[gdsii_arrow] add some TODO notes

breaking change, but properties are seldom used by anyone afaik

masque/file/gdsii.py

@@ -21,6 +21,7 @@ Notes:
 """
 from typing import IO, cast, Any
 from collections.abc import Iterable, Mapping, Callable
+from types import MappingProxyType
 import io
 import mmap
 import logging
@@ -52,6 +53,8 @@ path_cap_map = {
     4: Path.Cap.SquareCustom,
     }
 
+RO_EMPTY_DICT: Mapping[int, bytes] = MappingProxyType({})
+
 
 def rint_cast(val: ArrayLike) -> NDArray[numpy.int32]:
     return numpy.rint(val).astype(numpy.int32)
@@ -399,11 +402,15 @@ def _mrefs_to_grefs(refs: dict[str | None, list[Ref]]) -> list[klamath.library.R
     return grefs
 
 
-def _properties_to_annotations(properties: dict[int, bytes]) -> annotations_t:
+def _properties_to_annotations(properties: Mapping[int, bytes]) -> annotations_t:
+    if not properties:
+        return None
     return {str(k): [v.decode()] for k, v in properties.items()}
 
 
-def _annotations_to_properties(annotations: annotations_t, max_len: int = 126) -> dict[int, bytes]:
+def _annotations_to_properties(annotations: annotations_t, max_len: int = 126) -> Mapping[int, bytes]:
+    if annotations is None:
+        return RO_EMPTY_DICT
     cum_len = 0
     props = {}
     for key, vals in annotations.items():

