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@ -27,22 +27,32 @@ visitor_function_t = Callable[['Pattern', Tuple['Pattern'], Dict, numpy.ndarray]
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class Pattern:
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"""
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2D layout consisting of some set of shapes and references to other Pattern objects
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(via SubPattern). Shapes are assumed to inherit from .shapes.Shape or provide equivalent
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functions.
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:var shapes: List of all shapes in this Pattern. Elements in this list are assumed to inherit
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from Shape or provide equivalent functions.
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:var subpatterns: List of all SubPattern objects in this Pattern. Multiple SubPattern objects
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may reference the same Pattern object.
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:var name: An identifier for this object. Not necessarily unique.
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2D layout consisting of some set of shapes, labels, and references to other Pattern objects
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(via SubPattern and GridRepetition). Shapes are assumed to inherit from
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masque.shapes.Shape or provide equivalent functions.
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"""
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__slots__ = ('shapes', 'labels', 'subpatterns', 'name', 'locked')
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shapes: List[Shape]
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""" List of all shapes in this Pattern.
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Elements in this list are assumed to inherit from Shape or provide equivalent functions.
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"""
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labels: List[Label]
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""" List of all labels in this Pattern. """
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subpatterns: List[SubPattern or GridRepetition]
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""" List of all objects referencing other patterns in this Pattern.
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Examples are SubPattern (gdsii "instances") or GridRepetition (gdsii "arrays")
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Multiple objects in this list may reference the same Pattern object
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(multiple instances of the same object).
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"""
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name: str
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""" A name for this pattern """
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locked: bool
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""" When the pattern is locked, no changes may be made. """
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def __init__(self,
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name: str = '',
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@ -55,11 +65,12 @@ class Pattern:
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Basic init; arguments get assigned to member variables.
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Non-list inputs for shapes and subpatterns get converted to lists.
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:param shapes: Initial shapes in the Pattern
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:param labels: Initial labels in the Pattern
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:param subpatterns: Initial subpatterns in the Pattern
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:param name: An identifier for the Pattern
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:param locked: Whether to lock the pattern after construction
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Args:
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shapes: Initial shapes in the Pattern
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labels: Initial labels in the Pattern
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subpatterns: Initial subpatterns in the Pattern
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name: An identifier for the Pattern
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locked: Whether to lock the pattern after construction
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"""
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self.unlock()
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if isinstance(shapes, list):
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@ -106,8 +117,11 @@ class Pattern:
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Appends all shapes, labels and subpatterns from other_pattern to self's shapes,
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labels, and supbatterns.
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:param other_pattern: The Pattern to append
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:return: self
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Args:
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other_pattern: The Pattern to append
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Returns:
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self
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"""
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self.subpatterns += other_pattern.subpatterns
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self.shapes += other_pattern.shapes
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@ -125,16 +139,19 @@ class Pattern:
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given entity_func returns True.
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Self is _not_ altered, but shapes, labels, and subpatterns are _not_ copied.
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:param shapes_func: Given a shape, returns a boolean denoting whether the shape is a member
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of the subset. Default always returns False.
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:param labels_func: Given a label, returns a boolean denoting whether the label is a member
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of the subset. Default always returns False.
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:param subpatterns_func: Given a subpattern, returns a boolean denoting if it is a member
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of the subset. Default always returns False.
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:param recursive: If True, also calls .subset() recursively on patterns referenced by this
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pattern.
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:return: A Pattern containing all the shapes and subpatterns for which the parameter
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functions return True
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Args:
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shapes_func: Given a shape, returns a boolean denoting whether the shape is a member
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of the subset. Default always returns False.
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labels_func: Given a label, returns a boolean denoting whether the label is a member
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of the subset. Default always returns False.
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subpatterns_func: Given a subpattern, returns a boolean denoting if it is a member
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of the subset. Default always returns False.
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recursive: If True, also calls .subset() recursively on patterns referenced by this
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pattern.
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Returns:
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A Pattern containing all the shapes and subpatterns for which the parameter
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functions return True
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"""
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def do_subset(src):
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pat = Pattern(name=src.name)
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@ -163,12 +180,17 @@ class Pattern:
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It is only applied to any given pattern once, regardless of how many times it is
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referenced.
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:param func: Function which accepts a Pattern, and returns a pattern.
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:param memo: Dictionary used to avoid re-running on multiply-referenced patterns.
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Stores {id(pattern): func(pattern)} for patterns which have already been processed.
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Default None (no already-processed patterns).
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:return: The result of applying func() to this pattern and all subpatterns.
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:raises: PatternError if called on a pattern containing a circular reference.
