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])