.. _mesh_conversion: *************** Mesh Conversion *************** .. _mesh_converter: Mesh Converter ============== The command-line tool ``MpasMeshConverter.x`` and the corresponding wrapper function :py:func:`mpas_tools.mesh.conversion.convert` are used to convert a dataset describing cell locations, vertex locations, and connectivity between cells and vertices into a valid MPAS mesh following the `MPAS mesh specification `_. Example call to the command-line tool: .. code-block:: $ planar_hex --nx 4 --ny 4 --dc 10e3 -o base_mesh.nc $ MpasMeshConverter.x base_mesh.nc mesh.nc This example uses the ``planar_hex`` tool to generate a small, doubly periodic MPAS mesh with 10-km cells, then uses the MPAS mesh converter to make sure the resulting mesh adheres to the mesh specification. ``MpasMeshConverter.x`` takes two arguments, the input and output mesh files, and will prompt the user for file names if these arguments are not supplied. The same example in a python script can be accomplished with: .. code-block:: python from mpas_tools.planar_hex import make_planar_hex_mesh from mpas_tools.mesh.conversion import convert from mpas_tools.io import write_netcdf ds = make_planar_hex_mesh(nx=4, ny=4, dc=10e3, nonperiodic_x=False, nonperiodic_y=False) ds = convert(ds) write_netcdf(ds, 'mesh.nc') Regardless of which of these methods is used, the input mesh must define the following dimensions, variables and global attributes (not the dimension sizes are merely examples from the mesh generated in the previous examples): .. code-block:: netcdf mesh { dimensions: nCells = 16 ; nVertices = 32 ; vertexDegree = 3 ; variables: double xCell(nCells) ; double yCell(nCells) ; double zCell(nCells) ; double xVertex(nVertices) ; double yVertex(nVertices) ; double zVertex(nVertices) ; int cellsOnVertex(nVertices, vertexDegree) ; double meshDensity(nCells) ; // global attributes: :on_a_sphere = "NO" ; :sphere_radius = 0. ; :is_periodic = "YES" ; The variable ``meshDensity`` is required for historical reasons and is passed unchanged to the resulting mesh. It can contain all ones (or zeros), the resolution of the mesh in kilometers, or whatever field the user wishes. The following global attributes are optional but will be passed on to the resulting mesh: .. code-block:: // global attributes: :x_period = 40000. ; :y_period = 34641.0161513775 ; :history = "Tue May 26 20:58:10 2020: /home/xylar/miniconda3/envs/mpas/bin/planar_hex --nx 4 --ny 4 --dc 10e3 -o base_mesh.nc" ; The ``file_id`` global attribute is also optional and is preserved in the resulting mesh as ``parent_id``. A new ``file_id`` (a random hash tag) is generated by the mesh converter. The resulting dataset has all the dimensions and variables required for an MPAS mesh. The mesh converter also generates a file called ``graph.info`` that is used to create graph partitions from tools like `Metis `_. This file is not stored by default when the python ``cull`` function is used but can be specified with the ``graphInfoFileName`` argument. The python function also takes a ``logger`` that can be used to capture the output that would otherwise go to the screen via ``stdout`` and ``stderr``. .. _cell_culler: Cell Culler =========== The command-line tool ``MpasCellCuller.x`` and the corresponding wrapper function :py:func:`mpas_tools.mesh.conversion.cull` are used to cull cells from a mesh based on the ``cullCell`` field in the input dataset and/or the provided masks. The contents of the ``cullCell`` field is merge with the mask(s) from a masking dataset and the inverse of the mask(s) from an inverse-masking dataset. Then, a preserve-masking dataset is used to determine where cells should *not* be culled. Example call to the command-line tool, assuming you start with a spherical mesh called ``base_mesh.nc`` (not the doubly periodic planar mesh from the examples above): .. code-block:: $ merge_features -c natural_earth -b region -n "Land Coverage" -o land.geojson $ MpasMaskCreator.x base_mesh.nc land.nc -f land.geojson $ MpasCellCuller.x base_mesh.nc culled_mesh.nc -m land.nc This example uses the ``merge_features`` tool from the ``geometric_features`` conda package to get a geojson file containing land coverage. Then, it uses the mask creator (see the next section) to create a mask on the MPAS mesh that is one inside this region and zero outside. Finally, it culls the base mesh to only those cells where the mask is zero (i.e. the mask indicates which cells are to be removed). The same example in a python script can be accomplished with: .. code-block:: python import xarray from geometric_features import GeometricFeatures from mpas_tools.mesh.conversion import mask, cull gf = GeometricFeatures() fcLandCoverage = gf.read(componentName='natural_earth', objectType='region', featureNames=['Land Coverage']) dsBaseMesh = xarray.open_dataset('base_mesh.nc') dsLandMask = mask(dsBaseMesh, fcMask=fcLandCoverage) dsCulledMesh = conversion.cull(dsBaseMesh, dsMask=dsLandMask) write_netcdf(dsCulledMesh, 'culled_mesh.nc') Here is the full usage of ``MpasCellCuller.x``: .. code-block:: MpasCellCuller.x [input_name] [output_name] [[-m/-i/-p] masks_name] [-c] input_name: This argument specifies the input MPAS mesh. output_name: This argument specifies the output culled MPAS mesh. If not specified, it defaults to culled_mesh.nc, but it is required if additional arguments are specified. -m/-i/-p: These arguments control how a set of masks is used when culling a mesh. The -m argument applies a mask to cull based on (i.e. where the mask is 1, the mesh will be culled). The -i argument applies the inverse mask to cull based on (i.e. where the mask is 0, the mesh will be culled). The -p argument forces any marked cells to not be culled. If this argument is specified, the masks_name argument is required -c: Output the mapping from old to new mesh (cellMap) in cellMapForward.txt, and output the reverse mapping from new to old mesh in cellMapBackward.txt. .. _mask_creator: Mask Creator ============ The command-line tool ``MpasMaskCreator.x`` and the corresponding wrapper function :py:func:`mpas_tools.mesh.conversion.mask` are used to create a set of region masks either from mask features or from seed points to be used to flood fill a contiguous block of cells. Examples usage of the mask creator can be found above under the Cell Culler. Here is the full usage of ``MpasMaskCreator.x``: .. code-block:: MpasMaskCreator.x in_file out_file [ [-f/-s] file.geojson ] [--positive_lon] in_file: This argument defines the input file that masks will be created for. out_file: This argument defines the file that masks will be written to. -s file.geojson: This argument pair defines a set of points (from the geojson point definition) that will be used as seed points in a flood fill algorithim. This is useful when trying to remove isolated cells from a mesh. -f file.geojson: This argument pair defines a set of geojson features (regions, transects, or points) that will be converted into masks / lists. --positive_lon: It is unlikely that you want this argument. In rare cases when using a non-standard geojson file where the logitude ranges from 0 to 360 degrees (with the prime meridian at 0 degrees), use this flag. If this flag is not set, the logitude range is -180-180 with 0 degrees being the prime meridian, which is the case for standar geojson files including all features from the geometric_feature repo. The fact that longitudes in the input MPAS mesh range from 0 to 360 is not relevant to this flag, as latitude and longitude are recomputed internally from Cartesian coordinates. Whether this flag is passed in or not, any longitudes written are in the 0-360 range. .. _py_mask_creation: Mask Creation with Python Multiprocessing ========================================= The Mask Creator is a serial code, and the algorithms it uses to find points in a region or cells, edges and vertices along a transect are not particularly efficient or sophisticated. To provide better efficiency and to enable more sophisticated algorithms (now and in the future), a set of related python functions has been developed to provide much (but not all) of the functionality of the C++ Mask Creator described