def output(stream, peaks, chrom, w=73, strand='+', ):
"Outputs peaks to a stream"
logger.debug('writing %s peaks on strand %s' % (commify(len(peaks)), strand))
for mid, value in peaks:
start, end = mid - w, mid + w
stream.write("%s\t%d\t%d\t.\t%f\t%s\n" % (chrom, start, end, value, strand))
def transform(inpname, size, outname=None):
"""
Transforms reads stored in bedfile to a genetrack input file.
Requires at least 6 bed columns to access the strand.
"""
logger.debug('input %s' % inpname)
logger.debug('output %s' % outname)
reader = csv.reader(open(inpname), delimiter='\t')
# unwind the iterator
list(takewhile(lambda x: x[0].startswith('#'), reader))
output = file(outname, 'wt')
output.write('##gff-version 3\n')
output.write('# created with eland2gff on %s\n' % inpname)
output.write('# fixed read lenght of %s\n' % size)
for row in reader:
chrom, start, strand = row[10], int(row[12]), row[13]
end = start + size
if strand == 'F':
strand = '+'
else:
strand = '-'
result = map(str, [chrom, '.', '.', start, end, '.', strand, '.', '.'])
output.write("%s\n" % '\t'.join(result))
output.close()
def predict(inpname, outname, options):
"""
Generate the peak predictions on a genome wide scale
"""
if options.strand == TWOSTRAND:
logger.info('operating in twostrand mode')
if options.index:
index = hdflib.PositionalData(fname='', index=inpname, nobuild=True, workdir=options.workdir)
else:
index = hdflib.PositionalData(fname=inpname, nobuild=True, workdir=options.workdir)
fp = file(outname, 'wt')
for label in index.labels:
table = index.table(label)
size = table.cols.idx[-1]
info = util.commify(size)
logger.info('predicting on %s of total size %s' % (label, info))
lo = 0
hi = min( (size, options.maxsize) )
while True:
if lo >= size:
break
perc = '%.1f%%' % (100.0*lo/size)
logger.info('processing %s %s:%s (%s)' % (label, lo, hi, perc))
# get the data
res = index.query(start=lo, end=hi, label=label)
# exclusion zone
w = options.exclude/2
def predict(x, y):
fx, fy = fitlib.gaussian_smoothing(x=x, y=y, sigma=options.sigma, epsilon=options.level )
peaks = fitlib.detect_peaks(x=fx, y=fy )
if options.mode != 'all':
peaks = fitlib.select_peaks(peaks=peaks, exclusion=options.exclude, threshold=options.level)
return peaks
if options.strand == TWOSTRAND:
# operates in two strand mode
for yval, strand in [ (res.fwd, '+'), (res.rev, '-') ]:
logger.debug('processing strand %s' % strand)
peaks = predict(x=res.idx, y=yval)
output(stream=fp, peaks=peaks, chrom=label, w=w, strand=strand)
else:
# combine strands
peaks = predict(x=res.idx, y=res.val)
output(stream=fp, peaks=peaks, chrom=label, w=w, strand='+')
# switching to a higher interval
lo = hi
hi += options.maxsize
fp.close()
def fix_site_settings():
"""
On first run the site information may not be correct.
