def test_selectivity():
Selectivity = namedtuple("Selectivity",
("best_frequency", "spread", "gain"))
# center, spread for each unit, starting at 440 Hz A and 3 semitones up from there
s_units = [
Selectivity(music.key_to_frequency(49), 1, 0.8),
Selectivity(music.key_to_frequency(52), 1, 0.8)
]
stim = ABAStimulus()
network = TonotopicNetwork(s_units, stim)
network.update_all(10)
assert network.units[0].r > network.units[1].r
def test_build_stimulus_currents():
stim = ABAStimulus()
# center, spread for each unit, starting at 440 Hz A and 3 semitones up from there
s_units = [
Selectivity(music.key_to_frequency(49), 1, 0.6),
Selectivity(music.key_to_frequency(46), 1, 0.6)
]
stim = ABAStimulus(a_semitone=49, df=3)
network = SynapticNetwork(selectivities=s_units, stimulus=stim)
out = network.build_stimulus_currents()
assert out[0][0] > out[1][0]
assert out[0][50] < out[1][50]
def test_pars_list():
Selectivity = namedtuple("Selectivity",
("best_frequency", "spread", "gain"))
pars_list = [{"tau": 100., "the": .4}, None]
# center, spread for each unit, starting at 440 Hz A and 3 semitones up from there
s_units = [
Selectivity(music.key_to_frequency(49), 1.1, 0.8),
Selectivity(music.key_to_frequency(49), 1, 0.8)
]
stim = ABAStimulus()
network = TonotopicNetwork(s_units, stim, pars_list=pars_list)
network.update_all(10)
# print(network.units[0].__dict__)
# print(network.units[1].__dict__)
assert network.units[0].r < network.units[1].r
def __init__(self, name=None, best_frequency=440., spread=4., **kwargs):
"""
all the same things as WCUnit, but also has a bf and selectivity
Args:
best_frequency (float): the frequency (in hz) that best excites the cell
spread (float): inverse of selectivity, in terms of semitones to standard deviations
**kwargs:
"""
if not name:
name = "S" + str(best_frequency) + "-" + str(spread)
WCUnit.__init__(self, name=name, **kwargs)
key = music.frequency_to_key(best_frequency)
if music.key_to_frequency(key) != best_frequency:
print(
"warning- cell's response has been shifted to nearest semitone"
)
self.best_frequency = best_frequency
self.spread = spread
self.tuning_curve = self.fq_tuning_curve()
def test_key_to_frequency(self):
key = 49
self.assertEqual(440, utility.key_to_frequency(key))
ndf["tax"] = sim.tax
ulst.append(ndf)
df_out = pd.concat(ulst)
return df_out
if __name__ == '__main__':
tic = datetime.datetime.now()
stim = ABAStimulus()
Selectivity = namedtuple("Selectivity", ("best_frequency", "spread", "gain"))
# center, spread for each unit, starting at 440 Hz A and 3 semitones up from there
s_units = [
Selectivity(music.key_to_frequency(49), 1, 0.6),
Selectivity(music.key_to_frequency(52), 1, 0.6)
]
stim = ABAStimulus(a_semitone=49, df=3)
network = TonotopicNetwork(s_units, stim)
sim = TonotopicTripletsSimulation(network=network)
sim.run()
data = sim.traces_to_df()
g = sns.FacetGrid(data, col='unit', col_wrap=1)
g.map_dataframe(plot_generic_traces)
# f1 = generic_plot(sim.tax, np.array(sim.traces.values()))
toc = datetime.datetime.now()
print (toc - tic).microseconds / 10e6
# #plt.show()
def test_key_to_frequency(self):
key = 49
self.assertEqual(440, utility.key_to_frequency(key))
def test_fq_tuning_curve():
actual = fq_tuning_curve()
assert actual[49] == 1
assert music.key_to_frequency(49) == 440
def test_weird_fq_tuning_curve():
# note: it always goes to the nearest semitone
actual = fq_tuning_curve(num_tones=10, center=442.42)
assert actual[49] == 1
assert music.key_to_frequency(49) == 440