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bc88015 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 | """Native ASCENT and ZephIR table adapters with conservative annotation provenance."""
from __future__ import annotations
import numpy as np
import pandas as pd
XYZ = ["x_voxel", "y_voxel", "z_voxel"]
TRUSTED = {"human_corrected", "manually_verified", "verified_checkpoint"}
def _physical(df, spacing_xyz):
for axis, scale in zip("xyz", spacing_xyz or [None] * 3, strict=True):
df[f"{axis}_um"] = df[f"{axis}_voxel"] * scale if scale is not None else np.nan
df["coordinate_system"] = "source_microscope"
df["annotation_confidence"] = np.nan
df["validity"] = True
return df
def ascent_tracks(path, recording_id, *, predicted=False):
"""Read native ZYX CSV; physical spacing is from Zenodo 17561700 metadata."""
df = pd.read_csv(path).rename(
columns={
"TrackID": "track_id",
"ObjectID": "detection_id",
"t": "frame_index",
"x": "x_voxel",
"y": "y_voxel",
"z": "z_voxel",
}
)
required = {"track_id", "detection_id", "frame_index", *XYZ}
if not required.issubset(df.columns):
raise ValueError(f"Missing ASCENT fields: {sorted(required - set(df.columns))}")
df["recording_id"] = recording_id
df["annotation_type"] = "algorithm_prediction" if predicted else "human_corrected"
df["source_provenance"] = "ASCENT-PRED" if predicted else "ASCENT-GT-proofread-ZephIR"
df["label_set"] = "prediction" if predicted else "reference"
df["track_id"] = df.track_id.astype(str)
df["detection_id"] = df.detection_id.map(lambda i: f"{'pred' if predicted else 'gt'}:{i}")
return _physical(df, [0.243, 0.243, 1.5])
def zephir_tracks(path, metadata, recording_id):
"""Retain scorer codes; only documented ZEIR results are classified as predictions.
Other codes remain unknown until their human/algorithm provenance is verified.
Normalized coordinates map to voxel index x*size-0.5 using the native viewer
convention. No voxel spacing is inferred from the neuron arrangement.
"""
import h5py
with h5py.File(path, "r") as h:
df = pd.DataFrame({k: h[k][()] for k in h})
df = df.rename(
columns={
"t_idx": "frame_index",
"worldline_id": "track_id",
"id": "detection_id",
"provenance": "source_provenance",
}
)
df["source_provenance"] = df.source_provenance.map(lambda s: s.decode("utf-8"))
for axis in "xyz":
df[f"{axis}_normalized"] = df[axis]
df[f"{axis}_voxel"] = df[axis] * metadata[f"shape_{axis}"] - 0.5
df = df.drop(columns=axis)
df["annotation_type"] = np.where(
df.source_provenance == "ZEIR", "algorithm_prediction", "unknown"
)
df["recording_id"] = recording_id
df["label_set"] = "source_mixed"
df["track_id"] = df.track_id.astype(str)
df["detection_id"] = df.detection_id.map(lambda i: f"source:{i}")
df = _physical(df, None)
normalized = df[[f"{axis}_normalized" for axis in "xyz"]].to_numpy(dtype=float)
df["validity"] = (
np.isfinite(normalized).all(axis=1)
& (normalized >= 0).all(axis=1)
& (normalized <= 1).all(axis=1)
)
return df
def validate_tracks(df, *, shape_xyz=None):
"""Reject ambiguous assignments and nonfinite observed points; never clamp or interpolate."""
required = {
"recording_id",
"frame_index",
"track_id",
"detection_id",
*XYZ,
"annotation_type",
"label_set",
"validity",
}
if not required.issubset(df.columns):
raise ValueError(f"Missing fields: {sorted(required - set(df.columns))}")
if df.empty:
raise ValueError("Empty tracking table")
valid = df.loc[df.validity]
scope = ["recording_id", "label_set", "frame_index"]
if valid.duplicated(scope + ["track_id"]).any():
raise ValueError("Duplicate track assignments")
if valid.duplicated(scope + ["detection_id"]).any():
raise ValueError("Duplicate detection assignments")
if valid.track_id.isna().any() or valid.detection_id.isna().any():
raise ValueError("Missing identity on a valid observation")
if not np.isfinite(valid[XYZ].to_numpy(dtype=float)).all():
raise ValueError("Nonfinite observed coordinates")
if (valid.frame_index < 0).any() or (valid.frame_index % 1 != 0).any():
raise ValueError("Invalid frame index")
# Centers use pixel boundaries [-0.5, size-0.5], including ZephIR normalized endpoints.
if shape_xyz is not None:
points = valid[XYZ].to_numpy(dtype=float)
if ((points < -0.5) | (points > np.asarray(shape_xyz) - 0.5)).any():
raise ValueError("Spatial coordinates outside source bounds")
return {
"observations": len(valid),
"frames": int(valid.frame_index.nunique()),
"tracks": int(valid.track_id.nunique()),
"annotation_types": {
str(k): int(v) for k, v in valid.annotation_type.value_counts().items()
},
}
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