Contents¶

Copyright (C) 2017 Alexandros Avdis & Jon Hill

Permission is granted to copy, distribute and/or modify this document under the terms of the GNU Free Documentation License, Version 1.3  or any later version published by the Free Software Foundation; with no Invariant Sections, no Front-Cover Texts, and no Back-Cover Texts. A copy of the license is included in the section entitled “GNU  Free Documentation License”.

Welcome to the qmesh synoptic manual.

qmesh is an unstructured triangular mesh generator for geophysical domains. It is suited to mesh generation over topologically two-dimensional domains, typically used in coastal and ocean modelling. Typical examples can be seen in the Academic papers section.

qmesh was developed by Alexandros Avdis and Jon Hill, primarily to meet our meshing needs. We developed qmesh with an open-source philosophy in mind, so you are welcome to view the qmesh code and use qmesh, under the GNU General Public License, version 3. As the project grew and matured, the code was reorganised into various modules and packages. Following the traditional Python practice, each package is self-contained and includes documentation. However, the documentation of the packages is best described as a technical reference. Therefore, we have developed this synoptic manual where we aim to provide a brief but complete description of the general architecture of qmesh, development practices, installation procedures, an overview of further documentation and literature as well as a few tutorials that will enable the novice to make a quick start.

In particular, this manual is organised as follows:

The Introduction is a broad descriptions of the aims and objectives behind qmesh development.

The Architecture is discussed in the second chapter, describing the user interfaces, the packages and the qmesh modules.

The third chapter outlines the development and distribution methods, including a description of the testing framework and release practices. The chapter includes verbatim copies of the licences attached to qmesh code and documentation.

Chapter four includes instructions on qmesh Installation.

Chapter five outlines sources of Further documentation and Tutorials. Other literature sources, such as Academic papers, are also listed.

Introduction¶

Computational models are increasingly used to study complex geophysical fluid dynamics and its interaction with biological and geochemical processes [luo_et_al:2012]. The potential insight from simulations has made such models a valuable tool in scientific research as well as engineering. Simulations are used to assess the impact of anthropogenic changes, the vulnerability of infrastructure to natural hazards [hunter_et_al:2008][hill:2014] and in hydrocarbon exploration and sequestration research [wells_et_al:2010][mitchell_et_al:2011][collins_et_al:2017]. qmesh was built to facilitate one of the first stages in computational geophysical modelling: the specification of the simulation domain and its tessellation into discrete elements, commonly referred to as mesh generation. The predictive accuracy of simulations can be significantly affected by the mesh resolution, gradation and shape of mesh elements, collectively identified as “mesh quality”. Therefore, generation of high-quality meshes is fundamental to ocean and coastal modelling.

The paradigm of mesh generation in large-scale geophysical modelling can be broadly described as a two–step procedure. In the first step, the domain is defined in a topologically two-dimensional space, bounded by geophysical contours such as shorelines and open boundaries [Gorman_et_al:2006]. A finite, two-dimensional reference surface is thus defined, often on a geodetic datum, and a mesh is generated over this reference surface. If a two-dimensional approximation, such as the depth-averaged shallow water equations is sufficient, mesh-generation is complete. When a three-dimensional approximation is required, the second step is the projection of the surface mesh vertices at successive levels towards the domain floor, thus creating three-dimensional elements [Gorman_et_al:2006]. The second step requires little user intervention, so the first step has become a synonym for mesh generation, in the large–scale geophysical modelling context.

Despite the reduction of the relevant dimensions to just two, the production of quality meshes for geophysical domains can be an elaborate procedure [Gorman_et_al:2006][Gorman_et_al:2008][heinzer:2012][berry_et_al:2014][prodanovic:2015]. A significant number of data sources must be combined to compose a geometrically complex domain, and the geometry of geophysical domains is one of the most widely known examples of fractal geometry in nature [Mandelbrot_et_al:1983]. Geometrical length–scales across four orders of magnitude is typical in ocean modelling: For example in ocean modelling, the simulation domain size can span hundreds of kilometres, while the smallest bays can be tens of metres long. An even smaller scale may be relevant when the domain must accurately represent coastal infrastructure such as piers, pylons or embankments [xia_et_al:2010][avdis_etal:2016]. In addition to domain geometry, the flow typically exhibits a large range of scales, with many transient flow features, such as internal waves or jets, appearing due to the geometric complexity of the domain [bentley:subm_2016]. Therefore, a mesh must represent very complex domains with element sizes across a broad range of scales, with smaller elements in areas that require a higher fidelity, while gradation across element sizes must be smooth. Unstructured meshes are increasingly favoured in many models as they tend to satisfy the above requirements with relative ease [danilov:2013].

High–quality generic unstructured mesh generation packages are available, enabling routine generation of meshes. However, the interface of most mesh generators is based on Computer Aided Design and Manufacture (CAD–CAM). Such interfaces have been developed for describing geometries produced by manufacturing and construction processes and do not facilitate use or manipulation of geographical data [keith_turner:2005]. In particular, the fractal nature of ocean and coastal domains, as well as the various conventions used in geodetic coordinate reference systems are not natively expressed in CAD–CAM systems [keith_turner:2005][avdis_etal:2016]. Combining shoreline or bathymetry data is a typical example; one may have to combine several global, national and regional data sets as well as data from very high–resolution surveys over relatively small regions. Each data–set can also differ at least in terms of extents, resolution, coordinate reference system and vertical zero–datums. Unlike CAD–CAM, Geographical Information Systems (GIS) have been developed specifically for storing and analysing geographical information. GIS packages are robust and widely used in research as well as in operational and strategic planning contexts, where resource management, hazard mitigation and infrastructure development are a few examples. Therefore, GIS packages are ideal for supplying mesh generators with the geospatial data they require.

Another important feature of GIS systems is their capability to interact with databases, allowing concurrent data analysis and manipulation. Databases can also be used to record data origin and evolution, termed data provenance [Jacobs_etal:2015]. However, databases in GIS systems are typically not maintained in a manner pertinent to scientific research, where the primary aim of data provenance is to show reproducibility [Jacobs_etal:2015]. The practice of Research Data Management (RDM) is aimed at addressing data provenance, attribution and reproducibility. A record of data and software depended upon is usually required as evidence of reproducibility, including scientific data and software used in preparation of simulations. Therefore, the reproducibility of numerical simulations relies, at least in part, on the ability to exactly reproduce the underlying mesh. The integration of RDM and mesh generation was motivated by the increased attention on the reproducibility of scientific computation [LeVeque_etal_2012], perhaps as much as open–source software. Also, public research bodies are adopting policies on data and software output from publicly–funded research to be made readily available, and provenance to be clearly identified [European_Commission2012-lu][European_Commission2012-cr][rcuk:2015]. In the industrial sector reproducibility, data archiving and data provenance are viewed as efficient modelling practices. Industry and governing bodies are also bound by regulatory frameworks which require public accesibility to data during the planning phase [directive_2003/35/EC][UKstatue_1999_No_3445][UKstatue_2007_No_1518][UKstatue_2008_No_55], as well as after commisioning [directive_2003/4/EC][UKstatue_2004_No_3391], especially when data pertains to environmental impact of infrastructure.

Here we present the qmesh package, interfacing GIS with a mesh–generator and online data repositories. We link the abstractions offered by mesh generators and GIS packages and build tools that facilitate mesh–generation for coastal flow modelling. Unlike existing integrations of GIS and mesh generators, qmesh was principally developed as an object–oriented software library, accessible through an Application Programming Interface (API). In qmesh GIS capability is implemented through the use of existing and robust GIS implementations as generic libraries, rather than building extensions to a particular GIS implementation, such as is described in [CHC:1998][Gorman_et_al:2008][heinzer:2012][berry_et_al:2014][prodanovic:2015][remacle:2016]. qmesh is thus a library with which command line utilities and a graphical interface have also been developed. The broader aim of qmesh development is the creation of robust, efficient, operational and user–friendly tools for mesh generation over geophysical domains. The qmesh design was centred around providing the following requirements:

Facilitation of domain geometry and mesh element size definitions;

An intuitive way of specifying boundary conditions and parameterizations;

The ability to use various geodetic coordinate reference systems;

Promotion of the reproducibility of output and citation of data provenance

Provision of an open–source and tested package.

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Matrin R Wells, Peter A Allison, Matthew D Piggott, Gary J Hampson, Christopher C Pain, and Gerard J Gorman. Tidal modelling of an ancient tide-dominated seaway, Part 1: Model validation and application to global Cretaceous (Aptian) tides. Journal of Sedimentary Research, 80:393–410, 2010. doi:10.2110/jsr.2010.044.

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[18]	Jon Hill, Gareth S Collins, Alexandros Avdis, Stephan C Kramer, and Matthew D Piggott. How does multiscale modelling and inclusion of realistic palaeobathymetry affect numerical simulation of the Storegga Slide tsunami? Ocean Modelling, 83:11–25, 2014. doi:10.1016/j.ocemod.2014.08.007.

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[22]	Daniel S Collins, Alexandros Avdis, Peter A Allison, H D Johnson, Jon Hill, Matthew D Piggott, M H A Hassan, and A R Damit. Tidal dynamics and mangrove carbon sequestration during the Oligo–Miocene in the south china sea. Nature communications, 2017. doi:10.1038/ncomms15698.

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Architecture¶

The user perspective of mesh–generation packages is centred around specification of two parts: domain geometry and mesh element size. Encoding domain geometry and mesh element size is a useful paradigm for describing meshes for large–scale geophysical modelling [Gorman_et_al:2006], as it organises the necessary information in a conceptually clear way. We here follow the conventional norm in ocean modelling, discussed in the Introduction , where meshes are produced in a topologically two–dimensional space [Gorman_et_al:2006] and the domain is bound by various contours, typically topographic contours, and arbitrary lines. The two primary data structures of GIS are used to describe linear features and field data. A vector data structure can represent points, lines and regions on a reference surface, while a raster data structure encapsulates the discrete representation of fields. The analogue to the abstraction used to drive mesh generators is clear: the domain geometry can be described with a vector data structure, while a raster can express the element size metric. Thus, the obvious route to interfacing mesh generators with GIS is to provide a translation of GIS data structures into the corresponding structures native to the mesh generator software. The data structure translation is at the heart of the qmesh package. The translation is done with little user intervention, as the user typically interacts with the GIS package and parts of the qmesh package that facilitate specification of domain geometry and mesh element size. To meet the demands around data archiving, publication and reproducibility a Research Data Management (RDM) tool is included in the qmesh package. The RDM tool facilitates the process of publishing all resources, including output such as the mesh, to online, persistent and citable repositories.

