def bandpass_kaiser(ntaps, lowcut, highcut, fs, width):
nyq = 0.5 * fs
atten = kaiser_atten(ntaps, width / nyq)
beta = kaiser_beta(atten)
taps = firwin(ntaps, [lowcut, highcut], nyq=nyq, pass_zero=False,
window=('kaiser', beta), scale=False)
return taps
def bandpass_kaiser(ntaps, lowcut, highcut, fs, width):
nyq = 0.5 * fs
atten = kaiser_atten(ntaps, width / nyq)
beta = kaiser_beta(atten)
taps = firwin(ntaps, [lowcut, highcut], nyq=nyq, pass_zero=False,
window=('kaiser', beta), scale=False)
return taps
def bandpass_kaiser(self, channel,freqs=(1, 15)):
ntaps = 16
nyq = 0.5 * self.rate
width = 1.6
atten = signal.kaiser_atten(ntaps, width / nyq)
beta = signal.kaiser_beta(atten)
taps = signal.firwin(ntaps, [freqs[0], freqs[1]], nyq=nyq, pass_zero=False,
window=('kaiser', beta), scale=False)
newsig = signal.filtfilt(taps_kaiser16, 1.0, self.single_sweep_data[channel], padlen=500)
return newsig
def bandpass_kaiser(ntaps, lowcut, highcut, fs, width):
nyq = 0.5 * fs
atten = kaiser_atten(ntaps, width / nyq)
beta = kaiser_beta(atten)
taps = firwin(
ntaps,
[lowcut, highcut],
nyq=nyq,
pass_zero="bandpass",
window=("kaiser", beta),
scale=True,
)
return taps
def bandpass_kaiser(self, channel, freqs=(1, 15)):
ntaps = 16
nyq = 0.5 * self.rate
width = 1.6
atten = signal.kaiser_atten(ntaps, width / nyq)
beta = signal.kaiser_beta(atten)
taps = signal.firwin(ntaps, [freqs[0], freqs[1]],
nyq=nyq,
pass_zero=False,
window=('kaiser', beta),
scale=False)
newsig = signal.filtfilt(taps_kaiser16,
1.0,
self.single_sweep_data[channel],
padlen=500)
return newsig
def test_kaiser_atten():
a = kaiser_atten(1, 1.0)
assert_equal(a, 7.95)
a = kaiser_atten(2, 1/np.pi)
assert_equal(a, 2.285 + 7.95)
def construct_fir_filter(rate, frequencies, gains, order, phase, window,
design):
""" Construct coeffs of FIR filter.
Args:
rate (float): Nominal sampling rate of the input data.
order (int): Filter order
frequencies (list): Transition frequencies in Hz.
design (str|'firwin2'): Design of the transfert function of the filter.
phase (str|`linear`): Phase response ("zero", "zero-double" or "minimum").
window (float|`hamming`): The window to use in FIR design, ("hamming", "hann", or "blackman").
Returns:
array h. FIR coeffs.
Notes:
Adapted from mne.filters, see the documentation of:
* `mne.filters <https://martinos.org/mne/stable/generated/mne.filter.construct_fir_filter.html>`_
* `scipy.signal.firwin2 <https://docs.scipy.org/doc/scipy-0.14.0/reference/generated/scipy.signal.firwin2.html>`_
"""
nyq = rate / 2.
if design == 'firwin2':
from scipy.signal import firwin2 as design
else:
# not implemented yet
raise ValueError(
'firwin, remez and firls have not been implemented yet ')
# issue a warning if attenuation is less than this
min_att_db = 12 if phase == 'minimum' else 20
if frequencies[0] != 0 or frequencies[-1] != nyq:
raise ValueError(
'freq must start at 0 and end an Nyquist (%s), got %s' %
(nyq, frequencies))
gains = np.array(gains)
if window == "kaiser":
diffs = np.diff(frequencies)
width = min(diffs[diffs > 0])
beta = signal.kaiser_beta(signal.kaiser_atten(order, width / nyq))
window = ("kaiser", beta)
# check zero phase length
N = int(order)
if N % 2 == 0:
if phase == 'zero':
LOGGER.info('filter_length must be odd if phase="zero", '
'got %s' % N)
N += 1
elif phase == 'zero-double' and gains[-1] == 1:
N += 1
# construct symmetric (linear phase) filter
if phase == 'minimum':
h = design(N * 2 - 1, frequencies, gains, fs=rate, window=window)
h = signal.minimum_phase(h)
else:
h = design(N, frequencies, gains, fs=rate, window=window)
assert h.size == N
att_db, att_freq = _filter_attenuation(h, frequencies, gains)
if phase == 'zero-double':
att_db += 6
if att_db < min_att_db:
att_freq *= rate / 2.
LOGGER.info('Attenuation at stop frequency %0.1fHz is only %0.1fdB. '
'Increase filter_length for higher attenuation.' %
(att_freq, att_db))
return h
def test_kaiser_atten():
a = kaiser_atten(1, 1.0)
assert_equal(a, 7.95)
a = kaiser_atten(2, 1/np.pi)
assert_equal(a, 2.285 + 7.95)
def cpu_version(self, numtaps, width):
return signal.kaiser_atten(numtaps, width)
