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{
"cells": [
{
"metadata": {
"ExecuteTime": {
"end_time": "2025-05-14T20:03:21.888211Z",
"start_time": "2025-05-14T20:03:12.632050Z"
}
},
"cell_type": "code",
"source": [
"import torch\n",
"import sys\n",
"import os.path as osp\n",
"import os\n",
"import sys\n",
"import numpy as np\n",
"from src.dataset.dataset import SimpleIterDataset, EventDataset, EventDatasetCollection\n",
"from src.utils.utils import to_filelist\n",
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"import matplotlib\n",
"matplotlib.rc('font', size=13)\n",
"from src.plotting.plot_event import plot_event_comparison\n",
"from src.dataset.functions_data import concat_events\n",
"from src.utils.paths import get_path\n",
"from dotenv import load_dotenv\n",
"load_dotenv()"
],
"id": "6bae9707acf4a848",
"outputs": [
{
"data": {
"text/plain": [
"True"
]
},
"execution_count": 1,
"metadata": {},
"output_type": "execute_result"
}
],
"execution_count": 1
},
{
"metadata": {
"ExecuteTime": {
"end_time": "2025-05-14T20:03:22.129786Z",
"start_time": "2025-05-14T20:03:22.120105Z"
}
},
"cell_type": "code",
"source": [
"\n",
"def remove_from_list(lst):\n",
" out = []\n",
" for item in lst:\n",
" if item in [\"hgcal\", \"data.txt\", \"test_file.root\"]:\n",
" continue\n",
" out.append(item)\n",
" return out\n",
"\n",
"#path = \"/eos/user/g/gkrzmanc/jetclustering/data/SVJ_std_UL2018_scouting_test_large/SVJ_mMed-700GeV_mDark-20GeV_rinv-0.7_alpha-peak\"\n",
"def get_iter(path_to_ds):\n",
" return iter(EventDatasetCollection(path_to_ds, args=None))\n",
"\n",
"inputs = {\n",
" \"Delphes\": [\"Delphes_020425_test_PU_PFfix_part0/SVJ_mZprime-900_mDark-20_rinv-0.3_alpha-peak\"],\n",
" \"CMS FullSim\": [\"Feb26_2025_E1000_N500_noPartonFilter_GluonFix_FullF_part0/PFNano_s-channel_mMed-900_mDark-20_rinv-0.3_alpha-peak_13TeV-pythia8_n-1000\",\n",
" \"Feb26_2025_E1000_N500_noPartonFilter_GluonFix_FullF_part1/PFNano_s-channel_mMed-900_mDark-20_rinv-0.3_alpha-peak_13TeV-pythia8_n-1000\",\n",
" \"Feb26_2025_E1000_N500_noPartonFilter_GluonFix_FullF_part2/PFNano_s-channel_mMed-900_mDark-20_rinv-0.3_alpha-peak_13TeV-pythia8_n-1000\",\n",
" \"Feb26_2025_E1000_N500_noPartonFilter_GluonFix_FullF_part3/PFNano_s-channel_mMed-900_mDark-20_rinv-0.3_alpha-peak_13TeV-pythia8_n-1000\",\n",
" \"Feb26_2025_E1000_N500_noPartonFilter_GluonFix_FullF_part4/PFNano_s-channel_mMed-900_mDark-20_rinv-0.3_alpha-peak_13TeV-pythia8_n-1000\"]\n",
"}\n"
],
"id": "e7a7ef680143801e",
"outputs": [],
"execution_count": 2
},
{
"metadata": {
"ExecuteTime": {
"end_time": "2025-05-14T20:03:27.596552Z",
"start_time": "2025-05-14T20:03:27.359469Z"
}
},
"cell_type": "code",
"source": [
"datasets = {\n",
" key: get_iter([get_path(x, \"preprocessed_data\") for x in value]) for key, value in inputs.items()\n",
"}"
],
"id": "1549361c5b028634",
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Getting query for path Delphes_020425_test_PU_PFfix_part0/SVJ_mZprime-900_mDark-20_rinv-0.3_alpha-peak | Preproc. data root= /work/gkrzmanc/jetclustering/preprocessed_data\n",
"get_pfcands_key\n",
"Getting query for path Feb26_2025_E1000_N500_noPartonFilter_GluonFix_FullF_part0/PFNano_s-channel_mMed-900_mDark-20_rinv-0.3_alpha-peak_13TeV-pythia8_n-1000 | Preproc. data root= /work/gkrzmanc/jetclustering/preprocessed_data\n",
"Getting query for path Feb26_2025_E1000_N500_noPartonFilter_GluonFix_FullF_part1/PFNano_s-channel_mMed-900_mDark-20_rinv-0.3_alpha-peak_13TeV-pythia8_n-1000 | Preproc. data root= /work/gkrzmanc/jetclustering/preprocessed_data\n",
"Getting query for path Feb26_2025_E1000_N500_noPartonFilter_GluonFix_FullF_part2/PFNano_s-channel_mMed-900_mDark-20_rinv-0.3_alpha-peak_13TeV-pythia8_n-1000 | Preproc. data root= /work/gkrzmanc/jetclustering/preprocessed_data\n",
"Getting query for path Feb26_2025_E1000_N500_noPartonFilter_GluonFix_FullF_part3/PFNano_s-channel_mMed-900_mDark-20_rinv-0.3_alpha-peak_13TeV-pythia8_n-1000 | Preproc. data root= /work/gkrzmanc/jetclustering/preprocessed_data\n",
"Getting query for path Feb26_2025_E1000_N500_noPartonFilter_GluonFix_FullF_part4/PFNano_s-channel_mMed-900_mDark-20_rinv-0.3_alpha-peak_13TeV-pythia8_n-1000 | Preproc. data root= /work/gkrzmanc/jetclustering/preprocessed_data\n",
"get_pfcands_key\n",
"get_pfcands_key\n",
"get_pfcands_key\n",
"get_pfcands_key\n",
"get_pfcands_key\n"
]
}
],
"execution_count": 3
},
{
"metadata": {
"ExecuteTime": {
"end_time": "2025-04-30T09:52:58.871921Z",
