def _prepare(self):
if len(PUnitPhases()) != len(self):
df = pd.DataFrame(PUnitPhases().fetch())
df['phase'] = [circ.mean(e) for e in df.phases]
df['jitter'] = [circ.std(ph) for ph in df.phases]
self.insert([e.to_dict() for _, e in df.ix[:, ('fish_id', 'cell_id', 'phase', 'jitter')].iterrows()],
skip_duplicates=True)
def test_std():
data = np.array([
1.80044838, 2.02938314, 1.03534016, 4.84225057, 1.54256458, 5.19290675,
2.18474784, 4.77054777, 1.51736933, 0.72727580
])
s = pycircstat.std(data)
assert_allclose(1.4657, s, atol=0.001, rtol=0.001)
def _prepare(self):
if len(PUnitPhases()) != len(self):
df = pd.DataFrame(PUnitPhases().fetch())
df['phase'] = [circ.mean(e) for e in df.phases]
def center(x):
x['phase'] = circ.center(x.phase)
return x
df = df.groupby('fish_id').apply(center)
df['jitter'] = [circ.std(ph) for ph in df.phases]
self.insert([e.to_dict() for _, e in df.ix[:, ('fish_id', 'cell_id', 'phase', 'jitter')].iterrows()],
skip_duplicates=True)
def plot(self):
# plot mean phase of spikes to show that they are fish dependent
df = pd.DataFrame(self.fetch())
df['eod'] = [1 / np.median(np.diff(e)) for e in df.eod_times]
df['cmean'] = [circ.mean(e) for e in df.phases]
df['jitter'] = [circ.std(ph) / 2 / np.pi / e for ph, e in zip(df.phases, df.eod)]
model = ols('cmean ~ C(fish_id)', data=df).fit()
table = sm.stats.anova_lm(model)
print(table)
sns.factorplot('fish_id', 'cmean', data=df, kind='bar')
g = sns.pairplot(df.ix[:, ('cmean', 'jitter', 'fish_id')], hue='fish_id')
plt.show()
def _make_tuples(self, key):
print('Processing', key['cell_id'])
sampling_rate, eod = (Baseline() & key).fetch1['samplingrate', 'eod']
dt = 1. / sampling_rate
trials = Baseline.LocalEODPeaksTroughs() * Baseline.SpikeTimes() & key
aggregated_spikes = np.hstack([s / 1000 - p[0] * dt for s, p in zip(*trials.fetch['times', 'peaks'])])
aggregated_spikes %= 1 / eod
aggregated_spikes *= eod * 2 * np.pi # normalize to 2*pi
key['base_var'], key['base_mean'], key['base_std'] = \
circ.var(aggregated_spikes), circ.mean(aggregated_spikes), circ.std(aggregated_spikes)
self.insert1(key)
def _make_tuples(self, key):
print('Processing', key['cell_id'])
sampling_rate, eod = (Baseline() & key).fetch1('samplingrate', 'eod')
dt = 1. / sampling_rate
trials = Baseline.LocalEODPeaksTroughs() * Baseline.SpikeTimes() & key
aggregated_spikes = np.hstack([
s / 1000 - p[0] * dt
for s, p in zip(*trials.fetch('times', 'peaks'))
])
aggregated_spikes %= 1 / eod
aggregated_spikes *= eod * 2 * np.pi # normalize to 2*pi
key['base_var'], key['base_mean'], key['base_std'] = \
circ.var(aggregated_spikes), circ.mean(aggregated_spikes), circ.std(aggregated_spikes)
self.insert1(key)
def _make_tuples(self, key):
print('Processing', key['cell_id'], 'run', key['run_id'], )
if SecondOrderSignificantPeaks() & dict(key, eod_coeff=1, stimulus_coeff=0, baseline_coeff=0, refined=1):
eod, vs = (SecondOrderSignificantPeaks() & dict(key, eod_coeff=1, stimulus_coeff=0, baseline_coeff=0,
refined=1)).fetch1['frequency', 'vector_strength']
elif SecondOrderSignificantPeaks() & dict(key, eod_coeff=1, stimulus_coeff=0, baseline_coeff=0, refined=0):
eod, vs = (SecondOrderSignificantPeaks() & dict(key, eod_coeff=1, stimulus_coeff=0, baseline_coeff=0,
