def biolowhigh(signal, sampling_rate, low, high, mode='includehigh'):
if mode == 'includehigh': #think i included this for testing purposes
b, a = st.get_filter(ftype='butter',
band='highpass',
order=8,
frequency=low,
sampling_rate=sampling_rate)
aux, _ = st._filter_signal(b,
a,
signal=signal,
check_phase=True,
axis=0)
if mode == 'nohigh':
aux = signal
# low pass filter
b, a = st.get_filter(ftype='butter',
band='lowpass',
order=16,
frequency=high,
sampling_rate=sampling_rate)
filtered, _ = st._filter_signal(b, a, signal=aux, check_phase=True, axis=0)
return filtered
def butter_filter(self, signal, cutoff_frequency, framerate):
"""
:param signal: .wav data
:return: butter filtered data
"""
if (cutoff_frequency == "" or cutoff_frequency == "0"):
cutoff_frequency = 1000
cutoff_frequency = int(cutoff_frequency)
b, a = st.get_filter(ftype="butter",
order=2,
band="lowpass",
frequency=cutoff_frequency,
sampling_rate=framerate)
filtered, zf = st._filter_signal(b, a, signal, check_phase=True)
max_value = max(filtered)
filtered = [i / max_value for i in filtered]
return np.array(filtered)
def plot_filter(ftype='FIR',
band='lowpass',
order=None,
frequency=None,
sampling_rate=1000.,
path=None,
show=True,
**kwargs):
"""Plot the frequency response of the filter specified with the given
parameters.
Parameters
----------
ftype : str
Filter type:
* Finite Impulse Response filter ('FIR');
* Butterworth filter ('butter');
* Chebyshev filters ('cheby1', 'cheby2');
* Elliptic filter ('ellip');
* Bessel filter ('bessel').
band : str
Band type:
* Low-pass filter ('lowpass');
* High-pass filter ('highpass');
* Band-pass filter ('bandpass');
* Band-stop filter ('bandstop').
order : int
Order of the filter.
frequency : int, float, list, array
Cutoff frequencies; format depends on type of band:
* 'lowpass' or 'bandpass': single frequency;
* 'bandpass' or 'bandstop': pair of frequencies.
sampling_rate : int, float, optional
Sampling frequency (Hz).
path : str, optional
If provided, the plot will be saved to the specified file.
show : bool, optional
If True, show the plot immediately.
``**kwargs`` : dict, optional
Additional keyword arguments are passed to the underlying
scipy.signal function.
"""
# get filter
b, a = st.get_filter(ftype=ftype,
band=band,
order=order,
frequency=frequency,
sampling_rate=sampling_rate,
**kwargs)
# plot
fig = _plot_filter(b, a, sampling_rate)
# make layout tight
fig.tight_layout()
# save to file
if path is not None:
path = utils.normpath(path)
root, ext = os.path.splitext(path)
ext = ext.lower()
if ext not in ['png', 'jpg']:
path = root + '.png'
fig.savefig(path, dpi=200, bbox_inches='tight')
# show
if show:
plt.show()
else:
# close
plt.close(fig)
def plot_filter(ftype='FIR',
band='lowpass',
order=None,
frequency=None,
sampling_rate=1000.,
path=None,
show=True, **kwargs):
"""Plot the frequency response of the filter specified with the given
parameters.
Parameters
----------
ftype : str
Filter type:
* Finite Impulse Response filter ('FIR');
* Butterworth filter ('butter');
* Chebyshev filters ('cheby1', 'cheby2');
* Elliptic filter ('ellip');
* Bessel filter ('bessel').
band : str
Band type:
* Low-pass filter ('lowpass');
* High-pass filter ('highpass');
* Band-pass filter ('bandpass');
* Band-stop filter ('bandstop').
order : int
Order of the filter.
frequency : int, float, list, array
Cutoff frequencies; format depends on type of band:
* 'lowpass' or 'bandpass': single frequency;
* 'bandpass' or 'bandstop': pair of frequencies.
sampling_rate : int, float, optional
Sampling frequency (Hz).
path : str, optional
If provided, the plot will be saved to the specified file.
show : bool, optional
If True, show the plot immediately.
``**kwargs`` : dict, optional
Additional keyword arguments are passed to the underlying
scipy.signal function.
"""
# get filter
b, a = st.get_filter(ftype=ftype,
band=band,
order=order,
frequency=frequency,
sampling_rate=sampling_rate, **kwargs)
# plot
fig = _plot_filter(b, a, sampling_rate)
# make layout tight
fig.tight_layout()
# save to file
if path is not None:
path = utils.normpath(path)
root, ext = os.path.splitext(path)
ext = ext.lower()
if ext not in ['png', 'jpg']:
path = root + '.png'
fig.savefig(path, dpi=200, bbox_inches='tight')
# show
if show:
plt.show()
else:
# close
plt.close(fig)
data_eeg = utl.load(filename)[:, 0:14]
######################################################
# calculates logarithm of the PSDs of the five bands #
# theta, slow_alpha, alpha, beta, gamma #
# window size = 1.0s (128 samples) #
######################################################
signal = data_eeg
rate = 128.
window_size = 1.0
overlap = .5
# high pass filter
b, a = st.get_filter(ftype='butter',
band='highpass',
order=8,
frequency=2,
sampling_rate=rate)
aux, _ = st._filter_signal(b, a, signal=signal, check_phase=True, axis=0)
# low pass filter
b, a = st.get_filter(ftype='butter',
band='lowpass',
order=16,
frequency=47,
sampling_rate=rate)
filtered, _ = st._filter_signal(b, a, signal=aux, check_phase=True, axis=0)
plt.plot(signal[:, 0])