These are chat archives for thunder-project/thunder
modelfileis where you specify the stimulus values that you used to get the response magnitudes (which is sounds like you computed from a previous regression analysis). So if you used 12 angles, this this would be a vector containing those 12 angles. It could alternatively be a string with the path to a
.matfile that contains said parameters in a variable that is specified by another optional argument,
var. I think this is where the name comes form...it might make more sense to call it
stimparams, as that's the common denominator here.
@jwittenbach :point_up: April 21 2015 1:55 AM ah so is it literally the case of something like this:
circ = nmp.array((300, 90, 120, 330, 0, 240, 270, 210, 60, 30, 150, 180), dtype=nmp.int) tuningmodel = TuningModel.load(circ, 'circular')
ok and from that i'm assuming the output 'center' is a spatial map of tuning values (each of the angles in circ) ?
TuningModel, you'll also need a
Serieswhere each record contains the "responses" to those stimuli. Then you can call
Seriesto fit that model independently on each record of the
Series(e.g. each record might be a voxel or a neuron).
circ = nmp.array((300, 90, 120, 330, 0, 240, 270, 210, 60, 30, 150, 180), dtype=nmp.int) tuningmodel = TuningModel.load(nmp.radians(circ), 'circular').fit(responses)
Seriesof responses to the given stimulus parameters.
tuningmodelwill be a
Serieswhere each record contains the mean and variance of the fitted von Mises tuning curve
centerand the variances by
centers = tuningmodel['center']
boto(the library for accessing S3), and in the process added one configuration setting to fix a bug handling bucket names with periods in them https://github.com/thunder-project/thunder/blob/17b218c638acf9a460f5fcf49f4313e7ecfe4e2e/python/thunder/utils/ec2.py#L219
/root/.botoand remove this bit:
[s3] calling_format = boto.s3.connection.OrdinaryCallingFormat