Add Pattern.dfs()

Depth-first traversal with hierarchy and transform tracking
lethe/HEAD
Jan Petykiewicz 5 years ago
parent 76f213a7ce
commit 194a90fe7a

@ -15,12 +15,15 @@ from .subpattern import SubPattern
from .repetition import GridRepetition
from .shapes import Shape, Polygon
from .label import Label
from .utils import rotation_matrix_2d, vector2
from .utils import rotation_matrix_2d, vector2, normalize_mirror
from .error import PatternError
__author__ = 'Jan Petykiewicz'
visitor_function_t = Callable[['Pattern', Tuple['Pattern'], Dict, numpy.ndarray], 'Pattern']
class Pattern:
"""
2D layout consisting of some set of shapes and references to other Pattern objects
@ -165,6 +168,78 @@ class Pattern:
pat = memo[pat_id]
return pat
def dfs(self,
visit_before: visitor_function_t = None,
visit_after: visitor_function_t = None,
transform: numpy.ndarray or bool or None = False ,
memo: Dict = None,
hierarchy: Tuple['Pattern'] = (),
) -> 'Pattern':
"""
Experimental convenience function.
Performs a depth-first traversal of this pattern and its subpatterns.
At each pattern in the tree, the following sequence is called:
```
current_pattern = visit_before(current_pattern, **vist_args)
for sp in current_pattern.subpatterns]
sp.pattern = sp.pattern.df(visit_before, visit_after, updated_transform,
memo, (current_pattern,) + hierarchy)
current_pattern = visit_after(current_pattern, **visit_args)
```
where `visit_args` are
`hierarchy`: (top_pattern, L1_pattern, L2_pattern, ..., parent_pattern)
tuple of all parent-and-higher patterns
`transform`: numpy.ndarray containing cumulative
[x_offset, y_offset, rotation (rad), mirror_x (0 or 1)]
for the instance being visited
`memo`: Arbitrary dict (not altered except by visit_*())
:param visit_before: Function to call before traversing subpatterns.
Should accept a Pattern and **visit_args, and return the (possibly modified)
pattern. Default None (not called).
:param visit_after: Function to call after traversing subpatterns.
Should accept a Pattern and **visit_args, and return the (possibly modified)
pattern. Default None (not called).
:param transform: Initial value for `visit_args['transform']`.
Can be `False`, in which case the transform is not calculated.
`True` or `None` is interpreted as [0, 0, 0, 0].
:param memo: Arbitrary dict for use by visit_*() functions. Default None (empty dict).
:param hierarchy: Tuple of patterns specifying the hierarchy above the current pattern.
Appended to the start of the generated `visit_args['hierarchy']`.
Default is an empty tuple.
"""
if memo is None:
memo = {}
if transform is None or transform is True:
transform = numpy.zeros(4)
if self in hierarchy:
raise PatternError('.dfs() called on pattern with circular reference')
pat = self
if visit_before is not None:
pat = visit_before(pat, hierarchy=hierarchy, memo=memo, transform=transform)
for subpattern in self.subpatterns:
if transform is not False:
mirror_x, angle = normalize_mirror(subpattern.mirrored)
angle += subpattern.rotation
sp_transform = transform + numpy.hstack((subpattern.offset, angle, mirror_x))
sp_transform[3] %= 2
else:
sp_transform = False
subpattern.pattern = subpattern.pattern.dfs(visit_before=visit_before,
visit_after=visit_after,
transform=sp_transform,
memo=memo,
hierarchy=hierarchy + (self,))
if visit_after is not None:
pat = visit_after(pat, hierarchy=hierarchy, memo=memo, transform=transform)
return pat
def polygonize(self,
poly_num_points: int = None,
poly_max_arclen: float = None,

@ -57,6 +57,20 @@ def rotation_matrix_2d(theta: float) -> numpy.ndarray:
[numpy.sin(theta), +numpy.cos(theta)]])
def normalize_mirror(mirrored: Tuple[bool, bool]) -> Tuple[bool, float]:
mirrored_x, mirrored_y = mirrored
if mirrored_x and mirrored_y:
angle = numpy.pi
mirror_x = False
elif mirrored_x:
angle = 0
mirror_x = True
elif mirror_y:
angle = numpy.pi
mirror_x = True
return mirror_x, angle
def remove_duplicate_vertices(vertices: numpy.ndarray, closed_path: bool = True) -> numpy.ndarray:
duplicates = (vertices == numpy.roll(vertices, 1, axis=0)).all(axis=1)
if not closed_path:

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