def test_shape(self):
"""Is the transformation doing well?"""
from salem import read_shapefile
so = read_shapefile(get_demo_file('Hintereisferner.shp'))
sref = read_shapefile(get_demo_file('Hintereisferner_UTM.shp'))
st = gis.transform_geopandas(so, to_crs=sref.crs)
self.assertFalse(st is so)
assert_allclose(st.geometry[0].exterior.coords,
sref.geometry[0].exterior.coords)
sti = gis.transform_geopandas(so, to_crs=sref.crs, inplace=True)
self.assertTrue(sti is so)
assert_allclose(so.geometry[0].exterior.coords,
sref.geometry[0].exterior.coords)
assert_allclose(sti.geometry[0].exterior.coords,
sref.geometry[0].exterior.coords)
g = Grid(nxny=(1, 1), dxdy=(1, 1), x0y0=(10., 46.), proj=wgs84)
so = read_shapefile(get_demo_file('Hintereisferner.shp'))
st = gis.transform_geopandas(so, to_crs=g)
ref = np.array(so.geometry[0].exterior.coords)
ref = ref - np.floor(ref)
assert_allclose(ref, st.geometry[0].exterior.coords)
# round trip
so_back = gis.transform_geopandas(st, to_crs=so.crs)
assert_allclose(so_back.geometry[0].exterior.coords,
so.geometry[0].exterior.coords)
def set_roi(self, shape=None, geometry=None, crs=wgs84, grid=None,
corners=None, noerase=False):
"""Set a region of interest for the dataset.
If set succesfully, a ROI is simply a mask of the same size as the
dataset's grid, obtained with the .roi attribute.
I haven't decided yet if the data should be masekd out when a ROI
has been set.
Parameters
----------
shape: path to a shapefile
geometry: a shapely geometry
crs: the crs of the geometry
grid: a Grid object
corners: a ((x0, y0), (x1, y1)) tuple of the corners of the square
to subset the dataset to. The coordinates are not expressed in
wgs84, set the crs keyword
noerase: set to true in order to add the new ROI to the previous one
"""
# The rois are always defined on the original grids, but of course
# we take that into account when a subset is set (see roi
# decorator below)
ogrid = self._ogrid
# Initial mask
if noerase and (self.roi is not None):
mask = self.roi
else:
mask = np.zeros((ogrid.ny, ogrid.nx), dtype=np.int16)
# Several cases
if shape is not None:
gdf = sio.read_shapefile(shape)
gis.transform_geopandas(gdf, to_crs=ogrid.corner_grid,
inplace=True)
if rasterio is None:
raise ImportError('This feature needs rasterio')
from rasterio.features import rasterize
with rasterio.Env():
mask = rasterize(gdf.geometry, out=mask)
if geometry is not None:
geom = gis.transform_geometry(geometry, crs=crs,
to_crs=ogrid.corner_grid)
if rasterio is None:
raise ImportError('This feature needs rasterio')
from rasterio.features import rasterize
with rasterio.Env():
mask = rasterize(np.atleast_1d(geom), out=mask)
if grid is not None:
_tmp = np.ones((grid.ny, grid.nx), dtype=np.int16)
mask = ogrid.map_gridded_data(_tmp, grid, out=mask).filled(0)
if corners is not None:
cgrid = self._ogrid.center_grid
xy0, xy1 = corners
x0, y0 = cgrid.transform(*xy0, crs=crs, nearest=True)
x1, y1 = cgrid.transform(*xy1, crs=crs, nearest=True)
mask[np.min([y0, y1]):np.max([y0, y1])+1,
np.min([x0, x1]):np.max([x0, x1])+1] = 1
self.roi = mask
def test_shape(self):
"""Is the transformation doing well?"""
from salem import read_shapefile
so = read_shapefile(get_demo_file('Hintereisferner.shp'))
sref = read_shapefile(get_demo_file('Hintereisferner_UTM.shp'))
st = gis.transform_geopandas(so, to_crs=sref.crs)
self.assertFalse(st is so)
assert_allclose(st.geometry[0].exterior.coords,
sref.geometry[0].exterior.coords)
sti = gis.transform_geopandas(so, to_crs=sref.crs, inplace=True)
self.assertTrue(sti is so)
assert_allclose(so.geometry[0].exterior.coords,
sref.geometry[0].exterior.coords)
assert_allclose(sti.geometry[0].exterior.coords,
sref.geometry[0].exterior.coords)
g = Grid(nxny=(1, 1), dxdy=(1, 1), ll_corner=(10., 46.), proj=wgs84)
so = read_shapefile(get_demo_file('Hintereisferner.shp'))
st = gis.transform_geopandas(so, to_crs=g)
ref = np.array(so.geometry[0].exterior.coords)
ref = ref - np.floor(ref)
assert_allclose(ref, st.geometry[0].exterior.coords)
def set_roi(self, shape=None, geometry=None, crs=wgs84, grid=None,
corners=None, noerase=False):
"""Set a region of interest for the dataset.
