def plots(sessions_A, sessions_B, RL_agent, LR_agent = None, title = None,
max_change_LR = 0.001, max_change_RL = 0.01,
test_time = 20, parallel = False, test_data = None):
if test_data:
RL_fit_A = test_data['RL_fit']['fit_A']
RL_fit_B = test_data['RL_fit']['fit_B']
LR_fit_A = test_data['LR_fit']['fit_A']
LR_fit_B = test_data['LR_fit']['fit_B']
title = test_data['title']
else:
RL_fit_A = mf.fit_population(sessions_A, RL_agent, max_change = max_change_RL, parallel = parallel)
RL_fit_B = mf.fit_population(sessions_B, RL_agent, max_change = max_change_RL, parallel = parallel)
LR_fit_A = mf.fit_population(sessions_A, LR_agent, max_change = max_change_LR, parallel = parallel)
LR_fit_B = mf.fit_population(sessions_B, LR_agent, max_change = max_change_LR, parallel = parallel)
trial_rate_comparison(sessions_A, sessions_B, test_time, 1, title)
reversal_comparison(sessions_A, sessions_B, 2, title)
rp.scatter_plot_comp(LR_fit_A, LR_fit_B, fig_no = 3)
p.title(title)
rp.pop_fit_comparison(RL_fit_A, RL_fit_B, fig_no = 4, normalize = False)
p.suptitle(title)
abs_preference_comparison(sessions_A, sessions_B, RL_fit_A, RL_fit_B, RL_agent, 5, title)
