diff --git a/pysheds/_sgrid.py b/pysheds/_sgrid.py index 7ec23da..32427a4 100644 --- a/pysheds/_sgrid.py +++ b/pysheds/_sgrid.py @@ -313,7 +313,7 @@ def _d8_catchment_recursion(ix, catch, fdir, offsets, r_dirmap): @njit(boolean[:,:](int64[:,:], UniTuple(int64, 2), UniTuple(int64, 8)), cache=True) def _d8_catchment_recur_numba(fdir, pour_point, dirmap): - catch = np.zeros(fdir.shape, dtype=np.bool8) + catch = np.zeros(fdir.shape, dtype=np.bool_) offset = fdir.shape[1] i, j = pour_point ix = (i * offset) + j @@ -327,7 +327,7 @@ def _d8_catchment_recur_numba(fdir, pour_point, dirmap): @njit(boolean[:,:](int64[:,:], UniTuple(int64, 2), UniTuple(int64, 8))) def _d8_catchment_iter_numba(fdir, pour_point, dirmap): - catch = np.zeros(fdir.shape, dtype=np.bool8) + catch = np.zeros(fdir.shape, dtype=np.bool_) offset = fdir.shape[1] i, j = pour_point ix = (i * offset) + j @@ -370,7 +370,7 @@ def _dinf_catchment_recursion(ix, catch, fdir_0, fdir_1, offsets, r_dirmap): @njit(boolean[:,:](int64[:,:], int64[:,:], UniTuple(int64, 2), UniTuple(int64, 8)), cache=True) def _dinf_catchment_recur_numba(fdir_0, fdir_1, pour_point, dirmap): - catch = np.zeros(fdir_0.shape, dtype=np.bool8) + catch = np.zeros(fdir_0.shape, dtype=np.bool_) dirmap = np.array(dirmap) offset = fdir_0.shape[1] i, j = pour_point @@ -387,7 +387,7 @@ def _dinf_catchment_recur_numba(fdir_0, fdir_1, pour_point, dirmap): @njit(boolean[:,:](int64[:,:], int64[:,:], UniTuple(int64, 2), UniTuple(int64, 8)), cache=True) def _dinf_catchment_iter_numba(fdir_0, fdir_1, pour_point, dirmap): - catch = np.zeros(fdir_0.shape, dtype=np.bool8) + catch = np.zeros(fdir_0.shape, dtype=np.bool_) dirmap = np.array(dirmap) offset = fdir_0.shape[1] i, j = pour_point @@ -421,7 +421,7 @@ def _dinf_catchment_iter_numba(fdir_0, fdir_1, pour_point, dirmap): def _mfd_catchment_iter_numba(fdir, pour_point): _, m, n = fdir.shape mn = m * n - catch = np.zeros((m, n), dtype=np.bool8) + catch = np.zeros((m, n), dtype=np.bool_) i, j = pour_point ix = (i * n) + j offsets = np.array([-n, 1 - n, 1, @@ -542,7 +542,7 @@ def _dinf_accumulation_recursion(startnode, endnode, acc, fdir_0, fdir_1, def _dinf_accumulation_recur_numba(acc, fdir_0, fdir_1, indegree, startnodes, props_0, props_1): n = startnodes.size - visited = np.zeros(acc.shape, dtype=np.bool8) + visited = np.zeros(acc.shape, dtype=np.bool_) for k in range(n): startnode = startnodes.flat[k] endnode_0 = fdir_0.flat[startnode] @@ -611,7 +611,7 @@ def _dinf_accumulation_eff_recursion(startnode, endnode, acc, fdir_0, fdir_1, def _dinf_accumulation_eff_numba(acc, fdir_0, fdir_1, indegree, startnodes, props_0, props_1, eff): n = startnodes.size - visited = np.zeros(acc.shape, dtype=np.bool8) + visited = np.zeros(acc.shape, dtype=np.bool_) for k in range(n): startnode = startnodes.flat[k] endnode_0 = fdir_0.flat[startnode] @@ -742,7 +742,7 @@ def _d8_flow_distance_recursion(ix, fdir, visits, dist, weights, r_dirmap, @njit(float64[:,:](int64[:,:], float64[:,:], UniTuple(int64, 2), UniTuple(int64, 8)), cache=True) def _d8_flow_distance_recur_numba(fdir, weights, pour_point, dirmap): - visits = np.zeros(fdir.shape, dtype=np.bool8) + visits = np.zeros(fdir.shape, dtype=np.bool_) dist = np.full(fdir.shape, np.inf, dtype=np.float64) r_dirmap = np.array([dirmap[4], dirmap[5], dirmap[6], dirmap[7], dirmap[0], dirmap[1], @@ -760,7 +760,7 @@ def _d8_flow_distance_recur_numba(fdir, weights, pour_point, dirmap): @njit(float64[:,:](int64[:,:], float64[:,:], UniTuple(int64, 2), UniTuple(int64, 8)), cache=True) def _d8_flow_distance_iter_numba(fdir, weights, pour_point, dirmap): - visits = np.zeros(fdir.shape, dtype=np.bool8) + visits = np.zeros(fdir.shape, dtype=np.bool_) dist = np.full(fdir.shape, np.inf, dtype=np.float64) r_dirmap = np.array([dirmap[4], dirmap[5], dirmap[6], dirmap[7], dirmap[0], dirmap[1], @@ -818,7 +818,7 @@ def _dinf_flow_distance_recursion(ix, fdir_0, fdir_1, visits, dist, cache=True) def _dinf_flow_distance_recur_numba(fdir_0, fdir_1, weights_0, weights_1, pour_point, dirmap): - visits = np.zeros(fdir_0.shape, dtype=np.bool8) + visits = np.zeros(fdir_0.shape, dtype=np.bool_) dist = np.full(fdir_0.shape, np.inf, dtype=np.float64) r_dirmap = np.array([dirmap[4], dirmap[5], dirmap[6], dirmap[7], dirmap[0], dirmap[1], @@ -839,7 +839,7 @@ def _dinf_flow_distance_recur_numba(fdir_0, fdir_1, weights_0, weights_1, def _dinf_flow_distance_iter_numba(fdir_0, fdir_1, weights_0, weights_1, pour_point, dirmap): dist = np.full(fdir_0.shape, np.inf, dtype=np.float64) - visited = np.zeros(fdir_0.shape, dtype=np.bool8) + visited = np.zeros(fdir_0.shape, dtype=np.bool_) r_dirmap = np.array([dirmap[4], dirmap[5], dirmap[6], dirmap[7], dirmap[0], dirmap[1], dirmap[2], dirmap[3]]) @@ -883,7 +883,7 @@ def _mfd_flow_distance_iter_numba(fdir, pour_point, weights): _, m, n = fdir.shape mn = m * n dist = np.full((m, n), np.inf, dtype=np.float64) - visited = np.zeros((m, n), dtype=np.bool8) + visited = np.zeros((m, n), dtype=np.bool_) i, j = pour_point ix = (i * n) + j offsets = np.array([-n, 1 - n, 1, @@ -1020,9 +1020,9 @@ def _mfd_reverse_distance_iter_numba(rdist, fdir, indegree, startnodes, weights) def _par_get_candidates_numba(dem, inside): n = inside.size offset = dem.shape[1] - fdirs_defined = np.zeros(dem.shape, dtype=np.bool8) - flats = np.zeros(dem.shape, dtype=np.bool8) - higher_cells = np.zeros(dem.shape, dtype=np.bool8) + fdirs_defined = np.zeros(dem.shape, dtype=np.bool_) + flats = np.zeros(dem.shape, dtype=np.bool_) + higher_cells = np.zeros(dem.shape, dtype=np.bool_) offsets = np.array([-offset, 1 - offset, 1, 1 + offset, offset, - 1 + offset, - 1, - 1 - offset]) @@ -1656,7 +1656,7 @@ def _dinf_fix_cycles_recursion(node, fdir_0, fdir_1, ancestor, cache=True) def _dinf_fix_cycles_numba(fdir_0, fdir_1, max_cycle_size): n = fdir_0.size - visited = np.zeros(fdir_0.shape, dtype=np.bool8) + visited = np.zeros(fdir_0.shape, dtype=np.bool_) depth = 0 for node in range(n): _dinf_fix_cycles_recursion(node, fdir_0, fdir_1, node, @@ -1820,7 +1820,7 @@ def _construct_matching(fdir, dirmap): def _find_pits_numba(dem, inside): n = inside.size offset = dem.shape[1] - pits = np.zeros(dem.shape, dtype=np.bool8) + pits = np.zeros(dem.shape, dtype=np.bool_) offsets = np.array([-offset, 1 - offset, 1, 1 + offset, offset, - 1 + offset, - 1, - 1 - offset]) diff --git a/pysheds/sgrid.py b/pysheds/sgrid.py index 1093b0c..04c3cbb 100644 --- a/pysheds/sgrid.py +++ b/pysheds/sgrid.py @@ -706,7 +706,7 @@ def catchment(self, x, y, fdir, pour_value=None, dirmap=(64, 128, 1, 2, 4, 8, 16 ------- catch : Raster Raster indicating cells that lie in the catchment. The dtype will be - np.bool8, unless `pour_value` is specified, in which case the dtype will + np.bool_, unless `pour_value` is specified, in which case the dtype will be the smallest dtype capable of representing the pour value. """ if routing.lower() == 'd8': @@ -1282,7 +1282,7 @@ def compute_hand(self, fdir, dem, mask, dirmap=(64, 128, 1, 2, 4, 8, 16, 32), else: raise ValueError('Routing method must be one of: `d8`, `dinf`, `mfd`') dem_overrides = {'dtype' : np.float64, 'nodata' : dem.nodata} - mask_overrides = {'dtype' : np.bool8, 'nodata' : False} + mask_overrides = {'dtype' : np.bool_, 'nodata' : False} kwargs.update(fdir_overrides) fdir = self._input_handler(fdir, **kwargs) kwargs.update(dem_overrides) @@ -1412,7 +1412,7 @@ def extract_river_network(self, fdir, mask, dirmap=(64, 128, 1, 2, 4, 8, 16, 32) fdir_overrides = {'dtype' : np.int64, 'nodata' : fdir.nodata} else: raise NotImplementedError('Only implemented for `d8` routing.') - mask_overrides = {'dtype' : np.bool8, 'nodata' : False} + mask_overrides = {'dtype' : np.bool_, 'nodata' : False} kwargs.update(fdir_overrides) fdir = self._input_handler(fdir, **kwargs) kwargs.update(mask_overrides) @@ -1495,7 +1495,7 @@ def extract_profiles(self, fdir, mask, dirmap=(64, 128, 1, 2, 4, 8, 16, 32), fdir_overrides = {'dtype' : np.int64, 'nodata' : fdir.nodata} else: raise NotImplementedError('Only implemented for `d8` routing.') - mask_overrides = {'dtype' : np.bool8, 'nodata' : False} + mask_overrides = {'dtype' : np.bool_, 'nodata' : False} kwargs.update(fdir_overrides) fdir = self._input_handler(fdir, **kwargs) kwargs.update(mask_overrides) @@ -1559,7 +1559,7 @@ def stream_order(self, fdir, mask, dirmap=(64, 128, 1, 2, 4, 8, 16, 32), fdir_overrides = {'dtype' : np.int64, 'nodata' : fdir.nodata} else: raise NotImplementedError('Only implemented for `d8` routing.') - mask_overrides = {'dtype' : np.bool8, 'nodata' : False} + mask_overrides = {'dtype' : np.bool_, 'nodata' : False} kwargs.update(fdir_overrides) fdir = self._input_handler(fdir, **kwargs) kwargs.update(mask_overrides) @@ -2119,7 +2119,7 @@ def detect_depressions(self, dem, **kwargs): kwargs.update(input_overrides) dem = self._input_handler(dem, **kwargs) filled_dem = self.fill_depressions(dem, **kwargs) - depressions = np.zeros(filled_dem.shape, dtype=np.bool8) + depressions = np.zeros(filled_dem.shape, dtype=np.bool_) depressions[dem != filled_dem] = True depressions[np.isnan(dem) | np.isnan(filled_dem)] = False depressions = self._output_handler(data=depressions, @@ -2382,7 +2382,7 @@ def snap_to_mask(self, mask, xy, return_dist=False, **kwargs): assert isinstance(mask, Raster) except: raise TypeError('`mask` must be a Raster instance.') - mask_overrides = {'dtype' : np.bool8, 'nodata' : False} + mask_overrides = {'dtype' : np.bool_, 'nodata' : False} kwargs.update(mask_overrides) mask = self._input_handler(mask, **kwargs) affine = mask.affine @@ -2416,9 +2416,9 @@ def _get_nodata_cells(self, data): raise TypeError('Data must be a Raster.') nodata = data.nodata if np.isnan(nodata): - nodata_cells = np.isnan(data).astype(np.bool8) + nodata_cells = np.isnan(data).astype(np.bool_) else: - nodata_cells = (data == nodata).astype(np.bool8) + nodata_cells = (data == nodata).astype(np.bool_) return nodata_cells def _pop_rim(self, data, nodata=0): diff --git a/pysheds/sview.py b/pysheds/sview.py index a8e088d..311c3cc 100644 --- a/pysheds/sview.py +++ b/pysheds/sview.py @@ -334,7 +334,7 @@ def __init__(self, affine=Affine(1., 0., 0., 0., 1., 0.), shape=(1,1), self.crs = crs self.nodata = nodata if mask is None: - self.mask = np.ones(shape, dtype=np.bool8) + self.mask = np.ones(shape, dtype=np.bool_) else: self.mask = mask @@ -385,10 +385,10 @@ def mask(self): @mask.setter def mask(self, new_mask): try: - assert (np.min_scalar_type(new_mask) <= np.dtype(np.bool8)) + assert (np.min_scalar_type(new_mask) <= np.dtype(np.bool_)) except: raise TypeError('`mask` must be of boolean type') - new_mask = np.asarray(new_mask).astype(np.bool8) + new_mask = np.asarray(new_mask).astype(np.bool_) self._mask = new_mask @property @@ -621,7 +621,7 @@ def _view_multiraster(cls, data, target_view, data_view=None, interpolation='nea k, m, n = data.shape if out is None: out = np.empty((k, *target_view.shape), dtype=dtype) - out_mask = np.ones((k, *target_view.shape), dtype=np.bool8) + out_mask = np.ones((k, *target_view.shape), dtype=np.bool_) for i in range(k): slice_viewfinder = ViewFinder(affine=data_view.affine, mask=data_view.mask[i], nodata=data_view.nodata, crs=data_view.crs) @@ -971,4 +971,3 @@ def _view_different_crs(cls, view, data, data_view, target_view, interpolation=' else: raise ValueError('Interpolation method must be one of: `nearest`, `linear`') return view -