code stringlengths 3 6.57k |
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np.loadtxt(fname, dtype=np.float32) |
np.int32(data[:, 2:]) |
print('Parsed %r' % fname) |
parse_charlies_output(self, fname=None) |
np.loadtxt(fname, dtype=int) |
write_spc_app_input(self) |
self.get_spikes_sortedby('id') |
self.get_component_matrix() |
open(r'C:\home\mspacek\Desktop\Work\SPC\Weizmann\spc_app\spc_app_input.txt', 'w') |
f.write('AFFX\tNAME\t') |
f.write('s%d\t' % spike['id']) |
f.write('\n') |
enumerate(['Vpp', 'dt', 'x0', 'y0', 'sx', 'sy', 'theta']) |
f.write(param+'\t'+param+'\t') |
f.write('%f\t' % val) |
f.write('\n') |
f.close() |
hcluster(self, t=1.0) |
distrib (dt, s1, s2) |
self.get_spikes_sortedby('id') |
self.get_component_matrix() |
print(X) |
fclusterdata(X, t=t, method='single', metric='euclidean') |
self.get_ids(cids, spikes) |
export2Charlie(self, fname='spike_data', onlymaxchan=False, nchans=3, npoints=32) |
datapoints
(1/4, 3/4 wrt spike time) |
np.log2(npoints) |
update_wave(self.stream) |
intround(self.spikes[0].wave.data.shape[-1] / 4) |
np.empty(dims, dtype=np.float32) |
np.arange(len(dm.data) |
np.asarray(dm.coords) |
list(self.spikes) |
sids.sort() |
np.asarray([chani]) |
d2s.argsort() |
spike.update_wave(self.stream) |
np.zeros(data.shape[-1], data.dtype) |
row.extend(data[ti-npoints/4:ti+npoints*3/4]) |
str(datetime.datetime.now() |
dt.split('.') |
dt.replace(' ', '_') |
dt.replace(':', '.') |
np.savetxt(fname, output, fmt='%.1f', delimiter=' ') |
match(self, templates=None, weighting='signal', sort=True) |
disk.
(First match is slow, subsequent ones are ~ 15X faster.) |
sum(err**2) |
abs(template_signal) |
self.templates.values() |
sys.stdout.write('matching') |
time.time() |
len(self.spikes) |
template.get_stdev() |
self.spikes.values() |
spike.update_wave(tw) |
np.asarray([templatewave.data, spikewave.data]) |
np.abs(tsdata) |
max(axis=0) |
sum(axis=None) |
template.err.append((spike.id, intround(err) |
np.asarray(template.err, dtype=np.int64) |
len(template.err) |
argsort() |
sys.stdout.write('.') |
print('\nmatch took %.3f sec' % (time.time() |
Neuron(object) |
__init__(self, sort, id=None) |
WaveForm() |
np.array([], dtype=int) |
get_chans(self) |
self.update_wave() |
property(get_chans) |
get_chan(self) |
self.update_wave() |
self.wave.data.ptp(axis=1) |
argmax() |
property(get_chan) |
get_nspikes(self) |
len(self.sids) |
property(get_nspikes) |
__getstate__(self) |
self.__dict__.copy() |
d.pop('X', None) |
d.pop('Xhash', None) |
get_wave(self) |
self.update_wave() |
self.update_wave() |
update_wave(self) |
len(self.sids) |
RuntimeError("n%d has no spikes and its waveform can't be updated" % self.id) |
sort.get_mean_wave(self.sids, nid=self.id) |
sort.twts.copy() |
__sub__(self, other) |
getCommonWaveData(self, otherchan, otherchans, otherwavedata) |
np.intersect1d(self.chans, otherchans, assume_unique=True) |
len(chans) |
ValueError('No common chans') |
ValueError("maxchans aren't part of common chans") |
self.chans.searchsorted(chans) |
otherchans.searchsorted(chans) |
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