removed stuff from biorseo

-results_*
-build_BiORSEO_docker_image_ubuntu18.sh
-deploy_BiORSEO_docker_image_linux.sh
-INSTALL.md
-Readme.md
-benchmark_results/
-doc/

-FROM ubuntu:bionic
-
-# installing dependencies
-RUN apt-get update -yq && \
-    apt-get upgrade -y && \
-    apt-get install -y python3-dev python3-pip openjdk-11-jre libgsl23 libgslcblas0 libboost-program-options-dev libboost-filesystem-dev && \
-    rm -rf /var/lib/apt/lists/*
-
-# compiled biorseo
-COPY . /biorseo 
-# ViennaRNA installer
-ADD "https://www.tbi.univie.ac.at/RNA/download/ubuntu/ubuntu_18_04/viennarna_2.4.14-1_amd64.deb" /
-# jar3d archive
-ADD http://rna.bgsu.edu/data/jar3d/models/jar3d_2014-12-11.jar /
-
-# install codes
-RUN dpkg -i /viennarna_2.4.14-1_amd64.deb && \
-    apt-get install -f          && \
-    \
-    pip3 install networkx numpy regex wrapt biopython /biorseo/BayesPairing && \
-    \
-    cd / && \
-    rm -rf /biorseo/BayesPairing /ViennaRNA-2.4.13 /ViennaRNA-2.4.13.tar.gz
-WORKDIR /biorseo
\ No newline at end of file

-#!/usr/bin/python3
-#coding=utf-8
-
-# typical usage : ./benchmark.py data/sec_structs/verified_secondary_structures_database.dbn data/sec_structs/pseudoknots.dbn data/sec_structs/applications.dbn
-
-# the .dbn files should be formatted the following way:
-# > header of the sequence (somecode)
-# ACGUACGUACGUACGUACGU
-# ...(((...((...))))).
-# > header of the next sequence (somecode2)
-# ACGUACGUACGGGCGUACGU
-# ...(((..........))).
-
-from sys import argv
-from scipy import stats
-from tqdm import tqdm
-import subprocess
-from os import path, makedirs, getcwd, chdir, devnull
-import numpy as np
-import matplotlib.pyplot as plt
-from matplotlib.lines import Line2D
-from math import sqrt, ceil
-from multiprocessing import Pool, cpu_count, Manager, Value
-import multiprocessing
-import multiprocessing.pool
-import ast, time
-import pickle
-
-# ================== DEFINITION OF THE PATHS ==============================
-
-biorseoDir = path.realpath(".")
-jar3dexec = "/local/local/localopt/jar3d_2014-12-11.jar"
-bypdir = biorseoDir + "/BayesPairing/bayespairing/src"
-byp2dir = biorseoDir + "/BayesPairing2/bayespairing/src"
-moipdir = "/local/local/localopt/RNAMoIP/Src/RNAMoIP.py"
-biokopdir = "/local/local/localopt/biokop/biokop"
-runDir = path.dirname(path.realpath(__file__))
-bpRNAFile = argv[1]
-PseudobaseFile = argv[2]
-StudyCaseFile = argv[3]
-outputDir = biorseoDir + "/benchmark_results/"
-HLmotifDir = biorseoDir + "/data/modules/BGSU/HL/3.2/lib"
-ILmotifDir = biorseoDir + "/data/modules/BGSU/IL/3.2/lib"
-descfolder = biorseoDir + "/data/modules/DESC"
-rinfolder = biorseoDir + "/data/modules/RIN/Subfiles/"
-
-# Create some folders to store the results
-subprocess.call(["mkdir", "-p", outputDir])
-subprocess.call(["mkdir", "-p", outputDir + "PK/"])
-subprocess.call(["mkdir", "-p", outputDir + "noPK/"])
-
-n_launched = Value('i', 0)
-n_finished = Value('i', 0)
-n_skipped = Value('i', 0)
-ncores = cpu_count()
-
-# ================== CLASSES AND FUNCTIONS ================================
-
-class NoDaemonProcess(multiprocessing.Process):
-	@property
-	def daemon(self):
-		return False
-
-	@daemon.setter
-	def daemon(self, value):
-		pass
-
-
-class NoDaemonContext(type(multiprocessing.get_context())):
-	Process = NoDaemonProcess
-
-# We sub-class multiprocessing.pool.Pool instead of multiprocessing.Pool
-# because the latter is only a wrapper function, not a proper class.
-class MyPool(multiprocessing.pool.Pool):
-	def __init__(self, *args, **kwargs):
-		kwargs['context'] = NoDaemonContext()
-		super(MyPool, self).__init__(*args, **kwargs)
-
-
-class Job:
-	def __init__(self, results, command=[], function=None, args=[], how_many_in_parallel=0, priority=1, timeout=None, checkFunc=None, checkArgs=[], label=""):
-		self.cmd_ = command
-		self.func_ = function
-		self.args_ = args
-		self.checkFunc_ = checkFunc
-		self.checkArgs_ = checkArgs
-		self.results_file = results
-		self.priority_ = priority
-		self.timeout_ = timeout
-		self.comp_time = -1 # -1 is not executed yet
-		self.label = label
-		if not how_many_in_parallel:
-			self.nthreads = ncores
-		elif how_many_in_parallel == -1:
-			self.nthreads = ncores - 1
-		else:
-			self.nthreads = how_many_in_parallel
-		self.useless_bool = False
-
-	def __str__(self):
-		if self.func_ is None:
-			s = f"{self.priority_}({self.nthreads}) [{self.comp_time}]\t{j.label:25}" + " ".join(self.cmd_)
-		else:
-			s = f"{self.priority_}({self.nthreads}) [{self.comp_time}]\t{j.label:25}{self.func_.__name__}(" + " ".join([str(a) for a in self.args_]) + ")"
-		return s
-
-
-class Loop:
-	def __init__(self, rna_name, header, subsequence, looptype, position):
-		self.rna_name = rna_name
-		self.header = header
-		self.seq = subsequence
-		self.type = looptype
-		self.position = position
-
-	def get_header(self):
-		return self.header
-
-	def subsequence(self):
-		return self.seq
-
-
-class InsertionSite:
-	def __init__(self, loop, csv_line):
-		# BEWARE : jar3d csv output is crap because of java's locale settings.
-		# On french OSes, it uses commas to delimit the fields AND as floating point delimiters !!
-		# Parse with caution, and check what the csv output files look like on your system...
-		info = csv_line.split(',')
-		self.loop = loop  # the Loop object that has been searched with jar3d
-		# position of the loop's components, so the motif's ones, in the query sequence.
-		self.position = loop.position
-		# Motif model identifier of the RNA 3D Motif Atlas
-		self.atlas_id = info[2]
-		# alignment score of the subsequence to the motif model
-		self.score = int(float(info[4]))
-		# should the motif model be inverted to fit the sequence ?
-		self.rotation = int(info[-2])
-
-	def __lt__(self, other):
-		return self.score < other.score
-
-	def __gt__(self, other):
-		return self.score > other.score
-
-
-class Method:
-	def __init__(self, parent_rna, tool, data_source=None, placement_method=None, obj_func=None, PK=True, flat=False):
-		self.parent_rna = parent_rna
-
-		# defintion of the method (theoretical)
-		self.tool = tool
-		self.data_source = data_source
-		self.placement_method = placement_method
-		self.func = obj_func
-		self.allow_pk = PK
-		self.label = self.get_label()
-
-		# things related to execution
-		self.joblist = []
-		self.flat = flat
-		self.build_job_list()
-
-		# descriptors of the results set:
-		self.predictions = []
-		self.scores = []
-		self.ninsertions = []
-		self.max_mcc = 0
-		self.min_mcc = 0
-		self.avg_mcc = 0
-		self.max_f1 = 0
-		self.min_f1 = 0
-		self.avg_f1 = 0
-		self.best_pred = ""
-		self.n_pred = 0
-		self.ratio = 0  # ratio of the number of inserted motifs in the best solution on the max number of inserted motifs for this RNA
-
-	def get_label(self):
-		if self.tool == "biorseo":
-			if self.allow_pk:
-				return f"{self.data_source}-{self.placement_method}-{self.func}"
-			else:
-				return f"{self.data_source}-{self.placement_method}-{self.func}-noPK"
-		else:
-			return self.tool
-
-	def build_job_list(self):
-		# PRIORITIES :
-		# 1/	RNAsubopt
-		# 2/	mv
-		# 3/	Jar3D, BayesPairing, RNA-MoIP
-		# 4/	BiORSEO
-		# 5/	BiokoP
-		basename = self.parent_rna.basename
-		fasta = outputDir+basename+".fa"
-
-		# Things that require RNAsubopt calculations
-		if self.tool in ["RNAsubopt", "RNA-MoIP (1by1)", "RNA-MoIP (chunk)"] or self.placement_method == "Jar3d":
-			if f"{basename} RNAsubopt" in issues:
-				return
-			self.joblist.append(Job(command=["RNAsubopt", "-i", fasta, "--outfile="+ basename + ".subopt"],
-									priority=1, how_many_in_parallel=1 if self.flat else 0,
-									results = outputDir + "noPK/" +  basename + ".subopt",
-									label=f"{basename} RNAsubopt"))
-			self.joblist.append(Job(command=["mv", basename + ".subopt", outputDir + "noPK/"],
-									priority=2, how_many_in_parallel=1 if self.flat else 0,
-									results = outputDir + "noPK/" +  basename + ".subopt",
-									label=f"{basename} mv"))
-
-		# Find modules using Jar3d or BayesPairing:
-		if self.placement_method == "Jar3d":
-			if f"{basename} BGSU-Jar3d" in issues:
-				return
-			self.joblist.append(Job(function=launch_JAR3D, args=[instance.seq_, basename],
-									priority=3, how_many_in_parallel=1,
-									results = outputDir + basename + ".bgsu_jar3d.csv",
-									label=f"{basename} BGSU-Jar3d"))
-
-		if self.placement_method == "ByP":
-			if self.data_source == "DESC" :
-				module_type_arg = "rna3dmotif"
-			elif self.data_source == "RIN" :
-				module_type_arg = "carnaval"
-			else:
-				module_type_arg = "3dmotifatlas"
-
-			if module_type_arg != "carnaval":
-				if f"{basename} {self.data_source}-ByP" in issues:
-					return
-				# self.joblist.append(Job(function=launch_BayesPairing2, args=[module_type_arg, instance.seq_, instance.header_, basename],
-				# 						how_many_in_parallel=1 if self.flat else -1, priority=3,
-				# 						results = outputDir + basename + f".{self.data_source.lower()}_byp2.csv",
-				# 						label=f"{basename} {self.data_source}-ByP"))
-				self.joblist.append(Job(function=launch_BayesPairing, args=[module_type_arg, instance.seq_, instance.header_, basename],
-										how_many_in_parallel=1 if self.flat else -1, priority=3,
-										results = outputDir + basename + f".{self.data_source.lower()}_byp.csv",
-										label=f"{basename} {self.data_source}-ByP"))
-
-		if f"{basename} {self.label}" in issues:
-			return
-
-		if self.tool == "biorseo":
-			c = [ biorseoDir+"/bin/biorseo", "-s", fasta ]
-			if self.placement_method == "D.P.":
-				if self.data_source == "RIN" :
-					results_file = outputDir+f"{'' if self.allow_pk else 'no'}PK/"+basename+f".biorseo_rin_raw_{self.func}"
-					c += [ "-x", rinfolder]
-				else:
-					results_file = outputDir+f"{'' if self.allow_pk else 'no'}PK/"+basename+f".biorseo_desc_raw_{self.func}"
-					c += [ "-d", descfolder]
-			elif self.placement_method == "ByP":
-				results_file = outputDir+f"{'' if self.allow_pk else 'no'}PK/"+basename+f".biorseo_{self.data_source.lower()}_{self.placement_method.lower()}_{self.func}"
-				c += ["--bayespaircsv", outputDir+basename+f".{self.data_source.lower()}_{self.placement_method.lower()}.csv"]
-			else:
-				results_file = outputDir+f"{'' if self.allow_pk else 'no'}PK/"+basename+f".biorseo_{self.data_source.lower()}_{self.placement_method.lower()}_{self.func}"
-				c += ["--jar3dcsv", outputDir+basename+f".{self.data_source.lower()}_{self.placement_method.lower()}.csv"]
-			c += ["-o", results_file, "--func", self.func]
-			if not self.allow_pk:
-				c += ["-n"]
-			self.joblist.append(Job(command=c, priority=4, timeout=3600,
-									how_many_in_parallel=1 if self.flat else 10,
-									results = results_file,
-									label=f"{basename} {self.label}"))
-
-		if self.tool == "RNA-MoIP (chunk)":
-			self.joblist.append(Job(function=launch_RNAMoIP, args=[instance.seq_, instance.header_, basename, False],
-									priority=3, how_many_in_parallel=1 if self.flat else 0,
-									timeout=3600, results = outputDir + f"noPK/{basename}.moipc",
-									label=f"{basename} {self.label}"))
-
-		if self.tool == "RNA-MoIP (1by1)":
-			self.joblist.append(Job(function=launch_RNAMoIP, args=[instance.seq_, instance.header_, basename, True],
-									priority=3, how_many_in_parallel=1 if self.flat else 0,
-									timeout=3600,
-									results = outputDir + f"noPK/{basename}.moip",
-									label=f"{basename} {self.label}"))
-
-		if self.tool == "Biokop":
-			self.joblist.append(Job(command=[biokopdir, "-n1", "-i", fasta, "-o", outputDir + f"PK/{basename}.biok"],
-									priority=5, timeout=3600,
-									how_many_in_parallel=1 if self.flat else 3,
-									results = outputDir + f"PK/{basename}.biok",
-									label=f"{basename} {self.label}"))
-
-	def get_comp_times(self):
-		s = ""
-		for j in self.joblist:
-			s += f"{j.comp_time:.1f} + "
-		return s[:-3]
-
-
-class RNA:
-	def __init__(self, filename, header, seq, struct):
-		self.seq_ = seq
-		self.header_ = header
-		self.true2d = struct
-		self.basename = filename
-		self.methods = []
-		self.meth_idx = {}
-		# subprocess.call(["rm", "-f", outputDir + self.basename + "*"])
-		# subprocess.call(["rm", "-f", outputDir + "PK/" + self.basename + "*"])
-		# subprocess.call(["rm", "-f", outputDir + "noPK/" + self.basename + "*"])
-
-		if not path.isfile(outputDir + self.basename + ".fa"):
-			rna = open(outputDir + self.basename + ".fa", "w")
-			rna.write(">"+self.header_+'\n')
-			rna.write(self.seq_+'\n')
-			rna.close()
-
-	def add_method_evaluation(self, *args, **kwargs):
-		new_m = Method(*args, **kwargs)
-		self.meth_idx[new_m.label] = len(self.methods)
-		self.methods.append(new_m)
-
-	def evaluate(self, verbose=False):
-		if verbose:
-			print("{:<24}{}".format("True 2D", self.true2d))
-
-		for m in self.methods:
-			if len(m.predictions):
-				mccs = []
-				f1s = []
-				m.n_pred = len(m.predictions)
-
-				# List unique solutions
-				sec_structs = []
-				for p in m.predictions:
-					if not ')' in p: # ignore flat solutions
-						m.n_pred -= 1
-						continue
-					ss = p.split('\t')[0].split(' ')[0]
-					if ss not in sec_structs:
-						sec_structs.append(p.split('\t')[0])
-					else:
-						m.n_pred -= 1
-						continue
-					f1s.append(f1_score(*compare_two_structures(self.true2d, p)))
-					mccs.append(mattews_corr_coeff(*compare_two_structures(self.true2d, p)))
-
-				if len(mccs):
-					m.max_mcc = max(mccs)
-					m.min_mcc = min(mccs)
-					m.avg_mcc = sum(mccs)/float(len(mccs))
-					m.best_pred = sec_structs[mccs.index(m.max_mcc)]
-				if len(f1s):
-					m.max_f1 = max(f1s)
-					m.min_f1 = min(f1s)
-					m.avg_f1 = sum(f1s)/float(len(f1s))
-
-				for p,n in zip(m.predictions, m.ninsertions):
-					if not ')' in p: # ignore linear structures
-						continue
-					# if several structures have the max_MCC
-					if m.max_mcc == mattews_corr_coeff(*compare_two_structures(self.true2d, p)):
-						# if one of them has a higher ratio, update the ratio
-						if max(m.ninsertions) > 0 and float(n)/max(m.ninsertions) > m.ratio:
-							m.ratio = float(n)/max(m.ninsertions)
-							m.best_pred = p
-
-				if verbose:
-					print(f"{m.label:<21}\t{m.best_pred}\t{m.max_mcc:.2f}\t{m.n_pred}\t{m.get_comp_times()}")
-
-	def get_method(self, label):
-		return self.methods[self.meth_idx[label]]
-
-	def load_biokop_results(self):
-		if path.isfile(outputDir + "PK/" + self.basename + ".biok"):
-			rna = open(outputDir + "PK/" + self.basename + ".biok", "r")
-			lines = rna.readlines()
-			rna.close()
-			for i in range(1, len(lines)-1):
-				ss = lines[i].split(' ')[0]
-				if ss not in self.get_method("Biokop").predictions:
-					self.get_method("Biokop").predictions.append(ss)
-
-	def load_RNAsubopt_results(self):
-		if not path.isfile(outputDir + "noPK/" + self.basename + ".subopt"):
-			return
-		rna = open(outputDir + "noPK/" + self.basename + ".subopt", "r")
-		lines = rna.readlines()
-		rna.close()
-		for i in range(2, len(lines)):
-			ss = lines[i].split(' ')[0]
-			if ss not in self.get_method("RNAsubopt").predictions:
-				self.get_method("RNAsubopt").predictions.append(ss)
-
-	def load_RNAMoIP_results(self):
-		if not path.isfile(outputDir + "noPK/" + self.basename + ".moipc"):
-			return
-		rna = open(outputDir + "noPK/" + self.basename + ".moipc", "r")
-		lines = rna.readlines()
-		rna.close()
-		method = self.get_method("RNA-MoIP (chunk)")
-		for i in range(2, len(lines)):
-			method.predictions.append(lines[i].split('\t')[0])
-			method.ninsertions.append(int(lines[i].split('\t')[1]))
-			method.scores.append(float(lines[i].split('\t')[2][:-1]))
-		rna = open(outputDir + "noPK/" + self.basename + ".moip", "r")
-		lines = rna.readlines()
-		rna.close()
-		method = self.get_method("RNA-MoIP (1by1)")
-		for i in range(2, len(lines)):
-			method.predictions.append(lines[i].split('\t')[0])
-			method.ninsertions.append(int(lines[i].split('\t')[1]))
-			method.scores.append(float(lines[i].split('\t')[2][:-1]))
-
-	def load_biorseo_results(self, filename, method):
-		if path.isfile(filename):
-			rna = open(filename, "r")
-			lines = rna.readlines()
-			rna.close()
-			for i in range(2, len(lines)):
-				ss = lines[i].split(' ')[0].split('\t')[0]
-				# if ss not in method.predictions:
-				method.predictions.append(ss)
-				method.ninsertions.append(lines[i].count('+'))
-		# else:
-		#     print(filename, "not found !")
-
-	def load_results(self, include_noPK=False):
-		if "RNAsubopt" in self.meth_idx.keys():
-			self.load_RNAsubopt_results()
-		if "RNA-MoIP (1by1)" in self.meth_idx.keys():
-			self.load_RNAMoIP_results()
-		if "Biokop" in self.meth_idx.keys():
-			self.load_biokop_results()
-		if "DESC-D.P.-A" in self.meth_idx.keys():
-			self.load_biorseo_results(outputDir + "PK/" + self.basename + ".biorseo_desc_raw_A", self.get_method("DESC-D.P.-A"))
-		if "DESC-D.P.-B" in self.meth_idx.keys():
-			self.load_biorseo_results(outputDir + "PK/" + self.basename + ".biorseo_desc_raw_B", self.get_method("DESC-D.P.-B"))
-		if "DESC-ByP-A" in self.meth_idx.keys():
-			self.load_biorseo_results(outputDir + "PK/" + self.basename + ".biorseo_desc_byp_A", self.get_method("DESC-ByP-A"))
-		if "DESC-ByP-B" in self.meth_idx.keys():
-			self.load_biorseo_results(outputDir + "PK/" + self.basename + ".biorseo_desc_byp_B", self.get_method("DESC-ByP-B"))
-		if "DESC-ByP-C" in self.meth_idx.keys():
-			self.load_biorseo_results(outputDir + "PK/" + self.basename + ".biorseo_desc_byp_C", self.get_method("DESC-ByP-C"))
-		if "DESC-ByP-D" in self.meth_idx.keys():
-			self.load_biorseo_results(outputDir + "PK/" + self.basename + ".biorseo_desc_byp_D", self.get_method("DESC-ByP-D"))
-		if "BGSU-ByP-A" in self.meth_idx.keys():
-			self.load_biorseo_results(outputDir + "PK/" + self.basename + ".biorseo_bgsu_byp_A", self.get_method("BGSU-ByP-A"))
-		if "BGSU-ByP-B" in self.meth_idx.keys():
-			self.load_biorseo_results(outputDir + "PK/" + self.basename + ".biorseo_bgsu_byp_B", self.get_method("BGSU-ByP-B"))
-		if "BGSU-ByP-C" in self.meth_idx.keys():
-			self.load_biorseo_results(outputDir + "PK/" + self.basename + ".biorseo_bgsu_byp_C", self.get_method("BGSU-ByP-C"))
-		if "BGSU-ByP-D" in self.meth_idx.keys():
-			self.load_biorseo_results(outputDir + "PK/" + self.basename + ".biorseo_bgsu_byp_D", self.get_method("BGSU-ByP-D"))
-		if "BGSU-Jar3d-A" in self.meth_idx.keys():
-			self.load_biorseo_results(outputDir + "PK/" + self.basename + ".biorseo_bgsu_jar3d_A", self.get_method("BGSU-Jar3d-A"))
-		if "BGSU-Jar3d-B" in self.meth_idx.keys():
-			self.load_biorseo_results(outputDir + "PK/" + self.basename + ".biorseo_bgsu_jar3d_B", self.get_method("BGSU-Jar3d-B"))
-		if "BGSU-Jar3d-C" in self.meth_idx.keys():
-			self.load_biorseo_results(outputDir + "PK/" + self.basename + ".biorseo_bgsu_jar3d_C", self.get_method("BGSU-Jar3d-C"))
-		if "BGSU-Jar3d-B" in self.meth_idx.keys():
-			self.load_biorseo_results(outputDir + "PK/" + self.basename + ".biorseo_bgsu_jar3d_D", self.get_method("BGSU-Jar3d-D"))
-
-		if "RIN-D.P.-A" in self.meth_idx.keys():
-			self.load_biorseo_results(outputDir + "PK/" + self.basename + ".biorseo_rin_raw_A", self.get_method("RIN-D.P.-A"))
-		if "RIN-D.P.-B" in self.meth_idx.keys():
-			self.load_biorseo_results(outputDir + "PK/" + self.basename + ".biorseo_rin_raw_B", self.get_method("RIN-D.P.-B"))
-
-		if include_noPK:
-			if "DESC-D.P.-A-noPK" in self.meth_idx.keys():
-				self.load_biorseo_results(outputDir + "noPK/" + self.basename + ".biorseo_desc_raw_A", self.get_method("DESC-D.P.-A-noPK"))
-			if "DESC-D.P.-B-noPK" in self.meth_idx.keys():
-				self.load_biorseo_results(outputDir + "noPK/" + self.basename + ".biorseo_desc_raw_B", self.get_method("DESC-D.P.-B-noPK"))
-			if "DESC-ByP-A-noPK" in self.meth_idx.keys():
-				self.load_biorseo_results(outputDir + "noPK/" + self.basename + ".biorseo_desc_byp_A", self.get_method("DESC-ByP-A-noPK"))
-			if "DESC-ByP-B-noPK" in self.meth_idx.keys():
-				self.load_biorseo_results(outputDir + "noPK/" + self.basename + ".biorseo_desc_byp_B", self.get_method("DESC-ByP-B-noPK"))
-			if "DESC-ByP-C-noPK" in self.meth_idx.keys():
-				self.load_biorseo_results(outputDir + "noPK/" + self.basename + ".biorseo_desc_byp_C", self.get_method("DESC-ByP-C-noPK"))
-			if "DESC-ByP-D-noPK" in self.meth_idx.keys():
-				self.load_biorseo_results(outputDir + "noPK/" + self.basename + ".biorseo_desc_byp_D", self.get_method("DESC-ByP-D-noPK"))
-			if "BGSU-ByP-A-noPK" in self.meth_idx.keys():
-				self.load_biorseo_results(outputDir + "noPK/" + self.basename + ".biorseo_bgsu_byp_A", self.get_method("BGSU-ByP-A-noPK"))
-			if "BGSU-ByP-B-noPK" in self.meth_idx.keys():
-				self.load_biorseo_results(outputDir + "noPK/" + self.basename + ".biorseo_bgsu_byp_B", self.get_method("BGSU-ByP-B-noPK"))
-			if "BGSU-ByP-C-noPK" in self.meth_idx.keys():
-				self.load_biorseo_results(outputDir + "noPK/" + self.basename + ".biorseo_bgsu_byp_C", self.get_method("BGSU-ByP-C-noPK"))
-			if "BGSU-ByP-D-noPK" in self.meth_idx.keys():
-				self.load_biorseo_results(outputDir + "noPK/" + self.basename + ".biorseo_bgsu_byp_D", self.get_method("BGSU-ByP-D-noPK"))
-			if "BGSU-Jar3d-A-noPK" in self.meth_idx.keys():
-				self.load_biorseo_results(outputDir + "noPK/" + self.basename + ".biorseo_bgsu_jar3d_A", self.get_method("BGSU-Jar3d-A-noPK"))
-			if "BGSU-Jar3d-B-noPK" in self.meth_idx.keys():
-				self.load_biorseo_results(outputDir + "noPK/" + self.basename + ".biorseo_bgsu_jar3d_B", self.get_method("BGSU-Jar3d-B-noPK"))
-			if "BGSU-Jar3d-C-noPK" in self.meth_idx.keys():
-				self.load_biorseo_results(outputDir + "noPK/" + self.basename + ".biorseo_bgsu_jar3d_C", self.get_method("BGSU-Jar3d-C-noPK"))
-			if "BGSU-Jar3d-B-noPK" in self.meth_idx.keys():
-				self.load_biorseo_results(outputDir + "noPK/" + self.basename + ".biorseo_bgsu_jar3d_D", self.get_method("BGSU-Jar3d-D-noPK"))
-
-			if "RIN-D.P.-A-noPK" in self.meth_idx.keys():
-				self.load_biorseo_results(outputDir + "noPK/" + self.basename + ".biorseo_rin_raw_A", self.get_method("RIN-D.P.-A-noPK"))
-			if "RIN-D.P.-B-noPK" in self.meth_idx.keys():
-				self.load_biorseo_results(outputDir + "noPK/" + self.basename + ".biorseo_rin_raw_B", self.get_method("RIN-D.P.-B-noPK"))
-
-	def has_complete_results(self, with_PK):
-		if not with_PK:
-			if not path.isfile(outputDir + "noPK/" + self.basename + ".subopt"): return False
-			if not path.isfile(outputDir + "noPK/" + self.basename + ".moip"): return False
-			if not path.isfile(outputDir + "noPK/" + self.basename + ".moipc"): return False
-			if not path.isfile(outputDir + "noPK/" + self.basename + ".biorseo_desc_raw_A"): return False
-			if not path.isfile(outputDir + "noPK/" + self.basename + ".biorseo_desc_raw_B"): return False
-			if not path.isfile(outputDir + "noPK/" + self.basename + ".biorseo_desc_byp_A"): return False
-			if not path.isfile(outputDir + "noPK/" + self.basename + ".biorseo_desc_byp_B"): return False
-			if not path.isfile(outputDir + "noPK/" + self.basename + ".biorseo_desc_byp_C"): return False
-			if not path.isfile(outputDir + "noPK/" + self.basename + ".biorseo_desc_byp_D"): return False
-			if not path.isfile(outputDir + "noPK/" + self.basename + ".biorseo_bgsu_byp_A"): return False
-			if not path.isfile(outputDir + "noPK/" + self.basename + ".biorseo_bgsu_byp_B"): return False
-			if not path.isfile(outputDir + "noPK/" + self.basename + ".biorseo_bgsu_byp_C"): return False
-			if not path.isfile(outputDir + "noPK/" + self.basename + ".biorseo_bgsu_byp_D"): return False
-			if not path.isfile(outputDir + "noPK/" + self.basename + ".biorseo_bgsu_jar3d_A"): return False
-			if not path.isfile(outputDir + "noPK/" + self.basename + ".biorseo_bgsu_jar3d_B"): return False
-			if not path.isfile(outputDir + "noPK/" + self.basename + ".biorseo_bgsu_jar3d_C"): return False
-			if not path.isfile(outputDir + "noPK/" + self.basename + ".biorseo_bgsu_jar3d_D"): return False
-			if not path.isfile(outputDir + "noPK/" + self.basename + ".biorseo_rin_raw_A"): return False
-			if not path.isfile(outputDir + "noPK/" + self.basename + ".biorseo_rin_raw_B"): return False
-
-			return True
-		else:
-			if not path.isfile(outputDir + "PK/" + self.basename + ".biok"): return False
-			if not path.isfile(outputDir + "PK/" + self.basename + ".biorseo_desc_raw_A"): return False
-			if not path.isfile(outputDir + "PK/" + self.basename + ".biorseo_desc_raw_B"): return False
-			if not path.isfile(outputDir + "PK/" + self.basename + ".biorseo_desc_byp_A"): return False
-			if not path.isfile(outputDir + "PK/" + self.basename + ".biorseo_desc_byp_B"): return False
-			if not path.isfile(outputDir + "PK/" + self.basename + ".biorseo_desc_byp_C"): return False
-			if not path.isfile(outputDir + "PK/" + self.basename + ".biorseo_desc_byp_D"): return False
-			if not path.isfile(outputDir + "PK/" + self.basename + ".biorseo_bgsu_byp_A"): return False
-			if not path.isfile(outputDir + "PK/" + self.basename + ".biorseo_bgsu_byp_B"): return False
-			if not path.isfile(outputDir + "PK/" + self.basename + ".biorseo_bgsu_byp_C"): return False
-			if not path.isfile(outputDir + "PK/" + self.basename + ".biorseo_bgsu_byp_D"): return False
-			if not path.isfile(outputDir + "PK/" + self.basename + ".biorseo_bgsu_jar3d_A"): return False
-			if not path.isfile(outputDir + "PK/" + self.basename + ".biorseo_bgsu_jar3d_B"): return False
-			if not path.isfile(outputDir + "PK/" + self.basename + ".biorseo_bgsu_jar3d_C"): return False
-			if not path.isfile(outputDir + "PK/" + self.basename + ".biorseo_bgsu_jar3d_D"): return False
-			if not path.isfile(outputDir + "PK/" + self.basename + ".biorseo_rin_raw_A"): return False
-			if not path.isfile(outputDir + "PK/" + self.basename + ".biorseo_rin_raw_B"): return False
-
-			return True
-
-
-def init(arg1, arg2, arg3):
-	global n_launched, n_finished, n_skipped
-	n_launched = arg1
-	n_finished = arg2
-	n_skipped = arg3
-
-def execute_job(j):
-	global n_launched, n_skipped, n_finished
-
-	# Check if you really need to execute it
-	if path.isfile(j.results_file) or ((j.checkFunc_ is not None) and j.checkFunc_(*j.checkArgs_)):
-		# skip it
-		with n_skipped.get_lock():
-			n_skipped.value += 1
-			n_finished.value += 1
-		print(f"[{n_launched.value+n_skipped.value}/{jobcount}]\tSkipping {j.label} (already finished)")
-		return (0, 0)
-
-
-	# Add the job to log file and run
-	with n_launched.get_lock():
-		n_launched.value += 1
-	if len(j.cmd_):
-		logfile = open(runDir + "/log_of_the_run.sh", 'a')
-		logfile.write(" ".join(j.cmd_))
-		logfile.write("\n")
-		logfile.close()
-		print(f"[{n_launched.value+n_skipped.value}/{jobcount}]\t{j.label}")
-		start_time = time.time()
-		r = subprocess.run(j.cmd_, timeout=j.timeout_)
-		end_time = time.time()
-		if r.returncode != 0:
-			if r.stderr is not None:
-				print(r.stderr, flush=True)
-			print(f"[{n_launched.value+n_skipped.value}/{jobcount}]\tIssue faced with {j.label}, skipping it and adding it to known issues (if not known).")
