--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/src/merge_metadata.py	Mon Feb 09 12:17:40 2015 -0500
@@ -0,0 +1,454 @@
+#!/usr/bin/env python
+#####################################################################################
+#Copyright (C) <2012>
+#
+#Permission is hereby granted, free of charge, to any person obtaining a copy of
+#this software and associated documentation files (the "Software"), to deal in the
+#Software without restriction, including without limitation the rights to use, copy,
+#modify, merge, publish, distribute, sublicense, and/or sell copies of the Software,
+#and to permit persons to whom the Software is furnished to do so, subject to
+#the following conditions:
+#
+#The above copyright notice and this permission notice shall be included in all copies
+#or substantial portions of the Software.
+#
+#THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED,
+#INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A
+#PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT
+#HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION
+#OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE
+#SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
+#
+# This file is a component of the MaAsLin (Multivariate Associations Using Linear Models), 
+# authored by the Huttenhower lab at the Harvard School of Public Health
+# (contact Timothy Tickle, ttickle@hsph.harvard.edu).
+#####################################################################################
+"""
+Examples
+~~~~~~~~
+
+``metadata.txt``::
+
+	-	Y	Z
+	a	1	x
+	b	0	y
+	c		z
+
+``data.pcl``::
+
+	-	a	b	c
+	A|B	1	2	3
+	A|C	4	5	6
+	D|E	7	8	9
+
+``Examples``::
+
+	$ merge_metadata.py metadata.txt < data.pcl
+	sample	a	b	c
+	Y	1	0
+	Z	x	y	z
+	A	0.416667	0.466667	0.5
+	A|B	0.0833333	0.133333	0.166667
+	A|C	0.333333	0.333333	0.333333
+	D|E	0.583333	0.533333	0.5
+
+	$ merge_metadata.py metadata.txt -t 0 < data.pcl
+	sample	a	b	c
+	Y	1	0
+	Z	x	y	z
+	A|B	0.0833333	0.133333	0.166667
+	A|C	0.333333	0.333333	0.333333
+	D|E	0.583333	0.533333	0.5
+
+	$ merge_metadata.py metadata.txt -t 1 < data.pcl
+	sample	a	b	c
+	Y	1	0
+	Z	x	y	z
+	A	0.416667	0.466667	0.5
+	D	0.583333	0.533333	0.5
+
+	$ merge_metadata.py metadata.txt -t 0 -n < data.pcl
+	sample	a	b	c
+	Y	1	0
+	Z	x	y	z
+	A|B	1	2	3
+	A|C	4	5	6
+	D|E	7	8	9
+
+	$ merge_metadata.py metadata.txt -t 0 -m 0.8 -s "-" < data.pcl
+	sample	b	c
+	Y	0	-
+	Z	y	z
+	A|B	0.133333	0.166667
+	A|C	0.333333	0.333333
+	D|E	0.533333	0.5
+
+	$ merge_metadata.py -t 0 < data.pcl
+	sample	a	b	c
+	A|B	1	2	3
+	A|C	4	5	6
+	D|E	7	8	9
+
+.. testsetup::
+
+	from merge_metadata import *
+"""
+
+import argparse
+import blist
+import csv
+import re
+import sys
+
+c_dTarget	= 1.0
+c_fRound	= False
+
+class CClade:
+	
+	def __init__( self ):
+		
+		self.m_hashChildren = {}
+		self.m_adValues = None
+	
+	def get( self, astrClade ):
+		
+		return self.m_hashChildren.setdefault(
+			astrClade[0], CClade( ) ).get( astrClade[1:] ) if astrClade else self
+			
+	def set( self, adValues ):
+		
+		self.m_adValues = blist.blist( [0] ) * len( adValues )
+		for i, d in enumerate( adValues ):
+			if d:
+				self.m_adValues[i] = d
+		
+	def impute( self ):
+		
+		if not self.m_adValues:
+			for pChild in self.m_hashChildren.values( ):
+				adChild = pChild.impute( )
+				if self.m_adValues:
+					for i in range( len( adChild or [] ) ):
+						if adChild[i]:
+							self.m_adValues[i] += adChild[i]
+				elif adChild:
+					self.m_adValues = adChild[:] 
+					
+		return self.m_adValues
+	
+	def _freeze( self, hashValues, iTarget, astrClade, iDepth, fLeaves ):
+		
+		fHit = ( not iTarget ) or ( ( fLeaves and ( iDepth == iTarget ) ) or ( ( not fLeaves ) and ( iDepth <= iTarget ) ) )
+		iDepth += 1
+		setiRet = set()
+		if self.m_hashChildren:
+			for strChild, pChild in self.m_hashChildren.items( ):
+				setiRet |= pChild._freeze( hashValues, iTarget, astrClade + [strChild], iDepth, fLeaves )
+			setiRet = set( ( i + 1 ) for i in setiRet )
+		else:
+			setiRet.add( 0 )
+		if iTarget < 0:
+			if fLeaves:
+				fHit = -( iTarget + 1 ) in setiRet
+			else:
+				fHit = -( iTarget + 1 ) <= max( setiRet )
+		if astrClade and self.m_adValues and fHit:
+			hashValues["|".join( astrClade )] = self.m_adValues
+		return setiRet
+	
+	def freeze( self, hashValues, iTarget, fLeaves ):
+		
+		self._freeze( hashValues, iTarget, [], 0, fLeaves )
+	
+	def _repr( self, strClade ):
+
+		strRet = "<"
+		if strClade:
+			strRet += "%s %s" % (strClade, self.m_adValues)
+			if self.m_hashChildren:
+				strRet += " "
+		if self.m_hashChildren:
+			strRet += " ".join( p._repr( s ) for (s, p) in self.m_hashChildren.items( ) )
+		
+		return ( strRet + ">" )
+		
+	def __repr__( self ):
+		
+		return self._repr( "" )
+
+"""
+pTree = CClade( )
+pTree.get( ("A", "B") ).set( [1, 2, 3] )
+pTree.get( ("A", "C") ).set( [4, 5, 6] )
+pTree.get( ("D", "E") ).set( [7, 8, 9] )
+iTaxa = 0
+if iTaxa:
+	pTree.impute( )
+hashFeatures = {}
+pTree.freeze( hashFeatures, iTaxa )
+print( pTree )
+print( hashFeatures )
+sys.exit( 0 )
+#"""
+
+def merge_metadata( aastrMetadata, aastrData, ostm, fNormalize, strMissing, astrExclude, dMin, iTaxa, fLeaves ):
+	"""
+	Joins and outputs a data matrix with a metadata matrix, optionally normalizing and filtering it.
+	A pipe-delimited taxonomy hierarchy can also be dynamically added or removed.
+	
+	:param	aastrMetadata:	Split lines from which metadata are read.
+	:type	aastrMetadata:	collection of string collections
+	:param	aastrData:		Split lines from which data are read.
+	:type	aastrData:		collection of string collections
+	:param	ostm:			Output stream to which joined rows are written.
+	:type	ostm:			output stream
+	:param	fNormalize:		If true, divide data values by column sums.
+	:type	fNormalize:		bool
+	:param	strMissing:		Representation for missing metadata values.
+	:type	strMissing:		str
+	:param	astrExclude:	Lines from which excluded IDs are read.
+	:type	astrExclude:	collection of strings
+	:param	dMin:			Minimum fraction of maximum value for per-column quality control.
+	:type	dMin:			bool
+	:param	iTaxa:			Depth of taxonomy to be computed, -1 = leaves only, 0 = no change
+	:type	iTaxa:			int
+	:param	fLeaves:		Output only leaves, not complete taxonomy; ignored if taxa = 0
+	:type	fLeaves:		bool
+
+	Metadata are optional; if not provided, data will be optionally normalized or its taxonomy
+	modified as requested.  Metadata are provided one row per sample, data one column per
+	sample, both files tab-delimited text with one header row and one header column.
+	
+	Metadata IDs that do not match data IDs are discarded, and data IDs without corresponding
+	metadata IDs are given missing values.  Missing data values are always treated (and output)
+	as zero.
+	
+	Per-column quality control is performed if the requested minimum fraction is greater than
+	zero.  Specifically, for each column i, the row j containing the maximum value d is
+	identified.  If d is less than the minimum fraction of row j's maximum value over all columns,
+	the entire column i is removed.
+	
+	A taxonomy hierarchy will be calculated by default if row IDs are pipe-delimited, i.e. of
+	the form A|B|C.  All parent clades are computed by default, e.g. A|B and A, save when
+	they would be identical to a more specific child clade.  Negative values are counted from the
+	bottom (right) of the hierarchy rather than the top.  The special value of 0 deactivates
+	hierarchy calculation.
+	
+	>>> aastrMetadata = [[t.strip( ) for t in s] for s in ("-YZ", "a1x", "b0y", "c z")]
+	>>> aastrData = [s.split( ) for s in ( \
+		"-	a	b	c",		\
+		"A|B	1	2	3",	\
+		"A|C	4	5	6",	\
+		"D|E	7	8	9")]
+	>>> merge_metadata( aastrMetadata, aastrData, sys.stdout, True, "", [], 0.01, -1, False ) #doctest: +NORMALIZE_WHITESPACE
+	sample	a	b	c
+	Y	1	0
+	Z	x	y	z
+	A	0.416667	0.466667	0.5
+	A|B	0.0833333	0.133333	0.166667
+	A|C	0.333333	0.333333	0.333333
+	D|E	0.583333	0.533333	0.5
+
+	>>> merge_metadata( aastrMetadata, aastrData, sys.stdout, True, "", [], 0.01, -1, True ) #doctest: +NORMALIZE_WHITESPACE
+	sample	a	b	c
+	Y	1	0
+	Z	x	y	z
+	A|B	0.0833333	0.133333	0.166667
+	A|C	0.333333	0.333333	0.333333
+	D|E	0.583333	0.533333	0.5
+
+	>>> merge_metadata( aastrMetadata, aastrData, sys.stdout, True, "", [], 0, 0, True ) #doctest: +NORMALIZE_WHITESPACE
+	sample	a	b	c
+	Y	1	0
+	Z	x	y	z
+	A|B	0.0833333	0.133333	0.166667
+	A|C	0.333333	0.333333	0.333333
+	D|E	0.583333	0.533333	0.5
+
+	>>> merge_metadata( aastrMetadata, aastrData, sys.stdout, True, "", [], 0, 1, False ) #doctest: +NORMALIZE_WHITESPACE
+	sample	a	b	c
+	Y	1	0
+	Z	x	y	z
+	A	0.416667	0.466667	0.5
+	D	0.583333	0.533333	0.5
+
+	>>> merge_metadata( aastrMetadata, aastrData, sys.stdout, True, "", [], 0, -1, True ) #doctest: +NORMALIZE_WHITESPACE
+	sample	a	b	c
+	Y	1	0
+	Z	x	y	z
+	A|B	0.0833333	0.133333	0.166667
+	A|C	0.333333	0.333333	0.333333
+	D|E	0.583333	0.533333	0.5
+
+	>>> merge_metadata( aastrMetadata, aastrData, sys.stdout, False, "", [], 0, 0, True ) #doctest: +NORMALIZE_WHITESPACE
+	sample	a	b	c
+	Y	1	0
+	Z	x	y	z
+	A|B	1	2	3
+	A|C	4	5	6
+	D|E	7	8	9
+
+	>>> merge_metadata( aastrMetadata, aastrData, sys.stdout, True, "-", [], 0.8, 0, True ) #doctest: +NORMALIZE_WHITESPACE
+	sample	b	c
+	Y	0	-
+	Z	y	z
+	A|B	0.133333	0.166667
+	A|C	0.333333	0.333333
+	D|E	0.533333	0.5
+
+	>>> merge_metadata( None, aastrData, sys.stdout, False, "", [], 0, 0, True ) #doctest: +NORMALIZE_WHITESPACE
+	sample	a	b	c
+	A|B	1	2	3
+	A|C	4	5	6
+	D|E	7	8	9
+
