[gdsii_arrow] add gdsii_arrow
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masque/file/gdsii_arrow.py
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453
masque/file/gdsii_arrow.py
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# ruff: noqa: ARG001, F401
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"""
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GDSII file format readers and writers using the `TODO` library.
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Note that GDSII references follow the same convention as `masque`,
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with this order of operations:
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1. Mirroring
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2. Rotation
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3. Scaling
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4. Offset and array expansion (no mirroring/rotation/scaling applied to offsets)
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Scaling, rotation, and mirroring apply to individual instances, not grid
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vectors or offsets.
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Notes:
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* absolute positioning is not supported
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* PLEX is not supported
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* ELFLAGS are not supported
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* GDS does not support library- or structure-level annotations
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* GDS creation/modification/access times are set to 1900-01-01 for reproducibility.
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* Gzip modification time is set to 0 (start of current epoch, usually 1970-01-01)
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TODO writing
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TODO warn on boxes, nodes
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"""
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from typing import IO, cast, Any
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from collections.abc import Iterable, Mapping, Callable
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import io
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import mmap
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import logging
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import pathlib
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import gzip
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import string
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from pprint import pformat
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import numpy
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from numpy.typing import ArrayLike, NDArray
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from numpy.testing import assert_equal
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import pyarrow
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from pyarrow.cffi import ffi
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from .utils import is_gzipped, tmpfile
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from .. import Pattern, Ref, PatternError, LibraryError, Label, Shape
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from ..shapes import Polygon, Path, PolyCollection
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from ..repetition import Grid
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from ..utils import layer_t, annotations_t
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from ..library import LazyLibrary, Library, ILibrary, ILibraryView
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logger = logging.getLogger(__name__)
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clib = ffi.dlopen('/home/jan/projects/klamath-rs/target/release/libklamath_rs_ext.so')
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ffi.cdef('void read_path(char* path, struct ArrowArray* array, struct ArrowSchema* schema);')
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path_cap_map = {
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0: Path.Cap.Flush,
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1: Path.Cap.Circle,
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2: Path.Cap.Square,
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4: Path.Cap.SquareCustom,
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}
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def rint_cast(val: ArrayLike) -> NDArray[numpy.int32]:
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return numpy.rint(val).astype(numpy.int32)
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def _read_to_arrow(
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filename: str | pathlib.Path,
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*args,
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**kwargs,
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) -> pyarrow.Array:
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path = pathlib.Path(filename)
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path.resolve()
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ptr_array = ffi.new('struct ArrowArray[]', 1)
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ptr_schema = ffi.new('struct ArrowSchema[]', 1)
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clib.read_path(str(path).encode(), ptr_array, ptr_schema)
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iptr_schema = int(ffi.cast('uintptr_t', ptr_schema))
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iptr_array = int(ffi.cast('uintptr_t', ptr_array))
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arrow_arr = pyarrow.Array._import_from_c(iptr_array, iptr_schema)
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return arrow_arr
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def readfile(
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filename: str | pathlib.Path,
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*args,
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**kwargs,
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) -> tuple[Library, dict[str, Any]]:
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"""
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Wrapper for `read()` that takes a filename or path instead of a stream.
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Will automatically decompress gzipped files.
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Args:
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filename: Filename to save to.
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*args: passed to `read()`
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**kwargs: passed to `read()`
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"""
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arrow_arr = _read_to_arrow(filename)
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assert len(arrow_arr) == 1
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results = read_arrow(arrow_arr[0])
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return results
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def read_arrow(
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libarr: pyarrow.Array,
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raw_mode: bool = True,
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) -> tuple[Library, dict[str, Any]]:
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"""
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# TODO check GDSII file for cycles!
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Read a gdsii file and translate it into a dict of Pattern objects. GDSII structures are
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translated into Pattern objects; boundaries are translated into polygons, and srefs and arefs
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are translated into Ref objects.
