Merge branch 'master' of https://gitlab.psi.ch/sf-mx/crystfel_tools

cool_tools/chip_uc_gather.py

@@ -0,0 +1,214 @@
+#!/usr/bin/env python3
+
+# author J.Beale
+
+"""
+# aim
+order crystel hits from a .stream output and calcualte the unit-cell volume
+
+# usage
+python chip_uc_gather.py -s <path to stream file>
+                           -a <size of swissfel acq>
+                           -c <chip szie>
+                           -o <output>
+
+# output
+csv file of aperture, mean, no-of-hits
+input for uc_analysis.py
+"""
+
+# modules
+import re
+import argparse
+import pandas as pd
+import numpy as np
+import os
+
+def extract_chunks( input_file ):
+
+    # setup
+    chunk_df = pd.DataFrame()
+    event_no = []
+    chunks = []
+    hits = []
+    collect_lines = False
+    # Open the input file for reading
+    with open(input_file, 'r') as f:
+        for line in f:
+
+            # Check for the start condition
+            if line.startswith('----- Begin chunk -----'):
+                hit = False
+                collect_lines = True
+                chunk_lines = []
+            if collect_lines:
+                chunk_lines.append(line)
+            
+            # find image_no
+            if line.startswith( "Image serial number:" ):
+                event_search = re.findall( r"Image serial number:\s(\d+)", line )
+                event = int(event_search[0])
+                event_no.append( event )
+            
+            # is there a hit in chunk
+            if line.startswith( "Cell parameters" ):
+                hit = True
+
+            if line.startswith('----- End chunk -----'):
+                collect_lines = False  # Stop collecting lines
+                chunks.append( chunk_lines )
+                hits.append( hit )
+    
+    chunk_df[ "chunks" ] = chunks
+    chunk_df[ "event" ] = event_no
+    chunk_df[ "hit" ] = hits
+
+    return chunk_df
+
+
+def scrub_acq( chunk ):
+
+    for line in chunk:
+        # example
+        # "Image filename: /sf/cristallina/data/p21981/raw/run0141-hewl_cover_filter_30um/data/acq0001.JF17T16V01j.h5"
+        if line.startswith( "Image filename" ):
+            try: 
+                pattern = r"data/acq(\d+)\.JF"
+                acq = re.findall( pattern, line )
+                if AttributeError:
+                    return int(acq[0])
+            except AttributeError:
+                print( "scrub acquistion error" )
+                return np.nan
+
+def scrub_cell( chunk ):
+
+    for line in chunk:
+        if line.startswith( "Cell parameters" ):
+            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, line )
+                if AttributeError:
+                    return cell_lst
+            except AttributeError:
+                print( "scrub_cells error" )
+                return np.nan
+
+def V_unit( a, b, c, alpha, beta, gamma ):
+    
+    cos_alpha = np.cos(alpha * (np.pi / 180))
+    cos_beta = np.cos(beta * (np.pi / 180))
+    cos_gamma = np.cos(gamma * (np.pi / 180))
+    m_matrix = np.array([[a * a            , a * b * cos_gamma, a * c * cos_beta ], \
+                         [a * b * cos_gamma, b * b            , b * c * cos_alpha], \
+                         [a * c * cos_beta , b * c * cos_alpha, c * c            ]])
+    m_matrix_det = np.linalg.det(m_matrix)
+    V_unit = np.sqrt(m_matrix_det)
+    return V_unit
+
+def main( input_file, acq_size, chip_size, output ):
+
+    # extract chunks
+    print( "finding chucks" )
+    chunk_df = extract_chunks( input_file )
+    # display no. of chunks
+    print( "found {0} chunks".format( len(chunk_df) ) )
+    print( "found {0} crystals".format( chunk_df.hit.sum() ) )
+    print( "done" )
+
+    # find unit cells
+    print( "geting unit cell data from from chunks" )
+    xtal_df = pd.DataFrame()
+    counter = 0
+    for index, row in chunk_df.iterrows():
+
+        chunk, hit, event = row[ "chunks" ], row[ "hit" ], row[ "event" ]
+
+        if hit:
+
+            # calc image number
+            acq = scrub_acq( chunk )
+            image_no = ( acq - 1 )*acq_size + event
+
+            # get unit cells
+            cell_lst = scrub_cell( chunk )
+
+            # concat results
+            cols = [ "a", "b", "c", "alpha", "beta", "gamma" ]
+            xtal_df_1 = pd.DataFrame( cell_lst, columns=cols )
+
+            # set datatype
+            xtal_df_1 = xtal_df_1.astype( float )
+
+            # calculate 
+            xtal_df_1[ "image_no" ] = image_no
+            xtal_df_1[ "uc_vol" ] = V_unit( xtal_df_1.a.values[0],
+                                            xtal_df_1.b.values[0],
+                                            xtal_df_1.c.values[0],
+                                            xtal_df_1.alpha.values[0],
+                                            xtal_df_1.beta.values[0],
+                                            xtal_df_1.gamma.values[0] )
+            xtal_df = pd.concat( ( xtal_df, xtal_df_1 ) )
+
+            # add count and print every 1000s
+            counter = counter + 1
+            if counter % 1000 == 0:
+                print( counter, end='\r' )
+    print( "done" )
+
+    print( "merging multiple crystals" )
+    # average across multiple hits
+    uc_df = xtal_df.groupby( "image_no" ).mean()
+    uc_df[ "hits" ] = xtal_df.groupby( "image_no" ).count().a
+    print( xtal_df.groupby( "image_no" ).count().a )
+
+    # remove abc
+    uc_df = uc_df[ [ "uc_vol", "hits" ] ]
+    print( uc_df )
+
+    # stretch index to include blanks
+    chip_index = np.arange( 1, chip_size+1 )
+    uc_df = uc_df.reindex( chip_index )
+
+    print( "outputing to csv" )
+    # output to file
+    file_name = "{0}_uc.csv".format( output )
+    uc_df.to_csv( file_name, sep="," )
+    print( "done" )
+
+def list_of_floats(arg):
+    return list(map(int, arg.split(',')))
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        "-s",
+        "--stream",
+        help="input stream file",
+        required=True,
+        type=os.path.abspath
+    )
+    parser.add_argument(
+        "-a",
+        "--acq_size",
+        help="size of acquistions used when collecting data. default is 1000 images per acquisition",
+        default=1000,
+        type=int
+    )
+    parser.add_argument(
+        "-c",
+        "--chip_size",
+        help="total number of wells in the chip. default = 26244",
+        default=26244,
+        type=int
+    )
+    parser.add_argument(
+        "-o",
+        "--output",
+        help="output file name.",
+        required=True,
+        type=str
+    )   
+    args = parser.parse_args()
+    # run main
+    main( args.stream, args.acq_size, args.chip_size, args.output )

