# something other than the image we're going to reconstruct, but
# since this is just proof of concept, we'll go ahead
cx = kx.copy()
cy = ky.copy()
calib = k.copy()
# calib = np.tile(calib[:, None, :], (1, 2, 1))
calib = calib[:, None, :] # middle axis is the through-time dim
# Undersample: R=2
k[::2] = 0
# Reconstruct with Non-Cartesian GRAPPA
res = ttgrappa(
kx, ky, k, cx, cy, calib, kernel_size=25, coil_axis=-1)
# Let's take a look
sos = lambda x0: np.sqrt(np.sum(np.abs(x0)**2, axis=-1))
gridder0 = lambda x0: gridder(kx, ky, x0, sx=sx, sy=sx)
plt.subplot(1, 3, 1)
plt.imshow(sos(gridder0(k)))
plt.title('Undersampled')
plt.subplot(1, 3, 2)
plt.imshow(sos(gridder0(kspace.reshape((-1, nc)))))
plt.title('True')
plt.subplot(1, 3, 3)
plt.imshow(sos(gridder0(res)))
plt.title('Through-time GRAPPA')
plt.show()
# Do the thing
t0 = time()
bart_imspace = bart_nufft(bart_k)
bart_time = time() - t0
# Check it out
plt.figure()
plt.imshow(sos(bart_imspace))
plt.title('BART NUFFT')
plt.xlabel('Recon: %g sec' % bart_time)
plt.show(block=False)
# The phantominator module also supports arbitrary kspace
# sampling for multiple coils:
kx, ky = radial(sx, spokes)
k = kspace_shepp_logan(kx, ky, ncoil=nc)
# We will prefer a gridding approach to keep things simple. The
# helper function gridder wraps scipy.interpolate.griddata():
t0 = time()
grid_imspace = gridder(kx, ky, k, sx, sx, os=os, method=method)
grid_time = time() - t0
# Take a gander
plt.figure()
plt.imshow(sos(grid_imspace))
plt.title('scipy.interpolate.griddata')
plt.xlabel('Recon: %g sec' % grid_time)
plt.show()
t0 = time()
res_cart = grog(kx, ky, k, 2 * N, 2 * N, Gx, Gy)
print('Gridded in %g seconds' % (time() - t0))
# Now back to radial (inverse GROG)
res_radial = grog(kx,
ky,
np.reshape(res_cart, (-1, nc), order='F'),
2 * N,
2 * N,
Gx,
Gy,
inverse=True)
# Make sure we gridded something recognizable
nx, ny = 1, 3
plt.subplot(nx, ny, 1)
plt.imshow(sos(gridder(kx, ky, k, N, N)))
plt.title('Radial Truth')
plt.subplot(nx, ny, 2)
N2 = int(N / 2)
plt.imshow(sos(ifft(res_cart))[N2:-N2, N2:-N2])
plt.title('GROG Cartesian')
plt.subplot(nx, ny, 3)
plt.imshow(sos(gridder(kx, ky, res_radial, N, N)))
plt.title('GROG Radial (Inverse)')
plt.show()
hysplit_file_list = utils.get_hysplit_files(run_date=run_date, run_hours=hours)
utils.write_control_file(start_time=run_date,
coords=location_list,
hyfile_list=hysplit_file_list,
hours=hours,
vertical_type=vertical_type,
init_height=init_height,
tdumpdir=utils.trajectory_dir)
print 'calling HYSPLIT'
call(utils.HYSPLIT_call, shell=False, cwd=utils.HYSPLIT_workdir)
if __name__ == "__main__":
grid_coords = utils.gridder([27, -130], [36, -137], [41, -128], [32, -121],
numlats=5, numlons=6)
# utils.plot_gridpoints(grid_coords)
date_list = [dt.datetime(2015, 7, 01) + dt.timedelta(x) for x in range(0, 60)]
loop = lt(len(date_list))
fig, ax, m_ax = utils.make_map_plot()
# date_list = [dt.datetime(2015, 8, 19) + dt.timedelta(x) for x in range(0, 2)]
for i, date in enumerate(date_list):
loop.update(i)
if False:
run_trajectories(date, grid_coords)
if True:
t_name = os.path.join(utils.trajectory_dir,
'tdump'+date.strftime('%Y%m%dH%H%M'))
sx, spokes, nc = 256, 256, 8
traj = bart(1, 'traj -r -x%d -y%d' % (sx, spokes))
kx, ky = traj[0, ...].real.flatten(), traj[1, ...].real.flatten()
# Use BART to get Shepp-Logan and sensitivity maps
t0 = time()
k = bart(1, 'phantom -k -s%d -t' % nc, traj).reshape((-1, nc))
print('Took %g seconds to simulate %d coils' % (time() - t0, nc))
sens = bart(1, 'phantom -S%d -x%d' % (nc, sx)).squeeze()
# Undersample
ku = k.copy()
ku[::2] = 0
# Take a looksie
sos = lambda x0: np.sqrt(np.sum(np.abs(x0)**2, axis=-1))
plt.subplot(1, 3, 1)
plt.imshow(sos(gridder(kx, ky, k, sx, sx)))
plt.title('Truth')
plt.subplot(1, 3, 2)
plt.imshow(sos(gridder(kx, ky, ku, sx, sx)))
plt.title('Undersampled')
plt.subplot(1, 3, 3)
res = pars(kx, ky, ku, sens, kernel_radius=.8)
plt.imshow(sos(res))
plt.title('PARS')
plt.show()
def _gridder0(x0):
return gridder(kx, ky, x0, sx=sx, sy=sx)