# -*- coding: utf-8 -*-

# Licensed under a 3-clause BSD style license - see LICENSE.rst

"""

A "grab bag" of relatively small general-purpose utilities that don't have

a clear module/package to live in.

"""

import abc

import contextlib

import difflib

import inspect

import json

import os

import signal

import sys

import traceback

import unicodedata

import locale

import threading

import re

import urllib.request

from itertools import zip_longest

from contextlib import contextmanager

from collections import defaultdict, OrderedDict

__all__ = ['isiterable', 'silence', 'format_exception', 'NumpyRNGContext',

'find_api_page', 'is_path_hidden', 'walk_skip_hidden',

'JsonCustomEncoder', 'indent', 'InheritDocstrings',

'OrderedDescriptor', 'OrderedDescriptorContainer', 'set_locale',

'ShapedLikeNDArray', 'check_broadcast', 'IncompatibleShapeError',

'dtype_bytes_or_chars']

def isiterable(obj):

"""Returns `True` if the given object is iterable."""

try:

iter(obj)

return True

except TypeError:

return False

def indent(s, shift=1, width=4):

"""Indent a block of text. The indentation is applied to each line."""

indented = '\n'.join(' ' * (width * shift) + l if l else ''

for l in s.splitlines())

if s[-1] == '\n':

indented += '\n'

return indented

class _DummyFile:

"""A noop writeable object."""

def write(self, s):

pass

@contextlib.contextmanager

def silence():

"""A context manager that silences sys.stdout and sys.stderr."""

old_stdout = sys.stdout

old_stderr = sys.stderr

sys.stdout = _DummyFile()

sys.stderr = _DummyFile()

yield

sys.stdout = old_stdout

sys.stderr = old_stderr

def format_exception(msg, *args, **kwargs):

"""

Given an exception message string, uses new-style formatting arguments

``{filename}``, ``{lineno}``, ``{func}`` and/or ``{text}`` to fill in

information about the exception that occurred. For example:

try:

1/0

except:

raise ZeroDivisionError(

format_except('A divide by zero occurred in {filename} at '

'line {lineno} of function {func}.'))

Any additional positional or keyword arguments passed to this function are

also used to format the message.

.. note::

This uses `sys.exc_info` to gather up the information needed to fill

in the formatting arguments. Since `sys.exc_info` is not carried

outside a handled exception, it's not wise to use this

outside of an ``except`` clause - if it is, this will substitute

'<unkown>' for the 4 formatting arguments.

"""

tb = traceback.extract_tb(sys.exc_info()[2], limit=1)

if len(tb) > 0:

filename, lineno, func, text = tb[0]

else:

filename = lineno = func = text = '<unknown>'

return msg.format(*args, filename=filename, lineno=lineno, func=func,

text=text, **kwargs)

class NumpyRNGContext:

"""

A context manager (for use with the ``with`` statement) that will seed the

numpy random number generator (RNG) to a specific value, and then restore

the RNG state back to whatever it was before.

This is primarily intended for use in the astropy testing suit, but it

may be useful in ensuring reproducibility of Monte Carlo simulations in a

science context.

Parameters

----------

seed : int

The value to use to seed the numpy RNG

Examples

--------

A typical use case might be::

with NumpyRNGContext(<some seed value you pick>):

from numpy import random

randarr = random.randn(100)

... run your test using `randarr` ...

#Any code using numpy.random at this indent level will act just as it

#would have if it had been before the with statement - e.g. whatever

#the default seed is.

"""

def __init__(self, seed):

self.seed = seed

def __enter__(self):

from numpy import random

self.startstate = random.get_state()

random.seed(self.seed)

def __exit__(self, exc_type, exc_value, traceback):

from numpy import random

random.set_state(self.startstate)

def find_api_page(obj, version=None, openinbrowser=True, timeout=None):

"""

Determines the URL of the API page for the specified object, and

optionally open that page in a web browser.

.. note::

You must be connected to the internet for this to function even if

``openinbrowser`` is `False`, unless you provide a local version of

the documentation to ``version`` (e.g., ``file:///path/to/docs``).

