Merge branch 'pr/15'

biosppy/__init__.py

@@ -1,13 +1,19 @@
 # -*- coding: utf-8 -*-
 """
-    biosppy
-    -------
+biosppy
+-------
 
-    A toolbox for biosignal processing written in Python.
+A toolbox for biosignal processing written in Python.
 
-    :copyright: (c) 2015 by Instituto de Telecomunicacoes
-    :license: BSD 3-clause, see LICENSE for more details.
+:copyright: (c) 2015-2017 by Instituto de Telecomunicacoes
+:license: BSD 3-clause, see LICENSE for more details.
 """
 
+# compat
+from __future__ import absolute_import, division, print_function
+
 # get version
 from .version import version as __version__
+
+# allow lazy loading
+from .signals import bvp, ecg, eda, eeg, emg, resp, tools

biosppy/biometrics.py

@@ -1,20 +1,25 @@
 # -*- coding: utf-8 -*-
 """
-    biosppy.biometrics
-    ------------------
-
-    This module provides classifier interfaces for identity recognition
-    (biometrics) applications. The core API methods are:
-    * enroll: add a new subject;
-    * dismiss: remove an existing subject;
-    * identify: determine the identity of collected biometric dataset;
-    * authenticate: verify the identity of collected biometric dataset.
-
-    :copyright: (c) 2015 by Instituto de Telecomunicacoes
-    :license: BSD 3-clause, see LICENSE for more details.
+biosppy.biometrics
+------------------
+
+This module provides classifier interfaces for identity recognition
+(biometrics) applications. The core API methods are:
+* enroll: add a new subject;
+* dismiss: remove an existing subject;
+* identify: determine the identity of collected biometric dataset;
+* authenticate: verify the identity of collected biometric dataset.
+
+:copyright: (c) 2015-2017 by Instituto de Telecomunicacoes
+:license: BSD 3-clause, see LICENSE for more details.
 """
 
 # Imports
+# compat
+from __future__ import absolute_import, division, print_function
+from six.moves import range
+import six
+
 # built-in
 import collections
 
@@ -247,7 +252,7 @@ def list_subjects(self):
 
         """
 
-        subjects = self._subject2label.keys()
+        subjects = list(self._subject2label)
 
         return subjects
 
@@ -356,7 +361,7 @@ def batch_train(self, data=None):
         if data is None:
             raise TypeError("Please specify the data to train.")
 
-        for sub, val in data.iteritems():
+        for sub, val in six.iteritems(data):
             if val is None:
                 try:
                     self.dismiss(sub, deferred=True)
@@ -399,10 +404,10 @@ def update_thresholds(self, fraction=1.):
 
         # gather data to test
         data = {}
-        for subject, label in self._subject2label.iteritems():
+        for subject, label in six.iteritems(self._subject2label):
             # select a random fraction of the training data
             aux = self.io_load(label)
-            indx = range(len(aux))
+            indx = list(range(len(aux)))
             use, _ = utils.random_fraction(indx, fraction, sort=True)
 
             data[subject] = aux[use]
@@ -411,7 +416,7 @@ def update_thresholds(self, fraction=1.):
         _, res = self.evaluate(data, ths)
 
         # choose thresholds at EER
-        for subject, label in self._subject2label.iteritems():
+        for subject, label in six.iteritems(self._subject2label):
             EER_auth = res['subject'][subject]['authentication']['rates']['EER']
             self.set_auth_thr(label, EER_auth[self.EER_IDX, 0], ready=True)
 
@@ -639,7 +644,7 @@ def evaluate(self, data, thresholds=None, show=False):
             thresholds = self.get_thresholds()
 
         # get subjects
-        subjects = filter(lambda item: self.check_subject(item), data.keys())
+        subjects = [item for item in data if self.check_subject(item)]
         if len(subjects) == 0:
             raise ValueError("No enrolled subjects in test set.")
 
@@ -722,15 +727,15 @@ def cross_validation(cls, data, labels, cv, thresholds=None, **kwargs):
                 lbl = labels[item]
                 train_idx[lbl].append(item)
 
-            train_data = {sub: data[idx] for sub, idx in train_idx.iteritems()}
+            train_data = {sub: data[idx] for sub, idx in six.iteritems(train_idx)}
 
             # test data set
             test_idx = collections.defaultdict(list)
             for item in test:
                 lbl = labels[item]
                 test_idx[lbl].append(item)
 
-            test_data = {sub: data[idx] for sub, idx in test_idx.iteritems()}
+            test_data = {sub: data[idx] for sub, idx in six.iteritems(test_idx)}
 
