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The recommended install instructions will provide these dependencies for you through an Anaconda distribution

From the rest of the official LSST software stack, it depends on 

• base
• pex_config
• catalogs_generation

There are additional python, third-party dependencies, all of which can be installed through the LSST software distribution package manager. 

These additional packages are: 

• numpy
• scipy
• matplotlib
• SqlAlchemy
• healpy
• palpy
• pyephem

and depending on what kind of database you'd like to use, there will be some additional dependency on a python SQL driver for your database. SQLite compatibility is built into python, and LSST provides MySQL and SQLServer compatibility.

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Recommended Installation Instructions

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The recommended (simplest) approach is to use the newest W14 LSST software stack, and use the LSST-managed provided version of anaconda python. (Note that you can still See here if you want to use your own python instead - if you prefer this method, instructions for this custom procedure are included on this page). 

AnchorLSST anaconda pythonLSST anaconda pythonStart 1. Start by installing the necessary parts of the LSST software stack and the LSST-installed anaconda. 

Here, In these instructions we assume you are installing the stack in the directory ~/lsst, but feel free to replace that with any directory you like, however the install directory can be any place in the file system, including a place visible to all users.

mkdir ~/lsst
cd ~/lsst
curl -O http://sw.lsstcorp.org/eupspkg/newinstall.sh
bash newinstall.sh

Answer the prompts for newinstall.sh – in particular, choose 'yes' to have LSST install and manage Anaconda python. The newinstall.sh script will then install Anaconda python for you and declare this to eups (LSST's software package manager). 

After running 'bash newinstall.sh' above, then enter (if you use .csh) 

Choose yes when prompted to install Anaconda.  Choose yes when prompted to install git.  These packages will not interfere with your system installed versions.

 2. Set up the environment and install the simulations code and data.

Source the appropriate shell script and use eups (and here for more info) to install the software and data. 

source ~/lsst/loadLSST.csh 
eups distrib install lsst_sims -t sims

this This will install MAF all packages currently in the catalogs simulations framework and metrics analysis framework and all it's dependencies, and declare them to eups (the LSST software package manager). 

Alternatively, if you use .bash, then enter 'source loadLSST.sh' (and substitute loadLSST.sh everywhere for loadLSST.csh below). The rest of these instructions also assume .csh shell .. if you use .bash instead, simply translate 'setenv' into 'EXPORT' statements. 

The following is convenient to add a file that is easy to source when you log in, so that the LSST stack and MAF can be set up and ready to go in one step.  (Adding these to your .cshrc is optional). (Note that you do NOT have to define LSST_HOME before sourcing the loadLSST.csh file).

source ~/lsst/loadLSST.csh
setup sims_maf

dependencies.  Currently the complete list of installed packages is:

• base – LSST import utilities
• boost– Third party C++ package
• cfitsio– FITSIO I/O library
• daf_base – LSST data access framework 
• doxygen– Documentation suite
• freetds– OpenSource implementation for Tabular Data Streams (needed by pymssql)
• healpy– Python bindings for HealPix 
• palpy– Python binidings for PAL (Positional Astronomy Library)
• pex_config – LSST configuration package
• pex_exceptions – LSST exception handling package.
• pex_policy – Another LSST configuration package
• pyephem– Ephemeris generation code
• pyfits– Python bindings for interacting with FITS files
• pykg_config– Pure python implementation of pkg_config needed to install healpy on Mac OSX
• pymssql– Python bindings for talking to MS-SqlServer databases (needed to access UW base catalogs)
• scons– Software construction  tool
• sconsUtils – LSST utilities for scons
• sims_catalogs_generation – Code for querying databases for catalog data
• sims_catalogs_measures – Code for constructing observed catalogs including variable photometry and astrometry
• sims_dustmaps – SFD dust maps
• sims_maf – Metrics Analysis Framework
• sims_sed_library – Library of SED data for simulated catalogs
• swig– Library for auto-generating python bindings for C++ code
• throughputs – Nominal throughput curves for SDSS and LSST systems
• utils – LSST utilities package

Any of the above packages and all its dependencies can be installed by replacing lsst_sims with the appropriate package name (e.g. sims_maf) in the above code snippet.  Installation is now complete.  See package specific pages for documentation.

 DONE!  You are ready to try out the MAF examples.