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Update references to new SpikeInterface/MEArec
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README.md

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[![Build Status](https://github.com/alejoe91/MEArec/actions/workflows/python-package.yml/badge.svg)](https://github.com/alejoe91/MEArec/actions/workflows/python-package.yml/badge.svg) [![PyPI version](https://badge.fury.io/py/MEArec.svg)](https://badge.fury.io/py/MEArec)
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[![Build Status](https://github.com/SpikeInterface/MEArec/actions/workflows/python-package.yml/badge.svg)](https://github.com/SpikeInterface/MEArec/actions/workflows/python-package.yml/badge.svg) [![PyPI version](https://badge.fury.io/py/MEArec.svg)](https://badge.fury.io/py/MEArec)
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# MEArec: Fast and customizable simulation of extracellular recordings on Multi-Electrode-Arrays
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MEArec is a package for generating biophysical extracellular neural recording on Multi-Electrode Arrays (MEA). The recording generations combines a Extracellular Action Potentials (EAP) templates generation and spike trains generation. The recordings are built by convoluting and modulating EAP templates with spike trains and adding noise.
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To clone this repo open your terminal and run:
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`git clone https://github.com/alejoe91/MEArec.git`
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`git clone https://github.com/SpikeInterface/MEArec.git`
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## Installation
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docs/generate_recordings.rst

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the excitatory or inhibitory set. In this example from L5 cells, all cells contining LBC (Large Basket Cells) will be
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marked as inhibitory, and so on.
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If you use custom cell models, you should overwrite this section as shown in
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`this notebook <https://github.com/alejoe91/MEArec/blob/master/notebooks/generate_recordings_with_allen_models.ipynb>`_
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`this notebook <https://github.com/SpikeInterface/MEArec/blob/master/notebooks/generate_recordings_with_allen_models.ipynb>`_
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using cell models from `Allen database <https://celltypes.brain-map.org/>`_.
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Cell-types parameters section summary

docs/installation.rst

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.. code-block:: python
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git clone https://github.com/alejoe91/MEArec
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git clone https://github.com/SpikeInterface/MEArec
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cd MEArec
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pip install -e .
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docs/usage.rst

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* spike_traces: (n_spiketrains, n_samples) clean spike trace for each spike train
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* info: dictionary with parameters used
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There are several notebooks available `here <https://github.com/alejoe91/MEArec/tree/master/notebooks>`_
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There are several notebooks available `here <https://github.com/SpikeInterface/MEArec/tree/master/notebooks>`_
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that show MEArec applications.
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Plotting

pyproject.toml

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[project.urls]
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homepage = "https://github.com/alejoe91/MEArec"
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repository = "https://github.com/alejoe91/MEArec"
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homepage = "https://github.com/SpikeInterface/MEArec"
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repository = "https://github.com/SpikeInterface/MEArec"
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documentation = "https://mearec.readthedocs.io/"
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