def _surface_metadata(sconfig):
metadata = {}
if "x_resolution" in sconfig:
metadata["x_resolution"] = float(sconfig["x_resolution"])
else:
metadata["x_coordinates"] = tuple(map(float, config.string_to_list(sconfig["x_coordinates"])))
if "y_resolution" in sconfig:
metadata["y_resolution"] = float(sconfig["y_resolution"])
else:
metadata["y_coordinates"] = tuple(map(float, config.string_to_list(sconfig["y_coordinates"])))
return metadata
def __init__(self, name, model_metadata, config):
"""Constructor.
Args:
name (str)
Name of block.
model_metadata (ModelMetadata)
Metadata for model domain containing the block.
config (dict)
Block parameters as dictionary.
Keys:
- x_resolution: Resolution in x-direction (m) if uniform resolution in x-direction.
- x_coordinates: Array of x coordinates (m) if variable resolution in x-direction.
- y_resolution: Resolution in y-direction (m) if uniform resolution in y-direction.
- y_coordinates: Array of y coordinates (m) if variable resolution in y-direction.
- z_resolution: Resolution in z-direction (m) if uniform resolution in z-direction.
- z_top: Elevation of top of block (m) if uniform resolution in z-direction.
- z_bot: Elevation of bottom of block (m) if uniform resolution in z-direction.
- z_coordinates: Array of z coordinates (m) if variable resolution in z-direction.
- z_top_offset: Vertical offset of top set of points below top of block (m) (used to avoid roundoff errors).
- chunk_size: Dimensions of dataset chunk (should be about 10Kb - 1Mb)
"""
self.name = name
self.model_metadata = model_metadata
if "x_resolution" in config:
self.x_resolution = float(config["x_resolution"])
self.x_coordinates = None
else:
self.x_resolution = None
self.x_coordinates = tuple(
map(float, string_to_list(config["x_coordinates"])))
if "y_resolution" in config:
self.y_resolution = float(config["y_resolution"])
self.y_coordinates = None
else:
self.y_resolution = None
self.y_coordinates = tuple(
map(float, string_to_list(config["y_coordinates"])))
if "z_resolution" in config:
self.z_resolution = float(config["z_resolution"])
self.z_coordinates = None
self.z_top = float(config["z_top"])
self.z_bot = float(config["z_bot"])
else:
self.z_resolution = None
self.z_coordinates = tuple(
map(float, string_to_list(config["z_coordinates"])))
self.z_top = numpy.max(self.z_coordinates)
self.z_bot = numpy.min(self.z_coordinates)
self.z_top_offset = float(config["z_top_offset"])
self.chunk_size = tuple(map(int, string_to_list(config["chunk_size"])))
def _initialize(self, config):
"""Setup model.
Args:
config (dict)
Model configuration.
"""
self.metadata = ModelMetadata(config)
for name in string_to_list(config["domain"]["blocks"]):
block = Block(name, self.metadata, config[name])
self.blocks.append(block)
self.storage = HDF5Storage(config["geomodelgrids"]["filename"])
if "top_surface" in config:
self.top_surface = Surface("top_surface", self.metadata,
config["top_surface"], self.storage)
else:
self.top_surface = None
if "topography_bathymetry" in config:
self.topo_bathy = Surface("topography_bathymetry", self.metadata,
config["topography_bathymetry"],
self.storage)
else:
self.topo_bathy = None
def _block_metadata(bconfig):
metadata = {}
if "x_resolution" in bconfig:
metadata["x_resolution"] = float(bconfig["x_resolution"])
else:
metadata["x_coordinates"] = tuple(map(float, config.string_to_list(bconfig["x_coordinates"])))
if "y_resolution" in bconfig:
metadata["y_resolution"] = float(bconfig["y_resolution"])
else:
metadata["y_coordinates"] = tuple(map(float, config.string_to_list(bconfig["y_coordinates"])))
if "z_resolution" in bconfig:
metadata["z_resolution"] = float(bconfig["z_resolution"])
metadata["z_top"] = float(bconfig["z_top"])
metadata["z_bot"] = float(bconfig["z_bot"])
else:
metadata["z_coordinates"] = tuple(map(float, config.string_to_list(bconfig["z_coordinates"])))
metadata["z_top"] = numpy.max(metadata["z_coordinates"])
return metadata
def __init__(self, name, model_metadata, config, storage):
"""Constructor.
Args:
name (str)
Name of surface.
model_metadata (ModelMetadata)
Metadata for model domain associated with block.
config (dict)
Keys:
- x_resolution: Resolution in x-direction (m) if uniform resolution in x-direction.
- x_coordinates: Array of x coordinates (m) if variable resolution in x-direction.
- y_resolution: Resolution in y-direction (m) if uniform resolution in y-direction.
- y_coordinates: Array of y coordinates (m) if variable resolution in y-direction.
- chunk_size: Dimensions of dataset chunk (should be about 10Kb - 1Mb)
storage (HDF5Storage)
Storage interface.
"""
self.name = name
self.model_metadata = model_metadata
if "x_resolution" in config:
self.x_resolution = float(config["x_resolution"])
self.x_coordinates = None
else:
self.x_resolution = None
self.x_coordinates = tuple(
map(float, string_to_list(config["x_coordinates"])))
if "y_resolution" in config:
self.y_resolution = float(config["y_resolution"])
self.y_coordinates = None
else:
self.y_resolution = None
self.y_coordinates = tuple(
map(float, string_to_list(config["y_coordinates"])))
self.chunk_size = tuple(map(int, string_to_list(config["chunk_size"])))
self.storage = storage
def get_topography_bathymetry(self, points):
"""Query EarthVision model for elevation of topoggraphy or bathymetry at points.
Args:
points (numpy.array [Nx,Ny])
Numpy array with coordinates of points in model coordinates.
Returns:
Numpy array [Nx,Ny] of elevation of topography or bathymetry at points.
"""
POINTS_FILENAME = "topography_bathymetry_points.dat" # Must have .dat suffix.
ELEV_FILENAME = "topography_bathymetry_elev.dat" # Must have .dat suffix.
points_abspath = os.path.join(self.model_dir, POINTS_FILENAME)
elev_abspath = os.path.join(self.model_dir, ELEV_FILENAME)
scale = units.length_scale(self.config["earthvision"]["xy_units"])
numpy.savetxt(points_abspath,
points.reshape((-1, points.shape[2])) / scale,
fmt="%16.8e")
elev = -1.0e+20 * numpy.ones(points.shape[0:2])
for grd_filename in string_to_list(
self.config["earthvision"]["topography_bathymetry_2grd"]):
formula = "{filename_out}<elev> = bakint({ev2grd}, {filename_in}<x>, {filename_in}<y>);".format(
filename_in=POINTS_FILENAME,
filename_out=ELEV_FILENAME,
ev2grd=grd_filename)
elev_grd = self.api.ev_fp(formula,
elev_abspath).reshape(points.shape[0:2])
elev_grd *= units.length_scale(
self.config["earthvision"]["elev_units"])
elev = numpy.maximum(elev_grd, elev)
os.remove(points_abspath)
os.remove(elev_abspath)
return elev.reshape((elev.shape[0], elev.shape[1], 1))
def __init__(self, config):
"""Constructor.
Args:
config (dict)
Dictionary with model configuration.
Keys:
- title: Title of model.
- id: Model id.
- description: Description of model.
- keywords: List of keywords describing model.
- history: Description of model creation.
- comment: Additional comments about model.
- version: Model version number.
- creator_name: Name of person creating GeoModelGrids model.
- creator_email: Email of creator.
- creator_institution: Institution of creator.
- acknowledgement: Acknowledgement for model.
- authors: List of model authors.
- references: List of references for model.
- repository_name: Name of repository holding model.
- repository_url: URL of repository.
- repository_doi: Digital Object Identifier for repository.
- license: Name of the license for model.
- coordsys
- crs
- origin_x
- origin_y
- y_azimuth
- data
- values
- units
- layout
- domain
- dim_x
- dim_y
- dim_z
"""
self.title = config["geomodelgrids"]["title"]
self.id = config["geomodelgrids"]["id"]
self.description = config["geomodelgrids"]["description"]
self.keywords = string_to_list(config["geomodelgrids"]["keywords"])
self.history = config["geomodelgrids"]["history"]
self.comment = config["geomodelgrids"]["comment"]
self.version = config["geomodelgrids"]["version"]
self.creator_name = config["geomodelgrids"]["creator_name"]
self.creator_email = config["geomodelgrids"]["creator_email"]
self.creator_institution = config["geomodelgrids"][
"creator_institution"]
self.acknowledgement = config["geomodelgrids"]["acknowledgement"]
self.authors = string_to_list(config["geomodelgrids"]["authors"],
delimiter="|")
self.references = string_to_list(config["geomodelgrids"]["references"],
delimiter="|")
self.repository_name = config["geomodelgrids"]["repository_name"]
self.repository_url = config["geomodelgrids"]["repository_url"]
self.repository_doi = config["geomodelgrids"]["repository_doi"]
self.license = config["geomodelgrids"]["license"]
# Data
self.data_values = string_to_list(config["data"]["values"])
self.data_units = string_to_list(config["data"]["units"])
if config["data"]["layout"] in ["vertex"]:
self.data_layout = config["data"]["layout"]
else:
raise NotImplementedError(
"Currently only vertex-based data are implemented.")
# Coordinate system
self.crs = config["coordsys"]["crs"]
self.origin_x = float(config["coordsys"]["origin_x"])
self.origin_y = float(config["coordsys"]["origin_y"])
self.y_azimuth = float(config["coordsys"]["y_azimuth"])
# Domain dimensions
self.dim_x = float(config["domain"]["dim_x"])
self.dim_y = float(config["domain"]["dim_y"])
self.dim_z = float(config["domain"]["dim_z"])
# Auxiliary data
if "auxiliary" in config["data"]:
import json
self.auxiliary = json.loads(config["data"]["auxiliary"])
else:
self.auxiliary = {}
def setUp(self):
def _surface_metadata(sconfig):
metadata = {}
if "x_resolution" in sconfig:
metadata["x_resolution"] = float(sconfig["x_resolution"])
else:
metadata["x_coordinates"] = tuple(map(float, config.string_to_list(sconfig["x_coordinates"])))
if "y_resolution" in sconfig:
metadata["y_resolution"] = float(sconfig["y_resolution"])
else:
metadata["y_coordinates"] = tuple(map(float, config.string_to_list(sconfig["y_coordinates"])))
return metadata
def _block_metadata(bconfig):
metadata = {}
if "x_resolution" in bconfig:
metadata["x_resolution"] = float(bconfig["x_resolution"])
else:
metadata["x_coordinates"] = tuple(map(float, config.string_to_list(bconfig["x_coordinates"])))
if "y_resolution" in bconfig:
metadata["y_resolution"] = float(bconfig["y_resolution"])
else:
metadata["y_coordinates"] = tuple(map(float, config.string_to_list(bconfig["y_coordinates"])))
if "z_resolution" in bconfig:
metadata["z_resolution"] = float(bconfig["z_resolution"])
metadata["z_top"] = float(bconfig["z_top"])
metadata["z_bot"] = float(bconfig["z_bot"])
else:
metadata["z_coordinates"] = tuple(map(float, config.string_to_list(bconfig["z_coordinates"])))
metadata["z_top"] = numpy.max(metadata["z_coordinates"])
return metadata
model_config = config.get_config([self.CONFIG_FILENAME])
auxiliary = {"int_value": 1, "float_value": 2.0, "str_value": "abc"}
self.metadata = {
"filename": model_config["geomodelgrids"]["filename"],
"title": model_config["geomodelgrids"]["title"],
"id": model_config["geomodelgrids"]["id"],
"description": model_config["geomodelgrids"]["description"],
"keywords": config.string_to_list(model_config["geomodelgrids"]["keywords"]),
"history": model_config["geomodelgrids"]["history"],
"comment": model_config["geomodelgrids"]["comment"],
"version": model_config["geomodelgrids"]["version"],
"creator_name": model_config["geomodelgrids"]["creator_name"],
"creator_institution": model_config["geomodelgrids"]["creator_institution"],
"creator_email": model_config["geomodelgrids"]["creator_email"],
"acknowledgement": model_config["geomodelgrids"]["acknowledgement"],
"authors": config.string_to_list(model_config["geomodelgrids"]["authors"], delimiter="|"),
"references": config.string_to_list(model_config["geomodelgrids"]["references"], delimiter="|"),
"repository_name": model_config["geomodelgrids"]["repository_name"],
"repository_url": model_config["geomodelgrids"]["repository_url"],
"repository_doi": model_config["geomodelgrids"]["repository_doi"],
"license": model_config["geomodelgrids"]["license"],
"data_values": config.string_to_list(model_config["data"]["values"]),
"data_units": config.string_to_list(model_config["data"]["units"]),
"data_layout": model_config["data"]["layout"],
"crs": model_config["coordsys"]["crs"],
"origin_x": float(model_config["coordsys"]["origin_x"]),
"origin_y": float(model_config["coordsys"]["origin_y"]),
"y_azimuth": float(model_config["coordsys"]["y_azimuth"]),
"dim_x": float(model_config["domain"]["dim_x"]),
"dim_y": float(model_config["domain"]["dim_y"]),
"dim_z": float(model_config["domain"]["dim_z"]),
"auxiliary": numpy.string_(json.dumps(auxiliary, sort_keys=True)),
"top_surface": _surface_metadata(model_config["top_surface"]),
"topography_bathymetry": _surface_metadata(model_config["topography_bathymetry"]),
"blocks": {
"top": _block_metadata(model_config["top"]),
"bottom": _block_metadata(model_config["bottom"]),
}
}
def get_values(self, block, top_surface, topo_bathy, batch=None):
"""Get values at points.
Args:
block (Block)
Block information.
top_surface (Surface)
Elevation of top surface of model.
topo_bathy (Surface)
Elevation of topography or bathymetry used to define depth.
batch (BatchGenerator3D)
Current batch of points in block.
"""
def _varxy_index_fn(coordinates):
x_grid = numpy.array(coordinates)
x_grid.sort()
x_round = -int(numpy.log10(numpy.min(numpy.diff(x_grid))))
return x_grid.searchsorted(round(x, x_round))
if batch:
raise NotImplementedError(
"Batches with CSV files are not implemented.")
comment_flag = self.config["csv"].get("comment_flag", "#")
csv_data = numpy.loadtxt(self.config["csv"]["filename"],
comments=comment_flag)
model_x, model_y, model_z = self._to_model_xyz(csv_data)
value_names = string_to_list(self.config["data"]["values"])
value_columns = [
int(self.config["csv.columns"][value]) for value in value_names
]
if block.x_resolution:
def get_xindex(x, block):
return round(x / block.x_resolution)
else:
def get_xindex(x, block):
return _varxy_index_fn(block.x_coordinates)
if block.y_resolution:
def get_yindex(y, block):
return round(y / block.y_resolution)
else:
def get_yindex(y, block):
return _varxy_index_fn(block.y_coordinates)
if block.z_resolution:
def get_zindex(z, block):
return round(z / block.z_resolution)
else:
z_grid = numpy.array(block.z_coordinates)
z_grid.sort()
z_round = -int(numpy.log10(numpy.min(numpy.diff(z_grid))))
def get_zindex(z, block):
return len(z_grid) - 1 - z_grid.searchsorted(round(z, z_round))
nx, ny, nz = block.get_dims()
values = NODATA_VALUE * numpy.ones(
(nx, ny, nz, len(value_names)), dtype=numpy.float32)
for row, x, y, z in zip(csv_data, model_x, model_y, model_z):
ix = get_xindex(x, block)
iy = get_yindex(y, block)
iz = get_zindex(z, block)
row_values = row[value_columns]
values[ix, iy, iz, :] = row_values
return values
