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 dpss_cached(length,half_bandwidth_parameter):
'''
Get a collection of DPSS tapers. The number of tapers equals the
half bandwidth parameter times two. For legacy reasons the tapers
are returned transposed such that the first dimension indexes
tapers rather than time.
The advantage of using this function is that computing DPSS is
expensive. This function caches the results in RAM.
Parameters
----------
length : integer
length of the domain for which to compute the DPSS
half_bandwidth_parameter : number
The number of is the half_bandwidth_parameter*2
Returns
-------
ndarray:
tapers.T a transposed list of DPSS tapers
ndarray:
taper eigenvalues ( weights )
'''
tapers,eigen = dpss(length,half_bandwidth_parameter)
return tapers.T,eigen
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 fftppc_biased_multitaper(snippits,Fs=1000,k=4):
# some precision trouble
# use quad precition
# PPC doesn't care about scale so also rescale?
#nippits = array(snippits,dtype=__PPC_FP_TYPE__)
#snippits = snippits/std(snippits)
M,window = shape(snippits)
print(M,window)
if M<=window: warn('WARNING SAMPLES SEEM TRANSPOSED?')
#print('BROKE FOR MATLAB CHECK REMOVE +1 on K KEEP ALL TAPERS')
#tapers = dpss(window,NW=0.499*(k+1),k=(k+1))[0][:,:-1]
tapers = dpss(window,NW=0.499*k,k=k)[0]
results = []
unit = lambda x:x/abs(x)
average = [mean(unit(fft(snippits*taper)),0) for taper in tapers.T]
raw = mean([abs(x)**2 for x in average],0)
phases = angle(mean([exp(2j*pi*angle(x)) for x in average],0))
freqs = fftfreq(window,1./Fs)
return freqs[:(window+1)/2], raw[:(window+1)/2], phases[:(window+1)/2]
def test_dpss():
dpss(64, 2.5, 4)
def slepian(n_samp, num_tapers):
return dpss(N = n_samp, NW=int((num_tapers+1)/2), k=None)[0]
def test_dpss():
dpss(64, 2.5, 4)
