dr_tools.py

from __future__ import division, with_statement, print_function, unicode_literals, absolute_import

lastmodified = "31 May 2022"

class BufferedOutfile:
	# creates a temporary file for writing, then moves that file to the output filename, or deletes the temporary file if an exception is raised
	def __init__(self, outfile, mode='w', tmp_suffix=''):
		self.fh = None # filehandle, use this for writing to
		self.tmp_outf = None # filename, to use for writing to also set mode=None
		self.final_outf = outfile #internal
		self.tmp_suffix = tmp_suffix #internal
		self.mode = mode #internal
	def __enter__(self):
		import tempfile, os
		
		# make sure it's the right file system
		tmpfolder = tempfile.gettempdir()
		outfolder = os.path.dirname(os.path.abspath(self.final_outf))
		if os.lstat(tmpfolder).st_dev != os.lstat(outfolder).st_dev:
			tmpfolder = outfolder
		
		# create and open the temporary file
		self.tmp_outf = tempfile.mkstemp(self.tmp_suffix, dir=tmpfolder)[1]
		if self.mode is not None: self.fh = open(self.tmp_outf, self.mode)
		return self
	def __exit__(self, exc_type, exc_value, exc_traceback):
		import os
		if self.fh is not None: self.fh.close()
		try:
			if exc_type is None: # successful
				os.rename(self.tmp_outf, self.final_outf)
		except IOError:
			os.remove(self.tmp_outf)
			raise
		if exc_type is not None:
			os.remove(self.tmp_outf)
		return False

def memorypercent():
	try: import psutil
	except: return 0.0
	mem = 0.0
	for p in psutil.get_process_list():
		try:mem += p.get_memory_percent()
		except: pass
	return mem
	

def rank(sortby, handleties=0):
	""" return rank for each value in sortby, in the same order """
	zippedin = list(zip(sortby, list(range(len(sortby)))))
	if handleties:
		zippedin.sort()
		ranks = list(range(len(zippedin)))
		lastsameindex = -1
		lastsamevalue = None
		for ii in range(len(zippedin)):
			if zippedin[ii][0] != lastsamevalue:
				if lastsameindex != -1:
					targetrank = sum(ranks[lastsameindex:ii])/float(ii-lastsameindex)
					for jj in range(lastsameindex, ii):
						ranks[jj] = targetrank
				lastsameindex = ii
				lastsamevalue = zippedin[ii][0]
		if lastsameindex != -1:
			ii = len(zippedin)
			targetrank = sum(ranks[lastsameindex:ii])/float(ii-lastsameindex)
			for jj in range(lastsameindex, ii):
				ranks[jj] = targetrank
	else:
		import random
		random.shuffle(zippedin)
		zippedin.sort(key=lambda o:o[0])
		ranks = list(range(len(zippedin)))
	zippedout = list(zip([z[1] for z in zippedin], ranks))
	zippedout.sort()
	return [z[1] for z in zippedout]

def globalFDR(pvalues):
	""" returns list of global FDR values (corrected p values) in same order as input list
	    Uses Benjamini-Hochberg method """
	pnum = len(pvalues)
	pi = 1
	zippedin = list(zip(pvalues, list(range(pnum))))
	zippedin.sort()
	fdr = [z[0] for z in zippedin]
	for rank in range(pnum-1,0,-1):
		index = rank-1
		fdr[index] = min(fdr[index+1], pi*zippedin[index][0]*pnum/rank)
	zippedout = list(zip([z[1] for z in zippedin], fdr))
	zippedout.sort()
	return [z[1] for z in zippedout]
	
def esttrue(pvalues, minp=0.5, maxp=0.9):
	""" crudely estimate true number of non-null-hypothesis-followers from p-value distribution
	    will underestimate, unsure if it works """
	undermin = sum([1.0 for v in pvalues if v <= minp])
	undermax = sum([1.0 for v in pvalues if v <= maxp])
	return 1-(undermax-undermin)/undermax/(maxp-minp)*maxp
	
def PtoZ(p):
	from scipy.special import erfinv
	from math import sqrt
	return sqrt(2.0)*erfinv(2.0*p-1.0)
	
def ZtoP(Z):
	from scipy.special import erf
	from math import sqrt
	return 0.5*(1.0+erf(Z/sqrt(2.0)))

def combinedP(pvalues, weights=None):
	""" takes arrays of p-values (preferably 1-sided) and weights (sample sizes if equal variance), returns p-value (1-sided if 1-sided input) """
	from math import sqrt
	Zs = [PtoZ(p) for p in pvalues]
	if weights is None:
		Zcombined = sum([Z for Z in Zs])/sqrt(len(Zs))
	else:
		Zcombined = sum([w*Z for w,Z in zip(weights, Zs)])/sqrt(sum([w**2 for w in weights]))	
	return ZtoP(Zcombined)

def Ztest(values, popstd, popmean=0):
	""" two-tailed test if values are from a normal distributed with standard deviation popstd and mean popmean """
	import numpy, math
	Z = math.sqrt(len(values))*(numpy.mean(values)-popmean)/popstd
	return 2.0*ZtoP(-abs(Z))

def ftest(*args):
	""" input: lists of counts, output: p-value """
	import ctools
	if len(args) == 4 and not hasattr(args[0], '__iter__'):
		return ctools.ftest(args[0], args[1], args[2], args[3])
	else:
		array = list(args[0])
		dim = len(array)
		for a in args[1:]:
			if len(a) != dim: raise ValueError
			array += list(a)
		return ctools.ftest3(array, dim)

def bootstrap(func, datatuple, controls=1000, confidence=0.95, nullval=0, processes=1, give_median=False):
	if processes == 1:
		arr = _bootstrap_loop(func, datatuple, controls)
	else:
		import multiprocessing
		arr=[]
		jobs=[]
		cLeft = controls
		q = multiprocessing.Queue()
		for pi in range(processes, 0, -1):
			jobs.append(multiprocessing.Process(target=_bootstrap_loop, args=(func, datatuple, cLeft//pi, q)))
			cLeft -= cLeft//pi
		for job in jobs: job.start()	
		for job in jobs: arr.extend(q.get())
	if hasattr(arr[0], '__iter__'):
		parr = []
		minvarr = []
		maxvarr = []
		medvarr = []
		for a in map(list,list(zip(*arr))):
			p, minv, maxv, medv = _bootstrap_points(a, confidence, controls, nullval)
			minvarr.append(minv)
			maxvarr.append(maxv)
			medvarr.append(medv)
			parr.append(p)
		if give_median:
			return tuple(parr), tuple(minvarr), tuple(maxvarr), tuple(medvarr), tuple(func(*datatuple))
		else:
			return tuple(parr), tuple(minvarr), tuple(maxvarr)
	else:
		p, minv, maxv, medv = _bootstrap_points(arr, confidence, controls, nullval)
		if give_median:
			return p, minv, maxv, medv, func(*datatuple)
		else:
			return p, minv, maxv

