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"start_time": "2025-03-19T14:08:40.892203Z"
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"cell_type": "code",
"source": [
"import pickle\n",
"import torch\n",
"import os\n",
"import matplotlib.pyplot as plt\n",
"from src.utils.paths import get_path\n",
"from src.utils.utils import CPU_Unpickler\n",
"from pathlib import Path\n",
"import fastjet\n",
"from src.dataset.dataset import EventDataset\n",
"import numpy as np\n",
"filename = get_path(\"/work/gkrzmanc/jetclustering/results/train/Eval_no_pid_eval_1_2025_03_05_14_41_16/eval_1.pkl\", \"results\")\n",
"# for rinv=0.7, see /work/gkrzmanc/jetclustering/results/train/Test_betaPt_BC_rinv07_2025_01_03_15_38_58\n",
"\n",
"result = CPU_Unpickler(open(filename, \"rb\")).load()\n",
"dataset = EventDataset.from_directory(result[\"filename\"], mmap=True)\n"
],
"id": "862bda2d2f12153f",
"outputs": [
{
"ename": "FileNotFoundError",
"evalue": "[Errno 2] No such file or directory: '/work/gkrzmanc/jetclustering/results/train/Eval_no_pid_eval_1_2025_03_05_14_41_16/eval_1.pkl'",
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"\u001B[0;31m---------------------------------------------------------------------------\u001B[0m",
"\u001B[0;31mFileNotFoundError\u001B[0m Traceback (most recent call last)",
"Cell \u001B[0;32mIn[1], line 14\u001B[0m\n\u001B[1;32m 11\u001B[0m filename \u001B[38;5;241m=\u001B[39m get_path(\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124m/work/gkrzmanc/jetclustering/results/train/Eval_no_pid_eval_1_2025_03_05_14_41_16/eval_1.pkl\u001B[39m\u001B[38;5;124m\"\u001B[39m, \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mresults\u001B[39m\u001B[38;5;124m\"\u001B[39m)\n\u001B[1;32m 12\u001B[0m \u001B[38;5;66;03m# for rinv=0.7, see /work/gkrzmanc/jetclustering/results/train/Test_betaPt_BC_rinv07_2025_01_03_15_38_58\u001B[39;00m\n\u001B[0;32m---> 14\u001B[0m result \u001B[38;5;241m=\u001B[39m CPU_Unpickler(\u001B[38;5;28;43mopen\u001B[39;49m\u001B[43m(\u001B[49m\u001B[43mfilename\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[38;5;124;43mrb\u001B[39;49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[43m)\u001B[49m)\u001B[38;5;241m.\u001B[39mload()\n\u001B[1;32m 15\u001B[0m dataset \u001B[38;5;241m=\u001B[39m EventDataset\u001B[38;5;241m.\u001B[39mfrom_directory(result[\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mfilename\u001B[39m\u001B[38;5;124m\"\u001B[39m], mmap\u001B[38;5;241m=\u001B[39m\u001B[38;5;28;01mTrue\u001B[39;00m)\n",
"File \u001B[0;32m/work/gkrzmanc/1gatr/lib/python3.10/site-packages/IPython/core/interactiveshell.py:324\u001B[0m, in \u001B[0;36m_modified_open\u001B[0;34m(file, *args, **kwargs)\u001B[0m\n\u001B[1;32m 317\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m file \u001B[38;5;129;01min\u001B[39;00m {\u001B[38;5;241m0\u001B[39m, \u001B[38;5;241m1\u001B[39m, \u001B[38;5;241m2\u001B[39m}:\n\u001B[1;32m 318\u001B[0m \u001B[38;5;28;01mraise\u001B[39;00m \u001B[38;5;167;01mValueError\u001B[39;00m(\n\u001B[1;32m 319\u001B[0m \u001B[38;5;124mf\u001B[39m\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mIPython won\u001B[39m\u001B[38;5;124m'\u001B[39m\u001B[38;5;124mt let you open fd=\u001B[39m\u001B[38;5;132;01m{\u001B[39;00mfile\u001B[38;5;132;01m}\u001B[39;00m\u001B[38;5;124m by default \u001B[39m\u001B[38;5;124m\"\u001B[39m\n\u001B[1;32m 320\u001B[0m \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mas it is likely to crash IPython. If you know what you are doing, \u001B[39m\u001B[38;5;124m\"\u001B[39m\n\u001B[1;32m 321\u001B[0m \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124myou can use builtins\u001B[39m\u001B[38;5;124m'\u001B[39m\u001B[38;5;124m open.\u001B[39m\u001B[38;5;124m\"\u001B[39m\n\u001B[1;32m 322\u001B[0m )\n\u001B[0;32m--> 324\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[43mio_open\u001B[49m\u001B[43m(\u001B[49m\u001B[43mfile\u001B[49m\u001B[43m,\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",
