def signalPlot(t, sig, ax, timeUnits="ms", nticks=3, nThetaTicks=None, **kw):
'''
Plot a customized signal plot into an axis.
Parameters
----------
t : numpy array
Time array
sig : numpy array
Signal values
ax : matplotlib.axes object
Axes to plot into
xlabel : string
X label
ylabel : string
Y label
timeUnits : string
Time units to be appended to xlabe
leg : list of strings or None
Legend to print. If None, do not print any legend
nticks : int
Number of ticks on the yaxis
'''
xlabel = kw.pop('xlabel', 'Time (%s)' % timeUnits)
ylabel = kw.pop('ylabel', '')
ylabelPos = kw.pop('ylabelPos', -0.22)
xmargin = kw.pop('xmargin', 0)
zeroLine = kw.pop('zeroLine', True)
plt.hold('on')
globalAxesSettings(ax)
if nThetaTicks is not None:
xticks = np.linspace(t[0], t[-1], nThetaTicks)
ax.set_xticks(xticks)
plt.grid(b=True, which='major', axis='x')
else:
ax.xaxis.set_major_locator(ti.MaxNLocator(4))
ax.yaxis.set_major_locator(ti.MaxNLocator(nticks-1))
ax.xaxis.set_minor_locator(ti.AutoMinorLocator(2))
ax.plot(t, sig, **kw)
ax.set_xlabel(xlabel)
ax.text(ylabelPos, 0.5, ylabel, va='center', ha='center',
transform=ax.transAxes, rotation=90)
ax.spines['top'].set_visible(False)
ax.spines['right'].set_visible(False)
w = t[-1] - t[0]
ax.set_xlim([t[0] - xmargin*w, t[-1] + xmargin*w])
if (zeroLine):
color = rcp['grid.color']
ls = rcp['grid.linestyle']
lw = rcp['grid.linewidth']
alphsa = rcp['grid.alpha']
ax.axhline(0, ls=ls, lw=lw, color=color)
def plotConnHistogram(val, **kw):
'''
Plot a histogram of connection weights with some predetermined formatting.
Parameters
----------
val : numpy array
A 1D array of connetions.
**kw
Keyword arguments to the hist function. See code for some additional
keyword arguments.
'''
# keyword arguments
kw['bins'] = kw.get('bins', 20)
#kw['edgecolor'] = kw.get('edgecolor', 'none')
ax = kw.pop('ax', plt.gca())
xlabel = kw.pop('xlabel', 'g (nS)')
ylabel = kw.pop('ylabel', 'Count')
title = kw.pop('title', '')
locators = kw.pop('locators', {})
ylabelPos = kw.pop('ylabelPos', -0.2)
globalAxesSettings(ax)
n, _, _ = ax.hist(val, **kw)
print(np.max(n))
ax.set_xlabel(xlabel)
ax.text(ylabelPos,
0.5,
ylabel,
rotation=90,
transform=ax.transAxes,
va='center',
ha='right')
ax.set_title(title)
# tick formatting
x_major = locators.get('x_major', ti.MaxNLocator(3))
x_minor = locators.get('x_minor', ti.AutoMinorLocator(2))
y_major = locators.get('y_major', ti.LinearLocator(2))
y_minor = locators.get('y_minor', ti.AutoMinorLocator(4))
ax.xaxis.set_major_locator(x_major)
ax.yaxis.set_major_locator(y_major)
ax.xaxis.set_minor_locator(x_minor)
ax.yaxis.set_minor_locator(y_minor)
ax.spines['top'].set_visible(False)
ax.spines['right'].set_visible(False)
return ax
def plotConnHistogram(val, **kw):
'''
Plot a histogram of connection weights with some predetermined formatting.
Parameters
----------
val : numpy array
A 1D array of connetions.
**kw
Keyword arguments to the hist function. See code for some additional
keyword arguments.
'''
# keyword arguments
kw['bins'] = kw.get('bins', 20)
#kw['edgecolor'] = kw.get('edgecolor', 'none')
ax = kw.pop('ax', plt.gca())
xlabel = kw.pop('xlabel', 'g (nS)')
ylabel = kw.pop('ylabel', 'Count')
title = kw.pop('title', '')
locators = kw.pop('locators', {})
ylabelPos = kw.pop('ylabelPos', -0.2)
globalAxesSettings(ax)
n, _, _ = ax.hist(val, **kw)
print(np.max(n))
ax.set_xlabel(xlabel)
ax.text(ylabelPos, 0.5, ylabel, rotation=90, transform=ax.transAxes,
va='center', ha='right')
ax.set_title(title)
# tick formatting
x_major = locators.get('x_major', ti.MaxNLocator(3))
x_minor = locators.get('x_minor', ti.AutoMinorLocator(2))
y_major = locators.get('y_major', ti.LinearLocator(2))
y_minor = locators.get('y_minor', ti.AutoMinorLocator(4))
ax.xaxis.set_major_locator(x_major)
ax.yaxis.set_major_locator(y_major)
ax.xaxis.set_minor_locator(x_minor)
ax.yaxis.set_minor_locator(y_minor)
ax.spines['top'].set_visible(False)
ax.spines['right'].set_visible(False)
return ax
def EIPlot(E, I, labelx=None, labely="", holdVal='on', timeUnits="ms",
leg=['middle', 'edge'], nticks=3):
'''
Plot a double plot of E and I variable.
