def test_reductions_1D(dtype): x = np.arange(5).astype(dtype) a = da.from_array(x, chunks=(2,)) reduction_1d_test(da.sum, a, np.sum, x) reduction_1d_test(da.prod, a, np.prod, x) reduction_1d_test(da.mean, a, np.mean, x) reduction_1d_test(da.var, a, np.var, x) reduction_1d_test(da.std, a, np.std, x) reduction_1d_test(da.min, a, np.min, x, False) reduction_1d_test(da.max, a, np.max, x, False) reduction_1d_test(da.any, a, np.any, x, False) reduction_1d_test(da.all, a, np.all, x, False) reduction_1d_test(da.nansum, a, np.nansum, x) with ignoring(AttributeError): reduction_1d_test(da.nanprod, a, np.nanprod, x) reduction_1d_test(da.nanmean, a, np.mean, x) reduction_1d_test(da.nanvar, a, np.var, x) reduction_1d_test(da.nanstd, a, np.std, x) reduction_1d_test(da.nanmin, a, np.nanmin, x, False) reduction_1d_test(da.nanmax, a, np.nanmax, x, False) assert eq(da.argmax(a, axis=0), np.argmax(x, axis=0)) assert eq(da.argmin(a, axis=0), np.argmin(x, axis=0)) assert eq(da.nanargmax(a, axis=0), np.nanargmax(x, axis=0)) assert eq(da.nanargmin(a, axis=0), np.nanargmin(x, axis=0)) assert eq(da.argmax(a, axis=0, split_every=2), np.argmax(x, axis=0)) assert eq(da.argmin(a, axis=0, split_every=2), np.argmin(x, axis=0)) assert eq(da.nanargmax(a, axis=0, split_every=2), np.nanargmax(x, axis=0)) assert eq(da.nanargmin(a, axis=0, split_every=2), np.nanargmin(x, axis=0))
def test_reductions_2D_int(): x = np.arange(1, 122).reshape((11, 11)).astype('i4') a = da.from_array(x, chunks=(4, 4)) reduction_2d_test(da.sum, a, np.sum, x) reduction_2d_test(da.prod, a, np.prod, x) reduction_2d_test(da.mean, a, np.mean, x) reduction_2d_test(da.var, a, np.var, x, False) # Difference in dtype algo reduction_2d_test(da.std, a, np.std, x, False) # Difference in dtype algo reduction_2d_test(da.min, a, np.min, x, False) reduction_2d_test(da.max, a, np.max, x, False) reduction_2d_test(da.any, a, np.any, x, False) reduction_2d_test(da.all, a, np.all, x, False) reduction_2d_test(da.nansum, a, np.nansum, x) with ignoring(AttributeError): reduction_2d_test(da.nanprod, a, np.nanprod, x) reduction_2d_test(da.nanmean, a, np.mean, x) reduction_2d_test(da.nanvar, a, np.nanvar, x, False) # Difference in dtype algo reduction_2d_test(da.nanstd, a, np.nanstd, x, False) # Difference in dtype algo reduction_2d_test(da.nanmin, a, np.nanmin, x, False) reduction_2d_test(da.nanmax, a, np.nanmax, x, False) assert eq(da.argmax(a, axis=0), np.argmax(x, axis=0)) assert eq(da.argmin(a, axis=0), np.argmin(x, axis=0)) assert eq(da.nanargmax(a, axis=0), np.nanargmax(x, axis=0)) assert eq(da.nanargmin(a, axis=0), np.nanargmin(x, axis=0)) assert eq(da.argmax(a, axis=1), np.argmax(x, axis=1)) assert eq(da.argmin(a, axis=1), np.argmin(x, axis=1)) assert eq(da.nanargmax(a, axis=1), np.nanargmax(x, axis=1)) assert eq(da.nanargmin(a, axis=1), np.nanargmin(x, axis=1))
