def _main():
args = _parse_cli_args()
check_input(args.file)
outfile = args.out
if not outfile:
outfile = os.path.splitext(args.file)[0] + ".h5fit"
check_output(outfile, args.yes)
histogram = Histogram.from_h5hist(args.file)
histogram = histogram.cropped_to_adc_cuts()
popts, pstds = bisection_fit(histogram.pcx, histogram.counts, n=2)
copyfile(args.file, outfile)
with h5py.File(outfile, "a") as f:
fit = f.require_group("Fit")
f.attrs["basefile"] = os.path.basename(args.file)
fit.create_dataset("p", data=popts)
fit.create_dataset("perr", data=pstds)
sys.exit(0)
def _main():
args = _parse_cli_args()
check_input(args.file)
outfile = args.out
if not outfile:
outfile = os.path.splitext(args.file)[0] + ".h5hist"
check_output(outfile, args.yes)
if not args.ychannel:
hist, ex = hist1d_from_mpa_data(args.file,
args.xchannel,
nxbins=args.nx)
kind = "1D"
else:
hist, ex, ey = hist2d_from_mpa_data(args.file,
args.xchannel,
args.ychannel,
nxbins=args.nx,
nybins=args.ny)
kind = "2D"
with h5py.File(args.file, "r") as f:
datafile = f.attrs.get("datafile", None)
if not datafile:
datafile = os.path.basename(args.file)
with h5py.File(outfile, "w") as f:
f.attrs["datafile"] = datafile
f.attrs["basefile"] = os.path.basename(args.file)
f.attrs["kind"] = kind
f.attrs["xchannel"] = args.xchannel
f.create_dataset("EX", data=ex)
f.create_dataset("HIST", data=hist)
if kind == "2D":
f.attrs["orientation"] = "x = dim0/rows, y = dim1/cols"
f.attrs["ychannel"] = args.ychannel
f.create_dataset("EY", data=ey)
sys.exit(0)
def _main():
parser = argparse.ArgumentParser(
description="Convert an MPA-3 list file into HDF5 format.",
parents=[default_argparser]
)
args = parser.parse_args()
fin = check_input(args.file)
fout = args.out
if not fout:
fout = fin.replace(".lst", ".h5")
fout = check_output(fout, args.yes)
convert(fin, fout)
sys.exit(0)
def _main():
args = _parse_cli_args()
check_input(args.file)
save_as = {
".pdf": args.pdf,
".pgf": args.pgf,
".png": args.png,
".eps": args.eps,
}
if args.out:
out_name, ext = os.path.splitext(args.out)
if ext:
save_as[ext] = True
else:
out_name, _ = os.path.splitext(args.file)
if not any(save_as.values()):
save_as[".pdf"] = True
for ext, do_save in save_as.items():
if do_save:
check_output(out_name + ext, args.yes)
with h5py.File(args.file, "r") as f:
popts = f["Fit"]["p"][:]
pstds = f["Fit"]["perr"][:]
histogram = Histogram.from_h5hist(args.file)
histogram = histogram.cropped_to_adc_cuts()
synth_histogram = make_synth_histogram(histogram.pcx, histogram, popts,
pstds)
with plt.rc_context(rc={'lines.markersize': 1}):
_ = fit_overview_plot(histogram, synth_histogram, popts, pstds)
for ext, do_save in save_as.items():
if do_save:
plt.savefig(out_name + ext)
sys.exit(0)
def _main():
parser = argparse.ArgumentParser(
parents=[default_argparser],
description="Create a report sheet for a given measurement."