"start_time": "2025-04-30T09:52:58.833485Z"
}
},
"cell_type": "code",
"source": "e.final_parton_level_particles.pid",
"id": "baf454ab625e31d0",
"outputs": [
{
"ename": "NameError",
"evalue": "name 'e' is not defined",
"output_type": "error",
"traceback": [
"\u001B[0;31m---------------------------------------------------------------------------\u001B[0m",
"\u001B[0;31mNameError\u001B[0m Traceback (most recent call last)",
"Cell \u001B[0;32mIn[8], line 1\u001B[0m\n\u001B[0;32m----> 1\u001B[0m \u001B[43me\u001B[49m\u001B[38;5;241m.\u001B[39mfinal_parton_level_particles\u001B[38;5;241m.\u001B[39mpid\n",
"\u001B[0;31mNameError\u001B[0m: name 'e' is not defined"
]
}
],
"execution_count": 8
},
{
"metadata": {
"ExecuteTime": {
"end_time": "2025-04-30T12:04:56.009469Z",
"start_time": "2025-04-30T12:04:55.991970Z"
}
},
"cell_type": "code",
"source": "e = next(datasets[\"Delphes\"])",
"id": "9240584690041d12",
"outputs": [],
"execution_count": 4
},
{
"metadata": {
"ExecuteTime": {
"end_time": "2025-05-13T08:51:59.698564Z",
"start_time": "2025-05-13T08:51:58.939680Z"
}
},
"cell_type": "code",
"source": [
"pid_masses = {}\n",
"for i in range(100):\n",
" e = next(datasets[\"CMS FullSim\"])\n",
" for i in range(len(e.pfcands)):\n",
" pid = e.pfcands.pid[i].item()\n",
" if pid not in pid_masses:\n",
" pid_masses[pid] = []\n",
" pid_masses[pid].append(e.pfcands.mass[i].item())"
],
"id": "f16775ce378fd545",
"outputs": [],
"execution_count": 6
},
{
"metadata": {},
"cell_type": "code",
"source": "e = next(datasets[\"CMS FullSim\"])",
"id": "3b87ced12eea20ac",
"outputs": [],
"execution_count": null
},
{
"metadata": {},
"cell_type": "code",
"source": "pid_masses[211]",
"id": "b7c865969840fb02",
"outputs": [],
"execution_count": null
},
{
"metadata": {
"ExecuteTime": {
"end_time": "2025-05-14T20:03:31.983545Z",
"start_time": "2025-05-14T20:03:31.961690Z"
}
},
"cell_type": "code",
"source": [
"from tqdm import tqdm\n",
"ch_pids = torch.tensor([211, -211])\n",
"nh_pids = torch.tensor([130, 2112.0])\n",
"def get_stats(ds):\n",
" LE_pfcands_PID= []\n",
" result = {\n",
" \"n_pfcands\": [],\n",
" \"pfcands_pt\": [],\n",
" \"pfcands_eta\": [],\n",
" \"pfcands_phi\": [],\n",
" \"pfcands_pid\": [],\n",
" \"pfcands_mass\": [],\n",
" \"n_genp\": [],\n",
" \"n_parton_level\": [],\n",
" \"genp_pt\": [],\n",
" \"parton_level_pt\": [],\n",
" \"pt_ch\": [], # low-pt CH\n",
" \"pt_nh\": [], # low-pt NH\n",
" \"pt_gamma\": [],\n",
" \"E_vis\": [],\n",
" \"n_ch\": [],\n",
" \"n_nh\": [],\n",
" \"n_gamma\": []\n",
" # \"n_dq\": []\n",
" }\n",
" for _ in tqdm(range(10000)):\n",
" event = next(ds)\n",
" result[\"n_pfcands\"].append(len(event.pfcands))\n",
" result[\"pfcands_pt\"] += torch.log10(event.pfcands.pt).tolist()\n",
" result[\"pfcands_eta\"] += event.pfcands.eta.tolist()\n",
" result[\"pfcands_phi\"] += event.pfcands.phi.tolist()\n",
" result[\"pfcands_pid\"] += event.pfcands.pid.tolist()\n",
" result[\"pfcands_mass\"] += event.pfcands.mass.tolist()\n",
" result[\"n_genp\"].append(len(event.final_gen_particles))\n",
" result[\"n_parton_level\"].append(len(event.final_parton_level_particles))\n",
" result[\"genp_pt\"] += torch.log10(event.final_gen_particles.pt).tolist()\n",
" result[\"parton_level_pt\"] += torch.log10(event.final_parton_level_particles.pt).tolist()\n",
"# result[\"pt_ch\"] += event.pfcands.pt[event.pfcands.pid.isin(ch_pids)].tolist()\n",
" #result[\"n_dq\"].append(len(event.matrix_element_gen_particles))\n",
" result[\"pt_ch\"] += torch.log10(event.pfcands.pt[torch.isin(event.pfcands.pid, ch_pids)]).tolist()\n",
" result[\"pt_nh\"] += torch.log10(event.pfcands.pt[torch.isin(event.pfcands.pid, nh_pids)]).tolist()\n",
" result[\"pt_gamma\"] += torch.log10(event.pfcands.pt[event.pfcands.pid == 22]).tolist()\n",
" result[\"E_vis\"].append(torch.sum(event.pfcands.E).item())\n",
" result[\"n_ch\"].append(torch.isin(event.pfcands.pid, ch_pids).sum().item())\n",
" result[\"n_nh\"].append(torch.isin(event.pfcands.pid, nh_pids).sum().item())\n",
" result[\"n_gamma\"].append((event.pfcands.pid == 22).sum().item())\n",
" return result, LE_pfcands_PID"
],
"id": "e0d491f2943f20e9",
"outputs": [],
"execution_count": 4
},
{
"metadata": {
"ExecuteTime": {
"end_time": "2025-05-14T20:04:32.479877Z",
"start_time": "2025-05-14T20:03:32.248813Z"
}
},
"cell_type": "code",
"source": [
"results = {\n",
" key: get_stats(value)[0] for key, value in datasets.items()\n",
"}\n",
"\n",
"#results_PID = {\n",