-			with n_launched.get_lock():
-				n_launched.value -= 1
-			with n_skipped.get_lock():
-				n_skipped.value += 1
-			if j.label not in issues:
-				issues.add(j.label)
-				with open("benchmark_results/known_issues.txt", "a") as iss:
-					iss.write(j.label+"\n")
-	elif j.func_ is not None:
-		print(f"[{n_launched.value+n_skipped.value}/{jobcount}]\t{j.func_.__name__}({', '.join([str(a) for a in j.args_])})")
-		start_time = time.time()
-		r = j.func_(*j.args_)
-		end_time = time.time()
-
-	# Job is finished
-	with n_finished.get_lock():
-		n_finished.value += 1
-	t = end_time - start_time
-	return (t,r)
-
-def launch_JAR3D_worker(loop):
-	# write motif to a file
-	newpath = getcwd()+'/' + loop.rna_name + '/'+ loop.header[1:]
-	if not path.exists(newpath):
-		makedirs(newpath)
-	chdir(newpath)
-	filename = loop.header[1:]+".fasta"
-	fasta = open(filename, 'w')
-	fasta.write('>'+loop.get_header()+'\n'+loop.subsequence()+'\n')
-	fasta.close()
-
-	# Launch Jar3D on it
-	if loop.type == 'h':
-		cmd = ["java", "-jar", jar3dexec, filename, HLmotifDir+"/all.txt", loop.header[1:]+".HLloop.csv", loop.header[1:]+".HLseq.csv"]
-	else:
-		cmd = ["java", "-jar", jar3dexec, filename, ILmotifDir+"/all.txt", loop.header[1:]+".ILloop.csv", loop.header[1:]+".ILseq.csv"]
-
-	logfile = open(runDir + "/log_of_the_run.sh", 'a')
-	logfile.write(' '.join(cmd)+"\n")
-	logfile.close()
-	subprocess.run(cmd, stdout=subprocess.DEVNULL)
-
-	# Retrieve results
-	insertion_sites = []
-	if loop.type == 'h':
-		capstype = "HL"
-	else:
-		capstype = "IL"
-	csv = open(loop.header[1:]+".%sseq.csv" % capstype, 'r')
-	l = csv.readline()
-	while l:
-		if "true" in l:
-			insertion_sites.append(InsertionSite(loop, l))
-		l = csv.readline()
-	csv.close()
-
-	return insertion_sites
-
-def launch_JAR3D(seq_, basename):
-	rnasubopt_preds = []
-
-	# Extracting probable loops from RNA-subopt structures
-	rna = open(outputDir + f"noPK/{basename}.subopt", "r")
-	lines = rna.readlines()
-	rna.close()
-	for i in range(2, len(lines)):
-		ss = lines[i].split(' ')[0]
-		if ss not in rnasubopt_preds:
-			rnasubopt_preds.append(ss)
-	HLs = []
-	ILs = []
-	for ss in rnasubopt_preds:
-		loop_candidates = enumerate_loops(ss)
-		for loop_candidate in loop_candidates:
-			if len(loop_candidate) == 1 and loop_candidate not in HLs:
-				HLs.append(loop_candidate)
-			if len(loop_candidate) == 2 and loop_candidate not in ILs:
-				ILs.append(loop_candidate)
-
-	# Retrieve subsequences corresponding to the possible loops
-	loops = []
-	for i, l in enumerate(HLs):
-		loops.append(Loop(basename, ">HL%d" % (i+1), seq_[l[0][0]-1:l[0][1]], "h", l))
-	for i, l in enumerate(ILs):
-		loops.append(Loop(basename, ">IL%d" % (i+1), seq_[l[0][0]-1:l[0][1]]+'*'+seq_[l[1][0]-1:l[1][1]], "i", l))
-
-	# Scanning loop subsequences against motif database
-	if not path.exists(basename):
-		makedirs(basename)
-	p = Pool(processes=ncores)
-	insertion_sites = [x for y in p.map(launch_JAR3D_worker, loops) for x in y]
-	p.close()
-	p.join()
-	insertion_sites.sort(reverse=True)
-	subprocess.call(["rm", "-r", basename])
-
-	# Writing results to CSV file
-	c = 0
-	resultsfile = open(outputDir+basename+".bgsu_jar3d.csv", "w")
-	resultsfile.write("Motif,Rotation,Score,Start1,End1,Start2,End2\n")
-	for site in insertion_sites:
-		if site.score > 10:
-			c += 1
-			string = "FOUND with score %d:\t\t possible insertion of motif " % site.score + site.atlas_id
-			if site.rotation:
-				string += " (reversed)"
-			string += (" on " + site.loop.get_header() + " at positions")
-		resultsfile.write(site.atlas_id+',' + str(bool(site.rotation))+",%d" % site.score+',')
-		positions = [','.join([str(y) for y in x]) for x in site.position]
-		if len(positions) == 1:
-			positions.append("-,-")
-		resultsfile.write(','.join(positions)+'\n')
-	resultsfile.close()
-
-def launch_BayesPairing(module_type, seq_, header_, basename):
-
-	cmd = ["python3", "-W", "ignore", "parse_sequences.py","-seq",outputDir + basename + ".fa", "-d", module_type, "-interm","1"]
-
-	logfile = open(runDir + "/log_of_the_run.sh", 'a')
-	logfile.write(" ".join(cmd))
-	logfile.write("\n")
-	logfile.close()
-
-	chdir(bypdir)
-	out = subprocess.check_output(cmd).decode('utf-8')
-	BypLog = out.split('\n')
-	idx = 0
-	l = BypLog[idx]
-	while l[:3] != "PUR":
-		idx += 1
-		l = BypLog[idx]
-	insertion_sites = [ x for x in ast.literal_eval(l.split(":")[1][1:])]
-	if module_type=="carnaval":
-		rna = open(outputDir + basename + ".rin_byp.csv", "w")
-	elif module_type=="rna3dmotif":
-		rna = open(outputDir + basename + ".desc_byp.csv", "w")
-	else:
-		rna = open(outputDir + basename + ".bgsu_byp.csv", "w")
-	rna.write("Motif,Score,Start1,End1,Start2,End2...\n")
-	for i,module in enumerate(insertion_sites):
-		if len(module):
-			for (score, positions, sequence) in zip(*[iter(module)]*3):
-				pos = []
-				q = -2
-				for p in positions:
-					if p-q > 1:
-						pos.append(q)
-						pos.append(p)
-					q = p
-				pos.append(q)
-				rna.write(module_type+str(i)+','+str(int(score)))
-				for (p,q) in zip(*[iter(pos[1:])]*2):
-					if q>p:
-						rna.write(','+str(p)+','+str(q))
-				rna.write('\n')
-	rna.close()
-
-def launch_BayesPairing2(module_type, seq_, header_, basename):
-
-	if module_type=="rna3dmotif":
-		BP2_type = "rna3dmotif"
-	else:
-		BP2_type = "ALL"
-
-	cmd = ["python3", "-W", "ignore", "parse_sequences.py", "-seq", outputDir+basename+".fa", "-samplesize", "1000", "-d", BP2_type, "-o", "output"]
-
-	logfile = open(runDir + "/log_of_the_run.sh", 'a')
-	logfile.write(" ".join(cmd))
-	logfile.write("\n")
-	logfile.close()
-
-	chdir(byp2dir)
-
-	#subprocess.run(cmd)
-	subprocess.check_output(cmd)
-	filename = "../output/output.pickle"
-
-	objects = []
-	with (open(filename, "rb")) as openfile:
-		while True:
-			try:
-				objects.append(pickle.load(openfile))
-			except EOFError:
-				#print("EOFError while opening ../output/output.pickle for BP2")
-				break
-
-	if module_type=="rna3dmotif":
-		rna = open(outputDir + basename + ".desc_byp2.csv", "w")
-	else:
-		rna = open(outputDir + basename + ".bgsu_byp2.csv", "w")
-	rna.write("Motif,Score,Start1,End1,Start2,End2...\n")
-
-	for i in objects[0][list(objects[0].keys())[0]][0]:
-		for line in objects[0][list(objects[0].keys())[0]][0][i]:
-			if abs(line[2]) <= 2.3 : #default treshold of BP2
-				str_line = module_type + str(i) + "," + str(round(line[2],3))
-				for pos in line[3]:
-					str_line += "," + str(pos[0]) + "," + str(pos[-1])
-
-				rna.write(str_line + "\n")
-
-	rna.close()
-
-def launch_RNAMoIP_worker(c):
-	# launch gurobi
-	try:
-		out = subprocess.check_output(c).decode("utf-8")
-	except subprocess.CalledProcessError as e:
-		print(e.output)
-		exit()
-	gurobiLog = out.split('\n')
-
-	# parse output
-	idx = 0
-	l = gurobiLog[idx]
-	solution = ""
-	nsolutions = 0
-	while l != "Corrected secondary structure:" and l != " NO SOLUTIONS!":
-		if l[:19] == "Optimal solution nb:":
-			nsolutions = int(l.split(' ')[-1])
-		idx += 1
-		l = gurobiLog[idx]
-	if nsolutions > 1:
-		print("WARNING: RNA-MoIP found several solutions !")
-	if l == "Corrected secondary structure:":
-		idx+=1
-		solution =  gurobiLog[idx][1:]
-		idx += 1
-		motifs = []
-		while gurobiLog[idx].count('.'):
-			motif = gurobiLog[idx].split('-')[1]
-			if motif not in motifs:
-				motifs.append(motif)
-			idx += 1
-		nmotifs = len(motifs)
-		score = float(gurobiLog[-2][1:-1])
-	else:
-		solution = ""
-		nmotifs = 0
-		score = 0
-
-	return solution, nmotifs, score
-
-def launch_RNAMoIP(seq_, header_, basename, one_by_one):
-	RNAMoIP = moipdir
-
-	# read RNAsubopt predictions
-	rnasubopt_preds = []
-	rna = open(outputDir + f"noPK/{basename}.subopt", "r")
-	lines = rna.readlines()
-	rna.close()
-	for i in range(2, len(lines)):
-		ss = lines[i].split(' ')[0]
-		if ss not in rnasubopt_preds:
-			rnasubopt_preds.append(ss)
-	if one_by_one:
-		logfile = open(runDir + "/log_of_the_run.sh", 'a')
-		rna = open(outputDir + f"noPK/{basename}.moip", "w")
-		rna.write(header_+'\n')
-		rna.write(seq_+'\n')
-		for ss in rnasubopt_preds:
-			c = ["python2", RNAMoIP, "-s", f"{seq_}", "-ss", f"{ss}", "-d", descfolder]
-			logfile.write(" ".join(c)+'\n')
-			solution, nmotifs, score = launch_RNAMoIP_worker(c)
-			rna.write("{}\t{}\t{}\n".format(solution, nmotifs, score))
-		rna.close()
-		logfile.close()
-	else:
-		subopts = open(f"{runDir}/{basename}.temp_subopt", "w")
-		for ss in rnasubopt_preds:
-			subopts.write(ss + '\n')
-		subopts.close()
-		c = ["python2", RNAMoIP, "-s", f"{seq_}", "-ss", f"{runDir}/{basename}.temp_subopt", "-d", descfolder]
-
-		logfile = open(runDir + "/log_of_the_run.sh", 'a')
-		logfile.write(" ".join(c))
-		logfile.write("\n")
-		logfile.close()
-
-		solution, nmotifs, score = launch_RNAMoIP_worker(c)
-		rna = open(outputDir + f"noPK/{basename}.moipc", "w")
-		rna.write(header_+'\n')
-		rna.write(seq_+'\n')
-		rna.write(f"{solution}\t{nmotifs}\t{score}\n")
-		rna.close()
-		subprocess.call(["rm", f"{runDir}/{basename}.temp_subopt"])
-
-def mattews_corr_coeff(tp, tn, fp, fn):
-	if (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 accuracy(tp, tn, fp, fn):
-	return (tp+tn)/(tp+fp+tn+fn)
-
-def recall_sensitivity(tp, tn, fp, fn):
-	return tp/(tp+fn)
-
-def specificity(tp, tn, fp, fn):
-	return tn/(tn+fp)
-
-def precision_ppv(tp, tn, fp, fn):
-	return tp/(tp+fp)
-
-def npv(tp, tn, fp, fn):
-	return tn/(tn+fn)
-
-def f1_score(tp, tn, fp, fn):
-	return 2*tp/(2*tp+fp+fn)
-
-def dbn_to_basepairs(structure):
-	parenthesis = []
-	brackets = []
-	braces = []
-	rafters = []
-	basepairs = []
-	As = []
-	Bs = []
-	try:
-		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()))
-	except IndexError: # pop from empty list
-		print("Error in structure :", structure)
-		exit(0)
-	return basepairs
-
-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 enumerate_loops(s):
-	def resort(unclosedLoops):
-		loops.insert(len(loops)-1-unclosedLoops, loops[-1])
-		loops.pop(-1)
-
-	opened = []
-	openingStart = []
-	closingStart = []
-	loops = []
-	loopsUnclosed = 0
-	consecutiveOpenings = []
-	if s[0] == '(':
-		consecutiveOpenings.append(1)
-	consecutiveClosings = 0
-
-	lastclosed = -1
-	previous = ''
-	for i in range(len(s)):
-
-		# If we arrive on an unpaired segment
-		if s[i] == '.':
-			if previous == '(':
-				openingStart.append(i-1)
-			if previous == ')':
-				closingStart.append(i-1)
-
-		# Opening basepair
-		if s[i] == '(':
-			if previous == '(':
-				consecutiveOpenings[-1] += 1
-			else:
-				consecutiveOpenings.append(1)
-			if previous == ')':
-				closingStart.append(i-1)
-
-			# We have something like (...(
-			if len(openingStart) and openingStart[-1] == opened[-1]:
-				# Create a new loop starting with this component.
-				loops.append([(openingStart[-1], i)])
-				openingStart.pop(-1)
-				loopsUnclosed += 1
-			# We have something like )...( or even )(
-			if len(closingStart) and closingStart[-1] == lastclosed:
-				# Append a component to existing multiloop
-				loops[-1].append((closingStart[-1], i))
-				closingStart.pop(-1)
-
-			opened.append(i)
-
-		# Closing basepair
-		if s[i] == ')':
-			if previous == ')':
-				consecutiveClosings += 1
-			else:
-				consecutiveClosings = 1
-			# This is not supposed to happen in real data, but whatever.
-			if previous == '(':
-				openingStart.append(i-1)
-
-			# We have something like (...) or ()
-			if len(openingStart) and openingStart[-1] == opened[-1]:
-				# Create a new loop, and save it as already closed (HL)
-				loops.append([(openingStart[-1], i)])
-				openingStart.pop(-1)
-				resort(loopsUnclosed)
-			# We have something like )...)
-			if len(closingStart) and closingStart[-1] == lastclosed:
-				# Append a component to existing multiloop and close it.
-				loops[-1].append((closingStart[-1], i))
-				closingStart.pop(-1)
-				loopsUnclosed -= 1
-				resort(loopsUnclosed)
-
-			if i+1 < len(s):
-				if s[i+1] != ')':  # We are on something like: ).
-					# an openingStart has not been correctly detected, like in ...((((((...)))...)))
-					if consecutiveClosings < consecutiveOpenings[-1]:
-						# Create a new loop (uncompleted)
-						loops.append([(opened[-2], opened[-1])])
-						loopsUnclosed += 1
-
-					# We just completed an HL+stem, like ...(((...))).., we can forget its info
-					if consecutiveClosings == consecutiveOpenings[-1]:
-						consecutiveClosings = 0
-						consecutiveOpenings.pop(-1)
-					else:  # There are still several basepairs to remember, forget only the processed ones, keep the others
-						consecutiveOpenings[-1] -= consecutiveClosings
-						consecutiveClosings = 0
-
-				else:  # We are on something like: ))
-					# we are on an closingStart that cannot be correctly detected, like in ...(((...(((...))))))
-					if consecutiveClosings == consecutiveOpenings[-1]:
-						# Append a component to the uncomplete loop and close it.
-						loops[-1].append((i, i+1))
-						loopsUnclosed -= 1
-						resort(loopsUnclosed)
-						# Forget the info about the processed stem.
-						consecutiveClosings = 0
-						consecutiveOpenings.pop(-1)
-
-			opened.pop(-1)
-			lastclosed = i
-
-		previous = s[i]
-		# print(i,"=",s[i],"\t", "consec. Op=", consecutiveOpenings,"Cl=",consecutiveClosings)
-
-	return(loops)
-
-ignored_nt_dict = {}
-def is_canonical_nts(seq):
-	for c in seq[:-1]:
-		if c not in "ACGU":
-			if c in ignored_nt_dict.keys():
-				ignored_nt_dict[c] += 1
-			else:
-				ignored_nt_dict[c] = 1
-			return False
-	return True
-
-def is_canonical_bps(struct):
-	if "()" in struct:
-		return False
-	if "(.)" in struct:
-		return False
-	if "(..)" in struct:
-		return False
-	if "[]" in struct:
-		return False
-	if "[.]" in struct:
-		return False
-	if "[..]" in struct:
-		return False
-	return True
-
-def is_all(n, tot):
-	if n == tot:
-		return "\033[32m%d\033[0m/%d" % (n, tot)
-	else:
-		return "\033[91m%d\033[0m/%d" % (n, tot)
-
-def load_from_dbn(file, header_style=3):
-	container = []
-	counter = 0
-
-	db = open(file, "r")
-	c = 0
-	header = ""
-	seq = ""
-	struct = ""
-	while True:
-		l = db.readline()
-		if l == "":
-			break
-		c += 1
-		c = c % 3
-		if c == 1:
-			header = l[:-1]
-		if c == 2:
-			seq = l[:-1].upper()
-		if c == 0:
-			struct = l[:-1]
-			n = len(seq)
-
-			if n < 10 or n > 100:
-				continue  # ignore too short and too long RNAs
-			if not '(' in struct:
-				continue # ignore linear structures
-			if is_canonical_nts(seq) and is_canonical_bps(struct):
-				# keeps what's inside brackets at the end as the filename
-				if header_style == 1: container.append(RNA(header.replace('/', '_').split('(')[-1][:-1], header, seq, struct))
-				# keeps what's inside square brackets at the end as the filename
-				if header_style == 2: container.append(RNA(header.replace('/', '_').split('[')[-1][:-41], header, seq, struct))
-				# keeps all the header as filename
-				if header_style == 3: container.append(RNA(header[1:], header, seq, struct))
-				if '[' in struct: counter += 1
-	db.close()
-	return container, counter
-
-def get_bpRNA_statistics(include_noPK=True):
-
-	print("\nLoading bpRNA results from files...")
