def test_sigmoid():
"""Test sigmoidal fitting and generation
"""
n_pts = 1000
x = np.random.randn(n_pts)
p0 = (0., 1., 0., 1.)
y = ea.sigmoid(x, *p0)
assert_true(np.all(np.logical_and(y <= 1, y >= 0)))
p = ea.fit_sigmoid(x, y)
assert_allclose(p, p0, atol=1e-4, rtol=1e-4)
y += np.random.rand(n_pts) * 0.01
p = ea.fit_sigmoid(x, y)
assert_allclose(p, p0, atol=0.1, rtol=0.1)
def test_sigmoid():
"""Test sigmoidal fitting and generation."""
n_pts = 1000
x = np.random.randn(n_pts)
p0 = (0., 1., 0., 1.)
y = ea.sigmoid(x, *p0)
assert_true(np.all(np.logical_and(y <= 1, y >= 0)))
p = ea.fit_sigmoid(x, y)
assert_allclose(p, p0, atol=1e-4, rtol=1e-4)
p = ea.fit_sigmoid(x, y, (0, 1, None, None), ('upper', 'lower'))
assert_allclose(p, p0, atol=1e-4, rtol=1e-4)
y += np.random.rand(n_pts) * 0.01
p = ea.fit_sigmoid(x, y)
assert_allclose(p, p0, atol=0.1, rtol=0.1)
def test_sigmoid():
"""Test sigmoidal fitting and generation."""
n_pts = 1000
x = np.random.RandomState(0).randn(n_pts)
p0 = (0., 1., 0., 1.)
y = ea.sigmoid(x, *p0)
assert np.all(np.logical_and(y <= 1, y >= 0))
p = ea.fit_sigmoid(x, y)
assert_allclose(p, p0, atol=1e-4, rtol=1e-4)
with pytest.warns(None): # scipy convergence
p = ea.fit_sigmoid(x, y, (0, 1, None, None), ('upper', 'lower'))
assert_allclose(p, p0, atol=1e-4, rtol=1e-4)
y += np.random.rand(n_pts) * 0.01
p = ea.fit_sigmoid(x, y)
assert_allclose(p, p0, atol=0.1, rtol=0.1)
def test_sigmoid():
"""Test sigmoidal fitting and generation."""
n_pts = 1000
x = np.random.RandomState(0).randn(n_pts)
p0 = (0., 1., 0., 1.)
y = ea.sigmoid(x, *p0)
assert np.all(np.logical_and(y <= 1, y >= 0))
p = ea.fit_sigmoid(x, y)
assert_allclose(p, p0, atol=1e-4, rtol=1e-4)
with pytest.warns(None): # scipy convergence
p = ea.fit_sigmoid(x, y, (0, 1, None, None), ('upper', 'lower'))
assert_allclose(p, p0, atol=1e-4, rtol=1e-4)
y += np.random.rand(n_pts) * 0.01
p = ea.fit_sigmoid(x, y)
assert_allclose(p, p0, atol=0.1, rtol=0.1)
from expyfun.analyze import sigmoid
# Make a callback function that prints to the console, rather than log file
def callback(event_type, value=None, timestamp=None):
print((str(event_type) + ':').ljust(40) + str(value))
# Define parameters for modeled human subject (sigmoid probability)
true_thresh = 115
slope = 0.8
chance = 0.08 # if you don't hear it, you don't respond
# Make a tracker that uses the weighted up-down procedure to find 75%
tr = TrackerMHW(callback, 0, 120, base_step=5, start_value=80)
# Initialize human state
rng = np.random.RandomState(1)
# Do the task until the tracker stops
while not tr.stopped:
tr.respond(rng.rand() < sigmoid(
tr.x_current - true_thresh, lower=chance, slope=slope))
# Plot the results
fig, ax, lines = tr.plot()
lines += tr.plot_thresh()
ax.set_title(
'Adaptive track of model human (true threshold is {})'.format(true_thresh))
# acquired.
# initialize two tracker objects--one for each start value
tr_ud = [TrackerUD(callback, up, down, step_size_up, step_size_down,
stop_reversals, stop_trials, sv, change_indices,
change_rule, x_min, x_max) for sv in start_value]
# initialize TrackerDealer object
td = TrackerDealer(callback, tr_ud, max_lag, pace_rule, rng_dealer)
# Initialize human state
rng_human = np.random.RandomState(1) # random seed for modeled subject
for _, level in td:
# Get information of which trial type is next and what the level is at
# that time from TrackerDealer
td.respond(rng_human.rand() < sigmoid(level - true_thresh,
lower=chance, slope=slope))
##############################################################################
# Plotting the Results
# ---------------------------
axes = plt.subplots(2, 1)[1]
for i in [0, 1]:
fig, ax, lines = td.trackers.ravel()[i].plot(ax=axes[i], n_skip=4)
ax.legend(loc='best')
ax.set_title('Adaptive track with start value {} (true threshold '
'is {})'.format(start_value[i], true_thresh))
fig.tight_layout()
def likelihood_3(params, x, y, upper, slope, lower):
return np.sum((y - sigmoid(x, lower=lower, upper=upper,
midpt=params[0], slope=slope))**2)
def likelihood(params, x, y, lower):
return np.sum((y - sigmoid(x, lower=lower, upper=params[2],
midpt=params[0], slope=params[1]))**2)
if w:
print(':(')
# do individual condition fits
for (p, c, i) in zip(data, ['C0', 'C1'], range(2)):
mid = [ok[0]]
[temp, _, _, _, w] = fmin(likelihood_3, mid,
(conditions, p, ok[2], ok[1], lower),
full_output=True, disp=False)
params.append(temp)
if w:
print(':\'(')
thresholds = np.array([np.power(10, m / 20) for m in [params[0][0], params[1][0]]])
thresholds = np.array([-1. / ok[1] * np.log((ok[2] - .5) / (.75 - .5) - 1) + p[0] for p in params])
thresholds = np.power(10, thresholds / 20)
perform_imp = (sigmoid(20 * np.log10(thresholds[0]), lower=.5, upper=ok[2], midpt=params[1][0], slope=ok[1]) - .75) * 100
# %% save the data
data = dict(responses=responses[-1],
thresholds=thresholds,
ok = ok,
params=params,
percent=percent,
perform_imp = perform_imp)
write_hdf5((path + 'percent_' + subject), data, overwrite=True)
# %% plot an example
if subject=='016':
from collections import OrderedDict
from matplotlib.transforms import blended_transform_factory
perform_imp = np.empty((len(subjects), 1))
data_resamp = np.empty((len(subjects), 1000, 2, 5))
responses = []
biases = []
d_prime = np.empty((len(subjects), 5))
stem_perimp = np.empty((len(subjects), 1000, 2))
stem_thimp = np.empty((len(subjects), 1000, 2))
for i, s in enumerate(subjects):
data = read_hdf5(path + 'percent_' + s)
responses.append(data['responses'])
thresholds[i] = data['thresholds']
per_cor[i] = data['percent']
ok = data['ok']
params = data['params']
curves[i] = [sigmoid(20 * np.log10(x_plot_fit), lower=0.5, upper=ok[2],
midpt=p[0], slope=ok[1]) for p in params]
perform_imp[i] = data['perform_imp']
lapse[i] = [ok[2]]
slope[i] = [ok[1]]
thresh[i] = [params[0], params[1]]
percent = np.mean(per_cor, 0)
inds = np.argsort(thresholds[:, 0])
# %% Plot improvement in threshold at central threshold
dpi_fig = 200
dpi = 600
ms = 7
