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micropython-npyfile

Support for Numpy files (.npy) for MicroPython. Simple persistence of multi-dimensional numeric array data, and interoperability with Numpy/CPython et.c.

Was initially written to be used with emlearn-micropython, a Machine Learning and Digital Signal Processing library for MicroPython.

Features

• Reading & writing .npy files with numeric data (see below for Limitations)
• Streaming/chunked reading & writing
• No external dependencies. Uses array.array
• Written in pure Python. Compatible with CPython, CircuitPython, et.c.

Installing

This package can be installed using mip.

For example:

mpremote mip install github:jonnor/micropython-npyfile

Or just copy the npyfile.py file to your MicroPython device.

Usage

Save a file (simple)

import array
import npyfile

shape = (10, 4)
data = array.array('f', (1.0 for _ in range(shape[0]*shape[1])))

npyfile.save('mydata.npy', data, shape)

Load a file (simple)

import npyfile
shape, data = npyfile.load('mydata.npy')

print(shape)
print(data)

Streaming read

Streaming/chunked reading can be used to keep memory usage low.

import npyfile

with npyfile.Reader('mydata.npy') as reader:

    # Metadata available on the reader object
    print(reader.shape, reader.typecode, reader.itemsize)

    # NOTE: assumes input is 2d. Pick chunksize in another way if not
    chunksize = reader.shape[1]
    for chunk in reader.read_data_chunks(chunksize):
        print(len(chunk), chunk)

More examples:

• Streaming matching data from two files: two_streams.py

Streaming write

Streaming/chunked writing can be used to keep memory usage low.

See implementation of npyfile.save(), in npyfile.py

Reading .npz files

.npz files are ZIP archives. They can be read using an implementation of the zipfile module, such as jonnor/micropython-zipfile. Both uncompressed and DEFLATE compressed files are supported.

For example code, see read_npz.py.

Saving portable files with numpy

numpy.save defaults to allowing pickle support. This makes is very easy to accidentially save numpy.array objects that use pickle for serialization, instead of just numeric data. Such files will not be loadable by npyfile (or most other .npy readers).

To avoid this, when saving numeric arrays, always use:

numpy.save(PATH, DATA, allow_pickle=False)

Limitations

• Only little-endian is supported, not big-endian
• Only C data order is supported, not Fortran
• Strings are not supported
• Complex numbers not supported
• Pickled data is not supported

NOT TESTED on unsupported/malformed/malicious inputs.

TODO

Contributions welcomed!

TODO:

• Add tests for unsupported/malformed inputs

Developing

Running tests on host

Install the Unix/Window port of MicroPython. Then run:

MICROPYPATH=./ micropython tests/test_npyfile.py

The tests can also be ran under CPython

PYTHONPATH=./ python tests/test_npyfile.py

Running tests on device

Connect a MicroPython device via USB.

Copy over the data

mpremote cp npyfile.py :
mpremote -r cp tests/ :
mpremote run tests/test_npyfile.py