def test_run_pre_post_optimizations(self):
"Tests execution of run. Other tests should check correctness."
t = np.arange(0, 1, 0.01)
S = np.random.random(t.size)
oscN, nH = 2, 1
paramN = 3 + nH * (oscN - 1)
opt_maxiter = 10
options = {
"niter": 10,
"nwalkers": 20,
"PREOPTIMIZE": True,
"POSTOPTIMIZE": True,
"opt_maxiter": opt_maxiter
}
kursl = KurslMethod(nH=nH, max_osc=oscN, **options)
self.assertTrue(kursl.PREOPTIMIZE, "With preoptmize")
self.assertTrue(kursl.POSTOPTIMIZE, "With postoptmize")
self.assertEqual(kursl.opt_maxiter, opt_maxiter)
theta = kursl.run(t, S)
self.assertEqual(theta.shape, (oscN, paramN))
self.assertTrue(kursl.oscN, oscN)
self.assertTrue(kursl.nH, nH)
self.assertTrue(
np.all(theta == kursl.theta_init),
"After computing make sure it is assigned.\n"
"Received\n{}\nGot\n{}".format(theta, kursl.theta_init))
def test_run_custom_theta(self):
t = np.arange(0, 1, 0.01)
S = np.random.random(t.size)
oscN, nH = 2, 2
theta_init = np.random.random((oscN, 3 + nH * (oscN - 1)))
theta_init = np.array([
[10, 0, 2, 0, 0],
[20, 0, 5, 0, 1],
])
options = {"niter": 10, "nwalkers": 20}
kursl = KurslMethod(nH=nH, **options)
theta = kursl.run(t, S, theta_init=theta_init)
self.assertEqual(theta.shape, theta_init.shape,
"Shape shouldn't change")
self.assertEqual(kursl.nH, nH)
self.assertEqual(kursl.oscN, theta_init.shape[0])
self.assertEqual(kursl.paramN, theta_init.shape[1])
#!/usr/bin/python
# encoding: UTF-8
import logging
import numpy as np
import pylab as plt
import sys
from kursl import KurslMethod
# Import signal
S = np.loadtxt("examples/epilepsy_signal.txt")
fs = 173.61 # Hz
t = np.linspace(0, S.size / fs, S.size)
# Set verbose logging
logger = logging.getLogger(__file__)
logging.basicConfig(stream=sys.stdout, level=logging.DEBUG)
# Define KurslMethod
kursl = KurslMethod(nH=2, max_osc=4, nwalkers=60, niter=100, energy_ratio=0.4)
theta = kursl.run(t, S)
_, _, s_rec = kursl.model(t, theta)
signal = np.sum(s_rec, axis=0)
plt.plot(t, S)
plt.plot(t[:-1], signal)
plt.show()