"# key: get_stats(value)[1] for key, value in datasets.items()\n",
"#}"
],
"id": "87c6ab0ccf50fa58",
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"100%|██████████| 10000/10000 [00:25<00:00, 397.09it/s]\n",
"100%|██████████| 10000/10000 [00:35<00:00, 285.44it/s]\n"
]
}
],
"execution_count": 5
},
{
"metadata": {
"ExecuteTime": {
"end_time": "2025-05-13T09:10:44.582809Z",
"start_time": "2025-05-13T09:10:44.554540Z"
}
},
"cell_type": "code",
"source": [
"ev = next(datasets[\"Delphes\"])\n",
"print(ev.pfcands.pid)\n",
"print(ev.pfcands.eta)"
],
"id": "60d9dfce90fc3301",
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"tensor([ -211., -211., 211., 211., -211., -211., -211., 2212., 211.,\n",
" 321., 211., -211., 211., 211., -211., 211., 321., -321.,\n",
" -211., -211., 13., -211., -211., -211., -211., 211., 211.,\n",
" 2212., -2212., 211., 211., -211., 211., -211., -321., 2212.,\n",
" -211., 321., -211., 211., -2212., -211., 211., 211., -211.,\n",
" 211., 211., -211., 211., -211., 2212., -211., 211., 321.,\n",
" 2212., 211., -2212., 211., -211., -211., 211., 211., 211.,\n",
" -211., 211., -211., -211., -211., -211., -211., 211., 211.,\n",
" 211., 211., -2212., 211., -211., 211., 321., -321., 211.,\n",
" -211., -211., -211., -211., -211., -211., 2212., 211., 211.,\n",
" 22., 22., 22., 22., 22., 22., 22., 22., 22.,\n",
" 22., 22., 22., 22., 22., 22., 22., 22., 22.,\n",
" 22., 22., 22., 22., 22., 22., 22., 22., 22.,\n",
" 22., 22., 22., 22., 22., 22., 22., 22., 22.,\n",
" 22., 22., 22., 22., 22., 22., 22., 22., 22.,\n",
" 22., 22., 22., 22., 22., 22., 22., 22., 22.,\n",
" 22., 22., 22., 22., 22., 22., 22., 22., 22.,\n",
" 22., 22., 22., 22., 22., 22., 22., 22., 22.,\n",
" 22., 22., 22., 22., 22., 22., 22., 22., 22.,\n",
" 22., 22., 22., 22., 22., 22., 22., 22., 22.,\n",
" 22., 22., 22., 22., 22., 22., 22., 22., 22.,\n",
" 22., 22., 22., 22., 22., 22., 22., 22., 22.,\n",
" 22., 22., 22., 22., 22., 22., 22., 22., 22.,\n",
" 22., 22., 22., 22., 22., 22., 22., 22., 22.,\n",
" 22., 22., 22., 22., 22., 22., 22., 22., 22.,\n",
" 22., 22., 22., 22., 22., 22., 22., 22., 22.,\n",
" 22., 22., 22., 22., 22., 22., 22., 22., 22.,\n",
" 22., 22., 22., 22., 22., 22., 22., 22., 22.,\n",
" 22., 22., 22., 22., 22., 22., 22., 22., 22.,\n",
" 22., 22., 22., 22., 22., 22., 22., 22., 22.,\n",
" 22., 22., 22., 22., 22., 22., 22., 22., 22.,\n",
" 22., 22., 22., 22., 22., 22., 22., 22., 0.,\n",
" 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
" 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
" 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
" 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
" 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
" 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
" 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
" 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
" 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
" 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
" 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
" 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
" 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
" 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
" 0., 0., 0., 0.], dtype=torch.float64)\n",
"tensor([-2.3296e+00, -2.3419e+00, -2.2888e+00, -2.2427e+00, -2.0035e+00,\n",
" -1.9182e+00, -1.7687e+00, -1.6575e+00, -1.5705e+00, -1.5510e+00,\n",
" -1.5122e+00, -1.5308e+00, -1.4512e+00, -1.3991e+00, -1.4467e+00,\n",
" -1.3438e+00, -1.3369e+00, -1.3319e+00, -1.3057e+00, -1.3777e+00,\n",
" -1.3628e+00, -1.1651e+00, -7.1665e-01, -7.5901e-01, -6.2174e-01,\n",
" -5.3450e-01, -5.8215e-01, -4.3081e-01, -3.0314e-01, -2.6323e-01,\n",
" -2.2304e-01, -1.8626e-01, -6.1589e-02, -2.1350e-02, -3.8317e-02,\n",
" 8.4537e-02, 7.1163e-02, 8.2808e-02, 1.0621e-01, 9.5661e-02,\n",
" 1.2104e-01, 2.8841e-01, 2.9900e-01, 3.1743e-01, 2.8026e-01,\n",
" 4.7656e-01, 9.7945e-01, 1.0045e+00, 9.7821e-01, 1.1411e+00,\n",
" 1.1461e+00, 1.2028e+00, 1.1635e+00, 1.3571e+00, 1.3081e+00,\n",
" 1.3183e+00, 1.4194e+00, 1.4447e+00, 1.4046e+00, 1.4660e+00,\n",
" 1.4236e+00, 1.5504e+00, 1.4970e+00, 1.6392e+00, 1.7054e+00,\n",
" 1.7276e+00, 1.7297e+00, 1.6749e+00, 1.6981e+00, 1.8182e+00,\n",
" 1.7623e+00, 1.8918e+00, 1.8611e+00, 1.8781e+00, 2.0039e+00,\n",
" 2.0201e+00, 2.0069e+00, 1.9426e+00, 1.9770e+00, 1.9896e+00,\n",
" 2.1409e+00, 2.0636e+00, 2.3024e+00, 2.2780e+00, 2.1801e+00,\n",
" 2.1831e+00, 2.2532e+00, 2.2503e+00, 2.3362e+00, 2.3564e+00,\n",
