#!/usr/bin/env python

# Alevin and Kallisto currently output MTX files and gene labels in a manner
# inconsistent with the old-style 10X conventions. In both cases the matrix
# must be transposed, and gene indentifier columns duplicated

from __future__ import print_function
from collections import defaultdict
from struct import Struct
import pandas as pd
import gzip
import sys
import os
from scipy.io import mmread,mmwrite
from scipy.sparse import *
from shutil import copyfile
import pathlib
import numpy as np
import argparse

parser = argparse.ArgumentParser(description='Convert Alevin or Kallisto MTX outputs to 10X .mtx.')
parser.add_argument('mtx', help = 'MTX-format matrix file')
parser.add_argument('genes', help = 'Gene names text file')
parser.add_argument('barcodes', help = 'Barcodes file')
parser.add_argument('mtx_out', help = 'Output directory for converted results')
parser.add_argument('--cell_prefix', dest='cell_prefix', default='', help = 'Prefix to apply to cell barcodes')
args = parser.parse_args() 

quant_file=args.mtx
cb_file=args.barcodes
gene_file=args.genes
mtx_out=args.mtx_out
cell_prefix=args.cell_prefix

if not os.path.exists(quant_file):
    print("quant file {} doesn't exist".format( quant_file ))
    sys.exit(1)

if not os.path.exists(cb_file):
    print("cell barcodes file: {} doesn't exist".format( cb_file ))
    sys.exit(1)

if not os.path.exists(gene_file):
    print("genes file: {} doesn't exist".format( gene_file))
    sys.exit(1)

# Read gene and cell labels, apply cell prefix

cb_names = [cell_prefix + s for s in pd.read_csv(cb_file, header=None)[0].values]
gene_names = pd.read_csv(gene_file, header=None)[0].values
umi_counts = mmread( quant_file )
    
# Write outputs to a .mtx file readable by tools expecting 10X outputs.
# Barcodes file works as-is, genes need to be two-column, duplicating the
# identifiers. Matrix itself needs to have genes by row, so we transpose. 

pathlib.Path(mtx_out).mkdir(parents=True, exist_ok=True)
mmwrite('%s/matrix.mtx' % mtx_out, umi_counts.transpose()) 

genes_frame = pd.DataFrame([ gene_names, gene_names]).transpose()
genes_frame.to_csv(path_or_buf='%s/genes.tsv' % mtx_out, index=False, sep="\t", header = False)

with open('%s/barcodes.tsv' % mtx_out, 'w') as f:
    f.write("\n".join(cb_names))    
    f.write("\n")