text: """
  Functions that ignore NaN.
  
  Functions
  ---------
  
  - `nanmin` -- minimum non-NaN value
  - `nanmax` -- maximum non-NaN value
  - `nanargmin` -- index of minimum non-NaN value
  - `nanargmax` -- index of maximum non-NaN value
  - `nansum` -- sum of non-NaN values
  - `nanmean` -- mean of non-NaN values
  - `nanvar` -- variance of non-NaN values
  - `nanstd` -- standard deviation of non-NaN values
  
  """
  from __future__ import division, absolute_import, print_function
  
  import warnings
  import numpy as np
  
  
  __all__ = [
      'nansum', 'nanmax', 'nanmin', 'nanargmax', 'nanargmin', 'nanmean',
      'nanvar', 'nanstd'
      ]
  
  
  def _replace_nan(a, val):
      """
      If `a` is of inexact type, make a copy of `a`, replace NaNs with
      the `val` value, and return the copy together with a boolean mask
      marking the locations where NaNs were present. If `a` is not of
      inexact type, do nothing and return `a` together with a mask of None.
  
      Parameters
      ----------
      a : array-like
          Input array.
      val : float
          NaN values are set to val before doing the operation.
  
      Returns
      -------
      y : ndarray
          If `a` is of inexact type, return a copy of `a` with the NaNs
          replaced by the fill value, otherwise return `a`.
      mask: {bool, None}
          If `a` is of inexact type, return a boolean mask marking locations of
          NaNs, otherwise return None.
  
      """
      is_new = not isinstance(a, np.ndarray)
      if is_new:
          a = np.array(a)
      if not issubclass(a.dtype.type, np.inexact):
          return a, None
      if not is_new:
          # need copy
          a = np.array(a, subok=True)
  
      mask = np.isnan(a)
      np.copyto(a, val, where=mask)
      return a, mask
  
  
  def _copyto(a, val, mask):
      """
      Replace values in `a` with NaN where `mask` is True.  This differs from
      copyto in that it will deal with the case where `a` is a numpy scalar.
  
      Parameters
      ----------
      a : ndarray or numpy scalar
          Array or numpy scalar some of whose values are to be replaced
          by val.
      val : numpy scalar
          Value used a replacement.
      mask : ndarray, scalar
          Boolean array. Where True the corresponding element of `a` is
          replaced by `val`. Broadcasts.
  
      Returns
      -------
      res : ndarray, scalar
          Array with elements replaced or scalar `val`.
  
      """
      if isinstance(a, np.ndarray):
          np.copyto(a, val, where=mask, casting='unsafe')
      else:
          a = a.dtype.type(val)
      return a
  
  
  def _divide_by_count(a, b, out=None):
      """
      Compute a/b ignoring invalid results. If `a` is an array the division
      is done in place. If `a` is a scalar, then its type is preserved in the
      output. If out is None, then then a is used instead so that the
      division is in place. Note that this is only called with `a` an inexact
      type.
  
      Parameters
      ----------
      a : {ndarray, numpy scalar}
          Numerator. Expected to be of inexact type but not checked.
      b : {ndarray, numpy scalar}
          Denominator.
      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.
  
      Returns
      -------
      ret : {ndarray, numpy scalar}
          The return value is a/b. If `a` was an ndarray the division is done
          in place. If `a` is a numpy scalar, the division preserves its type.
  
      """
      with np.errstate(invalid='ignore'):
          if isinstance(a, np.ndarray):
              if out is None:
                  return np.divide(a, b, out=a, casting='unsafe')
              else:
                  return np.divide(a, b, out=out, casting='unsafe')
          else:
              if out is None:
                  return a.dtype.type(a / b)
              else:
                  # This is questionable, but currently a numpy scalar can
                  # be output to a zero dimensional array.
                  return np.divide(a, b, out=out, casting='unsafe')
  
  
  def nanmin(a, axis=None, out=None, keepdims=False):
      """
      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
  
      """
      if not isinstance(a, np.ndarray) or type(a) is np.ndarray:
          # Fast, but not safe for subclasses of ndarray
          res = np.fmin.reduce(a, axis=axis, out=out, keepdims=keepdims)
          if np.isnan(res).any():
              warnings.warn("All-NaN axis encountered", RuntimeWarning)
      else:
          # Slow, but safe for subclasses of ndarray
          a, mask = _replace_nan(a, +np.inf)
          res = np.amin(a, axis=axis, out=out, keepdims=keepdims)
          if mask is None:
              return res
  
