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Stock Market data, fetched from Yahoo and Google

by William Stein

import urllib

class Day:
    def __init__(self, date, open, high, low, close, volume): = date; self.high=float(high); self.low=float(low); self.close=float(close)
    def __repr__(self):
        return '%10s %4.2f %4.2f %4.2f %4.2f %10d'%(,, self.high, 
                   self.low, self.close, self.volume)

class Stock:
    def __init__(self, symbol):
        self.symbol = symbol.upper()

    def __repr__(self):
        return "%s (%s)"%(self.symbol,['price'])
    def yahoo(self):
        url = '' % (self.symbol, 'l1c1va2xj1b4j4dyekjm3m4rr5p5p6s7')
        values = urllib.urlopen(url).read().strip().strip('"').split(',')
        data = {}
        data['price'] = values[0]
        data['change'] = values[1]
        data['volume'] = values[2]
        data['avg_daily_volume'] = values[3]
        data['stock_exchange'] = values[4]
        data['market_cap'] = values[5]
        data['book_value'] = values[6]
        data['ebitda'] = values[7]
        data['dividend_per_share'] = values[8]
        data['dividend_yield'] = values[9]
        data['earnings_per_share'] = values[10]
        data['52_week_high'] = values[11]
        data['52_week_low'] = values[12]
        data['50day_moving_avg'] = values[13]
        data['200day_moving_avg'] = values[14]
        data['price_earnings_ratio'] = values[15]
        data['price_earnings_growth_ratio'] = values[16]
        data['price_sales_ratio'] = values[17]
        data['price_book_ratio'] = values[18]
        data['short_ratio'] = values[19]
        return data

    def historical(self):
            return self.__historical
        except AttributeError:
        symbol = self.symbol
        def get_data(exchange):
             name = get_remote_file(''%(exchange, symbol.upper()), 
             return open(name).read()
        R = get_data('NASDAQ')
        if "Bad Request" in R:
             R = get_data("NYSE")
        R = R.splitlines()
        headings = R[0].split(',')
        self.__historical = []
            for x in reversed(R[1:]):
                date, opn, high, low, close, volume = x.split(',')
                self.__historical.append(Day(date, opn,high,low,close,volume))
        except ValueError:
        self.__historical = Sequence(self.__historical,cr=True,universe=lambda x:x)
        return self.__historical

    def plot_average(self, spline_samples=10):
        d = self.historical()
        if len(d) == 0:
            return text('no historical data at Google Finance about %s'%self.symbol, (0,3))
        avg = list(enumerate([(z.high+z.low)/2 for z in d]))
        P = line(avg) + points(avg, rgbcolor='black', pointsize=4) + \
                 text(self.symbol, (len(d)*1.05, d[-1].low), horizontal_alignment='right', rgbcolor='black')
        if spline_samples > 0:
            k = 250//spline_samples
            spl = spline([avg[i*k] for i in range(len(d)//k)] + [avg[-1]])
            P += plot(spl, (0,len(d)+30), color=(0.7,0.7,0.7))
        return P

    def plot_diff(self):
        d = self.historical()
        if len(d) == 0:
            return text('no historical data at Google Finance about %s'%self.symbol, (0,3))
        diff = [] 
        for i in range(1, len(d)):
             z1 = d[i]; z0 = d[i-1]
             diff.append((i, (z1.high+z1.low)/2 - (z0.high + z0.low)/2))
        P = line(diff,thickness=0.5) + points(diff, rgbcolor='black', pointsize=4) + \
                 text(self.symbol, (len(d)*1.05, 0), horizontal_alignment='right', rgbcolor='black')
        return P

symbols = ['bsc', 'vmw', 'sbux', 'aapl', 'amzn', 'goog', 'wfmi', 'msft', 'yhoo', 'ebay', 'java', 'rht', ]; symbols.sort()
stocks = dict([(s,Stock(s)) for s in symbols])

def data(symbol = symbols, other_symbol='', spline_samples=(8,[0..15])):
     if other_symbol != '':
         symbol = other_symbol
     S = Stock(symbol)
     html('<h1 align=center><font color="darkred">%s</font></h1>'%S)
     S.plot_average(spline_samples).save('avg.png', figsize=[10,2])
     S.plot_diff().save('diff.png', figsize=[10,2])

     Y =
     k = Y.keys(); k.sort()
     html('Price during last 52 weeks:<br>Grey line is a spline through %s points (do not take seriously!):<br> <img src="cell://avg.png">'%spline_samples)
     html('Difference from previous day:<br> <img src="cell://diff.png">')
     html('<table align=center>' + '\n'.join('<tr><td>%s</td><td>%s</td></tr>'%(k[i], Y[k[i]]) for i in range(len(k))) + '</table>')


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.

from scipy.optimize import leastsq
import urllib2 as U
import scipy.stats as Stat
import time
current_year = time.localtime().tm_year
co2data = U.urlopen('').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 != '']
npi = RDF(pi)
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)):
    htmls1 = '<h3>CO2 monthly averages at Mauna Loa (interpolated), from NOAA/ESRL data</h3>'
    htmls2 = '<h4>'+cdate+'</h4>'
    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
        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))
    c_max = max([q[1] for q in sel_data])
    c_min = min([q[1] for q in sel_data])
    show(outplot, xmin = start_date, ymin = c_min-2, axes = True, xmax = end_date, ymax = c_max+3, frame = False)


Arctic sea ice extent data plot, fetched from NSIDC

by Marshall Hampton

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']
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.skipinitialspace = True
    icedata_f = urllib2.urlopen(''%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="">National Snow and Ice Data Center</a>')
    show(lp+reg, figsize = [7,4])


Pie Chart from the Google Chart API

by Harald Schilly

# Google Chart API:
import urllib2 as inet
from pylab import imshow
def gChart(title="Google Chart API plots Pie Charts!", color1=Color('purple'), color2=Color('black'), color3=Color('yellow'), val1=slider(0,1,.05,.5), val2=slider(0,1,.05,.3), val3=slider(0,1,.05,0.1), label=("Maths Physics Chemistry")):
    url = ""
    url += '&chtt=%s&chts=000000,25'%title.replace(" ","+")
    url += '&chco=%s'%(','.join([color1.html_color()[1:],color2.html_color()[1:],color3.html_color()[1:]]))
    url += '&chl=%s'%label.replace(" ","|")
    url += '&chd=t:%s'%(','.join(map(str,[val1,val2,val3])))
    print url
    html('<div style="border:3px dashed;text-align:center;padding:50px 0 50px 0"><img src="%s"></div>'%url)