def _bootstrap_points(arr, confidence, controls, nullval):
	if hasattr(confidence, '__iter__'):
		minv, maxv = list(zip(*[pointinarray(arr, [(1-c)/2, (1+c)/2]) for c in confidence]))
		medv = pointinarray(arr, 0.5)
	else:
		minv, maxv, medv = pointinarray(arr, [(1-confidence)/2, (1+confidence)/2, 0.5])
	p = sum(v <= nullval for v in arr)/float(controls)
	return p, minv, maxv, medv

def _bootstrap_loop(func, datatuple, controls, q=None):
	import numpy
	arr = []
	for ci in range(controls):
		resamplings = tuple([data[i] for i in numpy.random.randint(0,len(data),len(data))] for data in datatuple)
		arr.append(func(*resamplings))
	if q is None:
		return arr
	else:
		q.put(arr)

def p_from_distr(vals, distr):
	# same as return [sum(d < v for d in distr)/float(len(distr)) for v in vals], but faster
	envals = sorted((v,i) for i,v in enumerate(vals))
	p_arr = []
	distr = sorted(distr)
	ldistr = float(len(distr))
	r = 0
	p_arr = [-1 for v in vals]
	for v,i in envals:
		try:
			while distr[r] < v:
				r += 1
		except IndexError: pass
		p_arr[i] = 1 - r/ldistr
		if p_arr[i] == 0:
			p_arr[i] = 1/ldistr
	return p_arr

def variance_shrinkage_t_test(values1, values2, params=(0.5, 0.9), permutN=100):
	"""
	values1, values2 = NxM matrices, where N=number of replicates, M=number of tests
	params = (fraction from shrinkage, quantile to use for shrinkage) (default 0.5, 0.9)
	permutN = as high as possible (but makes it take more time)
	"""
	import numpy, math
	means1 = numpy.mean(values1, axis=1)
	means2 = numpy.mean(values2, axis=1)
	def new_var(values):
		variance = numpy.var(values, axis=1)
		c = pointinarray(variance, params[1])
		return variance * (1-params[0]) + c * params[0], c
	def new_var_perm(values, c):
		variance = numpy.var(values, axis=1)
		return variance * (1-params[0]) + c * params[0]
	def t_like_values(val1, val2, combined_variance):
		# ignore the factor 2/sqrt(2/n), since it's constant
		return (numpy.mean(val1, axis=1)-numpy.mean(val2, axis=1))/combined_variance
	var1, c1 = new_var(values1)
	var2, c2 = new_var(values2)
	t_like_real = t_like_values(values1, values2, var1+var2)
	stackval = numpy.hstack((values1, values2))
	n1 = len(values1[0])
	nboth = len(stackval[0])
	def permut_t():
		i_p = numpy.random.permutation(nboth)
		val1 = numpy.hstack(tuple(stackval[:,i:i+1] for i in i_p[:n1]))
		val2 = numpy.hstack(tuple(stackval[:,i:i+1] for i in i_p[n1:]))
		return t_like_values(val1, val2, new_var_perm(val1, c1) + new_var_perm(val2, c2))
	t_like_distr = [t for i in range(permutN) for t in permut_t()]
	return [1-2*abs(0.5-p) for p in p_from_distr(t_like_real, t_like_distr)]

def strip_end_zeros(number):
	string = str(number)
	if '.' in string or ',' in string:
		while string.endswith('0'): string = string[:-1]
		if string.endswith('.') or string.endswith(','): string = string[:-1]
	return string

class Parsed_rpkms(dict):
	def __init__(self, infiles, counts):
		# contains the table of values and names as dictionary with 'symbols', 'IDs' or sample name a key
		self.samples = []
		self.filenames = infiles
		self.allmappedreads = []
		self.normalizationreads = []
		self.is_counts = counts
		self.symbol_to_index = dict()
		self.ID_to_index = dict()
	
	def to_dataframe(self, indexname='symbols'):
		import pandas
		return pandas.DataFrame(dict((s,self[s]) for s in self.samples), index=(None if indexname is None else self[indexname]))

def writeexpr(filename, rpkm_expr, counts_expr=None, samples=None, row_indices=None, extra_comment_lines=[]):
	import sys, time
	if samples is None: samples = rpkm_expr.samples
	if row_indices is None: row_indices = range(len(rpkm_expr['symbols']))
	with open(filename, 'w') as outfh:
		print(join('#samples', samples), file=outfh)
		totalreadsD = dict(zip(rpkm_expr.samples, rpkm_expr.allmappedreads))
		normreadsD = dict(zip(rpkm_expr.samples, rpkm_expr.normalizationreads))
		if rpkm_expr.allmappedreads != []:
			print(join('#allmappedreads', [totalreadsD.get(s, 0) for s in samples]), file=outfh)
		if rpkm_expr.normalizationreads != []:
			print(join('#normalizationreads', [normreadsD.get(s, 0) for s in samples]), file=outfh)
		print(join('#arguments', ' '.join(sys.argv), 'time: '+time.asctime()), file=outfh)
		for line in extra_comment_lines:
			if not line[0] == '#': line = '#' + line
			line = line.rstrip('\r\n')
			print(line, file=outfh)
		for i in row_indices:
			symbol = rpkm_expr['symbols'][i]
			ID = rpkm_expr['IDs'][i]
			values_rpkm = (rpkm_expr[s][i] for s in samples)
			if counts_expr is None:
				print(join(symbol, ID, values_rpkm), file=outfh)
			else:
				values_reads = (counts_expr[s][i] for s in samples)
				print(join(symbol, ID, values_rpkm, map(strip_end_zeros, values_reads)), file=outfh)

def exprfromdataframe(data, is_counts=False):
    values = Parsed_rpkms(['exprfromdataframe'], is_counts)
    values.samples = data.columns
    values['symbols'] = list(data.index)
    values['IDs'] = ['' for sym in values['symbols']]
    values.symbol_to_index = dict((S, i) for i, S in enumerate(values['symbols']))
    for col in values.samples:
        values[col] = list(data[col])
    return values

