release notes of v0.7.0 and deprecation warnings (#300)

Author: dizcza

doc/conf.py

@@ -161,7 +161,7 @@
 # Add any paths that contain custom static files (such as style sheets) here,
 # relative to this directory. They are copied after the builtin static files,
 # so a file named "default.css" will overwrite the builtin "default.css".
-html_static_path = ['_static']
+# html_static_path = ['_static']
 
 # If not '', a 'Last updated on:' timestamp is inserted at every page bottom,
 # using the given strftime format.

doc/release_notes.rst

@@ -2,6 +2,52 @@
 Release Notes
 *************
 
+Elephant 0.7.0 release notes
+============================
+
+Breaking changes
+----------------
+* [gpfa] GPFA dimensionality reduction method is rewritten in easy-to-use scikit-learn class style format (https://github.com/NeuralEnsemble/elephant/pull/287):
+
+.. code-block:: python
+
+    gpfa = GPFA(bin_size=20*pq.ms, x_dim=8)
+    results = gpfa.fit_transform(spiketrains, returned_data=['xorth', 'xsm'])
+
+New tutorials
+-------------
+* GPFA dimensionality reduction method: https://elephant.readthedocs.io/en/latest/tutorials/gpfa.html
+* Unitary Event Analysis of coordinated spiking activity: https://elephant.readthedocs.io/en/latest/tutorials/unitary_event_analysis.html
+* (Introductory) statistics module: https://elephant.readthedocs.io/en/latest/tutorials/statistics.html
+
+Deprecations
+------------
+* **Python 2.7 support will be dropped on Dec 31, 2020.** Please switch to Python 3.6, 3.7, or 3.8.
+* [spike train generation] `homogeneous_poisson_process_with_refr_period()`, introduced in v0.6.4, is deprecated and will be deleted in v0.8.0. Use `homogeneous_poisson_process(refractory_period=...)` instead.
+* [pandas bridge] pandas\_bridge module is deprecated and will be deleted in v0.8.0.
+
+New features
+------------
+* New documentation style, guidelines, tutorials, and more (https://github.com/NeuralEnsemble/elephant/pull/294).
+* Python 3.8 support (https://github.com/NeuralEnsemble/elephant/pull/282).
+* [spike train generation] Added `refractory_period` flag in `homogeneous_poisson_process()` (https://github.com/NeuralEnsemble/elephant/pull/292) and `inhomogeneous_poisson_process()` (https://github.com/NeuralEnsemble/elephant/pull/295) functions. The default is `refractory_period=None`, meaning no refractoriness.
+* [spike train correlation] `cross_correlation_histogram()` supports different t_start and t_stop of input spiketrains.
+* [waveform features] `waveform_width()` function extracts the width (trough-to-peak TTP) of a waveform (https://github.com/NeuralEnsemble/elephant/pull/279).
+* [signal processing] Added `scaleopt` flag in `pairwise_cross_correlation()` to mimic the behavior of Matlab's `xcorr()` function (https://github.com/NeuralEnsemble/elephant/pull/277). The default is `scaleopt=unbiased` to be consistent with the previous versions of Elephant.
+* [spike train surrogates] Joint-ISI dithering method via `JointISI` class (https://github.com/NeuralEnsemble/elephant/pull/275).
+
+Bug fixes
+---------
+* [spike train correlation] Fix CCH Border Correction (https://github.com/NeuralEnsemble/elephant/pull/298). Now, the border correction in `cross_correlation_histogram()` correctly reflects the number of bins used for the calculation at each lag. The correction factor is now unity at full overlap.
+* [phase analysis] `spike_triggered_phase()` incorrect behavior when the spike train and the analog signal had different time units (https://github.com/NeuralEnsemble/elephant/pull/270).
+
+Performance
+-----------
+* [spade] SPADE x7 speedup (https://github.com/NeuralEnsemble/elephant/pull/280, https://github.com/NeuralEnsemble/elephant/pull/285, https://github.com/NeuralEnsemble/elephant/pull/286). Moreover, SPADE is now able to handle all surrogate types that are available in Elephant, as well as more types of statistical corrections.
+* [conversion] Fast & memory-efficient `covariance()` and Pearson `corrcoef()` (https://github.com/NeuralEnsemble/elephant/pull/274). Added flag `fast=True` by default in both functions.
+* [conversion] Use fast fftconvolve instead of np.correlate in `cross_correlation_histogram()` (https://github.com/NeuralEnsemble/elephant/pull/273).
+
+
 Elephant 0.6.4 release notes
 ============================
 

elephant/spike_train_generation.py

@@ -412,6 +412,20 @@ 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

@@ -288,6 +288,25 @@ 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):