def rotate_image_wcs(ima_name,
ima_folder="",
outwcs_folder=None,
rotangle=0.,
**kwargs):
"""Routine to remove potential Nan around an image and reconstruct
an optimal WCS reference image. The rotation angle is provided as a way
to optimise the extent of the output image, removing Nan along X and Y
at that angle.
Args:
ima_name (str): input image name. No default.
ima_folder (str): input image folder ['']
outwcs_folder (str): folder where to write the output frame. Default is
None which means that it will use the folder of the input image.
rotangle (float): rotation angle in degrees [0]
**kwargs:
in_suffix (str): in suffix to remove from name ['prealign']
out_suffix (str): out suffix to add to name ['rotwcs']
margin_factor (float): factor to extend the image [1.1]
Returns:
"""
# Reading the input names and setting output folder
fullname = joinpath(ima_folder, ima_name)
ima_folder, ima_name = os.path.split(fullname)
if outwcs_folder is None:
outwcs_folder = ima_folder
# Suffix
in_suffix = kwargs.pop("in_suffix", "prealign")
out_suffix = kwargs.pop("out_suffix", "rotwcs")
# Get margin if needed
margin_factor = kwargs.pop("margin_factor", 1.1)
extend_fraction = np.maximum(0., (margin_factor - 1.))
upipe.print_info("Will use a {:5.2f}% extra margin".format(
extend_fraction * 100.))
# Opening the image via mpdaf
imawcs = Image(fullname)
extra_pixels = (np.array(imawcs.shape) * extend_fraction).astype(np.int)
# New dimensions and extend current image
new_dim = tuple(np.array(imawcs.shape).astype(np.int) + extra_pixels)
ima_ext = imawcs.regrid(newdim=new_dim,
refpos=imawcs.get_start(),
refpix=tuple(extra_pixels / 2.),
newinc=imawcs.get_step()[0] * 3600.)
# Copy and rotate WCS
new_wcs = copy.deepcopy(ima_ext.wcs)
upipe.print_info("Rotating WCS by {} degrees".format(rotangle))
new_wcs.rotate(rotangle)
# New rotated image
ima_rot = Image(data=np.nan_to_num(ima_ext.data), wcs=new_wcs)
# Then resample the image using the initial one as your reference
ima_rot_resampled = ima_rot.align_with_image(ima_ext, flux=True)
# Crop NaN
ima_rot_resampled.crop()
# get the new header with wcs and rotate back
finalwcs = ima_rot_resampled.wcs
finalwcs.rotate(-rotangle)
# create the final image
final_rot_image = Image(data=ima_rot_resampled.data, wcs=finalwcs)
# Save image
if isinstance(in_suffix,
str) and in_suffix != "" and in_suffix in ima_name:
out_name = ima_name.replace(in_suffix, out_suffix)
else:
name, extension = os.path.splitext(ima_name)
out_suffix = add_string(out_suffix)
out_name = "{0}{1}{2}".format(name, out_suffix, extension)
# write output
final_rot_image.write(joinpath(outwcs_folder, out_name))
return outwcs_folder, out_name
def write(self, path=None, erase=False):
"""Save the current session in a folder that will have the name of the
ORIGIN object (self.name).
The ORIGIN.load(folder, newname=None) method will be used to load a
session. The parameter newname will let the user to load a session but
continue in a new one.
Parameters
----------
path : str
Path where the folder (self.name) will be stored.
erase : bool
Remove the folder if it exists.
"""
self._loginfo("Writing...")
# adapt session if path changes
if path is not None and path != self.path:
if not os.path.exists(path):
raise ValueError(f"path does not exist: {path}")
self.path = path
outpath = os.path.join(path, self.name)
# copy outpath to the new path
shutil.copytree(self.outpath, outpath)
self.outpath = outpath
self._setup_logfile(self.logger)
if erase:
shutil.rmtree(self.outpath)
os.makedirs(self.outpath, exist_ok=True)
# PSF
if isinstance(self.PSF, list):
for i, psf in enumerate(self.PSF):
cube = Cube(data=psf, mask=np.ma.nomask, copy=False)
cube.write(os.path.join(self.outpath,
"cube_psf_%02d.fits" % i))
else:
cube = Cube(data=self.PSF, mask=np.ma.nomask, copy=False)
cube.write(os.path.join(self.outpath, "cube_psf.fits"))
if self.wfields is not None:
for i, wfield in enumerate(self.wfields):
im = Image(data=wfield, mask=np.ma.nomask)
im.write(os.path.join(self.outpath, "wfield_%02d.fits" % i))
if self.ima_white is not None:
self.ima_white.write("%s/ima_white.fits" % self.outpath)
for step in self.steps.values():
step.dump(self.outpath)
# parameters in .yaml
with open(f"{self.outpath}/{self.name}.yaml", "w") as stream:
dump_yaml(self.param, stream)
# step3 - saving this manually for now
if self.nbAreas is not None:
