def _homogenize_params(self, other, maxdiff=1):
"""
Return triple with a tuple of indices (in self and other, respectively),
factors and constants at these frequencies.
Parameters
----------
other : CallistoSpectrogram
Spectrogram to be homogenized with the current one.
maxdiff : float
Threshold for which frequencies are considered equal.
"""
pairs_indices = [(x, y) for x, y, d in minimal_pairs(self.freq_axis, other.freq_axis) if d <= maxdiff]
pairs_data = [(self[n_one, :], other[n_two, :]) for n_one, n_two in pairs_indices]
# XXX: Maybe unnecessary.
pairs_data_gaussian = [(gaussian_filter1d(a, 15), gaussian_filter1d(b, 15)) for a, b in pairs_data]
# If we used integer arithmetic, we would accept more invalid
# values.
pairs_data_gaussian64 = np.float64(pairs_data_gaussian)
least = [leastsq(self._to_minimize(a, b), [1, 0])[0] for a, b in pairs_data_gaussian64]
factors = [x for x, y in least]
constants = [y for x, y in least]
return pairs_indices, factors, constants
def _homogenize_params(self, other, maxdiff=1):
"""
Return triple with a tuple of indices (in self and other, respectively),
factors and constants at these frequencies.
Parameters
----------
other : CallistoSpectrogram
Spectrogram to be homogenized with the current one.
maxdiff : float
Threshold for which frequencies are considered equal.
"""
pairs_indices = [
(x, y) for x, y, d in minimal_pairs(self.freq_axis, other.freq_axis)
if d <= maxdiff
]
pairs_data = [
(self[n_one, :], other[n_two, :]) for n_one, n_two in pairs_indices
]
# XXX: Maybe unnecessary.
pairs_data_gaussian = [
(gaussian_filter1d(a, 15), gaussian_filter1d(b, 15))
for a, b in pairs_data
]
# If we used integer arithmetic, we would accept more invalid
# values.
pairs_data_gaussian64 = np.float64(pairs_data_gaussian)
least = [
leastsq(self._to_minimize(a,b), [1, 0])[0]
for a, b in pairs_data_gaussian64
]
factors = [x for x, y in least]
constants = [y for x, y in least]
return pairs_indices, factors, constants
