# Data integration and metadata annotation

Repository for integrating data in HDF5 from various sources and managing metadata updates in the integrated files. 

Includes tools and workflows for comprehensive data integration and automated metadata review processes.

## Installation

Follow these steps to install and set up the project:

1. Download our GitLab repository in your GitLab folder, or alternatively open a Git Bash terminal and run the following commands: 
  ```
  cd Path/to/GitLab
  git clone https://gitlab.psi.ch/5505/data-integration-and-metadata-annotation.git
  ```

2. Open an Anaconda Prompt (Anaconda3) as administrator, and set the current directory to the path of the project's folder.

3. Create the project's environment `multiphase_chemistry_env` by running the following command:
  ```
  conda env create -f environment.yml
  ```

### Working with Jupyter Notebook on the `multiphase_chemistry_env`

1. Open an Anaconda Prompt as a regular user, ensure that `multiphase_chemistry_env` is in the list of available enviroments and activate it by running the following commands:
  ```
  conda env list
  conda activate multiphase_chemistry_env
  ```
2. Register the associated kernel in Jupyter by running:

  ```
  python -m ipykernel install --user --name multiphase_chemistry_env --display-name "Python (multiphase_chemistry_env)"  
  ```
3. Start a Jupyter Notebook by running the command:
  ```
  jupyter notebook
  ```
and select the `multiphase_chemistry_env` environment from the kernel options.

### Working with Visual Studio Code (VS Code) on the `multiphase_chemistry_env`
1. Open the project in VS Code, click on the Python interpreter in the status bar and choose the `multiphase_chemistry_env` environment.

## Data integration workflow

## Metadata review workflow
    - review through branches
    - updating files with metadata in Openbis

## Metadata 
| **Attribute**           | **CF Equivalent**           | **Definition**                                                                                                                                                     |
|-------------------------|-----------------------------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| campaign_name           |     -    | Denotes a range of possible campaigns, including laboratory and field experiments, beamtime, smog chamber studies, etc., related to atmospheric chemistry research.                                                                |
| project                 | -            | Denotes a valid name of the project under which the data was collected or produced.                                                                                                                                                                         |
| contact                    | contact (specifically E-mail address)           | Denotes the name of data producer who conducted the experiment or carried out the project that produced the raw dataset (or an aggragated dataset with multiple owners)                                                              | 
| description             | title (only info about content), comment (too broad in scope), source          | Provides a short description of methods and processing steps used to arrive at the current version of the dataset.                                                    | 
| experiment              | -         | Denotes a valid name of the specific experiment or study that generated the data.                                                                                           | 
| actris_level            | -       | Indicates the processing level of the data within the ACTRIS (Aerosol, Clouds and Trace Gases Research Infrastructure) framework.                                   | 
| dataset_startdate       | -         | Denotes the start datetime of the dataset collection.                                                                                                                    |
| dataset_enddate         | -           | Denotes the end datetime of the dataset collection.                                                                                                                      |                          
| processing_filename     | - | Denotes the name of the file used to process an initial version (e.g, original version) of the dataset into a processed dataset. | 
| processing_date         | -                | The date when the data processing was  completed.                                                                                                                    |                                 |


    
### Specifying a compound attribute in yaml language.
Consider the compound attribute *relative_humidity*, which has subattributes *value*, *units*, *range*, and *definition*. The yaml description of
such an attribute is as follows:
```yaml
relative_humidity:
  value: 65
  units: percentage
  range: '[0,100]'
  definition: 'Relative humidity represents the amount of water vapor present in the air relative to the maximum amount of water vapor the air can hold at a given temperature.'  
```
### Deleting or renaming a compound attribute in yaml language.
  - Assume the attribute *relative_humidity* already exists. Then it should be displayed as follows with the subattribute *rename_as*. This can be set differently to suggest a renaming of the attribute. 
  - To suggest deletion of an attribute, we are required to add a subattribute *delete* with value as *true*. Below for example, the
  attribute *relative_ humidity* is suggested to be deleted. Otherwise if *delete* is set as *false*, it will have no effect.
```yaml
relative_humidity:
  delete: true # we added this line in the review process
  rename_as: relative_humidity
  value: 65
  units: percentage
  range: '[0,100]'
  definition: 'Relative humidity represents the amount of water vapor present in the air relative to the maximum amount of water vapor the air can hold at a given temperature.'

