715 lines
24 KiB
Python
715 lines
24 KiB
Python
"""
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Library class for managing unique name->pattern mappings and
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deferred loading or creation.
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"""
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from typing import List, Dict, Callable, TypeVar, Generic, Type, TYPE_CHECKING
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from typing import Any, Tuple, Union, Iterator, Mapping, MutableMapping, Set, Optional, Sequence
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import logging
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import copy
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import base64
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import struct
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import re
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from pprint import pformat
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from collections import defaultdict
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from abc import ABCMeta, abstractmethod
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import numpy
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from numpy.typing import ArrayLike, NDArray, NDArray
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from .error import LibraryError, PatternError
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from .utils import rotation_matrix_2d, normalize_mirror
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from .shapes import Shape, Polygon
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from .label import Label
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if TYPE_CHECKING:
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from .pattern import Pattern
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logger = logging.getLogger(__name__)
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visitor_function_t = Callable[['Pattern', Tuple['Pattern'], Dict, NDArray[numpy.float64]], 'Pattern']
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L = TypeVar('L', bound='Library')
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ML = TypeVar('ML', bound='MutableLibrary')
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LL = TypeVar('LL', bound='LazyLibrary')
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class Library(Mapping[str, Pattern], metaclass=ABCMeta):
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# inherited abstract functions
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#def __getitem__(self, key: str) -> 'Pattern':
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#def __iter__(self) -> Iterator[str]:
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#def __len__(self) -> int:
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#__contains__, keys, items, values, get, __eq__, __ne__ supplied by Mapping
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def __repr__(self) -> str:
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return '<Library with keys ' + repr(list(self.keys())) + '>'
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def referenced_patterns(
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self,
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tops: Union[str, Sequence[str]],
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skip: Optional[Set[Optional[str]]] = None,
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) -> Set[Optional[str]]:
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"""
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Get the set of all pattern names referenced by `top`. Recursively traverses into any refs.
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Args:
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top: Name of the top pattern(s) to check.
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skip: Memo, set patterns which have already been traversed.
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Returns:
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Set of all referenced pattern names
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"""
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if skip is None:
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skip = set([None])
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if isinstance(tops, str):
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tops = (tops,)
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# Get referenced patterns for all tops
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targets = set()
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for top in set(tops):
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targets |= self[top].referenced_patterns()
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# Perform recursive lookups, but only once for each name
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for target in targets - skip:
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assert(target is not None)
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self.referenced_patterns(target, skip)
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skip.add(target)
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return targets
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# TODO maybe not for immutable?
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def subtree(
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self,
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tops: Union[str, Sequence[str]],
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) -> Library:
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"""
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Return a new `Library`, containing only the specified patterns and the patterns they
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reference (recursively).
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Args:
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tops: Name(s) of patterns to keep
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Returns:
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A `WrapROLibrary` containing only `tops` and the patterns they reference.
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"""
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keep: Set[str] = self.referenced_patterns(tops) - set((None,)) # type: ignore
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filtered = {kk: vv for kk, vv in self.items() if kk in keep}
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new = WrapROLibrary(filtered)
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return new
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def polygonize(
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self: L,
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poly_num_points: Optional[int] = None,
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poly_max_arclen: Optional[float] = None,
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) -> L:
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"""
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Calls `.polygonize(...)` on each pattern in this library.
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Arguments are passed on to `shape.to_polygons(...)`.
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Args:
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poly_num_points: Number of points to use for each polygon. Can be overridden by
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`poly_max_arclen` if that results in more points. Optional, defaults to shapes'
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internal defaults.
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poly_max_arclen: Maximum arclength which can be approximated by a single line
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segment. Optional, defaults to shapes' internal defaults.
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Returns:
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self
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"""
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for pat in self.values():
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pat.polygonize(poly_num_points, poly_max_arclen)
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return self
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def manhattanize(
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self: L,
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grid_x: ArrayLike,
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grid_y: ArrayLike,
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) -> L:
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"""
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Calls `.manhattanize(grid_x, grid_y)` on each pattern in this library.