if self.testO2 is not None:
for area in range(1, self.nbAreas + 1):
np.savetxt("%s/testO2_%d.txt" % (self.outpath, area),
self.testO2[area - 1])
if self.histO2 is not None:
for area in range(1, self.nbAreas + 1):
np.savetxt("%s/histO2_%d.txt" % (self.outpath, area),
self.histO2[area - 1])
if self.binO2 is not None:
for area in range(1, self.nbAreas + 1):
np.savetxt("%s/binO2_%d.txt" % (self.outpath, area),
self.binO2[area - 1])
self._loginfo("Current session saved in %s", self.outpath)
def xav_explorer(ima, ima_err, name, plot=False):
F_min = flux_min(ima, name)
IP2, JP2, gauss_im, I2, J2, pcont2, IPd, JPd, IPs, JPs = HIIrecover_loop(
ima, ima_err, name=name, plot=False, F_min=0, p=0)
gfitim, cont2, param = HIIplot2(IPs, JPs, gauss_im, ima, param=False)
JP3, IP3, tcont = background_interpolation(ima, name)
grid = interpolate_continuum(ima=ima,
JP=JP2,
IP=IP2,
I=I2,
J=J2,
pcont=pcont2,
cont=cont2,
JP3=JP3,
IP3=IP3,
tcont=tcont,
plot=False)
gfitim2 = HIIplot_cont(gfitim, grid, ima)
#res = Image(data=(ima.data - gfitim.data), wcs=ima.wcs)
p = 0
print(p)
gfitim, gfitim2, IP2, JP2, IPd, JPd, grid, param = loop2(IP2,
JP2,
I2,
J2,
pcont2,
cont2,
gfitim,
gfitim2,
ima,
ima_err,
IPd,
JPd,
param,
JP3,
IP3,
tcont,
name,
plot=False)
res2 = Image(data=(ima.data - gfitim2.data), wcs=ima.wcs)
catal = cat(param, name)
I_list = chi_histogram(param, plot=True)
b = 0
for k in range(0, len(I_list)):
catal.remove_row(I_list[k] - b)
b += 1
#param2 = read(cat=catal)
#gftim = HIIplot(ima, param2)
gfitim2 = HIIplot_cont(gfitim, grid, ima)
#ima = Image(filename=DATA_PATH + '{}_flux_drz.fits'.format(name))
ima2 = ima[:, :]
#if name == 'ASASSN14jg':
# ima2=ima2[40:290, 20:260]
chi2_global = chi_square(subima=ima2,
subima_err=ima_err,
gfitim=gfitim2,
parameter=1)
alpha = catal['alpha']
#Table with important data for plotting HII regions
data = [len(alpha), chi2_global, F_min]
CreateTable(name, OUTPUT_TABLES_PATH, data)
print(IPd, JPd)
if plot == True:
# fig, ax = plt.subplots(2, 3, constrained_layout=True)
fig, ax = plt.subplots(2, 3, figsize=(12, 8))
fig.suptitle('# {} HII regions - Chi2 {}'.format(
len(alpha), np.round(chi2_global, 2)),
fontsize=16)
ima.plot(scale='log',
colorbar='v',
ax=ax[0, 0],
vmin=0,
vmax=0.9 * np.amax(ima.data),
zscale=False,
title=r'flux = %s [10$^{-16}$ cgs]' %
(np.round(np.sum(ima.data), 2)))
ax[0, 1].scatter(IPd, JPd, color='red', marker='.', linewidth=0.01)
#res = Image(data=(ima.data - gfitim.data), wcs=ima.wcs)
gfitim.plot(scale='log',
vmin=0,
vmax=0.9 * np.amax(ima.data),
colorbar='v',
ax=ax[0, 1],
zscale=False,
title=r'flux = %s [10$^{-16}$ cgs]' %
(np.round(np.sum(gfitim.data), 2)))
gfitim2.plot(scale='log',
vmin=0,
vmax=0.9 * np.amax(ima.data),
colorbar='v',
ax=ax[0, 2],
zscale=False,
title=r'flux = %s [10$^{-16}$ cgs]' %
(np.round(np.sum(gfitim2.data), 2)))
grid.plot(scale='log',
vmin=0,
vmax=0.9 * np.amax(ima.data),
colorbar='v',
ax=ax[1, 0],
zscale=False,
title=r'flux = %s [10$^{-16}$ cgs]' %
(np.round(np.sum(grid.data), 2)))
res2 = Image(data=(ima.data - gfitim2.data), wcs=ima.wcs)
print('RESIDUALS: mean = {}, median = {}, std = {}'.format(
np.round(np.mean(np.ravel(res2.data)), 2),
np.round(np.median(np.ravel(res2.data)), 2),
np.round(np.std(np.ravel(res2.data)), 2))) #
#F_min
# res2.plot(vmin=np.amin(res2.data), vmax=np.amax(res2.data), colorbar='v', ax=ax[1,1], zscale=False, title=r'flux = %s [10$^{-20}$ cgs]'%( np.round(np.sum(res2.data),2)))
print("F_min" + str(F_min))
res2.plot(vmin=-3 * F_min,
vmax=3 * F_min,
colorbar='v',
ax=ax[1, 1],
zscale=False,
title=r'flux = %s [10$^{-16}$ cgs]' %
(np.round(np.sum(res2.data), 2)))
res2.plot(scale='log',
vmin=0,
vmax=0.9 * np.amax(ima.data),
colorbar='v',
ax=ax[1, 2],
zscale=False,
title='mean = {}, median = {}, std = {}'.format(
np.round(np.mean(np.ravel(res2.data)), 3),
np.round(np.median(np.ravel(res2.data)), 3),
np.round(np.std(np.ravel(res2.data)), 3)))
fig.tight_layout()
fig.savefig(OUTPUT_IMAGES_PATH + 'HII_recover_{}.pdf'.format(name),
bbox_inches='tight',
transparent=True)
fig.savefig(OUTPUT_IMAGES_PATH + 'HII_recover_{}.png'.format(name),
bbox_inches='tight',
transparent=True)
plt.close()
#res.write('res')
gfitim.write(name + '_gfitim_xav_fm.fits')
gfitim2.write(name + '_gfitim2_xav_fm.fits')
grid.write(name + '_grid_xav_fm.fits')
res2.write(name + '_res2_xav_fm.fits')
return catal, grid, res2, gfitim, gfitim2, chi2_global
