Python array manipulation and computational library
Numarray provides array manipulation and computational capabilities
similar to those found in IDL, Matlab, or Octave. Using numarray, it is
possible to write many efficient numerical data processing applications
directly in Python without using any C, C++ or Fortran code (as well as
doing such analysis interactively within Python or PyRAF). For algorithms
that are not well suited for efficient computation using array facilities
it is possible to write C functions (and eventually Fortran) that can
read and write numarray arrays that can be called from Python.
See the Doc directory for more numarray documentation, including
CHANGES for this release and installation instructions.
See http://sourceforge.net/projects/numpy for the source forge website
for Numeric and Numarray featuring CVS, Bug Tracking, Downloads, etc.
If you make a submission to a tracker, make sure that it has a
"Numarray" prefix, i.e. "Numarray Bugs" and not just "Bugs".
These are the LaTeX-sources for the numarray manual.
In order to process these files you need the python CVS sources and
the whole toolchain to generate python docs.
In this local directory create the following links:
icons -> <python cvs>/dist/src/Doc/html/icons
mkhowto -> <python cvs>/dist/src/Doc/tools/mkhowto
mkinfo -> <python cvs>/dist/src/Doc/tools/mkinfo
Calling mkhowto creates th
- write all chapters :)
- create module index
- extend glossary (standing request:)
- update index (standing request:)
- Put part's outline on same page as the "Part X".
See __LICENSE__ in Lib/__init__.py for the numarray license or view it
at the Python prompt as follo
Please see file LICENSE.txt in the source distribu
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