annotate SMART/Java/Python/clusterize.py @ 62:8c42a6d7ffd4

Added simple test BED file.

#! /usr/bin/env python
#
# Copyright INRA-URGI 2009-2010
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from commons.core.writer.WriterChooser import WriterChooser
"""Clusterize a set of transcripts"""

import os, os.path, random
from optparse import OptionParser
from heapq import heappush, heappop
from commons.core.parsing.ParserChooser import ParserChooser
from commons.core.writer.Gff3Writer import Gff3Writer
from SMART.Java.Python.structure.Transcript import Transcript
from SMART.Java.Python.ncList.NCListFilePickle import NCListFileUnpickle
from SMART.Java.Python.ncList.FileSorter import FileSorter
from SMART.Java.Python.misc.Progress import Progress
from SMART.Java.Python.misc.UnlimitedProgress import UnlimitedProgress

class Clusterize(object):

	def __init__(self, verbosity):
		self.parsers           = {}
		self.sortedFileNames   = {}
		self.normalize         = False
		self.presorted         = False
		self.distance          = 1
		self.collinear         = False
		self.nbWritten         = 0
		self.nbMerges          = 0
		self.verbosity         = verbosity
		self.splittedFileNames = {}
		self.chromosomes       = set()

	def __del__(self):
		for fileName1 in self.splittedFileNames:
			for fileName2 in self.splittedFileNames[fileName1].values():
				os.remove(fileName2)

	def setInputFiles(self, fileNames, format):
		parserChooser = ParserChooser(self.verbosity)
		parserChooser.findFormat(format)
		for fileName in fileNames:
			self.parsers[fileName] = parserChooser.getParser(fileName)
			self.sortedFileNames[fileName] = "%s_sorted_%d.pkl" % (os.path.splitext(fileName)[0], random.randint(1, 100000))
			if "SMARTTMPPATH" in os.environ:
				self.sortedFileNames[fileName] = os.path.join(os.environ["SMARTTMPPATH"], os.path.basename(self.sortedFileNames[fileName]))

	def setOutputFileName(self, fileName, format="gff3", title="S-MART", feature="transcript", featurePart="exon"):
		writerChooser = WriterChooser()
		writerChooser.findFormat(format)
		self.writer = writerChooser.getWriter(fileName)
		self.writer.setTitle(title)
		self.writer.setFeature(feature)
		self.writer.setFeaturePart(featurePart)

	def setDistance(self, distance):
		self.distance = distance

	def setColinear(self, collinear):
		self.collinear = collinear

	def setNormalize(self, normalize):
		self.normalize = normalize
		
	def setPresorted(self, presorted):
		self.presorted = presorted

	def _sortFiles(self):
		if self.presorted:
			return
		for fileName, parser in self.parsers.iteritems():
			fs = FileSorter(parser, self.verbosity-4)
			fs.perChromosome(True)
			fs.setPresorted(self.presorted)
			fs.setOutputFileName(self.sortedFileNames[fileName])
			fs.sort()
			self.splittedFileNames[fileName] = fs.getOutputFileNames()
			self.nbElementsPerChromosome     = fs.getNbElementsPerChromosome()
			self.nbElements                  = fs.getNbElements()
			self.chromosomes.update(self.splittedFileNames[fileName].keys())
		
	def _iterate(self):
		progress = UnlimitedProgress(10000, "Reading input file", self.verbosity)
		transcripts = []
		heap        = []
		parsersSets = []
		if self.chromosomes:
			parsersSets.append(self.parsers.values())
		else:
			for chromosome in self.chromosomes:
				parsersSets.append([self.splittedFileNames[fileName][chromosome] for fileName in self.splittedFileNames if chromosome in self.splittedFileNames[fileName]])
		for parsers in parsersSets:
			for parser in parsers:
				iterator = parser.getIterator()
				for transcript in iterator:
					if transcript.__class__.__name__ == "Mapping":
						transcript = transcript.getTranscript()
					heappush(heap, (transcript.getChromosome(), transcript.getStart(), -transcript.getEnd(), transcript, iterator))
					break
			while heap:
				chromosome, start, end, newTranscript, iterator = heappop(heap)
				for transcript in iterator:
					if transcript.__class__.__name__ == "Mapping":
						transcript = transcript.getTranscript()
					heappush(heap, (transcript.getChromosome(), transcript.getStart(), -transcript.getEnd(), transcript, iterator))
					break
				newTranscripts = []
				if newTranscript.__class__.__name__ == "Mapping":
					newTranscript = newTranscript.getTranscript()
				for oldTranscript in transcripts:
					if self._checkOverlap(newTranscript, oldTranscript):
						self._merge(newTranscript, oldTranscript)
					elif self._checkPassed(newTranscript, oldTranscript):
						self._write(oldTranscript)
					else:
						newTranscripts.append(oldTranscript)
				newTranscripts.append(newTranscript)
				transcripts = newTranscripts
				progress.inc()
			for transcript in transcripts:
				self._write(transcript)
		progress.done()

	def _merge(self, transcript1, transcript2):
		self.nbMerges += 1
		transcript2.setDirection(transcript1.getDirection())
		transcript1.merge(transcript2)

	def _write(self, transcript):
		self.nbWritten += 1
		self.writer.addTranscript(transcript)

	def _checkOverlap(self, transcript1, transcript2):
		if transcript1.getChromosome() != transcript2.getChromosome():
			return False
		if self.collinear and transcript1.getDirection() != transcript2.getDirection():
			return False
		if transcript1.getDistance(transcript2) > self.distance:
			return False
		return True

	def _checkPassed(self, transcript1, transcript2):
		return ((transcript1.getChromosome() != transcript2.getChromosome()) or (transcript1.getDistance(transcript2) > self.distance))

	def run(self):
		self._sortFiles()
		self._iterate()
		self.writer.close()
		if self.verbosity > 0:
			print "# input:   %d" % (self.nbElements)
			print "# written: %d (%d%% overlaps)" % (self.nbWritten, 0 if (self.nbElements == 0) else ((float(self.nbWritten) / self.nbElements) * 100))
			print "# merges:  %d" % (self.nbMerges)
		

if __name__ == "__main__":
	description = "Clusterize v1.0.3: clusterize the data which overlap. [Category: Merge]"

	parser = OptionParser(description = description)
	parser.add_option("-i", "--inputs",       dest="inputFileNames", action="store",				     type="string", help="input files (separated by commas) [compulsory] [format: string]")
	parser.add_option("-f", "--format",       dest="format",		 action="store",				     type="string", help="format of file [format: transcript file format]")
	parser.add_option("-o", "--output",       dest="outputFileName", action="store",				     type="string", help="output file [compulsory] [format: output file in transcript format given by -u]")
	parser.add_option("-u", "--outputFormat", dest="outputFormat",   action="store",      default="gff",		        type="string", help="output file format [format: transcript file format]")
	parser.add_option("-c", "--collinear",    dest="collinear",      action="store_true", default=False,				help="merge collinear transcripts only [format: bool] [default: false]")
	parser.add_option("-d", "--distance",     dest="distance",       action="store",      default=0,     type="int",    help="max. distance between two transcripts to be merged [format: int] [default: 0]")
	parser.add_option("-n", "--normalize",    dest="normalize",      action="store_true", default=False,				help="normalize the number of reads per cluster by the number of mappings per read [format: bool] [default: false]")
	parser.add_option("-s", "--sorted",       dest="sorted",		 action="store_true", default=False,				help="input is already sorted [format: bool] [default: false]")
	parser.add_option("-v", "--verbosity",    dest="verbosity",      action="store",      default=1,     type="int",    help="trace level [format: int] [default: 1]")
	(options, args) = parser.parse_args()

	c = Clusterize(options.verbosity)
	c.setInputFiles(options.inputFileNames.split(","), options.format)
	c.setOutputFileName(options.outputFileName, options.outputFormat)
	c.setColinear(options.collinear)
	c.setDistance(options.distance)
	c.setNormalize(options.normalize)
	c.setPresorted(options.sorted)
	c.run()