def loadexpr(infiles, counts=False):
	"""
	loads from output of rpkmforgenes.py
	"""
	if isinstance(infiles, str): infiles = [infiles]
	values = Parsed_rpkms(infiles, counts)
	numsymbols = None
	for infile in infiles:
		samples = None
		with open(infile, 'r') as infh:
			for line in infh:
				p = line.rstrip('\r\n').split('\t')
				if p[0] == '#samples':
					samples = p[1:]
					values.update(dict((n,[]) for n in samples))
					values['symbols'] = []
					values['IDs'] = []
					indexstart = 2+len(samples) if counts else 2
					values.samples.extend(samples)
				elif samples is None and p[0] == 'gene_name' and p[1] == 'geneID':
					samples = p[2:]
					if samples[:len(samples)//2] == samples[len(samples)//2:]:
						samples = samples[:len(samples)//2]
					values.update(dict((n,[]) for n in samples))
					values['symbols'] = []
					values['IDs'] = []
					indexstart = 2+len(samples) if counts else 2
					values.samples.extend(samples)
				elif p[0] == '#allmappedreads':
					values.allmappedreads.extend([float(v) for v in p[1:]])
				elif p[0] == '#normalizationreads':
					values.normalizationreads.extend([float(v) for v in p[1:]])
				elif line.startswith('#'):
					continue
				else:
					for s,v in zip(samples, p[indexstart:]):
						try:
							values[s].append(None if v == '-1' else float(v))
						except:
							import sys
							#print('Problem line:', repr(line), file=sys.stderr)
							raise
					values['symbols'].append(p[0])
					values['IDs'].append(p[1])
		if not (numsymbols is None or numsymbols == len(values['symbols'])):
			raise Exception('Mismatch in number of gene symbols between files')
		numsymbols = len(values['symbols'])
	
	# prepare some dictionaries
	values.symbol_to_index = dict((s, i) for i, S in enumerate(values['symbols']) for s in S.split('+'))
	values.symbol_to_index.update(dict((S, i) for i, S in enumerate(values['symbols'])))
	values.ID_to_index = dict((s, i) for i, S in enumerate(values['IDs']) for s in S.split('+'))
	values.ID_to_index.update(dict((S, i) for i, S in enumerate(values['IDs'])))
	
	return values

def getsequence(chromosome, start, end, genomedir, filesuffix=".fa"):
	""" returns nucleotide sequence string, for 0-based inclusive to exclusive interval """
	import os
	chromosomefile = os.path.join(genomedir, chromosome + filesuffix)
	cfileh = open(chromosomefile, "r")
	if start < 0: start = 0
	
	global chromosomefile_infodict
	try: chromosomefile_infodict
	except: chromosomefile_infodict = {}
	try: offset, linelength, seqlength = chromosomefile_infodict[chromosomefile]
	except:
		line1 = cfileh.readline(1000)
		if len(line1) < 1000 and line1[0] == '>': offset = len(line1)
		else:
			cfileh.seek(0)
			offset = 0
		line2 = cfileh.readline(1000)
		if len(line2) < 1000:
			linelength = len(line2)
			seqlength = len(line2.rstrip())
		else:
			linelength = 0
			seqlength = 0
		chromosomefile_infodict[chromosomefile] = offset, linelength, seqlength
	if linelength == 0:
		startfilepos = start + offset
		endfilepos = end + offset
	else:
		startfilepos = offset + (start // seqlength)*linelength + (start % seqlength)
		endfilepos = offset + (end // seqlength)*linelength + (end % seqlength)	
	cfileh.seek(startfilepos, 0)
	seq = ''.join(cfileh.read(endfilepos-startfilepos).split())
	cfileh.close()
	return seq

def reverseDNA(seq_in):
	""" returns nucleotide sequence string """
	sequencetools_reverseDNAdict = {"A":"T", "C":"G", "G":"C", "T":"A", "R":"Y","Y":"R","K":"M","M":"K","S":"S","W":"W","B":"V","D":"H","H":"D","V":"B","N":"N", "a":"t","c":"g","g":"c","t":"a","n":"n","\n":"\n"}
	seq_out = ""
	for bi in range(len(seq_in)):
		seq_out = sequencetools_reverseDNAdict[seq_in[bi]] + seq_out
	return seq_out
	
def expandsequence(sequence):
	return expandsequences([sequence])
	
def expandsequences(sequences):
	""" returns array of sequences """
	expanddict = {"A":"A", "C":"C", "G":"G", "T":"T", "R":"GA", "Y":"TC", "K":"GT", "M":"AC", "S":"GC", "W":"AT", "B":"GTC", "D":"GAT", "H":"ACT", "V":"GCA", "N":"ACGT"}
	seq_out = []
	for seqin in sequences:
		seq_l = [""]
		for bi in range(len(seqin)):
			expanded = expanddict[seqin[bi]]
			oldsequences = seq_l
			seq_l = []
			for ei in range(len(expanded)):
				for seq in oldsequences:
					seq_l.append(seq + expanded[ei])
		seq_out += seq_l
	return seq_out
	
def tocolour(seq):
	""" returns colourspace sequence string """
	plainseq = seq.upper()
	colourdict = {"AA":"0", "CC":"0", "GG":"0", "TT":"0", "CA":"1", "AC":"1", "GT":"1", "TG":"1", "GA":"2", "AG":"2", "TC":"2", "CT":"2", "TA":"3", "AT":"3", "CG":"3", "GC":"3"}
	colourseq = ""
	for pos in range(len(plainseq)-1):
		try:
			colour = colourdict[plainseq[pos:pos+2]]
		except:
			colour = "."
		colourseq += colour
	return plainseq[0] + colourseq
	
def fromcolour(seq):
	colour0 = {"A":"A", "C":"C", "G":"G", "T":"T"}
	colour1 = {"A":"C", "C":"A", "G":"T", "T":"G"}
	colour2 = {"G":"A", "A":"G", "T":"C", "C":"T"}
	colour3 = {"T":"A", "A":"T", "C":"G", "G":"C"}
	
	cs_sequence = seq
	pos = 1
	prevbase = cs_sequence[0]
	seq = prevbase
	while pos < len(cs_sequence):
		colour = cs_sequence[pos]
		if colour == "0":
			nextbase = colour0[prevbase]
		elif colour == "1":
			nextbase = colour1[prevbase]
		elif colour == "2":
			nextbase = colour2[prevbase]
		elif colour == "3":
			nextbase = colour3[prevbase]
		else:
			seq += "N"
			return seq
		seq += nextbase
		prevbase = nextbase
		pos += 1
	return seq
	