above. Computing MPAS Region Masks --------------------------- The function :py:func:`mpas_tools.mesh.mask.compute_mpas_region_masks()` or the ``compute_mpas_region_masks`` command-line tool can be used to create region masks on cells, edges and/or vertices given an MPAS mesh :py:class:`xarray.Dataset` ``dsMesh`` and a :py:class:`geometric_features.FeatureCollection` ``fcMask`` containing regions. The resulting masks, in the variable ``regionCellMasks``, are 1 where the center of the polygon corresponding to the cell, edge or vertex (see the `MPAS Mesh Specification `_) are inside the given region and 0 where they are outside. This function is far more useful if the user provides a :py:class:`multiprocessing.Pool` in the ``pool`` argument. ``pool`` should be created at the beginning of the calling code (when memory usage is small), possibly with :py:func:`mpas_tools.parallel.create_pool()`, and terminated (:py:meth:`multiprocessing.Pool.terminate`) before the code has finished. The same pool can be used in multiple calls to this and the other Python-based masking functions. If ``pool = None`` (the default), the masks are computed in serial, which will likely be frustratingly slow. The ``chunkSize`` argument can be used to control how much work (how many cells, edges or vertices) each process computes on at one time. A very small ``chunkSize`` will incur a high overhead, while a very large ``chunkSize`` will lead to poor load balancing and infrequent progress updates (if ``showProgress = True``). The default ``chunkSize`` of 1000 seems to perform well across a wide variety of mesh sizes and processor counts. It is a good idea to provide a ``logger`` (see :ref:`logging`) to get some output as the mask creation is progressing. For efficiency, large shapes (e.g. the global coastline) are divided into smaller "tiles". This subdivision is controlled with ``subdivisionThreshold``, which should be set to a minimum size in degrees (latitude or longitude). If a shape is larger than this, it will be divided into tiles. The underlying algorithm first check bounding boxes of the resulting shapes against points before performing the more time-consuming step of determining if the point is inside the shape. The default value of 30 degrees performs much better than no subdivision for large shapes (again, such as the global coastline), but alternative values have not yet been explored. The resulting variables are: - ``regionCellMasks(nCells, nRegions)`` - a cell mask (1 for inside and 0 for outside the region) for each region - ``regionEdgeMasks(nEdges, nRegions)`` - an edge mask for each region - ``regionVertexMasks(nVertices, nRegions)`` - a vertex mask for each region - ``regionNames(nRegions, string64)`` - the names of the regions NetCDF fill values are used for invalid mask values, so ``nCellsInRegion``, etc. are not produced. The command-line tool takes the following arguments: .. code-block:: $ compute_mpas_region_masks --help usage: compute_mpas_region_masks [-h] -m MESH_FILE_NAME -g GEOJSON_FILE_NAME -o MASK_FILE_NAME [-t MASK_TYPES [MASK_TYPES ...]] [-c CHUNK_SIZE] [--show_progress] [-s SUBDIVISION] [--process_count PROCESS_COUNT] [--multiprocessing_method MULTIPROCESSING_METHOD] optional arguments: -h, --help show this help message and exit -m MESH_FILE_NAME, --mesh_file_name MESH_FILE_NAME An MPAS mesh file -g GEOJSON_FILE_NAME, --geojson_file_name GEOJSON_FILE_NAME An Geojson file containing mask regions -o MASK_FILE_NAME, --mask_file_name MASK_FILE_NAME An output MPAS region masks file -t MASK_TYPES [MASK_TYPES ...], --mask_types MASK_TYPES [MASK_TYPES ...] Which type(s) of masks to make: cell, edge or vertex. Default is cell and vertex. -c CHUNK_SIZE, --chunk_size CHUNK_SIZE The number of cells, vertices or edges that are processed in one operation --show_progress Whether to show a progress bar -s SUBDIVISION, --subdivision SUBDIVISION A threshold in degrees (lon or lat) above which the mask region will be subdivided into smaller polygons for faster intersection checking --process_count PROCESS_COUNT The number of processes to use