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Args:
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grid_x: List of allowed x-coordinates for the Manhattanized polygon edges.
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grid_y: List of allowed y-coordinates for the Manhattanized polygon edges.
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Returns:
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self
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"""
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for pat in self.values():
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pat.manhattanize(grid_x, grid_y)
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return self
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def flatten(
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self,
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tops: Union[str, Sequence[str]],
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) -> Dict[str, 'Pattern']:
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"""
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Removes all refs and adds equivalent shapes.
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Also flattens all referenced patterns.
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Args:
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tops: The pattern(s) to flattern.
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Returns:
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{name: flat_pattern} mapping for all flattened patterns.
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"""
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if isinstance(tops, str):
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tops = (tops,)
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flattened: Dict[str, Optional['Pattern']] = {}
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def flatten_single(name) -> None:
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flattened[name] = None
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pat = self[name].deepcopy()
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for ref in pat.refs:
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target = ref.target
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if target is None:
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continue
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if target not in flattened:
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flatten_single(target)
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if flattened[target] is None:
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raise PatternError(f'Circular reference in {name} to {target}')
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p = ref.as_pattern(pattern=flattened[target])
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pat.append(p)
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pat.refs.clear()
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flattened[name] = pat
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for top in tops:
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flatten_single(top)
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assert(None not in flattened.values())
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return flattened # type: ignore
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def get_name(
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self,
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name: str = '__',
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sanitize: bool = True,
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max_length: int = 32,
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quiet: bool = False,
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) -> str:
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"""
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Find a unique name for the pattern.
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This function may be overridden in a subclass or monkey-patched to fit the caller's requirements.
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Args:
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name: Preferred name for the pattern. Default '__'.
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sanitize: Allows only alphanumeric charaters and _?$. Replaces invalid characters with underscores.
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max_length: Names longer than this will be truncated.
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quiet: If `True`, suppress log messages.
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Returns:
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Name, unique within this library.
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"""
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if sanitize:
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# Remove invalid characters
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sanitized_name = re.compile(r'[^A-Za-z0-9_\?\$]').sub('_', name)
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else:
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sanitized_name = name
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ii = 0
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suffixed_name = sanitized_name
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while suffixed_name in self or suffixed_name == '':
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suffix = base64.b64encode(struct.pack('>Q', ii), b'$?').decode('ASCII')
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suffixed_name = sanitized_name + '$' + suffix[:-1].lstrip('A')
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ii += 1
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if len(suffixed_name) > max_length:
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if name == '':
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raise LibraryError(f'No valid pattern names remaining within the specified {max_length=}')
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cropped_name = self.get_name(sanitized_name[:-1], sanitize=sanitize, max_length=max_length, quiet=True)
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else:
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cropped_name = suffixed_name
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if not quiet:
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logger.info(f'Requested name "{name}" changed to "{cropped_name}"')
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return cropped_name
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def find_toplevel(self) -> List[str]:
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"""
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Return the list of all patterns that are not referenced by any other pattern in the library.
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Returns:
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A list of pattern names in which no pattern is referenced by any other pattern.
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"""
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names = set(self.keys())
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not_toplevel: Set[Optional[str]] = set()
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for name in names:
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not_toplevel |= set(sp.target for sp in self[name].refs)
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toplevel = list(names - not_toplevel)
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return toplevel
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VVV = TypeVar('VVV')
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class MutableLibrary(Generic[VVV], Library, metaclass=ABCMeta):
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# inherited abstract functions
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#def __getitem__(self, key: str) -> 'Pattern':
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#def __iter__(self) -> Iterator[str]:
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#def __len__(self) -> int:
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@abstractmethod
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def __setitem__(self, key: str, value: VVV) -> None: # TODO
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pass
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@abstractmethod
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def __delitem__(self, key: str) -> None:
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pass
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@abstractmethod
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def _set(self, key: str, value: 'Pattern') -> None:
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pass
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@abstractmethod
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def _merge(self, other: Mapping[str, 'Pattern'], key: str) -> None:
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pass
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def add(
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self: ML,
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other: Mapping[str, 'Pattern'],
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use_ours: Callable[[str], bool] = lambda name: False,
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use_theirs: Callable[[str], bool] = lambda name: False,
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) -> ML:
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"""
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Add keys from another library into this one.
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Args:
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other: The library to insert keys from
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use_ours: Decision function for name conflicts, called with cell name.
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Should return `True` if the value from `self` should be used.
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use_theirs: Decision function for name conflicts. Same format as `use_ours`.
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Should return `True` if the value from `other` should be used.
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`use_ours` takes priority over `use_theirs`.
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Returns:
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self
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"""
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duplicates = set(self.keys()) & set(other.keys())
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keep_ours = set(name for name in duplicates if use_ours(name))
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keep_theirs = set(name for name in duplicates - keep_ours if use_theirs(name))
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conflicts = duplicates - keep_ours - keep_theirs
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if conflicts:
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raise LibraryError('Unresolved duplicate keys encountered in library merge: ' + pformat(conflicts))
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for key in set(other.keys()) - keep_ours:
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self._merge(other, key)
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return self
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#TODO maybe also in immutable case?
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def dfs(
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self: ML,
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top: str,
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visit_before: Optional[visitor_function_t] = None,
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visit_after: Optional[visitor_function_t] = None,
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transform: Union[ArrayLike, bool, None] = False,
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memo: Optional[Dict] = None,
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hierarchy: Tuple[str, ...] = (),
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) -> ML:
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"""
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Convenience function.
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Performs a depth-first traversal of a pattern and its referenced patterns.
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At each pattern in the tree, the following sequence is called:
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```
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current_pattern = visit_before(current_pattern, **vist_args)
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for sp in current_pattern.refs]
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self.dfs(sp.target, visit_before, visit_after, updated_transform,
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memo, (current_pattern,) + hierarchy)
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current_pattern = visit_after(current_pattern, **visit_args)
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```
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where `visit_args` are
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`hierarchy`: (top_pattern, L1_pattern, L2_pattern, ..., parent_pattern)
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tuple of all parent-and-higher patterns
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`transform`: numpy.ndarray containing cumulative
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[x_offset, y_offset, rotation (rad), mirror_x (0 or 1)]
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for the instance being visited
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`memo`: Arbitrary dict (not altered except by `visit_before()` and `visit_after()`)
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Args:
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top: Name of the pattern to start at (root node of the tree).
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visit_before: Function to call before traversing refs.
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Should accept a `Pattern` and `**visit_args`, and return the (possibly modified)
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pattern. Default `None` (not called).
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visit_after: Function to call after traversing refs.
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Should accept a `Pattern` and `**visit_args`, and return the (possibly modified)
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pattern. Default `None` (not called).
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transform: Initial value for `visit_args['transform']`.
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Can be `False`, in which case the transform is not calculated.
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`True` or `None` is interpreted as `[0, 0, 0, 0]`.
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memo: Arbitrary dict for use by `visit_*()` functions. Default `None` (empty dict).
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hierarchy: Tuple of patterns specifying the hierarchy above the current pattern.
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Appended to the start of the generated `visit_args['hierarchy']`.
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Default is an empty tuple.
