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Contents

Idea

Create some plots related to the Carbon cycle and do some simple time analysis in both time and frequency domain using Sage and its built in support for time series.

Details

Mauna loa

Diagrams

The classic Keeling curve, plotted upto jan 2011 in Sage with default aspect ratio for Sage List_plot:

Here is a plot of the whole series but showing season corrected values : (also default aspect ratio)

I think it can be better with setting it default aspect ratio to one, so we’ll try that as well and add some major grid lines:

Trend - season corrected also with SAGE aspect ratio one:

Here is one with the code below showing the interpolated date moving average over six months and also axes labels:

Let us look at some typical properties of time series, the Hurst exponent and autocorrelation to detect degree of randomness and tendence: tendence. The Hurst exponent is 0.23 and it indicates that the CO2 time series has a tendency to decrease between values will probably be followed by an increase.

Draft Code

Trying to see if the TimeSeries in Sage, can be used better instead, by eg using moving averages. The code below reflects the plotting with aspect ratio equal to one and also using Timeseries for months larger than two. I am just checking that it could also be used for one

# based on http://wiki.sagemath.org/interact/web made by Marshall Hampton CC 3.0 
import urllib2 as U
import time

current_year = time.localtime().tm_year
co2data = U.urlopen('ftp://ftp.cmdl.noaa.gov/ccg/co2/trends/co2_mm_mlo.txt').readlines()
datalines = []
for a_line in co2data:
    if a_line.find('Creation:') != -1:
        cdate = a_line
    if a_line[0] != '#':
        temp = a_line.replace('\n','').split(' ')
        temp = [float(q) for q in temp if q != '']
        datalines.append(temp)
@interact
def azimco2viewer(month = [1,2,6,12,24],trend= True): 
    if trend:
        co2_data = [q[5] for q in datalines]
    else:
        co2_data = [q[4] for q in datalines]
    yr_data = [q[2] for q in datalines]    
    if month>2:
        co2ts = finance.TimeSeries(co2_data)
        co2ts = co2ts.simple_moving_average(month)
        co2_data = co2ts.list()    
    lp = list_plot(zip(yr_data,co2_data), plotjoined=True, rgbcolor=(1,0,0))
    lp.axes_labels(['$year$','$CO_2ppm$'])
    lp.show(axes = True, figsize = [5,7], aspect_ratio=1.0,gridlines=True)