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
