Removed deprecation warnings from 0.7.0

elephant/__init__.py

@@ -28,7 +28,6 @@
                gpfa)
 
 try:
-    from . import pandas_bridge
     from . import asset
     from . import spade
 except ImportError:

elephant/spike_train_generation.py

@@ -412,20 +412,6 @@ def interval_generator_refractory(size):
     return spiketrain
 
 
-def homogeneous_poisson_process_with_refr_period(rate,
-                                                 refr_period=2. * pq.ms,
-                                                 t_start=0.0 * pq.ms,
-                                                 t_stop=1000.0 * pq.ms,
-                                                 as_array=False):
-    warnings.warn("homogeneous_poisson_process_with_refr_period() function is "
-                  "deprecated and will be deleted in v0.8.0 release. Use "
-                  "homogeneous_poisson_process(refractory_period=...) instead",
-                  DeprecationWarning)
-    return homogeneous_poisson_process(rate=rate, t_start=t_start,
-                                       t_stop=t_stop, as_array=as_array,
-                                       refractory_period=refr_period)
-
-
 def inhomogeneous_poisson_process(rate, as_array=False,
                                   refractory_period=None):
     """

elephant/test/test_spike_train_generation.py

@@ -288,25 +288,6 @@ def test_invalid(self):
         # no units provided for refractory_period
         self.assertRaises(ValueError, hpp, rate=rate, refractory_period=2)
 
-    def test_deprecated_homogeneous_poisson_process_with_refr_period(self):
-        # TODO: remove in v0.8.0
-        rate = 10 * Hz
-        t_start = 17 * ms
-        t_stop = 2 * s
-        refractory_period = 3 * ms
-        np.random.seed(28)
-        spiketrain = stgen.homogeneous_poisson_process(
-                rate=rate, t_start=t_start, t_stop=t_stop,
-                refractory_period=refractory_period)
-        np.random.seed(28)
-        spiketrain_depr = stgen.homogeneous_poisson_process_with_refr_period(
-            rate=rate, refr_period=refractory_period, t_start=t_start,
-            t_stop=t_stop
-        )
-        assert_array_almost_equal(spiketrain_depr.times, spiketrain.times)
-        self.assertAlmostEqual(spiketrain_depr.t_start, spiketrain.t_start)
-        self.assertAlmostEqual(spiketrain_depr.t_stop, spiketrain.t_stop)
-
 
 class InhomogeneousPoissonProcessTestCase(unittest.TestCase):
     def setUp(self):

elephant/test/test_unitary_event_analysis.py

@@ -115,17 +115,6 @@ def setUp(self):
                                      [0, 1, 1, 1, 1],
                                      [1, 1, 0, 1, 0]]])
 
-    @unittest.skipUnless(python_version_major == 3, "assertWarns requires 3.2")
-    def test_deprecated_N(self):
-        n_patterns = 10
-        m = np.random.randint(low=0, high=2, size=(n_patterns, 2))
-        with self.assertWarns(DeprecationWarning):
-            # check *args
-            h = ue.hash_from_pattern(m, n_patterns)
-        with self.assertWarns(DeprecationWarning):
-            # check **kwargs
-            h = ue.hash_from_pattern(m, N=n_patterns)
-
     def test_hash_default(self):
         m = np.array([[0, 0, 0], [1, 0, 0], [0, 1, 0], [0, 0, 1], [1, 1, 0],
                       [1, 0, 1], [0, 1, 1], [1, 1, 1]])

elephant/unitary_event_analysis.py

@@ -55,7 +55,6 @@
 
 import sys
 import warnings
-from functools import wraps
 
 import neo
 import numpy as np
@@ -66,26 +65,6 @@
 from elephant.utils import is_binary
 
 
-def decorate_deprecated_N(func):
-    @wraps(func)
-    def decorated_func(*args, **kwargs):
-        N = None
-        if 'N' in kwargs:
-            N = kwargs.pop('N')
-        elif len(args) > 1 and isinstance(args[1], int):
-            args = list(args)
-            N = args.pop(1)
-        if N is not None:
-            warnings.warn("'N' is deprecated in '{func_name}' and will be "
-                          "removed in the next Elephant release. Now 'N' is "
-                          "extracted from the data shape.".format(
-                              func_name=func.__name__), DeprecationWarning)
-        return func(*args, **kwargs)
-
-    return decorated_func
-
-
-@decorate_deprecated_N
 def hash_from_pattern(m, base=2):
     """
     Calculate for a spike pattern or a matrix of spike patterns
@@ -208,7 +187,6 @@ def inverse_hash_from_pattern(h, N, base=2):
     return m
 
 
-@decorate_deprecated_N
 def n_emp_mat(mat, pattern_hash, base=2):
     """
     Count the occurrences of spike coincidence patterns in the given spike
@@ -267,7 +245,6 @@ def n_emp_mat(mat, pattern_hash, base=2):
     return N_emp, indices
 
 
-@decorate_deprecated_N
 def n_emp_mat_sum_trial(mat, pattern_hash):
     """
     Calculate empirical number of observed patterns, summed across trials.
@@ -336,7 +313,6 @@ def n_emp_mat_sum_trial(mat, pattern_hash):
     return N_emp, idx_trials
 
 
-@decorate_deprecated_N
 def _n_exp_mat_analytic(mat, pattern_hash):
     """
     Calculates the expected joint probability for each spike pattern
@@ -357,7 +333,6 @@ def _n_exp_mat_analytic(mat, pattern_hash):
     return np.prod(pmat, axis=0) * float(mat.shape[1])
 
 
-@decorate_deprecated_N
 def _n_exp_mat_surrogate(mat, pattern_hash, n_surr=1):
     """
     Calculates the expected joint probability for each spike pattern with spike
@@ -374,7 +349,6 @@ def _n_exp_mat_surrogate(mat, pattern_hash, n_surr=1):
     return N_exp_array
 
 
-@decorate_deprecated_N
 def n_exp_mat(mat, pattern_hash, method='analytic', n_surr=1):
     """
     Calculates the expected joint probability for each spike pattern.
@@ -682,7 +656,6 @@ def _winpos(t_start, t_stop, winsize, winstep, position='left-edge'):
     return ts_winpos
 
 
-@decorate_deprecated_N
 def _UE(mat, pattern_hash, method='analytic_TrialByTrial', n_surr=1):
     """
     Return the default results of unitary events analysis