def wd_fig(fig,
loc,
ldict,
cdict,
what,
rpos=None,
xlim=[-1, 51],
ylim=[0, 1],
xticks=range(0, 51, 5),
yticks=np.arange(0., 1.1, 0.2),
clr='blue',
label=''):
nest.ResetKernel()
l = tp.CreateLayer(ldict)
tp.ConnectLayers(l, l, cdict)
ax = fig.add_subplot(loc)
if rpos is None:
rn = nest.GetLeaves(l)[0][:1] # first node
else:
rn = tp.FindNearestElement(l, rpos)
conns = nest.GetConnections(rn)
cstat = nest.GetStatus(conns)
vals = np.array([sd[what] for sd in cstat])
tgts = [sd['target'] for sd in cstat]
locs = np.array(tp.GetPosition(tgts))
ax.plot(locs[:, 0], vals, 'o', mec='none', mfc=clr, label=label)
ax.set_xlim(xlim)
ax.set_ylim(ylim)
ax.set_xticks(xticks)
ax.set_yticks(yticks)
def recordElectrodeEnviromentLazy(network, folder):
neurons_x_position_distance = 0.5/(network.parameters['Columns']/2.)
neurons_x_position = [(i, 0.) for i in np.arange(0., 0.5, neurons_x_position_distance*2.)]
#Add y-Positions
neurons_x_position += ([(0., i) for i in np.arange(0., 0.5, neurons_x_position_distance*2.)])
#Add diagonal
neurons_x_position += ([(i, i) for i in np.arange(0., 0.5, neurons_x_position_distance*2.)])
neurons_x_position = tp.FindNearestElement(network.l, neurons_x_position)
neurons_position = tp.GetPosition(neurons_x_position)
for position in neurons_position:
plt.close()
magic.recordElectrodeEnviroment(network.df_ex, position[0], position[1], 0.05, 0.05)
plt.savefig(folder + '/excitatory_neurons/electrode_enviroment' + str(round(position[0], 4)) + ', ' + str(round(position[1], 4)) + '.png',
dpi=300)
plt.close()
magic.recordElectrodeEnviroment(network.df_in, position[0], position[1], 0.05, 0.05)
plt.savefig(folder + '/inhibitory_neurons/electrode_enviroment' + str(round(position[0], 4)) + ', ' + str(round(position[1], 4)) + '.png',
dpi=300)
def plot_connections(ax, src, tgt, pops_list, red_conn_dens):
# z-positions
z0 = pops_list.index(src)
z1 = z0 + (pops_list.index(tgt) - z0)
# x,y-positions
if src == 'STIM':
xyloc = [0., 0.]
elif src == tgt:
xyloc = [0.8, 0.8]
elif src == 'EX':
xyloc = [0.8, -0.8]
elif src == 'IN':
xyloc = [-0.8, -0.8]
srcid = tp.FindNearestElement(pops[src]['layer'], xyloc, False)
srcloc = tp.GetPosition(srcid)[0]
tgtsloc = np.array(tp.GetTargetPositions(srcid, pops[tgt]['layer'])[0])
# targets do not get picked in the same order;
# they are sorted here for reproducibility
tgtsloc = tgtsloc[np.argsort(tgtsloc[:, 0])]
tgtsloc_show = tgtsloc[0:len(tgtsloc):red_conn_dens]
for tgtloc in tgtsloc_show:
ax.plot([srcloc[0], tgtloc[0]], [srcloc[1], tgtloc[1]], [z0, z1],
c=pops[src]['conn_color_light'],
linewidth=1.)
# highlight target
ax.plot(xs=[tgtloc[0]],
ys=[tgtloc[1]],
zs=[z1],
marker='o',
markeredgecolor='none',
markersize=2,
color=pops[src]['conn_color_dark'],
alpha=1.)
# to be printed on top
dots = [tgtsloc_show, z1, pops[src]['conn_color_dark'], 'none', 2]
srcdot = [srcloc, z0, 'white', 'black', 3]
return dots, srcdot
def test_FindElements(self):
"""Interface and result check for finding nearest element.
This function is Py only, so we also need to check results."""
# nodes at [-1,0,1]x[-1,0,1], column-wise
ldict = {
'elements': 'iaf_neuron',
'rows': 3,
'columns': 3,
'extent': (3., 3.)
}
nest.ResetKernel()
l = topo.CreateLayer(ldict)
# single location at center
n = topo.FindNearestElement(l, (0., 0.))
self.assertEqual(n, (6, ))
# single location, two layers
n = topo.FindNearestElement(l * 2, (0., 0.))
self.assertEqual(n, (6, 6))
# two locations, one layer
n = topo.FindNearestElement(l, ((0., 0.), (1., 1.)))
self.assertEqual(n, (6, 8))
# two locations, two layers
n = topo.FindNearestElement(l * 2, ((0., 0.), (1., 1.)))
self.assertEqual(n, ((6, 8), ) * 2)
# several closest locations, not all
n = topo.FindNearestElement(l, (0.5, 0.5))
self.assertEqual(len(n), 1)
self.assertEqual(1, sum(n[0] == k for k in (5, 6, 8, 9)))
# several closest locations, all
n = topo.FindNearestElement(l, (0.5, 0.5), find_all=True)
self.assertEqual(len(n), 1)
self.assertEqual(n, ((5, 6, 8, 9), ))
# complex case
n = topo.FindNearestElement(l * 2, ((0., 0.), (0.5, 0.5)),
find_all=True)
self.assertEqual(n, (((6, ), (5, 6, 8, 9)), ) * 2)
def test_FindElements(self):
"""Interface and result check for finding nearest element.
