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Compute proportion of information used for theta plots

master
tforest 2023-11-29 16:03:15 +01:00
parent 23fef174f2
commit 87bef76e28
1 changed files with 11 additions and 11 deletions
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22
swp2.py
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@ -262,14 +262,6 @@ def plot_all_epochs_thetafolder(folder_path, mu, tgen, title = "Title", theta_sc
# divide by N0 # divide by N0
y[i] = y[i]/N0 y[i] = y[i]/N0
x[i] = x[i]/N0 x[i] = x[i]/N0
sum_theta_i = 0
print(epoch, x, y)
for i in range(2, len(y)-1):
sum_theta_i=y[i] / (i-1)
prop = []
for k in range(2, len(y)-1):
prop.append(y[k+1] / (k - 1) / sum_theta_i)
#print(epoch, prop)
plt.plot(x, y, 'o', linestyle = "-", alpha=0.75, lw=2, label = str(epoch)+' BrkPt | Lik='+greatest_likelihood) plt.plot(x, y, 'o', linestyle = "-", alpha=0.75, lw=2, label = str(epoch)+' BrkPt | Lik='+greatest_likelihood)
if theta_scale: if theta_scale:
plt.xlabel("Coal. time") plt.xlabel("Coal. time")
@ -294,15 +286,15 @@ def plot_all_epochs_thetafolder(folder_path, mu, tgen, title = "Title", theta_sc
# number of monomorphic sites # number of monomorphic sites
L = L_stored L = L_stored
S0 = L-S S0 = L-S
print("SFS", SFS_stored) # print("SFS", SFS_stored)
print("S", S, "L", L, "S0=", S0) # print("S", S, "L", L, "S0=", S0)
# compute Ln # compute Ln
Ln = log_facto(S+S0) - log_facto(S0) + np.log(float(S0)/(S+S0)) * S0 Ln = log_facto(S+S0) - log_facto(S0) + np.log(float(S0)/(S+S0)) * S0
for xi in range(0, len(SFS_stored)): for xi in range(0, len(SFS_stored)):
p_i = SFS_stored[xi] / float(S+S0) p_i = SFS_stored[xi] / float(S+S0)
Ln += np.log(p_i) * SFS_stored[xi] - log_facto(SFS_stored[xi]) Ln += np.log(p_i) * SFS_stored[xi] - log_facto(SFS_stored[xi])
res = Ln res = Ln
print(res) # print(res)
# basic plot likelihood # basic plot likelihood
plt.figure(figsize=(5000/my_dpi, 2800/my_dpi), dpi=my_dpi) plt.figure(figsize=(5000/my_dpi, 2800/my_dpi), dpi=my_dpi)
plt.rcParams['font.size'] = '18' plt.rcParams['font.size'] = '18'
@ -362,6 +354,14 @@ def plot_test_theta(folder_path, mu, tgen, title = "Title", theta_scale = True,
N0 = y[0] N0 = y[0]
for i in range(len(y)): for i in range(len(y)):
y[i] = y[i]/N0 y[i] = y[i]/N0
# compute the proportion of information used at each bin of the SFS
sum_theta_i = 0
for i in range(2, len(y)-1):
sum_theta_i+=y[i] / (i-1)
prop = []
for k in range(2, len(y)-1):
prop.append(y[k] / (k - 1) / sum_theta_i)
# plot
plt.plot(x, y, 'o', linestyle="dotted", alpha=0.75, lw=2, label = str(epoch)+' brks') plt.plot(x, y, 'o', linestyle="dotted", alpha=0.75, lw=2, label = str(epoch)+' brks')
plt.xlabel("# breaks") plt.xlabel("# breaks")
plt.ylabel("theta") plt.ylabel("theta")