def define_wcs(skypos, skyrange, verbose=0, pixsz=0.000416666666666667):
"""
Define the world coordinate system (WCS).
:param skypos: The right ascension and declination, in degrees.
:type skypos: list
:param skyrange: Extent of the region of interest, in degrees.
:type skyrange: list
:param verbose: Verbosity level, a value of 0 is minimum verbosity.
:type verbose: int
:param pixsz: Size of a GALEX pixel, in degrees.
:type pixsz: float
:returns: astropy.wcs WCS Object -- The WCS information.
"""
if verbose:
mc.print_inline('Defining World Coordinate System (WCS).')
# NAXIS = 2
wcs = pywcs.WCS(naxis=2)
imsz = gxt.deg2pix(skypos, skyrange)
wcs.wcs.cdelt = np.array([-pixsz, pixsz])
wcs.wcs.ctype = ['RA---TAN', 'DEC--TAN']
wcs.wcs.crpix = [(imsz[1]/2.)+0.5, (imsz[0]/2.)+0.5]
wcs.wcs.crval = skypos
return wcs
def define_wcs(skypos, skyrange, verbose=0, pixsz=0.000416666666666667):
"""
Define the world coordinate system (WCS).
:param skypos: The right ascension and declination, in degrees.
:type skypos: list
:param skyrange: Extent of the region of interest, in degrees.
:type skyrange: list
:param verbose: Verbosity level, a value of 0 is minimum verbosity.
:type verbose: int
:param pixsz: Size of a GALEX pixel, in degrees.
:type pixsz: float
:returns: astropy.wcs WCS Object -- The WCS information.
"""
if verbose:
mc.print_inline('Defining World Coordinate System (WCS).')
# NAXIS = 2
wcs = pywcs.WCS(naxis=2)
imsz = gxt.deg2pix(skypos, skyrange)
wcs.wcs.cdelt = np.array([-pixsz, pixsz])
wcs.wcs.ctype = ['RA---TAN', 'DEC--TAN']
wcs.wcs.crpix = [(imsz[1]/2.)+0.5, (imsz[0]/2.)+0.5]
wcs.wcs.crval = skypos
return wcs
def integrate_map(band, skypos, tranges, skyrange, verbose=0, memlight=None,
hdu=None, retries=100, response=False, detsize=1.1):
"""
Integrate an image over some number of time ranges. Use a reduced
memory optimization (at the expense of more web queries) if requested.
:param band: The band to use, either 'FUV' or 'NUV'.
:type band: str
:param skypos: The right ascension and declination, in degrees.
:type skypos: list
:param tranges: Set of time ranges to retrieve the photon events, in
GALEX time seconds.
:type tranges: list
:param skyrange: RA and Dec extents of the region of interest in degrees.
:type skyrange: list
:param verbose: Verbosity level, a value of 0 is minimum verbosity.
:type verbose: int
:param memlight: Reduce memory usage by breaking query into smaller
segments of this size in seconds.
:type memlight: float
:param hdu: An existing HDU to modify.
:type hdu: bool
:param retries: Number of query retries to attempt before giving up.
:type retries: int
:param response: Apply the response correction.
:type response: bool
:param detsize: Effective diameter, in degrees, of the field-of-view.
:type detsize: float
:returns: numpy.ndarray - The integrated image.
"""
imsz = gxt.deg2pix(skypos, skyrange)
img = np.zeros(np.array(imsz, dtype='int32'))
for trange in tranges:
img += makemap(band, skypos, trange, skyrange,
response=response, verbose=verbose, detsize=detsize)
if response: # Intensity maps == average countrate maps.
expt=np.sum([dbt.compute_exptime(band,trange) for trange in tranges])
if expt>0:
img/=expt
return img
def makemap(band, skypos, trange, skyrange, response=False, verbose=0,
detsize=1.1):
"""
Generate a single image frame.
:param band: The band to use, either 'FUV' or 'NUV'.
:type band: str
:param skypos: The right ascension and declination, in degrees.
:type skypos: list
:param trange: Minimum and maximum time to use, in GALEX time seconds.
:type trange: list
:param skyrange: RA and Dec extent of the region of interest in degrees.
:type skyrange: list
:param response: Apply the response correction.
:type response: bool
:param verbose: Verbosity level, a value of 0 is minimum verbosity.
:type verbose: int
:param detsize: Effective diameter, in degrees, of the field-of-view.
:type detsize: float
:returns: numpy.ndarray - The bi-dimensional histogram of ra and dec.
