469 lines
14 KiB
Python
469 lines
14 KiB
Python
#!/usr/bin/env python3
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# authors T.Mason and J.Beale
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"""
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# aim
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to refine the detector distance using crystfel
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- naming covention = #.###/#.###.stream
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# usage
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python detector-distance-refinement.py -l <path to lst file generated by daq>
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-g <path to geom file>
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-d central clen to refine around
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-c cell_file
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-s sample size
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# output
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plot files of the analysis and a suggest for the clen
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"""
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# modules
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import pandas as pd
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import subprocess
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import os, errno
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import regex as re
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import numpy as np
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import matplotlib.pyplot as plt
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import time
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import argparse
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def h5_sample( lst, sample ):
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# create sample of images from run
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# read h5.lst - note - removes // from image column
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cols = [ "h5", "image" ]
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sample_df = pd.read_csv( lst, sep="\s//", engine="python", names=cols )
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# take defined sample
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sample_df = sample_df.sample( sample )
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# sort list
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sample_df = sample_df.sort_index()
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# re-add // to image columm
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sample_df[ "image" ] = "//" + sample_df.image.astype(str)
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# write sample to file
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sample_file = "h5_{0}_sample.lst".format( sample )
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sample_df.to_csv( sample_file, sep=" ", index=False, header=False )
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# return sample file name
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return sample_file
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def geom_amend( lab6_geom_file, clen ):
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# read lab6 geom
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lab6_geom = open( lab6_geom_file, "r" )
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# use regex to find clen and replace with new
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# clen example => clen = 0.1217
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clen_geom = re.sub( "clen = 0\.\d+", "clen = {0}".format( clen ), lab6_geom.read() )
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# close lab6 geom file
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lab6_geom.close()
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# write new clen_geom to file
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clen_geom_file = "{0}.geom".format( clen )
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geom = open( clen_geom_file, "w" )
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geom.write( clen_geom )
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geom.close()
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# return clen_geom file name
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return clen_geom_file
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def write_crystfel_run( clen, sample_h5_file, clen_geom_file, cell_file ):
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# crystfel file name
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cryst_run_file = "{0}_cryst_run.sh".format( clen )
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# write file
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run_sh = open( cryst_run_file, "w" )
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run_sh.write( "#!/bin/sh\n\n" )
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run_sh.write( "module purge\n" )
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run_sh.write( "module load crystfel/0.10.2\n" )
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run_sh.write( "indexamajig -i {0} \\\n".format( sample_h5_file ) )
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run_sh.write( " --output={0}.stream \\\n".format( clen ) )
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run_sh.write( " --geometry={0}\\\n".format( clen_geom_file ) )
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run_sh.write( " --pdb={0} \\\n".format( cell_file ) )
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run_sh.write( " --indexing=xgandalf-latt-cell --peaks=peakfinder8 \\\n" )
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run_sh.write( " --threshold=15 --min-snr=10 --int-radius=3,5,9 \\\n" )
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run_sh.write( " -j 36 --no-multi --no-retry --max-res=3000 --min-pix-count=2 --min-res=85\n\n" )
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run_sh.close()
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# make file executable
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subprocess.call( [ "chmod", "+x", "{0}".format( cryst_run_file ) ] )
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# return crystfel file name
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return cryst_run_file
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def make_sample( lst, sample ):
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# make sample list
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print("making {0} sample of images".format(sample))
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sample_h5 = h5_sample(lst, sample)
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sample_h5_file = "{0}/{1}".format(cwd, sample_h5)
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print("done")
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return sample_h5_file
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def make_process_dir(proc_dir):
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# make process directory
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try:
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os.makedirs( proc_dir )
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except OSError as e:
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if e.errno != errno.EEXIST:
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raise
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def make_step_range(centre_clen, step_size, steps):
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# make list of clen steps above and below the central clen
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print( "make clen array around {0}".format( centre_clen ) )
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step_range = step_size*steps
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bottom_step = centre_clen-step_range/2
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top_step = bottom_step+step_range
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step_range = np.arange( bottom_step, top_step, step_size )
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step_range = step_range.round( 6 ) # important - otherwise np gives your .99999999 instead of 1 somethimes
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print( "done" )
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return step_range
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def check_job_status(username):
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# wait for jobs to complete
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jobs_completed = False
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while not jobs_completed:
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# Get the status of the jobs using "squeue"
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result = subprocess.run(['squeue', '--user', '{0}'.format(username)], stdout=subprocess.PIPE)
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output = result.stdout.decode('utf-8')
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# Check if there are no jobs running for the user
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if '{0}'.format(username) not in output:
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jobs_completed = True
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else:
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# Sleep for some time and check again
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print("waiting for jobs to finish")
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time.sleep(30) # sleep for 30 seconds
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print("All jobs completed.")
