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{
"cells": [
{
"metadata": {
"ExecuteTime": {
"end_time": "2025-04-10T09:06:33.918985Z",
"start_time": "2025-04-10T09:06:30.413364Z"
}
},
"cell_type": "code",
"source": [
"import os\n",
"import ast\n",
"import glob\n",
"import functools\n",
"import math\n",
"import torch\n",
"\n",
"from torch.utils.data import DataLoader\n",
"from src.logger.logger import _logger, _configLogger\n",
"from src.dataset.dataset import EventDatasetCollection, EventDataset\n",
"from src.utils.import_tools import import_module\n",
"from src.dataset.functions_graph import graph_batch_func\n",
"from src.dataset.functions_data import concat_events\n",
"from src.utils.paths import get_path\n"
],
"id": "e3d819c470939dc",
"outputs": [],
"execution_count": 1
},
{
"metadata": {
"ExecuteTime": {
"end_time": "2025-04-10T09:06:34.311191Z",
"start_time": "2025-04-10T09:06:34.004349Z"
}
},
"cell_type": "code",
"source": [
"train_data = EventDataset.from_directory(\"/work/gkrzmanc/jetclustering/preprocessed_data/scouting_PFNano_signals1/SVJ_hadronic_std3/s-channel_mMed-1100_mDark-20_rinv-0.3_alpha-peak_13TeV-pythia8_n-2000\", mmap=True)\n",
"print(\"N events:\", len(train_data))\n",
"train_loader = DataLoader(\n",
" train_data,\n",
" batch_size=8,\n",
" drop_last=True,\n",
" pin_memory=True,\n",
" num_workers=1,\n",
" collate_fn=concat_events,\n",
" persistent_workers=1\n",
" )\n"
],
"id": "8c937e192df126b9",
"outputs": [
{
"ename": "FileNotFoundError",
"evalue": "[Errno 2] No such file or directory: '/work/gkrzmanc/jetclustering/preprocessed_data/scouting_PFNano_signals1/SVJ_hadronic_std3/s-channel_mMed-1100_mDark-20_rinv-0.3_alpha-peak_13TeV-pythia8_n-2000'",
"output_type": "error",
"traceback": [
"\u001B[0;31m---------------------------------------------------------------------------\u001B[0m",
"\u001B[0;31mFileNotFoundError\u001B[0m Traceback (most recent call last)",
"Cell \u001B[0;32mIn[2], line 1\u001B[0m\n\u001B[0;32m----> 1\u001B[0m train_data \u001B[38;5;241m=\u001B[39m \u001B[43mEventDataset\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mfrom_directory\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[38;5;124;43m/work/gkrzmanc/jetclustering/preprocessed_data/scouting_PFNano_signals1/SVJ_hadronic_std3/s-channel_mMed-1100_mDark-20_rinv-0.3_alpha-peak_13TeV-pythia8_n-2000\u001B[39;49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mmmap\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;28;43;01mTrue\u001B[39;49;00m\u001B[43m)\u001B[49m\n\u001B[1;32m 2\u001B[0m \u001B[38;5;28mprint\u001B[39m(\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mN events:\u001B[39m\u001B[38;5;124m\"\u001B[39m, \u001B[38;5;28mlen\u001B[39m(train_data))\n\u001B[1;32m 3\u001B[0m train_loader \u001B[38;5;241m=\u001B[39m DataLoader(\n\u001B[1;32m 4\u001B[0m train_data,\n\u001B[1;32m 5\u001B[0m batch_size\u001B[38;5;241m=\u001B[39m\u001B[38;5;241m8\u001B[39m,\n\u001B[0;32m (...)\u001B[0m\n\u001B[1;32m 10\u001B[0m persistent_workers\u001B[38;5;241m=\u001B[39m\u001B[38;5;241m1\u001B[39m\n\u001B[1;32m 11\u001B[0m )\n",
"File \u001B[0;32m/work/gkrzmanc/jetclustering/code/src/dataset/dataset.py:377\u001B[0m, in \u001B[0;36mEventDataset.from_directory\u001B[0;34m(dir, mmap, model_clusters_file, model_output_file, include_model_jets_unfiltered, fastjet_R, parton_level, gen_level, aug_soft, seed, aug_collinear)\u001B[0m\n\u001B[1;32m 374\u001B[0m \u001B[38;5;129m@staticmethod\u001B[39m\n\u001B[1;32m 375\u001B[0m \u001B[38;5;28;01mdef\u001B[39;00m\u001B[38;5;250m \u001B[39m\u001B[38;5;21mfrom_directory\u001B[39m(\u001B[38;5;28mdir\u001B[39m, mmap\u001B[38;5;241m=\u001B[39m\u001B[38;5;28;01mTrue\u001B[39;00m, model_clusters_file\u001B[38;5;241m=\u001B[39m\u001B[38;5;28;01mNone\u001B[39;00m, model_output_file\u001B[38;5;241m=\u001B[39m\u001B[38;5;28;01mNone\u001B[39;00m, include_model_jets_unfiltered\u001B[38;5;241m=\u001B[39m\u001B[38;5;28;01mFalse\u001B[39;00m, fastjet_R\u001B[38;5;241m=\u001B[39m\u001B[38;5;28;01mNone\u001B[39;00m, parton_level\u001B[38;5;241m=\u001B[39m\u001B[38;5;28;01mFalse\u001B[39;00m, gen_level\u001B[38;5;241m=\u001B[39m\u001B[38;5;28;01mFalse\u001B[39;00m, aug_soft\u001B[38;5;241m=\u001B[39m\u001B[38;5;28;01mFalse\u001B[39;00m, seed\u001B[38;5;241m=\u001B[39m\u001B[38;5;241m0\u001B[39m, aug_collinear\u001B[38;5;241m=\u001B[39m\u001B[38;5;28;01mFalse\u001B[39;00m):\n\u001B[1;32m 376\u001B[0m result \u001B[38;5;241m=\u001B[39m {}\n\u001B[0;32m--> 377\u001B[0m \u001B[38;5;28;01mfor\u001B[39;00m file \u001B[38;5;129;01min\u001B[39;00m \u001B[43mos\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mlistdir\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;28;43mdir\u001B[39;49m\u001B[43m)\u001B[49m:\n\u001B[1;32m 378\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m file \u001B[38;5;241m==\u001B[39m \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mmetadata.pkl\u001B[39m\u001B[38;5;124m\"\u001B[39m:\n\u001B[1;32m 379\u001B[0m metadata \u001B[38;5;241m=\u001B[39m pickle\u001B[38;5;241m.\u001B[39mload(\u001B[38;5;28mopen\u001B[39m(os\u001B[38;5;241m.\u001B[39mpath\u001B[38;5;241m.\u001B[39mjoin(\u001B[38;5;28mdir\u001B[39m, file), \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mrb\u001B[39m\u001B[38;5;124m\"\u001B[39m))\n",
"\u001B[0;31mFileNotFoundError\u001B[0m: [Errno 2] No such file or directory: '/work/gkrzmanc/jetclustering/preprocessed_data/scouting_PFNano_signals1/SVJ_hadronic_std3/s-channel_mMed-1100_mDark-20_rinv-0.3_alpha-peak_13TeV-pythia8_n-2000'"
]
}
],
"execution_count": 2
},
{
"metadata": {
"ExecuteTime": {
"end_time": "2024-12-19T17:19:10.625599Z",
"start_time": "2024-12-19T17:19:10.457272Z"
}
},
"cell_type": "code",
"source": "b = next(iter(train_loader))",
"id": "6ddf6f2f4c0f3c37",
"outputs": [],
"execution_count": 3
},
{
"metadata": {
"ExecuteTime": {
"end_time": "2024-12-19T17:19:20.974583Z",
"start_time": "2024-12-19T17:19:20.965173Z"
}
},
"cell_type": "code",
"source": "b.pfcands.pid.unique()",
"id": "5f2a6492abfac378",
"outputs": [
{
"data": {
"text/plain": [
"tensor([-211., -13., -11., 13., 22., 130., 211.])"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"execution_count": 5
},
{
"metadata": {
"ExecuteTime": {
"end_time": "2024-12-18T14:37:13.995401Z",
"start_time": "2024-12-18T14:37:13.986881Z"
}
},
"cell_type": "code",
"source": "dir(b)",
"id": "df59e9660ebabc72",
"outputs": [
{
"data": {
"text/plain": [
"['MET',\n",
" '__class__',\n",
" '__delattr__',\n",
" '__dict__',\n",
" '__dir__',\n",
" '__doc__',\n",
" '__eq__',\n",
" '__format__',\n",
" '__ge__',\n",
" '__getattribute__',\n",
" '__gt__',\n",
" '__hash__',\n",
" '__init__',\n",
" '__init_subclass__',\n",
" '__le__',\n",
" '__len__',\n",
" '__lt__',\n",
" '__module__',\n",
" '__ne__',\n",
" '__new__',\n",
" '__reduce__',\n",
" '__reduce_ex__',\n",
" '__repr__',\n",
" '__setattr__',\n",
" '__sizeof__',\n",
" '__str__',\n",
" '__subclasshook__',\n",
" '__weakref__',\n",
" 'evt_collections',\n",
" 'fatjets',\n",
" 'genjets',\n",
" 'init_attrs',\n",
" 'jets',\n",
" 'matrix_element_gen_particles',\n",
" 'n_events',\n",
" 'offline_pfcands',\n",
" 'pfcands',\n",
" 'serialize',\n",
" 'special_pfcands']"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"execution_count": 5
},
{
"metadata": {
"ExecuteTime": {
"end_time": "2024-12-18T14:37:14.126468Z",
"start_time": "2024-12-18T14:37:14.116496Z"
}
},
