tsdat.pipeline.pipelines

Classes

IngestPipeline	Pipeline class designed to read in raw, unstandardized time series data and enhance
TransformationPipeline	Pipeline class designed to read in standardized time series data and enhance

class tsdat.pipeline.pipelines.IngestPipeline

Bases: tsdat.pipeline.base.Pipeline

Pipeline class designed to read in raw, unstandardized time series data and enhance its quality and usability by converting it into a standard format, embedding metadata, applying quality checks and controls, generating reference plots, and saving the data in an accessible format so it can be used later in scientific analyses or in higher-level tsdat Pipelines.

Class Methods

get_ancillary_filepath	Returns the path to where an ancillary file should be saved so that it can be
hook_customize_dataset	Code hook to customize the retrieved dataset prior to qc being applied.
hook_finalize_dataset	Code hook to finalize the dataset after qc is applied but before it is saved.
hook_plot_dataset	Code hook to create plots for the data which runs after the dataset has been saved.
run	Runs the data pipeline on the provided inputs.

Method Descriptions

get_ancillary_filepath(title: str, extension: str = 'png', **kwargs: Any) → pathlib.Path

Returns the path to where an ancillary file should be saved so that it can be synced to the storage area automatically.

Parameters:

• title (str) – The title to use for the plot filename. Should only contain alphanumeric and ‘_’ characters.

• extension (str, optional) – The file extension. Defaults to “png”.

Returns:

Path – The ancillary filepath.

hook_customize_dataset(dataset: xarray.Dataset) → xarray.Dataset

Code hook to customize the retrieved dataset prior to qc being applied.

Parameters:

dataset (xr.Dataset) – The output dataset structure returned by the retriever API.

Returns:

xr.Dataset – The customized dataset.

hook_finalize_dataset(dataset: xarray.Dataset) → xarray.Dataset

Code hook to finalize the dataset after qc is applied but before it is saved.

Parameters:

dataset (xr.Dataset) – The output dataset returned by the retriever API and modified by the hook_customize_dataset user code hook.

Returns:

xr.Dataset – The finalized dataset, ready to be saved.

hook_plot_dataset(dataset: xarray.Dataset)

Code hook to create plots for the data which runs after the dataset has been saved.

Parameters:

dataset (xr.Dataset) – The dataset to plot.

run(inputs: List[str], **kwargs: Any) → xarray.Dataset

Runs the data pipeline on the provided inputs.

Parameters:

• inputs (List[str]) – A list of input keys that the pipeline’s Retriever class

• pipeline. (can use to load data into the) –

Returns:

xr.Dataset – The processed dataset.

class tsdat.pipeline.pipelines.TransformationPipeline

Bases: IngestPipeline

Pipeline class designed to read in standardized time series data and enhance its quality and usability by combining multiple sources of data, using higher-level processing techniques, etc.

class Parameters

Bases: pydantic.BaseModel

datastreams: List[str]

A list of datastreams that the pipeline should be configured to run for. Datastreams should include the location and data level information.

parameters: TransformationPipeline.Parameters

retriever: tsdat.io.retrievers.StorageRetriever

Class Methods

hook_customize_input_datasets	Code hook to customize any input datasets prior to datastreams being combined
run	Runs the data pipeline on the provided inputs.

Method Descriptions

hook_customize_input_datasets(input_datasets: Dict[str, xarray.Dataset], **kwargs: Any) → Dict[str, xarray.Dataset]

Code hook to customize any input datasets prior to datastreams being combined and data converters being run.

Parameters:

input_datasets (Dict[str, xr.Dataset]) – The dictionary of input key (str) to input dataset. Note that for transformation pipelines, input keys != input filename, rather each input key is a combination of the datastream and date range used to pull the input data from the storage retriever.

Returns:

Dict[str, xr.Dataset] – The customized input datasets.

run(inputs: List[str], **kwargs: Any) → xarray.Dataset

Runs the data pipeline on the provided inputs.

Parameters:

inputs (List[str]) – A 2-element list of start-date, end-date that the pipeline should process.

Returns:

xr.Dataset – The processed dataset.