diff --git a/reduction_tools/stream_stats.py b/reduction_tools/stream_stats.py
new file mode 100644
index 0000000..35ed361
--- /dev/null
+++ b/reduction_tools/stream_stats.py
@@ -0,0 +1,134 @@
+#!/usr/bin/env python3
+
+# author J.Beale
+
+"""
+# aim
+a quick a dirty script to see how your indexing went
+will spit out useful parameters like the number images, indexing rate etc
+
+# usage
+python stream_stats <path to stream file>
+
+# output
+text in line - no files
+chunks, crystals, indexing rate, mean res, obs, unit cell parameters
+"""
+import numpy as np
+import pandas as pd
+import regex as re
+import sys
+
+def count_chunks( stream ):
+
+    # get number of chunks
+    # example - ----- Begin chunk -----
+    # count them
+    try:
+        pattern = r"-----\sBegin\schunk\s-----"
+        chunks = re.findall( pattern, stream )
+        if AttributeError:
+            return len( chunks )
+    except AttributeError:
+        return np.nan
+
+def scrub_cells( 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"
+        cell_lst = re.findall( pattern, stream )
+        xtals = len( cell_lst )
+        if AttributeError:
+            return cell_lst, xtals
+    except AttributeError:
+        return np.nan
+
+def scrub_res( stream ):
+
+    # get diffraction limit
+    # example - diffraction_resolution_limit = 4.07 nm^-1 or 2.46 A
+    # scrub res_lst or return np.nan
+    try:
+        pattern = r"diffraction_resolution_limit\s=\s\d\.\d+\snm\^-1\sor\s(\d\.\d+)\sA"
+        res_lst = re.findall( pattern, stream )
+        if AttributeError:
+            return res_lst
+    except AttributeError:
+        return np.nan
+
+def scrub_obs( stream ):
+
+    # get number of reflections
+    # example - num_reflections = 308
+    # scrub reflections or return np.nan
+    try:
+        pattern = r"num_reflections\s=\s(\d+)"
+        obs_lst = re.findall( pattern, stream )
+        if AttributeError:
+            return obs_lst
+    except AttributeError:
+        return np.nan
+
+def main( stream_pwd ):
+
+    print( "reading stream file" )
+    # open stream file
+    stream = open( stream_pwd, "r" ).read()
+    print( "done" )
+
+    print( "scrubing data" )
+    # get total number chunks
+    chunks = count_chunks( stream )
+
+    # get list of cells
+    cell_lst, xtals = scrub_cells( stream )
+
+    # get list of cells
+    res_lst = scrub_res( stream )
+
+    # get list of cells
+    obs_lst = scrub_obs( stream )
+    print( "done" )
+
+    # res_df
+    cols = [ "a", "b", "c", "alpha", "beta", "gamma" ]
+    df = pd.DataFrame( cell_lst, columns=cols )
+    df[ "resolution" ] = res_lst
+    df[ "obs" ] = obs_lst
+
+    # convert all to floats
+    df = df.astype(float)
+
+    print( "calculating stream stats" )
+    # stats
+    index_rate = round( xtals/chunks*100, 2 )
+    mean_res, std_res = round( df.resolution.mean(), 2 ), round( df.resolution.std(), 2 )
+    median_res = df.resolution.median()
+    mean_obs, std_obs = round( df.obs.mean(), 2 ), round( df.obs.std(), 2)
+    mean_a, std_a = round( df.a.mean()*10, 2 ), round( df.a.std()*10, 2 )
+    mean_b, std_b = round( df.b.mean()*10, 2 ), round( df.b.std()*10, 2 )
+    mean_c, std_c = round( df.c.mean()*10, 2 ), round( df.c.std()*10, 2 )
+    mean_alpha, std_alpha = round( df.alpha.mean(), 2 ), round( df.alpha.std(), 2 )
+    mean_beta, std_beta = round(df.beta.mean(), 2 ), round( df.beta.std(), 2 )
+    mean_gamma, std_gamma = round( df.gamma.mean(), 2 ), round( df.gamma.std(), 2 )
+
+    # results
+    print( "image = {0}".format( chunks ) )
+    print( "crystals = {0}".format( xtals ) )
+    print( "indexing rate = {0} %".format( index_rate ) )
+    print( "mean resolution = {0} +/- {1} A".format( mean_res, std_res ) )
+    print( "median resolution = {0} A".format( median_res  ) )
+    print( "mean observations = {0} +/- {1}".format( mean_obs, std_obs ) )
+    print( "mean a = {0} +/- {1} A".format( mean_a, std_a ) )
+    print( "mean b = {0} +/- {1} A".format( mean_b, std_b ) )
+    print( "mean c = {0} +/- {1} A".format( mean_c, std_c ) )
+    print( "mean alpha = {0} +/- {1} A".format( mean_alpha, std_alpha ) )
+    print( "mean beta = {0} +/- {1} A".format( mean_beta, std_beta ) )
+    print( "mean gamma = {0} +/- {1} A".format( mean_gamma, std_gamma ) )
+
+if __name__ == "__main__":
+    stream_pwd = sys.argv[1]
+    main( stream_pwd )