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 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 region_of_interest(self, shape=None, geometry=None, grid=None,
corners=None, crs=wgs84, roi=None,
all_touched=False):
"""Computes a region of interest (ROI).
A ROI is simply a mask of the same size as the grid.
Parameters
----------
shape : str
path to a shapefile
geometry : geometry
a shapely geometry (don't forget the crs keyword)
grid : Grid
a Grid object which extent will form the ROI
corners : tuple
a ((x0, y0), (x1, y1)) tuple of the corners of the square
to subset the dataset to (don't forget the crs keyword)
crs : crs, default wgs84
coordinate reference system of the geometry and corners
roi : ndarray
add the new region_of_interest to a previous one (useful for
multiple geometries for example)
all_touched : boolean
pass-through argument for rasterio.features.rasterize, indicating
that all grid cells which are clipped by the shapefile defining
the region of interest should be included (default=False)
"""
# Initial mask
if roi is not None:
mask = np.array(roi, dtype=np.int16)
else:
mask = np.zeros((self.ny, self.nx), dtype=np.int16)
# Collect keyword arguments, overriding anything the user
# inadvertently added
rasterize_kws = dict(out=mask, all_touched=all_touched)
# Several cases
if shape is not None:
import pandas as pd
inplace = False
if not isinstance(shape, pd.DataFrame):
from salem.sio import read_shapefile
shape = read_shapefile(shape)
inplace = True
# corner grid is needed for rasterio
shape = transform_geopandas(shape, to_crs=self.corner_grid,
inplace=inplace)
import rasterio
with rasterio.Env():
mask = rasterio.features.rasterize(shape.geometry,
**rasterize_kws)
if geometry is not None:
import rasterio
# corner grid is needed for rasterio
geom = transform_geometry(geometry, crs=crs,
to_crs=self.corner_grid)
with rasterio.Env():
mask = rasterio.features.rasterize(np.atleast_1d(geom),
**rasterize_kws)
if grid is not None:
_tmp = np.ones((grid.ny, grid.nx), dtype=np.int16)
mask = self.map_gridded_data(_tmp, grid, out=mask).filled(0)
if corners is not None:
cgrid = self.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
return mask
def region_of_interest(self, shape=None, geometry=None, grid=None,
corners=None, crs=wgs84, roi=None):
"""Computes a region of interest (ROI).
A ROI is simply a mask of the same size as the grid.
Parameters
----------
shape : str
path to a shapefile
geometry : geometry
a shapely geometry (don't forget the crs keyword)
grid : Grid
a Grid object which extent will form the ROI
corners : tuple
a ((x0, y0), (x1, y1)) tuple of the corners of the square
to subset the dataset to (don't forget the crs keyword)
crs : crs, default wgs84
coordinate reference system of the geometry and corners
roi : ndarray
add the new region_of_interest to a previous one (useful for
multiple geometries for example)
"""
# Initial mask
if roi is not None:
mask = np.array(roi, dtype=np.int16)
else:
mask = np.zeros((self.ny, self.nx), dtype=np.int16)
# Several cases
if shape is not None:
import pandas as pd
inplace = False
if not isinstance(shape, pd.DataFrame):
from salem.sio import read_shapefile
shape = read_shapefile(shape)
inplace = True
# corner grid is needed for rasterio
shape = transform_geopandas(shape, to_crs=self.corner_grid,
inplace=inplace)
import rasterio
with rasterio.Env():
mask = rasterio.features.rasterize(shape.geometry, out=mask)
if geometry is not None:
import rasterio
# corner grid is needed for rasterio
geom = transform_geometry(geometry, crs=crs,
to_crs=self.corner_grid)
with rasterio.Env():
mask = rasterio.features.rasterize(np.atleast_1d(geom),
out=mask)
if grid is not None:
_tmp = np.ones((grid.ny, grid.nx), dtype=np.int16)
mask = self.map_gridded_data(_tmp, grid, out=mask).filled(0)
if corners is not None:
cgrid = self.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
return mask
