{ "cells": [ { "cell_type": "code", "execution_count": 15, "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": 16, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "41978 8523\n", "92337\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[\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", "notelectrons = allcolumns[\n", " (~allcolumns.isElectron) & (allcolumns.fromB) & (~allcolumns.lost)\n", "]\n", "\n", "print(ak.num(found, axis=0), ak.num(lost, axis=0))\n", "print(ak.num(notelectrons, axis=0))\n", "# ak.count(found, axis=None)" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [], "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\"])" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "data": { "image/png": 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pzX8Sf5/e7u4HNXf4o0zamydsezaT9v1M2plM2pOZtDuZtDcyaZ/MpH2YSbtba7uyvJxMK4r1TJ65burdmtvl5MpeVy6Wj2bS8rGU5QvJtKJ4sdY+y/JSJs/cccpJl6MortbKsf52dctQT/123/z+ck6KpW6+w+yz6TzbKEP++Ka/g7nv7qiN+tgOs79Rf59G3Z7y/Xa98yunl3HTdFovesc8f1G+VrnuX/zz3+0/43cSy/+/xPLfe6532TcT46PPPl65uLoPTY1pqpZXj2NSfd4g54HUuoP0p+k4Xho6j8HkxonHDVLuQc+r1XkMUhcVbW5ATRzXwfXWf6osVfXRRLkHzaOqngb9Xg3SZqrWbWJ/KeNpB/0ZtN8Y5Ni2daya2N+gmoi5iXZOs3Z2dmJjYyO63W50Op2eH+ccKh65Fup0OrG3t9doLFP1zpDFxcXkbNDDy3d3d+Pg4CB2dnZiZWUlnnjiiWPPx71161bPNhFx9Eum3d3dvtYBAACYNMZNAACn1P0xfRKuXr0a29vbsbKyEpcuXYputxtLS0s977I7/AHN5ubm0ef69evD18cjpurOkJT9/f1YWVk5+vf29nZsb29Ht9uN7e3tuHbtWiwtLcXt27djfn7+6NdQc3Nzyfz6Weck77//fvzoR7k7DPI+/vGPx8c//vHa2wMAQD/u3bsX9+7dq739+++/32A0tGUSx03GTADA+HwY/T4hpOp6xTXwyZp6dGtTpn4yZGdnJ+bn5ytvve50OrG9vR1LS0tx/vz5WFtbixs3bsTt27cjIuLs2erHGR0cHPS1zkm+9KUv9VmKapcvX44rV64MlQcAAJxkY2Mj1tc9JmCWTeq4yZgJABifv4yI1/ta81Of+h/aDaVJE/JC86Ye3dqkqZ8M2djYiBs3bmTXWV5ejuXl5eh2uxER8dRTT0VExJ071e9amJ+f72udk7z++uvx+c9//sT1UvzCCQCAUfja174Wzz//fO3tv/Od7wz9R23aNanjJmMmAGB8/mlE/GZfa/7wh1/rWeYaOK/qvSCHUo9u3dnZOfphTm77uqZ6MmRtbS2uX7/e18