             # instantiate classifier
             clf = cls(**kwargs)
@@ -832,8 +837,8 @@ def _prepare(self, data, targets=None):
 
         # target class labels
         if targets is None:
-            targets = self._subject2label.values()
-        elif isinstance(targets, basestring):
+            targets = list(self._subject2label.values())
+        elif isinstance(targets, six.string_types):
             targets = [targets]
 
         return data
@@ -975,7 +980,7 @@ def _authenticate(self, data, label, threshold):
         # select based on subject label
         aux = []
         ns = len(dists)
-        for i in xrange(ns):
+        for i in range(ns):
             aux.append(dists[i, train_labels[i, :] == label])
 
         dists = np.array(aux)
@@ -984,12 +989,12 @@ def _authenticate(self, data, label, threshold):
         dists = dists[:, :self.k]
 
         decision = np.zeros(ns, dtype='bool')
-        for i in xrange(ns):
+        for i in range(ns):
             # compare distances to threshold
             count = np.sum(dists[i, :] <= threshold)
 
             # decide accept
-            if count > (self.k / 2):
+            if count > (self.k // 2):
                 decision[i] = True
 
         return decision
@@ -1014,8 +1019,8 @@ def _get_thresholds(self):
             return np.linspace(self.min_thr, 1., 100)
 
         maxD = []
-        for _ in xrange(3):
-            for label in self._subject2label.values():
+        for _ in range(3):
+            for label in list(six.itervalues(self._subject2label)):
                 # randomly select samples
                 aux = self.io_load(label)
                 ind = np.random.randint(0, aux.shape[0], 3)
@@ -1064,14 +1069,14 @@ def _identify(self, data, threshold=None):
         ns = len(dists)
 
         labels = []
-        for i in xrange(ns):
+        for i in range(ns):
             lbl, _ = majority_rule(train_labels[i, :], random=True)
 
             # compare distances to threshold
             count = np.sum(dists[i, :] <= thrFcn(lbl))
 
             # decide
-            if count > (self.k / 2):
+            if count > (self.k // 2):
                 # accept
                 labels.append(lbl)
             else:
@@ -1102,8 +1107,8 @@ def _prepare(self, data, targets=None):
 
         # target class labels
         if targets is None:
-            targets = self._subject2label.values()
-        elif isinstance(targets, basestring):
+            targets = list(six.itervalues(self._subject2label))
+        elif isinstance(targets, six.string_types):
             targets = [targets]
 
         dists = []
@@ -1415,7 +1420,7 @@ def _authenticate(self, data, label, threshold):
         aux = aux[sel, :]
 
         decision = []
-        for i in xrange(ns):
+        for i in range(ns):
             # determine majority
             predMax, count = majority_rule(aux[:, i], random=True)
             rate = float(count) / norm
@@ -1483,7 +1488,7 @@ def _identify(self, data, threshold=None):
             norm = 1.0
 
         labels = []
-        for i in xrange(ns):
+        for i in range(ns):
             # determine majority
             predMax, count = majority_rule(aux[:, i], random=True)
             rate = float(count) / norm
@@ -1524,11 +1529,11 @@ def _prepare(self, data, targets=None):
 
         # target class labels
         if self._nbSubjects == 1:
-            pairs = self._models.keys()
+            pairs = list(self._models)
         else:
             if targets is None:
-                pairs = self._models.keys()
-            elif isinstance(targets, basestring):
+                pairs = list(self._models)
+            elif isinstance(targets, six.string_types):
                 labels = list(
                     set(self._subject2label.values()) - set([targets]))
                 pairs = [[targets, lbl] for lbl in labels]
@@ -1563,9 +1568,9 @@ def _train(self, enroll=None, dismiss=None):
             dismiss = []
 
         # process dismiss
-        pairs = self._models.keys()
+        pairs = list(self._models)
         for t in dismiss:
-            pairs = filter(lambda p: t in p, pairs)
+            pairs = [p for p in pairs if t in p]
 
         for p in pairs:
             self._del_clf(p)
@@ -1590,11 +1595,11 @@ def _train(self, enroll=None, dismiss=None):
 
         # check singles
         if self._nbSubjects == 1:
-            label = self._subject2label.values()[0]
+            label = list(six.itervalues(self._subject2label))[0]
             X = self.io_load(label)
             self._get_single_clf(X, label)
         elif self._nbSubjects > 1:
-            aux = filter(lambda p: '' in p, self._models.keys())
+            aux = [p for p in self._models if '' in p]
             if len(aux) != 0:
                 for p in aux:
                     self._del_clf(p)
@@ -1850,7 +1855,7 @@ def get_subject_results(results=None,
     R = np.zeros(nth, dtype='float')
     CM = []
 
-    for i in xrange(nth):  # for each threshold
+    for i in range(nth):  # for each threshold
         # authentication
         for k, lbl in enumerate(subject_idx):  # for each subject
             subject_tst = subjects[k]
@@ -2163,7 +2168,7 @@ def combination(results=None, weights=None):
         weights = {}
 
     # compile results to find all classes
-    vec = results.values()
+    vec = list(six.itervalues(results))
     if len(vec) == 0:
         raise CombinationError("No keys found.")
 
@@ -2182,13 +2187,13 @@ def combination(results=None, weights=None):
         # multi-class
         counts = np.zeros(nb, dtype='float')
 
-        for n in results.iterkeys():
+        for n in results:
             # ensure array
             res = np.array(results[n])
             ns = float(len(res))
 
             # get count for each unique class
-            for i in xrange(nb):
+            for i in range(nb):
                 aux = float(np.sum(res == unq[i]))
                 w = weights.get(n, 1.)
                 counts[i] += ((aux / ns) * w)

biosppy/clustering.py

@@ -1,16 +1,21 @@
 # -*- coding: utf-8 -*-
 """
-    biosppy.clustering
-    ------------------
+biosppy.clustering
+------------------
 
-    This module provides various unsupervised machine learning (clustering)
-    algorithms.
+This module provides various unsupervised machine learning (clustering)
+algorithms.
 
-    :copyright: (c) 2015 by Instituto de Telecomunicacoes
-    :license: BSD 3-clause, see LICENSE for more details.
+:copyright: (c) 2015-2017 by Instituto de Telecomunicacoes
+:license: BSD 3-clause, see LICENSE for more details.
 """
 
 # Imports
+# compat
+from __future__ import absolute_import, division, print_function
+from six.moves import map, range, zip
+import six
+
 # 3rd party
 import numpy as np
 import scipy.cluster.hierarchy as sch
@@ -134,7 +139,7 @@ def hierarchical(data=None,
                        'ward', 'weighted']:
         raise ValueError("Unknown linkage criterion '%r'." % linkage)
 
-    if not isinstance(metric, basestring):
+    if not isinstance(metric, six.string_types):
         raise TypeError("Please specify the distance metric as a string.")
 
     N = len(data)
@@ -405,19 +410,19 @@ def create_coassoc(ensemble=None, N=None):
     nparts = len(ensemble)
     assoc = 0
     for part in ensemble:
-        nsamples = np.array([len(part[key]) for key in part.iterkeys()])
-        dim = np.sum(nsamples * (nsamples - 1)) / 2
+        nsamples = np.array([len(part[key]) for key in part])
+        dim = np.sum(nsamples * (nsamples - 1)) // 2
 
         I = np.zeros(dim)
         J = np.zeros(dim)
         X = np.ones(dim)
         ntriplets = 0
 
-        for v in part.itervalues():
+        for v in six.itervalues(part):
             nb = len(v)
             if nb > 0:
-                for h in xrange(nb):
-                    for f in xrange(h + 1, nb):
+                for h in range(nb):
+                    for f in range(h + 1, nb):
                         I[ntriplets] = v[h]
                         J[ntriplets] = v[f]
                         ntriplets += 1
@@ -539,7 +544,7 @@ def mdist_templates(data=None,
         clusters = {0: np.arange(len(data), dtype='int')}
 
     # cluster labels
-    ks = clusters.keys()
+    ks = list(clusters)
 
     # remove the outliers' cluster, if present
     if '-1' in ks:
@@ -631,7 +636,7 @@ def centroid_templates(data=None, clusters=None, ntemplates=1):
         raise TypeError("Please specify a data clustering.")
 
     # cluster labels
-    ks = clusters.keys()
+    ks = list(clusters)
 
     # remove the outliers' cluster, if present
     if '-1' in ks:
@@ -839,7 +844,7 @@ def outliers_dmean(data=None,
             outliers.append(i)
 
     outliers = np.unique(outliers)
-    normal = np.setdiff1d(range(len(data)), outliers, assume_unique=True)
+    normal = np.setdiff1d(list(range(len(data))), outliers, assume_unique=True)
 
     # output
     clusters = {-1: outliers, 0: normal}
@@ -987,7 +992,7 @@ def _merge_clusters(clusters):
 
     """
 
-    keys = clusters.keys()
+    keys = list(clusters)
 
     # outliers
     if -1 in keys: