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    "info": {
        "author": "Kieran Leschinski, Oliver Czoske, Miguel Verdugo",
        "author_email": "kieran.leschinski@unive.ac.at, oliver.czoske@univie.ac.at, miguel.verdugo@univie.ac.at",
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        "classifiers": [
            "Intended Audience :: Science/Research",
            "License :: OSI Approved :: MIT License",
            "Operating System :: OS Independent",
            "Programming Language :: Python :: 3",
            "Topic :: Scientific/Engineering :: Astronomy"
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        "description": "# SimCADO - the instrument data simulator for MICADO\n\n[![Documentation Status](https://readthedocs.org/projects/simcado/badge/?version=latest)](https://simcado.readthedocs.io/en/latest/?badge=latest)\n[![Testing Status](https://travis-ci.org/astronomyk/SimCADO.svg?branch=master)](https://travis-ci.org/astronomyk/SimCADO.svg?branch=master)\n\n\nDocumentation for SimCADO can be found here:\n\n[https://simcado.readthedocs.io/en/latest/](https://simcado.readthedocs.io/en/latest/)\n\n## SimCADO in a nutshell\nSimCADO is a python package designed to simulate the effects of the Atmosphere, E-ELT, and MICADO instrument on incoming light. The current version (v0.2) can simulate the MICADO imaging modi (4mas and 1.5mas per pixel in the wavelength range 0.7\u00b5m to 2.5\u00b5m).\n\n### Reference Material\n\n* The inner workings of SimCADO are described in detail in [Leschinski et al. (2016)](https://arxiv.org/pdf/1609.01480v1.pdf)\n\n* The current status of MICADO is described in [Davies et al. (2016)](https://arxiv.org/pdf/1607.01954.pdf)\n\n\n## Downloading and Installing\nFor more information, see the the documentation\n\n**SimCADO has only been tested in Python 3.x**. \n\nIt is highly recommended to use Python 3, however the basics of generating images will still work in Python 2.7. We cannot guarantee this though. See the [Features](Features.md) page for more info on which functions with which Python version.\n\nThe quick way:\n\n    $ pip install simcado\n\nThe **first time** in python \n\n    >>> import simcado\n    >>> simcado.get_extras()\n    >>>\n    >>> # !! Only works in Python 3 - See Downloads section\n    >>> simcado.install_noise_cube()\n\n### Keeping SimCADO updated\n\nAs MICADO developes, the data files that SimCADO uses will also be updated. Therefore before you do any major work with SimCADO we *HIGHLY* recommend calling:\n\n    >>> simcado.get_extras()\n\n\n## Running a simulation in 3 lines\n\n**the keyword OBS_EXPTIME has been replaced by OBS_DIT in the latest version of SimCADO**\n\nThe easiest way to run a simulation is to create, or load, a Source object and then call the `.run()` command. If you specify a filename, the resulting image will be output to a FITS file under that name. If you do not specify a filename, the output will be returned to the console/notebook as an `astropy.io.fits.HDUList` object.\n\nTo begin, we will import the simcado module (assuming it is already installed).\n\n    >>> import simcado\n\nAt the very least, we need to create a `Source` object which contains both spatial and spectral information on our object of interest. Here we use the built-in command `.source.source_1E4_Msun_cluster()` to create a `Source`-object for a 10000-Msun stellar cluster. (See [Creating Sources](examples/Source.md) for more information).\n\n    >>> src = simcado.source.source_1E4_Msun_cluster()\n\nWe now pass the `source` object through SimCADO. This is as easy as calling `.run()`. If we specify a `filename`, SimCADO will write the output to disk in the form of a FITS file. If no `filename` is given, then SimCADO returns an `astropy.io.fits` object to the console/notebook.\n\n    >>> simcado.run(src, filename=\"my_first_sim.fits\")\n\n### Changing simulation parameters\n\nThe `sim.run()` also takes any [configuration keywords](Keywords.md) as parameters for running the simulation. For example, the default exposure time for the simulation is 60 seconds, however this can be increased of decreased by using the keyword `OBS_DIT` (and/or combining it with `OBS_NDIT`). A stacked 6x 10 minute observation sequence would look like:\n\n    >>> simcado.run(src, filename=\"my_first_sim.fits\", OBS_DIT=600, OBS_NDIT=6)\n\nThat's it. Of course SimCADO can also go in the other direction, providing many more levels of complexity, but for that the reader is directed to the examples pages and/or the [API](API/_build/index.html) documentation\n\n### SimCADO building blocks\nFor a brief explanation of how SimCADO works and which classes are relevant, please see either the [Getting Started](GettingStarted.md) or [SimCADO in depth](deep_stuff/SimCADO.md) section.\n\n## Bugs and Issues\n\nWe freely admit that there may still be several bugs that we have not found. If you come across an buggy part of SimCADO, *please please* tell us. We can't make SimCADO better if we don't know about things.\n\nThe preferable option is to open an issue on our Github page: [astronomyk/SimCADO/issues](https://github.com/astronomyk/SimCADO/issues), or you can contact either one of us directly.\n\nPlease always include the output of\n\n    >>> simcado.bug_report()\n\n\n## Contact\n\nFor questions and complaints alike, please contact the authors:\n\n* [kieran.leschinski@univie.ac.at]()\n* [oliver.czoske@univie.ac.at]()\n* [miguel.verdugo@univie.ac.at]()\n\n**Developers (Vienna):** Kieran Leschinski, Oliver Czoske, Miguel Verdugo\n\n**Data Flow Team Leader (Gronigen):** Gijs Verdoes Kleijn\n\n**MICADO home office (MPE):** http://www.mpe.mpg.de/ir/micado\n\n\n",
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