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Improve type annotations based on mypy errors

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This commit is contained in:
Jan Petykiewicz 2020-05-11 19:09:35 -07:00
commit 157df47884
13 changed files with 151 additions and 117 deletions
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18
masque/repetition.py
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@ -12,6 +12,8 @@ from numpy import pi
from .error import PatternError, PatternLockedError
from .utils import is_scalar, rotation_matrix_2d, vector2
if TYPE_CHECKING:
from . import Pattern
# TODO need top-level comment about what order rotation/scale/offset/mirror/array are applied
@ -51,7 +53,7 @@ class GridRepetition:
_scale: float
""" Scaling factor applied to individual instances in the grid (not the grid vectors) """
_mirrored: List[bool]
_mirrored: numpy.ndarray # ndarray[bool]
""" Whether to mirror individual instances across the x and y axes
(Applies to individual instances in the grid, not the grid vectors)
"""
@ -64,7 +66,7 @@ class GridRepetition:
_a_count: int
""" Number of instances along the direction specified by the `a_vector` """
_b_vector: numpy.ndarray or None
_b_vector: Optional[numpy.ndarray]
""" Vector `[x, y]` specifying a second lattice vector for the grid.
Specifies center-to-center spacing between adjacent elements.
Can be `None` for a 1D array.
@ -80,14 +82,14 @@ class GridRepetition:
""" If `True`, disallows changes to the GridRepetition """
def __init__(self,
pattern: 'Pattern',
pattern: Optional['Pattern'],
a_vector: numpy.ndarray,
a_count: int,
b_vector: numpy.ndarray = None,
b_vector: Optional[numpy.ndarray] = None,
b_count: int = 1,
offset: vector2 = (0.0, 0.0),
rotation: float = 0.0,
mirrored: List[bool] = None,
mirrored: Optional[Sequence[bool]] = None,
dose: float = 1.0,
scale: float = 1.0,
locked: bool = False):
@ -155,7 +157,7 @@ class GridRepetition:
locked=self.locked)
return new
def __deepcopy__(self, memo: Dict = None) -> 'GridReptition':
def __deepcopy__(self, memo: Dict = None) -> 'GridRepetition':
memo = {} if memo is None else memo
new = copy.copy(self).unlock()
new.pattern = copy.deepcopy(self.pattern, memo)
@ -230,11 +232,11 @@ class GridRepetition:
# Mirrored property
@property
def mirrored(self) -> List[bool]:
def mirrored(self) -> numpy.ndarray: # ndarray[bool]
return self._mirrored
@mirrored.setter
def mirrored(self, val: List[bool]):
def mirrored(self, val: Sequence[bool]):
if is_scalar(val):
raise PatternError('Mirrored must be a 2-element list of booleans')
self._mirrored = numpy.array(val, dtype=bool, copy=True)