          # Check for all-NaN axis
          mask = np.all(mask, axis=axis, keepdims=keepdims)
          if np.any(mask):
              res = _copyto(res, mask, np.nan)
              warnings.warn("All-NaN axis encountered", RuntimeWarning)
      return res
  
  
  def nanmax(a, axis=None, out=None, keepdims=False):
      """
      Return the maximum of an array or maximum 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 maximum is desired. If `a` is not an
          array, a conversion is attempted.
      axis : int, optional
          Axis along which the maximum is computed. The default is to compute
          the maximum 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
      -------
      nanmax : 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
      --------
      nanmin :
          The minimum value of an array along a given axis, ignoring any NaNs.
      amax :
          The maximum value of an array along a given axis, propagating any NaNs.
      fmax :
          Element-wise maximum of two arrays, ignoring any NaNs.
      maximum :
          Element-wise maximum of two arrays, propagating any NaNs.
      isnan :
          Shows which elements are Not a Number (NaN).
      isfinite:
          Shows which elements are neither NaN nor infinity.
  
      amin, fmin, minimum
  
      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.max.
  
      Examples
      --------
      >>> a = np.array([[1, 2], [3, np.nan]])
      >>> np.nanmax(a)
      3.0
      >>> np.nanmax(a, axis=0)
      array([ 3.,  2.])
      >>> np.nanmax(a, axis=1)
      array([ 2.,  3.])
  
      When positive infinity and negative infinity are present:
  
      >>> np.nanmax([1, 2, np.nan, np.NINF])
      2.0
      >>> np.nanmax([1, 2, np.nan, np.inf])
      inf
  
      """
      if not isinstance(a, np.ndarray) or type(a) is np.ndarray:
          # Fast, but not safe for subclasses of ndarray
          res = np.fmax.reduce(a, axis=axis, out=out, keepdims=keepdims)
          if np.isnan(res).any():
              warnings.warn("All-NaN slice encountered", RuntimeWarning)
      else:
          # Slow, but safe for subclasses of ndarray
          a, mask = _replace_nan(a, -np.inf)
          res = np.amax(a, axis=axis, out=out, keepdims=keepdims)
          if mask is None:
              return res
  
          # Check for all-NaN axis
          mask = np.all(mask, axis=axis, keepdims=keepdims)
          if np.any(mask):
              res = _copyto(res, mask, np.nan)
              warnings.warn("All-NaN axis encountered", RuntimeWarning)
      return res
  
  
  def nanargmin(a, axis=None):
      """
      Return the indices of the minimum values in the specified axis ignoring
      NaNs. For all-NaN slices ``ValueError`` is raised. Warning: the results
      cannot be trusted if a slice contains only NaNs and Infs.
  
      Parameters
      ----------
      a : array_like
          Input data.
      axis : int, optional
          Axis along which to operate.  By default flattened input is used.
  
      Returns
      -------
      index_array : ndarray
          An array of indices or a single index value.
  
      See Also
      --------
      argmin, nanargmax
  
      Examples
      --------
      >>> a = np.array([[np.nan, 4], [2, 3]])
      >>> np.argmin(a)
      0
      >>> np.nanargmin(a)
      2
      >>> np.nanargmin(a, axis=0)
      array([1, 1])
      >>> np.nanargmin(a, axis=1)
      array([1, 0])
  
      """
      a, mask = _replace_nan(a, np.inf)
      res = np.argmin(a, axis=axis)
      if mask is not None:
          mask = np.all(mask, axis=axis)
          if np.any(mask):
              raise ValueError("All-NaN slice encountered")
      return res
  
  
  def nanargmax(a, axis=None):
      """
      Return the indices of the maximum values in the specified axis ignoring
      NaNs. For all-NaN slices ``ValueError`` is raised. Warning: the
      results cannot be trusted if a slice contains only NaNs and -Infs.
  
  
      Parameters
      ----------
      a : array_like
          Input data.
      axis : int, optional
          Axis along which to operate.  By default flattened input is used.
  
      Returns
      -------
      index_array : ndarray
          An array of indices or a single index value.
  
      See Also
      --------
      argmax, nanargmin
  
      Examples
      --------
      >>> a = np.array([[np.nan, 4], [2, 3]])
      >>> np.argmax(a)
      0
      >>> np.nanargmax(a)
      1
      >>> np.nanargmax(a, axis=0)
      array([1, 0])
      >>> np.nanargmax(a, axis=1)
      array([1, 1])
  
      """
      a, mask = _replace_nan(a, -np.inf)
      res = np.argmax(a, axis=axis)
      if mask is not None:
          mask = np.all(mask, axis=axis)
          if np.any(mask):
              raise ValueError("All-NaN slice encountered")
      return res
  
  
  def nansum(a, axis=None, dtype=None, out=None, keepdims=0):
      """
      Return the sum of array elements over a given axis treating Not a
      Numbers (NaNs) as zero.
  
