def __init__(self,
reference_genome=None,
bed_file=None,
bam_file=None,
target_proximity=2000,
background_threshold=20,
tile_size=1000):
Flounder.__init__(self)
logging.info("Preparing reference genome")
self.ref = ReferenceGenome(reference_genome)
logging.info("Preparing BED coordinates")
self.bed = BedHandler(bed_file)
self.bed.set_reference(self.ref)
self.bed.set_target_proximity(target_proximity)
logging.info("setting BAM file")
self.bam = BamHandler(bam_file)
self.background_threshold = background_threshold
self.target_proximity = target_proximity
self.tile_size = tile_size
def __init__(self, fast5, bam, reference, modification="5mC",
threshold=0.85, context = "CG", args=None):
Flounder.__init__(self)
if args is not None:
if isinstance(args, argparse.Namespace):
self.argparse(args)
if isinstance(args, dict):
self.dictparse(args)
include_flounder(args)
# functions in module but outside of class may require these info
self.fast5 = fast5
self.reference = reference
self.bam = bam
self.modification = modification
self.threshold = threshold
self.context = context
self.index = hashlib.md5(
f" {self.fast5} {self.reference.fasta.filename} "
"{self.bam.bam} {self.modification} {self.context} "
"{self.threshold} ".encode()).hexdigest()[0:7]
logging.info(f"workflow_index == [{self.index}]")
def include_flounder(args):
"""
Set a flounder context visible in module global space for data caching.
Parameters
----------
args : TYPE
DESCRIPTION.
Returns
-------
None.
"""
# setup a Flounder for this workflow ...
global flounder
flounder = Flounder()
if isinstance(args, argparse.Namespace):
flounder.argparse(args)
if isinstance(args, dict):
flounder.dictparse(args)
def __init__(self, spec):
Flounder.__init__(self)
self.spec = spec
self.lines = None