Release v0.9.0 (NeuralEnsemble#379)

* added viziphant info

* don't test deprecated ChannelIndex

* added python 3.9 support

* neo 0.9; python 3 only

.gitignore

@@ -82,3 +82,6 @@ ignored/
 
 # data
 *.nix
+
+# neo logs
+**/logs

.travis.yml

@@ -9,19 +9,6 @@ addons:
 
 matrix:
   include:
-    - name: "conda 2.7"
-      python: 2.7
-      env: DISTRIB="conda"
-      before_install: sed -i 's/conda-forge/conda/g' requirements/environment.yml
-
-    - name: "pip 2.7"
-      python: 2.7
-      env: DISTRIB="pip"
-
-    - name: "pip 3.5"
-      python: 3.5
-      env: DISTRIB="pip"
-
     - name: "pip 3.6 requirements-extras"
       python: 3.6
       env: DISTRIB="pip"
@@ -40,8 +27,8 @@ matrix:
       python: 3.8
       env: DISTRIB="conda"
 
-    - name: "pip 3.8"
-      python: 3.8
+    - name: "pip 3.9"
+      python: 3.9
       env: DISTRIB="pip"
 
     - name: "docs"
@@ -60,8 +47,7 @@ matrix:
 install:
   - if [[ "${DISTRIB}" == "conda" ]];
     then
-      py_major=${TRAVIS_PYTHON_VERSION:0:1};
-      wget https://repo.continuum.io/miniconda/Miniconda${py_major}-latest-Linux-x86_64.sh -O miniconda.sh;
+      wget https://repo.continuum.io/miniconda/Miniconda3-latest-Linux-x86_64.sh -O miniconda.sh;
       bash miniconda.sh -b -p $HOME/miniconda;
       source "$HOME/miniconda/etc/profile.d/conda.sh";
       conda config --set always_yes yes;

README.md

@@ -1,10 +1,10 @@
 # Elephant - Electrophysiology Analysis Toolkit
 
-![](https://travis-ci.org/NeuralEnsemble/elephant.png?branch=master "Unit Test Status")
-![](https://coveralls.io/repos/NeuralEnsemble/elephant/badge.png "Unit Test Coverage")
-![](https://readthedocs.org/projects/elephant/badge/?version=latest "Documentation Status")
-![](https://img.shields.io/pypi/v/elephant)
-![](https://img.shields.io/pypi/dm/elephant)
+[![Build Status](https://travis-ci.org/NeuralEnsemble/elephant.svg?branch=master)](https://travis-ci.org/NeuralEnsemble/elephant)
+[![Coverage Status](https://coveralls.io/repos/github/NeuralEnsemble/elephant/badge.svg?branch=master)](https://coveralls.io/github/NeuralEnsemble/elephant?branch=master)
+[![Documentation Status](https://readthedocs.org/projects/elephant/badge/?version=latest)](https://elephant.readthedocs.io/en/latest/?badge=latest)
+[![![PyPi]](https://img.shields.io/pypi/v/elephant)](https://pypi.org/project/elephant/)
+[![Statistics](https://img.shields.io/pypi/dm/elephant)](https://seladb.github.io/StarTrack-js/#/preload?r=neuralensemble,elephant)
 [![Gitter](https://badges.gitter.im/python-elephant/community.svg)](https://gitter.im/python-elephant/community?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge)
 
 *Elephant* package analyses all sorts of neurophysiological data:
@@ -18,6 +18,13 @@ spike trains, LFP, analog signals. The input-output data format is either
 * Mailing list: https://groups.google.com/group/neuralensemble
 
 
+#### Visualization of Elephant analysis objects
+
+Viziphant package (https://github.com/INM-6/viziphant) is developed by Elephant
+team for plotting and visualization of the output of Elephant functions in a
+few lines of code.
+
+
 #### License
 
 Modified BSD License, see [LICENSE.txt](LICENSE.txt) for details.

