| Name | python-graphblas JSON |
| Version |
2023.12.0
JSON |
| download |
| home_page | |
| Summary | Python library for GraphBLAS: high-performance sparse linear algebra for scalable graph analytics |
| upload_time | 2023-12-13 19:32:48 |
| maintainer | |
| docs_url | None |
| author | Jim Kitchen, Python-graphblas contributors |
| requires_python | >=3.9 |
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| keywords |
graphblas
graph
sparse
matrix
lagraph
suitesparse
networks
graph theory
mathematics
network
discrete mathematics
math
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
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| coveralls test coverage |
No coveralls.
|
[](https://numfocus.org)
[](https://github.com/pyOpenSci/software-review/issues/81)
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<br>
[](https://anaconda.org/conda-forge/python-graphblas)
[](https://pypi.python.org/pypi/python-graphblas/)
[](https://pypi.python.org/pypi/python-graphblas/)
[](https://github.com/python-graphblas/python-graphblas/blob/main/LICENSE)
<br>
[](https://github.com/python-graphblas/python-graphblas/actions)
[](https://python-graphblas.readthedocs.io/en/latest/)
[](https://codecov.io/gh/python-graphblas/python-graphblas)
[](https://doi.org/10.5281/zenodo.7328791)
[](https://mybinder.org/v2/gh/python-graphblas/python-graphblas/HEAD?filepath=notebooks%2FIntro%20to%20GraphBLAS%20%2B%20SSSP%20example.ipynb)
Python library for GraphBLAS: high-performance sparse linear algebra for scalable graph analytics.
For algorithms, see
[`graphblas-algorithms`](https://github.com/python-graphblas/graphblas-algorithms).
- **Documentation:** [https://python-graphblas.readthedocs.io/](https://python-graphblas.readthedocs.io/)
- **FAQ:** [https://python-graphblas.readthedocs.io/en/stable/getting_started/faq.html](https://python-graphblas.readthedocs.io/en/stable/getting_started/faq.html)
- **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:** [python-graphblas#247](https://github.com/python-graphblas/python-graphblas/issues/247) or [https://scientific-python.org/calendars/](https://scientific-python.org/calendars/)
- **Chat via Discord:** [https://discord.com/invite/vur45CbwMz](https://discord.com/invite/vur45CbwMz) in the [#graphblas channel](https://discord.com/channels/786703927705862175/1024732940233605190)
<p float="left">
<img src="https://raw.githubusercontent.com/python-graphblas/python-graphblas/main/docs/_static/img/draw-example.png" width="231" align="top" alt="Directed graph", title="Directed graph"/>
<img src="https://raw.githubusercontent.com/python-graphblas/python-graphblas/main/docs/_static/img/repr-matrix.png" width="231" align="top" alt="Adjacency matrix" title="Adjacency matrix"/>
</p>
## Install
Install the latest version of Python-graphblas via conda:
```
$ conda install -c conda-forge python-graphblas
```
or pip:
```
$ pip install python-graphblas[default]
```
This will also install the [SuiteSparse:GraphBLAS](https://github.com/DrTimothyAldenDavis/GraphBLAS) compiled C library.
We currently support the [GraphBLAS C API 2.0 specification](https://graphblas.org/docs/GraphBLAS_API_C_v2.0.0.pdf).
### Optional Dependencies
The following are not required by python-graphblas, but may be needed for certain functionality to work.
- `pandas` – required for nicer `__repr__`;
- `matplotlib` – required for basic plotting of graphs;
- `scipy` – used in `io` module to read/write `scipy.sparse` format;
- `networkx` – used in `io` module to interface with `networkx` graphs;
- `fast-matrix-market` - for faster read/write of Matrix Market files with `gb.io.mmread` and `gb.io.mmwrite`.
## 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 2.0 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.
## Relation to other network analysis libraries
Python-graphblas aims to provide an efficient and consistent expression
of graph operations using linear algebra. This allows the development of
high-performance implementations of existing and new graph algorithms
(also see [`graphblas-algorithms`](https://github.com/python-graphblas/graphblas-algorithms)).
While end-to-end analysis can be done using `python-graphblas`, users
might find that other libraries in the Python ecosystem provide a more
convenient high-level interface for data pre-processing and transformation
(e.g. `pandas`, `scipy.sparse`), visualization (e.g. `networkx`, `igraph`),
interactive exploration and analysis (e.g. `networkx`, `igraph`) or for
algorithms that are not (yet) implemented in `graphblas-algorithms` (e.g.
