"""D-infinity Height Above Nearest Drainage (HAND).
Uses D-inf angle decomposition for downstream tracing. At each cell,
the dominant neighbor (higher weight) is followed to find the nearest
stream cell. HAND = elevation - drain_elevation.
Algorithm
---------
CPU : Kahn's BFS topological sort with reverse propagation of drain_elev.
In-degrees use both D-inf neighbors; reverse pass follows dominant.
GPU : CuPy-via-CPU.
Dask: iterative tile sweep with BoundaryStore exit-label propagation.
"""
from __future__ import annotations
import math
import numpy as np
import xarray as xr
try:
import dask.array as da
except ImportError:
da = None
from xrspatial.hydro.flow_accumulation_dinf import _angle_to_neighbors
from xrspatial.hydro.flow_path_dinf import _angle_to_neighbors_py
from xrspatial.hydro._boundary_store import BoundaryStore
from xrspatial.hydro.watershed_dinf import (
_dominant_offset_py,
_preprocess_tiles,
_to_numpy_f64,
)
from xrspatial.utils import (
_validate_raster,
has_cuda_and_cupy,
is_cupy_array,
is_dask_cupy,
ngjit,
)
# =====================================================================
# Memory guards
# =====================================================================
#
# CPU peak working set per pixel for ``_hand_dinf_cpu``:
# in_degree : int32 -> 4
# valid : int8 -> 1
# is_stream : int8 -> 1
# drain_elev : float64 -> 8
# hand_out : float64 -> 8
# order_r : int64 -> 8
# order_c : int64 -> 8
# Total ~38 bytes/pixel. Caller-provided ``flow_dir``, ``flow_accum``,
# and ``elevation`` arrays already live in RAM before the kernel runs
# and are not double-counted here.
_BYTES_PER_PIXEL = 38
# GPU peak working set per pixel for ``_hand_dinf_cupy``: that path
# copies fd/fa/elev to host via ``.get()`` then runs ``_hand_dinf_cpu``.
# Host working set is dominated by the same 38 B/px as the numpy path;
# on the device we keep the three input arrays (3 * float64 = 24 B/px)
# and the output (float64 = 8 B/px) -- 32 B/px total on the GPU side,
# but the input copies already exist before dispatch, so the marginal
# device allocation is 8 B/px. Use 32 B/px as a conservative budget
# mirroring the d8 and mfd siblings.
_GPU_BYTES_PER_PIXEL = 32
def _available_memory_bytes():
"""Best-effort estimate of available host memory in bytes."""
try:
with open('/proc/meminfo', 'r') as f:
for line in f:
if line.startswith('MemAvailable:'):
return int(line.split()[1]) * 1024 # kB -> bytes
except (OSError, ValueError, IndexError):
pass
try:
import psutil
return psutil.virtual_memory().available
except (ImportError, AttributeError):
pass
return 2 * 1024 ** 3
def _available_gpu_memory_bytes():
"""Best-effort estimate of free GPU memory in bytes.
Returns 0 if CuPy / CUDA is unavailable or the query fails -- callers
use that as a sentinel meaning "no GPU info, skip the guard".
"""
try:
import cupy as _cp
free, _total = _cp.cuda.runtime.memGetInfo()
return int(free)
except Exception:
return 0
def _check_memory(height, width):
"""Raise MemoryError if the HAND kernel would exceed 50% of RAM."""
required = int(height) * int(width) * _BYTES_PER_PIXEL
available = _available_memory_bytes()
if required > 0.5 * available:
raise MemoryError(
f"hand_dinf on a {height}x{width} grid requires "
f"~{required / 1e9:.1f} GB of working memory but only "
f"~{available / 1e9:.1f} GB is available. Use a "
f"dask-backed DataArray for out-of-core processing."
)
def _check_gpu_memory(height, width):
"""Raise MemoryError if the CuPy kernel would exceed 50% of free GPU RAM.
Skips the check (returns silently) when ``_available_gpu_memory_bytes``
cannot determine the free memory -- e.g. on hosts without CUDA, where
the kernel will fail at the cupy.asarray boundary anyway.
"""
available = _available_gpu_memory_bytes()
if available <= 0:
return
required = int(height) * int(width) * _GPU_BYTES_PER_PIXEL
if required > 0.5 * available:
raise MemoryError(
f"hand_dinf on a {height}x{width} grid requires "
f"~{required / 1e9:.1f} GB of GPU working memory but only "
f"~{available / 1e9:.1f} GB is free on the active device. "
f"Use a dask+cupy DataArray for out-of-core processing."
)
# =====================================================================
# CPU kernel
# =====================================================================
@ngjit
def _hand_dinf_cpu(flow_dir, flow_accum, elevation, H, W, threshold):
"""Compute HAND via Kahn's BFS with D-inf angle decomposition."""
in_degree = np.zeros((H, W), dtype=np.int32)
valid = np.zeros((H, W), dtype=np.int8)
is_stream = np.zeros((H, W), dtype=np.int8)
drain_elev = np.empty((H, W), dtype=np.float64)
hand_out = np.empty((H, W), dtype=np.float64)
for r in range(H):
for c in range(W):
v = flow_dir[r, c]
if v == v: # not NaN
valid[r, c] = 1
fa = flow_accum[r, c]
if fa == fa and fa >= threshold:
is_stream[r, c] = 1
drain_elev[r, c] = elevation[r, c]
else:
drain_elev[r, c] = np.nan
else:
drain_elev[r, c] = np.nan
hand_out[r, c] = np.nan
# In-degrees: both D-inf neighbors contribute
for r in range(H):
for c in range(W):
if valid[r, c] == 0:
continue
dy1, dx1, w1, dy2, dx2, w2 = _angle_to_neighbors(flow_dir[r, c])
if w1 > 0.0:
nr, nc = r + dy1, c + dx1
if 0 <= nr < H and 0 <= nc < W and valid[nr, nc] == 1:
in_degree[nr, nc] += 1
if w2 > 0.0:
nr, nc = r + dy2, c + dx2
if 0 <= nr < H and 0 <= nc < W and valid[nr, nc] == 1:
in_degree[nr, nc] += 1
# BFS topological order
order_r = np.empty(H * W, dtype=np.int64)
order_c = np.empty(H * W, dtype=np.int64)
head = np.int64(0)
tail = np.int64(0)
for r in range(H):
for c in range(W):
if valid[r, c] == 1 and in_degree[r, c] == 0:
order_r[tail] = r
order_c[tail] = c
tail += 1
while head < tail:
r = order_r[head]
c = order_c[head]
head += 1
dy1, dx1, w1, dy2, dx2, w2 = _angle_to_neighbors(flow_dir[r, c])
if w1 > 0.0:
nr, nc = r + dy1, c + dx1
if 0 <= nr < H and 0 <= nc < W and valid[nr, nc] == 1:
in_degree[nr, nc] -= 1
if in_degree[nr, nc] == 0:
order_r[tail] = nr
order_c[tail] = nc
tail += 1
if w2 > 0.0:
nr, nc = r + dy2, c + dx2
if 0 <= nr < H and 0 <= nc < W and valid[nr, nc] == 1:
in_degree[nr, nc] -= 1
if in_degree[nr, nc] == 0:
order_r[tail] = nr
order_c[tail] = nc
tail += 1
# Reverse pass: propagate drain_elev via dominant neighbor
for i in range(tail - 1, -1, -1):
r = order_r[i]
c = order_c[i]
if is_stream[r, c] == 1:
continue
dy1, dx1, w1, dy2, dx2, w2 = _angle_to_neighbors(flow_dir[r, c])
if w1 <= 0.0 and w2 <= 0.0:
drain_elev[r, c] = elevation[r, c]
continue
if w1 >= w2:
ddy, ddx = dy1, dx1
else:
ddy, ddx = dy2, dx2
nr, nc = r + ddy, c + ddx
if nr < 0 or nr >= H or nc < 0 or nc >= W:
drain_elev[r, c] = elevation[r, c]
continue
if valid[nr, nc] == 0:
drain_elev[r, c] = elevation[r, c]
continue
de = drain_elev[nr, nc]
if de == de:
drain_elev[r, c] = de
else:
drain_elev[r, c] = elevation[r, c]
for r in range(H):
for c in range(W):
if valid[r, c] == 1:
hand_out[r, c] = elevation[r, c] - drain_elev[r, c]
else:
hand_out[r, c] = np.nan
return hand_out
# =====================================================================
# CuPy backend
# =====================================================================
def _hand_dinf_cupy(fd_data, fa_data, elev_data, threshold):
import cupy as cp
fd_np = fd_data.get().astype(np.float64)
fa_np = fa_data.get().astype(np.float64)
el_np = elev_data.get().astype(np.float64)
H, W = fd_np.shape
out = _hand_dinf_cpu(fd_np, fa_np, el_np, H, W, threshold)
return cp.asarray(out)
# =====================================================================
# Dask tile kernel
# =====================================================================
@ngjit
def _hand_dinf_drain_elev_tile(flow_dir, flow_accum, elevation, h, w,
threshold,
exit_top, exit_bottom, exit_left, exit_right,
exit_tl, exit_tr, exit_bl, exit_br):
"""Compute drain_elev for a D-inf tile (for boundary propagation)."""
