Merge pull request #339 from hameerabbasi/clip-fix

Fix signature for clip.

docs/generated/sparse.clip.rst

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

docs/generated/sparse.rst

@@ -29,6 +29,8 @@ API
 
     concatenate
 
+    clip
+
     diagonal
 
     diagonalize

sparse/_common.py

@@ -2,6 +2,7 @@
 
 from ._utils import check_compressed_axes
 from ._coo import (
+    clip,
     tensordot,
     dot,
     matmul,

sparse/_coo/__init__.py

@@ -5,6 +5,7 @@
     dot,
     matmul,
     concatenate,
+    clip,
     stack,
     triu,
     tril,
@@ -33,6 +34,7 @@
     "dot",
     "matmul",
     "concatenate",
+    "clip",
     "stack",
     "triu",
     "tril",

sparse/_coo/common.py

@@ -1384,3 +1384,49 @@ def _diagonal_idx(coordlist, axis1, axis2, offset):
             if coordlist[axis1][i] + offset == coordlist[axis2][i]
         ]
     )
+
+
+def clip(a, a_min=None, a_max=None, out=None):
+    """
+    Clip (limit) the values in the array.
+
+    Return an array whose values are limited to ``[min, max]``. One of min
+    or max must be given.
+
+    Parameters
+    ----------
+    a: 
+    a_min : scalar or `SparseArray` or `None`
+        Minimum value. If `None`, clipping is not performed on lower
+        interval edge.
+    a_max : scalar or `SparseArray` or `None`
+        Maximum value. If `None`, clipping is not performed on upper
+        interval edge.
+    out : SparseArray, optional
+        If provided, the results will be placed in this array. It may be
+        the input array for in-place clipping. `out` must be of the right
+        shape to hold the output. Its type is preserved.
+
+    Returns
+    -------
+    clipped_array : SparseArray
+        An array with the elements of `self`, but where values < `min` are
+        replaced with `min`, and those > `max` with `max`.
+
+    Examples
+    --------
+    >>> import sparse
+    >>> x = sparse.COO.from_numpy([0, 0, 0, 1, 2, 3])
+    >>> sparse.clip(x, a_min=1).todense()  # doctest: +NORMALIZE_WHITESPACE
+    array([1, 1, 1, 1, 2, 3])
+    >>> sparse.clip(x, a_max=1).todense()  # doctest: +NORMALIZE_WHITESPACE
+    array([0, 0, 0, 1, 1, 1])
+    >>> sparse.clip(x, a_min=1, a_max=2).todense() # doctest: +NORMALIZE_WHITESPACE
+    array([1, 1, 1, 1, 2, 2])
+
+    See also
+    --------
+    numpy.clip : Equivalent NumPy function
+    """
+    a = asCOO(a, name="clip")
+    return a.clip(a_min, a_max)

sparse/_coo/core.py

@@ -2133,39 +2133,16 @@ def round(self, decimals=0, out=None):
     round_ = round
 
     def clip(self, min=None, max=None, out=None):
-        """Clip (limit) the values in the array.
+        """
+        Clip (limit) the values in the array.
 
         Return an array whose values are limited to ``[min, max]``. One of min
         or max must be given.
 
-        Parameters
-        ----------
-        min : scalar or array_like or `None`
-            Minimum value. If `None`, clipping is not performed on lower
-            interval edge.
-        max : scalar or array_like or `None`
-            Maximum value. If `None`, clipping is not performed on upper
-            interval edge.
-        out : COO, optional
-            If provided, the results will be placed in this array. It may be
-            the input array for in-place clipping. `out` must be of the right
-            shape to hold the output. Its type is preserved.
-
-        Returns
-        -------
-        clipped_array : COO
-            An array with the elements of `self`, but where values < `min` are
-            replaced with `min`, and those > `max` with `max`.
-
-        Examples
+        See Also
         --------
-        >>> x = COO.from_numpy([0, 0, 0, 1, 2, 3])
-        >>> x.clip(min=1).todense()  # doctest: +NORMALIZE_WHITESPACE
-        array([1, 1, 1, 1, 2, 3])
-        >>> x.clip(max=1).todense()  # doctest: +NORMALIZE_WHITESPACE
-        array([0, 0, 0, 1, 1, 1])
-        >>> x.clip(min=1, max=2).todense() # doctest: +NORMALIZE_WHITESPACE
-        array([1, 1, 1, 1, 2, 2])
+        sparse.clip : For full documentation and more details.
+        numpy.clip : Equivalent NumPy function.
         """
         if min is None and max is None:
             raise ValueError("One of max or min must be given.")