Refactor of CN and IC module #4
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This refactor will make it easier for me to integrate the EFR simulations into the Bayesian regression models I'm running. Main changes include: * Breaking out common operations (e.g., calculating the bilinear filter and inserting delays) into their own functions. * Updating the code to better handle N-dimensional arrays. Original version of the code assumes that the AN and CN data will only be 2 dimensional with dimensions (time x CF). This was causing problems because my simulation is using four dimensions (stimulus x subject x CF x time). * Time should generally be the *last* dimension due to Python coding conventions (in Matlab, time is usually the *first* dimension, but this has to do with Matlab being Fortran based vs. Python/Numpy being C/C++ based). * The run_model2018.py has been updated to reflect the dimension reordering of the ic_cn2018 module. Eventually I will submit changes reflecting the CF x time ordering for additional modules. These changes have been verified against the original code and shown to yield identical results for CN and IC outputs.
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This refactor will make it easier for me to integrate the EFR
simulations into the Bayesian regression models I'm running. Main
changes include:
and inserting delays) into their own functions.
version of the code assumes that the AN and CN data will only be 2
dimensional with dimensions (time x CF). This was causing problems
because my simulation is using four dimensions (stimulus x subject x
CF x time).
conventions (in Matlab, time is usually the first dimension, but
this has to do with Matlab being Fortran based vs. Python/Numpy being
C/C++ based).
reordering of the ic_cn2018 module. Eventually I will submit changes
reflecting the CF x time ordering for additional modules.
These changes have been verified against the original code and shown to
yield identical results for CN and IC outputs.