"\u001B[0;31mFileNotFoundError\u001B[0m: [Errno 2] No such file or directory: '/work/gkrzmanc/jetclustering/results/train/Eval_no_pid_eval_1_2025_03_05_14_41_16/eval_1.pkl'"
]
}
],
"execution_count": 1
},
{
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"source": "",
"id": "9087767e438ba4e7",
"outputs": [],
"execution_count": null
},
{
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"id": "d5c75efc6fdc491c",
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},
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"source": [
"# plotly 3d plot of result[\"pred\"], colored by result[\"GT_cluster\"]\n",
"from src.plotting.plot_coordinates import plot_coordinates\n",
"filt = result[\"event_idx\"] == 15\n",
"# normalized coordinates\n",
"norm_coords = result[\"pred\"][filt, 1:4] #/ np.linalg.norm(result[\"pred\"][filt, 1:4] , axis=1 ,keepdims=1)\n",
"pt = torch.tensor(result[\"pt\"][filt])\n",
"plot_coordinates(norm_coords, torch.tensor(result[\"pt\"][filt]), torch.tensor(result[\"GT_cluster\"][filt])).show()\n"
],
"id": "d584df4044d8a585",
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[('X', (32, 1)), ('Y', (32, 1)), ('Z', (32, 1)), ('tIdx', (32, 1)), ('pt', (32, 1))]\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/tmp/ipykernel_51221/2041003322.py:6: UserWarning:\n",
"\n",
"To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
"\n",
"/tmp/ipykernel_51221/2041003322.py:7: UserWarning:\n",
"\n",
"To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
"\n"
]
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" \"\"\"\n",
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"\n",
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"metadata": {
"ExecuteTime": {
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"start_time": "2025-03-05T09:21:31.222552Z"
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],
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"filename = get_path(\"/work/gkrzmanc/jetclustering/results/train/Eval_no_pid_eval_2025_03_04_15_54_50/eval_0.pkl\", \"results\")\n",
"# for rinv=0.7, see /work/gkrzmanc/jetclustering/results/train/Test_betaPt_BC_rinv07_2025_01_03_15_38_58\n",
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"result = CPU_Unpickler(open(filename, \"rb\")).load()\n",
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"/work/gkrzmanc/jetclustering/code/src/utils/utils.py:91: FutureWarning:\n",
"\n",
"You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
"\n",
"/tmp/ipykernel_40067/2366440542.py:11: UserWarning:\n",
"\n",
"To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
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"\n",
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"\n",
"/work/gkrzmanc/1gatr/lib/python3.10/site-packages/sklearn/utils/deprecation.py:151: FutureWarning:\n",
"\n",
"'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
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"def get_distance_matrix(v):\n",
" # compute the cosine similarity between vectors in matrix, fast format\n",
" # v is a numpy array\n",
" # returns a numpy array\n",
" dot_product = np.dot(v, v.T)\n",
" magnitude = np.sqrt(np.sum(np.square(v), axis=1))\n",
" magnitude = magnitude[:, np.newaxis]\n",
