import numpy as np

import matplotlib.pyplot as plot

import scikits.timeseries as ts

import scikits.timeseries.lib.plotlib as tplot

from scikits.timeseries.lib.moving_funcs import *

import scipy as sp

from StaticGateway import StaticGateway

import traceback

from Rynir import Rynir

from DmGateway import DmGateway

from Parser import Parser

rynir = Rynir()

#time_series = ["gunni_Awesom","leh3", "yef","1eh3"] # 1. er ologlegur, 2. ekki til, 3. of stor, 4 ok

#time_series = "yef"

#time_series = 'yfu|6zj=1'

#time_series = '16z3|ib9=3b'

time_series = 'xja|67k=3'

#time_series = 'x7p|61x=2'

#time_series = "1eh3" #british fatalities in afghanistan

#time_series = "1d8b|wzl=6" #crude oil

#time_series = "18ax|l2g=1w:l2h=2:l2i=12" #avocado

#time_series = "1ctt|wtr=3t:wts=f:wtt=1" #age population brazil

#time_series = "1bdn|twc=6:twe=k" #elecricity generation

#time_series = "w3x|6bx=3"

#time_series = "17tl|kqb=3" #rising slope, not interesting

#time_series = "1bcz|tuh=1o:tui=b:tuj=k" #oil import from iraq

#time_series = "yef" #?

#queryStringList = ["1eh3", "1d8b|wzl=6", "18ax|l2g=1w:l2h=2:l2i=12", "1ctt|wtr=3t:wts=f:wtt=1", "1bdn|twc=6:twe=k", "17tl|kqb=3", "1bcz|tuh=1o:tui=b:tuj=k", "1968"]

#report = rynir.analyze(time_series)

#report = rynir.analyze(queryStringList)

try:

for x in report:

for i in x:

print i

except:

pass

#debugging plot below, make sure parameters match the ones actually used

gate = DmGateway()

#gate = StaticGateway("staticDataSet.json")

ps = Parser()

dset = ps.parse(time_series)

series = ts.time_series(dset[1].getMaskedArray(),

start_date=ts.Date(freq='year', year=1, month=1))

#print series.count

fig = tplot.tsfigure()

# 111 = width, height, subplots

fsp = fig.add_tsplot(111)

fsp.tsplot(series, '-')

np.seterr(invalid='raise')

try:

avg = mov_average(series, 20)

std = mov_std(series, 20)

except Exception as error:

print error

traceback.print_exc()

print series

print avg

print std

lowerlim = avg+std*2

upperlim = avg-std*2

print lowerlim

print upperlim

fsp.tsplot(lowerlim, '-r')

fsp.tsplot(upperlim, '-r')

#for item in report[0]:

#for x in item.listOfFlags:

##print "-----Flag: " + str(x[0])

##print "--index: " + str(x[2])

##print "place flag at : " + str(x[2]) + ", " + str(series[x[2]])

#fsp.annotate(x[1], xy=(x[2], dset[1][x[2]]),

#xytext=(x[2]+5, dset[1][x[2]]+5), arrowprops=dict(facecolor='black', shrink=0.05),)

#fsp.annotate('raggi', xy=(50,20), xytext=(60, 20), arrowprops=dict(facecolor='black', shrink=0.05),)

#fsp.plot(dset[1].getMaskedArray())

#fsp.tsplot(series, '-')

plot.show()

#plot.savefig('Kaup i kaupthing - timalina og bond')

#draw the series from the parser, solid line

#fsp.tsplot(series, '-')

"""

#draw moving average, framesize 5, dotted green line

avg = mov_average(series, 13)

#avg9 = mov_average(series, 9)

#avg13 = mov_average(series, 13)

avgexp = mov_average_expw(series, 13)

std = mov_std(series, 13)

#std9 = mov_std(series, 9)

#std13 = mov_std(series, 13)

lowerlim = avg+std*2

upperlim = avg-std*2

#lowerlim9 = avg9+std9*2

#upperlim9 = avg9-std9*2

#lowerlim13 = avg13+std13*2

#upperlim13 = avg13-std13*2

fsp.tsplot(lowerlim, '--r')

fsp.tsplot(upperlim, '--r')

#fsp.tsplot(lowerlim9, '--y')

#fsp.tsplot(upperlim9, '--y')

#fsp.tsplot(lowerlim13, '--g')

#fsp.tsplot(upperlim13, '--g')

fsp.tsplot(avg, '--r')

fsp.tsplot(avgexp, '--y')

#fsp.tsplot(avg9, '--y')

#fsp.tsplot(avg13, '--g')

#t = sp.fft(dset[1].getMaskedArray())

#fftSer = ts.time_series(sp.fft(dset[1].getMaskedArray()),

# start_date=ts.Date(freq=dset.granularity, year=int(dset[0][0][0:4]), month=1))

#fsp.tsplot(fftSer, '--y')

#print t

"""

#fig = plot.figure(1, figsize=(8,5))

#ax = fig.add_subplot(111, autoscale_on=True)#, xlim=(-1,5), ylim=(-4,3))

#print series

"""

from scipy import linspace, polyval, polyfit, sqrt, stats, randn

from pylab import plot, title, show , legend

print dset

n=50

t=linspace(-5,5,n)

a=0.8

b=-4

x=polyval([a,b],dset[1]4)

xn=x+randn(n)

(ar,br)=polyfit(t,xn,1)

xr=polyval([ar,br],t)

err=sqrt(sum((xr-xn)**2)/n)

print('Linear regression using polyfit')

print('parameters: a=%.2f b=%.2f \nregression: a=%.2f b=%.2f, ms error= %.3f' % (a,b,ar,br,err))

#matplotlib ploting

title('Linear Regression Example')

plot(t,x,'g.--')

plot(t,xn,'k.')

plot(t,xr,'r.-')

legend(['original','plus noise', 'regression'])

show()

#Linear regression using stats.linregress

(a_s,b_s,r,tt,stderr)=stats.linregress(t,xn)

print('Linear regression using stats.linregress')

print('parameters: a=%.2f b=%.2f \nregression: a=%.2f b=%.2f, std error= %.3f' % (a,b,a_s,b_s,stderr))

"""