The GIS package chosen is QGIS [QGIS_software], the mesh generator is Gmsh [Geuzaine2009-dd] and the PyRDM software library [Jacobs_etal:2014] was used to integrate research data management [Jacobs_etal:2015]. The main reasons for choosing QGIS, Gmsh and PyRDM, are robustness, extensibility and permissive licences. Specifically, Gmsh is a robust mesh generator featuring a CAD–CAM interface, and has been used for generating meshes in various scientific and engineering domains, including geophysical domains [lambrechts_et_al:2008][Geuzaine2009-dd]. QGIS is a widely used GIS platform, with an active community of users and developers, and has been used as a user interface to mesh generation in past efforts [heinzer:2012][prodanovic:2015]. The functionality of QGIS is available to the user as a standard GIS system with a rich graphical interface and as an object–oriented Python module. Therefore, QGIS is a solid framework on which to develop complex applications that require GIS methods. Also, QGIS provides a framework for using such applications as extensions, via the QGIS graphical user interface. Finally, QGIS, Gmsh and PyRDM are released under the GNU General Public Licence, making possible the use of qmesh in an academic or industrial context, free of charge.

Fig. 1 presents an overview of the architecture of qmesh and conveys the usual work–flow. As shown, qmesh is composed of four modules, named vector, raster, mesh and publish. The purpose of the modules vector and raster is to facilitate the definition of the domain geometry and mesh–size metric and to interface qmesh to QGIS. The translation between GIS and mesh–generator data structures is performed by the mesh module, thus interfacing qmesh to Gmsh. The RDM functionality is implemented by the publish module, interfacing qmesh to online repositories and enabling identification and publication of data. Apart from just conceptual, the modules shown in Fig. 1 are also the Python modules of the qmesh implementation and are discussed in detail in section Module design and implementation. To allow access to the qmesh modules from a variety of environments three different User Interfaces have been developed, discussed in The qmesh interfaces and packages. The interfaces are implemented as separate Python packages, to facilitate distribution, installation and namespace organisation.

Schematic of qmesh library architecture. The arrows indicate data–flow when qmesh is used.

The qmesh interfaces and packages¶

qmesh can be used in a graphical as well as a programmatic environment, through three different user interfaces:

A Python–based Application Programming Interface (API).
A Linux Terminal Command Line Interface (CLI).
A Graphical User Interface (GUI).

Each interface is implemented as a separate Python package. We have avoided the creation of a single monolithic package to minimise dependencies, to orthogonalize the module definition to the User-Interface definition (to the extent possible), but also to more narrowly define the purpose of each package and thus help in more targeted testing. Thus, the following packages compose what collectively can be identified as qmesh:

qmesh. This is the “core” qmesh package, other APIs/packages require the qmesh package as a dependency. According to the Python documentation, Python packages are a way of structuring the Python module namespace by using dotted module names. Thus, the package presents a Python API and contains the implementation of the modules shown in Fig. 1, accessible as qmesh.vector, qmesh.raster, qmesh.mesh and qmesh.publish.
qmesh-cli. This package contains the code needed to access the qmesh functionality from a linux terminal.
qmesh-qgis-plugins. This package contains the code necessary to access the qmesh functionality from within QGIS, using a graphical interface.

Application Programming Interface¶

The Python-based API is an integral part of the qmesh implementation. It can be used to build scripts that automate series of operations, but its primary purpose is to allow the use of qmesh as a software library. It is the most powerfull and flexible of the qmesh interfaces, due to the power and flexibility of Python, its interactive shells (e.g. [PER-GRA:2007]) and numerous extensions.

Command Line Interface¶

The command line interface consists of a set of utilities each with well–defined input and output, making each utility a separate program. However, their purpose is to be used as diagnostic tools or, to automate operations which do not require a graphical interface.

Graphical User Interface¶

The graphical user interface is a QGIS extension. This way qmesh can be used via the QGIS application and in combination with other QGIS functionality. The GUI has been designed to allow access to the qmesh package with ease and little knowledge of the qmesh design.

Module design and implementation¶

The qmesh implementation follows the object-oriented programming paradigm. Thus, each of the modules shown in Fig. 1 contain class definitions that facilitate the aim of the module. In this section, we describe the modules in more detail and summarise the classes typically used. A complete listing of all definitions in each module can be found in the API Rerefence.

The vector module¶

The vector module is used to construct a complete definition of the domain geometry in terms of domain boundaries (lines) and domain surfaces (areas). Surfaces are defined in terms of lines, so the definition of surfaces is automated by methods in the vector module such that only the boundary lines need to be supplied. Other methods allow for essential geometric operations such as checking for erroneous geometries (i.e. intersecting shorelines) or the removal of small islands and lakes, based on a threshold surface area specified by the user. The necessity of shoreline processing in ocean modelling is discussed in [Gorman_et_al:2008], where the Terreno project used GMT [Wessel_Walter:1991] to affect shoreline processing. In qmesh however, geometry processing is done primarily through the QGIS software library, also allowing use of extensive functionality built-into the GIS platform. In addition to geometry definition, methods for identifying separate parts of the domain geometry are necessary. For example, open boundaries are associated with different boundary conditions to shorelines. The qmesh user can assign numerical identifiers to separate lines and apply different boundary conditions to separate boundaries. Numerical IDs can also be assigned to surfaces, allowing the identification of areas where different numerical treatments or parameterizations must be applied, as shown in the Tutorials. The QGIS library is used to store and retrieve the digital IDs as standardised feature attributes. The output of the vector module uses the ESRI shapefile [esri:1998] vector data–structure, which also supports storage of the ID feature attributes. This way the module output as well as IDs can be visualised, assigned and edited with any GIS platform.

The raster module¶

The aim of the raster module is to facilitate construction of raster fields that describe the desired element edge length distribution over the domain. For example, the element size might be chosen to be smaller in areas of shallow water, steep bathymetry and areas of significant variation in bathymetry slope. Therefore an optimal element size distribution is typically expressed as a function of bathymetry, its gradient and Hessian and the distance to boundaries [legrand_et_al:2000][legrand:2007][lambrechts_et_al:2008]. The raster module facilitates application of various mathematical operators to be applied to raster data such as derivatives, methods for combining raster fields such as pointwise minimum and maximum operators, but also methods for calculating the distance function raster from any given vector feature. The latter is useful when specifying a mesh size gradation towards specific features in the domain: for example, the element size gradually becoming smaller as a coastline or a tidal turbine is approached. A generic method has been implemented, aimed towards the construction of element size raster fields based on the distance from a given vector feature (lines, polygons or points). This kind of operation is expressed by the arrows between the raster and vector modules inside qmesh, in Fig. 1. As with the vector module, the output of the raster module uses GIS raster data structures enabling visualisation and editing of the output via the GIS system.

The mesh module¶

The mesh module is used to translate the domain and mesh element size definitions into Gmsh data structures and, as suggested in figure Fig. 1 can be used to convert the mesh into a vector data–structure. Such functionality enables mesh visualisation using QGIS, and in particular to over–lay the mesh on other data. Various qualities of the mesh can thus be assessed and the work–flow can be restarted towards improving the mesh. The meshing module also allows the user to specify the coordinate reference system of the output mesh, which need not be the same as that of the domain geometry and mesh–metric raster. Coordinates are reprojected to the target coordinate reference system before the data is passed to the mesh generator. The reprojection procedure uses the QGIS library; this way meshes can be obtained in all cartographic projections that QGIS supports and identifies via an EPSG code. The output mesh is two–dimensional and the EPSG specification describes the dimensions, including their units. As a particular case, the output mesh can be constructed in a three–dimensional space, where the mesh vertices lie on a sphere, using specific Gmsh functionality described in [lambrechts_et_al:2008][remacle:2016]. The vertex coordinates are specified in terms of a Cartesian reference system whose origin lies at the sphere centre, the z–axis is the axis of rotation and the x–axis intersects the surface of the sphere at 0^o longitude and 0^o latitude. Meshes thus constructed can be used to perform global simulations or simulations over large areas [Gorman_et_al:2006][lambrechts_et_al:2008][wells_et_al:2010][mitchell_et_al:2011][hill:2014][remacle:2016].

The publish module¶

The aim of the publish module is to facilitate provenance description and reproducibility of qmesh output. Broadly, the specific version of qmesh used to produce the mesh and all of the input data sources are stored in an online repository. In general data provenance may seem intractable since data and software are often stored in a non–persistent way and are not easily accessible. However, given the increasing importance of data provenance, online data repositories with efficient storage and access controls such as Zenodo and figshare, are becoming popular means of archiving and dissemination. Also, such services incorporate meta–data as means of describing hosted data and minting a unique Digital Object Identifier (DOI). The DOI is a standardised [ISO26324:2012] citable identifier and is aimed to be assigned to digital objects, stored in a persistent way in open repositories. Therefore, DOI is a widely–adopted identifier for digitally stored data, be that a scientific publication, the output of scientific computations or records from experiments and observations. Given the wide range of data sources that can be combined during mesh generation for realistic geophysical domains, the task of manually maintaining the provenance information of all the relevant data files can be time–consuming and error–prone. As shown in figure Fig. 1 the publish module interfaces with the qmesh development repository, via PyRDM [Jacobs_etal:2014][Jacobs_etal:2015], to identify the exact version of qmesh used. A query is then made with the repository hosting service to establish if this version of qmesh has already been uploaded and assigned a DOI. A similar query is performed for each input data source. Each unpublished item is then uploaded and a new DOI is minted and assigned to the entire dataset. The dataset also includes citations, in the form of meta–data, of the DOI markers of already published items. The various DOI markers can be thought of as nodes of a tree, and the citations are the tree connections, a similar concept to scientific publications. Also, the output can be archived in a private repository, without a DOI, to facilitate archival of commercially sensitive information.

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[2]	QGIS Development Team. QGIS Geographic Information System. Open Source Geospatial Foundation, 2009. URL: http://qgis.osgeo.org.

[3]	(1, 2) Christophe Geuzaine and Jean-François Remacle. Gmsh: A 3–D finite element mesh generator with built-in pre- and post-processing facilities. International Journal of Numerical Methods in Engineering, 79(11):1309–1331, 2009. doi:10.1002/nme.2579.

[4]	(1, 2) Christian T Jacobs, Alexandros Avdis, Gerard J Gorman, and Matthew D Piggott. PyRDM: A Python-based library for automating the management and online publication of scientific software and data. Journal of Open Research Software, 2(1):e28, 2014. doi:10.5334/jors.bj.

[5]	(1, 2) C. T. Jacobs, A. Avdis, S. L. Mouradian, and M. D. Piggott. Integrating Research Data Management into Geographical Information Systems. In Proceedings of the 5th International Workshop on Semantic Digital Archives. 2015. URL: http://hdl.handle.net/10044/1/28557.

