#!/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 # 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 scrub_cells( line ): # get uc values from stream file # example - Cell parameters 7.71784 7.78870 3.75250 nm, 90.19135 90.77553 90.19243 deg pattern = r"Cell\sparameters\s(\d+\.\d+)\s(\d+\.\d+)\s(\d+\.\d+)\snm,\s(\d+\.\d+)\s(\d+\.\d+)\s(\d+\.\d+)\sdeg" a = re.search( pattern, line ).group(1) b = re.search( pattern, line ).group(2) c = re.search( pattern, line ).group(3) alpha = re.search( pattern, line ).group(4) beta = re.search( pattern, line ).group(5) gamma = re.search( pattern, line ).group(6) return [ a, b, c, alpha, beta, gamma ] def scrub_res( stream ): # get diffraction limit # example - diffraction_resolution_limit = 4.07 nm^-1 or 2.46 A pattern = r"diffraction_resolution_limit\s=\s\d\.\d+\snm\^-1\sor\s(\d+\.\d+)\sA" res = re.search( pattern, stream ).group(1) return res def scrub_obs( stream ): # get number of reflections # example - num_reflections = 308 pattern = r"num_reflections\s=\s(\d+)" obs = re.search( pattern, stream ).group(1) return obs def main( stream_pwd ): chunks = 0 xtals = 0 cells = [] obs_list = [] res_list = [] print( "scrubing data" ) # open stream file with open( stream_pwd ) as stream: for line in stream: # count chunks if line.startswith( "----- Begin chunk -----" ): chunks = chunks + 1 # get cell if line.startswith( "Cell parameters" ): cell = scrub_cells( line ) cells.append( cell ) xtals = xtals + 1 # get res if line.startswith( "diffraction_resolution_limit" ): res = scrub_res( line ) res_list.append( res ) # get obs if line.startswith( "num_reflections" ): obs = scrub_obs( line ) obs_list.append( obs ) if chunks % 1000 == 0: print( "scrubbed {0} chunks".format( chunks ), end='\r' ) # res_df cols = [ "a", "b", "c", "alpha", "beta", "gamma" ] df = pd.DataFrame( cells, columns=cols ) df[ "resolution" ] = res_list df[ "obs" ] = obs_list # 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 )