Install#

Package installation#

Stable (with conda)

This is our recommend installation method! Follow the steps below to start using cogsworth!

Create a new empty conda environment for cogsworth:
```
conda create --name cogsworth python=3.10
```
Activate the environment by running:
```
conda activate cogsworth
```
Install cogsworth by running:
```
pip install cogsworth
```
OPTIONAL: If you want to install some of the cogsworth extras (this is necessary for some tutorials and examples, particularly those on observables predictions and postprocessing hydrodynamical simulations) then you can do so by running:
```
pip install 'cogsworth[extras]'
```

and you should be all set! Now it’s time to learn about Getting Started with cogsworth.

Stable (without conda)

We don’t recommend installing cogsworth without a conda environment but if you prefer to do it this way then all you need to do is run:

pip install cogsworth

OPTIONALLY if you want to install some of the cogsworth extras (this is necessary for some tutorials and examples, particularly those on observables predictions and postprocessing hydrodynamical simulations) then you can do so by instead running:

pip install 'cogsworth[extras]'

and you should be all set! Now it’s time to learn about Getting Started with cogsworth.

Development (from GitHub)

Warning

We don’t guarantee that there won’t be mistakes or bugs in the development version, use at your own risk!

The latest development version is available directly from our GitHub Repo. To start, clone the repository onto your machine:

git clone https://github.com/TomWagg/cogsworth
cd cogsworth

Next, we recommend that you create a Conda environment for working with cogsworth. You can do this by running:

conda create --name cogsworth python=3.10

And then activate the environment by running:

conda activate cogsworth

At this point, all that’s left to do is install cogsworth!:

pip install .

OPTIONALLY if you want to install some of the cogsworth extras (this is necessary for some tutorials and examples, particularly those on observables predictions and postprocessing hydrodynamical simulations) then you can do so by instead running:

pip install '.[extras]'

and you should be all set! Now it’s time to learn about Getting Started with cogsworth.

Tip

If you also want to work with Jupyter notebooks then you’ll also need to install jupyter/ipython to this environment!

Windows Installation

Installation of cogsworth on Windows without Windows Subsystem for Linux (WSL) is not supported. This is because some of the dependencies of cogsworth are not available on Windows. If you are using Windows, we recommend using WSL to install cogsworth. You can learn more about installing it here.

Dependencies#

Core Dependencies

Install via: pip install cogsworth

The core dependencies for a basic cogsworth installation are listed below.

cosmic for binary population synthesis
gala for galactic dynamics
numpy for vectorised operations
pandas for handling COSMIC dataframes
matplotlib for plotting
scipy for statistical distrubtions and integration
astropy for coordinate transformations

Development Dependencies

Install via: pip install ‘cogsworth[all]’

For developers of cogsworth there are also additional dependencies for testing (pytest, coverage, etc.) and documentation building (sphinx, nbspinx, etc.).

Most users do not need these dependencies.

Optional Dependencies

Install via: pip install ‘cogsworth[extras]’

In addition to the core dependencies, there are a number of optional dependencies that are required for some of the tutorials and examples in the documentation.

Observables predictions:

isochrones for applying bolometric corrections using stellar isochrones
dustmaps for accounting for dust extinction
gaiaunlimited for applying the empirical Gaia selection function

Postprocessing hydrodynamical simulations:

pynbody for reading and manipulating hydrodynamical simulations

LISA gravitational wave sources:

legwork for calculating LISA gravitational wave signals

Action-based galactic potentials:

agama for action-based galactic potentials

Data downloads for observables#

If you want to make predictions for observables with cogsworth then you’ll need to download some data files. Specifically, dustmaps requires the actual dust map files and gaiaunlimited needs data for the empirical gaia selection function. Below are some code snippets for downloading these files.

Dust maps#

If you’d like to apply dust maps then you’ll need to download the right files. This will depend on the dust map that you use, but here’s an example for Bayestar2019 that downloads the files if you don’t have them already:

import os
import dustmaps.bayestar
from dustmaps.std_paths import data_dir
bayestar_path = os.path.join(data_dir(), 'bayestar', '{}.h5'.format("bayestar2019"))
if not os.path.exists(bayestar_path):
    dustmaps.bayestar.fetch()

Gaia empirical selection function#

If you’d like to use cogsworth to make predictions for which stars are observable by Gaia then you’ll need to run the following to ensure there’s a directory for the files:

import os
gaia_unlimited_path = os.path.join(os.path.expanduser('~'), ".gaiaunlimited")
if not os.path.isdir(gaia_unlimited_path):
    os.mkdir(gaia_unlimited_path)