TEw88iO1w/9Sul+fn5vtY5yWOPPRaPP179QjkAAJgUwz5q6LHHHmswGpo2yeMmYyYAYHx+Jfr983jV9crsXAPf+8WnrsEeFzboo1ubNLWTIYeVcvhivn6cO3fu2H8ffX7t4b8XFhb6Wodm3Yj/Mpl24b/7fzNb/n0m7S8yadW/Xnvg1zNpZzJpEREfZNI+WjPf3PML7yZTyvJyMq0oco/CyHUN6Vjy+3sxk2dd6TjL8oWaeaaPQ1leqplnWu445OrzJLny54998+q2w2HKX0eujdZtT0VxNZNnve9L/badNuq6bmufddt2G+0w/x0c7fGtW75R9xVtGUf7rmOS+m1mi3HT5FkvisrlK+WnepZd+ee/W53J5deql5/7cu+ytxOBlG9WL//mW4kNKrL4XsX+IqK6iKlAqpY/2XcMg6+fiqNXUbxUubwsn+t7eaoPrzo/tdnfD3I+bOLcmaqjtoznerbqeA/WZqo00WZS9VGdRyrmUbeZ0eYxaN21JdU2quIb5Lim9zdY+QaJoy2D7m/UbXc2bETEaMYcdR7d2qSPNJrbiOzs7ERE7602h7dzV9nd3T2acZqbm4tOp9PzUpbDX0BduHChr3UAAAAmlXETAAAn+1pE/HCIT3/vXYmo9+jWJk3dnSE3b96MjY2NWFlZiWvXrh0t39vbO/rV0Ve/+tV49tln49KlB7/i3tnZibNnz8by8vLR+tevX4+FhYXY398/ut1mc3MzNjc3Y25uru91AAAAJo1xEwAA/fn4Lz519fe4sLqPbm3SVE2GdLvdWFpaiog49lyxQ++9915ERJw9ezY2NjZid3c3Op1OLC0txfb29rF1O51O7O3txdraWszPz8f+/n6sra0du0Wnn3UAAAAmiXETAACTZJhHtzZpqiZDOp1OlGV54nqP3qKdy++k23L6WQcAAGBSGDcBADApco9uTU2OPPzo1iZN1WQIAAAAAAAw+Zp6dGtTTIYAAAAAAACNafLRrU0xGcJI/efxvyfT/qL4F5kt/y6TdjeTdiaT9nYm7UeZtJP8OJOW+8q9O8Q+qxXFi8m0srxcM8+rmbT1zJbpsudiyeeZO75puXqpKx9nWv2y18+37rFvQ/12mK6bNuo0/116IZN2qVYsoz5GJ9XLJMVTt20P832qY5KO76zvb1z7HKVZLx/MsvWi6Fn2F+Vrlete+cLv9i68lTifnEv0C7fe7F1WPF29bmp5+WTFwtzYqcogeVyoWPbtyjXT59eq/aVUr1uV96D9b3V8qf29VLH0i5XrluWXh4yh3XNJ9T6HL8t0qm7ng7SvQY5Vat1B2kFZPtf3/h7k3dt2B81jEjRRd20aZH9txjxsHE3EMCnHZHp8GBEfjGGfvZp+dGsTPjKyPQEAAAAAAIyBO0MAAAAAAGDqfRipOzXa3ed0cGcIAAAAAAAw00yGAAAAAAAAM81jsgAAAAAAYOpNzgvUJ5HJkBY988wzcebMmcq01dXVWF1dHXFEo/GJH72XTLv7qS9mtvxfM2l3kyll+YfJtKL4eibPnB/X3C4i97UqyxeSaUXx4hD7bFZRrCfTyvJyze1yZa+3v5w2ypBTP870ca+b58n7bL6+66obS/12WPfYN39iH3Vdt6WNckxLnnX7iza08b2epjY6SeeRWba1tRVbW1uVaXfvpq/VIGdWxkzrRVG5/Mrvlb0LizcTufyoYlli/HTrpUQeb/cuKp/sf92IiEit3ytR7Mo8yvK5RB6DnE9T48lUWXql4hhEUbyWyHv01++jimHQfabqaBCpck/KmKUqjiZia6LcbdZRE9+hWdfEOGFSjvcgqvuCyW7P/XANPFtMhrTo1VdfjU6nM+4wAABgaLk/THe73VhYWBhxRMwCYyYAYJJN3zXw/Rj9nRr3R7y/+kyGAAAAAAAACS9FxJ8m0n46ykCGYjIEAAAAAABIeO4Xnyp/ExH/xQhjqc9kCAAAAAAATD0vUM/5yLgDAAAAAAAAaJM7Q6ilyE2jlf9LJvHdTFrzzbEsn6+1XVGsZ1I/WXufRfFiZsv0LGpZXs7us97+0urur43tcmUoyxcajyWnbiw5ue3y7XD0x6kN+WOfLn8bZagbyyyYpDYxTer3zaNt26fBqPsE4PRaL4rK5Vc+Ww6Qy5OJ5W/3uWxQ1fsry+pHXRTFS33HUZZPJ/L4RsXSL1euWy0Vc3UeVefWVD+eOg8P0u+n4hhWdd0PXpZB1m3ifDdI/Q9iUs7Fg8TRTPsafn9N5D3IPkddR01oM45JabujlurDRq2tPqm6zXxv6Hyb586QHHeGAAAAAAAAM82dIQAAAAAAMPXux+jv1Lg/4v3V584QAAAAAABgppkMAQAAAAAAZprHZAEAAAAAwNTzAvUckyEkFcVfZVJfT6aU5aVMni/WiqUsX8jkuV4zz8u10oaRK0c70p1Rrt7aKn8do66zUZd9Wo5DxOjjqbu/NuJsI89pOvY0a1ra6GnnOwp8vyh6ll0pyuqV33mzevk3vz3AHt+uWPbFxLpvVC5tpn/qjSOVb6qvHDaOsnxuqO2nV1UbaMYgx2TQ4+q8+EvTMmYap0mJeVLimCWT0ne39ze93ny73W4sLFxrZX+0w2QIAAAAAABMPS9Qz/HOEAAAAAAAYKa5M6RFzzzzTJw5c6YybXV1NVZXV0ccEQAA1LO1tRVbW1uVaXfv3h1xNMwKYyYAYJK5Bp4tJkNa9Oqrr0an0xl3GAAAMLTcH6YfPC95YcQRMQuMmQCASTZ918BeoJ7jMVkAAAAAAMBMc2cISWX5n2VSc2m5PF+oF0w2z8uN5zkr1M3kc4xOL8ceJpvvKJwexf+dSvlh76LyzcS6byWWv92bxQD9S1GsVy5vs48aJO+2yjJouZvIYxKkYht1WSa5jhg/7QMm3YfRzp0a3/jFp8q9FvbXDpMhAAAAAABAwoVffKq8GRH/zQhjqc9kCAAAAAAATD3vDMnxzhAAAAAAAGCmmQwBAAAAAABmmsdkAQAAAADA1Lsfo39s1f0R768+kyEAAADMvKJYr1xelperNygfr1hYtSwi4unE8i+fFFZWMrYpNEhZmij3pNTdwO1uyHUBgDSTIQAAAAAAMPW8QD3HO0MAAAAAAICZZjIEAAAAAACYaR6TBQAAAAAAU88L1HPcGQIAAAAAAMw0d4YAAAAAAMDU8wL1HJMhLXrmmWfizJkzlWmrq6uxuro64ogAAKCera2t2Nraqky7e/fuiKNhVrQ1ZiqK9Z5lZXm5Vl4wDO0OYLq5Bp4tJkNa9Oqrr0an0xl3GAAAMLTcH6a73W4sLCyMOCJmgTETADDJpu8a2DtDckyGAAAAAAAACX8eEf9bIu1nowxkKCZDAAAAAACAhK/84lPldkT88Qhjqc9kCAAAAAAATD0vUM/5yLgDAAAAAAAAaJM7QwAAAJgpZXl53CEAAIyBO0Ny3BkCAAAAAADMNJMhAAAAAADATPOYLAAAAAAAmHofxugfW+UxWQAAAAAAABPBnSEAAAAAADD1vEA9x50hAAAAAADATDMZ0oKf/exnx/5Ls+7duxdXrlyJe/fujTuUmaR+26V+26V+26V+26V+26V+2+X6l0FpM2n6q2rqJU3dVFMv1dRLmrqppl7SJvN65n788r0ho/rcH0nJmmAypAWT+UWYHffu3Yv19XWdcEvUb7vUb7vUb7vUb7vUb7vUb7tc/zIobSZNf1VNvaSpm2rqpZp6SVM31dRLmuuZ6eOdIS36/d///fjEJz5Rmba6uhqrq6sjjggAAOrZ2tqKra2tyrSf/OQnI46GWWHMBABMMtfAs8VkSIv+7M/+LH77t3973GEAAMDQcn+Y/ta3vhVf+tKXRhwRs8CYCQCYZNN3DewF6jkekwUAAAAAAMw0d4YAAAAAAMDUO3yBetO+FRF/mUgb9Z0o9ZkMAQAAAAAAEn77F58q/y4i/scRxlKfx2QBAAAAAAAzzZ0hAAAAAAAw9bxAPcedIRNsa2tr5vJpKpYmTFK9NJWP+m03n0mq34jJKtMkxdKUSSrTJMXSlEkq0yTF0pRJKtOk5dME9QvjVbe9jnq7Ybcd5f6mZbu6pqV84+iLp6WM2kyz2w1jWsqozTS7XV3jOIdOS90wJiWNe/3118uIKF9//fWh8nn66acbiWeS8mkijx/+8IdlRJQ//OEPxx7LpOWjftvNZ5Lqt6l41G+78ajfduNRv+3GM0n120Q+6rfdfJq6/uX0GKbN1G2vo96u7rbD9FfTUjfqpdnt1E019VJt2GuiaShj3e20mWrjqJdhth3ldpN0Dby3t1dGRBnxz8qIzRF//lkZEeXe3t64q+FEHpPVh263GxsbGzE/Px8HBwextLQUy8vL4w4LAABgYhg3AQAwyUyGnGB/fz8WFhZib28vOp1OREQ89dRTcefOnbh48eKYowMAABg/4yYAgEngnSE53hlygpWVlVhcXDy6oI+IWFtbi5WVlTFGBQAAMDmMmwAAmHQmQzIODg7i5s2bsbS0dGz5uXPnIiLi2rVr4wgLAABgYhg3AQAwDUyGZNy6dSsiIubn548tP/y10+7u7shjAgAAmCTGTQAAk+LDMX2mg3eGZOzv70dExNzcXDb9UT/96U8jIuJv/uZvhtr/T37yk+h2u0PlERFx9+7dicmniTzef//9iIj4zne+E4899thYY5m0fNRvu/lMUv02FY/6bTce9dtuPOq33XgmqX6byEf95v3kJz+Jb33rW7W3P7zuPbwO5nSpM24aZsz0D//wD0ft9WMf+1h87GMf62u7ut+XYb5ndbYdpr8adRlHuZ16SVM31dRLtWGviaahjHW302aqjaNehtl2kO1+9rOfxc9+9rOIOH590a/JvAb+wSnZZ00lSZcuXSojotzb2+tJi4hyfn6+cruXX365jAgfHx8fHx8fHx+fU/V5+eWX275EZwLVGTcZM/n4+Pj4+PjMymcSroG/+93vlp/4xCfGVgef+MQnyu9+97vjroYTuTMk46mnnoqIiDt37lSmP3ob+KGvfOUr8fLLL8dnP/vZ+NVf/dXa+x/kV04AAFDXw7+Kq+OnP/1pvPPOO/GVr3ylwaiYFnXGTcZMAMC4zdI18Oc+97l488034wc/GM9dGp/5zGfic5/73Fj2PQiTIRmHF+0HBwfZ9Ed95jOfiT/4gz9oKywAAICJUWfcZMwEANCsz33uc1MxITFOXqCece7cuYjofcbt4b8XFhZGHhMAAMAkMW4CAGAamAzJmJubi06nE7u7u8eW37x5MyIiLly4MI6wAAAAJoZxEwAA08BkyAmuX78eN2/ePPYrp83Nzdjc3Iy5ubnxBXYKPfpLM/qn7sbPMWBWaMv1qbvxcwygPcZNwzuNfdRpLHOT1B91nda2c1rL3QR1x6woyrIsxx3EpOt2u7GxsRHz8/Oxv78fS0tLcfHixex6BwcHsbS0FMvLy2OIeLLt7OzExsZGdLvd6HQ6sbm5GYuLiz3rFUVx7N+dTif29vaO/t1PfZ/WYzKqujtt9bu0tHT0C8dH7e7uHmvH2u/JDg4OYmNjIyIe/LHkUaNsp7NY1yfVr754OCfVb4S+eBi5+tUXN6OfPkA/zKD6GTedhmPtHJvm3NjLee0BY4NqrunTXI9Xcx2d5vqXiIgoacTt27fLiCj39vaOls3Pz5fb29tjjGrybG5ulouLi+X29nZ56dKlMiLKiCh3d3ePrbe9vV1evHix3NzcPPo8XLf91PdpPSajqrvTVr+3b98u5+fny83NzXJ7e/voc9iOH6b9nmx3d7dcXl4uI6K8ePFiT/oo2+ks1vVJ9asvHs5J9VuW+uJh5OpXX9yMfvoA/TBtOA3H2jk2zbmxl/PaA8YG1VzTp7ker+Y6Os31L4dMhjRkcXGxXFxcPLZse3u7p0M57ZaXl4/9e29vr4yInrp79N+P6qe+T+sxGVXdnbb63d7eLt97772e5Ycn1Idpv/1LXbyOsp3Ocl2n6ldf3Izc4EtfPLyq+tUXN6OfPkA/TBtOw7F2jk1zbuzlvHacsUE11/RprseruY7u5fqXQ94Z0oCDg4O4efNmLC0tHVt+7ty5iIi4du3aOMKaODdv3uy5Ra/T6USn0zn27MGdnZ24detWnD9/vrLu+qnv03pMRlV3p7F+L168WPm861deeSXOnz9/9G/td3ijbKensa71xe3TF7dHXzy8fvoA/TBtOA3H2jk2zbmxmvPayZyTqulv8vQ5vU5zf+P6l4eZDGnArVu3IiJifn7+2PJOpxMRD567R8Ti4mJPHR16ePnu7m4cHBzEzs5OrKysxBNPPHHsmYb91PdpPSajqrvTWr+POjg4iG63GxcuXDhapv0Ob5Tt9DTWtb64ffri0dIXD6afPkA/TBtOw7F2jk1zbuyf89pxzknV9Dd5+pz+nJb+xvUvDzMZ0oDDWcSqGdaH06m2v79/bBZ6e3s7yrKMvb29uHjx4tFLhA7rsZ/6Pq3HZFR1d1rr91Hf+MY3otPpHKsH7Xd4o2ynp72uH6Yvbo6+eLT0xc14uA/QD9OG03ysnWOdGwfhvHacc9Jg9DcP6HP6c9r7G9e/p5PJkAbcvn07IiLOnj1bmX5wcDDCaKbLzs5OzM/Px8WLF3vSOp1ObG9vx40bNyIiYm1tLSL6q+/TfkzarrvTXr+Hbty4Ec8++2xlmvZb3yjb6Wmv60P64nboi0dDXzy8R/sA/TBtOK3H2jn2OOfGkzmvHeec1D/9TS99Tt5p7m9c/55eJkMa8NRTT0VExJ07dyrTU7diEbGxsXHUsaYsLy/H8vJydLvdiOivvh2TB9qqO/X7y+dJLi8vZ9fTfgc3ynZ62uv6kL64Xfri9uiLm/FoH6Afpg2n9Vg7x1ZzbqzmvNbLOal/+ps0fU6v097fuP49vUyGNOCwkaZm7jTiamtra3H9+vW+6mdpaemok+invh2TX2qj7tTvgxdwPVwXOdrvYEbZTk97XUfoi0dFX9wOffHwqvoA/TBtOI3H2jk2z7mxl/NaL+ek/uhvTqbPOe409zeuf083kyENOHfuXET0PtPt8N8LCwsjj2nSXbt2LZaWlo5eDtSPw3rup74dk+Oarjv1G/HKK6+c+AuKh2m//RtlOz3tda0vHi19cfP0xcNJ9QH6Ydpw2o61c2x/nBuPc17r5Zx0Mv1N//Q5v3Ra+xvXv5gMacDc3Fx0Op3Y3d09tvzmzZsREXHhwoVxhDWxdnZ2IiJicXHx2PLDW+6q7O7uxsrKSkT0V9+OyS+1UXfq90E7Tj1b81Ha72BG2U5Pc13ri0dLX9wOfXF9uT5AP0wbTtOxdo7tj3NjL+e1Xs5Jefqb/ulzjjuN/Y3rXyIioqQRe3t7ZUSUt2/fPlo2Pz9fbm5ujjGqybO7u1t2Op1ye3v72OfixYvl9vZ2ube3V3Y6nWP1duPGjfLixYvH8umnvk/bMRl13Z22+n3YjRs3yrm5uZ7l2u9g3nvvvTIieuqnLEfbTme1rnP1qy8eXqp+9cXNyLXfQ/ri+k7qA8pSP0w7TsOxdo7t5dzYn9N+XjM2qOaaPs31eDXX0dVc/3KoKMuybHJy5TTrdruxsbER8/Pzsb+/H0tLS3Hx4sVxhzUxut1u9lav9957LyIizp8/H7du3Ypz585Fp9OJpaWlnlnbw/xOqu/TdEwODg5GXnenqX4fdv78+Th79mxsb28fWz6OYzCtut1ubG9vx7Vr12Jubi6uX78ei4uLMTc3d2ydUbXTWavrXP3qi4eXq1998fD66R8i9MV19dMHHNa1fpg2zPKxdo6t5tzYn9N8XjM2qOaaPs31eDXX0dVc//IwkyEAAAAAAMBM884QAAAAAABgppkMAQAAAAAAZprJEAAAAAAAYKaZDAEAAAAAAGaayRAAAAAAAGCmmQwBAAAAAABmmskQAAAAAABgppkMAQAAAAAAZprJEAAAGIG1tbUoiiKKoognnnginnjiieS/i6KI/f39cYccERHXrl07Ftv58+djaWkpnnrqqVhZWYmDg4NxhwgAwARy/cukMRkCAAAjcHBwEIuLi/Hee+8dfRYXFyMi4vr16/Hee+9FWZaxt7d3tP4kuHjxYly4cOHo/2/cuBG7u7uxu7sb165di4WFhYmJFQCAyeH6l0ljMgQAAEZke3s75ubmsut0Op24dOnSaALq0+Gv9JaWlo6Wzc/Px+LiYuzv78fNmzfHFRoAABPM9S+T5FfGHQAAAJwGS0tLMT8/39e6KysrLUczmMPB3uEv+Q4dDmwn5ZEGAABMDte/TBp3hgAAwAgsLy/3ve78/HzMz8/Hzs5OLC0txc2bN4+eXbyyshI7OztHzzHudrsR8WDAdv78+aPnGj+s2+0ee9bx2tpa37Ec5j8/P9/zq77DtE6n03d+AACcDq5/mTTuDAEAgAm0s7MTa2trsb+/fzQQm5+fj1u3bsX29vbRM4sPLS4uHg0gH9btdmNtbS12d3eP8j1//nwcHBzE9vb2iXG88sorR/k/mu9hbI+mAQDAoFz/0jZ3hgAAwARaXl4+elzA3NxcbG5uxt7e3tELJquevXz27NmeZV/96ldjc3PzWL5zc3Nx7dq1vl78ePiIgMPnJR8cHMTOzk78zu/8TszNzR0NMgEAYBiuf2mbO0MAAGBCHQ74vvCFL9Tafn9/P7rdbmxsbFSm37p1K/urtoODg6NHAbzyyiuxsbERZ8+ejfn5+djc3IyLFy/WigsAAKq4/qVNJkMAAGDCVf0Krh+HA7kbN27U2v7wV3GdTqd2HgAAMCjXv7TBY7IAAGBG7e/vH/vvoA4fAfDss882FhMAALTF9S85JkMAAGBGzc/PR0T0vFTy0OEv31IO070gEgCAaeD6lxyTIQAAMGZ37twZeJtPf/rTEXH8V2+H/3/4YsjDQdza2trRIwMOXbt2LZv//v5+7O/vx9zcXHQ6nYHjAwCAFNe/jIN3hgAAwJg8Onh71OHyqvTDAdra2lrMzc3F/v5+7O3tRcSDX7QtLS3F7u5uXLp0Ka5evRoLCwuxvLwcX/jCF2J3dzc6nU72BZCHv4o7d+5czdIBAMBxrn8ZJ3eGAADAiN28eTNWVlaOBoMbGxuxtrZ27FduOzs7sb29HREPBnyP/pJtcXExNjc3486dO3H+/Pm4fft2bG9vx/z8fFy6dCk2NzcjImJzczM2Nzdjfn7+KM/z588fpVe5du1arK2tRUTErVu3TvwVHQAA5Lj+ZRIUZVmW4w4CAAAAAACgLe4MAQAAAAAAZprJEAAAAAAAYKaZDAEAAAAAAGaayRAAAAAAAGCmmQwBAAAAAABmmskQAAAAAABgppkMAQAAAAAAZprJEAAAAAAAYKaZDAEAAAAAAGaayRAAAAAAAGCmmQwBAAAAAABmmskQAAAAAABgppkMAQAAAAAAZtr/Dzn7ayAougxXAAAAAElFTkSuQmCC", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "endVeloP_found = ak.to_numpy(found[\"p_end_velo\"])\n", "trueP_found = ak.to_numpy(found[\"p\"])\n", "\n", "endVeloP_lost = ak.to_numpy(lost[\"p_end_velo\"])\n", "trueP_lost = ak.to_numpy(lost[\"p\"])\n", "\n", "endVeloP_notelectrons = ak.to_numpy(notelectrons[\"p_end_velo\"])\n", "trueP_notelectrons = ak.to_numpy(notelectrons[\"p\"])\n", "\n", "stretch_factor = ak.num(trueP_lost, axis=0) / ak.num(trueP_found, axis=0)\n", "\n", "nbins = 100\n", "vmax = 200\n", "\n", "fig, ((ax0, ax1)) = plt.subplots(nrows=1, ncols=2, figsize=(20, 8))\n", "\n", "a0 = ax0.hist2d(\n", " trueP_found,\n", " endVeloP_found,\n", " density=False,\n", " bins=nbins,\n", " cmap=plt.cm.jet,\n", " cmin=1,\n", " vmax=vmax,\n", " range=[[0, 20000], [0, 20000]],\n", ")\n", "ax0.set_xlabel(f\"True $P$\")\n", "ax0.set_ylabel(f\"EndVelo $P$\")\n", "ax0.set_title(f\"found P\")\n", "\n", "a1 = ax1.hist2d(\n", " # trueP_notelectrons,\n", " # endVeloP_notelectrons,\n", " trueP_lost,\n", " endVeloP_lost,\n", " density=False,\n", " bins=nbins,\n", " cmap=plt.cm.jet,\n", " cmin=1,\n", " vmax=vmax * stretch_factor,\n", " range=[[0, 20000], [0, 20000]],\n", ")\n", "ax1.set_xlabel(f\"True $P$\")\n", "ax1.set_ylabel(f\"EndVelo $P$\")\n", "ax1.set_title(f\"lost P\")\n", "\n", "plt.suptitle(\"Momentum at Creation and EndVelo\")\n", "plt.colorbar(a0[3], ax=ax1)\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "a0 = plt.hist2d(\n", " trueP_found,\n", " endVeloP_found,\n", " density=False,\n", " bins=nbins,\n", " cmap=plt.cm.jet,\n", " cmin=1,\n", " vmax=200,\n", " range=[[0, 20000], [0, 20000]],\n", ")\n", "plt.xlabel(f\"True $P$ [MeV]\")\n", "plt.ylabel(f\"EndVelo $P$ [MeV]\")\n", "plt.title(f\"found P\")\n", "plt.colorbar(a0[3])\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 }