:py:mod:`alhambra.fastreduceD` ============================== .. py:module:: alhambra.fastreduceD Module Contents --------------- Classes ~~~~~~~ .. autoapisummary:: alhambra.fastreduceD.FTile alhambra.fastreduceD.FTilesArray alhambra.fastreduceD.TileWithUse alhambra.fastreduceD.FGlueList alhambra.fastreduceD.FTileList alhambra.fastreduceD._FastTileSet Functions ~~~~~~~~~ .. autoapisummary:: alhambra.fastreduceD._fdg alhambra.fastreduceD._ffakedouble_n alhambra.fastreduceD._ffakesingle alhambra.fastreduceD.ptins alhambra.fastreduceD.gmatch alhambra.fastreduceD.gcomp alhambra.fastreduceD.findmovetiles alhambra.fastreduceD._2go_moveandfill alhambra.fastreduceD._2go_findtrialmoves alhambra.fastreduceD._2go_checkandmove alhambra.fastreduceD.is_2go_nn alhambra.fastreduceD.gen_2go_maps alhambra.fastreduceD.gen_2go_profile alhambra.fastreduceD.is_2go_equiv alhambra.fastreduceD.is_22go_equiv alhambra.fastreduceD.fta_to_ft alhambra.fastreduceD.isatamequiv alhambra.fastreduceD.tilemerge alhambra.fastreduceD._findpotentialtilemerges alhambra.fastreduceD._findpotentialgluemerges alhambra.fastreduceD._recfix alhambra.fastreduceD._tilereduce alhambra.fastreduceD._gluereduce alhambra.fastreduceD._single_reduce_tiles alhambra.fastreduceD._single_reduce_ends alhambra.fastreduceD.reduce_tiles alhambra.fastreduceD.reduce_ends Attributes ~~~~~~~~~~ .. autoapisummary:: alhambra.fastreduceD.TMap alhambra.fastreduceD.TAU alhambra.fastreduceD.other alhambra.fastreduceD.uU alhambra.fastreduceD.uN alhambra.fastreduceD.uI alhambra.fastreduceD.uO alhambra.fastreduceD.uB alhambra.fastreduceD.uP alhambra.fastreduceD.invertuse alhambra.fastreduceD.usedict alhambra.fastreduceD.Equiv alhambra.fastreduceD.RSEL alhambra.fastreduceD.FTI alhambra.fastreduceD.FTS alhambra.fastreduceD.HSEL alhambra.fastreduceD.VSEL alhambra.fastreduceD.directions .. py:data:: TMap .. py:class:: FTile .. py:attribute:: name :type: str .. py:attribute:: used :type: bool .. py:attribute:: glues :type: numpy.typing.NDArray[numpy.int64] .. py:attribute:: use :type: numpy.typing.NDArray[numpy.int64] .. py:attribute:: color :type: Any .. py:attribute:: structure :type: Type[alhambra.tiles.Tile] .. py:attribute:: dfake :type: int .. py:attribute:: sfake :type: int .. py:class:: FTilesArray .. py:attribute:: color :type: Any .. py:attribute:: use :type: numpy.ndarray[Any, numpy.dtype[numpy.int64]] .. py:attribute:: glues :type: numpy.ndarray[Any, numpy.dtype[numpy.int64]] .. py:attribute:: name :type: numpy.ndarray[Any, numpy.dtype[numpy.str0]] .. py:attribute:: used :type: numpy.ndarray[Any, numpy.dtype[numpy.bool8]] .. py:attribute:: structure :type: numpy.ndarray[Any, numpy.dtype[numpy.str0]] .. py:attribute:: dfake :type: numpy.ndarray[Any, numpy.dtype[numpy.int64]] .. py:attribute:: sfake :type: numpy.ndarray[Any, numpy.dtype[numpy.int64]] .. py:method:: __len__() -> int .. py:method:: from_ftiles(ftiles: Iterable[FTile]) -> FTilesArray :classmethod: .. py:data:: TAU :value: 2 .. py:data:: other :value: [2, 3, 0, 1] .. py:class:: TileWithUse Bases: :py:obj:`Protocol` Base class for protocol classes. Protocol classes are defined as:: class Proto(Protocol): def meth(self) -> int: ... Such classes are primarily used with static type checkers that recognize structural subtyping (static duck-typing), for example:: class C: def meth(self) -> int: return 0 def func(x: Proto) -> int: return x.meth() func(C()) # Passes static type check See PEP 544 for details. Protocol classes decorated with @typing.runtime_checkable act as simple-minded runtime protocols that check only the presence of given attributes, ignoring their type signatures. Protocol classes can be generic, they are defined as:: class GenProto(Protocol[T]): def meth(self) -> T: ... .. py:property:: use :type: list[int] .. py:data:: uU :value: 0 .. py:data:: uN :value: 1 .. py:data:: uI :value: 2 .. py:data:: uO :value: 3 .. py:data:: uB :value: 4 .. py:data:: uP :value: 