def autocorrs(self, clusters=None, bin_width=0.0015, limit=0.03,
figsize=None):
""" Plots of autocorrelations of clustered spike times.
**Keywords**:
*clusters*: list or iterable
List of clusters to plot
*bin_width*: float
Width of bins in the autocorrelation calculation
*limit*: float
Time limit over which to calculate the autocorrelation
"""
cls = self.clusters.select(clusters)
cl_times = cls.times()
colors = plt.get_colors(max(self.clusters.keys()) + 1, self.cm)
fig, axes = plt.generate_axes(len(cls), 4, num=3, figsize=figsize,
sharex=True)
for ax, cl in zip(axes, cl_times):
ax.clear()
tstamps = cl_times[cl]
plt.autocorr(tstamps, ax=ax, color=colors[cl],
bin_width=bin_width, limit=limit)
ax.set_title('Cluster {}'.format(cl))
ax.set_xlabel('Lag (ms)')
fig.tight_layout()
return fig
def spikes(self, clusters=None, limit=50, figsize=None):
""" Generates plots of clustered spike waveforms.
"""
cls = self.clusters.select(clusters)
cl_spikes = cls.spikes()
colors = plt.get_colors(max(self.clusters.keys()) + 1, self.cm)
fig, axes = plt.generate_axes(len(cls),
4,
num=2,
sharex=True,
figsize=figsize)
for ax, cl in zip(axes, cl_spikes):
ax.clear()
spks = cl_spikes[cl]
plt.spikes(spks,
ax=ax,
color=colors[cl],
patch_size=spks.shape[1] / 4)
ax.set_title('Cluster {}'.format(cl))
ax.set_ylabel('Voltage (uV)')
ax.set_xticklabels('')
fig.tight_layout()
return fig
def scatter(self,
clusters=None,
components=[1, 2, 3],
limit=500,
figsize=(16, 5),
s=5):
""" Generates a scatter plot in feature space of the clustered data.
"""
from scipy.misc import comb
from itertools import combinations
components = [c - 1 for c in components]
feats, col_array = self._scatter_helper(clusters, limit)
N_plots = int(comb(len(components), 2, exact=True))
fig, axes = plt.generate_axes(N_plots, ncols=3, num=1, figsize=figsize)
for ax, (x, y) in zip(axes, combinations(components, 2)):
ax.clear() # Clear the axes before replotting
plt.scatter(feats[:, x], feats[:, y], colors=col_array, ax=ax, s=s)
ax.set_xlabel("Component {}".format(x + 1))
ax.set_ylabel("Component {}".format(y + 1))
fig.tight_layout()
return fig
def feature_trace(self, dimension, clusters=None, marker='o', color='k', xlims=(0,4000), figsize=(9,6)):
clusters = self.clusters.select(clusters)
fig, axes = plt.generate_axes(len(clusters), 2, figsize=figsize)
for cl, ax in zip(clusters, axes):
plt.feature_trace(clusters[cl]['feats'][:, dimension],
clusters[cl]['times'],
ax=ax, color=color, marker=marker, xlims=xlims)
ax.set_title('Cluster {}'.format(cl))
fig.tight_layout()
return fig
def timestamps(self, clusters=None, color='k', xlims=(0,4000), figsize=(9,6)):
""" Plot the timestamps for clusters. """
clusters = self.clusters.select(clusters)
times = clusters.times()
fig, axes = plt.generate_axes(len(clusters), 2, figsize=figsize)
for cl, ax in zip(times, axes):
plt.timestamps(times[cl], ax=ax, color=color, xlims=xlims)
ax.set_title('Cluster {}'.format(cl))
fig.tight_layout()
return fig
def timestamps(self,
clusters=None,
color='k',
xlims=(0, 4000),
figsize=(9, 6)):
""" Plot the timestamps for clusters. """
clusters = self.clusters.select(clusters)
times = clusters.times()
fig, axes = plt.generate_axes(len(clusters), 2, figsize=figsize)
for cl, ax in zip(times, axes):
plt.timestamps(times[cl], ax=ax, color=color, xlims=xlims)
ax.set_title('Cluster {}'.format(cl))
fig.tight_layout()
return fig
def scatter(self, clusters=None, components=[1,2,3], limit=500, figsize=(16,5), s=5):
""" Generates a scatter plot in feature space of the clustered data.
"""
from scipy.misc import comb
from itertools import combinations
components = [ c-1 for c in components ]
feats, col_array = self._scatter_helper(clusters, limit)
N_plots = int(comb(len(components), 2, exact=True))
fig, axes = plt.generate_axes(N_plots, ncols=3, num=1, figsize=figsize)
for ax, (x,y) in zip(axes,combinations(components,2)):
ax.clear() # Clear the axes before replotting
plt.scatter(feats[:,x], feats[:,y], colors=col_array, ax=ax, s=s)
ax.set_xlabel("Component {}".format(x+1))
ax.set_ylabel("Component {}".format(y+1))
fig.tight_layout()
return fig
def feature_trace(self,
dimension,
clusters=None,
marker='o',
color='k',
xlims=(0, 4000),
figsize=(9, 6)):
clusters = self.clusters.select(clusters)
fig, axes = plt.generate_axes(len(clusters), 2, figsize=figsize)
for cl, ax in zip(clusters, axes):
plt.feature_trace(clusters[cl]['feats'][:, dimension],
clusters[cl]['times'],
ax=ax,
color=color,
marker=marker,
xlims=xlims)
ax.set_title('Cluster {}'.format(cl))
fig.tight_layout()
return fig
def spikes(self, clusters=None, limit=50, figsize=None):
""" Generates plots of clustered spike waveforms.
"""
cls = self.clusters.select(clusters)
cl_spikes = cls.spikes()
colors = plt.get_colors(max(self.clusters.keys()) + 1, self.cm)
fig, axes = plt.generate_axes(len(cls), 4, num=2, sharex=True,
figsize=figsize)
for ax, cl in zip(axes, cl_spikes):
ax.clear()
spks = cl_spikes[cl]
plt.spikes(spks, ax=ax, color=colors[cl], patch_size=spks.shape[1]/4)
ax.set_title('Cluster {}'.format(cl))
ax.set_ylabel('Voltage (uV)')
ax.set_xticklabels('')
fig.tight_layout()
return fig
def autocorrs(self,
clusters=None,
bin_width=0.0015,
limit=0.03,
figsize=None):
""" Plots of autocorrelations of clustered spike times.
**Keywords**:
*clusters*: list or iterable
List of clusters to plot
*bin_width*: float
Width of bins in the autocorrelation calculation
*limit*: float
Time limit over which to calculate the autocorrelation
"""
cls = self.clusters.select(clusters)
cl_times = cls.times()
colors = plt.get_colors(max(self.clusters.keys()) + 1, self.cm)
fig, axes = plt.generate_axes(len(cls),
4,
num=3,
figsize=figsize,
sharex=True)
for ax, cl in zip(axes, cl_times):
ax.clear()
tstamps = cl_times[cl]
plt.autocorr(tstamps,
ax=ax,
color=colors[cl],
bin_width=bin_width,
limit=limit)
ax.set_title('Cluster {}'.format(cl))
ax.set_xlabel('Lag (ms)')
fig.tight_layout()
return fig
