Source code for sym.ops.pose3.lie_group_ops

# -----------------------------------------------------------------------------
# This file was autogenerated by symforce from template:
#     ops/CLASS/lie_group_ops.py.jinja
# Do NOT modify by hand.
# -----------------------------------------------------------------------------

# ruff: noqa: PLR0915, F401, PLW0211, PLR0914

import math
import typing as T

import numpy

import sym


[docs]class LieGroupOps(object): """ Python LieGroupOps implementation for :py:class:`symforce.geo.pose3.Pose3`. """
[docs] @staticmethod def from_tangent(vec, epsilon): # type: (numpy.ndarray, float) -> sym.Pose3 # Total ops: 15 # Input arrays if vec.shape == (6,): vec = vec.reshape((6, 1)) elif vec.shape != (6, 1): raise IndexError( "vec is expected to have shape (6, 1) or (6,); instead had shape {}".format( vec.shape ) ) # Intermediate terms (3) _tmp0 = math.sqrt(epsilon**2 + vec[0, 0] ** 2 + vec[1, 0] ** 2 + vec[2, 0] ** 2) _tmp1 = (1.0 / 2.0) * _tmp0 _tmp2 = math.sin(_tmp1) / _tmp0 # Output terms _res = [0.0] * 7 _res[0] = _tmp2 * vec[0, 0] _res[1] = _tmp2 * vec[1, 0] _res[2] = _tmp2 * vec[2, 0] _res[3] = math.cos(_tmp1) _res[4] = vec[3, 0] _res[5] = vec[4, 0] _res[6] = vec[5, 0] return sym.Pose3.from_storage(_res)
[docs] @staticmethod def to_tangent(a, epsilon): # type: (sym.Pose3, float) -> numpy.ndarray # Total ops: 17 # Input arrays _a = a.data # Intermediate terms (2) _tmp0 = min(abs(_a[3]), 1 - epsilon) _tmp1 = ( 2 * (2 * min(0, (0.0 if _a[3] == 0 else math.copysign(1, _a[3]))) + 1) * math.acos(_tmp0) / math.sqrt(1 - _tmp0**2) ) # Output terms _res = numpy.zeros(6) _res[0] = _a[0] * _tmp1 _res[1] = _a[1] * _tmp1 _res[2] = _a[2] * _tmp1 _res[3] = _a[4] _res[4] = _a[5] _res[5] = _a[6] return _res
[docs] @staticmethod def retract(a, vec, epsilon): # type: (sym.Pose3, numpy.ndarray, float) -> sym.Pose3 # Total ops: 48 # Input arrays _a = a.data if vec.shape == (6,): vec = vec.reshape((6, 1)) elif vec.shape != (6, 1): raise IndexError( "vec is expected to have shape (6, 1) or (6,); instead had shape {}".format( vec.shape ) ) # Intermediate terms (9) _tmp0 = math.sqrt(epsilon**2 + vec[0, 0] ** 2 + vec[1, 0] ** 2 + vec[2, 0] ** 2) _tmp1 = (1.0 / 2.0) * _tmp0 _tmp2 = math.cos(_tmp1) _tmp3 = math.sin(_tmp1) / _tmp0 _tmp4 = _a[3] * _tmp3 _tmp5 = _a[2] * _tmp3 _tmp6 = _tmp3 * vec[2, 0] _tmp7 = _a[0] * _tmp3 _tmp8 = _a[1] * _tmp3 # Output terms _res = [0.0] * 7 _res[0] = _a[0] * _tmp2 + _a[1] * _tmp6 + _tmp4 * vec[0, 0] - _tmp5 * vec[1, 0] _res[1] = _a[1] * _tmp2 + _tmp4 * vec[1, 0] + _tmp5 * vec[0, 0] - _tmp7 * vec[2, 0] _res[2] = _a[2] * _tmp2 + _a[3] * _tmp6 + _tmp7 * vec[1, 0] - _tmp8 * vec[0, 0] _res[3] = -_a[2] * _tmp6 + _a[3] * _tmp2 - _tmp7 * vec[0, 0] - _tmp8 * vec[1, 0] _res[4] = _a[4] + vec[3, 0] _res[5] = _a[5] + vec[4, 0] _res[6] = _a[6] + vec[5, 0] return sym.Pose3.from_storage(_res)
[docs] @staticmethod def local_coordinates(a, b, epsilon): # type: (sym.Pose3, sym.Pose3, float) -> numpy.ndarray # Total ops: 50 # Input arrays _a = a.data _b = b.data # Intermediate terms (4) _tmp0 = -_a[0] * _b[0] - _a[1] * _b[1] - _a[2] * _b[2] _tmp1 = _a[3] * _b[3] _tmp2 = min(1 - epsilon, abs(_tmp0 - _tmp1)) _tmp3 = ( 2 * (2 * min(0, (0.0 if -_tmp0 + _tmp1 == 0 else math.copysign(1, -_tmp0 + _tmp1))) + 1) * math.acos(_tmp2) / math.sqrt(1 - _tmp2**2) ) # Output terms _res = numpy.zeros(6) _res[0] = _tmp3 * (-_a[0] * _b[3] - _a[1] * _b[2] + _a[2] * _b[1] + _a[3] * _b[0]) _res[1] = _tmp3 * (_a[0] * _b[2] - _a[1] * _b[3] - _a[2] * _b[0] + _a[3] * _b[1]) _res[2] = _tmp3 * (-_a[0] * _b[1] + _a[1] * _b[0] - _a[2] * _b[3] + _a[3] * _b[2]) _res[3] = -_a[4] + _b[4] _res[4] = -_a[5] + _b[5] _res[5] = -_a[6] + _b[6] return _res
[docs] @staticmethod def interpolate(a, b, alpha, epsilon): # type: (sym.Pose3, sym.Pose3, float, float) -> sym.Pose3 # Total ops: 108 # Input arrays _a = a.data _b = b.data # Intermediate terms (17) _tmp0 = -_a[0] * _b[1] + _a[1] * _b[0] - _a[2] * _b[3] + _a[3] * _b[2] _tmp1 = _a[0] * _b[2] - _a[1] * _b[3] - _a[2] * _b[0] + _a[3] * _b[1] _tmp2 = -_a[0] * _b[0] - _a[1] * _b[1] - _a[2] * _b[2] _tmp3 = _a[3] * _b[3] _tmp4 = min(1 - epsilon, abs(_tmp2 - _tmp3)) _tmp5 = math.acos(_tmp4) _tmp6 = 2 * min(0, (0.0 if -_tmp2 + _tmp3 == 0 else math.copysign(1, -_tmp2 + _tmp3))) + 1 _tmp7 = 1 - _tmp4**2 _tmp8 = 4 * _tmp5**2 * _tmp6**2 * alpha**2 / _tmp7 _tmp9 = -_a[0] * _b[3] - _a[1] * _b[2] + _a[2] * _b[1] + _a[3] * _b[0] _tmp10 = math.sqrt(_tmp0**2 * _tmp8 + _tmp1**2 * _tmp8 + _tmp8 * _tmp9**2 + epsilon**2) _tmp11 = (1.0 / 2.0) * _tmp10 _tmp12 = 2 * _tmp5 * _tmp6 * alpha * math.sin(_tmp11) / (_tmp10 * math.sqrt(_tmp7)) _tmp13 = _tmp0 * _tmp12 _tmp14 = _tmp1 * _tmp12 _tmp15 = math.cos(_tmp11) _tmp16 = _tmp12 * _tmp9 # Output terms _res = [0.0] * 7 _res[0] = _a[0] * _tmp15 + _a[1] * _tmp13 - _a[2] * _tmp14 + _a[3] * _tmp16 _res[1] = -_a[0] * _tmp13 + _a[1] * _tmp15 + _a[2] * _tmp16 + _a[3] * _tmp14 _res[2] = _a[0] * _tmp14 - _a[1] * _tmp16 + _a[2] * _tmp15 + _a[3] * _tmp13 _res[3] = -_a[0] * _tmp16 - _a[1] * _tmp14 - _a[2] * _tmp13 + _a[3] * _tmp15 _res[4] = _a[4] + alpha * (-_a[4] + _b[4]) _res[5] = _a[5] + alpha * (-_a[5] + _b[5]) _res[6] = _a[6] + alpha * (-_a[6] + _b[6]) return sym.Pose3.from_storage(_res)