| |
- __builtin__.object
-
- broadcast
- busdaycalendar
- dtype
- flatiter
- generic
-
- bool_
- datetime64
- flexible
-
- character
-
- string_(__builtin__.str, character)
- unicode_(__builtin__.unicode, character)
- void
-
- numpy.core.records.record
- number
-
- inexact
-
- complexfloating
-
- complex128(complexfloating, __builtin__.complex)
- complex256
- complex64
- floating
-
- float128
- float16
- float32
- float64(floating, __builtin__.float)
- integer
-
- signedinteger
-
- int16
- int32
- int64(signedinteger, __builtin__.int)
- int64(signedinteger, __builtin__.int)
- int8
- timedelta64
- unsignedinteger
-
- uint16
- uint32
- uint64
- uint64
- uint8
- object_
- ndarray
-
- numpy.core.defchararray.chararray
- numpy.core.memmap.memmap
- numpy.core.records.recarray
- numpy.matrixlib.defmatrix.matrix
- nditer
- ufunc
- numpy._import_tools.PackageLoader
- numpy.core.getlimits.finfo
- numpy.core.getlimits.iinfo
- numpy.core.machar.MachAr
- numpy.core.numeric.errstate
- numpy.lib._datasource.DataSource
- numpy.lib.function_base.vectorize
- numpy.lib.index_tricks.ndenumerate
- numpy.lib.index_tricks.ndindex
- numpy.lib.polynomial.poly1d
- __builtin__.str(__builtin__.basestring)
-
- string_(__builtin__.str, character)
- __builtin__.unicode(__builtin__.basestring)
-
- unicode_(__builtin__.unicode, character)
- exceptions.DeprecationWarning(exceptions.Warning)
-
- ModuleDeprecationWarning
- exceptions.RuntimeWarning(exceptions.Warning)
-
- numpy.core.numeric.ComplexWarning
- numpy.lib.nanfunctions.NanWarning
- exceptions.UserWarning(exceptions.Warning)
-
- numpy.lib.polynomial.RankWarning
- numpy.core.records.format_parser
class DataSource(__builtin__.object) |
|
DataSource(destpath='.')
A generic data source file (file, http, ftp, ...).
DataSources can be local files or remote files/URLs. The files may
also be compressed or uncompressed. DataSource hides some of the low-level
details of downloading the file, allowing you to simply pass in a valid
file path (or URL) and obtain a file object.
Parameters
----------
destpath : str or None, optional
Path to the directory where the source file gets downloaded to for use.
If `destpath` is None, a temporary directory will be created.
The default path is the current directory.
Notes
-----
URLs require a scheme string (``http://``) to be used, without it they
will fail::
>>> repos = DataSource()
>>> repos.exists('www.google.com/index.html')
False
>>> repos.exists('http://www.google.com/index.html')
True
Temporary directories are deleted when the DataSource is deleted.
Examples
--------
::
>>> ds = DataSource('/home/guido')
>>> urlname = 'http://www.google.com/index.html'
>>> gfile = ds.open('http://www.google.com/index.html') # remote file
>>> ds.abspath(urlname)
'/home/guido/www.google.com/site/index.html'
>>> ds = DataSource(None) # use with temporary file
>>> ds.open('/home/guido/foobar.txt')
<open file '/home/guido.foobar.txt', mode 'r' at 0x91d4430>
>>> ds.abspath('/home/guido/foobar.txt')
'/tmp/tmpy4pgsP/home/guido/foobar.txt' |
|
Methods defined here:
- __del__(self)
- __init__(self, destpath='.')
- Create a DataSource with a local path at destpath.
- abspath(self, path)
- Return absolute path of file in the DataSource directory.
If `path` is an URL, then `abspath` will return either the location
the file exists locally or the location it would exist when opened
using the `open` method.
Parameters
----------
path : str
Can be a local file or a remote URL.
Returns
-------
out : str
Complete path, including the `DataSource` destination directory.
Notes
-----
The functionality is based on `os.path.abspath`.
- exists(self, path)
- Test if path exists.
Test if `path` exists as (and in this order):
- a local file.
- a remote URL that has been downloaded and stored locally in the
`DataSource` directory.
- a remote URL that has not been downloaded, but is valid and accessible.
Parameters
----------
path : str
Can be a local file or a remote URL.
Returns
-------
out : bool
True if `path` exists.
Notes
-----
When `path` is an URL, `exists` will return True if it's either stored
locally in the `DataSource` directory, or is a valid remote URL.
`DataSource` does not discriminate between the two, the file is accessible
if it exists in either location.
- open(self, path, mode='r')
- Open and return file-like object.
If `path` is an URL, it will be downloaded, stored in the `DataSource`
directory and opened from there.
Parameters
----------
path : str
Local file path or URL to open.
mode : {'r', 'w', 'a'}, optional
Mode to open `path`. Mode 'r' for reading, 'w' for writing, 'a' to
append. Available modes depend on the type of object specified by
`path`. Default is 'r'.
Returns
-------
out : file object
File object.
Data descriptors defined here:
- __dict__
- dictionary for instance variables (if defined)
- __weakref__
- list of weak references to the object (if defined)
|
class MachAr(__builtin__.object) |
|
Diagnosing machine parameters.
Attributes
----------
ibeta : int
Radix in which numbers are represented.
it : int
Number of base-`ibeta` digits in the floating point mantissa M.
machep : int
Exponent of the smallest (most negative) power of `ibeta` that,
added to 1.0, gives something different from 1.0
eps : float
Floating-point number ``beta**machep`` (floating point precision)
negep : int
Exponent of the smallest power of `ibeta` that, substracted
from 1.0, gives something different from 1.0.
epsneg : float
Floating-point number ``beta**negep``.
iexp : int
Number of bits in the exponent (including its sign and bias).
minexp : int
Smallest (most negative) power of `ibeta` consistent with there
being no leading zeros in the mantissa.
xmin : float
Floating point number ``beta**minexp`` (the smallest [in
magnitude] usable floating value).
maxexp : int
Smallest (positive) power of `ibeta` that causes overflow.
xmax : float
``(1-epsneg) * beta**maxexp`` (the largest [in magnitude]
usable floating value).
irnd : int
In ``range(6)``, information on what kind of rounding is done
in addition, and on how underflow is handled.
ngrd : int
Number of 'guard digits' used when truncating the product
of two mantissas to fit the representation.
epsilon : float
Same as `eps`.
tiny : float
Same as `xmin`.
huge : float
Same as `xmax`.
precision : float
``- int(-log10(eps))``
resolution : float
``- 10**(-precision)``
Parameters
----------
float_conv : function, optional
Function that converts an integer or integer array to a float
or float array. Default is `float`.
int_conv : function, optional
Function that converts a float or float array to an integer or
integer array. Default is `int`.
float_to_float : function, optional
Function that converts a float array to float. Default is `float`.
Note that this does not seem to do anything useful in the current
implementation.
float_to_str : function, optional
Function that converts a single float to a string. Default is
``lambda v:'%24.16e' %v``.
title : str, optional
Title that is printed in the string representation of `MachAr`.
See Also
--------
finfo : Machine limits for floating point types.
iinfo : Machine limits for integer types.
References
----------
.. [1] Press, Teukolsky, Vetterling and Flannery,
"Numerical Recipes in C++," 2nd ed,
Cambridge University Press, 2002, p. 31. |
|
Methods defined here:
- __init__(self, float_conv=<type 'float'>, int_conv=<type 'int'>, float_to_float=<type 'float'>, float_to_str=<function <lambda>>, title='Python floating point number')
- float_conv - convert integer to float (array)
int_conv - convert float (array) to integer
float_to_float - convert float array to float
float_to_str - convert array float to str
title - description of used floating point numbers
- __str__(self)
Data descriptors defined here:
- __dict__
- dictionary for instance variables (if defined)
- __weakref__
- list of weak references to the object (if defined)
|
class PackageLoader(__builtin__.object) |
| |
Methods defined here:
- __call__(self, *packages, **options)
- Load one or more packages into parent package top-level namespace.
This function is intended to shorten the need to import many
subpackages, say of scipy, constantly with statements such as
import scipy.linalg, scipy.fftpack, scipy.etc...
Instead, you can say:
import scipy
scipy.pkgload('linalg','fftpack',...)
or
scipy.pkgload()
to load all of them in one call.
If a name which doesn't exist in scipy's namespace is
given, a warning is shown.
Parameters
----------
*packages : arg-tuple
the names (one or more strings) of all the modules one
wishes to load into the top-level namespace.
verbose= : integer
verbosity level [default: -1].
verbose=-1 will suspend also warnings.
force= : bool
when True, force reloading loaded packages [default: False].
postpone= : bool
when True, don't load packages [default: False]
- __init__(self, verbose=False, infunc=False)
- Manages loading packages.
- error(self, mess)
- get_pkgdocs(self)
- Return documentation summary of subpackages.
- log(self, mess)
- warn(self, mess)
Data descriptors defined here:
- __dict__
- dictionary for instance variables (if defined)
- __weakref__
- list of weak references to the object (if defined)
|
bool8 = class bool_(generic) |
|
Numpy's Boolean type. Character code: ``?``. Alias: bool8 |
|
- Method resolution order:
- bool_
- generic
- __builtin__.object
Methods defined here:
- __and__(...)
- x.__and__(y) <==> x&y
- __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
- __nonzero__(...)
- x.__nonzero__() <==> x != 0
- __or__(...)
- x.__or__(y) <==> x|y
- __rand__(...)
- x.__rand__(y) <==> y&x
- __ror__(...)
- x.__ror__(y) <==> y|x
- __rxor__(...)
- x.__rxor__(y) <==> y^x
- __xor__(...)
- x.__xor__(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
Methods inherited from generic:
- __abs__(...)
- x.__abs__() <==> abs(x)
- __add__(...)
- x.__add__(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
- __oct__(...)
- x.__oct__() <==> oct(x)
- __pos__(...)
- x.__pos__() <==> +x
- __pow__(...)
- x.__pow__(y[, z]) <==> pow(x, y[, z])
- __radd__(...)
- x.__radd__(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
- __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
- __setstate__(...)
- __str__(...)
- x.__str__() <==> str(x)
- __sub__(...)
- x.__sub__(y) <==> x-y
- __truediv__(...)
- x.__truediv__(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:
* {'<', 'L'} - little endian
* {'>', 'B'} - big endian
* {'=', 'N'} - native order
* 'S' - swap dtype from current to opposite endian
* {'|', '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.
- 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 bool_(generic) |
|
Numpy's Boolean type. Character code: ``?``. Alias: bool8 |
|
- Method resolution order:
- bool_
- generic
- __builtin__.object
Methods defined here:
- __and__(...)
- x.__and__(y) <==> x&y
- __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
- __nonzero__(...)
- x.__nonzero__() <==> x != 0
- __or__(...)
- x.__or__(y) <==> x|y
- __rand__(...)
- x.__rand__(y) <==> y&x
- __ror__(...)
- x.__ror__(y) <==> y|x
- __rxor__(...)
- x.__rxor__(y) <==> y^x
- __xor__(...)
- x.__xor__(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
Methods inherited from generic:
- __abs__(...)
- x.__abs__() <==> abs(x)
- __add__(...)
- x.__add__(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
- __oct__(...)
- x.__oct__() <==> oct(x)
- __pos__(...)
- x.__pos__() <==> +x
- __pow__(...)
- x.__pow__(y[, z]) <==> pow(x, y[, z])
- __radd__(...)
- x.__radd__(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
- __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
- __setstate__(...)
- __str__(...)
- x.__str__() <==> str(x)
- __sub__(...)
- x.__sub__(y) <==> x-y
- __truediv__(...)
- x.__truediv__(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:
* {'<', 'L'} - little endian
* {'>', 'B'} - big endian
* {'=', 'N'} - native order
* 'S' - swap dtype from current to opposite endian
* {'|', '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.
- 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 broadcast(__builtin__.object) |
|
Produce an object that mimics broadcasting.
Parameters
----------
in1, in2, ... : array_like
Input parameters.
Returns
-------
b : broadcast object
Broadcast the input parameters against one another, and
return an object that encapsulates the result.
Amongst others, it has ``shape`` and ``nd`` properties, and
may be used as an iterator.
Examples
--------
Manually adding two vectors, using broadcasting:
>>> x = np.array([[1], [2], [3]])
>>> y = np.array([4, 5, 6])
>>> b = np.broadcast(x, y)
>>> out = np.empty(b.shape)
>>> out.flat = [u+v for (u,v) in b]
>>> out
array([[ 5., 6., 7.],
[ 6., 7., 8.],
[ 7., 8., 9.]])
Compare against built-in broadcasting:
>>> x + y
array([[5, 6, 7],
[6, 7, 8],
[7, 8, 9]]) |
|
Methods defined here:
- __iter__(...)
- x.__iter__() <==> iter(x)
- next(...)
- x.next() -> the next value, or raise StopIteration
- reset(...)
- reset()
Reset the broadcasted result's iterator(s).
Parameters
----------
None
Returns
-------
None
Examples
--------
>>> x = np.array([1, 2, 3])
>>> y = np.array([[4], [5], [6]]
>>> b = np.broadcast(x, y)
>>> b.index
0
>>> b.next(), b.next(), b.next()
((1, 4), (2, 4), (3, 4))
>>> b.index
3
>>> b.reset()
>>> b.index
0
Data descriptors defined here:
- index
- current index in broadcasted result
Examples
--------
>>> x = np.array([[1], [2], [3]])
>>> y = np.array([4, 5, 6])
>>> b = np.broadcast(x, y)
>>> b.index
0
>>> b.next(), b.next(), b.next()
((1, 4), (1, 5), (1, 6))
>>> b.index
3
- iters
- tuple of iterators along ``self``'s "components."
Returns a tuple of `numpy.flatiter` objects, one for each "component"
of ``self``.
See Also
--------
numpy.flatiter
Examples
--------
>>> x = np.array([1, 2, 3])
>>> y = np.array([[4], [5], [6]])
>>> b = np.broadcast(x, y)
>>> row, col = b.iters
>>> row.next(), col.next()
(1, 4)
- nd
- Number of dimensions of broadcasted result.
Examples
--------
>>> x = np.array([1, 2, 3])
>>> y = np.array([[4], [5], [6]])
>>> b = np.broadcast(x, y)
>>> b.nd
2
- numiter
- Number of iterators possessed by the broadcasted result.
Examples
--------
>>> x = np.array([1, 2, 3])
>>> y = np.array([[4], [5], [6]])
>>> b = np.broadcast(x, y)
>>> b.numiter
2
- shape
- Shape of broadcasted result.
Examples
--------
>>> x = np.array([1, 2, 3])
>>> y = np.array([[4], [5], [6]])
>>> b = np.broadcast(x, y)
>>> b.shape
(3, 3)
- size
- Total size of broadcasted result.
Examples
--------
>>> x = np.array([1, 2, 3])
>>> y = np.array([[4], [5], [6]])
>>> b = np.broadcast(x, y)
>>> b.size
9
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
|
class busdaycalendar(__builtin__.object) |
|
busdaycalendar(weekmask='1111100', holidays=None)
A business day calendar object that efficiently stores information
defining valid days for the busday family of functions.
The default valid days are Monday through Friday ("business days").
A busdaycalendar object can be specified with any set of weekly
valid days, plus an optional "holiday" dates that always will be invalid.
Once a busdaycalendar object is created, the weekmask and holidays
cannot be modified.
.. versionadded:: 1.7.0
Parameters
----------
weekmask : str or array_like of bool, optional
A seven-element array indicating which of Monday through Sunday are
valid days. May be specified as a length-seven list or array, like
[1,1,1,1,1,0,0]; a length-seven string, like '1111100'; or a string
like "Mon Tue Wed Thu Fri", made up of 3-character abbreviations for
weekdays, optionally separated by white space. Valid abbreviations
are: Mon Tue Wed Thu Fri Sat Sun
holidays : array_like of datetime64[D], optional
An array of dates to consider as invalid dates, no matter which
weekday they fall upon. Holiday dates may be specified in any
order, and NaT (not-a-time) dates are ignored. This list is
saved in a normalized form that is suited for fast calculations
of valid days.
Returns
-------
out : busdaycalendar
A business day calendar object containing the specified
weekmask and holidays values.
See Also
--------
is_busday : Returns a boolean array indicating valid days.
busday_offset : Applies an offset counted in valid days.
busday_count : Counts how many valid days are in a half-open date range.
Attributes
----------
Note: once a busdaycalendar object is created, you cannot modify the
weekmask or holidays. The attributes return copies of internal data.
weekmask : (copy) seven-element array of bool
holidays : (copy) sorted array of datetime64[D]
Examples
--------
>>> # Some important days in July
... bdd = np.busdaycalendar(
... holidays=['2011-07-01', '2011-07-04', '2011-07-17'])
>>> # Default is Monday to Friday weekdays
... bdd.weekmask
array([ True, True, True, True, True, False, False], dtype='bool')
>>> # Any holidays already on the weekend are removed
... bdd.holidays
array(['2011-07-01', '2011-07-04'], dtype='datetime64[D]') |
|
Methods defined here:
- __init__(...)
- x.__init__(...) initializes x; see help(type(x)) for signature
Data descriptors defined here:
- holidays
- A copy of the holiday array indicating additional invalid days.
- weekmask
- A copy of the seven-element boolean mask indicating valid days.
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
|
byte = 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
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__(...)
- __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:
* {'<', 'L'} - little endian
* {'>', 'B'} - big endian
* {'=', 'N'} - native order
* 'S' - swap dtype from current to opposite endian
* {'|', '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.
- 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
|
bytes_ = class string_(__builtin__.str, character) |
| |
- Method resolution order:
- string_
- __builtin__.str
- __builtin__.basestring
- character
- flexible
- 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 __builtin__.str:
- __add__(...)
- x.__add__(y) <==> x+y
- __contains__(...)
- x.__contains__(y) <==> y in x
- __format__(...)
- S.__format__(format_spec) -> string
Return a formatted version of S as described by format_spec.
- __getattribute__(...)
- x.__getattribute__('name') <==> x.name
- __getitem__(...)
- x.__getitem__(y) <==> x[y]
- __getnewargs__(...)
- __getslice__(...)
- x.__getslice__(i, j) <==> x[i:j]
Use of negative indices is not supported.
- __len__(...)
- x.__len__() <==> len(x)
- __mod__(...)
- x.__mod__(y) <==> x%y
- __mul__(...)
- x.__mul__(n) <==> x*n
- __rmod__(...)
- x.__rmod__(y) <==> y%x
- __rmul__(...)
- x.__rmul__(n) <==> n*x
- __sizeof__(...)
- S.__sizeof__() -> size of S in memory, in bytes
- capitalize(...)
- S.capitalize() -> string
Return a copy of the string S with only its first character
capitalized.
- center(...)
- S.center(width[, fillchar]) -> string
Return S centered in a string of length width. Padding is
done using the specified fill character (default is a space)
- count(...)
- S.count(sub[, start[, end]]) -> int
Return the number of non-overlapping occurrences of substring sub in
string S[start:end]. Optional arguments start and end are interpreted
as in slice notation.
- decode(...)
- S.decode([encoding[,errors]]) -> object
Decodes S using the codec registered for encoding. encoding defaults
to the default encoding. errors may be given to set a different error
handling scheme. Default is 'strict' meaning that encoding errors raise
a UnicodeDecodeError. Other possible values are 'ignore' and 'replace'
as well as any other name registered with codecs.register_error that is
able to handle UnicodeDecodeErrors.
- encode(...)
- S.encode([encoding[,errors]]) -> object
Encodes S using the codec registered for encoding. encoding defaults
to the default encoding. errors may be given to set a different error
handling scheme. Default is 'strict' meaning that encoding errors raise
a UnicodeEncodeError. Other possible values are 'ignore', 'replace' and
'xmlcharrefreplace' as well as any other name registered with
codecs.register_error that is able to handle UnicodeEncodeErrors.
- endswith(...)
- S.endswith(suffix[, start[, end]]) -> bool
Return True if S ends with the specified suffix, False otherwise.
With optional start, test S beginning at that position.
With optional end, stop comparing S at that position.
suffix can also be a tuple of strings to try.
- expandtabs(...)
- S.expandtabs([tabsize]) -> string
Return a copy of S where all tab characters are expanded using spaces.
If tabsize is not given, a tab size of 8 characters is assumed.
- find(...)
- S.find(sub [,start [,end]]) -> int
Return the lowest index in S where substring sub is found,
such that sub is contained within S[start:end]. Optional
arguments start and end are interpreted as in slice notation.
Return -1 on failure.
- format(...)
- S.format(*args, **kwargs) -> string
Return a formatted version of S, using substitutions from args and kwargs.
The substitutions are identified by braces ('{' and '}').
- index(...)
- S.index(sub [,start [,end]]) -> int
Like S.find() but raise ValueError when the substring is not found.
- isalnum(...)
- S.isalnum() -> bool
Return True if all characters in S are alphanumeric
and there is at least one character in S, False otherwise.
