You may test if everything is working by telling NEMS to download some sample auditory stimulus-response data, use a simple linear-nonlinear model (which should taking about 2 minutes to fit), and then save the results locally:
python demo_script.py
Or open demo_script.py
in an editor to work through each step of
the fit.
If you are just hacking around, please put your code in the scripts
directory --
it's for any one-off analysis or snippet of code that isn't yet ready
for reuse by other people. We recommend using scripts/demo_script.py
as a guide for the types of operations that you may find useful.
There is an :doc:`xforms <xforms>` system for batching and saving
analyses in a way that you can reload later; this was what we used in
the scripts/fit_model.py
command. We also have a good
tutorial/template at scripts/demo_script.py
. We recommend beginners
make a copy of it and edit it as needed. You can run it with:
python scripts/demo_script.py
If your data is in :doc:`Recording <recordings>` format, put it in the
recordings
directory. If it is not yet in recording form, you may
want to make a conversion script in scripts
that is able convert
your data from your custom format into :doc:`Signals <signals>` and
:doc:`Recordings <recordings>`.
Below is some pseudocode for a script that converts your data from a
custom format and then saves it as a Signal
to make it easier for other
people to use.
from nems0.signal import Signal
numpy_array = load_my_custom_data_format(...)
sig = Signal(data=numpy_array,
name='mysignal',
recording='some-string',
fs=200 # Hz
)
sig.save('recordings/my-new-recording/my-new-signal')
Once you have fit a model to a recording, you can save the resulting
files either locally in your NEMS/results
directory, or to a remote
:doc:`database <database>` server.