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rp_lqar_full_er_260526/data_prepped.pt

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+version https://git-lfs.github.com/spec/v1
+oid sha256:b0cb796251dc4cc0b73be329ca16b62dde7e11af5908b77c9c68d6699fbe1258
+size 13940002266

rp_lqar_full_er_260526/gen.py

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+import json
+import torch
+import numpy as np
+import pyg4lego
+from pathlib import Path
+_PATH = Path(__file__).resolve().parent
+from legofmt.data.dataloaders import LEGODataset
+from legofmt.data.prep import DataPrep
+
+pos = torch.tensor([-50.0, 0.0, 0.0], dtype=torch.float64).numpy()
+mom = torch.tensor([1.0, 0.0, 0.0], dtype=torch.float64).numpy()
+energy = torch.tensor([300.0], dtype=torch.float64).numpy()
+density =  torch.linspace(0.5, 10.0, steps=500, dtype=torch.float64).numpy()
+size = torch.tensor([100.0], dtype=torch.float64).numpy()
+pdgids_in = torch.tensor([-11, 11, 22], dtype=torch.int32).numpy()
+
+data = pyg4lego.run_simulation(40000, pos, mom, energy, random_gun=True, density=density, size=size, random_energy=True, SourceParticles=pdgids_in, random_energy_emin=10., random_energy_emax=300.)
+
+def to_tensors(obj):
+    if isinstance(obj, dict):
+        return {k: to_tensors(v) for k, v in obj.items()}
+    if isinstance(obj, np.ndarray):
+        return torch.from_numpy(obj)
+    return obj
+
+data = to_tensors(data)
+
+dataset = LEGODataset(
+    data=data, cutoff_mev=10, min_particles=0, device="cpu",
+)
+
+ntokens = dataset.target.shape[1] + 2  # +1 for e_dep, +1 for density
+
+pdgids = dataset.target[..., -1].flatten().nan_to_num().unique()
+pdgids = pdgids[(0 != pdgids) & (pdgids.abs() < 1_000_000_000)].tolist()
+
+meta_dict = {
+        "ntokens": ntokens,
+        "particles": pdgids,
+        "particles_in": pdgids_in.tolist()
+    }
+
+config = {
+  "model_conf": {
+      "manifold": "ProductManifold([Euclidean(), Sphere()], (3, 3))",
+      "proj_en": "in_frac_log",
+      "model_args": {"in_dim": 6},
+  },
+}
+
+dprep = DataPrep(config)
+data_prepped = dprep.prep(dataset.full_data)
+
+with open(_PATH / "meta.json", "w") as f:
+    json.dump(meta_dict, f, ensure_ascii=True, indent=4)
+
+torch.save(data_prepped, _PATH / "data_prepped.pt")

rp_lqar_full_er_260526/meta.json

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+{
+    "ntokens": 17,
+    "particles": [
+        -211.0,
+        -14.0,
+        -13.0,
+        -11.0,
+        11.0,
+        12.0,
+        14.0,
+        22.0,
+        211.0,
+        2112.0,
+        2212.0
+    ],
+    "particles_in": [
+        -11,
+        11,
+        22
+    ]
+}