# pynacollada 🍍 🥥 🍹
Collaborative platform for high-level analysis with pynapple. 


- [pynacollada](#pynacollada)
  * [Getting Started](#getting-started)
    + [Requirements](#requirements)
    + [Installation](#installation)
  * [Libraries](#libraries)
    + [Neural Ensembles](#neural-ensembles)
    + [Manifolds](#manifolds)
    + [EEG processing](#eeg-processing)
    + [PETH](#peth)
    + [Brain state scoring](#brain-state-scoring)
    + [Graphics](#graphics)
    + [Position tracking](#position-tracking)

> **Note**
> See the analysis of the pynapple paper [here](https://github.com/PeyracheLab/pynacollada/tree/main/pynacollada/Pynapple%20Paper%20Figures)


## Getting Started


### Requirements

-   Python 3.6+
-   [pynapple](https://github.com/PeyracheLab/pynapple)
-   scikit-learn
-   seaborn

### Installation

<!-- pynacco can be installed with pip:

``` {.sourceCode .shell}
$ pip install pynapple==0.2.0a1
```
 -->
Directly from the source code:

``` {.sourceCode .shell}
$ # clone the repository
$ git clone https://github.com/PeyracheLab/pynacollada.git
$ cd pynacollada
$ # Install in editable mode with `-e` or, equivalently, `--editable`
$ pip install -e .
```

## Libraries

|                         | **Jupyter notebook / scripts**                                                                                                                                                                                             | **Description**                                                                                          | **Contributors** |
|-------------------------|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|----------------------------------------------------------------------------------------------------------|------------------|
| **_Neural Ensembles_**  | [Neuralensemble_tutorial_replay1.ipynb](pynacollada/neural_ensemble/Neuralensemble_tutorial_replay1.ipynb)  <br/> [pynacollada_tutorial_replay_short.py](pynacollada/neural_ensemble/pynacollada_tutorial_replay_short.py) | How to use pynapple to compute sleep reactivation, step by step.                                         | Adrien Peyrache  |
| **_Manifolds_**         | [Tutorial_manifold_ring.ipynb](pynacollada/neural_manifold/Tutorial_manifold_ring.ipynb)                                                                                                                                   | How to project a ring manifold with head-direction neurons.                                              | Guillaume Viejo  |
| **_EEG processing_**    | [Tutorial_ripple_detection.ipynb](pynacollada/eeg_processing/Tutorial_ripple_detection.ipynb) <br/> [Tutorial_ripple_detection.py](pynacollada/eeg_processing/Tutorial_ripple_detection.ipynb)                             | How to detect ripples in CA1 step by steps.                                                              | Guillaume Viejo  |
| **_PETH_**              | [Tutorial_PETH_Ripples.ipynb](pynacollada/PETH/Tutorial_PETH_Ripples.ipynb)                                                                                                                                                | How to make a peri-event time histogramm and raster plots around ripples.                                | Guillaume Viejo  |
| **_Position tracking_** | [Tutorial_DeepLabCut_Path_Segmentation.ipynb](pynacollada/position_tracking/DLC_process_position.ipynb)                                                                                                                    | How to segment the path of a mouse running in a radial-arm maze with position extracted with DeepLabCut. | Dhruv Mehrotra   |
| **_Waveform processing_** | [load_mean_waveforms.ipynb](pynacollada/waveform_processing/load_mean_waveforms.ipynb)                                                                                                                    | How to extract the mean waveforms from a binary file. | Sofia Skromne Carrasco   |