Remove unused functions for swp2

swp2.py

@@ -3,13 +3,11 @@ import os
 import numpy as np
 import math
 import json
-import io
-from scipy.special import gammaln
-from matplotlib.backends.backend_pdf import PdfPages
-from matplotlib.ticker import MaxNLocator
-from mpl_toolkits.axes_grid1.inset_locator import inset_axes
-from matplotlib.ticker import MultipleLocator
+
 def log_facto(k):
+    """
+    Using the Stirling's approximation
+    """
     k = int(k)
     if k > 1e6:
         return k * np.log(k) - k + np.log(2*math.pi*k)/2
@@ -18,14 +16,6 @@ def log_facto(k):
         val += np.log(i)
     return val
 
-def log_facto_1(k):
-    startf = 1 # start of factorial sequence
-    stopf  = int(k+1) # end of of factorial sequence
-
-    q = gammaln(range(startf+1, stopf+1)) # n! = G(n+1)
-
-    return q[-1]
-
 def return_x_y_from_stwp_theta_file(stwp_theta_file, breaks, mu, tgen, relative_theta_scale = False):
     with open(stwp_theta_file, "r") as swp_file:
         # Read the first line
@@ -128,51 +118,6 @@ def return_x_y_from_stwp_theta_file(stwp_theta_file, breaks, mu, tgen, relative_
     #     #     x[i] = x[i]/N0
     return x,y,likelihood,thetas,sfs,L
 
-def return_x_y_from_stwp_theta_file_as_is(stwp_theta_file, breaks, mu, tgen, relative_theta_scale = False):
-    with open(stwp_theta_file, "r") as swp_file:
-        # Read the first line
-        line = swp_file.readline()
-        L = float(line.split()[2])
-        rands = swp_file.readline()
-        line = swp_file.readline()
-        # skip empty lines before SFS
-        while line == "\n":
-            line = swp_file.readline()
-        sfs = np.array(line.split()).astype(float)
-        # Process lines until the end of the file
-        while line:
-            # check at each line
-            if line.startswith("dim") :
-                dim = int(line.split()[1])
-                if dim == breaks+1:
-                    likelihood = line.split()[5]
-                    groups = line.split()[6:6+dim]
-                    theta_site = line.split()[6+dim:6+dim+1+dim]
-                elif dim < breaks+1:
-                    line = swp_file.readline()
-                    continue
-                elif dim > breaks+1:
-                    break
-                    #return 0,0,0
-            # Read the next line
-            line = swp_file.readline()
-    #### END of parsing
-    # quit this file if the number of dimensions is incorrect
-    if dim < breaks+1:
-        return 0,0
-    # get n, the last bin of the last group
-    # revert the list of groups as the most recent times correspond
-    # to the closest and last leafs of the coal. tree.
-    groups = groups[::-1]
-    theta_site = theta_site[::-1]
-
-    thetas = {}
-
-    for i in range(len(groups)):
-        groups[i] = groups[i].split(',')
-        # print(groups[i], len(groups[i]))
-        thetas[i] = [float(theta_site[i]), groups[i], likelihood]
-    return thetas, sfs
 
 def plot_k_epochs_thetafolder(folder_path, mu, tgen, breaks = 2, title = "Title", theta_scale = True):
     scenari = {}
@@ -180,7 +125,7 @@ def plot_k_epochs_thetafolder(folder_path, mu, tgen, breaks = 2, title = "Title"
     for file_name in os.listdir(folder_path):
         if os.path.isfile(os.path.join(folder_path, file_name)):
             # Perform actions on each file
-            x,y,likelihood,sfs,L = return_x_y_from_stwp_theta_file(folder_path+file_name, breaks = breaks,
+            x, y, likelihood, theta, sfs, L = return_x_y_from_stwp_theta_file(folder_path+file_name, breaks = breaks,
                                                              tgen = tgen,
                                      mu = mu, relative_theta_scale = theta_scale)
             if x == 0 or y == 0:
@@ -396,7 +341,7 @@ def save_k_theta(folder_path, mu, tgen, title = "Title", theta_scale = True,
         cpt +=1
         if os.path.isfile(os.path.join(folder_path, file_name)):
             for k in range(breaks_max):
-                thetas,sfs = return_x_y_from_stwp_theta_file_as_is(folder_path+file_name, breaks = k,
+                x,y,likelihood,thetas,sfs,L = return_x_y_from_stwp_theta_file(folder_path+file_name, breaks = k,
                                                                  tgen = tgen,
                                                                  mu = mu, relative_theta_scale = theta_scale)
                 if thetas == 0:
@@ -591,7 +536,7 @@ def plot_test_theta(folder_path, mu, tgen, title = "Title", theta_scale = True,
         cpt +=1
         if os.path.isfile(os.path.join(folder_path, file_name)):
             for k in range(breaks_max):
-                thetas,sfs = return_x_y_from_stwp_theta_file_as_is(folder_path+file_name, breaks = k,
+                x, y, likelihood, theta, sfs, L = return_x_y_from_stwp_theta_file(folder_path+file_name, breaks = k,
                                                                  tgen = tgen,
                                                                  mu = mu, relative_theta_scale = theta_scale)
                 if thetas == 0:
@@ -751,7 +696,7 @@ def combined_plot(folder_path, mu, tgen, breaks, title = "Title", theta_scale =
     # # plot_all_epochs_thetafolder(folder_path, mu, tgen, title, theta_scale, ax = None)
     # # plot_test_theta(folder_path, mu, tgen, title, theta_scale, breaks_max = breaks, ax = None)
     # # plt.clf()
-    # save_k_theta(folder_path, mu, tgen, title, theta_scale, breaks_max = breaks, output = title+"_plotdata.json")
+    save_k_theta(folder_path, mu, tgen, title, theta_scale, breaks_max = breaks, output = title+"_plotdata.json")
     with open(title+"_plotdata.json", 'r') as json_file:
         loaded_data = json.load(json_file)
     # plot page 1 of summary