This function updates the site to the correct values
"""
site, flag = Site.objects.get_or_create( id=settings.SITE_ID )
if site.domain != settings.SITE_DOMAIN:
site.domain, site.name=settings.SITE_DOMAIN, settings.SITE_NAME
logger.debug('modifying site domain:%s name: %s' % (site.domain, site.name) )
site.save()
def get_project( user, pid, write=True ):
"Returns a project for a given user"
try:
member = models.Member.objects.get( user=user, project__id=pid )
project = member.project
project.role = member.role
project.is_manager = (project.role == status.MANAGER)
except ObjectDoesNotExist, exc:
logger.debug( exc )
raise AccessError("You may not access this project")
def get_result(user, rid):
"Returns a result for a given user"
try:
# get the result
result = models.Result.objects.get(id=rid)
# verifies access rights
data = get_data(user, did=result.data.id, write=False)
except ObjectDoesNotExist, exc:
logger.debug( exc )
raise AccessError("You may not access this project")
def get_data( user, did, write=True ):
"Returns a project for a given user"
try:
data = models.Data.objects.get(id=did)
member = models.Member.objects.get( user=user, project=data.project )
project = member.project
project.role = member.role
project.is_manager = (project.role == status.MANAGER)
data.write_access = project.is_manager or (data.owner==user)
except ObjectDoesNotExist, exc:
logger.debug( exc )
raise AccessError("You may not access this project")
def output(
stream,
peaks,
chrom,
w=73,
strand='+',
):
"Outputs peaks to a stream"
logger.debug('writing %s peaks on strand %s' %
(commify(len(peaks)), strand))
for mid, value in peaks:
start, end = mid - w, mid + w
stream.write("%s\t%d\t%d\t.\t%f\t%s\n" %
(chrom, start, end, value, strand))
def load_users(fname, options):
"Loads users into the database"
if not os.path.isfile(fname):
logger.error('file not found %s' % fname)
return
if options.flush:
"Resets the database with fresh values"
flush_database()
# alters site settings
fix_site_settings()
# this will be the admin password
passwd = settings.SECRET_KEY
# check the lenght of secret key in non debug modes
if not settings.DEBUG and len(passwd) < 5:
msg = 'The value of the SECRET_KEY setting is too short. Please make it longer!'
logger.error(msg)
sys.exit()
# shorcut to user creatgion
user_get = User.objects.get_or_create
# read the file and create the users based on the data in it
stream = file(fname)
for row in csv.DictReader( stream ):
username, email, first_name, last_name = row['username'], row['email'], row['first_name'], row['last_name']
is_superuser = (row['is_superuser'] == 'yes')
user, flag = user_get(username=username, email=email, first_name=first_name, last_name=last_name, is_superuser=is_superuser, is_staff=is_superuser)
if flag:
logger.debug( 'created user: %s' % user.get_full_name() )
if username in ('admin', 'demo', 'public'):
# admin user will get the secret key as password
user.set_password(passwd)
else:
if options.test_mode:
# in testmode we will set known passwords
# used during functional testing
user.set_password( passwd + 'X')
else:
# all other users will need to reset their passwords
user.set_unusable_password()
user.save()
def upload_processor(request, pid):
"Handles the actual data upload"
user = request.user
if user.is_authenticated() and user.username!='public':
if 'upload' in request.POST:
count = 0
for i in range(50): # take at most 50 files
key = 'File%s' % i
if key in request.FILES:
count += 1
stream = request.FILES[key]
name = html.chop_dirname( stream.name )
logger.debug('%s uploaded file %s' % (user.username, name) )
authorize.create_data(user=user, pid=pid, stream=stream, name=name, info='no information')
user.message_set.create(message="Uploaded %s files" % count)
if 'simple' in request.POST:
return html.redirect("/project/view/%s/" % pid)
# this is needed only because the JUPload applet makes a HEAD request
return html.response('SUCCESS\n')
def __init__(self, fname, workdir=None, update=False, nobuild=False, index=None):
"""
Create the PositionalData
"""
self.fname = fname
self.db = None
# split the incoming name to find the real name, and base directory
basedir, basename = os.path.split(self.fname)
# the index may be stored in the workdir if it was specified
basedir = workdir or basedir
# this is the HDF index name that the file operates on
self.index = index or conf.path_join(basedir, "%s.hdf" % basename)
# debug messages
logger.debug("file path %s" % self.fname)
logger.debug("index path %s" % self.index)
# no building permitted
if nobuild and missing(self.index):
raise Exception("No autobuild allowed and no index found at %s" % self.index)
# creating indices if these are missing or an update is forced
if update or missing(self.index):
self.build()
# operates on the HDF file
self.db = openFile(self.index, mode="r")
self.root = self.db.root
# shows the internal labels
logger.debug("index labels -> %s" % self.labels)
def __init__(self,
fname,
workdir=None,
update=False,
nobuild=False,
index=None):
"""
Create the PositionalData
"""
self.fname = fname
self.db = None
# split the incoming name to find the real name, and base directory
basedir, basename = os.path.split(self.fname)
# the index may be stored in the workdir if it was specified
basedir = workdir or basedir
# this is the HDF index name that the file operates on
self.index = index or conf.path_join(basedir, '%s.hdf' % basename)
# debug messages
logger.debug('file path %s' % self.fname)
logger.debug('index path %s' % self.index)
# no building permitted
if nobuild and missing(self.index):
raise Exception('No autobuild allowed and no index found at %s' %
self.index)
# creating indices if these are missing or an update is forced
if update or missing(self.index):
self.build()
# operates on the HDF file
self.db = openFile(self.index, mode='r')
self.root = self.db.root
# shows the internal labels
logger.debug('index labels -> %s' % self.labels)
fwd, rev, val = 1, 0, 1
elif strand == '-':
# on reverse strand, 5' is at end
idx = int(end) - shift
fwd, rev, val = 0, 1, 1
else:
# no strand specified, generate interval centers
idx = (int(start)+int(end))/2
fwd, rev, val = 0, 0, 1
# it is essential be able to sort the index as a string!
fp.write('%s\t%012d\t%s\t%s\t%s\n' % (chrom, idx, fwd, rev, val))
fp.close()
linet = util.commify(linec)
logger.debug("parsing %s lines finished in %s" % (linet, timer.report()))
# if it is producing coverage then it will expand reads into full intervaals
# now let the sorting commence
cmd = "sort %s > %s" % (flat, sorted)
logger.debug("sorting into '%s'" % sorted)
os.system(cmd)
logger.debug("sorting finished in %s" % timer.report() )
logger.debug("consolidating into '%s'" % outname)
consolidate( sorted, outname, format=format)
logger.debug("consolidate finished in %s" % timer.report() )
logger.debug("output saved to '%s'" % outname)
logger.debug("full conversion finished in %s" % full.report() )
def transform(inpname, outname, format, shift=0, index=False, options=None):
"""
Transforms reads stored in bedfile to a genetrack input file.
Requires at least 6 bed columns to access the strand.
"""
# detect file formats
if format == "BED":
CHROM, START, END, STRAND = 0, 1, 2, 5
elif format == "GFF":
CHROM, START, END, STRAND = 0, 3, 4, 6
else:
raise Exception('Invalid file format' % format)
# two sanity checks, one day someone will thank me
if format == 'BED' and inpname.endswith('gff'):
raise Exception('BED format on a gff file?')
if format == 'GFF' and inpname.endswith('bed'):
raise Exception('GFF format on a bed file?')
# find the basename of the outputname
basename = os.path.basename(outname)
# two files store intermediate results
flat = conf.tempdata( '%s.flat' % basename )
sorted = conf.tempdata( '%s.sorted' % basename )
# check for track information on first line,
# much faster this way than conditional checking on each line
fp = file(inpname, 'rU')
first = fp.readline()
fp.close()
# create the reader
reader = csv.reader(file(inpname, 'rU'), delimiter='\t')
# skip if trackline exists
if first.startswith == 'track':
reader.next()
# unwind the comments
list(takewhile(lambda x: x[0].startswith('#'), reader))
# copious timing info for those who enjoy these
timer, full = util.Timer(), util.Timer()
logger.debug("parsing '%s'" % inpname)
logger.debug("output to '%s'" % outname)
# create the unsorted output file and apply corrections
logger.debug("unsorted flat file '%s'" % flat)
fp = file(flat, 'wt')
for linec, row in enumerate(reader):
try:
chrom, start, end, strand = row[CHROM], row[START], row[END], row[STRAND]
except Exception, exc:
first = row[0][0]
# may be hitting the end of the file with other comments
if first == '>':
break # hit the sequence content of the gff file
elif first == '#':
continue # hit upon some comments
else:
logger.error(row)
raise Exception(exc)
if strand == '+':
# on forward strand, 5' is at start
idx = int(start) + shift
fwd, rev, val = 1, 0, 1
elif strand == '-':
# on reverse strand, 5' is at end
idx = int(end) - shift
fwd, rev, val = 0, 1, 1
else:
# no strand specified, generate interval centers
idx = (int(start)+int(end))/2
fwd, rev, val = 0, 0, 1
# it is essential be able to sort the index as a string!
fp.write('%s\t%012d\t%s\t%s\t%s\n' % (chrom, idx, fwd, rev, val))
help="how many jobs to run in parallel"
)
# flushes all content away, drops all database content!
parser.add_option(
'--server', action="store_true",
dest="server", default=False,
help="runs as a server and invokes the jobrunner at every delay seconds",
)
# parse the argument list
options, args = parser.parse_args()
logger.disable(options.verbosity)
# missing file names
if options.server and not options.delay:
parser.print_help()
else:
if options.server:
logger.info('server mode, delay=%ss' % options.delay)
while 1:
# this is used to start multiple jobs with cron (at every minute but
# having them actually start up at smaller increments
time.sleep(options.delay)
execute(limit=options.limit)
if not options.server:
break
else:
logger.debug( 'jobserver waiting %ss' % options.delay)
return projects
def get_project( user, pid, write=True ):
"Returns a project for a given user"
try:
member = models.Member.objects.get( user=user, project__id=pid )
project = member.project
project.role = member.role
project.is_manager = (project.role == status.MANAGER)
except ObjectDoesNotExist, exc:
logger.debug( exc )
raise AccessError("You may not access this project")
# write access check on by default
if write and not project.is_manager:
logger.debug( 'write access with invalid role' )
raise AccessError('You may not change this project')
return member.project
def get_data( user, did, write=True ):
"Returns a project for a given user"
try:
data = models.Data.objects.get(id=did)
member = models.Member.objects.get( user=user, project=data.project )
project = member.project
project.role = member.role
project.is_manager = (project.role == status.MANAGER)
data.write_access = project.is_manager or (data.owner==user)
except ObjectDoesNotExist, exc:
logger.debug( exc )
def predict(inpname, outname, options):
"""
Generate the peak predictions on a genome wide scale
"""
if options.strand == TWOSTRAND:
logger.info('operating in twostrand mode')
if options.index:
index = hdflib.PositionalData(fname='',
index=inpname,
nobuild=True,
workdir=options.workdir)
else:
index = hdflib.PositionalData(fname=inpname,
nobuild=True,
workdir=options.workdir)
fp = file(outname, 'wt')
for label in index.labels:
table = index.table(label)
size = table.cols.idx[-1]
info = util.commify(size)
logger.info('predicting on %s of total size %s' % (label, info))
lo = 0
hi = min((size, options.maxsize))
while True:
if lo >= size:
break
perc = '%.1f%%' % (100.0 * lo / size)
logger.info('processing %s %s:%s (%s)' % (label, lo, hi, perc))
# get the data
res = index.query(start=lo, end=hi, label=label)
# exclusion zone
w = options.exclude / 2
def predict(x, y):
fx, fy = fitlib.gaussian_smoothing(x=x,
y=y,
sigma=options.sigma,
epsilon=options.level)
peaks = fitlib.detect_peaks(x=fx, y=fy)
if options.mode != 'all':
peaks = fitlib.select_peaks(peaks=peaks,
exclusion=options.exclude,
threshold=options.level)
return peaks
if options.strand == TWOSTRAND:
# operates in two strand mode
for yval, strand in [(res.fwd, '+'), (res.rev, '-')]:
logger.debug('processing strand %s' % strand)
peaks = predict(x=res.idx, y=yval)
output(stream=fp,
peaks=peaks,
chrom=label,
w=w,
strand=strand)
else:
# combine strands
peaks = predict(x=res.idx, y=res.val)
output(stream=fp, peaks=peaks, chrom=label, w=w, strand='+')
# switching to a higher interval
lo = hi
hi += options.maxsize
fp.close()