" -2.3982e+00, -2.3817e+00, -2.3568e+00, -2.3634e+00, -2.3399e+00,\n",
" -2.2820e+00, -2.2661e+00, -2.2641e+00, -2.2505e+00, -2.2231e+00,\n",
" -2.1852e+00, -2.1669e+00, -2.0865e+00, -2.0821e+00, -2.0730e+00,\n",
" -2.0419e+00, -2.0387e+00, -2.0028e+00, -2.0106e+00, -1.9600e+00,\n",
" -1.9628e+00, -1.9500e+00, -1.9509e+00, -1.9362e+00, -1.9166e+00,\n",
" -1.9285e+00, -1.9020e+00, -1.9019e+00, -1.9127e+00, -1.8655e+00,\n",
" -1.8309e+00, -1.8309e+00, -1.8203e+00, -1.8035e+00, -1.8080e+00,\n",
" -1.8014e+00, -1.7744e+00, -1.7268e+00, -1.6515e+00, -1.6413e+00,\n",
" -1.5739e+00, -1.5823e+00, -1.5328e+00, -1.4862e+00, -1.4698e+00,\n",
" -1.4443e+00, -1.3977e+00, -1.3856e+00, -1.3577e+00, -1.3433e+00,\n",
" -1.3439e+00, -1.3355e+00, -1.3105e+00, -1.3017e+00, -1.3038e+00,\n",
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" -1.1503e+00, -1.1485e+00, -1.1530e+00, -1.0762e+00, -1.0490e+00,\n",
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" -4.6473e-01, -4.3213e-01, -3.8526e-01, -3.8403e-01, -3.6908e-01,\n",
" -3.5098e-01, -1.7645e-01, -1.5925e-01, -1.5969e-01, -1.3599e-01,\n",
" -7.9646e-02, -7.6998e-02, 9.3435e-03, 1.2693e-02, 1.2792e-03,\n",
" 3.0444e-02, 3.9928e-02, 3.7316e-02, 5.5591e-02, 7.2189e-02,\n",
" 7.8277e-02, 1.1180e-01, 1.2563e-01, 1.4195e-01, 1.4067e-01,\n",
" 1.5710e-01, 1.6616e-01, 1.7680e-01, 1.8215e-01, 2.8873e-01,\n",
" 2.9478e-01, 3.0073e-01, 3.0561e-01, 3.2253e-01, 3.2248e-01,\n",
" 3.4815e-01, 3.8435e-01, 4.4838e-01, 4.8266e-01, 5.8324e-01,\n",
" 6.1958e-01, 6.4221e-01, 6.6830e-01, 7.0539e-01, 7.3842e-01,\n",
" 7.5799e-01, 8.0994e-01, 8.0769e-01, 8.4399e-01, 8.9490e-01,\n",
" 8.9686e-01, 8.9048e-01, 9.2633e-01, 1.0361e+00, 1.0936e+00,\n",
" 1.0812e+00, 1.1154e+00, 1.1470e+00, 1.1460e+00, 1.1656e+00,\n",
" 1.1700e+00, 1.1798e+00, 1.1765e+00, 1.1860e+00, 1.2078e+00,\n",
" 1.2313e+00, 1.2569e+00, 1.2722e+00, 1.2794e+00, 1.2748e+00,\n",
" 1.2947e+00, 1.2940e+00, 1.3166e+00, 1.3193e+00, 1.3669e+00,\n",
" 1.4039e+00, 1.3981e+00, 1.4154e+00, 1.4412e+00, 1.6286e+00,\n",
" 1.6277e+00, 1.6349e+00, 1.6406e+00, 1.6420e+00, 1.6641e+00,\n",
" 1.6672e+00, 1.6568e+00, 1.6972e+00, 1.7200e+00, 1.7401e+00,\n",
" 1.7746e+00, 1.7978e+00, 1.8077e+00, 1.8001e+00, 1.8443e+00,\n",
" 1.8455e+00, 1.8621e+00, 1.8777e+00, 1.9216e+00, 1.9216e+00,\n",
" 1.9337e+00, 1.9447e+00, 1.9463e+00, 1.9688e+00, 1.9943e+00,\n",
" 2.0017e+00, 2.0304e+00, 2.0290e+00, 2.1523e+00, 2.1873e+00,\n",
" 2.1857e+00, 2.2069e+00, 2.2201e+00, 2.2317e+00, 2.2487e+00,\n",
" 2.2689e+00, 2.2658e+00, 2.2984e+00, 2.3055e+00, 2.3153e+00,\n",
" 2.3277e+00, 2.3164e+00, -2.3537e+00, -2.3556e+00, -2.3951e+00,\n",
" -2.2735e+00, -2.2700e+00, -2.2406e+00, -2.2392e+00, -2.2806e+00,\n",
" -2.2649e+00, -2.2264e+00, -2.1847e+00, -2.1721e+00, -2.2998e+00,\n",
" -2.3074e+00, -2.2730e+00, -2.0876e+00, -2.1041e+00, -2.1365e+00,\n",
" -2.0971e+00, -2.1630e+00, -2.0427e+00, -1.9508e+00, -2.0174e+00,\n",
" -1.9918e+00, -1.9762e+00, -1.9464e+00, -2.0405e+00, -1.9158e+00,\n",
" -1.8576e+00, -1.9262e+00, -1.8580e+00, -1.8908e+00, -1.7837e+00,\n",
" -1.7404e+00, -1.8266e+00, -1.7896e+00, -1.7744e+00, -1.7861e+00,\n",
" -1.6578e+00, -1.7214e+00, -1.5828e+00, -1.6093e+00, -1.5578e+00,\n",
" -1.5591e+00, -1.4846e+00, -1.4993e+00, -1.5091e+00, -1.4577e+00,\n",
" -1.4351e+00, -1.3813e+00, -1.3467e+00, -1.3686e+00, -1.2636e+00,\n",
" -1.2187e+00, -1.2381e+00, -1.0476e+00, -1.0450e+00, -9.5719e-01,\n",
" -6.6280e-01, -6.7957e-01, -5.2311e-01, -3.9292e-01, -3.4694e-01,\n",
" -2.7141e-01, -2.1141e-01, -1.9284e-01, -2.9014e-02, -8.2851e-02,\n",
" 1.1202e-01, 2.6212e-01, 3.3185e-01, 2.7445e-01, 3.0566e-01,\n",
" 4.7407e-01, 5.3634e-01, 7.0303e-01, 8.4515e-01, 8.8403e-01,\n",
" 9.6265e-01, 1.0807e+00, 1.2028e+00, 1.2579e+00, 1.2459e+00,\n",
" 1.3814e+00, 1.4318e+00, 1.4390e+00, 1.5167e+00, 1.4945e+00,\n",
" 1.6372e+00, 1.5679e+00, 1.5791e+00, 1.7265e+00, 1.6831e+00,\n",
" 1.7620e+00, 1.8049e+00, 1.7676e+00, 1.8118e+00, 1.9101e+00,\n",
" 1.8410e+00, 1.8918e+00, 1.8759e+00, 1.8738e+00, 1.9979e+00,\n",
" 1.9510e+00, 1.9407e+00, 1.9732e+00, 2.0328e+00, 1.9553e+00,\n",
" 1.9690e+00, 2.1409e+00, 2.0641e+00, 2.1046e+00, 2.1248e+00,\n",
" 2.1466e+00, 2.1669e+00, 2.1427e+00, 2.0848e+00, 2.0757e+00,\n",
" 2.1897e+00, 2.2124e+00, 2.1922e+00, 2.2634e+00, 2.1988e+00,\n",
" 2.2759e+00, 2.3182e+00, 2.1913e+00, 2.2827e+00, 2.2021e+00,\n",
" 2.2547e+00, 2.2670e+00, 2.3976e+00], dtype=torch.float64)\n"
]
}
],
"execution_count": 32
},
{
"metadata": {