+	>>> merge_metadata( aastrMetadata, aastrData, sys.stdout, True, "", ["b"], 0.01, -1, False ) #doctest: +NORMALIZE_WHITESPACE
+	sample	a	c
+	Y	1
+	Z	x	z
+	A	0.416667	0.5
+	A|B	0.0833333	0.166667
+	A|C	0.333333	0.333333
+	D|E	0.583333	0.5
+	"""
+
+        #Put metadata in a dictionary
+        #{"First line element",["line element 2","line element 3","line element 4"]}
+        #If there is no metadata then 
+	astrMetadata = None
+	hashMetadata = {}
+	for astrLine in ( aastrMetadata or [] ):
+		if astrMetadata:
+			hashMetadata[astrLine[0]] = astrLine[1:]
+		else:
+			astrMetadata = astrLine[1:]
+	
+	astrHeaders = adSeqs = iCol = None
+	pTree = CClade( )
+	aastrRaw = []
+	for astrLine in aastrData:
+		if astrHeaders:
+			if ( astrLine[0] == "EWEIGHT" ) or ( astrLine[0] == "total" ) or \
+				( len( astrLine ) < 2 ):
+				continue
+			try:
+				adCounts = [( float(strCur) if len( strCur.strip( ) ) else 0 ) for
+					strCur in astrLine[iCol:]]
+			except ValueError:
+				aastrRaw.append( astrLine )
+				continue
+			for i in range( len( adCounts ) ):
+				adSeqs[i] += adCounts[i]
+			if ( iCol > 1 ) and ( astrLine[0] != astrLine[1] ):
+				if astrLine[1].find( astrLine[0] ) >= 0:
+					astrLine[0] = astrLine[1]
+				else:
+					astrLine[0] += " " + astrLine[1]
+			pTree.get( astrLine[0].split( "|" ) ).set( adCounts )
+		else:
+			iCol = 2 if ( astrLine[1].upper( ) == "NAME" ) else 1
+			astrHeaders = [strCur.replace( " ", "_" ) for strCur in astrLine[iCol:]]
+			adSeqs = [0] * len( astrHeaders )
+			
+	if iTaxa:
+		pTree.impute( )
+	hashFeatures = {}
+	pTree.freeze( hashFeatures, iTaxa, fLeaves )
+	setstrFeatures = hashFeatures.keys( )
+	
+	afOmit = [False] * len( astrHeaders )
+	if dMin > 0:
+		aadData = list(hashFeatures.values( ))
+		for i in range( len( astrHeaders ) ):
+			iMax = max( range( len( aadData ) ), key = lambda j: aadData[j][i] )
+			dMaxUs = aadData[iMax][i]
+			dMaxThem = max( aadData[iMax][j] for j in ( range( i ) + range( i + 1, len( astrHeaders ) ) ) )
+			if dMaxUs < ( dMin * dMaxThem ):
+				sys.stderr.write( "Omitting: %s\n" % astrHeaders[i] )
+				afOmit[i] = True
+	
+	if astrExclude:
+		setstrExclude = set(s.strip( ) for s in astrExclude)
+		for i in range( len( astrHeaders ) ):
+			if ( not afOmit[i] ) and ( astrHeaders[i] in setstrExclude ):
+				afOmit[i] = True
+	
+	adMult = [( ( c_dTarget / d ) if ( fNormalize and ( d > 0 ) ) else 1 ) for d in adSeqs]
+	for strFeature, adCounts in hashFeatures.items( ):
+		for i in range( len( adCounts ) ):
+			if adCounts[i]:
+				adCounts[i] *= adMult[i]
+				if c_fRound:
+					adCounts[i] = round( adCounts[i] )
+	for strFeature, adCounts in hashFeatures.items( ):
+		astrFeature = strFeature.strip( ).split( "|" )
+		while len( astrFeature ) > 1:
+			astrFeature = astrFeature[:-1]
+			strParent = "|".join( astrFeature )
+			adParent = hashFeatures.get( strParent )
+			if adParent == adCounts:
+				del hashFeatures[strParent]
+				setstrFeatures.remove( strParent )
