def _get_raster_spacing(coords):
"""Get the row and column spacing of a raster grid.
Parameters
----------
root : netcdf_file
A NetCDF object.
Returns
-------
(dy, dx)
Spacing of grid rows and columns.
"""
spacing = np.empty(len(coords), dtype=np.float64)
for (axis, coord) in enumerate(coords):
coord_spacing = np.diff(coord, axis=axis)
if not np.all(coord_spacing == coord_spacing.flat[0]):
raise NotRasterGridError()
spacing[axis] = coord_spacing.flat[0]
if not np.all(spacing == spacing[0]):
raise NotRasterGridError()
return spacing[0]
def read_netcdf(nc_file, reshape=False, just_grid=False):
"""
Reads the NetCDF file *nc_file*, and writes it to the fields of a new
RasterModelGrid, which it then returns.
Check the names of the fields in the returned grid with
grid.at_nodes.keys().
"""
try:
root = nc.netcdf_file(nc_file, 'r', version=2)
except TypeError:
root = nc4.Dataset(nc_file, 'r', format='NETCDF4')
shape = _read_netcdf_grid_shape(root)
spacing = _read_netcdf_grid_spacing(root)
assert (len(shape) == 2)
assert (len(spacing) == 2)
if spacing[0] != spacing[1]:
raise NotRasterGridError()
grid = RasterModelGrid(num_rows=shape[0], num_cols=shape[1], dx=spacing[0])
if not just_grid:
fields = _read_netcdf_structured_data(root)
for (name, values) in fields.items():
grid.add_field('node', name, values)
root.close()
return grid
def _get_raster_spacing(coords):
spacing = np.empty(len(coords), dtype=np.float64)
for (axis, coord) in enumerate(coords):
coord_spacing = np.diff(coord, axis=axis)
try:
assert (np.all(coord_spacing == coord_spacing.flat[0]))
except AssertionError:
raise NotRasterGridError()
spacing[axis] = coord_spacing.flat[0]
try:
assert (np.all(spacing == spacing[0]))
except AssertionError:
raise NotRasterGridError()
else:
return spacing[0]
def read_netcdf(nc_file, just_grid=False):
"""Read a GEBCO-formatted NetCDF file.
Reads the NetCDF file *nc_file*, and writes it to the fields of a new
RasterModelGrid, which it then returns. Check the names of the fields
in the returned grid with grid.at_nodes.keys().
Parameters
----------
nc_file : str
Name of the NetCDF file.
just_grid : bool, optional
If ``True``, just read the grid information and forget the data.
Otherwise add the data as fields.
Returns
-------
RasterModelGrid
A newly-created :any:`RasterModelGrid`.
"""
try:
root = nc.netcdf_file(nc_file, 'r', version=2)
except TypeError:
root = nc4.Dataset(nc_file, 'r', format='NETCDF4')
shape = _read_netcdf_grid_shape(root)
spacing = _read_netcdf_grid_spacing(root)
assert len(shape) == 2
assert len(spacing) == 2
if spacing[0] != spacing[1]:
raise NotRasterGridError()
grid = RasterModelGrid(shape, spacing=spacing)
if not just_grid:
fields = _read_netcdf_structured_data(root)
for (name, values) in fields.items():
grid.add_field('node', name, values)
root.close()
return grid
def read_netcdf(
nc_file,
grid=None,
name=None,
just_grid=False,
halo=0,
nodata_value=-9999.0,
):
"""Create a :class:`~.RasterModelGrid` from a netcdf file.
Create a new :class:`~.RasterModelGrid` from the netcdf file, *nc_file*.
If the netcdf file also contains data, add that data to the grid's fields.
To create a new grid without any associated data from the netcdf file,
set the *just_grid* keyword to ``True``.
A halo can be added with the keyword *halo*.
If you want the fields to be added to an existing grid, it can be passed
to the keyword argument *grid*.
Parameters
----------
nc_file : str
Name of a netcdf file.
grid : *grid* , optional
Adds data to an existing *grid* instead of creating a new one.
name : str, optional
Add only fields with NetCDF variable name to the grid. Default is to
add all NetCDF varibles to the grid.
just_grid : boolean, optional
Create a new grid but don't add value data.
halo : integer, optional
Adds outer border of depth halo to the *grid*.
nodata_value : float, optional
Value that indicates an invalid value. Default is -9999.
Returns
-------
:class:`~.RasterModelGrid`
A newly-created :class:`~.RasterModelGrid`.
Examples
--------
Import :func:`read_netcdf` and the path to an example netcdf file included
with landlab.
