def _project_reference_hdu(self, name_hdr, muse_hdu=None):
"""Project the reference image onto the MUSE field
"""
if self.use_montage:
# The original way. Sometimes this introduces an offset
hdu_repr = montage.reproject_hdu(self.reference_hdu,
header=name_hdr,
exact_size=True)
else:
# The mpdaf way
if muse_hdu is not None:
wcs_ref = WCS(hdr=self.reference_hdu.header)
ima_ref = Image(data=self.reference_hdu.data, wcs=wcs_ref)
wcs_muse = WCS(hdr=muse_hdu.header)
ima_muse = Image(data=muse_hdu.data, wcs=wcs_muse)
ima_ref = ima_ref.align_with_image(ima_muse)
hdu_repr = ima_ref.get_data_hdu()
else:
hdu_repre = None
print(
"Warning: provide target HDU when not using montage to reproject"
)
return hdu_repr
def test_create_psf_cube():
src = Source.from_file(get_data_file('sdetect', 'origin-00026.fits'))
cube = Cube(get_data_file('sdetect', 'subcub_mosaic.fits'))
src.add_FSF(cube)
wcs = src.images['MUSE_WHITE'].wcs
shape = src.images['MUSE_WHITE'].shape
# a, b, beta, field = src.get_FSF()
a = 0.862
b = -3.46e-05
beta = 2.8
psf = b * cube.wave.coord() + a
# Gaussian
gauss = create_psf_cube(psf.shape + shape, psf, wcs=wcs)
im = Image(data=gauss[0], wcs=wcs)
res = im.gauss_fit()
assert_almost_equal(wcs.sky2pix(res.center)[0], [12., 12.])
assert np.allclose(res.fwhm, psf[0])
assert np.allclose(res.flux, 1.0)
# Moffat
moff = create_psf_cube(psf.shape + shape, psf, wcs=wcs, beta=beta)
im = Image(data=moff[0], wcs=wcs)
res = im.moffat_fit()
assert_almost_equal(wcs.sky2pix(res.center)[0], [12., 12.])
assert np.allclose(res.fwhm, psf[0])
assert np.allclose(res.flux, 1.0, atol=1e-2)
def test_convolve():
"""Image class: testing discrete convolution method."""
shape = (12, 25)
wcs = WCS(cdelt=(1.0, 1.0), crval=(0.0, 0.0), shape=shape)
data = np.zeros(shape)
data[7, 5] = 1.0
mask = np.zeros(shape, dtype=bool)
mask[5, 3] = True
ima = Image(wcs=wcs, data=data, mask=mask, copy=False)
# Create a symmetric convolution kernel with an even number of elements
# along one dimension and and odd number along the other dimension.
# Make the kernel symmetric around (shape-1)//2. This requires that
# the final column be all zeros.
kern = np.array([[0.1, 0.25, 0.1, 0.0],
[0.25, 0.50, 0.25, 0.0],
[0.1, 0.25, 0.1, 0.0]])
# The image should consist of a copy of the convolution kernel, centered
# such that pixels (kern.shape-1)//2 is at pixel 7,5 of data.
expected_data = np.ma.array(data=np.zeros(shape), mask=mask)
expected_data.data[6:9, 4:8] = kern
res = ima.convolve(kern)
assert_masked_allclose(res.data, expected_data)
res = ima.convolve(Image(data=kern))
assert_masked_allclose(res.data, expected_data)
res = ima.fftconvolve(kern)
assert_masked_allclose(res.data, expected_data, atol=1e-15)
def test_convolve():
shape = (3, 12, 25)
data = np.zeros(shape)
data[:, 7, 5] = 1.0
mask = np.zeros(shape, dtype=bool)
mask[:, 5, 3] = True
c = generate_cube(data=data, mask=mask, shape=shape,
wave=WaveCoord(crval=1, cunit=u.angstrom))
# Create a symmetric convolution kernel with an even number of elements
# along one dimension and and odd number along the other dimension.
# Make the kernel symmetric around (shape-1)//2. This requires that
# the final column be all zeros.
kern = np.array([[[0.1, 0.25, 0.1, 0.0],
[0.25, 0.50, 0.25, 0.0],
[0.1, 0.25, 0.1, 0.0]]])
# The image should consist of a copy of the convolution kernel, centered
# such that pixels (kern.shape-1)//2 is at pixel 7,5 of data.
expected_data = ma.array(data=np.zeros(shape), mask=mask)
expected_data.data[:, 6:9, 4:8] = kern
res = c.convolve(kern)
assert_masked_allclose(res.data, expected_data, atol=1e-15)
res = c.convolve(Image(data=kern))
assert_masked_allclose(res.data, expected_data, atol=1e-15)
res = c.fftconvolve(kern)
assert_masked_allclose(res.data, expected_data, atol=1e-15)
def psf_photometry(sources, path, size=15):
if True:
image = Image(path)
print('start photometry')
psf = []
for s in sources:
x, y = s['ra'], s['dec']
img = image.truncate(y - size / 3600, y + size / 3600,
x - size / 3600, x + size / 3600)
seg = img.segment()[0]
fit = seg.moffat_fit(plot=False)
psf.append(
(s['id'], fit.center[0], fit.center[1], fit.err_center[0],
fit.err_center[1], fit.flux, fit.err_flux, fit.peak,
fit.err_peak, fit.fwhm[0], fit.fwhm[1], fit.err_fwhm[0],
fit.err_fwhm[1], fit.n, fit.err_n, fit.rot, fit.err_rot,
fit.cont, fit.err_cont))
print(seg)
psf = Table(rows=psf,
names=[
'id', 'center_x', 'center_y', 'center_x_err',
'center_y_err', 'flux', 'flux_err', 'peak', 'peak_err',
'fwhm_major', 'fwhm_minor', 'fwhm_major_err',
'fwhm_minor_err', 'beta', 'beta_err', 'rotation',
'rotation_err', 'continuum', 'continuum_err'
])
psf['mag'] = -2.5 * np.log10(psf['flux'])
psf['mag_err'] = np.abs(2.5 /
(psf['flux'] * np.log(10))) * psf['flux_err']
return psf
def test_dtype():
"""Image class: testing dtype."""
wcs = WCS(cdelt=(0.2, 0.3), crval=(8.5, 12), shape=(40, 30), deg=True)
data = np.zeros((40, 30))
data[19, 14] = 1
ima = Image(wcs=wcs, data=data, dtype=int)
ima2 = ima.fftconvolve_gauss(center=None,
flux=1.,
fwhm=(20000., 10000.),
peak=False,
rot=60.,
factor=1,
unit_center=u.deg,
unit_fwhm=u.arcsec)
g = ima2.gauss_fit(verbose=False)
assert_almost_equal(g.fwhm[0], 20000, 2)
assert_almost_equal(g.fwhm[1], 10000, 2)
assert_almost_equal(g.center[0], 8.5)
assert_almost_equal(g.center[1], 12)
assert_equal(ima2.dtype, np.float64)
ima3 = ima2.resample(newdim=(32, 24),
newstart=None,
newstep=ima2.get_step(unit=u.arcsec) * 0.8)
assert_equal(ima3.dtype, np.float64)
def test_fftconvolve():
"""Image class: testing FFT convolution method."""
