def encode(self, bnd):
'''
Return spike counts corresponding to given direction and encoding model.
Parameters
----------
bnd :BinnedData
uses bnd.pos, bnd.bin_edges
'''
pos = bnd.pos
time = bnd.bin_edges
ntask, nrep, nedge, ndim = pos.shape
model = self.model
assert(ndim == 3)
nbin = nedge - 1
lshape = ntask, nrep, nbin
rate = np.zeros(lshape, dtype=float)
assert(model == 'kd') # only model currently implemented
rate += self.B['k'] # baseline
dr = get_dir(pos)
rate += np.dot(dr, self.B['D']) # direction
rate += np.dot(pos[...,:-1,:], self.B['P']) # position
speed = get_speed(pos, time, tax=2, spax=-1)
rate += self.B['s'] * speed
return np.exp(rate)
def _format_for_gam2(count, time, pos):
assert np.rank(count) == np.rank(time) == (np.rank(pos) - 1)
assert count.shape[0] == time.shape[0] == pos.shape[0]
assert (count.shape[1] + 1) == time.shape[1] == pos.shape[1]
y = count.flatten()[:,None]
npt = y.size
tax, spax = 1, -1
# don't use real time as won't compare equivalent portions of trials
# t = edge2cen(time, axis=tax) # (ntrial, nbin)
# subtract offset to get all relative times
#t = (t - t[:,:1]).flatten()[:,None]
# instead use bin numbers
ntrial, nbin = count.shape
t = np.tile(np.arange(nbin, dtype=float)[None], (ntrial, 1))
t = t.flatten()[:,None]
# also create trial numbers for pulling out appropriate later on
# these don't correspond to original trial numbers because original trials
# have been permuted before getting here
tr = np.tile(np.arange(ntrial), (nbin, 1)).T.flatten()
d = kin.get_dir(pos, tax=tax, spax=spax).reshape(npt, 3)
p = edge2cen(pos, axis=tax).reshape(npt, 3)
v = kin.get_vel(pos, time, tax=tax, spax=spax).reshape(npt, 3)
sp = kin.get_speed(pos, time, tax=tax, spax=spax).flatten()[:,None]
# q is a second direction set-of-columns for deviance calculation
q = kin.get_dir(pos, tax=tax, spax=spax).reshape(npt, 3)
return np.concatenate([y,t,d,p,v,sp,q], axis=1), tr
def encode(self, bnd):
'''
Return spike counts corresponding to given direction and encoding model.
Parameters
----------
bnd :BinnedData
uses bnd.pos, bnd.bin_edges
'''
pos = bnd.pos
time = bnd.bin_edges
ntask, nrep, nedge, ndim = pos.shape
model = self.model
assert (ndim == 3)
nbin = nedge - 1
lshape = ntask, nrep, nbin
rate = np.zeros(lshape, dtype=float)
assert (model == 'kd') # only model currently implemented
rate += self.B['k'] # baseline
dr = get_dir(pos)
rate += np.dot(dr, self.B['D']) # direction
rate += np.dot(pos[..., :-1, :], self.B['P']) # position
speed = get_speed(pos, time, tax=2, spax=-1)
rate += self.B['s'] * speed
return np.exp(rate)
def _format_for_gam(count, time, pos):
'''
Format data for gam_predict_cv, i.e. an (n, 12) array
Parameters
----------
count : ndarray
spike counts, shape (ntrial, nbin)
time : ndarray
bin_edges, shape (ntrial, nbin + 1)
pos : ndarray
positions at `bin_edges`, shape (ntrial, nbin + 1, 3)
Returns
-------
formatted_data : ndarray
shape (ntrial * nbin, 12)
dimension 1 is [count, t, dx, dy, dz, px, py, pz, vx, vy, vz, sp]
'''
assert np.rank(count) == np.rank(time) == (np.rank(pos) - 1)
assert count.shape[0] == time.shape[0] == pos.shape[0]
assert (count.shape[1] + 1) == time.shape[1] == pos.shape[1]
y = count.flatten()[:,None]
npt = y.size
tax, spax = 1, -1
# don't use real time as won't compare equivalent portions of trials
# t = edge2cen(time, axis=tax) # (ntrial, nbin)
# subtract offset to get all relative times
#t = (t - t[:,:1]).flatten()[:,None]
# instead use bin numbers
ntrial, nbin = count.shape
t = np.tile(np.arange(nbin, dtype=float)[None], (ntrial, 1))
t = t.flatten()[:,None]
d = kin.get_dir(pos, tax=tax, spax=spax).reshape(npt, 3)
p = edge2cen(pos, axis=tax).reshape(npt, 3)
v = kin.get_vel(pos, time, tax=tax, spax=spax).reshape(npt, 3)
sp = kin.get_speed(pos, time, tax=tax, spax=spax).flatten()[:,None]
# q is a second direction set-of-columns for deviance calculation
q = kin.get_dir(pos, tax=tax, spax=spax).reshape(npt, 3)
return np.concatenate([y,t,d,p,v,sp,q], axis=1)