"ExecuteTime": {
"end_time": "2025-04-30T10:10:59.379157Z",
"start_time": "2025-04-30T10:10:58.996269Z"
}
},
"cell_type": "code",
"source": [
"import pandas as pd\n",
"for key in results:\n",
" print(\"#######\", key, \"#######\")\n",
" pids = results[key][\"pfcands_pid\"]\n",
" print(pd.Series(pids).value_counts(normalize=True))\n",
" print(\"dq\", pd.Series(results[key][\"n_dq\"]).value_counts())"
],
"id": "1fbd0a62f4b32c62",
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"####### Delphes #######\n",
"211.0 0.591530\n",
"22.0 0.257798\n",
"2112.0 0.150672\n",
"Name: proportion, dtype: float64\n"
]
},
{
"ename": "KeyError",
"evalue": "'n_dq'",
"output_type": "error",
"traceback": [
"\u001B[0;31m---------------------------------------------------------------------------\u001B[0m",
"\u001B[0;31mKeyError\u001B[0m Traceback (most recent call last)",
"Cell \u001B[0;32mIn[35], line 6\u001B[0m\n\u001B[1;32m 4\u001B[0m pids \u001B[38;5;241m=\u001B[39m results[key][\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mpfcands_pid\u001B[39m\u001B[38;5;124m\"\u001B[39m]\n\u001B[1;32m 5\u001B[0m \u001B[38;5;28mprint\u001B[39m(pd\u001B[38;5;241m.\u001B[39mSeries(pids)\u001B[38;5;241m.\u001B[39mvalue_counts(normalize\u001B[38;5;241m=\u001B[39m\u001B[38;5;28;01mTrue\u001B[39;00m))\n\u001B[0;32m----> 6\u001B[0m \u001B[38;5;28mprint\u001B[39m(\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mdq\u001B[39m\u001B[38;5;124m\"\u001B[39m, pd\u001B[38;5;241m.\u001B[39mSeries(\u001B[43mresults\u001B[49m\u001B[43m[\u001B[49m\u001B[43mkey\u001B[49m\u001B[43m]\u001B[49m\u001B[43m[\u001B[49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[38;5;124;43mn_dq\u001B[39;49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[43m]\u001B[49m)\u001B[38;5;241m.\u001B[39mvalue_counts())\n",
"\u001B[0;31mKeyError\u001B[0m: 'n_dq'"
]
}
],
"execution_count": 35
},
{
"metadata": {
"ExecuteTime": {
"end_time": "2025-05-14T20:11:07.612392Z",
"start_time": "2025-05-14T20:11:03.681914Z"
}
},
"cell_type": "code",
"source": [
"bins = {\n",
" \"n_pfcands\": np.linspace(0, 600, 50),\n",
" \"pfcands_pt\": np.linspace(-0.6, 2, 200),\n",
" \"pfcands_eta\": np.linspace(-2.4, 2.4, 200),\n",
" \"pfcands_phi\": np.linspace(-3.14, 3.14, 200),\n",
" \"pfcands_mass\": np.linspace(0, 2, 100),\n",
" \"n_genp\": np.linspace(0,600,50),\n",
" \"n_parton_level\": np.linspace(0,600,50),\n",
" \"genp_pt\": np.linspace(-1, 3, 200),\n",
" \"parton_level_pt\": np.linspace(-1, 3, 200),\n",
" \"pt_ch\": np.linspace(-1, 3, 200),\n",
" \"pt_nh\": np.linspace(-1, 3, 200),\n",
" \"pt_gamma\": np.linspace(-1, 3, 200),\n",
" \"E_vis\": np.linspace(500, 10000, 200),\n",
" \"n_gamma\": np.linspace(0, 1200, 200),\n",
" \"n_ch\": np.linspace(0, 1200, 200),\n",
" \"n_nh\": np.linspace(0, 1200, 200),\n",
" #\"n_dq\": np.linspace(0, 3, 3)\n",
"}\n",
"fig, ax = plt.subplots(10, 2, figsize=(10, 20))\n",
"for key in results:\n",
" for i, (k, v) in enumerate(results[key].items()):\n",
" if k == \"pfcands_pid\":\n",
" continue\n",
" ax[i // 2, i % 2].hist(v, bins=bins[k], alpha=0.5, label=key, density=\"pt\" in k)\n",
" ax[i // 2, i % 2].set_title(k)\n",
" if k == \"pfcands_pt\" or \"mass\" in k:# or \"_pt\" in k:\n",
" if not k == \"pfcands_pt\":\n",
" ax[i//2, i%2].set_yscale(\"log\")\n",
" #ax[i//2, i%2].set_xscale(\"log\")\n",
" ax[i // 2, i % 2].legend()\n",
"\n",
"fig.show()\n"
],
"id": "d819dae53f16cf8",
"outputs": [
{
"ename": "KeyboardInterrupt",
"evalue": "",
"output_type": "error",
"traceback": [
"\u001B[0;31m---------------------------------------------------------------------------\u001B[0m",
"\u001B[0;31mKeyboardInterrupt\u001B[0m Traceback (most recent call last)",
"Cell \u001B[0;32mIn[12], line 25\u001B[0m\n\u001B[1;32m 23\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m k \u001B[38;5;241m==\u001B[39m \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mpfcands_pid\u001B[39m\u001B[38;5;124m\"\u001B[39m:\n\u001B[1;32m 24\u001B[0m \u001B[38;5;28;01mcontinue\u001B[39;00m\n\u001B[0;32m---> 25\u001B[0m \u001B[43max\u001B[49m\u001B[43m[\u001B[49m\u001B[43mi\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;241;43m/\u001B[39;49m\u001B[38;5;241;43m/\u001B[39;49m\u001B[43m \u001B[49m\u001B[38;5;241;43m2\u001B[39;49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mi\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;241;43m%\u001B[39;49m\u001B[43m \u001B[49m\u001B[38;5;241;43m2\u001B[39;49m\u001B[43m]\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mhist\u001B[49m\u001B[43m(\u001B[49m\u001B[43mv\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mbins\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mbins\u001B[49m\u001B[43m[\u001B[49m\u001B[43mk\u001B[49m\u001B[43m]\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43malpha\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;241;43m0.5\u001B[39;49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mlabel\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mkey\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mdensity\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[38;5;124;43mpt\u001B[39;49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[43m \u001B[49m\u001B[38;5;129;43;01min\u001B[39;49;00m\u001B[43m \u001B[49m\u001B[43mk\u001B[49m\u001B[43m)\u001B[49m\n\u001B[1;32m 26\u001B[0m ax[i \u001B[38;5;241m/\u001B[39m\u001B[38;5;241m/\u001B[39m \u001B[38;5;241m2\u001B[39m, i \u001B[38;5;241m%\u001B[39m \u001B[38;5;241m2\u001B[39m]\u001B[38;5;241m.\u001B[39mset_title(k)\n\u001B[1;32m 27\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m k \u001B[38;5;241m==\u001B[39m \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mpfcands_pt\u001B[39m\u001B[38;5;124m\"\u001B[39m \u001B[38;5;129;01mor\u001B[39;00m \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mmass\u001B[39m\u001B[38;5;124m\"\u001B[39m \u001B[38;5;129;01min\u001B[39;00m k:\u001B[38;5;66;03m# or \"_pt\" in k:\u001B[39;00m\n",
"File \u001B[0;32m/work/gkrzmanc/1gatr/lib/python3.10/site-packages/matplotlib/_api/deprecation.py:453\u001B[0m, in \u001B[0;36mmake_keyword_only.<locals>.wrapper\u001B[0;34m(*args, **kwargs)\u001B[0m\n\u001B[1;32m 447\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28mlen\u001B[39m(args) \u001B[38;5;241m>\u001B[39m name_idx:\n\u001B[1;32m 448\u001B[0m warn_deprecated(\n\u001B[1;32m 449\u001B[0m since, message\u001B[38;5;241m=\u001B[39m\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mPassing the \u001B[39m\u001B[38;5;132;01m%(name)s\u001B[39;00m\u001B[38;5;124m \u001B[39m\u001B[38;5;132;01m%(obj_type)s\u001B[39;00m\u001B[38;5;124m \u001B[39m\u001B[38;5;124m\"\u001B[39m\n\u001B[1;32m 450\u001B[0m \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mpositionally is deprecated since Matplotlib \u001B[39m\u001B[38;5;132;01m%(since)s\u001B[39;00m\u001B[38;5;124m; the \u001B[39m\u001B[38;5;124m\"\u001B[39m\n\u001B[1;32m 451\u001B[0m \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mparameter will become keyword-only in \u001B[39m\u001B[38;5;132;01m%(removal)s\u001B[39;00m\u001B[38;5;124m.\u001B[39m\u001B[38;5;124m\"\u001B[39m,\n\u001B[1;32m 452\u001B[0m name\u001B[38;5;241m=\u001B[39mname, obj_type\u001B[38;5;241m=\u001B[39m\u001B[38;5;124mf\u001B[39m\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mparameter of \u001B[39m\u001B[38;5;132;01m{\u001B[39;00mfunc\u001B[38;5;241m.\u001B[39m\u001B[38;5;18m__name__\u001B[39m\u001B[38;5;132;01m}\u001B[39;00m\u001B[38;5;124m()\u001B[39m\u001B[38;5;124m\"\u001B[39m)\n\u001B[0;32m--> 453\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[43mfunc\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[43margs\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[43mkwargs\u001B[49m\u001B[43m)\u001B[49m\n",
"File \u001B[0;32m/work/gkrzmanc/1gatr/lib/python3.10/site-packages/matplotlib/__init__.py:1521\u001B[0m, in \u001B[0;36m_preprocess_data.<locals>.inner\u001B[0;34m(ax, data, *args, **kwargs)\u001B[0m\n\u001B[1;32m 1518\u001B[0m \u001B[38;5;129m@functools\u001B[39m\u001B[38;5;241m.\u001B[39mwraps(func)\n\u001B[1;32m 1519\u001B[0m \u001B[38;5;28;01mdef\u001B[39;00m\u001B[38;5;250m \u001B[39m\u001B[38;5;21minner\u001B[39m(ax, \u001B[38;5;241m*\u001B[39margs, data\u001B[38;5;241m=\u001B[39m\u001B[38;5;28;01mNone\u001B[39;00m, \u001B[38;5;241m*\u001B[39m\u001B[38;5;241m*\u001B[39mkwargs):\n\u001B[1;32m 1520\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m data \u001B[38;5;129;01mis\u001B[39;00m \u001B[38;5;28;01mNone\u001B[39;00m:\n\u001B[0;32m-> 1521\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[43mfunc\u001B[49m\u001B[43m(\u001B[49m\n\u001B[1;32m 1522\u001B[0m \u001B[43m \u001B[49m\u001B[43max\u001B[49m\u001B[43m,\u001B[49m\n\u001B[1;32m 1523\u001B[0m \u001B[43m \u001B[49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[38;5;28;43mmap\u001B[39;49m\u001B[43m(\u001B[49m\u001B[43mcbook\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43msanitize_sequence\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43margs\u001B[49m\u001B[43m)\u001B[49m\u001B[43m,\u001B[49m\n\u001B[1;32m 