Reference

spector.indices

A sparse boolean array, i.e., set of indices.

Provides a memory efficient set interface, with optimized conversion between numpy arrays.

Parameters:

Name	Type	Description	Default
keys	Iterable	optional iterable of keys	required

__doc__ = 'A sparse boolean array, i.e., set of indices.\n\n Provides a memory efficient set interface, with optimized conversion between numpy arrays.\n\n Args:\n keys (Iterable): optional iterable of keys\n ' class

str(object='') -> str str(bytes_or_buffer[, encoding[, errors]]) -> str

Create a new string object from the given object. If encoding or errors is specified, then the object must expose a data buffer that will be decoded using the given encoding and error handler. Otherwise, returns the result of object.str() (if defined) or repr(object). encoding defaults to 'utf-8'. errors defaults to 'strict'.

__pyx_vtable__ = <capsule object NULL at 0x7fa1bb847e70> class

Capsule objects let you wrap a C "void *" pointer in a Python object. They're a way of passing data through the Python interpreter without creating your own custom type.

Capsules are used for communication between extension modules. They provide a way for an extension module to export a C interface to other extension modules, so that extension modules can use the Python import mechanism to link to one another.

__array__(dtype=int64, copy=None) method descriptor

Return keys as numpy array.

__contains__(key) method descriptor

Return bool(key in self).

__eq__(value) method descriptor

Return self==value.

__ge__(value) method descriptor

Return self>=value.

__gt__(value) method descriptor

Return self>value.

__init__(*args, **kwargs) method descriptor

Initialize self. See help(type(self)) for accurate signature.

__ior__(value) method descriptor

Return self|=value.

__isub__(value) method descriptor

Return self-=value.

__iter__() method descriptor

Implement iter(self).

__ixor__(value) method descriptor

Return self^=value.

__le__(value) method descriptor

Return self<=value.

__len__() method descriptor

Return len(self).

__lt__(value) method descriptor

Return self<value.

__matmul__(object) method descriptor

Return binary dot product, i.e., intersection count.

__ne__(value) method descriptor

Return self!=value.

__new__(*args, **kwargs) builtin

Create and return a new object. See help(type) for accurate signature.

__rand__(value) method descriptor

Return value&self.

__repr__() method descriptor

Return repr(self).

__rmatmul__(value) method descriptor

Return value@self.

__ror__(value) method descriptor

Return value|self.

__rsub__(value) method descriptor

Return value-self.

__rxor__(value) method descriptor

Return value^self.

add(key) method descriptor

Add an index key.

clear() method descriptor

Remove all indices.

difference(*others) method descriptor

Return the difference of sets as a new set.

discard(key) method descriptor

Remove an index key, if present.

dot(*others) method descriptor

Return the intersection count of sets.

fromdense(values) classmethod

Return indices from a dense array representation.

intersection(*others) method descriptor

Return the intersection of sets as a new set.

isdisjoint(other) method descriptor

Return whether two indices have a null intersection.

todense(minlength=0, dtype=bool) method descriptor

Return a dense array representation of indices.

union(*others) method descriptor

Return the union of sets as a new set.

update(*others) method descriptor

Update from indices, arrays, or iterables.

spector.vector.vector

A sparse array of index keys mapped to numeric values.

Provides a memory efficient Counter interface, with optimized conversion between numpy arrays.

Parameters:

Name	Type	Description	Default
keys	Iterable[int]	optional iterable of keys	required
values		optional scalar or iterable of values	required

__doc__ = 'A sparse array of index keys mapped to numeric values.\n\n Provides a memory efficient Counter interface, with optimized conversion between numpy arrays.\n\n Args:\n keys (Iterable[int]): optional iterable of keys\n values: optional scalar or iterable of values\n ' class

str(object='') -> str str(bytes_or_buffer[, encoding[, errors]]) -> str

Create a new string object from the given object. If encoding or errors is specified, then the object must expose a data buffer that will be decoded using the given encoding and error handler. Otherwise, returns the result of object.str() (if defined) or repr(object). encoding defaults to 'utf-8'. errors defaults to 'strict'.

__pyx_vtable__ = <capsule object NULL at 0x7fa1bb977ab0> class

Capsule objects let you wrap a C "void *" pointer in a Python object. They're a way of passing data through the Python interpreter without creating your own custom type.

Capsules are used for communication between extension modules. They provide a way for an extension module to export a C interface to other extension modules, so that extension modules can use the Python import mechanism to link to one another.

__abs__() method descriptor

abs(self)

__contains__(key) method descriptor

Return bool(key in self).

__delitem__(key) method descriptor

Delete self[key].

