text: """Automatically adapted for numpy Sep 19, 2005 by convertcode.py
  
  """
  import functools
  import warnings
  
  __all__ = ['iscomplexobj', 'isrealobj', 'imag', 'iscomplex',
             'isreal', 'nan_to_num', 'real', 'real_if_close',
             'typename', 'asfarray', 'mintypecode', 'asscalar',
             'common_type']
  
  import numpy.core.numeric as _nx
  from numpy.core.numeric import asarray, asanyarray, isnan, zeros
  from numpy.core.overrides import set_module
  from numpy.core import overrides
  from .ufunclike import isneginf, isposinf
  
  
  array_function_dispatch = functools.partial(
      overrides.array_function_dispatch, module='numpy')
  
  
  _typecodes_by_elsize = 'GDFgdfQqLlIiHhBb?'
  
  
  @set_module('numpy')
  def mintypecode(typechars, typeset='GDFgdf', default='d'):
      """
      Return the character for the minimum-size type to which given types can
      be safely cast.
  
      The returned type character must represent the smallest size dtype such
      that an array of the returned type can handle the data from an array of
      all types in `typechars` (or if `typechars` is an array, then its
      dtype.char).
  
      Parameters
      ----------
      typechars : list of str or array_like
          If a list of strings, each string should represent a dtype.
          If array_like, the character representation of the array dtype is used.
      typeset : str or list of str, optional
          The set of characters that the returned character is chosen from.
          The default set is 'GDFgdf'.
      default : str, optional
          The default character, this is returned if none of the characters in
          `typechars` matches a character in `typeset`.
  
      Returns
      -------
      typechar : str
          The character representing the minimum-size type that was found.
  
      See Also
      --------
      dtype, sctype2char, maximum_sctype
  
      Examples
      --------
      >>> np.mintypecode(['d', 'f', 'S'])
      'd'
      >>> x = np.array([1.1, 2-3.j])
      >>> np.mintypecode(x)
      'D'
  
      >>> np.mintypecode('abceh', default='G')
      'G'
  
      """
      typecodes = ((isinstance(t, str) and t) or asarray(t).dtype.char
                   for t in typechars)
      intersection = set(t for t in typecodes if t in typeset)
      if not intersection:
          return default
      if 'F' in intersection and 'd' in intersection:
          return 'D'
      return min(intersection, key=_typecodes_by_elsize.index)
  
  
  def _asfarray_dispatcher(a, dtype=None):
      return (a,)
  
  
  @array_function_dispatch(_asfarray_dispatcher)
  def asfarray(a, dtype=_nx.float_):
      """
      Return an array converted to a float type.
  
      Parameters
      ----------
      a : array_like
          The input array.
      dtype : str or dtype object, optional
          Float type code to coerce input array `a`.  If `dtype` is one of the
          'int' dtypes, it is replaced with float64.
  
      Returns
      -------
      out : ndarray
          The input `a` as a float ndarray.
  
      Examples
      --------
      >>> np.asfarray([2, 3])
      array([2.,  3.])
      >>> np.asfarray([2, 3], dtype='float')
      array([2.,  3.])
      >>> np.asfarray([2, 3], dtype='int8')
      array([2.,  3.])
  
      """
      if not _nx.issubdtype(dtype, _nx.inexact):
          dtype = _nx.float_
      return asarray(a, dtype=dtype)
  
  
  def _real_dispatcher(val):
      return (val,)
  
  
  @array_function_dispatch(_real_dispatcher)
  def real(val):
      """
      Return the real part of the complex argument.
  
      Parameters
      ----------
      val : array_like
          Input array.
  
      Returns
      -------
      out : ndarray or scalar
          The real component of the complex argument. If `val` is real, the type
          of `val` is used for the output.  If `val` has complex elements, the
          returned type is float.
  
