NumPy doc（四）

    float_ = class float64(floating, __builtin__.float)     |  64-bit floating-point number. Character code 'd'. Python float compatible.     |       |  Method resolution order:     |      float64     |      floating     |      inexact     |      number     |      generic     |      __builtin__.float     |      __builtin__.object     |       |  Methods defined here:     |       |  __eq__(...)     |      x.__eq__(y) <==> x==y     |       |  __ge__(...)     |      x.__ge__(y) <==> x>=y     |       |  __gt__(...)     |      x.__gt__(y) <==> x>y     |       |  __le__(...)     |      x.__le__(y) <==> x<=y     |       |  __lt__(...)     |      x.__lt__(y) <==> x<y     |       |  __ne__(...)     |      x.__ne__(y) <==> x!=y     |       |  __repr__(...)     |      x.__repr__() <==> repr(x)     |       |  __str__(...)     |      x.__str__() <==> str(x)     |       |  ----------------------------------------------------------------------     |  Data and other attributes defined here:     |       |  __new__ = <built-in method __new__ of type object>     |      T.__new__(S, ...) -> a new object with type S, a subtype of T     |       |  ----------------------------------------------------------------------     |  Methods inherited from generic:     |       |  __abs__(...)     |      x.__abs__() <==> abs(x)     |       |  __add__(...)     |      x.__add__(y) <==> x+y     |       |  __and__(...)     |      x.__and__(y) <==> x&y     |       |  __array__(...)     |      sc.__array__(|type) return 0-dim array     |       |  __array_wrap__(...)     |      sc.__array_wrap__(obj) return scalar from array     |       |  __copy__(...)     |       |  __deepcopy__(...)     |       |  __div__(...)     |      x.__div__(y) <==> x/y     |       |  __divmod__(...)     |      x.__divmod__(y) <==> divmod(x, y)     |       |  __float__(...)     |      x.__float__() <==> float(x)     |       |  __floordiv__(...)     |      x.__floordiv__(y) <==> x//y     |       |  __format__(...)     |      NumPy array scalar formatter     |       |  __getitem__(...)     |      x.__getitem__(y) <==> x[y]     |       |  __hex__(...)     |      x.__hex__() <==> hex(x)     |       |  __int__(...)     |      x.__int__() <==> int(x)     |       |  __invert__(...)     |      x.__invert__() <==> ~x     |       |  __long__(...)     |      x.__long__() <==> long(x)     |       |  __lshift__(...)     |      x.__lshift__(y) <==> x<<y     |       |  __mod__(...)     |      x.__mod__(y) <==> x%y     |       |  __mul__(...)     |      x.__mul__(y) <==> x*y     |       |  __neg__(...)     |      x.__neg__() <==> -x     |       |  __nonzero__(...)     |      x.__nonzero__() <==> x != 0     |       |  __oct__(...)     |      x.__oct__() <==> oct(x)     |       |  __or__(...)     |      x.__or__(y) <==> x|y     |       |  __pos__(...)     |      x.__pos__() <==> +x     |       |  __pow__(...)     |      x.__pow__(y[, z]) <==> pow(x, y[, z])     |       |  __radd__(...)     |      x.__radd__(y) <==> y+x     |       |  __rand__(...)     |      x.__rand__(y) <==> y&x     |       |  __rdiv__(...)     |      x.__rdiv__(y) <==> y/x     |       |  __rdivmod__(...)     |      x.__rdivmod__(y) <==> divmod(y, x)     |       |  __reduce__(...)     |       |  __rfloordiv__(...)     |      x.__rfloordiv__(y) <==> y//x     |       |  __rlshift__(...)     |      x.__rlshift__(y) <==> y<<x     |       |  __rmod__(...)     |      x.__rmod__(y) <==> y%x     |       |  __rmul__(...)     |      x.__rmul__(y) <==> y*x     |       |  __ror__(...)     |      x.__ror__(y) <==> y|x     |       |  __rpow__(...)     |      y.__rpow__(x[, z]) <==> pow(x, y[, z])     |       |  __rrshift__(...)     |      x.__rrshift__(y) <==> y>>x     |       |  __rshift__(...)     |      x.__rshift__(y) <==> x>>y     |       |  __rsub__(...)     |      x.__rsub__(y) <==> y-x     |       |  __rtruediv__(...)     |      x.__rtruediv__(y) <==> y/x     |       |  __rxor__(...)     |      x.__rxor__(y) <==> y^x     |       |  __setstate__(...)     |       |  __sizeof__(...)     |       |  __sub__(...)     |      x.__sub__(y) <==> x-y     |       |  __truediv__(...)     |      x.__truediv__(y) <==> x/y     |       |  __xor__(...)     |      x.__xor__(y) <==> x^y     |       |  all(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  any(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  argmax(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  argmin(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  argsort(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  astype(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  byteswap(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class so as to     |      provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  choose(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  clip(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  compress(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  conj(...)     |       |  conjugate(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  copy(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  cumprod(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  cumsum(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  diagonal(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  dump(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  dumps(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  fill(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  flatten(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  getfield(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  item(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  itemset(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  max(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  mean(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  min(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  newbyteorder(...)     |      newbyteorder(new_order='S')     |           |      Return a new `dtype` with a different byte order.     |           |      Changes are also made in all fields and sub-arrays of the data type.     |           |      The `new_order` code can be any from the following:     |           |      * 'S' - swap dtype from current to opposite endian     |      * {'<', 'L'} - little endian     |      * {'>', 'B'} - big endian     |      * {'=', 'N'} - native order     |      * {'|', 'I'} - ignore (no change to byte order)     |           |      Parameters     |      ----------     |      new_order : str, optional     |          Byte order to force; a value from the byte order specifications     |          above.  The default value ('S') results in swapping the current     |          byte order. The code does a case-insensitive check on the first     |          letter of `new_order` for the alternatives above.  For example,     |          any of 'B' or 'b' or 'biggish' are valid to specify big-endian.     |           |           |      Returns     |      -------     |      new_dtype : dtype     |          New `dtype` object with the given change to the byte order.     |       |  nonzero(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  prod(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  ptp(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  put(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  ravel(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  repeat(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  reshape(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  resize(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  round(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  searchsorted(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  setfield(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  setflags(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class so as to     |      provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  sort(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  squeeze(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  std(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  sum(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  swapaxes(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  take(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  tobytes(...)     |       |  tofile(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  tolist(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  tostring(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  trace(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  transpose(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  var(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  view(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  ----------------------------------------------------------------------     |  Data descriptors inherited from generic:     |       |  T     |      transpose     |       |  __array_interface__     |      Array protocol: Python side     |       |  __array_priority__     |      Array priority.     |       |  __array_struct__     |      Array protocol: struct     |       |  base     |      base object     |       |  data     |      pointer to start of data     |       |  dtype     |      get array data-descriptor     |       |  flags     |      integer value of flags     |       |  flat     |      a 1-d view of scalar     |       |  imag     |      imaginary part of scalar     |       |  itemsize     |      length of one element in bytes     |       |  nbytes     |      length of item in bytes     |       |  ndim     |      number of array dimensions     |       |  real     |      real part of scalar     |       |  shape     |      tuple of array dimensions     |       |  size     |      number of elements in the gentype     |       |  strides     |      tuple of bytes steps in each dimension     |       |  ----------------------------------------------------------------------     |  Methods inherited from __builtin__.float:     |       |  __coerce__(...)     |      x.__coerce__(y) <==> coerce(x, y)     |       |  __getattribute__(...)     |      x.__getattribute__('name') <==> x.name     |       |  __getformat__(...)     |      float.__getformat__(typestr) -> string     |           |      You probably don't want to use this function.  It exists mainly to be     |      used in Python's test suite.     |           |      typestr must be 'double' or 'float'.  This function returns whichever of     |      'unknown', 'IEEE, big-endian' or 'IEEE, little-endian' best describes the     |      format of floating point numbers used by the C type named by typestr.     |       |  __getnewargs__(...)     |       |  __hash__(...)     |      x.__hash__() <==> hash(x)     |       |  __setformat__(...)     |      float.__setformat__(typestr, fmt) -> None     |           |      You probably don't want to use this function.  It exists mainly to be     |      used in Python's test suite.     |           |      typestr must be 'double' or 'float'.  fmt must be one of 'unknown',     |      'IEEE, big-endian' or 'IEEE, little-endian', and in addition can only be     |      one of the latter two if it appears to match the underlying C reality.     |           |      Override the automatic determination of C-level floating point type.     |      This affects how floats are converted to and from binary strings.     |       |  __trunc__(...)     |      Return the Integral closest to x between 0 and x.     |       |  as_integer_ratio(...)     |      float.as_integer_ratio() -> (int, int)     |           |      Return a pair of integers, whose ratio is exactly equal to the original     |      float and with a positive denominator.     |      Raise OverflowError on infinities and a ValueError on NaNs.     |           |      >>> (10.0).as_integer_ratio()     |      (10, 1)     |      >>> (0.0).as_integer_ratio()     |      (0, 1)     |      >>> (-.25).as_integer_ratio()     |      (-1, 4)     |       |  fromhex(...)     |      float.fromhex(string) -> float     |           |      Create a floating-point number from a hexadecimal string.     |      >>> float.fromhex('0x1.ffffp10')     |      2047.984375     |      >>> float.fromhex('-0x1p-1074')     |      -4.9406564584124654e-324     |       |  hex(...)     |      float.hex() -> string     |           |      Return a hexadecimal representation of a floating-point number.     |      >>> (-0.1).hex()     |      '-0x1.999999999999ap-4'     |      >>> 3.14159.hex()     |      '0x1.921f9f01b866ep+1'     |       |  is_integer(...)     |      Return True if the float is an integer.        class floating(inexact)     |  Method resolution order:     |      floating     |      inexact     |      number     |      generic     |      __builtin__.object     |       |  Methods inherited from generic:     |       |  __abs__(...)     |      x.__abs__() <==> abs(x)     |       |  __add__(...)     |      x.__add__(y) <==> x+y     |       |  __and__(...)     |      x.__and__(y) <==> x&y     |       |  __array__(...)     |      sc.__array__(|type) return 0-dim array     |       |  __array_wrap__(...)     |      sc.__array_wrap__(obj) return scalar from array     |       |  __copy__(...)     |       |  __deepcopy__(...)     |       |  __div__(...)     |      x.__div__(y) <==> x/y     |       |  __divmod__(...)     |      x.__divmod__(y) <==> divmod(x, y)     |       |  __eq__(...)     |      x.__eq__(y) <==> x==y     |       |  __float__(...)     |      x.__float__() <==> float(x)     |       |  __floordiv__(...)     |      x.__floordiv__(y) <==> x//y     |       |  __format__(...)     |      NumPy array scalar formatter     |       |  __ge__(...)     |      x.__ge__(y) <==> x>=y     |       |  __getitem__(...)     |      x.__getitem__(y) <==> x[y]     |       |  __gt__(...)     |      x.__gt__(y) <==> x>y     |       |  __hex__(...)     |      x.__hex__() <==> hex(x)     |       |  __int__(...)     |      x.__int__() <==> int(x)     |       |  __invert__(...)     |      x.__invert__() <==> ~x     |       |  __le__(...)     |      x.__le__(y) <==> x<=y     |       |  __long__(...)     |      x.__long__() <==> long(x)     |       |  __lshift__(...)     |      x.__lshift__(y) <==> x<<y     |       |  __lt__(...)     |      x.__lt__(y) <==> x<y     |       |  __mod__(...)     |      x.__mod__(y) <==> x%y     |       |  __mul__(...)     |      x.__mul__(y) <==> x*y     |       |  __ne__(...)     |      x.__ne__(y) <==> x!=y     |       |  __neg__(...)     |      x.__neg__() <==> -x     |       |  __nonzero__(...)     |      x.__nonzero__() <==> x != 0     |       |  __oct__(...)     |      x.__oct__() <==> oct(x)     |       |  __or__(...)     |      x.__or__(y) <==> x|y     |       |  __pos__(...)     |      x.__pos__() <==> +x     |       |  __pow__(...)     |      x.__pow__(y[, z]) <==> pow(x, y[, z])     |       |  __radd__(...)     |      x.__radd__(y) <==> y+x     |       |  __rand__(...)     |      x.__rand__(y) <==> y&x     |       |  __rdiv__(...)     |      x.__rdiv__(y) <==> y/x     |       |  __rdivmod__(...)     |      x.__rdivmod__(y) <==> divmod(y, x)     |       |  __reduce__(...)     |       |  __repr__(...)     |      x.__repr__() <==> repr(x)     |       |  __rfloordiv__(...)     |      x.__rfloordiv__(y) <==> y//x     |       |  __rlshift__(...)     |      x.__rlshift__(y) <==> y<<x     |       |  __rmod__(...)     |      x.__rmod__(y) <==> y%x     |       |  __rmul__(...)     |      x.__rmul__(y) <==> y*x     |       |  __ror__(...)     |      x.__ror__(y) <==> y|x     |       |  __rpow__(...)     |      y.__rpow__(x[, z]) <==> pow(x, y[, z])     |       |  __rrshift__(...)     |      x.__rrshift__(y) <==> y>>x     |       |  __rshift__(...)     |      x.__rshift__(y) <==> x>>y     |       |  __rsub__(...)     |      x.__rsub__(y) <==> y-x     |       |  __rtruediv__(...)     |      x.__rtruediv__(y) <==> y/x     |       |  __rxor__(...)     |      x.__rxor__(y) <==> y^x     |       |  __setstate__(...)     |       |  __sizeof__(...)     |       |  __str__(...)     |      x.__str__() <==> str(x)     |       |  __sub__(...)     |      x.__sub__(y) <==> x-y     |       |  __truediv__(...)     |      x.__truediv__(y) <==> x/y     |       |  __xor__(...)     |      x.__xor__(y) <==> x^y     |       |  all(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  any(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  argmax(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  argmin(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  argsort(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  astype(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  byteswap(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class so as to     |      provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  choose(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  clip(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  compress(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  conj(...)     |       |  conjugate(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  copy(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  cumprod(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  cumsum(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  diagonal(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  dump(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  dumps(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  fill(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  flatten(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  getfield(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  item(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  itemset(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  max(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  mean(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  min(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  newbyteorder(...)     |      newbyteorder(new_order='S')     |           |      Return a new `dtype` with a different byte order.     |           |      Changes are also made in all fields and sub-arrays of the data type.     |           |      The `new_order` code can be any from the following:     |           |      * 'S' - swap dtype from current to opposite endian     |      * {'<', 'L'} - little endian     |      * {'>', 'B'} - big endian     |      * {'=', 'N'} - native order     |      * {'|', 'I'} - ignore (no change to byte order)     |           |      Parameters     |      ----------     |      new_order : str, optional     |          Byte order to force; a value from the byte order specifications     |          above.  The default value ('S') results in swapping the current     |          byte order. The code does a case-insensitive check on the first     |          letter of `new_order` for the alternatives above.  For example,     |          any of 'B' or 'b' or 'biggish' are valid to specify big-endian.     |           |           |      Returns     |      -------     |      new_dtype : dtype     |          New `dtype` object with the given change to the byte order.     |       |  nonzero(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  prod(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  ptp(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  put(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  ravel(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  repeat(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  reshape(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  resize(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  round(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  searchsorted(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  setfield(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  setflags(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class so as to     |      provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  sort(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  squeeze(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  std(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  sum(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  swapaxes(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  take(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  tobytes(...)     |       |  tofile(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  tolist(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  tostring(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  trace(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  transpose(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  var(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  view(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  ----------------------------------------------------------------------     |  Data descriptors inherited from generic:     |       |  T     |      transpose     |       |  __array_interface__     |      Array protocol: Python side     |       |  __array_priority__     |      Array priority.     |       |  __array_struct__     |      Array protocol: struct     |       |  base     |      base object     |       |  data     |      pointer to start of data     |       |  dtype     |      get array data-descriptor     |       |  flags     |      integer value of flags     |       |  flat     |      a 1-d view of scalar     |       |  imag     |      imaginary part of scalar     |       |  itemsize     |      length of one element in bytes     |       |  nbytes     |      length of item in bytes     |       |  ndim     |      number of array dimensions     |       |  real     |      real part of scalar     |       |  shape     |      tuple of array dimensions     |       |  size     |      number of elements in the gentype     |       |  strides     |      tuple of bytes steps in each dimension        class format_parser     |  Class to convert formats, names, titles description to a dtype.     |       |  After constructing the format_parser object, the dtype attribute is     |  the converted data-type:     |  ``dtype = format_parser(formats, names, titles).dtype``     |       |  Attributes     |  ----------     |  dtype : dtype     |      The converted data-type.     |       |  Parameters     |  ----------     |  formats : str or list of str     |      The format description, either specified as a string with     |      comma-separated format descriptions in the form ``'f8, i4, a5'``, or     |      a list of format description strings  in the form     |      ``['f8', 'i4', 'a5']``.     |  names : str or list/tuple of str     |      The field names, either specified as a comma-separated string in the     |      form ``'col1, col2, col3'``, or as a list or tuple of strings in the     |      form ``['col1', 'col2', 'col3']``.     |      An empty list can be used, in that case default field names     |      ('f0', 'f1', ...) are used.     |  titles : sequence     |      Sequence of title strings. An empty list can be used to leave titles     |      out.     |  aligned : bool, optional     |      If True, align the fields by padding as the C-compiler would.     |      Default is False.     |  byteorder : str, optional     |      If specified, all the fields will be changed to the     |      provided byte-order.  Otherwise, the default byte-order is     |      used. For all available string specifiers, see `dtype.newbyteorder`.     |       |  See Also     |  --------     |  dtype, typename, sctype2char     |       |  Examples     |  --------     |  >>> np.format_parser(['f8', 'i4', 'a5'], ['col1', 'col2', 'col3'],     |  ...                  ['T1', 'T2', 'T3']).dtype     |  dtype([(('T1', 'col1'), '<f8'), (('T2', 'col2'), '<i4'),     |         (('T3', 'col3'), '|S5')])     |       |  `names` and/or `titles` can be empty lists. If `titles` is an empty list,     |  titles will simply not appear. If `names` is empty, default field names     |  will be used.     |       |  >>> np.format_parser(['f8', 'i4', 'a5'], ['col1', 'col2', 'col3'],     |  ...                  []).dtype     |  dtype([('col1', '<f8'), ('col2', '<i4'), ('col3', '|S5')])     |  >>> np.format_parser(['f8', 'i4', 'a5'], [], []).dtype     |  dtype([('f0', '<f8'), ('f1', '<i4'), ('f2', '|S5')])     |       |  Methods defined here:     |       |  __init__(self, formats, names, titles, aligned=False, byteorder=None)        class generic(__builtin__.object)     |  Base class for numpy scalar types.     |       |  Class from which most (all?) numpy scalar types are derived.  For     |  consistency, exposes the same API as `ndarray`, despite many     |  consequent attributes being either "get-only," or completely irrelevant.     |  This is the class from which it is strongly suggested users should derive     |  custom scalar types.     |       |  Methods defined here:     |       |  __abs__(...)     |      x.__abs__() <==> abs(x)     |       |  __add__(...)     |      x.__add__(y) <==> x+y     |       |  __and__(...)     |      x.__and__(y) <==> x&y     |       |  __array__(...)     |      sc.__array__(|type) return 0-dim array     |       |  __array_wrap__(...)     |      sc.__array_wrap__(obj) return scalar from array     |       |  __copy__(...)     |       |  __deepcopy__(...)     |       |  __div__(...)     |      x.__div__(y) <==> x/y     |       |  __divmod__(...)     |      x.__divmod__(y) <==> divmod(x, y)     |       |  __eq__(...)     |      x.__eq__(y) <==> x==y     |       |  __float__(...)     |      x.__float__() <==> float(x)     |       |  __floordiv__(...)     |      x.__floordiv__(y) <==> x//y     |       |  __format__(...)     |      NumPy array scalar formatter     |       |  __ge__(...)     |      x.__ge__(y) <==> x>=y     |       |  __getitem__(...)     |      x.__getitem__(y) <==> x[y]     |       |  __gt__(...)     |      x.__gt__(y) <==> x>y     |       |  __hex__(...)     |      x.__hex__() <==> hex(x)     |       |  __int__(...)     |      x.__int__() <==> int(x)     |       |  __invert__(...)     |      x.__invert__() <==> ~x     |       |  __le__(...)     |      x.__le__(y) <==> x<=y     |       |  __long__(...)     |      x.__long__() <==> long(x)     |       |  __lshift__(...)     |      x.__lshift__(y) <==> x<<y     |       |  __lt__(...)     |      x.__lt__(y) <==> x<y     |       |  __mod__(...)     |      x.__mod__(y) <==> x%y     |       |  __mul__(...)     |      x.__mul__(y) <==> x*y     |       |  __ne__(...)     |      x.__ne__(y) <==> x!=y     |       |  __neg__(...)     |      x.__neg__() <==> -x     |       |  __nonzero__(...)     |      x.__nonzero__() <==> x != 0     |       |  __oct__(...)     |      x.__oct__() <==> oct(x)     |       |  __or__(...)     |      x.__or__(y) <==> x|y     |       |  __pos__(...)     |      x.__pos__() <==> +x     |       |  __pow__(...)     |      x.__pow__(y[, z]) <==> pow(x, y[, z])     |       |  __radd__(...)     |      x.__radd__(y) <==> y+x     |       |  __rand__(...)     |      x.__rand__(y) <==> y&x     |       |  __rdiv__(...)     |      x.__rdiv__(y) <==> y/x     |       |  __rdivmod__(...)     |      x.__rdivmod__(y) <==> divmod(y, x)     |       |  __reduce__(...)     |       |  __repr__(...)     |      x.__repr__() <==> repr(x)     |       |  __rfloordiv__(...)     |      x.__rfloordiv__(y) <==> y//x     |       |  __rlshift__(...)     |      x.__rlshift__(y) <==> y<<x     |       |  __rmod__(...)     |      x.__rmod__(y) <==> y%x     |       |  __rmul__(...)     |      x.__rmul__(y) <==> y*x     |       |  __ror__(...)     |      x.__ror__(y) <==> y|x     |       |  __rpow__(...)     |      y.__rpow__(x[, z]) <==> pow(x, y[, z])     |       |  __rrshift__(...)     |      x.__rrshift__(y) <==> y>>x     |       |  __rshift__(...)     |      x.__rshift__(y) <==> x>>y     |       |  __rsub__(...)     |      x.__rsub__(y) <==> y-x     |       |  __rtruediv__(...)     |      x.__rtruediv__(y) <==> y/x     |       |  __rxor__(...)     |      x.__rxor__(y) <==> y^x     |       |  __setstate__(...)     |       |  __sizeof__(...)     |       |  __str__(...)     |      x.__str__() <==> str(x)     |       |  __sub__(...)     |      x.__sub__(y) <==> x-y     |       |  __truediv__(...)     |      x.__truediv__(y) <==> x/y     |       |  __xor__(...)     |      x.__xor__(y) <==> x^y     |       |  all(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  any(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  argmax(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  argmin(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  argsort(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  astype(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  byteswap(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class so as to     |      provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  choose(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  clip(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  compress(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  conj(...)     |       |  conjugate(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  copy(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  cumprod(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  cumsum(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  diagonal(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  dump(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  dumps(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  fill(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  flatten(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  getfield(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  item(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  itemset(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  max(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  mean(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  min(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  newbyteorder(...)     |      newbyteorder(new_order='S')     |           |      Return a new `dtype` with a different byte order.     |           |      Changes are also made in all fields and sub-arrays of the data type.     |           |      The `new_order` code can be any from the following:     |           |      * 'S' - swap dtype from current to opposite endian     |      * {'<', 'L'} - little endian     |      * {'>', 'B'} - big endian     |      * {'=', 'N'} - native order     |      * {'|', 'I'} - ignore (no change to byte order)     |           |      Parameters     |      ----------     |      new_order : str, optional     |          Byte order to force; a value from the byte order specifications     |          above.  The default value ('S') results in swapping the current     |          byte order. The code does a case-insensitive check on the first     |          letter of `new_order` for the alternatives above.  For example,     |          any of 'B' or 'b' or 'biggish' are valid to specify big-endian.     |           |           |      Returns     |      -------     |      new_dtype : dtype     |          New `dtype` object with the given change to the byte order.     |       |  nonzero(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  prod(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  ptp(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  put(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  ravel(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  repeat(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  reshape(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  resize(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  round(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  searchsorted(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  setfield(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  setflags(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class so as to     |      provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  sort(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  squeeze(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  std(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  sum(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  swapaxes(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  take(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  tobytes(...)     |       |  tofile(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  tolist(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  tostring(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  trace(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  transpose(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  var(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  view(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  ----------------------------------------------------------------------     |  Data descriptors defined here:     |       |  T     |      transpose     |       |  __array_interface__     |      Array protocol: Python side     |       |  __array_priority__     |      Array priority.     |       |  __array_struct__     |      Array protocol: struct     |       |  base     |      base object     |       |  data     |      pointer to start of data     |       |  dtype     |      get array data-descriptor     |       |  flags     |      integer value of flags     |       |  flat     |      a 1-d view of scalar     |       |  imag     |      imaginary part of scalar     |       |  itemsize     |      length of one element in bytes     |       |  nbytes     |      length of item in bytes     |       |  ndim     |      number of array dimensions     |       |  real     |      real part of scalar     |       |  shape     |      tuple of array dimensions     |       |  size     |      number of elements in the gentype     |       |  strides     |      tuple of bytes steps in each dimension        half = class float16(floating)     |  Method resolution order:     |      float16     |      floating     |      inexact     |      number     |      generic     |      __builtin__.object     |       |  Methods defined here:     |       |  __eq__(...)     |      x.__eq__(y) <==> x==y     |       |  __ge__(...)     |      x.__ge__(y) <==> x>=y     |       |  __gt__(...)     |      x.__gt__(y) <==> x>y     |       |  __hash__(...)     |      x.__hash__() <==> hash(x)     |       |  __le__(...)     |      x.__le__(y) <==> x<=y     |       |  __lt__(...)     |      x.__lt__(y) <==> x<y     |       |  __ne__(...)     |      x.__ne__(y) <==> x!=y     |       |  __repr__(...)     |      x.__repr__() <==> repr(x)     |       |  __str__(...)     |      x.__str__() <==> str(x)     |       |  ----------------------------------------------------------------------     |  Data and other attributes defined here:     |       |  __new__ = <built-in method __new__ of type object>     |      T.__new__(S, ...) -> a new object with type S, a subtype of T     |       |  ----------------------------------------------------------------------     |  Methods inherited from generic:     |       |  __abs__(...)     |      x.__abs__() <==> abs(x)     |       |  __add__(...)     |      x.__add__(y) <==> x+y     |       |  __and__(...)     |      x.__and__(y) <==> x&y     |       |  __array__(...)     |      sc.__array__(|type) return 0-dim array     |       |  __array_wrap__(...)     |      sc.__array_wrap__(obj) return scalar from array     |       |  __copy__(...)     |       |  __deepcopy__(...)     |       |  __div__(...)     |      x.__div__(y) <==> x/y     |       |  __divmod__(...)     |      x.__divmod__(y) <==> divmod(x, y)     |       |  __float__(...)     |      x.__float__() <==> float(x)     |       |  __floordiv__(...)     |      x.__floordiv__(y) <==> x//y     |       |  __format__(...)     |      NumPy array scalar formatter     |       |  __getitem__(...)     |      x.__getitem__(y) <==> x[y]     |       |  __hex__(...)     |      x.__hex__() <==> hex(x)     |       |  __int__(...)     |      x.__int__() <==> int(x)     |       |  __invert__(...)     |      x.__invert__() <==> ~x     |       |  __long__(...)     |      x.__long__() <==> long(x)     |       |  __lshift__(...)     |      x.__lshift__(y) <==> x<<y     |       |  __mod__(...)     |      x.__mod__(y) <==> x%y     |       |  __mul__(...)     |      x.__mul__(y) <==> x*y     |       |  __neg__(...)     |      x.__neg__() <==> -x     |       |  __nonzero__(...)     |      x.__nonzero__() <==> x != 0     |       |  __oct__(...)     |      x.__oct__() <==> oct(x)     |       |  __or__(...)     |      x.__or__(y) <==> x|y     |       |  __pos__(...)     |      x.__pos__() <==> +x     |       |  __pow__(...)     |      x.__pow__(y[, z]) <==> pow(x, y[, z])     |       |  __radd__(...)     |      x.__radd__(y) <==> y+x     |       |  __rand__(...)     |      x.__rand__(y) <==> y&x     |       |  __rdiv__(...)     |      x.__rdiv__(y) <==> y/x     |       |  __rdivmod__(...)     |      x.__rdivmod__(y) <==> divmod(y, x)     |       |  __reduce__(...)     |       |  __rfloordiv__(...)     |      x.__rfloordiv__(y) <==> y//x     |       |  __rlshift__(...)     |      x.__rlshift__(y) <==> y<<x     |       |  __rmod__(...)     |      x.__rmod__(y) <==> y%x     |       |  __rmul__(...)     |      x.__rmul__(y) <==> y*x     |       |  __ror__(...)     |      x.__ror__(y) <==> y|x     |       |  __rpow__(...)     |      y.__rpow__(x[, z]) <==> pow(x, y[, z])     |       |  __rrshift__(...)     |      x.__rrshift__(y) <==> y>>x     |       |  __rshift__(...)     |      x.__rshift__(y) <==> x>>y     |       |  __rsub__(...)     |      x.__rsub__(y) <==> y-x     |       |  __rtruediv__(...)     |      x.__rtruediv__(y) <==> y/x     |       |  __rxor__(...)     |      x.__rxor__(y) <==> y^x     |       |  __setstate__(...)     |       |  __sizeof__(...)     |       |  __sub__(...)     |      x.__sub__(y) <==> x-y     |       |  __truediv__(...)     |      x.__truediv__(y) <==> x/y     |       |  __xor__(...)     |      x.__xor__(y) <==> x^y     |       |  all(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  any(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  argmax(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  argmin(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  argsort(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  astype(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  byteswap(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class so as to     |      provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  choose(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  clip(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  compress(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  conj(...)     |       |  conjugate(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  copy(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  cumprod(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  cumsum(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  diagonal(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  dump(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  dumps(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  fill(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  flatten(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  getfield(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  item(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  itemset(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  max(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  mean(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  min(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  newbyteorder(...)     |      newbyteorder(new_order='S')     |           |      Return a new `dtype` with a different byte order.     |           |      Changes are also made in all fields and sub-arrays of the data type.     |           |      The `new_order` code can be any from the following:     |           |      * 'S' - swap dtype from current to opposite endian     |      * {'<', 'L'} - little endian     |      * {'>', 'B'} - big endian     |      * {'=', 'N'} - native order     |      * {'|', 'I'} - ignore (no change to byte order)     |           |      Parameters     |      ----------     |      new_order : str, optional     |          Byte order to force; a value from the byte order specifications     |          above.  The default value ('S') results in swapping the current     |          byte order. The code does a case-insensitive check on the first     |          letter of `new_order` for the alternatives above.  For example,     |          any of 'B' or 'b' or 'biggish' are valid to specify big-endian.     |           |           |      Returns     |      -------     |      new_dtype : dtype     |          New `dtype` object with the given change to the byte order.     |       |  nonzero(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  prod(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  ptp(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  put(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  ravel(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  repeat(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  reshape(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  resize(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  round(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  searchsorted(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  setfield(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  setflags(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class so as to     |      provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  sort(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  squeeze(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  std(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  sum(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  swapaxes(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  take(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  tobytes(...)     |       |  tofile(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  tolist(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  tostring(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  trace(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  transpose(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  var(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  view(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  ----------------------------------------------------------------------     |  Data descriptors inherited from generic:     |       |  T     |      transpose     |       |  __array_interface__     |      Array protocol: Python side     |       |  __array_priority__     |      Array priority.     |       |  __array_struct__     |      Array protocol: struct     |       |  base     |      base object     |       |  data     |      pointer to start of data     |       |  dtype     |      get array data-descriptor     |       |  flags     |      integer value of flags     |       |  flat     |      a 1-d view of scalar     |       |  imag     |      imaginary part of scalar     |       |  itemsize     |      length of one element in bytes     |       |  nbytes     |      length of item in bytes     |       |  ndim     |      number of array dimensions     |       |  real     |      real part of scalar     |       |  shape     |      tuple of array dimensions     |       |  size     |      number of elements in the gentype     |       |  strides     |      tuple of bytes steps in each dimension        class iinfo(__builtin__.object)     |  iinfo(type)     |       |  Machine limits for integer types.     |       |  Attributes     |  ----------     |  min : int     |      The smallest integer expressible by the type.     |  max : int     |      The largest integer expressible by the type.     |       |  Parameters     |  ----------     |  int_type : integer type, dtype, or instance     |      The kind of integer data type to get information about.     |       |  See Also     |  --------     |  finfo : The equivalent for floating point data types.     |       |  Examples     |  --------     |  With types:     |       |  >>> ii16 = np.iinfo(np.int16)     |  >>> ii16.min     |  -32768     |  >>> ii16.max     |  32767     |  >>> ii32 = np.iinfo(np.int32)     |  >>> ii32.min     |  -2147483648     |  >>> ii32.max     |  2147483647     |       |  With instances:     |       |  >>> ii32 = np.iinfo(np.int32(10))     |  >>> ii32.min     |  -2147483648     |  >>> ii32.max     |  2147483647     |       |  Methods defined here:     |       |  __init__(self, int_type)     |       |  __repr__(self)     |       |  __str__(self)     |      String representation.     |       |  ----------------------------------------------------------------------     |  Data descriptors defined here:     |       |  __dict__     |      dictionary for instance variables (if defined)     |       |  __weakref__     |      list of weak references to the object (if defined)     |       |  max     |      Maximum value of given dtype.     |       |  min     |      Minimum value of given dtype.        class inexact(number)     |  Method resolution order:     |      inexact     |      number     |      generic     |      __builtin__.object     |       |  Methods inherited from generic:     |       |  __abs__(...)     |      x.__abs__() <==> abs(x)     |       |  __add__(...)     |      x.__add__(y) <==> x+y     |       |  __and__(...)     |      x.__and__(y) <==> x&y     |       |  __array__(...)     |      sc.__array__(|type) return 0-dim array     |       |  __array_wrap__(...)     |      sc.__array_wrap__(obj) return scalar from array     |       |  __copy__(...)     |       |  __deepcopy__(...)     |       |  __div__(...)     |      x.__div__(y) <==> x/y     |       |  __divmod__(...)     |      x.__divmod__(y) <==> divmod(x, y)     |       |  __eq__(...)     |      x.__eq__(y) <==> x==y     |       |  __float__(...)     |      x.__float__() <==> float(x)     |       |  __floordiv__(...)     |      x.__floordiv__(y) <==> x//y     |       |  __format__(...)     |      NumPy array scalar formatter     |       |  __ge__(...)     |      x.__ge__(y) <==> x>=y     |       |  __getitem__(...)     |      x.__getitem__(y) <==> x[y]     |       |  __gt__(...)     |      x.__gt__(y) <==> x>y     |       |  __hex__(...)     |      x.__hex__() <==> hex(x)     |       |  __int__(...)     |      x.__int__() <==> int(x)     |       |  __invert__(...)     |      x.__invert__() <==> ~x     |       |  __le__(...)     |      x.__le__(y) <==> x<=y     |       |  __long__(...)     |      x.__long__() <==> long(x)     |       |  __lshift__(...)     |      x.__lshift__(y) <==> x<<y     |       |  __lt__(...)     |      x.__lt__(y) <==> x<y     |       |  __mod__(...)     |      x.__mod__(y) <==> x%y     |       |  __mul__(...)     |      x.__mul__(y) <==> x*y     |       |  __ne__(...)     |      x.__ne__(y) <==> x!=y     |       |  __neg__(...)     |      x.__neg__() <==> -x     |       |  __nonzero__(...)     |      x.__nonzero__() <==> x != 0     |       |  __oct__(...)     |      x.__oct__() <==> oct(x)     |       |  __or__(...)     |      x.__or__(y) <==> x|y     |       |  __pos__(...)     |      x.__pos__() <==> +x     |       |  __pow__(...)     |      x.__pow__(y[, z]) <==> pow(x, y[, z])     |       |  __radd__(...)     |      x.__radd__(y) <==> y+x     |       |  __rand__(...)     |      x.__rand__(y) <==> y&x     |       |  __rdiv__(...)     |      x.__rdiv__(y) <==> y/x     |       |  __rdivmod__(...)     |      x.__rdivmod__(y) <==> divmod(y, x)     |       |  __reduce__(...)     |       |  __repr__(...)     |      x.__repr__() <==> repr(x)     |       |  __rfloordiv__(...)     |      x.__rfloordiv__(y) <==> y//x     |       |  __rlshift__(...)     |      x.__rlshift__(y) <==> y<<x     |       |  __rmod__(...)     |      x.__rmod__(y) <==> y%x     |       |  __rmul__(...)     |      x.__rmul__(y) <==> y*x     |       |  __ror__(...)     |      x.__ror__(y) <==> y|x     |       |  __rpow__(...)     |      y.__rpow__(x[, z]) <==> pow(x, y[, z])     |       |  __rrshift__(...)     |      x.__rrshift__(y) <==> y>>x     |       |  __rshift__(...)     |      x.__rshift__(y) <==> x>>y     |       |  __rsub__(...)     |      x.__rsub__(y) <==> y-x     |       |  __rtruediv__(...)     |      x.__rtruediv__(y) <==> y/x     |       |  __rxor__(...)     |      x.__rxor__(y) <==> y^x     |       |  __setstate__(...)     |       |  __sizeof__(...)     |       |  __str__(...)     |      x.__str__() <==> str(x)     |       |  __sub__(...)     |      x.__sub__(y) <==> x-y     |       |  __truediv__(...)     |      x.__truediv__(y) <==> x/y     |       |  __xor__(...)     |      x.__xor__(y) <==> x^y     |       |  all(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  any(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  argmax(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  argmin(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  argsort(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  astype(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  byteswap(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class so as to     |      provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  choose(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  clip(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  compress(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  conj(...)     |       |  conjugate(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  copy(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  cumprod(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  cumsum(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  diagonal(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  dump(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  dumps(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  fill(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  flatten(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  getfield(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  item(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  itemset(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  max(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  mean(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  min(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  newbyteorder(...)     |      newbyteorder(new_order='S')     |           |      Return a new `dtype` with a different byte order.     |           |      Changes are also made in all fields and sub-arrays of the data type.     |           |      The `new_order` code can be any from the following:     |           |      * 'S' - swap dtype from current to opposite endian     |      * {'<', 'L'} - little endian     |      * {'>', 'B'} - big endian     |      * {'=', 'N'} - native order     |      * {'|', 'I'} - ignore (no change to byte order)     |           |      Parameters     |      ----------     |      new_order : str, optional     |          Byte order to force; a value from the byte order specifications     |          above.  The default value ('S') results in swapping the current     |          byte order. The code does a case-insensitive check on the first     |          letter of `new_order` for the alternatives above.  For example,     |          any of 'B' or 'b' or 'biggish' are valid to specify big-endian.     |           |           |      Returns     |      -------     |      new_dtype : dtype     |          New `dtype` object with the given change to the byte order.     |       |  nonzero(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  prod(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  ptp(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  put(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  ravel(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  repeat(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  reshape(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  resize(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  round(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  searchsorted(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  setfield(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  setflags(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class so as to     |      provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  sort(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  squeeze(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  std(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  sum(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  swapaxes(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  take(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  tobytes(...)     |       |  tofile(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  tolist(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  tostring(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  trace(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  transpose(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  var(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  view(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  ----------------------------------------------------------------------     |  Data descriptors inherited from generic:     |       |  T     |      transpose     |       |  __array_interface__     |      Array protocol: Python side     |       |  __array_priority__     |      Array priority.     |       |  __array_struct__     |      Array protocol: struct     |       |  base     |      base object     |       |  data     |      pointer to start of data     |       |  dtype     |      get array data-descriptor     |       |  flags     |      integer value of flags     |       |  flat     |      a 1-d view of scalar     |       |  imag     |      imaginary part of scalar     |       |  itemsize     |      length of one element in bytes     |       |  nbytes     |      length of item in bytes     |       |  ndim     |      number of array dimensions     |       |  real     |      real part of scalar     |       |  shape     |      tuple of array dimensions     |       |  size     |      number of elements in the gentype     |       |  strides     |      tuple of bytes steps in each dimension        int0 = class int64(signedinteger)     |  64-bit integer. Character code 'l'. Python int compatible.     |       |  Method resolution order:     |      int64     |      signedinteger     |      integer     |      number     |      generic     |      __builtin__.object     |       |  Methods defined here:     |       |  __eq__(...)     |      x.__eq__(y) <==> x==y     |       |  __ge__(...)     |      x.__ge__(y) <==> x>=y     |       |  __gt__(...)     |      x.__gt__(y) <==> x>y     |       |  __hash__(...)     |      x.__hash__() <==> hash(x)     |       |  __index__(...)     |      x[y:z] <==> x[y.__index__():z.__index__()]     |       |  __le__(...)     |      x.__le__(y) <==> x<=y     |       |  __lt__(...)     |      x.__lt__(y) <==> x<y     |       |  __ne__(...)     |      x.__ne__(y) <==> x!=y     |       |  ----------------------------------------------------------------------     |  Data and other attributes defined here:     |       |  __new__ = <built-in method __new__ of type object>     |      T.__new__(S, ...) -> a new object with type S, a subtype of T     |       |  ----------------------------------------------------------------------     |  Data descriptors inherited from integer:     |       |  denominator     |      denominator of value (1)     |       |  numerator     |      numerator of value (the value itself)     |       |  ----------------------------------------------------------------------     |  Methods inherited from generic:     |       |  __abs__(...)     |      x.__abs__() <==> abs(x)     |       |  __add__(...)     |      x.__add__(y) <==> x+y     |       |  __and__(...)     |      x.__and__(y) <==> x&y     |       |  __array__(...)     |      sc.__array__(|type) return 0-dim array     |       |  __array_wrap__(...)     |      sc.__array_wrap__(obj) return scalar from array     |       |  __copy__(...)     |       |  __deepcopy__(...)     |       |  __div__(...)     |      x.__div__(y) <==> x/y     |       |  __divmod__(...)     |      x.__divmod__(y) <==> divmod(x, y)     |       |  __float__(...)     |      x.__float__() <==> float(x)     |       |  __floordiv__(...)     |      x.__floordiv__(y) <==> x//y     |       |  __format__(...)     |      NumPy array scalar formatter     |       |  __getitem__(...)     |      x.__getitem__(y) <==> x[y]     |       |  __hex__(...)     |      x.__hex__() <==> hex(x)     |       |  __int__(...)     |      x.__int__() <==> int(x)     |       |  __invert__(...)     |      x.__invert__() <==> ~x     |       |  __long__(...)     |      x.__long__() <==> long(x)     |       |  __lshift__(...)     |      x.__lshift__(y) <==> x<<y     |       |  __mod__(...)     |      x.__mod__(y) <==> x%y     |       |  __mul__(...)     |      x.__mul__(y) <==> x*y     |       |  __neg__(...)     |      x.__neg__() <==> -x     |       |  __nonzero__(...)     |      x.__nonzero__() <==> x != 0     |       |  __oct__(...)     |      x.__oct__() <==> oct(x)     |       |  __or__(...)     |      x.__or__(y) <==> x|y     |       |  __pos__(...)     |      x.__pos__() <==> +x     |       |  __pow__(...)     |      x.__pow__(y[, z]) <==> pow(x, y[, z])     |       |  __radd__(...)     |      x.__radd__(y) <==> y+x     |       |  __rand__(...)     |      x.__rand__(y) <==> y&x     |       |  __rdiv__(...)     |      x.__rdiv__(y) <==> y/x     |       |  __rdivmod__(...)     |      x.__rdivmod__(y) <==> divmod(y, x)     |       |  __reduce__(...)     |       |  __repr__(...)     |      x.__repr__() <==> repr(x)     |       |  __rfloordiv__(...)     |      x.__rfloordiv__(y) <==> y//x     |       |  __rlshift__(...)     |      x.__rlshift__(y) <==> y<<x     |       |  __rmod__(...)     |      x.__rmod__(y) <==> y%x     |       |  __rmul__(...)     |      x.__rmul__(y) <==> y*x     |       |  __ror__(...)     |      x.__ror__(y) <==> y|x     |       |  __rpow__(...)     |      y.__rpow__(x[, z]) <==> pow(x, y[, z])     |       |  __rrshift__(...)     |      x.__rrshift__(y) <==> y>>x     |       |  __rshift__(...)     |      x.__rshift__(y) <==> x>>y     |       |  __rsub__(...)     |      x.__rsub__(y) <==> y-x     |       |  __rtruediv__(...)     |      x.__rtruediv__(y) <==> y/x     |       |  __rxor__(...)     |      x.__rxor__(y) <==> y^x     |       |  __setstate__(...)     |       |  __sizeof__(...)     |       |  __str__(...)     |      x.__str__() <==> str(x)     |       |  __sub__(...)     |      x.__sub__(y) <==> x-y     |       |  __truediv__(...)     |      x.__truediv__(y) <==> x/y     |       |  __xor__(...)     |      x.__xor__(y) <==> x^y     |       |  all(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  any(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  argmax(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  argmin(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  argsort(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  astype(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  byteswap(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class so as to     |      provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  choose(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  clip(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  compress(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  conj(...)     |       |  conjugate(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  copy(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  cumprod(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  cumsum(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  diagonal(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  dump(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  dumps(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  fill(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  flatten(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  getfield(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  item(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  itemset(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  max(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  mean(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  min(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  newbyteorder(...)     |      newbyteorder(new_order='S')     |           |      Return a new `dtype` with a different byte order.     |           |      Changes are also made in all fields and sub-arrays of the data type.     |           |      The `new_order` code can be any from the following:     |           |      * 'S' - swap dtype from current to opposite endian     |      * {'<', 'L'} - little endian     |      * {'>', 'B'} - big endian     |      * {'=', 'N'} - native order     |      * {'|', 'I'} - ignore (no change to byte order)     |           |      Parameters     |      ----------     |      new_order : str, optional     |          Byte order to force; a value from the byte order specifications     |          above.  The default value ('S') results in swapping the current     |          byte order. The code does a case-insensitive check on the first     |          letter of `new_order` for the alternatives above.  For example,     |          any of 'B' or 'b' or 'biggish' are valid to specify big-endian.     |           |           |      Returns     |      -------     |      new_dtype : dtype     |          New `dtype` object with the given change to the byte order.     |       |  nonzero(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  prod(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  ptp(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  put(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  ravel(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  repeat(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  reshape(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  resize(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  round(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  searchsorted(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  setfield(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  setflags(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class so as to     |      provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  sort(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  squeeze(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  std(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  sum(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  swapaxes(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  take(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  tobytes(...)     |       |  tofile(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  tolist(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  tostring(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  trace(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  transpose(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  var(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  view(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  ----------------------------------------------------------------------     |  Data descriptors inherited from generic:     |       |  T     |      transpose     |       |  __array_interface__     |      Array protocol: Python side     |       |  __array_priority__     |      Array priority.     |       |  __array_struct__     |      Array protocol: struct     |       |  base     |      base object     |       |  data     |      pointer to start of data     |       |  dtype     |      get array data-descriptor     |       |  flags     |      integer value of flags     |       |  flat     |      a 1-d view of scalar     |       |  imag     |      imaginary part of scalar     |       |  itemsize     |      length of one element in bytes     |       |  nbytes     |      length of item in bytes     |       |  ndim     |      number of array dimensions     |       |  real     |      real part of scalar     |       |  shape     |      tuple of array dimensions     |       |  size     |      number of elements in the gentype     |       |  strides     |      tuple of bytes steps in each dimension        class int16(signedinteger)     |  16-bit integer. Character code ``h``. C short compatible.     |       |  Method resolution order:     |      int16     |      signedinteger     |      integer     |      number     |      generic     |      __builtin__.object     |       |  Methods defined here:     |       |  __eq__(...)     |      x.__eq__(y) <==> x==y     |       |  __ge__(...)     |      x.__ge__(y) <==> x>=y     |       |  __gt__(...)     |      x.__gt__(y) <==> x>y     |       |  __hash__(...)     |      x.__hash__() <==> hash(x)     |       |  __index__(...)     |      x[y:z] <==> x[y.__index__():z.__index__()]     |       |  __le__(...)     |      x.__le__(y) <==> x<=y     |       |  __lt__(...)     |      x.__lt__(y) <==> x<y     |       |  __ne__(...)     |      x.__ne__(y) <==> x!=y     |       |  ----------------------------------------------------------------------     |  Data and other attributes defined here:     |       |  __new__ = <built-in method __new__ of type object>     |      T.__new__(S, ...) -> a new object with type S, a subtype of T     |       |  ----------------------------------------------------------------------     |  Data descriptors inherited from integer:     |       |  denominator     |      denominator of value (1)     |       |  numerator     |      numerator of value (the value itself)     |       |  ----------------------------------------------------------------------     |  Methods inherited from generic:     |       |  __abs__(...)     |      x.__abs__() <==> abs(x)     |       |  __add__(...)     |      x.__add__(y) <==> x+y     |       |  __and__(...)     |      x.__and__(y) <==> x&y     |       |  __array__(...)     |      sc.__array__(|type) return 0-dim array     |       |  __array_wrap__(...)     |      sc.__array_wrap__(obj) return scalar from array     |       |  __copy__(...)     |       |  __deepcopy__(...)     |       |  __div__(...)     |      x.__div__(y) <==> x/y     |       |  __divmod__(...)     |      x.__divmod__(y) <==> divmod(x, y)     |       |  __float__(...)     |      x.__float__() <==> float(x)     |       |  __floordiv__(...)     |      x.__floordiv__(y) <==> x//y     |       |  __format__(...)     |      NumPy array scalar formatter     |       |  __getitem__(...)     |      x.__getitem__(y) <==> x[y]     |       |  __hex__(...)     |      x.__hex__() <==> hex(x)     |       |  __int__(...)     |      x.__int__() <==> int(x)     |       |  __invert__(...)     |      x.__invert__() <==> ~x     |       |  __long__(...)     |      x.__long__() <==> long(x)     |       |  __lshift__(...)     |      x.__lshift__(y) <==> x<<y     |       |  __mod__(...)     |      x.__mod__(y) <==> x%y     |       |  __mul__(...)     |      x.__mul__(y) <==> x*y     |       |  __neg__(...)     |      x.__neg__() <==> -x     |       |  __nonzero__(...)     |      x.__nonzero__() <==> x != 0     |       |  __oct__(...)     |      x.__oct__() <==> oct(x)     |       |  __or__(...)     |      x.__or__(y) <==> x|y     |       |  __pos__(...)     |      x.__pos__() <==> +x     |       |  __pow__(...)     |      x.__pow__(y[, z]) <==> pow(x, y[, z])     |       |  __radd__(...)     |      x.__radd__(y) <==> y+x     |       |  __rand__(...)     |      x.__rand__(y) <==> y&x     |       |  __rdiv__(...)     |      x.__rdiv__(y) <==> y/x     |       |  __rdivmod__(...)     |      x.__rdivmod__(y) <==> divmod(y, x)     |       |  __reduce__(...)     |       |  __repr__(...)     |      x.__repr__() <==> repr(x)     |       |  __rfloordiv__(...)     |      x.__rfloordiv__(y) <==> y//x     |       |  __rlshift__(...)     |      x.__rlshift__(y) <==> y<<x     |       |  __rmod__(...)     |      x.__rmod__(y) <==> y%x     |       |  __rmul__(...)     |      x.__rmul__(y) <==> y*x     |       |  __ror__(...)     |      x.__ror__(y) <==> y|x     |       |  __rpow__(...)     |      y.__rpow__(x[, z]) <==> pow(x, y[, z])     |       |  __rrshift__(...)     |      x.__rrshift__(y) <==> y>>x     |       |  __rshift__(...)     |      x.__rshift__(y) <==> x>>y     |       |  __rsub__(...)     |      x.__rsub__(y) <==> y-x     |       |  __rtruediv__(...)     |      x.__rtruediv__(y) <==> y/x     |       |  __rxor__(...)     |      x.__rxor__(y) <==> y^x     |       |  __setstate__(...)     |       |  __sizeof__(...)     |       |  __str__(...)     |      x.__str__() <==> str(x)     |       |  __sub__(...)     |      x.__sub__(y) <==> x-y     |       |  __truediv__(...)     |      x.__truediv__(y) <==> x/y     |       |  __xor__(...)     |      x.__xor__(y) <==> x^y     |       |  all(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  any(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  argmax(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  argmin(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  argsort(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  astype(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  byteswap(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class so as to     |      provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  choose(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  clip(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  compress(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  conj(...)     |       |  conjugate(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  copy(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  cumprod(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  cumsum(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  diagonal(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  dump(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  dumps(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  fill(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  flatten(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  getfield(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  item(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  itemset(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  max(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  mean(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  min(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  newbyteorder(...)     |      newbyteorder(new_order='S')     |           |      Return a new `dtype` with a different byte order.     |           |      Changes are also made in all fields and sub-arrays of the data type.     |           |      The `new_order` code can be any from the following:     |           |      * 'S' - swap dtype from current to opposite endian     |      * {'<', 'L'} - little endian     |      * {'>', 'B'} - big endian     |      * {'=', 'N'} - native order     |      * {'|', 'I'} - ignore (no change to byte order)     |           |      Parameters     |      ----------     |      new_order : str, optional     |          Byte order to force; a value from the byte order specifications     |          above.  The default value ('S') results in swapping the current     |          byte order. The code does a case-insensitive check on the first     |          letter of `new_order` for the alternatives above.  For example,     |          any of 'B' or 'b' or 'biggish' are valid to specify big-endian.     |           |           |      Returns     |      -------     |      new_dtype : dtype     |          New `dtype` object with the given change to the byte order.     |       |  nonzero(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  prod(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  ptp(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  put(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  ravel(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  repeat(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  reshape(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  resize(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  round(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  searchsorted(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  setfield(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  setflags(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class so as to     |      provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  sort(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  squeeze(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  std(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  sum(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  swapaxes(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  take(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  tobytes(...)     |       |  tofile(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  tolist(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  tostring(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  trace(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  transpose(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  var(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  view(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  ----------------------------------------------------------------------     |  Data descriptors inherited from generic:     |       |  T     |      transpose     |       |  __array_interface__     |      Array protocol: Python side     |       |  __array_priority__     |      Array priority.     |       |  __array_struct__     |      Array protocol: struct     |       |  base     |      base object     |       |  data     |      pointer to start of data     |       |  dtype     |      get array data-descriptor     |       |  flags     |      integer value of flags     |       |  flat     |      a 1-d view of scalar     |       |  imag     |      imaginary part of scalar     |       |  itemsize     |      length of one element in bytes     |       |  nbytes     |      length of item in bytes     |       |  ndim     |      number of array dimensions     |       |  real     |      real part of scalar     |       |  shape     |      tuple of array dimensions     |       |  size     |      number of elements in the gentype     |       |  strides     |      tuple of bytes steps in each dimension        class int32(signedinteger, __builtin__.int)     |  32-bit integer. Character code 'i'. C int compatible.     |       |  Method resolution order:     |      int32     |      signedinteger     |      integer     |      number     |      generic     |      __builtin__.int     |      __builtin__.object     |       |  Methods defined here:     |       |  __eq__(...)     |      x.__eq__(y) <==> x==y     |       |  __ge__(...)     |      x.__ge__(y) <==> x>=y     |       |  __gt__(...)     |      x.__gt__(y) <==> x>y     |       |  __index__(...)     |      x[y:z] <==> x[y.__index__():z.__index__()]     |       |  __le__(...)     |      x.__le__(y) <==> x<=y     |       |  __lt__(...)     |      x.__lt__(y) <==> x<y     |       |  __ne__(...)     |      x.__ne__(y) <==> x!=y     |       |  ----------------------------------------------------------------------     |  Data and other attributes defined here:     |       |  __new__ = <built-in method __new__ of type object>     |      T.__new__(S, ...) -> a new object with type S, a subtype of T     |       |  ----------------------------------------------------------------------     |  Data descriptors inherited from integer:     |       |  denominator     |      denominator of value (1)     |       |  numerator     |      numerator of value (the value itself)     |       |  ----------------------------------------------------------------------     |  Methods inherited from generic:     |       |  __abs__(...)     |      x.__abs__() <==> abs(x)     |       |  __add__(...)     |      x.__add__(y) <==> x+y     |       |  __and__(...)     |      x.__and__(y) <==> x&y     |       |  __array__(...)     |      sc.__array__(|type) return 0-dim array     |       |  __array_wrap__(...)     |      sc.__array_wrap__(obj) return scalar from array     |       |  __copy__(...)     |       |  __deepcopy__(...)     |       |  __div__(...)     |      x.__div__(y) <==> x/y     |       |  __divmod__(...)     |      x.__divmod__(y) <==> divmod(x, y)     |       |  __float__(...)     |      x.__float__() <==> float(x)     |       |  __floordiv__(...)     |      x.__floordiv__(y) <==> x//y     |       |  __format__(...)     |      NumPy array scalar formatter     |       |  __getitem__(...)     |      x.__getitem__(y) <==> x[y]     |       |  __hex__(...)     |      x.__hex__() <==> hex(x)     |       |  __int__(...)     |      x.__int__() <==> int(x)     |       |  __invert__(...)     |      x.__invert__() <==> ~x     |       |  __long__(...)     |      x.__long__() <==> long(x)     |       |  __lshift__(...)     |      x.__lshift__(y) <==> x<<y     |       |  __mod__(...)     |      x.__mod__(y) <==> x%y     |       |  __mul__(...)     |      x.__mul__(y) <==> x*y     |       |  __neg__(...)     |      x.__neg__() <==> -x     |       |  __nonzero__(...)     |      x.__nonzero__() <==> x != 0     |       |  __oct__(...)     |      x.__oct__() <==> oct(x)     |       |  __or__(...)     |      x.__or__(y) <==> x|y     |       |  __pos__(...)     |      x.__pos__() <==> +x     |       |  __pow__(...)     |      x.__pow__(y[, z]) <==> pow(x, y[, z])     |       |  __radd__(...)     |      x.__radd__(y) <==> y+x     |       |  __rand__(...)     |      x.__rand__(y) <==> y&x     |       |  __rdiv__(...)     |      x.__rdiv__(y) <==> y/x     |       |  __rdivmod__(...)     |      x.__rdivmod__(y) <==> divmod(y, x)     |       |  __reduce__(...)     |       |  __repr__(...)     |      x.__repr__() <==> repr(x)     |       |  __rfloordiv__(...)     |      x.__rfloordiv__(y) <==> y//x     |       |  __rlshift__(...)     |      x.__rlshift__(y) <==> y<<x     |       |  __rmod__(...)     |      x.__rmod__(y) <==> y%x     |       |  __rmul__(...)     |      x.__rmul__(y) <==> y*x     |       |  __ror__(...)     |      x.__ror__(y) <==> y|x     |       |  __rpow__(...)     |      y.__rpow__(x[, z]) <==> pow(x, y[, z])     |       |  __rrshift__(...)     |      x.__rrshift__(y) <==> y>>x     |       |  __rshift__(...)     |      x.__rshift__(y) <==> x>>y     |       |  __rsub__(...)     |      x.__rsub__(y) <==> y-x     |       |  __rtruediv__(...)     |      x.__rtruediv__(y) <==> y/x     |       |  __rxor__(...)     |      x.__rxor__(y) <==> y^x     |       |  __setstate__(...)     |       |  __sizeof__(...)     |       |  __str__(...)     |      x.__str__() <==> str(x)     |       |  __sub__(...)     |      x.__sub__(y) <==> x-y     |       |  __truediv__(...)     |      x.__truediv__(y) <==> x/y     |       |  __xor__(...)     |      x.__xor__(y) <==> x^y     |       |  all(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  any(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  argmax(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  argmin(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  argsort(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  astype(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  byteswap(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class so as to     |      provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  choose(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  clip(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  compress(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  conj(...)     |       |  conjugate(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  copy(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  cumprod(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  cumsum(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  diagonal(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  dump(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  dumps(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  fill(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  flatten(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  getfield(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  item(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  itemset(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  max(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  mean(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  min(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  newbyteorder(...)     |      newbyteorder(new_order='S')     |           |      Return a new `dtype` with a different byte order.     |           |      Changes are also made in all fields and sub-arrays of the data type.     |           |      The `new_order` code can be any from the following:     |           |      * 'S' - swap dtype from current to opposite endian     |      * {'<', 'L'} - little endian     |      * {'>', 'B'} - big endian     |      * {'=', 'N'} - native order     |      * {'|', 'I'} - ignore (no change to byte order)     |           |      Parameters     |      ----------     |      new_order : str, optional     |          Byte order to force; a value from the byte order specifications     |          above.  The default value ('S') results in swapping the current     |          byte order. The code does a case-insensitive check on the first     |          letter of `new_order` for the alternatives above.  For example,     |          any of 'B' or 'b' or 'biggish' are valid to specify big-endian.     |           |           |      Returns     |      -------     |      new_dtype : dtype     |          New `dtype` object with the given change to the byte order.     |       |  nonzero(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  prod(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  ptp(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  put(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  ravel(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  repeat(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  reshape(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  resize(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  round(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  searchsorted(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  setfield(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  setflags(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class so as to     |      provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  sort(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  squeeze(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  std(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  sum(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  swapaxes(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  take(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  tobytes(...)     |       |  tofile(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  tolist(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  tostring(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  trace(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  transpose(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  var(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  view(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  ----------------------------------------------------------------------     |  Data descriptors inherited from generic:     |       |  T     |      transpose     |       |  __array_interface__     |      Array protocol: Python side     |       |  __array_priority__     |      Array priority.     |       |  __array_struct__     |      Array protocol: struct     |       |  base     |      base object     |       |  data     |      pointer to start of data     |       |  dtype     |      get array data-descriptor     |       |  flags     |      integer value of flags     |       |  flat     |      a 1-d view of scalar     |       |  imag     |      imaginary part of scalar     |       |  itemsize     |      length of one element in bytes     |       |  nbytes     |      length of item in bytes     |       |  ndim     |      number of array dimensions     |       |  real     |      real part of scalar     |       |  shape     |      tuple of array dimensions     |       |  size     |      number of elements in the gentype     |       |  strides     |      tuple of bytes steps in each dimension     |       |  ----------------------------------------------------------------------     |  Methods inherited from __builtin__.int:     |       |  __cmp__(...)     |      x.__cmp__(y) <==> cmp(x,y)     |       |  __coerce__(...)     |      x.__coerce__(y) <==> coerce(x, y)     |       |  __getattribute__(...)     |      x.__getattribute__('name') <==> x.name     |       |  __getnewargs__(...)     |       |  __hash__(...)     |      x.__hash__() <==> hash(x)     |       |  __trunc__(...)     |      Truncating an Integral returns itself.     |       |  bit_length(...)     |      int.bit_length() -> int     |           |      Number of bits necessary to represent self in binary.     |      >>> bin(37)     |      '0b100101'     |      >>> (37).bit_length()     |      6        class int64(signedinteger)     |  64-bit integer. Character code 'l'. Python int compatible.     |       |  Method resolution order:     |      int64     |      signedinteger     |      integer     |      number     |      generic     |      __builtin__.object     |       |  Methods defined here:     |       |  __eq__(...)     |      x.__eq__(y) <==> x==y     |       |  __ge__(...)     |      x.__ge__(y) <==> x>=y     |       |  __gt__(...)     |      x.__gt__(y) <==> x>y     |       |  __hash__(...)     |      x.__hash__() <==> hash(x)     |       |  __index__(...)     |      x[y:z] <==> x[y.__index__():z.__index__()]     |       |  __le__(...)     |      x.__le__(y) <==> x<=y     |       |  __lt__(...)     |      x.__lt__(y) <==> x<y     |       |  __ne__(...)     |      x.__ne__(y) <==> x!=y     |       |  ----------------------------------------------------------------------     |  Data and other attributes defined here:     |       |  __new__ = <built-in method __new__ of type object>     |      T.__new__(S, ...) -> a new object with type S, a subtype of T     |       |  ----------------------------------------------------------------------     |  Data descriptors inherited from integer:     |       |  denominator     |      denominator of value (1)     |       |  numerator     |      numerator of value (the value itself)     |       |  ----------------------------------------------------------------------     |  Methods inherited from generic:     |       |  __abs__(...)     |      x.__abs__() <==> abs(x)     |       |  __add__(...)     |      x.__add__(y) <==> x+y     |       |  __and__(...)     |      x.__and__(y) <==> x&y     |       |  __array__(...)     |      sc.__array__(|type) return 0-dim array     |       |  __array_wrap__(...)     |      sc.__array_wrap__(obj) return scalar from array     |       |  __copy__(...)     |       |  __deepcopy__(...)     |       |  __div__(...)     |      x.__div__(y) <==> x/y     |       |  __divmod__(...)     |      x.__divmod__(y) <==> divmod(x, y)     |       |  __float__(...)     |      x.__float__() <==> float(x)     |       |  __floordiv__(...)     |      x.__floordiv__(y) <==> x//y     |       |  __format__(...)     |      NumPy array scalar formatter     |       |  __getitem__(...)     |      x.__getitem__(y) <==> x[y]     |       |  __hex__(...)     |      x.__hex__() <==> hex(x)     |       |  __int__(...)     |      x.__int__() <==> int(x)     |       |  __invert__(...)     |      x.__invert__() <==> ~x     |       |  __long__(...)     |      x.__long__() <==> long(x)     |       |  __lshift__(...)     |      x.__lshift__(y) <==> x<<y     |       |  __mod__(...)     |      x.__mod__(y) <==> x%y     |       |  __mul__(...)     |      x.__mul__(y) <==> x*y     |       |  __neg__(...)     |      x.__neg__() <==> -x     |       |  __nonzero__(...)     |      x.__nonzero__() <==> x != 0     |       |  __oct__(...)     |      x.__oct__() <==> oct(x)     |       |  __or__(...)     |      x.__or__(y) <==> x|y     |       |  __pos__(...)     |      x.__pos__() <==> +x     |       |  __pow__(...)     |      x.__pow__(y[, z]) <==> pow(x, y[, z])     |       |  __radd__(...)     |      x.__radd__(y) <==> y+x     |       |  __rand__(...)     |      x.__rand__(y) <==> y&x     |       |  __rdiv__(...)     |      x.__rdiv__(y) <==> y/x     |       |  __rdivmod__(...)     |      x.__rdivmod__(y) <==> divmod(y, x)     |       |  __reduce__(...)     |       |  __repr__(...)     |      x.__repr__() <==> repr(x)     |       |  __rfloordiv__(...)     |      x.__rfloordiv__(y) <==> y//x     |       |  __rlshift__(...)     |      x.__rlshift__(y) <==> y<<x     |       |  __rmod__(...)     |      x.__rmod__(y) <==> y%x     |       |  __rmul__(...)     |      x.__rmul__(y) <==> y*x     |       |  __ror__(...)     |      x.__ror__(y) <==> y|x     |       |  __rpow__(...)     |      y.__rpow__(x[, z]) <==> pow(x, y[, z])     |       |  __rrshift__(...)     |      x.__rrshift__(y) <==> y>>x     |       |  __rshift__(...)     |      x.__rshift__(y) <==> x>>y     |       |  __rsub__(...)     |      x.__rsub__(y) <==> y-x     |       |  __rtruediv__(...)     |      x.__rtruediv__(y) <==> y/x     |       |  __rxor__(...)     |      x.__rxor__(y) <==> y^x     |       |  __setstate__(...)     |       |  __sizeof__(...)     |       |  __str__(...)     |      x.__str__() <==> str(x)     |       |  __sub__(...)     |      x.__sub__(y) <==> x-y     |       |  __truediv__(...)     |      x.__truediv__(y) <==> x/y     |       |  __xor__(...)     |      x.__xor__(y) <==> x^y     |       |  all(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  any(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  argmax(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  argmin(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  argsort(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  astype(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  byteswap(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class so as to     |      provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  choose(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  clip(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  compress(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  conj(...)     |       |  conjugate(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  copy(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  cumprod(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  cumsum(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  diagonal(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  dump(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  dumps(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  fill(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  flatten(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  getfield(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  item(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  itemset(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  max(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  mean(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  min(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  newbyteorder(...)     |      newbyteorder(new_order='S')     |           |      Return a new `dtype` with a different byte order.     |           |      Changes are also made in all fields and sub-arrays of the data type.     |           |      The `new_order` code can be any from the following:     |           |      * 'S' - swap dtype from current to opposite endian     |      * {'<', 'L'} - little endian     |      * {'>', 'B'} - big endian     |      * {'=', 'N'} - native order     |      * {'|', 'I'} - ignore (no change to byte order)     |           |      Parameters     |      ----------     |      new_order : str, optional     |          Byte order to force; a value from the byte order specifications     |          above.  The default value ('S') results in swapping the current     |          byte order. The code does a case-insensitive check on the first     |          letter of `new_order` for the alternatives above.  For example,     |          any of 'B' or 'b' or 'biggish' are valid to specify big-endian.     |           |           |      Returns     |      -------     |      new_dtype : dtype     |          New `dtype` object with the given change to the byte order.     |       |  nonzero(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  prod(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  ptp(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  put(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  ravel(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  repeat(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  reshape(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  resize(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  round(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  searchsorted(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  setfield(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  setflags(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class so as to     |      provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  sort(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  squeeze(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  std(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  sum(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  swapaxes(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  take(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  tobytes(...)     |       |  tofile(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  tolist(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  tostring(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  trace(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  transpose(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  var(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  view(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  ----------------------------------------------------------------------     |  Data descriptors inherited from generic:     |       |  T     |      transpose     |       |  __array_interface__     |      Array protocol: Python side     |       |  __array_priority__     |      Array priority.     |       |  __array_struct__     |      Array protocol: struct     |       |  base     |      base object     |       |  data     |      pointer to start of data     |       |  dtype     |      get array data-descriptor     |       |  flags     |      integer value of flags     |       |  flat     |      a 1-d view of scalar     |       |  imag     |      imaginary part of scalar     |       |  itemsize     |      length of one element in bytes     |       |  nbytes     |      length of item in bytes     |       |  ndim     |      number of array dimensions     |       |  real     |      real part of scalar     |       |  shape     |      tuple of array dimensions     |       |  size     |      number of elements in the gentype     |       |  strides     |      tuple of bytes steps in each dimension        class int8(signedinteger)     |  8-bit integer. Character code ``b``. C char compatible.     |       |  Method resolution order:     |      int8     |      signedinteger     |      integer     |      number     |      generic     |      __builtin__.object     |       |  Methods defined here:     |       |  __eq__(...)     |      x.__eq__(y) <==> x==y     |       |  __ge__(...)     |      x.__ge__(y) <==> x>=y     |       |  __gt__(...)     |      x.__gt__(y) <==> x>y     |       |  __hash__(...)     |      x.__hash__() <==> hash(x)     |       |  __index__(...)     |      x[y:z] <==> x[y.__index__():z.__index__()]     |       |  __le__(...)     |      x.__le__(y) <==> x<=y     |       |  __lt__(...)     |      x.__lt__(y) <==> x<y     |       |  __ne__(...)     |      x.__ne__(y) <==> x!=y     |       |  ----------------------------------------------------------------------     |  Data and other attributes defined here:     |       |  __new__ = <built-in method __new__ of type object>     |      T.__new__(S, ...) -> a new object with type S, a subtype of T     |       |  ----------------------------------------------------------------------     |  Data descriptors inherited from integer:     |       |  denominator     |      denominator of value (1)     |       |  numerator     |      numerator of value (the value itself)     |       |  ----------------------------------------------------------------------     |  Methods inherited from generic:     |       |  __abs__(...)     |      x.__abs__() <==> abs(x)     |       |  __add__(...)     |      x.__add__(y) <==> x+y     |       |  __and__(...)     |      x.__and__(y) <==> x&y     |       |  __array__(...)     |      sc.__array__(|type) return 0-dim array     |       |  __array_wrap__(...)     |      sc.__array_wrap__(obj) return scalar from array     |       |  __copy__(...)     |       |  __deepcopy__(...)     |       |  __div__(...)     |      x.__div__(y) <==> x/y     |       |  __divmod__(...)     |      x.__divmod__(y) <==> divmod(x, y)     |       |  __float__(...)     |      x.__float__() <==> float(x)     |       |  __floordiv__(...)     |      x.__floordiv__(y) <==> x//y     |       |  __format__(...)     |      NumPy array scalar formatter     |       |  __getitem__(...)     |      x.__getitem__(y) <==> x[y]     |       |  __hex__(...)     |      x.__hex__() <==> hex(x)     |       |  __int__(...)     |      x.__int__() <==> int(x)     |       |  __invert__(...)     |      x.__invert__() <==> ~x     |       |  __long__(...)     |      x.__long__() <==> long(x)     |       |  __lshift__(...)     |      x.__lshift__(y) <==> x<<y     |       |  __mod__(...)     |      x.__mod__(y) <==> x%y     |       |  __mul__(...)     |      x.__mul__(y) <==> x*y     |       |  __neg__(...)     |      x.__neg__() <==> -x     |       |  __nonzero__(...)     |      x.__nonzero__() <==> x != 0     |       |  __oct__(...)     |      x.__oct__() <==> oct(x)     |       |  __or__(...)     |      x.__or__(y) <==> x|y     |       |  __pos__(...)     |      x.__pos__() <==> +x     |       |  __pow__(...)     |      x.__pow__(y[, z]) <==> pow(x, y[, z])     |       |  __radd__(...)     |      x.__radd__(y) <==> y+x     |       |  __rand__(...)     |      x.__rand__(y) <==> y&x     |       |  __rdiv__(...)     |      x.__rdiv__(y) <==> y/x     |       |  __rdivmod__(...)     |      x.__rdivmod__(y) <==> divmod(y, x)     |       |  __reduce__(...)     |       |  __repr__(...)     |      x.__repr__() <==> repr(x)     |       |  __rfloordiv__(...)     |      x.__rfloordiv__(y) <==> y//x     |       |  __rlshift__(...)     |      x.__rlshift__(y) <==> y<<x     |       |  __rmod__(...)     |      x.__rmod__(y) <==> y%x     |       |  __rmul__(...)     |      x.__rmul__(y) <==> y*x     |       |  __ror__(...)     |      x.__ror__(y) <==> y|x     |       |  __rpow__(...)     |      y.__rpow__(x[, z]) <==> pow(x, y[, z])     |       |  __rrshift__(...)     |      x.__rrshift__(y) <==> y>>x     |       |  __rshift__(...)     |      x.__rshift__(y) <==> x>>y     |       |  __rsub__(...)     |      x.__rsub__(y) <==> y-x     |       |  __rtruediv__(...)     |      x.__rtruediv__(y) <==> y/x     |       |  __rxor__(...)     |      x.__rxor__(y) <==> y^x     |       |  __setstate__(...)     |       |  __sizeof__(...)     |       |  __str__(...)     |      x.__str__() <==> str(x)     |       |  __sub__(...)     |      x.__sub__(y) <==> x-y     |       |  __truediv__(...)     |      x.__truediv__(y) <==> x/y     |       |  __xor__(...)     |      x.__xor__(y) <==> x^y     |       |  all(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  any(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  argmax(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  argmin(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  argsort(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  astype(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  byteswap(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class so as to     |      provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  choose(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  clip(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  compress(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  conj(...)     |       |  conjugate(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  copy(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  cumprod(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  cumsum(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  diagonal(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  dump(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  dumps(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  fill(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  flatten(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  getfield(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  item(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  itemset(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  max(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  mean(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  min(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  newbyteorder(...)     |      newbyteorder(new_order='S')     |           |      Return a new `dtype` with a different byte order.     |           |      Changes are also made in all fields and sub-arrays of the data type.     |           |      The `new_order` code can be any from the following:     |           |      * 'S' - swap dtype from current to opposite endian     |      * {'<', 'L'} - little endian     |      * {'>', 'B'} - big endian     |      * {'=', 'N'} - native order     |      * {'|', 'I'} - ignore (no change to byte order)     |           |      Parameters     |      ----------     |      new_order : str, optional     |          Byte order to force; a value from the byte order specifications     |          above.  The default value ('S') results in swapping the current     |          byte order. The code does a case-insensitive check on the first     |          letter of `new_order` for the alternatives above.  For example,     |          any of 'B' or 'b' or 'biggish' are valid to specify big-endian.     |           |           |      Returns     |      -------     |      new_dtype : dtype     |          New `dtype` object with the given change to the byte order.     |       |  nonzero(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  prod(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  ptp(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  put(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  ravel(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  repeat(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  reshape(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  resize(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  round(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  searchsorted(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  setfield(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  setflags(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class so as to     |      provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  sort(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  squeeze(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  std(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  sum(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  swapaxes(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  take(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  tobytes(...)     |       |  tofile(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  tolist(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  tostring(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  trace(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  transpose(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  var(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  view(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  ----------------------------------------------------------------------     |  Data descriptors inherited from generic:     |       |  T     |      transpose     |       |  __array_interface__     |      Array protocol: Python side     |       |  __array_priority__     |      Array priority.     |       |  __array_struct__     |      Array protocol: struct     |       |  base     |      base object     |       |  data     |      pointer to start of data     |       |  dtype     |      get array data-descriptor     |       |  flags     |      integer value of flags     |       |  flat     |      a 1-d view of scalar     |       |  imag     |      imaginary part of scalar     |       |  itemsize     |      length of one element in bytes     |       |  nbytes     |      length of item in bytes     |       |  ndim     |      number of array dimensions     |       |  real     |      real part of scalar     |       |  shape     |      tuple of array dimensions     |       |  size     |      number of elements in the gentype     |       |  strides     |      tuple of bytes steps in each dimension        int_ = class int32(signedinteger, __builtin__.int)     |  32-bit integer. Character code 'i'. C int compatible.     |       |  Method resolution order:     |      int32     |      signedinteger     |      integer     |      number     |      generic     |      __builtin__.int     |      __builtin__.object     |       |  Methods defined here:     |       |  __eq__(...)     |      x.__eq__(y) <==> x==y     |       |  __ge__(...)     |      x.__ge__(y) <==> x>=y     |       |  __gt__(...)     |      x.__gt__(y) <==> x>y     |       |  __index__(...)     |      x[y:z] <==> x[y.__index__():z.__index__()]     |       |  __le__(...)     |      x.__le__(y) <==> x<=y     |       |  __lt__(...)     |      x.__lt__(y) <==> x<y     |       |  __ne__(...)     |      x.__ne__(y) <==> x!=y     |       |  ----------------------------------------------------------------------     |  Data and other attributes defined here:     |       |  __new__ = <built-in method __new__ of type object>     |      T.__new__(S, ...) -> a new object with type S, a subtype of T     |       |  ----------------------------------------------------------------------     |  Data descriptors inherited from integer:     |       |  denominator     |      denominator of value (1)     |       |  numerator     |      numerator of value (the value itself)     |       |  ----------------------------------------------------------------------     |  Methods inherited from generic:     |       |  __abs__(...)     |      x.__abs__() <==> abs(x)     |       |  __add__(...)     |      x.__add__(y) <==> x+y     |       |  __and__(...)     |      x.__and__(y) <==> x&y     |       |  __array__(...)     |      sc.__array__(|type) return 0-dim array     |       |  __array_wrap__(...)     |      sc.__array_wrap__(obj) return scalar from array     |       |  __copy__(...)     |       |  __deepcopy__(...)     |       |  __div__(...)     |      x.__div__(y) <==> x/y     |       |  __divmod__(...)     |      x.__divmod__(y) <==> divmod(x, y)     |       |  __float__(...)     |      x.__float__() <==> float(x)     |       |  __floordiv__(...)     |      x.__floordiv__(y) <==> x//y     |       |  __format__(...)     |      NumPy array scalar formatter     |       |  __getitem__(...)     |      x.__getitem__(y) <==> x[y]     |       |  __hex__(...)     |      x.__hex__() <==> hex(x)     |       |  __int__(...)     |      x.__int__() <==> int(x)     |       |  __invert__(...)     |      x.__invert__() <==> ~x     |       |  __long__(...)     |      x.