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itmlogic

itmlogic: The Irregular Terrain Model by Longley and Rice - Published in JOSS (2020)

https://github.com/edwardoughton/itmlogic

Science Score: 95.0%

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    CITATION.cff file
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    Found 9 DOI reference(s) in README and JOSS metadata
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    Links to: joss.theoj.org, zenodo.org
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    1 of 4 committers (25.0%) from academic institutions
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    Published in Journal of Open Source Software
Last synced: 6 months ago · JSON representation

Repository

Longley-Rice Irregular Terrain Model (itmlogic)

Basic Info
Statistics
  • Stars: 62
  • Watchers: 7
  • Forks: 24
  • Open Issues: 8
  • Releases: 1
Created over 6 years ago · Last pushed 9 months ago
Metadata Files
Readme Contributing License

README.md

itmlogic – Longley-Rice Irregular Terrain Model

Coverage Status DOI DOI

itmlogic is a Python implementation of the classic Longley-Rice propagation model (v1.2.2) and capable of estimating the signal propagation effects resulting from irregular terrain.

Software citation

  • Oughton, E.J., Russell, T., Johnson, J., Yardim, C., Kusuma, J., 2020. itmlogic: The Irregular Terrain Model by Longley and Rice. Journal of Open Source Software 5, 2266. https://doi.org/10.21105/joss.02266

Software purpose

This Python repo implements the model properties and algorithm defined in:

  • Hufford, G. A., A. G. Longley, and W. A. Kissick (1982), A guide to the use of the ITS Irregular Terrain Model in the area prediction mode, NTIA Report 82-100. (NTIS Order No. PB82-217977)
  • Hufford, G. A. (1995) The ITS Irregular Terrain Model, version 1.2.2, the Algorithm.

itmlogic enables you to account for the radio propagation impacts occuring from irregular terrain (hills, mountains etc.). For example, the image below shows the terrain undulation between the Crystal Palace (South London) transmitter and Mursley, Buckinghamshire, England. Such estimates enable the engineering design of many types of wireless radio systems, including 4G and 5G Radio Access Networks and wireless backhaul connections.

Terrain profile slice: Crystal Palace (South London) to Mursley

Example

Setup and configuration

All code for itmlogic is written in Python (Python>=3.7).

See requirements.txt for a full list of dependencies.

Conda

The recommended installation method is to use conda, which handles packages and virtual environments, along with the conda-forge channel which has a host of pre-built libraries and packages.

Create a conda environment called itmlogic:

conda create --name itmlogic python=3.7 gdal

Activate it (run this each time you switch projects):

conda activate itmlogic

First, install optional packages:

conda install numpy fiona shapely rtree rasterio pyproj tqdm pytest rasterstats pandas matplotlib

Once in the new environment, to install itmlogic clone this repository and either run:

python setup.py install

Or:

python setup.py develop

You can first run the tests to make sure everything is working correctly:

python -m pytest

Quick start

If you want to quickly generate results run using point-to-point mode run:

python scripts/p2p.py

Or using area prediction mode run:

python scripts/area.py

Results can then be visualized using:

python vis/vis.py

Example results - Point-to-point mode

Example

Example results - Area mode

Example

Documentation

For more information, see the itmlogic readthedocs documentation.

Background

The model was developed by the Institute for Telecommunication Sciences (ITS) for frequencies between 20 MHz and 20 GHz (named for Anita Longley & Phil Rice, 1968), and as a general purpose model can be applied to a large variety of engineering problems. Based on both electromagnetic theory and empirical statistical analyses of both terrain features and radio measurements, the Longley-Rice Irregular Terrain Model predicts the median attenuation of a radio signal as a function of distance and the variability of signal in time and in space.

The original NTIA disclaimer states:

The ITM software was developed by NTIA. NTIA does not make any warranty of any kind, express, implied or statutory, including, without limitation, the implied warranty of merchantability, fitness for a particular purpose, non-infringement and data accuracy. NTIA does not warrant or make any representations regarding the use of the software or the results thereof, including but not limited to the correctness, accuracy, reliability or usefulness of the software or the results. You can use, copy, modify, and redistribute the NTIA-developed software upon your acceptance of these terms and conditions and upon your express agreement to provide appropriate acknowledgments of NTIA's ownership of and development of the software by keeping this exact text present in any copied or derivative works.

Thanks for the support

The software repository itmlogic was written and developed at the Environmental Change Institute, University of Oxford within the EPSRC-sponsored MISTRAL programme (EP/N017064/1), as part of the Infrastructure Transition Research Consortium

Contributors

  • Edward J. Oughton (University of Oxford) (Software Engineering Lead)
  • Tom Russell (University of Oxford) (Software Engineering)
  • Joel Johnson (The Ohio State University) (ITM Modeling Lead)
  • Caglar Yardim (The Ohio State University) (ITM Modeling)
  • Julius Kusuma (Facebook Research) (ITM Modeling)

If you find an error or have a question, please submit an issue.