in_degree = np.zeros((h, w), dtype=np.int32)
valid = np.zeros((h, w), dtype=np.int8)
is_stream = np.zeros((h, w), dtype=np.int8)
drain_elev = np.empty((h, w), dtype=np.float64)
known = np.zeros((h, w), dtype=np.int8)
for r in range(h):
for c in range(w):
v = flow_dir[r, c]
if v == v:
valid[r, c] = 1
fa = flow_accum[r, c]
if fa == fa and fa >= threshold:
is_stream[r, c] = 1
drain_elev[r, c] = elevation[r, c]
known[r, c] = 1
else:
drain_elev[r, c] = np.nan
else:
drain_elev[r, c] = np.nan
# Apply exit labels at boundaries where dominant neighbor exits tile
for c in range(w):
if valid[0, c] == 1 and known[0, c] == 0:
dy1, dx1, w1, dy2, dx2, w2 = _angle_to_neighbors(flow_dir[0, c])
if w1 <= 0.0 and w2 <= 0.0:
continue
if w1 >= w2:
ddy = dy1
else:
ddy = dy2
if 0 + ddy < 0:
el = exit_top[c]
if el == el:
drain_elev[0, c] = el
known[0, c] = 1
else:
drain_elev[0, c] = elevation[0, c]
known[0, c] = 1
for c in range(w):
if valid[h - 1, c] == 1 and known[h - 1, c] == 0:
dy1, dx1, w1, dy2, dx2, w2 = _angle_to_neighbors(flow_dir[h - 1, c])
if w1 <= 0.0 and w2 <= 0.0:
continue
if w1 >= w2:
ddy = dy1
else:
ddy = dy2
if h - 1 + ddy >= h:
el = exit_bottom[c]
if el == el:
drain_elev[h - 1, c] = el
known[h - 1, c] = 1
else:
drain_elev[h - 1, c] = elevation[h - 1, c]
known[h - 1, c] = 1
for r in range(h):
if valid[r, 0] == 1 and known[r, 0] == 0:
dy1, dx1, w1, dy2, dx2, w2 = _angle_to_neighbors(flow_dir[r, 0])
if w1 <= 0.0 and w2 <= 0.0:
continue
if w1 >= w2:
ddx = dx1
else:
ddx = dx2
if 0 + ddx < 0:
el = exit_left[r]
if el == el:
drain_elev[r, 0] = el
known[r, 0] = 1
else:
drain_elev[r, 0] = elevation[r, 0]
known[r, 0] = 1
for r in range(h):
if valid[r, w - 1] == 1 and known[r, w - 1] == 0:
dy1, dx1, w1, dy2, dx2, w2 = _angle_to_neighbors(flow_dir[r, w - 1])
if w1 <= 0.0 and w2 <= 0.0:
continue
if w1 >= w2:
ddx = dx1
else:
ddx = dx2
if w - 1 + ddx >= w:
el = exit_right[r]
if el == el:
drain_elev[r, w - 1] = el
known[r, w - 1] = 1
else:
drain_elev[r, w - 1] = elevation[r, w - 1]
known[r, w - 1] = 1
# Corner overrides
if valid[0, 0] == 1 and known[0, 0] == 0:
dy1, dx1, w1, dy2, dx2, w2 = _angle_to_neighbors(flow_dir[0, 0])
if not (w1 <= 0.0 and w2 <= 0.0):
if w1 >= w2:
ddy, ddx = dy1, dx1
else:
ddy, ddx = dy2, dx2
if 0 + ddy < 0 and 0 + ddx < 0:
if exit_tl == exit_tl:
drain_elev[0, 0] = exit_tl
known[0, 0] = 1
if valid[0, w - 1] == 1 and known[0, w - 1] == 0:
dy1, dx1, w1, dy2, dx2, w2 = _angle_to_neighbors(flow_dir[0, w - 1])
if not (w1 <= 0.0 and w2 <= 0.0):
if w1 >= w2:
ddy, ddx = dy1, dx1
else:
ddy, ddx = dy2, dx2
if 0 + ddy < 0 and w - 1 + ddx >= w:
if exit_tr == exit_tr:
drain_elev[0, w - 1] = exit_tr
known[0, w - 1] = 1
if valid[h - 1, 0] == 1 and known[h - 1, 0] == 0:
dy1, dx1, w1, dy2, dx2, w2 = _angle_to_neighbors(flow_dir[h - 1, 0])
if not (w1 <= 0.0 and w2 <= 0.0):
if w1 >= w2:
ddy, ddx = dy1, dx1
else:
ddy, ddx = dy2, dx2
if h - 1 + ddy >= h and 0 + ddx < 0:
if exit_bl == exit_bl:
drain_elev[h - 1, 0] = exit_bl
known[h - 1, 0] = 1
if valid[h - 1, w - 1] == 1 and known[h - 1, w - 1] == 0:
dy1, dx1, w1, dy2, dx2, w2 = _angle_to_neighbors(flow_dir[h - 1, w - 1])
if not (w1 <= 0.0 and w2 <= 0.0):
if w1 >= w2:
ddy, ddx = dy1, dx1
else:
ddy, ddx = dy2, dx2
if h - 1 + ddy >= h and w - 1 + ddx >= w:
if exit_br == exit_br:
drain_elev[h - 1, w - 1] = exit_br
known[h - 1, w - 1] = 1
# In-degrees (non-known cells only)
for r in range(h):
for c in range(w):
if valid[r, c] == 0 or known[r, c] == 1:
continue
dy1, dx1, w1, dy2, dx2, w2 = _angle_to_neighbors(flow_dir[r, c])
if w1 > 0.0:
nr, nc = r + dy1, c + dx1
if 0 <= nr < h and 0 <= nc < w:
if valid[nr, nc] == 1 and known[nr, nc] == 0:
in_degree[nr, nc] += 1
if w2 > 0.0:
nr, nc = r + dy2, c + dx2
if 0 <= nr < h and 0 <= nc < w:
if valid[nr, nc] == 1 and known[nr, nc] == 0:
in_degree[nr, nc] += 1
# BFS
order_r = np.empty(h * w, dtype=np.int64)
order_c = np.empty(h * w, dtype=np.int64)
head = np.int64(0)
tail = np.int64(0)
for r in range(h):
for c in range(w):
if valid[r, c] == 1 and known[r, c] == 0 and in_degree[r, c] == 0:
order_r[tail] = r
order_c[tail] = c
tail += 1
while head < tail:
r = order_r[head]
c = order_c[head]
head += 1
dy1, dx1, w1, dy2, dx2, w2 = _angle_to_neighbors(flow_dir[r, c])
if w1 > 0.0:
nr, nc = r + dy1, c + dx1
if 0 <= nr < h and 0 <= nc < w and valid[nr, nc] == 1 and known[nr, nc] == 0:
in_degree[nr, nc] -= 1
if in_degree[nr, nc] == 0:
order_r[tail] = nr
order_c[tail] = nc
tail += 1
if w2 > 0.0:
nr, nc = r + dy2, c + dx2
if 0 <= nr < h and 0 <= nc < w and valid[nr, nc] == 1 and known[nr, nc] == 0:
in_degree[nr, nc] -= 1
if in_degree[nr, nc] == 0:
order_r[tail] = nr
order_c[tail] = nc
tail += 1
# Reverse pass
for i in range(tail - 1, -1, -1):
r = order_r[i]
c = order_c[i]
dy1, dx1, w1, dy2, dx2, w2 = _angle_to_neighbors(flow_dir[r, c])
if w1 <= 0.0 and w2 <= 0.0:
drain_elev[r, c] = elevation[r, c]
continue
if w1 >= w2:
ddy, ddx = dy1, dx1
else:
ddy, ddx = dy2, dx2
nr, nc = r + ddy, c + ddx
if nr < 0 or nr >= h or nc < 0 or nc >= w:
drain_elev[r, c] = elevation[r, c]
continue
if valid[nr, nc] == 0:
drain_elev[r, c] = elevation[r, c]
continue
de = drain_elev[nr, nc]
if de == de:
drain_elev[r, c] = de
else:
drain_elev[r, c] = elevation[r, c]
return drain_elev
@ngjit
def _hand_dinf_tile_kernel(flow_dir, flow_accum, elevation, h, w, threshold,
exit_top, exit_bottom, exit_left, exit_right,
exit_tl, exit_tr, exit_bl, exit_br):
"""HAND tile kernel: returns HAND values (elevation - drain_elev)."""
