theoritical logLn swp2
tforest
parent
463b9e63f9
commit
2253ce5f67
138
swp2.py
138
swp2.py
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@ -1,12 +1,38 @@
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import matplotlib.pyplot as plt
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import os
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import numpy as np
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import math
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from scipy.special import gammaln
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from matplotlib.backends.backend_pdf import PdfPages
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def log_facto(k):
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k = int(k)
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if k > 1e6:
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return k * np.log(k) - k + np.log(2*math.pi*k)/2
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val = 0
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for i in range(2, k+1):
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val += np.log(i)
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return val
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def log_facto_1(k):
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startf = 1 # start of factorial sequence
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stopf = int(k+1) # end of of factorial sequence
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q = gammaln(range(startf+1, stopf+1)) # n! = G(n+1)
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return q[-1]
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def return_x_y_from_stwp_theta_file(stwp_theta_file, breaks, mu, tgen, relative_theta_scale = False):
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with open(stwp_theta_file, "r") as swp_file:
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# Read the first line
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line = swp_file.readline()
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L = float(line.split()[2])
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rands = swp_file.readline()
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line = swp_file.readline()
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# skip empty lines before SFS
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while line == "\n":
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line = swp_file.readline()
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sfs = np.array(line.split()).astype(float)
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# Process lines until the end of the file
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while line:
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# check at each line
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@ -27,7 +53,7 @@ def return_x_y_from_stwp_theta_file(stwp_theta_file, breaks, mu, tgen, relative_
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#### END of parsing
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# quit this file if the number of dimensions is incorrect
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if dim < breaks+1:
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return 0,0,0
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return 0,0,0,0,0
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# get n, the last bin of the last group
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# revert the list of groups as the most recent times correspond
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# to the closest and last leafs of the coal. tree.
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@ -90,13 +116,19 @@ def return_x_y_from_stwp_theta_file(stwp_theta_file, breaks, mu, tgen, relative_
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# # # divide by N0
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# # y[i] = y[i]/N0
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# # x[i] = x[i]/N0
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return x,y,likelihood
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return x,y,likelihood,sfs,L
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def return_x_y_from_stwp_theta_file_as_is(stwp_theta_file, breaks, mu, tgen, relative_theta_scale = False):
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with open(stwp_theta_file, "r") as swp_file:
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# Read the first line
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line = swp_file.readline()
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L = float(line.split()[2])
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rands = swp_file.readline()
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line = swp_file.readline()
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# skip empty lines before SFS
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while line == "\n":
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line = swp_file.readline()
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sfs = np.array(line.split()).astype(float)
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# Process lines until the end of the file
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while line:
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# check at each line
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@ -117,7 +149,7 @@ def return_x_y_from_stwp_theta_file_as_is(stwp_theta_file, breaks, mu, tgen, rel
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#### END of parsing
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# quit this file if the number of dimensions is incorrect
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if dim < breaks+1:
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return 0,0,0
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return 0,0
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# get n, the last bin of the last group
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# revert the list of groups as the most recent times correspond
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# to the closest and last leafs of the coal. tree.
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@ -130,7 +162,7 @@ def return_x_y_from_stwp_theta_file_as_is(stwp_theta_file, breaks, mu, tgen, rel
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groups[i] = groups[i].split(',')
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#print(groups[i], len(groups[i]))
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thetas[i] = [float(theta_site[i]), groups[i], likelihood]
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return thetas
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return thetas, sfs
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def plot_k_epochs_thetafolder(folder_path, mu, tgen, breaks = 2, title = "Title", theta_scale = True):
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scenari = {}
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@ -138,7 +170,7 @@ def plot_k_epochs_thetafolder(folder_path, mu, tgen, breaks = 2, title = "Title"
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for file_name in os.listdir(folder_path):
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if os.path.isfile(os.path.join(folder_path, file_name)):
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# Perform actions on each file
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x,y,likelihood = return_x_y_from_stwp_theta_file(folder_path+file_name, breaks = breaks,
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x,y,likelihood,sfs,L = return_x_y_from_stwp_theta_file(folder_path+file_name, breaks = breaks,
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tgen = tgen,
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mu = mu, relative_theta_scale = theta_scale)
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if x == 0 or y == 0:
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@ -189,15 +221,17 @@ def plot_all_epochs_thetafolder(folder_path, mu, tgen, title = "Title", theta_sc
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breaks = 0
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cpt +=1
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if os.path.isfile(os.path.join(folder_path, file_name)):
