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@ -1,78 +1,47 @@
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"""
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Repetitions provides support for efficiently nesting multiple identical
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instances of a Pattern in the same parent Pattern.
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Repetitions provide support for efficiently representing multiple identical
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instances of an object .
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"""
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from typing import Union, List, Dict, Tuple, Optional, Sequence, TYPE_CHECKING, Any
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import copy
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from abc import ABCMeta, abstractmethod
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import numpy
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from numpy import pi
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from .error import PatternError, PatternLockedError
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from .utils import is_scalar, rotation_matrix_2d, vector2
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if TYPE_CHECKING:
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from . import Pattern
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from .utils import rotation_matrix_2d, vector2, AutoSlots
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from .traits import LockableImpl, Copyable, Scalable, Rotatable, Mirrorable
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# TODO need top-level comment about what order rotation/scale/offset/mirror/array are applied
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class GridRepetition:
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class Repetition(Copyable, Rotatable, Mirrorable, Scalable, metaclass=ABCMeta):
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"""
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GridRepetition provides support for efficiently embedding multiple copies of a `Pattern`
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into another `Pattern` at regularly-spaced offsets.
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Note that rotation, scaling, and mirroring are applied to individual instances of the
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pattern, not to the grid vectors.
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The order of operations is
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1. A single refernce instance to the target pattern is mirrored
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2. The single instance is rotated.
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3. The instance is scaled by the scaling factor.
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4. The instance is shifted by the provided offset
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(no mirroring/scaling/rotation is applied to the offset).
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5. Additional copies of the instance will appear at coordinates specified by
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`(offset + aa * a_vector + bb * b_vector)`, with `aa in range(0, a_count)`
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and `bb in range(0, b_count)`. All instance locations remain unaffected by
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mirroring/scaling/rotation, though each instance's data will be transformed
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relative to the instance's location (i.e. relative to the contained pattern's
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(0, 0) point).
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Interface common to all objects which specify repetitions
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"""
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__slots__ = ('_pattern',
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'_offset',
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'_rotation',
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'_dose',
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'_scale',
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'_mirrored',
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'_a_vector',
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__slots__ = ()
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@property
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@abstractmethod
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def displacements(self) -> numpy.ndarray:
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"""
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An Nx2 ndarray specifying all offsets generated by this repetition
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"""
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pass
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class Grid(LockableImpl, Repetition, metaclass=AutoSlots):
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"""
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`Grid` describes a 2D grid formed by two basis vectors and two 'counts' (sizes).
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The second basis vector and count (`b_vector` and `b_count`) may be omitted,
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which makes the grid describe a 1D array.
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Note that the offsets in either the 2D or 1D grids do not have to be axis-aligned.
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"""
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__slots__ = ('_a_vector',
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'_b_vector',
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'_a_count',
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'_b_count',
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'identifier',
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'locked')
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_pattern: Optional['Pattern']
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""" The `Pattern` being instanced """
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_offset: numpy.ndarray
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""" (x, y) offset for the base instance """
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_dose: float
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""" Scaling factor applied to the dose """
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_rotation: float
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""" Rotation of the individual instances in the grid (not the grid vectors).
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Radians, counterclockwise.
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"""
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_scale: float
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""" Scaling factor applied to individual instances in the grid (not the grid vectors) """
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_mirrored: numpy.ndarray # ndarray[bool]
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""" Whether to mirror individual instances across the x and y axes
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(Applies to individual instances in the grid, not the grid vectors)
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"""
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'_b_count')
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_a_vector: numpy.ndarray
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""" Vector `[x, y]` specifying the first lattice vector of the grid.
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@ -91,28 +60,14 @@ class GridRepetition:
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_b_count: int
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""" Number of instances along the direction specified by the `b_vector` """
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identifier: Tuple[Any, ...]