      In Numpy versions <= 1.8 Nan is returned for slices that are all-NaN or
      empty. In later versions zero is returned.
  
      Parameters
      ----------
      a : array_like
          Array containing numbers whose sum is desired. If `a` is not an
          array, a conversion is attempted.
      axis : int, optional
          Axis along which the sum is computed. The default is to compute the
          sum of the flattened array.
      dtype : data-type, optional
          The type of the returned array and of the accumulator in which the
          elements are summed.  By default, the dtype of `a` is used.  An
          exception is when `a` has an integer type with less precision than
          the platform (u)intp. In that case, the default will be either
          (u)int32 or (u)int64 depending on whether the platform is 32 or 64
          bits. For inexact inputs, dtype must be inexact.
  
          .. versionadded:: 1.8.0
      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. The casting of NaN to integer can yield
          unexpected results.
  
          .. versionadded:: 1.8.0
      keepdims : bool, optional
          If 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 `arr`.
  
          .. versionadded:: 1.8.0
  
      Returns
      -------
      y : ndarray or numpy scalar
  
      See Also
      --------
      numpy.sum : Sum across array propagating NaNs.
      isnan : Show which elements are NaN.
      isfinite: Show which elements are not NaN or +/-inf.
  
      Notes
      -----
      If both positive and negative infinity are present, the sum will be Not
      A Number (NaN).
  
      Numpy integer arithmetic is modular. If the size of a sum exceeds the
      size of an integer accumulator, its value will wrap around and the
      result will be incorrect. Specifying ``dtype=double`` can alleviate
      that problem.
  
      Examples
      --------
      >>> np.nansum(1)
      1
      >>> np.nansum([1])
      1
      >>> np.nansum([1, np.nan])
      1.0
      >>> a = np.array([[1, 1], [1, np.nan]])
      >>> np.nansum(a)
      3.0
      >>> np.nansum(a, axis=0)
      array([ 2.,  1.])
      >>> np.nansum([1, np.nan, np.inf])
      inf
      >>> np.nansum([1, np.nan, np.NINF])
      -inf
      >>> np.nansum([1, np.nan, np.inf, -np.inf]) # both +/- infinity present
      nan
  
      """
      a, mask = _replace_nan(a, 0)
      return np.sum(a, axis=axis, dtype=dtype, out=out, keepdims=keepdims)
  
  
  def nanmean(a, axis=None, dtype=None, out=None, keepdims=False):
      """
      Compute the arithmetic mean along the specified axis, ignoring NaNs.
  
      Returns the average of the array elements.  The average is taken over
      the flattened array by default, otherwise over the specified axis.
      `float64` intermediate and return values are used for integer inputs.
  
      For all-NaN slices, NaN is returned and a `RuntimeWarning` is raised.
  
      .. versionadded:: 1.8.0
  
      Parameters
      ----------
      a : array_like
          Array containing numbers whose mean is desired. If `a` is not an
          array, a conversion is attempted.
      axis : int, optional
          Axis along which the means are computed. The default is to compute
          the mean of the flattened array.
      dtype : data-type, optional
          Type to use in computing the mean.  For integer inputs, the default
          is `float64`; for inexact inputs, it is the same as the input
          dtype.
      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.
      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 `arr`.
  
      Returns
      -------
      m : ndarray, see dtype parameter above
          If `out=None`, returns a new array containing the mean values,
          otherwise a reference to the output array is returned. Nan is
          returned for slices that contain only NaNs.
  
      See Also
      --------
      average : Weighted average
      mean : Arithmetic mean taken while not ignoring NaNs
      var, nanvar
  
      Notes
      -----
      The arithmetic mean is the sum of the non-NaN elements along the axis
      divided by the number of non-NaN elements.
  
      Note that for floating-point input, the mean is computed using the same
      precision the input has.  Depending on the input data, this can cause
      the results to be inaccurate, especially for `float32`.  Specifying a
      higher-precision accumulator using the `dtype` keyword can alleviate
      this issue.
  
