[wip] add poly_collection shape

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jan 2024-10-17 18:04:25 -07:00
parent 73193473df
commit cc59ae9ab5

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@ -0,0 +1,210 @@
from typing import Any, cast, Iterable
from collections.abc import Sequence
import copy
import functools
import numpy
from numpy import pi
from numpy.typing import NDArray, ArrayLike
from . import Shape, normalized_shape_tuple
from ..error import PatternError
from ..repetition import Repetition
from ..utils import is_scalar, rotation_matrix_2d, annotations_lt, annotations_eq, rep2key
from ..utils import remove_colinear_vertices, remove_duplicate_vertices, annotations_t
@functools.total_ordering
class PolyCollection(Shape):
"""
A collection of polygons, consisting of list of vertex arrays (N_m x 2 ndarrays) which specify
implicitly-closed boundaries, and an offset.
Note that the setter for `PolyCollection.vertex_list` creates a copy of the
passed vertex coordinates.
A `normalized_form(...)` is available, but can be quite slow with lots of vertices.
"""
__slots__ = (
'_vertex_lists',
# Inherited
'_offset', '_repetition', '_annotations',
)
_vertex_lists: list[NDArray[numpy.float64]]
""" List of ndarrays (N_m x 2) of vertices `[ [[x0, y0], [x1, y1], ...] ]` """
# vertex_lists property
@property
def vertex_lists(self) -> Any: # mypy#3004 NDArray[numpy.float64]:
"""
Vertices of the polygons (ist of ndarrays (N_m x 2) `[ [[x0, y0], [x1, y1], ...] ]`
When setting, note that a copy will be made,
"""
return self._vertex_lists
@vertex_lists.setter
def vertex_lists(self, val: ArrayLike) -> None:
val = [numpy.array(vv, dtype=float) for vv in val]
for ii, vv in enumerate(val):
if len(vv.shape) < 2 or vv.shape[1] != 2:
raise PatternError(f'vertex_lists contents must be an Nx2 arrays (polygon #{ii} fails)')
if vv.shape[0] < 3:
raise PatternError(f'vertex_lists contents must have at least 3 vertices (Nx2 where N>2) (polygon ${ii} has shape {vv.shape})')
self._vertices = val
# xs property
@property
def xs(self) -> NDArray[numpy.float64]:
"""
All vertex x coords as a 1D ndarray
"""
return self.vertices[:, 0]
def __init__(
self,
vertex_lists: Iterable[ArrayLike],
*,
offset: ArrayLike = (0.0, 0.0),
rotation: float = 0.0,
repetition: Repetition | None = None,
annotations: annotations_t | None = None,
raw: bool = False,
) -> None:
if raw:
assert isinstance(vertex_lists, list)
assert all(isinstance(vv, numpy.ndarray) for vv in vertex_lists)
assert isinstance(offset, numpy.ndarray)
self._vertex_lists = vertex_lists
self._offset = offset
self._repetition = repetition
self._annotations = annotations if annotations is not None else {}
else:
self.vertices = vertices
self.offset = offset
self.repetition = repetition
self.annotations = annotations if annotations is not None else {}
self.rotate(rotation)
def __deepcopy__(self, memo: dict | None = None) -> 'PolyCollection':
memo = {} if memo is None else memo
new = copy.copy(self)
new._offset = self._offset.copy()
new._vertex_lists = [vv.copy() for vv in self._vertex_lists]
new._annotations = copy.deepcopy(self._annotations)
return new
def __eq__(self, other: Any) -> bool:
return (
type(self) is type(other)
and numpy.array_equal(self.offset, other.offset)
and all(numpy.array_equal(ss, oo) for ss, oo in zip(self.vertices, other.vertices))
and self.repetition == other.repetition
and annotations_eq(self.annotations, other.annotations)
)
def __lt__(self, other: Shape) -> bool:
if type(self) is not type(other):
if repr(type(self)) != repr(type(other)):
return repr(type(self)) < repr(type(other))
return id(type(self)) < id(type(other))
other = cast(PolyCollection, other)
for vv, oo in zip(self.vertices, other.vertices):
if not numpy.array_equal(vv, oo):
min_len = min(vv.shape[0], oo.shape[0])
eq_mask = vv[:min_len] != oo[:min_len]
eq_lt = vv[:min_len] < oo[:min_len]
eq_lt_masked = eq_lt[eq_mask]
if eq_lt_masked.size > 0:
return eq_lt_masked.flat[0]
return vv.shape[0] < oo.shape[0]
if len(self.vertex_lists) != len(other.vertex_lists):
return len(self.vertex_lists) < len(other.vertex_lists):
if not numpy.array_equal(self.offset, other.offset):
return tuple(self.offset) < tuple(other.offset)
if self.repetition != other.repetition:
return rep2key(self.repetition) < rep2key(other.repetition)
return annotations_lt(self.annotations, other.annotations)
def pop_as_polygon(self, index: int) -> 'Polygon':
"""
Remove one polygon from the list, and return it as a `Polygon` object.
