#!/usr/bin/python

# author J.Beale

"""
# aim
to process a batch of data very fast by splitting it into a number of chunks and submitting 
these jobs separately to the cluster

# usage
python crystfel_split.py -l <path-to-list-file>
                         -k <chunk-size>
                         -g <path-to-geom-file>                          
                         -c <path-to-cell-file>
# note                   -p True/False will make a progress bar

# output
a series of stream files from crystfel in the current working directory
"""

# modules
import pandas as pd
import subprocess
import os, errno
import time
import argparse
from tqdm import tqdm
import regex as re

def h5_split( lst, chunk_size ):

    # read h5.lst - note - removes // from image column
    # scrub file name
    lst_name = os.path.basename( lst )

    cols = [ "h5", "image" ]
    df = pd.read_csv( lst, sep="\s//", engine="python", names=cols )

    # re-add // to image columm and drop other columns
    df[ "h5_path" ] = df.h5 + " //" + df.image.astype( str )
    df = df[ [ "h5_path" ] ]

    # split df into a lst
    list_df = [df[i:i + chunk_size] for i in range( 0, len(df), chunk_size)]

    return list_df

def write_crystfel_run( proc_dir, name, chunk, chunk_lst_file, geom_file, cell_file ):

    # crystfel file name
    cryst_run_file = "{0}/{1}_{2}.sh".format( proc_dir, name, chunk )
    # write file
    run_sh = open( cryst_run_file, "w" )
    run_sh.write( "#!/bin/sh\n\n" )
    run_sh.write( "module purge\n" )
    run_sh.write( "module load crystfel/0.10.2\n" )
    run_sh.write( "indexamajig -i {0} \\\n".format( chunk_lst_file ) )
    run_sh.write( "  --output={0}_{1}.stream \\\n".format( name, chunk ) )
    run_sh.write( "  --geometry={0}\\\n".format( geom_file ) )
    run_sh.write( "  --pdb={0} \\\n".format( cell_file ) )
    run_sh.write( "  --indexing=xgandalf-latt-cell --peaks=peakfinder8 \\\n" )
    run_sh.write( "  --threshold=15 --min-snr=10 --int-radius=3,5,9 \\\n" )
    run_sh.write( "  -j 32 --no-multi --no-retry --max-res=3000 --min-pix-count=2 --min-res=85\\\n" )
    run_sh.close()

    # make file executable
    subprocess.call( [ "chmod", "+x", "{0}".format( cryst_run_file ) ] )

    # return crystfel file name
    return cryst_run_file

def make_process_dir( proc_dir ):
    # make process directory
    try:
        os.makedirs( proc_dir )
    except OSError as e:
        if e.errno != errno.EEXIST:
            raise

def submit_job( job_file ):

    # submit the job
    submit_cmd = ["sbatch",  "--cpus-per-task=32", "--" ,job_file]
    job_output = subprocess.check_output(submit_cmd)

    # scrub job id from - example Submitted batch job 742403
    pattern = r"Submitted batch job (\d+)"
    job_id = re.search( pattern, job_output.decode().strip() ).group(1)

    return int(job_id)

def wait_for_jobs( job_ids, total_jobs ):

    with tqdm(total=total_jobs, desc="Jobs Completed", unit="job") as pbar:
        while job_ids:
            completed_jobs = set()
            for job_id in job_ids:
                status_cmd = ["squeue", "-h", "-j", str(job_id)]
                status = subprocess.check_output(status_cmd)
                if not status:
                    completed_jobs.add(job_id)
                    pbar.update(1)
            job_ids.difference_update(completed_jobs)
            time.sleep(30)

def run_splits( cwd, name, lst, chunk_size, geom_file, cell_file, progress ):

    print( "reading SwissFEL lst file" )
    print( "creating {0} image chunks of lst".format( chunk_size ) )
    list_df = h5_split( lst, chunk_size )
    print( "DONE" )

    # set chunk counter
    chunk = 0

    # create job list
    job_list = []

    print( "creating crystfel jobs for individual chunks" )
    for chunk_lst in list_df:

        print( "chunk {0} = {1} images".format( chunk, len( chunk_lst ) ) ) 
        # define process directory
        proc_dir = "{0}/{1}/{1}_{2}".format( cwd, name, chunk )

        # make process directory
        make_process_dir(proc_dir)

        # write list to file
        chunk_lst_file = "{0}/{1}_{2}.lst".format( proc_dir, name, chunk )
        chunk_lst.to_csv( chunk_lst_file, index=False, header=False )

        # write crystfel file
        cryst_run_file = write_crystfel_run( proc_dir, name, chunk, chunk_lst_file, geom_file, cell_file )

        # add crystfel run file to list
        job_list.append( cryst_run_file )

        # increase chunk counter
        chunk = chunk +1
    print( "DONE" )

    # submitted job set
    submitted_job_ids = set()
    # submit jobs
    for job_file in job_list:
        job_id = submit_job( job_file )
        submitted_job_ids.add( job_id )
        print(f"Job submitted: { job_id }")

    # include progress bar if required
    if progress==True:
        wait_for_jobs(submitted_job_ids, len(job_list))
        print("slurm processing done")

if __name__ == "__main__":
    parser = argparse.ArgumentParser()
    parser.add_argument(
        "-l",
        "--lst_file",
        help="file from SwissFEL output to be processed quickly",
        type=os.path.abspath
    )
    parser.add_argument(
        "-k",
        "--chunk_size",
        help="how big should each chunk be? - the bigger the chunk, the fewer jobs, the slower it will be",
        type=int,
        default=1000
    )
    parser.add_argument(
        "-g",
        "--geom_file",
        help="path to geom file to be used in the refinement",
        type=os.path.abspath
    )
    parser.add_argument(
        "-c",
        "--cell_file",
        help="path to cell file of the crystals used in the refinement",
        type=os.path.abspath
    )
    parser.add_argument(
        "-n",
        "--job_name",
        help="the name of the job to be done",
        type=str,
        default="split"
    )
    parser.add_argument(
        "-p",
        "--progress",
        help="gives you the option of also having a progress bar",
        type=bool,
        default=True
    )
    args = parser.parse_args()
    # run geom converter
    cwd = os.getcwd()
    run_splits( cwd, args.job_name, args.lst_file, args.chunk_size, args.geom_file, args.cell_file, args.progress )