#!/usr/bin/env python3

# authors T. Mason and J. Beale


# modules
import pandas as pd
import subprocess
import os, errno
import regex as re
import numpy as np
import matplotlib.pyplot as plt
import time

def h5_sample( lst, sample ):

    # create sample of images from run
    # read h5.lst - note - removes // from image column
    cols = [ "h5", "image" ]
    sample_df = pd.read_csv( lst, sep="\s//", engine="python", names=cols )

    # take defined sample
    sample_df = sample_df.sample( sample )

    # sort list
    sample_df = sample_df.sort_index()

    # re-add // to image columm
    sample_df[ "image" ] = "//" + sample_df.image.astype(str)

    # write sample to file
    sample_file = "h5_{0}_sample.lst".format( sample )
    sample_df.to_csv( sample_file, sep=" ", index=False, header=False )

    # return sample file name
    return sample_file

def geom_amend( lab6_geom_file, clen ):

    # read lab6 geom
    lab6_geom = open( lab6_geom_file, "r" )

    # use regex to find clen and replace with new
    # clen example => clen = 0.1217
    clen_geom = re.sub( "clen = 0\.\d+", "clen = {0}".format( clen ), lab6_geom.read() )

    # close lab6 geom file
    lab6_geom.close()

    # write new clen_geom to file
    clen_geom_file = "{0}.geom".format( clen )
    geom = open( clen_geom_file, "w" )
    geom.write( clen_geom )
    geom.close()

    # return clen_geom file name
    return clen_geom_file

def write_crystfel_run( clen, sample_h5_file, clen_geom_file, cell_file ):

    # crystfel file name
    cryst_run_file = "{0}_cryst_run.sh".format( clen )

    # 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( sample_h5_file ) )
    run_sh.write( "  --output={0}.stream \\\n".format( clen ) )
    run_sh.write( "  --geometry={0}\\\n".format( clen_geom_file ) )
    run_sh.write( "  --pdb={0} \\\n".format( cell_file ) )
    run_sh.write( "  --indexing=xgandalf-latt-cell --peaks=peakfinder8 \\\n" )
    run_sh.write( "  --integration=rings-grad --tolerance=10.0,10.0,10.0,2,3,2 --threshold=10 --min-snr=5 --int-radius=2,3,6 \\\n" )
    run_sh.write( "  -j 36 --no-multi --no-retry --check-peaks --max-res=3000 --min-pix-count=1 --local-bg-radius=4 --min-res=85\n\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_sample(lst, sample):
    
    # set current working directory
    os.chdir("/sf/cristallina/data/p20590/work/process/jhb/detector_refinement")
    cwd = os.getcwd()
    
    # make sample list
    print("making {0} sample of images".format(sample))
    sample_h5 = h5_sample(lst, sample)
    sample_h5_file = "{0}/{1}".format(cwd, sample_h5)
    print("done")
    return cwd, sample_h5_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 make_step_range(centre_clen, step_size, steps):
    # make list of clen steps above and below the central clen
    print( "make clen array around {0}".format( centre_clen ) )
    step_range = step_size*steps
    bottom_step = centre_clen-step_range/2
    top_step = bottom_step+step_range
    step_range = np.arange( bottom_step, top_step, step_size )
    step_range = step_range.round( 6 ) # important - otherwise np gives your .99999999 instead of 1 somethimes
    print( "done" )
    
    return step_range
                
def check_job_status(username):
    # wait for jobs to complete
    jobs_completed = False
    while not jobs_completed:
       # Get the status of the jobs using "squeue"
       result = subprocess.run(['squeue', '--user', '{0}'.format(username)], stdout=subprocess.PIPE)
       output = result.stdout.decode('utf-8')

       # Check if there are no jobs running for the user
       if '{0}'.format(username) not in output:
           jobs_completed = True
       else:
           # Sleep for some time and check again
           print("waiting for jobs to finish")
           time.sleep(30)  # sleep for 30 seconds

    print("All jobs completed.")
        
def scrub_clen( stream_pwd ):

