diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000..72471a4 --- /dev/null +++ b/.gitignore @@ -0,0 +1,3 @@ +.ipynb_checkpoints/ +__pycache__/ +.ipytest_cache/ diff --git a/ServiceX stuff/ElectronData_1.1.ipynb b/ServiceX stuff/ElectronData_1.1.ipynb new file mode 100644 index 0000000..f5a4aca --- /dev/null +++ b/ServiceX stuff/ElectronData_1.1.ipynb @@ -0,0 +1,275 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Fetching simple lepton data\n", + "\n", + "This demo uses ServiceX to fetch electron data from an ATLAS Z->ee and an Z->$\\mu\\mu$ xAOD dataset." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Setup\n", + "\n", + "This demo requires a version of `servicex` up and running, as well as two ports forwarded for the service. We begin by setting up a configuration file for ServiceX, named `.servicex`. The file should be located either in the execution directory, or the user home directory (this would be ~/home for Linux or MacOS systems and the User directory for Windows systems).\n", + "\n", + "The contents of the file should be:\n", + "\n", + "```\n", + "api_endpoint:\n", + " endpoint: endpoint here\n", + " username: username here\n", + " password: password here\n", + "\n", + " minio_endpoint: localhost:9000\n", + " minio_username: miniouser\n", + " minio_password: leftfoot1\n", + "```\n", + "\n", + "Replace the fields with the appropriate ServiceX information and credentials (if you don't have these, you may request credentials from `http://rc1-xaod-servicex.uc.ssl-hep.org/`). From here, we import our dependencies and prepare to begin using the software." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "from servicex import ServiceXDataset\n", + "from servicex.minio_adaptor import MinioAdaptor\n", + "from servicex.servicex_adaptor import ServiceXAdaptor\n", + "from func_adl_xAOD import ServiceXDatasetSource\n", + "\n", + "\n", + "import uproot_methods\n", + "import matplotlib.pyplot as plt\n", + "\n", + "import datetime" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here, we decide which datasets we're interested in. Notice that the commented lines would allow us to manually pass credentials and configuration to ServiceX if we had not set up a configuration file previously." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "def create_dataset(dataset_name):\n", + " sxdataset = ServiceXDataset(dataset_name)\n", + " \n", + " return sxdataset\n", + " \n", + "zee_dataset = create_dataset('mc15_13TeV:mc15_13TeV.361106.PowhegPythia8EvtGen_AZNLOCTEQ6L1_Zee.merge.DAOD_STDM3.e3601_s2576_s2132_r6630_r6264_p2363_tid05630052_00')\n", + "zmm_dataset = create_dataset('mc15_13TeV:mc15_13TeV.361107.PowhegPythia8EvtGen_AZNLOCTEQ6L1_Zmumu.merge.DAOD_STDM3.e3601_s2576_s2132_r6630_r6264_p2363_tid05630078_00')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Function definitions" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "def retrieve_data(dataset):\n", + " data = ServiceXDatasetSource(dataset) \\\n", + " .Select('lambda e: (e.Electrons(\"Electrons\"), e.Muons(\"Muons\"))') \\\n", + " .Select('lambda ls: (ls[0].Select(lambda e: e.pt()), ls[0].Select(lambda e: e.eta()), \\\n", + " ls[0].Select(lambda e: e.phi()), ls[0].Select(lambda e: e.e()), \\\n", + " ls[1].Select(lambda m: m.pt()), ls[1].Select(lambda m: m.eta()), \\\n", + " ls[1].Select(lambda m: m.phi()), ls[1].Select(lambda m: m.e()))') \\\n", + " .AsAwkwardArray(('ElePt', 'EleEta', 'ElePhi', 'EleE', 'MuPt', 'MuEta', 'MuPhi', 'MuE')) \\\n", + " .value()\n", + " return data\n", + "\n", + "def four_vectorize(leptons_per_event, lepton_type):\n", + " four_vector = uproot_methods.TLorentzVectorArray.from_ptetaphi(\n", + " leptons_per_event[bytes(f\"{lepton_type}Pt\", 'utf-8')], leptons_per_event[bytes(f\"{lepton_type}Eta\", 'utf-8')],\n", + " leptons_per_event[bytes(f\"{lepton_type}Phi\", 'utf-8')], leptons_per_event[bytes(f\"{lepton_type}E\", 'utf-8')],\n", + " )\n", + " \n", + " return four_vector\n", + "\n", + "def organize_leptons(dataset, lepton_type):\n", + " v_leptons = four_vectorize(dataset, lepton_type)\n", + " v_leptons = v_leptons[v_leptons.counts >= 2]\n", + " dileptons = v_leptons[:, 0] + v_leptons[:, 1]\n", + " \n", + " return dileptons\n", + "\n", + "def plot_data(dielectrons, dimuons):\n", + " plt.figure(figsize=(12, 6))\n", + " plt.hist(dielectrons.mass/1000.0, bins=100, range=(0,200))\n", + " plt.title('Di-Electron