def main(directory, psfex_file, image, prefix, output_file):
print("\nExecuting Python script pipeline_fors2/9-psf.py, with:")
print(f"\tmain data directory {directory}")
print(f"\tPSFEx file {psfex_file}")
print(f"\timage file {image}")
print(f"\toutput file {output_file}")
print(f"\tfilter prefix {prefix}")
print()
_, pix_scale = ff.get_pixel_scale(image)
pix_scale = float(pix_scale)
fwhm_pix = float(
ff.get_header_attribute(file=psfex_file, attribute='PSF_FWHM', ext=1))
fwhm_deg = fwhm_pix * pix_scale
fwhm_arcsec = fwhm_deg * 3600.
param_dict = {
f'{prefix}_pixel_scale': pix_scale,
f'{prefix}_fwhm_pix': fwhm_pix,
f'{prefix}_fwhm_deg': fwhm_deg,
f'{prefix}_fwhm_arcsec': fwhm_arcsec,
}
p.add_params(file=directory + '/' + output_file, params=param_dict)
p.add_params(file=directory + '/' + 'output_paths',
params={f'{prefix}_psf_model': psfex_file})
p.yaml_to_json(directory + '/' + output_file)
def plot_gal_params(
hdu: fits.HDUList, ras: Union[list, np.ndarray, float], decs: Union[list, np.ndarray, float],
a: Union[list, np.ndarray, float], b: Union[list, np.ndarray, float],
theta: Union[list, np.ndarray, float], colour: str = 'white',
show_centre: bool = False,
label: str = None, world: bool = True, world_axes: bool = True, **kwargs):
"""
:param hdu:
:param ras: In degrees.
:param decs: In degrees.
:param a: In degrees.
:param b: In degrees.
:param theta: In degrees, apparently.
:param colour:
:param offset:
:param show_centre:
:return:
"""
# TODO: Rename parameters to reflect acceptance of pixel coordinates.
_, pix_scale = ff.get_pixel_scale(hdu)
n_y, n_x = hdu[0].data.shape
header = hdu[0].header
wcs_image = wcs.WCS(header=header)
if world:
xs, ys = wcs_image.all_world2pix(ras, decs, 0)
else:
xs = np.array(ras)
ys = np.array(decs)
theta = np.array(theta)
# Convert to photutils angle format
theta = u.world_angle_se_to_pu(theta, rot_angle=ff.get_rotation_angle(header))
a = u.dequantify(a)
b = u.dequantify(b)
for i, x in enumerate(xs):
u.debug_print(2, "plotting.plot_gal_params(): x, ys[i] ==", x, ys[i])
if a[i] != 0 and b[i] != 0:
if world_axes:
ellipse = photutils.EllipticalAperture((x, ys[i]), a=a[i] / pix_scale, b=b[i] / pix_scale,
theta=theta[i])
else:
ellipse = photutils.EllipticalAperture((x, ys[i]), a=a[i], b=b[i], theta=theta[i])
ellipse.plot(**kwargs)
line_label = None
else:
line_label = label
if show_centre:
plt.plot((0.0, n_x), (ys[i], ys[i]), c=colour, label=line_label)
plt.plot((x, x), (0.0, n_y), c=colour)
def distance_bar(hdu: fits.hdu.HDUList, ang_size_distance: float, x: float, y: float, frame: int,
colour: str = 'white', length: float = None, angle_length: float = None, spread: float = 1.,
reverse_y=False):
"""
Draw a projected distance bar on your plot.
:param hdu:
:param ang_size_distance: Cosmological angular size distance, in parsecs.
:param length: In parsecs, the length of bar you want to show. One of
:param angle_length: In arcsecs, the length of the bar you want to show.
:param x: Position of bar in plot.
:param y: Position of bar in plot.
:param colour: Colour of bar and text.
:return:
"""
if angle_length is length is None:
raise ValueError('Either length or angle_length must be provided.')
if angle_length is not None and length is not None:
raise ValueError('Only one of length or angle_length can be provided.')