cool_tools/uc_analysis.py

@@ -0,0 +1,64 @@
+#!/usr/bin/env python3
+
+# author J.Beale
+
+"""
+# aim
+compile results from 3 chips to give mean and std.
+
+# usage
+python uc_analysis.py <path to csv 1> <path to csv 2> <path to csv 3> output
+
+# output
+compile csv of 3 chips and gives a per aperture output of the hits, mean uc and std. uc
+"""
+
+# modules
+import pandas as pd
+import numpy as np
+import sys
+
+def compile_inputs( csv_lst, output ):
+
+    print( "compiling results" )
+    # overall inputs
+    compiled_df = pd.DataFrame()
+    merged_df = pd.DataFrame()
+
+    count=1
+    for csv in csv_lst:
+        
+        uc_vol = "vol_{0}".format( count )
+        hits = "hits_{0}".format( count )
+        cols = [ uc_vol, hits ]
+        csv_df = pd.read_csv( csv,
+                                skiprows=1,
+                                names=cols,
+                                index_col=0,
+                                sep=","
+                                )
+
+        compiled_df = pd.concat( ( compiled_df, csv_df ), axis=1 )
+
+        count = count +1
+
+    # merge hits
+    merged_df[ "hits" ] = compiled_df[ [ "hits_1", "hits_2", "hits_3" ] ].sum(axis=1)
+    merged_df[ "vol_mean" ] = compiled_df[ [ "vol_1", "vol_2", "vol_3" ] ].mean(axis=1)
+    merged_df[ "vol_std" ] = compiled_df[ [ "vol_1", "vol_2", "vol_3" ] ].std(axis=1)
+
+    # output results
+    file_name = "{0}_uc.csv".format( output )
+    merged_df.to_csv( file_name, sep="," )
+    print( "done" )
+
+if __name__ == "__main__":
+   
+    csv_lst = [ sys.argv[1],
+                sys.argv[2],
+                sys.argv[3]
+                ]
+    
+    print( sys.argv[1] )
+   
+    compile_inputs( csv_lst, sys.argv[4] )

reduction_tools/cat_lst.py

@@ -25,33 +25,46 @@ import pandas as pd
 import glob
 import os
 import numpy as np
+from sys import exit
 
-def concatenate_files( input_file_lst, output ):
+def concatenate_files( input_file_lst, output, label ):
 
-    output_file = "{0}.lst".format( output )
+    output_file = "{0}_{1}.lst".format( output, label )
 
+    lines = 0
     # create output file
     with open( output_file, "w" ) as output:
 
         # loop through input list - read and write to output file
-        for lst_file_pwd in input_file_lst:
+        for lst_file_pwd in input_file_lst.lst_pwd:
             
             # open and write to output file
             with open( lst_file_pwd, "r" ) as lst_file:
+                lines = lines + len( lst_file.readlines() )
                 output.write( lst_file.read() )
+                lst_file.close()
 
-def make_pwd( run_no, endstation, pgroup ):
+    output.close()
 
+    print( "written {0} images to {1}".format( lines, output_file ) )
+
+def make_pwd( run_no, endstation, pgroup, jfj ):
+
+    # if to determine folder for jfj/clara or old daq
+    if jfj == True:
+        lst_pwd = "/sf/{0}/data/{1}/res/run{2}*".format( endstation, pgroup, run_no )
+    else:
         # construct lst folder path
-    lst_pwd = "/sf/{0}/data/{1}/raw/".format( endstation, pgroup ) + "run" + run_no + "*/data"
+        lst_pwd = "/sf/{0}/data/{1}/raw/run{2}*/data".format( endstation, pgroup, run_no )
 
     return lst_pwd
 
 def find_lst( lst_dir, label ):
 
-    # if label = both, i.e. both lights and darks, set label to lst - so it's alwasy found
+    if label == "on" or label == "off":
-    if label == "both":
+        tail = "{0}.list".format( label )
-        label = "lst"
+    if label == "light" or label == "dark":
+        tail = "{0}.lst".format( label )
     
     # create df for all lst
     lst_dir_df = pd.DataFrame()
@@ -59,7 +72,8 @@ def find_lst( lst_dir, label ):
     # search for lst with appropriate labels
     for path, dirs, files in os.walk( lst_dir ):
         for name in files:
-            if name.endswith( ".lst" ):
+
+            if name.endswith( tail ):
 
                 # get lst pwd
                 lst_pwd = os.path.join( path, name )
@@ -76,7 +90,7 @@ def find_lst( lst_dir, label ):
     # return df lst from this directory
     return lst_dir_df
 
-def generate_lst_df( run_lst, endstation, label, pgroup ):
+def generate_lst_df( run_lst, endstation, label, pgroup, jfj ):
 
     # make run number df
     cols = [ "run_no" ]
@@ -85,12 +99,11 @@ def generate_lst_df( run_lst, endstation, label, pgroup ):
     range_df[ "run_no" ] = range_df.run_no.str.zfill(4)
 
     # make new column of list paths
-    range_df[ "lst_app_dir" ] = range_df[ "run_no" ].apply( lambda x: make_pwd( x, endstation, pgroup ) )
+    range_df[ "lst_app_dir" ] = range_df[ "run_no" ].apply( lambda x: make_pwd( x, endstation, pgroup, jfj ) )
 
     # make df of lsts to be concatenated
     lst_df = pd.DataFrame()
 
-
     for index, row in range_df.iterrows():
         
         # get approximate dir pwd
@@ -114,13 +127,18 @@ def generate_lst_df( run_lst, endstation, label, pgroup ):
 
     return lst_df
 
-def main( run_lst, endstation, label, pgroup, output_file ):
+def main( run_lst, endstation, label, pgroup, output_file, jfj ):
 
     # make df of lst files
-    lst_df = generate_lst_df( run_lst, endstation, label, pgroup )
+    lst_df = generate_lst_df( run_lst, endstation, label, pgroup, jfj )
+
+    # check to see if any files have been found
+    if lst_df.empty:
+        print( "no {0} lists were found in runs {1}".format( label, run_lst ) )
+        exit()
 
     # concatinate all lst file in lst_df
-    concatenate_files( lst_df.lst_pwd, output_file )
+    concatenate_files( lst_df, output_file, label )
 
 def range_of_runs(arg):
     return list(map(int, arg.split(',')))
@@ -153,20 +171,38 @@ if __name__ == "__main__":
         help="pgroup the data are collected in",
         type=str
         )
+    parser.add_argument(
+        "-j",
+        "--jfj",
+        help="was the Jungfraujoch/Clara data processing pipeline used to process your data. Default = True",
+        type=bool,
+        default=False
+        )
     parser.add_argument(
         "-l",
         "--label",
-        help="the label of the lst file, i.e. 'light', 'dark' or 'both'",
+        help="the activation label for the data. Not JFJ the labels should = 'light' or 'dark'. With JFJ the labels should = 'on' or 'off'.",
-        type=str,
+        type=str
-        required=True
         )
     parser.add_argument(
         "-o",
         "--output",
         help="name of output file",
         type=str,
+        default=None
         )
     args = parser.parse_args()
+    # JFJ on/off non-JFJ light/dark logic\
+    if args.label != "off" and args.label != "on" and args.label != "light" and args.label != "dark":
+        print( "label flag (-l) must = either 'on' or 'off' with JFJ = True, or 'light' or 'dark' and JFJ = False." )
+        exit()
+    print( args.jfj )
+    if ( args.label == "off" or args.label == "on" ) and args.jfj == False:
+        print( "JFJ uses 'on' and 'off' flags. Please check inputs and whether the new JFJ/Clara processing pipeline was used." )
+        exit()
+    if ( args.label == "light" or args.label == "dark" ) and args.jfj == True:
+        print( "The old daq uses 'light' and 'dark' flags. Please check inputs and whether the newJFJ/Clara processing pipeline was used." )
+        exit()
     # make continuous list from input range limits
     range = []
     if args.range is not None:
@@ -180,7 +216,12 @@ if __name__ == "__main__":
     runs = args.runs
     run_lst = range + runs
     print( "appending {0} lst files from runs {1}".format( args.label, run_lst ) )
+    # make default name
+    if not args.output:
+        output_name = "-".join( str(e) for e in run_lst )
+        output_name = "run{0}".format( output_name )
+    else:
+        output_name = args.output
     # run main
-    main( run_lst, args.endstation, args.label, args.pgroup, args.output )
+    main( run_lst, args.endstation, args.label, args.pgroup, output_name, args.jfj )
     print( "done" )
-

reduction_tools/crystfel_split.py

@@ -13,7 +13,7 @@ python crystfel_split.py -l <path-to-list-file>
                          -g <path-to-geom-file>                          
                          -c <path-to-cell-file>
                          -n <name-of-job>
-                         -e name of endstation
+                         -p photons or 
 
