def test_add_halo_where_halo_value_is_zero():
values = np.ones((3, 4), dtype=int)
with_halo = add_halo(values, halo_value=0)
assert np.all(with_halo == [
[0, 0, 0, 0, 0, 0],
[0, 1, 1, 1, 1, 0],
[0, 1, 1, 1, 1, 0],
[0, 1, 1, 1, 1, 0],
[0, 0, 0, 0, 0, 0],
])
def test_add_halo_where_halo_value_is_nan():
values = np.ones((2, 3), dtype=float)
with_halo = add_halo(values, halo=1, halo_value=np.nan)
assert np.all(with_halo[1:-1, 1:-1] == approx(values))
assert np.all(
np.isnan(with_halo) == [
[True, True, True, True, True],
[True, False, False, False, True],
[True, False, False, False, True],
[True, True, True, True, True],
])
def test_add_halo_where_halo_value_is_nan():
values = np.ones((2, 3), dtype=float)
with_halo = add_halo(values, halo=1, halo_value=np.nan)
assert np.all(with_halo[1:-1, 1:-1] == approx(values))
assert np.all(
np.isnan(with_halo)
== [
[True, True, True, True, True],
[True, False, False, False, True],
[True, False, False, False, True],
[True, True, True, True, True],
]
)
def test_add_halo_where_halo_value_is_zero():
values = np.ones((3, 4), dtype=int)
with_halo = add_halo(values, halo_value=0)
assert np.all(
with_halo
== [
[0, 0, 0, 0, 0, 0],
[0, 1, 1, 1, 1, 0],
[0, 1, 1, 1, 1, 0],
[0, 1, 1, 1, 1, 0],
[0, 0, 0, 0, 0, 0],
]
)
def test_add_halo_with_bigger_halo():
values = np.ones((2, 4), dtype=int)
with_halo = add_halo(values, halo=3, halo_value=0)
assert np.all(with_halo == [
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 1, 1, 1, 1, 0, 0, 0],
[0, 0, 0, 1, 1, 1, 1, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
])
def test_add_halo_with_bigger_halo():
values = np.ones((2, 4), dtype=int)
with_halo = add_halo(values, halo=3, halo_value=0)
assert np.all(
with_halo
== [
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 1, 1, 1, 1, 0, 0, 0],
[0, 0, 0, 1, 1, 1, 1, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
]
)
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
>>> from landlab.io.netcdf import NETCDF4_EXAMPLE_FILE
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(NETCDF4_EXAMPLE_FILE)
>>> grid.shape == (4, 3)
True
>>> grid.dy, grid.dx
(1.0, 1.0)
>>> list(grid.at_node.keys())
['surface__elevation']
>>> grid.at_node['surface__elevation']
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.
>>> from landlab.io.netcdf import NETCDF3_64BIT_EXAMPLE_FILE
>>> grid = read_netcdf(NETCDF3_64BIT_EXAMPLE_FILE)
>>> grid.shape == (4, 3)
True
>>> grid.dy, grid.dx
(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.))
>>> grid = read_netcdf(
... NETCDF4_EXAMPLE_FILE,
... grid=grid,
... halo=1,
... nodata_value=-1)
>>> grid.at_node['surface__elevation'].reshape(grid.shape)
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
try:
root = nc.netcdf_file(nc_file, "r", version=2)
except TypeError:
root = nc4.Dataset(nc_file, "r", format="NETCDF4")
try:
node_coords = _read_netcdf_structured_grid(root)
except ValueError:
if (len(root.variables["x"].dimensions) == 1) and (
len(root.variables["y"].dimensions) == 1
):
node_coords = _read_netcdf_raster_structured_grid(root)
else:
assert ValueError(
"x and y dimensions must both either be 2D "
"(nj, ni) or 1D (ni,) and (nj)."