[6]	(1, 2, 3, 4) Jonathan Lambrechts, Richard Comblen, Vincent Legat, Christophe Geuzaine, and Jean-François Remacle. Multiscale mesh generation on the sphere. Ocean Dynamics, 58(5-6):461–473, 8 October 2008.

[7]	Thomas J Heinzer, M Diane Williams, Emin C Dogrul, Tariq N Kadir, Charles F Brush, and Francis I Chung. Implementation of a feature-constraint mesh generation algorithm within a GIS. Comput. Geosci., 49:46–52, December 2012.

[8]	Pat Prodanovic. QGIS as a pre-and post-processor for TELEMAC: mesh generation and output visualization. In 22nd TELEMAC-MASCARET User Conference. 15 October 2015.

[9]	Fernando Pérez and Brian E. Granger. IPython: a system for interactive scientific computing. Computing in Science and Engineering, 9(3):21–29, May 2007. URL: http://ipython.org, doi:10.1109/MCSE.2007.53.

[10]	G J Gorman, M Piggott, M R Wells, C C Pain, and P A Allison. A systematic approach to unstructured mesh generation for ocean modelling using GMT and terreno. Computers and Geosciences, 34(12):1721–1731, December 2008.

[11]	Paul Wessel and Walter H F Smith. Free software helps map and display data. Eos Transactions AGU, 72(41):441–446, 1991.

[12]	ESRI. ESRI shapefile technical description. Technical Report J-7855, Environmental Systems Research Institute, July 1998.

[13]	S Legrand, V Legat, and E Deleersnijder. Delaunay mesh generation for an unstructured–grid ocean general circulation model. Ocean Modelling, 2(1–2):17–28, 2000.

[14]	Sébastien Legrand, Eric Deleersnijder, Eric Delhez, and Vincent Legat. Unstructured, anisotropic mesh generation for the northwestern european continental shelf, the continental slope and the neighbouring ocean. Continental Shelf Research, 27(9):1344–1356, 15 May 2007.

[15]	(1, 2) Jean-François Remacle and Jonathan Lambrechts. Fast and Robust Mesh Generation on the Sphere–Application to Coastal Domains. Procedia Engineering, 163:20–32, 2016.

[16]

Matrin R Wells, Peter A Allison, Matthew D Piggott, Gary J Hampson, Christopher C Pain, and Gerard J Gorman. Tidal modelling of an ancient tide-dominated seaway, Part 1: Model validation and application to global Cretaceous (Aptian) tides. Journal of Sedimentary Research, 80:393–410, 2010. doi:10.2110/jsr.2010.044.

[17]	Andrew J Mitchell, Peter A Allison, Gerald J Gorman, Matthew D Piggott, and Christopher C Pain. Tidal calculation in an ancient epicontinental sea: The Early Jurassic Laurasian Seaway. Geology, 39(3):207–210, 2011. doi:10.1130/G31496.1.

[18]	Jon Hill, Gareth S Collins, Alexandros Avdis, Stephan C Kramer, and Matthew D Piggott. How does multiscale modelling and inclusion of realistic palaeobathymetry affect numerical simulation of the Storegga Slide tsunami? Ocean Modelling, 83:11–25, 2014. doi:10.1016/j.ocemod.2014.08.007.

[19]	Technical Committee ISO/TC 46 (Information and documentation), Subcommittee SC 9 (Identification and description). ISO 26324:2012 Information and documentation – Digital object identifier system. Technical Report, International Organisation for Standardization, 2012.

Development, Distribution and Licence¶

Development and distribution repositories¶

Code development repositories¶

DOI repositories¶

Docker Cloud repositories¶

qmesh in the Python Package Index¶

The qmesh packages can be obtained through the Python Package Index (PyPI), also known as “The Cheeseshop”. The python pip package manager can be used to install the packages with all dependencies, details are given in the Installation section.

Testing¶

Unit testing¶

Regression testing¶

Integration testing¶

Version numbering¶

(Section in progress) The PEP 440 is followed to implement Semantic Versioning 2.0.0.

Licences¶

The qmesh code and documentation are open-source and are released under licences published by the Free Software Foundation. Verbatim copies of the licences are placed in the following sections. In specific:

The source code of all qmesh packages is released under the GNU General Public Licence, version 3 or (at your option) any later version. A copy of the full licence text is available in the section titled “GNU GENERAL PUBLIC LICENSE”.

All qmesh documentation, including this manual, is released under the terms of the GNU Free Documentation License, Version 1.3 or any later version published by the Free Software Foundation A copy of the full licence text is available in the section entitled “GNU Free Documentation License”.

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GNU Free Documentation License¶

Version 1.3, 3 November 2008

Everyone is permitted to copy and distribute verbatim copies of this license document, but changing it is not allowed.

PREAMBLE

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How to cite qmesh¶

It is good practice to cite sources, as it promotes provenance, attribution and reproducibility. The reason the qmesh code, as well as manuals, are available under Digital Object Identifiers (DOI) is to facilitate citation, but also to provide persistent sources where the code, containers and documentation can be accessed. A number of papers have been published in academic journals using or describing qmesh [hill:2014][Jacobs_etal:2015][avdis_etal:2016][Perez-Ortiz:2017][collins_et_al:2017][qmesh:2018].