# Get the angular pixel scale (degrees per pixel)
_, pix_angle_scale = ff.get_pixel_scale(hdu)
# Get the distance pixel scale (parsecs per pixel)
pix_length_scale = ff.projected_pix_scale(hdu=hdu, ang_size_distance=ang_size_distance)
if angle_length is None:
# Get the length of the bar in pixels.
pix_length = length / pix_length_scale
# Use the angular pixel scale to get the angular length of the bar
angle_length = pix_length * pix_angle_scale
else:
# Convert to degrees.
angle_length /= 3600
# Get the length of the bar in pixels.
pix_length = angle_length / pix_angle_scale
# Use the distance pixel scale to get the projected length of the bar.
length = pix_length * pix_length_scale
angle_length *= 3600
print('Projected length:', length, 'pc')
print('Angular size:', angle_length, 'arcsecs')
if reverse_y:
plt.plot((x, x + pix_length), (2 * frame - y, 2 * frame - y), c=colour)
plt.text(x, 2 * frame - y - spread, f'{np.round(length / 1000, 1)} kpc', color=colour)
plt.text(x, 2 * frame - y + 1.7 * spread, f'{int(angle_length)} arcsec', color=colour)
else:
plt.plot((x, x + pix_length), (y, y), c=colour)
plt.text(x, y + spread, f'{np.round(length / 1000, 1)} kpc', color=colour)
plt.text(x, y - 1.7 * spread, f'{int(angle_length)} arcsec', color=colour)
def plot_gal_params(hdu: fits.HDUList, ras: Union[list, np.ndarray, float], decs: Union[list, np.ndarray, float],
a: Union[list, np.ndarray, float], b: Union[list, np.ndarray, float],
theta: Union[list, np.ndarray, float], colour: str = 'white',
show_centre: bool = False,
label: str = None, world: bool = True, world_axes: bool = True, line_style='-'):
"""
:param hdu:
:param ras: In degrees.
:param decs: In degrees.
:param a: In degrees.
:param b: In degrees.
:param theta: In degrees, apparently.
:param colour:
:param offset:
:param show_centre:
:return:
"""
# TODO: Rename parameters to reflect acceptance of pixel coordinates.
_, pix_scale = ff.get_pixel_scale(hdu)
n_y, n_x = hdu[0].data.shape
header = hdu[0].header
wcs_image = wcs.WCS(header=header)
if world:
xs, ys = wcs_image.all_world2pix(ras, decs, 0)
else:
xs = np.array(ras)
ys = np.array(decs)
theta = np.array(theta)
theta = -theta + ff.get_rotation_angle(header)
# Convert to radians
theta = theta * np.pi / 180
for i, x in enumerate(xs):
if a[i] != 0 and b[i] != 0:
if world_axes:
ellipse = photutils.EllipticalAperture((x, ys[i]), a=a[i] / pix_scale, b=b[i] / pix_scale,
theta=theta[i])
else:
ellipse = photutils.EllipticalAperture((x, ys[i]), a=a[i], b=b[i], theta=theta[i])
ellipse.plot(color=colour, label=label, ls=line_style)
line_label = None
else:
line_label = label
if show_centre:
plt.plot((0.0, n_x), (ys[i], ys[i]), c=colour, label=line_label)
plt.plot((x, x), (0.0, n_y), c=colour)
def main(directory, psfex_file, image, prefix):
_, pix_scale = ff.get_pixel_scale(image)
pix_scale = float(pix_scale)
fwhm_pix = float(ff.get_header_attribute(file=psfex_file, attribute='PSF_FWHM', ext=1))
fwhm_deg = fwhm_pix * pix_scale
fwhm_arcsec = fwhm_deg * 3600.