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""" Arbitrary identifier, used internally by some `masque` functions. """
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locked: bool
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""" If `True`, disallows changes to the GridRepetition """
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def __init__(self,
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pattern: Optional['Pattern'],
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a_vector: numpy.ndarray,
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a_count: int,
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b_vector: Optional[numpy.ndarray] = None,
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b_count: Optional[int] = 1,
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offset: vector2 = (0.0, 0.0),
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rotation: float = 0.0,
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mirrored: Optional[Sequence[bool]] = None,
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dose: float = 1.0,
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scale: float = 1.0,
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locked: bool = False,
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identifier: Tuple[Any, ...] = ()):
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locked: bool = False,):
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"""
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Args:
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pattern: Pattern to reference.
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a_vector: First lattice vector, of the form `[x, y]`.
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Specifies center-to-center spacing between adjacent instances.
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a_count: Number of elements in the a_vector direction.
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@ -121,14 +76,7 @@ class GridRepetition:
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Can be omitted when specifying a 1D array.
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b_count: Number of elements in the `b_vector` direction.
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Should be omitted if `b_vector` was omitted.
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offset: (x, y) offset applied to all instances.
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rotation: Rotation (radians, counterclockwise) applied to each instance.
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Relative to each instance's (0, 0).
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mirrored: Whether to mirror individual instances across the x and y axes.
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dose: Scaling factor applied to the dose.
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scale: Scaling factor applied to the instances' geometry.
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locked: Whether the `GridRepetition` is locked after initialization.
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identifier: Arbitrary tuple, used internally by some `masque` functions.
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locked: Whether the `Grid` is locked after initialization.
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Raises:
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PatternError if `b_*` inputs conflict with each other
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@ -144,132 +92,31 @@ class GridRepetition:
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b_vector = numpy.array([0.0, 0.0])
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if a_count < 1:
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raise PatternError('Repetition has too-small a_count: '
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'{}'.format(a_count))
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raise PatternError(f'Repetition has too-small a_count: {a_count}')
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if b_count < 1:
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raise PatternError('Repetition has too-small b_count: '
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'{}'.format(b_count))
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raise PatternError(f'Repetition has too-small b_count: {b_count}')
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object.__setattr__(self, 'locked', False)
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self.a_vector = a_vector
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self.b_vector = b_vector
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self.a_count = a_count
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self.b_count = b_count
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self.identifier = identifier
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self.pattern = pattern
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self.offset = offset
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self.rotation = rotation
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self.dose = dose
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self.scale = scale
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if mirrored is None:
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mirrored = [False, False]
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self.mirrored = mirrored
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self.locked = locked
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def __setattr__(self, name, value):
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if self.locked and name != 'locked':
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raise PatternLockedError()
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object.__setattr__(self, name, value)
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def __copy__(self) -> 'GridRepetition':
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new = GridRepetition(pattern=self.pattern,
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a_vector=self.a_vector.copy(),
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b_vector=copy.copy(self.b_vector),
