def inspect(vastdir, resultdir, fitsdir, apikey, stars):
"""generate image+txt file to inspect a certain star on wrong ucac/localid nrs """
reading.trash_and_recreate_dir(Path(resultdir) / "inspect")
ref_jd, _, _, reference_frame = reading.extract_reference_frame(vastdir)
_, shapex, shapey = reading.get_fits_data(Path(fitsdir, Path(reference_frame).name))
refframes: List[RefFrame] = [
RefFrame(
ref_jd,
Path(vastdir) / "new-image.fits",
Path(fitsdir, Path(reference_frame).name),
)
]
# construct star descriptions
sds = utils_sd.construct_star_descriptions(vastdir, None)
star_catalog = do_calibration.create_star_descriptions_catalog(sds)
if args.radecs:
main_vast.read_and_tag_radec(args.radecs, sds)
if args.localids:
stardict = main_vast.get_localid_to_sd_dict(sds)
main_vast.read_and_tag_localid(args.localids, stardict)
if args.stars:
stars = sorted(list(map(lambda x: int(x), stars)))
else:
stars = sorted([x.local_id for x in sds if x.get_metadata("SELECTEDTAG")])
for starid in stars:
process(
vastdir,
resultdir,
fitsdir,
apikey,
shapex,
shapey,
starid,
ref_jd,
reference_frame,
sds,
star_catalog,
refframes,
)
def update_img(record: ImageRecord):
data, shapex, shapey = reading.get_fits_data(Path(fitsdir, record.file))
backgr = data.mean()
data = data.reshape(shapex, shapey)
data = np.pad(data, (padding, padding),
"constant",
constant_values=(backgr, backgr))
cropdata = data[record.y - fullcrop + padding:record.y + fullcrop +
padding, record.x - fullcrop + padding:record.x +
fullcrop + padding, ]
rotcrop = ndimage.interpolation.rotate(cropdata, record.rotation)
rotcrop = crop_center(rotcrop, crop, crop)
# fig = plt.figure(figsize=(15, 15), dpi=80, facecolor='w', edgecolor='k')
# rotx, roty = rotcrop.shape
# target_app = CircularAperture((rotx // 2, roty // 2), r=5.)
# target_app.plot()
# data[record.y-cropsize:record.y+cropsize,record.x-cropsize:record.x+cropsize] = 0
# ax.imshow(rotcrop, cmap='gray', origin='lower', norm=LogNorm())
pbar.update(1)
im.set_data(rotcrop)
ax.set_title(f"JD: {record.jd}")
def run_standard_field_charts(
star_descriptions: StarDescriptionList,
wcs,
fieldchartsdirs,
reference_fits_frame,
comp_stars: ComparisonStars,
):
# setting the font size for titles/axes
plt.rcParams.update({"axes.titlesize": "large", "axes.labelsize": "large"})
fits_data, _, _ = reading.get_fits_data(reference_fits_frame)
fits_data_blank, _, _ = reading.get_fits_data(reference_fits_frame, blank_data=True)
SHOW_UPSILON = False
# if SHOW_UPSILON:
# candidates = do_calibration.get_candidates(0.5)
# TODO hand labeled stars
# hand_candidates_descr = do_calibration.get_star_descriptions(init.wwcra_certain_candidates)
all_stars_descr = star_descriptions
# if SHOW_UPSILON:
# big_green = set_custom_label(comparison_ star_descr, 'comp')
# small_red = set_custom_label(apass_star_descr, [o.vmag for o in apass_star_descr])
# big_green = set_custom_label(vsx_star_descr, [o.match['catalog_dict']['name'] for o in vsx_star_descr])
# small_red = set_custom_label(hand_candidates_descr, [o.local_id for o in hand_candidates_descr])
# big_green = set_aavso_id_label(vsx_star_descr)
# small_red = set_local_id_label(hand_candidates_descr)
# all stars get a blank label
all_stars_no_label = set_custom_label(all_stars_descr, "")
# field chart with all detections
logging.info("Plotting field chart with all detected stars...")
