"""Stream link segmentation for D-infinity flow direction grids.
Assigns a unique positive integer ID to each stream "link" -- the
contiguous segment of stream cells between junctions, headwaters, and
outlets. This is the D-inf counterpart to the D8 ``stream_link`` module.
A **link-start cell** is either a headwater (in-degree 0 among stream
cells) or a junction (in-degree >= 2). Each link-start cell gets a
position-based ID: ``row * width + col + 1``. Downstream non-junction
cells inherit their upstream neighbor's link_id via Kahn's BFS.
Because D-inf angles can direct flow to two neighbours (the pair
bracketing the continuous angle), a cell may have up to two downstream
neighbours rather than exactly one as in D8.
Algorithm
---------
CPU : Kahn's BFS topological sort among stream cells -- O(N_stream).
GPU : iterative frontier peeling with pull-based kernels.
Dask: iterative tile sweep with boundary propagation, same pattern
as ``stream_link.py``.
"""
from __future__ import annotations
import math
import numpy as np
import xarray as xr
from numba import cuda
try:
import cupy
except ImportError:
class cupy: # type: ignore[no-redef]
ndarray = False
try:
import dask.array as da
except ImportError:
da = None
from xrspatial.utils import (
_validate_raster,
cuda_args,
has_cuda_and_cupy,
is_cupy_array,
is_dask_cupy,
ngjit,
)
from xrspatial.hydro._boundary_store import BoundaryStore
from xrspatial.dataset_support import supports_dataset
from xrspatial.hydro.stream_order_d8 import _to_numpy_f64
from xrspatial.hydro.stream_order_dinf import (
_dinf_downstream,
_NB_DY,
_NB_DX,
_preprocess_stream_tiles_dinf,
_make_stream_mask_np_dinf,
)
# =====================================================================
# Memory guards
# =====================================================================
#
# CPU peak working set per pixel for ``_stream_link_dinf_cpu``:
# link_id : float64 -> 8
# in_degree : int32 -> 4
# orig_indeg : int32 -> 4
# queue_r : int64 -> 8
# queue_c : int64 -> 8
# Total ~32 bytes/pixel. D-inf encodes one continuous downstream angle
# per cell, so there is no (8, H, W) per-neighbor weight buffer like in
# the MFD variant -- the working set matches the d8 budget. The
# caller-provided ``flow_dir`` and ``flow_accum`` arrays already live in
# RAM before the kernel runs and are not double-counted here.
_BYTES_PER_PIXEL = 32
# GPU peak working set per pixel for ``_stream_link_dinf_cupy``:
# angles_f64 : float64 -> 8
# stream_mask_i8 : int8 -> 1
# in_degree : int32 -> 4
# orig_indeg : int32 -> 4
# state : int32 -> 4
# link_id : float64 -> 8
# Total ~29 bytes/pixel. The ``fa_cp`` input copy adds another 8 B/px
# on the device. Use 40 B/px as a conservative budget covering both.
_GPU_BYTES_PER_PIXEL = 40
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 kernel would exceed 50% of available RAM."""
required = int(height) * int(width) * _BYTES_PER_PIXEL
available = _available_memory_bytes()
if required > 0.5 * available:
raise MemoryError(
f"stream_link_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"stream_link_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 _stream_link_dinf_cpu(angles, stream_mask, height, width):
"""Kahn's BFS link ID assignment among stream cells (D-inf).
Parameters
----------
angles : (H, W) float64 -- D-inf flow direction angles
stream_mask : (H, W) int8
height, width : int
Returns
-------
link_id : (H, W) float64
"""
nb_dy = np.array([0, -1, -1, -1, 0, 1, 1, 1], dtype=np.int64)
nb_dx = np.array([1, 1, 0, -1, -1, -1, 0, 1], dtype=np.int64)
pi_over_4 = math.pi / 4.0
link_id = np.empty((height, width), dtype=np.float64)
in_degree = np.zeros((height, width), dtype=np.int32)
# Initialise
for r in range(height):
for c in range(width):
if stream_mask[r, c] == 0:
link_id[r, c] = np.nan
else:
link_id[r, c] = 0.0
# Compute in-degrees from D-inf angles
for r in range(height):
for c in range(width):
if stream_mask[r, c] == 0:
continue
theta = angles[r, c]
if theta != theta: # NaN
continue
if theta < 0.0: # pit
continue
k = int(theta / pi_over_4)
if k >= 8:
k = 7
k2 = (k + 1) % 8
alpha = theta - k * pi_over_4
frac1 = (pi_over_4 - alpha) / pi_over_4
frac2 = alpha / pi_over_4
if frac1 > 0.0:
nr = r + nb_dy[k]
nc = c + nb_dx[k]
if 0 <= nr < height and 0 <= nc < width:
if stream_mask[nr, nc] == 1:
in_degree[nr, nc] += 1
if frac2 > 0.0:
nr2 = r + nb_dy[k2]
nc2 = c + nb_dx[k2]
if 0 <= nr2 < height and 0 <= nc2 < width:
if stream_mask[nr2, nc2] == 1:
in_degree[nr2, nc2] += 1
# Store original in-degree to identify junctions
orig_indeg = np.empty((height, width), dtype=np.int32)
for r in range(height):
for c in range(width):
orig_indeg[r, c] = in_degree[r, c]
# BFS queue
queue_r = np.empty(height * width, dtype=np.int64)
queue_c = np.empty(height * width, dtype=np.int64)
head = np.int64(0)
tail = np.int64(0)
# Enqueue headwaters (stream cells with in_degree == 0)
for r in range(height):
for c in range(width):
if stream_mask[r, c] == 1 and in_degree[r, c] == 0:
link_id[r, c] = float(r * width + c + 1)
queue_r[tail] = r
queue_c[tail] = c
tail += 1
while head < tail:
r = queue_r[head]
c = queue_c[head]
head += 1
theta = angles[r, c]
if theta != theta:
continue
if theta < 0.0:
continue
k = int(theta / pi_over_4)
if k >= 8:
k = 7
k2 = (k + 1) % 8
alpha = theta - k * pi_over_4
frac1 = (pi_over_4 - alpha) / pi_over_4
frac2 = alpha / pi_over_4
# Propagate to neighbor k
if frac1 > 0.0:
nr = r + nb_dy[k]
nc = c + nb_dx[k]
if 0 <= nr < height and 0 <= nc < width:
if stream_mask[nr, nc] == 1:
if orig_indeg[nr, nc] >= 2:
in_degree[nr, nc] -= 1
if in_degree[nr, nc] == 0:
link_id[nr, nc] = float(nr * width + nc + 1)
queue_r[tail] = nr
queue_c[tail] = nc
tail += 1
else:
if link_id[nr, nc] == 0.0:
link_id[nr, nc] = link_id[r, c]
in_degree[nr, nc] -= 1
if in_degree[nr, nc] == 0:
queue_r[tail] = nr
queue_c[tail] = nc
tail += 1
# Propagate to neighbor k2
if frac2 > 0.0:
nr2 = r + nb_dy[k2]
nc2 = c + nb_dx[k2]
if 0 <= nr2 < height and 0 <= nc2 < width:
if stream_mask[nr2, nc2] == 1:
if orig_indeg[nr2, nc2] >= 2:
in_degree[nr2, nc2] -= 1
if in_degree[nr2, nc2] == 0:
link_id[nr2, nc2] = float(
nr2 * width + nc2 + 1)
queue_r[tail] = nr2
queue_c[tail] = nc2
tail += 1
else:
if link_id[nr2, nc2] == 0.0:
link_id[nr2, nc2] = link_id[r, c]
in_degree[nr2, nc2] -= 1
if in_degree[nr2, nc2] == 0:
queue_r[tail] = nr2
queue_c[tail] = nc2
tail += 1
return link_id
# =====================================================================
# GPU kernels
# =====================================================================
@cuda.jit(device=True)
def _dinf_nb_dy(k):
if k == 0:
return 0
elif k == 1:
return -1
elif k == 2:
return -1
elif k == 3:
return -1
elif k == 4:
return 0
elif k == 5:
return 1
elif k == 6:
return 1
else:
return 1
@cuda.jit(device=True)
def _dinf_nb_dx(k):
if k == 0:
return 1
elif k == 1:
return 1
elif k == 2:
return 0
elif k == 3:
return -1
elif k == 4:
return -1
elif k == 5:
return -1
elif k == 6:
return 0
else:
return 1
@cuda.jit(device=True)
def _dinf_angle_to_k(theta):
if theta != theta: # NaN
return -1
if theta < 0.0:
return -1
pi_over_4 = 0.7853981633974483
k = int(theta / pi_over_4)
if k >= 8:
k = 7
return k
@cuda.jit
def _stream_link_dinf_init_gpu(angles, stream_mask, in_degree, state,
link_id, H, W):
"""Initialise GPU arrays and compute in-degrees from D-inf angles."""