Mass')\n", + " plt.xlabel('$m_{ee}$ [GeV]')\n", + " plt.ylabel('Count')\n", + " plt.show()\n", + "\n", + " plt.figure(figsize=(12,6))\n", + " plt.hist(dimuons.mass/1000.0, bins=100, range=(0,200))\n", + " plt.title('Di-Muon Mass')\n", + " plt.xlabel('$m_{\\mu\\mu}$ [GeV]')\n", + " plt.ylabel('Count')\n", + " plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "At this point, we are ready to ask ServiceX to fetch our data." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "zee_retrieved_data = retrieve_data(zee_dataset)\n", + "zmm_retrieved_data = retrieve_data(zmm_dataset)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "After this, we are interested in plotting the muon and electron counts for the `z->ee` dataset. We analyze the raw datasets and convert the information to the counts, and then we plot the data." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "zee_die = organize_leptons(zee_retrieved_data, 'Ele')\n", + "zee_dim = organize_leptons(zee_retrieved_data, 'Mu')\n", + "\n", + "plot_data(zee_die, zee_dim)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Finally, we do the same as above for the z->$\\mu\\mu$ dataset. As expected, the shapes switch and we find a exponentially decaying count for the electrons, but a peak for the muons." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "f:\\program files (x86)\\python 3.7\\lib\\site-packages\\numpy\\lib\\histograms.py:839: RuntimeWarning: invalid value encountered in greater_equal\n", + " keep = (tmp_a >= first_edge)\n", + "f:\\program files (x86)\\python 3.7\\lib\\site-packages\\numpy\\lib\\histograms.py:840: RuntimeWarning: invalid value encountered in less_equal\n", + " keep &= (tmp_a <= last_edge)\n" + ] + }, + { + "data": { + "image/png": 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JUocM4JIkSVKHDOCSJElShwzgkiRJUocM4JIkSVKHDOCSJElShwzgkiRJUocM4JIkSVKHDOCSJElShwzgkiRJUocM4JIkSVKHDOCSJElSh+ZOdwc0fUZWXzWwvnntaR33RJIk6cDhCLgkSZLUIQO4JEmS1CEDuCRJktQhA7gkSZLUIQO4JEmS1CEDuCRJktShoQbwJJuT3Jbk1iQbW+2wJNckubs9H9q3/rlJNiW5K8mpffWT2nY2JflAkrT6QUkub/UbkowMc38kSZKkfdXFCPjPV9XLq2pxe70auLaqFgHXttckOR5YDpwALAU+lGROa3M+sApY1B5LW30l8GhVHQe8Hzivg/2RJEmS9tp0fBHPMuC1bXkd8HngXa1+WVU9CdybZBNwcpLNwMFVdT1AkouB04