Given the independent peer-review process typical of academic journals, a paper is a source you can confidently cite as a robustly reviewed description of qmesh. Indeed, the availability of code and data through persistent repositories with a minted DOI is motivated by greater transparency, reproducibility and attribution of scientific research. Also, qmesh is an open source project. The source code and documentation are available free of charge, so appropriate attribution is the only way to support the project’s longevity.

Therefore, it is recommended to:

Include the qmesh version number in the citation.
Cite the qmesh paper [qmesh:2018].
Cite this manual.
Cite the code or container, using the DOI.
State the gmsh version, with appropriate references [Geuzaine2009-dd].
State the qgis version, with appropriate references [QGIS_software].

For example, a possible way to cite all of the above, when the Python API package was used is:

In this study the meshes were generated using the qmesh Python package (version 0.9) [qmesh:2018], which interfaces gmsh (version 2.8) [Geuzaine2009-dd] and qgis (version ) [QGIS_software]

If you want to use qmesh as a library in your code, and in particular redistribute qmesh code as part of another project, pay attention to the code and documentation licences.

[1]	G J Gorman, M D Piggott, C C Pain, C R E de Oliveira, A P Umpleby, and A J H Goddard. Optimisation based bathymetry approximation through constrained unstructured mesh adaptivity. Ocean Model., 12(3–4):436–452, 2006.

[2]	(1, 2) QGIS Development Team. QGIS Geographic Information System. Open Source Geospatial Foundation, 2009. URL: http://qgis.osgeo.org.

[3]	(1, 2) Christophe Geuzaine and Jean-François Remacle. Gmsh: A 3–D finite element mesh generator with built-in pre- and post-processing facilities. International Journal of Numerical Methods in Engineering, 79(11):1309–1331, 2009. doi:10.1002/nme.2579.

[4]	Christian T Jacobs, Alexandros Avdis, Gerard J Gorman, and Matthew D Piggott. PyRDM: A Python-based library for automating the management and online publication of scientific software and data. Journal of Open Research Software, 2(1):e28, 2014. doi:10.5334/jors.bj.

[5]	C. T. Jacobs, A. Avdis, S. L. Mouradian, and M. D. Piggott. Integrating Research Data Management into Geographical Information Systems. In Proceedings of the 5th International Workshop on Semantic Digital Archives. 2015. URL: http://hdl.handle.net/10044/1/28557.

[6]	Jonathan Lambrechts, Richard Comblen, Vincent Legat, Christophe Geuzaine, and Jean-François Remacle. Multiscale mesh generation on the sphere. Ocean Dynamics, 58(5-6):461–473, 8 October 2008.

[7]	Thomas J Heinzer, M Diane Williams, Emin C Dogrul, Tariq N Kadir, Charles F Brush, and Francis I Chung. Implementation of a feature-constraint mesh generation algorithm within a GIS. Comput. Geosci., 49:46–52, December 2012.

[8]	Pat Prodanovic. QGIS as a pre-and post-processor for TELEMAC: mesh generation and output visualization. In 22nd TELEMAC-MASCARET User Conference. 15 October 2015.

[9]	Fernando Pérez and Brian E. Granger. IPython: a system for interactive scientific computing. Computing in Science and Engineering, 9(3):21–29, May 2007. URL: http://ipython.org, doi:10.1109/MCSE.2007.53.

[10]	G J Gorman, M Piggott, M R Wells, C C Pain, and P A Allison. A systematic approach to unstructured mesh generation for ocean modelling using GMT and terreno. Computers and Geosciences, 34(12):1721–1731, December 2008.

[11]	Paul Wessel and Walter H F Smith. Free software helps map and display data. Eos Transactions AGU, 72(41):441–446, 1991.

[12]	ESRI. ESRI shapefile technical description. Technical Report J-7855, Environmental Systems Research Institute, July 1998.

[13]	S Legrand, V Legat, and E Deleersnijder. Delaunay mesh generation for an unstructured–grid ocean general circulation model. Ocean Modelling, 2(1–2):17–28, 2000.

[14]	Sébastien Legrand, Eric Deleersnijder, Eric Delhez, and Vincent Legat. Unstructured, anisotropic mesh generation for the northwestern european continental shelf, the continental slope and the neighbouring ocean. Continental Shelf Research, 27(9):1344–1356, 15 May 2007.

[15]	Jean-François Remacle and Jonathan Lambrechts. Fast and Robust Mesh Generation on the Sphere–Application to Coastal Domains. Procedia Engineering, 163:20–32, 2016.

[16]

Matrin R Wells, Peter A Allison, Matthew D Piggott, Gary J Hampson, Christopher C Pain, and Gerard J Gorman. Tidal modelling of an ancient tide-dominated seaway, Part 1: Model validation and application to global Cretaceous (Aptian) tides. Journal of Sedimentary Research, 80:393–410, 2010. doi:10.2110/jsr.2010.044.

[17]	Andrew J Mitchell, Peter A Allison, Gerald J Gorman, Matthew D Piggott, and Christopher C Pain. Tidal calculation in an ancient epicontinental sea: The Early Jurassic Laurasian Seaway. Geology, 39(3):207–210, 2011. doi:10.1130/G31496.1.

[18]	Jon Hill, Gareth S Collins, Alexandros Avdis, Stephan C Kramer, and Matthew D Piggott. How does multiscale modelling and inclusion of realistic palaeobathymetry affect numerical simulation of the Storegga Slide tsunami? Ocean Modelling, 83:11–25, 2014. doi:10.1016/j.ocemod.2014.08.007.

