Fixing proportion labels

swp2.py

 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
-import io
 from mpl_toolkits.axes_grid1.inset_locator import inset_axes
 from matplotlib.ticker import MultipleLocator
 def log_facto(k):
     my_dpi = 300
     plt.figure(figsize=(5000/my_dpi, 2800/my_dpi), dpi=my_dpi)
     plt.plot(x, y, 'r-', lw=2, label = 'Lik='+greatest_likelihood)
-    plt.xlim(1e-3, 1)
-    plt.ylim(0, 10)
     #plt.yscale('log')
     plt.xscale('log')
     plt.grid(True,which="both", linestyle='--', alpha = 0.3)
         fnt_size = 12
         # plt.rcParams['font.size'] = fnt_size
         ax1 = ax[0,0]
-    #ax1.set_xlim(1e-3, 1)
     ax1.set_yscale('log')
     ax1.set_xscale('log')
     ax1.grid(True,which="both", linestyle='--', alpha = 0.3)
         # years
         plt.set_xlabel("Time (years)", fontsize=fnt_size)
         plt.set_ylabel("Individuals (N)", fontsize=fnt_size)
-        ax1.set_xlim(1e-5, 1)
     # plt.rcParams['font.size'] = fnt_size
     # print(fnt_size, "rcParam font.size=", plt.rcParams['font.size'])
     ax1.legend(handles = plot_handles, loc='best', fontsize = fnt_size*0.5)
     # return plots
     return ax
 