i, j = cuda.grid(2)
if i >= H or j >= W:
return
if stream_mask[i, j] == 0:
state[i, j] = 0
link_id[i, j] = 0.0
return
state[i, j] = 1
link_id[i, j] = 0.0
theta = angles[i, j]
k = _dinf_angle_to_k(theta)
if k < 0:
return
pi_over_4 = 0.7853981633974483
k2 = (k + 1) % 8
alpha = theta - k * pi_over_4
frac1 = (pi_over_4 - alpha) / pi_over_4
frac2 = alpha / pi_over_4
if frac1 > 0.0:
ni = i + _dinf_nb_dy(k)
nj = j + _dinf_nb_dx(k)
if 0 <= ni < H and 0 <= nj < W and stream_mask[ni, nj] == 1:
cuda.atomic.add(in_degree, (ni, nj), 1)
if frac2 > 0.0:
ni2 = i + _dinf_nb_dy(k2)
nj2 = j + _dinf_nb_dx(k2)
if 0 <= ni2 < H and 0 <= nj2 < W and stream_mask[ni2, nj2] == 1:
cuda.atomic.add(in_degree, (ni2, nj2), 1)
@cuda.jit
def _stream_link_dinf_save_indeg(in_degree, orig_indeg, stream_mask, H, W):
"""Copy in_degree to orig_indeg after init."""
i, j = cuda.grid(2)
if i >= H or j >= W:
return
if stream_mask[i, j] == 1:
orig_indeg[i, j] = in_degree[i, j]
@cuda.jit
def _stream_link_dinf_find_ready(in_degree, orig_indeg, state, link_id,
changed, H, W):
"""Finalize previous frontier, mark new frontier cells."""
i, j = cuda.grid(2)
if i >= H or j >= W:
return
if state[i, j] == 2:
state[i, j] = 3
if state[i, j] == 1 and in_degree[i, j] == 0:
state[i, j] = 2
if orig_indeg[i, j] == 0 or orig_indeg[i, j] >= 2:
link_id[i, j] = float(i * W + j + 1)
cuda.atomic.add(changed, 0, 1)
@cuda.jit
def _stream_link_dinf_pull(angles, stream_mask, in_degree, orig_indeg,
state, link_id, H, W):
"""Active cells pull link_id from frontier D-inf neighbours."""
i, j = cuda.grid(2)
if i >= H or j >= W:
return
if state[i, j] != 1:
return
pi_over_4 = 0.7853981633974483
for nb in range(8):
dy = _dinf_nb_dy(nb)
dx = _dinf_nb_dx(nb)
ni = i + dy
nj = j + dx
if ni < 0 or ni >= H or nj < 0 or nj >= W:
continue
if state[ni, nj] != 2:
continue
if stream_mask[ni, nj] == 0:
continue
nb_theta = angles[ni, nj]
nb_k = _dinf_angle_to_k(nb_theta)
if nb_k < 0:
continue
nb_k2 = (nb_k + 1) % 8
nb_alpha = nb_theta - nb_k * pi_over_4
nb_frac1 = (pi_over_4 - nb_alpha) / pi_over_4
nb_frac2 = nb_alpha / pi_over_4
flows_to_us = False
if nb_frac1 > 0.0:
ti = ni + _dinf_nb_dy(nb_k)
tj = nj + _dinf_nb_dx(nb_k)
if ti == i and tj == j:
flows_to_us = True
if not flows_to_us and nb_frac2 > 0.0:
ti2 = ni + _dinf_nb_dy(nb_k2)
tj2 = nj + _dinf_nb_dx(nb_k2)
if ti2 == i and tj2 == j:
flows_to_us = True
if flows_to_us:
in_degree[i, j] -= 1
if orig_indeg[i, j] < 2 and link_id[i, j] == 0.0:
link_id[i, j] = link_id[ni, nj]
def _stream_link_dinf_cupy(angles_data, stream_mask_data):
"""GPU driver for D-inf stream link computation."""
