Source code for mars.tensor.reduction.nanmin

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from ... import opcodes as OperandDef
from ..datasource import tensor as astensor
from .core import TensorReduction, TensorReductionMixin


class TensorNanMin(TensorReduction, TensorReductionMixin):
    _op_type_ = OperandDef.NANMIN
    _func_name = 'nanmin'

    def __init__(self, axis=None, keepdims=None, combine_size=None, stage=None, **kw):
        stage = self._rewrite_stage(stage)
        super().__init__(_axis=axis, _keepdims=keepdims,
                         _combine_size=combine_size, stage=stage, **kw)


[docs]def nanmin(a, axis=None, out=None, keepdims=None, combine_size=None): """ Return minimum of a tensor 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 Tensor containing numbers whose minimum is desired. If `a` is not an tensor, a conversion is attempted. axis : int, optional Axis along which the minimum is computed. The default is to compute the minimum of the flattened tensor. out : Tensor, optional Alternate output tensor 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. 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`. If the value is anything but the default, then `keepdims` will be passed through to the `min` method of sub-classes of `Tensor`. If the sub-classes methods does not implement `keepdims` any exceptions will be raised. combine_size: int, optional The number of chunks to combine. Returns ------- nanmin : Tensor An tensor with the same shape as `a`, with the specified axis removed. If `a` is a 0-d tensor, or if axis is None, a tensor 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 ----- Mars 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 mt.min. Examples -------- >>> import mars.tensor as mt >>> a = mt.array([[1, 2], [3, mt.nan]]) >>> mt.nanmin(a).execute() 1.0 >>> mt.nanmin(a, axis=0).execute() array([ 1., 2.]) >>> mt.nanmin(a, axis=1).execute() array([ 1., 3.]) When positive infinity and negative infinity are present: >>> mt.nanmin([1, 2, mt.nan, mt.inf]).execute() 1.0 >>> mt.nanmin([1, 2, mt.nan, mt.NINF]).execute() -inf """ a = astensor(a) op = TensorNanMin(axis=axis, dtype=a.dtype, keepdims=keepdims, combine_size=combine_size) return op(a, out=out)