With new catalogues arriving such as the Gaia DR1, containing more than a billion objects, new methods of handling and visualizing these data volumes are needed. We show that by calculating statistics on a regular (N-dimensional) grid, visualizations of a billion objects can be done within a second on a modern desktop computer. This is achieved using memory mapping of hdf5 files together with a simple binning algorithm, which are part of a Python library called vaex. This enables efficient exploration or large datasets interactively, making science exploration of large catalogues feasible. Vaex is a Python library and an application, which allows for interactive exploration and visualization. The motivation for developing vaex is the catalo...
International audienceContext. The first Gaia data release (DR1) delivered a catalogue of astrometry...
International audienceContext. The first Gaia data release (DR1) delivered a catalogue of astrometry...
International audienceContext. The first Gaia data release (DR1) delivered a catalogue of astrometry...
With new catalogues arriving such as the Gaia DR1, containing more than a billion objects, new metho...
With new catalogues arriving such as the Gaia DR1, containing more than a billion objects, new metho...
With new catalogues arriving such as the Gaia DR1, containing more than a billion objects, new metho...
With new catalogues arriving such as the Gaia DR1, containing more than a billion objects, new metho...
We present a new Python library, called vaex, intended to handle extremely large tabular datasets su...
We present a new Python library, called vaex, intended to handle extremely large tabular datasets su...
We present a new Python library, called vaex, intended to handle extremely large tabular datasets su...
We present a new Python library, called vaex, intended to handle extremely large tabular datasets su...
We present a new Python library, called vaex, intended to handle extremely large tabular datasets su...
We present a new Python library, called vaex, intended to handle extremely large tabular datasets su...
We present a new Python library, called vaex, intended to handle extremely large tabular datasets su...
International audienceApache Spark is a Big Data framework for working on large distributed datasets...
International audienceContext. The first Gaia data release (DR1) delivered a catalogue of astrometry...
International audienceContext. The first Gaia data release (DR1) delivered a catalogue of astrometry...
International audienceContext. The first Gaia data release (DR1) delivered a catalogue of astrometry...
With new catalogues arriving such as the Gaia DR1, containing more than a billion objects, new metho...
With new catalogues arriving such as the Gaia DR1, containing more than a billion objects, new metho...
With new catalogues arriving such as the Gaia DR1, containing more than a billion objects, new metho...
With new catalogues arriving such as the Gaia DR1, containing more than a billion objects, new metho...
We present a new Python library, called vaex, intended to handle extremely large tabular datasets su...
We present a new Python library, called vaex, intended to handle extremely large tabular datasets su...
We present a new Python library, called vaex, intended to handle extremely large tabular datasets su...
We present a new Python library, called vaex, intended to handle extremely large tabular datasets su...
We present a new Python library, called vaex, intended to handle extremely large tabular datasets su...
We present a new Python library, called vaex, intended to handle extremely large tabular datasets su...
We present a new Python library, called vaex, intended to handle extremely large tabular datasets su...
International audienceApache Spark is a Big Data framework for working on large distributed datasets...
International audienceContext. The first Gaia data release (DR1) delivered a catalogue of astrometry...
International audienceContext. The first Gaia data release (DR1) delivered a catalogue of astrometry...
International audienceContext. The first Gaia data release (DR1) delivered a catalogue of astrometry...