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a_count=self.a_count,
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b_count=self.b_count,
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offset=self.offset.copy(),
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rotation=self.rotation,
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dose=self.dose,
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scale=self.scale,
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mirrored=self.mirrored.copy(),
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locked=self.locked)
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def __copy__(self) -> 'Grid':
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new = Grid(a_vector=self.a_vector.copy(),
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b_vector=copy.copy(self.b_vector),
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a_count=self.a_count,
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b_count=self.b_count,
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locked=self.locked)
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return new
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def __deepcopy__(self, memo: Dict = None) -> 'GridRepetition':
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def __deepcopy__(self, memo: Dict = None) -> 'Grid':
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memo = {} if memo is None else memo
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new = copy.copy(self).unlock()
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new.pattern = copy.deepcopy(self.pattern, memo)
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new.locked = self.locked
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return new
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# pattern property
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@property
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def pattern(self) -> Optional['Pattern']:
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return self._pattern
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@pattern.setter
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def pattern(self, val: Optional['Pattern']):
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from .pattern import Pattern
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if val is not None and not isinstance(val, Pattern):
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raise PatternError('Provided pattern {} is not a Pattern object or None!'.format(val))
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self._pattern = val
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# offset property
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@property
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def offset(self) -> numpy.ndarray:
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return self._offset
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@offset.setter
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def offset(self, val: vector2):
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if self.locked:
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raise PatternLockedError()
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if not isinstance(val, numpy.ndarray):
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val = numpy.array(val, dtype=float)
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if val.size != 2:
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raise PatternError('Offset must be convertible to size-2 ndarray')
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self._offset = val.flatten().astype(float)
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# dose property
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@property
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def dose(self) -> float:
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return self._dose
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@dose.setter
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def dose(self, val: float):
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if not is_scalar(val):
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raise PatternError('Dose must be a scalar')
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if not val >= 0:
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raise PatternError('Dose must be non-negative')
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self._dose = val
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# scale property
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@property
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def scale(self) -> float:
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return self._scale
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@scale.setter
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def scale(self, val: float):
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if not is_scalar(val):
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raise PatternError('Scale must be a scalar')
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if not val > 0:
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raise PatternError('Scale must be positive')
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self._scale = val
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# Rotation property [ccw]
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@property
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def rotation(self) -> float:
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return self._rotation
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@rotation.setter
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def rotation(self, val: float):
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if not is_scalar(val):
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raise PatternError('Rotation must be a scalar')
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self._rotation = val % (2 * pi)
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# Mirrored property
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@property
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def mirrored(self) -> numpy.ndarray: # ndarray[bool]