param_dict = {f'{prefix}_pixel_scale': pix_scale,
f'{prefix}_fwhm_pix': fwhm_pix,
f'{prefix}_fwhm_deg': fwhm_deg,
f'{prefix}_fwhm_arcsec': fwhm_arcsec,
}
p.add_params(file=directory + '/output_values', params=param_dict)
p.yaml_to_json(directory + '/output_values')
def main(data_dir, data_title, origin, destination, all_synths):
print("\nExecuting Python script pipeline_fors2/5-background_subtract.py, with:")
print(f"\tepoch {data_title}")
print(f"\torigin directory {origin}")
print(f"\tdestination directory {destination}")
print()
methods = ["ESO backgrounds only", "SExtractor backgrounds only", "polynomial fit", "Gaussian fit", "median value"]
if all_synths:
frame = 56
method = "polynomial fit"
degree = 5
do_mask = True
local = True
global_sub = False
trim_image = False
recorrect_subbed = True
eso_back = False
else:
frame = 200
# frame_arcsec = 30 * units.arcsec
# frame_deg = frame_arcsec.to(units.deg)
eso_back = False
_, method = u.select_option(message="Please select the background subtraction method.", options=methods,
default="polynomial fit")
degree = None
if method == "polynomial fit":
degree = u.user_input(message=f"Please enter the degree of {method} to use:", typ=int, default=3)
elif method == "ESO backgrounds only":
eso_back = True
do_mask = False
if method not in ["ESO backgrounds only", "SExtractor backgrounds only", "median value"]:
do_mask = u.select_yn(message="Mask sources using SExtractor catalogue?", default=True)
if method in ["polynomial fit", "Gaussian fit"]:
local = u.select_yn(message="Use a local fit?", default=True)
else:
local = False
global_sub = False
trim_image = False
recorrect_subbed = False
if local:
global_sub = u.select_yn(message="Subtract local fit from entire image?", default="n")
if not global_sub:
trim_image = u.select_yn(message="Trim images to subtracted region?", default="y")
recorrect_subbed = u.select_yn(message="Re-normalise background of subtracted region?", default="y")
# if not eso_back and method != "SExtractor backgrounds only":
# eso_back = u.select_yn(message="Subtract ESO Reflex fitted backgrounds first?", default=False)
outputs = p.object_output_params(data_title, instrument='FORS2')
data_dir = u.check_trailing_slash(data_dir)
destination = u.check_trailing_slash(destination)
destination = data_dir + destination
u.mkdir_check_nested(destination)
origin = u.check_trailing_slash(origin)
science_origin = data_dir + origin + "science/"
print(science_origin)
filters = outputs['filters']
frb_params = p.object_params_frb(obj=data_title[:-2])
epoch_params = p.object_params_fors2(obj=data_title)
background_origin_eso = ""
if eso_back:
background_origin_eso = data_dir + "/" + origin + "/backgrounds/"
if method == "SExtractor backgrounds only":
background_origin = f"{data_dir}{origin}backgrounds_sextractor/"
elif method == "polynomial fit":
background_origin = f"{destination}backgrounds/" # f"{destination}backgrounds_{method.replace(' ', '')}_degree_{degree}_local_{local}_globalsub_{global_sub}/"
else:
background_origin = f"{destination}backgrounds/" # f"{destination}backgrounds_{method.replace(' ', '')}_local_{local}_globalsub_{global_sub}/"
trimmed_path = ""
if trim_image:
trimmed_path = f"{data_dir}{origin}trimmed_to_background/"
u.mkdir_check_nested(trimmed_path)
ra = frb_params["burst_ra"]
dec = frb_params["burst_dec"]
if all_synths:
ras = epoch_params["test_synths"]["ra"]
decs = epoch_params["test_synths"]["dec"]
else:
ras = [ra]
decs = [dec]
for fil in filters:
trimmed_path_fil = ""
if trim_image:
trimmed_path_fil = f"{trimmed_path}{fil}/"
u.mkdir_check(trimmed_path_fil)
background_fil_dir = f"{background_origin}{fil}/"
u.mkdir_check_nested(background_fil_dir)
science_destination_fil = f"{destination}science/{fil}/"
u.mkdir_check_nested(science_destination_fil)
files = os.listdir(science_origin + fil + "/")
for file_name in files:
if file_name.endswith('.fits'):
new_file = file_name.replace("norm", "bg_sub")
new_path = f"{science_destination_fil}/{new_file}"
print("NEW_PATH:", new_path)
science = science_origin + fil + "/" + file_name
# First subtract ESO Reflex background images
# frame = (frame_deg / f.get_pixel_scale(file=science, astropy_units=True)[1]).to(f.pix).value
if eso_back:
background_eso = background_origin_eso + fil + "/" + file_name.replace("SCIENCE_REDUCED",
"PHOT_BACKGROUND_SCI")
ff.subtract_file(file=science, sub_file=background_eso, output=new_path)
science_image = new_path
if method != "ESO backgrounds only":
print(ra, dec)
print("Science image:", science)
science_image = fits.open(science)
print("Science file:", science_image)
wcs_this = WCS(header=science_image[0].header)
if method == "SExtractor backgrounds only":
background = background_origin + fil + "/" + file_name + "_back.fits"
print("Background image:", background)
else:
if method == "median value":
print(science_image[0].data.shape)
_, background_value, _ = sigma_clipped_stats(science_image[0].data)
background = deepcopy(science_image)
background[0].data = np.full(shape=science_image[0].data.shape, fill_value=background_value)
background_path = background_origin + fil + "/" + file_name.replace("SCIENCE_REDUCED",
"PHOT_BACKGROUND_MEDIAN")
