Hi clolern2,
> - for some reason a TZ has been inserted
Datetime values are stored as numbers. Timezone info is added when that numbers
are converted again into datetimes for labelling.
> - graphs have white space buffers on either side of the X-axix
You can use axes.xlim in order to adjust it.
> - points on X-axis are separated by the hour, instead of values in datetime
> object
Matplotlib automagically chooses a format depending on the scale but you
can specify a format:
import matplotlib.dates as mdates
...
xaxis.set_major_formatter(mdates.DateFormatter('%Y-%b-%d %H:%M'))
Goyo
El mié, 11-02-2009 a las 09:38 -0800, collern2 escribió:
> Hi,
>
> I've managed to take the contents of my CSV file and display it with
> matplotlib. I'm having some issues with the way my X-axis is being
> displayed.
>
> For the X-axis, I pass in a list that filled with datetime objects, an
> example of one element on the list:
>
> datetime.datetime(2007, 12, 17, 20, 28, 15),
>
> Issues (please see the attached cpu.png:
>
> - for some reason a TZ has been inserted
> - graphs have white space buffers on either side of the X-axix
> - points on X-axis are separated by the hour, instead of values in datetime
> object
>
> I have tried many variations of plotdate, etc. If someone could please point
> me in the right direction.
>
> Thanks
>
> =================
> Code http://www.nabble.com/file/p21958283/cpu.png
> =================
>
> #!/usr/bin/env python
>
> import csv
> import sys
> import matplotlib.pyplot as plt
> import datetime
>
> new_list = []
> time = []
> cpu = []
>
> fileReader = csv.reader(open("sample.csv", "rb"))
> for row in fileReader:
> new_list.append(row)
>
> # Converts papatimes time format into dattime
> def time_split(current_line):
> # splits papastats datetime format in useable python list
> dt = datetime.datetime.strptime(current_line[0],"%Y/%m/%d %H:%M:%S")
> time.append(dt)
>
> def cpu_calc(current_line):
> cpu.append(current_line[11].rstrip("%"))
>
> #Iterate over list of CSV values
> for i in new_list[1:]:
> time_split(i)
> cpu_calc(i)
>
> plt.plot(time, cpu, 'b-')
> #plt.plot_date(time, cpu, fmt='b-', xdate=False, ydate=False, tz=None)
>
> plt.xlabel('Time')
> plt.ylabel('CPU %')
> plt.title('Daily CPU Usage')
> plt.grid(True)
> plt.grid(alpha=0.2, color='black', linestyle='-', linewidth=0.1)
> plt.show()
>
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