-
-	# load results in objects
-	for instance in tqdm(bpRNAContainer, desc="bpRNA instances"):
-		instance.load_results(include_noPK=True)
-		instance.evaluate()
-
-	RNAs_fully_predicted_noPK = [ x for x in bpRNAContainer if x.has_complete_results(with_PK=False) ]
-
-	# Get max MCCs for each method without PK, and see who is complete
-	x_noPK = [
-		[ rna.get_method("RNAsubopt").max_mcc if rna.get_method("RNAsubopt").n_pred else print(rna.basename, "has no RNAsubopt structure (linear)") for rna in bpRNAContainer if rna.get_method("RNAsubopt").n_pred ],
-		[ rna.get_method("RNA-MoIP (1by1)").max_mcc if rna.get_method("RNA-MoIP (1by1)").n_pred else print(rna.basename, "has no RNA-MoIP (1by1)") for rna in bpRNAContainer if rna.get_method("RNA-MoIP (1by1)").n_pred ],
-		[ rna.get_method("RNA-MoIP (chunk)").max_mcc if rna.get_method("RNA-MoIP (chunk)").n_pred else print(rna.basename, "has no RNA-MoIP (chunk)") for rna in bpRNAContainer if rna.get_method("RNA-MoIP (chunk)").n_pred ],
-		[ rna.get_method("DESC-D.P.-A-noPK").max_mcc  if rna.get_method("DESC-D.P.-A-noPK").n_pred else print(rna.basename, "has no DESC-D.P.-A-noPK") for rna in bpRNAContainer if rna.get_method("DESC-D.P.-A-noPK").n_pred ],
-		[ rna.get_method("DESC-D.P.-B-noPK").max_mcc  if rna.get_method("DESC-D.P.-B-noPK").n_pred else print(rna.basename, "has no DESC-D.P.-B-noPK") for rna in bpRNAContainer if rna.get_method("DESC-D.P.-B-noPK").n_pred ],
-		[ rna.get_method("DESC-ByP-A-noPK").max_mcc  if rna.get_method("DESC-ByP-A-noPK").n_pred else print(rna.basename, "has no DESC-ByP-A-noPK") for rna in bpRNAContainer if rna.get_method("DESC-ByP-A-noPK").n_pred ],
-		[ rna.get_method("DESC-ByP-B-noPK").max_mcc  if rna.get_method("DESC-ByP-B-noPK").n_pred else print(rna.basename, "has no DESC-ByP-B-noPK") for rna in bpRNAContainer if rna.get_method("DESC-ByP-B-noPK").n_pred ],
-		[ rna.get_method("DESC-ByP-C-noPK").max_mcc  if rna.get_method("DESC-ByP-C-noPK").n_pred else print(rna.basename, "has no DESC-ByP-C-noPK") for rna in bpRNAContainer if rna.get_method("DESC-ByP-C-noPK").n_pred ],
-		[ rna.get_method("DESC-ByP-D-noPK").max_mcc  if rna.get_method("DESC-ByP-D-noPK").n_pred else print(rna.basename, "has no DESC-ByP-D-noPK") for rna in bpRNAContainer if rna.get_method("DESC-ByP-D-noPK").n_pred ],
-		[ rna.get_method("BGSU-Jar3d-A-noPK").max_mcc  if rna.get_method("BGSU-Jar3d-A-noPK").n_pred else print(rna.basename, "has no BGSU-Jar3d-A-noPK") for rna in bpRNAContainer if rna.get_method("BGSU-Jar3d-A-noPK").n_pred ],
-		[ rna.get_method("BGSU-Jar3d-B-noPK").max_mcc  if rna.get_method("BGSU-Jar3d-B-noPK").n_pred else print(rna.basename, "has no BGSU-Jar3d-B-noPK") for rna in bpRNAContainer if rna.get_method("BGSU-Jar3d-B-noPK").n_pred ],
-		[ rna.get_method("BGSU-Jar3d-C-noPK").max_mcc  if rna.get_method("BGSU-Jar3d-C-noPK").n_pred else print(rna.basename, "has no BGSU-Jar3d-C-noPK") for rna in bpRNAContainer if rna.get_method("BGSU-Jar3d-C-noPK").n_pred ],
-		[ rna.get_method("BGSU-Jar3d-D-noPK").max_mcc  if rna.get_method("BGSU-Jar3d-D-noPK").n_pred else print(rna.basename, "has no BGSU-Jar3d-D-noPK") for rna in bpRNAContainer if rna.get_method("BGSU-Jar3d-D-noPK").n_pred ],
-		[ rna.get_method("BGSU-ByP-A-noPK").max_mcc  if rna.get_method("BGSU-ByP-A-noPK").n_pred else print(rna.basename, "has no BGSU-ByP-A-noPK") for rna in bpRNAContainer if rna.get_method("BGSU-ByP-A-noPK").n_pred ],
-		[ rna.get_method("BGSU-ByP-B-noPK").max_mcc  if rna.get_method("BGSU-ByP-B-noPK").n_pred else print(rna.basename, "has no BGSU-ByP-B-noPK") for rna in bpRNAContainer if rna.get_method("BGSU-ByP-B-noPK").n_pred ],
-		[ rna.get_method("BGSU-ByP-C-noPK").max_mcc  if rna.get_method("BGSU-ByP-C-noPK").n_pred else print(rna.basename, "has no BGSU-ByP-C-noPK") for rna in bpRNAContainer if rna.get_method("BGSU-ByP-C-noPK").n_pred ],
-		[ rna.get_method("BGSU-ByP-D-noPK").max_mcc  if rna.get_method("BGSU-ByP-D-noPK").n_pred else print(rna.basename, "has no BGSU-ByP-D-noPK") for rna in bpRNAContainer if rna.get_method("BGSU-ByP-D-noPK").n_pred ],
-		[ rna.get_method("RIN-D.P.-A-noPK").max_mcc  if rna.get_method("RIN-D.P.-A-noPK").n_pred else print(rna.basename, "has no RIN-D.P.-A-noPK") for rna in bpRNAContainer if rna.get_method("RIN-D.P.-A-noPK").n_pred ],
-		[ rna.get_method("RIN-D.P.-B-noPK").max_mcc  if rna.get_method("RIN-D.P.-B-noPK").n_pred else print(rna.basename, "has no RIN-D.P.-B-noPK") for rna in bpRNAContainer if rna.get_method("RIN-D.P.-B-noPK").n_pred ],
-	]
-
-	x_noPK_fully = [
-		[ rna.get_method("RNAsubopt").max_mcc for rna in RNAs_fully_predicted_noPK ],
-		[ rna.get_method("RNA-MoIP (1by1)").max_mcc for rna in RNAs_fully_predicted_noPK ],
-		[ rna.get_method("RNA-MoIP (chunk)").max_mcc for rna in RNAs_fully_predicted_noPK ],
-		[ rna.get_method("DESC-D.P.-A-noPK").max_mcc for rna in RNAs_fully_predicted_noPK ],
-		[ rna.get_method("DESC-D.P.-B-noPK").max_mcc for rna in RNAs_fully_predicted_noPK ],
-		[ rna.get_method("DESC-ByP-A-noPK").max_mcc  for rna in RNAs_fully_predicted_noPK ],
-		[ rna.get_method("DESC-ByP-B-noPK").max_mcc  for rna in RNAs_fully_predicted_noPK ],
-		[ rna.get_method("DESC-ByP-C-noPK").max_mcc  for rna in RNAs_fully_predicted_noPK ],
-		[ rna.get_method("DESC-ByP-D-noPK").max_mcc  for rna in RNAs_fully_predicted_noPK ],
-		[ rna.get_method("BGSU-Jar3d-A-noPK").max_mcc  for rna in RNAs_fully_predicted_noPK ],
-		[ rna.get_method("BGSU-Jar3d-B-noPK").max_mcc  for rna in RNAs_fully_predicted_noPK ],
-		[ rna.get_method("BGSU-Jar3d-C-noPK").max_mcc  for rna in RNAs_fully_predicted_noPK ],
-		[ rna.get_method("BGSU-Jar3d-D-noPK").max_mcc  for rna in RNAs_fully_predicted_noPK ],
-		[ rna.get_method("BGSU-ByP-A-noPK").max_mcc  for rna in RNAs_fully_predicted_noPK ],
-		[ rna.get_method("BGSU-ByP-B-noPK").max_mcc  for rna in RNAs_fully_predicted_noPK ],
-		[ rna.get_method("BGSU-ByP-C-noPK").max_mcc  for rna in RNAs_fully_predicted_noPK ],
-		[ rna.get_method("BGSU-ByP-D-noPK").max_mcc  for rna in RNAs_fully_predicted_noPK ],
-		[ rna.get_method("RIN-D.P.-A-noPK").max_mcc for rna in RNAs_fully_predicted_noPK ],
-		[ rna.get_method("RIN-D.P.-B-noPK").max_mcc for rna in RNAs_fully_predicted_noPK ],
-	]  # We ensure having the same number of RNAs in every sample by discarding the ones for which computations did not ended/succeeded.
-
-
-	print()
-	print("Without PK:")
-	print("%s RNAsubopt predictions" % is_all(len(x_noPK[0]), bpRNA_tot))
-	print("%s RNA-MoIP 1 by 1 predictions" % is_all(len(x_noPK[1]), bpRNA_tot))
-	print("%s RNA-MoIP chunk predictions" % is_all(len(x_noPK[2]), bpRNA_tot))
-	print("%s biorseo + DESC + Patternmatch + f1A predictions" % is_all(len(x_noPK[3]), bpRNA_tot))
-	print("%s biorseo + DESC + Patternmatch + f1B predictions" % is_all(len(x_noPK[4]), bpRNA_tot))
-	print("%s biorseo + DESC + BayesPairing + f1A predictions" % is_all(len(x_noPK[5]), bpRNA_tot))
-	print("%s biorseo + DESC + BayesPairing + f1B predictions" % is_all(len(x_noPK[6]), bpRNA_tot))
-	print("%s biorseo + DESC + BayesPairing + f1C predictions" % is_all(len(x_noPK[7]), bpRNA_tot))
-	print("%s biorseo + DESC + BayesPairing + f1D predictions" % is_all(len(x_noPK[8]), bpRNA_tot))
-	print("%s biorseo + BGSU + JAR3D + f1A predictions" % is_all(len(x_noPK[9]), bpRNA_tot))
-	print("%s biorseo + BGSU + JAR3D + f1B predictions" % is_all(len(x_noPK[10]), bpRNA_tot))
-	print("%s biorseo + BGSU + JAR3D + f1C predictions" % is_all(len(x_noPK[11]), bpRNA_tot))
-	print("%s biorseo + BGSU + JAR3D + f1D predictions" % is_all(len(x_noPK[12]), bpRNA_tot))
-	print("%s biorseo + BGSU + BayesPairing + f1A predictions" % is_all(len(x_noPK[13]), bpRNA_tot))
-	print("%s biorseo + BGSU + BayesPairing + f1B predictions" % is_all(len(x_noPK[14]), bpRNA_tot))
-	print("%s biorseo + BGSU + BayesPairing + f1C predictions" % is_all(len(x_noPK[15]), bpRNA_tot))
-	print("%s biorseo + BGSU + BayesPairing + f1D predictions" % is_all(len(x_noPK[16]), bpRNA_tot))
-	print("%s biorseo + RIN + Patternmatch + f1A predictions" % is_all(len(x_noPK[17]), bpRNA_tot))
-	print("%s biorseo + RIN + Patternmatch + f1B predictions" % is_all(len(x_noPK[18]), bpRNA_tot))
-
-	print("==> %s ARN were predicted with all methods successful." % is_all(len(x_noPK_fully[0]), bpRNA_tot) )
-
-	# Stat tests
-	# Search if all methods are equal in positions with Friedman test:
-	test = stats.friedmanchisquare(*x_noPK_fully)
-	print("Friedman test without PK: H0 = 'The position parameter of all distributions is equal', p-value = ", test.pvalue)
-	# ==> No they are not, but none does better, no need to test one further.
-	test = stats.wilcoxon(x_noPK_fully[2], x_noPK_fully[3])
-	print("Wilcoxon signed rank test with PK: H0 = 'The position parameter of RNA-MoIP and RawA are equal', p-value = ", test.pvalue)
-	test = stats.wilcoxon(x_noPK_fully[2], x_noPK_fully[4])
-	print("Wilcoxon signed rank test with PK: H0 = 'The position parameter of RNA-MoIP and RawB are equal', p-value = ", test.pvalue)
-
-
-	RNAs_fully_predicted_PK = [ x for x in bpRNAContainer if x.has_complete_results(with_PK=True) ]
-
-	# Get max MCCs for each method with PK, and see who is complete
-	x_PK = [
-		[ rna.get_method("Biokop").max_mcc if rna.get_method("Biokop").n_pred else print(rna.basename, "has no Biokop") for rna in bpRNAContainer if rna.get_method("Biokop").n_pred ],
-		[ rna.get_method("DESC-D.P.-A").max_mcc if rna.get_method("DESC-D.P.-A").n_pred else print(rna.basename, "has no DESC-D.P.-A") for rna in bpRNAContainer if rna.get_method("DESC-D.P.-A").n_pred ],
-		[ rna.get_method("DESC-D.P.-B").max_mcc if rna.get_method("DESC-D.P.-B").n_pred else print(rna.basename, "has no DESC-D.P.-B") for rna in bpRNAContainer if rna.get_method("DESC-D.P.-B").n_pred ],
-		[ rna.get_method("DESC-ByP-A").max_mcc  if rna.get_method("DESC-ByP-A").n_pred else print(rna.basename, "has no DESC-ByP-A") for rna in bpRNAContainer if rna.get_method("DESC-ByP-A").n_pred ],
-		[ rna.get_method("DESC-ByP-B").max_mcc  if rna.get_method("DESC-ByP-B").n_pred else print(rna.basename, "has no DESC-ByP-B") for rna in bpRNAContainer if rna.get_method("DESC-ByP-B").n_pred ],
-		[ rna.get_method("DESC-ByP-C").max_mcc  if rna.get_method("DESC-ByP-C").n_pred else print(rna.basename, "has no DESC-ByP-C") for rna in bpRNAContainer if rna.get_method("DESC-ByP-C").n_pred ],
-		[ rna.get_method("DESC-ByP-D").max_mcc  if rna.get_method("DESC-ByP-D").n_pred else print(rna.basename, "has no DESC-ByP-D") for rna in bpRNAContainer if rna.get_method("DESC-ByP-D").n_pred ],
-		[ rna.get_method("BGSU-Jar3d-A").max_mcc  if rna.get_method("BGSU-Jar3d-A").n_pred else print(rna.basename, "has no BGSU-Jar3d-A") for rna in bpRNAContainer if rna.get_method("BGSU-Jar3d-A").n_pred ],
-		[ rna.get_method("BGSU-Jar3d-B").max_mcc  if rna.get_method("BGSU-Jar3d-B").n_pred else print(rna.basename, "has no BGSU-Jar3d-B") for rna in bpRNAContainer if rna.get_method("BGSU-Jar3d-B").n_pred ],
-		[ rna.get_method("BGSU-Jar3d-C").max_mcc  if rna.get_method("BGSU-Jar3d-C").n_pred else print(rna.basename, "has no BGSU-Jar3d-C") for rna in bpRNAContainer if rna.get_method("BGSU-Jar3d-C").n_pred ],
-		[ rna.get_method("BGSU-Jar3d-D").max_mcc  if rna.get_method("BGSU-Jar3d-D").n_pred else print(rna.basename, "has no BGSU-Jar3d-D") for rna in bpRNAContainer if rna.get_method("BGSU-Jar3d-D").n_pred ],
-		[ rna.get_method("BGSU-ByP-A").max_mcc  if rna.get_method("BGSU-ByP-A").n_pred else print(rna.basename, "has no BGSU-ByP-A") for rna in bpRNAContainer if rna.get_method("BGSU-ByP-A").n_pred ],
-		[ rna.get_method("BGSU-ByP-B").max_mcc  if rna.get_method("BGSU-ByP-B").n_pred else print(rna.basename, "has no BGSU-ByP-B") for rna in bpRNAContainer if rna.get_method("BGSU-ByP-B").n_pred ],
-		[ rna.get_method("BGSU-ByP-C").max_mcc  if rna.get_method("BGSU-ByP-C").n_pred else print(rna.basename, "has no BGSU-ByP-C") for rna in bpRNAContainer if rna.get_method("BGSU-ByP-C").n_pred ],
-		[ rna.get_method("BGSU-ByP-D").max_mcc  if rna.get_method("BGSU-ByP-D").n_pred else print(rna.basename, "has no BGSU-ByP-D") for rna in bpRNAContainer if rna.get_method("BGSU-ByP-D").n_pred ],
-		[ rna.get_method("RIN-D.P.-A").max_mcc if rna.get_method("RIN-D.P.-A").n_pred else print(rna.basename, "has no RIN-D.P.-A") for rna in bpRNAContainer if rna.get_method("RIN-D.P.-A").n_pred ],
-		[ rna.get_method("RIN-D.P.-B").max_mcc if rna.get_method("RIN-D.P.-B").n_pred else print(rna.basename, "has no RIN-D.P.-B") for rna in bpRNAContainer if rna.get_method("RIN-D.P.-B").n_pred ],
-	]
-
-	# We ensure having the same number of RNAs in every sample by discarding the one for which computations did not ended/succeeded.
-	x_PK_fully = [
-		[ rna.get_method("Biokop").max_mcc for rna in RNAs_fully_predicted_PK ],
-		[ rna.get_method("DESC-D.P.-A").max_mcc for rna in RNAs_fully_predicted_PK ],
-		[ rna.get_method("DESC-D.P.-B").max_mcc for rna in RNAs_fully_predicted_PK ],
-		[ rna.get_method("DESC-ByP-A").max_mcc  for rna in RNAs_fully_predicted_PK ],
-		[ rna.get_method("DESC-ByP-B").max_mcc  for rna in RNAs_fully_predicted_PK ],
-		[ rna.get_method("DESC-ByP-C").max_mcc  for rna in RNAs_fully_predicted_PK ],
-		[ rna.get_method("DESC-ByP-D").max_mcc  for rna in RNAs_fully_predicted_PK ],
-		[ rna.get_method("BGSU-Jar3d-A").max_mcc  for rna in RNAs_fully_predicted_PK ],
-		[ rna.get_method("BGSU-Jar3d-B").max_mcc  for rna in RNAs_fully_predicted_PK ],
-		[ rna.get_method("BGSU-Jar3d-C").max_mcc  for rna in RNAs_fully_predicted_PK ],
-		[ rna.get_method("BGSU-Jar3d-D").max_mcc  for rna in RNAs_fully_predicted_PK ],
-		[ rna.get_method("BGSU-ByP-A").max_mcc  for rna in RNAs_fully_predicted_PK ],
-		[ rna.get_method("BGSU-ByP-B").max_mcc  for rna in RNAs_fully_predicted_PK ],
-		[ rna.get_method("BGSU-ByP-C").max_mcc  for rna in RNAs_fully_predicted_PK ],
-		[ rna.get_method("BGSU-ByP-D").max_mcc  for rna in RNAs_fully_predicted_PK ],
-		[ rna.get_method("RIN-D.P.-A").max_mcc for rna in RNAs_fully_predicted_PK ],
-		[ rna.get_method("RIN-D.P.-B").max_mcc for rna in RNAs_fully_predicted_PK ],
-	]
-
-
-
-	print()
-	print("With PK:")
-	print("%s Biokop predictions" % is_all(len(x_PK[0]), bpRNA_tot))
-	print("%s biorseo + DESC + Patternmatch + f1A predictions" % is_all(len(x_PK[1]), bpRNA_tot))
-	print("%s biorseo + DESC + Patternmatch + f1B predictions" % is_all(len(x_PK[2]), bpRNA_tot))
-	print("%s biorseo + DESC + BayesPairing + f1A predictions" % is_all(len(x_PK[3]), bpRNA_tot))
-	print("%s biorseo + DESC + BayesPairing + f1B predictions" % is_all(len(x_PK[4]), bpRNA_tot))
-	print("%s biorseo + DESC + BayesPairing + f1C predictions" % is_all(len(x_PK[5]), bpRNA_tot))
-	print("%s biorseo + DESC + BayesPairing + f1D predictions" % is_all(len(x_PK[6]), bpRNA_tot))
-	print("%s biorseo + BGSU + JAR3D + f1A predictions" % is_all(len(x_PK[7]), bpRNA_tot))
-	print("%s biorseo + BGSU + JAR3D + f1B predictions" % is_all(len(x_PK[8]), bpRNA_tot))
-	print("%s biorseo + BGSU + JAR3D + f1C predictions" % is_all(len(x_PK[9]), bpRNA_tot))
-	print("%s biorseo + BGSU + JAR3D + f1D predictions" % is_all(len(x_PK[10]), bpRNA_tot))
-	print("%s biorseo + BGSU + BayesPairing + f1A predictions" % is_all(len(x_PK[11]), bpRNA_tot))
-	print("%s biorseo + BGSU + BayesPairing + f1B predictions" % is_all(len(x_PK[12]), bpRNA_tot))
-	print("%s biorseo + BGSU + BayesPairing + f1C predictions" % is_all(len(x_PK[13]), bpRNA_tot))
-	print("%s biorseo + BGSU + BayesPairing + f1D predictions" % is_all(len(x_PK[14]), bpRNA_tot))
-	print("%s biorseo + RIN + Patternmatch + f1A predictions" % is_all(len(x_PK[15]), bpRNA_tot))
-	print("%s biorseo + RIN + Patternmatch + f1B predictions" % is_all(len(x_PK[16]), bpRNA_tot))
-
-	print("==> %s ARN were predicted with all methods successful." % is_all(len(x_PK_fully[0]), bpRNA_tot) )
-
-	# stat tests
-	# First, search if all methods are equal in positions with Friedman test:
-	test = stats.friedmanchisquare(*x_PK_fully)
-	print("Friedman test with PK: H0 = 'The position parameter of all distributions is equal', p-value = ", test.pvalue)
-	# it looks like some methods do better. Let's test the difference:
-	test = stats.wilcoxon(x_PK_fully[0], x_PK_fully[1])
-	print("Wilcoxon signed rank test with PK: H0 = 'The position parameter of Biokop and RawA are equal', p-value = ", test.pvalue)
-	test = stats.wilcoxon(x_PK_fully[0], x_PK_fully[2])
-	print("Wilcoxon signed rank test with PK: H0 = 'The position parameter of Biokop and RawB are equal', p-value = ", test.pvalue)
-	test = stats.wilcoxon(x_PK_fully[0], x_PK_fully[7])
-	print("Wilcoxon signed rank test with PK: H0 = 'The position parameter of Biokop and Jar3dA are equal', p-value = ", test.pvalue)
-	test = stats.wilcoxon(x_PK_fully[0], x_PK_fully[8])
-	print("Wilcoxon signed rank test with PK: H0 = 'The position parameter of Biokop and Jar3dB are equal', p-value = ", test.pvalue)
-	test = stats.wilcoxon(x_PK_fully[0], x_PK_fully[9])
-	print("Wilcoxon signed rank test with PK: H0 = 'The position parameter of Biokop and Jar3dC are equal', p-value = ", test.pvalue)
-	test = stats.wilcoxon(x_PK_fully[0], x_PK_fully[10])
-	print("Wilcoxon signed rank test with PK: H0 = 'The position parameter of Biokop and Jar3dD are equal', p-value = ", test.pvalue)
-	
-	n = [
-		[ rna.get_method("Biokop").n_pred for rna in bpRNAContainer if rna.get_method("Biokop").n_pred ],
-		[ rna.get_method("RNAsubopt").n_pred  for rna in bpRNAContainer if rna.get_method("RNAsubopt").n_pred ],
-		[ rna.get_method("RNA-MoIP (1by1)").n_pred for rna in bpRNAContainer if rna.get_method("RNA-MoIP (1by1)").n_pred ],
-		[ rna.get_method("RNA-MoIP (chunk)").n_pred for rna in bpRNAContainer if rna.get_method("RNA-MoIP (chunk)").n_pred ],
-		[ rna.get_method("DESC-D.P.-A").n_pred for rna in bpRNAContainer if rna.get_method("DESC-D.P.-A").n_pred ],
-		[ rna.get_method("DESC-D.P.-B").n_pred for rna in bpRNAContainer if rna.get_method("DESC-D.P.-B").n_pred ],
-		[ rna.get_method("DESC-ByP-A").n_pred for rna in bpRNAContainer if rna.get_method("DESC-ByP-A").n_pred ],
-		[ rna.get_method("DESC-ByP-B").n_pred for rna in bpRNAContainer if rna.get_method("DESC-ByP-B").n_pred ],
-		[ rna.get_method("DESC-ByP-C").n_pred for rna in bpRNAContainer if rna.get_method("DESC-ByP-C").n_pred ],
-		[ rna.get_method("DESC-ByP-D").n_pred for rna in bpRNAContainer if rna.get_method("DESC-ByP-D").n_pred ],
-		[ rna.get_method("BGSU-Jar3d-A").n_pred for rna in bpRNAContainer if rna.get_method("BGSU-Jar3d-A").n_pred ],
-		[ rna.get_method("BGSU-Jar3d-B").n_pred for rna in bpRNAContainer if rna.get_method("BGSU-Jar3d-B").n_pred ],
-		[ rna.get_method("BGSU-Jar3d-C").n_pred for rna in bpRNAContainer if rna.get_method("BGSU-Jar3d-C").n_pred ],
-		[ rna.get_method("BGSU-Jar3d-D").n_pred for rna in bpRNAContainer if rna.get_method("BGSU-Jar3d-D").n_pred ],
-		[ rna.get_method("BGSU-ByP-A").n_pred for rna in bpRNAContainer if rna.get_method("BGSU-ByP-A").n_pred ],
-		[ rna.get_method("BGSU-ByP-B").n_pred for rna in bpRNAContainer if rna.get_method("BGSU-ByP-B").n_pred ],
-		[ rna.get_method("BGSU-ByP-C").n_pred for rna in bpRNAContainer if rna.get_method("BGSU-ByP-C").n_pred ],
-		[ rna.get_method("BGSU-ByP-D").n_pred for rna in bpRNAContainer if rna.get_method("BGSU-ByP-D").n_pred ],
-		[ rna.get_method("RIN-D.P.-A").n_pred for rna in bpRNAContainer if rna.get_method("RIN-D.P.-A").n_pred ],
-		[ rna.get_method("RIN-D.P.-B").n_pred for rna in bpRNAContainer if rna.get_method("RIN-D.P.-B").n_pred ],
-	]
-
-	r = [
-		[ rna.get_method("RNA-MoIP (1by1)").ratio for rna in bpRNAContainer if rna.get_method("RNA-MoIP (1by1)").n_pred > 1 ],
-		[ rna.get_method("DESC-D.P.-A").ratio for rna in bpRNAContainer if rna.get_method("DESC-D.P.-A").n_pred > 1 ],
-		[ rna.get_method("DESC-D.P.-B").ratio for rna in bpRNAContainer if rna.get_method("DESC-D.P.-B").n_pred > 1 ],
-		[ rna.get_method("DESC-ByP-A").ratio for rna in bpRNAContainer if rna.get_method("DESC-ByP-A").n_pred > 1 ],
-		[ rna.get_method("DESC-ByP-B").ratio for rna in bpRNAContainer if rna.get_method("DESC-ByP-B").n_pred > 1 ],
-		[ rna.get_method("DESC-ByP-C").ratio for rna in bpRNAContainer if rna.get_method("DESC-ByP-C").n_pred > 1 ],
-		[ rna.get_method("DESC-ByP-D").ratio for rna in bpRNAContainer if rna.get_method("DESC-ByP-D").n_pred > 1 ],
-		[ rna.get_method("BGSU-Jar3d-A").ratio for rna in bpRNAContainer if rna.get_method("BGSU-Jar3d-A").n_pred > 1 ],
-		[ rna.get_method("BGSU-Jar3d-B").ratio for rna in bpRNAContainer if rna.get_method("BGSU-Jar3d-B").n_pred > 1 ],
-		[ rna.get_method("BGSU-Jar3d-C").ratio for rna in bpRNAContainer if rna.get_method("BGSU-Jar3d-C").n_pred > 1 ],
-		[ rna.get_method("BGSU-Jar3d-D").ratio for rna in bpRNAContainer if rna.get_method("BGSU-Jar3d-D").n_pred > 1 ],
-		[ rna.get_method("BGSU-ByP-A").ratio for rna in bpRNAContainer if rna.get_method("BGSU-ByP-A").n_pred > 1 ],
-		[ rna.get_method("BGSU-ByP-B").ratio for rna in bpRNAContainer if rna.get_method("BGSU-ByP-B").n_pred > 1 ],
-		[ rna.get_method("BGSU-ByP-C").ratio for rna in bpRNAContainer if rna.get_method("BGSU-ByP-C").n_pred > 1 ],
-		[ rna.get_method("BGSU-ByP-D").ratio for rna in bpRNAContainer if rna.get_method("BGSU-ByP-D").n_pred > 1 ],
-		[ rna.get_method("RIN-D.P.-A").ratio for rna in bpRNAContainer if rna.get_method("RIN-D.P.-A").n_pred > 1 ],
-		[ rna.get_method("RIN-D.P.-B").ratio for rna in bpRNAContainer if rna.get_method("RIN-D.P.-B").n_pred > 1 ],
-	]
-
-	max_i = [
-		[ max(rna.get_method("RNA-MoIP (1by1)").ninsertions) for rna in bpRNAContainer if rna.get_method("RNA-MoIP (1by1)").n_pred ],
-		[ max(rna.get_method("RNA-MoIP (chunk)").ninsertions) for rna in bpRNAContainer if rna.get_method("RNA-MoIP (chunk)").n_pred ],
-		[ max(rna.get_method("DESC-D.P.-A").ninsertions) for rna in bpRNAContainer if rna.get_method("DESC-D.P.-A").n_pred ],
-		[ max(rna.get_method("DESC-D.P.-B").ninsertions) for rna in bpRNAContainer if rna.get_method("DESC-D.P.-B").n_pred ],
-		[ max(rna.get_method("DESC-ByP-A").ninsertions) for rna in bpRNAContainer if rna.get_method("DESC-ByP-A").n_pred ],
-		[ max(rna.get_method("DESC-ByP-B").ninsertions) for rna in bpRNAContainer if rna.get_method("DESC-ByP-B").n_pred ],
-		[ max(rna.get_method("DESC-ByP-C").ninsertions) for rna in bpRNAContainer if rna.get_method("DESC-ByP-C").n_pred ],
-		[ max(rna.get_method("DESC-ByP-D").ninsertions) for rna in bpRNAContainer if rna.get_method("DESC-ByP-D").n_pred ],
-		[ max(rna.get_method("BGSU-Jar3d-A").ninsertions) for rna in bpRNAContainer if rna.get_method("BGSU-Jar3d-A").n_pred ],
-		[ max(rna.get_method("BGSU-Jar3d-B").ninsertions) for rna in bpRNAContainer if rna.get_method("BGSU-Jar3d-B").n_pred ],
-		[ max(rna.get_method("BGSU-Jar3d-C").ninsertions) for rna in bpRNAContainer if rna.get_method("BGSU-Jar3d-C").n_pred ],
-		[ max(rna.get_method("BGSU-Jar3d-D").ninsertions) for rna in bpRNAContainer if rna.get_method("BGSU-Jar3d-D").n_pred ],
-		[ max(rna.get_method("BGSU-ByP-A").ninsertions) for rna in bpRNAContainer if rna.get_method("BGSU-ByP-A").n_pred ],
-		[ max(rna.get_method("BGSU-ByP-B").ninsertions) for rna in bpRNAContainer if rna.get_method("BGSU-ByP-B").n_pred ],
-		[ max(rna.get_method("BGSU-ByP-C").ninsertions) for rna in bpRNAContainer if rna.get_method("BGSU-ByP-C").n_pred ],
-		[ max(rna.get_method("BGSU-ByP-D").ninsertions) for rna in bpRNAContainer if rna.get_method("BGSU-ByP-D").n_pred ],
-		[ max(rna.get_method("RIN-D.P.-A").ninsertions) for rna in bpRNAContainer if rna.get_method("RIN-D.P.-A").n_pred ],
-		[ max(rna.get_method("RIN-D.P.-B").ninsertions) for rna in bpRNAContainer if rna.get_method("RIN-D.P.-B").n_pred ],
-	]
-	
-	return x_noPK_fully, x_PK_fully, n, r, max_i
-
-def get_Pseudobase_statistics():
-
-	# load results in objects
-	print("\nLoading Pseudobase results from files...")
-	for instance in tqdm(PseudobaseContainer, desc="Pseudobase instances"):
-		instance.load_results()
-		instance.evaluate()
-
-	RNAs_fully_predicted_Pseudobase = [ x for x in PseudobaseContainer if x.has_complete_results(with_PK=True)]
-
-	x_pseudobase = [
-		[ rna.get_method("Biokop").max_mcc if rna.get_method("Biokop").n_pred else print(rna.basename, "has no") for rna in PseudobaseContainer if rna.get_method("Biokop").n_pred ],
-		[ rna.get_method("RNAsubopt").max_mcc if rna.get_method("RNAsubopt").n_pred else print(rna.basename, "has no") for rna in PseudobaseContainer if rna.get_method("RNAsubopt").n_pred ],
-		[ rna.get_method("RNA-MoIP (1by1)").max_mcc if rna.get_method("RNA-MoIP (1by1)").n_pred else print(rna.basename, "has no") for rna in PseudobaseContainer if rna.get_method("RNA-MoIP (1by1)").n_pred ],
-		[ rna.get_method("RNA-MoIP (chunk)").max_mcc if rna.get_method("RNA-MoIP (chunk)").n_pred else print(rna.basename, "has no") for rna in PseudobaseContainer if rna.get_method("RNA-MoIP (chunk)").n_pred ],
-		[ rna.get_method("DESC-D.P.-A").max_mcc if rna.get_method("DESC-D.P.-A").n_pred else print(rna.basename, "has no") for rna in PseudobaseContainer if rna.get_method("DESC-D.P.-A").n_pred ],
-		[ rna.get_method("DESC-D.P.-B").max_mcc if rna.get_method("DESC-D.P.-B").n_pred else print(rna.basename, "has no") for rna in PseudobaseContainer if rna.get_method("DESC-D.P.-B").n_pred ],
-		[ rna.get_method("DESC-ByP-A").max_mcc if rna.get_method("DESC-ByP-A").n_pred else print(rna.basename, "has no") for rna in PseudobaseContainer if rna.get_method("DESC-ByP-A").n_pred ],
-		[ rna.get_method("DESC-ByP-B").max_mcc if rna.get_method("DESC-ByP-B").n_pred else print(rna.basename, "has no") for rna in PseudobaseContainer if rna.get_method("DESC-ByP-B").n_pred ],
-		[ rna.get_method("DESC-ByP-C").max_mcc if rna.get_method("DESC-ByP-C").n_pred else print(rna.basename, "has no") for rna in PseudobaseContainer if rna.get_method("DESC-ByP-C").n_pred ],
-		[ rna.get_method("DESC-ByP-D").max_mcc if rna.get_method("DESC-ByP-D").n_pred else print(rna.basename, "has no") for rna in PseudobaseContainer if rna.get_method("DESC-ByP-D").n_pred ],
-		[ rna.get_method("BGSU-Jar3d-A").max_mcc if rna.get_method("BGSU-Jar3d-A").n_pred else print(rna.basename, "has no") for rna in PseudobaseContainer if rna.get_method("BGSU-Jar3d-A").n_pred ],
-		[ rna.get_method("BGSU-Jar3d-B").max_mcc if rna.get_method("BGSU-Jar3d-B").n_pred else print(rna.basename, "has no") for rna in PseudobaseContainer if rna.get_method("BGSU-Jar3d-B").n_pred ],
-		[ rna.get_method("BGSU-Jar3d-C").max_mcc if rna.get_method("BGSU-Jar3d-C").n_pred else print(rna.basename, "has no") for rna in PseudobaseContainer if rna.get_method("BGSU-Jar3d-C").n_pred ],
-		[ rna.get_method("BGSU-Jar3d-D").max_mcc if rna.get_method("BGSU-Jar3d-D").n_pred else print(rna.basename, "has no") for rna in PseudobaseContainer if rna.get_method("BGSU-Jar3d-D").n_pred ],
-		[ rna.get_method("BGSU-ByP-A").max_mcc if rna.get_method("BGSU-ByP-A").n_pred else print(rna.basename, "has no") for rna in PseudobaseContainer if rna.get_method("BGSU-ByP-A").n_pred ],
-		[ rna.get_method("BGSU-ByP-B").max_mcc if rna.get_method("BGSU-ByP-B").n_pred else print(rna.basename, "has no") for rna in PseudobaseContainer if rna.get_method("BGSU-ByP-B").n_pred ],
-		[ rna.get_method("BGSU-ByP-C").max_mcc if rna.get_method("BGSU-ByP-C").n_pred else print(rna.basename, "has no") for rna in PseudobaseContainer if rna.get_method("BGSU-ByP-C").n_pred ],
-		[ rna.get_method("BGSU-ByP-D").max_mcc if rna.get_method("BGSU-ByP-D").n_pred else print(rna.basename, "has no") for rna in PseudobaseContainer if rna.get_method("BGSU-ByP-D").n_pred ],
-		[ rna.get_method("RIN-D.P.-A").max_mcc if rna.get_method("RIN-D.P.-A").n_pred else print(rna.basename, "has no") for rna in PseudobaseContainer if rna.get_method("RIN-D.P.-A").n_pred ],
-		[ rna.get_method("RIN-D.P.-B").max_mcc if rna.get_method("RIN-D.P.-B").n_pred else print(rna.basename, "has no") for rna in PseudobaseContainer if rna.get_method("RIN-D.P.-B").n_pred ],
-	]
-
-	# We ensure having the same number of RNAs in every sample by discarding the one for which computations did not ended/succeeded.
-	x_pseudobase_fully = [
-		[ rna.get_method("Biokop").max_mcc for rna in RNAs_fully_predicted_Pseudobase],
-		[ rna.get_method("RNAsubopt").max_mcc for rna in RNAs_fully_predicted_Pseudobase],
-		[ rna.get_method("RNA-MoIP (1by1)").max_mcc for rna in RNAs_fully_predicted_Pseudobase],
-		[ rna.get_method("RNA-MoIP (chunk)").max_mcc for rna in RNAs_fully_predicted_Pseudobase],
-		[ rna.get_method("DESC-D.P.-A").max_mcc for rna in RNAs_fully_predicted_Pseudobase],
-		[ rna.get_method("DESC-D.P.-B").max_mcc for rna in RNAs_fully_predicted_Pseudobase],
-		[ rna.get_method("DESC-ByP-A").max_mcc  for rna in RNAs_fully_predicted_Pseudobase],
-		[ rna.get_method("DESC-ByP-B").max_mcc  for rna in RNAs_fully_predicted_Pseudobase],
-		[ rna.get_method("DESC-ByP-C").max_mcc  for rna in RNAs_fully_predicted_Pseudobase],
-		[ rna.get_method("DESC-ByP-D").max_mcc  for rna in RNAs_fully_predicted_Pseudobase],
-		[ rna.get_method("BGSU-Jar3d-A").max_mcc  for rna in RNAs_fully_predicted_Pseudobase],
-		[ rna.get_method("BGSU-Jar3d-B").max_mcc  for rna in RNAs_fully_predicted_Pseudobase],
-		[ rna.get_method("BGSU-Jar3d-C").max_mcc  for rna in RNAs_fully_predicted_Pseudobase],
-		[ rna.get_method("BGSU-Jar3d-D").max_mcc  for rna in RNAs_fully_predicted_Pseudobase],
-		[ rna.get_method("BGSU-ByP-A").max_mcc  for rna in RNAs_fully_predicted_Pseudobase],
-		[ rna.get_method("BGSU-ByP-B").max_mcc  for rna in RNAs_fully_predicted_Pseudobase],
-		[ rna.get_method("BGSU-ByP-C").max_mcc  for rna in RNAs_fully_predicted_Pseudobase],
-		[ rna.get_method("BGSU-ByP-D").max_mcc  for rna in RNAs_fully_predicted_Pseudobase],
-		[ rna.get_method("RIN-D.P.-A").max_mcc for rna in RNAs_fully_predicted_Pseudobase],
-		[ rna.get_method("RIN-D.P.-B").max_mcc for rna in RNAs_fully_predicted_Pseudobase],
-	]
-
-
-	print()
-	print("With PK:")
-	print("%s Biokop predictions" % is_all(len(x_pseudobase[0]), Pseudobase_tot))
-	print("%s RNAsubopt predictions" % is_all(len(x_pseudobase[1]), Pseudobase_tot))
-	print("%s RNA-MoIP 1 by 1 predictions" % is_all(len(x_pseudobase[2]), Pseudobase_tot))
-	print("%s RNA-MoIP chunk predictions" % is_all(len(x_pseudobase[3]), Pseudobase_tot))
-	print("%s biorseo + DESC + Patternmatch + f1A predictions" % is_all(len(x_pseudobase[4]), Pseudobase_tot))
-	print("%s biorseo + DESC + Patternmatch + f1B predictions" % is_all(len(x_pseudobase[5]), Pseudobase_tot))
-	print("%s biorseo + DESC + BayesPairing + f1A predictions" % is_all(len(x_pseudobase[6]), Pseudobase_tot))
-	print("%s biorseo + DESC + BayesPairing + f1B predictions" % is_all(len(x_pseudobase[7]), Pseudobase_tot))
-	print("%s biorseo + DESC + BayesPairing + f1C predictions" % is_all(len(x_pseudobase[8]), Pseudobase_tot))
-	print("%s biorseo + DESC + BayesPairing + f1D predictions" % is_all(len(x_pseudobase[9]), Pseudobase_tot))
-	print("%s biorseo + BGSU + JAR3D + f1A predictions" % is_all(len(x_pseudobase[10]), Pseudobase_tot))
-	print("%s biorseo + BGSU + JAR3D + f1B predictions" % is_all(len(x_pseudobase[11]), Pseudobase_tot))
-	print("%s biorseo + BGSU + JAR3D + f1C predictions" % is_all(len(x_pseudobase[12]), Pseudobase_tot))
-	print("%s biorseo + BGSU + JAR3D + f1D predictions" % is_all(len(x_pseudobase[13]), Pseudobase_tot))
-	print("%s biorseo + BGSU + BayesPairing + f1A predictions" % is_all(len(x_pseudobase[14]), Pseudobase_tot))
-	print("%s biorseo + BGSU + BayesPairing + f1B predictions" % is_all(len(x_pseudobase[15]), Pseudobase_tot))
-	print("%s biorseo + BGSU + BayesPairing + f1C predictions" % is_all(len(x_pseudobase[16]), Pseudobase_tot))
-	print("%s biorseo + BGSU + BayesPairing + f1D predictions" % is_all(len(x_pseudobase[17]), Pseudobase_tot))
-	print("%s biorseo + RIN + Patternmatch + f1A predictions" % is_all(len(x_pseudobase[18]), Pseudobase_tot))
-	print("%s biorseo + RIN + Patternmatch + f1B predictions" % is_all(len(x_pseudobase[19]), Pseudobase_tot))
-
-	print("==> %s ARN were predicted with all methods successful." % is_all(len(x_pseudobase_fully[0]), Pseudobase_tot) )
-
-	# stat tests
-	# First, search if all methods are equal in positions with Friedman test:
-	test = stats.friedmanchisquare(*x_pseudobase_fully)
-	print("Friedman test with PK: H0 = 'The position parameter of all distributions is equal', p-value = ", test.pvalue)
-	# it looks like some methods do better. Let's test the difference:
-	test = stats.wilcoxon(x_pseudobase_fully[0], x_pseudobase_fully[4])
-	print("Wilcoxon signed rank test with PK: H0 = 'The position parameter of Biokop and RawA are equal', p-value = ", test.pvalue)
-	test = stats.wilcoxon(x_pseudobase_fully[0], x_pseudobase_fully[5])
-	print("Wilcoxon signed rank test with PK: H0 = 'The position parameter of Biokop and RawB are equal', p-value = ", test.pvalue)
-	test = stats.wilcoxon(x_pseudobase_fully[0], x_pseudobase_fully[10])
-	print("Wilcoxon signed rank test with PK: H0 = 'The position parameter of Biokop and Jar3dA are equal', p-value = ", test.pvalue)
-	test = stats.wilcoxon(x_pseudobase_fully[0], x_pseudobase_fully[11])
-	print("Wilcoxon signed rank test with PK: H0 = 'The position parameter of Biokop and Jar3dB are equal', p-value = ", test.pvalue)
-	test = stats.wilcoxon(x_pseudobase_fully[0], x_pseudobase_fully[12])
-	print("Wilcoxon signed rank test with PK: H0 = 'The position parameter of Biokop and Jar3dC are equal', p-value = ", test.pvalue)
-	test = stats.wilcoxon(x_pseudobase_fully[0], x_pseudobase_fully[13])
-	print("Wilcoxon signed rank test with PK: H0 = 'The position parameter of Biokop and Jar3dD are equal', p-value = ", test.pvalue)
-	return x_pseudobase_fully
-
-def print_StudyCase_results():
-	print("\nLoading study case results from files...")
-
-	# load results in objects
-	for instance in StudycaseContainer:
-		instance.load_results()
-		instance.evaluate(verbose=True)
-
-# ================= EXTRACTION OF STRUCTURES FROM FILES ===============================
-
-if __name__ == '__main__':
-
-	print("> Loading files...", flush=True)
-	bpRNAContainer, bpRNA_pk_counter = load_from_dbn(bpRNAFile, header_style=1)
-	PseudobaseContainer, Pseudobase_pk_counter = load_from_dbn(PseudobaseFile, header_style=3)
-	StudycaseContainer, StudyCase_pk_counter = load_from_dbn(StudyCaseFile, header_style=1)
-
-	for nt, number in ignored_nt_dict.items():
-		print("\t> ignored %d sequences because of char %c" % (number, nt))
-
-	bpRNA_tot = len(bpRNAContainer)
-	Pseudobase_tot = len(PseudobaseContainer)
-	StudyCase_tot = len(StudycaseContainer)
-	print("\t> Loaded %d RNAs of length between 10 and 100 from RNA Strand. %d of them contain pseudoknots." % (bpRNA_tot, bpRNA_pk_counter))
-	print("\t> Loaded %d RNAs of length between 10 and 100 from Pseudobase. %d of them contain pseudoknots." % (Pseudobase_tot, Pseudobase_pk_counter))
-	print("\t> Loaded %d RNAs of length between 10 and 100 from study case. %d of them contain pseudoknots." % (StudyCase_tot, StudyCase_pk_counter))
-
-	issues = set()
-	if path.isfile("benchmark_results/known_issues.txt"):
-		with open("benchmark_results/known_issues.txt") as f:
-			issues = set([ j[:-1] for j in f.readlines() ])
-		print(f"\t> Ignoring {len(issues)} known failing jobs.")
-
-	#================= PREDICTION OF STRUCTURES ===============================
-
-	#define job list
-	print("> Defining jobs...")
-	fulljoblist = []
-	joblabel_list = set()
-
-	if path.isfile("containers.pickle"):
-		with open("containers.pickle", "rb") as cont:
-			bpRNAContainer, PseudobaseContainer = pickle.load(cont)
-	else:
-		for instance in tqdm(bpRNAContainer, desc="bpRNA jobs"):
-			instance.add_method_evaluation(instance, "RNAsubopt", flat=False)
-			instance.add_method_evaluation(instance, "Biokop")
-			instance.add_method_evaluation(instance, "RNA-MoIP (1by1)")
-			instance.add_method_evaluation(instance, "RNA-MoIP (chunk)")
-			instance.add_method_evaluation(instance, tool="biorseo", data_source="DESC", placement_method="D.P.",  obj_func="A", PK=False)
-			instance.add_method_evaluation(instance, tool="biorseo", data_source="DESC", placement_method="D.P.",  obj_func="A", PK=True)
-			instance.add_method_evaluation(instance, tool="biorseo", data_source="DESC", placement_method="D.P.",  obj_func="B", PK=False)
-			instance.add_method_evaluation(instance, tool="biorseo", data_source="DESC", placement_method="D.P.",  obj_func="B", PK=True)
-			instance.add_method_evaluation(instance, tool="biorseo", data_source="DESC", placement_method="ByP",   obj_func="A", PK=False)
-			instance.add_method_evaluation(instance, tool="biorseo", data_source="DESC", placement_method="ByP",   obj_func="A", PK=True)
-			instance.add_method_evaluation(instance, tool="biorseo", data_source="DESC", placement_method="ByP",   obj_func="B", PK=False)
-			instance.add_method_evaluation(instance, tool="biorseo", data_source="DESC", placement_method="ByP",   obj_func="B", PK=True)
-			instance.add_method_evaluation(instance, tool="biorseo", data_source="DESC", placement_method="ByP",   obj_func="C", PK=False)
-			instance.add_method_evaluation(instance, tool="biorseo", data_source="DESC", placement_method="ByP",   obj_func="C", PK=True)
-			instance.add_method_evaluation(instance, tool="biorseo", data_source="DESC", placement_method="ByP",   obj_func="D", PK=False)
-			instance.add_method_evaluation(instance, tool="biorseo", data_source="DESC", placement_method="ByP",   obj_func="D", PK=True)
-			instance.add_method_evaluation(instance, tool="biorseo", data_source="BGSU", placement_method="ByP",   obj_func="A", PK=False)
-			instance.add_method_evaluation(instance, tool="biorseo", data_source="BGSU", placement_method="ByP",   obj_func="A", PK=True)
-			instance.add_method_evaluation(instance, tool="biorseo", data_source="BGSU", placement_method="ByP",   obj_func="B", PK=False)
-			instance.add_method_evaluation(instance, tool="biorseo", data_source="BGSU", placement_method="ByP",   obj_func="B", PK=True)
-			instance.add_method_evaluation(instance, tool="biorseo", data_source="BGSU", placement_method="ByP",   obj_func="C", PK=False)
-			instance.add_method_evaluation(instance, tool="biorseo", data_source="BGSU", placement_method="ByP",   obj_func="C", PK=True)
-			instance.add_method_evaluation(instance, tool="biorseo", data_source="BGSU", placement_method="ByP",   obj_func="D", PK=False)
-			instance.add_method_evaluation(instance, tool="biorseo", data_source="BGSU", placement_method="ByP",   obj_func="D", PK=True)
-			instance.add_method_evaluation(instance, tool="biorseo", data_source="BGSU", placement_method="Jar3d", obj_func="A", PK=False)
-			instance.add_method_evaluation(instance, tool="biorseo", data_source="BGSU", placement_method="Jar3d", obj_func="A", PK=True)
-			instance.add_method_evaluation(instance, tool="biorseo", data_source="BGSU", placement_method="Jar3d", obj_func="B", PK=False)
-			instance.add_method_evaluation(instance, tool="biorseo", data_source="BGSU", placement_method="Jar3d", obj_func="B", PK=True)
-			instance.add_method_evaluation(instance, tool="biorseo", data_source="BGSU", placement_method="Jar3d", obj_func="C", PK=False)
-			instance.add_method_evaluation(instance, tool="biorseo", data_source="BGSU", placement_method="Jar3d", obj_func="C", PK=True)
-			instance.add_method_evaluation(instance, tool="biorseo", data_source="BGSU", placement_method="Jar3d", obj_func="D", PK=False)
-			instance.add_method_evaluation(instance, tool="biorseo", data_source="BGSU", placement_method="Jar3d", obj_func="D", PK=True)
-			instance.add_method_evaluation(instance, tool="biorseo", data_source="RIN",  placement_method="D.P.",  obj_func="A", PK=False)
-			instance.add_method_evaluation(instance, tool="biorseo", data_source="RIN",  placement_method="D.P.",  obj_func="A", PK=True)
-			instance.add_method_evaluation(instance, tool="biorseo", data_source="RIN",  placement_method="D.P.",  obj_func="B", PK=False)
-			instance.add_method_evaluation(instance, tool="biorseo", data_source="RIN",  placement_method="D.P.",  obj_func="B", PK=True)
-
-			for method in instance.methods:
-				for i in range(len(method.joblist)):
-					j = method.joblist[i]
-					if j.label in joblabel_list: # look for a duplicate job (Jar3d, BayesPairing, RNAsubopt...)
-						# for index, job in enumerate(fulljoblist):
-						# 	if job.label == j.label:
-						# 		method.joblist[i] = fulljoblist[index] # point to the previous occurrence
-						# 		break
-						continue
-					else:
-						fulljoblist.append(j)
-						joblabel_list.add(j.label)
-
-		for instance in tqdm(PseudobaseContainer, desc="Pseudobase jobs"):
-			instance.add_method_evaluation(instance, "RNAsubopt", flat=False)
-			instance.add_method_evaluation(instance, "Biokop")
-			instance.add_method_evaluation(instance, "RNA-MoIP (1by1)")
-			instance.add_method_evaluation(instance, "RNA-MoIP (chunk)")
-			instance.add_method_evaluation(instance, tool="biorseo", data_source="DESC", placement_method="D.P.", obj_func="A")
-			instance.add_method_evaluation(instance, tool="biorseo", data_source="DESC", placement_method="D.P.", obj_func="B")
-			instance.add_method_evaluation(instance, tool="biorseo", data_source="DESC", placement_method="ByP", obj_func="A")
-			instance.add_method_evaluation(instance, tool="biorseo", data_source="DESC", placement_method="ByP", obj_func="B")
-			instance.add_method_evaluation(instance, tool="biorseo", data_source="DESC", placement_method="ByP", obj_func="C")
-			instance.add_method_evaluation(instance, tool="biorseo", data_source="DESC", placement_method="ByP", obj_func="D")
-			instance.add_method_evaluation(instance, tool="biorseo", data_source="BGSU", placement_method="ByP", obj_func="A")
-			instance.add_method_evaluation(instance, tool="biorseo", data_source="BGSU", placement_method="ByP", obj_func="B")
-			instance.add_method_evaluation(instance, tool="biorseo", data_source="BGSU", placement_method="ByP", obj_func="C")
-			instance.add_method_evaluation(instance, tool="biorseo", data_source="BGSU", placement_method="ByP", obj_func="D")
-			instance.add_method_evaluation(instance, tool="biorseo", data_source="BGSU", placement_method="Jar3d", obj_func="A")
-			instance.add_method_evaluation(instance, tool="biorseo", data_source="BGSU", placement_method="Jar3d", obj_func="B")
-			instance.add_method_evaluation(instance, tool="biorseo", data_source="BGSU", placement_method="Jar3d", obj_func="C")
-			instance.add_method_evaluation(instance, tool="biorseo", data_source="BGSU", placement_method="Jar3d", obj_func="D")
-			instance.add_method_evaluation(instance, tool="biorseo", data_source="RIN", placement_method="D.P.", obj_func="A")
-			instance.add_method_evaluation(instance, tool="biorseo", data_source="RIN", placement_method="D.P.", obj_func="B")
-
-			for method in instance.methods:
-				for i in range(len(method.joblist)):
-					j = method.joblist[i]
-					if j.label in joblabel_list: # look for a duplicate job (Jar3d, BayesPairing, RNAsubopt...)
-						# for index, job in enumerate(fulljoblist):
-						# 	if job.label == j.label:
-						# 		method.joblist[i] = fulljoblist[index] # point to the previous occurrence
-						# 		break
-						continue
-					else:
-						fulljoblist.append(j)
-						joblabel_list.add(j.label)
-
-		with open("containers.pickle", "wb") as cont:
-			pickle.dump((bpRNAContainer, PseudobaseContainer), cont)
-
-	for instance in StudycaseContainer: # We need to define these separately because we do not want concurrency, to measure proper run times.
-		instance.add_method_evaluation(instance, "RNAsubopt", flat=True)
-		instance.add_method_evaluation(instance, "Biokop", flat=True)
-		instance.add_method_evaluation(instance, "RNA-MoIP (1by1)", flat=True)
-		instance.add_method_evaluation(instance, "RNA-MoIP (chunk)", flat=True)
-		instance.add_method_evaluation(instance, tool="biorseo", data_source="DESC", placement_method="D.P.", obj_func="A", flat=True)
-		instance.add_method_evaluation(instance, tool="biorseo", data_source="DESC", placement_method="D.P.", obj_func="B", flat=True)
-		instance.add_method_evaluation(instance, tool="biorseo", data_source="DESC", placement_method="ByP", obj_func="A", flat=True)
-		instance.add_method_evaluation(instance, tool="biorseo", data_source="DESC", placement_method="ByP", obj_func="B", flat=True)
-		instance.add_method_evaluation(instance, tool="biorseo", data_source="DESC", placement_method="ByP", obj_func="C", flat=True)
-		instance.add_method_evaluation(instance, tool="biorseo", data_source="DESC", placement_method="ByP", obj_func="D", flat=True)
-		instance.add_method_evaluation(instance, tool="biorseo", data_source="BGSU", placement_method="ByP", obj_func="A", flat=True)
-		instance.add_method_evaluation(instance, tool="biorseo", data_source="BGSU", placement_method="ByP", obj_func="B", flat=True)
-		instance.add_method_evaluation(instance, tool="biorseo", data_source="BGSU", placement_method="ByP", obj_func="C", flat=True)
-		instance.add_method_evaluation(instance, tool="biorseo", data_source="BGSU", placement_method="ByP", obj_func="D", flat=True)
-		instance.add_method_evaluation(instance, tool="biorseo", data_source="BGSU", placement_method="Jar3d", obj_func="A", flat=True)
-		instance.add_method_evaluation(instance, tool="biorseo", data_source="BGSU", placement_method="Jar3d", obj_func="B", flat=True)
-		instance.add_method_evaluation(instance, tool="biorseo", data_source="BGSU", placement_method="Jar3d", obj_func="C", flat=True)
-		instance.add_method_evaluation(instance, tool="biorseo", data_source="BGSU", placement_method="Jar3d", obj_func="D", flat=True)
-		instance.add_method_evaluation(instance, tool="biorseo", data_source="RIN", placement_method="D.P.", obj_func="A", flat=True)
-		instance.add_method_evaluation(instance, tool="biorseo", data_source="RIN", placement_method="D.P.", obj_func="B", flat=True)
-
-		for method in instance.methods:
-			for i in range(len(method.joblist)):
-				j = method.joblist[i]
-				if j.label in joblabel_list: # look for a duplicate job (Jar3d, BayesPairing, RNAsubopt...)
