def test_gcrs_self_transform_closeby():
"""
Tests GCRS self transform for objects which are nearby and thus
have reasonable parallax.
Moon positions were originally created using JPL DE432s ephemeris.
The two lunar positions (one geocentric, one at a defined location)
are created via a transformation from ICRS to two different GCRS frames.
We test that the GCRS-GCRS self transform can correctly map one GCRS
frame onto the other.
"""
t = Time("2014-12-25T07:00")
moon_geocentric = SkyCoord(GCRS(318.10579159*u.deg,
-11.65281165*u.deg,
365042.64880308*u.km, obstime=t))
# this is the location of the Moon as seen from La Palma
obsgeoloc = [-5592982.59658935, -63054.1948592, 3059763.90102216]*u.m
obsgeovel = [4.59798494, -407.84677071, 0.]*u.m/u.s
moon_lapalma = SkyCoord(GCRS(318.7048445*u.deg,
-11.98761996*u.deg,
369722.8231031*u.km,
obstime=t,
obsgeoloc=obsgeoloc,
obsgeovel=obsgeovel))
transformed = moon_geocentric.transform_to(moon_lapalma.frame)
delta = transformed.separation_3d(moon_lapalma)
assert_allclose(delta, 0.0*u.m, atol=1*u.m)
def proper_motion(g0, g1):
"""Proper motion from two `Geom` instances.
Parameters
----------
g0, g1 : Geom
Two positions of the target. `g0.date < g1.date` is assumed.
Returns
-------
mu : Quantity
The proper motion.
phi : Angle
The position angle of the proper motion.
"""
from astropy.coordinates import SkyCoord
c0 = SkyCoord(ra=g0.ra, dec=g0.dec, frame='icrs')
c1 = SkyCoord(ra=g1.ra, dec=g1.dec, frame='icrs')
dt = (g1.date - g0.date).jd * u.day
mu = (c0.separation(c1) / dt).to(u.arcsec / u.hr)
phi = c0.position_angle(c1).to(u.deg)
return mu, phi
def find_uniques(spec_data, remove_imacs=False, nearenough_sep=5*u.arcsec):
import collections
# MATCH ON COORDINATES
scs = SkyCoord(spec_data['RA'], spec_data['DEC'], unit=u.deg)
idx1, idx2, sep2d, _ = scs.search_around_sky(scs, nearenough_sep)
# now contruct the groups from the pairs
grpdct = {}
grpi = 0
for i1, i2 in zip(idx1, idx2):
if i1 in grpdct:
if i2 in grpdct:
# combine the two groups by assigning grp2 items to grp1
# this block is by far the slowest part so if the data size grows it should be optimized
grp1 = grpdct[i1]
grp2 = grpdct[i2]
if grp1 != grp2:
to_set_to_1 = [i for i, grp in grpdct.iteritems() if grp==grp2]
for i in to_set_to_1:
grpdct[i] = grp1
else:
#add i2 to the group i1 is already in
grpdct[i2] = grpdct[i1]
else:
if i2 in grpdct:
#add i1 to the group i2 is already in
grpdct[i2] = grpdct[i1]
else:
# add them both to a new group
grpdct[i1] = grpdct[i2] = grpi
grpi += 1
grpnum_to_group_members = collections.defaultdict(list)
for k, v in grpdct.iteritems():
grpnum_to_group_members[v].append(k)
# convert the members into arrays
grpnum_to_group_members = {k:np.array(v) for k, v in grpnum_to_group_members.iteritems()}
# identify which is the "best" spectrum (meaning the first zq=4 spectrum)
idxs_to_keep = []
new_repeats = []
for grpnum, allmembers in grpnum_to_group_members.iteritems():
if remove_imacs:
members = allmembers[spec_data['TELNAME'][allmembers]!='IMACS']
if len(members)==0:
continue
else:
members = allmembers
idxs_to_keep.append(members[np.argsort(spec_data['ZQUALITY'][members])[-1]])
new_repeats.append('+'.join(np.unique(spec_data['SPEC_REPEAT'][members])))
# now build the output table from the input
unique_objs = spec_data[np.array(idxs_to_keep)]
del unique_objs['SPEC_REPEAT']
unique_objs['SPEC_REPEAT'] = new_repeats
return unique_objs
def _calculate_rotation_angle(reg_coordinate_frame, header):
"""Calculates the rotation angle from the region to the header's frame
This attempts to be compatible with the implementation used by SAOImage
DS9. In particular, this measures the rotation of the north axis as
measured at the center of the image, and therefore requires a
`~astropy.io.fits.Header` object with defined 'NAXIS1' and 'NAXIS2'
keywords.
Parameters
----------
reg_coordinate_frame : str
Coordinate frame used by the region file
header : `~astropy.io.fits.Header` instance
Header describing the image
Returns
-------
y_axis_rot : float
Degrees by which the north axis in the region's frame is rotated when
transformed to pixel coordinates
"""
new_wcs = WCS(header)
region_frame = SkyCoord(
'0d 0d',
frame=reg_coordinate_frame,
obstime='J2000')
region_frame = SkyCoord(
'0d 0d',
frame=reg_coordinate_frame,
obstime='J2000',
equinox=region_frame.equinox)
origin = SkyCoord.from_pixel(
header['NAXIS1']/2,
header['NAXIS2']/2,
wcs=new_wcs,
origin=1).transform_to(region_frame)
offset = proj_plane_pixel_scales(new_wcs)[1]
origin_x, origin_y = origin.to_pixel(new_wcs, origin=1)
origin_lon = origin.data.lon.degree
origin_lat = origin.data.lat.degree
offset_point = SkyCoord(
origin_lon, origin_lat+offset, unit='degree',
frame=origin.frame.name, obstime='J2000')
offset_x, offset_y = offset_point.to_pixel(new_wcs, origin=1)
north_rot = np.arctan2(
offset_y-origin_y,
offset_x-origin_x) / np.pi*180.
cdelt = new_wcs.wcs.get_cdelt()
if (cdelt > 0).all() or (cdelt < 0).all():
return north_rot - 90
else:
return -(north_rot - 90)
def calculate(self): ephem_location = ephem.Observer() ephem_location.lat = self.location.latitude.to(u.rad) / u.rad ephem_location.lon = self.location.longitude.to(u.rad) / u.rad ephem_location.elevation = self.location.height / u.meter ephem_location.date = ephem.Date(self.time.datetime) if self.data is None: self.alt = Latitude([], unit=u.deg) self.az = Longitude([], unit=u.deg) self.names = Column([], dtype=np.str) self.vmag = Column([]) else: ra = Longitude(self.data["ra"], u.h) dec = Latitude(self.data["dec"], u.deg) c = SkyCoord(ra, dec, frame='icrs') altaz = c.transform_to(AltAz(obstime=self.time, location=self.location)) self.alt = altaz.alt self.az = altaz.az self.names = self.data['name'] self.vmag = self.data['mag'] for ephemeris in self.ephemerides: ephemeris.compute(ephem_location) self.vmag = np.insert(self.vmag, [0], ephemeris.mag) self.alt = np.insert(self.alt, [0], (ephemeris.alt.znorm * u.rad).to(u.deg)) self.az = np.insert(self.az, [0], (ephemeris.az * u.rad).to(u.deg)) self.names = np.insert(self.names, [0], ephemeris.name) return self.names, self.vmag, self.alt, self.az
def exp_dead_new(file_num, name_file, imsz, wcs, flat_list, foc_list, asp_solution, dead, cut, flat_idx, step, out_path, return_dict):
print imsz
count = np.zeros(imsz)
x_lim = imsz[0]
y_lim = imsz[1]
length = flat_list[0].shape[0]
half_len = length/2.
print half_len
l = imsz[0]/10
start = foc_list[0,1]-half_len
print foc_list.shape
print start.shape
ox = np.repeat(np.arange(l)+start,length+1000)
oy = np.tile(np.arange(length+1000)+foc_list[0,0]-half_len-500,l)
omask = (ox>=0) & (ox<imsz[0]) & (oy>=0) & (oy<imsz[1])
ox = ox[omask]
oy = oy[omask]
gl,gb = wcs.all_pix2world(oy,ox,0)
c = SkyCoord(gl*u.degree, gb*u.degree, frame='galactic')
rd = c.transform_to(FK5)
for i in range(asp_solution.shape[0]):
hrflat = flat_list[flat_idx[i]]
foc = foc_list[i,:]#wcs.sip_pix2foc(wcs.wcs_world2pix(coo,1),1)
if (foc[1]+half_len)>=(start+l):
print 'update'
start = foc[1]-half_len
ox = np.repeat(np.arange(l)+start,length+1000)
oy = np.tile(np.arange(length+1000)+foc[0]-half_len-500,l)
omask = (ox>=0) & (ox<imsz[0]) & (oy>=0) & (oy<imsz[1])
if np.sum(omask)==0:
break
ox = ox[omask]
oy = oy[omask]
gl,gb = wcs.all_pix2world(oy,ox,0)
c = SkyCoord(gl*u.degree, gb*u.degree, frame='galactic')
rd = c.transform_to(FK5)
fmask = (ox>=(foc[1]-length/2)) & (ox<(foc[1]+length/2)) & (oy>=(foc[0]-length/2)) & (oy<(foc[0]+length/2))
if np.sum(fmask)==0:
continue
x = ox[fmask]
y = oy[fmask]
xi, eta = gn.gnomfwd_simple(rd.ra.deg[fmask], rd.dec.deg[fmask],
asp_solution[i,1], asp_solution[i,2], -asp_solution[i,3],1/36000.,0.)
px = ((xi/36000.)/(1.25/2.)*(1.25/(800* 0.001666))+1.)/2.*length
py = ((eta/36000.)/(1.25/2.)*(1.25/(800* 0.001666))+1.)/2.*length
pmask = (px>=0) & (px<length) & (py>=0) & (py<length)
if np.sum(pmask)==0:
continue
count[x[pmask].astype(int),y[pmask].astype(int)] += \
hrflat[px[pmask].astype(int),py[pmask].astype(int)]*step*(1-dead[i])*cut[i]
if i%100==0:
with open('/scratch/dw1519/galex/fits/scan_map/%s_gal_sec_exp_tmp%d.dat'%(name_file, file_num),'w') as f:
f.write('%d'%i)
print i
print '%d done'%file_num
#return_dict[file_num] = count
np.save('%s/%s_gal_sec_exp_tmp%d.npy'%(out_path, name_file, file_num), count)
def test_make_source_designation():
# Crab pulsar position for HESS
coordinate = SkyCoord('05h34m31.93830s +22d00m52.1758s', frame='icrs')
strrep = coordinate_iau_format(coordinate, ra_digits=4)
assert strrep == '0534+220'
# PKS 2155-304 AGN position for 2FGL
coordinate = SkyCoord('21h58m52.06511s -30d13m32.1182s', frame='icrs')
strrep = coordinate_iau_format(coordinate, ra_digits=5)
assert strrep == '2158.8-3013'
# Check the example from Section 3.2.1 of the IAU spec:
# http://cdsweb.u-strasbg.fr/Dic/iau-spec.html
icrs = SkyCoord('00h51m09.38s -42d26m33.8s', frame='icrs')
fk4 = icrs.transform_to('fk4')
strrep = coordinate_iau_format(icrs, ra_digits=6)
assert strrep == '005109-4226.5'
strrep = coordinate_iau_format(fk4, ra_digits=6)
assert strrep == '004848-4242.8'
strrep = coordinate_iau_format(fk4, ra_digits=4)
assert strrep == '0048-427'
strrep = coordinate_iau_format(fk4, ra_digits=4, dec_digits=2)
assert strrep == '0048-42'
# Check that array coordinate input works
coordinates = SkyCoord(ra=[10.68458, 83.82208],
dec=[41.26917, -5.39111],
unit=('deg', 'deg'))
strreps = coordinate_iau_format(coordinates, ra_digits=5, prefix='HESS J')
assert strreps == ['HESS J0042.7+4116', 'HESS J0535.2-0523']
def hextile(image,radius):
pos=[]
hs=radius*np.sqrt(3)
hdus=fits.open(image)
hdu=flatten(hdus)
maxy,maxx=hdu.data.shape
w=WCS(hdu.header)
print 'Hex tiling image'
# co-ords of bottom left of image
ra_c,dec_c=w.wcs_pix2world(maxx/2,maxy/2,0)
ra_factor=np.cos(dec_c*np.pi/180.0)
ra_ll,dec_ll=w.wcs_pix2world(0,0,0)
ra_lr,dec_lr=w.wcs_pix2world(maxx,0,0)
ra_ul,dec_ul=w.wcs_pix2world(0,maxy,0)
c_c=SkyCoord(ra_c*u.degree,dec_c*u.degree,frame='icrs')
c_ll=SkyCoord(ra_ll*u.degree,dec_ll*u.degree,frame='icrs')
c_lr=SkyCoord(ra_lr*u.degree,dec_lr*u.degree,frame='icrs')
dra,ddec=[v.value for v in c_c.spherical_offsets_to(c_ll)]
nha=dra*2/hs
print 'Number of hexes across',nha
c_ul=SkyCoord(ra_ul*u.degree,dec_ul*u.degree,frame='icrs')
dra,ddec=[v.value for v in c_c.spherical_offsets_to(c_ul)]
nhu=2*ddec/hs
print 'Number of hexes up',nhu
nha=int(0.5+nha)
nhu=int(0.5+nhu)
for j in range(nhu):
for i in range(nha):
xc=(1.0*maxx*(i+(j % 2)*0.5))/nha
yc=(maxy*(j+0.5))/nhu
ra_p,dec_p=w.wcs_pix2world(xc,yc,0)
pos.append((float(ra_p),float(dec_p)))
return ra_factor,pos
def assign_id(file1, file2):
"""
Preconditions: Expects 2 files read as astropy Tables. Files must have RA
and Dec columns.
Postconditions: Fills the DataNum column in the second file with the
DataNum of the closest RA/Dec match in the first file.
"""
ra1 = file1['RA']
dec1 = file1['Dec']
ra2 = file2['RA']
dec2 = file2['Dec']
# returns two catalogs comparing file2 to file 1
c = SkyCoord(ra=ra1*u.degree, dec=dec1*u.degree)
catalog = SkyCoord(ra=ra2*u.degree, dec=dec2*u.degree)
idx, d2d, d3d = c.match_to_catalog_3d(catalog)
# some of the matches are likely to be duplicates and not within a
# reasonable distance to be the same star
# return an array of true's and false's where match is within specified
# range (2 arcsec)
good_matches = d2d < 2*u.arcsec
# get all matches that are within 2 arcsec of the target
idx2 = idx[good_matches]
# apply file1's dataname to file2's dataname at the indexes specified by
# idx2
file2['DataNum'][idx2] = file1['DataNum'][good_matches]
# now have 2 files with the DataName column matching for stars with RA/Dec
# close enough
return file2
def make_coreqso_table(dr14qso,ebosstarg):
if isinstance(dr14qso,str):
dr14qso = Table.read(dr14qso)
if isinstance(ebosstarg,str):
ebosstarg = Table.read(ebosstarg)
#
dr14coo = SkyCoord(dr14qso['RA'],dr14qso['DEC'],unit=u.deg)
# restrict to CORE quasar targets
ii = np.where(ebosstarg['EBOSS_TARGET1'] & (1<<10) > 0)[0]
ebosstarg = ebosstarg[ii]
ebosstargcoo = SkyCoord(ebosstarg['RA'],ebosstarg['DEC'],unit=u.deg)
# now identify confirmed quasars from DR14 in the target list
m1,m2,sep,_ = dr14coo.search_around_sky(ebosstargcoo,2*u.arcsec)
# for some reason there is a repeated entry...
_,ii = np.unique(m1,return_index=True)
dr14qso = dr14qso[m2[ii]]
# just a sanity check
jj = np.where(dr14qso['EXTINCTION']>0)[0]
assert np.allclose(dr14qso['EXTINCTION'][jj],
ebosstarg['EXTINCTION'][m1[ii[jj]]],atol=1e-3)
# extract all the WISE columns from targeting
wisecols = ['W1_MAG','W1_MAG_ERR',
'W1_NANOMAGGIES','W1_NANOMAGGIES_IVAR',
'W2_NANOMAGGIES','W2_NANOMAGGIES_IVAR',
'HAS_WISE_PHOT']
# overwriting the DR14Q flux fields because they have invalid entries
for k in wisecols + ['EXTINCTION','PSFFLUX','PSFFLUX_IVAR']:
dr14qso[k] = ebosstarg[k][m1[ii]]
dr14qso.write('ebosscore_dr14q.fits',overwrite=True)
def transform(self, input_coords):
"""
Transform one set of coordinates to another
"""
if self.same_frames:
return input_coords
x_in, y_in = input_coords[:, 0], input_coords[:, 1]
try:
c_in = SkyCoord(x_in, y_in, unit=(u.deg, u.deg),
frame=self.input_system)
except: # Astropy < 1.0
c_in = SkyCoord(x_in, y_in, unit=(u.deg, u.deg),
frame=self.input_system.name,
**dict((key, getattr(self.input_system, key))
for key in self.input_system.get_frame_attr_names().keys()))
c_out = c_in.transform_to(self.output_system)
if issubclass(c_out.representation, (SphericalRepresentation, UnitSphericalRepresentation)):
lon = c_out.data.lon.deg
lat = c_out.data.lat.deg
else:
lon = c_out.spherical.lon.deg
lat = c_out.spherical.lat.deg
return np.concatenate((lon[:, np.newaxis], lat[:, np.newaxis]), axis=1)
def match_sky(reference_data,match_data,reference_radec=['ra','dec'],match_radec=['ra','dec']):
'''---Find the matches between 2 sets of ra+dec points---
Inputs:
-------
reference_data: usually the catlogue we wish to match to (eg. galaxies in GZ).
match_data: usually a subsidiary dataset, eg. detections in AFALFA, WISE, ...
reference_radec, match_radec: names of the columns that contain ra+dec (in degrees).
Outputs:
--------
ids: 3 column catalogue of 'match index', 'reference index' and 'separations' (in degrees).
