Metadata-Version: 2.1
Name: lccserver
Version: 0.2.7
Summary: A light curve collection server framework.
Home-page: https://github.com/waqasbhatti/lcc-server
Author: Waqas Bhatti
Author-email: waqas.afzal.bhatti@gmail.com
License: MIT
Description: [![Build Status](https://ci.wbhatti.org/buildStatus/icon?job=lcc-server)](https://ci.wbhatti.org/job/lcc-server)
        
        LCC-Server is a Python framework to serve collections of light curves. The code
        here forms the basis for the [HAT data server](https://data.hatsurveys.org). See
        the [installation notes](#installation) below for how to install and
        configure the server.
        
        - [Features](#features)
        - [Installation](#installation)
        - [SQLite requirement](#sqlite-requirement)
        - [Using the server](#using-the-server)
        - [Documentation](#documentation)
        - [Changelog](#changelog)
        - [Screenshots](#screenshots)
        - [License](#license)
        
        
        ## Features
        
        LCC-Server includes the following functionality:
        
        - collection of light curves from various projects into a single output format
          (text CSV files)
        - HTTP API and an interactive frontend for searching over multiple light curve
          collections by:
          - spatial cone search near specified coordinates
          - full-text search on object names, descriptions, tags, name resolution using
            SIMBAD's SESAME resolver for individual objects, and for open clusters,
            nebulae, etc.
          - queries based on applying filters to database columns of object properties,
            e.g. object names, magnitudes, colors, proper motions, variability and
            object type tags, variability indices, etc.
          - cross-matching to uploaded object lists with object IDs and coordinates
        - HTTP API for generating datasets from search results asychronously and
          interactive frontend for browsing these, caching results from searches, and
          generating output zip bundles containing search results and all matching light
          curves
        - HTTP API and interactive frontend for detailed information per object,
          including light curve plots, external catalog info, and period-finding results
          plus phased LCs if available
        - Access controls for all generated datasets, and support for user sign-ins and
          sign-ups
        
        
        ## Installation
        
        **NOTE:** Python >= 3.6 is required. Use of a virtualenv is recommended;
        something like this will work well:
        
        ```bash
        $ python3 -m venv lcc
        $ source lcc/bin/activate
        ```
        
        This package is [available on PyPI](https://pypi.org/project/lccserver). Install
        it with the virtualenv activated:
        
        ```bash
        $ pip install numpy  # to set up Fortran bindings for dependencies
        $ pip install lccserver  # add --pre to install unstable versions
        ```
        
        To install the latest version from Github:
        
        ```bash
        $ git clone https://github.com/waqasbhatti/lcc-server
        $ cd lcc-server
        $ pip install -e .
        ```
        
        If you're on Linux or MacOS, you can install the
        [uvloop](https://github.com/MagicStack/uvloop) package to optionally speed up
        some of the eventloop bits:
        
        ```bash
        $ pip install uvloop
        ```
        
        ## SQLite requirement
        
        The LCC-Server relies on the fact that the system SQLite library is new enough
        to contain the `fts5` full-text search module. For some older Enterprise Linux
        systems, this isn't the case. To get the LCC-Server and its tests running on
        these systems, you'll have to install a newer version of the [SQLite
        amalgamation](https://sqlite.org/download.html). I recommend downloading the
        tarball with autoconf so it's easy to install; e.g. for SQLite 3.27.2, use this
        file:
        [sqlite-autoconf-3270200.tar.gz](https://sqlite.org/2019/sqlite-autoconf-3270200.tar.gz).
        
        To install at the default location `/usr/local/lib`:
        
        ```bash
        $ tar xvf sqlite-autoconf-3270200.tar.gz
        $ ./configure
        $ make
        $ sudo make install
        ```
        
        Then, override the default location that Python uses for its SQLite library
        using `LD_LIBRARY_PATH`:
        
        ```bash
        $ export LD_LIBRARY_PATH='/usr/local/lib'
        
        # create a virtualenv using Python 3
        # here I've installed Python 3.7 to /opt/python37
        $ /opt/python37/bin/python3 -m venv env
        
        # activate the virtualenv, launch Python, and check if we've got a newer SQLite
        $ source env/bin/activate
        (env) $ python3
        Python 3.7.0 (default, Jun 28 2018, 15:17:26)
        [GCC 4.8.5 20150623 (Red Hat 4.8.5-28)] on linux
        Type "help", "copyright", "credits" or "license" for more information.
        >>> import sqlite3
        >>> sqlite3.sqlite_version
        '3.27.2'
        ```
        
        You can then run the LCC-Server using this virtualenv. You can use an
        `Environment` directive in the systemd service files to add in the
        `LD_LIBRARY_PATH` override before launching the server.
        
        
        ## Using the server
        
        Some post-installation setup is required to begin serving light curves. In
        particular, you will need to set up a base directory where LCC-Server can work
        from and various sub-directories.
        
        To make this process easier, there's an interactive CLI available when you
        install LCC-Server. This will be in your `$PATH` as
        [`lcc-server`](https://github.com/waqasbhatti/lcc-server/blob/master/lccserver/cli.py).
        
        A Jupyter notebook walkthough using this CLI to stand up an LCC-Server instance,
        with example light curves, can be found in the **astrobase-notebooks** repo:
        [lcc-server-setup.ipynb](https://github.com/waqasbhatti/astrobase-notebooks/blob/master/lcc-server-setup.ipynb)
        ([Jupyter
        nbviewer](https://nbviewer.jupyter.org/github/waqasbhatti/astrobase-notebooks/blob/master/lcc-server-setup.ipynb)).
        
        
        ## Documentation
        
        - Documentation for how to use the server for searching LC collections is hosted
        at the HAT data server instance: https://data.hatsurveys.org/docs.
        - The HTTP API is documented at: https://data.hatsurveys.org/docs/api.
        - A standalone Python module that serves as an LCC-Server HTTP API client is
          available in the astrobase repository:
          [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)).
        
        Server docs are automatically generated from the
        [server-docs](https://github.com/waqasbhatti/lcc-server/tree/master/lccserver/server-docs)
        directory in the git repository. Sphinx-based documentation for the Python
        modules is on the TODO list and will be linked here when done.
        
        
        ## Changelog
        
        Please see: https://github.com/waqasbhatti/lcc-server/blob/master/CHANGELOG.md
        for a list of changes applicable to tagged release versions.
        
        
        ## Screenshots
        
        ### The search interface
        
        [![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)
        
        ### Datasets from search results
        
        [![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)
        
        ### Per-object information
        
        [![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)
        
        
        ## License
        
        LCC-Server is provided under the MIT License. See the LICENSE file for the full
        text.
        
Keywords: astronomy
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: License :: OSI Approved :: MIT License
Classifier: Intended Audience :: Science/Research
Classifier: Topic :: Scientific/Engineering :: Astronomy
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 3 :: Only
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Requires-Python: >=3.6
Description-Content-Type: text/markdown
Provides-Extra: postgres