-					# for index, job in enumerate(fulljoblist):
-					# 	if job.label == j.label:
-					# 		method.joblist[i] = fulljoblist[index] # point to the previous occurrence
-					# 		break
-					continue
-				else:
-					fulljoblist.append(j)
-					joblabel_list.add(j.label)
-
-	# # sort jobs in a tree structure
-	# jobs = {}
-	# jobcount = len(fulljoblist)
-	# for job in fulljoblist:
-	# 	if job.priority_ not in jobs.keys():
-	# 		jobs[job.priority_] = {}
-	# 	if job.nthreads not in jobs[job.priority_].keys():
-	# 		print(f"New job priority/concurrency: {job.priority_} {job.nthreads}")
-	# 		jobs[job.priority_][job.nthreads] = []
-	# 	jobs[job.priority_][job.nthreads].append(job)
-	# nprio = max(jobs.keys())
-	# # for each priority level
-	# for i in range(1,nprio+1):
-	# 	if i not in jobs.keys(): continue # ignore this priority level if no job available
-	# 	different_thread_numbers = [n for n in jobs[i].keys()]
-	# 	different_thread_numbers.sort()
-	# 	print("processing jobs of priority", i)
-	# 	# jobs should be processed 1 by 1, 2 by 2, or n by n depending on their definition
-	# 	for n in different_thread_numbers:
-	# 		bunch = jobs[i][n]
-	# 		if not len(bunch): continue # ignore if no jobs should be processed n by n
-	# 		print("using", n, "processes:")
-	# 		try :
-	# 			# execute jobs of priority i that should be processed n by n:
-	# 			p = MyPool(initializer = init, initargs = (n_launched, n_finished, n_skipped), processes=n, maxtasksperchild=10)
-	# 			raw_results = p.map(execute_job, bunch)
-	# 			p.close()
-	# 			p.join()
-
-	# 			# extract computation times
-	# 			times = [ r[0] for r in raw_results ]
-	# 			for j, t in zip(bunch, times):
-	# 				j.comp_time = t
-
-	# 		except (subprocess.TimeoutExpired) :
-	# 			print("Skipping, took more than 3600s")
-	# 			pass
-
-
-	# ================= Statistics ========================
-
-	if path.isfile("pickleresults.pickle"):
-		with open("pickleresults.pickle", "rb") as rf:
-			t = pickle.load(rf)
-		x_noPK_fully, x_PK_fully, n, r, max_i, x_pseudobase_fully = t
-	else:
-		x_noPK_fully, x_PK_fully, n, r, max_i = get_bpRNA_statistics()
-		x_pseudobase_fully = get_Pseudobase_statistics()
-		with open("pickleresults.pickle", "wb") as rf:
-			pickle.dump((x_noPK_fully, x_PK_fully, n, r, max_i, x_pseudobase_fully), rf)
-	# print_StudyCase_results()
-
-	# ================= PLOTS OF RESULTS =======================================
-
-	colors = [
-		'#911eb4', #purple
-		'#000075', #navy
-		'#ffe119', '#ffe119', # yellow
-		'#e6194B', '#e6194B', #red
-		'#3cb44b', '#3cb44b', '#3cb44b', '#3cb44b', #green
-		'#4363d8', '#4363d8', '#4363d8', '#4363d8', #blue
-		'#3cb44b', '#3cb44b', '#3cb44b', '#3cb44b', # green
-		'#bbbbff', '#bbbbff' # grey-blue
-	]
-
-	def plot_best_MCCs(x_noPK_fully, x_PK_fully, x_pseudobase_fully):
-
-		print("Best MCCs...")
-		labels = [
-			"Biokop  \n",
-			"RNA\nsubopt", "RNA-\nMoIP\n1by1", "RNA-\nMoIP\nchunk",
-			"$f_{1A}$", "$f_{1B}$",
-			"$f_{1A}$", "$f_{1B}$", "$f_{1C}$", "$f_{1D}$",
-			"$f_{1A}$", "$f_{1B}$", "$f_{1C}$", "$f_{1D}$",
-			"$f_{1A}$", "$f_{1B}$", "$f_{1C}$", "$f_{1D}$",
-			"$f_{1A}$", "$f_{1B}$",
-		]
-
-		fig, axes = plt.subplots(nrows=3, ncols=1, figsize=(10,5), dpi=150)
-		fig.suptitle(" \n ")
-		fig.subplots_adjust(left=0.1, right=0.97, top=0.83, bottom=0.05)
-
-
-
-		# Line 1 : no Pseudoknots
-		xpos = [ 1+x for x in range(len(x_noPK_fully)) ] # skip Biokop's column
-		vplot = axes[0].violinplot(x_noPK_fully, showmeans=False, showmedians=False, showextrema=False,
-								   points=len(x_noPK_fully[0]), positions=xpos)
-		axes[0].set_xticks(xpos)
-		for patch, color in zip(vplot['bodies'], colors[1:]):
-			patch.set_facecolor(color)
-			patch.set_edgecolor(color)
-			patch.set_alpha(0.5)
-		quartile1, medians, quartile3 = np.percentile(x_noPK_fully, [25, 50, 75], axis=1)
-		axes[0].scatter(xpos, medians, marker='o', color='k', s=30, zorder=3)
-		axes[0].vlines(xpos, quartile1, quartile3, color='k', linestyle='-', lw=1)
-		for x, y1, y2 in zip(xpos, quartile1, quartile3):
-			bar1 = Line2D([x-0.1, x+0.1], [y1, y1], color="k", lw=1)
-			bar2 = Line2D([x-0.1, x+0.1], [y2, y2], color="k", lw=1)
-			axes[0].add_line(bar1)
-			axes[0].add_line(bar2)
-		axes[0].set_ylabel("(A)\nmax MCC\n(%d RNAs)" % (len(x_noPK_fully[0])), fontsize=12)
-
-		# Line 2 : Pseudoknots supported
-		xpos = [ 0 ] + [ i for i in range(4,20) ]
-		vplot = axes[1].violinplot(x_PK_fully, showmeans=False, showmedians=False, showextrema=False,
-								   points=len(x_PK_fully[0]), positions=xpos)
-		for patch, color in zip(vplot['bodies'], colors[:1] + colors[4:]):
-			patch.set_facecolor(color)
-			patch.set_edgecolor(color)
-			patch.set_alpha(0.5)
-		quartile1, medians, quartile3 = np.percentile(x_PK_fully, [25, 50, 75], axis=1)
-		axes[1].scatter(xpos, medians, marker='o', color='k', s=30, zorder=3)
-		axes[1].vlines(xpos, quartile1, quartile3, color='k', linestyle='-', lw=1)
-		for x, y1, y2 in zip(xpos, quartile1, quartile3):
-			bar1 = Line2D([x-0.1, x+0.1], [y1, y1], color="k", lw=1)
-			bar2 = Line2D([x-0.1, x+0.1], [y2, y2], color="k", lw=1)
-			axes[1].add_line(bar1)
-			axes[1].add_line(bar2)
-		axes[1].set_ylabel("(B)\nmax MCC\n(%d RNAs)" % (len(x_PK_fully[0])), fontsize=12)
-
-		# Line 3 : all methods on pseudoknotted dataset
-		xpos = [ x for x in range(len(x_pseudobase_fully)) ]
-		vplot = axes[2].violinplot(x_pseudobase_fully, showmeans=False, showmedians=False, showextrema=False,
-								   points=len(x_pseudobase_fully[0]), positions=xpos)
-		for patch, color in zip(vplot['bodies'], colors):
-			patch.set_facecolor(color)
-			patch.set_edgecolor(color)
-			patch.set_alpha(0.5)
-		quartile1, medians, quartile3 = np.percentile(x_pseudobase_fully, [25, 50, 75], axis=1)
-		axes[2].scatter(xpos, medians, marker='o', color='k', s=30, zorder=3)
-		axes[2].vlines(xpos, quartile1, quartile3, color='k', linestyle='-', lw=1)
-		for x, y1, y2 in zip(xpos, quartile1, quartile3):
-			bar1 = Line2D([x-0.1, x+0.1], [y1, y1], color="k", lw=1)
-			bar2 = Line2D([x-0.1, x+0.1], [y2, y2], color="k", lw=1)
-			axes[2].add_line(bar1)
-			axes[2].add_line(bar2)
-		axes[2].set_ylabel("(C)\nmax MCC\n(%d RNAs)" % (len(x_pseudobase_fully[0])), fontsize=12)
-
-		for ax in axes:
-			ax.set_ylim((0.0, 1.01))
-			ax.set_xlim((-1, 20))
-			yticks = [ i/10 for i in range(0, 11, 2) ]
-			ax.set_yticks(yticks)
-			for y in yticks:
-				ax.axhline(y=y, color="grey", linestyle="--", linewidth=1)
-			ax.tick_params(top=False, bottom=False, labeltop=False, labelbottom=False)
-			ax.set_xticks([i for i in range(20)])
-		axes[0].tick_params(top=True, bottom=False, labeltop=True, labelbottom=False)
-		axes[0].set_xticklabels(labels)
-		for i, tick in enumerate(axes[0].xaxis.get_major_ticks()):
-			if i<4: # Reduce size of Biokop, RNAsubopt and RNA-MoIP labels to stay readable
-				tick.label2.set_fontsize(10)
-			else:
-				tick.label2.set_fontsize(12)
-
-	def plot_more_info():
-		# ======= number of solutions, insertion ratio, etc ========================
-
-		# Figure : number of solutions
-		print("Number of solutions...")
-		plt.figure(figsize=(9,2.5), dpi=80)
-		plt.suptitle(" \n ")
-		plt.subplots_adjust(left=0.05, right=0.97, top=0.6, bottom=0.05)
-		xpos = [ x for x in range(len(n)) ]
-		for y in [ 10*x for x in range(8) ]:
-			plt.axhline(y=y, color="grey", linestyle="-", linewidth=0.5)
-		plt.axhline(y=1, color="grey", linestyle="-", linewidth=0.5)
-		vplot = plt.violinplot(n, showmeans=False, showmedians=False, showextrema=False, points=len(n[0]), positions=xpos)
-		for patch, color in zip(vplot['bodies'], colors):
-			patch.set_facecolor(color)
-			patch.set_edgecolor(color)
-			patch.set_alpha(0.5)
-		labels = [
-			"Biokop",
-			"RNAsubopt","RNA-MoIP\n1by1", "RNA-MoIP\nchunk",
-			"$f_{1A}$", "$f_{1B}$",
-			"$f_{1A}$", "$f_{1B}$", "$f_{1C}$", "$f_{1D}$",
-			"$f_{1A}$", "$f_{1B}$", "$f_{1C}$", "$f_{1D}$",
-			"$f_{1A}$", "$f_{1B}$", "$f_{1C}$", "$f_{1D}$",
-			"$f_{1A}$", "$f_{1B}$"
-		]
-		plt.xlim((-1,20))
-		plt.tick_params(top=False, bottom=False, labeltop=False, labelbottom=False)
-		plt.xticks([ i for i in range(len(labels))], labels)
-		plt.tick_params(top=True, bottom=False, labeltop=True, labelbottom=False)
-		for i, tick in enumerate(plt.gca().xaxis.get_major_ticks()):
-			if i<4: # Reduce size of RNA-MoIP labels to stay readable
-				# tick.label2.set_fontsize(8)
-				tick.label2.set_rotation(90)
-			else:
-				tick.label2.set_fontsize(12)
-		plt.yticks([ 20*x for x in range(3) ])
-		plt.ylim((0,40))
-		plt.savefig("number_of_solutions.png")
-
-		# Figure : max number of insertions and ratio
-		fig, axes = plt.subplots(nrows=2, ncols=1, figsize=(10,4), dpi=80)
-		fig.suptitle(" \n ")
-		fig.subplots_adjust(left=0.09, right=0.99, top=0.7, bottom=0.05)
-		
-		# Figure : max inserted
-		print("Max inserted...")
-		xpos = [ x for x in range(18) ]
-		axes[0].set_yticks([ 5*x for x in range(3) ])
-		for y in [ 2*x for x in range(7) ]:
-			axes[0].axhline(y=y, color="grey", linestyle="-", linewidth=0.5)
-		vplot = axes[0].violinplot(max_i, showmeans=False, showmedians=False, showextrema=False, points=len(max_i[0]), positions=xpos)
-		for patch, color in zip(vplot['bodies'], colors[2:]):
-			patch.set_facecolor(color)
-			patch.set_edgecolor(color)
-			patch.set_alpha(0.5)
-		axes[0].set_ylabel("(A)", fontsize=12)
-
-		# Figure : insertion ratio
-		print("Ratio of insertions...")
-		xpos = [ 0 ] + [ x for x in range(2, 1+len(r)) ]
-		axes[1].set_ylim((-0.01, 1.01))
-		yticks = [ 0, 0.5, 1.0 ]
-		axes[1].set_yticks(yticks)
-		for y in yticks:
-			axes[1].axhline(y=y, color="grey", linestyle="-", linewidth=0.5)
-		vplot = axes[1].violinplot(r, showmeans=False, showmedians=False, showextrema=False, points=len(r[0]), positions=xpos)
-		for patch, color in zip(vplot['bodies'], [colors[2]] + colors[4:]):
-			patch.set_facecolor(color)
-			patch.set_edgecolor(color)
-			patch.set_alpha(0.5)
-		for i,x in enumerate(xpos):
-			axes[1].annotate(str(len(r[i])), (x-0.25, 0.05), fontsize=8)
-		axes[1].set_ylabel("(B)", fontsize=12)
-
-		labels = labels[2:]
-		for ax in axes:
-			ax.set_xlim((-1,18))
-			ax.tick_params(top=False, bottom=False, labeltop=False, labelbottom=False)
-			ax.set_xticks([ i for i in range(18)])
-		axes[0].tick_params(top=True, bottom=False, labeltop=True, labelbottom=False)
-		axes[0].set_xticklabels(labels)
-		for i, tick in enumerate(axes[0].xaxis.get_major_ticks()):
-			if i<2: # Reduce size of RNA-MoIP labels to stay readable
-				# tick.label2.set_fontsize(9)
-				tick.label2.set_rotation(90)
-			else:
-				tick.label2.set_fontsize(12)
-
-	def compare_subopt_MoIP():
-		# ================== MCC performance ====================================
-
-		plt.figure(figsize=(10,4), dpi=80)
-		bpRNAContainer.sort(key=lambda x: x.get_method("RNA-MoIP (chunk)").max_mcc)
-
-		x = [
-			[ rna.get_method("RNA-MoIP (chunk)").max_mcc for rna in bpRNAContainer ],
-			[ rna.get_method("RNA-MoIP (1by1)").max_mcc for rna in bpRNAContainer ],
-			[ rna.get_method("RNAsubopt").max_mcc for rna in bpRNAContainer ]
-		]
-		diffs = [
-			[ x[1][i] - x[0][i] for i in range(len(x[0])) ], # 1by1 - chunk
-			[ x[1][i] - x[2][i] for i in range(len(x[0])) ], # 1by1 - subopt
-			[ x[0][i] - x[2][i] for i in range(len(x[0])) ]  # chunk - subopt
-		]
-
-		plt.subplot(121)
-		colors = [ 'firebrick','goldenrod', 'xkcd:blue']
-		labels = ["RNA-MoIP 'chunk' MCC (1 solution)", "Best RNA-MoIP '1by1' MCC", "Best RNAsubopt MCC" ]
-		for y, col, lab in zip(x, colors, labels):
-			plt.scatter(range(len(y)), y, color=col, label=lab, marker='o', s=2)
-		plt.axvline(x=0, color='black', linewidth=1)
-		plt.xlabel("RNA Strand verified structures (10 <  nt  < 100)")
-		plt.ylabel("Mattews Correlation Coefficient")
-		plt.ylim((-0.05,1.05))
-		plt.title("(a) Performance of the prediction method")
-		plt.legend(loc="lower right")
-
-		plt.subplot(122)
-		plt.axhline(y=0, color="black")
-		plt.boxplot(diffs)
-		plt.ylabel("Difference in max MCC")
-		plt.title("(b) Difference between prediction methods")
-		plt.xticks([1,2,3], ["MoIP '1by1'\n-\nMoIP 'chunk'", "MoIP '1by1'\n-\nRNAsubopt", "MoIP 'chunk'\n-\nRNAsubopt"])
-		plt.subplots_adjust(wspace=0.25, bottom=0.2, left=0.1, right=0.99)
-
-	plot_best_MCCs(x_noPK_fully, x_PK_fully, x_pseudobase_fully)
-	plt.savefig("best_MCCs.png")
-	plot_more_info()
-	plt.savefig("detailed_stats.png")
-	compare_subopt_MoIP()
-	plt.savefig("compare_subopt_MOIP.png")

->All values represent delta G in units of deka cal/mol (kcal/mol*100)
->Stacking 5' X1 Y1 3'
->         3' X2 Y2 5'
->X1X2 = AU CG GC UA GU UG (row headings)
->Y1Y2 = AU CG GC UA GU UG (column headings)
- -90 -220 -210 -110  -60 -140 
--210 -330 -240 -210 -140 -210 
--240 -340 -330 -220 -150 -250 
--130 -240 -210  -90 -100 -130 
--130 -250 -210 -140  -50  130 
--100 -150 -140  -60   30  -50 
->Hairpin Loop Energies: size = 1,2,3,..,30
-   0    0  570  560  560  540  590  560  640  650  660  670  680  690  690  700  710  710  720  720  730  730  740  740  750  750  750  760  760  770 
->Bulge loop Energies: size = 1,2,3,..,30
- 380  280  320  360  400  440  460  470  480  490  500  510  520  530  540  540  550  550  560  570  570  580  580  580  590  590  600  600  600  610 
->Interior Loop Energies: size = 1,2,3,..,30
-   0    0    0  170  180  200  220  230  240  250  260  270  280  290  300  300  310  310  320  330  330  340  340  340  350  350  360  360  360  370 
->NINIO asymmetry Terms: m1, m2, m3, m4, max
->Energy = MAX[ max, asymmetry*m#] where # = MIN(4,L1,L2)
->and L1, L2 are lengths of each side of loop, 
->and asymmetry = |L1-L2|
-  50   50   50   50  300
->Triloops  5' .. 3' (Not listed equals 0 bonus)
-AAAAU     0
->Tetraloops  5' .. 3' (Not listed equals 0 bonus)
-AGAAAU -200
-AGCAAU -150
-AGUAAU -150
-AGUGAU -150
-CGAAAG -300
-CGAAGG -250
-CGAGAG -200
-CGCAAG -300
-CGCGAG -250
-CGGAAG -300
-CGGGAG -150
-CGUAAG -200
-CGUGAG -300
-CUAACG -200
-CUACGG -250
-CUUCGG -300
-GGAAAC -300
-GGAAGC -150
-GGAGAC -300
-GGCAAC -250
-GGCGAC -150
-GGGAAC -150
-GGGAGC -150
-GGGGAC -300
-GGUGAC -300
-GUGAAC -150
-UGAAAA -150
-UGAAAG -200
-UGAGAG -250
-UGGAAA -150
->Mismatch HP:
->Columns 5'-AU,CG,GC,UA,GU,UG-3'
->rows 5'-AA,AC,AG,AU,CA,..,UU-3'
- -30 -150 -110  -50   20  -50 
- -50 -150 -150  -30  -50  -30 
- -30 -140 -130  -60  -30  -60 
- -30 -180 -210  -50  -30  -50 
- -10 -100 -110  -20  -10  -20 
- -20  -90  -70  -10  -20  -10 
--150 -290 -240 -120 -150 -170 
- -20  -80  -50    0  -20    0 
--110 -220 -240 -140  -90  -80 
--120 -200 -290 -120 -110 -120 
- -20 -160 -140  -70  -30  -30 
-  20 -110 -120  -20    0  -70 
- -30 -170 -190  -30  -30  -60 
- -30 -140 -100  -10  -30  -10 
- -60 -180 -220  -50  -40  -60 
--110 -200 -150  -80 -110  -80 
->Mismatch Interior:
->Columns 5'-AU,CG,GC,UA,GU,UG-3'
->rows 5'-AA,AC,AG,AU,CA,..,UU-3'
-  70    0    0   70   70   70 
-  70    0    0   70   70   70 
- -40 -110 -110  -40  -40  -40 
-  70    0    0   70   70   70 
-  70    0    0   70   70   70 
-  70    0    0   70   70   70 
-  70    0    0   70   70   70 
-  70    0    0   70   70   70 
- -40 -110 -110  -40  -40  -40 
-  70    0    0   70   70   70 
-  70    0    0   70   70   70 
-  70    0    0   70   70   70 
-  70    0    0   70   70   70 
-  70    0    0   70   70   70 
-  70    0    0   70   70   70 
-   0  -70  -70    0    0    0 
->Dangle Energies: 5' X1 Y 3'
->                 3' X2 . 5'
->Columns: Y = A, C, G, U
->Rows = X1X2 = AU,CG,GC,UA,GU,UG
- -80  -50  -80  -60 
--170  -80 -170 -120 
--110  -40 -130  -60 
- -70  -10  -70  -10 
- -80  -50  -80  -60 
- -70  -10  -70  -10 
->Dangle Energies: 5' X1 . 3'
->                 3' X2 Y 5'
->Columns: Y = A, C, G, U
->Rows = X1X2 = AU,CG,GC,UA,GU,UG
- -30  -10  -20  -20 
- -20  -30    0    0 
- -50  -30  -20  -10 
- -30  -30  -40  -20 
- -30  -10  -20  -20 
- -30  -30  -40  -20 
->Multiloop terms: ALPHA_1, ALPHA_2, ALPHA_3
->ML penalty = ALPHA_1 + s * ALPHA_2 + u *ALPHA_3
->s = # stems adjacent to ML, u = unpaired bases in ML
- 340   40    0
->AT_PENALTY: 
->Penalty for non GC pairs that terminate a helix
-  50
->Interior Loops 1x1
->CG..AU = 5'- C X A 3'
->         3'- G Y U 5'
->Rows: X = A C G U (X constant for a row)
->Columns: Y = A C G U (Y constant in column)
-AU..AU
- 170  170  170  170 
- 170  170  170  170 
- 170  170  -40  170 
- 170  170  170  150 
-AU..CG
- 110  110  110  110 
- 110  110  110  110 
- 110  110 -100  110 
- 110  110  110  100 
-AU..GC
- 110  110  110  110 
- 110  110  110  110 
- 110  110 -100  110 
- 110  110  110  110 
-AU..UA
- 170  170  170  170 
- 170  170  170  170 
- 170  170  -40  170 
- 170  170  170  120 
-AU..GU
- 170  170  170  170 
- 170  170  170  170 
- 170  170  -40  170 
- 170  170  170  170 
-AU..UG
- 170  170  170  170 
- 170  170  170  170 
- 170  170  -40  170 
- 170  170  170  170 
-CG..AU
- 110  110  110  110 
- 110  110  110  110 
- 110  110 -100  110 
- 110  110  110  110 
-CG..CG
-  40  -40   40   40 
-  30   50   40   50 
- -10   40 -170   40 
-  40    0   40  -30 
-CG..GC
- 110   40   40   40 
-  40   40   40   40 
-  40   40 -140   40 
-  40   40   40   40 
-CG..UA
- 110  110  110  110 
- 110  110  110  110 
- 110  110 -100  110 
- 110  110  110  110 
-CG..GU
- 110  110  110  110 
- 110  110  110  110 
- 110  110 -100  110 
- 110  110  110  110 
-CG..UG
- 110  110  110  110 
- 110  110  110  110 
- 110  110 -100  110 
- 110  110  110  110 
-GC..AU
- 110  110  110  110 
- 110  110  110  110 
- 110  110 -100  110 
- 110  110  110  110 
-GC..CG
-  80   40   40   40 
-  40   40   40   40 
-  40   40 -210   40 
-  40   40   40  -70 
-GC..GC
-  40   30  -10   40 
- -40   50   40    0 
-  40   40 -170   40 
-  40   50   40  -30 
-GC..UA
- 110  110  110  110 
- 110  110  110  110 
- 110  110 -100  110 
- 110  110  110  100 
-GC..GU
- 110  110  110  110 
- 110  110  110  110 
- 110  110 -100  110 
- 110  110  110  110 
-GC..UG
- 110  110  110  110 
- 110  110  110  110 
- 110  110 -100  110 
- 110  110  110  110 
-UA..AU
- 170  170  170  170 
- 170  170  170  170 
- 170  170  -40  170 
- 170  170  170  180 
-UA..CG
- 110  110  110  110 
- 110  110  110  110 
- 110  110 -100  110 
- 110  110  110  110 
-UA..GC
- 110  110  110  110 
- 110  110  110  110 
- 110  110 -100  110 
- 110  110  110  110 
-UA..UA
- 170  170  170  170 
- 170  170  170  170 
- 170  170  -40  170 
- 170  170  170  150 
-UA..GU
- 170  170  170  170 
- 170  170  170  170 
- 170  170  -40  170 
- 170  170  170  170 
-UA..UG
- 170  170  170  170 
- 170  170  170  170 
- 170  170  -40  170 
- 170  170  170  170 
-GU..AU
- 170  170  170  170 
- 170  170  170  170 
- 170  170  -40  170 
- 170  170  170  170 
-GU..CG
- 110  110  110  110 
- 110  110  110  110 
- 110  110 -100  110 
- 110  110  110  110 
-GU..GC
- 110  110  110  110 
- 110  110  110  110 
- 110  110 -100  110 
- 110  110  110  110 
-GU..UA
- 170  170  170  170 
- 170  170  170  170 
- 170  170  -40  170 
- 170  170  170  170 
-GU..GU
- 170  170  170  170 
- 170  170  170  170 
- 170  170  -40  170 
- 170  170  170  170 
-GU..UG
- 170  170  170  170 
- 170  170  170  170 
- 170  170  -40  170 
- 170  170  170  170 
-UG..AU
- 170  170  170  170 
- 170  170  170  170 
- 170  170  -40  170 
- 170  170  170  170 
-UG..CG
- 110  110  110  110 
- 110  110  110  110 
- 110  110 -100  110 
- 110  110  110  110 
-UG..GC
- 110  110  110  110 
- 110  110  110  110 
- 110  110 -100  110 
- 110  110  110  110 
-UG..UA
- 170  170  170  170 
- 170  170  170  170 
- 170  170  -40  170 
- 170  170  170  170 
-UG..GU
- 170  170  170  170 
- 170  170  170  170 
- 170  170  -40  170 
- 170  170  170  170 
-UG..UG
- 170  170  170  170 
- 170  170  170  170 
- 170  170  -40  170 
- 170  170  170  170 
->Interior Loops 2x2
->CG.AG..AU = 5'- C A G A -3'
->            3'- G Y X U -5'
->Rows: X = A C G U (X constant for a row)
->Columns: Y = A C G U (Y constant in column)
-AU.AA..AU
- 280  260  150  200 
- 230  220  110  200 
- 170  160   50  200 
- 200  200  200  200 
-AU.AC..AU
- 280  260  150  200 
- 340  260  250  200 
- 200  200  200  200 
- 340  260  250  200 
-AU.AG..AU
- 170  160   50  200 
- 200  200  200  200 
- 210  200   90  200 
- 100  -20   50  200 
-AU.AU..AU
- 200  200  200  200 
- 310  230  220  200 
- 220  110  180  200 
- 290  180  250  200 
-AU.CA..AU
- 250  310  200  310 
- 210  200  200  200 
- 150  240  200  240 
- 200  200  200  200 
-AU.CC..AU
- 250  250  200  250 
- 250  250  200  250 
- 200  200  200  200 
- 250  250  200  250 
-AU.CG..AU
- 150  240  200  240 
- 200  200  200  200 
- 190  240  200  240 
- -30   70  200   70 
-AU.CU..AU
- 200  200  200  200 
- 220  220  200  220 
- 100  190  200  190 
- 170  160  200  160 
-AU.GA..AU
- 150  200  210  230 
- 110  200  160   90 
-  50  200  100  210 
- 200  200  200  200 
-AU.GC..AU
- 150  200  210  130 
- 250  200  270  230 
- 200  200  200  200 
- 250  200  270  230 
-AU.GG..AU
-  50  200  100  210 
- 200  200  200  200 
-  90  200  140  170 
-  50  200   30 -150 
-AU.GU..AU
- 200  200  200  200 
- 220  200  240  200 
- 180  200  150  -20 
- 250  200  220  230 
-AU.UA..AU
- 200  310  130  270 