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x, y, likelihood = return_x_y_from_stwp_theta_file(folder_path+file_name, breaks = breaks,
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x, y, likelihood, sfs, L = return_x_y_from_stwp_theta_file(folder_path+file_name, breaks = breaks,
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tgen = tgen,
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mu = mu, relative_theta_scale = theta_scale)
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SFS_stored = sfs
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L_stored = L
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while not (x == 0 and y == 0):
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if breaks not in epochs.keys():
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epochs[breaks] = {}
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epochs[breaks][likelihood] = x,y
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breaks += 1
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x,y,likelihood = return_x_y_from_stwp_theta_file(folder_path+file_name, breaks = breaks,
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x,y,likelihood,sfs,L = return_x_y_from_stwp_theta_file(folder_path+file_name, breaks = breaks,
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tgen = tgen,
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mu = mu, relative_theta_scale = theta_scale)
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print("\n*******\n"+title+"\n--------\n"+"mu="+str(mu)+"\ntgen="+str(tgen)+"\nbreaks="+str(breaks)+"\n*******\n")
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@ -208,7 +242,7 @@ def plot_all_epochs_thetafolder(folder_path, mu, tgen, title = "Title", theta_sc
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plt.figure(figsize=(5000/my_dpi, 2800/my_dpi), dpi=my_dpi)
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plt.xlim(1e-3, 1)
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#plt.ylim(0, 10)
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#plt.yscale('log')
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plt.yscale('log')
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plt.xscale('log')
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plt.grid(True,which="both", linestyle='--', alpha = 0.3)
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brkpt_lik = []
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@ -228,7 +262,15 @@ def plot_all_epochs_thetafolder(folder_path, mu, tgen, title = "Title", theta_sc
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# divide by N0
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y[i] = y[i]/N0
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x[i] = x[i]/N0
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plt.plot(x, y, '-', alpha=0.75, lw=2, label = str(epoch)+' BrkPt | Lik='+greatest_likelihood)
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sum_theta_i = 0
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print(epoch, x, y)
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for i in range(2, len(y)-1):
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sum_theta_i=y[i] / (i-1)
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prop = []
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for k in range(2, len(y)-1):
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prop.append(y[k+1] / (k - 1) / sum_theta_i)
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#print(epoch, prop)
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plt.plot(x, y, 'o', linestyle = "-", alpha=0.75, lw=2, label = str(epoch)+' BrkPt | Lik='+greatest_likelihood)
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if theta_scale:
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plt.xlabel("Coal. time")
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plt.ylabel("Pop. size scaled by N0")
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@ -246,35 +288,67 @@ def plot_all_epochs_thetafolder(folder_path, mu, tgen, title = "Title", theta_sc
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plt.title(title)
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plt.savefig(title+'_b'+str(breaks)+'.pdf')
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# plot likelihood against nb of breakpoints
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# best possible likelihood from SFS
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# Segregating sites
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S = sum(SFS_stored)
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# number of monomorphic sites
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L = L_stored
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S0 = L-S
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print("SFS", SFS_stored)
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print("S", S, "L", L, "S0=", S0)
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# compute Ln
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Ln = log_facto(S+S0) - log_facto(S0) + np.log(float(S0)/(S+S0)) * S0
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for xi in range(0, len(SFS_stored)):
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p_i = SFS_stored[xi] / float(S+S0)
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Ln += np.log(p_i) * SFS_stored[xi] - log_facto(SFS_stored[xi])
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res = Ln
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print(res)
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# basic plot likelihood
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plt.figure(figsize=(5000/my_dpi, 2800/my_dpi), dpi=my_dpi)
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plt.rcParams['font.size'] = '18'
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AIC = 2*(len(brkpt_lik)+1)+2*np.array(brkpt_lik)[:, 1].astype(float)
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plt.plot(np.array(brkpt_lik)[:, 0], AIC, 'o', linestyle = "dotted", lw=2)
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plt.axhline(y=106)
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plt.plot(np.array(brkpt_lik)[:, 0], np.array(brkpt_lik)[:, 1].astype(float), 'o', linestyle = "dotted", lw=2)
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# plt.ylim(0,100)
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# plt.axhline(y=res)
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plt.yscale('log')
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plt.xlabel("# breakpoints", fontsize=20)
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plt.ylabel("$-\log\mathcal{L}$")
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#plt.legend(loc='upper right')
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plt.title(title)
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plt.savefig(title+'_Breakpts_Likelihood.pdf')
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# AIC
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plt.figure(figsize=(5000/my_dpi, 2800/my_dpi), dpi=my_dpi)
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plt.rcParams['font.size'] = '18'
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AIC = 2*(len(brkpt_lik)+1)+2*np.array(brkpt_lik)[:, 1].astype(float)
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plt.plot(np.array(brkpt_lik)[:, 0], AIC, 'o', linestyle = "dotted", lw=2)
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# plt.axhline(y=106)
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plt.yscale('log')
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plt.xlabel("# breakpoints", fontsize=20)
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plt.ylabel("AIC")
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#plt.legend(loc='upper right')
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plt.title(title)
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plt.savefig(title+'_Breakpts_Likelihood_AIC.pdf')
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def plot_test_theta(folder_path, mu, tgen, title = "Title", theta_scale = True, breaks_max = 6):
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def plot_test_theta(folder_path, mu, tgen, title = "Title", theta_scale = True, breaks_max = 5):
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"""
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Use theta values as is to do basic plots.
