VERSION_RE = "([0-9\.]+)"
USER_RE = "([a-zA-Z0-9]+)"
LANES_PER_FLOWCELL = 8
-LANE_LIST = range(1, LANES_PER_FLOWCELL+1)
+LANE_LIST = range(1, LANES_PER_FLOWCELL + 1)
from htsworkflow.util.alphanum import alphanum
from htsworkflow.util.ethelp import indent, flatten
else:
return self.gerald.runfolder_name
runfolder_name = property(_get_runfolder_name)
-
+
def get_elements(self):
"""
make one master xml file from all of our sub-components.
if self._name is None:
tmax = max(self.image_analysis.time, self.bustard.time, self.gerald.time)
timestamp = time.strftime('%Y-%m-%d', time.localtime(tmax))
- self._name = 'run_'+self.flowcell_id+"_"+timestamp+'.xml'
+ self._name = 'run_' + self.flowcell_id + "_" + timestamp + '.xml'
return self._name
name = property(_get_run_name)
def save(self, destdir=None):
if destdir is None:
destdir = ''
- logging.info("Saving run report "+ self.name)
+ logging.info("Saving run report " + self.name)
xml = self.get_elements()
indent(xml)
dest_pathname = os.path.join(destdir, self.name)
logging.info('Searching for runs in ' + datadir)
runs = []
# scan for firecrest directories
- for firecrest_pathname in glob(os.path.join(datadir,"*Firecrest*")):
+ for firecrest_pathname in glob(os.path.join(datadir, "*Firecrest*")):
logging.info('Found firecrest in ' + datadir)
image_analysis = firecrest.firecrest(firecrest_pathname)
if image_analysis is None:
image_analysis = ipar.ipar(ipar_pathname)
if image_analysis is None:
logging.warn(
- "%s is an empty or invalid IPAR directory" %(ipar_pathname,)
+ "%s is an empty or invalid IPAR directory" % (ipar_pathname,)
)
else:
scan_post_image_analysis(
bustard_dir = os.path.abspath(os.path.join(gerald_dir, '..'))
image_dir = os.path.abspath(os.path.join(gerald_dir, '..', '..'))
- runfolder_dir = os.path.abspath(os.path.join(image_dir, '..','..'))
-
+ runfolder_dir = os.path.abspath(os.path.join(image_dir, '..', '..'))
+
logging.info('--- use-run detected options ---')
logging.info('runfolder: %s' % (runfolder_dir,))
logging.info('image_dir: %s' % (image_dir,))
# leaf directory should be an IPAR or firecrest directory
data_dir, short_image_dir = os.path.split(image_dir)
logging.info('data_dir: %s' % (data_dir,))
- logging.info('short_iamge_dir: %s' %(short_image_dir,))
+ logging.info('short_iamge_dir: %s' % (short_image_dir,))
# guess which type of image processing directory we have by looking
# in the leaf directory name
p.image_analysis = image_run
p.bustard = base_calling_run
p.gerald = gerald_run
-
+
logging.info('Constructed PipelineRun from %s' % (gerald_dir,))
return p
def summarize_lane(gerald, lane_id):
report = []
summary_results = gerald.summary.lane_results
- for end in range(len(summary_results)):
+ for end in range(len(summary_results)):
eland_result = gerald.eland_results.results[end][lane_id]
report.append("Sample name %s" % (eland_result.sample_name))
report.append("Lane id %s end %s" % (eland_result.lane_id, end))
if hasattr(eland_result, 'match_codes'):
mc = eland_result.match_codes
nm = mc['NM']
- nm_percent = float(nm)/eland_result.reads * 100
+ nm_percent = float(nm) / eland_result.reads * 100
qc = mc['QC']
- qc_percent = float(qc)/eland_result.reads * 100
+ qc_percent = float(qc) / eland_result.reads * 100
report.append("No Match: %d (%2.2g %%)" % (nm, nm_percent))
report.append("QC Failed: %d (%2.2g %%)" % (qc, qc_percent))
plot_target_path = os.path.join(cycle_dir, 'Plots')
if os.path.exists(plot_html):
- logging.debug("Saving %s" % ( plot_html, ))
- logging.debug("Saving %s" % ( plot_images, ))
+ logging.debug("Saving %s" % (plot_html,))
+ logging.debug("Saving %s" % (plot_images,))
shutil.copy(plot_html, cycle_dir)
if not os.path.exists(plot_target_path):
- os.mkdir(plot_target_path)