def loadlist(infile, index=None, func=None, ignore='#', ignorelines=0):
	""" returns array of strings """
	infileh = open(infile, "r")
	for i in range(ignorelines):
		infileh.readline()
	outarray = [l.rstrip() for l in infileh.readlines()]
	infileh.close()
	if ignore is not None:
		outarray = [l for l in outarray if not l.startswith(ignore)]
	if index is not None:
		outarray = [l.split("\t")[index] for l in outarray]	
	if func is not None:
		outarray = list(map(func, outarray))
	return outarray
	
def printlist(outfile, inlist, method="w"):
	outfileh = open(outfile, method)
	for string in inlist:
		print(string, file=outfileh)
	outfileh.close()
	
def histogramheights(array, start, end, step, cumulative=0, fractions=False):
	""" returns 2 arrays: x and y """
	binpositions = []
	binheights = []
	pos = start
	arraylen = 0
	while pos <= end:
		binpositions.append(pos)
		binheights.append(0)
		pos += step
	for element in array:
		bin = int((element-start)/step)
		arraylen += 1
		if cumulative > 0:
			for bi in range(bin, len(binheights)):
				try:
					assert bi >= 0
					binheights[bi] += 1
				except: pass
		elif cumulative < 0:
			for bi in range(0, bin+1):
				try:
					assert bi >= 0
					binheights[bi] += 1
				except: pass
		else:
			try:
				assert bin >= 0
				binheights[bin] += 1
			except: pass
	if fractions:
		return (binpositions, [h/arraylen for h in binheights])
	else:
		return (binpositions, binheights)
	
def bin(array, start, end, step, cumulative=0, fractions=False):
	return histogramheights(array, start, end, step, cumulative, fractions)
	
def mixcolours(colours, weights):
	cout = "#"
	for si in [1,3,5]:	# red, green, blue
		cvals = [int(c[si:si+2], 16) for c in colours]
		mixed = max(0,min(255,int(0.5+sum([cvals[i]*weights[i] for i in range(len(cvals))]))))
		outstr = "%X" % mixed
		if len(outstr) == 1: outstr = "0" + outstr
		cout += outstr
	return cout

def rainbowmix(fraction, stops=['#fe0000', '#00fe00', '#0000fe', '#000000', 'f0f0f0', '#fe0000', '#0000fe', '#f0f0f0', '#00fe00', '#000000']):
	n = len(stops)
	mf = 1-(fraction*n)%1
	lstop = int(fraction*n)
	return mixcolours([stops[lstop%n], stops[(lstop+1)%n]], [mf, 1-mf])

def randomcolour():
	import random
	def hexconv(n):
		r = hex(n)[2:]
		return r if len(r) == 2 else '0'+r
	return '#' + ''.join([hexconv(random.randint(0,255)) for c in 'rgb'])

def chisquare(observed, total):
	""" returns fold enrichment, p-value """
	from scipy import stats
	import numpy
	expected = [float(v)*sum(observed)/sum(total) for v in total]
	obs = numpy.asarray(observed)
	obs = obs.astype(float)
	exp = numpy.asarray(expected)
	exp = exp.astype(float)
	fc = [observed[ii]/expected[ii] if expected[ii] != 0 else 0.0 for ii in range(len(observed))]
	return fc, stats.chisquare(obs, exp)[1]

def chisquaretable(a, b, c, d):
	fc, p = chisquare([a,b], [c+a,d+b])
	return fc[0], p

def permutationtest(func, a, b, controls=1000):
	""" returns 2-sided p-value for func(a) = func(b) """
	import random
	arr = [0 for i in range(controls+1)]
	arr[0] = abs(func(a) - func(b))
	c = a + b
	lena = len(a)
	for i in range(1, controls+1):
		random.shuffle(c)
		arr[i] = abs(func(c[:lena]) - func(c[lena:]))
	r = rank(arr)
	return 1-float(r[0])/(controls+1)

def pointinarray(array, quantile):
	#sortedarray = array[:]
	#sortedarray.sort()
	import numpy
	sortedarray = numpy.array(sorted(array))
	def _point(q):
		indexlow = int(q*(len(sortedarray)-1))
		fl = q*(len(sortedarray)-1)-indexlow
		if indexlow == len(sortedarray) - 1:
			return float(array[indexlow])
		else:
			return float(sortedarray[indexlow]*(1-fl)+sortedarray[indexlow+1]*fl)
	if isinstance(quantile, list):
		return list(map(_point, quantile))
	else:
		return _point(quantile)

def permutationtest_confint(func, a, b, confidence=0.95, controls=1000):
	""" returns func(a)-func(b), min, max, assumes equal distribution except func (eg mean) """
	import random
	pvalue = confidence
	if pvalue > 1: pvalue /= 100.0
	if pvalue > 0.5: pvalue = 1-pvalue
	arr = [0 for i in range(controls)]
	arr_0 = func(a) - func(b)
	c = a + b
	lena = len(a)
	for i in range(controls):
		random.shuffle(c)
		arr[i] = func(c[:lena]) - func(c[lena:])
	return arr_0+pointinarray(arr,pvalue/2.0), arr_0, arr_0+pointinarray(arr,1-pvalue/2.0)

def adjWald(a, b, confidence=0.95):
	""" calculate confidence interval for a/b, where a and b are integers """
	# http://measuringux.com/AdjustedWald.htm
	n = float(b)
	p = a/n
	z2 = PtoZ((1+confidence)/2)**2
	padj = (n*p + z2/2)/(n+z2)
	nadj = n + z2
	d = (z2*padj*(1-padj)/nadj)**0.5
	return padj-d, padj+d

def bootstrap_confint(func, a, b=None, confidence=0.95, resamplings=1000):
	""" returns min, max; dose not assume similar distribution, requires >20 values in a and b """
	import random
	pvalue = confidence
	if pvalue > 1: pvalue /= 100.0
	if pvalue > 0.5: pvalue = 1-pvalue
	arr = []
	if b is None:
		for i in range(resamplings):
			rs_a = [random.choice(a) for j in range(len(a))]
			arr.append(func(rs_a))
	else:
		for i in range(resamplings):
			rs_a = [random.choice(a) for j in range(len(a))]
			rs_b = [random.choice(b) for j in range(len(b))]
			arr.append(func(rs_a)-func(rs_b))
	return pointinarray(arr,pvalue/2.0), pointinarray(arr,1-pvalue/2.0)