- 200  200  -10  120 
- 200  240  110  240 
- 200  200  200  200 
-AU.UC..AU
- 200  250   30  170 
- 200  250  130  170 
- 200  200  200  200 
- 200  250  130  170 
-AU.UG..AU
- 200  240  110  240 
- 200  200  200  200 
- 200  240   70  200 
- 200   70 -250   70 
-AU.UU..AU
- 200  200  200  200 
- 200  220  100  140 
- 200  190 -120  190 
- 200  160  130   80 
-AU.AA..CG
- 210  200   90  200 
- 190  170   60  200 
-  10    0 -110  200 
- 200  200  200  200 
-AU.AC..CG
- 180  170   60  200 
- 250  170  160  200 
- 200  200  200  200 
- 150   70   70  200 
-AU.AG..CG
-  70   60  -50  200 
- 200  200  200  200 
- 180  160   50  200 
-   0 -120  -50  200 
-AU.AU..CG
- 200  200  200  200 
- 250  180  170  200 
-  40  -80  -10  200 
- 210  100  170  200 
-AU.CA..CG
- 190  240  200  240 
- 160  160  200  160 
- -10   80  200   80 
- 200  200  200  200 
-AU.CC..CG
- 160  150  200  150 
- 160  160  200  160 
- 200  200  200  200 
-  60   60  200   60 
-AU.CG..CG
-  50  140  200  140 
- 200  200  200  200 
- 150  210  200  210 
--130  -30  200  -30 
-AU.CU..CG
- 200  200  200  200 
- 170  160  200  160 
- -90   10  200   10 
-  90   80  200   80 
-AU.GA..CG
-  90  200  140  170 
-  60  200  120   40 
--110  200  -60   50 
- 200  200  200  200 
-AU.GC..CG
-  60  200  110   40 
- 160  200  180  140 
- 200  200  200  200 
-  70  200   80   50 
-AU.GG..CG
- -50  200    0  110 
- 200  200  200  200 
-  50  200  110  130 
- -50  200  -70 -250 
-AU.GU..CG
- 200  200  200  200 
- 170  200  180  150 
- -10  200  -30 -210 
- 170  200  140  150 
-AU.UA..CG
- 200  240   70  200 
- 200  160  -50   80 
- 200   80  -50   80 
- 200  200  200  200 
-AU.UC..CG
- 200  150  -60   70 
- 200  160   50   80 
- 200  200  200  200 
- 200   60  -50  -20 
-AU.UG..CG
- 200  140   10  150 
- 200  200  200  200 
- 200  210   40  170 
- 200  -30 -350  -30 
-AU.UU..CG
- 200  200  200  200 
- 200  160   50   80 
- 200   10 -310   10 
- 200   80   50    0 
-AU.AA..GC
- 200  190   80  200 
- 190  180   70  200 
- 100   90  -20  200 
- 200  200  200  200 
-AU.AC..GC
- 240  220  110  200 
- 280  210  200  200 
- 200  200  200  200 
- 270  190  180  200 
-AU.AG..GC
- 100   90  -20  200 
- 200  200  200  200 
- 180  160   50  200 
-  30  -80  -10  200 
-AU.AU..GC
- 200  200  200  200 
- 270  190  180  200 
- 180   70  140  200 
- 220  100  180  200 
-AU.CA..GC
- 180  230  200  230 
- 170  160  200  160 
-  80  170  200  170 
- 200  200  200  200 
-AU.CC..GC
- 210  210  200  210 
- 200  190  200  190 
- 200  200  200  200 
- 180  180  200  180 
-AU.CG..GC
-  80  170  200  170 
- 200  200  200  200 
- 150  210  200  210 
- -90    0  200    0 
-AU.CU..GC
- 200  200  200  200 
- 180  180  200  180 
-  60  150  200  150 
-  90   90  200   90 
-AU.GA..GC
-  80  200  130  160 
-  70  200  120   50 
- -20  200   30  140 
- 200  200  200  200 
-AU.GC..GC
- 110  200  170   90 
- 200  200  210  180 
- 200  200  200  200 
- 180  200  200  160 
-AU.GG..GC
- -20  200   30  140 
- 200  200  200  200 
-  50  200  110  130 
- -10  200  -40 -210 
-AU.GU..GC
- 200  200  200  200 
- 180  200  200  160 
- 140  200  110  -60 
- 180  200  150  160 
-AU.UA..GC
- 200  230   60  190 
- 200  160  -50   80 
- 200  170   40  180 
- 200  200  200  200 
-AU.UC..GC
- 200  210    0  130 
- 200  190   80  110 
- 200  200  200  200 
- 200  180   70  100 
-AU.UG..GC
- 200  170   40  180 
- 200  200  200  200 
- 200  210   40  170 
- 200    0 -310    0 
-AU.UU..GC
- 200  200  200  200 
- 200  180   70  100 
- 200  150 -160  160 
- 200   90   60   10 
-AU.AA..UA
- 280  260  150  200 
- 250  240  130  200 
- 150  140   30  200 
- 200  200  200  200 
-AU.AC..UA
- 260  250  140  200 
- 310  230  220  200 
- 200  200  200  200 
- 310  230  220  200 
-AU.AG..UA
- 150  140   30  200 
- 200  200  200  200 
- 210  190   80  200 
- 130   20   90  200 
-AU.AU..UA
- 200  200  200  200 
- 310  230  220  200 
- 230  120  190  200 
- 270  150  220  200 
-AU.CA..UA
- 250  310  200  310 
- 230  220  200  220 
- 130  220  200  220 
- 200  200  200  200 
-AU.CC..UA
- 240  230  200  230 
- 220  220  200  220 
- 200  200  200  200 
- 220  220  200  220 
-AU.CG..UA
- 130  220  200  220 
- 200  200  200  200 
- 180  240  200  240 
-  10  100  200  100 
-AU.CU..UA
- 200  200  200  200 
- 220  220  200  220 
- 110  200  200  200 
- 140  140  200  140 
-AU.GA..UA
- 150  200  210  230 
- 130  200  180  110 
-  30  200   80  190 
- 200  200  200  200 
-AU.GC..UA
- 140  200  190  120 
- 220  200  240  200 
- 200  200  200  200 
- 220  200  240  200 
-AU.GG..UA
-  30  200   80  190 
- 200  200  200  200 
-  80  200  140  160 
-  90  200   70 -110 
-AU.GU..UA
- 200  200  200  200 
- 220  200  240  200 
- 190  200  160  -10 
- 220  200  200  200 
-AU.UA..UA
- 200  310  130  270 
- 200  220   10  140 
- 200  220   90  220 
- 200  200  200  200 
-AU.UC..UA
- 200  230   20  150 
- 200  220  100  140 
- 200  200  200  200 
- 200  220  100  140 
-AU.UG..UA
- 200  220   90  220 
- 200  200  200  200 
- 200  240   70  200 
- 200  100 -210  110 
-AU.UU..UA
- 200  200  200  200 
- 200  220  100  140 
- 200  200 -110  200 
- 200  140  110   60 
-AU.AA..GU
- 280  260  150  200 
- 230  220  110  200 
- 170  160   50  200 
- 200  200  200  200 
-AU.AC..GU
- 280  260  150  200 
- 340  260  250  200 
- 200  200  200  200 
- 340  260  250  200 
-AU.AG..GU
- 170  160   50  200 
- 200  200  200  200 
- 210  200   90  200 
- 100  -20   50  200 
-AU.AU..GU
- 200  200  200  200 
- 310  230  220  200 
- 220  110  180  200 
- 290  180  250  200 
-AU.CA..GU
- 250  310  200  310 
- 210  200  200  200 
- 150  240  200  240 
- 200  200  200  200 
-AU.CC..GU
- 250  250  200  250 
- 250  250  200  250 
- 200  200  200  200 
- 250  250  200  250 
-AU.CG..GU
- 150  240  200  240 
- 200  200  200  200 
- 190  240  200  240 
- -30   70  200   70 
-AU.CU..GU
- 200  200  200  200 
- 220  220  200  220 
- 100  190  200  190 
- 170  160  200  160 
-AU.GA..GU
- 150  200  210  230 
- 110  200  160   90 
-  50  200  100  210 
- 200  200  200  200 
-AU.GC..GU
- 150  200  210  130 
- 250  200  270  230 
- 200  200  200  200 
- 250  200  270  230 
-AU.GG..GU
-  50  200  100  210 
- 200  200  200  200 
-  90  200  140  170 
-  50  200   30 -150 
-AU.GU..GU
- 200  200  200  200 
- 220  200  240  200 
- 180  200  150  -20 
- 250  200  220  230 
-AU.UA..GU
- 200  310  130  270 
- 200  200  -10  120 
- 200  240  110  240 
- 200  200  200  200 
-AU.UC..GU
- 200  250   30  170 
- 200  250  130  170 
- 200  200  200  200 
- 200  250  130  170 
-AU.UG..GU
- 200  240  110  240 
- 200  200  200  200 
- 200  240   70  200 
- 200   70 -250   70 
-AU.UU..GU
- 200  200  200  200 
- 200  220  100  140 
- 200  190 -120  190 
- 200  160  130   80 
-AU.AA..UG
- 280  260  150  200 
- 250  240  130  200 
- 150  140   30  200 
- 200  200  200  200 
-AU.AC..UG
- 260  250  140  200 
- 310  230  220  200 
- 200  200  200  200 
- 310  230  220  200 
-AU.AG..UG
- 150  140   30  200 
- 200  200  200  200 
- 210  190   80  200 
- 130   20   90  200 
-AU.AU..UG
- 200  200  200  200 
- 310  230  220  200 
- 230  120  190  200 
- 270  150  220  200 
-AU.CA..UG
- 250  310  200  310 
- 230  220  200  220 
- 130  220  200  220 
- 200  200  200  200 
-AU.CC..UG
- 240  230  200  230 
- 220  220  200  220 
- 200  200  200  200 
- 220  220  200  220 
-AU.CG..UG
- 130  220  200  220 
- 200  200  200  200 
- 180  240  200  240 
-  10  100  200  100 
-AU.CU..UG
- 200  200  200  200 
- 220  220  200  220 
- 110  200  200  200 
- 140  140  200  140 
-AU.GA..UG
- 150  200  210  230 
- 130  200  180  110 
-  30  200   80  190 
- 200  200  200  200 
-AU.GC..UG
- 140  200  190  120 
- 220  200  240  200 
- 200  200  200  200 
- 220  200  240  200 
-AU.GG..UG
-  30  200   80  190 
- 200  200  200  200 
-  80  200  140  160 
-  90  200   70 -110 
-AU.GU..UG
- 200  200  200  200 
- 220  200  240  200 
- 190  200  160  -10 
- 220  200  200  200 
-AU.UA..UG
- 200  310  130  270 
- 200  220   10  140 
- 200  220   90  220 
- 200  200  200  200 
-AU.UC..UG
- 200  230   20  150 
- 200  220  100  140 
- 200  200  200  200 
- 200  220  100  140 
-AU.UG..UG
- 200  220   90  220 
- 200  200  200  200 
- 200  240   70  200 
- 200  100 -210  110 
-AU.UU..UG
- 200  200  200  200 
- 200  220  100  140 
- 200  200 -110  200 
- 200  140  110   60 
-CG.AA..AU
- 200  240  100  200 
- 160  190   60  200 
- 100  130    0  200 
- 200  200  200  200 
-CG.AC..AU
- 200  240  100  200 
- 260  240  200  200 
- 200  200  200  200 
- 260  240  200  200 
-CG.AG..AU
- 100  130    0  200 
- 200  200  200  200 
- 140  170   40  200 
-  20  -40    0  200 
-CG.AU..AU
- 200  200  200  200 
- 230  210  170  200 
- 150   80  130  200 
- 220  150  200  200 
-CG.CA..AU
- 190  280  200  270 
- 150  180  200  160 
-  90  220  200  200 
- 200  200  200  200 
-CG.CC..AU
- 190  220  200  210 
- 190  220  200  210 
- 200  200  200  200 
- 190  220  200  210 
-CG.CG..AU
-  90  220  200  200 
- 200  200  200  200 
- 130  220  200  200 
- -90   40  200   30 
-CG.CU..AU
- 200  200  200  200 
- 160  190  200  180 
-  40  170  200  150 
- 110  140  200  120 
-CG.GA..AU
- 100  200  180  180 
-  60  200  130   40 
-   0  200   70  160 
- 200  200  200  200 
-CG.GC..AU
- 100  200  180   80 
- 200  200  240  180 
- 200  200  200  200 
- 200  200  240  180 
-CG.GG..AU
-   0  200   70  160 
- 200  200  200  200 
-  40  200  110  120 
-   0  200    0 -200 
-CG.GU..AU
- 200  200  200  200 
- 170  200  210  150 
- 130  200  120  -70 
- 200  200  190  180 
-CG.UA..AU
- 200  270   30  220 
- 200  160 -110   70 
- 200  200   10  190 
- 200  200  200  200 
-CG.UC..AU
- 200  210  -70  120 
- 200  210   30  120 
- 200  200  200  200 
- 200  210   30  120 
-CG.UG..AU
- 200  200   10  190 
- 200  200  200  200 
- 200  200  -30  150 
- 200   30 -350   20 
-CG.UU..AU
- 200  200  200  200 
- 200  180    0   90 
- 200  150 -220  150 
- 200  120   30   30 
-CG.AA..CG
-  50   60    0  200 
- 110  150  -70  200 
- -30   10 -160  200 
- 200  200  200  200 
-CG.AC..CG
- 110  110 -100  200 
- 170  150  -60  200 
- 200  200  200  200 
-  70   50   20  200 
-CG.AG..CG
-  40   50  -70  200 
- 200  200  200  200 
- 100  140    0  200 
-  10  -70  -80  200 
-CG.AU..CG
- 200  200  200  200 
- 180  150  120  200 
- -50  -60  -60  200 
- 150    0   90  200 
-CG.CA..CG
- 130  220  200  200 
- 100  130  200  120 
- -70   70  200   40 
- 200  200  200  200 
-CG.CC..CG
- 100  190  200  110 
- 100  130  200  120 
- 200  200  200  200 
-   0   30  200  170 
-CG.CG..CG
-  70   70  200  100 
- 200  200  200  200 
-  90  180  200  170 
--190  -30  200  -70 
-CG.CU..CG
- 200  200  200  200 
- 110  140  200  120 
--150  -20  200  -30 
- -20  -10  200   20 
-CG.GA..CG
- -20  200  110   90 
- -40  200   90    0 
--170  200  -90   30 
- 200  200  200  200 
-CG.GC..CG
-  70  200   80  -10 
- 110  200  150  100 
- 200  200  200  200 
-  20  200   50    0 
-CG.GG..CG
- -50  200  -20   60 
- 200  200  200  200 
-   0  200   80   90 
- -90  200 -100 -300 
-CG.GU..CG
- 200  200  200  200 
- 120  200  150  100 
--130  200  -60 -240 
-  90  200  110   60 
-CG.UA..CG
- 200  200  -10  140 
- 200  120 -160   30 
- 200   40 -160   50 
- 200  200  200  200 
-CG.UC..CG
- 200  110 -160   30 
- 200  120  -60   30 
- 200  200  200  200 
- 200   20 -160   10 
-CG.UG..CG
- 200   50  -60  140 
- 200  200  200  200 
- 200  170  -70  120 
- 200  -70 -440 -100 
-CG.UU..CG
- 200  200  200  200 
- 200  120  -50   30 
- 200  -10 -410   10 
- 200   40 -100   60 
-CG.AA..GC
- 130  160   30  200 
- 120  150   20  200 
-  30   60  -70  200 
- 200  200  200  200 
-CG.AC..GC
- 160  200   60  200 
- 210  180  150  200 
- 200  200  200  200 
- 190  170  130  200 
-CG.AG..GC
-  30   60  -70  200 
- 200  200  200  200 
- 100  140    0  200 
- -40 -110  -60  200 
-CG.AU..GC
- 200  200  200  200 
- 190  170  130  200 
- 110   40   90  200 
- 140   80  130  200 
-CG.CA..GC
- 120  210  200  190 
- 110  140  200  120 
-  20  150  200  130 
- 200  200  200  200 
-CG.CC..GC
- 150  180  200  170 
- 140  170  200  150 
- 200  200  200  200 
- 120  150  200  140 
-CG.CG..GC
-  20  150  200  130 
- 200  200  200  200 
-  90  180  200  170 
--150  -20  200  -40 
-CG.CU..GC
- 200  200  200  200 
- 120  150  200  140 
-   0  130  200  110 
-  30   60  200   50 
-CG.GA..GC
-  30  200  100  110 
-  20  200   90    0 
- -70  200    0   90 
- 200  200  200  200 
-CG.GC..GC
-  60  200  140   40 
- 150  200  180  130 
- 200  200  200  200 
- 130  200  170  110 
-CG.GG..GC
- -70  200    0   90 
- 200  200  200  200 
-   0  200   80   90 
- -60  200  -70 -260 
-CG.GU..GC
- 200  200  200  200 
- 130  200  170  110 
-  90  200   90 -110 
- 130  200  120  110 
-CG.UA..GC
- 200  190  -40  140 
- 200  120 -150   30 
- 200  130  -60  130 
- 200  200  200  200 
-CG.UC..GC
- 200  170 -110   80 
- 200  150  -20   60 
- 200  200  200  200 
- 200  140  -40   50 
-CG.UG..GC
- 200  130  -60  130 
- 200  200  200  200 
- 200  170  -70  120 
- 200  -40 -420  -50 
-CG.UU..GC
- 200  200  200  200 
- 200  140  -40   50 
- 200  110 -260  110 
- 200   50  -50  -40 
-CG.AA..UA
- 200  240  100  200 
- 180  210   80  200 
-  80  110  -20  200 
- 200  200  200  200 
-CG.AC..UA
- 190  220   90  200 
- 230  210  170  200 
- 200  200  200  200 
- 230  210  170  200 
-CG.AG..UA
-  80  110  -20  200 
- 200  200  200  200 
- 130  170   30  200 
-  60    0   40  200 
-CG.AU..UA
- 200  200  200  200 
- 230  210  170  200 
- 160   90  140  200 
- 190  130  180  200 
-CG.CA..UA
- 190  280  200  270 
- 170  200  200  180 
-  70  200  200  180 
- 200  200  200  200 
-CG.CC..UA
- 180  210  200  190 
- 160  190  200  180 
- 200  200  200  200 
- 160  190  200  180 
-CG.CG..UA
-  70  200  200  180 
- 200  200  200  200 
- 120  210  200  200 
- -50   80  200   70 
-CG.CU..UA
- 200  200  200  200 
- 160  190  200  180 
-  50  180  200  160 
-  80  110  200  100 
-CG.GA..UA
- 100  200  180  180 
-  80  200  150   60 
- -20  200   50  140 
- 200  200  200  200 
-CG.GC..UA
-  90  200  160   70 
- 170  200  210  150 
- 200  200  200  200 
- 170  200  210  150 
-CG.GG..UA
- -20  200   50  140 
- 200  200  200  200 
-  30  200  110  110 
-  40  200   40 -160 
-CG.GU..UA
- 200  200  200  200 
- 170  200  210  150 
- 140  200  130  -60 
- 180  200  170  160 
-CG.UA..UA
- 200  270   30  220 
- 200  180  -90   90 
- 200  180  -10  180 
- 200  200  200  200 
-CG.UC..UA
- 200  190  -80  100 
- 200  180    0   90 
- 200  200  200  200 
- 200  180    0   90 
-CG.UG..UA
- 200  180  -10  180 
- 200  200  200  200 
- 200  200  -40  150 
- 200   70 -310   60 
-CG.UU..UA
- 200  200  200  200 
- 200  180    0   90 
- 200  160 -210  160 
- 200  100    0   10 
-CG.AA..GU
- 200  240  100  200 
- 160  190   60  200 
- 100  130    0  200 
- 200  200  200  200 
-CG.AC..GU
- 200  240  100  200 
- 260  240  200  200 
- 200  200  200  200 
- 260  240  200  200 
-CG.AG..GU
- 100  130    0  200 
- 200  200  200  200 
- 140  170   40  200 
-  20  -40    0  200 
-CG.AU..GU
- 200  200  200  200 
- 230  210  170  200 
- 150   80  130  200 
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-UG.UU..AU
- 200  200  200  200 
- 200  250   70  160 
- 200  220 -160  220 
- 200  190   90  110 
-UG.AA..CG
- 210  210  110  200 
- 190  190   80  200 
-  10   10  -90  200 
- 200  200  200  200 
-UG.AC..CG
- 180  180   80  200 
- 250  190  180  200 
- 200  200  200  200 
- 150   90   90  200 
-UG.AG..CG
-  70   70  -30  200 
- 200  200  200  200 
- 180  180   70  200 
-   0 -100  -30  200 
-UG.AU..CG
- 200  200  200  200 
- 250  190  190  200 
-  40  -60   10  200 
- 210  110  190  200 
-UG.CA..CG
- 170  270  200  240 
- 140  190  200  160 
- -30  110  200   80 
- 200  200  200  200 
-UG.CC..CG
- 140  180  200  150 
- 140  190  200  160 
- 200  200  200  200 
-  40   90  200   60 
-UG.CG..CG
-  30  170  200  140 
- 200  200  200  200 
- 130  240  200  210 
--150    0  200  -30 
-UG.CU..CG
- 200  200  200  200 
- 150  190  200  160 
--110   40  200   10 
-  70  110  200   80 
-UG.GA..CG
- 110  200  150  160 
-  80  200  120   30 
- -90  200  -50   40 
- 200  200  200  200 
-UG.GC..CG
-  80  200  120   30 
- 180  200  180  130 
- 200  200  200  200 
-  90  200   80   40 
-UG.GG..CG
- -30  200   10  100 
- 200  200  200  200 
-  70  200  110  120 
- -30  200  -70 -260 
-UG.GU..CG
- 200  200  200  200 
- 190  200  190  140 
-  10  200  -30 -220 
- 190  200  150  140 
-UG.UA..CG
- 200  270   30  230 
- 200  190  -90  100 
- 200  110  -90  110 
- 200  200  200  200 
-UG.UC..CG
- 200  180 -100  100 
- 200  190   10  100 
- 200  200  200  200 
- 200   90  -90    0 
-UG.UG..CG
- 200  170  -30  170 
- 200  200  200  200 
- 200  240    0  190 
- 200    0 -390  -10 
-UG.UU..CG
- 200  200  200  200 
- 200  190   10  110 
- 200   40 -350   30 
- 200  110   10   30 
-UG.AA..GC
- 200  200  100  200 
- 190  190   90  200 
- 100  100    0  200 
- 200  200  200  200 
-UG.AC..GC
- 240  240  130  200 
- 280  220  220  200 
- 200  200  200  200 
- 270  210  200  200 
-UG.AG..GC
- 100  100    0  200 
- 200  200  200  200 
- 180  180   70  200 
-  30  -70   10  200 
-UG.AU..GC
- 200  200  200  200 
- 270  210  200  200 
- 180   80  160  200 
- 220  120  190  200 
-UG.CA..GC
- 160  260  200  230 
- 150  190  200  160 
-  60  200  200  170 
- 200  200  200  200 
-UG.CC..GC
- 190  240  200  210 
- 180  220  200  190 
- 200  200  200  200 
- 160  210  200  180 
-UG.CG..GC
-  60  200  200  170 
- 200  200  200  200 
- 130  240  200  210 
--110   30  200    0 
-UG.CU..GC
- 200  200  200  200 
- 160  210  200  180 
-  40  180  200  150 
-  70  120  200   90 
-UG.GA..GC
- 100  200  140  150 
-  90  200  130   40 
-   0  200   40  130 
- 200  200  200  200 
-UG.GC..GC
- 130  200  170   80 
- 220  200  220  170 
- 200  200  200  200 
- 200  200  200  150 
-UG.GG..GC
-   0  200   40  130 
- 200  200  200  200 
-  70  200  110  120 
-  10  200  -30 -220 
-UG.GU..GC
- 200  200  200  200 
- 200  200  200  150 
- 160  200  120  -70 
- 190  200  150  150 
-UG.UA..GC
- 200  260   20  220 
- 200  190  -90  110 
- 200  200    0  200 
- 200  200  200  200 
-UG.UC..GC
- 200  240  -40  150 
- 200  220   40  140 
- 200  200  200  200 
- 200  210   30  120 
-UG.UG..GC
- 200  200    0  200 
- 200  200  200  200 
- 200  240    0  190 
- 200   30 -350   30 
-UG.UU..GC
- 200  200  200  200 
- 200  210   30  120 
- 200  180 -200  180 
- 200  120   20   30 
-UG.AA..UA
- 280  280  170  200 
- 250  250  150  200 
- 150  150   50  200 
- 200  200  200  200 
-UG.AC..UA
- 260  260  160  200 
- 310  250  240  200 
- 200  200  200  200 
- 310  250  240  200 
-UG.AG..UA
- 150  150   50  200 
- 200  200  200  200 
- 210  210  100  200 
- 130   30  110  200 
-UG.AU..UA
- 200  200  200  200 
- 310  250  240  200 
- 230  130  210  200 
- 270  170  240  200 
-UG.CA..UA
- 230  340  200  310 
- 210  250  200  220 
- 110  250  200  220 
- 200  200  200  200 
-UG.CC..UA
- 220  260  200  230 
- 200  250  200  220 
- 200  200  200  200 
- 200  250  200  220 
-UG.CG..UA
- 110  250  200  220 
- 200  200  200  200 
- 160  270  200  240 
- -10  130  200  100 
-UG.CU..UA
- 200  200  200  200 
- 200  250  200  220 
-  90  230  200  200 
- 120  170  200  140 
-UG.GA..UA
- 170  200  210  220 
- 150  200  190  100 
-  50  200   90  180 
- 200  200  200  200 
-UG.GC..UA
- 160  200  200  110 
- 240  200  240  190 
- 200  200  200  200 
- 240  200  240  190 
-UG.GG..UA
-  50  200   90  180 
- 200  200  200  200 
- 100  200  140  150 
- 110  200   70 -120 
-UG.GU..UA
- 200  200  200  200 
- 240  200  240  190 
- 210  200  170  -20 
- 240  200  200  190 
-UG.UA..UA
- 200  340  100  290 
- 200  250  -30  170 
- 200  250   50  250 
- 200  200  200  200 
-UG.UC..UA
- 200  260  -20  180 
- 200  250   70  160 
- 200  200  200  200 
- 200  250   70  160 
-UG.UG..UA
- 200  250   50  250 
- 200  200  200  200 
- 200  270   30  220 
- 200  130 -250  130 
-UG.UU..UA
- 200  200  200  200 
- 200  250   70  160 
- 200  230 -150  230 
- 200  170   70   80 
-UG.AA..GU
- 280  280  170  200 
- 230  230  130  200 
- 170  170   70  200 
- 200  200  200  200 
-UG.AC..GU
- 280  280  170  200 
- 340  280  270  200 
- 200  200  200  200 
- 340  280  270  200 
-UG.AG..GU
- 170  170   70  200 
- 200  200  200  200 
- 210  210  110  200 
- 100    0   70  200 
-UG.AU..GU
- 200  200  200  200 
- 310  250  240  200 
- 220  120  200  200 
- 290  190  270  200 
-UG.CA..GU
- 230  340  200  310 
- 190  230  200  200 
- 130  270  200  240 
- 200  200  200  200 
-UG.CC..GU
- 230  280  200  250 
- 230  280  200  250 
- 200  200  200  200 
- 230  280  200  250 
-UG.CG..GU
- 130  270  200  240 
- 200  200  200  200 
- 170  270  200  240 
- -50  100  200   70 
-UG.CU..GU
- 200  200  200  200 
- 200  250  200  220 
-  80  220  200  190 
- 150  190  200  160 
-UG.GA..GU
- 170  200  210  220 
- 130  200  170   80 
-  70  200  110  200 
- 200  200  200  200 
-UG.GC..GU
- 170  200  210  120 
- 270  200  270  220 
- 200  200  200  200 
- 270  200  270  220 
-UG.GG..GU
-  70  200  110  200 
- 200  200  200  200 
- 110  200  150  160 
-  70  200   30 -160 
-UG.GU..GU
- 200  200  200  200 
- 240  200  240  190 
- 200  200  160  -30 
- 270  200  230  220 
-UG.UA..GU