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Additional library info is returned in a dict, containing:
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'name': name of the library
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'meters_per_unit': number of meters per database unit (all values are in database units)
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'logical_units_per_unit': number of "logical" units displayed by layout tools (typically microns)
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per database unit
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Args:
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stream: Stream to read from.
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raw_mode: If True, constructs shapes in raw mode, bypassing most data validation, Default True.
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Returns:
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- dict of pattern_name:Patterns generated from GDSII structures
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- dict of GDSII library info
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"""
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library_info = _read_header(libarr)
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layer_names_np = libarr['layers'].values.to_numpy().view('i2').reshape((-1, 2))
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layer_tups = [tuple(pair) for pair in layer_names_np]
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cell_ids = libarr['cells'].values.field('id').to_numpy()
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cell_names = libarr['cell_names'].as_py()
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def get_geom(libarr: pyarrow.Array, geom_type: str) -> dict[str, Any]:
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el = libarr['cells'].values.field(geom_type)
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elem = dict(
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offsets = el.offsets.to_numpy(),
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xy_arr = el.values.field('xy').values.to_numpy().reshape((-1, 2)),
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xy_off = el.values.field('xy').offsets.to_numpy() // 2,
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layer_inds = el.values.field('layer').to_numpy(),
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prop_off = el.values.field('properties').offsets.to_numpy(),
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prop_key = el.values.field('properties').values.field('key').to_numpy(),
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prop_val = el.values.field('properties').values.field('value').to_pylist(),
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)
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return elem
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rf = libarr['cells'].values.field('refs')
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refs = dict(
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offsets = rf.offsets.to_numpy(),
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targets = rf.values.field('target').to_numpy(),
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xy = rf.values.field('xy').to_numpy().view('i4').reshape((-1, 2)),
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invert_y = rf.values.field('invert_y').fill_null(False).to_numpy(zero_copy_only=False),
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angle_rad = numpy.rad2deg(rf.values.field('angle_deg').fill_null(0).to_numpy()),
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scale = rf.values.field('mag').fill_null(1).to_numpy(),
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rep_valid = rf.values.field('repetition').is_valid().to_numpy(zero_copy_only=False),
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rep_xy0 = rf.values.field('repetition').field('xy0').fill_null(0).to_numpy().view('i4').reshape((-1, 2)),
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rep_xy1 = rf.values.field('repetition').field('xy1').fill_null(0).to_numpy().view('i4').reshape((-1, 2)),
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rep_counts = rf.values.field('repetition').field('counts').fill_null(0).to_numpy().view('i2').reshape((-1, 2)),
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prop_off = rf.values.field('properties').offsets.to_numpy(),
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prop_key = rf.values.field('properties').values.field('key').to_numpy(),
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prop_val = rf.values.field('properties').values.field('value').to_pylist(),
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)
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txt = libarr['cells'].values.field('texts')
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texts = dict(
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offsets = txt.offsets.to_numpy(),
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layer_inds = txt.values.field('layer').to_numpy(),
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xy = txt.values.field('xy').to_numpy().view('i4').reshape((-1, 2)),
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string = txt.values.field('string').to_pylist(),
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prop_off = txt.values.field('properties').offsets.to_numpy(),
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prop_key = txt.values.field('properties').values.field('key').to_numpy(),
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prop_val = txt.values.field('properties').values.field('value').to_pylist(),
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)
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elements = dict(
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boundaries = get_geom(libarr, 'boundaries'),
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paths = get_geom(libarr, 'paths'),
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boxes = get_geom(libarr, 'boxes'),
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nodes = get_geom(libarr, 'nodes'),
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texts = texts,
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refs = refs,
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)
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paths = libarr['cells'].values.field('paths')
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elements['paths'].update(dict(
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width = paths.values.field('width').fill_null(0).to_numpy(),
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path_type = paths.values.field('path_type').fill_null(0).to_numpy(),
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extensions = numpy.stack((
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paths.values.field('extension_start').fill_null(0).to_numpy(),
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paths.values.field('extension_end').fill_null(0).to_numpy(),
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), axis=-1),
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))
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global_args = dict(
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cell_names = cell_names,
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layer_tups = layer_tups,
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raw_mode = raw_mode,
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)
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mlib = Library()
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for cc in range(len(libarr['cells'])):
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name = cell_names[cell_ids[cc]]
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pat = Pattern()
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_boundaries_to_polygons(pat, global_args, elements['boundaries'], cc)
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_gpaths_to_mpaths(pat, global_args, elements['paths'], cc)
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_grefs_to_mrefs(pat, global_args, elements['refs'], cc)
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_texts_to_labels(pat, global_args, elements['texts'], cc)
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mlib[name] = pat
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return mlib, library_info
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def _read_header(libarr: pyarrow.Array) -> dict[str, Any]:
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"""
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Read the file header and create the library_info dict.
