figure_folder = '../figures/' data_dir = '../data_new' prefix = '/20140820_' N_list = [20000] #,20000] mu_list = [1e-6, 2e-6, 4e-6, 8e-6, 16e-6, 32e-6, 64e-6, 128e-6] #nflip_list = [0.02,0.04, 0.08, 0.16] gamma_list = [1.0] #, 2.0,3.0, 5.0] omega_list = [0.3] nflip_list = [0.04, 0.08] sdt_list = [ 1, 100 ] #determines whether 2 genomes are sampled every generation, or 200 every 100 gen pred, norm_pred, run_stats = AU.load_prediction_data(prefix, N_list, mu_list, nflip_list, sdt_list, return_mean=True) valdt = 200 ssize = 200 D = 0.2 L = 2000 mean_fitness_true_fitness_spearman_i = -4 for gamma in gamma_list: for omega in omega_list: for sdt in [1, 100]: plt.figure(figsize=(10, 6)) ax = plt.subplot(111) #plt.title(r'\omega='+str(omega)+',\;dt='+str(sdt)+'$')
cols = ['b', 'r', 'g', 'c', 'm', 'k', 'y'] cols+=cols figure_folder = '../figures/' data_dir = '../data_new' prefix= '/20140820_' N_list = [20000] #,20000] mu_list = [1e-6,2e-6, 4e-6, 8e-6, 16e-6, 32e-6, 64e-6] #,128e-6] #nflip_list = [0.02,0.04, 0.08, 0.16] offset = 2 # multiply by 2 to transform to pairwise distance m_list = 2.0**np.arange(-6,4, 1) * 0.5 m_to_plot = m_list[offset:-2] nflip = 0.08 #, 0.16] sdt_list = [1,100] #determines whether 2 genomes are sampled every generation, or 200 every 100 gen pred, norm_pred, run_stats, corrcoef = AU.load_prediction_data(prefix, N_list, mu_list, [nflip], sdt_list, return_mean=True, polarizer=True) pred_I, norm_pred_I, run_stats_I = AU.load_prediction_data(prefix, N_list, mu_list, [nflip], sdt_list, return_mean=True, polarizer=False) D,gamma,omega = 0.2,1.0,0.3 valdt = 200 ssize = 200 L=2000 mean_fitness_true_fitness_spearman_i = -4 ################################################################### ### correlation vs pairwise diversity ################################################################### for sdt in [1,100]:
line_styles = ["-", "--", "-."] cols = ["b", "r", "g", "c", "m", "k", "y"] cols += cols figure_folder = "../figures/" data_dir = "../data_new" prefix = "/20140820_" N_list = [20000] # ,20000] mu_list = [1e-6, 2e-6, 4e-6, 8e-6, 16e-6, 32e-6, 64e-6, 128e-6] # nflip_list = [0.02,0.04, 0.08, 0.16] gamma_list = [1.0] # , 2.0,3.0, 5.0] omega_list = [0.3] nflip_list = [0.04, 0.08] sdt_list = [1, 100] # determines whether 2 genomes are sampled every generation, or 200 every 100 gen pred, norm_pred, run_stats = AU.load_prediction_data(prefix, N_list, mu_list, nflip_list, sdt_list, return_mean=True) valdt = 200 ssize = 200 D = 0.2 L = 2000 mean_fitness_true_fitness_spearman_i = -4 for gamma in gamma_list: for omega in omega_list: for sdt in [1, 100]: plt.figure(figsize=(10, 6)) ax = plt.subplot(111) # plt.title(r'\omega='+str(omega)+',\;dt='+str(sdt)+'$') for di, D in enumerate([0.2, 0.5]): pred_label = ssize, gamma, D, omega, valdt