Metadata-Version: 1.1
Name: skymapper
Version: 0.3
Summary: Mapping astronomical survey data on the sky, handsomely
Home-page: https://github.com/pmelchior/skymapper
Author: Peter Melchior
Author-email: peter.m.melchior@gmail.com
License: MIT
Description-Content-Type: text/markdown
Description: [![PyPI](https://img.shields.io/pypi/v/skymapper.svg)](https://pypi.python.org/pypi/skymapper/)
        [![License](https://img.shields.io/github/license/pmelchior/skymapper.svg)](https://github.com/pmelchior/skymapper/blob/master/LICENSE.md)
        
        # Skymapper
        
        *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, find the optimal projection for a given list of RA/Dec coordinations and [creating a density map](examples/example1.py) from a catalog in a few lines:
        
        ```python
        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, crit=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
        sep = 15
        map.grid(sep=sep)
        
        # make density plot
        nside = 32
        mappable = map.density(ra, dec, nside=nside)
        cb = map.colorbar(mappable, cb_label="$n_g$ [arcmin$^{-2}$]")
        
        # add scatter plot
        map.scatter(ra_scatter, dec_scatter, s=size_scatter, edgecolor='k', facecolor='None')
        
        # focus on relevant region
        map.focus(ra, dec)
        ```
        
        ![Random density in DES footprint](https://github.com/pmelchior/skymapper/raw/master/examples/example1.png)
        
        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` for binning and interpolating samples (RA, Dec, and optional an associated value)
        * `text` (with an optional `direction in ['parallel','meridian']` argument to align along either graticule)
        
        as well as special functions
        
        * `footprint` to show the region covered by a survey
        * `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 in healpix cells
        * `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 setup.py install` or get the latest release with
        
        ```
        pip install skymapper
        ```
        
        Dependencies:
        
        * numpy
        * scipy
        * matplotlib
        * healpy
        
        For survey footprints, you'll need [`pymangle`](https://github.com/esheldon/pymangle).
        
        ## Background
        
        The essential parts of the workflow are
        
        1. Creating the `Projection`, e.g. `Hammer`, `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) and submit a pull request.
        
        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](http://pmelchior.net/blog/map-projections-for-surveys.html)).
        
        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:
        
        
        ```python
        crit = skm.projection.stdDistortion
        proj = skm.Albers.optimize(ra, dec, crit=crit)
        ```
        
        This optimizes the `Albers` projection 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 projection
        
        For constructing your own projection, derive from [`Projection`](skymapper/projection.py). You'll see that every projection needs to implement at least the first of these methods: 
        
        * `transform` to map from RA/Dec to map coordinates x/y
        * `invert` to map from x/y to RA/Dec (if not implemented defaults to basic and slow BFGS inversion)
        
        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`.
        
        ### Defining a survey
        
        To make it easy to share survey footprints (and other details), we can hold them in a common place. To create one you only need to derive a class from [`Survey`](skymapper/survey/__init__.py), which only needs to implement one method:
        
        ​	`def contains(self, ra, dec)` to determine whether RA, Dec are inside the footprint
        
        If this looks like the [`pymangle`](https://github.com/esheldon/pymangle) interface: it should. That means that you can avoid the overhead of having to define a survey and pass a `pymangle.Mangle` object directly to `footprint()`.
        
        ### Limitation(s)
        
        The combination of `Map` and `Projection` is *not* a [matplotlib transformation](http://matplotlib.org/users/transforms_tutorial.html). 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 split the problem: making use of matplotlib for lower-level graphics primitive and layering the 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.
Keywords: visualization,map projection,matplotlib
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: License :: OSI Approved :: MIT License
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Education
Classifier: Intended Audience :: Science/Research
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python
Classifier: Topic :: Scientific/Engineering :: Astronomy
Requires: matplotlib
Requires: numpy
Requires: scipy
Requires: healpy
