import xarray as xr
import numpy
from scipy.spatial import KDTree
import shapely.geometry
import shapely.ops
from shapely.geometry import box, Polygon, MultiPolygon, GeometryCollection
from shapely.strtree import STRtree
import progressbar
from functools import partial
import argparse
from igraph import Graph
from geometric_features import read_feature_collection
from mpas_tools.transects import subdivide_great_circle, \
lon_lat_to_cartesian, cartesian_to_lon_lat
from mpas_tools.parallel import create_pool
from mpas_tools.io import write_netcdf
from mpas_tools.logging import LoggingContext
from mpas_tools.cime.constants import constants
[docs]def compute_mpas_region_masks(dsMesh, fcMask, maskTypes=('cell', 'vertex'),
logger=None, pool=None, chunkSize=1000,
showProgress=False, subdivisionThreshold=30.):
"""
Use shapely and processes to create a set of masks from a feature collection
made up of regions (polygons)
Parameters
----------
dsMesh : xarray.Dataset
An MPAS mesh on which the masks should be created
fcMask : geometric_features.FeatureCollection
A feature collection containing features to use to create the mask
maskTypes : tuple of {'cell', 'edge', 'vertex'}, optional
Which type(s) of masks to make. Masks are created based on whether
the latitude and longitude associated with each of these locations
(e.g. ``dsMesh.latCell`` and ``dsMesh.lonCell`` for ``'cell'``) are
inside or outside of the regions in ``fcMask``.
logger : logging.Logger, optional
A logger for the output if not stdout
pool : multiprocessing.Pool, optional
A pool for performing multiprocessing
chunkSize : int, optional
The number of cells, vertices or edges that are processed in one
operation. Experimentation has shown that 1000 is a reasonable
compromise between dividing the work into sufficient subtasks to
distribute the load and having sufficient work for each thread.
showProgress : bool, optional
Whether to show a progress bar
subdivisionThreshold : float, optional
A threshold in degrees (lon or lat) above which the mask region will
be subdivided into smaller polygons for faster intersection checking
Returns
-------
dsMask : xarray.Dataset
The masks
"""
suffixes = {'cell': 'Cell', 'edge': 'Edge', 'vertex': 'Vertex'}
dims = {'cell': 'nCells', 'edge': 'nEdges', 'vertex': 'nVertices'}
dsMasks = xr.Dataset()
for maskType in maskTypes:
suffix = suffixes[maskType]
dim = dims[maskType]
lonName = 'lon{}'.format(suffix)
latName = 'lat{}'.format(suffix)
lat = numpy.rad2deg(dsMesh[latName].values)
# transform longitudes to [-180, 180)
lon = numpy.mod(numpy.rad2deg(dsMesh[lonName].values) + 180.,
360.) - 180.
if logger is not None:
logger.info(' Computing {} masks:'.format(maskType))
# create shapely geometry for lon and lat
points = [shapely.geometry.Point(x, y) for x, y in zip(lon, lat)]
regionNames, masks, properties, nChar = _compute_region_masks(
fcMask, points, logger, pool, chunkSize, showProgress,
subdivisionThreshold)
nPoints = len(points)
if logger is not None:
logger.info(' Adding masks to dataset...')
nRegions = len(regionNames)
# create a new data array for masks
masksVarName = 'region{}Masks'.format(suffix)
dsMasks[masksVarName] = \
((dim, 'nRegions'), numpy.zeros((nPoints, nRegions), dtype=int))
for index in range(nRegions):
mask = masks[index]
dsMasks[masksVarName][:, index] = numpy.array(mask, dtype=int)
if 'regionNames' not in dsMasks:
# create a new data array for mask names
dsMasks['regionNames'] = (('nRegions',),
numpy.zeros((nRegions,),
dtype='|S{}'.format(nChar)))
for index in range(nRegions):
dsMasks['regionNames'][index] = regionNames[index]
for propertyName in properties:
if propertyName not in dsMasks:
dsMasks[propertyName] = (('nRegions',),
properties[propertyName])
if logger is not None:
logger.info(' Done.')
return dsMasks
def entry_point_compute_mpas_region_masks():
""" Entry point for ``compute_mpas_region_masks()``"""
parser = argparse.ArgumentParser()
parser.add_argument("-m", "--mesh_file_name", dest="mesh_file_name",
type=str, required=True,
help="An MPAS mesh file")
parser.add_argument("-g", "--geojson_file_name",
dest="geojson_file_name", type=str, required=True,
help="An Geojson file containing mask regions")
parser.add_argument("-o", "--mask_file_name", dest="mask_file_name",
type=str, required=True,
help="An output MPAS region masks file")
parser.add_argument("-t", "--mask_types", nargs='+', dest="mask_types",
type=str,
help="Which type(s) of masks to make: cell, edge or "
"vertex. Default is cell and vertex.")
