1. Open C:\Users\ValgaevO\OneDrive - AIT\Documents\OneNote Notebooks
2. Double click on "template_notebook"
3. Create new OneNote notebook from a template

1. Create new ToDo plan

1. Open CONDA prompt (miniconda)
2. Create conda enviroments with any python versions required for the project 
	a. conda env list
	b. conda create  --name MYPROJECT-ENVIROMENT_py3-XX python==3.XX
	c. conda activate MYPROJECT-ENVIROMENT_py3-XX

3. Run: cd C:\Users\ValgaevO\Lokal documents\01_code\01_ait-projects
4. Initiate project with CCDS template: ccds https://github.com/q909/template_project
5. Run: cd MY_PROJECT_DIR
6. Create POETRY enviroment 
	a. Init poetry enviroment (MINICONDA in cd MY_PROJECT_DIR):  poetry install
	b. Add new required package to poetry toml: poetry add XXX
7. Open VSCode
8. Select MYPROJECT-ENVIROMENT_py3-XX CONDA enviroment in VSCode
9. Open and run notebook

1. Init GIT repo: 
	a. VSCode 
	b. git init (in PROJECTDIR)
2. Create a remote repo online (GitHub or GitLab)
3. Check .gitignore file
4. Add GIT remote: git remote add origin https://github.com/q909/XXXXXXX or https://gitlab-intern.ait.ac.at/ValgaevO/XXXXXXXX
5. Publish branch and sync with remote
6. Get the changes from the remote: git pull origin master
7. Commit: git commit -m "this is my first commit"

├── LICENSE            <- Open-source license if one is chosen
├── Makefile           <- Makefile with convenience commands like `make data` or `make train`
├── README.md          <- The top-level README for developers using this project.
├── data
│   ├── external       <- Data from third party sources.
│   ├── interim        <- Intermediate data that has been transformed.
│   ├── processed      <- The final, canonical data sets for modeling.
│   └── raw            <- The original, immutable data dump.
│
├── docs               <- A default mkdocs project; see www.mkdocs.org for details
│
├── models             <- Trained and serialized models, model predictions, or model summaries
│
├── notebooks          <- Jupyter notebooks. Naming convention is a number (for ordering),
│                         the creator's initials, and a short `-` delimited description, e.g.
│                         `1.0-jqp-initial-data-exploration`.
│
├── pyproject.toml     <- Project configuration file with package metadata for 
│                         {{ cookiecutter.module_name }} and configuration for tools like black
│
├── references         <- Data dictionaries, manuals, and all other explanatory materials.
│
├── reports            <- Generated analysis as HTML, PDF, LaTeX, etc.
│   └── figures        <- Generated graphics and figures to be used in reporting
│
├── requirements.txt   <- The requirements file for reproducing the analysis environment, e.g.
│                         generated with `pip freeze > requirements.txt`
│
├── setup.cfg          <- Configuration file for flake8
│
└── {{ cookiecutter.module_name }}   <- Source code for use in this project.
    │
    ├── __init__.py             <- Makes {{ cookiecutter.module_name }} a Python module
    │
    ├── config.py               <- Store useful variables and configuration
    │
    ├── dataset.py              <- Scripts to download or generate data
    │
    ├── features.py             <- Code to create features for modeling
    │
    ├── modeling                
    │   ├── __init__.py 
    │   ├── predict.py          <- Code to run model inference with trained models          
    │   └── train.py            <- Code to train models
    │
    └── plots.py                <- Code to create visualizations