[19]	Technical Committee ISO/TC 46 (Information and documentation), Subcommittee SC 9 (Identification and description). ISO 26324:2012 Information and documentation – Digital object identifier system. Technical Report, International Organisation for Standardization, 2012.

[20]

Alexandros Avdis, T Christian Jacobs, Leo Simon Mouradian, Jon Hill, and D Matthew Piggott. Meshing ocean domains for coastal engineering applications. In M Papadrakakis, V Papadopoulos, G Stefanou, and V Plevris, editors, The European Community on Computational Methods in Applied Sciences and Engineering (ECCOMAS) VII Congress Proceedings, Crete, Greece, June 5–10. 2016. doi:10.6084/m9.figshare.4524629.

[21]	A Pèrez-Ortiz, AGL Borthwick, J McNaughton, and A Avdis. Characterization of the tidal resource in rathlin sound. Renewable Energy, 2017. doi:10.1016/j.renene.2017.04.026.

[22]	Daniel S Collins, Alexandros Avdis, Peter A Allison, H D Johnson, Jon Hill, Matthew D Piggott, M H A Hassan, and A R Damit. Tidal dynamics and mangrove carbon sequestration during the Oligo–Miocene in the south china sea. Nature communications, 2017. doi:10.1038/ncomms15698.

[23]	(1, 2, 3) Alexandros Avdis, Adam S Candy, Jon Hill, Stephan C Kramer, and Matthew D Piggott. Efficient unstructured mesh generation for marine renewable energy applications. Renewable Energy, 116:842–856, 2018. doi:10.1016/j.renene.2017.09.058.

Installation¶

Dependencies¶

qmesh relies on the following packages:

QGIS
gmsh
GFD_basisChange_tools
PyRDM
setuptools-qmesh

Installing from PyPI¶

The easiest way to install qmesh and qmesh-cli is through the Python Package Index. To install the qmesh python API type the following at the command line:

pip install qmesh

If your system has pip installed, the above command will install all dependencies themselves distributed through PyPI. The install procedure will also check for gmsh and qgis and will halt if those are not installed. In your system is missing qgis, it is best to install it through your distributions package manager as documented in the qmesh web-page. If your system is missing gmsh, the best option is to obtain a compiled binary from the gmsh web-page.

To install the qmesh cli :

pip install qmesh-cli

An advantage to installing via PyPI, is the ease to upgrade and remove. Should you wish to upgrade, type the following at a terminal:

pip install --upgrade SomePackage <qmesh-package>

To remove type the following:

pip uninstall <qmesh-package>

Docker containers¶

Docker containers can be thought of as virtual machines, and provide isolation and control of a working environment. It is easy to construct docker images where the operating system has all the necessary dependencies installed. The images are typically made accessible by uploading to online registries. Docker hub is a popular docker registry, where developers upload images, organised into repositories. For example, the qmesh repository houses all qmesh images, where each image is identified by a unique image tag. Some images are intended for testing and development, and the environment is set-up with a minimal set of dependencies to provide a clean “sand-box”. Other images provide a full qmesh installation and are aimed at facilitating use of qmesh when a user’s environment makes installation hard or impossible. You can see all qmesh images in the qmesh repository tags page. Note that you must have docker installed on your system, preferably thought a package manager to use docker images.

However, since docker provides a user with a bare-bone system, it puts the task of system administration onto the container user. Administering a Linux system can be demanding for users who just want to run qmesh, rather than spend time administering a system. The package qmeshcontainers provides the command-line utility qmeshcontainer, the latter is meant to facilitate creating and running qmesh containers. The package qmeshcontainers is available from the `Python Package Index https://pypi.org/project/qmeshcontainers/`_.

A complete description of the qmeshcontainer utility is available in the qmeshcontainers manual, a brief outline of installation and use is presented here.

To install qmeshcontainers issue the following at the command line:

sudo pip install qmeshcontainers

The primary usage scenario of qmesh containers, is that the user will have prepared the input files in some way and want to run qmesh. In the commands below, it is assumed that the input files are located in the directory mesh.

From the command line of your host (computer) navigate to the mesh directory and enter the directory. To create and run a qmesh container issue the following at the command line:

qmeshcontainer -mwd

The above will place you inside the qmesh container, giving you a Bash prompt. If you see any warning messages about user or group details mismatch, pay attention to them, but you do not have to take any action. As the qmeshcontainers utility is preparing the container, it tries to match your user set-up (including user and group IDs) so it can access and write files that match your profile into the mesh directory. The warning messages are produced in case the utility cannot fully replicate your user set-up. The above qmeshcontainer command will also make the directory mesh available inside the container. Any changes or deletions you make to the mesh directory are propagated to the host, so pay attention to any file deletions or changes you might make. Navigate into the mesh directory in the container bash prompt:

cd mesh

Assuming that the qmesh mesh generation script is called mesh_generation the following command at the container bash prompt will execute the script:

python mesh_generation

Review the qmesh script output to troubleshoot any problems and rerun the script to obtain the mesh you want. You can leave the container running, edit the input files on the host machine, and return to the terminal window with the container bash prompt to re-run the meshing script. Similarly, you can use gmsh or qgis on the host machine to visualise the mesh you have produced. Once you have obtained the mesh you want you can exit the container by typing exit or with the keyboard shortcut Ctrl + d.

For more information on the qmeshcontainer utility, including various user options, please consult the qmesh containers manual.

