1 """Convert srf and qseq archive files to fastqs
5 from pprint import pformat,pprint
8 from urlparse import urljoin, urlparse
10 from htsworkflow.pipelines.sequences import scan_for_sequences, \
11 update_model_sequence_library
12 from htsworkflow.pipelines.samplekey import SampleKey
13 from htsworkflow.pipelines import qseq2fastq
14 from htsworkflow.pipelines import srf2fastq
15 from htsworkflow.pipelines import desplit_fastq
16 from htsworkflow.submission.fastqname import FastqName
17 from htsworkflow.util.rdfhelp import get_model, dump_model, load_into_model, \
20 from htsworkflow.util.rdfns import *
21 from htsworkflow.util.conversion import parse_flowcell_id
23 from django.conf import settings
24 from django.template import Context, loader
28 LOGGER = logging.getLogger(__name__)
30 COMPRESSION_EXTENSIONS = {
35 class CondorFastqExtract(object):
36 def __init__(self, host, sequences_path,
41 """Extract fastqs from results archive
44 host (str): root of the htsworkflow api server
45 apidata (dict): id & key to post to the server
46 sequences_path (str): root of the directory tree to scan for files
47 log_path (str): where to put condor log files
48 compression (str): one of 'gzip', 'bzip2'
49 force (bool): do we force overwriting current files?
52 self.model = get_model(model)
53 self.sequences_path = sequences_path
54 self.log_path = log_path
55 self.compression=compression
57 LOGGER.info("CondorFastq host={0}".format(self.host))
58 LOGGER.info("CondorFastq sequences_path={0}".format(self.sequences_path))
59 LOGGER.info("CondorFastq log_path={0}".format(self.log_path))
60 LOGGER.info("Compression {0}".format(self.compression))
62 def create_scripts(self, result_map ):
64 Generate condor scripts to build any needed fastq files
67 result_map: htsworkflow.submission.results.ResultMap()
69 template_map = {'srf': 'srf.condor',
70 'qseq': 'qseq.condor',
71 'split_fastq': 'split_fastq.condor',
75 pythonpath = os.environ.get('PYTHONPATH', None)
76 if pythonpath is not None:
77 env = "PYTHONPATH=%s" % (pythonpath,)
78 condor_entries = self.build_condor_arguments(result_map)
79 for script_type in template_map.keys():
80 template = loader.get_template(template_map[script_type])
81 variables = {'python': sys.executable,
82 'logdir': self.log_path,
84 'args': condor_entries[script_type],
85 'root_url': self.host,
87 context = Context(variables)
89 with open(script_type + '.condor','w+') as outstream:
90 outstream.write(template.render(context))
92 def build_condor_arguments(self, result_map):
93 condor_entries = {'srf': [],
97 conversion_funcs = {'srf': self.condor_srf_to_fastq,
98 'qseq': self.condor_qseq_to_fastq,
99 'split_fastq': self.condor_desplit_fastq
101 sequences = self.find_archive_sequence_files(result_map)
102 needed_targets = self.update_fastq_targets(result_map, sequences)
104 for target_pathname, available_sources in needed_targets.items():
105 LOGGER.debug(' target : %s' % (target_pathname,))
106 LOGGER.debug(' candidate sources: %s' % (available_sources,))
107 for condor_type in available_sources.keys():
108 conversion = conversion_funcs.get(condor_type, None)
109 if conversion is None:
110 errmsg = "Unrecognized type: {0} for {1}"
111 LOGGER.error(errmsg.format(condor_type,
112 pformat(available_sources)))
114 sources = available_sources.get(condor_type, None)
116 if sources is not None:
117 condor_entries.setdefault(condor_type, []).append(
118 conversion(sources, target_pathname))
120 LOGGER.warn(" need file %s", target_pathname)
122 return condor_entries
124 def find_archive_sequence_files(self, result_map):
