def _plot(self):
feats = self._get_features()
labels = self.cluster_src.labels
if self.show_cells =='all':
show_labels = list(np.unique(labels))
if 0 in show_labels: show_labels.remove(0)
else:
show_labels = self.show_cells
data_range = None if self._autoscale else [0,1]
plotting.plot_features(feats, labels, show_cells=show_labels,
datarange=data_range, fig=self.fig)
def _plot(self):
feats = self._get_features()
labels = self.cluster_src.labels
if self.show_cells == 'all':
show_labels = list(np.unique(labels))
if 0 in show_labels: show_labels.remove(0)
else:
show_labels = self.show_cells
data_range = None if self._autoscale else [0, 1]
plotting.plot_features(feats,
labels,
show_cells=show_labels,
datarange=data_range,
fig=self.fig)
#!/usr/bin/env python
#coding=utf-8
from spike_sort.io.filters import PyTablesFilter
from spike_sort import extract
from spike_sort import features
from spike_sort import cluster
from spike_sort.ui import plotting
import os
dataset = '/SubjectA/session01/el1'
datapath = '../../../data/tutorial.h5'
io_filter = PyTablesFilter(datapath)
raw = io_filter.read_sp(dataset)
spt = extract.detect_spikes(raw, contact=3, thresh='auto')
sp_win = [-0.2, 0.8]
spt = extract.align_spikes(raw, spt, sp_win, type="max", resample=10)
sp_waves = extract.extract_spikes(raw, spt, sp_win)
sp_feats = features.combine(
(features.fetP2P(sp_waves), features.fetPCs(sp_waves)))
clust_idx = cluster.cluster("gmm", sp_feats, 4)
plotting.plot_features(sp_feats, clust_idx)
plotting.show()
io_filter.close()
#!/usr/bin/env python
# -*- coding: utf-8 -*-
from spike_sort.io.filters import PyTablesFilter
from spike_sort import extract
from spike_sort import features
from spike_sort import cluster
from spike_sort.ui import plotting
import os
dataset = '/SubjectA/session01/el1'
datapath = '../../../data/tutorial.h5'
io_filter = PyTablesFilter(datapath)
raw = io_filter.read_sp(dataset)
spt = extract.detect_spikes(raw, contact=3, thresh='auto')
sp_win = [-0.2, 0.8]
spt = extract.align_spikes(raw, spt, sp_win, type="max", resample=10)
sp_waves = extract.extract_spikes(raw, spt, sp_win)
sp_feats = features.combine(
(
features.fetP2P(sp_waves),
features.fetPCA(sp_waves)
)
)
plotting.plot_features(sp_feats)
plotting.show()
# -*- coding: utf-8 -*-
from spike_sort.io.filters import PyTablesFilter
from spike_sort import extract
from spike_sort import features
from spike_sort import cluster
from spike_sort.ui import plotting
import os
dataset = '/SubjectA/session01/el1'
datapath = '../../../data/tutorial.h5'
io_filter = PyTablesFilter(datapath)
raw = io_filter.read_sp(dataset)
spt = extract.detect_spikes(raw, contact=3, thresh='auto')
sp_win = [-0.2, 0.8]
spt = extract.align_spikes(raw, spt, sp_win, type="max", resample=10)
sp_waves = extract.extract_spikes(raw, spt, sp_win)
sp_feats = features.combine(
(
features.fetP2P(sp_waves),
features.fetPCA(sp_waves)
)
)
clust_idx = cluster.cluster("gmm",sp_feats,4)
plotting.plot_features(sp_feats, clust_idx)
plotting.show()
io_filter.close()