wcs = WCS(cdelt=(0.2, 0.3), crval=(8.5, 12), shape=(40, 30), deg=True)
data = np.zeros((40, 30))
data[19, 14] = 1
ima = Image(wcs=wcs, data=data)
ima2 = ima.fftconvolve_gauss(center=None,
flux=1.,
fwhm=(20000., 10000.),
peak=False,
rot=60.,
factor=1,
unit_center=u.deg,
unit_fwhm=u.arcsec)
g = ima2.gauss_fit(verbose=False)
assert_almost_equal(g.fwhm[0], 20000, 2)
assert_almost_equal(g.fwhm[1], 10000, 2)
assert_almost_equal(g.center[0], 8.5)
assert_almost_equal(g.center[1], 12)
ima2 = ima.fftconvolve_moffat(center=None,
flux=1.,
a=10000,
q=1,
n=2,
peak=False,
rot=60.,
factor=1,
unit_center=u.deg,
unit_a=u.arcsec)
m = ima2.moffat_fit(verbose=False)
assert_almost_equal(m.center[0], 8.5)
assert_almost_equal(m.center[1], 12)
def test_ee():
"""Image class: testing ensquared energy."""
wcs = WCS()
data = np.ones(shape=(6, 5)) * 2
image1 = Image(data=data, wcs=wcs)
image1.mask_region((2, 2), (1.5, 1.5),
inside=False,
unit_center=None,
unit_radius=None)
assert image1.ee() == 9 * 2
assert image1.ee(frac=True) == 1.0
ee = image1.ee(center=(2, 2), unit_center=None, radius=1, unit_radius=None)
assert ee == 4 * 2
r, eer = image1.eer_curve(center=(2, 2),
unit_center=None,
unit_radius=None,
cont=0)
assert r[1] == 1.0
assert eer[1] == 1.0
size = image1.ee_size(center=(2, 2),
unit_center=None,
unit_size=None,
cont=0)
assert_almost_equal(size[0], 1.775)
def __get__(self, obj, owner=None):
if obj is None:
return
try:
val = obj.__dict__[self.label]
except KeyError:
return
if isinstance(val, str):
if os.path.isfile(val):
kind = self.kind
if kind == 'cube':
val = Cube(val)
if kind == 'image':
val = Image(val)
elif kind == 'table':
val = _format_cat(Table.read(val))
elif kind == 'array':
val = np.loadtxt(val, ndmin=1)
elif kind == 'spectra':
val = load_spectra(val)
obj.__dict__[self.label] = val
else:
val = None
return val
def test_rebin():
"""Image class: testing rebin methods."""
wcs = WCS(crval=(0, 0))
data = np.arange(30).reshape(6, 5)
image1 = Image(data=data, wcs=wcs, var=np.ones(data.shape) * 0.5)
image1.mask_region((2, 2), (1.5, 1.5),
inside=False,
unit_center=None,
unit_radius=None)
# The test data array looks as follows:
#
# ---- ---- ---- ---- ----
# ---- 6.0 7.0 8.0 ----
# ---- 11.0 12.0 13.0 ----
# ---- 16.0 17.0 18.0 ----
# ---- ---- ---- ---- ----
# ---- ---- ---- ---- ----
#
# Where ---- signifies a masked value.
#
# After reducing both dimensions by a factor of 2, we should
# get a data array of the following 6 means of 4 pixels each:
#
# ---- ---- => 6/1 ---- ---- => (7+8)/2
# ---- 6.0 7.0 8.0
#
# ---- 11.0 => (11+16)/2 12.0 13.0 => (12+13+17+18)/4
# ---- 16.0 17.0 18.0
#
# ---- ---- => ---- ---- ---- => ----
# ---- ---- ---- ----
expected = np.ma.array(data=[[6.0, 7.5], [13.5, 15], [0.0, 0.0]],
mask=[[False, False], [False, False], [True, True]])
image2 = image1.rebin(2)
assert_masked_allclose(image2.data, expected)
image2 = image1.rebin(factor=(2, 2))
assert_masked_allclose(image2.data, expected)
# The variances of the original pixels were all 0.5, so taking the
# mean of N of these should give the mean a variance of 0.5/N.
# Given the number of pixels averaged in each of the above means,
# we thus expect the variance array to look as follows.
expected = np.ma.array(data=[[0.5, 0.25], [0.25, 0.125], [0.0, 0.0]],
mask=[[False, False], [False, False], [True, True]])
assert_masked_allclose(image2.var, expected)
# Check the WCS information.
start = image2.get_start()
assert start[0] == 0.5
assert start[1] == 0.5
def open_image(self, image_folder="./", image_name=None) :
"""Open the image
"""
self.image_folder = image_folder
self._isImage = True
self.image_name = image_name
if (imagein is None) | (not os.path.isfile(joinpath(image_folder, imagein))):
self._isImage = False
else :
self._isImage = True
self.image_galaxy = Image(joinpath(image_folder, image_name))
def test_background(a370II):
"""Image class: testing background value"""
wcs = WCS()
data = np.ones(shape=(6, 5)) * 2
image1 = Image(data=data, wcs=wcs)
(background, std) = image1.background()
assert background == 2
assert std == 0
(background, std) = a370II[1647:1732, 618:690].background()
# compare with IRAF results
assert (background - std < 1989) & (background + std > 1989)
def test_arithmetric():
"""Spectrum class: testing arithmetic functions"""
wave = WaveCoord(crpix=2.0, cdelt=3.0, crval=0.5, cunit=u.nm)
spectrum1 = Spectrum(data=np.array([0.5, 1, 2, 3, 4, 5, 6, 7, 8, 9]),
wave=wave)
spectrum2 = spectrum1 > 6 # [-,-,-,-,-,-,-,7,8,9]