doc/conf.py

@@ -342,8 +342,10 @@
 #epub_tocdup = True
 
 
-# Example configuration for intersphinx: refer to the Python standard library.
-intersphinx_mapping = {'http://docs.python.org/': None}
+# configuration for intersphinx: refer to Viziphant
+intersphinx_mapping = {
+    'viziphant': ('https://viziphant.readthedocs.io/en/latest/', None)
+}
 
 # Use more reliable mathjax source
 mathjax_path = 'https://cdn.mathjax.org/mathjax/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML'

doc/index.rst

@@ -20,6 +20,17 @@ Quantity is a Numpy-wrapper package for handling physical quantities like
 seconds, milliseconds, Hz, volts, etc. Quantity is used in both Neo and
 Elephant.
 
+
+**Visualization of Elephant analysis objects**
+
+`Viziphant <https://viziphant.readthedocs.io/en/latest/>`_ package is developed
+by Elephant team and provides a high-level API to easily generate plots and
+interactive visualizations of neuroscientific data and analysis results.
+The API uses and extends the same structure as in Elephant to ensure intuitive
+usage for scientists that are used to Elephant.
+
+
+
 Table of Contents
 -----------------
 

doc/maintainers_guide.rst

@@ -128,10 +128,12 @@ Making a release
    :file:`doc/modules.rst` and make a file with a short description in
    :file:`doc/reference/<modulename>.rst`.
 
-5. Remove :file:`elephant/spade_src/fim.so`. Otherwise, it'll be included in
+5. Push the commit with release notes and version updated to github.
+
+6. Remove :file:`elephant/spade_src/fim.so`. Otherwise, it'll be included in
    the built package (it should be downloaded at pip install).
 
-6. Build a source package and upload it to PyPi.
+7. Build a source package and upload it to PyPi.
 
    Build a source package (see `Packaging Python Projects
    <https://packaging.python.org/tutorials/packaging-projects/#generating-distribution-archives>`_)::
@@ -144,7 +146,7 @@ Making a release
 
     $ python -m twine upload dist/elephant-X.Y.Z.tar.gz
 
-7. Finally, make a release on GitHub UI page and copy-paste the release notes.
+8. Finally, make a release on GitHub UI page and copy-paste the release notes.
    Then tag the release in the Git repository and push it::
 
     $ git tag <version>

doc/reference/conversion.rst

@@ -3,4 +3,3 @@ BinnedSpikeTrain (conversion)
 =============================
 
 .. automodule:: elephant.conversion
-   :members:

doc/reference/gpfa.rst

@@ -3,15 +3,3 @@ Gaussian-Process Factor Analysis (GPFA)
 =======================================
 
 .. automodule:: elephant.gpfa.gpfa
-   :members:
-
-
-Tutorial
---------
-
-:doc:`View tutorial <../tutorials/gpfa>`
-
-Run tutorial interactively:
-
-.. image:: https://mybinder.org/badge.svg
-   :target: https://mybinder.org/v2/gh/NeuralEnsemble/elephant/master?filepath=doc/tutorials/gpfa.ipynb

doc/reference/spade.rst

@@ -3,4 +3,3 @@ Spike Pattern Detection and Evaluation (SPADE)
 ==============================================
 
 .. automodule:: elephant.spade
-   :members:

doc/reference/spike_train_correlation.rst

@@ -9,5 +9,3 @@ Spike train correlation
 
 
 .. automodule:: elephant.spike_train_correlation
-   :members:
-   :exclude-members: cch, sttc

