def standard_run(study_id,
tree_id,
seqaln,
mattype,
workdir,
configfi):
if os.path.isfile("{}/scrape.p".format(workdir)):
sys.stdout.write("Readloading from pickled scrapefile")
scraper = pickle.load(open("{}/scrape.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)
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)
#Prune sequnces 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()
#Mapping identifiers between OpenTree and NCBI requires and identifier dict object
ids = IdDicts(conf, workdir="example")
#Now combine the data, the ids, and the configuration into a single physcraper scrape object
scraper = PhyscraperScrape(data_obj, ids, conf)
#run the ananlyses
scraper.run_blast()
scraper.read_blast()
scraper.remove_identical_seqs()
scraper.generate_streamed_alignment()
while scraper.repeat == 1:
scraper.run_blast()
scraper.read_blast()
scraper.remove_identical_seqs()
scraper.generate_streamed_alignment()
"tmp",
study_id = study_id,
tree_id = tree_id,
phylesystem_loc = conf.phylesystem_loc)
data_obj.prune_short()
data_obj.write_files()
data_obj.write_labelled()
ids = IdDicts(conf, "tmp")
scraper = PhyscraperScrape(data_obj, ids, conf)
scraper.run_blast()
scraper.read_blast()
scraper.remove_identical_seqs()
scraper.generate_streamed_alignment()
scraper.run_blast()
scraper.read_blast()
scraper.remove_identical_seqs()
scraper.generate_streamed_alignment()
#otu_json = "tests/minitest_otu.json"
#treefile = "tests/minitest.tre"
#info_obj2 = StudyInfo(seqaln,
# mattype,
#"tmp"
# otu_json = otu_json,
