# modules
import pandas as pd
import regex as re
import os
import numpy as np
import matplotlib.pyplot as plt


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 main( 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.
    results_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()

        # put stats in results df
        stats = [ { "clen" : clen,
                    "indexed" : indexed,
                    "std_a" : std_a,
                    "std_b" : std_b,
                    "std_c" : std_c
                } ]
        results_df_1 = pd.DataFrame( stats )
        results_df = pd.concat( ( results_df, results_df_1 ) )

        print( "done" )

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

    # plot results
    fig, ax1 = plt.subplots()

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

    # instantiate a second axes that shares the same x-axis
    ax2 = ax1.twinx()

    # std_a plot
    color = "tab:blue"
    ax2.set_ylabel( "st.deviation", color=color )
    ax2.plot( results_df.clen, results_df.std_a, color=color )
    ax2.tick_params(axis='y', labelcolor=color)

    # std_b plot
    ax2.plot( results_df.clen, results_df.std_b, color=color )
    ax2.tick_params(axis='y', labelcolor=color)

    # std_b plot
    ax2.plot( results_df.clen, results_df.std_c, color=color )
    ax2.tick_params(axis='y', labelcolor=color)

    fig.tight_layout()  # otherwise the right y-label is slightly clipped
    plt.show()


# variables
top_dir = "/sf/cristallina/data/p20590/work/process/jhb/detector_refinement/coarse_scan"


main( top_dir )