136 lines
3.8 KiB
Python
136 lines
3.8 KiB
Python
"""
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Various helper functions
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"""
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from typing import Any, Union, Tuple, Sequence
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import numpy
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# Type definitions
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vector2 = Union[numpy.ndarray, Tuple[float, float], Sequence[float]]
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layer_t = Union[int, Tuple[int, int], str]
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def is_scalar(var: Any) -> bool:
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"""
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Alias for 'not hasattr(var, "__len__")'
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Args:
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var: Checks if `var` has a length.
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"""
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return not hasattr(var, "__len__")
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def get_bit(bit_string: Any, bit_id: int) -> bool:
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"""
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Interprets bit number `bit_id` from the right (lsb) of `bit_string` as a boolean
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Args:
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bit_string: Bit string to test
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bit_id: Bit number, 0-indexed from the right (lsb)
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Returns:
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Boolean value of the requested bit
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"""
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return bit_string & (1 << bit_id) != 0
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def set_bit(bit_string: Any, bit_id: int, value: bool) -> Any:
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"""
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Returns `bit_string`, with bit number `bit_id` set to boolean `value`.
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Args:
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bit_string: Bit string to alter
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bit_id: Bit number, 0-indexed from right (lsb)
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value: Boolean value to set bit to
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Returns:
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Altered `bit_string`
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"""
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mask = (1 << bit_id)
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bit_string &= ~mask
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if value:
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bit_string |= mask
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return bit_string
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def rotation_matrix_2d(theta: float) -> numpy.ndarray:
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"""
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2D rotation matrix for rotating counterclockwise around the origin.
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Args:
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theta: Angle to rotate, in radians
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Returns:
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rotation matrix
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"""
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return numpy.array([[numpy.cos(theta), -numpy.sin(theta)],
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[numpy.sin(theta), +numpy.cos(theta)]])
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def normalize_mirror(mirrored: Sequence[bool]) -> Tuple[bool, float]:
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"""
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Converts 0-2 mirror operations `(mirror_across_x_axis, mirror_across_y_axis)`
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into 0-1 mirror operations and a rotation
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Args:
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mirrored: `(mirror_across_x_axis, mirror_across_y_axis)`
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Returns:
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`mirror_across_x_axis` (bool) and
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`angle_to_rotate` in radians
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"""
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mirrored_x, mirrored_y = mirrored
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mirror_x = (mirrored_x != mirrored_y) #XOR
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angle = numpy.pi if mirrored_y else 0
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return mirror_x, angle
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def remove_duplicate_vertices(vertices: numpy.ndarray, closed_path: bool = True) -> numpy.ndarray:
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"""
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Given a list of vertices, remove any consecutive duplicates.
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Args:
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vertices: `[[x0, y0], [x1, y1], ...]`
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closed_path: If True, `vertices` is interpreted as an implicity-closed path
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(i.e. the last vertex will be removed if it is the same as the first)
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Returns:
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`vertices` with no consecutive duplicates.
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"""
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duplicates = (vertices == numpy.roll(vertices, 1, axis=0)).all(axis=1)
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if not closed_path:
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duplicates[0] = False
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return vertices[~duplicates]
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def remove_colinear_vertices(vertices: numpy.ndarray, closed_path: bool = True) -> numpy.ndarray:
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"""
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Given a list of vertices, remove any superflous vertices (i.e.
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those which lie along the line formed by their neighbors)
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Args:
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vertices: Nx2 ndarray of vertices
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closed_path: If `True`, the vertices are assumed to represent an implicitly
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closed path. If `False`, the path is assumed to be open. Default `True`.
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Returns:
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`vertices` with colinear (superflous) vertices removed.
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"""
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vertices = numpy.array(vertices)
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# Check for dx0/dy0 == dx1/dy1
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dv = numpy.roll(vertices, -1, axis=0) - vertices # [y1-y0, y2-y1, ...]
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dxdy = dv * numpy.roll(dv, 1, axis=0)[:, ::-1] #[[dx0*(dy_-1), (dx_-1)*dy0], dx1*dy0, dy1*dy0]]
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dxdy_diff = numpy.abs(numpy.diff(dxdy, axis=1))[:, 0]
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err_mult = 2 * numpy.abs(dxdy).sum(axis=1) + 1e-40
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slopes_equal = (dxdy_diff / err_mult) < 1e-15
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if not closed_path:
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slopes_equal[[0, -1]] = False
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return vertices[~slopes_equal]
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