import matplotlib.mlab as mlb
import matplotlib.pyplot as plt
from scikits.statsmodels.sandbox import tsa
import scikits.talkbox as stb
import scikits.talkbox.spectral.basic as stbs
ar = [1., -0.7]#[1,0,0,0,0,0,0,-0.7]
ma = [1., 0.3]
ar = np.convolve([1.]+[0]*50 +[-0.6], ar)
n_startup = 1000
# throwing away samples at beginning makes sample more "stationary"
xo = tsa.arma_generate_sample(ar,ma,n_startup+200)
x = xo[n_startup:]
def arma_periodogram(ar, ma, worn=None):
w, h = signal.freqz(ma, ar, **kwds)
sd = np.abs(h)**2/np.sqrt(2*np.pi)
if np.sum(np.isnan(h)) > 0:
# this happens with unit root or seasonal unit root'
print 'Warning: nan in frequency response h'
return w, sd
plt.figure()
plt.plot(x)
rescale = 1
'''generates some ARMA random samples and saves to python module file
'''
import numpy as np
from scikits.statsmodels.sandbox import tsa
from maketests_mlabwrap import HoldIt
if __name__ == '__main__':
filen = 'savedrvs.py'
np.set_printoptions(precision=14, linewidth=100)
# check arma to return same as random.normal
np.random.seed(10000)
xo = tsa.arma_generate_sample([1], [1], nsample=100)
xo2 = np.round(xo*1000).astype(int)
np.random.seed(10000)
rvs = np.random.normal(size=100)
rvs2 = np.round(xo*1000).astype(int)
assert (xo2==rvs2).all()
nsample = 1000
data = HoldIt('rvsdata')
np.random.seed(10000)
xo = tsa.arma_generate_sample([1, -0.8, 0.5], [1], nsample=nsample)
data.xar2 = np.round(xo*1000).astype(int)
np.random.seed(10000)
xo = np.random.normal(size=nsample)
data.xnormal = np.round(xo*1000).astype(int)