Parameters

----------

obj

The object to open the docs for or its fully-qualified name

(as a str).

version : str

The doc version - either a version number like '0.1', 'dev' for

the development/latest docs, or a URL to point to a specific

location that should be the *base* of the documentation. Defaults to

latest if you are on aren't on a release, otherwise, the version you

are on.

openinbrowser : bool

If `True`, the `webbrowser` package will be used to open the doc

page in a new web browser window.

timeout : number, optional

The number of seconds to wait before timing-out the query to

the astropy documentation. If not given, the default python

stdlib timeout will be used.

Returns

-------

url : str

The loaded URL

Raises

------

ValueError

If the documentation can't be found

"""

import webbrowser

from zlib import decompress

if (not isinstance(obj, str) and

hasattr(obj, '__module__') and

hasattr(obj, '__name__')):

obj = obj.__module__ + '.' + obj.__name__

elif inspect.ismodule(obj):

obj = obj.__name__

if version is None:

from .. import version

if version.release:

version = 'v' + version.version

else:

version = 'dev'

if '://' in version:

if version.endswith('index.html'):

baseurl = version[:-10]

elif version.endswith('/'):

baseurl = version

else:

baseurl = version + '/'

elif version == 'dev' or version == 'latest':

baseurl = 'http://devdocs.astropy.org/'

else:

baseurl = 'http://docs.astropy.org/en/{vers}/'.format(vers=version)

if timeout is None:

uf = urllib.request.urlopen(baseurl + 'objects.inv')

else:

uf = urllib.request.urlopen(baseurl + 'objects.inv', timeout=timeout)

try:

oiread = uf.read()

# need to first read/remove the first four lines, which have info before

# the compressed section with the actual object inventory

idx = -1

headerlines = []

for _ in range(4):

oldidx = idx

idx = oiread.index(b'\n', oldidx + 1)

headerlines.append(oiread[(oldidx+1):idx].decode('utf-8'))

# intersphinx version line, project name, and project version

ivers, proj, vers, compr = headerlines

if 'The remainder of this file is compressed using zlib' not in compr:

raise ValueError('The file downloaded from {0} does not seem to be'

'the usual Sphinx objects.inv format. Maybe it '

'has changed?'.format(baseurl + 'objects.inv'))

compressed = oiread[(idx+1):]

finally:

uf.close()

decompressed = decompress(compressed).decode('utf-8')

resurl = None

for l in decompressed.strip().splitlines():

ls = l.split()

name = ls[0]

loc = ls[3]

if loc.endswith('$'):

loc = loc[:-1] + name

if name == obj:

resurl = baseurl + loc

break

if resurl is None:

raise ValueError('Could not find the docs for the object {obj}'.format(obj=obj))

elif openinbrowser:

webbrowser.open(resurl)

return resurl

def signal_number_to_name(signum):

"""

Given an OS signal number, returns a signal name. If the signal

number is unknown, returns ``'UNKNOWN'``.

"""

# Since these numbers and names are platform specific, we use the

# builtin signal module and build a reverse mapping.

signal_to_name_map = dict((k, v) for v, k in signal.__dict__.items()

if v.startswith('SIG'))

return signal_to_name_map.get(signum, 'UNKNOWN')

if sys.platform == 'win32':

import ctypes

def _has_hidden_attribute(filepath):

"""

Returns True if the given filepath has the hidden attribute on

MS-Windows. Based on a post here:

http://stackoverflow.com/questions/284115/cross-platform-hidden-file-detection

"""

if isinstance(filepath, bytes):

filepath = filepath.decode(sys.getfilesystemencoding())

try:

attrs = ctypes.windll.kernel32.GetFileAttributesW(filepath)

result = bool(attrs & 2) and attrs != -1

except AttributeError:

result = False

return result

else:

def _has_hidden_attribute(filepath):

return False

def is_path_hidden(filepath):

"""

Determines if a given file or directory is hidden.

