def plot_grid(self, filename=None, show=False, plot_radii=[], xy_lim=None):
if self.uv_grid is None:
self.grid_uvw_coords()
grid_size = self.uv_grid.shape[0]
wavelength = const.c.value / self.freq_hz
fov_rad = Imager.uv_cellsize_to_fov(self.grid_cell_size_m / wavelength,
grid_size)
extent = Imager.grid_extent_wavelengths(degrees(fov_rad), grid_size)
extent = np.array(extent) * wavelength
fig, ax = plt.subplots(figsize=(8, 8), ncols=1, nrows=1)
fig.subplots_adjust(left=0.125,
bottom=0.1,
right=0.9,
top=0.9,
wspace=0.2,
hspace=0.2)
image = self.uv_grid.real
options = dict(interpolation='nearest',
cmap='gray_r',
extent=extent,
origin='lower')
im = ax.imshow(image,
norm=ImageNormalize(stretch=LogStretch()),
**options)
divider = make_axes_locatable(ax)
cax = divider.append_axes("right", size="2%", pad=0.03)
cbar = ax.figure.colorbar(im, cax=cax)
cbar.set_label('baselines per pixel')
cbar.ax.tick_params(labelsize='small')
ticks = np.arange(5) * round(image.max() / 5)
ticks = np.append(ticks, image.max())
cbar.set_ticks(ticks, update_ticks=True)
for r in plot_radii:
ax.add_artist(plt.Circle((0, 0), r, fill=False, color='r'))
ax.set_xlabel('uu (m)')
ax.set_ylabel('vv (m)')
ax.grid(True)
if xy_lim is not None:
ax.set_xlim(-xy_lim, xy_lim)
ax.set_ylim(-xy_lim, xy_lim)
if filename is not None:
label = ''
if 'ska1_v5' in filename:
label = 'SKA1 v5'
if 'model' in filename:
label = 'Model ' + str(
re.search(r'\d+', os.path.basename(filename)).group())
ax.text(0.02, 0.95, label, weight='bold', transform=ax.transAxes)
fig.savefig(filename)
if show:
plt.show()
if filename is not None or show:
plt.close(fig)
else:
return fig
def plot_grid(self, filename=None, show=False, plot_radii=[],
x_lim=None, y_lim=None):
if self.uv_grid is None:
self.grid_uvw_coords()
grid_size = self.uv_grid.shape[0]
wavelength = const.c.value / self.freq_hz
fov_rad = Imager.uv_cellsize_to_fov(self.grid_cell_size_m / wavelength,
grid_size)
extent = Imager.grid_extent_wavelengths(degrees(fov_rad), grid_size)
extent = np.array(extent) * wavelength
fig, ax = plt.subplots(figsize=(8, 8), ncols=1, nrows=1)
fig.subplots_adjust(left=0.125, bottom=0.1, right=0.9, top=0.9,
wspace=0.2, hspace=0.2)
image = self.uv_grid.real
options = dict(interpolation='nearest', cmap='gray_r', extent=extent,
origin='lower')
im = ax.imshow(image, norm=ImageNormalize(stretch=LogStretch()),
**options)
divider = make_axes_locatable(ax)
cax = divider.append_axes("right", size="2%", pad=0.03)
cbar = ax.figure.colorbar(im, cax=cax)
cbar.set_label('baselines per pixel')
cbar.ax.tick_params(labelsize='small')
ticks = np.arange(5) * round(image.max() / 5)
ticks = np.append(ticks, image.max())
cbar.set_ticks(ticks, update_ticks=True)
for r in plot_radii:
ax.add_artist(plt.Circle((0, 0), r, fill=False, color='r'))
ax.set_xlabel('uu (m)')
ax.set_ylabel('vv (m)')
if not x_lim is None:
ax.set_xlim(x_lim)
if not y_lim is None:
ax.set_ylim(y_lim)
if filename is not None:
fig.savefig(filename)
if show:
plt.show()
if filename is not None or show:
plt.close(fig)
else:
return fig
def eval_psf(self, im_size=None, fov_deg=None, plot2d=False, plot1d=True):
if self.uu_m is None:
self.gen_uvw_coords()
