diff --git a/methods/__pycache__/fit_linear_regression_model.cpython-310.pyc b/methods/__pycache__/fit_linear_regression_model.cpython-310.pyc new file mode 100644 index 0000000..2ef69ac Binary files /dev/null and b/methods/__pycache__/fit_linear_regression_model.cpython-310.pyc differ diff --git a/methods/fit_linear_regression_model.py b/methods/fit_linear_regression_model.py new file mode 100644 index 0000000..c4a4655 --- /dev/null +++ b/methods/fit_linear_regression_model.py @@ -0,0 +1,91 @@ +import awkward as ak +from sklearn.preprocessing import PolynomialFeatures +from sklearn.linear_model import LinearRegression +from sklearn.model_selection import train_test_split +from sklearn.metrics import mean_squared_error +import numpy as np + + +def fit_linear_regression_model( + array: ak.Array, + target_feat: str, + features: list[str], + degree: int, + keep: list[str] = None, + keep_only_linear_in: str = "", + remove: list[str] = None, + include_bias: bool = False, + fit_intercept: bool = False, + test_size=0.2, + random_state=42, +) -> tuple[LinearRegression, list[str], np.array, np.array]: + """Wrapper around sklearn's LinearRegression with PolynomialFeatures. + + Args: + array (ak.Array): The data. + target_feat (str): Target feature to be fitted. + features (list[str]): Features the target depends on. + degree (int): Highest order of the polynomial. + keep (list[str], optional): Monomials to keep. Defaults to None. + keep_only_linear_in (str, optional): Keep only terms that are linear in this feature. Defaults to "". + remove (list[str], optional): Monomials to remove. Defaults to None. + include_bias (bool, optional): Inlcude bias term in polynomial. Defaults to False. + fit_intercept (bool, optional): Fit zeroth order. Defaults to False. + test_size (float, optional): Fraction of data used for testing. Defaults to 0.2. + random_state (int, optional): Defaults to 42. + + Raises: + NotImplementedError: Simultaneous removing and keeping is not implemented. + + Returns: + tuple[LinearRegression, list[str]]: The linear regression object and the kept features. + """ + data = np.column_stack([ak.to_numpy(array[feat]) for feat in features]) + target = ak.to_numpy(array[target_feat]) + X_train, X_test, y_train, y_test = train_test_split( + data, + target, + test_size=test_size, + random_state=random_state, + ) + poly = PolynomialFeatures(degree=degree, include_bias=include_bias) + X_train_model = poly.fit_transform(X_train) + X_test_model = poly.fit_transform(X_test) + poly_features = poly.get_feature_names_out(input_features=features) + if not remove: + if keep: + remove = [i for i, f in enumerate(poly_features) if f not in keep] + elif keep_only_linear_in: + # remove everything that's not linear in variable + # the corrections should vanish + remove = [ + i + for i, f in enumerate(poly_features) + if (keep_only_linear_in not in f) or (keep_only_linear_in + "^" in f) + ] + else: + remove = [] + elif remove and keep: + raise NotImplementedError + X_train_model = np.delete(X_train_model, remove, axis=1) + X_test_model = np.delete(X_test_model, remove, axis=1) + poly_features = np.delete(poly_features, remove) + + lin_reg = LinearRegression(fit_intercept=fit_intercept) + lin_reg.fit(X_train_model, y_train) + y_pred_test = lin_reg.predict(X_test_model) + print(f"Parameterisation for {target_feat}:") + print("intercept=", lin_reg.intercept_) + print( + "coef=", + dict( + zip( + poly_features, + lin_reg.coef_, + ), + ), + ) + print("r2 score=", lin_reg.score(X_test_model, y_test)) + print("RMSE =", mean_squared_error(y_test, y_pred_test, squared=False)) + print() + return (lin_reg, poly_features, y_test, y_pred_test) diff --git a/trackinglosses_rad_length.ipynb b/trackinglosses_rad_length.ipynb deleted file mode 100644 index c2529ec..0000000 --- a/trackinglosses_rad_length.ipynb +++ /dev/null @@ -1,200 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 30, - "metadata": {}, - "outputs": [], - "source": [ - "import