" return dot_product / (magnitude * magnitude.T)\n",
"\n",
"def get_distance_matrix_Lorentz(v):\n",
" # Lorentz cosine similarity distance metric\n",
" # Lorentz dot product:\n",
" dot_product = np.outer(coords[:, 0], coords[:, 0]) - np.outer(coords[:, 1], coords[:, 1]) - np.outer(coords[:, 2], coords[:, 2]) - np.outer(coords[:, 3], coords[:, 3])\n",
" #magnitude = np.sqrt(np.abs(np.sum(np.square(v), axis=1)))\n",
" # lorentz magnitude\n",
" magnitude = np.sqrt(np.abs(v[:, 0]**2 - v[:, 1]**2 - v[:, 2] ** 2 - v[:, 3]**2))\n",
" magnitude = magnitude[:, np.newaxis]\n",
" return dot_product / (magnitude * magnitude.T)"
],
"id": "2dd7ac91e991f2ea",
"outputs": [],
"execution_count": 7
},
{
"metadata": {
"ExecuteTime": {
"end_time": "2025-01-20T11:10:19.367089Z",
"start_time": "2025-01-20T11:10:19.258556Z"
}
},
"cell_type": "code",
"source": [
"\n",
"import sklearn\n",
"\n",
"def cosine_similarity(vec1, vec2):\n",
" # Ensure the vectors are numpy arrays\n",
" vec1 = np.array(vec1)\n",
" vec2 = np.array(vec2)\n",
"\n",
" # Compute the dot product and the magnitudes\n",
" dot_product = np.dot(vec1, vec2)\n",
" magnitude_vec1 = np.linalg.norm(vec1)\n",
" magnitude_vec2 = np.linalg.norm(vec2)\n",
"\n",
" # Compute cosine similarity\n",
" if magnitude_vec1 == 0 or magnitude_vec2 == 0:\n",
" return 0.0 # Handle edge case where a vector has zero magnitude\n",
" return dot_product / (magnitude_vec1 * magnitude_vec2)\n",
"\n",
"def lorentz_norm(vec1, vec2):\n",
" diff = vec1-vec2\n",
" norm_squared = np.abs(diff[0]**2 - diff[1]**2 - diff[2] ** 2 - diff[3]**2)\n",
" return np.sqrt(norm_squared)\n",
"\n",
"\n",
"clusterer = hdbscan.HDBSCAN(min_cluster_size=5, min_samples=10, cluster_selection_epsilon=0.1)\n",
"from time import time\n",
"coords = result[\"pred\"][filt, 1:4]\n",
"t0 = time()\n",
"cluster_labels = clusterer.fit_predict(coords)\n",
"t1 = time()\n",
"print(t1-t0)\n",
"plot_coordinates(result[\"pred\"][filt, 1:4], result[\"pt\"][filt], torch.tensor(cluster_labels)).show()\n"
],
"id": "a910d5bb50d552a9",
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0.010918378829956055\n",
"[('X', (141, 1)), ('Y', (141, 1)), ('Z', (141, 1)), ('tIdx', (141, 1)), ('pt', (141, 1))]\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/work/gkrzmanc/1gatr/lib/python3.10/site-packages/sklearn/utils/deprecation.py:151: FutureWarning:\n",
"\n",
"'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
"\n",
"/work/gkrzmanc/1gatr/lib/python3.10/site-packages/sklearn/utils/deprecation.py:151: FutureWarning:\n",
"\n",
"'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
"\n"
]
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"metadata": {
"ExecuteTime": {
"end_time": "2025-01-20T11:14:21.791759Z",
"start_time": "2025-01-20T11:14:21.687717Z"
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"source": [
"clusterer = hdbscan.HDBSCAN(min_cluster_size=5, min_samples=15, cluster_selection_epsilon=0.1, metric=\"precomputed\")\n",
"from time import time\n",
"t0 = time()\n",
"\n",
"cluster_labels = clusterer.fit_predict(get_distance_matrix(np.array(norm_coords.double())))\n",
"print(cluster_labels.shape, \"cluster_labels.shape\")\n",
"t1 = time()\n",
"print(t1-t0)\n",
"#plot_coordinates(result[\"pred\"][filt, 1:4], result[\"pt\"][filt], torch.tensor(cluster_labels)).show()\n"
],
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"name": "stdout",
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"text": [
"(141,) cluster_labels.shape\n",
"0.011168956756591797\n",
"[('X', (141, 1)), ('Y', (141, 1)), ('Z', (141, 1)), ('tIdx', (141, 1)), ('pt', (141, 1))]\n"
