You cannot select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
712 lines
24 KiB
Python
712 lines
24 KiB
Python
"""
|
|
Library class for managing unique name->pattern mappings and
|
|
deferred loading or creation.
|
|
"""
|
|
from typing import List, Dict, Callable, TypeVar, Type, TYPE_CHECKING
|
|
from typing import Any, Tuple, Union, Iterator, Mapping, MutableMapping, Set, Optional, Sequence
|
|
import logging
|
|
import copy
|
|
import base64
|
|
import struct
|
|
import re
|
|
from pprint import pformat
|
|
from collections import defaultdict
|
|
from abc import ABCMeta, abstractmethod
|
|
|
|
import numpy
|
|
from numpy.typing import ArrayLike, NDArray, NDArray
|
|
|
|
from .error import LibraryError, PatternError
|
|
from .utils import rotation_matrix_2d, normalize_mirror
|
|
from .shapes import Shape, Polygon
|
|
from .label import Label
|
|
|
|
if TYPE_CHECKING:
|
|
from .pattern import Pattern
|
|
|
|
|
|
logger = logging.getLogger(__name__)
|
|
|
|
|
|
visitor_function_t = Callable[['Pattern', Tuple['Pattern'], Dict, NDArray[numpy.float64]], 'Pattern']
|
|
L = TypeVar('L', bound='Library')
|
|
ML = TypeVar('ML', bound='MutableLibrary')
|
|
#LL = TypeVar('LL', bound='LazyLibrary')
|
|
|
|
|
|
class Library(Mapping[str, Pattern], metaclass=ABCMeta):
|
|
# inherited abstract functions
|
|
#def __getitem__(self, key: str) -> 'Pattern':
|
|
#def __iter__(self) -> Iterator[str]:
|
|
#def __len__(self) -> int:
|
|
|
|
#__contains__, keys, items, values, get, __eq__, __ne__ supplied by Mapping
|
|
|
|
def __repr__(self) -> str:
|
|
return '<Library with keys ' + repr(list(self.keys())) + '>'
|
|
|
|
def referenced_patterns(
|
|
self,
|
|
tops: Union[str, Sequence[str]],
|
|
skip: Optional[Set[Optional[str]]] = None,
|
|
) -> Set[Optional[str]]:
|
|
"""
|
|
Get the set of all pattern names referenced by `top`. Recursively traverses into any subpatterns.
|
|
|
|
Args:
|
|
top: Name of the top pattern(s) to check.
|
|
skip: Memo, set patterns which have already been traversed.
|
|
|
|
Returns:
|
|
Set of all referenced pattern names
|
|
"""
|
|
if skip is None:
|
|
skip = set([None])
|
|
|
|
if isinstance(tops, str):
|
|
tops = (tops,)
|
|
|
|
# Get referenced patterns for all tops
|
|
targets = set()
|
|
for top in set(tops):
|
|
targets |= self[top].referenced_patterns()
|
|
|
|
# Perform recursive lookups, but only once for each name
|
|
for target in targets - skip:
|
|
assert(target is not None)
|
|
self.referenced_patterns(target, skip)
|
|
skip.add(target)
|
|
|
|
return targets
|
|
|
|
# TODO maybe not for immutable?
|
|
def subtree(
|
|
self,
|
|
tops: Union[str, Sequence[str]],
|
|
) -> WrapLibrary:
|
|
"""
|
|
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
|
|
|
|
filtered = {kk: vv for kk, vv in self.items() if kk in keep}
|
|
new = WrapLibrary(filtered)
|
|
return new
|
|
|
|
def polygonize(
|
|
self: L,
|
|
poly_num_points: Optional[int] = None,
|
|
poly_max_arclen: Optional[float] = None,
|
|
) -> L:
|
|
"""
|
|
Calls `.polygonize(...)` on each pattern in this library.
|
|
Arguments are passed on to `shape.to_polygons(...)`.
|
|
|
|
Args:
|
|
poly_num_points: Number of points to use for each polygon. Can be overridden by
|
|
`poly_max_arclen` if that results in more points. Optional, defaults to shapes'
|
|
internal defaults.
|
|
poly_max_arclen: Maximum arclength which can be approximated by a single line
|
|
segment. Optional, defaults to shapes' internal defaults.
|
|
|
|
Returns:
|
|
self
|
|
"""
|
|
for pat in self.values():
|
|
pat.polygonize(poly_num_points, poly_max_arclen)
|
|
return self
|
|
|
|
def manhattanize(
|
|
self: L,
|
|
grid_x: ArrayLike,
|
|
grid_y: ArrayLike,
|
|
) -> L:
|
|
"""
|
|
Calls `.manhattanize(grid_x, grid_y)` on each pattern in this library.
|
|
|
|
Args:
|
|
grid_x: List of allowed x-coordinates for the Manhattanized polygon edges.
|
|
grid_y: List of allowed y-coordinates for the Manhattanized polygon edges.
|
|
|
|
Returns:
|
|
self
|
|
"""
|
|
for pat in self.values():
|
|
pat.manhattanize(grid_x, grid_y)
|
|
return self
|
|
|
|
def flatten(
|
|
self,
|
|
tops: Union[str, Sequence[str]],
|
|
) -> Dict[str, 'Pattern']:
|
|
"""
|
|
Removes all subpatterns and adds equivalent shapes.
|
|
Also flattens all subpatterns.
|
|
|
|
Args:
|
|
tops: The pattern(s) to flattern.
|
|
|
|
Returns:
|
|
{name: flat_pattern} mapping for all flattened patterns.
|
|
"""
|
|
if isinstance(tops, str):
|
|
tops = (tops,)
|
|
|
|
flattened: Dict[str, Optional['Pattern']] = {}
|
|
|
|
def flatten_single(name) -> None:
|
|
flattened[name] = None
|
|
pat = self[name].deepcopy()
|
|
|
|
for subpat in pat.subpatterns:
|
|
target = subpat.target
|
|
if target is None:
|
|
continue
|
|
|
|
if target not in flattened:
|
|
flatten_single(target)
|
|
if flattened[target] is None:
|
|
raise PatternError(f'Circular reference in {name} to {target}')
|
|
|
|
p = subpat.as_pattern(pattern=flattened[target])
|
|
pat.append(p)
|
|
|
|
pat.subpatterns.clear()
|
|
flattened[name] = pat
|
|
|
|
for top in tops:
|
|
flatten_single(top)
|
|
|
|
assert(None not in flattened.values())
|
|
return flattened # type: ignore
|
|
|
|
def get_name(
|
|
self,
|
|
name: str = '__',
|
|
sanitize: bool = True,
|
|
max_length: int = 32,
|
|
quiet: bool = False,
|
|
) -> str:
|
|
"""
|
|
Find a unique name for the pattern.
|
|
|
|
This function may be overridden in a subclass or monkey-patched to fit the caller's requirements.
|
|
|
|
Args:
|
|
name: Preferred name for the pattern. Default '__'.
|
|
sanitize: Allows only alphanumeric charaters and _?$. Replaces invalid characters with underscores.
|
|
max_length: Names longer than this will be truncated.
|
|
quiet: If `True`, suppress log messages.
|
|
|
|
Returns:
|
|
Name, unique within this library.
|
|
"""
|
|
if sanitize:
|
|
# Remove invalid characters
|
|
sanitized_name = re.compile(r'[^A-Za-z0-9_\?\$]').sub('_', name)
|
|
else:
|
|
sanitized_name = name
|
|
|
|
ii = 0
|
|
suffixed_name = sanitized_name
|
|
while suffixed_name in self or suffixed_name == '':
|
|
suffix = base64.b64encode(struct.pack('>Q', ii), b'$?').decode('ASCII')
|
|
|
|
suffixed_name = sanitized_name + '$' + suffix[:-1].lstrip('A')
|
|
ii += 1
|
|
|
|
if len(suffixed_name) > max_length:
|
|
if name == '':
|
|
raise LibraryError(f'No valid pattern names remaining within the specified {max_length=}')
|
|
|
|
cropped_name = self.get_name(sanitized_name[:-1], sanitize=sanitize, max_length=max_length, quiet=True)
|
|
else:
|
|
cropped_name = suffixed_name
|
|
|
|
if not quiet:
|
|
logger.info(f'Requested name "{name}" changed to "{cropped_name}"')
|
|
|
|
return cropped_name
|
|
|
|
def find_toplevel(self) -> List[str]:
|
|
"""
|
|
Return the list of all patterns that are not referenced by any other pattern in the library.
