def conn_figure_3d(fig,
layer,
connd,
targets=None,
showmask=True,
xticks=range(-5, 6),
yticks=range(-5, 6),
xlim=[-5.5, 5.5],
ylim=[-5.5, 5.5]):
if targets is None:
targets = ((nest.FindCenterElement(layer), 'red'), )
nest.PlotLayer(layer, fig=fig, nodesize=20, nodecolor=(.5, .5, 1.))
for src, clr in targets:
if showmask:
mask = connd['mask']
else:
mask = None
nest.PlotTargets(src,
layer,
fig=fig,
mask=mask,
probability_parameter=None,
src_size=250,
tgt_color=clr,
tgt_size=60,
probability_cmap='Greens')
ax = fig.gca()
# ax.set_aspect('equal', 'box')
plt.draw()
def test_PlotLayer(self):
"""Test plotting layer."""
nest.ResetKernel()
layer = nest.Create('iaf_psc_alpha',
positions=nest.spatial.grid(shape=[3, 3],
extent=[2., 2.],
edge_wrap=True))
nest.PlotLayer(layer)
plotted_datapoints = plt.gca().collections[-1].get_offsets().data
reference_datapoints = nest.GetPosition(layer)
self.assertTrue(np.allclose(plotted_datapoints, reference_datapoints))
def conn_figure(fig,
layer,
connd,
targets=None,
showmask=True,
kern=None,
xticks=range(-5, 6),
yticks=range(-5, 6),
xlim=[-5.5, 5.5],
ylim=[-5.5, 5.5]):
if targets is None:
targets = ((nest.FindCenterElement(layer), 'red'), )
nest.PlotLayer(layer, fig=fig, nodesize=60)
for src, clr in targets:
if showmask:
mask = connd['mask']
else:
mask = None
nest.PlotTargets(src,
layer,
fig=fig,
mask=mask,
probability_parameter=kern,
src_size=250,
tgt_color=clr,
tgt_size=20,
mask_color='red',
probability_cmap='Greens')
beautify_layer(layer,
fig,
xlim=xlim,
ylim=ylim,
xticks=xticks,
yticks=yticks,
xlabel='',
ylabel='')
fig.gca().grid(False)
#
# For reference purposes, e.g., printing, we collect all populations
# in a tuple:
populations = (retina, TpRelay, TpInter, Rp) + tuple(
Vp_h_layers.values()) + tuple(Vp_v_layers.values())
##############################################################################
# Inspection
# ----------
#
# We can now look at the network using `PrintNodes`:
nest.PrintNodes()
# We can also try to plot a single layer in a network. All layers have
# equal positions of the nodes.
nest.PlotLayer(Rp)
plt.title('Layer Rp')
##############################################################################
# Synapse models
# =-------------
#
# Actual synapse dynamics, e.g., properties such as the synaptic time
# course, time constants, reversal potentials, are properties of
# neuron models in NEST and we set them in section `Neuron models`_
# above. When we refer to *synapse models* in NEST, we actually mean
# connectors which store information about connection weights and
# delays, as well as port numbers at the target neuron (``rport``)
# and implement synaptic plasticity. The latter two aspects are not
# relevant here.
#
ax.grid(True)
ax.set_axisbelow(True)
ax.set_xlabel(xlabel)
ax.set_ylabel(ylabel)
return
# --------------------------------------------------
nest.ResetKernel()
#{ layer1 #}
l = nest.Create('iaf_psc_alpha', positions=nest.spatial.grid(shape=[5, 5]))
#{ end #}
fig = nest.PlotLayer(l, nodesize=50)
beautify_layer(l, fig, xlabel='x-axis (columns)', ylabel='y-axis (rows)')
ax = fig.gca()
tx = []
for r in range(5):
tx.append(
ax.text(0.65,
0.4 - r * 0.2,
str(r),
horizontalalignment='center',
verticalalignment='center'))
tx.append(
ax.text(-0.4 + r * 0.2,
0.65,
str(r),
horizontalalignment='center',
"""
import nest
import matplotlib.pyplot as plt
nest.ResetKernel()
pos = nest.spatial.free(
[nest.random.uniform(-0.75, 0.75),
nest.random.uniform(-0.5, 0.5)],
extent=[2., 1.5])
l1 = nest.Create('iaf_psc_alpha', 12, positions=pos)
nest.PrintNodes()
nest.PlotLayer(l1, nodesize=50)
# beautify
plt.axis([-1.0, 1.0, -0.75, 0.75])
plt.axes().set_aspect('equal', 'box')
plt.axes().set_xticks((-0.75, -0.25, 0.25, 0.75))
plt.axes().set_yticks((-0.5, 0, 0.5))
plt.grid(True)
plt.xlabel('Extent: 2.0')
plt.ylabel('Extent: 1.5')
plt.show()
# plt.savefig('grid_iaf_irr.png')
ax.set_axisbelow(True)
ax.set_xlabel(xlabel)
ax.set_ylabel(ylabel)
return
# --------------------------------------------------
nest.ResetKernel()
#{ layer1 #}
layer = nest.Create('iaf_psc_alpha',
positions=nest.spatial.grid(shape=[5, 5]))
#{ end #}
fig = nest.PlotLayer(layer, nodesize=50)
beautify_layer(layer, fig, xlabel='x-axis (columns)', ylabel='y-axis (rows)')
ax = fig.gca()
tx = []
for r in range(5):
tx.append(ax.text(0.65, 0.4 - r * 0.2, str(r),
horizontalalignment='center',
verticalalignment='center'))
tx.append(ax.text(-0.4 + r * 0.2, 0.65, str(r),
horizontalalignment='center',
verticalalignment='center'))
# For bbox_extra_artists, see
# https://github.com/matplotlib/matplotlib/issues/351
# plt.savefig('../user_manual_figures/layer1.png', bbox_inches='tight',
# bbox_extra_artists=tx)
BCCN Tutorial @ CNS*09
Hans Ekkehard Plesser, UMB
"""
import nest
import matplotlib.pyplot as plt
import numpy as np
for ctr in [(0.0, 0.0), (-2.0, 2.0), (0.5, 1.0)]:
plt.figure()
nest.ResetKernel()
l1 = nest.Create('iaf_psc_alpha',
positions=nest.spatial.grid(shape=[4, 3],
extent=[2., 1.5],
center=ctr))
nest.PlotLayer(l1, nodesize=50, fig=plt.gcf())
# beautify
plt.axis([-3, 3, -3, 3])
plt.axes().set_aspect('equal', 'box')
plt.axes().set_xticks(np.arange(-3.0, 3.1, 1.0))
plt.axes().set_yticks(np.arange(-3.0, 3.1, 1.0))
plt.grid(True)
plt.xlabel('4 Columns, Extent: 1.5, Center: %.1f' % ctr[0])
plt.ylabel('2 Rows, Extent: 1.0, Center: %.1f' % ctr[1])
plt.show()
# plt.savefig('grid_iaf_oc_{}_{}.png'.format(ctr[0], ctr[1]))
