# Prepare for Clustering
#===============================================================================
# Separate all reads into separate files for each group so they are clustered individually
files2cluster, path = Preprocess.trim_reads(mode='separate', n=1)
#===============================================================================
# Cluster Data
#===============================================================================
# For Clustering
#------------------------------------------------------------------------------
# Default vars for clustering
default_vars = { 'c_thresh' : 0.90,
'n_filter' : 8,
'maskN' : False}
# Varibles to change, 1 dictionary per run
run_parameters = [
{ 'c_thresh' : 1.0},
{ 'c_thresh' : 0.90},
]
Cluster = ClusterClass(infiles=files2cluster, inpath=path, defaults=default_vars)
Cluster.c = c
Cluster.db = db
out_list = Cluster.run_batch_cdhit_clustering(run_parameters, threads=1)
## Display Summary
f = open("indel_test.fasta", "w")
seq1 = Seq(seq_generator(100))
seq2 = seq1[1:] + "T"
seq3 = seq1[:50] + seq1[51:] + "G"
seq4 = seq1[:25] + "T" + seq1[25:-1]
seq5 = seq1[:75] + "A" + seq1[75:-1]
seqR1 = SeqRecord(seq1, id="original")
seqR2 = SeqRecord(seq2, id="deletion0")
seqR3 = SeqRecord(seq3, id="deletion50")
seqR4 = SeqRecord(seq4, id="insertion25")
seqR5 = SeqRecord(seq5, id="insertion75")
SeqIO.write([seqR1, seqR2, seqR3, seqR4, seqR5], f, "fasta")
f.flush()
f.close()
path = os.getcwd()
params = {"c_thresh": 0.90, "n_filter": 8, "threads": 1, "mem": 0, "maskN": False, "outfile_postfix": "-clustered"}
C = ClusterClass(infiles="indel_test.fasta", inpath=path, defaults=params)
out = C.run_single_cdhit_clustering()
with open(out[0], "r") as outf:
for line in outf:
print line
