Pipeline Configuration

The pipeline config file pipeline.yaml describes the configuration of your pipeline:

1. Triggers - which file input file patterns should trigger this pipeline

2. Pipeline Class - dotted class name of the pipeline to use

3. Dependent Config Files - which yaml files to use for the retriever, dataset, quality management, and storage

Each pipeline template will include a starter pipeline config file in the config folder. It will work out of the box, but the configuration should be tweaked according to the specifics of your pipeline. Consult the Getting Started section for more information on getting started with a template.

Note

To prevent redundancy, Tsdat config files are designed to be shared across multiple pipelines. In the pipeline config file, you can specify a shared config file to use (ie., shared/config/dataset.yaml) and then override specific values in the overrides section.

An annotated example of an ingest pipeline config file is provided below:

#  Name of the Ingest Pipeline to use
classname: tsdat.pipeline.ingest.IngestPipeline

#  Regex patterns that should trigger this pipeline
triggers:
  - .*example_pipeline.*\.csv

# Retriever config
retriever:
  path: pipelines/example_pipeline/config/retriever.yaml

# Dataset config.  In this example, we use a dataset.yaml file that is shared across multiple pipelines,
# but we override one global attribute specifying a different location and we add one additional variable attribute.
dataset:
  path: shared/config/dataset.yaml
  overrides:
    /attrs/location_id: sgp
    /data_vars/first/attrs/new_attribute: please add this attribute

# Quality config - shared across multiple pipelines
quality:
  path: shared/config/default-quality.yaml

# Storage config - shared across multiple pipelines
storage:
  path: shared/config/storage.yaml

Overrides

You may have noticed the overrides option used in the dataset configuration. This option can be used to override or add values in the source configuration file. Here we are changing the location_id global attribute to “sgp” and adding a new attribute to the data variable named “first”. Overrides enhance the reusability of configuration files, allowing you to define a base configuration file and override specific features of it as needed for instruments at different sites.

Consider the example pipelines/lidar/config/dataset.yaml file below:

attrs:
  title: My Dataset
  location_id: sgp
  dataset_name: lidar
  data_level: b1
coords:
  time:
    dims: [time]
    dtype: datetime64[s]
    attrs:
      units: Seconds since 1970-01-01 00:00:00
data_vars:
  wind_speed:
    dims: [time]
    dtype: float
    attrs:
      units: m/s
      valid_range: [0, 30]

The pipeline.yaml file can define overrides as follows:

dataset:
  path: pipelines/lidar/config/dataset.yaml
  overrides:

    # Changing existing properties via dictionary access
    /attrs/location_id: hou

    # Adding properties / attributes via dictionary access
    /data_vars/wind_speed/attrs/comment: This adds a 'comment' attribute!

    # Adding new variables
    /data_vars/wind_dir:
      dims: [time]
      dtype: float
      attrs:
        units: deg
        comment: This is a brand new variable called 'wind_dir'

    # Changing properties by array index
    /data_variables/wind_speed/attrs/valid_range/1: 50

This is equivalent to defining a new dataset.yaml file as follows:

attrs:
  title: My Dataset
  location_id: hou
  dataset_name: lidar
  data_level: b1
coords:
  time:
    dims: [time]
    dtype: datetime64[s]
    attrs:
      units: Seconds since 1970-01-01 00:00:00
data_vars:
  wind_speed:
    dims: [time]
    dtype: float
    attrs:
      units: m/s
      valid_range: [0, 50]
      comment: This adds a 'comment' attribute!
  wind_dir:
    dims: [time]
    dtype: float
    attrs:
      units: deg
      comment: This is a brand new variable called 'wind_dir'