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570 lines
18 KiB
Python
570 lines
18 KiB
Python
"""
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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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"""
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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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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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# 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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"""
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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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"""
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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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'_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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_a_vector: numpy.ndarray
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""" Vector `[x, y]` specifying the first lattice vector of the grid.
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Specifies center-to-center spacing between adjacent elements.
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"""
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_a_count: int
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""" Number of instances along the direction specified by the `a_vector` """
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_b_vector: Optional[numpy.ndarray]
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""" Vector `[x, y]` specifying a second lattice vector for the grid.
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Specifies center-to-center spacing between adjacent elements.
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Can be `None` for a 1D array.
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"""
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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: 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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"""
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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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b_vector: Second lattice vector, of the form `[x, y]`.
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Specifies center-to-center spacing between adjacent instances.
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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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Raises:
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PatternError if `b_*` inputs conflict with each other
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or `a_count < 1`.
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"""
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if b_vector is None:
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if b_count > 1:
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raise PatternError('Repetition has b_count > 1 but no b_vector')
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else:
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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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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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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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return new
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def __deepcopy__(self, memo: Dict = None) -> 'GridRepetition':
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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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return self._a_vector
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@a_vector.setter
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def a_vector(self, val: vector2):
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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('a_vector must be convertible to size-2 ndarray')
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self._a_vector = val.flatten().astype(float)
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# b_vector property
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@property
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def b_vector(self) -> numpy.ndarray:
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return self._b_vector
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@b_vector.setter
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def b_vector(self, val: vector2):
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if not isinstance(val, numpy.ndarray):
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val = numpy.array(val, dtype=float, copy=True)
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if val.size != 2:
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raise PatternError('b_vector must be convertible to size-2 ndarray')
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self._b_vector = val.flatten()
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# a_count property
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@property
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def a_count(self) -> int:
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return self._a_count
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@a_count.setter
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def a_count(self, val: int):
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if val != int(val):
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raise PatternError('a_count must be convertable to an int!')
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self._a_count = int(val)
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# b_count property
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@property
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def b_count(self) -> int:
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return self._b_count
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@b_count.setter
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def b_count(self, val: int):
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if val != int(val):
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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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"""
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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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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 = self.pattern.deepcopy().deepunlock()
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newPat.translate_elements(offset)
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patterns.append(newPat)
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combined = patterns[0]
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for p in patterns[1:]:
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combined.append(p)
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combined.scale_by(self.scale)
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[combined.mirror(ax) for ax, do in enumerate(self.mirrored) if do]
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combined.rotate_around((0.0, 0.0), self.rotation)
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combined.translate_elements(self.offset)
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combined.scale_element_doses(self.dose)
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return combined
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def translate(self, offset: vector2) -> 'GridRepetition':
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"""
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Translate by the given offset
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Args:
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offset: `[x, y]` to translate by
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Returns:
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self
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"""
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self.offset += offset
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return self
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def rotate_around(self, pivot: vector2, rotation: float) -> 'GridRepetition':
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"""
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Rotate the array around a point
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Args:
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pivot: Point `[x, y]` to rotate around
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rotation: Angle to rotate by (counterclockwise, radians)
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Returns:
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self
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"""
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pivot = numpy.array(pivot, dtype=float)
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self.translate(-pivot)
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self.offset = numpy.dot(rotation_matrix_2d(rotation), self.offset)
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self.rotate(rotation)
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self.translate(+pivot)
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return self
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def rotate(self, rotation: float) -> 'GridRepetition':
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"""
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Rotate around (0, 0)
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Args:
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rotation: Angle to rotate by (counterclockwise, radians)
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Returns:
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self
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"""
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self.rotate_elements(rotation)
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self.a_vector = numpy.dot(rotation_matrix_2d(rotation), self.a_vector)
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if self.b_vector is not None:
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self.b_vector = numpy.dot(rotation_matrix_2d(rotation), self.b_vector)
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return self
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def rotate_elements(self, rotation: float) -> 'GridRepetition':
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"""
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Rotate each element around its origin
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Args:
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rotation: Angle to rotate by (counterclockwise, radians)
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Returns:
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self
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"""
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self.rotation += rotation
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return self
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def mirror(self, axis: int) -> 'GridRepetition':
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"""
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Mirror the GridRepetition across an axis.