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"""
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library_info = dict(
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name = libarr['lib_name'],
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meters_per_unit = libarr['meters_per_db_unit'],
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logical_units_per_unit = libarr['user_units_per_db_unit'],
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)
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return library_info
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def _grefs_to_mrefs(
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pat: Pattern,
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global_args: dict[str, Any],
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elem: dict[str, Any],
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cc: int,
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) -> None:
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cell_names = global_args['cell_names']
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elem_off = elem['offsets'] # which elements belong to each cell
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xy = elem['xy']
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prop_key = elem['prop_key']
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prop_val = elem['prop_val']
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targets = elem['targets']
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elem_count = elem_off[cc + 1] - elem_off[cc]
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elem_slc = slice(elem_off[cc], elem_off[cc] + elem_count + 1) # +1 to capture ending location for last elem
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prop_offs = elem['prop_off'][elem_slc] # which props belong to each element
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elem_invert_y = elem['invert_y'][elem_slc][:elem_count]
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elem_angle_rad = elem['angle_rad'][elem_slc][:elem_count]
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elem_scale = elem['scale'][elem_slc][:elem_count]
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elem_rep_xy0 = elem['rep_xy0'][elem_slc][:elem_count]
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elem_rep_xy1 = elem['rep_xy1'][elem_slc][:elem_count]
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elem_rep_counts = elem['rep_counts'][elem_slc][:elem_count]
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rep_valid = elem['rep_valid'][elem_slc][:elem_count]
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for ee in range(elem_count):
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target = cell_names[targets[ee]]
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offset = xy[ee]
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mirr = elem_invert_y[ee]
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rot = elem_angle_rad[ee]
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mag = elem_scale[ee]
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rep: None | Grid = None
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if rep_valid[ee]:
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a_vector = elem_rep_xy0[ee]
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b_vector = elem_rep_xy1[ee]
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a_count, b_count = elem_rep_counts[ee]
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rep = Grid(a_vector=a_vector, b_vector=b_vector, a_count=a_count, b_count=b_count)
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annotations: None | dict[str, list[int | float | str]] = None
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prop_ii, prop_ff = prop_offs[ee], prop_offs[ee + 1]
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if prop_ii < prop_ff:
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annotations = {str(prop_key[off]): [prop_val[off]] for off in range(prop_ii, prop_ff)}
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ref = Ref(offset=offset, mirrored=mirr, rotation=rot, scale=mag, repetition=rep, annotations=annotations)
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pat.refs[target].append(ref)
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def _texts_to_labels(
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pat: Pattern,
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global_args: dict[str, Any],
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elem: dict[str, Any],
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cc: int,
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) -> None:
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elem_off = elem['offsets'] # which elements belong to each cell
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xy = elem['xy']
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layer_tups = global_args['layer_tups']
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layer_inds = elem['layer_inds']
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prop_key = elem['prop_key']
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prop_val = elem['prop_val']
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elem_count = elem_off[cc + 1] - elem_off[cc]
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elem_slc = slice(elem_off[cc], elem_off[cc] + elem_count + 1) # +1 to capture ending location for last elem