uproot\t\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "from mpl_toolkits import mplot3d\n", - "import awkward as ak\n", - "from scipy.optimize import curve_fit\n", - "%matplotlib inline" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "40402 10099\n", - "50501\n" - ] - } - ], - "source": [ - "file = uproot.open(\n", - " \"tracking_losses_ntuple_B_default_radlength_endVelo.root:PrDebugTrackingLosses.PrDebugTrackingTool/Tuple;1\"\n", - ")\n", - "\n", - "# selektiere nur elektronen von B->K*ee\n", - "allcolumns = file.arrays()\n", - "found = allcolumns[\n", - " (allcolumns.isElectron) & (~allcolumns.lost) & (allcolumns.fromB)\n", - "] # B: 9056\n", - "lost = allcolumns[\n", - " (allcolumns.isElectron) & (allcolumns.lost) & (allcolumns.fromB)\n", - "] # B: 1466\n", - "\n", - "electrons = allcolumns[(allcolumns.isElectron) & (allcolumns.fromB)]\n", - "\n", - "print(ak.num(found, axis=0), ak.num(lost, axis=0))\n", - "print(ak.num(electrons,axis=0))\n", - "# ak.count(found, axis=None)" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "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", - "rad_length_lost = ak.to_numpy(lost[\"rad_length_frac\"])\n", - "eta_lost = ak.to_numpy(lost[\"eta\"])\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": 33, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "\n", - "\n", - "plt.hist(rad_length_lost,bins=100,density=True,alpha=0.5,color=\"darkorange\",histtype=\"bar\",label=\"lost\",range=[0,1])\n", - "plt.hist(rad_length_found,bins=100,density=True,alpha=0.5,color=\"blue\",histtype=\"bar\",label=\"found\",range=[0,1])\n", - "plt.xlim(0,1)\n", - "#plt.yscale(\"log\")\n", - "plt.title(\"radiation length fraction\")\n", - "plt.xlabel(f\"$x/X_0$\")\n", - "plt.ylabel(\"a.u.\")\n", - "\n", - "plt.legend()\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "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=[[0,0.5],[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=[[0,0.5],[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\")\n", - "plt.colorbar(a0[3], ax=ax1)\n", - "\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "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 -} diff --git a/trackinglosses_rad_length_beginVelo.ipynb b/trackinglosses_rad_length_beginVelo.ipynb new file mode 100644 index 0000000..5491d8c --- /dev/null +++ b/trackinglosses_rad_length_beginVelo.ipynb @@ -0,0 +1,346 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [], + "source": [ + "import uproot\t\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import pylab\n", + "from mpl_toolkits import mplot3d\n", + "import awkward as ak\n", + "from scipy.optimize import curve_fit\n", + "from methods.fit_linear_regression_model import fit_linear_regression_model\n", + "import sklearn\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "40402 10099\n", + "50501\n" + ] + } + ], + "source": [ + "file = uproot.open(\n", + " \"tracking_losses_ntuple_B_default_radlength_beginVelo.root:PrDebugTrackingLosses.PrDebugTrackingTool/Tuple;1\"\n", + ")\n", + "\n", + "# selektiere nur elektronen von B->K*ee\n", + "allcolumns = file.arrays()\n", + "found = allcolumns[\n", + " (allcolumns.isElectron) & (~allcolumns.lost) & (allcolumns.fromB)\n", + "] # B: 9056\n", + "lost = allcolumns[\n", + " (allcolumns.isElectron) & (allcolumns.lost) & (allcolumns.fromB)\n", + "] # B: 1466\n", + "\n", + "electrons = allcolumns[(allcolumns.isElectron) & (allcolumns.fromB)]\n", + "\n", + "print(ak.num(found, axis=0), ak.num(lost, axis=0))\n", + "print(ak.num(electrons, axis=0))\n", + "# ak.count(found, axis=None)" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "stretch factor: 0.24996287312509283\n" + ] + } + ], + "source": [ + "rad_length_found = ak.to_numpy(found[\"rad_length_frac\"])\n", + "eta_found = ak.to_numpy(found[\"eta\"])\n", + "phi_found = ak.to_numpy(found[\"phi\"])\n", + "\n", + "rad_length_lost = ak.to_numpy(lost[\"rad_length_frac\"])\n", + "eta_lost = ak.to_numpy(lost[\"eta\"])\n", + "phi_lost = ak.to_numpy(lost[\"phi\"])\n", + "\n", + "stretch_factor = ak.num(eta_lost, axis=0) / ak.num(eta_found, axis=0)\n", + "print(\"stretch factor: \", stretch_factor)" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.hist(\n", + " rad_length_lost,\n", + " bins=100,\n", + " density=True,\n", + " alpha=0.5,\n", + " color=\"darkorange\",\n", + " histtype=\"bar\",\n", + " label=\"lost\",\n", + " range=[0, 1],\n", + ")\n", + "plt.hist(\n", + " rad_length_found,\n", + " bins=100,\n", + " density=True,\n", + " alpha=0.5,\n", + " color=\"blue\",\n", + " histtype=\"bar\",\n", + " label=\"found\",\n", + " range=[0, 1],\n", + ")\n", + "plt.xlim(0, 1)\n", + "# plt.yscale(\"log\")\n", + "plt.title(\"radiation length fraction beginVelo\")\n", + "plt.xlabel(f\"$x/X_0$\")\n", + "plt.ylabel(\"a.u.\")\n", + "\n", + "plt.legend()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "nbins = 100\n", + "vmax = 60\n", + "\n", + "fig, ((ax0, ax1)) = plt.subplots(nrows=1, ncols=2, figsize=(20, 8))\n", + "\n", + "a0 = ax0.hist2d(\n", + " rad_length_found,\n", + " eta_found,\n", + " density=False,\n", + " bins=nbins,\n", + " cmap=plt.cm.jet,\n", + " cmin=1,\n", + " vmax=vmax,\n", + " range=[[0, 0.6], [2, 5]],\n", + ")\n", + "ax0.set_xlabel(f\"$x/X_0$\")\n", + "ax0.set_ylabel(f\"$\\eta$\")\n", + "ax0.set_title(f\"found $\\eta$ rad_length_frac\")\n", + "\n", + "a1 = ax1.hist2d(\n", + " rad_length_lost,\n", + " eta_lost,\n", + " density=False,\n", + " bins=nbins,\n", + " cmap=plt.cm.jet,\n", + " cmin=1,\n", + " vmax=vmax * stretch_factor,\n", + " range=[[0, 0.6], [2, 5]],\n", + ")\n", + "ax1.set_xlabel(f\"$x/X_0$\")\n", + "ax1.set_ylabel(f\"$\\eta$\")\n", + "ax1.set_title(f\"lost $\\eta$ rad_length_frac\")\n", + "# ax1.set(xlim=(0,4000), ylim=(-1000,1000))\n", + "\n", + "plt.suptitle(\"radiation length fraction and eta beginVelo\")\n", + "plt.colorbar(a0[3], ax=ax1)\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [], + "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", + "})" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Parameterisation for rad_length_frac:\n", + "intercept= 0.0\n", + "coef= {'1': 0.4397780582886272, 'x': -0.00033503537608265674, 'y': -4.789321464958182e-05, 'tx': 0.29235639572920724, 'ty': 0.04582618208302177, 'qop': -6.415064049320476, 'x^2': -1.7148780985188313e-05, 'x y': 6.213092137729638e-05, 'x tx': 0.0357792373581695, 'x ty': -0.03383507316198001, 'x qop': 5.411983741236269, 'y^2': -0.0001816058133305051, 'y tx': -0.060157737422332554, 'y ty': 0.3005860193729231, 'y qop': -0.5258168180545822, 'tx^2': -20.581151289333324, 'tx ty': 33.044369657406456, 'tx qop': -4029.9409136973454, 'ty^2': -125.62371943789807, 'ty qop': 353.08874965548245, 'qop^2': -3.455196781164501, 'x^3': 1.5371780097645036e-07, 'x^2 y': -1.2426481494632311e-06, 'x^2 tx': -0.0003102207951011151, 'x^2 ty': 0.0008294673378711963, 'x^2 qop': -0.0013130710648701907, 'x y^2': 1.3249247615395102e-07, 'x y tx': 0.0018733641082853674, 'x y ty': 0.0011459295349262105, 'x y qop': 0.0019703515773188207, 'x tx^2': 0.20678893428535178, 'x tx ty': -1.2036256002683805, 'x tx qop': 0.5976253324202965, 'x ty^2': -1.0782570211818505, 'x ty qop': 102.9459912574048, 'x qop^2': -399.16918894718305, 'y^3': 6.233244675968308e-07, 'y^2 tx': -0.0013994133271424403, 'y^2 ty': -0.001494031769823323, 'y^2 qop': 0.000739172959467016, 'y tx^2': -0.7290175680182052, 'y tx ty': 1.243439836769458, 'y tx qop': -105.55527881418587, 'y ty^2': 1.1837378224511292, 'y ty qop': -0.7657500120637686, 'y qop^2': 337.9089334089693, 'tx^3': -46.002810282557306, 'tx^2 ty': 453.57769058127064, 'tx^2 qop': -38.01772137810428, 'tx ty^2': -37.626648454968965, 'tx ty qop': 1.8221207378503694, 'tx qop^2': -0.40208255096589585, 'ty^3': -309.9803925733283, 'ty^2 qop': 15.736234283455065, 'ty qop^2': 0.6119680938280082, 'qop^3': -0.0011848472786331894, 'x^4': 1.4258219493967772e-08, 'x^3 y': -2.866883619390137e-09, 'x^3 tx': -4.622450717306492e-05, 'x^3 ty': -9.82760022623097e-05, 'x^3 