TheUniversityOfSheffield

Code for the internship @The University of Sheffield.

How-to:

1. Run 'data_generation.py' which exploits 'data_generator.py' to generate data starting from the indicated subject folder. It relies on "data_generation_outlier_detection.ipynb" in the notebooks folder
2. Run 'main.py' which exploits 'weather_analysis.py' to run a weather analysis on cadence etc, generating a statistics file in the output path indicated as argument in the command line. It corresponds to 'weather_analysis.ipynb' experimental pipeline.

Where-to:

• notebooks contains the experimental notebooks that visually represent the pipeline to generate the files, step by step. '10s_windowing.ipynb' is the first attempt to work with threshold detection, formalized in 'weather_analysis.py'. Outlier detection pipeline can be visually exploited in 'data_generation_outlier_detection.ipynb'.
• .py files are the files designated to work in a command-line fashion from your terminal.

If you are stucked in the .py files please refer to the corresponding notebooks

Requirements for python libraries:

• data_generator.py :
  • os, pandas, numpy, gzip, shutil