[Scipy-svn] r6785 - trunk/scipy/io/docs

scipy-svn@scip... scipy-svn@scip...
Sun Sep 12 12:21:38 CDT 2010


Author: ptvirtan
Date: 2010-09-12 12:21:38 -0500 (Sun, 12 Sep 2010)
New Revision: 6785

Removed:
   trunk/scipy/io/docs/numpyio.README
Log:
DEP: remove unused numpyio doc file

Deleted: trunk/scipy/io/docs/numpyio.README
===================================================================
--- trunk/scipy/io/docs/numpyio.README	2010-09-12 17:21:25 UTC (rev 6784)
+++ trunk/scipy/io/docs/numpyio.README	2010-09-12 17:21:38 UTC (rev 6785)
@@ -1,174 +0,0 @@
-
-This source file and makefile are intended to be used with python with
-Numerical extensions.
-
-To install:
-
-1) copy Makefile.pre.in from your python configuration directory 
-(e.g. /usr/lib/python1.5/config/Makefile.pre.in) to this directory.  
-2) make -f Makefile.pre.in boot
-3) make
-
-4) install in a directory on your Python path.
-
-executing make once compiles both sigtools and numpyio.
-
-There is a module called mIO.py that defines MATLAB-like binary file 
-interface using numpyio.  It is a recommended front-end for numpyio and 
-imported into signaltools.py
-
-Usage:
-
-import mIO
-
-fid = mIO.fopen('somefile','r','ieee-le')   # little-endian
-somedata = fid.fread(number_of_els,type)   # type can be all kinds of things
-                                          # like int32, float, complex, etc.
-                                          # check mIO.py for details
-
-# somedata is 1-D array of number_of_els (set the shape to whatever you want
-
-There are useful methods called fort_write and fort_read to this object 
-that allow you to use the struct module syntax to read in FORTRAN records into
-a list and write FORTRAN records.  
-
-
-Any Questions or problems or bug-reports send to
-Oliphant.Travis@altavista.net
-
-
-Background:
-
-  Once compiled, numpyio is a loadable module that can be used in
-python for reading and writing arbitrary binary data to and from
-Numerical Python arrays.  I work in Medical Imaging and often have
-large data sets to manipulate.  I came from a background of using
-MATLAB but only having doubles to work with really puts a crimp on the
-sizes of the data sets I could manipulate.  The fact that Numerical
-Python has more data types defined than doubles encouraged me to try
-it out.  I have been very impressed with its speed and utility, but I
-needed some way to read large data sets from an arbitrary binary file
-into Numerical Python arrays.  I didn't see any obvious way to do this
-so I wrote an extension module.  Although there is not much
-documentation, having the sources available is ultimately better than
-documentation.  
-
-
-Description:
-
-The module defines 5 methods for reading and writing NumPy arrays:
-
-********************************************************************
-
-g = numpyio.fread( fid, Num, read_type { mem_type, byteswap})
-
-     fid =       open file pointer object (i.e. from fid = open("filename") )
-     Num =       number of elements to read of type read_type
-     read_type = a character in 'cb1silfdFD' (PyArray types)
-                 describing how to interpret bytes on disk.
-OPTIONAL
-     mem_type =  a character (PyArray type) describing what kind of
-                 PyArray to return in g.   Default = read_type
-     byteswap =  0 for no byteswapping or a 1 to byteswap (to handle
-                 different endianness).    Default = 0
-
-************************************************************************
-
-numpyio.fwrite( fid, Num, myarray { write_type, byteswap} )
- 
-     fid =       open file stream
-     Num =       number of elements to write
-     myarray =   NumPy array holding the data to write (will be
-                 written as if ravel(myarray) was passed)
-OPTIONAL
-     write_type = character ('cb1silfdFD') describing how to write the 
-                  data (what datatype to use)  Default = type of
-                  myarray.
-     byteswap =   0 or 1 to determine if byteswapping occurs on write.
-                  Default = 0.
-
-
-These are the main routines, note that mem_type or write_type is
-specified then a blind typecast is done with no checking to see if it
-makes sense to do so.  I'm trusting the user knows what she wants to
-do.
-
-Three support routines are also included.
-
-************************************
-
-numpyio.bswap(myarray)
-
-     myarray = an array whose elements you want to byteswap.
-
-     This does an inplace byte-swap so that myarray is changed in
-     memory.
-
-*********************************************************
-
-out = numpyio.packbits(myarray)
-
-     myarray = an array whose (assumed binary) elements you want to
-               pack into bits (must be of integer type, 'cb1sl')
-
-     This routine packs the elements of a binary-valued dataset into a
-     1-D NumPy array of type PyArray_UBYTE ('b') whose bits correspond to
-     the logical (0 or nonzero) value of the input elements. 
-
-     If myarray has more dimensions than 2 it packs each slice (rows*columns)
-     separately.  The number of elements per slice (rows*columns) is 
-     important to know to be able to unpack the data later.
-
-     Example:
-  >>> a = array([[[1,0,1],
-  ...             [0,1,0]],
-  ...            [[1,1,0],
-  ...             [0,0,1]]])
-  >>> b = numpyio.packbits(a)
-  >>> b
-  array([168, 196], 'b')
-
-      Note that 168 = 128 + 32 + 8
-                196 = 128 + 64 + 4
-
-
-*****************************************************************
-
-out = numpyio.unpackbits(myarray, elements_per_slice {, out_type} )
-
-     myarray =        Array of integer type ('cb1sl') whose least
-                      significant byte is a bit-field for the
-                      resulting output array.
-
-     elements_per_slice = Necessary for interpretation of myarray.
-                          This is how many elements in the
-                          rows*columns of original packed structure.
-
-OPTIONAL
-     out_type =       The type of output array to populate with 1's
-                      and 0's.  Must be an integer type.
-
-
-The output array will be a 1-D array of 1's and zero's 
-
-Example:  (See above)  (It prints out a nice message saying how your
-	                machine interprets multibyte numbers.)
-
-  >>> c = numpyio.unpackbits(b,6)
-  This is a little-endian machine
-  >>> c
-  array([1, 0, 1, 0, 1, 0, 1, 1, 0, 0, 0, 1],'b')  
-
-********************************************************************
-
-
-Enjoy,
-
-Travis
-
-
-
-
-
-
-



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