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"""
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cpt = 0
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epochs = {}
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for file_name in os.listdir(folder_path):
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cpt +=1
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if os.path.isfile(os.path.join(folder_path, file_name)):
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for k in range(breaks_max):
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thetas = return_x_y_from_stwp_theta_file_as_is(folder_path+file_name, breaks = k,
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thetas,sfs = return_x_y_from_stwp_theta_file_as_is(folder_path+file_name, breaks = k,
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tgen = tgen,
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mu = mu, relative_theta_scale = theta_scale)
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if thetas[0] == 0:
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if thetas == 0:
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continue
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epochs[k] = thetas
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print("\n*******\n"+title+"\n--------\n"+"mu="+str(mu)+"\ntgen="+str(tgen)+"\nbreaks="+str(k)+"\n*******\n")
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print(cpt, "theta file(s) have been scanned.")
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# intialize figure
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# intialize figure 1
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my_dpi = 300
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plt.figure(figsize=(5000/my_dpi, 2800/my_dpi), dpi=my_dpi)
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for epoch, theta in epochs.items():
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@ -294,7 +368,7 @@ def plot_test_theta(folder_path, mu, tgen, title = "Title", theta_scale = True,
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plt.ylabel("theta")
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plt.legend(loc='upper right')
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plt.savefig(title+'_test'+str(k)+'.pdf')
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# fig 2
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# fig 2 & 3
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plt.figure(figsize=(5000/my_dpi, 2800/my_dpi), dpi=my_dpi)
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for epoch, theta in epochs.items():
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groups = np.array(list(theta.values()), dtype=object)[:, 1].tolist()
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@ -308,29 +382,41 @@ def plot_test_theta(folder_path, mu, tgen, title = "Title", theta_scale = True,
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N0 = y[0]
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for i in range(len(y)):
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y[i] = y[i]/N0
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#
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x_2 = []
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T = 0
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# k allant de de 14 à 2
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for i in range(len(x)):
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x[i] = int(x[i])
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#print(x[2])
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# compute the times as: theta_k / (k*(k-1))
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for i in range(0, len(x)):
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k = x[i]
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#print(k, y[k-2])
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#theta_k = y[k] / (k*(k-1))
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T += y[i] / (x[i]*(x[i]-1))
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x_2.append(T)
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# Plotting (fig 2)
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plt.plot(x_2, y, 'o', linestyle="dotted", alpha=0.75, lw=2, label = str(epoch)+' brks')
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plt.xlabel("# breaks")
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plt.ylabel("theta")
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plt.legend(loc='upper right')
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plt.savefig(title+'_test'+str(k)+'.pdf')
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# Plotting (fig 3) which is the same but log scale for x
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plt.plot(x_2, y, 'o', linestyle="dotted", alpha=0.75, lw=2, label = str(epoch)+' brks')
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plt.xscale('log')
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plt.xlabel("# breaks")
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plt.ylabel("theta")
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plt.legend(loc='upper right')
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plt.savefig(title+'_test'+str(k)+'.pdf')
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#
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plt.savefig(title+'_test'+str(k)+'_log.pdf')
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def save_multi_image(filename):
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pp = PdfPages(filename)
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fig_nums = plt.get_fignums()
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figs = [plt.figure(n) for n in fig_nums]
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for fig in figs:
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fig.savefig(pp, format='pdf')
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pp.close()
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def combined_plot(folder_path, mu, tgen, breaks, title = "Title", theta_scale = True):
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plot_all_epochs_thetafolder(folder_path, mu, tgen, title, theta_scale)
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plot_test_theta(folder_path, mu, tgen, title, theta_scale, breaks_max = breaks)
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save_multi_image(title+"_combined.pdf")
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if __name__ == "__main__":
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if len(sys.argv) != 4:
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