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Here is a table listing the current tensor frameworks that support the format:
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| Language | Framework | Status | Notes |
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| -------- | ------ | ------ | ----- |
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| C++ |[binsparse-reference-impl](https://github.com/GraphBLAS/binsparse-reference-impl)| under development | converts between binsparse V1.0 and custom in-memory sparse matrices |
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| Julia |[Finch.jl](https://willowahrens.io/Finch.jl/dev/fileio/)| under development | converts between binsparse V1.0 and V2.0 and Finch matrices and tensors |
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| Python |[binsparse-python](https://github.com/ivirshup/binsparse-python)| under development | converts between binsparse V1.0 and scipy.sparse matrices |
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### Editing
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The working version of the specification can be found under `spec/latest/index.bs`.
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<pre class='metadata'>
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Title: Binary Sparse Format Specification
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Title: Binary Sparse Format Specification Version 0.1
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Shortname: binsparse
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Level: 1
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Status: LS-COMMIT
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```json
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{
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"binsparse": {
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"version": "0.1",
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"format": "CSC",
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"shape": [10, 12],
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"data_types": {
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</div>
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Version {#key_version}
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----------------------
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Version indicates the version of the Binsparse specification used here.
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This is a two digit specifier of the form `major.minor`.
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Any minor updates should be backwards compatible with the previous version, e.g. must be a superset of the previous versions within the major release series.
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Major versions may break backwards compatibility.
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Shape {#key_shape}
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------------------
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:: Array of size `number_of_elements` containing stored values.
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The element of the vector located at index `i, j` has scalar value
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`values[j * number_of_rows + i]`.
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`values[i + j * number_of_rows]`.
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#### DMAT #### {#DMAT_format}
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```json
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{
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"version": "0.1",
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"format": {
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"level": "dense",
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"rank": 1,
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