1524\u001B[0m \u001B[43m \u001B[49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[43m{\u001B[49m\u001B[43mk\u001B[49m\u001B[43m:\u001B[49m\u001B[43m \u001B[49m\u001B[43mcbook\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43msanitize_sequence\u001B[49m\u001B[43m(\u001B[49m\u001B[43mv\u001B[49m\u001B[43m)\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;28;43;01mfor\u001B[39;49;00m\u001B[43m \u001B[49m\u001B[43mk\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mv\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;129;43;01min\u001B[39;49;00m\u001B[43m \u001B[49m\u001B[43mkwargs\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mitems\u001B[49m\u001B[43m(\u001B[49m\u001B[43m)\u001B[49m\u001B[43m}\u001B[49m\u001B[43m)\u001B[49m\n\u001B[1;32m 1526\u001B[0m bound \u001B[38;5;241m=\u001B[39m new_sig\u001B[38;5;241m.\u001B[39mbind(ax, \u001B[38;5;241m*\u001B[39margs, \u001B[38;5;241m*\u001B[39m\u001B[38;5;241m*\u001B[39mkwargs)\n\u001B[1;32m 1527\u001B[0m auto_label \u001B[38;5;241m=\u001B[39m (bound\u001B[38;5;241m.\u001B[39marguments\u001B[38;5;241m.\u001B[39mget(label_namer)\n\u001B[1;32m 1528\u001B[0m \u001B[38;5;129;01mor\u001B[39;00m bound\u001B[38;5;241m.\u001B[39mkwargs\u001B[38;5;241m.\u001B[39mget(label_namer))\n",
"File \u001B[0;32m/work/gkrzmanc/1gatr/lib/python3.10/site-packages/matplotlib/axes/_axes.py:7006\u001B[0m, in \u001B[0;36mAxes.hist\u001B[0;34m(self, x, bins, range, density, weights, cumulative, bottom, histtype, align, orientation, rwidth, log, color, label, stacked, **kwargs)\u001B[0m\n\u001B[1;32m 7003\u001B[0m stacked \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;01mTrue\u001B[39;00m\n\u001B[1;32m 7005\u001B[0m \u001B[38;5;66;03m# Massage 'x' for processing.\u001B[39;00m\n\u001B[0;32m-> 7006\u001B[0m x \u001B[38;5;241m=\u001B[39m \u001B[43mcbook\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m_reshape_2D\u001B[49m\u001B[43m(\u001B[49m\u001B[43mx\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;124;43m'\u001B[39;49m\u001B[38;5;124;43mx\u001B[39;49m\u001B[38;5;124;43m'\u001B[39;49m\u001B[43m)\u001B[49m\n\u001B[1;32m 7007\u001B[0m nx \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mlen\u001B[39m(x) \u001B[38;5;66;03m# number of datasets\u001B[39;00m\n\u001B[1;32m 7009\u001B[0m \u001B[38;5;66;03m# Process unit information. _process_unit_info sets the unit and\u001B[39;00m\n\u001B[1;32m 7010\u001B[0m \u001B[38;5;66;03m# converts the first dataset; then we convert each following dataset\u001B[39;00m\n\u001B[1;32m 7011\u001B[0m \u001B[38;5;66;03m# one at a time.\u001B[39;00m\n",
"File \u001B[0;32m/work/gkrzmanc/1gatr/lib/python3.10/site-packages/matplotlib/cbook.py:1409\u001B[0m, in \u001B[0;36m_reshape_2D\u001B[0;34m(X, name)\u001B[0m\n\u001B[1;32m 1407\u001B[0m \u001B[38;5;28;01melse\u001B[39;00m:\n\u001B[1;32m 1408\u001B[0m is_1d \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;01mFalse\u001B[39;00m\n\u001B[0;32m-> 1409\u001B[0m xi \u001B[38;5;241m=\u001B[39m \u001B[43mnp\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43masanyarray\u001B[49m\u001B[43m(\u001B[49m\u001B[43mxi\u001B[49m\u001B[43m)\u001B[49m\n\u001B[1;32m 1410\u001B[0m nd \u001B[38;5;241m=\u001B[39m np\u001B[38;5;241m.\u001B[39mndim(xi)\n\u001B[1;32m 1411\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m nd \u001B[38;5;241m>\u001B[39m \u001B[38;5;241m1\u001B[39m:\n",
"\u001B[0;31mKeyboardInterrupt\u001B[0m: "
]
},
{
"data": {
"text/plain": [
"<Figure size 1000x2000 with 20 Axes>"
],
"image/png": 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"
},
"metadata": {},
"output_type": "display_data"
}
],
"execution_count": 12
},
{
"metadata": {
"ExecuteTime": {
"end_time": "2025-04-30T09:56:02.626682Z",
"start_time": "2025-04-30T09:56:02.572415Z"
}
},
"cell_type": "code",
"source": "min(results[\"Delphes\"][\"pfcands_pt\"])",
"id": "379df7edfcf5942d",
"outputs": [
{
"data": {
"text/plain": [
"-0.3010289602264827"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"execution_count": 17
},
{
"metadata": {
"ExecuteTime": {
"end_time": "2025-04-30T09:56:04.424861Z",
"start_time": "2025-04-30T09:56:04.395017Z"
}
},
"cell_type": "code",
"source": "min(results[\"CMS FullSim\"][\"pfcands_pt\"])\n",
"id": "ddc61b4dc9883c28",
"outputs": [
{
"data": {
"text/plain": [
"-0.22177806941733907"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"execution_count": 18