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return self._mirrored
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@mirrored.setter
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def mirrored(self, val: Sequence[bool]):
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if is_scalar(val):
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raise PatternError('Mirrored must be a 2-element list of booleans')
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self._mirrored = numpy.array(val, dtype=bool, copy=True)
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# a_vector property
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@property
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def a_vector(self) -> numpy.ndarray:
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@ -320,69 +167,15 @@ class GridRepetition:
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raise PatternError('b_count must be convertable to an int!')
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self._b_count = int(val)
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def as_pattern(self) -> 'Pattern':
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@property
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def displacements(self) -> numpy.ndarray:
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aa, bb = numpy.meshgrid(numpy.arange(self.a_count), numpy.arange(self.b_count), indexing='ij')
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return (aa.flat[:, None] * self.a_vector[None, :] +
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bb.flat[:, None] * self.b_vector[None, :])
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def rotate(self, rotation: float) -> 'Grid':
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"""
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Returns a copy of self.pattern which has been scaled, rotated, repeated, etc.
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etc. according to this `GridRepetition`'s properties.
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Returns:
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A copy of self.pattern which has been scaled, rotated, repeated, etc.
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etc. according to this `GridRepetition`'s properties.
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"""
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assert(self.pattern is not None)
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patterns = []
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pat = self.pattern.deepcopy().deepunlock()
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pat.scale_by(self.scale)
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[pat.mirror(ax) for ax, do in enumerate(self.mirrored) if do]
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pat.rotate_around((0.0, 0.0), self.rotation)
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pat.translate_elements(self.offset)
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pat.scale_element_doses(self.dose)
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combined = type(pat)(name='__GridRepetition__')
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for a in range(self.a_count):
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for b in range(self.b_count):
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offset = a * self.a_vector + b * self.b_vector
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|
|
|
newPat = pat.deepcopy()
|
|
|
|
|
newPat.translate_elements(offset)
|
|
|
|
|
combined.append(newPat)
|
|
|
|
|
|
|
|
|
|
return combined
|
|
|
|
|
|
|
|
|
|
def translate(self, offset: vector2) -> 'GridRepetition':
|
|
|
|
|
"""
|
|
|
|
|
Translate by the given offset
|
|
|
|
|
|
|
|
|
|
Args:
|
|
|
|
|
offset: `[x, y]` to translate by
|
|
|
|
|
|
|
|
|
|
Returns:
|
|
|
|
|
self
|
|
|
|
|
"""
|
|
|
|
|
self.offset += offset
|
|
|
|
|
return self
|
|
|
|
|
|
|
|
|
|
def rotate_around(self, pivot: vector2, rotation: float) -> 'GridRepetition':
|
|
|
|
|
"""
|
|
|
|
|
Rotate the array around a point
|
|
|
|
|
|
|
|
|
|
Args:
|
|
|
|
|
pivot: Point `[x, y]` to rotate around
|
|
|
|
|
rotation: Angle to rotate by (counterclockwise, radians)
|
|
|
|
|
|
|
|
|
|
Returns:
|
|
|
|
|
self
|
|
|
|
|
"""
|
|
|
|
|
pivot = numpy.array(pivot, dtype=float)
|
|
|
|
|
self.translate(-pivot)
|
|
|
|
|
self.offset = numpy.dot(rotation_matrix_2d(rotation), self.offset)
|
|
|
|
|
self.rotate(rotation)
|
|
|
|
|
self.translate(+pivot)
|
|
|
|
|
return self
|
|
|
|
|
|
|
|
|
|
def rotate(self, rotation: float) -> 'GridRepetition':
|
|
|
|
|
"""
|
|
|
|
|
Rotate around (0, 0)
|
|
|
|
|
Rotate lattice vectors (around (0, 0))
|
|
|
|
|
|
|
|
|
|
Args:
|
|
|
|
|
rotation: Angle to rotate by (counterclockwise, radians)
|
|
|
|
|
@ -390,28 +183,14 @@ class GridRepetition:
|
|
|
|
|
Returns:
|
|
|
|
|
self
|
|
|
|
|
"""
|
|
|
|
|
self.rotate_elements(rotation)
|
|
|
|
|
self.a_vector = numpy.dot(rotation_matrix_2d(rotation), self.a_vector)
|
|
|
|
|
if self.b_vector is not None:
|
|
|
|
|
self.b_vector = numpy.dot(rotation_matrix_2d(rotation), self.b_vector)
|
|
|
|
|
return self
|
|
|
|
|
|
|
|
|
|
def rotate_elements(self, rotation: float) -> 'GridRepetition':
|
|
|
|
|
def mirror(self, axis: int) -> 'Grid':
|
|
|
|
|
"""
|
|
|
|
|
Rotate each element around its origin
|
|
|
|
|
|
|
|
|
|
Args:
|
|
|
|
|
rotation: Angle to rotate by (counterclockwise, radians)
|
|
|
|
|
|
|
|
|
|
Returns:
|
|
|
|
|
self
|
|
|
|
|
"""
|
|
|
|
|
self.rotation += rotation
|
|
|
|
|
return self
|
|
|
|
|
|
|
|
|
|
def mirror(self, axis: int) -> 'GridRepetition':
|
|
|
|
|
"""
|
|
|
|
|
Mirror the GridRepetition across an axis.
|
|
|
|
|
Mirror the Grid across an axis.
|
|
|
|
|
|
|
|
|
|
Args:
|
|
|
|
|
axis: Axis to mirror across.
|
|
|
|
|
@ -420,43 +199,30 @@ class GridRepetition:
|
|
|
|
|
Returns:
|
|
|
|
|
self
|
|
|
|
|
"""
|
|
|
|
|
self.mirror_elements(axis)
|
|
|
|
|
self.a_vector[1-axis] *= -1
|
|
|
|
|
if self.b_vector is not None:
|
|
|
|
|
self.b_vector[1-axis] *= -1
|
|
|
|
|
return self
|
|
|
|
|
|
|
|
|
|
def mirror_elements(self, axis: int) -> 'GridRepetition':
|
|
|
|
|
"""
|
|
|
|
|
Mirror each element across an axis relative to its origin.
|
|
|
|
|
|
|
|
|
|
Args:
|
|
|
|
|
axis: Axis to mirror across.