# +
spectrum3 = spectrum1 + spectrum2
assert spectrum3.data.data[3] == 3
assert spectrum3.data.data[8] == 16
spectrum3 = 4.2 + spectrum1
assert spectrum3.data.data[3] == 3 + 4.2
# -
spectrum3 = spectrum1 - spectrum2
assert spectrum3.data.data[3] == 3
assert spectrum3.data.data[8] == 0
spectrum3 = spectrum1 - 4.2
assert spectrum3.data.data[8] == 8 - 4.2
# *
spectrum3 = spectrum1 * spectrum2
assert spectrum3.data.data[8] == 64
spectrum3 = 4.2 * spectrum1
assert spectrum3.data.data[9] == 9 * 4.2
# /
spectrum3 = spectrum1 / spectrum2
# divide functions that have a validity domain returns the masked constant
# whenever the input is masked or falls outside the validity domain.
assert spectrum3.data.data[8] == 1
spectrum3 = 1.0 / (4.2 / spectrum1)
assert spectrum3.data.data[5] == 5 / 4.2
# with cube
wcs = WCS()
cube1 = Cube(data=np.ones(shape=(10, 6, 5)), wave=wave, wcs=wcs)
cube2 = spectrum1 + cube1
sp1data = spectrum1.data[:, np.newaxis, np.newaxis]
assert_array_almost_equal(cube2.data, sp1data + cube1.data)
cube2 = spectrum1 - cube1
assert_array_almost_equal(cube2.data, sp1data - cube1.data)
cube2 = spectrum1 * cube1
assert_array_almost_equal(cube2.data, sp1data * cube1.data)
cube2 = spectrum1 / cube1
assert_array_almost_equal(cube2.data, sp1data / cube1.data)
# spectrum * image
data = np.ones(shape=(6, 5)) * 2
image1 = Image(data=data, wcs=wcs)
cube2 = spectrum1 * image1
assert_array_almost_equal(cube2.data,
sp1data * image1.data[np.newaxis, :, :])
def error_ponderation(subima_err):
(l, k) = np.shape(subima_err)
xc, yc = l / 2, k / 2
L = []
for i in range(0, l):
for j in range(0, k):
L.append((1 / l) * ((i - xc)**2 + (j - yc)**2)**0.5)
L = np.array(L)
L = np.reshape(L, (l, k))
subima_err = Image(data=(subima_err.data + L), wcs=subima_err.wcs)
return subima_err
def a370II():
"""Return a test image from a real observation """
# The CD matrix of the above image includes a small shear term which means
# that the image can't be displayed accurately with rectangular pixels. All
# of the functions in MPDAF assume rectangular pixels, so replace the CD
# matrix with a similar one that doesn't have a shear component.
ima = Image(get_data_file('obj', 'a370II.fits'))
ima.wcs.set_cd(
np.array([[2.30899476e-5, -5.22301199e-5],
[-5.22871997e-5, -2.30647413e-5]]))
return ima
def test_peak(a370II):
"""Image class: testing peak research"""
wcs = WCS()
data = np.ones(shape=(6, 5)) * 2
image1 = Image(data=data, wcs=wcs)
image1.data[2, 3] = 8
p = image1.peak()
assert p['p'] == 2
assert p['q'] == 3
p = a370II.peak(center=(790, 875), radius=20, plot=False, unit_center=None,
unit_radius=None)
assert_almost_equal(p['p'], 793.1, 1)
assert_almost_equal(p['q'], 875.9, 1)
def test_segmap():
segfile = get_data_file('segmap', 'segmap.fits')
img = Image(segfile)
refdata = np.arange(14)
for arg in (segfile, img, img.data):
segmap = Segmap(arg)
assert segmap.img.shape == (90, 90)
assert str(segmap.img.data.dtype) == '>i8'
assert np.max(segmap.img._data) == 13
assert_array_equal(np.unique(segmap.img._data), refdata)
assert_array_equal(segmap.copy().img.data, segmap.img.data)
cmap = segmap.cmap()
assert cmap.N == 14 # nb of values in the segmap
def createSemiRealSource(srcData, cube, SNR):
"""
Create a Source object with a cube of data mixing real noise from a MUSE (sub-)cube and signal
of a simulated halo object. The object is centered in the real MUSE subcube
"""
dec, ra = cube.wcs.pix2sky([cube.shape[1] // 2, cube.shape[2] // 2])[0]
lmbda = cube.wave.coord(cube.shape[0] // 2)
cube.data = cube.data + SNR * srcData
src = Source.from_data(4000,
ra,
dec,
origin='Simulated',
cubes={'MUSE_CUBE': cube})
src.add_line(['LBDA_OBS', 'LINE'], [lmbda, "LYALPHA"])
src.images['TRUTH_DET_BIN_ALL'] = Image(data=obj.maskSources > 0)
return src
def cut_save(filename, position, size):
#Load image for data and wht, also wcs
hdu = fits.open(os.getcwd() + '/DATA/' + filename)
hdr = hdu[0].header
data = hdu[0].data
wcs_data = WCS(hdr)
try:
#Make the cutout, including the wcs
cutout_data = Cutout2D(data,
position=position,
size=size,
wcs=wcs_data)
hdu[0].data = cutout_data
#Plotting cutout image
plt.imshow(cutout_data, origin='lower', cmap='v', norm=LogNorm())
plt.show()
#Update header with the cutout WCS
data.header.update(cutout_data.wcs.to_header())
#Write the cutout to a new FITS file
cutout_filename = 'cut_' + filename
hdu.writeto(os.getcwd() + '/DATA/' + cutout_filename, overwrite=True)
except:
ima = Image(os.getcwd() + '/DATA/' + filename)
x_1, x_2 = hdr['CRPIX1'] - size[0], hdr['CRPIX2'] + size[0]
y_1, y_2 = hdr['CRPIX1'] - size[1], hdr['CRPIX2'] + size[1]
ima_cut = Image.copy(ima[x_1:x_2, y_1:y_2])
hdu[0].data = ima_cut.data
#Plotting cutout image
plt.imshow(ima_cut.data,
origin='lower',
cmap='viridis',
norm=LogNorm())
plt.show()
#Write the cutout to a new FITS file
cutout_filename = 'cut_' + filename
hdu.writeto(os.getcwd() + '/DATA/' + cutout_filename, overwrite=True)
hdu.close()
def elliptic_aperture_pix(filename,
position,
ellipticity,
theta,
r_end=9,
plot=True):
#Opening FITS, getting data and header
ima = Image(os.getcwd() + '/DATA/' + filename + '.fits')
hdr = ima.data_header
data = ima.data.copy()
#Galaxy center position and position for apertures
center = (hdr['CRPIX1'], hdr['CRPIX2'])
position = position + center
#Defining radius for circular apertures
radius = 50 * np.arange(1, r_end)
aperture = [
EllipticalAperture(position, i, i * ellipticity, theta) for i in radius
]
if plot == True:
#Plot galaxy image with apertures
fig, ax = plt.subplots(sharex=True, sharey=True)
ima.data[ima.data < 0] = 0
ima.plot(ax,
scale='log',
vmin=0,
vmax=0.9 * np.amax(ima.data),
colorbar='v')
for i in range(len(aperture)):
aperture[i].plot(ax, color='white', lw=2)
ax.set_title(r'Original image - NGC 3614')
#plt.savefig('Image_apertures', dpi = 200)
#Table with apertures data (flux sum)
phot_table = aperture_photometry(data, aperture)
for col in phot_table.colnames:
phot_table[col].info.format = '%.8g'
print(phot_table)
return data, position, aperture
def interpolate_continuum(ima,
JP,
IP,
I,
J,
pcont,
cont,
JP3,
IP3,
tcont,
plot=False,
F_min=0):
(h, w) = np.shape(ima)
coords = []
map_data = ima.data
for i in range(0, len(IP)):
if cont[i] > 0:
IP3.append(int(IP[i]))
JP3.append(int(JP[i]))
tcont.append(cont[i])
for i in range(0, len(I)):
if pcont[i] > 0:
IP3.append(int(I[i]))
JP3.append(int(J[i]))
tcont.append(pcont[i])
(n, m, l, k) = (np.amax(IP3), np.amax(JP3), np.amin(IP3), np.amin(JP3))
x = np.linspace(0, h - 1, h)
y = np.linspace(0, w - 1, w)
X, Y = np.meshgrid(x, y)
grid_z0 = interpolate.griddata((JP3, IP3),
np.array(tcont), (X, Y),
method='nearest')
# plt.imshow(grid_z0.T, origin='lower')
grid = Image(data=(grid_z0.T), wcs=ima.wcs)
grid.gaussian_filter(sigma=5, inplace=True)
if plot:
fig = plt.figure()
grid.plot(scale='log', vmin=0, vmax=np.amax(ima.data), colorbar='v')
return grid
def Image_conv(im, tab, unmask=True):
""" Defines the convolution between an Image object and an array.
