Data Access Methods
This section describes alternative methods for accessing and manipulating data in Eratos, building on the foundation set by basic data access methods.
Ideal for users requiring deeper data integration, custom processing pipelines, or package issues this guide covers the following access methods:
Jump back into the "Accessing and Using Data: getting started" tutorial to learn the functionality of Eratos Xarray.
This section contains several fundamental data queries to help you access and understand any given dataset. These methods utilise SDK functionality directly.
For data that is housed in Senaps Datastreams, such as IoT sensor data, use this guide to learn how to view, access, and interact with it.
Note:The following 3 methods are extensions of Eratos Xarray capabilities that gives you more control and scalability, utilizing the Eratos SDK directly.
This method allows users to retrieve time-series data for specific points of interest. It's particularly useful for monitoring environmental changes, asset performance, or any temporal data evolution at given geographical locations.
This method provides a way to access geospatial grid data at specified times as an array. It's useful for analyzing spatial data over time, supporting applications like tracking climate change impacts, urban development, or agricultural land use changes.
Focuses on extracting a 3D subset of data as an array, enabling detailed analysis of volumetric data. This function is invaluable for users working with complex datasets, such as atmospheric models, geological surveys, or multi-layered spatial data.
Updated 8 days ago