def test_reductions_2D_nans(): # chunks are a mix of some/all/no NaNs x = np.full((4, 4), np.nan) x[:2, :2] = np.array([[1, 2], [3, 4]]) x[2, 2] = 5 x[3, 3] = 6 a = da.from_array(x, chunks=(2, 2)) reduction_2d_test(da.sum, a, np.sum, x, False, False) reduction_2d_test(da.prod, a, np.prod, x, False, False) reduction_2d_test(da.mean, a, np.mean, x, False, False) reduction_2d_test(da.var, a, np.var, x, False, False) reduction_2d_test(da.std, a, np.std, x, False, False) reduction_2d_test(da.min, a, np.min, x, False, False) reduction_2d_test(da.max, a, np.max, x, False, False) reduction_2d_test(da.any, a, np.any, x, False, False) reduction_2d_test(da.all, a, np.all, x, False, False) reduction_2d_test(da.nansum, a, np.nansum, x, False, False) reduction_2d_test(da.nanprod, a, np.nanprod, x, False, False) reduction_2d_test(da.nanmean, a, np.nanmean, x, False, False) with pytest.warns(None): # division by 0 warning reduction_2d_test(da.nanvar, a, np.nanvar, x, False, False) with pytest.warns(None): # division by 0 warning reduction_2d_test(da.nanstd, a, np.nanstd, x, False, False) with pytest.warns(None): # all NaN axis warning reduction_2d_test(da.nanmin, a, np.nanmin, x, False, False) with pytest.warns(None): # all NaN axis warning reduction_2d_test(da.nanmax, a, np.nanmax, x, False, False) assert_eq(da.argmax(a), np.argmax(x)) assert_eq(da.argmin(a), np.argmin(x)) with pytest.warns(None): # all NaN axis warning assert_eq(da.nanargmax(a), np.nanargmax(x)) with pytest.warns(None): # all NaN axis warning assert_eq(da.nanargmin(a), np.nanargmin(x)) assert_eq(da.argmax(a, axis=0), np.argmax(x, axis=0)) assert_eq(da.argmin(a, axis=0), np.argmin(x, axis=0)) with pytest.warns(None): # all NaN axis warning assert_eq(da.nanargmax(a, axis=0), np.nanargmax(x, axis=0)) with pytest.warns(None): # all NaN axis warning assert_eq(da.nanargmin(a, axis=0), np.nanargmin(x, axis=0)) assert_eq(da.argmax(a, axis=1), np.argmax(x, axis=1)) assert_eq(da.argmin(a, axis=1), np.argmin(x, axis=1)) with pytest.warns(None): # all NaN axis warning assert_eq(da.nanargmax(a, axis=1), np.nanargmax(x, axis=1)) with pytest.warns(None): # all NaN axis warning assert_eq(da.nanargmin(a, axis=1), np.nanargmin(x, axis=1))
def test_reductions_2D_nans(): # chunks are a mix of some/all/no NaNs x = np.full((4, 4), np.nan) x[:2, :2] = np.array([[1, 2], [3, 4]]) x[2, 2] = 5 x[3, 3] = 6 a = da.from_array(x, chunks=(2, 2)) reduction_2d_test(da.sum, a, np.sum, x, False, False) reduction_2d_test(da.prod, a, np.prod, x, False, False) reduction_2d_test(da.mean, a, np.mean, x, False, False) reduction_2d_test(da.var, a, np.var, x, False, False) reduction_2d_test(da.std, a, np.std, x, False, False) reduction_2d_test(da.min, a, np.min, x, False, False) reduction_2d_test(da.max, a, np.max, x, False, False) reduction_2d_test(da.any, a, np.any, x, False, False) reduction_2d_test(da.all, a, np.all, x, False, False) reduction_2d_test(da.nansum, a, np.nansum, x, False, False) reduction_2d_test(da.nanprod, a, nanprod, x, False, False) reduction_2d_test(da.nanmean, a, np.nanmean, x, False, False) with