from __future__ import annotations
import logging
import os
from typing import TYPE_CHECKING
from dataclasses import dataclass
from functools import cached_property
import numpy as np
from numba import jit, generated_jit
try:
from coloredlogs import ColoredFormatter as Formatter
except ImportError:
from logging import Formatter
__version__ = '0.2.0'
logger = logging.getLogger(__name__)
handler = logging.StreamHandler()
logger.addHandler(handler)
handler.setFormatter(Formatter('%(name)s %(levelname)s %(message)s'))
try:
level = os.environ.get('COSCONLOGLEVEL', logging.WARNING)
logger.setLevel(level=level)
except ValueError:
logger.setLevel(level=logging.WARNING)
logger.error(f'Unknown COSCONLOGLEVEL {level}, set to default WARNING.')
[docs]@jit(nopython=True, nogil=True, cache=True)
def lastcol_quat_to_canonical(quat: np.ndarray[np.float_]) -> np.ndarray[np.float_]:
"""Convert from real-part-in-last-column to real-part-in-first-column"""
return np.ascontiguousarray(quat[..., np.array([3, 0, 1, 2])])
[docs]@jit(nopython=True, nogil=True, cache=True)
def canonical_quat_to_lastcol(quat: np.ndarray[np.float_]) -> np.ndarray[np.float_]:
"""Convert from real-part-in-first-column to real-part-in-last-column"""
return np.ascontiguousarray(quat[..., np.array([1, 2, 3, 0])])
[docs]@jit(nopython=True, nogil=True, cache=True)
def float64_to_complex128(array: np.ndarray[np.float64]) -> np.ndarray[np.complex128]:
return array.view(np.complex128)
[docs]@jit(nopython=True, nogil=True, cache=True)
def float32_to_complex64(array: np.ndarray[np.float32]) -> np.ndarray[np.complex64]:
return array.view(np.complex64)
[docs]@jit(nopython=True, nogil=True, cache=True)
def complex128_to_float64(array: np.ndarray[np.complex128]) -> np.ndarray[np.float64]:
return array.view(np.float64)
[docs]@jit(nopython=True, nogil=True, cache=True)
def complex64_to_float32(array: np.ndarray[np.complex64]) -> np.ndarray[np.float32]:
return array.view(np.float32)
[docs]@generated_jit(nopython=True, nogil=True, cache=True)
def float_to_complex(array: np.ndarray[np.float_]) -> np.ndarray[np.complex_]:
dtype = str(array.dtype)
if dtype == 'float64':
return float64_to_complex128
elif dtype == 'float32':
return float32_to_complex64
[docs]@generated_jit(nopython=True, nogil=True, cache=True)
def complex_to_float(array: np.ndarray[np.complex_]) -> np.ndarray[np.float_]:
dtype = str(array.dtype)
if dtype == 'complex128':
return complex128_to_float64
elif dtype == 'complex64':
return complex64_to_float32
[docs]@jit(nopython=True, nogil=True, cache=True)
def mul(p: np.ndarray[np.complex_], q: np.ndarray[np.complex_]) -> np.ndarray[np.complex_]:
"""Perform quarternion multiplication using complex multiplication"""
A = p[..., 0]
B = p[..., 1]
C = q[..., 0]
D = q[..., 1]
real_i_part = A * C - B * np.conjugate(D)
jk_part = B * np.conjugate(C) + A * D
return np.stack((real_i_part, jk_part), -1)
[docs]@jit(nopython=True, nogil=True, cache=True)
def matmul(p: np.ndarray[np.complex_], q: np.ndarray[np.complex_]) -> np.ndarray[np.complex_]:
"""Perform quarternion matrix multiplication using complex matrix multiplication"""
A = np.ascontiguousarray(p[..., 0])
B = np.ascontiguousarray(p[..., 1])
C = np.ascontiguousarray(q[..., 0])
D = np.ascontiguousarray(q[..., 1])
real_i_part = A @ C - B @ np.conjugate(D)
jk_part = B @ np.conjugate(C) + A @ D
return np.stack((real_i_part, jk_part), -1)
[docs]@jit(nopython=True, nogil=True, cache=True)
def quat_to_rotation_matrix(quats: np.ndarray[np.complex_]) -> np.ndarray[np.float_]:
"""Convert quaternion to rotation matrix.
