Cubes & Clouds

Concepts
What is a Platform?3 Topics1 Quiz 
What is a data cube?4 Topics1 Quiz

Open Science, Open Data and the FAIR Principles6 Topics3 Quizzes

DiscoveryData Discovery

Data Properties

Sample Lesson: Data Access8 Topics1 Quiz

Data Formats & Performance

Process & ShareData Processing

System Scaling

Result Validation

Result Sharing
Reduce
The reduce_dimension
process collapses a whole dimension of the datacube. It does so by using some sort of reducer, which is a function that calculates a single result from an amount of values, as e.g. mean()
, min()
and max()
are. For example we can reduce the time dimension (t
) of a timeseries by calculating the mean value of all timesteps for each pixel. We are left with a cube that has no time dimension, because all values of that dimension are compressed into a single mean value. The same goes for e.g. the spatial dimensions: If we calculate the mean along the x
and y
dimensions, we are left without any spatial dimensions, but a mean value for each instance that previously was a raster is returned. In the image below, the dimensions that are reduced are crossed out in the result.
::: tip Simplified reduce([🥬, 🥒, 🍅, 🧅], prepare) => 🥗
:::
Think of it as a waste press that does math instead of using brute force. Given a representation of our example datacube, let’s see how it is affected.
Learn how to use Reduce operators with this interactive exercise: