103,133 research outputs found
Science teachers' transformations of the use of computer modeling in the classroom: using research to inform training
This paper, from the UK group in the STTIS (Science Teacher Training in an Information Society) project, describes research into the nature of teachers' transformations of computer modeling, and the development of related teacher training materials. Eight teacher case studies help to identify factors that favor or hinder the take-up of innovative computer tools in science classes, and to show how teachers incorporate these tools in the curriculum. The training materials use the results to provide activities enabling teachers to learn about the tools and about the outcomes of the research into their implementation, and help them to take account of these ideas in their own implementation of the innovations
Vaex: Big Data exploration in the era of Gaia
We present a new Python library called vaex, to handle extremely large
tabular datasets, such as astronomical catalogues like the Gaia catalogue,
N-body simulations or any other regular datasets which can be structured in
rows and columns. Fast computations of statistics on regular N-dimensional
grids allows analysis and visualization in the order of a billion rows per
second. We use streaming algorithms, memory mapped files and a zero memory copy
policy to allow exploration of datasets larger than memory, e.g. out-of-core
algorithms. Vaex allows arbitrary (mathematical) transformations using normal
Python expressions and (a subset of) numpy functions which are lazily evaluated
and computed when needed in small chunks, which avoids wasting of RAM. Boolean
expressions (which are also lazily evaluated) can be used to explore subsets of
the data, which we call selections. Vaex uses a similar DataFrame API as
Pandas, a very popular library, which helps migration from Pandas.
Visualization is one of the key points of vaex, and is done using binned
statistics in 1d (e.g. histogram), in 2d (e.g. 2d histograms with colormapping)
and 3d (using volume rendering). Vaex is split in in several packages:
vaex-core for the computational part, vaex-viz for visualization mostly based
on matplotlib, vaex-jupyter for visualization in the Jupyter notebook/lab based
in IPyWidgets, vaex-server for the (optional) client-server communication,
vaex-ui for the Qt based interface, vaex-hdf5 for hdf5 based memory mapped
storage, vaex-astro for astronomy related selections, transformations and
memory mapped (column based) fits storage. Vaex is open source and available
under MIT license on github, documentation and other information can be found
on the main website: https://vaex.io, https://docs.vaex.io or
https://github.com/maartenbreddels/vaexComment: 14 pages, 8 figures, Submitted to A&A, interactive version of Fig 4:
https://vaex.io/paper/fig
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