`networkx`, `igraph`, `scipy.sparse.csgraph`). To facilitate communication with
other libraries, `graphblas.io` contains multiple connectors, see the
following section.
## Import/Export connectors to the Python ecosystem
`graphblas.io` contains functions for converting to and from:
```python
import graphblas as gb
# 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)
# numpy arrays can use `from_dense` and `to_dense` on Vector and Matrix
v = gb.Vector.from_dense(m)
m = v.to_dense()
A = gb.Matrix.from_dense(m, missing_value=0)
m = A.to_dense(fill_value=0)
```
Raw data
{
"_id": null,
"home_page": "",
"name": "python-graphblas",
"maintainer": "",
"docs_url": null,
"requires_python": ">=3.9",
"maintainer_email": "Erik Welch <erik.n.welch@gmail.com>, Jim Kitchen <jim22k@gmail.com>, Sultan Orazbayev <contact@econpoint.com>",
"keywords": "graphblas,graph,sparse,matrix,lagraph,suitesparse,Networks,Graph Theory,Mathematics,network,discrete mathematics,math",
"author": "Jim Kitchen, Python-graphblas contributors",
"author_email": "Erik Welch <erik.n.welch@gmail.com>",
"download_url": "https://files.pythonhosted.org/packages/10/b7/30eff1ab98fabb466d3c30fd7f4e0abbd18a3a54e737cf897241b1898904/python-graphblas-2023.12.0.tar.gz",
"platform": null,
"description": "\n\n[](https://numfocus.org)\n[](https://github.com/pyOpenSci/software-review/issues/81)\n[](https://discord.com/invite/vur45CbwMz)\n<br>\n[](https://anaconda.org/conda-forge/python-graphblas)\n[](https://pypi.python.org/pypi/python-graphblas/)\n[](https://pypi.python.org/pypi/python-graphblas/)\n[](https://github.com/python-graphblas/python-graphblas/blob/main/LICENSE)\n<br>\n[](https://github.com/python-graphblas/python-graphblas/actions)\n[](https://python-graphblas.readthedocs.io/en/latest/)\n[](https://codecov.io/gh/python-graphblas/python-graphblas)\n[](https://doi.org/10.5281/zenodo.7328791)\n[](https://mybinder.org/v2/gh/python-graphblas/python-graphblas/HEAD?filepath=notebooks%2FIntro%20to%20GraphBLAS%20%2B%20SSSP%20example.ipynb)\n\nPython library for GraphBLAS: high-performance sparse linear algebra for scalable graph analytics.\nFor algorithms, see\n[`graphblas-algorithms`](https://github.com/python-graphblas/graphblas-algorithms).\n\n- **Documentation:** [https://python-graphblas.readthedocs.io/](https://python-graphblas.readthedocs.io/)\n - **FAQ:** [https://python-graphblas.readthedocs.io/en/stable/getting_started/faq.html](https://python-graphblas.readthedocs.io/en/stable/getting_started/faq.html)\n - **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)\n - **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)\n- **Source:** [https://github.com/python-graphblas/python-graphblas](https://github.com/python-graphblas/python-graphblas)\n- **Bug reports:** [https://github.com/python-graphblas/python-graphblas/issues](https://github.com/python-graphblas/python-graphblas/issues)\n- **Github discussions:** [https://github.com/python-graphblas/python-graphblas/discussions](https://github.com/python-graphblas/python-graphblas/discussions)\n- **Weekly community call:** [python-graphblas#247](https://github.com/python-graphblas/python-graphblas/issues/247) or [https://scientific-python.org/calendars/](https://scientific-python.org/calendars/)\n- **Chat via Discord:** [https://discord.com/invite/vur45CbwMz](https://discord.com/invite/vur45CbwMz) in the [#graphblas channel](https://discord.com/channels/786703927705862175/1024732940233605190)\n\n<p float=\"left\">\n <img src=\"https://raw.githubusercontent.com/python-graphblas/python-graphblas/main/docs/_static/img/draw-example.png\" width=\"231\" align=\"top\" alt=\"Directed graph\", title=\"Directed graph\"/>\n <img src=\"https://raw.githubusercontent.com/python-graphblas/python-graphblas/main/docs/_static/img/repr-matrix.png\" width=\"231\" align=\"top\" alt=\"Adjacency matrix\" title=\"Adjacency matrix\"/>\n</p>\n\n## Install\nInstall the latest version of Python-graphblas via conda:\n```\n$ conda install -c conda-forge python-graphblas\n```\nor pip:\n```\n$ pip install python-graphblas[default]\n```\nThis will also install the [SuiteSparse:GraphBLAS](https://github.com/DrTimothyAldenDavis/GraphBLAS) compiled C library.