API reference#
This page provides an auto-generated summary of TEEHR’s API. For more details and examples, refer to the User Guide part of the documentation.
The Evaluation Class#
The top-level class for interacting with and exploring a TEEHR Evaluation.
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The Evaluation class. |
Creating and Managing an Evaluation#
Methods for creating, cloning, and configuring an Evaluation.
Create a study from the standard template. |
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List the evaluations available on S3. |
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Fetch the study data from S3. |
Clean temporary files. |
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Enable logging. |
The Evaluation Dataset#
Classes for creating, describing, and querying the Evaluation dataset tables.
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Base table class. |
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Access methods to units table. |
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Access methods to variables table. |
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Access methods to attributes table. |
Access methods to configurations table. |
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Access methods to locations table. |
Access methods to location attributes table. |
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Access methods to location crosswalks table. |
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Access methods to primary timeseries table. |
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Access methods to secondary timeseries table. |
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Access methods to joined timeseries table. |
Fetching NWM and USGS data#
Methods for fetching NWM and USGS data from external sources.
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Fetch USGS gage data and load into the TEEHR dataset. |
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Fetch NWM retrospective point data and load into the TEEHR dataset. |
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Fetch operational NWM point data and load into the TEEHR dataset. |
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Fetch NWM retrospective gridded data, calculate zonal statistics (currently only mean is available) of selected variable for given zones, and load into the TEEHR dataset. |
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Fetch NWM operational gridded data, calculate zonal statistics (currently only mean is available) of selected variable for given zones, and load into the TEEHR dataset. |
Metric Functions#
Functions for calculating metrics.
Contains UDFs for deterministic metric calculations in Spark queries. |
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Signature functions. |
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Functions for probabilistic metric calculations in Spark queries. |
Metric and Bootstrap Models#
Classes for defining and customizing metrics and bootstrap models.
Define and customize determinisitic metrics. |
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Define and customize signature metrics. |
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Define and customize probalistic metrics. |
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Container class for bootstrap sampling classes. |
Calculated Field Models#
Classes for defining and customizing user-defined field models.
Row level Calculated Fields. |
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Timeseries aware calculated fields. |
Visualization#
Methods for visualizing Evaluation data.
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Extends pandas DataFrame objects with visualization methods. |