Phil 7.28.20

GPT-2 Agents

Book

  • Read Michelle’s comments and pretty much agree with everything

GOES

  • Digging into coordinate frame transformations
  • It looks like these are some potential good libraries:
  • Starting with Transforms3d. It looks like it does most of the things I think I need to see?
    • aff – 4×4 affine matrix for operating on homogenous coordinates of shape (4,) or (4, N);
    • mat – 3×3 transformation matrix for operating on non-homogenous coordinate vectors of shape (3,) or (3, N). A rotation matrix is an example of a transformation matrix;
    • euler – euler angles – sequence of three scalars giving rotations about defined axes;
    • axangle – axis angle – axis (vector) and angle (scalar) giving axis around which to rotate and angle of rotation;
    • quat – quaternion – shape (4,);
    • rfnorm : reflection in plane defined by normal (vector) and optional point (vector);
    • zfdir : zooms encoded by factor (scalar) and direction (vector)
    • zdir – factor (scalar), direction (vector) pair to specify 3D zoom matrix;
    • striu : shears encoded by vector giving triangular portion above diagonal of NxN array (for ND transformation)
    • sadn : shears encoded by angle scalar, direction vector, normal vector (with optional point vector)
  • Scipy also has some good stuff in their spatial transformations library, particularly SLERP
  • Transforms3d doesn’t seem to have a SLERP function, but pyquaternion does. Going to try some more experiments. I think this is right? Need to plot:
    Earth vec, rotated 90 degrees
    
    degree = 90
    	q = +0.707 +0.000i +0.707j +0.000k
    	x = [0.00, 0.00, -1.00]
    	y = [0.00, 1.00, 0.00]
    	z = [1.00, 0.00, 0.00]
    
    Yaw vec, rotated 45 degrees
    
    degree = 45
    	q = +0.924 +0.000i +0.000j +0.383k
    	x = [0.71, 0.71, 0.00]
    	y = [-0.71, 0.71, 0.00]
    	z = [0.00, 0.00, 1.00]
    
    Composite vec
    	x = [0.00, 0.71, -0.71]
    	y = [0.00, 0.71, 0.71]
    	z = [1.00, 0.00, 0.00]

     

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