      See Also
      --------
      real_if_close, imag, angle
  
      Examples
      --------
      >>> a = np.array([1+2j, 3+4j, 5+6j])
      >>> a.real
      array([1.,  3.,  5.])
      >>> a.real = 9
      >>> a
      array([9.+2.j,  9.+4.j,  9.+6.j])
      >>> a.real = np.array([9, 8, 7])
      >>> a
      array([9.+2.j,  8.+4.j,  7.+6.j])
      >>> np.real(1 + 1j)
      1.0
  
      """
      try:
          return val.real
      except AttributeError:
          return asanyarray(val).real
  
  
  def _imag_dispatcher(val):
      return (val,)
  
  
  @array_function_dispatch(_imag_dispatcher)
  def imag(val):
      """
      Return the imaginary part of the complex argument.
  
      Parameters
      ----------
      val : array_like
          Input array.
  
      Returns
      -------
      out : ndarray or scalar
          The imaginary component of the complex argument. If `val` is real,
          the type of `val` is used for the output.  If `val` has complex
          elements, the returned type is float.
  
      See Also
      --------
      real, angle, real_if_close
  
      Examples
      --------
      >>> a = np.array([1+2j, 3+4j, 5+6j])
      >>> a.imag
      array([2.,  4.,  6.])
      >>> a.imag = np.array([8, 10, 12])
      >>> a
      array([1. +8.j,  3.+10.j,  5.+12.j])
      >>> np.imag(1 + 1j)
      1.0
  
      """
      try:
          return val.imag
      except AttributeError:
          return asanyarray(val).imag
  
  
  def _is_type_dispatcher(x):
      return (x,)
  
  
  @array_function_dispatch(_is_type_dispatcher)
  def iscomplex(x):
      """
      Returns a bool array, where True if input element is complex.
  
      What is tested is whether the input has a non-zero imaginary part, not if
      the input type is complex.
  
      Parameters
      ----------
      x : array_like
          Input array.
  
      Returns
      -------
      out : ndarray of bools
          Output array.
  
      See Also
      --------
      isreal
      iscomplexobj : Return True if x is a complex type or an array of complex
                     numbers.
  
      Examples
      --------
      >>> np.iscomplex([1+1j, 1+0j, 4.5, 3, 2, 2j])
      array([ True, False, False, False, False,  True])
  
      """
      ax = asanyarray(x)
      if issubclass(ax.dtype.type, _nx.complexfloating):
          return ax.imag != 0
      res = zeros(ax.shape, bool)
      return res[()]   # convert to scalar if needed
  
  
  @array_function_dispatch(_is_type_dispatcher)
  def isreal(x):
      """
      Returns a bool array, where True if input element is real.
  
      If element has complex type with zero complex part, the return value
      for that element is True.
  
      Parameters
      ----------
      x : array_like
          Input array.
  
      Returns
      -------
      out : ndarray, bool
          Boolean array of same shape as `x`.
  
      See Also
      --------
      iscomplex
      isrealobj : Return True if x is not a complex type.
  
      Examples
      --------
      >>> np.isreal([1+1j, 1+0j, 4.5, 3, 2, 2j])
      array([False,  True,  True,  True,  True, False])
  
      """
      return imag(x) == 0
  
  
  @array_function_dispatch(_is_type_dispatcher)
  def iscomplexobj(x):
      """
      Check for a complex type or an array of complex numbers.
  
      The type of the input is checked, not the value. Even if the input
      has an imaginary part equal to zero, `iscomplexobj` evaluates to True.
  
      Parameters
      ----------
      x : any
          The input can be of any type and shape.
  
      Returns
      -------
      iscomplexobj : bool
          The return value, True if `x` is of a complex type or has at least
          one complex element.
  
      See Also
      --------
      isrealobj, iscomplex
  
      Examples
      --------
      >>> np.iscomplexobj(1)
      False
      >>> np.iscomplexobj(1+0j)
      True
      >>> np.iscomplexobj([3, 1+0j, True])
      True
  
      """
      try:
          dtype = x.dtype
          type_ = dtype.type
      except AttributeError:
          type_ = asarray(x).dtype.type
      return issubclass(type_, _nx.complexfloating)
  
  
  @array_function_dispatch(_is_type_dispatcher)
  def isrealobj(x):
      """
      Return True if x is a not complex type or an array of complex numbers.
  