fig = plot_it(
[all_stars_no_label],
[4.0],
[False],
fits_data,
wcs,
"All detected stars",
PADDING,
annotate=False,
)
save(
fig, fieldchartsdirs + "all_detections_{}_stars".format(len(all_stars_no_label))
)
# vsx stars get their aavso id label
vsx_descr = utils.get_stars_with_metadata(star_descriptions, "VSX")
vsx_labeled = set_aavso_id_label(vsx_descr)
# field chart with all vsx stars
logging.info("Plotting field chart with all VSX variable stars...")
fig = plot_it(
[vsx_labeled], [10.0], [False], fits_data, wcs, "All VSX stars", PADDING
)
save(fig, fieldchartsdirs + "vsx_stars_{}".format(len(vsx_labeled)))
# field chart with all vsx stars without the background
logging.info(
"Plotting field chart with all VSX variable stars without reference field..."
)
fig = plot_it(
[vsx_labeled],
[10.0],
[False],
fits_data_blank,
wcs,
"VSX without background",
PADDING,
)
save(fig, fieldchartsdirs + "vsx_stars_no_ref_{}".format(len(vsx_labeled)))
# field chart with only the background
logging.info("Plotting field chart with only the reference field...")
fig, _ = get_plot_with_background_data(fits_data, 0, "Reference frame")
save(fig, fieldchartsdirs + "only_ref")
# candidate stars get their local id label
candidate_descr = utils.get_stars_with_metadata(
star_descriptions, "CANDIDATE", exclude=["VSX"]
)
candidate_labeled = set_local_id_label(candidate_descr)
# localid/radec inputfile stars get their local id label
selected_desc = utils.get_stars_with_metadata(star_descriptions, "SITE")
selected_no_vsx_descr = utils.get_stars_with_metadata(
star_descriptions, "SITE", exclude=["VSX"]
)
selected_no_vsx_labeled = set_custom_label(
selected_no_vsx_descr,
[x.get_metadata("SITE").our_name for x in selected_no_vsx_descr],
)
selected_count = len(selected_no_vsx_labeled)
# field chart with all vsx stars + candidates + radeccatalog
logging.info("Plotting field chart with all VSX variable stars + candidate vars...")
fig = plot_it(
[vsx_labeled, candidate_labeled],
[10.0, 5.0],
[False, True],
fits_data,
wcs,
"VSX stars + candidate stars",
PADDING,
)
save(
fig,
fieldchartsdirs
+ f"vsx_{len(vsx_labeled)}_and_candidates_{len(candidate_labeled)}",
)
# field chart with all vsx stars + localid/radec inputfile
logging.info(
f"Plotting field chart with all VSX variable stars + {selected_count} selected vars..."
)
fig = plot_it(
[vsx_labeled, selected_no_vsx_labeled],
[10.0, 5.0],
[False, True],
fits_data,
wcs,
"VSX stars + selected stars",
PADDING,
)
save(
fig,
fieldchartsdirs
+ "vsx_{}_and_selected_{}".format(len(vsx_labeled), selected_count),
)
# compstars get their vmag as label
# comp_stars_descr = comp_stars.star_descriptions
# comp_stars_labeled = set_custom_label(comp_stars_descr, [x.vmag for x in comp_stars_descr])
logging.info(
f"Plotting field chart for each of the {selected_count} selected stars"
)
# Plotting finder charts for the site
# field charts for each individually selected starfile star
for star in tqdm.tqdm(selected_desc, desc="Field chart for each star"):
filtered_compstars, check_star = do_compstars.filter_comparison_stars(
star, comp_stars
)
filtered_compstars_sds = filtered_compstars.star_descriptions
check_star_sd = check_star.star_descriptions
compstars_labeled = set_custom_label(
filtered_compstars_sds,
[x.get_metadata("UCAC4").vmag for x in filtered_compstars_sds],
