Metadata-Version: 2.1
Name: python-graphblas
Version: 2022.11.1
Summary: Python library for GraphBLAS: high-performance sparse linear algebra for scalable graph analytics
Home-page: https://github.com/python-graphblas/python-graphblas
Author: Erik Welch and Jim Kitchen
Author-email: erik.n.welch@gmail.com,jim22k@gmail.com
License: Apache License 2.0
Description: # Python-graphblas
        
        [![conda-forge](https://img.shields.io/conda/vn/conda-forge/python-graphblas.svg)](https://anaconda.org/conda-forge/python-graphblas)
        [![pypi](https://img.shields.io/pypi/v/python-graphblas.svg)](https://pypi.python.org/pypi/python-graphblas/)
        [![License](https://img.shields.io/badge/License-Apache%202.0-blue.svg)](https://github.com/python-graphblas/python-graphblas/blob/main/LICENSE)
        [![Tests](https://github.com/python-graphblas/python-graphblas/workflows/Tests/badge.svg?branch=main)](https://github.com/python-graphblas/python-graphblas/actions)
        [![Docs](https://readthedocs.org/projects/python-graphblas/badge/?version=latest)](https://python-graphblas.readthedocs.io/en/latest/)
        [![Coverage](https://coveralls.io/repos/python-graphblas/python-graphblas/badge.svg?branch=main)](https://coveralls.io/r/python-graphblas/python-graphblas)
        [![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.7328791.svg)](https://doi.org/10.5281/zenodo.7328791)
        [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/python-graphblas/python-graphblas/HEAD?filepath=notebooks%2FIntro%20to%20GraphBLAS%20%2B%20SSSP%20example.ipynb)
        [![Discord](https://img.shields.io/badge/Chat-Discord-blue)](https://discord.com/invite/vur45CbwMz)
        
        Python library for GraphBLAS: high-performance sparse linear algebra for scalable graph analytics.
        
        - **Documentation:** [https://python-graphblas.readthedocs.io/](https://python-graphblas.readthedocs.io/)
          - **GraphBLAS C API:** [https://graphblas.org/docs/GraphBLAS_API_C_v2.0.0.pdf](https://graphblas.org/docs/GraphBLAS_API_C_v2.0.0.pdf)
          - **SuiteSparse:GraphBLAS User Guide:** [https://github.com/DrTimothyAldenDavis/GraphBLAS/raw/stable/Doc/GraphBLAS_UserGuide.pdf](https://github.com/DrTimothyAldenDavis/GraphBLAS/raw/stable/Doc/GraphBLAS_UserGuide.pdf)
        - **Source:** [https://github.com/python-graphblas/python-graphblas](https://github.com/python-graphblas/python-graphblas)
        - **Bug reports:** [https://github.com/python-graphblas/python-graphblas/issues](https://github.com/python-graphblas/python-graphblas/issues)
        - **Github discussions:** [https://github.com/python-graphblas/python-graphblas/discussions](https://github.com/python-graphblas/python-graphblas/discussions)
        - **Weekly community call:** [https://github.com/python-graphblas/python-graphblas/issues/247](https://github.com/python-graphblas/python-graphblas/issues/247)
        - **Chat via Discord:** [https://discord.com/invite/vur45CbwMz](https://discord.com/invite/vur45CbwMz) in the [#graphblas channel](https://discord.com/channels/786703927705862175/1024732940233605190)
        
        ## Install
        Install the latest version of Python-graphblas via conda:
        ```
        $ conda install -c conda-forge python-graphblas
        ```
        or pip:
        ```
        $ pip install python-graphblas
        ```
        This will also install the [SuiteSparse:GraphBLAS](https://github.com/DrTimothyAldenDavis/GraphBLAS) compiled C library.
        
        ## Description
        Currently works with [SuiteSparse:GraphBLAS](https://github.com/DrTimothyAldenDavis/GraphBLAS), but the goal is to make it work with all implementations of the GraphBLAS spec.
        
        The approach taken with this library is to follow the C-API specification as closely as possible while making improvements
        allowed with the Python syntax. Because the spec always passes in the output object to be written to, we follow the same,
        which is very different from the way Python normally operates. In fact, many who are familiar with other Python data
        libraries (numpy, pandas, etc) will find it strange to not create new objects for every call.
        
        At the highest level, the goal is to separate output, mask, and accumulator on the left side of the assignment
        operator `=` and put the computation on the right side. Unfortunately, that approach doesn't always work very well
        with how Python handles assignment, so instead we (ab)use the left-shift `<<` notation to give the same flavor of
        assignment. This opens up all kinds of nice possibilities.
        
        This is an example of how the mapping works:
        ```C
        // C call
        GrB_Matrix_mxm(M, mask, GrB_PLUS_INT64, GrB_MIN_PLUS_INT64, A, B, NULL)
        ```
        ```python
        # Python call
        M(mask.V, accum=binary.plus) << A.mxm(B, semiring.min_plus)
        ```
        
        The expression on the right `A.mxm(B)` creates a delayed object which does no computation. Once it is used in the
        `<<` expression with `M`, the whole thing is translated into the equivalent GraphBLAS call.
        
        Delayed objects also have a `.new()` method which can be used to force computation and return a new
        object. This is convenient and often appropriate, but will create many unnecessary objects if used in a loop. It
        also loses the ability to perform accumulation with existing results. For best performance, following the standard
        GraphBLAS approach of (1) creating the object outside the loop and (2) using the object repeatedly within each loop
        is a much better approach, even if it doesn't feel very Pythonic.
        
