Isaure_benchmark.py
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import time
import subprocess
import os
import os.path
from math import sqrt, ceil
import numpy as np
import matplotlib.pyplot as plt
log_path = "test.log"
log = open(log_path, 'a')
def run_test(cmd, log):
log.write(time.asctime(time.localtime(time.time())) + " : Run process \"" + cmd + "\"\n")
log.flush()
process = subprocess.Popen(cmd.split(' ') ,shell=False,stdout=subprocess.PIPE, stderr=subprocess.STDOUT)
# Poll process.stdout to show stdout live
while process.poll() is None:
output = process.stdout.readline()
if output:
log.write(output.decode())
log.flush()
rc = process.poll()
#create the command line to run BiORSEO with the modules library of CaRNAval
def create_command_rin(path, name, function, estimator):
cmd = ("python3 " + path + "/biorseo.py -i " +
path + "/data/fasta/" +
name + ".fa " +
"-O results/ " +
"--carnaval " +
"--patternmatch " +
"--func " + function + " --" + estimator + " -v " +
" --biorseo-dir " + path + " " +
"--modules-path " + path + "/data/modules/RIN/Subfiles")
return cmd
#create the command line to run BiORSEO with the modules library of RNA3dMotifs Atlas
def create_command_bgsu(path, name, function, estimator):
cmd = ("python3 " + path + "/biorseo.py -i " +
path + "/data/fasta/" +
name + ".fa " +
"-O results/ " +
"--3dmotifatlas " +
"--jar3d " +
"--func " + function + " --" + estimator + " -v " +
"--jar3d-exec " + " /local/local/localopt/jar3d_2014-12-11.jar" +
" --biorseo-dir " + path + " " +
"--modules-path " + path + "/data/modules/BGSU")
return cmd
#create the command line to run BiORSEO with the modules library of RNA3dMotifs
def create_command_desc(path, name, function, estimator):
cmd = ("python3 " + path + "/biorseo.py -i " +
path + "/data/fasta/" +
name + ".fa " +
"-O results/ " +
"--rna3dmotifs " +
"--patternmatch " +
"--func " + function + " --" + estimator + " -v " +
" --biorseo-dir " + path + " " +
"--modules-path " + path + "/data/modules/DESC")
return cmd
#create the command line to run BiORSEO with the motifs library of Isaure in .json
def create_command_isaure(path, name, function, estimator):
cmd = ("python3 " + path + "/biorseo.py -i " +
path + "/data/fasta/" +
name + ".fa " +
"-O results/ " +
"--contacts " +
"--patternmatch " +
"--func " + function + " --" + estimator +
" --biorseo-dir " + path + " " +
"--modules-path " + path + "/data/modules/ISAURE/bibliotheque_a_lire")
return cmd
#execute the command line correspondin to the information put in the argument.
def execute_command(path, function, estimator, true_ctc, true_str, list_ctc, list_str, modules):
if (modules == 'desc'):
cmd = create_command_desc(path, name, function, estimator)
elif (modules == 'bgsu'):
cmd = create_command_bgsu(path, name, function, estimator)
elif (modules == 'rin'):
cmd = create_command_rin(path, name, function, estimator)
elif (modules == 'isaure'):
cmd = create_command_isaure(path, name, function, estimator)
os.system(cmd)
"""file_path = "results/test_" + name + ".json_pm" + function + "_" + estimator
if os.path.isfile(file_path):
tab = write_mcc_in_file(name, true_ctc, true_str, estimator, function)
list_ctc.append(tab[0])
list_str.append(tab[1])"""
#Retrieves the list of structures predicted by Biorseo for each sequence in the benchmark.txt file
def get_list_str_by_seq(name, estimator, function, list_str, true_str, modules):
if modules == 'bgsu':
extension = ".jar3d"
elif modules == 'desc':
extension = ".desc_pm"
elif modules == 'rin':