Tutorial¶

A Crash course in GIS¶

If you are an expert in GIS, you can skim this section. However, do not completely skip it, as we have included here a number of tips and tricks with GIS, that will save you a lot or time and effort when creating new meshes.

The GIS software we have used in this section is QGIS, because it is open-source and free, thus it is accesible to everyone. However, the concepts are applicable to any GIS suite. Indeed, the two most fundamental datatypes in GIS are vector and raster data, and their correspondence to mesh-generator data-structures was discussed in the Introduction and in [qmesh:2018].

Lets examine an example of each data-structure. Create a folder for the purpose of this example, named GIS_crash_course and inside that place the following two files:

The filtered and subsampled GEBCO global bathymetry, which you can download from `figshare <>`_.

This is an example of a raster file, where the global bathymetry and topography is represented as data over a regular grid: a number of points along the two geodetic coordinates, longitude and latitude define “pixels” and values attached to each of these points give the value of the elevation at that point.

The global 0-meter elevation contour, which you can download from `figshare <>`_.

This is an example of a vector file, where a sequence of points defines a line.

Place both files inside the GIS_crash_course folder, and open QGIS. You should have a window similar to Fig. 2, below.

The Qgis Window.

Lets examine the major areas and tools highlighted in figure Fig. 2, starting with the canvas, and layers windows. Each vector or raster file is represented in GIS as a separate layer. The purpose of the canvas is to overlay all layers, and facilitate contextual comparison and editing of the layers. Lets add the files we downloaded to see how the canvas and layers windows change. You can add the bathymetry via the Add Raster Layer button, also highlighted in Fig. 2.

Global meshes¶

Meshing the Mediterranean Sea¶

Meshing the North Sea¶

Meshing the Severn Estuary¶

Meshing the Seas around the Orkney and Shetland Islands¶

Further documentation¶

API Rerefence¶

CLI Rerefence¶

GUI Rerefence¶

Developer Documentation¶

Academic papers¶

Papers on qmesh¶

Avdis, A. & Candy A. S. & Hill J. & Kramer C. S. & Piggott M. D., “Efficient unstructured mesh generation for marine renewable energy applications”, Renewable Energy, Volume 116, Part A, February 2018, Pages 842-856, https://doi.org/10.1016/j.renene.2017.09.058
Jacobs C. T. & Avdis A. & Mouradian S. L. & Piggott M. D., “Integrating Research Data Management into Geographical Information Systems”, 5th International Workshop on Semantic Digital Archives (SDA), 2015, http://hdl.handle.net/10044/1/28557, http://ceur-ws.org/Vol-1529/

Papers using qmesh¶

Collins D. S. & Avdis A. & Allison P. A. & Johnson H. D. & Hill J. & Piggott M. D., Hassan M. H. A. & Damit A. R., “Tidal dynamics and mangrove carbon sequestration during the Oligo–Miocene in the South China Sea”, Nature Communications, Volume 8, Article number: 15698, 2017, https://doi.org/10.1038/ncomms15698
Pérez-Ortiz A. & Borthwick G. L. A. & McNaughton J. & Avdis A., “Characterization of the tidal resource in Rathlin Sound”, Renewable Energy, Volume 114, Part A, December 2017, Pages 229-243, https://doi.org/10.1016/j.renene.2017.04.026

Tutorials¶

Basic invocation and initialisation¶

initialisation, version numbers git sha keys in all APIs

Mesh generation for simulations of tidal flow with a turbine array in the Inner Sound¶

Here we demonstrate how qmesh can be used for high-resolution simulations in the context of renewable energy generation with tidal turbine farms. .. A mesh is constructed in a domain encompassing Northern Scotland, the Orkney and Shetland islands, as shown in

A dataset containing the complete definition of the domain geometry can be downloaded from figshare. A python script that uses this data can be found in the qmesh (API core) code repository and is a good example of the qmesh API: qmesh/examples/OrkneyShetland_UTM30/OrkneyShetland_UTM30.py run this file with python OrkneyShetland_UTM30.py. You will hopefully obtain a mesh similar to the one in figshare, but your mesh will not be identical to it. Differences in platforms, dependecy versions as well as differences in qmesh versions will prevent identical meshes.

Contents¶

Introduction¶

Architecture¶

The qmesh interfaces and packages¶

Application Programming Interface¶

Command Line Interface¶

Graphical User Interface¶

Module design and implementation¶

The vector module¶

The raster module¶

The mesh module¶

The publish module¶

Development, Distribution and Licence¶

Development and distribution repositories¶

Code development repositories¶

DOI repositories¶

Docker Cloud repositories¶

qmesh in the Python Package Index¶

Testing¶

Unit testing¶

Regression testing¶

Integration testing¶

Version numbering¶

Licences¶

GNU GENERAL PUBLIC LICENSE¶

GNU Free Documentation License¶

How to cite qmesh¶

Installation¶

Dependencies¶

Installing from PyPI¶

Docker containers¶

Tutorial¶

A Crash course in GIS¶

Global meshes¶

Meshing the Mediterranean Sea¶

Meshing the North Sea¶

Meshing the Severn Estuary¶

Meshing the Seas around the Orkney and Shetland Islands¶

Further documentation¶

API Rerefence¶

CLI Rerefence¶

GUI Rerefence¶

Developer Documentation¶

Academic papers¶

Papers on qmesh¶

Papers using qmesh¶

Tutorials¶

Basic invocation and initialisation¶

Mesh generation for simulations of tidal flow with a turbine array in the Inner Sound¶

Indices and tables¶