def test_mle_fit_psycho(self):
expected = {
'weibull50': (np.array([0.0034045, 3.9029162,
.1119576]), -334.1149693046583),
'weibull': (np.array([0.00316341, 1.72552866,
0.1032307]), -261.235178611311),
'erf_psycho': (np.array([-9.78747259, 10.,
0.15967605]), -193.0509031440323),
'erf_psycho_2gammas':
(np.array([-11.45463779, 9.9999999, 0.24117732,
0.0270835]), -147.02380025592902)
}
for model in self.test_data.keys():
pars, L = psy.mle_fit_psycho(self.test_data[model],
P_model=model,
nfits=10)
expected_pars, expected_L = expected[model]
self.assertTrue(np.allclose(expected_pars, pars, atol=1e-3),
f'unexpected pars for {model}')
self.assertTrue(np.isclose(expected_L, L, atol=1e-3),
f'unexpected likelihood for {model}')
# Test on of the models with function pars
params = {
'parmin': np.array([-5., 10., 0.]),
'parmax': np.array([5., 15., .1]),
'parstart': np.array([0., 11., 0.1]),
'nfits': 5
}
model = 'erf_psycho'
pars, L = psy.mle_fit_psycho(self.test_data[model],
P_model=model,
**params)
expected = [-5, 15, 0.1]
self.assertTrue(np.allclose(expected, pars, rtol=.01),
f'unexpected pars for {model}')
self.assertTrue(np.isclose(-195.55603, L, atol=1e-5),
f'unexpected likelihood for {model}')
def fit_psychfunc(df):
choicedat = df.groupby('signedContrast').agg({
'trial': 'max',
'choice2': 'mean'
}).reset_index()
pars, L = psy.mle_fit_psycho(
choicedat.values.transpose(),
P_model='erf_psycho_2gammas',
parstart=np.array(
[choicedat['signedContrast'].mean(), 20., 0.05, 0.05]),
parmin=np.array([choicedat['signedContrast'].min(), 0., 0., 0.]),
parmax=np.array([choicedat['signedContrast'].max(), 100., 1, 1]))
df2 = {
'bias': pars[0],
'threshold': pars[1],
'lapselow': pars[2],
'lapsehigh': pars[3]
}
return pd.DataFrame(df2, index=[0])
#bias = -10.
#threshold = 20.
#gamma = .1
# fake experimental data given those parameters
#ratio = psychofit.erf_psycho([bias, threshold, gamma],edgeMid)
# In[10]:
cc = edgeMid # contrasts
nn = histTact + histDCS # number of trials at each contrast
#ntrials = 40
#nn = ntrials*np.ones((np.shape(ratio)))
pp = ratio # proportion "rightward"
pars, L = psychofit.mle_fit_psycho(np.vstack((cc, nn, pp)), 'erf_psycho',
np.array([-100, 10., 0.5]),
np.array([-200., 0.1, 0.]),
np.array([20., 500., 1]), 50)
plt.figure(figsize=(10, 10))
plt.plot(cc, pp, 'ko', mfc='k', markersize=15)
plt.plot(
np.arange(-550, 150, 0.1),
psychofit.erf_psycho(pars, np.arange(-550, 150, 0.1)),
'-b',
linewidth=7,
label=' threshold = {:2.0f} \n slope = {:2.0f} \n lapse = {:.01f}'.format(
*pars))
plt.legend()
#plt.xlim([-100,100])
plt.ylim([-0.1, 1.1])
plt.plot((0, 0), (0, 1), 'k:')
def plot_psychometric(df, color='black', ax=None, **kwargs):
"""
Plots psychometric data for a given DataFrame of behavioural trials
If the data contains more than six different contrasts (or > three per side)
the data are fit with an erf function. The x-axis is percent contrast and
the y-axis is the proportion of 'rightward choices', i.e. trials where the
subject turned the wheel clockwise to threshold.
Example:
df = alf.load_behaviour('2018-09-11_1_Mouse1', r'\\server\SubjectData')
plot_psychometric(df)
Args:
df (DataFrame): DataFrame constructed from an ALF trials object.
ax (Axes): Axes to plot to. If None, a new figure is created.
