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    "info": {
        "author": "Waqas Bhatti",
        "author_email": "waqas.afzal.bhatti@gmail.com",
        "bugtrack_url": null,
        "classifiers": [
            "Development Status :: 4 - Beta",
            "Intended Audience :: Science/Research",
            "License :: OSI Approved :: MIT License",
            "Operating System :: OS Independent",
            "Programming Language :: Python :: 3",
            "Programming Language :: Python :: 3.6",
            "Programming Language :: Python :: 3.7",
            "Programming Language :: Python :: 3 :: Only",
            "Topic :: Scientific/Engineering :: Astronomy"
        ],
        "description": "[![Build Status](https://ci.wbhatti.org/buildStatus/icon?job=lcc-server)](https://ci.wbhatti.org/job/lcc-server)\n\nLCC-Server is a Python framework to serve collections of light curves. The code\nhere forms the basis for the [HAT data server](https://data.hatsurveys.org). See\nthe [installation notes](#installation) below for how to install and\nconfigure the server.\n\n- [Features](#features)\n- [Installation](#installation)\n- [SQLite requirement](#sqlite-requirement)\n- [Using the server](#using-the-server)\n- [Documentation](#documentation)\n- [Changelog](#changelog)\n- [Screenshots](#screenshots)\n- [License](#license)\n\n\n## Features\n\nLCC-Server includes the following functionality:\n\n- collection of light curves from various projects into a single output format\n  (text CSV files)\n- HTTP API and an interactive frontend for searching over multiple light curve\n  collections by:\n  - spatial cone search near specified coordinates\n  - full-text search on object names, descriptions, tags, name resolution using\n    SIMBAD's SESAME resolver for individual objects, and for open clusters,\n    nebulae, etc.\n  - queries based on applying filters to database columns of object properties,\n    e.g. object names, magnitudes, colors, proper motions, variability and\n    object type tags, variability indices, etc.\n  - cross-matching to uploaded object lists with object IDs and coordinates\n- HTTP API for generating datasets from search results asychronously and\n  interactive frontend for browsing these, caching results from searches, and\n  generating output zip bundles containing search results and all matching light\n  curves\n- HTTP API and interactive frontend for detailed information per object,\n  including light curve plots, external catalog info, and period-finding results\n  plus phased LCs if available\n- Access controls for all generated datasets, and support for user sign-ins and\n  sign-ups\n\n\n## Installation\n\n**NOTE:** Python >= 3.6 is required. Use of a virtualenv is recommended;\nsomething like this will work well:\n\n```bash\n$ python3 -m venv lcc\n$ source lcc/bin/activate\n```\n\nThis package is [available on PyPI](https://pypi.org/project/lccserver). Install\nit with the virtualenv activated:\n\n```bash\n$ pip install numpy  # to set up Fortran bindings for dependencies\n$ pip install lccserver  # add --pre to install unstable versions\n```\n\nTo install the latest version from Github:\n\n```bash\n$ git clone https://github.com/waqasbhatti/lcc-server\n$ cd lcc-server\n$ pip install -e .\n```\n\nIf you're on Linux or MacOS, you can install the\n[uvloop](https://github.com/MagicStack/uvloop) package to optionally speed up\nsome of the eventloop bits:\n\n```bash\n$ pip install uvloop\n```\n\n## SQLite requirement\n\nThe LCC-Server relies on the fact that the system SQLite library is new enough\nto contain the `fts5` full-text search module. For some older Enterprise Linux\nsystems, this isn't the case. To get the LCC-Server and its tests running on\nthese systems, you'll have to install a newer version of the [SQLite\namalgamation](https://sqlite.org/download.html). I recommend downloading the\ntarball with autoconf so it's easy to install; e.g. for SQLite 3.27.2, use this\nfile:\n[sqlite-autoconf-3270200.tar.gz](https://sqlite.org/2019/sqlite-autoconf-3270200.tar.gz).\n\nTo install at the default location `/usr/local/lib`:\n\n```bash\n$ tar xvf sqlite-autoconf-3270200.tar.gz\n$ ./configure\n$ make\n$ sudo make install\n```\n\nThen, override the default location that Python uses for its SQLite library\nusing `LD_LIBRARY_PATH`:\n\n```bash\n$ export LD_LIBRARY_PATH='/usr/local/lib'\n\n# create a virtualenv using Python 3\n# here I've installed Python 3.7 to /opt/python37\n$ /opt/python37/bin/python3 -m venv env\n\n# activate the virtualenv, launch Python, and check if we've got a newer SQLite\n$ source env/bin/activate\n(env) $ python3\nPython 3.7.0 (default, Jun 28 2018, 15:17:26)\n[GCC 4.8.5 20150623 (Red Hat 4.8.5-28)] on linux\nType \"help\", \"copyright\", \"credits\" or \"license\" for more information.\n>>> import sqlite3\n>>> sqlite3.sqlite_version\n'3.27.2'\n```\n\nYou can then run the LCC-Server using this virtualenv. You can use an\n`Environment` directive in the systemd service files to add in the\n`LD_LIBRARY_PATH` override before launching the server.\n\n\n## Using the server\n\nSome post-installation setup is required to begin serving light curves. In\nparticular, you will need to set up a base directory where LCC-Server can work\nfrom and various sub-directories.\n\nTo make this process easier, there's an interactive CLI available when you\ninstall LCC-Server. This will be in your `$PATH` as\n[`lcc-server`](https://github.com/waqasbhatti/lcc-server/blob/master/lccserver/cli.py).\n\nA Jupyter notebook walkthough using this CLI to stand up an LCC-Server instance,\nwith example light curves, can be found in the **astrobase-notebooks** repo:\n[lcc-server-setup.ipynb](https://github.com/waqasbhatti/astrobase-notebooks/blob/master/lcc-server-setup.ipynb)\n([Jupyter\nnbviewer](https://nbviewer.jupyter.org/github/waqasbhatti/astrobase-notebooks/blob/master/lcc-server-setup.ipynb)).\n\n\n## Documentation\n\n- Documentation for how to use the server for searching LC collections is hosted\nat the HAT data server instance: https://data.hatsurveys.org/docs.\n- The HTTP API is documented at: https://data.hatsurveys.org/docs/api.\n- A standalone Python module that serves as an LCC-Server HTTP API client is\n  available in the astrobase repository:\n  [lccs.py](https://github.com/waqasbhatti/astrobase/blob/master/astrobase/services/lccs.py) ([Docs](https://astrobase.readthedocs.io/en/latest/astrobase.services.lccs.html#module-astrobase.services.lccs)).\n\nServer docs are automatically generated from the\n[server-docs](https://github.com/waqasbhatti/lcc-server/tree/master/lccserver/server-docs)\ndirectory in the git repository. Sphinx-based documentation for the Python\nmodules is on the TODO list and will be linked here when done.\n\n\n## Changelog\n\nPlease see: https://github.com/waqasbhatti/lcc-server/blob/master/CHANGELOG.md\nfor a list of changes applicable to tagged release versions.\n\n\n## Screenshots\n\n### The search interface\n\n[![LCC server search interface](https://raw.githubusercontent.com/waqasbhatti/lcc-server/master/docs/search-th.png)](https://raw.githubusercontent.com/waqasbhatti/lcc-server/master/docs/search-montage.png)\n\n### Datasets from search results\n\n[![LCC server results display](https://raw.githubusercontent.com/waqasbhatti/lcc-server/master/docs/results-th.png)](https://raw.githubusercontent.com/waqasbhatti/lcc-server/master/docs/results-montage.png)\n\n### Per-object information\n\n[![LCC server object info](https://raw.githubusercontent.com/waqasbhatti/lcc-server/master/docs/objectinfo-th.png)](https://raw.githubusercontent.com/waqasbhatti/lcc-server/master/docs/objectinfo-montage.png)\n\n\n## License\n\nLCC-Server is provided under the MIT License. See the LICENSE file for the full\ntext.\n\n\n",
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