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Use ArrayLike and NDArray wherever possible. Some type fixes and some related corner cases

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jan 2022-02-23 15:47:38 -08:00
commit a4fe3d9e2e
20 changed files with 291 additions and 224 deletions
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masque/file/gdsii.py
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@ -30,7 +30,8 @@ import logging
import pathlib
import gzip
import numpy # type: ignore
import numpy
from numpy.typing import NDArray
import klamath
from klamath import records
@ -369,10 +370,12 @@ def _subpatterns_to_refs(subpatterns: List[SubPattern]) -> List[klamath.library.
properties = _annotations_to_properties(subpat.annotations, 512)
if isinstance(rep, Grid):
xy = numpy.array(subpat.offset) + [
b_vector = rep.b_vector if rep.b_vector is not None else numpy.zeros(2)
b_count = rep.b_count if rep.b_count is not None else 1
xy: NDArray[numpy.float64] = numpy.array(subpat.offset) + [
[0, 0],
rep.a_vector * rep.a_count,
rep.b_vector * rep.b_count,
b_vector * b_count,
]
aref = klamath.library.Reference(struct_name=encoded_name,
xy=numpy.round(xy).astype(int),