Installation

TL;DR

The recommended way to install is using pip:

python -m pip install --only-binary galpy galpy

This should install a fully-working version of galpy for Python versions >=3.8. If this fails, please open an issue on the galpy GitHub page, making sure to specify your platform and Python version. Then read on at Detailed installation instructions to learn how to install galpy when the above fails.

The recommended way to install in non-conda environments (e.g., venv) is using pip:

python -m pip install --only-binary galpy galpy

If you are using conda, especially if you used it to install numpy, the recommended way to install is using conda:

conda install -c conda-forge gsl galpy

These should install a fully-working version of galpy for Python versions >=3.8. If this fails, please open an issue on the galpy GitHub page, making sure to specify your platform and Python version. Then read on at Detailed installation instructions to learn how to install galpy when the above fails.

The recommended way to install is using conda:

conda install -c conda-forge gsl galpy

Note that on Windows it is necessary to explicitly install the GNU Scientific Library (GSL) in this way.

For more info on installation options, jump to the detailed instructions below: Detailed installation instructions

Dependencies

galpy requires the packaging, numpy, scipy, and matplotlib packages; these must be installed or the code will not be able to be imported. All installation methods described on this page will automatically install these required dependencies.

Optional dependencies are:

  • astropy for Quantity support (used throughout galpy when installed),

  • astroquery for the Orbit.from_name initialization method (to initialize using a celestial object’s name),

  • tqdm for displaying a progress bar for certain operations (e.g., orbit integration of multiple objects at once)

  • numexpr for plotting arbitrary expressions of Orbit quantities,

  • numba for speeding up the evaluation of certain functions when using C orbit integration,

  • JAX for use of constant-anisotropy DFs in galpy.df.constantbetadf, and

  • pynbody for use of SnapshotRZPotential and InterpSnapshotRZPotential.

Detailed installation instructions

If you are reading this, either the simple installation instructions at the top of this page did not work, you are trying to install the latest bleeding-edge version, or you are trying to set up a development installation. For info on setting up a development installation, see Development installation. In this section, we cover how to install the latest release or the latest bleeding-edge version for use on different platforms.

Warning

When using conda to install the GSL, make sure to install it from the conda-forge channel using conda install -c conda-forge gsl. If you install the GSL from the anaconda channel, it will often not work with galpy. In an environment.yml file, use - conda-forge::gsl.

As discussed in the TL;DR section above, the simplest and quickest way to install the latest galpy release on Linux is to use pip:

python -m pip install --only-binary galpy galpy

Alternatively, you can install both the GSL and galpy using conda:

conda install -c conda-forge gsl galpy

To compile galpy from source, you will first need to install the GSL. The easiest way to do this is using your package manager. On Linux distributions with apt-get, do:

apt-get install libgsl0-dev

or on distros with yum, do:

yum install gsl-devel

Alternatively, you can use conda to install the GSL and use conda to manage your Python environment. Install the GSL in your preferred environment with:

conda install -c conda-forge gsl

Once you have installed the GSL, compile galpy from source using:

export CFLAGS="$CFLAGS -I`gsl-config --prefix`/include"
export LDFLAGS="$LDFLAGS -L`gsl-config --prefix`/lib"
python -m pip install --no-binary galpy galpy

The commands in this section so far all install the latest release. If you want to install the latest bleeding-edge version, you have two options. If the installation in the TL;DR works for you, you can download a binary wheel for the latest main branch version on GitHub, which is available here. To install these wheels, download the relevant version for your operating system and Python version and do:

python -m pip install WHEEL_FILE.whl

These wheels have stable ...latest... names, so you can embed them in workflows that should always be using the latest version of galpy (e.g., to test your code against the latest development version).

If this doesn’t work, follow the steps above to install the GSL, define the relevant environment variables, and then install from source using:

python -m pip install git+https://github.com/jobovy/galpy.git#egg=galpy

You can also download the source code or clone the repository, navigate to the top-level directory, and install using:

python -m pip install .