__long__() <==> long(x)     |       |  __lshift__(...)     |      x.__lshift__(y) <==> x<<y     |       |  __mod__(...)     |      x.__mod__(y) <==> x%y     |       |  __mul__(...)     |      x.__mul__(y) <==> x*y     |       |  __neg__(...)     |      x.__neg__() <==> -x     |       |  __nonzero__(...)     |      x.__nonzero__() <==> x != 0     |       |  __oct__(...)     |      x.__oct__() <==> oct(x)     |       |  __or__(...)     |      x.__or__(y) <==> x|y     |       |  __pos__(...)     |      x.__pos__() <==> +x     |       |  __pow__(...)     |      x.__pow__(y[, z]) <==> pow(x, y[, z])     |       |  __radd__(...)     |      x.__radd__(y) <==> y+x     |       |  __rand__(...)     |      x.__rand__(y) <==> y&x     |       |  __rdiv__(...)     |      x.__rdiv__(y) <==> y/x     |       |  __rdivmod__(...)     |      x.__rdivmod__(y) <==> divmod(y, x)     |       |  __reduce__(...)     |       |  __repr__(...)     |      x.__repr__() <==> repr(x)     |       |  __rfloordiv__(...)     |      x.__rfloordiv__(y) <==> y//x     |       |  __rlshift__(...)     |      x.__rlshift__(y) <==> y<<x     |       |  __rmod__(...)     |      x.__rmod__(y) <==> y%x     |       |  __rmul__(...)     |      x.__rmul__(y) <==> y*x     |       |  __ror__(...)     |      x.__ror__(y) <==> y|x     |       |  __rpow__(...)     |      y.__rpow__(x[, z]) <==> pow(x, y[, z])     |       |  __rrshift__(...)     |      x.__rrshift__(y) <==> y>>x     |       |  __rshift__(...)     |      x.__rshift__(y) <==> x>>y     |       |  __rsub__(...)     |      x.__rsub__(y) <==> y-x     |       |  __rtruediv__(...)     |      x.__rtruediv__(y) <==> y/x     |       |  __rxor__(...)     |      x.__rxor__(y) <==> y^x     |       |  __setstate__(...)     |       |  __sizeof__(...)     |       |  __str__(...)     |      x.__str__() <==> str(x)     |       |  __sub__(...)     |      x.__sub__(y) <==> x-y     |       |  __truediv__(...)     |      x.__truediv__(y) <==> x/y     |       |  __xor__(...)     |      x.__xor__(y) <==> x^y     |       |  all(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  any(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  argmax(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  argmin(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  argsort(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  astype(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  byteswap(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class so as to     |      provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  choose(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  clip(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  compress(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  conj(...)     |       |  conjugate(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  copy(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  cumprod(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  cumsum(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  diagonal(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  dump(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  dumps(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  fill(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  flatten(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  getfield(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  item(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  itemset(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  max(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  mean(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  min(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  newbyteorder(...)     |      newbyteorder(new_order='S')     |           |      Return a new `dtype` with a different byte order.     |           |      Changes are also made in all fields and sub-arrays of the data type.     |           |      The `new_order` code can be any from the following:     |           |      * 'S' - swap dtype from current to opposite endian     |      * {'<', 'L'} - little endian     |      * {'>', 'B'} - big endian     |      * {'=', 'N'} - native order     |      * {'|', 'I'} - ignore (no change to byte order)     |           |      Parameters     |      ----------     |      new_order : str, optional     |          Byte order to force; a value from the byte order specifications     |          above.  The default value ('S') results in swapping the current     |          byte order. The code does a case-insensitive check on the first     |          letter of `new_order` for the alternatives above.  For example,     |          any of 'B' or 'b' or 'biggish' are valid to specify big-endian.     |           |           |      Returns     |      -------     |      new_dtype : dtype     |          New `dtype` object with the given change to the byte order.     |       |  nonzero(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  prod(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  ptp(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  put(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  ravel(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  repeat(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  reshape(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  resize(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  round(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  searchsorted(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  setfield(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  setflags(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class so as to     |      provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  sort(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  squeeze(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  std(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  sum(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  swapaxes(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  take(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  tobytes(...)     |       |  tofile(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  tolist(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  tostring(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  trace(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  transpose(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  var(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  view(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  ----------------------------------------------------------------------     |  Data descriptors inherited from generic:     |       |  T     |      transpose     |       |  __array_interface__     |      Array protocol: Python side     |       |  __array_priority__     |      Array priority.     |       |  __array_struct__     |      Array protocol: struct     |       |  base     |      base object     |       |  data     |      pointer to start of data     |       |  dtype     |      get array data-descriptor     |       |  flags     |      integer value of flags     |       |  flat     |      a 1-d view of scalar     |       |  imag     |      imaginary part of scalar     |       |  itemsize     |      length of one element in bytes     |       |  nbytes     |      length of item in bytes     |       |  ndim     |      number of array dimensions     |       |  real     |      real part of scalar     |       |  shape     |      tuple of array dimensions     |       |  size     |      number of elements in the gentype     |       |  strides     |      tuple of bytes steps in each dimension     |       |  ----------------------------------------------------------------------     |  Methods inherited from __builtin__.int:     |       |  __cmp__(...)     |      x.__cmp__(y) <==> cmp(x,y)     |       |  __coerce__(...)     |      x.__coerce__(y) <==> coerce(x, y)     |       |  __getattribute__(...)     |      x.__getattribute__('name') <==> x.name     |       |  __getnewargs__(...)     |       |  __hash__(...)     |      x.__hash__() <==> hash(x)     |       |  __trunc__(...)     |      Truncating an Integral returns itself.     |       |  bit_length(...)     |      int.bit_length() -> int     |           |      Number of bits necessary to represent self in binary.     |      >>> bin(37)     |      '0b100101'     |      >>> (37).bit_length()     |      6        intc = class int32(signedinteger, __builtin__.int)     |  Method resolution order:     |      int32     |      signedinteger     |      integer     |      number     |      generic     |      __builtin__.int     |      __builtin__.object     |       |  Methods defined here:     |       |  __eq__(...)     |      x.__eq__(y) <==> x==y     |       |  __ge__(...)     |      x.__ge__(y) <==> x>=y     |       |  __gt__(...)     |      x.__gt__(y) <==> x>y     |       |  __index__(...)     |      x[y:z] <==> x[y.__index__():z.__index__()]     |       |  __le__(...)     |      x.__le__(y) <==> x<=y     |       |  __lt__(...)     |      x.__lt__(y) <==> x<y     |       |  __ne__(...)     |      x.__ne__(y) <==> x!=y     |       |  ----------------------------------------------------------------------     |  Data and other attributes defined here:     |       |  __new__ = <built-in method __new__ of type object>     |      T.__new__(S, ...) -> a new object with type S, a subtype of T     |       |  ----------------------------------------------------------------------     |  Data descriptors inherited from integer:     |       |  denominator     |      denominator of value (1)     |       |  numerator     |      numerator of value (the value itself)     |       |  ----------------------------------------------------------------------     |  Methods inherited from generic:     |       |  __abs__(...)     |      x.__abs__() <==> abs(x)     |       |  __add__(...)     |      x.__add__(y) <==> x+y     |       |  __and__(...)     |      x.__and__(y) <==> x&y     |       |  __array__(...)     |      sc.__array__(|type) return 0-dim array     |       |  __array_wrap__(...)     |      sc.__array_wrap__(obj) return scalar from array     |       |  __copy__(...)     |       |  __deepcopy__(...)     |       |  __div__(...)     |      x.__div__(y) <==> x/y     |       |  __divmod__(...)     |      x.__divmod__(y) <==> divmod(x, y)     |       |  __float__(...)     |      x.__float__() <==> float(x)     |       |  __floordiv__(...)     |      x.__floordiv__(y) <==> x//y     |       |  __format__(...)     |      NumPy array scalar formatter     |       |  __getitem__(...)     |      x.__getitem__(y) <==> x[y]     |       |  __hex__(...)     |      x.__hex__() <==> hex(x)     |       |  __int__(...)     |      x.__int__() <==> int(x)     |       |  __invert__(...)     |      x.__invert__() <==> ~x     |       |  __long__(...)     |      x.__long__() <==> long(x)     |       |  __lshift__(...)     |      x.__lshift__(y) <==> x<<y     |       |  __mod__(...)     |      x.__mod__(y) <==> x%y     |       |  __mul__(...)     |      x.__mul__(y) <==> x*y     |       |  __neg__(...)     |      x.__neg__() <==> -x     |       |  __nonzero__(...)     |      x.__nonzero__() <==> x != 0     |       |  __oct__(...)     |      x.__oct__() <==> oct(x)     |       |  __or__(...)     |      x.__or__(y) <==> x|y     |       |  __pos__(...)     |      x.__pos__() <==> +x     |       |  __pow__(...)     |      x.__pow__(y[, z]) <==> pow(x, y[, z])     |       |  __radd__(...)     |      x.__radd__(y) <==> y+x     |       |  __rand__(...)     |      x.__rand__(y) <==> y&x     |       |  __rdiv__(...)     |      x.__rdiv__(y) <==> y/x     |       |  __rdivmod__(...)     |      x.__rdivmod__(y) <==> divmod(y, x)     |       |  __reduce__(...)     |       |  __repr__(...)     |      x.__repr__() <==> repr(x)     |       |  __rfloordiv__(...)     |      x.__rfloordiv__(y) <==> y//x     |       |  __rlshift__(...)     |      x.__rlshift__(y) <==> y<<x     |       |  __rmod__(...)     |      x.__rmod__(y) <==> y%x     |       |  __rmul__(...)     |      x.__rmul__(y) <==> y*x     |       |  __ror__(...)     |      x.__ror__(y) <==> y|x     |       |  __rpow__(...)     |      y.__rpow__(x[, z]) <==> pow(x, y[, z])     |       |  __rrshift__(...)     |      x.__rrshift__(y) <==> y>>x     |       |  __rshift__(...)     |      x.__rshift__(y) <==> x>>y     |       |  __rsub__(...)     |      x.__rsub__(y) <==> y-x     |       |  __rtruediv__(...)     |      x.__rtruediv__(y) <==> y/x     |       |  __rxor__(...)     |      x.__rxor__(y) <==> y^x     |       |  __setstate__(...)     |       |  __sizeof__(...)     |       |  __str__(...)     |      x.__str__() <==> str(x)     |       |  __sub__(...)     |      x.__sub__(y) <==> x-y     |       |  __truediv__(...)     |      x.__truediv__(y) <==> x/y     |       |  __xor__(...)     |      x.__xor__(y) <==> x^y     |       |  all(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  any(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  argmax(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  argmin(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  argsort(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  astype(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  byteswap(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class so as to     |      provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  choose(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  clip(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  compress(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  conj(...)     |       |  conjugate(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  copy(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  cumprod(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  cumsum(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  diagonal(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  dump(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  dumps(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  fill(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  flatten(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  getfield(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  item(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  itemset(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  max(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  mean(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  min(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  newbyteorder(...)     |      newbyteorder(new_order='S')     |           |      Return a new `dtype` with a different byte order.     |           |      Changes are also made in all fields and sub-arrays of the data type.     |           |      The `new_order` code can be any from the following:     |           |      * 'S' - swap dtype from current to opposite endian     |      * {'<', 'L'} - little endian     |      * {'>', 'B'} - big endian     |      * {'=', 'N'} - native order     |      * {'|', 'I'} - ignore (no change to byte order)     |           |      Parameters     |      ----------     |      new_order : str, optional     |          Byte order to force; a value from the byte order specifications     |          above.  The default value ('S') results in swapping the current     |          byte order. The code does a case-insensitive check on the first     |          letter of `new_order` for the alternatives above.  For example,     |          any of 'B' or 'b' or 'biggish' are valid to specify big-endian.     |           |           |      Returns     |      -------     |      new_dtype : dtype     |          New `dtype` object with the given change to the byte order.     |       |  nonzero(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  prod(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  ptp(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  put(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  ravel(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  repeat(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  reshape(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  resize(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  round(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  searchsorted(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  setfield(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  setflags(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class so as to     |      provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  sort(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  squeeze(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  std(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  sum(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  swapaxes(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  take(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  tobytes(...)     |       |  tofile(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  tolist(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  tostring(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  trace(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  transpose(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  var(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  view(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  ----------------------------------------------------------------------     |  Data descriptors inherited from generic:     |       |  T     |      transpose     |       |  __array_interface__     |      Array protocol: Python side     |       |  __array_priority__     |      Array priority.     |       |  __array_struct__     |      Array protocol: struct     |       |  base     |      base object     |       |  data     |      pointer to start of data     |       |  dtype     |      get array data-descriptor     |       |  flags     |      integer value of flags     |       |  flat     |      a 1-d view of scalar     |       |  imag     |      imaginary part of scalar     |       |  itemsize     |      length of one element in bytes     |       |  nbytes     |      length of item in bytes     |       |  ndim     |      number of array dimensions     |       |  real     |      real part of scalar     |       |  shape     |      tuple of array dimensions     |       |  size     |      number of elements in the gentype     |       |  strides     |      tuple of bytes steps in each dimension     |       |  ----------------------------------------------------------------------     |  Methods inherited from __builtin__.int:     |       |  __cmp__(...)     |      x.__cmp__(y) <==> cmp(x,y)     |       |  __coerce__(...)     |      x.__coerce__(y) <==> coerce(x, y)     |       |  __getattribute__(...)     |      x.__getattribute__('name') <==> x.name     |       |  __getnewargs__(...)     |       |  __hash__(...)     |      x.__hash__() <==> hash(x)     |       |  __trunc__(...)     |      Truncating an Integral returns itself.     |       |  bit_length(...)     |      int.bit_length() -> int     |           |      Number of bits necessary to represent self in binary.     |      >>> bin(37)     |      '0b100101'     |      >>> (37).bit_length()     |      6        class integer(number)     |  Method resolution order:     |      integer     |      number     |      generic     |      __builtin__.object     |       |  Data descriptors defined here:     |       |  denominator     |      denominator of value (1)     |       |  numerator     |      numerator of value (the value itself)     |       |  ----------------------------------------------------------------------     |  Methods inherited from generic:     |       |  __abs__(...)     |      x.__abs__() <==> abs(x)     |       |  __add__(...)     |      x.__add__(y) <==> x+y     |       |  __and__(...)     |      x.__and__(y) <==> x&y     |       |  __array__(...)     |      sc.__array__(|type) return 0-dim array     |       |  __array_wrap__(...)     |      sc.__array_wrap__(obj) return scalar from array     |       |  __copy__(...)     |       |  __deepcopy__(...)     |       |  __div__(...)     |      x.__div__(y) <==> x/y     |       |  __divmod__(...)     |      x.__divmod__(y) <==> divmod(x, y)     |       |  __eq__(...)     |      x.__eq__(y) <==> x==y     |       |  __float__(...)     |      x.__float__() <==> float(x)     |       |  __floordiv__(...)     |      x.__floordiv__(y) <==> x//y     |       |  __format__(...)     |      NumPy array scalar formatter     |       |  __ge__(...)     |      x.__ge__(y) <==> x>=y     |       |  __getitem__(...)     |      x.__getitem__(y) <==> x[y]     |       |  __gt__(...)     |      x.__gt__(y) <==> x>y     |       |  __hex__(...)     |      x.__hex__() <==> hex(x)     |       |  __int__(...)     |      x.__int__() <==> int(x)     |       |  __invert__(...)     |      x.__invert__() <==> ~x     |       |  __le__(...)     |      x.__le__(y) <==> x<=y     |       |  __long__(...)     |      x.__long__() <==> long(x)     |       |  __lshift__(...)     |      x.__lshift__(y) <==> x<<y     |       |  __lt__(...)     |      x.__lt__(y) <==> x<y     |       |  __mod__(...)     |      x.__mod__(y) <==> x%y     |       |  __mul__(...)     |      x.__mul__(y) <==> x*y     |       |  __ne__(...)     |      x.__ne__(y) <==> x!=y     |       |  __neg__(...)     |      x.__neg__() <==> -x     |       |  __nonzero__(...)     |      x.__nonzero__() <==> x != 0     |       |  __oct__(...)     |      x.__oct__() <==> oct(x)     |       |  __or__(...)     |      x.__or__(y) <==> x|y     |       |  __pos__(...)     |      x.__pos__() <==> +x     |       |  __pow__(...)     |      x.__pow__(y[, z]) <==> pow(x, y[, z])     |       |  __radd__(...)     |      x.__radd__(y) <==> y+x     |       |  __rand__(...)     |      x.__rand__(y) <==> y&x     |       |  __rdiv__(...)     |      x.__rdiv__(y) <==> y/x     |       |  __rdivmod__(...)     |      x.__rdivmod__(y) <==> divmod(y, x)     |       |  __reduce__(...)     |       |  __repr__(...)     |      x.__repr__() <==> repr(x)     |       |  __rfloordiv__(...)     |      x.__rfloordiv__(y) <==> y//x     |       |  __rlshift__(...)     |      x.__rlshift__(y) <==> y<<x     |       |  __rmod__(...)     |      x.__rmod__(y) <==> y%x     |       |  __rmul__(...)     |      x.__rmul__(y) <==> y*x     |       |  __ror__(...)     |      x.__ror__(y) <==> y|x     |       |  __rpow__(...)     |      y.__rpow__(x[, z]) <==> pow(x, y[, z])     |       |  __rrshift__(...)     |      x.__rrshift__(y) <==> y>>x     |       |  __rshift__(...)     |      x.__rshift__(y) <==> x>>y     |       |  __rsub__(...)     |      x.__rsub__(y) <==> y-x     |       |  __rtruediv__(...)     |      x.__rtruediv__(y) <==> y/x     |       |  __rxor__(...)     |      x.__rxor__(y) <==> y^x     |       |  __setstate__(...)     |       |  __sizeof__(...)     |       |  __str__(...)     |      x.__str__() <==> str(x)     |       |  __sub__(...)     |      x.__sub__(y) <==> x-y     |       |  __truediv__(...)     |      x.__truediv__(y) <==> x/y     |       |  __xor__(...)     |      x.__xor__(y) <==> x^y     |       |  all(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  any(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  argmax(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  argmin(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  argsort(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  astype(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  byteswap(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class so as to     |      provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  choose(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  clip(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  compress(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  conj(...)     |       |  conjugate(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  copy(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  cumprod(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  cumsum(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  diagonal(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  dump(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  dumps(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  fill(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  flatten(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  getfield(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  item(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  itemset(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  max(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  mean(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  min(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  newbyteorder(...)     |      newbyteorder(new_order='S')     |           |      Return a new `dtype` with a different byte order.     |           |      Changes are also made in all fields and sub-arrays of the data type.     |           |      The `new_order` code can be any from the following:     |           |      * 'S' - swap dtype from current to opposite endian     |      * {'<', 'L'} - little endian     |      * {'>', 'B'} - big endian     |      * {'=', 'N'} - native order     |      * {'|', 'I'} - ignore (no change to byte order)     |           |      Parameters     |      ----------     |      new_order : str, optional     |          Byte order to force; a value from the byte order specifications     |          above.  The default value ('S') results in swapping the current     |          byte order. The code does a case-insensitive check on the first     |          letter of `new_order` for the alternatives above.  For example,     |          any of 'B' or 'b' or 'biggish' are valid to specify big-endian.     |           |           |      Returns     |      -------     |      new_dtype : dtype     |          New `dtype` object with the given change to the byte order.     |       |  nonzero(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  prod(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  ptp(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  put(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  ravel(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  repeat(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  reshape(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  resize(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  round(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  searchsorted(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  setfield(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  setflags(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class so as to     |      provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  sort(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  squeeze(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  std(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  sum(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  swapaxes(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  take(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  tobytes(...)     |       |  tofile(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  tolist(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  tostring(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  trace(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  transpose(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  var(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  view(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  ----------------------------------------------------------------------     |  Data descriptors inherited from generic:     |       |  T     |      transpose     |       |  __array_interface__     |      Array protocol: Python side     |       |  __array_priority__     |      Array priority.     |       |  __array_struct__     |      Array protocol: struct     |       |  base     |      base object     |       |  data     |      pointer to start of data     |       |  dtype     |      get array data-descriptor     |       |  flags     |      integer value of flags     |       |  flat     |      a 1-d view of scalar     |       |  imag     |      imaginary part of scalar     |       |  itemsize     |      length of one element in bytes     |       |  nbytes     |      length of item in bytes     |       |  ndim     |      number of array dimensions     |       |  real     |      real part of scalar     |       |  shape     |      tuple of array dimensions     |       |  size     |      number of elements in the gentype     |       |  strides     |      tuple of bytes steps in each dimension        intp = class int64(signedinteger)     |  64-bit integer. Character code 'l'. Python int compatible.     |       |  Method resolution order:     |      int64     |      signedinteger     |      integer     |      number     |      generic     |      __builtin__.object     |       |  Methods defined here:     |       |  __eq__(...)     |      x.__eq__(y) <==> x==y     |       |  __ge__(...)     |      x.__ge__(y) <==> x>=y     |       |  __gt__(...)     |      x.__gt__(y) <==> x>y     |       |  __hash__(...)     |      x.__hash__() <==> hash(x)     |       |  __index__(...)     |      x[y:z] <==> x[y.__index__():z.__index__()]     |       |  __le__(...)     |      x.__le__(y) <==> x<=y     |       |  __lt__(...)     |      x.__lt__(y) <==> x<y     |       |  __ne__(...)     |      x.__ne__(y) <==> x!=y     |       |  ----------------------------------------------------------------------     |  Data and other attributes defined here:     |       |  __new__ = <built-in method __new__ of type object>     |      T.__new__(S, ...) -> a new object with type S, a subtype of T     |       |  ----------------------------------------------------------------------     |  Data descriptors inherited from integer:     |       |  denominator     |      denominator of value (1)     |       |  numerator     |      numerator of value (the value itself)     |       |  ----------------------------------------------------------------------     |  Methods inherited from generic:     |       |  __abs__(...)     |      x.__abs__() <==> abs(x)     |       |  __add__(...)     |      x.__add__(y) <==> x+y     |       |  __and__(...)     |      x.__and__(y) <==> x&y     |       |  __array__(...)     |      sc.__array__(|type) return 0-dim array     |       |  __array_wrap__(...)     |      sc.__array_wrap__(obj) return scalar from array     |       |  __copy__(...)     |       |  __deepcopy__(...)     |       |  __div__(...)     |      x.__div__(y) <==> x/y     |       |  __divmod__(...)     |      x.__divmod__(y) <==> divmod(x, y)     |       |  __float__(...)     |      x.__float__() <==> float(x)     |       |  __floordiv__(...)     |      x.__floordiv__(y) <==> x//y     |       |  __format__(...)     |      NumPy array scalar formatter     |       |  __getitem__(...)     |      x.__getitem__(y) <==> x[y]     |       |  __hex__(...)     |      x.__hex__() <==> hex(x)     |       |  __int__(...)     |      x.__int__() <==> int(x)     |       |  __invert__(...)     |      x.__invert__() <==> ~x     |       |  __long__(...)     |      x.__long__() <==> long(x)     |       |  __lshift__(...)     |      x.__lshift__(y) <==> x<<y     |       |  __mod__(...)     |      x.__mod__(y) <==> x%y     |       |  __mul__(...)     |      x.__mul__(y) <==> x*y     |       |  __neg__(...)     |      x.__neg__() <==> -x     |       |  __nonzero__(...)     |      x.__nonzero__() <==> x != 0     |       |  __oct__(...)     |      x.__oct__() <==> oct(x)     |       |  __or__(...)     |      x.__or__(y) <==> x|y     |       |  __pos__(...)     |      x.__pos__() <==> +x     |       |  __pow__(...)     |      x.__pow__(y[, z]) <==> pow(x, y[, z])     |       |  __radd__(...)     |      x.__radd__(y) <==> y+x     |       |  __rand__(...)     |      x.__rand__(y) <==> y&x     |       |  __rdiv__(...)     |      x.__rdiv__(y) <==> y/x     |       |  __rdivmod__(...)     |      x.__rdivmod__(y) <==> divmod(y, x)     |       |  __reduce__(...)     |       |  __repr__(...)     |      x.__repr__() <==> repr(x)     |       |  __rfloordiv__(...)     |      x.__rfloordiv__(y) <==> y//x     |       |  __rlshift__(...)     |      x.__rlshift__(y) <==> y<<x     |       |  __rmod__(...)     |      x.__rmod__(y) <==> y%x     |       |  __rmul__(...)     |      x.__rmul__(y) <==> y*x     |       |  __ror__(...)     |      x.__ror__(y) <==> y|x     |       |  __rpow__(...)     |      y.__rpow__(x[, z]) <==> pow(x, y[, z])     |       |  __rrshift__(...)     |      x.__rrshift__(y) <==> y>>x     |       |  __rshift__(...)     |      x.__rshift__(y) <==> x>>y     |       |  __rsub__(...)     |      x.__rsub__(y) <==> y-x     |       |  __rtruediv__(...)     |      x.__rtruediv__(y) <==> y/x     |       |  __rxor__(...)     |      x.__rxor__(y) <==> y^x     |       |  __setstate__(...)     |       |  __sizeof__(...)     |       |  __str__(...)     |      x.__str__() <==> str(x)     |       |  __sub__(...)     |      x.__sub__(y) <==> x-y     |       |  __truediv__(...)     |      x.__truediv__(y) <==> x/y     |       |  __xor__(...)     |      x.__xor__(y) <==> x^y     |       |  all(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  any(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  argmax(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  argmin(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  argsort(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  astype(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  byteswap(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class so as to     |      provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  choose(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  clip(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  compress(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  conj(...)     |       |  conjugate(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  copy(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  cumprod(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  cumsum(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  diagonal(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  dump(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  dumps(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  fill(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  flatten(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  getfield(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  item(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  itemset(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  max(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  mean(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  min(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  newbyteorder(...)     |      newbyteorder(new_order='S')     |           |      Return a new `dtype` with a different byte order.     |           |      Changes are also made in all fields and sub-arrays of the data type.     |           |      The `new_order` code can be any from the following:     |           |      * 'S' - swap dtype from current to opposite endian     |      * {'<', 'L'} - little endian     |      * {'>', 'B'} - big endian     |      * {'=', 'N'} - native order     |      * {'|', 'I'} - ignore (no change to byte order)     |           |      Parameters     |      ----------     |      new_order : str, optional     |          Byte order to force; a value from the byte order specifications     |          above.  The default value ('S') results in swapping the current     |          byte order. The code does a case-insensitive check on the first     |          letter of `new_order` for the alternatives above.  For example,     |          any of 'B' or 'b' or 'biggish' are valid to specify big-endian.     |           |           |      Returns     |      -------     |      new_dtype : dtype     |          New `dtype` object with the given change to the byte order.     |       |  nonzero(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  prod(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  ptp(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  put(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  ravel(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  repeat(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  reshape(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  resize(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  round(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  searchsorted(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  setfield(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  setflags(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class so as to     |      provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  sort(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  squeeze(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  std(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  sum(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  swapaxes(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  take(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  tobytes(...)     |       |  tofile(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  tolist(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  tostring(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  trace(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  transpose(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  var(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  view(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  ----------------------------------------------------------------------     |  Data descriptors inherited from generic:     |       |  T     |      transpose     |       |  __array_interface__     |      Array protocol: Python side     |       |  __array_priority__     |      Array priority.     |       |  __array_struct__     |      Array protocol: struct     |       |  base     |      base object     |       |  data     |      pointer to start of data     |       |  dtype     |      get array data-descriptor     |       |  flags     |      integer value of flags     |       |  flat     |      a 1-d view of scalar     |       |  imag     |      imaginary part of scalar     |       |  itemsize     |      length of one element in bytes     |       |  nbytes     |      length of item in bytes     |       |  ndim     |      number of array dimensions     |       |  real     |      real part of scalar     |       |  shape     |      tuple of array dimensions     |       |  size     |      number of elements in the gentype     |       |  strides     |      tuple of bytes steps in each dimension        longcomplex = class complex128(complexfloating)     |  Composed of two 64 bit floats     |       |  Method resolution order:     |      complex128     |      complexfloating     |      inexact     |      number     |      generic     |      __builtin__.object     |       |  Methods defined here:     |       |  __complex__(...)     |       |  __eq__(...)     |      x.__eq__(y) <==> x==y     |       |  __float__(...)     |      x.__float__() <==> float(x)     |       |  __ge__(...)     |      x.__ge__(y) <==> x>=y     |       |  __gt__(...)     |      x.__gt__(y) <==> x>y     |       |  __hash__(...)     |      x.__hash__() <==> hash(x)     |       |  __hex__(...)     |      x.__hex__() <==> hex(x)     |       |  __int__(...)     |      x.__int__() <==> int(x)     |       |  __le__(...)     |      x.__le__(y) <==> x<=y     |       |  __long__(...)     |      x.__long__() <==> long(x)     |       |  __lt__(...)     |      x.__lt__(y) <==> x<y     |       |  __ne__(...)     |      x.__ne__(y) <==> x!=y     |       |  __oct__(...)     |      x.__oct__() <==> oct(x)     |       |  __repr__(...)     |      x.__repr__() <==> repr(x)     |       |  __str__(...)     |      x.__str__() <==> str(x)     |       |  ----------------------------------------------------------------------     |  Data and other attributes defined here:     |       |  __new__ = <built-in method __new__ of type object>     |      T.__new__(S, ...) -> a new object with type S, a subtype of T     |       |  ----------------------------------------------------------------------     |  Methods inherited from generic:     |       |  __abs__(...)     |      x.__abs__() <==> abs(x)     |       |  __add__(...)     |      x.__add__(y) <==> x+y     |       |  __and__(...)     |      x.__and__(y) <==> x&y     |       |  __array__(...)     |      sc.__array__(|type) return 0-dim array     |       |  __array_wrap__(...)     |      sc.__array_wrap__(obj) return scalar from array     |       |  __copy__(...)     |       |  __deepcopy__(...)     |       |  __div__(...)     |      x.__div__(y) <==> x/y     |       |  __divmod__(...)     |      x.__divmod__(y) <==> divmod(x, y)     |       |  __floordiv__(...)     |      x.__floordiv__(y) <==> x//y     |       |  __format__(...)     |      NumPy array scalar formatter     |       |  __getitem__(...)     |      x.__getitem__(y) <==> x[y]     |       |  __invert__(...)     |      x.__invert__() <==> ~x     |       |  __lshift__(...)     |      x.__lshift__(y) <==> x<<y     |       |  __mod__(...)     |      x.__mod__(y) <==> x%y     |       |  __mul__(...)     |      x.__mul__(y) <==> x*y     |       |  __neg__(...)     |      x.__neg__() <==> -x     |       |  __nonzero__(...)     |      x.__nonzero__() <==> x != 0     |       |  __or__(...)     |      x.__or__(y) <==> x|y     |       |  __pos__(...)     |      x.__pos__() <==> +x     |       |  __pow__(...)     |      x.__pow__(y[, z]) <==> pow(x, y[, z])     |       |  __radd__(...)     |      x.__radd__(y) <==> y+x     |       |  __rand__(...)     |      x.__rand__(y) <==> y&x     |       |  __rdiv__(...)     |      x.__rdiv__(y) <==> y/x     |       |  __rdivmod__(...)     |      x.__rdivmod__(y) <==> divmod(y, x)     |       |  __reduce__(...)     |       |  __rfloordiv__(...)     |      x.__rfloordiv__(y) <==> y//x     |       |  __rlshift__(...)     |      x.__rlshift__(y) <==> y<<x     |       |  __rmod__(...)     |      x.__rmod__(y) <==> y%x     |       |  __rmul__(...)     |      x.__rmul__(y) <==> y*x     |       |  __ror__(...)     |      x.__ror__(y) <==> y|x     |       |  __rpow__(...)     |      y.__rpow__(x[, z]) <==> pow(x, y[, z])     |       |  __rrshift__(...)     |      x.__rrshift__(y) <==> y>>x     |       |  __rshift__(...)     |      x.__rshift__(y) <==> x>>y     |       |  __rsub__(...)     |      x.__rsub__(y) <==> y-x     |       |  __rtruediv__(...)     |      x.__rtruediv__(y) <==> y/x     |       |  __rxor__(...)     |      x.__rxor__(y) <==> y^x     |       |  __setstate__(...)     |       |  __sizeof__(...)     |       |  __sub__(...)     |      x.__sub__(y) <==> x-y     |       |  __truediv__(...)     |      x.__truediv__(y) <==> x/y     |       |  __xor__(...)     |      x.__xor__(y) <==> x^y     |       |  all(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  any(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  argmax(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  argmin(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  argsort(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  astype(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  byteswap(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class so as to     |      provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  choose(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  clip(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  compress(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  conj(...)     |       |  conjugate(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  copy(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  cumprod(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  cumsum(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  diagonal(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  dump(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  dumps(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  fill(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  flatten(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  getfield(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  item(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  itemset(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  max(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  mean(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  min(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  newbyteorder(...)     |      newbyteorder(new_order='S')     |           |      Return a new `dtype` with a different byte order.     |           |      Changes are also made in all fields and sub-arrays of the data type.     |           |      The `new_order` code can be any from the following:     |           |      * 'S' - swap dtype from current to opposite endian     |      * {'<', 'L'} - little endian     |      * {'>', 'B'} - big endian     |      * {'=', 'N'} - native order     |      * {'|', 'I'} - ignore (no change to byte order)     |           |      Parameters     |      ----------     |      new_order : str, optional     |          Byte order to force; a value from the byte order specifications     |          above.  The default value ('S') results in swapping the current     |          byte order. The code does a case-insensitive check on the first     |          letter of `new_order` for the alternatives above.  For example,     |          any of 'B' or 'b' or 'biggish' are valid to specify big-endian.     |           |           |      Returns     |      -------     |      new_dtype : dtype     |          New `dtype` object with the given change to the byte order.     |       |  nonzero(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  prod(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  ptp(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  put(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  ravel(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  repeat(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  reshape(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  resize(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  round(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  searchsorted(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  setfield(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  setflags(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class so as to     |      provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  sort(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  squeeze(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  std(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  sum(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  swapaxes(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  take(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  tobytes(...)     |       |  tofile(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  tolist(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  tostring(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  trace(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  transpose(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  var(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  view(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  ----------------------------------------------------------------------     |  Data descriptors inherited from generic:     |       |  T     |      transpose     |       |  __array_interface__     |      Array protocol: Python side     |       |  __array_priority__     |      Array priority.     |       |  __array_struct__     |      Array protocol: struct     |       |  base     |      base object     |       |  data     |      pointer to start of data     |       |  dtype     |      get array data-descriptor     |       |  flags     |      integer value of flags     |       |  flat     |      a 1-d view of scalar     |       |  imag     |      imaginary part of scalar     |       |  itemsize     |      length of one element in bytes     |       |  nbytes     |      length of item in bytes     |       |  ndim     |      number of array dimensions     |       |  real     |      real part of scalar     |       |  shape     |      tuple of array dimensions     |       |  size     |      number of elements in the gentype     |       |  strides     |      tuple of bytes steps in each dimension        longdouble = class float64(floating)     |  Method resolution order:     |      float64     |      floating     |      inexact     |      number     |      generic     |      __builtin__.object     |       |  Methods defined here:     |       |  __eq__(...)     |      x.__eq__(y) <==> x==y     |       |  __float__(...)     |      x.__float__() <==> float(x)     |       |  __ge__(...)     |      x.__ge__(y) <==> x>=y     |       |  __gt__(...)     |      x.__gt__(y) <==> x>y     |       |  __hash__(...)     |      x.__hash__() <==> hash(x)     |       |  __hex__(...)     |      x.__hex__() <==> hex(x)     |       |  __int__(...)     |      x.__int__() <==> int(x)     |       |  __le__(...)     |      x.__le__(y) <==> x<=y     |       |  __long__(...)     |      x.__long__() <==> long(x)     |       |  __lt__(...)     |      x.__lt__(y) <==> x<y     |       |  __ne__(...)     |      x.__ne__(y) <==> x!=y     |       |  __oct__(...)     |      x.__oct__() <==> oct(x)     |       |  __repr__(...)     |      x.__repr__() <==> repr(x)     |       |  __str__(...)     |      x.__str__() <==> str(x)     |       |  ----------------------------------------------------------------------     |  Data and other attributes defined here:     |       |  __new__ = <built-in method __new__ of type object>     |      T.__new__(S, ...) -> a new object with type S, a subtype of T     |       |  ----------------------------------------------------------------------     |  Methods inherited from generic:     |       |  __abs__(...)     |      x.__abs__() <==> abs(x)     |       |  __add__(...)     |      x.__add__(y) <==> x+y     |       |  __and__(...)     |      x.__and__(y) <==> x&y     |       |  __array__(...)     |      sc.__array__(|type) return 0-dim array     |       |  __array_wrap__(...)     |      sc.__array_wrap__(obj) return scalar from array     |       |  __copy__(...)     |       |  __deepcopy__(...)     |       |  __div__(...)     |      x.__div__(y) <==> x/y     |       |  __divmod__(...)     |      x.__divmod__(y) <==> divmod(x, y)     |       |  __floordiv__(...)     |      x.__floordiv__(y) <==> x//y     |       |  __format__(...)     |      NumPy array scalar formatter     |       |  __getitem__(...)     |      x.__getitem__(y) <==> x[y]     |       |  __invert__(...)     |      x.__invert__() <==> ~x     |       |  __lshift__(...)     |      x.__lshift__(y) <==> x<<y     |       |  __mod__(...)     |      x.__mod__(y) <==> x%y     |       |  __mul__(...)     |      x.__mul__(y) <==> x*y     |       |  __neg__(...)     |      x.__neg__() <==> -x     |       |  __nonzero__(...)     |      x.__nonzero__() <==> x != 0     |       |  __or__(...)     |      x.__or__(y) <==> x|y     |       |  __pos__(...)     |      x.__pos__() <==> +x     |       |  __pow__(...)     |      x.__pow__(y[, z]) <==> pow(x, y[, z])     |       |  __radd__(...)     |      x.__radd__(y) <==> y+x     |       |  __rand__(...)     |      x.__rand__(y) <==> y&x     |       |  __rdiv__(...)     |      x.__rdiv__(y) <==> y/x     |       |  __rdivmod__(...)     |      x.__rdivmod__(y) <==> divmod(y, x)     |       |  __reduce__(...)     |       |  __rfloordiv__(...)     |      x.__rfloordiv__(y) <==> y//x     |       |  __rlshift__(...)     |      x.__rlshift__(y) <==> y<<x     |       |  __rmod__(...)     |      x.__rmod__(y) <==> y%x     |       |  __rmul__(...)     |      x.__rmul__(y) <==> y*x     |       |  __ror__(...)     |      x.__ror__(y) <==> y|x     |       |  __rpow__(...)     |      y.__rpow__(x[, z]) <==> pow(x, y[, z])     |       |  __rrshift__(...)     |      x.__rrshift__(y) <==> y>>x     |       |  __rshift__(...)     |      x.__rshift__(y) <==> x>>y     |       |  __rsub__(...)     |      x.__rsub__(y) <==> y-x     |       |  __rtruediv__(...)     |      x.__rtruediv__(y) <==> y/x     |       |  __rxor__(...)     |      x.__rxor__(y) <==> y^x     |       |  __setstate__(...)     |       |  __sizeof__(...)     |       |  __sub__(...)     |      x.__sub__(y) <==> x-y     |       |  __truediv__(...)     |      x.__truediv__(y) <==> x/y     |       |  __xor__(...)     |      x.__xor__(y) <==> x^y     |       |  all(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  any(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  argmax(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  argmin(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  argsort(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  astype(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  byteswap(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class so as to     |      provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  choose(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  clip(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  compress(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  conj(...)     |       |  conjugate(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  copy(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  cumprod(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  cumsum(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  diagonal(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  dump(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  dumps(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  fill(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  flatten(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  getfield(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  item(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  itemset(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  max(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  mean(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  min(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  newbyteorder(...)     |      newbyteorder(new_order='S')     |           |      Return a new `dtype` with a different byte order.     |           |      Changes are also made in all fields and sub-arrays of the data type.     |           |      The `new_order` code can be any from the following:     |           |      * 'S' - swap dtype from current to opposite endian     |      * {'<', 'L'} - little endian     |      * {'>', 'B'} - big endian     |      * {'=', 'N'} - native order     |      * {'|', 'I'} - ignore (no change to byte order)     |           |      Parameters     |      ----------     |      new_order : str, optional     |          Byte order to force; a value from the byte order specifications     |          above.  The default value ('S') results in swapping the current     |          byte order. The code does a case-insensitive check on the first     |          letter of `new_order` for the alternatives above.  For example,     |          any of 'B' or 'b' or 'biggish' are valid to specify big-endian.     |           |           |      Returns     |      -------     |      new_dtype : dtype     |          New `dtype` object with the given change to the byte order.     |       |  nonzero(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  prod(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  ptp(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  put(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  ravel(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  repeat(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  reshape(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  resize(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  round(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  searchsorted(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  setfield(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  setflags(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class so as to     |      provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  sort(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  squeeze(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  std(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  sum(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  swapaxes(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  take(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  tobytes(...)     |       |  tofile(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  tolist(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  tostring(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  trace(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  transpose(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  var(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  view(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  ----------------------------------------------------------------------     |  Data descriptors inherited from generic:     |       |  T     |      transpose     |       |  __array_interface__     |      Array protocol: Python side     |       |  __array_priority__     |      Array priority.     |       |  __array_struct__     |      Array protocol: struct     |       |  base     |      base object     |       |  data     |      pointer to start of data     |       |  dtype     |      get array data-descriptor     |       |  flags     |      integer value of flags     |       |  flat     |      a 1-d view of scalar     |       |  imag     |      imaginary part of scalar     |       |  itemsize     |      length of one element in bytes     |       |  nbytes     |      length of item in bytes     |       |  ndim     |      number of array dimensions     |       |  real     |      real part of scalar     |       |  shape     |      tuple of array dimensions     |       |  size     |      number of elements in the gentype     |       |  strides     |      tuple of bytes steps in each dimension        longfloat = class float64(floating)     |  Method resolution order:     |      float64     |      floating     |      inexact     |      number     |      generic     |      __builtin__.object     |       |  Methods defined here:     |       |  __eq__(...)     |      x.__eq__(y) <==> x==y     |       |  __float__(...)     |      x.__float__() <==> float(x)     |       |  __ge__(...)     |      x.__ge__(y) <==> x>=y     |       |  __gt__(...)     |      x.__gt__(y) <==> x>y     |       |  __hash__(...)     |      x.__hash__() <==> hash(x)     |       |  __hex__(...)     |      x.__hex__() <==> hex(x)     |       |  __int__(...)     |      x.__int__() <==> int(x)     |       |  __le__(...)     |      x.__le__(y) <==> x<=y     |       |  __long__(...)     |      x.__long__() <==> long(x)     |       |  __lt__(...)     |      x.__lt__(y) <==> x<y     |       |  __ne__(...)     |      x.__ne__(y) <==> x!=y     |       |  __oct__(...)     |      x.__oct__() <==> oct(x)     |       |  __repr__(...)     |      x.__repr__() <==> repr(x)     |       |  __str__(...)     |      x.__str__() <==> str(x)     |       |  ----------------------------------------------------------------------     |  Data and other attributes defined here:     |       |  __new__ = <built-in method __new__ of type object>     |      T.__new__(S, ...) -> a new object with type S, a subtype of T     |       |  ----------------------------------------------------------------------     |  Methods inherited from generic:     |       |  __abs__(...)     |      x.__abs__() <==> abs(x)     |       |  __add__(...)     |      x.__add__(y) <==> x+y     |       |  __and__(...)     |      x.__and__(y) <==> x&y     |       |  __array__(...)     |      sc.__array__(|type) return 0-dim array     |       |  __array_wrap__(...)     |      sc.__array_wrap__(obj) return scalar from array     |       |  __copy__(...)     |       |  __deepcopy__(...)     |       |  __div__(...)     |      x.__div__(y) <==> x/y     |       |  __divmod__(...)     |      x.__divmod__(y) <==> divmod(x, y)     |       |  __floordiv__(...)     |      x.__floordiv__(y) <==> x//y     |       |  __format__(...)     |      NumPy array scalar formatter     |       |  __getitem__(...)     |      x.__getitem__(y) <==> x[y]     |       |  __invert__(...)     |      x.__invert__() <==> ~x     |       |  __lshift__(...)     |      x.__lshift__(y) <==> x<<y     |       |  __mod__(...)     |      x.__mod__(y) <==> x%y     |       |  __mul__(...)     |      x.__mul__(y) <==> x*y     |       |  __neg__(...)     |      x.__neg__() <==> -x     |       |  __nonzero__(...)     |      x.__nonzero__() <==> x != 0     |       |  __or__(...)     |      x.__or__(y) <==> x|y     |       |  __pos__(...)     |      x.__pos__() <==> +x     |       |  __pow__(...)     |      x.__pow__(y[, z]) <==> pow(x, y[, z])     |       |  __radd__(...)     |      x.__radd__(y) <==> y+x     |       |  __rand__(...)     |      x.__rand__(y) <==> y&x     |       |  __rdiv__(...)     |      x.__rdiv__(y) <==> y/x     |       |  __rdivmod__(...)     |      x.__rdivmod__(y) <==> divmod(y, x)     |       |  __reduce__(...)     |       |  __rfloordiv__(...)     |      x.__rfloordiv__(y) <==> y//x     |       |  __rlshift__(...)     |      x.__rlshift__(y) <==> y<<x     |       |  __rmod__(...)     |      x.__rmod__(y) <==> y%x     |       |  __rmul__(...)     |      x.__rmul__(y) <==> y*x     |       |  __ror__(...)     |      x.__ror__(y) <==> y|x     |       |  __rpow__(...)     |      y.__rpow__(x[, z]) <==> pow(x, y[, z])     |       |  __rrshift__(...)     |      x.__rrshift__(y) <==> y>>x     |       |  __rshift__(...)     |      x.__rshift__(y) <==> x>>y     |       |  __rsub__(...)     |      x.__rsub__(y) <==> y-x     |       |  __rtruediv__(...)     |      x.__rtruediv__(y) <==> y/x     |       |  __rxor__(...)     |      x.__rxor__(y) <==> y^x     |       |  __setstate__(...)     |       |  __sizeof__(...)     |       |  __sub__(...)     |      x.__sub__(y) <==> x-y     |       |  __truediv__(...)     |      x.__truediv__(y) <==> x/y     |       |  __xor__(...)     |      x.__xor__(y) <==> x^y     |       |  all(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  any(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  argmax(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  argmin(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  argsort(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  astype(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  byteswap(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class so as to     |      provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  choose(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  clip(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  compress(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  conj(...)     |       |  conjugate(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  copy(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  cumprod(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  cumsum(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  diagonal(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  dump(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  dumps(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  fill(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  flatten(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  getfield(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  item(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  itemset(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  max(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  mean(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  min(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  newbyteorder(...)     |      newbyteorder(new_order='S')     |           |      Return a new `dtype` with a different byte order.     |           |      Changes are also made in all fields and sub-arrays of the data type.     |           |      The `new_order` code can be any from the following:     |           |      * 'S' - swap dtype from current to opposite endian     |      * {'<', 'L'} - little endian     |      * {'>', 'B'} - big endian     |      * {'=', 'N'} - native order     |      * {'|', 'I'} - ignore (no change to byte order)     |           |      Parameters     |      ----------     |      new_order : str, optional     |          Byte order to force; a value from the byte order specifications     |          above.  The default value ('S') results in swapping the current     |          byte order. The code does a case-insensitive check on the first     |          letter of `new_order` for the alternatives above.  For example,     |          any of 'B' or 'b' or 'biggish' are valid to specify big-endian.     |           |           |      Returns     |      -------     |      new_dtype : dtype     |          New `dtype` object with the given change to the byte order.     |       |  nonzero(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  prod(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  ptp(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  put(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  ravel(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  repeat(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  reshape(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  resize(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  round(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  searchsorted(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  setfield(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  setflags(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class so as to     |      provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  sort(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  squeeze(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  std(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  sum(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  swapaxes(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  take(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  tobytes(...)     |       |  tofile(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  tolist(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  tostring(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  trace(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  transpose(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  var(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  view(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  ----------------------------------------------------------------------     |  Data descriptors inherited from generic:     |       |  T     |      transpose     |       |  __array_interface__     |      Array protocol: Python side     |       |  __array_priority__     |      Array priority.     |       |  __array_struct__     |      Array protocol: struct     |       |  base     |      base object     |       |  data     |      pointer to start of data     |       |  dtype     |      get array data-descriptor     |       |  flags     |      integer value of flags     |       |  flat     |      a 1-d view of scalar     |       |  imag     |      imaginary part of scalar     |       |  itemsize     |      length of one element in bytes     |       |  nbytes     |      length of item in bytes     |       |  ndim     |      number of array dimensions     |       |  real     |      real part of scalar     |       |  shape     |      tuple of array dimensions     |       |  size     |      number of elements in the gentype     |       |  strides     |      tuple of bytes steps in each dimension        longlong = class int64(signedinteger)     |  64-bit integer. Character code 'l'. Python int compatible.     |       |  Method resolution order:     |      int64     |      signedinteger     |      integer     |      number     |      generic     |      __builtin__.object     |       |  Methods defined here:     |       |  __eq__(...)     |      x.__eq__(y) <==> x==y     |       |  __ge__(...)     |      x.__ge__(y) <==> x>=y     |       |  __gt__(...)     |      x.__gt__(y) <==> x>y     |       |  __hash__(...)     |      x.__hash__() <==> hash(x)     |       |  __index__(...)     |      x[y:z] <==> x[y.__index__():z.__index__()]     |       |  __le__(...)     |      x.__le__(y) <==> x<=y     |       |  __lt__(...)     |      x.__lt__(y) <==> x<y     |       |  __ne__(...)     |      x.__ne__(y) <==> x!=y     |       |  ----------------------------------------------------------------------     |  Data and other attributes defined here:     |       |  __new__ = <built-in method __new__ of type object>     |      T.__new__(S, ...) -> a new object with type S, a subtype of T     |       |  ----------------------------------------------------------------------     |  Data descriptors inherited from integer:     |       |  denominator     |      denominator of value (1)     |       |  numerator     |      numerator of value (the value itself)     |       |  ----------------------------------------------------------------------     |  Methods inherited from generic:     |       |  __abs__(...)     |      x.__abs__() <==> abs(x)     |       |  __add__(...)     |      x.__add__(y) <==> x+y     |       |  __and__(...)     |      x.__and__(y) <==> x&y     |       |  __array__(...)     |      sc.__array__(|type) return 0-dim array     |       |  __array_wrap__(...)     |      sc.__array_wrap__(obj) return scalar from array     |       |  __copy__(...)     |       |  __deepcopy__(...)     |       |  __div__(...)     |      x.__div__(y) <==> x/y     |       |  __divmod__(...)     |      x.__divmod__(y) <==> divmod(x, y)     |       |  __float__(...)     |      x.__float__() <==> float(x)     |       |  __floordiv__(...)     |      x.__floordiv__(y) <==> x//y     |       |  __format__(...)     |      NumPy array scalar formatter     |       |  __getitem__(...)     |      x.__getitem__(y) <==> x[y]     |       |  __hex__(...)     |      x.__hex__() <==> hex(x)     |       |  __int__(...)     |      x.__int__() <==> int(x)     |       |  __invert__(...)     |      x.__invert__() <==> ~x     |       |  __long__(...)     |      x.__long__() <==> long(x)     |       |  __lshift__(...)     |      x.__lshift__(y) <==> x<<y     |       |  __mod__(...)     |      x.__mod__(y) <==> x%y     |       |  __mul__(...)     |      x.__mul__(y) <==> x*y     |       |  __neg__(...)     |      x.__neg__() <==> -x     |       |  __nonzero__(...)     |      x.__nonzero__() <==> x != 0     |       |  __oct__(...)     |      x.__oct__() <==> oct(x)     |       |  __or__(...)     |      x.__or__(y) <==> x|y     |       |  __pos__(...)     |      x.__pos__() <==> +x     |       |  __pow__(...)     |      x.__pow__(y[, z]) <==> pow(x, y[, z])     |       |  __radd__(...)     |      x.__radd__(y) <==> y+x     |       |  __rand__(...)     |      x.__rand__(y) <==> y&x     |       |  __rdiv__(...)     |      x.__rdiv__(y) <==> y/x     |       |  __rdivmod__(...)     |      x.__rdivmod__(y) <==> divmod(y, x)     |       |  __reduce__(...)     |       |  __repr__(...)     |      x.__repr__() <==> repr(x)     |       |  __rfloordiv__(...)     |      x.__rfloordiv__(y) <==> y//x     |       |  __rlshift__(...)     |      x.__rlshift__(y) <==> y<<x     |       |  __rmod__(...)     |      x.__rmod__(y) <==> y%x     |       |  __rmul__(...)     |      x.__rmul__(y) <==> y*x     |       |  __ror__(...)     |      x.__ror__(y) <==> y|x     |       |  __rpow__(...)     |      y.__rpow__(x[, z]) <==> pow(x, y[, z])     |       |  __rrshift__(...)     |      x.__rrshift__(y) <==> y>>x     |       |  __rshift__(...)     |      x.__rshift__(y) <==> x>>y     |       |  __rsub__(...)     |      x.__rsub__(y) <==> y-x     |       |  __rtruediv__(...)     |      x.__rtruediv__(y) <==> y/x     |       |  __rxor__(...)     |      x.__rxor__(y) <==> y^x     |       |  __setstate__(...)     |       |  __sizeof__(...)     |       |  __str__(...)     |      x.__str__() <==> str(x)     |       |  __sub__(...)     |      x.__sub__(y) <==> x-y     |       |  __truediv__(...)     |      x.__truediv__(y) <==> x/y     |       |  __xor__(...)     |      x.__xor__(y) <==> x^y     |       |  all(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  any(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  argmax(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  argmin(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  argsort(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  astype(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  byteswap(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class so as to     |      provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  choose(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  clip(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  compress(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  conj(...)     |       |  conjugate(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  copy(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  cumprod(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  cumsum(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  diagonal(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  dump(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  dumps(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  fill(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  flatten(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  getfield(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  item(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  itemset(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  max(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  mean(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  min(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  newbyteorder(...)     |      newbyteorder(new_order='S')     |           |      Return a new `dtype` with a different byte order.     |           |      Changes are also made in all fields and sub-arrays of the data type.     |           |      The `new_order` code can be any from the following:     |           |      * 'S' - swap dtype from current to opposite endian     |      * {'<', 'L'} - little endian     |      * {'>', 'B'} - big endian     |      * {'=', 'N'} - native order     |      * {'|', 'I'} - ignore (no change to byte order)     |           |      Parameters     |      ----------     |      new_order : str, optional     |          Byte order to force; a value from the byte order specifications     |          above.  The default value ('S') results in swapping the current     |          byte order. The code does a case-insensitive check on the first     |          letter of `new_order` for the alternatives above.  For example,     |          any of 'B' or 'b' or 'biggish' are valid to specify big-endian.     |           |           |      Returns     |      -------     |      new_dtype : dtype     |          New `dtype` object with the given change to the byte order.     |       |  nonzero(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  prod(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  ptp(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  put(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  ravel(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  repeat(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  reshape(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  resize(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  round(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  searchsorted(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  setfield(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  setflags(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class so as to     |      provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  sort(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  squeeze(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  std(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  sum(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  swapaxes(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  take(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  tobytes(...)     |       |  tofile(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  tolist(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  tostring(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  trace(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  transpose(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  var(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  view(...)     |      Not implemented (virtual attribute)     |           |      Class generic exists solely to derive numpy scalars from, and possesses,     |      albeit unimplemented, all the attributes of the ndarray class     |      so as to provide a uniform API.     |           |      See Also     |      --------     |      The corresponding attribute of the derived class of interest.     |       |  ----------------------------------------------------------------------     |  Data descriptors inherited from generic:     |       |  T     |      transpose     |       |  __array_interface__     |      Array protocol: Python side     |       |  __array_priority__     |      Array priority.     |       |  __array_struct__     |      Array protocol: struct     |       |  base     |      base object     |       |  data     |      pointer to start of data     |       |  dtype     |      get array data-descriptor     |       |  flags     |      integer value of flags     |       |  flat     |      a 1-d view of scalar     |       |  imag     |      imaginary part of scalar     |       |  itemsize     |      length of one element in bytes     |       |  nbytes     |      length of item in bytes     |       |  ndim     |      number of array dimensions     |       |  real     |      real part of scalar     |       |  shape     |      tuple of array dimensions     |       |  size     |      number of elements in the gentype     |       |  strides     |      tuple of bytes steps in each dimension        class matrix(numpy.ndarray)     |  matrix(data, dtype=None, copy=True)     |       |  Returns a matrix from an array-like object, or from a string of data.     |  A matrix is a specialized 2-D array that retains its 2-D nature     |  through operations.  It has certain special operators, such as ``*``     |  (matrix multiplication) and ``**`` (matrix power).     |       |  Parameters     |  ----------     |  data : array_like or string     |     If `data` is a string, it is interpreted as a matrix with commas     |     or spaces separating columns, and semicolons separating rows.     |  dtype : data-type     |     Data-type of the output matrix.     |  copy : bool     |     If `data` is already an `ndarray`, then this flag determines     |     whether the data is copied (the default), or whether a view is     |     constructed.     |       |  See Also     |  --------     |  array     |       |  Examples     |  --------     |  >>> a = np.matrix('1 2; 3 4')     |  >>> print a     |  [[1 2]     |   [3 4]]     |       |  >>> np.matrix([[1, 2], [3, 4]])     |  matrix([[1, 2],     |          [3, 4]])     |       |  Method resolution order:     |      matrix     |      numpy.ndarray     |      __builtin__.object     |       |  Methods defined here:     |       |  __array_finalize__(self, obj)     |       |  __getitem__(self, index)     |       |  __imul__(self, other)     |       |  __ipow__(self, other)     |       |  __mul__(self, other)     |       |  __pow__(self, other)     |       |  __repr__(self)     |       |  __rmul__(self, other)     |       |  __rpow__(self, other)     |       |  __str__(self)     |       |  all(self, axis=None, out=None)     |      Test whether all matrix elements along a given axis evaluate to True.     |           |      Parameters     |      ----------     |      See `numpy.all` for complete descriptions     |           |      See Also     |      --------     |      numpy.all     |           |      Notes     |      -----     |      This is the same as `ndarray.all`, but it returns a `matrix` object.     |           |      Examples     |      --------     |      >>> x = np.matrix(np.arange(12).reshape((3,4))); x     |      matrix([[ 0,  1,  2,  3],     |              [ 4,  5,  6,  7],     |              [ 8,  9, 10, 11]])     |      >>> y = x[0]; y     |      matrix([[0, 1, 2, 3]])     |      >>> (x == y)     |      matrix([[ True,  True,  True,  True],     |              [False, False, False, False],     |              [False, False, False, False]], dtype=bool)     |      >>> (x == y).all()     |      False     |      >>> (x == y).all(0)     |      matrix([[False, False, False, False]], dtype=bool)     |      >>> (x == y).all(1)     |      matrix([[ True],     |              [False],     |              [False]], dtype=bool)     |       |  any(self, axis=None, out=None)     |      Test whether any array element along a given axis evaluates to True.     |           |      Refer to `numpy.any` for full documentation.     |           |      Parameters     |      ----------     |      axis : int, optional     |          Axis along which logical OR is performed     |      out : ndarray, optional     |          Output to existing array instead of creating new one, must have     |          same shape as expected output     |           |      Returns     |      -------     |          any : bool, ndarray     |              Returns a single bool if `axis` is ``None``; otherwise,     |              returns `ndarray`     |       |  argmax(self, axis=None, out=None)     |      Indices of the maximum values along an axis.     |           |      Parameters     |      ----------     |      See `numpy.argmax` for complete descriptions     |           |      See Also     |      --------     |      numpy.argmax     |           |      Notes     |      -----     |      This is the same as `ndarray.argmax`, but returns a `matrix` object     |      where `ndarray.argmax` would return an `ndarray`.     |           |      Examples     |      --------     |      >>> x = np.matrix(np.arange(12).reshape((3,4))); x     |      matrix([[ 0,  1,  2,  3],     |              [ 4,  5,  6,  7],     |              [ 8,  9, 10, 11]])     |      >>> x.argmax()     |      11     |      >>> x.argmax(0)     |      matrix([[2, 2, 2, 2]])     |      >>> x.argmax(1)     |      matrix([[3],     |              [3],     |              [3]])     |       |  argmin(self, axis=None, out=None)     |      Return the indices of the minimum values along an axis.     |           |      Parameters     |      ----------     |      See `numpy.argmin` for complete descriptions.     |           |      See Also     |      --------     |      numpy.argmin     |           |      Notes     |      -----     |      This is the same as `ndarray.argmin`, but returns a `matrix` object     |      where `ndarray.argmin` would return an `ndarray`.     |           |      Examples     |      --------     |      >>> x = -np.matrix(np.arange(12).reshape((3,4))); x     |      matrix([[  0,  -1,  -2,  -3],     |              [ -4,  -5,  -6,  -7],     |              [ -8,  -9, -10, -11]])     |      >>> x.argmin()     |      11     |      >>> x.argmin(0)     |      matrix([[2, 2, 2, 2]])     |      >>> x.argmin(1)     |      matrix([[3],     |              [3],     |              [3]])     |       |  flatten(self, order='C')     |      Return a flattened copy of the matrix.     |           |      All `N` elements of the matrix are placed into a single row.     |           |      Parameters     |      ----------     |      order : {'C', 'F', 'A'}, optional     |          Whether to flatten in C (row-major), Fortran (column-major) order,     |          or preserve the C/Fortran ordering from `m`.     |          The default is 'C'.     |           |      Returns     |      -------     |      y : matrix     |          A copy of the matrix, flattened to a `(1, N)` matrix where `N`     |          is the number of elements in the original matrix.     |           |      See Also     |      --------     |      ravel : Return a flattened array.     |      flat : A 1-D flat iterator over the matrix.     |           |      Examples     |      --------     |      >>> m = np.matrix([[1,2], [3,4]])     |      >>> m.flatten()     |      matrix([[1, 2, 3, 4]])     |      >>> m.flatten('F')     |      matrix([[1, 3, 2, 4]])     |       |  getA(self)     |      Return `self` as an `ndarray` object.     |           |      Equivalent to ``np.asarray(self)``.     |           |      Parameters     |      ----------     |      None     |           |      Returns     |      -------     |      ret : ndarray     |          `self` as an `ndarray`     |           |      Examples     |      --------     |      >>> x = np.matrix(np.arange(12).reshape((3,4))); x     |      matrix([[ 0,  1,  2,  3],     |              [ 4,  5,  6,  7],     |              [ 8,  9, 10, 11]])     |      >>> x.getA()     |      array([[ 0,  1,  2,  3],     |             [ 4,  5,  6,  7],     |             [ 8,  9, 10, 11]])     |       |  getA1(self)     |      Return `self` as a flattened `ndarray`.     |           |      Equivalent to ``np.asarray(x).ravel()``     |           |      Parameters     |      ----------     |      None     |           |      Returns     |      -------     |      ret : ndarray     |          `self`, 1-D, as an `ndarray`     |           |      Examples     |      --------     |      >>> x = np.matrix(np.arange(12).reshape((3,4))); x     |      matrix([[ 0,  1,  2,  3],     |              [ 4,  5,  6,  7],     |              [ 8,  9, 10, 11]])     |      >>> x.getA1()     |      array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11])     |       |  getH(self)     |      Returns the (complex) conjugate transpose of `self`.     |           |      Equivalent to ``np.transpose(self)`` if `self` is real-valued.     |           |      Parameters     |      ----------     |      None     |           |      Returns     |      -------     |      ret : matrix object     |          complex conjugate transpose of `self`     |           |      Examples     |      --------     |      >>> x = np.matrix(np.arange(12).reshape((3,4)))     |      >>> z = x - 1j*x; z     |      matrix([[  0. +0.j,   1. -1.j,   2. -2.j,   3. -3.j],     |              [  4. -4.j,   5. -5.j,   6. -6.j,   7. -7.j],     |              [  8. -8.j,   9. -9.j,  10.-10.j,  11.-11.j]])     |      >>> z.getH()     |      matrix([[  0. +0.j,   4. +4.j,   8. +8.j],     |              [  1. +1.j,   5. +5.j,   9. +9.j],     |              [  2. +2.j,   6. +6.j,  10.+10.j],     |              [  3. +3.j,   7. +7.j,  11.+11.j]])     |       |  getI(self)     |      Returns the (multiplicative) inverse of invertible `self`.     |           |      Parameters     |      ----------     |      None     |           |      Returns     |      -------     |      ret : matrix object     |          If `self` is non-singular, `ret` is such that ``ret * self`` ==     |          ``self * ret`` == ``np.matrix(np.eye(self[0,:].size)`` all return     |          ``True``.     |           |      Raises     |      ------     |      numpy.linalg.LinAlgError: Singular matrix     |          If `self` is singular.     |           |      See Also     |      --------     |      linalg.inv     |           |      Examples     |      --------     |      >>> m = np.matrix('[1, 2; 3, 4]'); m     |      matrix([[1, 2],     |              [3, 4]])     |      >>> m.getI()     |      matrix([[-2. ,  1. ],     |              [ 1.5, -0.5]])     |      >>> m.getI() * m     |      matrix([[ 1.,  0.],     |              [ 0.,  1.]])     |       |  getT(self)     |      Returns the transpose of the matrix.     |           |      Does *not* conjugate!  For the complex conjugate transpose, use ``.H``.     |           |      Parameters     |      ----------     |      None     |           |      Returns     |      -------     |      ret : matrix object     |          The (non-conjugated) transpose of the matrix.     |           |      See Also     |      --------     |      transpose, getH     |           |      Examples     |      --------     |      >>> m = np.matrix('[1, 2; 3, 4]')     |      >>> m     |      matrix([[1, 2],     |              [3, 4]])     |      >>> m.getT()     |      matrix([[1, 3],     |              [2, 4]])     |       |  max(self, axis=None, out=None)     |      Return the maximum value along an axis.     |           |      Parameters     |      ----------     |      See `amax` for complete descriptions     |           |      See Also     |      --------     |      amax, ndarray.max     |           |      Notes     |      -----     |      This is the same as `ndarray.max`, but returns a `matrix` object     |      where `ndarray.max` would return an ndarray.     |           |      Examples     |      --------     |      >>> x = np.matrix(np.arange(12).reshape((3,4))); x     |      matrix([[ 0,  1,  2,  3],     |              [ 4,  5,  6,  7],     |              [ 8,  9, 10, 11]])     |      >>> x.max()     |      11     |      >>> x.max(0)     |      matrix([[ 8,  9, 10, 11]])     |      >>> x.max(1)     |      matrix([[ 3],     |              [ 7],     |              [11]])     |       |  mean(self, axis=None, dtype=None, out=None)     |      Returns the average of the matrix elements along the given axis.     |           |      Refer to `numpy.mean` for full documentation.     |           |      See Also     |      --------     |      numpy.mean     |           |      Notes     |      -----     |      Same as `ndarray.mean` except that, where that returns an `ndarray`,     |      this returns a `matrix` object.     |           |      Examples     |      --------     |      >>> x = np.matrix(np.arange(12).reshape((3, 4)))     |      >>> x     |      matrix([[ 0,  1,  2,  3],     |              [ 4,  5,  6,  7],     |              [ 8,  9, 10, 11]])     |      >>> x.mean()     |      5.5     |      >>> x.mean(0)     |      matrix([[ 4.,  5.,  6.,  7.]])     |      >>> x.mean(1)     |      matrix([[ 1.5],     |              [ 5.5],     |              [ 9.5]])     |       |  min(self, axis=None, out=None)     |      Return the minimum value along an axis.     |           |      Parameters     |      ----------     |      See `amin` for complete descriptions.     |           |      See Also     |      --------     |      amin, ndarray.min     |           |      Notes     |      -----     |      This is the same as `ndarray.min`, but returns a `matrix` object     |      where `ndarray.min` would return an ndarray.     |           |      Examples     |      --------     |      >>> x = -np.matrix(np.arange(12).reshape((3,4))); x     |      matrix([[  0,  -1,  -2,  -3],     |              [ -4,  -5,  -6,  -7],     |              [ -8,  -9, -10, -11]])     |      >>> x.min()     |      -11     |      >>> x.min(0)     |      matrix([[ -8,  -9, -10, -11]])     |      >>> x.min(1)     |      matrix([[ -3],     |              [ -7],     |              [-11]])     |       |  prod(self, axis=None, dtype=None, out=None)     |      Return the product of the array elements over the given axis.     |           |      Refer to `prod` for full documentation.     |           |      See Also     |      --------     |      prod, ndarray.prod     |           |      Notes     |      -----     |      Same as `ndarray.prod`, except, where that returns an `ndarray`, this     |      returns a `matrix` object instead.     |           |      Examples     |      --------     |      >>> x = np.matrix(np.arange(12).reshape((3,4))); x     |      matrix([[ 0,  1,  2,  3],     |              [ 4,  5,  6,  7],     |              [ 8,  9, 10, 11]])     |      >>> x.prod()     |      0     |      >>> x.prod(0)     |      matrix([[  0,  45, 120, 231]])     |      >>> x.prod(1)     |      matrix([[   0],     |              [ 840],     |              [7920]])     |       |  ptp(self, axis=None, out=None)     |      Peak-to-peak (maximum - minimum) value along the given axis.     |           |      Refer to `numpy.ptp` for full documentation.     |           |      See Also     |      --------     |      numpy.ptp     |           |      Notes     |      -----     |      Same as `ndarray.ptp`, except, where that would return an `ndarray` object,     |      this returns a `matrix` object.     |           |      Examples     |      --------     |      >>> x = np.matrix(np.arange(12).reshape((3,4))); x     |      matrix([[ 0,  1,  2,  3],     |              [ 4,  5,  6,  7],     |              [ 8,  9, 10, 11]])     |      >>> x.ptp()     |      11     |      >>> x.ptp(0)     |      matrix([[8, 8, 8, 8]])     |      >>> x.ptp(1)     |      matrix([[3],     |              [3],     |              [3]])     |       |  ravel(self, order='C')     |      Return a flattened matrix.     |           |      Refer to `numpy.ravel` for more documentation.     |           |      Parameters     |      ----------     |      order : {'C', 'F', 'A', 'K'}, optional     |          The elements of `m` are read using this index order. 'C' means to     |          index the elements in C-like order, with the last axis index     |          changing fastest, back to the first axis index changing slowest.     |          'F' means to index the elements in Fortran-like index order, with     |          the first index changing fastest, and the last index changing     |          slowest. Note that the 'C' and 'F' options take no account of the     |          memory layout of the underlying array, and only refer to the order     |          of axis indexing.  'A' means to read the elements in Fortran-like     |          index order if `m` is Fortran *contiguous* in memory, C-like order     |          otherwise.  'K' means to read the elements in the order they occur     |          in memory, except for reversing the data when strides are negative.     |          By default, 'C' index order is used.     |           |      Returns     |      -------     |      ret : matrix     |          Return the matrix flattened to shape `(1, N)` where `N`     |          is the number of elements in the original matrix.     |          A copy is made only if necessary.     |           |      See Also     |      --------     |      matrix.flatten : returns a similar output matrix but always a copy     |      matrix.flat : a flat iterator on the array.     |      numpy.ravel : related function which returns an ndarray     |       |  squeeze(self, axis=None)     |      Return a possibly reshaped matrix.     |           |      Refer to `numpy.squeeze` for more documentation.     |           |      Parameters     |      ----------     |      axis : None or int or tuple of ints, optional     |          Selects a subset of the single-dimensional entries in the shape.     |          If an axis is selected with shape entry greater than one,     |          an error is raised.     |           |      Returns     |      -------     |      squeezed : matrix     |          The matrix, but as a (1, N) matrix if it had shape (N, 1).     |           |      See Also     |      --------     |      numpy.squeeze : related function     |           |      Notes     |      -----     |      If `m` has a single column then that column is returned     |      as the single row of a matrix.  Otherwise `m` is returned.     |      The returned matrix is always either `m` itself or a view into `m`.     |      Supplying an axis keyword argument will not affect the returned matrix     |      but it may cause an error to be raised.     |           |      Examples     |      --------     |      >>> c = np.matrix([[1], [2]])     |      >>> c     |      matrix([[1],     |              [2]])     |      >>> c.squeeze()     |      matrix([[1, 2]])     |      >>> r = c.T     |      >>> r     |      matrix([[1, 2]])     |      >>> r.squeeze()     |      matrix([[1, 2]])     |      >>> m = np.matrix([[1, 2], [3, 4]])     |      >>> m.squeeze()     |      matrix([[1, 2],     |              [3, 4]])     |       |  std(self, axis=None, dtype=None, out=None, ddof=0)     |      Return the standard deviation of the array elements along the given axis.     |           |      Refer to `numpy.std` for full documentation.     |           |      See Also     |      --------     |      numpy.std     |           |      Notes     |      -----     |      This is the same as `ndarray.std`, except that where an `ndarray` would     |      be returned, a `matrix` object is returned instead.     |           |      Examples     |      --------     |      >>> x = np.matrix(np.arange(12).reshape((3, 4)))     |      >>> x     |      matrix([[ 0,  1,  2,  3],     |              [ 4,  5,  6,  7],     |              [ 8,  9, 10, 11]])     |      >>> x.std()     |      3.4520525295346629     |      >>> x.std(0)     |      matrix([[ 3.26598632,  3.26598632,  3.26598632,  3.26598632]])     |      >>> x.std(1)     |      matrix([[ 1.11803399],     |              [ 1.11803399],     |              [ 1.11803399]])     |       |  sum(self, axis=None, dtype=None, out=None)     |      Returns the sum of the matrix elements, along the given axis.     |           |      Refer to `numpy.sum` for full documentation.     |           |      See Also     |      --------     |      numpy.sum     |           |      Notes     |      -----     |      This is the same as `ndarray.sum`, except that where an `ndarray` would     |      be returned, a `matrix` object is returned instead.     |           |      Examples     |      --------     |      >>> x = np.matrix([[1, 2], [4, 3]])     |      >>> x.sum()     |      10     |      >>> x.sum(axis=1)     |      matrix([[3],     |              [7]])     |      >>> x.sum(axis=1, dtype='float')     |      matrix([[ 3.],     |              [ 7.]])     |      >>> out = np.zeros((1, 2), dtype='float')     |      >>> x.sum(axis=1, dtype='float', out=out)     |      matrix([[ 3.],     |              [ 7.]])     |       |  tolist(self)     |      Return the matrix as a (possibly nested) list.     |           |      See `ndarray.tolist` for full documentation.     |           |      See Also     |      --------     |      ndarray.tolist     |           |      Examples     |      --------     |      >>> x = np.matrix(np.arange(12).reshape((3,4))); x     |      matrix([[ 0,  1,  2,  3],     |              [ 4,  5,  6,  7],     |              [ 8,  9, 10, 11]])     |      >>> x.tolist()     |      [[0, 1, 2, 3], [4, 5, 6, 7], [8, 9, 10, 11]]     |       |  var(self, axis=None, dtype=None, out=None, ddof=0)     |      Returns the variance of the matrix elements, along the given axis.     |           |      Refer to `numpy.var` for full documentation.     |           |      See Also     |      --------     |      numpy.var     |           |      Notes     |      -----     |      This is the same as `ndarray.var`, except that where an `ndarray` would     |      be returned, a `matrix` object is returned instead.     |           |      Examples     |      --------     |      >>> x = np.matrix(np.arange(12).reshape((3, 4)))     |      >>> x     |      matrix([[ 0,  1,  2,  3],     |              [ 4,  5,  6,  7],     |              [ 8,  9, 10, 11]])     |      >>> x.var()     |      11.916666666666666     |      >>> x.var(0)     |      matrix([[ 10.66666667,  10.66666667,  10.66666667,  10.66666667]])     |      >>> x.var(1)     |      matrix([[ 1.25],     |              [ 1.25],     |              [ 1.25]])     |       |  ----------------------------------------------------------------------     |  Static methods defined here:     |       |  __new__(subtype, data, dtype=None, copy=True)     |       |  ----------------------------------------------------------------------     |  Data descriptors defined here:     |       |  A     |      Return `self` as an `ndarray` object.     |           |      Equivalent to ``np.asarray(self)``.     |           |      Parameters     |      ----------     |      None     |           |      Returns     |      -------     |      ret : ndarray     |          `self` as an `ndarray`     |           |      Examples     |      --------     |      >>> x = np.matrix(np.arange(12).reshape((3,4))); x     |      matrix([[ 0,  1,  2,  3],     |              [ 4,  5,  6,  7],     |              [ 8,  9, 10, 11]])     |      >>> x.getA()     |      array([[ 0,  1,  2,  3],     |             [ 4,  5,  6,  7],     |             [ 8,  9, 10, 11]])     |       |  A1     |      Return `self` as a flattened `ndarray`.     |           |      Equivalent to ``np.asarray(x).ravel()``     |           |      Parameters     |      ----------     |      None     |           |      Returns     |      -------     |      ret : ndarray     |          `self`, 1-D, as an `ndarray`     |           |      Examples     |      --------     |      >>> x = np.matrix(np.arange(12).reshape((3,4))); x     |      matrix([[ 0,  1,  2,  3],     |              [ 4,  5,  6,  7],     |              [ 8,  9, 10, 11]])     |      >>> x.getA1()     |      array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11])     |       |  H     |      Returns the (complex) conjugate transpose of `self`.     |           |      Equivalent to ``np.transpose(self)`` if `self` is real-valued.     |           |      Parameters     |      ----------     |      None     |           |      Returns     |      -------     |      ret : matrix object     |          complex conjugate transpose of `self`     |           |      Examples     |      --------     |      >>> x = np.matrix(np.arange(12).reshape((3,4)))     |      >>> z = x - 1j*x; z     |      matrix([[  0. +0.j,   1. -1.j,   2. -2.j,   3. -3.j],     |              [  4. -4.j,   5. -5.j,   6. -6.j,   7. -7.j],     |              [  8. -8.j,   9. -9.j,  10.-10.j,  11.-11.j]])     |      >>> z.getH()     |      matrix([[  0. +0.j,   4. +4.j,   8. +8.j],     |              [  1. +1.j,   5. +5.j,   9. +9.j],     |              [  2. +2.j,   6. +6.j,  10.+10.j],     |              [  3. +3.j,   7. +7.j,  11.+11.j]])     |       |  I     |      Returns the (multiplicative) inverse of invertible `self`.     |           |      Parameters     |      ----------     |      None     |           |      Returns     |      -------     |      ret : matrix object     |          If `self` is non-singular, `ret` is such that ``ret * self`` ==     |          ``self * ret`` == ``np.matrix(np.eye(self[0,:].size)`` all return     |          ``True``.     |           |      Raises     |      ------     |      numpy.linalg.LinAlgError: Singular matrix     |          If `self` is singular.     |           |      See Also     |      --------     |      linalg.inv     |           |      Examples     |      --------     |      >>> m = np.matrix('[1, 2; 3, 4]'); m     |      matrix([[1, 2],     |              [3, 4]])     |      >>> m.getI()     |      matrix([[-2. ,  1. ],     |              [ 1.5, -0.5]])     |      >>> m.getI() * m     |      matrix([[ 1.,  0.],     |              [ 0.,  1.]])     |       |  T     |      Returns the transpose of the matrix.     |           |      Does *not* conjugate!  For the complex conjugate transpose, use ``.H``.     |           |      Parameters     |      ----------     |      None     |           |      Returns     |      -------     |      ret : matrix object     |          The (non-conjugated) transpose of the matrix.     |           |      See Also     |      --------     |      transpose, getH     |           |      Examples     |      --------     |      >>> m = np.matrix('[1, 2; 3, 4]')     |      >>> m     |      matrix([[1, 2],     |              [3, 4]])     |      >>> m.getT()     |      matrix([[1, 3],     |              [2, 4]])     |       |  __dict__     |      dictionary for instance variables (if defined)     |       |  ----------------------------------------------------------------------     |  Data and other attributes defined here:     |       |  __array_priority__ = 10.0     |       |  ----------------------------------------------------------------------     |  Methods inherited from numpy.ndarray:     |       |  __abs__(...)     |      x.__abs__() <==> abs(x)     |       |  __add__(...)     |      x.__add__(y) <==> x+y     |       |  __and__(...)     |      x.__and__(y) <==> x&y     |       |  __array__(...)     |      a.__array__(|dtype) -> reference if type unchanged, copy otherwise.     |           |      Returns either a new reference to self if dtype is not given or a new array     |      of provided data type if dtype is different from the current dtype of the     |      array.     |       |  __array_prepare__(...)     |      a.__array_prepare__(obj) -> Object of same type as ndarray object obj.     |       |  __array_wrap__(...)     |      a.__array_wrap__(obj) -> Object of same type as ndarray object a.     |       |  __contains__(...)     |      x.__contains__(y) <==> y in x     |       |  __copy__(...)     |      a.__copy__([order])     |           |      Return a copy of the array.     |           |      Parameters     |      ----------     |      order : {'C', 'F', 'A'}, optional     |          If order is 'C' (False) then the result is contiguous (default).     |          If order is 'Fortran' (True) then the result has fortran order.     |          If order is 'Any' (None) then the result has fortran order     |          only if the array already is in fortran order.     |       |  __deepcopy__(...)     |      a.__deepcopy__() -> Deep copy of array.     |           |      Used if copy.deepcopy is called on an array.     |       |  __delitem__(...)     |      x.__delitem__(y) <==> del x[y]     |       |  __delslice__(...)     |      x.__delslice__(i, j) <==> del x[i:j]     |           |      Use of negative indices is not supported.     |       |  __div__(...)     |      x.__div__(y) <==> x/y     |       |  __divmod__(...)     |      x.__divmod__(y) <==> divmod(x, y)     |       |  __eq__(...)     |      x.__eq__(y) <==> x==y     |       |  __float__(...)     |      x.__float__() <==> float(x)     |       |  __floordiv__(...)     |      x.__floordiv__(y) <==> x//y     |       |  __ge__(...)     |      x.__ge__(y) <==> x>=y     |       |  __getslice__(...)     |      x.__getslice__(i, j) <==> x[i:j]     |           |      Use of negative indices is not supported.     |       |  __gt__(...)     |      x.__gt__(y) <==> x>y     |       |  __hex__(...)     |      x.__hex__() <==> hex(x)     |       |  __iadd__(...)     |      x.__iadd__(y) <==> x+=y     |       |  __iand__(...)     |      x.__iand__(y) <==> x&=y     |       |  __idiv__(...)     |      x.__idiv__(y) <==> x/=y     |       |  __ifloordiv__(...)     |      x.__ifloordiv__(y) <==> x//=y     |       |  __ilshift__(...)     |      x.__ilshift__(y) <==> x<<=y     |       |  __imod__(...)     |      x.__imod__(y) <==> x%=y     |       |  __index__(...)     |      x[y:z] <==> x[y.__index__():z.__index__()]     |       |  __int__(...)     |      x.__int__() <==> int(x)     |       |  __invert__(...)     |      x.__invert__() <==> ~x     |       |  __ior__(...)     |      x.__ior__(y) <==> x|=y     |       |  __irshift__(...)     |      x.__irshift__(y) <==> x>>=y     |       |  __isub__(...)     |      x.__isub__(y) <==> x-=y     |       |  __iter__(...)     |      x.__iter__() <==> iter(x)     |       |  __itruediv__(...)     |      x.__itruediv__(y) <==> x/=y     |       |  __ixor__(...)     |      x.__ixor__(y) <==> x^=y     |       |  __le__(...)     |      x.__le__(y) <==> x<=y     |       |  __len__(...)     |      x.__len__() <==> len(x)     |       |  __long__(...)     |      x.__long__() <==> long(x)     |       |  __lshift__(...)     |      x.__lshift__(y) <==> x<<y     |       |  __lt__(...)     |      x.__lt__(y) <==> x<y     |       |  __mod__(...)     |      x.