https://github.com/ywx649999311/eztao

A Python Toolkit for AGN Time Series Analysis using CARMA models
https://github.com/ywx649999311/eztao

agn astronomy carma drw gaussian-processes time-series

Last synced: 6 days ago
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A Python Toolkit for AGN Time Series Analysis using CARMA models

• Host: GitHub
• URL: https://github.com/ywx649999311/eztao
• Owner: ywx649999311
• License: mit
• Created: 2020-04-29T19:50:06.000Z (over 4 years ago)
• Default Branch: master
• Last Pushed: 2024-09-18T01:21:32.000Z (3 months ago)
• Last Synced: 2024-12-09T21:36:31.549Z (15 days ago)
• Topics: agn, astronomy, carma, drw, gaussian-processes, time-series
• Language: Python
• Homepage:
• Size: 11.6 MB
• Stars: 18
• Watchers: 4
• Forks: 9
• Open Issues: 10
• Metadata Files:
  • Readme: README.md
  • Changelog: Changelog.rst
  • License: LICENSE

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README

        
![tests](https://github.com/ywx649999311/EzTao/workflows/tests/badge.svg)

[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/ywx649999311/EzTao/v0.4.3?filepath=docs/notebooks)

[![Documentation Status](https://readthedocs.org/projects/eztao/badge/?version=latest)](https://eztao.readthedocs.io/en/latest/)



# EzTao (易道)

**EzTao** is a toolkit for conducting AGN time-series/variability analysis, mainly utilizing the continuous-time auto-regressive moving average model (CARMA)

## Installation

```

pip install git+https://github.com/ywx649999311/EzTao.git

```

### Dependencies

>```

>python = ">=3.8,<3.12"

>celerite = ">= 0.3.0"

>scipy = "> 1.5.0"

>numba = ">= 0.57.0"

>matplotlib = ">=3.3.3"

>emcee = ">=0.3.0"

>```

## Quick Examples

Let's first simulate a DRW/CARMA(1,0) process with a variance of 0.3^2 and a relaxation timescale of 100 days. This time series will have a total of 200 data points and span 10 years.

```python

import numpy as np

import matplotlib.pyplot as plt

from eztao.carma import DRW_term

from eztao.ts import gpSimRand

# define a DRW kernel & and simulate a process

amp = 0.2

tau = 100

DRW_kernel = DRW_term(np.log(amp), np.log(tau))

t, y, yerr = gpSimRand(DRW_kernel, 10, 365*10, 200)

# now, plot it

fig, ax = plt.subplots(1,1, dpi=150, figsize=(8,3))

ax.errorbar(t, y, yerr, fmt='.')

...

```

![drw_sim](include/drw_sim.png)

We can fit the simulated time series to the DRW model and see how well we can recover the input parameters.

```python

from eztao.ts import drw_fit

best_fit = drw_fit(t, y, yerr)

print(f'Best-fit DRW parameters: {best_fit}')

```

```shell

Best-fit DRW parameters: [0.17356983 88.36262467]

```

How does the power spectrum density (PSD) compare?

```python

from eztao.carma import gp_psd

# get psd functions

true_psd = gp_psd(DRW_kernel)

best_psd = gp_psd(DRW_term(*np.log(best_fit)))

# plot

fig, ax = plt.subplots(1,1, dpi=150, figsize=(6,3))

freq = np.logspace(-5, 2)

ax.plot(freq, true_psd(freq), label='Input PSD')

ax.plot(freq, best_psd(freq), label='Best-fit PSD')

...

```

![drw_psd](include/drw_psd.png)

__Note:__ How well the input and best-fit PSD match is up to how good the best-fit parameters are, which is highly influenced by the quality of the input time series.

For more examples, please check out the [online documentation](https://eztao.readthedocs.io/en/latest/) or run the tutorial notebooks at ->

[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/ywx649999311/EzTao/v0.4.0?filepath=docs/notebooks).

## Development

`poetry` is used to solve dependencies and to build/publish this package. Below shows how setup the environment for development (assuming you already have `poetry` installed on your machine). _**Warning:**_ `poetry` is having issue installing `llvmlite = 0.34.0` (used for `eztao = ^0.4.0`) under Python 3.9. The issue disappears for Python 3.8.

1. Clone this repository, and enter the repository folder.

2. Create a python virtual environment and activate it (the virtual environment name must be '.venv').

    ```

    python -m venv .venv

    source .venv/bin/activate

    ```

3. Install dependencies and **EzTao** in editable mode.

   ```

   poetry install

   ```

Now you should be ready to start adding new features. Be sure to checkout the normal practice regarding how to use `poetry` on its website. When you are ready to push, also make sure the poetry.lock file is checked-in if any dependency has changed.

## Citation

We are working on a paper to describe the full implementation of **EzTao**. In the meantime, if you find **EzTao** useful for your research, please consider acknowledging **EzTao** using the following:

```

@MISC{Yu2022,

       author = {{Yu}, Weixiang and {Richards}, Gordon T.},

        title = "{EzTao: Easier CARMA Modeling}",

     keywords = {Software},

 howpublished = {Astrophysics Source Code Library, record ascl:2201.001},

         year = 2022,

        month = jan,

          eid = {ascl:2201.001},

        pages = {ascl:2201.001},

archivePrefix = {ascl},

       eprint = {2201.001},

       adsurl = {https://ui.adsabs.harvard.edu/abs/2022ascl.soft01001Y},

      adsnote = {Provided by the SAO/NASA Astrophysics Data System}

}

```