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Returns:
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self
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"""
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if memo is None:
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memo = {}
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if transform is None or transform is True:
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transform = numpy.zeros(4)
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elif transform is not False:
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transform = numpy.array(transform)
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if top in hierarchy:
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raise PatternError('.dfs() called on pattern with circular reference')
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pat = self[top]
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if visit_before is not None:
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pat = visit_before(pat, hierarchy=hierarchy, memo=memo, transform=transform) # type: ignore
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for ref in pat.refs:
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if transform is not False:
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sign = numpy.ones(2)
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if transform[3]:
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sign[1] = -1
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xy = numpy.dot(rotation_matrix_2d(transform[2]), ref.offset * sign)
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mirror_x, angle = normalize_mirror(ref.mirrored)
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angle += ref.rotation
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sp_transform = transform + (xy[0], xy[1], angle, mirror_x)
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sp_transform[3] %= 2
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else:
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sp_transform = False
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if ref.target is None:
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continue
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self.dfs(
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top=ref.target,
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visit_before=visit_before,
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visit_after=visit_after,
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transform=sp_transform,
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memo=memo,
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hierarchy=hierarchy + (top,),
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)
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if visit_after is not None:
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pat = visit_after(pat, hierarchy=hierarchy, memo=memo, transform=transform) # type: ignore
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self._set(top, pat)
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return self
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def dedup(
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self: ML,
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norm_value: int = int(1e6),
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exclude_types: Tuple[Type] = (Polygon,),
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label2name: Optional[Callable[[Tuple], str]] = None,
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threshold: int = 2,
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) -> ML:
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"""
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Iterates through all `Pattern`s. Within each `Pattern`, it iterates
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over all shapes, calling `.normalized_form(norm_value)` on them to retrieve a scale-,
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offset-, and rotation-independent form. Each shape whose normalized form appears
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more than once is removed and re-added using `Ref` objects referencing a newly-created
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`Pattern` containing only the normalized form of the shape.
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Note:
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The default norm_value was chosen to give a reasonable precision when using
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integer values for coordinates.
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Args:
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norm_value: Passed to `shape.normalized_form(norm_value)`. Default `1e6` (see function
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note)
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exclude_types: Shape types passed in this argument are always left untouched, for
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speed or convenience. Default: `(shapes.Polygon,)`
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label2name: Given a label tuple as returned by `shape.normalized_form(...)`, pick
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a name for the generated pattern. Default `self.get_name('_shape')`.
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threshold: Only replace shapes with refs if there will be at least this many
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instances.
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Returns:
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self
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"""
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# This currently simplifies globally (same shape in different patterns is
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# merged into the same ref target).
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from .pattern import Pattern
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if exclude_types is None:
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exclude_types = ()
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if label2name is None:
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label2name = lambda label: self.get_name('_shape')
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shape_counts: MutableMapping[Tuple, int] = defaultdict(int)
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shape_funcs = {}
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### First pass ###
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# Using the label tuple from `.normalized_form()` as a key, check how many of each shape
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# are present and store the shape function for each one
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for pat in tuple(self.values()):
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for i, shape in enumerate(pat.shapes):
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if not any(isinstance(shape, t) for t in exclude_types):
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label, _values, func = shape.normalized_form(norm_value)
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shape_funcs[label] = func
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shape_counts[label] += 1
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shape_pats = {}
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for label, count in shape_counts.items():
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if count < threshold:
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continue
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shape_func = shape_funcs[label]
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shape_pat = Pattern(shapes=[shape_func()])
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shape_pats[label] = shape_pat
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### Second pass ###
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for pat in tuple(self.values()):
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# Store `[(index_in_shapes, values_from_normalized_form), ...]` for all shapes which
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# are to be replaced.
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# The `values` are `(offset, scale, rotation)`.
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shape_table: MutableMapping[Tuple, List] = defaultdict(list)
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for i, shape in enumerate(pat.shapes):
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if any(isinstance(shape, t) for t in exclude_types):
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continue
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label, values, _func = shape.normalized_form(norm_value)
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if label not in shape_pats:
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continue
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shape_table[label].append((i, values))
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# For repeated shapes, create a `Pattern` holding a normalized shape object,
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# and add `pat.refs` entries for each occurrence in pat. Also, note down that
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# we should delete the `pat.shapes` entries for which we made `Ref`s.
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shapes_to_remove = []
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for label in shape_table:
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target = label2name(label)
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for i, values in shape_table[label]:
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offset, scale, rotation, mirror_x = values
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pat.ref(target=target, offset=offset, scale=scale,
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rotation=rotation, mirrored=(mirror_x, False))
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shapes_to_remove.append(i)
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# Remove any shapes for which we have created refs.
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for i in sorted(shapes_to_remove, reverse=True):
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del pat.shapes[i]
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for ll, pp in shape_pats.items():
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self._set(label2name(ll), pp)
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return self
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def wrap_repeated_shapes(
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self: ML,
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name_func: Optional[Callable[['Pattern', Union[Shape, Label]], str]] = None,
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) -> ML:
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"""
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Wraps all shapes and labels with a non-`None` `repetition` attribute
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into a `Ref`/`Pattern` combination, and applies the `repetition`
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to each `Ref` instead of its contained shape.
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Args:
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name_func: Function f(this_pattern, shape) which generates a name for the
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wrapping pattern. Default is `self.get_name('_rep')`.
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Returns:
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self
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"""
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from .pattern import Pattern
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if name_func is None:
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name_func = lambda _pat, _shape: self.get_name('_rep')
|
|
|
|
for pat in tuple(self.values()):
|
|
new_shapes = []
|
|
for shape in pat.shapes:
|
|
if shape.repetition is None:
|
|
new_shapes.append(shape)
|
|
continue
|
|
|
|
name = name_func(pat, shape)
|
|
self._set(name, Pattern(shapes=[shape]))
|
|
pat.ref(name, repetition=shape.repetition)
|
|
shape.repetition = None
|
|
pat.shapes = new_shapes
|
|
|
|
new_labels = []
|
|
for label in pat.labels:
|
|
if label.repetition is None:
|
|
new_labels.append(label)
|
|
continue
|
|
name = name_func(pat, label)
|
|
self._set(name, Pattern(labels=[label]))
|
|
pat.ref(name, repetition=label.repetition)
|
|
label.repetition = None
|
|
pat.labels = new_labels
|
|
|
|
return self
|
|
|
|
def subtree(
|
|
self: ML,
|
|
tops: Union[str, Sequence[str]],
|
|
) -> ML:
|
|
"""
|
|
Return a new `Library`, containing only the specified patterns and the patterns they
|
|
reference (recursively).
|
|
|
|
Args:
|
|
tops: Name(s) of patterns to keep
|
|
|
|
Returns:
|
|
A `Library` containing only `tops` and the patterns they reference.
|
|
"""
|
|
keep: Set[str] = self.referenced_patterns(tops) - set((None,)) # type: ignore
|
|
|
|
new = type(self)()
|
|
for key in keep:
|
|
new._merge(self, key)
|
|
return new
|
|
|
|
|
|
class WrapROLibrary(Library):
|
|
mapping: Mapping[str, 'Pattern']
|
|
|
|
def __init__(
|
|
self,
|
|
mapping: Mapping[str, 'Pattern'],
|
|
) -> None:
|
|
self.mapping = mapping
|
|
|
|
def __getitem__(self, key: str) -> 'Pattern':
|
|
return self.mapping[key]
|
|
|
|
def __iter__(self) -> Iterator[str]:
|
|
return iter(self.mapping)
|
|
|
|
def __len__(self) -> int:
|
|
return len(self.mapping)
|
|
|
|
def __repr__(self) -> str:
|
|
return f'<WrapROLibrary ({type(self.mapping)}) with keys ' + repr(list(self.keys())) + '>'
|
|
|
|
|
|
class WrapLibrary(MutableLibrary):
|
|
mapping: MutableMapping[str, 'Pattern']
|
|
|
|
def __init__(
|
|
self,
|
|
mapping: MutableMapping[str, 'Pattern'],
|
|
) -> None:
|
|
self.mapping = mapping
|
|
|
|
def __getitem__(self, key: str) -> 'Pattern':
|
|
return self.mapping[key]
|
|
|
|
def __iter__(self) -> Iterator[str]:
|
|
return iter(self.mapping)
|
|
|
|
def __len__(self) -> int:
|
|
return len(self.mapping)
|
|
|
|
def __setitem__(self, key: str, value: 'Pattern') -> None:
|
|
self.mapping[key] = value
|
|
|
|
def __delitem__(self, key: str) -> None:
|
|
del self.mapping[key]
|
|
|
|
def _set(self, key: str, value: 'Pattern') -> None:
|
|
self[key] = value
|
|
|
|
def _merge(self, other: Mapping[str, 'Pattern'], key: str) -> None:
|
|
self[key] = other[key]
|
|
|
|
def __repr__(self) -> str:
|
|
return f'<WrapLibrary ({type(self.mapping)}) with keys ' + repr(list(self.keys())) + '>'
|
|
|
|
|
|
class LazyLibrary(MutableLibrary):
|
|
"""
|
|
This class is usually used to create a library of Patterns by mapping names to
|
|
functions which generate or load the relevant `Pattern` object as-needed.
