Module mpython.core.base_types
Classes
class AnyMatlabArray-
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class AnyMatlabArray(MatlabType): """Base class for all matlab-like arrays (numeric, cell, struct).""" @property def as_num(self): raise TypeError( f"Cannot interpret a {type(self).__name__} as a numeric array" ) @property def as_cell(self): raise TypeError( f"Cannot interpret a {type(self).__name__} as a cell" ) @property def as_struct(self): raise TypeError( f"Cannot interpret a {type(self).__name__} as a struct" ) # TODO: `as_obj` for object arrays?Base class for all matlab-like arrays (numeric, cell, struct).
Ancestors
Subclasses
Instance variables
prop as_cell-
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@property def as_cell(self): raise TypeError( f"Cannot interpret a {type(self).__name__} as a cell" ) prop as_num-
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@property def as_num(self): raise TypeError( f"Cannot interpret a {type(self).__name__} as a numeric array" ) prop as_struct-
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@property def as_struct(self): raise TypeError( f"Cannot interpret a {type(self).__name__} as a struct" )
Inherited members
class MatlabType-
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class MatlabType: """Generic type for objects that have an exact matlab equivalent.""" @classmethod def from_any(cls, other, **kwargs): """ Convert python/matlab objects to `MatlabType` objects (`Cell`, `Struct`, `Array`, `MatlabClass`). !!! warning "Conversion is performed in-place when possible." """ # Circular import Array = _imports.Array Cell = _imports.Cell MatlabClass = _imports.MatlabClass MatlabFunction = _imports.MatlabFunction SparseArray = _imports.SparseArray Struct = _imports.Struct AnyDelayedArray = _imports.AnyDelayedArray # Conversion rules: # - we do not convert to matlab's own array types # (`matlab.double`, etc); # - we do not convert to types that can be passed directly to # the matlab runtime; # - instead, we convert to python types that mimic matlab types. _from_any = partial(cls.from_any, **kwargs) _runtime = kwargs.pop("_runtime", None) if isinstance(other, MatlabType): if isinstance(other, AnyDelayedArray): other._error_is_not_finalized() return other if isinstance(other, dict): if "type__" in other: type__ = other["type__"] if type__ == "none": # MPython returns this when catching a function # that should return no values but is asked for one. return None elif type__ == "emptystruct": return Struct.from_shape([0]) elif type__ == "structarray": # MPython returns a list of dictionaries in data__ # and the array shape in size__. return Struct._from_runtime(other, _runtime) elif type__ == "cell": # MPython returns a list of dictionaries in data__ # and the array shape in size__. return Cell._from_runtime(other, _runtime) elif type__ == "object": # MPython returns the object's fields serialized # in a dictionary. return MatlabClass._from_runtime(other, _runtime) elif type__ == "sparse": # MPython returns the coordinates and values in a dict. return SparseArray._from_runtime(other, _runtime) elif type__ == "char": # Character array that is not a row vector # (row vector are converted to str automatically) # MPython returns all rows in a (F-ordered) cell in data__ # Let's use the cell constructor to return a cellstr. # -> A cellstr is a column vector, not a row vector size = np.asarray(other["size__"]).tolist()[0] size = size[:-1] + [1] other["type__"] = "cell" other["size__"] = np.asarray([size]) return Cell._from_runtime(other, _runtime) else: raise ValueError("Don't know what to do with type", type__) else: other = type(other)( zip(other.keys(), map(_from_any, other.values())) ) return Struct.from_any(other) if isinstance(other, (list, tuple, set)): # nested tuples are cells of cells, not cell arrays if _runtime: return Cell._from_runtime(other, _runtime) else: return Cell.from_any(other) if isinstance(other, (np.ndarray, int, float, complex, bool)): # [array of] numbers -> Array if _runtime: return Array._from_runtime(other, _runtime) else: return Array.from_any(other) if isinstance(other, str): return other if isinstance(other, bytes): return other.decode() if other is None: # This can happen when matlab code is called without `nargout` return other matlab = _import_matlab() if matlab and isinstance(other, matlab.object): return MatlabFunction._from_runtime(other, _runtime) if type(other) in _matlab_array_types(): return Array._from_runtime(other, _runtime) if hasattr(other, "__iter__"): # Iterable -> let's try to make it a cell return cls.from_any(list(other), _runtime=_runtime) raise TypeError(f"Cannot convert {type(other)} into a matlab object.") @classmethod def _from_runtime(cls, obj, _runtime): return cls.from_any(obj, _runtime=_runtime) @classmethod def _to_runtime(cls, obj): """ Convert object to representation that the matlab runtime understands. """ to_runtime = cls._to_runtime from ..utils import sparse # FIXME: Circular import if isinstance(obj, MatlabType): # class / structarray / cell return obj._as_runtime() elif isinstance(obj, (list, tuple, set)): return type(obj)(map(to_runtime, obj)) elif isinstance(obj, dict): if "type__" in obj: return obj return type(obj)(zip(obj.keys(), map(to_runtime, obj.values()))) elif isinstance(obj, np.ndarray): obj = np.asarray(obj) if obj.dtype in (object, dict): shape, dtype = obj.shape, obj.dtype obj = np.fromiter(map(to_runtime, obj.flat), dtype=dtype) obj = obj.reshape(shape) return obj.tolist() return obj elif sparse and isinstance(obj, sparse.sparray): SparseArray = _imports.SparseArray return SparseArray.from_any(obj)._as_runtime() else: # TODO: do we want to raise if the type is not supported by matlab? # # Valid types for matlab bindings: # - bool, int, float, complex, str, bytes, bytearray # # Valid matlab types that we have already dealt with: # - list, tuple, set, dict, ndarray # # All other values/types are invalid (including `None`!) return obj def _as_runtime(self): raise NotImplementedError def _as_matlab_object(self): # Backward compatibility # FIXME: Or just keep `_as_matlab_object` and remove `_as_runtime`? return self._as_runtime()Generic type for objects that have an exact matlab equivalent.
Subclasses
Static methods
def from_any(other, **kwargs)-
Convert python/matlab objects to
MatlabTypeobjects (Cell,Struct,Array,MatlabClass).Conversion is performed in-place when possible.