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{ "cells": [ { "cell_type": "code", "execution_count": 1, "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": 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": [ "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": 4, "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": 5, "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": 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': 0.19830920321074946, '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": 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 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": 10, "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": 11, "metadata": {}, "outputs": [], "source": [ "df = pd.DataFrame(\n", " {\n", " \"phi\": phi_a * 90.0 / np.pi,\n", " \"eta\": eta_a,\n", " \"rad_length_frac\": rad_length_frac_a,\n", " }\n", ")\n", "df = df.round({\"phi\": 0, \"eta\": 1, \"rad_length_frac\": 4})" ] }, { "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>1.5</th>\n", " <td>0.000000</td>\n", " <td>0.000000</td>\n", " <td>0.000000</td>\n", " <td>0.000000</td>\n", " <td>0.000000</td>\n", " <td>0.000000</td>\n", " <td>0.000000</td>\n", " <td>0.000000</td>\n", " <td>0.000000</td>\n", " <td>0.000000</td>\n", " <td>...</td>\n", " <td>0.000000</td>\n", " <td>0.000000</td>\n", " <td>0.000000</td>\n", " <td>0.000000</td>\n", " <td>0.000000</td>\n", " <td>0.000000</td>\n", " <td>0.000000</td>\n", " <td>0.000000</td>\n", " <td>0.000000</td>\n", " <td>0.000000</td>\n", " </tr>\n", " <tr>\n", " <th>1.6</th>\n", " <td>0.000000</td>\n", " <td>0.745900</td>\n", " <td>0.000000</td>\n", " <td>0.746800</td>\n", " <td>0.000000</td>\n", " <td>0.000000</td>\n", " <td>0.000000</td>\n", " <td>0.000000</td>\n", " <td>0.000000</td>\n", " <td>0.000000</td>\n", " <td>...</td>\n", " <td>0.000000</td>\n", " <td>0.000000</td>\n", " <td>0.000000</td>\n", " <td>0.000000</td>\n", " <td>0.000000</td>\n", " <td>0.000000</td>\n", " <td>0.000000</td>\n", " <td>0.000000</td>\n", " <td>0.000000</td>\n", " <td>0.000000</td>\n", " </tr>\n", " <tr>\n", " <th>1.7</th>\n", " <td>0.000000</td>\n", " <td>1.178400</td>\n", " <td>0.295200</td>\n", " <td>0.743500</td>\n", " <td>0.379000</td>\n", " <td>0.614800</td>\n", " <td>1.693300</td>\n", " <td>0.000000</td>\n", " <td>0.930075</td>\n", " <td>0.111500</td>\n", " <td>...</td>\n", " <td>0.381300</td>\n", " <td>0.000000</td>\n", " <td>0.000000</td>\n", " <td>0.380250</td>\n", " <td>1.114300</td>\n", " <td>0.730200</td>\n", " <td>0.420600</td>\n", " <td>0.000000</td>\n", " <td>0.377950</td>\n", " <td>0.000000</td>\n", " </tr>\n", " <tr>\n", " <th>1.8</th>\n", " <td>0.381100</td>\n", " <td>0.386050</td>\n", " <td>0.243450</td>\n", " <td>0.168900</td>\n", " <td>0.000000</td>\n", " <td>0.229833</td>\n", " <td>0.229900</td>\n", " <td>0.390667</td>\n", " <td>0.165900</td>\n", " <td>0.327267</td>\n", " <td>...</td>\n", " <td>0.223500</td>\n", " <td>0.236243</td>\n", " <td>0.883980</td>\n", " <td>0.270350</td>\n", " <td>1.366700</td>\n", " <td>0.160850</td>\n", " <td>0.000000</td>\n", " <td>0.272100</td>\n", " <td>0.234500</td>\n", " <td>0.124500</td>\n", " </tr>\n", " <tr>\n", " <th>1.9</th>\n", " <td>0.153000</td>\n", " <td>0.000000</td>\n", " <td>0.142200</td>\n", " <td>0.239467</td>\n", " <td>0.295500</td>\n", " <td>0.150900</td>\n", " <td>0.273500</td>\n", " <td>0.161000</td>\n", " <td>0.867600</td>\n", " <td>0.000000</td>\n", " <td>...</td>\n", " <td>0.220200</td>\n", " <td>0.171267</td>\n", " <td>0.163900</td>\n", " <td>0.151325</td>\n", " <td>0.154617</td>\n", " <td>0.251200</td>\n", " <td>0.139100</td>\n", " <td>0.191800</td>\n", " <td>0.224867</td>\n", " <td>0.144000</td>\n", " </tr>\n", " <tr>\n", " <th>2.0</th>\n", " <td>0.151000</td>\n", " <td>0.137790</td>\n", " <td>0.139500</td>\n", " <td>0.141687</td>\n", " <td>0.162550</td>\n", " <td>0.160067</td>\n", " <td>0.161450</td>\n", " <td>0.149060</td>\n", " <td>0.169400</td>\n", " <td>0.176289</td>\n", " <td>...</td>\n", " <td>0.172440</td>\n", " <td>0.176475</td>\n", " <td>0.153450</td>\n", " <td>0.152857</td>\n", " <td>0.178971</td>\n", " <td>0.154686</td>\n", " <td>0.141500</td>\n", " <td>0.140043</td>\n", " <td>0.141740</td>\n", " <td>0.134250</td>\n", " </tr>\n", " <tr>\n", " <th>2.1</th>\n", " <td>0.132200</td>\n", " <td>0.137630</td>\n", " <td>0.147300</td>\n", " <td>0.136700</td>\n", " <td>0.139863</td>\n", " <td>0.165500</td>\n", " <td>0.150780</td>\n", " <td>0.149290</td>\n", " <td>0.152229</td>\n", " <td>0.168450</td>\n", " <td>...</td>\n", " <td>0.165614</td>\n", " <td>0.155900</td>\n", " <td>0.150020</td>\n", " <td>0.148483</td>\n", " <td>0.160091</td>\n", " <td>0.141550</td>\n", " <td>0.143433</td>\n", " <td>0.137018</td>\n", " <td>0.137937</td>\n", " <td>0.137000</td>\n", " </tr>\n", " <tr>\n", " <th>2.2</th>\n", " <td>0.138267</td>\n", " <td>0.136160</td>\n", " <td>0.137342</td>\n", " <td>0.136750</td>\n", " <td>0.138395</td>\n", " <td>0.161375</td>\n", " <td>0.155864</td>\n", " <td>0.150350</td>\n", " <td>0.148888</td>\n", " <td>0.153114</td>\n", " <td>...</td>\n", " <td>0.155750</td>\n", " <td>0.149120</td>\n", " <td>0.148875</td>\n", " <td>0.158500</td>\n", " <td>0.159117</td>\n", " <td>0.139737</td>\n", " <td>0.136467</td>\n", " <td>0.136900</td>\n", " <td>0.136238</td>\n", " <td>0.146150</td>\n", " </tr>\n", " <tr>\n", " <th>2.3</th>\n", " <td>0.140120</td>\n", " <td>0.135150</td>\n", " <td>0.136392</td>\n", " <td>0.136000</td>\n", " <td>0.135283</td>\n", " <td>0.138733</td>\n", " <td>0.162330</td>\n", " <td>0.156182</td>\n", " <td>0.147925</td>\n", " <td>0.145092</td>\n", " <td>...</td>\n", " <td>0.159367</td>\n", " <td>0.148900</td>\n", " <td>0.155467</td>\n", " <td>0.160300</td>\n", " <td>0.145629</td>\n", " <td>0.145200</td>\n", " <td>0.139894</td>\n", " <td>0.141271</td>\n", " <td>0.138073</td>\n", " <td>0.141620</td>\n", " </tr>\n", " <tr>\n", " <th>2.4</th>\n", " <td>0.146343</td>\n", " <td>0.137150</td>\n", " <td>0.140820</td>\n", " <td>0.137143</td>\n", " <td>0.134425</td>\n", " <td>0.138600</td>\n", " <td>0.153840</td>\n", " <td>0.162650</td>\n", " <td>0.154400</td>\n", " <td>0.144275</td>\n", " <td>...</td>\n", " <td>0.143763</td>\n", " <td>0.151444</td>\n", " <td>0.157156</td>\n", " <td>0.153878</td>\n", " <td>0.138533</td>\n", " <td>0.138200</td>\n", " <td>0.138525</td>\n", " <td>0.136667</td>\n", " <td>0.135871</td>\n", " <td>0.139700</td>\n", " </tr>\n", " <tr>\n", " <th>2.5</th>\n", " <td>0.138550</td>\n", " <td>0.137725</td>\n", " <td>0.135490</td>\n", " <td>0.134900</td>\n", " <td>0.136400</td>\n", " <td>0.137218</td>\n", " <td>0.139250</td>\n", " <td>0.156625</td>\n", " <td>0.159414</td>\n", " <td>0.147300</td>\n", " <td>...</td>\n", " <td>0.149229</td>\n", " <td>0.158000</td>\n", " <td>0.151414</td>\n", " <td>0.143550</td>\n", " <td>0.135358</td>\n", " <td>0.136343</td>\n", " <td>0.134927</td>\n", " <td>0.134337</td>\n", " <td>0.136756</td>\n", " <td>0.136367</td>\n", " </tr>\n", " <tr>\n", " <th>2.6</th>\n", " <td>0.137725</td>\n", " <td>0.134624</td>\n", " <td>0.135183</td>\n", " <td>0.134150</td>\n", " <td>0.135617</td>\n", " <td>0.134246</td>\n", " <td>0.135311</td>\n", " <td>0.144350</td>\n", " <td>0.158150</td>\n", " <td>0.156129</td>\n", " <td>...