- isalpha(...)
- S.isalpha() -> bool
Return True if all characters in S are alphabetic
and there is at least one character in S, False otherwise.
- isdigit(...)
- S.isdigit() -> bool
Return True if all characters in S are digits
and there is at least one character in S, False otherwise.
- islower(...)
- S.islower() -> bool
Return True if all cased characters in S are lowercase and there is
at least one cased character in S, False otherwise.
- isspace(...)
- S.isspace() -> bool
Return True if all characters in S are whitespace
and there is at least one character in S, False otherwise.
- istitle(...)
- S.istitle() -> bool
Return True if S is a titlecased string and there is at least one
character in S, i.e. uppercase characters may only follow uncased
characters and lowercase characters only cased ones. Return False
otherwise.
- isupper(...)
- S.isupper() -> bool
Return True if all cased characters in S are uppercase and there is
at least one cased character in S, False otherwise.
- join(...)
- S.join(iterable) -> string
Return a string which is the concatenation of the strings in the
iterable. The separator between elements is S.
- ljust(...)
- S.ljust(width[, fillchar]) -> string
Return S left-justified in a string of length width. Padding is
done using the specified fill character (default is a space).
- lower(...)
- S.lower() -> string
Return a copy of the string S converted to lowercase.
- lstrip(...)
- S.lstrip([chars]) -> string or unicode
Return a copy of the string S with leading whitespace removed.
If chars is given and not None, remove characters in chars instead.
If chars is unicode, S will be converted to unicode before stripping
- partition(...)
- S.partition(sep) -> (head, sep, tail)
Search for the separator sep in S, and return the part before it,
the separator itself, and the part after it. If the separator is not
found, return S and two empty strings.
- replace(...)
- S.replace(old, new[, count]) -> string
Return a copy of string S with all occurrences of substring
old replaced by new. If the optional argument count is
given, only the first count occurrences are replaced.
- rfind(...)
- S.rfind(sub [,start [,end]]) -> int
Return the highest index in S where substring sub is found,
such that sub is contained within S[start:end]. Optional
arguments start and end are interpreted as in slice notation.
Return -1 on failure.
- rindex(...)
- S.rindex(sub [,start [,end]]) -> int
Like S.rfind() but raise ValueError when the substring is not found.
- rjust(...)
- S.rjust(width[, fillchar]) -> string
Return S right-justified in a string of length width. Padding is
done using the specified fill character (default is a space)
- rpartition(...)
- S.rpartition(sep) -> (head, sep, tail)
Search for the separator sep in S, starting at the end of S, and return
the part before it, the separator itself, and the part after it. If the
separator is not found, return two empty strings and S.
- rsplit(...)
- S.rsplit([sep [,maxsplit]]) -> list of strings
Return a list of the words in the string S, using sep as the
delimiter string, starting at the end of the string and working
to the front. If maxsplit is given, at most maxsplit splits are
done. If sep is not specified or is None, any whitespace string
is a separator.
- rstrip(...)
- S.rstrip([chars]) -> string or unicode
Return a copy of the string S with trailing whitespace removed.
If chars is given and not None, remove characters in chars instead.
If chars is unicode, S will be converted to unicode before stripping
- split(...)
- S.split([sep [,maxsplit]]) -> list of strings
Return a list of the words in the string S, using sep as the
delimiter string. If maxsplit is given, at most maxsplit
splits are done. If sep is not specified or is None, any
whitespace string is a separator and empty strings are removed
from the result.
- splitlines(...)
- S.splitlines(keepends=False) -> list of strings
Return a list of the lines in S, breaking at line boundaries.
Line breaks are not included in the resulting list unless keepends
is given and true.
- startswith(...)
- S.startswith(prefix[, start[, end]]) -> bool
Return True if S starts with the specified prefix, False otherwise.
With optional start, test S beginning at that position.
With optional end, stop comparing S at that position.
prefix can also be a tuple of strings to try.
- strip(...)
- S.strip([chars]) -> string or unicode
Return a copy of the string S with leading and trailing
whitespace removed.
If chars is given and not None, remove characters in chars instead.
If chars is unicode, S will be converted to unicode before stripping
- swapcase(...)
- S.swapcase() -> string
Return a copy of the string S with uppercase characters
converted to lowercase and vice versa.
- title(...)
- S.title() -> string
Return a titlecased version of S, i.e. words start with uppercase
characters, all remaining cased characters have lowercase.
- translate(...)
- S.translate(table [,deletechars]) -> string
Return a copy of the string S, where all characters occurring
in the optional argument deletechars are removed, and the
remaining characters have been mapped through the given
translation table, which must be a string of length 256 or None.
If the table argument is None, no translation is applied and
the operation simply removes the characters in deletechars.
- upper(...)
- S.upper() -> string
Return a copy of the string S converted to uppercase.
- zfill(...)
- S.zfill(width) -> string
Pad a numeric string S with zeros on the left, to fill a field
of the specified width. The string S is never truncated.
Methods inherited from generic:
- __abs__(...)
- x.__abs__() <==> abs(x)
- __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
- __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
- __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
- __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__(...)
- __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:
* {'<', 'L'} - little endian
* {'>', 'B'} - big endian
* {'=', 'N'} - native order
* 'S' - swap dtype from current to opposite endian
* {'|', '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.
- 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
|
cdouble = class complex128(complexfloating, __builtin__.complex) |
|
Composed of two 64 bit floats |
|
- Method resolution order:
- complex128
- complexfloating
- inexact
- number
- generic
- __builtin__.complex
- __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__(...)
- __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:
* {'<', 'L'} - little endian
* {'>', 'B'} - big endian
* {'=', 'N'} - native order
* 'S' - swap dtype from current to opposite endian
* {'|', '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.
- 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__.complex:
- __coerce__(...)
- x.__coerce__(y) <==> coerce(x, y)
- __getattribute__(...)
- x.__getattribute__('name') <==> x.name
- __getnewargs__(...)
- __hash__(...)
- x.__hash__() <==> hash(x)
|
cfloat = class complex128(complexfloating, __builtin__.complex) |
|
Composed of two 64 bit floats |
|
- Method resolution order:
- complex128
- complexfloating
- inexact
- number
- generic
- __builtin__.complex
- __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__(...)
- __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:
* {'<', 'L'} - little endian
* {'>', 'B'} - big endian
* {'=', 'N'} - native order
* 'S' - swap dtype from current to opposite endian
* {'|', '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.
- 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__.complex:
- __coerce__(...)
- x.__coerce__(y) <==> coerce(x, y)
- __getattribute__(...)
- x.__getattribute__('name') <==> x.name
- __getnewargs__(...)
- __hash__(...)
- x.__hash__() <==> hash(x)
|
class character(flexible) |
| |
- Method resolution order:
- character
- flexible
- 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__(...)
- __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:
* {'<', 'L'} - little endian
* {'>', 'B'} - big endian
* {'=', 'N'} - native order
* 'S' - swap dtype from current to opposite endian
* {'|', '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.
- 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 chararray(numpy.ndarray) |
|
chararray(shape, itemsize=1, unicode=False, buffer=None, offset=0,
strides=None, order=None)
Provides a convenient view on arrays of string and unicode values.
.. note::
The `chararray` class exists for backwards compatibility with
Numarray, it is not recommended for new development. Starting from numpy
1.4, if one needs arrays of strings, it is recommended to use arrays of
`dtype` `object_`, `string_` or `unicode_`, and use the free functions
in the `numpy.char` module for fast vectorized string operations.
Versus a regular Numpy array of type `str` or `unicode`, this
class adds the following functionality:
1) values automatically have whitespace removed from the end
when indexed
2) comparison operators automatically remove whitespace from the
end when comparing values
3) vectorized string operations are provided as methods
(e.g. `.endswith`) and infix operators (e.g. ``"+", "*", "%"``)
chararrays should be created using `numpy.char.array` or
`numpy.char.asarray`, rather than this constructor directly.
This constructor creates the array, using `buffer` (with `offset`
and `strides`) if it is not ``None``. If `buffer` is ``None``, then
constructs a new array with `strides` in "C order", unless both
``len(shape) >= 2`` and ``order='Fortran'``, in which case `strides`
is in "Fortran order".
Methods
-------
astype
argsort
copy
count
decode
dump
dumps
encode
endswith
expandtabs
fill
find
flatten
getfield
index
isalnum
isalpha
isdecimal
isdigit
islower
isnumeric
isspace
istitle
isupper
item
join
ljust
lower
lstrip
nonzero
put
ravel
repeat
replace
reshape
resize
rfind
rindex
rjust
rsplit
rstrip
searchsorted
setfield
setflags
sort
split
splitlines
squeeze
startswith
strip
swapaxes
swapcase
take
title
tofile
tolist
tostring
translate
transpose
upper
view
zfill
Parameters
----------
shape : tuple
Shape of the array.
itemsize : int, optional
Length of each array element, in number of characters. Default is 1.
unicode : bool, optional
Are the array elements of type unicode (True) or string (False).
Default is False.
buffer : int, optional
Memory address of the start of the array data. Default is None,
in which case a new array is created.
offset : int, optional
Fixed stride displacement from the beginning of an axis?
Default is 0. Needs to be >=0.
strides : array_like of ints, optional
Strides for the array (see `ndarray.strides` for full description).
Default is None.
order : {'C', 'F'}, optional
The order in which the array data is stored in memory: 'C' ->
"row major" order (the default), 'F' -> "column major"
(Fortran) order.
Examples
--------
>>> charar = np.chararray((3, 3))
>>> charar[:] = 'a'
>>> charar
chararray([['a', 'a', 'a'],
['a', 'a', 'a'],
['a', 'a', 'a']],
dtype='|S1')
>>> charar = np.chararray(charar.shape, itemsize=5)
>>> charar[:] = 'abc'
>>> charar
chararray([['abc', 'abc', 'abc'],
['abc', 'abc', 'abc'],
['abc', 'abc', 'abc']],
dtype='|S5') |
|
- Method resolution order:
- chararray
- numpy.ndarray
- __builtin__.object
Methods defined here:
- __add__(self, other)
- Return (self + other), that is string concatenation,
element-wise for a pair of array_likes of str or unicode.
See also
--------
add
- __array_finalize__(self, obj)
- __eq__(self, other)
- Return (self == other) element-wise.
See also
--------
equal
- __ge__(self, other)
- Return (self >= other) element-wise.
See also
--------
greater_equal
- __getitem__(self, obj)
- __gt__(self, other)
- Return (self > other) element-wise.
See also
--------
greater
- __le__(self, other)
- Return (self <= other) element-wise.
See also
--------
less_equal
- __lt__(self, other)
- Return (self < other) element-wise.
See also
--------
less
- __mod__(self, i)
- Return (self % i), that is pre-Python 2.6 string formatting
(iterpolation), element-wise for a pair of array_likes of `string_`
or `unicode_`.
See also
--------
mod
- __mul__(self, i)
- Return (self * i), that is string multiple concatenation,
element-wise.
See also
--------
multiply
- __ne__(self, other)
- Return (self != other) element-wise.
See also
--------
not_equal
- __radd__(self, other)
- Return (other + self), that is string concatenation,
element-wise for a pair of array_likes of `string_` or `unicode_`.
See also
--------
add
- __rmod__(self, other)
- __rmul__(self, i)
- Return (self * i), that is string multiple concatenation,
element-wise.
See also
--------
multiply
- argsort(self, axis=-1, kind='quicksort', order=None)
- 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
- capitalize(self)
- Return a copy of `self` with only the first character of each element
capitalized.
See also
--------
char.capitalize
- center(self, width, fillchar=' ')
- Return a copy of `self` with its elements centered in a
string of length `width`.
See also
--------
center
- count(self, sub, start=0, end=None)
- Returns an array with the number of non-overlapping occurrences of
substring `sub` in the range [`start`, `end`].
See also
--------
char.count
- decode(self, encoding=None, errors=None)
- Calls `str.decode` element-wise.
See also
--------
char.decode
- encode(self, encoding=None, errors=None)
- Calls `str.encode` element-wise.
See also
--------
char.encode
- endswith(self, suffix, start=0, end=None)
- Returns a boolean array which is `True` where the string element
in `self` ends with `suffix`, otherwise `False`.
See also
--------
char.endswith
- expandtabs(self, tabsize=8)
- Return a copy of each string element where all tab characters are
replaced by one or more spaces.
See also
--------
char.expandtabs
- find(self, sub, start=0, end=None)
- For each element, return the lowest index in the string where
substring `sub` is found.
See also
--------
char.find
- index(self, sub, start=0, end=None)
- Like `find`, but raises `ValueError` when the substring is not found.
See also
--------
char.index
- isalnum(self)
- Returns true for each element if all characters in the string
are alphanumeric and there is at least one character, false
otherwise.
See also
--------
char.isalnum
- isalpha(self)
- Returns true for each element if all characters in the string
are alphabetic and there is at least one character, false
otherwise.
See also
--------
char.isalpha
- isdecimal(self)
- For each element in `self`, return True if there are only
decimal characters in the element.
See also
--------
char.isdecimal
- isdigit(self)
- Returns true for each element if all characters in the string are
digits and there is at least one character, false otherwise.
See also
--------
char.isdigit
- islower(self)
- Returns true for each element if all cased characters in the
string are lowercase and there is at least one cased character,
false otherwise.
See also
--------
char.islower
- isnumeric(self)
- For each element in `self`, return True if there are only
numeric characters in the element.
See also
--------
char.isnumeric
- isspace(self)
- Returns true for each element if there are only whitespace
characters in the string and there is at least one character,
false otherwise.
See also
--------
char.isspace
- istitle(self)
- Returns true for each element if the element is a titlecased
string and there is at least one character, false otherwise.
See also
--------
char.istitle
- isupper(self)
- Returns true for each element if all cased characters in the
string are uppercase and there is at least one character, false
otherwise.
See also
--------
char.isupper
- join(self, seq)
- Return a string which is the concatenation of the strings in the
sequence `seq`.
See also
--------
char.join
- ljust(self, width, fillchar=' ')
- Return an array with the elements of `self` left-justified in a
string of length `width`.
See also
--------
char.ljust
- lower(self)
- Return an array with the elements of `self` converted to
lowercase.
See also
--------
char.lower
- lstrip(self, chars=None)
- For each element in `self`, return a copy with the leading characters
removed.
See also
--------
char.lstrip
- partition(self, sep)
- Partition each element in `self` around `sep`.
See also
--------
partition
- replace(self, old, new, count=None)
- For each element in `self`, return a copy of the string with all
occurrences of substring `old` replaced by `new`.
See also
--------
char.replace
- rfind(self, sub, start=0, end=None)
- For each element in `self`, return the highest index in the string
where substring `sub` is found, such that `sub` is contained
within [`start`, `end`].
See also
--------
char.rfind
- rindex(self, sub, start=0, end=None)
- Like `rfind`, but raises `ValueError` when the substring `sub` is
not found.
See also
--------
char.rindex
- rjust(self, width, fillchar=' ')
- Return an array with the elements of `self`
right-justified in a string of length `width`.
See also
--------
char.rjust
- rpartition(self, sep)
- Partition each element in `self` around `sep`.
See also
--------
rpartition
- rsplit(self, sep=None, maxsplit=None)
- For each element in `self`, return a list of the words in
the string, using `sep` as the delimiter string.
See also
--------
char.rsplit
- rstrip(self, chars=None)
- For each element in `self`, return a copy with the trailing
characters removed.
See also
--------
char.rstrip
- split(self, sep=None, maxsplit=None)
- For each element in `self`, return a list of the words in the
string, using `sep` as the delimiter string.
See also
--------
char.split
- splitlines(self, keepends=None)
- For each element in `self`, return a list of the lines in the
element, breaking at line boundaries.
See also
--------
char.splitlines
- startswith(self, prefix, start=0, end=None)
- Returns a boolean array which is `True` where the string element
in `self` starts with `prefix`, otherwise `False`.
See also
--------
char.startswith
- strip(self, chars=None)
- For each element in `self`, return a copy with the leading and
trailing characters removed.
See also
--------
char.strip
- swapcase(self)
- For each element in `self`, return a copy of the string with
uppercase characters converted to lowercase and vice versa.
See also
--------
char.swapcase
- title(self)
- For each element in `self`, return a titlecased version of the
string: words start with uppercase characters, all remaining cased
characters are lowercase.
See also
--------
char.title
- translate(self, table, deletechars=None)
- For each element in `self`, return a copy of the string where
all characters occurring in the optional argument
`deletechars` are removed, and the remaining characters have
been mapped through the given translation table.
See also
--------
char.translate
- upper(self)
- Return an array with the elements of `self` converted to
uppercase.
See also
--------
char.upper
- zfill(self, width)
- Return the numeric string left-filled with zeros in a string of
length `width`.
See also
--------
char.zfill
Static methods defined here:
- __new__(subtype, shape, itemsize=1, unicode=False, buffer=None, offset=0, strides=None, order='C')
Data descriptors defined here:
- __dict__
- dictionary for instance variables (if defined)
Methods inherited from numpy.ndarray:
- __abs__(...)
- x.__abs__() <==> abs(x)
- __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)
- __float__(...)
- x.__float__() <==> float(x)
- __floordiv__(...)
- x.__floordiv__(y) <==> x//y
- __getslice__(...)
- x.__getslice__(i, j) <==> x[i:j]
Use of negative indices is not supported.
- __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
- __len__(...)
- x.__len__() <==> len(x)
- __long__(...)
- x.__long__() <==> long(x)
- __lshift__(...)
- x.__lshift__(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])
- __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
- __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)
- __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)
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)
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='quickselect', 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
- 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`.
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(a_min, a_max, out=None)
Return an array whose values are limited to ``[a_min, a_max]``.
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.
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 C (row-major), Fortran (column-major) 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)
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)
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
above. `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
- prod(...)
- a.prod(axis=None, dtype=None, out=None)
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 : list, optional
When `a` is an array with fields defined, this argument specifies
which fields to compare first, second, etc. Not all fields need be
specified.
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)
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)
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
- 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.tostring())``.
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 a Python string containing the raw data bytes in the array.
Constructs a Python string showing a copy of the raw contents of
data memory. The string 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.
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 : str
A Python string exhibiting a copy of `a`'s raw data.
Examples
--------
>>> x = np.array([[0, 1], [2, 3]])
>>> x.tostring()
'\x00\x00\x00\x00\x01\x00\x00\x00\x02\x00\x00\x00\x03\x00\x00\x00'
>>> x.tostring('C') == x.tostring()
True
>>> x.tostring('F')
'\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)
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 record array:
>>> 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_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
|
clongdouble = class complex256(complexfloating) |
|
Composed of two 128 bit floats |
|
- Method resolution order:
- complex256
- 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__(...)
- __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:
* {'<', 'L'} - little endian
* {'>', 'B'} - big endian
* {'=', 'N'} - native order
* 'S' - swap dtype from current to opposite endian
* {'|', '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.
- 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
|
clongfloat = class complex256(complexfloating) |
|
Composed of two 128 bit floats |
|
- Method resolution order:
- complex256
- 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__(...)
- __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:
* {'<', 'L'} - little endian
* {'>', 'B'} - big endian
* {'=', 'N'} - native order
* 'S' - swap dtype from current to opposite endian
* {'|', '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.
- 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 complex128(complexfloating, __builtin__.complex) |
|
Composed of two 64 bit floats |
|
- Method resolution order:
- complex128
- complexfloating
- inexact
- number
- generic
- __builtin__.complex
- __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__(...)
- __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:
* {'<', 'L'} - little endian
* {'>', 'B'} - big endian
* {'=', 'N'} - native order
* 'S' - swap dtype from current to opposite endian
* {'|', '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.
- 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__.complex:
- __coerce__(...)
- x.__coerce__(y) <==> coerce(x, y)
- __getattribute__(...)
- x.__getattribute__('name') <==> x.name
- __getnewargs__(...)
- __hash__(...)
- x.__hash__() <==> hash(x)
|
class complex256(complexfloating) |
|
Composed of two 128 bit floats |
|
- Method resolution order:
- complex256
- 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__(...)
- __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:
* {'<', 'L'} - little endian
* {'>', 'B'} - big endian
* {'=', 'N'} - native order
* 'S' - swap dtype from current to opposite endian
* {'|', '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.
- 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 complex64(complexfloating) |
|
Composed of two 32 bit floats |
|
- Method resolution order:
- complex64
- complexfloating
- inexact
- number
- generic
- __builtin__.object
Methods defined here:
- __complex__(...)
- __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__(...)
- __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:
* {'<', 'L'} - little endian
* {'>', 'B'} - big endian
* {'=', 'N'} - native order
* 'S' - swap dtype from current to opposite endian
* {'|', '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.
- 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
|
complex_ = class complex128(complexfloating, __builtin__.complex) |
|
Composed of two 64 bit floats |
|
- Method resolution order:
- complex128
- complexfloating
- inexact
- number
- generic
- __builtin__.complex
- __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__(...)
