lyncs-DDalphaAMG 0.1.2
A Python interface to the DDalphaAMG multigrid solver library
Keywords: solver,library,interface,Python,multigrid,DDalphaAMG

# A Python interface to the DDalphaAMG multigrid solver library

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This package provides a Python interface to DDalphaAMG.
[DDalphaAMG] is a solver library for inverting Wilson Clover and Twisted Mass fermions from lattice QCD.
It provides an implementation of an adaptive aggregation-based algebraic multigrid ($\alpha$AMG) method.

[DDalphaAMG]: https://github.com/sbacchio/DDalphaAMG

## Installation

**NOTE**: lyncs_DDalphaAMG requires a working MPI installation.
This can be installed via `apt-get`:

```bash
sudo apt-get install libopenmpi-dev openmpi-bin
```

OR using `conda`:

```bash
conda install -c anaconda mpi4py
```

The package can be installed via `pip`:

```bash
pip install [--user] lyncs_DDalphaAMG
```

## Documentation

The functions provided by the [DDalphaAMG API](https://github.com/sbacchio/DDalphaAMG/blob/master/src/DDalphaAMG.h) are available in the Solver class `lyncs_DDalphaAMG.Solver`.
Please, use `help(Solver)` to see an overview of the functionalities.
In the following we present some examples on the usage of the package.


```python
from lyncs_DDalphaAMG import Solver

# Creating the solver
solver = Solver(global_lattice=[4, 4, 4, 4],
       	 	kappa=0.125)

# Reading the configurations
conf = solver.read_configuration("test/conf.random")
plaq = solver.set_configuration(conf)
print("Plaquette:", plaq)

# Computing the solution of a random vector
vector = solver.random()
result = solver.solve(vector)
```

The above example is run locally and all the fields are numpy arrays.

The Solver class is also Dask compatible and by giving a distributed communicator,
as in the following example, the solver instance is now distributed remotely on
the dask workers and all the fields are Dask arrays.

```python
from lyncs_mpi import Client

# Creating a client with 4 workers
client = Client(num_workers = 4)
comm = client.create_comm()
procs = [2, 2, 1, 1]
comm = comms.create_cart(procs)

solver = Solver(global_lattice=[4, 4, 4, 4],
       	 	comm = comm,
       	 	kappa=0.125)
		
# Continues as above
```