parser.add_argument("-c", "--chunk_size", dest="chunk_size", type=int,
default=1000,
help="The number of cells, vertices or edges that are "
"processed in one operation")
parser.add_argument("--show_progress", dest="show_progress",
action="store_true",
help="Whether to show a progress bar")
parser.add_argument("-s", "--subdivision", dest="subdivision", type=float,
default=30.,
help="A threshold in degrees (lon or lat) above which "
"the mask region will be subdivided into smaller "
"polygons for faster intersection checking")
parser.add_argument(
"--process_count", required=False, dest="process_count", type=int,
help="The number of processes to use to compute masks. The "
"default is to use all available cores")
parser.add_argument(
"--multiprocessing_method", dest="multiprocessing_method",
default='forkserver',
help="The multiprocessing method use for python mask creation "
"('fork', 'spawn' or 'forkserver')")
parser.add_argument("--format", dest="format", type=str,
help="NetCDF file format")
parser.add_argument("--engine", dest="engine", type=str,
help="NetCDF output engine")
args = parser.parse_args()
dsMesh = xr.open_dataset(args.mesh_file_name, decode_cf=False,
decode_times=False)
fcMask = read_feature_collection(args.geojson_file_name)
pool = create_pool(process_count=args.process_count,
method=args.multiprocessing_method)
if args.mask_types is None:
args.mask_types = ('cell', 'vertex')
with LoggingContext('compute_mpas_region_masks') as logger:
dsMasks = compute_mpas_region_masks(
dsMesh=dsMesh, fcMask=fcMask, maskTypes=args.mask_types,
logger=logger, pool=pool, chunkSize=args.chunk_size,
showProgress=args.show_progress,
subdivisionThreshold=args.subdivision)
write_netcdf(dsMasks, args.mask_file_name, format=args.format,
engine=args.engine)
[docs]def compute_mpas_transect_masks(dsMesh, fcMask, earthRadius,
maskTypes=('cell', 'edge', 'vertex'),
logger=None, pool=None, chunkSize=1000,
showProgress=False, subdivisionResolution=10e3,
addEdgeSign=False):
"""
Use shapely and processes to create a set of masks from a feature
collection made up of transects (line strings)
Parameters
----------
dsMesh : xarray.Dataset
An MPAS mesh on which the masks should be created
fcMask : geometric_features.FeatureCollection
A feature collection containing features to use to create the mask
earthRadius : float
The radius of the earth in meters
maskTypes : tuple of {'cell', 'edge', 'vertex'}, optional
Which type(s) of masks to make. Masks are created based on whether
the latitude and longitude associated with each of these locations
(e.g. ``dsMesh.latCell`` and ``dsMesh.lonCell`` for ``'cell'``) are
inside or outside of the transects in ``fcMask``.
logger : logging.Logger, optional
A logger for the output if not stdout
pool : multiprocessing.Pool, optional
A pool for performing multiprocessing
chunkSize : int, optional
The number of cells, vertices or edges that are processed in one
operation. Experimentation has shown that 1000 is a reasonable
compromise between dividing the work into sufficient subtasks to
distribute the load and having sufficient work for each thread.
showProgress : bool, optional
Whether to show a progress bar
subdivisionResolution : float, optional
The maximum resolution (in meters) of segments in a transect. If a
transect is too coarse, it will be subdivided. Pass ``None`` for no
subdivision.
addEdgeSign : bool, optional
Whether to add the ``edgeSign`` variable, which requires significant
extra computation
Returns
-------
dsMask : xarray.Dataset
The masks
"""
suffixes = {'cell': 'Cell', 'edge': 'Edge', 'vertex': 'Vertex'}
dims = {'cell': 'nCells', 'edge': 'nEdges', 'vertex': 'nVertices'}
dsMasks = xr.Dataset()
for maskType in maskTypes:
suffix = suffixes[maskType]
dim = dims[maskType]
if logger is not None:
logger.info(' Computing {} masks:'.format(maskType))
polygons, nPolygons, duplicatePolygons = \
_get_polygons(dsMesh, maskType)
transectNames, masks, properties, nChar, shapes = \
_compute_transect_masks(fcMask, polygons, logger, pool, chunkSize,
showProgress, subdivisionResolution,
earthRadius)
if logger is not None:
if addEdgeSign and maskType == 'edge':
logger.info(' Adding masks and edge signs to dataset...')
else:
logger.info(' Adding masks to dataset...')
nTransects = len(transectNames)
# create a new data array for masks
masksVarName = 'transect{}Masks'.format(suffix)
dsMasks[masksVarName] = \
((dim, 'nTransects'),
numpy.zeros((nPolygons, nTransects), dtype=int))
if addEdgeSign and maskType == 'edge':
dsMasks['transectEdgeMaskSigns'] = \
((dim, 'nTransects'),
numpy.zeros((nPolygons, nTransects), dtype=int))
for index in range(nTransects):
maskAndDuplicates = masks[index]
mask = maskAndDuplicates[0:nPolygons]
mask[duplicatePolygons] = \
numpy.logical_or(mask[duplicatePolygons],
maskAndDuplicates[nPolygons:])
dsMasks[masksVarName][:, index] = numpy.array(mask, dtype=int)
if addEdgeSign and maskType == 'edge':
print(transectNames[index])
dsMasks['transectEdgeMaskSigns'][:, index] = \
_compute_edge_sign(dsMesh, mask, shapes[index])
if 'transectNames' not in dsMasks:
# create a new data array for mask names
dsMasks['transectNames'] = \
(('nTransects',), numpy.zeros((nTransects,),
dtype='|S{}'.format(nChar)))
for index in range(nTransects):
dsMasks['transectNames'][index] = transectNames[index]
for propertyName in properties:
if propertyName not in dsMasks:
dsMasks[propertyName] = (('nTransects',),
properties[propertyName])
if logger is not None:
logger.info(' Done.')
