Refactor dmas scripts/funs

+kernel/gen_dmas_grid.m

@@ -1,18 +1,18 @@
 
-% GEN_DMA_DMA_GRID  Evaluate kernel/transfer functions for DMA-DMA. 
+% GEN_DMAS_GRID  Evaluate kernel/transfer functions for DMA-DMA. 
 %  This function exploits the grid structure of the data and 
 %  reconstruction domain to speed computation.
 % 
-%  A = kernel.gen_pma_dma_grid(GRID_B,GRID_I) uses the data grid, GRID_B,
+%  A = kernel.gen_dmas_grid(GRID_B,GRID_I) uses the data grid, GRID_B,
 %  and integration grid, GRID_I, which is generally higher resolution. 
 % 
-%  A = kernel.gen_pma_dma_grid(GRID_B,GRID_I,PROP_DMA1) uses the 
+%  A = kernel.gen_dmas_grid(GRID_B,GRID_I,PROP_DMA1) uses the 
 %  pre-computed DMA properties in PROP_DMA1 for the first DMA. 
 % 
-%  A = kernel.gen_pma_dma_grid(GRID_B,GRID_I,PROP_DMA1,PROP_DMA2) adds an
+%  A = kernel.gen_dmas_grid(GRID_B,GRID_I,PROP_DMA1,PROP_DMA2) adds an
 %  inputs for the properties of the second DMA. 
 % 
-%  A = kernel.gen_pma_dma_grid(...,B_NEUT) adds a boolean flag of whether
+%  A = kernel.gen_dmas_grid(...,B_NEUT) adds a boolean flag of whether
 %  the particles are reneutralized. 
 %  
 %  ------------------------------------------------------------------------
@@ -24,7 +24,7 @@
 % 
 %  AUTHOR: Timothy Sipkens, 2018-11-27
 
-function A = gen_dma_dma_grid(grid_b, grid_i, prop_dma1, prop_dma2, b_neut, varargin)
+function A = gen_dmas_grid(grid_b, grid_i, prop_dma1, prop_dma2, b_neut, varargin)
 
 if ~exist('prop_dma1','var'); prop_dma1 = []; end
 if ~exist('prop_dma2','var'); prop_dma2 = []; end

main_dma_dma.m → main_dmas.m

@@ -1,9 +1,8 @@
 
-% MAIN_DMA_DMA  Script for doing tandem DMA inversion.
+% MAIN_DMAS  Script for doing tandem DMA inversion.
 % This case is for a HTDMA setup also showing growth factors.
 % 
 % Author: Timothy Sipkens, 2022-06-28
-%=========================================================================%
 
 clear;
 clc;
@@ -31,7 +30,7 @@
 
 %== Generate x vector on coarser grid ====================================%
 %   This will be used later to gauge accuracy of reconstructions
-n_x = [70,80]; % number of elements per dimension in x
+n_x = [20,32]; % number of elements per dimension in x
     % [20,32]; % used for plotting projections of basis functions
     % [40,64]; % used in evaluating previous versions of regularization
 
@@ -63,8 +62,8 @@
 prop_dma = kernel.prop_dma
 
 % Generate A matrix based on grid for x and b.
-A = kernel.gen_dma_dma_grid(grid_b, grid_x, prop_dma, prop_dma);
-A2 = kernel.gen_dma_dma_grid(grid_b, grid_x, prop_dma, prop_dma, 0);
+A = kernel.gen_dmas_grid(grid_b, grid_x, prop_dma, prop_dma);
+A2 = kernel.gen_dmas_grid(grid_b, grid_x, prop_dma, prop_dma, 0);
 
 idx = round(2 * grid_b.Ne / 3);
 grid_b.elements(idx,:)