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Args:
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func: Function which accepts a Pattern, and returns a pattern.
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memo: Dictionary used to avoid re-running on multiply-referenced patterns.
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Stores `{id(pattern): func(pattern)}` for patterns which have already been processed.
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Default `None` (no already-processed patterns).
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Returns:
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The result of applying func() to this pattern and all subpatterns.
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Raises:
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PatternError if called on a pattern containing a circular reference.
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"""
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if memo is None:
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memo = {}
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@ -212,19 +234,24 @@ class Pattern:
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for the instance being visited
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`memo`: Arbitrary dict (not altered except by visit_*())
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:param visit_before: Function to call before traversing subpatterns.
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Args:
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visit_before: Function to call before traversing subpatterns.
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Should accept a `Pattern` and `**visit_args`, and return the (possibly modified)
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pattern. Default `None` (not called).
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visit_after: Function to call after traversing subpatterns.
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Should accept a Pattern and **visit_args, and return the (possibly modified)
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pattern. Default None (not called).
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:param visit_after: Function to call after traversing subpatterns.
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Should accept a Pattern and **visit_args, and return the (possibly modified)
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pattern. Default None (not called).
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:param transform: Initial value for `visit_args['transform']`.
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pattern. Default `None` (not called).
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transform: Initial value for `visit_args['transform']`.
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Can be `False`, in which case the transform is not calculated.
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`True` or `None` is interpreted as [0, 0, 0, 0].
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:param memo: Arbitrary dict for use by visit_*() functions. Default None (empty dict).
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:param hierarchy: Tuple of patterns specifying the hierarchy above the current pattern.
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`True` or `None` is interpreted as `[0, 0, 0, 0]`.
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memo: Arbitrary dict for use by `visit_*()` functions. Default `None` (empty dict).
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hierarchy: Tuple of patterns specifying the hierarchy above the current pattern.
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Appended to the start of the generated `visit_args['hierarchy']`.
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Default is an empty tuple.
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Returns:
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The result, including `visit_before(self, ...)` and `visit_after(self, ...)`.
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Note that `self` may also be altered!
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"""
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if memo is None:
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memo = {}
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@ -267,16 +294,19 @@ class Pattern:
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poly_max_arclen: float = None,
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) -> 'Pattern':
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"""
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Calls .to_polygons(...) on all the shapes in this Pattern and any referenced patterns,
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Calls `.to_polygons(...)` on all the shapes in this Pattern and any referenced patterns,
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replacing them with the returned polygons.
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Arguments are passed directly to shape.to_polygons(...).
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Arguments are passed directly to `shape.to_polygons(...)`.
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:param poly_num_points: Number of points to use for each polygon. Can be overridden by
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poly_max_arclen if that results in more points. Optional, defaults to shapes'
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internal defaults.
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:param poly_max_arclen: Maximum arclength which can be approximated by a single line
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Args:
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poly_num_points: Number of points to use for each polygon. Can be overridden by
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`poly_max_arclen` if that results in more points. Optional, defaults to shapes'
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internal defaults.
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poly_max_arclen: Maximum arclength which can be approximated by a single line
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segment. Optional, defaults to shapes' internal defaults.
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:return: self
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Returns:
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self
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"""
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old_shapes = self.shapes
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self.shapes = list(itertools.chain.from_iterable(
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@ -291,12 +321,15 @@ class Pattern:
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grid_y: numpy.ndarray,
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) -> 'Pattern':
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"""
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Calls .polygonize() and .flatten on the pattern, then calls .manhattanize() on all the
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Calls `.polygonize()` and `.flatten()` on the pattern, then calls `.manhattanize()` on all the
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resulting shapes, replacing them with the returned Manhattan polygons.
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:param grid_x: List of allowed x-coordinates for the Manhattanized polygon edges.
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:param grid_y: List of allowed y-coordinates for the Manhattanized polygon edges.
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:return: self
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Args:
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grid_x: List of allowed x-coordinates for the Manhattanized polygon edges.
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grid_y: List of allowed y-coordinates for the Manhattanized polygon edges.
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Returns:
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self
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"""
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self.polygonize().flatten()
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@ -311,21 +344,25 @@ class Pattern:
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exclude_types: Tuple[Shape] = (Polygon,)
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) -> 'Pattern':
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"""
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Iterates through this Pattern and all referenced Patterns. Within each Pattern, it iterates
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over all shapes, calling .normalized_form(norm_value) on them to retrieve a scale-,
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Iterates through this `Pattern` and all referenced `Pattern`s. Within each `Pattern`, it iterates
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over all shapes, calling `.normalized_form(norm_value)` on them to retrieve a scale-,
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offset-, dose-, and rotation-independent form. Each shape whose normalized form appears
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more than once is removed and re-added using subpattern objects referencing a newly-created
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Pattern containing only the normalized form of the shape.
