nit

bob/pad/base/script/det.py

@@ -6,6 +6,7 @@ from bob.extension.scripts.click_helper import verbosity_option
 from bob.bio.base.score import load
 from . import figure
 
+
 @click.command()
 @common_options.scores_argument(min_arg=2, force_eval=True, nargs=-1)
 @common_options.output_plot_file_option(default_out='det.pdf')
@@ -31,23 +32,23 @@ from . import figure
               help='If False, will annotate the plots hypothetically, instead '
               'of with real data values of the calculated error rates.')
 @click.pass_context
-def det(ctx, scores, criteria,  real_data, **kwargs):
-    """Plot DET
+def det(ctx, scores, criteria, real_data, **kwargs):
+  """Plot DET
 
-    You need to provide 2 or 4 scores
-    files for each PAD system in this order:
+  You need to provide 2 or 4 scores
+  files for each PAD system in this order:
 
-    \b
-    * licit development scores
-    * licit evaluation scores
-    * spoof development scores (when ``--no-spoof`` is False (default))
-    * spoof evaluation scores (when ``--no-spoof`` is False (default))
+  \b
+  * licit development scores
+  * licit evaluation scores
+  * spoof development scores (when ``--no-spoof`` is False (default))
+  * spoof evaluation scores (when ``--no-spoof`` is False (default))
 
 
-    Examples:
-        $ bob pad det --no-spoof dev-scores eval-scores
+  Examples:
+      $ bob pad det --no-spoof dev-scores eval-scores
 
-        $ bob pad det {licit,spoof}/scores-{dev,eval}
-    """
-    process = figure.Det(ctx, scores, True, load.split, criteria, real_data)
-    process.run()
+      $ bob pad det {licit,spoof}/scores-{dev,eval}
+  """
+  process = figure.Det(ctx, scores, True, load.split, criteria, real_data)
+  process.run()

bob/pad/base/script/epc.py

@@ -6,6 +6,7 @@ from bob.extension.scripts.click_helper import verbosity_option
 from bob.bio.base.score import load
 from . import figure
 
+
 @click.command()
 @common_options.scores_argument(min_arg=2, force_eval=True, nargs=-1)
 @common_options.output_plot_file_option(default_out='epc.pdf')
@@ -49,6 +50,7 @@ def epc(ctx, scores, **kwargs):
     process = figure.Epc(ctx, scores, True, load.split)
     process.run()
 
+
 @click.command()
 @common_options.scores_argument(min_arg=2, force_eval=True, nargs=-1)
 @common_options.output_plot_file_option(default_out='epsc.pdf')

bob/pad/base/script/error_utils.py

 #!/usr/bin/env python
-#Ivana Chingovska <ivana.chingovska@idiap.ch>
-#Fri Dec  7 12:33:37 CET 2012
+# Ivana Chingovska <ivana.chingovska@idiap.ch>
+# Fri Dec  7 12:33:37 CET 2012
 """Utility functions for computation of EPSC curve and related measurement"""
 
-import os
-import sys
 import bob.measure
 import numpy
-import argparse
 
 
 def calc_pass_rate(threshold, attacks):
@@ -69,7 +66,7 @@ def weighted_neg_error_rate_criteria(data,
         if beta == 0.5:
             return abs(far_w - frr)
         else:
-            #return abs(far_w - frr)
+            # return abs(far_w - frr)
             return abs((1 - beta) * frr - beta * far_w)
 
     elif criteria == 'min-hter':
@@ -137,7 +134,7 @@ def weighted_negatives_threshold(licit_neg,
                                  beta=0.5,
                                  criteria='eer'):
     """Calculates the threshold for achieving the given criteria between the
-    FAR_w and the FRR, given the single value for the weight parameter 
+    FAR_w and the FRR, given the single value for the weight parameter
     balancing between impostors and spoofing attacks and a single value for the
     parameter beta balancing between the real accesses and the negatives
     (impostors and spoofing attacks)
@@ -205,14 +202,14 @@ def epsc_thresholds(licit_neg,
     spoofing attacks. If None, it is going to span the full range [0,1].
     Otherwise, can be set to a fixed value or a list of values.
     - beta - the value of the parameter beta, balancing between real accesses
-    and all the negatives (zero-effort impostors and spoofing attacks). If 
+    and all the negatives (zero-effort impostors and spoofing attacks). If
     None, it is going to span the full range [0,1]. Otherwise, can be set to a
     fixed value or a list of values.
 
