Please visit the `Github repository <https://github.com/appier/h5sparse>`_
for more information.
h5sparse
========
.. image:: https://img.shields.io/travis/appier/h5sparse/master.svg
:target: https://travis-ci.org/appier/h5sparse
.. image:: https://img.shields.io/pypi/v/h5sparse.svg
:target: https://pypi.python.org/pypi/h5sparse
.. image:: https://img.shields.io/pypi/l/h5sparse.svg
:target: https://pypi.python.org/pypi/h5sparse
Scipy sparse matrix in HDF5.
Installation
------------
.. code:: bash
pip install h5sparse
Testing
-------
- for single environment:
.. code:: bash
python setup.py test
- for all environments:
.. code:: bash
tox
Examples
--------
Create dataset
**************
.. code:: python
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'])
In [5]: # you can also create dataset using the formats that original h5py accepts
...: with h5sparse.File("test.h5") as h5f:
...: h5f.create_dataset('sparse/matrix3', data=[1,2,3])
Read dataset
************
.. code:: python
In [6]: h5f = h5sparse.File("test.h5")
In [7]: h5f['sparse/matrix'][1:3]
Out[7]:
<2x3 sparse matrix of type '<class 'numpy.float64'>'
with 1 stored elements in Compressed Sparse Row format>
In [8]: h5f['sparse/matrix'][1:3].toarray()
Out[8]:
array([[ 0., 0., 1.],
[ 0., 0., 0.]])
In [9]: h5f['sparse']['matrix'][1:3].toarray()
Out[9]:
array([[ 0., 0., 1.],
[ 0., 0., 0.]])
In [10]: h5f['sparse']['matrix'][2:].toarray()
Out[10]:
array([[ 0., 0., 0.],
[ 1., 1., 0.]])
In [11]: h5f['sparse']['matrix'][:2].toarray()
Out[11]:
array([[ 0., 1., 0.],
[ 0., 0., 1.]])
In [12]: h5f['sparse']['matrix'][-2:].toarray()
Out[12]:
array([[ 0., 0., 0.],
[ 1., 1., 0.]])
In [13]: h5f['sparse']['matrix'][:-2].toarray()
Out[13]:
array([[ 0., 1., 0.],
[ 0., 0., 1.]])
In [14]: h5f['sparse']['matrix'][()].toarray()
Out[14]:
array([[ 0., 1., 0.],
[ 0., 0., 1.],
[ 0., 0., 0.],
[ 1., 1., 0.]])
In [15]: import h5py
In [16]: h5py_h5f = h5py.File("test.h5")
In [17]: h5sparse.Group(h5py_h5f.id)['sparse/matrix'][()]
Out[17]:
<4x3 sparse matrix of type '<class 'numpy.float64'>'
with 4 stored elements in Compressed Sparse Row format>
In [18]: h5sparse.Group(h5py_h5f['sparse'].id)['matrix'][()]
Out[18]:
<4x3 sparse matrix of type '<class 'numpy.float64'>'
with 4 stored elements in Compressed Sparse Row format>
In [19]: h5sparse.Dataset(h5py_h5f['sparse/matrix'])[()]
Out[19]:
<4x3 sparse matrix of type '<class 'numpy.float64'>'
with 4 stored elements in Compressed Sparse Row format>
Append dataset
**************
.. code:: python
In [20]: to_append = ss.csr_matrix([[0, 1, 1],
...: [1, 0, 0]],
...: dtype=np.float64)
In [21]: h5f.create_dataset('matrix', data=sparse_matrix, chunks=(100000,),
...: maxshape=(None,))
In [22]: h5f['matrix'].append(to_append)
In [23]: h5f['matrix'][()]
Out[23]:
<6x3 sparse matrix of type '<class 'numpy.float64'>'
with 7 stored elements in Compressed Sparse Row format>
In [24]: h5f['matrix'][()].toarray()
Out[24]:
array([[ 0., 1., 0.],
[ 0., 0., 1.],
[ 0., 0., 0.],
[ 1., 1., 0.],
[ 0., 1., 1.],
[ 1., 0., 0.]])