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`Pattern` containing only the normalized form of the shape.
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Note that the default norm_value was chosen to give a reasonable precision when converting
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to GDSII, which uses integer values for pixel coordinates.
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Note:
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The default norm_value was chosen to give a reasonable precision when converting
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to GDSII, which uses integer values for pixel coordinates.
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:param recursive: Whether to call recursively on self's subpatterns. Default True.
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:param norm_value: Passed to shape.normalized_form(norm_value). Default 1e6 (see function
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Args:
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recursive: Whether to call recursively on self's subpatterns. Default `True`.
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norm_value: Passed to `shape.normalized_form(norm_value)`. Default `1e6` (see function
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note about GDSII)
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:param exclude_types: Shape types passed in this argument are always left untouched, for
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speed or convenience. Default: (Shapes.Polygon,)
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:return: self
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exclude_types: Shape types passed in this argument are always left untouched, for
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speed or convenience. Default: `(shapes.Polygon,)`
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Returns:
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self
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"""
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if exclude_types is None:
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@ -337,9 +374,9 @@ class Pattern:
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norm_value=norm_value,
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exclude_types=exclude_types)
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# Create a dict which uses the label tuple from .normalized_form() as a key, and which
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# stores (function_to_create_normalized_shape, [(index_in_shapes, values), ...]), where
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# values are the (offset, scale, rotation, dose) values as calculated by .normalized_form()
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# Create a dict which uses the label tuple from `.normalized_form()` as a key, and which
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# stores `(function_to_create_normalized_shape, [(index_in_shapes, values), ...])`, where
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# values are the `(offset, scale, rotation, dose)` values as calculated by `.normalized_form()`
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shape_table = defaultdict(lambda: [None, list()])
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for i, shape in enumerate(self.shapes):
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if not any((isinstance(shape, t) for t in exclude_types)):
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@ -348,9 +385,9 @@ class Pattern:
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shape_table[label][1].append((i, values))
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# Iterate over the normalized shapes in the table. If any normalized shape occurs more than
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# once, create a Pattern holding a normalized shape object, and add self.subpatterns
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# once, create a `Pattern` holding a normalized shape object, and add `self.subpatterns`
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# entries for each occurrence in self. Also, note down that we should delete the
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# self.shapes entries for which we made SubPatterns.
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# `self.shapes` entries for which we made SubPatterns.
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shapes_to_remove = []
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for label in shape_table:
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if len(shape_table[label][1]) > 1:
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@ -374,21 +411,23 @@ class Pattern:
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"""
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Represents the pattern as a list of polygons.
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Deep-copies the pattern, then calls .polygonize() and .flatten() on the copy in order to
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Deep-copies the pattern, then calls `.polygonize()` and `.flatten()` on the copy in order to
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generate the list of polygons.
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:return: A list of (Ni, 2) numpy.ndarrays specifying vertices of the polygons. Each ndarray
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is of the form [[x0, y0], [x1, y1],...].
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Returns:
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A list of `(Ni, 2)` `numpy.ndarray`s specifying vertices of the polygons. Each ndarray
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is of the form `[[x0, y0], [x1, y1],...]`.
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"""
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pat = self.deepcopy().deepunlock().polygonize().flatten()
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return [shape.vertices + shape.offset for shape in pat.shapes]
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def referenced_patterns_by_id(self) -> Dict[int, 'Pattern']:
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"""
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Create a dictionary of {id(pat): pat} for all Pattern objects referenced by this
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Create a dictionary of `{id(pat): pat}` for all Pattern objects referenced by this
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Pattern (operates recursively on all referenced Patterns as well)
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:return: Dictionary of {id(pat): pat} for all referenced Pattern objects
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Returns:
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Dictionary of `{id(pat): pat}` for all referenced Pattern objects
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"""
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ids = {}
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for subpat in self.subpatterns:
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@ -399,11 +438,12 @@ class Pattern:
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def get_bounds(self) -> Union[numpy.ndarray, None]:
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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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Return a `numpy.ndarray` containing `[[x_min, y_min], [x_max, y_max]]`, corresponding to the
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extent of the Pattern's contents in each dimension.
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Returns None if the Pattern is empty.
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Returns `None` if the Pattern is empty.