pytest.warns(None): # division by 0 warning reduction_2d_test(da.nanvar, a, np.nanvar, x, False, False) with pytest.warns(None): # division by 0 warning reduction_2d_test(da.nanstd, a, np.nanstd, x, False, False) with pytest.warns(None): # all NaN axis warning reduction_2d_test(da.nanmin, a, np.nanmin, x, False, False) with pytest.warns(None): # all NaN axis warning reduction_2d_test(da.nanmax, a, np.nanmax, x, False, False) assert_eq(da.argmax(a), np.argmax(x)) assert_eq(da.argmin(a), np.argmin(x)) with pytest.warns(None): # all NaN axis warning assert_eq(da.nanargmax(a), np.nanargmax(x)) with pytest.warns(None): # all NaN axis warning assert_eq(da.nanargmin(a), np.nanargmin(x)) assert_eq(da.argmax(a, axis=0), np.argmax(x, axis=0)) assert_eq(da.argmin(a, axis=0), np.argmin(x, axis=0)) with pytest.warns(None): # all NaN axis warning assert_eq(da.nanargmax(a, axis=0), np.nanargmax(x, axis=0)) with pytest.warns(None): # all NaN axis warning assert_eq(da.nanargmin(a, axis=0), np.nanargmin(x, axis=0)) assert_eq(da.argmax(a, axis=1), np.argmax(x, axis=1)) assert_eq(da.argmin(a, axis=1), np.argmin(x, axis=1)) with pytest.warns(None): # all NaN axis warning assert_eq(da.nanargmax(a, axis=1), np.nanargmax(x, axis=1)) with pytest.warns(None): # all NaN axis warning assert_eq(da.nanargmin(a, axis=1), np.nanargmin(x, axis=1))
def test_reductions_1D_int(): x = np.arange(5).astype('i4') a = da.from_array(x, chunks=(2, )) reduction_1d_test(da.sum, a, np.sum, x) reduction_1d_test(da.prod, a, np.prod, x) reduction_1d_test(da.mean, a, np.mean, x) reduction_1d_test(da.var, a, np.var, x) reduction_1d_test(da.std, a, np.std, x) reduction_1d_test(da.min, a, np.min, x, False) reduction_1d_test(da.max, a, np.max, x, False) reduction_1d_test(da.any, a, np.any, x, False) reduction_1d_test(da.all, a, np.all, x, False) reduction_1d_test(da.nansum, a, np.nansum, x) with ignoring(AttributeError): reduction_1d_test(da.nanprod, a, np.nanprod, x) reduction_1d_test(da.nanmean, a, np.mean, x) reduction_1d_test(da.nanvar, a, np.var, x) reduction_1d_test(da.nanstd, a, np.std, x) reduction_1d_test(da.nanmin, a, np.nanmin, x, False) reduction_1d_test(da.nanmax, a, np.nanmax, x, False) assert eq(da.argmax(a, axis=0), np.argmax(x, axis=0)) assert eq(da.argmin(a, axis=0), np.argmin(x, axis=0)) assert eq(da.nanargmax(a, axis=0), np.nanargmax(x, axis=0)) assert eq(da.nanargmin(a, axis=0), np.nanargmin(x, axis=0))
