# Getting Started

The __galileoQC__ package is run from a Jupyterlab notebook. You should create a virtual working environment (called `whizz` in this documentation) with python venv.

## Installation

To use __galileoQC__, first install it into your `whizz` environment using pip (conda installations of __galileoQC__ can work but conda has not been tested as fully as pip):

```bash
(whizz) $ pip install galileoQC
```

Then start up a jupyterlab notebook. For more information about this, see [^Jupyter] . The Jupyterlab-Desktop app [^JupyterApp] also works.

For more information on installation see [Install Details](#installs-target).

```{toctree}
:maxdepth: 2
:hidden:
install_details.md
```

## Session Setup

In a jupyterlab notebook, set up your session with the following commands.

Firstly import the necessary Python packages ...

```python
from pathlib import Path # useful for file names
%matplotlib widget # needed to, for example, zoom into plots
```

... then import __galileoQC__ ... 

```python
import galileoQC as qc
```

... and finally set the path to your field and plan data. This might look something like this:

```python
# Setup the path to the acquired data, ...
data_root = r'.source/tutorials/'
dx = Path(data_root + r'CanobieData/Canobie.xyz')
dh = dx.with_suffix('.hdf5')

# ..., and plan data.
plan_root = data_root
px = Path(plan_root + r'CanobieData/CanobiePlan.xyz')
ph = px.with_suffix('.hdf5')
```

The variables `dx`, `dh`, `px` and `ph` are often used to represent the four key data filenames. Of course, you can use any variable names for your projects but it is worth noting these because you will see them occasionally in the documentation.

If you want to see how to use __galileoQC__, go to [Tutorials](#tutorials-target) and follow along with the examples. You can also download the python tutorial notebooks and example data from [github](https://github.com/GeoscienceAustralia/galileoQC/tree/main/docs/source/tutorials) and experiment with the functions yourself.

Now you are ready to QC your data!

[^Jupyter]: <https://jupyter.org>

[^JupyterApp]: <https://github.com/jupyterlab/jupyterlab-desktop>


## Installation on NCI Australia

Finally, some users might want to install __galileoQC__ on their **NCI** (National Computational InfraStructure) environment. The following notes might prove useful.

__WARNING - some of the following information might be out of date.__

Using the instructions in this [link](https://opus.nci.org.au/display/Help/3.3+Using+a+custom+Python+virtual+environment+in+JupyterLab), set up a `python3` environment in your specific location. Due to memory/space limitations for each user, it is suggested that you make this custom python environment in your project directory in NCI. The environment will have a folder with the same name in your working directory after it is built:

For the QAQC python scripts, we need to install these extra `python3` packages within our new python environment:
- &nbsp; Xarray (version 2022.12.0 or higher)
- &nbsp; Rioxarray
- &nbsp; netCDF4
- &nbsp; Ipympl
- &nbsp; Colorcet
- &nbsp; Filebrowser
- &nbsp; Matplotlib(version 3.6.2) 

In addition to the above packages, two customised scripts called `graphics.py` and `colors.py` can be downloaded from [here](https://github.com/jobar8/graphics). These packages should be placed in `site_packages` available in your python environment.

Also make sure that you have all the following packages as well - if anything missing:

- &nbsp; H5py
- &nbsp; Verde
- &nbsp; Pooch

Finally, in order to run the current QAQC python scripts on NCI, the following steps need to be taken:

- &nbsp; Analyst need to have an NCI account
- &nbsp; Login to https://ood.nci.org.au/pun/sys/dashboard
- &nbsp; Choose Virtual Desktop 
- &nbsp; Except project that needs to be changed to your active project the rest of settings should be accepted as default 
- &nbsp; Launch the VDI app
- &nbsp; From the menu choose the Terminal 
- &nbsp; Navigate to your working directory 
- &nbsp; Load `python3` package available in NCI
``` 
[user@ood-vn30 user]$ module load python3/3.9.2
	Loading python3/3.9.2
	 Loading requirement: intel-mkl/2020.4.304
```
In your working directory you will have two active subfolders:
- &nbsp; Directory that contains your code and notebooks
- &nbsp; Directory that contains your specific python environment
Load your specific `python3` environment for instance loading environment called QA 	
```
[user@ood-vn30 user]$ . QA/bin/activate
     (QA) [user@ood-vn30 user]$
```
- &nbsp;  Run jupyter notebook in your VDI using the command:
```
Jupyter notebook
```

At this stage, you will be redirected to a web browser that you can load the specific notebook and quality check your airborne gravity dataset.