Hi John,
I did some change in the load function, it's help for me so perhaps some
other people will appreciate it. I add the possibility to choose which
columns inside a file you want use.
ex: I have a file like:
1 2 3
1 2 3
1 2 3
and I want use only the columns 2 and 3:
so I can do this with the new load function:
load('toto.dat',columns=[2,3])
Perhaps you can arrange this to have something a little bit cleaner (I'm
a beginner with python).
I add the change to have the matrix transpose too to correct the problem
I hab before to recuperate the columns and not the lines when you are doing:
x,y,z = load('toto.dat')
Thanks,
Nicolas
ps: I add something inside the doc but you had to correct this too for a
correct english or/and more understandable text if you include these
changes.
------------------------------------------------------------
def load(fname,comments='%',columns=None):
"""
Load ASCII data from fname into an array and return the array.
The data must be regular, same number of values in every row
fname can be a filename or a file handle.
A character for to delimit the comments can be use (optional),
the default is the matlab character '%'.
An second optional argument can be add, to tell which columns you
want use in the file. This arguments is a list who contains the
number of columns beggining by 1.
matfile data is not currently supported, but see
Nigel Wade's matfile ftp://ion.le.ac.uk/matfile/matfile.tar.gz
Example usage:
x,y = load('test.dat') # data in two columns
X = load('test.dat') # a matrix of data
x = load('test.dat') # a single column of data
x = load('test.dat,'#') # the character use like a comment delimiter
is '#'
"""
if is_string_like(fname):
fh = file(fname)
elif hasattr(fname, 'seek'):
fh = fname
else:
raise ValueError('fname must be a string or file handle')
X = []
numCols = None
for line in fh:
line = line[:line.find(comments)].strip()
if not len(line): continue
row = [float(val) for val in line.split()]
thisLen = len(row)
if numCols is not None and thisLen != numCols:
raise ValueError('All rows must have the same number of
columns')
if columns is not None:
row = [row[i] for i in (array(columns)-1)]
X.append(row)
X = array(X)
r,c = X.shape
if r==1 or c==1:
X.shape = max([r,c]),
return transpose(X)
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