import cupy as cp
H, W = angles_data.shape
angles_f64 = angles_data.astype(cp.float64)
stream_mask_i8 = stream_mask_data.astype(cp.int8)
in_degree = cp.zeros((H, W), dtype=cp.int32)
orig_indeg = cp.zeros((H, W), dtype=cp.int32)
state = cp.zeros((H, W), dtype=cp.int32)
link_id = cp.zeros((H, W), dtype=cp.float64)
changed = cp.zeros(1, dtype=cp.int32)
griddim, blockdim = cuda_args((H, W))
_stream_link_dinf_init_gpu[griddim, blockdim](
angles_f64, stream_mask_i8, in_degree, state, link_id, H, W)
_stream_link_dinf_save_indeg[griddim, blockdim](
in_degree, orig_indeg, stream_mask_i8, H, W)
max_iter = H * W
for _ in range(max_iter):
changed[0] = 0
_stream_link_dinf_find_ready[griddim, blockdim](
in_degree, orig_indeg, state, link_id, changed, H, W)
if int(changed[0]) == 0:
break
_stream_link_dinf_pull[griddim, blockdim](
angles_f64, stream_mask_i8, in_degree, orig_indeg,
state, link_id, H, W)
link_id = cp.where(stream_mask_i8 == 0, cp.nan, link_id)
return link_id
# =====================================================================
# GPU tile kernel for dask+cupy
# =====================================================================
@cuda.jit
def _stream_link_dinf_find_ready_tile(in_degree, orig_indeg, state, link_id,
changed, H, W,
row_off, col_off, total_w):
"""Tile-aware find_ready: uses global coordinates for link IDs."""
i, j = cuda.grid(2)
if i >= H or j >= W:
return
if state[i, j] == 2:
state[i, j] = 3
if state[i, j] == 1 and in_degree[i, j] == 0:
state[i, j] = 2
if orig_indeg[i, j] == 0 or orig_indeg[i, j] >= 2:
gi = row_off + i
gj = col_off + j
link_id[i, j] = float(gi * total_w + gj + 1)
cuda.atomic.add(changed, 0, 1)
def _stream_link_dinf_tile_cupy(angles_data, stream_mask_data,
seed_id_top, seed_id_bottom,
seed_id_left, seed_id_right,
seed_ext_top, seed_ext_bottom,
seed_ext_left, seed_ext_right,
row_offset, col_offset, total_width):
"""GPU seeded D-inf stream link for a single tile."""
