Python plot_featuresの例

コード例 #1

ファイル: components.py プロジェクト: belevtsoff/SpikeSort

 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)

コード例 #2

ファイル: components.py プロジェクト: nickponvert/SpikeSort

 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)

コード例 #3

ファイル: tutorial_clusters.py プロジェクト: nickponvert/SpikeSort

#!/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()

コード例 #4

ファイル: tutorial_features.py プロジェクト: mxhit-j/SpikeSort

#!/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()

コード例 #5

ファイル: tutorial_clusters.py プロジェクト: btel/SpikeSort

# -*- 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()