>>> from landlab.io.netcdf import read_netcdf
Create a new grid from the netcdf file. The example grid is a uniform
rectilinear grid with 4 rows and 3 columns of nodes with unit spacing.
The data file also contains data defined at the nodes for the grid for
a variable called, *surface__elevation*.
>>> grid = read_netcdf("test-netcdf4.nc") # doctest: +SKIP
>>> grid.shape == (4, 3) # doctest: +SKIP
True
>>> grid.dy, grid.dx # doctest: +SKIP
(1.0, 1.0)
>>> list(grid.at_node.keys()) # doctest: +SKIP
['surface__elevation']
>>> grid.at_node['surface__elevation'] # doctest: +SKIP
array([ 0., 1., 2., 3., 4., 5., 6., 7., 8., 9., 10.,
11.])
:func:`read_netcdf` will try to determine the format of the netcdf file.
For example, the same call will also work for *netcdf3*-formatted files.
>>> grid = read_netcdf("test-netcdf3-64bit.nc") # doctest: +SKIP
>>> grid.shape == (4, 3) # doctest: +SKIP
True
>>> grid.dy, grid.dx # doctest: +SKIP
(1.0, 1.0)
A more complicated example might add data with a halo to an existing grid.
Note that the lower left corner must be specified correctly for the data
and the grid to align correctly.
>>> from landlab import RasterModelGrid
>>> grid = RasterModelGrid((6, 5), xy_of_lower_left=(-1., -1.)) # doctest: +SKIP
>>> grid = read_netcdf(
... "test-netcdf4.nc",
... grid=grid,
... halo=1,
... nodata_value=-1,
... ) # doctest: +SKIP
>>> grid.at_node['surface__elevation'].reshape(grid.shape) # doctest: +SKIP
array([[ -1., -1., -1., -1., -1.],
[ -1., 0., 1., 2., -1.],
[ -1., 3., 4., 5., -1.],
[ -1., 6., 7., 8., -1.],
[ -1., 9., 10., 11., -1.],
[ -1., -1., -1., -1., -1.]])
"""
from landlab import RasterModelGrid
dataset = xr.open_dataset(nc_file)
if isinstance(name, str):
names = {name}
elif name is None:
names = set(dataset.variables)
else:
names = set(name)
# test if the input is a raster (x and y) are only 1-D instead of 2D.
if len(dataset["x"].shape) == 1:
y, x = np.meshgrid(dataset["y"], dataset["x"], indexing="ij")
else:
x = dataset["x"]
y = dataset["y"]
dx = np.diff(x, axis=1)
dy = np.diff(y, axis=0)
if np.all(dx == dx[0, 0]) and np.all(dy == dy[0, 0]):
xy_spacing = (dx[0, 0], dy[0, 0])
else:
raise NotRasterGridError()
shape = x.shape
xy_of_lower_left = (
x[0, 0] - halo * xy_spacing[0],
y[0, 0] - halo * xy_spacing[1],
)
if grid is None:
grid = RasterModelGrid(shape,
xy_spacing=xy_spacing,
xy_of_lower_left=xy_of_lower_left)
else:
if grid.shape != (shape[0] + 2 * halo, shape[1] + 2 * halo):
raise MismatchGridDataSizeError(
shape[0] + 2 * halo * shape[1] + 2 * halo,
grid.number_of_node_rows * grid.number_of_node_columns,
)
if (grid.dx, grid.dy) != xy_spacing:
raise MismatchGridXYSpacing((grid.dx, grid.dy), xy_spacing)
if grid.xy_of_lower_left != xy_of_lower_left:
raise MismatchGridXYLowerLeft(grid.xy_of_lower_left,
xy_of_lower_left)
if not just_grid:
fields, grid_mapping_dict = _read_netcdf_structured_data(dataset)
for (field_name, values) in fields.items():
# add halo if necessary
if halo > 0:
values = add_halo(values.reshape(shape),
halo=halo,
halo_value=nodata_value).reshape((-1, ))
# add only the requested fields.
if (name is None) or (field_name == name):
add_field = True
else:
add_field = False
if add_field:
grid.add_field(field_name, values, at="node", clobber=True)
if (name is not None) and (name not in grid.at_node):
raise ValueError(
"Specified field {name} was not in provided NetCDF.".format(
name=name))
ignore = {"x", "y"}
for name in names - ignore:
values = dataset.variables[name].values
if halo > 0:
values = add_halo(values.reshape(shape),
halo=halo,
halo_value=nodata_value).reshape((-1, ))
grid.add_field(name, values, at="node", clobber=True)
return grid