A Numeric Transform operations toolset
This module provides a toolset to do fast matrix/quaternion/vector operations. For practical reasons, most functions are written to accept asymmetrical inputs, meaning that calculations will be made element wise even if array sizes differ, and reuse the last entry of the smallest input to complete the calculation.
Numpy, Scipy and Numba python modules.
- Eric Vignola ([email protected])
import numpy as np
from transforms import vector
# declare two arrays of 10 million vectors
V0 = vector.random((10**7,3))
V1 = vector.random((10**7,3))
# slerp 10 million vectors 1:1 element wise, half way
slerp = vector.slerp(V0, V1, 0.5)
# slerp 10 million vectors to a common vector, half way
slerp = vector.slerp(V0, V1[0], 0.5)
# slerp 10 million vectors 1:1 element wise, with a random ratio for each
blend = np.random.random(10**7)
slerp = vector.slerp(V0, V1, blend)
axisAngleToEuler quaternionAdd vectorArcToEuler
axisAngleToMatrix quaternionConjugate vectorArcToMatrix
axisAngleToQuaternion quaternionDot vectorArcToQuaternion
eulerSlerp quaternionInverse vectorCross
eulerToEuler quaternionMultiply vectorDot
eulerToMatrix quaternionNegate vectorLerp
eulerToQuaternion quaternionNormalize vectorMagnitude
matrixIdentity quaternionSlerp vectorNormalize
matrixInterpolate quaternionSub vectorSlerp
matrixInverse quaternionToEuler vectorToEuler
matrixLocal quaternionToMatrix vectorToMatrix
matrixMultiply randomAngle vectorToQuaternion
matrixNormalize randomEuler
matrixPointMultiply randomMatrix
matrixSlerp randomQuaternion
matrixToEuler randomVector
matrixToQuaternion
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