def test_wrap_and_check(self):
from pyresample import utils
lons1 = np.arange(-135., +135, 50.)
lats = np.ones_like(lons1) * 70.
new_lons, new_lats = utils.check_and_wrap(lons1, lats)
self.assertIs(lats, new_lats)
self.assertTrue(np.isclose(lons1, new_lons).all())
lons2 = np.where(lons1 < 0, lons1 + 360, lons1)
new_lons, new_lats = utils.check_and_wrap(lons2, lats)
self.assertIs(lats, new_lats)
# after wrapping lons2 should look like lons1
self.assertTrue(np.isclose(lons1, new_lons).all())
lats2 = lats + 25.
self.assertRaises(ValueError, utils.check_and_wrap, lons1, lats2)
def test_wrap_and_check(self):
from pyresample import utils
lons1 = np.arange(-135., +135, 50.)
lats = np.ones_like(lons1) * 70.
new_lons, new_lats = utils.check_and_wrap(lons1, lats)
self.assertIs(lats, new_lats)
self.assertTrue(np.isclose(lons1, new_lons).all())
lons2 = np.where(lons1 < 0, lons1 + 360, lons1)
new_lons, new_lats = utils.check_and_wrap(lons2, lats)
self.assertIs(lats, new_lats)
# after wrapping lons2 should look like lons1
self.assertTrue(np.isclose(lons1, new_lons).all())
lats2 = lats + 25.
self.assertRaises(ValueError, utils.check_and_wrap, lons1, lats2)
def remap_data(self, dataarray, grid=None, **kwargs):
"""remaps from another grid to the current grid of self using pyresample.
it assumes that the dimensions are ordered in ROW,COL,CHANNEL per
pyresample docs
Parameters
----------
grid : pyresample grid (SwathDefinition or AreaDefinition)
Description of parameter `grid`.
da : ndarray or xarray DataArray
Description of parameter `dset`.
radius_of_influence : float or integer
radius of influcence for pyresample in meters.
Returns
-------
xarray.DataArray
resampled object on current grid.
"""
from pyresample import utils
from .util import resample
from .util.interp_util import lonlat_to_swathdefinition as llsd
# from .grids import get_generic_projection_from_proj4
# check to see if grid is supplied
dataarray = _rename_to_monet_latlon(dataarray)
lons_t, lats_t = utils.check_and_wrap(self.obj.longitude.values,
self.obj.latitude.values)
self.obj = _rename_to_monet_latlon(self.obj)
lons_s, lats_s = utils.check_and_wrap(dataarray.longitude.values,
dataarray.latitude.values)
target = llsd(longitude=lons_t, latitude=lats_t)
source = llsd(latitude=lats_s, longitude=lons_s)
# target = get_generic_projection_from_proj4(
# self.obj.latitude, self.object.longitude, self.obj.proj4_srs)
# source_grid = get_generic_projection_from_proj4(
# dataarray.latitude, dataarray.longitude, dataarray.proj4_srs)
if grid is None: # grid is assumed to be in da.area
out = resample.resample_dataset(dataarray, source, target,
**kwargs)
else:
out = resample.resample_dataset(dataarray, grid, target, **kwargs)
return out
def nearest_latlon(self, lat=None, lon=None, **kwargs):
"""Short summary.
Parameters
----------
lat : type
Description of parameter `lat`.
lon : type
Description of parameter `lon`.
**kwargs : type
Description of parameter `**kwargs`.
Returns
-------
type
Description of returned object.
"""
try:
from pyresample import utils
from .util.interp_util import nearest_point_swathdefinition as npsd
from .util.interp_util import lonlat_to_swathdefinition as llsd
has_pyresample = True
except ImportError:
has_pyresample = False
if has_pyresample:
lons, lats = utils.check_and_wrap(self.obj.longitude.values,
self.obj.latitude.values)
swath = llsd(longitude=lons, latitude=lats)
pswath = npsd(longitude=float(lon), latitude=float(lat))
row, col = utils.generate_nearest_neighbour_linesample_arrays(
swath, pswath, float(1e6))
y, x = row[0][0], col[0][0]
return self.obj.isel(x=x, y=y)
else:
vars = pd.Series(self.obj.variables)
skip_keys = ['latitude', 'longitude', 'time', 'TFLAG']
loop_vars = vars.loc[~vars.isin(skip_keys)]
kwargs = self._check_kwargs_and_set_defaults(**kwargs)
kwargs['reuse_weights'] = True
orig = self.obj[loop_vars.iloc[0]].monet.nearest_latlon(
lat=lat, lon=lon, cleanup=False, **kwargs)
dset = orig.to_dataset()
dset.attrs = self.obj.attrs.copy()
for i in loop_vars[1:-1].values:
dset[i] = self.obj[i].monet.nearest_latlon(lat=lat,
lon=lon,
cleanup=False,
**kwargs)
i = loop_vars.values[-1]
dset[i] = self.obj[i].monet.nearest_latlon(lat=lat,
lon=lon,
cleanup=True,
**kwargs)
return dset
def window(self, lat_min, lon_min, lat_max, lon_max):
"""Function to window, ie select a specific region, given the lower left
latitude and longitude and the upper right latitude and longitude
Parameters
----------
lat_min : float
lower left latitude .
