def test_default_call_CEEMDAN():
T = np.arange(50)
S = np.cos(T*0.1)
max_imf = 2
ceemdan = ceemdan(trials=5)
ceemdan(S, T, max_imf)
def test_ceemdan_passingArgumentsViaDict(self):
trials = 10
noise_kind = 'uniform'
spline_kind = 'linear'
# Making sure that we are not testing default options
ceemdan = ceemdan()
self.assertFalse(ceemdan.trials==trials,
self.cmp_msg(ceemdan.trials, trials))
self.assertFalse(ceemdan.noise_kind==noise_kind,
self.cmp_msg(ceemdan.noise_kind, noise_kind))
self.assertFalse(ceemdan.EMD.spline_kind==spline_kind,
self.cmp_msg(ceemdan.EMD.spline_kind, spline_kind))
# Testing for passing attributes via params
params = {"trials": trials, "noise_kind": noise_kind,
"spline_kind": spline_kind}
ceemdan = ceemdan(**params)
self.assertTrue(ceemdan.trials==trials,
self.cmp_msg(ceemdan.trials, trials))
self.assertTrue(ceemdan.noise_kind==noise_kind,
self.cmp_msg(ceemdan.noise_kind, noise_kind))
self.assertTrue(ceemdan.EMD.spline_kind==spline_kind,
self.cmp_msg(ceemdan.EMD.spline_kind, spline_kind))
def test_ceemdan_noiseSeed(self):
T = np.linspace(0, 1, 100)
S = np.sin(2*np.pi*T+ 4**T) + np.cos( (T-0.4)**2)
# Compare up to machine epsilon
cmpMachEps = lambda x, y: np.abs(x-y)<=2*np.finfo(x.dtype).eps
ceemdan = ceemdan(trials=10)
# First run random seed
cIMF1 = ceemdan(S)
# Second run with defined seed, diff than first
ceemdan.noise_seed(12345)
cIMF2 = ceemdan(S)
# Extremly unlikely to have same seed, thus different results
msg_false = "Different seeds, expected different outcomes"
if cIMF1.shape == cIMF2.shape:
self.assertFalse(np.all(cmpMachEps(cIMF1,cIMF2)), msg_false)
# Third run with same seed as with 2nd
ceemdan.noise_seed(12345)
cIMF3 = ceemdan(S)
# Using same seeds, thus expecting same results
msg_true = "Used same seed, expected same results"
self.assertTrue(np.all(cmpMachEps(cIMF2,cIMF3)), msg_true)
def test_ceemdan_completeRun(self):
S = np.random.random(200)
ceemdan = ceemdan()
cIMFs = ceemdan(S)
self.assertTrue(cIMFs.shape[0]>1)
self.assertTrue(cIMFs.shape[1]==S.size)
self.assertTrue('pool' in ceemdan.__dict__)
def test_ceemdan_notParallel(self):
S = np.random.random(100)
ceemdan = ceemdan(parallel=False)
cIMFs = ceemdan(S)
self.assertTrue(cIMFs.shape[0]>1)
self.assertTrue(cIMFs.shape[1]==S.size)
self.assertFalse('pool' in ceemdan.__dict__)
def test_ceemdan_testMaxImf(self):
S = np.random.random(100)
ceemdan = ceemdan(trials=10)
max_imf = 1
cIMFs = ceemdan(S, max_imf=max_imf)
self.assertTrue(cIMFs.shape[0]==max_imf+1)
max_imf = 3
cIMFs = ceemdan(S, max_imf=max_imf)
self.assertTrue(cIMFs.shape[0]==max_imf+1)
def test_ceemdan_origianlSignal(self):
T = np.linspace(0, 1, 100)
S = 2*np.cos(3*np.pi*T) + np.cos(2*np.pi*T+ 4**T)
# Make a copy of S for comparsion
Scopy = np.copy(S)
# Compare up to machine epsilon
cmpMachEps = lambda x, y: np.abs(x-y)<=2*np.finfo(x.dtype).eps
ceemdan = ceemdan(trials=10)
ceemdan(S)
# The original signal should not be changed after the 'ceemdan' function.
msg_true = "Expected no change of the original signal"
self.assertTrue(np.all(cmpMachEps(Scopy,S)), msg_true)
def test_ceemdan_passingCustomEMD(self):
spline_kind = "linear"
params = {"spline_kind": spline_kind}
ceemdan = ceemdan()
self.assertFalse(ceemdan.EMD.spline_kind==spline_kind,
"Not"+self.cmp_msg(ceemdan.EMD.spline_kind, spline_kind))
from EMD import emd
emd = emd(**params)
ceemdan = ceemdan(ext_EMD=emd)
self.assertTrue(ceemdan.EMD.spline_kind==spline_kind,
self.cmp_msg(ceemdan.EMD.spline_kind, spline_kind))
def test_ceemdan_simpleRun():
T = np.linspace(0, 1, 100)
S = np.sin(2*np.pi*T)
config = {"processes": 1}
ceemdan = ceemdan(trials=10, max_imf=1, **config)
ceemdan.EMD.FIXE_H = 5
ceemdan.ceemdan(S)
def test_ceemdan_noiseKind_uniform():
ceemdan = ceemdan()
ceemdan.noise_kind = "uniform"
ceemdan.generate_noise(1., 100)
def test_ceemdan_constantEpsilon():
S = np.random.random(100)
ceemdan = ceemdan(trials=10, max_imf=2)
ceemdan.beta_progress = False
ceemdan(S)
def test_ceemdan_noiseKind_unknown(self):
ceemdan = ceemdan()
ceemdan.noise_kind = "bernoulli"
with self.assertRaises(ValueError):
ceemdan.generate_noise(1., 100)
