Think of it as something like a scientist’s journal. We believe that the very process of exploration is valuable in itself, and that this process should be saved so that other programmers (including yourself in six months time) can see what happened and learn by example. We try to debug our code, by exploring different combinations of inputs. We explore the behavior of an algorithm that we are developing, to see how it works with various kinds of data. We experiment with a new API that we haven’t used before, to understand exactly how it behaves. Exploratory programming is based on the observation that most of us spend most of our time as coders exploring and experimenting. Nbdev is a system for something that we call exploratory programming. We’ve already written a large programming library (fastai v2) using nbdev, as well as a range of smaller projects. It is a Python programming environment called nbdev, which allows you to create complete python packages, including tests and a rich documentation system, all in Jupyter Notebooks. My fast.ai colleague Sylvain Gugger and I have been working on a labor of love for the last couple of years. “I really do think is a huge step forward for programming environments”: Chris Lattner, inventor of Swift, LLVM, and Swift Playgrounds. An active EO Sign In account is required to log in to the Jupyter Notebook.Ī series of Jupyter Notebooks are made available, in order to understand how to exploit the API that provides the data access service for different types of datasets included in the ESA PDGS datacube.Nbdev: use Jupyter Notebooks for everything Written: by Jeremy Howard The Jupyter Notebooks are the key application that allows a direct interaction with live coding activity and data visualisation.

Furthermore the platform also provides the datasets that are involved in the data processing activities of the ACIX project.īesides the Graphic User Interface, it is possible to access and process the described data inside a specific Jupyter interactive computing environment, where the data can be handled exploiting the Python libraries made available for the users.

The ESA PDGS datacube service interface enables a pixel-based access to the data available in the European Space Agency dissemination services, including Heritage Missions (HM), Third Party Missions (TPM) and Earth Explorer (EE) data.

Jupyter notebook online