lint and a small bugfix on score generation

bob/measure/load.py

@@ -10,6 +10,7 @@ import numpy
 
 LOGGER = logging.getLogger('bob.measure')
 
+
 def split(filename):
     """split(filename) -> negatives, positives
 
@@ -36,7 +37,7 @@ def split(filename):
         columns = numpy.loadtxt(filename)
         neg_pos = columns[:, 0]
         scores = columns[:, 1]
-    except:
+    except Exception:
         LOGGER.error('''Cannot read {}. This file must be a two columns file with
                    the first column containing -1 or 1 (i.e. negative or
                    positive) and the second the scores
@@ -45,6 +46,7 @@ def split(filename):
     return (scores[numpy.where(neg_pos == -1)],
             scores[numpy.where(neg_pos == 1)])
 
+
 def split_files(filenames):
     """split_files
 

bob/measure/script/figure.py

@@ -196,7 +196,7 @@ class Metrics(MeasureBase):
                           threshold),
                        file=self.log_file)
         else:
-            click.echo("[Min. criterion: user provider] Threshold on "
+            click.echo("[Min. criterion: user provided] Threshold on "
                        "Development set `%s`: %e"
                        % (dev_file or title, threshold), file=self.log_file)
 

bob/measure/script/gen.py

 """Generate random scores.
 """
-import pkg_resources  # to make sure bob gets imported properly
 import os
 import logging
 import numpy
@@ -15,6 +14,7 @@ logger = logging.getLogger(__name__)
 NUM_NEG = 5000
 NUM_POS = 5000
 
+
 def gen_score_distr(mean_neg, mean_pos, sigma_neg=1, sigma_pos=1):
     """Generate scores from normal distributions
 
@@ -47,17 +47,17 @@ def gen_score_distr(mean_neg, mean_pos, sigma_neg=1, sigma_pos=1):
     return neg_scores, pos_scores
 
 
-def write_scores_to_file(pos, neg, filename):
+def write_scores_to_file(neg, pos, filename):
     """Writes score distributions into 2-column score files. For the format of
     the 2-column score files, please refer to Bob's documentation. See
     :py:func:`bob.measure.load.split`.
 
     Parameters
     ----------
-    pos : :py:class:`numpy.ndarray`
-        Scores for positive samples.
     neg : :py:class:`numpy.ndarray`
         Scores for negative samples.
+    pos : :py:class:`numpy.ndarray`
+        Scores for positive samples.
     filename : str
         The path to write the score to.
     """
@@ -72,6 +72,7 @@ def write_scores_to_file(pos, neg, filename):
             text = '-1 %f\n' % i if nan_dist(mt) > 0.01 else '1 nan\n'
             f.write(text)
 
+
 @click.command()
 @click.argument('outdir')
 @click.option('--mean-neg', default=-1, type=FLOAT, show_default=True)

bob/measure/utils.py

@@ -4,6 +4,7 @@ import numpy
 import scipy.stats
 import bob.core
 
+
 def remove_nan(scores):
     """remove_nan
 
@@ -28,6 +29,7 @@ def remove_nan(scores):
         logger.warning('Found {} NaNs in {} scores'.format(sum_nans, total))
     return scores[numpy.where(~nans)], sum_nans, total
 
+
 def get_fta(scores):
     """get_fta
         calculates the Failure To Acquire (FtA) rate, i.e. proportion of NaN(s)
@@ -53,6 +55,7 @@ def get_fta(scores):
     fta_total += total
     return ((neg, pos), fta_sum / fta_total)
 
+
 def get_fta_list(scores):
     """ Get FTAs for a list of scores
 
@@ -84,6 +87,7 @@ def get_fta_list(scores):
         fta_list.append(fta)
     return (neg_list, pos_list, fta_list)
 
+
 def get_thres(criter, neg, pos, far=None):
     """Get threshold for the given positive/negatives scores and criterion
 
@@ -116,6 +120,7 @@ def get_thres(criter, neg, pos, far=None):
     else:
         raise ValueError("Incorrect plotting criterion: ``%s``" % criter)
 
+
 def get_colors(n):
     """get_colors
     Get a list of matplotlib colors
@@ -135,7 +140,8 @@ def get_colors(n):
         cmap = pyplot.cm.get_cmap(name='magma')
         return [cmap(i) for i in numpy.linspace(0, 1.0, n + 1)]
 
-    return ['C0','C1','C2','C3','C4','C5','C6','C7','C8','C9']
+    return ['C0', 'C1', 'C2', 'C3', 'C4', 'C5', 'C6', 'C7', 'C8', 'C9']
+
 
 def get_linestyles(n, on=True):
     """Get a list of matplotlib linestyles
@@ -154,24 +160,25 @@ def get_linestyles(n, on=True):
         return [None] * n
 
     list_linestyles = [
-        (0, ()),                    #solid
-        (0, (1, 1)),                #densely dotted
-        (0, (5, 5)),                #dashed
-        (0, (5, 1)),                #densely dashed
-        (0, (3, 1, 1, 1, 1, 1)),    #densely dashdotdotted
-        (0, (3, 10, 1, 10, 1, 10)), #loosely dashdotdotted
-        (0, (3, 5, 1, 5, 1, 5)),    #dashdotdotted
-        (0, (3, 1, 1, 1)),          #densely dashdotted
-        (0, (1, 5)),                #dotted
-        (0, (3, 5, 1, 5)),          #dashdotted
-        (0, (5, 10)),               #loosely dashed
-        (0, (3, 10, 1, 10)),        #loosely dashdotted
-        (0, (1, 10))                #loosely dotted
+        (0, ()),  # solid
+        (0, (1, 1)),  # densely dotted
+        (0, (5, 5)),  # dashed
+        (0, (5, 1)),  # densely dashed
+        (0, (3, 1, 1, 1, 1, 1)),  # densely dashdotdotted
+        (0, (3, 10, 1, 10, 1, 10)),  # loosely dashdotdotted
+        (0, (3, 5, 1, 5, 1, 5)),  # dashdotdotted
+        (0, (3, 1, 1, 1)),  # densely dashdotted
+        (0, (1, 5)),  # dotted
+        (0, (3, 5, 1, 5)),  # dashdotted
+        (0, (5, 10)),  # loosely dashed
+        (0, (3, 10, 1, 10)),  # loosely dashdotted
+        (0, (1, 10))  # loosely dotted
     ]
     while n > len(list_linestyles):
         list_linestyles += list_linestyles
     return list_linestyles
 
+
 def confidence_for_indicator_variable(x, n, alpha=0.05):
     '''Calculates the confidence interval for proportion estimates
     The Clopper-Pearson interval method is used for estimating the confidence