Corefunctions

Corefunctionality for creating dataloaders objects from downloaded datasets

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get_default_dataset_path

 get_default_dataset_path ()

Create a directory in the user’s home directory for storing datasets

get_default_dataset_path()
Path('/Users/daniel/.tsfast/datasets')

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get_dataset_path

 get_dataset_path ()

Retrieves the tsfast dataset directory. Tries to read the path in the environment variable ‘TSFAST_PATH’, returns the default otherwise.

env_path = '/directory/.tsfast/'
os.environ['TSFAST_PATH'] = env_path
test_eq(get_dataset_path(),Path(env_path))
get_dataset_path()
Path('/directory/.tsfast')
os.environ['TSFAST_PATH'] = ''
get_dataset_path()
Path('/Users/daniel/.tsfast/datasets')

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clean_default_dataset_path

 clean_default_dataset_path ()

Removes the default directory where the datasets are stored


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create_dls_downl

 create_dls_downl (dataset=None, download_function=None, win_sz:int=100,
                   x:list=[], stp_sz:int=1, sub_seq_len:int=None,
                   bs:int=64, prediction:bool=False,
                   input_delay:bool=False, valid_stp_sz:int=None,
                   cached:bool=True, num_workers:int=5,
                   max_batches_training:int=300,
                   max_batches_valid:int=None, dls_id:str=None, **kwargs)
Type Default Details
dataset NoneType None path to the dataset directory, if not provided uses default
download_function NoneType None function
win_sz int 100
x list []
stp_sz int 1
sub_seq_len int None
bs int 64
prediction bool False
input_delay bool False
valid_stp_sz int None
cached bool True
num_workers int 5
max_batches_training int 300
max_batches_valid int None
dls_id str None
kwargs VAR_KEYWORD
# #| export
# @delegates(create_dls_downloadable,keep=True)
# def create_dls_wh(
#     dataset,
#     win_sz=100,
#     **kwargs):
#     'Dataloader for Wienerhammerstein Benchmark'
#     return create_dls_downloadable(dataset,
#         download_function=wiener_hammerstein,
#         u=['u0'],
#         y=['y0'],
#         **kwargs)

Wiener Hammerstein Benchmark Dataset

dls = create_dls_wh()
dls.show_batch(max_n=1)
dls = create_dls_wh_prediction()
dls.show_batch(max_n=1)

dls[-1].show_batch(max_n=1)

Silverbox Benchmark Dataset

dls = create_dls_silverbox()
dls.show_batch(max_n=1)
dls = create_dls_silverbox_prediction()
dls.show_batch(max_n=1)

Cascaded Tanks Benchmark Dataset

dls = create_dls_cascaded_tanks()
len(dls.items)
715
dls = create_dls_cascaded_tanks()
dls.show_batch(max_n=1)
dls = create_dls_cascaded_tanks_prediction()
dls.show_batch(max_n=1)

EMPS Benchmark Dataset

dls = create_dls_emps()
dls.show_batch(max_n=1)
dls = create_dls_emps_prediction()
dls.show_batch(max_n=1)

CED Benchmark Dataset

dls = create_dls_ced()
dls.show_batch(max_n=1)
dls = create_dls_ced_prediction()
dls.show_batch(max_n=1)

Wiener Hammerstein with Process Noise Benchmark Dataset

dls = create_dls_noisy_wh()
dls.show_batch(max_n=1)
dls = create_dls_noisy_wh_prediction()
dls.show_batch(max_n=1)

Industrial Robot Benchmark Dataset

dls = create_dls_robot_forward()
dls.show_batch(max_n=1)
dls = create_dls_robot_forward_prediction()
dls.show_batch(max_n=1)

dls = create_dls_robot_inverse()
dls.show_batch(max_n=1)
dls = create_dls_robot_inverse_prediction()
dls.show_batch(max_n=1)

Ship Benchmark Dataset

dls = create_dls_ship()
dls.show_batch(max_n=1)
dls = create_dls_ship_prediction()
dls.show_batch(max_n=1)

Quadrotor Pelican Dataset

dls = create_dls_quad_pelican()
dls.show_batch(max_n=1)
dls = create_dls_quad_pelican_prediction()
dls.show_batch(max_n=1)

Quadrotor PI Dataset

dls = create_dls_quad_pi()
dls.show_batch(max_n=1)
dls = create_dls_quad_pi_prediction()
dls.show_batch(max_n=1)

BROAD Dataset

dls = create_dls_broad()
dls.show_batch(max_n=1)
dls = create_dls_broad_prediction()
dls.show_batch(max_n=1)

All datasets