'''
reference_ra, reference_dec = [np.array(reference_data[i]) for i in reference_radec]
match_ra, match_dec = [np.array(match_data[i]) for i in match_radec]
reference_coord = SkyCoord(ra=reference_ra*u.degree, dec=reference_dec*u.degree)
match_coord = SkyCoord(ra=match_ra*u.degree, dec=match_dec*u.degree)
idx, sep, _ = match_coord.match_to_catalog_sky(reference_coord)
match_idx = np.arange(len(match_data))
ids = Table(np.array([match_idx,idx,sep.arcsecond]).T
,names=('match_index','reference_index','separation'))
print('{} galaxies in the reference catalogue'.format(len(reference_data)))
print('{} galaxies in the match catalogue'.format(len(match_data)))
print('---> {} matches in total'.format(len(ids)))
return ids
def beamcheck(beam_input, ras, decs):
"""
Creates a box and a cicle at the start and end of the obs then checks if the source is inside it
"""
found = False
#TODO: (BWM) this is just a coding prferences, but try to space out big chunks of code.
source = SkyCoord(ra=ras*u.degree, dec=decs*u.degree, frame='icrs')
obs, ra_beam, dec_beam, time_beam = beam_input #in degrees from metadata
ra_intial, ra_final, dec_top, dec_bot = calcbox( ra_beam, dec_beam, time_beam)
centrebeam_start = SkyCoord(ra=ra_intial*u.degree, dec=dec_beam*u.degree, frame='icrs')
centrebeam_end = SkyCoord(ra=ra_final*u.degree, dec=dec_beam*u.degree, frame='icrs')
#checks if the source is within 10 degrees of the start and end of the file
angdif_start = centrebeam_start.separation(source).degree
angdiff_start = float(angdif_start)
angdif_end = centrebeam_end.separation(source).degree
angdiff_end = float(angdif_end)
#loop is inaccurate for beams near the south pole
#check the circle beam at the start and end of the observation and the rectangle connecting them
if ra_intial > ra_final:
if (angdiff_start < 10.) or (angdiff_end < 10.) or \
( ( ((ras > ra_intial) and (ras < 360.)) or ((ras > 0.) and (ras < ra_final)) ) and \
((dec_top > decs) and (dec_bot < decs)) ):
found = True
#TODO: just fyi, if you are comparing a value to two limits, i.e. want to know if x>1 and x<10
# the equivalent in python is actually just: if 1<x<10: *do stuff*, rather than having to use a million "and"
else:
if (angdiff_start < 10.) or (angdiff_end < 10.) or \
( ((ras > ra_intial) and (ras < ra_final)) and ((dec_top > decs) and (dec_bot < decs)) ):
found = True
return found
def _convert_radec_to_altaz(ra, dec, lon, lat, height, time):
"""Convert a single position.
This is done for easy code sharing with other tools.
Astropy does support arrays of positions.
"""
radec = SkyCoord(ra, dec, unit='deg')
location = EarthLocation(lon=Angle(lon, 'deg'),
lat=Angle(lat, 'deg'),
height=height * u.km)
# Pressure = 0 is the default
obstime = Time(time, scale='utc')
# temperature = 0 * u.deg_C
# pressure = 0 * u.bar
# relative_humidity = ?
# obswl = ?
altaz_frame = AltAz(obstime=obstime, location=location)
# temperature=temperature, pressure=pressure)
altaz = radec.transform_to(altaz_frame)
az = altaz.az.deg
alt = altaz.alt.deg
return dict(az=az, alt=alt)
def convert_catalog(cfile):
tycho2 = pyfits.open('../data/tycho2.fits')[1].data
sfile=re.split('\.',cfile)[0]+'.txt'
print(sfile)
try:
df = load_data.load_catalog(sfile)
except IOError:
print('skip')
return None
c = SkyCoord(df['gl']*u.degree, df['gb']*u.degree, frame='galactic')
catalog = SkyCoord(tycho2['Glon']*u.degree, tycho2['Glat']*u.degree, frame='galactic')
idx, d2d, d3d = c.match_to_catalog_sky(catalog)
mask=d2d<0.001*u.degree
print(np.sum(mask))
dtype = np.dtype([('tycho_num', int), ('Glon', '>f4'), ('Glat', '>f4'), ('RAJ2000', '>f4'), ('DEJ2000', '>f4'),
('flux', float), ('nuv', float), ('gl', float), ('gb', float)])
matched_tycho = tycho2[idx[mask]]
matched_df = df[mask]
matched_catalog = np.core.records.fromarrays(np.array([idx[mask], matched_tycho['Glon'], matched_tycho['Glat'],
matched_tycho['RAJ2000'], matched_tycho['DEJ2000'], np.array(matched_df['FLUX_AUTO']),
np.array(matched_df['nuv']), np.array(matched_df['gl']),
np.array(matched_df['gb'])]), dtype=dtype)
print(matched_catalog.shape)
np.save(cfile, matched_catalog)
return matched_catalog
def test_wcsndmap_set_get_by_coord(npix, binsz, coordsys, proj, skydir, axes):
geom = WcsGeom.create(npix=npix, binsz=binsz, skydir=skydir,
proj=proj, coordsys=coordsys, axes=axes)
m = WcsNDMap(geom)
coords = m.geom.get_coord()
m.set_by_coord(coords, coords[0])
assert_allclose(coords[0], m.get_by_coord(coords))
if not geom.is_allsky:
coords[1][...] = 0.0
assert_allclose(
np.nan * np.ones(coords[0].shape), m.get_by_coord(coords))
# Test with SkyCoords
m = WcsNDMap(geom)
coords = m.geom.get_coord()
skydir = SkyCoord(coords[0], coords[1], unit='deg',
frame=coordsys_to_frame(geom.coordsys))
skydir_cel = skydir.transform_to('icrs')
skydir_gal = skydir.transform_to('galactic')
m.set_by_coord((skydir_gal,) + coords[2:], coords[0])
assert_allclose(coords[0], m.get_by_coord(coords))
assert_allclose(m.get_by_coord((skydir_cel,) + coords[2:]),
m.get_by_coord((skydir_gal,) + coords[2:]))
# Test with MapCoord
m = WcsNDMap(geom)
coords = m.geom.get_coord()
coords_dict = dict(lon=coords[0], lat=coords[1])
if axes:
for i, ax in enumerate(axes):
coords_dict[ax.name] = coords[i + 2]
map_coords = MapCoord.create(coords_dict, coordsys=coordsys)
m.set_by_coord(map_coords, coords[0])
assert_allclose(coords[0], m.get_by_coord(map_coords))
def box(coords, unit=None, expand=True):
""" Box (rectangle) containing all the `coords`
Returns
-------
(center:SkyCoord, ra_size:Angle, dec_size:Angle)
"""
unit_kwargs = {}
if unit is not None:
unit_kwargs['unit'] = unit
if isinstance(coords, Table):
try:
try:
coords = SkyCoord.guess_from_table(coords[['ra', 'dec']], **unit_kwargs)
except u.UnitsError:
coords = SkyCoord.guess_from_table(coords[['ra', 'dec']], unit=(u.hourangle, u.deg))
except (KeyError, AttributeError):
coords = SkyCoord.guess_from_table(coords, **unit_kwargs)
else:
coords = SkyCoord(coords, **unit_kwargs)
dra = coords.ra.max() - coords.ra.min()
ddec = coords.dec.max() - coords.dec.min()
cra = coords.ra.min() + dra / 2.0
cdec = coords.dec.min() + ddec / 2.0
if expand:
if isinstance(expand, bool):
expand = 1.1
dra *= expand
ddec *= expand
return SkyCoord(cra, cdec), dra, ddec
def compute_output_transform(refwcs, filename, fiducial):
"""Compute a simple FITS-type WCS transform
"""
x0, y0 = refwcs.backward_transform(*fiducial)
x1 = x0 + 1
y1 = y0 + 1
ra0, dec0 = refwcs(x0, y0)
ra_xdir, dec_xdir = refwcs(x1, y0)
ra_ydir, dec_ydir = refwcs(x0, y1)
position0 = SkyCoord(ra=ra0, dec=dec0, unit='deg')
position_xdir = SkyCoord(ra=ra_xdir, dec=dec_xdir, unit='deg')
position_ydir = SkyCoord(ra=ra_ydir, dec=dec_ydir, unit='deg')
offset_xdir = position0.spherical_offsets_to(position_xdir)
offset_ydir = position0.spherical_offsets_to(position_ydir)
xscale = np.abs(position0.separation(position_xdir).value)
yscale = np.abs(position0.separation(position_ydir).value)
scale = np.sqrt(xscale * yscale)
c00 = offset_xdir[0].value / scale
c01 = offset_xdir[1].value / scale
c10 = offset_ydir[0].value / scale
c11 = offset_ydir[1].value / scale
pc_matrix = AffineTransformation2D(matrix=[[c00, c01], [c10, c11]])
cdelt = Scale(scale) & Scale(scale)
return pc_matrix | cdelt
def get_tns_ra_dec(ra, dec, rad=15):
'''
Queries the TNS and obtains the targets reported for the specified RA, DEC position.
Provided that ASASSN targets are there, a 7 arcsec position error is expected.
By default we will use 10 arcsec.
ra: float
position in degrees
dec: float
position in degrees
rad: float, optional
Search radius in arcseconds.
'''
url = "https://wis-tns.weizmann.ac.il/search?&name=&ra={0}&decl={1}&radius={2}&coords_unit=arcsec&format=csv".format(ra, dec, rad)
cont_url = urlopen(url)
cont = cont_url.read()
t = Table.read(StringIO(cont), format='ascii.csv')
if len(t) > 0:
coords = np.array([t["RA"], t["DEC"]]).T
c = SkyCoord(coords, frame='icrs', unit=(u.hourangle, u.deg))
basecoord = SkyCoord(ra, dec, frame='icrs', unit=(u.deg, u.deg))
#In case there are several objects in the match radius, we select the closest one
dist = c.separation(basecoord)
closest = t[np.argmin(dist)]
else:
closest = None
return closest
def cam_to_tel():
# Coordinates in any fram can be given as a numpy array of the xyz positions
# e.g. in this case the position on pixels in the camera
pix_x = np.ones(2048) * u.m
pix_y = np.ones(2048) * u.m
# first define the camera frame
camera_frame = CameraFrame(focal_length=15 * u.m)
# create a coordinate in that frame
camera_coord = SkyCoord(pix_x, pix_y, frame=camera_frame)
# then use transform to function to convert to a new system making sure
# to give the required values for the conversion (these are not checked
# yet)
telescope_coord = camera_coord.transform_to(TelescopeFrame())
# Print coordinates in the new frame
print("Telescope Coordinate", telescope_coord)
# Transforming back is then easy
camera_coord2 = telescope_coord.transform_to(camera_frame)
# We can easily check the distance between 2 coordinates in the same frame
# In this case they should be the same
print("Separation", np.sum(camera_coord.separation_3d(camera_coord2)))
def test_get_skycoord():
m31 = SkyCoord(10.6847083*u.deg, 41.26875*u.deg)
m31_with_distance = SkyCoord(10.6847083*u.deg, 41.26875*u.deg, 780*u.kpc)
subaru = Observer.at_site('subaru')
time = Time("2016-01-22 12:00")
pos, vel = subaru.location.get_gcrs_posvel(time)
gcrs_frame = GCRS(obstime=Time("2016-01-22 12:00"), obsgeoloc=pos, obsgeovel=vel)
m31_gcrs = m31.transform_to(gcrs_frame)
m31_gcrs_with_distance = m31_with_distance.transform_to(gcrs_frame)
coo = get_skycoord(m31)
assert coo.is_equivalent_frame(ICRS())
with pytest.raises(TypeError) as exc_info:
len(coo)
coo = get_skycoord([m31])
assert coo.is_equivalent_frame(ICRS())
assert len(coo) == 1
coo = get_skycoord([m31, m31_gcrs])
assert coo.is_equivalent_frame(ICRS())
assert len(coo) == 2
coo = get_skycoord([m31_with_distance, m31_gcrs_with_distance])
assert coo.is_equivalent_frame(ICRS())
assert len(coo) == 2
coo = get_skycoord([m31, m31_gcrs, m31_gcrs_with_distance, m31_with_distance])
assert coo.is_equivalent_frame(ICRS())
assert len(coo) == 4
coo = get_skycoord([m31_gcrs, m31_gcrs_with_distance])
assert coo.is_equivalent_frame(m31_gcrs.frame)
assert len(coo) == 2
def calculateSeparation(ra1, dec1, ra2, dec2):
"""
Returns angular separation between two coordinates (all in degrees).
Parameters
----------
ra1 : float or numpy array
RA of coordinate 1 in degrees
dec1 : float or numpy array
Dec of coordinate 1 in degrees
ra2 : float
RA of coordinate 2 in degrees
dec2 : float
Dec of coordinate 2 in degrees
Returns
-------
separation : astropy Angle or numpy array
Angular separation in degrees
"""
from astropy.coordinates import SkyCoord
import astropy.units as u
coord1 = SkyCoord(ra1, dec1, unit=(u.degree, u.degree), frame='fk5')
coord2 = SkyCoord(ra2, dec2, unit=(u.degree, u.degree), frame='fk5')
return coord1.separation(coord2)
def test_against_pyephem():
"""Check that Astropy gives consistent results with one PyEphem example.
PyEphem: http://rhodesmill.org/pyephem/
See example input and output here:
https://gist.github.com/zonca/1672906
https://github.com/phn/pytpm/issues/2#issuecomment-3698679
"""
obstime = Time('2011-09-18 08:50:00')
location = EarthLocation(lon=Angle('-109d24m53.1s'),
lat=Angle('33d41m46.0s'),
height=30000. * u.m)
# We are using the default pressure and temperature in PyEphem
# relative_humidity = ?
# obswl = ?
altaz_frame = AltAz(obstime=obstime, location=location,
temperature=15 * u.deg_C, pressure=1.010 * u.bar)
altaz = SkyCoord('6.8927d -60.7665d', frame=altaz_frame)
radec_actual = altaz.transform_to('icrs')
radec_expected = SkyCoord('196.497518d -4.569323d', frame='icrs') # EPHEM
# radec_expected = SkyCoord('196.496220d -4.569390d', frame='icrs') # HORIZON
distance = radec_actual.separation(radec_expected).to('arcsec')
# TODO: why is this difference so large?
# It currently is: 31.45187984720655 arcsec
assert distance < 1e3 * u.arcsec
# Add assert on current Astropy result so that we notice if something changes
radec_expected = SkyCoord('196.495372d -4.560694d', frame='icrs')
distance = radec_actual.separation(radec_expected).to('arcsec')
# Current value: 0.0031402822944751997 arcsec
assert distance < 1 * u.arcsec
def do_stage(self, images):
for image in images:
self.setup_logging(image)
try:
# OFST-RA/DEC is the same as CAT-RA/DEC but includes user requested offset
requested_coords = SkyCoord(image.header['OFST-RA'], image.header['OFST-DEC'],
unit=(u.hour, u.deg), frame='icrs')
except ValueError as e:
try:
# Fallback to CAT-RA and CAT-DEC
requested_coords = SkyCoord(image.header['CAT-RA'], image.header['CAT-DEC'],
unit=(u.hour, u.deg), frame='icrs')
except:
self.logger.error(e, extra=self.logging_tags)
continue
# This only works assuming CRPIX is at the center of the image
solved_coords = SkyCoord(image.header['CRVAL1'], image.header['CRVAL2'],
unit=(u.deg, u.deg), frame='icrs')
angular_separation = solved_coords.separation(requested_coords).arcsec
logs.add_tag(self.logging_tags, 'PNTOFST', angular_separation)
if abs(angular_separation) > self.SEVERE_THRESHOLD:
self.logger.error('Pointing offset exceeds threshold', extra=self.logging_tags)
elif abs(angular_separation) > self.WARNING_THRESHOLD:
self.logger.warning('Pointing offset exceeds threshhold', extra=self.logging_tags)
image.header['PNTOFST'] = (
angular_separation, '[arcsec] offset of requested and solved center'
)
return images
def test_fk5_equinox_and_epoch_j2000_0_to_topocentric_observed():
"""
http://phn.github.io/pytpm/conversions.html#fk5-equinox-and-epoch-j2000-0-to-topocentric-observed
"""
# Observatory position for `kpno` from here:
# http://idlastro.gsfc.nasa.gov/ftp/pro/astro/observatory.pro
location = EarthLocation(lon=Angle('-111.598333d'),
lat=Angle('31.956389d'),
height=2093.093 * u.m) # TODO: height correct?
obstime = Time('2010-01-01 12:00:00')
# relative_humidity = ?
# obswl = ?
altaz_frame = AltAz(obstime=obstime, location=location,
temperature=0 * u.deg_C, pressure=0.781 * u.bar)
radec = SkyCoord('12h22m54.899s 15d49m20.57s', frame='fk5')
altaz_actual = radec.transform_to(altaz_frame)
altaz_expected = SkyCoord('264d55m06s 37d54m41s', frame='altaz')
# altaz_expected = SkyCoord('343.586827647d 15.7683070508d', frame='altaz')
# altaz_expected = SkyCoord('133.498195532d 22.0162383595d', frame='altaz')
distance = altaz_actual.separation(altaz_expected)
# print(altaz_actual)
# print(altaz_expected)
# print(distance)
"""TODO: Current output is completely incorrect ... xfailing this test for now.
<SkyCoord (AltAz: obstime=2010-01-01 12:00:00.000, location=(-1994497.7199061865, -5037954.447348028, 3357437.2294832403) m, pressure=781.0 hPa, temperature=0.0 deg_C, relative_humidity=0, obswl=1.0 micron):00:00.000, location=(-1994497.7199061865, -5037954.447348028, 3357437.2294832403) m, pressure=781.0 hPa, temperature=0.0 deg_C, relative_humidity=0, obswl=1.0 micron): az=133.4869896371561 deg, alt=67.97857990957701 deg>
<SkyCoord (AltAz: obstime=None, location=None, pressure=0.0 hPa, temperature=0.0 deg_C, relative_humidity=0, obswl=1.0 micron): az=264.91833333333335 deg, alt=37.91138888888889 deg>
68d02m45.732s
"""
assert distance < 1 * u.arcsec
def calculate(self): ephem_location = ephem.Observer() ephem_location.lat = self.location.latitude.to(u.rad) / u.rad ephem_location.lon = self.location.longitude.to(u.rad) / u.rad ephem_location.elevation = self.location.height / u.meter ephem_location.date = ephem.Date(self.time.datetime) if self.data is None: self.alt = Latitude([], unit=u.deg) self.az = Longitude([], unit=u.deg) self.names = Column([], dtype=np.str) self.vmag = Column([]) else: ra = Longitude((self.data['RAh'], self.data['RAm'], self.data['RAs']), u.h) dec = Latitude((np.core.defchararray.add(self.data['DE-'], self.data['DEd'].astype(str)).astype(int), self.data['DEm'], self.data['DEs']), u.deg) c = SkyCoord(ra, dec, frame='icrs') altaz = c.transform_to(AltAz(obstime=self.time, location=self.location)) self.alt = altaz.alt self.az = altaz.az self.names = self.data['Name'] self.vmag = self.data['Vmag'] for ephemeris in self.ephemerides: ephemeris.compute(ephem_location) self.vmag = self.vmag.insert(0, ephemeris.mag) self.alt = self.alt.insert(0, (ephemeris.alt.znorm * u.rad).to(u.deg)) self.az = self.az.insert(0, (ephemeris.az * u.rad).to(u.deg)) self.names = self.names.insert(0, ephemeris.name) return self.names, self.vmag, self.alt, self.az
def test_disk_distribution(diskclass, diskpar, n_expected):
'''This is a separate test from test_disk_radius, because it's a simpler
to write if we don't have to worry about the inner hole.
For the test itself: The results should be poisson distributed (or, for large
numbers this will be almost normal).
That makes testing it a little awkard in a short run time, thus the limits are
fairly loose.
This test is run for several extended sources, incl Gaussian. Stirctly speaking
it should fail for a Gaussian distribution, but if the sigma is large enough it
will pass a loose test (and still fail if things to catastrophically wrong,
e.g. some test circles are outside the source).