    # get clen from stream name
    # example - /sf/cristallina/data/p20590/work/process/jhb/detector_refinement/coarse_scan/0.115/0.115.stream
    # scrub clen and return - else nan
    try:
        pattern = r"0\.\d+/(0\.\d+)\.stream"
        re_search = re.search( pattern, stream_pwd )
        clen = re_search.group( 1 )
        if AttributeError:
            return float( clen )
    except AttributeError:
        return np.nan

def find_streams( top_dir ):

    # create df for streams
    stream_df = pd.DataFrame()

    # search for all files that end with .stream

    for path, dirs, files in os.walk( top_dir ):
        for name in files:
            if name.endswith( ".stream" ):

                # get stream pwd
                stream_pwd = os.path.join( path, name )

                # scrub clen from stream
                clen = scrub_clen( stream_pwd )

                # put clen and stream pwd into df
                data = [ {  "stream_pwd" : stream_pwd,
                            "clen" : clen
                         } ]
                stream_df_1 = pd.DataFrame( data )
                stream_df = pd.concat( ( stream_df, stream_df_1 ) )
    
    # sort df based on clen
    stream_df = stream_df.sort_values( by="clen" )

    # reset df index
    stream_df = stream_df.reset_index( drop=True )

    # return df of streams and clens
    return stream_df


def scrub_us( stream ):

    # get uc values from stream file
    # example - Cell parameters 7.71784 7.78870 3.75250 nm, 90.19135 90.77553 90.19243 deg
    # scrub clen and return - else nan
    try:
        pattern = r"Cell\sparameters\s(\d\.\d+)\s(\d\.\d+)\s(\d\.\d+)\snm,\s(\d+\.\d+)\s(\d+\.\d+)\s(\d+\.\d+)\sdeg"
        cells = re.findall( pattern, stream )
        if AttributeError:
            return cells
    except AttributeError:
        return np.nan
    
def scrub_helper(top_dir):
    # find stream files from process directory
    print( "finding stream files" )
    stream_df = find_streams( top_dir )
    print( "done" )

    # making results df for unit cell and index no.
    stats_df = pd.DataFrame()

    # loop through stream files and collect unit_cell information
    print( "looping through stream files to collect unit cell, indexed information" )
    for index, row in stream_df.iterrows():

        stream_pwd, clen = row[ "stream_pwd" ], row[ "clen" ]

        # open stream file
        print( "scrubbing stream for clen={0}".format( clen ) )
        stream = open( stream_pwd, "r" ).read()

        # scrub unit cell information
        cells = scrub_us( stream )

        # put cells in df
        cols = [ "a", "b", "c", "alpha", "beta", "gamma" ]
        cells_df = pd.DataFrame( cells, columns=cols )
        cells_df = cells_df.astype( float )

        # calc stats
        indexed = len( cells_df )
        std_a = cells_df.a.std()
        std_b = cells_df.b.std()
        std_c = cells_df.c.std()
        std_alpha = cells_df.alpha.std()
        std_beta = cells_df.beta.std()
        std_gamma = cells_df.gamma.std()

        # put stats in results df
        stats = [ { "clen" : clen,
                    "indexed" : indexed,
                    "std_a" : std_a,
                    "std_b" : std_b,
                    "std_c" : std_c,
                    "std_alpha" : std_alpha,
                    "std_beta" : std_beta,
                    "std_gamma" : std_gamma,
                } ]
        stats_df_1 = pd.DataFrame( stats )
        stats_df = pd.concat( ( stats_df, stats_df_1 ) )

        print( "done" )

    # reset index
    stats_df = stats_df.reset_index( drop=True )

    return stats_df

def find_clen_values(stats_df):

    def find_min_clen(col_name):
        min_val = stats_df[col_name].min()
        min_row = stats_df[stats_df[col_name] == min_val]
        min_clen = min_row['clen'].values[0]
        return min_val, min_clen

    min_alpha_val, min_alpha_clen = find_min_clen('std_alpha')
    min_beta_val, min_beta_clen = find_min_clen('std_beta')
    min_gamma_val, min_gamma_clen = find_min_clen('std_gamma')
    min_c_val, min_c_clen = find_min_clen('std_c')
    
    print(f"The value of clen for the minimum alpha value of {min_alpha_val} is {min_alpha_clen}")
    print(f"The value of clen for the minimum beta value of {min_beta_val} is {min_beta_clen}")
    print(f"The value of clen for the minimum gamma value of {min_gamma_val} is {min_gamma_clen}")
    print(f"The value of clen for the minimum c value of {min_c_val} is {min_c_clen}")
    
