load function a little bit faster

                Hi, I did some change (again) in the load

    > function to improve the speed when you're load some big
    > data file but you want use only some columns. I did all my
    > tests with a file with 9722 line and 16 columns. The
    > bench test file is after. I think that the result of the
    > bench are interesting:

    > I you want use 2 columns on the 16 the results are:

    > load matplotlib 0.58 load with columns choice 0.27 normal
    > load inside the new load version 0.58

    > We win a factor two. I know that depend totally from the
    > number of columns and that the change is not interesting
    > and more decrease the efficiency if you want use all the
    > data in your file but like the columns call is optionnal I
    > don't think that is point is crucial but I add a figure to
    > see the effect when you go to one to all the columns.

    > The load function is after.

Either there was an error i your cut and paste, or the reason your new
load function is faster is that it does nothing. Note the indentation

The second time you do "for line in fh" you clearly intend to be
handling the columns case, but it is inside the "if columns is None"
block.

It looks like the reason the columns version of load is faster is
because it's not doing anything...

JDH

    if columns is 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')
            X.append(row) else:
        for line in fh:
            line = line[:line.find(comments)].strip()
            if not len(line): continue
            row = [val for val in line.split()]
            row = [float(row[i]) for i in columns]
            thisLen = len(row)
            if numCols is not None and thisLen != numCols:
                raise ValueError('All rows must have the same number

···

Regards,

    > Nicolas

    > -----------------------------------------------

    > #!/usr/bin/env python # -*- coding: utf-8 -*-

    > from time import clock

    > t3 = clock() import load_2 Y=load_2.load('data') x=Y[:,0]
    > y=Y[:,1] t4 = clock() #print t4-t3 #print x,y

    > col = [0,6] t1 = clock() import load_matplotlib
    > X=load_matplotlib.load('data') #X = [X[:,i] for i in col]
    > x=X[:,0] y=X[:,1] t2 = clock() print 'load matplotlib',
    > t2-t1 #print X

    > t3 = clock() import load_2
    > X=load_2.load('data',columns=range(14)) x=Y[:,0] y=Y[:,1]
    > t4 = clock() print 'load with columns choice', t4-t3

    > t3 = clock() import load_2 Y=load_2.load('data') x=Y[:,0]
    > y=Y[:,1] t4 = clock() normal = t4-t3 print 'normal load ',
    > normal

    > time = [] for i in range(16): t3 = clock() import load_2
    > X=load_2.load('data',columns=range(i)) x=Y[:,0] y=Y[:,1]
    > t4 = clock() #print 'load with columns choice', t4-t3
    > time.append(t4-t3)

    > from pylab import * time = array(time)/normal

    > plot(range(16),time) xlabel('N columns (total = 16)')
    > ylabel('time columns /normal time') show()

    > ------------------------------------------------------------------
    > 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 0 (python style).

    > matfile data is not currently supported, but see
    > Nigel Wade's matfile
    > ftp://ion.le.ac.uk/matfile/matfile.tar.gz

    > Example usage:

    > X = load('test.dat') # data in two columns t =
    > X[:,0] y = X[:,1]

    > Alternatively, you can do

    > t,y = transpose(load('test.dat')) # for two column
    > data X = load('test.dat',[0,2]) # data in two columns
    > (columns 1 and 3 use in the file)

    > X = load('test.dat') # a matrix of data X =
    > load('test.dat',columns=[2,3]) # a matrix of data, only
    > columns 3 and 4 will be use x = load('test.dat') # a
    > single column of data

    > x = load('test.dat,'#') # the character use like a
    > comment delimiter is '#' """

    > # from numarray import array

    > fh = file(fname)

    > X = [] numCols = None if columns is 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') X.append(row) else: for
    > line in fh: line = line[:line.find(comments)].strip() if
    > not len(line): continue row = [val for val in
    > line.split()] row = [float(row[i]) for i in columns]
    > thisLen = len(row) if numCols is not None and thisLen !=
    > numCols: raise ValueError('All rows must have the same
    > number of columns') X.append(row)

    > X = array(X) r,c = X.shape if r==1 or c==1: X.shape =
    > max([r,c]), return X

It looks like the reason the columns version of load is faster is
because it's not doing anything...

It' not exactly true. I'm agree that the change is not big, but the difference comes from this two lines:

#row = [val for val in line.split()] #no change in float for all values

row = line.split() # dont need the loop so forgot the precedent line row = [float(row[i]) for i in columns] # float value

and in a fact there are a condition if:

the first is to keep exactly the same function than yours. The second part is to not transform all the element in float but only the columns choose and this change explain the difference...

Regards,

    Nicolas