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delete duplicate utils submodule

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jan 2023-01-13 16:08:17 -08:00
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masque/utils.py
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"""
Various helper functions
"""
from typing import Any, Union, Tuple, Sequence, Dict, List
from abc import ABCMeta
import numpy
from numpy.typing import NDArray, ArrayLike
# Type definitions
layer_t = Union[int, Tuple[int, int], str]
annotations_t = Dict[str, List[Union[int, float, str]]]
def is_scalar(var: Any) -> bool:
"""
Alias for 'not hasattr(var, "__len__")'
Args:
var: Checks if `var` has a length.
"""
return not hasattr(var, "__len__")
def get_bit(bit_string: Any, bit_id: int) -> bool:
"""
Interprets bit number `bit_id` from the right (lsb) of `bit_string` as a boolean
Args:
bit_string: Bit string to test
bit_id: Bit number, 0-indexed from the right (lsb)
Returns:
Boolean value of the requested bit
"""
return bit_string & (1 << bit_id) != 0
def set_bit(bit_string: Any, bit_id: int, value: bool) -> Any:
"""
Returns `bit_string`, with bit number `bit_id` set to boolean `value`.
Args:
bit_string: Bit string to alter
bit_id: Bit number, 0-indexed from right (lsb)
value: Boolean value to set bit to
Returns:
Altered `bit_string`
"""
mask = (1 << bit_id)
bit_string &= ~mask
if value:
bit_string |= mask
return bit_string
def rotation_matrix_2d(theta: float) -> NDArray[numpy.float64]:
"""
2D rotation matrix for rotating counterclockwise around the origin.
Args:
theta: Angle to rotate, in radians
Returns:
rotation matrix
"""
return numpy.array([[numpy.cos(theta), -numpy.sin(theta)],
[numpy.sin(theta), +numpy.cos(theta)]])
def normalize_mirror(mirrored: Sequence[bool]) -> Tuple[bool, float]:
"""
Converts 0-2 mirror operations `(mirror_across_x_axis, mirror_across_y_axis)`
into 0-1 mirror operations and a rotation
Args:
mirrored: `(mirror_across_x_axis, mirror_across_y_axis)`
Returns:
`mirror_across_x_axis` (bool) and
`angle_to_rotate` in radians
"""
mirrored_x, mirrored_y = mirrored
mirror_x = (mirrored_x != mirrored_y) # XOR
angle = numpy.pi if mirrored_y else 0
return mirror_x, angle
def remove_duplicate_vertices(vertices: ArrayLike, closed_path: bool = True) -> NDArray[numpy.float64]:
"""
Given a list of vertices, remove any consecutive duplicates.
Args:
vertices: `[[x0, y0], [x1, y1], ...]`
closed_path: If True, `vertices` is interpreted as an implicity-closed path
(i.e. the last vertex will be removed if it is the same as the first)
Returns:
`vertices` with no consecutive duplicates.
"""
vertices = numpy.array(vertices)
duplicates = (vertices == numpy.roll(vertices, 1, axis=0)).all(axis=1)
if not closed_path:
duplicates[0] = False
return vertices[~duplicates]
def remove_colinear_vertices(vertices: ArrayLike, closed_path: bool = True) -> NDArray[numpy.float64]:
"""
Given a list of vertices, remove any superflous vertices (i.e.
those which lie along the line formed by their neighbors)
Args:
vertices: Nx2 ndarray of vertices
closed_path: If `True`, the vertices are assumed to represent an implicitly
closed path. If `False`, the path is assumed to be open. Default `True`.
Returns:
`vertices` with colinear (superflous) vertices removed.
"""
vertices = remove_duplicate_vertices(vertices)
# Check for dx0/dy0 == dx1/dy1
dv = numpy.roll(vertices, -1, axis=0) - vertices # [y1-y0, y2-y1, ...]
dxdy = dv * numpy.roll(dv, 1, axis=0)[:, ::-1] # [[dx0*(dy_-1), (dx_-1)*dy0], dx1*dy0, dy1*dx0]]
dxdy_diff = numpy.abs(numpy.diff(dxdy, axis=1))[:, 0]
err_mult = 2 * numpy.abs(dxdy).sum(axis=1) + 1e-40
slopes_equal = (dxdy_diff / err_mult) < 1e-15
if not closed_path:
slopes_equal[[0, -1]] = False
return vertices[~slopes_equal]
class AutoSlots(ABCMeta):
"""
Metaclass for automatically generating __slots__ based on superclass type annotations.
Superclasses must set `__slots__ = ()` to make this work properly.
This is a workaround for the fact that non-empty `__slots__` can't be used
with multiple inheritance. Since we only use multiple inheritance with abstract
classes, they can have empty `__slots__` and their attribute type annotations
can be used to generate a full `__slots__` for the concrete class.
"""
def __new__(cls, name, bases, dctn):
parents = set()
for base in bases:
parents |= set(base.mro())
slots = tuple(dctn.get('__slots__', tuple()))
for parent in parents:
if not hasattr(parent, '__annotations__'):
continue
slots += tuple(getattr(parent, '__annotations__').keys())
dctn['__slots__'] = slots
return super().__new__(cls, name, bases, dctn)