From 6d4f1b3e64deff4a3542308dbc5c713d87a9579b Mon Sep 17 00:00:00 2001 From: Vincenzo Marciano Date: Wed, 19 Apr 2023 12:43:50 +0200 Subject: [PATCH] Update README.md --- README.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/README.md b/README.md index 77b036b..59bff6e 100644 --- a/README.md +++ b/README.md @@ -2,11 +2,11 @@ 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 "fist_test.ipynb" in the _notebooks_ folder +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 'first_test.ipynb'. +* _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_