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" [ 2.9706e-01, 9.5452e-01, 2.5266e-02],\n",
" [-3.1297e-01, -9.4976e-01, 1.9227e-03],\n",
" [-3.4379e-01, 9.3092e-01, 1.2330e-01],\n",
" [ 7.5591e-01, -6.5467e-01, 1.9844e-03],\n",
" [ 6.8384e-02, -9.9766e-01, 4.6762e-04]])"
]
},
"execution_count": 27,
"metadata": {},
"output_type": "execute_result"
}
],
"execution_count": 27
},
{
"metadata": {
"ExecuteTime": {
"end_time": "2025-01-15T13:26:47.541174Z",
"start_time": "2025-01-15T13:26:42.640256Z"
}
},
"cell_type": "code",
"source": [
"from src.jetfinder.basicjetfinder import basicjetfinder\n",
"from src.jetfinder.basicjetfinder_types import PseudoJet"
],
"id": "3d306ff55aba8f56",
"outputs": [],
"execution_count": 62
},
{
"metadata": {
"ExecuteTime": {
"end_time": "2025-02-12T11:30:57.051315Z",
"start_time": "2025-02-12T11:30:56.738611Z"
}
},
"cell_type": "code",
"source": "event = dataset[1]",
"id": "f22d322fe5c58d62",
"outputs": [],
"execution_count": 11
},
{
"metadata": {
"ExecuteTime": {
"end_time": "2025-02-12T11:30:57.798195Z",
"start_time": "2025-02-12T11:30:57.789047Z"
}
},
"cell_type": "code",
"source": [
"def get_pseudojets(event):\n",
" pseudojets = []\n",
" for i in range(len(event.pfcands)):\n",
" pseudojets.append(PseudoJet(event.pfcands.pxyz[i, 0].item(), event.pfcands.pxyz[i, 1].item(), event.pfcands.pxyz[i, 2].item(), event.pfcands.E[i].item()))\n",
" return pseudojets\n",
"def get_pseudojets_fastjet(event):\n",
" pseudojets = []\n",
" for i in range(len(event.pfcands)):\n",
" pseudojets.append(fastjet.PseudoJet(event.pfcands.pxyz[i, 0].item(), event.pfcands.pxyz[i, 1].item(), event.pfcands.pxyz[i, 2].item(), event.pfcands.E[i].item()))\n",
" return pseudojets"
],
"id": "d82147109550e95d",
"outputs": [],
"execution_count": 12
},
{
"metadata": {
"ExecuteTime": {
"end_time": "2025-01-15T13:26:48.214374164Z",
"start_time": "2025-01-05T10:22:51.003001Z"
}
},
"cell_type": "code",
"source": [
"pj = get_pseudojets(event)\n",
"init_jets, history = basicjetfinder(pj, return_raw=True)"
],
"id": "109f80517d6d8ebe",
"outputs": [],
"execution_count": 9
},
{
"metadata": {
"ExecuteTime": {
"end_time": "2025-01-15T13:26:48.218588253Z",
"start_time": "2025-01-05T10:22:51.189299Z"
}
},
"cell_type": "code",
"source": "pj[0].phi, pj[0].rap",
"id": "d447c0086cf78673",
"outputs": [
{
"data": {
"text/plain": [
"(5.176739997422374, -1.1044922730459195)"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"execution_count": 10
},
{
"metadata": {
"ExecuteTime": {
"end_time": "2025-01-15T13:26:48.220185691Z",
"start_time": "2025-01-05T10:22:51.295388Z"
}
},
"cell_type": "code",
"source": "event.pfcands.phi[0].item(), event.pfcands.eta[0].item()",
"id": "829888c538a224ce",
"outputs": [
{
"data": {
"text/plain": [
"(-1.1064453125, -1.1044921875)"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"execution_count": 11
},
{
"metadata": {
"ExecuteTime": {
"end_time": "2025-01-15T13:26:48.222836564Z",
"start_time": "2025-01-05T10:22:53.423595Z"
}
},
"cell_type": "code",
"source": [
"history.parent1[-1], history.parent2[-1]\n",
"history.jetp_index[305]"
],
"id": "5066f1dbfc75c415",
"outputs": [
{
"data": {
"text/plain": [
"305"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"execution_count": 12
},
{
"metadata": {
"ExecuteTime": {
"end_time": "2025-01-15T13:26:48.225401392Z",
"start_time": "2025-01-05T10:22:53.613292Z"
}
},
"cell_type": "code",
"source": "jets = basicjetfinder(pj, return_raw=0, ptmin=100)",
"id": "111438c86429cb73",
"outputs": [],
"execution_count": 13
},
{
"metadata": {
"ExecuteTime": {
"end_time": "2025-01-15T13:26:48.227766958Z",
"start_time": "2025-01-05T10:23:02.561963Z"
}
},
