def test_hfd(self):
ans = univariate.hfd(white_noise[:100], 10)
ref = 2.0703560530609164
self.assertAlmostEqual(ref, ans)
ans = univariate.hfd(np.cumsum(white_noise[:10000]), 20)
ref = 1.5369261439430244
self.assertAlmostEqual(ref, ans)
def test_hfd(self):
ans = univariate.hfd(white_noise[:100], 10)
ref = 2.0703560530609164
self.assertAlmostEqual(ref, ans)
ans = univariate.hfd(np.cumsum(white_noise[:10000]), 20)
ref = 1.5369261439430244
self.assertAlmostEqual(ref, ans)
from timeit import Timer
import pandas as pd
import numpy as np
import sys
MIN_EPOCH_N = 256 * 5
MAX_EPOCH_N = 256 * 30
EPOCH_STEP = 256 * 5
N_REPLICATES = 5
SPECT_ENT_BANDS = 2 ** np.arange(0,8)/2
fun_to_test = [
{"times":100,"name":"hfd", "is_original":True,"fun": lambda x: pyeeg.hfd(x,2**3)},
{"times":100,"name":"hfd", "is_original":False,"fun": lambda x: univ.hfd(x,2**3)},
{"times":100,"name":"hjorth", "is_original":True,"fun": lambda x: pyeeg.hjorth(x)},
{"times":100,"name":"hjorth", "is_original":False,"fun": lambda x: univ.hjorth(x)},
{"times":100,"name":"pfd", "is_original":True, "fun":lambda x: pyeeg.pfd(x)},
{"times":100,"name":"pfd", "is_original":False, "fun":lambda x: pyeeg.pfd(x)},
{"times":2,"name":"samp_ent", "is_original":True, "fun":lambda x: pyeeg.samp_entropy(x,2,1.5)},
{"times":10,"name":"samp_ent", "is_original":False, "fun":lambda x: univ.samp_entropy(x,2,1.5,relative_r=False)},
{"times":2,"name":"ap_ent", "is_original":True, "fun":lambda x: pyeeg.ap_entropy(x,2,1.5)},
{"times":10,"name":"ap_ent", "is_original":False, "fun":lambda x: univ.ap_entropy(x,2,1.5)},
{"times":10,"name":"svd_ent", "is_original":True, "fun":lambda x: pyeeg.svd_entropy(x,2,3)},
{"times":100,"name":"svd_ent", "is_original":False, "fun":lambda x: univ.svd_entropy(x,2,3)},
{"times":10,"name":"fisher_info", "is_original":True, "fun":lambda x: pyeeg.fisher_info(x,2,3)},
{"times":100, "name":"fisher_info", "is_original":False, "fun":lambda x: univ.fisher_info(x,2,3)},
{"times":100,"name":"spectral_entropy", "is_original":True, "fun":lambda x: pyeeg.spectral_entropy(x,SPECT_ENT_BANDS,256)},
{"times":100, "name":"spectral_entropy", "is_original":False, "fun":lambda x: univ.spectral_entropy(x,256, SPECT_ENT_BANDS)},
N_REPLICATES = 5
SPECT_ENT_BANDS = 2**np.arange(0, 8) / 2
fun_to_test = [
{
"times": 100,
"name": "hfd",
"is_original": True,
"fun": lambda x: pyeeg.hfd(x, 2**3)
},
{
"times": 100,
"name": "hfd",
"is_original": False,
"fun": lambda x: univ.hfd(x, 2**3)
},
{
"times": 100,
"name": "hjorth",
"is_original": True,
"fun": lambda x: pyeeg.hjorth(x)
},
{
"times": 100,
"name": "hjorth",
"is_original": False,
"fun": lambda x: univ.hjorth(x)
},
{
"times": 100,
