def reproject_to(self, reference_cube, projection_type='bicubic'):
"""
Spatially reprojects a `SpectralCube` onto a reference cube.
Parameters
----------
reference_cube : `SpectralCube`
Reference cube with the desired spatial projection.
projection_type : {'nearest-neighbor', 'bilinear',
'biquadratic', 'bicubic', 'flux-conserving'}
Specify method of reprojection. Default: 'bilinear'.
Returns
-------
reprojected_cube : `SpectralCube`
Cube spatially reprojected to the reference cube.
"""
from reproject import reproject
reference = reference_cube.data
shape_out = reference[0].shape
try:
wcs_in = self.wcs.dropaxis(2)
except:
wcs_in = self.wcs
try:
wcs_out = reference_cube.wcs.dropaxis(2)
except:
wcs_out = reference_cube.wcs
energy = self.energy
cube = self.data
new_cube = np.zeros(
(cube.shape[0], reference.shape[1], reference.shape[2]))
energy_slices = np.arange(cube.shape[0])
# TODO: Re-implement to reproject cubes directly without needing
# to loop over energies here. Errors with reproject when doing this
# first need to be understood and fixed.
for i in energy_slices:
array = cube[i]
data_in = (array.value, wcs_in)
new_cube[i] = reproject(data_in, wcs_out, shape_out,
projection_type)[0]
new_cube = Quantity(new_cube, array.unit)
# Create new wcs
header_in = self.wcs.to_header()
header_out = reference_cube.wcs.to_header()
# Keep output energy info the same as input, but changes spatial information
# So need to restore energy parameters to input values here
try:
header_out['CRPIX3'] = header_in['CRPIX3']
header_out['CDELT3'] = header_in['CDELT3']
header_out['CTYPE3'] = header_in['CTYPE3']
header_out['CRVAL3'] = header_in['CRVAL3']
except:
pass
wcs_out = WCS(header_out)
return SpectralCube(new_cube, wcs_out, energy)
def reproject_to(self, reference_cube, projection_type='bicubic'):
"""
Spatially reprojects a `SpectralCube` onto a reference cube.
Parameters
----------
reference_cube : `SpectralCube`
Reference cube with the desired spatial projection.
projection_type : {'nearest-neighbor', 'bilinear',
'biquadratic', 'bicubic', 'flux-conserving'}
Specify method of reprojection. Default: 'bilinear'.
Returns
-------
reprojected_cube : `SpectralCube`
Cube spatially reprojected to the reference cube.
"""
from reproject import reproject
reference = reference_cube.data
shape_out = reference[0].shape
try:
wcs_in = self.wcs.dropaxis(2)
except:
wcs_in = self.wcs
try:
wcs_out = reference_cube.wcs.dropaxis(2)
except:
wcs_out = reference_cube.wcs
energy = self.energy
cube = self.data
new_cube = np.zeros((cube.shape[0], reference.shape[1],
reference.shape[2]))
energy_slices = np.arange(cube.shape[0])
# TODO: Re-implement to reproject cubes directly without needing
# to loop over energies here. Errors with reproject when doing this
# first need to be understood and fixed.
for i in energy_slices:
array = cube[i]
data_in = (array.value, wcs_in)
new_cube[i] = reproject(data_in, wcs_out, shape_out, projection_type)[0]
new_cube = Quantity(new_cube, array.unit)
# Create new wcs
header_in = self.wcs.to_header()
header_out = reference_cube.wcs.to_header()
# Keep output energy info the same as input, but changes spatial information
# So need to restore energy parameters to input values here
try:
header_out['CRPIX3'] = header_in['CRPIX3']
header_out['CDELT3'] = header_in['CDELT3']
header_out['CTYPE3'] = header_in['CTYPE3']
header_out['CRVAL3'] = header_in['CRVAL3']
except:
pass
wcs_out = WCS(header_out)
return SpectralCube(new_cube, wcs_out, energy)
# os3hw.py
# Solution to Open Source week 3 homework -- working with filters & geoprocessing
# cgarrard 2-12-08
# import the modules
import ogr, sys, os, reproject
# set the working directory
os.chdir('d:/data/classes/python/os4')
# set the filenames
inFn = 'ut_counties.shp'