126 Find archived sequence files associated with our results.
128 self.import_libraries(result_map)
129 flowcell_ids = self.find_relevant_flowcell_ids()
130 self.import_sequences(flowcell_ids)
133 prefix libns: <http://jumpgate.caltech.edu/wiki/LibraryOntology#>
134 prefix rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#>
135 prefix xsd: <http://www.w3.org/2001/XMLSchema#>
137 select ?filenode ?filetype ?cycle ?lane_number ?read
139 ?flowcell ?flowcell_id ?read_length
140 ?flowcell_type ?flowcell_status
142 ?filenode libns:cycle ?cycle ;
143 libns:lane_number ?lane_number ;
145 libns:flowcell ?flowcell ;
146 libns:flowcell_id ?flowcell_id ;
147 libns:library ?library ;
148 libns:library_id ?library_id ;
149 libns:file_type ?filetype ;
150 a libns:IlluminaResult .
151 ?flowcell libns:read_length ?read_length ;
152 libns:flowcell_type ?flowcell_type .
153 OPTIONAL { ?flowcell libns:flowcell_status ?flowcell_status }
154 FILTER(?filetype != libns:sequencer_result)
157 LOGGER.debug("find_archive_sequence_files query: %s",
159 query = RDF.SPARQLQuery(query_text)
161 for r in query.execute(self.model):
162 library_id = fromTypedNode(r['library_id'])
163 if library_id in result_map:
164 seq = SequenceResult(r)
165 LOGGER.debug("Creating sequence result for library %s: %s",
171 def import_libraries(self, result_map):
172 for lib_id in result_map.keys():
173 lib_id_encoded = lib_id.encode('utf-8')
174 liburl = urljoin(self.host, 'library/%s/' % (lib_id_encoded,))
175 library = RDF.Node(RDF.Uri(liburl))
176 self.import_library(library)
178 def import_library(self, library):
179 """Import library data into our model if we don't have it already
181 q = RDF.Statement(library, rdfNS['type'], libraryOntology['Library'])
183 if not self.model.contains_statement(q):
185 load_into_model(self.model, 'rdfa', library)
186 LOGGER.debug("Did we import %s: %s", library.uri, present)
188 def find_relevant_flowcell_ids(self):
189 """Generate set of flowcell ids that had samples of interest on them
191 flowcell_query = RDF.SPARQLQuery("""
192 prefix libns: <http://jumpgate.caltech.edu/wiki/LibraryOntology#>
194 select distinct ?flowcell ?flowcell_id
196 ?library a libns:Library ;
197 libns:has_lane ?lane .
198 ?lane libns:flowcell ?flowcell .
199 ?flowcell libns:flowcell_id ?flowcell_id .
202 for r in flowcell_query.execute(self.model):
203 flowcell_ids.add( fromTypedNode(r['flowcell_id']) )
205 a_lane = self.model.get_target(r['flowcell'],
206 libraryOntology['has_lane'])
209 # we lack information about which lanes were on this flowcell
210 load_into_model(self.model, 'rdfa', r['flowcell'])
211 LOGGER.debug("Did we imported %s: %s" % (r['flowcell'].uri,
216 def import_sequences(self, flowcell_ids):
218 for f in flowcell_ids:
219 seq_dirs.append(os.path.join(self.sequences_path, str(f)))
220 sequences = scan_for_sequences(seq_dirs)
221 for seq in sequences:
222 seq.save_to_model(self.model, self.host)
223 update_model_sequence_library(self.model, self.host)
225 def update_fastq_targets(self, result_map, raw_files):
226 """Return list of fastq files that need to be built.
228 Also update model with link between illumina result files
229 and our target fastq file.
231 # find what targets we're missing
233 for seq in raw_files:
236 filename_attributes = {
237 'flowcell': seq.flowcell_id,
238 'lib_id': seq.library_id,
239 'lane': seq.lane_number,
242 'compression_extension': COMPRESSION_EXTENSIONS[self.compression],
243 'is_paired': seq.ispaired
246 fqName = FastqName(**filename_attributes)
248 result_dir = result_map[seq.library_id]
249 target_pathname = os.path.join(result_dir, fqName.filename)
250 if self.force or not os.path.exists(target_pathname):
251 t = needed_targets.setdefault(target_pathname, {})
252 t.setdefault(seq.filetype, []).append(seq)
253 self.add_target_source_links(target_pathname, seq)
254 return needed_targets
256 def add_target_source_links(self, target, seq):
257 """Add link between target pathname and the 'lane' that produced it
258 (note lane objects are now post demultiplexing.)
260 target_uri = 'file://' + target.encode('utf-8')
261 target_node = RDF.Node(RDF.Uri(target_uri))
262 source_stmt = RDF.Statement(target_node, dcNS['source'], seq.filenode)
263 self.model.add_statement(source_stmt)
265 def condor_srf_to_fastq(self, sources, target_pathname):
267 raise ValueError("srf to fastq can only handle one file")
270 if sources[0].flowcell_id == '30DY0AAXX':
274 'sources': [sources[0].path],
275 'pyscript': srf2fastq.__file__,
276 'flowcell': sources[0].flowcell_id,
277 'ispaired': sources[0].ispaired,
278 'target': target_pathname,
279 'target_right': target_pathname.replace('_r1.fastq', '_r2.fastq'),
284 def condor_qseq_to_fastq(self, sources, target_pathname):
286 for source in sources:
287 paths.append(source.path)
290 'pyscript': qseq2fastq.__file__,
291 'flowcell': sources[0].flowcell_id,
292 'target': target_pathname,
294 'ispaired': sources[0].ispaired,
295 'istar': len(sources) == 1,
298 def condor_desplit_fastq(self, sources, target_pathname):
300 for source in sources:
301 paths.append(source.path)
303 compression_argument = ''
305 compression_argument = '--'+self.compression
308 'pyscript': desplit_fastq.__file__,
309 'target': target_pathname,
310 'compression': compression_argument,
312 'ispaired': sources[0].ispaired,
316 def make_lane_dict(lib_db, lib_id):
318 Convert the lane_set in a lib_db to a dictionary
319 indexed by flowcell ID
322 for lane in lib_db[lib_id]['lane_set']:
323 flowcell_id, status = parse_flowcell_id(lane['flowcell'])
324 lane['flowcell'] = flowcell_id
325 result.append((lane['flowcell'], lane))
328 class SequenceResult(object):
329 """Convert the sparql query result from find_archive_sequence_files
331 def __init__(self, result):
332 self.filenode = result['filenode']
333 self._filetype = result['filetype']
334 self.cycle = fromTypedNode(result['cycle'])
335 self.lane_number = fromTypedNode(result['lane_number'])
336 self.read = fromTypedNode(result['read'])
337 if type(self.read) in types.StringTypes:
339 self.library = result['library']
340 self.library_id = fromTypedNode(result['library_id'])
341 self.flowcell = result['flowcell']
342 self.flowcell_id = fromTypedNode(result['flowcell_id'])
343 self.flowcell_type = fromTypedNode(result['flowcell_type'])
344 self.flowcell_status = fromTypedNode(result['flowcell_status'])
347 """is this sequence / flowcell 'good enough'"""
348 if self.flowcell_status is not None and \
349 self.flowcell_status.lower() == "failed":
352 isgood = property(_is_good)
354 def _get_ispaired(self):
355 if self.flowcell_type.lower() == "paired":
359 ispaired = property(_get_ispaired)
361 def _get_filetype(self):
362 return strip_namespace(libraryOntology, self._filetype)
363 filetype = property(_get_filetype)
366 url = urlparse(str(self.filenode.uri))
367 if url.scheme == 'file':
370 errmsg = u"Unsupported scheme {0} for {1}"
371 raise ValueError(errmsg.format(url.scheme, unicode(url)))
372 path = property(_get_path)
375 return "SequenceResult({0},{1},{2})".format(
377 str(self.library_id),
378 str(self.flowcell_id))