Parameters
----------
E : A pair (sig, times)
Excitatory variable.
I : A pair (sig, times)
Inhibitory variable.
holdVal : str
Value to pass on to the hold() function
'''
plt.hold('on')
ax = plt.subplot(211)
globalAxesSettings(plt.gca())
plt.gca().xaxis.set_major_locator(ti.MaxNLocator(4))
plt.gca().yaxis.set_major_locator(ti.MaxNLocator(nticks-1))
plt.plot(E[1], E[0])
if (labely != ""):
plt.ylabel("E cell " + labely)
plt.legend(leg)
plt.subplot(212, sharex=ax)
globalAxesSettings(plt.gca())
plt.gca().xaxis.set_major_locator(ti.MaxNLocator(4))
plt.gca().yaxis.set_major_locator(ti.MaxNLocator(nticks-1))
plt.plot(I[1], I[0])
if (labely != ""):
plt.ylabel("I cell " + labely)
if (labelx is None):
plt.xlabel('Time (%s)' % timeUnits)
elif (labelx != ""):
plt.xlabel(labelx)
plt.legend(leg)
plt.tight_layout(w_pad=1.0)
def plot2DWeightMatrix(C, **kw):
ax = kw.pop('ax', plt.gca())
xlabel = kw.pop('xlabel', 'Neuron #')
ylabel = kw.pop('ylabel', 'Neuron #')
labelpad = kw.pop('labelpad', None)
title = kw.pop('title', '')
kw['rasterized'] = kw.get('rasterized', True)
X = np.arange(C.shape[1] + 1)
Y = np.arange(C.shape[0] + 1)
globalAxesSettings(ax)
ax.pcolormesh(X, Y, C, **kw)
ax.set_xticks([0.5, X[-1] - 0.5])
ax.set_yticks([0.5, Y[-1] - 0.5])
ax.set_xticklabels([1, X[-1]])
ax.set_yticklabels([1, Y[-1]])
plt.axis('scaled')
ax.set_xlabel(xlabel, labelpad=labelpad)
ax.set_ylabel(ylabel, labelpad=labelpad)
ax.set_title(title, size='small')
return ax
def plot2DWeightMatrix(C, **kw):
ax = kw.pop('ax', plt.gca())
xlabel = kw.pop('xlabel', 'Neuron #')
ylabel = kw.pop('ylabel', 'Neuron #')
labelpad = kw.pop('labelpad', None)
title = kw.pop('title', '')
kw['rasterized'] = kw.get('rasterized', True)
X = np.arange(C.shape[1] + 1)
Y = np.arange(C.shape[0] + 1)
globalAxesSettings(ax)
ax.pcolormesh(X, Y, C, **kw)
ax.set_xticks([0.5, X[-1]-0.5])
ax.set_yticks([0.5, Y[-1]-0.5])
ax.set_xticklabels([1, X[-1]])
ax.set_yticklabels([1, Y[-1]])
plt.axis('scaled')
ax.set_xlabel(xlabel, labelpad=labelpad)
ax.set_ylabel(ylabel, labelpad=labelpad)
ax.set_title(title, size='small')
return ax
def parameterScape(fig, X, Y, C, fmts, sizes, cmaps, vranges, **kwargs):
"""
"""
ax = fig.gca()
defaultMargin = 1.0
xmargin = kwargs.pop('xmargin', defaultMargin)
ymargin = kwargs.pop('ymargin', defaultMargin)
legend = kwargs.pop('legend', None)
colorbars = kwargs.pop('colorbars', None)
sm = []
colors = []
for cIdx, c in enumerate(C):
m = ScalarMappable(cmap=cmaps[cIdx])
m.set_clim(vranges[cIdx])
m.set_array(c)
colors.append(m.to_rgba(c))
sm.append(m)
for yIdx, y in enumerate(Y):
for xIdx, x in enumerate(X):
for cIdx, c in enumerate(C):
if (np.isnan(c[yIdx, xIdx])):
continue
ax.plot(x,
y,
fmts[cIdx],
color=colors[cIdx][yIdx, xIdx, :],
markersize=sizes[cIdx],
**kwargs)
globalAxesSettings(ax)
ax.spines['top'].set_visible(False)
ax.spines['right'].set_visible(False)
ax.spines['bottom'].set_visible(False)
ax.spines['left'].set_visible(False)
ax.tick_params(bottom='off',
top='off',
left='off',
right='off',
length=0,
pad=-5)
ax.axis('scaled')
dx = X[1] - X[0]
dy = Y[1] - Y[0]
ax.set_xlim([X[0] - xmargin * dx, X[-1] + xmargin * dx])
ax.set_ylim([Y[0] - ymargin * dy, Y[-1] + ymargin * dy])
if (legend is not None):
legend_strings = legend[0]
legend_kw = legend[1]
if (legend_kw is None):
legend_kw = {}
parameterScapeLegend(ax, legend_strings, fmts, sizes, cmaps, kwargs,
**legend_kw)
if (colorbars is not None):
cax = plotColorbars(fig, sm, **colorbars)
else:
cax = None
return ax, cax