return dsMasks
def entry_point_compute_mpas_transect_masks():
""" Entry point for ``compute_mpas_transect_masks()``"""
parser = argparse.ArgumentParser()
parser.add_argument("-m", "--mesh_file_name", dest="mesh_file_name",
type=str, required=True,
help="An MPAS mesh file")
parser.add_argument("-g", "--geojson_file_name",
dest="geojson_file_name", type=str, required=True,
help="An Geojson file containing transects")
parser.add_argument("-o", "--mask_file_name", dest="mask_file_name",
type=str, required=True,
help="An output MPAS transect masks file")
parser.add_argument("-t", "--mask_types", nargs='+', dest="mask_types",
type=str,
help="Which type(s) of masks to make: cell, edge or "
"vertex. Default is cell, edge and vertex.")
parser.add_argument("-c", "--chunk_size", dest="chunk_size", type=int,
default=1000,
help="The number of cells, vertices or edges that are "
"processed in one operation")
parser.add_argument("--show_progress", dest="show_progress",
action="store_true",
help="Whether to show a progress bar")
parser.add_argument("-s", "--subdivision", dest="subdivision", type=float,
help="The maximum resolution (in meters) of segments "
"in a transect. If a transect is too coarse, it "
"will be subdivided. Default is no subdivision.")
parser.add_argument(
"--process_count", required=False, dest="process_count", type=int,
help="The number of processes to use to compute masks. The "
"default is to use all available cores")
parser.add_argument(
"--multiprocessing_method", dest="multiprocessing_method",
default='forkserver',
help="The multiprocessing method use for python mask creation "
"('fork', 'spawn' or 'forkserver')")
parser.add_argument("--add_edge_sign", dest="add_edge_sign",
action="store_true",
help="Whether to add the transectEdgeMaskSigns "
"variable")
parser.add_argument("--format", dest="format", type=str,
help="NetCDF file format")
parser.add_argument("--engine", dest="engine", type=str,
help="NetCDF output engine")
args = parser.parse_args()
dsMesh = xr.open_dataset(args.mesh_file_name, decode_cf=False,
decode_times=False)
fcMask = read_feature_collection(args.geojson_file_name)
pool = create_pool(process_count=args.process_count,
method=args.multiprocessing_method)
if args.mask_types is None:
args.mask_types = ('cell', 'edge', 'vertex')
earth_radius = constants['SHR_CONST_REARTH']
with LoggingContext('compute_mpas_transect_masks') as logger:
dsMasks = compute_mpas_transect_masks(
dsMesh=dsMesh, fcMask=fcMask, earthRadius=earth_radius,
maskTypes=args.mask_types, logger=logger, pool=pool,
chunkSize=args.chunk_size, showProgress=args.show_progress,
subdivisionResolution=args.subdivision,
addEdgeSign=args.add_edge_sign)
write_netcdf(dsMasks, args.mask_file_name, format=args.format,
engine=args.engine)
[docs]def compute_mpas_flood_fill_mask(dsMesh, fcSeed, logger=None, workers=-1):
"""
Flood fill from the given set of seed points to create a contiguous mask.
The flood fill operates using cellsOnCell, starting from the cells
whose centers are closest to the seed points.
Parameters
----------
dsMesh : xarray.Dataset
An MPAS mesh on which the masks should be created
fcSeed : geometric_features.FeatureCollection
A feature collection containing points at which to start the flood fill
logger : logging.Logger, optional
A logger for the output if not stdout
workers : int, optional
The number of threads used for finding nearest neighbors. The default
is all available threads (``workers=-1``)
Returns
-------
dsMask : xarray.Dataset
The masks
"""
dsMasks = xr.Dataset()
lat = numpy.rad2deg(dsMesh.latCell.values)
# transform longitudes to [-180, 180)
lon = numpy.mod(numpy.rad2deg(dsMesh.lonCell.values) + 180.,
360.) - 180.
if logger is not None:
logger.info(' Computing flood fill mask on cells:')
mask = _compute_seed_mask(fcSeed, lon, lat, workers)
cellsOnCell = dsMesh.cellsOnCell.values - 1
mask = _flood_fill_mask(mask, cellsOnCell)
if logger is not None:
logger.info(' Adding masks to dataset...')
# create a new data array for the mask
masksVarName = 'cellSeedMask'
dsMasks[masksVarName] = (('nCells',), numpy.array(mask, dtype=int))
if logger is not None:
logger.info(' Done.')
return dsMasks
def entry_point_compute_mpas_flood_fill_mask():
""" Entry point for ``compute_mpas_flood_fill_mask()``"""
parser = argparse.ArgumentParser()
parser.add_argument("-m", "--mesh_file_name", dest="mesh_file_name",
type=str, required=True,
help="An MPAS mesh file")
parser.add_argument("-g", "--geojson_file_name",
dest="geojson_file_name", type=str, required=True,
help="An Geojson file containing points at which to "
"start the flood fill")
parser.add_argument("-o", "--mask_file_name", dest="mask_file_name",
type=str, required=True,
help="An output MPAS region masks file")
parser.add_argument("--format", dest="format", type=str,
help="NetCDF file format")
parser.add_argument("--engine", dest="engine", type=str,
help="NetCDF output engine")
args = parser.parse_args()
dsMesh = xr.open_dataset(args.mesh_file_name, decode_cf=False,
decode_times=False)
fcSeed = read_feature_collection(args.geojson_file_name)
with LoggingContext('compute_mpas_flood_fill_mask') as logger:
dsMasks = compute_mpas_flood_fill_mask(
dsMesh=dsMesh, fcSeed=fcSeed, logger=logger)
write_netcdf(dsMasks, args.mask_file_name, format=args.format,
engine=args.engine)
[docs]def compute_lon_lat_region_masks(lon, lat, fcMask, logger=None, pool=None,
chunkSize=1000, showProgress=False,
subdivisionThreshold=30.):
"""
Use shapely and processes to create a set of masks from a feature
collection made up of regions (polygons) on a tensor lon/lat grid
Parameters
----------
lon : numpy.ndarray
A 1D array of longitudes in degrees between -180 and 180
lat : numpy.ndarray
A 1D array of latitudes in degrees between -90 and 90
fcMask : geometric_features.FeatureCollection
A feature collection containing features to use to create the mask
logger : logging.Logger, optional
A logger for the output if not stdout
pool : multiprocessing.Pool, optional
A pool for performing multiprocessing
chunkSize : int, optional
The number of cells, vertices or edges that are processed in one
operation. Experimentation has shown that 1000 is a reasonable
compromise between dividing the work into sufficient subtasks to
distribute the load and having sufficient work for each thread.
showProgress : bool, optional
Whether to show a progress bar
subdivisionThreshold : float, optional
A threshold in degrees (lon or lat) above which the mask region will
be subdivided into smaller polygons for faster intersection checking
Returns
-------
dsMask : xarray.Dataset
The masks
"""
dsMasks = xr.Dataset()
Lon, Lat = numpy.meshgrid(lon, lat)
shape = Lon.shape
Lon = Lon.ravel()
Lat = Lat.ravel()
# create shapely geometry for lon and lat
points = [shapely.geometry.Point(x, y) for x, y in zip(Lon, Lat)]
regionNames, masks, properties, nChar = _compute_region_masks(
fcMask, points, logger, pool, chunkSize, showProgress,
subdivisionThreshold)
nlon = len(lon)
nlat = len(lat)
if logger is not None:
logger.info(' Adding masks to dataset...')
nRegions = len(regionNames)
# create a new data array for masks
masksVarName = 'regionMasks'
dsMasks[masksVarName] = \
(('lat', 'lon', 'nRegions'), numpy.zeros((nlat, nlon, nRegions),
dtype=int))
for index in range(nRegions):
mask = masks[index]
dsMasks[masksVarName][:, :, index] = \
numpy.array(mask.reshape(shape), dtype=int)
# create a new data array for mask names
dsMasks['regionNames'] = (('nRegions',),
numpy.zeros((nRegions,),
dtype='|S{}'.format(nChar)))
for index in range(nRegions):
dsMasks['regionNames'][index] = regionNames[index]
for propertyName in properties:
if propertyName not in dsMasks:
dsMasks[propertyName] = (('nRegions',),
properties[propertyName])
if logger is not None:
logger.info(' Done.')
return dsMasks
def entry_point_compute_lon_lat_region_masks():
""" Entry point for ``compute_lon_lat_region_masks()``"""
parser = argparse.ArgumentParser()
parser.add_argument("-i", "--grid_file_name", dest="grid_file_name",
type=str, required=True,
help="An input lon/lat grid file")
parser.add_argument("--lon", dest="lon", default="lon", type=str,
help="The name of the longitude coordinate")
parser.add_argument("--lat", dest="lat", default="lat", type=str,
help="The name of the latitude coordinate")
parser.add_argument("-g", "--geojson_file_name",
dest="geojson_file_name", type=str, required=True,
help="An Geojson file containing mask regions")
parser.add_argument("-o", "--mask_file_name", dest="mask_file_name",
type=str, required=True,
help="An output MPAS region masks file")
parser.add_argument("-c", "--chunk_size", dest="chunk_size", type=int,
default=1000,
help="The number of grid points that are "
"processed in one operation")
parser.add_argument("--show_progress", dest="show_progress",
action="store_true",
help="Whether to show a progress bar")
parser.add_argument("-s", "--subdivision", dest="subdivision", type=float,
default=30.,
help="A threshold in degrees (lon or lat) above which "
"the mask region will be subdivided into smaller "
"polygons for faster intersection checking")
parser.add_argument(
"--process_count", required=False, dest="process_count", type=int,
help="The number of processes to use to compute masks. The "
"default is to use all available cores")
parser.add_argument(
"--multiprocessing_method", dest="multiprocessing_method",
default='forkserver',
help="The multiprocessing method use for python mask creation "
"('fork', 'spawn' or 'forkserver')")
parser.add_argument("--format", dest="format", type=str,
help="NetCDF file format")
parser.add_argument("--engine", dest="engine", type=str,
help="NetCDF output engine")
args = parser.parse_args()
dsGrid = xr.open_dataset(args.grid_file_name, decode_cf=False,
decode_times=False)
lon = dsGrid[args.lon].values
lat = dsGrid[args.lat].values
fcMask = read_feature_collection(args.geojson_file_name)
pool = create_pool(process_count=args.process_count,
method=args.multiprocessing_method)
with LoggingContext('compute_lon_lat_region_masks') as logger:
dsMasks = compute_lon_lat_region_masks(
lon=lon, lat=lat, fcMask=fcMask, logger=logger, pool=pool,
chunkSize=args.chunk_size, showProgress=args.show_progress,
subdivisionThreshold=args.subdivision)
write_netcdf(dsMasks, args.mask_file_name, format=args.format,
engine=args.engine)
def compute_projection_grid_region_masks(
lon, lat, fcMask, logger=None, pool=None, chunkSize=1000,
showProgress=False, subdivisionThreshold=30., xdim='x', ydim='y'):
"""
Use shapely and processes to create a set of masks from a feature
collection made up of regions (polygons) on a projection grid such as
a polar-stereographic grid.
Parameters
----------
lon : numpy.ndarray
A 2D array of longitudes in degrees between -180 and 180
lat : numpy.ndarray
A 2D array of latitudes in degrees between -90 and 90
fcMask : geometric_features.FeatureCollection
A feature collection containing features to use to create the mask
logger : logging.Logger, optional
A logger for the output if not stdout
pool : multiprocessing.Pool, optional
A pool for performing multiprocessing
chunkSize : int, optional
The number of cells, vertices or edges that are processed in one
operation. Experimentation has shown that 1000 is a reasonable
compromise between dividing the work into sufficient subtasks to
distribute the load and having sufficient work for each thread.
showProgress : bool, optional
Whether to show a progress bar
subdivisionThreshold : float, optional
A threshold in degrees (lon or lat) above which the mask region will
be subdivided into smaller polygons for faster intersection checking
xdim : str, optional
The name of the x dimension
ydim : str, optional
The name of the y dimension
Returns
-------
dsMask : xarray.Dataset
The masks
"""
dsMasks = xr.Dataset()
# make sure -180 <= lon < 180
lon = numpy.mod(lon + 180., 360.) - 180.
ny, nx = lon.shape
# create shapely geometry for lon and lat
points = [shapely.geometry.Point(x, y) for x, y in
zip(lon.ravel(), lat.ravel())]
regionNames, masks, properties, nChar = _compute_region_masks(
fcMask, points, logger, pool, chunkSize, showProgress,
subdivisionThreshold)
if logger is not None:
logger.info(' Adding masks to dataset...')
nRegions = len(regionNames)
# create a new data array for masks
masksVarName = 'regionMasks'
dsMasks[masksVarName] = \
((ydim, xdim, 'nRegions'), numpy.zeros((ny, nx, nRegions), dtype=int))
for index in range(nRegions):
mask = masks[index]
dsMasks[masksVarName][:, :, index] = \
numpy.array(mask.reshape((ny, nx)), dtype=int)
# create a new data array for mask names
dsMasks['regionNames'] = (('nRegions',),
numpy.zeros((nRegions,),
dtype='|S{}'.format(nChar)))
for index in range(nRegions):
dsMasks['regionNames'][index] = regionNames[index]
for propertyName in properties:
if propertyName not in dsMasks:
dsMasks[propertyName] = (('nRegions',),
properties[propertyName])
if logger is not None:
logger.info(' Done.')
return dsMasks
def entry_point_compute_projection_grid_region_masks():
""" Entry point for ``compute_projection_grid_region_masks()``"""
parser = argparse.ArgumentParser()
parser.add_argument("-i", "--grid_file_name", dest="grid_file_name",
type=str, required=True,
help="An input lon/lat grid file")
parser.add_argument("--lon", dest="lon", default="lon", type=str,
help="The name of the 2D longitude coordinate")
parser.add_argument("--lat", dest="lat", default="lat", type=str,
help="The name of the 2D latitude coordinate")
parser.add_argument("-g", "--geojson_file_name",
dest="geojson_file_name", type=str, required=True,
help="An Geojson file containing mask regions")
parser.add_argument("-o", "--mask_file_name", dest="mask_file_name",
type=str, required=True,
help="An output MPAS region masks file")
parser.add_argument("-c", "--chunk_size", dest="chunk_size", type=int,
default=1000,
help="The number of grid points that are "
"processed in one operation")
parser.add_argument("--show_progress", dest="show_progress",
action="store_true",
help="Whether to show a progress bar")
parser.add_argument("-s", "--subdivision", dest="subdivision", type=float,
default=30.,
help="A threshold in degrees (lon or lat) above which "
"the mask region will be subdivided into smaller "
"polygons for faster intersection checking")
parser.add_argument(
"--process_count", required=False, dest="process_count", type=int,
help="The number of processes to use to compute masks. The "
"default is to use all available cores")
parser.add_argument(
"--multiprocessing_method", dest="multiprocessing_method",
default='forkserver',
help="The multiprocessing method use for python mask creation "
"('fork', 'spawn' or 'forkserver')")
parser.add_argument("--format", dest="format", type=str,
help="NetCDF file format")
parser.add_argument("--engine", dest="engine", type=str,
help="NetCDF output engine")
args = parser.parse_args()
dsGrid = xr.open_dataset(args.grid_file_name, decode_cf=False,
decode_times=False)
lon = dsGrid[args.lon]
lat = dsGrid[args.lat]
ydim, xdim = lon.dims
fcMask = read_feature_collection(args.geojson_file_name)
pool = create_pool(process_count=args.process_count,
method=args.multiprocessing_method)
with LoggingContext('compute_lon_lat_region_masks') as logger:
dsMasks = compute_projection_grid_region_masks(
lon=lon.values, lat=lat.values, fcMask=fcMask, logger=logger,
pool=pool, chunkSize=args.chunk_size,
showProgress=args.show_progress,
subdivisionThreshold=args.subdivision, xdim=xdim, ydim=ydim)
write_netcdf(dsMasks, args.mask_file_name, format=args.format,
engine=args.engine)
def _compute_mask_from_shapes(shapes1, shapes2, func, pool, chunkSize,
showProgress):
"""
If multiprocessing, break shapes2 into chunks and use multiprocessing to
apply the given function one chunk at a time
"""
nShapes2 = len(shapes2)
if pool is None:
mask = func(shapes1, shapes2)
else:
nChunks = int(numpy.ceil(nShapes2 / chunkSize))
chunks = []
indices = [0]
for iChunk in range(nChunks):
start = iChunk * chunkSize
end = min((iChunk + 1) * chunkSize, nShapes2)
chunks.append(shapes2[start:end])
indices.append(end)
partial_func = partial(func, shapes1)
if showProgress:
widgets = [' ', progressbar.Percentage(), ' ',
progressbar.Bar(), ' ', progressbar.ETA()]
bar = progressbar.ProgressBar(widgets=widgets,
maxval=nChunks).start()
else:
bar = None
mask = numpy.zeros((nShapes2,), bool)
for iChunk, maskChunk in \
enumerate(pool.imap(partial_func, chunks)):
mask[indices[iChunk]:indices[iChunk + 1]] = maskChunk
if showProgress:
bar.update(iChunk + 1)
if showProgress:
bar.finish()
return mask
def _get_region_names_and_properties(fc):
regionNames = []
for feature in fc.features:
name = feature['properties']['name']
regionNames.append(name)
propertyNames = set()
for feature in fc.features:
for propertyName in feature['properties']:
if propertyName not in ['name', 'author', 'tags', 'component',
'object']:
propertyNames.add(propertyName)
properties = {}
for propertyName in propertyNames:
properties[propertyName] = []
for feature in fc.features:
if propertyName in feature['properties']:
propertyVal = feature['properties'][propertyName]
properties[propertyName].append(propertyVal)
else:
properties[propertyName].append('')
return regionNames, properties
def _compute_region_masks(fcMask, points, logger, pool, chunkSize,
showProgress, threshold):
"""
Build a region mask file from the given mesh and geojson file defining
a set of regions.
"""
regionNames, properties = _get_region_names_and_properties(fcMask)
masks = []
nChar = 0
for feature in fcMask.features:
name = feature['properties']['name']
if logger is not None:
logger.info(' {}'.format(name))
shape = shapely.geometry.shape(feature['geometry'])
shapes = _katana(shape, threshold=threshold)
mask = _compute_mask_from_shapes(
shapes1=shapes, shapes2=points, func=_contains,
pool=pool, chunkSize=chunkSize, showProgress=showProgress)
nChar = max(nChar, len(name))
masks.append(mask)
return regionNames, masks, properties, nChar
def _contains(shapes, points):
tree = STRtree(points)
mask = numpy.zeros(len(points), dtype=bool)
for shape in shapes:
indicesInShape = tree.query(shape, predicate='covers')
mask[indicesInShape] = True
return mask
def _katana(geometry, threshold, count=0, maxcount=250):
"""
From https://snorfalorpagus.net/blog/2016/03/13/splitting-large-polygons-for-faster-intersections/
Split a Polygon into two parts across it's shortest dimension
Copyright (c) 2016, Joshua Arnott
All rights reserved.
Redistribution and use in source and binary forms, with or without
modification, are permitted provided that the following conditions are met:
1. Redistributions of source code must retain the above copyright notice,
this list of conditions and the following disclaimer.
2. Redistributions in binary form must reproduce the above copyright notice,
this list of conditions and the following disclaimer in the documentation
and/or other materials provided with the distribution.
THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS “AS IS”
AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE
LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
POSSIBILITY OF SUCH DAMAGE.
"""
bounds = geometry.bounds
width = bounds[2] - bounds[0]
height = bounds[3] - bounds[1]
if max(width, height) <= threshold or count == maxcount:
# either the polygon is smaller than the threshold, or the maximum
# number of recursions has been reached
return [geometry]
if height >= width:
# split left to right
a = box(bounds[0], bounds[1], bounds[2], bounds[1]+height/2)
b = box(bounds[0], bounds[1]+height/2, bounds[2], bounds[3])
else:
# split top to bottom
a = box(bounds[0], bounds[1], bounds[0]+width/2, bounds[3])
b = box(bounds[0]+width/2, bounds[1], bounds[2], bounds[3])
result = []
for d in (a, b,):
c = geometry.intersection(d)
if isinstance(c, GeometryCollection):
c = c.geoms
else:
c = [c]
for e in c:
if isinstance(e, (Polygon, MultiPolygon)):
result.extend(_katana(e, threshold, count+1, maxcount))
if count > 0:
return result
# convert multipart into singlepart
final_result = []
for g in result:
if isinstance(g, MultiPolygon):
final_result.extend(g.geoms)
else:
final_result.append(g)
return final_result
def _compute_transect_masks(fcMask, polygons, logger, pool, chunkSize,
showProgress, subdivisionResolution, earthRadius):
"""
Build a transect mask file from the given mesh and geojson file defining
a set of transects.
"""
transectNames, properties = _get_region_names_and_properties(fcMask)
masks = []
shapes = []
nChar = 0
for feature in fcMask.features:
name = feature['properties']['name']
if logger is not None:
logger.info(' {}'.format(name))
geom_type = feature['geometry']['type']
if geom_type == 'LineString':
coordinates = [feature['geometry']['coordinates']]
elif geom_type == 'MultiLineString':
coordinates = feature['geometry']['coordinates']
else:
raise ValueError('Unexpected geometry type {}'.format(geom_type))
new_coords = []
for coord_index, coords in enumerate(coordinates):
if subdivisionResolution is None:
new_coords.append(coords)
else:
lon, lat = zip(*coords)
x, y, z = lon_lat_to_cartesian(
lon, lat, earthRadius, degrees=True)
x, y, z, _, _ = subdivide_great_circle(
x, y, z, subdivisionResolution, earthRadius)
lon, lat = cartesian_to_lon_lat(
x, y, z, earthRadius, degrees=True)
new_coords.append([list(a) for a in zip(lon, lat)])
if geom_type == 'LineString':
shape = shapely.geometry.LineString(new_coords[0])
else:
shape = shapely.geometry.MultiLineString(new_coords)
mask = _compute_mask_from_shapes(
shapes1=shape, shapes2=polygons, func=_intersects,
pool=pool, chunkSize=chunkSize, showProgress=showProgress)
nChar = max(nChar, len(name))
masks.append(mask)
shapes.append(shape)
return transectNames, masks, properties, nChar, shapes
def _intersects(shape, polygons):
tree = STRtree(polygons)
mask = numpy.zeros(len(polygons), dtype=bool)
indicesInShape = tree.query(shape, predicate='intersects')
mask[indicesInShape] = True
return mask
def _get_polygons(dsMesh, maskType):
if maskType == 'cell':
# polygons are vertices on cell
vertexIndices = dsMesh.verticesOnCell.values - 1
nVerticesOnCell = dsMesh.nEdgesOnCell.values
maxVertices = vertexIndices.shape[1]
for iVertex in range(maxVertices):
mask = iVertex >= nVerticesOnCell
# copy the last valid vertex
vertexIndices[mask, iVertex] = \
vertexIndices[mask, nVerticesOnCell[mask]-1]
lonVertex = dsMesh.lonVertex.values
latVertex = dsMesh.latVertex.values
lonCenter = dsMesh.lonCell.values
elif maskType == 'vertex':
# polygons are cells on vertex
vertexIndices = dsMesh.cellsOnVertex.values - 1
maxVertices = vertexIndices.shape[1]
firstValid = vertexIndices[:, 0]
for iVertex in range(1, maxVertices):
mask = firstValid < 0
firstValid[mask] = vertexIndices[mask, iVertex]
assert(numpy.all(firstValid >= 0))
for iVertex in range(maxVertices):
mask = vertexIndices[:, iVertex] < 0
vertexIndices[mask, iVertex] = firstValid[mask]
lonVertex = dsMesh.lonCell.values
latVertex = dsMesh.latCell.values
lonCenter = dsMesh.lonVertex.values
elif maskType == 'edge':
# oh, dear, this is a bit more complicated. Polygons are kites
# involving both vertices and cell centers
verticesOnEdge = dsMesh.verticesOnEdge - 1
cellsOnEdge = dsMesh.cellsOnEdge - 1
nEdges = dsMesh.sizes['nEdges']
nCells = dsMesh.sizes['nCells']
vertexIndices = -1 * numpy.ones((nEdges, 4), int)
vertexIndices[:, 0] = cellsOnEdge[:, 0]
vertexIndices[:, 1] = verticesOnEdge[:, 0] + nCells
vertexIndices[:, 2] = cellsOnEdge[:, 1]
vertexIndices[:, 3] = verticesOnEdge[:, 1] + nCells
# if there are invalid cells, just point to the next vertex; all
# vertices on cell should be valid
mask = vertexIndices[:, 0] < 0
vertexIndices[mask, 0] = vertexIndices[mask, 1]
mask = vertexIndices[:, 2] < 0
vertexIndices[mask, 2] = vertexIndices[mask, 3]
lonVertex = numpy.append(dsMesh.lonCell.values,
dsMesh.lonVertex.values)
latVertex = numpy.append(dsMesh.latCell.values,
dsMesh.latVertex.values)
lonCenter = dsMesh.lonEdge.values
else:
raise ValueError('Unknown mask type {}'.format(maskType))
assert numpy.all(vertexIndices >= 0)
latVertex = numpy.rad2deg(latVertex)
# transform longitudes to [-180, 180)
lonVertex = numpy.mod(numpy.rad2deg(lonVertex) + 180., 360.) - 180.
lonCenter = numpy.mod(numpy.rad2deg(lonCenter) + 180., 360.) - 180.
lon = lonVertex[vertexIndices]
lat = latVertex[vertexIndices]
lon, lat, duplicatePolygons = _copy_dateline_lon_lat_vertices(lon, lat,
lonCenter)
nPolygons = len(lonCenter)
polygons = []
for index in range(lon.shape[0]):
coords = zip(lon[index, :], lat[index, :])
polygons.append(shapely.geometry.Polygon(coords))
return polygons, nPolygons, duplicatePolygons
def _copy_dateline_lon_lat_vertices(lonVertex, latVertex, lonCenter):
nPolygons, _ = lonVertex.shape
lonDiff = lonVertex - lonCenter.reshape(nPolygons, 1)
# which polygons have vertices that are out of range to the west?
outOfRange = lonDiff < -180.
duplicatePolygonsEast = numpy.flatnonzero(numpy.any(outOfRange, axis=1))
lonVertex[outOfRange] += 360.
lonVertexToAdd = lonVertex[duplicatePolygonsEast, :] - 360.
latVertexToAdd = latVertex[duplicatePolygonsEast, :]
# which polygons have vertices that are out of range to the east?
outOfRange = lonDiff >= 180.
duplicatePolygonsWest = numpy.flatnonzero(numpy.any(outOfRange, axis=1))
lonVertex[outOfRange] -= 360.
lonVertexToAdd = numpy.append(lonVertexToAdd,
lonVertex[duplicatePolygonsWest, :] + 360.,
axis=0)
latVertexToAdd = numpy.append(latVertexToAdd,
latVertex[duplicatePolygonsWest, :],
axis=0)
lonVertex = numpy.append(lonVertex, lonVertexToAdd, axis=0)
latVertex = numpy.append(latVertex, latVertexToAdd, axis=0)
duplicatePolygons = numpy.append(duplicatePolygonsEast,
duplicatePolygonsWest)
# TODO: we still need to do something about cells that contain the poles
return lonVertex, latVertex, duplicatePolygons
def _compute_seed_mask(fcSeed, lon, lat, workers):
"""
Find the cell centers (points) closes to the given seed points and set
the resulting mask to 1 there
"""
points = numpy.vstack((lon, lat)).T
tree = KDTree(points)
mask = numpy.zeros(len(lon), dtype=int)
points = numpy.zeros((len(fcSeed.features), 2))
for index, feature in enumerate(fcSeed.features):
points[index, :] = feature['geometry']['coordinates']
_, indices = tree.query(points, workers=workers)
for index in indices:
mask[index] = 1
return mask
def _flood_fill_mask(mask, cellsOnCell):
"""
Flood fill starting with a mask of seed points
"""
maxNeighbors = cellsOnCell.shape[1]
while True:
neighbors = cellsOnCell[mask == 1, :]
maskCount = 0
for iNeighbor in range(maxNeighbors):
indices = neighbors[:, iNeighbor]
# we only want to mask valid neighbors and locations that aren't
# already masked
indices = indices[indices >= 0]
localMask = mask[indices] == 0
maskCount += numpy.count_nonzero(localMask)
indices = indices[localMask]
mask[indices] = 1
if maskCount == 0:
break
return mask
def _compute_edge_sign(dsMesh, edgeMask, shape):
""" Compute the edge sign along a transect """
edge_indices = numpy.flatnonzero(edgeMask)
voe = dsMesh.verticesOnEdge.isel(nEdges=edge_indices).values - 1
lon = numpy.rad2deg(dsMesh.lonVertex.values[voe])
lon = numpy.mod(lon + 180., 360.) - 180.
lat = numpy.rad2deg(dsMesh.latVertex.values[voe])
lonEdge = numpy.rad2deg(dsMesh.lonEdge.values[edge_indices])
lonEdge = numpy.mod(lonEdge + 180., 360.) - 180.
lon, lat, duplicate_edges = \
_copy_dateline_lon_lat_vertices(lon, lat, lonEdge)
nEdges = dsMesh.sizes['nEdges']
nVertices = dsMesh.sizes['nVertices']
# give periodic copies unique edge and vertex indices
edge_indices = numpy.append(edge_indices,
edge_indices[duplicate_edges] + nEdges)
voe = numpy.append(voe, voe[duplicate_edges, :] + nVertices, axis=0)
unique_vertices = numpy.unique(voe.ravel())
local_voe = numpy.zeros(voe.shape, dtype=int)
distance = []
unique_lon = []
unique_lat = []
for local_v, v in enumerate(unique_vertices):
local_mask = voe == v
x = lon[local_mask][0]
y = lat[local_mask][0]
distance.append(shape.project(shapely.geometry.Point(x, y)))
unique_lon.append(x)
unique_lat.append(y)
local_voe[local_mask] = local_v
graph = Graph(n=len(unique_vertices),
edges=zip(local_voe[:, 0], local_voe[:, 1]))
graph.vs['distance'] = distance
graph.vs['lon'] = unique_lon
graph.vs['lat'] = unique_lat
graph.vs['vertices'] = numpy.arange(len(unique_vertices))
graph.es['edges'] = edge_indices
graph.es['vertices'] = [(v0, v1) for v0, v1 in zip(voe[:, 0], voe[:, 1])]
edgeSign = numpy.zeros(edgeMask.shape, dtype=int)
clusters = graph.connected_components()
for cluster_index in range(len(clusters)):
cluster = clusters.subgraph(cluster_index)
distance = cluster.vs['distance']
if len(cluster.es) == 1:
edges = cluster.es.select(0)
edge = edges[0]
if edge.source_vertex['distance'] < edge.target_vertex['distance']:
sign = [1]
else:
sign = [-1]
else:
start = numpy.argmin(distance)
end = numpy.argmax(distance)
indices = \
cluster.get_shortest_paths(v=start, to=end, output='epath')[0]
edges = cluster.es.select(indices)
if len(edges) == 1:
edge = edges[0]
if edge.source_vertex['distance'] < \
edge.target_vertex['distance']:
sign = [1]
else:
sign = [-1]
else:
verts = numpy.array(edges['vertices'])
sign = numpy.zeros(len(indices), dtype=int)
for index in range(len(indices)-1):
if verts[index, 1] in verts[index+1, :]:
sign[index] = 1
elif verts[index, 0] in verts[index+1, :]:
sign[index] = -1
else:
raise ValueError('could not find vertex '
'{}'.format(index))
if verts[-1, 0] in verts[-2, :]:
sign[-1] = 1
elif verts[-1, 1] in verts[-2, :]:
sign[-1] = -1
else:
raise ValueError('could not find vertex -1')
sign = numpy.array(sign)
edge_indices = numpy.array(edges['edges'])
valid = numpy.array(edge_indices) < nEdges
edgeSign[edge_indices[valid]] = sign[valid]
duplicate = numpy.logical_not(valid)
edgeSign[edge_indices[duplicate] - nEdges] = sign[duplicate]
return edgeSign