|
|
|
|
|
(0: mirror across x-axis, 1: mirror across y-axis)
|
|
|
|
|
|
|
|
|
|
Returns:
|
|
|
|
|
self
|
|
|
|
|
"""
|
|
|
|
|
self.mirrored[axis] = not self.mirrored[axis]
|
|
|
|
|
self.rotation *= -1
|
|
|
|
|
return self
|
|
|
|
|
|
|
|
|
|
def get_bounds(self) -> Optional[numpy.ndarray]:
|
|
|
|
|
"""
|
|
|
|
|
Return a `numpy.ndarray` containing `[[x_min, y_min], [x_max, y_max]]`, corresponding to the
|
|
|
|
|
extent of the `GridRepetition` in each dimension.
|
|
|
|
|
Returns `None` if the contained `Pattern` is empty.
|
|
|
|
|
extent of the `Grid` in each dimension.
|
|
|
|
|
|
|
|
|
|
Returns:
|
|
|
|
|
`[[x_min, y_min], [x_max, y_max]]` or `None`
|
|
|
|
|
"""
|
|
|
|
|
if self.pattern is None:
|
|
|
|
|
return None
|
|
|
|
|
return self.as_pattern().get_bounds()
|
|
|
|
|
a_extent = self.a_vector * self.a_count
|
|
|
|
|
b_extent = self.b_vector * self.b_count if self.b_count != 0 else 0
|
|
|
|
|
|
|
|
|
|
def scale_by(self, c: float) -> 'GridRepetition':
|
|
|
|
|
corners = ((0, 0), a_extent, b_extent, a_extent + b_extent)
|
|
|
|
|
xy_min = numpy.min(corners, axis=0)
|
|
|
|
|
xy_max = numpy.min(corners, axis=0)
|
|
|
|
|
return numpy.array((xy_min, xy_max))
|
|
|
|
|
|
|
|
|
|
def scale_by(self, c: float) -> 'Grid':
|
|
|
|
|
"""
|
|
|
|
|
Scale the GridRepetition by a factor
|
|
|
|
|
Scale the Grid by a factor
|
|
|
|
|
|
|
|
|
|
Args:
|
|
|
|
|
c: scaling factor
|
|
|
|
|
@ -464,107 +230,116 @@ class GridRepetition:
|
|
|
|
|
Returns:
|
|
|
|
|
self
|
|
|
|
|
"""
|
|
|
|
|
self.scale_elements_by(c)
|
|
|
|
|
self.a_vector *= c
|
|
|
|
|
if self.b_vector is not None:
|
|
|
|
|
self.b_vector *= c
|
|
|
|
|
return self
|
|
|
|
|
|
|
|
|
|
def scale_elements_by(self, c: float) -> 'GridRepetition':
|
|
|
|
|
def lock(self) -> 'Grid':
|
|
|
|
|
"""
|
|
|
|
|
Scale each element by a factor
|
|
|
|
|
|
|
|
|
|
Args:
|
|
|
|
|
c: scaling factor
|
|
|
|
|
Lock the `Grid`, disallowing changes.
|
|
|
|
|
|
|
|
|
|
Returns:
|
|
|
|
|
self
|
|
|
|
|
"""
|
|
|
|
|
self.scale *= c
|
|
|
|
|
return self
|
|
|
|
|
|
|
|
|
|
def copy(self) -> 'GridRepetition':
|
|
|
|
|
"""
|
|
|
|
|
Return a shallow copy of the repetition.
|
|
|
|
|
|
|
|
|
|
Returns:
|
|
|
|
|
`copy.copy(self)`
|
|
|
|
|
"""
|
|
|
|
|
return copy.copy(self)
|
|
|
|
|
|
|
|
|
|
def deepcopy(self) -> 'GridRepetition':
|
|
|
|
|
"""
|
|
|
|
|
Return a deep copy of the repetition.
