def get_projected_speed(self, tno):
'''calculate speed of trial number `tno`, projected along task direction
Parameters
----------
tno : int
index of trial to calculate
Returns
-------
proj : ndarray
projected speed values, in m/s
Notes
-----
we have to do them one-at-a-time because positions has different
numbers of elements in each trial.
'''
trial = self.positions[tno]
pos = trial[:,0:3]
time = trial[:,3]
vel = kin.get_vel(pos, time, tax=0, spax=-1)
task_dir = unitvec(self.TargetPos[tno] - self.StartPos[tno])
proj = np.dot(vel, task_dir)
return proj
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 _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)
def prepare_regressors(count, pos, time, model=''):
'''
Parameters
----------
count : array_like
firing rates, shape (ntrial, nbin)
pos : array_like
positional data, shape (ntrial, nbin + 1, ndim)
these are positions at bin edges
time : array_like
bin edge times, shape (ntrial, nbin + 1)
model : string
model
Returns
-------
reg_vars : ndarray
reg_frs : ndarray
'''
count = np.asarray(count)
assert(np.rank(count) == 2)
ntrial, nbin = count.shape
assert(not np.any(np.isnan(count)))
pos = np.asarray(pos)
assert(np.rank(pos) == 3)
assert(pos.shape[0:2] == (ntrial, nbin + 1))
time = np.asarray(time)
assert(np.rank(time) == 2)
assert(time.shape == (ntrial, nbin + 1))
assert(type(model) == str)
#ntrial, nbin = count.shape
ndim = pos.shape[-1]
nvar = ntrial * nbin
endog = count.reshape(nvar) # should be equivalent to flatten
vels = None
exog = None
# add the time-invariant variables
if 'p' in model:
pos_flat = pos[:,1:,:].reshape(nvar, ndim)
exog = add_column(exog, pos_flat)
if 's' in model:
if vels == None:
vels = get_vel(pos, time, tax=1, spax=2)
spds = np.sqrt(np.sum(vels**2, axis=2))
spds_flat = spds.reshape(nvar)
exog = add_column(exog, spds_flat)
# do the potentially time-variant variables
if ('d' in model or 'v' in model):
if 'd' in model:
var = get_dir(pos, tax=1, spax=2)
elif 'v' in model:
if vels == None:
vels = get_vel(pos, time, tax=1, spax=2)
var = vels
if not 'X' in model: # time-static variable
var_flat = np.reshape(var, (nvar, ndim))
else: # time-dynamic variable
mask = np.eye(nbin)
var_flat = np.reshape(mask[None,...,None] * var[...,None,:],
(nvar, nbin * ndim))
exog = add_column(exog, var_flat)
# do the constant + dynamic direction model
if 'q' in model:
assert ('X' in model)
# for simplicity's sake, let us call this model 'kqX'
assert ('d' not in model)
assert ('v' not in model)
if 'k' in model:
exog = sm.tools.add_constant(exog, prepend=False)
offset = np.log(np.diff(time, axis=-1).reshape(nvar))
return endog, exog, offset