refined=0)).fetch1['frequency', 'vector_strength']
else:
eod = (Runs() & key).fetch1['eod']
aggregated_spikes = np.hstack(TrialAlign().load_trials(key))
aggregated_spikes %= 1 / eod
aggregated_spikes *= eod * 2 * np.pi # normalize to 2*pi
if len(aggregated_spikes) > 1:
key['stim_var'], key['stim_mean'], key['stim_std'] = \
circ.var(aggregated_spikes), circ.mean(aggregated_spikes), circ.std(aggregated_spikes)
self.insert1(key)
def _make_tuples(self, key):
print('Processing', key['cell_id'], 'run', key['run_id'])
if SecondOrderSignificantPeaks() & dict(
key, eod_coeff=1, stimulus_coeff=0, baseline_coeff=0,
refined=1):
eod, vs = (SecondOrderSignificantPeaks()
& dict(key,
eod_coeff=1,
stimulus_coeff=0,
baseline_coeff=0,
refined=1)).fetch1('frequency',
'vector_strength')
elif SecondOrderSignificantPeaks() & dict(
key, eod_coeff=1, stimulus_coeff=0, baseline_coeff=0,
refined=0):
eod, vs = (SecondOrderSignificantPeaks()
& dict(key,
eod_coeff=1,
stimulus_coeff=0,
baseline_coeff=0,
refined=0)).fetch1('frequency',
'vector_strength')
else:
eod = (Runs() & key).fetch1('eod')
aggregated_spikes = TrialAlign().load_trials(key)
if len(aggregated_spikes) == 0:
warn('TrialAlign returned no spikes. Skipping')
return
else:
aggregated_spikes = np.hstack(aggregated_spikes)
aggregated_spikes %= 1 / eod
aggregated_spikes *= eod * 2 * np.pi # normalize to 2*pi
if len(aggregated_spikes) > 1:
key['stim_var'], key['stim_mean'], key['stim_std'] = \
circ.var(aggregated_spikes), circ.mean(aggregated_spikes), circ.std(aggregated_spikes)
self.insert1(key)
def load_spikes(self, key, centered=True, plot=False):
if centered:
Phases = (RandomTrials.PhaseSet() * CenteredPUnitPhases()).project('phase', phase_cell='cell_id')
else:
Phases = (RandomTrials.PhaseSet() * UncenteredPUnitPhases()).project('phase', phase_cell='cell_id')
trials = Runs.SpikeTimes() * RandomTrials.TrialSet() * Phases * TrialAlign() & key
times, phase, align_times = trials.fetch['times', 'phase', 't0']
dt = 1. / (Runs() & trials).fetch1['samplingrate']
eod, duration = (Runs() & trials).fetch1['eod', 'duration']
rad2period = 1 / 2 / np.pi / eod
# get spikes, convert to s, align to EOD, add bootstrapped phase
print('Phase std', circ.std(phase), 'Centered', centered)
if plot:
figdir = 'figures/sanity/pyr_lif_stimulus/'
mkdir(figdir)
fig, ax = plt.subplots(2, 1, sharex=True)
spikes = [s / 1000 - t0 for s, t0 in zip(times, align_times)]
for i, s in enumerate(spikes):
ax[0].plot(s, 0 * s + i, '.k', ms=1)
ax[0].set_title('without phase variation')
spikes = [s / 1000 - t0 + ph * rad2period for s, t0, ph in zip(times, align_times, phase)]
for i, s in enumerate(spikes):
ax[1].plot(s, 0 * s + i, '.k', ms=1)
ax[1].set_title('with phase variation')
fig.savefig(figdir +
'alignments_{n_total}_{pyr_simul_id}_{repeat_id}_{centered}.pdf'.format(centered=centered, **key))
spikes = [s / 1000 - t0 + ph * rad2period for s, t0, ph in zip(times, align_times, phase)]
return spikes, dt, eod, duration
def test_std():
data = np.array([1.80044838, 2.02938314, 1.03534016, 4.84225057,
1.54256458, 5.19290675, 2.18474784,
4.77054777, 1.51736933, 0.72727580])
s = pycircstat.std(data)
assert_allclose(1.4657, s, atol=0.001, rtol=0.001)