qop': -0.0003703064328215433, 'x^2 y^2': -2.5736142106325133e-09, 'x^2 y tx': 0.00010255319807583874, 'x^2 y ty': 4.727919866809316e-05, 'x^2 y qop': -0.0006108544453127251, 'x^2 tx^2': 0.0554175462089006, 'x^2 tx ty': 0.16472559466997527, 'x^2 tx qop': 0.4783758545936343, 'x^2 ty^2': -0.029491297111002623, 'x^2 ty qop': 0.4072459238308874, 'x^2 qop^2': 1.274082272606741, 'x y^3': 1.019856199491187e-08, 'x y^2 tx': -4.0220230914655986e-05, 'x y^2 ty': -7.807169754414645e-05, 'x y^2 qop': 0.0009660222713137046, 'x y tx^2': -0.1655492341343554, 'x y tx ty': -0.026431127204681104, 'x y tx qop': 0.6220634937641649, 'x y ty^2': 0.09539033507193473, 'x y ty qop': -1.5617368820840163, 'x y qop^2': 9.235370076473936, 'x tx^3': -29.04572443637665, 'x tx^2 ty': -68.28508620169197, 'x tx^2 qop': -138.17259913877646, 'x tx ty^2': 35.45859347871301, 'x tx ty qop': -273.5835489656032, 'x tx qop^2': -13.43661305575325, 'x ty^3': -32.63704432223729, 'x ty^2 qop': 266.4268072225656, 'x ty qop^2': -19.06231959583929, 'x qop^3': 1.7675392248918753, 'y^4': -5.9619367220875574e-09, 'y^3 tx': 5.3697276143793715e-05, 'y^3 ty': 1.6576491020714457e-05, 'y^3 qop': -0.00017261869555795784, 'y^2 tx^2': 0.05014539344681879, 'y^2 tx ty': -0.07584181301500559, 'y^2 tx qop': -0.11650839062004614, 'y^2 ty^2': -0.015097852966895253, 'y^2 ty qop': 0.30517451156427916, 'y^2 qop^2': -6.650919392954738, 'y tx^3': 67.30430825382783, 'y tx^2 ty': -34.801544500318485, 'y tx^2 qop': -142.686910608299, 'y tx ty^2': 27.185653317937266, 'y tx ty qop': 439.1032304393983, 'y tx qop^2': -18.23933135674488, 'y ty^3': 4.265555264813383, 'y ty^2 qop': -131.08781810964697, 'y ty qop^2': 34.20534645433437, 'y qop^3': 0.11852127731033578, 'tx^4': 5649.699838454551, 'tx^3 ty': 329.9615549901753, 'tx^3 qop': -5.54031835759426, 'tx^2 ty^2': 584.460443200635, 'tx^2 ty qop': -0.7411483064667538, 'tx^2 qop^2': -0.03588219456135393, 'tx ty^3': 173.96226240131907, 'tx ty^2 qop': 1.0493642752682577, 'tx ty qop^2': -0.052660249222163585, 'tx qop^3': 0.001973073444663188, 'ty^4': 215.62061929608907, 'ty^3 qop': -0.5416245412488983, 'ty^2 qop^2': 0.09234573370962393, 'ty qop^3': 0.0002451739001199065, 'qop^4': 2.303474287996047e-05}\n", + "r2 score= 0.07032743800675567\n", + "RMSE = 0.1530771991719649\n", + "Type \n" + ] + } + ], + "source": [ + "lin_reg, features, xx0_test, xx0_predicted = fit_linear_regression_model(\n", + " data,\n", + " \"rad_length_frac\",\n", + " [\"x\", \"y\", \"tx\", \"ty\", \"qop\"],\n", + " 4,\n", + " include_bias=True,\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "nbins = 100\n", + "vmax = 40\n", + "\n", + "a0 = plt.hist2d(\n", + " xx0_test,\n", + " xx0_predicted,\n", + " density=False,\n", + " bins=nbins,\n", + " cmap=plt.cm.jet,\n", + " cmin=1,\n", + " vmax=vmax,\n", + " range=[[-0.2, 1.0], [-0.2, 1.0]],\n", + ")\n", + "plt.plot([-0.2, 1], [-0.2, 1], marker=\"\", alpha=0.8)\n", + "plt.xlabel(f\"True $x/X_0$\")\n", + "plt.ylabel(f\"Predicted $x/X_0$\")\n", + "plt.title(f\"found rad_length_frac\")\n", + "# ax1.set(xlim=(0,4000), ylim=(-1000,1000))\n", + "\n", + "plt.colorbar(a0[3])\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "test = ak.zip({\n", + " \"tx\": slopex,\n", + " \"ty\": slopey,\n", + " \"x\": x,\n", + " \"y\": y,\n", + " \"qop\": qop,\n", + "})\n", + "test = np.column_stack([ak.to_numpy(test[feat]) for feat in features])" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "tuner", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.12" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/trackinglosses_rad_length_endVelo.ipynb b/trackinglosses_rad_length_endVelo.ipynb new file mode 100644 index 0000000..0ed6969 --- /dev/null +++ b/trackinglosses_rad_length_endVelo.ipynb @@ -0,0 +1,317 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 55, + "metadata": {}, + "outputs": [], + "source": [ + "import uproot\t\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from mpl_toolkits import mplot3d\n", + "import awkward as ak\n", + "from scipy.optimize import curve_fit\n", + "from methods.fit_linear_regression_model import fit_linear_regression_model\n", + "import sklearn\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "40402 10099\n", + "50501\n" + ] + } + ], + "source": [ + "file = uproot.open(\n", + " \"tracking_losses_ntuple_B_default_radlength_endVelo.