+ os.mkdir(plot_target_path)
for plot_file in glob(plot_images):
shutil.copy(plot_file, plot_target_path)
else:
tar_cmd = ['tar', 'c'] + score_files
bzip_cmd = [ 'bzip2', '-9', '-c' ]
- tar_dest_name =os.path.join(cycle_dir, 'scores.tar.bz2')
+ tar_dest_name = os.path.join(cycle_dir, 'scores.tar.bz2')
tar_dest = open(tar_dest_name, 'w')
logging.info("Compressing score files from %s" % (scores_path,))
logging.info("Running tar: " + " ".join(tar_cmd[:10]))
logging.info("Running bzip2: " + " ".join(bzip_cmd))
- logging.info("Writing to %s" %(tar_dest_name,))
+ logging.info("Writing to %s" % (tar_dest_name,))
env = {'BZIP': '-9'}
tar = subprocess.Popen(tar_cmd, stdout=subprocess.PIPE, shell=False, env=env,
"""
# copy & bzip eland files
bz_commands = []
-
+
for lanes_dictionary in gerald_object.eland_results.results:
for eland_lane in lanes_dictionary.values():
source_name = eland_lane.pathname
dest_name = os.path.join(cycle_dir, name)
logging.info("Saving eland file %s to %s" % \
(source_name, dest_name))
-
+
if is_compressed(name):
- logging.info('Already compressed, Saving to %s' % (dest_name, ))
+ logging.info('Already compressed, Saving to %s' % (dest_name,))
shutil.copy(source_name, dest_name)
else:
# not compressed
#bzip = subprocess.Popen(args, stdout=bzip_dest)
#logging.info('Saving to %s' % (dest_name, ))
#bzip.wait()
-
+
if len(bz_commands) > 0:
q = QueueCommands(bz_commands, num_jobs)
q.run()
-
-
-def extract_results(runs, output_base_dir=None, site="individual", num_jobs=1):
+
+
+def extract_results(runs, output_base_dir=None, site="individual", num_jobs=1, raw_format='qseq'):
"""
Iterate over runfolders in runs extracting the most useful information.
* run parameters (in run-*.xml)
r.save(cycle_dir)
# save illumina flowcell status report
- save_flowcell_reports( os.path.join(r.image_analysis.pathname, '..'), cycle_dir )
-
+ save_flowcell_reports(os.path.join(r.image_analysis.pathname, '..'), cycle_dir)
+
# save stuff from bustard
# grab IVC plot
save_ivc_plot(r.bustard, cycle_dir)
# build base call saving commands
if site is not None:
lanes = []
- for lane in range(1,9):
+ for lane in range(1, 9):
if r.gerald.lanes[lane].analysis != 'none':
lanes.append(lane)
run_name = srf.pathname_to_run_name(r.pathname)
- srf_cmds = srf.make_qseq_commands(run_name, r.bustard.pathname, lanes, site, cycle_dir)
- srf.run_commands(r.bustard.pathname, srf_cmds, num_jobs)
+ if raw_format == 'qseq':
+ seq_cmds = srf.make_qseq_commands(run_name, r.bustard.pathname, lanes, site, cycle_dir)
+ elif raw_format == 'srf':
+ seq_cmds = srf.make_srf_commands(run_name, r.bustard.pathname, lanes, site, cycle_dir, 0)
+ else:
+ raise ValueError('Unknown --raw-format=%s' % (raw_format))
+ srf.run_commands(r.bustard.pathname, seq_cmds, num_jobs)
# save stuff from GERALD
# copy stuff out of the main run
# save summary file
save_summary_file(g, cycle_dir)
-
+
# compress eland result files
compress_eland_results(g, cycle_dir, num_jobs)
-
+
# md5 all the compressed files once we're done
md5_commands = srf.make_md5_commands(cycle_dir)
srf.run_commands(cycle_dir, md5_commands, num_jobs)
-
+
def rm_list(files, dry_run=True):
for f in files:
if os.path.exists(f):
else:
os.unlink(f)
else:
- logging.warn("%s doesn't exist."% (f,))
+ logging.warn("%s doesn't exist." % (f,))
def clean_runs(runs, dry_run=True):
"""
logging.info("Cleaning intermediate files")
# make clean_intermediate
if os.path.exists(os.path.join(run.image_analysis.pathname, 'Makefile')):
- clean_process = subprocess.Popen(['make', 'clean_intermediate'],
+ clean_process = subprocess.Popen(['make', 'clean_intermediate'],
cwd=run.image_analysis.pathname,)
clean_process.wait()
projects / htsworkflow.git / blobdiff