lars/giant
1
0
Fork 0
Code Issues 4 Pull Requests 2 Actions Packages Projects Releases Wiki Activity

Rename pre_energy/post_energy columns to pre_E/post_E

Browse Source 
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
This commit is contained in:
Lars Bogner
2026-06-17 14:31:41 +02:00
parent 877f83a5cd
commit d402cdace3
3 changed files with 7 additions and 7 deletions
Download Patch File Download Diff File Expand all files Collapse all files
giant/cli.py
+1 -1
View File
@@ -296,7 +296,7 @@ def predict(
"pre_x": chunk["pre_pos"][:, 0],
"pre_y": chunk["pre_pos"][:, 1],
"pre_z": chunk["pre_pos"][:, 2],
"pre_energy": chunk["pre_energy"],
"pre_E": chunk["pre_E"],
"pre_dir_x": chunk["pre_dir"][:, 0],
"pre_dir_y": chunk["pre_dir"][:, 1],
"pre_dir_z": chunk["pre_dir"][:, 2],
giant/data/loader.py
+4 -4
View File
@@ -21,13 +21,13 @@ def _df_to_dict(df: pd.DataFrame) -> dict[str, np.ndarray]:
"event_id": df["event_id"].to_numpy(),
"pdg": df["pdg"].to_numpy(dtype=np.int32),
"pre_pos": df[["pre_x", "pre_y", "pre_z"]].to_numpy(dtype=np.float32),
"pre_energy": df["pre_energy"].to_numpy(dtype=np.float32),
"pre_E": df["pre_E"].to_numpy(dtype=np.float32),
"pre_dir": df[["pre_dir_x", "pre_dir_y", "pre_dir_z"]].to_numpy(dtype=np.float32),
"material": df["material"].to_numpy(dtype=object),
"layer_id": df["layer_id"].to_numpy(dtype=np.int32),
"n_sec": df["child_track_ids"].apply(len).to_numpy(dtype=np.int32),
"step_length": df["step_length"].to_numpy(dtype=np.float32),
"delta_e": (df["pre_energy"] - df["post_energy"]).to_numpy(dtype=np.float32),
"delta_e": (df["pre_E"] - df["post_E"]).to_numpy(dtype=np.float32),
"edep": df["edep"].to_numpy(dtype=np.float32),
"post_dir": df[["post_dir_x", "post_dir_y", "post_dir_z"]].to_numpy(dtype=np.float32),
}
@@ -51,7 +51,7 @@ def iter_file_chunks(path: str | Path) -> Iterator[dict[str, np.ndarray]]:
_COND_COLS = [
"event_id", "pdg",
"pre_x", "pre_y", "pre_z", "pre_energy",
"pre_x", "pre_y", "pre_z", "pre_E",
"pre_dir_x", "pre_dir_y", "pre_dir_z",
"material", "layer_id", "child_track_ids",
]
@@ -62,7 +62,7 @@ def _cond_df_to_dict(df: pd.DataFrame) -> dict[str, np.ndarray]:
"event_id": df["event_id"].to_numpy(),
"pdg": df["pdg"].to_numpy(dtype=np.int32),
"pre_pos": df[["pre_x", "pre_y", "pre_z"]].to_numpy(dtype=np.float32),
"pre_energy": df["pre_energy"].to_numpy(dtype=np.float32),
"pre_E": df["pre_E"].to_numpy(dtype=np.float32),
"pre_dir": df[["pre_dir_x", "pre_dir_y", "pre_dir_z"]].to_numpy(dtype=np.float32),
"material": df["material"].to_numpy(dtype=object),
"layer_id": df["layer_id"].to_numpy(dtype=np.int32),
giant/data/transforms.py
+2 -2
View File
@@ -129,7 +129,7 @@ def build_cond_features(
"""Build conditioning arrays only — no target, no post-step variables."""
cond_cont = np.column_stack([
data["pre_pos"],
log_transform(data["pre_energy"]),
log_transform(data["pre_E"]),
data["pre_dir"],
data["layer_id"].astype(np.float32),
data["n_sec"].astype(np.float32),
@@ -168,7 +168,7 @@ def build_features(
cond_cont = np.column_stack([
data["pre_pos"],
log_transform(data["pre_energy"]),
log_transform(data["pre_E"]),
data["pre_dir"],
data["layer_id"].astype(np.float32),
data["n_sec"].astype(np.float32),