def crosscorrs(self, clusters=None, bin_width=0.0015, limit=0.03,
figsize=(9,5)):
""" Plots of cross-correlations of clustered spike times. """
times = self.clusters.select(clusters).times()
colors = plt.get_colors(max(self.clusters.keys()) + 1, self.cm)
# Set the number of rows and columns to plot
n_rows, n_cols = [len(times)]*2
fig, axes = plt.generate_crosscorr_axes(n_rows, n_cols, num=4,
figsize=figsize)
for (ii, jj) in axes:
ax = axes[(ii,jj)]
cl1, cl2 = times.keys()[ii], times.keys()[jj]
t1, t2 = times[cl1], times[cl2]
# Get the cross-correlation for different clusters
if ii != jj:
plt.crosscorr(t1, t2, ax=ax, bin_width=bin_width, limit=limit)
ax.set_xticklabels('')
ax.set_yticklabels('')
# If cluster 1 is the same as cluster 2, get the autocorrelation
else:
plt.autocorr(t1, ax=ax, color=colors[cl1],
bin_width=bin_width, limit=limit)
ax.set_ylabel('{}'.format(cl1))
ax.set_xticklabels('')
ax.set_yticklabels('')
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
def crosscorrs(self,
clusters=None,
bin_width=0.0015,
limit=0.03,
figsize=(9, 5)):
""" Plots of cross-correlations of clustered spike times. """
times = self.clusters.select(clusters).times()
colors = plt.get_colors(max(self.clusters.keys()) + 1, self.cm)
# Set the number of rows and columns to plot
n_rows, n_cols = [len(times)] * 2
fig, axes = plt.generate_crosscorr_axes(n_rows,
n_cols,
num=4,
figsize=figsize)
for (ii, jj) in axes:
ax = axes[(ii, jj)]
cl1, cl2 = times.keys()[ii], times.keys()[jj]
t1, t2 = times[cl1], times[cl2]
# Get the cross-correlation for different clusters
if ii != jj:
plt.crosscorr(t1, t2, ax=ax, bin_width=bin_width, limit=limit)
ax.set_xticklabels('')
ax.set_yticklabels('')
# If cluster 1 is the same as cluster 2, get the autocorrelation
else:
plt.autocorr(t1,
ax=ax,
color=colors[cl1],
bin_width=bin_width,
limit=limit)
ax.set_ylabel('{}'.format(cl1))
ax.set_xticklabels('')
ax.set_yticklabels('')
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