# Evaluate grid size and fov if needed.
if im_size is None:
b_max = self.r_uv_m.max()
grid_size = int(ceil(b_max / self.grid_cell_size_m)) * 2 + \
self.station_diameter_m
if grid_size % 2 == 1:
grid_size += 1
else:
grid_size = im_size
wavelength = const.c.value / self.freq_hz
if fov_deg is None:
cellsize_wavelengths = self.grid_cell_size_m / wavelength
fov_rad = Imager.uv_cellsize_to_fov(cellsize_wavelengths,
grid_size)
fov_deg = degrees(fov_rad)
else:
fov_deg = fov_deg
uu = self.uu_m / wavelength
vv = self.vv_m / wavelength
ww = self.ww_m / wavelength
amp = np.ones_like(uu, dtype='c8')
psf = Imager.make_image(uu, vv, ww, np.ones_like(uu, dtype='c8'),
fov_deg, grid_size)
extent = Imager.image_extent_lm(fov_deg, grid_size)
self.psf = psf
self.psf_fov_deg = fov_deg
# --- Plotting ----
if plot2d:
fig, ax = plt.subplots(figsize=(8, 8))
norm = SymLogNorm(linthresh=0.05, linscale=1.0, vmin=-0.05, vmax=0.5,
clip=False)
opts = dict(interpolation='nearest', origin='lower', cmap='gray_r',
extent=extent, norm=norm)
im = ax.imshow(psf, **opts)
divider = make_axes_locatable(ax)
cax = divider.append_axes("right", size="5%", pad=0.03)
cbar = ax.figure.colorbar(im, cax=cax)
cbar.ax.tick_params(labelsize='small')
ax.set_xlabel('l')
ax.set_ylabel('m')
plt.savefig('psf.png')
plt.show()
plt.close(fig)
if plot1d:
l, m = Imager.image_pixels(self.psf_fov_deg, grid_size)
r_lm = (l**2 + m**2)**0.5
r_lm = r_lm.flatten()
idx_sorted = np.argsort(r_lm)
r_lm = r_lm[idx_sorted]
psf_1d = self.psf.flatten()[idx_sorted]
psf_hwhm = (wavelength / (r_lm[-1] * 2.0)) / 2
fig, ax = plt.subplots(figsize=(8, 8))
ax.plot(r_lm, psf_1d, 'k.', ms=2, alpha=0.1)
ax.set_xscale('log')
ax.set_yscale('log')
fig.savefig('TEST_psf1d.png')
plt.close(fig)
num_bins = 100 # FIXME(BM) make this a function arg
psf_1d_mean = np.zeros(num_bins)
psf_1d_abs_mean = np.zeros(num_bins)
psf_1d_abs_max = np.zeros(num_bins)
psf_1d_min = np.zeros(num_bins)
psf_1d_max = np.zeros(num_bins)
psf_1d_std = np.zeros(num_bins)
bin_edges = np.linspace(r_lm[0], r_lm[-1], num_bins + 1)
# bin_edges = np.logspace(log10(r_lm[1]), log10(r_lm[-1]),
# num_bins + 1)
psf_1d_bin_r = (bin_edges[1:] + bin_edges[:-1]) / 2
bin_idx = np.digitize(r_lm, bin_edges)
for i in range(1, num_bins + 1):
values = psf_1d[bin_idx == i]
if values.size > 0:
psf_1d_mean[i - 1] = np.mean(values)
psf_1d_abs_mean[i - 1] = np.mean(np.abs(values))
psf_1d_abs_max[i - 1] = np.max(np.abs(values))
psf_1d_min[i - 1] = np.min(values)
psf_1d_max[i - 1] = np.max(values)
psf_1d_std[i - 1] = np.std(values)
fig, ax = plt.subplots(figsize=(8, 8))
ax.plot(psf_1d_bin_r, psf_1d_abs_mean, '-', c='b', lw=1, label='abs mean')
ax.plot(psf_1d_bin_r, psf_1d_abs_max, '-', c='r', lw=1, label='abs max')
ax.plot(psf_1d_bin_r, psf_1d_std, '-', c='g', lw=1, label='std')
# ax.set_ylim(-0.1, 0.5)
# ax.set_xlim(0, psf_1d_bin_r[-1] / 2**0.5)
# ax.set_xscale('log')
ax.set_yscale('log')
ax.set_ylim(1e-4, 1)
ax.set_xlim(0, psf_1d_bin_r[-1] / 2**0.5)
ax.set_xlabel('PSF radius (direction cosines)')
# ax.set_ylabel('PSF amplitude')
ax.set_title('Azimuthally averaged PSF (FoV: %.2f)' % fov_deg)
ax.legend()
plt.show()
plt.close(fig)
def eval_psf(self,
im_size=None,
fov_deg=None,
plot2d=False,
plot1d=True,
filename_root=None,
num_bins=100):
"""Evaluate and plot the PSF."""
if self.uu_m is None:
self.gen_uvw_coords()
