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simulation-toolkit

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nest-simulator
YounesBouhadjar
YounesBouhadjar commented Sep 5, 2021

In NEST3, the volume_transmitter is of type NodeCollection, but in the synaptic specification it's required to provide only a node ID of the vt, For example when running:

import nest
vt = nest.Create("volume_transmitter")
nest.CopyModel("stdp_dopamine_synapse", "stdp_dopa", {"vt": vt})

You get a type mismatch error. A workaround for this is to convert the NodeColletion to a li

syasini
syasini commented Aug 17, 2020

Currently, the canvas.cut_mask() method takes in the healpix map of an experiment footprint. It would be nice to allow the user to apply such cuts using experiment footprints. For example

canvas.cut_mask("CMB-S4", inplace=True) 
canvas.cut_mask("DESI", inplace=True) 

would limit the catalog to the common footprint between S4 and DESI.

TODO: Add masks from [cmb x galax

Speech Tester is a set of Python scripts conceived as an extension to HTK Automatic Speech Recognition system (Young et al., 2002). Speech Tester aims to help language researchers and engineers to measure the intelligibility of transformed audio signals. Here the termed “transform” may describe any process that alters a speech signal or its reception by a potential listener. For instance, any type of audio compression, noise background and/or reverberation, vocoding (to simulate cochlear implants), partial auditory loss, or any combination of these and other imaginable transformations. Intelligibility refers to the capacity of a listener to recognize a transformed signal. In the rest of this introduction we describe the motivation to develop Speech Tester to measure speech intelligibility.
  • Updated Dec 30, 2020

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