def test_reductions_2D(dtype): x = np.arange(1, 122).reshape((11, 11)).astype(dtype) a = da.from_array(x, chunks=(4, 4)) b = a.sum(keepdims=True) assert b._keys() == [[(b.name, 0, 0)]] reduction_2d_test(da.sum, a, np.sum, x) reduction_2d_test(da.prod, a, np.prod, x) reduction_2d_test(da.mean, a, np.mean, x) reduction_2d_test(da.var, a, np.var, x, False) # Difference in dtype algo reduction_2d_test(da.std, a, np.std, x, False) # Difference in dtype algo reduction_2d_test(da.min, a, np.min, x, False) reduction_2d_test(da.max, a, np.max, x, False) reduction_2d_test(da.any, a, np.any, x, False) reduction_2d_test(da.all, a, np.all, x, False) reduction_2d_test(da.nansum, a, np.nansum, x) with ignoring(AttributeError): reduction_2d_test(da.nanprod, a, np.nanprod, x) reduction_2d_test(da.nanmean, a, np.mean, x) reduction_2d_test(da.nanvar, a, np.nanvar, x, False) # Difference in dtype algo reduction_2d_test(da.nanstd, a, np.nanstd, x, False) # Difference in dtype algo reduction_2d_test(da.nanmin, a, np.nanmin, x, False) reduction_2d_test(da.nanmax, a, np.nanmax, x, False) assert eq(da.argmax(a, axis=0), np.argmax(x, axis=0)) assert eq(da.argmin(a, axis=0), np.argmin(x, axis=0)) assert eq(da.nanargmax(a, axis=0), np.nanargmax(x, axis=0)) assert eq(da.nanargmin(a, axis=0), np.nanargmin(x, axis=0)) assert eq(da.argmax(a, axis=1), np.argmax(x, axis=1)) assert eq(da.argmin(a, axis=1), np.argmin(x, axis=1)) assert eq(da.nanargmax(a, axis=1), np.nanargmax(x, axis=1)) assert eq(da.nanargmin(a, axis=1), np.nanargmin(x, axis=1)) assert eq(da.argmax(a, axis=0, split_every=2), np.argmax(x, axis=0)) assert eq(da.argmin(a, axis=0, split_every=2), np.argmin(x, axis=0)) assert eq(da.nanargmax(a, axis=0, split_every=2), np.nanargmax(x, axis=0)) assert eq(da.nanargmin(a, axis=0, split_every=2), np.nanargmin(x, axis=0)) assert eq(da.argmax(a, axis=1, split_every=2), np.argmax(x, axis=1)) assert eq(da.argmin(a, axis=1, split_every=2), np.argmin(x, axis=1)) assert eq(da.nanargmax(a, axis=1, split_every=2), np.nanargmax(x, axis=1)) assert eq(da.nanargmin(a, axis=1, split_every=2), np.nanargmin(x, axis=1))
def test_reductions_2D_nans(): # chunks are a mix of some/all/no NaNs x = np.full((4, 4), np.nan) x[:2, :2] = np.array([[1, 2], [3, 4]]) x[2, 2] = 5 x[3, 3] = 6 a = da.from_array(x, chunks=(2, 2)) reduction_2d_test(da.sum, a, np.sum, x, False, False) reduction_2d_test(da.prod, a, np.prod, x, False, False) reduction_2d_test(da.mean, a, np.mean, x, False, False) reduction_2d_test(da.var, a, np.var, x, False, False) reduction_2d_test(da.std, a, np.std, x, False, False) reduction_2d_test(da.min, a, np.min, x, False, False) reduction_2d_test(da.max, a, np.max, x, False, False) reduction_2d_test(da.any, a, np.any, x, False, False) reduction_2d_test(da.all, a, np.all, x, False, False) reduction_2d_test(da.nansum, a, np.nansum, x, False, False) reduction_2d_test(da.nanprod, a, np.nanprod, x, False, False) with