script python pour faire des analyses sur le nombre de motifs dans la librairie,…

… le nombre de motifs insérés et le nombre de solutions prédites

+import json
+import numpy as np
+import matplotlib.pyplot as plt
+import os.path
+
+# Creates a violin plot of the distribution of the number of 'motifs' in the Isaure pattern library
+def stats_library():
+    with open('/mnt/c/Users/natha/Documents/IBISC/biorseo2/biorseo/data/modules/ISAURE/motifs_28-05-2021.json') as f:
+        data = json.load(f)
+
+        nb_motifs = json.dumps(data).count("sequence")
+        print("nombre de motifs: " + str(nb_motifs))
+
+        tab_seq_length = []
+        tab_seq_length_with_delimiter = []
+        for i in range(1007):
+            test = str(i) in data
+            if test:
+                sequence = data[str(i)]["sequence"]
+                count_delimiter = sequence.count('&')
+                tab_seq_length.append(len(sequence) - count_delimiter)
+                tab_seq_length_with_delimiter.append(len(sequence))
+
+        data_to_plot = [np.array(tab_seq_length), np.array(tab_seq_length_with_delimiter)]
+        min1 = np.amin(data_to_plot[0])
+        max1 = np.amax(data_to_plot[0])
+        median1 = np.median(data_to_plot[0])
+
+        min2 = np.amin(data_to_plot[1])
+        max2 = np.amax(data_to_plot[1])
+        median2 = np.median(data_to_plot[1])
+        fig = plt.figure()
+
+        ax = fig.add_axes([0, 0, 1, 1])
+
+        label1 = "nombre de nucléotides" + "\n minimum: " + str(min1) + "    mediane: " + str(
+            median1) + "    maximum: " + str(max1)
+        label2 = "nombre de nucléotides + nombre de &" + "\n minimum: " + str(min2) + "    mediane: " + str(
+            median2) + "    maximum: " + str(max2)
+        labels = [label1, label2]
+        ax.set_xticks(np.arange(1, len(labels) + 1))
+        ax.set_xticklabels(labels)
+        ax.set_ylabel('longueurs des motifs')
+        ax.set_xlabel('motifs')
+
+        violins = ax.violinplot(data_to_plot, showmedians=True)
+
+        for partname in ('cbars', 'cmins', 'cmaxes', 'cmedians'):
+            vp = violins[partname]
+            vp.set_edgecolor('black')
+            vp.set_linewidth(1)
+
+        for v in violins['bodies']:
+            v.set_facecolor('red')
+        plt.title("Répartition des longueurs des motifs dans la bibliothèque (" + str(nb_motifs) + " motifs)")
+        plt.savefig("statistiques_motifs_Isaure.png", bbox_inches='tight')
+
+# Returns the list in half
+def get_half(list_name):
+    first_half = []
+    second_half = []
+    if len(list_name) % 2 == 0:
+        middle = len(list_name) / 2
+    else:
+        middle = len(list_name) / 2 + 0.5
+
+    for i in range(int(middle)):
+        first_half.append(list_name[i])
+
+    for i in range(int(middle)):
+        if i + int(middle) < len(list_name):
+            second_half.append(list_name[i + int(middle)])
+
+    return [first_half, second_half]
+
+# Returns the list of name of the sequence in the benchmark.dbn
+def get_list_name_bm():
+    path_file = '/mnt/c/Users/natha/Documents/IBISC/biorseo2/biorseo/data/modules/ISAURE/benchmark.dbn'
+    my_file = open(path_file, "r")
+    list_name = []
+    count = 0
+    for line in my_file:
+        if count % 4 == 0:
+            list_name.append(line[5:len(line) - 2])
+        count = count + 1
+    my_file.close()
+
+    return list_name
+
+# Returns a 2d array containing for each sequence of the benchmark the number of 'motifs' inserted of each solution
+def get_nb_motifs_by_seq(type_file):
+    list_name = get_list_name_bm()
+    tab = []
+    for name in list_name:
+        path_file = "/mnt/c/Users/natha/Documents/IBISC/biorseo2/biorseo/results/test_" + name + type_file
+        if os.path.exists(path_file):
+            list_nb_motifs = []
+            my_file = open(path_file, "r")
+            name = my_file.readline()
+            seq = my_file.readline()
+            count = 0
+            for line in my_file:
+                if count % 2 == 0:
+                    tab_split = line.split("+")
+                    list_nb_motifs.append(len(tab_split) - 1)
+                count = count + 1
+            my_file.close()
+            tab.append(list_nb_motifs)
+    return tab
+
+# Creates a violin plot that shows the distribution of the number of patterns per solution for each sequence of the benchmark
+def stats_nb_motifs_in_result(type_file):
+    list_name = get_list_name_bm()
+    tab = get_nb_motifs_by_seq(type_file)
+
+    list_median_str = []
+    for i in range(len(tab)):
+        list_median_str.append(np.median(tab[i]))
+
+    tab = [x for _, x in sorted(zip(list_median_str, tab))]
+    list_name = [x for _, x in sorted(zip(list_median_str, list_name))]
+
+    if (len(tab) % 2 == 0):
+        absciss = len(tab) / 2
+    else:
+        absciss = len(tab) / 2 + 0.5
+
+    divide_name = get_half(list_name)
+    divide_tab = get_half(tab)
+
+    arr = np.array(tab)
+    fig, ax = plt.subplots()
+    plt.figure(figsize=(15, 4), dpi=200)
+    plt.xticks(rotation=90)
+    violins = plt.violinplot(divide_tab[0], showmedians=True)
+    for i in range(int(len(divide_tab[0]))):
+        y = divide_tab[0][i]
+        x = np.random.normal(1 + i, 0.04, size=len(y))
+        plt.scatter(x, y)
+        plt.xticks(np.arange(1, len(divide_tab[0]) + 1), divide_name[0])
+
+    plt.xlabel('nom de la séquence')
+    plt.ylabel('nombre de motifs insérés dans la structure prédite')
+    plt.title("Répartition du nombre de motifs insérés par résultat pour chaque séquence")
+
+    for part in ('cbars', 'cmins', 'cmaxes', 'cmedians'):
+        vp = violins[part]
+        vp.set_edgecolor('black')
+        vp.set_linewidth(1)
+
+    for v in violins['bodies']:
+        v.set_facecolor('black')
+
+    plt.savefig("statistiques_nb_motifs_inseres_Isaure_" + type_file[8:] + "_1.png", bbox_inches='tight')
+
+    plt.figure(figsize=(15, 4), dpi=200)
+    plt.xticks(rotation=90)
+    violins = plt.violinplot(divide_tab[1], showmedians=True)
+    for i in range(int(len(divide_tab[1]))):
+        y = divide_tab[1][i]
+        x = np.random.normal(1 + i, 0.04, size=len(y))
+        plt.scatter(x, y)
+        plt.xticks(np.arange(1, len(divide_tab[1]) + 1), divide_name[1])
+
+    plt.xlabel('nom de la séquence')
+    plt.ylabel('nombre de motifs insérés dans la structure prédite')
+    plt.title("Répartition du nombre de motifs insérés par résultat pour chaque séquence (2ème partie)")
+
+    for part in ('cbars', 'cmins', 'cmaxes', 'cmedians'):
+        vp = violins[part]
+        vp.set_edgecolor('black')
+        vp.set_linewidth(1)
+
+    for v in violins['bodies']:
+        v.set_facecolor('black')
+    plt.savefig("statistiques_nb_motifs_inseres_Isaure_" + type_file[8:] + "_2.png", bbox_inches='tight')
+
+# Returns the grouping of the number of inserted 'motif' for all solutions of all sequences of the benchmark
+# according to the extension in argument
+def get_all_nb_motifs_by_type(type_file):
+    tab = get_nb_motifs_by_seq(type_file)
+    tab_all = []
+    for i in range(len(tab)):
+        for j in range(len(tab[i])):
+            tab_all.append(tab[i][j])
+    return tab_all
+
+# Create a figure containing the violin plot for MEA + function E, MEA + function F, MFE + function E and MFE + function F
+# Each violin plot show the distribution of the number of inserted 'motif' by solution
+def stats_nb_motifs_all():
+    list_name = get_list_name_bm()
+    tab_all_E_MEA = get_all_nb_motifs_by_type(".json_pmE_MEA")
+    tab_all_F_MEA = get_all_nb_motifs_by_type(".json_pmF_MEA")
+    tab_all_E_MFE = get_all_nb_motifs_by_type(".json_pmE_MFE")
+    tab_all_F_MFE = get_all_nb_motifs_by_type(".json_pmF_MFE")
+
+    data_to_plot = [tab_all_E_MEA, tab_all_F_MEA, tab_all_E_MFE, tab_all_F_MFE]
+    fig = plt.figure()
+    fig.set_size_inches(6, 3)
+    ax = fig.add_axes([0, 0, 1, 1])
+
+    labels = ['MEA + E', 'MEA + F', 'MFE + E', 'MFE + F']
+    ax.set_xticks(np.arange(1, len(labels) + 1))
+    ax.set_xticklabels(labels)
+    ax.set_xlabel("Répartition du nombre de motifs insérés par résultat pour chaque séquence")
+    ax.set_ylabel("nombre de motifs insérés dans la structure prédite")
+
+    violins = ax.violinplot(data_to_plot, showmedians=True)
+
+    for partname in ('cbars', 'cmins', 'cmaxes', 'cmedians'):
+        vp = violins[partname]
+        vp.set_edgecolor('black')
+        vp.set_linewidth(1)
+
+    for v in violins['bodies']:
+        v.set_facecolor("black")