def violin_plot(ax,data,pos, bp=False):
    '''
    create violin plots on an axis
    run with e.g violin_plot(pylab.axes(), [[3,4,5],[7,8]], [0, 1])
    '''
    # from http://pyinsci.blogspot.com/2009/09/violin-plot-with-matplotlib.html
    from matplotlib.patches import Rectangle
    from scipy.stats import gaussian_kde
    from numpy.random import normal
    from numpy import arange
    
    dist = max(pos)-min(pos)
    w = min(0.15*max(dist,1.0),0.5)
    for d,p in zip(data,pos):
        k = gaussian_kde(d) #calculates the kernel density
        m = k.dataset.min() #lower bound of violin
        M = k.dataset.max() #upper bound of violin
        x = arange(m,M,(M-m)/500.) # support for violin
        v = k.evaluate(x) #violin profile (density curve)
        v = v/v.max()*w #scaling the violin to the available space
        ax.fill_betweenx(x,p,v+p,facecolor='y',alpha=0.3)
        ax.fill_betweenx(x,p,-v+p,facecolor='y',alpha=0.3)
    if bp:
        #ax.boxplot(data,notch=1,positions=pos,vert=1)
        boxplotborders = [pointinarray(D, [0.25,0.5,0.75]) for D in data]
        for x, borders in zip(pos, boxplotborders):
            ax.add_patch(Rectangle((x-0.05, borders[0]), 0.1, borders[2]-borders[0], linewidth=0, facecolor='k'))
        ax.plot(pos, [d[1] for d in boxplotborders], 'wo')

def GOgenelist(GO_file, term, shortened=0):
	files = {"BP":"/home/danielr/ChIP-seq-Sox3/perGOcat/data/BP_goterm.txt","MF":"/home/danielr/ChIP-seq-Sox3/perGOcat/data/MF_goterm.txt", "CC":"/home/danielr/ChIP-seq-Sox3/perGOcat/data/CC_goterm.txt"}
	try: GO_file = files[GO_file]
	except: pass
	foundcat  = 0
	GO_fileh = open(GO_file, "r")
	for line in GO_fileh:
		p = line[:-1].split("\t")
		if p[0] == term or (shortened and term.upper().replace(" ","_") in p[0].upper().replace(" ","_")):
			GO_fileh.close()
			return p[1].split(";")
	GO_fileh.close()
	raise UserWarning("Did not find term " + term + " in " + GO_file)

def loadmotif(infile, trimstart=0, trimend=0):
	from TAMO import MotifTools
	lines = loadlist(infile)
	if lines[0] == "A\tC\tG\tT":
		ma = []
		for l in lines[1:]:
			p = l.split("\t")
			ma.append({'A':float(p[0]), 'C':float(p[1]), 'G':float(p[2]), 'T':float(p[3])})
		if trimend == 0: ma = ma[trimstart:]
		else: ma = ma[trimstart:-trimend]
		return MotifTools.Motif_from_counts(ma)
	elif lines[0][0] in 'ACGT':
		if trimend == 0: lines = lines[trimstart:]
		else: lines = lines[trimstart:-trimend]
		return MotifTools.Motif(lines)
	else:
		na = []
		for line in lines:
			na.append(list(map(int, line.split())))
		ma = []
		for i in range(len(na[0])):
			ma.append({'A':na[0][i], 'C':na[1][i], 'G':na[2][i], 'T':na[3][i]})
		return MotifTools.Motif_from_counts(ma)

def join(*args, **kwargs):
	""" returns tab-separated string """
	try: sep = kwargs["sep"]
	except: sep = "\t"
	array = []
	for a in args:
		iterable = hasattr(a, '__iter__')
		if iterable and isinstance(a, str): iterable = False
		try:
			if iterable and isinstance(a, unicode): iterable = False
		except NameError: pass
		if iterable: array.extend(a)
		else: array.append(a)
	return sep.join(map(str, array))

def split(line, sep='\t'):
	return line.rstrip('\r\n').split(sep)

def splitlines(infile, ignore='', sep='\t'):
	if infile.endswith('.gz'):
		import gzip
		infh = gzip.open(infile, 'r')
	else:
		infh = open(infile, 'rU')
	try:
		for line in infh:
			if ignore and line.startswith(ignore): continue
			yield line.rstrip('\r\n').split(sep)
	finally:
		infh.close()

class Cregion:
	allchromosomes = {}
	indexdict = {} # inverse of allchromosomes
	allwindows = []
	WL = 3000
	
	def __init__(self, chromosome, start, end=None, strand='?'):
		self.start = start
		if end == None: self.end = start
		else: self.end = end
		try: self.chrindex = Cregion.allchromosomes[chromosome+strand]
		except KeyError:
			self.chrindex = len(Cregion.allchromosomes)
			Cregion.allchromosomes[chromosome+strand] = self.chrindex
			Cregion.indexdict[self.chrindex] = chromosome+strand
			Cregion.allwindows.append([])
	
	def __hash__(self):
		return self.chrindex * self.start + hash(self.end)
	
	def addtowindows(self):
		# adds instance to Cregion.allwindows
		wchr = Cregion.allwindows[self.chrindex]
		if len(wchr) <= self.end//Cregion.WL: wchr.extend([[] for i in range(1+self.end//Cregion.WL-len(wchr))])
		for wi in range(self.start//Cregion.WL, self.end//Cregion.WL+1):
			wchr[wi].append(self)
		
	def getwindow(self):
		# returns list of Cregion instances which could overlap
		wchr = Cregion.allwindows[self.chrindex]
		s = min(len(wchr), self.start//Cregion.WL)
		e = min(len(wchr), self.end//Cregion.WL+1)
		return list(set([v for l in wchr[s:e] for v in l])) # flattens wchr[s:e], removes duplicates
		
	def overlaps(self, other):
		return self.start <= other.start < self.end or other.start <= self.start < other.end
	
	def overlapping(self):
		return [r for r in self.getwindow() if r.overlaps(self)]
	
	def getchromosome(self):
		return Cregion.indexdict[self.chrindex][:-1]
	
	def startingwithin(self):
		# returns list of Cregion instances whose start coordinate is within the region
		return [r for r in self.getwindow() if self.start <= r.start < self.end]
	
	def getstrand(self):
		strand = Cregion.indexdict[self.chrindex][-1]
		if strand == "?": raise Exception("No strand given")
		return strand
	
	def __repr__(self):
		return self.name(1, 0)
	
	def name(self, start_add=0, end_add=0):
		try:
			strand = self.getstrand()
		except:
			return self.getchromosome()+":"+str(self.start+start_add)+"-"+str(self.end+end_add)
		else:
			return self.getchromosome()+":"+str(self.start+start_add)+"-"+str(self.end+end_add)+":"+strand
	