)
assert len(node_coords) == 2
dx = _get_raster_spacing(node_coords)
xy_spacing = (dx, dx)
shape = node_coords[0].shape
xy_of_lower_left = (
node_coords[0].min() - halo * dx,
node_coords[1].min() - halo * dx,
)
if grid is not None:
if (grid.number_of_node_rows != shape[0] + 2 * halo) or (
grid.number_of_node_columns != 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 grid is None:
grid = RasterModelGrid(
shape, xy_spacing=xy_spacing, xy_of_lower_left=xy_of_lower_left
)
if not just_grid:
fields, grid_mapping_dict = _read_netcdf_structured_data(root)
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", noclobber=False)
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)
)
# save grid mapping
if grid_mapping_dict is not None:
grid.grid_mapping = grid_mapping_dict
root.close()
return grid
def test_add_halo_with_defaults():
values = np.arange(6, dtype=int).reshape((2, 3))
with_halo = add_halo(values)
assert np.all(with_halo[1:-1, 1:-1] == values)
assert values.dtype == with_halo.dtype
def test_add_halo_to_bool_array():
values = np.full((2, 3), True, dtype=bool)
with_halo = add_halo(values)
assert np.all(with_halo[1:-1, 1:-1] == values)
assert values.dtype == with_halo.dtype
def read_esri_ascii(asc_file, grid=None, reshape=False, name=None, halo=0):
"""Read :py:class:`~landlab.RasterModelGrid` from an ESRI ASCII file.
Read data from *asc_file*, an ESRI_ ASCII file, into a
:py:class:`~landlab.RasterModelGrid`. *asc_file* is either the name of
the data file or is a file-like object.
The grid and data read from the file are returned as a tuple
(*grid*, *data*) where *grid* is an instance of
:py:class:`~landlab.RasterModelGrid` and *data* is a numpy
array of doubles with that has been reshaped to have the number of rows
and columns given in the header.
.. _ESRI: http://resources.esri.com/help/9.3/arcgisengine/java/GP_ToolRef/spatial_analyst_tools/esri_ascii_raster_format.htm
Parameters
----------
asc_file : str of file-like
Data file to read.
reshape : boolean, optional
Reshape the returned array, otherwise return a flattened array.
name : str, optional
Add data to the grid as a named field.
grid : *grid* , optional
Adds data to an existing *grid* instead of creating a new one.
halo : integer, optional
Adds outer border of depth halo to the *grid*.
Returns
-------
(grid, data) : tuple
A newly-created RasterModel grid and the associated node data.
Raises
------
DataSizeError
Data are not the same size as indicated by the header file.
MismatchGridDataSizeError
If a grid is passed, and the size of the grid does not agree with the
size of the data.
MismatchGridXYSpacing
If a grid is passed, and the cellsize listed in the heading does not
match the grid dx and dy.
MismatchGridXYLowerLeft
If a grid is passed and the xllcorner and yllcorner do not match that
of the grid.
Examples
--------
Assume that fop is the name of a file that contains text below
(make sure you have your path correct):
ncols 3
nrows 4
xllcorner 1.
yllcorner 2.
cellsize 10.
NODATA_value -9999
0. 1. 2.
3. 4. 5.
6. 7. 8.
9. 10. 11.
--------
>>> from landlab.io import read_esri_ascii
>>> (grid, data) = read_esri_ascii('fop') # doctest: +SKIP
>>> #grid is an object of type RasterModelGrid with 4 rows and 3 cols
>>> #data contains an array of length 4*3 that is equal to
>>> # [9., 10., 11., 6., 7., 8., 3., 4., 5., 0., 1., 2.]
>>> (grid, data) = read_esri_ascii('fop', halo=1) # doctest: +SKIP
>>> #now the data has a nodata_value ring of -9999 around it. So array is
>>> # [-9999, -9999, -9999, -9999, -9999, -9999,
>>> # -9999, 9., 10., 11., -9999,
>>> # -9999, 6., 7., 8., -9999,
>>> # -9999, 3., 4., 5., -9999,
>>> # -9999, 0., 1., 2. -9999,
>>> # -9999, -9999, -9999, -9999, -9999, -9999]
"""
from ..grid import RasterModelGrid
# if the asc_file is provided as a string, open it and pass the pointer to
# _read_asc_header, and _read_asc_data
if isinstance(asc_file, six.string_types):
with open(asc_file, "r") as f:
header = read_asc_header(f)
data = _read_asc_data(f)
# otherwise, pass asc_file directly.
else:
header = read_asc_header(asc_file)
data = _read_asc_data(asc_file)
