use ArrayLike when accepting pseudo-ndarrays

masque/builder/devices.py

@@ -8,6 +8,7 @@ from collections import Counter
 
 import numpy        # type: ignore
 from numpy import pi
+from numpy.typing import ArrayLike
 
 from ..pattern import Pattern
 from ..subpattern import SubPattern
@@ -48,7 +49,7 @@ class Port(PositionableImpl, Rotatable, PivotableImpl, Copyable, Mirrorable, met
     """ Port types must match to be plugged together if both are non-zero """
 
     def __init__(self,
-                 offset: numpy.ndarray,
+                 offset: ArrayLike,
                  rotation: Optional[float],
                  ptype: str = 'unk',
                  ) -> None:
@@ -730,7 +731,7 @@ class Device(Copyable, Mirrorable):
         return s
 
 
-def rotate_offsets_around(offsets: numpy.ndarray, pivot: numpy.ndarray, angle: float) -> numpy.ndarray:
+def rotate_offsets_around(offsets: ArrayLike, pivot: ArrayLike, angle: float) -> numpy.ndarray:
     offsets -= pivot
     offsets[:] = (rotation_matrix_2d(angle) @ offsets.T).T
     offsets += pivot

masque/pattern.py

@@ -11,6 +11,7 @@ from collections import defaultdict
 
 import numpy        # type: ignore
 from numpy import inf
+from numpy.typing import ArrayLike
 # .visualize imports matplotlib and matplotlib.collections
 
 from .subpattern import SubPattern
@@ -333,8 +334,8 @@ class Pattern(LockableImpl, AnnotatableImpl, Mirrorable, metaclass=AutoSlots):
         return self
 
     def manhattanize(self: P,
-                     grid_x: numpy.ndarray,
+                     grid_x: ArrayLike,
-                     grid_y: numpy.ndarray,
+                     grid_y: ArrayLike,
                      ) -> P:
         """
         Calls `.polygonize()` and `.flatten()` on the pattern, then calls `.manhattanize()` on all the

masque/repetition.py

@@ -8,6 +8,7 @@ import copy
 from abc import ABCMeta, abstractmethod
 
 import numpy        # type: ignore
+from numpy.typing import ArrayLike
 
 from .error import PatternError
 from .utils import rotation_matrix_2d, vector2, AutoSlots
@@ -61,9 +62,9 @@ class Grid(LockableImpl, Repetition, metaclass=AutoSlots):
     """ Number of instances along the direction specified by the `b_vector` """
 
     def __init__(self,
-                 a_vector: numpy.ndarray,
+                 a_vector: ArrayLike,
                  a_count: int,
-                 b_vector: Optional[numpy.ndarray] = None,
+                 b_vector: Optional[ArrayLike] = None,
                  b_count: Optional[int] = 1,
                  locked: bool = False,):
         """
@@ -303,13 +304,13 @@ class Arbitrary(LockableImpl, Repetition, metaclass=AutoSlots):
         return self._displacements
 
     @displacements.setter
-    def displacements(self, val: Union[Sequence[Sequence[float]], numpy.ndarray]):
+    def displacements(self, val: ArrayLike):
         val = numpy.array(val, float)
         val = numpy.sort(val.view([('', val.dtype)] * val.shape[1]), 0).view(val.dtype)    # sort rows
         self._displacements = val
 
     def __init__(self,
-                 displacements: numpy.ndarray,
+                 displacements: ArrayLike,
                  locked: bool = False,):
         """
         Args:

masque/shapes/path.py

@@ -4,6 +4,7 @@ from enum import Enum
 
 import numpy        # type: ignore
 from numpy import pi, inf
+from numpy.typing import ArrayLike
 
 from . import Shape, normalized_shape_tuple, Polygon, Circle
 from .. import PatternError
@@ -102,7 +103,7 @@ class Path(Shape, metaclass=AutoSlots):
         return self._vertices
 
     @vertices.setter
-    def vertices(self, val: numpy.ndarray):
+    def vertices(self, val: ArrayLike):
         val = numpy.array(val, dtype=float)                 # TODO document that these might not be copied
         if len(val.shape) < 2 or val.shape[1] != 2:
             raise PatternError('Vertices must be an Nx2 array')
@@ -119,7 +120,7 @@ class Path(Shape, metaclass=AutoSlots):
         return self.vertices[:, 0]
 
     @xs.setter
-    def xs(self, val: numpy.ndarray):
+    def xs(self, val: ArrayLike):
         val = numpy.array(val, dtype=float).flatten()
         if val.size != self.vertices.shape[0]:
             raise PatternError('Wrong number of vertices')
@@ -134,18 +135,18 @@ class Path(Shape, metaclass=AutoSlots):
         return self.vertices[:, 1]
 
