def get_input():
args, params = cmdline_config_cxi_reader.get_all('extract_array',
'exporting an array')
params = params['extract_array']
print(args.filename)
return args, params
def get_input():
args, params = cmdline_config_cxi_reader.get_all(
'pos_refine',
'update the sample translations according to a least squares minimisation procedure',
exclude=['frames'])
params = params['pos_refine']
# frames, split by frame no.
roi = params['roi']
roi = (params['good_frames'], slice(roi[0], roi[1]), slice(roi[2], roi[3]))
params['frames'] = MpiArray_from_h5(args.filename,
params['frames'],
axis=0,
dtype=np.float64,
roi=roi)
#def MpiArray_from_h5(fnam, path, axis=0, dtype=None, roi = None):
#params['frames'] = params['frames'][params['good_frames']]
if rank != 0:
params['R_ss_fs'] = None
# offset positions
if rank == 0 and params['pixel_shifts'] is not None:
params['R_ss_fs'] += fm.steps_offset(params['R_ss_fs'],
params['pixel_shifts'])
params['R_ss_fs'] = MpiArray(params['R_ss_fs'])
params['R_ss_fs'].scatter(axis=0)
# set masked pixels to negative 1
for i in range(params['frames'].arr.shape[0]):
params['frames'].arr[i][~params['mask']] = -1
params['whitefield'][~params['mask']] = -1
if params['pixel_shifts'] is None:
params['pixel_shifts'] = np.zeros((2, ) + params['whitefield'].shape,
dtype=np.float64)
# add a regularization factor
shape = params['whitefield'].shape
reg = mk_reg(shape, params['reg'])
return args, params, reg
def get_input():
args, params = cmdline_config_cxi_reader.get_all(
'update_pixel_map',
'update the pixel shifts according to a least squares minimisation procedure',
exclude=['frames', 'whitefield', 'mask'])
params = params['update_pixel_map']
# split by ss pixels
####################
# special treatment for frames
roi = params['roi']
roi = (params['good_frames'], slice(roi[0], roi[1]), slice(roi[2], roi[3]))
# easy for the mask
params['frames'] = MpiArray_from_h5(args.filename,
params['frames'],
axis=1,
roi=roi,
dtype=np.float64)
params['mask'] = MpiArray_from_h5(args.filename,
params['mask'],
axis=0,
roi=roi[1:],
dtype=np.bool)
with h5py.File(args.filename, 'r') as f:
shape = f[params['whitefield']].shape
if len(shape) == 2:
params['whitefield'] = MpiArray_from_h5(args.filename,
params['whitefield'],
axis=0,
roi=roi[1:],
dtype=np.float64)
params['whitefield'].arr[~params['mask'].arr] = -1
else:
params['whitefield'] = MpiArray_from_h5(args.filename,
params['whitefield'],
axis=1,
roi=roi,
dtype=np.float64)
for i in range(params['whitefield'].arr.shape[0]):
params['whitefield'].arr[i][~params['mask'].arr] = -1
# set masked pixels to negative 1
for i in range(params['frames'].arr.shape[0]):
params['frames'].arr[i][~params['mask'].arr] = -1
# offset positions
if params['pixel_shifts'] is not None:
params['R_ss_fs'] += fm.steps_offset(params['R_ss_fs'],
params['pixel_shifts'])
# special treatment for the pixel_shifts
if params['pixel_shifts'] is None and rank == 0:
params['pixel_shifts'] = np.zeros(
(2, ) + params['whitefield'].shape[-2:], dtype=np.float64)
if rank != 0:
params['pixel_shifts'] = None
params['pixel_shifts'] = MpiArray(params['pixel_shifts'])
params['pixel_shifts'].scatter(axis=1)
return args, params
bad = (overlap < 2.0)
atlas[bad] = -1
atlas[~bad] /= (overlap[~bad] + 1.0e-5)
return atlas
if __name__ == '__main__':
# get input
###########
# get command line args and config
sc = 'fit_defocus_registration'
des = 'fit the sample to focus distance (and astigmatism) by registration of speckles.'
args, params = cmdline_parser.parse_cmdline_args(sc, des)
# now load the necessary data
args, params = cmdline_config_cxi_reader.get_all(sc, des)
params = params['fit_defocus_registration']
if ('pixel_shifts' in params) and (params['pixel_shifts'] is not None):
pix_shifts = params['pixel_shifts']
else:
pix_shifts = np.zeros((2, ) + params['whitefield'].shape[-2:],
dtype=np.float64)
z1 = params['defocus']
z2 = params['distance'] - params['defocus']
M = (z1 + z2) / z1
ze = z2 / M
print('mag :', M)
print('z effective:', ze)
if reg is not None:
x = np.arange(shape[0]) - shape[0] // 2
x = np.exp(-x**2 / (2. * reg**2))
y = np.arange(shape[1]) - shape[1] // 2
y = np.exp(-y**2 / (2. * reg**2))
reg = np.outer(x, y)
else:
reg = 1
return reg
if __name__ == '__main__':
# get input
###########
args, params = cmdline_config_cxi_reader.get_all(
'stitch',
'stitch frames together to form a merged view of the sample from projection images'
)
params = params['stitch']
if ('pixel_shifts' in params) and (params['pixel_shifts'] is not None):
pix_shifts = params['pixel_shifts']
else:
pix_shifts = np.zeros((2, ) + params['whitefield'].shape[-2:],
dtype=np.float64)
# Do Stuff
##########
# set masked pixels to negative 1
mask = params['mask'].astype(np.bool)
params['frames'] = params['frames'][params['good_frames']].astype(
np.float64)
def get_input():
args, params = cmdline_config_cxi_reader.get_all('make_whitefield',
'estimate the whitefield by taking the median value of every pixel')
params = params['make_whitefield']
return args, params