lon_min : float
lower left longitude.
lat_max : float
upper right latitude.
lon_max : float
upper right longitude.
Returns
-------
xr.DataSet
returns the windowed object.
"""
try:
from pyresample import utils
from .util.interp_util import nearest_point_swathdefinition as npsd
from .util.interp_util import lonlat_to_swathdefinition as llsd
from numpy import concatenate
has_pyresample = True
except ImportError:
has_pyresample = False
try:
if has_pyresample:
lons, lats = utils.check_and_wrap(self.obj.longitude.values,
self.obj.latitude.values)
swath = llsd(longitude=lons, latitude=lats)
pswath_ll = npsd(longitude=float(lon_min),
latitude=float(lat_min))
pswath_ur = npsd(longitude=float(lon_max),
latitude=float(lat_max))
row, col = utils.generate_nearest_neighbour_linesample_arrays(
swath, pswath_ll, float(1e6))
y_ll, x_ll = row[0][0], col[0][0]
row, col = utils.generate_nearest_neighbour_linesample_arrays(
swath, pswath_ur, float(1e6))
y_ur, x_ur = row[0][0], col[0][0]
if x_ur < x_ll:
x1 = self.obj.x.where(self.obj.x >= x_ll, drop=True).values
x2 = self.obj.x.where(self.obj.x <= x_ur, drop=True).values
xrange = concatenate([x1, x2]).astype(int)
self.obj['longitude'][:] = utils.wrap_longitudes(
self.obj.longitude.values)
# xrange = arange(float(x_ur), float(x_ll), dtype=int)
else:
xrange = slice(x_ll, x_ur)
if y_ur < y_ll:
y1 = self.obj.y.where(self.obj.y >= y_ll, drop=True).values
y2 = self.obj.y.where(self.obj.y <= y_ur, drop=True).values
yrange = concatenate([y1, y2]).astype(int)
else:
yrange = slice(y_ll, y_ur)
return self.obj.sel(x=xrange, y=yrange)
else:
raise ImportError
except ImportError:
print('Window functionality is unavailable without pyresample')
def nearest_latlon(self,
lat=None,
lon=None,
cleanup=True,
esmf=False,
**kwargs):
"""Uses xesmf to intepolate to a given latitude and longitude. Note
that the conservative method is not available.
Parameters
----------
lat : type
Description of parameter `lat`.
lon : type
Description of parameter `lon`.
**kwargs : type
Description of parameter `**kwargs`.
Returns
-------
type
Description of returned object.
"""
try:
from pyresample import geometry, utils
from .util.resample import resample_dataset
from .util.interp_util import nearest_point_swathdefinition as npsd
from .util.interp_util import lonlat_to_swathdefinition as llsd
has_pyresample = True
except ImportError:
has_pyresample = False
if esmf:
has_pyresample = False
from .util.interp_util import lonlat_to_xesmf
from .util.resample import resample_xesmf
try:
if lat is None or lon is None:
raise RuntimeError
except RuntimeError:
print('Must provide latitude and longitude')
if has_pyresample:
lons, lats = utils.check_and_wrap(self.obj.longitude.values,
self.obj.latitude.values)
swath = llsd(longitude=lons, latitude=lats)
pswath = npsd(longitude=float(lon), latitude=float(lat))
row, col = utils.generate_nearest_neighbour_linesample_arrays(
swath, pswath, float(1e6))
y, x = row[0][0], col[0][0]
return self.obj.sel(x=x, y=y)
else:
kwargs = self._check_kwargs_and_set_defaults(**kwargs)
self.obj = rename_latlon(self.obj)
target = lonlat_to_xesmf(longitude=lon, latitude=lat)
output = resample_xesmf(self.obj, target, **kwargs)
if cleanup:
output = resample_xesmf(self.obj,
target,
cleanup=True,
**kwargs)
return rename_latlon(output.squeeze())
