============= Release Notes ============= Elephant 0.10.0 release notes ============================= Documentation ------------- The documentation is revised and restructured by categories (https://github.com/NeuralEnsemble/elephant/pull/386) to simplify navigation on readthedocs and improve user experience. All citations used in Elephant are stored in a single [BibTex file](https://github.com/NeuralEnsemble/elephant/blob/master/doc/bib/elephant.bib). Optimizations ------------- CUDA and OpenCL support *********************** [Analysis of Sequences of Synchronous EvenTs](https://elephant.readthedocs.io/en/latest/reference/asset.html) has become the first module in Elephant that supports CUDA and OpenCL (https://github.com/NeuralEnsemble/elephant/pull/351, https://github.com/NeuralEnsemble/elephant/pull/404, https://github.com/NeuralEnsemble/elephant/pull/399). Whether you have an Nvidia GPU or just run the analysis on a laptop with a built-in Intel graphics card, the speed-up is **X100** and **X1000** compared to a single CPU core. The computations are optimized to a degree that you can analyse and look for spike patterns in real data in several minutes of compute time on a laptop. The installation instructions are described in the [install](https://elephant.readthedocs.io/en/latest/install.html) section. Other optimizations ******************* * Surrogates: sped up bin shuffling (https://github.com/NeuralEnsemble/elephant/pull/400) and reimplemented the continuous time version (https://github.com/NeuralEnsemble/elephant/pull/397) * Improved memory efficiency of creating a BinnedSpikeTrain (https://github.com/NeuralEnsemble/elephant/pull/395) New functionality and features ------------------------------ * Synchrofact detection (https://github.com/NeuralEnsemble/elephant/pull/322) is a method to detect highly synchronous spikes (at the level of sampling rate precision with an option to extend this to jittered synchrony) and annotate or optionally remove them. * Added `phase_locking_value`, `mean_phase_vector`, and `phase_difference` functions (https://github.com/NeuralEnsemble/elephant/pull/385/files) * BinnedSpikeTrain: - added `to_spike_trains` and `time_slice` functions (https://github.com/NeuralEnsemble/elephant/pull/390). Now you can slice a binned spike train as `bst[:, i:j]` or `bst.time_slice(t_start, t_stop)`. Also, with `to_spike_trains` function, you can generate a realization of spike trains that maps to the same BinnedSpikeTrain object when binned. - optional CSC format (https://github.com/NeuralEnsemble/elephant/pull/402) - the `copy` parameter (False by default) in the `binarize` function makes a *shallow* copy, if set to True, of the output BinnedSpikeTrain object (https://github.com/NeuralEnsemble/elephant/pull/402) * Granger causality tutorial notebook (https://github.com/NeuralEnsemble/elephant/pull/393) * Unitary Event Analysis support multiple pattern hashes (https://github.com/NeuralEnsemble/elephant/pull/387) Bug fixes --------- * Account for unidirectional spiketrain->segment links in synchrofact deletion (https://github.com/NeuralEnsemble/elephant/pull/398) * Joint-ISI dithering: fixed a bug regarding first ISI bin (https://github.com/NeuralEnsemble/elephant/pull/396) * Fix LvR values from being off when units are in seconds (https://github.com/NeuralEnsemble/elephant/pull/389) 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 **************************** New features ------------ * The `parallel` module is a new experimental module (https://github.com/NeuralEnsemble/elephant/pull/307) to run python functions concurrently. Supports native (pythonic) ProcessPollExecutor and MPI. Not limited to Elephant functional. * Added an optional `refractory_period` argument, set to None by default, to `dither_spikes` function (https://github.com/NeuralEnsemble/elephant/pull/297). * Added `cdf` and `icdf` functions in Kernel class to correctly estimate the median index, needed for `instantaneous_rate` function in statistics.py (https://github.com/NeuralEnsemble/elephant/pull/313). * Added an optional `center_kernel` argument, set to True by default (to behave as in Elephant <0.8.0 versions) to `instantaneous_rate` function in statistics.py (https://github.com/NeuralEnsemble/elephant/pull/313). New tutorials ------------- * Analysis of Sequences of Synchronous EvenTs (ASSET) tutorial: https://elephant.readthedocs.io/en/latest/tutorials/asset.html * Parallel module tutorial: https://elephant.readthedocs.io/en/latest/tutorials/parallel.html Optimization ------------ * Optimized ASSET runtime by a factor of 10 and more (https://github.com/NeuralEnsemble/elephant/pull/259, https://github.com/NeuralEnsemble/elephant/pull/333). Python 2.7 and 3.5 deprecation ------------------------------ Python 2.7 and 3.5 are deprecated and will not be maintained by the end of 2020. Switch to Python 3.6+. 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 **************************** 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 **************************** This release has been made for the "1st Elephant User Workshop" (https://www.humanbrainproject.eu/en/education/participatecollaborate/infrastructure-events-trainings/1st-elephant-user-workshop-accelerate-structured-and-reproducibl). Main features ------------- * neo v0.8.0 compatible New modules ----------- * GPFA - Gaussian-process factor analysis - dimensionality reduction method for neural trajectory visualization (https://github.com/NeuralEnsemble/elephant/pull/233). _Note: the API could change in the future._ Bug fixes --------- * [signal processing] Keep `array_annotations` in the output of signal processing functions (https://github.com/NeuralEnsemble/elephant/pull/258). * [SPADE] Fixed the calculation of the duration of a pattern in the output (https://github.com/NeuralEnsemble/elephant/pull/254). * [statistics] Fixed automatic kernel selection yields incorrect values (https://github.com/NeuralEnsemble/elephant/pull/246). Improvements ------------ * Vectorized `spike_time_tiling_coefficient()` function - got rid of a double for-loop (https://github.com/NeuralEnsemble/elephant/pull/244) * Reduced the number of warnings during the tests (https://github.com/NeuralEnsemble/elephant/pull/238). * Removed unused debug code in `spade/fast_fca.py` (https://github.com/NeuralEnsemble/elephant/pull/249). * Improved doc string of `covariance()` and `corrcoef()` (https://github.com/NeuralEnsemble/elephant/pull/260). Elephant 0.6.3 release notes **************************** July 22nd 2019 The release v0.6.3 is mostly about improving maintenance. New functions ------------- * `waveform_features` module * Waveform signal-to-noise ratio (https://github.com/NeuralEnsemble/elephant/pull/219). * Added support for Butterworth `sosfiltfilt` - numerically stable (in particular, higher order) filtering (https://github.com/NeuralEnsemble/elephant/pull/234). Bug fixes --------- * Fixed neo version typo in requirements file (https://github.com/NeuralEnsemble/elephant/pull/218) * Fixed broken docs (https://github.com/NeuralEnsemble/elephant/pull/230, https://github.com/NeuralEnsemble/elephant/pull/232) * Fixed issue with 32-bit arch (https://github.com/NeuralEnsemble/elephant/pull/229) Other changes ------------- * Added issue templates (https://github.com/NeuralEnsemble/elephant/pull/226) * Single VERSION file (https://github.com/NeuralEnsemble/elephant/pull/231) Elephant 0.6.2 release notes **************************** April 23rd 2019 New functions ------------- * `signal_processing` module * New functions to calculate the area under a time series and the derivative of a time series. Other changes ------------- * Added support to initialize binned spike train representations with a matrix * Multiple bug fixes Elephant 0.6.1 release notes **************************** April 1st 2019 New functions ------------- * `signal_processing` module * New function to calculate the cross-correlation function for analog signals. * `spade` module * Spatio-temporal spike pattern detection now includes the option to assess significance also based on time-lags of patterns, in addition to patterns size and frequency (referred to as 3D pattern spectrum). Other changes ------------- * This release fixes a number of compatibility issues in relation to API breaking changes in the Neo library. * Fixed error in STTC calculation (spike time tiling coefficient) * Minor bug fixes Elephant 0.6.0 release notes **************************** October 12th 2018 New functions ------------- * `cell_assembly_detection` module * New function to detect higher-order correlation structures such as patterns in parallel spike trains based on Russo et al, 2017. * **wavelet_transform()** function in `signal_prosessing.py` module * Function for computing wavelet transform of a given time series based on Le van Quyen et al. (2001) Other changes ------------- * Switched to multiple `requirements.txt` files which are directly read into the `setup.py` * `instantaneous_rate()` accepts now list of spiketrains * Minor bug fixes Elephant 0.5.0 release notes **************************** April 4nd 2018 New functions ------------- * `change_point_detection` module: * New function to detect changes in the firing rate * `spike_train_correlation` module: * New function to calculate the spike time tiling coefficient * `phase_analysis` module: * New function to extract spike-triggered phases of an AnalogSignal * `unitary_event_analysis` module: * Added new unit test to the UE function to verify the method based on data of a recent [Re]Science publication Other changes ------------- * Minor bug fixes Elephant 0.4.3 release notes **************************** March 2nd 2018 Other changes ------------- * Bug fixes in `spade` module: * Fixed an incompatibility with the latest version of an external library Elephant 0.4.2 release notes **************************** March 1st 2018 New functions ------------- * `spike_train_generation` module: * **inhomogeneous_poisson()** function * Modules for Spatio Temporal Pattern Detection (SPADE) `spade_src`: * Module SPADE: `spade.py` * Module `statistics.py`: * Added CV2 (coefficient of variation for non-stationary time series) * Module `spike_train_correlation.py`: * Added normalization in **cross-correlation histogram()** (CCH) Other changes ------------- * Adapted the `setup.py` to automatically install the spade modules including the compiled `C` files `fim.so` * Included testing environment for MPI in `travis.yml` * Changed function arguments in `current_source_density.py` to `neo.AnalogSignal` instead list of `neo.AnalogSignal` objects * Fixes to travis and setup configuration files * Fixed bug in ISI function `isi()`, `statistics.py` module * Fixed bug in `dither_spikes()`, `spike_train_surrogates.py` * Minor bug fixes Elephant 0.4.1 release notes **************************** March 23rd 2017 Other changes ------------- * Fix in `setup.py` to correctly import the current source density module Elephant 0.4.0 release notes **************************** March 22nd 2017 New functions ------------- * `spike_train_generation` module: * peak detection: **peak_detection()** * Modules for Current Source Density: `current_source_density_src` * Module Current Source Density: `KCSD.py` * Module for Inverse Current Source Density: `icsd.py` API changes ----------- * Interoperability between Neo 0.5.0 and Elephant * Elephant has adapted its functions to the changes in Neo 0.5.0, most of the functionality behaves as before * See Neo documentation for recent changes: http://neo.readthedocs.io/en/latest/whatisnew.html Other changes ------------- * Fixes to travis and setup configuration files. * Minor bug fixes. * Added module `six` for Python 2.7 backwards compatibility Elephant 0.3.0 release notes **************************** April 12st 2016 New functions ------------- * `spike_train_correlation` module: * cross correlation histogram: **cross_correlation_histogram()** * `spike_train_generation` module: * single interaction process (SIP): **single_interaction_process()** * compound Poisson process (CPP): **compound_poisson_process()** * `signal_processing` module: * analytic signal: **hilbert()** * `sta` module: * spike field coherence: **spike_field_coherence()** * Module to represent kernels: `kernels` module * Spike train metrics / dissimilarity / synchrony measures: `spike_train_dissimilarity` module * Unitary Event (UE) analysis: `unitary_event_analysis` module * Analysis of Sequences of Synchronous EvenTs (ASSET): `asset` module API changes ----------- * Function **instantaneous_rate()** now uses kernels as objects defined in the `kernels` module. The previous implementation of the function using the `make_kernel()` function is deprecated, but still temporarily available as `oldfct_instantaneous_rate()`. Other changes ------------- * Fixes to travis and readthedocs configuration files. Elephant 0.2.1 release notes **************************** February 18th 2016 Other changes ------------- Minor bug fixes. Elephant 0.2.0 release notes **************************** September 22nd 2015 New functions ------------- * Added covariance function **covariance()** in the `spike_train_correlation` module * Added complexity pdf **complexity_pdf()** in the `statistics` module * Added spike train extraction from analog signals via threshold detection the in `spike_train_generation` module * Added **coherence()** function for analog signals in the `spectral` module * Added **Cumulant Based Inference for higher-order of Correlation (CuBIC)** in the `cubic` module for correlation analysis of parallel recorded spike trains API changes ----------- * **Optimized kernel bandwidth** in `rate_estimation` function: Calculates the optimized kernel width when the paramter kernel width is specified as `auto` Other changes ------------- * **Optimized creation of sparse matrices**: The creation speed of the sparse matrix inside the `BinnedSpikeTrain` class is optimized * Added **Izhikevich neuron simulator** in the `make_spike_extraction_test_data` module * Minor improvements to the test and continous integration infrastructure