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prop_offs = elem['prop_off'][elem_slc] # which props belong to each element
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elem_layer_inds = layer_inds[elem_slc][:elem_count]
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elem_strings = elem['string'][elem_slc][:elem_count]
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for ee in range(elem_count):
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layer = layer_tups[elem_layer_inds[ee]]
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offset = xy[ee]
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string = elem_strings[ee]
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annotations: None | dict[str, list[int | float | str]] = None
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prop_ii, prop_ff = prop_offs[ee], prop_offs[ee + 1]
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if prop_ii < prop_ff:
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annotations = {str(prop_key[off]): [prop_val[off]] for off in range(prop_ii, prop_ff)}
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mlabel = Label(string=string, offset=offset, annotations=annotations)
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pat.labels[layer].append(mlabel)
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def _gpaths_to_mpaths(
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pat: Pattern,
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global_args: dict[str, Any],
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elem: dict[str, Any],
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cc: int,
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) -> None:
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elem_off = elem['offsets'] # which elements belong to each cell
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xy_val = elem['xy_arr']
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layer_tups = global_args['layer_tups']
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layer_inds = elem['layer_inds']
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prop_key = elem['prop_key']
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prop_val = elem['prop_val']
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elem_count = elem_off[cc + 1] - elem_off[cc]
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elem_slc = slice(elem_off[cc], elem_off[cc] + elem_count + 1) # +1 to capture ending location for last elem
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xy_offs = elem['xy_off'][elem_slc] # which xy coords belong to each element
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prop_offs = elem['prop_off'][elem_slc] # which props belong to each element
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elem_layer_inds = layer_inds[elem_slc][:elem_count]
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elem_widths = elem['width'][elem_slc][:elem_count]
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elem_path_types = elem['path_type'][elem_slc][:elem_count]
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elem_extensions = elem['extensions'][elem_slc][:elem_count]
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zeros = numpy.zeros((elem_count, 2))
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raw_mode = global_args['raw_mode']
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for ee in range(elem_count):
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layer = layer_tups[elem_layer_inds[ee]]
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vertices = xy_val[xy_offs[ee]:xy_offs[ee + 1]]
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width = elem_widths[ee]
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cap_int = elem_path_types[ee]
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cap = path_cap_map[cap_int]
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if cap_int == 4:
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cap_extensions = elem_extensions[ee]
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else:
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cap_extensions = None
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annotations: None | dict[str, list[int | float | str]] = None
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prop_ii, prop_ff = prop_offs[ee], prop_offs[ee + 1]
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if prop_ii < prop_ff:
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annotations = {str(prop_key[off]): [prop_val[off]] for off in range(prop_ii, prop_ff)}
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path = Path(vertices=vertices, offset=zeros[ee], annotations=annotations, raw=raw_mode,
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width=width, cap=cap,cap_extensions=cap_extensions)
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pat.shapes[layer].append(path)
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def _boundaries_to_polygons(
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pat: Pattern,
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global_args: dict[str, Any],
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elem: dict[str, Any],
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cc: int,
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) -> None:
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elem_off = elem['offsets'] # which elements belong to each cell
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xy_val = elem['xy_arr']
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layer_inds = elem['layer_inds']
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layer_tups = global_args['layer_tups']
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prop_key = elem['prop_key']
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prop_val = elem['prop_val']
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elem_count = elem_off[cc + 1] - elem_off[cc]
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elem_slc = slice(elem_off[cc], elem_off[cc] + elem_count + 1) # +1 to capture ending location for last elem
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xy_offs = elem['xy_off'][elem_slc] # which xy coords belong to each element
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xy_counts = xy_offs[1:] - xy_offs[:-1]
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prop_offs = elem['prop_off'][elem_slc] # which props belong to each element
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prop_counts = prop_offs[1:] - prop_offs[:-1]
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elem_layer_inds = layer_inds[elem_slc][:elem_count]
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order = numpy.argsort(elem_layer_inds, stable=True)
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unilayer_inds, unilayer_first, unilayer_count = numpy.unique(elem_layer_inds, return_index=True, return_counts=True)
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zeros = numpy.zeros((elem_count, 2))
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raw_mode = global_args['raw_mode']
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for layer_ind, ff, nn in zip(unilayer_inds, unilayer_first, unilayer_count, strict=True):
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ee_inds = order[ff:ff + nn]
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layer = layer_tups[layer_ind]
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propless_mask = prop_counts[ee_inds] == 0
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poly_count_on_layer = propless_mask.sum()
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if poly_count_on_layer == 1:
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propless_mask[:] = 0 # Never make a 1-element collection
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elif poly_count_on_layer > 1:
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propless_vert_counts = xy_counts[ee_inds[propless_mask]] - 1 # -1 to drop closing point
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vertex_lists = numpy.empty((propless_vert_counts.sum(), 2), dtype=numpy.float64)
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vertex_offsets = numpy.cumsum(numpy.concatenate([[0], propless_vert_counts]))
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for ii, ee in enumerate(ee_inds[propless_mask]):
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vo = vertex_offsets[ii]
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vertex_lists[vo:vo + propless_vert_counts[ii]] = xy_val[xy_offs[ee]:xy_offs[ee + 1] - 1]
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polys = PolyCollection(vertex_lists=vertex_lists, vertex_offsets=vertex_offsets, offset=zeros[ee])
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pat.shapes[layer].append(polys)
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# Handle single polygons
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for ee in ee_inds[~propless_mask]:
|
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layer = layer_tups[elem_layer_inds[ee]]
|
||||
vertices = xy_val[xy_offs[ee]:xy_offs[ee + 1] - 1] # -1 to drop closing point
|
||||
|
||||
annotations: None | dict[str, list[int | float | str]] = None
|
||||
prop_ii, prop_ff = prop_offs[ee], prop_offs[ee + 1]
|
||||
if prop_ii < prop_ff:
|
||||
annotations = {str(prop_key[off]): prop_val[off] for off in range(prop_ii, prop_ff)}
|
||||
|
||||
poly = Polygon(vertices=vertices, offset=zeros[ee], annotations=annotations, raw=raw_mode)
|
||||
pat.shapes[layer].append(poly)
|
||||
|
||||
|
||||
#def _properties_to_annotations(properties: pyarrow.Array) -> annotations_t:
|
||||
# return {prop['key'].as_py(): prop['value'].as_py() for prop in properties}
|
||||
|
||||
|
||||
def check_valid_names(
|
||||
names: Iterable[str],
|
||||
max_length: int = 32,
|
||||
) -> None:
|
||||
"""
|
||||
Check all provided names to see if they're valid GDSII cell names.
|
||||
|
||||
Args:
|
||||
names: Collection of names to check
|
||||
max_length: Max allowed length
|
||||
|
||||
"""
|
||||
allowed_chars = set(string.ascii_letters + string.digits + '_?$')
|
||||
|
||||
bad_chars = [
|
||||
name for name in names
|
||||
if not set(name).issubset(allowed_chars)
|
||||
]
|
||||
|
||||
bad_lengths = [
|
||||
name for name in names
|
||||
if len(name) > max_length
|
||||
]
|
||||
|
||||
if bad_chars:
|
||||
logger.error('Names contain invalid characters:\n' + pformat(bad_chars))
|
||||
|
||||
if bad_lengths:
|
||||
logger.error(f'Names too long (>{max_length}:\n' + pformat(bad_chars))
|
||||
|
||||
if bad_chars or bad_lengths:
|
||||
raise LibraryError('Library contains invalid names, see log above')
|
||||
Loading…
x
Reference in New Issue
Block a user