extension = ".rin_pm"
elif modules == 'json':
extension = ".json_pm"
file_path = "results/test_" + name + extension + function + "_" + estimator
if os.path.isfile(file_path):
path_benchmark = "data/modules/ISAURE/benchmark.txt"
max_mcc = get_mcc_structs_max(path_benchmark, name, estimator, function, extension, modules, true_str)
list_str.append(max_mcc)
# ================== Code from Louis Becquey Benchark.py ==============================
def dbn_to_basepairs(structure):
parenthesis = []
brackets = []
braces = []
rafters = []
basepairs = []
As = []
Bs = []
for i, c in enumerate(structure):
if c == '(':
parenthesis.append(i)
if c == '[':
brackets.append(i)
if c == '{':
braces.append(i)
if c == '<':
rafters.append(i)
if c == 'A':
As.append(i)
if c == 'B':
Bs.append(i)
if c == '.':
continue
if c == ')':
basepairs.append((i, parenthesis.pop()))
if c == ']':
basepairs.append((i, brackets.pop()))
if c == '}':
basepairs.append((i, braces.pop()))
if c == '>':
basepairs.append((i, rafters.pop()))
if c == 'a':
basepairs.append((i, As.pop()))
if c == 'b':
basepairs.append((i, Bs.pop()))
return basepairs
def compare_two_contacts(true_ctc, prediction):
tp = 0
fp = 0
tn = 0
fn = 0
for i in range(len(true_ctc)):
if true_ctc[i] == '*' and prediction[i] == '*':
tp += 1
elif true_ctc[i] == '.' and prediction[i] == '.':
tn += 1
elif true_ctc[i] == '.' and prediction[i] == '*':
fp += 1
elif true_ctc[i] == '*' and prediction[i] == '.':
fn += 1
return [tp, tn, fp, fn]
def compare_two_structures(true2d, prediction):
true_basepairs = dbn_to_basepairs(true2d)
pred_basepairs = dbn_to_basepairs(prediction)
tp = 0
fp = 0
tn = 0
fn = 0
for bp in true_basepairs:
if bp in pred_basepairs:
tp += 1
else:
fn += 1
for bp in pred_basepairs:
if bp not in true_basepairs:
fp += 1
tn = len(true2d) * (len(true2d) - 1) * 0.5 - fp - fn - tp
return [tp, tn, fp, fn]
def mattews_corr_coeff(tp, tn, fp, fn):
if ((tp + fp) * (tp + fn) * (tn + fp) * (tn + fn) == 0):
#print("warning: division by zero! no contact in the prediction")
#print("tp: " + str(tp) + " fp: " + str(fp) + " tn: " + str(tn) + " fn: " + str(fn))
return -1
elif (tp + fp == 0):
print("We have an issue : no positives detected ! (linear structure)")
return (tp * tn - fp * fn) / sqrt((tp + fp) * (tp + fn) * (tn + fp) * (tn + fn))
def f1_score(tp, tn, fp, fn):
return 2 * tp / (2 * tp + fp + fn)
def specificity(tp, tn, fp, fn):
return tn / (tn + fp)
# ================== Code from Louis Becquey Benchark.py ==============================
#Get the best MCC value for all prediction of the results file of the sequence in argument
def get_mcc_structs_max(path_benchmark, sequence_id, estimator, function, extension, modules, true_structure):
read_prd = open("results/test_" + sequence_id + extension + function + "_" + estimator, "r")
write = open("results/test_" + sequence_id + ".mcc_" + function + "_" + estimator + "_" + modules, "w")
max_mcc_str = -1;
title_exp = ">test_" + sequence_id + ": "
write.write(title_exp)
structure_exp = true_structure
write.write("structure 2d attendue:\n" + structure_exp + "\n")
title_prd = read_prd.readline()
structure_prd = read_prd.readline()
sequence_prd = structure_prd
while structure_prd:
structure_prd = read_prd.readline()
if (len(structure_prd) != 0):
write.write("\nstructure 2d predite:\n" + structure_prd[:len(sequence_prd)] + "\n")
mcc_tab = compare_two_structures(structure_exp, structure_prd[:len(sequence_prd)])
mcc_str = mattews_corr_coeff(mcc_tab[0], mcc_tab[1], mcc_tab[2], mcc_tab[3])
