def plotIToEBrokenAxis(sp,
gIdx,
neuronIdx,
trialNum=0,
axBoundaries=None,
axesProportions=(0.5, 0.5),
bottomLimits=None,
topLimits=None,
**kw):
if axBoundaries is None:
axBoundaries = [0, 0, 1, 1]
left, bottom, right, top = axBoundaries
title = kw.pop('title', 'E cell')
fig = kw.pop('fig', plt.gcf())
h = top - bottom
w = right - left
hBottom = h * axesProportions[0]
hTop = h * axesProportions[1]
axBottom = fig.add_axes(
Bbox.from_extents(left, bottom, right, bottom + hBottom))
axTop = fig.add_axes(Bbox.from_extents(left, top - hTop, right, top),
sharex=axBottom)
_, gI = aggr.computeYX(sp, iterList)
M = sp[0][gIdx][trialNum].data['g_EI']
conns = M[neuronIdx, :]
pconn.plotConnHistogram(conns, title=title, ax=axBottom, **kw)
kw['ylabel'] = ''
pconn.plotConnHistogram(conns, title=title, ax=axTop, **kw)
annG = gI[0, gIdx]
if annG - int(annG) == 0:
annG = int(annG)
#ann = '$g_I$ = {0} nS'.format(annG)
#fig.text(left+0.95*w, bottom+0.9*h, ann, ha='right', va='bottom',
# fontsize='x-small')
axBottom.set_xlim([0, annG])
axBottom.set_xticks([0, annG])
axBottom.xaxis.set_ticklabels([0, '$g_I$'])
axBottom.set_ylim(bottomLimits)
axBottom.set_yticks(bottomLimits)
axBottom.yaxis.set_minor_locator(ti.NullLocator())
axTop.set_ylim(topLimits)
axTop.set_yticks([topLimits[1]])
axTop.xaxis.set_visible(False)
axTop.spines['bottom'].set_visible(False)
divLen = 0.07
d = .015
kwargs = dict(transform=fig.transFigure, color='k', clip_on=False)
axBottom.plot((left - divLen * w, left + divLen * w),
(bottom + hBottom + d, bottom + hBottom - d), **kwargs)
axTop.plot((left - divLen * w, left + divLen * w),
(top - hTop + d, top - hTop - d), **kwargs)
return axBottom, axTop
def plotIToEBrokenAxis(sp, gIdx, neuronIdx, trialNum=0, axBoundaries=None,
axesProportions=(0.5, 0.5), bottomLimits=None,
topLimits=None, **kw):
if axBoundaries is None:
axBoundaries = [0, 0, 1, 1]
left, bottom, right, top = axBoundaries
title = kw.pop('title', 'E cell')
fig = kw.pop('fig', plt.gcf())
h = top - bottom
w = right - left
hBottom = h*axesProportions[0]
hTop = h*axesProportions[1]
axBottom = fig.add_axes(Bbox.from_extents(left, bottom, right, bottom +
hBottom))
axTop = fig.add_axes(Bbox.from_extents(left, top - hTop, right, top),
sharex=axBottom)
_, gI = aggr.computeYX(sp, iterList)
M = sp[0][gIdx][trialNum].data['g_EI']
conns = M[neuronIdx, :]
pconn.plotConnHistogram(conns, title=title, ax=axBottom, **kw)
kw['ylabel'] = ''
pconn.plotConnHistogram(conns, title=title, ax=axTop, **kw)
annG = gI[0, gIdx]
if annG - int(annG) == 0:
annG = int(annG)
#ann = '$g_I$ = {0} nS'.format(annG)
#fig.text(left+0.95*w, bottom+0.9*h, ann, ha='right', va='bottom',
# fontsize='x-small')
axBottom.set_xlim([0, annG])
axBottom.set_xticks([0, annG])
axBottom.xaxis.set_ticklabels([0, '$g_I$'])
axBottom.set_ylim(bottomLimits)
axBottom.set_yticks(bottomLimits)
axBottom.yaxis.set_minor_locator(ti.NullLocator())
axTop.set_ylim(topLimits)
axTop.set_yticks([topLimits[1]])
axTop.xaxis.set_visible(False)
axTop.spines['bottom'].set_visible(False)
divLen = 0.07
d = .015
kwargs = dict(transform=fig.transFigure, color='k', clip_on=False)
axBottom.plot((left-divLen*w, left+divLen*w), (bottom+hBottom + d,
bottom+hBottom - d),
**kwargs)
axTop.plot((left-divLen*w, left+divLen*w), (top-hTop + d, top-hTop - d),
**kwargs)
return axBottom, axTop
def plotIToE(sp, gIdx, neuronIdx, trialNum=0, **kw):
title = kw.pop('title', 'E cell')
gE, gI = aggr.computeYX(sp, iterList)
M = sp[0][gIdx][trialNum].data['g_EI']
conns = M[neuronIdx, :]
ax = pconn.plotConnHistogram(conns,
title=title, **kw)
annG = gI[0, gIdx]
if (annG - int(annG) == 0):
annG = int(annG)
ann = '$g_I$ = {0} nS'.format(annG)
ax.text(0.95, 0.9, ann, ha='right', va='bottom', fontsize='x-small',
transform=ax.transAxes)
ax.set_xlim([0, annG])
ax.set_xticks([0, annG])
def plotEToI(sp, gIdx, neuronIdx, trialNum=0, **kw):
title = kw.pop('title', 'I cell')
ylim = kw.pop('ylim', None)
gE, gI = aggr.computeYX(sp, iterList)
M = sp[0][gIdx][trialNum].data['g_IE']
conns = M[neuronIdx, :]
ax = pconn.plotConnHistogram(conns, title=title, **kw)
annG = gE[0, gIdx]
if (annG - int(annG) == 0):
annG = int(annG)
#ann = '$g_E$ = {0} nS'.format(annG)
#ax.text(0.95, 0.9, ann, ha='right', va='bottom', fontsize='x-small',
# transform=ax.transAxes)
ax.set_xlim([0, annG])
ax.set_xticks([0, annG])
ax.xaxis.set_ticklabels([0, '$g_E$'])
ax.set_ylim(ylim)
def plotEToI(sp, gIdx, neuronIdx, trialNum=0, **kw):
title = kw.pop('title', 'I cell')
ylim = kw.pop('ylim', None)
gE, gI = aggr.computeYX(sp, iterList)
M = sp[0][gIdx][trialNum].data['g_IE']
conns = M[neuronIdx, :]
ax = pconn.plotConnHistogram(conns,
title=title, **kw)
annG = gE[0, gIdx]
if (annG - int(annG) == 0):
annG = int(annG)
#ann = '$g_E$ = {0} nS'.format(annG)
#ax.text(0.95, 0.9, ann, ha='right', va='bottom', fontsize='x-small',
# transform=ax.transAxes)
ax.set_xlim([0, annG])
ax.set_xticks([0, annG])
ax.xaxis.set_ticklabels([0, '$g_E$'])
ax.set_ylim(ylim)
def plotIToE(sp, gIdx, neuronIdx, trialNum=0, **kw):
title = kw.pop('title', 'E cell')
gE, gI = aggr.computeYX(sp, iterList)
M = sp[0][gIdx][trialNum].data['g_EI']
conns = M[neuronIdx, :]
ax = pconn.plotConnHistogram(conns, title=title, **kw)
annG = gI[0, gIdx]
if (annG - int(annG) == 0):
annG = int(annG)
ann = '$g_I$ = {0} nS'.format(annG)
ax.text(0.95,
0.9,
ann,
ha='right',
va='bottom',
fontsize='x-small',
transform=ax.transAxes)
ax.set_xlim([0, annG])
ax.set_xticks([0, annG])