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Args:
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axis: Axis to mirror across.
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(0: mirror across x-axis, 1: mirror across y-axis)
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Returns:
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self
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"""
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self.mirror_elements(axis)
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self.a_vector[1-axis] *= -1
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if self.b_vector is not None:
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self.b_vector[1-axis] *= -1
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return self
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def mirror_elements(self, axis: int) -> 'GridRepetition':
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"""
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Mirror each element across an axis relative to its origin.
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Args:
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axis: Axis to mirror across.
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(0: mirror across x-axis, 1: mirror across y-axis)
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Returns:
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self
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"""
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self.mirrored[axis] = not self.mirrored[axis]
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self.rotation *= -1
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return self
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def get_bounds(self) -> Optional[numpy.ndarray]:
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"""
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Return a `numpy.ndarray` containing `[[x_min, y_min], [x_max, y_max]]`, corresponding to the
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extent of the `GridRepetition` in each dimension.
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Returns `None` if the contained `Pattern` is empty.
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Returns:
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`[[x_min, y_min], [x_max, y_max]]` or `None`
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"""
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if self.pattern is None:
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return None
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return self.as_pattern().get_bounds()
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def scale_by(self, c: float) -> 'GridRepetition':
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"""
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Scale the GridRepetition by a factor
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Args:
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c: scaling factor
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Returns:
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self
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"""
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self.scale_elements_by(c)
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self.a_vector *= c
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if self.b_vector is not None:
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self.b_vector *= c
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return self
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def scale_elements_by(self, c: float) -> 'GridRepetition':
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"""
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Scale each element by a factor
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Args:
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c: scaling factor
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Returns:
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self
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"""
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self.scale *= c
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return self
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def copy(self) -> 'GridRepetition':
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"""
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Return a shallow copy of the repetition.
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Returns:
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`copy.copy(self)`
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"""
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return copy.copy(self)
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def deepcopy(self) -> 'GridRepetition':
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"""
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Return a deep copy of the repetition.
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Returns:
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`copy.deepcopy(self)`
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"""
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return copy.deepcopy(self)
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def lock(self) -> 'GridRepetition':
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"""
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Lock the `GridRepetition`, disallowing changes.
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Returns:
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self
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"""
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self.offset.flags.writeable = False
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self.a_vector.flags.writeable = False
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self.mirrored.flags.writeable = False
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if self.b_vector is not None:
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self.b_vector.flags.writeable = False
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object.__setattr__(self, 'locked', True)
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return self
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def unlock(self) -> 'GridRepetition':
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"""
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Unlock the `GridRepetition`
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Returns:
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self
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"""
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self.offset.flags.writeable = True
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self.a_vector.flags.writeable = True
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self.mirrored.flags.writeable = True
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if self.b_vector is not None:
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self.b_vector.flags.writeable = True
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object.__setattr__(self, 'locked', False)
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return self
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def deeplock(self) -> 'GridRepetition':
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"""
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Recursively lock the `GridRepetition` and its contained pattern
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Returns:
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self
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"""
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assert(self.pattern is not None)
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self.lock()
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self.pattern.deeplock()
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return self
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def deepunlock(self) -> 'GridRepetition':
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"""
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Recursively unlock the `GridRepetition` and its contained pattern
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This is dangerous unless you have just performed a deepcopy, since
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the component parts may be reused elsewhere.
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Returns:
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self
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"""
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assert(self.pattern is not None)
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self.unlock()
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self.pattern.deepunlock()
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return self
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def __repr__(self) -> str:
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name = self.pattern.name if self.pattern is not None else None
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rotation = f' r{self.rotation*180/pi:g}' if self.rotation != 0 else ''
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scale = f' d{self.scale:g}' if self.scale != 1 else ''
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mirrored = ' m{:d}{:d}'.format(*self.mirrored) if self.mirrored.any() else ''
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dose = f' d{self.dose:g}' if self.dose != 1 else ''
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locked = ' L' if self.locked else ''
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bv = f', {self.b_vector}' if self.b_vector is not None else ''
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return (f'<GridRepetition "{name}" at {self.offset} {rotation}{scale}{mirrored}{dose}'
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f' {self.a_count}x{self.b_count} ({self.a_vector}{bv}){locked}>')
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