- __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:
* {'<', 'L'} - little endian
* {'>', 'B'} - big endian
* {'=', 'N'} - native order
* 'S' - swap dtype from current to opposite endian
* {'|', '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.
- 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__.complex:
- __coerce__(...)
- x.__coerce__(y) <==> coerce(x, y)
- __getattribute__(...)
- x.__getattribute__('name') <==> x.name
- __getnewargs__(...)
- __hash__(...)
- x.__hash__() <==> hash(x)
|
class complexfloating(inexact) |
| |
- Method resolution order:
- complexfloating
- 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__(...)
- __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:
* {'<', 'L'} - little endian
* {'>', 'B'} - big endian
* {'=', 'N'} - native order
* 'S' - swap dtype from current to opposite endian
* {'|', '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.
- 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
|
csingle = class complex64(complexfloating) |
|
Composed of two 32 bit floats |
|
- Method resolution order:
- complex64
- complexfloating
- inexact
- number
- generic
- __builtin__.object
Methods defined here:
- __complex__(...)
- __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__(...)
- __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:
* {'<', 'L'} - little endian
* {'>', 'B'} - big endian
* {'=', 'N'} - native order
* 'S' - swap dtype from current to opposite endian
* {'|', '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.
- 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 datetime64(generic) |
| |
- Method resolution order:
- datetime64
- 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__(...)
- __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:
* {'<', 'L'} - little endian
* {'>', 'B'} - big endian
* {'=', 'N'} - native order
* 'S' - swap dtype from current to opposite endian
* {'|', '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.
- 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
|
double = 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__(...)
- __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:
* {'<', 'L'} - little endian
* {'>', 'B'} - big endian
* {'=', 'N'} - native order
* 'S' - swap dtype from current to opposite endian
* {'|', '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.
- 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 dtype(__builtin__.object) |
|
dtype(obj, align=False, copy=False)
Create a data type object.
A numpy array is homogeneous, and contains elements described by a
dtype object. A dtype object can be constructed from different
combinations of fundamental numeric types.
Parameters
----------
obj
Object to be converted to a data type object.
align : bool, optional
Add padding to the fields to match what a C compiler would output
for a similar C-struct. Can be ``True`` only if `obj` is a dictionary
or a comma-separated string. If a struct dtype is being created,
this also sets a sticky alignment flag ``isalignedstruct``.
copy : bool, optional
Make a new copy of the data-type object. If ``False``, the result
may just be a reference to a built-in data-type object.
See also
--------
result_type
Examples
--------
Using array-scalar type:
>>> np.dtype(np.int16)
dtype('int16')
Record, one field name 'f1', containing int16:
>>> np.dtype([('f1', np.int16)])
dtype([('f1', '<i2')])
Record, one field named 'f1', in itself containing a record with one field:
>>> np.dtype([('f1', [('f1', np.int16)])])
dtype([('f1', [('f1', '<i2')])])
Record, two fields: the first field contains an unsigned int, the
second an int32:
>>> np.dtype([('f1', np.uint), ('f2', np.int32)])
dtype([('f1', '<u4'), ('f2', '<i4')])
Using array-protocol type strings:
>>> np.dtype([('a','f8'),('b','S10')])
dtype([('a', '<f8'), ('b', '|S10')])
Using comma-separated field formats. The shape is (2,3):
>>> np.dtype("i4, (2,3)f8")
dtype([('f0', '<i4'), ('f1', '<f8', (2, 3))])
Using tuples. ``int`` is a fixed type, 3 the field's shape. ``void``
is a flexible type, here of size 10:
>>> np.dtype([('hello',(np.int,3)),('world',np.void,10)])
dtype([('hello', '<i4', 3), ('world', '|V10')])
Subdivide ``int16`` into 2 ``int8``'s, called x and y. 0 and 1 are
the offsets in bytes:
>>> np.dtype((np.int16, {'x':(np.int8,0), 'y':(np.int8,1)}))
dtype(('<i2', [('x', '|i1'), ('y', '|i1')]))
Using dictionaries. Two fields named 'gender' and 'age':
>>> np.dtype({'names':['gender','age'], 'formats':['S1',np.uint8]})
dtype([('gender', '|S1'), ('age', '|u1')])
Offsets in bytes, here 0 and 25:
>>> np.dtype({'surname':('S25',0),'age':(np.uint8,25)})
dtype([('surname', '|S25'), ('age', '|u1')]) |
|
Methods defined here:
- __eq__(...)
- x.__eq__(y) <==> x==y
- __ge__(...)
- x.__ge__(y) <==> x>=y
- __getitem__(...)
- x.__getitem__(y) <==> x[y]
- __gt__(...)
- x.__gt__(y) <==> x>y
- __hash__(...)
- x.__hash__() <==> hash(x)
- __le__(...)
- x.__le__(y) <==> x<=y
- __len__(...)
- x.__len__() <==> len(x)
- __lt__(...)
- x.__lt__(y) <==> x<y
- __mul__(...)
- x.__mul__(n) <==> x*n
- __ne__(...)
- x.__ne__(y) <==> x!=y
- __reduce__(...)
- __repr__(...)
- x.__repr__() <==> repr(x)
- __rmul__(...)
- x.__rmul__(n) <==> n*x
- __setstate__(...)
- __str__(...)
- x.__str__() <==> str(x)
- 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.
Parameters
----------
new_order : string, optional
Byte order to force; a value from the byte order
specifications below. The default value ('S') results in
swapping the current byte order.
`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 code does a case-insensitive check on the first letter of
`new_order` for these alternatives. For example, any of '>'
or 'B' or 'b' or 'brian' are valid to specify big-endian.
Returns
-------
new_dtype : dtype
New dtype object with the given change to the byte order.
Notes
-----
Changes are also made in all fields and sub-arrays of the data type.
Examples
--------
>>> import sys
>>> sys_is_le = sys.byteorder == 'little'
>>> native_code = sys_is_le and '<' or '>'
>>> swapped_code = sys_is_le and '>' or '<'
>>> native_dt = np.dtype(native_code+'i2')
>>> swapped_dt = np.dtype(swapped_code+'i2')
>>> native_dt.newbyteorder('S') == swapped_dt
True
>>> native_dt.newbyteorder() == swapped_dt
True
>>> native_dt == swapped_dt.newbyteorder('S')
True
>>> native_dt == swapped_dt.newbyteorder('=')
True
>>> native_dt == swapped_dt.newbyteorder('N')
True
>>> native_dt == native_dt.newbyteorder('|')
True
>>> np.dtype('<i2') == native_dt.newbyteorder('<')
True
>>> np.dtype('<i2') == native_dt.newbyteorder('L')
True
>>> np.dtype('>i2') == native_dt.newbyteorder('>')
True
>>> np.dtype('>i2') == native_dt.newbyteorder('B')
True
Data descriptors defined here:
- alignment
- The required alignment (bytes) of this data-type according to the compiler.
More information is available in the C-API section of the manual.
- base
- byteorder
- A character indicating the byte-order of this data-type object.
One of:
=== ==============
'=' native
'<' little-endian
'>' big-endian
'|' not applicable
=== ==============
All built-in data-type objects have byteorder either '=' or '|'.
Examples
--------
>>> dt = np.dtype('i2')
>>> dt.byteorder
'='
>>> # endian is not relevant for 8 bit numbers
>>> np.dtype('i1').byteorder
'|'
>>> # or ASCII strings
>>> np.dtype('S2').byteorder
'|'
>>> # Even if specific code is given, and it is native
>>> # '=' is the byteorder
>>> import sys
>>> sys_is_le = sys.byteorder == 'little'
>>> native_code = sys_is_le and '<' or '>'
>>> swapped_code = sys_is_le and '>' or '<'
>>> dt = np.dtype(native_code + 'i2')
>>> dt.byteorder
'='
>>> # Swapped code shows up as itself
>>> dt = np.dtype(swapped_code + 'i2')
>>> dt.byteorder == swapped_code
True
- char
- A unique character code for each of the 21 different built-in types.
- descr
- Array-interface compliant full description of the data-type.
The format is that required by the 'descr' key in the
`__array_interface__` attribute.
- fields
- Dictionary of named fields defined for this data type, or ``None``.
The dictionary is indexed by keys that are the names of the fields.
Each entry in the dictionary is a tuple fully describing the field::
(dtype, offset[, title])
If present, the optional title can be any object (if it is a string
or unicode then it will also be a key in the fields dictionary,
otherwise it's meta-data). Notice also that the first two elements
of the tuple can be passed directly as arguments to the ``ndarray.getfield``
and ``ndarray.setfield`` methods.
See Also
--------
ndarray.getfield, ndarray.setfield
Examples
--------
>>> dt = np.dtype([('name', np.str_, 16), ('grades', np.float64, (2,))])
>>> print dt.fields
{'grades': (dtype(('float64',(2,))), 16), 'name': (dtype('|S16'), 0)}
- flags
- Bit-flags describing how this data type is to be interpreted.
Bit-masks are in `numpy.core.multiarray` as the constants
`ITEM_HASOBJECT`, `LIST_PICKLE`, `ITEM_IS_POINTER`, `NEEDS_INIT`,
`NEEDS_PYAPI`, `USE_GETITEM`, `USE_SETITEM`. A full explanation
of these flags is in C-API documentation; they are largely useful
for user-defined data-types.
- hasobject
- Boolean indicating whether this dtype contains any reference-counted
objects in any fields or sub-dtypes.
Recall that what is actually in the ndarray memory representing
the Python object is the memory address of that object (a pointer).
Special handling may be required, and this attribute is useful for
distinguishing data types that may contain arbitrary Python objects
and data-types that won't.
- isalignedstruct
- Boolean indicating whether the dtype is a struct which maintains
field alignment. This flag is sticky, so when combining multiple
structs together, it is preserved and produces new dtypes which
are also aligned.
- isbuiltin
- Integer indicating how this dtype relates to the built-in dtypes.
Read-only.
= ========================================================================
0 if this is a structured array type, with fields
1 if this is a dtype compiled into numpy (such as ints, floats etc)
2 if the dtype is for a user-defined numpy type
A user-defined type uses the numpy C-API machinery to extend
numpy to handle a new array type. See
:ref:`user.user-defined-data-types` in the Numpy manual.
= ========================================================================
Examples
--------
>>> dt = np.dtype('i2')
>>> dt.isbuiltin
1
>>> dt = np.dtype('f8')
>>> dt.isbuiltin
1
>>> dt = np.dtype([('field1', 'f8')])
>>> dt.isbuiltin
0
- isnative
- Boolean indicating whether the byte order of this dtype is native
to the platform.
- itemsize
- The element size of this data-type object.
For 18 of the 21 types this number is fixed by the data-type.
For the flexible data-types, this number can be anything.
- kind
- A character code (one of 'biufcSUV') identifying the general kind of data.
- metadata
- name
- A bit-width name for this data-type.
Un-sized flexible data-type objects do not have this attribute.
- names
- Ordered list of field names, or ``None`` if there are no fields.
The names are ordered according to increasing byte offset. This can be
used, for example, to walk through all of the named fields in offset order.
Examples
--------
>>> dt = np.dtype([('name', np.str_, 16), ('grades', np.float64, (2,))])
>>> dt.names
('name', 'grades')
- num
- A unique number for each of the 21 different built-in types.
These are roughly ordered from least-to-most precision.
- shape
- Shape tuple of the sub-array if this data type describes a sub-array,
and ``()`` otherwise.
- str
- The array-protocol typestring of this data-type object.
- subdtype
- Tuple ``(item_dtype, shape)`` if this `dtype` describes a sub-array, and
None otherwise.
The *shape* is the fixed shape of the sub-array described by this
data type, and *item_dtype* the data type of the array.
If a field whose dtype object has this attribute is retrieved,
then the extra dimensions implied by *shape* are tacked on to
the end of the retrieved array.
- type
- The type object used to instantiate a scalar of this data-type.
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
|
class errstate(__builtin__.object) |
|
errstate(**kwargs)
Context manager for floating-point error handling.
Using an instance of `errstate` as a context manager allows statements in
that context to execute with a known error handling behavior. Upon entering
the context the error handling is set with `seterr` and `seterrcall`, and
upon exiting it is reset to what it was before.
Parameters
----------
kwargs : {divide, over, under, invalid}
Keyword arguments. The valid keywords are the possible floating-point
exceptions. Each keyword should have a string value that defines the
treatment for the particular error. Possible values are
{'ignore', 'warn', 'raise', 'call', 'print', 'log'}.
See Also
--------
seterr, geterr, seterrcall, geterrcall
Notes
-----
The ``with`` statement was introduced in Python 2.5, and can only be used
there by importing it: ``from __future__ import with_statement``. In
earlier Python versions the ``with`` statement is not available.
For complete documentation of the types of floating-point exceptions and
treatment options, see `seterr`.
Examples
--------
>>> from __future__ import with_statement # use 'with' in Python 2.5
>>> olderr = np.seterr(all='ignore') # Set error handling to known state.
>>> np.arange(3) / 0.
array([ NaN, Inf, Inf])
>>> with np.errstate(divide='warn'):
... np.arange(3) / 0.
...
__main__:2: RuntimeWarning: divide by zero encountered in divide
array([ NaN, Inf, Inf])
>>> np.sqrt(-1)
nan
>>> with np.errstate(invalid='raise'):
... np.sqrt(-1)
Traceback (most recent call last):
File "<stdin>", line 2, in <module>
FloatingPointError: invalid value encountered in sqrt
Outside the context the error handling behavior has not changed:
>>> np.geterr()
{'over': 'warn', 'divide': 'warn', 'invalid': 'warn',
'under': 'ignore'} |
|
Methods defined here:
- __enter__(self)
- __exit__(self, *exc_info)
- __init__(self, **kwargs)
- # Note that we don't want to run the above doctests because they will fail
# without a from __future__ import with_statement
Data descriptors defined here:
- __dict__
- dictionary for instance variables (if defined)
- __weakref__
- list of weak references to the object (if defined)
|
class finfo(__builtin__.object) |
|
finfo(dtype)
Machine limits for floating point types.
Attributes
----------
eps : float
The smallest representable positive number such that
``1.0 + eps != 1.0``. Type of `eps` is an appropriate floating
point type.
epsneg : floating point number of the appropriate type
The smallest representable positive number such that
``1.0 - epsneg != 1.0``.
iexp : int
The number of bits in the exponent portion of the floating point
representation.
machar : MachAr
The object which calculated these parameters and holds more
detailed information.
machep : int
The exponent that yields `eps`.
max : floating point number of the appropriate type
The largest representable number.
maxexp : int
The smallest positive power of the base (2) that causes overflow.
min : floating point number of the appropriate type
The smallest representable number, typically ``-max``.
minexp : int
The most negative power of the base (2) consistent with there
being no leading 0's in the mantissa.
negep : int
The exponent that yields `epsneg`.
nexp : int
The number of bits in the exponent including its sign and bias.
nmant : int
The number of bits in the mantissa.
precision : int
The approximate number of decimal digits to which this kind of
float is precise.
resolution : floating point number of the appropriate type
The approximate decimal resolution of this type, i.e.,
``10**-precision``.
tiny : float
The smallest positive usable number. Type of `tiny` is an
appropriate floating point type.
Parameters
----------
dtype : float, dtype, or instance
Kind of floating point data-type about which to get information.
See Also
--------
MachAr : The implementation of the tests that produce this information.
iinfo : The equivalent for integer data types.
Notes
-----
For developers of NumPy: do not instantiate this at the module level.
The initial calculation of these parameters is expensive and negatively
impacts import times. These objects are cached, so calling ``finfo()``
repeatedly inside your functions is not a problem. |
|
Methods defined here:
- __repr__(self)
- __str__(self)
Static methods defined here:
- __new__(cls, dtype)
Data descriptors defined here:
- __dict__
- dictionary for instance variables (if defined)
- __weakref__
- list of weak references to the object (if defined)
|
class flatiter(__builtin__.object) |
|
Flat iterator object to iterate over arrays.
A `flatiter` iterator is returned by ``x.flat`` for any array `x`.
It allows iterating over the array as if it were a 1-D array,
either in a for-loop or by calling its `next` method.
Iteration is done in C-contiguous style, with the last index varying the
fastest. The iterator can also be indexed using basic slicing or
advanced indexing.
See Also
--------
ndarray.flat : Return a flat iterator over an array.
ndarray.flatten : Returns a flattened copy of an array.
Notes
-----
A `flatiter` iterator can not be constructed directly from Python code
by calling the `flatiter` constructor.
Examples
--------
>>> x = np.arange(6).reshape(2, 3)
>>> fl = x.flat
>>> type(fl)
<type 'numpy.flatiter'>
>>> for item in fl:
... print item
...
0
1
2
3
4
5
>>> fl[2:4]
array([2, 3]) |
|
Methods defined here:
- __array__(...)
- __array__(type=None) Get array from iterator
- __delitem__(...)
- x.__delitem__(y) <==> del x[y]
- __eq__(...)
- x.__eq__(y) <==> x==y
- __ge__(...)
- x.__ge__(y) <==> x>=y
- __getitem__(...)
- x.__getitem__(y) <==> x[y]
- __gt__(...)
- x.__gt__(y) <==> x>y
- __iter__(...)
- x.__iter__() <==> iter(x)
- __le__(...)
- x.__le__(y) <==> x<=y
- __len__(...)
- x.__len__() <==> len(x)
- __lt__(...)
- x.__lt__(y) <==> x<y
- __ne__(...)
- x.__ne__(y) <==> x!=y
- __setitem__(...)
- x.__setitem__(i, y) <==> x[i]=y
- copy(...)
- copy()
Get a copy of the iterator as a 1-D array.
Examples
--------
>>> x = np.arange(6).reshape(2, 3)
>>> x
array([[0, 1, 2],
[3, 4, 5]])
>>> fl = x.flat
>>> fl.copy()
array([0, 1, 2, 3, 4, 5])
- next(...)
- x.next() -> the next value, or raise StopIteration
Data descriptors defined here:
- base
- A reference to the array that is iterated over.
Examples
--------
>>> x = np.arange(5)
>>> fl = x.flat
>>> fl.base is x
True
- coords
- An N-dimensional tuple of current coordinates.
Examples
--------
>>> x = np.arange(6).reshape(2, 3)
>>> fl = x.flat
>>> fl.coords
(0, 0)
>>> fl.next()
0
>>> fl.coords
(0, 1)
- index
- Current flat index into the array.
Examples
--------
>>> x = np.arange(6).reshape(2, 3)
>>> fl = x.flat
>>> fl.index
0
>>> fl.next()
0
>>> fl.index
1
|
class flexible(generic) |
| |
- Method resolution order:
- flexible
- 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__(...)
- __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:
* {'<', 'L'} - little endian
* {'>', 'B'} - big endian
* {'=', 'N'} - native order
* 'S' - swap dtype from current to opposite endian
* {'|', '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.
- 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 float128(floating) |
|
128-bit floating-point number. Character code: 'g'. C long float
compatible. |
|
- Method resolution order:
- float128
- 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__(...)
- __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:
* {'<', 'L'} - little endian
* {'>', 'B'} - big endian
* {'=', 'N'} - native order
* 'S' - swap dtype from current to opposite endian
* {'|', '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.
- 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 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__(...)
- __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:
* {'<', 'L'} - little endian
* {'>', 'B'} - big endian
* {'=', 'N'} - native order
* 'S' - swap dtype from current to opposite endian
* {'|', '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.
- 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 float32(floating) |
|
32-bit floating-point number. Character code 'f'. C float compatible. |
|
- Method resolution order:
- float32
- 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__(...)
- __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:
* {'<', 'L'} - little endian
* {'>', 'B'} - big endian
* {'=', 'N'} - native order
* 'S' - swap dtype from current to opposite endian
* {'|', '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.
- 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 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__(...)
- __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:
* {'<', 'L'} - little endian
* {'>', 'B'} - big endian
* {'=', 'N'} - native order
* 'S' - swap dtype from current to opposite endian
* {'|', '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.
- 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.
|
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__(...)
- __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:
* {'<', 'L'} - little endian
* {'>', 'B'} - big endian
* {'=', 'N'} - native order
* 'S' - swap dtype from current to opposite endian
* {'|', '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.
- 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__(...)
- __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:
* {'<', 'L'} - little endian
* {'>', 'B'} - big endian
* {'=', 'N'} - native order
* 'S' - swap dtype from current to opposite endian
* {'|', '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.
- 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__(...)
- __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:
* {'<', 'L'} - little endian
* {'>', 'B'} - big endian
* {'=', 'N'} - native order
* 'S' - swap dtype from current to opposite endian
* {'|', '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.
- 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__(...)
- __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:
* {'<', 'L'} - little endian
* {'>', 'B'} - big endian
* {'=', 'N'} - native order
* 'S' - swap dtype from current to opposite endian
* {'|', '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.
- 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
----------
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__(...)
- __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:
* {'<', 'L'} - little endian
* {'>', 'B'} - big endian
* {'=', 'N'} - native order
* 'S' - swap dtype from current to opposite endian
* {'|', '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.
