From f5762a3b14550a738dc361c021eabc671bddfe3f Mon Sep 17 00:00:00 2001 From: Mike Grudic Date: Wed, 20 Dec 2023 14:12:15 -0800 Subject: [PATCH] readme --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 78589c1..6677610 100644 --- a/README.md +++ b/README.md @@ -165,7 +165,7 @@ print("RMS potential error: ", phi_error) pytreegrav's octree implementation can be used for efficient tree-based searches for ray-tracing of unstructured data. Currently implemented is the method ``ColumnDensity``, which calculates the integral of the density field to infinity along a grid of rays originating at each particle (defaulting to 6 rays). For example: ```python -columns = ColumnDensity(x, m, h, parallel=True) # shape (N,6) array of column densities along 6 rays oriented along cartesian axes +columns = ColumnDensity(x, m, h, parallel=True) # shape (N,6) array of column densities in 6 angular bins - this is fastest but least accurate columns_10 = ColumnDensity(x, m, h, rays=10, parallel=True) # shape (N, 10) array column densities along 10 random rays columns_random = ColumnDensity(x, m, h, randomize_rays=True, parallel=True) # can randomize the ray grid for each particle so that there are no correlated errors due to the angular discretization columns_custom = ColumnDensity(x, m, h, rays=np.random.normal(size=(100,3)), parallel=True) # can also pass an arbitrary set of rays for the raygrid; these need not be normalized