\n", " <td>0.178500</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>...</td>\n", " <td>NaN</td>\n", " <td>0.104250</td>\n", " <td>0.110900</td>\n", " <td>0.085500</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>0.081500</td>\n", " <td>NaN</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "<p>61 rows × 181 columns</p>\n", "</div>" ], "text/plain": [ "phi -90.0 -89.0 -88.0 -87.0 -86.0 -85.0 -84.0 \\\n", "eta \n", "4.0 NaN 0.137133 0.140800 0.137350 0.155200 0.160000 0.146800 \n", "4.1 0.1510 0.137575 0.141467 0.137575 0.152850 0.160033 0.176100 \n", "4.2 0.1322 0.137800 NaN 0.135900 0.141100 0.165500 0.149975 \n", "4.3 NaN 0.135900 0.136300 0.135900 0.137788 NaN 0.153367 \n", "4.4 0.1356 0.136075 0.139525 0.137800 0.138975 0.163250 0.153900 \n", "... ... ... ... ... ... ... ... \n", "9.6 NaN 0.092700 0.097750 0.099525 0.098250 NaN 0.092600 \n", "9.7 NaN NaN NaN 0.098700 0.093400 0.090750 0.090000 \n", "9.8 0.0922 0.092300 0.096600 0.089067 0.092600 NaN 0.080200 \n", "9.9 NaN 0.090800 0.084900 0.076200 NaN 0.085500 0.090000 \n", "10.0 NaN 0.191400 NaN 0.119100 NaN 0.178500 NaN \n", "\n", "phi -83.0 -82.0 -81.0 ... 81.0 82.0 83.0 \\\n", "eta ... \n", "4.0 0.146500 0.164300 0.177060 ... 0.18345 0.176550 0.149900 \n", "4.1 0.149350 0.173250 0.173386 ... 0.16720 0.169650 0.163200 \n", "4.2 0.149429 0.152883 0.174633 ... 0.16582 0.155200 0.153533 \n", "4.3 0.145900 0.146350 0.158700 ... 0.16390 0.148220 0.146367 \n", "4.4 0.149725 0.147800 0.156467 ... 0.15555 0.149933 0.150256 \n", "... ... ... ... ... ... ... ... \n", "9.6 0.097100 NaN 0.093400 ... NaN 0.090000 0.100500 \n", "9.7 0.088100 NaN NaN ... 0.09120 NaN NaN \n", "9.8 0.091950 0.090867 NaN ... NaN 0.076700 NaN \n", "9.9 0.098400 0.090000 NaN ... 0.30180 0.089000 0.088900 \n", "10.0 NaN NaN NaN ... NaN 0.104250 0.110900 \n", "\n", "phi 84.0 85.0 86.0 87.0 88.0 89.0 90.0 \n", "eta \n", "4.0 0.152733 0.161000 0.160900 0.13870 0.137400 0.137200 0.13425 \n", "4.1 0.153900 0.157467 0.151300 NaN 0.136850 0.138880 0.13610 \n", "4.2 0.147900 0.164650 0.140775 0.14170 0.137314 0.137133 0.13620 \n", "4.3 0.147600 0.156067 0.141640 0.14135 0.136250 0.135740 0.13870 \n", "4.4 0.158500 0.157440 0.137333 0.13620 0.136820 0.136467 0.14150 \n", "... ... ... ... ... ... ... ... \n", "9.6 0.095100 0.100500 NaN 0.09600 0.091850 0.094067 NaN \n", "9.7 0.093500 0.096200 0.097100 NaN 0.095400 NaN NaN \n", "9.8 0.087750 NaN NaN 0.09200 0.080300 0.100300 NaN \n", "9.9 NaN 0.090000 NaN NaN 0.081600 0.088767 NaN \n", "10.0 0.085500 NaN NaN NaN NaN 0.081500 NaN \n", "\n", "[61 rows x 181 columns]" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_pivoted" ] }, { "cell_type": "code", "execution_count": 30, "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<Figure size 1500x700 with 2 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig = plt.figure(figsize=(15, 7))\n", "ax = sns.heatmap(\n", " df_pivoted,\n", " robust=True,\n", " square=False,\n", " cmap=colormaps[\"rainbow\"],\n", " xticklabels=False,\n", " yticklabels=False,\n", " vmax=0.7,\n", " cbar_kws={\n", " \"label\": r\"$x/X_0$\",\n", " \"pad\": 0.005,\n", " \"shrink\": 1,\n", " \"ticks\": [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7],\n", " \"aspect\": 15,\n", " },\n", ")\n", "# ax.set_yticks([5, 15, 25, 35], [2, 3, 4, 5])\n", "ax.set_yticks([0, 20, 40, 60], [2, 3, 4, 5])\n", "ax.set_xticks([39, 89, 139], [-100, 0, 100]) # ([79, 179, 279], [-100, 0, 100])\n", "ax.set_xlabel(f\"$\\phi$ [deg]\")\n", "ax.set_ylabel(f\"$\\eta$\")\n", "ax.patch.set_edgecolor(\"black\")\n", "\n", "ax.patch.set_linewidth(2)\n", "# ax.set_yticklabels([])\n", "ax.invert_yaxis()\n", "# ax.set_title(\"EndVELO to EndUT $x/X_0$\", size=35)\n", "mplhep.lhcb.text(\n", " \"Simulation \\qquad \\qquad \\qquad EndVELO to EndUT $x/X_0$\", loc=0\n", ")\n", "# plt.show()\n", "plt.savefig(\n", " \"/work/cetin/Projektpraktikum/thesis/rad_length_frac_eta_phi_2.pdf\", format=\"PDF\"\n", ")" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [], "source": [ "# # make these smaller to increase the resolution\n", "# dy, dx = 0.1, 1.0\n", "\n", "# # generate 2 2d grids for the x & y bounds\n", "# y, x = np.mgrid[slice(1.5, 5 + dy, dy), slice(-180, 180 + dx, dx)]\n", "\n", "# plt.pcolormesh(x, y, df_pivoted, cmap=colormaps[\"jet\"], vmax=0.7)\n", "\n", "# plt.colorbar()\n", "# plt.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "tuner", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.10.12" } }, "nbformat": 4, "nbformat_minor": 2}
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