If set succesfully, a ROI is simply a mask of the same size as the
dataset's grid, obtained with the .roi attribute.
I haven't decided yet if the data should be masekd out when a ROI
has been set.
Parameters
----------
shape: path to a shapefile
geometry: a shapely geometry
crs: the crs of the geometry
grid: a Grid object
corners: a ((x0, y0), (x1, y1)) tuple of the corners of the square
to subset the dataset to. The coordinates are not expressed in
wgs84, set the crs keyword
noerase: set to true in order to add the new ROI to the previous one
"""
# The rois are always defined on the original grids, but of course
# we take that into account when a subset is set (see roi
# decorator below)
ogrid = self._ogrid
# Initial mask
if noerase and (self.roi is not None):
mask = self.roi
else:
mask = np.zeros((ogrid.ny, ogrid.nx), dtype=np.int16)
# Several cases
if shape is not None:
gdf = sio.read_shapefile(shape)
gis.transform_geopandas(gdf, to_crs=ogrid.corner_grid,
inplace=True)
with rasterio.Env():
mask = features.rasterize(gdf.geometry, out=mask)
if geometry is not None:
geom = gis.transform_geometry(geometry, crs=crs,
to_crs=ogrid.corner_grid)
with rasterio.Env():
mask = features.rasterize(np.atleast_1d(geom), out=mask)
if grid is not None:
_tmp = np.ones((grid.ny, grid.nx), dtype=np.int16)
mask = ogrid.map_gridded_data(_tmp, grid, out=mask).filled(0)
if corners is not None:
cgrid = self._ogrid.center_grid
xy0, xy1 = corners
x0, y0 = cgrid.transform(*xy0, crs=crs, nearest=True)
x1, y1 = cgrid.transform(*xy1, crs=crs, nearest=True)
mask[np.min([y0, y1]):np.max([y0, y1])+1,
np.min([x0, x1]):np.max([x0, x1])+1] = 1
self.roi = mask
def _memory_transform(shape_cpath, grid=None, grid_str=None):
"""Quick solution using joblib in order to not transform many times the
same shape (useful for maps).
Since grid is a complex object joblib seemed to have trouble with it,
so joblib is checking its cache according to grid_str while the job is
done with grid.
"""
shape = read_shapefile(shape_cpath, cached=True)
e = grid.extent_in_crs(crs=shape.crs)
p = np.nonzero(~((shape['min_x'] > e[1]) |
(shape['max_x'] < e[0]) |
(shape['min_y'] > e[3]) |
(shape['max_y'] < e[2])))
shape = shape.iloc[p]
shape = gis.transform_geopandas(shape, to_crs=grid, inplace=True)
return shape
def _memory_shapefile_to_grid(shape_cpath,
grid=None,
nxny=None,
pixel_ref=None,
x0y0=None,
dxdy=None,
proj=None):
"""Quick solution using joblib in order to not transform many times the
same shape (useful for maps).
Since grid is a complex object, joblib seems to have trouble with it.
So joblib is checking its cache according to the grid params while the job
is done with grid.
"""
shape = read_shapefile(shape_cpath, cached=True)
e = grid.extent_in_crs(crs=shape.crs)
p = np.nonzero(~(
(shape['min_x'].to_numpy() > e[1]) | (shape['max_x'].to_numpy() < e[0])
| (shape['min_y'].to_numpy() > e[3])
| (shape['max_y'].to_numpy() < e[2])))
shape = shape.iloc[p]
shape = gis.transform_geopandas(shape, to_crs=grid, inplace=True)
return shape
def set_shapefile(self,
shape=None,
countries=False,
oceans=False,
rivers=False,
lakes=False,
**kwargs):
"""Add a shapefile to the plot.
Salem is shipped with a few default settings for country borders,
oceans and rivers (set one at the time!)
set_shapefile() without argument will reset the map to zero shapefiles.
Parameters
----------
shape: the path to the shapefile to read
countries: if True, add country borders
oceans: if True, add oceans
rivers: if True, add rivers
lakes: if True, add lakes
kwargs: all keywords accepted by the corresponding collection.
For LineStrings::
linewidths, colors, linestyles, ...
