From 2130b015fd15100fbf2c4ed70952ea49a6d096fd Mon Sep 17 00:00:00 2001 From: jan Date: Fri, 3 Jul 2020 13:46:38 -0700 Subject: [PATCH] readme updates --- README.md | 18 +++++++++--------- 1 file changed, 9 insertions(+), 9 deletions(-) diff --git a/README.md b/README.md index 896001e..afca8ac 100644 --- a/README.md +++ b/README.md @@ -6,10 +6,10 @@ electromagnetic solver implemented in Python and OpenCL. **Capabilities:** * Arbitrary distributions of the following: - * Dielectric constant (```epsilon```) - * Magnetic permeabilty (```mu```) - * Perfect electric conductor (```PEC```) - * Perfect magnetic conductor (```PMC```) + * Dielectric constant (`epsilon`) + * Magnetic permeabilty (`mu`) + * Perfect electric conductor (`PEC`) + * Perfect magnetic conductor (`PMC`) * Variable-sized rectangular grids * Stretched-coordinate PMLs (complex cell sizes allowed) @@ -17,10 +17,10 @@ Currently, only periodic boundary conditions are included. PEC/PMC boundaries can be implemented by drawing PEC/PMC cells near the edges. Bloch boundary conditions are not included but wouldn't be very hard to add. -The default solver ```opencl_fdfd.cg_solver(...)``` located in main.py +The default solver `opencl_fdfd.cg_solver(...)` located in main.py implements the E-field wave operator directly (ie, as a list of OpenCL instructions rather than a matrix). Additionally, there is a slower -(and slightly more versatile) solver in ```csr.py``` which attempts to solve +(and slightly more versatile) solver in `csr.py` which attempts to solve an arbitrary sparse matrix in compressed sparse row (CSR) format using the same conjugate gradient method as the default solver. The CSR solver is significantly slower, but can be very useful for testing alternative @@ -49,14 +49,14 @@ pip install git+https://mpxd.net/code/jan/opencl_fdfd.git@release ## Use -See the documentation for ```opencl_fdfd.cg_solver(...)``` +See the documentation for `opencl_fdfd.cg_solver(...)` (located in ```main.py```) for details about how to call the solver. The FDFD arguments are identical to those in -```fdfd_tools.solvers.generic(...)```, and a few solver-specific +`meanas.solvers.generic(...)`, and a few solver-specific arguments are available. An alternate (slower) FDFD solver and a general gpu-based sparse matrix -solver is available in ```csr.py```. These aren't particularly +solver is available in `csr.py`. These aren't particularly well-optimized, and something like [MAGMA](http://icl.cs.utk.edu/magma/index.html) would probably be a better choice if you absolutely need to solve arbitrary sparse matrices