masque/file/gdsii_arrow.py

@@ -0,0 +1,440 @@
+"""
+GDSII file format readers and writers using the `TODO` library.
+
+Note that GDSII references follow the same convention as `masque`,
+  with this order of operations:
+   1. Mirroring
+   2. Rotation
+   3. Scaling
+   4. Offset and array expansion (no mirroring/rotation/scaling applied to offsets)
+
+  Scaling, rotation, and mirroring apply to individual instances, not grid
+   vectors or offsets.
+
+Notes:
+ * absolute positioning is not supported
+ * PLEX is not supported
+ * ELFLAGS are not supported
+ * GDS does not support library- or structure-level annotations
+ * GDS creation/modification/access times are set to 1900-01-01 for reproducibility.
+ * Gzip modification time is set to 0 (start of current epoch, usually 1970-01-01)
+
+ TODO writing
+ TODO warn on boxes, nodes
+"""
+from typing import IO, cast, Any
+from collections.abc import Iterable, Mapping, Callable
+import io
+import mmap
+import logging
+import pathlib
+import gzip
+import string
+from pprint import pformat
+
+import numpy
+from numpy.typing import ArrayLike, NDArray
+from numpy.testing import assert_equal
+import pyarrow
+from pyarrow.cffi import ffi
+
+from .utils import is_gzipped, tmpfile
+from .. import Pattern, Ref, PatternError, LibraryError, Label, Shape
+from ..shapes import Polygon, Path
+from ..repetition import Grid
+from ..utils import layer_t, annotations_t
+from ..library import LazyLibrary, Library, ILibrary, ILibraryView
+
+
+logger = logging.getLogger(__name__)
+
+clib = ffi.dlopen('/home/jan/projects/klamath-rs/target/release/libklamath_rs_ext.so')
+ffi.cdef('void read_path(char* path, struct ArrowArray* array, struct ArrowSchema* schema);')
+
+
+path_cap_map = {
+    0: Path.Cap.Flush,
+    1: Path.Cap.Circle,
+    2: Path.Cap.Square,
+    4: Path.Cap.SquareCustom,
+    }
+
+
+def rint_cast(val: ArrayLike) -> NDArray[numpy.int32]:
+    return numpy.rint(val).astype(numpy.int32)
+
+
+def _read_to_arrow(
+        filename: str | pathlib.Path,
+        *args,
+        **kwargs,
+        ) -> pyarrow.Array:
+    path = pathlib.Path(filename)
+    path.resolve()
+    ptr_array = ffi.new('struct ArrowArray[]', 1)
+    ptr_schema = ffi.new('struct ArrowSchema[]', 1)
+    clib.read_path(str(path).encode(), ptr_array, ptr_schema)
+
+    iptr_schema = int(ffi.cast('uintptr_t', ptr_schema))
+    iptr_array = int(ffi.cast('uintptr_t', ptr_array))
+    arrow_arr = pyarrow.Array._import_from_c(iptr_array, iptr_schema)
+
+    return arrow_arr
+
+
+def readfile(
+        filename: str | pathlib.Path,
+        *args,
+        **kwargs,
+        ) -> tuple[Library, dict[str, Any]]:
+    """
+    Wrapper for `read()` that takes a filename or path instead of a stream.
+
+    Will automatically decompress gzipped files.
+
+    Args:
+        filename: Filename to save to.
+        *args: passed to `read()`
+        **kwargs: passed to `read()`
+    """
+    arrow_arr = _read_to_arrow(filename)
+    assert len(arrow_arr) == 1
+
+    results = read_arrow(arrow_arr[0])
+
+    return results
+
+
+def read_arrow(
+        libarr: pyarrow.Array,
+        raw_mode: bool = True,
+        ) -> tuple[Library, dict[str, Any]]:
+    """
+    # TODO check GDSII file for cycles!
+    Read a gdsii file and translate it into a dict of Pattern objects. GDSII structures are
+     translated into Pattern objects; boundaries are translated into polygons, and srefs and arefs
+     are translated into Ref objects.
+
+    Additional library info is returned in a dict, containing:
+      'name': name of the library
+      'meters_per_unit': number of meters per database unit (all values are in database units)
+      'logical_units_per_unit': number of "logical" units displayed by layout tools (typically microns)
+                                per database unit
+
+    Args:
+        stream: Stream to read from.
+        raw_mode: If True, constructs shapes in raw mode, bypassing most data validation, Default True.
+
+    Returns:
+        - dict of pattern_name:Patterns generated from GDSII structures
+        - dict of GDSII library info
+    """
+    library_info = _read_header(libarr)
+
+    layer_names_np = libarr['layers'].values.to_numpy().view('i2').reshape((-1, 2))
+    layer_tups = [tuple(pair) for pair in layer_names_np]
+
+    cell_ids = libarr['cells'].values.field('id').to_numpy()
+    cell_names = libarr['cell_names'].as_py()
+
+    def get_geom(libarr: pyarrow.Array, geom_type: str) -> dict[str, Any]:
+        el = libarr['cells'].values.field(geom_type)
+        elem = dict(
+            offsets = el.offsets.to_numpy(),
+            xy_arr = el.values.field('xy').values.to_numpy().reshape((-1, 2)),
+            xy_off = el.values.field('xy').offsets.to_numpy() // 2,
+            layer_inds = el.values.field('layer').to_numpy(),
+            prop_off = el.values.field('properties').offsets.to_numpy(),
+            prop_key = el.values.field('properties').values.field('key').to_numpy(),