"""
I = np.array(
(
[1.j, 0.],
[0. , 1.],
[0., 1.j],
),
dtype=quats.dtype,
)
res = rotate_2d(quats, I)
return np.stack(
(
complex_to_float(res[0])[..., 1:],
complex_to_float(res[1])[..., 1:],
complex_to_float(res[2])[..., 1:],
),
-1,
)
[docs]@jit(nopython=True, nogil=True, cache=True)
def rotation_matrix_to_quat(m: np.ndarray[np.float_]) -> np.ndarray[np.complex_]:
"""Convert rotation matrix to quaternion.
See https://en.wikipedia.org/wiki/Rotation_matrix#Quaternion
"""
Q_xx = m[..., 0, 0]
Q_xy = m[..., 0, 1]
Q_xz = m[..., 0, 2]
Q_yx = m[..., 1, 0]
Q_yy = m[..., 1, 1]
Q_yz = m[..., 1, 2]
Q_zx = m[..., 2, 0]
Q_zy = m[..., 2, 1]
Q_zz = m[..., 2, 2]
# abs is for guarding against floating point error
wx = 0.5 * np.sqrt(np.abs(1. + Q_xx + Q_yy + Q_zz)) + \
(0.5j * np.sign(Q_zy - Q_yz)) * np.sqrt(np.abs(1. + Q_xx - Q_yy - Q_zz))
yz = (0.5 * np.sign(Q_xz - Q_zx)) * np.sqrt(np.abs(1. - Q_xx + Q_yy - Q_zz)) + \
(0.5j * np.sign(Q_yx - Q_xy)) * np.sqrt(np.abs(1. - Q_xx - Q_yy + Q_zz))
return np.stack((wx, yz), -1)
[docs]@jit(nopython=True, nogil=True, cache=True)
def conjugate(p: np.ndarray[np.complex_]) -> np.ndarray[np.complex_]:
res = np.empty_like(p)
res[..., 0] = np.conjugate(p[..., 0])
res[..., 1] = -p[..., 1]
return res
[docs]@jit(nopython=True, nogil=True, cache=True)
def norm(p: np.ndarray[np.complex_]) -> np.ndarray[np.float_]:
return (p * np.conjugate(p)).sum(axis=-1)
[docs]@jit(nopython=True, nogil=True, cache=True)
def abs(p: np.ndarray[np.complex_]) -> np.ndarray[np.float_]:
return np.sqrt(norm(p))
[docs]@jit(nopython=True, nogil=True, cache=True)
def inverse(p: np.ndarray[np.complex_]) -> np.ndarray[np.complex_]:
return conjugate(p) / norm(p).reshape(*p.shape[:-1], 1)
[docs]@jit(nopython=True, nogil=True, cache=True)
def rotate(p: np.ndarray[np.complex_], v: np.ndarray[np.complex_]) -> np.ndarray[np.complex_]:
"""Rotate v by p respecting Numpy broadcasting rule."""
p_inv = inverse(p)
return mul(mul(p, v), p_inv)
[docs]@jit(nopython=True, nogil=True, cache=True)
def rotate_2d(p: np.ndarray[np.complex_], v: np.ndarray[np.complex_]) -> List[np.ndarray[np.complex_]]:
"""Rotate each row of v by p and stack at an axis.
:param v: 2d-array
"""
p_inv = inverse(p)
return [mul(mul(p, row), p_inv) for row in v]
[docs]@jit(nopython=True, nogil=True, cache=True)
def quat_to_azimuthal_equidistant_projection_polar_with_orientation(quats: np.ndarray[np.complex_]) -> np.ndarray[np.float_]:
"""Convert from detector pointing to Azimuthal equidistant projection in polar coordinate with orientation.
Returned array is in radian,
has the last dimension with 3 elements,
1st as the angular distance to North pole,
2nd as the azimuth,
3rd as the orientation in angle.
"""
xz_axes = np.array(
(
[1.j, 0.],
[0., 1.j],
),
dtype=quats.dtype,
)
# rotation from boresight
orients, r = rotate_2d(quats, xz_axes)
ds = np.arccos(r[:, 1].imag)
angles = np.arctan2(r[:, 1].real, r[:, 0].imag)
return np.stack(
(
ds,
angles,
np.arctan2(orients[:, 1].real, orients[:, 0].imag)
),
-1,
)
[docs]@jit(nopython=True, nogil=True, cache=True)
def quat_to_azimuthal_equidistant_projection_with_orientation(quats: np.ndarray[np.complex_]) -> np.ndarray[np.float_]:
"""Convert from detector pointing to Azimuthal equidistant projection in cartesian coordinate with orientation.
Returned array is in radian,
has the last dimension with 3 elements,
1st as the horizontal angular position,
2nd as the vertical angular position,
3rd as the orientation in angle.
"""
xz_axes = np.array(
(
[1.j, 0.],
[0., 1.j],
),
dtype=quats.dtype,
)
# rotation from boresight
orients, r = rotate_2d(quats, xz_axes)
ds = np.arccos(r[:, 1].imag)
angles = np.arctan2(r[:, 1].real, r[:, 0].imag)
return np.stack(
(
ds * np.cos(angles),
ds * np.sin(angles),
np.arctan2(orients[:, 1].real, orients[:, 0].imag)
),
-1,
)
[docs]@jit(nopython=True, nogil=True, cache=True)
def azimuthal_equidistant_projection_polar_with_orientation_to_rotation_matrix(array: np.ndarray[np.float_]) -> np.ndarray[np.float_]:
"""Convert Azimuthal equidistant projection in polar coordinate with orientation to detector pointing.
Input array is in radian,
has the last dimension with 3 elements,
1st as the angular distance to North pole,
2nd as the azimuth,
3rd as the orientation in angle.
"""
theta = array[..., 0]
phi = array[..., 1]
alpha = array[..., 2]
sin_theta = np.sin(theta)
Rz = np.stack(
(
sin_theta * np.cos(phi),
sin_theta * np.sin(phi),
np.cos(theta),
),
-1,
)
# t is cot(beta)
t = -np.tan(theta) * np.cos(phi - alpha)
sin_beta = np.power(1. + np.square(t), -0.5)
Rx = np.stack(
(
sin_beta * np.cos(alpha),
sin_beta * np.sin(alpha),
t * sin_beta,
),
-1,
)
return np.stack((Rx, np.cross(Rz, Rx), Rz), -1)
[docs]@jit(nopython=True, nogil=True, cache=True)
def dist_spherical(p: np.ndarray[np.complex_], q: np.ndarray[np.complex_]) -> float:
"""Great circle distance between 2 detector quaternions."""
z = np.array([0., 1.j], dtype=p.dtype)
p_z = complex_to_float(rotate(p.reshape(1, 2), z))
q_z = complex_to_float(rotate(q.reshape(1, 2), z))
dot = (p_z[0, 1:] * q_z[0, 1:]).sum()
# protect against floating point error
if dot > 1.:
assert dot - 1. < 1e-3
return 0.
elif dot < -1.:
assert -dot - 1. < 1e-3
return np.pi
else:
return np.arccos(dot)
[docs]@jit(nopython=True, nogil=True, cache=True)
def dist_spherical_pairwise(ps: np.ndarray[np.complex_]) -> np.ndarray[np.float_]:
"""Pair-wise great circle distances between detector quaternions.
Assume input is a 1-dim array of quarternions (2d-array)
and return pairwise distance in 1d-array,
ordered in "row-major" and "j>i" directions.
E.g. for 3 detectors, [(0, 1), (0, 2), (1, 2)] ordering.
