Time Series Data
Learn about Time Series Datasets
Time series datasets are a container for individual data points. All data points within a time series dataset share the same data type, they share the same set of fields.
Additionally all time series datasets share a few common fields.
One of those, the time field enables you to perform time-based data queries on a dataset.
Overview
Here is a quick overview of the API for listing and accessing datasets which this page covers. Some usage examples for different use-cases are provided below.
| Method | API Reference | Description |
|---|---|---|
client.datasets | Listing datasets | List all available datasets. |
datasets.open_data.copernicus.sentinel1_sar | Accessing a dataset | Access a specific dataset. |
Listing datasets
You can use your Tilebox Python client instance to access the datasets available to you. For example, to access a dataset called dataset in a dataset group called some, you can use the following code.
Once you have your dataset object, you can then use it to list the available collections for the dataset.
Tip: if you’re using an IDE or an interactive environment with auto-complete you can use it on your client instance to
discover the datasets that are available to you. Type client. and trigger auto-complete after the dot to do so.
Common Fields
While the actual data fields between data points of different time series datasets can vary greatly, there are common fields that all time series datasets share.
The timestamp associated with each data point. Tilebox uses a milli-second precision for storing and indexing data points. Timestamps are always in UTC.
A Universally_unique_identifier (UUID) which uniquely identifies each datapoint. IDs are generated in such a way that sorting them lexicographically also means sorting them by their time field.
The time the data point was ingested into the Tilebox API. Timestamps are always in UTC.
These fields are present on all time series datasets. Together they make up the metadata of a datapoint. Each dataset also have its own set of fields that are specific to that dataset.
Tilebox is using milli-second precision timestamps for storing and indexing data points. If there are data points within one milli-second, they share the same timestamp. Each data point can contain any number of timestamp fields with an arbitrarily higher precision. For telemetry data for example it’s common to have timestamp fields using a nanosecond precision.
Example datapoint
Below is an example datapoint from a time series dataset in the form of an xarray.Dataset.
It contains the common fields. When using the Tilebox Python client library, you receive the data in this format.
<xarray.Dataset>
Dimensions: ()
Coordinates:
time datetime64[ns] 2023-03-12 16:45:23.532
id <U36 '0186d6b6-66cc-fcfd-91df-bbbff72499c3'
ingestion_time datetime64[ns] 2023-03-12 19:53:12.742
Data variables:
*empty*
The datatype <U36 represents a string of Unicode characters with a maximum length of 36.
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