Add full and full_like

docs/changelog.rst

@@ -4,6 +4,7 @@ Changelog
 Upcoming Release
 ----------------
 
+* Added :code:`sparse.full` and :code:`sparse.full_like` (:pr:`189`).
 * Added :code:`COO.clip` method (:pr:`185`).
 * Added :code:`COO.copy` method, and changed pickle of :code:`COO` to not
   include its cache (:pr:`184`).

docs/generated/sparse.full.rst

@@ -0,0 +1,6 @@
+full
+====
+
+.. currentmodule:: sparse
+
+.. autofunction:: full

docs/generated/sparse.full_like.rst

@@ -0,0 +1,6 @@
+full_like
+=========
+
+.. currentmodule:: sparse
+
+.. autofunction:: full_like

docs/generated/sparse.rst

@@ -27,12 +27,16 @@ API
 
     concatenate
 
-    eye
-
     dot
 
     elemwise
 
+    eye
+
+    full
+
+    full_like
+
     load_npz
 
     nanmax

sparse/coo/__init__.py

@@ -2,4 +2,4 @@
 from .umath import elemwise
 from .common import (tensordot, dot, concatenate, stack, triu, tril, where,
                      nansum, nanprod, nanmin, nanmax, nanreduce, roll, eye,
-                     zeros, zeros_like, ones, ones_like)
+                     full, full_like, zeros, zeros_like, ones, ones_like)

sparse/coo/common.py

@@ -768,6 +768,71 @@ def eye(N, M=None, k=0, dtype=float):
                sorted=True)
 
 
+def full(shape, fill_value, dtype=None):
+    """Return a COO array of given shape and type, filled with `fill_value`.
+
+    Parameters
+    ----------
+    shape : int or tuple of ints
+        Shape of the new array, e.g., ``(2, 3)`` or ``2``.
+    fill_value : scalar
+        Fill value.
+    dtype : data-type, optional
+        The desired data-type for the array. The default, `None`, means
+        `np.array(fill_value).dtype`.
+
+    Returns
+    -------
+    out : COO
+        Array of `fill_value` with the given shape and dtype.
+
+    Examples
+    --------
+    >>> full(5, 9).todense()  # doctest: +NORMALIZE_WHITESPACE
+    array([9, 9, 9, 9, 9])
+
+    >>> full((2, 2), 9, dtype=float).todense()  # doctest: +SKIP
+    array([[9., 9.],
+           [9., 9.]])
+    """
+    from .core import COO
+
+    if dtype is None:
+        dtype = np.array(fill_value).dtype
+    if not isinstance(shape, tuple):
+        shape = (shape,)
+    data = np.empty(0, dtype=dtype)
+    coords = np.empty((len(shape), 0), dtype=np.intp)
+    return COO(coords, data=data, shape=shape,
+               fill_value=fill_value, has_duplicates=False,
+               sorted=True)
+
+
+def full_like(a, fill_value, dtype=None):
+    """Return a full array with the same shape and type as a given array.
+
+    Parameters
+    ----------
+    a : array_like
+        The shape and data-type of the result will match those of `a`.
+    dtype : data-type, optional
+        Overrides the data type of the result.
+
+    Returns
+    -------
+    out : COO
+        Array of `fill_value` with the same shape and type as `a`.
+
+    Examples
+    --------
+    >>> x = np.ones((2, 3), dtype='i8')
+    >>> full_like(x, 9.0).todense()  # doctest: +NORMALIZE_WHITESPACE
+    array([[9, 9, 9],
+           [9, 9, 9]])
+    """
+    return full(a.shape, fill_value, dtype=(a.dtype if dtype is None else dtype))
+
+
 def zeros(shape, dtype=float):
     """Return a COO array of given shape and type, filled with zeros.
 
