now has stream_stats output and log file - loguru still needs work

reduction_tools/crystfel_split.py

@@ -18,6 +18,7 @@ python crystfel_split.py -l <path-to-list-file>
 
 # output
 a series of stream files from crystfel in the current working directory
+a log file with relavent info on the run
 """
 
 # modules
@@ -28,6 +29,93 @@ import time
 import argparse
 from tqdm import tqdm
 import regex as re
+import numpy as np
+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 ):
 
@@ -97,6 +185,7 @@ 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 ):
@@ -109,7 +198,7 @@ def submit_job( job_file ):
     pattern = r"Submitted batch job (\d+)"
     job_id = re.search( pattern, job_output.decode().strip() ).group(1)
 
-    return int(job_id)
+    return int( job_id )
 
 def wait_for_jobs( job_ids, total_jobs ):
 
@@ -123,14 +212,9 @@ def wait_for_jobs( job_ids, total_jobs ):
                     completed_jobs.add(job_id)
                     pbar.update(1)
             job_ids.difference_update(completed_jobs)
-            time.sleep(30)
+            time.sleep(5)
 
-def run_splits( cwd, name, lst, chunk_size, geom_file, cell_file ):
+def run_splits( list_df, cwd, name, lst, chunk_size, geom_file, cell_file ):
-
-    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
@@ -141,10 +225,9 @@ def run_splits( cwd, name, lst, chunk_size, geom_file, cell_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 )
 
@@ -164,7 +247,7 @@ def run_splits( cwd, name, lst, chunk_size, geom_file, cell_file ):
 
         # submit jobs
         job_id = submit_job( cryst_run_file )
-        print(f"Job submitted: { job_id }")
+        logger.info( f"Job submitted: { job_id }" )
         submitted_job_ids.add( job_id )
 
         # increase chunk counter
@@ -173,11 +256,20 @@ def run_splits( cwd, name, lst, chunk_size, geom_file, cell_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 ):
+
+    print( "reading SwissFEL lst file" )
+    print( "creating {0} image chunks of lst".format( chunk_size ) )
+    list_df = h5_split( lst, chunk_size )
     print( "DONE" )
 
-    # include progress bar if required
+    # run crystfel runs on individual splits
+    submitted_job_ids, chunk, stream_lst = run_splits( list_df, cwd, name, lst, chunk_size, geom_file, cell_file )
+
+    # monitor progress of jobs
     wait_for_jobs(submitted_job_ids, chunk)
-    print("slurm processing done")
     
     # make composite .stream file
     output_file = "{0}.stream".format( name )
@@ -190,14 +282,38 @@ def run_splits( cwd, name, lst, chunk_size, geom_file, cell_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}")
+                    logger.info( 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" )
+    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 ) )
 
 if __name__ == "__main__":
     parser = argparse.ArgumentParser()
@@ -212,7 +328,7 @@ if __name__ == "__main__":
         "--chunk_size",
         help="how big should each chunk be? - the bigger the chunk, the fewer jobs, the slower it will be",
         type=int,
-        default=2500
+        default=500
     )
     parser.add_argument(
         "-g",
@@ -234,6 +350,13 @@ if __name__ == "__main__":
         default="split"
     )
     args = parser.parse_args()
-    # run geom converter
+    # set current working directory
     cwd = os.getcwd()
-    run_splits( cwd, args.job_name, args.lst_file, args.chunk_size, args.geom_file, args.cell_file )
+    # set loguru
+    logfile = "{0}.log".format( args.job_name )
+    logger.add( logfile, level="DEBUG" )
+    # 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 )
+