import csv
from collections import Counter, OrderedDict
from hashlib import md5
import os.path
import tempfile
from subprocess import check_call
from gims_qa_tests import TaxTuple
#from qa_core import present

def run_kraken(sequenceFiles, database, kraken_options=dict(preload=None, threads=1)):
	"kraken options should be supplied as a dict to kraken_options; give a None value for valueless options"

	#hash the kraken db files
	md5hash = md5()
	#os.path.walk(database, lambda arg, dirname, files: [md5hash.update(open(os.path.join(database, dirname, f), 'rb').read()) for f in files], None)
	md5hash.update(open(os.path.join(database, 'database.kdb'), 'rb').read())

	db_hash = md5hash.hexdigest()


	with tempfile.NamedTemporaryFile('rU') as kraken_out:
		with tempfile.NamedTemporaryFile() as seq_data:
			#cat all the files together
			check_call("cat {files} > {seq_data.name}".format(
				files = " ".join([present(f) for f in sequenceFiles]),
				seq_data = seq_data
				), 
			shell=True)

			def make_option(key, value):
				if value is not None:
					return "--{} {}".format(key, value)
				return "--{}".format(key)

			#build options
			options = " ".join([make_option(k,v) for k,v in kraken_options.items()])

			#call kraken
			check_call("kraken {options} "
					"--db {database} "
					"--gzip-compressed "
					"--fastq {seq_data.name} "
					"--output {kraken_out.name}".format(**locals()), shell=True)

			counts = Counter()

			with tempfile.NamedTemporaryFile('rU') as kraken_report:
				check_call("kraken-translate --db {database} {kraken_out.name} > {kraken_report.name}".format(**locals()), shell=True)
				rows = csv.reader(kraken_report, delimiter='\t')
				for seq_id, taxon in rows:
					counts[taxon] += 1.0
					counts["total"] += 1.0

				#clean up all the temp files by releasing contexts
	
	top_results = counts.most_common(6) #total, plus five most common taxa
	total, total_count = top_results.pop(0)
	top_hit, top_hit_count = top_results.pop(0)
	next_hits = [h[0].replace(';', ' ') for h in top_results]
	next_hit_counts = [h[1] for h in top_results]

	top_hit_frac = top_hit_count / total_count
	next_hit_frac = [h / total_count for h in next_hit_counts]

	return TaxTuple(top_hit.replace(';', ' '), top_hit_frac, next_hits, next_hit_frac, db_hash)

def  runKrakenSeqtest(sequenceFiles,database):

	krakenoutoutpath = "/home/charles.strittmatter/output/kraken/"

	w = datetime.datetime.now()
        
	krakenout = "krakenOut" + w.strftime("%Y-%m-%d-%H-%M")
	krakenreport = "" +  krakenoutoutpath + "kraken_report_" + w.strftime("%Y-%m-%d-%H-%M")
	
	sequenceFiles[0]
	sequenceFiles[1]

	os.path.abspath(os.path.join("Input", sequenceFileName[0]))

	command = "kraken --preload "
	command += "--db " + database + ' '
	#if len(fastqPaths) > 1:
	command += "--paired " + os.path.abspath(os.path.join("sample/Input", sequenceFiles[0])) + " " + os.path.abspath(os.path.join("sample/Input", sequenceFiles[1])) 
	#  command += ' '.join(fastqPaths)
	command += " --output " + "/home/charles.strittmatter/output/kraken/" + krakenout
	
	print command

	check_call(command, shell=True)
	print "%s -- Kraken called..." % datetime.datetime.now()


	kraken_report_cmd = "kraken-report --db "
	kraken_report_cmd += database
	kraken_report_cmd += " " + krakenoutoutpath + krakenout
	kraken_report_cmd += " > " + krakenreport
	
	print kraken_report_cmd

	check_call(kraken_report_cmd, shell=True)




	parsed_kraken_rpt = "cat "+ krakenreport + " | grep \"$(printf '\tS\t')\" | grep -v '-' | head -n 5"

	kraken_rpt_lines = check_output(parsed_kraken_rpt, shell=True)

	split_kraken_rpt_line = re.split("\t|\n", kraken_rpt_lines)

