#!/usr/bin/python

# author J.Beale

"""
# aim
to merge .stream files and calculate statistics

# usage
python partialator.py -s <path-to-stream-file>
                       -n name (name of job - default = partialator)
                       -p pointgroup
                       -m model (unity or xsphere - default is unity)                       
                       -i iterations - number of iterations in partialator
                       -c <path-to-cell-file>
                       -b number of resolution bins - must be > 20
                       -r high-res limt. Needs a default. Default set to 1.3
                       -a max-adu. Default = 12000
                       -v ra reservation name if available

# output
- scaled/merged files
- an mtz file
- useful plots
- useful summerized .dat files
- log file of output
"""

# modules
from sys import exit
import pandas as pd
import numpy as np
import subprocess
import os, errno
import time
import argparse
from tqdm import tqdm
import regex as re
import matplotlib.pyplot as plt
from scipy.optimize import curve_fit
import warnings
warnings.filterwarnings( "ignore", category=RuntimeWarning ) 
from loguru import logger

def submit_job( job_file ):


    submit_cmd = [ "sbatch", "--" , job_file ]
    logger.info( "using slurm command = {0}".format( submit_cmd ) )


    job_output = subprocess.check_output( submit_cmd )
    logger.info( "submited job = {0}".format( job_output ) )

    # 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( 2 )

def run_compare_check( proc_dir, name, cell, shells, part_h_res ):

    # check file name
    check_run_file = "{0}/check_{1}.sh".format( proc_dir, name )

    # write file
    check_sh = open( check_run_file, "w" )
    check_sh.write( "#!/bin/sh\n\n" )
    check_sh.write( "module purge\n" )
    check_sh.write( "module use MX unstable\n" )
    check_sh.write( "module load crystfel/0.10.2-rhel8\n" )
    check_sh.write( "check_hkl --shell-file=mult.dat *.hkl -p {0} --nshells={1} --highres={2} &> check_hkl.log\n".format( cell, shells, part_h_res ) )
    check_sh.write( "check_hkl --ltest --ignore-negs --shell-file=ltest.dat *.hkl -p {0} --nshells={1} --highres={2} &>  ltest.log\n".format( cell, shells, part_h_res ) )
    check_sh.write( "check_hkl --wilson --shell-file=wilson.dat *.hkl -p {0} --nshells={1} --highres={2} &> wilson.log\n".format( cell, shells, part_h_res ) )
    check_sh.write( "compare_hkl --fom=Rsplit --shell-file=Rsplit.dat *.hkl1 *hkl2 -p {0} --nshells={1} --highres={2} &>  Rsplit.log\n".format( cell, shells, part_h_res ) )
    check_sh.write( "compare_hkl --fom=cc --shell-file=cc.dat *.hkl1 *hkl2  -p {0} --nshells={1} --highres={2} &> cc.log\n".format( cell, shells, part_h_res ) )
    check_sh.write( "compare_hkl --fom=ccstar --shell-file=ccstar.dat *.hkl1 *hkl2  -p {0} --nshells={1} --highres={2} &>  ccstar.log\n".format( cell, shells, part_h_res ) )
    check_sh.close()

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

    # add check script to log
    check_input = open( check_run_file, "r" )
    logger.info( "check input file =\n{0}".format( check_input.read() ) )
    check_input.close()

    # return check file name
    return check_run_file

def summary_stats( cc_dat, ccstar_dat, mult_dat, rsplit_dat, wilson_dat ):

    # read all files into pd
    # function to sort out different column names
    def read_dat( dat, var ):

        # different columns names of each dat file
        if var == "cc":
            cols = [ "d(nm)", "cc", "nref", "d", "min", "max" ]
        elif var == "ccstar":
            cols = [ "1(nm)", "ccstar", "nref", "d", "min", "max" ]
        elif var == "mult":
            cols = [ "d(nm)", "nref", "poss", "comp", "obs",
                    "mult", "snr", "I", "d", "min", "max" ]
        elif var == "rsplit":
            cols = [ "d(nm)", "rsplit", "nref", "d", "min", "max"  ]
        elif var == "wilson":
            cols = [ "bin", "s2", "d", "lnI", "nref" ]
        
        df = pd.read_csv( dat, names=cols, skiprows=1, sep="\s+" )

        return df

    # make df
    cc_df = read_dat( cc_dat, "cc" )
    ccstar_df = read_dat( ccstar_dat, "ccstar" )
    mult_df = read_dat( mult_dat, "mult" )
    rsplit_df = read_dat( rsplit_dat, "rsplit" )
    wilson_df = read_dat( wilson_dat, "wilson" )

    # remove unwanted cols
    cc_df = cc_df[ [ "cc" ] ]
    ccstar_df = ccstar_df[ [ "ccstar" ] ]
    rsplit_df = rsplit_df[ [ "rsplit" ] ]
    wilson_df = wilson_df[ [ "lnI" ] ]
    