Returns:
ax (Axes): The plot axes
"""
if len(df['signedContrast'].unique()) > 4:
df2 = df.groupby(['signedContrast']).agg({
'choice': 'count',
'choice2': 'mean'
}).reset_index()
df2.rename(columns={
"choice2": "fraction",
"choice": "ntrials"
},
inplace=True)
pars, L = psy.mle_fit_psycho(
df2.transpose().values, # extract the data from the df
P_model='erf_psycho_2gammas',
parstart=np.array([df2['signedContrast'].mean(), 20., 0.05, 0.05]),
parmin=np.array([df2['signedContrast'].min(), 0., 0., 0.]),
parmax=np.array([df2['signedContrast'].max(), 100., 1, 1]))
sns.lineplot(np.arange(-100, 100),
psy.erf_psycho_2gammas(pars, np.arange(-100, 100)),
color=color,
ax=ax)
# plot datapoints on top
sns.lineplot(x='signedContrast',
y='choice2',
err_style="bars",
linewidth=0,
linestyle='None',
mew=0.5,
marker='.',
ci=68,
data=df,
color=color,
ax=ax)
# Reduce the clutter
ax.set_xticks([-100, -50, 0, 50, 100])
ax.set_xticklabels(['-100', '-50', '0', '50', '100'])
ax.set_yticks([0, .5, 1])
# Set the limits
ax.set_xlim([-110, 110])
ax.set_ylim([-0.03, 1.03])
ax.set_xlabel('Contrast (%)')
return ax
def make(self, key):
key_psy = key.copy()
n_trials, n_correct_trials = (behavior.TrialSet & key).fetch1(
'n_trials', 'n_correct_trials')
key_psy['performance'] = n_correct_trials / n_trials
trials = behavior.TrialSet.Trial & key
trials_rt = trials.proj(rt='trial_response_time-trial_stim_on_time')
contrasts_left = np.unique(
trials.fetch('trial_stim_contrast_left'))[1:] # discard contrast 0
contrasts_right = np.unique(trials.fetch('trial_stim_contrast_right'))[
1:] # discard contrast 0
trials_no_contrast = trials & 'trial_stim_contrast_right=0' & 'trial_stim_contrast_left=0'
n_right_trials_stim_left = [len(trials & 'trial_stim_contrast_left={}'.format(contrast) & 'trial_response_choice="CCW"') \
for contrast in contrasts_left]
n_trials_stim_left = [len(trials & 'trial_stim_contrast_left={}'.format(contrast)) \
for contrast in contrasts_left]
n_right_trials_stim_right = [len(trials & 'trial_stim_contrast_right={}'.format(contrast) & 'trial_response_choice="CCW"') \
for contrast in contrasts_right]
n_trials_stim_right = [len(trials & 'trial_stim_contrast_right={}'.format(contrast)) \
for contrast in contrasts_right]
p_right_stim_left = np.divide(n_right_trials_stim_left,
n_trials_stim_left)
p_right_stim_right = np.divide(n_right_trials_stim_right,
n_trials_stim_right)
rt_stim_left = [(trials_rt & (trials & 'trial_stim_contrast_left={}'.format(contrast))).fetch('rt').mean() \
for contrast in contrasts_left]
rt_stim_right = [(trials_rt & (trials & 'trial_stim_contrast_right={}'.format(contrast))).fetch('rt').mean() \
for contrast in contrasts_right]
trials_no_contrast = trials & 'trial_stim_contrast_right=0' & 'trial_stim_contrast_left=0'
if trials_no_contrast:
key_psy['contrasts'] = np.hstack(
[np.negative(contrasts_left[::-1]), 0, contrasts_right]) * 100
p_right_no_contrast = len(trials_no_contrast
& 'trial_response_choice="CCW"') / len(
trials_no_contrast)
key_psy['prob_choose_right'] = np.hstack([
p_right_stim_left[::-1], p_right_no_contrast,
p_right_stim_right
])
n_total_trials = np.hstack([
n_trials_stim_left,
len(trials_no_contrast), n_trials_stim_right
])
rt_no_contrast = (trials_rt
& trials_no_contrast).fetch('rt').mean()
key_psy['rt'] = np.hstack(
[rt_stim_left, rt_no_contrast, rt_stim_right])
else:
key_psy['contrasts'] = np.hstack(
[np.negative(contrasts_left[::-1]), contrasts_right]) * 100
key_psy['prob_choose_right'] = np.hstack([
(p_right_stim_left[::-1]), p_right_stim_right
])
n_total_trials = np.hstack(
[n_trials_stim_left, n_trials_stim_right])
key_psy['rt'] = np.hstack([rt_stim_left, rt_stim_right])
pars, L = psy.mle_fit_psycho(np.vstack([key_psy['contrasts'], n_total_trials, key_psy['prob_choose_right']]), \
P_model='erf_psycho_2gammas', \
parstart=np.array([key_psy['contrasts'].mean(), 20., 0.05, 0.05]), \
parmin=np.array([key_psy['contrasts'].mean(), 0., 0., 0.]), \
parmax=np.array([key_psy['contrasts'].mean(), 100., 1, 1]))
print(pars)
key_psy['bias'] = pars[0]
key_psy['threshold'] = pars[1]
key_psy['lapse_low'] = pars[2]
key_psy['lapse_high'] = pars[3]
self.insert1(key_psy)