</td>\n", " <td>0.157864</td>\n", " <td>0.157836</td>\n", " <td>0.145880</td>\n", " <td>0.135780</td>\n", " <td>0.134319</td>\n", " <td>0.134755</td>\n", " <td>0.134533</td>\n", " <td>0.135633</td>\n", " <td>0.135900</td>\n", " <td>0.134450</td>\n", " </tr>\n", " <tr>\n", " <th>2.7</th>\n", " <td>0.135840</td>\n", " <td>0.135129</td>\n", " <td>0.132957</td>\n", " <td>0.132900</td>\n", " <td>0.133153</td>\n", " <td>0.132860</td>\n", " <td>0.135122</td>\n", " <td>0.136907</td>\n", " <td>0.148817</td>\n", " <td>0.159080</td>\n", " <td>...</td>\n", " <td>0.161543</td>\n", " <td>0.151389</td>\n", " <td>0.139600</td>\n", " <td>0.134067</td>\n", " <td>0.132920</td>\n", " <td>0.133156</td>\n", " <td>0.133244</td>\n", " <td>0.133837</td>\n", " <td>0.132917</td>\n", " <td>0.134400</td>\n", " </tr>\n", " <tr>\n", " <th>2.8</th>\n", " <td>0.146100</td>\n", " <td>0.139309</td>\n", " <td>0.138750</td>\n", " <td>0.138678</td>\n", " <td>0.136657</td>\n", " <td>0.135780</td>\n", " <td>0.135900</td>\n", " <td>0.133218</td>\n", " <td>0.140078</td>\n", " <td>0.150836</td>\n", " <td>...</td>\n", " <td>0.153625</td>\n", " <td>0.144011</td>\n", " <td>0.136418</td>\n", " <td>0.137729</td>\n", " <td>0.137500</td>\n", " <td>0.138112</td>\n", " <td>0.141614</td>\n", " <td>0.137290</td>\n", " <td>0.140292</td>\n", " <td>0.144650</td>\n", " </tr>\n", " <tr>\n", " <th>2.9</th>\n", " <td>0.138650</td>\n", " <td>0.137057</td>\n", " <td>0.140111</td>\n", " <td>0.136422</td>\n", " <td>0.135869</td>\n", " <td>0.136715</td>\n", " <td>0.138400</td>\n", " <td>0.133900</td>\n", " <td>0.140856</td>\n", " <td>0.138470</td>\n", " <td>...</td>\n", " <td>0.142667</td>\n", " <td>0.134730</td>\n", " <td>0.136569</td>\n", " <td>0.137010</td>\n", " <td>0.136192</td>\n", " <td>0.137589</td>\n", " <td>0.139643</td>\n", " <td>0.138133</td>\n", " <td>0.135671</td>\n", " <td>0.143737</td>\n", " </tr>\n", " <tr>\n", " <th>3.0</th>\n", " <td>0.140580</td>\n", " <td>0.132220</td>\n", " <td>0.132673</td>\n", " <td>0.132340</td>\n", " <td>0.132370</td>\n", " <td>0.132330</td>\n", " <td>0.132350</td>\n", " <td>0.132364</td>\n", " <td>0.132364</td>\n", " <td>0.132875</td>\n", " <td>...</td>\n", " <td>0.132560</td>\n", " <td>0.132367</td>\n", " <td>0.132367</td>\n", " <td>0.132330</td>\n", " <td>0.132321</td>\n", " <td>0.132323</td>\n", " <td>0.132336</td>\n", " <td>0.132500</td>\n", " <td>0.132250</td>\n", " <td>0.131733</td>\n", " </tr>\n", " <tr>\n", " <th>3.1</th>\n", " <td>0.140850</td>\n", " <td>0.133550</td>\n", " <td>0.132243</td>\n", " <td>0.133160</td>\n", " <td>0.132200</td>\n", " <td>0.135089</td>\n", " <td>0.133630</td>\n", " <td>0.135355</td>\n", " <td>0.136069</td>\n", " <td>0.135850</td>\n", " <td>...</td>\n", " <td>0.134755</td>\n", " <td>0.134214</td>\n", " <td>0.135983</td>\n", " <td>0.134942</td>\n", " <td>0.132553</td>\n", " <td>0.132840</td>\n", " <td>0.133667</td>\n", " <td>0.132217</td>\n", " <td>0.132188</td>\n", " <td>0.140175</td>\n", " </tr>\n", " <tr>\n", " <th>3.2</th>\n", " <td>0.141883</td>\n", " <td>0.138843</td>\n", " <td>0.137400</td>\n", " <td>0.138456</td>\n", " <td>0.135829</td>\n", " <td>0.134738</td>\n", " <td>0.133143</td>\n", " <td>0.132936</td>\n", " <td>0.133822</td>\n", " <td>0.134731</td>\n", " <td>...</td>\n", " <td>0.136450</td>\n", " <td>0.139473</td>\n", " <td>0.134975</td>\n", " <td>0.135812</td>\n", " <td>0.136360</td>\n", " <td>0.135514</td>\n", " <td>0.136362</td>\n", " <td>0.137678</td>\n", " <td>0.142022</td>\n", " <td>0.136033</td>\n", " </tr>\n", " <tr>\n", " <th>3.3</th>\n", " <td>0.139400</td>\n", " <td>0.136378</td>\n", " <td>0.137167</td>\n", " <td>0.134940</td>\n", " <td>0.132460</td>\n", " <td>0.136362</td>\n", " <td>0.133274</td>\n", " <td>0.132863</td>\n", " <td>0.136533</td>\n", " <td>0.130312</td>\n", " <td>...