+	
+	if astrMetadata:
+		for i in range( len( astrMetadata ) ):
+			hashFeatures[astrMetadata[i]] = astrCur = []
+			for strSubject in astrHeaders:
+				astrSubject = hashMetadata.get( strSubject )
+				if not astrSubject:
+					strSubject = re.sub( '_.*$', "", strSubject )
+					astrSubject = hashMetadata.get( strSubject, [] )
+				astrCur.append( astrSubject[i] if ( i < len( astrSubject ) ) else "" )
+	
+	astrFeatures = sorted( astrMetadata or [] ) + sorted( setstrFeatures )
+	aiHeaders = filter( lambda i: not afOmit[i], range( len( astrHeaders ) ) )
+	csvw = csv.writer( sys.stdout, csv.excel_tab )
+	csvw.writerow( ["sample"] + [astrHeaders[i] for i in aiHeaders] )
+	for iFeature in range( len( astrFeatures ) ):
+		strFeature = astrFeatures[iFeature]
+		adFeature = hashFeatures[strFeature]
+		astrValues = [adFeature[i] for i in aiHeaders]
+		for i in range( len( astrValues ) ):
+			strValue = astrValues[i]
+			if type( strValue ) in (int, float):
+				astrValues[i] = "%g" % astrValues[i]
+			elif ( not strValue ) or ( ( type( strValue ) == str ) and
+				( len( strValue ) == 0 ) ):
+				astrValues[i] = strMissing
+		csvw.writerow( [strFeature] + astrValues )
+
+	for astrRaw in aastrRaw:
+		csvw.writerow( [astrRaw[i] for i in aiHeaders] )
+
+argp = argparse.ArgumentParser( prog = "merge_metadata.py",
+	description = "Join a data matrix with a metadata matrix, optionally normalizing and filtering it.\n\n" +
+	"A pipe-delimited taxonomy hierarchy can also be dynamically added or removed." )
+argp.add_argument( "-n",		dest = "fNormalize",	action = "store_false",
+	help = "Don't normalize data values by column sums" )
+argp.add_argument( "-s",		dest = "strMissing",	metavar = "missing",
+	type = str,		default = " ",
+	help = "String representing missing metadata values" )
+argp.add_argument( "-m",		dest = "dMin",			metavar = "min",
+	type = float,	default = 0.01,
+	help = "Per-column quality control, minimum fraction of maximum value" )
+argp.add_argument( "-t",		dest = "iTaxa",			metavar = "taxa",
+	type = int,		default = -1,
+	help = "Depth of taxonomy to be computed, negative = from right, 0 = no change" )
+argp.add_argument( "-l",		dest = "fLeaves",		action = "store_true",
+	help = "Output only leaves, not complete taxonomy" )
+argp.add_argument( "-x",		dest = "istmExclude",	metavar = "exclude.txt",
+	type = file,
+	help = "File from which sample IDs to exclude are read" )
+argp.add_argument( "istmMetadata",	metavar = "metadata.txt",
+	type = file,	nargs = "?",
+	help = "File from which metadata is read" )
+__doc__ = "::\n\n\t" + argp.format_help( ).replace( "\n", "\n\t" ) + __doc__
+
+def _main( ):
+	args = argp.parse_args( )
+	merge_metadata( args.istmMetadata and csv.reader( args.istmMetadata, csv.excel_tab ),
+		csv.reader( sys.stdin, csv.excel_tab ), sys.stdout, args.fNormalize, args.strMissing,
+			args.istmExclude, args.dMin, args.iTaxa, args.fLeaves )
+	
+if __name__ == "__main__":
+	_main( )