def test_bad_georeference(self):
ok = global_pp()
bad = global_pp()
bad.coord('latitude').coord_system = None
bad.coord('longitude').coord_system = None
with self.assertRaises(ValueError):
regrid(bad, ok)
with self.assertRaises(ValueError):
regrid(ok, bad)
bad = global_pp()
bad.coord('latitude').coord_system = None
bad.coord('longitude').coord_system = GeogCS(6371000)
with self.assertRaises(ValueError):
regrid(bad, ok)
with self.assertRaises(ValueError):
regrid(ok, bad)
bad = global_pp()
bad.coord('latitude').coord_system = GeogCS(6370000)
bad.coord('longitude').coord_system = GeogCS(6371000)
with self.assertRaises(ValueError):
regrid(bad, ok)
with self.assertRaises(ValueError):
regrid(ok, bad)
def test_bad_georeference(self):
ok = global_pp()
bad = global_pp()
bad.coord('latitude').coord_system = None
bad.coord('longitude').coord_system = None
with self.assertRaises(ValueError):
regrid(bad, ok)
with self.assertRaises(ValueError):
regrid(ok, bad)
bad = global_pp()
bad.coord('latitude').coord_system = None
bad.coord('longitude').coord_system = GeogCS(6371000)
with self.assertRaises(ValueError):
regrid(bad, ok)
with self.assertRaises(ValueError):
regrid(ok, bad)
bad = global_pp()
bad.coord('latitude').coord_system = GeogCS(6370000)
bad.coord('longitude').coord_system = GeogCS(6371000)
with self.assertRaises(ValueError):
regrid(bad, ok)
with self.assertRaises(ValueError):
regrid(ok, bad)
def test_bad_units(self):
ok = global_pp()
bad = global_pp()
bad.coord('longitude').units = 'radians'
with self.assertRaises(ValueError):
regrid(bad, ok)
with self.assertRaises(ValueError):
regrid(ok, bad)
bad = Cube(np.arange(12, dtype=np.float32).reshape(3, 4))
cs = OSGB()
y_coord = DimCoord(range(3),
'projection_y_coordinate',
units='m',
coord_system=cs)
x_coord = DimCoord(range(4),
'projection_x_coordinate',
units='km',
coord_system=cs)
bad.add_dim_coord(y_coord, 0)
bad.add_dim_coord(x_coord, 1)
with self.assertRaises(ValueError):
regrid(bad, ok)
with self.assertRaises(ValueError):
regrid(ok, bad)
def setUp(self):
self.func_setup = (
'iris.analysis._regrid.'
'_regrid_weighted_curvilinear_to_rectilinear__prepare')
self.func_operate = (
'iris.analysis._regrid.'
'_regrid_weighted_curvilinear_to_rectilinear__perform')
# Define a test source grid and target grid, basically the same.
self.src_grid = global_pp()
self.tgt_grid = global_pp()
# Modify the names so we can tell them apart.
self.src_grid.rename('src_grid')
self.tgt_grid.rename('TARGET_GRID')
# Replace the source-grid x and y coords with equivalent 2d versions.
x_coord = self.src_grid.coord('longitude')
y_coord = self.src_grid.coord('latitude')
nx, = x_coord.shape
ny, = y_coord.shape
xx, yy = np.meshgrid(x_coord.points, y_coord.points)
self.src_grid.remove_coord(x_coord)
self.src_grid.remove_coord(y_coord)
x_coord_2d = AuxCoord(xx,
standard_name=x_coord.standard_name,
units=x_coord.units,
coord_system=x_coord.coord_system)
y_coord_2d = AuxCoord(yy,
standard_name=y_coord.standard_name,
units=y_coord.units,
coord_system=y_coord.coord_system)
self.src_grid.add_aux_coord(x_coord_2d, (0, 1))
self.src_grid.add_aux_coord(y_coord_2d, (0, 1))
self.weights = np.ones(self.src_grid.shape, self.src_grid.dtype)
# Define an actual, dummy cube for the internal partial result, so we
# can do a cubelist merge on it, which is too complicated to mock out.
self.dummy_slice_result = Cube([1])
def setUp(self):
self.func_setup = (
'iris.experimental.regrid.'