|
|
|
|
|
|
|
|
|
|
Returns:
|
|
|
|
|
`copy.deepcopy(self)`
|
|
|
|
|
"""
|
|
|
|
|
return copy.deepcopy(self)
|
|
|
|
|
|
|
|
|
|
def lock(self) -> 'GridRepetition':
|
|
|
|
|
"""
|
|
|
|
|
Lock the `GridRepetition`, disallowing changes.
|
|
|
|
|
|
|
|
|
|
Returns:
|
|
|
|
|
self
|
|
|
|
|
"""
|
|
|
|
|
self.offset.flags.writeable = False
|
|
|
|
|
self.a_vector.flags.writeable = False
|
|
|
|
|
self.mirrored.flags.writeable = False
|
|
|
|
|
if self.b_vector is not None:
|
|
|
|
|
self.b_vector.flags.writeable = False
|
|
|
|
|
object.__setattr__(self, 'locked', True)
|
|
|
|
|
LockableImpl.lock(self)
|
|
|
|
|
return self
|
|
|
|
|
|
|
|
|
|
def unlock(self) -> 'GridRepetition':
|
|
|
|
|
def unlock(self) -> 'Grid':
|
|
|
|
|
"""
|
|
|
|
|
Unlock the `GridRepetition`
|
|
|
|
|
Unlock the `Grid`
|
|
|
|
|
|
|
|
|
|
Returns:
|
|
|
|
|
self
|
|
|
|
|
"""
|
|
|
|
|
self.offset.flags.writeable = True
|
|
|
|
|
self.a_vector.flags.writeable = True
|
|
|
|
|
self.mirrored.flags.writeable = True
|
|
|
|
|
if self.b_vector is not None:
|
|
|
|
|
self.b_vector.flags.writeable = True
|
|
|
|
|
object.__setattr__(self, 'locked', False)
|
|
|
|
|
return self
|
|
|
|
|
|
|
|
|
|
def deeplock(self) -> 'GridRepetition':
|
|
|
|
|
"""
|
|
|
|
|
Recursively lock the `GridRepetition` and its contained pattern
|
|
|
|
|
|
|
|
|
|
Returns:
|
|
|
|
|
self
|
|
|
|
|
"""
|
|
|
|
|
assert(self.pattern is not None)
|
|
|
|
|
self.lock()
|
|
|
|
|
self.pattern.deeplock()
|
|
|
|
|
return self
|
|
|
|
|
|
|
|
|
|
def deepunlock(self) -> 'GridRepetition':
|
|
|
|
|
"""
|
|
|
|
|
Recursively unlock the `GridRepetition` and its contained pattern
|
|
|
|
|
|
|
|
|
|
This is dangerous unless you have just performed a deepcopy, since
|
|
|
|
|
the component parts may be reused elsewhere.
|
|
|
|
|
|
|
|
|
|
Returns:
|
|
|
|
|
self
|
|
|
|
|
"""
|
|
|
|
|
assert(self.pattern is not None)
|
|
|
|
|
self.unlock()
|
|
|
|
|
self.pattern.deepunlock()
|
|
|
|
|
LockableImpl.unlock(self)
|
|
|
|
|
return self
|
|
|
|
|
|
|
|
|
|
def __repr__(self) -> str:
|
|
|
|
|
name = self.pattern.name if self.pattern is not None else None
|
|
|
|
|
rotation = f' r{self.rotation*180/pi:g}' if self.rotation != 0 else ''
|
|
|
|
|
scale = f' d{self.scale:g}' if self.scale != 1 else ''
|
|
|
|
|
mirrored = ' m{:d}{:d}'.format(*self.mirrored) if self.mirrored.any() else ''
|
|
|
|
|
dose = f' d{self.dose:g}' if self.dose != 1 else ''
|
|
|
|
|
locked = ' L' if self.locked else ''
|
|
|
|
|
bv = f', {self.b_vector}' if self.b_vector is not None else ''
|
|
|
|
|
return (f'<GridRepetition "{name}" at {self.offset} {rotation}{scale}{mirrored}{dose}'
|
|
|
|
|
f' {self.a_count}x{self.b_count} ({self.a_vector}{bv}){locked}>')
|
|
|
|
