def get_sort_by_pd(bnd):
'''
Parameters
----------
bnd : cell.BinnedData instance
contains PSTHs (n, ...) and positions (n, ...) from construct_PSTHs
tasks : array_like
shape (n, 6), start and target positions of movements
Returns
-------
indices to sort PSTH tasks relative to PD.
'''
# bnd.ensure_flat_inplace()
tasks = bnd.tasks
starts, stops = tasks[:,:3], tasks[:,3:]
dirs = stops - starts
ndset = bnd.count.shape[2]
pds = np.zeros((ndset, 3))
for i in xrange(ndset):
count, pos, time = bnd.get_for_glm(i)
model = 'kd'
b, cov = glm_any_model(count, pos, time, model=model)
bdict = unpack_coefficients(b, model=model)
pds[i] = bdict['d'] / np.linalg.norm(bdict['d'])
angs = np.arccos(np.dot(pds, dirs.T))
return np.argsort(angs, axis=-1)
def test_glm_any_model():
nbin = 10
align = 'hold'
lag = 0.1 # s
ds = datasets['small']
dc = DataCollection(ds.get_files())
dc.add_unit(ds.get_units()[0], lag)
bnd = dc.make_binned(nbin=nbin, align=align)
ntask, nrep, nunit, nbin = bnd.shape
# make perfect test counts
# direction-only model
tp = radians([20, 10]) # degrees
b0 = log(10) # log(Hz)
pd = pol2cart(tp)
drn = kinematics.get_idir(bnd.pos, axis=2)
rate = exp(dot(drn, pd) + b0)
window_size = diff(bnd.bin_edges, axis=2)
count_mean = rate * window_size
count = poisson(count_mean)
count = count[:,:,None]
bnd.set_PSTHs(count)
# now fit data for pd
count, pos, time = bnd.get_for_regress()
bnom, bse_nom = glm_any_model(count, pos, time, model='kd')
pd_exp = unitvec(bnom['d'])
tp_exp = cart2pol(pd_exp)
acceptable_err = 0.05 # about 3 degrees absolute error
err = abs(tp - tp_exp)
assert_array_less(err, acceptable_err)