to compute masks. The default is to use all available cores --multiprocessing_method MULTIPROCESSING_METHOD The multiprocessing method use for python mask creation ('fork', 'spawn' or 'forkserver') Computing Transect Masks ------------------------ The function :py:func:`mpas_tools.mesh.mask.compute_mpas_transect_masks()` and the ``compute_mpas_transect_masks`` command-line tool are similar to the function for computing region masks. The function takes a :py:class:`geometric_features.FeatureCollection` ``fcMask`` that is made up of transects, rather than regions. One mask is produced for each feature in the collection, indicating where the transect intersects the cell, edge or vertex polygons (see the `MPAS Mesh Specification `_). The arguments ``logger``, ``pool``, ``chunkSize`` and ``showProgress`` are the same as for region-mask creation above. The argument ``subdivisionResolution`` is a length in meters, above which segments of the transect are subdivided to provide a better representation of the spherical path in longitude/latitude space. The default value of 10 km is typically good enough to capture distortion at typical MPAS mesh resolutions. The algorithm perform intersections in longitude/latitude space using the ``shapely`` library. Because ``shapely`` is designed for 2D shapes in a Cartesian plane, it is not designed for spherical coordinates. Care has been taken to handle periodicity at the dateline (antimeridian) but there may be issues with MPAS mesh polygons containing the north or south pole. If a user needs to handle a transect that is very close to the pole, it is likely worth contacting the developers to request modifications to the code to support this case. The resulting variables are: - ``transectCellMasks(nCells, nTransects)`` - a cell mask (1 if the transect intersects the cell and 0 if not) for each transect - ``transectEdgeMasks(nEdges, nTransects)`` - an edge mask for each transect - ``transectVertexMasks(nVertices, nTransects)`` - a vertex mask for each transect - ``transectNames(nTransects, string64)`` - the names of the transects We don't currently provide cell, edge or vertex indices (e.g. ``transectCellGlobalIDs``) for path along a transect. This is, in part, because the algorithm doesn't keep track of the relative order of points along a transect. This could be updated in the future if there is sufficient demand. The edge sign (``transectEdgeMaskSigns``) is computed only if ``addEdgeSign=True``, since this takes extra time to compute and isn't always needed. .. note:: While the default ``subdivisionResolution`` is 10 km for :py:func:`mpas_tools.mesh.mask.compute_mpas_transect_masks()`, the default behavior in the command-line tool ``compute_mpas_transect_masks`` is no subdivision because there is otherwise not a good way to specify at the command line that no subdivision is desired. Typically, users will want to request subdivision with something like ``-s 10e3`` The command-line tool takes the following arguments: .. code-block:: $ compute_mpas_transect_masks --help usage: compute_mpas_transect_masks [-h] -m MESH_FILE_NAME -g GEOJSON_FILE_NAME -o MASK_FILE_NAME [-t MASK_TYPES [MASK_TYPES ...]] [-c CHUNK_SIZE] [--show_progress] [-s SUBDIVISION] [--process_count PROCESS_COUNT] [--multiprocessing_method MULTIPROCESSING_METHOD] optional arguments: -h, --help show this help message and exit -m MESH_FILE_NAME, --mesh_file_name MESH_FILE_NAME An MPAS mesh file -g GEOJSON_FILE_NAME, --geojson_file_name GEOJSON_FILE_NAME An Geojson file containing transects -o MASK_FILE_NAME, --mask_file_name MASK_FILE_NAME An output MPAS transect masks file -t MASK_TYPES [MASK_TYPES ...], --mask_types MASK_TYPES [MASK_TYPES ...] Which type(s) of masks to make: cell, edge or vertex. Default is cell, edge and vertex. -c CHUNK_SIZE, --chunk_size CHUNK_SIZE The number of cells, vertices or edges that are processed in one operation --show_progress Whether to show a progress bar -s SUBDIVISION, --subdivision SUBDIVISION The maximum resolution (in meters) of segments in a transect. If a transect is too coarse, it will be