Designed to be used with the multiprocessing function
'FSF_convolution_multiprocessing'.
:param im: Image object
:type im: class 'mpdaf.obj.Image'
:param tab: array containing the convolution kernel
:type tab: array
:param unmask: if True use .data of masked array (faster computation)
:type unmask: bool
:return: array
:rtype: array
"""
if unmask is True:
res = ssl.fftconvolve(im.data.data, tab, 'full')
else:
res = ssl.fftconvolve(im.data, tab, 'full')
a, b = tab.shape
im_tmp = Image(data=res[int(a-1)//2:im.data.shape[0] + (a-1)//2,
(b-1)//2:im.data.shape[1]+(b-1)//2])
return im_tmp.data
def load(cls, folder, newname=None, loglevel=None, logcolor=None):
"""Load a previous session of ORIGIN.
ORIGIN.write() method saves a session in a folder that has the name of
the ORIGIN object (self.name).
Parameters
----------
folder : str
Folder name (with the relative path) where the ORIGIN data
have been stored.
newname : str
New name for this session. This parameter lets the user to load a
previous session but continue in a new one. If None, the user will
continue the loaded session.
loglevel : str
Level for the logger (by default reuse the saved level).
logcolor : bool
Use color for the logger levels.
"""
path = os.path.dirname(os.path.abspath(folder))
name = os.path.basename(folder)
with open(f"{folder}/{name}.yaml", "r") as stream:
param = load_yaml(stream)
if "FWHM PSF" in param:
FWHM_PSF = np.asarray(param["FWHM PSF"])
else:
FWHM_PSF = None
if "LBDA_FWHM PSF" in param:
LBDA_FWHM_PSF = np.asarray(param["LBDA FWHM PSF"])
else:
LBDA_FWHM_PSF = None
if os.path.isfile(param["PSF"]):
PSF = param["PSF"]
else:
if os.path.isfile("%s/cube_psf.fits" % folder):
PSF = "%s/cube_psf.fits" % folder
else:
PSF_files = glob.glob("%s/cube_psf_*.fits" % folder)
if len(PSF_files) == 0:
PSF = None
elif len(PSF_files) == 1:
PSF = PSF_files[0]
else:
PSF = sorted(PSF_files)
wfield_files = glob.glob("%s/wfield_*.fits" % folder)
if len(wfield_files) == 0:
wfields = None
else:
wfields = sorted(wfield_files)
# step0
if os.path.isfile("%s/ima_white.fits" % folder):
ima_white = Image("%s/ima_white.fits" % folder)
else:
ima_white = None
if newname is not None:
# copy outpath to the new path
shutil.copytree(os.path.join(path, name),
os.path.join(path, newname))
name = newname
loglevel = loglevel if loglevel is not None else param["loglevel"]
logcolor = logcolor if logcolor is not None else param["logcolor"]
obj = cls(
path=path,
name=name,
param=param,
imawhite=ima_white,
loglevel=loglevel,
logcolor=logcolor,
filename=param["cubename"],
fieldmap=param["fieldmap"],
wfields=wfields,
profiles=param["profiles"],
PSF=PSF,
FWHM_PSF=FWHM_PSF,
LBDA_FWHM_PSF=LBDA_FWHM_PSF,
)
for step in obj.steps.values():
step.load(obj.outpath)
# special case for step3
NbAreas = param.get("nbareas")
if NbAreas is not None:
if os.path.isfile("%s/testO2_1.txt" % folder):
obj.testO2 = [
np.loadtxt("%s/testO2_%d.txt" % (folder, area), ndmin=1)
for area in range(1, NbAreas + 1)
]
if os.path.isfile("%s/histO2_1.txt" % folder):
obj.histO2 = [
np.loadtxt("%s/histO2_%d.txt" % (folder, area), ndmin=1)
for area in range(1, NbAreas + 1)
]
if os.path.isfile("%s/binO2_1.txt" % folder):
obj.binO2 = [
np.loadtxt("%s/binO2_%d.txt" % (folder, area), ndmin=1)
for area in range(1, NbAreas + 1)
]
return obj
def plot_NB(self, src_ind, ax1=None, ax2=None, ax3=None):
"""Plot the narrow band images.
Parameters
----------
src_ind : int
Index of the object in self.Cat0.
ax1 : matplotlib.Axes
The Axes instance in which the NB image around the source is drawn.
ax2 : matplotlib.Axes
The Axes instance in which a other NB image for check is drawn.
ax3 : matplotlib.Axes
The Axes instance in which the difference is drawn.