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) & (allcolumns.fromB)]\n", + "\n", + "print(ak.num(found, axis=0), ak.num(lost, axis=0))\n", + "print(ak.num(electrons, axis=0))\n", + "# ak.count(found, axis=None)" + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "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", + "rad_length_lost = ak.to_numpy(lost[\"rad_length_frac\"])\n", + "eta_lost = ak.to_numpy(lost[\"eta\"])\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": 58, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.hist(\n", + " rad_length_lost,\n", + " bins=100,\n", + " density=True,\n", + " alpha=0.5,\n", + " color=\"darkorange\",\n", + " histtype=\"bar\",\n", + " label=\"lost\",\n", + " range=[0, 1],\n", + ")\n", + "plt.hist(\n", + " rad_length_found,\n", + " bins=100,\n", + " density=True,\n", + " alpha=0.5,\n", + " color=\"blue\",\n", + " histtype=\"bar\",\n", + " label=\"found\",\n", + " range=[0, 1],\n", + ")\n", + "plt.xlim(0, 1)\n", + "# plt.yscale(\"log\")\n", + "plt.title(\"radiation length fraction endVelo\")\n", + "plt.xlabel(f\"$x/X_0$\")\n", + "plt.ylabel(\"a.u.\")\n", + "\n", + "plt.legend()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 59, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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owiAn9o9zYt9cyY4lX8yOffSd12jQHvxNduiTf/29eyvzLTdmx57LieVIklN7awsAALA75y/uIvPjQyn3zrVSc35LezU1/Rvj3uHEWz+WXt2tx9LTz9+eng4AhXk5in9tktc0AQAAAAAAR8CRXxlx1113xbFj6U9MLCwsxMLCQsEtAgCAvVlZWYmVlZXU2MWLu3mCGb7OmAkAmGQHrw98dDewPvKTEWfPno1arVZ2MwAAYN/y/jDc6/ViZmam4BZxGBgzAQCTTB/44DjykxEAAAAAAHCwtV890lwqsiGZTEYAAAAAAEAhLsd4Xpv0j1490vxZRPy3Y6hzd0xGQBGyJiUjImZzYg/kxH78ndmxT2SH3vrxp3MKzfFfZ4de+fE3Zwe/J6fM33g2M5Qk35UZq1Q+kxM7nVPmqZzGAADAEXdrRn/5fFYf+/aUtMcz8p4cTvpAes7HM8ZPzeSmobQHHsyo7mcz0gGA0piMAAAAAACAQhzdDazfUHYDAAAAAACAw83KCAAAAAAAKMS49oy4Vp3lszICAAAAAAAYK5MRAAAAAADAWHlNExTh/8yJfTUn9hc5sed+Mzv25Hdlhv4y3p1T6EZm5MFP5Jz2z3Jip3NicWNmpFL5+bwTMyXJqT2dd6XOvMa+J6fOe/ZcJwAATL7b05Nnjw2ntTPyxpmhlK/c95OpOVv3p5dw0w3PDCf+3cX0zJ9OaVtExB2PpyTW0/PuQtZYYj/jEwAOKxtYAwAAAAAAjIWVEQAAAAAAUAgbWAMAAAAAAIyFyQgAAAAAAGCsvKYJAAAAAAAKcXQ3sDYZAUX4iZxYkhM7l1fod2WHbssOffi3H8uM/VlObfd9NDt2/4dyTvw3ObH4fF5wTyqV05mxJDl1jbPfkxP7UgntAQCAAp3v7i5/O62ve3LHpz8SX86IrKamHvvEcNpvf+TO1Lz/oPKvMsp+fCilUhlOi9hdfz0rb+XBlMT708cImWWkjiluzyijnpoOAJPAZAQAAAAAABTCyggAAAAAAOBA+rcR8VsZsReLbEgmkxEAAAAAAFCIl2M8KxXufPVIcz4i/sUY6tydN5TdAAAAAAAA4HA78isj7rrrrjh27FhqbGFhIRYWFgpuEQAA7M3KykqsrKykxi5evFhwazgsjJkAgEmmD3xwHPnJiLNnz0atViu7GQAAsG95fxju9XoxMzNTcIs4DIyZAIBJdvD6wDawBsYp7zPv3BezY9/8zuzY5/9dduxff0dm6Jc+fndm7L5/9Wh2mX+eHYq/yIl9/tmc4B/nxN6RE8srM1ulcvoaOd6TE3vvnspNklPXqBMAACbF4+nJt6b3aZN+fSitUulmlH37UMrHPttIzdlMfio1/eIN9w6l/U8f+V8z6vvO1NQkGW5z4T6dfj2zxhVpY4rs6wwAk+vI7hnx4osvXvVfDr5Lly7FAw88EJcuXSq7KYzUSxHxK1H8jDHj4mf1cHJfDx/39PDR/2W3fM+Uw+dvOVz3crju5XDdy+G6l2My+zOX4+ubWBd1XC7kK7sWkxET9Y3Ifly6dClOnz7tQ/3QeSki/o8wGXF4+Fk9nNzXw8c9PXz0f9kt3zPl8PlbDte9HK57OVz3crju5dCfmSxHdjICAAAAAAAohj0jAAAAAACgEEd3A2srIwAAAAAAgLGyMgKK8KWcWOWd2bHf+GLOed+RHTuRHfrBv/6FzNgH/8XvZcb+Kr4pu9D/PTsUcWNO7PbMSJJcuS7PPvts3HRTxDPPNOLGG6+UValkX9Ak+aHMWKVyOq+h1/CZfZybbq/tSZJTI24JAABEZPbPz6f3WyuVk0NpSVJPz/vgcFrz/T+VmvcfxyOp6Wf+bjjtD37lH6TXF1/bcTvi/tMR8UJERNx001JEvCkisvvdlepw2k1/+tXUvD9133AZ9861UvNmSRs3GBMcfFnjQfcWjoKtDayLrrN8VkYAAAAAAABjZWUEAAAAAAAUwp4RAAAAAAAAY2EyYkxWVlYObPkHue3jNta2J2O+LufHWP7vHdx7Oo69IF7Lz2o5XPfiyx431734ssfNPS2vfCjLKL+3J7GsSf3ZHW27Rth3/v0RXffOSIoZuUs/94sjLG101933e/FlTWKbRm0Sv8ZJbNOoTeLXOIltOlp+PyLOZBy/VGK7vs5kxJgc5EHqQW77uI237WO+Ln9pMiKdyYiyyh8n1734ssfNdS++7HFzT8srH8oyqX+gmMQ/wIzSxE5G/MGIrnt3JMWM3IufMBlRhsP+2eC6F1/OqMsapUn8GiexTZNpawPrUR/fGRH3ZRwfKeQru5aJ2zOi3W7H0tJS9Hq9qNVqsby8HPV6/Zrn9Xq9