	@staticmethod
	def clearwindows(new_windowsize=None):
		if new_windowsize is not None: Cregion.WL = new_windowsize
		Cregion.allwindows = [[] for c in Cregion.allchromosomes]
		
	@staticmethod
	def overlappingpoint(chromosome, pos, strand='?'):
		try:
			wchr = Cregion.allwindows[Cregion.allchromosomes[chromosome+strand]]
		except KeyError:
			return []
		s = pos//Cregion.WL
		try:
			return [r for r in wchr[s] if r.start <= pos < r.end]
		except IndexError:
			return []
	
	@staticmethod
	def closesttopoint(chromosome, pos, strand='?', mindist=0, maxdist=1e30, check_forward=True, check_backward=True):
		try:
			wchr = Cregion.allwindows[Cregion.allchromosomes[chromosome+strand]]
		except KeyError:
			return []
		s = pos//Cregion.WL
		windist = 0
		candidates = set()
		
		def closesttopoint_distance(r):
			if r.start <= pos <= r.end: return 0
			return min(abs(r.start-pos), abs(r.end-pos))
		
		while windist < 5+maxdist/Cregion.WL:
			new_candidates = set()
			if check_backward:
				try: new_candidates |= set(r for r in wchr[s-windist])
				except IndexError:pass
			if check_forward:
				try: new_candidates |= set(r for r in wchr[s+windist])
				except IndexError:pass
			new_candidates = set(r for r in new_candidates if mindist <= closesttopoint_distance(r) < maxdist)
			if candidates:
				candidates |= new_candidates
				break
			candidates |= new_candidates
			windist += 1
		
		if not candidates: return []
		closest_dist = min(closesttopoint_distance(r) for r in candidates)
		return [r for r in candidates if closesttopoint_distance(r) == closest_dist]

def flag(flags, default=None, array=None):
	if array == None:
		import sys
		argv = sys.argv
	else: argv = array
	if "--help" in argv: raise UserWarning("--help in arguments")
	for f in str(flags).split("/"):
		if f[0] != "-":
			try: return flagpos(int(f), None, argv)
			except: pass
		try:
			index = argv.index(f)
		except ValueError: continue
		try:
			return argv[index+1]
		except IndexError: raise UserWarning("No value after " + f + " in arguments")
	if default == None: raise UserWarning(str(flags) + " not in arguments")
	return default
	
def flagarray(flags, default=None, array=None):
	if array == None:
		import sys
		argv = sys.argv
	else: argv = array
	if "--help" in argv: raise UserWarning("--help in arguments")
	index = None
	for f in str(flags).split("/"):
		if f[0] != "-":
			v = []
			i = 0
			try:
				while 1:
					v.append(flagpos(int(f)+i, None, argv))
					i += 1
			except: pass
			if len(v) > 0: return v
		try:
			index = argv.index(f)
		except ValueError: continue
	if index == None:
		if default == None: raise UserWarning(str(flags) + " not in arguments")
		return default
	nextflag = index+1
	while nextflag < len(argv):
		if argv[nextflag][0] == "-" and len(argv[nextflag]) > 1 and argv[nextflag][1] not in "0123456789.": break
		nextflag += 1
	return argv[index+1:nextflag]
	
def ifflag(flags, present=1, absent=0, array=None):
	if array == None:
		import sys
		argv = sys.argv
	else: argv = array
	if "--help" in argv: raise UserWarning("--help in arguments")
	for f in str(flags).split("/"):
		if f in argv: return present
	return absent
	
def flagpos(index, default=None, array=None):
	if array == None:
		import sys
		argv = sys.argv
	else: argv = array
	if "--help" in argv: raise UserWarning("--help in arguments")
	try:
		value = argv[index]
		for v in argv[1:index+1]:
			if len(v) > 1 and v[0] == "-" and v[1] not in "0123456789.": raise IndexError
	except:
		if default == None:
			raise UserWarning("Argument " + str(index) + " is missing")
		else: return default
	return value


