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organised tools into directories - made 16M pyfai script work

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Beale John Henry
2023-03-22 14:03:00 +01:00
parent cfcc9b5941
commit b1ce7a2215
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clen_tools/distance-refinement.py Normal file
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# modules
import pandas as pd
import subprocess
import os, errno
import regex as re
import numpy as np
def h5_sample( lst, sample ):
# create sample of images from run
# read h5.lst - note - removes // from imade column
cols = [ "h5", "image" ]
sample_df = pd.read_csv( lst, sep="\s//", engine="python", names=cols )
# take defined sample
sample_df = sample_df.sample( sample )
# sort list
sample_df = sample_df.sort_index()
# re-add // to image columm
sample_df[ "image" ] = "//" + sample_df.image.astype(str)
# write sample to file
sample_file = "h5_{0}_sample.lst".format( sample )
sample_df.to_csv( sample_file, sep=" ", index=False, header=False )
# return sample file name
return sample_file
def geom_amend( lab6_geom_file, clen ):
# read lab6 geom
lab6_geom = open( lab6_geom_file, "r" )
# use regex to find clen and replace with new
# clen example => clen = 0.1217
clen_geom = re.sub( "clen = 0\.\d+", "clen = {0}".format( clen ), lab6_geom.read() )
# close lab6 geom file
lab6_geom.close()
# write new clen_geom to file
clen_geom_file = "{0}.geom".format( clen )
geom = open( clen_geom_file, "w" )
geom.write( clen_geom )
geom.close()
# return clen_geom file name
return clen_geom_file
def write_crystfel_run( clen, sample_h5_file, clen_geom_file, cell_file ):
# crystfel file name
cryst_run_file = "{0}_cryst_run.sh".format( clen )
# write file
run_sh = open( cryst_run_file, "w" )
run_sh.write( "#!/bin/sh\n\n" )
run_sh.write( "module purge\n" )
run_sh.write( "module load crystfel/0.10.2\n" )
# run_sh.write( "module use MX unstable\n" )
# run_sh.write( "module load gcc/4.8.5 hdf5_serial/1.10.3 xds/20210205 DirAx/1.17 pinkindexer/2021.08\n" )
# run_sh.write( "module load xgandalf/2021.08 fdip/2021.08 mosflm/7.3.0 crystfel/0.10.0 HDF5_bitshuffle/2018.05 HDF5_LZ4/2018.05 ccp4\n\n" )
run_sh.write( "indexamajig -i {0} \\\n".format( sample_h5_file ) )
run_sh.write( " --output={0}.stream \\\n".format( clen ) )
run_sh.write( " --geometry={0}\\\n".format( clen_geom_file ) )
run_sh.write( " --pdb={0} \\\n".format( cell_file ) )
run_sh.write( " --indexing=xgandalf-latt-cell --peaks=peakfinder8 \\\n" )
run_sh.write( " --integration=rings-grad --tolerance=10.0,10.0,10.0,2,3,2 --threshold=10 --min-snr=5 --int-radius=2,3,6 \\\n" )
run_sh.write( " -j 36 --no-multi --no-retry --check-peaks --max-res=3000 --min-pix-count=1 --local-bg-radius=4 --min-res=85\n\n" )
run_sh.close()
# make file executable
subprocess.call( [ "chmod", "+x", "{0}".format( cryst_run_file ) ] )
# return crystfel file name
return cryst_run_file
def main( lst, sample, lab6_geom_file, centre_clen, cell_file, steps, scan_name, step_size ):
# set current working directory
cwd = os.getcwd()
# make sample list
print( "making {0} sample of images".format( sample ) )
sample_h5 = h5_sample( lst, sample)
sample_h5_file = "{0}/{1}".format( cwd, sample_h5 )
print( "done" )
# make list of clen steps above and below the central clen
print( "make clen array around {0}".format( centre_clen ) )
step_range = step_size*steps
bottom_step = centre_clen-step_range/2
top_step = bottom_step+step_range
step_range = np.arange( bottom_step, top_step, step_size )
step_range = step_range.round( 4 ) # important - otherwise np gives your .99999999 instead of 1 somethimes
print( "done" )
# make directorys for results
print( "begin CrystFEL anaylsis of different clens" )
# loop to cycle through clen steps
for clen in step_range:
# move back to cwd
os.chdir( cwd )
print( "processing clen = {0}".format( clen ) )
# define process directory
proc_dir = "{0}/{1}/{2}".format( cwd, scan_name, clen )
# make process directory
try:
os.makedirs( proc_dir )
except OSError as e:
if e.errno != errno.EEXIST:
raise
# move to process directory
os.chdir( proc_dir )
# make geom file
print( "amend .geom file" )
clen_geom_file = geom_amend( lab6_geom_file, clen )
print( "done" )
# make crystfel run file
print( "make crystfel file" )
cryst_run_file = write_crystfel_run( clen, sample_h5_file, clen_geom_file, cell_file )
print( "done" )
# run crystfel file
print( "run crystFEL" )
#subprocess.call( [ "./{0}".format( cryst_run_file ) ] )
subprocess.call( [ "sbatch", "-p", "day", "--cpus-per-task=32", "--", "./{0}".format( cryst_run_file ) ] )
print( "done" )
#subprocess.call( [ "sbatch", "-p", "day", "--cpus-per-task=32", "--", "run{0}.sh".format( run.zfill(4) ) ] )
# variables
sample = 500
lst = "/sf/cristallina/data/p20590/work/process/jhb/detector_refinement/acq0001.JF17T16V01.dark.lst"
lab6_geom_file = "/sf/cristallina/data/p20590/work/process/jhb/detector_refinement/8M_p-op_c-op_p20590.geom"
centre_clen = 0.122 # in m
cell_file = "/sf/cristallina/data/p20590/work/process/jhb/detector_refinement/hewl.cell"
steps = 10
scan_name = "fine_scan"
step_size = 0.0005 # m - 0.001 = coarse scan, 0.0005 = fine
main( lst, sample, lab6_geom_file, centre_clen, cell_file, steps, scan_name, step_size )

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clen_tools/distance-scan-analysis.py Normal file
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# 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 )