# Local installation

## Run pATLAS locally

### Docker compose (recommended)

You can run pATLAS locally without much requirements by using [patlas-compose](https://github.com/bfrgoncalves/patlas-compose). This will automatically handle the installation of the version 1.5.2 of pATLAS and launch the service in a local instance. For that you just require:

* [docker](https://docs.docker.com/install/#supported-platforms)
* [docker-compose](https://docs.docker.com/compose/install/)

Then, follow this simple steps:

* Clone the repository [patlas-compose](https://github.com/bfrgoncalves/patlas-compose).

```
git clone https://github.com/bfrgoncalves/patlas-compose
```

* Enter the *patlas-compose* folder

```
cd patlas-compose
```

* Launch the compose:

```
docker-compose up
```

* Wait for the line `* Running on http://0.0.0.0:5000/ (Press CTRL+C to quit)` to show up, meaning that the service is now running.
* Access on `127.0.0.1:5000` or `0.0.0.0:5000`.

### From scratch (alternatively)

pATLAS can be run locally if you have PostgreSQL installed and configured. After, you just need to:

1\) Clone this repository:

```
git clone https://github.com/tiagofilipe12/pATLAS
```

2\) [Create your custom database version](/docs/api/database_creation.md#creating-a-custom-version-of-pATLAS) or [generate the default pATLAS database](/docs/api/database_creation.md) or [download sql file from version 1.6.1](https://github.com/tiagofilipe12/pATLAS/releases/tag/1.6.1) (the `tar.gz` archive). **Note:** if you download the sql file from version 1.6.1 you may skip steps 3 to 4 and **continue with step 5**.

3\) Make sure all the necessary files are in place.

* by default pATLAS generates a `import_to_vivagraph.json` file in

  the folder `<tag_provided_to_o_flag>/results`. Place this file in the

  `patlas/db_manager/db_app/static/json` folder.
* change session to read the new `import_to_vivagraph.json` file by

  changing from `false` to `true` a variable named `devel` in

  `patlas/db_manager/db_app/static/js/pATLASGlobals.js`

4\) Create the database that the front end will run:

```
createdb <your_database>
```

5\) [load the generated sql file](https://github.com/tiagofilipe12/pATLAS#database-import)

6\) Install backend dependencies:

```
# within the root directory of this repository
pip install -r requirements.txt
```

7\) Install frontend dependencies:

```
# change directory to static direcoty where `index.html` will look for
# its depdendenies
cd patlas/db_manager/db_app/static/
# then install them (package.json is located in this directory)
yarn install
```

8\) Compile node modules so that the html can understand, using webpack:

```
# You can also user a local installation of webpack.
# entry-point.js is the config file where all the imported modules are
# called
node_modules/webpack/bin/webpack.js entry-point.js
```

9\) Then execute the script `run.py`.

```
# within the root directory of this repository
cd patlas/db_manager
./run.py <your_database>
```

Note: the database name is utterly important to properly say to the frontend where to get the data.

10\) Go to `127.0.0.1:5000`.

## Optimization of the resources usage by the web page

Using the `devel = true` isn't very efficient, so you can allow the force directed graph to render in a `devel = true` session, then when you are satisfied pause the force layout using the buttons available in pATLAS and click at the same time `Shift+Ctrl+Space`. This will take a while but eventually it will generate a file named `filtered.json`. Once you have this file you can add it to the `patlas/db_manager/db_app/static/json` folder and change the `devel` variable to `false`. This will use the previously saved positions to render a pre rendered network.


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