'_regrid_weighted_curvilinear_to_rectilinear__prepare')
self.func_operate = (
'iris.experimental.regrid.'
'_regrid_weighted_curvilinear_to_rectilinear__perform')
# Define a test source grid and target grid, basically the same.
self.src_grid = global_pp()
self.tgt_grid = global_pp()
# Modify the names so we can tell them apart.
self.src_grid.rename('src_grid')
self.tgt_grid.rename('TARGET_GRID')
# Replace the source-grid x and y coords with equivalent 2d versions.
x_coord = self.src_grid.coord('longitude')
y_coord = self.src_grid.coord('latitude')
nx, = x_coord.shape
ny, = y_coord.shape
xx, yy = np.meshgrid(x_coord.points, y_coord.points)
self.src_grid.remove_coord(x_coord)
self.src_grid.remove_coord(y_coord)
x_coord_2d = AuxCoord(xx,
standard_name=x_coord.standard_name,
units=x_coord.units,
coord_system=x_coord.coord_system)
y_coord_2d = AuxCoord(yy,
standard_name=y_coord.standard_name,
units=y_coord.units,
coord_system=y_coord.coord_system)
self.src_grid.add_aux_coord(x_coord_2d, (0, 1))
self.src_grid.add_aux_coord(y_coord_2d, (0, 1))
self.weights = np.ones(self.src_grid.shape, self.src_grid.dtype)
# Define an actual, dummy cube for the internal partial result, so we
# can do a cubelist merge on it, which is too complicated to mock out.
self.dummy_slice_result = Cube([1])
def test_non_rectilinear_src(self):
ok = global_pp()
# Lat and/or lon missing
bad = global_pp()
bad.remove_coord('latitude')
with self.assertRaises(ValueError):
regrid(bad, ok)
with self.assertRaises(ValueError):
regrid(ok, bad)
bad = global_pp()
bad.remove_coord('longitude')
with self.assertRaises(ValueError):
regrid(bad, ok)
with self.assertRaises(ValueError):
regrid(ok, bad)
bad = global_pp()
bad.remove_coord('latitude')
bad.remove_coord('longitude')
with self.assertRaises(ValueError):
regrid(bad, ok)
with self.assertRaises(ValueError):
regrid(ok, bad)
# Lat/lon not a DimCoord
def demote_coord(coord_name):
bad = global_pp()
coord = bad.coord(coord_name)
dims = bad.coord_dims(coord)
bad.remove_coord(coord_name)
aux_coord = AuxCoord.from_coord(coord)
bad.add_aux_coord(aux_coord, dims)
with self.assertRaises(ValueError):
regrid(bad, ok)
with self.assertRaises(ValueError):
regrid(ok, bad)
demote_coord('latitude')
demote_coord('longitude')
# Lat/lon share a single dimension
bad = global_pp()
lat = bad.coord('latitude')
bad = bad[0, :lat.shape[0]]
bad.remove_coord('latitude')
bad.add_aux_coord(lat, 0)
with self.assertRaises(ValueError):
regrid(bad, ok)
with self.assertRaises(ValueError):
regrid(ok, bad)
def test_non_rectilinear_src(self):
ok = global_pp()
# Lat and/or lon missing
bad = global_pp()
bad.remove_coord('latitude')
with self.assertRaises(ValueError):
regrid(bad, ok)
with self.assertRaises(ValueError):
regrid(ok, bad)
bad = global_pp()
bad.remove_coord('longitude')
with self.assertRaises(ValueError):
regrid(bad, ok)
with self.assertRaises(ValueError):
regrid(ok, bad)
bad = global_pp()
bad.remove_coord('latitude')
bad.remove_coord('longitude')
with self.assertRaises(ValueError):
regrid(bad, ok)
with self.assertRaises(ValueError):
regrid(ok, bad)
# Lat/lon not a DimCoord
def demote_coord(coord_name):
bad = global_pp()