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:return: [[x_min, y_min], [x_max, y_max]] or None
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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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entries = self.shapes + self.subpatterns + self.labels
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if not entries:
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@ -428,13 +468,16 @@ class Pattern:
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Shape identifiers are changed to represent their original position in the
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pattern hierarchy:
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(L1_name (str), L1_index (int), L2_name, L2_index, ..., *original_shape_identifier)
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where L1_name is the first-level subpattern's name (e.g. self.subpatterns[0].pattern.name),
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L2_name is the next-level subpattern's name (e.g.
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self.subpatterns[0].pattern.subpatterns[0].pattern.name) and L1_index is an integer
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used to differentiate between multiple instance of the same (or same-named) subpatterns.
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`(L1_name (str), L1_index (int), L2_name, L2_index, ..., *original_shape_identifier)`
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where
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`L1_name` is the first-level subpattern's name (e.g. `self.subpatterns[0].pattern.name`),
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`L2_name` is the next-level subpattern's name (e.g.
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`self.subpatterns[0].pattern.subpatterns[0].pattern.name`) and
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`L1_index` is an integer used to differentiate between multiple instance ofi the same
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(or same-named) subpatterns.
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:return: self
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Returns:
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self
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"""
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subpatterns = copy.deepcopy(self.subpatterns)
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self.subpatterns = []
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@ -457,22 +500,28 @@ class Pattern:
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"""
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Translates all shapes, label, and subpatterns by the given offset.
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:param offset: Offset to translate by
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:return: self
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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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for entry in self.shapes + self.subpatterns + self.labels:
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entry.translate(offset)
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return self
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def scale_elements(self, scale: float) -> 'Pattern':
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def scale_elements(self, c: float) -> 'Pattern':
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""""
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Scales all shapes and subpatterns by the given value.
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:param scale: value to scale by
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:return: self
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Args:
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c: factor to scale by
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Returns:
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self
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"""
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for entry in self.shapes + self.subpatterns:
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entry.scale(scale)
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entry.scale(c)
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return self
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def scale_by(self, c: float) -> 'Pattern':
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@ -480,8 +529,11 @@ class Pattern:
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Scale this Pattern by the given value
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(all shapes and subpatterns and their offsets are scaled)
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:param c: value to scale by
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:return: self
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Args:
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c: factor to scale by
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Returns:
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self
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"""
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for entry in self.shapes + self.subpatterns:
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entry.offset *= c
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@ -494,9 +546,12 @@ class Pattern:
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"""
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Rotate the Pattern around the a location.
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:param pivot: Location to rotate around
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:param rotation: Angle to rotate by (counter-clockwise, radians)
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:return: self
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Args:
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pivot: (x, y) location to rotate around
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rotation: Angle to rotate by (counter-clockwise, radians)
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Returns:
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self
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"""
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pivot = numpy.array(pivot)
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self.translate_elements(-pivot)
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@ -509,8 +564,11 @@ class Pattern:
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"""
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Rotate the offsets of all shapes, labels, and subpatterns around (0, 0)
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:param rotation: Angle to rotate by (counter-clockwise, radians)
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:return: self
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Args:
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rotation: Angle to rotate by (counter-clockwise, radians)
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Returns:
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self
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"""
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for entry in self.shapes + self.subpatterns + self.labels:
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entry.offset = numpy.dot(rotation_matrix_2d(rotation), entry.offset)
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@ -520,8 +578,11 @@ class Pattern:
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"""
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Rotate each shape and subpattern around its center (offset)
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:param rotation: Angle to rotate by (counter-clockwise, radians)
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:return: self
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Args:
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rotation: Angle to rotate by (counter-clockwise, radians)
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Returns:
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self
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"""
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for entry in self.shapes + self.subpatterns:
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entry.rotate(rotation)
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@ -531,8 +592,12 @@ class Pattern:
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"""
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Mirror the offsets of all shapes, labels, and subpatterns across an axis
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:param axis: Axis to mirror across
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:return: self
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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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for entry in self.shapes + self.subpatterns + self.labels:
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entry.offset[axis - 1] *= -1
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@ -541,10 +606,14 @@ class Pattern:
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def mirror_elements(self, axis: int) -> 'Pattern':
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"""
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Mirror each shape and subpattern across an axis, relative to its
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center (offset)
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offset
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:param axis: Axis to mirror across
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:return: self
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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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for entry in self.shapes + self.subpatterns:
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entry.mirror(axis)
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@ -554,22 +623,29 @@ class Pattern:
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"""
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Mirror the Pattern across an axis
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:param axis: Axis to mirror across
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:return: self
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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.mirror_element_centers(axis)
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return self
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def scale_element_doses(self, factor: float) -> 'Pattern':
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def scale_element_doses(self, c: float) -> 'Pattern':
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"""
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Multiply all shape and subpattern doses by a factor
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:param factor: Factor to multiply doses by
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:return: self
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Args:
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c: Factor to multiply doses by
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Return:
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self
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"""
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for entry in self.shapes + self.subpatterns:
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entry.dose *= factor
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entry.dose *= c
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return self
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def copy(self) -> 'Pattern':
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@ -577,25 +653,26 @@ class Pattern:
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Return a copy of the Pattern, deep-copying shapes and copying subpattern
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entries, but not deep-copying any referenced patterns.
