1 """Convert srf and qseq archive files to fastqs
5 from pprint import pformat,pprint
8 from six.moves.urllib.parse 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
25 from django.utils.encoding import smart_str
29 LOGGER = logging.getLogger(__name__)
31 COMPRESSION_EXTENSIONS = {
36 class CondorFastqExtract(object):
37 def __init__(self, host, sequences_path,
42 """Extract fastqs from results archive
45 host (str): root of the htsworkflow api server
46 apidata (dict): id & key to post to the server
47 sequences_path (str): root of the directory tree to scan for files
48 log_path (str): where to put condor log files
49 compression (str): one of 'gzip', 'bzip2'
50 force (bool): do we force overwriting current files?
53 self.model = get_model(model)
54 self.sequences_path = sequences_path
55 self.log_path = log_path
56 self.compression=compression
58 LOGGER.info("CondorFastq host={0}".format(self.host))
59 LOGGER.info("CondorFastq sequences_path={0}".format(self.sequences_path))
60 LOGGER.info("CondorFastq log_path={0}".format(self.log_path))
61 LOGGER.info("Compression {0}".format(self.compression))
63 def create_scripts(self, result_map ):
65 Generate condor scripts to build any needed fastq files
68 result_map: htsworkflow.submission.results.ResultMap()
70 template_map = {'srf': 'srf.condor',
71 'qseq': 'qseq.condor',
72 'split_fastq': 'split_fastq.condor',
76 pythonpath = os.environ.get('PYTHONPATH', None)
77 if pythonpath is not None:
78 env = "PYTHONPATH=%s" % (pythonpath,)
79 condor_entries = self.build_condor_arguments(result_map)
80 for script_type in template_map.keys():
81 template = loader.get_template(template_map[script_type])
82 variables = {'python': sys.executable,
83 'logdir': self.log_path,
85 'args': condor_entries[script_type],
86 'root_url': self.host,
88 context = Context(variables)
90 with open(script_type + '.condor','w+') as outstream:
91 outstream.write(template.render(context))
93 def build_condor_arguments(self, result_map):
94 condor_entries = {'srf': [],
98 conversion_funcs = {'srf': self.condor_srf_to_fastq,
99 'qseq': self.condor_qseq_to_fastq,
100 'split_fastq': self.condor_desplit_fastq
102 sequences = self.find_archive_sequence_files(result_map)
103 needed_targets = self.update_fastq_targets(result_map, sequences)
105 for target_pathname, available_sources in needed_targets.items():
106 LOGGER.debug(' target : %s' % (target_pathname,))
107 LOGGER.debug(' candidate sources: %s' % (available_sources,))
108 for condor_type in available_sources.keys():
109 conversion = conversion_funcs.get(condor_type, None)
110 if conversion is None:
111 errmsg = "Unrecognized type: {0} for {1}"
112 LOGGER.error(errmsg.format(condor_type,
113 pformat(available_sources)))
115 sources = available_sources.get(condor_type, None)
117 if sources is not None:
118 condor_entries.setdefault(condor_type, []).append(
119 conversion(sources, target_pathname))
121 LOGGER.warn(" need file %s", target_pathname)
123 return condor_entries
125 def find_archive_sequence_files(self, result_map):
127 Find archived sequence files associated with our results.
129 self.import_libraries(result_map)
130 flowcell_ids = self.find_relevant_flowcell_ids()
131 self.import_sequences(flowcell_ids)
134 prefix libns: <http://jumpgate.caltech.edu/wiki/LibraryOntology#>
135 prefix rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#>
136 prefix xsd: <http://www.w3.org/2001/XMLSchema#>
138 select ?filenode ?filetype ?cycle ?lane_number ?read
140 ?flowcell ?flowcell_id ?read_length
141 ?flowcell_type ?flowcell_status
143 ?filenode libns:cycle ?cycle ;
144 libns:lane_number ?lane_number ;
146 libns:flowcell ?flowcell ;
147 libns:flowcell_id ?flowcell_id ;
148 libns:library ?library ;
149 libns:library_id ?library_id ;
150 libns:file_type ?filetype ;
151 a libns:IlluminaResult .
152 ?flowcell libns:read_length ?read_length ;
153 libns:flowcell_type ?flowcell_type .
154 OPTIONAL { ?flowcell libns:flowcell_status ?flowcell_status }
155 FILTER(?filetype != libns:sequencer_result)
158 LOGGER.debug("find_archive_sequence_files query: %s",
160 query = RDF.SPARQLQuery(query_text)
162 for r in query.execute(self.model):
163 library_id = fromTypedNode(r['library_id'])
164 if library_id in result_map:
165 seq = SequenceResult(r)
166 LOGGER.debug("Creating sequence result for library %s: %s",
172 def import_libraries(self, result_map):
173 for lib_id in result_map.keys():
174 lib_id_encoded = lib_id.encode('utf-8')
175 liburl = urljoin(self.host, 'library/%s/' % (lib_id_encoded,))
176 library = RDF.Node(RDF.Uri(liburl))
177 self.import_library(library)
179 def import_library(self, library):
180 """Import library data into our model if we don't have it already
182 q = RDF.Statement(library, rdfNS['type'], libraryOntology['Library'])
184 if not self.model.contains_statement(q):
186 load_into_model(self.model, 'rdfa', library)
187 LOGGER.debug("Did we import %s: %s", library.uri, present)
189 def find_relevant_flowcell_ids(self):
190 """Generate set of flowcell ids that had samples of interest on them
192 flowcell_query = RDF.SPARQLQuery("""
193 prefix libns: <http://jumpgate.caltech.edu/wiki/LibraryOntology#>
195 select distinct ?flowcell ?flowcell_id
197 ?library a libns:Library ;
198 libns:has_lane ?lane .
199 ?lane libns:flowcell ?flowcell .
200 ?flowcell libns:flowcell_id ?flowcell_id .
203 for r in flowcell_query.execute(self.model):
204 flowcell_ids.add( fromTypedNode(r['flowcell_id']) )
206 a_lane = self.model.get_target(r['flowcell'],
207 libraryOntology['has_lane'])
210 # we lack information about which lanes were on this flowcell
211 load_into_model(self.model, 'rdfa', r['flowcell'])
212 LOGGER.debug("Did we imported %s: %s" % (r['flowcell'].uri,
217 def import_sequences(self, flowcell_ids):
219 for f in flowcell_ids:
220 seq_dirs.append(os.path.join(self.sequences_path, str(f)))
221 sequences = scan_for_sequences(seq_dirs)
222 for seq in sequences:
223 seq.save_to_model(self.model, self.host)
224 update_model_sequence_library(self.model, self.host)
226 def update_fastq_targets(self, result_map, raw_files):
227 """Return list of fastq files that need to be built.
229 Also update model with link between illumina result files
230 and our target fastq file.
232 # find what targets we're missing
234 for seq in raw_files:
237 filename_attributes = {
238 'flowcell': seq.flowcell_id,
239 'lib_id': seq.library_id,
240 'lane': seq.lane_number,
243 'compression_extension': COMPRESSION_EXTENSIONS[self.compression],
244 'is_paired': seq.ispaired
247 fqName = FastqName(**filename_attributes)
249 result_dir = result_map[seq.library_id]
250 target_pathname = os.path.join(result_dir, fqName.filename)
251 if self.force or not os.path.exists(target_pathname):
252 t = needed_targets.setdefault(target_pathname, {})
253 t.setdefault(seq.filetype, []).append(seq)
254 self.add_target_source_links(target_pathname, seq)
255 return needed_targets
257 def add_target_source_links(self, target, seq):
258 """Add link between target pathname and the 'lane' that produced it
259 (note lane objects are now post demultiplexing.)
261 target_uri = 'file://' + smart_str(target)
262 target_node = RDF.Node(RDF.Uri(target_uri))
263 source_stmt = RDF.Statement(target_node, dcNS['source'], seq.filenode)
264 self.model.add_statement(source_stmt)
266 def condor_srf_to_fastq(self, sources, target_pathname):
268 raise ValueError("srf to fastq can only handle one file")
271 if sources[0].flowcell_id == '30DY0AAXX':
275 'sources': [sources[0].path],
276 'pyscript': srf2fastq.__file__,
277 'flowcell': sources[0].flowcell_id,
278 'ispaired': sources[0].ispaired,
279 'target': target_pathname,
280 'target_right': target_pathname.replace('_r1.fastq', '_r2.fastq'),
285 def condor_qseq_to_fastq(self, sources, target_pathname):
287 for source in sources:
288 paths.append(source.path)
291 'pyscript': qseq2fastq.__file__,
292 'flowcell': sources[0].flowcell_id,
293 'target': target_pathname,
295 'ispaired': sources[0].ispaired,
296 'istar': len(sources) == 1,
299 def condor_desplit_fastq(self, sources, target_pathname):
301 for source in sources:
302 paths.append(source.path)
304 compression_argument = ''
306 compression_argument = '--'+self.compression
309 'pyscript': desplit_fastq.__file__,
310 'target': target_pathname,
311 'compression': compression_argument,
313 'ispaired': sources[0].ispaired,
317 def make_lane_dict(lib_db, lib_id):
319 Convert the lane_set in a lib_db to a dictionary
320 indexed by flowcell ID
323 for lane in lib_db[lib_id]['lane_set']:
324 flowcell_id, status = parse_flowcell_id(lane['flowcell'])
325 lane['flowcell'] = flowcell_id
326 result.append((lane['flowcell'], lane))
329 class SequenceResult(object):
330 """Convert the sparql query result from find_archive_sequence_files
332 def __init__(self, result):
333 self.filenode = result['filenode']
334 self._filetype = result['filetype']
335 self.cycle = fromTypedNode(result['cycle'])
336 self.lane_number = fromTypedNode(result['lane_number'])
337 self.read = fromTypedNode(result['read'])
338 if type(self.read) in types.StringTypes:
340 self.library = result['library']
341 self.library_id = fromTypedNode(result['library_id'])
342 self.flowcell = result['flowcell']
343 self.flowcell_id = fromTypedNode(result['flowcell_id'])
344 self.flowcell_type = fromTypedNode(result['flowcell_type'])
345 self.flowcell_status = fromTypedNode(result['flowcell_status'])
348 """is this sequence / flowcell 'good enough'"""
349 if self.flowcell_status is not None and \
350 self.flowcell_status.lower() == "failed":
353 isgood = property(_is_good)
355 def _get_ispaired(self):
356 if self.flowcell_type.lower() == "paired":
360 ispaired = property(_get_ispaired)
362 def _get_filetype(self):
363 return strip_namespace(libraryOntology, self._filetype)
364 filetype = property(_get_filetype)
367 url = urlparse(str(self.filenode.uri))
368 if url.scheme == 'file':
371 errmsg = u"Unsupported scheme {0} for {1}"
372 raise ValueError(errmsg.format(url.scheme, unicode(url)))
373 path = property(_get_path)
376 return "SequenceResult({0},{1},{2})".format(
378 str(self.library_id),
379 str(self.flowcell_id))