From 1f8b985cfca3880f3b266647eb9c8f3f6c775a00 Mon Sep 17 00:00:00 2001 From: cetin Date: Mon, 9 Oct 2023 16:32:56 +0200 Subject: [PATCH] up and downstream --- B_rework.ipynb | 185 ++++++++++++++++--------------------------------- B_updown.ipynb | 118 ++++++++++++++++++------------- 2 files changed, 130 insertions(+), 173 deletions(-) diff --git a/B_rework.ipynb b/B_rework.ipynb index 1162460..9e8b390 100644 --- a/B_rework.ipynb +++ b/B_rework.ipynb @@ -280,100 +280,36 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": 61, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "sample size: 322\n", - "eff (cutoff = 0 ) = 0.9379 +/- 0.0135\n", - "sample size: 322\n", - "eff (cutoff = 25 ) = 0.9379 +/- 0.0135\n", - "sample size: 322\n", - "eff (cutoff = 50 ) = 0.9379 +/- 0.0135\n", - "sample size: 322\n", - "eff (cutoff = 75 ) = 0.9379 +/- 0.0135\n", - "sample size: 322\n", - "eff (cutoff = 100 ) = 0.9379 +/- 0.0135\n", - "sample size: 384\n", - "eff (cutoff = 125 ) = 0.9453 +/- 0.0116\n", - "sample size: 433\n", - "eff (cutoff = 150 ) = 0.9423 +/- 0.0112\n", - "sample size: 485\n", - "eff (cutoff = 175 ) = 0.9443 +/- 0.0104\n", - "sample size: 529\n", - "eff (cutoff = 200 ) = 0.949 +/- 0.0096\n", - "sample size: 581\n", - "eff (cutoff = 225 ) = 0.9501 +/- 0.009\n", - "sample size: 644\n", - "eff (cutoff = 250 ) = 0.9519 +/- 0.0084\n", - "sample size: 705\n", - "eff (cutoff = 275 ) = 0.9475 +/- 0.0084\n", - "sample size: 757\n", - "eff (cutoff = 300 ) = 0.9498 +/- 0.0079\n", - "sample size: 802\n", - "eff (cutoff = 325 ) = 0.9451 +/- 0.008\n", - "sample size: 846\n", - "eff (cutoff = 350 ) = 0.9433 +/- 0.008\n", - "sample size: 876\n", - "eff (cutoff = 375 ) = 0.9452 +/- 0.0077\n", - "sample size: 919\n", - "eff (cutoff = 400 ) = 0.9467 +/- 0.0074\n", - "sample size: 972\n", - "eff (cutoff = 425 ) = 0.9475 +/- 0.0072\n", - "sample size: 1019\n", - "eff (cutoff = 450 ) = 0.949 +/- 0.0069\n", - "sample size: 1067\n", - "eff (cutoff = 475 ) = 0.9475 +/- 0.0068\n", - "sample size: 1117\n", - "eff (cutoff = 500 ) = 0.9418 +/- 0.007\n", - "sample size: 1144\n", - "eff (cutoff = 525 ) = 0.9423 +/- 0.0069\n", - "sample size: 1184\n", - "eff (cutoff = 550 ) = 0.9409 +/- 0.0069\n", - "sample size: 1234\n", - "eff (cutoff = 575 ) = 0.9408 +/- 0.0067\n", - "sample size: 1268\n", - "eff (cutoff = 600 ) = 0.9416 +/- 0.0066\n", - "sample size: 1303\n", - "eff (cutoff = 625 ) = 0.9417 +/- 0.0065\n", - "sample size: 1342\n", - "eff (cutoff = 650 ) = 0.9404 +/- 0.0065\n", - "sample size: 1381\n", - "eff (cutoff = 675 ) = 0.9399 +/- 0.0064\n", - "sample size: 1416\n", - "eff (cutoff = 700 ) = 0.9407 +/- 0.0063\n", - "sample size: 1444\n", - "eff (cutoff = 725 ) = 0.9418 +/- 0.0062\n", - "sample size: 1484\n", - "eff (cutoff = 750 ) = 0.942 +/- 0.0061\n", - "sample size: 1523\n", - "eff (cutoff = 775 ) = 0.9402 +/- 0.0061\n", - "sample size: 1557\n", - "eff (cutoff = 800 ) = 0.939 +/- 0.0061\n", - "sample size: 1593\n", - "eff (cutoff = 825 ) = 0.9379 +/- 0.006\n", - "sample size: 1628\n", - "eff (cutoff = 850 ) = 0.9373 +/- 0.006\n", - "sample size: 1656\n", - "eff (cutoff = 875 ) = 0.9384 +/- 0.0059\n", - "sample size: 1690\n", - "eff (cutoff = 900 ) = 0.9385 +/- 0.0058\n", - "sample size: 1721\n", - "eff (cutoff = 925 ) = 0.9378 +/- 0.0058\n", - "sample size: 1745\n", - "eff (cutoff = 950 ) = 0.9381 +/- 0.0058\n", - "sample size: 1769\n", - "eff (cutoff = 975 ) = 0.9378 +/- 0.0057\n", - "sample size: 1796\n", - "eff (cutoff = 1000 ) = 0.9365 +/- 0.0058\n", - "sample size: 1838\n", - "eff (cutoff = 1025 ) = 0.9374 +/- 0.0056\n", + "cutoff = 100 MeV, sample size: 322\n", + "eff = 0.9379 +/- 0.0135\n", + "cutoff = 200 MeV, sample size: 481\n", + "eff = 0.948 +/- 0.0101\n", + "cutoff = 300 MeV, sample size: 627\n", + "eff = 0.949 +/- 0.0088\n", + "cutoff = 400 MeV, sample size: 739\n", + "eff = 0.9513 +/- 0.0079\n", + "cutoff = 500 MeV, sample size: 860\n", + "eff = 0.9477 +/- 0.0076\n", + "cutoff = 600 MeV, sample size: 973\n", + "eff = 0.9424 +/- 0.0075\n", + "cutoff = 700 MeV, sample size: 1106\n", + "eff = 0.9412 +/- 0.0071\n", + "cutoff = 800 MeV, sample size: 1188\n", + "eff = 0.9411 +/- 0.0068\n", + "cutoff = 900 MeV, sample size: 1288\n", + "eff = 0.9387 +/- 0.0067\n", + "cutoff = 1000 MeV, sample size: 1387\n", + "eff = 0.9416 +/- 0.0063\n", "\n", - "cutoff energy = 350MeV, sample size: 846\n", - "eff = 0.9433 +/- 0.008\n" + "cutoff energy = 350MeV, sample size: 693\n", + "eff = 0.9481 +/- 0.0084\n" ] } ], @@ -397,14 +333,15 @@ "\n", "\n", "\n", - "for cutoff_energy in range(0,1050,25):\n", - "\tnobrem_f = acc_brem_found[ak.all(acc_brem_found[\"brem_photons_pe\"]" ], "text/plain": [ - "" + "" ] }, - "execution_count": 37, + "execution_count": 62, "metadata": {}, "output_type": "execute_result" } @@ -477,13 +414,13 @@ "#if any photon of an electron has an energy higher the cutoff then it is included\n", "cutoff_energy=350\n", "\n", - "brem_found = acc_brem_found[ak.any(acc_brem_found[\"brem_photons_pe\"]>=cutoff_energy,axis=1)]\n", + "brem_found = acc_brem_found[ak.sum(acc_brem_found[\"brem_photons_pe\"],axis=-1,keepdims=False)>=cutoff_energy]\n", "energy_found = ak.to_numpy(brem_found[\"energy\"])\n", "eph_found = ak.to_numpy(ak.sum(brem_found[\"brem_photons_pe\"], axis=-1, keepdims=False))\n", "residual_found = energy_found - eph_found\n", "energyloss_found = eph_found/energy_found\n", "\n", - "brem_lost = acc_brem_lost[ak.any(acc_brem_lost[\"brem_photons_pe\"]>=cutoff_energy,axis=1)]\n", + "brem_lost = acc_brem_lost[ak.sum(acc_brem_lost[\"brem_photons_pe\"],axis=-1,keepdims=False)>=cutoff_energy]\n", "energy_lost = ak.to_numpy(brem_lost[\"energy\"])\n", "eph_lost = ak.to_numpy(ak.sum(brem_lost[\"brem_photons_pe\"], axis=-1, keepdims=False))\n", "residual_lost = energy_lost - eph_lost\n", @@ -495,15 +432,15 @@ }, { "cell_type": "code", - "execution_count": 38, + "execution_count": 63, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "mean energyloss relative to initial energy (found): 0.4107345449771658\n", - "mean energyloss relative to initial energy (lost): 0.7300783757368142\n" + "mean energyloss relative to initial energy (found): 0.40459562244424735\n", + "mean energyloss relative to initial energy (lost): 0.7244570697471976\n" ] } ], @@ -516,12 +453,12 @@ }, { "cell_type": "code", - "execution_count": 39, + "execution_count": 65, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -532,7 +469,7 @@ ], "source": [ "#in abhängigkeit von der energie der