        Descriptor flags are set on the appropriate elements to keep logic close to what it affects. Here is the same call
        with descriptor bits set. `ttcsr` indicates transpose the first and second matrices, complement the structure of the mask,
        and do a replacement on the output.
        ```C
        // C call
        GrB_Matrix_mxm(M, mask, GrB_PLUS_INT64, GrB_MIN_PLUS_INT64, A, B, desc.ttcsr)
        ```
        ```python
        # Python call
        M(~mask.S, accum=binary.plus, replace=True) << A.T.mxm(B.T, semiring.min_plus)
        ```
        
        The objects receiving the flag operations (A.T, ~mask, etc) are also delayed objects. They hold on to the state but
        do no computation, allowing the correct descriptor bits to be set in a single GraphBLAS call.
        
        **If no mask or accumulator is used, the call looks like this**:
        ```python
        M << A.mxm(B, semiring.min_plus)
        ```
        The use of `<<` to indicate updating is actually just syntactic sugar for a real `.update()` method. The above
        expression could be written as:
        ```python
        M.update(A.mxm(B, semiring.min_plus))
        ```
        
        ## Operations
        ```python
        M(mask, accum) << A.mxm(B, semiring)        # mxm
        w(mask, accum) << A.mxv(v, semiring)        # mxv
        w(mask, accum) << v.vxm(B, semiring)        # vxm
        M(mask, accum) << A.ewise_add(B, binaryop)  # eWiseAdd
        M(mask, accum) << A.ewise_mult(B, binaryop) # eWiseMult
        M(mask, accum) << A.kronecker(B, binaryop)  # kronecker
        M(mask, accum) << A.T                       # transpose
        ```
        ## Extract
        ```python
        M(mask, accum) << A[rows, cols]             # rows and cols are a list or a slice
        w(mask, accum) << A[rows, col_index]        # extract column
        w(mask, accum) << A[row_index, cols]        # extract row
        s = A[row_index, col_index].value           # extract single element
        ```
        ## Assign
        ```python
        M(mask, accum)[rows, cols] << A             # rows and cols are a list or a slice
        M(mask, accum)[rows, col_index] << v        # assign column
        M(mask, accum)[row_index, cols] << v        # assign row
        M(mask, accum)[rows, cols] << s             # assign scalar to many elements
        M[row_index, col_index] << s                # assign scalar to single element
                                                    # (mask and accum not allowed)
        del M[row_index, col_index]                 # remove single element
        ```
        ## Apply
        ```python
        M(mask, accum) << A.apply(unaryop)
        M(mask, accum) << A.apply(binaryop, left=s)   # bind-first
        M(mask, accum) << A.apply(binaryop, right=s)  # bind-second
        ```
        ## Reduce
        ```python
        v(mask, accum) << A.reduce_rowwise(op)      # reduce row-wise
        v(mask, accum) << A.reduce_columnwise(op)   # reduce column-wise
        s(accum) << A.reduce_scalar(op)
        s(accum) << v.reduce(op)
        ```
        ## Creating new Vectors / Matrices
        ```python
        A = Matrix.new(dtype, num_rows, num_cols)   # new_type
        B = A.dup()                                 # dup
        A = Matrix.from_coo([row_indices], [col_indices], [values])  # build
        ```
        ## New from delayed
        Delayed objects can be used to create a new object using `.new()` method
        ```python
        C = A.mxm(B, semiring).new()
        ```
        ## Properties
        ```python
        size = v.size                               # size
        nrows = M.nrows                             # nrows
        ncols = M.ncols                             # ncols
        nvals = M.nvals                             # nvals
        rindices, cindices, vals = M.to_coo()       # extractTuples
        ```
        ## Initialization
        There is a mechanism to initialize `graphblas` with a context prior to use. This allows for setting the backend to
        use as well as the blocking/non-blocking mode. If the context is not initialized, a default initialization will
        be performed automatically.
        ```python
        import graphblas as gb
        # Context initialization must happen before any other imports
        gb.init('suitesparse', blocking=True)
        
        # Now we can import other items from graphblas
        from graphblas import binary, semiring
        from graphblas import Matrix, Vector, Scalar
        ```
        ## Performant User Defined Functions
        Python-graphblas requires `numba` which enables compiling user-defined Python functions to native C for use in GraphBLAS.
        
        Example customized UnaryOp:
        ```python
        from graphblas import unary
        
        def force_odd_func(x):
            if x % 2 == 0:
                return x + 1
            return x
        
        unary.register_new('force_odd', force_odd_func)
        
        v = Vector.from_coo([0, 1, 3], [1, 2, 3])
        w = v.apply(unary.force_odd).new()
        w  # indexes=[0, 1, 3], values=[1, 3, 3]
        ```
        Similar methods exist for BinaryOp, Monoid, and Semiring.
        
        ## Import/Export connectors to the Python ecosystem
        `graphblas.io` contains functions for converting to and from:
        ```python
        import graphblas as gb
        
        # numpy arrays
        # 1-D array becomes Vector, 2-D array becomes Matrix
        A = gb.io.from_numpy(m)
        m = gb.io.to_numpy(A)
        
        # scipy.sparse matrices
        A = gb.io.from_scipy_sparse(m)
        m = gb.io.to_scipy_sparse(m, format='csr')
        
        # networkx graphs
        A = gb.io.from_networkx(g)
        g = gb.io.to_networkx(A)
        ```
        
Keywords: graphblas,graph,sparse,matrix,lagraph,suitesparse
Platform: UNKNOWN
Classifier: Development Status :: 5 - Production/Stable
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Operating System :: MacOS :: MacOS X
Classifier: Operating System :: POSIX :: Linux
Classifier: Operating System :: Microsoft :: Windows
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3 :: Only
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Scientific/Engineering :: Mathematics
Requires-Python: >=3.8
Description-Content-Type: text/markdown
Provides-Extra: repr
Provides-Extra: io
Provides-Extra: viz
Provides-Extra: test
Provides-Extra: complete
