A Python package to create neutral landscape models
NLMpy is a Python package for the creation of neutral landscape models that
are widely used by landscape ecologists to model ecological patterns across
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
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.
NLMpy neutral landscape models are produced as two-dimensional NumPy arrays, so the
results can be easily incorporated into broader Python workflows.
NLMpy to create a midpoint displacement neutral landscape model can be achieved with
only two lines of code:
from nlmpy import nlmpy nlm = nlmpy.mpd(nRow=50, nCol=50, h=0.75)
But as described in the software paper a wide variety of different patterns can be produced:
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
NLMpy is available on the Python Package Index, 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
NLMpy onto your computer, it does at least allow you to make use of the
NLMpy within a neighbouring Python script.
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. 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.
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