+    plt.savefig('repartition_nb_motifs.png', dpi=200, bbox_inches='tight')
+
+# Returns the list of the number of solutions and the list of the length of each sequence of the benchmark
+def get_nb_solutions_and_sizes_by_seq(type_file):
+    list_name = get_list_name_bm()
+    list_nb_solutions = []
+    list_size = []
+    for name in list_name:
+        path_file = "/mnt/c/Users/natha/Documents/IBISC/biorseo2/biorseo/results/test_" + name + type_file
+        if os.path.exists(path_file):
+            my_file = open(path_file, "r")
+            name = my_file.readline()
+            seq = my_file.readline()
+            list_size.append(len(seq))
+            count = 0
+            nb = 0
+            for line in my_file:
+                if count % 2 == 0:
+                    nb = nb + 1
+                count = count + 1
+            list_nb_solutions.append(nb)
+            my_file.close()
+    return [list_nb_solutions, list_size]
+
+# Creates 4 violin plots that shows the distribution of the number of solutions for each sequence of the benchmark
+def stats_nb_solutions():
+    list_name = get_list_name_bm()
+    tab_all_E_MEA = get_nb_solutions_and_sizes_by_seq(".json_pmE_MEA")[0]
+    tab_all_F_MEA = get_nb_solutions_and_sizes_by_seq(".json_pmF_MEA")[0]
+    tab_all_E_MFE = get_nb_solutions_and_sizes_by_seq(".json_pmE_MFE")[0]
+    tab_all_F_MFE = get_nb_solutions_and_sizes_by_seq(".json_pmF_MFE")[0]
+    data_to_plot = [tab_all_E_MEA, tab_all_F_MEA, tab_all_E_MFE, tab_all_F_MFE]
+
+    all_data = []
+    for i in range(len(data_to_plot)):
+        for j in range(len(data_to_plot[i])):
+            all_data.append(data_to_plot[i][j])
+
+    min = np.amin(all_data)
+    max = np.amax(all_data)
+    median = np.median(all_data)
+
+    fig = plt.figure()
+    fig.set_size_inches(6, 3)
+    ax = fig.add_axes([0, 0, 1, 1])
+
+    labels = ['MEA + E', 'MEA + F', 'MFE + E', 'MFE + F']
+    ax.set_xticks(np.arange(1, len(labels) + 1))
+    ax.set_xticklabels(labels)
+    ax.set_xlabel("Répartition du nombre solutions pour chaque séquence du benchmark" + "\n minimum: " + str(min) + "    mediane: " + str(
+            median) + "    maximum: " + str(max) + " (Pour l'ensemble des solutions)")
+    ax.set_ylabel("nombre de solutions (structures secondaires prédites)")
+
+    violins = ax.violinplot(data_to_plot, showmedians=True)
+
+    for partname in ('cbars', 'cmins', 'cmaxes', 'cmedians'):
+        vp = violins[partname]
+        vp.set_edgecolor('blue')
+        vp.set_linewidth(1)
+
+    for v in violins['bodies']:
+        v.set_facecolor("blue")
+    plt.savefig('repartition_nb_solutions.png', dpi=200, bbox_inches='tight')
+
+# Create a scatter plot showing the number of solutions according to the length of the sequence in the benchmark
+def stats_nb_solutions_by_seq_length():
+    list_name = get_list_name_bm()
+    x = []
+    y = []
+    tab_all_E_MEA = get_nb_solutions_and_sizes_by_seq(".json_pmE_MEA")
+    for i in range(len(tab_all_E_MEA[0])):
+        x.append(tab_all_E_MEA[1][i])
+        y.append(tab_all_E_MEA[0][i])
+    tab_all_F_MEA = get_nb_solutions_and_sizes_by_seq(".json_pmF_MEA")
+    for i in range(len(tab_all_F_MEA[0])):
+        x.append(tab_all_F_MEA[1][i])
+        y.append(tab_all_F_MEA[0][i])
+    tab_all_E_MFE = get_nb_solutions_and_sizes_by_seq(".json_pmE_MFE")
+    for i in range(len(tab_all_E_MFE[0])):
+        x.append(tab_all_E_MFE[1][i])
+        y.append(tab_all_E_MFE[0][i])
+    tab_all_F_MFE = get_nb_solutions_and_sizes_by_seq(".json_pmF_MFE")
+    for i in range(len(tab_all_F_MFE[0])):
+        x.append(tab_all_F_MFE[1][i])
+        y.append(tab_all_F_MFE[0][i])
+
+    plt.scatter(x, y, s=50, c='blue', marker='o', edgecolors='black')
+    plt.ylabel("nombre de solutions")
+    plt.xlabel("longueur de la séquence")
+    plt.title('nombre de structures prédites en fonction de la longueur de la séquence')
+    plt.savefig('nb_solutions_en_fonction_seq.png', dpi=200, bbox_inches='tight')
+
+"""stats_nb_motifs_in_result(".json_pmE_MEA")
+stats_nb_motifs_in_result(".json_pmF_MEA")
+stats_nb_motifs_in_result(".json_pmE_MFE")
+stats_nb_motifs_in_result(".json_pmF_MFE")
+stats_nb_motifs_all()
+stats_nb_solutions()"""
+stats_nb_solutions_by_seq_length()