import cupy as cp
H, W = angles_data.shape
angles_f64 = angles_data.astype(cp.float64)
stream_mask_i8 = stream_mask_data.astype(cp.int8)
in_degree = cp.zeros((H, W), dtype=cp.int32)
orig_indeg = cp.zeros((H, W), dtype=cp.int32)
state = cp.zeros((H, W), dtype=cp.int32)
link_id = cp.zeros((H, W), dtype=cp.float64)
changed = cp.zeros(1, dtype=cp.int32)
griddim, blockdim = cuda_args((H, W))
_stream_link_dinf_init_gpu[griddim, blockdim](
angles_f64, stream_mask_i8, in_degree, state, link_id, H, W)
_stream_link_dinf_save_indeg[griddim, blockdim](
in_degree, orig_indeg, stream_mask_i8, H, W)
# Add external in-degree counts to orig_indeg (for junction detection)
for r_idx, ext in [
((0, slice(None)), seed_ext_top),
((H - 1, slice(None)), seed_ext_bottom),
((slice(None), 0), seed_ext_left),
((slice(None), W - 1), seed_ext_right),
]:
ext_cp = cp.asarray(ext).astype(cp.int32)
is_stream = stream_mask_i8[r_idx] == 1
orig_indeg[r_idx] += cp.where(is_stream, ext_cp, 0)
# Pre-set link_ids for non-junction boundary cells with seed values
for r_idx, s_id in [
((0, slice(None)), seed_id_top),
((H - 1, slice(None)), seed_id_bottom),
((slice(None), 0), seed_id_left),
((slice(None), W - 1), seed_id_right),
]:
sid_cp = cp.asarray(s_id)
is_stream = stream_mask_i8[r_idx] == 1
non_junction = orig_indeg[r_idx] < 2
has_seed = sid_cp > 0
mask = is_stream & non_junction & has_seed
link_id[r_idx] = cp.where(mask, sid_cp, link_id[r_idx])
max_iter = H * W
for _ in range(max_iter):
changed[0] = 0
_stream_link_dinf_find_ready_tile[griddim, blockdim](
in_degree, orig_indeg, state, link_id, changed,
H, W, row_offset, col_offset, total_width)
if int(changed[0]) == 0:
break
_stream_link_dinf_pull[griddim, blockdim](
angles_f64, stream_mask_i8, in_degree, orig_indeg,
state, link_id, H, W)
link_id = cp.where(stream_mask_i8 == 0, cp.nan, link_id)
return link_id
# =====================================================================
# CPU tile kernel for dask
# =====================================================================
@ngjit
def _stream_link_dinf_tile_kernel(angles, stream_mask, h, w,
seed_id_top, seed_id_bottom,
seed_id_left, seed_id_right,
seed_ext_top, seed_ext_bottom,
seed_ext_left, seed_ext_right,
row_offset, col_offset, total_width):
"""Seeded BFS link assignment for a single D-inf tile.
Parameters
----------
angles : (h, w) float64 -- D-inf flow direction angles for this tile
stream_mask : (h, w) int8
seed_id_* : link_id values flowing in from neighbouring tiles.
seed_ext_* : count of external stream cells flowing into each
boundary cell (to compute correct orig_indeg).
row_offset, col_offset : global pixel offsets of this tile.
total_width : total number of columns in the full raster.
"""
nb_dy = np.array([0, -1, -1, -1, 0, 1, 1, 1], dtype=np.int64)
nb_dx = np.array([1, 1, 0, -1, -1, -1, 0, 1], dtype=np.int64)
pi_over_4 = math.pi / 4.0
link_id = np.empty((h, w), dtype=np.float64)
in_degree = np.zeros((h, w), dtype=np.int32)
# Initialise
for r in range(h):
for c in range(w):
if stream_mask[r, c] == 0:
link_id[r, c] = np.nan
else:
link_id[r, c] = 0.0
# Compute within-tile in-degrees
for r in range(h):
for c in range(w):
if stream_mask[r, c] == 0:
continue
theta = angles[r, c]
if theta != theta:
continue
if theta < 0.0:
continue
k = int(theta / pi_over_4)
if k >= 8:
k = 7
k2 = (k + 1) % 8
alpha = theta - k * pi_over_4
frac1 = (pi_over_4 - alpha) / pi_over_4
frac2 = alpha / pi_over_4
if frac1 > 0.0:
nr = r + nb_dy[k]
nc = c + nb_dx[k]
