ZeMA dataset API
An API for accessing the data in the ZeMA remaining-useful life dataset
- class zema_emc_annotated.dataset.ExtractionDataType(value)[source]
Identifiers of data types in ZeMA dataset
- class zema_emc_annotated.dataset.ZeMASamples(sample_size: SampleSize = SampleSize(idx_first_cycle=0, n_cycles=1, datapoints_per_cycle=1), normalize: bool = False, skip_hash_check: bool = False)[source]
Extracts requested number of samples of values with associated uncertainties
The underlying dataset is the annotated “Sensor data set of one electromechanical cylinder at ZeMA testbed (ZeMA DAQ and Smart-Up Unit)” by Dorst et al. [Dorst2021]. Each extracted sample will be cached in the download directory of the file, which is handled by
pooch.os_cache(), where<AppName>evaluates topooch. That way the concurrent retrieval of the same data is as performant as possible and can simply be left tozema_emc_annotated. Where ever the result ofZeMASamplesis needed in an external code base, it should be safe to call it over and over without causing unnecessary extractions or even downloads. The underlying mechanism is Python’s built-inpickle.- Parameters
sample_size (SampleSize, optional) – tuple containing information about which samples to extract, defaults to default of
SampleSizenormalize (bool, optional) – if
True, then values are centered around zero and values and uncertainties are scaled to values’ unit std, defaults toFalseskip_hash_check (bool, optional) – allow to circumvent strict hash checking during the retrieve of dataset file, to speed up concurrent calls as each check for the large file might take several seconds, defaults to
False
- uncertain_values
The collection of samples of values with associated uncertainties, will be of shape (
sample_size.n_cycles, 11 xsample_size.datapoints_per_cycle)- Type