As discussed in the TL;DR section above, the simplest and quickest way to install the latest galpy release on a Mac is to use pip:

python -m pip install --only-binary galpy galpy

However, if you are using conda and, particularly, if you used it to install numpy, issues can arise with multiple OpenMP runtimes and the best way to avoid those is to install using conda:

conda install -c conda-forge gsl galpy

To compile galpy from source, you will first need to install the GSL and OpenMP. The easiest way to do this is using Homebrew as:

brew install gsl libomp

Note that in order not to conflict with other OpenMP installations, the Homebrew version of libomp is “keg-only” and you have to set the environment variables to use it. You can do this by running:

export CFLAGS=-I$(brew --prefix)/include -I/usr/local/opt/libomp/include
export LDFLAGS=-L$(brew --prefix)/lib -L/usr/local/opt/libomp/lib
export LD_LIBRARY_PATH=$(brew --prefix)/lib:/usr/local/opt/libomp/lib

Note that the exact paths may vary depending on your system (run brew info libomp to get the correct paths).

Alternatively, you can use conda to install the GSL and OpenMP and use conda to manage your Python environment. Install the GSL and OpenMP in your preferred environment with:

conda install -c conda-forge gsl llvm-openmp

Then set the path and relevant environment variables using:

export CFLAGS="$CFLAGS -I`gsl-config --prefix`/include"
export LDFLAGS="$LDFLAGS -L`gsl-config --prefix`/lib"

Once you have installed the GSL and OpenMP, compile galpy from source using:

python -m pip install --no-binary galpy galpy

The commands in this section so far all install the latest release. If you want to install the latest bleeding-edge version, you have two options. If the installation in the TL;DR works for you, you can download a binary wheel for the latest main branch version on GitHub, which is available here. To install these wheels, download the relevant version for your operating system and Python version and do:

python -m pip install WHEEL_FILE.whl

These wheels have stable ...latest... names, so you can embed them in workflows that should always be using the latest version of galpy (e.g., to test your code against the latest development version).

If this doesn’t work, follow the steps above to install the GSL and OpenMP, define the relevant environment variables, and then install from source using:

python -m pip install git+https://github.com/jobovy/galpy.git#egg=galpy

You can also download the source code or clone the repository, navigate to the top-level directory, and install using:

python -m pip install .

As discussed in the TL;DR section above, the simplest and quickest way to install the latest galpy release on Windows is to use conda:

conda install -c conda-forge gsl galpy

If you want to install the latest bleeding-edge version, you have to install the GSL first as. In an existing conda environment, do:

conda install -c conda-forge gsl

while if you don’t want to use conda to manage your Python environment, you can do:

conda create -n gsl conda-forge::gsl
conda activate gsl

Either way, then set the path and relevant environment variables using:

set PATH=%PATH%;"$CONDA_PREFIX\\Library\\bin"
set INCLUDE=%CONDA_PREFIX%\Library\include;%INCLUDE%
set LIB=%CONDA_PREFIX%\Library\lib;%LIB%
set LIBPATH=%CONDA_PREFIX%\Library\lib;%LIBPATH%

in the CMD shell or:

$env:Path+="$env:CONDA_PREFIX\Library\bin"
$env:INCLUDE="$env:CONDA_PREFIX\Library\include"
$env:LIB="$env:CONDA_PREFIX\Library\lib"
$env:LIBPATH="$env:CONDA_PREFIX\Library\lib"

if you are using PowerShell. Note that you have to execute these commands from the conda environment such that the CONDA_PREFIX variable is set. To compile with OpenMP on Windows, you have to also install Intel OpenMP via:

conda install -c anaconda intel-openmp

Then you can deactivate the conda environment (but you don’t have to!).

With the GSL set up, you can then download a binary wheel for the latest main branch version on GitHub, which is available here. To install these wheels, download the relevant version for your operating system and Python version and do:

python -m pip install WHEEL_FILE.whl

You can also compile from source using:

python -m pip install git+https://github.com/jobovy/galpy.git#egg=galpy

or you can download the source code or clone the repository, navigate to the top-level directory, and install using:

python -m pip install .