if (max_mcc_str < mcc_str):
max_mcc_str = mcc_str
write.write("mcc: " + str(mcc_str) + "\n")
contacts_prd = read_prd.readline()
write.write("max mcc 2D:" + str(max_mcc_str))
read_prd.close()
write.close()
return max_mcc_str
#Create a file that store the information concerning the MCC value obtains for each prediction of the
#sequence in input
def write_mcc_in_file(sequence_id, true_contacts, true_structure, estimator, function):
read_prd = open("results/test_" + sequence_id + ".json_pm" + function + "_" + estimator, "r")
write = open("results/test_" + sequence_id + ".mcc_" + function + "_" + estimator, "w")
max_mcc_str = -1;
max_mcc_ctc = -1;
title_exp = ">test_" + sequence_id + ": "
write.write(title_exp)
contacts_exp = true_contacts
structure_exp = true_structure
write.write("structure 2d attendue:\n" + structure_exp + "\n")
write.write("contacts attendus:\n" + contacts_exp + "\n" + len(structure_exp) * "-")
title_prd = read_prd.readline()
structure_prd = read_prd.readline()
sequence_prd = structure_prd
while structure_prd:
structure_prd = read_prd.readline()
if (len(structure_prd) != 0):
write.write("\nstructure 2d predite:\n" + structure_prd[:len(sequence_prd)] + "\n")
mcc_tab = compare_two_structures(structure_exp, structure_prd[:len(sequence_prd)])
mcc_str = mattews_corr_coeff(mcc_tab[0], mcc_tab[1], mcc_tab[2], mcc_tab[3])
if (max_mcc_str < mcc_str):
max_mcc_str = mcc_str
write.write("mcc: " + str(mcc_str) + "\n")
contacts_prd = read_prd.readline()
write.write("\ncontacts predits:\n" + contacts_prd)
if (len(contacts_prd) == len(contacts_exp)):
mcc_tab = compare_two_contacts(contacts_exp, contacts_prd)
mcc_ctc = mattews_corr_coeff(mcc_tab[0], mcc_tab[1], mcc_tab[2], mcc_tab[3])
if (max_mcc_ctc < mcc_ctc):
max_mcc_ctc = mcc_ctc
write.write("mcc: " + str(mcc_ctc) + "\n\n")
else:
write.write("mcc: no expected contacts sequence or not same length between expected and predicted\n\n")
write.write("max mcc 2D:" + str(max_mcc_str))
write.write("max mcc ctc:" + str(max_mcc_ctc))
read_prd.close()
write.close()
return [max_mcc_ctc, max_mcc_str]
def set_axis_style(ax, labels):
ax.xaxis.set_tick_params(direction='out')
ax.xaxis.set_ticks_position('bottom')
ax.set_xticks(np.arange(1, len(labels) + 1))
ax.set_xticklabels(labels)
ax.set_xlim(0.25, len(labels) + 0.75)
ax.set_xlabel('Sample name')
#Create one violin plot to see the distribution of the best MCC (of the base pairing)
def visualization_best_mcc_str(list_struct2d, estimator, function, modules, color, lines_color):
print(estimator + " + " + function + ": ")
np_struct2d = np.array(list_struct2d)
data_to_plot = np_struct2d
median_2d = np.median(np_struct2d)
print("mediane 2D: " + str(median_2d) + "\n")
fig = plt.figure()
ax = fig.add_axes([0, 0, 1, 1])
labels = ['structure 2D']
ax.set_xticks(np.arange(1, len(labels) + 1))
ax.set_xticklabels(labels)
ax.set_xlabel(function)
ax.set_ylabel('MCC')
violins = ax.violinplot(data_to_plot, showmedians=True)
for partname in ('cbars', 'cmins', 'cmaxes', 'cmedians'):
vp = violins[partname]
vp.set_edgecolor(lines_color)
vp.set_linewidth(1)
for v in violins['bodies']:
v.set_facecolor(color)
plt.savefig('visualisation_28_09' + estimator + '_' + function + '_' + modules + '.png', bbox_inches='tight')
#Create 4 violin plot to see the distribution of the best MCC (of the base pairing)
def visualization_best_mcc_str_4_figures(list_struct2d, color, lines_color):
np_struct2d_1 = np.array(list_struct2d[0])
np_struct2d_2 = np.array(list_struct2d[1])
np_struct2d_3 = np.array(list_struct2d[2])