"cell_type": "code",
"source": [
"fig, ax = plt.subplots()\n",
"print(len(jets))\n",
"import numpy as np\n",
"pts = [jet.pt for jet in jets]\n",
"ax.hist(pts, bins=np.linspace(100, 300, 30))\n",
"fig.show()"
],
"id": "c9b6a5bae8fb77a0",
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"12\n"
]
},
{
"data": {
"text/plain": [
""
],
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"
},
"metadata": {},
"output_type": "display_data"
}
],
"execution_count": 15
},
{
"metadata": {
"ExecuteTime": {
"end_time": "2025-01-15T13:26:48.228831708Z",
"start_time": "2025-01-05T10:23:12.488535Z"
}
},
"cell_type": "code",
"source": "pts",
"id": "8173000a212c5561",
"outputs": [
{
"data": {
"text/plain": [
"[106.4914540483752,\n",
" 126.82878103484677,\n",
" 128.75701244217592,\n",
" 139.36815083799416,\n",
" 141.45818096650805,\n",
" 157.3921266403816,\n",
" 263.460560295077,\n",
" 510.77385760617676,\n",
" 1265.8454239380796,\n",
" 3240.9516711140454,\n",
" 9355.60575745164,\n",
" 12409.915133671686]"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"execution_count": 16
},
{
"metadata": {
"ExecuteTime": {
"end_time": "2025-01-15T13:26:48.232599293Z",
"start_time": "2025-01-04T21:42:24.952081Z"
}
},
"cell_type": "code",
"source": "history.parent1[-1], history.parent2[-1]\n",
"id": "ca12ef3d85430a6c",
"outputs": [
{
"data": {
"text/plain": [
"(305, -1)"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"execution_count": 14
},
{
"metadata": {
"ExecuteTime": {
"end_time": "2025-01-15T13:26:48.235836146Z",
"start_time": "2025-01-04T21:42:25.992473Z"
}
},
"cell_type": "code",
"source": "history.parent1",
"id": "648b5a6c3f3469db",
"outputs": [
{
"data": {
"text/plain": [
"array([ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1,\n",
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" -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1,\n",
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" 337, 338, 339, 340, 6, 341, 7, 343, 345, 346, 347, 348, 5,\n",
" 349, 351, 352, 353, 127, 350, 356, 354, 358, 13, 359, 357, 362,\n",
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" 9, 455, 453, 454, 458, 33, 460, 388, 459, 463, 464, 465, 25,\n",
" 466, 468, 469, 456, 470, 472, 473, 474, 475, 476, 477, 478, 479,\n",
" 480, 481, 482, 483, 484, 485, 486, 487, 488, 489, 462, 35, 491,\n",
" 461, 494, 495, 493, 497, 498, 499, 20, 496, 502, 500, 504, 503,\n",
" 506, 507, 505, 509, 510, 511, 512, 513, 514, 515, 516, 508, 518,\n",
" 519, 501, 520, 22, 521, 92, 522, 524, 526, 91, 528, 248, 530,\n",
" 527, 523, 65, 177, 533, 537, 538, 467, 539, 48, 40, 540, 93,\n",
" 541, 37, 547, 548, 549, 550, 551, 552, 553, 554, 555, 556, 557,\n",
" 558, 559, 560, 561, 542, 534, 57, 529, 564, 563, 568, 566, 570,\n",
" 571, 572, 573, 574, 575, 565, 577, 569, 185, 102, 581, 62, 583,\n",
" 52, 43, 586, 587, 588, 589, 590, 591, 582, 50, 594, 595, 596,\n",
" 597, 598, 599, 600, 27, 602, 603, 604, 98, 606, 607, 543, 544,\n",
" 608, 609, 567, 579, 614, 611, 605, 617, 593, 619, 620, 621, 89,\n",
" 623, 624, 625, 626, 627, 628, 629, 612, 631, 632, 633, 634, 635,\n",
" 636, 637, 610, 616, 640, 84, 192, 642, 644, 645, 646, 647, 545,\n",
" 649, 650, 651, 652, 653, 654, 190, 104, 657, 658, 659, 660, 661,\n",
" 49, 137, 664, 656, 126, 166, 305])"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"execution_count": 15
},
{
"metadata": {
"ExecuteTime": {
"end_time": "2025-01-15T13:26:48.239141583Z",
"start_time": "2025-01-04T21:42:40.173588Z"
}
},
"cell_type": "code",
"source": "history.parent2",
"id": "7f4ba4d2fac40d0b",
"outputs": [
{
"data": {
"text/plain": [
"array([ -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1,\n",
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" -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1,\n",
" -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1,\n",
" -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1,\n",
" -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1,\n",
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" -1, 239, -1, -1, -1, -1, -1])"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"execution_count": 16
},
{
"metadata": {
"ExecuteTime": {
"end_time": "2025-02-12T11:37:26.356164Z",
"start_time": "2025-02-12T11:37:26.334334Z"
}
},
"cell_type": "code",
"source": [
"import fastjet\n",
"jetdef = fastjet.JetDefinition(fastjet.antikt_algorithm, 0.8)\n",
"array = get_pseudojets_fastjet(event)\n",
"\n",
"cluster = fastjet.ClusterSequence(array, jetdef)\n",
"inc_jets = cluster.inclusive_jets()\n",
"for elem in inc_jets:\n",
" pt = elem.pt()\n",
" if pt < 100:\n",
" continue\n",
" print(\"pt:\", elem.pt(), \"eta:\", elem.rap(), \"phi:\", elem.phi(), \"m\", elem.m())\n",
"\n",
"\n",
"def get_jets(event, jetdef):\n",
" pt = []\n",
" eta = []\n",
" phis = []\n",
" mass = []\n",
" array = get_pseudojets_fastjet(event)\n",
"\n",
" cluster = fastjet.ClusterSequence(array, jetdef)\n",
" inc_jets = cluster.inclusive_jets()\n",
" for elem in inc_jets:\n",
" if elem.pt() < 100:\n",
" continue\n",
" #print(\"pt:\", elem.pt(), \"eta:\", elem.rap(), \"phi:\", elem.phi())ž\n",
" pt.append(elem.pt())\n",
" eta.append(elem.rap())\n",
" phi = elem.phi()\n",
" if phi > np.pi:\n",
" phi -= 2*np.pi\n",
" phis.append(phi)\n",
" mass.append(elem.m())\n",
" return pt, eta, phis, mass\n"
],
"id": "fe26fce41ce8a6e0",
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"pt: 170.28684882840693 eta: 1.4439809865649234 phi: 3.443388811838133 m 34.279811963116074\n"
]
}
],
"execution_count": 26
},
{
"metadata": {
"ExecuteTime": {
"end_time": "2025-02-12T11:37:27.360232Z",
"start_time": "2025-02-12T11:37:27.350919Z"
}
},
"cell_type": "code",
"source": "event.fatjets.pt.tolist(), event.fatjets.eta.tolist(), event.fatjets.phi.tolist()",
"id": "d1e783a1ef0b7d2",
"outputs": [
{
"data": {
"text/plain": [
"([170.28684997558594], [1.462087869644165], [-2.839796543121338])"
]
},
"execution_count": 27,
"metadata": {},
"output_type": "execute_result"
}
],
"execution_count": 27
},
{
"metadata": {
"ExecuteTime": {
"end_time": "2025-02-12T11:37:44.517906Z",
"start_time": "2025-02-12T11:37:44.500190Z"
}
},
"cell_type": "code",
"source": [
"jetdef = fastjet.JetDefinition(fastjet.antikt_algorithm, 0.8)\n",
"\n",
"jets = get_jets(event, jetdef)\n",
"print(jets)"
],
"id": "4a70177cc18e6294",
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"([170.28684882840693], [1.4439809865649234], [-2.8397964953414534], [34.279811963116074])\n"
]
}
],
"execution_count": 32
},
{
"metadata": {
"ExecuteTime": {
"end_time": "2025-01-15T13:26:48.246660551Z",
"start_time": "2025-01-08T13:38:47.767966Z"
}
},
"cell_type": "code",
"source": [
"model_clusters_file =\"/work/gkrzmanc/jetclustering/results/train/Test_betaPt_BC_2025_01_03_15_07_14/HDBSCAN_10_20.pkl\"\n",
"model_output_file = \"/work/gkrzmanc/jetclustering/results/train/Test_betaPt_BC_2025_01_03_15_07_14/eval_0.pkl\"\n"
],
"id": "2b10ddbf4eec8cb5",
"outputs": [],
"execution_count": 5
},
{
"metadata": {
"ExecuteTime": {
"end_time": "2025-01-15T13:26:48.248147149Z",
"start_time": "2025-01-08T13:38:47.884029Z"
}
},
"cell_type": "code",
"source": [
"ds = EventDataset.from_directory(\"/work/gkrzmanc/jetclustering/preprocessed_data/scouting_PFNano_signals2/SVJ_hadronic_std/s-channel_mMed-900_mDark-20_rinv-0.3\",\n",
