384 lines
13 KiB
Python
384 lines
13 KiB
Python
#!/usr/bin/python
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# author J.Beale
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"""
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# aim
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to merge .stream files and calculate statistics
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# usage
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python partialator.py -s <path-to-stream-file>
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-n name (name of job - default = partialator)
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-p pointgroup
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-m model (unity or xsphere - default is unity)
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-i iterations - number of iterations in partialator
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-c <path-to-cell-file>
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-b number of resolution bins - must be > 20
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-r high-res limt. Needs a default. Default set to 1.3
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-a max-adu. Default = 12000
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-v ra reservation name if available
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# output
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- scaled/merged files
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- an mtz file
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- useful plots
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- useful summerized .dat files
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- log file of output
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"""
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# modules
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from sys import exit
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import pandas as pd
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import numpy as np
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import subprocess
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import os, errno
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import time
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import argparse
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from tqdm import tqdm
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import regex as re
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import matplotlib.pyplot as plt
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from scipy.optimize import curve_fit
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import warnings
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warnings.filterwarnings( "ignore", category=RuntimeWarning )
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from loguru import logger
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def submit_job( job_file ):
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submit_cmd = [ "sbatch", "--" , job_file ]
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logger.info( "using slurm command = {0}".format( submit_cmd ) )
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job_output = subprocess.check_output( submit_cmd )
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logger.info( "submited job = {0}".format( job_output ) )
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# scrub job id from - example Submitted batch job 742403
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pattern = r"Submitted batch job (\d+)"
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job_id = re.search( pattern, job_output.decode().strip() ).group(1)
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return int( job_id )
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def wait_for_jobs( job_ids, total_jobs ):
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with tqdm( total=total_jobs, desc="Jobs Completed", unit="job" ) as pbar:
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while job_ids:
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completed_jobs = set()
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for job_id in job_ids:
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status_cmd = [ "squeue", "-h", "-j", str( job_id ) ]
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status = subprocess.check_output( status_cmd )
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if not status:
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completed_jobs.add( job_id )
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pbar.update( 1 )
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job_ids.difference_update( completed_jobs )
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time.sleep( 2 )
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def run_compare_check( proc_dir, name, cell, shells, part_h_res ):
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# check file name
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check_run_file = "{0}/check_{1}.sh".format( proc_dir, name )
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# write file
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check_sh = open( check_run_file, "w" )
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check_sh.write( "#!/bin/sh\n\n" )
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check_sh.write( "module purge\n" )
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check_sh.write( "module use MX unstable\n" )
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check_sh.write( "module load crystfel/0.10.2-rhel8\n" )
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check_sh.write( "check_hkl --shell-file=mult.dat *.hkl -p {0} --nshells={1} --highres={2} &> check_hkl.log\n".format( cell, shells, part_h_res ) )
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check_sh.write( "check_hkl --ltest --ignore-negs --shell-file=ltest.dat *.hkl -p {0} --nshells={1} --highres={2} &> ltest.log\n".format( cell, shells, part_h_res ) )
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check_sh.write( "check_hkl --wilson --shell-file=wilson.dat *.hkl -p {0} --nshells={1} --highres={2} &> wilson.log\n".format( cell, shells, part_h_res ) )
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check_sh.write( "compare_hkl --fom=Rsplit --shell-file=Rsplit.dat *.hkl1 *hkl2 -p {0} --nshells={1} --highres={2} &> Rsplit.log\n".format( cell, shells, part_h_res ) )
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check_sh.write( "compare_hkl --fom=cc --shell-file=cc.dat *.hkl1 *hkl2 -p {0} --nshells={1} --highres={2} &> cc.log\n".format( cell, shells, part_h_res ) )
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check_sh.write( "compare_hkl --fom=ccstar --shell-file=ccstar.dat *.hkl1 *hkl2 -p {0} --nshells={1} --highres={2} &> ccstar.log\n".format( cell, shells, part_h_res ) )
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check_sh.close()
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# make file executable
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subprocess.call( [ "chmod", "+x", "{0}".format( check_run_file ) ] )
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# add check script to log
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check_input = open( check_run_file, "r" )
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logger.info( "check input file =\n{0}".format( check_input.read() ) )
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check_input.close()
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# return check file name
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return check_run_file
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def summary_stats( cc_dat, ccstar_dat, mult_dat, rsplit_dat, wilson_dat ):
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# read all files into pd
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# function to sort out different column names
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def read_dat( dat, var ):
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# different columns names of each dat file
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if var == "cc":
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cols = [ "d(nm)", "cc", "nref", "d", "min", "max" ]
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elif var == "ccstar":
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cols = [ "1(nm)", "ccstar", "nref", "d", "min", "max" ]
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elif var == "mult":
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cols = [ "d(nm)", "nref", "poss", "comp", "obs",
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"mult", "snr", "I", "d", "min", "max" ]
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elif var == "rsplit":
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cols = [ "d(nm)", "rsplit", "nref", "d", "min", "max" ]