    # merge dfs
    stats_df = pd.concat( [ mult_df, cc_df, ccstar_df, rsplit_df, wilson_df ], axis=1, join="inner" )

    # make 1/d, 1/d^2 column
    stats_df[ "1_d" ] = 1 / stats_df.d
    stats_df[ "1_d2" ] = 1 / stats_df.d**2

    # change nan to 0 
    stats_df = stats_df.fillna(0)

    return stats_df

def get_metric( d2_series, cc_series, cut_off ):

    # Define the tanh function from scitbx
    def tanh(x, r, s0):
        z = (x - s0)/r
        return 0.5 * ( 1 - np.tanh(z) )

    def arctanh( y, r, s0 ):
        return r * np.arctanh( 1 - 2*y ) + s0

    # Fit the tanh to the data
    params, covariance = curve_fit( tanh, d2_series, cc_series )

    # Extract the fitted parameters
    r, s0 = params

    # calculate cut-off point 
    cc_stat = arctanh( cut_off, r, s0 )
    # covert back from 1/d2 to d
    cc_stat = np.sqrt( ( 1 / cc_stat ) )

    # get curve for plotting
    cc_tanh = tanh( d2_series, r, s0 )

    return round( cc_stat, 3 ), cc_tanh

def get_overall_cc():

    # open cc log file
    cc_log_file = open( "cc.log" )
    cc_log = cc_log_file.read()

    # regex example = Overall CC = 0.5970865
    overcc_pattern = r"Overall\sCC\s=\s(\d\.\d+)"
    try:
        overcc = re.search( overcc_pattern, cc_log ).group(1)
    except AttributeError as e:
        overcc = np.nan

    return overcc

def get_overall_rsplit():

    # open rsplit log file
    rsplit_log_file = open( "Rsplit.log" )
    rsplit_log = rsplit_log_file.read()

    # regex example = Overall Rsplit = 54.58 %
    overrsplit_pattern = r"Overall\sRsplit\s=\s(\d+\.\d+)"
    try:
        overrsplit = re.search( overrsplit_pattern, rsplit_log ).group(1)
    except AttributeError as e:
        overrsplit = np.nan

    return overrsplit

def get_b():

    # open rsplit log file
    wilson_log_file = open( "wilson.log" )
    wilson_log = wilson_log_file.read()

    # regex example = B = 41.63 A^2
    b_factor_pattern = r"B\s=\s(\d+\.\d+)\sA"
    try:
        b_factor = re.search( b_factor_pattern, wilson_log ).group(1)
    except AttributeError as e:
        b_factor = np.nan

    return b_factor

def summary_fig( name, stats_df, cc_tanh, ccstar_tanh, cc_cut, ccstar_cut ):

    def dto1_d( x ):
        return 1/x

    # plot results
    cc_fig, axs = plt.subplots(2, 2)
    cc_fig.suptitle( "cc and cc* vs resolution" )

    # cc plot
    color = "tab:red"
    axs[0,0].set_xlabel( "1/d (1/A)" )
    axs[0,0].set_ylabel( "CC", color=color )
    axs[0,0].set_ylim( 0, 1 )
    axs[0,0].axhline( y = 0.3, color="black", linestyle = "dashed" )
    # plot cc
    axs[0,0].plot( stats_df[ "1_d" ], stats_df.cc, color=color )
    # plot fit
    axs[0,0].plot( stats_df[ "1_d" ], cc_tanh, color="tab:grey", linestyle = "dashed" )
    sax1 = axs[0,0].secondary_xaxis( 'top', functions=( dto1_d, dto1_d ) )
    sax1.set_xlabel('d (A)')
    axs[0,0].tick_params( axis="y", labelcolor=color )
    axs[0,0].text( 0.1, 0.1, "CC0.5 @ 0.3 = {0}".format( cc_cut ), fontsize = 8 )

    # cc* plot
    color = "tab:blue"
    axs[0,1].set_xlabel( "1/d (1/A)" )
    axs[0,1].set_ylabel( "CC*", color=color )
    axs[0,1].set_ylim( 0, 1 )
    axs[0,1].axhline( y = 0.7, color="black", linestyle = "dashed" )
    axs[0,1].plot( stats_df[ "1_d" ], stats_df.ccstar, color=color )
    # plot fit
    axs[0,1].plot( stats_df[ "1_d" ], ccstar_tanh, color="tab:grey", linestyle = "dashed" )
    sax2 = axs[0,1].secondary_xaxis( 'top', functions=( dto1_d, dto1_d ) )
    sax2.set_xlabel('d (A)')
    axs[0,1].tick_params( axis='y', labelcolor=color )
    axs[0,1].text( 0.1, 0.1, "CC* @ 0.7 = {0}".format( ccstar_cut ) , fontsize = 8 )