# Next do background fitting.
else:
background = deepcopy(science_image)
background[0].data = np.zeros(background[0].data.shape)
background_path = background_origin + fil + "/" + file_name.replace("SCIENCE_REDUCED",
"PHOT_BACKGROUND_FITTED")
for i, ra in enumerate(ras):
dec = decs[i]
x, y = wcs_this.all_world2pix(ra, dec, 0)
print(x, y)
bottom, top, left, right = ff.subimage_edges(data=science_image[0].data, x=x, y=y,
frame=frame)
if do_mask:
# Produce a pixel mask that roughly masks out the true sources in the image so that
# they don't get fitted.
mask_max = 10
_, pixel_scale = ff.get_pixel_scale(science_image)
sextractor = Table.read(
f"{data_dir}analysis/sextractor/4-divided_by_exp_time/{fil}/{file_name.replace('.fits', '_psf-fit.cat')}",
format='ascii.sextractor')
weights = np.ones(shape=science_image[0].data.shape)
for obj in filter(
lambda o: left < o["X_IMAGE"] < right and bottom < o["Y_IMAGE"] < top,
sextractor):
mask_rad = min(int(obj["A_WORLD"] * obj["KRON_RADIUS"] / pixel_scale), mask_max)
x_prime = int(np.round(obj["X_IMAGE"]))
y_prime = int(np.round(obj["Y_IMAGE"]))
weights[y_prime - mask_rad:y_prime + mask_rad,
x_prime - mask_rad:x_prime + mask_rad] = 0.0
plt.imshow(weights, origin="lower")
plt.savefig(
background_origin + fil + "/" + file_name.replace("norm.fits", "mask.png"))
else:
weights = None
background_this = fit_background_fits(image=science_image,
model_type=method[:method.find(" ")],
deg=degree, local=local,
global_sub=global_sub,
centre_x=x, centre_y=y, frame=frame,
weights=weights)
background[0].data += background_this[0].data
if recorrect_subbed:
offset = get_median_background(image=science,
ra=epoch_params["renormalise_centre_ra"],
dec=epoch_params["renormalise_centre_dec"], frame=50,
show=False,
output=new_path[
:new_path.find("bg_sub")] + "renorm_patch_")
print("RECORRECT_SUBBED:", recorrect_subbed)
print("SUBTRACTING FROM BACKGROUND:", offset)
print(bottom, top, left, right)
print(background[0].data[bottom:top, left:right].shape)
print(np.median(background[0].data[bottom:top, left:right]))
background[0].data[bottom:top, left:right] -= offset
print(np.median(background[0].data[bottom:top, left:right]))
if trim_image:
print("TRIMMED_PATH_FIL:", trimmed_path_fil)
science_image = ff.trim_file(path=science_image, left=left, right=right, top=top,
bottom=bottom,
new_path=trimmed_path_fil + file_name.replace(
"norm.fits",
"trimmed_to_back.fits"))
print("Science after trim:", science_image)
background = ff.trim_file(path=background, left=left, right=right, top=top,
bottom=bottom,
new_path=background_path)
print("Writing background to:")
print(background_path)
background.writeto(background_path, overwrite=True)
print("SCIENCE:", science_image)
print("BACKGROUND:", background)
subbed = ff.subtract_file(file=science_image, sub_file=background, output=new_path)
# # TODO: check if regions overlap
#
# plt.hist(subbed[0].data[int(y - frame + 1):int(y + frame - 1),
# int(x - frame + 1):int(x + frame - 1)].flatten(),
# bins=10)
# plt.savefig(new_path[:new_path.find("bg_sub")] + "histplot.png")
# plt.close()
copyfile(data_dir + "/" + origin + "/" + data_title + ".log", destination + data_title + ".log")
u.write_log(path=destination + data_title + ".log",
action=f'Backgrounds subtracted using 4-background_subtract.py with method {method}\n')
def main(image, cat_path_1, cat_path_2, cat_name_1, cat_name_2, offset_x,
offset_y, psf):
if cat_name_1 == 'DES':
ra_name_1 = 'RA'
dec_name_1 = 'DEC'
else:
ra_name_1 = 'ra'
dec_name_1 = 'dec'
if cat_name_2 == 'DES':
ra_name_2 = 'RA'
dec_name_2 = 'DEC'
else:
ra_name_2 = 'ra'
dec_name_2 = 'dec'
if psf:
names = p.sextractor_names_psf()
else:
names = p.sextractor_names()
if cat_name_1 == 'SExtractor':
cat_1 = table.Table(np.genfromtxt(cat_path_1, names=names))
else:
cat_1 = table.Table()
cat_1 = cat_1.read(cat_path_1, format='ascii.csv')
if cat_name_2 == 'SExtractor':
cat_2 = table.Table(np.genfromtxt(cat_path_2, names=names))
else:
cat_2 = table.Table()
cat_2 = cat_2.read(cat_path_2, format='ascii.csv')
param_dict = {}
image = fits.open(image)
header = image[0].header
data = image[0].data
wcs_info = wcs.WCS(header=header)
cat_1['x'], cat_1['y'] = wcs_info.all_world2pix(cat_1[ra_name_1],
cat_1[dec_name_1], 0)
cat_2['x'], cat_2['y'] = wcs_info.all_world2pix(cat_2[ra_name_2],
cat_2[dec_name_2], 0)
norm = pl.nice_norm(data)
plt.subplot(projection=wcs_info)
plt.imshow(data, norm=norm, origin='lower', cmap='viridis')
plt.scatter(cat_1['x'], cat_1['y'], label=cat_name_1, c='violet')
plt.scatter(cat_2['x'], cat_2['y'], label=cat_name_2, c='red')
plt.legend()
plt.show()
scale_ra, scale_dec = ff.get_pixel_scale(image)
ra_corrected = cat_2[ra_name_2] + offset_x * scale_ra
dec_corrected = cat_2[dec_name_2] + offset_y * scale_ra
x, y = wcs_info.all_world2pix(ra_corrected, dec_corrected, 0)
norm = pl.nice_norm(data)
plt.subplot(projection=wcs_info)
plt.imshow(data, norm=norm, origin='lower', cmap='viridis')
plt.scatter(cat_1['x'], cat_1['y'], label=cat_name_1, c='violet')
plt.scatter(x, y, label=cat_name_2, c='red')
plt.legend()
plt.show()
image.close()
def tweak(sextractor_path: str,
destination: str,
image_path: str,
cat_path: str,
cat_name: str,
tolerance: float = 10.,
show: bool = False,
stars_only: bool = False,
manual: bool = False,
offset_x: float = None,
offset_y: float = None,
offsets_world: bool = False,
psf: bool = True,
specific_star: bool = False,
star_ra: float = None,
star_dec: float = None):
"""
For tweaking the astrometric solution of a fits image using a catalogue; either matches as many stars as possible
and uses the median offset, uses a single star at a specified position (specific_star=True) or a manual offset.
:param sextractor_path: Path to SExtractor-generated catalogue.
:param destination: Directory to write tweaked image to.
:param image_path: Path to image FITS file
:param cat_path: Path to file containing catalogue.
:param cat_name: Name of catalogue used.
:param tolerance: Tolerance, in pixels, within which matches will be accepted.
:param show: Plot matches onscreen?
:param stars_only: Only match using stars, determined using SExtractor's 'class_star' output.
:param manual: Use manual offset?
:param offset_x: Offset in x to use; only if 'manual' is set to True.
:param offset_y: Offset in y to use; only if 'manual' is set to True.
:param offsets_world: If True, interprets the offsets as being given in World Coordinates (RA and DEC)
:param psf: Use the PSF-fitting position?
:param specific_star: Use a specific star to tweak? This means, instead of finding the closest matches for many
stars, alignment is attempted with a single star nearest the given position.
:param star_ra: Right Ascension of star for single-star alignment.
:param star_dec: Declination of star for single-star alignment.
:return: None
"""
param_dict = {}
print(image_path)
image = fits.open(image_path)
header = image[0].header
data = image[0].data
wcs_info = wcs.WCS(header=header)
# Set the appropriate column names depending on the format of the catalogue to use.
if cat_name == 'DES':
ra_name = 'RA'
dec_name = 'DEC'
elif cat_name == 'Gaia' or cat_name == 'SDSS':
ra_name = 'ra'
dec_name = 'dec'
else:
if psf:
ra_name = 'ra_psf'
dec_name = 'dec_psf'
else:
ra_name = 'ra'
dec_name = 'dec'
if psf:
names = p.sextractor_names_psf()
sextractor_ra_name = 'ra_psf'
sextractor_dec_name = 'dec_psf'
else:
names = p.sextractor_names()
sextractor_ra_name = 'ra'
sextractor_dec_name = 'dec'
if show or not manual:
if cat_name == 'SExtractor':
cat = table.Table(np.genfromtxt(cat_path, names=names))
else:
cat = table.Table()
cat = cat.read(cat_path, format='ascii.csv')
sextracted = table.Table(np.genfromtxt(sextractor_path, names=names))
if stars_only:
sextracted = sextracted[sextracted['class_star'] > 0.9]
cat['x'], cat['y'] = wcs_info.all_world2pix(cat[ra_name],
cat[dec_name], 0)
x, y = wcs_info.all_world2pix(sextracted[sextractor_ra_name],
sextracted[sextractor_dec_name], 0)
norm = ImageNormalize(data,
interval=ZScaleInterval(),
stretch=SqrtStretch())
if show:
# plt.subplot(projection=wcs_info)
plt.imshow(data, norm=norm, origin='lower', cmap='viridis')
plt.scatter(x, y, label='SExtractor', c='violet')
plt.scatter(cat['x'], cat['y'], label=cat_name, c='red')
plt.legend()
plt.title('Pre-Correction')
plt.show()
if manual:
if offset_x is not None and offset_y is not None:
if not offsets_world:
scale_ra, scale_dec = ff.get_pixel_scale(image)
offset_ra = -offset_x * abs(scale_ra)
offset_dec = -offset_y * abs(scale_dec)
print(offset_x, offset_y)
else:
offset_ra = offset_x
offset_dec = offset_y
else:
print('Set offsets in epoch .yaml file')
offset_ra = offset_dec = None
elif specific_star:
if star_ra is not None and star_dec is not None:
print(star_ra, star_dec)
star_cat, d = u.find_object(x=star_ra,
y=star_dec,
x_search=cat[ra_name],
y_search=cat[dec_name])
print('MD:', d)
star_cat = cat[star_cat]
star_sex, d = u.find_object(
x=star_ra,
y=star_dec,
x_search=sextracted[sextractor_ra_name],
y_search=sextracted[sextractor_dec_name])
print('MD:', d)
star_sex = sextracted[star_sex]
offset_ra = (star_sex[sextractor_ra_name] - star_cat[ra_name])
offset_dec = (star_sex[sextractor_dec_name] - star_cat[dec_name])
else:
raise ValueError(
'If doing specific_star, must specify star_ra and star_dec')
else:
match_ids, match_ids_cat = u.match_cat(x_match=sextracted['x'],
y_match=sextracted['y'],
x_cat=cat['x'],
y_cat=cat['y'],
tolerance=tolerance)
sextracted = sextracted[match_ids]
cat = cat[match_ids_cat]
offsets_ra = sextracted[sextractor_ra_name] - cat[ra_name]
offsets_dec = sextracted[sextractor_dec_name] - cat[dec_name]
offset_ra = np.nanmedian(offsets_ra)
offset_dec = np.nanmedian(offsets_dec)
# TODO: This is quick and dirty and will only work if the image is approximately oriented along x=RA
# and y=DEC (CROTA ~ 0)
if offset_ra is not None and offset_dec is not None:
param_dict['offset_ra'] = float(offset_ra)
param_dict['offset_dec'] = float(offset_dec)
image = offset_astrometry(hdu=image,
offset_ra=offset_ra,