</td>\n", " <td>0.131400</td>\n", " <td>0.131879</td>\n", " <td>0.130691</td>\n", " <td>0.131740</td>\n", " <td>0.132938</td>\n", " <td>0.134318</td>\n", " <td>0.136760</td>\n", " <td>0.138513</td>\n", " <td>0.140400</td>\n", " <td>0.146788</td>\n", " </tr>\n", " <tr>\n", " <th>3.4</th>\n", " <td>0.141000</td>\n", " <td>0.130790</td>\n", " <td>0.129220</td>\n", " <td>0.129329</td>\n", " <td>0.130916</td>\n", " <td>0.129267</td>\n", " <td>0.128655</td>\n", " <td>0.132400</td>\n", " <td>0.128681</td>\n", " <td>0.128758</td>\n", " <td>...</td>\n", " <td>0.131280</td>\n", " <td>0.128478</td>\n", " <td>0.128667</td>\n", " <td>0.128831</td>\n", " <td>0.133727</td>\n", " <td>0.133914</td>\n", " <td>0.130500</td>\n", " <td>0.128700</td>\n", " <td>0.128457</td>\n", " <td>0.136000</td>\n", " </tr>\n", " <tr>\n", " <th>3.5</th>\n", " <td>0.133933</td>\n", " <td>0.140282</td>\n", " <td>0.135745</td>\n", " <td>0.147691</td>\n", " <td>0.127075</td>\n", " <td>0.134500</td>\n", " <td>0.130312</td>\n", " <td>0.146278</td>\n", " <td>0.128320</td>\n", " <td>0.142617</td>\n", " <td>...</td>\n", " <td>0.135367</td>\n", " <td>0.147464</td>\n", " <td>0.135347</td>\n", " <td>0.135480</td>\n", " <td>0.131614</td>\n", " <td>0.128662</td>\n", " <td>0.140411</td>\n", " <td>0.134522</td>\n", " <td>0.137387</td>\n", " <td>0.137213</td>\n", " </tr>\n", " <tr>\n", " <th>3.6</th>\n", " <td>0.157160</td>\n", " <td>0.150125</td>\n", " <td>0.190522</td>\n", " <td>0.163414</td>\n", " <td>0.153340</td>\n", " <td>0.182489</td>\n", " <td>0.146633</td>\n", " <td>0.174283</td>\n", " <td>0.164600</td>\n", " <td>0.156060</td>\n", " <td>...</td>\n", " <td>0.167818</td>\n", " <td>0.175760</td>\n", " <td>0.172300</td>\n", " <td>0.170070</td>\n", " <td>0.172709</td>\n", " <td>0.160782</td>\n", " <td>0.165338</td>\n", " <td>0.150207</td>\n", " <td>0.169028</td>\n", " <td>0.172525</td>\n", " </tr>\n", " <tr>\n", " <th>3.7</th>\n", " <td>0.212600</td>\n", " <td>0.203550</td>\n", " <td>0.182613</td>\n", " <td>0.217033</td>\n", " <td>0.196530</td>\n", " <td>0.192733</td>\n", " <td>0.196800</td>\n", " <td>0.196678</td>\n", " <td>0.194400</td>\n", " <td>0.193862</td>\n", " <td>...</td>\n", " <td>0.207025</td>\n", " <td>0.213778</td>\n", " <td>0.199458</td>\n", " <td>0.195767</td>\n", " <td>0.202582</td>\n", " <td>0.193050</td>\n", " <td>0.203717</td>\n", " <td>0.188890</td>\n", " <td>0.187186</td>\n", " <td>0.219900</td>\n", " </tr>\n", " <tr>\n", " <th>3.8</th>\n", " <td>0.258014</td>\n", " <td>0.226800</td>\n", " <td>0.214507</td>\n", " <td>0.220812</td>\n", " <td>0.212843</td>\n", " <td>0.213190</td>\n", " <td>0.214863</td>\n", " <td>0.214723</td>\n", " <td>0.225433</td>\n", " <td>0.221414</td>\n", " <td>...</td>\n", " <td>0.258750</td>\n", " <td>0.219029</td>\n", " <td>0.216213</td>\n", " <td>0.218565</td>\n", " <td>0.214589</td>\n", " <td>0.215342</td>\n", " <td>0.212015</td>\n", " <td>0.208669</td>\n", " <td>0.218762</td>\n", " <td>0.209350</td>\n", " </tr>\n", " <tr>\n", " <th>3.9</th>\n", " <td>0.312460</td>\n", " <td>0.259640</td>\n", " <td>0.229650</td>\n", " <td>0.263078</td>\n", " <td>0.252018</td>\n", " <td>0.237225</td>\n", " <td>0.237745</td>\n", " <td>0.297200</td>\n", " <td>0.298883</td>\n", " <td>0.219567</td>\n", " <td>...</td>\n", " <td>0.281725</td>\n", " <td>0.263557</td>\n", " <td>0.288811</td>\n", " <td>0.296427</td>\n", " <td>0.269145</td>\n", " <td>0.287975</td>\n", " <td>0.327073</td>\n", " <td>0.329788</td>\n", " <td>0.274791</td>\n", " <td>0.319000</td>\n", " </tr>\n", " <tr>\n", " <th>4.0</th>\n", " <td>0.261700</td>\n", " <td>0.265618</td>\n", " <td>0.158400</td>\n", " <td>0.276025</td>\n", " <td>0.353650</td>\n", " <td>0.274587</td>\n", " <td>0.289418</td>\n", " <td>0.250657</td>\n", " <td>0.247240</td>\n", " <td>0.233933</td>\n", " <td>...</td>\n", " <td>0.230525</td>\n", " <td>0.312200</td>\n", " <td>0.261683</td>\n", " <td>0.321733</td>\n", " <td>0.295550</td>\n", " <td>0.319608</td>\n", " <td>0.270450</td>\n", " <td>0.303856</td>\n", " <td>0.355400</td>\n", " <td>0.277650</td>\n", " </tr>\n", " <tr>\n", " <th>4.1</th>\n", " <td>0.141975</td>\n", " <td>0.146789</td>\n", " <td>0.188287</td>\n", " <td>0.179920</td>\n", " <td>0.178042</td>\n", " <td>0.198750</td>\n", " <td>0.169862</td>\n", " <td>0.183632</td>\n", " <td>0.133243</td>\n", " <td>0.188200</td>\n", " <td>...</td>\n", " <td>0.259920</td>\n", " <td>0.189321</td>\n", " <td>0.252600</td>\n", " <td>0.117550</td>\n", " <td>0.130433</td>\n", " <td>0.203973</td>\n", " <td>0.167662</td>\n", " <td>0.189260</td>\n", " <td>0.194700</td>\n", " <td>0.145700</td>\n", " </tr>\n", " <tr>\n", " <th>4.2</th>\n", " <td>0.208650</td>\n", " <td>0.172833</td>\n", " <td>0.240123</td>\n", " <td>0.270567</td>\n", " <td>0.174783</td>\n", " <td>0.187754</td>\n", " <td>0.257860</td>\n", " <td>0.179833</td>\n", " <td>0.161525</td>\n", " <td>0.129600</td>\n", " <td>...</td>\n", " <td>0.233350</td>\n", " <td>0.357600</td>\n", " <td>0.230143</td>\n", " <td>0.198875</td>\n", " <td>0.153750</td>\n", " <td>0.207388</td>\n", " <td>0.251690</td>\n", " <td>0.198325</td>\n", " <td>0.178300</td>\n", " <td>0.300800</td>\n", " </tr>\n", " <tr>\n", " <th>4.3</th>\n", " <td>0.488733</td>\n", " <td>0.894550</td>\n", " <td>0.438914</td>\n", " <td>0.666500</td>\n", " <td>0.436417</td>\n", " <td>0.223957</td>\n", " <td>0.315433</td>\n", " <td>0.287644</td>\n", " <td>0.346513</td>\n", " <td>0.350088</td>\n", " <td>...</td>\n", " <td>0.347413</td>\n", " <td>0.602767</td>\n", " <td>0.622100</td>\n", " <td>0.784133</td>\n", " <td>0.214943</td>\n", " <td>0.308667</td>\n", " <td>0.246850</td>\n", " <td>0.686620</td>\n", " <td>0.189040</td>\n", " <td>0.310000</td>\n", " </tr>\n", " <tr>\n", " <th>4.4</th>\n", " <td>0.104350</td>\n", " <td>0.507614</td>\n", " <td>1.530550</td>\n", " <td>0.909633</td>\n", " <td>0.000000</td>\n", " <td>0.779233</td>\n", " <td>0.098667</td>\n", " <td>1.691500</td>\n", " <td>0.099500</td>\n", " <td>0.609345</td>\n", " <td>...</td>\n", " <td>0.267200</td>\n", " <td>0.247160</td>\n", " <td>0.756825</td>\n", " <td>0.685763</td>\n", " <td>0.519725</td>\n", " <td>0.519360</td>\n", " <td>1.221350</td>\n", " <td>0.127637</td>\n", " <td>0.169967</td>\n", " <td>0.296950</td>\n", " </tr>\n", " <tr>\n", " <th>4.5</th>\n", " <td>0.000000</td>\n", " <td>0.104690</td>\n", " <td>0.118533</td>\n", " <td>0.121583</td>\n", " <td>0.092800</td>\n", " <td>0.092717</td>\n", " <td>0.100013</td>\n", " <td>0.100217</td>\n", " <td>0.103375</td>\n", " <td>0.108060</td>\n", " <td>...</td>\n", " <td>0.098950</td>\n", " <td>0.097314</td>\n", " <td>0.099140</td>\n", " <td>0.104000</td>\n", " <td>0.114250</td>\n", " <td>0.098533</td>\n", " <td>0.089100</td>\n", " <td>0.176213</td>\n", " <td>0.116700</td>\n", " <td>0.106100</td>\n", " </tr>\n", " <tr>\n", " <th>4.6</th>\n", " <td>0.104450</td>\n", " <td>0.103500</td>\n", " <td>0.095200</td>\n", " <td>0.096600</td>\n", " <td>0.100317</td>\n", " <td>0.097909</td>\n", " <td>0.099567</td>\n", " <td>0.095550</td>\n", " <td>0.101467</td>\n", " <td>0.096550</td>\n", " <td>...</td>\n", " <td>0.000000</td>\n", " <td>0.101150</td>\n", " <td>0.103700</td>\n", " <td>0.102917</td>\n", " <td>0.089000</td>\n", " <td>0.102150</td>\n", " <td>0.109400</td>\n", " <td>0.100788</td>\n", " <td>0.102500</td>\n", " <td>0.000000</td>\n", " </tr>\n", " <tr>\n", " <th>4.7</th>\n", " <td>0.094300</td>\n", " <td>0.101400</td>\n", " <td>0.099475</td>\n", " <td>0.098700</td>\n", " <td>0.095050</td>\n", " <td>0.095020</td>\n", " <td>0.095925</td>\n", " <td>0.099850</td>\n", " <td>0.000000</td>\n", " <td>0.098260</td>\n", " <td>...</td>\n", " <td>0.000000</td>\n", " <td>0.096500</td>\n", " <td>0.096100</td>\n", " <td>0.090350</td>\n", " <td>0.096900</td>\n", " <td>0.087700</td>\n", " <td>0.097950</td>\n", " <td>0.089450</td>\n", " <td>0.097233</td>\n", " <td>0.094300</td>\n", " </tr>\n", " <tr>\n", " <th>4.8</th>\n", " <td>0.000000</td>\n", " <td>0.094333</td>\n", " <td>0.097750</td>\n", " <td>0.099525</td>\n", " <td>0.094929</td>\n", " <td>0.089000</td>\n", " <td>0.092600</td>\n", " <td>0.095250</td>\n", " <td>0.095100</td>\n", " <td>0.094150</td>\n", " <td>...</td>\n", " <td>0.091200</td>\n", " <td>0.090000</td>\n", " <td>0.100500</td>\n", " <td>0.095100</td>\n", " <td>0.098800</td>\n", " <td>0.097100</td>\n", " <td>0.096000</td>\n", " <td>0.094133</td>\n", " <td>0.094067</td>\n", " <td>0.000000</td>\n", " </tr>\n", " <tr>\n", " <th>4.9</th>\n", " <td>0.092200</td>\n", " <td>0.092300</td>\n", " <td>0.091700</td>\n", " <td>0.088420</td>\n", " <td>0.092600</td>\n", " <td>0.089000</td>\n", " <td>0.085100</td>\n", " <td>0.090667</td>\n", " <td>0.090650</td>\n", " <td>0.000000</td>\n", " <td>...</td>\n", " <td>0.000000</td>\n", " <td>0.082850</td>\n", " <td>0.000000</td>\n", " <td>0.089667</td>\n", " <td>0.093100</td>\n", " <td>0.000000</td>\n", " <td>0.092000</td>\n", " <td>0.087850</td>\n", " <td>0.091650</td>\n", " <td>0.000000</td>\n", " </tr>\n", " <tr>\n", " <th>5.0</th>\n", " <td>0.000000</td>\n", " <td>0.141100</td>\n", " <td>0.076200</td>\n", " <td>0.119100</td>\n", " <td>0.000000</td>\n", " <td>0.178500</td>\n", " <td>0.090000</td>\n", " <td>0.098400</td>\n", " <td>0.000000</td>\n", " <td>0.000000</td>\n", " <td>...</td>\n", " <td>0.301800</td>\n", " <td>0.104250</td>\n", " <td>0.103567</td>\n", " <td>0.085500</td>\n", " <td>0.000000</td>\n", " <td>0.000000</td>\n", " <td>0.000000</td>\n", " <td>0.081600</td>\n", " <td>0.081500</td>\n", " <td>0.000000</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "<p>36 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", "1.5 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 \n", "1.6 0.000000 0.745900 0.000000 0.746800 0.000000 0.000000 0.000000 \n", "1.7 0.000000 1.178400 0.295200 0.743500 0.379000 0.614800 1.693300 \n", "1.8 0.381100 0.386050 0.243450 0.168900 0.000000 0.229833 0.229900 \n", "1.9 0.153000 0.000000 0.142200 0.239467 0.295500 0.150900 0.273500 \n", "2.0 0.151000 0.137790 0.139500 0.141687 0.162550 0.160067 0.161450 \n", "2.1 0.132200 0.137630 0.147300 0.136700 0.139863 0.165500 0.150780 \n", "2.2 0.138267 0.136160 0.137342 0.136750 0.138395 0.161375 0.155864 \n", "2.3 0.140120 0.135150 0.136392 0.136000 0.135283 0.138733 0.162330 \n", "2.4 0.146343 0.137150 0.140820 0.137143 0.134425 0.138600 0.153840 \n", "2.5 0.138550 0.137725 0.135490 0.134900 0.136400 0.137218 0.139250 \n", "2.6 0.137725 0.134624 0.135183 0.134150 0.135617 0.134246 0.135311 \n", "2.7 0.135840 0.135129 0.132957 0.132900 0.133153 0.132860 0.135122 \n", "2.8 0.146100 0.139309 0.138750 0.138678 0.136657 0.135780 0.135900 \n", "2.9 0.138650 0.137057 0.140111 0.136422 0.135869 0.136715 