def nanmin(a, axis=None, out=None, keepdims=False):
if not isinstance(a, np.ndarray) or type(a) is np.ndarray:<EOL><INDENT>res = np.fmin.reduce(a, axis=axis, out=out, keepdims=keepdims)<EOL>if np.isnan(res).any():<EOL><INDENT>warnings.warn("<STR_LIT>", RuntimeWarning)<EOL><DEDENT><DEDENT>else:<EOL><INDENT>a, mask = _replace_nan(a, +np.inf)<EOL>res = np.amin(a, axis=axis, out=out, keepdims=keepdims)<EOL>if mask is None:<EOL><INDENT>return res<EOL><DEDENT>mask = np.all(mask, axis=axis, keepdims=keepdims)<EOL>if np.any(mask):<EOL><INDENT>res = _copyto(res, np.nan, mask)<EOL>warnings.warn("<STR_LIT>", RuntimeWarning)<EOL><DEDENT><DEDENT>return res<EOL>
Return minimum of an array or minimum along an axis, ignoring any NaNs.
When all-NaN slices are encountered a ``RuntimeWarning`` is raised and
Nan is returned for that slice.

Parameters
----------
a : array_like
    Array containing numbers whose minimum is desired. If `a` is not an
    array, a conversion is attempted.
axis : int, optional
    Axis along which the minimum is computed. The default is to compute
    the minimum of the flattened array.
out : ndarray, optional
    Alternate output array in which to place the result.  The default
    is ``None``; if provided, it must have the same shape as the
    expected output, but the type will be cast if necessary.  See
    `doc.ufuncs` for details.

    .. versionadded:: 1.8.0
keepdims : bool, optional
    If this is set to True, the axes which are reduced are left in the
    result as dimensions with size one. With this option, the result
    will broadcast correctly against the original `a`.

    .. versionadded:: 1.8.0

Returns
-------
nanmin : ndarray
    An array with the same shape as `a`, with the specified axis
    removed.  If `a` is a 0-d array, or if axis is None, an ndarray
    scalar is returned.  The same dtype as `a` is returned.

See Also
--------
nanmax :
    The maximum value of an array along a given axis, ignoring any NaNs.
amin :
    The minimum value of an array along a given axis, propagating any NaNs.
fmin :
    Element-wise minimum of two arrays, ignoring any NaNs.
minimum :
    Element-wise minimum of two arrays, propagating any NaNs.
isnan :
    Shows which elements are Not a Number (NaN).
isfinite:
    Shows which elements are neither NaN nor infinity.

amax, fmax, maximum

Notes
-----
Numpy uses the IEEE Standard for Binary Floating-Point for Arithmetic
(IEEE 754). This means that Not a Number is not equivalent to infinity.
Positive infinity is treated as a very large number and negative
infinity is treated as a very small (i.e. negative) number.

If the input has a integer type the function is equivalent to np.min.

Examples
--------
>>> a = np.array([[1, 2], [3, np.nan]])
>>> np.nanmin(a)
1.0
>>> np.nanmin(a, axis=0)
array([ 1.,  2.])
>>> np.nanmin(a, axis=1)
array([ 1.,  3.])

When positive infinity and negative infinity are present:

>>> np.nanmin([1, 2, np.nan, np.inf])
1.0
>>> np.nanmin([1, 2, np.nan, np.NINF])
-inf