np_struct2d_4 = np.array(list_struct2d[3])
data_to_plot = [np_struct2d_1, np_struct2d_2, np_struct2d_3, np_struct2d_4]
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_xlim(0.25, len(labels) + 0.75)
ax.set_xlabel("motifs d'Isaure")
ax.set_ylabel('MCC (en fonction des appariements)')
violins = ax.violinplot(data_to_plot, showmedians=True)
for partname in ('cbars', 'cmins', 'cmaxes', 'cmedians'):
vp = violins[partname]
vp.set_edgecolor(lines_color)
vp.set_linewidth(1)
for v in violins['bodies']:
v.set_facecolor(color)
plt.savefig('visualisation_28_09_Isaure_E_F.png', dpi=200, bbox_inches='tight')
#Create 2 violin plot to see the distribution of the best MCC (one for the MCC of
# the base pairing and one for the contacts)
def visualization_best_mcc(list_struct2d, list_contacts, estimator, function, modules, color, lines_color):
print(estimator + " + " + function + ": ")
np_struct2d = np.array(list_struct2d)
np_contacts = np.array(list_contacts)
data_to_plot = [np_struct2d, np_contacts]
median_2d = np.median(np_struct2d)
median_ctc = np.median(np_contacts)
print("mediane 2D: " + str(median_2d) + "\n")
print("mediane ctc: " + str(median_ctc) + "\n")
fig = plt.figure()
ax = fig.add_axes([0, 0, 1, 1])
labels = ['structure 2D', 'contacts']
ax.set_xticks(np.arange(1, len(labels) + 1))
ax.set_xticklabels(labels)
ax.set_xlabel(function)
ax.set_ylabel('MCC')
violins = ax.violinplot(data_to_plot, showmedians=True)
for partname in ('cbars', 'cmins', 'cmaxes', 'cmedians'):
vp = violins[partname]
vp.set_edgecolor(lines_color)
vp.set_linewidth(1)
for v in violins['bodies']:
v.set_facecolor(color)
plt.savefig('visualisation_16_06_' + estimator + '_' + function + '_' + modules + '.png', bbox_inches='tight')
#Return the list of names of all the sequence in the benchmark.txt, the list of structures and the list
# of contacts predicted in the result file of each sequence.
# This function is only use for the result in .json_pm format files
def get_list_structs_contacts_all(path_benchmark, estimator, function):
myfile = open(path_benchmark, "r")
list_name = []
complete_list_struct2d = []
complete_list_contacts = []
name = myfile.readline()
contacts = myfile.readline()
seq = myfile.readline()
structure2d = myfile.readline()
count = 0
while seq:
name = name[6:].strip()
count = count + 1
file_path = "results/test_" + name + ".json_pm" + function + "_" + estimator
if os.path.isfile(file_path):
file_result = open(file_path, "r")
list_struct2d = []
list_contacts = []
list_name.append(name)
title_prd = file_result.readline()
structure_prd = file_result.readline()
sequence = structure_prd
while structure_prd:
structure_prd = file_result.readline()
if (len(structure_prd) != 0):
mcc_tab = compare_two_structures(structure2d, structure_prd[:len(sequence)])
mcc_str = mattews_corr_coeff(mcc_tab[0], mcc_tab[1], mcc_tab[2], mcc_tab[3])
list_struct2d.append(mcc_str)
contacts_prd = file_result.readline()
if (len(contacts_prd) == len(contacts)):
mcc_tab = compare_two_contacts(contacts, contacts_prd)
mcc_ctc = mattews_corr_coeff(mcc_tab[0], mcc_tab[1], mcc_tab[2], mcc_tab[3])
list_contacts.append(mcc_ctc)
complete_list_struct2d.append(list_struct2d)
complete_list_contacts.append(list_contacts)
name = myfile.readline()
contacts = myfile.readline()
seq = myfile.readline()
structure2d = myfile.readline()
return [list_name, complete_list_struct2d, complete_list_contacts]
myfile.close()
#Return the list of names of all the sequence in the benchmark.txt, the list of structures