- 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, __builtin__.int) |
|
64-bit integer. Character code 'l'. Python int compatible. |
|
- Method resolution order:
- int64
- 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
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__(...)
- __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:
* {'<', 'L'} - little endian
* {'>', 'B'} - big endian
* {'=', 'N'} - native order
* 'S' - swap dtype from current to opposite endian
* {'|', '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.
- 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
Data descriptors inherited from __builtin__.int:
- denominator
- the denominator of a rational number in lowest terms
- numerator
- the numerator of a rational number in lowest terms
|
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
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__(...)
- __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:
* {'<', 'L'} - little endian
* {'>', 'B'} - big endian
* {'=', 'N'} - native order
* 'S' - swap dtype from current to opposite endian
* {'|', '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.
- 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) |
|
32-bit integer. Character code 'i'. C int compatible. |
|
- Method resolution order:
- int32
- 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
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__(...)
- __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:
* {'<', 'L'} - little endian
* {'>', 'B'} - big endian
* {'=', 'N'} - native order
* 'S' - swap dtype from current to opposite endian
* {'|', '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.
- 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 int64(signedinteger, __builtin__.int) |
|
64-bit integer. Character code 'l'. Python int compatible. |
|
- Method resolution order:
- int64
- 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
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__(...)
- __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:
* {'<', 'L'} - little endian
* {'>', 'B'} - big endian
* {'=', 'N'} - native order
* 'S' - swap dtype from current to opposite endian
* {'|', '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.
- 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
Data descriptors inherited from __builtin__.int:
- denominator
- the denominator of a rational number in lowest terms
- numerator
- the numerator of a rational number in lowest terms
|
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
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__(...)
- __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:
* {'<', 'L'} - little endian
* {'>', 'B'} - big endian
* {'=', 'N'} - native order
* 'S' - swap dtype from current to opposite endian
* {'|', '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.
- 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 int64(signedinteger, __builtin__.int) |
|
64-bit integer. Character code 'l'. Python int compatible. |
|
- Method resolution order:
- int64
- 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
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__(...)
- __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:
* {'<', 'L'} - little endian
* {'>', 'B'} - big endian
* {'=', 'N'} - native order
* 'S' - swap dtype from current to opposite endian
* {'|', '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.
- 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
Data descriptors inherited from __builtin__.int:
- denominator
- the denominator of a rational number in lowest terms
- numerator
- the numerator of a rational number in lowest terms
|
intc = class int32(signedinteger) |
|
32-bit integer. Character code 'i'. C int compatible. |
|
- Method resolution order:
- int32
- 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
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__(...)
- __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:
* {'<', 'L'} - little endian
* {'>', 'B'} - big endian
* {'=', 'N'} - native order
* 'S' - swap dtype from current to opposite endian
* {'|', '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.
- 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 integer(number) |
| |
- Method resolution order:
- integer
- 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__(...)
- __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:
* {'<', 'L'} - little endian
* {'>', 'B'} - big endian
* {'=', 'N'} - native order
* 'S' - swap dtype from current to opposite endian
* {'|', '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.
- 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, __builtin__.int) |
|
64-bit integer. Character code 'l'. Python int compatible. |
|
- Method resolution order:
- int64
- 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
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__(...)
- __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:
* {'<', 'L'} - little endian
* {'>', 'B'} - big endian
* {'=', 'N'} - native order
* 'S' - swap dtype from current to opposite endian
* {'|', '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.
- 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
Data descriptors inherited from __builtin__.int:
- denominator
- the denominator of a rational number in lowest terms
- numerator
- the numerator of a rational number in lowest terms
|
longcomplex = class complex256(complexfloating) |
|
Composed of two 128 bit floats |
|
- Method resolution order:
- complex256
- 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__(...)
- __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:
* {'<', 'L'} - little endian
* {'>', 'B'} - big endian
* {'=', 'N'} - native order
* 'S' - swap dtype from current to opposite endian
* {'|', '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.
- 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 float128(floating) |
|
128-bit floating-point number. Character code: 'g'. C long float
compatible. |
|
- Method resolution order:
- float128
- 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__(...)
- __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:
* {'<', 'L'} - little endian
* {'>', 'B'} - big endian
* {'=', 'N'} - native order
* 'S' - swap dtype from current to opposite endian
* {'|', '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.
- 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 float128(floating) |
|
128-bit floating-point number. Character code: 'g'. C long float
compatible. |
|
- Method resolution order:
- float128
- 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__(...)
- __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:
* {'<', 'L'} - little endian
* {'>', 'B'} - big endian
* {'=', 'N'} - native order
* 'S' - swap dtype from current to opposite endian
* {'|', '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.
- 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, __builtin__.int) |
| |
- Method resolution order:
- int64
- 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
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__(...)
- __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:
* {'<', 'L'} - little endian
* {'>', 'B'} - big endian
* {'=', 'N'} - native order
* 'S' - swap dtype from current to opposite endian
* {'|', '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.
- 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
Data descriptors inherited from __builtin__.int:
- denominator
- the denominator of a rational number in lowest terms
- numerator
- the numerator of a rational number in lowest terms
|
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]])
- 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 `getH`.
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]])
- 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
- base array
- A1
- 1-d base array
- H
- hermitian (conjugate) transpose
- I
- inverse
- T
- transpose
- __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)
- __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='quickselect', 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`.
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(a_min, a_max, out=None)
Return an array whose values are limited to ``[a_min, a_max]``.
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.
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 C (row-major), Fortran (column-major) 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]])
- 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
above. `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 : list, optional
When `a` is an array with fields defined, this argument specifies
which fields to compare first, second, etc. Not all fields need be
specified.
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
- 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 : list, optional
When `a` is an array with fields defined, this argument specifies
which fields to compare first, second, etc. Not all fields need be
specified.
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
- 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
- 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.tostring())``.
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 a Python string containing the raw data bytes in the array.
Constructs a Python string showing a copy of the raw contents of
data memory. The string 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.
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 : str
A Python string exhibiting a copy of `a`'s raw data.
Examples
--------
>>> x = np.array([[0, 1], [2, 3]])
>>> x.tostring()
'\x00\x00\x00\x00\x01\x00\x00\x00\x02\x00\x00\x00\x03\x00\x00\x00'
>>> x.tostring('C') == x.tostring()
True
>>> x.tostring('F')
'\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 record array:
>>> 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
|
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.
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. The default offset is 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: C (row-major) or
Fortran (column-major). 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
-------
close
Close the memmap file.
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.
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)
- __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)
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)
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='quickselect', 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`.
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(a_min, a_max, out=None)
Return an array whose values are limited to ``[a_min, a_max]``.
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.
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 C (row-major), Fortran (column-major) 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)
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)
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
above. `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 : list, optional
When `a` is an array with fields defined, this argument specifies
which fields to compare first, second, etc. Not all fields need be
specified.
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)
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 : list, optional
When `a` is an array with fields defined, this argument specifies
which fields to compare first, second, etc. Not all fields need be
specified.
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)
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)
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
- 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.tostring())``.
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 a Python string containing the raw data bytes in the array.
Constructs a Python string showing a copy of the raw contents of
data memory. The string 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.
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 : str
A Python string exhibiting a copy of `a`'s raw data.
Examples
--------
>>> x = np.array([[0, 1], [2, 3]])
>>> x.tostring()
'\x00\x00\x00\x00\x01\x00\x00\x00\x02\x00\x00\x00\x03\x00\x00\x00'
>>> x.tostring('C') == x.tostring()
True
>>> x.tostring('F')
'\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)
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 record array:
>>> 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
|
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 or column-major 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)
- __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)
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)
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='quickselect', 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`.
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(a_min, a_max, out=None)
Return an array whose values are limited to ``[a_min, a_max]``.
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.
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 C (row-major), Fortran (column-major) 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)
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)
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
above. `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 : list, optional
When `a` is an array with fields defined, this argument specifies
which fields to compare first, second, etc. Not all fields need be
specified.
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)
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 : list, optional
When `a` is an array with fields defined, this argument specifies
which fields to compare first, second, etc. Not all fields need be
specified.
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)
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)
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
- 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.tostring())``.
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 a Python string containing the raw data bytes in the array.
Constructs a Python string showing a copy of the raw contents of
data memory. The string 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.
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 : str
A Python string exhibiting a copy of `a`'s raw data.
Examples
--------
>>> x = np.array([[0, 1], [2, 3]])
>>> x.tostring()
'\x00\x00\x00\x00\x01\x00\x00\x00\x02\x00\x00\x00\x03\x00\x00\x00'
>>> x.tostring('C') == x.tostring()
True
>>> x.tostring('F')
'\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)
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 record array:
>>> 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:
- __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
----------
a : 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)
|
class ndindex(__builtin__.object) |
|
An N-dimensional iterator object to index arrays.
Given the shape of an array, an `ndindex` instance iterates over
the N-dimensional index of the array. At each iteration a tuple
of indices is returned, the last dimension is iterated over first.
Parameters
----------
`*args` : ints
The size of each dimension of the array.
See Also
--------
ndenumerate, flatiter
Examples
--------
>>> for index in np.ndindex(3, 2, 1):
... print index
(0, 0, 0)
(0, 1, 0)
(1, 0, 0)
(1, 1, 0)
(2, 0, 0)
(2, 1, 0) |
|
Methods defined here:
- __init__(self, *shape)
- __iter__(self)
- __next__(self)
- Standard iterator method, updates the index and returns the index tuple.
Returns
-------
val : tuple of ints
Returns a tuple containing the indices of the current iteration.
- ndincr(self)
- Increment the multi-dimensional index by one.
This method is for backward compatibility only: do not use.
- next = __next__(self)
Data descriptors defined here:
- __dict__
- dictionary for instance variables (if defined)
- __weakref__
- list of weak references to the object (if defined)
|
class nditer(__builtin__.object) |
|
Efficient multi-dimensional iterator object to iterate over arrays.
To get started using this object, see the
:ref:`introductory guide to array iteration <arrays.nditer>`.
Parameters
----------
op : ndarray or sequence of array_like
The array(s) to iterate over.
flags : sequence of str, optional
Flags to control the behavior of the iterator.
* "buffered" enables buffering when required.
* "c_index" causes a C-order index to be tracked.
* "f_index" causes a Fortran-order index to be tracked.
* "multi_index" causes a multi-index, or a tuple of indices
with one per iteration dimension, to be tracked.
* "common_dtype" causes all the operands to be converted to
a common data type, with copying or buffering as necessary.
* "delay_bufalloc" delays allocation of the buffers until
a reset() call is made. Allows "allocate" operands to
be initialized before their values are copied into the buffers.
* "external_loop" causes the `values` given to be
one-dimensional arrays with multiple values instead of
zero-dimensional arrays.
* "grow_inner" allows the `value` array sizes to be made
larger than the buffer size when both "buffered" and
"external_loop" is used.
* "ranged" allows the iterator to be restricted to a sub-range
of the iterindex values.
* "refs_ok" enables iteration of reference types, such as
object arrays.
* "reduce_ok" enables iteration of "readwrite" operands
which are broadcasted, also known as reduction operands.
* "zerosize_ok" allows `itersize` to be zero.
op_flags : list of list of str, optional
This is a list of flags for each operand. At minimum, one of
"readonly", "readwrite", or "writeonly" must be specified.
* "readonly" indicates the operand will only be read from.
* "readwrite" indicates the operand will be read from and written to.
* "writeonly" indicates the operand will only be written to.
* "no_broadcast" prevents the operand from being broadcasted.
* "contig" forces the operand data to be contiguous.
* "aligned" forces the operand data to be aligned.
* "nbo" forces the operand data to be in native byte order.
* "copy" allows a temporary read-only copy if required.
* "updateifcopy" allows a temporary read-write copy if required.
* "allocate" causes the array to be allocated if it is None
in the `op` parameter.
* "no_subtype" prevents an "allocate" operand from using a subtype.
* "arraymask" indicates that this operand is the mask to use
for selecting elements when writing to operands with the
'writemasked' flag set. The iterator does not enforce this,
but when writing from a buffer back to the array, it only
copies those elements indicated by this mask.
* 'writemasked' indicates that only elements where the chosen
'arraymask' operand is True will be written to.
op_dtypes : dtype or tuple of dtype(s), optional
The required data type(s) of the operands. If copying or buffering
is enabled, the data will be converted to/from their original types.
order : {'C', 'F', 'A', 'K'}, optional
Controls the iteration order. '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. This also
affects the element memory order of "allocate" operands, as they
are allocated to be compatible with iteration order.
Default is 'K'.
casting : {'no', 'equiv', 'safe', 'same_kind', 'unsafe'}, optional
Controls what kind of data casting may occur when making a copy
or buffering. Setting this to 'unsafe' is not recommended,
as it can adversely affect accumulations.
* '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.
op_axes : list of list of ints, optional
If provided, is a list of ints or None for each operands.
The list of axes for an operand is a mapping from the dimensions
of the iterator to the dimensions of the operand. A value of
-1 can be placed for entries, causing that dimension to be
treated as "newaxis".
itershape : tuple of ints, optional
The desired shape of the iterator. This allows "allocate" operands
with a dimension mapped by op_axes not corresponding to a dimension
of a different operand to get a value not equal to 1 for that
dimension.
buffersize : int, optional
When buffering is enabled, controls the size of the temporary
buffers. Set to 0 for the default value.
Attributes
----------
dtypes : tuple of dtype(s)
The data types of the values provided in `value`. This may be
different from the operand data types if buffering is enabled.
finished : bool
Whether the iteration over the operands is finished or not.
has_delayed_bufalloc : bool
If True, the iterator was created with the "delay_bufalloc" flag,
and no reset() function was called on it yet.
has_index : bool
If True, the iterator was created with either the "c_index" or
the "f_index" flag, and the property `index` can be used to
retrieve it.
has_multi_index : bool
If True, the iterator was created with the "multi_index" flag,
and the property `multi_index` can be used to retrieve it.
index :
When the "c_index" or "f_index" flag was used, this property
provides access to the index. Raises a ValueError if accessed
and `has_index` is False.
iterationneedsapi : bool
Whether iteration requires access to the Python API, for example
if one of the operands is an object array.
iterindex : int
An index which matches the order of iteration.
itersize : int
Size of the iterator.
itviews :
Structured view(s) of `operands` in memory, matching the reordered
and optimized iterator access pattern.
multi_index :
When the "multi_index" flag was used, this property
provides access to the index. Raises a ValueError if accessed
accessed and `has_multi_index` is False.
ndim : int
The iterator's dimension.
nop : int
The number of iterator operands.
operands : tuple of operand(s)
The array(s) to be iterated over.
shape : tuple of ints
Shape tuple, the shape of the iterator.
value :
Value of `operands` at current iteration. Normally, this is a
tuple of array scalars, but if the flag "external_loop" is used,
it is a tuple of one dimensional arrays.
Notes
-----
`nditer` supersedes `flatiter`. The iterator implementation behind
`nditer` is also exposed by the Numpy C API.
The Python exposure supplies two iteration interfaces, one which follows
the Python iterator protocol, and another which mirrors the C-style
do-while pattern. The native Python approach is better in most cases, but
if you need the iterator's coordinates or index, use the C-style pattern.
Examples
--------
Here is how we might write an ``iter_add`` function, using the
Python iterator protocol::
def iter_add_py(x, y, out=None):
addop = np.add
it = np.nditer([x, y, out], [],
[['readonly'], ['readonly'], ['writeonly','allocate']])
for (a, b, c) in it:
addop(a, b, out=c)
return it.operands[2]
Here is the same function, but following the C-style pattern::
def iter_add(x, y, out=None):
addop = np.add
it = np.nditer([x, y, out], [],
[['readonly'], ['readonly'], ['writeonly','allocate']])
while not it.finished:
addop(it[0], it[1], out=it[2])
it.iternext()
return it.operands[2]
Here is an example outer product function::
def outer_it(x, y, out=None):
mulop = np.multiply
it = np.nditer([x, y, out], ['external_loop'],
[['readonly'], ['readonly'], ['writeonly', 'allocate']],
op_axes=[range(x.ndim)+[-1]*y.ndim,
[-1]*x.ndim+range(y.ndim),
None])
for (a, b, c) in it:
mulop(a, b, out=c)
return it.operands[2]
>>> a = np.arange(2)+1
>>> b = np.arange(3)+1
>>> outer_it(a,b)
array([[1, 2, 3],
[2, 4, 6]])
Here is an example function which operates like a "lambda" ufunc::
def luf(lamdaexpr, *args, **kwargs):
"luf(lambdaexpr, op1, ..., opn, out=None, order='K', casting='safe', buffersize=0)"
nargs = len(args)
op = (kwargs.get('out',None),) + args
it = np.nditer(op, ['buffered','external_loop'],
[['writeonly','allocate','no_broadcast']] +
[['readonly','nbo','aligned']]*nargs,
order=kwargs.get('order','K'),
casting=kwargs.get('casting','safe'),
buffersize=kwargs.get('buffersize',0))
while not it.finished:
it[0] = lamdaexpr(*it[1:])
it.iternext()
return it.operands[0]
>>> a = np.arange(5)
>>> b = np.ones(5)
>>> luf(lambda i,j:i*i + j/2, a, b)
array([ 0.5, 1.5, 4.5, 9.5, 16.5]) |
|
Methods defined here:
- __copy__(...)
- __delitem__(...)
- x.__delitem__(y) <==> del x[y]
- __delslice__(...)
- x.__delslice__(i, j) <==> del x[i:j]
Use of negative indices is not supported.
- __getitem__(...)
- x.__getitem__(y) <==> x[y]
- __getslice__(...)
- x.__getslice__(i, j) <==> x[i:j]
Use of negative indices is not supported.
- __init__(...)
- x.__init__(...) initializes x; see help(type(x)) for signature
- __iter__(...)
- x.__iter__() <==> iter(x)
- __len__(...)
- x.__len__() <==> len(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.
- copy(...)
- copy()
Get a copy of the iterator in its current state.
Examples
--------
>>> x = np.arange(10)
>>> y = x + 1
>>> it = np.nditer([x, y])
>>> it.next()
(array(0), array(1))
>>> it2 = it.copy()
>>> it2.next()
(array(1), array(2))
- debug_print(...)
- debug_print()
Print the current state of the `nditer` instance and debug info to stdout.
- enable_external_loop(...)
- enable_external_loop()
When the "external_loop" was not used during construction, but
is desired, this modifies the iterator to behave as if the flag
was specified.
- iternext(...)
- iternext()
Check whether iterations are left, and perform a single internal iteration
without returning the result. Used in the C-style pattern do-while
pattern. For an example, see `nditer`.
Returns
-------
iternext : bool
Whether or not there are iterations left.
- next(...)
- x.next() -> the next value, or raise StopIteration
- remove_axis(...)
- remove_axis(i)
Removes axis `i` from the iterator. Requires that the flag "multi_index"
be enabled.
- remove_multi_index(...)
- remove_multi_index()
When the "multi_index" flag was specified, this removes it, allowing
the internal iteration structure to be optimized further.
- reset(...)
- reset()
Reset the iterator to its initial state.
Data descriptors defined here:
- dtypes
- finished
- has_delayed_bufalloc
- has_index
- has_multi_index
- index
- iterationneedsapi
- iterindex
- iterrange
- itersize
- itviews
- multi_index
- ndim
- nop
- operands
- shape
- value
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
|
class number(generic) |
| |
- Method resolution order:
- 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__(...)
- __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:
* {'<', 'L'} - little endian
* {'>', 'B'} - big endian
* {'=', 'N'} - native order
* 'S' - swap dtype from current to opposite endian
* {'|', '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.
- 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
|
object0 = class object_(generic) |
|
Any Python object. Character code: 'O'. |
|
- Method resolution order:
- object_
- generic
- __builtin__.object
Methods defined here:
- __add__(...)
- x.__add__(y) <==> x+y
- __call__(...)
- x.__call__(...) <==> x(...)
- __contains__(...)
- x.__contains__(y) <==> y in x
- __delattr__(...)
- x.__delattr__('name') <==> del x.name
- __delitem__(...)
- x.__delitem__(y) <==> del x[y]
- __eq__(...)
- x.__eq__(y) <==> x==y
- __ge__(...)
- x.__ge__(y) <==> x>=y
- __getattribute__(...)
- x.__getattribute__('name') <==> x.name
- __getitem__(...)
- x.__getitem__(y) <==> x[y]
- __gt__(...)
- x.__gt__(y) <==> x>y
- __hash__(...)
- x.__hash__() <==> hash(x)
- __iadd__(...)
- x.__iadd__(y) <==> x+=y
- __imul__(...)
- x.__imul__(y) <==> x*=y
- __le__(...)
- x.__le__(y) <==> x<=y
- __len__(...)
- x.__len__() <==> len(x)
- __lt__(...)
- x.__lt__(y) <==> x<y
- __mul__(...)
- x.__mul__(n) <==> x*n
- __ne__(...)
- x.__ne__(y) <==> x!=y
- __rmul__(...)
- x.__rmul__(n) <==> n*x
- __setattr__(...)
- x.__setattr__('name', value) <==> x.name = value
- __setitem__(...)
- x.__setitem__(i, y) <==> x[i]=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
Methods inherited from generic:
- __abs__(...)
- x.__abs__() <==> abs(x)
- __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
- __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
- __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
- __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__(...)
- __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:
* {'<', 'L'} - little endian
* {'>', 'B'} - big endian
* {'=', 'N'} - native order
* 'S' - swap dtype from current to opposite endian
* {'|', '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.
- 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 object_(generic) |
|
Any Python object. Character code: 'O'. |
|
- Method resolution order:
- object_
- generic
- __builtin__.object
Methods defined here:
- __add__(...)
- x.__add__(y) <==> x+y
- __call__(...)
- x.__call__(...) <==> x(...)
- __contains__(...)
- x.__contains__(y) <==> y in x
- __delattr__(...)
- x.__delattr__('name') <==> del x.name
- __delitem__(...)
- x.__delitem__(y) <==> del x[y]
- __eq__(...)
- x.__eq__(y) <==> x==y
- __ge__(...)
- x.__ge__(y) <==> x>=y
- __getattribute__(...)
- x.__getattribute__('name') <==> x.name
- __getitem__(...)
- x.__getitem__(y) <==> x[y]
- __gt__(...)
- x.__gt__(y) <==> x>y
- __hash__(...)
- x.__hash__() <==> hash(x)
- __iadd__(...)
- x.__iadd__(y) <==> x+=y
- __imul__(...)
- x.__imul__(y) <==> x*=y
- __le__(...)
- x.__le__(y) <==> x<=y
- __len__(...)
- x.__len__() <==> len(x)
- __lt__(...)
- x.__lt__(y) <==> x<y
- __mul__(...)
- x.__mul__(n) <==> x*n
- __ne__(...)
- x.__ne__(y) <==> x!=y
- __rmul__(...)
- x.__rmul__(n) <==> n*x
- __setattr__(...)
- x.__setattr__('name', value) <==> x.name = value
- __setitem__(...)
- x.__setitem__(i, y) <==> x[i]=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
Methods inherited from generic:
- __abs__(...)
- x.__abs__() <==> abs(x)
- __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
- __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
- __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
- __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__(...)
- __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:
* {'<', 'L'} - little endian
* {'>', 'B'} - big endian
* {'=', 'N'} - native order
* 'S' - swap dtype from current to opposite endian
* {'|', '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.
- 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 poly1d(__builtin__.object) |
|
A one-dimensional polynomial class.
A convenience class, used to encapsulate "natural" operations on
polynomials so that said operations may take on their customary
form in code (see Examples).
Parameters
----------
c_or_r : array_like
The polynomial's coefficients, in decreasing powers, or if
the value of the second parameter is True, the polynomial's
roots (values where the polynomial evaluates to 0). For example,
``poly1d([1, 2, 3])`` returns an object that represents
:math:`x^2 + 2x + 3`, whereas ``poly1d([1, 2, 3], True)`` returns
one that represents :math:`(x-1)(x-2)(x-3) = x^3 - 6x^2 + 11x -6`.
r : bool, optional
If True, `c_or_r` specifies the polynomial's roots; the default
is False.
variable : str, optional
Changes the variable used when printing `p` from `x` to `variable`
(see Examples).
Examples
--------
Construct the polynomial :math:`x^2 + 2x + 3`:
>>> p = np.poly1d([1, 2, 3])
>>> print np.poly1d(p)
2
1 x + 2 x + 3
Evaluate the polynomial at :math:`x = 0.5`:
>>> p(0.5)
4.25
Find the roots:
>>> p.r
array([-1.+1.41421356j, -1.-1.41421356j])
>>> p(p.r)
array([ -4.44089210e-16+0.j, -4.44089210e-16+0.j])
These numbers in the previous line represent (0, 0) to machine precision
Show the coefficients:
>>> p.c
array([1, 2, 3])
Display the order (the leading zero-coefficients are removed):
>>> p.order
2
Show the coefficient of the k-th power in the polynomial
(which is equivalent to ``p.c[-(i+1)]``):
>>> p[1]
2
Polynomials can be added, subtracted, multiplied, and divided
(returns quotient and remainder):
>>> p * p
poly1d([ 1, 4, 10, 12, 9])
>>> (p**3 + 4) / p
(poly1d([ 1., 4., 10., 12., 9.]), poly1d([ 4.]))
``asarray(p)`` gives the coefficient array, so polynomials can be
used in all functions that accept arrays:
>>> p**2 # square of polynomial
poly1d([ 1, 4, 10, 12, 9])
>>> np.square(p) # square of individual coefficients
array([1, 4, 9])
The variable used in the string representation of `p` can be modified,
using the `variable` parameter:
>>> p = np.poly1d([1,2,3], variable='z')
>>> print p
2
1 z + 2 z + 3
Construct a polynomial from its roots:
>>> np.poly1d([1, 2], True)
poly1d([ 1, -3, 2])
This is the same polynomial as obtained by:
>>> np.poly1d([1, -1]) * np.poly1d([1, -2])
poly1d([ 1, -3, 2]) |
|
Methods defined here:
- __add__(self, other)
- __array__(self, t=None)
- __call__(self, val)
- __div__(self, other)
- __eq__(self, other)
- __getattr__(self, key)
- __getitem__(self, val)
- __init__(self, c_or_r, r=0, variable=None)
- __iter__(self)
- __len__(self)
- __mul__(self, other)
- __ne__(self, other)
- __neg__(self)
- __pos__(self)
- __pow__(self, val)
- __radd__(self, other)
- __rdiv__(self, other)
- __repr__(self)
- __rmul__(self, other)
- __rsub__(self, other)
- __rtruediv__ = __rdiv__(self, other)
- __setattr__(self, key, val)
- __setitem__(self, key, val)
- __str__(self)
- __sub__(self, other)
- __truediv__ = __div__(self, other)
- deriv(self, m=1)
- Return a derivative of this polynomial.
Refer to `polyder` for full documentation.
See Also
--------
polyder : equivalent function
- integ(self, m=1, k=0)
- Return an antiderivative (indefinite integral) of this polynomial.
Refer to `polyint` for full documentation.
See Also
--------
polyint : equivalent function
Data descriptors defined here:
- __dict__
- dictionary for instance variables (if defined)
- __weakref__
- list of weak references to the object (if defined)
Data and other attributes defined here:
- __hash__ = None
- coeffs = None
- order = None
- variable = None
|
class recarray(numpy.ndarray) |
|
Construct an ndarray that allows field access using attributes.
Arrays may have a data-types containing fields, analogous
to columns in a spread sheet. An example is ``[(x, int), (y, float)]``,
where each entry in the array is a pair of ``(int, float)``. Normally,
these attributes are accessed using dictionary lookups such as ``arr['x']``
and ``arr['y']``. Record arrays allow the fields to be accessed as members
of the array, using ``arr.x`` and ``arr.y``.
Parameters
----------
shape : tuple
Shape of output array.
dtype : data-type, optional
The desired data-type. By default, the data-type is determined
from `formats`, `names`, `titles`, `aligned` and `byteorder`.
formats : list of data-types, optional
A list containing the data-types for the different columns, e.g.
``['i4', 'f8', 'i4']``. `formats` does *not* support the new
convention of using types directly, i.e. ``(int, float, int)``.
Note that `formats` must be a list, not a tuple.
Given that `formats` is somewhat limited, we recommend specifying
`dtype` instead.
names : tuple of str, optional
The name of each column, e.g. ``('x', 'y', 'z')``.
buf : buffer, optional
By default, a new array is created of the given shape and data-type.
If `buf` is specified and is an object exposing the buffer interface,
the array will use the memory from the existing buffer. In this case,
the `offset` and `strides` keywords are available.
Other Parameters
----------------
titles : tuple of str, optional
Aliases for column names. For example, if `names` were
``('x', 'y', 'z')`` and `titles` is
``('x_coordinate', 'y_coordinate', 'z_coordinate')``, then
``arr['x']`` is equivalent to both ``arr.x`` and ``arr.x_coordinate``.
byteorder : {'<', '>', '='}, optional
Byte-order for all fields.
aligned : bool, optional
Align the fields in memory as the C-compiler would.
strides : tuple of ints, optional
Buffer (`buf`) is interpreted according to these strides (strides
define how many bytes each array element, row, column, etc.
occupy in memory).
offset : int, optional
Start reading buffer (`buf`) from this offset onwards.
order : {'C', 'F'}, optional
Row-major or column-major order.
Returns
-------
rec : recarray
Empty array of the given shape and type.
See Also
--------
rec.fromrecords : Construct a record array from data.
record : fundamental data-type for `recarray`.
format_parser : determine a data-type from formats, names, titles.
Notes
-----
This constructor can be compared to ``empty``: it creates a new record
array but does not fill it with data. To create a record array from data,
use one of the following methods:
1. Create a standard ndarray and convert it to a record array,
using ``arr.view(np.recarray)``
2. Use the `buf` keyword.
3. Use `np.rec.fromrecords`.
Examples
--------
Create an array with two fields, ``x`` and ``y``:
>>> x = np.array([(1.0, 2), (3.0, 4)], dtype=[('x', float), ('y', int)])
>>> x
array([(1.0, 2), (3.0, 4)],
dtype=[('x', '<f8'), ('y', '<i4')])
>>> x['x']
array([ 1., 3.])
View the array as a record array:
>>> x = x.view(np.recarray)
>>> x.x
array([ 1., 3.])
>>> x.y
array([2, 4])
Create a new, empty record array:
>>> np.recarray((2,),
... dtype=[('x', int), ('y', float), ('z', int)]) #doctest: +SKIP
rec.array([(-1073741821, 1.2249118382103472e-301, 24547520),
(3471280, 1.2134086255804012e-316, 0)],
dtype=[('x', '<i4'), ('y', '<f8'), ('z', '<i4')]) |
|
- Method resolution order:
- recarray
- numpy.ndarray
- __builtin__.object
Methods defined here:
- __getattribute__(self, attr)
- __getitem__(self, indx)
- __repr__(self)
- __setattr__(self, attr, val)
- field(self, attr, val=None)
- view(self, dtype=None, type=None)
Static methods defined here:
- __new__(subtype, shape, dtype=None, buf=None, offset=0, strides=None, formats=None, names=None, titles=None, byteorder=None, aligned=False, order='C')
Data descriptors defined here:
- __dict__
- dictionary for instance variables (if defined)
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
- __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.
- __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)
- __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)
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)
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='quickselect', 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`.
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(a_min, a_max, out=None)
Return an array whose values are limited to ``[a_min, a_max]``.
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.
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 C (row-major), Fortran (column-major) 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)
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)
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
above. `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 : list, optional
When `a` is an array with fields defined, this argument specifies
which fields to compare first, second, etc. Not all fields need be
specified.
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)
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 : list, optional
When `a` is an array with fields defined, this argument specifies
which fields to compare first, second, etc. Not all fields need be
specified.
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)
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)
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
- 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.tostring())``.
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 a Python string containing the raw data bytes in the array.
Constructs a Python string showing a copy of the raw contents of
data memory. The string 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.
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 : str
A Python string exhibiting a copy of `a`'s raw data.
Examples
--------
>>> x = np.array([[0, 1], [2, 3]])
>>> x.tostring()
'\x00\x00\x00\x00\x01\x00\x00\x00\x02\x00\x00\x00\x03\x00\x00\x00'
>>> x.tostring('C') == x.tostring()
True
>>> x.tostring('F')
'\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)
Returns the variance of the array elements, along given axis.
Refer to `numpy.var` for full documentation.
See Also
--------
numpy.var : equivalent function
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_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
|
class record(numpy.void) |
|
A data-type scalar that allows field access as attribute lookup. |
|
- Method resolution order:
- record
- numpy.void
- numpy.flexible
- numpy.generic
- __builtin__.object
Methods defined here:
- __getattribute__(self, attr)
- __repr__(self)
- __setattr__(self, attr, val)
- __str__(self)
- pprint(self)
- Pretty-print all fields.
Data descriptors defined here:
- __dict__
- dictionary for instance variables (if defined)
- __weakref__
- list of weak references to the object (if defined)
Methods inherited from numpy.void:
- __delitem__(...)
- x.__delitem__(y) <==> del x[y]
- __eq__(...)
- x.__eq__(y) <==> x==y
- __ge__(...)
- x.__ge__(y) <==> x>=y
- __getitem__(...)
- x.__getitem__(y) <==> x[y]
- __gt__(...)
- x.__gt__(y) <==> x>y
- __hash__(...)
- x.__hash__() <==> hash(x)
- __le__(...)
- x.__le__(y) <==> x<=y
- __len__(...)
- x.__len__() <==> len(x)
- __lt__(...)
- x.__lt__(y) <==> x<y
- __ne__(...)
- x.__ne__(y) <==> x!=y
- __setitem__(...)
- x.__setitem__(i, y) <==> x[i]=y
- getfield(...)
- setfield(...)
Data descriptors inherited from numpy.void:
- dtype
- dtype object
- flags
- integer value of flags
Data and other attributes inherited from numpy.void:
- __new__ = <built-in method __new__ of type object>
- T.__new__(S, ...) -> a new object with type S, a subtype of T
Methods inherited from numpy.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
- __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__(...)
- __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.
- 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:
* {'<', 'L'} - little endian
* {'>', 'B'} - big endian
* {'=', 'N'} - native order
* 'S' - swap dtype from current to opposite endian
* {'|', '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.
- 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.
- 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 numpy.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
- 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
|
short = 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
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__(...)
- __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:
* {'<', 'L'} - little endian
* {'>', 'B'} - big endian
* {'=', 'N'} - native order
* 'S' - swap dtype from current to opposite endian
* {'|', '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.
- 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 signedinteger(integer) |
| |
- Method resolution order:
- signedinteger
- integer
- 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__(...)
- __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:
* {'<', 'L'} - little endian
* {'>', 'B'} - big endian
* {'=', 'N'} - native order
* 'S' - swap dtype from current to opposite endian
* {'|', '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.
- 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
|
single = class float32(floating) |
|
32-bit floating-point number. Character code 'f'. C float compatible. |
|
- Method resolution order:
- float32
- 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__(...)
- __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:
* {'<', 'L'} - little endian
* {'>', 'B'} - big endian
* {'=', 'N'} - native order
* 'S' - swap dtype from current to opposite endian
* {'|', '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.
- 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
|
singlecomplex = class complex64(complexfloating) |
|
Composed of two 32 bit floats |
|
- Method resolution order:
- complex64
- complexfloating
- inexact
- number
- generic
- __builtin__.object
Methods defined here:
- __complex__(...)
- __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__(...)
- __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:
* {'<', 'L'} - little endian
* {'>', 'B'} - big endian
* {'=', 'N'} - native order
* 'S' - swap dtype from current to opposite endian
* {'|', '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.
- 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
|
str_ = class string_(__builtin__.str, character) |
| |
- Method resolution order:
- string_
- __builtin__.str
- __builtin__.basestring
- character
- flexible
- 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 __builtin__.str:
- __add__(...)
- x.__add__(y) <==> x+y
- __contains__(...)
- x.__contains__(y) <==> y in x
- __format__(...)
- S.__format__(format_spec) -> string
Return a formatted version of S as described by format_spec.
- __getattribute__(...)
- x.__getattribute__('name') <==> x.name
- __getitem__(...)
- x.__getitem__(y) <==> x[y]
- __getnewargs__(...)
- __getslice__(...)
- x.__getslice__(i, j) <==> x[i:j]
Use of negative indices is not supported.
- __len__(...)
- x.__len__() <==> len(x)
- __mod__(...)
- x.__mod__(y) <==> x%y
- __mul__(...)
- x.__mul__(n) <==> x*n
- __rmod__(...)
- x.__rmod__(y) <==> y%x
- __rmul__(...)
- x.__rmul__(n) <==> n*x
- __sizeof__(...)
- S.__sizeof__() -> size of S in memory, in bytes
- capitalize(...)
- S.capitalize() -> string
Return a copy of the string S with only its first character
capitalized.
- center(...)
- S.center(width[, fillchar]) -> string
Return S centered in a string of length width. Padding is
done using the specified fill character (default is a space)
- count(...)
- S.count(sub[, start[, end]]) -> int
Return the number of non-overlapping occurrences of substring sub in
string S[start:end]. Optional arguments start and end are interpreted
as in slice notation.
- decode(...)
- S.decode([encoding[,errors]]) -> object
Decodes S using the codec registered for encoding. encoding defaults
to the default encoding. errors may be given to set a different error
handling scheme. Default is 'strict' meaning that encoding errors raise
a UnicodeDecodeError. Other possible values are 'ignore' and 'replace'
as well as any other name registered with codecs.register_error that is
able to handle UnicodeDecodeErrors.
- encode(...)
- S.encode([encoding[,errors]]) -> object
Encodes S using the codec registered for encoding. encoding defaults
to the default encoding. errors may be given to set a different error
handling scheme. Default is 'strict' meaning that encoding errors raise
a UnicodeEncodeError. Other possible values are 'ignore', 'replace' and
'xmlcharrefreplace' as well as any other name registered with
codecs.register_error that is able to handle UnicodeEncodeErrors.
- endswith(...)
- S.endswith(suffix[, start[, end]]) -> bool
Return True if S ends with the specified suffix, False otherwise.
With optional start, test S beginning at that position.
With optional end, stop comparing S at that position.
suffix can also be a tuple of strings to try.
- expandtabs(...)
- S.expandtabs([tabsize]) -> string
Return a copy of S where all tab characters are expanded using spaces.
If tabsize is not given, a tab size of 8 characters is assumed.
- find(...)
- S.find(sub [,start [,end]]) -> int
Return the lowest index in S where substring sub is found,
such that sub is contained within S[start:end]. Optional
arguments start and end are interpreted as in slice notation.
Return -1 on failure.
- format(...)
- S.format(*args, **kwargs) -> string
Return a formatted version of S, using substitutions from args and kwargs.
The substitutions are identified by braces ('{' and '}').
- index(...)
- S.index(sub [,start [,end]]) -> int
Like S.find() but raise ValueError when the substring is not found.
- isalnum(...)
- S.isalnum() -> bool
Return True if all characters in S are alphanumeric
and there is at least one character in S, False otherwise.
- isalpha(...)
- S.isalpha() -> bool
Return True if all characters in S are alphabetic
and there is at least one character in S, False otherwise.
- isdigit(...)
- S.isdigit() -> bool
Return True if all characters in S are digits
and there is at least one character in S, False otherwise.
- islower(...)
- S.islower() -> bool
Return True if all cased characters in S are lowercase and there is
at least one cased character in S, False otherwise.
- isspace(...)
- S.isspace() -> bool
Return True if all characters in S are whitespace
and there is at least one character in S, False otherwise.
- istitle(...)
- S.istitle() -> bool
Return True if S is a titlecased string and there is at least one
character in S, i.e. uppercase characters may only follow uncased
characters and lowercase characters only cased ones. Return False
otherwise.
- isupper(...)
- S.isupper() -> bool
Return True if all cased characters in S are uppercase and there is
at least one cased character in S, False otherwise.
- join(...)
- S.join(iterable) -> string
Return a string which is the concatenation of the strings in the
iterable. The separator between elements is S.
- ljust(...)
- S.ljust(width[, fillchar]) -> string
Return S left-justified in a string of length width. Padding is
done using the specified fill character (default is a space).
- lower(...)
- S.lower() -> string
Return a copy of the string S converted to lowercase.
- lstrip(...)
- S.lstrip([chars]) -> string or unicode
Return a copy of the string S with leading whitespace removed.
If chars is given and not None, remove characters in chars instead.
If chars is unicode, S will be converted to unicode before stripping
- partition(...)
- S.partition(sep) -> (head, sep, tail)
Search for the separator sep in S, and return the part before it,
the separator itself, and the part after it. If the separator is not
found, return S and two empty strings.
- replace(...)
- S.replace(old, new[, count]) -> string
Return a copy of string S with all occurrences of substring
old replaced by new. If the optional argument count is
given, only the first count occurrences are replaced.
- rfind(...)
- S.rfind(sub [,start [,end]]) -> int
Return the highest index in S where substring sub is found,
such that sub is contained within S[start:end]. Optional
arguments start and end are interpreted as in slice notation.
Return -1 on failure.
- rindex(...)
- S.rindex(sub [,start [,end]]) -> int
Like S.rfind() but raise ValueError when the substring is not found.
- rjust(...)
- S.rjust(width[, fillchar]) -> string
Return S right-justified in a string of length width. Padding is
done using the specified fill character (default is a space)
- rpartition(...)
- S.rpartition(sep) -> (head, sep, tail)
Search for the separator sep in S, starting at the end of S, and return
the part before it, the separator itself, and the part after it. If the
separator is not found, return two empty strings and S.
- rsplit(...)
