205 lines
10 KiB
Python
205 lines
10 KiB
Python
"""Generate small SAR3D references by executing notebook cells 0 and 4.
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Requires the original, untracked Multi_antenna_3D_clean_v1.ipynb; a missing notebook
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is an error. NumPy/SciPy execute its function/class ASTs unchanged. Only parameter
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values are replaced; plotting imports and the interactive top-level runs are
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excluded. No production SAR implementation or hand-transcribed oracle is used.
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Run from the repository root:
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.venv/bin/python data_acq_and_processing/tests/generate_sar3d_reference.py
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"""
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from __future__ import annotations
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import argparse
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import ast
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import hashlib
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import json
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from pathlib import Path
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import tempfile
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import numpy as np
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import scipy
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ROOT = Path(__file__).resolve().parents[2]
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REFERENCE = Path(__file__).with_name("sar3d_reference.npz")
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def notebook_functions(notebook: Path, parameters: dict) -> tuple[dict, dict]:
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cells = json.loads(notebook.read_text(encoding="utf-8"))["cells"]
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namespace = {"__name__": __name__}
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hashes = {}
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for index in (0, 4):
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source = "".join(cells[index]["source"])
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hashes[str(index)] = hashlib.sha256(source.encode()).hexdigest()
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tree = ast.parse(source, filename=f"{notebook.name}:cell{index}")
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definitions = []
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for node in tree.body:
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if isinstance(node, (ast.FunctionDef, ast.ClassDef, ast.ImportFrom)):
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definitions.append(node)
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elif isinstance(node, ast.Import):
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if not any(alias.name.startswith(("matplotlib", "plotly")) for alias in node.names):
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definitions.append(node)
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elif isinstance(node, ast.Assign) and len(node.targets) == 1 and isinstance(node.targets[0], ast.Name):
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try:
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namespace[node.targets[0].id] = ast.literal_eval(node.value)
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except (ValueError, TypeError):
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pass # Dataset loading and the interactive run are supplied below.
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namespace.update(parameters)
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exec(compile(ast.Module(definitions, type_ignores=[]), f"{notebook.name}:cell{index}",
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"exec", dont_inherit=True), namespace)
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return namespace, hashes
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def scene() -> dict:
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"""Deterministic two-echo input with motion, vertical baselines and four pairs."""
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request = dict(
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tx=[[0, -.4, 0, 0], [1, .45, .2, .05]],
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rx=[[0, -.2, .1, 0], [1, .3, .35, .02]], f_start=1e9, f_stop=4e9,
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min_depth=1., max_depth=3., speed=.4, pitch=13., ground=.3,
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voxel=.4, radius=1, power=1.5, compensation=True, normalize=True,
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)
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# Match the float32 settings/geometry accepted by the native fixture.
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for key in ("tx", "rx"):
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request[key] = np.asarray(request[key], dtype=np.float32).astype(float).tolist()
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frequency = np.linspace(1e9, 4e9, 201, dtype=np.float32)
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members = []
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for frame, offset in enumerate((0, 180_000_000, 510_000_000)):
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traces = []
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start = 10_000_000_000 + offset
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for tx in range(2):
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for rx in range(2):
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samples = ((1 + .1 * rx) * np.exp(-2j * np.pi * frequency.astype(float)
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* (4.8 + .08 * tx - .03 * frame) / 3e8)
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+ .35 * np.exp(-2j * np.pi * frequency.astype(float) * (5.6 - .05 * rx) / 3e8)).astype(np.complex64)
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traces.append(dict(input=rx, output=tx, start=start, end=start + 165_000_000,
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frequency=frequency.tolist(), s21=np.column_stack((samples.real, samples.imag)).tolist()))
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members.append(dict(id=frame + 1, timestamp=start + 165_000_000, traces=traces))
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request.update(previous=members[:-1], latest=members[-1])
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return request
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def write_dataset(directory: Path, request: dict) -> list[Path]:
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"""Supply the notebook's original config_profile/meta/NPY input contract."""