@@ -793,14 +858,7 @@ def zeros(shape, dtype=float):
     array([[0, 0],
            [0, 0]])
     """
-    from .core import COO
-
-    if not isinstance(shape, tuple):
-        shape = (shape,)
-    data = np.empty(0, dtype=dtype)
-    coords = np.empty((len(shape), 0), dtype=np.intp)
-    return COO(coords, data=data, shape=shape, has_duplicates=False,
-               sorted=True)
+    return full(shape, 0, np.dtype(dtype))
 
 
 def zeros_like(a, dtype=None):
@@ -853,14 +911,7 @@ def ones(shape, dtype=float):
     array([[1, 1],
            [1, 1]])
     """
-    from .core import COO
-
-    if not isinstance(shape, tuple):
-        shape = (shape,)
-    data = np.empty(0, dtype=dtype)
-    coords = np.empty((len(shape), 0), dtype=np.intp)
-    return COO(coords, data=data, shape=shape, fill_value=1,
-               has_duplicates=False, sorted=True)
+    return full(shape, 1, np.dtype(dtype))
 
 
 def ones_like(a, dtype=None):

sparse/tests/test_coo.py

@@ -1808,25 +1808,37 @@ def test_eye(N, M):
         assert_eq(sparse.eye(N, M=M, k=k, dtype='i4'), np.eye(N, M=M, k=k, dtype='i4'))
 
 
-def test_zeros():
-    assert_eq(sparse.zeros(5), np.zeros(5))
-    assert_eq(sparse.zeros((5, 4)), np.zeros((5, 4)))
-    assert_eq(sparse.zeros((5, 4), dtype='i4'), np.zeros((5, 4), dtype='i4'))
+@pytest.mark.parametrize('funcname', ['ones', 'zeros'])
+def test_ones_zeros(funcname):
+    sp_func = getattr(sparse, funcname)
+    np_func = getattr(np, funcname)
 
+    assert_eq(sp_func(5), np_func(5))
+    assert_eq(sp_func((5, 4)), np_func((5, 4)))
+    assert_eq(sp_func((5, 4), dtype='i4'), np_func((5, 4), dtype='i4'))
+    assert_eq(sp_func((5, 4), dtype=None), np_func((5, 4), dtype=None))
+
+
+@pytest.mark.parametrize('funcname', ['ones_like', 'zeros_like'])
+def test_ones_zeros_like(funcname):
+    sp_func = getattr(sparse, funcname)
+    np_func = getattr(np, funcname)
 
-def test_zeros_like():
     x = np.ones((5, 5), dtype='i8')
-    assert_eq(sparse.zeros_like(x), np.zeros_like(x))
-    assert_eq(sparse.zeros_like(x, dtype='f8'), np.zeros_like(x, dtype='f8'))
+
+    assert_eq(sp_func(x), np_func(x))
+    assert_eq(sp_func(x, dtype='f8'), np_func(x, dtype='f8'))
+    assert_eq(sp_func(x, dtype=None), np_func(x, dtype=None))
 
 
-def test_ones():
-    assert_eq(sparse.ones(5), np.ones(5))
-    assert_eq(sparse.ones((5, 4)), np.ones((5, 4)))
-    assert_eq(sparse.ones((5, 4), dtype='i4'), np.ones((5, 4), dtype='i4'))
+def test_full():
+    assert_eq(sparse.full(5, 9), np.full(5, 9))
+    assert_eq(sparse.full(5, 9, dtype='f8'), np.full(5, 9, dtype='f8'))
+    assert_eq(sparse.full((5, 4), 9.5), np.full((5, 4), 9.5))
+    assert_eq(sparse.full((5, 4), 9.5, dtype='i4'), np.full((5, 4), 9.5, dtype='i4'))
 
 
-def test_ones_like():
+def test_full_like():
     x = np.zeros((5, 5), dtype='i8')
-    assert_eq(sparse.ones_like(x), np.ones_like(x))
-    assert_eq(sparse.ones_like(x, dtype='f8'), np.ones_like(x, dtype='f8'))
+    assert_eq(sparse.full_like(x, 9.5), np.full_like(x, 9.5))
+    assert_eq(sparse.full_like(x, 9.5, dtype='f8'), np.full_like(x, 9.5, dtype='f8'))