 # crystfel parameter may need some editing in the function - write_crystfel_run
 
@@ -24,11 +24,11 @@ a log file with .geom and evalation of indexing, cell etc
 """
 
 # modules
+import argparse
 import pandas as pd
 import subprocess
 import os, errno
 import time
-import argparse
 from tqdm import tqdm
 import regex as re
 import numpy as np
@@ -69,7 +69,7 @@ def scrub_res( stream ):
     # example - diffraction_resolution_limit = 4.07 nm^-1 or 2.46 A
     # scrub res_lst or return np.nan
     try:
-        pattern = r"diffraction_resolution_limit\s=\s\d\.\d+\snm\^-1\sor\s(\d\.\d+)\sA"
+        pattern = r"diffraction_resolution_limit\s=\s\d+\.\d+\snm\^-1\sor\s(\d+\.\d+)\sA"
         res_lst = re.findall( pattern, stream )
         if AttributeError:
             return res_lst
@@ -191,11 +191,23 @@ def make_process_dir( proc_dir ):
             logger.debug( "making directory error" )
             raise
 
-def submit_job( job_file ):
+def submit_job( job_file, reservation ):
 
     # submit the job
-    submit_cmd = ["sbatch",  "--cpus-per-task=32", "--" ,job_file]
+    if reservation:
+        print( "using a ra beamtime reservation = {0}".format( reservation ) )
+        logger.info( "using ra reservation to process data = {0}".format( reservation ) )
+        submit_cmd = [ "sbatch", "-p", "hour", "--reservation={0}".format( reservation ), "--cpus-per-task=32", "--" , job_file ]
+    else:
+        submit_cmd = [ "sbatch", "-p", "hour", "--cpus-per-task=32", "--" , job_file ]
+    logger.info( "using slurm command = {0}".format( submit_cmd ) )
+
+    try:
         job_output = subprocess.check_output( submit_cmd )
+        logger.info( "submited job = {0}".format( job_output ) )
+    except subprocess.CalledProcessError as e:
+        print( "please give the correct ra reservation or remove the -v from the arguements" )
+        exit()
 
     # scrub job id from - example Submitted batch job 742403
     pattern = r"Submitted batch job (\d+)"
@@ -217,7 +229,7 @@ def wait_for_jobs( job_ids, total_jobs ):
             job_ids.difference_update(completed_jobs)
             time.sleep(2)
 
-def run_splits( list_df, cwd, name, lst, chunk_size, geom_file, cell_file, threshold ):
+def run_splits( list_df, cwd, name, geom_file, cell_file, threshold, reservation ):
 
     # set chunk counter
     chunk = 0
@@ -249,8 +261,7 @@ def run_splits( list_df, cwd, name, lst, chunk_size, geom_file, cell_file, thres
         stream_lst.append( "{0}/{1}".format( proc_dir, stream_file ) )
 
         # submit jobs
-        job_id = submit_job( cryst_run_file )
+        job_id = submit_job( cryst_run_file, reservation  )
-        logger.info( f"Job submitted: { job_id }" )
         submitted_job_ids.add( job_id )
 
         # increase chunk counter
@@ -261,7 +272,7 @@ def run_splits( list_df, cwd, name, lst, chunk_size, geom_file, cell_file, thres
 
     return submitted_job_ids, chunk, stream_lst
 
-def main( cwd, name, lst, chunk_size, geom_file, cell_file, threshold ):
+def main( cwd, name, lst, chunk_size, geom_file, cell_file, threshold, reservation ):
 
     print( "reading SwissFEL lst file" )
     print( "creating {0} image chunks of lst".format( chunk_size ) )
@@ -270,7 +281,7 @@ def main( cwd, name, lst, chunk_size, geom_file, cell_file, threshold ):
 
     # run crystfel runs on individual splits
     print( "submitting jobs to cluster" )
-    submitted_job_ids, chunk, stream_lst = run_splits( list_df, cwd, name, lst, chunk_size, geom_file, cell_file, threshold )
+    submitted_job_ids, chunk, stream_lst = run_splits( list_df, cwd, name, geom_file, cell_file, threshold, reservation )
 
     # monitor progress of jobs
     time.sleep( 30 )
@@ -323,6 +334,20 @@ def main( cwd, name, lst, chunk_size, geom_file, cell_file, threshold ):
     logger.info( "mean beta = {0} +/- {1} A".format( mean_beta, std_beta ) )
     logger.info( "mean gamma = {0} +/- {1} A".format( mean_gamma, std_gamma ) )
 
+    print( "printing stats" )
+    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__":
     parser = argparse.ArgumentParser()
     parser.add_argument(
@@ -360,6 +385,20 @@ if __name__ == "__main__":
         type=str,
         default="split"
     )
+    parser.add_argument(
+        "-v",
+        "--reservation",
+        help="reservation name for ra cluster. Usually along the lines of P11111_2024-12-10",
+        type=str,
+        default=None
+    )
+    parser.add_argument(
+        "-p",
+        "--photons_or_energy",
+        help="determines the threshold to use for CrystFEL. Photons counts have always been used in Cristallina and are now used on Alvra from 01.11.2024. Please use 'energy' for Alvra before this.",
+        type=str,
+        default="photons"
+    )
     parser.add_argument(
         "-d",
         "--debug",
@@ -367,21 +406,9 @@ if __name__ == "__main__":
         type=bool,
         default=False
     )
-    parser.add_argument(
-        "-e",
-        "--endstation",
-        help="which endstation did you collect these data from, e.g., alvra or cristallina. Please over-write name depending on endstation.",
-        type=str,
-        default="cristallina"
-    )
     args = parser.parse_args()
     # set current working directory
     cwd = os.getcwd()
-    # set threshold based on endstation
-    if args.endstation == "alvra":
-        threshold = 3000
-    elif args.endstation == "cristallina":
-        threshold = 10
     # set loguru
     if not args.debug:
         logger.remove()
@@ -390,5 +417,10 @@ if __name__ == "__main__":
     # log geometry file
     geom = open( args.geom_file, "r" ).read()
     logger.info( geom )
-    main( cwd, args.job_name, args.lst_file, args.chunk_size, args.geom_file, args.cell_file, threshold )
+    # set threshold based on detector
+    if args.photons_or_energy == "energy":
+        threshold = 3000
+    elif args.photons_or_energy == "photons":
+        threshold = 15
+    main( cwd, args.job_name, args.lst_file, args.chunk_size, args.geom_file, args.cell_file, threshold, args.reservation )
 

reduction_tools/crystfel_split_var.py

@@ -27,12 +27,99 @@ a series of stream files from crystfel in the current working directory
 
