def test1():
"""Simple test to check if inverting an image can correctly recover
the baseline coordinates which the image was created with.
"""
# Make a FITS image from a freq baseline coordinates
uu_m = [0.0, 30.0, 50.0]
vv_m = np.zeros_like(uu_m)
amps = np.ones_like(uu_m, dtype='c16')
grid, image_filename = make_image(uu_m, vv_m, amps, freq_hz=const.c.value,
im_size=128, cell_size_uv_m=1,
image_root_name='TEST',
algorithm='FFT')
# Load created FITS image and extract the image data and header
# information relating to its coordinates.
hdu_list = fits.open(image_filename)
image = hdu_list[0].data.squeeze()
im_size = hdu_list[0].header['NAXIS1']
freq_hz = hdu_list[0].header['CRVAL3']
cell_size_deg = fabs(hdu_list[0].header['CDELT1'])
# FT the image back to the grid.
ft_image = np.fft.fftshift(np.fft.ifft2(np.fft.fftshift(image)))
# Get the image and grid coordiante extent (required for plotting)
wavelength = const.c.value / freq_hz
fov_deg = degrees(Imager.cellsize_to_fov(radians(cell_size_deg), im_size))
extent_lm = Imager.image_extent_lm(fov_deg, im_size)
extent_uv_m = Imager.grid_extent_wavelengths(fov_deg, im_size) * wavelength
# Plot the image, grid, and FT(image) to check if we have recovered
# the input positions correctly.
opts = dict(interpolation='nearest', origin='lower', cmap='gray_r')
fig, (ax1, ax2, ax3, ax4) = plt.subplots(figsize=(20, 6), ncols=4)
fig.subplots_adjust(left=0.05, bottom=0.05, right=0.95, top=0.98,
wspace=0.3, hspace=0.2)
plot_image(ax1, image, **dict(opts, extent=extent_lm, vmin=-3, vmax=3))
ax1.set_title('Image')
plot_image(ax2, grid.real, **dict(opts, extent=extent_uv_m))
ax2.set_title('Grid')
ax2.set_xlabel('uu (m)')
ax2.set_ylabel('vv (m)')
plot_image(ax3, ft_image.real, **dict(opts, extent=extent_uv_m))
ax3.set_title('FT(image).real')
ax3.set_xlabel('uu (m)')
ax3.set_ylabel('vv (m)')
plot_image(ax4, ft_image.imag, **dict(opts, extent=extent_uv_m))
ax4.set_title('FT(image).imag')
ax4.set_xlabel('uu (m)')
ax4.set_ylabel('vv (m)')
# Add markers for input positions
marker_opts = dict(radius=3, color='r', fill=None)
for pos in zip(uu_m, vv_m):
ax3.add_artist(plt.Circle(pos, **marker_opts))
ax3.add_artist(plt.Circle(-np.array(pos), **marker_opts))
ax2.add_artist(plt.Circle(pos, **marker_opts))
plt.show()
def make_image(uu_m,
vv_m,
amps,
freq_hz,
im_size,
cell_size_uv_m,
image_root_name,
algorithm='fft'):
"""Make a FITS image from the specified data.
This function is slightly unusual in that the image size is specified
using a grid pixel size rather than image pixel size or field-of-view.
Args:
uu_m (array_like, float): Baseline uu coordinates, in metres
vv_m (array_like, float): Baseline vv coordinates, in metres
amps (array_like, complex): Visibility amplitudes
freq_hz (float): Observation frequency, in Hz
im_size (int): Image dimension (assumes square image)
cell_size_uv_m (float): Image grid pixel separation, in metres
image_root_name (str): Output FITS image root name.
algorithm (str): Imaging algorithm to use (FFT or W-projection)
Returns:
(dict, str): Dictionary containing the grid used to make the image,
filename of the output fits image created.
"""
ra_deg, dec_deg = 0, 90
ww_m = np.zeros_like(uu_m)
image_type = 'I'
wavelength = const.c.value / freq_hz
uv_cellsize_wavelengths = cell_size_uv_m / wavelength
fov_rad = Imager.uv_cellsize_to_fov(uv_cellsize_wavelengths, im_size)
# Create FITS image
imager = Imager(precision='double')
imager.set(fov_deg=degrees(fov_rad),
size=im_size,
algorithm=algorithm,
weighting='natural',
image_type=image_type,
wprojplanes=-1,
output_root=image_root_name)
imager.set_vis_frequency(freq_hz)
imager.set_vis_phase_centre(ra_deg, dec_deg)
imager_data = imager.run(uu_m, vv_m, ww_m, amps, return_grids=1)
image_grid = imager_data['grids'].squeeze()
return image_grid, '%s_%s.fits' % (image_root_name, image_type)
def make_image(uu_m, vv_m, amps, freq_hz, im_size, cell_size_uv_m,
image_root_name, algorithm='fft'):
"""Make a FITS image from the specified data.
This function is slightly unusual in that the image size is specified
using a grid pixel size rather than image pixel size or field-of-view.
Args:
uu_m (array_like, float): Baseline uu coordiantes, in metres
vv_m (array_like, flaot): Baseline vv coordiantes, in metres
amps (array_like, complex): Visibility amplitudes
freq_hz (float): Observation frequency, in Hz
im_size (int): Image dimension (assumes square image)
cell_size_uv_m (float): Image grid pixel separation, in metres
image_root_name (str): Output FITS image root name.
algorithm (str): Imaging algorithm to use (FFT or W-projection)
Returns:
(dict, str): Dictionary containing the grid used to make the image,
filename of the output fits image created.
"""
ra_deg, dec_deg = 0, 90
ww_m = np.zeros_like(uu_m)
image_type = 'I'
wavelength = const.c.value / freq_hz
uv_cellsize_wavelengths = cell_size_uv_m / wavelength
fov_rad = Imager.uv_cellsize_to_fov(uv_cellsize_wavelengths, im_size)
# Create FITS image
imager = Imager(precision='double')
imager.set(fov_deg=degrees(fov_rad), size=im_size, algorithm=algorithm,
weighting='natural', image_type=image_type, wprojplanes=-1,
output_root=image_root_name)
imager.set_vis_frequency(freq_hz)
imager.set_vis_phase_centre(ra_deg, dec_deg)
imager_data = imager.run(uu_m, vv_m, ww_m, amps, return_grids=True)
image_grid = imager_data['grids'].squeeze()
return image_grid, '%s_%s.fits' % (image_root_name, image_type)
def test1():
"""Simple test to check if inverting an image can correctly recover
the baseline coordinates which the image was created with.
"""
# Make a FITS image from a freq baseline coordinates
uu_m = [0.0, 30.0, 50.0]
vv_m = np.zeros_like(uu_m)
amps = np.ones_like(uu_m, dtype='c16')
grid, image_filename = make_image(uu_m,
vv_m,
amps,
freq_hz=const.c.value,
im_size=128,
cell_size_uv_m=1,
image_root_name='TEST',
algorithm='FFT')
# Load created FITS image and extract the image data and header
# information relating to its coordinates.
hdu_list = fits.open(image_filename)
image = hdu_list[0].data.squeeze()
im_size = hdu_list[0].header['NAXIS1']
freq_hz = hdu_list[0].header['CRVAL3']
cell_size_deg = fabs(hdu_list[0].header['CDELT1'])
# FT the image back to the grid.
ft_image = np.fft.fftshift(np.fft.ifft2(np.fft.fftshift(image)))
# Get the image and grid coordiante extent (required for plotting)
wavelength = const.c.value / freq_hz
fov_deg = degrees(Imager.cellsize_to_fov(radians(cell_size_deg), im_size))
extent_lm = Imager.image_extent_lm(fov_deg, im_size)
extent_uv_m = Imager.grid_extent_wavelengths(fov_deg, im_size) * wavelength
# Plot the image, grid, and FT(image) to check if we have recovered
# the input positions correctly.
opts = dict(interpolation='nearest', origin='lower', cmap='gray_r')
fig, (ax1, ax2, ax3, ax4) = plt.subplots(figsize=(20, 6), ncols=4)
fig.subplots_adjust(left=0.05,
bottom=0.05,
right=0.95,
top=0.98,
wspace=0.3,
hspace=0.2)
plot_image(ax1, image, **dict(opts, extent=extent_lm, vmin=-3, vmax=3))
ax1.set_title('Image')
plot_image(ax2, grid.real, **dict(opts, extent=extent_uv_m))
ax2.set_title('Grid')
ax2.set_xlabel('uu (m)')
ax2.set_ylabel('vv (m)')
plot_image(ax3, ft_image.real, **dict(opts, extent=extent_uv_m))
ax3.set_title('FT(image).real')
ax3.set_xlabel('uu (m)')
ax3.set_ylabel('vv (m)')
plot_image(ax4, ft_image.imag, **dict(opts, extent=extent_uv_m))
ax4.set_title('FT(image).imag')
ax4.set_xlabel('uu (m)')
ax4.set_ylabel('vv (m)')
# Add markers for input positions
marker_opts = dict(radius=3, color='r', fill=None)
for pos in zip(uu_m, vv_m):
ax3.add_artist(plt.Circle(pos, **marker_opts))
ax3.add_artist(plt.Circle(-np.array(pos), **marker_opts))
ax2.add_artist(plt.Circle(pos, **marker_opts))
plt.show()