Folder structure

The folder structure for the itmlogic package is summarized as follows, and matches the box diagram highlighted in both the JOSS paper and the documentation:

+---src
|   +---itmlogic
|   |   |   lrprop.py
|   |   |   __init__.py
|   |   |
|   |   +---diffraction_attenuation
|   |   |       adiff.py
|   |   |       aknfe.py
|   |   |       fht.py
|   |   |
|   |   +---los_attenuation
|   |   |       alos.py
|   |   |
|   |   +---misc
|   |   |       qerf.py
|   |   |       qerfi.py
|   |   |       qtile.py
|   |   |
|   |   +---preparatory_subroutines
|   |   |       dlthx.py
|   |   |       hzns.py
|   |   |       qlra.py
|   |   |       qlrpfl.py
|   |   |       qlrps.py
|   |   |       zlsq1.py
|   |   |
|   |   +---scatter_attenuation
|   |   |       ahd.py
|   |   |       ascat.py
|   |   |       h0f.py
|   |   |
|   |   +---statistics
|   |   |       avar.py
|   |   |       curv.py

Owner

  • Name: Edward Oughton
  • Login: edwardoughton
  • Kind: user
  • Location: Fairfax, VA
  • Company: George Mason University, VA

Open-source data analytics for decision-making

JOSS Publication

itmlogic: The Irregular Terrain Model by Longley and Rice
Published
July 06, 2020
DOI
Volume 5, Issue 51, Page 2266
Authors
Edward J. Oughton ORCID
Environmental Change Institute, University of Oxford, Computer Laboratory, University of Cambridge
Tom Russell ORCID
Environmental Change Institute, University of Oxford
Joel Johnson
ElectroScience Laboratory, The Ohio State University
Caglar Yardim
ElectroScience Laboratory, The Ohio State University
Julius Kusuma
Facebook Connectivity Lab, Facebook Research
Editor
Daniel S. Katz ORCID
Tags
python mobile telecommunications propagation longley-rice
View PDF Review Thread Software Archive

GitHub Events

Total
  • Watch event: 6
  • Delete event: 1
  • Issue comment event: 1
  • Push event: 5
  • Pull request event: 4
  • Fork event: 2
  • Create event: 2
Last Year
  • Watch event: 6
  • Delete event: 1
  • Issue comment event: 1
  • Push event: 5
  • Pull request event: 4
  • Fork event: 2
  • Create event: 2

Committers

Last synced: 7 months ago

All Time
  • Total Commits: 216
  • Total Committers: 4
  • Avg Commits per committer: 54.0
  • Development Distribution Score (DDS): 0.153
Past Year
  • Commits: 5
  • Committers: 1
  • Avg Commits per committer: 5.0
  • Development Distribution Score (DDS): 0.0
Top Committers
Name Email Commits
ej550 e****n@g****m 183
Tom Russell t****l@g****m 29
jarmniku j****u@g****m 3
Daniel S. Katz d****z@i****g 1
Committer Domains (Top 20 + Academic)
ieee.org: 1

Issues and Pull Requests

Last synced: 6 months ago

All Time
  • Total issues: 19
  • Total pull requests: 58
  • Average time to close issues: 7 days
  • Average time to close pull requests: 10 days
  • Total issue authors: 13
  • Total pull request authors: 4
  • Average comments per issue: 2.05
  • Average comments per pull request: 0.0
  • Merged pull requests: 57
  • Bot issues: 0
  • Bot pull requests: 0
Past Year
  • Issues: 1
  • Pull requests: 2
  • Average time to close issues: N/A
  • Average time to close pull requests: 10 minutes
  • Issue authors: 1
  • Pull request authors: 2
  • Average comments per issue: 2.0
  • Average comments per pull request: 0.0
  • Merged pull requests: 1
  • Bot issues: 0
  • Bot pull requests: 0
View more stats
Top Authors
Issue Authors
  • garrettj403 (3)
  • WorksWellWithOthers (2)
  • aleksandrovivan (2)
  • edwardoughton (2)
  • ab3ap (2)
  • zadaal (1)
  • JAM5107k (1)
  • tomalrussell (1)
  • adagorret (1)
  • 0xCoto (1)
  • tbagnallMATM (1)
  • soramarcos (1)
  • gorlapraveen (1)
Pull Request Authors
  • edwardoughton (52)
  • tomalrussell (5)
  • jarmniku (2)
  • danielskatz (1)
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Packages

  • Total packages: 2
  • Total downloads:
    • pypi 88 last-month
  • Total dependent packages: 0
    (may contain duplicates)
  • Total dependent repositories: 1
    (may contain duplicates)
  • Total versions: 4
  • Total maintainers: 2
pypi.org: itmlogic

Longley-Rice irregular terrain propagation model

  • Versions: 3
  • Dependent Packages: 0
  • Dependent Repositories: 1
  • Downloads: 88 Last month
Rankings
Stargazers count: 9.8%
Dependent packages count: 10.1%
Forks count: 10.2%
Average: 17.5%
Dependent repos count: 21.6%
Downloads: 36.0%
Maintainers (2)
edwardoughton tomalrussell
Last synced: 6 months ago
conda-forge.org: itmlogic
  • Versions: 1
  • Dependent Packages: 0
  • Dependent Repositories: 0
Rankings
Dependent repos count: 34.0%
Stargazers count: 40.7%
Average: 42.3%
Forks count: 43.4%
Dependent packages count: 51.2%
Last synced: 6 months ago

Dependencies

requirements-docs.txt pypi
  • sphinx *
requirements.txt pypi
  • fiona *
  • numpy *
  • pyproj *
  • pytest >=4.6
  • rasterio *
  • rasterstats *
  • shapely *
  • simplejson *
  • tqdm *
setup.py pypi
  • eg *
  • numpy *
  • six >=1.7