'''
s = diskclass(coords=SkyCoord(213., -10., unit=u.deg), **diskpar)
photons = s.generate_photons(1e5)
n = np.empty(20)
for i in range(len(n)):
circ = SkyCoord((213. + np.random.uniform(-0.1, .1)) * u.degree,
(- 10. + np.random.uniform(-0.1, .1)) * u.degree)
d = circ.separation(SkyCoord(photons['ra'], photons['dec'], unit='deg'))
n[i] = (d < 5. * u.arcmin).sum()
s, p = normaltest(n)
# assert a p value here that is soo small that it's never going to be hit
# by chance.
assert p > .05
# better: Test number of expected photons matches
# Allow large variation so that this is not triggered by chance
assert np.isclose(n.mean(), n_expected, rtol=.2)
def mk_radecname(ra, dec, precision=0, prefix='',
shortform=False):
""" make a radec name from ra, dec e.g. HHMMSSsDDMMSS
"""
sep = ''
radec = SkyCoord(ra=ra * u.degree, dec=dec * u.degree)
if not shortform:
radecname = radec.to_string('hmsdms', decimal=False,
sep=sep,
precision=precision)
if shortform:
radec_string = radec.to_string('hmsdms', decimal=False,
sep=sep, precision=0)
radecname = radec_string[0:4] + radec_string[7:12]
radecname = np.core.defchararray.replace(radecname, ' ', '')
if prefix != '':
# radecname = prefix + radecname does not work
radecname = np.core.defchararray.add(prefix, radecname)
return str(radecname)
def __init__(self,obj,off=None,fits=None):
"""
obj Init the main object position
ra,dec either degree or hh,dd
off same for offset star
fits if set read from file
"""
#assume flots are in deg
if isinstance(obj['ra'],float):
self.obj=SkyCoord(obj['ra'],obj['dec'],unit='deg')
else:
self.obj=SkyCoord(obj['ra'],obj['dec'],unit=(u.hourangle, u.deg))
#reference
if(off):
if isinstance(off['ra'],float):
self.off=SkyCoord(off['ra'],off['dec'],unit='deg')
else:
self.off=SkyCoord(off['ra'],off['dec'],unit=(u.hourangle, u.deg))
try:
self.offmag=off['mag']
except:
self.offmag=0.0
#deal with image
if(fits):
self.loadimg(fits)
def detect_sources(file_info, cid, settings):
from astrotoyz.detect_sources import find_stars
import astrotoyz.viewer
session_vars.catalogs[cid] = None
hdulist = toyz.web.viewer.get_file(file_info)
wcs = astrotoyz.viewer.get_wcs(file_info, hdulist)
hdu = hdulist[int(file_info['frame'])]
settings['img_data'] = hdu.data
sources = find_stars(**settings)
catalog = Catalog(cid, file_info=file_info, data=sources)
catalog.dropna(inplace=True)
if wcs is not None:
from astropy.coordinates import SkyCoord
id_name = catalog.settings['data']['id_name']
ra_name = catalog.settings['data']['ra_name']
dec_name = catalog.settings['data']['dec_name']
wcs_array = wcs.all_pix2world(catalog['x'], catalog['y'], 1)
catalog[ra_name] = wcs_array[0]
catalog[dec_name] = wcs_array[1]
coords = SkyCoord(ra=wcs_array[0], dec=wcs_array[1], unit='deg')
catalog[id_name] = coords.to_string('hmsdms')
else:
sep = np.zeroes(shape=(catalog.shape[0],),dtype='|S1')
sep.fill(',')
new_id = np.core.defchararray.add(catalog['x'].values.astype('|S10'), sep)
new_id = np.core.defchararray.add(new_id,catalog['y'].values.astype('|S10'))
catalog[id_name] = new_id
catalog.set_index(id_name, inplace=True)
session_vars.catalogs[cid] = catalog;
print('finished detecting sources')
return catalog
def predict(self, hillas_dict, inst, pointing_alt, pointing_az):
'''
The function you want to call for the reconstruction of the
event. It takes care of setting up the event and consecutively
calls the functions for the direction and core position
reconstruction. Shower parameters not reconstructed by this
class are set to np.nan
Parameters
-----------
hillas_dict: dict
dictionary with telescope IDs as key and
HillasParametersContainer instances as values
inst : ctapipe.io.InstrumentContainer
instrumental description
pointing_alt: dict[astropy.coordinates.Angle]
dict mapping telescope ids to pointing altitude
pointing_az: dict[astropy.coordinates.Angle]
dict mapping telescope ids to pointing azimuth
Raises
------
TooFewTelescopesException
if len(hillas_dict) < 2
InvalidWidthException
if any width is np.nan or 0
'''
# filter warnings for missing obs time. this is needed because MC data has no obs time
warnings.filterwarnings(action='ignore',
category=MissingFrameAttributeWarning)
# stereoscopy needs at least two telescopes
if len(hillas_dict) < 2:
raise TooFewTelescopesException(
"need at least two telescopes, have {}".format(
len(hillas_dict)))
# check for np.nan or 0 width's as these screw up weights
if any(
[np.isnan(hillas_dict[tel]['width'].value)
for tel in hillas_dict]):
raise InvalidWidthException(
"A HillasContainer contains an ellipse of width==np.nan")
if any([hillas_dict[tel]['width'].value == 0 for tel in hillas_dict]):
raise InvalidWidthException(
"A HillasContainer contains an ellipse of width==0")
self.initialize_hillas_planes(hillas_dict, inst.subarray, pointing_alt,
pointing_az)
# algebraic direction estimate
direction, err_est_dir = self.estimate_direction()
alt = u.Quantity(list(pointing_alt.values()))
az = u.Quantity(list(pointing_az.values()))
if np.any(alt != alt[0]) or np.any(az != az[0]):
warnings.warn('Divergent pointing not supported')
telescope_pointing = SkyCoord(alt=alt[0], az=az[0], frame=AltAz())
# core position estimate using a geometric approach
core_pos = self.estimate_core_position(hillas_dict, telescope_pointing)
# container class for reconstructed showers
result = ReconstructedShowerContainer()
_, lat, lon = cartesian_to_spherical(*direction)
# estimate max height of shower
h_max = self.estimate_h_max()
# astropy's coordinates system rotates counter-clockwise.
# Apparently we assume it to be clockwise.
result.alt, result.az = lat, -lon
result.core_x = core_pos[0]
result.core_y = core_pos[1]
result.core_uncert = np.nan
result.tel_ids = [h for h in hillas_dict.keys()]
result.average_intensity = np.mean(
[h.intensity for h in hillas_dict.values()])
result.is_valid = True
result.alt_uncert = err_est_dir
result.az_uncert = np.nan
result.h_max = h_max
result.h_max_uncert = np.nan
result.goodness_of_fit = np.nan
return result
def circle_distance(x, y):
c1 = SkyCoord(x[0], x[1], frame='icrs', unit="deg")
c2 = SkyCoord(y[0], y[1], frame='icrs', unit="deg")
sep = c1.separation(c2)
return sep.deg
Dir = '/1/home/heh15/workingspace/Arp240/NGC5257/12CO10/'
imageDir = Dir + 'casa5.4/'
picDir = Dir + 'picture/'
regionDir = Dir + 'region/'
mom0file = imageDir + 'NGC5257_12CO10_combine_pbcor_2rms_mom0.fits'
mom1file = imageDir + 'NGC5257_12CO10_pbcor_cube_mom1.fits'
############################################################
# basic information
galaxy = 'NGC5257'
line = '12CO10'
position = SkyCoord(dec=50.4167 * u.arcmin,
ra=204.9706 * u.degree,
frame='icrs')
beamra = 204 * u.degree + 58 * u.arcmin + 30 * u.arcsec
beamdec = 50 * u.arcmin + 3 * u.arcsec
beamposition = SkyCoord(dec=beamdec, ra=beamra, frame='icrs')
beammajor = 1.986 * u.arcsec / 2.0
beamminor = 1.576 * u.arcsec / 2.0
pa = -71 * u.degree
############################################################
# function
def fits_import(fitsimage, item=0):
hdr = fits.open(fitsimage)[item].header
def addGal(self, gal_idx, mag_clm='MAG', z_clm='ZGAL', sens_galaxies=None,
magbins=None, SPL=None, Rcom_max=None, debug=False, tbinedges=None):
""" Adds a set of galaxies for clustering analysis from the main astropy Table
A random sample is also generated
Parameters
----------
gal_idx : ndarray (int or bool)
Indices of self.galaxy to add to the clustering analysis
mag_clm : str, optional
Name of the column for the galaxy magnitudes
z_clm : str, optional
Name of the column for the galaxy redshifts
sens_galaxies : np.recarray
array containing the shape of the sensitivity function of the galaxies being added
Used for generating sensitivity function instead of the added galaxies
Important to use when the subset is too small for an accurate sensitivity function
magbins : ndarray (optional)
SPL : dict (optional)
Contains the sensitivity function (CubicSpline's)
Rcom_max : float, optional
Maximum comoving separation of the survey in Mpc
tbinedges : ndarray
Only used for debug=True
Returns
-------
Updates self.galreal internally
"""
# Grab the galaxies
sub_gal = self.galaxies[gal_idx]
# Rename any columns here
sub_gal.rename_column(mag_clm,'MAG')
sub_gal.rename_column(z_clm,'ZGAL')
# Strip down to important columns only here, as desired
# Convert to numpy rec array
galnew = sub_gal.as_array().view(np.recarray)
# Calculate randoms
if sens_galaxies is None:
sens_galaxies = galnew
# Sensitivity function
if (SPL is None) or (magbins is None):
magbins, _, SPL = spline_sensitivity(sens_galaxies)
# Randoms
galnewrand = random_gal(galnew, self.igal_rand, magbins, SPL)
# Cut on Rcom_max?
if Rcom_max is not None:
rcoord = SkyCoord(ra=galnewrand.RA, dec=galnewrand.DEC, unit='deg')
angsep = self.coord.separation(rcoord)
# R comoving
Rcom = self.cosmo.kpc_comoving_per_arcmin(galnewrand.ZGAL) * angsep.to('arcmin')
# Cut
goodr = Rcom.to('Mpc').value < Rcom_max
galnewrand = galnewrand[goodr]
if debug:
gcoord = SkyCoord(ra=galnew.RA, dec=galnew.DEC, unit='deg')
gangsep = self.coord.separation(gcoord)
gRcom = self.cosmo.kpc_comoving_per_arcmin(galnew.ZGAL) * gangsep.to('arcmin')
faintR = galnewrand.MAG > 19.
faintg = galnew.MAG > 19.
#
from matplotlib import pyplot as plt
import matplotlib.gridspec as gridspec
plt.clf()
if True:
gs = gridspec.GridSpec(2,1)
ax = plt.subplot(gs[0])
ax.hist(gRcom[~faintg], color='k', bins=tbinedges, normed=1, label='DD', fill=False)
ax.hist(Rcom[goodr][~faintR], edgecolor='red', bins=tbinedges, normed=1, label='RR', fill=False)
# Faint
ax = plt.subplot(gs[1])
ax.hist(gRcom[faintg], color='k', bins=tbinedges, normed=1, label='DD', fill=False)
ax.hist(Rcom[goodr][faintR], edgecolor='red', bins=tbinedges, normed=1, label='RR', fill=False)
ax.set_ylabel('Faint')
ax.set_xlabel('Rcom (Mpc)')
else:
ax = plt.gca()
zbins = np.arange(0., 0.8, 0.025)
ax.hist(galnew.ZGAL[faintg], color='k', bins=zbins, normed=1, label='DD', fill=False)
ax.hist(galnewrand.ZGAL[faintR], edgecolor='red', bins=zbins, normed=1, label='RR', fill=False)
ax.set_xlabel('zGAL')
plt.show()
# Load me up
if self.galreal is None:
self.galreal = galnew # np rec array with galaxy properties
self.galrand = galnewrand
else:
galnew = galnew.astype(self.galreal.dtype)
self.galreal = np.append(self.galreal, galnew)
self.galreal = np.rec.array(self.galreal)
self.galrand = np.append(self.galrand, galnewrand)
self.galrand = np.rec.array(self.galrand)
def sky_section(self, bounds, radius = None, wrap_at_180 = True):
"""
Extract a sub section of the survey from the sky
Parameters
----------
bounds: `list` or `Quantity` or `SkyCoord`
if `list` or `Quantity` must be formatted as:
[min Galactic Longitude, max Galactic Longitude, min Galactic Latitude, max Galactic Latitude]
or
[center Galactic Longitude, center Galactic Latitude] and requires radius keyword to be set
default units of u.deg are assumed
if `SkyCoord', must be length 4 or length 1 or length 2
length 4 specifies 4 corners of rectangular shape
length 1 specifies center of circular region and requires radius keyword to be set
length 2 specifies two corners of rectangular region
radius: 'number' or 'Quantity', optional, must be keyword
sets radius of circular region
wrap_at_180: `bool`, optional, must be keyword
if True, wraps longitude angles at 180d
use if mapping accross Galactic Center
"""
if wrap_at_180:
wrap_at = "180d"
else:
wrap_at = "360d"
if not isinstance(bounds, u.Quantity) | isinstance(bounds, SkyCoord):
bounds *= u.deg
logging.warning("No units provided for bounds, assuming u.deg")
wham_coords = self.get_SkyCoord()
if isinstance(bounds, SkyCoord):
if len(bounds) == 1:
if radius is None:
raise TypeError("Radius must be provided if only a single coordinate is given")
elif not isinstance(radius, u.Quantity):
radius *= u.deg
logging.warning("No units provided for radius, assuming u.deg")
center = bounds
elif len(bounds) >= 2:
min_lon, max_lon = bounds.l.wrap_at(wrap_at).min(), bounds.l.wrap_at(wrap_at).max()
min_lat, max_lat = bounds.b.min(), bounds.l.max()
elif len(bounds) == 2:
if radius is None:
raise TypeError("Radius must be provided if only a single coordinate is given")
elif not isinstance(radius, u.Quantity):
radius *= u.deg
logging.warning("No units provided for radius, assuming u.deg")
center = SkyCoord(l = bounds[0], b = bounds[1], frame = 'galactic')
elif len(bounds) == 4:
min_lon, max_lon, min_lat, max_lat = Angle(bounds)
min_lon = min_lon.wrap_at(wrap_at)
max_lon = max_lon.wrap_at(wrap_at)
else:
raise TypeError("Input bounds and/or radius are not understood")
# rectangular extraction
if radius is None:
# Mask of points inside rectangular region
inside_mask = wham_coords.l.wrap_at(wrap_at) <= max_lon
inside_mask &= wham_coords.l.wrap_at(wrap_at) >= min_lon
inside_mask &= wham_coords.b <= max_lat
inside_mask &= wham_coords.b >= min_lat
else: # Circle extraction
# Compute Separation
# Warning to self: This is VERY slow
sep = wham_coords.separation(center)
# Mask of points inside circular region
inside_mask = sep <= radius
return self[inside_mask]
def create_blockvisibility_from_uvfits(fitsname, channum=None, ack=False, antnum=None):
""" Minimal UVFIT to BlockVisibility converter
The UVFITS format is much more general than the RASCIL BlockVisibility so we cut many corners.
Creates a list of BlockVisibility's, split by field and spectral window
:param fitsname: File name of UVFITS
:param channum: range of channels e.g. range(17,32), default is None meaning all
:param antnum: the number of antenna
:return:
"""
def ParamDict(hdul):
"Return the dictionary of the random parameters"
"""
The keys of the dictionary are the parameter names uppercased for
consistency. The values are the column numbers.
If multiple parameters have the same name (e.g., DATE) their
columns are entered as a list.
"""
pre=re.compile(r"PTYPE(?P<i>\d+)")
res={}
for k,v in hdul.header.items():
m=pre.match(k)
if m :
vu=v.upper()
if vu in res:
res[ vu ] = [ res[vu], int(m.group("i")) ]
else:
res[ vu ] = int(m.group("i"))
return res
# Open the file
with fits.open(fitsname) as hdul:
# Read Spectral Window
nspw = hdul[0].header['NAXIS5']
# Read Channel and Frequency Interval
freq_ref = hdul[0].header['CRVAL4']
mid_chan_freq = hdul[0].header['CRPIX4']
delt_freq = hdul[0].header['CDELT4']
# Real the number of channels in one spectral window
channels = hdul[0].header['NAXIS4']
freq = numpy.zeros([nspw, channels])
# Read Frequency or IF
freqhdulname="AIPS FQ"
sdhu = hdul.index_of(freqhdulname)
if_freq = hdul[sdhu].data['IF FREQ'].ravel()
for i in range(nspw):
temp = numpy.array([if_freq[i] + freq_ref+delt_freq* ff for ff in range(channels)])
freq[i,:] = temp[:]
freq_delt = numpy.ones(channels) * delt_freq
if channum is None:
channum = range(channels)
primary = hdul[0].data
# Read time
bvtimes = Time(hdul[0].data['DATE'], hdul[0].data['_DATE'], format='jd')
bv_times = numpy.unique(bvtimes.jd)
ntimes = len(bv_times)
# # Get Antenna
# blin = hdul[0].data['BASELINE']
antennahdulname="AIPS AN"
adhu = hdul.index_of(antennahdulname)
try:
antenna_name = hdul[adhu].data['ANNAME']
antenna_name = antenna_name.encode('ascii','ignore')
except:
antenna_name = None
antenna_xyz = hdul[adhu].data['STABXYZ']
antenna_mount = hdul[adhu].data['MNTSTA']
try:
antenna_diameter = hdul[adhu].data['DIAMETER']
except:
antenna_diameter = None
# To reading some UVFITS with wrong numbers of antenna
if antnum is not None:
if antenna_name is not None:
antenna_name = antenna_name[:antnum]
antenna_xyz = antenna_xyz[:antnum]
antenna_mount = antenna_mount[:antnum]
if antenna_diameter is not None:
antenna_diameter = antenna_diameter[:antnum]
nants = len(antenna_xyz)
# res= {}
# for i,row in enumerate(fin[ahdul].data):
# res[row.field("ANNAME") ] = i +1
# Get polarisation info
npol = hdul[0].header['NAXIS3']
corr_type = numpy.arange(hdul[0].header['NAXIS3']) - (hdul[0].header['CRPIX3'] - 1)
corr_type *= hdul[0].header['CDELT3']
corr_type += hdul[0].header['CRVAL3']
# xx yy xy yx
# These correspond to the CASA Stokes enumerations
if numpy.array_equal(corr_type, [1, 2, 3, 4]):
polarisation_frame = PolarisationFrame('stokesIQUV')
elif numpy.array_equal(corr_type, [-1, -2, -3, -4]):
polarisation_frame = PolarisationFrame('circular')
elif numpy.array_equal(corr_type, [-5, -6, -7, -8]):
polarisation_frame = PolarisationFrame('linear')
else:
raise KeyError("Polarisation not understood: %s" % str(corr_type))
configuration = Configuration(name='', data=None, location=None,
names=antenna_name, xyz=antenna_xyz, mount=antenna_mount, frame=None,
receptor_frame=polarisation_frame,
diameter=antenna_diameter)
# Get RA and DEC
phase_center_ra_degrees = numpy.float(hdul[0].header['CRVAL6'])
phase_center_dec_degrees = numpy.float(hdul[0].header['CRVAL7'])
# Get phasecentres
phasecentre = SkyCoord(ra=phase_center_ra_degrees * u.deg, dec=phase_center_dec_degrees * u.deg, frame='icrs', equinox='J2000')
# Get UVW
d=ParamDict(hdul[0])
if "UU" in d:
uu = hdul[0].data['UU']
vv = hdul[0].data['VV']
ww = hdul[0].data['WW']
else:
uu = hdul[0].data['UU---SIN']
vv = hdul[0].data['VV---SIN']
ww = hdul[0].data['WW---SIN']
_vis = hdul[0].data['DATA']
#_vis.shape = (nchan, ntimes, (nants*(nants-1)//2 ), npol, -1)
#self.vis = -(_vis[...,0] * 1.j + _vis[...,1])
row = 0
nchan = len(channum)
vis_list = list()
for spw_index in range(nspw):
bv_vis = numpy.zeros([ntimes, nants, nants, nchan, npol]).astype('complex')
bv_weight = numpy.zeros([ntimes, nants, nants, nchan, npol])
bv_uvw = numpy.zeros([ntimes, nants, nants, 3])
for time_index , time in enumerate(bv_times):
#restfreq = freq[channel_index]
for antenna1 in range(nants-1):
for antenna2 in range(antenna1 + 1, nants):
for channel_no, channel_index in enumerate(channum):
for pol_index in range(npol):
bv_vis[time_index, antenna2, antenna1, channel_no,pol_index] = complex(_vis[row,:,:,spw_index,channel_index, pol_index ,0],_vis[row,:,:,spw_index,channel_index,pol_index ,1])
bv_weight[time_index, antenna2, antenna1, channel_no, pol_index] = _vis[row,:,:,spw_index,channel_index,pol_index ,2]
bv_uvw[time_index, antenna2, antenna1, 0] = uu[row]* constants.c.value
bv_uvw[time_index, antenna2, antenna1, 1] = vv[row]* constants.c.value
bv_uvw[time_index, antenna2, antenna1, 2] = ww[row]* constants.c.value
row += 1
vis_list.append(BlockVisibility(uvw=bv_uvw,
time=bv_times,
frequency=freq[spw_index][channum],
channel_bandwidth=freq_delt[channum],
vis=bv_vis,
weight=bv_weight,
imaging_weight= bv_weight,
configuration=configuration,
phasecentre=phasecentre,
polarisation_frame=polarisation_frame))
return vis_list
def create_blockvisibility_from_ms(msname, channum=None, start_chan=None, end_chan=None, ack=False,
datacolumn='DATA', selected_sources=None, selected_dds=None):
""" Minimal MS to BlockVisibility converter
The MS format is much more general than the RASCIL BlockVisibility so we cut many corners. This requires casacore to be
installed. If not an exception ModuleNotFoundError is raised.