Parameters

----------

filepath : str

The path to a file or directory

Returns

-------

hidden : bool

Returns `True` if the file is hidden

"""

name = os.path.basename(os.path.abspath(filepath))

if isinstance(name, bytes):

is_dotted = name.startswith(b'.')

else:

is_dotted = name.startswith('.')

return is_dotted or _has_hidden_attribute(filepath)

def walk_skip_hidden(top, onerror=None, followlinks=False):

"""

A wrapper for `os.walk` that skips hidden files and directories.

This function does not have the parameter ``topdown`` from

`os.walk`: the directories must always be recursed top-down when

using this function.

See also

--------

os.walk : For a description of the parameters

"""

for root, dirs, files in os.walk(

top, topdown=True, onerror=onerror,

followlinks=followlinks):

# These lists must be updated in-place so os.walk will skip

# hidden directories

dirs[:] = [d for d in dirs if not is_path_hidden(d)]

files[:] = [f for f in files if not is_path_hidden(f)]

yield root, dirs, files

class JsonCustomEncoder(json.JSONEncoder):

"""Support for data types that JSON default encoder

does not do.

This includes:

* Numpy array or number

* Complex number

* Set

* Bytes

* astropy.UnitBase

* astropy.Quantity

Examples

--------

>>> import json

>>> import numpy as np

>>> from astropy.utils.misc import JsonCustomEncoder

>>> json.dumps(np.arange(3), cls=JsonCustomEncoder)

'[0, 1, 2]'

"""

def default(self, obj):

from .. import units as u

import numpy as np

if isinstance(obj, u.Quantity):

return dict(value=obj.value, unit=obj.unit.to_string())

if isinstance(obj, (np.number, np.ndarray)):

return obj.tolist()

elif isinstance(obj, complex):

return [obj.real, obj.imag]

elif isinstance(obj, set):

return list(obj)

elif isinstance(obj, bytes): # pragma: py3

return obj.decode()

elif isinstance(obj, (u.UnitBase, u.FunctionUnitBase)):

if obj == u.dimensionless_unscaled:

obj = 'dimensionless_unit'

else:

return obj.to_string()

return json.JSONEncoder.default(self, obj)

def strip_accents(s):

"""

Remove accents from a Unicode string.

This helps with matching "ångström" to "angstrom", for example.

"""

return ''.join(

c for c in unicodedata.normalize('NFD', s)

if unicodedata.category(c) != 'Mn')

def did_you_mean(s, candidates, n=3, cutoff=0.8, fix=None):

"""

When a string isn't found in a set of candidates, we can be nice

to provide a list of alternatives in the exception. This

convenience function helps to format that part of the exception.

Parameters

----------

s : str

candidates : sequence of str or dict of str keys

n : int

The maximum number of results to include. See

`difflib.get_close_matches`.

cutoff : float

In the range [0, 1]. Possibilities that don't score at least

that similar to word are ignored. See

`difflib.get_close_matches`.

fix : callable

A callable to modify the results after matching. It should

take a single string and return a sequence of strings

containing the fixed matches.

Returns

-------

message : str

Returns the string "Did you mean X, Y, or Z?", or the empty

string if no alternatives were found.

"""

if isinstance(s, str):

s = strip_accents(s)

s_lower = s.lower()

# Create a mapping from the lower case name to all capitalization

# variants of that name.

candidates_lower = {}

for candidate in candidates:

candidate_lower = candidate.lower()

candidates_lower.setdefault(candidate_lower, [])

candidates_lower[candidate_lower].append(candidate)

# The heuristic here is to first try "singularizing" the word. If

# that doesn't match anything use difflib to find close matches in

# original, lower and upper case.

if s_lower.endswith('s') and s_lower[:-1] in candidates_lower:

matches = [s_lower[:-1]]

else:

matches = difflib.get_close_matches(

s_lower, candidates_lower, n=n, cutoff=cutoff)

if len(matches):

capitalized_matches = set()

for match in matches:

capitalized_matches.update(candidates_lower[match])

matches = capitalized_matches

if fix is not None:

mapped_matches = []

for match in matches:

mapped_matches.extend(fix(match))

matches = mapped_matches

matches = list(set(matches))

matches = sorted(matches)

if len(matches) == 1:

matches = matches[0]

else:

matches = (', '.join(matches[:-1]) + ' or ' +

matches[-1])

return 'Did you mean {0}?'.format(matches)

return ''

class InheritDocstrings(type):

"""

This metaclass makes methods of a class automatically have their

docstrings filled in from the methods they override in the base

class.

If the class uses multiple inheritance, the docstring will be

chosen from the first class in the bases list, in the same way as

methods are normally resolved in Python. If this results in

selecting the wrong docstring, the docstring will need to be

explicitly included on the method.

For example::

>>> from astropy.utils.misc import InheritDocstrings

>>> class A(metaclass=InheritDocstrings):

... def wiggle(self):

... "Wiggle the thingamajig"

... pass

>>> class B(A):

... def wiggle(self):

... pass

>>> B.wiggle.__doc__

u'Wiggle the thingamajig'

"""

def __init__(cls, name, bases, dct):

def is_public_member(key):

return (

(key.startswith('__') and key.endswith('__')

and len(key) > 4) or

not key.startswith('_'))

for key, val in dct.items():

if ((inspect.isfunction(val) or inspect.isdatadescriptor(val)) and

is_public_member(key) and

val.__doc__ is None):

for base in cls.__mro__[1:]:

super_method = getattr(base, key, None)

if super_method is not None:

val.__doc__ = super_method.__doc__

break

super().__init__(name, bases, dct)

class OrderedDescriptor(metaclass=abc.ABCMeta):

"""

Base class for descriptors whose order in the class body should be

preserved. Intended for use in concert with the

`OrderedDescriptorContainer` metaclass.

Subclasses of `OrderedDescriptor` must define a value for a class attribute

called ``_class_attribute_``. This is the name of a class attribute on the

*container* class for these descriptors, which will be set to an

`~collections.OrderedDict` at class creation time. This

`~collections.OrderedDict` will contain a mapping of all class attributes

that were assigned instances of the `OrderedDescriptor` subclass, to the

instances themselves. See the documentation for

`OrderedDescriptorContainer` for a concrete example.

Optionally, subclasses of `OrderedDescriptor` may define a value for a

class attribute called ``_name_attribute_``. This should be the name of

an attribute on instances of the subclass. When specified, during

creation of a class containing these descriptors, the name attribute on

each instance will be set to the name of the class attribute it was

assigned to on the class.

.. note::

Although this class is intended for use with *descriptors* (i.e.

classes that define any of the ``__get__``, ``__set__``, or

``__delete__`` magic methods), this base class is not itself a

descriptor, and technically this could be used for classes that are

not descriptors too. However, use with descriptors is the original

intended purpose.

"""

# This id increments for each OrderedDescriptor instance created, so they

# are always ordered in the order they were created. Class bodies are

# guaranteed to be executed from top to bottom. Not sure if this is

# thread-safe though.

_nextid = 1

@property

@abc.abstractmethod

def _class_attribute_(self):

"""

Subclasses should define this attribute to the name of an attribute on

classes containing this subclass. That attribute will contain the mapping

of all instances of that `OrderedDescriptor` subclass defined in the class

body. If the same descriptor needs to be used with different classes,

each with different names of this attribute, multiple subclasses will be

needed.

"""

_name_attribute_ = None

"""

Subclasses may optionally define this attribute to specify the name of an

attribute on instances of the class that should be filled with the

instance's attribute name at class creation time.

"""

def __init__(self, *args, **kwargs):

# The _nextid attribute is shared across all subclasses so that

# different subclasses of OrderedDescriptors can be sorted correctly

# between themselves

self.__order = OrderedDescriptor._nextid

OrderedDescriptor._nextid += 1

super().__init__()

def __lt__(self, other):

"""

Defined for convenient sorting of `OrderedDescriptor` instances, which

are defined to sort in their creation order.

"""

if (isinstance(self, OrderedDescriptor) and

isinstance(other, OrderedDescriptor)):

try:

return self.__order < other.__order

except AttributeError:

raise RuntimeError(

'Could not determine ordering for {0} and {1}; at least '

'one of them is not calling super().__init__ in its '

'__init__.'.format(self, other))

else:

return NotImplemented

class OrderedDescriptorContainer(type):

"""

Classes should use this metaclass if they wish to use `OrderedDescriptor`

attributes, which are class attributes that "remember" the order in which

they were defined in the class body.

Every subclass of `OrderedDescriptor` has an attribute called

``_class_attribute_``. For example, if we have

.. code:: python

class ExampleDecorator(OrderedDescriptor):

_class_attribute_ = '_examples_'

Then when a class with the `OrderedDescriptorContainer` metaclass is

created, it will automatically be assigned a class attribute ``_examples_``

referencing an `~collections.OrderedDict` containing all instances of

``ExampleDecorator`` defined in the class body, mapped to by the names of

the attributes they were assigned to.

When subclassing a class with this metaclass, the descriptor dict (i.e.

``_examples_`` in the above example) will *not* contain descriptors

inherited from the base class. That is, this only works by default with

decorators explicitly defined in the class body. However, the subclass

*may* define an attribute ``_inherit_decorators_`` which lists

`OrderedDescriptor` classes that *should* be added from base classes.

See the examples section below for an example of this.

Examples

--------

>>> from astropy.utils import OrderedDescriptor, OrderedDescriptorContainer

>>> class TypedAttribute(OrderedDescriptor):

... \"\"\"

... Attributes that may only be assigned objects of a specific type,

... or subclasses thereof. For some reason we care about their order.

... \"\"\"

...

... _class_attribute_ = 'typed_attributes'

... _name_attribute_ = 'name'

... # A default name so that instances not attached to a class can

... # still be repr'd; useful for debugging

... name = '<unbound>'

...

... def __init__(self, type):

... # Make sure not to forget to call the super __init__

... super().__init__()

... self.type = type

...

... def __get__(self, obj, objtype=None):

... if obj is None:

... return self

... if self.name in obj.__dict__:

... return obj.__dict__[self.name]

... else:

... raise AttributeError(self.name)

...

... def __set__(self, obj, value):

... if not isinstance(value, self.type):

... raise ValueError('{0}.{1} must be of type {2!r}'.format(

... obj.__class__.__name__, self.name, self.type))

... obj.__dict__[self.name] = value

...

... def __delete__(self, obj):

... if self.name in obj.__dict__:

... del obj.__dict__[self.name]

... else:

... raise AttributeError(self.name)

...

... def __repr__(self):

... if isinstance(self.type, tuple) and len(self.type) > 1:

... typestr = '({0})'.format(

... ', '.join(t.__name__ for t in self.type))

... else:

... typestr = self.type.__name__

... return '<{0}(name={1}, type={2})>'.format(

... self.__class__.__name__, self.name, typestr)

...

Now let's create an example class that uses this ``TypedAttribute``::

>>> class Point2D(metaclass=OrderedDescriptorContainer):

... x = TypedAttribute((float, int))

... y = TypedAttribute((float, int))

...

... def __init__(self, x, y):

... self.x, self.y = x, y

...

>>> p1 = Point2D(1.0, 2.0)

>>> p1.x

1.0

>>> p1.y

2.0

>>> p2 = Point2D('a', 'b') # doctest: +IGNORE_EXCEPTION_DETAIL

Traceback (most recent call last):

...

ValueError: Point2D.x must be of type (float, int>)

We see that ``TypedAttribute`` works more or less as advertised, but

there's nothing special about that. Let's see what

`OrderedDescriptorContainer` did for us::

>>> Point2D.typed_attributes

OrderedDict([('x', <TypedAttribute(name=x, type=(float, int))>),

('y', <TypedAttribute(name=y, type=(float, int))>)])

If we create a subclass, it does *not* by default add inherited descriptors

to ``typed_attributes``::

>>> class Point3D(Point2D):

... z = TypedAttribute((float, int))

...