 # modules
 import pandas as pd
+import numpy as np
 import subprocess
 import os, errno
 import time
 import argparse
 from tqdm import tqdm
 import regex as re
+from loguru import logger
+
+def count_chunks( stream ):
+
+    # get number of chunks
+    # example - ----- Begin chunk -----
+    # count them
+    try:
+        pattern = r"-----\sBegin\schunk\s-----"
+        chunks = re.findall( pattern, stream )
+        if AttributeError:
+            return len( chunks )
+    except AttributeError:
+        logger.debug( "count_chunks error" )
+        return np.nan
+
+def scrub_cells( 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 )
+        if AttributeError:
+            return cell_lst, xtals
+    except AttributeError:
+        logger.debug( "scrub_cells error" )
+        return np.nan
+
+def scrub_res( stream ):
+
+    # get diffraction limit
+    # example - diffraction_resolution_limit = 4.07 nm^-1 or 2.46 A
+    # scrub res_lst or return np.nan
+    try:
+        pattern = r"diffraction_resolution_limit\s=\s\d+\.\d+\snm\^-1\sor\s(\d+\.\d+)\sA"
+        res_lst = re.findall( pattern, stream )
+        if AttributeError:
+            return res_lst
+    except AttributeError:
+        logger.debug( "scrub_res error" )
+        return np.nan
+
+def scrub_obs( stream ):
+
+    # get number of reflections
+    # example - num_reflections = 308
+    # scrub reflections or return np.nan
+    try:
+        pattern = r"num_reflections\s=\s(\d+)"
+        obs_lst = re.findall( pattern, stream )
+        if AttributeError:
+            return obs_lst
+    except AttributeError:
+        logger.debug( "scrub_obs error" )
+        return np.nan
+
+def calculate_stats( stream_pwd ):
+
+    # open stream file
+    stream = open( stream_pwd, "r" ).read()
+
+    # get total number chunks
+    chunks = count_chunks( stream )
+
+    # get list of cells
+    cell_lst, xtals = scrub_cells( stream )
+
+    # get list of cells
+    res_lst = scrub_res( stream )
+
+    # get list of cells
+    obs_lst = scrub_obs( stream )
+
+    # res_df
+    cols = [ "a", "b", "c", "alpha", "beta", "gamma" ]
+    df = pd.DataFrame( cell_lst, columns=cols )
+    df[ "resolution" ] = res_lst
+    df[ "obs" ] = obs_lst
+
+    # convert all to floats
+    df = df.astype(float)
+
+    return df, xtals, chunks
 
 def h5_split( lst, chunk_size ):
 
@@ -53,7 +140,8 @@ def h5_split( lst, chunk_size ):
     return list_df
 
 def write_crystfel_run( proc_dir, name, chunk, chunk_lst_file,
-                        geom_file, cell_file, threshold, min_snr,
+                        geom_file, cell_file, indexer, peakfinder,
+                        integrator, tolerance, threshold, min_snr,
                         int_rad, multi, retry, min_pix, bg_rad, min_res ):
 
     # stream file name
@@ -71,10 +159,11 @@ def write_crystfel_run( proc_dir, name, chunk, chunk_lst_file,
     run_sh.write( "  --output={0} \\\n".format( stream_file ) )
     run_sh.write( "  --geometry={0} \\\n".format( geom_file ) )
     run_sh.write( "  --pdb={0} \\\n".format( cell_file ) )
-    run_sh.write( "  --indexing=xgandalf-latt-cell \\\n" )
+    run_sh.write( "  --indexing={0} \\\n".format( indexer ) )
-    run_sh.write( "  --peaks=peakfinder8 \\\n" )
+    run_sh.write( "  --peaks={0} \\\n".format( peakfinder ) )
-    run_sh.write( "  --integration=rings-grad \\\n" )
+    run_sh.write( "  --integration={0} \\\n".format( integrator ) )
-    run_sh.write( "  --tolerance=10.0,10.0,10.0,2,3,2 \\\n" )
+    run_sh.write( "  --tolerance={0},{1},{2},{3},{4},{5} \\\n".format( tolerance[0], tolerance[1], tolerance[2],
+                                                                       tolerance[3], tolerance[4], tolerance[5] ) )
     run_sh.write( "  --threshold={0} \\\n".format( threshold ) )
     run_sh.write( "  --min-snr={0} \\\n".format( min_snr ) )
     run_sh.write( "  --int-radius={0},{1},{2} \\\n".format( int_rad[0], int_rad[1], int_rad[2] ) )
@@ -100,13 +189,26 @@ def make_process_dir( proc_dir ):
         os.makedirs( proc_dir )
     except OSError as e:
         if e.errno != errno.EEXIST:
+            logger.debug( "making directory error" )
             raise
 
-def submit_job( job_file ):
+def submit_job( job_file, reservation ):
 
     # submit the job
-    submit_cmd = ["sbatch",  "--cpus-per-task=32", "--" ,job_file]
+    if reservation:
+        print( "using a ra beamtime reservation = {0}".format( reservation ) )
+        logger.info( "using ra reservation to process data = {0}".format( reservation ) )
+        submit_cmd = [ "sbatch", "-p", "hour", "--reservation={0}".format( reservation ), "--cpus-per-task=32", "--" , job_file ]
+    else:
+        submit_cmd = [ "sbatch", "-p", "hour", "--cpus-per-task=32", "--" , job_file ]
+    logger.info( "using slurm command = {0}".format( submit_cmd ) )
+
+    try:
         job_output = subprocess.check_output( submit_cmd )
+        logger.info( "submited job = {0}".format( job_output ) )
+    except subprocess.CalledProcessError as e:
+        print( "please give the correct ra reservation or remove the -v from the arguements" )
+        exit()
 
     # scrub job id from - example Submitted batch job 742403
     pattern = r"Submitted batch job (\d+)"
@@ -126,16 +228,12 @@ def wait_for_jobs( job_ids, total_jobs ):
                     completed_jobs.add(job_id)
                     pbar.update(1)
             job_ids.difference_update(completed_jobs)
-            time.sleep(10)
+            time.sleep(2)
 
-def run_splits( cwd, name, lst, chunk_size, geom_file,
+def run_splits( list_df, cwd, name, geom_file, cell_file,
-                cell_file, progress, threshold, min_snr,
+               indexer, peakfinder, integrator, tolerance, threshold,
-                int_rad, multi, retry, min_pix ):
+               min_snr, int_rad, multi, retry, min_pix, bg_rad,
-
+               min_res, reservation ):
-    print( "reading SwissFEL lst file" )
-    print( "creating {0} image chunks of lst".format( chunk_size ) )
-    list_df = h5_split( lst, chunk_size )
-    print( "DONE" )
 
     # set chunk counter
     chunk = 0
@@ -146,10 +244,9 @@ def run_splits( cwd, name, lst, chunk_size, geom_file,
     # stream file list
     stream_lst = []
 
-    print( "creating crystfel jobs for individual chunks" )
     for chunk_lst in list_df:
 
-        print( "chunk {0} = {1} images".format( chunk, len( chunk_lst ) ) ) 
+        logger.info( "chunk {0} = {1} images".format( chunk, len( chunk_lst ) ) )
         # define process directory
         proc_dir = "{0}/{1}/{1}_{2}".format( cwd, name, chunk )
 
@@ -165,13 +262,13 @@ def run_splits( cwd, name, lst, chunk_size, geom_file,
 
         # write crystfel file and append path to list
         cryst_run_file, stream_file = write_crystfel_run( proc_dir, name, chunk, chunk_lst_file,
-                                                            geom_file, cell_file, threshold, min_snr, 
+                                                          geom_file, cell_file, indexer, peakfinder,
-                                                            int_rad, multi, retry, min_pix )
+                                                          integrator, tolerance, threshold, min_snr,
+                                                          int_rad, multi, retry, min_pix, bg_rad, min_res )
         stream_lst.append( "{0}/{1}".format( proc_dir, stream_file ) )
 
         # submit jobs
-        job_id = submit_job( cryst_run_file )
+        job_id = submit_job( cryst_run_file, reservation  )
-        print(f"Job submitted: { job_id }")
         submitted_job_ids.add( job_id )
 
         # increase chunk counter
@@ -180,15 +277,34 @@ def run_splits( cwd, name, lst, chunk_size, geom_file,
         # move back to top dir
         os.chdir( cwd )
 
+    return submitted_job_ids, chunk, stream_lst
+
+def main( cwd, name, lst, chunk_size, geom_file, cell_file, 
+          indexer, peakfinder, integrator, tolerance, threshold,
+          min_snr, int_rad, multi, retry, min_pix, bg_rad,
+          min_res, reservation ):
+
+    print( "reading SwissFEL lst file" )
+    print( "creating {0} image chunks of lst".format( chunk_size ) )
+    list_df = h5_split( lst, chunk_size )
     print( "DONE" )
 
