# Circular mask and flat probability, with edge wrapΒΆ

Create two populations of iaf_psc_alpha neurons on a 30x30 grid with edge_wrap, connect with circular mask, flat probability, visualize.

BCCN Tutorial @ CNS*09 Hans Ekkehard Plesser, UMB

```import matplotlib.pyplot as plt
import numpy as np
import nest

nest.ResetKernel()

pos = nest.spatial.grid(shape=[30, 30], extent=[3., 3.], edge_wrap=True)
```

create and connect two populations

```a = nest.Create('iaf_psc_alpha', positions=pos)
b = nest.Create('iaf_psc_alpha', positions=pos)

cdict = {'rule': 'pairwise_bernoulli',
'p': 0.5,
'mask': {'circular': {'radius': 0.5}}}

nest.Connect(a, b,
conn_spec=cdict,
syn_spec={'weight': nest.random.uniform(0.5, 2.)})

plt.clf()
```

plot targets of neurons in different grid locations

```# first, clear existing figure, get current figure
plt.clf()
fig = plt.gcf()

# plot targets of two source neurons into same figure, with mask
for src_index in [30 * 15 + 15, 0]:
# obtain node id for center
src = a[src_index:src_index + 1]
nest.PlotTargets(src, b, mask=cdict['mask'], fig=fig)

# beautify
plt.axes().set_xticks(np.arange(-1.5, 1.55, 0.5))
plt.axes().set_yticks(np.arange(-1.5, 1.55, 0.5))
plt.grid(True)
plt.axis([-2.0, 2.0, -2.0, 2.0])
plt.axes().set_aspect('equal', 'box')
plt.title('Connection targets')

plt.show()

# plt.savefig('connex_ew.pdf')
```

Total running time of the script: ( 0 minutes 0.000 seconds)

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