Saving and loading

stablebear provides a binary format for efficiently saving and loading tensors. All tensor types are supported, including PCF, numeric, point cloud, barcode, symmetric matrix tensors, etc.

Saving

Use save() to write a tensor to a file:

import stablebear as sb
from stablebear.random import noisy_sin

X = noisy_sin((100,), n_points=50)
sb.save(X, 'my_pcfs.sb')

You can also pass an open file object in binary write mode:

with open('my_pcfs.sb', 'wb') as f:
    sb.save(X, f)

Pickle support

All tensor types and standalone objects (Pcf, Barcode, DistanceMatrix, SymmetricMatrix) support Python’s pickle protocol. This means they work with pickle.dumps/pickle.loads, copy.deepcopy, and multiprocessing:

import pickle

data = pickle.dumps(X)
X_restored = pickle.loads(data)

Pickling uses stablebear’s binary format internally, so it is efficient and preserves dtype and shape.

Note

Many stablebear operations (distance matrices, reductions, etc.) are already parallelized internally using multithreading and GPU acceleration. Layering Python multiprocessing on top will most likely decrease performance in these cases due to process overhead and memory duplication.

Loading

Use load() to read a tensor back:

X = sb.load('my_pcfs.sb')

The returned tensor will be of the same type and dtype as what was saved. As with save, you can also pass an open file object:

with open('my_pcfs.sb', 'rb') as f:
    X = sb.load(f)