def test_GetChildren(self):
"""GetChildren"""
nest.ResetKernel()
model = 'iaf_neuron'
l = nest.LayoutNetwork(model, [2, 3])
topKids = [2, 6]
kids2 = [3, 4, 5]
kids6 = [7, 8, 9]
# test top level
self.assertEqual(nest.GetChildren(l), [topKids])
# test underlying level
self.assertEqual(nest.GetChildren([2, 6]), [kids2, kids6])
# test with empty dict
self.assertEqual(nest.GetChildren(l, properties={}), [topKids])
# local id of middle nodes
self.assertEqual(nest.GetChildren([2, 6], properties={'local_id': 2}),
[[4], [8]])
# selection by model type
self.assertEqual(nest.GetChildren(l, properties={'model': 'subnet'}),
[topKids])
self.assertEqual(nest.GetChildren([2], properties={'model': 'subnet'}),
[[]])
self.assertEqual(nest.GetChildren([2], properties={'model': model}),
[kids2])
def test_GetChildren(self):
"""GetChildren"""
nest.ResetKernel()
model = 'iaf_psc_alpha'
l = nest.LayoutNetwork(model, (2, 3))
topKids = (2, 6)
kids2 = (3, 4, 5)
kids6 = (7, 8, 9)
# test top level
self.assertEqual(nest.GetChildren(l), (topKids, ))
# test underlying level
self.assertEqual(nest.GetChildren((2, 6)), (kids2, kids6))
# test with empty dict
self.assertEqual(nest.GetChildren(l, properties={}), (topKids, ))
# local id of middle nodes
self.assertEqual(nest.GetChildren(
(2, 6), properties={'local_id': 2}), ((4, ), (8, )))
# selection by model type
self.assertEqual(nest.GetChildren(
l, properties={'model': 'subnet'}), (topKids, ))
self.assertEqual(nest.GetChildren(
(2, ), properties={'model': 'subnet'}), (tuple(), ))
self.assertEqual(nest.GetChildren(
(2, ), properties={'model': model}), (kids2, ))
def check_distance():
layer = tp.CreateLayer(l3d_specs)
tp.ConnectLayers(layer, layer, conn_dict1)
nodes = nest.GetChildren(layer)[0]
positions = tp.GetPosition(nodes)
target_positions = tp.GetTargetPositions(nodes, layer)
deltas = filter(
lambda x: x > 0.35,
[[get_delta(positions[i], pos2) for pos2 in target_positions[i]]
for i in range(len(positions))])
print len(deltas)
def MyPlotLayer(layer, ax=None, nodecolor='b', nodesize=20):
"""
Plot nodes in a layer.
This function plots only top-level nodes, not the content of composite nodes.
Note: You should not use this function in distributed simulations.
Parameters
----------
layer GID of layer to plot (as single-element list)
fig Matplotlib figure to plot to. If not given, a new figure is created [optional].
nodecolor Color for nodes [optional].
nodesize Marker size for nodes [optional].
Returns
-------
Matplotlib figure.
See also
--------
PlotTargets
"""
import matplotlib.pyplot as plt
if len(layer) != 1:
raise ValueError("layer must contain exactly one GID.")
