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some bug fixes and updated functionality, imporved plots, now also rsplit and mutl

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Beale John Henry
2023-11-03 09:52:13 +01:00
parent 5a2840ee3b
commit d43dce83ce
1 changed files with 106 additions and 34 deletions
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140
reduction_tools/partialator.py
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@@ -15,9 +15,11 @@ python partialator.py -s <path-to-stream-file>
-c <path-to-cell-file>
-b number of resolution bins - must be > 20
-r high-res limt. Needs a default. Default set to 1.3
-a max-adu. Default = 12000
# output
- scaled/merged files
- an mtz file
- useful plots
- useful summerized .dat files
"""
@@ -60,7 +62,7 @@ def wait_for_jobs( job_ids, total_jobs ):
job_ids.difference_update(completed_jobs)
time.sleep(2)
def run_partialator( proc_dir, name, stream, pointgroup, model, iterations, cell, shells, part_h_res ):
def run_partialator( proc_dir, name, stream, pointgroup, model, iterations, cell, shells, part_h_res, adu ):
# partialator file name
part_run_file = "{0}/partialator_{1}.sh".format( proc_dir, name )
@@ -74,13 +76,14 @@ def run_partialator( proc_dir, name, stream, pointgroup, model, iterations, cell
part_sh.write( " -o merged_{0}.hkl \\\n".format( name ) )
part_sh.write( " -y {0} \\\n".format( pointgroup ) )
part_sh.write( " --model={0} \\\n".format( model ) )
part_sh.write( " --max-adu={0} \\\n".format( adu ) )
part_sh.write( " --iterations={0}\n\n".format( iterations ) )
part_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 ) )
part_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 ) )
part_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 ) )
part_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 ) )
part_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 ) )
part_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 ) )
part_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 ) )
part_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 ) )
part_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 ) )
part_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 ) )
part_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 ) )
part_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 ) )
part_sh.close()
# make file executable
@@ -97,7 +100,7 @@ def make_process_dir( dir ):
if e.errno != errno.EEXIST:
raise
def summary_stats( cc_dat, ccstar_dat, mult_dat ):
def summary_stats( cc_dat, ccstar_dat, mult_dat, rsplit_dat ):
# read all files into pd
# function to sort out different column names
@@ -111,22 +114,28 @@ def summary_stats( cc_dat, ccstar_dat, mult_dat ):
elif var == "mult":
cols = [ "d(nm)", "nref", "poss", "comp", "obs",
"mult", "snr", "I", "d", "min", "max" ]
elif var == "rsplit":
cols = [ "d(nm)", "rsplit", "nref", "d", "min", "max" ]
df = pd.read_csv( dat, names=cols, skiprows=1, sep="\s+" )
print(df)
return df
# make df
cc_df = read_dat( cc_dat, "cc" )
ccstar_df = read_dat( ccstar_dat, "ccstar" )
mult_df = read_dat( mult_dat, "mult" )
rsplit_df = read_dat( rsplit_dat, "rsplit" )
# remove unwanted cols
cc_df = cc_df[ [ "cc" ] ]
ccstar_df = ccstar_df[ [ "ccstar" ] ]
rsplit_df = rsplit_df[ [ "rsplit" ] ]
# merge dfs
stats_df = pd.concat( [ mult_df, cc_df, ccstar_df], axis=1, join="inner" )
stats_df = pd.concat( [ mult_df, cc_df, ccstar_df, rsplit_df ], axis=1, join="inner" )
# make 1/d, 1/d^2 column
stats_df[ "1_d" ] = 1 / stats_df.d
@@ -136,7 +145,7 @@ def summary_stats( cc_dat, ccstar_dat, mult_dat ):
stats_df = stats_df[ [ "1_d", "1_d2", "d", "min",
"max", "nref", "poss",
"comp", "obs", "mult",
"snr", "I", "cc", "ccstar"] ]
"snr", "I", "cc", "ccstar", "rsplit" ] ]
# change nan to 0
stats_df = stats_df.fillna(0)
@@ -170,31 +179,82 @@ def get_metric( d2_series, cc_series, cut_off ):
def summary_fig( stats_df ):
# plot results
cc_fig, (ax1, ax2) = plt.subplots(1, 2)
cc_fig, axs = plt.subplots(2, 2)
cc_fig.suptitle( "cc and cc* vs resolution" )
# indexed images plot
# cc plot
color = "tab:red"
ax1.set_xlabel( "1/d2 (1/A)" )
ax1.set_ylabel("CC" )
ax1.axhline(y = 0.3, color="black", linestyle = "dashed")
ax1.plot(stats_df[ "1_d" ], stats_df.cc, color=color)
ax1.tick_params(axis="y", labelcolor=color)
axs[0,0].set_xlabel( "1/d (1/A)" )
axs[0,0].set_ylabel("CC" )
axs[0,0].set_ylim( 0, 1 )
axs[0,0].axhline(y = 0.3, color="black", linestyle = "dashed")
axs[0,0].plot(stats_df[ "1_d" ], stats_df.cc, color=color)