warnings.catch_warnings(): warnings.simplefilter("ignore", RuntimeWarning) reduction_2d_test(da.nanmean, a, np.nanmean, x, False, False) reduction_2d_test(da.nanvar, a, np.nanvar, x, False, False) reduction_2d_test(da.nanstd, a, np.nanstd, x, False, False) reduction_2d_test(da.nanmin, a, np.nanmin, x, False, False) reduction_2d_test(da.nanmax, a, np.nanmax, x, False, False) assert_eq(da.argmax(a), np.argmax(x)) assert_eq(da.argmin(a), np.argmin(x)) assert_eq(da.nanargmax(a), np.nanargmax(x)) assert_eq(da.nanargmin(a), np.nanargmin(x)) assert_eq(da.argmax(a, axis=0), np.argmax(x, axis=0)) assert_eq(da.argmin(a, axis=0), np.argmin(x, axis=0)) assert_eq(da.nanargmax(a, axis=0), np.nanargmax(x, axis=0)) assert_eq(da.nanargmin(a, axis=0), np.nanargmin(x, axis=0)) assert_eq(da.argmax(a, axis=1), np.argmax(x, axis=1)) assert_eq(da.argmin(a, axis=1), np.argmin(x, axis=1)) assert_eq(da.nanargmax(a, axis=1), np.nanargmax(x, axis=1)) assert_eq(da.nanargmin(a, axis=1), np.nanargmin(x, axis=1))
def test_reductions_2D_nans(): # chunks are a mix of some/all/no NaNs x = np.full((4, 4), np.nan) x[:2, :2] = np.array([[1, 2], [3, 4]]) x[2, 2] = 5 x[3, 3] = 6 a = da.from_array(x, chunks=(2, 2)) reduction_2d_test(da.sum, a, np.sum, x, False, False) reduction_2d_test(da.prod, a, np.prod, x, False, False) reduction_2d_test(da.mean, a, np.mean, x, False, False) reduction_2d_test(da.var, a, np.var, x, False, False) reduction_2d_test(da.std, a, np.std, x, False, False) reduction_2d_test(da.min, a, np.min, x, False, False) reduction_2d_test(da.max, a, np.max, x, False, False) reduction_2d_test(da.any, a, np.any, x, False, False) reduction_2d_test(da.all, a, np.all, x, False, False) reduction_2d_test(da.nansum, a, np.nansum, x, False, False) with ignoring(AttributeError): reduction_2d_test(da.nanprod, a, np.nanprod, x, False, False) reduction_2d_test(da.nanmean, a, np.nanmean, x, False, False) reduction_2d_test(da.nanvar, a, np.nanvar, x, False, False) reduction_2d_test(da.nanstd, a, np.nanstd, x, False, False) reduction_2d_test(da.nanmin, a, np.nanmin, x, False, False) reduction_2d_test(da.nanmax, a, np.nanmax, x, False, False) assert eq(da.argmax(a), np.argmax(x)) assert eq(da.argmin(a), np.argmin(x)) assert eq(da.nanargmax(a), np.nanargmax(x)) assert eq(da.nanargmin(a), np.nanargmin(x)) assert eq(da.argmax(a, axis=0), np.argmax(x, axis=0)) assert eq(da.argmin(a, axis=0), np.argmin(x, axis=0)) assert eq(da.nanargmax(a, axis=0), np.nanargmax(x, axis=0)) assert eq(da.nanargmin(a, axis=0), np.nanargmin(x, axis=0)) assert eq(da.argmax(a, axis=1), np.argmax(x, axis=1)) assert eq(da.argmin(a, axis=1), np.argmin(x, axis=1)) assert eq(da.nanargmax(a, axis=1), np.nanargmax(x, axis=1)) assert eq(da.nanargmin(a, axis=1), np.nanargmin(x, axis=1))
def test_nan(): x = np.array([[1, np.nan, 3, 4], [5, 6, 7, np.nan], [9, 10, 11, 12]]) d = da.from_array(x, chunks=(2, 2)) assert_eq(np.nansum(x), da.nansum(d)) assert_eq(np.nansum(x, axis=0), da.nansum(d, axis=0)) assert_eq(np.nanmean(x, axis=1), da.nanmean(d, axis=1)) assert_eq(np.nanmin(x, axis=1), da.nanmin(d, axis=1)) assert_eq(np.nanmax(x, axis=(0, 1)), da.nanmax(d, axis=(0, 1))) assert_eq(np.nanvar(x), da.nanvar(d)) assert_eq(np.nanstd(x, axis=0), da.nanstd(d, axis=0)) assert_eq(np.nanargmin(x, axis=0), da.nanargmin(d, axis=0)) assert_eq(np.nanargmax(x, axis=0), da.nanargmax(d, axis=0)) assert_eq(np.nanprod(x), da.nanprod(d))
def test_nan(): x = np.array([[1, np.nan, 3, 4], [5, 6, 7, np.nan], [9, 10, 11, 12]]) d = da.from_array(x, blockshape=(2, 2)) assert eq(np.nansum(x), da.nansum(d)) assert eq(np.nansum(x, axis=0), da.nansum(d, axis=0)) assert eq(np.nanmean(x, axis=1), da.nanmean(d, axis=1)) assert eq(np.nanmin(x, axis=1), da.nanmin(d, axis=1)) assert eq(np.nanmax(x, axis=(0, 1)), da.nanmax(d, axis=(0, 1))) assert eq(np.nanvar(x), da.nanvar(d)) assert eq(np.nanstd(x, axis=0), da.nanstd(d, axis=0)) assert eq(np.nanargmin(x, axis=0), da.nanargmin(d, axis=0)) assert eq(np.nanargmax(x, axis=0), da.nanargmax(d, axis=0)) with ignoring(AttributeError): assert eq(np.nanprod(x), da.nanprod(d))
def test_nan(): x = np.array([[1, np.nan, 3, 4], [5, 6, 7, np.nan], [9, 10, 11, 12]]) d = da.from_array(x, chunks=(2, 2)) assert_eq(np.nansum(x), da.nansum(d)) assert_eq(np.nansum(x, axis=0), da.nansum(d, axis=0)) assert_eq(np.nanmean(x, axis=1), da.nanmean(d, axis=1)) assert_eq(np.nanmin(x, axis=1), da.nanmin(d, axis=1)) assert_eq(np.nanmax(x, axis=(0, 1)), da.nanmax(d, axis=(0, 1))) assert_eq(np.nanvar(x), da.nanvar(d)) assert_eq(np.nanstd(x, axis=0), da.nanstd(d, axis=0)) assert_eq(np.nanargmin(x, axis=0), da.nanargmin(d, axis=0)) assert_eq(np.nanargmax(x, axis=0), da.nanargmax(d, axis=0)) assert_eq(nanprod(x), da.nanprod(d))
def test_reductions(): x = np.arange(5).astype('f4') a = da.from_array(x, blockshape=(2, )) assert eq(da.all(a), np.all(x)) assert eq(da.any(a), np.any(x)) assert eq(da.argmax(a, axis=0), np.argmax(x, axis=0)) assert eq(da.argmin(a, axis=0), np.argmin(x, axis=0)) assert eq(da.max(a), np.max(x)) assert eq(da.mean(a), np.mean(x)) assert eq(da.min(a), np.min(x)) assert eq(da.nanargmax(a, axis=0), np.nanargmax(x, axis=0)) assert eq(da.nanargmin(a, axis=0), np.nanargmin(x, axis=0)) assert eq(da.nanmax(a), np.nanmax(x)) assert eq(da.nanmin(a), np.nanmin(x)) assert eq(da.nansum(a), np.nansum(x)) assert eq(da.nanvar(a), np.nanvar(x)) assert eq(da.nanstd(a), np.nanstd(x))
def test_reductions(): x = np.arange(5).astype('f4') a = da.from_array(x, chunks=(2,)) assert eq(da.all(a), np.all(x)) assert eq(da.any(a), np.any(x)) assert eq(da.argmax(a, axis=0), np.argmax(x, axis=0)) assert eq(da.argmin(a, axis=0), np.argmin(x, axis=0)) assert eq(da.max(a), np.max(x)) assert eq(da.mean(a), np.mean(x)) assert eq(da.min(a), np.min(x)) assert eq(da.nanargmax(a, axis=0), np.nanargmax(x, axis=0)) assert eq(da.nanargmin(a, axis=0), np.nanargmin(x, axis=0)) assert eq(da.nanmax(a), np.nanmax(x)) assert eq(da.nanmin(a), np.nanmin(x)) assert eq(da.nansum(a), np.nansum(x)) assert eq(da.nanvar(a), np.nanvar(x)) assert eq(da.nanstd(a), np.nanstd(x))
def new_grid_mapping_from_coords( x_coords: xr.DataArray, y_coords: xr.DataArray, crs: Union[str, pyproj.crs.CRS], *, tile_size: Union[int, Tuple[int, int]] = None, tolerance: float = DEFAULT_TOLERANCE, ) -> GridMapping: crs = _normalize_crs(crs) assert_instance(x_coords, xr.DataArray, name='x_coords') assert_instance(y_coords, xr.DataArray, name='y_coords') assert_true(x_coords.ndim in (1, 2), 'x_coords and y_coords must be either 1D or 2D arrays') assert_instance(tolerance, float, name='tolerance') assert_true(tolerance > 0.0, 'tolerance must be greater zero') if x_coords.name and y_coords.name: xy_var_names = str(x_coords.name), str(y_coords.name) else: xy_var_names = _default_xy_var_names(crs) tile_size = _normalize_int_pair(tile_size, default=None) is_lon_360 = None # None means "not yet known" if crs.is_geographic: is_lon_360 = bool(np.any(x_coords > 180)) x_res = 0 y_res = 0 if x_coords.ndim == 1: # We have 1D x,y coordinates cls = Coords1DGridMapping assert_true(x_coords.size >= 2 and y_coords.size >= 2, 'sizes of x_coords and y_coords 1D arrays must be >= 2') size = x_coords.size, y_coords.size x_dim, y_dim = x_coords.dims[0], y_coords.dims[0] x_diff = _abs_no_zero(x_coords.diff(dim=x_dim).values) y_diff = _abs_no_zero(y_coords.diff(dim=y_dim).values) if not is_lon_360 and crs.is_geographic: is_anti_meridian_crossed = np.any(np.nanmax(x_diff) > 180) if is_anti_meridian_crossed: x_coords = to_lon_360(x_coords) x_diff = _abs_no_zero(x_coords.diff(dim=x_dim)) is_lon_360 = True x_res, y_res = x_diff[0], y_diff[0] x_diff_equal = np.allclose(x_diff, x_res, atol=tolerance) y_diff_equal = np.allclose(y_diff, y_res, atol=tolerance) is_regular = x_diff_equal and y_diff_equal if is_regular: x_res = round_to_fraction(x_res, 5, 0.25) y_res = round_to_fraction(y_res, 5, 0.25) else: x_res = round_to_fraction(float(np.nanmedian(x_diff)), 2, 0.5) y_res = round_to_fraction(float(np.nanmedian(y_diff)), 2, 0.5) if tile_size is None \ and x_coords.chunks is not None \ and y_coords.chunks is not None: tile_size = (max(0, *x_coords.chunks[0]), max(0, *y_coords.chunks[0])) # Guess j axis direction is_j_axis_up = bool(y_coords[0] < y_coords[-1]) else: # We have 2D x,y coordinates cls = Coords2DGridMapping assert_true( x_coords.shape == y_coords.shape, 'shapes of x_coords and y_coords' ' 2D arrays must be equal') assert_true( x_coords.dims == y_coords.dims, 'dimensions of x_coords and y_coords' ' 2D arrays must be equal') y_dim, x_dim = x_coords.dims height, width = x_coords.shape size = width, height x = da.asarray(x_coords) y = da.asarray(y_coords) x_x_diff = _abs_no_nan(da.diff(x, axis=1)) x_y_diff = _abs_no_nan(da.diff(x, axis=0)) y_x_diff = _abs_no_nan(da.diff(y, axis=1)) y_y_diff = _abs_no_nan(da.diff(y, axis=0)) if not is_lon_360 and crs.is_geographic: is_anti_meridian_crossed = da.any(da.max(x_x_diff) > 180) \ or da.any(da.max(x_y_diff) > 180) if is_anti_meridian_crossed: x_coords = to_lon_360(x_coords) x = da.asarray(x_coords) x_x_diff = _abs_no_nan(da.diff(x, axis=1)) x_y_diff = _abs_no_nan(da.diff(x, axis=0)) is_lon_360 = True is_regular = False if da.all(x_y_diff == 0) and da.all(y_x_diff == 0): x_res = x_x_diff[0, 0] y_res = y_y_diff[0, 0] is_regular = \ da.allclose(x_x_diff[0, :], x_res, atol=tolerance) \ and da.allclose(x_x_diff[-1, :], x_res, atol=tolerance) \ and da.allclose(y_y_diff[:, 0], y_res, atol=tolerance) \ and da.allclose(y_y_diff[:, -1], y_res, atol=tolerance) if not is_regular: # Let diff arrays have same shape as original by # doubling last rows and columns. x_x_diff_c = da.concatenate([x_x_diff, x_x_diff[:, -1:]], axis=1) y_x_diff_c = da.concatenate([y_x_diff, y_x_diff[:, -1:]], axis=1) x_y_diff_c = da.concatenate([x_y_diff, x_y_diff[-1:, :]], axis=0) y_y_diff_c = da.concatenate([y_y_diff, y_y_diff[-1:, :]], axis=0) # Find resolution via area x_abs_diff = da.sqrt(da.square(x_x_diff_c) + da.square(x_y_diff_c)) y_abs_diff = da.sqrt(da.square(y_x_diff_c) + da.square(y_y_diff_c)) if crs.is_geographic: # Convert degrees into meters x_abs_diff_r = da.radians(x_abs_diff) y_abs_diff_r = da.radians(y_abs_diff) x_abs_diff = _ER * da.cos(x_abs_diff_r) * y_abs_diff_r y_abs_diff = _ER * y_abs_diff_r xy_areas = (x_abs_diff * y_abs_diff).flatten() xy_areas = da.where(xy_areas > 0, xy_areas, np.nan) # Get indices of min and max area xy_area_index_min = da.nanargmin(xy_areas) xy_area_index_max = da.nanargmax(xy_areas) # Convert area to edge length xy_res_min = math.sqrt(xy_areas[xy_area_index_min]) xy_res_max = math.sqrt(xy_areas[xy_area_index_max]) # Empirically weight min more than max xy_res = 0.7 * xy_res_min + 0.3 * xy_res_max if crs.is_geographic: # Convert meters back into degrees # print(f'xy_res in meters: {xy_res}') xy_res = math.degrees(xy_res / _ER) # print(f'xy_res in degrees: {xy_res}') # Because this is an estimation, we can round to a nice number xy_res = round_to_fraction(xy_res, digits=1, resolution=0.5) x_res, y_res = float(xy_res), float(xy_res) if tile_size is None and x_coords.chunks is not None: j_chunks, i_chunks = x_coords.chunks tile_size = max(0, *i_chunks), max(0, *j_chunks) if tile_size is not None: tile_width, tile_height = tile_size x_coords = x_coords.chunk((tile_height, tile_width)) y_coords = y_coords.chunk((tile_height, tile_width)) # Guess j axis direction is_j_axis_up = np.all(y_coords[0, :] < y_coords[-1, :]) or None assert_true(x_res > 0 and y_res > 0, 'internal error: x_res and y_res could not be determined', exception_type=RuntimeError) x_res, y_res = _to_int_or_float(x_res), _to_int_or_float(y_res) x_res_05, y_res_05 = x_res / 2, y_res / 2 x_min = _to_int_or_float(x_coords.min() - x_res_05) y_min = _to_int_or_float(y_coords.min() - y_res_05) x_max = _to_int_or_float(x_coords.max() + x_res_05) y_max = _to_int_or_float(y_coords.max() + y_res_05) return cls(x_coords=x_coords, y_coords=y_coords, crs=crs, size=size, tile_size=tile_size, xy_bbox=(x_min, y_min, x_max, y_max), xy_res=(x_res, y_res), xy_var_names=xy_var_names, xy_dim_names=(str(x_dim), str(y_dim)), is_regular=is_regular, is_lon_360=is_lon_360, is_j_axis_up=is_j_axis_up)