|
return (f'<Grid {self.a_count}x{self.b_count} ({self.a_vector}{bv}){locked}>')
|
|
|
|
|
|
|
|
|
|
def __eq__(self, other: Any) -> bool:
|
|
|
|
|
if not isinstance(other, type(self)):
|
|
|
|
|
return False
|
|
|
|
|
if self.a_count != other.a_count or self.b_count != other.b_count:
|
|
|
|
|
return False
|
|
|
|
|
if any(self.a_vector[ii] != other.a_vector[ii] for ii in range(2)):
|
|
|
|
|
return False
|
|
|
|
|
if self.b_vector is None and other.b_vector is None:
|
|
|
|
|
return True
|
|
|
|
|
if self.b_vector is None or other.b_vector is None:
|
|
|
|
|
return False
|
|
|
|
|
if any(self.b_vector[ii] != other.b_vector[ii] for ii in range(2)):
|
|
|
|
|
return False
|
|
|
|
|
if self.locked != other.locked:
|
|
|
|
|
return False
|
|
|
|
|
return True
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
class Arbitrary(LockableImpl, Repetition, metaclass=AutoSlots):
|
|
|
|
|
"""
|
|
|
|
|
`Arbitrary` is a simple list of (absolute) displacements for instances.
|
|
|
|
|
|
|
|
|
|
Attributes:
|
|
|
|
|
displacements (numpy.ndarray): absolute displacements of all elements
|
|
|
|
|
`[[x0, y0], [x1, y1], ...]`
|
|
|
|
|
"""
|
|
|
|
|
|
|
|
|
|
_displacements: numpy.ndarray
|
|
|
|
|
""" List of vectors `[[x0, y0], [x1, y1], ...]` specifying the offsets
|
|
|
|
|
of the instances.
|
|
|
|
|
"""
|
|
|
|
|
|
|
|
|
|
locked: bool
|
|
|
|
|
""" If `True`, disallows changes to the object. """
|
|
|
|
|
|
|
|
|
|
@property
|
|
|
|
|
def displacements(self) -> numpy.ndarray:
|
|
|
|
|
return self._displacements
|
|
|
|
|
|
|
|
|
|
@displacements.setter
|
|
|
|
|
def displacements(self, val: Union[Sequence[Sequence[float]], numpy.ndarray]):
|
|
|
|
|
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 lock(self) -> 'Arbitrary':
|
|
|
|
|
"""
|
|
|
|
|
Lock the object, disallowing changes.
|
|
|
|
|
|
|
|
|
|
Returns:
|
|
|
|
|
self
|
|
|
|
|
"""
|
|
|
|
|
self._displacements.flags.writeable = False
|
|
|
|
|
LockableImpl.lock(self)
|
|
|
|
|
return self
|
|
|
|
|
|
|
|
|
|
def unlock(self) -> 'Arbitrary':
|
|
|
|
|
"""
|
|
|
|
|
Unlock the object
|
|
|
|
|
|
|
|
|
|
Returns:
|
|
|
|
|
self
|
|
|
|
|
"""
|
|
|
|
|
self._displacements.flags.writeable = True
|
|
|
|
|
LockableImpl.unlock(self)
|
|
|
|
|
return self
|
|
|
|
|
|
|
|
|
|
def __repr__(self) -> str:
|
|
|
|
|
locked = ' L' if self.locked else ''
|
|
|
|
|
return (f'<Arbitrary {len(self.displacements)}pts {locked}>')
|
|
|
|
|
|
|
|
|
|
def __eq__(self, other: Any) -> bool:
|
|
|
|
|
if not isinstance(other, type(self)):
|
|
|
|
|
return False
|
|
|
|
|
if self.locked != other.locked:
|
|
|
|
|
return False
|
|
|
|
|
return numpy.array_equal(self.displacements, other.displacements)
|
|
|
|
|
|
Jan Petykiewicz
4 years ago