grp = h5f.create_group('xpcs')
qtmp = grp.create_dataset('q_points', (3, N, N),
'f',
chunks=(1, N, N),
compression="gzip",
shuffle=True)
qtmp.attrs['wavelen'] = wavelen
qtmp.attrs['theta'] = [-10, 10]
qtmp.attrs['theta_units'] = 'degree'
qtmp.attrs['content'] = 'qx, qy, qz'
qtmp[0, :, :] = q_points[..., 0].reshape((N, N))
qtmp[1, :, :] = q_points[..., 1].reshape((N, N))
qtmp[2, :, :] = q_points[..., 2].reshape((N, N))
# read data
dset = grp.create_dataset('imgs', (Nsteps, N, N),
'f',
chunks=(1, N, N),
compression="gzip",
shuffle=True)
# turn the crank
t0 = time.time()
for i, fname in enumerate(binfiles):
print(fname)
pts = read_lammps_bin(fname)
img = mdscatter.dft(pts, q_points)
img = np.abs(img)**2
dset[i, :, :] = np.reshape(img, (N, N))
t1 = time.time() - t0
print('time taken = %f\n' % t1)
if __name__ == '__main__':
wavelen = 0.1
qvals = qvals(nrow = N, ncol = N)
outf = 'xpcs' + str(N).zfill(5) + '.h5'
h5f = h5py.File(outf, 'w')
grp = h5f.create_group('xpcs')
qtmp = grp.create_dataset('q_points', (3, N*N), 'f')
qtmp.attrs['wavelen'] = wavelen
qtmp.attrs['theta'] = [-10, 10]
qtmp.attrs['theta_units'] = 'degree'
# List data files sorted by step number.
steps = sorted(glob.glob('data/*.txt'), key=lambda name: int(name[8:-4]))
Nsteps = len(steps)
dset = grp.create_dataset('imgs', (Nsteps, N, N), 'f')
# turn the crank
t0 = time.time()
for i, npy in tqdm.tqdm(enumerate(steps), total=Nsteps):
# Data is id, x, y ,z. Drop the id column.
# Multiply by 16 to get the q-range for 8-ID-I setup.
pts = 16 * np.loadtxt(npy, skiprows=9)[:, 1:]
img = mdscatter.dft(pts, qvals)
img = np.abs(img)**2
dset[i,:,:] = np.reshape(img, (N, N))
t1 = time.time() - t0
print('time taken = %f\n' % t1)
h5f.close()
sdd = 4.
scale = 28
center = (0, 768)
beam_rad = 6 * scale
detector = Lambda750k()
qvecs = detector.qvectors(sdd, center, wavelen)
outf = 'xpcs_out.h5'
h5f = h5py.File(outf, 'w')
grp = h5f.create_group('xpcs')
qtmp = grp.create_dataset('q_points', (3, *detector.shape), 'f')
# read data
pattern = '3(\d){4}'
npys = filelist('/home/dkumar/Data/np_arrays', pattern)
Nsteps = len(npys)
dset = grp.create_dataset('imgs', (Nsteps, *detector.shape), 'f')
# turn the crank
t0 = time.time()
for i, npy in enumerate(npys):
pts = load_npy(npy, center=np.array([8, 8, 8]), scale=scale)
img = mdscatter.dft(pts, qvals, beam_rad)
img = np.abs(img)**2
img = np.reshape(img, detector.shape)
dset[i, :, :] = np.reshape(img, detector.shape)
t1 = time.time() - t0
print('time taken = %f\n' % t1)
h5f.close()