SciQLop: a tool suite to facilitate multi-mission data browsing and analysis

The SCIentific Qt application for Learning from Observations of Plasmas (SciQLop) project allows to easily discover, retrieve, plot and label in situ space physic measurements stored on remote servers such as Coordinated Data Analysis Web (CDAWeb) or Automated Multi-Dataset Analysis (AMDA). Analyzing data from a single instrument on a given mission can raise some technical difficulties such as finding where to get them, how to get them and sometimes how to read them. Thus building for example a machine-learning pipeline involving multiple instruments and even multiple spacecraft missions can be very challenging. Our goal here is to remove all these technical difficulties without sacrificing performances to allow scientist to focus on data analysis. The SciQLop project is composed of the following tools: Speasy: An easy to use Python package to retrieve data from remote servers with multi-layer cache support. Speasyₚroxy: A self-hostable, chainable remote cache for Speasy written as a simple Python package. Broni: A Python package which finds intersections between spacecraft trajectories and simple shapes or physical models such as magnetosheath. Orbit-viewer: A Python graphical user interface (GUI) for Broni. TSCat: A Python package used as backend for catalogs of events storage. TSCat-GUI: A Python graphical user interface (GUI). SciQLop-GUI: An extensible and efficient user interface to visualize and label time-series with an embedded IPYthon terminal. While some components are production ready and already used for science, SciQLop is still being developped and the landscape is moving quite fast. In this poster we will demonstrate how the SciQLop project makes masive in-situ data analysis simple and fast and we will also take the oportunity to exchange ideas with our users.