"""
imsz = gxt.deg2pix(skypos, skyrange)
photons = np.array(gQuery.getArray(
gQuery.skyrect(band, skypos[0], skypos[1], trange[0], trange[1],
skyrange[0], skyrange[1]), verbose=verbose),
dtype='float64')
try:
events = {'t':photons[:, 0]/tscale, 'ra':photons[:, 1],
'dec':photons[:, 2], 'xi':photons[:, 3], 'eta':photons[:, 4],
'x':photons[:, 5], 'y':photons[:, 6]}
except IndexError:
if verbose > 2:
print('No events found at {s} +/- {r} in {t}.'.format(
s=skypos, r=skyrange, t=trange))
return np.zeros(np.array(imsz, dtype='int32'))
# Trim the data on detsize
col, row = ct.xieta2colrow(events['xi'], events['eta'], band)
ix = np.where(gxt.aper2deg(4)*mc.distance(col, row, 400, 400) <= detsize)
n = len(ix[0])
m = len(col)
if n == 0:
return np.zeros(np.int(imsz))
for k in list(events.keys()):
events[k] = events[k][ix]
events = ct.hashresponse(band, events)
wcs = define_wcs(skypos, skyrange)
coo = list(zip(events['ra'], events['dec']))
foc = wcs.sip_pix2foc(wcs.wcs_world2pix(coo, 1), 1)
weights = 1./events['response'] if response else None
H, xedges, yedges = np.histogram2d(foc[:, 1]-0.5, foc[:, 0]-0.5, bins=imsz,
range=([[0, imsz[0]], [0, imsz[1]]]),
weights=weights)
return H
def integrate_map(band, skypos, tranges, skyrange, verbose=0, memlight=None,
hdu=None, retries=100, response=False, detsize=1.1):
"""
Integrate an image over some number of time ranges. Use a reduced
memory optimization (at the expense of more web queries) if requested.
:param band: The band to use, either 'FUV' or 'NUV'.
:type band: str
:param skypos: The right ascension and declination, in degrees.
:type skypos: list
:param tranges: Set of time ranges to retrieve the photon events, in
GALEX time seconds.
:type tranges: list
:param skyrange: RA and Dec extents of the region of interest in degrees.
:type skyrange: list
:param verbose: Verbosity level, a value of 0 is minimum verbosity.
:type verbose: int
:param memlight: Reduce memory usage by breaking query into smaller
segments of this size in seconds.
:type memlight: float
:param hdu: An existing HDU to modify.
:type hdu: bool
:param retries: Number of query retries to attempt before giving up.
:type retries: int
:param response: Apply the response correction.
:type response: bool
:param detsize: Effective diameter, in degrees, of the field-of-view.
:type detsize: float
:returns: numpy.ndarray - The integrated image.
"""
imsz = gxt.deg2pix(skypos, skyrange)
img = np.zeros(np.array(imsz, dtype='int32'))
for trange in tranges:
img += makemap(band, skypos, trange, skyrange,
response=response, verbose=verbose, detsize=detsize)
if response: # Intensity maps == average countrate maps.
expt=np.sum([dbt.compute_exptime(band,trange) for trange in tranges])
if expt>0:
img/=expt
return img
def makemap(band, skypos, trange, skyrange, response=False, verbose=0,
detsize=1.1):
"""
Generate a single image frame.
:param band: The band to use, either 'FUV' or 'NUV'.
:type band: str
:param skypos: The right ascension and declination, in degrees.
:type skypos: list
:param trange: Minimum and maximum time to use, in GALEX time seconds.
:type trange: list
:param skyrange: RA and Dec extent of the region of interest in degrees.
:type skyrange: list
:param response: Apply the response correction.
:type response: bool
:param verbose: Verbosity level, a value of 0 is minimum verbosity.
:type verbose: int
:param detsize: Effective diameter, in degrees, of the field-of-view.
:type detsize: float
:returns: numpy.ndarray - The bi-dimensional histogram of ra and dec.
"""
imsz = gxt.deg2pix(skypos, skyrange)
photons = np.array(gQuery.getArray(
gQuery.skyrect(band, skypos[0], skypos[1], trange[0], trange[1],
skyrange[0], skyrange[1]), verbose=verbose),
dtype='float64')
try:
events = {'t':photons[:, 0]/tscale, 'ra':photons[:, 1],
'dec':photons[:, 2], 'xi':photons[:, 3], 'eta':photons[:, 4],
'x':photons[:, 5], 'y':photons[:, 6]}
except IndexError:
if verbose > 2:
print('No events found at {s} +/- {r} in {t}.'.format(
s=skypos, r=skyrange, t=trange))
return np.zeros(np.array(imsz, dtype='int32'))
# Trim the data on detsize
col, row = ct.xieta2colrow(events['xi'], events['eta'], band)
ix = np.where(gxt.aper2deg(4)*mc.distance(col, row, 400, 400) <= detsize)
n = len(ix[0])
m = len(col)
if n == 0:
return np.zeros(np.int(imsz))
for k in list(events.keys()):
events[k] = events[k][ix]
events = ct.hashresponse(band, events)
wcs = define_wcs(skypos, skyrange)
coo = list(zip(events['ra'], events['dec']))
foc = wcs.sip_pix2foc(wcs.wcs_world2pix(coo, 1), 1)
weights = 1./events['response'] if response else None
H, xedges, yedges = np.histogram2d(foc[:, 1]-0.5, foc[:, 0]-0.5, bins=imsz,
range=([[0, imsz[0]], [0, imsz[1]]]),
weights=weights)
return H
def test_deg2pix_pole(self):
skypos,skyrange=[90.,90.],[1.,1.]
pix = gt.deg2pix(skypos,skyrange)
self.assertAlmostEqual(pix[0],2400.)
self.assertAlmostEqual(pix[1],21.)
def test_deg2pix_equator(self):
skypos,skyrange=[0.,0.],[1.,1.]
pix = gt.deg2pix(skypos,skyrange)
self.assertAlmostEqual(pix[0],2400.)
self.assertAlmostEqual(pix[1],2400.)