elektronen\n", - "plt.hist(energyloss_lost, bins=200, density=True, alpha=0.5, histtype='bar', color=\"darkorange\", label=\"lost\")\n", + "plt.hist(energyloss_lost, bins=100, density=True, alpha=0.5, histtype='bar', color=\"darkorange\", label=\"lost\")\n", "plt.hist(energyloss_found, bins=100, density=True, alpha=0.5, histtype='bar', color=\"blue\", label=\"found\")\n", "plt.xticks(np.arange(0,1.1,0.1), minor=True,)\n", "plt.yticks(np.arange(0,10,1), minor=True)\n", @@ -551,12 +488,12 @@ }, { "cell_type": "code", - "execution_count": 40, + "execution_count": 77, "metadata": {}, "outputs": [ { "data": { - "image/png": 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" ] @@ -569,14 +506,14 @@ "#energyloss in abh von der energie der elektronen\n", "fig, ((ax0, ax1)) = plt.subplots(nrows=1, ncols=2, figsize=(20,6))\n", "\n", - "a0=ax0.hist2d(energyloss_found, energy_found, bins=(np.linspace(0,1,70), np.linspace(0,1.5e5,105)), cmap=plt.cm.jet, cmin=1, vmax=7)\n", + "a0=ax0.hist2d(energyloss_found, energy_found, bins=(np.linspace(0,1,70), np.linspace(0,1.5e5,70)), cmap=plt.cm.jet, cmin=1, vmax=15)\n", "ax0.set_ylim(0,1.5e5)\n", "ax0.set_xlim(0,1)\n", "ax0.set_xlabel(r\"energyloss $E_\\gamma/E_0$\")\n", "ax0.set_ylabel(r\"$E_0$\")\n", "ax0.set_title(\"found energyloss wrt electron energy\")\n", "\n", - "a1=ax1.hist2d(energyloss_lost, energy_lost, bins=(np.linspace(0,1,70), np.linspace(0,1.5e5,105)), cmap=plt.cm.jet, cmin=1, vmax=7) \n", + "a1=ax1.hist2d(energyloss_lost, energy_lost, bins=(np.linspace(0,1,70), np.linspace(0,1.5e5,70)), cmap=plt.cm.jet, cmin=1, vmax=15) \n", "ax1.set_ylim(0,1.5e5)\n", "ax1.set_xlim(0,1)\n", "ax1.set_xlabel(r\"energyloss $E_\\gamma/E_0$\")\n", @@ -594,12 +531,12 @@ }, { "cell_type": "code", - "execution_count": 41, + "execution_count": 67, "metadata": {}, "outputs": [ { "data": { - "image/png": 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MHDjQ5MuXL8XPUxIdHW0kma+//toYY8xnn31mgoODzYABA0yzZs2c6cqVK2d69uzpUmdJZtSoUUnyfOqppywNmCVK6RiMHz/eeHl5mR07drhsT2yPlStXGmOMeeONN4wkc+bMmRTLSO7mwup5m5aBk5vFx8ebK1eumHLlyplnnnnGUp43stoGxiS9Obe679dff20kJfnD/c2qVKniclOZKPGadPfdd7tsP336tAkMDDT33nuvy/YjR44Yf39/l/OpV69eRpL5/PPPXdLee++9pkKFCqnGdeTIEePj42Oefvppl+3nzp0z4eHhLjf9VstJ/IPFwoULXdLt2LHDSDLvvfeec1vJkiWNt7e3+emnn1zSPvvss8bhcLgM3BhjTOvWrZPcdPfq1csULlzYXL582bltwoQJxsvLy+0NNgAA7tz8R1ur/0b/+eefRpKZMmVKqvmn1EdITnr66d9++61L2sqVK5vWrVs736elvyTJhIaGmr///tslbdeuXU1QUJA5deqUc1tCQoKpXLlykj94N2nSxNSsWdNl/yeffNKEhISYc+fOObfd3Dd74YUXkq3Pk08+aRwOh7MvYaVfX7VqVXPfffelmiY5L7/8sgkMDHT+EfvRRx81bdq0MdWrV3f+iOT33393uRdJrHNy/XVj0nb8jUn5GLRu3drcdtttJjY21mX7wIEDTUBAgDN9+/btk7T/zZI75wMCAkznzp1d0m3evNlISvfAyY2uXr1q4uLizG+//WYkmSVLlrjN82ZW2yC5ewmr+77yyivJ/uH+RqdOnUrxPiel+0Cr97bGXPtuBAQEmN9++8257eLFiyZ//vymf//+KcZlTNr76VbK6d+/v8mbN69LOmP+/z4zsT+f2O5ly5Z1GRgzxph69eqZ4sWLu/Tnz507ZwoUKOByb5mQkGDKlCljOnXq5LJ/27ZtTdmyZV0GegGkDY/qApCsn376SX/88YciIyPl5fX/l4q8efPqgQce0LZt25KdTm5Fs2bNFBwc7HxfpEgRFS5cWL/99psk6cKFC9qxY4fuv/9+BQQEONMFBwerQ4cObvPfsmWL/v77b/Xq1Uvx8fHO19WrV9WmTRvt2LEjyRT7jh07uryvXr26Ll26pJMnT0qSli9fLofDoYcfftglz/DwcNWoUSPJIndhYWG65557XLZt3LhRwcHBatOmjcv2Hj16JKnDk08+KUn66KOPnNumTZumatWq6e67706x7uvXr5ck57TnRF27dlVQUJDzUWU1a9aUn5+fHn/8cc2cOTPJYwwk6Y477tCZM2fUo0cPLVmyxPL08MaNGysgIEDR0dGSrk03b9q0qdq0aaMtW7bon3/+0dGjR3Xw4EG1aNEiyf4PPPCApXLcSe4YLF++XFWrVlXNmjVdjmPr1q1dpsnXq1dPktStWzd9/vnn+v33392Wl9HzNiXx8fEaN26cKleuLD8/P/n4+MjPz08HDx5M8ugLK6y2QUb2TXxEwVNPPZWeKjvdfC5s3bpVFy9eTHJ+Fy9eXPfcc0+SR/E5HI4kbV+9enXntSYlX375peLj4/XII4+41DMgIEBNmjRJ0kZWylm+fLny5cunDh06uORZs2ZNhYeHJ8mzevXqKl++vMu2jRs3qmrVqqpcubLL9uSuIYMHD9bJkye1YMECSdceQfH++++rXbt2bh93AQBAWln9Nzp//vwqW7asJk2apLfeekt79uxJ8kiptEprPz08PFx33HGHy7bk/t1OS3/pnnvuUVhYmMu2jRs36p577lHBggWd27y8vNStW7ckdRg8eLD27t2rzZs3S7r2CKxZs2apV69eyps3b4p1X79+vSpXrpykPr1795YxxnlvYKVff8cdd2jVqlV64YUX9NVXX+nixYsplnuj5s2b6+LFi9qyZYuka49LbtmypVq0aKG1a9c6t0lK0vdPrr+eXjcfg0uXLmndunXq3Lmz8uTJ43Ic7733Xl26dEnbtm2TdK3u3333nQYMGKAvv/xSZ8+edVve1q1bdenSJT300EMu2xs1aqSSJUumux4nT57UE088oeLFi8vHx0e+vr7O/NLa909LG2Rk31WrVql8+fLJ3tulxc19f6v3tolq1qypEiVKON8HBASofPnybvv+ae2nWyln+fLlatasmSIiIlzybNu2raRr14cbdezYUb6+vs73Fy5c0M6dO3XffffJz8/PuT1v3rxJ7ju8vLw0cOBALV++XEeOHJEk/fLLL1q9erUGDBiQ5PHpAKxj4ARAsv766y9JUtGiRZN8FhERoatXr+r06dPpyrtAgQJJtvn7+zs756dPn9bVq1cVHh6eJF1y22524sQJSVKXLl3k6+vr8powYYKMMfr7779TjSlxIbbEmE6cOCFjjIoUKZIkz23btiW5+Uiu3f766y8VKVIkyfaUtnXv3l0ffvihEhIStG/fPn3zzTcaOHBgqnX/66+/5OPjk2ThQofDofDwcOdxLVu2rKKjo1W4cGE99dRTKlu2rMqWLevyHN/IyEh98skn+u233/TAAw+ocOHCql+/vvMGKCUBAQFq3Lix8wZp3bp1atmypZo2baqEhAR98803zjyS61wn13bpkVw+J06c0L59+5Icw+DgYBljnMfx7rvv1uLFi51/PL/ttttUtWrVVJ/xnNHzNiVDhw7VyJEjdd9992nZsmX69ttvtWPHDtWoUcPyDe2NrLZBRvY9deqUvL29M1RvKekxdHddSvw8UZ48eVwGsaRr321363skXkPq1auXpK7z589P0kZWyjlx4oTOnDkjPz+/JHnGxMTYfg2pVauW7rrrLuczqJcvX67Dhw+7vYYAAJAeVv+NdjgcWrdunVq3bq2JEyeqdu3aKlSokAYNGqRz586lq+y09tPd3Ysk5pmW/lJG/93u1KmTSpUq5fx3e8aMGbpw4YLbH6H89ddfKbZ54ueStX79O++8o+eff16LFy9Ws2bNlD9/ft133306ePBgqjEkrk8SHR2tn3/+WYcPH3YOnHz77bc6f/68oqOjVaZMGZUuXdplX7v6/cnl9ddffyk+Pl5Tp05NchzvvfdeSXIexxEjRuiNN97Qtm3b1LZtWxUoUEDNmzfXzp07UywvsW3t7PtfvXpVrVq10hdffKHnnntO69at0/bt250DFGnt+6elDTKy76lTp3Tbbbelq843Su4YWrm3TWTlu52ctPbTrV5Dli1bliS/xDWL3F1DTp8+7byu3Sy5bX379lVgYKA++OADSdK7776rwMBAlzU6AaSdz60OAIBnSuwMHD9+PMlnf/zxh7y8vJy/6AkICEh2seX0LmAXFhYmh8OhmJiYJJ8lt+1mib/qmjp1qho0aJBsmuQ6G+7ydDgc+uabb5yDKje6eVtyv+ooUKCAtm/fnmR7SnUaPHiwZs2apSVLlmj16tXOxeRTU6BAAcXHx+vUqVMuHUxjjGJiYpwzKSTprrvu0l133aWEhATt3LlTU6dO1ZAhQ1SkSBE9+OCDkqQ+ffqoT58+unDhgr7++mtFRUWpffv2OnDgQKq/pGrevLlGjRql7du369ixY2rZsqWCg4NVr149rV27Vn/88YfKly+v4sWLJ9nXrl/EJJdPwYIFFRgYmOIigTf+IrBTp07q1KmTLl++rG3btmn8+PHq2bOnSpUqpYYNGybZNy3nbeIf2W/+3tzc+Zekzz77TI888ojGjRvnsv3PP/9Uvnz5kq1HatLSBundt1ChQkpISFBMTEyGbohvPoburkupxZ4Wifn85z//ydAvBm/Os0CBAlq9enWyn984C09K+RqSOKhzo5SuIYMGDVLXrl21e/duTZs2TeXLl1fLli3TET0AAKlLy7/RJUuW1McffyxJOnDggD7//HONHj1aV65ccf7RLy3S2k+3mmda+ksZ/Xfby8tLTz31lF588UW9+eabeu+999S8eXNVqFAh1TgLFCiQYpvfHKe7fn1QUJDGjBmjMWPG6MSJE87ZJx06dND//ve/FGPw8/PTnXfeqejoaN12220KDw9XtWrVVKZMGUnXFkBft26d2rdvn2RfO38Jf3NeYWFh8vb2VmRkZIoDUIkDOT4+Pho6dKiGDh2qM2fOKDo6Wi+++KJat26to0ePKk+ePEn2TTznU+r73zjDN6W+/833y99//72+++47zZgxQ7169XJu//nnn1OqdqrS0gYZ2bdQoUI6duxYumK8UXJ9f6v3thmR1n661TyrV6+u1157LdnPEwc3EyV3/jocDsvXkNDQUPXq1Uv//ve/NXz4cE2fPl09e/ZM1/0igP/HwAmAZFWoUEHFihXTnDlzNHz4cOc/5BcuXNDChQvVsGFDZweyVKlSOnnypE6cOOEckLhy5Yq+/PLLdJUdFBSkO+64Q1988YUmTZrk7GieO3dOy5Ytc7t/48aNlS9fPv3www+2/bq6ffv2ev311/X7778nO73eiiZNmujzzz/XqlWrnFN0JWnevHnJpq9Tp44aNWqkCRMm6Pvvv9fjjz+uoKCgVMto3ry5Jk6cqM8++0zPPPOMc/vChQt14cIFNW/ePMk+3t7eql+/vipWrKjZs2dr9+7dzoGTREFBQWrbtq2uXLmi++67T/v370/1D8otWrTQiy++qJEjR+q2225TxYoVnduXLl2qmJiYND2S68YZQIGBgZb3u1n79u01btw4FShQIMWbhOTKbtKkifLly6cvv/xSe/bsSXbgJC3nbZEiRRQQEKB9+/a5bF+yZEmSfB0OR5Ib/hUrVuj333/X7bffbqkON0pPG6R137Zt22r8+PF6//339corr6SYzsovwG7UsGFDBQYG6rPPPlPXrl2d248dO6b169erS5culvNKTevWreXj46NffvnFtkfHtW/fXvPmzVNCQoLq16+frjyaNGmiN954Qz/88IPL47pSuoZ07txZJUqU0LBhw7Rx40ZNnjyZqfoAgEyR3n+jy5cvr5dfflkLFy7U7t27ndvT0kewo5+eXJ7p7S8latKkiVauXKk///zTOYB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"text/plain": [ "
