def flatFiringRate(FR,
times,
labely=None,
labelx=None,
units="ms",
titleStr="",
nticks=2):
globalAxesSettings(mpl.gca())
N = len(FR) # no. of rows == no. of neurons
T, N_id = np.meshgrid(times, np.arange(N))
pcolormesh(T, N_id, FR)
loc = LinearLocator(nticks)
loc.tick_values(0, N - 1)
mpl.gca().yaxis.set_major_locator(loc)
if (labelx is None):
xlabel("Time (%s)" % units)
elif (labelx != ""):
xlabel(labelx)
if (labely is None):
ylabel("Neuron #")
elif (labely != ""):
ylabel(labely)
if (titleStr != ""):
title(titleStr)
axis('tight')
createColorbar(mpl.gca(), data=FR, label='Firing rate (Hz)')
tight_layout()
def scale_radar(array, n=6, kind='max'):
from matplotlib.ticker import MaxNLocator, LinearLocator
if array.ndim == 1:
array = np.atleast_2d(array).T
if kind == 'linear':
locator = LinearLocator(n)
else:
locator = MaxNLocator(n)
tvalues = ([
locator.tick_values(*aminmax)
for aminmax in zip(array.min(axis=0), array.max(axis=0))
])
aminmax = np.vstack((arr.min(), arr.max()) for arr in tvalues)
amins = aminmax[:, 0]
aptps = aminmax.ptp(axis=1)
ticks = np.vstack(tvalue[:n] for tvalue in tvalues)
arrays = (array - amins) / aptps
return arrays, ticks