	print "=====================" + krakenreport + "=========================="
	print split_kraken_rpt_line
	split_kraken_rpt_length = len(split_kraken_rpt_line)
	print split_kraken_rpt_line[5]+" "+split_kraken_rpt_line[0]+" "+split_kraken_rpt_line[4]
	taxLen = 6;
	taxIdIdx = 4;
	outputFile = "/home/charles.strittmatter/output/kraken/" + w.strftime("%Y-%m-%d-%H-%M")  + '_metadata_kraken.tsv'

 	metadata = "Platform        Run     asm_stats_contig_n50    asm_stats_length_bp     asm_stats_n_contig      attribute_package       bioproject_acc  bioproject_center       bioproject_title        biosample_acc   collected_by    sample_name     scientific_name serovar sra_center      strain  sub_species     tax-id"




	splitMetadata = metadata.split("\t")
	for i in range(0, len(split_kraken_rpt_line)):
			if split_kraken_rpt_line[i] == '':
					print "don't add to the metadata...split_kraken_rpt_line.pop(i)"
			elif i%(taxLen) == taxLen-1:
					print "i is "+str(i)+"under this condition: i%(taxLen) == taxLen-1"
					splitMetadata.insert(len(splitMetadata)-3, split_kraken_rpt_line[i].strip())
			elif i%(taxLen) == taxIdIdx:
					print "i is "+str(i)+"under this condition: i%(taxLen) == taxIdIdx"
					splitMetadata.insert(len(splitMetadata)-1, split_kraken_rpt_line[i].strip())
			elif i%(taxLen) == 0:
					print "i is "+str(i)+"under this condition: i%(taxLen) == 0"
					splitMetadata.insert(len(splitMetadata)-1, split_kraken_rpt_line[i].strip())

			splitMetadataLength = len(splitMetadata)
			first_kraken_percentage = splitMetadataLength-15
	with open(outputFile, 'wb') as f:
			#writer = csv.writer(f, delimiter="\t")

			#         print splitMetadata
			for i in xrange(splitMetadataLength-3, first_kraken_percentage-1, -3):
					print "Split metadata of i "+splitMetadata[i]
					for j in xrange(splitMetadataLength-3, first_kraken_percentage-1, -3):
							if float(splitMetadata[j]) > float(splitMetadata[i]):
									print "i: "+str(i)+" and j: "+str(j)
									print "swapping "+splitMetadata[i]+" with "+splitMetadata[j]
									old_hit_org = splitMetadata[i-1]
									old_hit_percentage = splitMetadata[i]
									old_hit_tx_id = splitMetadata[i+1]

									splitMetadata[i-1] = splitMetadata[j-1]
									splitMetadata[i] = splitMetadata[j]
									splitMetadata[i+1] = splitMetadata[j+1]

									splitMetadata[j-1] = old_hit_org
									splitMetadata[j] = old_hit_percentage
									splitMetadata[j+1] = old_hit_tx_id

	df = pandas.DataFrame(splitMetadata)

	print df


	# Here aree dataframe collumns for !

	print df.iat[0,0]
	print df.iat[1,0]
	print df.iat[3,0]
	print df.iat[4,0]
	print df.iat[6,0]
	print df.iat[7,0]
	print df.iat[9,0]
	print df.iat[10,0]

	OrganismOne = df.iat[0,0]
	OrganismTwo =  df.iat[3,0]
	OrganismThree = df.iat[6,0]
	OrganismFour =  df.iat[9,0]


	OrganismOnePer = df.iat[1,0]
	OrganismTwoPer = df.iat[4,0]
	OrganismThreePer = df.iat[7,0]
	OrganismFourPer = df.iat[10,0]


	df = {'OrganismOne':OrganismOne,
			'OrganismTwo':OrganismTwo,
			'OrganismThree':OrganismThree,
			'OrganismFour':OrganismFour,
			'OrganismOne%':OrganismOnePer,
			'OrganismTwo%':OrganismTwoPer,
			'OrganismThree%':OrganismThreePer,
			'OrganismFour%':OrganismFourPer }


	#Clean up files

	command = ""
	command += "rm " + krakenoutoutpath + krakenout

	print command

	check_call(command, shell=True)



	command = ""
	command += "rm " + outputFile

	print command

	return pandas.DataFrame(df, index=[0])