def test_dtype():
"""Image class: testing dtype."""
wcs = WCS(cdelt=(0.2, 0.3), crval=(8.5, 12), shape=(40, 30), deg=True)
data = np.zeros((40, 30))
data[19, 14] = 1
ima = Image(wcs=wcs, data=data, dtype=int)
ima2 = ima.fftconvolve_gauss(center=None,
flux=1.,
fwhm=(20000., 10000.),
peak=False,
rot=60.,
factor=1,
unit_center=u.deg,
unit_fwhm=u.arcsec)
g = ima2.gauss_fit(verbose=False)
assert_almost_equal(g.fwhm[0], 20000, 2)
assert_almost_equal(g.fwhm[1], 10000, 2)
assert_almost_equal(g.center[0], 8.5)
assert_almost_equal(g.center[1], 12)
assert_equal(ima2.dtype, np.float64)
ima3 = ima2.resample(newdim=(32, 24),
newstart=None,
newstep=ima2.get_step(unit=u.arcsec) * 0.8)
assert_equal(ima3.dtype, np.float64)
def test_fftconvolve():
"""Image class: testing FFT convolution method."""
wcs = WCS(cdelt=(0.2, 0.3), crval=(8.5, 12), shape=(40, 30), deg=True)
data = np.zeros((40, 30))
data[19, 14] = 1
ima = Image(wcs=wcs, data=data)
ima2 = ima.fftconvolve_gauss(center=None,
flux=1.,
fwhm=(20000., 10000.),
peak=False,
rot=60.,
factor=1,
unit_center=u.deg,
unit_fwhm=u.arcsec)
g = ima2.gauss_fit(verbose=False)
assert_almost_equal(g.fwhm[0], 20000, 2)
assert_almost_equal(g.fwhm[1], 10000, 2)
assert_almost_equal(g.center[0], 8.5)
assert_almost_equal(g.center[1], 12)
ima2 = ima.fftconvolve_moffat(center=None,
flux=1.,
a=10000,
q=1,
n=2,
peak=False,
rot=60.,
factor=1,
unit_center=u.deg,
unit_a=u.arcsec)
m = ima2.moffat_fit(verbose=False)
assert_almost_equal(m.center[0], 8.5)
assert_almost_equal(m.center[1], 12)
def convolve(self, basename, main_beam):
outName = string.split(basename, '.fits')[0]
outName = outName + '_cv.fits'
dat, baseheader = fint.openFile(basename)
basefile = fits.open(basename)
#baseheader = basefile[0].header
basedata = Image(basename)
if 'NAXIS3' in baseheader:
del baseheader['NAXIS3']
if 'NAXIS4' in baseheader:
del baseheader['NAXIS4']
aaa = basedata.data
beam = np.array([float(baseheader['BMAJ']), float(baseheader['BMIN'])])
print main_beam, beam
if main_beam[0] > beam[0] and main_beam[1] > beam[1]:
bx = np.sqrt(main_beam[0] * main_beam[0] - beam[0] * beam[0])
by = np.sqrt(main_beam[1] * main_beam[1] - beam[1] * beam[1])
#bx= main_beam[0]
#by = main_beam[1]
if 'CDELT1' in baseheader:
pix_size = -baseheader['CDELT1']
elif 'CD1_1' in baseheader:
pix_size = -baseheader['CD1_1']
beam_area = 2 * np.pi * beam[0] / 2.35482 * beam[1] / 2.35482
main_beam_area = 2 * np.pi * main_beam[0] / 2.35482 * main_beam[
1] / 2.35482
number_pix_beam = beam_area / (pix_size * pix_size)
#aaa = np.divide(aaa,number_pix_beam)
#aaa = np.squeeze(aaa)
#basedata.data = np.squeeze(basedata.data)
#basedata.data=aaa
#print basedata.shape
newdata = Image.fftconvolve_gauss(basedata,
center=None,
flux=1,
peak=True,
factor=1,
fwhm=(by, bx),
unit_center=u.degree,
unit_fwhm=u.degree,
inplace=False)
aaa = np.array(newdata.data)
result = np.divide(aaa, np.power(main_beam_area / beam_area, 2))
#result = np.multiply(aaa,main_beam_area/(pix_size*pix_size))
baseheader['BMAJ'] = main_beam[0]
baseheader['BMIN'] = main_beam[1]
fits.writeto(outName, result, baseheader, overwrite=True)
else:
outName = basename
return outName