+    # run crystfel runs on individual splits
+    print( "submitting jobs to cluster" )
+    submitted_job_ids, chunk, stream_lst = run_splits( list_df, cwd, name, geom_file, cell_file,
+                                                       indexer, peakfinder, integrator, tolerance, threshold,
+                                                       min_snr, int_rad, multi, retry, min_pix, bg_rad,
+                                                       min_res, reservation )
+
+    # monitor progress of jobs
+    time.sleep( 30 )
     wait_for_jobs( submitted_job_ids, chunk )
-    print("slurm processing done")
+    print( "done" )
     
     # make composite .stream file
     output_file = "{0}.stream".format( name )
 
-    print( "comp" )
+    print( "concatenating .streams from separate runs." )
     try:
         # Open the output file in 'append' mode
         with open(output_file, "a") as output:
@@ -197,110 +313,206 @@ def run_splits( cwd, name, lst, chunk_size, geom_file,
                     with open(file_name, "r") as input_file:
                         # Read the contents of the input file and append to the output file
                         output.write(input_file.read())
-                    print(f"Appended contents from {file_name} to {output_file}")
                 except FileNotFoundError:
-                    print(f"File {file_name} not found. Skipping.")
+                    logger.debug(f"File {file_name} not found. Skipping.")
     except IOError as e:
-        print(f"An error occurred while appending files: {e}")
+        logger.debug(f"An error occurred while appending files: {e}")
 
-    print( "DONE" )
+    print( "done" )
+    
+    df, xtals, chunks = calculate_stats( output_file )
+
+    # 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 )
+
+    logger.info( "image = {0}".format( chunks ) )
+    logger.info( "crystals = {0}".format( xtals ) )
+    logger.info( "indexing rate = {0} %".format( index_rate ) )
+    logger.info( "mean resolution = {0} +/- {1} A".format( mean_res, std_res ) )
+    logger.info( "median resolution = {0} A".format( median_res  ) )
+    logger.info( "mean observations = {0} +/- {1}".format( mean_obs, std_obs ) )
+    logger.info( "mean a = {0} +/- {1} A".format( mean_a, std_a ) )
+    logger.info( "mean b = {0} +/- {1} A".format( mean_b, std_b ) )
+    logger.info( "mean c = {0} +/- {1} A".format( mean_c, std_c ) )
+    logger.info( "mean alpha = {0} +/- {1} A".format( mean_alpha, std_alpha ) )
+    logger.info( "mean beta = {0} +/- {1} A".format( mean_beta, std_beta ) )
+    logger.info( "mean gamma = {0} +/- {1} A".format( mean_gamma, std_gamma ) )
+
+    print( "printing stats" )
+    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 ) )
 
 def list_of_ints(arg):
     return list(map(int, arg.split(',')))
 
+def list_of_floats(arg):
+    return list(map(float, arg.split(',')))
+
 if __name__ == "__main__":
     parser = argparse.ArgumentParser()
-    parser.add_argument(
-        "-l",
-        "--lst_file",
-        help="file from SwissFEL output to be processed quickly",
-        type=os.path.abspath
-    )
-    parser.add_argument(
-        "-k",
-        "--chunk_size",
-        help="how big should each chunk be? - the bigger the chunk, the fewer jobs, the slower it will be",
-        type=int,
-        default=1000
-    )
-    parser.add_argument(
-        "-g",
-        "--geom_file",
-        help="path to geom file to be used in the refinement",
-        type=os.path.abspath
-    )
-    parser.add_argument(
-        "-c",
-        "--cell_file",
-        help="path to cell file of the crystals used in the refinement",
-        type=os.path.abspath
-    )
     parser.add_argument(
         "-n",
         "--job_name",
-        help="the name of the job to be done",
+        help="the name of the job to be done. Default = split",
         type=str,
         default="split"
     )
+    parser.add_argument(
+        "-l",
+        "--lst_file",
+        help="file from SwissFEL output to be processed quickly. Requried.",
+        type=os.path.abspath,
+        required=True
+    )
+    parser.add_argument(
+        "-k",
+        "--chunk_size",
+        help="how big should each image split be? Default = 500. Fewer will be faster.",
+        type=int,
+        default=500
+    )
+    parser.add_argument(
+        "-g",
+        "--geom_file",
+        help="path to geom file to be used in the refinement. Requried.",
+        type=os.path.abspath,
+        required=True
+    )
+    parser.add_argument(
+        "-c",
+        "--cell_file",
+        help="path to cell file of the crystals used in the refinement. Requried.",
+        type=os.path.abspath,
+        required=True
+    )
+    parser.add_argument(
+        "-x",
+        "--indexer",
+        help="indexer to use. Default = xgandalf-latt-cell",
+        type=str,
+        default="xgandalf-latt-cell"
+    )
+    parser.add_argument(
+        "-f",
+        "--peakfinder",
+        help="peakfinder to use. Default = peakfinder8",
+        type=str,
+        default="peakfinder8"
+    )
+    parser.add_argument(
+        "-a",
+        "--integrator",
+        help="integrator to use. Default = rings-nocen-nograd",
+        type=str,
+        default="rings-nocen-nograd"
+    )
+    parser.add_argument(
+        "-y",
+        "--tolerance",
+        help="tolerance to use. Default = 10.0,10.0,10.0,2.0,3.0,2.0",
+        type=list_of_floats,
+        default=[10.0,10.0,10.0,2.0,3.0,2.0]
+    )
     parser.add_argument(
         "-t",
         "--threshold",
-        help="threshold for crystfel run - peaks must be above this to be found",
+        help="peaks must be above this to be found during spot-finding. Default = 20",
         type=int,
-        default=10
+        default=20
     )
     parser.add_argument(
         "-s",
         "--min_snr",
-        help="min-snr for crystfel run - peaks must to above this to be counted",
+        help="peaks must to above this to be counted. Default = 5.",
         type=int,
         default=5
     )
     parser.add_argument(
         "-i",
         "--int_radius",
-        help="int_rad for crystfel run - peaks must to above this to be counted",
+        help="integration ring radii. Default = 2,3,5 = 2 for spot and then 3 and 5 to calculate background.",
         type=list_of_ints,
-        default=[3,5,9]
+        default=[2,3,5]
     )
     parser.add_argument(
         "-m",
         "--multi",
-        help="multi crystfel flag, do you wnat to look for multiple lattices",
+        help="do you wnat to look for multiple lattices. Default = True",
         type=bool,
-        default=False
+        default=True
     )
     parser.add_argument(
         "-r",
         "--retry",
-        help="retry crystfel flag, do you want to retry failed indexing patterns",
+        help="do you want to retry failed indexing patterns. Default = False",
         type=bool,
         default=False
     )
     parser.add_argument(
-        "-x",
+        "-p",
         "--min_pix",
-        help="min-pix-count for crystfel runs, minimum number of pixels a spot should contain in peak finding",
+        help="minimum number of pixels a spot should contain in peak finding.Default = 2",
         type=int,
         default=2
     )
     parser.add_argument(
         "-b",
         "--bg_rad",
-        help="crystfel background radius flag, radius (in pixels) used for the estimation of the local background",
+        help="radius (in pixels) used for the estimation of the local background. Default = 4",
         type=int,
-        default=2
+        default=4
     )
     parser.add_argument(
         "-q",
         "--min-res",
-        help="m",
+        help="min-res for spot-finding in pixels. Default = 85.",
         type=int,
-        default=2
+        default=85
+    )
+    parser.add_argument(
+        "-v",
+        "--reservation",
+        help="reservation name for ra cluster. Usually along the lines of P11111_2024-12-10",
+        type=str,
+        default=None
+    )
+    parser.add_argument(
+        "-d",
+        "--debug",
+        help="output debug to terminal.",
+        type=bool,
+        default=False
     )
     args = parser.parse_args()
     # run geom converter
     cwd = os.getcwd()
+    # set loguru
+    if not args.debug:
+        logger.remove()
+    logfile = "{0}.log".format( args.job_name )
+    logger.add( logfile, format="{message}", level="INFO")
+    # log geometry file
+    geom = open( args.geom_file, "r" ).read()
+    logger.info( geom )
     if args.multi == True:
         multi = "multi"
     else:
@@ -309,7 +521,8 @@ if __name__ == "__main__":
         retry = "retry"
     else:
         retry = "no-retry"
-    run_splits( cwd, args.job_name, args.lst_file, args.chunk_size,
+    main( cwd, args.job_name, args.lst_file, args.chunk_size,
-                args.geom_file, args.cell_file,
+                args.geom_file, args.cell_file, args.indexer, args.peakfinder,
-                args.threshold, args.min_snr, args.int_radius, 
+                args.integrator, args.tolerance, args.threshold, 
-                multi, retry, args.min_pix )
+                args.min_snr, args.int_radius, multi, retry, args.min_pix, args.bg_rad,
+                args.min_res, args.reservation  )