      Examples
      --------
      >>> a = np.array([[1, np.nan], [3, 4]])
      >>> np.nanmean(a)
      2.6666666666666665
      >>> np.nanmean(a, axis=0)
      array([ 2.,  4.])
      >>> np.nanmean(a, axis=1)
      array([ 1.,  3.5])
  
      """
      arr, mask = _replace_nan(a, 0)
      if mask is None:
          return np.mean(arr, axis=axis, dtype=dtype, out=out, keepdims=keepdims)
  
      if dtype is not None:
          dtype = np.dtype(dtype)
      if dtype is not None and not issubclass(dtype.type, np.inexact):
          raise TypeError("If a is inexact, then dtype must be inexact")
      if out is not None and not issubclass(out.dtype.type, np.inexact):
          raise TypeError("If a is inexact, then out must be inexact")
  
      # The warning context speeds things up.
      with warnings.catch_warnings():
          warnings.simplefilter('ignore')
          cnt = np.sum(~mask, axis=axis, dtype=np.intp, keepdims=keepdims)
          tot = np.sum(arr, axis=axis, dtype=dtype, out=out, keepdims=keepdims)
          avg = _divide_by_count(tot, cnt, out=out)
  
      isbad = (cnt == 0)
      if isbad.any():
          warnings.warn("Mean of empty slice", RuntimeWarning)
          # NaN is the only possible bad value, so no further
          # action is needed to handle bad results.
      return avg
  
  
  def nanvar(a, axis=None, dtype=None, out=None, ddof=0, keepdims=False):
      """
      Compute the variance along the specified axis, while ignoring NaNs.
  
      Returns the variance of the array elements, a measure of the spread of
      a distribution.  The variance is computed for the flattened array by
      default, otherwise over the specified axis.
  
      For all-NaN slices or slices with zero degrees of freedom, NaN is
      returned and a `RuntimeWarning` is raised.
  
      .. versionadded:: 1.8.0
  
      Parameters
      ----------
      a : array_like
          Array containing numbers whose variance is desired.  If `a` is not an
          array, a conversion is attempted.
      axis : int, optional
          Axis along which the variance is computed.  The default is to compute
          the variance of the flattened array.
      dtype : data-type, optional
          Type to use in computing the variance.  For arrays of integer type
          the default is `float32`; for arrays of float types it is the same as
          the array type.
      out : ndarray, optional
          Alternate output array in which to place the result.  It must have
          the same shape as the expected output, but the type is cast if
          necessary.
      ddof : int, optional
          "Delta Degrees of Freedom": the divisor used in the calculation is
          ``N - ddof``, where ``N`` represents the number of non-NaN
          elements. By default `ddof` is zero.
      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 `arr`.
  
      Returns
      -------
      variance : ndarray, see dtype parameter above
          If `out` is None, return a new array containing the variance,
          otherwise return a reference to the output array. If ddof is >= the
          number of non-NaN elements in a slice or the slice contains only
          NaNs, then the result for that slice is NaN.
  
      See Also
      --------
      std : Standard deviation
      mean : Average
      var : Variance while not ignoring NaNs
      nanstd, nanmean
      numpy.doc.ufuncs : Section "Output arguments"
  
      Notes
      -----
      The variance is the average of the squared deviations from the mean,
      i.e.,  ``var = mean(abs(x - x.mean())**2)``.
  
      The mean is normally calculated as ``x.sum() / N``, where ``N = len(x)``.
      If, however, `ddof` is specified, the divisor ``N - ddof`` is used
      instead.  In standard statistical practice, ``ddof=1`` provides an
      unbiased estimator of the variance of a hypothetical infinite
      population.  ``ddof=0`` provides a maximum likelihood estimate of the
      variance for normally distributed variables.
  
      Note that for complex numbers, the absolute value is taken before
      squaring, so that the result is always real and nonnegative.
  
      For floating-point input, the variance is computed using the same
      precision the input has.  Depending on the input data, this can cause
      the results to be inaccurate, especially for `float32` (see example
      below).  Specifying a higher-accuracy accumulator using the ``dtype``
      keyword can alleviate this issue.
  
      Examples
      --------
      >>> a = np.array([[1, np.nan], [3, 4]])
      >>> np.var(a)
      1.5555555555555554
      >>> np.nanvar(a, axis=0)
      array([ 1.,  0.])
      >>> np.nanvar(a, axis=1)
      array([ 0.,  0.25])
  
      """
      arr, mask = _replace_nan(a, 0)
      if mask is None:
          return np.var(arr, axis=axis, dtype=dtype, out=out, ddof=ddof,
                        keepdims=keepdims)
  
      if dtype is not None:
          dtype = np.dtype(dtype)
      if dtype is not None and not issubclass(dtype.type, np.inexact):
          raise TypeError("If a is inexact, then dtype must be inexact")
      if out is not None and not issubclass(out.dtype.type, np.inexact):
          raise TypeError("If a is inexact, then out must be inexact")
  
      with warnings.catch_warnings():
          warnings.simplefilter('ignore')
  