coord = bad.coord(coord_name)
dims = bad.coord_dims(coord)
bad.remove_coord(coord_name)
aux_coord = AuxCoord.from_coord(coord)
bad.add_aux_coord(aux_coord, dims)
with self.assertRaises(ValueError):
regrid(bad, ok)
with self.assertRaises(ValueError):
regrid(ok, bad)
demote_coord('latitude')
demote_coord('longitude')
# Lat/lon share a single dimension
bad = global_pp()
lat = bad.coord('latitude')
bad = bad[0, :lat.shape[0]]
bad.remove_coord('latitude')
bad.add_aux_coord(lat, 0)
with self.assertRaises(ValueError):
regrid(bad, ok)
with self.assertRaises(ValueError):
regrid(ok, bad)
def test_geog_cs_circular(self):
cube = stock.global_pp()
assert cube.coord('longitude').circular
result = _xy_range(cube)
np.testing.assert_array_almost_equal(
result, ((0, 360), (-90, 90)), decimal=0)
def test_subsample(self):
src = global_pp()
grid = src[::2, ::3]
result = regrid(src, grid)
qplt.pcolormesh(result)
qplt.plt.gca().coastlines()
self.check_graphic()
def test_geog_cs_circular(self):
cube = stock.global_pp()
assert cube.coord('longitude').circular
result = _xy_range(cube)
np.testing.assert_array_almost_equal(result, ((0, 360), (-90, 90)),
decimal=0)
def _grid_subset(self):
# The destination grid points are entirely contained within the
# src grid points.
grid = global_pp()[:4, :5]
grid.coord('longitude').points = np.linspace(-3.182, -3.06, 5)
grid.coord('latitude').points = np.linspace(52.372, 52.44, 4)
return grid
def test_subsample(self):
src = global_pp()
grid = src[::2, ::3]
result = regrid(src, grid)
qplt.pcolormesh(result)
qplt.plt.gca().coastlines()
self.check_graphic()
def _grid_subset(self):
# The destination grid points are entirely contained within the
# src grid points.
grid = global_pp()[:4, :5]
grid.coord('longitude').points = np.linspace(-3.182, -3.06, 5)
grid.coord('latitude').points = np.linspace(52.372, 52.44, 4)
return grid
def test_grid_no_overlap(self):
# The destination grid points are NOT contained within the
# src grid points.
grid = global_pp()[:4, :4]
grid.coord('longitude').points = np.linspace(-3.3, -3.2, 4)
grid.coord('latitude').points = np.linspace(52.377, 52.43, 4)
result = regrid(self.src, grid)
self.assertCMLApproxData(result, RESULT_DIR + ('no_overlap.cml',))
def setUp(self):
self.src = global_pp()
# Subsample and shift the target grid so that we can see a visual
# difference between regridding scheme methods.
grid = self.src[1::2, 1::3]
grid.coord('latitude').points = grid.coord('latitude').points + 1
grid.coord('longitude').points = grid.coord('longitude').points + 1
self.grid = grid
def test_geog_cs_regional(self):
cube = stock.global_pp()
cube = cube[10:20, 20:30]
assert not cube.coord('longitude').circular
result = _xy_range(cube)
np.testing.assert_array_almost_equal(
result, ((75, 108.75), (42.5, 65)), decimal=0)
def setUp(self):
self.src = global_pp()
# Subsample and shift the target grid so that we can see a visual
# difference between regridding scheme methods.
grid = self.src[1::2, 1::3]
grid.coord('latitude').points = grid.coord('latitude').points + 1
grid.coord('longitude').points = grid.coord('longitude').points + 1
self.grid = grid
def test_grid_no_overlap(self):