if 0 <= nr < h and 0 <= nc < w:
if stream_mask[nr, nc] == 1:
in_degree[nr, nc] += 1
if frac2 > 0.0:
nr2 = r + nb_dy[k2]
nc2 = c + nb_dx[k2]
if 0 <= nr2 < h and 0 <= nc2 < w:
if stream_mask[nr2, nc2] == 1:
in_degree[nr2, nc2] += 1
# orig_indeg = within-tile + external (for junction detection only)
orig_indeg = np.empty((h, w), dtype=np.int32)
for r in range(h):
for c in range(w):
orig_indeg[r, c] = in_degree[r, c]
for c in range(w):
if stream_mask[0, c] == 1:
orig_indeg[0, c] += int(seed_ext_top[c])
if stream_mask[h - 1, c] == 1:
orig_indeg[h - 1, c] += int(seed_ext_bottom[c])
for r in range(h):
if stream_mask[r, 0] == 1:
orig_indeg[r, 0] += int(seed_ext_left[r])
if stream_mask[r, w - 1] == 1:
orig_indeg[r, w - 1] += int(seed_ext_right[r])
# BFS queue
queue_r = np.empty(h * w, dtype=np.int64)
queue_c = np.empty(h * w, dtype=np.int64)
head = np.int64(0)
tail = np.int64(0)
# Assign initial link_ids and enqueue cells with in_degree == 0
for r in range(h):
for c in range(w):
if stream_mask[r, c] != 1:
continue
if in_degree[r, c] != 0:
continue
if orig_indeg[r, c] == 0 or orig_indeg[r, c] >= 2:
gr = row_offset + r
gc = col_offset + c
link_id[r, c] = float(gr * total_width + gc + 1)
queue_r[tail] = r
queue_c[tail] = c
tail += 1
# Seed non-junction, non-headwater boundary cells
for c in range(w):
if stream_mask[0, c] == 1 and seed_id_top[c] > 0:
if orig_indeg[0, c] < 2:
link_id[0, c] = seed_id_top[c]
if stream_mask[h - 1, c] == 1 and seed_id_bottom[c] > 0:
if orig_indeg[h - 1, c] < 2:
link_id[h - 1, c] = seed_id_bottom[c]
for r in range(h):
if stream_mask[r, 0] == 1 and seed_id_left[r] > 0:
if orig_indeg[r, 0] < 2:
link_id[r, 0] = seed_id_left[r]
if stream_mask[r, w - 1] == 1 and seed_id_right[r] > 0:
if orig_indeg[r, w - 1] < 2:
link_id[r, w - 1] = seed_id_right[r]
while head < tail:
r = queue_r[head]
c = queue_c[head]
head += 1
theta = angles[r, c]
if theta != theta:
continue
if theta < 0.0:
continue
k = int(theta / pi_over_4)
if k >= 8:
k = 7
k2 = (k + 1) % 8
alpha = theta - k * pi_over_4
frac1 = (pi_over_4 - alpha) / pi_over_4
frac2 = alpha / pi_over_4
if frac1 > 0.0:
nr = r + nb_dy[k]
nc = c + nb_dx[k]
if 0 <= nr < h and 0 <= nc < w:
if stream_mask[nr, nc] == 1:
if orig_indeg[nr, nc] >= 2:
in_degree[nr, nc] -= 1
if in_degree[nr, nc] == 0:
gnr = row_offset + nr
gnc = col_offset + nc
link_id[nr, nc] = float(
gnr * total_width + gnc + 1)
queue_r[tail] = nr
queue_c[tail] = nc
tail += 1
else:
if link_id[nr, nc] == 0.0:
link_id[nr, nc] = link_id[r, c]
in_degree[nr, nc] -= 1
if in_degree[nr, nc] == 0:
queue_r[tail] = nr
queue_c[tail] = nc
tail += 1
if frac2 > 0.0:
nr2 = r + nb_dy[k2]
nc2 = c + nb_dx[k2]
if 0 <= nr2 < h and 0 <= nc2 < w:
if stream_mask[nr2, nc2] == 1:
if orig_indeg[nr2, nc2] >= 2:
in_degree[nr2, nc2] -= 1
if in_degree[nr2, nc2] == 0:
gnr2 = row_offset + nr2
gnc2 = col_offset + nc2
link_id[nr2, nc2] = float(
gnr2 * total_width + gnc2 + 1)
queue_r[tail] = nr2
queue_c[tail] = nc2
tail += 1
else:
if link_id[nr2, nc2] == 0.0:
link_id[nr2, nc2] = link_id[r, c]
in_degree[nr2, nc2] -= 1
if in_degree[nr2, nc2] == 0:
queue_r[tail] = nr2
queue_c[tail] = nc2
tail += 1
return link_id
# =====================================================================
# Dask seed computation
# =====================================================================
def _compute_link_seeds_dinf(iy, ix, boundaries, flow_bdry, mask_bdry,
chunks_y, chunks_x, n_tile_y, n_tile_x):
"""Compute seed arrays for tile (iy, ix) from neighbour boundaries.
