diff --git a/README.md b/README.md
index d5dcf80..fc47e44 100644
--- a/README.md
+++ b/README.md
@@ -4,25 +4,25 @@
 
 
 A library for differential collision detection between the following convex primitives:
-- polytopes 
+- polytopes
 - capsules
 - cylinders
 - cones
 - spheres
-- padded polygons 
+- padded polygons
 
-For more details, see our paper on [arXiv](https://arxiv.org/abs/2207.00669). 
+For more details, see our paper on [arXiv](https://arxiv.org/abs/2207.00669).
 
 ## Interface
-DCOL works by creating a struct for each shape, and calling a function to query a proximity value between them. 
-#### Primitives 
+DCOL works by creating a struct for each shape, and calling a function to query a proximity value between them.
+#### Primitives
 Each primitive is implemented as a struct in DCOL. The defining dimensions for each primitive is described in the [paper](https://arxiv.org/abs/2207.00669), and the primitives can be constructed as the following:
 ```julia
 import DCOL as dc
 
 polytype = dc.Polytope(A, b)   # polytope is described by Ax <= b
-capsule  = dc.Capsule(R, L)    # radius R, length L 
-cylinder = dc.Cylinder(R, L)   # radius R, length L 
+capsule  = dc.Capsule(R, L)    # radius R, length L
+cylinder = dc.Cylinder(R, L)   # radius R, length L
 cone     = dc.Cone(H, β)       # height H, half angle β
 sphere   = dc.Sphere(R)        # radius R
 polygon  = dc.Polygon(A, b, R) # polygon is described by Ay <= b, cushion radius R
@@ -42,14 +42,14 @@ P1.r = SA[1,2,3.0]    # position in world frame W
 P1.p = SA[0.0,0,0]    # MRP ᵂpᴮ
 ```
 #### Proximity Functions
-DCOL exposes a function `proximity` for collision detection, as well as `proximity_jacobian` for collision detection and derivatives. Two optional arguments are included that pertain to the optimization solver under the hood,  `verbose` turns on logging for this solver, and `pdip_tol` is the termination criteria. 
+DCOL exposes a function `proximity` for collision detection, as well as `proximity_jacobian` for collision detection and derivatives. Two optional arguments are included that pertain to the optimization solver under the hood,  `verbose` turns on logging for this solver, and `pdip_tol` is the termination criteria.
 ```julia
 α,x = dc.proximity(P1, P2; verbose = false, pdip_tol = 1e-6)
 α,x,J = dc.proximity_jacobian(P1, P2; verbose = false, pdip_tol = 1e-6)
 ```
 These functions output $\alpha$ as the proximity value, with the following significance:
-- $\alpha \leq 1$ means there **is** a collision between the two primitives 
-- $\alpha >1$ means there **is not** a collision between the two primitives 
+- $\alpha \leq 1$ means there **is** a collision between the two primitives
+- $\alpha >1$ means there **is not** a collision between the two primitives
 
 Also, returned is `x` which is the intersection point between the scaled shapes (see algorithm for significance), and a Jacobian `J` which is the following:
 
@@ -67,12 +67,12 @@ J &= \frac{\partial (x,\alpha) }{\partial (r_1,p_1,r_2,p_2)}
 \end{align*}
 $$
 
-## Visualizer 
+## Visualizer
 All of the primitives (both quaternion and MRP) can be visualized in [MeshCat](https://github.com/rdeits/MeshCat.jl). Below is an example of visualization for a cone:
 
 ```julia
-import DCOL as dc 
-import Meshcat as mc 
+import DCOL as dc
+import Meshcat as mc
 
 vis = mc.Visualizer()
 mc.open(vis)
@@ -82,7 +82,7 @@ cone.r = @SVector randn(3)
 cone.q = normalize((@SVector randn(4)))
 
 # build primitive scaled by α = 1.0
-dc.build_primitive!(vis, cone, :cone; α = 1.0,color = mc.RGBA(1,0,0,0.0))
+dc.build_primitive!(vis, cone, :cone; α = 1.0,color = mc.RGBA(1,0,0,1.0))
 
 # update position and attitude
 dc.update_pose!(vis[:cone],cone)
@@ -94,7 +94,7 @@ DCOL calculates the collision information between two primitives by solving for
 - $\alpha \in \mathbb{R}$, the scaling applied to each primitive
 - $x \in \mathbb{R}^3$, an intersection point in the world frame
 
-The following optimization problem solves for the minimum scaling α such that a point x exists in the scaled versions of two primitives P1 and P2. 
+The following optimization problem solves for the minimum scaling α such that a point x exists in the scaled versions of two primitives P1 and P2.
 
 $$
 \begin{align*}