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        "author": "Daniela Ruz",
        "author_email": "druzmieres@gmail.com",
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            "License :: OSI Approved :: GNU Lesser General Public License v3 (LGPLv3)",
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        "description": "# alerce\\_xmatch\n\nAPI to cross the information of astronomical catalogs with your local catalog or a pandas DataFrame with the astronomical position (ra, dec).\n\n## Installation\n\n### Dependencies\n\nThis libraries are specify in the `requirements.txt` file:\n\n````shell\ncertifi==2019.3.9\nnumpy==1.16.4\npandas==0.24.2\npython-dateutil==2.8.0\npytz==2019.1\nsix==1.12.0\nwebsocket-client==0.56.0\n````\n\n### Pip installation\n\n````shell\npip install alerce_xmatch\n````\n\n### Manual installation\n\nClone this repo, by executing:\n\n````shell\ngit clone https://github.com/alercebroker/xmatch_client.git\n````\n\nThen, change the current directory to this repo (generally by using `cd xmatch_client`) and execute:\n\n````shell\npython setup.py install\n````\n\n## Usage\n\n### Import lib\n\nThe library is called `alerce_xmatch`. To import to your current script (or jupyter notebook) use:\n\n````python\nimport alerce_xmatch\n````\n\nThe class and method available are:\n\n````python\nfrom alerce_xmatch import Catalog, crossmatch, OutputCols, TargetCatalogs\n````\n\n### Description\n\n#### To crossmatch and create a Catalog\n\nCatalogs object has a DataFrame with the astronomical position (in [ra, dec] format) and optionally an id (generally called oid) and more useful attributes. Also, it has an optional name. To create a Catalog object use:\n\n````python\nmy_catalog = Catalog(df=df, name=name)\n# By default df=None, and name=\"input_catalog\"\n````\n\nWith this catalog you can get the cross information by using the method:\n\n````python\nmy_catalog.crossmatch(<target>, output=OutputCols.BOTH, radec=True)\n````\n\nThe `<target>` parameter are available by importing `alerce_xmatch.TargetCatalogs` and the possibilities are:\n\n````python\nfrom alerce_xmatch import TargetCatalogs\n\n# Available catalogs\nTargetCatalogs.GAIA\nTargetCatalogs.ASASSN\nTargetCatalogs.CRTSNORTH\nTargetCatalogs.CRTSSOUTH\nTargetCatalogs.LINEAR\nTargetCatalogs.TNS\nTargetCatalogs.ZTF\n````\n\nThe other optional parameters are:\n\n##### Output columns:\n\nThe parameter `output`, define the columns on the output DataFrame of the resulting Catalog. This option are `OutputCols` as follow:\n\n````python\nfrom alerce_xmatch import OutputCols\n\nOutputCols.BOTH  # Show all the resilting columns\nOutputCols.TARGET  # Show the columns of the target catalog (Gaia, Asassn, ZTF, etc...)\nOutputCols.SOURCE # Show the columns of your catalog\n````\n\n##### Show ra, dec coordinates\n\nBy default `radec` is ` True`, which implies that the output DataFrame has the [ra, dec] position. You can hide this attribute (and lose it) setting `radec` to  `False`.\n\n#### To crossmatch a DataFrame\n\nIf you want to get and provide just the DataFrame with your info, use the crossmatch method provided as follow:\n\n````python\nimport pandas as pd\nfrom alerce_xmatch import crossmatch, TargetCatalogs, OutputCols\n\nsource_catalog = pd.DataFrame(<your data>)\n\nresult = crossmatch(source_catalog, TargetCatalogs.ZTF, output=OutputCols.BOTH, radec=True)\n````\n\nHere, the example target catalog is ZTF, and the optional parameters are used with its default value.\n\nThe output type, given a DataFrame, it is a pandas DataFrame. This static method can be used identically providing a Catalog as the `source_catalog` and the output type will be a Catalog object.",
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