nabu.io.reader module

class nabu.io.reader.Reader(sub_region=None)

Bases: object

Abstract class for various file readers.

Parameters:

sub_region (tuple, optional) – Coordinates in the form (start_x, end_x, start_y, end_y), to read a subset of each frame. It can be used for Regions of Interest (ROI). Indices start at zero !

get_data(data_url)

Get data from a silx.io.url.DataUrl

release()

Release the file if needed.

class nabu.io.reader.NPReader(sub_region=None, mmap=True)

Bases: Reader

Reader for NPY/NPZ files. Mostly used for internal development. Please refer to the documentation of nabu.io.reader.Reader

multi_load = True

get_data(data_url)

Get data from a silx.io.url.DataUrl

release()

Release the file if needed.

class nabu.io.reader.EDFReader(sub_region=None)

Bases: Reader

A class for reading series of EDF Files. Multi-frames EDF are not supported.

multi_load = False

read(fname)

get_data(data_url)

Get data from a silx.io.url.DataUrl

class nabu.io.reader.HDF5Reader(sub_region=None)

Bases: Reader

A class for reading a HDF5 File.

multi_load = True

get_data(data_url)

Get data from a silx.io.url.DataUrl

release()

Release the file if needed.

class nabu.io.reader.HDF5Loader(fname, data_path, sub_region=None, data_buffer=None, pre_allocate=True, dtype='f')

Bases: object

An alternative class to HDF5Reader where information is first passed at class instantiation

load_data(force_load=False, output=None)

class nabu.io.reader.VolReaderBase(*args, **kwargs)

Bases: object

Base class with common code for data readers (EDFStackReader, NXTomoReader, etc)

get_frames_indices()

class nabu.io.reader.NXTomoReader(filename, data_path='{entry}/instrument/detector/data', sub_region=None, image_key=0, output_dtype=<class 'numpy.float32'>, processing_func=None, processing_func_args=None, processing_func_kwargs=None)

Bases: VolReaderBase

Read a HDF5 file in NXTomo layout.

Parameters:

• filename (str) – Path to the file to read.

• data_path (str) – Path within the HDF5 file, eg. “entry/instrument/detector/data”. Default is {entry}/data/data where {entry} is a magic keyword for the first entry.

• sub_region (slice or tuple, optional) – Region to select within the data, once the “image key” selection has been done. If None, all the data (corresponding to image_key) is selected. If slice(start, stop) is provided, the selection is done along dimension 0. Otherwise, it must be a 3-tuple of slices in the form (slice(start_angle, end_angle, step_angle), slice(start_z, end_z, step_z), slice(start_x, end_x, step_x)) Each of the parameters can be None, in this case the default start and end are taken in each dimension.

• output_dtype (numpy.dtype, optional) – Output data type if the data memory is allocated by this class. Default is float32.

• image_key (int, or None, optional) – Image type to read (see NXTomo documentation). 0 for projections, 1 for flat-field, 2 for dark field. If set to None, all the data will be read.

• processing_func (callable, optional) – Function to be called on each loaded stack of images. If provided, this function first argument must be the source buffer (3D array: stack of raw images), and the second argument must be the destination buffer (3D array, stack of output images). It can be None.

• None. (The other parameters are passed to "processing_func" if this parameter is not) –

image_key_path = 'instrument/detector/image_key_control'

multiple_frames_per_file = True

load_data(output=None, convert_after_reading=True)

Read data.

Parameters:

• output (array-like, optional) – Destination 3D array that will hold the data. If provided, use this memory buffer instead of allocating the memory. Its shape must be compatible with the selection of ‘sub_region’ and ‘image_key’.

• conver_after_reading (bool, optional) – Whether to do the dtype conversion (if any, eg. uint16 to float32) after data reading. With using h5py’s read_direct(), reading from uint16 to float32 is extremely slow, so data type conversion should be done after reading. The drawback is that it requires more memory.

class nabu.io.reader.NXDarksFlats(filename, **nxtomoreader_kwargs)

Bases: object

get_raw_darks(force_reload=False, as_multiple_array=True)

get_raw_flats(force_reload=False, as_multiple_array=True)

get_reduced_darks(method='mean', force_reload=False, as_dict=False)

get_reduced_flats(method='median', force_reload=False, as_dict=False)

get_raw_current(h5_path='{entry}/control/data')

get_reduced_current(h5_path='{entry}/control/data', method='median')

class nabu.io.reader.EDFStackReader(filenames, sub_region=None, output_dtype=<class 'numpy.float32'>, n_reading_threads=1, processing_func=None, processing_func_args=None, processing_func_kwargs=None)

Bases: VolReaderBase

multiple_frames_per_file = False

load_data(output=None)

nabu.io.reader.load_images_stack_from_hdf5(fname, h5_data_path, sub_region=None)

Load a 3D dataset from a HDF5 file.

Parameters:

• fname (str) – File path

• h5_data_path (str) – Data path within the HDF5 file

• sub_region (tuple, optional) – Tuple indicating which sub-volume to load, in the form (xmin, xmax, ymin, ymax, zmin, zmax) where the 3D dataset has the python shape (N_Z, N_Y, N_X). This means that the data will be loaded as data[zmin:zmax, ymin:ymax, xmin:xmax].

nabu.io.reader.get_hdf5_dataset_shape(fname, h5_data_path, sub_region=None)

nabu.io.reader.get_hdf5_dataset_dtype(fname, h5_data_path)

nabu.io.reader.get_entry_from_h5_path(h5_path)

nabu.io.reader.check_virtual_sources_exist(fname, data_path)

nabu.io.reader.import_h5_to_dict(h5file, h5path, asarray=False)

Wrapper on top of silx.io.dictdump.dicttoh5 replacing “None” with None

Parameters:

• h5file (str) – File name

• h5path (str) – Path in the HDF5 file

• asarray (bool, optional) – Whether to convert each numeric value to an 0D array. Default is False.