On Wed, Jun 29, 2016 at 2:41 PM, Peter Otten <__pete...@web.de> wrote:
> Colin Ross wrote:
>
> > Good afternoon,
> >
> > I have a .dat file that is 214 rows by 65 columns that I would like to
> > read into python and plot the 1st column versus all other columns.
> >
> > My code:
> >
> > #########################################################################
> >
> > import numpy as np
> > import scipy
> > import pylab as pl
> > import matplotlib
> > from matplotlib.ticker import ScalarFormatter, FormatStrFormatter
> > import sys
> >
> > # Load in text from .dat file
> >
> > sed = np.loadtxt('spectra.dat', unpack = True)
>
> for flux in sed:
> pl.plot(wavelength, flux)
>
> > pl.xscale('log')
> >
> > pl.show()
> >
> > #########################################################################
> >
> > This is fine if I want to write out a separate line for each of the 65
> > columns, but I would like to simplify the code by looping over the data.
> > Can someone please help me formatting the loop correctly?
>
Thank you for the fast response! I have tried this and it outputs the
attached image. It seems to only plot 1 curve, when I would have expected
64?
>
> To understand why the above works (assuming it does) consider that for many
> data types
>
> for item in data:
> ... # use item
>
> is equivalent to
>
> for index in range(len(data)):
> item = data[index]
> ... # use item
>
> Unrolling the above for len(data) == 3 gives:
>
> item = data[0]
> ... # use first item
> item = data[1]
> ... # use second item
> item = data[2]
> ... # use third item
>
>
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