Hi Martin,
Thank for the relpy. What I have is a script that reads the data from
a large file then prints out the values listed in a particular column. What
I now need to do is have the information in that column plotted as the
number of rows vs. the mean value of all of the rows. What I have so far is
import matplotlib.pyplot as plt
masses = []
f = open( 'myfile.txt','r')
f.readline()
for line in f:
if line != ' ':
line = line.strip() # Strips end of line character
columns = line.split() # Splits into coloumn
mass = columns[8] # Column which contains mass values
mass = float(mass)
masses.append(mass)
print(mass)
plt.plot()
plt.show
I am thinking I can do something like
'y runs fron 0 to n where n == len(masses) '
x = 'mass_avg = sum(masses)/len(masses)'
Problem is I don' tknow how to have matplotlib do it with out giving me an
error about dimentions. I would also like to do this with out having to
write and read from another file. I alos need to to be able to work on files
with ddifering numbers of rows.
Thanks
mdekauwe wrote:
>
> I wasn't quite able to follow exactly what you wanted to do but maybe this
> will help. I am going to generate some "data" that I think sounds a bit
> like yours, write it to a file, clearly you already have this. Then I am
> going to read it back in and plot it, e.g.
>
> import matplotlib.pyplot as plt
> import numpy as np
>
> # Generate some data a little like yours, I think?
> # print it to a file, i.e. I am making your myfile.txt
> numrows = 100
> numcols = 8
> mass = np.random.normal(0, 1, (numrows * numcols)).reshape(numrows,
> numcols)
> f = open("myfile.txt", "w")
> for i in xrange(numrows):
> for j in xrange(numcols):
> print >>f, mass[i,j],
> print >> f
> f.close()
>
> # read the file back in
> mass = np.loadtxt("myfile.txt")
>
> # plot the 8th column
> fig = plt.figure()
> ax = fig.add_subplot(111)
> ax.plot(mass[:,7], 'r-o')
> ax.set_xlabel("Time")
> ax.set_ylabel("Mass")
> plt.show()
>
>
> I wasn't clear on the mean bit, but that is easy to do with numpy, e.g.
>
> mean_mass = np.mean(mass[:,8])
>
> etc.
>
> Numpy et al is great for stuff like this.
>
> Hope that helps,
>
> Martin
>
>
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