"""Command-line calibration of the pyfli simulator against an experimental decay cube."""
import argparse
import os
from .calibrator import DetectorSimCalibrator
from .config import CalibrationInputs, PixelSelectionCriteria
from .folder_calibration import calibrate_folder
[docs]
def parse_args(argv=None):
p = argparse.ArgumentParser(
description=__doc__, formatter_class=argparse.RawDescriptionHelpFormatter
)
p.add_argument(
"--data", required=True, help="raw 3D decay data (.sdt/.ptu/.tif/.npy/...)"
)
p.add_argument("--detector", required=True, choices=["continuous", "discrete"])
p.add_argument(
"--model", required=True, choices=["bi_exponential", "mono_exponential"]
)
p.add_argument(
"--irf",
default=None,
help="experimental IRF (one file, also in folder mode); omit to simulate it",
)
p.add_argument(
"--irf-mode", default="auto", choices=["auto", "experimental", "simulated"]
)
p.add_argument("--bg", default=None, help="background file/folder")
p.add_argument(
"--mask",
default=None,
help="boolean sample mask (folder mode: folder of masks named like the data files); omit for automatic",
)
p.add_argument(
"--reference",
default=None,
help="AlliGator / SPCImage folder or pyfli session .npy/folder",
)
p.add_argument(
"--reference-source",
default="auto",
choices=["auto", "alligator", "spcimage", "pyfli"],
)
p.add_argument(
"--reference-alpha", default="auto", choices=["auto", "intensity", "amplitude"]
)
p.add_argument("--laser-mhz", type=float, default=80.0)
p.add_argument("--bin-radius", type=int, default=0)
p.add_argument(
"--bit-depth", type=int, default=None, help="continuous detectors only"
)
p.add_argument("--jitter", default="auto", choices=["auto", "on", "off"])
p.add_argument("--mono-fraction", type=float, default=None)
p.add_argument("--prior-inflation", type=float, default=1.0)
p.add_argument("--reference-weight", type=float, default=0.05)
p.add_argument("--no-optimize-irf", action="store_true")
p.add_argument("--no-optimize-noise", action="store_true")
p.add_argument("--no-optimize-priors", action="store_true")
p.add_argument("--n-sim", type=int, default=2000)
p.add_argument("--n-exp", type=int, default=20000)
p.add_argument("--n-fit", type=int, default=1500)
p.add_argument(
"--n-bank",
type=int,
default=20000,
help="simulated pixels in the prior-fit bank",
)
p.add_argument(
"--rounds", type=int, default=3, help="alternating prior / IRF-noise rounds"
)
p.add_argument(
"--evals1",
type=int,
default=120,
help="IRF / noise evaluations (first round; later rounds half)",
)
p.add_argument(
"--evals2",
type=int,
default=1500,
help="prior evaluations per round (cheap, bank re-weighting)",
)
p.add_argument(
"--target-coverage",
type=float,
default=0.99,
help="fraction of experimental pixels that must lie inside the simulated distribution (0: best fit only)",
)
p.add_argument("--seed", type=int, default=0)
p.add_argument("--out", default=None)
p.add_argument(
"--weighting",
default="equal",
choices=["equal", "pixels"],
help="folder mode: weight of each experiment in the combined setup",
)
p.add_argument(
"--force",
action="store_true",
help="folder mode: recalibrate experiments that already have a config",
)
# pixel selection
p.add_argument("--min-signal", type=float, default=50.0)
p.add_argument("--snr-min", type=float, default=5.0)
p.add_argument("--flatness-sigma", type=float, default=5.0)
p.add_argument("--otsu", default="auto", choices=["auto", "on", "off"])
p.add_argument(
"--refine-mask",
action="store_true",
help="intersect --mask with the automatic criteria",
)
return p.parse_args(argv)
[docs]
def main(argv=None):
a = parse_args(argv)
shared = dict(
irf_mode=a.irf_mode,
bg_path=a.bg,
reference_source=a.reference_source,
reference_alpha=a.reference_alpha,
laser_freq_mhz=a.laser_mhz,
bin_radius=a.bin_radius,
bit_depth=a.bit_depth,
jitter={"auto": "auto", "on": True, "off": False}[a.jitter],
mono_fraction=a.mono_fraction,
prior_inflation=a.prior_inflation,
reference_weight=a.reference_weight,
optimize_irf=not a.no_optimize_irf,
optimize_noise=not a.no_optimize_noise,
optimize_priors=not a.no_optimize_priors,
n_sim_pixels=a.n_sim,
n_exp_pixels=a.n_exp,
n_fit_pixels=a.n_fit,
n_bank_pixels=a.n_bank,
bank_rounds=a.rounds,
max_evals_stage1=a.evals1,
max_evals_stage2=a.evals2,
target_coverage=a.target_coverage,
seed=a.seed,
selection=PixelSelectionCriteria(
min_signal_photons=a.min_signal,
snr_min=a.snr_min,
flatness_sigma=a.flatness_sigma,
use_otsu=a.otsu,
refine_user_mask=a.refine_mask,
),
)
if os.path.isdir(a.data):
if a.reference:
raise SystemExit(
"--reference belongs to one experiment; it cannot be used with a data folder"
)
if a.mask and not os.path.isdir(a.mask):
raise SystemExit(
"With a data folder, --mask must be a folder of masks named like the data files"
)
if a.irf and not os.path.isfile(a.irf):
raise SystemExit(
"With a data folder, --irf must be one IRF file shared by all experiments"
)
out = calibrate_folder(
a.data,
a.detector,
a.model,
output_root=a.out,
mask_folder=a.mask,
irf_path=a.irf,
force=a.force,
weighting=a.weighting,
**shared,
)
print(
f"\n{len(out['per_experiment'])} experiments calibrated (one config each):"
)
for name, path in out["per_experiment"].items():
print(f" {name}: {path} (mask: {out['masks'][name]})")
for name, err in out["failed"].items():
print(f" FAILED {name}: {err}")
if out["combined"]:
print(f"combined setup: {out['combined']['py']}")
return
inputs = CalibrationInputs(
data_path=a.data,
detector_type=a.detector,
model_type=a.model,
mask_path=a.mask,
irf_path=a.irf,
reference_path=a.reference,
output_dir=a.out,
**shared,
)
result = DetectorSimCalibrator(inputs).run()
print("\nCalibrated pyfli simulator config:")
for k, v in result.config.items():
print(f" {k}: {v}")
print(f"SimGenerator kwargs: {result.sim_generator_kwargs}")
v = result.validation
print(
f"distance initial -> best fit -> covering: {v['distance_initial']:.3f} -> {v['distance_best_fit']:.3f} -> {v['distance_final']:.3f}"
)
print(f"coverage of the experiment: {v['coverage_final']}")
print(f"FLIValidator: {v['fli_validator']}")
print(f"outputs: {result.output_dir}")
if __name__ == "__main__":
main()
