def test_minimal_pairs():
"""
This should return the pairs of elements from list1 and list2 with
minimal difference between their values.
"""
list1 = [0, 5, 10, 15, 20, 25]
list2 = [3, 12, 19, 21, 26, 29]
assert list(minimal_pairs(list1, list2)) == [(1, 0, 2), (2, 1, 2),
(4, 2, 1), (5, 4, 1)]
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 : `radiospectra.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 test_closest():
assert (list(minimal_pairs([50, 60],
[0, 10, 20, 30, 40, 51, 52])) == [(0, 5, 1),
(1, 6, 8)])
def test_minimum_pairs_end_diff():
assert (list(minimal_pairs([0, 1, 2, 8], [1, 2, 3, 4])) == [(1, 0, 0),
(2, 1, 0),
(3, 3, 4)])
def test_minimum_pairs_commotative():
A = [0, 1, 2]
B = [1, 2, 3]
first = list(minimal_pairs(A, B))
assert first == [(b, a, d) for a, b, d in minimal_pairs(B, A)]