|
|
|
|
Returns:
|
|
A list of pattern names in which no pattern is referenced by any other pattern.
|
|
"""
|
|
names = set(self.keys())
|
|
not_toplevel: Set[Optional[str]] = set()
|
|
for name in names:
|
|
not_toplevel |= set(sp.target for sp in self[name].subpatterns)
|
|
|
|
toplevel = list(names - not_toplevel)
|
|
return toplevel
|
|
|
|
|
|
class MutableLibrary(Library, metaclass=ABCMeta):
|
|
# inherited abstract functions
|
|
#def __getitem__(self, key: str) -> 'Pattern':
|
|
#def __iter__(self) -> Iterator[str]:
|
|
#def __len__(self) -> int:
|
|
|
|
@abstractmethod
|
|
def __setitem__(self, key: str, value: VVV) -> None: # TODO
|
|
pass
|
|
|
|
@abstractmethod
|
|
def __delitem__(self, key: str) -> None:
|
|
pass
|
|
|
|
@abstractmethod
|
|
def _set(self, key: str, value: 'Pattern') -> None:
|
|
pass
|
|
|
|
@abstractmethod
|
|
def _merge(self, other: Mapping[str, 'Pattern'], key: str) -> None:
|
|
pass
|
|
|
|
def add(
|
|
self: ML,
|
|
other: Mapping[str, 'Pattern'],
|
|
use_ours: Callable[[str], bool] = lambda name: False,
|
|
use_theirs: Callable[[str], bool] = lambda name: False,
|
|
) -> ML:
|
|
"""
|
|
Add keys from another library into this one.
|
|
|
|
Args:
|
|
other: The library to insert keys from
|
|
use_ours: Decision function for name conflicts, called with cell name.
|
|
Should return `True` if the value from `self` should be used.
|
|
use_theirs: Decision function for name conflicts. Same format as `use_ours`.
|
|
Should return `True` if the value from `other` should be used.
|
|
`use_ours` takes priority over `use_theirs`.
|
|
Returns:
|
|
self
|
|
"""
|
|
duplicates = set(self.keys()) & set(other.keys())
|
|
keep_ours = set(name for name in duplicates if use_ours(name))
|
|
keep_theirs = set(name for name in duplicates - keep_ours if use_theirs(name))
|
|
conflicts = duplicates - keep_ours - keep_theirs
|
|
|
|
if conflicts:
|
|
raise LibraryError('Unresolved duplicate keys encountered in library merge: ' + pformat(conflicts))
|
|
|
|
for key in set(other.keys()) - keep_ours:
|
|
self._merge(other, key)
|
|
|
|
return self
|
|
|
|
#TODO maybe also in immutable case?
|
|
def dfs(
|
|
self: ML,
|
|
top: str,
|
|
visit_before: Optional[visitor_function_t] = None,
|
|
visit_after: Optional[visitor_function_t] = None,
|
|
transform: Union[ArrayLike, bool, None] = False,
|
|
memo: Optional[Dict] = None,
|
|
hierarchy: Tuple[str, ...] = (),
|
|
) -> ML:
|
|
"""
|
|
Convenience function.
|
|
Performs a depth-first traversal of a pattern and its subpatterns.
|
|
At each pattern in the tree, the following sequence is called:
|
|
```
|
|
current_pattern = visit_before(current_pattern, **vist_args)
|
|
for sp in current_pattern.subpatterns]
|
|
self.dfs(sp.target, visit_before, visit_after, updated_transform,
|
|
memo, (current_pattern,) + hierarchy)
|
|
current_pattern = visit_after(current_pattern, **visit_args)
|
|
```
|
|
where `visit_args` are
|
|
`hierarchy`: (top_pattern, L1_pattern, L2_pattern, ..., parent_pattern)
|
|
tuple of all parent-and-higher patterns
|
|
`transform`: numpy.ndarray containing cumulative
|
|
[x_offset, y_offset, rotation (rad), mirror_x (0 or 1)]
|
|
for the instance being visited
|
|
`memo`: Arbitrary dict (not altered except by `visit_before()` and `visit_after()`)
|
|
|
|
Args:
|
|
top: Name of the pattern to start at (root node of the tree).
|
|
visit_before: Function to call before traversing subpatterns.
|
|
Should accept a `Pattern` and `**visit_args`, and return the (possibly modified)
|
|
pattern. Default `None` (not called).
|
|
visit_after: Function to call after traversing subpatterns.
|
|
Should accept a `Pattern` and `**visit_args`, and return the (possibly modified)
|
|
pattern. Default `None` (not called).
|
|
transform: Initial value for `visit_args['transform']`.
|
|
Can be `False`, in which case the transform is not calculated.
|
|
`True` or `None` is interpreted as `[0, 0, 0, 0]`.
|
|
memo: Arbitrary dict for use by `visit_*()` functions. Default `None` (empty dict).
|
|
hierarchy: Tuple of patterns specifying the hierarchy above the current pattern.
|
|
Appended to the start of the generated `visit_args['hierarchy']`.
|
|
Default is an empty tuple.
|
|
|
|
Returns:
|
|
self
|
|
"""
|
|
if memo is None:
|
|
memo = {}
|
|
|
|
if transform is None or transform is True:
|
|
transform = numpy.zeros(4)
|
|
elif transform is not False:
|
|
transform = numpy.array(transform)
|
|
|
|
if top in hierarchy:
|
|
raise PatternError('.dfs() called on pattern with circular reference')
|
|
|
|
pat = self[top]
|
|
if visit_before is not None:
|
|
pat = visit_before(pat, hierarchy=hierarchy, memo=memo, transform=transform) # type: ignore
|
|
|
|
for subpattern in pat.subpatterns:
|
|
if transform is not False:
|
|
sign = numpy.ones(2)
|
|
if transform[3]:
|
|
sign[1] = -1
|
|
xy = numpy.dot(rotation_matrix_2d(transform[2]), subpattern.offset * sign)
|
|
mirror_x, angle = normalize_mirror(subpattern.mirrored)
|
|
angle += subpattern.rotation
|
|
sp_transform = transform + (xy[0], xy[1], angle, mirror_x)
|
|
sp_transform[3] %= 2
|
|
else:
|
|
sp_transform = False
|
|
|
|
if subpattern.target is None:
|
|
continue
|
|
|
|
self.dfs(
|
|
top=subpattern.target,
|
|
visit_before=visit_before,
|
|
visit_after=visit_after,
|
|
transform=sp_transform,
|
|
memo=memo,
|
|
hierarchy=hierarchy + (top,),
|
|
)
|
|
|
|
if visit_after is not None:
|
|
pat = visit_after(pat, hierarchy=hierarchy, memo=memo, transform=transform) # type: ignore
|
|
|
|
self._set(top, pat)
|
|
return self
|
|
|
|
def subpatternize(
|
|
self: ML,
|
|
norm_value: int = int(1e6),
|
|
exclude_types: Tuple[Type] = (Polygon,),
|
|
label2name: Optional[Callable[[Tuple], str]] = None,
|
|
threshold: int = 2,
|
|
) -> ML:
|
|
"""
|
|
Iterates through all `Pattern`s. Within each `Pattern`, it iterates
|
|
over all shapes, calling `.normalized_form(norm_value)` on them to retrieve a scale-,
|
|
offset-, and rotation-independent form. Each shape whose normalized form appears
|
|
more than once is removed and re-added using subpattern objects referencing a newly-created
|
|
`Pattern` containing only the normalized form of the shape.
|
|
|
|
Note:
|
|
The default norm_value was chosen to give a reasonable precision when using
|
|
integer values for coordinates.
|
|
|
|
Args:
|
|
norm_value: Passed to `shape.normalized_form(norm_value)`. Default `1e6` (see function
|
|
note)
|
|
exclude_types: Shape types passed in this argument are always left untouched, for
|
|
speed or convenience. Default: `(shapes.Polygon,)`
|
|
label2name: Given a label tuple as returned by `shape.normalized_form(...)`, pick
|
|
a name for the generated pattern. Default `self.get_name('_shape')`.
|
|
threshold: Only replace shapes with subpatterns if there will be at least this many
|
|
instances.
|
|
|
|
Returns:
|
|
self
|
|
"""
|
|
# This currently simplifies globally (same shape in different patterns is
|
|
# merged into the same subpattern target).
|
|
|
|
from .pattern import Pattern
|
|
|
|
if exclude_types is None:
|
|
exclude_types = ()
|
|
|
|
if label2name is None:
|
|
label2name = lambda label: self.get_name('_shape')
|
|
|
|
|
|
shape_counts: MutableMapping[Tuple, int] = defaultdict(int)
|
|
shape_funcs = {}
|
|
|
|
### First pass ###
|
|
# Using the label tuple from `.normalized_form()` as a key, check how many of each shape
|
|
# are present and store the shape function for each one
|
|
for pat in tuple(self.values()):
|
|
for i, shape in enumerate(pat.shapes):
|
|
if not any(isinstance(shape, t) for t in exclude_types):
|
|
label, _values, func = shape.normalized_form(norm_value)
|
|
shape_funcs[label] = func
|
|
shape_counts[label] += 1
|
|
|
|
shape_pats = {}
|
|
for label, count in shape_counts.items():
|
|
if count < threshold:
|
|
continue
|
|
|
|
shape_func = shape_funcs[label]
|
|
shape_pat = Pattern(shapes=[shape_func()])
|
|
shape_pats[label] = shape_pat
|
|
|
|
### Second pass ###
|
|
for pat in tuple(self.values()):
|
|
# Store `[(index_in_shapes, values_from_normalized_form), ...]` for all shapes which
|
|
# are to be replaced.
|
|
# The `values` are `(offset, scale, rotation)`.
|
|
|
|
shape_table: MutableMapping[Tuple, List] = defaultdict(list)
|
|
for i, shape in enumerate(pat.shapes):
|
|
if any(isinstance(shape, t) for t in exclude_types):
|
|
continue
|
|
|
|
label, values, _func = shape.normalized_form(norm_value)
|
|
|
|
if label not in shape_pats:
|
|
continue
|
|
|
|
shape_table[label].append((i, values))
|
|
|
|
# For repeated shapes, create a `Pattern` holding a normalized shape object,
|
|
# and add `pat.subpatterns` entries for each occurrence in pat. Also, note down that
|
|
# we should delete the `pat.shapes` entries for which we made SubPatterns.
|
|
shapes_to_remove = []
|
|
for label in shape_table:
|
|
target = label2name(label)
|
|
for i, values in shape_table[label]:
|
|
offset, scale, rotation, mirror_x = values
|
|
pat.addsp(target=target, offset=offset, scale=scale,
|
|
rotation=rotation, mirrored=(mirror_x, False))
|
|
shapes_to_remove.append(i)
|
|
|
|
# Remove any shapes for which we have created subpatterns.
|
|
for i in sorted(shapes_to_remove, reverse=True):
|
|
del pat.shapes[i]
|
|
|
|
for ll, pp in shape_pats.items():
|
|
self._set(label2name(ll), pp)
|
|
|
|
return self
|
|
|
|
def wrap_repeated_shapes(
|
|
self: ML,
|
|
name_func: Optional[Callable[['Pattern', Union[Shape, Label]], str]] = None,
|
|
) -> ML:
|
|
"""
|
|
Wraps all shapes and labels with a non-`None` `repetition` attribute
|
|
into a `SubPattern`/`Pattern` combination, and applies the `repetition`
|
|
to each `SubPattern` instead of its contained shape.
|
|
|
|
Args:
|
|
name_func: Function f(this_pattern, shape) which generates a name for the
|
|
wrapping pattern. Default is `self.get_name('_rep')`.
|
|
|
|
Returns:
|
|
self
|
|
"""
|
|
from .pattern import Pattern
|
|
|
|
if name_func is None:
|
|
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.addsp(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.addsp(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.dict.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)')
|