[Feature] Trial handling in Elephant #579
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added 30 commits
July 8, 2022 14:48
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Hello @Moritz-Alexander-Kern! Thanks for updating this PR. We checked the lines you've touched for PEP 8 issues, and found:
Comment last updated at 2023-10-31 09:19:48 UTC |
…_spike_trains = False returns one analog signal
…ts class returns a neo SpikeTrainList
…e/trial_class # Conflicts: # elephant/trials.py
…nd pool_spike_trains=True
…=True and pool_spike_trains=False
Moritz-Alexander-Kern
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NeuralEnsemble:master
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This PR is the initial contribution to introduce a trial handling functionality into Elephant.
The PR includes the following changes:
Meta class:
Trials: This meta class is responsible for defining the behavior and structure of the trial objects in Elephant. It includes methods and attributes related to handling and manipulating trials.Implementations:
TrialsFromBlockandTrialsFromLists: These two implementations are specific ways to createTrialsobjects from different data structures.TrialsFromBlockmay create trial objects from a neo.block of data, whileTrialsFromListscreates trial objects from lists of data.Modification to
elephant.statistics.instantaneous_rate: Theinstantaneous_ratefunction in the Elephant library is modified to accept aTrialsobject as input. This modification enables the calculation of firing rates based on the values ofcross_spiketrainandcross_trial. The firing rate calculation can now be averaged over trials or spiketrains within a trial, or both.Overall, this PR adds trial handling functionality to the Elephant library, allowing users to work with trials and for now calculate averaged firing rates .