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use ArrayLike when accepting pseudo-ndarrays

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nolock
Jan Petykiewicz 3 years ago
parent 3aefa644e5
commit 4796676a4e
7 changed files with 31 additions and 24 deletions
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5
masque/builder/devices.py
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@ -8,6 +8,7 @@ from collections import Counter
import numpy # type: ignore import numpy # type: ignore
from numpy import pi from numpy import pi
from numpy.typing import ArrayLike
from ..pattern import Pattern from ..pattern import Pattern
from ..subpattern import SubPattern 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 """ """ Port types must match to be plugged together if both are non-zero """
def __init__(self, def __init__(self,
offset: numpy.ndarray, offset: ArrayLike,
rotation: Optional[float], rotation: Optional[float],
ptype: str = 'unk', ptype: str = 'unk',
) -> None: ) -> None:
@ -730,7 +731,7 @@ class Device(Copyable, Mirrorable):
return s 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 -= pivot
offsets[:] = (rotation_matrix_2d(angle) @ offsets.T).T offsets[:] = (rotation_matrix_2d(angle) @ offsets.T).T
offsets += pivot offsets += pivot

5
masque/pattern.py
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@ -11,6 +11,7 @@ from collections import defaultdict
import numpy # type: ignore import numpy # type: ignore
from numpy import inf from numpy import inf
from numpy.typing import ArrayLike
# .visualize imports matplotlib and matplotlib.collections # .visualize imports matplotlib and matplotlib.collections
from .subpattern import SubPattern from .subpattern import SubPattern
@ -333,8 +334,8 @@ class Pattern(LockableImpl, AnnotatableImpl, Mirrorable, metaclass=AutoSlots):
return self return self
def manhattanize(self: P, def manhattanize(self: P,
grid_x: numpy.ndarray, grid_x: ArrayLike,
grid_y: numpy.ndarray, grid_y: ArrayLike,
) -> P: ) -> P:
""" """
Calls `.polygonize()` and `.flatten()` on the pattern, then calls `.manhattanize()` on all the Calls `.polygonize()` and `.flatten()` on the pattern, then calls `.manhattanize()` on all the

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

11
masque/shapes/path.py
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@ -4,6 +4,7 @@ from enum import Enum
import numpy # type: ignore import numpy # type: ignore
from numpy import pi, inf from numpy import pi, inf
from numpy.typing import ArrayLike
from . import Shape, normalized_shape_tuple, Polygon, Circle from . import Shape, normalized_shape_tuple, Polygon, Circle
from .. import PatternError from .. import PatternError
@ -102,7 +103,7 @@ class Path(Shape, metaclass=AutoSlots):
return self._vertices return self._vertices
@vertices.setter @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 val = numpy.array(val, dtype=float) # TODO document that these might not be copied
if len(val.shape) < 2 or val.shape[1] != 2: if len(val.shape) < 2 or val.shape[1] != 2:
raise PatternError('Vertices must be an Nx2 array') raise PatternError('Vertices must be an Nx2 array')
@ -119,7 +120,7 @@ class Path(Shape, metaclass=AutoSlots):
return self.vertices[:, 0] return self.vertices[:, 0]
@xs.setter @xs.setter
def xs(self, val: numpy.ndarray): def xs(self, val: ArrayLike):
val = numpy.array(val, dtype=float).flatten() val = numpy.array(val, dtype=float).flatten()
if val.size != self.vertices.shape[0]: if val.size != self.vertices.shape[0]:
raise PatternError('Wrong number of vertices') raise PatternError('Wrong number of vertices')
@ -134,18 +135,18 @@ class Path(Shape, metaclass=AutoSlots):
return self.vertices[:, 1] return self.vertices[:, 1]
@ys.setter @ys.setter
def ys(self, val: numpy.ndarray): def ys(self, val: ArrayLike):
val = numpy.array(val, dtype=float).flatten() val = numpy.array(val, dtype=float).flatten()
if val.size != self.vertices.shape[0]: if val.size != self.vertices.shape[0]:
raise PatternError('Wrong number of vertices') raise PatternError('Wrong number of vertices')
self.vertices[:, 1] = val self.vertices[:, 1] = val
def __init__(self, def __init__(self,
vertices: numpy.ndarray, vertices: ArrayLike,
width: float = 0.0, width: float = 0.0,
*, *,
cap: PathCap = PathCap.Flush, cap: PathCap = PathCap.Flush,
cap_extensions: numpy.ndarray = None, cap_extensions: Optional[ArrayLike] = None,
offset: vector2 = (0.0, 0.0), offset: vector2 = (0.0, 0.0),
rotation: float = 0, rotation: float = 0,
mirrored: Sequence[bool] = (False, False), mirrored: Sequence[bool] = (False, False),