+            prop_val = el.values.field('properties').values.field('value').to_pylist(),
+            )
+        return elem
+
+    rf = libarr['cells'].values.field('refs')
+    refs = dict(
+        offsets = rf.offsets.to_numpy(),
+        targets = rf.values.field('target').to_numpy(),
+        xy = rf.values.field('xy').to_numpy().view('i4').reshape((-1, 2)),
+        invert_y = rf.values.field('invert_y').fill_null(False).to_numpy(zero_copy_only=False),
+        angle_rad = numpy.rad2deg(rf.values.field('angle_deg').fill_null(0).to_numpy()),
+        scale = rf.values.field('mag').fill_null(1).to_numpy(),
+        rep_valid = rf.values.field('repetition').is_valid().to_numpy(zero_copy_only=False),
+        rep_xy0 = rf.values.field('repetition').field('xy0').fill_null(0).to_numpy().view('i4').reshape((-1, 2)),
+        rep_xy1 = rf.values.field('repetition').field('xy1').fill_null(0).to_numpy().view('i4').reshape((-1, 2)),
+        rep_counts = rf.values.field('repetition').field('counts').fill_null(0).to_numpy().view('i2').reshape((-1, 2)),
+        prop_off = rf.values.field('properties').offsets.to_numpy(),
+        prop_key = rf.values.field('properties').values.field('key').to_numpy(),
+        prop_val = rf.values.field('properties').values.field('value').to_pylist(),
+        )
+
+    txt = libarr['cells'].values.field('texts')
+    texts = dict(
+        offsets = txt.offsets.to_numpy(),
+        layer_inds = txt.values.field('layer').to_numpy(),
+        xy = txt.values.field('xy').to_numpy().view('i4').reshape((-1, 2)),
+        string = txt.values.field('string').to_pylist(),
+        prop_off = txt.values.field('properties').offsets.to_numpy(),
+        prop_key = txt.values.field('properties').values.field('key').to_numpy(),
+        prop_val = txt.values.field('properties').values.field('value').to_pylist(),
+        )
+
+    elements = dict(
+        boundaries = get_geom(libarr, 'boundaries'),
+        paths = get_geom(libarr, 'paths'),
+        boxes = get_geom(libarr, 'boxes'),
+        nodes = get_geom(libarr, 'nodes'),
+        texts = texts,
+        refs = refs,
+        )
+
+    paths = libarr['cells'].values.field('paths')
+    elements['paths'].update(dict(
+        width = paths.values.field('width').to_numpy(),
+        path_type = paths.values.field('path_type').to_numpy(),
+        extensions = numpy.stack((
+            paths.values.field('extension_start').to_numpy(zero_copy_only=False),
+            paths.values.field('extension_end').to_numpy(zero_copy_only=False),
+            ), axis=-1),
+        ))
+
+    global_args = dict(
+        cell_names = cell_names,
+        layer_tups = layer_tups,
+        raw_mode = raw_mode,
+        )
+
+    mlib = Library()
+    for cc, cell in enumerate(libarr['cells']):
+        name = cell_names[cell_ids[cc]]
+        pat = read_cell(cc, cell, libarr['cell_names'], global_args=global_args, elements=elements)
+        mlib[name] = pat
+
+    return mlib, library_info
+
+
+def _read_header(libarr: pyarrow.Array) -> dict[str, Any]:
+    """
+    Read the file header and create the library_info dict.
+    """
+    library_info = dict(
+        name = libarr['lib_name'],
+        meters_per_unit = libarr['meters_per_db_unit'],
+        logical_units_per_unit = libarr['user_units_per_db_unit'],
+        )
+    return library_info
+
+
+def read_cell(
+        cc: int,
+        cellarr: pyarrow.Array,
+        cell_names: pyarrow.Array,
+        elements: dict[str, Any],
+        global_args: dict[str, Any],
+        ) -> Pattern:
+    """
+    TODO
+    Read elements from a GDS structure and build a Pattern from them.
+
+    Args:
+        stream: Seekable stream, positioned at a record boundary.
+                Will be read until an ENDSTR record is consumed.
+        name: Name of the resulting Pattern
+        raw_mode: If True, bypass per-shape data validation. Default True.
+
+    Returns:
+        A pattern containing the elements that were read.
+    """
+    pat = Pattern()
+
+    _boundaries_to_polygons(pat, global_args, elements['boundaries'], cc)
+    _gpaths_to_mpaths(pat, global_args, elements['paths'], cc)
+    _grefs_to_mrefs(pat, global_args, elements['refs'], cc)
+    _texts_to_labels(pat, global_args, elements['texts'], cc)
+
+    return pat
+
+
+def _grefs_to_mrefs(
+        pat: Pattern,
+        global_args: dict[str, Any],
+        elem: dict[str, Any],
+        cc: int,
+        ) -> None:
+    cell_names = global_args['cell_names']
+    elem_off = elem['offsets']      # which elements belong to each cell
+    xy = elem['xy']
+    prop_key = elem['prop_key']
+    prop_val = elem['prop_val']
+    targets = elem['targets']
+
+    rep_valid = elem['rep_valid']
+
+    elem_count = elem_off[cc + 1] - elem_off[cc]
+    elem_slc = slice(elem_off[cc], elem_off[cc] + elem_count + 1)   # +1 to capture ending location for last elem
+    prop_offs = elem['prop_off'][elem_slc]  # which props belong to each element