This function is Py only, so we also need to check results."""
# nodes at [-1,0,1]x[-1,0,1], column-wise
ldict = {
'elements': 'iaf_neuron',
'rows': 3,
'columns': 3,
'extent': [3., 3.]
}
nest.ResetKernel()
l = topo.CreateLayer(ldict)
# single location at center
n = topo.FindNearestElement(l, [0., 0.])
self.assertEqual(n, [6])
# single location, two layers
n = topo.FindNearestElement(l * 2, [0., 0.])
self.assertEqual(n, [6, 6])
# two locations, one layer
n = topo.FindNearestElement(l, [[0., 0.], [1., 1.]])
self.assertEqual(n, [6, 8])
# two locations, two layers
n = topo.FindNearestElement(l * 2, [[0., 0.], [1., 1.]])
self.assertEqual(n, [[6, 8]] * 2)
# several closest locations, not all
n = topo.FindNearestElement(l, [0.5, 0.5])
self.assertEqual(len(n), 1)
self.assertEqual(1, sum([n[0] == k for k in [5, 6, 8, 9]]))
# several closest locations, all
n = topo.FindNearestElement(l, [0.5, 0.5], find_all=True)
self.assertEqual(len(n), 1)
self.assertEqual(n, [[5, 6, 8, 9]])
# complex case
n = topo.FindNearestElement(l * 2, [[0., 0.], [0.5, 0.5]],
find_all=True)
self.assertEqual(n, [[[6], [5, 6, 8, 9]]] * 2)
'rectangular': {
'lower_left': [-2., -1.],
'upper_right': [2., 1.]
}
}
}
tp.ConnectLayers(l, l, conndict)
# { end #}
fig = plt.figure()
fig.add_subplot(121)
conn_figure(fig,
l,
conndict,
targets=((tp.FindCenterElement(l), 'red'),
(tp.FindNearestElement(l, [4., 5.]), 'yellow')))
# same another time, with periodic bcs
lpbc = tp.CreateLayer({
'rows': 11,
'columns': 11,
'extent': [11., 11.],
'elements': 'iaf_neuron',
'edge_wrap': True
})
tp.ConnectLayers(lpbc, lpbc, conndict)
fig.add_subplot(122)
conn_figure(fig,
lpbc,
conndict,
showmask=False,
def plot_connections(ax, pop, pops_list, red_conn_dens, dots):
# note that xyloc of connection is set here manually
# connections from pop to tagt
for tgt in pops[pop]['tgts']:
z0 = pops_list.index(pop)
z1 = z0 + (pops_list.index(tgt) - z0)
if pop == tgt or z0 <= z1:
if pop == tgt:
xyloc = [0.8, 0.8]
else:
xyloc = [-0.8, -0.8]
srcid = tp.FindNearestElement(pops[pop]['layer'], xyloc, False)
srcloc = tp.GetPosition(srcid)[0]
tgtsloc = np.array(
tp.GetTargetPositions(srcid, pops[tgt]['layer'])[0])
# targets do not get picked in the same order;
# they are sorted here for reproducibility
tgtsloc = tgtsloc[np.argsort(tgtsloc[:, 0])]
tgtsloc_show = tgtsloc[0:len(tgtsloc):red_conn_dens]
for tgtloc in tgtsloc_show:
plt.plot([srcloc[0], tgtloc[0]], [srcloc[1], tgtloc[1]],
[z0, z1],
c=pops[pop]['conn_color'],
linewidth=1,
alpha=0.1)
# highlight target
plt.plot(tgtloc[0],
tgtloc[1],
zs=[z1],
marker='o',
markeredgecolor='none',
markersize=2,
color=pops[pop]['conn_color'],
alpha=1.)
dots.append([srcloc, z0, 'white', 'black', 3])
if pop == 'IN' and tgt == 'EX': # final
dots.append(
[tgtsloc_show, z1, pops[pop]['conn_color'], 'none', 2])
# draw connections from src to pop
for src in pops_list:
z0 = pops_list.index(src)
z1 = z0 + (pops_list.index(pop) - z0)
if src != pop and (pop in pops[src]['tgts']) and z0 > z1:
if src == 'STIM':
xyloc = [0., 0.]
else:
xyloc = [0.8, -0.8]
srcid = tp.FindNearestElement(pops[src]['layer'], xyloc, False)
srcloc = tp.GetPosition(srcid)[0]
tgtsloc = np.array(
tp.GetTargetPositions(srcid, pops[pop]['layer'])[0])
tgtsloc = tgtsloc[np.argsort(tgtsloc[:, 0])]
tgtsloc_show = tgtsloc[0:len(tgtsloc):red_conn_dens]
for tgtloc in tgtsloc_show:
plt.plot([srcloc[0], tgtloc[0]], [srcloc[1], tgtloc[1]],
[z0, z1],
c=pops[src]['conn_color'],
linewidth=1,
alpha=0.1)
plt.plot(tgtloc[0],
tgtloc[1],
zs=[z1],
marker='o',
markeredgecolor='none',
markersize=2,
color=pops[src]['conn_color'],
alpha=1.)
dots.append([srcloc, z0, 'white', 'black', 3])
if src == 'STIM': # final
dots.append(
[tgtsloc_show, z1, pops[src]['conn_color'], 'none', 2])
return dots
