Package: tepimport
TEPImportUtility for quickly downloading and loading the Tennessee Eastman Process data set.Small Data SetThe data set is downloaded from the University of Illinois Large Scale Systems Research Laboratory. A copy of the license is included in the zip package downloaded from the site in the filereadme.txt.Large Data SetThe data set is downloaded from the Harvard Dataverse site. By downloading this data, you are agreeing to the terms set out in the Harvard Dataverse site.The terms can be found in the following link.https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/6C3JR1QuickstartInstall the package using pip.pip3 install tepimportImport the data sets into your codeimport tepimport
tep = tepimport.import_sets()Output the data setsfor name, train, test in tep:
    print(name)
    print(train)
    print(test)Downloading the DataYou will be automatically prompted to download the data when trying to import the files into your code. Alternatively, you can run the download module with the following command.python3 download.py [-h] [--url URL] [--path PATH] [--target TARGET] [--name NAME] [--use-local] [--cleanup] [--no-extract]

optional arguments:
-h, --help       show this help message and exit
--url URL        custom url to download the data from
--path PATH      the path to download the zip file to
--target TARGET  the target path to extract the zip file to
--name NAME      the name of the zip file
--use-local      extract a local copy of the zip file
--cleanup        delete the zip file after extracting it
--no-extract     download the zip without extracting itModule FunctionsThetepimport.pyfile provides the utilities for import the data sets into your code.data_exists_check() -> NoneCheck that the data set is present in the defined folder path and prompt the user to download the data set if it can't be found.set_folder_path(path: str) -> NoneChange the path of where to look for the data setsimport_data_set(file_name: str) -> np.ndarrayImport a data set file as a numpy arrayimport_sets(sets_to_import: tuple, check_data_exists: bool, skip_training: bool, skip_test: bool) -> listTakes a sequence of integers from 0-21 and returns a list of tuples
of (set name, training set, test set)

Parameters
----------
sets_to_import: iterable or int
    An iterable object containing integers in the range [0, 21] or a single integer in that range that indicate the data sets to be imported. By default all data sets will be imported.

check_data_exists: bool
    Checks that the data sets exist before attempting to import and prompt the user to download the data sets if they aren't found. Set to True by default.

skip_training: bool
    If true, don't import the training sets. Set to False by default.

skip_test: bool
    If true, don't import the test sets. Set to False by default.import_tep_sets(lagged_samples: int) -> tupleImports the normal operation training set and 4 of the commonly used test sets [IDV(0), IDV(4), IDV(5), and IDV(10)] with only the first 22 measured variables and first 11 manipulated variables. By default, 2 lagged copies are added to the data sets.add_lagged_samples(data: np.ndarray, lagged_samples: int) -> np.ndarrayTakes a matrix X of [x(1), x(2), ..., x(n)] of n samples where each sample x(i) = [x_1(i), x_2(i), ..., x_m(n)]^T contains m variables and returns a new matrix X* = [x*(1), x*(2), ..., x*(n - d)] of n - d samples where each sample x*(i) = [x_1(i + d), x_2(i + d), ..., x_m(i + d), x_1(i + d - 1), ..., x_2(i + d - 2), ..., x_m(i)] contains m(d + 1) variables where d is the number of lagged samples.