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{ "cells": [ { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "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 methods.fit_linear_regression_model import fit_linear_regression_model\n", "import sklearn\n", "import seaborn as sns\n", "import pandas as pd\n", "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": 3, "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": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "41978 8523\n", "49865\n" ] } ], "source": [ "file = uproot.open(\n", " \"tracking_losses_ntuple_B_EndVeloP.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 >= 1.5)\n", " & (np.abs(allcolumns.phi) < 3.142)]\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": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "stretch factor: 0.1943140448361755\n" ] } ], "source": [ "rad_length_found = ak.to_numpy(\n", " found[(found.eta <= 5.0) & (found.eta >= 1.5) & (np.abs(found.phi) < 3.142)][\n", " \"rad_length_frac\"\n", " ]\n", ")\n", "eta_found = ak.to_numpy(\n", " found[(found.eta <= 5.0) & (found.eta >= 1.5) & (np.abs(found.phi) < 3.142)][\"eta\"]\n", ")\n", "phi_found = ak.to_numpy(\n", " found[(found.eta <= 5.0) & (found.eta >= 1.5) & (np.abs(found.phi) < 3.142)][\"phi\"]\n", ")\n", "rad_length_lost = ak.to_numpy(\n", " lost[(lost.eta <= 5.0) & (lost.eta >= 1.5) & (np.abs(lost.phi) < 3.142)][\n", " \"rad_length_frac\"\n", " ]\n", ")\n", "eta_lost = ak.to_numpy(\n", " lost[(lost.eta <= 5.0) & (lost.eta >= 1.5) & (np.abs(lost.phi) < 3.142)][\"eta\"]\n", ")\n", "phi_lost = ak.to_numpy(\n", " lost[(lost.eta <= 5.0) & (lost.eta >= 1.5) & (np.abs(lost.phi) < 3.142)][\"phi\"]\n", ")\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": 6, "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 endVelo2endUT\")\n", "plt.xlabel(f\"$x/X_0$\")\n", "plt.ylabel(\"a.u.\")\n", "\n", "plt.legend()\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 7, "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": 8, "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': 7.616826922074438e-06, 'x^2 y ty': -3.91052449297824e-05, 'x^2 y qop': 9.93899828579223e-05, 'x^2 tx^2': -0.005400741368580057, 'x^2 tx ty': -0.009338160688408294, 'x^2 tx qop': -0.0017215190824096578, 'x^2 ty^2': 0.0007665795500993852, 'x^2 ty qop': 0.08528819041114723, 'x^2 qop^2': 8.037042310903203, 'x y^3': 8.933181749881669e-09, 'x y^2 tx': 1.766907321343325e-05, 'x y^2 ty': -2.1412010806409754e-05, 'x y^2 qop': -7.010215747540322e-05, 'x y tx^2': -0.0021778144582400415, 'x y tx ty': 0.0326584774738, 'x y tx qop': -0.1598215452174385, 'x y ty^2': 0.012945427966444779, 'x y ty qop': -0.23950569088511311, 'x y qop^2': -0.8775916738593352, 'x tx^3': 1.366672968587086, 'x tx^2 ty': 1.7459886700480327, 'x tx^2 qop': 0.4423601484422016, 'x tx ty^2': -1.0803356692637864, 'x tx ty qop': -0.0706577637682464, 'x tx qop^2': 0.006422119173581787, 'x ty^3': -2.2905272843167253, 'x ty^2 qop': -0.0063092971067729734, 'x ty qop^2': -0.001963650254414034, 'x qop^3': -1.0318719588655238e-06, 'y^4': -2.213189409516758e-09, 'y^3 tx': 7.716181404937572e-08, 'y^3 ty': 3.7462658548648164e-06, 'y^3 qop': -2.6897178570957402e-05, 'y^2 tx^2': -0.019391135282039867, 'y^2 tx ty': 0.003922857934752042, 'y^2 tx qop': 0.30048105074735626, 'y^2 ty^2': -0.0014404468920953982, 'y^2 ty qop': 0.017062949506976018, 'y^2 qop^2': -0.5172314152946776, 'y tx^3': 1.1761566789450086, 'y tx^2 ty': -1.8639649790914088, 'y tx^2 qop': -0.07088661078488609, 'y tx ty^2': -2.1282820437243197, 'y tx ty qop': -0.001276549939024397, 'y tx qop^2': -0.0019180156335069092, 'y ty^3': -0.06849699842395515, 'y ty^2 qop': -0.0351395250211265, 'y ty qop^2': -0.0005408300561230844, 'y qop^3': 4.1258598459708434e-06, 'tx^4': -0.02399482130004447, 'tx^3 ty': 0.010297903626621132, 'tx^3 qop': 0.0018304232474417028, 'tx^2 ty^2': -0.01163526658236639, 'tx^2 ty qop': -0.00029701477688915344, 'tx^2 qop^2': 2.0001744822333693e-06, 'tx ty^3': -0.014645131120788562, 'tx ty^2 qop': -2.1232731978440055e-05, 'tx ty qop^2': -3.4544969537609295e-06, 'tx qop^3': 8.78704309226661e-09, 'ty^4': -0.001422061237110601, 'ty^3 qop': -0.00017083649574085 "r2 score= -0.008270330873300091\n", "RMSE = 0.10823208615961777\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", " {\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 = 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": 9, "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": 10, "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": 11, "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': 3.819052794399519, 'x ty^2': -1.214138821848195, 'x ty qop': -4.179406422119741, 'x qop^2': -0.11723833703778899, 'y^3': -2.1060731703048674e-07, 'y^2 tx': -0.005136952674035623, 'y^2 ty': 0.0002523550890177065, 'y^2 qop': 0.020199755591199943, 'y tx^2': 0.8881296792413045, 'y tx ty': 2.1057062787476855, 'y tx qop': -4.322557205912296, 'y ty^2': -0.062185778412248593, 'y ty qop': -12.744670929978815, 'y qop^2': 0.10865361350283592, 'tx^3': -0.2772083890350773, 'tx^2 ty': -0.002155259913110253, 'tx^2 qop': 0.01973183125611695, 'tx ty^2': 0.2547275714314975, 'tx ty qop': -0.01267461996128659, 'tx qop^2': -0.0002315355231400779, 'ty^3': 0.12852489701010045, 'ty^2 qop': -0.03229168715651398, 'ty qop^2': 0.0001733290594162183, 'qop^3': -1.1131786479856305e-06, 'x^4': 1.4072631948636172e-09, 'x^3 y': -4.525309382774623e-08, 'x^3 tx': -5.048150129027817e-07, 'x^3 ty': 2.845994251238215e-07, 'x^3 qop': 6.161442924141475e-05, 'x^2 y^2': -1.8614020325102842e-08, 'x^2 y tx': 9.737839179148333e-05, 'x^2 y ty': -1.3038804363763035e-05, 'x^2 y qop': -8.415032085912991e-05, 'x^2 tx^2': -0.0010152847281566686, 'x^2 tx ty': -0.0032259545758118137, 'x^2 tx qop': -0.041785493301881166, 'x^2 ty^2': 0.013227443641328787, 'x^2 ty qop': 0.0035654519670473366, 'x^2 qop^2': 1.2621531315710728, 'x y^3': 3.906278722709544e-08, 'x y^2 tx': 6.858475435222999e-05, 'x y^2 ty': -5.862278080592809e-05, 'x y^2 qop': -0.00016236412225426912, 'x y tx^2': -0.06608751351613794, 'x y tx ty': -0.04864228625905696, 'x y tx qop': 0.06901548261804959, 'x y ty^2': 0.04159526642181612, 'x y ty qop': 0.28489071089527757, 'x y qop^2': 0.2535927965752249, 'x tx^3': 0.5018159233398294, 'x tx^2 ty': 0.9699678165589771, 'x tx^2 qop': 0.4741265130417677, 'x tx ty^2': 10.315681588678894, 'x tx ty qop': -0.22617149043686857, 'x tx qop^2': 0.00898570789717606, 'x ty^3': -12.737405175272935, 'x ty^2 qop': -0.014062351992903526, 'x ty qop^2': 0.005154120346378951, 'x qop^3': -4.609067543005094e-05, '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 "r2 score= 0.01281806793978646\n", "RMSE = 0.2644569540509028\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", " 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": 25, "metadata": {}, "outputs": [], "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})" ] }, { "cell_type": "code", "execution_count": 39, "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": 40, "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>3.1</th>\n", " <td>0.0000</td>\n", " <td>0.00000</td>\n", " <td>0.00000</td>\n", " <td>0.000000</td>\n", " <td>0.00000</td>\n", " <td>0.00000</td>\n", " <td>0.0000</td>\n", " <td>0.00000</td>\n", " <td>0.000000</td>\n", " <td>0.0000</td>\n", " <td>...</td>\n", " <td>0.0000</td>\n", " <td>0.00000</td>\n", " <td>0.0000</td>\n", " <td>0.00000</td>\n", " <td>0.0000</td>\n", " <td>0.0000</td>\n", " <td>0.0000</td>\n", " <td>0.00000</td>\n", " <td>0.000000</td>\n", " <td>0.0</td>\n", " </tr>\n", " <tr>\n", " <th>3.2</th>\n", " <td>0.0000</td>\n", " <td>0.00000</td>\n", " <td>0.00000</td>\n", " <td>0.000000</td>\n", " <td>0.00000</td>\n", " <td>0.00000</td>\n", " <td>0.0000</td>\n", " <td>0.00000</td>\n", " <td>0.000000</td>\n", " <td>0.0000</td>\n", " <td>...</td>\n", " <td>0.0000</td>\n", " <td>0.00000</td>\n", " <td>0.0000</td>\n", " <td>0.00000</td>\n", " <td>0.0000</td>\n", " <td>0.0000</td>\n", " <td>0.0000</td>\n", " <td>0.00000</td>\n", " <td>0.000000</td>\n", " <td>0.0</td>\n", " </tr>\n", " <tr>\n", " <th>3.3</th>\n", " <td>0.0000</td>\n", " <td>0.96215</td>\n", " <td>0.00000</td>\n", " <td>0.746800</td>\n", " <td>0.00000</td>\n", " <td>0.00000</td>\n", " <td>1.7263</td>\n", " <td>0.00000</td>\n", " <td>0.381400</td>\n", " <td>0.0000</td>\n", " <td>...</td>\n", " <td>0.3813</td>\n", " <td>0.00000</td>\n", " <td>0.0000</td>\n", " <td>0.00000</td>\n", " <td>0.0000</td>\n", " <td>0.0000</td>\n", " <td>0.0000</td>\n", " <td>0.00000</td>\n", " <td>0.000000</td>\n", " <td>0.0</td>\n", " </tr>\n", " <tr>\n", " <th>3.4</th>\n", " <td>0.0000</td>\n", " <td>0.00000</td>\n", " <td>0.00000</td>\n", " <td>0.743500</td>\n", " <td>0.37900</td>\n", " <td>0.11090</td>\n", " <td>1.6273</td>\n", " <td>0.00000</td>\n", " <td>0.380200</td>\n", " <td>0.1115</td>\n", " <td>...</td>\n", " <td>0.0000</td>\n", " <td>0.00000</td>\n", " <td>0.0000</td>\n", " <td>0.38025</td>\n", " <td>1.1143</td>\n", " <td>0.7429</td>\n", " <td>0.1109</td>\n", " <td>0.00000</td>\n", " <td>0.378400</td>\n", " <td>0.0</td>\n", " </tr>\n", " <tr>\n", " <th>3.5</th>\n", " <td>0.0000</td>\n", " <td>0.38280</td>\n", " <td>0.33605</td>\n", " <td>0.110300</td>\n", " <td>0.00000</td>\n", " <td>1.11870</td>\n", " <td>0.0000</td>\n", " <td>0.23330</td>\n", " <td>1.479350</td>\n", " <td>0.0000</td>\n", " <td>...</td>\n", " <td>0.1458</td>\n", " <td>0.39860</td>\n", " <td>0.0000</td>\n", " <td>0.39800</td>\n", " <td>0.0000</td>\n", " <td>0.7175</td>\n", " <td>0.7303</td>\n", " <td>0.00000</td>\n", " <td>0.243900</td>\n", " <td>0.0</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>0.0000</td>\n", " <td>0.09270</td>\n", " <td>0.09775</td>\n", " <td>0.099525</td>\n", " <td>0.09825</td>\n", " <td>0.00000</td>\n", " <td>0.0926</td>\n", " <td>0.09710</td>\n", " <td>0.000000</td>\n", " <td>0.0934</td>\n", " <td>...</td>\n", " <td>0.0000</td>\n", " <td>0.09000</td>\n", " <td>0.1005</td>\n", " <td>0.09510</td>\n", " <td>0.1005</td>\n", " <td>0.0000</td>\n", " <td>0.0960</td>\n", " <td>0.09185</td>\n", " <td>0.094067</td>\n", " <td>0.0</td>\n", " </tr>\n", " <tr>\n", " <th>9.7</th>\n", " <td>0.0000</td>\n", " <td>0.00000</td>\n", " <td>0.00000</td>\n", " <td>0.098700</td>\n", " <td>0.09340</td>\n", " <td>0.09075</td>\n", " <td>0.0900</td>\n", " <td>0.08810</td>\n", " <td>0.000000</td>\n", " <td>0.0000</td>\n", " <td>...</td>\n", " <td>0.0912</td>\n", " <td>0.00000</td>\n", " <td>0.0000</td>\n", " <td>0.09350</td>\n", " <td>0.0962</td>\n", " <td>0.0971</td>\n", " <td>0.0000</td>\n", " <td>0.09540</td>\n", " <td>0.000000</td>\n", " <td>0.0</td>\n", " </tr>\n", " <tr>\n", " <th>9.8</th>\n", " <td>0.0922</td>\n", " <td>0.09230</td>\n", " <td>0.09660</td>\n", " <td>0.089067</td>\n", " <td>0.09260</td>\n", " <td>0.00000</td>\n", " <td>0.0802</td>\n", " <td>0.09195</td>\n", " <td>0.090867</td>\n", " <td>0.0000</td>\n", " <td>...</td>\n", " <td>0.0000</td>\n", " <td>0.07670</td>\n", " <td>0.0000</td>\n", " <td>0.08775</td>\n", " <td>0.0000</td>\n", " <td>0.0000</td>\n", " <td>0.0920</td>\n", " <td>0.08030</td>\n", " <td>0.100300</td>\n", " <td>0.0</td>\n", " </tr>\n", " <tr>\n", " <th>9.9</th>\n", " <td>0.0000</td>\n", " <td>0.09080</td>\n", " <td>0.08490</td>\n", " <td>0.076200</td>\n", " <td>0.00000</td>\n", " <td>0.08550</td>\n", " <td>0.0900</td>\n", " <td>0.09840</td>\n", " <td>0.090000</td>\n", " <td>0.0000</td>\n", " <td>...</td>\n", " <td>0.3018</td>\n", " <td>0.08900</td>\n", " <td>0.0889</td>\n", " <td>0.00000</td>\n", " <td>0.0900</td>\n", " <td>0.0000</td>\n", " <td>0.0000</td>\n", " <td>0.08160</td>\n", " <td>0.088767</td>\n", " <td>0.0</td>\n", " </tr>\n", " <tr>\n", " <th>10.0</th>\n", " <td>0.0000</td>\n", " <td>0.19140</td>\n", " <td>0.00000</td>\n", " <td>0.119100</td>\n", " <td>0.00000</td>\n", " <td>0.17850</td>\n", " <td>0.0000</td>\n", " <td>0.00000</td>\n", " <td>0.000000</td>\n", " <td>0.0000</td>\n", " <td>...</td>\n", " <td>0.0000</td>\n", " <td>0.10425</td>\n", " <td>0.1109</td>\n", " <td>0.08550</td>\n", " <td>0.0000</td>\n", " <td>0.0000</td>\n", " <td>0.0000</td>\n", " <td>0.00000</td>\n", " <td>0.081500</td>\n", " <td>0.0</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "<p>70 rows × 181 columns</p>\n", "</div>" ], "text/plain": [ "phi -90.0 -89.0 -88.0 -87.0 -86.0 -85.0 -84.0 -83.0 \\\n", "eta \n", "3.1 0.0000 0.00000 0.00000 0.000000 0.00000 0.00000 0.0000 0.00000 \n", "3.2 0.0000 0.00000 0.00000 0.000000 0.00000 0.00000 0.0000 0.00000 \n", "3.3 0.0000 0.96215 0.00000 0.746800 0.00000 0.00000 1.7263 0.00000 \n", "3.4 0.0000 0.00000 0.00000 0.743500 0.37900 0.11090 1.6273 0.00000 \n", "3.5 0.0000 0.38280 0.33605 0.110300 0.00000 1.11870 0.0000 0.23330 \n", "... ... ... ... ... ... ... ... ... \n", "9.6 0.0000 0.09270 0.09775 0.099525 0.09825 0.00000 0.0926 0.09710 \n", "9.7 0.0000 0.00000 0.00000 0.098700 0.09340 0.09075 0.0900 0.08810 \n", "9.8 0.0922 0.09230 0.09660 0.089067 0.09260 0.00000 0.0802 0.09195 \n", "9.9 0.0000 0.09080 0.08490 0.076200 0.00000 0.08550 0.0900 0.09840 \n", "10.0 0.0000 0.19140 0.00000 0.119100 0.00000 0.17850 0.0000 0.00000 \n", "\n", "phi -82.0 -81.0 ... 81.0 82.0 83.0 84.0 85.0 86.0 \\\n", "eta ... \n", "3.1 0.000000 0.0000 ... 0.0000 0.00000 0.0000 0.00000 0.0000 0.0000 \n", "3.2 0.000000 0.0000 ... 0.0000 0.00000 0.0000 0.00000 0.0000 0.0000 \n", "3.3 0.381400 0.0000 ... 0.3813 0.00000 0.0000 0.00000 0.0000 0.0000 \n", "3.4 0.380200 0.1115 ... 0.0000 0.00000 0.0000 0.38025 1.1143 0.7429 \n", "3.5 1.479350 0.0000 ... 0.1458 0.39860 0.0000 0.39800 0.0000 0.7175 \n", "... ... ... ... ... ... ... ... ... ... \n", "9.6 0.000000 0.0934 ... 0.0000 0.09000 0.1005 0.09510 0.1005 0.0000 \n", "9.7 0.000000 0.0000 ... 0.0912 0.00000 0.0000 0.09350 0.0962 0.0971 \n", "9.8 0.090867 0.0000 ... 0.0000 0.07670 0.0000 0.08775 0.0000 0.0000 \n", "9.9 0.090000 0.0000 ... 0.3018 0.08900 0.0889 0.00000 0.0900 0.0000 \n", "10.0 0.000000 0.0000 ... 0.0000 0.10425 0.1109 0.08550 0.0000 0.0000 \n", "\n", "phi 87.0 88.0 89.0 90.0 \n", "eta \n", "3.1 0.0000 0.00000 0.000000 0.0 \n", "3.2 0.0000 0.00000 0.000000 0.0 \n", "3.3 0.0000 0.00000 0.000000 0.0 \n", "3.4 0.1109 0.00000 0.378400 0.0 \n", "3.5 0.7303 0.00000 0.243900 0.0 \n", "... ... ... ... ... \n", "9.6 0.0960 0.09185 0.094067 0.0 \n", "9.7 0.0000 0.09540 0.000000 0.0 \n", "9.8 0.0920 0.08030 0.100300 0.0 \n", "9.9 0.0000 0.08160 0.088767 0.0 \n", "10.0 0.0000 0.00000 0.081500 0.0 \n", "\n", "[70 rows x 181 columns]" ] }, "execution_count": 40, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_pivoted" ] }, { "cell_type": "code", "execution_count": 41, "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<Figure size 2100x700 with 2 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig = plt.figure(figsize=(21, 7))\n", "ax = sns.heatmap(\n", " df_pivoted,\n", " robust=True,\n", " square=True,\n", " cmap=colormaps[\"rainbow\"],\n", " xticklabels=False,\n", " yticklabels=False,\n", " vmax=0.7,\n", ")\n", "# ax.set_yticks([5, 15, 25, 35], [2, 3, 4, 5])\n", "ax.set_yticks([10, 30, 50, 70], [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", "\n", "# ax.set_yticklabels([])\n", "ax.invert_yaxis()\n", "ax.set_title(\"LHCb EndVelo to EndUT $x/X_0$\")\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "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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