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elif var == "wilson":
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cols = [ "bin", "s2", "d", "lnI", "nref" ]
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df = pd.read_csv( dat, names=cols, skiprows=1, sep="\s+" )
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return df
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# make df
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cc_df = read_dat( cc_dat, "cc" )
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ccstar_df = read_dat( ccstar_dat, "ccstar" )
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mult_df = read_dat( mult_dat, "mult" )
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rsplit_df = read_dat( rsplit_dat, "rsplit" )
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wilson_df = read_dat( wilson_dat, "wilson" )
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# remove unwanted cols
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cc_df = cc_df[ [ "cc" ] ]
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ccstar_df = ccstar_df[ [ "ccstar" ] ]
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rsplit_df = rsplit_df[ [ "rsplit" ] ]
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wilson_df = wilson_df[ [ "lnI" ] ]
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# merge dfs
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stats_df = pd.concat( [ mult_df, cc_df, ccstar_df, rsplit_df, wilson_df ], axis=1, join="inner" )
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# make 1/d, 1/d^2 column
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stats_df[ "1_d" ] = 1 / stats_df.d
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stats_df[ "1_d2" ] = 1 / stats_df.d**2
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# change nan to 0
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stats_df = stats_df.fillna(0)
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return stats_df
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def get_metric( d2_series, cc_series, cut_off ):
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# Define the tanh function from scitbx
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def tanh(x, r, s0):
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z = (x - s0)/r
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return 0.5 * ( 1 - np.tanh(z) )
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def arctanh( y, r, s0 ):
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return r * np.arctanh( 1 - 2*y ) + s0
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# Fit the tanh to the data
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params, covariance = curve_fit( tanh, d2_series, cc_series )
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# Extract the fitted parameters
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r, s0 = params
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# calculate cut-off point
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cc_stat = arctanh( cut_off, r, s0 )
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# covert back from 1/d2 to d
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cc_stat = np.sqrt( ( 1 / cc_stat ) )
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# get curve for plotting
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cc_tanh = tanh( d2_series, r, s0 )
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return round( cc_stat, 3 ), cc_tanh
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def get_overall_cc():
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# open cc log file
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cc_log_file = open( "cc.log" )
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cc_log = cc_log_file.read()
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# regex example = Overall CC = 0.5970865
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overcc_pattern = r"Overall\sCC\s=\s(\d\.\d+)"
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try:
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overcc = re.search( overcc_pattern, cc_log ).group(1)
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except AttributeError as e:
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overcc = np.nan
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return overcc
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def get_overall_rsplit():
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# open rsplit log file
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rsplit_log_file = open( "Rsplit.log" )
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rsplit_log = rsplit_log_file.read()
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# regex example = Overall Rsplit = 54.58 %
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overrsplit_pattern = r"Overall\sRsplit\s=\s(\d+\.\d+)"
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try:
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overrsplit = re.search( overrsplit_pattern, rsplit_log ).group(1)
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except AttributeError as e:
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overrsplit = np.nan
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return overrsplit
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def get_b():
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# open rsplit log file
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wilson_log_file = open( "wilson.log" )
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wilson_log = wilson_log_file.read()
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# regex example = B = 41.63 A^2
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b_factor_pattern = r"B\s=\s(\d+\.\d+)\sA"
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try:
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b_factor = re.search( b_factor_pattern, wilson_log ).group(1)
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except AttributeError as e:
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b_factor = np.nan
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return b_factor
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def summary_fig( name, stats_df, cc_tanh, ccstar_tanh, cc_cut, ccstar_cut ):
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def dto1_d( x ):
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return 1/x
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# plot results
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cc_fig, axs = plt.subplots(2, 2)
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cc_fig.suptitle( "cc and cc* vs resolution" )
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# cc plot
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color = "tab:red"
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axs[0,0].set_xlabel( "1/d (1/A)" )
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axs[0,0].set_ylabel( "CC", color=color )
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axs[0,0].set_ylim( 0, 1 )
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axs[0,0].axhline( y = 0.3, color="black", linestyle = "dashed" )
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# plot cc
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axs[0,0].plot( stats_df[ "1_d" ], stats_df.cc, color=color )
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# plot fit
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axs[0,0].plot( stats_df[ "1_d" ], cc_tanh, color="tab:grey", linestyle = "dashed" )
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sax1 = axs[0,0].secondary_xaxis( 'top', functions=( dto1_d, dto1_d ) )
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sax1.set_xlabel('d (A)')
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axs[0,0].tick_params( axis="y", labelcolor=color )
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axs[0,0].text( 0.1, 0.1, "CC0.5 @ 0.3 = {0}".format( cc_cut ), fontsize = 8 )
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# cc* plot
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color = "tab:blue"
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axs[0,1].set_xlabel( "1/d (1/A)" )
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axs[0,1].set_ylabel( "CC*", color=color )
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axs[0,1].set_ylim( 0, 1 )
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axs[0,1].axhline( y = 0.7, color="black", linestyle = "dashed" )
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axs[0,1].plot( stats_df[ "1_d" ], stats_df.ccstar, color=color )
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# plot fit
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axs[0,1].plot( stats_df[ "1_d" ], ccstar_tanh, color="tab:grey", linestyle = "dashed" )
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sax2 = axs[0,1].secondary_xaxis( 'top', functions=( dto1_d, dto1_d ) )
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sax2.set_xlabel('d (A)')