Whenever you run galpy, you have to adjust the PATH variable as above. These wheels have stable ...latest... names, so you can embed them in workflows that should always be using the latest version of galpy (e.g., to test your code against the latest development version).

Development installation

To install galpy for local development (i.e., you are changing the galpy source code), first fork the repository on GitHub to your own account and then clone it to your local machine:

git clone git@github.com:YOUR_GITHUB_USERNAME/galpy.git

Then, install the GSL and OpenMP as described in the relevant Detailed installation instructions section above. Then compile galpy from source:

python -m pip install -e .

Whenever you change the C code, you have to re-run the last command. Note that any development should happen on a branch with an informative name.

To test the code locally, install pytest:

pip install pytest

You might also need to make sure to install the optional dependencies as discussed here depending on which tests you want to run. Running the entire test code takes a long time and isn’t recommended (CI does that). Tests are arranged in files for large chunks of related functionality and you would typically run a single one of these locally. For example:

pytest -vxs tests/test_coords.py

The ‘-v’ flag is for verbose output, the ‘-x’ flag stops after the first failure, and the ‘-s’ flag prints output from print statements. You can also run a single test in a file, e.g.,:

pytest -vxs tests/test_coords.py::test_radec_to_lb_ngp

to just run the test_radec_to_lb_ngp test. You can also run tests with names that match a pattern, e.g.,:

pytest -vxs tests/test_coords.py -k "ngp"

to run all tests in test_coords.py that have ngp in their name.

galpy uses pre-commit to run a number of pre-commit checks on the code. To install pre-commit, do:

pip install pre-commit

and then run:

pre-commit install

to install the pre-commit hooks. These will run automatically whenever you commit.

More esoteric installations

Installing the TorusMapper code

Warning

The TorusMapper code is not part of any of galpy’s binary distributions (installed using conda or pip); if you want to gain access to the TorusMapper, you need to install from source as explained in this section and above.

Since v1.2, galpy contains a basic interface to the TorusMapper code of Binney & McMillan (2016). This interface uses a stripped-down version of the TorusMapper code, that is not bundled with the galpy code, but kept in a fork of the original TorusMapper code. Installation of the TorusMapper interface is therefore only possible when installing from source after downloading or cloning the galpy code and installing using pip install ..

To install the TorusMapper code, clone the galpy repository and before running the installation of galpy, navigate to the top-level galpy directory (which contains the setup.py file) and do:

git clone https://github.com/jobovy/Torus.git galpy/actionAngle/actionAngleTorus_c_ext/torus
cd galpy/actionAngle/actionAngleTorus_c_ext/torus
git checkout galpy
cd -

Then proceed to install galpy using the pip install . technique or its variants as usual.

Using galpy in web applications

galpy can be compiled to WebAssembly using the emscripten compiler. In particular, galpy is part of the pyodide Python distribution for the browser, meaning that galpy can be used on websites without user installation and it still runs at the speed of a compiled language. This powers, for example, the Try galpy interactive session on this documentation’s home page. Thus, it is easy to, e.g., build web-based, interactive galactic-dynamics examples or tutorials without requiring users to install the scientific Python stack and galpy itself.

galpy is included in versions >0.20 of pyodide, so galpy can be imported in any web context that uses pyodide (e.g., jupyterlite or pyscript). Python packages used in pyodide are compiled to the usual wheels, but for the emscripten compiler. Such a wheel for the latest development version of galpy is always available at galpy-latest-cp310-cp310-emscripten_wasm32.whl (note that this URL will change for future pyodide versions, which include emscripten version numbers in the wheel name). It can be used in pyodide for example as

>>> import pyodide_js
>>> await pyodide_js.loadPackage(['numpy','scipy','matplotlib','astropy',
        'future','setuptools',
        'https://www.galpy.org/wheelhouse/galpy-latest-cp310-cp310-emscripten_wasm32.whl'])

after which you can import galpy and do (almost) everything you can in the Python version of galpy (everything except for querying Simbad using Orbit.from_name and except for Orbit.animate). Note that depending on your context, you might have to just import pyodide to get the loadPackage function.

