[Scipy-svn] r5259 - trunk/doc/source/tutorial
scipy-svn@scip...
scipy-svn@scip...
Sun Dec 14 08:24:21 CST 2008
Author: jarrod.millman
Date: 2008-12-14 08:24:19 -0600 (Sun, 14 Dec 2008)
New Revision: 5259
Modified:
trunk/doc/source/tutorial/basic.rst
Log:
minor updates
Modified: trunk/doc/source/tutorial/basic.rst
===================================================================
--- trunk/doc/source/tutorial/basic.rst 2008-12-14 14:05:54 UTC (rev 5258)
+++ trunk/doc/source/tutorial/basic.rst 2008-12-14 14:24:19 UTC (rev 5259)
@@ -5,6 +5,8 @@
.. currentmodule:: numpy
+.. contents::
+
Interaction with Numpy
------------------------
@@ -14,12 +16,12 @@
functions (addition, subtraction, division) have been altered to not
raise exceptions if floating-point errors are encountered; instead,
NaN's and Inf's are returned in the arrays. To assist in detection of
-these events, several functions (:func:`isnan`, :func:`isfinite`,
-:func:`isinf`) are available.
+these events, several functions (:func:`sp.isnan`, :func:`sp.isfinite`,
+:func:`sp.isinf`) are available.
Finally, some of the basic functions like log, sqrt, and inverse trig
functions have been modified to return complex numbers instead of
-NaN's where appropriate (*i.e.* ``scipy.sqrt(-1)`` returns ``1j``).
+NaN's where appropriate (*i.e.* ``sp.sqrt(-1)`` returns ``1j``).
Top-level scipy routines
@@ -44,8 +46,8 @@
Type handling
^^^^^^^^^^^^^
-Note the difference between :func:`iscomplex` (:func:`isreal`) and
-:func:`iscomplexobj` (:func:`isrealobj`). The former command is
+Note the difference between :func:`sp.iscomplex`/:func:`sp.isreal` and
+:func:`sp.iscomplexobj`/:func:`sp.isrealobj`. The former command is
array based and returns byte arrays of ones and zeros providing the
result of the element-wise test. The latter command is object based
and returns a scalar describing the result of the test on the entire
@@ -55,32 +57,34 @@
complex number. While complex numbers and arrays have attributes that
return those values, if one is not sure whether or not the object will
be complex-valued, it is better to use the functional forms
-:func:`real` and :func:`imag` . These functions succeed for anything
+:func:`sp.real` and :func:`sp.imag` . These functions succeed for anything
that can be turned into a Numpy array. Consider also the function
-:func:`real_if_close` which transforms a complex-valued number with
+:func:`sp.real_if_close` which transforms a complex-valued number with
tiny imaginary part into a real number.
Occasionally the need to check whether or not a number is a scalar
(Python (long)int, Python float, Python complex, or rank-0 array)
occurs in coding. This functionality is provided in the convenient
-function :func:`isscalar` which returns a 1 or a 0.
+function :func:`sp.isscalar` which returns a 1 or a 0.
Finally, ensuring that objects are a certain Numpy type occurs often
enough that it has been given a convenient interface in SciPy through
-the use of the :obj:`cast` dictionary. The dictionary is keyed by the
+the use of the :obj:`sp.cast` dictionary. The dictionary is keyed by the
type it is desired to cast to and the dictionary stores functions to
-perform the casting. Thus, ``>>> a = cast['f'](d)`` returns an array
-of :class:`float32` from *d*. This function is also useful as an easy
-way to get a scalar of a certain type: ``>>> fpi = cast['f'](pi)``.
+perform the casting. Thus, ``sp.cast['f'](d)`` returns an array
+of :class:`sp.float32` from *d*. This function is also useful as an easy
+way to get a scalar of a certain type::
+ >>> sp.cast['f'](sp.pi)
+ array(3.1415927410125732, dtype=float32)
Index Tricks
^^^^^^^^^^^^
-Thre are some class instances that make special use of the slicing
+There are some class instances that make special use of the slicing
functionality to provide efficient means for array construction. This
-part will discuss the operation of :obj:`mgrid` , :obj:`ogrid` ,
-:obj:`r_` , and :obj:`c_` for quickly constructing arrays.
+part will discuss the operation of :obj:`sp.mgrid` , :obj:`sp.ogrid` ,
+:obj:`sp.r_` , and :obj:`sp.c_` for quickly constructing arrays.
One familiar with Matlab may complain that it is difficult to
construct arrays from the interactive session with Python. Suppose,
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