Args:
index: which polygon to pop
"""
verts = self.vertex_lists.pop(index)
return Polygon(
vertices=verts,
offset=self.offset,
repetition=self.repetition.copy(),
annotations=copy.deepcopy(self.annotations),
)
def to_polygons(
self,
num_vertices: int | None = None, # unused # noqa: ARG002
max_arclen: float | None = None, # unused # noqa: ARG002
) -> list['Polygon']:
return [Polygon(
vertices=vv,
offset=self.offset,
repetition=self.repetition.copy(),
annotations=copy.deepcopy(self.annotations),
) for vv in self.vertex_lists]
def get_bounds_single(self) -> NDArray[numpy.float64]: # TODO note shape get_bounds doesn't include repetition
mins = [numpy.min(vv, axis=0) for vv self.vertex_lists]
maxs = [numpy.max(vv, axis=0) for vv self.vertex_lists]
return numpy.vstack((self.offset + numpy.min(self.vertex_lists, axis=0),
self.offset + numpy.max(self.vertex_lists, axis=0)))
def rotate(self, theta: float) -> 'Polygon':
if theta != 0:
for vv in self.vertex_lists:
vv[:] = numpy.dot(rotation_matrix_2d(theta), vv.T).T
return self
def mirror(self, axis: int = 0) -> 'Polygon':
for vv in self.vertex_lists:
vv[:, axis - 1] *= -1
return self
def scale_by(self, c: float) -> 'Polygon':
for vv in self.vertex_lists:
vv *= c
return self
def normalized_form(self, norm_value: float) -> normalized_shape_tuple:
# Note: this function is going to be pretty slow for many-vertexed polygons, relative to
# other shapes
meanv = numpy.concatenate(self.vertex_lists).mean(axis=0)
zeroed_vertices = [vv - meanv for vv in self.vertex_lists]
offset = meanv + self.offset
scale = zeroed_vertices.std()
normed_vertices = zeroed_vertices / scale
_, _, vertex_axis = numpy.linalg.svd(zeroed_vertices)
rotation = numpy.arctan2(vertex_axis[0][1], vertex_axis[0][0]) % (2 * pi)
rotated_vertices = numpy.vstack([numpy.dot(rotation_matrix_2d(-rotation), v)
for v in normed_vertices])
# Reorder the vertices so that the one with lowest x, then y, comes first.
x_min = rotated_vertices[:, 0].argmin()
if not is_scalar(x_min):
y_min = rotated_vertices[x_min, 1].argmin()
x_min = cast(Sequence, x_min)[y_min]
reordered_vertices = numpy.roll(rotated_vertices, -x_min, axis=0)
# TODO: normalize mirroring?
return ((type(self), reordered_vertices.data.tobytes()),
(offset, scale / norm_value, rotation, False),
lambda: Polygon(reordered_vertices * norm_value))
def __repr__(self) -> str:
centroid = self.offset + numpy.concatenate(self.vertex_lists).mean(axis=0)
return f'<PolyCollection centroid {centroid} p{len(self.vertex_lists)}>'