Metadata-Version: 1.0
Name: nlmpy
Version: 1.0.0
Summary: A Python package to create neutral landscape models
Home-page: https://pypi.python.org/pypi/nlmpy
Author: Thomas R. Etherington, E. Penelope Holland, David O'Sullivan, Pierre Vigier
Author-email: etheringtont@landcareresearch.co.nz
License: MIT
Description: # NLMpy <img src="images/logo.png" align="right" width="200" />
        
        `NLMpy` is a Python package for the creation of neutral landscape models that 
        are widely used by landscape ecologists to model ecological patterns across 
        landscapes.  `NLMpy` can create both continuous patterns to represent landscape 
        characteristics such as elevation or moisture gradients, or categorical patterns 
        to represent landscape characteristics such as vegetation patches or land parcel 
        boundaries.
        
        `NLMpy` aims to:
        
        - be open-source so it can be easily adapted or developed for specific modelling requirements.
        - be cross-platform it can be used on any computer system.
        - bring together a wide range of neutral landscape model algorithms.
        - be easily integrated with geographic information system data.
        - enable novel combinations and integrations of different neutral landscape model algorithms.
        
        A full description of the package can be found in the accompanying 
        [software paper](https://besjournals.onlinelibrary.wiley.com/doi/full/10.1111/2041-210X.12308).
        
        ## Quick examples
        
        All the `NLMpy` neutral landscape models are produced as two-dimensional NumPy arrays, so the 
        results can be easily incorporated into broader Python workflows.
        
        Using `NLMpy` to create a midpoint displacement neutral landscape model can be achieved with 
        only two lines of code:
        
        ```python
        from nlmpy import nlmpy
        nlm = nlmpy.mpd(nRow=50, nCol=50, h=0.75)
        ```
        
        But as described in the [software paper](https://besjournals.onlinelibrary.wiley.com/doi/full/10.1111/2041-210X.12308) 
        a wide variety of different patterns can be produced:
        
        <img src="https://besjournals.onlinelibrary.wiley.com/cms/asset/2a1ff548-8d0f-45e3-af85-981370176795/mee312308-fig-0001-m.jpg" align="centre" width="1000" />
        
        ## Citation
        
        If you use `NLMpy` in your research we would be very grateful if you could please cite the 
        software using the following freely available software paper:
        
        [Etherington TR, Holland EP, O'Sullivan D (2015) NLMpy: a Python software package for 
        the creation of neutral landscape models within a general numerical framework. Methods in 
        Ecology and Evolution 6:164-168](https://besjournals.onlinelibrary.wiley.com/doi/full/10.1111/2041-210X.12308)
        
        ## Installation
        
        `NLMpy` is available on the [Python Package Index](https://pypi.python.org/pypi/nlmpy), so it can be installed using:
        
        ```
        pip install nlmpy
        ```
        
        If that does not work you could also simply move the `NLMpy.py` file to the same location 
        on your computer as a Python script that wants to import `NLMpy`, then when those scripts are 
        executed they will import all the `NLMpy` functions.  So while this approach does not 
        actually install `NLMpy` onto your computer, it does at least allow you to make use of the 
        functionality of `NLMpy` within a neighbouring Python script.
        
        ## Package dependencies
        
        - numpy
        - scipy
        - numba
        
        ## Community guidelines
        
        We very much welcome input from others\! If you find a bug, need some
        help, or can think of some extra functionality that would be useful,
        please raise an
        [issue](https://github.com/tretherington/nlmpy/issues). Better
        still, please feel free to fork the project and raise a pull request if
        you think and can fix a bug, clarify the documentation, or improve the
        functionality yourself.
        
Platform: UNKNOWN