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"description": "Please visit the `Github repository <https://github.com/appier/h5sparse>`_\nfor more information.\n\nh5sparse\n========\n.. image:: https://img.shields.io/travis/appier/h5sparse/master.svg\n :target: https://travis-ci.org/appier/h5sparse\n.. image:: https://img.shields.io/pypi/v/h5sparse.svg\n :target: https://pypi.python.org/pypi/h5sparse\n.. image:: https://img.shields.io/pypi/l/h5sparse.svg\n :target: https://pypi.python.org/pypi/h5sparse\n\nScipy sparse matrix in HDF5.\n\n\nInstallation\n------------\n.. code:: bash\n\n pip install h5sparse\n\n\nTesting\n-------\n- for single environment:\n\n .. code:: bash\n\n python setup.py test\n\n- for all environments:\n\n .. code:: bash\n\n tox\n\n\nExamples\n--------\n\nCreate dataset\n**************\n.. code:: python\n\n In [1]: import scipy.sparse as ss\n ...: import h5sparse\n ...: import numpy as np\n ...:\n\n In [2]: sparse_matrix = ss.csr_matrix([[0, 1, 0],\n ...: [0, 0, 1],\n ...: [0, 0, 0],\n ...: [1, 1, 0]],\n ...: dtype=np.float64)\n\n In [3]: # create dataset from scipy sparse matrix\n ...: with h5sparse.File(\"test.h5\") as h5f:\n ...: h5f.create_dataset('sparse/matrix', data=sparse_matrix)\n\n In [4]: # you can also create dataset from another dataset\n ...: with h5sparse.File(\"test.h5\") as h5f:\n ...: h5f.create_dataset('sparse/matrix2', data=h5f['sparse/matrix'])\n\n In [5]: # you can also create dataset using the formats that original h5py accepts\n ...: with h5sparse.File(\"test.h5\") as h5f:\n ...: h5f.create_dataset('sparse/matrix3', data=[1,2,3])\n\nRead dataset\n************\n.. code:: python\n\n In [6]: h5f = h5sparse.File(\"test.h5\")\n\n In [7]: h5f['sparse/matrix'][1:3]\n Out[7]:\n <2x3 sparse matrix of type '<class 'numpy.float64'>'\n with 1 stored elements in Compressed Sparse Row format>\n\n In [8]: h5f['sparse/matrix'][1:3].toarray()\n Out[8]:\n array([[ 0., 0., 1.],\n [ 0., 0., 0.]])\n\n In [9]: h5f['sparse']['matrix'][1:3].toarray()\n Out[9]:\n array([[ 0., 0., 1.],\n [ 0., 0., 0.]])\n\n In [10]: h5f['sparse']['matrix'][2:].toarray()\n Out[10]:\n array([[ 0., 0., 0.],\n [ 1., 1., 0.]])\n\n In [11]: h5f['sparse']['matrix'][:2].toarray()\n Out[11]:\n array([[ 0., 1., 0.],\n [ 0., 0., 1.]])\n\n In [12]: h5f['sparse']['matrix'][-2:].toarray()\n Out[12]:\n array([[ 0., 0., 0.],\n [ 1., 1., 0.]])\n\n In [13]: h5f['sparse']['matrix'][:-2].toarray()\n Out[13]:\n array([[ 0., 1., 0.],\n [ 0., 0., 1.]])\n\n In [14]: h5f['sparse']['matrix'][()].toarray()\n Out[14]:\n array([[ 0., 1., 0.],\n [ 0., 0., 1.],\n [ 0., 0., 0.],\n [ 1., 1., 0.]])\n\n In [15]: import h5py\n\n In [16]: h5py_h5f = h5py.File(\"test.h5\")\n\n In [17]: h5sparse.Group(h5py_h5f.id)['sparse/matrix'][()]\n Out[17]:\n <4x3 sparse matrix of type '<class 'numpy.float64'>'\n with 4 stored elements in Compressed Sparse Row format>\n\n In [18]: h5sparse.Group(h5py_h5f['sparse'].id)['matrix'][()]\n Out[18]:\n <4x3 sparse matrix of type '<class 'numpy.float64'>'\n with 4 stored elements in Compressed Sparse Row format>\n\n In [19]: h5sparse.Dataset(h5py_h5f['sparse/matrix'])[()]\n Out[19]:\n <4x3 sparse matrix of type '<class 'numpy.float64'>'\n with 4 stored elements in Compressed Sparse Row format>\n\nAppend dataset\n**************\n.. code:: python\n\n In [20]: to_append = ss.csr_matrix([[0, 1, 1],\n ...: [1, 0, 0]],\n ...: dtype=np.float64)\n\n In [21]: h5f.create_dataset('matrix', data=sparse_matrix, chunks=(100000,),\n ...: maxshape=(None,))\n\n In [22]: h5f['matrix'].append(to_append)\n\n In [23]: h5f['matrix'][()]\n Out[23]:\n <6x3 sparse matrix of type '<class 'numpy.float64'>'\n with 7 stored elements in Compressed Sparse Row format>\n\n In [24]: h5f['matrix'][()].toarray()\n Out[24]:\n array([[ 0., 1., 0.],\n [ 0., 0., 1.],\n [ 0., 0., 0.],\n [ 1., 1., 0.],\n [ 0., 1., 1.],\n [ 1., 0., 0.]])\n\n\n",
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