# The destination grid points are NOT contained within the
# src grid points.
grid = global_pp()[:4, :4]
grid.coord('longitude').points = np.linspace(-3.3, -3.2, 4)
grid.coord('latitude').points = np.linspace(52.377, 52.43, 4)
result = regrid(self.src, grid)
self.assertCMLApproxData(result, RESULT_DIR + ('no_overlap.cml', ))
def setUp(self):
src = global_pp()[::10, ::10]
level_height = AuxCoord(0, long_name='level_height', units='m',
attributes={'positive': 'up'})
sigma = AuxCoord(1, long_name='sigma')
surface_altitude = AuxCoord((src.data - src.data.min()) * 50,
'surface_altitude', units='m')
src.add_aux_coord(level_height)
src.add_aux_coord(sigma)
src.add_aux_coord(surface_altitude, [0, 1])
hybrid_height = HybridHeightFactory(level_height, sigma,
surface_altitude)
src.add_aux_factory(hybrid_height)
self.src = src
grid = global_pp()[:4, :4]
grid.coord('longitude').points = grid.coord('longitude').points - 5
self.grid = grid
def setUp(self):
src = global_pp()[::10, ::10]
level_height = AuxCoord(0, long_name='level_height', units='m',
attributes={'positive': 'up'})
sigma = AuxCoord(1, long_name='sigma')
surface_altitude = AuxCoord((src.data - src.data.min()) * 50,
'surface_altitude', units='m')
src.add_aux_coord(level_height)
src.add_aux_coord(sigma)
src.add_aux_coord(surface_altitude, [0, 1])
hybrid_height = HybridHeightFactory(level_height, sigma,
surface_altitude)
src.add_aux_factory(hybrid_height)
self.src = src
grid = global_pp()[:4, :4]
grid.coord('longitude').points = grid.coord('longitude').points - 5
self.grid = grid
def test_single_point(self):
src = self.src[0, 0]
grid = global_pp()[:1, :1]
# These coordinate values have been derived by converting the
# rotated coordinates of src[1, 1] into lat/lon by using cs2cs.
grid.coord('longitude').points = -3.144870
grid.coord('latitude').points = 52.406444
result = regrid(src, grid)
self.assertEqual(src.data[1, 1], result.data)
def test_single_point(self):
src = self.src[0, 0]
grid = global_pp()[:1, :1]
# These coordinate values have been derived by converting the
# rotated coordinates of src[1, 1] into lat/lon by using cs2cs.
grid.coord('longitude').points = -3.144870
grid.coord('latitude').points = 52.406444
result = regrid(src, grid)
self.assertEqual(src.data[1, 1], result.data)
def test_geog_cs_regional(self):
cube = stock.global_pp()
cube = cube[10:20, 20:30]
assert not cube.coord("longitude").circular
result = _xy_range(cube)
np.testing.assert_array_almost_equal(
result, ((75, 108.75), (42.5, 65)), decimal=0
)
def setUp(self):
self.src_grid = global_pp()
y = self.src_grid.coord('latitude')
x = self.src_grid.coord('longitude')
self.src_grid.remove_coord('latitude')
self.src_grid.remove_coord('longitude')
self.src_grid.add_aux_coord(y, 0)
self.src_grid.add_aux_coord(x, 1)
weights = np.ones(self.src_grid.shape, self.src_grid.dtype)
self.regridder = Regridder(self.src_grid, self.src_grid, weights)
def setUp(self):
self.src_grid = global_pp()
y = self.src_grid.coord('latitude')
x = self.src_grid.coord('longitude')
self.src_grid.remove_coord('latitude')
self.src_grid.remove_coord('longitude')
self.src_grid.add_aux_coord(y, 0)
self.src_grid.add_aux_coord(x, 1)
weights = np.ones(self.src_grid.shape, self.src_grid.dtype)
self.regridder = Regridder(self.src_grid, self.src_grid, weights)
def test_non_cube(self):
array = np.zeros((3, 4))
cube = global_pp()
with self.assertRaises(TypeError):
regrid(array, cube)
with self.assertRaises(TypeError):
regrid(cube, array)
with self.assertRaises(TypeError):
regrid(42, cube)
with self.assertRaises(TypeError):
regrid(cube, 42)
def test_grid_partial_overlap(self):
# The destination grid points are partially contained within the
# src grid points.
grid = global_pp()[:4, :4]
grid.coord('longitude').points = np.linspace(-3.3, -3.06, 4)
grid.coord('latitude').points = np.linspace(52.377, 52.43, 4)
for method in self.methods:
regridder = Regridder(self.src, grid, method, self.mode)
result = regridder(self.src)
cml = RESULT_DIR + ('{}_partial_overlap.cml'.format(method),)
self.assertCMLApproxData(result, cml)
def test_grid_partial_overlap(self):
# The destination grid points are partially contained within the
# src grid points.
grid = global_pp()[:4, :4]
grid.coord('longitude').points = np.linspace(-3.3, -3.06, 4)
grid.coord('latitude').points = np.linspace(52.377, 52.43, 4)
for method in self.methods:
regridder = Regridder(self.src, grid, method, self.mode)
result = regridder(self.src)
cml = RESULT_DIR + ('{}_partial_overlap.cml'.format(method), )
self.assertCMLApproxData(result, cml)
def demote_coord(coord_name):
bad = global_pp()
coord = bad.coord(coord_name)
dims = bad.coord_dims(coord)
bad.remove_coord(coord_name)
aux_coord = AuxCoord.from_coord(coord)
bad.add_aux_coord(aux_coord, dims)
with self.assertRaises(ValueError):
regrid(bad, ok)
with self.assertRaises(ValueError):
regrid(ok, bad)
def test_non_cube(self):
array = np.zeros((3, 4))
cube = global_pp()
with self.assertRaises(TypeError):
regrid(array, cube)
with self.assertRaises(TypeError):
regrid(cube, array)
with self.assertRaises(TypeError):
regrid(42, cube)
with self.assertRaises(TypeError):
regrid(cube, 42)
def setUp(self):
self.func = ('iris.experimental.regrid.'
'regrid_weighted_curvilinear_to_rectilinear')
self.ok = global_pp()
y = self.ok.coord('latitude')
x = self.ok.coord('longitude')
self.ok.remove_coord('latitude')
self.ok.remove_coord('longitude')
self.ok.add_aux_coord(y, 0)
self.ok.add_aux_coord(x, 1)
self.weights = np.ones(self.ok.shape, self.ok.dtype)
def demote_coord(coord_name):
bad = global_pp()
coord = bad.coord(coord_name)
dims = bad.coord_dims(coord)
bad.remove_coord(coord_name)
aux_coord = AuxCoord.from_coord(coord)
bad.add_aux_coord(aux_coord, dims)
with self.assertRaises(ValueError):
regrid(bad, ok)
with self.assertRaises(ValueError):
regrid(ok, bad)
def setUp(self):
self.src = global_pp()
_ = self.src.data
# Cast up to float64, to work around numpy<=1.8 bug with means of
# arrays of 32bit floats.
self.src.data = self.src.data.astype(np.float64)
# Subsample and shift the target grid so that we can see a visual
# difference between regridding scheme methods.
grid = self.src[1::2, 1::3]
grid.coord("latitude").points = grid.coord("latitude").points + 1
grid.coord("longitude").points = grid.coord("longitude").points + 1
self.grid = grid
def setUp(self):
path = tests.get_data_path(
('NetCDF', 'unstructured_grid', 'theta_nodal_xios.nc'))
self.src = iris.load_cube(path, 'Potential Temperature')
src_lat = self.src.coord('latitude')
src_lon = self.src.coord('longitude')
src_lat.coord_system = src_lon.coord_system = GeogCS(6370000)
src_lat.convert_units(Unit('degrees'))
src_lon.convert_units(Unit('degrees'))
self.global_grid = global_pp()
def setUp(self):
path = tests.get_data_path(
("NetCDF", "unstructured_grid", "theta_nodal_xios.nc"))
self.src = iris.load_cube(path, "Potential Temperature")
src_lat = self.src.coord("latitude")
src_lon = self.src.coord("longitude")
src_lat.coord_system = src_lon.coord_system = GeogCS(6370000)
src_lat.convert_units(Unit("degrees"))
src_lon.convert_units(Unit("degrees"))
self.global_grid = global_pp()
def setUp(self):
path = tests.get_data_path(('NetCDF', 'unstructured_grid',
'theta_nodal_xios.nc'))
self.src = iris.load_cube(path, 'Potential Temperature')
src_lat = self.src.coord('latitude')
src_lon = self.src.coord('longitude')
src_lat.coord_system = src_lon.coord_system = GeogCS(6370000)
src_lat.convert_units(Unit('degrees'))
src_lon.convert_units(Unit('degrees'))
self.global_grid = global_pp()
def setUp(self):
self.src = global_pp()
_ = self.src.data
# Cast up to float64, to work around numpy<=1.8 bug with means of
# arrays of 32bit floats.
self.src.data = self.src.data.astype(np.float64)
# Subsample and shift the target grid so that we can see a visual
# difference between regridding scheme methods.
grid = self.src[1::2, 1::3]
grid.coord('latitude').points = grid.coord('latitude').points + 1
grid.coord('longitude').points = grid.coord('longitude').points + 1
self.grid = grid
def test_bad_units(self):
ok = global_pp()
bad = global_pp()
bad.coord('longitude').units = 'radians'
with self.assertRaises(ValueError):
regrid(bad, ok)
with self.assertRaises(ValueError):
regrid(ok, bad)
bad = Cube(np.arange(12, dtype=np.float32).reshape(3, 4))
cs = OSGB()
y_coord = DimCoord(range(3), 'projection_y_coordinate', units='m',
coord_system=cs)
x_coord = DimCoord(range(4), 'projection_x_coordinate', units='km',
coord_system=cs)
bad.add_dim_coord(y_coord, 0)
bad.add_dim_coord(x_coord, 1)
with self.assertRaises(ValueError):
regrid(bad, ok)
with self.assertRaises(ValueError):
regrid(ok, bad)
def simpletest(do_savefig=True, do_showfig=True, savefig_file='./puffer.png'):
figure = make_puffersphere_figure()
axes = make_puffersphere_axes()
axes.stock_img()
data = istk.global_pp()
axes.coastlines()
qplt.contour(data)
draw_gridlines()
#axes.coastlines()
if do_savefig:
save_figure_for_puffersphere(figure=plt.gcf(), filename=savefig_file)
if do_showfig:
plt.show()
# Suck it back in
readback = rasterio.open(file_path)
data = readback.read(1)
print 'readback:'
print ' crs=', readback.crs
print ' width=', readback.width
print ' height=', readback.height
print ' data-shape=', data.shape
print ' data-dtype=', data.dtype
print ' data-min=', np.min(data)
print ' data-max=', np.max(data)
if __name__ == '__main__':
import iris.tests.stock as istk
test_cube = istk.global_pp()
print 'Original float file version:'
raster = RasterioContent(test_cube)
raster.save('tmp1.tif')
file_report('tmp1.tif')
# Let's rescale to 0..255, just for fun
datamin = np.min(test_cube.data)
datamax = np.max(test_cube.data)
newmin = 25.
newmax = 215.
ratio = (newmax - newmin) / (datamax - datamin)
test_cube.data = newmin + ratio * (test_cube.data - datamin)
# Let's also make it into bytes
test_cube.data = test_cube.data.astype(np.uint8)