)
checkstar_labeled = set_custom_label(
check_star_sd, f"Kmag={check_star_sd[0].get_metadata('UCAC4').vmag}"
)
starui: utils.StarUI = utils.get_star_or_catalog_name(star, suffix="")
fig = plot_it(
[[star], vsx_labeled, compstars_labeled, checkstar_labeled],
[7.0, 5.0, 3.0, 4.0],
[True, False, True, False],
fits_data,
wcs,
f"VSX stars + comp stars + {starui.catalog_name} (star {star.local_id})",
PADDING,
)
save(fig, f"{fieldchartsdirs}vsx_and_star_{starui.filename_no_ext}")
gc.collect()
def update_img(
star: StarDescription,
record: ImageRecord,
neighbours: List[StarDescription],
resultdir: str,
platesolved_file: str,
):
resultlines = []
fig = plt.figure(figsize=(36, 32), dpi=dpi, facecolor="w", edgecolor="k")
wcs = do_calibration.get_wcs(platesolved_file)
data, shapex, shapey = reading.get_fits_data(platesolved_file)
backgr = data.mean()
data = data.reshape(shapex, shapey)
data = np.pad(
data, (padding, padding), "constant", constant_values=(backgr, backgr)
)
starxy = SkyCoord.to_pixel(star.coords, wcs=wcs, origin=0)
# add main target
add_circle(record.x, record.y, 3, "b")
startoml = load_toml(star, resultdir)
star.vmag = startoml["vmag"]
resultlines.append(log_star(star, -1))
random_offset = False
offset1 = 70
offset2 = 10
# add neighbours
for idx, nstar in enumerate(neighbours):
add_pixels(nstar, wcs, 0)
add_circle(nstar.xpos, nstar.ypos, 4, "g")
if random_offset:
xrandoffset = random.randint(3, 4)
yrandoffset = random.randint(2, 3)
xsignrand = random.choice([-1.0, 1.0])
ysignrand = random.choice([-1.0, 1.0])
offset1 = xsignrand * xrandoffset
offset2 = ysignrand * yrandoffset
# https://matplotlib.org/3.1.0/api/_as_gen/matplotlib.pyplot.arrow.html
plt.annotate(
f"{idx}",
xy=(round(nstar.xpos), round(nstar.ypos)),
xycoords="data",
xytext=(offset1, offset2),
textcoords="offset points",
size=NEIGHBOUR_TEXT_SIZE,
color="red",
arrowprops=dict(arrowstyle="-", color="grey", alpha=0.2),
)
resultlines.append(log_star(nstar, idx))
# loading and painting ucac stars
radius = 0.08
ucac_stars: List[MinimalStarTuple] = ucac4.get_region_minimal_star_tuples(
star.coords.ra.deg, star.coords.dec.deg, radius
)
logging.info(f"Looping on {len(ucac_stars)} UCAC4 stars")
for ucac_star in ucac_stars:
coord = SkyCoord(ucac_star.ra, ucac_star.dec, unit="deg")
if star.coords.separation(coord).degree > radius:
continue
xy = SkyCoord.to_pixel(coord, wcs=wcs, origin=0)
x, y = round(xy[0].item(0)), round(xy[1].item(0))
add_circle(x, y, 2, "c")
plt.annotate(
f"{ucac_star.id[-3:]}",
xy=(x, y),
xycoords="data",
xytext=(2, 2),
textcoords="offset points",
size=UCAC4_TEXT_SIZE,
arrowprops=dict(arrowstyle="-", color="grey", alpha=0.2),
)
median = np.median(data)
# data = ndimage.interpolation.rotate(data, record.rotation)
plt.imshow(data, cmap="gray_r", origin="lower", vmin=0, vmax=min(median * 5, 65536))
starui = utils.get_star_or_catalog_name(star)
save_inspect_image = Path(
Path(resultdir) / "inspect",
f"{starui.filename_no_ext}_{Path(resultdir).name}_inspect.png",
)
save_inspect_txt = Path(
Path(resultdir) / "inspect",
f"{starui.filename_no_ext}_{Path(resultdir).name}_inspect.txt",
)
fig.savefig(save_inspect_image)
logging.info(f"Saved file as {save_inspect_image}.")
write_file(star, save_inspect_txt, resultlines)
plt.close(fig)
plt.clf()