subdivided. Default is no subdivision. --process_count PROCESS_COUNT The number of processes to use to compute masks. The default is to use all available cores --multiprocessing_method MULTIPROCESSING_METHOD The multiprocessing method use for python mask creation ('fork', 'spawn' or 'forkserver') --add_edge_sign Whether to add the transectEdgeMaskSigns variable Computing a Flood-fill Mask --------------------------- The function :py:func:`mpas_tools.mesh.mask.compute_mpas_flood_fill_mask()` and the command-line tool ``compute_mpas_flood_fill_mask`` fill in a mask, starting with the ocean points closest to the seed points given in :py:class:`geometric_features.FeatureCollection` ``fcSeed``. This algorithm runs in serial, and will be more efficient the more seed points are provided and the more widely scattered over the ocean they are. The resulting dataset contains a single variable: - ``cellSeedMask(nCells)`` - a cell mask that is 1 where the flood fill (following ``cellsOnCell``) propagated starting from the seed points and 0 elsewhere The command-line tool takes the following arguments: .. code-block:: $ compute_mpas_flood_fill_mask --help usage: compute_mpas_flood_fill_mask [-h] -m MESH_FILE_NAME -g GEOJSON_FILE_NAME -o MASK_FILE_NAME optional arguments: -h, --help show this help message and exit -m MESH_FILE_NAME, --mesh_file_name MESH_FILE_NAME An MPAS mesh file -g GEOJSON_FILE_NAME, --geojson_file_name GEOJSON_FILE_NAME An Geojson file containing points at which to start the flood fill -o MASK_FILE_NAME, --mask_file_name MASK_FILE_NAME An output MPAS region masks file Computing Lon/Lat Region Masks ------------------------------ The function :py:func:`mpas_tools.mesh.mask.compute_lon_lat_region_masks()` or the ``compute_lon_lat_region_masks`` command-line tool compute region masks on a longitude/latitude grid but are otherwise functionally very similar to the corresponding tools for compute MPAS region masks. The major difference is that 1D arrays of longitude and latitude are provided instead of an MPAS mesh dataset. There is no argument equivalent to the mask type for MPAS meshes. Instead, mask values are given at each point on the 2D longitude/latitude grid. All other arguments serve the same purpose as for the MPAS region mask creation described above. The command-line tool takes the following arguments: .. code-block:: $ compute_lon_lat_region_masks --help usage: compute_lon_lat_region_masks [-h] -i GRID_FILE_NAME [--lon LON] [--lat LAT] -g GEOJSON_FILE_NAME -o MASK_FILE_NAME [-c CHUNK_SIZE] [--show_progress] [-s SUBDIVISION] [--process_count PROCESS_COUNT] [--multiprocessing_method MULTIPROCESSING_METHOD] optional arguments: -h, --help show this help message and exit -i GRID_FILE_NAME, --grid_file_name GRID_FILE_NAME An input lon/lat grid file --lon LON The name of the longitude coordinate --lat LAT The name of the latitude coordinate -g GEOJSON_FILE_NAME, --geojson_file_name GEOJSON_FILE_NAME An Geojson file containing mask regions -o MASK_FILE_NAME, --mask_file_name MASK_FILE_NAME An output MPAS region masks file -c CHUNK_SIZE, --chunk_size CHUNK_SIZE The number of grid points that are processed in one operation --show_progress Whether to show a progress bar -s SUBDIVISION, --subdivision SUBDIVISION A threshold in degrees (lon or lat) above which the mask region will be subdivided into smaller polygons for faster intersection checking --process_count PROCESS_COUNT The number of processes to use to compute masks. The default is to use all available cores --multiprocessing_method MULTIPROCESSING_METHOD The multiprocessing method use for python mask creation ('fork', 'spawn' or 'forkserver') .. _merge_split: Merging and Splitting ===================== In order to support running `MPAS-Albany Land Ice (MALI) `_ with both Greenland and Antarctica at the same time, tools have been added to support merging and splitting MPAS meshes. Merging two meshes can be accomplished with :py:func:`mpas_tools.merge_grids.merge_grids()`: .. code-block:: python from mpas_tools.translate import translate from mpas_tools.merge_grids import merge_grids