- 200  340  100  290 
- 200  230  -50  150 
- 200  270   70  270 
- 200  200  200  200 
-UG.UC..GU
- 200  280    0  190 
- 200  280  100  190 
- 200  200  200  200 
- 200  280  100  190 
-UG.UG..GU
- 200  270   70  270 
- 200  200  200  200 
- 200  270   30  230 
- 200  100 -290   90 
-UG.UU..GU
- 200  200  200  200 
- 200  250   70  160 
- 200  220 -160  220 
- 200  190   90  110 
-UG.AA..UG
- 280  280  170  200 
- 250  250  150  200 
- 150  150   50  200 
- 200  200  200  200 
-UG.AC..UG
- 260  260  160  200 
- 310  250  240  200 
- 200  200  200  200 
- 310  250  240  200 
-UG.AG..UG
- 150  150   50  200 
- 200  200  200  200 
- 210  210  100  200 
- 130   30  110  200 
-UG.AU..UG
- 200  200  200  200 
- 310  250  240  200 
- 230  130  210  200 
- 270  170  240  200 
-UG.CA..UG
- 230  340  200  310 
- 210  250  200  220 
- 110  250  200  220 
- 200  200  200  200 
-UG.CC..UG
- 220  260  200  230 
- 200  250  200  220 
- 200  200  200  200 
- 200  250  200  220 
-UG.CG..UG
- 110  250  200  220 
- 200  200  200  200 
- 160  270  200  240 
- -10  130  200  100 
-UG.CU..UG
- 200  200  200  200 
- 200  250  200  220 
-  90  230  200  200 
- 120  170  200  140 
-UG.GA..UG
- 170  200  210  220 
- 150  200  190  100 
-  50  200   90  180 
- 200  200  200  200 
-UG.GC..UG
- 160  200  200  110 
- 240  200  240  190 
- 200  200  200  200 
- 240  200  240  190 
-UG.GG..UG
-  50  200   90  180 
- 200  200  200  200 
- 100  200  140  150 
- 110  200   70 -120 
-UG.GU..UG
- 200  200  200  200 
- 240  200  240  190 
- 210  200  170  -20 
- 240  200  200  190 
-UG.UA..UG
- 200  340  100  290 
- 200  250  -30  170 
- 200  250   50  250 
- 200  200  200  200 
-UG.UC..UG
- 200  260  -20  180 
- 200  250   70  160 
- 200  200  200  200 
- 200  250   70  160 
-UG.UG..UG
- 200  250   50  250 
- 200  200  200  200 
- 200  270   30  220 
- 200  130 -250  130 
-UG.UU..UG
- 200  200  200  200 
- 200  250   70  160 
- 200  230 -150  230 
- 200  170   70   80 
->Interior Loops 1x2
->CG.A..AU = 5'- C A   A -3'
->           3'- G Y X U -5'
->Rows: X = A C G U (X constant for a row)
->Columns: Y = A C G U (Y constant in column)
-AU.A..AU
- 390  370  310  550 
- 360  320  310  550 
- 250  210  190  550 
- 550  550  550  550 
-AU.C..AU
- 380  370  550  370 
- 370  400  550  370 
- 550  550  550  550 
- 400  340  550  370 
-AU.G..AU
- 320  550  230  550 
- 550  550  550  550 
- 230  550  370  550 
- 550  550  550  550 
-AU.U..AU
- 550  550  550  550 
- 550  370  550  280 
- 550  550  550  550 
- 550  320  550  270 
-AU.A..CG
- 320  300  240  480 
- 290  250  240  480 
- 180  140  120  480 
- 480  480  480  480 
-AU.C..CG
- 310  300  480  300 
- 300  330  480  300 
- 480  480  480  480 
- 330  270  480  300 
-AU.G..CG
- 250  480  160  480 
- 480  480  480  480 
- 160  480  300  480 
- 480  480  480  480 
-AU.U..CG
- 480  480  480  480 
- 480  300  480  210 
- 480  480  480  480 
- 480  250  480  200 
-AU.A..GC
- 320  300  240  480 
- 290  250  240  480 
- 180  140  120  480 
- 480  480  480  480 
-AU.C..GC
- 310  300  480  300 
- 300  330  480  300 
- 480  480  480  480 
- 330  270  480  300 
-AU.G..GC
- 250  480  160  480 
- 480  480  480  480 
- 160  480  300  480 
- 480  480  480  480 
-AU.U..GC
- 480  480  480  480 
- 480  300  480  210 
- 480  480  480  480 
- 480  250  480  200 
-AU.A..UA
- 390  370  310  550 
- 360  320  310  550 
- 250  210  190  550 
- 550  550  550  550 
-AU.C..UA
- 380  370  550  370 
- 370  400  550  370 
- 550  550  550  550 
- 400  340  550  370 
-AU.G..UA
- 320  550  230  550 
- 550  550  550  550 
- 230  550  370  550 
- 550  550  550  550 
-AU.U..UA
- 550  550  550  550 
- 550  370  550  280 
- 550  550  550  550 
- 550  320  550  270 
-AU.A..GU
- 390  370  310  550 
- 360  320  310  550 
- 250  210  190  550 
- 550  550  550  550 
-AU.C..GU
- 380  370  550  370 
- 370  400  550  370 
- 550  550  550  550 
- 400  340  550  370 
-AU.G..GU
- 320  550  230  550 
- 550  550  550  550 
- 230  550  370  550 
- 550  550  550  550 
-AU.U..GU
- 550  550  550  550 
- 550  370  550  280 
- 550  550  550  550 
- 550  320  550  270 
-AU.A..UG
- 390  370  310  550 
- 360  320  310  550 
- 250  210  190  550 
- 550  550  550  550 
-AU.C..UG
- 380  370  550  370 
- 370  400  550  370 
- 550  550  550  550 
- 400  340  550  370 
-AU.G..UG
- 320  550  230  550 
- 550  550  550  550 
- 230  550  370  550 
- 550  550  550  550 
-AU.U..UG
- 550  550  550  550 
- 550  370  550  280 
- 550  550  550  550 
- 550  320  550  270 
-CG.A..AU
- 320  300  240  480 
- 290  250  240  480 
- 180  140  120  480 
- 480  480  480  480 
-CG.C..AU
- 310  300  480  300 
- 300  330  480  300 
- 480  480  480  480 
- 330  270  480  300 
-CG.G..AU
- 250  480  160  480 
- 480  480  480  480 
- 160  480  300  480 
- 480  480  480  480 
-CG.U..AU
- 480  480  480  480 
- 480  300  480  210 
- 480  480  480  480 
- 480  250  480  200 
-CG.A..CG
- 230  220  110  400 
- 210  170  160  400 
-  80   60   40  400 
- 400  400  400  400 
-CG.C..CG
- 230  220  400  220 
- 220  250  400  220 
- 400  400  400  400 
- 250  190  400  220 
-CG.G..CG
- 170  400   80  400 
- 400  400  400  400 
-  80  400  220  400 
- 400  400  400  400 
-CG.U..CG
- 400  400  400  400 
- 400  220  400  150 
- 400  400  400  400 
- 400  170  400  120 
-CG.A..GC
- 240  220  160  400 
- 210  170  160  400 
- 100   60   40  400 
- 400  400  400  400 
-CG.C..GC
- 230  220  400  220 
- 220  250  400  220 
- 400  400  400  400 
- 250  190  400  220 
-CG.G..GC
- 170  400   80  400 
- 400  400  400  400 
-  80  400  220  400 
- 400  400  400  400 
-CG.U..GC
- 400  400  400  400 
- 400  220  400  130 
- 400  400  400  400 
- 400  170  400  120 
-CG.A..UA
- 320  300  240  480 
- 290  250  240  480 
- 180  140  120  480 
- 480  480  480  480 
-CG.C..UA
- 310  300  480  300 
- 300  330  480  300 
- 480  480  480  480 
- 330  270  480  300 
-CG.G..UA
- 250  480  160  480 
- 480  480  480  480 
- 160  480  300  480 
- 480  480  480  480 
-CG.U..UA
- 480  480  480  480 
- 480  300  480  210 
- 480  480  480  480 
- 480  250  480  200 
-CG.A..GU
- 320  300  240  480 
- 290  250  240  480 
- 180  140  120  480 
- 480  480  480  480 
-CG.C..GU
- 310  300  480  300 
- 300  330  480  300 
- 480  480  480  480 
- 330  270  480  300 
-CG.G..GU
- 250  480  160  480 
- 480  480  480  480 
- 160  480  300  480 
- 480  480  480  480 
-CG.U..GU
- 480  480  480  480 
- 480  300  480  210 
- 480  480  480  480 
- 480  250  480  200 
-CG.A..UG
- 320  300  240  480 
- 290  250  240  480 
- 180  140  120  480 
- 480  480  480  480 
-CG.C..UG
- 310  300  480  300 
- 300  330  480  300 
- 480  480  480  480 
- 330  270  480  300 
-CG.G..UG
- 250  480  160  480 
- 480  480  480  480 
- 160  480  300  480 
- 480  480  480  480 
-CG.U..UG
- 480  480  480  480 
- 480  300  480  210 
- 480  480  480  480 
- 480  250  480  200 
-GC.A..AU
- 320  300  240  480 
- 290  250  240  480 
- 180  140  120  480 
- 480  480  480  480 
-GC.C..AU
- 310  300  480  300 
- 300  330  480  300 
- 480  480  480  480 
- 330  270  480  300 
-GC.G..AU
- 250  480  160  480 
- 480  480  480  480 
- 160  480  300  480 
- 480  480  480  480 
-GC.U..AU
- 480  480  480  480 
- 480  300  480  210 
- 480  480  480  480 
- 480  250  480  200 
-GC.A..CG
- 240  220  160  400 
- 210  170  160  400 
- 100   60   40  400 
- 400  400  400  400 
-GC.C..CG
- 230  220  400  220 
- 220  250  400  220 
- 400  400  400  400 
- 250  190  400  220 
-GC.G..CG
- 170  400   80  400 
- 400  400  400  400 
-  80  400  220  400 
- 400  400  400  400 
-GC.U..CG
- 400  400  400  400 
- 400  220  400  130 
- 400  400  400  400 
- 400  170  400  120 
-GC.A..GC
- 250  220  210  400 
- 210  170  160  400 
- 120   60   40  400 
- 400  400  400  400 
-GC.C..GC
- 230  220  400  220 
- 220  250  400  220 
- 400  400  400  400 
- 250  190  400  220 
-GC.G..GC
- 170  400   80  400 
- 400  400  400  400 
-  80  400  220  400 
- 400  400  400  400 
-GC.U..GC
- 400  400  400  400 
- 400  220  400  120 
- 400  400  400  400 
- 400  170  400  120 
-GC.A..UA
- 320  300  240  480 
- 290  250  240  480 
- 180  140  120  480 
- 480  480  480  480 
-GC.C..UA
- 310  300  480  300 
- 300  330  480  300 
- 480  480  480  480 
- 330  270  480  300 
-GC.G..UA
- 250  480  160  480 
- 480  480  480  480 
- 160  480  300  480 
- 480  480  480  480 
-GC.U..UA
- 480  480  480  480 
- 480  300  480  210 
- 480  480  480  480 
- 480  250  480  200 
-GC.A..GU
- 320  300  240  480 
- 290  250  240  480 
- 180  140  120  480 
- 480  480  480  480 
-GC.C..GU
- 310  300  480  300 
- 300  330  480  300 
- 480  480  480  480 
- 330  270  480  300 
-GC.G..GU
- 250  480  160  480 
- 480  480  480  480 
- 160  480  300  480 
- 480  480  480  480 
-GC.U..GU
- 480  480  480  480 
- 480  300  480  210 
- 480  480  480  480 
- 480  250  480  200 
-GC.A..UG
- 320  300  240  480 
- 290  250  240  480 
- 180  140  120  480 
- 480  480  480  480 
-GC.C..UG
- 310  300  480  300 
- 300  330  480  300 
- 480  480  480  480 
- 330  270  480  300 
-GC.G..UG
- 250  480  160  480 
- 480  480  480  480 
- 160  480  300  480 
- 480  480  480  480 
-GC.U..UG
- 480  480  480  480 
- 480  300  480  210 
- 480  480  480  480 
- 480  250  480  200 
-UA.A..AU
- 390  370  310  550 
- 360  320  310  550 
- 250  210  190  550 
- 550  550  550  550 
-UA.C..AU
- 380  370  550  370 
- 370  400  550  370 
- 550  550  550  550 
- 400  340  550  370 
-UA.G..AU
- 320  550  230  550 
- 550  550  550  550 
- 230  550  370  550 
- 550  550  550  550 
-UA.U..AU
- 550  550  550  550 
- 550  370  550  280 
- 550  550  550  550 
- 550  320  550  270 
-UA.A..CG
- 320  300  240  480 
- 290  250  240  480 
- 180  140  120  480 
- 480  480  480  480 
-UA.C..CG
- 310  300  480  300 
- 300  330  480  300 
- 480  480  480  480 
- 330  270  480  300 
-UA.G..CG
- 250  480  160  480 
- 480  480  480  480 
- 160  480  300  480 
- 480  480  480  480 
-UA.U..CG
- 480  480  480  480 
- 480  300  480  210 
- 480  480  480  480 
- 480  250  480  200 
-UA.A..GC
- 320  300  240  480 
- 290  250  240  480 
- 180  140  120  480 
- 480  480  480  480 
-UA.C..GC
- 310  300  480  300 
- 300  330  480  300 
- 480  480  480  480 
- 330  270  480  300 
-UA.G..GC
- 250  480  160  480 
- 480  480  480  480 
- 160  480  300  480 
- 480  480  480  480 
-UA.U..GC
- 480  480  480  480 
- 480  300  480  210 
- 480  480  480  480 
- 480  250  480  200 
-UA.A..UA
- 390  370  310  550 
- 360  320  310  550 
- 250  210  190  550 
- 550  550  550  550 
-UA.C..UA
- 380  370  550  370 
- 370  400  550  370 
- 550  550  550  550 
- 400  340  550  370 
-UA.G..UA
- 320  550  230  550 
- 550  550  550  550 
- 230  550  370  550 
- 550  550  550  550 
-UA.U..UA
- 550  550  550  550 
- 550  370  550  280 
- 550  550  550  550 
- 550  320  550  270 
-UA.A..GU
- 390  370  310  550 
- 360  320  310  550 
- 250  210  190  550 
- 550  550  550  550 
-UA.C..GU
- 380  370  550  370 
- 370  400  550  370 
- 550  550  550  550 
- 400  340  550  370 
-UA.G..GU
- 320  550  230  550 
- 550  550  550  550 
- 230  550  370  550 
- 550  550  550  550 
-UA.U..GU
- 550  550  550  550 
- 550  370  550  280 
- 550  550  550  550 
- 550  320  550  270 
-UA.A..UG
- 390  370  310  550 
- 360  320  310  550 
- 250  210  190  550 
- 550  550  550  550 
-UA.C..UG
- 380  370  550  370 
- 370  400  550  370 
- 550  550  550  550 
- 400  340  550  370 
-UA.G..UG
- 320  550  230  550 
- 550  550  550  550 
- 230  550  370  550 
- 550  550  550  550 
-UA.U..UG
- 550  550  550  550 
- 550  370  550  280 
- 550  550  550  550 
- 550  320  550  270 
-GU.A..AU
- 390  370  310  550 
- 360  320  310  550 
- 250  210  190  550 
- 550  550  550  550 
-GU.C..AU
- 380  370  550  370 
- 370  400  550  370 
- 550  550  550  550 
- 400  340  550  370 
-GU.G..AU
- 320  550  230  550 
- 550  550  550  550 
- 230  550  370  550 
- 550  550  550  550 
-GU.U..AU
- 550  550  550  550 
- 550  370  550  280 
- 550  550  550  550 
- 550  320  550  270 
-GU.A..CG
- 320  300  240  480 
- 290  250  240  480 
- 180  140  120  480 
- 480  480  480  480 
-GU.C..CG
- 310  300  480  300 
- 300  330  480  300 
- 480  480  480  480 
- 330  270  480  300 
-GU.G..CG
- 250  480  160  480 
- 480  480  480  480 
- 160  480  300  480 
- 480  480  480  480 
-GU.U..CG
- 480  480  480  480 
- 480  300  480  210 
- 480  480  480  480 
- 480  250  480  200 
-GU.A..GC
- 320  300  240  480 
- 290  250  240  480 
- 180  140  120  480 
- 480  480  480  480 
-GU.C..GC
- 310  300  480  300 
- 300  330  480  300 
- 480  480  480  480 
- 330  270  480  300 
-GU.G..GC
- 250  480  160  480 
- 480  480  480  480 
- 160  480  300  480 
- 480  480  480  480 
-GU.U..GC
- 480  480  480  480 
- 480  300  480  210 
- 480  480  480  480 
- 480  250  480  200 
-GU.A..UA
- 390  370  310  550 
- 360  320  310  550 
- 250  210  190  550 
- 550  550  550  550 
-GU.C..UA
- 380  370  550  370 
- 370  400  550  370 
- 550  550  550  550 
- 400  340  550  370 
-GU.G..UA
- 320  550  230  550 
- 550  550  550  550 
- 230  550  370  550 
- 550  550  550  550 
-GU.U..UA
- 550  550  550  550 
- 550  370  550  280 
- 550  550  550  550 
- 550  320  550  270 
-GU.A..GU
- 390  370  310  550 
- 360  320  310  550 
- 250  210  190  550 
- 550  550  550  550 
-GU.C..GU
- 380  370  550  370 
- 370  400  550  370 
- 550  550  550  550 
- 400  340  550  370 
-GU.G..GU
- 320  550  230  550 
- 550  550  550  550 
- 230  550  370  550 
- 550  550  550  550 
-GU.U..GU
- 550  550  550  550 
- 550  370  550  280 
- 550  550  550  550 
- 550  320  550  270 
-GU.A..UG
- 390  370  310  550 
- 360  320  310  550 
- 250  210  190  550 
- 550  550  550  550 
-GU.C..UG
- 380  370  550  370 
- 370  400  550  370 
- 550  550  550  550 
- 400  340  550  370 
-GU.G..UG
- 320  550  230  550 
- 550  550  550  550 
- 230  550  370  550 
- 550  550  550  550 
-GU.U..UG
- 550  550  550  550 
- 550  370  550  280 
- 550  550  550  550 
- 550  320  550  270 
-UG.A..AU
- 390  370  310  550 
- 360  320  310  550 
- 250  210  190  550 
- 550  550  550  550 
-UG.C..AU
- 380  370  550  370 
- 370  400  550  370 
- 550  550  550  550 
- 400  340  550  370 
-UG.G..AU
- 320  550  230  550 
- 550  550  550  550 
- 230  550  370  550 
- 550  550  550  550 
-UG.U..AU
- 550  550  550  550 
- 550  370  550  280 
- 550  550  550  550 
- 550  320  550  270 
-UG.A..CG
- 320  300  240  480 
- 290  250  240  480 
- 180  140  120  480 
- 480  480  480  480 
-UG.C..CG
- 310  300  480  300 
- 300  330  480  300 
- 480  480  480  480 
- 330  270  480  300 
-UG.G..CG
- 250  480  160  480 
- 480  480  480  480 
- 160  480  300  480 
- 480  480  480  480 
-UG.U..CG
- 480  480  480  480 
- 480  300  480  210 
- 480  480  480  480 
- 480  250  480  200 
-UG.A..GC
- 320  300  240  480 
- 290  250  240  480 
- 180  140  120  480 
- 480  480  480  480 
-UG.C..GC
- 310  300  480  300 
- 300  330  480  300 
- 480  480  480  480 
- 330  270  480  300 
-UG.G..GC
- 250  480  160  480 
- 480  480  480  480 
- 160  480  300  480 
- 480  480  480  480 
-UG.U..GC
- 480  480  480  480 
- 480  300  480  210 
- 480  480  480  480 
- 480  250  480  200 
-UG.A..UA
- 390  370  310  550 
- 360  320  310  550 
- 250  210  190  550 
- 550  550  550  550 
-UG.C..UA
- 380  370  550  370 
- 370  400  550  370 
- 550  550  550  550 
- 400  340  550  370 
-UG.G..UA
- 320  550  230  550 
- 550  550  550  550 
- 230  550  370  550 
- 550  550  550  550 
-UG.U..UA
- 550  550  550  550 
- 550  370  550  280 
- 550  550  550  550 
- 550  320  550  270 
-UG.A..GU
- 390  370  310  550 
- 360  320  310  550 
- 250  210  190  550 
- 550  550  550  550 
-UG.C..GU
- 380  370  550  370 
- 370  400  550  370 
- 550  550  550  550 
- 400  340  550  370 
-UG.G..GU
- 320  550  230  550 
- 550  550  550  550 
- 230  550  370  550 
- 550  550  550  550 
-UG.U..GU
- 550  550  550  550 
- 550  370  550  280 
- 550  550  550  550 
- 550  320  550  270 
-UG.A..UG
- 390  370  310  550 
- 360  320  310  550 
- 250  210  190  550 
- 550  550  550  550 
-UG.C..UG
- 380  370  550  370 
- 370  400  550  370 
- 550  550  550  550 
- 400  340  550  370 
-UG.G..UG
- 320  550  230  550 
- 550  550  550  550 
- 230  550  370  550 
- 550  550  550  550 
-UG.U..UG
- 550  550  550  550 
- 550  370  550  280 
- 550  550  550  550 
- 550  320  550  270 
->POLYC - Penalty for poly C hairpins.
->First number is penalty for polyC triloop
->Second number is "slope", i.e. penalty per C in a non triloop
->Third number is "intercept", i.e. constant penalty for non triloops
- 140   30  160
->BETA - Pseudoknot energy parameters.  B1 B2 B3 B1M B1P
->B1 = Constant penalty for "open" pseudoknot
->B2 = penalty per pair in pseudoknot
->B3 = penalty per base in pseudoknot
->B1M = constant penalty for pseudoknot in a "closed" loop
->B1P = constant penalty for pseudoknot in a pseudoknot.
- 960   10   10 1500 1500
->BIMOLECULAR //TINOCO, 277
- 409

-#!/usr/bin/python3
-
-# This script's purpose is to extract information about the CaRNAval
-# RINS from a Python pickle object containing RINs from their RIN.py class.
-# We do this because the official JSON file is hard to understand, and Antoine Soulé
-# recommended the pickle.
-
-import networkx, os, pickle, subprocess, sys
-
-if __name__=="__main__":
-
-    
-    rin_DIR = os.getcwd() + "/../data/modules/RIN/"
-    filename = "CaRNAval_1_as_dictionnary.nxpickled"
-
-    # Check that we can find CaRNAval RINs, and load the dataset
-    try:
-        sys.path.append(os.path.abspath(rin_DIR))
-        import RIN
-    except ImportError:
-        # We have to download it
-        subprocess.run(["wget", '-O', '../data/modules/carnaval_dataset.zip', "http://carnaval.lri.fr/carnaval_dataset.zip"])
-        subprocess.run(["unzip", '-ou', '../data/modules/carnaval_dataset.zip', "carnaval_dataset/CaRNAval_1_as_dictionnary.nxpickled", "carnaval_dataset/RIN.py"])
-        subprocess.run(["rm", "-f", "../data/modules/RIN/", "../data/modules/carnaval_dataset.zip"])
-        subprocess.run(["mv", "carnaval_dataset/", "../data/modules/RIN/"])
-        sys.path.append(os.path.abspath(rin_DIR))
-        import RIN
-
-    try:
-        objects = []
-        with (open(rin_DIR+filename, "rb")) as openfile:
-            while True:
-                try:
-                    objects.append(pickle.load(openfile))
-                except EOFError:
-                    break
-        print("Dataset loaded")
-    except OSError:
-        print("File not found : " + rin_DIR + filename)
-        exit(1)
-
-    # Creation of a directory to extract RINs from the pickle file to individual files
-    try:
-        os.makedirs(rin_DIR + "Subfiles", exist_ok=True)
-    except OSError:
-        print("Creation of the directory %s failed" % (rin_DIR + "Subfiles"))
-        exit(1)
-
-    # Loop on every CaRNAval module and extract it from the Python object to flat text file
-    n_modules = len(objects[0]) # ? to
-    for i in range(1,1+n_modules):
-        motif = objects[0][i].graph
-        f = open(rin_DIR + "Subfiles/" + str(i-1) + ".txt", "w+")
-        f.write("ntA,ntB,long_range;...\n")
-
-        components = []
-        comp = []
-        nodes = list(motif)
-        nodes.sort()
-        for node in nodes:
-            if comp == []:
-                comp.append(node)
-            else:
-                if comp[-1] + 1 != node : #not the same component
-                    components.append(comp)
-                    comp = []
-                    comp.append(node)
-                else :
-                    comp.append(node)
-        components.append(comp)
-
-        #print(nodes)
-
-        basepairs = ""
-        edges = list(motif.edges())
-        for a in edges:
-            if motif.edges[a]['label'] == 'CWW' :
-                ntA = nodes.index(a[0])
-                ntB = nodes.index(a[1])
-
-                if ntA <= ntB :
-                    basepairs += str(ntA) + "," + str(ntB) + "," + str(motif.edges[a]['long_range']) + ";"
-
-        f.write(basepairs + "\n")
-        f.write("pos;k;seq\n")
-
-        num_nt = -1
-        for a in components:
-            seq = ""
-            data_comp = str(num_nt+1)
-            for b in a:
-                num_nt += 1
-
-                # sometimes in the nxpicled file, a node has the attribute "realnt", 
-                # and sometimes "real_nt", but it's the same thing
-                try:
-                    seq += motif.nodes[b]["realnt"]
-                except:
-                    seq += motif.nodes[b]["real_nt"]
-            data_comp += "," + str(num_nt) + ";" + str(len(a)) + ";" + seq + "\n"
-            f.write(data_comp)
-
-        f.close()
-        # print(str(i-1) + ".txt created")
-
-    print("Successfully parsed "+filename, ", now individual RINs are saved in Subfiles/ folder.", sep='')
-

-# ============================ IMPORTS ====================================
-import subprocess
-import time
-import resource
-
-# take a RNA sequence and cut it from 100 bases to actual length
-# then measure computation time, peak memory, and number of solutions for each length
-
-# This RNA is actually a 16S rRNA from PDB 1J5E.
