corrections concerning Python3

src/py/statiskit/core/data.py

@@ -64,7 +64,7 @@ def set_dispersion(self, dispersion):
 def wrapper_set_name(f):
     @wraps(f)
     def set_name(self, name):
-        if not isinstance(name, basestring):
+        if not isinstance(name, str):
             raise TypeError('expected basestring, but got {!r}'.format(type(name)))
         is_name = True
         try:
@@ -128,7 +128,7 @@ def __len__(self):
     @wraps(f1)
     def __getitem__(self, index):
         if isinstance(index, slice):
-            return [self[index] for index in xrange(*index.indices(len(self)))]
+            return [self[index] for index in range(*index.indices(len(self)))]
         else:
             if index < 0:
                 index += len(self)
@@ -173,7 +173,7 @@ def __str__(self):
         rows = [("", str(self.name))] + [(str(index), str(event)) if event is not None else (repr(index), '?') for index, event in enumerate(events) if index < controls.head] + [('...', '...')] + [(repr(index), repr(event)) if event is not None else (repr(index), '?') for index, event in enumerate(events) if index > max(len(events) - controls.tail, controls.head)]
     else:
         rows = [("", str(self.name))] + [(str(index), str(event)) if event is not None else (repr(index), '?') for index, event in enumerate(events)]
-    columns = zip(*rows)
+    columns = list(zip(*rows))
     maxima = [max(max(*[len(row) for row in column]), 3) + 2 for column in columns]
     string = []
     for index, row in enumerate(rows):
@@ -203,7 +203,7 @@ def _repr_html_(self):
 del _repr_html_
 
 def pdf_plot(self, axes=None, **kwargs):
-    from estimation import frequency_estimation, histogram_estimation
+    from .estimation import frequency_estimation, histogram_estimation
     sample_space = self.sample_space
     norm = kwargs.pop('norm', False)
     if isinstance(norm, bool):
@@ -236,7 +236,7 @@ def pdf_plot(self, axes=None, **kwargs):
 del pdf_plot
 
 def cdf_plot(self, axes=None, **kwargs):
-    from estimation import frequency_estimation
+    from .estimation import frequency_estimation
     sample_space = self.sample_space
     norm = kwargs.pop('norm', False)
     if isinstance(norm, bool):
@@ -260,7 +260,7 @@ def cdf_plot(self, axes=None, **kwargs):
 del cdf_plot
 
 def box_plot(self, axes=None, **kwargs):
-    from estimation import frequency_estimation
+    from .estimation import frequency_estimation
     sample_space = self.sample_space
     norm = kwargs.pop('norm', False)
     if isinstance(norm, bool):
@@ -486,7 +486,7 @@ def __repr__(self):
         rows = [[""] + [repr(component.name) for component in self.components]] + [[repr(index)] + [repr(uevent) if uevent is not None else '?' for uevent in mevent] for index, mevent in enumerate(events) if index < controls.head] + [['...'] + ['...'] * len(self.components)] + [[repr(index)] + [repr(uevent) if uevent is not None else '?' for uevent in mevent] for index, mevent in enumerate(events) if index > max(len(events) - controls.tail, controls.head)]
     else:
         rows = [[""] + [repr(component.name) for component in self.components]] + [[repr(index)] + [repr(uevent) if uevent is not None else '?' for uevent in mevent] for index, mevent in enumerate(events)]
-    columns = zip(*rows)
+    columns = list(zip(*rows))
     maxima = [max(max(*[len(row) for row in column]), 3) + 2 for column in columns]
     string = []
     for index, row in enumerate(rows):

src/py/statiskit/core/distribution.py

@@ -128,7 +128,7 @@ def _repr_latex_(self):
 def wrapper_probability(f):
     @wraps(f)
     def probability(self, event, **kwargs):
-        if isinstance(event, basestring):
+        if isinstance(event, str):
             event = CategoricalElementaryEvent(event)
         elif isinstance(event, int):
             event = DiscreteElementaryEvent(event)
@@ -164,7 +164,7 @@ def pdf_plot(self, axes=None, fmt='|', **kwargs):
     if axes is None:
         axes = pyplot.subplot(1,1,1)
     labels = getattr(self, 'ordered_values', getattr(self, 'values'))
-    x, labels = zip(*[(index, label) for index, label in enumerate(labels)])
+    x, labels = list(zip(*[(index, label) for index, label in enumerate(labels)]))
     y = [self.probability(label, log=False) for label in labels]
     if 'norm' in kwargs:
         norm = kwargs.pop('norm')
@@ -198,7 +198,7 @@ def wrapper(f):
     @wraps(f)
     def __init__(self, *args, **kwargs):
         f(self, args)
-        for attr in kwargs.keys():
+        for attr in list(kwargs.keys()):
             if hasattr(self, attr):
                 setattr(self, attr, kwargs.pop(attr))
             else:
@@ -229,15 +229,15 @@ def cdf_plot(self, axes=None, fmt='|', **kwargs):
     if axes is None:
         axes = pyplot.subplot(1,1,1)
     labels = self.ordered_values
-    x, labels = zip(*[(index, label) for index, label in enumerate(labels)])
+    x, labels = list(zip(*[(index, label) for index, label in enumerate(labels)]))
     y = self.pi
     if 'norm' in kwargs:
         norm = kwargs.pop('norm')
         y = [norm * p for p in y]
     else:
         y = [p for p in y]
     y = [y[i] for i in self.rank]
-    y = [sum(y[:i]) for i in xrange(1, len(y)+1)]
+    y = [sum(y[:i]) for i in range(1, len(y)+1)]
     if '|' in fmt:
         fmt = fmt.replace('|', '')
         width = kwargs.pop('width', .8)
@@ -281,7 +281,7 @@ def box_plot(self, axes=None, edgecolor="k", width=.5, vert=True, whiskers=(.09,
         axes.plot([pos-width/2., pos+width/2.], [qe, qe], color=edgecolor)
         axes.plot([pos, pos], [qb, q1], color=edgecolor)
         axes.plot([pos, pos], [q3, qe], color=edgecolor)
-        axes.set_yticks(range(len(values)))
+        axes.set_yticks(list(range(len(values))))
         axes.set_yticklabels(values)
     else:
         axes.bar(q1, width, q3-q1, pos-width/2., facecolor=facecolor, edgecolor=edgecolor)
@@ -290,7 +290,7 @@ def box_plot(self, axes=None, edgecolor="k", width=.5, vert=True, whiskers=(.09,
         axes.plot([qe, qe], [pos-width/2., pos+width/2.], color=edgecolor)
         axes.plot([qb, q1], [pos, pos], color=edgecolor)
         axes.plot([q3, qe], [pos, pos], color=edgecolor)
-        axes.set_xticks(range(len(values)))
+        axes.set_xticks(list(range(len(values))))
         axes.set_xticklabels(values)
     return axes
 
@@ -325,7 +325,7 @@ def pdf_plot(self, axes=None, fmt='|', **kwargs):
             kwargs['qmin'] = int(qmin)
         if 'qmax' not in kwargs and 'pmax' not in kwargs:
             kwargs['qmax'] = int(qmax)
-    x = kwargs.pop('quantiles', range(kwargs.pop('qmin', self.quantile(kwargs.pop('pmin', 0.025))), kwargs.pop('qmax', self.quantile(kwargs.pop('pmax', 0.975)))+1))
+    x = kwargs.pop('quantiles', list(range(kwargs.pop('qmin', self.quantile(kwargs.pop('pmin', 0.025))), kwargs.pop('qmax', self.quantile(kwargs.pop('pmax', 0.975)))+1)))
     y = [self.pdf(q) for q in x]
     if 'norm' in kwargs:
         norm = kwargs.pop('norm')
@@ -352,7 +352,7 @@ def cdf_plot(self, axes=None, fmt='o-', **kwargs):
             kwargs['qmin'] = int(qmin)
         if 'qmax' not in kwargs and 'pmax' not in kwargs:
             kwargs['qmax'] = int(qmax)
-    x = kwargs.pop('quantiles', range(kwargs.pop('qmin', self.quantile(kwargs.pop('pmin', 0.025))), kwargs.pop('qmax', self.quantile(kwargs.pop('pmax', 0.975)))+1))
+    x = kwargs.pop('quantiles', list(range(kwargs.pop('qmin', self.quantile(kwargs.pop('pmin', 0.025))), kwargs.pop('qmax', self.quantile(kwargs.pop('pmax', 0.975)))+1)))
     y = [self.cdf(q) for q in x]
     if 'norm' in kwargs:
         norm = kwargs.pop('norm')
@@ -887,7 +887,7 @@ def probability(self, *events, **kwargs):
         if not isinstance(event, MultivariateEvent):
             event = VectorEvent(len(events))
             for index, component in enumerate(events):
-                if isinstance(component, basestring):
+                if isinstance(component, str):
                     event[index] = CategoricalElementaryEvent(component)
                 elif isinstance(component, int):
                     event[index] = DiscreteElementaryEvent(component)
@@ -906,7 +906,7 @@ def probability(self, *events, **kwargs):
 MultivariateDistribution.probability = wrapper_probability(MultivariateDistribution.probability)
 
 def simulation(self, size):
-    return from_list(*map(list, zip(*[self.simulate() for index in range(size)])))
+    return from_list(*list(map(list, list(zip(*[self.simulate() for index in range(size)])))))
 
 MultivariateDistribution.simulation = simulation
 del simulation
@@ -1021,7 +1021,7 @@ def pdf_plot(self, axes=None, *args, **kwargs):
                     else:
                         skwargs = [{}] * self.nb_states
                     for index, (pi, observation) in enumerate(zip(self.pi, self.observations)):
-                        for key, value in kwargs.iteritems():
+                        for key, value in kwargs.items():
                             if not key in skwargs[index]:
                                 skwargs[index][key] = value
                         axes = observation.pdf_plot(axes=axes, norm=pi*norm, *args, **skwargs[index])

src/py/statiskit/core/estimation.py

@@ -179,7 +179,7 @@ def wrapper__getitem__(f):
         @wraps(f)
         def __getitem__(self, index):
             if isinstance(index, slice):
-                return [self[index] for index in xrange(*index.indices(len(self)))]
+                return [self[index] for index in range(*index.indices(len(self)))]
             else:
                 if index < 0:
                     index += len(self)
@@ -238,7 +238,7 @@ def wrapper__getitem__(f):
             @wraps(f)
             def __getitem__(self, index):
                 if isinstance(index, slice):
-                    return [self[index] for index in xrange(*index.indices(len(self)))]
+                    return [self[index] for index in range(*index.indices(len(self)))]
                 else:
                     if index < 0:
                         index += len(self)
@@ -254,7 +254,7 @@ def wrapper__setitem__(f):
             @wraps(f)
             def __setitem__(self, index, estimator):
                 if isinstance(index, slice):
-                    return [self[index] for index in xrange(*index.indices(len(self)))]
+                    return [self[index] for index in range(*index.indices(len(self)))]
                 else:
                     if index < 0:
                         index += len(self)
@@ -301,7 +301,7 @@ def __len__(self):
         @wraps(f1)
         def __getitem__(self, index):
             if isinstance(index, slice):
-                return [self[index] for index in xrange(*index.indices(len(self)))]
+                return [self[index] for index in range(*index.indices(len(self)))]
             else:
                 if index < 0:
                     index += len(self)
@@ -333,12 +333,12 @@ def _estimation(algo, data, mapping, **kwargs):
     try:
         algo = mapping[algo]()
     except KeyError:
-        raise ValueError('\'algo\' parameter, possible values are ' + ', '.join('"' + algo + '"' for algo in mapping.iterkeys()))
+        raise ValueError('\'algo\' parameter, possible values are ' + ', '.join('"' + algo + '"' for algo in mapping.keys()))
     except:
         raise
     if data:
         lazy = kwargs.pop('lazy', False)
-    for attr in kwargs.keys():
+    for attr in list(kwargs.keys()):
         if hasattr(algo, attr):
             setattr(algo, attr, kwargs.pop(attr))
         else:
@@ -503,7 +503,7 @@ def singular_selection(*args, **kwargs):
         mapping = dict(criterion = SingularDistributionSelection.CriterionEstimator)
         estimators = []
         for arg in args:
-            estimators.append(singular_selection(arg, **dict((key, value) for (key, value) in kwargs.iteritems() if key == "sum")))
+            estimators.append(singular_selection(arg, **dict((key, value) for (key, value) in kwargs.items() if key == "sum")))
         kwargs.pop('sum', None)
         return _estimation(algo, data, mapping, estimators=estimators, **kwargs)
 

src/py/statiskit/core/event.py

@@ -25,7 +25,7 @@
 del UnivariateEvent.get_event
 
 def type_to_event(event):
-    if isinstance(event, basestring):
+    if isinstance(event, str):
         event = CategoricalElementaryEvent(event)
     elif isinstance(event, int):
         event = DiscreteElementaryEvent(event)
@@ -263,7 +263,7 @@ def wrapper(f):
     @wraps(f)
     def __getitem__(self, index):
         if isinstance(index, slice):
-            return [self[_index] for _index in xrange(*index.indices(len(self)))]
+            return [self[_index] for _index in range(*index.indices(len(self)))]
         else:
             if index < 0:
                 index += len(self)
@@ -291,7 +291,7 @@ def wrapper(f):
     @wraps(f)
     def __setitem__(self, index, event):
         if isinstance(index, slice):
-            for _index, _event in zip(xrange(*index.indices(len(self))), event):
+            for _index, _event in zip(range(*index.indices(len(self))), event):
                 self[_index] = _event
         if index < 0:
             index += len(self)

src/py/statiskit/core/indicator.py

@@ -88,10 +88,10 @@ def location_estimation(algo='mean', data="univariate", **kwargs):
         try:
             algo = mapping[algo]()
         except KeyError:
-            raise ValueError('\'algo\' parameter, possible values are ' + ', '.join('"' + algo + '"' for algo in mapping.iterkeys()))
+            raise ValueError('\'algo\' parameter, possible values are ' + ', '.join('"' + algo + '"' for algo in mapping.keys()))
         except:
             raise
-        for attr in kwargs.keys():
+        for attr in list(kwargs.keys()):
             if hasattr(algo, attr):
                 setattr(algo, attr, kwargs.pop(attr))
         if data:
@@ -103,10 +103,10 @@ def location_estimation(algo='mean', data="univariate", **kwargs):
         try:
             algo = mapping[algo]()
         except KeyError:
-            raise ValueError('\'algo\' parameter, possible values are ' + ', '.join('"' + algo + '"' for algo in mapping.iterkeys()))
+            raise ValueError('\'algo\' parameter, possible values are ' + ', '.join('"' + algo + '"' for algo in mapping.keys()))
         except:
             raise
-        for attr in kwargs.keys():
+        for attr in list(kwargs.keys()):
             if hasattr(algo, attr):
                 setattr(algo, attr, kwargs.pop(attr))
         if isinstance(data, UnivariateData):
@@ -145,10 +145,10 @@ def dispersion_estimation(algo='variance', data="univariate", **kwargs):
         try:
             algo = mapping[algo]()
         except KeyError:
-            raise ValueError('\'algo\' parameter, possible values are ' + ', '.join('"' + algo + '"' for algo in mapping.iterkeys()))
+            raise ValueError('\'algo\' parameter, possible values are ' + ', '.join('"' + algo + '"' for algo in mapping.keys()))
         except:
             raise
-        for attr in kwargs.keys():
+        for attr in list(kwargs.keys()):
             if hasattr(algo, attr):
                 setattr(algo, attr, kwargs.pop(attr))
         if data:
@@ -160,10 +160,10 @@ def dispersion_estimation(algo='variance', data="univariate", **kwargs):
         try:
             algo = mapping[algo]()
         except KeyError:
-            raise ValueError('\'algo\' parameter, possible values are ' + ', '.join('"' + algo + '"' for algo in mapping.iterkeys()))
+            raise ValueError('\'algo\' parameter, possible values are ' + ', '.join('"' + algo + '"' for algo in mapping.keys()))
         except:
             raise
-        for attr in kwargs.keys():
+        for attr in list(kwargs.keys()):
             if hasattr(algo, attr):
                 setattr(algo, attr, kwargs.pop(attr))
         if isinstance(data, UnivariateData):

src/py/statiskit/core/io.py

@@ -13,7 +13,7 @@
 def read_csv(filepath, sep=None, header=False, **kwargs):
     """
     """
-    if sep and not isinstance(sep, basestring):
+    if sep and not isinstance(sep, str):
         raise TypeError('\'sep\' parameter')
     with open(filepath, 'r') as filehandler:
         lines = filehandler.readlines()
@@ -39,7 +39,7 @@ def write_csv(data, filepath, sep=' ', header=False, censored=True):
     """
     if not isinstance(data, MultivariateDataFrame):
         raise TypeError('\'data\' parameter')
-    if not isinstance(sep, basestring):
+    if not isinstance(sep, str):
         raise TypeError('\'sep\' parameter')
     with open(filepath, 'w') as filehandler:
         if header:

src/py/statiskit/core/sample_space.py

@@ -55,7 +55,7 @@ def __call__(self, event):
     """
     if event is None or isinstance(event, CategoricalEvent):
         return event
-    elif isinstance(event, basestring):
+    elif isinstance(event, str):
         event = event.strip()
         if event in ['', '?']:
             return None
@@ -105,7 +105,7 @@ def getter(self):
 
     @wraps(g)
     def setter(self, encoding):
-        if isinstance(encoding, basestring):
+        if isinstance(encoding, str):
             encoding = encoding_type.names[encoding.upper()]
         g(self, encoding)
 
@@ -145,7 +145,7 @@ def __call__(self, event):
         return event
     elif isinstance(event, int):
         return DiscreteElementaryEvent(event)
-    elif isinstance(event, basestring):
+    elif isinstance(event, str):
         event = event.replace('\n', '').strip(' ')
         if event in ['', '?']:
             return None
@@ -244,7 +244,7 @@ def __call__(self, event):
             return ContinuousElementaryEvent(event)
         else:
             return None
-    elif isinstance(event, basestring):
+    elif isinstance(event, str):
         event = event.replace('\n', '').strip(' ')
         if event in ['', '?']:
             return None

src/py/statiskit/core/singular.py

@@ -21,7 +21,7 @@ def probability(self, *events, **kwargs):
         if not isinstance(event, MultivariateEvent):
             event = VectorEvent(len(events))
             for index, component in enumerate(events):
-                if isinstance(component, basestring):
+                if isinstance(component, str):
                     event[index] = CategoricalElementaryEvent(component)
                 elif isinstance(component, int):
                     event[index] = DiscreteElementaryEvent(component)