import glob,os
from fastq_reader import Fastq_Reader
help_message = 'usage example: python check_hash_collisions.py -r 2 -i /project/home/hashed_reads/ -o /project/home/cluster_vectors/'
if __name__ == "__main__":
try:
opts, args = getopt.getopt(sys.argv[1:],'hr:i:o:',["--filerank=","inputdir=","outputdir="])
except:
print help_message
sys.exit(2)
for opt, arg in opts:
if opt in ('-h','--help'):
print help_message
sys.exit()
elif opt in ('-r',"--filerank"):
fr = int(arg)
elif opt in ('-i','--inputdir'):
inputdir = arg
if inputdir[-1] != '/':
inputdir += '/'
elif opt in ('-o','--outputdir'):
outputdir = arg
if outputdir[-1] != '/':
outputdir += '/'
hashobject = Fastq_Reader(inputdir,outputdir)
HashFiles = glob.glob(os.path.join(hashobject.input_path,'*.hashq.*'))
hashobject.infile = HashFiles[fr]
t,n,H = hashobject.collision_report()
print 'total hashed kmers:',t
print 'total pairwise collisions:',n
print 'collision histogram:',H
if opt in ('-h','--help'):
print help_message
sys.exit()
elif opt in ('-r',"--filerank"):
fr = int(arg)-1
elif opt in ('-i','--inputdir'):
inputdir = arg
if inputdir[-1] != '/':
inputdir += '/'
elif opt in ('-o','--outputdir'):
outputdir = arg
if outputdir[-1] != '/':
outputdir += '/'
hashobject = Fastq_Reader(inputdir,outputdir)
Hashq_Files = glob.glob(os.path.join(hashobject.input_path,'*.hashq.*'))
hashobject.infile = Hashq_Files[fr]
hashobject.outfile = hashobject.output_path + 'intermediate_clusters/' + str(fr)
hashobject.global_weights = np.load(hashobject.output_path + 'global_weights.npy')
global_weight_sum = hashobject.global_weights.sum(dtype=np.float64)
Cluster_Files = glob.glob(os.path.join(hashobject.output_path,'*.cluster.npy'))
Cluster_Files = [(int(cf[cf.rfind('/')+1:cf.index('.')]),cf) for cf in Cluster_Files]
cluster_sizes = np.load(hashobject.output_path+'kmer_cluster_sizes.npy')
total_set_size = 0
cluster_weights = []
cluster_keys = []
outpart = 0
for ci,cf in Cluster_Files:
# ignore super clusters and super small clusters
if cluster_sizes[ci] < 0.2*2**hashobject.hash_size:
cw = np.load(cf)
cw_sum_prob = hashobject.global_weights[cw].sum(dtype=np.float64)/global_weight_sum
