def test_make_sugrid(self):
ra_sub, dec_sub = self.system1.get_subgrid(subgrid_res=2)
ra_sub = util.array2image(ra_sub)
ra, dec = self.system1.get_cutout_coords
cos_dec = np.cos(self.system1.dec / 360 * 2 * np.pi)
ra = util.array2image(ra*cos_dec)
ra_resized = util.averaging(ra_sub, len(ra_sub), len(ra_sub)/2)
assert ra[0][0] == ra_resized[0][0]
assert ra[9][7] == ra_resized[9][7]
def get_psf_errors(self, kernel, sigma_bkg, star_list):
"""
returns a error map of sigma prop Intensity for a stacked psf estimation
:param psf_kwargs:
:param star_list:
:return:
"""
psf_size = len(kernel)
kernel_mean = util.image2array(kernel)
weights = np.zeros(len(star_list))
cov_i = np.zeros((psf_size**2, psf_size**2))
num_stars = len(star_list)
for i in range(0, num_stars):
star_list_i = star_list[i].copy()
star = util.cut_edges(star_list_i, psf_size)
weights[i] = np.sum(star)
rel_array = np.array(
[util.image2array(star) / weights[i] - kernel_mean])
a = (rel_array.T).dot(rel_array)
cov_i += a
factor = 1. / (num_stars)
#weights_sum = sum(weights)
sigma2_stack = factor * util.array2image(np.diag(cov_i))
psf_stack = copy.deepcopy(kernel)
sigma2_stack_new = sigma2_stack # - (data_kwargs['sigma_background']**2/weights_sum)
sigma2_stack_new[np.where(sigma2_stack_new < 0)] = 0
psf_stack[np.where(psf_stack < sigma_bkg)] = sigma_bkg
error_map = sigma2_stack_new / (psf_stack)**2
#error_map[np.where(error_map < psf_stack**2/data_kwargs['reduced_noise'])] = 0
# n = len(error_map)
#error_map[(n-1)/2-1:(n-1)/2+2,(n-1)/2-1:(n-1)/2+2] += 0
#error_map = filters.gaussian_filter(error_map, sigma=0.5)
return error_map
def test_cutout(self):
"""
tests the cutout routine and whether the header is also changed accordingly
"""
xw = 50 # number of pixel to be cutout in x-axis
yw = 50 # number of pixel to be cutout in y-axis
img, head, exp_map, ra_coords, dec_coords = self.system1._cutout(self.fits_filename, self.ra, self.dec, xw, yw)
ra_coords = util.array2image(ra_coords)
dec_coords = util.array2image(dec_coords)
assert head['NAXIS1'] == 2*xw
assert head['NAXIS2'] == 2*yw
assert len(img) == 2*yw
assert len(img[0]) == 2*xw
assert len(ra_coords) == yw*2
assert len(ra_coords[0]) == xw*2
cos_dec = np.cos(self.system1.dec / 360 * 2 * np.pi)
print(cos_dec)
print(np.sqrt((ra_coords[yw, xw]*cos_dec)**2 + dec_coords[yw, xw]**2))
ra, dec = self.system1.map_pix2coord(xw, yw)
npt.assert_almost_equal(ra_coords[yw, xw]*cos_dec, ra, decimal=5)
npt.assert_almost_equal(dec_coords[yw, xw], dec, decimal=5)
x,
y,
n_max,
beta,
1.,
center_x=0,
center_y=0)
# reconstruct M31 with the shapelet coefficients
image_reconstructed = shapeletSet.function(x,
y,
param_list,
n_max,
beta,
center_x=0,
center_y=0)
image_reconstructed_2d = util.array2image(
image_reconstructed) # map 1d data vector in 2d image
plt.matshow(np.log10(image_reconstructed_2d),
origin='lower') #,vmin=-5, vmax=1)
plt.colorbar()
plt.show()
pyfits.PrimaryHDU(image_reconstructed_2d).writeto(
'shapelet/{0}_shapelet.fits'.format(name), overwrite=True)
import pickle
output = open('shapelet/{0}.pkl'.format(name), 'wb')
passvar = {
'n_max': n_max,
'beta': beta,
'param_list': param_list,
'numPix_large': len(data)
}