   """
     step_size = 1 / float(points)
 
-    if omega == None:
+    if omega is None:
         omega = numpy.array([(i * step_size) for i in range(points + 1)])
     elif not isinstance(omega, list) and not isinstance(
             omega, tuple) and not isinstance(omega, numpy.ndarray):
@@ -220,7 +217,7 @@ def epsc_thresholds(licit_neg,
     else:
         omega = numpy.array(omega)
 
-    if beta == None:
+    if beta is None:
         beta = numpy.array([(i * step_size) for i in range(points + 1)])
     elif not isinstance(beta, list) and not isinstance(
             beta, tuple) and not isinstance(beta, numpy.ndarray):
@@ -288,8 +285,8 @@ negative samples (impostors and spoofing attacks).
         spoof_neg, spoof_pos,
         threshold)  # calculate test frr @ threshold (spoof scenario)
 
-    frr = farfrr_licit[
-        1]  # we can take this value from farfrr_spoof as well, it doesn't matter
+    # we can take this value from farfrr_spoof as well, it doesn't matter
+    frr = farfrr_licit[1]
     far = farfrr_licit[0]
     sfar = farfrr_spoof[0]
 
@@ -302,8 +299,8 @@ negative samples (impostors and spoofing attacks).
 
 def epsc_error_rates(licit_neg, licit_pos, spoof_neg, spoof_pos, thresholds,
                      omega, beta):
-    """Calculates several error rates: FAR_w and WER_wb for the given weights 
-    (omega and beta) and thresholds (the thresholds need to be computed first 
+    """Calculates several error rates: FAR_w and WER_wb for the given weights
+    (omega and beta) and thresholds (the thresholds need to be computed first
     using the method: epsc_thresholds() before passing to this method)
 
     Parameters
@@ -350,8 +347,8 @@ def epsc_error_rates(licit_neg, licit_pos, spoof_neg, spoof_pos, thresholds,
 
 def all_error_rates(licit_neg, licit_pos, spoof_neg, spoof_pos, thresholds,
                     omega, beta):
-    """Calculates several error rates: FAR_w and WER_wb for the given weights 
-    (omega and beta) and thresholds (the thresholds need to be computed first 
+    """Calculates several error rates: FAR_w and WER_wb for the given weights
+    (omega and beta) and thresholds (the thresholds need to be computed first
     using the method: epsc_thresholds() before passing to this method)
 
     Parameters
@@ -427,7 +424,7 @@ def calc_aue(licit_neg,
     - h_bound - higher bound of integration
     - points - number of points to calculate EPSC
     - criteria - the decision threshold criteria ('eer', 'wer' or 'min-hter')
-    - var_param - name of the parameter which is varied on the abscissa 
+    - var_param - name of the parameter which is varied on the abscissa
     ('omega' or 'beta')
   """
 

bob/pad/base/script/evaluate.py

@@ -3,10 +3,10 @@ experiements
 """
 import click
 from bob.measure.script import common_options
-from bob.extension.scripts.click_helper import (verbosity_option, bool_option)
-from bob.bio.base.score import load
+from bob.extension.scripts.click_helper import verbosity_option
 import bob.bio.base.script.commands as bio_commands
-from . import (figure, histograms, metrics, det, fmr_iapmr, epc)
+from . import (histograms, metrics, det, fmr_iapmr, epc)
+
 
 @click.command()
 @common_options.scores_argument(nargs=-1)
@@ -99,8 +99,8 @@ def vuln(ctx, scores, **kwargs):
 
     \b
     1. Computes bob pad vuln_metrics
-    2. Plots EPC, EPSC, vulnerability histograms, fmr vs IAPMR to a multi-page PDF
-       file
+    2. Plots EPC, EPSC, vulnerability histograms, fmr vs IAPMR to a multi-page
+       PDF file
 