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profile = {"gpr": {}, "run": {"combos": []}}
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for kind, position in (("tx", "output_pos"), ("rx", "input_pos")):
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profile["gpr"][f"{kind}_geometry"] = [
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{position: int(index), "x_m": x, "y_m": y, "z_m": z}
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for index, x, y, z in request[kind]
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]
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profile["run"]["combos"] = [
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{"input": trace["input"], "output": trace["output"]} for trace in request["latest"]["traces"]
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]
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(directory / "config_profile.json").write_text(json.dumps(profile), encoding="utf-8")
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snapshots = []
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for index, member in enumerate([*request["previous"], request["latest"]]):
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snapshot = directory / f'{index:04d}_id{member["id"]}_ns{member["timestamp"]}'
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snapshot.mkdir()
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metadata = {"traces": []}
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for trace in member["traces"]:
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stem = f'i{trace["input"]}_o{trace["output"]}'
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values = np.asarray(trace["s21"], dtype=np.float32)
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np.save(snapshot / f"{stem}_s21.npy", values[:, 0] + 1j * values[:, 1])
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np.save(snapshot / f"{stem}_freq.npy", np.asarray(trace["frequency"], dtype=np.float32))
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metadata["traces"].append(dict(input=trace["input"], output=trace["output"],
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capture_start_ns=trace["start"], capture_end_ns=trace["end"]))
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(snapshot / "meta.json").write_text(json.dumps(metadata), encoding="utf-8")
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snapshots.append(snapshot)
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return snapshots
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def reference(notebook: Path, request: dict, directory: Path) -> tuple[dict, dict]:
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snapshots = write_dataset(directory, request)
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center = snapshots[max(len(snapshots) - 1 - request["radius"], 0)]
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parameters = dict(
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INPUT_IDX=[int(row[0]) for row in request["rx"]],
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OUTPUT_IDX=[int(row[0]) for row in request["tx"]],
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F_START=request["f_start"], F_STOP=request["f_stop"],
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MIN_DEPTH=request["min_depth"], MAX_DEPTH=request["max_depth"],
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PAIR_NORM_DEPTH_MIN=request["min_depth"], PAIR_NORM_DEPTH_MAX=request["max_depth"],
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SPEED_M_S=request["speed"], LOOK_ANGLE_DEG=request["pitch"],
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BP_GROUND_DEPTH_M=request["ground"], COMP_RANGE_POWER=request["power"],
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SAR_VOXEL_M=(request["voxel"],) * 3, SAR_FRAME_RADIUS=request["radius"],
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SAR_USE_COMPENSATION=request["compensation"], SAR_NORMALIZE_EACH_FRAME=request["normalize"],
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SAR_SCAN_ROOT=directory, DATA_PATH=center, BG_PATH=directory, v=3e8,
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)
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ns, hashes = notebook_functions(notebook, parameters)
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tx, rx, order, tx_order, pair_count = ns["load_antenna_config_profile"](
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directory / "config_profile.json", ns["INPUT_IDX"], ns["OUTPUT_IDX"])
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ns["N_tx"], ns["N_rx"] = len(tx), len(rx)
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for kind, positions in (("tx", tx), ("rx", rx)):
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for axis, values in zip("xyz", ns["positions_to_xyz"](positions).T):
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ns[f"{axis}_{kind}"] = values
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antenna_x = np.concatenate((ns["x_tx"], ns["x_rx"]))
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ns["x_grid_bp"] = np.linspace(antenna_x.min() - 2, antenna_x.max() + 2, ns["NX_BP"])
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sweep_time, _, _ = ns["estimate_sweep_time_from_meta"](directory, n_pairs=pair_count)
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config = ns["MotionConfig"](
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speed_m_s=ns["SPEED_M_S"], look_angle_deg=ns["LOOK_ANGLE_DEG"],
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tx_sweep_time_s=sweep_time, frame_acq_time_s=sweep_time * pair_count,
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pair_order_phys=order, tx_order_full=tuple(tx_order),
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n_freq_hint=len(request["latest"]["traces"][0]["frequency"]),
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apply_pair_motion_correction=ns["APPLY_PAIR_MOTION_CORRECTION"],
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apply_freq_phase_correction=ns["APPLY_FREQ_PHASE_CORRECTION"],
