def design_sosmat_low_pass_high_pass_bounds(order, band_edges, sample_rate):
"""Returns matrix containing sos coeffs of low and highpass.
The cutoff frequencies are placed at the first and last band edge.
.. note:: This funtion is not used anymore.
Parameters
----------
order : int
Order of the band pass filters.
band_edges : ndarray
Band edge frequencies for the low an highpass.
sample_rate : scalar
Sample rate.
Returns
-------
sosdict : ndarray
Second order section coefficients,
the first column contains the low pass coefs
and the second column contains the highpass coeffs.
"""
sosmat = zeros((0.5 * order * 6, 2))
band_edges_normalized = to_normalized_frequencies(band_edges, sample_rate)
sosmat[:, 0] = butter_sos('lowpass', order,
band_edges_normalized[0]).flatten()
sosmat[:, 1] = butter_sos('highpass', order,
band_edges_normalized[-1]).flatten()
return sosmat
def design_sosmat_low_pass_high_pass_bounds(order, band_edges, sample_rate):
"""Returns matrix containing sos coeffs of low and highpass.
The cutoff frequencies are placed at the first and last band edge.
.. note:: This funtion is not used anymore.
Parameters
----------
order : int
Order of the band pass filters.
band_edges : ndarray
Band edge frequencies for the low an highpass.
sample_rate : scalar
Sample rate.
Returns
-------
sosdict : ndarray
Second order section coefficients,
the first column contains the low pass coefs
and the second column contains the highpass coeffs.
"""
sosmat = zeros((0.5*order*6, 2))
band_edges_normalized = to_normalized_frequencies(band_edges, sample_rate)
sosmat[:, 0] = butter_sos('lowpass', order,
band_edges_normalized[0]).flatten()
sosmat[:, 1] = butter_sos('highpass', order,
band_edges_normalized[-1]).flatten()
return sosmat
def test_bandstop(self):
sosmat = bw.butter_sos('bandstop', 2, self.v1, self.v2)
self.assertTrue(np.all(np.isfinite(sosmat)))
x, y, F, Y = sf.freqz(sosmat, sample_rate=self.sample_rate, plot=False)
mask = np.logical_and(F >= self.v1 * self.sample_rate,
F <= self.v2 * self.sample_rate)
self.assertTrue(np.all(np.abs(Y[mask]) <= 1.0 / np.sqrt(2)))
def test_highpass(self):
sosmat = bw.butter_sos('highpass', 2, self.v1)
self.assertTrue(np.all(np.isfinite(sosmat)))
x, y, F, Y = sf.freqz(
sosmat, sample_rate=self.sample_rate, plot=False)
mask = np.logical_and(F <= self.v1*self.sample_rate, F >=0)
self.assertTrue(np.all(np.abs(Y[mask]) <= 1.0/np.sqrt(2)))
def test_lowpass(self):
sosmat = bw.butter_sos('lowpass', 2, self.v1)
self.assertTrue(np.all(np.isfinite(sosmat)))
x, y, F, Y = sf.freqz(
sosmat, sample_rate=self.sample_rate, plot=False)
mask = F >= self.v1*self.sample_rate
self.assertTrue(np.all(np.abs(Y[mask]) <= 1.0/np.sqrt(2)))
def test_bandstop(self):
sosmat = bw.butter_sos('bandstop', 2, self.v1, self.v2)
self.assertTrue(np.all(np.isfinite(sosmat)))
x, y, F, Y = sf.freqz(
sosmat, sample_rate=self.sample_rate, plot=False)
mask = np.logical_and(F >= self.v1*self.sample_rate,
F <= self.v2*self.sample_rate)
self.assertTrue(np.all(np.abs(Y[mask]) <= 1.0/np.sqrt(2)))
def design_sosmat_band_passes(order, band_edges, sample_rate,
edge_correction_percent=0.0):
"""Return matrix containig sos coeffs of bandpasses.
Parameters
----------
order : int
Order of the band pass filters.
band_edges : ndarray
Band edge frequencies for the bandpasses.
sample_rate : scalar
Sample frequency.
edge_correction_percent : scalar
Percentage for the correction of the bandedges.
Float between -100 % and 100 %.
It can be helpfull dependent on the used filter order.
p > 0 widens the band passes.
Returns
-------
sosmat : ndarray
Second order section coefficients.
Each column is one band pass cascade of coefficients.
"""
num_coeffs_biquad_bandpass = 6
num_coeffs_cascade = order * num_coeffs_biquad_bandpass
num_bands = len(band_edges) - 1
sosmat = zeros((num_coeffs_cascade, num_bands))
band_edges_normalized = to_normalized_frequencies(band_edges, sample_rate)
p_lower = (1 - edge_correction_percent*1e-2)
p_upper = (1 + edge_correction_percent*1e-2)
for i, (lower_freq, upper_freq) in enumerate(zip(
band_edges_normalized[:-1],
band_edges_normalized[1:])):
sos = butter_sos('bandpass',
order,
p_lower*lower_freq,
p_upper*upper_freq)
sosmat[:, i] = sos.flatten()
return sosmat
def design_sosmat_band_passes(order,
band_edges,
sample_rate,
edge_correction_percent=0.0):
"""Return matrix containig sos coeffs of bandpasses.
Parameters
----------
order : int
Order of the band pass filters.
band_edges : ndarray
Band edge frequencies for the bandpasses.
sample_rate : scalar
Sample frequency.
edge_correction_percent : scalar
Percentage for the correction of the bandedges.
Float between -100 % and 100 %.
It can be helpfull dependent on the used filter order.
p > 0 widens the band passes.
Returns
-------
sosmat : ndarray
Second order section coefficients.
Each column is one band pass cascade of coefficients.
"""
num_coeffs_biquad_bandpass = 6
num_coeffs_cascade = order * num_coeffs_biquad_bandpass
num_bands = len(band_edges) - 1
sosmat = zeros((num_coeffs_cascade, num_bands))
band_edges_normalized = to_normalized_frequencies(band_edges, sample_rate)
p_lower = (1 - edge_correction_percent * 1e-2)
p_upper = (1 + edge_correction_percent * 1e-2)
for i, (lower_freq, upper_freq) in enumerate(
zip(band_edges_normalized[:-1], band_edges_normalized[1:])):
sos = butter_sos('bandpass', order, p_lower * lower_freq,
p_upper * upper_freq)
sosmat[:, i] = sos.flatten()
return sosmat
def test_highpass(self):
sosmat = bw.butter_sos('highpass', 2, self.v1)
self.assertTrue(np.all(np.isfinite(sosmat)))
x, y, F, Y = sf.freqz(sosmat, sample_rate=self.sample_rate, plot=False)
mask = np.logical_and(F <= self.v1 * self.sample_rate, F >= 0)
self.assertTrue(np.all(np.abs(Y[mask]) <= 1.0 / np.sqrt(2)))
def test_lowpass(self):
sosmat = bw.butter_sos('lowpass', 2, self.v1)
self.assertTrue(np.all(np.isfinite(sosmat)))
x, y, F, Y = sf.freqz(sosmat, sample_rate=self.sample_rate, plot=False)
mask = F >= self.v1 * self.sample_rate
self.assertTrue(np.all(np.abs(Y[mask]) <= 1.0 / np.sqrt(2)))