+
+    for ee in range(elem_count):
+        target = cell_names[targets[ee]]
+        offset = xy[ee]
+        mirr = elem['invert_y'][ee]
+        rot = elem['angle_rad'][ee]
+        mag = elem['scale'][ee]
+
+        rep: None | Grid = None
+        if rep_valid[ee]:
+            a_vector = elem['rep_xy0'][ee]
+            b_vector = elem['rep_xy1'][ee]
+            a_count, b_count = elem['rep_counts'][ee]
+            rep = Grid(a_vector=a_vector, b_vector=b_vector, a_count=a_count, b_count=b_count)
+
+        annotations: None | dict[int, str] = None
+        prop_ii, prop_ff = prop_offs[ee], prop_offs[ee + 1]
+        if prop_ii < prop_ff:
+            annotations = {prop_key[off]: prop_val[off] for off in range(prop_ii, prop_ff)}
+
+        ref = Ref(offset=offset, mirrored=mirr, rotation=rot, scale=mag, repetition=rep, annotations=annotations)
+        pat.refs[target].append(ref)
+
+
+def _texts_to_labels(
+        pat: Pattern,
+        global_args: dict[str, Any],
+        elem: dict[str, Any],
+        cc: int,
+        ) -> None:
+    elem_off = elem['offsets']      # which elements belong to each cell
+    xy = elem['xy']
+    layer_tups = global_args['layer_tups']
+    layer_inds = elem['layer_inds']
+    prop_key = elem['prop_key']
+    prop_val = elem['prop_val']
+
+    elem_count = elem_off[cc + 1] - elem_off[cc]
+    elem_slc = slice(elem_off[cc], elem_off[cc] + elem_count + 1)   # +1 to capture ending location for last elem
+    prop_offs = elem['prop_off'][elem_slc]  # which props belong to each element
+
+    for ee in range(elem_count):
+        layer = layer_tups[layer_inds[ee]]
+        offset = xy[ee]
+        string = elem['string'][ee]
+
+        annotations: None | dict[int, str] = None
+        prop_ii, prop_ff = prop_offs[ee], prop_offs[ee + 1]
+        if prop_ii < prop_ff:
+            annotations = {prop_key[off]: prop_val[off] for off in range(prop_ii, prop_ff)}
+
+        mlabel = Label(string=string, offset=offset, annotations=annotations)
+        pat.labels[layer].append(mlabel)
+
+
+def _gpaths_to_mpaths(
+        pat: Pattern,
+        global_args: dict[str, Any],
+        elem: dict[str, Any],
+        cc: int,
+        ) -> None:
+    elem_off = elem['offsets']      # which elements belong to each cell
+    xy_val = elem['xy_arr']
+    layer_tups = global_args['layer_tups']
+    layer_inds = elem['layer_inds']
+    prop_key = elem['prop_key']
+    prop_val = elem['prop_val']
+
+    elem_count = elem_off[cc + 1] - elem_off[cc]
+    elem_slc = slice(elem_off[cc], elem_off[cc] + elem_count + 1)   # +1 to capture ending location for last elem
+    xy_offs = elem['xy_off'][elem_slc]      # which xy coords belong to each element
+    prop_offs = elem['prop_off'][elem_slc]  # which props belong to each element
+
+    zeros = numpy.zeros((elem_count, 2))
+    raw_mode = global_args['raw_mode']
+    for ee in range(elem_count):
+        elem_ind = elem_off[cc] + ee
+        layer = layer_tups[layer_inds[ee]]
+        vertices = xy_val[xy_offs[ee]:xy_offs[ee + 1]]
+        width = elem['width'][elem_ind]
+        cap_int = elem['path_type'][elem_ind]
+        cap = path_cap_map[cap_int]
+        if cap_int == 4:
+            cap_extensions = elem['extensions'][elem_ind]
+        else:
+            cap_extensions = None
+
+        annotations: None | dict[int, str] = None
+        prop_ii, prop_ff = prop_offs[ee], prop_offs[ee + 1]
+        if prop_ii < prop_ff:
+            annotations = {prop_key[off]: prop_val[off] for off in range(prop_ii, prop_ff)}
+
+        path = Path(vertices=vertices, offset=zeros[ee], annotations=annotations, raw=raw_mode,
+            width=width, cap=cap,cap_extensions=cap_extensions)
+        pat.shapes[layer].append(path)
+
+
+def _boundaries_to_polygons(
+        pat: Pattern,
+        global_args: dict[str, Any],
+        elem: dict[str, Any],
+        cc: int,
+        ) -> None:
+    elem_off = elem['offsets']      # which elements belong to each cell
+    xy_val = elem['xy_arr']
+    layer_tups = global_args['layer_tups']
+    layer_inds = elem['layer_inds']
+    prop_key = elem['prop_key']
+    prop_val = elem['prop_val']
+
+    elem_count = elem_off[cc + 1] - elem_off[cc]
+    elem_slc = slice(elem_off[cc], elem_off[cc] + elem_count + 1)   # +1 to capture ending location for last elem
+    xy_offs = elem['xy_off'][elem_slc]      # which xy coords belong to each element
+    prop_offs = elem['prop_off'][elem_slc]  # which props belong to each element
+
+    zeros = numpy.zeros((elem_count, 2))
+    raw_mode = global_args['raw_mode']
+    for ee in range(elem_count):
+        layer = layer_tups[layer_inds[ee]]
+        vertices = xy_val[xy_offs[ee]:xy_offs[ee + 1] - 1]    # -1 to drop closing point
+
+        annotations: None | dict[int, str] = None
+        prop_ii, prop_ff = prop_offs[ee], prop_offs[ee + 1]
+        if prop_ii < prop_ff:
+            annotations = {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')

