NumPy (NPY)#

This reader supports reading and writing of numpy arrays in the .npy format, which is a binary file format for storing numpy arrays. This reader supports reading large npy files lazily and in a distributed fashion.

Note

This format doesn’t support storing metadata like scale, units, etc.

API functions#

rsciio.numpy.file_reader(filename, lazy=False, chunks='auto', navigation_axes=None, **kwargs)#

Read data from npy files.

Parameters:
filenamestr, pathlib.Path

Filename of the file to read or corresponding pathlib.Path.

lazybool, default=False

Whether to open the file lazily or not. The file will stay open until closed in compute() or closed manually. get_file_handle() can be used to access the file handler and close it manually.

chunkstuple of int or None, default=None

Define the chunking used for saving the dataset. If None, calculates chunks for the signal, with preferably at least one chunk per signal space.

navigation_axeslist, optional

List of axes that should be treated as navigation axes. If not provided, all axes will be treated as signal axes.

**kwargsdict, optional

Pass keyword arguments to the numpy.load(), when lazy is False, otherwise to rsciio.utils.distributed.memmap_distributed().

Returns:
list of dict

List of dictionaries containing the following fields:

  • ‘data’ – multidimensional numpy.ndarray or dask.array.Array

  • ‘axes’ – list of dictionaries describing the axes containing the fields ‘name’, ‘units’, ‘index_in_array’, and either ‘size’, ‘offset’, and ‘scale’ or a numpy array ‘axis’ containing the full axes vector

  • ‘metadata’ – dictionary containing the parsed metadata

  • ‘original_metadata’ – dictionary containing the full metadata tree from the input file

When the file contains several datasets, each dataset will be loaded as separate dictionary.

Notes

This plugin does not support metadata like scale, units, etc.

Examples

To load a numpy file with lazy loading and specified chunks:

>>> from rsciio.numpy import file_reader
>>> d = file_reader('data.npy', lazy=True, chunks=("auto", "auto", 250, 250), navigation_axes=[0, 1])
>>> d[0]['data']
dask.array<array, shape=(1000, 1000, 500, 500), dtype=float64, chunksize=(100, 100, 250, 250)>
rsciio.numpy.file_writer(filename, signal, **kwargs)#

Write data to npy files.

Parameters:
filenamestr, pathlib.Path

Filename of the file to write to or corresponding pathlib.Path.

signaldict

Dictionary containing the signal object. Should contain the following fields:

  • ‘data’ – multidimensional numpy array

  • ‘axes’ – list of dictionaries describing the axes containing the fields ‘name’, ‘units’, ‘index_in_array’, and either ‘size’, ‘offset’, and ‘scale’ or a numpy array ‘axis’ containing the full axes vector

  • ‘metadata’ – dictionary containing the metadata tree

**kwargsdict, optional

Additional keyword arguments passed to numpy.save().

Notes

This plugin does not support metadata like scale, units, etc.