def mP(a, tab, Aligner, sc):
manager = multiprocessing.Manager()
alignments = manager.list()
cores = int(input('Inserisci il numero di processori: '))
if cores > multiprocessing.cpu_count():
cores = multiprocessing.cpu_count()
print("Superato il numero massimo di processori,", str(cores),
"in uso")
else:
print(str(cores), "processori in uso")
processes = []
data = ReadFile.SPARKreadFile(sc)
dict = [x["SEQ"] for x in data.rdd.collect()]
#dict = ReadFile.HengLireadFile() #Heng Li
chunk_size = len(dict) / cores
slices = Chunks(dict, math.ceil(chunk_size))
for i, s in enumerate(slices):
procname = 'processor' + str(i)
p = multiprocessing.Process(target=Alignment.mPalignment,
args=(a, tab, Aligner, s, alignments,
procname))
p.start()
processes.append(p)
for p in processes:
p.join()
DF = spark.createDataFrame(alignments)
DataFrame = DF.join(data, on=['seq'], how='inner')
return DataFrame
from pyspark.shell import sqlContext
import Alignment
import createBam
import MultiProcess
from pyspark import SparkContext, SparkConf
from timeit import default_timer as timer
import os
import ReadFile
import SparkAligner
import Aligner
import pickle
import HashTable
from datetime import datetime
sc = SparkContext.getOrCreate()
data = ReadFile.SPARKreadFile(sc)
dict = [x["SEQ"] for x in data.rdd.collect()]
#CODICE CON MAPPY ===================================================================================================================
a = mp.Aligner("reference.fa", preset="map-ont")
alignmentsS = []
# alignmentsH = []
tab = str.maketrans('ACTG', 'TGAC')
AlignerS = namedtuple('SEQ', [
'contig', 'flag', 'seq', 'pos', 'mapq', 'cigar', 'is_primary', 'MDtag',
'cstag'
]) #SPARK
# AlignerH = namedtuple('SEQ', ['contig', 'Rname', 'flag', 'pos', 'mapq', 'cigar', 'seq', 'is_primary', 'MDtag', 'cstag','basequal']) #Heng Li
# startMP = timer()
# DataFrameMP = MultiProcess.mP(a, tab, AlignerS, sc) #MULTIPROCESSORE SPARK
