A Python-based wrapper for Noise Model Simulation (NMSIM). Eases the creation of input files and joining data from GIS databases. Improves overall flexibility of the tool for diverse applications.
“The Purpose of a System is What it Does.” -- Stafford Beer, 2001
This repository provides a scripting toolkit for the development of acoustic propagation models with Noise Model Simulation (NMSIM) software. At the request of the National Park Service, NMSIM was updated in 2014 to use the Nord2000 propagation algorithm, "an advanced ray-tracing noise model that includes refraction by the atmosphere and diffraction around terrain." The primary purpose of NMSIM-Python
is to sidestep NMSIM's arcane Graphical User Interface (GUI) while continuing to leverage Nord2000's extensive functionality, standardization, speed, and physical accuracy.
The eventual goal of NMSIM-Python
is to provide an overarching NMSIM
class corresponding to the Control File (see Figure 1, .nms) which describes the parameters for an acoustic propagation model scenario. In this vision, a NMSIM
object allows the user to gracefully implement inter-related modelling scenarios. By modifying input parameters, users can generate a specific sequence of acoustic propagation predictions. Such a tool enables the user to iterate over whichever dimension(s) of a civic problem are in question (e.g., properties of the noise source, trajectory, receiver location, etc.). Such focus could greatly expedite mitigation work, ground-truthing or experiments in perception.
For now, though, this repository exists only as:
(1) a set of loosely-connected python modules
(2) a few selectJupyter
notebooks
(3) anarcpy
toolbox meant for use with ArcGIS Pro
You can think of NMSIM-Python
as an “exploded view” of the modelling process, with functions to actualize each input, intermediary, or output filetype used by the software (Figure 1). Current design favors scripters working in a project-driven, maximally-flexible environment. The Jupyter
notebooks demonstrate this style of flexible use [note: they are only truly functional for National Park Service employees due to data-sharing issues. Please contact me if you would like to try them - I can figure out how to send you the appropriate data.] For a rich example of NMSIM-Python
in use, see Kirby Heck's work with DENA-overflights
.
Figure 1.) An "exploded view" the NMSIM modelling process as an information flow graph. The fundamental architecture is colored in blue. Useful output types are colored in amber, their respective raw NMSIM outputs in yellow, and input or intermediary file types in beige. Jupyter notebooks are green. Arcpy toolboxes are red.
The second purpose of NMSIM-Python
is to promote records retention for models used in planning or compliance processes. Ideally, public models are publicly available alongside the documents that implement them. Thus, NMSIM-Python
aims to support the storage of model objects for future reuse (i.e., as a pickle
or other filetype).
Note: NMSIM-Python
is a library in active development and therefore should be considered an unstable tool. For the same reason it would greatly benefit from the contributions of open-source programmers. It could also benefit from curious physicists/geographers/ecologists who have an interest in software testing and application. True batching (i.e., flexible compilation of batch files) should also be implemented, but currently isn’t. Batching isn’t purposeful for models containing sequences of isolated events, but as soon as overlapping events feature in a simulation batching is required. However, a batching routine would subsume the NMSIM
Class, so writing it will have to wait until the class is available first!
NMSIM models represent three coupled systems:
(1) the landscape and atmosphere which
(2) noise sources move over/through
(3) apprehended by the ear that hears: be in animal, human, or metal
Considering the multiphysics involved -- and the RAM-limited era in which NMSIM was originally developed -- there are lots of files required to operate the software. This section attempts to provide a brief list of every file type shown in Figure 1. Such knowledge is key to scripting in NMSIM-Python
as well as understanding limitations of the toolkit.
FlightTrackBuilder.exe
module, which is geometrically arcane (and therefore extremely tedious.)Site-based Model [read: 3D spectrogram representation] (.tis) resulting from control file. One use of these data could be time-aligning model spectrograms with GPS data or acoustic measurements. Further description is outside of the scope of this README, but site-based models provide a much-needed recourse for the validation of field studies or compliance efforts.
Grid-based Model [read: 4D spectral raster representation] (.tig) resulting from control file. Reducing the dimensionality of the 4D spectral raster into a 2D metric raster is required for mapping (or basically any) purpose. Dimensionality reduction is identical to summarizing any one-third octave band acoustic time-series record with a simpler metric.
The following section demonstrates use of NMSIM-Python
to model within a site-based paradigm. In this example there is a single receiver which 'observes' the acoustic morphology of a propeller aircraft as it transits the landscape along a specific trajectory. The model results in a spectrogram (i.e., 2D representation of sound in time and frequency).
Figure 2.) NMSIM modelling via a site-based paradigm. Like Figure 1, the process is represented as an information flow graph. User-based inputs are shown as green arrows on the left margin. These lead to intermediary inputs, which are finally organized together into the fundamental NMSIM Control File for this model. In turn, the Control File is referenced by the Batch File, which is ultimately used by NMSIM.
This project is in the worldwide public domain. As stated in CONTRIBUTING:
This project is in the public domain within the United States, and copyright and related rights in the work worldwide are waived through the CC0 1.0 Universal public domain dedication.
All contributions to this project will be released under the CC0 dedication. By submitting a pull request, you are agreeing to comply with this waiver of copyright interest.