diff --git a/distance-scan-analysis.py b/distance-scan-analysis.py
deleted file mode 100644
index 7bcbadc..0000000
--- a/distance-scan-analysis.py
+++ /dev/null
@@ -1,155 +0,0 @@
-
-
-# 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 )