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## Usage example
```python
import numpy as np
import questplus as qp
# Stimulus domain.
intensities = np.arange(start=-3.5, stop=-0.5+0.25, step=0.25)
stim_domain = dict(intensity=intensities)
# Parameter domain.
thresholds = intensities.copy()
slopes = np.linspace(0.5, 15, 5)
lower_asymptotes = np.linspace(0.01, 0.5, 5)
lapse_rate = 0.01
param_domain = dict(threshold=thresholds,
slope=slopes,
lower_asymptote=lower_asymptotes,
lapse_rate=lapse_rate)
# Outcome (response) domain.
responses = ['Yes', 'No']
outcome_domain = dict(response=responses)
# Further parameters.
func = 'weibull'
stim_scale = 'log10'
stim_selection_method = 'min_entropy'
param_estimation_method = 'mean'
# Initialize the QUEST+ staircase.
q = qp.QuestPlus(stim_domain=stim_domain,
func=func,
stim_scale=stim_scale,
param_domain=param_domain,
outcome_domain=outcome_domain,
stim_selection_method=stim_selection_method,
param_estimation_method=param_estimation_method)
trial_count = 20
for current_trial_number in range(1, trial_count+1):
next_stim = q.next_stim
print(f'Please present stimulus {next_stim}.')
# Retrieve response
# ...
# outcome = dict(response='Yes') or
# outcome = dict(response='No')
q.update(stim=next_stim, outcome=outcome)
print(f'\nParameter estimates:\n')
for param_name, value in q.param_estimate.items():
print(f' {param_name}: {value:.3f}')
```
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