JayKimDevolved/deepseek

c011401 verified 3 months ago

12 kB

	"""
	========
	Glossary
	========

	.. glossary::

	along an axis
	Axes are defined for arrays with more than one dimension. A
	2-dimensional array has two corresponding axes: the first running
	vertically downwards across rows (axis 0), and the second running
	horizontally across columns (axis 1).

	Many operation can take place along one of these axes. For example,
	we can sum each row of an array, in which case we operate along
	columns, or axis 1::

	>>> x = np.arange(12).reshape((3,4))

	>>> x
	array([[ 0, 1, 2, 3],
	[ 4, 5, 6, 7],
	[ 8, 9, 10, 11]])

	>>> x.sum(axis=1)
	array([ 6, 22, 38])

	array
	A homogeneous container of numerical elements. Each element in the
	array occupies a fixed amount of memory (hence homogeneous), and
	can be a numerical element of a single type (such as float, int
	or complex) or a combination (such as ``(float, int, float)``). Each
	array has an associated data-type (or ``dtype``), which describes
	the numerical type of its elements::

	>>> x = np.array([1, 2, 3], float)

	>>> x
	array([ 1., 2., 3.])

	>>> x.dtype # floating point number, 64 bits of memory per element
	dtype('float64')


	# More complicated data type: each array element is a combination of
	# and integer and a floating point number
	>>> np.array([(1, 2.0), (3, 4.0)], dtype=[('x', int), ('y', float)])
	array([(1, 2.0), (3, 4.0)],
	dtype=[('x', '<i4'), ('y', '<f8')])

	Fast element-wise operations, called `ufuncs`_, operate on arrays.

	array_like
	Any sequence that can be interpreted as an ndarray. This includes
	nested lists, tuples, scalars and existing arrays.

	attribute
	A property of an object that can be accessed using ``obj.attribute``,
	e.g., ``shape`` is an attribute of an array::

	>>> x = np.array([1, 2, 3])
	>>> x.shape
	(3,)

	BLAS
	`Basic Linear Algebra Subprograms <http://en.wikipedia.org/wiki/BLAS>`_

	broadcast
	NumPy can do operations on arrays whose shapes are mismatched::

	>>> x = np.array([1, 2])
	>>> y = np.array([[3], [4]])

	>>> x
	array([1, 2])

	>>> y
	array([[3],
	[4]])

	>>> x + y
	array([[4, 5],
	[5, 6]])

	See `doc.broadcasting`_ for more information.

	C order
	See `row-major`

	column-major
	A way to represent items in a N-dimensional array in the 1-dimensional
	computer memory. In column-major order, the leftmost index "varies the
	fastest": for example the array::

	[[1, 2, 3],
	[4, 5, 6]]

	is represented in the column-major order as::

	[1, 4, 2, 5, 3, 6]

	Column-major order is also known as the Fortran order, as the Fortran
	programming language uses it.

	decorator
	An operator that transforms a function. For example, a ``log``
	decorator may be defined to print debugging information upon
	function execution::

	>>> def log(f):
	... def new_logging_func(args, *kwargs):
	... print "Logging call with parameters:", args, kwargs
	... return f(args, *kwargs)
	...
	... return new_logging_func

	Now, when we define a function, we can "decorate" it using ``log``::

	>>> @log
	... def add(a, b):
	... return a + b

	Calling ``add`` then yields:

	>>> add(1, 2)
	Logging call with parameters: (1, 2) {}
	3

	dictionary
	Resembling a language dictionary, which provides a mapping between
	words and descriptions thereof, a Python dictionary is a mapping
	between two objects::

	>>> x = {1: 'one', 'two': [1, 2]}

	Here, `x` is a dictionary mapping keys to values, in this case
	the integer 1 to the string "one", and the string "two" to
	the list ``[1, 2]``. The values may be accessed using their
	corresponding keys::

	>>> x[1]
	'one'

	>>> x['two']
	[1, 2]

	Note that dictionaries are not stored in any specific order. Also,
	most mutable (see immutable below) objects, such as lists, may not
	be used as keys.

	For more information on dictionaries, read the
	`Python tutorial <http://docs.python.org/tut>`_.

	Fortran order
	See `column-major`

	flattened
	Collapsed to a one-dimensional array. See `ndarray.flatten`_ for details.

	immutable
	An object that cannot be modified after execution is called
	immutable. Two common examples are strings and tuples.

	instance
	A class definition gives the blueprint for constructing an object::

	>>> class House(object):
	... wall_colour = 'white'

	Yet, we have to build a house before it exists::

	>>> h = House() # build a house

	Now, ``h`` is called a ``House`` instance. An instance is therefore
	a specific realisation of a class.

	iterable
	A sequence that allows "walking" (iterating) over items, typically
	using a loop such as::

	>>> x = [1, 2, 3]
	>>> [item**2 for item in x]
	[1, 4, 9]

	It is often used in combintion with ``enumerate``::
	>>> keys = ['a','b','c']
	>>> for n, k in enumerate(keys):
	... print "Key %d: %s" % (n, k)
	...
	Key 0: a
	Key 1: b
	Key 2: c

	list
	A Python container that can hold any number of objects or items.
	The items do not have to be of the same type, and can even be
	lists themselves::

	>>> x = [2, 2.0, "two", [2, 2.0]]

	The list `x` contains 4 items, each which can be accessed individually::

	>>> x[2] # the string 'two'
	'two'

	>>> x[3] # a list, containing an integer 2 and a float 2.0
	[2, 2.0]