Creates a list of BlockVisibility's, split by field and spectral window
Reading of a subset of channels is possible using either start_chan and end_chan or channnum. Using start_chan
and end_chan is preferred since it only reads the channels required. Channum is more flexible and can be used to
read a random list of channels.
:param msname: File name of MS
:param channum: range of channels e.g. range(17,32), default is None meaning all
:param start_chan: Starting channel to read
:param end_chan: End channel to read
:return:
"""
try:
from casacore.tables import table # pylint: disable=import-error
except ModuleNotFoundError:
raise ModuleNotFoundError("casacore is not installed")
try:
from rascil.processing_components.visibility import msv2
except ModuleNotFoundError:
raise ModuleNotFoundError("cannot import msv2")
tab = table(msname, ack=ack)
log.debug("create_blockvisibility_from_ms: %s" % str(tab.info()))
if selected_sources is None:
fields = numpy.unique(tab.getcol('FIELD_ID'))
else:
fieldtab = table('%s/FIELD' % msname, ack=False)
sources = fieldtab.getcol('NAME')
fields = list()
for field, source in enumerate(sources):
if source in selected_sources: fields.append(field)
assert len(fields) > 0, "No sources selected"
if selected_dds is None:
dds = numpy.unique(tab.getcol('DATA_DESC_ID'))
else:
dds = selected_dds
log.debug("create_blockvisibility_from_ms: Reading unique fields %s, unique data descriptions %s" % (
str(fields), str(dds)))
vis_list = list()
for field in fields:
ftab = table(msname, ack=ack).query('FIELD_ID==%d' % field, style='')
for dd in dds:
meta = {'MSV2':{'FIELD_ID': field, 'DATA_DESC_ID':dd}}
ms = ftab.query('DATA_DESC_ID==%d' % dd, style='')
assert ms.nrows() > 0, "Empty selection for FIELD_ID=%d and DATA_DESC_ID=%d" % (field, dd)
log.debug("create_blockvisibility_from_ms: Found %d rows" % (ms.nrows()))
# The TIME column has descriptor:
# {'valueType': 'double', 'dataManagerType': 'IncrementalStMan', 'dataManagerGroup': 'TIME',
# 'option': 0, 'maxlen': 0, 'comment': 'Modified Julian Day',
# 'keywords': {'QuantumUnits': ['s'], 'MEASINFO': {'type': 'epoch', 'Ref': 'UTC'}}}
otime = ms.getcol('TIME')
datacol = ms.getcol(datacolumn, nrow=1)
datacol_shape = list(datacol.shape)
channels = datacol.shape[-2]
log.debug("create_blockvisibility_from_ms: Found %d channels" % (channels))
if channum is None:
if start_chan is not None and end_chan is not None:
try:
log.debug("create_blockvisibility_from_ms: Reading channels from %d to %d" %
(start_chan, end_chan))
blc = [start_chan, 0]
trc = [end_chan, datacol_shape[-1] - 1]
channum = range(start_chan, end_chan+1)
ms_vis = ms.getcolslice(datacolumn, blc=blc, trc=trc)
ms_weight = ms.getcol('WEIGHT')
except IndexError:
raise IndexError("channel number exceeds max. within ms")
else:
log.debug("create_blockvisibility_from_ms: Reading all %d channels" % (channels))
try:
channum = range(channels)
ms_vis = ms.getcol(datacolumn)[:, channum, :]
ms_weight = ms.getcol('WEIGHT')
channum = range(channels)
except IndexError:
raise IndexError("channel number exceeds max. within ms")
else:
log.debug("create_blockvisibility_from_ms: Reading channels %s " % (channum))
channum = range(channels)
try:
ms_vis = ms.getcol(datacolumn)[:, channum, :]
ms_weight = ms.getcol('WEIGHT')[:, :]
except IndexError:
raise IndexError("channel number exceeds max. within ms")
uvw = -1 * ms.getcol('UVW')
antenna1 = ms.getcol('ANTENNA1')
antenna2 = ms.getcol('ANTENNA2')
integration_time = ms.getcol('INTERVAL')
# time = Time((time-integration_time/2.0)/86400+ 2400000.5,format='jd',scale='utc').utc.value
time = (otime - integration_time / 2.0)
start_time = numpy.min(time)/86400.0
end_time = numpy.max(time)/86400.0
log.debug("create_blockvisibility_from_ms: Observation from %s to %s" %
(Time(start_time, format='mjd').iso, Time(end_time, format='mjd').iso))
# Now get info from the subtables
spwtab = table('%s/SPECTRAL_WINDOW' % msname, ack=False)
cfrequency = spwtab.getcol('CHAN_FREQ')[dd][channum]
cchannel_bandwidth = spwtab.getcol('CHAN_WIDTH')[dd][channum]
nchan = cfrequency.shape[0]
# Get polarisation info
npol = 4
poltab = table('%s/POLARIZATION' % msname, ack=False)
corr_type = poltab.getcol('CORR_TYPE')
# These correspond to the CASA Stokes enumerations
if numpy.array_equal(corr_type[0], [1, 2, 3, 4]):
polarisation_frame = PolarisationFrame('stokesIQUV')
elif numpy.array_equal(corr_type[0], [5, 6, 7, 8]):
polarisation_frame = PolarisationFrame('circular')
elif numpy.array_equal(corr_type[0], [9, 10, 11, 12]):
polarisation_frame = PolarisationFrame('linear')
elif numpy.array_equal(corr_type[0], [9]):
npol = 1
polarisation_frame = PolarisationFrame('stokesI')
else:
raise KeyError("Polarisation not understood: %s" % str(corr_type))
# Get configuration
anttab = table('%s/ANTENNA' % msname, ack=False)
nants = anttab.nrows()
mount = anttab.getcol('MOUNT')
names = anttab.getcol('NAME')
diameter = anttab.getcol('DISH_DIAMETER')
xyz = anttab.getcol('POSITION')
configuration = Configuration(name='', data=None, location=None,
names=names, xyz=xyz, mount=mount, frame=None,
receptor_frame=ReceptorFrame("linear"),
diameter=diameter)
# Get phasecentres
fieldtab = table('%s/FIELD' % msname, ack=False)
pc = fieldtab.getcol('PHASE_DIR')[field, 0, :]
source = fieldtab.getcol('NAME')[field]
phasecentre = SkyCoord(ra=pc[0] * u.rad, dec=pc[1] * u.rad, frame='icrs', equinox='J2000')
time_index_row = numpy.zeros_like(time, dtype='int')
time_last = time[0]
time_index = 0
for row, _ in enumerate(time):
if time[row] > time_last + integration_time[row]:
assert time[row] > time_last, "MS is not time-sorted - cannot convert"
time_index += 1
time_last = time[row]
time_index_row[row] = time_index
ntimes = time_index + 1
bv_times = numpy.zeros([ntimes])
bv_vis = numpy.zeros([ntimes, nants, nants, nchan, npol]).astype('complex')
bv_weight = numpy.zeros([ntimes, nants, nants, nchan, npol])
bv_imaging_weight = numpy.zeros([ntimes, nants, nants, nchan, npol])
bv_uvw = numpy.zeros([ntimes, nants, nants, 3])
bv_integration_time = numpy.zeros([ntimes])
for row, _ in enumerate(time):
time_index = time_index_row[row]
bv_times[time_index] = time[row]
bv_vis[time_index, antenna2[row], antenna1[row], ...] = ms_vis[row, ...]
bv_weight[time_index, antenna2[row], antenna1[row], :, ...] = ms_weight[row, numpy.newaxis, ...]
bv_imaging_weight[time_index, antenna2[row], antenna1[row], :, ...] = ms_weight[row, numpy.newaxis, ...]
bv_uvw[time_index, antenna2[row], antenna1[row], :] = uvw[row, :]
bv_integration_time[time_index] = integration_time[row]
vis_list.append(BlockVisibility(uvw=bv_uvw,
time=bv_times,
frequency=cfrequency,
channel_bandwidth=cchannel_bandwidth,
vis=bv_vis,
weight=bv_weight,
integration_time = bv_integration_time,
imaging_weight=bv_imaging_weight,
configuration=configuration,
phasecentre=phasecentre,
polarisation_frame=polarisation_frame,
source=source, meta=meta))
tab.close()
return vis_list
def main(images,
regions,
colors,
labels,
shrdsfile=None,
fluxlimit=0.,
wisefile=None,
sigma=0.,
levels=[5., 10., 20., 50., 100.]):
"""
Plot some regions on top of some images with specified colors,
and create PDF.
Inputs:
images = list of fits image names to plot
regions = list of region filenames to plot
colors = what colors to plot each region
labels = label for regions
shrdsfile = if not None, path to SHRDS candidates data file
This will plot the SHRDS candidate regions on top
fluxlimit = only plot WISE regions brighter than this peak
continuum flux density (mJy/beam)
wisefile = if not None, path to WISE positions data file
This will plot the WISE regions on top
sigma = if > 0., will plot colormap with contours at
levels * sigma
levels = list of contour levels
Returns:
Nothing
"""
levels = np.array(levels)
outimages = []
for image in images:
#
# Open fits file, generate WCS
#
hdu = fits.open(image)[0]
wcs = WCS(hdu.header)
#
# Generate figure
#
plt.ioff()
fig = plt.figure()
wcs_celest = wcs.sub(['celestial'])
ax = plt.subplot(projection=wcs_celest)
ax.set_title(image.replace('.fits', ''))
# image
cax = ax.imshow(hdu.data[0, 0],
origin='lower',
interpolation='none',
cmap='viridis')
# contours
if sigma > 0.:
con = ax.contour(hdu.data[0, 0],
origin='lower',
levels=levels * sigma,
colors='k',
linewidths=0.2)
xlen, ylen = hdu.data[0, 0].shape
ax.coords[0].set_major_formatter('hh:mm:ss')
ax.set_xlabel('RA (J2000)')
ax.set_ylabel('Declination (J2000)')
#
# Adjust limits
#
ax.set_xlim(0.1 * xlen, 0.9 * xlen)
ax.set_ylim(0.1 * ylen, 0.9 * ylen)
#
# Plot colorbar
#
cbar = fig.colorbar(cax, fraction=0.046, pad=0.04)
cbar.set_label('Flux Density (Jy/beam)')
#
# Plot beam, if it is defined
#
pixsize = hdu.header['CDELT2'] # deg
if 'BMAJ' in hdu.header.keys():
beam_maj = hdu.header['BMAJ'] / pixsize # pix
beam_min = hdu.header['BMIN'] / pixsize # pix
beam_pa = hdu.header['BPA']
ellipse = Ellipse((1. / 8. * xlen, 1. / 8. * ylen),
beam_min,
beam_maj,
angle=beam_pa,
fill=True,
zorder=10,
hatch='///',
edgecolor='black',
facecolor='white')
ax.add_patch(ellipse)
#
# Plot regions
#
for reg, col, lab in zip(regions, colors, labels):
if not os.path.exists(reg):
continue
# read second line in region file
with open(reg, 'r') as f:
f.readline()
data = f.readline()
# handle point region
if 'ellipse' in data:
splt = data.split(' ')
RA = splt[1].replace('[[', '').replace(',', '')
RA_h, RA_m, RA_s = RA.split(':')
RA = '{0}h{1}m{2}s'.format(RA_h, RA_m, RA_s)
dec = splt[2].replace('],', '')
dec_d, dec_m, dec_s, dec_ss = dec.split('.')
dec = '{0}d{1}m{2}.{3}s'.format(dec_d, dec_m, dec_s,
dec_ss)
coord = SkyCoord(RA, dec)
ax.plot(coord.ra.value,
coord.dec.value,
'+',
color=col,
markersize=10,
transform=ax.get_transform('world'),
label=lab)
# handle point region
elif 'poly' in data:
splt = data.split('[[')[1]
splt = splt.split(']]')[0]
parts = splt.split(' ')
RAs = []
decs = []
for ind in range(0, len(parts), 2):
RA = parts[ind].replace('[', '').replace(',', '')
RA_h, RA_m, RA_s = RA.split(':')
RA = '{0}h{1}m{2}s'.format(RA_h, RA_m, RA_s)
dec = parts[ind + 1].replace('],', '')
dec_d, dec_m, dec_s, dec_ss = dec.split('.')
dec = '{0}d{1}m{2}.{3}s'.format(
dec_d, dec_m, dec_s, dec_ss)
coord = SkyCoord(RA, dec)
RAs.append(coord.ra.value)
decs.append(coord.dec.value)
RAs.append(RAs[0])
decs.append(decs[0])
ax.plot(RAs,
decs,
marker=None,
linestyle='solid',
color=col,
transform=ax.get_transform('world'),
label=lab,
zorder=110)
#
# Add regions legend
#
if len(regions) > 0:
region_legend = plt.legend(loc='upper right', fontsize=10)
ax.add_artist(region_legend)
#
# Plot SHRDS candidate regions
#
if shrdsfile is not None:
shrdsdata = np.genfromtxt(shrdsfile,
dtype=None,
delimiter=',',
encoding='UTF-8',
usecols=(0, 1, 4, 5, 6, 7),
skip_header=1,
names=('name', 'GName', 'RA', 'Dec',
'diameter', 'flux'))
RA = np.zeros(len(shrdsdata))
Dec = np.zeros(len(shrdsdata))
for i, dat in enumerate(shrdsdata):
parts = [float(part) for part in dat['RA'].split(':')]
RA[i] = 360. / 24. * (parts[0] + parts[1] / 60. +
parts[2] / 3600.)
parts = [float(part) for part in dat['Dec'].split(':')]
Dec[i] = np.abs(parts[0]) + parts[1] / 60. + parts[2] / 3600.
if '-' in dat['Dec']:
Dec[i] = -1. * Dec[i]
# limit only to regions with centers within image
corners = wcs_celest.calc_footprint()
min_RA = np.min(corners[:, 0])
max_RA = np.max(corners[:, 0])
RA_range = max_RA - min_RA
#min_RA += RA_range
#max_RA -= RA_range
min_Dec = np.min(corners[:, 1])
max_Dec = np.max(corners[:, 1])
Dec_range = max_Dec - min_Dec
#min_Dec += Dec_range
#max_Dec -= Dec_range
good = (min_RA < RA) & (RA < max_RA) & (min_Dec < Dec) & (
Dec < max_Dec) & (shrdsdata['flux'] > fluxlimit)
# plot them
shrdsdata = shrdsdata[good]
RA = RA[good]
Dec = Dec[good]
for R, D, dat in zip(RA, Dec, shrdsdata):
xpos, ypos = wcs_celest.wcs_world2pix(R, D, 1)
size = dat['diameter'] / 3600. / pixsize
ell = Ellipse((xpos, ypos),
size,
size,
color='m',
fill=False,
linestyle='dashed',
zorder=105)
ax.add_patch(ell)
ax.text(R,
D,
dat['GName'],
transform=ax.get_transform('world'),
fontsize=10,
zorder=105)
#
# Plot WISE regions
#
if wisefile is not None:
wisedata = np.genfromtxt(wisefile,
dtype=None,
names=True,
encoding='UTF-8')
# limit only to regions with centers within image
corners = wcs_celest.calc_footprint()
min_RA = np.min(corners[:, 0])
max_RA = np.max(corners[:, 0])
RA_range = max_RA - min_RA
#min_RA += RA_range
#max_RA -= RA_range
min_Dec = np.min(corners[:, 1])
max_Dec = np.max(corners[:, 1])
Dec_range = max_Dec - min_Dec
#min_Dec += Dec_range
#max_Dec -= Dec_range
good = (min_RA < wisedata['RA']) & (wisedata['RA'] < max_RA) & (
min_Dec < wisedata['Dec']) & (wisedata['Dec'] < max_Dec)
# plot them
wisedata = wisedata[good]
for dat in wisedata:
xpos, ypos = wcs_celest.wcs_world2pix(dat['RA'], dat['Dec'], 1)
size = dat['Size'] * 2. / 3600. / pixsize
ell = Ellipse((xpos, ypos),
size,
size,
color='y',
fill=False,
linestyle='dashed',
zorder=100)
ax.add_patch(ell)
ax.text(dat['RA'],
dat['Dec'],
dat['GName'],
transform=ax.get_transform('world'),
fontsize=10,
zorder=100)
#
# Add WISE+SHRDS legend
#
if shrdsfile is not None or wisefile is not None:
patches = []
if shrdsfile is not None:
ell = Ellipse((0, 0),
0.1,
0.1,
color='m',
fill=False,
linestyle='dashed',
label='SHRDS Candidates')
patches.append(ell)
if wisefile is not None:
ell = Ellipse((0, 0),
0.1,
0.1,
color='y',
fill=False,
linestyle='dashed',
label='WISE Catalog')
patches.append(ell)
wise_legend = plt.legend(handles=patches,
loc='lower right',
fontsize=10,
handler_map={Ellipse: HandlerEllipse()})
ax.add_artist(wise_legend)
#
# Re-scale to fit, then save
#
fig.savefig(image.replace('.fits', '.reg.pdf'), bbox_inches='tight')
plt.close(fig)
plt.ion()
outimages.append(image.replace('.fits', '.reg.pdf'))
def pointing_radec(self):
"""Pointing positions as ICRS (`~astropy.coordinates.SkyCoord`)."""
