archeo 1.9.9
Bayesian framework for inferring natal kick, ancestral masses and spins of black holes.
License: MIT
Requires Python: <4.0,>=3.11
Keywords: black-holes, gravitational-waves, black-hole-archeology

# ARCHEO

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Archeo is a Python package designed to infer the natal kick, ancestral masses, and spins of black holes in the Pair-instability Supernova (PISN) gap,
with a particular focus on hierarchical black hole formation.

## Basic Usage with Command Line Interface (CLI)

We provide a command line interface (CLI) for archeo, which allows users to generate preset priors and visualize prior distributions easily.

```
> python -m archeo --help

Usage: python -m archeo [OPTIONS] COMMAND [ARGS]...

  Command line interface for archeo

Options:
  --help  Show this message and exit.

Commands:
  generate-preset-prior  Generate a preset prior.
  visualize-prior        Visualize the prior distribution.
```

In the following,
we will introduce the available commands in the CLI.

### Generate a Preset Prior with CLI

We provide a command to generate preset priors, which can be used for further analysis.

```
> python -m archeo generate-preset-prior --help
Usage: python -m archeo generate-preset-prior [OPTIONS]

  Generate a preset prior.

Options:
  -n, --name TEXT        Preset prior name, available values are default,
                         agnostic_precessing_spin, agnostic_aligned_spin,
                         precessing_spin, aligned_spin,
                         positively_aligned_spin.
  -o, --output-dir TEXT  Directory to save the generated prior configuration.
  --help                 Show this message and exit.
```

Here is an example of how to generate a preset prior using the CLI:

```bash
python -m archeo generate-preset-prior
```

This command will generate the default prior configuration and save it in the current directory.
Note that the default prior is an aligned spin prior with only 1000 samples.
So we expect it to be fast to generate (within 1 minute).
To generate other priors, you can specify the `--name` option with one of the available values.
For example,

```bash
python -m archeo generate-preset-prior --name agnostic_precessing_spin
```

### Visualize the Prior Distribution with CLI

We provide a command to visualize the generated ancestral prior distribution.

```
> python -m archeo visualize-prior --help
Usage: python -m archeo visualize-prior [OPTIONS]

  Visualize the prior distribution.

Options:
  -f, --filepath TEXT    Path to the prior data.  [required]
  -o, --output-dir TEXT  Directory to save the visualization output.
  --help                 Show this message and exit.
```

Here is an example of how to visualize the prior distribution using the CLI:

```bash
python -m archeo visualize-prior --filepath ./prior.parquet
```

This command will read the prior data from `prior.parquet` and save the visualization output in the current directory.
Note that the visualizations include:
- Animation of how distributions (various parameters) change over kick magnitude constraint.
- 2D histogram of the mass-spin distribution.
- Kick distribution for each spin-bin (binwidth=0.1).

## Ancestral Parameter Estimation

The following example demonstrates how to use the package to visualize the prior and posterior distributions of a single event.

```python
import archeo

# Load the mass/spin samples from a file (usually PE results from LVK)
# They are expected to be a list of floats
mass_posterior = [68.0, 71.4, ..., 91.4]
spin_posterior = [0.31, 0.54, ..., 0.64]

# Create a prior
prior = archeo.Prior.from_config("precessing_spin")
# Create a posterior from the samples and the prior
posterior = prior.to_posterior(mass_posterior, spin_posterior)

# Visualize the prior and the posterior
archeo.visualize_prior_distribution(prior, output_dir="./")
archeo.visualize_posterior_estimation({"GW190521": posterior}, output_dir="./")
```

## Available Preset Priors

This table provides an overview of the different prior configurations available in archeo.

| Name | Samples  | Fits Model | Spin Aligned | Only Up-Aligned Spin | $\chi_1$ | $\chi_2$ | $\phi_1$ [rad] | $\phi_2$ [rad] | $\theta_1$ [rad] | $\theta_2$ [rad] | $m_1 [M_\odot]$ | $m_2 [M_\odot]$ | $q$ | Uniform in $q$ |
|------------------------------------|-----------|------------------|----|-----|-------|-------|------------|------------|-----------|-----------|---------|---------|-------|-----|
| default (tiny_aligned_spin)        | 5,000     | NRSur3dq8Remnant | ✅ | ❌ | 0 - 1 | 0 - 1 | 0 - $2\pi$ | 0 - $2\pi$ | 0 - $\pi$ | 0 - $\pi$ | 5 - 200 | 5 - 200 | 1 - 6 | ❌ |
| agnostic_precessing_spin           | 2,000,000 | NRSur7dq4Remnant | ❌ | ❌ | 0 - 1 | 0 - 1 | 0 - $2\pi$ | 0 - $2\pi$ | 0 - $\pi$ | 0 - $\pi$ | 5 - 200 | 5 - 200 | 1 - 6 | ❌ |
| agnostic_aligned_spin              | 2,000,000 | NRSur3dq8Remnant | ✅ | ❌ | 0 - 1 | 0 - 1 | 0 - $2\pi$ | 0 - $2\pi$ | 0 - $\pi$ | 0 - $\pi$ | 5 - 200 | 5 - 200 | 1 - 6 | ❌ |
| precessing_spin                    | 2,000,000 | NRSur7dq4Remnant | ❌ | ❌ | 0 - 1 | 0 - 1 | 0 - $2\pi$ | 0 - $2\pi$ | 0 - $\pi$ | 0 - $\pi$ | 5 - 65  | 5 - 65  | 1 - 6 | ❌ |
| aligned_spin                       | 2,000,000 | NRSur3dq8Remnant | ✅ | ❌ | 0 - 1 | 0 - 1 | 0 - $2\pi$ | 0 - $2\pi$ | 0 - $\pi$ | 0 - $\pi$ | 5 - 65  | 5 - 65  | 1 - 6 | ❌ |
| positively_aligned_spin            | 2,000,000 | NRSur3dq8Remnant | ✅ | ✅ | 0 - 1 | 0 - 1 | 0 - $2\pi$ | 0 - $2\pi$ | 0 - $\pi$ | 0 - $\pi$ | 5 - 65  | 5 - 65  | 1 - 6 | ❌ |

## Configure your own prior

Check out the preset priors in [quick.py](https://github.com/wyhwong/archeo/blob/main/src/archeo/preset/quick.py). From that, one should be able to create their own prior by following the same structure.

## Try our UI

Archeo also provides a simple web-based user interface to visualize the distributions of remnant properties.
To run the UI locally, simply run the following command:

```bash
pip3 install archeo[ui]
python3 -m archeo.ui
```

Then the UI will be available at [localhost:8501](http://localhost:8501).

You may also try our [demo version](https://archeo.streamlit.app/) online, which is hosted on Streamlit Community Cloud.

## Getting Help

The code is maintained by [Henry Wong](https://github.com/wyhwong) under [Juan Calderon Bustillo](https://git.ligo.org/juan.calderonbustillo)'s supervision. You can find the [list of contributors](https://github.com/wyhwong/archeo/graphs/contributors) here. Please report bugs by raising an issue on our [GitHub](https://github.com/wyhwong/archeo) repository.

## License

Archeo has a MIT License - see the [LICENSE](https://github.com/wyhwong/archeo/blob/main/LICENSE).