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|
%global _empty_manifest_terminate_build 0
Name: python-pyamg
Version: 5.0.0
Release: 1
Summary: PyAMG: Algebraic Multigrid Solvers in Python
License: MIT
URL: https://github.com/pyamg/pyamg
Source0: https://mirrors.nju.edu.cn/pypi/web/packages/dd/22/e8c569797fc2ffb6c5115237eeb60f48201d63792d8a829b0f997839cafa/pyamg-5.0.0.tar.gz
Requires: python3-numpy
Requires: python3-scipy
%description
[](https://github.com/pyamg/pyamg/actions?query=workflow%3ACI+branch%3Amain)
[](https://pypi.python.org/pypi/pyamg/)
[](https://codecov.io/gh/pyamg/pyamg)
[](https://doi.org/10.21105/joss.04142)
# Installation
PyAMG requires `numpy` and `scipy`
```
pip install pyamg
```
or from source:
```
pip install .
```
(`python setup.py install` will no longer work)
or with conda (see details below)
```
conda config --add channels conda-forge
conda install pyamg
```
# Introduction
PyAMG is a library of **Algebraic Multigrid (AMG)** solvers with a convenient Python interface.
PyAMG is currently developed and maintained by
[Luke Olson](http://lukeo.cs.illinois.edu),
[Jacob Schroder](https://www.unm.edu/~jbschroder), and
[Ben Southworth](https://arxiv.org/a/southworth_b_1.html).
The organization of the project can be found in [`organization.md`](organization.md) and
examples of use can be found in [`pyamg-examples`](https://github.com/pyamg/pyamg-examples).
**Acknowledgements:**
PyAMG was created by
[Nathan Bell](http://wnbell.com/),
[Luke Olson](http://lukeo.cs.illinois.edu), and
[Jacob Schroder](https://www.unm.edu/~jbschroder).
Portions of the project were partially supported by the NSF under award DMS-0612448.
# Citing
If you use PyAMG in your work, please consider using the following citation:
<pre>
@article{BeOlSc2022,
author = {Nathan Bell and Luke N. Olson and Jacob Schroder},
title = {{PyAMG}: Algebraic Multigrid Solvers in Python},
journal = {Journal of Open Source Software},
year = {2022},
publisher = {The Open Journal},
volume = {7},
number = {72},
pages = {4142},
doi = {10.21105/joss.04142},
url = {https://doi.org/10.21105/joss.04142},
}
</pre>
# Getting Help
- For documentation see [http://pyamg.readthedocs.io/en/latest/](http://pyamg.readthedocs.io/en/latest/).
- Create an [issue](https://github.com/pyamg/pyamg/issues).
- Look at the [Tutorial](https://github.com/pyamg/pyamg/wiki/Tutorial) or the [examples](https://github.com/pyamg/pyamg-examples) (for instance the [0_start_here](https://github.com/pyamg/pyamg-examples/blob/main/0_start_here/demo.py) example).
- Run the unit tests (`pip install pytest`):
- With PyAMG installed and from a non-source directory:
```python
import pyamg
pyamg.test()
```
- From the PyAMG source directory and installed (e.g. with `pip install -e .`):
```python
pytest .
```
# What is AMG?
AMG is a multilevel technique for solving large-scale linear systems with optimal or near-optimal efficiency. Unlike geometric multigrid, AMG requires little or no geometric information about the underlying problem and develops a sequence of coarser grids directly from the input matrix. This feature is especially important for problems discretized on unstructured meshes and irregular grids.
# PyAMG Features
PyAMG features implementations of:
- **Ruge-Stuben (RS)** or *Classical AMG*
- AMG based on **Smoothed Aggregation (SA)**
and experimental support for:
- **Adaptive Smoothed Aggregation (αSA)**
- **Compatible Relaxation (CR)**
The predominant portion of PyAMG is written in Python with a smaller amount of supporting C++ code for performance critical operations.
# Example Usage
PyAMG is easy to use! The following code constructs a two-dimensional Poisson problem and solves the resulting linear system with Classical AMG.
````python
import pyamg
import numpy as np
A = pyamg.gallery.poisson((500,500), format='csr') # 2D Poisson problem on 500x500 grid
ml = pyamg.ruge_stuben_solver(A) # construct the multigrid hierarchy
print(ml) # print hierarchy information
b = np.random.rand(A.shape[0]) # pick a random right hand side
x = ml.solve(b, tol=1e-10) # solve Ax=b to a tolerance of 1e-10
print("residual: ", np.linalg.norm(b-A*x)) # compute norm of residual vector
````
Program output:
<pre>
multilevel_solver
Number of Levels: 9
Operator Complexity: 2.199
Grid Complexity: 1.667
Coarse Solver: 'pinv2'
level unknowns nonzeros
0 250000 1248000 [45.47%]
1 125000 1121002 [40.84%]
2 31252 280662 [10.23%]
3 7825 70657 [ 2.57%]
4 1937 17971 [ 0.65%]
5 483 4725 [ 0.17%]
6 124 1352 [ 0.05%]
7 29 293 [ 0.01%]
8 7 41 [ 0.00%]
residual: 1.24748994988e-08
</pre>
# Conda
More information can be found at [conda-forge/pyamg-feedstock](https://github.com/conda-forge/pyamg-feedstock).
*Linux:*
[](https://circleci.com/gh/conda-forge/pyamg-feedstock)
*OSX:*
[](https://travis-ci.org/conda-forge/pyamg-feedstock)
*Windows:*
[](https://ci.appveyor.com/project/conda-forge/pyamg-feedstock/branch/master)
*Version:*
[](https://anaconda.org/conda-forge/pyamg)
*Downloads:*
[](https://anaconda.org/conda-forge/pyamg)
Installing `pyamg` from the `conda-forge` channel can be achieved by adding `conda-forge` to your channels with:
```
conda config --add channels conda-forge
```
Once the `conda-forge` channel has been enabled, `pyamg` can be installed with:
```
conda install pyamg
```
It is possible to list all of the versions of `pyamg` available on your platform with:
```
conda search pyamg --channel conda-forge
```
%package -n python3-pyamg
Summary: PyAMG: Algebraic Multigrid Solvers in Python
Provides: python-pyamg
BuildRequires: python3-devel
BuildRequires: python3-setuptools
BuildRequires: python3-pip
BuildRequires: python3-cffi
BuildRequires: gcc
BuildRequires: gdb
%description -n python3-pyamg
[](https://github.com/pyamg/pyamg/actions?query=workflow%3ACI+branch%3Amain)
[](https://pypi.python.org/pypi/pyamg/)
[](https://codecov.io/gh/pyamg/pyamg)
[](https://doi.org/10.21105/joss.04142)
# Installation
PyAMG requires `numpy` and `scipy`
```
pip install pyamg
```
or from source:
```
pip install .
```
(`python setup.py install` will no longer work)
or with conda (see details below)
```
conda config --add channels conda-forge
conda install pyamg
```
# Introduction
PyAMG is a library of **Algebraic Multigrid (AMG)** solvers with a convenient Python interface.
PyAMG is currently developed and maintained by
[Luke Olson](http://lukeo.cs.illinois.edu),
[Jacob Schroder](https://www.unm.edu/~jbschroder), and
[Ben Southworth](https://arxiv.org/a/southworth_b_1.html).
The organization of the project can be found in [`organization.md`](organization.md) and
examples of use can be found in [`pyamg-examples`](https://github.com/pyamg/pyamg-examples).
**Acknowledgements:**
PyAMG was created by
[Nathan Bell](http://wnbell.com/),
[Luke Olson](http://lukeo.cs.illinois.edu), and
[Jacob Schroder](https://www.unm.edu/~jbschroder).
Portions of the project were partially supported by the NSF under award DMS-0612448.
# Citing
If you use PyAMG in your work, please consider using the following citation:
<pre>
@article{BeOlSc2022,
author = {Nathan Bell and Luke N. Olson and Jacob Schroder},
title = {{PyAMG}: Algebraic Multigrid Solvers in Python},
journal = {Journal of Open Source Software},
year = {2022},
publisher = {The Open Journal},
volume = {7},
number = {72},
pages = {4142},
doi = {10.21105/joss.04142},
url = {https://doi.org/10.21105/joss.04142},
}
</pre>
# Getting Help
- For documentation see [http://pyamg.readthedocs.io/en/latest/](http://pyamg.readthedocs.io/en/latest/).
- Create an [issue](https://github.com/pyamg/pyamg/issues).
- Look at the [Tutorial](https://github.com/pyamg/pyamg/wiki/Tutorial) or the [examples](https://github.com/pyamg/pyamg-examples) (for instance the [0_start_here](https://github.com/pyamg/pyamg-examples/blob/main/0_start_here/demo.py) example).
- Run the unit tests (`pip install pytest`):
- With PyAMG installed and from a non-source directory:
```python
import pyamg
pyamg.test()
```
- From the PyAMG source directory and installed (e.g. with `pip install -e .`):
```python
pytest .
```
# What is AMG?
AMG is a multilevel technique for solving large-scale linear systems with optimal or near-optimal efficiency. Unlike geometric multigrid, AMG requires little or no geometric information about the underlying problem and develops a sequence of coarser grids directly from the input matrix. This feature is especially important for problems discretized on unstructured meshes and irregular grids.
# PyAMG Features
PyAMG features implementations of:
- **Ruge-Stuben (RS)** or *Classical AMG*
- AMG based on **Smoothed Aggregation (SA)**
and experimental support for:
- **Adaptive Smoothed Aggregation (αSA)**
- **Compatible Relaxation (CR)**
The predominant portion of PyAMG is written in Python with a smaller amount of supporting C++ code for performance critical operations.
# Example Usage
PyAMG is easy to use! The following code constructs a two-dimensional Poisson problem and solves the resulting linear system with Classical AMG.
````python
import pyamg
import numpy as np
A = pyamg.gallery.poisson((500,500), format='csr') # 2D Poisson problem on 500x500 grid
ml = pyamg.ruge_stuben_solver(A) # construct the multigrid hierarchy
print(ml) # print hierarchy information
b = np.random.rand(A.shape[0]) # pick a random right hand side
x = ml.solve(b, tol=1e-10) # solve Ax=b to a tolerance of 1e-10
print("residual: ", np.linalg.norm(b-A*x)) # compute norm of residual vector
````
Program output:
<pre>
multilevel_solver
Number of Levels: 9
Operator Complexity: 2.199
Grid Complexity: 1.667
Coarse Solver: 'pinv2'
level unknowns nonzeros
0 250000 1248000 [45.47%]
1 125000 1121002 [40.84%]
2 31252 280662 [10.23%]
3 7825 70657 [ 2.57%]
4 1937 17971 [ 0.65%]
5 483 4725 [ 0.17%]
6 124 1352 [ 0.05%]
7 29 293 [ 0.01%]
8 7 41 [ 0.00%]
residual: 1.24748994988e-08
</pre>
# Conda
More information can be found at [conda-forge/pyamg-feedstock](https://github.com/conda-forge/pyamg-feedstock).
*Linux:*
[](https://circleci.com/gh/conda-forge/pyamg-feedstock)
*OSX:*
[](https://travis-ci.org/conda-forge/pyamg-feedstock)
*Windows:*
[](https://ci.appveyor.com/project/conda-forge/pyamg-feedstock/branch/master)
*Version:*
[](https://anaconda.org/conda-forge/pyamg)
*Downloads:*
[](https://anaconda.org/conda-forge/pyamg)
Installing `pyamg` from the `conda-forge` channel can be achieved by adding `conda-forge` to your channels with:
```
conda config --add channels conda-forge
```
Once the `conda-forge` channel has been enabled, `pyamg` can be installed with:
```
conda install pyamg
```
It is possible to list all of the versions of `pyamg` available on your platform with:
```
conda search pyamg --channel conda-forge
```
%package help
Summary: Development documents and examples for pyamg
Provides: python3-pyamg-doc
%description help
[](https://github.com/pyamg/pyamg/actions?query=workflow%3ACI+branch%3Amain)
[](https://pypi.python.org/pypi/pyamg/)
[](https://codecov.io/gh/pyamg/pyamg)
[](https://doi.org/10.21105/joss.04142)
# Installation
PyAMG requires `numpy` and `scipy`
```
pip install pyamg
```
or from source:
```
pip install .
```
(`python setup.py install` will no longer work)
or with conda (see details below)
```
conda config --add channels conda-forge
conda install pyamg
```
# Introduction
PyAMG is a library of **Algebraic Multigrid (AMG)** solvers with a convenient Python interface.
PyAMG is currently developed and maintained by
[Luke Olson](http://lukeo.cs.illinois.edu),
[Jacob Schroder](https://www.unm.edu/~jbschroder), and
[Ben Southworth](https://arxiv.org/a/southworth_b_1.html).
The organization of the project can be found in [`organization.md`](organization.md) and
examples of use can be found in [`pyamg-examples`](https://github.com/pyamg/pyamg-examples).
**Acknowledgements:**
PyAMG was created by
[Nathan Bell](http://wnbell.com/),
[Luke Olson](http://lukeo.cs.illinois.edu), and
[Jacob Schroder](https://www.unm.edu/~jbschroder).
Portions of the project were partially supported by the NSF under award DMS-0612448.
# Citing
If you use PyAMG in your work, please consider using the following citation:
<pre>
@article{BeOlSc2022,
author = {Nathan Bell and Luke N. Olson and Jacob Schroder},
title = {{PyAMG}: Algebraic Multigrid Solvers in Python},
journal = {Journal of Open Source Software},
year = {2022},
publisher = {The Open Journal},
volume = {7},
number = {72},
pages = {4142},
doi = {10.21105/joss.04142},
url = {https://doi.org/10.21105/joss.04142},
}
</pre>
# Getting Help
- For documentation see [http://pyamg.readthedocs.io/en/latest/](http://pyamg.readthedocs.io/en/latest/).
- Create an [issue](https://github.com/pyamg/pyamg/issues).
- Look at the [Tutorial](https://github.com/pyamg/pyamg/wiki/Tutorial) or the [examples](https://github.com/pyamg/pyamg-examples) (for instance the [0_start_here](https://github.com/pyamg/pyamg-examples/blob/main/0_start_here/demo.py) example).
- Run the unit tests (`pip install pytest`):
- With PyAMG installed and from a non-source directory:
```python
import pyamg
pyamg.test()
```
- From the PyAMG source directory and installed (e.g. with `pip install -e .`):
```python
pytest .
```
# What is AMG?
AMG is a multilevel technique for solving large-scale linear systems with optimal or near-optimal efficiency. Unlike geometric multigrid, AMG requires little or no geometric information about the underlying problem and develops a sequence of coarser grids directly from the input matrix. This feature is especially important for problems discretized on unstructured meshes and irregular grids.
# PyAMG Features
PyAMG features implementations of:
- **Ruge-Stuben (RS)** or *Classical AMG*
- AMG based on **Smoothed Aggregation (SA)**
and experimental support for:
- **Adaptive Smoothed Aggregation (αSA)**
- **Compatible Relaxation (CR)**
The predominant portion of PyAMG is written in Python with a smaller amount of supporting C++ code for performance critical operations.
# Example Usage
PyAMG is easy to use! The following code constructs a two-dimensional Poisson problem and solves the resulting linear system with Classical AMG.
````python
import pyamg
import numpy as np
A = pyamg.gallery.poisson((500,500), format='csr') # 2D Poisson problem on 500x500 grid
ml = pyamg.ruge_stuben_solver(A) # construct the multigrid hierarchy
print(ml) # print hierarchy information
b = np.random.rand(A.shape[0]) # pick a random right hand side
x = ml.solve(b, tol=1e-10) # solve Ax=b to a tolerance of 1e-10
print("residual: ", np.linalg.norm(b-A*x)) # compute norm of residual vector
````
Program output:
<pre>
multilevel_solver
Number of Levels: 9
Operator Complexity: 2.199
Grid Complexity: 1.667
Coarse Solver: 'pinv2'
level unknowns nonzeros
0 250000 1248000 [45.47%]
1 125000 1121002 [40.84%]
2 31252 280662 [10.23%]
3 7825 70657 [ 2.57%]
4 1937 17971 [ 0.65%]
5 483 4725 [ 0.17%]
6 124 1352 [ 0.05%]
7 29 293 [ 0.01%]
8 7 41 [ 0.00%]
residual: 1.24748994988e-08
</pre>
# Conda
More information can be found at [conda-forge/pyamg-feedstock](https://github.com/conda-forge/pyamg-feedstock).
*Linux:*
[](https://circleci.com/gh/conda-forge/pyamg-feedstock)
*OSX:*
[](https://travis-ci.org/conda-forge/pyamg-feedstock)
*Windows:*
[](https://ci.appveyor.com/project/conda-forge/pyamg-feedstock/branch/master)
*Version:*
[](https://anaconda.org/conda-forge/pyamg)
*Downloads:*
[](https://anaconda.org/conda-forge/pyamg)
Installing `pyamg` from the `conda-forge` channel can be achieved by adding `conda-forge` to your channels with:
```
conda config --add channels conda-forge
```
Once the `conda-forge` channel has been enabled, `pyamg` can be installed with:
```
conda install pyamg
```
It is possible to list all of the versions of `pyamg` available on your platform with:
```
conda search pyamg --channel conda-forge
```
%prep
%autosetup -n pyamg-5.0.0
%build
%py3_build
%install
%py3_install
install -d -m755 %{buildroot}/%{_pkgdocdir}
if [ -d doc ]; then cp -arf doc %{buildroot}/%{_pkgdocdir}; fi
if [ -d docs ]; then cp -arf docs %{buildroot}/%{_pkgdocdir}; fi
if [ -d example ]; then cp -arf example %{buildroot}/%{_pkgdocdir}; fi
if [ -d examples ]; then cp -arf examples %{buildroot}/%{_pkgdocdir}; fi
pushd %{buildroot}
if [ -d usr/lib ]; then
find usr/lib -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/lib64 ]; then
find usr/lib64 -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/bin ]; then
find usr/bin -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/sbin ]; then
find usr/sbin -type f -printf "/%h/%f\n" >> filelist.lst
fi
touch doclist.lst
if [ -d usr/share/man ]; then
find usr/share/man -type f -printf "/%h/%f.gz\n" >> doclist.lst
fi
popd
mv %{buildroot}/filelist.lst .
mv %{buildroot}/doclist.lst .
%files -n python3-pyamg -f filelist.lst
%dir %{python3_sitearch}/*
%files help -f doclist.lst
%{_docdir}/*
%changelog
* Tue Apr 25 2023 Python_Bot <Python_Bot@openeuler.org> - 5.0.0-1
- Package Spec generated
|
