npy-append-array 0.9.12

Create Numpy NPY files that are larger than the main memory

NpyAppendArray

Create Numpy NPY files by appending on the zero axis. The main application is to efficiently create arrays which are larger than the main memory:

1. Embedded devices might have limited memory
2. Certain workflows (e.g. Deep Learning) may require to handle large amounts of data

After creation, the file can then be read with memory mapping, e.g. by adding mmap_mode="r".

Installation

conda install -c conda-forge npy-append-array

or

pip install npy-append-array

Usage

from npy_append_array import NpyAppendArray
import numpy as np

arr1 = np.array([[1,2],[3,4]])
arr2 = np.array([[1,2],[3,4],[5,6]])

filename = 'out.npy'

with NpyAppendArray(filename) as npaa:
    npaa.append(arr1)
    npaa.append(arr2)
    npaa.append(arr2)
    
data = np.load(filename, mmap_mode="r")

print(data)

Implementation Details

Appending to an array created by np.save might be possible under certain circumstances, since the .npy total header byte count is required to be evenly divisible by 64. Thus, there might be some spare space to grow the shape entry in the array descriptor. However, this is not guaranteed and might randomly fail. Initialize the array with NpyAppendArray(filename) directly (see above) so the header will be created with 64 byte of spare header space for growth.

Will 64 byte extra header space cover my needs?

It allows for up to 10^64 >= 2^212 array entries or data bits. Indeed, this is less than the number of atoms in the universe. However, fully populating such an array, due to limits imposed by quantum mechanics, would require more energy than would be needed to boil the oceans, compare

https://hbfs.wordpress.com/2009/02/10/to-boil-the-oceans

Therefore, the extra header space might cover your needs.

Limitations

1. Only tested with Linux. For Windows, consider using WSL (version 2 or above).
2. NotImplementedError thrown when Fortran order is used.
3. NPY version 3 is unsupported because there is no numpy.lib.format.read_array_header_3_0 function defined in https://numpy.org/devdocs/reference/generated/numpy.lib.format.html
4. Just like with numpy.load/numpy.save, multithreaded read/write is unsupported