0.138400 \n", "3.0 0.140580 0.132220 0.132673 0.132340 0.132370 0.132330 0.132350 \n", "3.1 0.140850 0.133550 0.132243 0.133160 0.132200 0.135089 0.133630 \n", "3.2 0.141883 0.138843 0.137400 0.138456 0.135829 0.134738 0.133143 \n", "3.3 0.139400 0.136378 0.137167 0.134940 0.132460 0.136362 0.133274 \n", "3.4 0.141000 0.130790 0.129220 0.129329 0.130916 0.129267 0.128655 \n", "3.5 0.133933 0.140282 0.135745 0.147691 0.127075 0.134500 0.130312 \n", "3.6 0.157160 0.150125 0.190522 0.163414 0.153340 0.182489 0.146633 \n", "3.7 0.212600 0.203550 0.182613 0.217033 0.196530 0.192733 0.196800 \n", "3.8 0.258014 0.226800 0.214507 0.220812 0.212843 0.213190 0.214863 \n", "3.9 0.312460 0.259640 0.229650 0.263078 0.252018 0.237225 0.237745 \n", "4.0 0.261700 0.265618 0.158400 0.276025 0.353650 0.274587 0.289418 \n", "4.1 0.141975 0.146789 0.188287 0.179920 0.178042 0.198750 0.169862 \n", "4.2 0.208650 0.172833 0.240123 0.270567 0.174783 0.187754 0.257860 \n", "4.3 0.488733 0.894550 0.438914 0.666500 0.436417 0.223957 0.315433 \n", "4.4 0.104350 0.507614 1.530550 0.909633 0.000000 0.779233 0.098667 \n", "4.5 0.000000 0.104690 0.118533 0.121583 0.092800 0.092717 0.100013 \n", "4.6 0.104450 0.103500 0.095200 0.096600 0.100317 0.097909 0.099567 \n", "4.7 0.094300 0.101400 0.099475 0.098700 0.095050 0.095020 0.095925 \n", "4.8 0.000000 0.094333 0.097750 0.099525 0.094929 0.089000 0.092600 \n", "4.9 0.092200 0.092300 0.091700 0.088420 0.092600 0.089000 0.085100 \n", "5.0 0.000000 0.141100 0.076200 0.119100 0.000000 0.178500 0.090000 \n", "\n", "phi -83.0 -82.0 -81.0 ... 81.0 82.0 83.0 \\\n", "eta ... \n", "1.5 0.000000 0.000000 0.000000 ... 0.000000 0.000000 0.000000 \n", "1.6 0.000000 0.000000 0.000000 ... 0.000000 0.000000 0.000000 \n", "1.7 0.000000 0.930075 0.111500 ... 0.381300 0.000000 0.000000 \n", "1.8 0.390667 0.165900 0.327267 ... 0.223500 0.236243 0.883980 \n", "1.9 0.161000 0.867600 0.000000 ... 0.220200 0.171267 0.163900 \n", "2.0 0.149060 0.169400 0.176289 ... 0.172440 0.176475 0.153450 \n", "2.1 0.149290 0.152229 0.168450 ... 0.165614 0.155900 0.150020 \n", "2.2 0.150350 0.148888 0.153114 ... 0.155750 0.149120 0.148875 \n", "2.3 0.156182 0.147925 0.145092 ... 0.159367 0.148900 0.155467 \n", "2.4 0.162650 0.154400 0.144275 ... 0.143763 0.151444 0.157156 \n", "2.5 0.156625 0.159414 0.147300 ... 0.149229 0.158000 0.151414 \n", "2.6 0.144350 0.158150 0.156129 ... 0.157864 0.157836 0.145880 \n", "2.7 0.136907 0.148817 0.159080 ... 0.161543 0.151389 0.139600 \n", "2.8 0.133218 0.140078 0.150836 ... 0.153625 0.144011 0.136418 \n", "2.9 0.133900 0.140856 0.138470 ... 0.142667 0.134730 0.136569 \n", "3.0 0.132364 0.132364 0.132875 ... 0.132560 0.132367 0.132367 \n", "3.1 0.135355 0.136069 0.135850 ... 0.134755 0.134214 0.135983 \n", "3.2 0.132936 0.133822 0.134731 ... 0.136450 0.139473 0.134975 \n", "3.3 0.132863 0.136533 0.130312 ... 0.131400 0.131879 0.130691 \n", "3.4 0.132400 0.128681 0.128758 ... 0.131280 0.128478 0.128667 \n", "3.5 0.146278 0.128320 0.142617 ... 0.135367 0.147464 0.135347 \n", "3.6 0.174283 0.164600 0.156060 ... 0.167818 0.175760 0.172300 \n", "3.7 0.196678 0.194400 0.193862 ... 0.207025 0.213778 0.199458 \n", "3.8 0.214723 0.225433 0.221414 ... 0.258750 0.219029 0.216213 \n", "3.9 0.297200 0.298883 0.219567 ... 0.281725 0.263557 0.288811 \n", "4.0 0.250657 0.247240 0.233933 ... 0.230525 0.312200 0.261683 \n", "4.1 0.183632 0.133243 0.188200 ... 0.259920 0.189321 0.252600 \n", "4.2 0.179833 0.161525 0.129600 ... 0.233350 0.357600 0.230143 \n", "4.3 0.287644 0.346513 0.350088 ... 0.347413 0.602767 0.622100 \n", "4.4 1.691500 0.099500 0.609345 ... 