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axs[0,1].tick_params( axis='y', labelcolor=color )
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axs[0,1].text( 0.1, 0.1, "CC* @ 0.7 = {0}".format( ccstar_cut ) , fontsize = 8 )
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# rsplit plot
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color = "tab:green"
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axs[1,0].set_xlabel( "1/d (1/A)" )
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axs[1,0].set_ylabel( "Rsplit", color=color )
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axs[1,0].plot( stats_df[ "1_d" ], stats_df.rsplit, color=color )
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sax3 = axs[1,0].secondary_xaxis( 'top', functions=( dto1_d, dto1_d ) )
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sax3.set_xlabel( 'd (A)' )
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axs[1,0].tick_params( axis='y', labelcolor=color )
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# wilson plot
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color = "tab:purple"
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axs[1,1].set_xlabel( "1/d**2 (1/A**2)" )
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axs[1,1].set_ylabel( "lnI", color=color )
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axs[1,1].plot( stats_df[ "1_d2" ], stats_df.lnI, color=color )
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axs[1,1].tick_params( axis='y', labelcolor=color )
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# save figure
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plt.tight_layout()
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plt.savefig( "{0}_plots.png".format( name ) )
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def main( cwd, name, cell, shells, part_h_res ):
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# submitted job set
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submitted_job_ids = set()
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# now run the check and compare scripts
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print( "running check/compare" )
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check_run_file = run_compare_check( cwd, name, cell, shells, part_h_res )
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check_job_id = submit_job( check_run_file )
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print( f"job submitted: {0}".format( check_job_id ) )
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submitted_job_ids.add( check_job_id )
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time.sleep(10)
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wait_for_jobs( submitted_job_ids, 1 )
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print( "done" )
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# stats files names
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cc_dat = "cc.dat"
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ccstar_dat = "ccstar.dat"
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mult_dat = "mult.dat"
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rsplit_dat = "Rsplit.dat"
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wilson_dat = "wilson.dat"
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# make summary data table
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stats_df = summary_stats( cc_dat, ccstar_dat, mult_dat, rsplit_dat, wilson_dat )
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logger.info( "stats table from .dat file =\n{0}".format( stats_df.to_string() ) )
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print_df = stats_df[ [ "1_d", "d", "min",
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"max", "nref", "poss",
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"comp", "obs", "mult",
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"snr", "I", "rsplit", "cc", "ccstar" ] ]
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print_df.to_csv( "{0}_summary_table.csv".format( name ), sep="\t", index=False )
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# calculate cc metrics
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cc_cut, cc_tanh = get_metric( stats_df[ "1_d2" ], stats_df.cc, 0.3 )
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ccstar_cut, ccstar_tanh = get_metric( stats_df[ "1_d2" ], stats_df.ccstar, 0.7 )
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print( "resolution at CC0.5 at 0.3 = {0}".format( cc_cut ) )
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print( "resolution at CC* at 0.7 = {0}".format( ccstar_cut ) )
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logger.info( "resolution at CC0.5 at 0.3 = {0}".format( cc_cut ) )
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logger.info( "resolution at CC* at 0.7 = {0}".format( ccstar_cut ) )
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# scrub other metrics
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overcc = get_overall_cc()
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overrsplit = get_overall_rsplit()
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b_factor = get_b()
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logger.info( "overall CC0.5 = {0}".format( overcc ) )
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logger.info( "overall Rsplit = {0}".format( overrsplit ) )
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logger.info( "overall B = {0}".format( b_factor ) )
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# show plots
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summary_fig( name, stats_df, cc_tanh, ccstar_tanh, cc_cut, ccstar_cut )
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# move back to top dir
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os.chdir( cwd )
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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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"-n",
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"--name",
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help="name of check.",
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type=str,
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required=True
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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 CrystFEL cell file for partialator.",
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type=os.path.abspath,
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required=True
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)
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parser.add_argument(
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"-b",
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"--bins",
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help="number of resolution bins to use. Should be more than 20. Default = 20.",
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type=int,
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default=20
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)
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parser.add_argument(
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"-r",
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"--resolution",
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help="high res limit - need something here. Default set to 1.3.",
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type=float,
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default=1.3
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)
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parser.add_argument(
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"-d",
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"--debug",
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help="output debug to terminal.",
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type=bool,
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default=False
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)
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args = parser.parse_args()
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# set loguru
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if not args.debug:
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logger.remove()
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logfile = "{0}.log".format( args.name )
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logger.add( logfile, format="{message}", level="INFO")
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# run main
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cwd = os.getcwd()
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print( "top working directory = {0}".format( cwd ) )
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main( cwd, args.name, args.cell_file, args.bins, args.resolution )
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