nextflow.enable.dsl=2


process CONPLEX {
    container "${params.container_conplex}"
    containerOptions "${params.containerOptions}"
    publishDir "${params.outdir}/${params.project_name}/receptors", mode: 'copy'
    // // Temporarily disabled debug prints
    // debug true
    maxForks 5
    // scratch true //deletes workdir after successful completion

    input:
     path smi_drug
     path smi_metabolite
     path mut_fasta

    output:
     path "*drug_scores.tsv", emit: drug
     path "*significant_interactions.tsv", emit: interactions
     
    script:
    """
    workdir=`pwd`

    #concat mutated and reference fasta files. If it has name with _2 its mutated.
    #after test is don retrun this
    cat $mut_fasta /home/omic/ConPLex/MANE_referent_transcipt_reference.fasta > \$workdir/all_protein.fasta

    ### Test
    #head -n 100 /home/omic/ConPLex/MANE_referent_transcipt_reference.fasta > \$workdir/test.fasta
    #cat $mut_fasta \$workdir/test.fasta > \$workdir/all_protein.fasta
    ###

    . activate conplex-dti

     #transform smi for conplex
    python3 -c "
import sys
from rdkit import Chem
import numpy as np
import pandas as pd

def converter(drug_csv, outname):
    #open file to write drug/matabolite smi 
    out_file = open(outname + '.txt', 'w')
    
    #load drug csv; only has drug in separate lines
    drug_smi_list = np.array(pd.read_csv(drug_csv)['SMILES']).tolist()
    #original drugs are anotated with drug_{}
    for n, smi in enumerate(drug_smi_list):
        out_file.write('{}\\tdrug_{}\\n'.format(smi, n))
 
    out_file.close()

converter('$smi_drug', 'drag')
    "
    #concatinate drag and metabolites
    cat drag.txt $smi_metabolite > \$workdir/smiles.smi
    #conplex predicition 1. round 
    python3 /home/omic/ConPLex/conplex.py --fastas \$workdir/all_protein.fasta --smis \$workdir/smiles.smi --screening_batch_size ${params.screening_batch_size} --outdir \$workdir --split_write_df 'no'
    #### this part is use to test all alternative transcripts (this part can be skipped for now, cuz we will use it for tissue specific analysis later) 
    ##get fasta for 2. round transcripts for proteins above threshold
    #python3 /home/omic/ConPLex/get_round_2.py --threshold ${params.threshold} --workdir \$workdir --round 1 
    ##conplex all transcipt for protrin above threshold
    #python3 /home/omic/ConPLex/conplex.py --fastas \$workdir/round_2.fasta --smis \$workdir/smiles.smi --screening_batch_size ${params.screening_batch_size} --outdir \$workdir --split_write_df 'no'
    ####
    #get data for second run
    python3 /home/omic/ConPLex/get_round_2.py --threshold ${params.threshold} --workdir \$workdir --round 2  
    """
}

process NETWORK_ENRICHMENT {
  container "${params.container_conplex}"
  containerOptions "${params.containerOptions}"
  publishDir "${params.outdir}/${params.project_name}/receptors", mode: 'copy'
  // // Temporarily disabled debug prints
  // debug true
  maxForks 5

  input:
    path interactions

  output:
    path "*_network_enrichment.tsv", emit: network_enrichment
    path "*_network_interactions.tsv", emit: network_interactions

  script:
    """
#!/opt/conda/envs/conplex-dti/bin/python 
import pandas as pd
import requests

interaction_data = pd.read_csv('$interactions', sep = '\\t')
interaction_data =  interaction_data.sort_values('conplex_score', ascending=False)

#get list of enst above threshold
protein_network_list = '%0d'.join(list(pd.unique(interaction_data[interaction_data['conplex_score'] > ${params.protein_network_threshold}]['transcipt'])))

#get urls
url_network = f'https://string-db.org/api/tsv/network?identifiers={protein_network_list}&species=9606'
url_enrichment = f'https://string-db.org/api/tsv/enrichment?identifiers={protein_network_list}&species=9606'

#get enrichment dataframe
r = requests.get(url_enrichment)
lines = r.text.split('\\n') # pull the text from the response object and split based on new lines
data = [l.split('\\t') for l in lines] # split each line into its components based on tabs
enrichment_df = pd.DataFrame(data[1:-1], columns = data[0]) # convert to dataframe using the first row as the column names; drop empty, final row
#get protein interaction dataframe
r = requests.get(url_network)
lines = r.text.split('\\n') # pull the text from the response object and split based on new lines
data = [l.split('\\t') for l in lines] # split each line into its components based on tabs
# convert to dataframe using the first row as the column names; drop empty, final row
interactions_df = pd.DataFrame(data[1:-1], columns = data[0]) 
# dataframe with the preferred names of the two proteins and the score of the interaction
#interactions_df = interactions_df[['preferredName_A', 'preferredName_B', 'score']]  

#output name
output_name = '$interactions'.split('/')[-1].split('_significant_interactions.tsv')[0]

#save
enrichment_df.to_csv(f'{output_name }_network_enrichment.tsv',sep='\\t')
interactions_df.to_csv(f'{output_name }_network_interactions.tsv',sep='\\t')
    """
}