Installation FAQ

I get warnings like “galpyWarning: libgalpy C extension module not loaded, because libgalpy.so image was not found”

This typically means that the GNU Scientific Library (GSL) was unavailable during galpy’s installation, causing the C extensions not to be compiled. Most of the galpy code will still run, but slower because it will run in pure Python. The code requires GSL versions >= 1.14. If you believe that the correct GSL version is installed for galpy, check that the library can be found during installation (see below).

I get warnings like “libgalpy C extension module not loaded, because of error ‘dlopen(……/site-packages/libgalpy.cpython-310-darwin.so, 0x0006): Library not loaded: ‘@rpath/libgsl.25.dylib’ etc.”

This happens when galpy was successfully compiled against the GSL, but the GSL is not available at runtime. This mainly happens when you installed a binary package (e.g,, using conda or a Windows wheel from pip) and you don’t have the GSL or the correct version available locally.

For example, this commonly happens when you have installed the GSL using conda from the anaconda channel, which often happens because most people have the defaults channel at higher priority than the conda-forge channel. Use:

conda list gsl

to check the channel from which the GSL was installed. If it was not the conda-forge channel, uninstall the GSL:

conda uninstall gsl

and re-install from conda-forge:

conda install -c conda-forge gsl

I get the warning “galpyWarning: libgalpy_actionAngleTorus C extension module not loaded, because libgalpy_actionAngleTorus.so image was not found”

This is typically because the TorusMapper code was not compiled, because it was unavailable during installation. This code is only necessary if you want to use galpy.actionAngle.actionAngleTorus. See above for instructions on how to install the TorusMapper code. Note that in recent versions of galpy, you should not be getting this warning, unless you set verbose=True in the configuration file.

How do I install the GSL?

The easiest way to install this is using its Anaconda build:

conda install -c conda-forge gsl

If you do not want to go that route, on a Mac, the next easiest way to install the GSL is using Homebrew as:

brew install gsl

You should be able to check your version using (on Mac/Linux):

gsl-config --version

On Linux distributions with apt-get, the GSL can be installed using:

apt-get install libgsl0-dev

or on distros with yum, do:

yum install gsl-devel

On Windows, using conda-forge to install the GSL is your best bet, but note that this doesn’t mean that you have to use conda for the rest of your Python environment. You can simply use a conda environment for the GSL, while using pip to install galpy and other packages. However, in that case, you need to add the relevant conda environment to your PATH. So, for example, you can install the GSL as:

conda create -n gsl conda-forge::gsl
conda activate gsl

and then set the path using:

set PATH=%PATH%;"$CONDA_PREFIX\\Library\\bin"

in the CMD shell or:

$env:Path+="$env:CONDA_PREFIX\Library\bin"

if you are using PowerShell. Note that you have to execute these commands from the conda environment such that the CONDA_PREFIX variable is set. You also still have to set the INCLUDE, LIB, and LIBPATH environment variables as discussed in detailed_installation_win (also from the conda environment). Then you can deactivate the conda environment and install galpy using, e.g., pip. Whenever you run galpy, you have to adjust the PATH variable as above.

The galpy installation fails because of C compilation errors

galpy’s installation from source can fail due to compilation errors, which look like:

error: command 'gcc' failed with exit status 1

or:

error: command 'clang' failed with exit status 1

or:

error: command 'cc' failed with exit status 1

This is typically because the compiler cannot locate the GSL header files or the GSL library. You can tell the installation about where you’ve installed the GSL library by defining (for example, when the GSL was installed under /usr; the LD_LIBRARY_PATH part of this may or may not be necessary depending on your system):

export CFLAGS=-I/usr/include
export LDFLAGS=-L/usr/lib
export LD_LIBRARY_PATH=/usr/lib

or:

setenv CFLAGS -I/usr/include
setenv LDFLAGS -L/usr/lib
setenv LD_LIBRARY_PATH /usr/lib

depending on your shell type (change the actual path to the include and lib directories that have the gsl directory). If you already have CFLAGS, LDFLAGS, and LD_LIBRARY_PATH defined you just have to add the '-I/usr/include', '-L/usr/lib', and '/usr/lib' to them.

If you are on a Mac or UNIX system (e.g., Linux), you can find the correct CFLAGS and LDFLAGS/LD_LIBRARY_path entries by doing:

gsl-config --cflags
gsl-config --prefix

where you should add /lib to the output of the latter.

I have defined CFLAGS, LDFLAGS, and LD_LIBRARY_PATH, but the compiler does not seem to include these and still returns with errors

This typically happens if you install using sudo, but have defined the CFLAGS etc. environment variables without using sudo. Try using sudo -E instead, which propagates your own environment variables to the sudo user.

I’m having issues with OpenMP

galpy uses OpenMP to parallelize various of the computations done in C. galpy can be installed without OpenMP by specifying the option --no-openmp when running the installation commands above:

pip install . --install-option="--no-openmp"

or when using pip as follows:

pip install -U --no-deps --install-option="--no-openmp" git+https://github.com/jobovy/galpy.git#egg=galpy

This might be useful if one is using the clang compiler, which is the new default on macs with OS X (>= 10.8), but does not support OpenMP. clang might lead to errors in the installation of galpy such as:

ld: library not found for -lgomp

clang: error: linker command failed with exit code 1 (use -v to see invocation)

If you get these errors, you can use the commands given above to install without OpenMP, or specify to use gcc by specifying the CC and LDSHARED environment variables to use gcc. Note that recent versions of galpy attempt to automatically detect OpenMP support, so using --no-openmp should not typically be necessary even on Macs.

Configuration file

Since v1.2, galpy uses a configuration file to set a small number of configuration variables. This configuration file is parsed using ConfigParser/configparser. It is currently used:

  • to set a default set of distance and velocity scales (ro and vo throughout galpy) for conversion between physical and internal galpy unit

  • to decide whether to use seaborn plotting with galpy’s defaults (which affects all plotting after importing galpy.util.plot),

  • to specify whether output from functions or methods should be given as an astropy Quantity with units as much as possible or not, and whether or not to use astropy’s coordinate transformations (these are typically somewhat slower than galpy’s own coordinate transformations, but they are more accurate and more general)

  • to set the level of verbosity of galpy’s warning system (the default verbose=False turns off non-crucial warnings).

  • To set options related to whether or not to check for new versions of galpy (do-check= False turns all such checks off; check-non-interactive sets whether or not to do the version check in non-interactive (script) sessions; check-non-interactive sets the cadence of how often to check for version updates in non-interactive sessions [in days; interactive sessions always check]; last-non-interactive-check is an internal variable to store when the last check occurred)

The current configuration file therefore looks like this:

[normalization]
ro = 8.
vo = 220.

[plot]
seaborn-bovy-defaults = False

[astropy]
astropy-units = False
astropy-coords = True

[warnings]
verbose = False

[version-check]
do-check = True
check-non-interactive = True
check-non-interactive-every = 1
last-non-interactive-check = 2000-01-01

where ro is the distance scale specified in kpc, vo the velocity scale in km/s, and the setting is to not return output as a Quantity. These are the current default settings.

A user-wide configuration file should be located at $HOME/.galpyrc. This user-wide file can be overridden by a $PWD/.galpyrc file in the current directory. If no configuration file is found, the code will automatically write the default configuration to $HOME/.galpyrc. Thus, after installing galpy, you can simply use some of its simplest functionality (e.g., integrate an orbit), and after this the default configuration file will be present at $HOME/.galpyrc. If you want to change any of the settings (for example, you want Quantity output), you can edit this file. The default configuration file can also be found here.