Hi Xavier,

This is a weakness in the implementation of SelectNodesByMask. It returns a plain NodeCollection without any spatial metadata, so the returned NodeCollection does not represent a layer. Could you create a Github issue about this?

Finding a good solution for this is not entirely trivial. One solution would be to create a NodeCollection that contains copies of the positions of all the nodes that are selected. This is technically straightforward but for large selections, it could lead to noticeable memory overhead, at least if those collections are long-lived. Representing the selection as a collection of sliced connections can become complicated for layers with free node placement where likely each node would be a slice of its own.

We should discuss potential use cases to find out what would be the best solution.

Best,

Hans Ekkehard

-- 

Prof. Dr. Hans Ekkehard Plesser

Department of Data Science

Faculty of Science and Technology

Norwegian University of Life Sciences

PO Box 5003, 1432 Aas, Norway

Phone +47 6723 1560

Email hans.ekkehard.plesser@nmbu.no

Home http://arken.nmbu.no/~plesser

From: Xavier Otazu <xotazu@cvc.uab.cat>
Date: Wednesday, 13 March 2024 at 17:20
To: users@nest-simulator.org <users@nest-simulator.org>
Subject: [NEST Users] SelectNodesByMask() and DumpLayerConnections() combination problem

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Hello,

Looking at the documentation, I understand that the output of SelectNodesByMask() is a NodeCollection, and the 'layer' input parameters of DumpLayerConnections() is also a NodeCollection. Hence, I understand that I can combine these two parameters, but when I do it (see code below) I receive the error: nest.lib.hl_api_exceptions.LayerExpected: LayerExpected in SLI function DumpLayerConnections_os_g_g_l

Am I missing something?

Thanks a lot in advance!

Xavier
--------

import nest

# Layers creation

pos = nest.spatial.grid(shape = [100,100] )

input_l = nest.Create('iaf_psc_alpha', positions=pos)
layer_0 = nest.Create('iaf_psc_alpha', positions=pos)

conn_neur = {'rule':'pairwise_bernoulli', 'mask': {'grid':{'shape':[10,10]}} }
syn_0 = {'synapse_model': 'static_synapse'}

nest.Connect(input_l, layer_0, conn_neur, syn_0)

# GetNodesByMask

mask_specs = {'lower_left':[-0.25,-0.25], 'upper_right':[0.25,0.25]}
mask_obj = nest.CreateMask(masktype='rectangular', specs=mask_specs, anchor=[0.0,0.0])
center_neur = nest.SelectNodesByMask(layer_0,[0.0,0.0],mask_obj)
nest.DumpLayerConnections(input_l,center_neur, 'static_synapse', 'conn.txt')
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