"""
if self.Cat0 is None:
raise ValueError("Run the step 05 to initialize self.Cat0")
if ax1 is None and ax2 is None and ax3 is None:
fig, (ax1, ax2, ax3) = plt.subplots(1, 3, figsize=(12, 4))
# Coordinates of the source
x0 = self.Cat0[src_ind]["x0"]
y0 = self.Cat0[src_ind]["y0"]
z0 = self.Cat0[src_ind]["z0"]
# Larger spatial ranges for the plots
longxy0 = 20
y01 = max(0, y0 - longxy0)
y02 = min(self.shape[1], y0 + longxy0 + 1)
x01 = max(0, x0 - longxy0)
x02 = min(self.shape[2], x0 + longxy0 + 1)
# Coordinates in this window
y00 = y0 - y01
x00 = x0 - x01
# spectral profile
num_prof = self.Cat0[src_ind]["profile"]
profil0 = self.profiles[num_prof]
# length of the spectral profile
profil1 = profil0[profil0 > 1e-13]
long0 = profil1.shape[0]
# half-length of the spectral profile
longz = long0 // 2
# spectral range
intz1 = max(0, z0 - longz)
intz2 = min(self.shape[0], z0 + longz + 1)
# subcube for the plot
cube_test_plot = self.cube_raw[intz1:intz2, y01:y02, x01:x02]
wcs = self.wcs[y01:y02, x01:x02]
# controle cube
nb_ranges = 3
if (z0 + longz + nb_ranges * long0) < self.shape[0]:
intz1c = intz1 + nb_ranges * long0
intz2c = intz2 + nb_ranges * long0
else:
intz1c = intz1 - nb_ranges * long0
intz2c = intz2 - nb_ranges * long0
cube_controle_plot = self.cube_raw[intz1c:intz2c, y01:y02, x01:x02]
# (1/sqrt(2)) * difference of the 2 sububes
diff_cube_plot = (1 / np.sqrt(2)) * (cube_test_plot -
cube_controle_plot)
if ax1 is not None:
ax1.plot(x00, y00, "m+")
ima_test_plot = Image(data=cube_test_plot.sum(axis=0), wcs=wcs)
title = "cube test - (%d,%d)\n" % (x0, y0)
title += "lambda=%d int=[%d,%d[" % (z0, intz1, intz2)
ima_test_plot.plot(colorbar="v", title=title, ax=ax1)
ax1.get_xaxis().set_visible(False)
ax1.get_yaxis().set_visible(False)
if ax2 is not None:
ax2.plot(x00, y00, "m+")
ima_controle_plot = Image(data=cube_controle_plot.sum(axis=0),
wcs=wcs)
title = "check - (%d,%d)\n" % (x0, y0) + "int=[%d,%d[" % (intz1c,
intz2c)
ima_controle_plot.plot(colorbar="v", title=title, ax=ax2)
ax2.get_xaxis().set_visible(False)
ax2.get_yaxis().set_visible(False)
if ax3 is not None:
ax3.plot(x00, y00, "m+")
ima_diff_plot = Image(data=diff_cube_plot.sum(axis=0), wcs=wcs)
title = "Difference narrow band - (%d,%d)\n" % (
x0, y0) + "int=[%d,%d[" % (
intz1c,
intz2c,
)
ima_diff_plot.plot(colorbar="v", title=title, ax=ax3)
ax3.get_xaxis().set_visible(False)
ax3.get_yaxis().set_visible(False)
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 convolve(self, basename, main_beam):
outName = string.split(basename, '.fits')[0]
outName = outName + '_cv.fits'
dat, baseheader = fint.openFile(basename)
basefile = fits.open(basename)
#baseheader = basefile[0].header
basedata = Image(basename)
if 'NAXIS3' in baseheader:
del baseheader['NAXIS3']
if 'NAXIS4' in baseheader:
del baseheader['NAXIS4']
aaa = basedata.data
beam = np.array([float(baseheader['BMAJ']), float(baseheader['BMIN'])])
print main_beam, beam
if main_beam[0] > beam[0] and main_beam[1] > beam[1]:
bx = np.sqrt(main_beam[0] * main_beam[0] - beam[0] * beam[0])
by = np.sqrt(main_beam[1] * main_beam[1] - beam[1] * beam[1])
#bx= main_beam[0]
#by = main_beam[1]
if 'CDELT1' in baseheader:
pix_size = -baseheader['CDELT1']
elif 'CD1_1' in baseheader:
pix_size = -baseheader['CD1_1']
beam_area = 2 * np.pi * beam[0] / 2.35482 * beam[1] / 2.35482
main_beam_area = 2 * np.pi * main_beam[0] / 2.35482 * main_beam[
1] / 2.35482
number_pix_beam = beam_area / (pix_size * pix_size)
#aaa = np.divide(aaa,number_pix_beam)
#aaa = np.squeeze(aaa)
#basedata.data = np.squeeze(basedata.data)
#basedata.data=aaa
#print basedata.shape
newdata = Image.fftconvolve_gauss(basedata,
center=None,
flux=1,
peak=True,
factor=1,
fwhm=(by, bx),
unit_center=u.degree,
unit_fwhm=u.degree,
inplace=False)
aaa = np.array(newdata.data)
result = np.divide(aaa, np.power(main_beam_area / beam_area, 2))
#result = np.multiply(aaa,main_beam_area/(pix_size*pix_size))
baseheader['BMAJ'] = main_beam[0]
baseheader['BMIN'] = main_beam[1]
fits.writeto(outName, result, baseheader, overwrite=True)
else:
outName = basename
return outName
def test_mask():
"""Image class: testing mask functionalities"""
wcs = WCS()
data = np.ones(shape=(6, 5)) * 2
# A region of half-width=1 and half-height=1 should have a size of
# 2x2 pixels. A 2x2 region of pixels has a center at the shared
# corner of the 4 pixels, and the closest corner to the requested
# center of 2.1,1.8 is 2.5,1.5, so we expect the square of unmasked pixels
# to be pixels 2,3 along the Y axis, and pixels 1,2 along the X axis.
image1 = Image(data=data, wcs=wcs)
image1.mask_region((2.1, 1.8), (1, 1), inside=False, unit_center=None,
unit_radius=None)
expected_mask = np.array([[1, 1, 1, 1, 1],
[1, 1, 1, 1, 1],
[1, 0, 0, 1, 1],
[1, 0, 0, 1, 1],
[1, 1, 1, 1, 1],
[1, 1, 1, 1, 1]], dtype=bool)
assert_array_equal(image1._mask, expected_mask)
# Test that inside=True gives the opposite result
image1.unmask()
image1.mask_region((2.1, 1.8), (1, 1), inside=True, unit_center=None,
unit_radius=None)
assert_array_equal(image1._mask, ~expected_mask)
# And test with a rotation, 90° so should give the same result
image1.unmask()
image1.mask_region((2.1, 1.8), (1, 1), inside=True, unit_center=None,
unit_radius=None, posangle=90)
assert_array_equal(image1._mask, ~expected_mask)
# Try exactly the same experiment as the above, except that the center
# and size of the region are specified in world-coordinates instead of
# pixels.
wcs = WCS(deg=True)
image1 = Image(data=data, wcs=wcs)
image1.mask_region(wcs.pix2sky([2.1, 1.8]), (3600, 3600), inside=False)
assert_array_equal(image1._mask, expected_mask)
# And same with a rotation
image1.unmask()
image1.mask_region(wcs.pix2sky([2.1, 1.8]), (3600, 3600), inside=True,
posangle=90)
assert_array_equal(image1._mask, ~expected_mask)
# Mask around a region of half-width and half-height 1.1 pixels,
# specified in arcseconds, centered close to pixel 2.4,3.8. This
# ideally corresponds to a region of 2.2x2.2 pixels. The closest
# possible size is 2x2 pixels. A region of 2x2 pixels has its
# center at the shared corner of these 4 pixels, and the nearest
# corner to the desired central index of (2.4,3.8) is (2.5,3.5).
# So all of the image should be masked, except for a 2x2 area of
# pixel indexes 2,3 along the Y axis and pixel indexes 3,4 along
# the X axis.
image1.unmask()
image1.mask_region(wcs.pix2sky([2.4, 3.8]), 1.1 * 3600.0, inside=False)
expected_mask = np.array([[1, 1, 1, 1, 1],
[1, 1, 1, 1, 1],
[1, 1, 1, 0, 0],
[1, 1, 1, 0, 0],
[1, 1, 1, 1, 1],
[1, 1, 1, 1, 1]], dtype=bool)
assert_array_equal(image1._mask, expected_mask)
