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Update README.md #130

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31 changes: 16 additions & 15 deletions README.md
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Original file line number Diff line number Diff line change
@@ -1,5 +1,5 @@
> **Warning**
> **SWC Ephys is not sufficiently tested to be used in analysis. This release is only for testing. Do not use for your final analyses.**
> **Spikewrap is not sufficiently tested to be used in analysis. This release is only for testing. Do not use for your final analyses.**

> **Warning** **Limitations**
> - works only on SpikeGLX recordings with 1 gate, 1 trigger, 1 probe (per run, e.g. g0, t0, imec0)
Expand All @@ -9,6 +9,7 @@
> - no options to remove potentially large intermediate files
> - installation / running on HPC is a bit clunky. In future this can be simplified with SLURM jobs organised under the hood and setting up a HPC module.
> - untested!
> - The documentation is currently outdated.


# Features
Expand All @@ -23,7 +24,7 @@ Sorting requires a NVIDIA GPU and so is currently only available using the SWC's

To install locally, clone the repository to your local machine using git.

`git clone [email protected]:neuroinformatics-unit/swc_ephys.git`
`git clone [email protected]:neuroinformatics-unit/spikewrap.git`

Change directory to the repo and install using

Expand All @@ -37,7 +38,7 @@ or if using the zsh shell

`pip install -e ".[dev]"`

After installation, the module can be imported with `import swc_ephys`.
After installation, the module can be imported with `import spikewrap`.

## Running on the HPC

Expand All @@ -49,23 +50,23 @@ To connect and run on the HPC (e.g. from Windows, macOS or Linux terminal):

`ssh hpc-gw1`

The first time using, it is necessary to steup and install `swc_ephys`. It is strongly recommended to make a new conda environment on the HPC, before installing `swc_ephys`.
The first time using, it is necessary to steup and install `spikewrap`. It is strongly recommended to make a new conda environment on the HPC, before installing `spikewrap`.

`module load miniconda`

`conda create --name swc_ephys python=3.10`
`conda create --name spikewrap python=3.10`

`conda activate swc_ephys`
`conda activate spikewrap`

and install swc_ephys and it's dependencies:
and install spikewrap and it's dependencies:

`mkdir ~/git-repos`

`cd ~/git-repos`

`git clone https://github.com/JoeZiminski/swc_ephys.git`
`git clone https://github.com/JoeZiminski/spikewrap.git`

`cd swc_ephys`
`cd spikewrap`

`pip install -e .`

Expand All @@ -77,13 +78,13 @@ Before running, it is necessary to request use of a GPU node on the HPC to run s

`module load miniconda`

`conda activate swc_ephys`
`conda activate spikewrap`

`python my_pipeline_script.py`

# Quick Start Guide

SWC Ephys (currently) expects input data to be stored in a `rawdata` folder. A subject (e.g. mouse) data should be stored in the `rawdata` folder and contain SpikeGLX output format (example below). **Currently, only recordings with 1 gate, 1 trigger and 1 probe are supported (i.e. index 0 for all gate, trigger probe, `g0`, `t0` and `imec0`)**.
Spikewrap (currently) expects input data to be stored in a `rawdata` folder. A subject (e.g. mouse) data should be stored in the `rawdata` folder and contain SpikeGLX output format (example below). **Currently, only recordings with 1 gate, 1 trigger and 1 probe are supported (i.e. index 0 for all gate, trigger probe, `g0`, `t0` and `imec0`)**.

```
└── rawdata/
Expand All @@ -100,7 +101,7 @@ SWC Ephys (currently) expects input data to be stored in a `rawdata` folder. A s
Example code to analyse this data in this format is below:

```
from swc_ephys.pipeline.full_pipeline import run_full_pipeline
from spikewrap.pipeline.full_pipeline import run_full_pipeline

base_path = "/ceph/neuroinformatics/neuroinformatics/scratch/ece_ephys_learning"

Expand Down Expand Up @@ -155,7 +156,7 @@ Output are the saved preprocessed data, spike sorting results as well as a list

**preprocessed**:

- Binary-format spikeinterface recording from the final preprocessing step (`si_recording`) 2) `data_class.pkl` swc_ephys internal use.
- Binary-format spikeinterface recording from the final preprocessing step (`si_recording`) 2) `data_class.pkl` spikewrap internal use.

**-sorting output (e.g. kilosort2_5-sorting, multiple sorters can be run)**:

Expand Down Expand Up @@ -203,8 +204,8 @@ Configuration files are structured as a dictionary where keys indicate the order
Visualising preprocesing output can be run locally to inspect output of preprocessing routines. To visualise preprocessing outputs:

```
from swc_ephys.pipeline.preprocess import preprocess
from swc_ephys.pipeline.visualise import visualise
from spikewrap.pipeline.preprocess import preprocess
from spikewrap.pipeline.visualise import visualise

base_path = "/ceph/neuroinformatics/neuroinformatics/scratch/ece_ephys_learning"
sub_name = "1110925"
Expand Down