For Polygons::
alpha, edgecolor, facecolor, fill, linestyle, linewidth, color, ...
"""
# See if the user wanted defaults settings
if oceans:
kwargs.setdefault('facecolor', (0.36862745, 0.64313725, 0.8))
kwargs.setdefault('edgecolor', 'none')
kwargs.setdefault('alpha', 1)
return self.set_shapefile(shapefiles['oceans'], **kwargs)
if rivers:
kwargs.setdefault('color', (0.08984375, 0.65625, 0.8515625))
return self.set_shapefile(shapefiles['rivers'], **kwargs)
if lakes:
kwargs.setdefault('color', (0.08984375, 0.65625, 0.8515625))
return self.set_shapefile(shapefiles['lakes'], **kwargs)
if countries:
kwargs.setdefault('zorder', 1.5)
return self.set_shapefile(shapefiles['world_borders'], **kwargs)
# Defaults
if not all(k in kwargs for k in ('facecolor', 'edgecolor')):
kwargs.setdefault('color', 'k')
# Reset?
if shape is None:
self._collections = []
return
# Transform
if isinstance(shape, pd.DataFrame):
shape = gis.transform_geopandas(shape, to_crs=self.grid)
else:
shape = sio.read_shapefile_to_grid(shape, grid=self.grid)
if len(shape) == 0:
return
# Different collection for each type
geomtype = shape.iloc[0].geometry.type
if 'Polygon' in geomtype:
patches = []
for g in shape.geometry:
if 'Multi' in g.type:
for gg in g:
patches.append(PolygonPatch(gg))
else:
patches.append(PolygonPatch(g))
kwargs.setdefault('facecolor', 'none')
if 'color' in kwargs:
kwargs.setdefault('edgecolor', kwargs['color'])
del kwargs['color']
self._collections.append(PatchCollection(patches, **kwargs))
elif 'LineString' in geomtype:
lines = []
for g in shape.geometry:
if 'Multi' in g.type:
for gg in g:
lines.append(np.array(gg))
else:
lines.append(np.array(g))
self._collections.append(LineCollection(lines, **kwargs))
else:
raise NotImplementedError(geomtype)
def set_shapefile(self, shape=None, countries=False, oceans=False,
rivers=False, **kwargs):
"""Add a shapefile to the plot.
Salem is shipped with a few default settings for country borders,
oceans and rivers (set one at the time!)
set_shapefile() without argument will reset the map to zero shapefiles.
Parameters
----------
shape: the path to the shapefile to read
countries: if True, add country borders
oceans: if True, add oceans
rivers: if True, add rivers
kwargs: all keywords accepted by the corresponding collection.
For LineStrings::
linewidths, colors, linestyles, ...
For Polygons::
alpha, edgecolor, facecolor, fill, linestyle, linewidth, color, ...
"""
# See if the user wanted defaults settings
if oceans:
kwargs.setdefault('facecolor', (0.36862745, 0.64313725, 0.8))
kwargs.setdefault('edgecolor', 'none')
kwargs.setdefault('alpha', 1)
return self.set_shapefile(shapefiles['oceans'], **kwargs)
if rivers:
kwargs.setdefault('colors', (0.08984375, 0.65625, 0.8515625))
return self.set_shapefile(shapefiles['rivers'], **kwargs)
if countries:
kwargs.setdefault('zorder', 50)
return self.set_shapefile(shapefiles['world_borders'], **kwargs)
# Reset?
if shape is None:
self._collections = []
return
# Transform
if isinstance(shape, pd.DataFrame):
shape = gis.transform_geopandas(shape, to_crs=self.grid)
else:
shape = sio.read_shapefile_to_grid(shape, grid=self.grid)
if len(shape) == 0:
return
# Different collection for each type
geomtype = shape.iloc[0].geometry.type
if 'Polygon' in geomtype:
patches = []
for g in shape.geometry:
if 'Multi' in g.type:
for gg in g:
patches.append(PolygonPatch(gg))
else:
patches.append(PolygonPatch(g))
kwargs.setdefault('facecolor', 'none')
if 'color' in kwargs:
kwargs.setdefault('edgecolor', kwargs['color'])
del kwargs['color']
self._collections.append(PatchCollection(patches, **kwargs))
elif 'LineString' in geomtype:
lines = []
for g in shape.geometry:
if 'Multi' in g.type:
for gg in g:
lines.append(np.array(gg))
else:
lines.append(np.array(g))
self._collections.append(LineCollection(lines, **kwargs))
else:
raise NotImplementedError(geomtype)