def plotTestImage(imageArray):
interval = ZScaleInterval()
limits = interval.get_limits(imageArray)
sources = locateStarsInImage(imageArray)
positions = np.transpose((sources['xcentroid'], sources['ycentroid']))
apertures = CircularAperture(positions, r=4.)
norm = ImageNormalize(stretch=SqrtStretch())
plt.imshow(imageArray,
cmap='Greys',
origin='lower',
norm=norm,
interpolation='nearest',
vmin=limits[0],
vmax=limits[1])
apertures.plot(color='red', lw=1.5, alpha=0.5)
plt.show()
def compute_eff_radii(image, plot=False):
max_pix_rad = np.min(image.shape) // 2
radius = np.arange(3, max_pix_rad, 3)
fake_image = np.zeros_like(image)
fake_image[max_pix_rad // 2:(3 * max_pix_rad) // 2,
max_pix_rad // 2:(3 * max_pix_rad) //
2] = image[max_pix_rad // 2:(3 * max_pix_rad) // 2,
max_pix_rad // 2:(3 * max_pix_rad) // 2]
com_x, com_y = centroid_2dg(fake_image)
aperture_sum = []
for rad_i in radius:
aperture = CircularAperture((com_x, com_y), r=rad_i)
phot_table = aperture_photometry(image, aperture)
aperture_sum.append(phot_table['aperture_sum'].value)
aperture_sum = np.array(aperture_sum).squeeze()
norm_aperture_sum = aperture_sum / aperture_sum[-1]
half_mass_rad = np.interp(0.5, norm_aperture_sum, radius)
half_mass_aperture = CircularAperture((com_x, com_y), r=half_mass_rad)
two_half_mass_aperture = CircularAperture((com_x, com_y),
r=2 * half_mass_rad)
half_mass_table = aperture_photometry(image, half_mass_aperture)
two_half_mass_table = aperture_photometry(image, two_half_mass_aperture)
if plot:
fig = plt.figure()
plt.imshow(np.log10(image))
plt.colorbar(label='log(image)')
plt.plot(com_x, com_y, '+', markersize=8, color='c')
half_mass_aperture.plot(color='r', lw=2, label=r'Half mass rad')
two_half_mass_aperture.plot(color='orange',
lw=2,
label=r'Two half mass rad')
plt.annotate(
'Tot mass={:.2}\nHalf mass={:.2}\nTwoHalf mass={:.2}'.format(
float(aperture_sum[-1]),
float(half_mass_table['aperture_sum'].value),
float(two_half_mass_table['aperture_sum'].value)),
xy=(.1, 1),
xycoords='axes fraction',
va='bottom')
plt.legend()
plt.close()
return half_mass_aperture, two_half_mass_aperture, fig
else:
return half_mass_aperture, two_half_mass_aperture
def compute_circular_aperture(self, centre, radius, flux_return=False,
plot=False):
from photutils.aperture import CircularAperture#, aperture_photometry
aperture = CircularAperture(centre, r=radius)
aperture_mask = aperture.to_mask()
aperture_mask = np.array(
aperture_mask.to_image(self.wcs.celestial.array_shape), dtype=bool)
if flux_return:
integrated_flux = np.nansum(self.flux[:, aperture_mask], axis=1)
integrated_no_cov = np.nansum(self.flux_error[:, aperture_mask]**2, axis=1)
## Accounting for covariance errors
if aperture_mask[aperture_mask].size<100:
integrated_flux_cov = integrated_no_cov*(
1+1.62*np.log10(aperture_mask[aperture_mask].size))
integrated_flux_err = np.sqrt(integrated_flux_cov)
else:
integrated_flux_cov = integrated_no_cov*4.2
integrated_flux_err = np.sqrt(integrated_flux_cov)
else:
integrated_flux = None
integrated_flux_err = None
if plot:
fig = plt.figure()
ax = fig.add_subplot(111)
ax.imshow(self.flux[self.wl.size//2, :, :], cmap='gist_earth_r')
aperture.plot(lw=1, color='r')
ax.annotate(r'$R_e={:4.3}~(Kpc)$'.format(self.eff_radius_physical),
xy=(.9,.95), xycoords='axes fraction', va='top', ha='right')
ax.annotate(r'$R_e={:4.3}~(arcsec)$'.format(self.eff_radius),
xy=(.9,.9), xycoords='axes fraction', va='top', ha='right')
aperture.plot(lw=1, color='r')
ax.annotate(r'$R_e={:4.3}~(pix)$'.format(self.eff_radius_pix),
xy=(.9,.85), xycoords='axes fraction', va='top', ha='right')
ax.annotate(r'$R_a={:4.3}~(pix)$'.format(radius),
xy=(.9,.80), xycoords='axes fraction', va='top', ha='right',
color='r')
aperture.plot(lw=1, color='r')
# plt.savefig('bpt_apertures/'+name_i+'.png')
plt.show()
plt.close()
return aperture, aperture_mask, integrated_flux, integrated_flux_err, fig
else:
return aperture, aperture_mask, integrated_flux, integrated_flux_err
flux_companion = aperture_photometry(data[0], [aperture, annulus])
bkg_mean = flux_companion['aperture_sum_1'] / annulus.area
bkg_sum_in_companion = bkg_mean * aperture.area
print(flux_companion)
print("bkg_mean =", bkg_mean[0], "\naperture.area =", aperture.area,
"\nannulus.area =", annulus.area)
print("bkg_sum_in_companion =", bkg_sum_in_companion[0])
flux_companion_origin = flux_companion[
'aperture_sum_0'] - bkg_sum_in_companion
print("flux companion origin =", flux_companion_origin[0])
norm = simple_norm(data, 'sqrt', percent=99)
plt.imshow(data[0], norm=norm, interpolation='nearest')
ap_patches = aperture.plot(color='white',
lw=2,
label='Photometry aperture')
ann_patches = annulus.plot(color='red',
lw=2,
label='Background annulus')
#handles=(ap_patches[0],ann_patches[0])
plt.legend(loc=(0.17, 0.05),
facecolor='#458989',
labelcolor='white',
prop={
'weight': 'bold',
'size': 11
})
plt.xlim(20, 170)
plt.ylim(20, 256)
#plt.savefig('./Origin_Companion_Flux.png')
r_out = r_in + 15
annulus = CircularAnnulus(positions, r_in=r_in, r_out=r_out)
# plot image with apertures
y_or_n = input('Do you wish to display the image and appertures? ')
if (y_or_n[0] == 'y') or (y_or_n[0] == 'Y'):
plt.figure(1)
plt.clf()
# label the sources
mylabels = sources['id']
for idx, txt in enumerate(mylabels):
plt.annotate(txt, (positions[idx, 0], positions[idx, 1]))
plt.imshow(np.log(imdata), cmap='Greys')
apertures.plot(color='red', lw=1.5, alpha=0.5)
annulus.plot(color='green', lw=1.5, alpha=0.5)
plt.show()
# sum signal in source apertures
aper_ann = [apertures, annulus]
phot_table = aperture_photometry(imdata, aper_ann)
# Find the background per pixel from the annulus
print('\nMean background per pixel:')
phot_table['mean_bkg'] = phot_table['aperture_sum_1'] / annulus.area
phot_table['id', 'mean_bkg'].pprint()
# Compute the background signal contribution to the aperture
bkg_signal = phot_table['mean_bkg'] * apertures.area
source_counts = phot_table['aperture_sum_0'] - bkg_signal