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features.py
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features.py
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# -*- coding: utf-8 -*-
# features.py
# author : Antoine Passemiers
from spectral import *
from utils import checkDropOutsByChannel
import numpy as np
import nolds # non-linear measures for dynamical systems
class Feature:
def __init__(self):
self.n_electrodes = 16
self.fs = 400
def __len__(self):
return self.n_electrodes
class FeatureLogSpectrum(Feature):
coherences = list()
def __init__(self, *args, **kwargs):
Feature.__init__(self, *args, **kwargs)
self.n_coef = 144
def __len__(self):
return self.n_electrodes * self.n_coef
def process(self, signals):
features = np.empty(self.__len__(), dtype=float)
for i in range(self.n_electrodes):
has_nan = checkDropOutsByChannel(signals[:, i])
if not has_nan:
features[(i * self.n_coef):((i + 1) * self.n_coef)
] = LogSpectrum(signals[:, i], self.n_coef)
else:
features[(i * self.n_coef):((i + 1) * self.n_coef)] = np.nan
return features
class FeatureLyapunovExponent(Feature):
def __init__(self, *args, **kwargs):
Feature.__init__(self, *args, **kwargs)
def process(self, signals):
features = np.empty(self.__len__(), dtype=np.float64)
for i in range(self.__len__()):
has_nan = checkDropOutsByChannel(signals[:, i])
features[i] = nolds.lyap_r(
signals[:, i]) if not has_nan else np.nan
return features
class FeatureHurstExponent(Feature):
def __init__(self, *args, **kwargs):
Feature.__init__(self, *args, **kwargs)
def process(self, signals):
features = np.empty(self.__len__(), dtype=np.float64)
for i in range(self.__len__()):
has_nan = checkDropOutsByChannel(signals[:, i])
features[i] = nolds.hurst_rs(
signals[:, i]) if not has_nan else np.nan
return features
class FeatureSTE(Feature):
def __init__(self, *args, **kwargs):
Feature.__init__(self, *args, **kwargs)
def process(self, signals):
features = np.empty(self.__len__(), dtype=np.float64)
for i in range(self.__len__()):
has_nan = checkDropOutsByChannel(signals[:, i])
features[i] = STE(signals[:, i]) if not has_nan else np.nan
return features
class FeatureZeroCrossings(Feature):
def __init__(self, *args, **kwargs):
Feature.__init__(self, *args, **kwargs)
def process(self, signals):
features = np.empty(self.__len__(), dtype=np.float64)
for i in range(self.__len__()):
has_nan = checkDropOutsByChannel(signals[:, i])
features[i] = ZCR(signals[:, i]) if not has_nan else np.nan
return features
class FeatureSpectralEntropy(Feature):
def __init__(self, *args, **kwargs):
Feature.__init__(self, *args, **kwargs)
def process(self, signals):
features = np.empty(self.__len__(), dtype=np.float64)
for i in range(self.__len__()):
has_nan = checkDropOutsByChannel(signals[:, i])
features[i] = PowerSpectralEntropy(
signals[:, i]) if not has_nan else np.nan
return features
class FeatureSpectralCoherence(Feature):
coherences = list()
def __init__(self, *args, **kwargs):
Feature.__init__(self, *args, **kwargs)
self.architecture = "circular"
self.band = all_bands["all"]
self.electrode_ids = list(range(16)) + [0]
FeatureSpectralCoherence.coherences.append(self)
def __len__(self):
n_bands = len(ALL_BANDS_400_HZ)
if self.architecture == "circular":
return self.n_electrodes * n_bands
else:
return self.n_electrodes * (self.n_electrodes - 1) * n_bands / 2
def config(self, architecture="circular", band="all"):
self.architecture = architecture
self.band = all_bands[band]
return self
def process(self, signals):
features = np.empty(self.__len__(), dtype=float)
n_bands = len(ALL_BANDS_400_HZ)
if self.architecture == "circular":
# autosp = AutospectralDensities(signals, self.fs)
for i in range(self.n_electrodes):
has_nan = checkDropOutsByChannel(signals[:, i])
coherences = SpectralCoherence(
signals, self.electrode_ids[i], self.electrode_ids[i + 1], fs=self.fs)
for j in range(n_bands):
features[i * n_bands + j] = coherences[ALL_BANDS_400_HZ[j]
].mean() if not has_nan else np.nan
elif self.architecture == "full":
has_nan = np.zeros(self.n_electrodes, dtype=np.bool)
for i in range(self.n_electrodes):
has_nan[i] = checkDropOutsByChannel(signals[:, i])
p = 0
for i in range(self.n_electrodes):
for k in range(self.n_electrodes):
if i < k:
coherences = SpectralCoherence(
signals, i, k, fs=self.fs)
for j in range(n_bands):
features[p * n_bands + j] = coherences[ALL_BANDS_400_HZ[j]
].mean() if not (has_nan[i] or has_nan[k]) else np.nan
p += 1
else:
NotImplementedError()
return features
class FeatureSet:
def __init__(self, n_electrodes, fs=400):
self.features = list()
self.n = 0
self.n_electrodes = n_electrodes
self.fs = fs
def add(self, feature):
feature.fs = self.fs
feature.n_electrodes = self.n_electrodes
self.features.append(feature)
self.n += len(feature)
def set_n_electrodes(self, n_electrodes):
self.n_electrodes = n_electrodes
self.n = 0
for feature in self.features:
feature.fs = self.fs
feature.n_electrodes = self.n_electrodes
self.n += len(feature)
def getFeatures(self):
return self.features
def __len__(self):
return self.n
def __getitem__(self, key):
return self.features[key]