reduction_tools/make_mtz.py

@@ -5,6 +5,7 @@
 """
 # aim
 to make an mtz from an hkl file output from partialator or process_hkl
+runs f2mtz and then truncate for create an mtz with other intensities and structure factors
 
 # usage to make mtz from manually entered lengths and angles
 python make_mtz.py -i <path to .hkl file from partialator>
@@ -14,6 +15,8 @@ python make_mtz.py -i <path to .hkl file from partialator>
                    -d dataset name in mtz
                    -g spacegroup
                    -c list of cell lengths and angles to use - 59.3,59.3,153.1,90.0,90.0,90.0
+                   -r number of residues
+                   -A resolution range - e.g. 40.0,2.0
 
 # usage to make mtz from the mean angles and lengths in stream file
 python make_mtz.py -i <path to .hkl file from partialator>
@@ -22,6 +25,8 @@ python make_mtz.py -i <path to .hkl file from partialator>
                    -x xtal name in mtz
                    -d dataset name in mtz
                    -g spacegroup
+                   -r number of residues
+                   -A resolution range - e.g. 40.0,2.0
                    -s <path to stream file>
                    -u True
 
@@ -29,8 +34,11 @@ python make_mtz.py -i <path to .hkl file from partialator>
 python make_mtz.py -s <path to stream file>
 
 # output
-- .mtz file
+- .mtz file - just intensities
-- .html log file
+- f2mtz.log file
+- _F.mtz file - intensities and structure factors
+- cuts data to desired resolution
+- truncate.log file
 """
 
 # modules
@@ -72,27 +80,49 @@ def get_mean_cell( stream_file ):
 
     return mean_cell, len(cell_lst)
 
-def make_mtz( hklout_file, mtzout_file, project, crystal, dataset, cell, spacegroup ):
+def make_mtz( hklout_file, mtzout_file, project, crystal, dataset, cell, spacegroup, residues, res_range ):
 
     # make_mtz file name
     mtz_run_file = "make_mtz.sh"
 
+    # make F file name
+    Fout_file = os.path.splitext( mtzout_file )[0] + "_F.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\n" )
-    mtz_sh.write( "f2mtz HKLIN {0} HKLOUT {1} > out.html << EOF\n".format( hklout_file, mtzout_file ) )
+    mtz_sh.write( "f2mtz HKLIN {0} HKLOUT {1} << EOF_hkl > f2mtz.log\n".format( hklout_file, mtzout_file ) )
     mtz_sh.write( "TITLE Reflections from CrystFEL\n" )
     mtz_sh.write( "NAME PROJECT {0} CRYSTAL {1} DATASET {2}\n".format( project, crystal, dataset ) )
     mtz_sh.write( "CELL {0} {1} {2} {3} {4} {5}\n".format( cell[0], cell[1], cell[2], cell[3], cell[4], cell[5] ) )
     mtz_sh.write( "SYMM {0}\n".format( spacegroup ) )
     mtz_sh.write( "SKIP 3\n" )
-    mtz_sh.write( "LABOUT H K L IMEAN SIGIMEAN\n" )
+    mtz_sh.write( "LABOUT H K L I_stream SIGI_stream\n" )
     mtz_sh.write( "CTYPE  H H H J        Q\n" )
     mtz_sh.write( "FORMAT '(3(F4.0,1X),F10.2,10X,F10.2)'\n" )
     mtz_sh.write( "SKIP 3\n" )
-    mtz_sh.write( "EOF" )
+    mtz_sh.write( "EOF_hkl\n\n\n" )
+    mtz_sh.write( "echo 'done'\n" )
+    mtz_sh.write( "echo 'I and SIGI from CrystFEL stream saved as I_stream and SIGI_stream'\n" )
+    mtz_sh.write( "echo 'I filename = {0}'\n\n\n".format( mtzout_file ) )
+    mtz_sh.write( "echo 'running truncate'\n" )
+    mtz_sh.write( "echo 'setting resolution range to {0}-{1}'\n".format( res_range[0], res_range[1] ) )
+    mtz_sh.write( "echo 'assuming that there are {0}' in assymetric unit\n\n\n".format( residues ) )
+    mtz_sh.write( "truncate HKLIN {0} HKLOUT {1} << EOF_F > truncate.log\n".format( mtzout_file, Fout_file ) )
+    mtz_sh.write( "truncate YES\n" )
+    mtz_sh.write( "anomalous NO\n" )
+    mtz_sh.write( "nresidue {0}\n".format( residues ) )
+    mtz_sh.write( "resolution {0} {1}\n".format( res_range[0], res_range[1] ) )
+    mtz_sh.write( "plot OFF\n" )
+    mtz_sh.write( "labin IMEAN=I_stream SIGIMEAN=SIGI_stream\n" )
+    mtz_sh.write( "labout F=F_stream SIGF=SIGF_stream\n" )
+    mtz_sh.write( "end\n" )
+    mtz_sh.write( "EOF_F\n\n\n" )
+    mtz_sh.write( "echo 'done'\n" )
+    mtz_sh.write( "echo 'I_stream and SIGI_stream from f2mtz converted to F_stream and F_stream'\n" )
+    mtz_sh.write( "echo 'F filename = {0} (contains both Is and Fs)'".format( Fout_file ) )
     mtz_sh.close()
 
     # make file executable
@@ -118,7 +148,7 @@ def cut_hkl_file( hklin_file, hklout_file ):
     
     hklout.close()
 
-def main( hklin_file, hklout_file, mtzout, project, crystal, dataset, cell, spacegroup ):
+def main( hklin_file, hklout_file, mtzout, project, crystal, dataset, cell, spacegroup, residues, res_range ):
 