from mpas_tools.planar_hex import make_planar_hex_mesh from mpas_tools.io import write_netcdf dsMesh1 = make_planar_hex_mesh(nx=10, ny=10, dc=1000., nonperiodic_x=True, nonperiodic_y=True) dsMesh2 = make_planar_hex_mesh(nx=10, ny=10, dc=1000., nonperiodic_x=True, nonperiodic_y=True) translate(dsMesh2, xOffset=20000., yOffset=0.) write_netcdf(dsMesh1, 'mesh1.nc') write_netcdf(dsMesh2, 'mesh2.nc') merge_grids(infile1='mesh1.nc', infile2='mesh2.nc', outfile='merged_mesh.nc') Typically, it will only make sense to merge non-periodic meshes in this way. Later, perhaps during analysis or visualization, it can be useful to split apart the merged meshes. This can be done with :py:func:`mpas_tools.split_grids.split_grids()` .. code-block:: python from mpas_tools.translate import translate from mpas_tools.split_grids import split_grids from mpas_tools.planar_hex import make_planar_hex_mesh from mpas_tools.io import write_netcdf dsMesh1 = make_planar_hex_mesh(nx=10, ny=10, dc=1000., nonperiodic_x=True, nonperiodic_y=True) dsMesh2 = make_planar_hex_mesh(nx=10, ny=10, dc=1000., nonperiodic_x=True, nonperiodic_y=True) translate(dsMesh2, xOffset=20000., yOffset=0.) write_netcdf(dsMesh1, 'mesh1.nc') write_netcdf(dsMesh2, 'mesh2.nc') split_grids(infile='merged_mesh.nc', outfile1='split_mesh1.nc', outfile='split_mesh2.nc') Merging meshes can also be accomplished with the ``merge_grids`` command-line tool: .. code-block:: none $ merge_grids --help usage: merge_grids [-h] [-o FILENAME] FILENAME1 FILENAME2 Tool to merge 2 MPAS non-contiguous meshes together into a single file positional arguments: FILENAME1 File name for first mesh to merge FILENAME2 File name for second mesh to merge optional arguments: -h, --help show this help message and exit -o FILENAME The merged mesh file Similarly, ``split_grids`` can be used to to split meshes: .. code-block:: none $ split_grids --help usage: split_grids [-h] [-1 FILENAME] [-2 FILENAME] [--nCells NCELLS] [--nEdges NEDGES] [--nVertices NVERTICES] [--maxEdges MAXEDGES1 MAXEDGES2] MESHFILE Tool to split 2 previously merged MPAS non-contiguous meshes into separate files. Typical usage is: split_grids.py -1 outfile1.nc -2 outfile2.nc infile The optional arguments for nCells, nEdges, nVertices, and maxEdges should generally not be required as this information is saved in the combined mesh file as global attributes by the merge_grids.py script. positional arguments: MESHFILE Mesh file to split optional arguments: -h, --help show this help message and exit -1 FILENAME, --outfile1 FILENAME File name for first mesh output (default: mesh1.nc) -2 FILENAME, --outfile2 FILENAME File name for second mesh output (default: mesh2.nc) --nCells NCELLS The number of cells in the first mesh (default: the value specified in MESHFILE global attribute merge_point) --nEdges NEDGES The number of edges in the first mesh (default: the value specified in MESHFILE global attribute merge_point) --nVertices NVERTICES The number of vertices in the first mesh (default: the value specified in MESHFILE global attribute merge_point) --maxEdges MAXEDGES1 MAXEDGES2 The number of maxEdges in each mesh (default: the value specified in MESHFILE global attribute merge_point OR: will use MESHFILE maxEdges dimension and assume same for both) .. _mesh_translation: Translation =========== A planar mesh can be translated in x, y or both by calling :py:func:`mpas_tools.translate.translate()`: .. code-block:: python from mpas_tools.translate import translate from mpas_tools.planar_hex import make_planar_hex_mesh dsMesh = make_planar_hex_mesh(nx=10, ny=20, dc=1000., nonperiodic_x=False, nonperiodic_y=False) translate(dsMesh, xOffset=1000., yOffset=2000.) This creates a periodic, planar mesh and then translates it by 1 km in x and 2 km in y. .. note:: All the functions in the ``mpas_tools.translate`` module modify the mesh inplace, rather than returning a new ``xarray.Dataset`` object. This is in contrast to typical ``xarray`` functions and methods. A mesh can be translated so that its center is at ``x = 0.``, ``y = 0.