return SkyCoord(self["RA_PNT"], self["DEC_PNT"], unit="deg", frame="icrs")
#ratio = np.mean((1.-dead[ratio_mask])/(tec[ratio_mask]/fec[ratio_mask]))
ratio = np.median(1. - dead[ratio_mask]) / np.median(
tec[ratio_mask] / fec[ratio_mask])
print 'ratio:{0}'.format(ratio)
dead[fec_mask] = 1. - ratio * tec[fec_mask] / fec[fec_mask]
dead_tmp = dead[ix_tmp]
dead_list.append(dead_tmp)
cut_list.append(cut[ix_cut, 1])
#asp_old = np.load('../data/photon_list/%s_asp.npy'%name)
#sky_data = SkyCoord(asp_old[:,1:3], unit='deg', frame=FK5, equinox='J2000.0')
#calculate the image size and location
#asp_map = np.load('{0}/{1}{2}_asp.npy'.format(guide_path, name, guide_suffix))
asp_map = np.load('{0}/{1}_asp.npy'.format(guide_path, name))
sky_data = SkyCoord(asp_map[:, 1:3],
unit='deg',
frame=FK5,
equinox='J2000.0')
gal = sky_data.transform_to(Galactic)
asprta = np.concatenate(
(np.array([gal.l.deg]).T, np.array([gal.b.deg]).T), axis=1)
gal_l.append(np.mean(asprta[:, 0]))
asp_solution = np.concatenate(asp_solution_list, axis=0)
sky_data = SkyCoord(asp_solution[:, 1:3],
unit='deg',
frame=FK5,
equinox='J2000.0')
gal = sky_data.transform_to(Galactic)
asp_solution[:, 1:3] = np.concatenate(
(np.array([gal.l.deg]).T, np.array([gal.b.deg]).T), axis=1)
def handle_grb(v, pretend=False):
"""
Handles the actual VOEvent parsing, generating observations if appropriate.
:param v: string in VOEvent XML format
:param pretend: Boolean, True if we don't want to actually schedule the observations.
:return: None
"""
log.debug("processing GRB {0}".format(v.attrib['ivorn']))
# trigger = False
if 'SWIFT' in v.attrib['ivorn']:
# compute the trigger id
trig_id = "SWIFT_" + v.attrib['ivorn'].split('_')[-1].split('-')[0]
# #The following should never be hit because of the checks made in is_grb.
# grbid = v.find(".//Param[@name='GRB_Identified']").attrib['value']
# if grbid != 'true':
# log.debug("SWIFT alert but not a GRB")
# handlers.send_email(from_address='*****@*****.**',
# to_addresses=DEBUG_NOTIFY_LIST,
# subject='GRB_fermi_swift debug notification for trigger: %s' % trig_id,
# msg_text=DEBUG_EMAIL_TEMPLATE % "SWIFT alert but not a GRB",
# attachments=[('voevent.xml', voeventparse.dumps(v))])
#
# return
log.debug("SWIFT GRB trigger detected")
this_trig_type = "SWIFT"
# If the star tracker looses it's lock then we can't trust any of the locations so we ignore this alert.
startrack_lost_lock = v.find(
".//Param[@name='StarTrack_Lost_Lock']").attrib['value']
# convert 'true' to True, and everything else to false
startrack_lost_lock = startrack_lost_lock.lower() == 'true'
log.debug("StarLock OK? {0}".format(not startrack_lost_lock))
if startrack_lost_lock:
log.debug("The SWIFT star tracker lost it's lock")
handlers.send_email(
from_address='*****@*****.**',
to_addresses=DEBUG_NOTIFY_LIST,
subject='GRB_fermi_swift debug notification for trigger: %s' %
trig_id,
msg_text=DEBUG_EMAIL_TEMPLATE %
"SWIFT alert for GRB, but with StarTrack_Lost_Lock",
attachments=[('voevent.xml', voeventparse.dumps(v))])
return
# cache the event using the trigger id
if trig_id not in xml_cache:
grb = GRB(event=v)
grb.trigger_id = trig_id
xml_cache[trig_id] = grb
else:
grb = xml_cache[trig_id]
grb.add_event(v)
trig_time = float(
v.find(".//Param[@name='Integ_Time']").attrib['value'])
if trig_time < LONG_SHORT_LIMIT:
grb.debug("Probably a short GRB: t={0} < 2".format(trig_time))
grb.short = True
grb.vcsmode = SWIFT_SHORT_TRIGGERS_IN_VCSMODE
trigger = True
else:
grb.debug("Probably a long GRB: t={0} > 2".format(trig_time))
grb.short = False
grb.vcsmode = SWIFT_LONG_TRIGGERS_IN_VCSMODE
trigger = True
elif "Fermi" in v.attrib['ivorn']:
log.debug("Fermi GRB notice detected")
# cache the event using the trigger id
trig_id = "Fermi_" + v.attrib['ivorn'].split('_')[-2]
this_trig_type = v.attrib['ivorn'].split('_')[1] # Flt, Gnd, or Fin
if trig_id not in xml_cache:
grb = GRB(event=v)
grb.trigger_id = trig_id
xml_cache[trig_id] = grb
else:
grb = xml_cache[trig_id]
grb.add_event(v)
# Not all alerts have trigger times.
# eg Fermi#GBM_Gnd_Pos
if this_trig_type == 'Flt':
trig_time = float(
v.find(".//Param[@name='Trig_Timescale']").attrib['value'])
if trig_time < LONG_SHORT_LIMIT:
grb.short = True
grb.debug("Possibly a short GRB: t={0}".format(trig_time))
else:
msg = "Probably not a short GRB: t={0}".format(trig_time)
grb.debug(msg)
grb.debug("Not Triggering")
handlers.send_email(
from_address='*****@*****.**',
to_addresses=DEBUG_NOTIFY_LIST,
subject='GRB_fermi_swift debug notification for trigger: %s'
% trig_id,
msg_text=DEBUG_EMAIL_TEMPLATE %
'\n'.join([str(x) for x in grb.loglist]),
attachments=[('voevent.xml', voeventparse.dumps(v))])
return # don't trigger
most_likely = int(
v.find(".//Param[@name='Most_Likely_Index']").attrib['value'])
# ignore things that don't have GRB as best guess
if most_likely == 4:
grb.debug("MOST_LIKELY = GRB")
prob = int(
v.find(
".//Param[@name='Most_Likely_Prob']").attrib['value'])
# ignore things that don't reach our probability threshold
if prob > FERMI_POBABILITY_THRESHOLD:
grb.debug("Prob(GRB): {0}% > {1}".format(
prob, FERMI_POBABILITY_THRESHOLD))
trigger = True
else:
msg = "Prob(GRB): {0}% <{1}".format(
prob, FERMI_POBABILITY_THRESHOLD)
grb.debug(msg)
grb.debug("Not Triggering")
handlers.send_email(
from_address='*****@*****.**',
to_addresses=DEBUG_NOTIFY_LIST,
subject=
'GRB_fermi_swift debug notification for trigger: %s' %
trig_id,
msg_text=DEBUG_EMAIL_TEMPLATE %
'\n'.join([str(x) for x in grb.loglist]),
attachments=[('voevent.xml', voeventparse.dumps(v))])
return
else:
msg = "MOST_LIKELY != GRB"
grb.debug(msg)
grb.debug("Not Triggering")
handlers.send_email(
from_address='*****@*****.**',
to_addresses=DEBUG_NOTIFY_LIST,
subject='GRB_fermi_swift debug notification for trigger: %s'
% trig_id,
msg_text=DEBUG_EMAIL_TEMPLATE %
'\n'.join([str(x) for x in grb.loglist]),
attachments=[('voevent.xml', voeventparse.dumps(v))])
return
else:
# for Gnd/Fin we trigger if we already triggered on the Flt position
grb.debug("Gnd/Flt message -> reverting to Flt trigger")
trigger = grb.triggered
else:
msg = "Not a Fermi or SWIFT GRB."
log.debug(msg)
log.debug("Not Triggering")
handlers.send_email(from_address='*****@*****.**',
to_addresses=DEBUG_NOTIFY_LIST,
subject='GRB_fermi_swift debug notification',
msg_text=DEBUG_EMAIL_TEMPLATE % msg,
attachments=[('voevent.xml', voeventparse.dumps(v))
])
return
if not trigger:
grb.debug("Not Triggering")
handlers.send_email(
from_address='*****@*****.**',
to_addresses=DEBUG_NOTIFY_LIST,
subject='GRB_fermi_swift debug notification for trigger: %s' %
trig_id,
msg_text=DEBUG_EMAIL_TEMPLATE %
'\n'.join([str(x) for x in grb.loglist]),
attachments=[('voevent.xml', voeventparse.dumps(v))])
return
# get current position
ra, dec, err = handlers.get_position_info(v)
# add it to the list of positions
grb.add_pos((ra, dec, err))
grb.debug("RA {0}, Dec {1}, err {2}".format(ra, dec, err))
if not grb.vcsmode:
req_time_min = 30
else:
grb.debug('Reducing request time to %d for VCS observation' %
SWIFT_SHORT_VCS_TIME)
req_time_min = SWIFT_SHORT_VCS_TIME
# check repointing just for tests
# last_pos = grb.get_pos(-2)
# if None not in last_pos:
# grb.info("Old position: RA {0}, Dec {1}, err {2}".format(*last_pos))
#
# pos_diff = SkyCoord(ra=last_pos[0], dec=last_pos[1], unit=astropy.units.degree, frame='icrs').separation(
# SkyCoord(ra=ra, dec=dec, unit=astropy.units.degree, frame='icrs')).degree
# if pos_diff < REPOINTING_LIMIT:
# grb.info("New position is {0} deg from previous (less than constraint of {1} deg)".format(pos_diff,
# REPOINTING_LIMIT))
# grb.info("Not triggering")
# handlers.send_email(from_address='*****@*****.**',
# to_addresses=DEBUG_NOTIFY_LIST,
# subject='GRB_fermi_swift debug notification',
# msg_text=DEBUG_EMAIL_TEMPLATE % '\n'.join([str(x) for x in grb.loglist]),
# attachments=[('voevent.xml', voeventparse.dumps(v))])
# return
# else:
# grb.info("New position is {0} deg from previous (greater than constraint of {1} deg".format(pos_diff,
# REPOINTING_LIMIT))
# grb.info("Attempting trigger")
# end tests
# look at the schedule
obslist = triggerservice.obslist(obstime=1800)
if obslist is not None and len(obslist) > 0:
grb.debug("Currently observing:")
grb.debug(str(obslist))
# are we currently observing *this* GRB?
obs = str(
obslist[0][1]) # in case the obslist is returning unicode strings
obs_group_id = obslist[0][
5] # The group ID of the first observation in the list returned
grb.debug("obs {0}, trig {1}".format(obs, trig_id))
# Same GRB trigger from same telescope
if trig_id in obs:
# if obs == trig_id:
# update the schedule!
grb.info("Already observing this GRB")
last_pos = grb.get_pos(-2)
grb.info(
"Old position: RA {0}, Dec {1}, err {2}".format(*last_pos))
pos_diff = SkyCoord(ra=last_pos[0],
dec=last_pos[1],
unit=astropy.units.degree,
frame='icrs').separation(
SkyCoord(ra=ra,
dec=dec,
unit=astropy.units.degree,
frame='icrs')).degree
grb.info("New position is {0} deg from previous".format(pos_diff))
if pos_diff < REPOINTING_LIMIT:
grb.info("(less than constraint of {0} deg)".format(
REPOINTING_LIMIT))
grb.info("Not triggering")
handlers.send_email(
from_address='*****@*****.**',
to_addresses=DEBUG_NOTIFY_LIST,
subject='GRB_fermi_swift debug notification',
msg_text=DEBUG_EMAIL_TEMPLATE %
'\n'.join([str(x) for x in grb.loglist]),
attachments=[('voevent.xml', voeventparse.dumps(v))])
return
grb.info(
"(greater than constraint of {0}deg)".format(REPOINTING_LIMIT))
if "SWIFT" in trig_id:
grb.info("Updating SWIFT observation with new coords")
pass
elif "Fermi" in trig_id:
prev_type = grb.last_trig_type
if this_trig_type == 'Flt' and (prev_type in ['Gnd', 'Fin']):
msg = "{0} positions have precedence over {1}".format(
prev_type, this_trig_type)
grb.info(msg)
grb.info("Not triggering")
handlers.send_email(
from_address='*****@*****.**',
to_addresses=DEBUG_NOTIFY_LIST,
subject=
'GRB_fermi_swift debug notification for trigger: %s' %
trig_id,
msg_text=DEBUG_EMAIL_TEMPLATE %
'\n'.join([str(x) for x in grb.loglist]),
attachments=[('voevent.xml', voeventparse.dumps(v))])
return
elif this_trig_type == 'Gnd' and prev_type == 'Fin':
msg = "{0} positions have precedence over {1}".format(
prev_type, this_trig_type)
grb.info(msg)
grb.info("Not triggering")
handlers.send_email(
from_address='*****@*****.**',
to_addresses=DEBUG_NOTIFY_LIST,
subject=
'GRB_fermi_swift debug notification for trigger: %s' %
trig_id,
msg_text=DEBUG_EMAIL_TEMPLATE %
'\n'.join([str(x) for x in grb.loglist]),
attachments=[('voevent.xml', voeventparse.dumps(v))])
return
else:
grb.info("Triggering {0} to replace {1}".format(
this_trig_type, prev_type))
# shorten the observing time requested so we are ~30mins total (for non VCS).
# If this is a VCS mode observation, don't shorten the time - if the previous trigger was
# in VCS mode, we won't be able to interrupt it, and if it wasn't, we still want the normal
# length of a VCS trigger.
if (grb.first_trig_time is not None) and not grb.vcsmode:
req_time_min = 30 - (Time.now() -
grb.first_trig_time).sec // 60
grb.debug('Set requested time to %d' % req_time_min)
# if we are observing a SWIFT trigger but not the trigger we just received
elif 'SWIFT' in obs:
if "SWIFT" in trig_id:
if obs in xml_cache:
prev_short = xml_cache[obs].short
else:
prev_short = False # best bet if we don't know
grb.info("Curently observing a SWIFT trigger")
if grb.short and not prev_short:
grb.info("Interrupting with a short SWIFT GRB")
else:
grb.info("Not interrupting previous observation")
handlers.send_email(
from_address='*****@*****.**',
to_addresses=DEBUG_NOTIFY_LIST,
subject=
'GRB_fermi_swift debug notification for trigger: %s' %
trig_id,
msg_text=DEBUG_EMAIL_TEMPLATE %
'\n'.join([str(x) for x in grb.loglist]),
attachments=[('voevent.xml', voeventparse.dumps(v))])
return
else:
grb.info("Not interrupting previous obs")
handlers.send_email(
from_address='*****@*****.**',
to_addresses=DEBUG_NOTIFY_LIST,
subject='GRB_fermi_swift debug notification for trigger: %s'
% trig_id,
msg_text=DEBUG_EMAIL_TEMPLATE %
'\n'.join([str(x) for x in grb.loglist]),
attachments=[('voevent.xml', voeventparse.dumps(v))])
return
# if we are observing a FERMI trigger but not the trigger we just received
elif 'Fermi' in obs:
# SWIFT > Fermi
if "SWIFT" in trig_id:
grb.info("Replacing a Fermi trigger with a SWIFT trigger")
else:
grb.info(
"Currently observing a different Fermi trigger, not interrupting"
)
handlers.send_email(
from_address='*****@*****.**',
to_addresses=DEBUG_NOTIFY_LIST,
subject='GRB_fermi_swift debug notification for trigger: %s'
% trig_id,
msg_text=DEBUG_EMAIL_TEMPLATE %
'\n'.join([str(x) for x in grb.loglist]),
attachments=[('voevent.xml', voeventparse.dumps(v))])
return
else:
grb.info("Not currently observing any GRBs")
else:
grb.debug("Current schedule empty")
emaildict = {
'triggerid':
grb.trigger_id,
'trigtime':
Time.now().iso,
'ra':
Angle(grb.ra[-1],
unit=astropy.units.deg).to_string(unit=astropy.units.hour,
sep=':'),
'dec':
Angle(grb.dec[-1],
unit=astropy.units.deg).to_string(unit=astropy.units.deg,
sep=':'),
'err':
grb.err[-1]
}
email_text = EMAIL_TEMPLATE % emaildict
email_subject = EMAIL_SUBJECT_TEMPLATE % grb.trigger_id
# Do the trigger
result = grb.trigger_observation(
ttype=this_trig_type,
obsname=trig_id,
time_min=req_time_min,
pretend=pretend,
project_id=PROJECT_ID,
secure_key=SECURE_KEY,
email_tolist=NOTIFY_LIST,
email_text=email_text,
email_subject=email_subject,
creator='VOEvent_Auto_Trigger: GRB_Fermi_swift=%s' % __version__,
voevent=voeventparse.dumps(v))
if result is None:
handlers.send_email(
from_address='*****@*****.**',
to_addresses=DEBUG_NOTIFY_LIST,
subject='GRB_fermi_swift debug notification for trigger: %s' %
trig_id,
msg_text=DEBUG_EMAIL_TEMPLATE %
'\n'.join([str(x) for x in grb.loglist]),
attachments=[('voevent.xml', voeventparse.dumps(v))])
def pointing_galactic(self):
"""Pointing positions as Galactic (`~astropy.coordinates.SkyCoord`)."""
return SkyCoord(
self["GLON_PNT"], self["GLAT_PNT"], unit="deg", frame="galactic"
)
def get_trilegal(filename,
ra,
dec,
folder='.',
galactic=False,
filterset='kepler_2mass',
area=1,
magnum=1,
maglim=27,
binaries=False,
trilegal_version='1.7',
sigma_AV=0.1):
"""
Calls the TRILEGAL web form simulation and downloads the file.