__mod__(y) <==> x%y     |       |  __ne__(...)     |      x.__ne__(y) <==> x!=y     |       |  __neg__(...)     |      x.__neg__() <==> -x     |       |  __nonzero__(...)     |      x.__nonzero__() <==> x != 0     |       |  __oct__(...)     |      x.__oct__() <==> oct(x)     |       |  __or__(...)     |      x.__or__(y) <==> x|y     |       |  __pos__(...)     |      x.__pos__() <==> +x     |       |  __radd__(...)     |      x.__radd__(y) <==> y+x     |       |  __rand__(...)     |      x.__rand__(y) <==> y&x     |       |  __rdiv__(...)     |      x.__rdiv__(y) <==> y/x     |       |  __rdivmod__(...)     |      x.__rdivmod__(y) <==> divmod(y, x)     |       |  __reduce__(...)     |      a.__reduce__()     |           |      For pickling.     |       |  __rfloordiv__(...)     |      x.__rfloordiv__(y) <==> y//x     |       |  __rlshift__(...)     |      x.__rlshift__(y) <==> y<<x     |       |  __rmod__(...)     |      x.__rmod__(y) <==> y%x     |       |  __ror__(...)     |      x.__ror__(y) <==> y|x     |       |  __rrshift__(...)     |      x.__rrshift__(y) <==> y>>x     |       |  __rshift__(...)     |      x.__rshift__(y) <==> x>>y     |       |  __rsub__(...)     |      x.__rsub__(y) <==> y-x     |       |  __rtruediv__(...)     |      x.__rtruediv__(y) <==> y/x     |       |  __rxor__(...)     |      x.__rxor__(y) <==> y^x     |       |  __setitem__(...)     |      x.__setitem__(i, y) <==> x[i]=y     |       |  __setslice__(...)     |      x.__setslice__(i, j, y) <==> x[i:j]=y     |           |      Use  of negative indices is not supported.     |       |  __setstate__(...)     |      a.__setstate__(version, shape, dtype, isfortran, rawdata)     |           |      For unpickling.     |           |      Parameters     |      ----------     |      version : int     |          optional pickle version. If omitted defaults to 0.     |      shape : tuple     |      dtype : data-type     |      isFortran : bool     |      rawdata : string or list     |          a binary string with the data (or a list if 'a' is an object array)     |       |  __sizeof__(...)     |       |  __sub__(...)     |      x.__sub__(y) <==> x-y     |       |  __truediv__(...)     |      x.__truediv__(y) <==> x/y     |       |  __xor__(...)     |      x.__xor__(y) <==> x^y     |       |  argpartition(...)     |      a.argpartition(kth, axis=-1, kind='introselect', order=None)     |           |      Returns the indices that would partition this array.     |           |      Refer to `numpy.argpartition` for full documentation.     |           |      .. versionadded:: 1.8.0     |           |      See Also     |      --------     |      numpy.argpartition : equivalent function     |       |  argsort(...)     |      a.argsort(axis=-1, kind='quicksort', order=None)     |           |      Returns the indices that would sort this array.     |           |      Refer to `numpy.argsort` for full documentation.     |           |      See Also     |      --------     |      numpy.argsort : equivalent function     |       |  astype(...)     |      a.astype(dtype, order='K', casting='unsafe', subok=True, copy=True)     |           |      Copy of the array, cast to a specified type.     |           |      Parameters     |      ----------     |      dtype : str or dtype     |          Typecode or data-type to which the array is cast.     |      order : {'C', 'F', 'A', 'K'}, optional     |          Controls the memory layout order of the result.     |          'C' means C order, 'F' means Fortran order, 'A'     |          means 'F' order if all the arrays are Fortran contiguous,     |          'C' order otherwise, and 'K' means as close to the     |          order the array elements appear in memory as possible.     |          Default is 'K'.     |      casting : {'no', 'equiv', 'safe', 'same_kind', 'unsafe'}, optional     |          Controls what kind of data casting may occur. Defaults to 'unsafe'     |          for backwards compatibility.     |           |            * 'no' means the data types should not be cast at all.     |            * 'equiv' means only byte-order changes are allowed.     |            * 'safe' means only casts which can preserve values are allowed.     |            * 'same_kind' means only safe casts or casts within a kind,     |              like float64 to float32, are allowed.     |            * 'unsafe' means any data conversions may be done.     |      subok : bool, optional     |          If True, then sub-classes will be passed-through (default), otherwise     |          the returned array will be forced to be a base-class array.     |      copy : bool, optional     |          By default, astype always returns a newly allocated array. If this     |          is set to false, and the `dtype`, `order`, and `subok`     |          requirements are satisfied, the input array is returned instead     |          of a copy.     |           |      Returns     |      -------     |      arr_t : ndarray     |          Unless `copy` is False and the other conditions for returning the input     |          array are satisfied (see description for `copy` input paramter), `arr_t`     |          is a new array of the same shape as the input array, with dtype, order     |          given by `dtype`, `order`.     |           |      Notes     |      -----     |      Starting in NumPy 1.9, astype method now returns an error if the string     |      dtype to cast to is not long enough in 'safe' casting mode to hold the max     |      value of integer/float array that is being casted. Previously the casting     |      was allowed even if the result was truncated.     |           |      Raises     |      ------     |      ComplexWarning     |          When casting from complex to float or int. To avoid this,     |          one should use ``a.real.astype(t)``.     |           |      Examples     |      --------     |      >>> x = np.array([1, 2, 2.5])     |      >>> x     |      array([ 1. ,  2. ,  2.5])     |           |      >>> x.astype(int)     |      array([1, 2, 2])     |       |  byteswap(...)     |      a.byteswap(inplace)     |           |      Swap the bytes of the array elements     |           |      Toggle between low-endian and big-endian data representation by     |      returning a byteswapped array, optionally swapped in-place.     |           |      Parameters     |      ----------     |      inplace : bool, optional     |          If ``True``, swap bytes in-place, default is ``False``.     |           |      Returns     |      -------     |      out : ndarray     |          The byteswapped array. If `inplace` is ``True``, this is     |          a view to self.     |           |      Examples     |      --------     |      >>> A = np.array([1, 256, 8755], dtype=np.int16)     |      >>> map(hex, A)     |      ['0x1', '0x100', '0x2233']     |      >>> A.byteswap(True)     |      array([  256,     1, 13090], dtype=int16)     |      >>> map(hex, A)     |      ['0x100', '0x1', '0x3322']     |           |      Arrays of strings are not swapped     |           |      >>> A = np.array(['ceg', 'fac'])     |      >>> A.byteswap()     |      array(['ceg', 'fac'],     |            dtype='|S3')     |       |  choose(...)     |      a.choose(choices, out=None, mode='raise')     |           |      Use an index array to construct a new array from a set of choices.     |           |      Refer to `numpy.choose` for full documentation.     |           |      See Also     |      --------     |      numpy.choose : equivalent function     |       |  clip(...)     |      a.clip(min=None, max=None, out=None)     |           |      Return an array whose values are limited to ``[min, max]``.     |      One of max or min must be given.     |           |      Refer to `numpy.clip` for full documentation.     |           |      See Also     |      --------     |      numpy.clip : equivalent function     |       |  compress(...)     |      a.compress(condition, axis=None, out=None)     |           |      Return selected slices of this array along given axis.     |           |      Refer to `numpy.compress` for full documentation.     |           |      See Also     |      --------     |      numpy.compress : equivalent function     |       |  conj(...)     |      a.conj()     |           |      Complex-conjugate all elements.     |           |      Refer to `numpy.conjugate` for full documentation.     |           |      See Also     |      --------     |      numpy.conjugate : equivalent function     |       |  conjugate(...)     |      a.conjugate()     |           |      Return the complex conjugate, element-wise.     |           |      Refer to `numpy.conjugate` for full documentation.     |           |      See Also     |      --------     |      numpy.conjugate : equivalent function     |       |  copy(...)     |      a.copy(order='C')     |           |      Return a copy of the array.     |           |      Parameters     |      ----------     |      order : {'C', 'F', 'A', 'K'}, optional     |          Controls the memory layout of the copy. 'C' means C-order,     |          'F' means F-order, 'A' means 'F' if `a` is Fortran contiguous,     |          'C' otherwise. 'K' means match the layout of `a` as closely     |          as possible. (Note that this function and :func:numpy.copy are very     |          similar, but have different default values for their order=     |          arguments.)     |           |      See also     |      --------     |      numpy.copy     |      numpy.copyto     |           |      Examples     |      --------     |      >>> x = np.array([[1,2,3],[4,5,6]], order='F')     |           |      >>> y = x.copy()     |           |      >>> x.fill(0)     |           |      >>> x     |      array([[0, 0, 0],     |             [0, 0, 0]])     |           |      >>> y     |      array([[1, 2, 3],     |             [4, 5, 6]])     |           |      >>> y.flags['C_CONTIGUOUS']     |      True     |       |  cumprod(...)     |      a.cumprod(axis=None, dtype=None, out=None)     |           |      Return the cumulative product of the elements along the given axis.     |           |      Refer to `numpy.cumprod` for full documentation.     |           |      See Also     |      --------     |      numpy.cumprod : equivalent function     |       |  cumsum(...)     |      a.cumsum(axis=None, dtype=None, out=None)     |           |      Return the cumulative sum of the elements along the given axis.     |           |      Refer to `numpy.cumsum` for full documentation.     |           |      See Also     |      --------     |      numpy.cumsum : equivalent function     |       |  diagonal(...)     |      a.diagonal(offset=0, axis1=0, axis2=1)     |           |      Return specified diagonals. In NumPy 1.9 the returned array is a     |      read-only view instead of a copy as in previous NumPy versions.  In     |      NumPy 1.10 the read-only restriction will be removed.     |           |      Refer to :func:`numpy.diagonal` for full documentation.     |           |      See Also     |      --------     |      numpy.diagonal : equivalent function     |       |  dot(...)     |      a.dot(b, out=None)     |           |      Dot product of two arrays.     |           |      Refer to `numpy.dot` for full documentation.     |           |      See Also     |      --------     |      numpy.dot : equivalent function     |           |      Examples     |      --------     |      >>> a = np.eye(2)     |      >>> b = np.ones((2, 2)) * 2     |      >>> a.dot(b)     |      array([[ 2.,  2.],     |             [ 2.,  2.]])     |           |      This array method can be conveniently chained:     |           |      >>> a.dot(b).dot(b)     |      array([[ 8.,  8.],     |             [ 8.,  8.]])     |       |  dump(...)     |      a.dump(file)     |           |      Dump a pickle of the array to the specified file.     |      The array can be read back with pickle.load or numpy.load.     |           |      Parameters     |      ----------     |      file : str     |          A string naming the dump file.     |       |  dumps(...)     |      a.dumps()     |           |      Returns the pickle of the array as a string.     |      pickle.loads or numpy.loads will convert the string back to an array.     |           |      Parameters     |      ----------     |      None     |       |  fill(...)     |      a.fill(value)     |           |      Fill the array with a scalar value.     |           |      Parameters     |      ----------     |      value : scalar     |          All elements of `a` will be assigned this value.     |           |      Examples     |      --------     |      >>> a = np.array([1, 2])     |      >>> a.fill(0)     |      >>> a     |      array([0, 0])     |      >>> a = np.empty(2)     |      >>> a.fill(1)     |      >>> a     |      array([ 1.,  1.])     |       |  getfield(...)     |      a.getfield(dtype, offset=0)     |           |      Returns a field of the given array as a certain type.     |           |      A field is a view of the array data with a given data-type. The values in     |      the view are determined by the given type and the offset into the current     |      array in bytes. The offset needs to be such that the view dtype fits in the     |      array dtype; for example an array of dtype complex128 has 16-byte elements.     |      If taking a view with a 32-bit integer (4 bytes), the offset needs to be     |      between 0 and 12 bytes.     |           |      Parameters     |      ----------     |      dtype : str or dtype     |          The data type of the view. The dtype size of the view can not be larger     |          than that of the array itself.     |      offset : int     |          Number of bytes to skip before beginning the element view.     |           |      Examples     |      --------     |      >>> x = np.diag([1.+1.j]*2)     |      >>> x[1, 1] = 2 + 4.j     |      >>> x     |      array([[ 1.+1.j,  0.+0.j],     |             [ 0.+0.j,  2.+4.j]])     |      >>> x.getfield(np.float64)     |      array([[ 1.,  0.],     |             [ 0.,  2.]])     |           |      By choosing an offset of 8 bytes we can select the complex part of the     |      array for our view:     |           |      >>> x.getfield(np.float64, offset=8)     |      array([[ 1.,  0.],     |         [ 0.,  4.]])     |       |  item(...)     |      a.item(*args)     |           |      Copy an element of an array to a standard Python scalar and return it.     |           |      Parameters     |      ----------     |      \*args : Arguments (variable number and type)     |           |          * none: in this case, the method only works for arrays     |            with one element (`a.size == 1`), which element is     |            copied into a standard Python scalar object and returned.     |           |          * int_type: this argument is interpreted as a flat index into     |            the array, specifying which element to copy and return.     |           |          * tuple of int_types: functions as does a single int_type argument,     |            except that the argument is interpreted as an nd-index into the     |            array.     |           |      Returns     |      -------     |      z : Standard Python scalar object     |          A copy of the specified element of the array as a suitable     |          Python scalar     |           |      Notes     |      -----     |      When the data type of `a` is longdouble or clongdouble, item() returns     |      a scalar array object because there is no available Python scalar that     |      would not lose information. Void arrays return a buffer object for item(),     |      unless fields are defined, in which case a tuple is returned.     |           |      `item` is very similar to a[args], except, instead of an array scalar,     |      a standard Python scalar is returned. This can be useful for speeding up     |      access to elements of the array and doing arithmetic on elements of the     |      array using Python's optimized math.     |           |      Examples     |      --------     |      >>> x = np.random.randint(9, size=(3, 3))     |      >>> x     |      array([[3, 1, 7],     |             [2, 8, 3],     |             [8, 5, 3]])     |      >>> x.item(3)     |      2     |      >>> x.item(7)     |      5     |      >>> x.item((0, 1))     |      1     |      >>> x.item((2, 2))     |      3     |       |  itemset(...)     |      a.itemset(*args)     |           |      Insert scalar into an array (scalar is cast to array's dtype, if possible)     |           |      There must be at least 1 argument, and define the last argument     |      as *item*.  Then, ``a.itemset(*args)`` is equivalent to but faster     |      than ``a[args] = item``.  The item should be a scalar value and `args`     |      must select a single item in the array `a`.     |           |      Parameters     |      ----------     |      \*args : Arguments     |          If one argument: a scalar, only used in case `a` is of size 1.     |          If two arguments: the last argument is the value to be set     |          and must be a scalar, the first argument specifies a single array     |          element location. It is either an int or a tuple.     |           |      Notes     |      -----     |      Compared to indexing syntax, `itemset` provides some speed increase     |      for placing a scalar into a particular location in an `ndarray`,     |      if you must do this.  However, generally this is discouraged:     |      among other problems, it complicates the appearance of the code.     |      Also, when using `itemset` (and `item`) inside a loop, be sure     |      to assign the methods to a local variable to avoid the attribute     |      look-up at each loop iteration.     |           |      Examples     |      --------     |      >>> x = np.random.randint(9, size=(3, 3))     |      >>> x     |      array([[3, 1, 7],     |             [2, 8, 3],     |             [8, 5, 3]])     |      >>> x.itemset(4, 0)     |      >>> x.itemset((2, 2), 9)     |      >>> x     |      array([[3, 1, 7],     |             [2, 0, 3],     |             [8, 5, 9]])     |       |  newbyteorder(...)     |      arr.newbyteorder(new_order='S')     |           |      Return the array with the same data viewed with a different byte order.     |           |      Equivalent to::     |           |          arr.view(arr.dtype.newbytorder(new_order))     |           |      Changes are also made in all fields and sub-arrays of the array data     |      type.     |           |           |           |      Parameters     |      ----------     |      new_order : string, optional     |          Byte order to force; a value from the byte order specifications     |          below. `new_order` codes can be any of:     |           |          * 'S' - swap dtype from current to opposite endian     |          * {'<', 'L'} - little endian     |          * {'>', 'B'} - big endian     |          * {'=', 'N'} - native order     |          * {'|', 'I'} - ignore (no change to byte order)     |           |          The default value ('S') results in swapping the current     |          byte order. The code does a case-insensitive check on the first     |          letter of `new_order` for the alternatives above.  For example,     |          any of 'B' or 'b' or 'biggish' are valid to specify big-endian.     |           |           |      Returns     |      -------     |      new_arr : array     |          New array object with the dtype reflecting given change to the     |          byte order.     |       |  nonzero(...)     |      a.nonzero()     |           |      Return the indices of the elements that are non-zero.     |           |      Refer to `numpy.nonzero` for full documentation.     |           |      See Also     |      --------     |      numpy.nonzero : equivalent function     |       |  partition(...)     |      a.partition(kth, axis=-1, kind='introselect', order=None)     |           |      Rearranges the elements in the array in such a way that value of the     |      element in kth position is in the position it would be in a sorted array.     |      All elements smaller than the kth element are moved before this element and     |      all equal or greater are moved behind it. The ordering of the elements in     |      the two partitions is undefined.     |           |      .. versionadded:: 1.8.0     |           |      Parameters     |      ----------     |      kth : int or sequence of ints     |          Element index to partition by. The kth element value will be in its     |          final sorted position and all smaller elements will be moved before it     |          and all equal or greater elements behind it.     |          The order all elements in the partitions is undefined.     |          If provided with a sequence of kth it will partition all elements     |          indexed by kth of them into their sorted position at once.     |      axis : int, optional     |          Axis along which to sort. Default is -1, which means sort along the     |          last axis.     |      kind : {'introselect'}, optional     |          Selection algorithm. Default is 'introselect'.     |      order : str or list of str, optional     |          When `a` is an array with fields defined, this argument specifies     |          which fields to compare first, second, etc.  A single field can     |          be specified as a string, and not all fields need be specified,     |          but unspecified fields will still be used, in the order in which     |          they come up in the dtype, to break ties.     |           |      See Also     |      --------     |      numpy.partition : Return a parititioned copy of an array.     |      argpartition : Indirect partition.     |      sort : Full sort.     |           |      Notes     |      -----     |      See ``np.partition`` for notes on the different algorithms.     |           |      Examples     |      --------     |      >>> a = np.array([3, 4, 2, 1])     |      >>> a.partition(a, 3)     |      >>> a     |      array([2, 1, 3, 4])     |           |      >>> a.partition((1, 3))     |      array([1, 2, 3, 4])     |       |  put(...)     |      a.put(indices, values, mode='raise')     |           |      Set ``a.flat[n] = values[n]`` for all `n` in indices.     |           |      Refer to `numpy.put` for full documentation.     |           |      See Also     |      --------     |      numpy.put : equivalent function     |       |  repeat(...)     |      a.repeat(repeats, axis=None)     |           |      Repeat elements of an array.     |           |      Refer to `numpy.repeat` for full documentation.     |           |      See Also     |      --------     |      numpy.repeat : equivalent function     |       |  reshape(...)     |      a.reshape(shape, order='C')     |           |      Returns an array containing the same data with a new shape.     |           |      Refer to `numpy.reshape` for full documentation.     |           |      See Also     |      --------     |      numpy.reshape : equivalent function     |       |  resize(...)     |      a.resize(new_shape, refcheck=True)     |           |      Change shape and size of array in-place.     |           |      Parameters     |      ----------     |      new_shape : tuple of ints, or `n` ints     |          Shape of resized array.     |      refcheck : bool, optional     |          If False, reference count will not be checked. Default is True.     |           |      Returns     |      -------     |      None     |           |      Raises     |      ------     |      ValueError     |          If `a` does not own its own data or references or views to it exist,     |          and the data memory must be changed.     |           |      SystemError     |          If the `order` keyword argument is specified. This behaviour is a     |          bug in NumPy.     |           |      See Also     |      --------     |      resize : Return a new array with the specified shape.     |           |      Notes     |      -----     |      This reallocates space for the data area if necessary.     |           |      Only contiguous arrays (data elements consecutive in memory) can be     |      resized.     |           |      The purpose of the reference count check is to make sure you     |      do not use this array as a buffer for another Python object and then     |      reallocate the memory. However, reference counts can increase in     |      other ways so if you are sure that you have not shared the memory     |      for this array with another Python object, then you may safely set     |      `refcheck` to False.     |           |      Examples     |      --------     |      Shrinking an array: array is flattened (in the order that the data are     |      stored in memory), resized, and reshaped:     |           |      >>> a = np.array([[0, 1], [2, 3]], order='C')     |      >>> a.resize((2, 1))     |      >>> a     |      array([[0],     |             [1]])     |           |      >>> a = np.array([[0, 1], [2, 3]], order='F')     |      >>> a.resize((2, 1))     |      >>> a     |      array([[0],     |             [2]])     |           |      Enlarging an array: as above, but missing entries are filled with zeros:     |           |      >>> b = np.array([[0, 1], [2, 3]])     |      >>> b.resize(2, 3) # new_shape parameter doesn't have to be a tuple     |      >>> b     |      array([[0, 1, 2],     |             [3, 0, 0]])     |           |      Referencing an array prevents resizing...     |           |      >>> c = a     |      >>> a.resize((1, 1))     |      Traceback (most recent call last):     |      ...     |      ValueError: cannot resize an array that has been referenced ...     |           |      Unless `refcheck` is False:     |           |      >>> a.resize((1, 1), refcheck=False)     |      >>> a     |      array([[0]])     |      >>> c     |      array([[0]])     |       |  round(...)     |      a.round(decimals=0, out=None)     |           |      Return `a` with each element rounded to the given number of decimals.     |           |      Refer to `numpy.around` for full documentation.     |           |      See Also     |      --------     |      numpy.around : equivalent function     |       |  searchsorted(...)     |      a.searchsorted(v, side='left', sorter=None)     |           |      Find indices where elements of v should be inserted in a to maintain order.     |           |      For full documentation, see `numpy.searchsorted`     |           |      See Also     |      --------     |      numpy.searchsorted : equivalent function     |       |  setfield(...)     |      a.setfield(val, dtype, offset=0)     |           |      Put a value into a specified place in a field defined by a data-type.     |           |      Place `val` into `a`'s field defined by `dtype` and beginning `offset`     |      bytes into the field.     |           |      Parameters     |      ----------     |      val : object     |          Value to be placed in field.     |      dtype : dtype object     |          Data-type of the field in which to place `val`.     |      offset : int, optional     |          The number of bytes into the field at which to place `val`.     |           |      Returns     |      -------     |      None     |           |      See Also     |      --------     |      getfield     |           |      Examples     |      --------     |      >>> x = np.eye(3)     |      >>> x.getfield(np.float64)     |      array([[ 1.,  0.,  0.],     |             [ 0.,  1.,  0.],     |             [ 0.,  0.,  1.]])     |      >>> x.setfield(3, np.int32)     |      >>> x.getfield(np.int32)     |      array([[3, 3, 3],     |             [3, 3, 3],     |             [3, 3, 3]])     |      >>> x     |      array([[  1.00000000e+000,   1.48219694e-323,   1.48219694e-323],     |             [  1.48219694e-323,   1.00000000e+000,   1.48219694e-323],     |             [  1.48219694e-323,   1.48219694e-323,   1.00000000e+000]])     |      >>> x.setfield(np.eye(3), np.int32)     |      >>> x     |      array([[ 1.,  0.,  0.],     |             [ 0.,  1.,  0.],     |             [ 0.,  0.,  1.]])     |       |  setflags(...)     |      a.setflags(write=None, align=None, uic=None)     |           |      Set array flags WRITEABLE, ALIGNED, and UPDATEIFCOPY, respectively.     |           |      These Boolean-valued flags affect how numpy interprets the memory     |      area used by `a` (see Notes below). The ALIGNED flag can only     |      be set to True if the data is actually aligned according to the type.     |      The UPDATEIFCOPY flag can never be set to True. The flag WRITEABLE     |      can only be set to True if the array owns its own memory, or the     |      ultimate owner of the memory exposes a writeable buffer interface,     |      or is a string. (The exception for string is made so that unpickling     |      can be done without copying memory.)     |           |      Parameters     |      ----------     |      write : bool, optional     |          Describes whether or not `a` can be written to.     |      align : bool, optional     |          Describes whether or not `a` is aligned properly for its type.     |      uic : bool, optional     |          Describes whether or not `a` is a copy of another "base" array.     |           |      Notes     |      -----     |      Array flags provide information about how the memory area used     |      for the array is to be interpreted. There are 6 Boolean flags     |      in use, only three of which can be changed by the user:     |      UPDATEIFCOPY, WRITEABLE, and ALIGNED.     |           |      WRITEABLE (W) the data area can be written to;     |           |      ALIGNED (A) the data and strides are aligned appropriately for the hardware     |      (as determined by the compiler);     |           |      UPDATEIFCOPY (U) this array is a copy of some other array (referenced     |      by .base). When this array is deallocated, the base array will be     |      updated with the contents of this array.     |           |      All flags can be accessed using their first (upper case) letter as well     |      as the full name.     |           |      Examples     |      --------     |      >>> y     |      array([[3, 1, 7],     |             [2, 0, 0],     |             [8, 5, 9]])     |      >>> y.flags     |        C_CONTIGUOUS : True     |        F_CONTIGUOUS : False     |        OWNDATA : True     |        WRITEABLE : True     |        ALIGNED : True     |        UPDATEIFCOPY : False     |      >>> y.setflags(write=0, align=0)     |      >>> y.flags     |        C_CONTIGUOUS : True     |        F_CONTIGUOUS : False     |        OWNDATA : True     |        WRITEABLE : False     |        ALIGNED : False     |        UPDATEIFCOPY : False     |      >>> y.setflags(uic=1)     |      Traceback (most recent call last):     |        File "<stdin>", line 1, in <module>     |      ValueError: cannot set UPDATEIFCOPY flag to True     |       |  sort(...)     |      a.sort(axis=-1, kind='quicksort', order=None)     |           |      Sort an array, in-place.     |           |      Parameters     |      ----------     |      axis : int, optional     |          Axis along which to sort. Default is -1, which means sort along the     |          last axis.     |      kind : {'quicksort', 'mergesort', 'heapsort'}, optional     |          Sorting algorithm. Default is 'quicksort'.     |      order : str or list of str, optional     |          When `a` is an array with fields defined, this argument specifies     |          which fields to compare first, second, etc.  A single field can     |          be specified as a string, and not all fields need be specified,     |          but unspecified fields will still be used, in the order in which     |          they come up in the dtype, to break ties.     |           |      See Also     |      --------     |      numpy.sort : Return a sorted copy of an array.     |      argsort : Indirect sort.     |      lexsort : Indirect stable sort on multiple keys.     |      searchsorted : Find elements in sorted array.     |      partition: Partial sort.     |           |      Notes     |      -----     |      See ``sort`` for notes on the different sorting algorithms.     |           |      Examples     |      --------     |      >>> a = np.array([[1,4], [3,1]])     |      >>> a.sort(axis=1)     |      >>> a     |      array([[1, 4],     |             [1, 3]])     |      >>> a.sort(axis=0)     |      >>> a     |      array([[1, 3],     |             [1, 4]])     |           |      Use the `order` keyword to specify a field to use when sorting a     |      structured array:     |           |      >>> a = np.array([('a', 2), ('c', 1)], dtype=[('x', 'S1'), ('y', int)])     |      >>> a.sort(order='y')     |      >>> a     |      array([('c', 1), ('a', 2)],     |            dtype=[('x', '|S1'), ('y', '<i4')])     |       |  swapaxes(...)     |      a.swapaxes(axis1, axis2)     |           |      Return a view of the array with `axis1` and `axis2` interchanged.     |           |      Refer to `numpy.swapaxes` for full documentation.     |           |      See Also     |      --------     |      numpy.swapaxes : equivalent function     |       |  take(...)     |      a.take(indices, axis=None, out=None, mode='raise')     |           |      Return an array formed from the elements of `a` at the given indices.     |           |      Refer to `numpy.take` for full documentation.     |           |      See Also     |      --------     |      numpy.take : equivalent function     |       |  tobytes(...)     |      a.tobytes(order='C')     |           |      Construct Python bytes containing the raw data bytes in the array.     |           |      Constructs Python bytes showing a copy of the raw contents of     |      data memory. The bytes object can be produced in either 'C' or 'Fortran',     |      or 'Any' order (the default is 'C'-order). 'Any' order means C-order     |      unless the F_CONTIGUOUS flag in the array is set, in which case it     |      means 'Fortran' order.     |           |      .. versionadded:: 1.9.0     |           |      Parameters     |      ----------     |      order : {'C', 'F', None}, optional     |          Order of the data for multidimensional arrays:     |          C, Fortran, or the same as for the original array.     |           |      Returns     |      -------     |      s : bytes     |          Python bytes exhibiting a copy of `a`'s raw data.     |           |      Examples     |      --------     |      >>> x = np.array([[0, 1], [2, 3]])     |      >>> x.tobytes()     |      b'\x00\x00\x00\x00\x01\x00\x00\x00\x02\x00\x00\x00\x03\x00\x00\x00'     |      >>> x.tobytes('C') == x.tobytes()     |      True     |      >>> x.tobytes('F')     |      b'\x00\x00\x00\x00\x02\x00\x00\x00\x01\x00\x00\x00\x03\x00\x00\x00'     |       |  tofile(...)     |      a.tofile(fid, sep="", format="%s")     |           |      Write array to a file as text or binary (default).     |           |      Data is always written in 'C' order, independent of the order of `a`.     |      The data produced by this method can be recovered using the function     |      fromfile().     |           |      Parameters     |      ----------     |      fid : file or str     |          An open file object, or a string containing a filename.     |      sep : str     |          Separator between array items for text output.     |          If "" (empty), a binary file is written, equivalent to     |          ``file.write(a.tobytes())``.     |      format : str     |          Format string for text file output.     |          Each entry in the array is formatted to text by first converting     |          it to the closest Python type, and then using "format" % item.     |           |      Notes     |      -----     |      This is a convenience function for quick storage of array data.     |      Information on endianness and precision is lost, so this method is not a     |      good choice for files intended to archive data or transport data between     |      machines with different endianness. Some of these problems can be overcome     |      by outputting the data as text files, at the expense of speed and file     |      size.     |       |  tostring(...)     |      a.tostring(order='C')     |           |      Construct Python bytes containing the raw data bytes in the array.     |           |      Constructs Python bytes showing a copy of the raw contents of     |      data memory. The bytes object can be produced in either 'C' or 'Fortran',     |      or 'Any' order (the default is 'C'-order). 'Any' order means C-order     |      unless the F_CONTIGUOUS flag in the array is set, in which case it     |      means 'Fortran' order.     |           |      This function is a compatibility alias for tobytes. Despite its name it returns bytes not strings.     |           |      Parameters     |      ----------     |      order : {'C', 'F', None}, optional     |          Order of the data for multidimensional arrays:     |          C, Fortran, or the same as for the original array.     |           |      Returns     |      -------     |      s : bytes     |          Python bytes exhibiting a copy of `a`'s raw data.     |           |      Examples     |      --------     |      >>> x = np.array([[0, 1], [2, 3]])     |      >>> x.tobytes()     |      b'\x00\x00\x00\x00\x01\x00\x00\x00\x02\x00\x00\x00\x03\x00\x00\x00'     |      >>> x.tobytes('C') == x.tobytes()     |      True     |      >>> x.tobytes('F')     |      b'\x00\x00\x00\x00\x02\x00\x00\x00\x01\x00\x00\x00\x03\x00\x00\x00'     |       |  trace(...)     |      a.trace(offset=0, axis1=0, axis2=1, dtype=None, out=None)     |           |      Return the sum along diagonals of the array.     |           |      Refer to `numpy.trace` for full documentation.     |           |      See Also     |      --------     |      numpy.trace : equivalent function     |       |  transpose(...)     |      a.transpose(*axes)     |           |      Returns a view of the array with axes transposed.     |           |      For a 1-D array, this has no effect. (To change between column and     |      row vectors, first cast the 1-D array into a matrix object.)     |      For a 2-D array, this is the usual matrix transpose.     |      For an n-D array, if axes are given, their order indicates how the     |      axes are permuted (see Examples). If axes are not provided and     |      ``a.shape = (i[0], i[1], ... i[n-2], i[n-1])``, then     |      ``a.transpose().shape = (i[n-1], i[n-2], ... i[1], i[0])``.     |           |      Parameters     |      ----------     |      axes : None, tuple of ints, or `n` ints     |           |       * None or no argument: reverses the order of the axes.     |           |       * tuple of ints: `i` in the `j`-th place in the tuple means `a`'s     |         `i`-th axis becomes `a.transpose()`'s `j`-th axis.     |           |       * `n` ints: same as an n-tuple of the same ints (this form is     |         intended simply as a "convenience" alternative to the tuple form)     |           |      Returns     |      -------     |      out : ndarray     |          View of `a`, with axes suitably permuted.     |           |      See Also     |      --------     |      ndarray.T : Array property returning the array transposed.     |           |      Examples     |      --------     |      >>> a = np.array([[1, 2], [3, 4]])     |      >>> a     |      array([[1, 2],     |             [3, 4]])     |      >>> a.transpose()     |      array([[1, 3],     |             [2, 4]])     |      >>> a.transpose((1, 0))     |      array([[1, 3],     |             [2, 4]])     |      >>> a.transpose(1, 0)     |      array([[1, 3],     |             [2, 4]])     |       |  view(...)     |      a.view(dtype=None, type=None)     |           |      New view of array with the same data.     |           |      Parameters     |      ----------     |      dtype : data-type or ndarray sub-class, optional     |          Data-type descriptor of the returned view, e.g., float32 or int16. The     |          default, None, results in the view having the same data-type as `a`.     |          This argument can also be specified as an ndarray sub-class, which     |          then specifies the type of the returned object (this is equivalent to     |          setting the ``type`` parameter).     |      type : Python type, optional     |          Type of the returned view, e.g., ndarray or matrix.  Again, the     |          default None results in type preservation.     |           |      Notes     |      -----     |      ``a.view()`` is used two different ways:     |           |      ``a.view(some_dtype)`` or ``a.view(dtype=some_dtype)`` constructs a view     |      of the array's memory with a different data-type.  This can cause a     |      reinterpretation of the bytes of memory.     |           |      ``a.view(ndarray_subclass)`` or ``a.view(type=ndarray_subclass)`` just     |      returns an instance of `ndarray_subclass` that looks at the same array     |      (same shape, dtype, etc.)  This does not cause a reinterpretation of the     |      memory.     |           |      For ``a.view(some_dtype)``, if ``some_dtype`` has a different number of     |      bytes per entry than the previous dtype (for example, converting a     |      regular array to a structured array), then the behavior of the view     |      cannot be predicted just from the superficial appearance of ``a`` (shown     |      by ``print(a)``). It also depends on exactly how ``a`` is stored in     |      memory. Therefore if ``a`` is C-ordered versus fortran-ordered, versus     |      defined as a slice or transpose, etc., the view may give different     |      results.     |           |           |      Examples     |      --------     |      >>> x = np.array([(1, 2)], dtype=[('a', np.int8), ('b', np.int8)])     |           |      Viewing array data using a different type and dtype:     |           |      >>> y = x.view(dtype=np.int16, type=np.matrix)     |      >>> y     |      matrix([[513]], dtype=int16)     |      >>> print type(y)     |      <class 'numpy.matrixlib.defmatrix.matrix'>     |           |      Creating a view on a structured array so it can be used in calculations     |           |      >>> x = np.array([(1, 2),(3,4)], dtype=[('a', np.int8), ('b', np.int8)])     |      >>> xv = x.view(dtype=np.int8).reshape(-1,2)     |      >>> xv     |      array([[1, 2],     |             [3, 4]], dtype=int8)     |      >>> xv.mean(0)     |      array([ 2.,  3.])     |           |      Making changes to the view changes the underlying array     |           |      >>> xv[0,1] = 20     |      >>> print x     |      [(1, 20) (3, 4)]     |           |      Using a view to convert an array to a recarray:     |           |      >>> z = x.view(np.recarray)     |      >>> z.a     |      array([1], dtype=int8)     |           |      Views share data:     |           |      >>> x[0] = (9, 10)     |      >>> z[0]     |      (9, 10)     |           |      Views that change the dtype size (bytes per entry) should normally be     |      avoided on arrays defined by slices, transposes, fortran-ordering, etc.:     |           |      >>> x = np.array([[1,2,3],[4,5,6]], dtype=np.int16)     |      >>> y = x[:, 0:2]     |      >>> y     |      array([[1, 2],     |             [4, 5]], dtype=int16)     |      >>> y.view(dtype=[('width', np.int16), ('length', np.int16)])     |      Traceback (most recent call last):     |        File "<stdin>", line 1, in <module>     |      ValueError: new type not compatible with array.     |      >>> z = y.copy()     |      >>> z.view(dtype=[('width', np.int16), ('length', np.int16)])     |      array([[(1, 2)],     |             [(4, 5)]], dtype=[('width', '<i2'), ('length', '<i2')])     |       |  ----------------------------------------------------------------------     |  Data descriptors inherited from numpy.ndarray:     |       |  __array_interface__     |      Array protocol: Python side.     |       |  __array_struct__     |      Array protocol: C-struct side.     |       |  base     |      Base object if memory is from some other object.     |           |      Examples     |      --------     |      The base of an array that owns its memory is None:     |           |      >>> x = np.array([1,2,3,4])     |      >>> x.base is None     |      True     |           |      Slicing creates a view, whose memory is shared with x:     |           |      >>> y = x[2:]     |      >>> y.base is x     |      True     |       |  ctypes     |      An object to simplify the interaction of the array with the ctypes     |      module.     |           |      This attribute creates an object that makes it easier to use arrays     |      when calling shared libraries with the ctypes module. The returned     |      object has, among others, data, shape, and strides attributes (see     |      Notes below) which themselves return ctypes objects that can be used     |      as arguments to a shared library.     |           |      Parameters     |      ----------     |      None     |           |      Returns     |      -------     |      c : Python object     |          Possessing attributes data, shape, strides, etc.     |           |      See Also     |      --------     |      numpy.ctypeslib     |           |      Notes     |      -----     |      Below are the public attributes of this object which were documented     |      in "Guide to NumPy" (we have omitted undocumented public attributes,     |      as well as documented private attributes):     |           |      * data: A pointer to the memory area of the array as a Python integer.     |        This memory area may contain data that is not aligned, or not in correct     |        byte-order. The memory area may not even be writeable. The array     |        flags and data-type of this array should be respected when passing this     |        attribute to arbitrary C-code to avoid trouble that can include Python     |        crashing. User Beware! The value of this attribute is exactly the same     |        as self._array_interface_['data'][0].     |           |      * shape (c_intp*self.ndim): A ctypes array of length self.ndim where     |        the basetype is the C-integer corresponding to dtype('p') on this     |        platform. This base-type could be c_int, c_long, or c_longlong     |        depending on the platform. The c_intp type is defined accordingly in     |        numpy.ctypeslib. The ctypes array contains the shape of the underlying     |        array.     |           |      * strides (c_intp*self.ndim): A ctypes array of length self.ndim where     |        the basetype is the same as for the shape attribute. This ctypes array     |        contains the strides information from the underlying array. This strides     |        information is important for showing how many bytes must be jumped to     |        get to the next element in the array.     |           |      * data_as(obj): Return the data pointer cast to a particular c-types object.     |        For example, calling self._as_parameter_ is equivalent to     |        self.data_as(ctypes.c_void_p). Perhaps you want to use the data as a     |        pointer to a ctypes array of floating-point data:     |        self.data_as(ctypes.POINTER(ctypes.c_double)).     |           |      * shape_as(obj): Return the shape tuple as an array of some other c-types     |        type. For example: self.shape_as(ctypes.c_short).     |           |      * strides_as(obj): Return the strides tuple as an array of some other     |        c-types type. For example: self.strides_as(ctypes.c_longlong).     |           |      Be careful using the ctypes attribute - especially on temporary     |      arrays or arrays constructed on the fly. For example, calling     |      ``(a+b).ctypes.data_as(ctypes.c_void_p)`` returns a pointer to memory     |      that is invalid because the array created as (a+b) is deallocated     |      before the next Python statement. You can avoid this problem using     |      either ``c=a+b`` or ``ct=(a+b).ctypes``. In the latter case, ct will     |      hold a reference to the array until ct is deleted or re-assigned.     |           |      If the ctypes module is not available, then the ctypes attribute     |      of array objects still returns something useful, but ctypes objects     |      are not returned and errors may be raised instead. In particular,     |      the object will still have the as parameter attribute which will     |      return an integer equal to the data attribute.     |           |      Examples     |      --------     |      >>> import ctypes     |      >>> x     |      array([[0, 1],     |             [2, 3]])     |      >>> x.ctypes.data     |      30439712     |      >>> x.ctypes.data_as(ctypes.POINTER(ctypes.c_long))     |      <ctypes.LP_c_long object at 0x01F01300>     |      >>> x.ctypes.data_as(ctypes.POINTER(ctypes.c_long)).contents     |      c_long(0)     |      >>> x.ctypes.data_as(ctypes.POINTER(ctypes.c_longlong)).contents     |      c_longlong(4294967296L)     |      >>> x.ctypes.shape     |      <numpy.core._internal.c_long_Array_2 object at 0x01FFD580>     |      >>> x.ctypes.shape_as(ctypes.c_long)     |      <numpy.core._internal.c_long_Array_2 object at 0x01FCE620>     |      >>> x.ctypes.strides     |      <numpy.core._internal.c_long_Array_2 object at 0x01FCE620>     |      >>> x.ctypes.strides_as(ctypes.c_longlong)     |      <numpy.core._internal.c_longlong_Array_2 object at 0x01F01300>     |       |  data     |      Python buffer object pointing to the start of the array's data.     |       |  dtype     |      Data-type of the array's elements.     |           |      Parameters     |      ----------     |      None     |           |      Returns     |      -------     |      d : numpy dtype object     |           |      See Also     |      --------     |      numpy.dtype     |           |      Examples     |      --------     |      >>> x     |      array([[0, 1],     |             [2, 3]])     |      >>> x.dtype     |      dtype('int32')     |      >>> type(x.dtype)     |      <type 'numpy.dtype'>     |       |  flags     |      Information about the memory layout of the array.     |           |      Attributes     |      ----------     |      C_CONTIGUOUS (C)     |          The data is in a single, C-style contiguous segment.     |      F_CONTIGUOUS (F)     |          The data is in a single, Fortran-style contiguous segment.     |      OWNDATA (O)     |          The array owns the memory it uses or borrows it from another object.     |      WRITEABLE (W)     |          The data area can be written to.  Setting this to False locks     |          the data, making it read-only.  A view (slice, etc.) inherits WRITEABLE     |          from its base array at creation time, but a view of a writeable     |          array may be subsequently locked while the base array remains writeable.     |          (The opposite is not true, in that a view of a locked array may not     |          be made writeable.  However, currently, locking a base object does not     |          lock any views that already reference it, so under that circumstance it     |          is possible to alter the contents of a locked array via a previously     |          created writeable view onto it.)  Attempting to change a non-writeable     |          array raises a RuntimeError exception.     |      ALIGNED (A)     |          The data and all elements are aligned appropriately for the hardware.     |      UPDATEIFCOPY (U)     |          This array is a copy of some other array. When this array is     |          deallocated, the base array will be updated with the contents of     |          this array.     |      FNC     |          F_CONTIGUOUS and not C_CONTIGUOUS.     |      FORC     |          F_CONTIGUOUS or C_CONTIGUOUS (one-segment test).     |      BEHAVED (B)     |          ALIGNED and WRITEABLE.     |      CARRAY (CA)     |          BEHAVED and C_CONTIGUOUS.     |      FARRAY (FA)     |          BEHAVED and F_CONTIGUOUS and not C_CONTIGUOUS.     |           |      Notes     |      -----     |      The `flags` object can be accessed dictionary-like (as in ``a.flags['WRITEABLE']``),     |      or by using lowercased attribute names (as in ``a.flags.writeable``). Short flag     |      names are only supported in dictionary access.     |           |      Only the UPDATEIFCOPY, WRITEABLE, and ALIGNED flags can be changed by     |      the user, via direct assignment to the attribute or dictionary entry,     |      or by calling `ndarray.setflags`.     |           |      The array flags cannot be set arbitrarily:     |           |      - UPDATEIFCOPY can only be set ``False``.     |      - ALIGNED can only be set ``True`` if the data is truly aligned.     |      - WRITEABLE can only be set ``True`` if the array owns its own memory     |        or the ultimate owner of the memory exposes a writeable buffer     |        interface or is a string.     |           |      Arrays can be both C-style and Fortran-style contiguous simultaneously.     |      This is clear for 1-dimensional arrays, but can also be true for higher     |      dimensional arrays.     |           |      Even for contiguous arrays a stride for a given dimension     |      ``arr.strides[dim]`` may be *arbitrary* if ``arr.shape[dim] == 1``     |      or the array has no elements.     |      It does *not* generally hold that ``self.strides[-1] == self.itemsize``     |      for C-style contiguous arrays or ``self.strides[0] == self.itemsize`` for     |      Fortran-style contiguous arrays is true.     |       |  flat     |      A 1-D iterator over the array.     |           |      This is a `numpy.flatiter` instance, which acts similarly to, but is not     |      a subclass of, Python's built-in iterator object.     |           |      See Also     |      --------     |      flatten : Return a copy of the array collapsed into one dimension.     |           |      flatiter     |           |      Examples     |      --------     |      >>> x = np.arange(1, 7).reshape(2, 3)     |      >>> x     |      array([[1, 2, 3],     |             [4, 5, 6]])     |      >>> x.flat[3]     |      4     |      >>> x.T     |      array([[1, 4],     |             [2, 5],     |             [3, 6]])     |      >>> x.T.flat[3]     |      5     |      >>> type(x.flat)     |      <type 'numpy.flatiter'>     |           |      An assignment example:     |           |      >>> x.flat = 3; x     |      array([[3, 3, 3],     |             [3, 3, 3]])     |      >>> x.flat[[1,4]] = 1; x     |      array([[3, 1, 3],     |             [3, 1, 3]])     |       |  imag     |      The imaginary part of the array.     |           |      Examples     |      --------     |      >>> x = np.sqrt([1+0j, 0+1j])     |      >>> x.imag     |      array([ 0.        ,  0.70710678])     |      >>> x.imag.dtype     |      dtype('float64')     |       |  itemsize     |      Length of one array element in bytes.     |           |      Examples     |      --------     |      >>> x = np.array([1,2,3], dtype=np.float64)     |      >>> x.itemsize     |      8     |      >>> x = np.array([1,2,3], dtype=np.complex128)     |      >>> x.itemsize     |      16     |       |  nbytes     |      Total bytes consumed by the elements of the array.     |           |      Notes     |      -----     |      Does not include memory consumed by non-element attributes of the     |      array object.     |           |      Examples     |      --------     |      >>> x = np.zeros((3,5,2), dtype=np.complex128)     |      >>> x.nbytes     |      480     |      >>> np.prod(x.shape) * x.itemsize     |      480     |       |  ndim     |      Number of array dimensions.     |           |      Examples     |      --------     |      >>> x = np.array([1, 2, 3])     |      >>> x.ndim     |      1     |      >>> y = np.zeros((2, 3, 4))     |      >>> y.ndim     |      3     |       |  real     |      The real part of the array.     |           |      Examples     |      --------     |      >>> x = np.sqrt([1+0j, 0+1j])     |      >>> x.real     |      array([ 1.        ,  0.70710678])     |      >>> x.real.dtype     |      dtype('float64')     |           |      See Also     |      --------     |      numpy.real : equivalent function     |       |  shape     |      Tuple of array dimensions.     |           |      Notes     |      -----     |      May be used to "reshape" the array, as long as this would not     |      require a change in the total number of elements     |           |      Examples     |      --------     |      >>> x = np.array([1, 2, 3, 4])     |      >>> x.shape     |      (4,)     |      >>> y = np.zeros((2, 3, 4))     |      >>> y.shape     |      (2, 3, 4)     |      >>> y.shape = (3, 8)     |      >>> y     |      array([[ 0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.],     |             [ 0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.],     |             [ 0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.]])     |      >>> y.shape = (3, 6)     |      Traceback (most recent call last):     |        File "<stdin>", line 1, in <module>     |      ValueError: total size of new array must be unchanged     |       |  size     |      Number of elements in the array.     |           |      Equivalent to ``np.prod(a.shape)``, i.e., the product of the array's     |      dimensions.     |           |      Examples     |      --------     |      >>> x = np.zeros((3, 5, 2), dtype=np.complex128)     |      >>> x.size     |      30     |      >>> np.prod(x.shape)     |      30     |       |  strides     |      Tuple of bytes to step in each dimension when traversing an array.     |           |      The byte offset of element ``(i[0], i[1], ..., i[n])`` in an array `a`     |      is::     |           |          offset = sum(np.array(i) * a.strides)     |           |      A more detailed explanation of strides can be found in the     |      "ndarray.rst" file in the NumPy reference guide.     |           |      Notes     |      -----     |      Imagine an array of 32-bit integers (each 4 bytes)::     |           |        x = np.array([[0, 1, 2, 3, 4],     |                      [5, 6, 7, 8, 9]], dtype=np.int32)     |           |      This array is stored in memory as 40 bytes, one after the other     |      (known as a contiguous block of memory).  The strides of an array tell     |      us how many bytes we have to skip in memory to move to the next position     |      along a certain axis.  For example, we have to skip 4 bytes (1 value) to     |      move to the next column, but 20 bytes (5 values) to get to the same     |      position in the next row.  As such, the strides for the array `x` will be     |      ``(20, 4)``.     |           |      See Also     |      --------     |      numpy.lib.stride_tricks.as_strided     |           |      Examples     |      --------     |      >>> y = np.reshape(np.arange(2*3*4), (2,3,4))     |      >>> y     |      array([[[ 0,  1,  2,  3],     |              [ 4,  5,  6,  7],     |              [ 8,  9, 10, 11]],     |             [[12, 13, 14, 15],     |              [16, 17, 18, 19],     |              [20, 21, 22, 23]]])     |      >>> y.strides     |      (48, 16, 4)     |      >>> y[1,1,1]     |      17     |      >>> offset=sum(y.strides * np.array((1,1,1)))     |      >>> offset/y.itemsize     |      17     |           |      >>> x = np.reshape(np.arange(5*6*7*8), (5,6,7,8)).transpose(2,3,1,0)     |      >>> x.strides     |      (32, 4, 224, 1344)     |      >>> i = np.array([3,5,2,2])     |      >>> offset = sum(i * x.strides)     |      >>> x[3,5,2,2]     |      813     |      >>> offset / x.itemsize     |      813     |       |  ----------------------------------------------------------------------     |  Data and other attributes inherited from numpy.ndarray:     |       |  __hash__ = None        class memmap(numpy.ndarray)     |  Create a memory-map to an array stored in a *binary* file on disk.     |       |  Memory-mapped files are used for accessing small segments of large files     |  on disk, without reading the entire file into memory.  Numpy's     |  memmap's are array-like objects.  This differs from Python's ``mmap``     |  module, which uses file-like objects.     |       |  This subclass of ndarray has some unpleasant interactions with     |  some operations, because it doesn't quite fit properly as a subclass.     |  An alternative to using this subclass is to create the ``mmap``     |  object yourself, then create an ndarray with ndarray.__new__ directly,     |  passing the object created in its 'buffer=' parameter.     |       |  This class may at some point be turned into a factory function     |  which returns a view into an mmap buffer.     |       |  Delete the memmap instance to close.     |       |       |  Parameters     |  ----------     |  filename : str or file-like object     |      The file name or file object to be used as the array data buffer.     |  dtype : data-type, optional     |      The data-type used to interpret the file contents.     |      Default is `uint8`.     |  mode : {'r+', 'r', 'w+', 'c'}, optional     |      The file is opened in this mode:     |       |      +------+-------------------------------------------------------------+     |      | 'r'  | Open existing file for reading only.                        |     |      +------+-------------------------------------------------------------+     |      | 'r+' | Open existing file for reading and writing.                 |     |      +------+-------------------------------------------------------------+     |      | 'w+' | Create or overwrite existing file for reading and writing.  |     |      +------+-------------------------------------------------------------+     |      | 'c'  | Copy-on-write: assignments affect data in memory, but       |     |      |      | changes are not saved to disk.  The file on disk is         |     |      |      | read-only.                                                  |     |      +------+-------------------------------------------------------------+     |       |      Default is 'r+'.     |  offset : int, optional     |      In the file, array data starts at this offset. Since `offset` is     |      measured in bytes, it should normally be a multiple of the byte-size     |      of `dtype`. When ``mode != 'r'``, even positive offsets beyond end of     |      file are valid; The file will be extended to accommodate the     |      additional data. By default, ``memmap`` will start at the beginning of     |      the file, even if ``filename`` is a file pointer ``fp`` and     |      ``fp.tell() != 0``.     |  shape : tuple, optional     |      The desired shape of the array. If ``mode == 'r'`` and the number     |      of remaining bytes after `offset` is not a multiple of the byte-size     |      of `dtype`, you must specify `shape`. By default, the returned array     |      will be 1-D with the number of elements determined by file size     |      and data-type.     |  order : {'C', 'F'}, optional     |      Specify the order of the ndarray memory layout:     |      :term:`row-major`, C-style or :term:`column-major`,     |      Fortran-style.  This only has an effect if the shape is     |      greater than 1-D.  The default order is 'C'.     |       |  Attributes     |  ----------     |  filename : str     |      Path to the mapped file.     |  offset : int     |      Offset position in the file.     |  mode : str     |      File mode.     |       |  Methods     |  -------     |  flush     |      Flush any changes in memory to file on disk.     |      When you delete a memmap object, flush is called first to write     |      changes to disk before removing the object.     |       |       |  Notes     |  -----     |  The memmap object can be used anywhere an ndarray is accepted.     |  Given a memmap ``fp``, ``isinstance(fp, numpy.ndarray)`` returns     |  ``True``.     |       |  Memory-mapped arrays use the Python memory-map object which     |  (prior to Python 2.5) does not allow files to be larger than a     |  certain size depending on the platform. This size is always < 2GB     |  even on 64-bit systems.     |       |  When a memmap causes a file to be created or extended beyond its     |  current size in the filesystem, the contents of the new part are     |  unspecified. On systems with POSIX filesystem semantics, the extended     |  part will be filled with zero bytes.     |       |  Examples     |  --------     |  >>> data = np.arange(12, dtype='float32')     |  >>> data.resize((3,4))     |       |  This example uses a temporary file so that doctest doesn't write     |  files to your directory. You would use a 'normal' filename.     |       |  >>> from tempfile import mkdtemp     |  >>> import os.path as path     |  >>> filename = path.join(mkdtemp(), 'newfile.dat')     |       |  Create a memmap with dtype and shape that matches our data:     |       |  >>> fp = np.memmap(filename, dtype='float32', mode='w+', shape=(3,4))     |  >>> fp     |  memmap([[ 0.,  0.,  0.,  0.],     |          [ 0.,  0.,  0.,  0.],     |          [ 0.,  0.,  0.,  0.]], dtype=float32)     |       |  Write data to memmap array:     |       |  >>> fp[:] = data[:]     |  >>> fp     |  memmap([[  0.,   1.,   2.,   3.],     |          [  4.,   5.,   6.,   7.],     |          [  8.,   9.,  10.,  11.]], dtype=float32)     |       |  >>> fp.filename == path.abspath(filename)     |  True     |       |  Deletion flushes memory changes to disk before removing the object:     |       |  >>> del fp     |       |  Load the memmap and verify data was stored:     |       |  >>> newfp = np.memmap(filename, dtype='float32', mode='r', shape=(3,4))     |  >>> newfp     |  memmap([[  0.,   1.,   2.,   3.],     |          [  4.,   5.,   6.,   7.],     |          [  8.,   9.,  10.,  11.]], dtype=float32)     |       |  Read-only memmap:     |       |  >>> fpr = np.memmap(filename, dtype='float32', mode='r', shape=(3,4))     |  >>> fpr.flags.writeable     |  False     |       |  Copy-on-write memmap:     |       |  >>> fpc = np.memmap(filename, dtype='float32', mode='c', shape=(3,4))     |  >>> fpc.flags.writeable     |  True     |       |  It's possible to assign to copy-on-write array, but values are only     |  written into the memory copy of the array, and not written to disk:     |       |  >>> fpc     |  memmap([[  0.,   1.,   2.,   3.],     |          [  4.,   5.,   6.,   7.],     |          [  8.,   9.,  10.,  11.]], dtype=float32)     |  >>> fpc[0,:] = 0     |  >>> fpc     |  memmap([[  0.,   0.,   0.,   0.],     |          [  4.,   5.,   6.,   7.],     |          [  8.,   9.,  10.,  11.]], dtype=float32)     |       |  File on disk is unchanged:     |       |  >>> fpr     |  memmap([[  0.,   1.,   2.,   3.],     |          [  4.,   5.,   6.,   7.],     |          [  8.,   9.,  10.,  11.]], dtype=float32)     |       |  Offset into a memmap:     |       |  >>> fpo = np.memmap(filename, dtype='float32', mode='r', offset=16)     |  >>> fpo     |  memmap([  4.,   5.,   6.,   7.,   8.,   9.,  10.,  11.], dtype=float32)     |       |  Method resolution order:     |      memmap     |      numpy.ndarray     |      __builtin__.object     |       |  Methods defined here:     |       |  __array_finalize__(self, obj)     |       |  flush(self)     |      Write any changes in the array to the file on disk.     |           |      For further information, see `memmap`.     |           |      Parameters     |      ----------     |      None     |           |      See Also     |      --------     |      memmap     |       |  ----------------------------------------------------------------------     |  Static methods defined here:     |       |  __new__(subtype, filename, dtype=<type 'numpy.uint8'>, mode='r+', offset=0, shape=None, order='C')     |       |  ----------------------------------------------------------------------     |  Data descriptors defined here:     |       |  __dict__     |      dictionary for instance variables (if defined)     |       |  ----------------------------------------------------------------------     |  Data and other attributes defined here:     |       |  __array_priority__ = -100.0     |       |  ----------------------------------------------------------------------     |  Methods inherited from numpy.ndarray:     |       |  __abs__(...)     |      x.__abs__() <==> abs(x)     |       |  __add__(...)     |      x.__add__(y) <==> x+y     |       |  __and__(...)     |      x.__and__(y) <==> x&y     |       |  __array__(...)     |      a.__array__(|dtype) -> reference if type unchanged, copy otherwise.     |           |      Returns either a new reference to self if dtype is not given or a new array     |      of provided data type if dtype is different from the current dtype of the     |      array.     |       |  __array_prepare__(...)     |      a.__array_prepare__(obj) -> Object of same type as ndarray object obj.     |       |  __array_wrap__(...)     |      a.__array_wrap__(obj) -> Object of same type as ndarray object a.     |       |  __contains__(...)     |      x.__contains__(y) <==> y in x     |       |  __copy__(...)     |      a.__copy__([order])     |           |      Return a copy of the array.     |           |      Parameters     |      ----------     |      order : {'C', 'F', 'A'}, optional     |          If order is 'C' (False) then the result is contiguous (default).     |          If order is 'Fortran' (True) then the result has fortran order.     |          If order is 'Any' (None) then the result has fortran order     |          only if the array already is in fortran order.     |       |  __deepcopy__(...)     |      a.__deepcopy__() -> Deep copy of array.     |           |      Used if copy.deepcopy is called on an array.     |       |  __delitem__(...)     |      x.__delitem__(y) <==> del x[y]     |       |  __delslice__(...)     |      x.__delslice__(i, j) <==> del x[i:j]     |           |      Use of negative indices is not supported.     |       |  __div__(...)     |      x.__div__(y) <==> x/y     |       |  __divmod__(...)     |      x.__divmod__(y) <==> divmod(x, y)     |       |  __eq__(...)     |      x.__eq__(y) <==> x==y     |       |  __float__(...)     |      x.__float__() <==> float(x)     |       |  __floordiv__(...)     |      x.__floordiv__(y) <==> x//y     |       |  __ge__(...)     |      x.__ge__(y) <==> x>=y     |       |  __getitem__(...)     |      x.__getitem__(y) <==> x[y]     |       |  __getslice__(...)     |      x.__getslice__(i, j) <==> x[i:j]     |           |      Use of negative indices is not supported.     |       |  __gt__(...)     |      x.__gt__(y) <==> x>y     |       |  __hex__(...)     |      x.__hex__() <==> hex(x)     |       |  __iadd__(...)     |      x.__iadd__(y) <==> x+=y     |       |  __iand__(...)     |      x.__iand__(y) <==> x&=y     |       |  __idiv__(...)     |      x.__idiv__(y) <==> x/=y     |       |  __ifloordiv__(...)     |      x.__ifloordiv__(y) <==> x//=y     |       |  __ilshift__(...)     |      x.__ilshift__(y) <==> x<<=y     |       |  __imod__(...)     |      x.__imod__(y) <==> x%=y     |       |  __imul__(...)     |      x.__imul__(y) <==> x*=y     |       |  __index__(...)     |      x[y:z] <==> x[y.__index__():z.__index__()]     |       |  __int__(...)     |      x.__int__() <==> int(x)     |       |  __invert__(...)     |      x.__invert__() <==> ~x     |       |  __ior__(...)     |      x.__ior__(y) <==> x|=y     |       |  __ipow__(...)     |      x.__ipow__(y) <==> x**=y     |       |  __irshift__(...)     |      x.__irshift__(y) <==> x>>=y     |       |  __isub__(...)     |      x.__isub__(y) <==> x-=y     |       |  __iter__(...)     |      x.__iter__() <==> iter(x)     |       |  __itruediv__(...)     |      x.__itruediv__(y) <==> x/=y     |       |  __ixor__(...)     |      x.__ixor__(y) <==> x^=y     |       |  __le__(...)     |      x.__le__(y) <==> x<=y     |       |  __len__(...)     |      x.__len__() <==> len(x)     |       |  __long__(...)     |      x.__long__() <==> long(x)     |       |  __lshift__(...)     |      x.__lshift__(y) <==> x<<y     |       |  __lt__(...)     |      x.__lt__(y) <==> x<y     |       |  __mod__(...)     |      x.__mod__(y) <==> x%y     |       |  __mul__(...)     |      x.__mul__(y) <==> x*y     |       |  __ne__(...)     |      x.__ne__(y) <==> x!=y     |       |  __neg__(...)     |      x.__neg__() <==> -x     |       |  __nonzero__(...)     |      x.__nonzero__() <==> x != 0     |       |  __oct__(...)     |      x.__oct__() <==> oct(x)     |       |  __or__(...)     |      x.__or__(y) <==> x|y     |       |  __pos__(...)     |      x.__pos__() <==> +x     |       |  __pow__(...)     |      x.__pow__(y[, z]) <==> pow(x, y[, z])     |       |  __radd__(...)     |      x.__radd__(y) <==> y+x     |       |  __rand__(...)     |      x.__rand__(y) <==> y&x     |       |  __rdiv__(...)     |      x.__rdiv__(y) <==> y/x     |       |  __rdivmod__(...)     |      x.__rdivmod__(y) <==> divmod(y, x)     |       |  __reduce__(...)     |      a.__reduce__()     |           |      For pickling.     |       |  __repr__(...)     |      x.__repr__() <==> repr(x)     |       |  __rfloordiv__(...)     |      x.__rfloordiv__(y) <==> y//x     |       |  __rlshift__(...)     |      x.__rlshift__(y) <==> y<<x     |       |  __rmod__(...)     |      x.__rmod__(y) <==> y%x     |       |  __rmul__(...)     |      x.__rmul__(y) <==> y*x     |       |  __ror__(...)     |      x.__ror__(y) <==> y|x     |       |  __rpow__(...)     |      y.__rpow__(x[, z]) <==> pow(x, y[, z])     |       |  __rrshift__(...)     |      x.__rrshift__(y) <==> y>>x     |       |  __rshift__(...)     |      x.__rshift__(y) <==> x>>y     |       |  __rsub__(...)     |      x.__rsub__(y) <==> y-x     |       |  __rtruediv__(...)     |      x.__rtruediv__(y) <==> y/x     |       |  __rxor__(...)     |      x.__rxor__(y) <==> y^x     |       |  __setitem__(...)     |      x.__setitem__(i, y) <==> x[i]=y     |       |  __setslice__(...)     |      x.__setslice__(i, j, y) <==> x[i:j]=y     |           |      Use  of negative indices is not supported.     |       |  __setstate__(...)     |      a.__setstate__(version, shape, dtype, isfortran, rawdata)     |           |      For unpickling.     |           |      Parameters     |      ----------     |      version : int     |          optional pickle version. If omitted defaults to 0.     |      shape : tuple     |      dtype : data-type     |      isFortran : bool     |      rawdata : string or list     |          a binary string with the data (or a list if 'a' is an object array)     |       |  __sizeof__(...)     |       |  __str__(...)     |      x.__str__() <==> str(x)     |       |  __sub__(...)     |      x.__sub__(y) <==> x-y     |       |  __truediv__(...)     |      x.__truediv__(y) <==> x/y     |       |  __xor__(...)     |      x.__xor__(y) <==> x^y     |       |  all(...)     |      a.all(axis=None, out=None, keepdims=False)     |           |      Returns True if all elements evaluate to True.     |           |      Refer to `numpy.all` for full documentation.     |           |      See Also     |      --------     |      numpy.all : equivalent function     |       |  any(...)     |      a.any(axis=None, out=None, keepdims=False)     |           |      Returns True if any of the elements of `a` evaluate to True.     |           |      Refer to `numpy.any` for full documentation.     |           |      See Also     |      --------     |      numpy.any : equivalent function     |       |  argmax(...)     |      a.argmax(axis=None, out=None)     |           |      Return indices of the maximum values along the given axis.     |           |      Refer to `numpy.argmax` for full documentation.     |           |      See Also     |      --------     |      numpy.argmax : equivalent function     |       |  argmin(...)     |      a.argmin(axis=None, out=None)     |           |      Return indices of the minimum values along the given axis of `a`.     |           |      Refer to `numpy.argmin` for detailed documentation.     |           |      See Also     |      --------     |      numpy.argmin : equivalent function     |       |  argpartition(...)     |      a.argpartition(kth, axis=-1, kind='introselect', order=None)     |           |      Returns the indices that would partition this array.     |           |      Refer to `numpy.argpartition` for full documentation.     |           |      .. versionadded:: 1.8.0     |           |      See Also     |      --------     |      numpy.argpartition : equivalent function     |       |  argsort(...)     |      a.argsort(axis=-1, kind='quicksort', order=None)     |           |      Returns the indices that would sort this array.     |           |      Refer to `numpy.argsort` for full documentation.     |           |      See Also     |      --------     |      numpy.argsort : equivalent function     |       |  astype(...)     |      a.astype(dtype, order='K', casting='unsafe', subok=True, copy=True)     |           |      Copy of the array, cast to a specified type.     |           |      Parameters     |      ----------     |      dtype : str or dtype     |          Typecode or data-type to which the array is cast.     |      order : {'C', 'F', 'A', 'K'}, optional     |          Controls the memory layout order of the result.     |          'C' means C order, 'F' means Fortran order, 'A'     |          means 'F' order if all the arrays are Fortran contiguous,     |          'C' order otherwise, and 'K' means as close to the     |          order the array elements appear in memory as possible.     |          Default is 'K'.     |      casting : {'no', 'equiv', 'safe', 'same_kind', 'unsafe'}, optional     |          Controls what kind of data casting may occur. Defaults to 'unsafe'     |          for backwards compatibility.     |           |            * 'no' means the data types should not be cast at all.     |            * 'equiv' means only byte-order changes are allowed.     |            * 'safe' means only casts which can preserve values are allowed.     |            * 'same_kind' means only safe casts or casts within a kind,     |              like float64 to float32, are allowed.     |            * 'unsafe' means any data conversions may be done.     |      subok : bool, optional     |          If True, then sub-classes will be passed-through (default), otherwise     |          the returned array will be forced to be a base-class array.     |      copy : bool, optional     |          By default, astype always returns a newly allocated array. If this     |          is set to false, and the `dtype`, `order`, and `subok`     |          requirements are satisfied, the input array is returned instead     |          of a copy.     |           |      Returns     |      -------     |      arr_t : ndarray     |          Unless `copy` is False and the other conditions for returning the input     |          array are satisfied (see description for `copy` input paramter), `arr_t`     |          is a new array of the same shape as the input array, with dtype, order     |          given by `dtype`, `order`.     |           |      Notes     |      -----     |      Starting in NumPy 1.9, astype method now returns an error if the string     |      dtype to cast to is not long enough in 'safe' casting mode to hold the max     |      value of integer/float array that is being casted. Previously the casting     |      was allowed even if the result was truncated.     |           |      Raises     |      ------     |      ComplexWarning     |          When casting from complex to float or int. To avoid this,     |          one should use ``a.real.astype(t)``.     |           |      Examples     |      --------     |      >>> x = np.array([1, 2, 2.5])     |      >>> x     |      array([ 1. ,  2. ,  2.5])     |           |      >>> x.astype(int)     |      array([1, 2, 2])     |       |  byteswap(...)     |      a.byteswap(inplace)     |           |      Swap the bytes of the array elements     |           |      Toggle between low-endian and big-endian data representation by     |      returning a byteswapped array, optionally swapped in-place.     |           |      Parameters     |      ----------     |      inplace : bool, optional     |          If ``True``, swap bytes in-place, default is ``False``.     |           |      Returns     |      -------     |      out : ndarray     |          The byteswapped array. If `inplace` is ``True``, this is     |          a view to self.     |           |      Examples     |      --------     |      >>> A = np.array([1, 256, 8755], dtype=np.int16)     |      >>> map(hex, A)     |      ['0x1', '0x100', '0x2233']     |      >>> A.byteswap(True)     |      array([  256,     1, 13090], dtype=int16)     |      >>> map(hex, A)     |      ['0x100', '0x1', '0x3322']     |           |      Arrays of strings are not swapped     |           |      >>> A = np.array(['ceg', 'fac'])     |      >>> A.byteswap()     |      array(['ceg', 'fac'],     |            dtype='|S3')     |       |  choose(...)     |      a.choose(choices, out=None, mode='raise')     |           |      Use an index array to construct a new array from a set of choices.     |           |      Refer to `numpy.choose` for full documentation.     |           |      See Also     |      --------     |      numpy.choose : equivalent function     |       |  clip(...)     |      a.clip(min=None, max=None, out=None)     |           |      Return an array whose values are limited to ``[min, max]``.     |      One of max or min must be given.     |           |      Refer to `numpy.clip` for full documentation.     |           |      See Also     |      --------     |      numpy.clip : equivalent function     |       |  compress(...)     |      a.compress(condition, axis=None, out=None)     |           |      Return selected slices of this array along given axis.     |           |      Refer to `numpy.compress` for full documentation.     |           |      See Also     |      --------     |      numpy.compress : equivalent function     |       |  conj(...)     |      a.conj()     |           |      Complex-conjugate all elements.     |           |      Refer to `numpy.conjugate` for full documentation.     |           |      See Also     |      --------     |      numpy.conjugate : equivalent function     |       |  conjugate(...)     |      a.conjugate()     |           |      Return the complex conjugate, element-wise.     |           |      Refer to `numpy.conjugate` for full documentation.     |           |      See Also     |      --------     |      numpy.conjugate : equivalent function     |       |  copy(...)     |      a.copy(order='C')     |           |      Return a copy of the array.     |           |      Parameters     |      ----------     |      order : {'C', 'F', 'A', 'K'}, optional     |          Controls the memory layout of the copy. 'C' means C-order,     |          'F' means F-order, 'A' means 'F' if `a` is Fortran contiguous,     |          'C' otherwise. 'K' means match the layout of `a` as closely     |          as possible. (Note that this function and :func:numpy.copy are very     |          similar, but have different default values for their order=     |          arguments.)     |           |      See also     |      --------     |      numpy.copy     |      numpy.copyto     |           |      Examples     |      --------     |      >>> x = np.array([[1,2,3],[4,5,6]], order='F')     |           |      >>> y = x.copy()     |           |      >>> x.fill(0)     |           |      >>> x     |      array([[0, 0, 0],     |             [0, 0, 0]])     |           |      >>> y     |      array([[1, 2, 3],     |             [4, 5, 6]])     |           |      >>> y.flags['C_CONTIGUOUS']     |      True     |       |  cumprod(...)     |      a.cumprod(axis=None, dtype=None, out=None)     |           |      Return the cumulative product of the elements along the given axis.     |           |      Refer to `numpy.cumprod` for full documentation.     |           |      See Also     |      --------     |      numpy.cumprod : equivalent function     |       |  cumsum(...)     |      a.cumsum(axis=None, dtype=None, out=None)     |           |      Return the cumulative sum of the elements along the given axis.     |           |      Refer to `numpy.cumsum` for full documentation.     |           |      See Also     |      --------     |      numpy.cumsum : equivalent function     |       |  diagonal(...)     |      a.diagonal(offset=0, axis1=0, axis2=1)     |           |      Return specified diagonals. In NumPy 1.9 the returned array is a     |      read-only view instead of a copy as in previous NumPy versions.  In     |      NumPy 1.10 the read-only restriction will be removed.     |           |      Refer to :func:`numpy.diagonal` for full documentation.     |           |      See Also     |      --------     |      numpy.diagonal : equivalent function     |       |  dot(...)     |      a.dot(b, out=None)     |           |      Dot product of two arrays.     |           |      Refer to `numpy.dot` for full documentation.     |           |      See Also     |      --------     |      numpy.dot : equivalent function     |           |      Examples     |      --------     |      >>> a = np.eye(2)     |      >>> b = np.ones((2, 2)) * 2     |      >>> a.dot(b)     |      array([[ 2.,  2.],     |             [ 2.,  2.]])     |           |      This array method can be conveniently chained:     |           |      >>> a.dot(b).dot(b)     |      array([[ 8.,  8.],     |             [ 8.,  8.]])     |       |  dump(...)     |      a.dump(file)     |           |      Dump a pickle of the array to the specified file.     |      The array can be read back with pickle.load or numpy.load.     |           |      Parameters     |      ----------     |      file : str     |          A string naming the dump file.     |       |  dumps(...)     |      a.dumps()     |           |      Returns the pickle of the array as a string.     |      pickle.loads or numpy.loads will convert the string back to an array.     |           |      Parameters     |      ----------     |      None     |       |  fill(...)     |      a.fill(value)     |           |      Fill the array with a scalar value.     |           |      Parameters     |      ----------     |      value : scalar     |          All elements of `a` will be assigned this value.     |           |      Examples     |      --------     |      >>> a = np.array([1, 2])     |      >>> a.fill(0)     |      >>> a     |      array([0, 0])     |      >>> a = np.empty(2)     |      >>> a.fill(1)     |      >>> a     |      array([ 1.,  1.])     |       |  flatten(...)     |      a.flatten(order='C')     |           |      Return a copy of the array collapsed into one dimension.     |           |      Parameters     |      ----------     |      order : {'C', 'F', 'A'}, optional     |          Whether to flatten in row-major (C-style) or     |          column-major (Fortran-style) order or preserve the     |          C/Fortran ordering from `a`.  The default is 'C'.     |           |      Returns     |      -------     |      y : ndarray     |          A copy of the input array, flattened to one dimension.     |           |      See Also     |      --------     |      ravel : Return a flattened array.     |      flat : A 1-D flat iterator over the array.     |           |      Examples     |      --------     |      >>> a = np.array([[1,2], [3,4]])     |      >>> a.flatten()     |      array([1, 2, 3, 4])     |      >>> a.flatten('F')     |      array([1, 3, 2, 4])     |       |  getfield(...)     |      a.getfield(dtype, offset=0)     |           |      Returns a field of the given array as a certain type.     |           |      A field is a view of the array data with a given data-type. The values in     |      the view are determined by the given type and the offset into the current     |      array in bytes. The offset needs to be such that the view dtype fits in the     |      array dtype; for example an array of dtype complex128 has 16-byte elements.     |      If taking a view with a 32-bit integer (4 bytes), the offset needs to be     |      between 0 and 12 bytes.     |           |      Parameters     |      ----------     |      dtype : str or dtype     |          The data type of the view. The dtype size of the view can not be larger     |          than that of the array itself.     |      offset : int     |          Number of bytes to skip before beginning the element view.     |           |      Examples     |      --------     |      >>> x = np.diag([1.+1.j]*2)     |      >>> x[1, 1] = 2 + 4.j     |      >>> x     |      array([[ 1.+1.j,  0.+0.j],     |             [ 0.+0.j,  2.+4.j]])     |      >>> x.getfield(np.float64)     |      array([[ 1.,  0.],     |             [ 0.,  2.]])     |           |      By choosing an offset of 8 bytes we can select the complex part of the     |      array for our view:     |           |      >>> x.getfield(np.float64, offset=8)     |      array([[ 1.,  0.],     |         [ 0.,  4.]])     |       |  item(...)     |      a.item(*args)     |           |      Copy an element of an array to a standard Python scalar and return it.     |           |      Parameters     |      ----------     |      \*args : Arguments (variable number and type)     |           |          * none: in this case, the method only works for arrays     |            with one element (`a.size == 1`), which element is     |            copied into a standard Python scalar object and returned.     |           |          * int_type: this argument is interpreted as a flat index into     |            the array, specifying which element to copy and return.     |           |          * tuple of int_types: functions as does a single int_type argument,     |            except that the argument is interpreted as an nd-index into the     |            array.     |           |      Returns     |      -------     |      z : Standard Python scalar object     |          A copy of the specified element of the array as a suitable     |          Python scalar     |           |      Notes     |      -----     |      When the data type of `a` is longdouble or clongdouble, item() returns     |      a scalar array object because there is no available Python scalar that     |      would not lose information. Void arrays return a buffer object for item(),     |      unless fields are defined, in which case a tuple is returned.     |           |      `item` is very similar to a[args], except, instead of an array scalar,     |      a standard Python scalar is returned. This can be useful for speeding up     |      access to elements of the array and doing arithmetic on elements of the     |      array using Python's optimized math.     |           |      Examples     |      --------     |      >>> x = np.random.randint(9, size=(3, 3))     |      >>> x     |      array([[3, 1, 7],     |             [2, 8, 3],     |             [8, 5, 3]])     |      >>> x.item(3)     |      2     |      >>> x.item(7)     |      5     |      >>> x.item((0, 1))     |      1     |      >>> x.item((2, 2))     |      3     |       |  itemset(...)     |      a.itemset(*args)     |           |      Insert scalar into an array (scalar is cast to array's dtype, if possible)     |           |      There must be at least 1 argument, and define the last argument     |      as *item*.  Then, ``a.itemset(*args)`` is equivalent to but faster     |      than ``a[args] = item``.  The item should be a scalar value and `args`     |      must select a single item in the array `a`.     |           |      Parameters     |      ----------     |      \*args : Arguments     |          If one argument: a scalar, only used in case `a` is of size 1.     |          If two arguments: the last argument is the value to be set     |          and must be a scalar, the first argument specifies a single array     |          element location. It is either an int or a tuple.     |           |      Notes     |      -----     |      Compared to indexing syntax, `itemset` provides some speed increase     |      for placing a scalar into a particular location in an `ndarray`,     |      if you must do this.  However, generally this is discouraged:     |      among other problems, it complicates the appearance of the code.     |      Also, when using `itemset` (and `item`) inside a loop, be sure     |      to assign the methods to a local variable to avoid the attribute     |      look-up at each loop iteration.     |           |      Examples     |      --------     |      >>> x = np.random.randint(9, size=(3, 3))     |      >>> x     |      array([[3, 1, 7],     |             [2, 8, 3],     |             [8, 5, 3]])     |      >>> x.itemset(4, 0)     |      >>> x.itemset((2, 2), 9)     |      >>> x     |      array([[3, 1, 7],     |             [2, 0, 3],     |             [8, 5, 9]])     |       |  max(...)     |      a.max(axis=None, out=None)     |           |      Return the maximum along a given axis.     |           |      Refer to `numpy.amax` for full documentation.     |           |      See Also     |      --------     |      numpy.amax : equivalent function     |       |  mean(...)     |      a.mean(axis=None, dtype=None, out=None, keepdims=False)     |           |      Returns the average of the array elements along given axis.     |           |      Refer to `numpy.mean` for full documentation.     |           |      See Also     |      --------     |      numpy.mean : equivalent function     |       |  min(...)     |      a.min(axis=None, out=None, keepdims=False)     |           |      Return the minimum along a given axis.     |           |      Refer to `numpy.amin` for full documentation.     |           |      See Also     |      --------     |      numpy.amin : equivalent function     |       |  newbyteorder(...)     |      arr.newbyteorder(new_order='S')     |           |      Return the array with the same data viewed with a different byte order.     |           |      Equivalent to::     |           |          arr.view(arr.dtype.newbytorder(new_order))     |           |      Changes are also made in all fields and sub-arrays of the array data     |      type.     |           |           |           |      Parameters     |      ----------     |      new_order : string, optional     |          Byte order to force; a value from the byte order specifications     |          below. `new_order` codes can be any of:     |           |          * 'S' - swap dtype from current to opposite endian     |          * {'<', 'L'} - little endian     |          * {'>', 'B'} - big endian     |          * {'=', 'N'} - native order     |          * {'|', 'I'} - ignore (no change to byte order)     |           |          The default value ('S') results in swapping the current     |          byte order. The code does a case-insensitive check on the first     |          letter of `new_order` for the alternatives above.  For example,     |          any of 'B' or 'b' or 'biggish' are valid to specify big-endian.     |           |           |      Returns     |      -------     |      new_arr : array     |          New array object with the dtype reflecting given change to the     |          byte order.     |       |  nonzero(...)     |      a.nonzero()     |           |      Return the indices of the elements that are non-zero.     |           |      Refer to `numpy.nonzero` for full documentation.     |           |      See Also     |      --------     |      numpy.nonzero : equivalent function     |       |  partition(...)     |      a.partition(kth, axis=-1, kind='introselect', order=None)     |           |      Rearranges the elements in the array in such a way that value of the     |      element in kth position is in the position it would be in a sorted array.     |      All elements smaller than the kth element are moved before this element and     |      all equal or greater are moved behind it. The ordering of the elements in     |      the two partitions is undefined.     |           |      .. versionadded:: 1.8.0     |           |      Parameters     |      ----------     |      kth : int or sequence of ints     |          Element index to partition by. The kth element value will be in its     |          final sorted position and all smaller elements will be moved before it     |          and all equal or greater elements behind it.     |          The order all elements in the partitions is undefined.     |          If provided with a sequence of kth it will partition all elements     |          indexed by kth of them into their sorted position at once.     |      axis : int, optional     |          Axis along which to sort. Default is -1, which means sort along the     |          last axis.     |      kind : {'introselect'}, optional     |          Selection algorithm. Default is 'introselect'.     |      order : str or list of str, optional     |          When `a` is an array with fields defined, this argument specifies     |          which fields to compare first, second, etc.  A single field can     |          be specified as a string, and not all fields need be specified,     |          but unspecified fields will still be used, in the order in which     |          they come up in the dtype, to break ties.     |           |      See Also     |      --------     |      numpy.partition : Return a parititioned copy of an array.     |      argpartition : Indirect partition.     |      sort : Full sort.     |           |      Notes     |      -----     |      See ``np.partition`` for notes on the different algorithms.     |           |      Examples     |      --------     |      >>> a = np.array([3, 4, 2, 1])     |      >>> a.partition(a, 3)     |      >>> a     |      array([2, 1, 3, 4])     |           |      >>> a.partition((1, 3))     |      array([1, 2, 3, 4])     |       |  prod(...)     |      a.prod(axis=None, dtype=None, out=None, keepdims=False)     |           |      Return the product of the array elements over the given axis     |           |      Refer to `numpy.prod` for full documentation.     |           |      See Also     |      --------     |      numpy.prod : equivalent function     |       |  ptp(...)     |      a.ptp(axis=None, out=None)     |           |      Peak to peak (maximum - minimum) value along a given axis.     |           |      Refer to `numpy.ptp` for full documentation.     |           |      See Also     |      --------     |      numpy.ptp : equivalent function     |       |  put(...)     |      a.put(indices, values, mode='raise')     |           |      Set ``a.flat[n] = values[n]`` for all `n` in indices.     |           |      Refer to `numpy.put` for full documentation.     |           |      See Also     |      --------     |      numpy.put : equivalent function     |       |  ravel(...)     |      a.ravel([order])     |           |      Return a flattened array.     |           |      Refer to `numpy.ravel` for full documentation.     |           |      See Also     |      --------     |      numpy.ravel : equivalent function     |           |      ndarray.flat : a flat iterator on the array.     |       |  repeat(...)     |      a.repeat(repeats, axis=None)     |           |      Repeat elements of an array.     |           |      Refer to `numpy.repeat` for full documentation.     |           |      See Also     |      --------     |      numpy.repeat : equivalent function     |       |  reshape(...)     |      a.reshape(shape, order='C')     |           |      Returns an array containing the same data with a new shape.     |           |      Refer to `numpy.reshape` for full documentation.     |           |      See Also     |      --------     |      numpy.reshape : equivalent function     |       |  resize(...)     |      a.resize(new_shape, refcheck=True)     |           |      Change shape and size of array in-place.     |           |      Parameters     |      ----------     |      new_shape : tuple of ints, or `n` ints     |          Shape of resized array.     |      refcheck : bool, optional     |          If False, reference count will not be checked. Default is True.     |           |      Returns     |      -------     |      None     |           |      Raises     |      ------     |      ValueError     |          If `a` does not own its own data or references or views to it exist,     |          and the data memory must be changed.     |           |      SystemError     |          If the `order` keyword argument is specified. This behaviour is a     |          bug in NumPy.     |           |      See Also     |      --------     |      resize : Return a new array with the specified shape.     |           |      Notes     |      -----     |      This reallocates space for the data area if necessary.     |           |      Only contiguous arrays (data elements consecutive in memory) can be     |      resized.     |           |      The purpose of the reference count check is to make sure you     |      do not use this array as a buffer for another Python object and then     |      reallocate the memory. However, reference counts can increase in     |      other ways so if you are sure that you have not shared the memory     |      for this array with another Python object, then you may safely set     |      `refcheck` to False.     |           |      Examples     |      --------     |      Shrinking an array: array is flattened (in the order that the data are     |      stored in memory), resized, and reshaped:     |           |      >>> a = np.array([[0, 1], [2, 3]], order='C')     |      >>> a.resize((2, 1))     |      >>> a     |      array([[0],     |             [1]])     |           |      >>> a = np.array([[0, 1], [2, 3]], order='F')     |      >>> a.resize((2, 1))     |      >>> a     |      array([[0],     |             [2]])     |           |      Enlarging an array: as above, but missing entries are filled with zeros:     |           |      >>> b = np.array([[0, 1], [2, 3]])     |      >>> b.resize(2, 3) # new_shape parameter doesn't have to be a tuple     |      >>> b     |      array([[0, 1, 2],     |             [3, 0, 0]])     |           |      Referencing an array prevents resizing...     |           |      >>> c = a     |      >>> a.resize((1, 1))     |      Traceback (most recent call last):     |      ...     |      ValueError: cannot resize an array that has been referenced ...     |           |      Unless `refcheck` is False:     |           |      >>> a.resize((1, 1), refcheck=False)     |      >>> a     |      array([[0]])     |      >>> c     |      array([[0]])     |       |  round(...)     |      a.round(decimals=0, out=None)     |           |      Return `a` with each element rounded to the given number of decimals.     |           |      Refer to `numpy.around` for full documentation.     |           |      See Also     |      --------     |      numpy.around : equivalent function     |       |  searchsorted(...)     |      a.searchsorted(v, side='left', sorter=None)     |           |      Find indices where elements of v should be inserted in a to maintain order.     |           |      For full documentation, see `numpy.searchsorted`     |           |      See Also     |      --------     |      numpy.searchsorted : equivalent function     |       |  setfield(...)     |      a.setfield(val, dtype, offset=0)     |           |      Put a value into a specified place in a field defined by a data-type.     |           |      Place `val` into `a`'s field defined by `dtype` and beginning `offset`     |      bytes into the field.     |           |      Parameters     |      ----------     |      val : object     |          Value to be placed in field.     |      dtype : dtype object     |          Data-type of the field in which to place `val`.     |      offset : int, optional     |          The number of bytes into the field at which to place `val`.     |           |      Returns     |      -------     |      None     |           |      See Also     |      --------     |      getfield     |           |      Examples     |      --------     |      >>> x = np.eye(3)     |      >>> x.getfield(np.float64)     |      array([[ 1.,  0.,  0.],     |             [ 0.,  1.,  0.],     |             [ 0.,  0.,  1.]])     |      >>> x.setfield(3, np.int32)     |      >>> x.getfield(np.int32)     |      array([[3, 3, 3],     |             [3, 3, 3],     |             [3, 3, 3]])     |      >>> x     |      array([[  1.00000000e+000,   1.48219694e-323,   1.48219694e-323],     |             [  1.48219694e-323,   1.00000000e+000,   1.48219694e-323],     |             [  1.48219694e-323,   1.48219694e-323,   1.00000000e+000]])     |      >>> x.setfield(np.eye(3), np.int32)     |      >>> x     |      array([[ 1.,  0.,  0.],     |             [ 0.,  1.,  0.],     |             [ 0.,  0.,  1.]])     |       |  setflags(...)     |      a.setflags(write=None, align=None, uic=None)     |           |      Set array flags WRITEABLE, ALIGNED, and UPDATEIFCOPY, respectively.     |           |      These Boolean-valued flags affect how numpy interprets the memory     |      area used by `a` (see Notes below). The ALIGNED flag can only     |      be set to True if the data is actually aligned according to the type.     |      The UPDATEIFCOPY flag can never be set to True. The flag WRITEABLE     |      can only be set to True if the array owns its own memory, or the     |      ultimate owner of the memory exposes a writeable buffer interface,     |      or is a string. (The exception for string is made so that unpickling     |      can be done without copying memory.)     |           |      Parameters     |      ----------     |      write : bool, optional     |          Describes whether or not `a` can be written to.     |      align : bool, optional     |          Describes whether or not `a` is aligned properly for its type.     |      uic : bool, optional     |          Describes whether or not `a` is a copy of another "base" array.     |           |      Notes     |      -----     |      Array flags provide information about how the memory area used     |      for the array is to be interpreted. There are 6 Boolean flags     |      in use, only three of which can be changed by the user:     |      UPDATEIFCOPY, WRITEABLE, and ALIGNED.     |           |      WRITEABLE (W) the data area can be written to;     |           |      ALIGNED (A) the data and strides are aligned appropriately for the hardware     |      (as determined by the compiler);     |           |      UPDATEIFCOPY (U) this array is a copy of some other array (referenced     |      by .base). When this array is deallocated, the base array will be     |      updated with the contents of this array.     |           |      All flags can be accessed using their first (upper case) letter as well     |      as the full name.     |           |      Examples     |      --------     |      >>> y     |      array([[3, 1, 7],     |             [2, 0, 0],     |             [8, 5, 9]])     |      >>> y.flags     |        C_CONTIGUOUS : True     |        F_CONTIGUOUS : False     |        OWNDATA : True     |        WRITEABLE : True     |        ALIGNED : True     |        UPDATEIFCOPY : False     |      >>> y.setflags(write=0, align=0)     |      >>> y.flags     |        C_CONTIGUOUS : True     |        F_CONTIGUOUS : False     |        OWNDATA : True     |        WRITEABLE : False     |        ALIGNED : False     |        UPDATEIFCOPY : False     |      >>> y.setflags(uic=1)     |      Traceback (most recent call last):     |        File "<stdin>", line 1, in <module>     |      ValueError: cannot set UPDATEIFCOPY flag to True     |       |  sort(...)     |      a.sort(axis=-1, kind='quicksort', order=None)     |           |      Sort an array, in-place.     |           |      Parameters     |      ----------     |      axis : int, optional     |          Axis along which to sort. Default is -1, which means sort along the     |          last axis.     |      kind : {'quicksort', 'mergesort', 'heapsort'}, optional     |          Sorting algorithm. Default is 'quicksort'.     |      order : str or list of str, optional     |          When `a` is an array with fields defined, this argument specifies     |          which fields to compare first, second, etc.  