\nWe currently support the [GraphBLAS C API 2.0 specification](https://graphblas.org/docs/GraphBLAS_API_C_v2.0.0.pdf).\n\n### Optional Dependencies\n\nThe following are not required by python-graphblas, but may be needed for certain functionality to work.\n\n- `pandas` \u2013 required for nicer `__repr__`;\n- `matplotlib` \u2013 required for basic plotting of graphs;\n- `scipy` \u2013 used in `io` module to read/write `scipy.sparse` format;\n- `networkx` \u2013 used in `io` module to interface with `networkx` graphs;\n- `fast-matrix-market` - for faster read/write of Matrix Market files with `gb.io.mmread` and `gb.io.mmwrite`.\n\n## Description\nCurrently works with [SuiteSparse:GraphBLAS](https://github.com/DrTimothyAldenDavis/GraphBLAS), but the goal is to make it work with all implementations of the GraphBLAS spec.\n\nThe approach taken with this library is to follow the C-API 2.0 specification as closely as possible while making improvements\nallowed with the Python syntax. Because the spec always passes in the output object to be written to, we follow the same,\nwhich is very different from the way Python normally operates. In fact, many who are familiar with other Python data\nlibraries (numpy, pandas, etc) will find it strange to not create new objects for every call.\n\nAt the highest level, the goal is to separate output, mask, and accumulator on the left side of the assignment\noperator `=` and put the computation on the right side. Unfortunately, that approach doesn't always work very well\nwith how Python handles assignment, so instead we (ab)use the left-shift `<<` notation to give the same flavor of\nassignment. This opens up all kinds of nice possibilities.\n\nThis is an example of how the mapping works:\n```C\n// C call\nGrB_Matrix_mxm(M, mask, GrB_PLUS_INT64, GrB_MIN_PLUS_INT64, A, B, NULL)\n```\n```python\n# Python call\nM(mask.V, accum=binary.plus) << A.mxm(B, semiring.min_plus)\n```\n\nThe expression on the right `A.mxm(B)` creates a delayed object which does no computation. Once it is used in the\n`<<` expression with `M`, the whole thing is translated into the equivalent GraphBLAS call.\n\nDelayed objects also have a `.new()` method which can be used to force computation and return a new\nobject. This is convenient and often appropriate, but will create many unnecessary objects if used in a loop. It\nalso loses the ability to perform accumulation with existing results. For best performance, following the standard\nGraphBLAS approach of (1) creating the object outside the loop and (2) using the object repeatedly within each loop\nis a much better approach, even if it doesn't feel very Pythonic.\n\nDescriptor flags are set on the appropriate elements to keep logic close to what it affects. Here is the same call\nwith descriptor bits set. `ttcsr` indicates transpose the first and second matrices, complement the structure of the mask,\nand do a replacement on the output.\n```C\n// C call\nGrB_Matrix_mxm(M, mask, GrB_PLUS_INT64, GrB_MIN_PLUS_INT64, A, B, desc.ttcsr)\n```\n```python\n# Python call\nM(~mask.S, accum=binary.plus, replace=True) << A.T.mxm(B.T, semiring.min_plus)\n```\n\nThe objects receiving the flag operations (A.T, ~mask, etc) are also delayed objects. They hold on to the state but\ndo no computation, allowing the correct descriptor bits to be set in a single GraphBLAS call.\n\n**If no mask or accumulator is used, the call looks like this**:\n```python\nM << A.mxm(B, semiring.min_plus)\n```\nThe use of `<<` to indicate updating is actually just syntactic sugar for a real `.update()` method. The above\nexpression could be written as:\n```python\nM.update(A.mxm(B, semiring.min_plus))\n```\n\n## Operations\n```python\nM(mask, accum) << A.mxm(B, semiring) # mxm\nw(mask, accum) << A.mxv(v, semiring) # mxv\nw(mask, accum) << v.vxm(B, semiring) # vxm\nM(mask, accum) << A.ewise_add(B, binaryop) # eWiseAdd\nM(mask, accum) << A.ewise_mult(B, binaryop) # eWiseMult\nM(mask, accum) << A.kronecker(B, binaryop) # kronecker\nM(mask, accum) << A.T # transpose\n```\n## Extract\n```python\nM(mask, accum) << A[rows, cols] # rows and cols are a list or a slice\nw(mask, accum) << A[rows, col_index] # extract column\nw(mask, accum) << A[row_index, cols] # extract row\ns = A[row_index, col_index].value # extract single element\n```\n## Assign\n```python\nM(mask, accum)[rows, cols] << A # rows and cols are a list or a slice\nM(mask, accum)[rows, col_index] << v # assign column\nM(mask, accum)[row_index, cols] << v # assign row\nM(mask, accum)[rows, cols] << s # assign scalar to many elements\nM[row_index, col_index] << s # assign scalar to single element\n # (mask and accum not allowed)\ndel M[row_index, col_index] # remove single element\n```\n## Apply\n```python\nM(mask, accum) << A.apply(unaryop)\nM(mask, accum) << A.apply(binaryop, left=s) # bind-first\nM(mask, accum) << A.apply(binaryop, right=s) # bind-second\n```\n## Reduce\n```python\nv(mask, accum) << A.reduce_rowwise(op) # reduce row-wise\nv(mask, accum) << A.reduce_columnwise(op) # reduce column-wise\ns(accum) << A.reduce_scalar(op)\ns(accum) << v.reduce(op)\n```\n## Creating new Vectors / Matrices\n```python\nA = Matrix.new(dtype, num_rows, num_cols) # new_type\nB = A.dup() # dup\nA = Matrix.from_coo([row_indices], [col_indices], [values]) # build\n```\n## New from delayed\nDelayed objects can be used to create a new object using `.new()` method\n```python\nC = A.mxm(B, semiring).new()\n```\n## Properties\n```python\nsize = v.size # size\nnrows = M.nrows # nrows\nncols = M.ncols # ncols\nnvals = M.nvals # nvals\nrindices, cindices, vals = M.to_coo() # extractTuples\n```\n## Initialization\nThere is a mechanism to initialize `graphblas` with a context prior to use. This allows for setting the backend to\nuse as well as the blocking/non-blocking mode. If the context is not initialized, a default initialization will\nbe performed automatically.\n```python\nimport graphblas as gb\n\n# Context initialization must happen before any other imports\ngb.init(\"suitesparse\", blocking=True)\n\n# Now we can import other items from graphblas\nfrom graphblas import binary, semiring\nfrom graphblas import Matrix, Vector, Scalar\n```\n## Performant User Defined Functions\nPython-graphblas requires `numba` which enables compiling user-defined Python functions to native C for use in GraphBLAS.\n\nExample customized UnaryOp:\n```python\nfrom graphblas import unary\n\ndef force_odd_func(x):\n if x % 2 == 0:\n return x + 1\n return x\n\nunary.register_new(\"force_odd\", force_odd_func)\n\nv = Vector.from_coo([0, 1, 3], [1, 2, 3])\nw = v.apply(unary.force_odd).new()\nw # indexes=[0, 1, 3], values=[1, 3, 3]\n```\nSimilar methods exist for BinaryOp, Monoid, and Semiring.\n\n## Relation to other network analysis libraries\nPython-graphblas aims to provide an efficient and consistent expression\nof graph operations using linear algebra. This allows the development of\nhigh-performance implementations of existing and new graph algorithms\n(also see [`graphblas-algorithms`](https://github.com/python-graphblas/graphblas-algorithms)).\n\nWhile end-to-end analysis can be done using `python-graphblas`, users\nmight find that other libraries in the Python ecosystem provide a more\nconvenient high-level interface for data pre-processing and transformation\n(e.g. `pandas`, `scipy.sparse`), visualization (e.g. `networkx`, `igraph`),\ninteractive exploration and analysis (e.g. `networkx`, `igraph`) or for\nalgorithms that are not (yet) implemented in `graphblas-algorithms` (e.g.\n`networkx`, `igraph`, `scipy.sparse.csgraph`). To facilitate communication with\nother libraries, `graphblas.io` contains multiple connectors, see the\nfollowing section.\n\n## Import/Export connectors to the Python ecosystem\n`graphblas.io` contains functions for converting to and from:\n```python\nimport graphblas as gb\n\n# scipy.sparse matrices\nA = gb.io.from_scipy_sparse(m)\nm = gb.io.to_scipy_sparse(m, format=\"csr\")\n\n# networkx graphs\nA = gb.io.from_networkx(g)\ng = gb.io.to_networkx(A)\n\n# numpy arrays can use `from_dense` and `to_dense` on Vector and Matrix\nv = gb.Vector.from_dense(m)\nm = v.to_dense()\n\nA = gb.Matrix.from_dense(m, missing_value=0)\nm = A.to_dense(fill_value=0)\n```\n",
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