axs[0,0].tick_params(axis="y", labelcolor=color)
# label color
# cc* plot
color = "tab:blue"
ax2.set_xlabel( "1/d (1/A)" )
ax2.set_ylabel("CC*", color=color)
ax2.axhline(y = 0.7, color="black", linestyle = "dashed")
ax2.plot(stats_df[ "1_d" ], stats_df.ccstar, color=color)
ax2.tick_params(axis='y', labelcolor=color)
plt.show()
axs[0,1].set_xlabel( "1/d (1/A)" )
axs[0,1].set_ylabel("CC*", color=color)
axs[0,1].set_ylim( 0, 1 )
axs[0,1].axhline(y = 0.7, color="black", linestyle = "dashed")
axs[0,1].plot(stats_df[ "1_d" ], stats_df.ccstar, color=color)
axs[0,1].tick_params(axis='y', labelcolor=color)
# rsplit plot
color = "tab:green"
axs[1,0].set_xlabel( "1/d (1/A)" )
axs[1,0].set_ylabel("Rsplit", color=color)
axs[1,0].plot(stats_df[ "1_d" ], stats_df.rsplit, color=color)
axs[1,0].tick_params(axis='y', labelcolor=color)
# rsplit plot
color = "tab:purple"
axs[1,1].set_xlabel( "1/d (1/A)" )
axs[1,1].set_ylabel("Multiplicity", color=color)
axs[1,1].plot(stats_df[ "1_d" ], stats_df.mult, color=color)
axs[1,1].tick_params(axis='y', labelcolor=color)
# save figure
plt.tight_layout()
plt.savefig("cc_curves.png")
plt.savefig("plots.png")
def main( cwd, name, stream, pointgroup, model, iterations, cell, shells, part_h_res ):
def get_mean_cell( 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 )
except AttributeError:
return np.nan
cols = [ "a", "b", "c", "alpha", "beta", "gamma" ]
cell_df = pd.DataFrame( cell_lst, columns=cols )
mean_a = round( cell_df.a.mean()*10, 3 )
mean_b = round( cell_df.b.mean()*10, 3 )
mean_c = round( cell_df.c.mean()*10, 3 )
mean_alpha = round( cell_df.alpha.mean(), 3 )
mean_beta = round( cell_df.beta.mean(), 3 )
mean_gamma = round( cell_df.gamma.mean(), 3 )
return mean_a, mean_b, mean_c, mean_alpha, mean_beta, mean_gamma
def make_mtz( hkl, mtz ):
# write file
mtz_sh = open( mtz_run_file, "w" )
mtz_sh.write( "#!/bin/sh\n\n" )
mtz_sh.write( "module purge\n" )
mtz_sh.write( "module load ccp4/8.0\n" )
mtz_sh.write( "f2mtz HKLIN {0} HKLOUT {1}\n",format( hkl, mtz ) )
mtz_sh.write( "TITLE Reflections from " )
mtz_sh.close()
def main( cwd, name, stream, pointgroup, model, iterations, cell, shells, part_h_res, adu ):
print( "begin job" )
# submitted job set
@@ -209,7 +269,7 @@ def main( cwd, name, stream, pointgroup, model, iterations, cell, shells, part_h
print( "making partialator files" )
# make partialator run file
part_run_file = run_partialator( part_dir, name, stream, pointgroup, model, iterations, cell, shells, part_h_res )
part_run_file = run_partialator( part_dir, name, stream, pointgroup, model, iterations, cell, shells, part_h_res, adu )
# submit job
job_id = submit_job( part_run_file )
@@ -225,9 +285,10 @@ def main( cwd, name, stream, pointgroup, model, iterations, cell, shells, part_h
cc_dat = "cc.dat"
ccstar_dat = "ccstar.dat"
mult_dat = "mult.dat"
rsplit_dat = "rsplit.dat"
# make summary data table
stats_df = summary_stats( cc_dat, ccstar_dat, mult_dat )
stats_df = summary_stats( cc_dat, ccstar_dat, mult_dat, rsplit_dat )
print( stats_df.to_string() )
print_df = stats_df[ [ "1_d", "d", "min",
"max", "nref", "poss",
@@ -252,21 +313,23 @@ if __name__ == "__main__":
parser.add_argument(
"-n",
"--name",
help="name of partialator run, also name of folder where data will be processed. Default = partialator",
help="name of partialator run, also name of folder where data will be processed.",
type=str,
default="partialator"
required=True
)
parser.add_argument(
"-s",
"--stream_file",
help="path to stream file",
type=os.path.abspath
type=os.path.abspath,
required=True
)
parser.add_argument(
"-p",
"--pointgroup",
help="pointgroup used by CrystFEL for partialator run",
type=str
type=str,
required=True
)
parser.add_argument(
"-m",
@@ -286,7 +349,8 @@ if __name__ == "__main__":
"-c",
"--cell_file",
help="path to CrystFEL cell file for partialator",
type=os.path.abspath
type=os.path.abspath,
required=True
)
parser.add_argument(
"-b",
@@ -302,11 +366,19 @@ if __name__ == "__main__":
type=float,
default=1.3
)
parser.add_argument(
"-a",
"--max_adu",
help="maximum detector counts to allow. Default is 12000",
type=int,
default=12000
)
args = parser.parse_args()
# run main
cwd = os.getcwd()
print( "top working directory = {0}".format( cwd ) )
main( cwd, args.name, args.stream_file, args.pointgroup, args.model, args.iterations, args.cell_file, args.bins, args.resolution )
main( cwd, args.name, args.stream_file, args.pointgroup, args.model, args.iterations, args.cell_file, args.bins, args.resolution, args.max_adu )