# get layer extent and center, x and y
xext, yext = nest.GetStatus(layer, 'topology')[0]['extent'][:2]
xctr, yctr = nest.GetStatus(layer, 'topology')[0]['center'][:2]
# extract position information, transpose to list of x and y positions
xpos, ypos = zip(*GetPosition(nest.GetChildren(layer)))
if not ax:
ax = pylab.subplot(111)
ax.scatter(xpos, ypos, s=nodesize, facecolor=nodecolor, edgecolor='none')
_draw_extent(ax, xctr, yctr, xext, yext)
random.uniform(-0.5, 0.5)
] for j in range(1000)]
l1 = topo.CreateLayer({
'extent': [1.5, 1.5, 1.5], # must specify 3d extent AND center
'center': [0., 0., 0.],
'positions': pos,
'elements': 'iaf_neuron'
})
# visualize
#xext, yext = nest.GetStatus(l1, 'topology')[0]['extent']
#xctr, yctr = nest.GetStatus(l1, 'topology')[0]['center']
# extract position information, transpose to list of x, y and z positions
xpos, ypos, zpos = zip(*topo.GetPosition(nest.GetChildren(l1)[0]))
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
ax.scatter(xpos, ypos, zpos, s=15, facecolor='b', edgecolor='none')
# Gaussian connections in full volume [-0.75,0.75]**3
topo.ConnectLayers(
l1, l1, {
'connection_type': 'divergent',
'allow_autapses': False,
'mask': {
'volume': {
'lower_left': [-0.75, -0.75, -0.75],
'upper_right': [0.75, 0.75, 0.75]
}
},
'allow_multapses': False,
'allow_autapses': False,
'connection_type': 'convergent',
'mask': {
'rectangular': {
'lower_left': [-0.5, -0.5],
'upper_right': [0.5, 0.5]
}
},
'kernel': 0.5,
'sources': {
'model': 'iaf_neuron'
},
'targets': {
'model': 'iaf_neuron'
}
}
tp.ConnectLayers(l, l, projections)
tp.ConnectLayers(l_dummy1, l, projections)
tp.ConnectLayers(l_dummy2, l, projections)
if nest.version() == 'NEST 2.0.0':
sources = nest.GetChildren(l)
if nest.version() == 'NEST 2.2.1':
sources = nest.GetChildren(l)[0]
t = time.time()
targets = tp.GetTargetNodes(sources, l)
print time.time() - t
'connection_type': 'convergent',
'mask': {
'rectangular': {
'lower_left': [-0.5, -0.5],
'upper_right': [0.5, 0.5]
}
},
'kernel': 0.5,
'sources': {
'model': 'iaf_neuron'
},
'targets': {
'model': 'iaf_neuron'
}
}
tp.ConnectLayers(l, l, projections)
tp.ConnectLayers(l_dummy1, l, projections)
tp.ConnectLayers(l_dummy2, l, projections)
tp.ConnectLayers(l_dummy3, l, projections)
t = time.time()
targets = tp.GetTargetNodes(nest.GetChildren(l)[0], l)
m = []
for trg in targets:
m.append(len(trg))
print time.time() - t
print len(m)
#print tp.FindCenterElement(l)
#print nest.GetChildren(l)
#print nest.GetLeaves(l)
GIDs and neuron positions are written to file.
'''
layer_in = tp.CreateLayer(layerdict_in)
layer_ex = tp.CreateLayer(layerdict_ex)
layer_stim = tp.CreateLayer(layerdict_stim)
tp.DumpLayerNodes(
layer_ex, os.path.join(spike_output_path, label_positions + '-0.dat'))
tp.DumpLayerNodes(
layer_in, os.path.join(spike_output_path, label_positions + '-1.dat'))
tp.DumpLayerNodes(
layer_stim, os.path.join(spike_output_path,
label_positions + '-2.dat'))
nodes_ex = nest.GetChildren(layer_ex)[0] # nodes of ex/in neurons
nodes_in = nest.GetChildren(layer_in)[0]
nodes_stim = nest.GetChildren(layer_stim)[0]
'''
Distribute initial membrane voltages.
'''
for neurons in [nodes_ex, nodes_in]:
for neuron in neurons:
nest.SetStatus([neuron], {'V_m': theta * random.rand()})
'''
Create spike detectors for recording from the excitatory and the
inhibitory populations and a poisson generator as noise source.
The spike detectors are configured for writing to file.
'''
random.uniform(-0.5, 0.5)
] for j in range(1000)]
l1 = topo.CreateLayer({
'extent': [1.5, 1.5, 1.5], # must specify 3d extent AND center
'center': [0., 0., 0.],
'positions': pos,
'elements': 'iaf_psc_alpha'
})
# visualize
# xext, yext = nest.GetStatus(l1, 'topology')[0]['extent']
# xctr, yctr = nest.GetStatus(l1, 'topology')[0]['center']
# l1_children is a work-around until NEST 3.0 is released
l1_children = nest.GetChildren(l1)[0]
# extract position information, transpose to list of x, y and z positions
xpos, ypos, zpos = zip(*topo.GetPosition(l1_children))
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
ax.scatter(xpos, ypos, zpos, s=15, facecolor='b', edgecolor='none')
# Gaussian connections in full volume [-0.75,0.75]**3
topo.ConnectLayers(
l1, l1, {
'connection_type': 'divergent',
'allow_autapses': False,
'mask': {
'volume': {
'lower_left': [-0.75, -0.75, -0.75],