[Scipy-svn] r6785 - trunk/scipy/io/docs
scipy-svn at scipy.org
scipy-svn at scipy.org
Sun Sep 12 13:21:38 EDT 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 at 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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