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Mapping astronomical survey data on the sky, handsomely

Project description

PyPI License


A collection of matplotlib instructions to map astronomical survey data from the celestial sphere onto 2D.

The purpose of this package is to facilitate interactive work as well as the the creation of publication-quality plots with a python-based workflow many astronomers are accustomed to. The primary motivation is a truthful representation of samples and fields from the curved sky in planar figures, which becomes relevant when sizable portions of the sky are observed.

What can it do? For instance, creating a density map from a catalog in a few lines:

import skymapper as skm

# define the best Albers projection for the footprint
# minimizing the variation in distortion
crit = skm.stdDistortion
proj = skm.Albers.optimize(ra, dec, reduce_fct=crit)

# construct map: will hold figure and projection
# the outline of the sphere can be styled with kwargs for matplotlib Polygon
map = skm.Map(proj)

# add graticules, separated by 15 deg
# the lines can be styled with kwargs for matplotlib Line2D
# additional arguments for formatting the graticule labels

# make density plot
nside = 32
mappable = map.density(ra, dec, nside=nside, clip_path=footprint)
cb = map.colorbar(mappable, cb_label="$n_g$ [arcmin$^{-2}$]")

# add footprint
map.footprint("DES", zorder=20, edgecolor='#2222B2', facecolor='None', lw=1)

Random density in DES footprint

For exploratory work, you can zoom and pan, also scroll in/out (google-maps style). The map will automatically update the location of the graticule labels, which are not regularly spaced.

The syntax mimics matplotlib as closely as possible. Currently supported are canonical plotting functions

  • plot
  • scatter
  • hexbin
  • text (with an optional direction in ['parallel','meridian'] argument to align along a graticule)

as well as special functions

  • footprint to load a RA/Dec list and turn it into a closed polygon
  • vertex to plot a list of simple convex polygons
  • healpix to plot a healpix map as a list of polygons
  • density to create a density map (see example above)
  • interpolate to interpolate given samples over their convex hull
  • extrapolate to generate a field from samples over the entire sky or a subregion

Installation and Prerequisites

You can either clone the repo and install by python install or get the latest release with

pip install skymapper


  • numpy
  • scipy
  • matplotlib

For the healpix methods, you'll need healpy.


The essential parts of the workflow are

  1. Creating the Projection, e.g. Albers
  2. Setting up a Map to hold the projection and matplotlib figure, ax, ...
  3. Add data to the map

Several map projections are available, the full list is stored in the dictionary projection_register. If the projection you want isn't included, open an issue, or better: create it yourself (see below).

Since most ground-based surveys have predominant East-West coverage, we suggest using conic projections, in particular the equal-area Albers conic (an discussion why exactly that one is here).

Map projections can preserve sky area, angles, or distances, but never all three. That means defining a suitable projection must be a compromise. For most applications, sizes should exactly be preserved, which means that angles and distances may not be. The optimal projection for a given list of ra, dec can be found by calling:

crit = skm.projection.stdDistortion
proj = skm.Albers.optimize(ra, dec, reduce_fct=crit)

This optimizes the Albers parameters to minimize the variance of the map distortion (i.e. the apparent ellipticity of a true circle on the sky). Alternative criteria are e.g. maxDistortion or stdScale (for projections that are not equal-area.)

Defining a survey

To make it easy to share survey footprints and optimal projections, we can hold them in a common place. To create one you only need to derive a class from Survey, which only needs to implement two methods:

  • getFootprint to return a RA, Dec list of the footprint
  • getConfigfile to return the full path of a pickled map configuration, saved by

Defining a projection

For constructing your own projection, derive from Projection. You'll see that every projection needs to implement three methods:

  • transform to map from RA/Dec to map coordinates x/y
  • invert to map from x/y to RA/Dec
  • contains to test whether a given x/y is a valid map coordinate

If the projection has several parameters, you will want to create a special @classmethod optimize because the default one only determines the best RA reference. An example for that is given in e.g. ConicProjection.optimize.


The projection is not a matplotlib transformation. Among several reasons, it is very difficult (maybe impossible) to work with the matplotlib.Axes that are not rectangles or ellipses. So, we decided to make use of matplotlib for lower-level graphics primitive and to layer to map-making on top of it. This way, we can control e.g. the interpolation method on the sphere or the location of the tick labels in a way consistent with visual expectations from hundreds of years of cartography. While skymapper tries to follow matplotlib conventions very closely, some methods may not work as expected. Open an issue if you think you found such a case.

In particular, we'd appreciate help to make sure that the interactive features work well on all matplotlib backends.

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