},
{
"metadata": {
"ExecuteTime": {
"end_time": "2025-04-30T09:56:12.573254Z",
"start_time": "2025-04-30T09:56:12.545197Z"
}
},
"cell_type": "code",
"source": "min(results[\"CMS FullSim\"][\"pfcands_eta\"])\n",
"id": "1205b61b7f7623d3",
"outputs": [
{
"data": {
"text/plain": [
"-2.3984375"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"execution_count": 19
},
{
"metadata": {
"ExecuteTime": {
"end_time": "2025-05-13T09:08:14.486690Z",
"start_time": "2025-05-13T09:08:14.481653Z"
}
},
"cell_type": "code",
"source": "np.array(results[\"Delphes\"][\"n_nh\"])\n",
"id": "9f80bcfce6445fae",
"outputs": [
{
"data": {
"text/plain": [
"array([0, 0, 0, ..., 0, 0, 0])"
]
},
"execution_count": 27,
"metadata": {},
"output_type": "execute_result"
}
],
"execution_count": 27
},
{
"metadata": {
"ExecuteTime": {
"end_time": "2025-04-30T09:56:18.234763Z",
"start_time": "2025-04-30T09:56:18.131626Z"
}
},
"cell_type": "code",
"source": "t = torch.tensor(results[\"CMS FullSim\"][\"pfcands_pt\"])",
"id": "f412edaf53f77bad",
"outputs": [],
"execution_count": 21
},
{
"metadata": {
"ExecuteTime": {
"end_time": "2025-04-30T09:56:18.506369Z",
"start_time": "2025-04-30T09:56:18.494594Z"
}
},
"cell_type": "code",
"source": "t[t<0.222]",
"id": "88b97beb3a57c575",
"outputs": [
{
"data": {
"text/plain": [
"tensor([ 0.2212, 0.2194, 0.2191, ..., -0.2158, -0.2172, -0.2200])"
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"execution_count": 22
},
{
"metadata": {
"ExecuteTime": {
"end_time": "2025-04-30T09:56:18.878548Z",
"start_time": "2025-04-30T09:56:18.777117Z"
}
},
"cell_type": "code",
"source": [
"import pandas as pd\n",
"for key in results_PID:\n",
" print(key, \"number of PFCands in sample:\", len(results_PID[key]))\n",
" print(pd.value_counts(pd.Series(results_PID[key]), normalize=False))\n"
],
"id": "461362524bad047f",
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Delphes number of PFCands in sample: 426711\n",
"211.0 306595\n",
"22.0 95529\n",
"2112.0 24587\n",
"Name: count, dtype: int64\n",
"CMS FullSim number of PFCands in sample: 149427\n",
" 22.0 75853\n",
" 130.0 28642\n",
" 211.0 22569\n",
"-211.0 22363\n",
"Name: count, dtype: int64\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/tmp/ipykernel_61141/3118960101.py:4: FutureWarning: pandas.value_counts is deprecated and will be removed in a future version. Use pd.Series(obj).value_counts() instead.\n",
" print(pd.value_counts(pd.Series(results_PID[key]), normalize=False))\n"
]
}
],
"execution_count": 23
},
{
"metadata": {
"ExecuteTime": {
"end_time": "2025-05-14T20:12:22.593341Z",
"start_time": "2025-05-14T20:12:10.328335Z"
}
},
"cell_type": "code",
"source": [
"fig, ax = plt.subplots(1, 2, figsize=(10, 5))\n",
"ax[0].hist(results[\"Delphes\"][\"n_pfcands\"], bins=bins[\"n_pfcands\"], histtype=\"step\", density=True, label=\"PFCands\")\n",
"ax[1].hist(results[\"Delphes\"][\"pfcands_pt\"], bins=bins[\"pfcands_pt\"], histtype=\"step\", density=True, label=\"PFCands\")\n",
"ax[1].hist(results[\"Delphes\"][\"genp_pt\"], bins=bins[\"genp_pt\"], histtype=\"step\", density=True, label=\"Final-state particles\")\n",
"ax[0].hist(results[\"Delphes\"][\"n_genp\"], bins=bins[\"n_genp\"], histtype=\"step\", density=True, label=\"Final-state particles\")\n",
"ax[0].hist(results[\"Delphes\"][\"n_parton_level\"], bins=bins[\"n_parton_level\"], histtype=\"step\", density=True, label=\"Parton-level particles\")\n",
"ax[1].hist(results[\"Delphes\"][\"parton_level_pt\"], bins=bins[\"parton_level_pt\"], histtype=\"step\", density=True, label=\"Parton-level particles\")\n",
"\n",
"ax[0].set_ylabel(\"Density\")\n",
"ax[0].set_xlabel(\"Number of particles\")\n",
"ax[1].set_ylabel(\"Density\")\n",
"ax[1].set_xlabel(r\"$log_{10}(p_T)$\")\n",
"ax[0].grid()\n",
"ax[0].legend()\n",
"ax[1].grid()\n",
"ax[1].legend()\n",
"fig.tight_layout()\n",
"fig.show()\n",
"fig.savefig(\"/work/gkrzmanc/jetclustering/plot_dataset_stats_900_03.pdf\")"
],
"id": "71a35d13854c9396",
"outputs": [
{
"data": {
"text/plain": [
"<Figure size 1000x500 with 2 Axes>"
],
"image/png": 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"
},
"metadata": {},
"output_type": "display_data"
}
],
"execution_count": 14
},
{
"metadata": {},
"cell_type": "code",
"outputs": [],
"execution_count": null,
"source": "",
"id": "26173542a2a972b6"
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
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