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 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_