outFn = 'ut_counties_utm.shp'
# set the epsg codes
inEPSG = 4269
outEPSG = 26912
# outEPSG = 9999999
# reproject using the reproject method in the reproject module
reproject.reproject(inFn, outFn, inEPSG, outEPSG)
from montage_wrapper import reproject as reproject_montage
hdu = fits.open('MSX_E.fits')[0]
wcs_in = WCS(hdu.header)
print('{0:10s} {1:9s} {2:9s}'.format(' header', 'reproject', ' montage '))
print('---------- --------- ---------')
for header in glob.glob('*.hdr'):
name = header.split('.')[0]
# With 'reproject' package
header_out = fits.Header.fromtextfile(header)
wcs_out = WCS(header_out)
time1 = time.time()
array_out = reproject(hdu, wcs_out, shape_out=hdu.data.shape, projection_type='flux-conserving')
time2 = time.time()
fits.writeto(name + '_reproject.fits', array_out, header_out, clobber=True)
# With montage
time3 = time.time()
reproject_montage('MSX_E.fits', name + '_montage.fits', header=header)
time4 = time.time()
print("{0:10s} {1:9.3f} {2:9.3f}".format(name, time2-time1, time4-time3))
from montage_wrapper import reproject as reproject_montage
hdu = fits.open('data_small.fits')[0]
wcs_in = WCS(hdu.header)
print('{0:10s} {1:9s} {2:9s}'.format(' header', 'reproject', ' montage '))
print('---------- --------- ---------')
header = 'small_eq.hdr'
name = 'small'
# With 'reproject' package
header_out = fits.Header.fromtextfile(header)
wcs_out = WCS(header_out)
time1 = time.time()
array_out = reproject(hdu, wcs_out, shape_out=(header_out['NAXIS2'], header_out['NAXIS1']), projection_type='flux-conserving')
time2 = time.time()
fits.writeto(name + '_reproject.fits', array_out, header_out, clobber=True)
# With montage
time3 = time.time()
reproject_montage('data_small.fits', name + '_montage.fits', header=header, exact_size=True)
time4 = time.time()
print("{0:10s} {1:9.3f} {2:9.3f}".format(name, time2-time1, time4-time3))
def time_flux_conserving(self):
reproject(self.hdu_in,
self.header_out_size,
projection_type='flux-conserving')
def time_bilinear(self):
reproject(self.hdu_in,
self.header_out_size,
projection_type='bilinear')
def time_flux_conserving(self):
reproject(self.hdu_in, self.header_out_size, projection_type='flux-conserving')
def time_bilinear(self):
reproject(self.hdu_in, self.header_out_size, projection_type='bilinear')
beam1 = Beam.from_fits_header(epoch1[0].header)
epoch1[0].header = epoch1header = wcs.WCS(epoch1[0].header).sub([wcs.WCSSUB_CELESTIAL]).to_header()
epoch1header['NAXIS1'] = epoch1[0].data.shape[1]
epoch1header['NAXIS2'] = epoch1[0].data.shape[0]
wcs1 = wcs.WCS(epoch1[0].header).sub([wcs.WCSSUB_CELESTIAL])
epoch3 = fits.open(paths.dpath("W51C_ACarray_continuum_4096_both_uniform_contsplit.clean.image.fits"))
beam3 = Beam.from_fits_header(epoch3[0].header)
epoch3[0].data = epoch3[0].data.squeeze()
wcs3 = wcs.WCS(epoch3[0].header).sub([wcs.WCSSUB_CELESTIAL])
epoch3header = wcs3.to_header()
epoch3header['NAXIS'] = 2
epoch3header['NAXIS1'] = epoch3[0].data.shape[1]
epoch3header['NAXIS2'] = epoch3[0].data.shape[0]
#epoch1reproj = FITS_tools.hcongrid.hcongrid(epoch1[0].data, epoch1header, epoch3header)
epoch1reproj, footprint = reproject.reproject(epoch1[0], epoch3header)
scalefactor = (beam3.sr/beam1.sr).value
xshift,yshift, ex, ey = image_registration.chi2_shift(epoch3[0].data, epoch1reproj*scalefactor, err=0.001)
center = coordinates.SkyCoord(290.92443*u.deg, 14.515755*u.deg, frame='fk5')
xx,yy = wcs1.wcs_world2pix([[center.fk4.ra.deg, center.fk4.dec.deg]], 0)[0]
subim1 = epoch1[0].data[yy-10:yy+10, xx-10:xx+10]
wcs1sub = wcs1[yy-10:yy+10, xx-10:xx+10]
xx,yy = wcs3.wcs_world2pix([[center.fk5.ra.deg, center.fk5.dec.deg]], 0)[0]
wcs3sub = wcs3[yy-10:yy+10, xx-10:xx+10]
subim3 = epoch3[0].data[yy-10:yy+10, xx-10:xx+10]
gf1 = gaussfitter.gaussfit(subim1)
gf3 = gaussfitter.gaussfit(subim3)