" ] @@ -643,7 +580,7 @@ }, { "cell_type": "code", - "execution_count": 42, + "execution_count": 68, "metadata": {}, "outputs": [], "source": [ @@ -672,7 +609,7 @@ }, { "cell_type": "code", - "execution_count": 43, + "execution_count": 69, "metadata": {}, "outputs": [], "source": [ @@ -715,7 +652,7 @@ }, { "cell_type": "code", - "execution_count": 44, + "execution_count": 70, "metadata": {}, "outputs": [ { @@ -739,7 +676,7 @@ "" ] }, - "execution_count": 44, + "execution_count": 70, "metadata": {}, "output_type": "execute_result" } @@ -751,7 +688,7 @@ }, { "cell_type": "code", - "execution_count": 45, + "execution_count": 71, "metadata": {}, "outputs": [], "source": [ @@ -810,7 +747,7 @@ }, { "cell_type": "code", - "execution_count": 46, + "execution_count": 72, "metadata": {}, "outputs": [ { @@ -834,7 +771,7 @@ "" ] }, - "execution_count": 46, + "execution_count": 72, "metadata": {}, "output_type": "execute_result" } @@ -855,7 +792,7 @@ }, { "cell_type": "code", - "execution_count": 47, + "execution_count": 73, "metadata": {}, "outputs": [ { @@ -935,7 +872,7 @@ }, { "cell_type": "code", - "execution_count": 48, + "execution_count": 74, "metadata": {}, "outputs": [ { @@ -986,7 +923,7 @@ }, { "cell_type": "code", - "execution_count": 49, + "execution_count": 75, "metadata": {}, "outputs": [ { diff --git a/B_updown.ipynb b/B_updown.ipynb index 77a0308..0735c36 100644 --- a/B_updown.ipynb +++ b/B_updown.ipynb @@ -409,7 +409,7 @@ }, { "cell_type": "code", - "execution_count": 134, + "execution_count": 148, "metadata": {}, "outputs": [ { @@ -417,8 +417,8 @@ "output_type": "stream", "text": [ "\n", - "upstream: cutoff energy = 200MeV, sample size: 1279\n", - "eff = 0.921 +/- 0.008\n" + "upstream: cutoff energy = 350MeV, sample size: 1562\n", + "eff = 0.9181 +/- 0.007\n" ] } ], @@ -428,9 +428,13 @@ "\n", "\n", "\n", - "for cutoff_energy in range(0,1000,50):\n", - "\tup_nobrem_f = upstream_found[ak.all(upstream_found[\"brem_photons_pe\"]" ] @@ -514,16 +528,21 @@ "source": [ "#plot efficiencies wrt cutoff energy\n", "fig, ax = plt.subplots(nrows=1,ncols=2,figsize=(18,6))\n", - "x_ = np.linspace(0,1000,20)\n", + "x_ = np.arange(50,1050,step=50)\n", "ax[0].scatter(x_,up_efficiencies)\n", "ax[0].set(xlabel=\"cutoff energy [MeV]\",ylabel=r\"$\\epsilon$\",title=\"upstream\", ylim=[0.85,0.95])\n", "ax[0].set_yticks(np.arange(0.85,0.96,step=0.01),minor=False)\n", + "ax[0].set_xticks(np.arange(0,1100,step=50),minor=True)\n", "ax[0].grid()\n", "\n", "ax[1].scatter(x_,down_efficiencies)\n", "ax[1].set(xlabel=\"cutoff energy [MeV]\",ylabel=r\"$\\epsilon$\",title=\"downstream\", ylim=[0.85,0.95])\n", "ax[1].set_yticks(np.arange(0.85,0.96,step=0.01),minor=False)\n", - "ax[1].grid()\n", + "ax[1].set_xticks(np.arange(0,1100,step=50),minor=True)\n", + "ax[1].grid(True)\n", + "\n", + "fig.suptitle(r\"$e^\\pm$ from $B\\rightarrow K^\\ast ee$, $p>5$GeV, nobrem electrons\")\n", + "\n", "\n", "plt.show()" ] @@ -537,47 +556,48 @@ }, { "cell_type": "code", - "execution_count": 137, + "execution_count": 164, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "upstream eff = 0.852 +/- 0.004\n", - "downstream eff = 0.84 +/- 0.005\n" + "brem vertices\n", + "upstream eff = 0.851 +/- 0.004\n", + "downstream