https://github.com/temuller/atlas_object
https://github.com/temuller/atlas_object
Last synced: 5 months ago
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- Host: GitHub
- URL: https://github.com/temuller/atlas_object
- Owner: temuller
- License: mit
- Created: 2022-06-09T16:11:02.000Z (about 4 years ago)
- Default Branch: main
- Last Pushed: 2022-07-13T13:00:25.000Z (almost 4 years ago)
- Last Synced: 2025-09-27T06:06:26.639Z (9 months ago)
- Language: Jupyter Notebook
- Size: 768 KB
- Stars: 1
- Watchers: 1
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# ATLAS Object
Package for "cleaning" ATLAS light curves by doing variance-weighted rolling mean or sigma clipping.
[](https://github.com/temuller/atlas_object)
[](https://github.com/temuller/atlas_object/blob/master/LICENSE)
[](https://pypi.org/project/atlas_object/)
[](https://zenodo.org/badge/latestdoi/501723601)
___
## Conda environment
It is recommended to create an environment before installing ATLAS Object:
```code
conda create -n atlas pip
conda activate atlas
pip install atlas_object
```
## Usage example
First, make sure that your data has the same output format as given by the ATLAS API. One can easily plot the light curves:
```code
import numpy as np
import matplotlib.pyplot as plt
import atlas_object as ao
# let's download a test file
ao.utils.download_test_data()
lc_file = 'test_lc.csv' # downloaded ATLAS forced photometry file
obj = ao.atlas_object(lc_file)
obj.plot_lcs(58600, 58800) # the user can choose the x-axis range
```
The user can choose to do a sigma clipping within the rolling mean:
```code
sigclip_kwargs = {'n_sigma':3}
obj.rolling(3, center=False, sigma_clip=True, **sigclip_kwargs)
obj.plot_lcs(58600, 58800)
```
``sigclip_kwargs`` needs to have the same input parameters as ``obj.sigma_clip()``. All the changes occur on ``obj.lcs``, while ``obj.init_lcs`` contains the initial light curves.
The user also has access to the indices of the data removed by the sigma clipping, for each band (e.g. ``obj.lcs.o.indices``):
```code
mags = np.empty(0)
fig, ax = plt.subplots(figsize=(8, 6))
for filt in 'co':
lc = obj.init_lcs[filt]
time = lc.time
mag = lc.mag
mag_err = lc.mag_err
mask = ~obj.lcs[filt].indices
ax.errorbar(time, mag, mag_err,
fmt='o', c=lc.color, mec='k',
alpha=0.2)
ax.errorbar(time[mask], mag[mask], mag_err[mask],
fmt='o', label=filt, c=lc.color, mec='k'
)
mags = np.r_[mags, mag]
ax.set_ylabel('Appartent Magnitude', fontsize=18)
ax.set_xlabel('MJD', fontsize=18)
ax.tick_params(labelsize=18)
ax.set_ylim(mags.min()-0.5, mags.max()+0.5)
ax.set_xlim(58600, 58800)
ax.invert_yaxis()
ax.legend(fontsize=18)
plt.show()
```
## ATLAS forced photometry
For information about public ATLAS forced photometry, check: https://fallingstar-data.com/forcedphot/.
For specific information about the data, check: https://fallingstar-data.com/forcedphot/resultdesc/.
## Contributing
To contribute, either open an issue or send a pull request (prefered option). You can also contact me directly.
## Citing ATLAS Object
If you make use of ATLAS Object, please cite:
```code
@software{tomas_e_muller_bravo_2022_6821107,
author = {Tomás E. Müller Bravo},
title = {temuller/atlas\_object: First release!},
month = jul,
year = 2022,
publisher = {Zenodo},
version = {v0.1.0},
doi = {10.5281/zenodo.6821107},
url = {https://doi.org/10.5281/zenodo.6821107}
}
```