gistfile1.py

import pylab
import random
import pandas as pd
import numpy as np

duration = 100
meanIncrease = 3.0
stDevIncrease = 3.
sdi = stDevIncrease

## Here we generate a fictional time series, for a
## variable that generally increases over time but
## has significant noise.

for j in range(100):
    x = range(duration)
    y = []
    yNow = 0

    tstype = random.randint(0,2) # 0, 1, 2
    print tstype
    if tstype == 0:
        for i in x:
            if i < 25:
                mi = 0.5
                sdi = 0.3
            elif i < 50:
                mi = 0.
                sdi = 0.2
            elif i < 70:
                mi = 1.
                sdi = 0.2
            else:
                mi = -1.
                sdi = 0.2
            nextDelta = random.normalvariate(mi, sdi)
            yNow += nextDelta
            y.append( max(0, min(yNow, 20)) )
    elif tstype == 1:
        for i in x:
            if i < 25:
                mi = 0.5
                sdi = 0.3
            elif i < 50:
                mi = -0.5
                sdi = 0.3
            elif i < 70:
                mi = 1.
                sdi = 0.3
            else:
                mi = -1.
                sdi = 0.3
            nextDelta = random.normalvariate(mi, sdi)
            yNow += nextDelta
            y.append( max(0, min(yNow, 20)) )
    else:
        for i in x:
            if i < 25:
                mi = 0.5
                sdi = 0.3
            elif i < 50:
                mi = -0.5
                sdi = 0.3
            elif i < 70:
                if yNow < 10:
                    mi = 1.5
                else:
                    mi = 0.
                sdi = 0.1
            else:
                if yNow < 10:
                    mi = 1.5
                else:
                    mi = 0.
                sdi = 0.1
            nextDelta = random.normalvariate(mi, sdi)
            yNow += nextDelta
            y.append( max(0, min(yNow, 20)) )

    yy = pd.Series(y)
    when = pd.date_range(start=pd.datetime(2013,1,1), freq='S', periods=len(x))
    xxt = pd.Series(yy.values, index=when)
    print xxt
    xxt.to_csv('./xxt.csv')

    pylab.plot(x,y)

pylab.xlabel("Time")
pylab.ylabel("Value")
pylab.savefig("lineGraph.png")
pylab.show()