# There is no reason for halo to be negative.
# Assume that if a negative value is given it should be 0.
if halo <= 0:
shape = (header["nrows"], header["ncols"])
if data.size != shape[0] * shape[1]:
raise DataSizeError(shape[0] * shape[1], data.size)
else:
shape = (header["nrows"] + 2 * halo, header["ncols"] + 2 * halo)
# check to see if a nodata_value was given. If not, assign -9999.
if "nodata_value" in header.keys():
nodata_value = header["nodata_value"]
else:
header["nodata_value"] = -9999.0
nodata_value = header["nodata_value"]
if data.size != (shape[0] - 2 * halo) * (shape[1] - 2 * halo):
raise DataSizeError(shape[0] * shape[1], data.size)
xy_spacing = (header["cellsize"], header["cellsize"])
xy_of_lower_left = (
header["xllcorner"] - halo * header["cellsize"],
header["yllcorner"] - halo * header["cellsize"],
)
data = np.flipud(data)
if halo > 0:
data = add_halo(
data.reshape(header["nrows"], header["ncols"]),
halo=halo,
halo_value=nodata_value,
).reshape((-1,))
if not reshape:
data = data.flatten()
if grid is not None:
if (grid.number_of_node_rows != shape[0]) or (
grid.number_of_node_columns != shape[1]
):
raise MismatchGridDataSizeError(
shape[0] * shape[1],
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 grid is None:
grid = RasterModelGrid(
shape, xy_spacing=xy_spacing, xy_of_lower_left=xy_of_lower_left
)
if name:
grid.add_field("node", name, data)
return (grid, data)
def test_add_halo_to_float_array():
values = np.arange(6, dtype=float).reshape((2, 3))
with_halo = add_halo(values)
assert np.all(with_halo[1:-1, 1:-1] == approx(values))
assert values.dtype == with_halo.dtype
def test_add_halo_with_defaults():
values = np.arange(6, dtype=int).reshape((2, 3))
with_halo = add_halo(values)
assert np.all(with_halo[1:-1, 1:-1] == values)
assert values.dtype == with_halo.dtype
def test_add_halo_with_zero_halo():
values = np.ones((2, 3), dtype=int)
with_halo = add_halo(values, halo=0)
assert np.all(with_halo == values)
def read_esri_ascii(asc_file, grid=None, reshape=False, name=None, halo=0):
"""Read :py:class:`~landlab.RasterModelGrid` from an ESRI ASCII file.
Read data from *asc_file*, an ESRI_ ASCII file, into a
:py:class:`~landlab.RasterModelGrid`. *asc_file* is either the name of
the data file or is a file-like object.
The grid and data read from the file are returned as a tuple
(*grid*, *data*) where *grid* is an instance of
:py:class:`~landlab.RasterModelGrid` and *data* is a numpy
array of doubles with that has been reshaped to have the number of rows
and columns given in the header.
.. _ESRI: http://resources.esri.com/help/9.3/arcgisengine/java/GP_ToolRef/spatial_analyst_tools/esri_ascii_raster_format.htm
Parameters
----------
asc_file : str of file-like
Data file to read.
reshape : boolean, optional
Reshape the returned array, otherwise return a flattened array.
name : str, optional
Add data to the grid as a named field.
grid : *grid* , optional
Adds data to an existing *grid* instead of creating a new one.
halo : integer, optional
Adds outer border of depth halo to the *grid*.
Returns
-------
(grid, data) : tuple
A newly-created RasterModel grid and the associated node data.
Raises
------
DataSizeError
Data are not the same size as indicated by the header file.
MismatchGridDataSizeError
If a grid is passed, and the size of the grid does not agree with the
size of the data.
MismatchGridXYSpacing
If a grid is passed, and the cellsize listed in the heading does not
match the grid dx and dy.
MismatchGridXYLowerLeft
If a grid is passed and the xllcorner and yllcorner do not match that
of the grid.
Examples
--------
Assume that fop is the name of a file that contains text below
(make sure you have your path correct):
ncols 3
nrows 4
xllcorner 1.
yllcorner 2.
cellsize 10.
NODATA_value -9999
0. 1. 2.
3. 4. 5.
6. 7. 8.
9. 10. 11.
--------
>>> from landlab.io import read_esri_ascii
>>> (grid, data) = read_esri_ascii('fop') # doctest: +SKIP
>>> #grid is an object of type RasterModelGrid with 4 rows and 3 cols
>>> #data contains an array of length 4*3 that is equal to
>>> # [9., 10., 11., 6., 7., 8., 3., 4., 5., 0., 1., 2.]
>>> (grid, data) = read_esri_ascii('fop', halo=1) # doctest: +SKIP
>>> #now the data has a nodata_value ring of -9999 around it. So array is
>>> # [-9999, -9999, -9999, -9999, -9999, -9999,
>>> # -9999, 9., 10., 11., -9999,
>>> # -9999, 6., 7., 8., -9999,
>>> # -9999, 3., 4., 5., -9999,
>>> # -9999, 0., 1., 2. -9999,
>>> # -9999, -9999, -9999, -9999, -9999, -9999]
"""
from ..grid import RasterModelGrid
# if the asc_file is provided as a string, open it and pass the pointer to
# _read_asc_header, and _read_asc_data
if isinstance(asc_file, (str, pathlib.Path)):
with open(asc_file, "r") as f:
header = read_asc_header(f)
data = _read_asc_data(f)
# otherwise, pass asc_file directly.
else:
header = read_asc_header(asc_file)
data = _read_asc_data(asc_file)
# There is no reason for halo to be negative.
# Assume that if a negative value is given it should be 0.
if halo <= 0:
shape = (header["nrows"], header["ncols"])
if data.size != shape[0] * shape[1]:
raise DataSizeError(shape[0] * shape[1], data.size)
else:
shape = (header["nrows"] + 2 * halo, header["ncols"] + 2 * halo)
# check to see if a nodata_value was given. If not, assign -9999.
if "nodata_value" in header.keys():
nodata_value = header["nodata_value"]
else:
header["nodata_value"] = -9999.0
nodata_value = header["nodata_value"]
if data.size != (shape[0] - 2 * halo) * (shape[1] - 2 * halo):
raise DataSizeError(shape[0] * shape[1], data.size)
xy_spacing = (header["cellsize"], header["cellsize"])
xy_of_lower_left = (
header["xllcorner"] - halo * header["cellsize"],
header["yllcorner"] - halo * header["cellsize"],
)
data = np.flipud(data)
if halo > 0:
data = add_halo(
data.reshape(header["nrows"], header["ncols"]),
halo=halo,
halo_value=nodata_value,
).reshape((-1, ))
if not reshape:
data = data.flatten()
if grid is not None:
if (grid.number_of_node_rows !=
shape[0]) or (grid.number_of_node_columns != shape[1]):
raise MismatchGridDataSizeError(
shape[0] * shape[1],
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 grid is None:
grid = RasterModelGrid(shape,
xy_spacing=xy_spacing,
xy_of_lower_left=xy_of_lower_left)
if name:
grid.add_field(name, data, at="node")
return (grid, data)
def test_add_halo_to_bool_array():
values = np.full((2, 3), True, dtype=bool)
with_halo = add_halo(values)
assert np.all(with_halo[1:-1, 1:-1] == values)
assert values.dtype == with_halo.dtype
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
def test_add_halo_with_zero_halo():
values = np.ones((2, 3), dtype=int)
with_halo = add_halo(values, halo=0)
assert np.all(with_halo == values)
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
try:
root = nc.netcdf_file(nc_file, "r", version=2)
except TypeError:
root = nc4.Dataset(nc_file, "r", format="NETCDF4")
try:
node_coords = _read_netcdf_structured_grid(root)
except ValueError:
if (len(root.variables["x"].dimensions) == 1) and (len(
root.variables["y"].dimensions) == 1):
node_coords = _read_netcdf_raster_structured_grid(root)
else:
assert ValueError("x and y dimensions must both either be 2D "
"(nj, ni) or 1D (ni,) and (nj).")
assert len(node_coords) == 2
dx = _get_raster_spacing(node_coords)
xy_spacing = (dx, dx)
shape = node_coords[0].shape
xy_of_lower_left = (
node_coords[0].min() - halo * dx,
node_coords[1].min() - halo * dx,
)
if grid is not None:
if (grid.number_of_node_rows != shape[0] + 2 * halo) or (
grid.number_of_node_columns != 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 grid is None:
grid = RasterModelGrid(shape,
xy_spacing=xy_spacing,
xy_of_lower_left=xy_of_lower_left)
if not just_grid:
fields, grid_mapping_dict = _read_netcdf_structured_data(root)
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))
# save grid mapping
if grid_mapping_dict is not None:
grid.grid_mapping = grid_mapping_dict
root.close()
return grid
def test_add_halo_to_float_array():
values = np.arange(6, dtype=float).reshape((2, 3))
with_halo = add_halo(values)
assert np.all(with_halo[1:-1, 1:-1] == approx(values))
assert values.dtype == with_halo.dtype