      The type of the input is checked, not the value. So even if the input
      has an imaginary part equal to zero, `isrealobj` evaluates to False
      if the data type is complex.
  
      Parameters
      ----------
      x : any
          The input can be of any type and shape.
  
      Returns
      -------
      y : bool
          The return value, False if `x` is of a complex type.
  
      See Also
      --------
      iscomplexobj, isreal
  
      Examples
      --------
      >>> np.isrealobj(1)
      True
      >>> np.isrealobj(1+0j)
      False
      >>> np.isrealobj([3, 1+0j, True])
      False
  
      """
      return not iscomplexobj(x)
  
  #-----------------------------------------------------------------------------
  
  def _getmaxmin(t):
      from numpy.core import getlimits
      f = getlimits.finfo(t)
      return f.max, f.min
  
  
  def _nan_to_num_dispatcher(x, copy=None, nan=None, posinf=None, neginf=None):
      return (x,)
  
  
  @array_function_dispatch(_nan_to_num_dispatcher)
  def nan_to_num(x, copy=True, nan=0.0, posinf=None, neginf=None):
      """
      Replace NaN with zero and infinity with large finite numbers (default
      behaviour) or with the numbers defined by the user using the `nan`, 
      `posinf` and/or `neginf` keywords.
  
      If `x` is inexact, NaN is replaced by zero or by the user defined value in
      `nan` keyword, infinity is replaced by the largest finite floating point 
      values representable by ``x.dtype`` or by the user defined value in 
      `posinf` keyword and -infinity is replaced by the most negative finite 
      floating point values representable by ``x.dtype`` or by the user defined 
      value in `neginf` keyword.
  
      For complex dtypes, the above is applied to each of the real and
      imaginary components of `x` separately.
  
      If `x` is not inexact, then no replacements are made.
  
      Parameters
      ----------
      x : scalar or array_like
          Input data.
      copy : bool, optional
          Whether to create a copy of `x` (True) or to replace values
          in-place (False). The in-place operation only occurs if
          casting to an array does not require a copy.
          Default is True.
          
          .. versionadded:: 1.13
      nan : int, float, optional
          Value to be used to fill NaN values. If no value is passed 
          then NaN values will be replaced with 0.0.
          
          .. versionadded:: 1.17
      posinf : int, float, optional
          Value to be used to fill positive infinity values. If no value is 
          passed then positive infinity values will be replaced with a very
          large number.
          
          .. versionadded:: 1.17
      neginf : int, float, optional
          Value to be used to fill negative infinity values. If no value is 
          passed then negative infinity values will be replaced with a very
          small (or negative) number.
          
          .. versionadded:: 1.17
  
          
  
      Returns
      -------
      out : ndarray
          `x`, with the non-finite values replaced. If `copy` is False, this may
          be `x` itself.
  
      See Also
      --------
      isinf : Shows which elements are positive or negative infinity.
      isneginf : Shows which elements are negative infinity.
      isposinf : Shows which elements are positive infinity.
      isnan : Shows which elements are Not a Number (NaN).
      isfinite : Shows which elements are finite (not NaN, not infinity)
  
      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.
  
      Examples
      --------
      >>> np.nan_to_num(np.inf)
      1.7976931348623157e+308
      >>> np.nan_to_num(-np.inf)
      -1.7976931348623157e+308
      >>> np.nan_to_num(np.nan)
      0.0
      >>> x = np.array([np.inf, -np.inf, np.nan, -128, 128])
      >>> np.nan_to_num(x)
      array([ 1.79769313e+308, -1.79769313e+308,  0.00000000e+000, # may vary
             -1.28000000e+002,  1.28000000e+002])
      >>> np.nan_to_num(x, nan=-9999, posinf=33333333, neginf=33333333)
      array([ 3.3333333e+07,  3.3333333e+07, -9.9990000e+03, 
             -1.2800000e+02,  1.2800000e+02])
      >>> y = np.array([complex(np.inf, np.nan), np.nan, complex(np.nan, np.inf)])
      array([  1.79769313e+308,  -1.79769313e+308,   0.00000000e+000, # may vary
           -1.28000000e+002,   1.28000000e+002])
      >>> np.nan_to_num(y)
      array([  1.79769313e+308 +0.00000000e+000j, # may vary
               0.00000000e+000 +0.00000000e+000j,
               0.00000000e+000 +1.79769313e+308j])
      >>> np.nan_to_num(y, nan=111111, posinf=222222)
      array([222222.+111111.j, 111111.     +0.j, 111111.+222222.j])
      """
      x = _nx.array(x, subok=True, copy=copy)
      xtype = x.dtype.type
  