    return min_alpha_clen, min_beta_clen, min_gamma_clen, min_c_clen, min_alpha_val, min_beta_val, min_gamma_val, min_c_val


def plot_indexed_std(stats_df, ax1, ax2):
    # indexed images plot
    color = "tab:red"
    ax1.set_xlabel("clen")
    ax1.set_ylabel("indexed", color=color)
    ax1.plot(stats_df.clen, stats_df.indexed, color=color)
    ax1.tick_params(axis="y", labelcolor=color)

    # label color
    color = "tab:blue"
    ax2.set_ylabel("a,b,c st.deviation", color=color)
    ax2.tick_params(axis='y', labelcolor=color)

    # std_a plot
    color = "lightsteelblue"
    ax2.plot(stats_df.clen, stats_df.std_a, color=color)

    # std_b plot
    color = "cornflowerblue"
    ax2.plot(stats_df.clen, stats_df.std_b, color=color)

    # std_c plot
    color = "royalblue"
    ax2.plot(stats_df.clen, stats_df.std_c, color=color)


def plot_indexed_std_alpha_beta_gamma(stats_df, ax1, ax2):
    # indexed images plot
    color = "tab:red"
    ax1.set_xlabel("clen")
    ax1.set_ylabel("indexed", color=color)
    ax1.plot(stats_df.clen, stats_df.indexed, color=color)
    ax1.tick_params(axis="y", labelcolor=color)

    # label color
    color = "tab:green"
    ax2.set_ylabel("alpha, beta, gamma st.deviation", color=color)
    ax2.tick_params(axis='y', labelcolor=color)

    # std_alpha plot
    color = "limegreen"
    ax2.plot(stats_df.clen, stats_df.std_alpha, color=color)

    # std_beta plot
    color = "darkgreen"
    ax2.plot(stats_df.clen, stats_df.std_beta, color=color)

    # std_gamma plot
    color = "green"
    ax2.plot(stats_df.clen, stats_df.std_gamma, color=color)
        
def main_coarse( lst, sample, lab6_geom_file, centre_clen, cell_file, steps_coarse, scan_name_coarse, step_size_coarse ):

    #make sample list
    cwd, sample_h5_file = make_sample(lst, sample)
    
    # make list of clen steps above and below the central clen
    step_range = make_step_range(centre_clen, step_size_coarse, steps_coarse)
    
    # make directorys for results
    print( "begin CrystFEL anaylsis of different clens" )

    # loop to cycle through clen steps
    for clen in step_range:

        # move back to cwd
        os.chdir( cwd )

        print( "processing clen = {0}".format( clen ) )
        # define process directory
        proc_dir = "{0}/{1}/{2}".format( cwd, scan_name_coarse, clen )

        # make process directory
        make_process_dir(proc_dir)
        
        # move to process directory
        os.chdir( proc_dir )

        # make geom file
        clen_geom_file = geom_amend( lab6_geom_file, clen )

        # make crystfel run file
        cryst_run_file = write_crystfel_run( clen, sample_h5_file, clen_geom_file, cell_file )  

        # run crystfel file
        subprocess.call( [ "sbatch", "-p", "day", "--cpus-per-task=32", "--", "./{0}".format( cryst_run_file ) ] )
        print( "done" )

    #wait for jobs to complete
    check_job_status(username)

def main_fine( lst, lab6_geom_file, centre_clen, cell_file, steps_fine, scan_name_fine, step_size_fine ):

    # set current working directory
    os.chdir("/sf/cristallina/data/p20590/work/process/jhb/detector_refinement")
    cwd = os.getcwd()
    
    #define the sample_h5_file location for this function
    sample_h5 = "h5_{0}_sample.lst".format(sample)
    sample_h5_file = "{0}/{1}".format(cwd, sample_h5)

    # make list of clen steps above and below the central clen
    step_range = make_step_range(centre_clen, step_size_fine, steps_fine)

