def __init__(self,
file_names,
open_option="r+",
use_corrected=False,
overwrite=False,
batch=False):
super(BaseHDF5Analyser, self).__init__()
file_name = hadd(file_names)
if not os.path.exists(file_name):
print("File not found", file_name)
raise IOError
self.overwrite = overwrite
self.batch = batch
self.input_file = h5py.File(file_name, open_option)
if use_corrected:
if corrected_images_group in self.input_file:
self.images = self.input_file[corrected_images_group]
else:
warnings.warn("""
Could not find corrected images!
Using the raw ones.""")
self.images = self.input_file[raw_images_group]
else:
try:
self.images = self.input_file[raw_images_group]
except KeyError:
print(raw_images_group, "not found!\n\n")
raise
self.output_directory = self.input_file.require_group(
post_processing_dirname)
self.n_images = len(self.images)
if self.overwrite and self.output_name() in self.output_directory:
del self.output_directory[self.output_name()]
def __init__(self, image_files, flat_files, pixel, roi, steps, periods,
extension, overwrite):
self.overwrite = overwrite
self.image_array = get_projection_stack(image_files, pixel, roi,
overwrite)
self.flat_image = get_projection_stack(flat_files, pixel, roi,
overwrite)
open_option = "a"
self.input_file = h5py.File(hadd(image_files), open_option)
output_names = self.set_names()
self.output_directory = self.input_file.require_group(
post_processing_dirname)
self.export_name = hadd(image_files).replace("hdf5", extension)
self.n_steps = steps
self.n_periods = periods
self.n_flats = self.flat_image.shape[0] // self.n_steps
self.extension = extension
#Overwrite if necessary
if self.overwrite:
self.exists_in_file = False
for name in output_names:
if name in self.output_directory:
del self.output_directory[name]
self.initialize_reconstruction()
else:
images = {}
for name in output_names:
if name in self.output_directory:
images[name] = self.output_directory[name]
if len(images) == 3:
#All three images were saved, don't recalculate them
self.absorption_image = self.output_directory[
self.absorption_image_name]
self.differential_phase_image = self.output_directory[
self.differential_phase_image_name]
self.dark_field_image = self.output_directory[
self.dark_field_image_name]
self.exists_in_file = True
else:
self.exists_in_file = False
self.initialize_reconstruction()
'--dataset',
metavar='DATASET',
nargs='+',
default=['postprocessing/stack_pixel_509'],
help='dataset(s) in the HDF5 file to be exported')
commandline_parser.add_argument('--show',
action='store_true',
help='show each image.')
if __name__ == '__main__':
print_version(commandline_parser.prog)
args = commandline_parser.parse_args()
extension = args.format
dataset_names = args.dataset
file_name = hadd(args.file)
input_file = h5py.File(file_name, "r")
n = len(dataset_names)
print('saving {0} images:'.format(n))
for i, name in enumerate(dataset_names):
try:
image_array = input_file[name]
except KeyError:
print(name, "not found!\n\n")
raise
output_name = "{0}.{1}".format(name, extension)
without_slashes = output_name.replace("/", "_")
full_output_path = file_name.replace(".hdf5", "_" + without_slashes)
print(full_output_path)
plt.imsave(full_output_path, image_array)
if args.show:
def __init__(self, extension, file_name, *args, **kwargs):
self.extension = extension.lower()
self.parent_dir, _ = os.path.splitext(hadd(file_name))
self.image_dir = os.path.join(self.parent_dir, self.extension)
super(ImageConverter, self).__init__(file_name, *args, **kwargs)
plt.ylabel("visibility $2 a_1 / a_0$ ($\\%$)")
mean_visibility = mean_visibility[0]
line = plt.axhline(y=mean_visibility, color='r')
plt.legend([line], [
"average visibility: {0:.2f} $\\%$".format(mean_visibility * 100)
])
else:
plt.errorbar(np.arange(1, mean_visibility.shape[0] + 1),
mean_visibility,
yerr=std_dev_visibility,
fmt='o')
plt.xlim(0, mean_visibility.shape[0] + 1)
plt.xlabel("image number")
plt.ylabel("average visibility $2 a_1 / a_0$ ($\\%$)")
axis.yaxis.set_major_formatter(
FuncFormatter(lambda x, pos=0: "{0:.2%}".format(x)))
plt.tight_layout()
if not args.batch:
plt.ion()
plt.show()
input("Press ENTER to quit.")
"""Save to hdf5 file"""
output_object = np.vstack((pixels, visibility))
output_name = "postprocessing/visibility_{0}".format(args.pixel)
output_file_name = hadd(args.file)
output_file = h5py.File(output_file_name)
if output_name in output_file:
del output_file[output_name]
output_file.create_dataset(output_name, data=output_object)
print("Saved", os.path.join(output_file_name, output_name))