A single field can     |          be specified as a string, and not all fields need be specified,     |          but unspecified fields will still be used, in the order in which     |          they come up in the dtype, to break ties.     |           |      See Also     |      --------     |      numpy.sort : Return a sorted copy of an array.     |      argsort : Indirect sort.     |      lexsort : Indirect stable sort on multiple keys.     |      searchsorted : Find elements in sorted array.     |      partition: Partial sort.     |           |      Notes     |      -----     |      See ``sort`` for notes on the different sorting algorithms.     |           |      Examples     |      --------     |      >>> a = np.array([[1,4], [3,1]])     |      >>> a.sort(axis=1)     |      >>> a     |      array([[1, 4],     |             [1, 3]])     |      >>> a.sort(axis=0)     |      >>> a     |      array([[1, 3],     |             [1, 4]])     |           |      Use the `order` keyword to specify a field to use when sorting a     |      structured array:     |           |      >>> a = np.array([('a', 2), ('c', 1)], dtype=[('x', 'S1'), ('y', int)])     |      >>> a.sort(order='y')     |      >>> a     |      array([('c', 1), ('a', 2)],     |            dtype=[('x', '|S1'), ('y', '<i4')])     |       |  squeeze(...)     |      a.squeeze(axis=None)     |           |      Remove single-dimensional entries from the shape of `a`.     |           |      Refer to `numpy.squeeze` for full documentation.     |           |      See Also     |      --------     |      numpy.squeeze : equivalent function     |       |  std(...)     |      a.std(axis=None, dtype=None, out=None, ddof=0, keepdims=False)     |           |      Returns the standard deviation of the array elements along given axis.     |           |      Refer to `numpy.std` for full documentation.     |           |      See Also     |      --------     |      numpy.std : equivalent function     |       |  sum(...)     |      a.sum(axis=None, dtype=None, out=None, keepdims=False)     |           |      Return the sum of the array elements over the given axis.     |           |      Refer to `numpy.sum` for full documentation.     |           |      See Also     |      --------     |      numpy.sum : equivalent function     |       |  swapaxes(...)     |      a.swapaxes(axis1, axis2)     |           |      Return a view of the array with `axis1` and `axis2` interchanged.     |           |      Refer to `numpy.swapaxes` for full documentation.     |           |      See Also     |      --------     |      numpy.swapaxes : equivalent function     |       |  take(...)     |      a.take(indices, axis=None, out=None, mode='raise')     |           |      Return an array formed from the elements of `a` at the given indices.     |           |      Refer to `numpy.take` for full documentation.     |           |      See Also     |      --------     |      numpy.take : equivalent function     |       |  tobytes(...)     |      a.tobytes(order='C')     |           |      Construct Python bytes containing the raw data bytes in the array.     |           |      Constructs Python bytes showing a copy of the raw contents of     |      data memory. The bytes object can be produced in either 'C' or 'Fortran',     |      or 'Any' order (the default is 'C'-order). 'Any' order means C-order     |      unless the F_CONTIGUOUS flag in the array is set, in which case it     |      means 'Fortran' order.     |           |      .. versionadded:: 1.9.0     |           |      Parameters     |      ----------     |      order : {'C', 'F', None}, optional     |          Order of the data for multidimensional arrays:     |          C, Fortran, or the same as for the original array.     |           |      Returns     |      -------     |      s : bytes     |          Python bytes exhibiting a copy of `a`'s raw data.     |           |      Examples     |      --------     |      >>> x = np.array([[0, 1], [2, 3]])     |      >>> x.tobytes()     |      b'\x00\x00\x00\x00\x01\x00\x00\x00\x02\x00\x00\x00\x03\x00\x00\x00'     |      >>> x.tobytes('C') == x.tobytes()     |      True     |      >>> x.tobytes('F')     |      b'\x00\x00\x00\x00\x02\x00\x00\x00\x01\x00\x00\x00\x03\x00\x00\x00'     |       |  tofile(...)     |      a.tofile(fid, sep="", format="%s")     |           |      Write array to a file as text or binary (default).     |           |      Data is always written in 'C' order, independent of the order of `a`.     |      The data produced by this method can be recovered using the function     |      fromfile().     |           |      Parameters     |      ----------     |      fid : file or str     |          An open file object, or a string containing a filename.     |      sep : str     |          Separator between array items for text output.     |          If "" (empty), a binary file is written, equivalent to     |          ``file.write(a.tobytes())``.     |      format : str     |          Format string for text file output.     |          Each entry in the array is formatted to text by first converting     |          it to the closest Python type, and then using "format" % item.     |           |      Notes     |      -----     |      This is a convenience function for quick storage of array data.     |      Information on endianness and precision is lost, so this method is not a     |      good choice for files intended to archive data or transport data between     |      machines with different endianness. Some of these problems can be overcome     |      by outputting the data as text files, at the expense of speed and file     |      size.     |       |  tolist(...)     |      a.tolist()     |           |      Return the array as a (possibly nested) list.     |           |      Return a copy of the array data as a (nested) Python list.     |      Data items are converted to the nearest compatible Python type.     |           |      Parameters     |      ----------     |      none     |           |      Returns     |      -------     |      y : list     |          The possibly nested list of array elements.     |           |      Notes     |      -----     |      The array may be recreated, ``a = np.array(a.tolist())``.     |           |      Examples     |      --------     |      >>> a = np.array([1, 2])     |      >>> a.tolist()     |      [1, 2]     |      >>> a = np.array([[1, 2], [3, 4]])     |      >>> list(a)     |      [array([1, 2]), array([3, 4])]     |      >>> a.tolist()     |      [[1, 2], [3, 4]]     |       |  tostring(...)     |      a.tostring(order='C')     |           |      Construct Python bytes containing the raw data bytes in the array.     |           |      Constructs Python bytes showing a copy of the raw contents of     |      data memory. The bytes object can be produced in either 'C' or 'Fortran',     |      or 'Any' order (the default is 'C'-order). 'Any' order means C-order     |      unless the F_CONTIGUOUS flag in the array is set, in which case it     |      means 'Fortran' order.     |           |      This function is a compatibility alias for tobytes. Despite its name it returns bytes not strings.     |           |      Parameters     |      ----------     |      order : {'C', 'F', None}, optional     |          Order of the data for multidimensional arrays:     |          C, Fortran, or the same as for the original array.     |           |      Returns     |      -------     |      s : bytes     |          Python bytes exhibiting a copy of `a`'s raw data.     |           |      Examples     |      --------     |      >>> x = np.array([[0, 1], [2, 3]])     |      >>> x.tobytes()     |      b'\x00\x00\x00\x00\x01\x00\x00\x00\x02\x00\x00\x00\x03\x00\x00\x00'     |      >>> x.tobytes('C') == x.tobytes()     |      True     |      >>> x.tobytes('F')     |      b'\x00\x00\x00\x00\x02\x00\x00\x00\x01\x00\x00\x00\x03\x00\x00\x00'     |       |  trace(...)     |      a.trace(offset=0, axis1=0, axis2=1, dtype=None, out=None)     |           |      Return the sum along diagonals of the array.     |           |      Refer to `numpy.trace` for full documentation.     |           |      See Also     |      --------     |      numpy.trace : equivalent function     |       |  transpose(...)     |      a.transpose(*axes)     |           |      Returns a view of the array with axes transposed.     |           |      For a 1-D array, this has no effect. (To change between column and     |      row vectors, first cast the 1-D array into a matrix object.)     |      For a 2-D array, this is the usual matrix transpose.     |      For an n-D array, if axes are given, their order indicates how the     |      axes are permuted (see Examples). If axes are not provided and     |      ``a.shape = (i[0], i[1], ... i[n-2], i[n-1])``, then     |      ``a.transpose().shape = (i[n-1], i[n-2], ... i[1], i[0])``.     |           |      Parameters     |      ----------     |      axes : None, tuple of ints, or `n` ints     |           |       * None or no argument: reverses the order of the axes.     |           |       * tuple of ints: `i` in the `j`-th place in the tuple means `a`'s     |         `i`-th axis becomes `a.transpose()`'s `j`-th axis.     |           |       * `n` ints: same as an n-tuple of the same ints (this form is     |         intended simply as a "convenience" alternative to the tuple form)     |           |      Returns     |      -------     |      out : ndarray     |          View of `a`, with axes suitably permuted.     |           |      See Also     |      --------     |      ndarray.T : Array property returning the array transposed.     |           |      Examples     |      --------     |      >>> a = np.array([[1, 2], [3, 4]])     |      >>> a     |      array([[1, 2],     |             [3, 4]])     |      >>> a.transpose()     |      array([[1, 3],     |             [2, 4]])     |      >>> a.transpose((1, 0))     |      array([[1, 3],     |             [2, 4]])     |      >>> a.transpose(1, 0)     |      array([[1, 3],     |             [2, 4]])     |       |  var(...)     |      a.var(axis=None, dtype=None, out=None, ddof=0, keepdims=False)     |           |      Returns the variance of the array elements, along given axis.     |           |      Refer to `numpy.var` for full documentation.     |           |      See Also     |      --------     |      numpy.var : equivalent function     |       |  view(...)     |      a.view(dtype=None, type=None)     |           |      New view of array with the same data.     |           |      Parameters     |      ----------     |      dtype : data-type or ndarray sub-class, optional     |          Data-type descriptor of the returned view, e.g., float32 or int16. The     |          default, None, results in the view having the same data-type as `a`.     |          This argument can also be specified as an ndarray sub-class, which     |          then specifies the type of the returned object (this is equivalent to     |          setting the ``type`` parameter).     |      type : Python type, optional     |          Type of the returned view, e.g., ndarray or matrix.  Again, the     |          default None results in type preservation.     |           |      Notes     |      -----     |      ``a.view()`` is used two different ways:     |           |      ``a.view(some_dtype)`` or ``a.view(dtype=some_dtype)`` constructs a view     |      of the array's memory with a different data-type.  This can cause a     |      reinterpretation of the bytes of memory.     |           |      ``a.view(ndarray_subclass)`` or ``a.view(type=ndarray_subclass)`` just     |      returns an instance of `ndarray_subclass` that looks at the same array     |      (same shape, dtype, etc.)  This does not cause a reinterpretation of the     |      memory.     |           |      For ``a.view(some_dtype)``, if ``some_dtype`` has a different number of     |      bytes per entry than the previous dtype (for example, converting a     |      regular array to a structured array), then the behavior of the view     |      cannot be predicted just from the superficial appearance of ``a`` (shown     |      by ``print(a)``). It also depends on exactly how ``a`` is stored in     |      memory. Therefore if ``a`` is C-ordered versus fortran-ordered, versus     |      defined as a slice or transpose, etc., the view may give different     |      results.     |           |           |      Examples     |      --------     |      >>> x = np.array([(1, 2)], dtype=[('a', np.int8), ('b', np.int8)])     |           |      Viewing array data using a different type and dtype:     |           |      >>> y = x.view(dtype=np.int16, type=np.matrix)     |      >>> y     |      matrix([[513]], dtype=int16)     |      >>> print type(y)     |      <class 'numpy.matrixlib.defmatrix.matrix'>     |           |      Creating a view on a structured array so it can be used in calculations     |           |      >>> x = np.array([(1, 2),(3,4)], dtype=[('a', np.int8), ('b', np.int8)])     |      >>> xv = x.view(dtype=np.int8).reshape(-1,2)     |      >>> xv     |      array([[1, 2],     |             [3, 4]], dtype=int8)     |      >>> xv.mean(0)     |      array([ 2.,  3.])     |           |      Making changes to the view changes the underlying array     |           |      >>> xv[0,1] = 20     |      >>> print x     |      [(1, 20) (3, 4)]     |           |      Using a view to convert an array to a recarray:     |           |      >>> z = x.view(np.recarray)     |      >>> z.a     |      array([1], dtype=int8)     |           |      Views share data:     |           |      >>> x[0] = (9, 10)     |      >>> z[0]     |      (9, 10)     |           |      Views that change the dtype size (bytes per entry) should normally be     |      avoided on arrays defined by slices, transposes, fortran-ordering, etc.:     |           |      >>> x = np.array([[1,2,3],[4,5,6]], dtype=np.int16)     |      >>> y = x[:, 0:2]     |      >>> y     |      array([[1, 2],     |             [4, 5]], dtype=int16)     |      >>> y.view(dtype=[('width', np.int16), ('length', np.int16)])     |      Traceback (most recent call last):     |        File "<stdin>", line 1, in <module>     |      ValueError: new type not compatible with array.     |      >>> z = y.copy()     |      >>> z.view(dtype=[('width', np.int16), ('length', np.int16)])     |      array([[(1, 2)],     |             [(4, 5)]], dtype=[('width', '<i2'), ('length', '<i2')])     |       |  ----------------------------------------------------------------------     |  Data descriptors inherited from numpy.ndarray:     |       |  T     |      Same as self.transpose(), except that self is returned if     |      self.ndim < 2.     |           |      Examples     |      --------     |      >>> x = np.array([[1.,2.],[3.,4.]])     |      >>> x     |      array([[ 1.,  2.],     |             [ 3.,  4.]])     |      >>> x.T     |      array([[ 1.,  3.],     |             [ 2.,  4.]])     |      >>> x = np.array([1.,2.,3.,4.])     |      >>> x     |      array([ 1.,  2.,  3.,  4.])     |      >>> x.T     |      array([ 1.,  2.,  3.,  4.])     |       |  __array_interface__     |      Array protocol: Python side.     |       |  __array_struct__     |      Array protocol: C-struct side.     |       |  base     |      Base object if memory is from some other object.     |           |      Examples     |      --------     |      The base of an array that owns its memory is None:     |           |      >>> x = np.array([1,2,3,4])     |      >>> x.base is None     |      True     |           |      Slicing creates a view, whose memory is shared with x:     |           |      >>> y = x[2:]     |      >>> y.base is x     |      True     |       |  ctypes     |      An object to simplify the interaction of the array with the ctypes     |      module.     |           |      This attribute creates an object that makes it easier to use arrays     |      when calling shared libraries with the ctypes module. The returned     |      object has, among others, data, shape, and strides attributes (see     |      Notes below) which themselves return ctypes objects that can be used     |      as arguments to a shared library.     |           |      Parameters     |      ----------     |      None     |           |      Returns     |      -------     |      c : Python object     |          Possessing attributes data, shape, strides, etc.     |           |      See Also     |      --------     |      numpy.ctypeslib     |           |      Notes     |      -----     |      Below are the public attributes of this object which were documented     |      in "Guide to NumPy" (we have omitted undocumented public attributes,     |      as well as documented private attributes):     |           |      * data: A pointer to the memory area of the array as a Python integer.     |        This memory area may contain data that is not aligned, or not in correct     |        byte-order. The memory area may not even be writeable. The array     |        flags and data-type of this array should be respected when passing this     |        attribute to arbitrary C-code to avoid trouble that can include Python     |        crashing. User Beware! The value of this attribute is exactly the same     |        as self._array_interface_['data'][0].     |           |      * shape (c_intp*self.ndim): A ctypes array of length self.ndim where     |        the basetype is the C-integer corresponding to dtype('p') on this     |        platform. This base-type could be c_int, c_long, or c_longlong     |        depending on the platform. The c_intp type is defined accordingly in     |        numpy.ctypeslib. The ctypes array contains the shape of the underlying     |        array.     |           |      * strides (c_intp*self.ndim): A ctypes array of length self.ndim where     |        the basetype is the same as for the shape attribute. This ctypes array     |        contains the strides information from the underlying array. This strides     |        information is important for showing how many bytes must be jumped to     |        get to the next element in the array.     |           |      * data_as(obj): Return the data pointer cast to a particular c-types object.     |        For example, calling self._as_parameter_ is equivalent to     |        self.data_as(ctypes.c_void_p). Perhaps you want to use the data as a     |        pointer to a ctypes array of floating-point data:     |        self.data_as(ctypes.POINTER(ctypes.c_double)).     |           |      * shape_as(obj): Return the shape tuple as an array of some other c-types     |        type. For example: self.shape_as(ctypes.c_short).     |           |      * strides_as(obj): Return the strides tuple as an array of some other     |        c-types type. For example: self.strides_as(ctypes.c_longlong).     |           |      Be careful using the ctypes attribute - especially on temporary     |      arrays or arrays constructed on the fly. For example, calling     |      ``(a+b).ctypes.data_as(ctypes.c_void_p)`` returns a pointer to memory     |      that is invalid because the array created as (a+b) is deallocated     |      before the next Python statement. You can avoid this problem using     |      either ``c=a+b`` or ``ct=(a+b).ctypes``. In the latter case, ct will     |      hold a reference to the array until ct is deleted or re-assigned.     |           |      If the ctypes module is not available, then the ctypes attribute     |      of array objects still returns something useful, but ctypes objects     |      are not returned and errors may be raised instead. In particular,     |      the object will still have the as parameter attribute which will     |      return an integer equal to the data attribute.     |           |      Examples     |      --------     |      >>> import ctypes     |      >>> x     |      array([[0, 1],     |             [2, 3]])     |      >>> x.ctypes.data     |      30439712     |      >>> x.ctypes.data_as(ctypes.POINTER(ctypes.c_long))     |      <ctypes.LP_c_long object at 0x01F01300>     |      >>> x.ctypes.data_as(ctypes.POINTER(ctypes.c_long)).contents     |      c_long(0)     |      >>> x.ctypes.data_as(ctypes.POINTER(ctypes.c_longlong)).contents     |      c_longlong(4294967296L)     |      >>> x.ctypes.shape     |      <numpy.core._internal.c_long_Array_2 object at 0x01FFD580>     |      >>> x.ctypes.shape_as(ctypes.c_long)     |      <numpy.core._internal.c_long_Array_2 object at 0x01FCE620>     |      >>> x.ctypes.strides     |      <numpy.core._internal.c_long_Array_2 object at 0x01FCE620>     |      >>> x.ctypes.strides_as(ctypes.c_longlong)     |      <numpy.core._internal.c_longlong_Array_2 object at 0x01F01300>     |       |  data     |      Python buffer object pointing to the start of the array's data.     |       |  dtype     |      Data-type of the array's elements.     |           |      Parameters     |      ----------     |      None     |           |      Returns     |      -------     |      d : numpy dtype object     |           |      See Also     |      --------     |      numpy.dtype     |           |      Examples     |      --------     |      >>> x     |      array([[0, 1],     |             [2, 3]])     |      >>> x.dtype     |      dtype('int32')     |      >>> type(x.dtype)     |      <type 'numpy.dtype'>     |       |  flags     |      Information about the memory layout of the array.     |           |      Attributes     |      ----------     |      C_CONTIGUOUS (C)     |          The data is in a single, C-style contiguous segment.     |      F_CONTIGUOUS (F)     |          The data is in a single, Fortran-style contiguous segment.     |      OWNDATA (O)     |          The array owns the memory it uses or borrows it from another object.     |      WRITEABLE (W)     |          The data area can be written to.  Setting this to False locks     |          the data, making it read-only.  A view (slice, etc.) inherits WRITEABLE     |          from its base array at creation time, but a view of a writeable     |          array may be subsequently locked while the base array remains writeable.     |          (The opposite is not true, in that a view of a locked array may not     |          be made writeable.  However, currently, locking a base object does not     |          lock any views that already reference it, so under that circumstance it     |          is possible to alter the contents of a locked array via a previously     |          created writeable view onto it.)  Attempting to change a non-writeable     |          array raises a RuntimeError exception.     |      ALIGNED (A)     |          The data and all elements are aligned appropriately for the hardware.     |      UPDATEIFCOPY (U)     |          This array is a copy of some other array. When this array is     |          deallocated, the base array will be updated with the contents of     |          this array.     |      FNC     |          F_CONTIGUOUS and not C_CONTIGUOUS.     |      FORC     |          F_CONTIGUOUS or C_CONTIGUOUS (one-segment test).     |      BEHAVED (B)     |          ALIGNED and WRITEABLE.     |      CARRAY (CA)     |          BEHAVED and C_CONTIGUOUS.     |      FARRAY (FA)     |          BEHAVED and F_CONTIGUOUS and not C_CONTIGUOUS.     |           |      Notes     |      -----     |      The `flags` object can be accessed dictionary-like (as in ``a.flags['WRITEABLE']``),     |      or by using lowercased attribute names (as in ``a.flags.writeable``). Short flag     |      names are only supported in dictionary access.     |           |      Only the UPDATEIFCOPY, WRITEABLE, and ALIGNED flags can be changed by     |      the user, via direct assignment to the attribute or dictionary entry,     |      or by calling `ndarray.setflags`.     |           |      The array flags cannot be set arbitrarily:     |           |      - UPDATEIFCOPY can only be set ``False``.     |      - ALIGNED can only be set ``True`` if the data is truly aligned.     |      - WRITEABLE can only be set ``True`` if the array owns its own memory     |        or the ultimate owner of the memory exposes a writeable buffer     |        interface or is a string.     |           |      Arrays can be both C-style and Fortran-style contiguous simultaneously.     |      This is clear for 1-dimensional arrays, but can also be true for higher     |      dimensional arrays.     |           |      Even for contiguous arrays a stride for a given dimension     |      ``arr.strides[dim]`` may be *arbitrary* if ``arr.shape[dim] == 1``     |      or the array has no elements.     |      It does *not* generally hold that ``self.strides[-1] == self.itemsize``     |      for C-style contiguous arrays or ``self.strides[0] == self.itemsize`` for     |      Fortran-style contiguous arrays is true.     |       |  flat     |      A 1-D iterator over the array.     |           |      This is a `numpy.flatiter` instance, which acts similarly to, but is not     |      a subclass of, Python's built-in iterator object.     |           |      See Also     |      --------     |      flatten : Return a copy of the array collapsed into one dimension.     |           |      flatiter     |           |      Examples     |      --------     |      >>> x = np.arange(1, 7).reshape(2, 3)     |      >>> x     |      array([[1, 2, 3],     |             [4, 5, 6]])     |      >>> x.flat[3]     |      4     |      >>> x.T     |      array([[1, 4],     |             [2, 5],     |             [3, 6]])     |      >>> x.T.flat[3]     |      5     |      >>> type(x.flat)     |      <type 'numpy.flatiter'>     |           |      An assignment example:     |           |      >>> x.flat = 3; x     |      array([[3, 3, 3],     |             [3, 3, 3]])     |      >>> x.flat[[1,4]] = 1; x     |      array([[3, 1, 3],     |             [3, 1, 3]])     |       |  imag     |      The imaginary part of the array.     |           |      Examples     |      --------     |      >>> x = np.sqrt([1+0j, 0+1j])     |      >>> x.imag     |      array([ 0.        ,  0.70710678])     |      >>> x.imag.dtype     |      dtype('float64')     |       |  itemsize     |      Length of one array element in bytes.     |           |      Examples     |      --------     |      >>> x = np.array([1,2,3], dtype=np.float64)     |      >>> x.itemsize     |      8     |      >>> x = np.array([1,2,3], dtype=np.complex128)     |      >>> x.itemsize     |      16     |       |  nbytes     |      Total bytes consumed by the elements of the array.     |           |      Notes     |      -----     |      Does not include memory consumed by non-element attributes of the     |      array object.     |           |      Examples     |      --------     |      >>> x = np.zeros((3,5,2), dtype=np.complex128)     |      >>> x.nbytes     |      480     |      >>> np.prod(x.shape) * x.itemsize     |      480     |       |  ndim     |      Number of array dimensions.     |           |      Examples     |      --------     |      >>> x = np.array([1, 2, 3])     |      >>> x.ndim     |      1     |      >>> y = np.zeros((2, 3, 4))     |      >>> y.ndim     |      3     |       |  real     |      The real part of the array.     |           |      Examples     |      --------     |      >>> x = np.sqrt([1+0j, 0+1j])     |      >>> x.real     |      array([ 1.        ,  0.70710678])     |      >>> x.real.dtype     |      dtype('float64')     |           |      See Also     |      --------     |      numpy.real : equivalent function     |       |  shape     |      Tuple of array dimensions.     |           |      Notes     |      -----     |      May be used to "reshape" the array, as long as this would not     |      require a change in the total number of elements     |           |      Examples     |      --------     |      >>> x = np.array([1, 2, 3, 4])     |      >>> x.shape     |      (4,)     |      >>> y = np.zeros((2, 3, 4))     |      >>> y.shape     |      (2, 3, 4)     |      >>> y.shape = (3, 8)     |      >>> y     |      array([[ 0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.],     |             [ 0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.],     |             [ 0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.]])     |      >>> y.shape = (3, 6)     |      Traceback (most recent call last):     |        File "<stdin>", line 1, in <module>     |      ValueError: total size of new array must be unchanged     |       |  size     |      Number of elements in the array.     |           |      Equivalent to ``np.prod(a.shape)``, i.e., the product of the array's     |      dimensions.     |           |      Examples     |      --------     |      >>> x = np.zeros((3, 5, 2), dtype=np.complex128)     |      >>> x.size     |      30     |      >>> np.prod(x.shape)     |      30     |       |  strides     |      Tuple of bytes to step in each dimension when traversing an array.     |           |      The byte offset of element ``(i[0], i[1], ..., i[n])`` in an array `a`     |      is::     |           |          offset = sum(np.array(i) * a.strides)     |           |      A more detailed explanation of strides can be found in the     |      "ndarray.rst" file in the NumPy reference guide.     |           |      Notes     |      -----     |      Imagine an array of 32-bit integers (each 4 bytes)::     |           |        x = np.array([[0, 1, 2, 3, 4],     |                      [5, 6, 7, 8, 9]], dtype=np.int32)     |           |      This array is stored in memory as 40 bytes, one after the other     |      (known as a contiguous block of memory).  The strides of an array tell     |      us how many bytes we have to skip in memory to move to the next position     |      along a certain axis.  For example, we have to skip 4 bytes (1 value) to     |      move to the next column, but 20 bytes (5 values) to get to the same     |      position in the next row.  As such, the strides for the array `x` will be     |      ``(20, 4)``.     |           |      See Also     |      --------     |      numpy.lib.stride_tricks.as_strided     |           |      Examples     |      --------     |      >>> y = np.reshape(np.arange(2*3*4), (2,3,4))     |      >>> y     |      array([[[ 0,  1,  2,  3],     |              [ 4,  5,  6,  7],     |              [ 8,  9, 10, 11]],     |             [[12, 13, 14, 15],     |              [16, 17, 18, 19],     |              [20, 21, 22, 23]]])     |      >>> y.strides     |      (48, 16, 4)     |      >>> y[1,1,1]     |      17     |      >>> offset=sum(y.strides * np.array((1,1,1)))     |      >>> offset/y.itemsize     |      17     |           |      >>> x = np.reshape(np.arange(5*6*7*8), (5,6,7,8)).transpose(2,3,1,0)     |      >>> x.strides     |      (32, 4, 224, 1344)     |      >>> i = np.array([3,5,2,2])     |      >>> offset = sum(i * x.strides)     |      >>> x[3,5,2,2]     |      813     |      >>> offset / x.itemsize     |      813     |       |  ----------------------------------------------------------------------     |  Data and other attributes inherited from numpy.ndarray:     |       |  __hash__ = None        class ndarray(__builtin__.object)     |  ndarray(shape, dtype=float, buffer=None, offset=0,     |          strides=None, order=None)     |       |  An array object represents a multidimensional, homogeneous array     |  of fixed-size items.  An associated data-type object describes the     |  format of each element in the array (its byte-order, how many bytes it     |  occupies in memory, whether it is an integer, a floating point number,     |  or something else, etc.)     |       |  Arrays should be constructed using `array`, `zeros` or `empty` (refer     |  to the See Also section below).  The parameters given here refer to     |  a low-level method (`ndarray(...)`) for instantiating an array.     |       |  For more information, refer to the `numpy` module and examine the     |  the methods and attributes of an array.     |       |  Parameters     |  ----------     |  (for the __new__ method; see Notes below)     |       |  shape : tuple of ints     |      Shape of created array.     |  dtype : data-type, optional     |      Any object that can be interpreted as a numpy data type.     |  buffer : object exposing buffer interface, optional     |      Used to fill the array with data.     |  offset : int, optional     |      Offset of array data in buffer.     |  strides : tuple of ints, optional     |      Strides of data in memory.     |  order : {'C', 'F'}, optional     |      Row-major (C-style) or column-major (Fortran-style) order.     |       |  Attributes     |  ----------     |  T : ndarray     |      Transpose of the array.     |  data : buffer     |      The array's elements, in memory.     |  dtype : dtype object     |      Describes the format of the elements in the array.     |  flags : dict     |      Dictionary containing information related to memory use, e.g.,     |      'C_CONTIGUOUS', 'OWNDATA', 'WRITEABLE', etc.     |  flat : numpy.flatiter object     |      Flattened version of the array as an iterator.  The iterator     |      allows assignments, e.g., ``x.flat = 3`` (See `ndarray.flat` for     |      assignment examples; TODO).     |  imag : ndarray     |      Imaginary part of the array.     |  real : ndarray     |      Real part of the array.     |  size : int     |      Number of elements in the array.     |  itemsize : int     |      The memory use of each array element in bytes.     |  nbytes : int     |      The total number of bytes required to store the array data,     |      i.e., ``itemsize * size``.     |  ndim : int     |      The array's number of dimensions.     |  shape : tuple of ints     |      Shape of the array.     |  strides : tuple of ints     |      The step-size required to move from one element to the next in     |      memory. For example, a contiguous ``(3, 4)`` array of type     |      ``int16`` in C-order has strides ``(8, 2)``.  This implies that     |      to move from element to element in memory requires jumps of 2 bytes.     |      To move from row-to-row, one needs to jump 8 bytes at a time     |      (``2 * 4``).     |  ctypes : ctypes object     |      Class containing properties of the array needed for interaction     |      with ctypes.     |  base : ndarray     |      If the array is a view into another array, that array is its `base`     |      (unless that array is also a view).  The `base` array is where the     |      array data is actually stored.     |       |  See Also     |  --------     |  array : Construct an array.     |  zeros : Create an array, each element of which is zero.     |  empty : Create an array, but leave its allocated memory unchanged (i.e.,     |          it contains "garbage").     |  dtype : Create a data-type.     |       |  Notes     |  -----     |  There are two modes of creating an array using ``__new__``:     |       |  1. If `buffer` is None, then only `shape`, `dtype`, and `order`     |     are used.     |  2. If `buffer` is an object exposing the buffer interface, then     |     all keywords are interpreted.     |       |  No ``__init__`` method is needed because the array is fully initialized     |  after the ``__new__`` method.     |       |  Examples     |  --------     |  These examples illustrate the low-level `ndarray` constructor.  Refer     |  to the `See Also` section above for easier ways of constructing an     |  ndarray.     |       |  First mode, `buffer` is None:     |       |  >>> np.ndarray(shape=(2,2), dtype=float, order='F')     |  array([[ -1.13698227e+002,   4.25087011e-303],     |         [  2.88528414e-306,   3.27025015e-309]])         #random     |       |  Second mode:     |       |  >>> np.ndarray((2,), buffer=np.array([1,2,3]),     |  ...            offset=np.int_().itemsize,     |  ...            dtype=int) # offset = 1*itemsize, i.e. skip first element     |  array([2, 3])     |       |  Methods defined here:     |       |  __abs__(...)     |      x.__abs__() <==> abs(x)     |       |  __add__(...)     |      x.__add__(y) <==> x+y     |       |  __and__(...)     |      x.__and__(y) <==> x&y     |       |  __array__(...)     |      a.__array__(|dtype) -> reference if type unchanged, copy otherwise.     |           |      Returns either a new reference to self if dtype is not given or a new array     |      of provided data type if dtype is different from the current dtype of the     |      array.     |       |  __array_prepare__(...)     |      a.__array_prepare__(obj) -> Object of same type as ndarray object obj.     |       |  __array_wrap__(...)     |      a.__array_wrap__(obj) -> Object of same type as ndarray object a.     |       |  __contains__(...)     |      x.__contains__(y) <==> y in x     |       |  __copy__(...)     |      a.__copy__([order])     |           |      Return a copy of the array.     |           |      Parameters     |      ----------     |      order : {'C', 'F', 'A'}, optional     |          If order is 'C' (False) then the result is contiguous (default).     |          If order is 'Fortran' (True) then the result has fortran order.     |          If order is 'Any' (None) then the result has fortran order     |          only if the array already is in fortran order.     |       |  __deepcopy__(...)     |      a.__deepcopy__() -> Deep copy of array.     |           |      Used if copy.deepcopy is called on an array.     |       |  __delitem__(...)     |      x.__delitem__(y) <==> del x[y]     |       |  __delslice__(...)     |      x.__delslice__(i, j) <==> del x[i:j]     |           |      Use of negative indices is not supported.     |       |  __div__(...)     |      x.__div__(y) <==> x/y     |       |  __divmod__(...)     |      x.__divmod__(y) <==> divmod(x, y)     |       |  __eq__(...)     |      x.__eq__(y) <==> x==y     |       |  __float__(...)     |      x.__float__() <==> float(x)     |       |  __floordiv__(...)     |      x.__floordiv__(y) <==> x//y     |       |  __ge__(...)     |      x.__ge__(y) <==> x>=y     |       |  __getitem__(...)     |      x.__getitem__(y) <==> x[y]     |       |  __getslice__(...)     |      x.__getslice__(i, j) <==> x[i:j]     |           |      Use of negative indices is not supported.     |       |  __gt__(...)     |      x.__gt__(y) <==> x>y     |       |  __hex__(...)     |      x.__hex__() <==> hex(x)     |       |  __iadd__(...)     |      x.__iadd__(y) <==> x+=y     |       |  __iand__(...)     |      x.__iand__(y) <==> x&=y     |       |  __idiv__(...)     |      x.__idiv__(y) <==> x/=y     |       |  __ifloordiv__(...)     |      x.__ifloordiv__(y) <==> x//=y     |       |  __ilshift__(...)     |      x.__ilshift__(y) <==> x<<=y     |       |  __imod__(...)     |      x.__imod__(y) <==> x%=y     |       |  __imul__(...)     |      x.__imul__(y) <==> x*=y     |       |  __index__(...)     |      x[y:z] <==> x[y.__index__():z.__index__()]     |       |  __int__(...)     |      x.__int__() <==> int(x)     |       |  __invert__(...)     |      x.__invert__() <==> ~x     |       |  __ior__(...)     |      x.__ior__(y) <==> x|=y     |       |  __ipow__(...)     |      x.__ipow__(y) <==> x**=y     |       |  __irshift__(...)     |      x.__irshift__(y) <==> x>>=y     |       |  __isub__(...)     |      x.__isub__(y) <==> x-=y     |       |  __iter__(...)     |      x.__iter__() <==> iter(x)     |       |  __itruediv__(...)     |      x.__itruediv__(y) <==> x/=y     |       |  __ixor__(...)     |      x.__ixor__(y) <==> x^=y     |       |  __le__(...)     |      x.__le__(y) <==> x<=y     |       |  __len__(...)     |      x.__len__() <==> len(x)     |       |  __long__(...)     |      x.__long__() <==> long(x)     |       |  __lshift__(...)     |      x.__lshift__(y) <==> x<<y     |       |  __lt__(...)     |      x.__lt__(y) <==> x<y     |       |  __mod__(...)     |      x.__mod__(y) <==> x%y     |       |  __mul__(...)     |      x.__mul__(y) <==> x*y     |       |  __ne__(...)     |      x.__ne__(y) <==> x!=y     |       |  __neg__(...)     |      x.__neg__() <==> -x     |       |  __nonzero__(...)     |      x.__nonzero__() <==> x != 0     |       |  __oct__(...)     |      x.__oct__() <==> oct(x)     |       |  __or__(...)     |      x.__or__(y) <==> x|y     |       |  __pos__(...)     |      x.__pos__() <==> +x     |       |  __pow__(...)     |      x.__pow__(y[, z]) <==> pow(x, y[, z])     |       |  __radd__(...)     |      x.__radd__(y) <==> y+x     |       |  __rand__(...)     |      x.__rand__(y) <==> y&x     |       |  __rdiv__(...)     |      x.__rdiv__(y) <==> y/x     |       |  __rdivmod__(...)     |      x.__rdivmod__(y) <==> divmod(y, x)     |       |  __reduce__(...)     |      a.__reduce__()     |           |      For pickling.     |       |  __repr__(...)     |      x.__repr__() <==> repr(x)     |       |  __rfloordiv__(...)     |      x.__rfloordiv__(y) <==> y//x     |       |  __rlshift__(...)     |      x.__rlshift__(y) <==> y<<x     |       |  __rmod__(...)     |      x.__rmod__(y) <==> y%x     |       |  __rmul__(...)     |      x.__rmul__(y) <==> y*x     |       |  __ror__(...)     |      x.__ror__(y) <==> y|x     |       |  __rpow__(...)     |      y.__rpow__(x[, z]) <==> pow(x, y[, z])     |       |  __rrshift__(...)     |      x.__rrshift__(y) <==> y>>x     |       |  __rshift__(...)     |      x.__rshift__(y) <==> x>>y     |       |  __rsub__(...)     |      x.__rsub__(y) <==> y-x     |       |  __rtruediv__(...)     |      x.__rtruediv__(y) <==> y/x     |       |  __rxor__(...)     |      x.__rxor__(y) <==> y^x     |       |  __setitem__(...)     |      x.__setitem__(i, y) <==> x[i]=y     |       |  __setslice__(...)     |      x.__setslice__(i, j, y) <==> x[i:j]=y     |           |      Use  of negative indices is not supported.     |       |  __setstate__(...)     |      a.__setstate__(version, shape, dtype, isfortran, rawdata)     |           |      For unpickling.     |           |      Parameters     |      ----------     |      version : int     |          optional pickle version. If omitted defaults to 0.     |      shape : tuple     |      dtype : data-type     |      isFortran : bool     |      rawdata : string or list     |          a binary string with the data (or a list if 'a' is an object array)     |       |  __sizeof__(...)     |       |  __str__(...)     |      x.__str__() <==> str(x)     |       |  __sub__(...)     |      x.__sub__(y) <==> x-y     |       |  __truediv__(...)     |      x.__truediv__(y) <==> x/y     |       |  __xor__(...)     |      x.__xor__(y) <==> x^y     |       |  all(...)     |      a.all(axis=None, out=None, keepdims=False)     |           |      Returns True if all elements evaluate to True.     |           |      Refer to `numpy.all` for full documentation.     |           |      See Also     |      --------     |      numpy.all : equivalent function     |       |  any(...)     |      a.any(axis=None, out=None, keepdims=False)     |           |      Returns True if any of the elements of `a` evaluate to True.     |           |      Refer to `numpy.any` for full documentation.     |           |      See Also     |      --------     |      numpy.any : equivalent function     |       |  argmax(...)     |      a.argmax(axis=None, out=None)     |           |      Return indices of the maximum values along the given axis.     |           |      Refer to `numpy.argmax` for full documentation.     |           |      See Also     |      --------     |      numpy.argmax : equivalent function     |       |  argmin(...)     |      a.argmin(axis=None, out=None)     |           |      Return indices of the minimum values along the given axis of `a`.     |           |      Refer to `numpy.argmin` for detailed documentation.     |           |      See Also     |      --------     |      numpy.argmin : equivalent function     |       |  argpartition(...)     |      a.argpartition(kth, axis=-1, kind='introselect', order=None)     |           |      Returns the indices that would partition this array.     |           |      Refer to `numpy.argpartition` for full documentation.     |           |      .. versionadded:: 1.8.0     |           |      See Also     |      --------     |      numpy.argpartition : equivalent function     |       |  argsort(...)     |      a.argsort(axis=-1, kind='quicksort', order=None)     |           |      Returns the indices that would sort this array.     |           |      Refer to `numpy.argsort` for full documentation.     |           |      See Also     |      --------     |      numpy.argsort : equivalent function     |       |  astype(...)     |      a.astype(dtype, order='K', casting='unsafe', subok=True, copy=True)     |           |      Copy of the array, cast to a specified type.     |           |      Parameters     |      ----------     |      dtype : str or dtype     |          Typecode or data-type to which the array is cast.     |      order : {'C', 'F', 'A', 'K'}, optional     |          Controls the memory layout order of the result.     |          'C' means C order, 'F' means Fortran order, 'A'     |          means 'F' order if all the arrays are Fortran contiguous,     |          'C' order otherwise, and 'K' means as close to the     |          order the array elements appear in memory as possible.     |          Default is 'K'.     |      casting : {'no', 'equiv', 'safe', 'same_kind', 'unsafe'}, optional     |          Controls what kind of data casting may occur. Defaults to 'unsafe'     |          for backwards compatibility.     |           |            * 'no' means the data types should not be cast at all.     |            * 'equiv' means only byte-order changes are allowed.     |            * 'safe' means only casts which can preserve values are allowed.     |            * 'same_kind' means only safe casts or casts within a kind,     |              like float64 to float32, are allowed.     |            * 'unsafe' means any data conversions may be done.     |      subok : bool, optional     |          If True, then sub-classes will be passed-through (default), otherwise     |          the returned array will be forced to be a base-class array.     |      copy : bool, optional     |          By default, astype always returns a newly allocated array. If this     |          is set to false, and the `dtype`, `order`, and `subok`     |          requirements are satisfied, the input array is returned instead     |          of a copy.     |           |      Returns     |      -------     |      arr_t : ndarray     |          Unless `copy` is False and the other conditions for returning the input     |          array are satisfied (see description for `copy` input paramter), `arr_t`     |          is a new array of the same shape as the input array, with dtype, order     |          given by `dtype`, `order`.     |           |      Notes     |      -----     |      Starting in NumPy 1.9, astype method now returns an error if the string     |      dtype to cast to is not long enough in 'safe' casting mode to hold the max     |      value of integer/float array that is being casted. Previously the casting     |      was allowed even if the result was truncated.     |           |      Raises     |      ------     |      ComplexWarning     |          When casting from complex to float or int. To avoid this,     |          one should use ``a.real.astype(t)``.     |           |      Examples     |      --------     |      >>> x = np.array([1, 2, 2.5])     |      >>> x     |      array([ 1. ,  2. ,  2.5])     |           |      >>> x.astype(int)     |      array([1, 2, 2])     |       |  byteswap(...)     |      a.byteswap(inplace)     |           |      Swap the bytes of the array elements     |           |      Toggle between low-endian and big-endian data representation by     |      returning a byteswapped array, optionally swapped in-place.     |           |      Parameters     |      ----------     |      inplace : bool, optional     |          If ``True``, swap bytes in-place, default is ``False``.     |           |      Returns     |      -------     |      out : ndarray     |          The byteswapped array. If `inplace` is ``True``, this is     |          a view to self.     |           |      Examples     |      --------     |      >>> A = np.array([1, 256, 8755], dtype=np.int16)     |      >>> map(hex, A)     |      ['0x1', '0x100', '0x2233']     |      >>> A.byteswap(True)     |      array([  256,     1, 13090], dtype=int16)     |      >>> map(hex, A)     |      ['0x100', '0x1', '0x3322']     |           |      Arrays of strings are not swapped     |           |      >>> A = np.array(['ceg', 'fac'])     |      >>> A.byteswap()     |      array(['ceg', 'fac'],     |            dtype='|S3')     |       |  choose(...)     |      a.choose(choices, out=None, mode='raise')     |           |      Use an index array to construct a new array from a set of choices.     |           |      Refer to `numpy.choose` for full documentation.     |           |      See Also     |      --------     |      numpy.choose : equivalent function     |       |  clip(...)     |      a.clip(min=None, max=None, out=None)     |           |      Return an array whose values are limited to ``[min, max]``.     |      One of max or min must be given.     |           |      Refer to `numpy.clip` for full documentation.     |           |      See Also     |      --------     |      numpy.clip : equivalent function     |       |  compress(...)     |      a.compress(condition, axis=None, out=None)     |           |      Return selected slices of this array along given axis.     |           |      Refer to `numpy.compress` for full documentation.     |           |      See Also     |      --------     |      numpy.compress : equivalent function     |       |  conj(...)     |      a.conj()     |           |      Complex-conjugate all elements.     |           |      Refer to `numpy.conjugate` for full documentation.     |           |      See Also     |      --------     |      numpy.conjugate : equivalent function     |       |  conjugate(...)     |      a.conjugate()     |           |      Return the complex conjugate, element-wise.     |           |      Refer to `numpy.conjugate` for full documentation.     |           |      See Also     |      --------     |      numpy.conjugate : equivalent function     |       |  copy(...)     |      a.copy(order='C')     |           |      Return a copy of the array.     |           |      Parameters     |      ----------     |      order : {'C', 'F', 'A', 'K'}, optional     |          Controls the memory layout of the copy. 'C' means C-order,     |          'F' means F-order, 'A' means 'F' if `a` is Fortran contiguous,     |          'C' otherwise. 'K' means match the layout of `a` as closely     |          as possible. (Note that this function and :func:numpy.copy are very     |          similar, but have different default values for their order=     |          arguments.)     |           |      See also     |      --------     |      numpy.copy     |      numpy.copyto     |           |      Examples     |      --------     |      >>> x = np.array([[1,2,3],[4,5,6]], order='F')     |           |      >>> y = x.copy()     |           |      >>> x.fill(0)     |           |      >>> x     |      array([[0, 0, 0],     |             [0, 0, 0]])     |           |      >>> y     |      array([[1, 2, 3],     |             [4, 5, 6]])     |           |      >>> y.flags['C_CONTIGUOUS']     |      True     |       |  cumprod(...)     |      a.cumprod(axis=None, dtype=None, out=None)     |           |      Return the cumulative product of the elements along the given axis.     |           |      Refer to `numpy.cumprod` for full documentation.     |           |      See Also     |      --------     |      numpy.cumprod : equivalent function     |       |  cumsum(...)     |      a.cumsum(axis=None, dtype=None, out=None)     |           |      Return the cumulative sum of the elements along the given axis.     |           |      Refer to `numpy.cumsum` for full documentation.     |           |      See Also     |      --------     |      numpy.cumsum : equivalent function     |       |  diagonal(...)     |      a.diagonal(offset=0, axis1=0, axis2=1)     |           |      Return specified diagonals. In NumPy 1.9 the returned array is a     |      read-only view instead of a copy as in previous NumPy versions.  In     |      NumPy 1.10 the read-only restriction will be removed.     |           |      Refer to :func:`numpy.diagonal` for full documentation.     |           |      See Also     |      --------     |      numpy.diagonal : equivalent function     |       |  dot(...)     |      a.dot(b, out=None)     |           |      Dot product of two arrays.     |           |      Refer to `numpy.dot` for full documentation.     |           |      See Also     |      --------     |      numpy.dot : equivalent function     |           |      Examples     |      --------     |      >>> a = np.eye(2)     |      >>> b = np.ones((2, 2)) * 2     |      >>> a.dot(b)     |      array([[ 2.,  2.],     |             [ 2.,  2.]])     |           |      This array method can be conveniently chained:     |           |      >>> a.dot(b).dot(b)     |      array([[ 8.,  8.],     |             [ 8.,  8.]])     |       |  dump(...)     |      a.dump(file)     |           |      Dump a pickle of the array to the specified file.     |      The array can be read back with pickle.load or numpy.load.     |           |      Parameters     |      ----------     |      file : str     |          A string naming the dump file.     |       |  dumps(...)     |      a.dumps()     |           |      Returns the pickle of the array as a string.     |      pickle.loads or numpy.loads will convert the string back to an array.     |           |      Parameters     |      ----------     |      None     |       |  fill(...)     |      a.fill(value)     |           |      Fill the array with a scalar value.     |           |      Parameters     |      ----------     |      value : scalar     |          All elements of `a` will be assigned this value.     |           |      Examples     |      --------     |      >>> a = np.array([1, 2])     |      >>> a.fill(0)     |      >>> a     |      array([0, 0])     |      >>> a = np.empty(2)     |      >>> a.fill(1)     |      >>> a     |      array([ 1.,  1.])     |       |  flatten(...)     |      a.flatten(order='C')     |           |      Return a copy of the array collapsed into one dimension.     |           |      Parameters     |      ----------     |      order : {'C', 'F', 'A'}, optional     |          Whether to flatten in row-major (C-style) or     |          column-major (Fortran-style) order or preserve the     |          C/Fortran ordering from `a`.  The default is 'C'.     |           |      Returns     |      -------     |      y : ndarray     |          A copy of the input array, flattened to one dimension.     |           |      See Also     |      --------     |      ravel : Return a flattened array.     |      flat : A 1-D flat iterator over the array.     |           |      Examples     |      --------     |      >>> a = np.array([[1,2], [3,4]])     |      >>> a.flatten()     |      array([1, 2, 3, 4])     |      >>> a.flatten('F')     |      array([1, 3, 2, 4])     |       |  getfield(...)     |      a.getfield(dtype, offset=0)     |           |      Returns a field of the given array as a certain type.     |           |      A field is a view of the array data with a given data-type. The values in     |      the view are determined by the given type and the offset into the current     |      array in bytes. The offset needs to be such that the view dtype fits in the     |      array dtype; for example an array of dtype complex128 has 16-byte elements.     |      If taking a view with a 32-bit integer (4 bytes), the offset needs to be     |      between 0 and 12 bytes.     |           |      Parameters     |      ----------     |      dtype : str or dtype     |          The data type of the view. The dtype size of the view can not be larger     |          than that of the array itself.     |      offset : int     |          Number of bytes to skip before beginning the element view.     |           |      Examples     |      --------     |      >>> x = np.diag([1.+1.j]*2)     |      >>> x[1, 1] = 2 + 4.j     |      >>> x     |      array([[ 1.+1.j,  0.+0.j],     |             [ 0.+0.j,  2.+4.j]])     |      >>> x.getfield(np.float64)     |      array([[ 1.,  0.],     |             [ 0.,  2.]])     |           |      By choosing an offset of 8 bytes we can select the complex part of the     |      array for our view:     |           |      >>> x.getfield(np.float64, offset=8)     |      array([[ 1.,  0.],     |         [ 0.,  4.]])     |       |  item(...)     |      a.item(*args)     |           |      Copy an element of an array to a standard Python scalar and return it.     |           |      Parameters     |      ----------     |      \*args : Arguments (variable number and type)     |           |          * none: in this case, the method only works for arrays     |            with one element (`a.size == 1`), which element is     |            copied into a standard Python scalar object and returned.     |           |          * int_type: this argument is interpreted as a flat index into     |            the array, specifying which element to copy and return.     |           |          * tuple of int_types: functions as does a single int_type argument,     |            except that the argument is interpreted as an nd-index into the     |            array.     |           |      Returns     |      -------     |      z : Standard Python scalar object     |          A copy of the specified element of the array as a suitable     |          Python scalar     |           |      Notes     |      -----     |      When the data type of `a` is longdouble or clongdouble, item() returns     |      a scalar array object because there is no available Python scalar that     |      would not lose information. Void arrays return a buffer object for item(),     |      unless fields are defined, in which case a tuple is returned.     |           |      `item` is very similar to a[args], except, instead of an array scalar,     |      a standard Python scalar is returned. This can be useful for speeding up     |      access to elements of the array and doing arithmetic on elements of the     |      array using Python's optimized math.     |           |      Examples     |      --------     |      >>> x = np.random.randint(9, size=(3, 3))     |      >>> x     |      array([[3, 1, 7],     |             [2, 8, 3],     |             [8, 5, 3]])     |      >>> x.item(3)     |      2     |      >>> x.item(7)     |      5     |      >>> x.item((0, 1))     |      1     |      >>> x.item((2, 2))     |      3     |       |  itemset(...)     |      a.itemset(*args)     |           |      Insert scalar into an array (scalar is cast to array's dtype, if possible)     |           |      There must be at least 1 argument, and define the last argument     |      as *item*.  Then, ``a.itemset(*args)`` is equivalent to but faster     |      than ``a[args] = item``.  The item should be a scalar value and `args`     |      must select a single item in the array `a`.     |           |      Parameters     |      ----------     |      \*args : Arguments     |          If one argument: a scalar, only used in case `a` is of size 1.     |          If two arguments: the last argument is the value to be set     |          and must be a scalar, the first argument specifies a single array     |          element location. It is either an int or a tuple.     |           |      Notes     |      -----     |      Compared to indexing syntax, `itemset` provides some speed increase     |      for placing a scalar into a particular location in an `ndarray`,     |      if you must do this.  However, generally this is discouraged:     |      among other problems, it complicates the appearance of the code.     |      Also, when using `itemset` (and `item`) inside a loop, be sure     |      to assign the methods to a local variable to avoid the attribute     |      look-up at each loop iteration.     |           |      Examples     |      --------     |      >>> x = np.random.randint(9, size=(3, 3))     |      >>> x     |      array([[3, 1, 7],     |             [2, 8, 3],     |             [8, 5, 3]])     |      >>> x.itemset(4, 0)     |      >>> x.itemset((2, 2), 9)     |      >>> x     |      array([[3, 1, 7],     |             [2, 0, 3],     |             [8, 5, 9]])     |       |  max(...)     |      a.max(axis=None, out=None)     |           |      Return the maximum along a given axis.     |           |      Refer to `numpy.amax` for full documentation.     |           |      See Also     |      --------     |      numpy.amax : equivalent function     |       |  mean(...)     |      a.mean(axis=None, dtype=None, out=None, keepdims=False)     |           |      Returns the average of the array elements along given axis.     |           |      Refer to `numpy.mean` for full documentation.     |           |      See Also     |      --------     |      numpy.mean : equivalent function     |       |  min(...)     |      a.min(axis=None, out=None, keepdims=False)     |           |      Return the minimum along a given axis.     |           |      Refer to `numpy.amin` for full documentation.     |           |      See Also     |      --------     |      numpy.amin : equivalent function     |       |  newbyteorder(...)     |      arr.newbyteorder(new_order='S')     |           |      Return the array with the same data viewed with a different byte order.     |           |      Equivalent to::     |           |          arr.view(arr.dtype.newbytorder(new_order))     |           |      Changes are also made in all fields and sub-arrays of the array data     |      type.     |           |           |           |      Parameters     |      ----------     |      new_order : string, optional     |          Byte order to force; a value from the byte order specifications     |          below. `new_order` codes can be any of:     |           |          * 'S' - swap dtype from current to opposite endian     |          * {'<', 'L'} - little endian     |          * {'>', 'B'} - big endian     |          * {'=', 'N'} - native order     |          * {'|', 'I'} - ignore (no change to byte order)     |           |          The default value ('S') results in swapping the current     |          byte order. The code does a case-insensitive check on the first     |          letter of `new_order` for the alternatives above.  For example,     |          any of 'B' or 'b' or 'biggish' are valid to specify big-endian.     |           |           |      Returns     |      -------     |      new_arr : array     |          New array object with the dtype reflecting given change to the     |          byte order.     |       |  nonzero(...)     |      a.nonzero()     |           |      Return the indices of the elements that are non-zero.     |           |      Refer to `numpy.nonzero` for full documentation.     |           |      See Also     |      --------     |      numpy.nonzero : equivalent function     |       |  partition(...)     |      a.partition(kth, axis=-1, kind='introselect', order=None)     |           |      Rearranges the elements in the array in such a way that value of the     |      element in kth position is in the position it would be in a sorted array.     |      All elements smaller than the kth element are moved before this element and     |      all equal or greater are moved behind it. The ordering of the elements in     |      the two partitions is undefined.     |           |      .. versionadded:: 1.8.0     |           |      Parameters     |      ----------     |      kth : int or sequence of ints     |          Element index to partition by. The kth element value will be in its     |          final sorted position and all smaller elements will be moved before it     |          and all equal or greater elements behind it.     |          The order all elements in the partitions is undefined.     |          If provided with a sequence of kth it will partition all elements     |          indexed by kth of them into their sorted position at once.     |      axis : int, optional     |          Axis along which to sort. Default is -1, which means sort along the     |          last axis.     |      kind : {'introselect'}, optional     |          Selection algorithm. Default is 'introselect'.     |      order : str or list of str, optional     |          When `a` is an array with fields defined, this argument specifies     |          which fields to compare first, second, etc.  A single field can     |          be specified as a string, and not all fields need be specified,     |          but unspecified fields will still be used, in the order in which     |          they come up in the dtype, to break ties.     |           |      See Also     |      --------     |      numpy.partition : Return a parititioned copy of an array.     |      argpartition : Indirect partition.     |      sort : Full sort.     |           |      Notes     |      -----     |      See ``np.partition`` for notes on the different algorithms.     |           |      Examples     |      --------     |      >>> a = np.array([3, 4, 2, 1])     |      >>> a.partition(a, 3)     |      >>> a     |      array([2, 1, 3, 4])     |           |      >>> a.partition((1, 3))     |      array([1, 2, 3, 4])     |       |  prod(...)     |      a.prod(axis=None, dtype=None, out=None, keepdims=False)     |           |      Return the product of the array elements over the given axis     |           |      Refer to `numpy.prod` for full documentation.     |           |      See Also     |      --------     |      numpy.prod : equivalent function     |       |  ptp(...)     |      a.ptp(axis=None, out=None)     |           |      Peak to peak (maximum - minimum) value along a given axis.     |           |      Refer to `numpy.ptp` for full documentation.     |           |      See Also     |      --------     |      numpy.ptp : equivalent function     |       |  put(...)     |      a.put(indices, values, mode='raise')     |           |      Set ``a.flat[n] = values[n]`` for all `n` in indices.     |           |      Refer to `numpy.put` for full documentation.     |           |      See Also     |      --------     |      numpy.put : equivalent function     |       |  ravel(...)     |      a.ravel([order])     |           |      Return a flattened array.     |           |      Refer to `numpy.ravel` for full documentation.     |           |      See Also     |      --------     |      numpy.ravel : equivalent function     |           |      ndarray.flat : a flat iterator on the array.     |       |  repeat(...)     |      a.repeat(repeats, axis=None)     |           |      Repeat elements of an array.     |           |      Refer to `numpy.repeat` for full documentation.     |           |      See Also     |      --------     |      numpy.repeat : equivalent function     |       |  reshape(...)     |      a.reshape(shape, order='C')     |           |      Returns an array containing the same data with a new shape.     |           |      Refer to `numpy.reshape` for full documentation.     |           |      See Also     |      --------     |      numpy.reshape : equivalent function     |       |  resize(...)     |      a.resize(new_shape, refcheck=True)     |           |      Change shape and size of array in-place.     |           |      Parameters     |      ----------     |      new_shape : tuple of ints, or `n` ints     |          Shape of resized array.     |      refcheck : bool, optional     |          If False, reference count will not be checked. Default is True.     |           |      Returns     |      -------     |      None     |           |      Raises     |      ------     |      ValueError     |          If `a` does not own its own data or references or views to it exist,     |          and the data memory must be changed.     |           |      SystemError     |          If the `order` keyword argument is specified. This behaviour is a     |          bug in NumPy.     |           |      See Also     |      --------     |      resize : Return a new array with the specified shape.     |           |      Notes     |      -----     |      This reallocates space for the data area if necessary.     |           |      Only contiguous arrays (data elements consecutive in memory) can be     |      resized.     |           |      The purpose of the reference count check is to make sure you     |      do not use this array as a buffer for another Python object and then     |      reallocate the memory. However, reference counts can increase in     |      other ways so if you are sure that you have not shared the memory     |      for this array with another Python object, then you may safely set     |      `refcheck` to False.     |           |      Examples     |      --------     |      Shrinking an array: array is flattened (in the order that the data are     |      stored in memory), resized, and reshaped:     |           |      >>> a = np.array([[0, 1], [2, 3]], order='C')     |      >>> a.resize((2, 1))     |      >>> a     |      array([[0],     |             [1]])     |           |      >>> a = np.array([[0, 1], [2, 3]], order='F')     |      >>> a.resize((2, 1))     |      >>> a     |      array([[0],     |             [2]])     |           |      Enlarging an array: as above, but missing entries are filled with zeros:     |           |      >>> b = np.array([[0, 1], [2, 3]])     |      >>> b.resize(2, 3) # new_shape parameter doesn't have to be a tuple     |      >>> b     |      array([[0, 1, 2],     |             [3, 0, 0]])     |           |      Referencing an array prevents resizing...     |           |      >>> c = a     |      >>> a.resize((1, 1))     |      Traceback (most recent call last):     |      ...     |      ValueError: cannot resize an array that has been referenced ...     |           |      Unless `refcheck` is False:     |           |      >>> a.resize((1, 1), refcheck=False)     |      >>> a     |      array([[0]])     |      >>> c     |      array([[0]])     |       |  round(...)     |      a.round(decimals=0, out=None)     |           |      Return `a` with each element rounded to the given number of decimals.     |           |      Refer to `numpy.around` for full documentation.     |           |      See Also     |      --------     |      numpy.around : equivalent function     |       |  searchsorted(...)     |      a.searchsorted(v, side='left', sorter=None)     |           |      Find indices where elements of v should be inserted in a to maintain order.     |           |      For full documentation, see `numpy.searchsorted`     |           |      See Also     |      --------     |      numpy.searchsorted : equivalent function     |       |  setfield(...)     |      a.setfield(val, dtype, offset=0)     |           |      Put a value into a specified place in a field defined by a data-type.     |           |      Place `val` into `a`'s field defined by `dtype` and beginning `offset`     |      bytes into the field.     |           |      Parameters     |      ----------     |      val : object     |          Value to be placed in field.     |      dtype : dtype object     |          Data-type of the field in which to place `val`.     |      offset : int, optional     |          The number of bytes into the field at which to place `val`.     |           |      Returns     |      -------     |      None     |           |      See Also     |      --------     |      getfield     |           |      Examples     |      --------     |      >>> x = np.eye(3)     |      >>> x.getfield(np.float64)     |      array([[ 1.,  0.,  0.],     |             [ 0.,  1.,  0.],     |             [ 0.,  0.,  1.]])     |      >>> x.setfield(3, np.int32)     |      >>> x.getfield(np.int32)     |      array([[3, 3, 3],     |             [3, 3, 3],     |             [3, 3, 3]])     |      >>> x     |      array([[  1.00000000e+000,   1.48219694e-323,   1.48219694e-323],     |             [  1.48219694e-323,   1.00000000e+000,   1.48219694e-323],     |             [  1.48219694e-323,   1.48219694e-323,   1.00000000e+000]])     |      >>> x.setfield(np.eye(3), np.int32)     |      >>> x     |      array([[ 1.,  0.,  0.],     |             [ 0.,  1.,  0.],     |             [ 0.,  0.,  1.]])     |       |  setflags(...)     |      a.setflags(write=None, align=None, uic=None)     |           |      Set array flags WRITEABLE, ALIGNED, and UPDATEIFCOPY, respectively.     |           |      These Boolean-valued flags affect how numpy interprets the memory     |      area used by `a` (see Notes below). The ALIGNED flag can only     |      be set to True if the data is actually aligned according to the type.     |      The UPDATEIFCOPY flag can never be set to True. The flag WRITEABLE     |      can only be set to True if the array owns its own memory, or the     |      ultimate owner of the memory exposes a writeable buffer interface,     |      or is a string. (The exception for string is made so that unpickling     |      can be done without copying memory.)     |           |      Parameters     |      ----------     |      write : bool, optional     |          Describes whether or not `a` can be written to.     |      align : bool, optional     |          Describes whether or not `a` is aligned properly for its type.     |      uic : bool, optional     |          Describes whether or not `a` is a copy of another "base" array.     |           |      Notes     |      -----     |      Array flags provide information about how the memory area used     |      for the array is to be interpreted. There are 6 Boolean flags     |      in use, only three of which can be changed by the user:     |      UPDATEIFCOPY, WRITEABLE, and ALIGNED.     |           |      WRITEABLE (W) the data area can be written to;     |           |      ALIGNED (A) the data and strides are aligned appropriately for the hardware     |      (as determined by the compiler);     |           |      UPDATEIFCOPY (U) this array is a copy of some other array (referenced     |      by .base). When this array is deallocated, the base array will be     |      updated with the contents of this array.     |           |      All flags can be accessed using their first (upper case) letter as well     |      as the full name.     |           |      Examples     |      --------     |      >>> y     |      array([[3, 1, 7],     |             [2, 0, 0],     |             [8, 5, 9]])     |      >>> y.flags     |        C_CONTIGUOUS : True     |        F_CONTIGUOUS : False     |        OWNDATA : True     |        WRITEABLE : True     |        ALIGNED : True     |        UPDATEIFCOPY : False     |      >>> y.setflags(write=0, align=0)     |      >>> y.flags     |        C_CONTIGUOUS : True     |        F_CONTIGUOUS : False     |        OWNDATA : True     |        WRITEABLE : False     |        ALIGNED : False     |        UPDATEIFCOPY : False     |      >>> y.setflags(uic=1)     |      Traceback (most recent call last):     |        File "<stdin>", line 1, in <module>     |      ValueError: cannot set UPDATEIFCOPY flag to True     |       |  sort(...)     |      a.sort(axis=-1, kind='quicksort', order=None)     |           |      Sort an array, in-place.     |           |      Parameters     |      ----------     |      axis : int, optional     |          Axis along which to sort. Default is -1, which means sort along the     |          last axis.     |      kind : {'quicksort', 'mergesort', 'heapsort'}, optional     |          Sorting algorithm. Default is 'quicksort'.     |      order : str or list of str, optional     |          When `a` is an array with fields defined, this argument specifies     |          which fields to compare first, second, etc.  A single field can     |          be specified as a string, and not all fields need be specified,     |          but unspecified fields will still be used, in the order in which     |          they come up in the dtype, to break ties.     |           |      See Also     |      --------     |      numpy.sort : Return a sorted copy of an array.     |      argsort : Indirect sort.     |      lexsort : Indirect stable sort on multiple keys.     |      searchsorted : Find elements in sorted array.     |      partition: Partial sort.     |           |      Notes     |      -----     |      See ``sort`` for notes on the different sorting algorithms.     |           |      Examples     |      --------     |      >>> a = np.array([[1,4], [3,1]])     |      >>> a.sort(axis=1)     |      >>> a     |      array([[1, 4],     |             [1, 3]])     |      >>> a.sort(axis=0)     |      >>> a     |      array([[1, 3],     |             [1, 4]])     |           |      Use the `order` keyword to specify a field to use when sorting a     |      structured array:     |           |      >>> a = np.array([('a', 2), ('c', 1)], dtype=[('x', 'S1'), ('y', int)])     |      >>> a.sort(order='y')     |      >>> a     |      array([('c', 1), ('a', 2)],     |            dtype=[('x', '|S1'), ('y', '<i4')])     |       |  squeeze(...)     |      a.squeeze(axis=None)     |           |      Remove single-dimensional entries from the shape of `a`.     |           |      Refer to `numpy.squeeze` for full documentation.     |           |      See Also     |      --------     |      numpy.squeeze : equivalent function     |       |  std(...)     |      a.std(axis=None, dtype=None, out=None, ddof=0, keepdims=False)     |           |      Returns the standard deviation of the array elements along given axis.     |           |      Refer to `numpy.std` for full documentation.     |           |      See Also     |      --------     |      numpy.std : equivalent function     |       |  sum(...)     |      a.sum(axis=None, dtype=None, out=None, keepdims=False)     |           |      Return the sum of the array elements over the given axis.     |           |      Refer to `numpy.sum` for full documentation.     |           |      See Also     |      --------     |      numpy.sum : equivalent function     |       |  swapaxes(...)     |      a.swapaxes(axis1, axis2)     |           |      Return a view of the array with `axis1` and `axis2` interchanged.     |           |      Refer to `numpy.swapaxes` for full documentation.     |           |      See Also     |      --------     |      numpy.swapaxes : equivalent function     |       |  take(...)     |      a.take(indices, axis=None, out=None, mode='raise')     |           |      Return an array formed from the elements of `a` at the given indices.     |           |      Refer to `numpy.take` for full documentation.     |           |      See Also     |      --------     |      numpy.take : equivalent function     |       |  tobytes(...)     |      a.tobytes(order='C')     |           |      Construct Python bytes containing the raw data bytes in the array.     |           |      Constructs Python bytes showing a copy of the raw contents of     |      data memory. The bytes object can be produced in either 'C' or 'Fortran',     |      or 'Any' order (the default is 'C'-order). 'Any' order means C-order     |      unless the F_CONTIGUOUS flag in the array is set, in which case it     |      means 'Fortran' order.     |           |      .. versionadded:: 1.9.0     |           |      Parameters     |      ----------     |      order : {'C', 'F', None}, optional     |          Order of the data for multidimensional arrays:     |          C, Fortran, or the same as for the original array.     |           |      Returns     |      -------     |      s : bytes     |          Python bytes exhibiting a copy of `a`'s raw data.     |           |      Examples     |      --------     |      >>> x = np.array([[0, 1], [2, 3]])     |      >>> x.tobytes()     |      b'\x00\x00\x00\x00\x01\x00\x00\x00\x02\x00\x00\x00\x03\x00\x00\x00'     |      >>> x.tobytes('C') == x.tobytes()     |      True     |      >>> x.tobytes('F')     |      b'\x00\x00\x00\x00\x02\x00\x00\x00\x01\x00\x00\x00\x03\x00\x00\x00'     |       |  tofile(...)     |      a.tofile(fid, sep="", format="%s")     |           |      Write array to a file as text or binary (default).     |           |      Data is always written in 'C' order, independent of the order of `a`.     |      The data produced by this method can be recovered using the function     |      fromfile().     |           |      Parameters     |      ----------     |      fid : file or str     |          An open file object, or a string containing a filename.     |      sep : str     |          Separator between array items for text output.     |          If "" (empty), a binary file is written, equivalent to     |          ``file.write(a.tobytes())``.     |      format : str     |          Format string for text file output.     |          Each entry in the array is formatted to text by first converting     |          it to the closest Python type, and then using "format" % item.     |           |      Notes     |      -----     |      This is a convenience function for quick storage of array data.     |      Information on endianness and precision is lost, so this method is not a     |      good choice for files intended to archive data or transport data between     |      machines with different endianness. Some of these problems can be overcome     |      by outputting the data as text files, at the expense of speed and file     |      size.     |       |  tolist(...)     |      a.tolist()     |           |      Return the array as a (possibly nested) list.     |           |      Return a copy of the array data as a (nested) Python list.     |      Data items are converted to the nearest compatible Python type.     |           |      Parameters     |      ----------     |      none     |           |      Returns     |      -------     |      y : list     |          The possibly nested list of array elements.     |           |      Notes     |      -----     |      The array may be recreated, ``a = np.array(a.tolist())``.     |           |      Examples     |      --------     |      >>> a = np.array([1, 2])     |      >>> a.tolist()     |      [1, 2]     |      >>> a = np.array([[1, 2], [3, 4]])     |      >>> list(a)     |      [array([1, 2]), array([3, 4])]     |      >>> a.tolist()     |      [[1, 2], [3, 4]]     |       |  tostring(...)     |      a.tostring(order='C')     |           |      Construct Python bytes containing the raw data bytes in the array.     |           |      Constructs Python bytes showing a copy of the raw contents of     |      data memory. The bytes object can be produced in either 'C' or 'Fortran',     |      or 'Any' order (the default is 'C'-order). 'Any' order means C-order     |      unless the F_CONTIGUOUS flag in the array is set, in which case it     |      means 'Fortran' order.     |           |      This function is a compatibility alias for tobytes. Despite its name it returns bytes not strings.     |           |      Parameters     |      ----------     |      order : {'C', 'F', None}, optional     |          Order of the data for multidimensional arrays:     |          C, Fortran, or the same as for the original array.     |           |      Returns     |      -------     |      s : bytes     |          Python bytes exhibiting a copy of `a`'s raw data.     |           |      Examples     |      --------     |      >>> x = np.array([[0, 1], [2, 3]])     |      >>> x.tobytes()     |      b'\x00\x00\x00\x00\x01\x00\x00\x00\x02\x00\x00\x00\x03\x00\x00\x00'     |      >>> x.tobytes('C') == x.tobytes()     |      True     |      >>> x.tobytes('F')     |      b'\x00\x00\x00\x00\x02\x00\x00\x00\x01\x00\x00\x00\x03\x00\x00\x00'     |       |  trace(...)     |      a.trace(offset=0, axis1=0, axis2=1, dtype=None, out=None)     |           |      Return the sum along diagonals of the array.     |           |      Refer to `numpy.trace` for full documentation.     |           |      See Also     |      --------     |      numpy.trace : equivalent function     |       |  transpose(...)     |      a.transpose(*axes)     |           |      Returns a view of the array with axes transposed.     |           |      For a 1-D array, this has no effect. (To change between column and     |      row vectors, first cast the 1-D array into a matrix object.)     |      For a 2-D array, this is the usual matrix transpose.     |      For an n-D array, if axes are given, their order indicates how the     |      axes are permuted (see Examples). If axes are not provided and     |      ``a.shape = (i[0], i[1], ... i[n-2], i[n-1])``, then     |      ``a.transpose().shape = (i[n-1], i[n-2], ... i[1], i[0])``.     |           |      Parameters     |      ----------     |      axes : None, tuple of ints, or `n` ints     |           |       * None or no argument: reverses the order of the axes.     |           |       * tuple of ints: `i` in the `j`-th place in the tuple means `a`'s     |         `i`-th axis becomes `a.transpose()`'s `j`-th axis.     |           |       * `n` ints: same as an n-tuple of the same ints (this form is     |         intended simply as a "convenience" alternative to the tuple form)     |           |      Returns     |      -------     |      out : ndarray     |          View of `a`, with axes suitably permuted.     |           |      See Also     |      --------     |      ndarray.T : Array property returning the array transposed.     |           |      Examples     |      --------     |      >>> a = np.array([[1, 2], [3, 4]])     |      >>> a     |      array([[1, 2],     |             [3, 4]])     |      >>> a.transpose()     |      array([[1, 3],     |             [2, 4]])     |      >>> a.transpose((1, 0))     |      array([[1, 3],     |             [2, 4]])     |      >>> a.transpose(1, 0)     |      array([[1, 3],     |             [2, 4]])     |       |  var(...)     |      a.var(axis=None, dtype=None, out=None, ddof=0, keepdims=False)     |           |      Returns the variance of the array elements, along given axis.     |           |      Refer to `numpy.var` for full documentation.     |           |      See Also     |      --------     |      numpy.var : equivalent function     |       |  view(...)     |      a.view(dtype=None, type=None)     |           |      New view of array with the same data.     |           |      Parameters     |      ----------     |      dtype : data-type or ndarray sub-class, optional     |          Data-type descriptor of the returned view, e.g., float32 or int16. The     |          default, None, results in the view having the same data-type as `a`.     |          This argument can also be specified as an ndarray sub-class, which     |          then specifies the type of the returned object (this is equivalent to     |          setting the ``type`` parameter).     |      type : Python type, optional     |          Type of the returned view, e.g., ndarray or matrix.  Again, the     |          default None results in type preservation.     |           |      Notes     |      -----     |      ``a.view()`` is used two different ways:     |           |      ``a.view(some_dtype)`` or ``a.view(dtype=some_dtype)`` constructs a view     |      of the array's memory with a different data-type.  This can cause a     |      reinterpretation of the bytes of memory.     |           |      ``a.view(ndarray_subclass)`` or ``a.view(type=ndarray_subclass)`` just     |      returns an instance of `ndarray_subclass` that looks at the same array     |      (same shape, dtype, etc.)  This does not cause a reinterpretation of the     |      memory.     |           |      For ``a.view(some_dtype)``, if ``some_dtype`` has a different number of     |      bytes per entry than the previous dtype (for example, converting a     |      regular array to a structured array), then the behavior of the view     |      cannot be predicted just from the superficial appearance of ``a`` (shown     |      by ``print(a)``). It also depends on exactly how ``a`` is stored in     |      memory. Therefore if ``a`` is C-ordered versus fortran-ordered, versus     |      defined as a slice or transpose, etc., the view may give different     |      results.     |           |           |      Examples     |      --------     |      >>> x = np.array([(1, 2)], dtype=[('a', np.int8), ('b', np.int8)])     |           |      Viewing array data using a different type and dtype:     |           |      >>> y = x.view(dtype=np.int16, type=np.matrix)     |      >>> y     |      matrix([[513]], dtype=int16)     |      >>> print type(y)     |      <class 'numpy.matrixlib.defmatrix.matrix'>     |           |      Creating a view on a structured array so it can be used in calculations     |           |      >>> x = np.array([(1, 2),(3,4)], dtype=[('a', np.int8), ('b', np.int8)])     |      >>> xv = x.view(dtype=np.int8).reshape(-1,2)     |      >>> xv     |      array([[1, 2],     |             [3, 4]], dtype=int8)     |      >>> xv.mean(0)     |      array([ 2.,  3.])     |           |      Making changes to the view changes the underlying array     |           |      >>> xv[0,1] = 20     |      >>> print x     |      [(1, 20) (3, 4)]     |           |      Using a view to convert an array to a recarray:     |           |      >>> z = x.view(np.recarray)     |      >>> z.a     |      array([1], dtype=int8)     |           |      Views share data:     |           |      >>> x[0] = (9, 10)     |      >>> z[0]     |      (9, 10)     |           |      Views that change the dtype size (bytes per entry) should normally be     |      avoided on arrays defined by slices, transposes, fortran-ordering, etc.:     |           |      >>> x = np.array([[1,2,3],[4,5,6]], dtype=np.int16)     |      >>> y = x[:, 0:2]     |      >>> y     |      array([[1, 2],     |             [4, 5]], dtype=int16)     |      >>> y.view(dtype=[('width', np.int16), ('length', np.int16)])     |      Traceback (most recent call last):     |        File "<stdin>", line 1, in <module>     |      ValueError: new type not compatible with array.     |      >>> z = y.copy()     |      >>> z.view(dtype=[('width', np.int16), ('length', np.int16)])     |      array([[(1, 2)],     |             [(4, 5)]], dtype=[('width', '<i2'), ('length', '<i2')])     |       |  ----------------------------------------------------------------------     |  Data descriptors defined here:     |       |  T     |      Same as self.transpose(), except that self is returned if     |      self.ndim < 2.     |           |      Examples     |      --------     |      >>> x = np.array([[1.,2.],[3.,4.]])     |      >>> x     |      array([[ 1.,  2.],     |             [ 3.,  4.]])     |      >>> x.T     |      array([[ 1.,  3.],     |             [ 2.,  4.]])     |      >>> x = np.array([1.,2.,3.,4.])     |      >>> x     |      array([ 1.,  2.,  3.,  4.])     |      >>> x.T     |      array([ 1.,  2.,  3.,  4.])     |       |  __array_finalize__     |      None.     |       |  __array_interface__     |      Array protocol: Python side.     |       |  __array_priority__     |      Array priority.     |       |  __array_struct__     |      Array protocol: C-struct side.     |       |  base     |      Base object if memory is from some other object.     |           |      Examples     |      --------     |      The base of an array that owns its memory is None:     |           |      >>> x = np.array([1,2,3,4])     |      >>> x.base is None     |      True     |           |      Slicing creates a view, whose memory is shared with x:     |           |      >>> y = x[2:]     |      >>> y.base is x     |      True     |       |  ctypes     |      An object to simplify the interaction of the array with the ctypes     |      module.     |           |      This attribute creates an object that makes it easier to use arrays     |      when calling shared libraries with the ctypes module. The returned     |      object has, among others, data, shape, and strides attributes (see     |      Notes below) which themselves return ctypes objects that can be used     |      as arguments to a shared library.     |           |      Parameters     |      ----------     |      None     |           |      Returns     |      -------     |      c : Python object     |          Possessing attributes data, shape, strides, etc.     |           |      See Also     |      --------     |      numpy.ctypeslib     |           |      Notes     |      -----     |      Below are the public attributes of this object which were documented     |      in "Guide to NumPy" (we have omitted undocumented public attributes,     |      as well as documented private attributes):     |           |      * data: A pointer to the memory area of the array as a Python integer.     |        This memory area may contain data that is not aligned, or not in correct     |        byte-order. The memory area may not even be writeable. The array     |        flags and data-type of this array should be respected when passing this     |        attribute to arbitrary C-code to avoid trouble that can include Python     |        crashing. User Beware! The value of this attribute is exactly the same     |        as self._array_interface_['data'][0].     |           |      * shape (c_intp*self.ndim): A ctypes array of length self.ndim where     |        the basetype is the C-integer corresponding to dtype('p') on this     |        platform. This base-type could be c_int, c_long, or c_longlong     |        depending on the platform. The c_intp type is defined accordingly in     |        numpy.ctypeslib. The ctypes array contains the shape of the underlying     |        array.     |           |      * strides (c_intp*self.ndim): A ctypes array of length self.ndim where     |        the basetype is the same as for the shape attribute. This ctypes array     |        contains the strides information from the underlying array. This strides     |        information is important for showing how many bytes must be jumped to     |        get to the next element in the array.     |           |      * data_as(obj): Return the data pointer cast to a particular c-types object.     |        For example, calling self._as_parameter_ is equivalent to     |        self.data_as(ctypes.c_void_p). Perhaps you want to use the data as a     |        pointer to a ctypes array of floating-point data:     |        self.data_as(ctypes.POINTER(ctypes.c_double)).     |           |      * shape_as(obj): Return the shape tuple as an array of some other c-types     |        type. For example: self.shape_as(ctypes.c_short).     |           |      * strides_as(obj): Return the strides tuple as an array of some other     |        c-types type. For example: self.strides_as(ctypes.c_longlong).     |           |      Be careful using the ctypes attribute - especially on temporary     |      arrays or arrays constructed on the fly. For example, calling     |      ``(a+b).ctypes.data_as(ctypes.c_void_p)`` returns a pointer to memory     |      that is invalid because the array created as (a+b) is deallocated     |      before the next Python statement. You can avoid this problem using     |      either ``c=a+b`` or ``ct=(a+b).ctypes``. In the latter case, ct will     |      hold a reference to the array until ct is deleted or re-assigned.     |           |      If the ctypes module is not available, then the ctypes attribute     |      of array objects still returns something useful, but ctypes objects     |      are not returned and errors may be raised instead. In particular,     |      the object will still have the as parameter attribute which will     |      return an integer equal to the data attribute.     |           |      Examples     |      --------     |      >>> import ctypes     |      >>> x     |      array([[0, 1],     |             [2, 3]])     |      >>> x.ctypes.data     |      30439712     |      >>> x.ctypes.data_as(ctypes.POINTER(ctypes.c_long))     |      <ctypes.LP_c_long object at 0x01F01300>     |      >>> x.ctypes.data_as(ctypes.POINTER(ctypes.c_long)).contents     |      c_long(0)     |      >>> x.ctypes.data_as(ctypes.POINTER(ctypes.c_longlong)).contents     |      c_longlong(4294967296L)     |      >>> x.ctypes.shape     |      <numpy.core._internal.c_long_Array_2 object at 0x01FFD580>     |      >>> x.ctypes.shape_as(ctypes.c_long)     |      <numpy.core._internal.c_long_Array_2 object at 0x01FCE620>     |      >>> x.ctypes.strides     |      <numpy.core._internal.c_long_Array_2 object at 0x01FCE620>     |      >>> x.ctypes.strides_as(ctypes.c_longlong)     |      <numpy.core._internal.c_longlong_Array_2 object at 0x01F01300>     |       |  data     |      Python buffer object pointing to the start of the array's data.     |       |  dtype     |      Data-type of the array's elements.     |           |      Parameters     |      ----------     |      None     |           |      Returns     |      -------     |      d : numpy dtype object     |           |      See Also     |      --------     |      numpy.dtype     |           |      Examples     |      --------     |      >>> x     |      array([[0, 1],     |             [2, 3]])     |      >>> x.dtype     |      dtype('int32')     |      >>> type(x.dtype)     |      <type 'numpy.dtype'>     |       |  flags     |      Information about the memory layout of the array.     |           |      Attributes     |      ----------     |      C_CONTIGUOUS (C)     |          The data is in a single, C-style contiguous segment.     |      F_CONTIGUOUS (F)     |          The data is in a single, Fortran-style contiguous segment.     |      OWNDATA (O)     |          The array owns the memory it uses or borrows it from another object.     |      WRITEABLE (W)     |          The data area can be written to.  Setting this to False locks     |          the data, making it read-only.  A view (slice, etc.) inherits WRITEABLE     |          from its base array at creation time, but a view of a writeable     |          array may be subsequently locked while the base array remains writeable.     |          (The opposite is not true, in that a view of a locked array may not     |          be made writeable.  However, currently, locking a base object does not     |          lock any views that already reference it, so under that circumstance it     |          is possible to alter the contents of a locked array via a previously     |          created writeable view onto it.)  Attempting to change a non-writeable     |          array raises a RuntimeError exception.     |      ALIGNED (A)     |          The data and all elements are aligned appropriately for the hardware.     |      UPDATEIFCOPY (U)     |          This array is a copy of some other array. When this array is     |          deallocated, the base array will be updated with the contents of     |          this array.     |      FNC     |          F_CONTIGUOUS and not C_CONTIGUOUS.     |      FORC     |          F_CONTIGUOUS or C_CONTIGUOUS (one-segment test).     |      BEHAVED (B)     |          ALIGNED and WRITEABLE.     |      CARRAY (CA)     |          BEHAVED and C_CONTIGUOUS.     |      FARRAY (FA)     |          BEHAVED and F_CONTIGUOUS and not C_CONTIGUOUS.     |           |      Notes     |      -----     |      The `flags` object can be accessed dictionary-like (as in ``a.flags['WRITEABLE']``),     |      or by using lowercased attribute names (as in ``a.flags.writeable``). Short flag     |      names are only supported in dictionary access.     |           |      Only the UPDATEIFCOPY, WRITEABLE, and ALIGNED flags can be changed by     |      the user, via direct assignment to the attribute or dictionary entry,     |      or by calling `ndarray.setflags`.     |           |      The array flags cannot be set arbitrarily:     |           |      - UPDATEIFCOPY can only be set ``False``.     |      - ALIGNED can only be set ``True`` if the data is truly aligned.     |      - WRITEABLE can only be set ``True`` if the array owns its own memory     |        or the ultimate owner of the memory exposes a writeable buffer     |        interface or is a string.     |           |      Arrays can be both C-style and Fortran-style contiguous simultaneously.     |      This is clear for 1-dimensional arrays, but can also be true for higher     |      dimensional arrays.     |           |      Even for contiguous arrays a stride for a given dimension     |      ``arr.strides[dim]`` may be *arbitrary* if ``arr.shape[dim] == 1``     |      or the array has no elements.     |      It does *not* generally hold that ``self.strides[-1] == self.itemsize``     |      for C-style contiguous arrays or ``self.strides[0] == self.itemsize`` for     |      Fortran-style contiguous arrays is true.     |       |  flat     |      A 1-D iterator over the array.     |           |      This is a `numpy.flatiter` instance, which acts similarly to, but is not     |      a subclass of, Python's built-in iterator object.     |           |      See Also     |      --------     |      flatten : Return a copy of the array collapsed into one dimension.     |           |      flatiter     |           |      Examples     |      --------     |      >>> x = np.arange(1, 7).reshape(2, 3)     |      >>> x     |      array([[1, 2, 3],     |             [4, 5, 6]])     |      >>> x.flat[3]     |      4     |      >>> x.T     |      array([[1, 4],     |             [2, 5],     |             [3, 6]])     |      >>> x.T.flat[3]     |      5     |      >>> type(x.flat)     |      <type 'numpy.flatiter'>     |           |      An assignment example:     |           |      >>> x.flat = 3; x     |      array([[3, 3, 3],     |             [3, 3, 3]])     |      >>> x.flat[[1,4]] = 1; x     |      array([[3, 1, 3],     |             [3, 1, 3]])     |       |  imag     |      The imaginary part of the array.     |           |      Examples     |      --------     |      >>> x = np.sqrt([1+0j, 0+1j])     |      >>> x.imag     |      array([ 0.        ,  0.70710678])     |      >>> x.imag.dtype     |      dtype('float64')     |       |  itemsize     |      Length of one array element in bytes.     |           |      Examples     |      --------     |      >>> x = np.array([1,2,3], dtype=np.float64)     |      >>> x.itemsize     |      8     |      >>> x = np.array([1,2,3], dtype=np.complex128)     |      >>> x.itemsize     |      16     |       |  nbytes     |      Total bytes consumed by the elements of the array.     |           |      Notes     |      -----     |      Does not include memory consumed by non-element attributes of the     |      array object.     |           |      Examples     |      --------     |      >>> x = np.zeros((3,5,2), dtype=np.complex128)     |      >>> x.nbytes     |      480     |      >>> np.prod(x.shape) * x.itemsize     |      480     |       |  ndim     |      Number of array dimensions.     |           |      Examples     |      --------     |      >>> x = np.array([1, 2, 3])     |      >>> x.ndim     |      1     |      >>> y = np.zeros((2, 3, 4))     |      >>> y.ndim     |      3     |       |  real     |      The real part of the array.     |           |      Examples     |      --------     |      >>> x = np.sqrt([1+0j, 0+1j])     |      >>> x.real     |      array([ 1.        ,  0.70710678])     |      >>> x.real.dtype     |      dtype('float64')     |           |      See Also     |      --------     |      numpy.real : equivalent function     |       |  shape     |      Tuple of array dimensions.     |           |      Notes     |      -----     |      May be used to "reshape" the array, as long as this would not     |      require a change in the total number of elements     |           |      Examples     |      --------     |      >>> x = np.array([1, 2, 3, 4])     |      >>> x.shape     |      (4,)     |      >>> y = np.zeros((2, 3, 4))     |      >>> y.shape     |      (2, 3, 4)     |      >>> y.shape = (3, 8)     |      >>> y     |      array([[ 0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.],     |             [ 0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.],     |             [ 0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.]])     |      >>> y.shape = (3, 6)     |      Traceback (most recent call last):     |        File "<stdin>", line 1, in <module>     |      ValueError: total size of new array must be unchanged     |       |  size     |      Number of elements in the array.     |           |      Equivalent to ``np.prod(a.shape)``, i.e., the product of the array's     |      dimensions.     |           |      Examples     |      --------     |      >>> x = np.zeros((3, 5, 2), dtype=np.complex128)     |      >>> x.size     |      30     |      >>> np.prod(x.shape)     |      30     |       |  strides     |      Tuple of bytes to step in each dimension when traversing an array.     |           |      The byte offset of element ``(i[0], i[1], ..., i[n])`` in an array `a`     |      is::     |           |          offset = sum(np.array(i) * a.strides)     |           |      A more detailed explanation of strides can be found in the     |      "ndarray.rst" file in the NumPy reference guide.     |           |      Notes     |      -----     |      Imagine an array of 32-bit integers (each 4 bytes)::     |           |        x = np.array([[0, 1, 2, 3, 4],     |                      [5, 6, 7, 8, 9]], dtype=np.int32)     |           |      This array is stored in memory as 40 bytes, one after the other     |      (known as a contiguous block of memory).  The strides of an array tell     |      us how many bytes we have to skip in memory to move to the next position     |      along a certain axis.  For example, we have to skip 4 bytes (1 value) to     |      move to the next column, but 20 bytes (5 values) to get to the same     |      position in the next row.  As such, the strides for the array `x` will be     |      ``(20, 4)``.     |           |      See Also     |      --------     |      numpy.lib.stride_tricks.as_strided     |           |      Examples     |      --------     |      >>> y = np.reshape(np.arange(2*3*4), (2,3,4))     |      >>> y     |      array([[[ 0,  1,  2,  3],     |              [ 4,  5,  6,  7],     |              [ 8,  9, 10, 11]],     |             [[12, 13, 14, 15],     |              [16, 17, 18, 19],     |              [20, 21, 22, 23]]])     |      >>> y.strides     |      (48, 16, 4)     |      >>> y[1,1,1]     |      17     |      >>> offset=sum(y.strides * np.array((1,1,1)))     |      >>> offset/y.itemsize     |      17     |           |      >>> x = np.reshape(np.arange(5*6*7*8), (5,6,7,8)).transpose(2,3,1,0)     |      >>> x.strides     |      (32, 4, 224, 1344)     |      >>> i = np.array([3,5,2,2])     |      >>> offset = sum(i * x.strides)     |      >>> x[3,5,2,2]     |      813     |      >>> offset / x.itemsize     |      813     |       |  ----------------------------------------------------------------------     |  Data and other attributes defined here:     |       |  __hash__ = None     |       |  __new__ = <built-in method __new__ of type object>     |      T.__new__(S, ...) -> a new object with type S, a subtype of T        class ndenumerate(__builtin__.object)     |  Multidimensional index iterator.     |       |  Return an iterator yielding pairs of array coordinates and values.     |       |  Parameters     |  ----------     |  arr : ndarray     |    Input array.     |       |  See Also     |  --------     |  ndindex, flatiter     |       |  Examples     |  --------     |  >>> a = np.array([[1, 2], [3, 4]])     |  >>> for index, x in np.ndenumerate(a):     |  ...     print index, x     |  (0, 0) 1     |  (0, 1) 2     |  (1, 0) 3     |  (1, 1) 4     |       |  Methods defined here:     |       |  __init__(self, arr)     |       |  __iter__(self)     |       |  __next__(self)     |      Standard iterator method, returns the index tuple and array value.     |           |      Returns     |      -------     |      coords : tuple of ints     |          The indices of the current iteration.     |      val : scalar     |          The array element of the current iteration.     |       |  next = __next__(self)     |       |  ----------------------------------------------------------------------     |  Data descriptors defined here:     |       |  __dict__     |      dictionary for instance variables (if defined)     |       |  __weakref__     |      list of weak references to the object (if defined)