Release (#1)

* set up git LFS

* added RippleNet files

* add conda environment

* fix

* add .DS_Store to ignore list

* pip requirements file

* working on README

* prep release

Co-authored-by: Espen Hagen

.gitattributes

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+*.h5 filter=lfs diff=lfs merge=lfs -text
+*.pkl filter=lfs diff=lfs merge=lfs -text
+*.pdf filter=lfs diff=lfs merge=lfs -text
+*.csv filter=lfs diff=lfs merge=lfs -text

.gitignore

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 # Pyre type checker
 .pyre/
+
+# macos stuff
+.DS_Store

README.md

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-# RippleNet
+# RippleNet
+
+## Description
+
+This repository contains files for RippleNet, a recurrent neural network with LSTM
+layers for detecting sharp-wave ripples in single-channel LFP signals measured
+in hippocampus CA1.
+
+Author: Espen Hagen (https://github.com/espenhgn)
+
+LICENSE: https://github.com/espenhgn/RippleNet/blob/master/LICENSE
+
+## Clone
+
+These codes can be downloaded using git (www.git-scm.com):
+
+    cd <Repositories> # whatever download destination
+    git clone https://github.com/espenhgn/RippleNet
+    cd RippleNet
+
+Some binary files like `.h5` and `.pkl` may be tracked using Git LFS (https://git-lfs.github.com)
+
+## dependencies
+
+- `python>=3`
+- `numpy`
+- `scipy`
+- `ipympls`
+- `matplotlib`
+- `h5py`
+- `pandas`
+- `seaborn`
+- `notebook`
+- `jupyter`
+- `tensorflow>=2.0`
+- `tensorflow-gpu` (optional)
+
+Dependencies can be installed in your existing Python environment using
+the `requirements.txt` file and the `pip` utility:
+
+    pip install -r requirements.txt
+
+To install an Anaconda Python (www.anaconda.com) environment with these dependencies, issue
+
+    conda env create -f environment.yml
+    conda activate ripplenet
+
+This will not install `tensorflow-gpu` for hardware acceleration by default.
+
+
+## Binder
+
+You may mess around with the RippleNet notebooks on MyBinder.org:
+https://mybinder.org/v2/gh/espenhgn/RippleNet/master
+
+Retraining networks is not recommended (no GPU access)!
+
+## Files and folders:
+
+- `README.md`: This file
+- `LICENSE`: License file
+- `environment.yml`: Conda environment file
+- `RippleNet_training_bidirectional.ipynb`: Jupyter notebook for training bidirectional RippleNet
+- `RippleNet_training_unidirectional.ipynb`: Notebook for training unidirectional RippleNet
+- `RippleNet_manuscript_figures.ipynb`: Notebook for generating figures 2-7 in Hagen E. et al. (2020)
+- `RippleNet_timeseries_prediction.ipynb`: Notebook	for generating figures 8-11 in Hagen E. et al. (2020)
+- `RippleNet_interactive_prototype.ipynb`: Notebook with user-interactive detection and rejection of ripple events
+- `trained_networks/`
+    - `ripplenet_*directional_random_seed*.h5`: trained RippleNet instances of uni- or bidirectional types
+    - `ripplenet_*directional_best_random_seed*.h5`: best-performing model on validation set during training
+    - `ripplenet_*directional_history_random_seed*.csv`: training history (.csv format)
+    - `ripplenet_*directional_history_random_seed*.pkl`: training history (.pickle format)
+- `ripplenet/`
+    - `common.py`: shared methods and functions
+    - `models.py`: function declarations for `tensorflow.keras` models
+- `data/`
+    - `train_00.h5`: Training data set (mouse)
+    - `train_tingley_00.h5`: Training data set (rat)
+    - `validation_00.h5`: Validation data set (mouse)
+    - `validation_00.h5`: Validation data set (rat)
+    - `test_00.h5`: Test data set (mouse)
+    - `m4029_session1.h5`: Test data set (mouse, continuous)