# predicted in the result file of each sequence.
def get_list_structs_all(path_benchmark, estimator, function, modules):
if modules == 'bgsu':
extension = ".jar3d"
elif modules == 'desc':
extension = ".desc_pm"
elif modules == 'rin':
extension = ".rin_pm"
elif modules == 'json':
extension = ".json_pm"
myfile = open(path_benchmark, "r")
list_name = []
complete_list_struct2d = []
complete_list_contacts = []
name = myfile.readline()
contacts = myfile.readline()
seq = myfile.readline()
structure2d = myfile.readline()
count = 0
while seq:
name = name[6:].strip()
count = count + 1
file_path = "results/test_" + name + extension + function + "_" + estimator
if os.path.isfile(file_path):
file_result = open(file_path, "r")
list_struct2d = []
list_name.append(name)
title_prd = file_result.readline()
structure_prd = file_result.readline()
sequence = structure_prd
while structure_prd:
structure_prd = file_result.readline()
if (len(structure_prd) != 0):
mcc_tab = compare_two_structures(structure2d, structure_prd[:len(sequence)])
mcc_str = mattews_corr_coeff(mcc_tab[0], mcc_tab[1], mcc_tab[2], mcc_tab[3])
list_struct2d.append(mcc_str)
contacts_prd = file_result.readline()
complete_list_struct2d.append(list_struct2d)
name = myfile.readline()
contacts = myfile.readline()
seq = myfile.readline()
structure2d = myfile.readline()
return [list_name, complete_list_struct2d]
myfile.close()
#Return the list in argument in two lists, each list containing half of the list in argument
def get_half(list):
first_half = []
second_half = []
if (len(list) % 2 == 0):
middle = len(list) / 2
else:
middle = len(list) / 2 + 0.5
for i in range (int(middle)):
first_half.append(list[i])
for i in range (int(middle)):
if i + int(middle) < len(list):
second_half.append(list[i + int(middle)])
return [first_half, second_half]
#Create a boxplot with all the MCC (for the base pairing) obtains with all the prediction for each sequence,
# divide in two figures.
def visualization_all_mcc_str(path_benchmark, estimator, function, modules):
list_name = get_list_structs_all(path_benchmark, estimator, function, modules)[0]
tab_struct2d = get_list_structs_all(path_benchmark, estimator, function, modules)[1]
min = 20
max = 0
max_i = 0
min_i = 0
for i in range(len(tab_struct2d)):
if (len(tab_struct2d[i]) > max):
max = len(tab_struct2d[i])
max_i = i
if (len(tab_struct2d[i]) < min):
min = len(tab_struct2d[i])
min_i = i
print("max: " + list_name[max_i] + " " + str(max) + " min: " + list_name[min_i] + " " + str(min) + "\n")
np_struct2d = np.array(tab_struct2d)
size = len(tab_struct2d)
list_median_str = []
for i in range(size):
list_median_str.append(np.median(np_struct2d[i]))
all_str = []
for i in range(size):
for j in range(len(np_struct2d[i])):
all_str.append(np_struct2d[i][j])
"""print("mediane struct" + estimator + " + " + function + " : " + str(np.median(all_str)))
print("ecart struct" + estimator + " + " + function + " : " + str(np.std(all_str)) + "\n")"""
data = [x for _, x in sorted(zip(list_median_str, tab_struct2d))]
boxName = [x for _, x in sorted(zip(list_median_str, list_name))]
if (len(data) % 2 == 0):
absciss = len(data) / 2
else:
absciss = len(data) / 2 + 0.5
divide_tab_name = get_half(boxName)
divide_tab_data = get_half(data)
plt.figure(figsize=(15,4),dpi=200)
plt.xticks(rotation=90)
plt.boxplot(divide_tab_data[0], medianprops=dict(color='black'))
for i in range(int(absciss)):
y =data[i]
x = np.random.normal(1 + i, 0.04, size=len(y))
plt.scatter(x, y)
plt.xticks(np.arange(1, absciss + 1), divide_tab_name[0])
plt.xlabel('nom de la séquence')
plt.ylabel('MCC (appariements)')
plt.savefig('visualisation_all_mcc_' + estimator + "_" + function + "_" + modules + '.png', bbox_inches='tight')
plt.figure(figsize=(15, 4), dpi=200)
plt.xticks(rotation=90)
plt.boxplot(divide_tab_data[1], medianprops=dict(color='black'))
for i in range(len(data)):
if i + int(absciss) < len(data):
y = data[i + int(absciss)]
x = np.random.normal(1 + i, 0.04, size=len(y))
plt.scatter(x, y)
plt.xticks(np.arange(1, absciss + 1), divide_tab_name[1])
plt.xlabel('nom de la séquence')
plt.ylabel('MCC')
plt.savefig('visualisation_all_mcc_' + estimator + "_" + function + "_" + modules + '_2.png', bbox_inches='tight')