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def scrub_clen( stream_pwd ):
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# get clen from stream name
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# example - /sf/cristallina/data/p20590/work/process/jhb/detector_refinement/coarse_scan/0.115/0.115.stream
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# scrub clen and return - else nan
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try:
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pattern = r"0\.\d+/(0\.\d+)\.stream"
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re_search = re.search( pattern, stream_pwd )
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clen = re_search.group( 1 )
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if AttributeError:
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return float( clen )
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except AttributeError:
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return 1
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def find_streams( top_dir ):
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# create df for streams
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stream_df = pd.DataFrame()
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# search for all files that end with .stream
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for path, dirs, files in os.walk( top_dir ):
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for name in files:
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if name.endswith( ".stream" ):
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# get stream pwd
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stream_pwd = os.path.join( path, name )
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# scrub clen from stream
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clen = scrub_clen( stream_pwd )
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# put clen and stream pwd into df
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data = [ { "stream_pwd" : stream_pwd,
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"clen" : clen
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} ]
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stream_df_1 = pd.DataFrame( data )
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stream_df = pd.concat( ( stream_df, stream_df_1 ) )
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# sort df based on clen
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stream_df = stream_df.sort_values( by="clen" )
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# reset df index
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stream_df = stream_df.reset_index( drop=True )
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# return df of streams and clens
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return stream_df
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def scrub_us( stream ):
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# get uc values from stream file
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# example - Cell parameters 7.71784 7.78870 3.75250 nm, 90.19135 90.77553 90.19243 deg
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# scrub clen and return - else nan
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try:
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pattern = r"Cell\sparameters\s(\d\.\d+)\s(\d\.\d+)\s(\d\.\d+)\snm,\s(\d+\.\d+)\s(\d+\.\d+)\s(\d+\.\d+)\sdeg"
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cells = re.findall( pattern, stream )
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if AttributeError:
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return cells
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except AttributeError:
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return np.nan
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def scrub_helper( top_dir ):
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# find stream files from process directory
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print( "finding stream files" )
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stream_df = find_streams( top_dir )
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print( "done" )
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# making results df for unit cell and index no.
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stats_df = pd.DataFrame()
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# loop through stream files and collect unit_cell information
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print( "looping through stream files to collect unit cell, indexed information" )
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for index, row in stream_df.iterrows():
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stream_pwd, clen = row[ "stream_pwd" ], row[ "clen" ]
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# open stream file
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print( "scrubbing stream for clen={0}".format( clen ) )
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stream = open( stream_pwd, "r" ).read()
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# scrub unit cell information
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cells = scrub_us( stream )
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# put cells in df
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cols = [ "a", "b", "c", "alpha", "beta", "gamma" ]
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cells_df = pd.DataFrame( cells, columns=cols )
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cells_df = cells_df.astype( float )
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# calc stats
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indexed = len( cells_df )
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std_a = cells_df.a.std()
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std_b = cells_df.b.std()
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std_c = cells_df.c.std()
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std_alpha = cells_df.alpha.std()
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std_beta = cells_df.beta.std()
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std_gamma = cells_df.gamma.std()
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# put stats in results df
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stats = [ { "clen" : clen,
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"indexed" : indexed,
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"std_a" : std_a,
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"std_b" : std_b,
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"std_c" : std_c,
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"std_alpha" : std_alpha,
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"std_beta" : std_beta,
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"std_gamma" : std_gamma,
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} ]
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stats_df_1 = pd.DataFrame( stats )
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stats_df = pd.concat( ( stats_df, stats_df_1 ) )
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print( "done" )
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# reset index
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stats_df = stats_df.reset_index( drop=True )
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return stats_df
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def find_clen_values(stats_df):
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def find_min_clen(col_name):
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min_val = stats_df[col_name].min()
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min_row = stats_df[stats_df[col_name] == min_val]
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min_clen = min_row['clen'].values[0]
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return min_val, min_clen
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min_alpha_val, min_alpha_clen = find_min_clen('std_alpha')
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min_beta_val, min_beta_clen = find_min_clen('std_beta')
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min_gamma_val, min_gamma_clen = find_min_clen('std_gamma')
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min_c_val, min_c_clen = find_min_clen('std_c')
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print("The value of clen for the minimum alpha value of {} is {}".format(min_alpha_val, min_alpha_clen))
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print("The value of clen for the minimum beta value of {} is {}".format(min_beta_val, min_beta_clen))
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print("The value of clen for the minimum gamma value of {} is {}".format(min_gamma_val, min_gamma_clen))
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print("The value of clen for the minimum c value of {} is {}".format(min_c_val, min_c_clen))
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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
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def plot_indexed_std( stats_df, ax1, ax2 ):
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# indexed images plot
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color = "tab:red"
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ax1.set_xlabel("clen")
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ax1.set_ylabel("indexed", color=color)
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ax1.plot(stats_df.clen, stats_df.indexed, color=color)
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ax1.tick_params(axis="y", labelcolor=color)
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# label color
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color = "tab:blue"
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ax2.set_ylabel("a,b,c st.deviation", color=color)
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ax2.tick_params(axis='y', labelcolor=color)
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# std_a plot
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color = "lightsteelblue"
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ax2.plot(stats_df.clen, stats_df.std_a, color=color)
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# std_b plot
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color = "cornflowerblue"