+def save_k_theta(folder_path, mu, tgen, title = "Title", theta_scale = True,
+    breaks_max = 10, output = None):
+    """
+    Save theta values as is to do basic plots.
+    """
+    cpt = 0
+    epochs = {}
+    len_sfs = 0
+    for file_name in os.listdir(folder_path):
+        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,
+                                                                 tgen = tgen,
+                                                                 mu = mu, relative_theta_scale = theta_scale)
+                if thetas == 0:
+                    continue
+                if len(thetas)-1 != k:
+                    continue
+                if k not in epochs.keys():
+                    epochs[k] = {}
+                likelihood = str(eval(thetas[k][2]))
+                epochs[k][likelihood] = thetas
+                #epochs[k] = thetas
+    print("\n*******\n"+title+"\n--------\n"+"mu="+str(mu)+"\ntgen="+str(tgen)+"\nbreaks="+str(k)+"\n*******\n")
+    print(cpt, "theta file(s) have been scanned.")
+    plots = []
+    best_epochs = {}
+    for epoch in epochs:
+        likelihoods = []
+        for key in epochs[epoch].keys():
+            likelihoods.append(key)
+        likelihoods.sort()
+        minLogLn = str(likelihoods[0])
+        best_epochs[epoch] = epochs[epoch][minLogLn]
+    for epoch, theta in best_epochs.items():
+        groups = np.array(list(theta.values()), dtype=object)[:, 1].tolist()
+        x = []
+        y = []
+        thetas = np.array(list(theta.values()), dtype=object)[:, 0]
+        for i,group in enumerate(groups):
+            x += group[::-1]
+            y += list(np.repeat(thetas[i], len(group)))
+            if epoch == 0:
+                N0 = y[0]
+                # compute the proportion of information used at each bin of the SFS
+                sum_theta_i = 0
+                for i in range(2, len(y)+2):
+                    sum_theta_i+=y[i-2] / (i-1)
+                prop = []
+                for k in range(2, len(y)+2):
+                    prop.append(y[k-2] / (k - 1) / sum_theta_i)
+                prop = prop[::-1]
+        # normalise to N0 (N0 of epoch1)
+        for i in range(len(y)):
+            y[i] = y[i]/N0
+        # x_plot, y_plot = plot_straight_x_y(x, y)
+        p = x, y
+        # add plot to the list of all plots to superimpose
+        plots.append(p)
+    cumul = 0
+    prop_cumul = []
+    for val in prop:
+        prop_cumul.append(val+cumul)
+        cumul = val+cumul
+    prop = prop_cumul
+
+    lines_fig2 = []
+    for epoch, theta in best_epochs.items():
+        groups = np.array(list(theta.values()), dtype=object)[:, 1].tolist()
+        x = []
+        y = []
+        thetas = np.array(list(theta.values()), dtype=object)[:, 0]
+        for i,group in enumerate(groups):
+            x += group[::-1]
+            y += list(np.repeat(thetas[i], len(group)))
+            if epoch == 0:
+                N0 = y[0]
+        for i in range(len(y)):
+            y[i] = y[i]/N0
+        x_2 = []
+        T = 0
+        for i in range(len(x)):
+            x[i] = int(x[i])
+        # compute the times as: theta_k / (k*(k-1))
+        for i in range(0, len(x)):
+            T += y[i] / (x[i]*(x[i]-1))
+            x_2.append(T)
+        # Save plotting (fig 2)
+        x_2 = [0]+x_2
+        y = [y[0]]+y
+        # x2_plot, y2_plot = plot_straight_x_y(x_2, y)
+        p2 = x_2, y
+        lines_fig2.append(p2)
+
+    saved_plots = {"raw_stairs":plots, "scaled_stairs":lines_fig2,
+                    "prop":prop}
+    if output == None:
+        output = title+"_plotdata.json"
+    with open(output, 'w') as json_file:
+        json.dump(saved_plots, json_file)
+    return saved_plots
+
+def plot_raw_stairs(plot_lines, plot_lines2, prop, title, ax = None, n_ticks = 10):
+    # multiple fig
+    if ax is None:
+        # intialize figure 1
+        my_dpi = 300
+        fnt_size = 18
+        # plt.rcParams['font.size'] = fnt_size
+        fig, ax1 = plt.subplots(figsize=(5000/my_dpi, 2800/my_dpi), dpi=my_dpi)
+    else:
+        fnt_size = 12
+        # plt.rcParams['font.size'] = fnt_size
+        ax1 = ax[0, 1]
+        plt.subplots_adjust(wspace=0.3, hspace=0.3)
+    plots = []
+
+    for epoch, plot in enumerate(plot_lines):
+        x,y = plot
+        x_plot, y_plot = plot_straight_x_y(x,y)
+        p, = ax1.plot(x_plot, y_plot, 'o', linestyle="-", alpha=0.75, lw=2, label = str(epoch)+' brks')
+
+        # add plot to the list of all plots to superimpose
+        plots.append(p)
+    x_ticks = x
+    # print(x_ticks)
+    #print(prop, "\n", sum(prop))
+    #ax.legend(handles=[p0]+plots)
+    ax1.set_xlabel("# bin", fontsize=fnt_size)
+    # Set the x-axis locator to reduce the number of ticks to 10
+    ax1.set_ylabel("theta", fontsize=fnt_size)
+    ax1.set_title("Title", fontsize=fnt_size)
+    ax1.legend(handles=plots, loc='best', fontsize = fnt_size*0.5)
+    ax1.set_xticks(x_ticks)
+    step = len(x_ticks)//(n_ticks-1)
+    values = x_ticks[::step]
+    new_prop = []
+    for val in values:
+        new_prop.append(prop[int(val)-2])
+    new_prop = new_prop[::-1]
+    ax1.set_xticks(values)
+    ax1.set_xticklabels([f'{values[k]}\n{val:.2f}' for k, val in enumerate(new_prop)], fontsize = fnt_size*0.8)
+    if ax is None:
+        plt.savefig(title+'_raw'+str(k)+'.pdf')
+    # fig 2 & 3
+    if ax is None:
+        fig2, ax2 = plt.subplots(figsize=(5000/my_dpi, 2800/my_dpi), dpi=my_dpi)
+        fig3, ax3 = plt.subplots(figsize=(5000/my_dpi, 2800/my_dpi), dpi=my_dpi)
+    else:
+        # plt.rcParams['font.size'] = fnt_size
+        # place of plots on the grid
+        ax2 = ax[1,0]
+        ax3 = ax[1,1]
+    lines_fig2 = []
+    lines_fig3 = []
+    #plt.figure(figsize=(5000/my_dpi, 2800/my_dpi), dpi=my_dpi)
+    for epoch, plot in enumerate(plot_lines2):
+        x,y=plot
+        x2_plot, y2_plot = plot_straight_x_y(x,y)
+        p2, = ax2.plot(x2_plot, y2_plot, 'o', linestyle="-", alpha=0.75, lw=2, label = str(epoch)+' brks')
+        lines_fig2.append(p2)
+        # Plotting (fig 3) which is the same but log scale for x
+        p3, = ax3.plot(x2_plot, y2_plot, 'o', linestyle="-", alpha=0.75, lw=2, label = str(epoch)+' brks')
+        lines_fig3.append(p3)
+    ax2.set_xlabel("Relative scale", fontsize=fnt_size)
+    ax2.set_ylabel("theta", fontsize=fnt_size)
+    ax2.set_title("Title", fontsize=fnt_size)
+    ax2.legend(handles=lines_fig2, loc='best', fontsize = fnt_size*0.5)
+    if ax is None:
+        plt.savefig(title+'_plot2_'+str(k)+'.pdf')
+    ax3.set_xscale('log')
+    ax3.set_yscale('log')
+    ax3.set_xlabel("log Relative scale", fontsize=fnt_size)
+    ax3.set_ylabel("theta", fontsize=fnt_size)
+    ax3.set_title("Title", fontsize=fnt_size)
+    ax3.legend(handles=lines_fig3, loc='best', fontsize = fnt_size*0.5)
+    if ax is None:
+        plt.savefig(title+'_plot3_'+str(k)+'_log.pdf')
+        plt.clf()
+    # return plots
+    return ax
+
 def plot_test_theta(folder_path, mu, tgen, title = "Title", theta_scale = True, breaks_max = 10, ax = None, n_ticks = 10):
     """
     Use theta values as is to do basic plots.
                     continue
                 if k not in epochs.keys():
                     epochs[k] = {}
-                likelihood = thetas[k][2]
+                likelihood = str(eval(thetas[k][2]))
                 epochs[k][likelihood] = thetas
                 #epochs[k] = thetas
     print("\n*******\n"+title+"\n--------\n"+"mu="+str(mu)+"\ntgen="+str(tgen)+"\nbreaks="+str(k)+"\n*******\n")
     for epoch in epochs:
         likelihoods = []
         for key in epochs[epoch].keys():
-            likelihoods.append(float(key))
+            likelihoods.append(key)
         likelihoods.sort()
         minLogLn = str(likelihoods[0])
         best_epochs[epoch] = epochs[epoch][minLogLn]
 