offset_dec=offset_dec,
output=destination)
if show and not manual:
wcs_info = wcs.WCS(header=header)
cat['x'], cat['y'] = wcs_info.all_world2pix(
cat[ra_name], cat[dec_name], 0)
x, y = wcs_info.all_world2pix(
sextracted[sextractor_ra_name] - offset_ra,
sextracted[sextractor_dec_name] - offset_dec, 0)
if show:
plt.subplot(projection=wcs_info)
plt.imshow(data, norm=norm, origin='lower', cmap='viridis')
plt.scatter(x, y, label='SExtractor', c='violet')
plt.scatter(cat['x'], cat['y'], label=cat_name, c='red')
plt.legend()
plt.title('Post-Correction')
plt.show()
image.close()
return param_dict
def distance_bar(hdu: fits.hdu.HDUList, ang_size_distance: float, x: float, y: float, frame: int,
length: float = None, angle_length: float = None, spread: float = 1.,
reverse_y=False, fancy: bool = False, x_bar: float = None, line_kwargs: dict = {},
text_kwargs: dict = {},
fancy_line_kwargs: dict = {"lw": 0.5, "color": "red"}):
"""
Draw a projected distance bar on your plot.
:param hdu:
:param ang_size_distance: Cosmological angular size distance, in parsecs.
:param length: In parsecs, the length of bar you want to show. One of
:param angle_length: In arcsecs, the length of the bar you want to show.
:param x: Position of bar in plot.
:param y: Position of bar in plot.
:return:
"""
if angle_length is length is None:
raise ValueError('Either length or angle_length must be provided.')
if angle_length is not None and length is not None:
raise ValueError('Only one of length or angle_length can be provided.')
# Get the angular pixel scale (degrees per pixel)
_, pix_angle_scale = ff.get_pixel_scale(hdu)
# Get the distance pixel scale (parsecs per pixel)
pix_length_scale = ff.projected_pix_scale(hdu=hdu, ang_size_distance=ang_size_distance)
if angle_length is None:
# Get the length of the bar in pixels.
pix_length = length / pix_length_scale
# Use the angular pixel scale to get the angular length of the bar
angle_length = pix_length * pix_angle_scale
else:
# Convert to degrees.
angle_length /= 3600
# Get the length of the bar in pixels.
pix_length = angle_length / pix_angle_scale
# Use the distance pixel scale to get the projected length of the bar.
length = pix_length * pix_length_scale
angle_length *= 3600
print('Projected length:', length, 'pc')
print('Angular size:', angle_length, 'arcsecs')
if "lw" in line_kwargs:
lw = line_kwargs["lw"]
else:
lw = 1
if x_bar is None:
x_bar = x
if reverse_y:
x_left = x_bar
x_right = x_bar + pix_length
y_bar = 2 * frame - y
y_text_kpc = 2 * frame - y - spread
y_text_arcsec = 2 * frame - y + 1.7 * spread
else:
x_left = x_bar + 0.5
x_right = x_bar + 0.5 + pix_length
y_bar = y
y_text_kpc = y + spread
y_text_arcsec = y - 1.7 * spread
plt.plot((x_left, x_right), (y_bar, y_bar), **line_kwargs)
if fancy:
# Large bar left endcap
plt.plot((x_left, x_left), (y_bar + spread / 3, y_bar - spread / 3), **line_kwargs)
# Large bar right endcap
plt.plot((x_right, x_right), (y_bar + spread / 3, y_bar - spread / 3), **line_kwargs)
# Small bar
plt.plot((x_left, x_right), (y_bar, y_bar), **fancy_line_kwargs)
# Small bar left endcap
plt.plot((x_left, x_left), (y_bar + spread / 3, y_bar - spread / 3), **fancy_line_kwargs)
# Small bar right endcap
plt.plot((x_right, x_right), (y_bar + spread / 3, y_bar - spread / 3), **fancy_line_kwargs)
plt.text(x, y_text_kpc, f'{np.round(length / 1000, 1)} kpc', **text_kwargs)
plt.text(x, y_text_arcsec, f'{int(angle_length)} arcsec', **text_kwargs)
def main(field, subtraction_path, epoch, instrument):
comparison_name = f'{field}_{epoch}'
