def test_pcovar():
p = pcovar(data_cosine(), 15, NFFT=4096, scale_by_freq=True)
p()
p = pcovar(marple_data, 15, NFFT=4096)
p()
print(p.get_converted_psd('centerdc'))
return p.psd
def test_pmtm():
data = data_cosine(N=64, A=0.1, sampling=1024, freq=200)
res = pmtm(data, 2.5, 4, show=False)
res = pmtm(data, 2.5, show=False)
res = pmtm(data, 2.5, show=False, method="eigen")
res = pmtm(data, 2.5, show=False, method="unity")
res = pmtm(data, 2.5, method="eigen", show=True)
res = pmtm(data, 2.5, method="adapt", show=True)
#res = pmtm(data, 2.5, show=False, method="eigen", show=True)
# e and v must be provided together
try:
res = pmtm(data, 2.5, show=False, e=1, v=None)
assert False
except:
assert True
# provide v and e
v, e = dpss(64, 4, 2)
pmtm(marple_data, NW=4, k=2, v=v, e=e)
try:
pmtm(marple_data, NW=None, k=2)
assert False
except:
assert True
def test_pmtm():
data = data_cosine(N=64, A=0.1, sampling=1024, freq=200)
res = pmtm(data, 2.5, 4, show=False)
res = pmtm(data, 2.5, show=False)
res = pmtm(data, 2.5, show=False, method="eigen")
res = pmtm(data, 2.5, show=False, method="unity")
res = pmtm(data, 2.5, method="eigen", show=True)
res = pmtm(data, 2.5, method="adapt", show=True)
#res = pmtm(data, 2.5, show=False, method="eigen", show=True)
# e and v must be provided together
try:
res = pmtm(data, 2.5, show=False, e=1, v=None)
assert False
except:
assert True
# provide v and e
v,e = dpss(64,4,2)
pmtm(marple_data, NW=4, k=2, v=v, e=e);
try:
pmtm(marple_data, NW=None, k=2);
assert False
except:
assert True
def test_pcovar():
p = pcovar(data_cosine(), 15, NFFT=4096, scale_by_freq=True)
p()
print(p)
p = pcovar(marple_data, 15, NFFT=4096)
p()
print(p)
print(p.get_converted_psd('centerdc'))
return p.psd
def test_pmusic():
p = pmusic(marple_data, 15, NSIG=11)
p()
p = pmusic(data_cosine(), 15, NSIG=11, verbose=True)
p()
print(p)
# test verbosity of the _get_signal_space function
spectrum_set_level("DEBUG")
pmusic(data_two_freqs(), 15, threshold=1)()
pmusic(data_two_freqs(), 15, NSIG=11, verbose=True)()
pmusic(data_two_freqs(), 15, criteria="mdl")()
#
pmusic(data_two_freqs(), 15, NSIG=0)()
def test_pmusic():
p = pmusic(marple_data, 15, NSIG=11)
p()
p = pmusic(data_cosine(), 15, NSIG=11)
p()
print(p)
def test_eigen_parameters():
psd, s = ev(data_cosine(), 15)
psd, s = ev(data_cosine(), 15, NSIG=11)
psd, s = ev(data_cosine(), 15, threshold=2)
def test_pmtm():
data = data_cosine(N=64, A=0.1, sampling=1024, freq=200)
res = pmtm(data, 2.5, 4, show=False)
res = pmtm(data, 2.5, show=False)
def test_eigen_parameters():
psd, s = ev(data_cosine(), 15)
psd, s = ev(data_cosine(), 15, NSIG=11)
psd, s = ev(data_cosine(), 15, threshold=2)
def test_pev():
p = pev(marple_data, 15, NSIG=11)
p()
p = pev(data_cosine(), 15, NSIG=11, verbose=True)
p()
print(p)
hamming = Window(len(x), name='hamming')
f, pxx = sci.periodogram(x,
window=hamming.data,
fs=Fs,
nfft=len(x),
scaling='spectrum')
pwrest = pxx.max()
idx = pxx.argmax()
plt.subplot(312)
plt.plot(f, pxx)
plt.title('Periodograma')
plt.xlabel('Frequência')
plt.ylabel('Potência (W)')
plt.grid()
plt.axis([0, 2 * fc, 0, A**2])
print('A potência máxima ocorre em ', f[idx], ' Hz')
print('A potência estimada é', pwrest)
plt.subplot(313)
#construindo todo o procedimento com as funções da spectrum
import spectrum as spec
data = spec.data_cosine(N=len(x), A=10, sampling=Fs, freq=fc)
p = spec.Periodogram(x, sampling=Fs, window='hamming')
p.run() #Recomputa a psd caso 'x' tenha sido alterado
p.plot()
plt.title("Periodograma (dB) da Spectrum")
plt.tight_layout()
plt.show()