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Scipy sparse matrix in HDF5.

Project description

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h5sparse

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Scipy sparse matrix in HDF5.

Installation

pip install h5sparse

Testing

  • for single environment:

    python setup.py test
  • for all environments:

    tox

Examples

Create dataset

In [1]: import scipy.sparse as ss
   ...: import h5sparse
   ...: import numpy as np
   ...:

In [2]: sparse_matrix = ss.csr_matrix([[0, 1, 0],
   ...:                                [0, 0, 1],
   ...:                                [0, 0, 0],
   ...:                                [1, 1, 0]],
   ...:                               dtype=np.float64)

In [3]: # create dataset from scipy sparse matrix
   ...: with h5sparse.File("test.h5") as h5f:
   ...:     h5f.create_dataset('sparse/matrix', data=sparse_matrix)

In [4]: # you can also create dataset from another dataset
   ...: with h5sparse.File("test.h5") as h5f:
   ...:     h5f.create_dataset('sparse/matrix2', data=h5f['sparse/matrix'])

Read dataset

In [5]: h5f = h5sparse.File("test.h5")

In [6]: h5f['sparse/matrix'][1:3]
Out[6]:
<2x3 sparse matrix of type '<class 'numpy.float64'>'
        with 1 stored elements in Compressed Sparse Row format>

In [7]: h5f['sparse/matrix'][1:3].toarray()
Out[7]:
array([[ 0.,  0.,  1.],
       [ 0.,  0.,  0.]])

In [8]: h5f['sparse']['matrix'][1:3].toarray()
Out[8]:
array([[ 0.,  0.,  1.],
       [ 0.,  0.,  0.]])

In [9]: h5f['sparse']['matrix'][2:].toarray()
Out[9]:
array([[ 0.,  0.,  0.],
       [ 1.,  1.,  0.]])

In [10]: h5f['sparse']['matrix'][:2].toarray()
Out[10]:
array([[ 0.,  1.,  0.],
       [ 0.,  0.,  1.]])

In [11]: h5f['sparse']['matrix'][-2:].toarray()
Out[11]:
array([[ 0.,  0.,  0.],
       [ 1.,  1.,  0.]])

In [12]: h5f['sparse']['matrix'][:-2].toarray()
Out[12]:
array([[ 0.,  1.,  0.],
       [ 0.,  0.,  1.]])

In [13]: h5f['sparse']['matrix'].value.toarray()
Out[13]:
array([[ 0.,  1.,  0.],
       [ 0.,  0.,  1.],
       [ 0.,  0.,  0.],
       [ 1.,  1.,  0.]])

In [15]: import h5py

In [16]: h5f = h5py.File("test.h5")

In [18]: h5sparse.Group(h5f)['sparse/matrix'].value
Out[18]:
<4x3 sparse matrix of type '<class 'numpy.float64'>'
        with 4 stored elements in Compressed Sparse Row format>

In [19]: h5sparse.Group(h5f['sparse'])['matrix'].value
Out[19]:
<4x3 sparse matrix of type '<class 'numpy.float64'>'
        with 4 stored elements in Compressed Sparse Row format>

In [21]: h5sparse.Dataset(h5f['sparse/matrix']).value
Out[21]:
<4x3 sparse matrix of type '<class 'numpy.float64'>'
        with 4 stored elements in Compressed Sparse Row format>

Append dataset

In [22]: to_append = ss.csr_matrix([[0, 1, 1],
    ...:                            [1, 0, 0]],
    ...:                           dtype=np.float64)

In [23]: h5f.create_dataset('matrix', data=sparse_matrix, chunks=(100000,),
    ...:                    maxshape=(None,))

In [24]: h5f['matrix'].append(to_append)

In [25]: h5f['matrix'].value
Out[25]:
<6x3 sparse matrix of type '<class 'numpy.float64'>'
        with 7 stored elements in Compressed Sparse Row format>

In [26]: h5f['matrix'].value.toarray()
Out[26]:
array([[ 0.,  1.,  0.],
       [ 0.,  0.,  1.],
       [ 0.,  0.,  0.],
       [ 1.,  1.,  0.],
       [ 0.,  1.,  1.],
       [ 1.,  0.,  0.]])

Version scheme

We use semantic versioning.

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