-# http://ndbserver.rutgers.edu/service/ndb/atlas/summary
-seq = "UUUGUUGGAGAGUUUGAUCCUGGCUCAGGGUGAACGCUGGCGGCGUGCCUAAGACAUGCAAGUCGUGCGGGCCGCGGGGUUUUACUCCGUGGUCAGCGGCGGACGGGUGAGUAACGCGUGGGUGACCUACCCGGAAGAGGGGGACAACCCGGGGAAACUCGGGCUAAUCCCCCAUGUGGACCCGCCCCUUGGGGUGUGUCCAAAGGGCUUUGCCCGCUUCCGGAUGGGCCCGCGUCCCAUCAGCUAGUUGGUGGGGUAAUGGCCCACCAAGGCGACGACGGGUAGCCGGUCUGAGAGGAUGGCCGGCCACAGGGGCACUGAGACACGGGCCCCACUCCUACGGGAGGCAGCAGUUAGGAAUCUUCCGCAAUGGGCGCAAGCCUGACGGAGCGACGCCGCUUGGAGGAAGAAGCCCUUCGGGGUGUAAACUCCUGAACCCGGGACGAAACCCCCGACGAGGGGACUGACGGUACCGGGGUAAUAGCGCCGGCCAACUCCGUGCCAGCAGCCGCGGUAAUACGGAGGGCGCGAGCGUUACCCGGAUUCACUGGGCGUAAAGGGCGUGUAGGCGGCCUGGGGCGUCCCAUGUGAAAGACCACGGCUCAACCGUGGGGGAGCGUGGGAUACGCUCAGGCUAGACGGUGGGAGAGGGUGGUGGAAUUCCCGGAGUAGCGGUGAAAUGCGCAGAUACCGGGAGGAACGCCGAUGGCGAAGGCAGCCACCUGGUCCACCCGUGACGCUGAGGCGCGAAAGCGUGGGGAGCAAACCGGAUUAGAUACCCGGGUAGUCCACGCCCUAAACGAUGCGCGCUAGGUCUCUGGGUCUCCUGGGGGCCGAAGCUAACGCGUUAAGCGCGCCGCCUGGGGAGUACGGCCGCAAGGCUGAAACUCAAAGGAAUUGACGGGGGCCCGCACAAGCGGUGGAGCAUGUGGUUUAAUUCGAAGCAACGCGAAGAACCUUACCAGGCCUUGACAUGCUAGGGAACCCGGGUGAAAGCCUGGGGUGCCCCGCGAGGGGAGCCCUAGCACAGGUGCUGCAUGGCCGUCGUCAGCUCGUGCCGUGAGGUGUUGGGUUAAGUCCCGCAACGAGCGCAACCCCCGCCGUUAGUUGCCAGCGGUUCGGCCGGGCACUCUAACGGGACUGCCCGCGAAAGCGGGAGGAAGGAGGGGACGACGUCUGGUCAGCAUGGCCCUUACGGCCUGGGCGACACACGUGCUACAAUGCCCACUACAAAGCGAUGCCACCCGGCAACGGGGAGCUAAUCGCAAAAAGGUGGGCCCAGUUCGGAUUGGGGUCUGCAACCCGACCCCAUGAAGCCGGAAUCGCUAGUAAUCGCGGAUCAGCCAUGCCGCGGUGAAUACGUUCCCGGGCCUUGUACACACCGCCCGUCACGCCAUGGGAGCGGGCUCUACCCGAAGUCGCCGGGAGCCUACGGGCAGGCGCCGAGGGUAGGGCCCGUGACUGGGGCGAAGUCGUAACAAGGUAGCUGUACCGGAAGGUGCGGCUGGAUCACCUCCUUUCU"
-
-step = 100
-n = len(seq)
-
-while step < len(seq)+50:
-	sub_seq = seq[0:(min(step,n))]
-
-	# write the sequence to file
-	fasta = open("data/fasta/ZDFS33.fa", 'w')
-	fasta.write(">__'ZDFS33 : 0-" + str(len(sub_seq)) + "'\n" + sub_seq)
-	fasta.close()
-
-	# run biorseo on it, with default options
-	cmd = ["./bin/biorseo", "-d", "./data/modules/DESC", "-s", "./ZDFS33.fa", "-v"]
-	old_time = time.time()
-	output = subprocess.check_output(cmd, stderr=subprocess.DEVNULL).decode("utf-8").split("\n")[-5:]
-	run_time = time.time() - old_time
-	max_ram = resource.getrusage(resource.RUSAGE_CHILDREN).ru_maxrss
-
-	for line in output :
-		if "Quitting because combinatorial issues" in line :
-			nb_sol = -1
-		elif "solutions kept" in line :
-			nb_sol = line.split(",")[1].split()[0]
-
-	print(len(sub_seq), "first nucleotides :", nb_sol, "solutions in", run_time, "seconds, using", max_ram, "kb of RAM")
-
-	step += 50

-#!/bin/bash
-
-echo "WARNING: The purpose of this file is to document how the docker image was built.";
-echo "You cannot execute it directly, because of licensing reasons. Please get your own:";
-echo "- CPLEX academic version: cplex_installer_12.8_Student.bin";
-echo "- Nupack header files: nupack_3.2.2.tar.gz";
-exit 0;
-
-cd ../
-THISDIR="$( cd "$( dirname "${BASH_SOURCE[0]}" )" >/dev/null 2>&1 && pwd )"
-
-####################################################### Dependencies ##############################################################
-sudo apt install -y clang-7 cmake make automake libboost-program-options-dev libboost-filesystem-dev openjdk-11-jre
-sudo update-alternatives --install /usr/bin/clang++ clang++ /usr/bin/clang++-7 100
-sudo update-alternatives --install /usr/bin/clang clang /usr/bin/clang-7 100
-
-# CPLEX: only to build biorseo
-# HERE YOU SHOULD GET YOUR OWN cplex_installer_12.8_Student.bin ! I am not allowed to share mine anymore.
-chmod +x cplex_installer_12.8_Student.bin
-printf "4\n\n1\n\n\n\n\n" | sudo ./cplex_installer_12.8_Student.bin
-rm cplex_installer_12.8_Student.bin
-
-# Eigen: only to build biorseo (no need to give it to the docker image)
-wget http://bitbucket.org/eigen/eigen/get/3.3.7.tar.gz -O eigen_src.tar.gz
-tar -xf eigen_src.tar.gz
-cd eigen-eigen-323c052e1731
-mkdir build
-cd build
-cmake ..
-sudo make install
-cd ../..
-rm -rf eigen_src.tar.gz eigen-eigen-323c052e1731
-
-# Nupack: only to build biorseo (no need to give it to the docker image)
-#curl -u yourname@yourUni.com:yourPassword http://www.nupack.org/downloads/serve_file/nupack3.2.2.tar.gz --output nupack3.2.2.tar.gz
-tar -xf nupack3.2.2.tar.gz
-cd nupack3.2.2
-mkdir build
-cd build
-cmake ..
-make -j8
-sudo make install
-cd ../..
-sudo cp nupack3.2.2/src/thermo/*.h /usr/local/include/nupack/thermo/
-rm -rf nupack3.2.2.tar.gz nupack3.2.2/
-
-# BayesPairing: install on the docker image (done by the Dockerfile)
-git clone http://jwgitlab.cs.mcgill.ca/sarrazin/rnabayespairing.git BayesPairing
-
-######################################################### Build Biorseo ###########################################################
-# build here, install later on the docker image (done by the Dockerfile)
-mkdir -p results
-make -j 8
-make clean
-rm -rf doc/ obj/
-
-######################################################## Build Docker container ##################################################
-# Execute the Dockerfile and build the image
-docker build . -t biorseo

-
-#!/bin/bash
-######################################################## RNA modules ##############################################################
-
-cd ../
-
-# Rna3Dmotifs data
-mkdir -p data/modules/DESC
-wget https://github.com/McGill-CSB/RNAMoIP/raw/master/CATALOGUE.tgz
-tar -xvzf CATALOGUE.tgz 
-mv No_Redondance_DESC/*.desc data/modules/DESC/
-rm -r No_Redondance_VIEW3D No_Redondance_DESC CATALOGUE.tgz
-
-# The RNA 3D Motif Atlas
-mkdir -p data/modules/BGSU
-wget http://rna.bgsu.edu/data/jar3d/models/HL/HL_3.2_models.zip
-unzip HL_3.2_models.zip
-mv HL data/modules/BGSU
-rm HL_3.2_models.zip
-wget http://rna.bgsu.edu/data/jar3d/models/IL/IL_3.2_models.zip
-unzip IL_3.2_models.zip
-mv IL data/modules/BGSU
-rm IL_3.2_models.zip
-
-# Install BayesPairing
-sudo -H pip3 install --upgrade pip
-sudo -H pip3 install networkx numpy regex wrapt biopython
-git clone http://jwgitlab.cs.mcgill.ca/sarrazin/rnabayespairing.git BayesPairing
-cd BayesPairing
-sudo -H pip3 install .
-
-# Train Bayes Pairing (it has been installed on the image and the source has been deleted, we train the models now, and will remount it as volume at run time)
-cd bayespairing/src
-python3 parse_sequences.py -d rna3dmotif -seq ACACGGGGUAAGAGCUGAACGCAUCUAAGCUCGAAACCCACUUGGAAAAGAGACACCGCCGAGGUCCCGCGUACAAGACGCGGUCGAUAGACUCGGGGUGUGCGCGUCGAGGUAACGAGACGUUAAGCCCACGAGCACUAACAGACCAAAGCCAUCAU -ss ".................................................................((...............)xxxx(...................................................)xxx).............."
-python3 parse_sequences.py -d 3dmotifatlas -seq ACACGGGGUAAGAGCUGAACGCAUCUAAGCUCGAAACCCACUUGGAAAAGAGACACCGCCGAGGUCCCGCGUACAAGACGCGGUCGAUAGACUCGGGGUGUGCGCGUCGAGGUAACGAGACGUUAAGCCCACGAGCACUAACAGACCAAAGCCAUCAU -ss ".................................................................((...............)xxxx(...................................................)xxx).............."
-cd ../../..
-
-######################################################## Run it ##############################################################
-
-# docker run -v `pwd`/data/modules:/modules -v `pwd`/BayesPairing/bayespairing:/byp -v `pwd`/results:/biorseo/results biorseo ./biorseo.py -i /biorseo/data/fasta/applications.fa --rna3dmotifs --patternmatch --func B
\ No newline at end of file

-#!/usr/bin/python3
-# Created by Louis Becquey, louis.becquey@univ-evry.fr, Oct 2019
-# This script processes files containing RNA structures obtained from bi-objective
-# optimization programs, and a dot-bracket database of reference structures, to plot
-# where are the best solutions in the Pareto set.
-#
-# The result files should follow this kind of format:
-# for Biokop: (option --biokop)
-# Structure        Free energy score       Expected accuracy score
-# (((...(((...)))))) <tab> obj1_value <tab> obj2_value
-# (((............))) <tab> obj1_value <tab> obj2_value
-# ((((((...)))...))) <tab> obj1_value <tab> obj2_value
-# ...
-#
-# for BiORSEO: (options --biorseo_**stuff**)
-# >Header of the sequence
-# GGCACAGAGUUAUGUGCC
-# (((...(((...)))))) + Motif1 + Motif2 <tab> obj1_value <tab> obj2_value
-# (((............))) <tab> obj1_value <tab> obj2_value
-# ((((((...)))...))) + Motif1 <tab> obj1_value <tab> obj2_value
-#
-# typical Biokop usage:
-# python3 pareto_visualizer.py --biokop --folder path/to/your/results/folder --database path/to/the/database_file.dbn
-# typical Biorseo usage:
-# python3 pareto_visualizer.py --folder path/to/your/results/folder --database path/to/the/database_file.dbn
-#
-
-from math import sqrt
-import numpy as np
-import matplotlib.pyplot as plt
-from matplotlib import cm
-import scipy.stats as st
-import sys
-import os
-import subprocess
-import getopt
-
-
-class SecStruct:
-    def __init__(self, dot_bracket, obj1_value, obj2_value):
-        self.dbn = dot_bracket
-        self.objectives = [obj1_value, obj2_value]
-        self.basepair_list = self.get_basepairs()
-        self.length = len(dot_bracket)
-
-    def get_basepairs(self):
-        parenthesis = []
-        brackets = []
-        braces = []
-        rafters = []
-        basepairs = []
-        As = []
-        Bs = []
-        for i, c in enumerate(self.dbn):
-            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 get_MCC_with(self, reference_structure):
-        # Get true and false positives and negatives
-        tp = 0
-        fp = 0
-        tn = 0
-        fn = 0
-        for bp in reference_structure.basepair_list:
-            if bp in self.basepair_list:
-                tp += 1
-            else:
-                fn += 1
-        for bp in self.basepair_list:
-            if bp not in reference_structure.basepair_list:
-                fp += 1
-        tn = reference_structure.length * (reference_structure.length - 1) * 0.5 - fp - fn - tp
-
-        # Compute MCC
-        if (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))
-
-
-class Pareto:
-    def __init__(self, list_of_structs, reference):
-        self.predictions = list_of_structs
-        self.true_structure = reference
-        self.n_pred = len(list_of_structs)
-        self.max_obj1 = max([s.objectives[0] for s in self.predictions])
-        self.max_obj2 = max([s.objectives[1] for s in self.predictions])
-        self.index_of_best = self.find_best_solution()
-
-    def find_best_solution(self):
-        # returns the index of the solution of the Pareto set which is the closest
-        # to the real 2D structure (the one with the max MCC)
-        max_i = -1
-        max_mcc = -1
-        for i, s in enumerate(self.predictions):
-            mcc = s.get_MCC_with(self.true_structure)
-            if mcc > max_mcc:
-                max_mcc = mcc
-                max_i = i
-        return max_i
-
-    def get_normalized_coords(self):
-        # retrieves the objective values of the best solution and normlizes them
-        coords = self.predictions[self.index_of_best].objectives
-        if self.max_obj1:  # avoid divide by zero if all solutions are 0
-            x = coords[0] / self.max_obj1
-        else:
-            x = 0.5
-        if self.max_obj2:  # avoid divide by zero if all solutions are 0
-            y = coords[1] / self.max_obj2
-        else:
-            y = 0.5
-        return (x, y)
-
-
-class RNA:
-    def __init__(self, filename, header, seq, struct):
-        self.seq_ = seq
-        self.header_ = header
-        self.struct_ = struct
-        self.basename_ = filename
-
-
-ignored_nt_dict = {}
-
-
-def is_canonical_nts(seq):
-    for c in seq[:-1]:
-        if c not in "ACGU":
-            if c in ignored_nt_dict.keys():
-                ignored_nt_dict[c] += 1
-            else:
-                ignored_nt_dict[c] = 1
-            return False
-    return True
-
-
-def is_canonical_bps(struct):
-    if "()" in struct:
-        return False
-    if "(.)" in struct:
-        return False
-    if "(..)" in struct:
-        return False
-    if "[]" in struct:
-        return False
-    if "[.]" in struct:
-        return False
-    if "[..]" in struct:
-        return False
-    return True
-
-def load_from_dbn(file, header_style=3):
-    container = []
-    pkcounter = 0
-
-    db = open(file, "r")
-    c = 0
-    header = ""
-    seq = ""
-    struct = ""
-    while True:
-        l = db.readline()
-        if l == "":
-            break
-        c += 1
-        c = c % 3
-        if c == 1:
-            header = l[:-1]
-        if c == 2:
-            seq = l[:-1].upper()
-        if c == 0:
-            struct = l[:-1]
-            n = len(seq)
-
-            if n < 10 or n > 100:
-                continue  # ignore too short and too long RNAs
-            if is_canonical_nts(seq) and is_canonical_bps(struct) and '(' in struct:
-                if header_style == 1: container.append(RNA(header.replace('/', '_').split('(')[-1][:-1], header, seq, struct))
-                if header_style == 2: container.append(RNA(header.replace('/', '_').split('[')[-1][:-41], header, seq, struct))
-                if header_style == 3: container.append(RNA(header[1:], header, seq, struct))
-                if '[' in struct: pkcounter += 1
-    db.close()
-    return container, pkcounter
-
-
-def parse_biokop(folder, basename, ext=".biok"):
-    solutions = []
-    err = 0
-    if os.path.isfile(os.path.join(folder, basename + ext)):
-        rna = open(os.path.join(folder, basename + ext), "r")
-        lines = rna.readlines()
-        rna.close()
-        different_2ds = []
-        for s in lines[1:]:
-            if s == '\n':
-                continue
-            splitted = s.split('\t')
-            db2d = splitted[0]
-            if db2d not in different_2ds:
-                different_2ds.append(db2d)
-            # here is a negative sign because Biokop actually minimizes -MEA instead
-            # of maximizing MEA : we switch back to MEA
-            solutions.append(SecStruct(db2d, -float(splitted[2][:-1]), -float(splitted[1]))) # MEA first, MFE second
-
-        # check the range of MEA in this pareto set
-        min_mea = solutions[0].objectives[0]
-        max_mea = min_mea
-        for s in solutions:
-            mea = s.objectives[0]
-            if mea < min_mea:
-                min_mea = mea
-            if mea > max_mea:
-                max_mea = mea
-
-        # normalize so the minimum MEA of the set is 0
-        for i in range(len(solutions)):
-            solutions[i].objectives[0] -= min_mea
-
-        if len(different_2ds) > 1:
-            return solutions, err
-        else:
-            print("[%s] \033[36mWARNING: ignoring this RNA, only one 2D solution is found.\033[0m" % (basename))
-            err = 1
-    return None, err
-
-
-def parse_biorseo(folder, basename, ext):
-    solutions = []
-    err = 0
-    if os.path.isfile(os.path.join(folder, basename + ext)):
-        rna = open(os.path.join(folder, basename + ext), "r")
-        lines = rna.readlines()
-        rna.close()
-        different_2ds = []
-        for s in lines[2:]:
-            if s == '\n':
-                continue
-            splitted = s.split('\t')
-            db2d = splitted[0].split(' ')[0]
-            if db2d not in different_2ds:
-                different_2ds.append(db2d)
-            solutions.append(SecStruct(db2d, float(splitted[2][:-1]), float(splitted[1]))) # put MEA first, modules in 2nd (y axis)
-        if len(different_2ds) > 1:
-            return solutions, err
-        else:
-            print("[%s] \033[36mWARNING: ignoring this RNA, only one 2D solution is found.\033[0m" % (basename))
-            err = 1
-    return None, err
-
-
-def prettify_biorseo(code):
-    name = ""
-    if "bgsu" in code:
-        name += "RNA 3D Motif Atlas + "
-    elif "rin" in code:
-        name += "CaRNAval + "
-    else:
-        name += "Rna3Dmotifs + "
-    if "raw" in code:
-        name += "Direct P.M."
-    if "byp" in code:
-        name += "BPairing"
-    if "jar3d" in code:
-        name += "Jar3d"
-    # name += " + $f_{1" + code[-1] + "}$"
-    return name
-
-def process_extension(ax, pos, ext, nsolutions=False, xlabel="Best solution performs\nwell on obj1", ylabel="Best solution performs\n well on obj2"):
-    points = []
-    sizes = []
-    skipped = 0
-    for rna in RNAcontainer:
-        # Extracting the predictions from the results file
-        solutions, err = parse(results_folder, rna.basename_, ext)
-        if solutions is None:
-            if err == 0:
-                skipped += 1
-            continue
-        reference = SecStruct(rna.struct_, float("inf"), float("inf"))
-        pset = Pareto(solutions, reference)
-        points.append(pset.get_normalized_coords())
-        sizes.append(pset.n_pred)
-        print("[%s] Loaded %d solutions in a Pareto set, max(obj1)=%f, max(obj2)=%f" % (rna.basename_, pset.n_pred, pset.max_obj1, pset.max_obj2))
-    print("Loaded %d points on %d." % (len(points), len(RNAcontainer)-skipped))
-
-    x = np.array([p[0] for p in points])
-    y = np.array([p[1] for p in points])
-    xmin, xmax = 0, 1
-    ymin, ymax = 0, 1
-    xx, yy = np.mgrid[xmin:xmax:100j, ymin:ymax:100j]
-    positions = np.vstack([xx.ravel(), yy.ravel()])
-    values = np.vstack([x, y])
-    kernel = st.gaussian_kde(values)
-    f = np.reshape(kernel(positions).T, xx.shape)
-    ax[pos].axhline(y=0, alpha=0.2, color='black')
-    ax[pos].axhline(y=1, alpha=0.2, color='black')
-    ax[pos].axvline(x=0, alpha=0.2, color='black')
-    ax[pos].axvline(x=1, alpha=0.2, color='black')
-    ax[pos].contourf(xx, yy, f, cmap=cm.Blues, alpha=0.5)
-    ax[pos].scatter(x, y, s=25, alpha=0.1)
-    ax[pos].set_xlim((-0.1, 1.1))
-    ax[pos].set_ylim((-0.1, 1.1))
-    ax[pos].set_title(prettify_biorseo(ext[1:]), fontsize=10)
-    ax[pos].annotate("(" + str(len(points)) + '/' + str(len(RNAcontainer)-skipped) + " RNAs)", (0.08, 0.15))
-    ax[pos].set_xlabel(xlabel)
-    ax[pos].set_ylabel(ylabel)
-
-    if nsolutions:
-        ax[pos + 1].hist(sizes, bins=range(0, max(sizes) + 1, 2), histtype='bar')
-        ax[pos + 1].set_xlim((0, max(sizes) + 2))
-        ax[pos + 1].set_xticks(range(0, max(sizes), 10))
-        ax[pos + 1].set_xticklabels(range(0, max(sizes), 10), rotation=90)
-        ax[pos + 1].set_xlabel("# solutions")
-        ax[pos + 1].set_ylabel("# RNAs")
-
-
-if __name__ == "__main__":
-    try:
-        opts, args = getopt.getopt( sys.argv[1:], "", 
-                                [  "biorseo_desc_byp_A", "biorseo_desc_byp_B",
-                                    "biorseo_desc_byp_C", "biorseo_desc_byp_D",
-                                    "biorseo_bgsu_byp_A", "biorseo_bgsu_byp_B",
-                                    "biorseo_bgsu_byp_C", "biorseo_bgsu_byp_D",
-                                    "biorseo_desc_raw_A", "biorseo_desc_raw_B",
-                                    "biorseo_bgsu_jar3d_A", "biorseo_bgsu_jar3d_B",
-                                    "biorseo_bgsu_jar3d_C", "biorseo_bgsu_jar3d_D",
-                                    "biorseo_rin_raw_A", "biorseo_rin_raw_B",
-                                    "biokop", "folder=", "database=", "output="
-                                ])
-    except getopt.GetoptError as err:
-        print(err)
-        sys.exit(2)
-
-    results_folder = "."
-    extension = "all"
-    outputf = ""
-    for opt, arg in opts:
-        if opt == "--biokop":
-            extension = ".biok"
-            parse = parse_biokop
-        elif opt == "--folder":
-            results_folder = arg
-        elif opt == "--database":
-            database = arg
-        elif opt == "--output":
-            outputf = arg
-        else:
-            extension = '.' + opt[2:]
-            parse = parse_biorseo
-
-    RNAcontainer, _ = load_from_dbn(database)
-
-    if results_folder[-1] != '/':
-        results_folder = results_folder + '/'
-    if outputf == "":
-        outputf = results_folder
-    if outputf[-1] != '/':
-        outputf = outputf + '/'
-
-    if extension == "all":
-        parse = parse_biorseo
-        fig, ax = plt.subplots(4,5,figsize=(12,10), sharex=True, sharey=True)
-        ax = ax.flatten()
-        process_extension(ax, 0, ".biorseo_desc_raw_A",     ylabel="Normalized $f_{1A}$", xlabel="Normalized MEA")
-        process_extension(ax, 1, ".biorseo_rin_raw_A",      ylabel="Normalized $f_{1A}$", xlabel="Normalized MEA")
-        process_extension(ax, 2, ".biorseo_desc_byp_A",     ylabel="Normalized $f_{1A}$", xlabel="Normalized MEA")
-        process_extension(ax, 3, ".biorseo_bgsu_byp_A",     ylabel="Normalized $f_{1A}$", xlabel="Normalized MEA")
-        process_extension(ax, 4, ".biorseo_bgsu_jar3d_A",   ylabel="Normalized $f_{1A}$", xlabel="Normalized MEA")
-        ax[0].set_title(prettify_biorseo("biorseo_desc_raw_A"), fontsize=10)
-        ax[1].set_title(prettify_biorseo("biorseo_rin_raw_A"), fontsize=10)
-        ax[2].set_title(prettify_biorseo("biorseo_desc_byp_A"), fontsize=10)
-        ax[3].set_title(prettify_biorseo("biorseo_bgsu_byp_A"), fontsize=10)
-        ax[4].set_title(prettify_biorseo("biorseo_bgsu_jar3d_A"), fontsize=10)
-
-        process_extension(ax, 5, ".biorseo_desc_raw_B",     ylabel="Normalized $f_{1B}$", xlabel="Normalized MEA")
-        process_extension(ax, 6, ".biorseo_rin_raw_B",      ylabel="Normalized $f_{1B}$", xlabel="Normalized MEA")
-        process_extension(ax, 7, ".biorseo_desc_byp_B",     ylabel="Normalized $f_{1B}$", xlabel="Normalized MEA")
-        process_extension(ax, 8, ".biorseo_bgsu_byp_B",     ylabel="Normalized $f_{1B}$", xlabel="Normalized MEA")
-        process_extension(ax, 9, ".biorseo_bgsu_jar3d_B",   ylabel="Normalized $f_{1B}$", xlabel="Normalized MEA")
-
-        process_extension(ax, 12, ".biorseo_desc_byp_C",   ylabel="Normalized $f_{1C}$", xlabel="Normalized MEA")
-        process_extension(ax, 13, ".biorseo_bgsu_byp_C",   ylabel="Normalized $f_{1C}$", xlabel="Normalized MEA")
-        process_extension(ax, 14, ".biorseo_bgsu_jar3d_C", ylabel="Normalized $f_{1C}$", xlabel="Normalized MEA")
-        ax[10].axis("off")
-        ax[11].axis("off")
-
-        process_extension(ax, 17, ".biorseo_desc_byp_D",   ylabel="Normalized $f_{1D}$", xlabel="Normalized MEA")
-        process_extension(ax, 18, ".biorseo_bgsu_byp_D",   ylabel="Normalized $f_{1D}$", xlabel="Normalized MEA")
-        process_extension(ax, 19, ".biorseo_bgsu_jar3d_D", ylabel="Normalized $f_{1D}$", xlabel="Normalized MEA")
-        ax[15].axis("off")
-        ax[16].axis("off")
-        for a in ax:
-            a.label_outer()
-        plt.subplots_adjust(bottom=0.05, top=0.95, left=0.07, right=0.98, hspace=0.1, wspace = 0.05)
-        plt.savefig("pareto_visualizer.png")  
-    else:
-        fig, ax = plt.subplots(2,1, figsize=(6,10))
-        plt.subplots_adjust(bottom=0.12, top=0.9, left=0.15, right=0.9, hspace=0.4)
-        if extension == ".biok":
-            process_extension(ax, 0, extension, nsolutions=True, ylabel="Normalized MFE", xlabel="Normalized MEA")
-        else:
-            process_extension(ax, 0, extension, nsolutions=False)
-        plt.savefig("pareto_visualizer_ext.png")
\ No newline at end of file