Object-oriented linear and nonlinear coordinate system transforms.
- A collection of different types of coordinate system transform classes with full unit test coverage
- Easy methods for mapping coordinate data through these transforms
- Transform composition and simplification
- Transforms intelligently map data types including numpy arrays, lists, etc.
Wishlist:
- Automatic generation of composite transforms from a coordinate system graph
- Coordinate arrays that know which coordinate system they live in to handle automatic mapping
- Conversion of transforms between ITK, Qt, scikit-image, vispy, etc.
- Numba, cuda optimization
Scale and translate 2D coordinates:
import numpy as np
from coorx import *
coords = np.array([
[ 0, 0],
[ 1, 2],
[20, 21],
])
tr = STTransform(scale=(10, 1), offset=(5, 5))
tr.map(coords)
# returns:
# [ [ 5., 5.],
# [ 15., 7.],
# [205., 26.] ]
Compose multiple transforms together:
tr1 = STTransform(scale=(1, 10, 100))
tr2 = AffineTransform(dims=3)
tr2.rotate(90, axis=(0, 0, 1))
tr3 = CompositeTransform([tr2, tr1])
tr3.map(coords)
- import bilinear, SRT transforms from pyqtgraph
- import coordinate system graph handling from vispy
- make coordinate system dimensionality explicit
- unit tests against ITK output
Coorx is adapted from code originally written for VisPy (vispy.org), inspired by the nice transform classes in ITK, and maintained by the Allen Institute for Brain Science.