- S.rsplit([sep [,maxsplit]]) -> list of strings
Return a list of the words in the string S, using sep as the
delimiter string, starting at the end of the string and working
to the front. If maxsplit is given, at most maxsplit splits are
done. If sep is not specified or is None, any whitespace string
is a separator.
- rstrip(...)
- S.rstrip([chars]) -> string or unicode
Return a copy of the string S with trailing whitespace removed.
If chars is given and not None, remove characters in chars instead.
If chars is unicode, S will be converted to unicode before stripping
- split(...)
- S.split([sep [,maxsplit]]) -> list of strings
Return a list of the words in the string S, using sep as the
delimiter string. If maxsplit is given, at most maxsplit
splits are done. If sep is not specified or is None, any
whitespace string is a separator and empty strings are removed
from the result.
- splitlines(...)
- S.splitlines(keepends=False) -> list of strings
Return a list of the lines in S, breaking at line boundaries.
Line breaks are not included in the resulting list unless keepends
is given and true.
- startswith(...)
- S.startswith(prefix[, start[, end]]) -> bool
Return True if S starts with the specified prefix, False otherwise.
With optional start, test S beginning at that position.
With optional end, stop comparing S at that position.
prefix can also be a tuple of strings to try.
- strip(...)
- S.strip([chars]) -> string or unicode
Return a copy of the string S with leading and trailing
whitespace removed.
If chars is given and not None, remove characters in chars instead.
If chars is unicode, S will be converted to unicode before stripping
- swapcase(...)
- S.swapcase() -> string
Return a copy of the string S with uppercase characters
converted to lowercase and vice versa.
- title(...)
- S.title() -> string
Return a titlecased version of S, i.e. words start with uppercase
characters, all remaining cased characters have lowercase.
- translate(...)
- S.translate(table [,deletechars]) -> string
Return a copy of the string S, where all characters occurring
in the optional argument deletechars are removed, and the
remaining characters have been mapped through the given
translation table, which must be a string of length 256 or None.
If the table argument is None, no translation is applied and
the operation simply removes the characters in deletechars.
- upper(...)
- S.upper() -> string
Return a copy of the string S converted to uppercase.
- zfill(...)
- S.zfill(width) -> string
Pad a numeric string S with zeros on the left, to fill a field
of the specified width. The string S is never truncated.
Methods inherited from generic:
- __abs__(...)
- x.__abs__() <==> abs(x)
- __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
- __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
- __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
- __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__(...)
- __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:
* {'<', 'L'} - little endian
* {'>', 'B'} - big endian
* {'=', 'N'} - native order
* 'S' - swap dtype from current to opposite endian
* {'|', '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.
- 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
|
string0 = class string_(__builtin__.str, character) |
| |
- Method resolution order:
- string_
- __builtin__.str
- __builtin__.basestring
- character
- flexible
- 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 __builtin__.str:
- __add__(...)
- x.__add__(y) <==> x+y
- __contains__(...)
- x.__contains__(y) <==> y in x
- __format__(...)
- S.__format__(format_spec) -> string
Return a formatted version of S as described by format_spec.
- __getattribute__(...)
- x.__getattribute__('name') <==> x.name
- __getitem__(...)
- x.__getitem__(y) <==> x[y]
- __getnewargs__(...)
- __getslice__(...)
- x.__getslice__(i, j) <==> x[i:j]
Use of negative indices is not supported.
- __len__(...)
- x.__len__() <==> len(x)
- __mod__(...)
- x.__mod__(y) <==> x%y
- __mul__(...)
- x.__mul__(n) <==> x*n
- __rmod__(...)
- x.__rmod__(y) <==> y%x
- __rmul__(...)
- x.__rmul__(n) <==> n*x
- __sizeof__(...)
- S.__sizeof__() -> size of S in memory, in bytes
- capitalize(...)
- S.capitalize() -> string
Return a copy of the string S with only its first character
capitalized.
- center(...)
- S.center(width[, fillchar]) -> string
Return S centered in a string of length width. Padding is
done using the specified fill character (default is a space)
- count(...)
- S.count(sub[, start[, end]]) -> int
Return the number of non-overlapping occurrences of substring sub in
string S[start:end]. Optional arguments start and end are interpreted
as in slice notation.
- decode(...)
- S.decode([encoding[,errors]]) -> object
Decodes S using the codec registered for encoding. encoding defaults
to the default encoding. errors may be given to set a different error
handling scheme. Default is 'strict' meaning that encoding errors raise
a UnicodeDecodeError. Other possible values are 'ignore' and 'replace'
as well as any other name registered with codecs.register_error that is
able to handle UnicodeDecodeErrors.
- encode(...)
- S.encode([encoding[,errors]]) -> object
Encodes S using the codec registered for encoding. encoding defaults
to the default encoding. errors may be given to set a different error
handling scheme. Default is 'strict' meaning that encoding errors raise
a UnicodeEncodeError. Other possible values are 'ignore', 'replace' and
'xmlcharrefreplace' as well as any other name registered with
codecs.register_error that is able to handle UnicodeEncodeErrors.
- endswith(...)
- S.endswith(suffix[, start[, end]]) -> bool
Return True if S ends with the specified suffix, False otherwise.
With optional start, test S beginning at that position.
With optional end, stop comparing S at that position.
suffix can also be a tuple of strings to try.
- expandtabs(...)
- S.expandtabs([tabsize]) -> string
Return a copy of S where all tab characters are expanded using spaces.
If tabsize is not given, a tab size of 8 characters is assumed.
- find(...)
- S.find(sub [,start [,end]]) -> int
Return the lowest index in S where substring sub is found,
such that sub is contained within S[start:end]. Optional
arguments start and end are interpreted as in slice notation.
Return -1 on failure.
- format(...)
- S.format(*args, **kwargs) -> string
Return a formatted version of S, using substitutions from args and kwargs.
The substitutions are identified by braces ('{' and '}').
- index(...)
- S.index(sub [,start [,end]]) -> int
Like S.find() but raise ValueError when the substring is not found.
- isalnum(...)
- S.isalnum() -> bool
Return True if all characters in S are alphanumeric
and there is at least one character in S, False otherwise.
- isalpha(...)
- S.isalpha() -> bool
Return True if all characters in S are alphabetic
and there is at least one character in S, False otherwise.
- isdigit(...)
- S.isdigit() -> bool
Return True if all characters in S are digits
and there is at least one character in S, False otherwise.
- islower(...)
- S.islower() -> bool
Return True if all cased characters in S are lowercase and there is
at least one cased character in S, False otherwise.
- isspace(...)
- S.isspace() -> bool
Return True if all characters in S are whitespace
and there is at least one character in S, False otherwise.
- istitle(...)
- S.istitle() -> bool
Return True if S is a titlecased string and there is at least one
character in S, i.e. uppercase characters may only follow uncased
characters and lowercase characters only cased ones. Return False
otherwise.
- isupper(...)
- S.isupper() -> bool
Return True if all cased characters in S are uppercase and there is
at least one cased character in S, False otherwise.
- join(...)
- S.join(iterable) -> string
Return a string which is the concatenation of the strings in the
iterable. The separator between elements is S.
- ljust(...)
- S.ljust(width[, fillchar]) -> string
Return S left-justified in a string of length width. Padding is
done using the specified fill character (default is a space).
- lower(...)
- S.lower() -> string
Return a copy of the string S converted to lowercase.
- lstrip(...)
- S.lstrip([chars]) -> string or unicode
Return a copy of the string S with leading whitespace removed.
If chars is given and not None, remove characters in chars instead.
If chars is unicode, S will be converted to unicode before stripping
- partition(...)
- S.partition(sep) -> (head, sep, tail)
Search for the separator sep in S, and return the part before it,
the separator itself, and the part after it. If the separator is not
found, return S and two empty strings.
- replace(...)
- S.replace(old, new[, count]) -> string
Return a copy of string S with all occurrences of substring
old replaced by new. If the optional argument count is
given, only the first count occurrences are replaced.
- rfind(...)
- S.rfind(sub [,start [,end]]) -> int
Return the highest index in S where substring sub is found,
such that sub is contained within S[start:end]. Optional
arguments start and end are interpreted as in slice notation.
Return -1 on failure.
- rindex(...)
- S.rindex(sub [,start [,end]]) -> int
Like S.rfind() but raise ValueError when the substring is not found.
- rjust(...)
- S.rjust(width[, fillchar]) -> string
Return S right-justified in a string of length width. Padding is
done using the specified fill character (default is a space)
- rpartition(...)
- S.rpartition(sep) -> (head, sep, tail)
Search for the separator sep in S, starting at the end of S, and return
the part before it, the separator itself, and the part after it. If the
separator is not found, return two empty strings and S.
- rsplit(...)
- S.rsplit([sep [,maxsplit]]) -> list of strings
Return a list of the words in the string S, using sep as the
delimiter string, starting at the end of the string and working
to the front. If maxsplit is given, at most maxsplit splits are
done. If sep is not specified or is None, any whitespace string
is a separator.
- rstrip(...)
- S.rstrip([chars]) -> string or unicode
Return a copy of the string S with trailing whitespace removed.
If chars is given and not None, remove characters in chars instead.
If chars is unicode, S will be converted to unicode before stripping
- split(...)
- S.split([sep [,maxsplit]]) -> list of strings
Return a list of the words in the string S, using sep as the
delimiter string. If maxsplit is given, at most maxsplit
splits are done. If sep is not specified or is None, any
whitespace string is a separator and empty strings are removed
from the result.
- splitlines(...)
- S.splitlines(keepends=False) -> list of strings
Return a list of the lines in S, breaking at line boundaries.
Line breaks are not included in the resulting list unless keepends
is given and true.
- startswith(...)
- S.startswith(prefix[, start[, end]]) -> bool
Return True if S starts with the specified prefix, False otherwise.
With optional start, test S beginning at that position.
With optional end, stop comparing S at that position.
prefix can also be a tuple of strings to try.
- strip(...)
- S.strip([chars]) -> string or unicode
Return a copy of the string S with leading and trailing
whitespace removed.
If chars is given and not None, remove characters in chars instead.
If chars is unicode, S will be converted to unicode before stripping
- swapcase(...)
- S.swapcase() -> string
Return a copy of the string S with uppercase characters
converted to lowercase and vice versa.
- title(...)
- S.title() -> string
Return a titlecased version of S, i.e. words start with uppercase
characters, all remaining cased characters have lowercase.
- translate(...)
- S.translate(table [,deletechars]) -> string
Return a copy of the string S, where all characters occurring
in the optional argument deletechars are removed, and the
remaining characters have been mapped through the given
translation table, which must be a string of length 256 or None.
If the table argument is None, no translation is applied and
the operation simply removes the characters in deletechars.
- upper(...)
- S.upper() -> string
Return a copy of the string S converted to uppercase.
- zfill(...)
- S.zfill(width) -> string
Pad a numeric string S with zeros on the left, to fill a field
of the specified width. The string S is never truncated.
Methods inherited from generic:
- __abs__(...)
- x.__abs__() <==> abs(x)
- __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
- __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
- __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
- __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__(...)
- __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:
* {'<', 'L'} - little endian
* {'>', 'B'} - big endian
* {'=', 'N'} - native order
* 'S' - swap dtype from current to opposite endian
* {'|', '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.
- 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 string_(__builtin__.str, character) |
| |
- Method resolution order:
- string_
- __builtin__.str
- __builtin__.basestring
- character
- flexible
- 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 __builtin__.str:
- __add__(...)
- x.__add__(y) <==> x+y
- __contains__(...)
- x.__contains__(y) <==> y in x
- __format__(...)
- S.__format__(format_spec) -> string
Return a formatted version of S as described by format_spec.
- __getattribute__(...)
- x.__getattribute__('name') <==> x.name
- __getitem__(...)
- x.__getitem__(y) <==> x[y]
- __getnewargs__(...)
- __getslice__(...)
- x.__getslice__(i, j) <==> x[i:j]
Use of negative indices is not supported.
- __len__(...)
- x.__len__() <==> len(x)
- __mod__(...)
- x.__mod__(y) <==> x%y
- __mul__(...)
- x.__mul__(n) <==> x*n
- __rmod__(...)
- x.__rmod__(y) <==> y%x
- __rmul__(...)
- x.__rmul__(n) <==> n*x
- __sizeof__(...)
- S.__sizeof__() -> size of S in memory, in bytes
- capitalize(...)
- S.capitalize() -> string
Return a copy of the string S with only its first character
capitalized.
- center(...)
- S.center(width[, fillchar]) -> string
Return S centered in a string of length width. Padding is
done using the specified fill character (default is a space)
- count(...)
- S.count(sub[, start[, end]]) -> int
Return the number of non-overlapping occurrences of substring sub in
string S[start:end]. Optional arguments start and end are interpreted
as in slice notation.
- decode(...)
- S.decode([encoding[,errors]]) -> object
Decodes S using the codec registered for encoding. encoding defaults
to the default encoding. errors may be given to set a different error
handling scheme. Default is 'strict' meaning that encoding errors raise
a UnicodeDecodeError. Other possible values are 'ignore' and 'replace'
as well as any other name registered with codecs.register_error that is
able to handle UnicodeDecodeErrors.
- encode(...)
- S.encode([encoding[,errors]]) -> object
Encodes S using the codec registered for encoding. encoding defaults
to the default encoding. errors may be given to set a different error
handling scheme. Default is 'strict' meaning that encoding errors raise
a UnicodeEncodeError. Other possible values are 'ignore', 'replace' and
'xmlcharrefreplace' as well as any other name registered with
codecs.register_error that is able to handle UnicodeEncodeErrors.
- endswith(...)
- S.endswith(suffix[, start[, end]]) -> bool
Return True if S ends with the specified suffix, False otherwise.
With optional start, test S beginning at that position.
With optional end, stop comparing S at that position.
suffix can also be a tuple of strings to try.
- expandtabs(...)
- S.expandtabs([tabsize]) -> string
Return a copy of S where all tab characters are expanded using spaces.
If tabsize is not given, a tab size of 8 characters is assumed.
- find(...)
- S.find(sub [,start [,end]]) -> int
Return the lowest index in S where substring sub is found,
such that sub is contained within S[start:end]. Optional
arguments start and end are interpreted as in slice notation.
Return -1 on failure.
- format(...)
- S.format(*args, **kwargs) -> string
Return a formatted version of S, using substitutions from args and kwargs.
The substitutions are identified by braces ('{' and '}').
- index(...)
- S.index(sub [,start [,end]]) -> int
Like S.find() but raise ValueError when the substring is not found.
- isalnum(...)
- S.isalnum() -> bool
Return True if all characters in S are alphanumeric
and there is at least one character in S, False otherwise.
- isalpha(...)
- S.isalpha() -> bool
Return True if all characters in S are alphabetic
and there is at least one character in S, False otherwise.
- isdigit(...)
- S.isdigit() -> bool
Return True if all characters in S are digits
and there is at least one character in S, False otherwise.
- islower(...)
- S.islower() -> bool
Return True if all cased characters in S are lowercase and there is
at least one cased character in S, False otherwise.
- isspace(...)
- S.isspace() -> bool
Return True if all characters in S are whitespace
and there is at least one character in S, False otherwise.
- istitle(...)
- S.istitle() -> bool
Return True if S is a titlecased string and there is at least one
character in S, i.e. uppercase characters may only follow uncased
characters and lowercase characters only cased ones. Return False
otherwise.
- isupper(...)
- S.isupper() -> bool
Return True if all cased characters in S are uppercase and there is
at least one cased character in S, False otherwise.
- join(...)
- S.join(iterable) -> string
Return a string which is the concatenation of the strings in the
iterable. The separator between elements is S.
- ljust(...)
- S.ljust(width[, fillchar]) -> string
Return S left-justified in a string of length width. Padding is
done using the specified fill character (default is a space).
- lower(...)
- S.lower() -> string
Return a copy of the string S converted to lowercase.
- lstrip(...)
- S.lstrip([chars]) -> string or unicode
Return a copy of the string S with leading whitespace removed.
If chars is given and not None, remove characters in chars instead.
If chars is unicode, S will be converted to unicode before stripping
- partition(...)
- S.partition(sep) -> (head, sep, tail)
Search for the separator sep in S, and return the part before it,
the separator itself, and the part after it. If the separator is not
found, return S and two empty strings.
- replace(...)
- S.replace(old, new[, count]) -> string
Return a copy of string S with all occurrences of substring
old replaced by new. If the optional argument count is
given, only the first count occurrences are replaced.
- rfind(...)
- S.rfind(sub [,start [,end]]) -> int
Return the highest index in S where substring sub is found,
such that sub is contained within S[start:end]. Optional
arguments start and end are interpreted as in slice notation.
Return -1 on failure.
- rindex(...)
- S.rindex(sub [,start [,end]]) -> int
Like S.rfind() but raise ValueError when the substring is not found.
- rjust(...)
- S.rjust(width[, fillchar]) -> string
Return S right-justified in a string of length width. Padding is
done using the specified fill character (default is a space)
- rpartition(...)
- S.rpartition(sep) -> (head, sep, tail)
Search for the separator sep in S, starting at the end of S, and return
the part before it, the separator itself, and the part after it. If the
separator is not found, return two empty strings and S.
- rsplit(...)
- S.rsplit([sep [,maxsplit]]) -> list of strings
Return a list of the words in the string S, using sep as the
delimiter string, starting at the end of the string and working
to the front. If maxsplit is given, at most maxsplit splits are
done. If sep is not specified or is None, any whitespace string
is a separator.
- rstrip(...)
- S.rstrip([chars]) -> string or unicode
Return a copy of the string S with trailing whitespace removed.
If chars is given and not None, remove characters in chars instead.
If chars is unicode, S will be converted to unicode before stripping
- split(...)
- S.split([sep [,maxsplit]]) -> list of strings
Return a list of the words in the string S, using sep as the
delimiter string. If maxsplit is given, at most maxsplit
splits are done. If sep is not specified or is None, any
whitespace string is a separator and empty strings are removed
from the result.
- splitlines(...)
- S.splitlines(keepends=False) -> list of strings
Return a list of the lines in S, breaking at line boundaries.
Line breaks are not included in the resulting list unless keepends
is given and true.
- startswith(...)
- S.startswith(prefix[, start[, end]]) -> bool
Return True if S starts with the specified prefix, False otherwise.
With optional start, test S beginning at that position.
With optional end, stop comparing S at that position.
prefix can also be a tuple of strings to try.
- strip(...)
- S.strip([chars]) -> string or unicode
Return a copy of the string S with leading and trailing
whitespace removed.
If chars is given and not None, remove characters in chars instead.
If chars is unicode, S will be converted to unicode before stripping
- swapcase(...)
- S.swapcase() -> string
Return a copy of the string S with uppercase characters
converted to lowercase and vice versa.
- title(...)
- S.title() -> string
Return a titlecased version of S, i.e. words start with uppercase
characters, all remaining cased characters have lowercase.
- translate(...)
- S.translate(table [,deletechars]) -> string
Return a copy of the string S, where all characters occurring
in the optional argument deletechars are removed, and the
remaining characters have been mapped through the given
translation table, which must be a string of length 256 or None.
If the table argument is None, no translation is applied and
the operation simply removes the characters in deletechars.
- upper(...)
- S.upper() -> string
Return a copy of the string S converted to uppercase.
- zfill(...)
- S.zfill(width) -> string
Pad a numeric string S with zeros on the left, to fill a field
of the specified width. The string S is never truncated.
Methods inherited from generic:
- __abs__(...)
- x.__abs__() <==> abs(x)
- __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
- __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
- __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
- __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__(...)
- __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:
* {'<', 'L'} - little endian
* {'>', 'B'} - big endian
* {'=', 'N'} - native order
* 'S' - swap dtype from current to opposite endian
* {'|', '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.
- 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 timedelta64(signedinteger) |
| |
- Method resolution order:
- timedelta64
- 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)
- __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__(...)
- __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:
* {'<', 'L'} - little endian
* {'>', 'B'} - big endian
* {'=', 'N'} - native order
* 'S' - swap dtype from current to opposite endian
* {'|', '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.
- 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
|
ubyte = class uint8(unsignedinteger) |
| |
- Method resolution order:
- uint8
- unsignedinteger
- 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
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__(...)
- __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:
* {'<', 'L'} - little endian
* {'>', 'B'} - big endian
* {'=', 'N'} - native order
* 'S' - swap dtype from current to opposite endian
* {'|', '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.
- 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 ufunc(__builtin__.object) |
|
Functions that operate element by element on whole arrays.
To see the documentation for a specific ufunc, use np.info(). For
example, np.info(np.sin). Because ufuncs are written in C
(for speed) and linked into Python with NumPy's ufunc facility,
Python's help() function finds this page whenever help() is called
on a ufunc.
A detailed explanation of ufuncs can be found in the "ufuncs.rst"
file in the NumPy reference guide.
Unary ufuncs:
=============
op(X, out=None)
Apply op to X elementwise
Parameters
----------
X : array_like
Input array.
out : array_like
An array to store the output. Must be the same shape as `X`.
Returns
-------
r : array_like
`r` will have the same shape as `X`; if out is provided, `r`
will be equal to out.