>>> Point3D.typed_attributes

OrderedDict([('z', <TypedAttribute(name=z, type=(float, int))>)])

However, if we specify ``_inherit_descriptors_`` from ``Point2D`` then

it will do so::

>>> class Point3D(Point2D):

... _inherit_descriptors_ = (TypedAttribute,)

... z = TypedAttribute((float, int))

...

>>> Point3D.typed_attributes

OrderedDict([('x', <TypedAttribute(name=x, type=(float, int))>),

('y', <TypedAttribute(name=y, type=(float, int))>),

('z', <TypedAttribute(name=z, type=(float, int))>)])

.. note::

Hopefully it is clear from these examples that this construction

also allows a class of type `OrderedDescriptorContainer` to use

multiple different `OrderedDescriptor` classes simultaneously.

"""

_inherit_descriptors_ = ()

def __init__(cls, cls_name, bases, members):

descriptors = defaultdict(list)

seen = set()

inherit_descriptors = ()

descr_bases = {}

for mro_cls in cls.__mro__:

for name, obj in mro_cls.__dict__.items():

if name in seen:

# Checks if we've already seen an attribute of the given

# name (if so it will override anything of the same name in

# any base class)

continue

seen.add(name)

if (not isinstance(obj, OrderedDescriptor) or

(inherit_descriptors and

not isinstance(obj, inherit_descriptors))):

# The second condition applies when checking any

# subclasses, to see if we can inherit any descriptors of

# the given type from subclasses (by default inheritance is

# disabled unless the class has _inherit_descriptors_

# defined)

continue

if obj._name_attribute_ is not None:

setattr(obj, obj._name_attribute_, name)

# Don't just use the descriptor's class directly; instead go

# through its MRO and find the class on which _class_attribute_

# is defined directly. This way subclasses of some

# OrderedDescriptor *may* override _class_attribute_ and have

# its own _class_attribute_, but by default all subclasses of

# some OrderedDescriptor are still grouped together

# TODO: It might be worth clarifying this in the docs

if obj.__class__ not in descr_bases:

for obj_cls_base in obj.__class__.__mro__:

if '_class_attribute_' in obj_cls_base.__dict__:

descr_bases[obj.__class__] = obj_cls_base

descriptors[obj_cls_base].append((obj, name))

break

else:

# Make sure to put obj first for sorting purposes

obj_cls_base = descr_bases[obj.__class__]

descriptors[obj_cls_base].append((obj, name))

if not getattr(mro_cls, '_inherit_descriptors_', False):

# If _inherit_descriptors_ is undefined then we don't inherit

# any OrderedDescriptors from any of the base classes, and

# there's no reason to continue through the MRO

break

else:

inherit_descriptors = mro_cls._inherit_descriptors_

for descriptor_cls, instances in descriptors.items():

instances.sort()

instances = OrderedDict((key, value) for value, key in instances)

setattr(cls, descriptor_cls._class_attribute_, instances)

super().__init__(cls_name, bases, members)

LOCALE_LOCK = threading.Lock()

@contextmanager

def set_locale(name):

"""

Context manager to temporarily set the locale to ``name``.

An example is setting locale to "C" so that the C strtod()

function will use "." as the decimal point to enable consistent

numerical string parsing.

Note that one cannot nest multiple set_locale() context manager

statements as this causes a threading lock.

This code taken from https://stackoverflow.com/questions/18593661/how-do-i-strftime-a-date-object-in-a-different-locale.

Parameters

==========

name : str

Locale name, e.g. "C" or "fr_FR".

"""

name = str(name)

with LOCALE_LOCK:

saved = locale.setlocale(locale.LC_ALL)

if saved == name:

# Don't do anything if locale is already the requested locale

yield

else:

try:

locale.setlocale(locale.LC_ALL, name)

yield

finally:

locale.setlocale(locale.LC_ALL, saved)

class ShapedLikeNDArray(metaclass=abc.ABCMeta):

"""Mixin class to provide shape-changing methods.

The class proper is assumed to have some underlying data, which are arrays

or array-like structures. It must define a ``shape`` property, which gives

the shape of those data, as well as an ``_apply`` method that creates a new

instance in which a `~numpy.ndarray` method has been applied to those.