)
args = parser.parse_args()
stub = os.path.splitext(os.path.basename(args.file))[0]
outfile = args.out
if not outfile:
outfile = stub + ".md"
check_output(outfile, args.yes)
plots = _PLOTS.substitute(
fit_adc2_adc3_dr2=f"./{stub}_DR2_ADC2_ADC3_fit.png",
hist_adc1=f"./{stub}_ADC1.png",
hist_adc2=f"./{stub}_ADC2.png",
hist_adc3=f"./{stub}_ADC3.png",
hist_adc2_adc1=f"./{stub}_ADC2_ADC1.png",
hist_adc2_adc3_dr1=f"./{stub}_DR1_ADC2_ADC3.png",
hist_adc2_adc3_dr2=f"./{stub}_DR2_ADC2_ADC3.png",
hist_adc2_adc1_dr2=f"./{stub}_DR2_ADC2_ADC1.png",
)
if not args.meta:
meta = "N/A"
else:
from pickle import load
meta_data = load(open(args.meta, "rb"))
stringify_meta(meta_data)
meta = _META.substitute(**meta_data)
content = _TEMPLATE.substitute(
FILENAME=f"{stub}.lst", META=meta, PLOTS=plots
)
with open(outfile, "w") as f:
f.write(content)
def _main():
args = _parse_cli_args()
check_input(args.file)
save_as = {
".pdf": args.pdf,
".pgf": args.pgf,
".png": args.png,
".eps": args.eps,
}
if args.out:
out_name, ext = os.path.splitext(args.out)
if ext:
save_as[ext] = True
else:
out_name, _ = os.path.splitext(args.file)
if not any(save_as.values()):
save_as[".pdf"] = True
for ext, do_save in save_as.items():
if do_save:
check_output(out_name + ext, args.yes)
# with h5py.File(args.file, "r") as f:
# kind = f.attrs["kind"]
# if kind == "1D":
# _fig = hist1d_from_h5hist(f, scaling=args.linear)
# elif kind == "2D":
# _fig = hist2d_from_h5hist(f, scaling=args.linear)
hist = Histogram.from_h5hist(args.file, metafile=args.meta)
fig = hist.plot()
for ext, do_save in save_as.items():
if do_save:
plt.savefig(out_name + ext)
sys.exit(0)
from _common import (default_argparser, read_orchestration_csv, check_input,
check_output)
if __name__ == "__main__":
parser = argparse.ArgumentParser(
description=
"Extract metadata from orchestration file and save as pickle object.",
parents=[default_argparser],
)
parser.add_argument("lst",
type=str,
help="Filename corresponding to lst file")
args = parser.parse_args()
if not args.out:
out = args.lst[:-4] + ".meta"
else:
out = args.out
check_input(args.file)
check_output(out, yes=args.yes)
df = read_orchestration_csv(args.file)
df = df.set_index("FILE")
with open(out, "wb") as f:
pickle.dump(dict(df.loc[args.lst]), f)
sys.exit(0)
def _main():
args = _parse_cli_args()
check_input(args.file)
outfile = args.out
if not outfile:
outfile = os.path.splitext(args.file)[0] + ".h5roi"
check_output(outfile, args.yes)
kind_of_roi = [args.strip, args.rect, args.poly]
assert sum(bool(a) for a in kind_of_roi) == 1, "Choose exactly one type of ROI!"
if args.poly:
vx = np.array(args.poly[::2])
vy = np.array(args.poly[1::2])
_check_roi_2d_poly = _make_check_roi_2d_poly(vx, vy)
with h5py.File(args.file, "r") as fin, h5py.File(outfile, "w") as fout:
datafile = fin.attrs.get("datafile", None)
if not datafile:
datafile = os.path.basename(args.file)
fout["datafile"] = datafile
eve_in = fin["EVENTS"]
xchan = eve_in[args.xchannel]
ychan = eve_in[args.ychannel] if args.ychannel else None
fin.copy("CFG", fout)
eve_out = fout.create_group("EVENTS")
roiinf = fout.create_group("ROI")
for name, vec in eve_in.items():
stor = eve_out.create_dataset(name, (2,), maxshape=vec.shape, dtype=vec.dtype)
n = eve_in["TIME"].len()
chunk_size = TYPICAL_DASK_CHUNK
stor_pos = 0
for k in tqdm(range(n//chunk_size+1), desc="Processing"):
if (k+1)*chunk_size + 1 < n:
slc = np.s_[k*chunk_size:(k+1)*chunk_size + 1]
else:
slc = np.s_[k*chunk_size:]
xarr = xchan[slc]
if ychan:
yarr = ychan[slc]
if args.strip:
in_roi = _check_roi_1d(xarr, min(args.strip), max(args.strip))
kind = "1D"
roiargs = str(args.strip)
if args.rect:
in_roi = _check_roi_2d_rect(
xarr, args.rect[0], args.rect[1], yarr, args.rect[2], args.rect[3]
)
kind = "2DRect"
roiargs = str(args.rect)
if args.poly:
in_roi = _check_roi_2d_poly(xarr, yarr)
kind = "2DPoly"
roiargs = str(args.rect)
for name, vec in eve_in.items():
stor = eve_out[name]
filt = vec[slc][in_roi]
stor.resize(stor_pos+filt.size, axis=0)
stor[stor_pos:stor_pos+filt.size] = filt
stor_pos += filt.size
roiinf.attrs["kind"] = kind
roiinf.attrs["roiargs"] = roiargs
roiinf.attrs["xchannel"] = args.xchannel
roiinf.attrs["basefile"] = os.path.basename(args.file)
if args.ychannel:
roiinf.attrs["ychannel"] = args.ychannel
sys.exit(0)