adjust dbcare command output

src/epmt/epmt_cmd_dbcare.py

@@ -24,8 +24,9 @@ def _get_dead_row_stats():
         "ORDER BY relname"
     )
     result = orm_raw_sql(sql)
-    rows = result.fetchall()
-    return rows
+    logger.debug('result = %s', result)
+    #logger.debug('type(result) = %s', type(result))
+    return result
 
 
 def _vacuum_tables():
@@ -47,7 +48,9 @@ def _vacuum_tables():
                         'last_vacuum=%s last_autovacuum=%s',
                         row[0], row[1], row[2], row[3], row[4])
     except Exception as e:
-        logger.warning('could not query dead row stats: %s', e)
+        logger.error('could not query dead row stats: %s', e)
+        raise Exception from e
+
 
     # VACUUM requires autocommit — use a raw DBAPI connection
     raw_conn = engine.raw_connection()

src/epmt/epmt_cmd_help.py

@@ -1,7 +1,6 @@
 """
 EPMT help command module - provides help functionality.
 """
-# from __future__ import print_function
 from inspect import signature
 from sys import stderr
 

src/epmt/epmt_cmd_list.py

@@ -2,7 +2,6 @@
 EPMT list command module - handles job listing functionality.
 """
 
-# from __future__ import print_function
 from sys import stderr
 from logging import getLogger
 

src/epmt/epmt_cmd_show.py

@@ -2,12 +2,12 @@
 EPMT show command module - handles job display functionality.
 """
 
-# from __future__ import print_function
 from logging import getLogger
-logger = getLogger(__name__)
 
 import epmt.epmt_query as eq
 
+logger = getLogger(__name__)
+
 def epmt_show_job(jobid, key=None):
     if isinstance(jobid, list):
         jobid = jobid[0]

src/epmt/epmt_concat.py

@@ -1,4 +1,3 @@
-#!/usr/bin/env python3
 '''
 Data Flow:
 Always Check for and Store comments
@@ -18,7 +17,6 @@
   Jobid: `goodSample_123`
 '''
 
-from __future__ import unicode_literals
 from epmt.epmtlib import epmt_logging_init, logfn
 
 from sys import exit as sysexit
@@ -375,35 +373,3 @@ def csvjoiner(indir,
     return False, None, badfiles_renamed
 
 
-if __name__ == '__main__':
-    # inl: remove this entry point?
-    from argparse import ArgumentParser
-    parser = ArgumentParser(description="Concatenate CSV files. It returns 0 on success and < 0 on error")
-    parser.add_argument(
-        'files', nargs='+', metavar='FILE',
-        help='Two or more CSV files to concatenate OR a directory containing CSV files')
-    parser.add_argument(
-        '-v', '--verbose',
-        action="count",
-        default=0,
-        help="increase verbosity")
-    parser.add_argument(
-        '-o', '--output-file',
-        help="Name of the output file, determined from input if not specified",
-        default='')
-    parser.add_argument(
-        '-e', '--error',
-        action='store_true',
-        help="Exit at the first sign of trouble",
-        default=False)
-    parser.add_argument(
-        '-E', '--error-dir',
-        help="Name of the directory to save files with errors, disabled if not specified",
-        default='')
-    args = parser.parse_args()
-    retval, of, bf = csvjoiner(debug=args.verbose,
-                               indir=(args.files[0] if len(args.files) == 1 else args.files),
-                               outfile=args.output_file,
-                               keep_going=not args.error,
-                               errdir=args.error_dir)
-    sysexit(0 if retval else -1)

src/epmt/epmt_convert_csv.py

@@ -384,8 +384,3 @@ def extract_jobid_from_collated_csv(collated_csv):
     '''
     return collated_csv.split('papiex')[-1].split('-')[1]
 
-
-if __name__ == "__main__":
-    import sys
-    epmt_logging_init(intlvl=2)
-    convert_csv_in_tar(sys.argv[1], sys.argv[2] if len(sys.argv) > 2 else '')

src/epmt/epmt_daemon.py

@@ -1,7 +1,7 @@
 """
 EPMT daemon module - handles background daemon functionality.
 """
-# from __future__ import print_function
+
 from getpass import getuser
 from os import path, kill, unlink, getppid
 from sys import exit as sysexit

src/epmt/epmt_rootcause.py

@@ -88,57 +88,57 @@ def rootcause(ref, input, features, methods=[rootcause_zscore]):
     return False, None, None
 
 
-if __name__ == "__main__":
-    # Synthesize 10 feature names
-    n_features = 6
-    features = [f'{x:c}' for x in range(97, 97 + n_features)]
-    print("Features:\n", features)
+#if __name__ == "__main__":
+#    # Synthesize 10 feature names
+#    n_features = 6
+#    features = [f'{x:c}' for x in range(97, 97 + n_features)]
+#    print("Features:\n", features)
 
-    def multiple():
-        '''
-        Check with multiple outliers
-        Narrow range of reference values
-        '''
-        np.random.seed(104)
-        random_reference_df = pd.DataFrame(np.random.randint(50, 100, size=(100, n_features)), columns=features)
-        print("Reference:\n", random_reference_df.head())
-        # Wider range input values for test set
-        random_input_df = pd.DataFrame(np.random.randint(25, 125, size=(1, n_features)), columns=features)
-        print("Input:\n", random_input_df.head())
-        retval, df, dct = rootcause(random_reference_df, random_input_df, features)
-        print("Retval:\n", retval)
-        print("Result:\n", df)
-        print("Result:\n", dct)
+def ex_multiple(features, n_features):
+    '''
+    Check with multiple outliers
+    Narrow range of reference values
+    '''
+    np.random.seed(104)
+    random_reference_df = pd.DataFrame(np.random.randint(50, 100, size=(100, n_features)), columns=features)
+    print("Reference:\n", random_reference_df.head())
+    # Wider range input values for test set
+    random_input_df = pd.DataFrame(np.random.randint(25, 125, size=(1, n_features)), columns=features)
+    print("Input:\n", random_input_df.head())
+    retval, df, dct = rootcause(random_reference_df, random_input_df, features)
+    print("Retval:\n", retval)
+    print("Result:\n", df)
+    print("Result:\n", dct)
 
-    def none():
-        '''
-        Check with no outliers
-        Narrow range of reference values
-        '''
-        np.random.seed(103)
-        random_reference_df = pd.DataFrame(np.random.randint(50, 100, size=(100, n_features)), columns=features)
-        print("Reference:\n", random_reference_df.head())
-        # Wider range input values for test set
-        random_input_df = pd.DataFrame(np.random.randint(25, 125, size=(1, n_features)), columns=features)
-        print("Input:\n", random_input_df.head())
-        retval, df, dct = rootcause(random_reference_df, random_input_df, features)
-        print("Retval:\n", retval)
-        print("Result:\n", df)
-        print("Result:\n", dct)
+def ex_none(features, n_features):
+    '''
+    Check with no outliers
+    Narrow range of reference values
+    '''
+    np.random.seed(103)
+    random_reference_df = pd.DataFrame(np.random.randint(50, 100, size=(100, n_features)), columns=features)
+    print("Reference:\n", random_reference_df.head())
+    # Wider range input values for test set
+    random_input_df = pd.DataFrame(np.random.randint(25, 125, size=(1, n_features)), columns=features)
+    print("Input:\n", random_input_df.head())
+    retval, df, dct = rootcause(random_reference_df, random_input_df, features)
+    print("Retval:\n", retval)
+    print("Result:\n", df)
+    print("Result:\n", dct)
+
+def ex_one(features, n_features):
+    '''
+    Check with one outlier
+    Narrow range of reference values
+    '''
+    np.random.seed(102)
+    random_reference_df = pd.DataFrame(np.random.randint(50, 100, size=(100, n_features)), columns=features)
+    print("Reference:\n", random_reference_df.head())
+    # Wider range input values for test set
+    random_input_df = pd.DataFrame(np.random.randint(25, 125, size=(1, n_features)), columns=features)
+    print("Input:\n", random_input_df.head())
+    retval, df, dct = rca(random_reference_df, random_input_df, features)
+    print("Retval:\n", retval)
+    print("Result:\n", df)
+    print("Result:\n", dct)
 
-    def one():
-        '''
-        Check with one outlier
-        Narrow range of reference values
-        '''
-        np.random.seed(102)
-        random_reference_df = pd.DataFrame(np.random.randint(50, 100, size=(100, n_features)), columns=features)
-        print("Reference:\n", random_reference_df.head())
-        # Wider range input values for test set
-        random_input_df = pd.DataFrame(np.random.randint(25, 125, size=(1, n_features)), columns=features)
-        print("Input:\n", random_input_df.head())
-        retval, df, dct = rca(random_reference_df, random_input_df, features)
-        print("Retval:\n", retval)
-        print("Result:\n", df)
-        print("Result:\n", dct)
-    one()

src/epmt/epmtlib.py

@@ -1224,7 +1224,3 @@ def set_signal_handlers(signals=[], handler=None):
         logger.debug('Finished restoring signal handlers to defaults')
     else:
         logger.debug('Finished setting up signal handlers')
-
-
-if __name__ == "__main__":
-    print(version_str(True))