Vaex is an open source program and Python library to visualize and explore large tabular datasets. It can calculate statistics such as mean, sum, count, standard deviation etc, on an N-dimensional grid up to a billion (109) objects/rows per second.
The original motivation to develop Vaex is the Gaia astronomical catalogue containing over a billion stars (at least for data release 1, DR1). Vaex can visualize the complete Gaia catalogue in one second.
- Graphical interface for most common uses cases.
- Visualize and explore big tabular data interactively
- Renders histograms, density plots and volume rendering plots for visualization in the order of a billion (109) objects in the order of 1 second.
- Explore the dataset by using visual queries and Boolean expressions to visualize subsets of the data.
- Statistics such as mean, sum, count, standard deviation etc, can all be calculated on an N-dimensional grid.
- For exploration it supports selection in 1 and 2d, but it can also analyze the columns (dimensions) to find subspaces which are richer in information than others.
- Overplot vectors, for instance mean motions, tensors (for instance mean velocity dispersion tensor).
- Custom expressions, e.g. log(sqrt(x**2+y**2)), calculated on the fly.
- Uses memory mapping, zero memory copy policy and lazy computations for best performance. Memory mapped files avoids unnecessary reading and copying of data. Binning or aggregating the data on a grid, using simple optimized algorithms. Columnar storage of data avoids reading unnecessary data and enables maximum performance of hard drives.
- Publish quality output (using matplotlib).
- Linked views: selecting in 1 view will also select it in different views.
- Data formats supported:
- hdf5 (Hierarchical Data Format): gadget, Vaex’s own format;
- hdf5 from Amuse;
- fits bintable;
- VOtable over SAMP;
- gadget native format.
- Client/server architecture: Delegate computations to a remote server.
The Vaex library generates the same plots and more, and offers integration with Jupyter/IPython notebook.
- pip and conda installable.
- Make custom plot and statistics.
- Calculate statistics on a N-dimensional grid and visualize it.
- Create interactive Jupyter/IPython notebooks.
- Publication quality plots with matplotlib.
- Interactive plots with bqplot or Bokeh.
- Combine the notebook with the graphical interface in one kernel.
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