"cell_type": "code",
"source": "len(b.pfcands), b.pfcands.batch_number",
"id": "d286a98188187c6",
"outputs": [
{
"data": {
"text/plain": [
"(2023, tensor([ 0, 331, 636, 821, 1044, 1404, 1619, 1787, 2023]))"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"execution_count": 6
},
{
"metadata": {
"ExecuteTime": {
"end_time": "2024-12-18T14:41:22.295628Z",
"start_time": "2024-12-18T14:41:22.277888Z"
}
},
"cell_type": "code",
"source": [
"def get_idx_for_event(obj, i):\n",
" return obj.batch_number[i], obj.batch_number[i+1]\n",
"\n",
"def get_labels(pfcands):\n",
" labels = torch.zeros(len(pfcands)).long()\n",
" R = 0.8\n",
" for i in range(len(b)):\n",
" s, e = get_idx_for_event(b.matrix_element_gen_particles, i)\n",
" dq_eta = b.matrix_element_gen_particles.eta[s:e]\n",
" dq_phi = b.matrix_element_gen_particles.phi[s:e]\n",
" # dq_pt = b.matrix_element_gen_particles.pt[s:e] # Maybe we can somehow weigh the loss by pt?\n",
" s, e = get_idx_for_event(pfcands, i)\n",
" pfcands_eta = pfcands.eta[s:e]\n",
" pfcands_phi = pfcands.phi[s:e]\n",
" # calculate the distance matrix between each dark quark and pfcands\n",
" dist_matrix = torch.cdist(\n",
" torch.stack([dq_eta, dq_phi], dim=1),\n",
" torch.stack([pfcands_eta, pfcands_phi], dim=1),\n",
" p=2\n",
" )\n",
" dist_matrix = dist_matrix.T\n",
" closest_quark_dist, closest_quark_idx = dist_matrix.min(dim=1)\n",
" closest_quark_idx[closest_quark_dist > R] = -1\n",
" labels[s:e] = closest_quark_idx\n",
" return labels"
],
"id": "1311033e4b878a6b",
"outputs": [],
"execution_count": 22
},
{
"metadata": {
"ExecuteTime": {
"end_time": "2024-12-29T11:28:01.367425Z",
"start_time": "2024-12-29T11:28:01.348995Z"
}
},
"cell_type": "code",
"source": "",
"id": "878ebfeda196f1ea",
"outputs": [],
"execution_count": 1
},
{
"metadata": {},
"cell_type": "code",
"outputs": [],
"execution_count": null,
"source": [
"import os\n",
"from src.dataset.dataset import SimpleIterDataset, EventDataset\n",
"from src.utils.utils import to_filelist\n",
"from src.utils.paths import get_path\n",
"\n",
"class Args:\n",
" def __init__(self):\n",
" self.data_train = datasets\n",
" self.data_val = datasets\n",
" #self.data_train = files_train\n",
" self.data_config = \"config_files/config_jets_1_delphes.yaml\"\n",
" self.extra_selection = None\n",
" self.train_val_split = 1.0\n",
" self.data_fraction = 1\n",
" self.file_fraction = 1\n",
" self.fetch_by_files = False\n",
" self.fetch_step = 1\n",
" self.steps_per_epoch = None\n",
" self.in_memory = False\n",
" self.local_rank = None\n",
" self.copy_inputs = False\n",
" self.no_remake_weights = False\n",
" self.batch_size = 10\n",
" self.num_workers = 0\n",
" self.demo = False\n",
" self.laplace = False\n",
" self.diffs = False\n",
" self.class_edges = False\n",
"args = Args()\n",
"train_range = (0, args.train_val_split)\n",
"train_file_dict, train_files = to_filelist(args, 'train')\n",
"train_data = SimpleIterDataset(train_file_dict, args.data_config, for_training=True,\n",
" extra_selection=args.extra_selection,\n",
" remake_weights=True,\n",
" load_range_and_fraction=(train_range, args.data_fraction),\n",
" file_fraction=args.file_fraction,\n",
" fetch_by_files=args.fetch_by_files,\n",
" fetch_step=args.fetch_step,\n",
" infinity_mode=False,\n",
" in_memory=args.in_memory,\n",
" async_load=False,\n",
" name='train', jets=True)\n",
"iterator = iter(train_data)"
],
"id": "7475933352961325"
},
{
"metadata": {},
"cell_type": "code",
"outputs": [],
"execution_count": null,
"source": "",
"id": "5759f10b89b33d82"
}
],
"metadata": {
"kernelspec": {
"name": "python3",
"language": "python",
"display_name": "Python 3 (ipykernel)"
}
},
"nbformat": 5,
"nbformat_minor": 9
}
|