0.267200 0.247160 0.756825 \n", "4.5 0.100217 0.103375 0.108060 ... 0.098950 0.097314 0.099140 \n", "4.6 0.095550 0.101467 0.096550 ... 0.000000 0.101150 0.103700 \n", "4.7 0.099850 0.000000 0.098260 ... 0.000000 0.096500 0.096100 \n", "4.8 0.095250 0.095100 0.094150 ... 0.091200 0.090000 0.100500 \n", "4.9 0.090667 0.090650 0.000000 ... 0.000000 0.082850 0.000000 \n", "5.0 0.098400 0.000000 0.000000 ... 0.301800 0.104250 0.103567 \n", "\n", "phi 84.0 85.0 86.0 87.0 88.0 89.0 90.0 \n", "eta \n", "1.5 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 \n", "1.6 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 \n", "1.7 0.380250 1.114300 0.730200 0.420600 0.000000 0.377950 0.000000 \n", "1.8 0.270350 1.366700 0.160850 0.000000 0.272100 0.234500 0.124500 \n", "1.9 0.151325 0.154617 0.251200 0.139100 0.191800 0.224867 0.144000 \n", "2.0 0.152857 0.178971 0.154686 0.141500 0.140043 0.141740 0.134250 \n", "2.1 0.148483 0.160091 0.141550 0.143433 0.137018 0.137937 0.137000 \n", "2.2 0.158500 0.159117 0.139737 0.136467 0.136900 0.136238 0.146150 \n", "2.3 0.160300 0.145629 0.145200 0.139894 0.141271 0.138073 0.141620 \n", "2.4 0.153878 0.138533 0.138200 0.138525 0.136667 0.135871 0.139700 \n", "2.5 0.143550 0.135358 0.136343 0.134927 0.134337 0.136756 0.136367 \n", "2.6 0.135780 0.134319 0.134755 0.134533 0.135633 0.135900 0.134450 \n", "2.7 0.134067 0.132920 0.133156 0.133244 0.133837 0.132917 0.134400 \n", "2.8 0.137729 0.137500 0.138112 0.141614 0.137290 0.140292 0.144650 \n", "2.9 0.137010 0.136192 0.137589 0.139643 0.138133 0.135671 0.143737 \n", "3.0 0.132330 0.132321 0.132323 0.132336 0.132500 0.132250 0.131733 \n", "3.1 0.134942 0.132553 0.132840 0.133667 0.132217 0.132188 0.140175 \n", "3.2 0.135812 0.136360 0.135514 0.136362 0.137678 0.142022 0.136033 \n", "3.3 0.131740 0.132938 0.134318 0.136760 0.138513 0.140400 0.146788 \n", "3.4 0.128831 0.133727 0.133914 0.130500 0.128700 0.128457 0.136000 \n", "3.5 0.135480 0.131614 0.128662 0.140411 0.134522 0.137387 0.137213 \n", "3.6 0.170070 0.172709 0.160782 0.165338 0.150207 0.169028 0.172525 \n", "3.7 0.195767 0.202582 0.193050 0.203717 0.188890 0.187186 0.219900 \n", "3.8 0.218565 0.214589 0.215342 0.212015 0.208669 0.218762 0.209350 \n", "3.9 0.296427 0.269145 0.287975 0.327073 0.329788 0.274791 0.319000 \n", "4.0 0.321733 0.295550 0.319608 0.270450 0.303856 0.355400 0.277650 \n", "4.1 0.117550 0.130433 0.203973 0.167662 0.189260 0.194700 0.145700 \n", "4.2 0.198875 0.153750 0.207388 0.251690 0.198325 0.178300 0.300800 \n", "4.3 0.784133 0.214943 0.308667 0.246850 0.686620 0.189040 0.310000 \n", "4.4 0.685763 0.519725 0.519360 1.221350 0.127637 0.169967 0.296950 \n", "4.5 0.104000 0.114250 0.098533 0.089100 0.176213 0.116700 0.106100 \n", "4.6 0.102917 0.089000 0.102150 0.109400 0.100788 0.102500 0.000000 \n", "4.7 0.090350 0.096900 0.087700 0.097950 0.089450 0.097233 0.094300 \n", "4.8 0.095100 0.098800 0.097100 0.096000 0.094133 0.094067 0.000000 \n", "4.9 0.089667 0.093100 0.000000 0.092000 0.087850 0.091650 0.000000 \n", "5.0 0.085500 0.000000 0.000000 0.000000 0.081600 0.081500 0.000000 \n", "\n", "[36 rows x 181 columns]" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_pivoted" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<Figure size 640x480 with 2 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "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", ")\n", "ax.set_yticks([5, 15, 25, 35], [2, 3, 4, 5])\n", "ax.set_xticks([39, 89, 139],\n", " [-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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