      isscalar = (x.ndim == 0)
  
      if not issubclass(xtype, _nx.inexact):
          return x[()] if isscalar else x
  
      iscomplex = issubclass(xtype, _nx.complexfloating)
  
      dest = (x.real, x.imag) if iscomplex else (x,)
      maxf, minf = _getmaxmin(x.real.dtype)
      if posinf is not None:
          maxf = posinf
      if neginf is not None:
          minf = neginf
      for d in dest:
          idx_nan = isnan(d)
          idx_posinf = isposinf(d)
          idx_neginf = isneginf(d)
          _nx.copyto(d, nan, where=idx_nan)
          _nx.copyto(d, maxf, where=idx_posinf)
          _nx.copyto(d, minf, where=idx_neginf)
      return x[()] if isscalar else x
  
  #-----------------------------------------------------------------------------
  
  def _real_if_close_dispatcher(a, tol=None):
      return (a,)
  
  
  @array_function_dispatch(_real_if_close_dispatcher)
  def real_if_close(a, tol=100):
      """
      If input is complex with all imaginary parts close to zero, return 
      real parts.
  
      "Close to zero" is defined as `tol` * (machine epsilon of the type for
      `a`).
  
      Parameters
      ----------
      a : array_like
          Input array.
      tol : float
          Tolerance in machine epsilons for the complex part of the elements
          in the array.
  
      Returns
      -------
      out : ndarray
          If `a` is real, the type of `a` is used for the output.  If `a`
          has complex elements, the returned type is float.
  
      See Also
      --------
      real, imag, angle
  
      Notes
      -----
      Machine epsilon varies from machine to machine and between data types
      but Python floats on most platforms have a machine epsilon equal to
      2.2204460492503131e-16.  You can use 'np.finfo(float).eps' to print
      out the machine epsilon for floats.
  
      Examples
      --------
      >>> np.finfo(float).eps
      2.2204460492503131e-16 # may vary
  
      >>> np.real_if_close([2.1 + 4e-14j, 5.2 + 3e-15j], tol=1000)
      array([2.1, 5.2])
      >>> np.real_if_close([2.1 + 4e-13j, 5.2 + 3e-15j], tol=1000)
      array([2.1+4.e-13j, 5.2 + 3e-15j])
  
      """
      a = asanyarray(a)
      if not issubclass(a.dtype.type, _nx.complexfloating):
          return a
      if tol > 1:
          from numpy.core import getlimits
          f = getlimits.finfo(a.dtype.type)
          tol = f.eps * tol
      if _nx.all(_nx.absolute(a.imag) < tol):
          a = a.real
      return a
  
  
  def _asscalar_dispatcher(a):
      # 2018-10-10, 1.16
      warnings.warn('np.asscalar(a) is deprecated since NumPy v1.16, use '
                    'a.item() instead', DeprecationWarning, stacklevel=3)
      return (a,)
  
  
  @array_function_dispatch(_asscalar_dispatcher)
  def asscalar(a):
      """
      Convert an array of size 1 to its scalar equivalent.
  
      .. deprecated:: 1.16
  
          Deprecated, use `numpy.ndarray.item()` instead.
  
      Parameters
      ----------
      a : ndarray
          Input array of size 1.
  
      Returns
      -------
      out : scalar
          Scalar representation of `a`. The output data type is the same type
          returned by the input's `item` method.
  