drain_elev = _hand_dinf_drain_elev_tile(
flow_dir, flow_accum, elevation, h, w, threshold,
exit_top, exit_bottom, exit_left, exit_right,
exit_tl, exit_tr, exit_bl, exit_br)
out = np.empty((h, w), dtype=np.float64)
for r in range(h):
for c in range(w):
v = flow_dir[r, c]
if v == v:
out[r, c] = elevation[r, c] - drain_elev[r, c]
else:
out[r, c] = np.nan
return out
# =====================================================================
# Dask iterative tile sweep
# =====================================================================
def _compute_exit_labels(iy, ix, boundaries, flow_bdry,
chunks_y, chunks_x, n_tile_y, n_tile_x):
"""Same exit-label pattern as watershed_dinf but propagating drain_elev."""
from xrspatial.hydro.watershed_dinf import _compute_exit_labels as _ws_compute
return _ws_compute(iy, ix, boundaries, flow_bdry,
chunks_y, chunks_x, n_tile_y, n_tile_x)
def _process_tile_hand(iy, ix, flow_dir_da, flow_accum_da, elev_da,
boundaries, flow_bdry, threshold,
chunks_y, chunks_x, n_tile_y, n_tile_x):
fd_chunk = np.asarray(
flow_dir_da.blocks[iy, ix].compute(), dtype=np.float64)
fa_chunk = np.asarray(
flow_accum_da.blocks[iy, ix].compute(), dtype=np.float64)
el_chunk = np.asarray(
elev_da.blocks[iy, ix].compute(), dtype=np.float64)
h, w = fd_chunk.shape
exits = _compute_exit_labels(
iy, ix, boundaries, flow_bdry,
chunks_y, chunks_x, n_tile_y, n_tile_x)
drain_elev = _hand_dinf_drain_elev_tile(
fd_chunk, fa_chunk, el_chunk, h, w, threshold, *exits)
new_top = drain_elev[0, :].copy()
new_bottom = drain_elev[-1, :].copy()
new_left = drain_elev[:, 0].copy()
new_right = drain_elev[:, -1].copy()
changed = False
for side, new in (('top', new_top), ('bottom', new_bottom),
('left', new_left), ('right', new_right)):
old = boundaries.get(side, iy, ix).copy()
with np.errstate(invalid='ignore'):
mask = ~(np.isnan(old) & np.isnan(new))
if mask.any():
diff = old[mask] != new[mask]
if np.any(diff):
changed = True
break
boundaries.set('top', iy, ix, new_top)
boundaries.set('bottom', iy, ix, new_bottom)
boundaries.set('left', iy, ix, new_left)
boundaries.set('right', iy, ix, new_right)
return changed
def _hand_dinf_dask(flow_dir_da, flow_accum_da, elev_da, threshold):
chunks_y = flow_dir_da.chunks[0]
chunks_x = flow_dir_da.chunks[1]
n_tile_y = len(chunks_y)
n_tile_x = len(chunks_x)
flow_bdry = _preprocess_tiles(flow_dir_da, chunks_y, chunks_x)
flow_bdry = flow_bdry.snapshot()
boundaries = BoundaryStore(chunks_y, chunks_x, fill_value=np.nan)
max_iterations = max(n_tile_y, n_tile_x) * 2 + 10
for _iteration in range(max_iterations):
any_changed = False
for iy in range(n_tile_y):
for ix in range(n_tile_x):
c = _process_tile_hand(
iy, ix, flow_dir_da, flow_accum_da, elev_da,
boundaries, flow_bdry, threshold,
chunks_y, chunks_x, n_tile_y, n_tile_x)
if c:
any_changed = True
for iy in reversed(range(n_tile_y)):
for ix in reversed(range(n_tile_x)):
c = _process_tile_hand(
iy, ix, flow_dir_da, flow_accum_da, elev_da,
boundaries, flow_bdry, threshold,
chunks_y, chunks_x, n_tile_y, n_tile_x)
if c:
any_changed = True
if not any_changed:
break
boundaries = boundaries.snapshot()
def _tile_fn(fd_block, fa_block, el_block, block_info=None):
if block_info is None or 0 not in block_info:
return np.full(fd_block.shape, np.nan, dtype=np.float64)
iy, ix = block_info[0]['chunk-location']
h, w = fd_block.shape
exits = _compute_exit_labels(
iy, ix, boundaries, flow_bdry,
chunks_y, chunks_x, n_tile_y, n_tile_x)
return _hand_dinf_tile_kernel(
np.asarray(fd_block, dtype=np.float64),
np.asarray(fa_block, dtype=np.float64),
np.asarray(el_block, dtype=np.float64),
h, w, threshold, *exits)
return da.map_blocks(
_tile_fn,
flow_dir_da, flow_accum_da, elev_da,
dtype=np.float64,
meta=np.array((), dtype=np.float64),
)
def _hand_dinf_dask_cupy(flow_dir_da, flow_accum_da, elev_da, threshold):
import cupy as cp
fd_np = flow_dir_da.map_blocks(
lambda b: b.get(), dtype=flow_dir_da.dtype,
meta=np.array((), dtype=flow_dir_da.dtype))
fa_np = flow_accum_da.map_blocks(
lambda b: b.get(), dtype=flow_accum_da.dtype,
meta=np.array((), dtype=flow_accum_da.dtype))
el_np = elev_da.map_blocks(
lambda b: b.get(), dtype=elev_da.dtype,
meta=np.array((), dtype=elev_da.dtype))
result = _hand_dinf_dask(fd_np, fa_np, el_np, threshold)
return result.map_blocks(
cp.asarray, dtype=result.dtype,
meta=cp.array((), dtype=result.dtype))
# =====================================================================
# Public API
# =====================================================================
[docs]
def hand_dinf(flow_dir_dinf: xr.DataArray,
flow_accum: xr.DataArray,
elevation: xr.DataArray,
threshold: float = 100,
name: str = 'hand_dinf') -> xr.DataArray:
"""Compute HAND using D-infinity flow direction.
Parameters
----------
flow_dir_dinf : xarray.DataArray
2D D-infinity flow direction grid.
flow_accum : xarray.DataArray
2D flow accumulation grid.
elevation : xarray.DataArray
2D elevation grid.
threshold : float, default 100
Minimum flow accumulation to define a stream cell.
name : str, default 'hand_dinf'
Name of output DataArray.
Returns
-------
xarray.DataArray
2D float64 HAND grid. Stream cells have HAND = 0.
"""
_validate_raster(flow_dir_dinf, func_name='hand_dinf', name='flow_dir_dinf')
_validate_raster(flow_accum, func_name='hand_dinf', name='flow_accum')
_validate_raster(elevation, func_name='hand_dinf', name='elevation')
if not np.isfinite(threshold):
raise ValueError(
"threshold must be a finite number, got %s" % threshold
)
fd_data = flow_dir_dinf.data
fa_data = flow_accum.data
el_data = elevation.data
if isinstance(fd_data, np.ndarray):
_check_memory(*fd_data.shape)
fd = fd_data.astype(np.float64)
fa = np.asarray(fa_data, dtype=np.float64)
el = np.asarray(el_data, dtype=np.float64)
H, W = fd.shape
out = _hand_dinf_cpu(fd, fa, el, H, W, float(threshold))
elif has_cuda_and_cupy() and is_cupy_array(fd_data):
_check_gpu_memory(*fd_data.shape)
_check_memory(*fd_data.shape)
out = _hand_dinf_cupy(fd_data, fa_data, el_data, float(threshold))
elif has_cuda_and_cupy() and is_dask_cupy(flow_dir_dinf):
out = _hand_dinf_dask_cupy(fd_data, fa_data, el_data, float(threshold))
elif da is not None and isinstance(fd_data, da.Array):
out = _hand_dinf_dask(fd_data, fa_data, el_data, float(threshold))
else:
raise TypeError(f"Unsupported array type: {type(fd_data)}")
return xr.DataArray(out,
name=name,
coords=flow_dir_dinf.coords,
dims=flow_dir_dinf.dims,
attrs=flow_dir_dinf.attrs)
