easyScience is a Python/QML framework for data analysis being developed to speed-up the time from data collection to publication. Users will be able to both view and model their data using a variety of popular field dependent engines or calculators within a friendly graphical interface or through a Jupyter notebooks.
Current success stories are for the fields of powder neutron diffraction and reflectometry where profiles based on user defined models are simulated and refinement against experimental data.
Anyone is free to use easyScience-based applications and the source code is openly shared on GitHub.
easyScience-based applications works across operating systems: macOS, Windows, or Linux.
easyScience-based applications have intuitive tabbed interface with a clear workflow, built-in step-by-step user guides and video tutorials.
easyScience-based applications are distributed as an all-in-one package that includes all the dependencies and can be installed with only a few clicks.
easyScience-based applications integrate existing technique-specific libraries, to cover different functionality. Minimization can be done with multiple packages such as lmfit, bumps and DFO-LS.
In easyScience-based applications, you can modify any parameter manually or with a sidebar slider, and the simulated model curve is automatically recalculated in real time.