      Examples
      --------
      >>> np.asscalar(np.array([24]))
      24
      """
      return a.item()
  
  #-----------------------------------------------------------------------------
  
  _namefromtype = {'S1': 'character',
                   '?': 'bool',
                   'b': 'signed char',
                   'B': 'unsigned char',
                   'h': 'short',
                   'H': 'unsigned short',
                   'i': 'integer',
                   'I': 'unsigned integer',
                   'l': 'long integer',
                   'L': 'unsigned long integer',
                   'q': 'long long integer',
                   'Q': 'unsigned long long integer',
                   'f': 'single precision',
                   'd': 'double precision',
                   'g': 'long precision',
                   'F': 'complex single precision',
                   'D': 'complex double precision',
                   'G': 'complex long double precision',
                   'S': 'string',
                   'U': 'unicode',
                   'V': 'void',
                   'O': 'object'
                   }
  
  @set_module('numpy')
  def typename(char):
      """
      Return a description for the given data type code.
  
      Parameters
      ----------
      char : str
          Data type code.
  
      Returns
      -------
      out : str
          Description of the input data type code.
  
      See Also
      --------
      dtype, typecodes
  
      Examples
      --------
      >>> typechars = ['S1', '?', 'B', 'D', 'G', 'F', 'I', 'H', 'L', 'O', 'Q',
      ...              'S', 'U', 'V', 'b', 'd', 'g', 'f', 'i', 'h', 'l', 'q']
      >>> for typechar in typechars:
      ...     print(typechar, ' : ', np.typename(typechar))
      ...
      S1  :  character
      ?  :  bool
      B  :  unsigned char
      D  :  complex double precision
      G  :  complex long double precision
      F  :  complex single precision
      I  :  unsigned integer
      H  :  unsigned short
      L  :  unsigned long integer
      O  :  object
      Q  :  unsigned long long integer
      S  :  string
      U  :  unicode
      V  :  void
      b  :  signed char
      d  :  double precision
      g  :  long precision
      f  :  single precision
      i  :  integer
      h  :  short
      l  :  long integer
      q  :  long long integer
  
      """
      return _namefromtype[char]
  
  #-----------------------------------------------------------------------------
  
  #determine the "minimum common type" for a group of arrays.
  array_type = [[_nx.half, _nx.single, _nx.double, _nx.longdouble],
                [None, _nx.csingle, _nx.cdouble, _nx.clongdouble]]
  array_precision = {_nx.half: 0,
                     _nx.single: 1,
                     _nx.double: 2,
                     _nx.longdouble: 3,
                     _nx.csingle: 1,
                     _nx.cdouble: 2,
                     _nx.clongdouble: 3}
  
  
  def _common_type_dispatcher(*arrays):
      return arrays
  
  
  @array_function_dispatch(_common_type_dispatcher)
  def common_type(*arrays):
      """
      Return a scalar type which is common to the input arrays.
  
      The return type will always be an inexact (i.e. floating point) scalar
      type, even if all the arrays are integer arrays. If one of the inputs is
      an integer array, the minimum precision type that is returned is a
      64-bit floating point dtype.
  
      All input arrays except int64 and uint64 can be safely cast to the
      returned dtype without loss of information.
  
      Parameters
      ----------
      array1, array2, ... : ndarrays
          Input arrays.
  
      Returns
      -------
      out : data type code
          Data type code.
  
      See Also
      --------
      dtype, mintypecode
  
      Examples
      --------
      >>> np.common_type(np.arange(2, dtype=np.float32))
      <class 'numpy.float32'>
      >>> np.common_type(np.arange(2, dtype=np.float32), np.arange(2))
      <class 'numpy.float64'>
      >>> np.common_type(np.arange(4), np.array([45, 6.j]), np.array([45.0]))
      <class 'numpy.complex128'>
  
      """
      is_complex = False
      precision = 0
      for a in arrays:
          t = a.dtype.type
          if iscomplexobj(a):
              is_complex = True
          if issubclass(t, _nx.integer):
              p = 2  # array_precision[_nx.double]
          else:
              p = array_precision.get(t, None)
              if p is None:
                  raise TypeError("can't get common type for non-numeric array")
          precision = max(precision, p)
      if is_complex:
          return array_type[1][precision]
      else:
          return array_type[0][precision]
  