	It is also possible to select more than one item at a time,
	using slicing::

	>>> x[0:2] # or, equivalently, x[:2]
	[2, 2.0]

	In code, arrays are often conveniently expressed as nested lists::


	>>> np.array([[1, 2], [3, 4]])
	array([[1, 2],
	[3, 4]])

	For more information, read the section on lists in the `Python
	tutorial <http://docs.python.org/tut>`_. For a mapping
	type (key-value), see dictionary.

	mask
	A boolean array, used to select only certain elements for an operation::

	>>> x = np.arange(5)
	>>> x
	array([0, 1, 2, 3, 4])

	>>> mask = (x > 2)
	>>> mask
	array([False, False, False, True, True], dtype=bool)

	>>> x[mask] = -1
	>>> x
	array([ 0, 1, 2, -1, -1])

	masked array
	Array that suppressed values indicated by a mask::

	>>> x = np.ma.masked_array([np.nan, 2, np.nan], [True, False, True])
	>>> x
	masked_array(data = [-- 2.0 --],
	mask = [ True False True],
	fill_value = 1e+20)
	<BLANKLINE>

	>>> x + [1, 2, 3]
	masked_array(data = [-- 4.0 --],
	mask = [ True False True],
	fill_value = 1e+20)
	<BLANKLINE>


	Masked arrays are often used when operating on arrays containing
	missing or invalid entries.

	matrix
	A 2-dimensional ndarray that preserves its two-dimensional nature
	throughout operations. It has certain special operations, such as ``*``
	(matrix multiplication) and ``**`` (matrix power), defined::

	>>> x = np.mat([[1, 2], [3, 4]])

	>>> x
	matrix([[1, 2],
	[3, 4]])

	>>> x**2
	matrix([[ 7, 10],
	[15, 22]])

	method
	A function associated with an object. For example, each ndarray has a
	method called ``repeat``::

	>>> x = np.array([1, 2, 3])

	>>> x.repeat(2)
	array([1, 1, 2, 2, 3, 3])

	ndarray
	See array.

	reference
	If ``a`` is a reference to ``b``, then ``(a is b) == True``. Therefore,
	``a`` and ``b`` are different names for the same Python object.

	row-major
	A way to represent items in a N-dimensional array in the 1-dimensional
	computer memory. In row-major order, the rightmost index "varies
	the fastest": for example the array::

	[[1, 2, 3],
	[4, 5, 6]]

	is represented in the row-major order as::

	[1, 2, 3, 4, 5, 6]

	Row-major order is also known as the C order, as the C programming
	language uses it. New Numpy arrays are by default in row-major order.

	self
	Often seen in method signatures, ``self`` refers to the instance
	of the associated class. For example:

	>>> class Paintbrush(object):
	... color = 'blue'
	...
	... def paint(self):
	... print "Painting the city %s!" % self.color
	...
	>>> p = Paintbrush()
	>>> p.color = 'red'
	>>> p.paint() # self refers to 'p'
	Painting the city red!

	slice
	Used to select only certain elements from a sequence::

	>>> x = range(5)
	>>> x
	[0, 1, 2, 3, 4]

	>>> x[1:3] # slice from 1 to 3 (excluding 3 itself)
	[1, 2]

	>>> x[1:5:2] # slice from 1 to 5, but skipping every second element
	[1, 3]

	>>> x[::-1] # slice a sequence in reverse
	[4, 3, 2, 1, 0]

	Arrays may have more than one dimension, each which can be sliced
	individually::

	>>> x = np.array([[1, 2], [3, 4]])
	>>> x
	array([[1, 2],
	[3, 4]])

	>>> x[:, 1]
	array([2, 4])

	tuple
	A sequence that may contain a variable number of types of any
	kind. A tuple is immutable, i.e., once constructed it cannot be
	changed. Similar to a list, it can be indexed and sliced::

	>>> x = (1, 'one', [1, 2])

	>>> x
	(1, 'one', [1, 2])

	>>> x[0]
	1

	>>> x[:2]
	(1, 'one')

	A useful concept is "tuple unpacking", which allows variables to
	be assigned to the contents of a tuple::

	>>> x, y = (1, 2)
	>>> x, y = 1, 2

	This is often used when a function returns multiple values:

	>>> def return_many():
	... return 1, 'alpha', None

	>>> a, b, c = return_many()
	>>> a, b, c
	(1, 'alpha', None)

	>>> a
	1
	>>> b
	'alpha'

	ufunc
	Universal function. A fast element-wise array operation. Examples include
	``add``, ``sin`` and ``logical_or``.

	view
	An array that does not own its data, but refers to another array's
	data instead. For example, we may create a view that only shows
	every second element of another array::

	>>> x = np.arange(5)
	>>> x
	array([0, 1, 2, 3, 4])

	>>> y = x[::2]
	>>> y
	array([0, 2, 4])

	>>> x[0] = 3 # changing x changes y as well, since y is a view on x
	>>> y
	array([3, 2, 4])

	wrapper
	Python is a high-level (highly abstracted, or English-like) language.
	This abstraction comes at a price in execution speed, and sometimes
	it becomes necessary to use lower level languages to do fast
	computations. A wrapper is code that provides a bridge between
	high and the low level languages, allowing, e.g., Python to execute
	code written in C or Fortran.

	Examples include ctypes, SWIG and Cython (which wraps C and C++)
	and f2py (which wraps Fortran).

	"""
	from __future__ import division, absolute_import, print_function