WFpaimq1GoPBIBqNRszOzhbQYtiBf5QT+7nffPV/no9IfvPqWOW7ss9LvpgdO/fO4bQXIuJcxCun35x52ic//n2ZsdloZ9f3yhsjBlnBZ7PPiz/JjFQqW7GvRUTETTd1IuL6iIhIkh/KOe90ZixJTg2lve99j8TnPnfq1XMfyWnrO3LKzW7P+96XXWZaewAAdsK4if3K6ze/3la/9bV95ytlpM0EZHwf/uzr/r0Z8b74XGrW92akf+hjw2mP/HJEnEuvMtUTO8+a/vVFRNw+lPLMda3XpVyIZ65rxb2zr0+PiPWMYm9NHx+8901XX3cOh1GMB/fycwxFyvoe9f14dE3UZMSZM2ei0+lEs9mMjY2NOHPmTDQajeh0Orkd636/HzMzM7G+vh61Wi0iIqanp2NzczPm5+eLaj4AAMDYGTcBABxkNrCeCE8++WR0Op2Yn5+P5eXlWF+/8rjA8vJy7nnNZjPq9fp2hzoiYnFxMZrN5ljbCwAAUDTjJgAADqKJmYzodrtDnedarRa1Wi36/X7meYPBILrdbjQajavST5w4ERERq6uro28sAABACYybAAA4qCZmMqJer0e1Wk2NZaVHRJw7dy41z9bTPp1OZ0QtBAAAKJdxEwDAQTeOzat3cpRvovaMSNPv93OXDW89/TM1NZUbf70XXnghIiL+6I/+aF/te+Mb3xhvfOMbh9IvXrwYvV5vX2XnGWf5B7Xtzz//fEREPPXUU3HDDTeMvPyIfbT9P+QF/+LV/774mv9/VZJX119nh176m+G05GLES73ctny599XM2OfiUk59lyO+nNXW57PPe/3Xm+qFV//bj4g3RURc4x58OTOSdt7V9/R8Trl/t6ty08sfLT+r2Vz34sse93113Ysv2z0tp+y88l988cV48cUX91zuVr93qx/M4TKOcdOox0yj/NmZxLJGVc6oP3+z25Xdb369rfOHy/rzlLzH0wt5/XAiuRh/1fvb1Kx/HJdT07+cMsy5+HJELyU94v9Nb8eFN6WVHFfGYxERX42IrTH+8Nd3xfUZZbzWy1fSLqRc+5yhVZqLie/3nTrMnw3pZe3+5zjCdS+rrKN53dO/R3dS11ZZh7MPnP777/DVmSKZYGtra0mtVsvNc/LkySQikvX19aFYRCTVajX1vIcffjiJCIfD4XA4HA6H40gdDz/88Ej66kyOcY2bjJkcDofD4XAclmMS+sBf+MIXkuuvv760a3D99dcnX/jCF0q9BhO9MmJpaSnW1tZy80xPT0dExObmZmo8a6nyhz/84Xj44Yfj7W9/e7zpTWlPRuxM1soIAAAYpf0+FfbCCy/EV77ylfjwhz88wlYxCcY1bjJmAgDKdpj6wO9617vi85//fPzt35azSuGWW26Jd73rXaXUvWViJyMWFxfjoYceyn3vacTXO82DwSA3/nq33HJLfP/3f/++2ggAAFCmcY6bjJkAAEbrXe96V+kTAmWamA2sX2t1dTUajcb2Zmp5Tpw4ERHD7zjd+vfMzMzoGwgAAFAy4yYAAA6SiZuMaLfbERFRr9evSs/a2GRqaipqtVp0Op2r0rvdbkRE3H333WNoJQAAQHmMmwAAOGgqSZIkZTdiS7fbjcXFxWg2m1elr6+vx8zMTMzPz0e/349GoxGtVmu7493r9WJmZiY2Nja2lxdPT09Hs9mMkydPFv51AAAcFf1+/5qvhwFGy7gJAA4v/WsOs4nZM6LX60Wj0YiIGOpUR0RcuHAhIq6843Rzc/Oqd53WarVYX1+PxcXFqFarsb6+HlNTU/H0009Hs9mMRqMRs7OzO2rD0tJSVKvVGAwGOz6P8dvPvRkMBrG0tBQREcvLy+NsJru01/vabrdjaWkper1e1Gq1WF5eHnoqkHKM4p5Wq9Wr/nBC+Ubx+7Hb7cbc3Nz273PKtZ97WqlUrvr3Vj+Mco3i57Tf728/bT8/Px9TU1NjaCn7Napx05vf/ObodrvxLd/yLbGxsRHtdtuYqQDGNeUw7iiHsUE59N3LoX9dDn3gAyg5ZDY2NpKISNbX17fTqtVq0mq1xnIe47efe9PpdJLZ2dkkIpL5+flxNpNd2ut9XV5eTur1etJqtZKTJ08mEZFERNLpdMbdZK5hr/e01Wol8/PzSafTSTqdTlKr1ZKISDY2NsbdZHZgVL8fq9VqMjU1NermsQf7uadbP6/Ly8vbx2vLoRz7/Tnd2NhIZmdnk3q97rP3iDBmKodxTTmMO8phbFAOffdy6F+XQx/4YDp0kxH1ej2p1+tXpbVareRa8y57PY/xG8W90WmfPHu9r7Ozs1f9e319PYmIobIo3l7v6fLy8lX/3rqna2trI28juzeKz+CTJ08m9XrdgGZC7Oee+qydTPu5p+vr68nU1JR+0hFjzFQO45pyGHeUw9igHPru5dC/Loc+8ME0cRtY78dgMIhut7u9bHnLiRMnIiJidXV1pOcxfu7N4bTX+9rtdoeWpNdqtajVatHv98fTWHZkPz+rr39H9daSyFqtNtpGsmuj+Azudrvx1re+1f2cEPu5p+12O86dOxdzc3N+/06Q/dzTwWAQd9555/YrMDgajJnK4fqVw7ijHMYG5dB3L4f+dTn0gQ+uQzUZce7cuYiIoU1etj5EO53OSM9j/Nybw2mv97Ver2du4mRzp3KN8me13W7H8vKyezoBRnFfW62WTVEnyH7uaafTicFgEO12O5rNZtx8883R7XbH11h2ZD/3dHFxMQaDgXfPHzHGTOVw/cph3FEOY4Ny6LuXQ/+6HPrAB9ehmozYekIha6ORrCcY9noe4+feHE6jvq/9fj/m5ub22yz2YVT3dHFxcXvzKcq33/u6uLiogzdh9nNPW61WJEkS6+vrMT8/v71BnN/F5drPPd16YqzT6cTMzEzcfPPN7ukRYMxUDtevHMYd5TA2KIe+ezn0r8uhD3xwHarJiI2NjYiIOH78eGp8MBiM9DzGz705nEZ5X9vtdlSr1Zifnx9F09ijUdzTM2fORL/fj8FgYJnqhNjPfe31evHWt77V4HHCjOJntVarRavVirW1tYi4MnClPHu9p71eLyKu3M9msxnr6+uxvr4e/X4/pqen9bEOMWOmcrh+5TDuKIexQTn03cuhf10OfeCD61BNRkxPT0dExObmZmo860N1r+cxfu7N4TTK+7q0tLT9C5vyjOKenjx5MtbW1qLT6cTU1JSncibAfu7r0tKSJd4TaJSfv7OzszE7O7vdoacce72nW09+NZvN7TyvfW/u0tLSqJvKhDBmKofrVw7jjnIYG5RD370c+tfl0Ac+uA7VZMTWN1HWLNa13vm42/MYP/fmcBrVfV1cXIyHHnrI98EEGOXPar1ej/n5eUskJ8Be7+vi4uL2MtfXHhFx1f9TvFH/Xm00GpkDAIqx13uataS9Xq9HhFfGHGbGTOVw/cph3FEOY4Ny6LuXQ/+6HPrAB9c3lN2AUdraMf313zhb/56ZmRnpeYyfe3M4jeK+rq6uRqPR2N6ciHKN+mf1tttuM9ibAHu9r91uN86cOZMam56ejlqtFuvr6yNsKTs1jt+rW2VSjv32f7eWuL9e1pJ3Dj5jpnK4fuUw7iiHsUE59N3LoX9dDn3gg+tQrYyYmpqKWq02tGP61k70d99990jPY/zcm8Npv/e13W5HxNdnrrdYylieUf+s9vv9oftL8fZ6X9fX1yNJkquOkydPxtTU1PYGbZRj1D+rnU4nms3myNrH7u2n/1uv17fzbdl6uswfRg8vY6ZyuH7lMO4oh7FBOfTdy6F/XQ594AMsOWTW19eTiEg2Nja206rVarK8vLz9742NjaRarSadTmdX51GOvd7TLRcuXEgiIpmfny+kvezMXu9rp9NJarVa0mq1rjrm5+eTVqtV6NfA1fZyTy9cuJDMzs4ma2trV+Wp1+vFNZxc+/0M3nLy5MlkampqrG1lZ/ZyT9fX15NarXZVnrW1Nb9bJ8R++7+vTVteXk5qtVoxDac0xkzlMK4ph3FHOYwNyqHvXg7963LoAx9Mh+o1TRGxvXxscXExqtVq9Pv9WFxcjPn5+e08g8EgNjc3r3qv2E7Ooxx7vacRV55Y2dqE5tFHH41GoxH1ej3zHXEUZy/3tdfrRaPRiIhIfVLgwoULhbSddHu5p1NTUzEYDOKHf/iHo9VqRaPRiGq1OvR0A+XZz2cwk2kv97Rarcbx48djaWkpOp1O1Gq1aDQa279jKdco+r9ra2vbn8megDz8jJnKYVxTDuOOchgblEPfvRz61+XQBz6YKkmSJGU3AgAAAAAAOLwO1Z4RAAAAAADA5DEZAQAAAAAAjJXJCAAAAAAAYKxMRgAAAAAAAGNlMgIAAAAAABgrkxEAAAAAAMBYmYwAAAAAAADGymQEAAAAAAAwViYjAABgAvX7/bKbAAAAhdH/PfxMRgAAwASam5uLwWBQdjMAAKAQ+r+Hn8kIAACYML1eL6rVakxNTUXElafEFhcXo1KpRKVSiZtvvjnOnDmznb/b7cb09PR2rNvtltRyAADYPf3fo6GSJElSdiMAAICvazabMTc3F/V6/ar0ubm5aLfbMTs7G2tra0Oxfr8f6+vrRTYVAAD2Tf/3aDAZAQAAE2Z6ejo2NjaG0nu9XszMzMTU1FRcuHBhO73dbsfi4mLqOQAAMOn0f48Gr2kCAIAJ0m63h54I21Kr1aJWq8VgMNheit7r9WJxcTE6nU6RzQQAgJHQ/z06TEYAAMAEeeSRR6LZbGbGt2KtVisGg0HMzc3F2tpaVKvVopoIAAAjo/97dJiMAACAMdh6Yuvmm2++anA1NzcXN998c/T7/aFzBoNB9Pv9qNVqmeXefffdEXHlCbI777wzlpeXc/MDAEAR9H+5FntGAADAGDUajeh2u5EkyfbGfP1+P+bn54fyrq6uxmAwiJMnT+aWmbeRHwAAlEn/lyzfUHYDAADgMJubm4tutxvNZjMWFxdzl5O3Wq147LHHrlnmVhm9Xi8zT7PZjOnp6Xj66afjtttui9nZ2d03HgAAdkn/lywmIwAAYIy2NuObmprKHYj1+/04fvx4TE1N5ZbXbrej2+1GtVqNfr8fvV5vaJn63NxcVKvV7SfMZmZmYmpqKnNjQAAAGBX9X7LYMwIAAMZoawCW9o7c12q1Wrkb90V8/T28jz322FUb+b1Wv9+Pdrt9VVn33HNPLC8v76X5AACwK/q/ZLFnBAAAjNHi4mJ0u90YDAaxsbGRmW96ejo3PhgMYmZmJtbW1qJWq8VgMIibb745IiJe26Vvt9sxNzd3VVq3241GoxEXLly45pNnAACwH/q/ZLEyAgAAxqTdbkej0Yhmsxn9fn/76bDXPyXW7XavuYT8zjvvjFartb0kfWpqavs9uO12ezvfk08+OTTgOn78eEREbG5u7uvrAQCAPPq/5DEZAQAAI9Tv9+PMmTPRbrdjc3Mz6vX69kCr1WrFmTNntgdHW661RL3RaES1Wh0asDUajYiIWFpa2k4bDAZD5b+2bQAAMEr6v+yUyQgAABihXq8XS0tL8eSTT8b8/HxEXHlv7uzsbKyurka9Xh96cittE76IK0+MTU9PR7fbjV6vd9UTYO12e/t9ub1eL+bm5rbzZz0BlreBIAAA7IX+LztlzwgAACjR6upqDAaDOHny5EjKy3tnrq4/AABl0/89ur6h7AYAAMBR1mq14rHHHhtZeVtPmPX7/e0nwbKePAMAgKLp/x5dXtMEAAAl6ff7cfz48aFl6/uxtST+tUvaH3nkkVheXh5ZHQAAsBf6v0eb1zQBAEBJFhcX47bbbovZ2dmRl91sNmN6ejqefvrpmJ6e3n5/LwAAlEX/92gzGQEAACWZm5uLtbW1spsBAACF0P892kxGAAAAAAAAY2XPCAAAAAAAYKxMRgAAAAAAAGNlMgIAAAAAABgrkxEAAAAAAMBYmYwAAAAAAADGymQEAAAAAAAwViYjAAAAAACAsTIZAQAAAAAAjNX/D88I19j1r7eLAAAAAElFTkSuQmCC", + "text/plain": [ + "
" + ] + }, + "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": 60, + "metadata": {}, + "outputs": [], + "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", + "})" + ] + }, + { + "cell_type": "code", + "execution_count": 61, + "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': -6.713410801676869e-05, 'ty^2 qop^2': -2.7595247487299285e-06, 'ty qop^3': -1.972159538523153e-08, 'qop^4': 3.222747801976328e-10, 'x^5': -3.2453151277422876e-11, 'x^4 y': -7.538414337204813e-13, 'x^4 tx': 9.439335535432747e-08, 'x^4 ty': 1.5053041968471348e-07, 'x^4 qop': 2.0621745686622006e-06, 'x^3 y^2': -6.107114813858061e-12, 'x^3 y tx': -1.4186378294311908e-07, 'x^3 y ty': 9.335861888004615e-07, 'x^3 y qop': 4.389303884896889e-06, 'x^3 tx^2': -0.00010056331721829473, 'x^3 tx ty': -0.000368816675172811, 'x^3 tx qop': -0.0031371932512015794, 'x^3 ty^2': -0.0006215843383633401, 'x^3 ty qop': -0.0040682949745989485, 'x^3 qop^2': -0.01783210821802376, 'x^2 y^3': -2.1280754936015e-12, 'x^2 y^2 tx': -9.287590581052996e-07, 'x^2 y^2 ty': -4.159245552415314e-07, 'x^2 y^2 qop': -3.762232883230965e-06, 'x^2 y tx^2': 0.0003560931930516667, 'x^2 y tx ty': -0.00014974182212146823, 'x^2 y tx qop': -0.002168783048351754, 'x^2 y ty^2': 0.0005557263565684947, 'x^2 y ty qop': -0.0027368181509398255, 'x^2 y qop^2': -0.021251765155328187, 'x^2 tx^3': 0.04636097936049924, 'x^2 tx^2 ty': 0.19264721978393362, 'x^2 tx^2 qop': 1.1201214250508411, 'x^2 tx ty^2': 0.2717131602944406, 'x^2 tx ty qop': 1.0069243735131281, 'x^2 tx qop^2': 0.0017781753799746996, 'x^2 ty^3': -0.17716467804753896, 'x^2 ty^2 qop': -0.7967627886628681, 'x^2 ty qop^2': -0.020752836202331357, 'x^2 qop^3': -0.0003102149541338055, 'x y^4': -3.716849050761084e-11, 'x y^3 tx': 4.2096182362172385e-07, 'x y^3 ty': 9.343710843934261e-08, 'x y^3 qop': -4.85417695898771e-06, 'x y^2 tx^2': 0.0007723490259714336, 'x y^2 tx ty': -0.000465174042059667, 'x y^2 tx qop': 0.0083072051829175, 