# Work out a usable grid size.
if im_size is None:
b_max = self.r_uv_m.max()
grid_size = int(ceil(b_max / self.grid_cell_size_m)) * 2 + \
self.station_diameter_m
if grid_size % 2 == 1:
grid_size += 1
else:
grid_size = im_size
# Work out the FoV
wavelength = const.c.value / self.freq_hz
if fov_deg is None:
cellsize_wavelengths = self.grid_cell_size_m / wavelength
fov_rad = Imager.uv_cellsize_to_fov(cellsize_wavelengths,
grid_size)
fov_deg = degrees(fov_rad)
else:
fov_deg = fov_deg
uu = self.uu_m / wavelength
vv = self.vv_m / wavelength
ww = self.ww_m / wavelength
amp = np.ones_like(uu, dtype='c8')
# _r = (uu**2 + vv**2)**0.5 * wavelength
# amp[_r <= 1e3] = 0.0
psf = Imager.make_image(uu,
vv,
ww,
amp,
fov_deg,
grid_size,
weighting='Natural')
extent = Imager.image_extent_lm(fov_deg, grid_size)
self.psf = psf
self.psf_fov_deg = fov_deg
# --- Plotting ----
if plot2d:
fig, ax = plt.subplots(figsize=(8, 8))
norm = SymLogNorm(linthresh=0.005,
linscale=1.0,
vmin=-0.005,
vmax=1.0,
clip=False)
opts = dict(interpolation='nearest',
origin='lower',
cmap='inferno',
extent=extent,
norm=norm)
# opts = dict(interpolation='nearest', origin='lower', cmap='gray_r',
# extent=extent)
im = ax.imshow(psf, **opts)
divider = make_axes_locatable(ax)
cax = divider.append_axes("right", size="5%", pad=0.03)
cbar = ax.figure.colorbar(im, cax=cax)
cbar.ax.tick_params(labelsize='small')
ax.set_xlabel('l')
ax.set_ylabel('m')
if filename_root:
label = ''
if 'ska1_v5' in filename_root:
label = 'SKA1 v5'
if 'model' in filename_root:
label = 'Model ' + str(
re.search(r'\d+',
os.path.basename(filename_root)).group())
ax.text(0.02,
0.95,
label,
color='white',
weight='bold',
transform=ax.transAxes)
plt.savefig('%s_2d_%.1f.png' % (filename_root, fov_deg))
else:
plt.show()
plt.close(fig)
if plot1d:
l, m = Imager.image_pixels(self.psf_fov_deg, grid_size)
r_lm = (l**2 + m**2)**0.5
r_lm = r_lm.flatten()
idx_sorted = np.argsort(r_lm)
r_lm = r_lm[idx_sorted]
psf_1d = self.psf.flatten()[idx_sorted]
psf_hwhm = (wavelength / (r_lm[-1] * 2.0)) / 2
# fig, ax = plt.subplots(figsize=(8, 8))
# ax.plot(r_lm, psf_1d, 'k.', ms=2, alpha=0.1)
# ax.set_xscale('log')
# ax.set_yscale('log')
# fig.savefig('TEST_psf1d.png')
# plt.close(fig)
psf_1d_mean = np.zeros(num_bins)
psf_1d_abs_mean = np.zeros(num_bins)
psf_1d_abs_max = np.zeros(num_bins)
psf_1d_min = np.zeros(num_bins)
psf_1d_max = np.zeros(num_bins)
psf_1d_std = np.zeros(num_bins)
psf_1d_rms = np.zeros(num_bins)
bin_edges = np.linspace(r_lm[0], r_lm[-1] * 2**(-0.5),
num_bins + 1)
# bin_edges = np.logspace(log10(r_lm[1]), log10(r_lm[-1]),
# num_bins + 1)
psf_1d_bin_r = (bin_edges[1:] + bin_edges[:-1]) / 2
bin_idx = np.digitize(r_lm, bin_edges)
for i in range(1, num_bins + 1):
values = psf_1d[bin_idx == i]
if values.size > 0:
psf_1d_mean[i - 1] = np.mean(values)
psf_1d_abs_mean[i - 1] = np.mean(np.abs(values))
psf_1d_abs_max[i - 1] = np.max(np.abs(values))
psf_1d_min[i - 1] = np.min(values)
psf_1d_max[i - 1] = np.max(values)
psf_1d_std[i - 1] = np.std(values)
psf_1d_rms[i - 1] = np.mean(values**2)**0.5
self.psf_1d['r'] = psf_1d_bin_r
self.psf_1d['min'] = psf_1d_min
self.psf_1d['max'] = psf_1d_max
self.psf_1d['mean'] = psf_1d_mean
self.psf_1d['std'] = psf_1d_std
self.psf_1d['rms'] = psf_1d_rms
self.psf_1d['abs_mean'] = psf_1d_abs_mean
self.psf_1d['abs_max'] = psf_1d_abs_max
fig, ax = plt.subplots(figsize=(8, 8))
ax.plot(psf_1d_bin_r,
psf_1d_abs_mean,
'-',
c='b',
lw=1,
label='abs mean')
ax.plot(psf_1d_bin_r,
psf_1d_abs_max,
'-',
c='r',
lw=1,
label='abs max')
ax.plot(psf_1d_bin_r, psf_1d_std, '-', c='g', lw=1, label='std')
# ax.set_ylim(-0.1, 0.5)
# ax.set_xlim(0, psf_1d_bin_r[-1] / 2**0.5)
# ax.set_xscale('log')
ax.set_yscale('log')
ax.set_ylim(1e-4, 1)
ax.set_xlim(0, psf_1d_bin_r[-1] / 2**0.5)
ax.set_xlabel('PSF radius (direction cosines)')
# ax.set_ylabel('PSF amplitude')
ax.set_title('Azimuthally averaged PSF (FoV: %.2f)' % fov_deg)
ax.legend()
if filename_root:
plt.savefig('%s_1d_%.1f.png' % (filename_root, fov_deg))
else:
plt.show()
plt.close(fig)