```

# How to Extend DIMA’s File Reading Capabilities for New Instruments

We now explain how to extend DIMA's file-reading capabilities by adding support for a new instrument. The process involves adding instrument-specific files and registering the new instrument's file reader.

## 1. Create Instrument Files
You need to add two files for the new instrument:

- A **YAML file** that contains the instrument-specific description terms. 
  - **Location**: `instruments/dictionaries/`
  
- A **Python file** that reads the instrument's data files (e.g., JSON files).
  - **Location**: `instruments/readers/`

**Example:**
- **YAML file**: `ACSM_TOFWARE_flags.yaml`
- **Python file**: `flag_reader.py` (reads `flag.json` files from the new instrument).

## 2. Register the New Instrument Reader
To enable DIMA to recognize the new instrument's file reader, update the **filereader registry**:

1. Open the file: `instruments/readers/filereader_registry.py`.
2. Add an entry to register the new instrument's reader.

**Example:**
```python
# Import the new reader
from instruments.readers.flag_reader import read_jsonflag_as_dict
# Register the new instrument in the registry
file_extensions.append('.json') 
file_readers.update({'ACSM_TOFWARE_flags_json' : lambda x: read_jsonflag_as_dict(x)})
```

## -------------------
## Getting started

To make it easy for you to get started with GitLab, here's a list of recommended next steps.

Already a pro? Just edit this README.md and make it your own. Want to make it easy? [Use the template at the bottom](#editing-this-readme)!

## Add your files

- [ ] [Create](https://docs.gitlab.com/ee/user/project/repository/web_editor.html#create-a-file) or [upload](https://docs.gitlab.com/ee/user/project/repository/web_editor.html#upload-a-file) files
- [ ] [Add files using the command line](https://docs.gitlab.com/ee/gitlab-basics/add-file.html#add-a-file-using-the-command-line) or push an existing Git repository with the following command:

```
cd existing_repo
git remote add origin https://gitlab.psi.ch/5505/functionspython.git
git branch -M main
git push -uf origin main
```


```
cd existing_repo
git remote add origin https://gitlab.psi.ch/5505/functionspython.git
git branch -M main
git push -uf origin main
```

## Integrate with your tools

## Integrate with your tools

- [ ] [Set up project integrations](https://gitlab.psi.ch/5505/functionspython/-/settings/integrations)

## Collaborate with your team

- [ ] [Invite team members and collaborators](https://docs.gitlab.com/ee/user/project/members/)
- [ ] [Create a new merge request](https://docs.gitlab.com/ee/user/project/merge_requests/creating_merge_requests.html)
- [ ] [Automatically close issues from merge requests](https://docs.gitlab.com/ee/user/project/issues/managing_issues.html#closing-issues-automatically)
- [ ] [Enable merge request approvals](https://docs.gitlab.com/ee/user/project/merge_requests/approvals/)
- [ ] [Automatically merge when pipeline succeeds](https://docs.gitlab.com/ee/user/project/merge_requests/merge_when_pipeline_succeeds.html)

## Test and Deploy

Use the built-in continuous integration in GitLab.

- [ ] [Get started with GitLab CI/CD](https://docs.gitlab.com/ee/ci/quick_start/index.html)
- [ ] [Analyze your code for known vulnerabilities with Static Application Security Testing(SAST)](https://docs.gitlab.com/ee/user/application_security/sast/)
- [ ] [Deploy to Kubernetes, Amazon EC2, or Amazon ECS using Auto Deploy](https://docs.gitlab.com/ee/topics/autodevops/requirements.html)
- [ ] [Use pull-based deployments for improved Kubernetes management](https://docs.gitlab.com/ee/user/clusters/agent/)
- [ ] [Set up protected environments](https://docs.gitlab.com/ee/ci/environments/protected_environments.html)

***

# Editing this README

When you're ready to make this README your own, just edit this file and use the handy template below (or feel free to structure it however you want - this is just a starting point!).  Thank you to [makeareadme.com](https://www.makeareadme.com/) for this template.

## Suggestions for a good README
Every project is different, so consider which of these sections apply to yours. The sections used in the template are suggestions for most open source projects. Also keep in mind that while a README can be too long and detailed, too long is better than too short. If you think your README is too long, consider utilizing another form of documentation rather than cutting out information.

## Name
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