<jupyter_start><jupyter_text>Simulation participant 1<jupyter_code>def sim1_condition(params, n_trials):
    """Simulates a diffusion process over an entire condition."""
    itr = int(n_trials/4)
    drift, boundary, beta, t_e_mean, t_m_mean = params
    choicert = np.empty(n_trials)
    N200 = np.empty(n_trials)
    cond_spat = np.empty(n_trials)
    cond_coher = np.empty(n_trials) 
    np.random.seed(123)
    for i in range(itr):
        choicert[i], N200[i] = diffusion_trial(drift[0], boundary, beta, t_e_mean[0], t_m_mean)
        choicert[itr + i], N200[itr + i] = diffusion_trial(drift[0], boundary, beta, t_e_mean[1], t_m_mean)
        choicert[2*itr + i], N200[2*itr + i] = diffusion_trial(drift[1], boundary, beta, t_e_mean[0], t_m_mean)
        choicert[3*itr + i], N200[3*itr + i] = diffusion_trial(drift[1], boundary, beta, t_e_mean[1], t_m_mean)
    
    cond_spat = np.concatenate([np.repeat(1,itr),np.repeat(2,itr), np.repeat(1,itr),np.repeat(2,itr)])
    cond_coher = np.concatenate([np.repeat(1,itr),np.repeat(1,itr), np.repeat(2,itr),np.repeat(2,itr)])
    conds = np.concatenate([np.repeat(1,itr), np.repeat(2,itr), np.repeat(3,itr), np.repeat(4,itr)])
    
    return choicert, cond_spat, cond_coher, conds, N200

choicert, cond_spat,cond_coher, conds, N200  = sim1_condition(params=[[2.5, 1.5], 1.5, .5,  [.3, .5], .4], n_trials=288)
sim1_dic = {'y':choicert, 'cond_spat':cond_spat, 'cond_coher':cond_coher, 'conds':conds, 'n200lat': N200}
sim1_df = pd.DataFrame(sim1_dic)
sim1_df.to_csv('sim1.csv', index=False)<jupyter_output><empty_output><jupyter_text>Simulation participant 2<jupyter_code>def sim2_condition(params, n_trials):
    """Simulates a diffusion process over an entire condition."""
    itr = int(n_trials/4)
    drift, boundary, beta, t_e_mean, t_m_mean = params
    choicert = np.empty(n_trials)
    N200 = np.empty(n_trials)
    cond_spat = np.empty(n_trials)
    cond_coher = np.empty(n_trials)
    np.random.seed(123)
    for i in range(itr):
        choicert[i], N200[i] = diffusion_trial(drift[0], boundary, beta, t_e_mean, t_m_mean[0])
        choicert[itr + i], N200[itr + i] = diffusion_trial(drift[0], boundary, beta, t_e_mean, t_m_mean[1])
        choicert[2*itr + i], N200[2*itr + i] = diffusion_trial(drift[1], boundary, beta, t_e_mean, t_m_mean[0])
        choicert[3*itr + i], N200[3*itr + i] = diffusion_trial(drift[1], boundary, beta, t_e_mean, t_m_mean[1])
    
    cond_spat = np.concatenate([np.repeat(1,itr),np.repeat(2,itr), np.repeat(1,itr),np.repeat(2,itr)])
    cond_coher = np.concatenate([np.repeat(1,itr),np.repeat(1,itr), np.repeat(2,itr),np.repeat(2,itr)])
    conds = np.concatenate([np.repeat(1,itr), np.repeat(2,itr), np.repeat(3,itr), np.repeat(4,itr)])
    
    return choicert, cond_spat, cond_coher, conds, N200

choicert, cond_spat,cond_coher, conds, N200  = sim2_condition(params=[[2.5, 1.5], 1.5, .5,  .3, [.4, .6]], n_trials=500)
sim2_dic = {'y':choicert, 'cond_spat':cond_spat, 'cond_coher':cond_coher, 'conds':conds, 'n200lat': N200}
sim2_df = pd.DataFrame(sim2_dic)
sim2_df.to_csv('sim2.csv', index=False)<jupyter_output><empty_output><jupyter_text>Simulation participant 3<jupyter_code>def sim3_condition(params, n_trials):
    """Simulates a diffusion process over an entire condition."""
    itr = int(n_trials/4)
    drift, boundary, beta, t_e_mean, t_m_mean = params
    choicert = np.empty(n_trials)
    N200 = np.empty(n_trials)
    cond_spat = np.empty(n_trials)
    cond_coher = np.empty(n_trials)
    np.random.seed(123)
    for i in range(itr):
        choicert[i], N200[i] = diffusion_trial(drift[0], boundary, beta, t_e_mean[0], t_m_mean[0])
        choicert[itr + i], N200[itr + i] = diffusion_trial(drift[0], boundary, beta, t_e_mean[1], t_m_mean[1])
        choicert[2*itr + i], N200[2*itr + i] = diffusion_trial(drift[1], boundary, beta, t_e_mean[0], t_m_mean[0])
        choicert[3*itr + i], N200[3*itr + i] = diffusion_trial(drift[1], boundary, beta, t_e_mean[1], t_m_mean[1])
    
    cond_spat = np.concatenate([np.repeat(1,itr),np.repeat(2,itr), np.repeat(1,itr),np.repeat(2,itr)])
    cond_coher = np.concatenate([np.repeat(1,itr),np.repeat(1,itr), np.repeat(2,itr),np.repeat(2,itr)])
    conds = np.concatenate([np.repeat(1,itr), np.repeat(2,itr), np.repeat(3,itr), np.repeat(4,itr)])
    
    return choicert, cond_spat, cond_coher, conds, N200

choicert, cond_spat,cond_coher, conds, N200  = sim3_condition(params=[[2.5, 1.5], 1.5, .5,  [.3, .5], [.4, .6]], n_trials=500)
sim3_dic = {'y':choicert, 'cond_spat':cond_spat, 'cond_coher':cond_coher, 'conds':conds, 'n200lat': N200}
sim3_df = pd.DataFrame(sim3_dic)
sim3_df.to_csv('sim3.csv', index=False)<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.17366290219475816, 'fraction_numerical': 0.049648412529298955, 'mean_word_length': 5.010243277848912, 'pattern_counts': {'":': 0, '<': 13, '<?xml version=': 0, '>': 13, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'chain_length': '3', 'dir': 'jupyter-structured-clean-dedup', 'id': '65168', 'license': 'MIT', 'n_tokens_mistral': 2116, 'n_tokens_neox': 2084, 'n_words': 323, 'path': 'Neuro-Cognitive Models/Runs/Simulation_run/Data_simulation.ipynb', 'repo_name': 'AGhaderi/spatial_attenNCM', 'size': '4693'}