def get_pupil_data(sub, run):
"""Get a preprocessed pupil trace for one subject and one avmovie run """
dm = io.readtxt(TRACE_SRC.format(sub=sub, run=run))
dm = ops.auto_type(dm)
dm.pupil_size @= lambda i: np.nan if i == 0 else i
dm.luminance @= lambda i: np.nan if i == 0 else i
dm.pupil_size = srs._interpolate(ops.z(dm.pupil_size))
dm.luminance = srs._interpolate(ops.z(dm.luminance))
if REMOVE_LUMINANCE_FROM_PUPIL:
i, s1, s2 = np.polyfit(dm.pupil_size, dm.luminance, deg=2)
dm.pupil_size -= i + s1 * dm.luminance + s2 * dm.luminance ** 2
return deconv_hrf(flt(dm.pupil_size))
def test_z():
dm = DataMatrix(length=5)
dm.a = range(-2, 3)
dm.z = ops.z(dm.a)
for x, y in zip(dm.z, [-1.26, -0.63, 0, .63, 1.26]):
assert (abs(x - y) < .1)
def process_frame(run, frame, lm, cm):
dm = DataMatrix(length=len(SUBJECTS))
dm.frame = frame
dm.sub = IntColumn
dm.x = FloatColumn
dm.y = FloatColumn
dm.pupil = FloatColumn
dm.luminance = FloatColumn
dm.change = FloatColumn
print('Run {}, frame {}'.format(run, frame))
for row, sub in zip(dm, SUBJECTS):
_dm = _get_subject_data(sub, run)
_dm.pupil = ops.z(_dm.pupil)
try:
_row = (_dm.frame == frame)[0]
except IndexError:
continue
row.sub = sub
x = min(1279, max(0, _row.x))
y = min(546, max(0, _row.y))
if not x and not y:
row.x = np.nan
row.y = np.nan
row.pupil = np.nan
row.luminance = np.nan
row.change = np.nan
else:
row.x = x
row.y = y
row.pupil = _row.pupil
row.luminance = lm[int(y), int(x)]
row.change = cm[int(y), int(x)]
return dm
def test_z(): dm = DataMatrix(length=5) dm.a = range(-2,3) dm.z = ops.z(dm.a) for x, y in zip(dm.z, [-1.26, -0.63, 0, .63, 1.26]): assert(abs(x-y) < .1)
def test_z():
dm = DataMatrix(length=5)
dm.a = range(-2, 3)
dm.z = ops.z(dm.a)
for test, ref in zip(dm.z, [-1.26, -0.63, 0, .63, 1.26]):
assert (math.isclose(test, ref, abs_tol=.01))
# Add a non-numeric value, which should be ignored and its z value should
# be NAN.
dm.length = 6
dm.z = ops.z(dm.a)
assert (dm.z[5] != dm.z[5])
for test, ref in zip(dm.z[:-1], [-1.26, -0.63, 0, .63, 1.26]):
assert (math.isclose(test, ref, abs_tol=.01))
# If there is no variability, the z-scores should be NAN
dm.a = 2
dm.z = ops.z(dm.a)
assert (all(ref != ref for ref in dm.z))
def get_trace_data(sub, run, tracename):
"""A generic function to get a simple trace."""
dm = io.readtxt(TRACE_SRC.format(sub=sub, run=run))
dm = ops.auto_type(dm)
dm[tracename] @= lambda i: np.nan if i == 0 else i
dm[tracename] = srs._interpolate(ops.z(dm[tracename]))
return deconv_hrf(flt(dm[tracename]))