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pair_motion_correction_mode=ns["PAIR_MOTION_CORRECTION_MODE"],
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motion_phase_sign=ns["MOTION_PHASE_SIGN"],
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)
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timestamps, _ = ns["compute_pair_timestamps_for_snapshot"](center, config)
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result = ns["sar_compute_volume"](center, None, config, timestamps, verbose=False)
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volume, grid = result["volume"], result["grid"]
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iy, iz, ix = ns["sar_global_max_index"](volume, grid)
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expected = {axis: grid[axis] for axis in "xyz"}
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expected.update(front=volume.max(axis=1), side=volume.max(axis=2), top=volume.max(axis=0),
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peak=np.array([result["level"], grid["x"][ix], grid["y"][iy], grid["z"][iz]]))
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return expected, hashes
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def generate(notebook: Path, output: Path) -> None:
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# Read up front so a missing notebook cannot produce an incomplete artifact.
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notebook.read_text(encoding="utf-8")
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cases = [
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dict(compensation=True, normalize=True),
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dict(compensation=True, normalize=False, speed=-.2, pitch=-7., ground=.5),
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dict(compensation=False, normalize=True, f_start=1.3e9, f_stop=3.7e9),
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dict(compensation=False, normalize=False, speed=0., pitch=0., radius=0),
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]
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arrays, configurations = {}, []
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for index, settings in enumerate(cases):
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request = scene()
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request.update(settings)
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for key in ("min_depth", "max_depth", "speed", "pitch", "ground", "voxel", "power"):
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request[key] = float(np.float32(request[key]))
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if index == 1:
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# A separate, unequal capture window per output instead of interleaving.
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for member in [*request["previous"], request["latest"]]:
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for trace in member["traces"]:
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if trace["output"] == 0:
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trace["end"] = trace["start"] + 80_000_000
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else:
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trace["start"] += 95_000_000
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with tempfile.TemporaryDirectory(prefix="sar3d_notebook_") as directory:
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expected, hashes = reference(notebook, request, Path(directory))
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if index == 0:
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arrays["request"] = np.array(json.dumps(request, separators=(",", ":")))
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configurations.append(dict(
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settings={key: request[key] for key in settings},
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windows=[[[trace["start"], trace["end"]] for trace in member["traces"]]
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for member in [*request["previous"], request["latest"]]],
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))
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arrays.update({f"case_{index}_{key}": value for key, value in expected.items()})
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arrays["cases"] = np.array(json.dumps(configurations, separators=(",", ":")))
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arrays["provenance"] = np.array(json.dumps(dict(
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notebook=notebook.name, cell_source_sha256=hashes, cells=[0, 4], cases=len(cases),
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numpy=np.__version__, scipy=scipy.__version__,
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method="Unchanged notebook AST functions; max projections of sar_compute_volume output",
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), sort_keys=True))
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output.parent.mkdir(parents=True, exist_ok=True)
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np.savez_compressed(output, **arrays)
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print(f"Wrote {output}: {output.stat().st_size} bytes, {len(cases)} notebook references")
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if __name__ == "__main__":
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parser = argparse.ArgumentParser(description=__doc__)
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parser.add_argument("--notebook", type=Path, default=ROOT / "Multi_antenna_3D_clean_v1.ipynb")
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parser.add_argument("--output", type=Path, default=REFERENCE)
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args = parser.parse_args()
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generate(args.notebook, args.output)
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