Sparse (csc and csr) and dense arrays backed by on-disk storage in Zarr or HDF5. Allows accessing slices of larger than memory arrays. Inspired by h5sparse.
Examples
import backedarray as ba
import scipy.sparse
import numpy as np
import h5py
import zarrCreate Dataset
csr_matrix = scipy.sparse.random(100, 50, format="csr", density=0.2)
dense_array = csr_matrix.toarray()HDF5 Backend
# Write sparse matrix in csc or csr format to hdf5 file
h5_csr_path = 'csr.h5'
with h5py.File(h5_csr_path, "w") as f:
ba.write_sparse(f.create_group("X"), csr_matrix)# Write 2-d numpy array to hdf5
h5_dense_path = 'dense.h5'
with h5py.File(h5_dense_path, "w") as f:
f["X"] = dense_arrayZarr Backend
# Write sparse matrix in csc or csr format to zarr file
zarr_csr_path = 'csr.zarr'
with zarr.open(zarr_csr_path, mode="w") as f:
ba.write_sparse(f.create_group("X"), csr_matrix) # Write 2-d numpy array to zarr format
zarr_dense_path = 'dense.zarr'
with zarr.open(zarr_dense_path, mode="w") as f:
f["X"] = dense_arrayRead Dataset
HDF5 Backend
h5_csr_file = h5py.File(h5_csr_path, "r")
h5_csr_disk = ba.open(h5_csr_file["X"])
h5_dense_file = h5py.File(h5_dense_path, "r")
h5_dense_disk = ba.open(h5_dense_file["X"])Zarr Backend
zarr_csr_disk = ba.open(zarr.open(zarr_csr_path)["X"])
zarr_dense_disk = ba.open(zarr.open(zarr_dense_path)["X"])Numpy Style Indexing
zarr_csr_disk[1:3].toarray()array([[0. , 0.25620103, 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0.57643237, 0.7628611 , 0. , 0. ,
0. , 0.99872378, 0. , 0. , 0. ,
0. , 0. , 0. , 0.82040632, 0. ,
0.09788999, 0. , 0. , 0.67186548, 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0.24171919, 0. , 0. ,
0. , 0. , 0.5893689 , 0. , 0. ],
[0. , 0. , 0. , 0. , 0.1650544 ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0.98852861, 0. , 0.01475572,
0. , 0.82875194, 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0.28405987, 0. , 0. , 0.72342298,
0. , 0. , 0. , 0.12985154, 0. ]])
zarr_dense_disk[-2:]array([[0.51141143, 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0.87214978,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0.95867897, 0. , 0.00825137,
0. , 0. , 0. , 0. , 0. ,
0. , 0.29541905, 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0.68913921, 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0.87239577, 0. , 0.93164802, 0. , 0. ],
[0. , 0. , 0. , 0.04102313, 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0.81888661, 0. , 0. , 0. , 0. ,
0. , 0. , 0.18858683, 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0.83726992, 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0.60594181,
0.61483901, 0. , 0. , 0.37080615, 0.62691013]])
h5_csr_disk[2:].toarray()array([[0. , 0. , 0. , ..., 0. , 0.12985154,
0. ],
[0. , 0. , 0.56872386, ..., 0. , 0. ,
0.36926708],
[0. , 0. , 0.75702799, ..., 0.97589322, 0. ,
0.34865313],
...,
[0. , 0.14634835, 0. , ..., 0. , 0. ,
0. ],
[0.51141143, 0. , 0. , ..., 0.93164802, 0. ,
0. ],
[0. , 0. , 0. , ..., 0. , 0.37080615,
0.62691013]])
h5_csr_disk[...].toarray()array([[0. , 0. , 0. , ..., 0. , 0. ,
0. ],
[0. , 0.25620103, 0. , ..., 0.5893689 , 0. ,
0. ],
[0. , 0. , 0. , ..., 0. , 0.12985154,
0. ],
...,
[0. , 0.14634835, 0. , ..., 0. , 0. ,
0. ],
[0.51141143, 0. , 0. , ..., 0.93164802, 0. ,
0. ],
[0. , 0. , 0. , ..., 0. , 0.37080615,
0.62691013]])
h5_dense_disk[:2]array([[0. , 0. , 0. , 0. , 0. ,
0.71493443, 0.20460768, 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0.68284516,
0. , 0. , 0. , 0. , 0. ,
0.93012152, 0. , 0. , 0.2165738 , 0. ,
0. , 0. , 0.93954512, 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0.1808206 , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ],
[0. , 0.25620103, 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0.57643237, 0.7628611 , 0. , 0. ,
0. , 0.99872378, 0. , 0. , 0. ,
0. , 0. , 0. , 0.82040632, 0. ,
0.09788999, 0. , 0. , 0.67186548, 0. ,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0.24171919, 0. , 0. ,
0. , 0. , 0.5893689 , 0. , 0. ]])
h5_csr_file.close()
h5_dense_file.close()Append
zarr_csr_disk.append(csr_matrix)
np.testing.assert_array_equal(zarr_csr_disk[...].toarray(), scipy.sparse.vstack((csr_matrix, csr_matrix)).toarray())Read h5ad files created using anndata
%%bash
if [ ! -f "pbmc3k.h5ad" ]; then
wget https://raw.githubusercontent.com/chanzuckerberg/cellxgene/main/example-dataset/pbmc3k.h5ad
fiimport anndata.experimental
with h5py.File('pbmc3k.h5ad', 'r') as f:
obs = anndata.experimental.read_elem(f['obs'])
var = anndata.experimental.read_elem(f['var'])
X = ba.open(f['X'])1 maintainer
Release files for backedarray 0.0.1
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| backedarray-0.0.1.tar.gz | 10.1 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| backedarray-0.0.1-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 19.7 kB
Release files / backedarray-0.0.1.tar.gz
| Download URL | backedarray-0.0.1.tar.gz |
|---|---|
| Size | 10.1 kB |
| Tags | Source |
|
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| Uploaded via |
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Release files / backedarray-0.0.1-py3-none-any.whl
| Download URL | backedarray-0.0.1-py3-none-any.whl |
|---|---|
| Size | 9.7 kB |
| Tags | Python 3 |
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