def test_grdlandmask_fails():
"""
Check that grdlandmask fails correctly when region and spacing are not
given.
"""
with pytest.raises(GMTInvalidInput):
grdlandmask()
def test_grdlandmask_no_outgrid():
"""
Test grdlandmask with no set outgrid.
"""
temp_grid = grdlandmask(spacing=1, region=[-5, 5, -5, 5])
assert temp_grid.dims == ("lat", "lon")
assert temp_grid.gmt.gtype == 1 # Geographic grid
assert temp_grid.gmt.registration == 0 # Pixel registration
assert temp_grid.min() == 0
assert temp_grid.max() == 1
def test_grdlandmask_no_outgrid(expected_grid):
"""
Test grdlandmask with no set outgrid.
"""
result = grdlandmask(spacing=1, region=[125, 130, 30, 35])
# check information of the output grid
assert isinstance(result, xr.DataArray)
assert result.gmt.gtype == 1 # Geographic grid
assert result.gmt.registration == 0 # Gridline registration
# check information of the output grid
xr.testing.assert_allclose(a=result, b=expected_grid)
def test_grdlandmask_outgrid(expected_grid):
"""
Creates a grid land mask with an outgrid argument.
"""
with GMTTempFile(suffix=".nc") as tmpfile:
result = grdlandmask(outgrid=tmpfile.name,
spacing=1,
region=[125, 130, 30, 35])
assert result is None # return value is None
assert os.path.exists(path=tmpfile.name) # check that outgrid exists
temp_grid = load_dataarray(tmpfile.name)
xr.testing.assert_allclose(a=temp_grid, b=expected_grid)
def test_grdlandmask_outgrid():
"""
Creates a grid land mask with an outgrid argument.
"""
with GMTTempFile(suffix=".nc") as tmpfile:
result = grdlandmask(outgrid=tmpfile.name,
spacing=1,
region=[-5, 5, -5, 5])
assert result is None # return value is None
assert os.path.exists(path=tmpfile.name) # check that outgrid exists
result = (grdinfo(grid=tmpfile.name, force_scan=0,
per_column="n").strip().split())
assert result == [
"-5", "5", "-5", "5", "0", "1", "1", "1", "11", "11", "0", "1"
]
def filter_vectors_to_land_only(region, elon, nlat, e, n):
maskfile = pygmt.grdlandmask(region=region, spacing='10m', resolution='i');
points, newelon, newnlat, newe, newn = [], [], [], [], [];
for i in range(len(elon)):
points.append(np.array([elon[i], nlat[i]])); # build np array of points
points = np.array(points);
if len(points) == 0:
return [], [], [], [];
values = pygmt.grdtrack(points, maskfile); # values is a pandas DataFrame
for i, item in enumerate(values[2]): # column 2 is the grdtrack output
if item > 0.8: # if grdtrack gives something on land
newelon.append(elon[i]);
newnlat.append(nlat[i]);
newe.append(e[i]);
newn.append(n[i]);
return newelon, newnlat, newe, newn;
the ``maskvalues`` parameter.
"""
import pygmt
fig = pygmt.Figure()
# Define region of interest
region = [-65, -40, -40, -20]
# Assign a value of 0 for all water masses and a value of 1 for all land
# masses.
# Use shoreline data with (l)ow resolution and set the grid spacing to
# 5 arc minute in x and y direction.
grid = pygmt.grdlandmask(region=region,
spacing="5m",
maskvalues=[0, 1],
resolution="l")
# Plot clipped grid
fig.basemap(region=region, projection="M12c", frame=True)
# Define a colormap to be used for two categories, define the range of the
# new discrete CPT using series=(lowest_value, highest_value, interval),
# use color_model="+cwater,land" to write the discrete color palette
# "batlow" in categorical format and add water/land as annotations for the
# colorbar.
pygmt.makecpt(cmap="batlow", series=(0, 1, 1), color_model="+cwater,land")
fig.grdimage(grid=grid, cmap=True)
fig.colorbar(position="JMR+o0.5c/0c+w8c")