     # remove final lines from crystfel hkl out
     print( "removing final lines from crystfel hklin" )
@@ -129,7 +159,7 @@ def main( hklin_file, hklout_file, mtzout, project, crystal, dataset, cell, spac
     print( "making mtz" )
     print( "using cell constants\n{0} {1} {2} A {3} {4} {5} deg".format( cell[0], cell[1], cell[2], cell[3], cell[4], cell[5] ) )
     print( "Titles in mtz will be:\nPROJECT {0} CRYSTAL {1} DATASET {2}".format( project, crystal, dataset ) )
-    make_mtz( hklout_file, mtzout, project, crystal, dataset, cell, spacegroup )
+    make_mtz( hklout_file, mtzout, project, crystal, dataset, cell, spacegroup, residues, res_range )
     print( "done" )
 
 def list_of_floats(arg):
@@ -182,6 +212,18 @@ if __name__ == "__main__":
         help="list of complete cell length and angles, e.g. 59.3,59.3,153.1,90.0,90.0,90.0. They all should be floats",
         type=list_of_floats
     )
+    parser.add_argument(
+        "-r",
+        "--residues",
+        help="number of residues for truncate, e.g., hewl = 129",
+        type=int
+    )
+    parser.add_argument(
+        "-A",
+        "--resolution_range",
+        help="list of 2 floats - low res then high res cut off, e.g., 50.0,1.3",
+        type=list_of_floats
+    )
     parser.add_argument(
         "-s",
         "--stream_file",
@@ -200,7 +242,7 @@ if __name__ == "__main__":
     if args.stream_file:
         print( "reading stream file" )
         cell, xtals = get_mean_cell( args.stream_file )
-        print( "found {0} xtats".format( xtals ) )
+        print( "found {0} xtals".format( xtals ) )
         print( "mean lengths = {0}, {1}, {2} A".format( cell[0], cell[1], cell[2] ) )
         print( "mean angles = {0}, {1}, {2} deg".format( cell[3], cell[4], cell[5] ) )
         print( "# for input to make_mtz = {0},{1},{2},{3},{4},{5}".format( cell[0], cell[1], cell[2], cell[3], cell[4], cell[5] ) )
@@ -210,11 +252,11 @@ if __name__ == "__main__":
             mtzout = args.mtzout
         else:
             mtzout = os.path.splitext( args.hklin )[0] + ".mtz"
-        main( args.hklin, hklout_file, mtzout, args.project, args.crystal, args.dataset, cell, args.spacegroup )
+        main( args.hklin, hklout_file, mtzout, args.project, args.crystal, args.dataset, cell, args.spacegroup, args.residues, args.resolution_range )
     if args.stream_file == None and args.use_stream == False:
         hklout_file = os.path.splitext( args.hklin )[0] + "_cut.hkl"
         if args.mtzout:
             mtzout = args.mtzout
         else:
             mtzout = os.path.splitext( args.hklin )[0] + ".mtz"
-        main( args.hklin, hklout_file, mtzout, args.project, args.crystal, args.dataset, args.cell, args.spacegroup )
+        main( args.hklin, hklout_file, mtzout, args.project, args.crystal, args.dataset, args.cell, args.spacegroup, args.residues, args.resolution_range )

reduction_tools/partialator.py

@@ -16,15 +16,18 @@ python partialator.py -s <path-to-stream-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
+                       -R ra reservation name if available
 
 # output
 - scaled/merged files
 - an mtz file
 - useful plots
 - useful summerized .dat files
+- log file of output
 """
 
 # modules
+from sys import exit
 import pandas as pd
 import numpy as np
 import subprocess
@@ -35,12 +38,27 @@ from tqdm import tqdm
 import regex as re
 import matplotlib.pyplot as plt
 from scipy.optimize import curve_fit
+import warnings
+warnings.filterwarnings( "ignore", category=RuntimeWarning ) 
+from loguru import logger
 
-def submit_job( job_file ):
+def submit_job( job_file, reservation ):
 
     # submit the job
-    submit_cmd = ["sbatch",  "--cpus-per-task=32", "--" ,job_file]
+    if reservation:
+        print( "using a ra beamtime reservation = {0}".format( reservation ) )
+        logger.info( "using ra reservation to process data = {0}".format( reservation ) )
+        submit_cmd = [ "sbatch", "-p", "day", "--reservation={0}".format( reservation ), "--cpus-per-task=32", "--" , job_file ]
+    else:
+        submit_cmd = [ "sbatch", "-p", "day", "--cpus-per-task=32", "--" , job_file ]
+    logger.info( "using slurm command = {0}".format( submit_cmd ) )
+
+    try:
         job_output = subprocess.check_output( submit_cmd )
+        logger.info( "submited job = {0}".format( job_output ) )
+    except subprocess.CalledProcessError as e:
+        print( "please give the correct ra reservation or remove the -R from the arguements" )
+        exit()
 
     # scrub job id from - example Submitted batch job 742403
     pattern = r"Submitted batch job (\d+)"
@@ -78,6 +96,7 @@ def run_partialator( proc_dir, name, stream, pointgroup, model, iterations, cell
     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( "  -j 32 \\\n" )
     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 ) )
@@ -90,6 +109,11 @@ def run_partialator( proc_dir, name, stream, pointgroup, model, iterations, cell
     # make file executable
     subprocess.call( [ "chmod", "+x", "{0}".format( part_run_file ) ] )
 
+    # add partialator script to log
+    part_input = open( part_run_file, "r" )
+    logger.info( "partialator input file =\n{0}".format( part_input.read() ) )
+    part_input.close()
+
     # return partialator file name
     return part_run_file
 
@@ -101,7 +125,7 @@ def make_process_dir( dir ):
         if e.errno != errno.EEXIST:
             raise
 
-def summary_stats( cc_dat, ccstar_dat, mult_dat, rsplit_dat ):
+def summary_stats( cc_dat, ccstar_dat, mult_dat, rsplit_dat, wilson_dat ):
 
     # read all files into pd
     # function to sort out different column names
@@ -117,37 +141,33 @@ def summary_stats( cc_dat, ccstar_dat, mult_dat, rsplit_dat ):
                     "mult", "snr", "I", "d", "min", "max" ]
         elif var == "rsplit":
             cols = [ "d(nm)", "rsplit", "nref", "d", "min", "max"  ]
+        elif var == "wilson":
+            cols = [ "bin", "s2", "d", "lnI", "nref" ]
         
         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" )
+    wilson_df = read_dat( wilson_dat, "wilson" )
 
     # remove unwanted cols
     cc_df = cc_df[ [ "cc" ] ]
     ccstar_df = ccstar_df[ [ "ccstar" ] ]
     rsplit_df = rsplit_df[ [ "rsplit" ] ]
+    wilson_df = wilson_df[ [ "lnI" ] ]
     
     # merge dfs
-    stats_df = pd.concat( [ mult_df, cc_df, ccstar_df, rsplit_df ], axis=1, join="inner" )
+    stats_df = pd.concat( [ mult_df, cc_df, ccstar_df, rsplit_df, wilson_df ], axis=1, join="inner" )
 
     # make 1/d, 1/d^2 column
     stats_df[ "1_d" ] = 1 / stats_df.d
     stats_df[ "1_d2" ] = 1 / stats_df.d**2
 
-    # reorder cols
-    stats_df = stats_df[ [  "1_d", "1_d2", "d", "min",
-                            "max", "nref", "poss",
-                            "comp", "obs", "mult",
-                            "snr", "I", "cc", "ccstar", "rsplit" ] ]
-
     # change nan to 0 
     stats_df = stats_df.fillna(0)
 
@@ -171,13 +191,21 @@ def get_metric( d2_series, cc_series, cut_off ):
 
     # calculate cut-off point 
     cc_stat = arctanh( cut_off, r, s0 )
-
     # covert back from 1/d2 to d
     cc_stat = np.sqrt( ( 1 / cc_stat ) )
 
-    return cc_stat
+    # get curve for plotting
+    cc_tanh = tanh( d2_series, r, s0 )
 
-def summary_fig( stats_df ):
+    return round( cc_stat, 3 ), cc_tanh
+
+def summary_fig( stats_df, cc_tanh, ccstar_tanh, cc_cut, ccstar_cut ):
+
+    def dto1_d( x ):
+        return 1/x
+        
+    def dto1_d2( x ):
+        return 1/x**2
 