Furthermore, for consistency with `~numpy.ndarray`, it is recommended to

define a setter for the ``shape`` property, which, like the

`~numpy.ndarray.shape` property allows in-place reshaping the internal data

(and, unlike the ``reshape`` method raises an exception if this is not

possible).

This class also defines default implementations for ``ndim`` and ``size``

properties, calculating those from the ``shape``. These can be overridden

by subclasses if there are faster ways to obtain those numbers.

"""

# Note to developers: if new methods are added here, be sure to check that

# they work properly with the classes that use this, such as Time and

# BaseRepresentation, i.e., look at their ``_apply`` methods and add

# relevant tests. This is particularly important for methods that imply

# copies rather than views of data (see the special-case treatment of

# 'flatten' in Time).

@property

@abc.abstractmethod

def shape(self):

"""The shape of the instance and underlying arrays."""

@abc.abstractmethod

def _apply(method, *args, **kwargs):

"""Create a new instance, with ``method`` applied to underlying data.

The method is any of the shape-changing methods for `~numpy.ndarray`

(``reshape``, ``swapaxes``, etc.), as well as those picking particular

elements (``__getitem__``, ``take``, etc.). It will be applied to the

underlying arrays (e.g., ``jd1`` and ``jd2`` in `~astropy.time.Time`),

with the results used to create a new instance.

Parameters

----------

method : str

Method to be applied to the instance's internal data arrays.

args : tuple

Any positional arguments for ``method``.

kwargs : dict

Any keyword arguments for ``method``.

"""

@property

def ndim(self):

"""The number of dimensions of the instance and underlying arrays."""

return len(self.shape)

@property

def size(self):

"""The size of the object, as calculated from its shape."""

size = 1

for sh in self.shape:

size *= sh

return size

@property

def isscalar(self):

return self.shape == ()

def __len__(self):

if self.isscalar:

raise TypeError("Scalar {0!r} object has no len()"

.format(self.__class__.__name__))

return self.shape[0]

def __bool__(self):

"""Any instance should evaluate to True, except when it is empty."""

return self.size > 0

def __getitem__(self, item):

try:

return self._apply('__getitem__', item)

except IndexError:

if self.isscalar:

raise TypeError('scalar {0!r} object is not subscriptable.'

.format(self.__class__.__name__))

else:

raise

def __iter__(self):

if self.isscalar:

raise TypeError('scalar {0!r} object is not iterable.'

.format(self.__class__.__name__))

# We cannot just write a generator here, since then the above error

# would only be raised once we try to use the iterator, rather than

# upon its definition using iter(self).

def self_iter():

for idx in range(len(self)):

yield self[idx]

return self_iter()

def copy(self, *args, **kwargs):

"""Return an instance containing copies of the internal data.

Parameters are as for :meth:`~numpy.ndarray.copy`.

"""

return self._apply('copy', *args, **kwargs)

def reshape(self, *args, **kwargs):

"""Returns an instance containing the same data with a new shape.

Parameters are as for :meth:`~numpy.ndarray.reshape`. Note that it is

not always possible to change the shape of an array without copying the

data (see :func:`~numpy.reshape` documentation). If you want an error

to be raise if the data is copied, you should assign the new shape to

the shape attribute (note: this may not be implemented for all classes

using ``ShapedLikeNDArray``).

"""

return self._apply('reshape', *args, **kwargs)

def ravel(self, *args, **kwargs):

"""Return an instance with the array collapsed into one dimension.

Parameters are as for :meth:`~numpy.ndarray.ravel`. Note that it is

not always possible to unravel an array without copying the data.

If you want an error to be raise if the data is copied, you should

should assign shape ``(-1,)`` to the shape attribute.

"""

return self._apply('ravel', *args, **kwargs)

def flatten(self, *args, **kwargs):

"""Return a copy with the array collapsed into one dimension.

Parameters are as for :meth:`~numpy.ndarray.flatten`.

"""

return self._apply('flatten', *args, **kwargs)

def transpose(self, *args, **kwargs):

"""Return an instance with the data transposed.