[finalize-scores] Backup scores, speed up help

bob/pad/base/script/finalize_scores.py

 """Finalizes the scores that are produced by spoof.py
 """
 import click
-import numpy
-import logging
-from bob.extension.scripts.click_helper import (
-    verbosity_option, log_parameters)
+from bob.extension.scripts.click_helper import log_parameters
+from bob.extension.scripts.click_helper import verbosity_option
 
-logger = logging.getLogger(__name__)
 
-
-@click.command(name='finalize-scores', epilog='''\b
+@click.command(
+    name="finalize-scores",
+    epilog="""\b
 Examples:
   $ bin/bob pad finalize_scores /path/to/scores-dev
   $ bin/bob pad finalize_scores /path/to/scores-{dev,eval}
-''')
-@click.argument('scores', type=click.Path(exists=True, dir_okay=False),
-                nargs=-1)
-@click.option('-m', '--method', default='mean',
-              type=click.Choice(['mean', 'min', 'max']), show_default=True,
-              help='The method to use when finalizing the scores.')
+""",
+)
+@click.argument("scores", type=click.Path(exists=True, dir_okay=False), nargs=-1)
+@click.option(
+    "-m",
+    "--method",
+    default="mean",
+    type=click.Choice(["mean", "min", "max"]),
+    show_default=True,
+    help="The method to use when finalizing the scores.",
+)
+@click.option("--backup/--no-backup", default=True, help="Whether to backup scores.")
 @verbosity_option()
-def finalize_scores(scores, method, **kwargs):
-    """Finalizes the scores given by spoof.py
+def finalize_scores(scores, method, backup, verbose):
+    """Finalizes the scores given by bob pad vanilla-pad
     When using bob.pad.base, Algorithms can produce several score values for
     each unique sample. You can use this script to average (or min/max) these
     scores to have one final score per sample.
 
-    The conversion is done in-place. The order of scores will change.
+    The conversion is done in-place (original files will be backed up).
+    The order of scores will change.
     """
+    import logging
+
+    import numpy
+
+    logger = logging.getLogger(__name__)
     log_parameters(logger)
 
-    mean = {'mean': numpy.nanmean, 'max': numpy.nanmax, 'min': numpy.nanmin}[method]
+    mean = {"mean": numpy.nanmean, "max": numpy.nanmax, "min": numpy.nanmin}[method]
 
     for path in scores:
         new_lines = []
         with open(path) as f:
             old_lines = f.readlines()
-            old_lines.sort()
+
+        if backup:
+            with open(f"{path}.bak", "w") as f:
+                f.writelines(old_lines)
+
+        old_lines.sort()
+
         for i, line in enumerate(old_lines):
             uniq, s = line.strip().rsplit(maxsplit=1)
             s = float(s)
@@ -47,14 +63,13 @@ def finalize_scores(scores, method, **kwargs):
             if uniq == last_line:
                 last_scores.append(s)
             else:
-                new_lines.append('{} {}\n'.format(
-                    last_line, mean(last_scores)))
+                new_lines.append("{} {}\n".format(last_line, mean(last_scores)))
                 last_scores = [s]
 
             last_line = uniq
 
         else:  # this else is for the for loop
-            new_lines.append('{} {}\n'.format(last_line, mean(last_scores)))
+            new_lines.append("{} {}\n".format(last_line, mean(last_scores)))
 
-        with open(path, 'w') as f:
+        with open(path, "w") as f:
             f.writelines(new_lines)