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Revision 10 as of 2012-05-09 04:27:02
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= Sage Interactions - Calculus =
goto [:interact:interact main page]

= Web applications =

== Stock Market data, fetched from Yahoo and Google ==
= Sage Interactions - Web applications =
goto [[interact|interact main page]]

<<TableOfContents>>

== Stock Market data, fetched from Yahoo and Google FIXME ==
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{{{ {{{#!sagecell
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attachment:stocks.png {{attachment:stocks.png}}
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{{{ {{{#!sagecell
from scipy.optimize import leastsq
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import time
current_year = time.localtime().tm_year
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trdf = RealField(16)
@interact
def mauna_loa_co2(start_date = slider(1958,2010,1,1958), end_date = slider(1958, 2010,1,2009)):
npi = RDF(pi)
@interact(layout=[['start_date'],['end_date'],['show_linear_fit','show_nonlinear_fit']])
def mauna_loa_co2(start_date = slider(1958,current_year,1,1958), end_date = slider(1958, current_year,1,current_year-1), show_linear_fit = checkbox(default=True), show_nonlinear_fit = checkbox(default=False)):
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    sel_data = [[q[2],q[4]] for q in datalines if start_date < q[2] < end_date]     html(htmls1+htmls2)
    sel_data = [[q[2],q[4]] for q in datalines if start_date <= q[2] <= end_date]
    outplot = list_plot(sel_data, plotjoined=True, rgbcolor=(1,0,0))
    if show_nonlinear_fit:
        def powerlaw(t,a):
            return sel_data[0][1] + a[0]*(t-sel_data[0][0])^(a[1])
        def res_fun(a):
            return [q[1]-powerlaw(q[0],a) for q in sel_data]
        def fitcos(t,a):
            return a[0]*cos(t*2*npi+a[1])+a[2]*cos(t*4*npi+a[3])
        def res_fun2(a):
            return [q[1]-fitcos(q[0],a) for q in resids]
        a1 = leastsq(res_fun,[1/2.4,1.3])[0]
        resids = [[q[0],q[1] - powerlaw(q[0],a1)] for q in sel_data]
        a2 = leastsq(res_fun2, [3,0,1,0])[0]
        r2_plot = list_plot([[q[0],powerlaw(q[0],a1)+fitcos(q[0],a2)] for q in resids], rgbcolor='green',plotjoined=True)
        outplot = outplot + r2_plot
        var('t')
        formula1 = '%.2f+%.2f(t - %d)^%.2f'%(sel_data[0][1],a1[0],sel_data[0][0],a1[1])
        formula2 = '%.2fcos(2 pi t + %.2f)+%.2f cos(4 pi t + %.2f)'%(a2[0],a2[1],a2[2],a2[3])
        html('Nonlinear fit: <br>%s<br>'%(formula1+'+'+formula2))
    if show_linear_fit:
        slope, intercept, r, ttprob, stderr = Stat.linregress(sel_data)
        outplot = outplot + plot(slope*x+intercept,start_date,end_date)
        html('Linear regression slope: %.2f ppm/year; correlation coefficient: %.2f'%(slope,r))
    var('x,y')
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    slope, intercept, r, ttprob, stderr = Stat.linregress(sel_data)
    html(htmls1+htmls2+'<h4>Linear regression slope: ' + str(trdf(slope)) + ' ppm/year; correlation coefficient: ' + str(trdf(r)) + '</h4>')
    var('x,y')
    show(list_plot(sel_data, plotjoined=True, rgbcolor=(1,0,0)) + plot(slope*x+intercept,start_date,end_date), xmin = start_date, ymin = c_min-2, axes = True, xmax = end_date, ymax = c_max+3, frame = False)
}}}
attachment:co2c.png
    show(outplot, xmin = start_date, ymin = c_min-2, axes = True, xmax = end_date, ymax = c_max+3, frame = False)
}}}
{{attachment:co2c.png}}

== Arctic sea ice extent data plot, fetched from NSIDC ==
by Marshall Hampton

{{{#!sagecell
import urllib2, csv
months = ['Jan','Feb','Mar','Apr','May','Jun','Jul','Aug','Sep','Oct','Nov','Dec']
longmonths = ['January','February','March','April','May','June','July','August','September','October','November','December']
@interact
def iceplotter(month = selector(zip(range(1,13),longmonths),default = (4, 'April'),label="Month")):
    month_str = months[month-1] + '/N_%02d_area.txt'%(month)
    dialect=csv.excel
    dialect.skipinitialspace = True
    icedata_f = urllib2.urlopen('ftp://sidads.colorado.edu/DATASETS/NOAA/G02135/%s'%month_str)
    cr = csv.reader(icedata_f,delimiter=' ', dialect=dialect)
    icedata = list(cr)
    icedata = [x for x in icedata[1:] if len(x)==6 and N(x[5])>0]
    
    lp = list_plot([[N(x[0]),N(x[4])] for x in icedata])
    
    def lin_regress(xdata, ydata):
        xmean = N(mean(xdata))
        ymean = N(mean(ydata))
        xm = vector(RDF,[q-xmean for q in xdata])
        ym = vector(RDF,[q-ymean for q in ydata])
        xy = xm.inner_product(ym)
        xx = xm.inner_product(xm)
        slope = xy/xx
        intercept = ymean - slope*xmean
        return slope, intercept
    
    years = [N(x[0]) for x in icedata]
    ice = [N(x[4]) for x in icedata]
    slope, inter = lin_regress(years,ice)
    reg = plot(lambda x:slope*x+inter,(min(years),max(years)))
    html('<h3>Extent of Arctic sea ice coverage in %s, %d - %d</h3>'%(longmonths[month-1],min(years),max(years)))
    html('Data from the <a href="http://nsidc.org/">National Snow and Ice Data Center</a>')
    show(lp+reg, figsize = [7,4])
}}}
{{attachment:seaice.png}}
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{{{ {{{#!sagecell
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attachment:interact_with_google_chart_api.png {{attachment:interact_with_google_chart_api.png}}

Sage Interactions - Web applications

goto interact main page

Stock Market data, fetched from Yahoo and Google FIXME

by William Stein

stocks.png

CO2 data plot, fetched from NOAA

by Marshall Hampton

While support for R is rapidly improving, scipy.stats has a lot of useful stuff too. This only scratches the surface.

co2c.png

Arctic sea ice extent data plot, fetched from NSIDC

by Marshall Hampton

seaice.png

Pie Chart from the Google Chart API

by Harald Schilly

interact_with_google_chart_api.png

interact/web (last edited 2020-06-01 18:37:46 by kcrisman)