params = p.object_params_frb(field)
ra_burst = params['burst_ra']
dec_burst = params['burst_dec']
burst_err_a = params['burst_err_a']
burst_err_b = params['burst_err_b']
burst_err_theta = params['burst_err_theta']
comparison_params = p.object_params_instrument(comparison_name, instrument)
subtraction_path = f'{params["data_dir"]}subtraction/{subtraction_path}'
filters = params['filters']
for f in filters:
f_0 = f[0]
destination_path_filter = f'{subtraction_path}{f}/'
template_epoch = params['template_epoch']
comparison_extinction = comparison_params[f_0 + '_ext_up']
cat_generated_file = filter(lambda file: file[-15:] == '_comparison.csv',
os.listdir(f'{destination_path_filter}')).__next__()
comparison_image_file = filter(lambda file: file[-24:] == '_comparison_aligned.fits',
os.listdir(f'{destination_path_filter}')).__next__()
difference_image_file = filter(lambda file: file[-16:] == '_difference.fits',
os.listdir(f'{destination_path_filter}')).__next__()
cat_generated_path = f'{destination_path_filter}{cat_generated_file}'
cat_sextractor_path = f'{destination_path_filter}difference.cat'
comparison_image_path = f'{destination_path_filter}{comparison_image_file}'
difference_image_path = f'{destination_path_filter}{difference_image_file}'
cat_generated = table.Table.read(cat_generated_path, format='ascii.csv')
cat_sextractor = table.Table(np.genfromtxt(cat_sextractor_path, names=p.sextractor_names()))
difference_image = fits.open(difference_image_path)
comparison_image = fits.open(comparison_image_path)
comparison_header = comparison_image[0].header
exp_time = comparison_header['EXPTIME']
comparison_zeropoint, _, airmass, _ = ph.select_zeropoint(comparison_name, f, instrument=instrument)
plotting.plot_all_params(image=difference_image, cat=cat_sextractor, show=False)
for obj in cat_generated:
plt.scatter(obj['x_0'], obj['y_0'], c='white')
plt.show()
_, pix_scale = ff.get_pixel_scale(comparison_image)
match_ids_sextractor, match_ids_generated = u.match_cat(x_match=cat_sextractor['ra'],
y_match=cat_sextractor['dec'],
x_cat=cat_generated['ra'],
y_cat=cat_generated['dec'],
tolerance=3 * pix_scale)
matches_sextractor = cat_sextractor[match_ids_sextractor]
matches_generated = cat_generated[match_ids_generated]
plotting.plot_all_params(image=difference_image, cat=matches_sextractor, show=False)
# plt.scatter(x_burst, y_burst, c='white')
plotting.plot_gal_params(hdu=difference_image, ras=[ra_burst], decs=[dec_burst], a=[burst_err_a],
b=[burst_err_b], theta=[burst_err_theta], colour='blue', show_centre=True)
for obj in matches_generated:
plt.scatter(obj['x_0'], obj['y_0'], c='white')
plt.show()
matches_sextractor['mag_sextractor'], _, _ = ph.magnitude_complete(flux=matches_sextractor['flux_aper'],
exp_time=exp_time, airmass=airmass,
zeropoint=comparison_zeropoint,
ext=comparison_extinction)
matches = table.hstack([matches_generated, matches_sextractor], table_names=['generated', 'sextracted'])
matches['delta_mag'] = matches['mag_sextractor'] - matches['mag']
delta_mag = np.median(matches['delta_mag'])
matches.write(f'{destination_path_filter}{field}_{epoch}-{template_epoch}_difference_matched_sources.csv',
format='ascii.csv', overwrite=True)
print(f'{len(matches)} matches / {len(cat_generated)} generated = '
f'{100 * len(matches) / len(cat_generated)} % ')
print(f'Faintest recovered: {max(matches["mag"])} generated; '
f'{max(matches_sextractor["mag_sextractor"])} sextracted')
print('Median delta mag:', delta_mag)
plt.scatter(matches['mag'], matches['delta_mag'])
plt.show()
difference_image.close()