    # rsplit plot
    color = "tab:green"
    axs[1,0].set_xlabel( "1/d (1/A)" )
    axs[1,0].set_ylabel( "Rsplit", color=color )
    axs[1,0].plot( stats_df[ "1_d" ], stats_df.rsplit, color=color )
    sax3 = axs[1,0].secondary_xaxis( 'top', functions=( dto1_d, dto1_d ) )
    sax3.set_xlabel( 'd (A)' )
    axs[1,0].tick_params( axis='y', labelcolor=color )

    # wilson plot
    color = "tab:purple"
    axs[1,1].set_xlabel( "1/d**2 (1/A**2)" )
    axs[1,1].set_ylabel( "lnI", color=color )
    axs[1,1].plot( stats_df[ "1_d2" ], stats_df.lnI, color=color )
    axs[1,1].tick_params( axis='y', labelcolor=color )

    # save figure
    plt.tight_layout()
    plt.savefig( "{0}_plots.png".format( name ) )

def main( cwd, name, cell, shells, part_h_res ):

    # submitted job set
    submitted_job_ids = set()
   
    # now run the check and compare scripts
    print( "running check/compare" )
    check_run_file = run_compare_check( cwd, name, cell, shells, part_h_res )
    check_job_id = submit_job( check_run_file )
    print( f"job submitted: {0}".format( check_job_id ) )
    submitted_job_ids.add( check_job_id )
    time.sleep(10)
    wait_for_jobs( submitted_job_ids, 1 )
    print( "done" )

    # stats files names
    cc_dat = "cc.dat"
    ccstar_dat = "ccstar.dat"
    mult_dat = "mult.dat"
    rsplit_dat = "Rsplit.dat"
    wilson_dat = "wilson.dat"

    # make summary data table
    stats_df = summary_stats( cc_dat, ccstar_dat, mult_dat, rsplit_dat, wilson_dat  )
    logger.info( "stats table from .dat file =\n{0}".format( stats_df.to_string() ) )
    print_df = stats_df[ [  "1_d", "d", "min",
                            "max", "nref", "poss",
                            "comp", "obs", "mult",
                            "snr", "I", "rsplit", "cc", "ccstar" ] ]
    print_df.to_csv( "{0}_summary_table.csv".format( name ), sep="\t", index=False )

    # calculate cc metrics
    cc_cut, cc_tanh = get_metric( stats_df[ "1_d2" ], stats_df.cc, 0.3 )
    ccstar_cut, ccstar_tanh = get_metric( stats_df[ "1_d2" ], stats_df.ccstar, 0.7 )
    print( "resolution at CC0.5 at 0.3 = {0}".format( cc_cut ) )
    print( "resolution at CC* at 0.7 = {0}".format( ccstar_cut ) )
    logger.info( "resolution at CC0.5 at 0.3 = {0}".format( cc_cut ) )
    logger.info( "resolution at CC* at 0.7 = {0}".format( ccstar_cut ) )

    # scrub other metrics

    overcc = get_overall_cc()
    overrsplit = get_overall_rsplit()
    b_factor = get_b()

    logger.info( "overall CC0.5 = {0}".format( overcc ) )
    logger.info( "overall Rsplit = {0}".format( overrsplit ) )
    logger.info( "overall B = {0}".format( b_factor ) )

    # show plots
    summary_fig( name, stats_df, cc_tanh, ccstar_tanh, cc_cut, ccstar_cut )

    # move back to top dir
    os.chdir( cwd )

if __name__ == "__main__":
    parser = argparse.ArgumentParser()
    parser.add_argument(
        "-n",
        "--name",
        help="name of check.",
        type=str,
        required=True
    )
    parser.add_argument(
        "-c",
        "--cell_file",
        help="path to CrystFEL cell file for partialator.",
        type=os.path.abspath,
        required=True
    )
    parser.add_argument(
        "-b",
        "--bins",
        help="number of resolution bins to use. Should be more than 20. Default = 20.",
        type=int,
        default=20
    )
    parser.add_argument(
        "-r",
        "--resolution",
        help="high res limit - need something here. Default set to 1.3.",
        type=float,
        default=1.3
    )
    parser.add_argument(
        "-d",
        "--debug",
        help="output debug to terminal.",
        type=bool,
        default=False
    )
    args = parser.parse_args()
    # set loguru
    if not args.debug:
        logger.remove()
    logfile = "{0}.log".format( args.name )
    logger.add( logfile, format="{message}", level="INFO")
    # run main
    cwd = os.getcwd()
    print( "top working directory = {0}".format( cwd ) )
    main( cwd, args.name, args.cell_file, args.bins, args.resolution )