          # Compute mean
          cnt = np.sum(~mask, axis=axis, dtype=np.intp, keepdims=True)
          avg = np.sum(arr, axis=axis, dtype=dtype, keepdims=True)
          avg = _divide_by_count(avg, cnt)
  
          # Compute squared deviation from mean.
          arr -= avg
          arr = _copyto(arr, 0, mask)
          if issubclass(arr.dtype.type, np.complexfloating):
              sqr = np.multiply(arr, arr.conj(), out=arr).real
          else:
              sqr = np.multiply(arr, arr, out=arr)
  
          # Compute variance.
          var = np.sum(sqr, axis=axis, dtype=dtype, out=out, keepdims=keepdims)
          if var.ndim < cnt.ndim:
              # Subclasses of ndarray may ignore keepdims, so check here.
              cnt = cnt.squeeze(axis)
          dof = cnt - ddof
          var = _divide_by_count(var, dof)
  
      isbad = (dof <= 0)
      if np.any(isbad):
          warnings.warn("Degrees of freedom <= 0 for slice.", RuntimeWarning)
          # NaN, inf, or negative numbers are all possible bad
          # values, so explicitly replace them with NaN.
          var = _copyto(var, np.nan, isbad)
      return var
  
  
  def nanstd(a, axis=None, dtype=None, out=None, ddof=0, keepdims=False):
      """
      Compute the standard deviation along the specified axis, while
      ignoring NaNs.
  
      Returns the standard deviation, a measure of the spread of a
      distribution, of the non-NaN array elements. The standard deviation is
      computed for the flattened array by default, otherwise over the
      specified axis.
  
      For all-NaN slices or slices with zero degrees of freedom, NaN is
      returned and a `RuntimeWarning` is raised.
  
      .. versionadded:: 1.8.0
  
      Parameters
      ----------
      a : array_like
          Calculate the standard deviation of the non-NaN values.
      axis : int, optional
          Axis along which the standard deviation is computed. The default is
          to compute the standard deviation of the flattened array.
      dtype : dtype, optional
          Type to use in computing the standard deviation. For arrays of
          integer type the default is float64, for arrays of float types it
          is the same as the array type.
      out : ndarray, optional
          Alternative output array in which to place the result. It must have
          the same shape as the expected output but the type (of the
          calculated values) will be cast if necessary.
      ddof : int, optional
          Means Delta Degrees of Freedom.  The divisor used in calculations
          is ``N - ddof``, where ``N`` represents the number of non-NaN
          elements.  By default `ddof` is zero.
      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 `arr`.
  
      Returns
      -------
      standard_deviation : ndarray, see dtype parameter above.
          If `out` is None, return a new array containing the standard
          deviation, otherwise return a reference to the output array. If
          ddof is >= the number of non-NaN elements in a slice or the slice
          contains only NaNs, then the result for that slice is NaN.
  
      See Also
      --------
      var, mean, std
      nanvar, nanmean
      numpy.doc.ufuncs : Section "Output arguments"
  
      Notes
      -----
      The standard deviation is the square root of the average of the squared
      deviations from the mean: ``std = sqrt(mean(abs(x - x.mean())**2))``.
  
      The average squared deviation is normally calculated as
      ``x.sum() / N``, where ``N = len(x)``.  If, however, `ddof` is
      specified, the divisor ``N - ddof`` is used instead. In standard
      statistical practice, ``ddof=1`` provides an unbiased estimator of the
      variance of the infinite population. ``ddof=0`` provides a maximum
      likelihood estimate of the variance for normally distributed variables.
      The standard deviation computed in this function is the square root of
      the estimated variance, so even with ``ddof=1``, it will not be an
      unbiased estimate of the standard deviation per se.
  
      Note that, for complex numbers, `std` takes the absolute value before
      squaring, so that the result is always real and nonnegative.
  
      For floating-point input, the *std* is computed using the same
      precision the input has. Depending on the input data, this can cause
      the results to be inaccurate, especially for float32 (see example
      below).  Specifying a higher-accuracy accumulator using the `dtype`
      keyword can alleviate this issue.
  
      Examples
      --------
      >>> a = np.array([[1, np.nan], [3, 4]])
      >>> np.nanstd(a)
      1.247219128924647
      >>> np.nanstd(a, axis=0)
      array([ 1.,  0.])
      >>> np.nanstd(a, axis=1)
      array([ 0.,  0.5])
  
      """
      var = nanvar(a, axis=axis, dtype=dtype, out=out, ddof=ddof,
                   keepdims=keepdims)
      if isinstance(var, np.ndarray):
          std = np.sqrt(var, out=var)
      else:
          std = var.dtype.type(np.sqrt(var))
      return std
  