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See also: Pattern.deepcopy()
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See also: `Pattern.deepcopy()`
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:return: A copy of the current Pattern.
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Returns:
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A copy of the current Pattern.
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"""
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return copy.copy(self)
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def deepcopy(self) -> 'Pattern':
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"""
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Convenience method for copy.deepcopy(pattern)
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Convenience method for `copy.deepcopy(pattern)`
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:return: A deep copy of the current Pattern.
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Returns:
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A deep copy of the current Pattern.
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"""
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return copy.deepcopy(self)
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def is_empty(self) -> bool:
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"""
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Returns true if the Pattern contains no shapes, labels, or subpatterns.
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:return: True if the pattern is empty.
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Returns:
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True if the pattern is contains no shapes, labels, or subpatterns.
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"""
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return (len(self.subpatterns) == 0 and
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len(self.shapes) == 0 and
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@ -603,9 +680,11 @@ class Pattern:
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def lock(self) -> 'Pattern':
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"""
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Lock the pattern
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Lock the pattern, raising an exception if it is modified.
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Also see `deeplock()`.
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:return: self
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Returns:
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self
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"""
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object.__setattr__(self, 'locked', True)
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return self
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@ -614,16 +693,18 @@ class Pattern:
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"""
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Unlock the pattern
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:return: self
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Returns:
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self
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"""
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object.__setattr__(self, 'locked', False)
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return self
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def deeplock(self) -> 'Pattern':
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"""
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Recursively lock the pattern, all referenced shapes, subpatterns, and labels
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Recursively lock the pattern, all referenced shapes, subpatterns, and labels.
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:return: self
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Returns:
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self
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"""
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self.lock()
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for ss in self.shapes + self.labels:
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@ -634,11 +715,13 @@ class Pattern:
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def deepunlock(self) -> 'Pattern':
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"""
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Recursively unlock the pattern, all referenced shapes, subpatterns, and labels
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Recursively unlock the pattern, all referenced shapes, subpatterns, and labels.
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This is dangerous unless you have just performed a deepcopy!
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This is dangerous unless you have just performed a deepcopy, since anything
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you change will be changed everywhere it is referenced!
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:return: self
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Return:
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self
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"""
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self.unlock()
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for ss in self.shapes + self.labels:
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@ -650,10 +733,13 @@ class Pattern:
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@staticmethod
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def load(filename: str) -> 'Pattern':
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"""
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Load a Pattern from a file
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Load a Pattern from a file using pickle
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:param filename: Filename to load from
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:return: Loaded Pattern
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Args:
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filename: Filename to load from
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Returns:
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Loaded Pattern
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"""
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with open(filename, 'rb') as f:
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pattern = pickle.load(f)
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@ -662,10 +748,13 @@ class Pattern:
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def save(self, filename: str) -> 'Pattern':
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"""
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Save the Pattern to a file
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Save the Pattern to a file using pickle
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:param filename: Filename to save to
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:return: self
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Args:
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filename: Filename to save to
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Returns:
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self
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"""
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with open(filename, 'wb') as f:
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pickle.dump(self, f, protocol=pickle.HIGHEST_PROTOCOL)
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@ -679,12 +768,16 @@ class Pattern:
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"""
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Draw a picture of the Pattern and wait for the user to inspect it
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Imports matplotlib.
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Imports `matplotlib`.
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:param offset: Coordinates to offset by before drawing
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:param line_color: Outlines are drawn with this color (passed to matplotlib PolyCollection)
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:param fill_color: Interiors are drawn with this color (passed to matplotlib PolyCollection)
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:param overdraw: Whether to create a new figure or draw on a pre-existing one
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Note that this can be slow; it is often faster to export to GDSII and use
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klayout or a different GDS viewer!
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Args:
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offset: Coordinates to offset by before drawing
|
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|
line_color: Outlines are drawn with this color (passed to `matplotlib.collections.PolyCollection`)
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fill_color: Interiors are drawn with this color (passed to `matplotlib.collections.PolyCollection`)
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overdraw: Whether to create a new figure or draw on a pre-existing one
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"""
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# TODO: add text labels to visualize()
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from matplotlib import pyplot
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