     # plot results
     cc_fig, axs = plt.subplots(2, 2)
@@ -186,11 +214,17 @@ def summary_fig( stats_df ):
     # cc plot
     color = "tab:red"
     axs[0,0].set_xlabel( "1/d (1/A)" )
-    axs[0,0].set_ylabel("CC" )
+    axs[0,0].set_ylabel( "CC", color=color )
     axs[0,0].set_ylim( 0, 1 )
     axs[0,0].axhline( y = 0.3, color="black", linestyle = "dashed" )
+    # plot cc
     axs[0,0].plot( stats_df[ "1_d" ], stats_df.cc, color=color )
+    # plot fit
+    axs[0,0].plot( stats_df[ "1_d" ], cc_tanh, color="tab:grey", linestyle = "dashed" )
+    sax1 = axs[0,0].secondary_xaxis( 'top', functions=( dto1_d, dto1_d ) )
+    sax1.set_xlabel('d (A)')
     axs[0,0].tick_params( axis="y", labelcolor=color )
+    axs[0,0].text( 0.1, 0.1, "CC @ 0.2 = {0}".format( cc_cut ), fontsize = 8 )
 
     # cc* plot
     color = "tab:blue"
@@ -199,54 +233,38 @@ def summary_fig( stats_df ):
     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 )
+    # plot fit
+    axs[0,1].plot( stats_df[ "1_d" ], ccstar_tanh, color="tab:grey", linestyle = "dashed" )
+    sax2 = axs[0,1].secondary_xaxis( 'top', functions=( dto1_d, dto1_d ) )
+    sax2.set_xlabel('d (A)')
     axs[0,1].tick_params( axis='y', labelcolor=color )
+    axs[0,1].text( 0.1, 0.1, "CC* @ 0.7 = {0}".format( ccstar_cut ) , fontsize = 8 )
 
     # 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 )
+    sax3 = axs[1,0].secondary_xaxis( 'top', functions=( dto1_d, dto1_d ) )
+    sax3.set_xlabel('d (A)')
     axs[1,0].tick_params( axis='y', labelcolor=color )
 
 
-    # rsplit plot
+    # wilson plot
     color = "tab:purple"
-    axs[1,1].set_xlabel( "1/d (1/A)" )
+    axs[1,1].set_xlabel( "1/d**2 (1/A**2)" )
-    axs[1,1].set_ylabel("Multiplicity", color=color)
+    axs[1,1].set_ylabel( "lnI", color=color )
-    axs[1,1].plot(stats_df[ "1_d" ], stats_df.mult, color=color)
+    axs[1,1].plot( stats_df[ "1_d2" ], stats_df.lnI, color=color )
+    sax4 = axs[1,1].secondary_xaxis( 'top', functions=( dto1_d2, dto1_d2 ) )
+    sax4.set_xlabel( "d (A)" )
     axs[1,1].tick_params( axis='y', labelcolor=color )
 
     # save figure
     plt.tight_layout()
     plt.savefig( "plots.png" )
 
-def get_mean_cell( stream ):
+def main( cwd, name, stream, pointgroup, model, iterations, cell, shells, part_h_res, adu, reservation ):
 
-    # 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 main( cwd, name, stream, pointgroup, model, iterations, cell, shells, part_h_res, adu ):
-
-    print( "begin job" )
     # submitted job set
     submitted_job_ids = set()
 
@@ -257,18 +275,17 @@ def main( cwd, name, stream, pointgroup, model, iterations, cell, shells, part_h
     # move to part directory
     os.chdir( part_dir )
 
-    print( "making partialator files" )
+    print( "making partialator file" )
     # make partialator run file
     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 )
+    job_id = submit_job( part_run_file, reservation )
     print(f"job submitted: {0}".format( job_id ) )
     submitted_job_ids.add( job_id )
-    print( "DONE" )
 
     # use progress bar to track job completion
-    time.sleep(30)
+    time.sleep(10)
     wait_for_jobs(submitted_job_ids, 1 )
     print("slurm processing done")
 
@@ -277,10 +294,11 @@ def main( cwd, name, stream, pointgroup, model, iterations, cell, shells, part_h
     ccstar_dat = "ccstar.dat"
     mult_dat = "mult.dat"
     rsplit_dat = "Rsplit.dat"
+    wilson_dat = "wilson.dat"
 
     # make summary data table
-    stats_df = summary_stats( cc_dat, ccstar_dat, mult_dat, rsplit_dat )
+    stats_df = summary_stats( cc_dat, ccstar_dat, mult_dat, rsplit_dat, wilson_dat  )
-    print( stats_df.to_string() )
+    logger.info( "stats table from .dat file =\n{0}".format( stats_df.to_string() ) )
     print_df = stats_df[ [  "1_d", "d", "min",
                             "max", "nref", "poss",
                             "comp", "obs", "mult",
@@ -288,13 +306,15 @@ def main( cwd, name, stream, pointgroup, model, iterations, cell, shells, part_h
     print_df.to_csv( "summary_table.csv", sep="\t", index=False )
 
     # calculate cc metrics
-    cc_cut = get_metric( stats_df[ "1_d2" ], stats_df.cc, 0.3 )
+    cc_cut, cc_tanh = get_metric( stats_df[ "1_d2" ], stats_df.cc, 0.3 )
-    ccstar_cut = get_metric( stats_df[ "1_d2" ], stats_df.ccstar, 0.7 )
+    ccstar_cut, ccstar_tanh = get_metric( stats_df[ "1_d2" ], stats_df.ccstar, 0.7 )
     print( "resolution at CC0.5 at 0.3 = {0}".format( cc_cut ) )
     print( "resolution at CC* at 0.7 = {0}".format( ccstar_cut ) )
+    logger.info( "resolution at CC0.5 at 0.3 = {0}".format( cc_cut ) )
+    logger.info( "resolution at CC* at 0.7 = {0}".format( ccstar_cut ) )
 
     # show plots
-    summary_fig( stats_df )
+    summary_fig( stats_df, cc_tanh, ccstar_tanh, cc_cut, ccstar_cut )
 
     # move back to top dir
     os.chdir( cwd )
@@ -361,13 +381,33 @@ if __name__ == "__main__":
         "-a",
         "--max_adu",
         help="maximum detector counts to allow. Default is 12000.",
-        type=int
+        type=int,
+        default=12000
+    )
+    parser.add_argument(
+        "-R",
+        "--reservation",
+        help="reservation name for ra cluster. Usually along the lines of P11111_2024-12-10",
+        type=str,
+        default=None
+    )
+    parser.add_argument(
+        "-d",
+        "--debug",
+        help="output debug to terminal.",
+        type=bool,
+        default=False
     )
     args = parser.parse_args()
+    # set loguru
+    if not args.debug:
+        logger.remove()
+    logfile = "{0}.log".format( args.name )
+    logger.add( logfile, format="{message}", level="INFO")
     # 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, args.max_adu )
+    main( cwd, args.name, args.stream_file, args.pointgroup, args.model, args.iterations, args.cell_file, args.bins, args.resolution, args.max_adu, args.reservation )
 
 
 

reduction_tools/stream_stats.py

@@ -52,7 +52,7 @@ def scrub_res( stream ):
     # example - diffraction_resolution_limit = 4.07 nm^-1 or 2.46 A
     # scrub res_lst or return np.nan
     try:
-        pattern = r"diffraction_resolution_limit\s=\s\d\.\d+\snm\^-1\sor\s(\d\.\d+)\sA"
+        pattern = r"diffraction_resolution_limit\s=\s\d\.\d+\snm\^-1\sor\s(\d+\.\d+)\sA"
         res_lst = re.findall( pattern, stream )
         if AttributeError:
             return res_lst