{ "info": { "author": "Michael Medford", "author_email": "michaelmedford@berkeley.edu", "bugtrack_url": null, "classifiers": [ "Intended Audience :: Science/Research", "License :: OSI Approved :: MIT License", "Operating System :: MacOS", "Operating System :: POSIX", "Operating System :: Unix", "Programming Language :: Python :: 3.6", "Topic :: Scientific/Engineering :: Astronomy" ], "description": "# zort : ZTF Object Reader Tool\n\n## Getting Started\n\nThe ZTF Object Reader Tool, ```zort```, is set of functions to organize and \naccess the ZTF Public Data Release lightcurves across multiple colors. \n\n### ZTF Public Data Release Lightcurves\n\nInstructions for downloading and extracting ZTF Public Data Release Lightcurves \ncan be found at: https://www.ztf.caltech.edu/page/dr1#12c\n\nThe ZTF Public Data Release Lightcurves are generated through spatially \ncross-matching individual epoch photometric catalogs. Catalogs are \npre-filtered to be (1) the same ZTF observation field ID, (2) the same CCD \nreadout channel, and (3) the same photometric color. Spatially coincidence\nobservations in these catalogs are all labelled as objects and saved to a \ncommon ascii file along with the observation data for each epoch of the object. \nThese files are consolidated such that all objects sharing a common ZTF \nobservation field ID reside in the same file.\n\n```zort``` refers to these files with extension ```*.txt``` as \n**lightcurve files**.\n\n### Features\n\n```zort``` provides facilitates the reading and inspection of lightcurves in \nthe ZTF Public Data Release. The features of ```zort``` include:\n- Seamless looping through ZTF lightcurves for custom filtering, where \ninteresting objects can be saved and recovered by only their file location\n- Consolidating g-band and R-band lightcurves of a single source that are \notherwise labelled as two separate objects by pairing objects as \"siblings\"\n- Plotting lightcurves in multiple colors for visual inspection\n\n### Installation\n\nPreferred method is through pip:\n\n```\npip install zort\n```\n\nLatest version can also be installed from github:\n```\ngit clone https://github.com/MichaelMedford/zort.git\ncd zort\npython setup.py install\n```\n\n### Terminology\n- **lightcurve file**: Files included in the ZTF Public Data Release containing \nepoch photometry for spatially coincidence observations\n- **object**: A collection of spatially coincident \nobservations in a single color. Objects include IDs, sky locations (in right \nascension and declination) and colors (g-band and R-band).\n- **lightcurve**: Observation epochs of an object. Lightcurve observations \ninclude dates, magnitudes and magnitude errors.\n- **rcid map**: Information on the organization of the lightcurve files \nrequired for faster object access.\n- **sibling**: A spatially coincident object in a different color \noriginating from the same astrophysical source.\n\n### Initialization\n\n```zort``` requires two additional data products per lightcurve file \n(```*.txt```) in order to make object discovery and multiple color \nconsolidation faster. Object files (```*.objects```) contain all of the \nmetadata for each object in a lightcurve file. RCID map files \n(```*.rcid_map```) contain lightcurve file metadata that facilitates faster \nmatching of multiple colors for individual objects. ```zort``` requires that \neach lightcurve file has a corresponding object file and RCID map file.\n\nTo generate object files and RCID map files for a directory of lightcurve \nfiles, run\n```\nzort-initialize -lightcurve-file-directory=LIGHTCURVE_FILE_DIRECTORY -single\n```\nor if mpi4py is installed then launch multiple instances of \n```\nzort-initialize -lightcurve-file-directory=LIGHTCURVE_FILE_DIRECTORY -parallel\n```\n\nIf each lightcurve file does not have an object file and an RCID map then \n```zort``` will not be able to locate siblings \n\n## Examples\n\n### Extracting Lightcurves\n\n```zort``` is designed to provide you with easy access to all of the \nlightcurves in a lightcurve file for applying filters and saving interesting \nobjects. The preferred method for inspecting lightcurves is through a for-loop.\n\nA filter is created that returns True for interesting objects. This filter \ncan involve simply cuts on object properties or complicated model fitting to \nthe full observation data in the object's lightcurve\n```\ndef my_interesting_filter(obj):\n cond1 = obj.nepochs >= 20\n cond2 = min(obj.lightcurve.mag) <= 17.0\n if cond1 and cond2:\n return True\n else:\n return False\n```\n\nWhen a lightcurve file is looped over, it returns each object in the lightcurve\nfile. Interesting objects can be gathered into a list and saved to disk using the \n```save_objects``` function.\n```\nfilename = 'lightcurve_file_example.txt'\ninteresting_objects = []\n\nfrom zort.lightcurveFile import LightcurveFile\nfor obj in LightcurveFile(filename):\n if my_interesting_filter(obj):\n interesting_objects.append(obj)\n\nfrom zort.object import save_objects\nsave_objects('objects.list', interesting_objects)\n```\n\nObjects and their lightcurves can be retrieved from a saved list by using the \n```load_objects``` function. Each object comes loaded with its metadata and \nlightcurve, easily previewed by printing the object and lightcurve attribute. \n```\nfrom zort.object import load_objects\ninteresting_objects = load_objects('objects.list')\nfor obj in interesting_objects:\n print(obj)\n print(obj.lightcurve)\n``` \n\nObjects can also be extracted in parallel by instantiating the LightcurveFile \nclass with a rank and size. This could be done through mpi4py, or other \nparallelization packages. The LightcurveFile class simply needs to be told \nthe rank of the parallel process and the total number, or size, of the parallel\nprocesses. \n```\nfrom mpi4py import MPI\ncomm = MPI.COMM_WORLD\nrank = comm.rank\nsize = comm.size\n\nfilename = 'lightcurve_file_example.txt'\ninteresting_objects = []\n\nfrom zort.lightcurveFile import LightcurveFile\nfor obj in LightcurveFile(filename, proc_rank=rank, proc_size=size):\n if my_interesting_filter(obj):\n interesting_objects.append(obj)\n\nfrom zort.object import save_objects\nsave_objects('objects.%i.list' % rank, interesting_objects)\n```\n\nSetting the ```proc_rank``` and ```proc_size``` parameters will cause the \niterator to uniquely send different objects to each parallel process without \nloading all of the objects into memory for each process. This allows for \napplying a filter to all of the objects in a lightcurve file without \noverloading memory.\n\n### Matching multiple colors for an object\n\nEach object is defined as a spatially coincidence series of observations that \nshare a (1) ZTF observation field ID, (2) CCD readout channel, and (3) \nphotometric filter. This labels multiple colors of the same astrophysical \nsource as separate ZTF objects with separate object IDs. The ZTF Public Data \nRelease does not provide any native support for pairing these objects as \nmultiple colors of the same source.\n\n```zort``` supports searching for and saving multiple colors for the same \nsource. The ZTF Public Data Release contains observations in g-band \n(filterid=1) and R-band (filterid=2). Each object can therefore have one \nadditional object that comes from the same astrophysical source but is in a \ndifferent color. These matching objects are labelled as \"siblings\" and can \nbe both discovered and saved with ```zort```.\n\nThe sibling for each object can be located by simply running an object's \n```locate_sibling``` method. Running\n\n```\nfilename = 'field000245_ra357.03053to5.26702_dec-27.96964to-20.4773.txt'\nbuffer_position = 6852\nobj = Object(filename, buffer_position)\nobj.locate_sibling()\n```\n\nresults in\n```\nLocating sibling for ZTF Object 245101100000025\n-- Object location: 4.74852, -26.23583 ...\n** sibling file missing! **\n-- Searching between buffers 17749819 and 18135260\n---- Sibling found at 4.74851, -26.23581 !\n---- Original Color: 1 | Sibling Color: 2\n---- Sibling saved\n``` \n\nThe sibling is saved in a ```*.siblings``` file that can be later recalled. This \nwas the first time that a sibling was located for this lightcurve file and \ntherefore a new sibling file was generated. Now that the sibling has been \nlocated, running\n\n```\nobj.locate_sibling()\n```\n\nresults in\n```\nLocating sibling for ZTF Object 245101100000025\n-- Object location: 4.74852, -26.23583 ...\n-- Loading sibling...\n-- Sibling loaded!\n``` \n\nAn object's sibling is itself another object and can be accessed through the \nsibling attribute.\n\n```\nprint(obj)\nFilename: field000245_ra357.03053to5.26702_dec-27.96964to-20.4773.txt\nBuffer Position: 6852\nObject ID: 245101100000025\nColor: g\nRa/Dec: (4.74852, -26.23583)\n22 Epochs passing quality cuts\n\nprint(obj.sibling)\nFilename: field000245_ra357.03053to5.26702_dec-27.96964to-20.4773.txt\nBuffer Position: 126136890\nObject ID: 245201100000047\nColor: r\nRa/Dec: (4.74851, -26.23581)\n22 Epochs passing quality cuts\n```\n\nThe default tolerance for matching two objecs as siblings is is 2.0\". However \nthis can be altered by changing ```obj.sibling_tol_as``` prior to runnning\n```obj.locate_sibling()``` for the first time. \n\n### Plotting lightcurves\n\nA lightcurve plot can be generated for any object using the \n```obj.plot_lightcurve()``` method.\n![](https://raw.githubusercontent.com/MichaelMedford/zort/master/example_images/field000245_ra357.03053to5.26702_dec-27.96964to-20.4773.txt-6852-lc.png)\n\nA lightcurve plot including an object's sibling \ncand be generated using the ```obj.plot_lightcurves()``` method.\n![](https://raw.githubusercontent.com/MichaelMedford/zort/master/example_images/field000245_ra357.03053to5.26702_dec-27.96964to-20.4773.txt-6852-lc-with_sibling.png)\n\n## Requirements\n* Python 3.6\n\n## Authors\n* Michael Medford \n\n## Citation\n[![DOI](https://zenodo.org/badge/200887248.svg)](https://zenodo.org/badge/latestdoi/200887248)\n\n\n", "description_content_type": "text/markdown", "docs_url": null, "download_url": 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