Module mpython.sparse_array

Classes

class SparseArray (*args, **kwargs)
Expand source code 
class SparseArray(_SparseMixin, Array):
    """
    Matlab sparse arrays (dense backend).

    ```python
    # Instantiate from size
    SparseArray(N, M, ...)
    SparseArray([N, M, ...])
    SparseArray.from_shape([N, M, ...])

    # Instantiate from existing sparse or dense array
    SparseArray(other_array)
    SparseArray.from_any(other_array)

    # Other options
    SparseArray(..., dtype=None, *, copy=None)
    ```

    !!! warning
        Lists or vectors of integers can be interpreted as shapes
        or as dense arrays to copy. They are interpreted as shapes
        by the `SparseArray` constructor. To ensure that they are
        interpreted as dense arrays to copy, usse `SparseArray.from_any`.

    !!! note
        This is not really a sparse array, but a dense array that gets
        converted to a sparse array when passed to matlab.
    """

    def to_dense(self) -> "Array":
        return np.ndarray.view(self, Array)

    @classmethod
    def from_coo(cls, values, indices, shape=None, **kw) -> "SparseArray":
        """
        Build a sparse array from indices and values.

        Parameters
        ----------
        values : (N,) ArrayLike
            Values to set at each index.
        indices : (D, N) ArrayLike
            Indices of nonzero elements.
        shape : list[int] | None
            Shape of the array.
        dtype : np.dtype | None
            Target data type. Same as `values` by default.

        Returns
        -------
        array : SparseArray
            New array.
        """
        dtype = kw.get("dtype", None)
        indices = np.asarray(indices)
        values = np.asarray(values, dtype=dtype)
        if shape is None:
            shape = (1 + indices.max(-1)).astype(np.uint64).tolist()
        if dtype is None:
            dtype = values.dtype
        obj = cls.from_shape(shape, dtype=dtype)
        obj[tuple(indices)] = values
        return obj

Matlab sparse arrays (dense backend).

# Instantiate from size
SparseArray(N, M, ...)
SparseArray([N, M, ...])
SparseArray.from_shape([N, M, ...])

# Instantiate from existing sparse or dense array
SparseArray(other_array)
SparseArray.from_any(other_array)

# Other options
SparseArray(..., dtype=None, *, copy=None)

Warning

Lists or vectors of integers can be interpreted as shapes or as dense arrays to copy. They are interpreted as shapes by the SparseArray constructor. To ensure that they are interpreted as dense arrays to copy, usse Array.from_any().

Note

This is not really a sparse array, but a dense array that gets converted to a sparse array when passed to matlab.

Ancestors

Static methods

def from_coo(values, indices, shape=None, **kw) ‑> SparseArray

Build a sparse array from indices and values.

Parameters

values : (N,) ArrayLike
Values to set at each index.
indices : (D, N) ArrayLike
Indices of nonzero elements.
shape : list[int] | None
Shape of the array.
dtype : np.dtype | None
Target data type. Same as values by default.

Returns

array : SparseArray
New array.

Methods

def to_dense(self) ‑> Array
Expand source code 
def to_dense(self) -> "Array":
    return np.ndarray.view(self, Array)

Inherited members