__eq__(value) method descriptor

Return self==value.

__ge__(value) method descriptor

Return self>=value.

__getitem__(key) method descriptor

Return self[key].

__gt__(value) method descriptor

Return self>value.

__iadd__(value) method descriptor

Return self+=value.

__imul__(value) method descriptor

Return self*=value.

__init__(*args, **kwargs) method descriptor

Initialize self. See help(type(self)) for accurate signature.

__ior__(value) method descriptor

Return self|=value.

__ipow__(value) method descriptor

Return self**=value.

__isub__(value) method descriptor

Return self-=value.

__iter__() method descriptor

Implement iter(self).

__itruediv__(value) method descriptor

Return self/=value.

__ixor__(value) method descriptor

Return self^=value.

__le__(value) method descriptor

Return self<=value.

__len__() method descriptor

Return len(self).

__lt__(value) method descriptor

Return self<value.

__matmul__(object) method descriptor

Return vector dot product.

__ne__(value) method descriptor

Return self!=value.

__neg__() method descriptor

-self

__new__(*args, **kwargs) builtin

Create and return a new object. See help(type) for accurate signature.

__pow__(value, mod=None) method descriptor

Return pow(self, value, mod).

__rand__(value) method descriptor

Return value&self.

__repr__() method descriptor

Return repr(self).

__rmatmul__(value) method descriptor

Return value@self.

__ror__(value) method descriptor

Return value|self.

__rpow__(value, mod=None) method descriptor

Return pow(value, self, mod).

__rxor__(value) method descriptor

Return value^self.

__setitem__(key, value) method descriptor

Set self[key] to value.

argmax(**kwargs) method descriptor

Return key with maximum value.

argmin(**kwargs) method descriptor

Return key with minimum value.

argpartition(kth, **kwargs) method descriptor

Return keys partitioned by values.

argsort(**kwargs) method descriptor

Return keys sorted by values.

clear() method descriptor

Remove all items.

difference(*others) method descriptor

Provisional set difference; return vector without keys.

equal(other) method descriptor

Return whether vectors are equal as scalar bool; == is element-wise.

filter(ufunc, *args, **kwargs) method descriptor

Return element-wise array of keys from applying predicate across vectors.

fromdense(values) classmethod

Return vector from a dense array representation.

items() method descriptor

Return zipped keys and values.

keys() method descriptor

Return keys as numpy array.

map(ufunc, *args, **kwargs) method descriptor

Return element-wise array of values from applying function across vectors.

max(initial=None, **kwargs) method descriptor

Return maximum value.

maximum(value) method descriptor

Return element-wise maximum vector.

min(initial=None, **kwargs) method descriptor

Return minimum value.

minimum(value) method descriptor

Return element-wise minimum vector.

sum(initial=0.0, dtype=float, **kwargs) method descriptor

Return sum of values.

todense(minlength=0, dtype=float) method descriptor

Return a dense array representation of vector.

update(keys, values=1.0) method descriptor

Update from vector, arrays, mapping, or keys with scalar.

values(dtype=float) method descriptor

Return values as numpy array.

spector.matrix.matrix

Bases: defaultdict

A sparse vector of vectors.

Parameters:

Name	Type	Description	Default
data	Iterable		()

Source code in spector/matrix.py

class matrix(collections.defaultdict):
    """A sparse vector of vectors.

    Args:
        data (Iterable):
    """

    def __init__(self, data=(), copy=True):
        super().__init__(vector)
        (self if copy else super()).update(data)

    @classmethod
    def cast(cls, data) -> 'matrix':
        return cls(data, copy=False)

    @property
    def row(self) -> np.ndarray:
        """COO format row index array of the matrix"""
        return np.concatenate([np.full(len(self[key]), key) for key in self])

    @property
    def col(self) -> np.ndarray:
        """COO format column index array of the matrix"""
        return np.concatenate([vec.keys() for vec in self.values()])

    @property
    def data(self) -> np.ndarray:
        """COO format data array of the matrix"""
        return np.concatenate([vec.values() for vec in self.values()])

    def update(self, data):
        """Update from mapping or iterable."""
        if isinstance(data, Mapping):
            for key in data:
                self[key].update(data[key])
        else:
            for key, value in data:
                self[key].update(value)

    def __iadd__(self, other):
        if isinstance(other, Mapping):
            for key in other:
                self[key] += other[key]
        else:
            self.map(vector.__iadd__, other)
        return self

    def __add__(self, other) -> 'matrix':
        return type(self)(self).__iadd__(other)

    def __imul__(self, other):
        if isinstance(other, Mapping):
            for key in set(self).difference(other):
                del self[key]
            for key in self:
                self[key] *= other[key]
        else:
            self.map(vector.__imul__, other)
        return self