Binary ufuncs:
==============
op(X, Y, out=None)
Apply `op` to `X` and `Y` elementwise. May "broadcast" to make
the shapes of `X` and `Y` congruent.
The broadcasting rules are:
* Dimensions of length 1 may be prepended to either array.
* Arrays may be repeated along dimensions of length 1.
Parameters
----------
X : array_like
First input array.
Y : array_like
Second input array.
out : array_like
An array to store the output. Must be the same shape as the
output would have.
Returns
-------
r : array_like
The return value; if out is provided, `r` will be equal to out. |
|
Methods defined here:
- __call__(...)
- x.__call__(...) <==> x(...)
- __repr__(...)
- x.__repr__() <==> repr(x)
- __str__(...)
- x.__str__() <==> str(x)
- accumulate(...)
- accumulate(array, axis=0, dtype=None, out=None)
Accumulate the result of applying the operator to all elements.
For a one-dimensional array, accumulate produces results equivalent to::
r = np.empty(len(A))
t = op.identity # op = the ufunc being applied to A's elements
for i in range(len(A)):
t = op(t, A[i])
r[i] = t
return r
For example, add.accumulate() is equivalent to np.cumsum().
For a multi-dimensional array, accumulate is applied along only one
axis (axis zero by default; see Examples below) so repeated use is
necessary if one wants to accumulate over multiple axes.
Parameters
----------
array : array_like
The array to act on.
axis : int, optional
The axis along which to apply the accumulation; default is zero.
dtype : data-type code, optional
The data-type used to represent the intermediate results. Defaults
to the data-type of the output array if such is provided, or the
the data-type of the input array if no output array is provided.
out : ndarray, optional
A location into which the result is stored. If not provided a
freshly-allocated array is returned.
Returns
-------
r : ndarray
The accumulated values. If `out` was supplied, `r` is a reference to
`out`.
Examples
--------
1-D array examples:
>>> np.add.accumulate([2, 3, 5])
array([ 2, 5, 10])
>>> np.multiply.accumulate([2, 3, 5])
array([ 2, 6, 30])
2-D array examples:
>>> I = np.eye(2)
>>> I
array([[ 1., 0.],
[ 0., 1.]])
Accumulate along axis 0 (rows), down columns:
>>> np.add.accumulate(I, 0)
array([[ 1., 0.],
[ 1., 1.]])
>>> np.add.accumulate(I) # no axis specified = axis zero
array([[ 1., 0.],
[ 1., 1.]])
Accumulate along axis 1 (columns), through rows:
>>> np.add.accumulate(I, 1)
array([[ 1., 1.],
[ 0., 1.]])
- at(...)
- at(a, indices, b=None)
Performs unbuffered in place operation on operand 'a' for elements
specified by 'indices'. For addition ufunc, this method is equivalent to
`a[indices] += b`, except that results are accumulated for elements that
are indexed more than once. For example, `a[[0,0]] += 1` will only
increment the first element once because of buffering, whereas
`add.at(a, [0,0], 1)` will increment the first element twice.
Parameters
----------
a : array_like
The array to perform in place operation on.
indices : array_like or tuple
Array like index object or slice object for indexing into first
operand. If first operand has multiple dimensions, indices can be a
tuple of array like index objects or slice objects.
b : array_like
Second operand for ufuncs requiring two operands. Operand must be
broadcastable over first operand after indexing or slicing.
Examples
--------
Set items 0 and 1 to their negative values:
>>> a = np.array([1, 2, 3, 4])
>>> np.negative.at(a, [0, 1])
>>> print(a)
array([-1, -2, 3, 4])
::
Increment items 0 and 1, and increment item 2 twice:
>>> a = np.array([1, 2, 3, 4])
>>> np.add.at(a, [0, 1, 2, 2], 1)
>>> print(a)
array([2, 3, 5, 4])
::
Add items 0 and 1 in first array to second array,
and store results in first array:
>>> a = np.array([1, 2, 3, 4])
>>> b = np.array([1, 2])
>>> np.add.at(a, [0, 1], b)
>>> print(a)
array([2, 4, 3, 4])
- outer(...)
- outer(A, B)
Apply the ufunc `op` to all pairs (a, b) with a in `A` and b in `B`.
Let ``M = A.ndim``, ``N = B.ndim``. Then the result, `C`, of
``op.outer(A, B)`` is an array of dimension M + N such that:
.. math:: C[i_0, ..., i_{M-1}, j_0, ..., j_{N-1}] =
op(A[i_0, ..., i_{M-1}], B[j_0, ..., j_{N-1}])
For `A` and `B` one-dimensional, this is equivalent to::
r = empty(len(A),len(B))
for i in range(len(A)):
for j in range(len(B)):
r[i,j] = op(A[i], B[j]) # op = ufunc in question
Parameters
----------
A : array_like
First array
B : array_like
Second array
Returns
-------
r : ndarray
Output array
See Also
--------
numpy.outer
Examples
--------
>>> np.multiply.outer([1, 2, 3], [4, 5, 6])
array([[ 4, 5, 6],
[ 8, 10, 12],
[12, 15, 18]])
A multi-dimensional example:
>>> A = np.array([[1, 2, 3], [4, 5, 6]])
>>> A.shape
(2, 3)
>>> B = np.array([[1, 2, 3, 4]])
>>> B.shape
(1, 4)
>>> C = np.multiply.outer(A, B)
>>> C.shape; C
(2, 3, 1, 4)
array([[[[ 1, 2, 3, 4]],
[[ 2, 4, 6, 8]],
[[ 3, 6, 9, 12]]],
[[[ 4, 8, 12, 16]],
[[ 5, 10, 15, 20]],
[[ 6, 12, 18, 24]]]])
- reduce(...)
- reduce(a, axis=0, dtype=None, out=None, keepdims=False)
Reduces `a`'s dimension by one, by applying ufunc along one axis.
Let :math:`a.shape = (N_0, ..., N_i, ..., N_{M-1})`. Then
:math:`ufunc.reduce(a, axis=i)[k_0, ..,k_{i-1}, k_{i+1}, .., k_{M-1}]` =
the result of iterating `j` over :math:`range(N_i)`, cumulatively applying
ufunc to each :math:`a[k_0, ..,k_{i-1}, j, k_{i+1}, .., k_{M-1}]`.
For a one-dimensional array, reduce produces results equivalent to:
::
r = op.identity # op = ufunc
for i in range(len(A)):
r = op(r, A[i])
return r
For example, add.reduce() is equivalent to sum().
Parameters
----------
a : array_like
The array to act on.
axis : None or int or tuple of ints, optional
Axis or axes along which a reduction is performed.
The default (`axis` = 0) is perform a reduction over the first
dimension of the input array. `axis` may be negative, in
which case it counts from the last to the first axis.
.. versionadded:: 1.7.0
If this is `None`, a reduction is performed over all the axes.
If this is a tuple of ints, a reduction is performed on multiple
axes, instead of a single axis or all the axes as before.
For operations which are either not commutative or not associative,
doing a reduction over multiple axes is not well-defined. The
ufuncs do not currently raise an exception in this case, but will
likely do so in the future.
dtype : data-type code, optional
The type used to represent the intermediate results. Defaults
to the data-type of the output array if this is provided, or
the data-type of the input array if no output array is provided.
out : ndarray, optional
A location into which the result is stored. If not provided, a
freshly-allocated array is returned.
keepdims : bool, optional
If this is set to True, the axes which are reduced are left
in the result as dimensions with size one. With this option,
the result will broadcast correctly against the original `arr`.
Returns
-------
r : ndarray
The reduced array. If `out` was supplied, `r` is a reference to it.
Examples
--------
>>> np.multiply.reduce([2,3,5])
30
A multi-dimensional array example:
>>> X = np.arange(8).reshape((2,2,2))
>>> X
array([[[0, 1],
[2, 3]],
[[4, 5],
[6, 7]]])
>>> np.add.reduce(X, 0)
array([[ 4, 6],
[ 8, 10]])
>>> np.add.reduce(X) # confirm: default axis value is 0
array([[ 4, 6],
[ 8, 10]])
>>> np.add.reduce(X, 1)
array([[ 2, 4],
[10, 12]])
>>> np.add.reduce(X, 2)
array([[ 1, 5],
[ 9, 13]])
- reduceat(...)
- reduceat(a, indices, axis=0, dtype=None, out=None)
Performs a (local) reduce with specified slices over a single axis.
For i in ``range(len(indices))``, `reduceat` computes
``ufunc.reduce(a[indices[i]:indices[i+1]])``, which becomes the i-th
generalized "row" parallel to `axis` in the final result (i.e., in a
2-D array, for example, if `axis = 0`, it becomes the i-th row, but if
`axis = 1`, it becomes the i-th column). There are two exceptions to this:
* when ``i = len(indices) - 1`` (so for the last index),
``indices[i+1] = a.shape[axis]``.
* if ``indices[i] >= indices[i + 1]``, the i-th generalized "row" is
simply ``a[indices[i]]``.
The shape of the output depends on the size of `indices`, and may be
larger than `a` (this happens if ``len(indices) > a.shape[axis]``).
Parameters
----------
a : array_like
The array to act on.
indices : array_like
Paired indices, comma separated (not colon), specifying slices to
reduce.
axis : int, optional
The axis along which to apply the reduceat.
dtype : data-type code, optional
The type used to represent the intermediate results. Defaults
to the data type of the output array if this is provided, or
the data type of the input array if no output array is provided.
out : ndarray, optional
A location into which the result is stored. If not provided a
freshly-allocated array is returned.
Returns
-------
r : ndarray
The reduced values. If `out` was supplied, `r` is a reference to
`out`.
Notes
-----
A descriptive example:
If `a` is 1-D, the function `ufunc.accumulate(a)` is the same as
``ufunc.reduceat(a, indices)[::2]`` where `indices` is
``range(len(array) - 1)`` with a zero placed
in every other element:
``indices = zeros(2 * len(a) - 1)``, ``indices[1::2] = range(1, len(a))``.
Don't be fooled by this attribute's name: `reduceat(a)` is not
necessarily smaller than `a`.
Examples
--------
To take the running sum of four successive values:
>>> np.add.reduceat(np.arange(8),[0,4, 1,5, 2,6, 3,7])[::2]
array([ 6, 10, 14, 18])
A 2-D example:
>>> x = np.linspace(0, 15, 16).reshape(4,4)
>>> x
array([[ 0., 1., 2., 3.],
[ 4., 5., 6., 7.],
[ 8., 9., 10., 11.],
[ 12., 13., 14., 15.]])
::
# reduce such that the result has the following five rows:
# [row1 + row2 + row3]
# [row4]
# [row2]
# [row3]
# [row1 + row2 + row3 + row4]
>>> np.add.reduceat(x, [0, 3, 1, 2, 0])
array([[ 12., 15., 18., 21.],
[ 12., 13., 14., 15.],
[ 4., 5., 6., 7.],
[ 8., 9., 10., 11.],
[ 24., 28., 32., 36.]])
::
# reduce such that result has the following two columns:
# [col1 * col2 * col3, col4]
>>> np.multiply.reduceat(x, [0, 3], 1)
array([[ 0., 3.],
[ 120., 7.],
[ 720., 11.],
[ 2184., 15.]])
Data descriptors defined here:
- identity
- The identity value.
Data attribute containing the identity element for the ufunc, if it has one.
If it does not, the attribute value is None.
Examples
--------
>>> np.add.identity
0
>>> np.multiply.identity
1
>>> np.power.identity
1
>>> print np.exp.identity
None
- nargs
- The number of arguments.
Data attribute containing the number of arguments the ufunc takes, including
optional ones.
Notes
-----
Typically this value will be one more than what you might expect because all
ufuncs take the optional "out" argument.
Examples
--------
>>> np.add.nargs
3
>>> np.multiply.nargs
3
>>> np.power.nargs
3
>>> np.exp.nargs
2
- nin
- The number of inputs.
Data attribute containing the number of arguments the ufunc treats as input.
Examples
--------
>>> np.add.nin
2
>>> np.multiply.nin
2
>>> np.power.nin
2
>>> np.exp.nin
1
- nout
- The number of outputs.
Data attribute containing the number of arguments the ufunc treats as output.
Notes
-----
Since all ufuncs can take output arguments, this will always be (at least) 1.
Examples
--------
>>> np.add.nout
1
>>> np.multiply.nout
1
>>> np.power.nout
1
>>> np.exp.nout
1
- ntypes
- The number of types.
The number of numerical NumPy types - of which there are 18 total - on which
the ufunc can operate.
See Also
--------
numpy.ufunc.types
Examples
--------
>>> np.add.ntypes
18
>>> np.multiply.ntypes
18
>>> np.power.ntypes
17
>>> np.exp.ntypes
7
>>> np.remainder.ntypes
14
- signature
- types
- Returns a list with types grouped input->output.
Data attribute listing the data-type "Domain-Range" groupings the ufunc can
deliver. The data-types are given using the character codes.
See Also
--------
numpy.ufunc.ntypes
Examples
--------
>>> np.add.types
['??->?', 'bb->b', 'BB->B', 'hh->h', 'HH->H', 'ii->i', 'II->I', 'll->l',
'LL->L', 'qq->q', 'QQ->Q', 'ff->f', 'dd->d', 'gg->g', 'FF->F', 'DD->D',
'GG->G', 'OO->O']
>>> np.multiply.types
['??->?', 'bb->b', 'BB->B', 'hh->h', 'HH->H', 'ii->i', 'II->I', 'll->l',
'LL->L', 'qq->q', 'QQ->Q', 'ff->f', 'dd->d', 'gg->g', 'FF->F', 'DD->D',
'GG->G', 'OO->O']
>>> np.power.types
['bb->b', 'BB->B', 'hh->h', 'HH->H', 'ii->i', 'II->I', 'll->l', 'LL->L',
'qq->q', 'QQ->Q', 'ff->f', 'dd->d', 'gg->g', 'FF->F', 'DD->D', 'GG->G',
'OO->O']
>>> np.exp.types
['f->f', 'd->d', 'g->g', 'F->F', 'D->D', 'G->G', 'O->O']
>>> np.remainder.types
['bb->b', 'BB->B', 'hh->h', 'HH->H', 'ii->i', 'II->I', 'll->l', 'LL->L',
'qq->q', 'QQ->Q', 'ff->f', 'dd->d', 'gg->g', 'OO->O']
|
uint = class uint64(unsignedinteger) |
| |
- Method resolution order:
- uint64
- unsignedinteger
- 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
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__(...)
- __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:
* {'<', 'L'} - little endian
* {'>', 'B'} - big endian
* {'=', 'N'} - native order
* 'S' - swap dtype from current to opposite endian
* {'|', '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.
- 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
|
uint0 = class uint64(unsignedinteger) |
| |
- Method resolution order:
- uint64
- unsignedinteger
- 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
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__(...)
- __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:
* {'<', 'L'} - little endian
* {'>', 'B'} - big endian
* {'=', 'N'} - native order
* 'S' - swap dtype from current to opposite endian
* {'|', '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.
- 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 uint16(unsignedinteger) |
| |
- Method resolution order:
- uint16
- unsignedinteger
- 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
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__(...)
- __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:
* {'<', 'L'} - little endian
* {'>', 'B'} - big endian
* {'=', 'N'} - native order
* 'S' - swap dtype from current to opposite endian
* {'|', '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.
- 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 uint32(unsignedinteger) |
| |
- Method resolution order:
- uint32
- unsignedinteger
- 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
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__(...)
- __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:
* {'<', 'L'} - little endian
* {'>', 'B'} - big endian
* {'=', 'N'} - native order
* 'S' - swap dtype from current to opposite endian
* {'|', '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.
- 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 uint64(unsignedinteger) |
| |
- Method resolution order:
- uint64
- unsignedinteger
- 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
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__(...)
- __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:
* {'<', 'L'} - little endian
* {'>', 'B'} - big endian
* {'=', 'N'} - native order
* 'S' - swap dtype from current to opposite endian
* {'|', '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.
- 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 uint8(unsignedinteger) |
| |
- Method resolution order:
- uint8
- unsignedinteger
- 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
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__(...)
- __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:
* {'<', 'L'} - little endian
* {'>', 'B'} - big endian
* {'=', 'N'} - native order
* 'S' - swap dtype from current to opposite endian
* {'|', '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.
- 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
|
uintc = class uint32(unsignedinteger) |
| |
- Method resolution order:
- uint32
- unsignedinteger
- 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
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__(...)
- __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:
* {'<', 'L'} - little endian
* {'>', 'B'} - big endian
* {'=', 'N'} - native order
* 'S' - swap dtype from current to opposite endian
* {'|', '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.
- 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
|
uintp = class uint64(unsignedinteger) |
| |
- Method resolution order:
- uint64
- unsignedinteger
- 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
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__(...)
- __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:
* {'<', 'L'} - little endian
* {'>', 'B'} - big endian
* {'=', 'N'} - native order
* 'S' - swap dtype from current to opposite endian
* {'|', '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.
- 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
|
ulonglong = class uint64(unsignedinteger) |
| |
- Method resolution order:
- uint64
- unsignedinteger
- 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
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__(...)
- __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:
* {'<', 'L'} - little endian
* {'>', 'B'} - big endian
* {'=', 'N'} - native order
* 'S' - swap dtype from current to opposite endian
* {'|', '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.
- 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
|
unicode0 = class unicode_(__builtin__.unicode, character) |
| |
- Method resolution order:
- unicode_
- __builtin__.unicode
- __builtin__.basestring
- character
- flexible
- 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 __builtin__.unicode:
- __add__(...)
- x.__add__(y) <==> x+y
- __contains__(...)
- x.__contains__(y) <==> y in x
- __format__(...)
- S.__format__(format_spec) -> unicode
Return a formatted version of S as described by format_spec.
- __getattribute__(...)
- x.__getattribute__('name') <==> x.name
- __getitem__(...)
- x.__getitem__(y) <==> x[y]
- __getnewargs__(...)
- __getslice__(...)
- x.__getslice__(i, j) <==> x[i:j]
Use of negative indices is not supported.
- __len__(...)
- x.__len__() <==> len(x)
- __mod__(...)
- x.__mod__(y) <==> x%y
- __mul__(...)
- x.__mul__(n) <==> x*n
- __rmod__(...)
- x.__rmod__(y) <==> y%x
- __rmul__(...)
- x.__rmul__(n) <==> n*x
- __sizeof__(...)
- S.__sizeof__() -> size of S in memory, in bytes
- capitalize(...)
- S.capitalize() -> unicode
Return a capitalized version of S, i.e. make the first character
have upper case and the rest lower case.
- center(...)
- S.center(width[, fillchar]) -> unicode
Return S centered in a Unicode string of length width. Padding is
done using the specified fill character (default is a space)
- count(...)
- S.count(sub[, start[, end]]) -> int
Return the number of non-overlapping occurrences of substring sub in
Unicode string S[start:end]. Optional arguments start and end are
interpreted as in slice notation.
- decode(...)
- S.decode([encoding[,errors]]) -> string or unicode
Decodes S using the codec registered for encoding. encoding defaults
to the default encoding. errors may be given to set a different error
handling scheme. Default is 'strict' meaning that encoding errors raise
a UnicodeDecodeError. Other possible values are 'ignore' and 'replace'
as well as any other name registered with codecs.register_error that is
able to handle UnicodeDecodeErrors.
- encode(...)
- S.encode([encoding[,errors]]) -> string or unicode
Encodes S using the codec registered for encoding. encoding defaults
to the default encoding. errors may be given to set a different error
handling scheme. Default is 'strict' meaning that encoding errors raise
a UnicodeEncodeError. Other possible values are 'ignore', 'replace' and
'xmlcharrefreplace' as well as any other name registered with
codecs.register_error that can handle UnicodeEncodeErrors.
- endswith(...)
- S.endswith(suffix[, start[, end]]) -> bool
Return True if S ends with the specified suffix, False otherwise.
With optional start, test S beginning at that position.
With optional end, stop comparing S at that position.
suffix can also be a tuple of strings to try.
- expandtabs(...)
- S.expandtabs([tabsize]) -> unicode
Return a copy of S where all tab characters are expanded using spaces.
If tabsize is not given, a tab size of 8 characters is assumed.
- find(...)
- S.find(sub [,start [,end]]) -> int
Return the lowest index in S where substring sub is found,
such that sub is contained within S[start:end]. Optional
arguments start and end are interpreted as in slice notation.
Return -1 on failure.
- format(...)
- S.format(*args, **kwargs) -> unicode
Return a formatted version of S, using substitutions from args and kwargs.
The substitutions are identified by braces ('{' and '}').
- index(...)
- S.index(sub [,start [,end]]) -> int
Like S.find() but raise ValueError when the substring is not found.
- isalnum(...)
- S.isalnum() -> bool
Return True if all characters in S are alphanumeric
and there is at least one character in S, False otherwise.
- isalpha(...)
- S.isalpha() -> bool
Return True if all characters in S are alphabetic
and there is at least one character in S, False otherwise.