masque/file/oasis.py

@@ -671,6 +671,8 @@ def repetition_masq2fata(
 
 def annotations_to_properties(annotations: annotations_t) -> list[fatrec.Property]:
     #TODO determine is_standard based on key?
+    if annotations is None:
+        return []
     properties = []
     for key, values in annotations.items():
         vals = [AString(v) if isinstance(v, str) else v

masque/pattern.py

@@ -332,7 +332,7 @@ class Pattern(PortList, AnnotatableImpl, Mirrorable):
             ))
 
         self.ports = dict(sorted(self.ports.items()))
-        self.annotations = dict(sorted(self.annotations.items()))
+        self.annotations = dict(sorted(self.annotations.items())) if self.annotations is not None else None
 
         return self
 
@@ -354,10 +354,13 @@ class Pattern(PortList, AnnotatableImpl, Mirrorable):
         for layer, lseq in other_pattern.labels.items():
             self.labels[layer].extend(lseq)
 
-        annotation_conflicts = set(self.annotations.keys()) & set(other_pattern.annotations.keys())
-        if annotation_conflicts:
-            raise PatternError(f'Annotation keys overlap: {annotation_conflicts}')
-        self.annotations.update(other_pattern.annotations)
+        if other_pattern.annotations is not None:
+            if self.annotations is None:
+                self.annotations = {}
+            annotation_conflicts = set(self.annotations.keys()) & set(other_pattern.annotations.keys())
+            if annotation_conflicts:
+                raise PatternError(f'Annotation keys overlap: {annotation_conflicts}')
+            self.annotations.update(other_pattern.annotations)
 
         port_conflicts = set(self.ports.keys()) & set(other_pattern.ports.keys())
         if port_conflicts:
@@ -415,7 +418,7 @@ class Pattern(PortList, AnnotatableImpl, Mirrorable):
         elif default_keep:
             pat.refs = copy.copy(self.refs)
 
-        if annotations is not None:
+        if annotations is not None and self.annotations is not None:
             pat.annotations = {k: v for k, v in self.annotations.items() if annotations(k, v)}
         elif default_keep:
             pat.annotations = copy.copy(self.annotations)

masque/shapes/polygon.py

@@ -107,7 +107,8 @@ class Polygon(Shape):
             self.offset = offset
             self.repetition = repetition
             self.annotations = annotations if annotations is not None else {}
-        self.rotate(rotation)
+        if rotation:
+            self.rotate(rotation)
 
     def __deepcopy__(self, memo: dict | None = None) -> 'Polygon':
         memo = {} if memo is None else memo

masque/utils/curves.py

@@ -5,7 +5,7 @@ from numpy import pi
 try:
     from numpy import trapezoid
 except ImportError:
-    from numpy import trapz as trapezoid
+    from numpy import trapz as trapezoid        # type:ignore
 
 
 def bezier(

masque/utils/types.py

@@ -5,7 +5,7 @@ from typing import Protocol
 
 
 layer_t = int | tuple[int, int] | str
-annotations_t = dict[str, list[int | float | str]]
+annotations_t = dict[str, list[int | float | str]] | None
 
 
 class SupportsBool(Protocol):