# Create a boxplot with all the MCC (for the base pairing and the contacts) obtains with all
# the prediction for each sequence, divide in two figures.
def visualization_all_mcc(path_benchmark, estimator, function, modules):
list_name = get_list_structs_contacts_all(path_benchmark, estimator, function)[0]
tab_struct2d = get_list_structs_contacts_all(path_benchmark, estimator, function)[1]
tab_contacts = get_list_structs_contacts_all(path_benchmark, estimator, function)[2]
min = 20
max = 0
max_i = 0
min_i = 0
for i in range(len(tab_struct2d)):
if (len(tab_struct2d[i]) > max):
max = len(tab_struct2d[i])
max_i = i
if (len(tab_struct2d[i]) < min):
min = len(tab_struct2d[i])
min_i = i
print("max: " + list_name[max_i] + " " + str(max) + " min: " + list_name[min_i] + " " + str(min) + "\n")
np_struct2d = np.array(tab_struct2d)
size = len(tab_struct2d)
list_median_str = []
for i in range(size):
list_median_str.append(np.median(np_struct2d[i]))
all_str = []
for i in range(size):
for j in range(len(np_struct2d[i])):
all_str.append(np_struct2d[i][j])
"""print("mediane struct" + estimator + " + " + function + " : " + str(np.median(all_str)))
print("ecart struct" + estimator + " + " + function + " : " + str(np.std(all_str)) + "\n")"""
data = [x for _, x in sorted(zip(list_median_str, tab_struct2d))]
boxName = [x for _, x in sorted(zip(list_median_str, list_name))]
if (len(data) % 2 == 0):
absciss = len(data) / 2
else:
absciss = len(data) / 2 + 0.5
divide_tab_name = get_half(boxName)
divide_tab_data = get_half(data)
plt.figure(figsize=(15,4),dpi=200)
plt.xticks(rotation=90)
plt.boxplot(divide_tab_data[0], medianprops=dict(color='black'))
for i in range(int(absciss)):
y =data[i]
x = np.random.normal(1 + i, 0.04, size=len(y))
plt.scatter(x, y)
plt.xticks(np.arange(1, absciss + 1), divide_tab_name[0])
plt.xlabel('nom de la séquence')
plt.ylabel('MCC (appariements)')
plt.savefig('visualisation_128arn_structure2d_' + estimator + "_" + function + "_" + modules + '.png', bbox_inches='tight')
plt.figure(figsize=(15, 4), dpi=200)
plt.xticks(rotation=90)
plt.boxplot(divide_tab_data[1], medianprops=dict(color='black'))
for i in range(len(data)):
if i + int(absciss) < len(data):
y = data[i + int(absciss)]
x = np.random.normal(1 + i, 0.04, size=len(y))
plt.scatter(x, y)
plt.xticks(np.arange(1, absciss + 1), divide_tab_name[1])
plt.xlabel('nom de la séquence')
plt.ylabel('MCC')
plt.savefig('visualisation_128arn_structure2d_' + estimator + "_" + function + "_" + modules + '_2.png', bbox_inches='tight')
np_contacts = np.array(tab_contacts)
size = len(tab_contacts)
list_median_ctc = []
for i in range(size):
list_median_ctc.append(np.median(np_contacts[i]))
all_ctc = []
for i in range(size):
for j in range(len(np_contacts[i])):
all_ctc.append(np_contacts[i][j])
"""print("mediane ctc" + estimator + " + " + function + " : " + str(np.median(all_ctc)))
print("ecart ctc" + estimator + " + " + function + " : " + str(np.std(all_ctc)) + "\n")"""
data = [x for _, x in sorted(zip(list_median_ctc, tab_contacts))]
boxName = [x for _, x in sorted(zip(list_median_ctc, list_name))]
if (len(data) % 2 == 0) :
absciss = len(data)/2
else :
absciss = len(data)/2 + 0.5
divide_tab_name = get_half(boxName)
divide_tab_data = get_half(data)
plt.figure(figsize=(15, 4), dpi=200)
plt.xticks(rotation=90)
plt.boxplot(divide_tab_data[0], medianprops=dict(color='black'))
for i in range(int(absciss)):
y = data[i]
x = np.random.normal(1 + i, 0.04, size=len(y))
plt.scatter(x, y)
plt.xticks(np.arange(1, absciss + 1), divide_tab_name[0])
plt.xlabel('nom de la séquence')
plt.ylabel('MCC (contacts)')
plt.savefig('visualisation_128arn_contacts_' + estimator + "_" + function + '.png', bbox_inches='tight')
plt.figure(figsize=(15, 4), dpi=200)
plt.xticks(rotation=90)
plt.boxplot(divide_tab_data[1], medianprops=dict(color='black'))
for i in range(len(data)):
if i + int(absciss) < len(data) :
y = data[i + int(absciss)]
x = np.random.normal(1 + i, 0.04, size=len(y))
plt.scatter(x, y)
plt.xticks(np.arange(1, absciss + 1), divide_tab_name[1])
plt.xlabel('nom de la séquence')
plt.ylabel('MCC')
plt.savefig('visualisation_128arn_contacts_' + estimator + "_" + function + '_2.png', bbox_inches='tight')
