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petercorkepetercorke
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types definition depends on python version
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‎spatialmath/base/_types_38.py

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# for Python <= 3.8
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from typing import overload, Union, List, Tuple, Type, TextIO, Any, Callable, Optional
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from typing import Literal as L
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# array like
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# these are input to many functions in spatialmath.base, and can be a list, tuple or
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# ndarray. The elements are generally float, but some functions accept symbolic
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# arguments as well, which leads to a NumPy array with dtype=object
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#
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# The variants like ArrayLike2 indicate that a list, tuple or ndarray of
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# length 2 is expected. Static checking of tuple length is possible but not a lists.
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# This might be possible in future versions of Python, but for now it is a hint to the
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# coder about what is expected
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from numpy.typing import DTypeLike, ArrayLike, NDArray
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# from typing import TypeVar
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# NDArray = TypeVar('NDArray')
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ArrayLike = Union[float, List[float], Tuple[float], NDArray]
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ArrayLike2 = Union[List, Tuple[float,float], NDArray]
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ArrayLike3 = Union[List,Tuple[float,float,float], NDArray]
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ArrayLike4 = Union[List,Tuple[float,float,float,float], NDArray]
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ArrayLike6 = Union[List,Tuple[float,float,float,float,float,float], NDArray]
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# real vectors
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R2 = NDArray # R^2
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R3 = NDArray # R^3
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R4 = NDArray # R^4
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R6 = NDArray # R^6
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# real matrices
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R2x2 = NDArray # R^{3x3} matrix
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R3x3 = NDArray # R^{3x3} matrix
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R4x4 = NDArray # R^{4x4} matrix
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R6x6 = NDArray # R^{6x6} matrix
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R1x3 = NDArray # R^{1x3} row vector
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R3x1 = NDArray # R^{3x1} column vector
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R1x2 = NDArray # R^{1x2} row vector
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R2x1 = NDArray # R^{2x1} column vector
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Points2 = NDArray # R^{2xN} matrix
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Points3 = NDArray # R^{2xN} matrix
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RNx3 = NDArray # R^{Nx3} matrix
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# Lie group elements
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SO2Array = NDArray # SO(2) rotation matrix
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SE2Array = NDArray # SE(2) rigid-body transform
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SO3Array = NDArray # SO(3) rotation matrix
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SE3Array = NDArray # SE(3) rigid-body transform
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# Lie algebra elements
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so2Array = NDArray # so(2) Lie algebra of SO(2), skew-symmetrix matrix
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se2Array = NDArray # se(2) Lie algebra of SE(2), augmented skew-symmetrix matrix
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so3Array = NDArray # so(3) Lie algebra of SO(3), skew-symmetrix matrix
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se3Array = NDArray # se(3) Lie algebra of SE(3), augmented skew-symmetrix matrix
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# quaternion arrays
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QuaternionArray = NDArray
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UnitQuaternionArray = NDArray
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Rn = Union[R2,R3]
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SOnArray = Union[SO2Array,SO3Array]
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SEnArray = Union[SE2Array,SE3Array]
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sonArray = Union[so2Array,so3Array]
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senArray = Union[se2Array,se3Array]
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‎spatialmath/base/_types_39.py

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# for Python >= 3.9
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from typing import overload, Union, List, Tuple, Type, TextIO, Any, Callable, Optional
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from typing import Literal as L
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from numpy import ndarray, dtype, floating
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from numpy.typing import NDArray, DTypeLike
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# array like
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# these are input to many functions in spatialmath.base, and can be a list, tuple or
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# ndarray. The elements are generally float, but some functions accept symbolic
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# arguments as well, which leads to a NumPy array with dtype=object
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#
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# The variants like ArrayLike2 indicate that a list, tuple or ndarray of
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# length 2 is expected. Static checking of tuple length is possible but not a lists.
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# This might be possible in future versions of Python, but for now it is a hint to the
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# coder about what is expected
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ArrayLike = Union[float, List[float], Tuple[float], ndarray[Any, dtype[floating]]]
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ArrayLike2 = Union[List, Tuple[float,float], ndarray[Tuple[L[2,]], dtype[floating]]]
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ArrayLike3 = Union[List,Tuple[float,float,float],ndarray[Tuple[L[3,]], dtype[floating]]]
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ArrayLike4 = Union[List,Tuple[float,float,float,float],ndarray[Tuple[L[4,]], dtype[floating]]]
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ArrayLike6 = Union[List,Tuple[float,float,float,float,float,float],ndarray[Tuple[L[6,]], dtype[floating]]]
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# real vectors
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R2 = ndarray[Tuple[L[2,]], dtype[floating]] # R^2
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R3 = ndarray[Tuple[L[3,]], dtype[floating]] # R^3
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R4 = ndarray[Tuple[L[4,]], dtype[floating]] # R^3
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R6 = ndarray[Tuple[L[6,]], dtype[floating]] # R^6
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# real matrices
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R2x2 = ndarray[Tuple[L[2,2]], dtype[floating]] # R^{2x2} matrix
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R3x3 = ndarray[Tuple[L[3,3]], dtype[floating]] # R^{3x3} matrix
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R4x4 = ndarray[Tuple[L[4,4]], dtype[floating]] # R^{4x4} matrix
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R6x6 = ndarray[Tuple[L[6,6]], dtype[floating]] # R^{6x6} matrix
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R1x3 = ndarray[Tuple[L[1,3]], dtype[floating]] # R^{1x3} row vector
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R3x1 = ndarray[Tuple[L[3,1]], dtype[floating]] # R^{3x1} column vector
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R1x2 = ndarray[Tuple[L[1,2]], dtype[floating]] # R^{1x2} row vector
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R2x1 = ndarray[Tuple[L[2,1]], dtype[floating]] # R^{2x1} column vector
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Points2 = ndarray[(2,Any), dtype[floating]] # R^{2xN} matrix
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Points3 = ndarray[(3,Any), dtype[floating]] # R^{2xN} matrix
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RNx3 = ndarray[(Any,3), dtype[floating]] # R^{Nx3} matrix
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def a(x:Points2):
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return x
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import numpy as np
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b = np.zeros((2,10))
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z=a('sss')
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z=a(b)
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# Lie group elements
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SO2Array = ndarray[Tuple[L[2,2]], dtype[floating]] # SO(2) rotation matrix
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SE2Array = ndarray[Tuple[L[3,3]], dtype[floating]] # SE(2) rigid-body transform
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SO3Array = ndarray[Tuple[L[3,3]], dtype[floating]] # SO(3) rotation matrix
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SE3Array = ndarray[Tuple[L[4,4]], dtype[floating]] # SE(3) rigid-body transform
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# Lie algebra elements
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so2Array = ndarray[Tuple[L[2,2]], dtype[floating]] # so(2) Lie algebra of SO(2), skew-symmetrix matrix
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se2Array = ndarray[Tuple[L[3,3]], dtype[floating]] # se(2) Lie algebra of SE(2), augmented skew-symmetrix matrix
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so3Array = ndarray[Tuple[L[3,3]], dtype[floating]] # so(3) Lie algebra of SO(3), skew-symmetrix matrix
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se3Array = ndarray[Tuple[L[4,4]], dtype[floating]] # se(3) Lie algebra of SE(3), augmented skew-symmetrix matrix
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# quaternion arrays
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QuaternionArray = ndarray[Tuple[L[4,]], dtype[floating]]
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UnitQuaternionArray = ndarray[Tuple[L[4,]], dtype[floating]]
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Rn = Union[R2,R3]
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SOnArray = Union[SO2Array,SO3Array]
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SEnArray = Union[SE2Array,SE3Array]
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sonArray = Union[so2Array,so3Array]
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senArray = Union[se2Array,se3Array]

‎spatialmath/base/sm_types.py

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‎spatialmath/base/types.py

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import sys
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if sys.version_info.minor > 8:
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from spatialmath.base._types_39 import *
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else:
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from spatialmath.base._types_38 import *

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