Parameters
----------
filename : string
Output filename. If extension not provided, it will be added.
ra : float
Coordinate for line-of-sight simulation.
dec : float
Coordinate for line-of-sight simulation.
folder : string, optional
Folder to which to save file.
filterset : string, optional
Filter set for which to call TRILEGAL.
area : float, optional
Area of TRILEGAL simulation [sq. deg]
magnum : integer, optional
Bandpass number to limit source magnitudes in.
maglim : integer, optional
Limiting magnitude in ``magnum`` bandpass of the ``filterset``.
binaries : boolean, optional
Whether to have TRILEGAL include binary stars. Default ``False``.
trilegal_version : float, optional
Version of the TRILEGAL API to call. Default ``'1.7'``.
sigma_AV : float, optional
Fractional spread in A_V along the line of sight.
"""
if galactic:
l, b = ra, dec
else:
try:
c = SkyCoord(ra, dec)
except UnitsError:
c = SkyCoord(ra, dec, unit='deg')
l, b = (c.galactic.l.value, c.galactic.b.value)
if os.path.isabs(filename):
folder = ''
if not re.search(r'\.dat$', filename):
outfile = '{}/{}.dat'.format(folder, filename)
else:
outfile = '{}/{}'.format(folder, filename)
AV = get_AV_infinity(l, b, frame='galactic')
trilegal_webcall(trilegal_version, l, b, area, binaries, AV, sigma_AV,
filterset, magnum, maglim, outfile)
return AV
def select_observations(self, selection=None):
"""Select subset of observations.
Returns a new observation table representing the subset.
There are 3 main kinds of selection criteria, according to the
value of the **type** keyword in the **selection** dictionary:
- sky regions
- time intervals (min, max)
- intervals (min, max) on any other parameter present in the
observation table, that can be casted into an
`~astropy.units.Quantity` object
Allowed selection criteria are interpreted using the following
keywords in the **selection** dictionary under the **type** key.
- ``sky_circle`` is a circular region centered in the coordinate
marked by the **lon** and **lat** keywords, and radius **radius**;
uses `~gammapy.catalog.select_sky_circle`
- ``time_box`` is a 1D selection criterion acting on the observation
start time (**TSTART**); the interval is set via the
**time_range** keyword; uses
`~gammapy.data.ObservationTable.select_time_range`
- ``par_box`` is a 1D selection criterion acting on any
parameter defined in the observation table that can be casted
into an `~astropy.units.Quantity` object; the parameter name
and interval can be specified using the keywords **variable** and
**value_range** respectively; min = max selects exact
values of the parameter; uses
`~gammapy.data.ObservationTable.select_range`
In all cases, the selection can be inverted by activating the
**inverted** flag, in which case, the selection is applied to keep all
elements outside the selected range.
A few examples of selection criteria are given below.
Parameters
----------
selection : dict
Dictionary with a few keywords for applying selection cuts.
Returns
-------
obs_table : `~gammapy.data.ObservationTable`
Observation table after selection.
Examples
--------
>>> selection = dict(type='sky_circle', frame='galactic',
... lon=Angle(0, 'deg'),
... lat=Angle(0, 'deg'),
... radius=Angle(5, 'deg'),
... border=Angle(2, 'deg'))
>>> selected_obs_table = obs_table.select_observations(selection)
>>> selection = dict(type='time_box',
... time_range=Time(['2012-01-01T01:00:00', '2012-01-01T02:00:00']))
>>> selected_obs_table = obs_table.select_observations(selection)
>>> selection = dict(type='par_box', variable='ALT',
... value_range=Angle([60., 70.], 'deg'))
>>> selected_obs_table = obs_table.select_observations(selection)
>>> selection = dict(type='par_box', variable='OBS_ID',
... value_range=[2, 5])
>>> selected_obs_table = obs_table.select_observations(selection)
>>> selection = dict(type='par_box', variable='N_TELS',
... value_range=[4, 4])
>>> selected_obs_table = obs_table.select_observations(selection)
"""
if "inverted" not in selection:
selection["inverted"] = False
if "partial_overlap" not in selection:
selection["partial_overlap"] = False
if selection["type"] == "sky_circle":
lon = Angle(selection["lon"], "deg")
lat = Angle(selection["lat"], "deg")
radius = Angle(selection["radius"])
if "border" in selection:
border = Angle(selection["border"])
else:
border = Angle(0, "deg")
region = SphericalCircleSkyRegion(
center=SkyCoord(lon, lat, frame=selection["frame"]),
radius=radius + border,
)
mask = region.contains(self.pointing_radec)
if selection["inverted"]:
mask = np.invert(mask)
return self[mask]
elif selection["type"] == "time_box":
return self.select_time_range(
selection["time_range"],
selection["partial_overlap"],
selection["inverted"],
)
elif selection["type"] == "par_box":
return self.select_range(
selection["variable"], selection["value_range"], selection["inverted"]
)
else:
raise ValueError(f"Invalid selection type: {selection['type']}")
def skycoord(self):
return SkyCoord(self.lon,
self.lat,
unit="deg",
frame=coordsys_to_frame(self.coordsys))
def batss_pointing_detect(obs_id, #should be BATSS_slew object?
ra, dec, # Source RA/Dec
eband_name, # Source energy band
err_rad): # Source error radius (arcmin, 90%)
'''
Run imaging and detection for BAT pointings before and after a given slew,
for given sky coordinates and energy band
'''
#Check input parameters
obs = [] # Initialize observation list
if isinstance(obs_id, list):
for obs_id0 in obs_id:
obs.append(BATSS_slew(obs_id0))
else:
obs.append(BATSS_slew(obs_id))
pos = SkyCoord(ra, dec, unit='deg')
coord_str = ('J'+pos.ra.to_string(unit='hour',pad=True,sep='',fields=2)
+(10*pos.dec).to_string(pad=True,sep='',fields=1,alwayssign=True))
coord_str_tex = coord_str[:5]+'$'+coord_str[5]+'$'+coord_str[6:] # TeX
eband = BATSS_eband(eband_name)
err_rad = err_rad * u.arcmin
# Input/Output directories
root = BATSS_dir.root
dataroot = './data/'
if not os.path.exists(dataroot):
os.makedirs(dataroot)
# Loop over BATSS observations
for obs0 in obs:
t0 = datetime.now()
##Time object with slew date
#obs_date = Time('20'+obs0.id[:2]+'-'+obs0.id[2:4]+'-'+obs0.id[4:6])
obs_date = datetime(int('20'+obs0.id[:2]), int(obs0.id[2:4]), int(obs0.id[4:6]))
print(f'{70*"="} {datetime.now():%c}')
print('BATSS Observation type and ID: ', obs0.type.upper(), obs0.id)
print('Coordinates to search (J2000): ',pos.to_string('hmsdms'))
print('Energy band: '+eband.name+' ('+eband.str_keV+')')
# Output directories
datadir = dataroot+obs0.type+'_'+obs0.id+'_'+coord_str+ '_'+eband.name+'/'
if not os.path.exists(datadir):
os.makedirs(datadir)
tempdir = datadir+'temp/'
if not os.path.exists(tempdir):
os.makedirs(tempdir)
# Initialize output txt file
txtfile = datadir+obs0.type+'_'+obs0.id+'_'+coord_str+ '_'+eband.name+'.txt'
f = open(txtfile, 'w')
f.write(f'{70*"="} {datetime.now():%c}\n')
f.write('BATSS Observation type and ID: '+obs0.type.upper()+' '+obs0.id+'\n')
f.write('Coordinates to search (J2000): '+pos.to_string('hmsdms')+'\n')
f.write('Energy band: '+eband.name+' ('+eband.str_keV+')'+'\n')
f.close()
#Input catalog file
catfile_in = tempdir+'batss.in.cat'
# CATNUM: Source number within catalog
# NAME: Source name
# RA_CAT/GLON_CAT: Catalogued source longitude
# DEC_CAT/GLAT_CAT: Catalogued source latitude
# RA_OBJ/GLON_OBJ: Source longitude (to be modified upon detection)
# DEC_OBJ/GLAT_OBJ: Source latitude (to be modified upon detection)
# ERR_RAD_BATSS: BATSS error radius (90%, deg)
cat_in_Table = Table(
{'CATNUM':[0],
'NAME':['BATSS_'+coord_str],
'RA_OBJ':[pos.ra.value] * pos.ra.unit,
'DEC_OBJ':[pos.dec.value] * pos.dec.unit,
'RA_CAT':[pos.ra.value] * pos.ra.unit,
'DEC_CAT':[pos.dec.value] * pos.ra.unit,
'ERR_RAD_BATSS':[err_rad.to_value(u.deg)] * u.deg},
names=('CATNUM','NAME','RA_OBJ','DEC_OBJ','RA_CAT','DEC_CAT',
'ERR_RAD_BATSS')) #Specifies column order
cat_in = fits.BinTableHDU(cat_in_Table, name='BAT_CATALOG')
cat_in.header.set('HDUNAME', 'BAT_CATALOG', 'Name of extension',
before='TTYPE1') #Necessary?
cat_in.header.set('HDUCLASS', 'CATALOG', 'Source catalog',
before='TTYPE1')
cat_in.header.comments['TTYPE1'] = 'Source number within catalog'
cat_in.header.set('TNULL1', -1, 'data null value', after='TFORM1')
cat_in.header.comments['TTYPE2'] = 'Source name'
cat_in.header.comments['TTYPE3'] = 'Detected source longitude'
cat_in.header.comments['TUNIT3'] = 'physical unit of field'
cat_in.header.set('TDISP3', 'F10.4', 'column display format',
after='TUNIT3')
cat_in.header.comments['TTYPE4'] = 'Detected source latitude'
cat_in.header.comments['TUNIT4'] = 'physical unit of field'
cat_in.header.set('TDISP4', 'F10.4', 'column display format',
after='TUNIT4')
cat_in.header.comments['TTYPE5'] = 'Catalogued source longitude'
cat_in.header.comments['TUNIT5'] = 'physical unit of field'
cat_in.header.set('TDISP5', 'F10.4', 'column display format',
after='TUNIT5')
cat_in.header.comments['TTYPE6'] = 'Catalogued source latitude'
cat_in.header.comments['TUNIT6'] = 'physical unit of field'
cat_in.header.set('TDISP6', 'F10.4', 'column display format',
after='TUNIT6')
cat_in.header.comments['TTYPE7'] = 'BATSS cat_in. error radius (90%)'
cat_in.header.comments['TUNIT7'] = 'physical unit of field'
cat_in.header.set('TDISP7', 'F6.4', 'column display format',
after='TUNIT7')
cat_in.writeto(catfile_in, overwrite=True)
# Get master FITS header for slew (archival by default)
flag_realtime = False
if os.path.exists(obs0.fitsfile):
hdrfile = obs0.fitsfile
hdrext = 0
else:
print('Warning: No archival master FITS file found for'
f' {obs0.type} {obs0.id}. Getting header info from queue file.')
if os.path.exists(obs0.queuefile):
hdrfile = obs0.queuefile
hdrext = obs0.type+'_'+obs0.id
else:
print('Warning: No archival queue file found for'
f' {obs0.type} {obs0.id}. Getting header info from'
f' real-time data')
flag_realtime = True
if os.path.exists(obs0.fitsfile_realtime):
hdrfile = obs0.fitsfile_realtime
hdrext = 0
else:
print('Warning: No real-time master FITS file found for'
f' {obs0.type} {obs0.id}. Getting header info from'
f' queue file.')
if os.path.exists(obs0.queuefile_realtime):
hdrfile = obs0.queuefile_realtime
hdrext = obs0.type+'_'+obs0.id
else:
raise IOError('Neither archival nor real-time files'
f' found for {obs0.type} {obs0.id}')
#fitsfile = obs0.fitsfile_realtime if flag_realtime else obs0.fitsfile
print('Header file: '+hdrfile)
print('Extension:')
print(hdrext)
try:
header = fits.getheader(hdrfile, hdrext)
except IOError as err:
raise IOError(err)
except:
print('Some other error! (hdrfile)')
# Partial coding map
pcfile = obs0.pcfile_realtime if flag_realtime else obs0.pcfile
try:
if not os.path.exists(pcfile):
# Try getting default partial coding map
print('Warning: Partial coding file ('
+('realtime' if flag_realtime else 'archival')
+') does not exist. Reading from default file.')
pcfile = BAT_pcfile_def()
print('Partial coding map file: '+pcfile)
pcmap, pchdr = fits.getdata(pcfile, header=True)
except IOError:
raise
except:
print('Some other error! (pcfile)')
else:
dims_pcmap = np.shape(pcmap)
# Attitude file
attfile = obs0.attfile_realtime if flag_realtime else obs0.attfile
try:
if not os.path.exists(attfile):
raise IOError('Attitude file ('+('realtime' if flag_realtime else 'archival')+') does not exist')
att = fits.getdata(obs0.attfile, 1)
except IOError:
raise
else:
flag_settled = 192 # (binary) FLAGS field for settled spacecraft
# Get time windows for preceding and following pointings
obs_t0 = header['BEG_SLEW'] #[MET]
gti_pre = {'start':0, 'stop':header['BEG_SLEW']} #[MET]
gti_pre_sod = {'start':0, 'stop':int(obs0.id[7:9])*3600 + int(obs0.id[10:12])*60 + int(obs0.id[13:15])} #[SOD]
gti_pos = {'start':header['END_SLEW'], 'stop':0} #[MET]
gti_pos_sod = {'start':gti_pre_sod['stop'] + int(obs0.id[17:20]), 'stop':0} #[SOD]
queuefile = obs0.queuefile_realtime if flag_realtime else obs0.queuefile
try:
with fits.open(queuefile) as queue_hdul:
w = np.array([hdu.name == 'SLEW_'+obs0.id for hdu in queue_hdul]).nonzero()[0]
assert len(w) == 1
except IOError:
raise
else:
w = w[0]
# Beginning of preceding pointing
if w == 1:
# Get slew from previous day
date_pre = obs_date - timedelta(days=1)
queuefile_pre = root + f'products/{date_pre.year:04}_{date_pre.month:02}/queue{"_realtime" if flag_realtime else ""}/queue_{date_pre.year % 100:02}{date_pre.month:02}{date_pre.day:02}_{obs0.type}.fits'
try:
with fits.open(queuefile_pre) as queue_pre_hdul:
wpre = len(queue_pre_hdul)
gti_pre_sod['start'] = -86400
except OSError:
print('File not found: '+queuefile_pre)
raise
else:
queuefile_pre = queuefile
queue_pre_hdul = queue_hdul
wpre = w-1
slew_id_pre = queue_pre_hdul[wpre].name[5:]
gti_pre['start'] = fits.getval(queuefile_pre, 'END_SLEW', ext=wpre)
gti_pre_sod['start'] += int(slew_id_pre[7:9])*3600 + int(slew_id_pre[10:12])*60 + int(slew_id_pre[13:15]) + int(slew_id_pre[17:20])
# End of following pointing
if w == len(queue_hdul):
# Get slew from following day
date_pre = obs_date + timedelta(days=1)
queuefile_pos = root + f'products/{date_pos.year:04}_{date_pos.month:02}/queue{"_realtime" if flag_realtime else ""}/queue_{date_pos.year % 100:02}{date_pos.month:02}{date_pos.day:02}_{obs0.type}.fits'
try:
with fits.open(queuefile_pos) as queue_pos_hdul:
wpos = len(queue_pos_hdul)
gti_pos_sod['stop'] = 86400
except OSError:
print('File not found: '+queuefile_pos)
raise
else:
queuefile_pos = queuefile
queue_pos_hdul = queue_hdul
wpos = w+1
slew_id_pos = queue_pos_hdul[wpos].name[5:]
gti_pos['stop'] = fits.getval(queuefile_pos, 'BEG_SLEW', ext=wpos)
gti_pos_sod['stop'] += (int(slew_id_pos[7:9])*3600 +
int(slew_id_pos[10:12])*60 + int(slew_id_pos[13:15]))
# Read AFST files for previous, current and following days
afst_obs_id = []
afst_yymmdd = []
afst_start_sod = []
afst_stop_sod = []
for d in [-1,0,1]:
date0 = obs_date + timedelta(days=d)
yymmdd = f'{date0.year % 100:02}{date0.month:02}{date0.day:02}'
afstfile = (root + f'products/{date0.year:04}_{date0.month:02}/'
f'afst/afst_{date0.year % 100:02}{date0.month:02}'
f'{date0.day:02}.html')
try:
with open(afstfile,'r') as f0:
afst_soup = BeautifulSoup(f0, features='lxml')
except OSError:
raise
tr = afst_soup.find_all('tr') #, features='lxml')
for tr0 in tr:
try:
afst_class = tr0['class'][0]
except KeyError:
continue
if afst_class == 'header':
continue
td0 = tr0.find_all('td')
start0 = td0[0].get_text(strip=True)
start_sod0 = (datetime(int(start0[:4]), int(start0[5:7]), int(start0[8:10])) - obs_date).days*86400 + int(start0[11:13])*3600 + int(start0[14:16])*60 + int(start0[17:19])
stop0 = td0[1].get_text(strip=True)
stop_sod0 = (datetime(int(stop0[:4]), int(stop0[5:7]), int(stop0[8:10])) - obs_date).days*86400 + int(stop0[11:13])*3600 + int(stop0[14:16])*60 + int(stop0[17:19])
afst_obs_id.append(td0[2].a.text.zfill(8) + td0[3].a.text.zfill(3))
afst_yymmdd.append(yymmdd)
afst_start_sod.append(start_sod0)
afst_stop_sod.append(stop_sod0)
point = Table({'obs_id':afst_obs_id, 'yymmdd':afst_yymmdd, 'start_sod':afst_start_sod, 'stop_sod':afst_stop_sod})
del afst_obs_id, afst_yymmdd, afst_start_sod, afst_stop_sod
# Get Observation IDs for preceding and following pointings
dt_pre = point['stop_sod'].clip(max=gti_pre_sod['stop']) - point['start_sod'].clip(min=gti_pre_sod['start'])
upre = np.argmax(dt_pre)
dt_pre = dt_pre[upre]
assert dt_pre > 0
obs_id_pre = point[upre]['obs_id']
yymmdd_pre = point[upre]['yymmdd']
dt_pos = point['stop_sod'].clip(max=gti_pos_sod['stop']) - point['start_sod'].clip(min=gti_pos_sod['start'])
upos = np.argmax(dt_pos)
dt_pos = dt_pos[upos]
assert dt_pos > 0
obs_id_pos = point[upos]['obs_id']
yymmdd_pos = point[upos]['yymmdd']
del point
# Save GTI files for preceding and following pointings
gtifile_pre = tempdir+obs0.type+'_'+obs0.id+'_pre.gti'
gti_pre_Table = Table({'START':[gti_pre['start']] * u.s, 'STOP':[gti_pre['stop']] * u.s}, names=('START','STOP'))
gtihdr_pre = BATSS_gtihdr(gti_pre_Table)
hdu_pre = fits.BinTableHDU(gti_pre_Table, header=gtihdr_pre)
hdu_pre.writeto(gtifile_pre, overwrite=True)
gtifile_pos = tempdir+obs0.type+'_'+obs0.id+'_pos.gti'
gti_pos_Table = Table({'START':[gti_pos['start']] * u.s, 'STOP':[gti_pos['stop']] * u.s}, names=('START','STOP'))
gtihdr_pos = BATSS_gtihdr(gti_pos_Table)
hdu_pos = fits.BinTableHDU(gti_pos_Table, header=gtihdr_pos)
hdu_pos.writeto(gtifile_pos, overwrite=True)
# Perform BATSURVEY analysis on preceding and following pointings
obs0.src_name = 'BATSS '+coord_str # Include BATSS source name
obs0.src_name_tex = 'BATSS '+coord_str_tex # TeX formatted
obs0.eband = eband
for flag_pre in [True, False]:
print(f'{70*"="} {datetime.now():%c}')
f = open(txtfile, 'a')
if flag_pre:
print('PRECEDING POINTING. ',end='')
f.write(f'\n{95*"="}\nPRECEDING POINTING. ')
prefix = 'pre'
gtifile = gtifile_pre
obs_id = obs_id_pre
yymmdd_point = yymmdd_pre
else:
print('FOLLOWING POINTING. ',end='')
f.write(f'\n{95*"="}\nFOLLOWING POINTING. ')
prefix = 'pos'
gtifile = gtifile_pos
obs_id = obs_id_pos
yymmdd_point = yymmdd_pos
yyyy_mm_point = '20'+yymmdd_point[:2]+'_'+yymmdd_point[2:4]
print(f'Observation ID: {obs_id}')
f.write(f'Observation ID: {obs_id}\n')
# Get coding fraction of source from attitude data
gti = fits.getdata(gtifile,1)
w = ((att['time'] >= gti['start'])
& (att['time'] <= gti['stop'])).nonzero()[0]
assert len(w) > 0
#print(f'Attitude records found within GTI: {len(w)}')
w0 = (att[w]['flags'] == flag_settled).nonzero()[0]
assert len(w0) > 0
w = w[w0]
#print(f'Settled records: {len(w)}')
w0 = (att[w]['obs_id'] == obs_id).nonzero()[0]
if len(w0) == 0:
str_out = ('WARNING: No settled attitude records found for'
f' Observation {obs_id}')
print(str_out)
f.write('\t'+str_out+'\n')
obs_id0, obs_id0_pos = np.unique(att[w]['obs_id'],
return_inverse=True)
obs_id0_cts = np.bincount(obs_id0_pos)
imax = obs_id0_cts.argmax()
str_out = (f'\tUsing most frequent Obs ID: {obs_id0[imax]}'
f' ({obs_id0_cts[imax]} records)')
print(str_out)
f.write(str_out+'\n')
obs_id = obs_id0[imax]
w0 = (obs_id0_pos == imax).nonzero()[0]
assert len(w0) > 0
del obs_id0, obs_id0_pos, obs_id0_cts, imax
w = w[w0]
w0 = w[len(w)//2]
ra0 = att[w0]['pointing'][0]
dec0 = att[w0]['pointing'][1]
roll0 = att[w0]['pointing'][2]
# Modify pchdr astrometry
pchdr = BAT_astrmod(pchdr, ra=ra0, dec=dec0, roll=roll0)
#fits.PrimaryHDU(pcmap, pchdr).writeto(datadir+'test_pchdr_'
# +prefix+'.fits', overwrite=True) #TEMP
pcwcs = wcs.WCS(pchdr)
pix = pcwcs.all_world2pix([[pos.ra.deg, pos.dec.deg]],
1)[0].round().astype(int)[::-1] # For [y,x] indexing!
pix = pix.clip(1, dims_pcmap) - 1
pcodefr0 = 100 * pcmap[pix[0], pix[1]]
str_out = f'Source coding fraction: {pcodefr0:6.2f}%. '
print(str_out, end='')
f.write(str_out)
if pcodefr0 == 0:
str_out = 'Pointing skipped'
print(str_out)
f.write(str_out+'\n')
if flag_pre:
obs0.cat_pre = []
else:
obs0.cat_pos = []
continue
print('Downloading pointing data... ', end='')
t1 = datetime.now()
obsdir = datadir+prefix+'_'+obs0.type+'_'+obs0.id+'/'
command = ['wget' # basic command
' -q' # turn off output
' -r -l0' # recursive retrieval (max depth 0)
' -nH' # no host-prefixed directories
' --cut-dirs=7' # also ignore 7 directories
' -np' # do not ascend to parent directory
f' --directory-prefix={obsdir}' # top directory for output
' --no-check-certificate' # don't check server certificate
' -c' # continue partial downloading
' -N' # use same timestamping as remote file
" -R'index*'" # reject all 'index*' files
' -erobots=off' # turn off Robots Exclusion Standard
' --retr-symlinks' # download symbolic links
' http://heasarc.gsfc.nasa.gov/FTP/swift/data/obs/'
f'{yyyy_mm_point}//{obs_id}/'+s for s in ['bat/','auxil/']]
for command0 in command:
subp.run(command0.split(' '))
str_out = f'({(datetime.now()-t1).seconds}s)'
print('done '+str_out)
f.write(f'Pointing data downloaded {str_out}\n')
f.close()
# Loop over DPH and SNAPSHOT imaging
datadir_in = obsdir
cat_tex = []
for flag_dph in [False, True]:
gti_ntries = 0
while gti_ntries < 2:
gti_ntries += 1
print(f'{70*"-"} {datetime.now():%c}')
print(('DPH' if flag_dph else 'SNAPSHOT')+' loop:')
print(f' GTI loop {gti_ntries}: '+
('Standard filtering' if gti_ntries == 1
else 'USERGTI filtering only'))
datadir_out = (obsdir+'results_'
+eband.name+('_dph' if flag_dph else '')+'/')
# BATSURVEY command
command = ['batsurvey',
datadir_in, datadir_out,
'energybins='+eband.str,
'elimits='+eband.str,
'incatalog='+catfile_in,
'ncleaniter=2', #Always clean DPH
# Apply DPH keyword
'timesep='+('DPH' if flag_dph else 'SNAPSHOT'),
'filtnames='+('all' if gti_ntries == 1
else ('global,pointing,filter_file,startracker,'
'st_lossfcn,data_flags,earthconstraints,'
'remove_midnight,occultation,usergti')),
'gtifile='+gtifile,
# Minimum exposure threshold
'expothresh=150.0']
print(' '.join(command))
subp.run(command)
# Find if master GTI file was created
gtifile_out = glob.glob(datadir_out+'gti/master.gti')
if len(gtifile_out) > 0:
if gti_ntries == 1:
gti_text = 'Standard'
elif gti_ntries == 2:
gti_text = 'Standard failed. USERGTI only'
break
else:
if gti_ntries == 1:
print('Standard GTI filtering failed. ', end='')
if flag_dph:
print('DPH binning does not work with '
'USERGTI. Aborting')
gti_text = ('Standard failed. DPH binning '
'does not work with USERGTI filtering')
break
else:
print('Standard GTI filtering failed.'
' Trying USERGTI only')
elif gti_ntries == 2:
print('Standard GTI and USERGTI filtering failed.'
' Aborting')
gti_text = 'Standard and USERGTI failed'
# Get output catalogs
cat_out = []
catfile_out = glob.glob(datadir_out+'point_*/point_*_2.cat')
catfile = (datadir+prefix+'_'+obs0.type+'_'+obs0.id
+'_'+coord_str+'_'+eband.name
+('_dph' if flag_dph else '')+'.cat')
if len(catfile_out) > 0:
print(('DPH' if flag_dph else 'SNAPSHOT')
+' catalogs found:', len(catfile_out))
else:
print('Warning: No '+('DPH' if flag_dph else 'SNAPSHOT')
+' catalogs found. Skipping')
for catfile_out0 in catfile_out:
print('Catalog file: '+catfile_out0)
t_ss = os.path.basename(catfile_out0).split('_')[1]
print(f' {t_ss[:4]}-{t_ss[4:7]}-{t_ss[7:9]}'
f':{t_ss[9:11]}...', end='')
cat0, hdr0 = fits.getdata(catfile_out0, 1, header=True)
cat0_name = cat0['name'].strip()
#cat0['name'] = cat0['name'].strip()
#cat0['rate'] /= 0.16 #[cts/cm2/sec]
#cat0['cent_rate'] /= 0.16
#cat0['rate_err'] /= 0.16
#cat0['bkg_var'] /= 0.16
w = (cat0_name == 'BATSS_'+coord_str).nonzero()[0]
if len(w) > 0:
cat0 = Table(cat0)
for w0 in w:
if len(cat_out) == 0:
cat0[w0]['CATNUM'] = 1
cat_out = Table(cat0[w0])
hdr_out = hdr0
hdr_out.remove('HISTORY', ignore_missing=True,
remove_all=True)
hdr_out['EXTNAME'] = 'BATSURVEY_CATALOG'
hdr_out['HDUNAME'] = 'BATSURVEY_CATALOG'
# Index for new sources in catalog
hdr_out['NEWSRCIN'] = 2
else:
cat0[w0]['CATNUM'] = hdr_out['NEWSRCIN']
hdr_out['NEWSRCIN'] += 1
cat_out.add_row(cat0[w0])
# Save catalog file
n_det = len(cat_out)
with open(txtfile,'a') as f:
f.write(f'\n{"DPH" if flag_dph else "SNAPSHOT"}'
' processing:\n')
f.write(f'GTI filtering: {gti_text}\n')
f.write(f'Detections: {n_det if n_det > 0 else "NONE"}\n')
if n_det > 0:
fits.BinTableHDU(cat_out, hdr_out).writeto(catfile,
overwrite=True)
print(f'Saved {n_det} detection(s) of'
f' BATSS_{coord_str} to file {catfile}')
f.write(' '.join([' #',
f'{"Time_start":23s}', f'{"Time_stop":23s}',
f'{"Exp[s]":7s}', f'{"CF[%]":6s}',
'S/N(pix)','S/N(fit)'])+'\n')
for cat0 in cat_out:
f.write(' '.join([f'{cat0["CATNUM"]:2}',
met2Time(cat0['TIME']).iso,
met2Time(cat0['TIME_STOP']).iso,
f'{cat0["EXPOSURE"]:7.1f}',
f'{100*cat0["PCODEFR"]:6.2f}',
f'{cat0["CENT_SNR"]:8.2f}',
f'{cat0["SNR"]:8.2f}'])
+'\n')
cat_tex.append({
'dt':cat0['TIME']-obs_t0,
'exp':cat0['EXPOSURE'],
'cf':100*cat0['PCODEFR'],
'cent_snr':cat0['CENT_SNR'],
'snr':cat0['SNR']
})
if flag_pre:
obs0.cat_pre = cat_tex
else:
obs0.cat_pos = cat_tex
str_out = ('\nDONE. Processing time: '
+str(datetime.now()-t0).split('.')[0])
print(str_out)
with open(txtfile, 'a') as f:
f.write(str_out+'\n')
print('Closed output text file: ', f.name)
return obs
response['hgc_y']) < 80.0:
area = response['area_atdiskcenter']
response_index = i
##############################################################################
# Next let's get the boundary of the coronal hole
ch = responses[response_index]
p1 = ch["hpc_boundcc"][9:-2]
p2 = p1.split(',')
p3 = [v.split(" ") for v in p2]
ch_date = parse_time(ch['event_starttime'])
##############################################################################
# The coronal hole was detected at different time than the AIA image was
# taken so we need to rotate it to the map observation time.
ch_boundary = SkyCoord([(float(v[0]), float(v[1])) * u.arcsec for v in p3],
obstime=ch_date,
frame=frames.Helioprojective)
rotated_ch_boundary = solar_rotate_coordinate(ch_boundary, time=aia_map.date)
##############################################################################
# Now let's plot the rotated coronal hole boundary on the AIA map, and fill
# it with hatching.
fig = plt.figure()
ax = plt.subplot(projection=aia_map)
aia_map.plot(axes=ax)
ax.plot_coord(rotated_ch_boundary, color='c')
ax.set_title('{:s}\n{:s}'.format(aia_map.name, ch['frm_specificid']))
plt.colorbar()
plt.show()
def f(x, coords):
"""Function to minimize"""
lon, lat = x
center = SkyCoord(lon * u.deg, lat * u.deg)
return np.sum(center.separation(coords).deg)
def test_docs_example():
# Test the example in astroplan/docs/tutorials/constraints.rst
target_table_string = """# name ra_degrees dec_degrees
Polaris 37.95456067 89.26410897
Vega 279.234734787 38.783688956
Albireo 292.68033548 27.959680072
Algol 47.042218553 40.955646675
Rigel 78.634467067 -8.201638365
Regulus 152.092962438 11.967208776"""
from astroplan import Observer, FixedTarget
from astropy.time import Time
subaru = Observer.at_site("Subaru")
time_range = Time(["2015-08-01 06:00", "2015-08-01 12:00"])
# Read in the table of targets
from astropy.io import ascii
target_table = ascii.read(target_table_string)
# Create astroplan.FixedTarget objects for each one in the table
from astropy.coordinates import SkyCoord
import astropy.units as u
targets = [
FixedTarget(coord=SkyCoord(ra=ra * u.deg, dec=dec * u.deg), name=name)
for name, ra, dec in target_table
]
from astroplan import Constraint, is_observable
class VegaSeparationConstraint(Constraint):
"""
Constraint the separation from Vega
"""
def __init__(self, min=None, max=None):
"""
min : `~astropy.units.Quantity` or `None` (optional)
Minimum acceptable separation between Vega and target. `None`
indicates no limit.
max : `~astropy.units.Quantity` or `None` (optional)
Minimum acceptable separation between Vega and target. `None`
indicates no limit.
"""
self.min = min if min is not None else 0 * u.deg
self.max = max if max is not None else 180 * u.deg
def compute_constraint(self, times, observer, targets):
vega = SkyCoord(ra=279.23473479 * u.deg, dec=38.78368896 * u.deg)
# Calculate separation between target and vega
# Targets are automatically converted to SkyCoord objects
# by __call__ before compute_constraint is called.
vega_separation = vega.separation(targets)
# Return an array that is True where the target is observable and
# False where it is not
return (self.min < vega_separation) & (vega_separation < self.max)
constraints = [VegaSeparationConstraint(min=5 * u.deg, max=30 * u.deg)]
observability = is_observable(constraints,
subaru,
targets,
time_range=time_range)
assert all(observability == [False, False, True, False, False, False])
def lonlat_to_skycoord(lon, lat, coordsys):
return SkyCoord(lon, lat, frame=coordsys_to_frame(coordsys), unit="deg")
# -*- coding: utf-8 -*-
"""
Created on Sat Aug 17 21:05:15 2019
@author: souza
"""
from astropy import units as u
from astropy.coordinates import SkyCoord
# c = SkyCoord('21 33 27.02 -00 49 23.7', unit=(u.hourangle, u.deg))
# print c
ra_dec = input('Type Ra (hour) and Dec (deg) between quotation marks: ')
print
ra_dec
c = SkyCoord(ra_dec, unit=(u.hourangle, u.deg))
print
c
def drizzle_images(label='macs0647-jd1',
ra=101.9822125,
dec=70.24326667,
pixscale=0.06,
size=10,
wcs=None,
pixfrac=0.8,
kernel='square',
theta=0,
half_optical_pixscale=False,
filters=[
'f160w', 'f140w', 'f125w', 'f105w', 'f110w', 'f098m',
'f850lp', 'f814w', 'f775w', 'f606w', 'f475w', 'f555w',
'f600lp', 'f390w', 'f350lp'
],
remove=True,
rgb_params=RGB_PARAMS,
master='grizli-jan2019',
aws_bucket='s3://grizli/CutoutProducts/',
scale_ab=21,
thumb_height=2.0,
sync_fits=True,
subtract_median=True,
include_saturated=True,
include_ir_psf=False,
show_filters=['visb', 'visr', 'y', 'j', 'h'],
combine_similar_filters=True):
"""
label='cp561356'; ra=150.208875; dec=1.850241667; size=40; filters=['f160w','f814w', 'f140w','f125w','f105w','f606w','f475w']
"""
import glob
import copy
import os
import numpy as np
import astropy.io.fits as pyfits
from astropy.coordinates import SkyCoord
import astropy.units as u
from drizzlepac.adrizzle import do_driz
import boto3
from grizli import prep, utils
from grizli.pipeline import auto_script
if isinstance(ra, str):
coo = SkyCoord('{0} {1}'.format(ra, dec), unit=(u.hour, u.deg))
ra, dec = coo.ra.value, coo.dec.value
if label is None:
try:
import mastquery.utils
label = mastquery.utils.radec_to_targname(
ra=ra,
dec=dec,
round_arcsec=(1 / 15, 1),
targstr='j{rah}{ram}{ras}{sign}{ded}{dem}{des}')
except:
label = 'grizli-cutout'
#master = 'cosmos'
#master = 'grizli-jan2019'
if master == 'grizli-jan2019':
parent = 's3://grizli/MosaicTools/'
s3 = boto3.resource('s3')
s3_client = boto3.client('s3')
bkt = s3.Bucket('grizli')
elif master == 'cosmos':
parent = 's3://grizli-preprocess/CosmosMosaic/'
s3 = boto3.resource('s3')
s3_client = boto3.client('s3')
bkt = s3.Bucket('grizli-preprocess')
else:
# Run on local files, e.g., "Prep" directory
parent = None
remove = False
for ext in ['_visits.fits', '_visits.npy', '_filter_groups.npy'][-1:]:
if (not os.path.exists('{0}{1}'.format(master, ext))) & (parent
is not None):
s3_path = parent.split('/')[-2]
s3_file = '{0}{1}'.format(master, ext)
print('{0}{1}'.format(parent, s3_file))
bkt.download_file(s3_path + '/' + s3_file,
s3_file,
ExtraArgs={"RequestPayer": "requester"})
#os.system('aws s3 cp {0}{1}{2} ./'.format(parent, master, ext))
#tab = utils.read_catalog('{0}_visits.fits'.format(master))
#all_visits = np.load('{0}_visits.npy'.format(master))[0]
if parent is not None:
groups = np.load('{0}_filter_groups.npy'.format(master),
allow_pickle=True)[0]
else:
# Reformat local visits.npy into a groups file
groups_files = glob.glob('*filter_groups.npy')
if len(groups_files) == 0:
visit_file = glob.glob('*visits.npy')[0]
visits, groups, info = np.load(visit_file)
visit_root = visit_file.split('_visits')[0]
visit_filters = np.array(
[v['product'].split('-')[-1] for v in visits])
groups = {}
for filt in np.unique(visit_filters):
groups[filt] = {}
groups[filt]['filter'] = filt
groups[filt]['files'] = []
groups[filt]['footprints'] = []
groups[filt]['awspath'] = None
ix = np.where(visit_filters == filt)[0]
for i in ix:
groups[filt]['files'].extend(visits[i]['files'])
groups[filt]['footprints'].extend(visits[i]['footprints'])
np.save('{0}_filter_groups.npy'.format(visit_root), [groups])
else:
groups = np.load(groups_files[0])[0]
#filters = ['f160w','f814w', 'f110w', 'f098m', 'f140w','f125w','f105w','f606w', 'f475w']
has_filts = []
lower_filters = [f.lower() for f in filters]
for filt in lower_filters:
if filt not in groups:
continue
visits = [copy.deepcopy(groups[filt])]
#visits[0]['reference'] = 'CarlosGG/ak03_j1000p0228/Prep/ak03_j1000p0228-f160w_drz_sci.fits'
visits[0]['product'] = label + '-' + filt
if wcs is None:
hdu = utils.make_wcsheader(ra=ra,
dec=dec,
size=size,
pixscale=pixscale,
get_hdu=True,
theta=theta)
h = hdu.header
else:
h = utils.to_header(wcs)
if (filt[:2] in ['f0', 'f1', 'g1']) | (not half_optical_pixscale):
#data = hdu.data
pass
else:
for k in ['NAXIS1', 'NAXIS2', 'CRPIX1', 'CRPIX2']:
h[k] *= 2
h['CRPIX1'] -= 0.5
h['CRPIX2'] -= 0.5
for k in ['CD1_1', 'CD1_2', 'CD2_1', 'CD2_2']:
if k in h:
h[k] /= 2
#data = np.zeros((h['NAXIS2'], h['NAXIS1']), dtype=np.int16)
#pyfits.PrimaryHDU(header=h, data=data).writeto('ref.fits', overwrite=True, output_verify='fix')
#visits[0]['reference'] = 'ref.fits'
print('\n\n###\nMake filter: {0}'.format(filt))
if (filt.upper() in ['F105W', 'F125W', 'F140W', 'F160W'
]) & include_ir_psf:
clean_i = False
else:
clean_i = remove
status = utils.drizzle_from_visit(visits[0],
h,
pixfrac=pixfrac,
kernel=kernel,
clean=clean_i,
include_saturated=include_saturated)
if status is not None:
sci, wht, outh = status
if subtract_median:
med = np.median(sci[sci != 0])
if not np.isfinite(med):
med = 0.