def MINE_MIC(X, Y):
	if MINE_MIC.m is None:
		import minepy.mine
		MINE_MIC.m = minepy.mine.MINE()
		# http://www.exploredata.net/Downloads/P-Value-Tables
		Pvalue_tables_pasted = {25:'''0.99884	0.000000256
0.94268	0.000011013
0.91884	0.000013061
0.90438	0.00001972
0.88865	0.000025355
0.86904	0.000032013
0.85445	0.000040209
0.84284	0.000050709
0.83884	0.0000566
0.83317	0.000067612
0.8249	0.0000776
0.81767	0.00009015
0.81124	0.000103979
0.80546	0.000119858
0.80465	0.000140346
0.80021	0.000154945
0.79701	0.000170311
0.79539	0.000211032
0.79095	0.00024535
0.77845	0.000272497
0.76874	0.000298108
0.76151	0.000330121
0.75884	0.000346768
0.75717	0.000383648
0.73923	0.000429235
0.72997	0.000459968
0.72373	0.000514006
0.7232	0.000526043
0.72298	0.000586228
0.72021	0.000596985
0.71539	0.000605948
0.71134	0.000650767
0.71007	0.000712233
0.69786	0.000788552
0.69583	0.000854628
0.69527	0.000869994
0.69342	0.00099062
0.69001	0.001021097
0.68683	0.00110382
0.6852	0.001117137
0.68416	0.001166566
0.68151	0.001273874
0.68144	0.001293851
0.67884	0.001366585
0.67676	0.001452125
0.67566	0.001473381
0.67328	0.001495663
0.6704	0.001512054
0.66889	0.00153792
0.66825	0.001683645
0.6675	0.001939752
0.66686	0.001966131
0.66559	0.001984314
0.66166	0.002011974
0.65923	0.00204245
0.659	0.002120819
0.65893	0.00232007
0.65582	0.002338254
0.65101	0.00235823
0.64632	0.002543395
0.64546	0.002571311
0.6442	0.00259308
0.64205	0.002819478
0.6417	0.00284304
0.64021	0.00286737
0.63939	0.002892212
0.63454	0.002918335
0.62973	0.002948556
0.62689	0.003184686
0.62627	0.003214907
0.62145	0.003249993
0.61904	0.003293275
0.61705	0.003345521
0.61583	0.003455391
0.61422	0.003500722
0.61261	0.003550662
0.61127	0.003658995
0.61046	0.00401524
0.60943	0.00429414
0.6078	0.004341264
0.60683	0.004386851
0.60602	0.004432181
0.60202	0.004470597
0.60158	0.004496464
0.59884	0.004717228
0.59728	0.004772291
0.59676	0.004794828
0.59358	0.005119828
0.59085	0.005185903
0.58756	0.005296797
0.5875	0.005336494
0.58507	0.005396935
0.58323	0.005466084
0.58275	0.006017738
0.58033	0.006212123
0.57982	0.006277174
0.57907	0.006657236
0.57663	0.006725104
0.57599	0.006804753
0.57356	0.006893878
0.57124	0.00699376
0.56806	0.0070898
0.56772	0.00719045
0.56657	0.007311076
0.56568	0.008334734
0.56546	0.008428213
0.56529	0.008549352
0.56379	0.008640782
0.56147	0.009422675
0.55853	0.009504629
0.55806	0.010344403
0.55731	0.010408174
0.55703	0.010517275
0.5565	0.010754174
0.55406	0.010890678
0.55348	0.011023342
0.55326	0.011795247
0.54926	0.012076708
0.5488	0.012232165
0.54696	0.012288765
0.54585	0.012393768
0.5418	0.012518236
0.5406	0.012618118
0.54053	0.012807124
0.5377	0.01289164
0.53578	0.013796208
0.53543	0.01465519
0.53538	0.014938444
0.53238	0.01502424
0.53035	0.015144866
0.53016	0.015411217
0.5296	0.015567442
0.5285	0.016822876
0.52683	0.016924551
0.52509	0.017060031
0.52312	0.017172206
0.52076	0.01728182
0.51948	0.017590428
0.51884	0.018349016
0.51676	0.018463752
0.51669	0.01861716
0.51549	0.0197512
0.51225	0.020036503
0.5115	0.020179154
0.51144	0.020355611
0.51109	0.020505946
0.50913	0.020864751
0.50582	0.021081674
0.50448	0.021265046
0.50367	0.021450211
0.50299	0.02174166
0.50282	0.021915045
0.50259	0.022146309
0.50241	0.022192408
0.50189	0.022242861
0.50184	0.022414453
0.50004	0.022644692
0.49923	0.022815003
0.49907	0.022911556
0.499	0.024890748
0.49767	0.025234187
0.49686	0.026575417
0.49564	0.02690938
0.49559	0.027084813
0.49431	0.02744618
0.49362	0.028004236
0.49356	0.028204255
0.49345	0.028385579
0.49264	0.028689321
0.49159	0.028973087
0.49124	0.029228682
0.49032	0.029520643
0.48968	0.02973859
0.48923	0.029818495
0.48916	0.029975745
0.4882	0.03005898
0.48806	0.03010892
0.48633	0.030276414
0.48568	0.03040498
0.48338	0.030796823
0.48257	0.030932559
0.4825	0.031272925
0.48205	0.031516482
0.48188	0.03159357
0.48106	0.034491417
0.48066	0.03512272
0.47991	0.035216455
0.4799	0.035297897
0.47962	0.038293576
0.47853	0.038382701
0.47747	0.038649564
0.47552	0.038753544
0.47488	0.03902937
0.47413	0.039077775
0.47228	0.041726429
0.47088	0.042136712
0.46887	0.042535982
0.4684	0.042887873
0.46811	0.043497919
0.46644	0.043670279
0.46642	0.0442793
0.46504	0.044713913
0.46445	0.045665093
0.46412	0.046110207
0.46377	0.046502306
0.46359	0.048711226
0.46088	0.049413982
0.45885	0.049836302
0.45873	0.050227377
0.45786	0.05052318
0.45637	0.050728065
0.45549	0.050858936
0.45532	0.051311989
0.45445	0.051750699
0.45295	0.052137676
0.44926	0.05231439
0.44786	0.055561566
0.44711	0.05999554''', 760:'''0.20037	0.000001281
0.1994	0.000002561
0.1975	0.000003842
0.19697	0.000005122
0.19623	0.000006403
0.19571	0.000007683
0.1953	0.000008964
0.19499	0.000010244
0.19459	0.000011525
0.19429	0.000012805
0.19403	0.000014086
0.19382	0.000015366
0.19371	0.000016647
0.19313	0.000017927
0.19293	0.000019208
0.19272	0.000020488
0.1925	0.000021769
0.19225	0.000023049
0.19185	0.00002433
0.19167	0.000025611
0.19146	0.000026891
0.19133	0.000028172
0.19125	0.000029452
0.19111	0.000030733
0.19088	0.000032013
0.1908	0.000033294
0.19069	0.000034574
0.19057	0.000035855
0.19045	0.000037135
0.19027	0.000038416
0.19013	0.000039696
0.19003	0.000040977
0.18995	0.000042257
0.18978	0.000043538
0.18964	0.000044818
0.18955	0.000046099
0.18949	0.000047379
0.18941	0.00004866
0.18927	0.000049941
0.18918	0.000051221
0.18912	0.000052502
0.18905	0.000053782
0.189	0.000055063
0.18891	0.000056343
0.18885	0.000057624
0.18879	0.000058904
0.18875	0.000060185
0.18864	0.000061465
0.18859	0.000062746
0.18853	0.000064026
0.18849	0.000065307
0.1884	0.000066587
0.1883	0.000067868
0.18825	0.000069148
0.18812	0.000070429
0.18807	0.00007171
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0.29752	0.054244922
0.29751	0.054268227
0.2975	0.05428974
0.29749	0.054310485
0.29748	0.054334047
0.29747	0.054355816
0.29746	0.054377585
0.29745	0.054401147
0.29744	0.054424196
0.29743	0.054443404
0.29742	0.054467991
0.29741	0.054485662
0.2974	0.054509992
0.29739	0.054528688
0.29738	0.05454892
0.29737	0.054573507
0.29736	0.054591946
0.29735	0.054617301
0.29734	0.054638558
0.29733	0.05466468
0.29732	0.05468645
0.29731	0.054704633
0.2973	0.05472589
0.29729	0.054754062
0.29728	0.054775575
0.29727	0.054795807
0.29726	0.054820649
0.29725	0.054842162
0.29724	0.054859834
0.29723	0.054880578
0.29722	0.05490747
0.29721	0.054929239
0.2972	0.05495152
0.29719	0.054973801
0.29718	0.054994034
0.29717	0.055017083
0.29716	0.05504295
0.29715	0.055064463
0.29714	0.055089561
0.29713	0.05511005
0.29712	0.055132075
0.29711	0.055151795
0.2971	0.055171772
0.29709	0.055193797
0.29708	0.055222737
0.29707	0.055244506
0.29706	0.055263714
0.29705	0.055284202
0.29704	0.055308276
0.29703	0.055335168
0.29702	0.055354888
0.29701	0.055377681
0.297	0.05539817
0.29699	0.05541789
0.29698	0.055440427
0.29697	0.055458611
0.29696	0.055483197
0.29695	0.055512393
0.29694	0.055536211
0.29693	0.05555337
0.29692	0.055575652
0.29691	0.055595372
0.2969	0.05561919
0.29689	0.055636861
0.29688	0.055660167
0.29687	0.055678351
0.29686	0.055698071
0.29685	0.055718303
0.29684	0.055747499
0.29683	0.055768756
0.29682	0.055790781
0.29681	0.055814599
0.2968	0.055836112
0.29679	0.055857625
0.29678	0.055879138
0.29677	0.055897578
0.29676	0.055919347
0.29675	0.055939579
0.29674	0.055965702
0.29673	0.055985166
0.29672	0.056007704
0.29671	0.056033314
0.2967	0.056055339
0.29669	0.056082999
0.29668	0.056111171
0.29667	0.056137038
0.29666	0.056160087
0.29665	0.056182368
0.29664	0.056203881
0.29663	0.056224882
0.29662	0.056247932
0.29661	0.056267908
0.2966	0.056295055
0.29659	0.056325276
0.29658	0.056345252
0.29657	0.056368814
0.29656	0.056391864
0.29655	0.056418755
0.29654	0.05644078
0.29653	0.056466135
0.29652	0.056488416
0.29651	0.056525039
0.2965	0.056551162
0.29649	0.056578053
0.29648	0.056601359
0.29647	0.056627738
0.29646	0.056651044
0.29645	0.056682801
0.29644	0.05670918
0.29643	0.056735303
0.29642	0.056761425
0.29641	0.056783707
0.2964	0.056809317
0.29639	0.056831599
0.29638	0.056854392
0.29637	0.056878466
0.29636	0.05690587
0.29635	0.056934041
0.29634	0.056961701
0.29633	0.05698475
0.29632	0.057009081
0.29631	0.057039813
0.2963	0.057061839
0.29629	0.057086681
0.29628	0.057118182
0.29627	0.057148659
0.29626	0.057174782
0.29625	0.057197831
0.29624	0.057227027
0.29623	0.05724854
0.29622	0.057268773
0.29621	0.057297201
0.2962	0.057319482
0.29619	0.057341507
0.29618	0.057366605
0.29617	0.057392472
0.29616	0.057415778
0.29615	0.057440876
0.29614	0.057463414
0.29613	0.057482878
0.29612	0.057509001
0.29611	0.057531794
0.2961	0.057553819
0.29609	0.057580454
0.29608	0.057605041
0.29607	0.057636542
0.29606	0.057663433
0.29605	0.057688275
0.29604	0.057714142
0.29603	0.057736167
0.29602	0.057761778
0.29601	0.057785596
0.296	0.057806341
0.29599	0.057826573
0.29598	0.057847061
0.29597	0.057878819
0.29596	0.057899819
0.29595	0.057923893
0.29594	0.057947199
0.29593	0.057971017
0.29592	0.057991506
0.29591	0.058016348
0.2959	0.058045032
0.29589	0.058067825
0.29588	0.058093436
0.29587	0.058120327
0.29586	0.058146962
0.29585	0.058171805
0.29584	0.05819383
0.29583	0.058222514
0.29582	0.058245563
0.29581	0.058269125
0.2958	0.058289614
0.29579	0.058313432
0.29578	0.058339811
0.29577	0.058361324
0.29576	0.058384117
0.29575	0.058405118
0.29574	0.058425862
0.29573	0.058454034
0.29572	0.058475291
0.29571	0.058497572
0.2957	0.058521646
0.29569	0.05854572
0.29568	0.058565697
0.29567	0.058590539
0.29566	0.058615125
0.29565	0.05864176
0.29564	0.058670444
0.29563	0.058692469
0.29562	0.058721922
0.29561	0.058753167
0.2956	0.058780314
0.29559	0.058811303
0.29558	0.058834865
0.29557	0.058859963
0.29556	0.05888583
0.29555	0.058907599
0.29554	0.058937051
0.29553	0.058968552
0.29552	0.058990834
0.29551	0.059013115
0.2955	0.05903514
0.29549	0.059059726
0.29548	0.059089691
0.29547	0.059113765
0.29546	0.059140912
0.29545	0.059164986
0.29544	0.059193926
0.29543	0.059224659
0.29542	0.059258721
0.29541	0.059288686
0.2954	0.059321467
0.29539	0.059347334
0.29538	0.059373201
0.29537	0.059402653
0.29536	0.059425191
0.29535	0.05945157
0.29534	0.05947257
0.29533	0.059503303
0.29532	0.059532499
0.29531	0.059559134
0.2953	0.059588587
0.29529	0.059611124
0.29528	0.05964032
0.29527	0.059662089
0.29526	0.059690005
0.29525	0.059713567
0.29524	0.059746092
0.29523	0.059779642
0.29522	0.059801667
0.29521	0.059824717
0.2952	0.059850071
0.29519	0.059878755
0.29518	0.059903854
0.29517	0.059931769
0.29516	0.059959173
0.29515	0.059988113'''
}
		MINE_MIC.Pvalue_tables = dict((key, dict(map(float, row.split('\t')[:2]) for row in table.splitlines())) for key, table in Pvalue_tables_pasted.items())
		MINE_MIC.Pvalue_tables_keys = sorted(MINE_MIC.Pvalue_tables.keys())
	MINE_MIC.m.compute_score(X, Y)
	mic = MINE_MIC.m.mic()
	try:
		n_table = get_closest_lower(len(X), MINE_MIC.Pvalue_tables_keys)
		mic_table = get_closest_lower(mic, MINE_MIC.Pvalue_tables[n_table].keys())
	except ValueError:
		pval=1
	else:
		pval = MINE_MIC.Pvalue_tables[n_table][mic_table]
	return mic, pval
MINE_MIC.m = None

def get_closest_lower(target, values):
	import bisect
	V = sorted(values)
	index = bisect.bisect(V, target)
	if index == 0: raise ValueError
	return V[index-1]