TOAR Gridding Tool
====

# About 

The TOARgridding projects data from the TOAD database (https://toar-data.fz-juelich.de/) onto a grid. 
The request to the database also allows a statistical analysis of the requested value.
The mean and standard deviation of all stations within a cell are computed. 

The tool handles the request to the database over the REST API and the subsequent processing.
The results of the gridding are provided as xarray objects for subsequent processing by the user.

This project is in beta with the intended basic functionalities. 
The documentation is work in progress.

# Requirements

TBD, see pyproject.toml

# Installation

Move to the folder you want to create download this project to.
We now need to download the source code (https://gitlab.jsc.fz-juelich.de/esde/toar-public/toargridding/-/tree/dev?ref_type=heads). Either as ZIP folder or via git:

## Download with GIT
Clone the project from its git repository:
```
git clone https://gitlab.jsc.fz-juelich.de/esde/toar-public/toargridding.git 
```
With git we need to checkout the development branch. Therefore we need to change to the project directory first:
```
cd toargridding
git checkout dev
```

## Installing Dependencies

The handling of required packages is done with poetry. So run poetry in the project directory:
```
poetry install
```

# How does this tool work?

This tool has two main parts. The first handles requests to the TOAR database and the analysis of the data.
The second part is the gridding, which is performed offline

## Request to TOAR Database with Statistical Analysis 

Requests are send to the analysis service of the TOAR database. This allows a selection of different stations base on their metadata and performing a statistical analysis.
Whenever a request is submitted, it will be processed. The returned status endpoint will point ot the results as soon as the process is finished. 
A request can take several hours, depending on time range and the number of requested stations. 
At the moment, there is no possibility implemented to check the status of a running job until it is finished (Date: 2024-05-14).

As soon as a request is finished, the status endpoint will not be valid forever. The data will be stored longer in a cache by the analysis service. As soon as the same request is submitted, first the cache is checked, if the results have already been calculated. The retrieval of the results from the cache can take some time, similar to the analysis.

There is no check, if a request is already running. Therefore, submitting a request multiple times, leads to additional load on the system and slows down all requests. 

The TOAR database has only a limited number of workers for performing a statistical analysis. Therefore, it is advised to run one request after another, especially for large requests covering a large number of stations and or a longer time.

## Gridding

The gridding uses a user defined grid to combine all stations in a cell.
Per cell mean, standard deviation and the number of stations are reported.

# Example

There are at the moment three example provided as jupyter notebooks (https://jupyter.org/).

Running them with the python environment produced by poetry can be done by
```
poetry run jupyter notebook
```

##  High level function
```
tests/produce_data_withOptional.ipynb 
```
Provides an example on how to download data, apply gridding and save the results as netCDF files.
The AnalysisServiceDownload caches already obtained data on the local machine.
This allows different griddings without the necessity to repeat the request to the TOARDB and subsequent download.

In total two requests are executed. 
The example uses a dictionary to pass additional arguments to the request to the TAOR database (here: station category from TOAR). 
A detailed list can be found at https://toar-data.fz-juelich.de/api/v2/#stationmeta

```
tests/produce_data_manyStations.ipynb
```
Uses a similar request, but without the restriction to the station type. Therefore, a much larger number of stations is requested (about 1000 compared to a few hundred, that have a "toar1_category" classification used in the previous example).
Therefore, this example is restricted to the calculation of "dma8epax".

## Retrieving data
```
tests/get_sample_data.ipynb 
```
Downloads data from the TOAR database. The extracted data are written to disc. No further processing or gridding is done. 

## Retrieving data
```
tests/get_sample_data_manual.ipynb 
```
Downloads data from the TOAR database with a manual creation of the request to the TOAR database.
As an example for addition parameters, the "toar1_category" is passed to the metadata object.
This example does not perform any gridding.

## Retrieving data and visualization
```
tests/quality_controll.ipynb
```
Notebook for downloading and visualization of data. 
The data are downloaded and reused for subsequent executions of this notebook.
The gridding is done on the downloaded data. Gridded data are not saved to disc. 

## Benchmarks Requests to TOAR Database

```
tests/benchmark.py
```
This script requests datasets with different durations (days to month) from the TOAR Database and saves them to disc.
It reports the duration for the different requests.
There is no gridding involved.
CAVE: This script can run several hours.


# Supported Grids

The first supported grid is a regular grid with longitude and latitude.

# Supported Variables

At the moment only a limited number of variables from the TOAR database is supported. 

# Supported Time intervals

At the moment time differences larger than one day are working, i.e. start and end=start+1day leads to crashes.

# Documentation of Source Code:

At the moment Carsten Hinz is working on a documentation of the source code, while getting familiar with it.
The aim is a brief overview on the functionalities and the arguments. As he personally does not like repetitions, 
the documentations might not match other style guides. 
It will definitely be possible to extend the documentation:-)

```
class example:
    """An example class

    A more detailed explanation of the purpose of this example class.
    """

    def __init__(self, varA : int, varB : str):
        """Constructor

        Attributes:
        varA:
            brief details and more context
        varB:
            same here.
        """
        [implementation]    
    
    def func1(self, att1, att2):
        """Brief

        details

        Attributes:
        -----------
        att1:
            brief/details
        att2:
            brief/details
        """

        [implementation]
        
```

```
@dataclass
class dataClass:
    """Brief description

    optional details

    Parameters
    ----------
    anInt:
        brief description
    anStr:
        brief description
    secStr:
        brief description (explanation of default value, if this seems necessary)
    """
    anInt : int
    anStr : str
    secStr : str = "Default value"
```