<filename>examples/plot_allpsd.py
"""
Spectral analysis of a two frequencies signal
==================================================


"""
###########################################################
# Context
# ----------

##############################################
# Example 
# --------
#
# In the following example, we use most of the methods available to 
# analyse an input signal made of the addition of two sinus and an 
# additive gaussian noise
import numpy
import spectrum
from spectrum import tools
from numpy.testing import assert_array_almost_equal
import pylab
data = spectrum.marple_data

from pylab import *
nn = numpy.arange(200)
xx = cos(0.257*pi*nn) + sin(0.2*pi*nn) + 0.01*randn(size(nn));

def create_all_psd():

    f = pylab.linspace(0, 1, 4096)

    pylab.figure(figsize=(12,8))

    # MA model
    p = spectrum.pma(xx, 64,128); p(); p.plot()
    """
    #ARMA 15 order
    a, b, rho = spectrum.arma_estimate(data, 15,15, 30)
    psd = spectrum.arma2psd(A=a,B=b, rho=rho)
    newpsd = tools.cshift(psd, len(psd)//2) # switch positive and negative freq
    pylab.plot(f, 10 * pylab.log10(newpsd/max(newpsd)), label='ARMA 15,15')
    """
    # <NAME>
    p = spectrum.pyule(xx, 7 , NFFT=4096, scale_by_freq=False); p.plot()
    # equivalent to
    # plot([x for x in p.frequencies()] , 10*log10(p.psd)); grid(True)

    #burg method
    p = spectrum.pburg(xx, 7, scale_by_freq=False);  p.plot()

    #pcovar
    p = spectrum.pcovar(xx, 7, scale_by_freq=False);  p.plot()

    #pmodcovar
    p = spectrum.pmodcovar(xx, 7, scale_by_freq=False); p.plot()

    # correlogram
    p = spectrum.pcorrelogram(xx, lag=60, NFFT=512, scale_by_freq=False); p.plot()

    # minvar
    p = spectrum.pminvar(xx, 7, NFFT=256, scale_by_freq=False); p.plot()

    # pmusic
    p = spectrum.pmusic(xx, 10,4, scale_by_freq=False); p.plot()

    # pmusic
    p = spectrum.pev(xx, 10, 4, scale_by_freq=False); p.plot()

    # periodogram
    p = spectrum.Periodogram(xx, scale_by_freq=False); p.plot()

    #
    legend( ["MA 32", "pyule 7", "pburg 7", "pcovar", "pmodcovar", "correlogram",
                "minvar", "pmusic", "pev", "periodgram"])


    pylab.ylim([-80,80])

create_all_psd()

<filename>examples/plot_allpsd.py
"""
Spectral analysis of a two frequencies signal
==================================================


"""
###########################################################
# Context
# ----------

##############################################
# Example 
# --------
#
# In the following example, we use most of the methods available to 
# analyse an input signal made of the addition of two sinus and an 
# additive gaussian noise
import numpy
import spectrum
from spectrum import tools
from numpy.testing import assert_array_almost_equal
import pylab
data = spectrum.marple_data

from pylab import *
nn = numpy.arange(200)
xx = cos(0.257*pi*nn) + sin(0.2*pi*nn) + 0.01*randn(size(nn));

def create_all_psd():

    f = pylab.linspace(0, 1, 4096)

    pylab.figure(figsize=(12,8))

    # MA model
    p = spectrum.pma(xx, 64,128); p(); p.plot()
    """
    #ARMA 15 order
    a, b, rho = spectrum.arma_estimate(data, 15,15, 30)
    psd = spectrum.arma2psd(A=a,B=b, rho=rho)
    newpsd = tools.cshift(psd, len(psd)//2) # switch positive and negative freq
    pylab.plot(f, 10 * pylab.log10(newpsd/max(newpsd)), label='ARMA 15,15')
    """
    # <NAME>
    p = spectrum.pyule(xx, 7 , NFFT=4096, scale_by_freq=False); p.plot()
    # equivalent to
    # plot([x for x in p.frequencies()] , 10*log10(p.psd)); grid(True)

    #burg method
    p = spectrum.pburg(xx, 7, scale_by_freq=False);  p.plot()

    #pcovar
    p = spectrum.pcovar(xx, 7, scale_by_freq=False);  p.plot()

    #pmodcovar
    p = spectrum.pmodcovar(xx, 7, scale_by_freq=False); p.plot()

    # correlogram
    p = spectrum.pcorrelogram(xx, lag=60, NFFT=512, scale_by_freq=False); p.plot()

    # minvar
    p = spectrum.pminvar(xx, 7, NFFT=256, scale_by_freq=False); p.plot()

    # pmusic
    p = spectrum.pmusic(xx, 10,4, scale_by_freq=False); p.plot()

    # pmusic
    p = spectrum.pev(xx, 10, 4, scale_by_freq=False); p.plot()

    # periodogram
    p = spectrum.Periodogram(xx, scale_by_freq=False); p.plot()

    #
    legend( ["MA 32", "pyule 7", "pburg 7", "pcovar", "pmodcovar", "correlogram",
                "minvar", "pmusic", "pev", "periodgram"])


    pylab.ylim([-80,80])

create_all_psd()

Spectral analysis of a two frequencies signal ================================================== ########################################################### # Context # ---------- ############################################## # Example # -------- # # In the following example, we use most of the methods available to # analyse an input signal made of the addition of two sinus and an # additive gaussian noise # MA model #ARMA 15 order     a, b, rho = spectrum.arma_estimate(data, 15,15, 30)     psd = spectrum.arma2psd(A=a,B=b, rho=rho)     newpsd = tools.cshift(psd, len(psd)//2) # switch positive and negative freq     pylab.plot(f, 10 * pylab.log10(newpsd/max(newpsd)), label='ARMA 15,15') # <NAME> # equivalent to # plot([x for x in p.frequencies()] , 10*log10(p.psd)); grid(True) #burg method #pcovar #pmodcovar # correlogram # minvar # pmusic # pmusic # periodogram #