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| == Stock Market data, fetched from Yahoo and Google == | == Stock Market data, fetched from Yahoo and Google FIXME == |
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| {{{ | (Need to fix plotting warnings as well as some stocks give index errors (like bsc, etc.) {{{#!sagecell |
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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) |
show(outplot, xmin = start_date, ymin = c_min-2, axes = True, xmax = end_date, ymax = c_max+3, frame = False) |
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| {{{ | {{{#!sagecell |
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| {{{ | {{{#!sagecell |
Sage Interactions - Web applications
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Contents
Stock Market data, fetched from Yahoo and Google FIXME
by William Stein
(Need to fix plotting warnings as well as some stocks give index errors (like bsc, etc.)
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.
Arctic sea ice extent data plot, fetched from NSIDC
by Marshall Hampton
Pie Chart from the Google Chart API
by Harald Schilly
