Illinois Tech SCS lab collaboration with FDSN department on human nutrition studies.
├── LICENSE
├── Makefile <- (Remove) Makefile with commands like `make data` or `make train`
├── README.md <- The top-level README for developers using this project.
├── data
│ ├── onboarded <- onboarded datasets.
│ ├── processed <- The final, canonical data sets for modeling.
│ └── raw <- The original datasets downloaded from drive.
│
├── docs <- (TODO) A default Sphinx project; see sphinx-doc.org for details
│
├── models <- (Remove) Trained and serialized models, model predictions, or model summaries
│
├── notebooks <- TODO: 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`.
│
├── references <- (Remove) Data dictionaries, manuals, and all other explanatory materials.
│
├── reports <- (Remove) 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.py <- (Remove) makes project pip installable (pip install -e .) so src can be imported
├── src <- Source code for use in this project.
├── __init__.py <- Makes src a Python module
│
├── onboarding <- Scripts to onboard the datasets and log the information about new dataset.
│ └── data_validation.py
│
├── preprocess <- Scripts to turn raw data into features for modeling
│ └── build_features.py
│
├── models <- TODO: Scripts to train models and then use trained models to make
│ │ predictions
│ ├── predict_model.py
│ └── train_model.py
│
└── visualization <- TODO: Scripts to create exploratory and results oriented visualizations
└── visualize.py
-
Clone the git repo with the below command:
git clone https://github.com/candiceT233/FDSN-CS.git -
Go to the FDSN-CS project folder
-
Download the datasets from the drive and save it to the data/raw folder.
-
Run the below python command to trigger the onboarding tool.
python src/onboarding/data_validation.py --newdata <data_set name> --keymap Data_Key-Map.xlsx --loglevel 1
Example:
python src/onboarding/data_validation.py --newdata MMTT_FRTS_.xlsx --keymap Data_Key-Map.xlsx --loglevel 1
INFO:__main__:MMTT_FRTS_.xlsx read successfully
INFO:__main__:Data_Key-Map.xlsx read successfully
Current file name: MMTT_FRTS_.xlsx
Example format: [study_name]-[data_type]-[suffix].csv
Enter the new name for the file:
INFO:__main__:File name of MMTT_FRTS_.xlsx is not changed
INFO:__main__:Subject renamed to Subject
INFO:__main__:Sequence renamed to Sequence
INFO:__main__:data/onboarded/MMTT_FRTS_.xlsx saved successfully
Project based on the cookiecutter data science project template. #cookiecutterdatascience