def load_ids_obj(conf, workdir):
"""
Generates the IdDict class object.
:param conf: Config Object of physcraper class
:param workdir: working directory
:return:
"""
if os.path.isfile("{}/id_pickle.p".format(workdir)):
sys.stdout.write("Reloading id dicts from {}\n".format(workdir))
ids = pickle.load(open("{}/id_pickle.p".format(workdir), "rb"))
else:
sys.stdout.write("setting up ID dictionaries\n")
sys.stdout.flush()
ids = IdDicts(conf, "{}/id_pickle.p".format(workdir))
ids.dump("{}/id_pickle.p".format(workdir))
return ids
def standard_run(study_id,
tree_id,
seqaln,
mattype,
workdir,
configfi,
ingroup_mrca=None,
shared_blast_folder=None):
"""looks for a json file to continue run, or builds and runs
new analysis for as long as new seqs are found
This is the wrapper function to start a PhyScraper run with tree and alignment ids from Open Tree of Life.
You need:
seqaln = ID of alignment file
mattype = the format name of you alignment
trfn = Id of phylogeny to update
workdir = define where your analysis files shall be stored
configfi = path to your config file
ingroup_mrca = define the mrca, by supplying the Open Tree of Life identifier of the clade of interest
shared_blast_folder = not necessary, if you want to share blast searches across runs (see documentation),
give the path to the folder with the shared runs.
"""
debug("Debugging mode is on")
conf = ConfigObj(configfi, interactive=False)
if os.path.isfile("{}/att_checkpoint.p".format(workdir)):
sys.stdout.write("Reloading data object from pickle file\n")
data_obj = pickle.load(open("{}/att_checkpoint.p".format(workdir), "rb"))
# scraper.repeat = 1
else:
sys.stdout.write("setting up Data Object\n")
sys.stdout.flush()
# read the config file into a configuration object
conf = ConfigObj(configfi, interactive=False)
aln = DnaCharacterMatrix.get(path=seqaln, schema=mattype)
# Generate an linked Alignment-Tree-Taxa object
data_obj = generate_ATT_from_phylesystem(aln=aln,
workdir=workdir,
study_id=study_id,
tree_id=tree_id,
phylesystem_loc=conf.phylesystem_loc,
ingroup_mrca=ingroup_mrca)
# Mapping identifiers between OpenTree and NCBI requires and identifier dict object
# ids = IdDicts(conf, workdir="example")
# Prune sequences below a certain length threshold
# This is particularly important when using loci that have been de-concatenated, as some are 0 length which causes problems.
data_obj.prune_short()
data_obj.write_files()
data_obj.write_labelled(label="^ot:ottTaxonName")
data_obj.write_otus("otu_info", schema="table")
data_obj.dump()
# Mapping identifiers between OpenTree and NCBI requires and identifier dict object
if os.path.isfile(conf.id_pickle):
sys.stdout.write("Reloading id dicts from {}\n".format(conf.id_pickle))
ids = pickle.load(open(conf.id_pickle, "rb"))
else:
sys.stdout.write("setting up id dictionaries\n")
sys.stdout.flush()
ids = IdDicts(conf, workdir=workdir)
ids.dump()
# Now combine the data, the ids, and the configuration into a single physcraper scrape object
scraper = PhyscraperScrape(data_obj, ids)
# run the analyses
if shared_blast_folder:
scraper.blast_subdir = shared_blast_folder
else:
shared_blast_folder = None
scraper.run_blast_wrapper(delay=14)
scraper.read_blast_wrapper(blast_dir=shared_blast_folder)
scraper.remove_identical_seqs()
scraper.generate_streamed_alignment()
while scraper.repeat == 1:
scraper.data.write_labelled(label="^ot:ottTaxonName")
scraper.data.write_otus("otu_info", schema="table")
if shared_blast_folder:
scraper.blast_subdir = shared_blast_folder
else:
shared_blast_folder = None
scraper.run_blast_wrapper(delay=14)
scraper.read_blast_wrapper(blast_dir=shared_blast_folder)
scraper.remove_identical_seqs()
scraper.generate_streamed_alignment()
# scraper.write_otu_info()
return scraper