9
masque/shapes/polygon.py
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@ -3,6 +3,7 @@ import copy
import numpy # type: ignore import numpy # type: ignore
from numpy import pi from numpy import pi
from numpy.typing import ArrayLike
from . import Shape, normalized_shape_tuple from . import Shape, normalized_shape_tuple
from .. import PatternError from .. import PatternError
@ -33,7 +34,7 @@ class Polygon(Shape, metaclass=AutoSlots):
return self._vertices return self._vertices
@vertices.setter @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 val = numpy.array(val, dtype=float) # TODO document that these might not be copied
if len(val.shape) < 2 or val.shape[1] != 2: if len(val.shape) < 2 or val.shape[1] != 2:
raise PatternError('Vertices must be an Nx2 array') raise PatternError('Vertices must be an Nx2 array')
@ -50,7 +51,7 @@ class Polygon(Shape, metaclass=AutoSlots):
return self.vertices[:, 0] return self.vertices[:, 0]
@xs.setter @xs.setter
def xs(self, val: numpy.ndarray): def xs(self, val: ArrayLike):
val = numpy.array(val, dtype=float).flatten() val = numpy.array(val, dtype=float).flatten()
if val.size != self.vertices.shape[0]: if val.size != self.vertices.shape[0]:
raise PatternError('Wrong number of vertices') raise PatternError('Wrong number of vertices')
@ -65,14 +66,14 @@ class Polygon(Shape, metaclass=AutoSlots):
return self.vertices[:, 1] return self.vertices[:, 1]
@ys.setter @ys.setter
def ys(self, val: numpy.ndarray): def ys(self, val: ArrayLike):
val = numpy.array(val, dtype=float).flatten() val = numpy.array(val, dtype=float).flatten()
if val.size != self.vertices.shape[0]: if val.size != self.vertices.shape[0]:
raise PatternError('Wrong number of vertices') raise PatternError('Wrong number of vertices')
self.vertices[:, 1] = val self.vertices[:, 1] = val
def __init__(self, def __init__(self,
vertices: numpy.ndarray, vertices: ArrayLike,
*, *,
offset: vector2 = (0.0, 0.0), offset: vector2 = (0.0, 0.0),
rotation: float = 0.0, rotation: float = 0.0,

9
masque/shapes/shape.py
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@ -2,6 +2,7 @@ from typing import List, Tuple, Callable, TypeVar, Optional, TYPE_CHECKING
from abc import ABCMeta, abstractmethod from abc import ABCMeta, abstractmethod
import numpy # type: ignore import numpy # type: ignore
from numpy.typing import ArrayLike
from ..traits import (PositionableImpl, LayerableImpl, DoseableImpl, from ..traits import (PositionableImpl, LayerableImpl, DoseableImpl,
Rotatable, Mirrorable, Copyable, Scalable, Rotatable, Mirrorable, Copyable, Scalable,
@ -93,8 +94,8 @@ class Shape(PositionableImpl, LayerableImpl, DoseableImpl, Rotatable, Mirrorable
---- Non-abstract methods ---- Non-abstract methods
''' '''
def manhattanize_fast(self, def manhattanize_fast(self,
grid_x: numpy.ndarray, grid_x: ArrayLike,
grid_y: numpy.ndarray, grid_y: ArrayLike,
) -> List['Polygon']: ) -> List['Polygon']:
""" """
Returns a list of polygons with grid-aligned ("Manhattan") edges approximating the shape. 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 return manhattan_polygons
def manhattanize(self, def manhattanize(self,
grid_x: numpy.ndarray, grid_x: ArrayLike,
grid_y: numpy.ndarray grid_y: ArrayLike,
) -> List['Polygon']: ) -> List['Polygon']:
""" """
Returns a list of polygons with grid-aligned ("Manhattan") edges approximating the shape. Returns a list of polygons with grid-aligned ("Manhattan") edges approximating the shape.

7
masque/utils.py
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@ -5,10 +5,11 @@ from typing import Any, Union, Tuple, Sequence, Dict, List
from abc import ABCMeta from abc import ABCMeta
import numpy # type: ignore import numpy # type: ignore
from numpy.typing import ArrayLike
# Type definitions # Type definitions
vector2 = Union[numpy.ndarray, Tuple[float, float], Sequence[float]] vector2 = ArrayLike
layer_t = Union[int, Tuple[int, int], str] layer_t = Union[int, Tuple[int, int], str]
annotations_t = Dict[str, List[Union[int, float, 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 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. 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] 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. Given a list of vertices, remove any superflous vertices (i.e.
those which lie along the line formed by their neighbors) those which lie along the line formed by their neighbors)

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