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This package contains utilities to help you follow along with the Bladesight tutorials.

pip install bladesight

All the datasets used in the tutorials are hosted on AWS on a public S3 bucket. This package makes it seamless to download and use these datasets. First import the Datasets object.

from bladesight import Datasets

You can list the available datasets by printing the using the list_datasets method.

>>> print(Datasets.online_datasets)
['bladesight-datasets/intro_to_btt/intro_to_btt_ch02']

You can then load a dataset by selecting the dataset like you would have selected a dictionary key, just by using the 'data/' prefix:

>>> my_dataset = Datasets['data/intro_to_btt/intro_to_btt_ch02']

You can then load the table by selecting the table like you would have selected a dictionary key, just by using the 'table/' prefix:

>>> df_proximity_probe = my_dataset['table/aluminium_blisk_1200_rpm']

The datasets have been downloaded to your local machine. You can find them in the following directory:

~/.bladesight/data

You'll see an attribution every time you open a dataset. This attribution is a link to the dataset's citation.

If you'd like your datasets to be accessible through this package, I'd be more than willing to convert it to the appropriate format and upload it.

Please email me at [email protected] to get the ball rolling.

I include all the functions that are used frequently throughout the tutorials in the bladesight package. This is to avoid cluttering the tutorials with code that is not relevant to the tutorial's objective.

from bladesight.btt.triggering_criteria import threshold_crossing_hysteresis_rising

This project is licensed under the terms of the MIT license.