GrW5v3tG1dAfxJklRVdbInQzbeF+VIkiRp9hr2CHgBn01yc5JVrXZUVW0FaM9HtvoC4IG+tltabUFbHlvfpU1V7QAeAw4fwn5IkiRJU2LYI+CvqaoHkxwJXJPk6xOsmwG1mqA+UZtdN9wL/6sAXvCCF0zcY0mSJGmIhjoCXlUPtueHgU8AJwMPJTkaoD0/3FbfAhzT13wh8GCrLxxQ36VNkrnAIcD2Af24oKoWV9Xi+fPnT83OSZIkSXthaAE8ybOTPHd0GfhF4GvABmBFW20FcGVb3gAsb3c2OZbexZY3tmkqjyc5pd395KwxbUa3dQZw3f4y/1uSJEn7p2FOQTkK+ES7Y+Bc4GNV9ekkNwHrk6wE7gfOBKiq25OsB+4AdgDnVNXOtq2zgYuAefQuvry61S8ELmkXbG6ndxcVSZIkacYaWgCvqnuAlw2oPwIsGafNGmDNgPpG4MQB9SdoAV6SJEmaDfwmTEmSJKlDBnBJkiSpQwZwSZIkqUMGcEmSJKlDBnBJkiSpQwZwSZIkqUMGcEmSJKlDBnBJkiSpQwZwSZIkqUMGcEmSJKlDBnBJkiSpQwZwSZIkqUOTCuBJXjOZmiRJkqSJTXYE/IOTrEmSJEmawNyJ3kzy08CrgflJfqfvrYOBOcPsmCRJkrQ/mjCAA88AntPWe25f/TvAGcPqlCRJkrS/mjCAV9UXgC8kuaiq7uuoT5IkSdJ+a3cj4KMOSnIBMNLfpqpeN4xOSZIkSfuryQbwvwA+DPwZsHN43ZEkSZL2b5MN4Duq6vyh9kSSJEk6AEz2NoR/neQ3kxyd5LDRx1B7JkmSJO2HJjsCvqI9/4e+WgEvmtruSJIkSfu3SQXwqjp22B2RJEmSDgSTCuBJzhpUr6qLp7Y7B6aR1VdNdxckSZLUkclOQXll3/IzgSXALYABXJIkSdoDk52C8tv9r5McAlwylB5JkiRJ+7HJ3gVlrO8Bi6ayI5IkSdKBYLJzwP+a3l1PAOYALwHWD6tTkiRJ0v5qsnPA39u3vAO4r6q2DKE/kiRJ0n5tUlNQquoLwNeB5wKHAj8YZqckSZKk/dWkAniSNwM3AmcCbwZuSHLGJNvOSfKVJJ9srw9Lck2Su9vzoX3rnptkU5K7kpzaVz8pyW3tvQ8kSasflOTyVr8hychkd1ySJEmaDpOdgvJ7wCur6mGAJPOBvwGumETbdwB3Age316uBa6tqbZLV7fW7khwPLAdOAJ4P/E2SF1fVTuB8YBXwZeBTwFLgamAl8GhVHZdkOXAe8JZJ7pPGMd59yTevPa3jnkiSJO1/JnsXlKeNhu/mkcm0TbIQOA34s77yMmBdW14HnN5Xv6yqnqyqe4FNwMlJjgYOrqrrq6ro3Xv89AHbugJYMjo6LkmSJM1Ekx0B/3SSzwCXttdvoTcSvTt/DPwuvbnjo46qqq0AVbU1yZGtvoDeCPeoLa32w7Y8tj7a5oG2rR1JHgMOB741yf2SJEmSOjVhAE9yHL3A/B+S/DLwM0CA64GP7qbtG4GHq+rmJK+dRF8GjVzXBPWJ2oztyyp6U1h4wQteMImuSJIkScOxu2kkfww8DlBVH6+q36mqf0tv9PuPd9P2NcCbkmwGLgNel+TPgYfatBLa8+jUli3AMX3tFwIPtvrCAfVd2iSZCxwCbB/bkaq6oKoWV9Xi+fPn76bbkiRJ0vDsLoCPVNVXxxaraiMwMlHDqjq3qhZW1Qi9iyuvq6pfATYAK9pqK4Ar2/IGYHm7s8mx9L5p88Y2XeXxJKe0+d1njWkzuq0z2mc8ZQRckiRJmil2Nwf8mRO8N28vP3MtsD7JSuB+erc2pKpuT7IeuIPel/2c0+6AAnA2cFH7zKvbA+BC4JIkm+iNfC/fyz5JkiRJndhdAL8pyb+qqj/tL7bwfPNkP6SqPg98vi0/AiwZZ701wJoB9Y3AiQPqT9ACvCRJkjQb7C6AvxP4RJK38ePAvRh4BvBLw+yYJEmStD+aMIBX1UPAq5P8PD8egb6qqq4bes8kSZKk/dCk7gNeVZ8DPjfkvkiSJEn7vcl+E6YkSZKkKWAAlyRJkjpkAJckSZI6ZACXJEmSOmQAlyRJkjpkAJckSZI6ZACXJEmSOmQAlyRJkjpkAJckSZI6ZACXJEmSOmQAlyRJkjpkAJckSZI6ZACXJEmSOmQAlyRJkjpkAJckSZI6ZACXJEmSOmQAlyRJkjpkAJckSZI6ZACXJEmSOmQAlyRJkjpkAJckSZI6ZACXJEmSOmQAlyRJkjpkAJckSZI6ZACXJEmSOmQAlyRJkjpkAJckSZI6NHdYG07yTOCLwEHtc66oqncnOQy4HBgBNgNvrqpHW5tzgZXATuDfVNVnWv0k4CJgHvAp4B1VVUkOAi4GTgIeAd5SVZuHtU8HupHVVw2sb157Wsc9kSRJmr2GOQL+JPC6qnoZ8HJgaZJTgNXAtVW1CLi2vSbJ8cBy4ARgKfChJHPats4HVgGL2mNpq68EHq2q44D3A+cNcX8kSZKkfTa0AF49320vn94eBSwD1rX6OuD0trwMuKyqnqyqe4FNwMlJjgYOrqrrq6rojXj3txnd1hXAkiQZ1j5JkiRJ+2qoc8CTzElyK/AwcE1V3QAcVVVbAdrzkW31BcADfc23tNqCtjy2vkubqtoBPAYcPqAfq5JsTLJx27ZtU7V7kiRJ0h4bagCvqp1V9XJgIb3R7BMnWH3QyHVNUJ+ozdh+XFBVi6tq8fz583fXbUmSJGloOrkLSlV9G/g8vbnbD7VpJbTnh9tqW4Bj+potBB5s9YUD6ru0STIXOATYPpSdkCRJkqbA0AJ4kvlJnteW5wG/AHwd2ACsaKutAK5syxuA5UkOSnIsvYstb2zTVB5Pckqb333WmDaj2zoDuK7NE5ckSZJmpKHdhhA4GljX7mTyNGB9VX0yyfXA+iQrgfuBMwGq6vYk64E7gB3AOVW1s23rbH58G8Kr2wPgQuCSJJvojXwvH+L+SJIkSftsaAG8qr4KvGJA/RFgyTht1gBrBtQ3Ak+ZP15VT9ACvCRJkjQb+E2YkiRJUocM4JIkSVKHDOCSJElShwzgkiRJUocM4JIkSVKHDOCSJElShwzgkiRJUocM4JIkSVKHDOCSJElShwzgkiRJUocM4JIkSVKHDOCSJElShwzgkiRJUofmTncHNPuNrL5qYH3z2tM67okkSdLM5wi4JEmS1CEDuCRJktQhA7gkSZLUIQO4JEmS1CEDuCRJktQhA7gkSZLUIQO4JEmS1CEDuCRJktQhA7gkSZLUIQO4JEmS1CEDuCRJktQhA7gkSZLUIQO4JEmS1CEDuCRJktQhA7gkSZLUoaEF8CTHJPlckjuT3J7kHa1+WJJrktzdng/ta3Nukk1J7kpyal/9pCS3tfc+kCStflCSy1v9hiQjw9ofSZIkaSoMcwR8B/DvquolwCnAOUmOB1YD11bVIuDa9pr23nLgBGAp8KEkc9q2zgdWAYvaY2mrrwQerarjgPcD5w1xfyRJkqR9NrQAXlVbq+qWtvw4cCewAFgGrGurrQNOb8vLgMuq6smquhfYBJyc5Gjg4Kq6vqoKuHhMm9FtXQEsGR0dlyRJkmaiuV18SJsa8grgBuCoqtoKvZCe5Mi22gLgy33NtrTaD9vy2PpomwfatnYkeQw4HPjWUHZEe2Rk9VUD65vXntZxTyRJkmaOoV+EmeQ5wF8C76yq70y06oBaTVCfqM3YPqxKsjHJxm3btu2uy5IkSdLQDDWAJ3k6vfD90ar6eCs/1KaV0J4fbvUtwDF9zRcCD7b6wgH1XdokmQscAmwf24+quqCqFlfV4vnz50/FrkmSJEl7ZZh3QQlwIXBnVb2v760NwIq2vAK4sq++vN3Z5Fh6F1ve2KarPJ7klLbNs8a0Gd3WGcB1bZ64JEmSNCMNcw74a4BfBW5Lcmur/UdgLbA+yUrgfuBMgKq6Pcl64A56d1A5p6p2tnZnAxcB84Cr2wN6Af+SJJvojXwvH+L+SJIkSftsaAG8qr7E4DnaAEvGabMGWDOgvhE4cUD9CVqAlyRJkmYDvwlTkiRJ6pABXJIkSeqQAVySJEnqkAFckiRJ6pABXJIkSeqQAVySJEnqkAFckiRJ6tAwv4hHGmhk9VXjvrd57Wkd9kSSJKl7joBLkiRJHTKAS5IkSR0ygEuSJEkdMoBLkiRJHTKAS5IkSR0ygEuSJEkdMoBLkiRJHTKAS5IkSR0ygEuSJEkd8pswNaOM9y2ZfkOmJEnaXzgCLkmSJHXIAC5JkiR1yAAuSZIkdcgALkmSJHXIAC5JkiR1yAAuSZIkdcjbEGpW8PaEkiRpf+EIuCRJktQhA7gkSZLUIQO4JEmS1CEDuCRJktQhL8LUrObFmZIkabYZ2gh4ko8keTjJ1/pqhyW5Jsnd7fnQvvfOTbIpyV1JTu2rn5TktvbeB5Kk1Q9Kcnmr35BkZFj7IkmSJE2VYU5BuQhYOqa2Gri2qhYB17bXJDkeWA6c0Np8KMmc1uZ8YBWwqD1Gt7kSeLSqjgPeD5w3tD2RJEmSpsjQAnhVfRHYPqa8DFjXltcBp/fVL6uqJ6vqXmATcHKSo4GDq+r6qirg4jFtRrd1BbBkdHRckiRJmqm6vgjzqKraCtCej2z1BcADfettabUFbXlsfZc2VbUDeAw4fNCHJlmVZGOSjdu2bZuiXZEkSZL23Ey5C8qgkeuaoD5Rm6cWqy6oqsVVtXj+/Pl72UVJkiRp33UdwB9q00pozw+3+hbgmL71FgIPtvrCAfVd2iSZCxzCU6e8SJIkSTNK17ch3ACsANa25yv76h9L8j7g+fQutryxqnYmeTzJKcANwFnAB8ds63rgDOC6Nk9c8vaEkiRpxhpaAE9yKfBa4IgkW4B30wve65OsBO4HzgSoqtuTrAfuAHYA51TVzraps+ndUWUecHV7AFwIXJJkE72R7+XD2hdJkiRpqgwtgFfVW8d5a8k4668B1gyobwROHFB/ghbgJUmSpNnCb8LUAcWpKZIkabrNlLugSJIkSQcEA7gkSZLUIQO4JEmS1CHngEs4N1ySJHXHEXBJkiSpQwZwSZIkqUMGcEmSJKlDzgGXJuDccEmSNNUcAZckSZI6ZACXJEmSOuQUFGkvODVFkiTtLUfAJUmSpA4ZwCVJkqQOOQVFmkJOTZEkSbvjCLgkSZLUIUfApQ44Mi5JkkY5Ai5JkiR1yAAuSZIkdcgpKNI0Gm9qCjg9RZKk/ZUj4JIkSVKHHAGXZigv3JQkaf9kAJdmGYO5JEmzm1NQJEmSpA45Ai7tJxwZlyRpdjCAS/s5g7kkSTOLAVw6QE10C8RBDOySJE0NA7ikSXEkXZKkqWEAl7RPDOaSJO2ZWR/AkywF/gcwB/izqlo7zV2SxJ5PcRmPQV6StL+Z1QE8yRzgfwKvB7YANyXZUFV3TG/PJE2VqQryEzHkS5K6NKsDOHAysKmq7gFIchmwDDCAS5q0LkL+IAZ/STowzfYAvgB4oO/1FuBV09QXSdoj0xX892f+USNpNpjtATwDavWUlZJVwKr28rtJ7hpqr8Z3BPCtafrs2cjjtWc8XnvG47VnZsXxynnT3YN/MCuO1wzi8dozHq89M53H64WDirM9gG8Bjul7vRB4cOxKVXUBcEFXnRpPko1VtXi6+zFbeLz2jMdrz3i89ozHa894vPaMx2vPeLz2zEw8Xk+b7g7so5uARUmOTfIMYDmwYUh+pfYAAAfWSURBVJr7JEmSJI1rVo+AV9WOJL8FfIbebQg/UlW3T3O3JEmSpHHN6gAOUFWfAj413f2YpGmfBjPLeLz2jMdrz3i89ozHa894vPaMx2vPeLz2zIw7Xql6yjWLkiRJkoZkts8BlyRJkmYVA3gHkixNcleSTUlWT3d/ZpokxyT5XJI7k9ye5B2t/p4k30xya3v8s+nu60yRZHOS29px2dhqhyW5Jsnd7fnQ6e7nTJDkJ/rOoVuTfCfJOz2/fizJR5I8nORrfbVxz6ck57bfZ3clOXV6ej29xjlm/z3J15N8Ncknkjyv1UeSfL/vXPvw9PV8eoxzvMb9GTzQz7Fxjtflfcdqc5JbW/2APr8myBAz+neYU1CGLMkc4P8Br6d328SbgLdWld/W2SQ5Gji6qm5J8lzgZuB04M3Ad6vqvdPawRkoyWZgcVV9q6/2h8D2qlrb/tA7tKreNV19nInaz+M36X1h16/j+QVAkp8DvgtcXFUnttrA8ynJ8cCl9L6J+PnA3wAvrqqd09T9aTHOMftF4Lp2g4DzANoxGwE+ObregWic4/UeBvwMeo4NPl5j3v8j4LGq+oMD/fyaIEP8GjP4d5gj4MN3MrCpqu6pqh8AlwHLprlPM0pVba2qW9ry48Cd9L7lVHtmGbCuLa+j9wtIu1oCfKOq7pvujswkVfVFYPuY8njn0zLgsqp6sqruBTbR+z13QBl0zKrqs1W1o738Mr3vphDjnmPjOeDPsYmOV5LQG6C6tNNOzVATZIgZ/TvMAD58C4AH+l5vwXA5rvaX/CuAG1rpt9p/537EKRW7KOCzSW5O75teAY6qqq3Q+4UEHDltvZu5lrPrP1qeX+Mb73zyd9rk/AZwdd/rY5N8JckXkvzsdHVqBhr0M+g5NrGfBR6qqrv7ap5fPCVDzOjfYQbw4cuAmvN+BkjyHOAvgXdW1XeA84F/DLwc2Ar80TR2b6Z5TVX9FPAG4Jz235WaQHpf1vUm4C9ayfNr7/g7bTeS/B6wA/hoK20FXlBVrwB+B/hYkoOnq38zyHg/g55jE3sruw4keH4xMEOMu+qAWufnlwF8+LYAx/S9Xgg8OE19mbGSPJ3eD85Hq+rjAFX1UFXtrKofAX/KAfZfkBOpqgfb88PAJ+gdm4faXLjROXEPT18PZ6Q3ALdU1UPg+TUJ451P/k6bQJIVwBuBt1W7yKr9V/cjbflm4BvAi6evlzPDBD+DnmPjSDIX+GXg8tGa59fgDMEM/x1mAB++m4BFSY5tI3DLgQ3T3KcZpc1nuxC4s6re11c/um+1XwK+NrbtgSjJs9uFJiR5NvCL9I7NBmBFW20FcOX09HDG2mXUyPNrt8Y7nzYAy5MclORYYBFw4zT0b8ZJshR4F/CmqvpeX31+uwCYJC+id8zumZ5ezhwT/Ax6jo3vF4CvV9WW0cKBfn6NlyGY4b/DZv03Yc507Wr43wI+A8wBPlJVt09zt2aa1wC/Ctw2elsl4D8Cb03ycnr/NbQZ+NfT070Z5yjgE73fOcwFPlZVn05yE7A+yUrgfuDMaezjjJLkWfTuRNR/Dv2h51dPkkuB1wJHJNkCvBtYy4DzqapuT7IeuIPeNItzDqS7U4wa55idCxwEXNN+Pr9cVW8Hfg74gyQ7gJ3A26tqshck7hfGOV6vHfQz6Dk2+HhV1YU89ToW8PwaL0PM6N9h3oZQkiRJ6pBTUCRJkqQOGcAlSZKkDhnAJUmSpA4ZwCVJkqQOGcAlSZKkDhnAJUmSpA4ZwCVJkqQOGcAlSU+RZCTJ9/u+2IIkRyX5WJJ7ktyc5Pokv7Sb7Xw+yaljau9M8qEk85LcmuQHSY4Y1r5I0kxjAJckjecbVfVy+Ieve/4r4ItV9aKqOonet/It3M02Lm3r9VsOXFpV32/bf3CK+y1JM5oBXJL2A0n+IsmfJPlSkvuS/EySi5P8vyQXTsFHvA74QVV9eLRQVfdV1Qf7+vArSW5so9r/K8kc4ArgjUkOauuMAM8HvjQFfZKkWckALkn7h5cC91TVzwDrgAuBdwEnAr88GoD3wQnALeO9meQlwFuA17RR7Z3A26rqEeBGYGlbdTlweVXVPvZHkmatudPdAUnSvknyTOB5wB+30veBC6tqa3v/e8APpvgz/yfwM/RGxV8JLAFOAm7qzVZhHvBwW310GsqV7fk3prIvkjTbGMAlafY7Abilqn7UXr8MOB8gyUJ6c6znJvl94FnAM6rqN5M8HdilNsFn3A78i9EXVXVOu3ByYysFWFdV5w5o+1fA+5L8FDCvqsYdSZekA4FTUCRp9nsp8H/7Xv8T4Ktt+WVteRW9UelvA89p7w2qjec64JlJzu6rPatv+VrgjCRHAiQ5LMkLAarqu8DngY/QGw2XpAOaI+CSNPu9lN4869HpKPOq6tH23mgYfwVwTlU92dduUG2gqqokpwPvT/K7wDbg7+nNM6eq7kjyn4DPJnka8EPgHOC+tolLgY/z1DuiSNIBJ14HI0n7vyT/HPiXwAPAdVX16UG1vvVHgE9W1Ykd9G0zsLiqvjXsz5KkmcAALkl6iiTHAH8LPDJ6L/AhfMY84HpgPvDSqto+jM+RpJnGAC5JkiR1yIswJUmSpA4ZwCVJkqQOGcAlSZKkDhnAJUmSpA4ZwCVJkqQOGcAlSZKkDhnAJUmSpA4ZwCVJkqQO/X9xrAai0Y9FywAAAABJRU5ErkJggg==\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "zmm_die = organize_leptons(zmm_retrieved_data, 'Ele')\n", + "zmm_dim = organize_leptons(zmm_retrieved_data, 'Mu')\n", + "\n", + "plot_data(zmm_die, zmm_dim)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "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": "3.7.7" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +}