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        "author": "Thomas R. Etherington, E. Penelope Holland, and David O'Sullivan",
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        "description": "NLMpy\n=====\n\nNLMpy is a Python package for the creation of neutral landscape models that\nare widely used in the modelling of ecological patterns and processes across\nlandscapes.\n\nNLMpy aims to provide several advantages over existing NLM software:\n\n* it is open-source so it can be easily adapted or developed for specific modelling requirements.\n* being cross-platform it can be used on any computer system.\n* it brings together a much wider range of NLM algorithms, including some that are not available elsewhere.\n* it can be combined with geographic information system (GIS) data.\n* it enables novel combinations and integrations of different NLM algorithms.\n* it can be embedded into larger modelling frameworks based on software that is capable of executing a Python script.  \n\nExample use\n===========\n\nUsing NLMpy to create and export a midpoint displacement NLM can be achieved with \nonly three lines of code:\n\n.. code:: python\n\n    >>> import nlmpy\n    >>> nlm = nlmpy.mpd(nRow=50, nCol=50, h=0.75)\n    >>> nlmpy.exportASCIIGrid(\"raster.asc\", nlm)\n\nPython 3.x example use\n======================\n\nDue to differences in absolute and relative importing between Python versions 2.x and 3.x NLMpy needs to be imported slightly differently in version 3.x:\n\n.. code:: python\n\n    >>> from nlmpy import nlmpy\n    >>> nlm = nlmpy.mpd(nRow=50, nCol=50, h=0.75)\n    >>> nlmpy.exportASCIIGrid(\"raster.asc\", nlm)\n\t\nPlease note that when executing the example scripts from the supplementary material provided with the published paper, if you are running Python 3.x you will need to modify the way NLMpy is imported by the script.\n\nOtherwise NLMPy should work fine in a 3.x environment and has been successfully tested with Python 3.5.2, Numpy 1.11.3, SciPy 0.18.1, Matplotlib 2.0.0, and NLMpy 0.1.4.\n\nDocumentation\n=============\n\n\nA full description of NLMpy is published in: Etherington TR, Holland EP, and \nO'Sullivan D (2015) NLMpy: a Python software package for the creation of \nneutral landscape models within a general numerical framework. Methods in \nEcology and Evolution 6(2):164-168 , which is freely available online  \n(http://bit.ly/14i4x7n).  \n\nThe journal website also holds example scripts and GIS data\n(http://bit.ly/1XUXjOF) that generate the figures in the paper.  There are \nalso some tutorial videos that provide some advice about installing \n(http://bit.ly/1qLfMjt) and using (http://bit.ly/2491u9n) NLMpy.\n\n\nDependencies\n============\n\nNLMpy was developed using:\n\n* Python 2.7.3\n* NumPy 1.8.0\n* SciPy 0.13.2\n* Matplotlib 1.3.1\n\nVersion History\n===============\n\n* 0.1.5 - added some advice to the read me about imprting NLMpy with Python 3.x\n* 0.1.4 - updated code to add compatability with Python 3.x\n* 0.1.3 - updated documentation\n* 0.1.2 - updated documentation\n* 0.1.1 - added the exportASCIIGrid function\n\nInstallation notes\n==================\n\nGetting a properly working installation of Python and associated packages can be a rather \nunintuitive task \u2013 especially if you are not overly familiar with software distribution \nmethods.  It is possible to build a Python installation by downloading Python itself \n(https://www.python.org/downloads/) and then adding required Python packages from the \nPython Package Index (https://pypi.python.org/pypi).  However, a much simpler approach \nthat I would recommend is using a scientific distribution of Python that comes with all \nthe packages you are most likely to require for scientific applications.\n\nPerhaps the two most popular scientific distributions of Python are the Anaconda \ndistribution (https://store.continuum.io/cshop/anaconda/) and the Canopy distribution \n(https://www.enthought.com/products/canopy/).  I would recommend either, as they are \nboth: cross-platform, free to download, and come with all the Python packages most \ncommonly required for scientific computing.  My personal preference is for the Anaconda \ndistribution, as it comes with Spyder (https://pythonhosted.org/spyder/) that is in my \nopinion the best environment for writing Python code (it is similar to Rstudio for people \nmore used to using R), and because in Canopy access to some of the Python packages I use \na lot requires additional licencing.  But in order to get NLMpy working either Anaconda or \nCanopy will work, as they both come with the NumPy (http://www.numpy.org/) and SciPy \n(http://www.scipy.org/) packages on which NLMpy depends, the Matplotlib \n(http://matplotlib.org/) package that the example scripts use to plot results, and the pip \n(https://pypi.python.org/pypi/pip) package that makes installation of NLMpy easy.\n\nOnce you have installed the Python version 2.7.x of either Anaconda or Canopy, the \ninstallation of NLMpy can be done from the command line using either a Terminal on \nOSX/UNIX or a Command Prompt on Windows.  At the command line you just need to run::\n\n    pip install nlmpy\n\nwhich will get the pip program to install NLMpy directly from the online Python Package \nIndex.\n\nYou may have a problem using this approach if you connect to the Internet via a proxy \nserver.  So while this approach works fine for me at home, I can\u2019t use it at work.  There \nis however an alternative approach.  You can go to the NLMpy Python Package Index page \n(https://pypi.python.org/pypi/nlmpy) and download the nlmpy-0.1.1.tar.gz package file.  \nWith the file downloaded to your computer you can then use pip to install NLMpy from this \nlocal file by running::\n\n    pip install /Users/username/Downloads/nlmpy-0.1.5.tar.gz\n\nthough you will obviously have to specify the directory location for the package file that \nis correct on your computer!\n\nIf you are unable to get either of those approaches to work, there is an easy workaround.  \nIf you decompress the nlmpy-0.1.5.tar.gz package file, you will find inside a nlmpy.py file \nthat contains all the NLMpy functions.  If you simply move this file to the same location \non your computer as a Python script that wants to import nlmpy, then when those scripts are \nexecuted they will import all the NLMpy functions.  So while this approach doesn\u2019t \nactually install NLMpy onto your computer, it does at least allow you to make use of the \nfunctionality of NLMpy within a neighbouring Python script.",
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