    # make directorys for results
    print( "begin CrystFEL anaylsis of different clens" )
    
    # loop to cycle through clen steps
    for clen in step_range:

        # move back to cwd
        os.chdir( cwd )

        print( "processing clen = {0}".format( clen ) )
        # define process directory
        proc_dir = "{0}/{1}/{2}".format( cwd, scan_name_fine, clen )

        # make process directory
        make_process_dir(proc_dir)
        
        # move to process directory
        os.chdir( proc_dir )

        # make geom file
        clen_geom_file = geom_amend( lab6_geom_file, clen )

        # make crystfel run file
        cryst_run_file = write_crystfel_run( clen, sample_h5_file, clen_geom_file, cell_file )   

        # run crystfel file
        subprocess.call( [ "sbatch", "-p", "day", "--cpus-per-task=32", "--", "./{0}".format( cryst_run_file ) ] )
        print( "done" )
        
    #wait for jobs to complete
    check_job_status(username)

def scrub_main_coarse( top_dir_coarse ):

    stats_df = scrub_helper(top_dir_coarse)

    #print clen for minimum alpha, beta, and gamma values
    min_alpha_clen, min_beta_clen, min_gamma_clen, min_c_clen, min_alpha_val, min_beta_val, min_gamma_val, min_c_val = find_clen_values(stats_df)
    
    # plot results
    fig, (ax1, ax3) = plt.subplots(1, 2)
    ax2 = ax1.twinx()
    ax4 = ax3.twinx()

    plot_indexed_std(stats_df, ax1, ax2)
    plot_indexed_std_alpha_beta_gamma(stats_df, ax3, ax4)

    fig.tight_layout()
    plt.show()
    
def scrub_main_fine( top_dir_fine ):

    stats_df = scrub_helper(top_dir_fine)

    #print clen for minimum alpha, beta, and gamma values
    min_alpha_clen, min_beta_clen, min_gamma_clen, min_c_clen, min_alpha_val, min_beta_val, min_gamma_val, min_c_val = find_clen_values(stats_df)
    
    #print suggested clen
    suggested_clen = (min_alpha_clen + min_beta_clen + min_gamma_clen )/3
    suggested_clen = round(suggested_clen, 4)
    print ("The suggested clen = {0}".format(suggested_clen))
    
    # plot results
    fig, (ax1, ax3) = plt.subplots(1, 2)
    ax2 = ax1.twinx()
    ax4 = ax3.twinx()

    plot_indexed_std(stats_df, ax1, ax2)
    plot_indexed_std_alpha_beta_gamma(stats_df, ax3, ax4)

    fig.tight_layout()
    plt.show()
    

#location to which the data from coarse and fine scans will be saved
#!!The scan_name must match the final folder name for the respective coarse or fine scans!!
top_dir =  "/sf/cristallina/data/p20590/work/process/jhb/detector_refinement"
scan_name_coarse = "coarse"
scan_name_fine = "fine"
top_dir_coarse = "{0}/{1}".format( top_dir, scan_name_coarse )
top_dir_fine = "{0}/{1}".format( top_dir, scan_name_fine )

#General parameters for the scans
lst = "/sf/cristallina/data/p20590/work/process/jhb/detector_refinement/acq0001.JF17T16V01.dark.lst"
lab6_geom_file = "/sf/cristallina/data/p20590/work/process/jhb/detector_refinement/8M_p-op_c-op_p20590.geom"
centre_clen = 0.122 # in m
cell_file = "/sf/cristallina/data/p20590/work/process/jhb/detector_refinement/hewl.cell"
username = "beale_j"  #note that the timer only checks if the user has ANY jobs running,
                      #so the user should ONLY be running the jobs related to this script on the cluster
                      #to avoid a very long wait

#stepping parameters for coarse and fine scan (generally not to be changed)
sample = 500
steps_coarse = 20
step_size_coarse = 0.0005 # m 
steps_fine = 50
step_size_fine = 0.00005 # m 

#Calling the functions
main_coarse( lst, sample, lab6_geom_file, centre_clen, cell_file, steps_coarse, scan_name_coarse, step_size_coarse )

scrub_main_coarse( top_dir_coarse )

main_fine( lst, lab6_geom_file, centre_clen, cell_file, steps_fine, scan_name_fine, step_size_fine )

scrub_main_fine( top_dir_fine )