print('\n\nMedian {0} = {1:.3f}\n\n'.format(filt, med))
outh['IMGMED'] = (med, 'Median subtracted from the image')
else:
med = 0.
outh['IMGMED'] = (0., 'Median subtracted from the image')
pyfits.writeto('{0}-{1}_drz_sci.fits'.format(label, filt),
data=sci,
header=outh,
overwrite=True,
output_verify='fix')
pyfits.writeto('{0}-{1}_drz_wht.fits'.format(label, filt),
data=wht,
header=outh,
overwrite=True,
output_verify='fix')
has_filts.append(filt)
if (filt.upper() in ['F105W', 'F125W', 'F140W', 'F160W'
]) & include_ir_psf:
from grizli.galfit.psf import DrizzlePSF
hdu = pyfits.open('{0}-{1}_drz_sci.fits'.format(label, filt),
mode='update')
flt_files = [] #visits[0]['files']
for i in range(1, 10000):
key = 'FLT{0:05d}'.format(i)
if key not in hdu[0].header:
break
flt_files.append(hdu[0].header[key])
dp = DrizzlePSF(flt_files=flt_files, driz_hdu=hdu[0])
psf = dp.get_psf(ra=dp.driz_wcs.wcs.crval[0],
dec=dp.driz_wcs.wcs.crval[1],
filter=filt.upper(),
pixfrac=dp.driz_header['PIXFRAC'],
kernel=dp.driz_header['KERNEL'],
wcs_slice=dp.driz_wcs,
get_extended=True,
verbose=False,
get_weight=False)
psf[1].header['EXTNAME'] = 'PSF'
#psf[1].header['EXTVER'] = filt
hdu.append(psf[1])
hdu.flush()
#psf.writeto('{0}-{1}_drz_sci.fits'.format(label, filt),
# overwrite=True, output_verify='fix')
#status = prep.drizzle_overlaps(visits, parse_visits=False, check_overlaps=True, pixfrac=pixfrac, skysub=False, final_wcs=True, final_wht_type='IVM', static=True, max_files=260, fix_wcs_system=True)
#
# if len(glob.glob('{0}-{1}*sci.fits'.format(label, filt))):
# has_filts.append(filt)
if combine_similar_filters:
combine_filters(label=label)
if remove:
os.system('rm *_fl*fits')
if len(has_filts) == 0:
return []
if rgb_params:
#auto_script.field_rgb(root=label, HOME_PATH=None, filters=has_filts, **rgb_params)
show_all_thumbnails(label=label,
thumb_height=thumb_height,
scale_ab=scale_ab,
close=True,
rgb_params=rgb_params,
filters=show_filters)
if aws_bucket:
#aws_bucket = 's3://grizli-cosmos/CutoutProducts/'
#aws_bucket = 's3://grizli/CutoutProducts/'
s3 = boto3.resource('s3')
s3_client = boto3.client('s3')
bkt = s3.Bucket(aws_bucket.split("/")[2])
aws_path = '/'.join(aws_bucket.split("/")[3:])
if sync_fits:
files = glob.glob('{0}*'.format(label))
else:
files = glob.glob('{0}*png'.format(label))
for file in files:
print('{0} -> {1}'.format(file, aws_bucket))
bkt.upload_file(file,
'{0}/{1}'.format(aws_path,
file).replace('//', '/'),
ExtraArgs={'ACL': 'public-read'})
#os.system('aws s3 sync --exclude "*" --include "{0}*" ./ {1} --acl public-read'.format(label, aws_bucket))
#os.system("""echo "<pre>" > index.html; aws s3 ls AWSBUCKETX --human-readable | sort -k 1 -k 2 | grep -v index | awk '{printf("%s %s",$1, $2); printf(" %6s %s ", $3, $4); print "<a href="$5">"$5"</a>"}'>> index.html; aws s3 cp index.html AWSBUCKETX --acl public-read""".replace('AWSBUCKETX', aws_bucket))
return has_filts
def setup(self):
self.region = SphericalCircleSkyRegion(
center=SkyCoord(10 * u.deg, 20 * u.deg), radius=10 * u.deg
)
from ..observer import Observer
from ..target import FixedTarget, get_skycoord
from ..constraints import (
AltitudeConstraint, AirmassConstraint, AtNightConstraint, is_observable,
is_always_observable, observability_table, time_grid_from_range,
GalacticLatitudeConstraint, SunSeparationConstraint,
MoonSeparationConstraint, MoonIlluminationConstraint, TimeConstraint,
LocalTimeConstraint, months_observable, max_best_rescale, min_best_rescale,
PhaseConstraint, PrimaryEclipseConstraint, SecondaryEclipseConstraint,
is_event_observable)
from ..periodic import EclipsingSystem
APY_LT104 = not minversion('astropy', '1.0.4')
vega = FixedTarget(coord=SkyCoord(ra=279.23473479 * u.deg,
dec=38.78368896 * u.deg),
name="Vega")
rigel = FixedTarget(coord=SkyCoord(ra=78.63446707 * u.deg,
dec=8.20163837 * u.deg),
name="Rigel")
polaris = FixedTarget(coord=SkyCoord(ra=37.95456067 * u.deg,
dec=89.26410897 * u.deg),
name="Polaris")
def test_at_night_basic():
subaru = Observer.at_site("Subaru")
time_ranges = [
Time(['2001-02-03 04:05:06', '2001-02-04 04:05:06']), # 1 day
Time(['2007-08-09 10:11:12', '2007-08-09 11:11:12'])
] # 1 hr
############################################################
# basic setting
PA = 213.3
incl = 0.43
xcenter = 159.09
ycenter = 158.29
# corresponds to 13h39m57.692s, 0d49'50.838"
# ra=13*15+39*15.0/60.0+57.692*15.0/3600.0; dec=49.0/60.0+50.838/3600.0
ra = 204.9903
dec = 0.8308
steps = (33.75 - 0.75) / 1.5 + 1
radius_arcsec = np.linspace(0.75, 33.75, steps)
radius_kpc = radius_arcsec * 0.48
size = radius_arcsec.shape[0] - 1
position = SkyCoord(dec=dec * u.degree, ra=ra * u.degree, frame='icrs')
rings = dict.fromkeys((range(size)))
rings_mask = dict.fromkeys((range(size)))
pixel_area = 0.3 * 0.3
pixel_sr = pixel_area / (60**2 * 180 / math.pi)**2
D = 99
majorbeam = 2.021
minorbeam = 1.610
beamarea = majorbeam * minorbeam * 1.1331
beamarea_pix = beamarea / 0.09
############################################################
# function
def fits_import(fitsimage, item=0):
hdr = fits.open(fitsimage)[item].header
def test_contains(self):
coord = SkyCoord([20.1, 22] * u.deg, 20 * u.deg)
mask = self.region.contains(coord)
assert_equal(mask, [True, False])
def aia171_test_submap(aia171_test_map):
bl = SkyCoord(-512 * u.arcsec, 100 * u.arcsec, frame=aia171_test_map.coordinate_frame)
ur = SkyCoord(-100 * u.arcsec, 400 * u.arcsec, frame=aia171_test_map.coordinate_frame)
return aia171_test_map.submap(bl, ur)
def get_altaz(obj_name, ipt_lon, ipt_lat, t=None):
#for html scrapping
#from lxml import html
#from bs4 import BeautifulSoup
#to place requests
import requests
import json
import astropy.units as u
from astropy.time import Time
from astropy.coordinates import SkyCoord, EarthLocation, Angle, Latitude, Longitude
from astroplan import FixedTarget, Observer
from astroquery.simbad import Simbad as simbad
import ephem
if t == None: t = Time.now()
## Set up the observer
obs_el = 100 * u.m
loc = EarthLocation.from_geodetic(ipt_lon, ipt_lat, obs_el)
my_site = Observer(name='My_Site', location=loc)
obs_lat = my_site.location.latitude
obs_lon = my_site.location.longitude
#observer for pyephem
ephem_site = ephem.Observer()
ephem_site.lon, ephem_site.lat = str(obs_lon.deg), str(obs_lat.deg)
ephem_site.date = ephem.Date(str(t.decimalyear))
##Get the object
#Check for planet-hood.
#if planet: resolve the individual planet with pyephem.
#else if satellite or ISS (or TIANGONG) scrap the appropriate websites and return info
#else query simbad
############
# Put in an auto-correct for kids
############
#just make it lower case for now
obj_name = obj_name.lower()
if obj_name in [
"sun", "mercury", "venus", "moon", "mars", "jupiter", "saturn",
"uranus", "neptune", "pluto"
]:
if obj_name == "sun": my_planet = ephem.Sun()
elif obj_name == "mercury": my_planet = ephem.Mercury()
elif obj_name == "venus": my_planet = ephem.Venus()
elif obj_name == "moon": my_planet = ephem.Moon()
elif obj_name == "mars": my_planet = ephem.Mars()
elif obj_name == "jupiter": my_planet = ephem.Jupiter()
elif obj_name == "saturn": my_planet = ephem.Saturn()
elif obj_name == "uranus": my_planet = ephem.Uranus()
elif obj_name == "neptune": my_planet = ephem.Neptune()
elif obj_name == "pluto": my_planet = ephem.Pluto()
my_planet.compute(ephem_site)
az = my_planet.az * 180 / 3.1415926535
alt = my_planet.alt * 180 / 3.1415926535
#here coded for just ISS but for all satellites we should have similar setups, probably poll site
elif (obj_name == "iss"):
#try a request for the iss from the open notify site. Gives current json data
page = requests.get("http://api.open-notify.org/iss-now.json")
issdata = page.json()
tstamp = issdata['timestamp']
isslat = issdata['iss_position']['latitude']
isslon = issdata['iss_position']['longitude']
#there are issues with just this amount of data as you do not know the altitude of the object
#here we fix it to 350 km
issheight = 350 * u.km
isslat = Latitude(isslat, unit=u.deg)
isslon = Longitude(isslon, unit=u.deg)
#there are issues however as this data does NOT contain the altitude so lets try scrapping the html
#the issue with fullissdata is that it contains information in NASA style units (M50 Cartesian & M50 Keplerian)
page = requests.get(
"http://spaceflight.nasa.gov/realdata/sightings/SSapplications/Post/JavaSSOP/orbit/ISS/SVPOST.html"
)
#fullissdata=html.fromstring(page.text)
#there are also other satellites liseted in, issue is parsing the information as I do not know what each field contains
#the issue here is that all sat data contains unknown units and uncertain which entries contain useful information
page = requests.get(
"http://www.celestrak.com/NORAD/elements/stations.txt")
allsatdata = page.text
c = SkyCoord(isslon, isslat, issheight)
my_target = FixedTarget(name='ISS', coord=c)
az = my_site.altaz(t, my_target).az.deg
alt = my_site.altaz(t, my_target).alt.deg
else:
try:
q = simbad.query_object(obj_name)
c = SkyCoord(q["RA"][0], q["DEC"][0], unit=(u.hourangle, u.deg))
my_star = FixedTarget(name='my_star', coord=c)
az = my_site.altaz(t, my_star).az.deg
alt = my_site.altaz(t, my_star).alt.deg
except:
print("Couldn't find Object in Database")
alt, az = 0, 0
return alt, az
gs=gridspec.GridSpec(n_row, n_column)
gs.update(left=0.03, right=0.97, bottom=0.03, top=0.97, wspace=0.2, hspace=0.2)
# Now reading the HD rgb image
im_data = np.flipud(skimage.io.imread(im_rgb_hd_file))
im_size= im_data.shape
avm=AVM.from_image(im_rgb_hd_file)
w = avm.to_wcs()
w.naxis1=im_size[1]
w.naxis2=im_size[0]
# Sort them by magnitude
cat_ngfs.sort('m_i')
cat_ngfs_coo = SkyCoord(cat_ngfs['RA'], cat_ngfs['DEC'], unit="deg")
for i in np.arange(len(cat_ngfs)):
print 'Processing NGFS dwarf ', cat_ngfs['ID'][i]
ax=plt.subplot(gs[i])
ax.set_aspect('equal')
ax.axis('off')
im_crop_coo=w.wcs_world2pix([[ cat_ngfs_coo.ra[i].deg,(cat_ngfs_coo.dec[i]+dwarf_zoom_radius).deg],[cat_ngfs_coo.ra[i].deg,(cat_ngfs_coo.dec[i]-dwarf_zoom_radius).deg]], 1)
im_crop_size=(np.abs(im_crop_coo[0,1]-im_crop_coo[1,1])*np.asarray([1.,1.])).astype(int)
im_crop_coo=(w.wcs_world2pix([[ cat_ngfs_coo.ra[i].deg, cat_ngfs_coo.dec[i].deg]], 1)[0]).astype(int)
im_crop_data=im_data[im_crop_coo[1]-im_crop_size[1]/2:im_crop_coo[1]+im_crop_size[1]/2,im_crop_coo[0]-im_crop_size[0]/2:im_crop_coo[0]+im_crop_size[0]/2]
skimage.io.imsave('dwarf_zoom.png', np.flipud(im_crop_data))
im_crop_size= im_crop_data.shape
def actualSetUp(self, add_errors=False, freqwin=3, block=False, dospectral=True, dopol=False, zerow=False,
makegcfcf=False):
self.npixel = 256
self.low = create_named_configuration('LOWBD2', rmax=750.0)
self.freqwin = freqwin
self.vis_list = list()
self.ntimes = 5
self.cellsize = 0.0005
# Choose the interval so that the maximum change in w is smallish
integration_time = numpy.pi * (24 / (12 * 60))
self.times = numpy.linspace(-integration_time * (self.ntimes // 2), integration_time * (self.ntimes // 2),
self.ntimes)
if freqwin > 1:
self.frequency = numpy.linspace(0.8e8, 1.2e8, self.freqwin)
self.channelwidth = numpy.array(freqwin * [self.frequency[1] - self.frequency[0]])
else:
self.frequency = numpy.array([1.0e8])
self.channelwidth = numpy.array([4e7])
if dopol:
self.vis_pol = PolarisationFrame('linear')
self.image_pol = PolarisationFrame('stokesIQUV')
f = numpy.array([100.0, 20.0, -10.0, 1.0])
else:
self.vis_pol = PolarisationFrame('stokesI')
self.image_pol = PolarisationFrame('stokesI')
f = numpy.array([100.0])
if dospectral:
flux = numpy.array([f * numpy.power(freq / 1e8, -0.7) for freq in self.frequency])
else:
flux = numpy.array([f])
self.phasecentre = SkyCoord(ra=+180.0 * u.deg, dec=-60.0 * u.deg, frame='icrs', equinox='J2000')
self.bvis_list = [rsexecute.execute(ingest_unittest_visibility)(self.low,
[self.frequency[freqwin]],
[self.channelwidth[freqwin]],
self.times,
self.vis_pol,
self.phasecentre, block=True,
zerow=zerow)
for freqwin, _ in enumerate(self.frequency)]
self.vis_list = [rsexecute.execute(convert_blockvisibility_to_visibility)(bvis) for bvis in self.bvis_list]
self.model_list = [rsexecute.execute(create_unittest_model, nout=freqwin)(self.vis_list[freqwin],
self.image_pol,
cellsize=self.cellsize,
npixel=self.npixel)
for freqwin, _ in enumerate(self.frequency)]
self.components_list = [rsexecute.execute(create_unittest_components)(self.model_list[freqwin],
flux[freqwin, :][numpy.newaxis, :],
single=True)
for freqwin, _ in enumerate(self.frequency)]
self.components_list = rsexecute.compute(self.components_list, sync=True)
self.model_list = [rsexecute.execute(insert_skycomponent, nout=1)(self.model_list[freqwin],
self.components_list[freqwin])
for freqwin, _ in enumerate(self.frequency)]
self.model_list = rsexecute.compute(self.model_list, sync=True)
self.vis_list = [rsexecute.execute(predict_skycomponent_visibility)(self.vis_list[freqwin],
self.components_list[freqwin])
for freqwin, _ in enumerate(self.frequency)]
centre = self.freqwin // 2
# Calculate the model convolved with a Gaussian.
self.model = self.model_list[centre]
self.cmodel = smooth_image(self.model)
if self.persist: export_image_to_fits(self.model, '%s/test_imaging_model.fits' % self.dir)
if self.persist: export_image_to_fits(self.cmodel, '%s/test_imaging_cmodel.fits' % self.dir)
if add_errors and block:
self.vis_list = [rsexecute.execute(insert_unittest_errors)(self.vis_list[i])
for i, _ in enumerate(self.frequency)]
self.components = self.components_list[centre]
if makegcfcf:
self.gcfcf = [create_awterm_convolutionfunction(self.model, nw=61, wstep=16.0,
oversampling=8,
support=64,
use_aaf=True)]
self.gcfcf_clipped = [(self.gcfcf[0][0], apply_bounding_box_convolutionfunction(self.gcfcf[0][1],
fractional_level=1e-3))]
self.gcfcf_joint = [create_awterm_convolutionfunction(self.model, nw=11, wstep=16.0,
oversampling=8,
support=64,
use_aaf=True)]
else:
self.gcfcf = None
self.gcfcf_clipped = None
self.gcfcf_joint = None