'x y^2 ty^2': -0.0001514875700367028, 'x y^2 ty qop': 0.0033210944559410355, 'x y^2 qop^2': -0.0016097785846472324, 'x y tx^3': -0.18644890104309755, 'x y tx^2 ty': -0.04212197105444866, 'x y tx^2 qop': 1.0626013948995083, 'x y tx ty^2': 0.09950829907042329, 'x y tx ty qop': -0.7011822284857461, 'x y tx qop^2': -0.02153879411999937, 'x y ty^3': 0.07920758108734982, 'x y ty^2 qop': -1.3677505196768507, 'x y ty qop^2': 0.005572049947548557, 'x y qop^3': 0.0003625259165958029, 'x tx^4': -7.806580145878633, 'x tx^3 ty': -0.48543671938056426, 'x tx^3 qop': 0.01223464815691587, 'x tx^2 ty^2': -0.7759844557140754, 'x tx^2 ty qop': 0.004830189695310542, 'x tx^2 qop^2': 6.943806457826217e-06, 'x tx ty^3': -0.2626842333297885, 'x tx ty^2 qop': -0.002218311914437031, 'x tx ty qop^2': -5.935445430100284e-05, 'x tx qop^3': -4.652385512477656e-07, 'x ty^4': -0.13391527053501218, 'x ty^3 qop': -0.004907072655857102, 'x ty^2 qop^2': 1.6619362532345914e-05, 'x ty qop^3': 8.142368541762968e-07, 'x qop^4': 4.653376619669799e-09, 'y^5': 3.825384453648439e-12, 'y^4 tx': -4.679158727149968e-10, 'y^4 ty': -5.908476596871992e-09, 'y^4 qop': 2.1690478767266708e-08, 'y^3 tx^2': -9.496214334184586e-05, 'y^3 tx ty': 7.42067773199526e-05, 'y^3 tx qop': 0.004042069781436391, 'y^3 ty^2': 1.3648094370263125e-06, 'y^3 ty qop': -9.095584346929984e-05, 'y^3 qop^2': 0.0026650743754950902, 'y^2 tx^3': -0.22830126100028328, 'y^2 tx^2 ty': 0.07976968562385643, 'y^2 tx^2 qop': -0.569375473311322, 'y^2 tx ty^2': -0.05752462027849215, 'y^2 tx ty qop': -1.3821142344540522, 'y^2 tx qop^2': 0.0030041051075548795, 'y^2 ty^3': 0.0007092199317686958, 'y^2 ty^2 qop': 0.08213843039582976, 'y^2 ty qop^2': -0.009533342523832138, 'y^2 qop^3': -4.410702217968634e-05, 'y tx^4': -0.4617640318570093, 'y tx^3 ty': -0.7726015660797079, 'y tx^3 qop': 0.004989400779050441, 'y tx^2 ty^2': -0.2543425991684884, 'y tx^2 ty qop': -0.001971807200332014, 'y tx^2 qop^2': -6.047144105356334e-05, 'y tx ty^3': -0.14996432803062354, 'y tx ty^2 qop': -0.00494485016520198, 'y tx ty qop^2': 1.250565568496303e-05, 'y tx qop^3': 7.906173456988411e-07, 'y ty^4': 0.023264047526106668, 'y ty^3 qop': 0.0014038996016424031, 'y ty^2 qop^2': -2.8280611787753928e-05, 'y ty qop^3': 3.0725068466100624e-07, 'y qop^4': -4.09432863754477e-09, 'tx^5': -0.052446331725343374, 'tx^4 ty': -0.0031642363210890884, 'tx^4 qop': 5.831594421042545e-05, 'tx^3 ty^2': -0.005193740457310502, 'tx^3 ty qop': 1.5211405438268112e-05, 'tx^3 qop^2': 7.987434860389955e-09, 'tx^2 ty^3': -0.0017266713029734364, 'tx^2 ty^2 qop': -3.2665518364513855e-06, 'tx^2 ty qop^2': -1.2886641260179494e-07, 'tx^2 qop^3': -7.431322766916425e-10, 'tx ty^4': -0.0009711418007784676, 'tx ty^3 qop': -1.1566774851255521e-05, 'tx ty^2 qop^2': 3.07405227672554e-08, 'tx ty qop^3': 1.5258831556369568e-09, 'tx qop^4': 6.567772758657935e-12, 'ty^5': 0.00011456709116180268, 'ty^4 qop': 6.736678103390532e-06, 'ty^3 qop^2': -6.391677860197088e-08, 'ty^2 qop^3': 8.915568061124554e-10, 'ty qop^4': -6.626331897050053e-12, 'qop^5': -2.2660425734485018e-13}\n", + "r2 score= 0.04993524171391073\n", + "RMSE = 0.12351226258714283\n", + "Type \n" + ] + } + ], + "source": [ + "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", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 62, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "nbins = 100\n", + "vmax = 50\n", + "\n", + "a0 = plt.hist2d(\n", + " xx0_test,\n", + " xx0_predicted,\n", + " density=False,\n", + " bins=nbins,\n", + " cmap=plt.cm.jet,\n", + " cmin=1,\n", + " vmax=vmax,\n", + " range=[[-0.1, 0.6], [-0.1, 0.6]],\n", + ")\n", + "plt.plot([-0.1, 0.6], [-0.1, 0.6], marker=\"\", alpha=0.8)\n", + "plt.xlabel(f\"True $x/X_0$\")\n", + "plt.ylabel(f\"Predicted $x/X_0$\")\n", + "plt.title(f\"found rad_length_frac\")\n", + "# ax1.set(xlim=(0,4000), ylim=(-1000,1000))\n", + "\n", + "plt.colorbar(a0[3])\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "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 +}