"""
# make sure it is 2d-array
n, _ = ps.shape
size = (n * (n - 1)) // 2
res = np.empty(size)
k = 0
for i in range(n):
for j in range(i + 1, n):
res[k] = dist_spherical(ps[i], ps[j])
k += 1
return res
[docs]@jit(nopython=True, nogil=True, cache=True)
def dist_spherical_pairwise_from_lastcol_array(ps: np.ndarray[np.float_]) -> np.ndarray[np.float_]:
return dist_spherical_pairwise(float_to_complex(lastcol_quat_to_canonical(ps)))
[docs]@dataclass
class Quaternion:
array_complex: np.ndarray[np.complex_]
def __post_init__(self):
assert self.array_complex.shape[-1] == 2
[docs] def clear_cache(self):
try:
del self.array
except AttributeError:
pass
try:
del self.lastcol_array
except AttributeError:
pass
try:
del self.azimuthal_equidistant_projection_polar_with_orientation
except AttributeError:
pass
try:
del self.azimuthal_equidistant_projection_with_orientation
except AttributeError:
pass
try:
del self.to_rotation_matrix
except AttributeError:
pass
@cached_property
def array(self) -> np.ndarray[np.float_]:
return complex_to_float(self.array_complex)
@cached_property
def lastcol_array(self) -> np.ndarray[np.float_]:
return canonical_quat_to_lastcol(complex_to_float(self.array_complex))
def __add__(self, other: Quaternion) -> Quaternion:
return Quaternion(self.array + other.array)
def __iadd__(self, other: Quaternion):
self.clear_cache()
self.array += other.array
def __mul__(self, other: Quaternion) -> Quaternion:
return Quaternion(mul(self.array_complex, other.array_complex))
def __imul__(self, other: Quaternion):
self.clear_cache()
self.array_complex = mul(self.array_complex, other.array_complex)
def __matmul__(self, other: Quaternion) -> Quaternion:
return Quaternion(matmul(self.array_complex, other.array_complex))
def __imatmul__(self, other: Quaternion) -> Quaternion:
self.clear_cache()
self.array_complex = matmul(self.array_complex, other.array_complex)
@property
def conjugate(self) -> Quaternion:
return Quaternion(conjugate(self.array))
@property
def norm(self) -> np.ndarray[np.float_]:
return norm(self.array)
@property
def abs(self) -> np.ndarray[np.float_]:
return abs(self.array)
@property
def normalize(self) -> Quaternion:
return Quaternion(self.array_complex / self.abs[..., None])
@property
def inverse(self) -> Quaternion:
return Quaternion(inverse(self.array_complex))
[docs] def rotate(self, other: Quaternion) -> Quaternion:
return Quaternion(rotate(self.array_complex, other.array_complex))
@cached_property
def azimuthal_equidistant_projection_polar_with_orientation(self) -> np.ndarray[np.float_]:
"""Convert from detector pointing to Azimuthal equidistant projection in polar coordinate with orientation.
Returned array is in radian,
has the last dimension with 3 elements,
1st as the angular distance to North pole,
2nd as the azimuth,
3rd as the orientation in angle.
"""
return quat_to_azimuthal_equidistant_projection_polar_with_orientation(self.array_complex)
@cached_property
def azimuthal_equidistant_projection_with_orientation(self) -> np.ndarray[np.float_]:
"""Convert from detector pointing to Azimuthal equidistant projection in cartesian coordinate with orientation.
Returned array is in radian,
has the last dimension with 3 elements,
1st as the horizontal angular position,
2nd as the vertical angular position,
3rd as the orientation in angle.
"""
return quat_to_azimuthal_equidistant_projection_with_orientation(self.array_complex)
[docs] @classmethod
def from_array(cls, array: np.ndarray[np.float_]) -> Quaternion:
"""Create Quaternion from real array with last axis as w, x, y, z.
"""
return cls(float_to_complex(array))
[docs] @classmethod
def from_lastcol_array(cls, array: np.ndarray[np.float_]) -> Quaternion:
"""Create Quaternion from real array with last axis as x, y, z, w.
Convention used in TOAST.
"""
return cls(float_to_complex(lastcol_quat_to_canonical(array)))
[docs] @classmethod
def from_rotation_matrix(cls, array: np.ndarray[np.float_]) -> Quaternion:
return cls(rotation_matrix_to_quat(array))
[docs] @classmethod
def from_azimuthal_equidistant_projection_polar_with_orientation(cls, array: np.ndarray[np.float_]) -> Quaternion:
m = azimuthal_equidistant_projection_polar_with_orientation_to_rotation_matrix(array)
return cls.from_rotation_matrix(m)
@cached_property
def to_rotation_matrix(self) -> np.ndarray[np.float_]:
return quat_to_rotation_matrix(self.array_complex)