def homogenize(self, other, maxdiff=1):
""" Return overlapping part of self and other as (self, other) tuple.
Homogenize intensities so that the images can be used with
combine_frequencies. Note that this works best when most of the
picture is signal, so use :py:meth:`in_interval` to select the subset
of your image before applying this method.
Parameters
----------
other : CallistoSpectrogram
Spectrogram to be homogenized with the current one.
maxdiff : float
Threshold for which frequencies are considered equal.
"""
one, two = self._overlap(other)
pairs_indices, factors, constants = one._homogenize_params(
two, maxdiff
)
# XXX: Maybe (xd.freq_axis[x] + yd.freq_axis[y]) / 2.
pairs_freqs = [one.freq_axis[x] for x, y in pairs_indices]
# XXX: Extrapolation does not work this way.
# XXX: Improve.
f1 = np.polyfit(pairs_freqs, factors, 3)
f2 = np.polyfit(pairs_freqs, constants, 3)
return (
one,
two * polyfun_at(f1, two.freq_axis)[:, np.newaxis] +
polyfun_at(f2, two.freq_axis)[:, np.newaxis]
)
def homogenize(self, other, maxdiff=1):
""" Return overlapping part of self and other as (self, other) tuple.
Homogenize intensities so that the images can be used with
combine_frequencies. Note that this works best when most of the
picture is signal, so use :py:meth:`in_interval` to select the subset
of your image before applying this method.
Parameters
----------
other : CallistoSpectrogram
Spectrogram to be homogenized with the current one.
maxdiff : float
Threshold for which frequencies are considered equal.
"""
one, two = self._overlap(other)
pairs_indices, factors, constants = one._homogenize_params(
two, maxdiff)
# XXX: Maybe (xd.freq_axis[x] + yd.freq_axis[y]) / 2.
pairs_freqs = [one.freq_axis[x] for x, y in pairs_indices]
# XXX: Extrapolation does not work this way.
# XXX: Improve.
f1 = np.polyfit(pairs_freqs, factors, 3)
f2 = np.polyfit(pairs_freqs, constants, 3)
return (one, two * polyfun_at(f1, two.freq_axis)[:, np.newaxis] +
polyfun_at(f2, two.freq_axis)[:, np.newaxis])
def test_polyfun_at():
"""
This should evaluate the polynomial x^3 + 5x^2 - 6x + 3 at x = 5.
"""
coeff = [1, 5, -6, 3]
assert util.polyfun_at(coeff, 5) == 223
def test_polyfun_at():
"""
This should evaluate the polynomial x^3 + 5x^2 - 6x + 3 at x = 5.
"""
coeff = [1, 5, -6, 3]
assert util.polyfun_at(coeff, 5) == 223
