Hi Ryan,
I think your solution will work thank you!! I did get an error though it is
" f.next() # You want to skip the first line, I guess.
AttributeError: '_io.TextIOWrapper' object has no attribute 'next' "
thank you
Khary
rcnelson wrote:
>
> If I understand your question correctly, I may have a solution to your
> problem. First of all, the statement below, when converted to Python
> code, will generate an array of numbers the same length of your masses
> list.
>> 'y runs fron 0 to n where n == len(masses) '
> However, this statement will give you a single number:
>> x = 'mass_avg = sum(masses)/len(masses)'
> You will not be able to plot these two objects because of the different
> sizes. If you are asking about a 'running' or cumulative mean, then you
> may
> want to use the cumulative sum function from Numpy (cumsum). To convert
> this
> into a cumulative average, you can do a simple division.
>
> Below is a modification to your script that incorporates this averaging
> technique. (I don't know why you want to print everything. Surely you
> can't
> see all of the data as the file gets processed. It is also a very slow
> operation... I'll just ignore those parts.)
>
> import numpy as np
> import matplotlib.pyplot as plt
> f = open('myfile.txt')
> f.next() # You want to skip the first line, I guess.
> mass = []
> for line in f:
> # This will skip the lines that are spaces.
> if line.isspace(): continue
> # The strip function is unnecessary. The defalut for the split
> function
> takes care of that.
> columns = line.split()
> # Don't call the float function every time. It's a waste.
> mass.append( columns[8] )
> # Here we can convert the list of strings into an array of floats with the
> dtype keyword.
> mass = np.array( mass, dtype='float')
> # Here's the cumulative average steps.
> mass_sum = np.cumsum(mass)
> mass_average = mass_sum/ np.arange(1, len(mass_sum) + 1)
> # If you only plot one array or list of values, they are assumed to be the
> y
> values.
> # The x values in that case are the indices of the y value array.
> plt.plot(mass_average)
> plt.show()
>
>
> Ryan
>
>
>> Message: 5
>> Date: Thu, 25 Aug 2011 11:15:57 -0700 (PDT)
>> From: surfcast23 <[email protected]>
>> Subject: Re: [Matplotlib-users] How do you Plot data generated by a
>> python script?
>> To: [email protected]
>> Message-ID: <[email protected]>
>> Content-Type: text/plain; charset=us-ascii
>>
>>
>> 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
>> >
>> >
>>
>> --
>> View this message in context:
>> http://old.nabble.com/How-do-you-Plot-data-generated-by-a-python-script--tp32328822p32336570.html
>> Sent from the matplotlib - users mailing list archive at Nabble.com.
>>
>>
>
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