def test_gmap_transformed():
dem = GeoTiff(get_demo_file('hef_srtm.tif'))
dem.set_subset(margin=-100)
dem = mercator_grid(center_ll=(10.76, 46.798444), extent=(10000, 7000))
i, j = dem.ij_coordinates
g = GoogleVisibleMap(x=i, y=j, crs=dem, size_x=500, size_y=400)
img = g.get_vardata()
m = Map(dem, countries=False)
with pytest.raises(ValueError):
m.set_data(img)
m.set_lonlat_contours(interval=0.025)
m.set_shapefile(get_demo_file('Hintereisferner.shp'),
linewidths=2,
edgecolor='darkred')
m.set_rgb(img, g.grid)
fig, ax = plt.subplots(1, 1)
m.visualize(ax=ax, addcbar=False)
plt.tight_layout()
return fig
def test_gmap_transformed():
dem = GeoTiff(get_demo_file('hef_srtm.tif'))
dem.set_subset(margin=-100)
dem = mercator_grid(center_ll=(10.76, 46.798444),
extent=(10000, 7000))
i, j = dem.ij_coordinates
g = GoogleVisibleMap(x=i, y=j, crs=dem, size_x=500, size_y=400)
img = g.get_vardata()
m = Map(dem, countries=False)
with pytest.raises(ValueError):
m.set_data(img)
m.set_lonlat_contours(interval=0.025)
m.set_shapefile(get_demo_file('Hintereisferner.shp'),
linewidths=2, edgecolor='darkred')
m.set_rgb(img, g.grid)
fig, ax = plt.subplots(1, 1)
m.visualize(ax=ax, addcbar=False)
plt.tight_layout()
return fig
def grid_tiff(file, grid):
g = GeoTiff(file)
# Spare memory
ex = grid.extent_in_crs(crs=wgs84) # l, r, b, t
g.set_subset(corners=((ex[0], ex[2]), (ex[1], ex[3])),
crs=wgs84, margin=10)
ls = g.get_vardata()
ls_on_grid = grid.lookup_transform(ls, grid=grid)
return ls_on_grid
def grid_tiff(file, grid):
g = GeoTiff(file)
# Spare memory
ex = grid.extent_in_crs(crs=wgs84) # l, r, b, t
g.set_subset(corners=((ex[0], ex[2]), (ex[1], ex[3])),
crs=wgs84,
margin=10)
ls = g.get_vardata()
ls_on_grid = grid.lookup_transform(ls, grid=grid)
return ls_on_grid
def set_topography(self, topo=None, crs=None, relief_factor=0.7, **kwargs):
"""Add topographical shading to the map.
Parameters
----------
topo: path to a geotiff file containing the topography, OR
2d data array
relief_factor: how strong should the shading be?
kwargs: any keyword accepted by salem.Grid.map_gridded_data (interp,ks)
Returns
-------
the topography if needed (bonus)
"""
if topo is None:
self._shading_base()
return
kwargs.setdefault('interp', 'spline')
if isinstance(topo, six.string_types):
_, ext = os.path.splitext(topo)
if ext.lower() == '.tif':
g = GeoTiff(topo)
# Spare memory
ex = self.grid.extent_in_crs(crs=wgs84) # l, r, b, t
g.set_subset(corners=((ex[0], ex[2]), (ex[1], ex[3])),
crs=wgs84,
margin=10)
z = g.get_vardata()
z[z < -999] = 0
z = self.grid.map_gridded_data(z, g.grid, **kwargs)
else:
raise ValueError(
'File extension not recognised: {}'.format(ext))
else:
z = self._check_data(topo, crs=crs, **kwargs)
# Gradient in m m-1
ddx = self.grid.dx
ddy = self.grid.dy
if self.grid.proj.is_latlong():
# we make a coarse approx of the avg dx on a sphere
_, lat = self.grid.ll_coordinates
ddx = np.mean(ddx * 111200 * np.cos(lat * np.pi / 180))
ddy *= 111200
dy, dx = np.gradient(z, ddy, ddx)
self._shading_base(dx - dy, relief_factor=relief_factor)
return z
def set_topography(self, topo=None, crs=None, relief_factor=0.7, **kwargs):
"""Add topographical shading to the map.
Parameters
----------
topo: path to a geotiff file containing the topography, OR
2d data array
relief_factor: how strong should the shading be?
kwargs: any keyword accepted by salem.Grid.map_gridded_data (interp,ks)
Returns
-------
the topography if needed (bonus)
"""
if topo is None:
self._shading_base()
return
kwargs.setdefault('interp', 'spline')
if isinstance(topo, six.string_types):
_, ext = os.path.splitext(topo)
if ext.lower() == '.tif':
g = GeoTiff(topo)
# Spare memory
ex = self.grid.extent_in_crs(crs=wgs84) # l, r, b, t
g.set_subset(corners=((ex[0], ex[2]), (ex[1], ex[3])),
crs=wgs84, margin=10)
z = g.get_vardata()
z[z < -999] = 0
z = self.grid.map_gridded_data(z, g.grid, **kwargs)
else:
raise ValueError('File extension not recognised: {}'
.format(ext))
else:
z = self._check_data(topo, crs=crs, **kwargs)
# Gradient in m m-1
ddx = self.grid.dx
ddy = self.grid.dy
if self.grid.proj.is_latlong():
# we make a coarse approx of the avg dx on a sphere
_, lat = self.grid.ll_coordinates
ddx = np.mean(ddx * 111200 * np.cos(lat * np.pi / 180))
ddy *= 111200
dy, dx = np.gradient(z, ddy, ddx)
self._shading_base(dx - dy, relief_factor=relief_factor)
return z
cached=True)
river = salem.read_shapefile('/users/global/cornkle/data/pythonWorkspace/proj_CEH/shapes/rivers/ne_10m_rivers_lake_centerlines.shp', cached=True)
lakes = salem.read_shapefile('/users/global/cornkle/data/pythonWorkspace/proj_CEH/shapes/lakes/ne_10m_lakes.shp', cached=True)
map.set_shapefile(lakes, edgecolor='k', facecolor='none', linewidth=2,)
srtm_on_ds = ds18_present.salem.lookup_transform(top)
t_on_ds = ds18_present.salem.transform(t)
t2_on_ds = ds18_present.salem.transform(t2)
grid = ds18_present.salem.grid
#deforestation
g = GeoTiff(lst)
ex = grid.extent_in_crs(crs=wgs84) # l, r, b, t
g.set_subset(corners=((ex[0], ex[2]), (ex[1], ex[3])),
crs=wgs84, margin=10)
ls = g.get_vardata()
ls = np.array(ls, dtype=float)
lst_on_ds = ds18_present.salem.lookup_transform(ls, grid=g.grid)
#evergreen forest
g = GeoTiff(vegfra)
ex = grid.extent_in_crs(crs=wgs84) # l, r, b, t
g.set_subset(corners=((ex[0], ex[2]), (ex[1], ex[3])),
crs=wgs84, margin=10)
ls = g.get_vardata()
ls = np.array(ls, dtype=float)
ls[ls >100] = np.nan
ls = grid.map_gridded_data(ls, g.grid)
vegfra_on_ds = ds18_present.salem.lookup_transform(ls, grid=grid)
edgecolor='k',
facecolor='none',
linewidth=2,
)
srtm = open_xr_dataset(get_demo_file('hef_srtm.tif'))
srtm_on_ds = ds.salem.lookup_transform(srtm)
grid = ds.salem.grid
srtm_on_ds = ds.salem.lookup_transform(top)
#t_on_ds = ds.salem.lookup_transform(t, grid=grid)
#
g = GeoTiff(lst)
# Spare memory
ex = grid.extent_in_crs(crs=wgs84) # l, r, b, t
g.set_subset(corners=((ex[0], ex[2]), (ex[1], ex[3])), crs=wgs84, margin=10)
ls = g.get_vardata()
ls = np.array(ls, dtype=float)
ls[ls > 200] = np.nan
ls = grid.map_gridded_data(ls, g.grid)
lst_on_ds = ds.salem.lookup_transform(ls, grid=grid)
g = GeoTiff(vegfra)
# Spare memory
ex = grid.extent_in_crs(crs=wgs84) # l, r, b, t
g.set_subset(corners=((ex[0], ex[2]), (ex[1], ex[3])), crs=wgs84, margin=10)
ls = g.get_vardata()
ls = np.array(ls, dtype=float)
ls[ls > 100] = np.nan
ls = grid.map_gridded_data(ls, g.grid)
vegfra_on_ds = ds.salem.lookup_transform(ls, grid=grid)