 def combined_plot(folder_path, mu, tgen, breaks, title = "Title", theta_scale = True):
     my_dpi = 300
-    # Add some extra space for the second axis at the bottom
-    #plt.rcParams['font.size'] = 18
-    fig, axs = plt.subplots(3, 2, figsize=(5000/my_dpi, 2970/my_dpi), dpi=my_dpi)
-    #plt.rcParams['font.size'] = 12
-    ax = plot_all_epochs_thetafolder(folder_path, mu, tgen, title, theta_scale, ax = axs)
-    ax = plot_test_theta(folder_path, mu, tgen, title, theta_scale, breaks_max = breaks, ax = axs)
-    # Adjust layout to prevent clipping of titles
-    plt.tight_layout()
-    # Adjust absolute space between the top and bottom rows
-    #plt.subplots_adjust(hspace=0.7)  # Adjust this value based on your requirement
-    # Save the entire grid as a single figure
-    plt.savefig(title+'_combined.pdf')
-    plt.clf()
-    # # second call for individual plots
-    # 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)
+    # # Add some extra space for the second axis at the bottom
+    # #plt.rcParams['font.size'] = 18
+    # fig, axs = plt.subplots(2, 2, figsize=(5000/my_dpi, 2970/my_dpi), dpi=my_dpi)
+    # #plt.rcParams['font.size'] = 12
+    # ax = plot_all_epochs_thetafolder(folder_path, mu, tgen, title, theta_scale, ax = axs)
+    # ax = plot_test_theta(folder_path, mu, tgen, title, theta_scale, breaks_max = breaks, ax = axs)
+    # # Adjust layout to prevent clipping of titles
+    # plt.tight_layout()
+    # # Adjust absolute space between the top and bottom rows
+    # #plt.subplots_adjust(hspace=0.7)  # Adjust this value based on your requirement
+    # # Save the entire grid as a single figure
+    # plt.savefig(title+'_combined.pdf')
     # plt.clf()
+    # # # second call for individual plots
+    # # 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")
+
+    with open(title+"_plotdata.json", 'r') as json_file:
+        loaded_data = json.load(json_file)
+
+    fig1, ax1 = plt.subplots(2, 2, figsize=(5000/my_dpi, 2970/my_dpi), dpi=my_dpi)
+    # plot_test_theta(folder_path, mu, tgen, title, theta_scale, breaks_max = breaks, ax = ax1)
+    ax1 = plot_raw_stairs(plot_lines = loaded_data['raw_stairs'], plot_lines2 = loaded_data['scaled_stairs'],
+                            prop = loaded_data['prop'], title = title, ax = ax1)
 
+    plt.savefig(title+'_raw_scaled.pdf')
+    fig1.clf()
 if __name__ == "__main__":
 
     if len(sys.argv) != 4: