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 loop(lst):
g = GeoTiff(lst)
ls = g.get_vardata()
lon = g.grid.ll_coordinates[0]
lat = g.grid.ll_coordinates[1]
ls = np.array(ls, dtype=float)
ls[ls == -9999] = np.nan
year = lst[-11:-7]
month = lst[-6:-4]
date = pd.Timestamp(year + '-' + month)
da = xr.DataArray(ls,
coords={
'time': date,
'lat': lat[:, 0],
'lon': lon[0, :]
},
dims=['lat', 'lon']) # [np.newaxis, :])
ds = xr.Dataset()
ds['precip'] = da
enc = {'precip': {'complevel': 5, 'zlib': True}}
out = lst.replace('.tif', '.nc')
ds.to_netcdf(path=out, mode='w', encoding=enc, format='NETCDF4')
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
def test_hef_from_array():
grid = mercator_grid(center_ll=(10.76, 46.798444), extent=(10000, 7000))
c = Map(grid, countries=False)
c.set_lonlat_contours(interval=0)
c.set_shapefile(get_demo_file('Hintereisferner_UTM.shp'))
dem = GeoTiff(get_demo_file('hef_srtm.tif'))
mytopo = dem.get_vardata()
c.set_topography(mytopo, crs=dem.grid, interp='spline')
fig, ax = plt.subplots(1, 1)
c.visualize(ax=ax, addcbar=False, title='From array')
plt.tight_layout()
return fig
def open_xr_dataset(file):
"""Thin wrapper around xarray's open_dataset.
This is needed because variables often have not enough georef attrs
to be understood alone, and datasets tend to loose their attrs with
operations...
Returns:
--------
xr.Dataset
"""
# if geotiff, use Salem
p, ext = os.path.splitext(file)
if (ext.lower() == '.tif') or (ext.lower() == '.tiff'):
from salem import GeoTiff
geo = GeoTiff(file)
# TODO: currently everything is loaded in memory (baaad)
da = xr.DataArray(geo.get_vardata(),
coords={'x': geo.grid.x_coord,
'y': geo.grid.y_coord},
dims=['y', 'x'])
ds = xr.Dataset()
ds.attrs['pyproj_srs'] = geo.grid.proj.srs
ds['data'] = da
ds['data'].attrs['pyproj_srs'] = geo.grid.proj.srs
return ds
# otherwise rely on xarray
ds = xr.open_dataset(file)
# did we get it? If not no need to go further
try:
grid = ds.salem.grid
except:
warnings.warn('File not recognised as Salem grid. Fall back to xarray',
RuntimeWarning)
return ds
# add cartesian coords for WRF
if 'west_east' in ds.variables:
ds['west_east'] = ds.salem.grid.x_coord
ds['south_north'] = ds.salem.grid.y_coord
# add pyproj string everywhere
ds.attrs['pyproj_srs'] = grid.proj.srs
for v in ds.data_vars:
ds[v].attrs['pyproj_srs'] = grid.proj.srs
return ds
def test_hef_from_array():
grid = mercator_grid(center_ll=(10.76, 46.798444),
extent=(10000, 7000))
c = Map(grid, countries=False)
c.set_lonlat_contours(interval=0)
c.set_shapefile(get_demo_file('Hintereisferner_UTM.shp'))
dem = GeoTiff(get_demo_file('hef_srtm.tif'))
mytopo = dem.get_vardata()
c.set_topography(mytopo, crs=dem.grid, interp='spline')
fig, ax = plt.subplots(1, 1)
c.visualize(ax=ax, addcbar=False, title='From array')
plt.tight_layout()
return fig
def test_read_to_grid(self):
g = GeoTiff(utils.get_demo_file('hef_srtm.tif'))
sf = utils.get_demo_file('Hintereisferner_UTM.shp')
df1 = read_shapefile_to_grid(sf, g.grid)
df2 = transform_geopandas(read_shapefile(sf), to_crs=g.grid)
assert_allclose(df1.geometry[0].exterior.coords,
df2.geometry[0].exterior.coords)
# test for caching
d = g.grid.to_dict()
# change key ordering by chance
d2 = dict((k, v) for k, v in d.items())
from salem.sio import _memory_shapefile_to_grid, cached_shapefile_path
shape_cpath = cached_shapefile_path(sf)
res = _memory_shapefile_to_grid.call_and_shelve(shape_cpath,
grid=g.grid,
**d)
h1 = res.argument_hash
res = _memory_shapefile_to_grid.call_and_shelve(shape_cpath,
grid=g.grid,
**d2)
h2 = res.argument_hash
self.assertEqual(h1, h2)
def test_read_to_grid(self):
g = GeoTiff(utils.get_demo_file('hef_srtm.tif'))
sf = utils.get_demo_file('Hintereisferner_UTM.shp')
df1 = read_shapefile_to_grid(sf, g.grid)
df2 = transform_geopandas(read_shapefile(sf), to_crs=g.grid)
assert_allclose(df1.geometry[0].exterior.coords,
df2.geometry[0].exterior.coords)
def test_hef_topo_withnan():
grid = mercator_grid(center_ll=(10.76, 46.798444), extent=(10000, 7000))
c = Map(grid, countries=False)
c.set_lonlat_contours(interval=10)
c.set_shapefile(get_demo_file('Hintereisferner_UTM.shp'))
dem = GeoTiff(get_demo_file('hef_srtm.tif'))
mytopo = dem.get_vardata()
h = c.set_topography(mytopo, crs=dem.grid, interp='spline')
c.set_lonlat_contours()
c.set_cmap(get_cmap('topo'))
c.set_plot_params(nlevels=256)
# Try with nan data
h[-100:, -100:] = np.NaN
c.set_data(h)
fig, ax = plt.subplots(1, 1)
c.visualize(ax=ax, title='color with NaN')
plt.tight_layout()
return fig
def test_hef_topo_withnan():
grid = mercator_grid(center_ll=(10.76, 46.798444),
extent=(10000, 7000))
c = Map(grid, countries=False)
c.set_lonlat_contours(interval=10)
c.set_shapefile(get_demo_file('Hintereisferner_UTM.shp'))
dem = GeoTiff(get_demo_file('hef_srtm.tif'))
mytopo = dem.get_vardata()
h = c.set_topography(mytopo, crs=dem.grid, interp='spline')
c.set_lonlat_contours()
c.set_cmap(get_cmap('topo'))
c.set_plot_params(nlevels=256)
# Try with nan data
h[-100:, -100:] = np.NaN
c.set_data(h)
fig, ax = plt.subplots(1, 1)
c.visualize(ax=ax, title='color with NaN')
plt.tight_layout()
return fig
def test_to_cartopy(self):
import cartopy.crs as ccrs
from salem import GeoNetcdf, GeoTiff
grid = gis.mercator_grid(center_ll=(11.38, 47.26),
extent=(2000000, 2000000))
p = gis.proj_to_cartopy(grid.proj)
assert isinstance(p, ccrs.TransverseMercator)
fuzzy_proj_tester(grid.proj, pyproj.Proj(p.proj4_params))
ds = GeoNetcdf(get_demo_file('wrfout_d01.nc'))
p = gis.proj_to_cartopy(ds.grid.proj)
assert isinstance(p, ccrs.LambertConformal)
fuzzy_proj_tester(ds.grid.proj, pyproj.Proj(p.proj4_params))
ds = GeoNetcdf(get_demo_file('wrf_mercator.nc'))
p = gis.proj_to_cartopy(ds.grid.proj)
assert isinstance(p, ccrs.Mercator)
fuzzy_proj_tester(ds.grid.proj, pyproj.Proj(p.proj4_params))
ds = GeoTiff(get_demo_file('himalaya.tif'))
p = gis.proj_to_cartopy(ds.grid.proj)
assert isinstance(p, ccrs.PlateCarree)
ds = GeoTiff(get_demo_file('hef_roi.tif'))
p = gis.proj_to_cartopy(ds.grid.proj)
assert isinstance(p, ccrs.PlateCarree)
fuzzy_proj_tester(ds.grid.proj, pyproj.Proj(p.proj4_params))
p = gis.proj_to_cartopy(wgs84)
assert isinstance(p, ccrs.PlateCarree)
p = gis.proj_to_cartopy(pyproj.Proj('+proj=utm +zone=15'))
assert isinstance(p, ccrs.UTM)
# this needs gdal
if gis.has_gdal:
p = gis.proj_to_cartopy(pyproj.Proj(init='epsg:26915'))
assert isinstance(p, ccrs.UTM)
def read_tiff(file):
g = GeoTiff(file)
ls = g.get_vardata()
return ls
# read the ocean shapefile (data from http://www.naturalearthdata.com)
oceans = salem.read_shapefile(salem.get_demo_file('ne_50m_ocean.shp'),
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)
cached=True)
map.set_shapefile(
lakes,
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)
def read_tiff(file):
g = GeoTiff(file)
ls = g.get_vardata()
return ls