doc/reference/spike_train_dissimilarity.rst

@@ -4,5 +4,3 @@ Spike train dissimilarity
 
 
 .. automodule:: elephant.spike_train_dissimilarity
-   :members:
-

doc/release_notes.rst

@@ -2,6 +2,75 @@
 Release Notes
 *************
 
+
+Elephant 0.9.0 release notes
+============================
+
+This release is titled to accompany the [2nd Elephant User Workshop](https://www.humanbrainproject.eu/en/education/participatecollaborate/infrastructure-events-trainings/2nd-elephant-user-workshop/)
+
+Viziphant
+---------
+Meet Viziphant, the visualization of Elephant analysis methods, at https://viziphant.readthedocs.io/en/latest/. This package provides support to easily plot and visualize the output of Elephant functions in a few lines of code.
+
+Provenance tracking
+-------------------
+Provenance is becoming a separate direction in Elephant. Many things are still to come, and we started with annotating `time_histogram`, `instantaneous_rate` and `cross_correlation_histogram` outputs to carry the information about the parameters these functions used. This allowed Viziphant, the visualization of Elephant analyses, to look for the `.annotations` dictionary of the output of these function to "understand" how the object has been generated and label the plot axes accordingly.
+
+New functionality and features
+------------------------------
+* Time-domain pairwise and conditional pairwise Granger causality measures (https://github.com/NeuralEnsemble/elephant/pull/332, https://github.com/NeuralEnsemble/elephant/pull/359)
+* Spike contrast function that measures the synchrony of spike trains (https://github.com/NeuralEnsemble/elephant/pull/354; thanks to @Broxy7 for bringing this in Elephant).
+* Revised local variability LvR (https://github.com/NeuralEnsemble/elephant/pull/346) as an alternative to the LV measure.
+* Three surrogate methods: Trial-shifting, Bin Shuffling, ISI dithering (https://github.com/NeuralEnsemble/elephant/pull/343).
+* Added a new function to generate spike trains: `inhomogeneous_gamma_process` (https://github.com/NeuralEnsemble/elephant/pull/339).
+* The output of `instantaneous_rate` function is now a 2D matrix of shape `(time, len(spiketrains))` (https://github.com/NeuralEnsemble/elephant/issues/363). Not only can the users assess the averaged instantaneous rate (`rates.mean(axis=1)`) but also explore how much the instantaneous rate deviates from trial to trial (`rates.std(axis=1)`) (originally asked in https://github.com/NeuralEnsemble/elephant/issues/363).
+
+Python 3 only
+-------------
+* Python 2.7 and 3.5 support is dropped. You can still however enjoy the features of Elephant v0.9.0 with Python 2.7 or 3.5 by installing Elephant from [this](https://github.com/NeuralEnsemble/elephant/tree/295c6bd7fea196cf9665a78649fafedab5840cfa) commit `pip install git+https://github.com/NeuralEnsemble/elephant@295c6bd7fea196cf9665a78649fafedab5840cfa#egg=elephant[extras]`
+* Added Python 3.9 support.
+
+Optimization
+------------
+* You have been asking for direct numpy support for years. Added `_t_start`, `_t_stop`, and `_bin_size` attributes of BinnedSpikeTrain are guaranteed to be of the same units and hence are unitless (https://github.com/NeuralEnsemble/elephant/pull/378). It doesn't mean though that you need to care about units on your own: `t_start`, `t_stop`, and `bin_size` properties are still quantities with units. The `.rescale()` method of a BinnedSpikeTrain rescales the internal units to new ones in-place. The following Elephant functions are optimized with unitless BinnedSpikeTrain:
+  - cross_correlation_histogram
+  - bin_shuffling (one of the surrogate methods)
+  - spike_train_timescale
+* X4 faster binning and overall BinnedSpikeTrain object creation (https://github.com/NeuralEnsemble/elephant/pull/368).
+* `instantaneous_rate` function is vectorized to work with a list of spike train trials rather than computing them in a loop (previously, `for spiketrain in spiketrains; do compute instantaneous_rate(spiketrain); done`), which brought X25 speedup (https://github.com/NeuralEnsemble/elephant/pull/362; thanks to @gyyang for the idea and original implementation).
+* Memory-efficient `zscore` function (https://github.com/NeuralEnsemble/elephant/pull/372).
+* Don't sort the input array in ISI function (https://github.com/NeuralEnsemble/elephant/pull/371), which reduces function algorithmic time complexity from `O(N logN)` to linear `O(N)`. Now, when the input time array is not sorted, a warning is shown.
+* Vectorized Current Source Density `generate_lfp` function (https://github.com/NeuralEnsemble/elephant/pull/358).
+
+Breaking changes
+----------------
+* mpi4py package is removed from the extra requirements to allow `pip install elephant[extras]` on machines without MPI installed system-wide. Refer to [MPI support](https://elephant.readthedocs.io/en/latest/install.html#mpi-support) installation page in elephant.
+* BinnedSpikeTrain (https://github.com/NeuralEnsemble/elephant/pull/368, https://github.com/NeuralEnsemble/elephant/pull/377):
+  - previously, when t_start/stop, if set manually, was outside of the shared time interval, only the shared [t_start_shared=max(t_start), t_stop_shared=min(t_stop)] interval was implicitly considered without any warnings. Now an error is thrown with a description on how to fix it.
+  - removed `lst_input`, `input_spiketrains`, `matrix_columns`, `matrix_rows` (in favor of the new attribute - `shape`), `tolerance`, `is_spiketrain`, `is_binned` attributes from BinnedSpikeTrain class. Part of them are confusing (e.g., `is_binned` was just the opposite of `is_spiketrain`, but one can erroneously think that it's data is clipped to 0 and 1), and part of them - `lst_input`, `input_spiketrains` input data - should not have been saved as attributes of an object in the first place because the input spike trains are not used after the sparse matrix is created.
+  - now the users can directly access `.sparse_matrix` attribute of BinnedSpikeTrain to do efficient (yet unsafe in general) operations. For this reason, `to_sparse_array()` function, which does not make a copy, as one could think of, is deprecated.
+* `instantaneous_rate` function (https://github.com/NeuralEnsemble/elephant/pull/362):
+  - in case of multiple input spike trains, the output of the instantaneous rate function is (always) a 2D matrix of shape `(time, len(spiketrains))` instead of a pseudo 1D array (previous behavior) of shape `(time, 1)` that contained the instantaneous rate summed across input spike trains;
+  - in case of multiple input spike trains, the user needs to manually provide the input kernel instead of `auto`, which is set by default, for the reason that it's currently not clear how to estimate the common kernel for a set of spike trains. If you have an idea how to do this, we`d appreciate if you let us know by [getting in touch with us](https://elephant.readthedocs.io/en/latest/get_in_touch.html).
+
+Other changes
+-------------
+* `waveform_snr` function now directly takes a 2D or 3D waveforms matrix rather than a spike train (deprecated behavior).
+* Added a warning in fanofactor function when the input spiketrains vary in their durations (https://github.com/NeuralEnsemble/elephant/pull/341).
+* SPADE: New way to count patterns for multiple testing (https://github.com/NeuralEnsemble/elephant/pull/347)
+* GPFA renamed 'xsm' -> 'latent_variable' and 'xorth' -> 'latent_variable_orth'
+
+Bug fixes
+---------
+* Instantaneous rate arrays were not centered at the origin for spike trains that are symmetric at t=0 with `center_kernel=True` option (https://github.com/NeuralEnsemble/elephant/pull/362).
+* The number of discarded spikes that fall into the last bin of a BinnedSpikeTrain object was incorrectly calculated (https://github.com/NeuralEnsemble/elephant/pull/368).
+* Fixed index selection in `spike_triggered_phase` (https://github.com/NeuralEnsemble/elephant/pull/382)
+* Fixed surrogates bugs:
+  - `joint-ISI` and `shuffle ISI` output spike trains were not sorted in time (https://github.com/NeuralEnsemble/elephant/pull/364);
+  - surrogates get arbitrary sampling_rate (https://github.com/NeuralEnsemble/elephant/pull/353), which relates to the provenance tracking issue;
+
+
+
 Elephant 0.8.0 release notes
 ============================
 
@@ -29,7 +98,6 @@ Breaking changes
 ----------------
 * Naming convention changes (`binsize` -> `bin_size`, etc.) in almost all Elephant functions (https://github.com/NeuralEnsemble/elephant/pull/316).
 