     @ys.setter
-    def ys(self, val: numpy.ndarray):
+    def ys(self, val: ArrayLike):
         val = numpy.array(val, dtype=float).flatten()
         if val.size != self.vertices.shape[0]:
             raise PatternError('Wrong number of vertices')
         self.vertices[:, 1] = val
 
     def __init__(self,
-                 vertices: numpy.ndarray,
+                 vertices: ArrayLike,
                  width: float = 0.0,
                  *,
                  cap: PathCap = PathCap.Flush,
-                 cap_extensions: numpy.ndarray = None,
+                 cap_extensions: Optional[ArrayLike] = None,
                  offset: vector2 = (0.0, 0.0),
                  rotation: float = 0,
                  mirrored: Sequence[bool] = (False, False),

masque/shapes/polygon.py

@@ -3,6 +3,7 @@ import copy
 
 import numpy        # type: ignore
 from numpy import pi
+from numpy.typing import ArrayLike
 
 from . import Shape, normalized_shape_tuple
 from .. import PatternError
@@ -33,7 +34,7 @@ class Polygon(Shape, metaclass=AutoSlots):
         return self._vertices
 
     @vertices.setter
-    def vertices(self, val: numpy.ndarray):
+    def vertices(self, val: ArrayLike):
         val = numpy.array(val, dtype=float)             # TODO document that these might not be copied
         if len(val.shape) < 2 or val.shape[1] != 2:
             raise PatternError('Vertices must be an Nx2 array')
@@ -50,7 +51,7 @@ class Polygon(Shape, metaclass=AutoSlots):
         return self.vertices[:, 0]
 
     @xs.setter
-    def xs(self, val: numpy.ndarray):
+    def xs(self, val: ArrayLike):
         val = numpy.array(val, dtype=float).flatten()
         if val.size != self.vertices.shape[0]:
             raise PatternError('Wrong number of vertices')
@@ -65,14 +66,14 @@ class Polygon(Shape, metaclass=AutoSlots):
         return self.vertices[:, 1]
 
     @ys.setter
-    def ys(self, val: numpy.ndarray):
+    def ys(self, val: ArrayLike):
         val = numpy.array(val, dtype=float).flatten()
         if val.size != self.vertices.shape[0]:
             raise PatternError('Wrong number of vertices')
         self.vertices[:, 1] = val
 
     def __init__(self,
-                 vertices: numpy.ndarray,
+                 vertices: ArrayLike,
                  *,
                  offset: vector2 = (0.0, 0.0),
                  rotation: float = 0.0,

masque/shapes/shape.py

@@ -2,6 +2,7 @@ from typing import List, Tuple, Callable, TypeVar, Optional, TYPE_CHECKING
 from abc import ABCMeta, abstractmethod
 
 import numpy        # type: ignore
+from numpy.typing import ArrayLike
 
 from ..traits import (PositionableImpl, LayerableImpl, DoseableImpl,
                       Rotatable, Mirrorable, Copyable, Scalable,
@@ -93,8 +94,8 @@ class Shape(PositionableImpl, LayerableImpl, DoseableImpl, Rotatable, Mirrorable
     ---- Non-abstract methods
     '''
     def manhattanize_fast(self,
-                          grid_x: numpy.ndarray,
+                          grid_x: ArrayLike,
-                          grid_y: numpy.ndarray,
+                          grid_y: ArrayLike,
                           ) -> List['Polygon']:
         """
         Returns a list of polygons with grid-aligned ("Manhattan") edges approximating the shape.
@@ -200,8 +201,8 @@ class Shape(PositionableImpl, LayerableImpl, DoseableImpl, Rotatable, Mirrorable
         return manhattan_polygons
 
     def manhattanize(self,
-                     grid_x: numpy.ndarray,
+                     grid_x: ArrayLike,
-                     grid_y: numpy.ndarray
+                     grid_y: ArrayLike,
                      ) -> List['Polygon']:
         """
         Returns a list of polygons with grid-aligned ("Manhattan") edges approximating the shape.

masque/utils.py

@@ -5,10 +5,11 @@ from typing import Any, Union, Tuple, Sequence, Dict, List
 from abc import ABCMeta
 
 import numpy        # type: ignore
+from numpy.typing import ArrayLike
 
 
 # Type definitions
-vector2 = Union[numpy.ndarray, Tuple[float, float], Sequence[float]]
+vector2 = ArrayLike
 layer_t = Union[int, Tuple[int, int], str]
 annotations_t = Dict[str, List[Union[int, float, str]]]
 
@@ -89,7 +90,7 @@ def normalize_mirror(mirrored: Sequence[bool]) -> Tuple[bool, float]:
     return mirror_x, angle
 
 
-def remove_duplicate_vertices(vertices: numpy.ndarray, closed_path: bool = True) -> numpy.ndarray:
+def remove_duplicate_vertices(vertices: ArrayLike, closed_path: bool = True) -> numpy.ndarray:
     """
     Given a list of vertices, remove any consecutive duplicates.
 
@@ -107,7 +108,7 @@ def remove_duplicate_vertices(vertices: numpy.ndarray, closed_path: bool = True)
     return vertices[~duplicates]
 
 
-def remove_colinear_vertices(vertices: numpy.ndarray, closed_path: bool = True) -> numpy.ndarray:
+def remove_colinear_vertices(vertices: ArrayLike, closed_path: bool = True) -> numpy.ndarray:
     """
     Given a list of vertices, remove any superflous vertices (i.e.
         those which lie along the line formed by their neighbors)