Merge pull request #464 from hameerabbasi/dok-ctor-scipy

Add DOK sparse constructor.

docs/generated/sparse.DOK.from_scipy_sparse.rst

@@ -0,0 +1,6 @@
+DOK.from\_scipy\_sparse
+=======================
+
+.. currentmodule:: sparse
+
+.. automethod:: DOK.from_scipy_sparse

docs/generated/sparse.DOK.rst

@@ -36,6 +36,8 @@ DOK
       DOK.from_coo
 
       DOK.from_numpy
+
+      DOK.from_scipy_sparse
 
       DOK.to_coo
 

sparse/_dok.py

@@ -3,6 +3,7 @@
 from collections.abc import Iterable
 
 import numpy as np
+import scipy.sparse
 from numpy.lib.mixins import NDArrayOperatorsMixin
 
 from ._slicing import normalize_index
@@ -106,6 +107,11 @@ def __init__(self, shape, data=None, dtype=None, fill_value=None):
             self._make_shallow_copy_of(ar)
             return
 
+        if isinstance(shape, scipy.sparse.spmatrix):
+            ar = DOK.from_scipy_sparse(shape)
+            self._make_shallow_copy_of(ar)
+            return
+
         self.dtype = np.dtype(dtype)
 
         if not data:
@@ -127,6 +133,32 @@ def __init__(self, shape, data=None, dtype=None, fill_value=None):
         else:
             raise ValueError("data must be a dict.")
 
+    @classmethod
+    def from_scipy_sparse(cls, x):
+        """
+        Create a :obj:`DOK` array from a :obj:`scipy.sparse.spmatrix`.
+
+        Parameters
+        ----------
+        x : scipy.sparse.spmatrix
+            The matrix to convert.
+
+        Returns
+        -------
+        DOK
+            The equivalent :obj:`DOK` array.
+
+        Examples
+        --------
+        >>> x = scipy.sparse.rand(6, 3, density=0.2)
+        >>> s = DOK.from_scipy_sparse(x)
+        >>> np.array_equal(x.todense(), s.todense())
+        True
+        """
+        from sparse import COO
+
+        return COO.from_scipy_sparse(x).asformat(cls)
+
     @classmethod
     def from_coo(cls, x):
         """

sparse/tests/test_dok.py

@@ -47,6 +47,15 @@ def test_convert_to_numpy():
     assert_eq(x, s)
 
 
+def test_convert_from_scipy_sparse():
+    import scipy.sparse
+
+    x = scipy.sparse.rand(6, 3, density=0.2)
+    s = DOK(x)
+
+    assert_eq(x, s)
+
+
 @pytest.mark.parametrize(
     "shape, data",
     [