Na}{

File Content: `glossary.py`

"""
========
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*.

   record array
       An `ndarray`_ with `structured data type`_ which has been subclassed as
       np.recarray and whose dtype is of type np.record, making the
       fields of its data type to be accessible by attribute.

   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])
   
   structured data type
       A data type composed of other datatypes
   
   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
Name	Size	Permission
__init__.py	574 bytes	0644
__init__.pyc	1080 bytes	0644
__init__.pyo	1080 bytes	0644
basics.py	7870 bytes	0644
basics.pyc	8097 bytes	0644
basics.pyo	8097 bytes	0644
broadcasting.py	5565 bytes	0644
broadcasting.pyc	5798 bytes	0644
broadcasting.pyo	5798 bytes	0644
byteswapping.py	5349 bytes	0644
byteswapping.pyc	5578 bytes	0644
byteswapping.pyo	5578 bytes	0644
constants.py	8954 bytes	0644
constants.pyc	8274 bytes	0644
constants.pyo	8274 bytes	0644
creation.py	5507 bytes	0644
creation.pyc	5736 bytes	0644
creation.pyo	5736 bytes	0644
glossary.py	12346 bytes	0644
glossary.pyc	12575 bytes	0644
glossary.pyo	12575 bytes	0644
indexing.py	15672 bytes	0644
indexing.pyc	15901 bytes	0644
indexing.pyo	15901 bytes	0644
internals.py	9673 bytes	0644
internals.pyc	9903 bytes	0644
internals.pyo	9903 bytes	0644
misc.py	6165 bytes	0644
misc.pyc	6390 bytes	0644
misc.pyo	6390 bytes	0644
structured_arrays.py	11442 bytes	0644
structured_arrays.pyc	11680 bytes	0644
structured_arrays.pyo	11680 bytes	0644
subclassing.py	20243 bytes	0644
subclassing.pyc	20475 bytes	0644
subclassing.pyo	20475 bytes	0644
ufuncs.py	5427 bytes	0644
ufuncs.pyc	5652 bytes	0644
ufuncs.pyo	5652 bytes	0644
File Content: glossary.py

File Content: `glossary.py`