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ax2.plot(stats_df.clen, stats_df.std_b, color=color)
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# std_c plot
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color = "royalblue"
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ax2.plot(stats_df.clen, stats_df.std_c, color=color)
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def plot_indexed_std_alpha_beta_gamma( stats_df, ax1, ax2 ):
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# indexed images plot
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color = "tab:red"
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ax1.set_xlabel("clen")
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ax1.set_ylabel("indexed", color=color)
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ax1.plot(stats_df.clen, stats_df.indexed, color=color)
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ax1.tick_params(axis="y", labelcolor=color)
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# label color
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color = "tab:green"
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ax2.set_ylabel("alpha, beta, gamma st.deviation", color=color)
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ax2.tick_params(axis='y', labelcolor=color)
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# std_alpha plot
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color = "limegreen"
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ax2.plot(stats_df.clen, stats_df.std_alpha, color=color)
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# std_beta plot
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color = "darkgreen"
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ax2.plot(stats_df.clen, stats_df.std_beta, color=color)
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# std_gamma plot
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color = "green"
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ax2.plot(stats_df.clen, stats_df.std_gamma, color=color)
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def scan( cwd, lst, sample, lab6_geom_file, centre_clen, cell_file, step_size ):
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# define coarse or fine scan
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if step_size == "coarse":
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steps = 20
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step_size = 0.0005 # m
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scan_name = "coarse"
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if step_size == "fine":
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steps = 50
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step_size = 0.00005 # m
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scan_name = "fine"
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#make sample list
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sample_h5_file = make_sample(lst, sample)
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# make list of clen steps above and below the central clen
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step_range = make_step_range(centre_clen, step_size, steps)
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# make directorys for results
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print( "begin CrystFEL anaylsis of different clens" )
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# loop to cycle through clen steps
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for clen in step_range:
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print( "processing clen = {0}".format( clen ) )
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# define process directory
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proc_dir = "{0}/{1}/{2}".format( cwd, scan_name, clen )
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# make process directory
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make_process_dir(proc_dir)
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# move to process directory
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os.chdir( proc_dir )
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# make geom file
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clen_geom_file = geom_amend( lab6_geom_file, clen )
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# make crystfel run file
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cryst_run_file = write_crystfel_run( clen, sample_h5_file, clen_geom_file, cell_file )
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# run crystfel file
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subprocess.call( [ "sbatch", "-p", "day", "--cpus-per-task=32", "--", "./{0}".format( cryst_run_file ) ] )
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print( "done" )
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# move back to cwd
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os.chdir( cwd )
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#wait for jobs to complete
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check_job_status(username)
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def scrub_scan( scan_top_dir, scan ):
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stats_df = scrub_helper(scan_top_dir)
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#print clen for minimum alpha, beta, and gamma values
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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)
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# print suggested clen
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suggested_clen = (min_alpha_clen + min_beta_clen + min_gamma_clen )/3
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suggested_clen = round(suggested_clen, 4)
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print ("The suggested clen = {0}".format(suggested_clen))
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# plot results
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fig, (ax1, ax3) = plt.subplots(1, 2)
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ax2 = ax1.twinx()
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ax4 = ax3.twinx()
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plot_indexed_std(stats_df, ax1, ax2)
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plot_indexed_std_alpha_beta_gamma(stats_df, ax3, ax4)
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fig.tight_layout()
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plt.savefig("{0}.png".format(scan))
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return suggested_clen
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def main( cwd, lst, sample, geom, centre_clen, cell_file ):
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top_dir_coarse = "{0}/coarse".format( cwd )
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scan( cwd, lst, sample, geom, centre_clen, cell_file, step_size="coarse" )
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suggested_clen = scrub_scan( top_dir_coarse, scan="coarse" )
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top_dir_fine = "{0}/fine".format( cwd )
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scan( cwd, lst, sample, geom, suggested_clen, cell_file, step_size="fine" )
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scrub_scan( top_dir_fine, scan="fine" )
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plt.show()
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if __name__ == "__main__":
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parser = argparse.ArgumentParser()
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parser.add_argument(
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"-l",
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"--lst",
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help="path to crystfel list file containing enough patterns for detector distance refinement",
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type=os.path.abspath
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)
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parser.add_argument(
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"-g",
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"--geom",
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help="path to geom file to be used in the refinement",
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type=os.path.abspath
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)
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parser.add_argument(
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"-d",
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"--central_distance",
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help="intial clen to use for refinement - usually from detector shift refinement",
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type=float
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)
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parser.add_argument(
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"-c",
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"--cell_file",
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help="path to cell file of the crystals used in the refinement",
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type=os.path.abspath
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)
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parser.add_argument(
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"-s",
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"--sample",
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help="sample size to use in the refinement",
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type=int,
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default=500
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)
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args = parser.parse_args()
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# run main
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username = os.getlogin()
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cwd = os.getcwd()
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print( "current username = {0}".format( username ) )
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print( "top working directory = {0}".format( cwd ) )
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main( cwd, args.lst, args.sample, args.geom, args.central_distance, args.cell_file )
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