-
 Elephant 0.7.0 release notes
 ============================
 

elephant/VERSION

@@ -1 +1 @@
-0.8.0
+0.9.0

elephant/conversion.py

@@ -6,14 +6,20 @@
 An example is the representation of a spike train as a sequence of 0-1 values
 (binned spike train).
 
+
+.. autosummary::
+    :toctree: toctree/conversion
+
+    BinnedSpikeTrain
+    binarize
+
 :copyright: Copyright 2014-2016 by the Elephant team, see `doc/authors.rst`.
 :license: BSD, see LICENSE.txt for details.
 """
 
 from __future__ import division, print_function, unicode_literals
 
 import warnings
-from copy import deepcopy
 
 import neo
 import numpy as np

elephant/gpfa/gpfa.py

@@ -33,6 +33,31 @@
 3) orthonormalization of the matrix C and the corresponding subspace, for
 visualization purposes: (c.f., `gpfa_core.orthonormalize()`)
 
+
+.. autosummary::
+    :toctree: toctree/gpfa
+
+    GPFA
+
+
+Visualization
+-------------
+Visualization of GPFA transforms is covered in Viziphant:
+https://viziphant.readthedocs.io/en/latest/modules.html
+
+
+Tutorial
+--------
+
+:doc:`View tutorial <../tutorials/gpfa>`
+
+Run tutorial interactively:
+
+.. image:: https://mybinder.org/badge.svg
+   :target: https://mybinder.org/v2/gh/NeuralEnsemble/elephant/master
+            ?filepath=doc/tutorials/gpfa.ipynb
+
+
 References
 ----------
 The code was ported from the MATLAB code based on Byron Yu's implementation.
@@ -59,6 +84,11 @@
 from elephant.utils import deprecated_alias
 
 
+__all__ = [
+    "GPFA"
+]
+
+
 class GPFA(sklearn.base.BaseEstimator):
     """
     Apply Gaussian process factor analysis (GPFA) to spike train data

elephant/spade.py

@@ -3,10 +3,27 @@
 statistical tests to detect and assess the statistical significance of repeated
 occurrences of spike sequences (spatio-temporal patterns, STP).
 
-Given a list of Neo Spiketrain objects, assumed to be recorded in parallel, the
-SPADE analysis can be applied as demonstrated in this short toy example of 10
-artificial spike trains of exhibiting fully synchronous events of order 10.
 
+.. autosummary::
+    :toctree: toctree/spade
+
+    spade
+    concepts_mining
+    pvalue_spectrum
+    test_signature_significance
+    approximate_stability
+    pattern_set_reduction
+    concept_output_to_patterns
+
+
+Visualization
+-------------
+Visualization of SPADE analysis is covered in Viziphant:
+https://viziphant.readthedocs.io/en/latest/modules.html
+
+
+Notes
+-----
 This modules relies on the implementation of the fp-growth algorithm contained
 in the file fim.so which can be found here (http://www.borgelt.net/pyfim.html)
 and should be available in the spade_src folder (elephant/spade_src/).
@@ -17,6 +34,10 @@
 
 Examples
 --------
+Given a list of Neo Spiketrain objects, assumed to be recorded in parallel, the
+SPADE analysis can be applied as demonstrated in this short toy example of 10
+artificial spike trains of exhibiting fully synchronous events of order 10.
+
 >>> from elephant.spade import spade
 >>> import elephant.spike_train_generation
 >>> import quantities as pq