def test_merge():
"""
This should return a sorted (from greatest to least) merged list
from list1 and list2.
"""
list1 = [13, 11, 9, 7, 5, 3, 1]
list2 = [14, 12, 10, 8, 6, 4, 2]
result = list(util.merge([list1, list2]))
assert result[::-1] == sorted(result)
assert list(util.merge([[], [1], []])) == [1]
def test_merge():
"""
This should return a sorted (from greatest to least) merged list
from list1 and list2.
"""
list1 = [13, 11, 9, 7, 5, 3, 1]
list2 = [14, 12, 10, 8, 6, 4, 2]
result = list(util.merge([list1, list2]))
assert result[::-1] == sorted(result)
assert list(util.merge([[], [1], []])) == [1]
def combine_frequencies(cls, specs):
""" Return new spectrogram that contains frequencies from all the
spectrograms in spec. Only returns time intersection of all of them.
Parameters
----------
spec : list
List of spectrograms of which to combine the frequencies into one.
"""
if not specs:
raise ValueError("Need at least one spectrogram.")
specs = cls.intersect_time(specs)
one = specs[0]
dtype_ = max(sp.dtype for sp in specs)
fsize = sum(sp.shape[0] for sp in specs)
new = np.zeros((fsize, one.shape[1]), dtype=dtype_)
freq_axis = np.zeros((fsize,))
for n, (data, row) in enumerate(merge(
[
[(sp, n) for n in xrange(sp.shape[0])] for sp in specs
],
key=lambda x: x[0].freq_axis[x[1]]
)):
new[n, :] = data[row, :]
freq_axis[n] = data.freq_axis[row]
params = {
'time_axis': one.time_axis, # Should be equal
'freq_axis': freq_axis,
'start': one.start,
'end': one.end,
't_delt': one.t_delt,
't_init': one.t_init,
't_label': one.t_label,
'f_label': one.f_label,
'content': one.content,
'instruments': _union(spec.instruments for spec in specs)
}
return common_base(specs)(new, **params)
def combine_frequencies(cls, specs):
""" Return new spectrogram that contains frequencies from all the
spectrograms in spec. Only returns time intersection of all of them.
Parameters
----------
spec : list
List of spectrograms of which to combine the frequencies into one.
"""
if not specs:
raise ValueError("Need at least one spectrogram.")
specs = cls.intersect_time(specs)
one = specs[0]
dtype_ = max(sp.dtype for sp in specs)
fsize = sum(sp.shape[0] for sp in specs)
new = np.zeros((fsize, one.shape[1]), dtype=dtype_)
freq_axis = np.zeros((fsize, ))
for n, (data, row) in enumerate(
merge([[(sp, n) for n in xrange(sp.shape[0])] for sp in specs],
key=lambda x: x[0].freq_axis[x[1]])):
new[n, :] = data[row, :]
freq_axis[n] = data.freq_axis[row]
params = {
'time_axis': one.time_axis, # Should be equal
'freq_axis': freq_axis,
'start': one.start,
'end': one.end,
't_delt': one.t_delt,
't_init': one.t_init,
't_label': one.t_label,
'f_label': one.f_label,
'content': one.content,
'instruments': _union(spec.instruments for spec in specs)
}
return common_base(specs)(new, **params)