# Mask outside an elliptical region centered at pixel 3.5,3.5.
# The boolean expected_mask array given below was a verified
# output of mask_ellipse() for the specified ellipse parameters.
data = np.ones(shape=(8, 8))
image1 = Image(data=data, wcs=wcs)
image1.mask_ellipse([3.5, 3.5], (2.5, 3.5), 45.0, unit_radius=None,
unit_center=None, inside=False)
expected_mask = np.array([
[1, 1, 1, 1, 1, 1, 1, 1],
[1, 1, 1, 0, 0, 0, 1, 1],
[1, 1, 0, 0, 0, 0, 0, 1],
[1, 0, 0, 0, 0, 0, 0, 1],
[1, 0, 0, 0, 0, 0, 0, 1],
[1, 0, 0, 0, 0, 0, 1, 1],
[1, 1, 0, 0, 0, 1, 1, 1],
[1, 1, 1, 1, 1, 1, 1, 1]],
dtype=bool)
assert_array_equal(image1._mask, expected_mask)
# Use np.where to select the masked pixels and check that mask_selection()
# then reproduces the same mask.
ksel = np.where(image1.data.mask)
image1.unmask()
image1.mask_selection(ksel)
assert_array_equal(image1._mask, expected_mask)
# Check inside=True
image1.unmask()
image1.mask_ellipse([3.5, 3.5], (2.5, 3.5), 45.0, unit_radius=None,
unit_center=None, inside=True)
assert_array_equal(image1._mask, ~expected_mask)
def create_source(
source_id,
source_table,
source_lines,
origin_params,
cube_cor_filename,
cube_std_filename,
mask_filename,
skymask_filename,
spectra_fits_filename,
segmaps,
version,
source_ts,
profile_fwhm,
*,
author="",
nb_fwhm=2,
expmap_filename=None,
save_to=None,
):
"""Create a MPDAF source.
This function create a MPDAF source object for the ORIGIN source.
Parameters
----------
source_id : int
Identifier for the source in the source and line tables.
source_table : astropy.table.Table
Catalogue of sources like the Cat3_sources one.
source_lines : astropy.table.Table
Catalogue of lines like the Cat3_lines one.
origin_params : dict
Dictionary of the parameters for the ORIGIN run.
cube_cor_filename : str
Name of the file containing the correlation cube of the ORIGIN run.
cube_std_filename : str
Name of the file containing the std cube of the ORIGIN run.
mask_filenam e: str
Name of the file containing the mask of the source.
skymask_filename : str
Name of the file containing the sky mask of the source.
spectra_fits_filename : str
Name of the FITS file containing the spectra of the lines.
segmaps : dict(str: str)
Dictionnary associating to a segmap type the associated FITS file name.
version : str
Version number stored in the source.
source_ts : str
Time stamp for when the source was created.
profile_fwhm : list of int
List of line profile FWHM in pixel. The index in the list is the
profile number.
author : str
Name of the author.
nb_fwhm : float
Factor multiplying the FWHM of the line to compute the width of the
narrow band image.
expmap_filename : str
Name of the file containing the exposure map. If not None, a cut-out
of the exposure map will be added to the source file.
save_to : str
If not None, the source will be saved to the given file.
Returns
-------
mpdaf.sdetect.Source or None
If save_to is used, the function returns None.
"""
logger = logging.getLogger(__name__)
# [0] is to get a Row not a table.
source_table = source_table.filled()
source_info = source_table[source_table["ID"] == source_id][0]
# The mask size is used for the cut-out size.
mask = Image(mask_filename)
mask_size = mask.shape[0]
data_cube = Cube(origin_params["cubename"], convert_float64=False)
origin = (
"ORIGIN",
origin_version,
os.path.basename(origin_params["cubename"]),
data_cube.primary_header.get("CUBE_V", ""),
)
source = Source.from_data(
source_info["ID"], source_info["ra"], source_info["dec"], origin
)
# Information about the source in the headers
source.header["SRC_V"] = version, "Source version"
source.header["SRC_TS"] = source_ts, "Timestamp of the source creation"
source.header["CAT3_TS"] = (
source_table.meta["CAT3_TS"],
"Timestamp of the catalog creation",
)
source.add_history("Source created with ORIGIN", author)
source.header["OR_X"] = source_info["x"], "x position in pixels"
source.header["OR_Y"] = source_info["y"], "y position in pixels"
source.header["OR_SEG"] = (
source_info["seg_label"],
"Label in the segmentation map",
)
source.header["OR_V"] = origin_version, "ORIGIN version"
source.header["OR_FLUX"] = source_info["flux"], "flux maximum in all lines"
source.header["OR_PMAX"] = (source_info["purity"], "maximum purity in all lines")
if not np.isnan(source_info["STD"]):
source.header["OR_STD"] = (source_info["STD"], "STD max value in all lines")
if not np.isnan(source_info["nsigSTD"]):
source.header["OR_nSTD"] = (
source_info["nsigSTD"],
"max of STD/std(STD) in all lines",
)
if not np.isnan(source_info["T_GLR"]):
source.header["OR_TGLR"] = (
source_info["T_GLR"],
"T_GLR max value in all lines",
)
if not np.isnan(source_info["nsigTGLR"]):
source.header["OR_nTGLR"] = (
source_info["nsigTGLR"],
"max of T_GLR/std(T_GLR) in all lines",
)
# source_header_keyword: (key_in_origin_param, description)
parameters_to_add = {
"OR_PROF": ("profiles", "OR input, spectral profiles"),
"OR_FSF": ("PSF", "OR input, FSF cube"),
"OR_THL%02d": ("threshold_list", "OR input threshold per area"),
"OR_NA": ("nbareas", "OR number of areas"),
"preprocessing": {"OR_DCT": ("dct_order", "OR input, DCT order")},
"areas": {
"OR_PFAA": ("pfa", "OR input, PFA used to create the area map"),
"OR_SIZA": ("maxsize", "OR input, maximum area size in pixels"),
"OR_MSIZA": ("minsize", "OR input, minimum area size in pixels"),
