NLMpy
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.
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:from nlmpy import nlmpynlm = 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:
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:
Installation
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 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. 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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