def test_eemd_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
config = {"processes": 1}
eemd = eemd(trials=10, **config)
# First run random seed
eIMF1 = eemd(S)
# Second run with defined seed, diff than first
eemd.noise_seed(12345)
eIMF2 = eemd(S)
# Extremly unlikely to have same seed, thus different results
msg_false = "Different seeds, expected different outcomes"
if eIMF1.shape == eIMF2.shape:
self.assertFalse(np.all(cmpMachEps(eIMF1, eIMF2)), msg_false)
# Third run with same seed as with 2nd
eemd.noise_seed(12345)
eIMF3 = eemd(S)
# Using same seeds, thus expecting same results
msg_true = "Used same seed, expected same results"
self.assertTrue(np.all(cmpMachEps(eIMF2, eIMF3)), msg_true)
def test_eemd_passingArgumentsViaDict(self):
trials = 10
noise_kind = 'uniform'
spline_kind = 'linear'
# Making sure that we are not testing default options
eemd = eemd()
self.assertFalse(eemd.trials == trials,
self.cmp_msg(eemd.trials, trials))
self.assertFalse(eemd.noise_kind == noise_kind,
self.cmp_msg(eemd.noise_kind, noise_kind))
self.assertFalse(eemd.EMD.spline_kind == spline_kind,
self.cmp_msg(eemd.EMD.spline_kind, spline_kind))
# Testing for passing attributes via params
params = {
"trials": trials,
"noise_kind": noise_kind,
"spline_kind": spline_kind
}
eemd = eemd(**params)
self.assertTrue(eemd.trials == trials,
self.cmp_msg(eemd.trials, trials))
self.assertTrue(eemd.noise_kind == noise_kind,
self.cmp_msg(eemd.noise_kind, noise_kind))
self.assertTrue(eemd.EMD.spline_kind == spline_kind,
self.cmp_msg(eemd.EMD.spline_kind, spline_kind))
def test_default_call_EEMD():
T = np.arange(50)
S = np.cos(T * 0.1)
max_imf = 2
eemd = eemd()
eemd(S, T, max_imf)
def test_eemd_passingCustomEMD(self):
spline_kind = "linear"
params = {"spline_kind": spline_kind}
eemd = eemd()
self.assertFalse(
eemd.EMD.spline_kind == spline_kind,
"Not" + self.cmp_msg(eemd.EMD.spline_kind, spline_kind))
from EMD import emd
emd = emd(**params)
eemd = eemd(ext_EMD=emd)
self.assertTrue(eemd.EMD.spline_kind == spline_kind,
self.cmp_msg(eemd.EMD.spline_kind, spline_kind))
def test_eemd_simpleRun(self):
T = np.linspace(0, 1, 100)
S = np.sin(2 * np.pi * T)
config = {"processes": 1}
eemd = eemd(trials=10, max_imf=1, **config)
eemd.EMD.FIXE_H = 5
eemd.eemd(S)
self.assertTrue('pool' in eemd.__dict__)
def test_eemd_notParallel(self):
S = np.random.random(100)
eemd = eemd(trials=5, max_imf=2, parallel=False)
eemd.EMD.FIXE_H = 2
eIMFs = eemd.eemd(S)
self.assertTrue(eIMFs.shape[0] > 0)
self.assertTrue(eIMFs.shape[1], len(S))
self.assertFalse('pool' in eemd.__dict__)
def test_eemd_unsupportedNoiseKind(self):
noise_kind = "whoever_supports_this_is_wrong"
eemd = eemd(noise_kind=noise_kind)
with self.assertRaises(ValueError):
eemd.generate_noise(1., 100)
from EMD import eemd import numpy as np import pylab as plt # Define signal t = np.linspace(0, 1, 200) sin = lambda x, p: np.sin(2 * np.pi * x * t + p) S = 3 * sin(18, 0.2) * (t - 0.2)**2 S += 5 * sin(11, 2.7) S += 3 * sin(14, 1.6) S += 1 * np.sin(4 * 2 * np.pi * (t - 0.8)**2) S += t**2.1 - t # Assign EEMD to `eemd` variable eemd = eemd() # Say we want detect extrema using parabolic method emd = eemd.EMD emd.extrema_detection = "parabol" # Execute EEMD on S eIMFs = eemd.eemd(S, t) nIMFs = eIMFs.shape[0] # Plot results plt.figure(figsize=(12, 9)) plt.subplot(nIMFs + 1, 1, 1) plt.plot(t, S, 'r') for n in range(nIMFs):