    def __mul__(self, other) -> dict:
        if isinstance(other, Mapping):
            return self.cast((key, self[key] * other[key]) for key in set(self).intersection(other))
        return self.map(vector.__mul__, other)

    def sum(self, axis: int | None = None):
        """Return sum of matrix elements across axis, by default both."""
        if axis in (0, -2):
            return functools.reduce(vector.__iadd__, self.values(), vector())
        if axis in (1, -1):
            return self.map(np.sum)
        if axis is None:
            return sum(map(np.sum, self.values()))
        raise np.exceptions.AxisError(axis, ndim=2)

    def map(self, func: Callable, *args, **kwargs) -> dict:
        """Return matrix with function applies across vectors."""
        result = {key: func(self[key], *args, **kwargs) for key in self}
        if all(isinstance(value, vector) for value in result.values()):
            return self.cast(result)
        return result

    def filter(self, func: Callable, *args, **kwargs) -> 'matrix':
        """Return matrix with function applies across vectors."""
        return self.cast((key, vec) for key, vec in self.items() if func(vec, *args, **kwargs))

    @classmethod
    def fromcoo(cls, row: Iterable, col: Iterable[int], data: Iterable[float]) -> 'matrix':
        """Return matrix from COOrdinate format arrays."""
        return cls.cast((key, vector(col, data)) for key, col, data in groupby(row, col, data))

    def transpose(self) -> 'matrix':
        """Return matrix with reversed dimensions."""
        return self.fromcoo(self.col, self.row, self.data)

    T = property(transpose)

    def __matmul__(self, other: 'matrix') -> 'matrix':
        other = other.transpose()
        return self.cast((key, vector(other.map(self[key].__matmul__))) for key in self)

col property

COO format column index array of the matrix

data property

COO format data array of the matrix

row property

COO format row index array of the matrix

filter(func, *args, **kwargs)

Return matrix with function applies across vectors.

Source code in spector/matrix.py

def filter(self, func: Callable, *args, **kwargs) -> 'matrix':
    """Return matrix with function applies across vectors."""
    return self.cast((key, vec) for key, vec in self.items() if func(vec, *args, **kwargs))

fromcoo(row, col, data) classmethod

Return matrix from COOrdinate format arrays.

Source code in spector/matrix.py

@classmethod
def fromcoo(cls, row: Iterable, col: Iterable[int], data: Iterable[float]) -> 'matrix':
    """Return matrix from COOrdinate format arrays."""
    return cls.cast((key, vector(col, data)) for key, col, data in groupby(row, col, data))

map(func, *args, **kwargs)

Return matrix with function applies across vectors.

Source code in spector/matrix.py

def map(self, func: Callable, *args, **kwargs) -> dict:
    """Return matrix with function applies across vectors."""
    result = {key: func(self[key], *args, **kwargs) for key in self}
    if all(isinstance(value, vector) for value in result.values()):
        return self.cast(result)
    return result

sum(axis=None)

Return sum of matrix elements across axis, by default both.

Source code in spector/matrix.py

def sum(self, axis: int | None = None):
    """Return sum of matrix elements across axis, by default both."""
    if axis in (0, -2):
        return functools.reduce(vector.__iadd__, self.values(), vector())
    if axis in (1, -1):
        return self.map(np.sum)
    if axis is None:
        return sum(map(np.sum, self.values()))
    raise np.exceptions.AxisError(axis, ndim=2)

transpose()

Return matrix with reversed dimensions.

Source code in spector/matrix.py

def transpose(self) -> 'matrix':
    """Return matrix with reversed dimensions."""
    return self.fromcoo(self.col, self.row, self.data)

update(data)

Update from mapping or iterable.

Source code in spector/matrix.py

def update(self, data):
    """Update from mapping or iterable."""
    if isinstance(data, Mapping):
        for key in data:
            self[key].update(data[key])
    else:
        for key, value in data:
            self[key].update(value)

spector.groupby(keys, *arrays)

Generate unique keys with associated groups.

Parameters:

Name	Type	Description	Default
keys	Iterable		required
*arrays	Iterable		()

Source code in spector/matrix.py

def groupby(keys: Iterable, *arrays) -> Iterator[tuple]:
    """Generate unique keys with associated groups.

    Args:
        keys:
        *arrays (Iterable):
    """
    arrays = tuple(map(np.asarray, arrays))
    try:
        items = _arggroupby(asiarray(keys))
    except TypeError:  # fallback to sorting
        items = arggroupby(keys)
    for key, values in items:
        yield key, *(arr[values] for arr in arrays)