},
"compute_PCA_threshold": {"OR_PFAT": ("pfa_test", "OR input, PFA test")},
"compute_greedy_PCA": {
"OR_FBG": ("Noise_population", "OR input: fraction of spectra estimated"),
"OR_ITMAX": ("itermax", "OR input, maximum number of iterations"),
},
"compute_TGLR": {"OR_NG": ("size", "OR input, connectivity size")},
"detection": {
"OR_DXY": ("tol_spat", "OR input, spatial tolerance for merging (pix)"),
"OR_DZ": ("tol_spec", "OR input, spectral tolerance for merging (pix)"),
},
"compute_spectra": {"OR_NXZ": ("grid_dxy", "OR input, grid Nxy")},
}
def add_keyword(keyword, param, description, params):
if param == "threshold_list" and param in params:
for idx, threshold in enumerate(params["threshold_list"]):
source.header[keyword % idx] = (float("%0.2f" % threshold), description)
elif param in params:
if params[param] is None:
source.header[keyword] = "", description
else:
source.header[keyword] = params[param], description
else:
logger.debug("Parameter %s absent of the parameter list.", param)
for keyword, val in parameters_to_add.items():
if isinstance(val, dict) and keyword in origin_params:
for key, val2 in val.items():
add_keyword(key, *val2, origin_params[keyword]["params"])
else:
add_keyword(keyword, *val, origin_params)
source.header["COMP_CAT"] = (
source_info["comp"],
"1/0 (1=Pre-detected in STD, 0=detected in CORREL)",
)
if source.COMP_CAT:
threshold_keyword, purity_keyword = "threshold_std", "purity_std"
else:
threshold_keyword, purity_keyword = "threshold", "purity"
source.header["OR_TH"] = (
float("%0.2f" % origin_params[threshold_keyword]),
"OR input, threshold",
)
source.header["OR_PURI"] = (
float("%0.2f" % origin_params[purity_keyword]),
"OR input, purity",
)
# Mini-cubes
source.add_cube(
data_cube, "MUSE_CUBE", size=mask_size, unit_size=None, add_white=True
)
# Add FSF with the full cube, to have the same shape as fieldmap, then we
# can work directly with the subcube
has_fsf = True
try:
source.add_FSF(data_cube, fieldmap=origin_params["fieldmap"])
except:
logger.debug('No FSF information found in the cube')
has_fsf = False
data_cube = source.cubes["MUSE_CUBE"]
if source.COMP_CAT:
cube_ori = Cube(cube_std_filename, convert_float64=False)
source.add_cube(cube_ori, "ORI_SNCUBE", size=mask_size, unit_size=None)
cube_ori = source.cubes["ORI_SNCUBE"]
else:
cube_ori = Cube(cube_cor_filename, convert_float64=False)
source.add_cube(cube_ori, "ORI_CORREL", size=mask_size, unit_size=None)
cube_ori = source.cubes["ORI_CORREL"]
# Table of sources around the exported sources.
radius = mask_size / 2
x_min, x_max = source_info["x"] - radius, source_info["x"] + radius
y_min, y_max = source_info["y"] - radius, source_info["y"] + radius
nearby_sources = (
(source_table["x"] >= x_min)
& (source_table["x"] <= x_max)
& (source_table["y"] >= y_min)
& (source_table["y"] <= y_max)
)
source.tables["ORI_CAT"] = source_table["ID", "ra", "dec"][nearby_sources]
# Maps
# The white map was added when adding the MUSE cube.
source.images["ORI_MAXMAP"] = cube_ori.max(axis=0)
# Using add_image, the image size is taken from the white map.
source.add_image(mask, "ORI_MASK_OBJ")
source.add_image(Image(skymask_filename), "ORI_MASK_SKY")
for segmap_type, segmap_filename in segmaps.items():
source.add_image(Image(segmap_filename), "ORI_SEGMAP_%s" % segmap_type)
if expmap_filename is not None:
source.add_image(Image(expmap_filename), "EXPMAP")
# Full source spectra
source.extract_spectra(
data_cube, obj_mask="ORI_MASK_OBJ", sky_mask="ORI_MASK_SKY", skysub=True
)
source.extract_spectra(
data_cube, obj_mask="ORI_MASK_OBJ", sky_mask="ORI_MASK_SKY", skysub=False
)
if source.COMP_CAT:
source.spectra["ORI_CORR"] = (
source.cubes["ORI_SNCUBE"] * source.images["ORI_MASK_OBJ"]
).mean(axis=(1, 2))
else:
source.spectra["ORI_CORR"] = (
source.cubes["ORI_CORREL"] * source.images["ORI_MASK_OBJ"]
).mean(axis=(1, 2))
# Add the FSF information to the source and use this information to compute
# the PSF weighted spectra.
if has_fsf:
try:
fsfmodel = source.get_FSF()
fwhm_fsf = fsfmodel.get_fwhm(data_cube.wave.coord())
beta_fsf = fsfmodel.get_beta(data_cube.wave.coord())
source.extract_spectra(
data_cube,
obj_mask="ORI_MASK_OBJ",
sky_mask="ORI_MASK_SKY",
skysub=True,
psf=fwhm_fsf,
beta=beta_fsf,
)
source.extract_spectra(
data_cube,
obj_mask="ORI_MASK_OBJ",
sky_mask="ORI_MASK_SKY",
skysub=False,
psf=fwhm_fsf,
beta=beta_fsf,
)
except:
# WIP to work with the new FSF model
has_fsf = False
# Per line data: the line table, the spectrum of each line, the narrow band
# map from the data and from the correlation cube.
# Content of the line table in the source
line_columns, line_units, line_fmt = zip(
*[
("NUM_LINE", None, None),
("RA_LINE", u.deg, ".2f"),
("DEC_LINE", u.deg, ".2f"),
("LBDA_OBS", u.Angstrom, ".2f"),
("FWHM", u.Angstrom, ".2f"),
("FLUX", u.erg / (u.s * u.cm ** 2), ".1f"),
("GLR", None, ".1f"),
("nGLR", None, ".1f"),
("PROF", None, None),
("PURITY", None, ".2f"),
]
)
# If the line is a complementary one, the GLR column is replace by STD
if source.COMP_CAT:
line_columns = list(line_columns)
line_columns[6] = "STD"
line_columns[7] = "nSTD"
# We put all the ORIGIN lines in an ORI_LINES tables but keep only the
# unique lines in the LINES tables.
source.add_table(source_lines, "ORI_LINES", select_in=None, col_dist=None)