" model_clusters_file=model_clusters_file, model_output_file=model_output_file)"
],
"id": "45e33b8f47a24359",
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/work/gkrzmanc/jetclustering/code/src/utils/utils.py:91: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
" return lambda b: torch.load(io.BytesIO(b), map_location='cpu')\n"
]
}
],
"execution_count": 6
},
{
"metadata": {
"ExecuteTime": {
"end_time": "2025-01-15T13:26:48.249374772Z",
"start_time": "2025-01-08T13:38:48.434363Z"
}
},
"cell_type": "code",
"source": [
"labels = CPU_Unpickler(open(\"/work/gkrzmanc/jetclustering/results/train/Test_betaPt_BC_2025_01_03_15_07_14/HDBSCAN_10_20.pkl\", \"rb\")).load()\n",
"labels"
],
"id": "9e23e7eff0c80073",
"outputs": [
{
"data": {
"text/plain": [
"array([ 0, 2, 2, ..., 1, 0, -1])"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"execution_count": 7
},
{
"metadata": {
"ExecuteTime": {
"end_time": "2025-01-15T13:26:48.250785451Z",
"start_time": "2025-01-08T13:38:48.566720Z"
}
},
"cell_type": "code",
"source": [
"from src.plotting.plot_event import plot_event\n",
"clist = ['#1f78b4', '#b3df8a', '#33a02c', '#fb9a99', '#e31a1c', '#fdbe6f', '#ff7f00', '#cab2d6', '#6a3d9a', '#ffff99', '#b15928']\n",
"colors = {\n",
" -1: \"gray\",\n",
" 0: clist[0],\n",
" 1: clist[1],\n",
" 2: clist[2],\n",
" 3: clist[3],\n",
" 4: clist[4],\n",
" 5: clist[5],\n",
" 6: clist[6],\n",
" 7: clist[7],\n",
"}\n",
"\n",
"idx = 4\n",
"c = [colors[i] for i in labels[result[\"event_idx\"] == idx]]\n",
"\n",
"plot_event(ds[idx], colors=c).show()\n",
"\n",
"print(ds[idx].model_jets.pt)"
],
"id": "349e83cf3a97bbd6",
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"tensor([427.9641, 391.8387])\n"
]
},
{
"data": {
"text/plain": [
""
],
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"
},
"metadata": {},
"output_type": "display_data"
}
],
"execution_count": 8
},
{
"metadata": {},
"cell_type": "code",
"outputs": [],
"execution_count": null,
"source": "",
"id": "4a2509d55bd64836"
},
{
"metadata": {
"ExecuteTime": {
"end_time": "2025-01-15T13:26:48.252545623Z",
"start_time": "2025-01-07T15:45:38.549155Z"
}
},
"cell_type": "code",
"source": [
"pt_model_jet = []\n",
"for i in range(200):\n",
" pt_model_jet += ds[i].model_jets.pt.tolist()"
],
"id": "831cb3f91dc26650",
"outputs": [
{
"ename": "NameError",
"evalue": "name 'ds' 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[4], line 3\u001B[0m\n\u001B[1;32m 1\u001B[0m pt_model_jet \u001B[38;5;241m=\u001B[39m []\n\u001B[1;32m 2\u001B[0m \u001B[38;5;28;01mfor\u001B[39;00m i \u001B[38;5;129;01min\u001B[39;00m \u001B[38;5;28mrange\u001B[39m(\u001B[38;5;241m200\u001B[39m):\n\u001B[0;32m----> 3\u001B[0m pt_model_jet \u001B[38;5;241m+\u001B[39m\u001B[38;5;241m=\u001B[39m \u001B[43mds\u001B[49m[i]\u001B[38;5;241m.\u001B[39mmodel_jets\u001B[38;5;241m.\u001B[39mpt\u001B[38;5;241m.\u001B[39mtolist()\n",
"\u001B[0;31mNameError\u001B[0m: name 'ds' is not defined"
]
}
],
"execution_count": 4
},
{
"metadata": {
"ExecuteTime": {
"end_time": "2025-01-15T13:26:48.253846859Z",
"start_time": "2025-01-07T15:45:47.111072Z"
}
},
"cell_type": "code",
"source": [
"fig, ax = plt.subplots()\n",
"ax.hist(pt_model_jet, bins=np.linspace(0, 300, 30))\n",
"fig.show()"
],
"id": "ac8f0f719e587403",
"outputs": [
{
"data": {
"text/plain": [
""
],
"image/png": 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},
"metadata": {},
"output_type": "display_data"
}
],
"execution_count": 6
},
{
"metadata": {
"ExecuteTime": {
"end_time": "2025-01-15T13:26:48.255093897Z",
"start_time": "2025-01-15T10:16:58.042882Z"
}
},
"cell_type": "code",
"source": [