- isdecimal(...)
- S.isdecimal() -> bool
Return True if there are only decimal characters in S,
False otherwise.
- isdigit(...)
- S.isdigit() -> bool
Return True if all characters in S are digits
and there is at least one character in S, False otherwise.
- islower(...)
- S.islower() -> bool
Return True if all cased characters in S are lowercase and there is
at least one cased character in S, False otherwise.
- isnumeric(...)
- S.isnumeric() -> bool
Return True if there are only numeric characters in S,
False otherwise.
- isspace(...)
- S.isspace() -> bool
Return True if all characters in S are whitespace
and there is at least one character in S, False otherwise.
- istitle(...)
- S.istitle() -> bool
Return True if S is a titlecased string and there is at least one
character in S, i.e. upper- and titlecase characters may only
follow uncased characters and lowercase characters only cased ones.
Return False otherwise.
- isupper(...)
- S.isupper() -> bool
Return True if all cased characters in S are uppercase and there is
at least one cased character in S, False otherwise.
- join(...)
- S.join(iterable) -> unicode
Return a string which is the concatenation of the strings in the
iterable. The separator between elements is S.
- ljust(...)
- S.ljust(width[, fillchar]) -> int
Return S left-justified in a Unicode string of length width. Padding is
done using the specified fill character (default is a space).
- lower(...)
- S.lower() -> unicode
Return a copy of the string S converted to lowercase.
- lstrip(...)
- S.lstrip([chars]) -> unicode
Return a copy of the string S with leading whitespace removed.
If chars is given and not None, remove characters in chars instead.
If chars is a str, it will be converted to unicode before stripping
- partition(...)
- S.partition(sep) -> (head, sep, tail)
Search for the separator sep in S, and return the part before it,
the separator itself, and the part after it. If the separator is not
found, return S and two empty strings.
- replace(...)
- S.replace(old, new[, count]) -> unicode
Return a copy of S with all occurrences of substring
old replaced by new. If the optional argument count is
given, only the first count occurrences are replaced.
- rfind(...)
- S.rfind(sub [,start [,end]]) -> int
Return the highest index in S where substring sub is found,
such that sub is contained within S[start:end]. Optional
arguments start and end are interpreted as in slice notation.
Return -1 on failure.
- rindex(...)
- S.rindex(sub [,start [,end]]) -> int
Like S.rfind() but raise ValueError when the substring is not found.
- rjust(...)
- S.rjust(width[, fillchar]) -> unicode
Return S right-justified in a Unicode string of length width. Padding is
done using the specified fill character (default is a space).
- rpartition(...)
- S.rpartition(sep) -> (head, sep, tail)
Search for the separator sep in S, starting at the end of S, and return
the part before it, the separator itself, and the part after it. If the
separator is not found, return two empty strings and S.
- rsplit(...)
- S.rsplit([sep [,maxsplit]]) -> list of strings
Return a list of the words in S, using sep as the
delimiter string, starting at the end of the string and
working to the front. If maxsplit is given, at most maxsplit
splits are done. If sep is not specified, any whitespace string
is a separator.
- rstrip(...)
- S.rstrip([chars]) -> unicode
Return a copy of the string S with trailing whitespace removed.
If chars is given and not None, remove characters in chars instead.
If chars is a str, it will be converted to unicode before stripping
- split(...)
- S.split([sep [,maxsplit]]) -> list of strings
Return a list of the words in S, using sep as the
delimiter string. If maxsplit is given, at most maxsplit
splits are done. If sep is not specified or is None, any
whitespace string is a separator and empty strings are
removed from the result.
- splitlines(...)
- S.splitlines(keepends=False) -> list of strings
Return a list of the lines in S, breaking at line boundaries.
Line breaks are not included in the resulting list unless keepends
is given and true.
- startswith(...)
- S.startswith(prefix[, start[, end]]) -> bool
Return True if S starts with the specified prefix, False otherwise.
With optional start, test S beginning at that position.
With optional end, stop comparing S at that position.
prefix can also be a tuple of strings to try.
- strip(...)
- S.strip([chars]) -> unicode
Return a copy of the string S with leading and trailing
whitespace removed.
If chars is given and not None, remove characters in chars instead.
If chars is a str, it will be converted to unicode before stripping
- swapcase(...)
- S.swapcase() -> unicode
Return a copy of S with uppercase characters converted to lowercase
and vice versa.
- title(...)
- S.title() -> unicode
Return a titlecased version of S, i.e. words start with title case
characters, all remaining cased characters have lower case.
- translate(...)
- S.translate(table) -> unicode
Return a copy of the string S, where all characters have been mapped
through the given translation table, which must be a mapping of
Unicode ordinals to Unicode ordinals, Unicode strings or None.
Unmapped characters are left untouched. Characters mapped to None
are deleted.
- upper(...)
- S.upper() -> unicode
Return a copy of S converted to uppercase.
- zfill(...)
- S.zfill(width) -> unicode
Pad a numeric string S with zeros on the left, to fill a field
of the specified width. The string S is never truncated.
Methods inherited from generic:
- __abs__(...)
- x.__abs__() <==> abs(x)
- __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
- __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
- __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
- __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__(...)
- __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:
* {'<', 'L'} - little endian
* {'>', 'B'} - big endian
* {'=', 'N'} - native order
* 'S' - swap dtype from current to opposite endian
* {'|', '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.
- 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 unicode_(__builtin__.unicode, character) |
| |
- Method resolution order:
- unicode_
- __builtin__.unicode
- __builtin__.basestring
- character
- flexible
- 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 __builtin__.unicode:
- __add__(...)
- x.__add__(y) <==> x+y
- __contains__(...)
- x.__contains__(y) <==> y in x
- __format__(...)
- S.__format__(format_spec) -> unicode
Return a formatted version of S as described by format_spec.
- __getattribute__(...)
- x.__getattribute__('name') <==> x.name
- __getitem__(...)
- x.__getitem__(y) <==> x[y]
- __getnewargs__(...)
- __getslice__(...)
- x.__getslice__(i, j) <==> x[i:j]
Use of negative indices is not supported.
- __len__(...)
- x.__len__() <==> len(x)
- __mod__(...)
- x.__mod__(y) <==> x%y
- __mul__(...)
- x.__mul__(n) <==> x*n
- __rmod__(...)
- x.__rmod__(y) <==> y%x
- __rmul__(...)
- x.__rmul__(n) <==> n*x
- __sizeof__(...)
- S.__sizeof__() -> size of S in memory, in bytes
- capitalize(...)
- S.capitalize() -> unicode
Return a capitalized version of S, i.e. make the first character
have upper case and the rest lower case.
- center(...)
- S.center(width[, fillchar]) -> unicode
Return S centered in a Unicode string of length width. Padding is
done using the specified fill character (default is a space)
- count(...)
- S.count(sub[, start[, end]]) -> int
Return the number of non-overlapping occurrences of substring sub in
Unicode string S[start:end]. Optional arguments start and end are
interpreted as in slice notation.
- decode(...)
- S.decode([encoding[,errors]]) -> string or unicode
Decodes S using the codec registered for encoding. encoding defaults
to the default encoding. errors may be given to set a different error
handling scheme. Default is 'strict' meaning that encoding errors raise
a UnicodeDecodeError. Other possible values are 'ignore' and 'replace'
as well as any other name registered with codecs.register_error that is
able to handle UnicodeDecodeErrors.
- encode(...)
- S.encode([encoding[,errors]]) -> string or unicode
Encodes S using the codec registered for encoding. encoding defaults
to the default encoding. errors may be given to set a different error
handling scheme. Default is 'strict' meaning that encoding errors raise
a UnicodeEncodeError. Other possible values are 'ignore', 'replace' and
'xmlcharrefreplace' as well as any other name registered with
codecs.register_error that can handle UnicodeEncodeErrors.
- endswith(...)
- S.endswith(suffix[, start[, end]]) -> bool
Return True if S ends with the specified suffix, False otherwise.
With optional start, test S beginning at that position.
With optional end, stop comparing S at that position.
suffix can also be a tuple of strings to try.
- expandtabs(...)
- S.expandtabs([tabsize]) -> unicode
Return a copy of S where all tab characters are expanded using spaces.
If tabsize is not given, a tab size of 8 characters is assumed.
- find(...)
- S.find(sub [,start [,end]]) -> int
Return the lowest index in S where substring sub is found,
such that sub is contained within S[start:end]. Optional
arguments start and end are interpreted as in slice notation.
Return -1 on failure.
- format(...)
- S.format(*args, **kwargs) -> unicode
Return a formatted version of S, using substitutions from args and kwargs.
The substitutions are identified by braces ('{' and '}').
- index(...)
- S.index(sub [,start [,end]]) -> int
Like S.find() but raise ValueError when the substring is not found.
- isalnum(...)
- S.isalnum() -> bool
Return True if all characters in S are alphanumeric
and there is at least one character in S, False otherwise.
- isalpha(...)
- S.isalpha() -> bool
Return True if all characters in S are alphabetic
and there is at least one character in S, False otherwise.
- isdecimal(...)
- S.isdecimal() -> bool
Return True if there are only decimal characters in S,
False otherwise.
- isdigit(...)
- S.isdigit() -> bool
Return True if all characters in S are digits
and there is at least one character in S, False otherwise.
- islower(...)
- S.islower() -> bool
Return True if all cased characters in S are lowercase and there is
at least one cased character in S, False otherwise.
- isnumeric(...)
- S.isnumeric() -> bool
Return True if there are only numeric characters in S,
False otherwise.
- isspace(...)
- S.isspace() -> bool
Return True if all characters in S are whitespace
and there is at least one character in S, False otherwise.
- istitle(...)
- S.istitle() -> bool
Return True if S is a titlecased string and there is at least one
character in S, i.e. upper- and titlecase characters may only
follow uncased characters and lowercase characters only cased ones.
Return False otherwise.
- isupper(...)
- S.isupper() -> bool
Return True if all cased characters in S are uppercase and there is
at least one cased character in S, False otherwise.
- join(...)
- S.join(iterable) -> unicode
Return a string which is the concatenation of the strings in the
iterable. The separator between elements is S.
- ljust(...)
- S.ljust(width[, fillchar]) -> int
Return S left-justified in a Unicode string of length width. Padding is
done using the specified fill character (default is a space).
- lower(...)
- S.lower() -> unicode
Return a copy of the string S converted to lowercase.
- lstrip(...)
- S.lstrip([chars]) -> unicode
Return a copy of the string S with leading whitespace removed.
If chars is given and not None, remove characters in chars instead.
If chars is a str, it will be converted to unicode before stripping
- partition(...)
- S.partition(sep) -> (head, sep, tail)
Search for the separator sep in S, and return the part before it,
the separator itself, and the part after it. If the separator is not
found, return S and two empty strings.
- replace(...)
- S.replace(old, new[, count]) -> unicode
Return a copy of S with all occurrences of substring
old replaced by new. If the optional argument count is
given, only the first count occurrences are replaced.
- rfind(...)
- S.rfind(sub [,start [,end]]) -> int
Return the highest index in S where substring sub is found,
such that sub is contained within S[start:end]. Optional
arguments start and end are interpreted as in slice notation.
Return -1 on failure.
- rindex(...)
- S.rindex(sub [,start [,end]]) -> int
Like S.rfind() but raise ValueError when the substring is not found.
- rjust(...)
- S.rjust(width[, fillchar]) -> unicode
Return S right-justified in a Unicode string of length width. Padding is
done using the specified fill character (default is a space).
- rpartition(...)
- S.rpartition(sep) -> (head, sep, tail)
Search for the separator sep in S, starting at the end of S, and return
the part before it, the separator itself, and the part after it. If the
separator is not found, return two empty strings and S.
- rsplit(...)
- S.rsplit([sep [,maxsplit]]) -> list of strings
Return a list of the words in S, using sep as the
delimiter string, starting at the end of the string and
working to the front. If maxsplit is given, at most maxsplit
splits are done. If sep is not specified, any whitespace string
is a separator.
- rstrip(...)
- S.rstrip([chars]) -> unicode
Return a copy of the string S with trailing whitespace removed.
If chars is given and not None, remove characters in chars instead.
If chars is a str, it will be converted to unicode before stripping
- split(...)
- S.split([sep [,maxsplit]]) -> list of strings
Return a list of the words in S, using sep as the
delimiter string. If maxsplit is given, at most maxsplit
splits are done. If sep is not specified or is None, any
whitespace string is a separator and empty strings are
removed from the result.
- splitlines(...)
- S.splitlines(keepends=False) -> list of strings
Return a list of the lines in S, breaking at line boundaries.
Line breaks are not included in the resulting list unless keepends
is given and true.
- startswith(...)
- S.startswith(prefix[, start[, end]]) -> bool
Return True if S starts with the specified prefix, False otherwise.
With optional start, test S beginning at that position.
With optional end, stop comparing S at that position.
prefix can also be a tuple of strings to try.
- strip(...)
- S.strip([chars]) -> unicode
Return a copy of the string S with leading and trailing
whitespace removed.
If chars is given and not None, remove characters in chars instead.
If chars is a str, it will be converted to unicode before stripping
- swapcase(...)
- S.swapcase() -> unicode
Return a copy of S with uppercase characters converted to lowercase
and vice versa.
- title(...)
- S.title() -> unicode
Return a titlecased version of S, i.e. words start with title case
characters, all remaining cased characters have lower case.
- translate(...)
- S.translate(table) -> unicode
Return a copy of the string S, where all characters have been mapped
through the given translation table, which must be a mapping of
Unicode ordinals to Unicode ordinals, Unicode strings or None.
Unmapped characters are left untouched. Characters mapped to None
are deleted.
- upper(...)
- S.upper() -> unicode
Return a copy of S converted to uppercase.
- zfill(...)
- S.zfill(width) -> unicode
Pad a numeric string S with zeros on the left, to fill a field
of the specified width. The string S is never truncated.
Methods inherited from generic:
- __abs__(...)
- x.__abs__() <==> abs(x)
- __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
- __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
- __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
- __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__(...)
- __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:
* {'<', 'L'} - little endian
* {'>', 'B'} - big endian
* {'=', 'N'} - native order
* 'S' - swap dtype from current to opposite endian
* {'|', '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.
- 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 unsignedinteger(integer) |
| |
- Method resolution order:
- unsignedinteger
- integer
- 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__(...)
- __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:
* {'<', 'L'} - little endian
* {'>', 'B'} - big endian
* {'=', 'N'} - native order
* 'S' - swap dtype from current to opposite endian
* {'|', '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.
- 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
|
ushort = class uint16(unsignedinteger) |
| |
- Method resolution order:
- uint16
- unsignedinteger
- 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
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__(...)
- __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:
* {'<', 'L'} - little endian
* {'>', 'B'} - big endian
* {'=', 'N'} - native order
* 'S' - swap dtype from current to opposite endian
* {'|', '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.
- 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 vectorize(__builtin__.object) |
|
vectorize(pyfunc, otypes='', doc=None, excluded=None, cache=False)
Generalized function class.
Define a vectorized function which takes a nested sequence
of objects or numpy arrays as inputs and returns a
numpy array as output. The vectorized function evaluates `pyfunc` over
successive tuples of the input arrays like the python map function,
except it uses the broadcasting rules of numpy.
The data type of the output of `vectorized` is determined by calling
the function with the first element of the input. This can be avoided
by specifying the `otypes` argument.
Parameters
----------
pyfunc : callable
A python function or method.
otypes : str or list of dtypes, optional
The output data type. It must be specified as either a string of
typecode characters or a list of data type specifiers. There should
be one data type specifier for each output.
doc : str, optional
The docstring for the function. If `None`, the docstring will be the
``pyfunc.__doc__``.
excluded : set, optional
Set of strings or integers representing the positional or keyword
arguments for which the function will not be vectorized. These will be
passed directly to `pyfunc` unmodified.
.. versionadded:: 1.7.0
cache : bool, optional
If `True`, then cache the first function call that determines the number
of outputs if `otypes` is not provided.
.. versionadded:: 1.7.0
Returns
-------
vectorized : callable
Vectorized function.
Examples
--------
>>> def myfunc(a, b):
... "Return a-b if a>b, otherwise return a+b"
... if a > b:
... return a - b
... else:
... return a + b
>>> vfunc = np.vectorize(myfunc)
>>> vfunc([1, 2, 3, 4], 2)
array([3, 4, 1, 2])
The docstring is taken from the input function to `vectorize` unless it
is specified
>>> vfunc.__doc__
'Return a-b if a>b, otherwise return a+b'
>>> vfunc = np.vectorize(myfunc, doc='Vectorized `myfunc`')
>>> vfunc.__doc__
'Vectorized `myfunc`'
The output type is determined by evaluating the first element of the input,
unless it is specified
>>> out = vfunc([1, 2, 3, 4], 2)
>>> type(out[0])
<type 'numpy.int32'>
>>> vfunc = np.vectorize(myfunc, otypes=[np.float])
>>> out = vfunc([1, 2, 3, 4], 2)
>>> type(out[0])
<type 'numpy.float64'>
The `excluded` argument can be used to prevent vectorizing over certain
arguments. This can be useful for array-like arguments of a fixed length
such as the coefficients for a polynomial as in `polyval`:
>>> def mypolyval(p, x):
... _p = list(p)
... res = _p.pop(0)
... while _p:
... res = res*x + _p.pop(0)
... return res
>>> vpolyval = np.vectorize(mypolyval, excluded=['p'])
>>> vpolyval(p=[1, 2, 3], x=[0, 1])
array([3, 6])
Positional arguments may also be excluded by specifying their position:
>>> vpolyval.excluded.add(0)
>>> vpolyval([1, 2, 3], x=[0, 1])
array([3, 6])
Notes
-----
The `vectorize` function is provided primarily for convenience, not for
performance. The implementation is essentially a for loop.
If `otypes` is not specified, then a call to the function with the first
argument will be used to determine the number of outputs. The results of
this call will be cached if `cache` is `True` to prevent calling the
function twice. However, to implement the cache, the original function must
be wrapped which will slow down subsequent calls, so only do this if your
function is expensive.
The new keyword argument interface and `excluded` argument support further
degrades performance. |
|
Methods defined here:
- __call__(self, *args, **kwargs)
- Return arrays with the results of `pyfunc` broadcast (vectorized) over
`args` and `kwargs` not in `excluded`.
- __init__(self, pyfunc, otypes='', doc=None, excluded=None, cache=False)
Data descriptors defined here:
- __dict__
- dictionary for instance variables (if defined)
- __weakref__
- list of weak references to the object (if defined)
|
class void(flexible) |
| |
- Method resolution order:
- void
- flexible
- generic
- __builtin__.object
Methods defined here:
- __delitem__(...)
- x.__delitem__(y) <==> del x[y]
- __eq__(...)
- x.__eq__(y) <==> x==y
- __ge__(...)
- x.__ge__(y) <==> x>=y
- __getitem__(...)
- x.__getitem__(y) <==> x[y]
- __gt__(...)
- x.__gt__(y) <==> x>y
- __hash__(...)
- x.__hash__() <==> hash(x)
- __le__(...)
- x.__le__(y) <==> x<=y
- __len__(...)
- x.__len__() <==> len(x)
- __lt__(...)
- x.__lt__(y) <==> x<y
- __ne__(...)
- x.__ne__(y) <==> x!=y
- __setitem__(...)
- x.__setitem__(i, y) <==> x[i]=y
- getfield(...)
- setfield(...)
Data descriptors defined here:
- dtype
- dtype object
- flags
- integer value of flags
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
- __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__(...)
- __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.
- 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:
* {'<', 'L'} - little endian
* {'>', 'B'} - big endian
* {'=', 'N'} - native order
* 'S' - swap dtype from current to opposite endian
* {'|', '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.
- 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.
- 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
- 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
|
void0 = class void(flexible) |
| |
- Method resolution order:
- void
- flexible
- generic
- __builtin__.object
Methods defined here:
- __delitem__(...)
- x.__delitem__(y) <==> del x[y]
- __eq__(...)
- x.__eq__(y) <==> x==y
- __ge__(...)
- x.__ge__(y) <==> x>=y
- __getitem__(...)
- x.__getitem__(y) <==> x[y]
- __gt__(...)
- x.__gt__(y) <==> x>y
- __hash__(...)
- x.__hash__() <==> hash(x)
- __le__(...)
- x.__le__(y) <==> x<=y
- __len__(...)
- x.__len__() <==> len(x)
- __lt__(...)
- x.__lt__(y) <==> x<y
- __ne__(...)
- x.__ne__(y) <==> x!=y
- __setitem__(...)
- x.__setitem__(i, y) <==> x[i]=y
- getfield(...)
- setfield(...)
Data descriptors defined here:
- dtype
- dtype object
- flags
- integer value of flags
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
- __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__(...)
- __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.
- 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:
* {'<', 'L'} - little endian
* {'>', 'B'} - big endian
* {'=', 'N'} - native order
* 'S' - swap dtype from current to opposite endian
* {'|', '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.
- 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.
- 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
- 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
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