# Most of those commands lines and this script work only for the benchmark_16-07-2021.json
# file provides by Isaure Chauvot de Beauchene. This script is means to automize the execution
# of BiORSEO for each sequence and the creation of figures.
# after compiling create_file.cpp in 'scripts' directory (compiling once is enough) :
# clang++ create_files.cpp -o create
#cmd = ("scripts/create")
# after compiling create_file.cpp in 'scripts' directory (compiling once is enough) :
# clang++ add_delimiter.cpp -o addDelimiter
#cmd0 = ("cppsrc/Scripts/addDelimiter")
# after compiling count_pattern.cpp in 'scripts' directory (compiling once is enough) :
# clang++ count_pattern.cpp -o countPattern
#cmd1 = ("cppsrc/Scripts/countPattern")
myfile = open("data/modules/ISAURE/benchmark.txt", "r")
name = myfile.readline()
contacts = myfile.readline()
seq = myfile.readline()
structure2d = myfile.readline()
list_struct2d = [[],[],[],[]]
# source path to the directory (for my computer)
path = "/mnt/c/Users/natha/Documents/IBISC/biorseo2/biorseo"
path2 = "/local/local/BiorseoNath"
while seq:
name = name[6:].strip()
print(name)
# after compiling delete_same_pdb.cpp in 'scripts' directory (compiling once is enough) :
# clang++ delete_same_pdb.cpp -o deletePdb
#cmd2 = ("cppsrc/Scripts/deletePdb " + name)
#os.system(cmd2)
"""get_list_str_by_seq(name, 'MEA', 'E', list_struct2d[0], structure2d, 'json')
get_list_str_by_seq(name, 'MEA', 'F', list_struct2d[1], structure2d, 'json')
get_list_str_by_seq(name, 'MFE', 'E', list_struct2d[2], structure2d, 'json')
get_list_str_by_seq(name, 'MFE', 'F', list_struct2d[3], structure2d, 'json')"""
name = myfile.readline()
contacts = myfile.readline()
seq = myfile.readline()
structure2d = myfile.readline()
#visualization_best_mcc_str_4_figures(list_struct2d, 'red', '#900C3F')
"""visualization_best_mcc(list_struct2d_A_MFE, list_contacts_A_MFE, 'MFE', 'A', 'rin', 'red', '#900C3F')
visualization_best_mcc(list_struct2d_B_MFE, list_contacts_B_MFE, 'MFE', 'B', 'rin', 'blue', '#0900FF')
visualization_best_mcc(list_struct2d_A_MEA, list_contacts_A_MEA, 'MEA', 'A', 'rin', 'red', '#900C3F')
visualization_best_mcc(list_struct2d_B_MEA, list_contacts_B_MEA, 'MEA', 'B', 'rin', 'blue', '#0900FF')"""
myfile.close()
path_benchmark = "data/modules/ISAURE/benchmark.txt"
visualization_all_mcc_str(path_benchmark, 'MEA', 'C', 'bgsu')
visualization_all_mcc_str(path_benchmark, 'MEA', 'D', 'bgsu')
visualization_all_mcc_str(path_benchmark, 'MFE', 'C', 'bgsu')
visualization_all_mcc_str(path_benchmark, 'MFE', 'D', 'bgsu')