|
|
|
|
The cache can be disabled by setting the `enable_cache` attribute to `False`.
|
|
"""
|
|
dict: Dict[str, Callable[[], 'Pattern']]
|
|
cache: Dict[str, 'Pattern']
|
|
enable_cache: bool = True
|
|
|
|
def __init__(self) -> None:
|
|
self.dict = {}
|
|
self.cache = {}
|
|
|
|
def __setitem__(self, key: str, value: Callable[[], 'Pattern']) -> None:
|
|
self.dict[key] = value
|
|
if key in self.cache:
|
|
del self.cache[key]
|
|
|
|
def __delitem__(self, key: str) -> None:
|
|
del self.dict[key]
|
|
if key in self.cache:
|
|
del self.cache[key]
|
|
|
|
def __getitem__(self, key: str) -> 'Pattern':
|
|
logger.debug(f'loading {key}')
|
|
if self.enable_cache and key in self.cache:
|
|
logger.debug(f'found {key} in cache')
|
|
return self.cache[key]
|
|
|
|
func = self.dict[key]
|
|
pat = func()
|
|
self.cache[key] = pat
|
|
return pat
|
|
|
|
def __iter__(self) -> Iterator[str]:
|
|
return iter(self.dict)
|
|
|
|
def __len__(self) -> int:
|
|
return len(self.dict)
|
|
|
|
def _set(self, key: str, value: 'Pattern') -> None:
|
|
self[key] = lambda: value
|
|
|
|
def _merge(self, other: Mapping[str, 'Pattern'], key: str) -> None:
|
|
if isinstance(other, LazyLibrary):
|
|
self.dict[key] = other.dict[key]
|
|
if key in other.cache:
|
|
self.cache[key] = other.cache[key]
|
|
else:
|
|
self._set(key, other[key])
|
|
|
|
def __repr__(self) -> str:
|
|
return '<LazyLibrary with keys ' + repr(list(self.keys())) + '>'
|
|
|
|
def precache(self: LL) -> LL:
|
|
"""
|
|
Force all patterns into the cache
|
|
|
|
Returns:
|
|
self
|
|
"""
|
|
for key in self.dict:
|
|
_ = self.dict.__getitem__(key)
|
|
return self
|
|
|
|
def clear_cache(self: LL) -> LL:
|
|
"""
|
|
Clear the cache of this library.
|
|
This is usually used before modifying or deleting cells, e.g. when merging
|
|
with another library.
|
|
|
|
Returns:
|
|
self
|
|
"""
|
|
self.cache.clear()
|
|
return self
|
|
|
|
def __deepcopy__(self, memo: Optional[Dict] = None) -> 'LazyLibrary':
|
|
raise LibraryError('LazyLibrary cannot be deepcopied (deepcopy doesn\'t descend into closures)')
|