5 .. py:data:: invertuse :value: [0, 1, 3, 2, 4, 5] .. py:data:: usedict .. py:data:: Equiv .. py:class:: FGlueList(glues: alhambra.glues.GlueList[alhambra.glues.Glue]) .. py:method:: blankequiv() -> Equiv .. py:method:: iseq(equiv: Equiv, a: int, b: int) -> bool .. py:method:: domerge(equiv: Equiv, a: int, b: int, preserveuse: bool = False) -> Equiv .. py:class:: FTileList(tiles: alhambra.tiles.TileList[alhambra.tiles.Tile], gluelist: FGlueList) .. py:attribute:: stiles :type: FTilesArray .. py:attribute:: htiles :type: FTilesArray .. py:attribute:: vtiles :type: FTilesArray .. py:data:: RSEL :value: (2, 3, 0, 1) .. py:data:: FTI :value: (1, 0, 1, 0) .. py:data:: FTS :value: () .. py:function:: _fdg(dir, gs1, gs2) .. py:function:: _ffakedouble_n(tn: int, sta, gluelist: FGlueList, outputonly: bool = True, *, dir4: Literal[True], equiv=None) -> tuple[list[FTile], list[FTile], list[FTile], list[FTile]] _ffakedouble_n(tn: int, sta, gluelist: FGlueList, outputonly: bool = True, *, dir4: Literal[False] = False, equiv=None) -> tuple[list[FTile], list[FTile]] .. py:data:: HSEL :value: ((1, 0, 5, 6), (2, 3, 4, 0)) .. py:data:: VSEL :value: ((1, 2, 0, 6), (0, 3, 4, 5)) .. py:function:: _ffakesingle(ftile: FTile, gluelist: FGlueList) -> Sequence[FTile] .. py:class:: _FastTileSet(tilesystem: alhambra.tilesets.TileSet) .. py:attribute:: gluelist :type: FGlueList .. py:attribute:: tilelist :type: FTileList .. py:method:: applyequiv(ts: alhambra.tilesets.TileSet, equiv) -> alhambra.tilesets.TileSet .. py:method:: togluemergespec(ts, equiv) .. py:function:: ptins(fts: _FastTileSet, equiv: Equiv | None = None, tau=2) -> list[list[numpy.ndarray[Any, numpy.dtype[numpy.int64]]]] Calculate potential tile attachments to input neighborhoods .. py:function:: gmatch(fts, g1, g2, equiv=None) .. py:function:: gcomp(fts, g1, g2, equiv=None) .. py:function:: findmovetiles(fts, tilei, direction, allin, allout) .. py:function:: _2go_moveandfill(fts: _FastTileSet, t: int, i: int, allin=True, allout=True, pdir=None, x=0, y=0, old_d=None) -> TMap .. py:function:: _2go_findtrialmoves(fts: _FastTileSet, tilei, direction, equiv=None) .. py:data:: directions :value: [(0,), (1, 0), (0, 1), ()] .. py:function:: _2go_checkandmove(fts, ct, x, y, i, tmap: TMap, exclude=tuple(), equiv=None) .. py:function:: is_2go_nn(fts: _FastTileSet, tn: int, un: int, equiv: numpy.ndarray[Any, numpy.dtype[numpy.int64]], tau: int = 2, tmaps: dict[int, TMap] | None = None, *, retall: Literal[True], also22go: bool = False) -> tuple[bool, tuple[Any, Any] | None] is_2go_nn(fts: _FastTileSet, tn: int, un: int, equiv: numpy.ndarray[Any, numpy.dtype[numpy.int64]], tau: int = 2, tmaps: dict[int, TMap] | None = None, *, retall: Literal[False] = False, also22go: bool = False) -> bool .. py:function:: gen_2go_maps(fts: _FastTileSet) -> dict[int, TMap] .. py:function:: gen_2go_profile(fts: _FastTileSet, equiv=None, tmaps=None, also22go: bool = False) .. py:function:: is_2go_equiv(fts: _FastTileSet, equiv: Equiv | None = None, tmaps=None, origsens=None) .. py:function:: is_22go_equiv(fts, equiv=None, ins2go=None, orig22go=None) .. py:function:: fta_to_ft(stiles, un, sel=None) .. py:function:: isatamequiv(fts, equiv, initptins=None) .. py:function:: tilemerge(fts, equiv, t1, t2, preserveuse=False) .. py:function:: _findpotentialtilemerges(fts, equiv) .. py:function:: _findpotentialgluemerges(fts, equiv) .. py:function:: _recfix(fts, equiv, tp, initptins, check2go=False, tmaps=None, orig2go=None, orig22go=None, check22go=False, checkld=False, chain=None, preserveuse=False) .. py:function:: _tilereduce(fts, equiv=None, check2go=False, initptins=None, check22go=False, checkld=False, preserveuse=False) .. py:function:: _gluereduce(fts: _FastTileSet, equiv: Equiv | None = None, check2go: bool = False, check22go: bool = False, checkld: bool = False, initptins=None, preserveuse: bool = False) .. py:function:: _single_reduce_tiles(p) .. py:function:: _single_reduce_ends(p) .. py:function:: reduce_tiles(tileset, preserve=('s22', 'ld'), tries=10, threads=1, returntype='equiv', best=1, key=None, initequiv=None) Apply tile reduction algorithm, preserving some set of properties, and using a multiprocessing pool. :param tileset: The system to reduce. :type tileset: TileSet :param preserve: The properties to preserve. Currently supported are 's1' for first order sensitivity, 's2' for second order sensitivity, 's22' for two-by-two sensitivity, 'ld' for small lattice defects, and 'gs' for glue sense (to avoid spurious hierarchical attachment). Default is currently ('s22', 'ld'). :type preserve: a tuple or list of strings, optional :param tries: The number of times to run the algorithm. :type tries: int, optional :param threads: The number of threads to use (using multiprocessing). :type threads: int, optional :param returntype: The type of object to return. If 'equiv', returns an array of glue equivalences (or list, if best != 1) that can be applied to the tileset with apply_equiv, or used for further reduction. If 'TileSet', return a TileSet with the equiv already applied (or a list, if best != 1). :type returntype: 'TileSet' or 'equiv' (default 'equiv') :param best: The number of systems to return. If 1, the result will be returned directly; if k > 1, a list will be returned of the best k results (per cmp); if k = None, a list of *all* results will be returned, sorted by cmp. (default 1) :type best: int or None, optional :param key: A comparison function for equivs, to sort the results. FIXME: documentation needed. Default (if None) here is to sort by number of glues in the system, regardless of number of tiles. :type key: function (ts, equiv1, equiv2) -> some number/comparable :param initequiv: If provided, the equivalence array to start from. If None, start from the tileset without any merged glues. :type initequiv: equiv :returns: **reduced** -- The reduced system/systems :rtype: single TileSet .. py:function:: reduce_ends(tileset, preserve=('s22', 'ld'), tries=10, threads=1, returntype='equiv', best=1, key=None, initequiv=None) Apply end reduction algorithm, preserving some set of properties, and using a multiprocessing pool. :param tileset: The system to reduce. :type tileset: TileSet :param preserve: The properties to preserve. Currently supported are 's1' for first order sensitivity, 's2' for second order sensitivity, 's22' for two-by-two sensitivity, 'ld' for small lattice defects, and 'gs' for glue sense (to avoid spurious hierarchical attachment). Default is currently ('s22', 'ld'). :type preserve: a tuple or list of strings, optional :param tries: The number of times to run the algorithm. :type tries: int, optional :param threads: The number of threads to use (using multiprocessing). :type threads: int, optional :param returntype: The type of object to return. If 'equiv', returns an array of glue equivalences (or list, if best != 1) that can be applied to the tileset with apply_equiv, or used for further reduction. If 'TileSet', return a TileSet with the equiv already applied (or a list, if best != 1). :type returntype: 'TileSet' or 'equiv' (default 'equiv') :param best: The number of systems to return. If 1, the result will be returned directly; if k > 1, a list will be returned of the best k results (per cmp); if k = None, a list of *all* results will be returned, sorted by cmp. (default 1) :type best: int or None, optional :param key: A comparison function for equivs, to sort the results. FIXME: documentation needed. Default (if None) here is to sort by number of glues in the system, regardless of number of tiles. :type key: function (ts, equiv1, equiv2) -> some number/comparable :param initequiv: If provided, the equivalence array to start from. If None, start from the tileset without any merged glues. :type initequiv: equiv :returns: **reduced** -- The reduced system/systems :rtype: single TileSet