`` with the function :py:func:`mpas_tools.translate.center()`: .. code-block:: python from mpas_tools.translate import center from mpas_tools.planar_hex import make_planar_hex_mesh dsMesh = make_planar_hex_mesh(nx=10, ny=20, dc=1000., nonperiodic_x=False, nonperiodic_y=False) center(dsMesh) A mesh can be translated so its center matches the center of another mesh by using :py:func:`mpas_tools.translate.center_on_mesh()`: .. code-block:: python from mpas_tools.translate import center_on_mesh from mpas_tools.planar_hex import make_planar_hex_mesh dsMesh1 = make_planar_hex_mesh(nx=10, ny=20, dc=1000., nonperiodic_x=False, nonperiodic_y=False) dsMesh2 = make_planar_hex_mesh(nx=20, ny=40, dc=2000., nonperiodic_x=False, nonperiodic_y=False) center_on_mesh(dsMesh2, dsMesh1) In this example, the coordinates of ``dsMesh2`` are altered so its center matches that of ``dsMesh1``. The functionality of all three of these functions is also available via the ``translate_planar_grid`` command-line tool: .. code-block:: none $ translate_planar_grid --help == Gathering information. (Invoke with --help for more details. All arguments are optional) Usage: translate_planar_grid [options] This script translates the coordinate system of the planar MPAS mesh specified with the -f flag. There are 3 possible methods to choose from: 1) shift the origin to the center of the domain 2) arbirary shift in x and/or y 3) shift to the center of the domain described in a separate file Options: -h, --help show this help message and exit -f FILENAME, --file=FILENAME MPAS planar grid file name. [default: grid.nc] -d FILENAME, --datafile=FILENAME data file name to which to match the domain center of. Uses xCell,yCell or, if those fields do not exist, will secondly try x1,y1 fields. -x SHIFT_VALUE user-specified shift in the x-direction. [default: 0.0] -y SHIFT_VALUE user-specified shift in the y-direction. [default: 0.0] -c shift so origin is at center of domain [default: False] Converting Between Mesh Formats =============================== MSH to MPAS NetCDF ------------------ ``jigsawpy`` produces meshes in ``.msh`` format that need to be converted to `NetCDF `_ files for use by MPAS components. A utility function :py:func:`mpas_tools.mesh.creation.jigsaw_to_netcdf.jigsaw_to_netcdf()` or the command-line utility ``jigsaw_to_netcdf`` are used for this purpose. In addition to the input ``.msh`` and output ``.nc`` files, the user must specify whether this is a spherical or planar mesh and, if it is spherical, provide the radius of the Earth in meters. Triangle to MPAS NetCDF ----------------------- Meshes in `Triangle `_ format can be converted to MPAS NetCDF format using :py:func:`mpas_tools.mesh.creation.triangle_to_netcdf.triangle_to_netcdf()` or the ``triangle_to_netcdf`` command-line tool. The user supplies the names of input ``.node`` and ``.ele`` files and the name of an output MPAS mesh file. MPAS NetCDF to Triangle ----------------------- MPAS meshes in NetCDF format can be converted to ``Triangle`` format using :py:func:`mpas_tools.mesh.creation.mpas_to_triangle.mpas_to_triangle()` or the ``mpas_to_triangle`` command-line tool. The user supplies the name of an input MPAS mesh file and the output prefix for the resulting Triangle ``.node`` and ``.ele`` files. MPAS NetCDF to SCRIP -------------------- The function :py:func:`mpas_tools.scrip.from_mpas.scrip_from_mpas()` can be used to convert an MPAS mesh file in NetCDF format to `SCRIP `_ format. SCRIP files are typically used to create mapping files used to interpolate between meshes. A command-line tools is also available for this purpose: .. code-block:: none $ scrip_from_mpas --help == Gathering information. (Invoke with --help for more details. All arguments are optional) Usage: scrip_from_mpas [options] This script takes an MPAS grid file and generates a SCRIP grid file. Options: -h, --help show this help message and exit -m FILENAME, --mpas=FILENAME MPAS grid file name used as input. [default: grid.nc] -s FILENAME, --scrip=FILENAME SCRIP grid file to output. [default: scrip.nc] -l, --landice If flag is on, landice masks will be computed and used.