{"id":41269751,"url":"https://github.com/temuller/atlas_object","last_synced_at":"2026-01-23T02:22:21.802Z","repository":{"id":57710918,"uuid":"501723601","full_name":"temuller/atlas_object","owner":"temuller","description":null,"archived":false,"fork":false,"pushed_at":"2022-07-13T13:00:25.000Z","size":786,"stargazers_count":1,"open_issues_count":0,"forks_count":1,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-09-27T06:06:26.639Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":null,"language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/temuller.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null}},"created_at":"2022-06-09T16:11:02.000Z","updated_at":"2022-10-14T08:53:55.000Z","dependencies_parsed_at":"2022-09-26T21:21:37.231Z","dependency_job_id":null,"html_url":"https://github.com/temuller/atlas_object","commit_stats":null,"previous_names":[],"tags_count":1,"template":false,"template_full_name":null,"purl":"pkg:github/temuller/atlas_object","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/temuller%2Fatlas_object","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/temuller%2Fatlas_object/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/temuller%2Fatlas_object/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/temuller%2Fatlas_object/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/temuller","download_url":"https://codeload.github.com/temuller/atlas_object/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/temuller%2Fatlas_object/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":28678136,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-01-23T01:00:35.747Z","status":"online","status_checked_at":"2026-01-23T02:00:08.296Z","response_time":59,"last_error":null,"robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":true,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":[],"created_at":"2026-01-23T02:22:21.296Z","updated_at":"2026-01-23T02:22:21.785Z","avatar_url":"https://github.com/temuller.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# ATLAS Object\n\nPackage for \"cleaning\" ATLAS light curves by doing variance-weighted rolling mean or sigma clipping.\n\n[![repo](https://img.shields.io/badge/GitHub-temuller%2Fatlas_object-blue.svg?style=flat)](https://github.com/temuller/atlas_object)\n[![license](http://img.shields.io/badge/license-MIT-blue.svg?style=flat)](https://github.com/temuller/atlas_object/blob/master/LICENSE)\n![Python Version](https://img.shields.io/badge/Python-3.8%2B-blue)\n[![PyPI](https://img.shields.io/pypi/v/atlas_object?label=PyPI\u0026logo=pypi\u0026logoColor=white)](https://pypi.org/project/atlas_object/)\n[![DOI](https://zenodo.org/badge/501723601.svg)](https://zenodo.org/badge/latestdoi/501723601)\n\n___\n## Conda environment\n\nIt is recommended to create an environment before installing ATLAS Object:\n\n```code\nconda create -n atlas pip\nconda activate atlas\npip install atlas_object\n```\n\n## Usage example\n\nFirst, make sure that your data has the same output format as given by the ATLAS API. One can easily plot the light curves:\n\n```code\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport atlas_object as ao\n\n# let's download a test file\nao.utils.download_test_data()\n\nlc_file = 'test_lc.csv'  # downloaded ATLAS forced photometry file\nobj = ao.atlas_object(lc_file)\nobj.plot_lcs(58600, 58800)  # the user can choose the x-axis range\n```\n![ATLAS1](static/atlas1.png)\n\nThe user can choose to do a sigma clipping within the rolling mean:\n\n```code\nsigclip_kwargs = {'n_sigma':3}\nobj.rolling(3, center=False, sigma_clip=True, **sigclip_kwargs)\nobj.plot_lcs(58600, 58800)\n```\n\n![ATLAS2](static/atlas2.png)\n\n``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.\n\nThe user also has access to the indices of the data removed by the sigma clipping, for each band (e.g. ``obj.lcs.o.indices``):\n\n```code\nmags = np.empty(0)\n\nfig, ax = plt.subplots(figsize=(8, 6))\nfor filt in 'co':\n    lc = obj.init_lcs[filt]\n    time = lc.time\n    mag = lc.mag\n    mag_err = lc.mag_err\n    mask = ~obj.lcs[filt].indices\n    ax.errorbar(time, mag, mag_err, \n                fmt='o', c=lc.color, mec='k',\n                alpha=0.2)\n    ax.errorbar(time[mask], mag[mask], mag_err[mask], \n                fmt='o', label=filt, c=lc.color, mec='k'\n               )\n    mags = np.r_[mags, mag]\n\nax.set_ylabel('Appartent Magnitude', fontsize=18)\nax.set_xlabel('MJD', fontsize=18)\nax.tick_params(labelsize=18)\nax.set_ylim(mags.min()-0.5, mags.max()+0.5)\nax.set_xlim(58600, 58800)\nax.invert_yaxis()\nax.legend(fontsize=18)\nplt.show()\n```\n\n![ATLAS3](static/atlas3.png)\n\n## ATLAS forced photometry\n\nFor information about public ATLAS forced photometry, check: https://fallingstar-data.com/forcedphot/.\nFor specific information about the data, check: https://fallingstar-data.com/forcedphot/resultdesc/.\n\n## Contributing\n\nTo contribute, either open an issue or send a pull request (prefered option). You can also contact me directly.\n\n\n## Citing ATLAS Object\n\nIf you make use of ATLAS Object, please cite:\n\n```code\n@software{tomas_e_muller_bravo_2022_6821107,\n  author       = {Tomás E. Müller Bravo},\n  title        = {temuller/atlas\\_object: First release!},\n  month        = jul,\n  year         = 2022,\n  publisher    = {Zenodo},\n  version      = {v0.1.0},\n  doi          = {10.5281/zenodo.6821107},\n  url          = {https://doi.org/10.5281/zenodo.6821107}\n} \n```\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Ftemuller%2Fatlas_object","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Ftemuller%2Fatlas_object","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Ftemuller%2Fatlas_object/lists"}