 
     You need to provide 4 score files for each biometric system in this order:

bob/pad/base/script/fmr_iapmr.py

@@ -6,6 +6,7 @@ from bob.extension.scripts.click_helper import verbosity_option
 from bob.bio.base.score import load
 from . import figure
 
+
 @click.command()
 @common_options.scores_argument(min_arg=2, force_eval=True, nargs=-1)
 @common_options.output_plot_file_option(default_out='fmr_iapmr.pdf')

bob/pad/base/script/histograms.py

@@ -6,6 +6,7 @@ from bob.extension.scripts.click_helper import (verbosity_option, bool_option)
 from bob.bio.base.score import load
 from . import figure
 
+
 @click.command()
 @common_options.scores_argument(nargs=-1)
 @common_options.title_option()
@@ -27,13 +28,13 @@ def hist(ctx, scores, evaluation, **kwargs):
     """ Plots histograms of Bona fida and PA along with threshold
     criterion.
 
-    You need to provide one or more development score file(s) for each experiment.
-    You can also provide eval files along with dev files. If only dev scores
-    are provided, you must use flag `--no-evaluation`.
+    You need to provide one or more development score file(s) for each
+    experiment. You can also provide eval files along with dev files. If only
+    dev scores are provided, you must use flag `--no-evaluation`.
 
-    By default, when eval-scores are given, only eval-scores histograms are 
+    By default, when eval-scores are given, only eval-scores histograms are
     displayed with threshold line
-    computed from dev-scores. If you want to display dev-scores distributions 
+    computed from dev-scores. If you want to display dev-scores distributions
     as well, use ``--show-dev`` option.
 
     Examples:
@@ -47,6 +48,7 @@ def hist(ctx, scores, evaluation, **kwargs):
     process = figure.HistPad(ctx, scores, evaluation, load.split)
     process.run()
 
+
 @click.command()
 @common_options.scores_argument(nargs=-1, min_arg=2)
 @common_options.title_option()
@@ -87,10 +89,10 @@ def vuln_hist(ctx, scores, evaluation, **kwargs):
     See :ref:`bob.pad.base.vulnerability` in the documentation for a guide on
     vulnerability analysis.
 
-    You need to provide one or more development score file(s) for each experiment.
-    You can also provide eval files along with dev files. If only dev-scores
-    are used set the flag `--no-evaluation`
-    is required in that case.
+    You need to provide one or more development score file(s) for each
+    experiment. You can also provide eval files along with dev files. If only
+    dev-scores are used set the flag `--no-evaluation` is required in that
+    case.
 
     By default, when eval-scores are given, only eval-scores histograms are
     displayed with threshold line

bob/pad/base/script/metrics.py

@@ -7,6 +7,7 @@ from bob.extension.scripts.click_helper import (verbosity_option,
 from bob.bio.base.score import load
 from . import figure
 
+
 @click.command(context_settings=dict(token_normalize_func=lambda x: x.lower()))
 @common_options.scores_argument(nargs=-1)
 @common_options.eval_option()
@@ -20,7 +21,8 @@ def metrics(ctx, scores, evaluation, **kwargs):
     """PAD ISO compliant metrics.
 
     Reports several metrics based on a selected thresholds on the development
-    set and apply them on evaluation sets (if provided). The used thresholds are:
+    set and apply them on evaluation sets (if provided). The used thresholds
+    are:
 
         bpcer20     When APCER is set to 5%.
 
@@ -52,6 +54,7 @@ def metrics(ctx, scores, evaluation, **kwargs):
     process = figure.Metrics(ctx, scores, evaluation, load.split)
     process.run()
 
+
 @click.command(context_settings=dict(token_normalize_func=lambda x: x.lower()))
 @common_options.scores_argument(min_arg=2, force_eval=True, nargs=-1)
 @common_options.eval_option()

bob/pad/base/script/pad.py

@@ -8,5 +8,5 @@ from click_plugins import with_plugins
 @with_plugins(pkg_resources.iter_entry_points('bob.pad.cli'))
 @click.group()
 def pad():
-    """Entry for bob.pad commands."""
+    """Presentation Attack Detection related commands."""
     pass