"v1 = np.array([1, 2, 2, 2])\n",
"v2 = np.array([3, 3, 2, 1])\n",
"from scipy.spatial.distance import minkowski"
],
"id": "d4a0921e620054b5",
"outputs": [],
"execution_count": 25
},
{
"metadata": {
"ExecuteTime": {
"end_time": "2025-01-15T13:26:48.256269976Z",
"start_time": "2025-01-15T10:16:59.189155Z"
}
},
"cell_type": "code",
"source": "minkowski(v1, v2, 2)",
"id": "f8dd3da5642a12bd",
"outputs": [
{
"data": {
"text/plain": [
"2.449489742783178"
]
},
"execution_count": 26,
"metadata": {},
"output_type": "execute_result"
}
],
"execution_count": 26
},
{
"metadata": {
"ExecuteTime": {
"end_time": "2025-01-15T13:26:48.257490963Z",
"start_time": "2025-01-15T10:17:12.055427Z"
}
},
"cell_type": "code",
"source": "diff = v2-v1",
"id": "b6a8812cb07ba219",
"outputs": [],
"execution_count": 27
},
{
"metadata": {
"ExecuteTime": {
"end_time": "2025-01-20T15:16:23.853431Z",
"start_time": "2025-01-20T15:16:23.118842Z"
}
},
"cell_type": "code",
"source": [
"filename = get_path(\"/work/gkrzmanc/jetclustering/results/train/Eval_LGATr_no_coords_loss_1_2025_01_18_15_21_00/eval_3.pkl\", \"results\")\n",
"# for rinv=0.7, see /work/gkrzmanc/jetclustering/results/train/Test_betaPt_BC_rinv07_2025_01_03_15_38_58\n",
"\n",
"result = CPU_Unpickler(open(filename, \"rb\")).load()\n",
"dataset = EventDataset.from_directory(result[\"filename\"], mmap=True)\n"
],
"id": "78667573e6fbbb9e",
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/work/gkrzmanc/jetclustering/code/src/utils/utils.py:91: FutureWarning:\n",
"\n",
"You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
"\n"
]
}
],
"execution_count": 34
},
{
"metadata": {
"ExecuteTime": {
"end_time": "2025-01-20T15:17:12.581333Z",
"start_time": "2025-01-20T15:17:12.483045Z"
}
},
"cell_type": "code",
"source": [
"# plotly 3d plot of result[\"pred\"], colored by result[\"GT_cluster\"]\n",
"from src.plotting.plot_coordinates import plot_coordinates\n",
"filt = result[\"event_idx\"] == 6\n",
"# normalized coordinates\n",
"norm_coords = result[\"pred\"][filt, 1:4] #/ np.linalg.norm(result[\"pred\"][filt, 1:4] , axis=1 ,keepdims=1)\n",
"plot_coordinates(norm_coords, torch.tensor(result[\"pt\"][filt]), torch.tensor(result[\"GT_cluster\"][filt])).show()\n"
],
"id": "6aa3604655d72882",
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[('X', (220, 1)), ('Y', (220, 1)), ('Z', (220, 1)), ('tIdx', (220, 1)), ('pt', (220, 1))]\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/tmp/ipykernel_48257/4193388651.py:6: UserWarning:\n",
"\n",
"To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
"\n"
]
},
{
"data": {
"application/vnd.plotly.v1+json": {
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tIdx=%{marker.color}",
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},
"execution_count": 39,
"metadata": {},
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}
],
"execution_count": 39
},
{
"metadata": {
"ExecuteTime": {
"end_time": "2025-01-20T15:19:21.065491Z",
"start_time": "2025-01-20T15:19:20.974966Z"
}
},
"cell_type": "code",
"source": "plot_coordinates(dataset[6].pfcands.pxyz, torch.tensor(result[\"pt\"][filt]), torch.tensor(result[\"GT_cluster\"][filt])).show()\n",
"id": "6e2a07e42eca8496",
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[('X', (220, 1)), ('Y', (220, 1)), ('Z', (220, 1)), ('tIdx', (220, 1)), ('pt', (220, 1))]\n"
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},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/tmp/ipykernel_48257/919745444.py:1: UserWarning:\n",
"\n",
"To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
"\n"
]
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