eff = 0.836 +/- 0.005\n" ] } ], "source": [ - "cutoff_energy=200\n", + "cutoff_energy=350\n", "#possibly: instead of checking if any photons exceed the cutoff, use the sum of all photon energies to separate nobrem and brem\n", "\n", - "upstream_brem_found = upstream_found[ak.any(upstream_found[\"brem_photons_pe\"]>=cutoff_energy,axis=1)]\n", + "upstream_brem_found = upstream_found[ak.sum(upstream_found[\"brem_photons_pe\"],axis=-1,keepdims=False)>=cutoff_energy]\n", "up_energy_found = ak.to_numpy(upstream_brem_found[\"energy\"])\n", "up_eph_found = ak.to_numpy(ak.sum(upstream_brem_found[\"brem_photons_pe\"], axis=-1, keepdims=False))\n", "up_residual_found = up_energy_found - up_eph_found\n", "up_energyloss_found = up_eph_found/up_energy_found\n", "\n", "\n", - "upstream_brem_lost = upstream_lost[ak.any(upstream_lost[\"brem_photons_pe\"]>=cutoff_energy,axis=1)]\n", + "upstream_brem_lost = upstream_lost[ak.sum(upstream_lost[\"brem_photons_pe\"],axis=-1,keepdims=False)>=cutoff_energy]\n", "up_energy_lost = ak.to_numpy(upstream_brem_lost[\"energy\"])\n", "up_eph_lost = ak.to_numpy(ak.sum(upstream_brem_lost[\"brem_photons_pe\"], axis=-1, keepdims=False))\n", "up_residual_lost = up_energy_lost - up_eph_lost\n", "up_energyloss_lost = up_eph_lost/up_energy_lost\n", "\n", "\n", - "print(\"upstream eff = \", np.round(t_eff(upstream_brem_found,upstream_brem_lost),3), \"+/-\", np.round(eff_err(upstream_brem_found, upstream_brem_lost),3))\n", + "print(\"brem vertices\\nupstream eff = \", np.round(t_eff(upstream_brem_found,upstream_brem_lost),3), \"+/-\", np.round(eff_err(upstream_brem_found, upstream_brem_lost),3))\n", "\n", "\n", - "downstream_brem_found = downstream_found[ak.any(downstream_found[\"brem_photons_pe\"]>=cutoff_energy,axis=1)]\n", + "downstream_brem_found = downstream_found[ak.sum(downstream_found[\"brem_photons_pe\"],axis=-1,keepdims=False)>=cutoff_energy]\n", "down_energy_found = ak.to_numpy(downstream_brem_found[\"energy\"])\n", "down_eph_found = ak.to_numpy(ak.sum(downstream_brem_found[\"brem_photons_pe\"], axis=-1, keepdims=False))\n", "down_residual_found = down_energy_found - down_eph_found\n", "down_energyloss_found = down_eph_found/down_energy_found\n", "\n", "\n", - "downstream_brem_lost = downstream_lost[ak.any(downstream_lost[\"brem_photons_pe\"]>=cutoff_energy,axis=1)]\n", + "downstream_brem_lost = downstream_lost[ak.sum(downstream_lost[\"brem_photons_pe\"],axis=-1,keepdims=False)>=cutoff_energy]\n", "down_energy_lost = ak.to_numpy(downstream_brem_lost[\"energy\"])\n", "down_eph_lost = ak.to_numpy(ak.sum(downstream_brem_lost[\"brem_photons_pe\"], axis=-1, keepdims=False))\n", "down_residual_lost = down_energy_lost - down_eph_lost\n", @@ -589,7 +609,7 @@ }, { "cell_type": "code", - "execution_count": 138, + "execution_count": 165, "metadata": {}, "outputs": [ { @@ -597,11 +617,11 @@ "output_type": "stream", "text": [ "upstream:\n", - "mean energyloss relative to initial energy (found): 0.3207102540525612\n", - "mean energyloss relative to initial energy (lost): 0.5602258293743071\n", + "mean energyloss relative to initial energy (found): 0.33078325542598164\n", + "mean energyloss relative to initial energy (lost): 0.5708618852236069\n", "downstream:\n", - "mean energyloss relative to initial energy (found): 0.17552539358035377\n", - "mean energyloss relative to initial energy (lost): 0.2870828762276071\n" + "mean energyloss relative to initial energy (found): 0.19104090843883118\n", + "mean energyloss relative to initial energy (lost): 0.3051594568487781\n" ] } ], @@ -615,12 +635,12 @@ }, { "cell_type": "code", - "execution_count": 139, + "execution_count": 166, "metadata": {}, "outputs": [ { "data": { - "image/png": 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"text/plain": [ "
" ] @@ -657,18 +677,18 @@ "\"\"\"\n", "most electrons lose little energy relative to their initial energy downstream\n", "\"\"\"\n", - "fig.suptitle(r\"$B\\rightarrow K^\\ast ee$, $p>5$GeV, only photons w/ brem_vtx_z$<9500$mm\")\n", + "fig.suptitle(r\"$B\\rightarrow K^\\ast ee$, $p>5$GeV, photons w/ brem_vtx_z$<9500$mm\")\n", "plt.show()" ] }, { "cell_type": "code", - "execution_count": 140, + "execution_count": 168, "metadata": {}, "outputs": [ { "data": { - "image/png": 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" ] @@ -705,12 +725,12 @@ }, { "cell_type": "code", - "execution_count": 141, + "execution_count": 169, "metadata": {}, "outputs": [ { "data": { - "image/png": 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" ] @@ -746,12 +766,12 @@ }, { "cell_type": "code", - "execution_count": 142, + "execution_count": 170, "metadata": {}, "outputs": [ { "data": { - "image/png": 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" ] @@ -791,12 +811,12 @@ }, { "cell_type": "code", - "execution_count": 143, + "execution_count": 171, "metadata": {}, "outputs": [ { "data": { - "image/png": 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98cQTSk5OdjwqVqyo4ODgDKeR+Oqrr3ThwoUMH88//7xb8nXttCQXL17Utm3b1L17dxUuXNix3dfXVzExMfrtt9/0008/Oe3TqVMnp+c1atSQzWZzmkbIz89PN910k3799dds87Rr1y5J0oIFC7Rjxw7t2LFDK1asUMeOHTVq1CgtWLAgw/3++OOPLKf6sNls8vPz05w5c5SUlKT77rtPJ0+ezPT4MTExSkxM1MaNG7PN87WmTp3q0votrt4XObl/srNjxw5Jf0/fYbfbHdv9/f110003ZTuFlyQVKVJEffv21cqVK3XixAm99tprOnz4sNq3b6+oqKhM83XzzTfrrbfe0t13363bb79dffv21ebNm1WqVCk9/fTT2R63QYMG8vf3dzwmTZokSUpMTNTatWt1zz33qFChQk5ldddddykxMVFbt251Sqt///76448/9OWXX0qSkpOTNWvWLN1+++2qUqVKtnnJKWOMJOt/c61atdIvv/yiw4cPKzExUZs2bVL79u3VsmVLrV69WpK0Zs0aBQYGOqbCS3P1GjuSVLduXSUmJurUqVOObbfeequmT5+uV155RVu3blVSUpLlc7v2fcXKdUlOTta4ceNUs2ZNBQQEyM/PTwEBATp06JB+/PHHdMdy9/tPmgcffNDp+X333Sc/Pz+tX78+XWxW5ZqT99Rr5SQNT17rnN6DCxYscHkqLMnaNcjNPZfGU/dSv379dOnSJc2bN8+xbdq0aQoMDFSvXr1cTue+++7TY489pqeeekqvvPKKnn/+ebVp08bl/a81ZMgQLVu2TJ9//rluueWWDGP27dunyMhIde3aVX/88Yf++c9/6uTJk1qyZIkefPBBp/sxM48//rgOHjyoIUOG6Pfff9exY8f0j3/8w1GGPj7/+zrFymeDzWbL9JjXvmYlVsr9veCpewkAgBuNThAAAJCpuLg47dmzR//9738VGBjo9OWtv7+/Ll26pCJFityQvJUqVcrpeXx8vIwx6bZLcqy7cObMGaftRYsWdXoeEBCgQoUKKSgoKN32xMTEbPOUtih5ly5d1LBhQzVs2FDt2rXT3Llz5evrqw8//DDD/UJDQ/Xhhx9m+0j7sqxr164qVqxYhscPCgrSxx9/rJiYGD377LOOL8jdydX7wlP3z86dO+Xv759unnRJOn78uMqWLWspvXPnzuns2bNKSEiQMUZFihSRn5+fy/sXKVJEnTp10r59+3Tp0iUVL15cwcHBGX5BNGfOHO3YsSPddTlz5oySk5M1ZcqUdOV01113SVK6zp3u3bvLbrdr2rRpkv5eRPfkyZO5WhA9OxcvXtSZM2cUFRVl+W+udevWkv7+knnTpk1KSkrSnXfeqdatWzvWHVizZo2aNWuWbsH4a+/3wMBASdKlS5cc2+bNm6fevXvr3//+t5o2baqiRYvq4YcfVlxcnMvnd+25WLkuw4cP10svvaS7775bS5Ys0bZt27Rjxw7Vq1fPKZ9p3P3+kyYyMtLpuZ+fn4oVK5bu/U/Kulxz8p56rZyk4clrnZN7cPv27Tp69KilThAr1yA391waT91LtWrVUqNGjRzvMSkpKZo1a5a6du2a7pjZ6devn5KSkuTn56ehQ4da2vdqr7zyit5//3198MEHat++faZx/v7+stvtSklJUUJCghISEnThwgXLeX7ttdc0c+ZMlSlTRuXKldMPP/ygkSNHSpJKly6d5f7XfjZIyvQ++PPPPyU5X0srsWlyey946l4CAOBGc711BwAACpxjx45J+ntR7mt/mZ3m6oVcr6drfwEZHh4uHx+fDBcqPX78uCSpePHiHs3T7t27VadOHfn6+jpt9/f3l6+vb4ZfhEp/f8n3yCOPZJn2smXLtHTpUnXv3l2ffvpphl/S7969W/Xq1ZOfn58+/PBDx8K6mzZtynCx9pxy9b74+eefXYqzaufOnSpevHi6L2W2bdumX375RS+99FK2aRw7dkyff/655s6dqx07dqh06dK6//779eGHH6phw4aW85Q2OsJms8nX11d33nmnVq1apRMnTjh9yVmzZk1JSreQb3h4uOPX8YMHD87wGBUrVnR6HhwcrJ49e+rDDz/UiRMn9PHHHys0NDTDziF3WbZsmVJSUtSiRQvLf3NlypRR1apVtWbNGlWoUEENGzZUkSJF1KpVKw0aNEjbtm3T1q1bNWbMmBzlrXjx4nrrrbf01ltv6ejRo/ryyy/17LPP6tSpU1qxYoVLaWT0vuLqdZk1a5YefvhhjRs3zun1P/7447p2FsfFxTl9OZucnKwzZ85k2HGaFXe8p3rqfTmn1zon9+D8+fNVtWpV1a5d2+X8WbkGubnnroe+fftq0KBB+vHHH/XLL7/oxIkT6tu3r6U0Ll68qJiYGFWtWlUnT57UI488osWLF1vOy/Tp0/XSSy9p9OjR6tevX5axNWrU0C+//KItW7Zozpw5eu211zRixAg1a9ZM999/v7p3756usyojzzzzjIYNG6ZDhw4pNDRU5cuX18CBAxUSEqIGDRpku//Vnw2SVKdOHX366adKTk52+hz//vvvJcnpPrMSCwAAskYnCAAAyFTaLwJtNluOvhi+nkJCQtS4cWMtWLBAEydOdPyKNzU1VbNmzXJ8+eUpCQkJ+uWXX9SqVat0ry1evFiJiYm64447cpz+hAkT1Llz50w7QNKO37ZtW0l/d6wsXLhQt956q7p06aLt27e79IWPK1y9Lzx1/+zcuVMJCQk6e/as48vllJQUPfPMM6pQoUKm07ScP39e06dP17x587R582aFh4fr3nvv1euvv67o6GinqU2siI+P19KlS1W/fn1Hx8xzzz2nr776Sv/4xz/0xRdfyN/fP8s0ChUqpJYtW2rPnj2qW7euAgICXDp2//799f7772vChAlavny5+vTpo0KFCuXoPLJz9OhRjRw5Una73fEloNW/udatW+uzzz5T2bJl1bFjR0lS1apVVa5cOY0aNUpJSUmOX+vnRrly5TRkyBCtXbtW3377rWN7RqMKsmLluthsNkf6aZYtW6bff/9dN910Uw7OImdmz57t9OXsZ599puTkZLVo0cJSOlaub2blej3elzO71pmxeg/Onz9f9913n6U85eYa5PS9wFN69uyp4cOHa/r06frll19UunRpx+dMmuz+rv7xj3/o6NGj2r59u/7zn/+oe/fuevPNN/Xkk0+6nI8VK1ZowIAB6tevn2JjY13er2nTpmratKneeustrV27VnPmzNELL7ygJ554QtHR0br//vv18MMPpxt9du35pXU4HD16VPPmzdOAAQOy3EfK+LPhnnvu0Ycffqj58+fr/vvvd8TOmDFDUVFRaty4sWOblVgAAJA1OkEAAECmKleurJYtW+rFF1/UhQsX1LhxYxljdOLECa1fv169e/e2/MWaJ40fP15t2rRRy5YtNXLkSAUEBOjdd9/V/v379emnn2Y5t3Zu7d69W8YYhYSEOOZrj4+P1+bNm/Xmm2+qbt26jik0cmLJkiUKDg7OdJqmtONf/cVbZGSkFi9erObNm6tLly7asGFDtl/auMLV+8Lq/WOz2RQdHa2vv/4602MfPnxYZ86cUbly5dSjRw+NGDFCiYmJ+uc//6ldu3bp66+/zvRLw127dunZZ59Vly5dtGjRInXo0CHbzolr9erVS+XKlVPDhg1VvHhxHTp0SJMmTdLJkyc1ffp0R1yzZs30zjvv6PHHH9ctt9yiRx99VLVq1XL8Kn7+/PmSpLCwMMc+b7/9tpo3b67bb79djz32mCpUqKDz58/r559/1pIlS7Ru3bp0+WnYsKHq1q2rt956S8aYTKfCcqVsr7Z//37HOgSnTp3SN998o2nTpsnX11cLFy5UiRIlJFn/m2vVqpXeffdd/fHHH3rrrbectk+bNk3h4eEu/br6WgkJCWrZsqV69eql6tWrKzQ01LEmT7du3RxxderUkfR3Wffu3Vv+/v6qVq1almm7el06deqk6dOnq3r16qpbt6527dqlCRMmqEyZMpbPJzcWLFggPz8/tWnTRgcOHNBLL72kevXqWf4iX3L9+mZWrqGhoW5/X3b1WmfGyj24d+9e/fe//7U0FZaU+2uQk/cCTylSpIjuueceTZ8+XWfPntXIkSPTdRhndf3//e9/a9asWZo2bZpq1aqlWrVqaciQIXrmmWfUrFkz3Xrrrdnm4fDhw+rRo4cqVaqkvn37plsT5eabb07XAXktX19ftW3bVm3bttX777+vZcuWac6cORo2bJgaN26c4WjJ/fv3a/78+WrYsKECAwP13Xff6bXXXlOVKlX08ssvO8W6+tnQoUMHtWnTRo899pjOnTunm266SZ9++qlWrFihWbNmOY0ktRILAACycSNWYwcAAHlHQkKCee6550zVqlVNUFCQCQ8PN/Xq1TOPP/64iY+P9+ix169fbySZzz//3LEtNjbWSDKnT5/OcJ9vvvnG3HnnnSYkJMQEBwebJk2amCVLljjFZJZG7969TUhISLo0o6OjTa1atbLM68SJE40kp0dISIi5+eabzauvvmouXrzo6mnnSNrxd+/ene61zz//3NhsNtOjRw+TmprqluO5el+4Gnf+/HkjyTzwwANZHvezzz4zkszmzZtNTEyMCQsLM6GhoaZr167mhx9+yDbPFy5cyM1pm/Hjx5v69esbu91ufH19TYkSJcw999xjtm/fnmH83r17Td++fU3FihVNYGCgCQoKMjfddJN5+OGHzdq1a9PFHz582PTr18+ULl3a+Pv7mxIlSpjbbrvNvPLKK5nm6e233zaSTM2aNTN83dWyNcaYadOmOd3DAQEBJiIiwkRHR5tx48aZU6dOpdvHlb+5NPHx8cbHx8eEhISYK1euOLbPnj3bSDLdunVzis/sbzUtn4cPHzbGGJOYmGj+8Y9/mLp165qwsDATHBxsqlWrZmJjY9P97T333HMmKirK+Pj4GElm/fr12b6vuHJd4uPjTf/+/U1ERIQpVKi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