# Table containing the information on the narrow band images.
nb_par_rows = []
hdulist = fits.open(spectra_fits_filename)
for line in source_lines[source_lines["merged_in"] == -9999]:
num_line, lbda_ori, prof = line[["num_line", "lbda", "profile"]]
fwhm_ori = profile_fwhm[prof] * data_cube.wave.get_step(unit=u.Angstrom)
if source.COMP_CAT:
glr_std = line["STD"]
nglr_std = line["nsigSTD"]
else:
glr_std = line["T_GLR"]
nglr_std = line["nsigTGLR"]
source.add_line(
cols=line_columns,
values=[
num_line,
line["ra"],
line["dec"],
lbda_ori,
fwhm_ori,
line["flux"],
glr_std,
nglr_std,
prof,
line["purity"],
],
units=line_units,
fmt=line_fmt,
desc=None,
)
if f"DATA{num_line}" in hdulist: # RB add test
source.spectra[f"ORI_SPEC_{num_line}"] = Spectrum(
hdulist=hdulist,
ext=(f"DATA{num_line}", f"STAT{num_line}"),
convert_float64=False,
)
source.add_narrow_band_image_lbdaobs(
data_cube,
f"NB_LINE_{num_line}",
lbda=lbda_ori,
width=nb_fwhm * fwhm_ori,
method="sum",
subtract_off=True,
margin=10.0,
fband=3.0,
)
nb_par_rows.append(
[f"NB_LINE_{num_line}", lbda_ori, nb_fwhm * fwhm_ori, 10.0, 3.0]
)
source.add_narrow_band_image_lbdaobs(
cube_ori,
f"ORI_CORR_{num_line}",
lbda=lbda_ori,
width=nb_fwhm * fwhm_ori,
method="max",
subtract_off=False,
)
# Compute the spectra weighted by the correlation map for the
# current line
tags = [f"ORI_CORR_{num_line}"]
source.extract_spectra(
data_cube,
obj_mask="ORI_MASK_OBJ",
sky_mask="ORI_MASK_SKY",
skysub=True,
tags_to_try=tags,
)
source.extract_spectra(
data_cube,
obj_mask="ORI_MASK_OBJ",
sky_mask="ORI_MASK_SKY",
skysub=False,
tags_to_try=tags,
)
# set REFSPEC to the spectrum weighted by the correlation map of the
# brightest line
num_max = source.lines["NUM_LINE"][np.argmax(source.lines["FLUX"])]
source.header["REFSPEC"] = f"ORI_CORR_{num_max}_SKYSUB"
hdulist.close()
nb_par = Table(
names=["LINE", "LBDA", "WIDTH", "MARGIN", "FBAND"],
dtype=["U20", float, float, float, float],
rows=nb_par_rows,
)
source.add_table(nb_par, "NB_PAR", select_in=None, col_dist=None)
if save_to is not None:
source.write(save_to)
else:
return source
s_n_mask = np.load('out_r4_3.npy')
all_pixels = []
for i in range(len(s_n_mask)):
posi = [s_n_mask[i][0], s_n_mask[i][1]]
all_pixels.append(posi)
all_pixels = np.asarray(all_pixels)
datacube = Cube('nor_cut_cube.fits')
xlen = len(datacube[0, :, 0].data)
ylen = len(datacube[0, 0, :].data[0])
wcs1 = WCS(crval=0, cdelt=0.2)
MyData = np.ones((xlen, ylen))
ima = Image(data=MyData, wcs=wcs1)
for i in range(xlen):
for j in range(ylen):
par = params.valuesdict()
incli = par['incli']
col_denst = par['col_dens']
h = par['height']
bes = par['dop_param']
v_max = par['vel_max']
h_vt = par['h_v']
csize = par['csize']
r_0t = par['r_0']
h_v = 10**h_vt
def run(
self,
orig,
threshold=None,
threshold_std=None,
tol_spat=3,
tol_spec=5,
maxdist_lines=2.5,
segmap=None,
):
if threshold is not None:
orig.threshold_correl = threshold
if threshold_std is not None:
orig.threshold_std = threshold_std
# detection on the correl cube
self._loginfo('Thresholding correl (>%.2f)', orig.threshold_correl)
z, y, x = np.where(orig.cube_local_max._data > orig.threshold_correl)
cat = Table([x, y, z], names=('x0', 'y0', 'z0'))
cat['comp'] = 0
cat['STD'] = np.nan
cat['T_GLR'] = orig.cube_local_max._data[z, y, x]
cat['profile'] = orig.cube_profile._data[z, y, x]
self._loginfo('%d detected lines', len(cat))
# detection on the std cube
self._loginfo('Thresholding std (>%.2f)', orig.threshold_std)
z, y, x = np.where(orig.cube_std_local_max._data > orig.threshold_std)
cat_std = Table([x, y, z], names=('x0', 'y0', 'z0'))
cat_std['comp'] = 1
cat_std['STD'] = orig.cube_std_local_max._data[z, y, x]
cat_std['T_GLR'] = np.nan
cat_std['profile'] = 0
self._loginfo('%d detected lines', len(cat_std))
# Store the raw detection catalog in Cat0
# Merge tables. vstack creates a masked Table, masking the missing
# values. But a bug with Astropy/Numpy 1.14 is causing the Cat1 table
# to be modified later by Cat2 operations, if it was not dumped before.
# So for now we transform the table to a non-masked one.
self.Cat0 = _format_cat(vstack([cat, cat_std]).filled())
# merging: remove detections in std close to the ones in correl
kdt_cor = cKDTree(np.array([cat['x0'], cat['y0'], cat['z0']]).T)
kdt_std = cKDTree(
np.array([cat_std['x0'], cat_std['y0'], cat_std['z0']]).T)
# find matches in std that are close to correl detections
matched = set(
itertools.chain.from_iterable(
kdt_cor.query_ball_tree(kdt_std, maxdist_lines)))
unmatched = list(set(range(len(cat_std))) - matched)
cat_std = cat_std[unmatched]
self._loginfo('kept %d lines from std after filtering', len(unmatched))
if segmap is not None:
self.logger.info('Overriding segmap_cont with the given one')
self.segmap_label = Image(segmap)
if self.segmap_label.shape != orig.shape[1:]:
raise ValueError(
'segmap does not have the same shape as the processed cube'
)
else:
self.logger.info(
'Using segmap_cont with an additional deblending step')
self.segmap_label = phot_deblend_sources(orig.ima_dct,
orig.segmap_cont.data,
npixels=5,
mode='linear')
cat = _format_cat(vstack([cat, cat_std]).filled())
cat['area'] = self.segmap_label._data[cat['y0'], cat['x0']]
self.logger.info('Spatio-spectral merging...')
cat = spatiospectral_merging(cat, tol_spat, tol_spec)
# add real coordinates and other useful info
z, y, x = cat['z0'].data, cat['y0'].data, cat['x0'].data
dec, ra = orig.wcs.pix2sky(np.stack((y, x)).T).T
cat.add_column(Column(name='ra', data=ra), index=0)
cat.add_column(Column(name='dec', data=dec), index=1)
cat.add_column(Column(name='lbda', data=orig.wave.coord(z)), index=2)
cat.rename_column('area', 'seg_label')
# Start ID numbering at 1
cat['imatch'] += 1
cat['imatch2'] += 1
# Relabel IDs sequentially
oldIDs = np.unique(cat['imatch'])
idmap = np.zeros(oldIDs.max() + 1, dtype=int)
idmap[oldIDs] = np.arange(1, len(oldIDs) + 1)
cat.add_column(Column(name='ID', data=idmap[cat['imatch']]), index=0)
cat.sort('ID')
self._loginfo('Purity estimation')
cat = purity_estimation(cat, orig.Pval, orig.Pval_comp)
cat_comp = cat[cat['comp'] == 1]
ns = len(set(cat['ID']))
ds = len(set(cat_comp['ID']) - set(cat['ID']))
nl = len(cat)
dl = len(cat_comp)
self.Cat1 = cat
msg = 'Save the catalog in self.Cat1 (%d [+%s] sources, %d [+%d] lines)'
self._loginfo(msg, ns, ds, nl, dl)