Returns (seed_id_top, seed_id_bottom, seed_id_left, seed_id_right,
seed_ext_top, seed_ext_bottom, seed_ext_left, seed_ext_right).
seed_id_* : link_id propagated from the neighbouring tile.
seed_ext_* : count of external stream cells flowing into each
boundary cell (needed to compute correct orig_indeg).
"""
nb_dy = _NB_DY
nb_dx = _NB_DX
tile_h = chunks_y[iy]
tile_w = chunks_x[ix]
seed_id_top = np.zeros(tile_w, dtype=np.float64)
seed_id_bottom = np.zeros(tile_w, dtype=np.float64)
seed_id_left = np.zeros(tile_h, dtype=np.float64)
seed_id_right = np.zeros(tile_h, dtype=np.float64)
seed_ext_top = np.zeros(tile_w, dtype=np.float64)
seed_ext_bottom = np.zeros(tile_w, dtype=np.float64)
seed_ext_left = np.zeros(tile_h, dtype=np.float64)
seed_ext_right = np.zeros(tile_h, dtype=np.float64)
# Top edge: bottom row of tile above
if iy > 0:
nb_fdir = flow_bdry.get('bottom', iy - 1, ix)
nb_mask = mask_bdry.get('bottom', iy - 1, ix)
nb_link = boundaries.get('bottom', iy - 1, ix)
w = len(nb_fdir)
for j in range(w):
if nb_mask[j] == 0:
continue
theta = nb_fdir[j]
if theta != theta or theta < 0.0:
continue
k1, f1, k2, f2 = _dinf_downstream(theta)
if k1 < 0:
continue
for kk, frac in [(k1, f1), (k2, f2)]:
if frac <= 0.0:
continue
dy = int(nb_dy[kk])
dx = int(nb_dx[kk])
if dy == 1: # flows south into our tile
tc = j + dx
if 0 <= tc < tile_w:
seed_ext_top[tc] += 1.0
lid = nb_link[j]
if lid > 0 and (seed_id_top[tc] == 0
or lid < seed_id_top[tc]):
seed_id_top[tc] = lid
# Bottom edge: top row of tile below
if iy < n_tile_y - 1:
nb_fdir = flow_bdry.get('top', iy + 1, ix)
nb_mask = mask_bdry.get('top', iy + 1, ix)
nb_link = boundaries.get('top', iy + 1, ix)
w = len(nb_fdir)
for j in range(w):
if nb_mask[j] == 0:
continue
theta = nb_fdir[j]
if theta != theta or theta < 0.0:
continue
k1, f1, k2, f2 = _dinf_downstream(theta)
if k1 < 0:
continue
for kk, frac in [(k1, f1), (k2, f2)]:
if frac <= 0.0:
continue
dy = int(nb_dy[kk])
dx = int(nb_dx[kk])
if dy == -1: # flows north into our tile
tc = j + dx
if 0 <= tc < tile_w:
seed_ext_bottom[tc] += 1.0
lid = nb_link[j]
if lid > 0 and (seed_id_bottom[tc] == 0
or lid < seed_id_bottom[tc]):
seed_id_bottom[tc] = lid
# Left edge: right column of tile to the left
if ix > 0:
nb_fdir = flow_bdry.get('right', iy, ix - 1)
nb_mask = mask_bdry.get('right', iy, ix - 1)
nb_link = boundaries.get('right', iy, ix - 1)
h = len(nb_fdir)
for i in range(h):
if nb_mask[i] == 0:
continue
theta = nb_fdir[i]
if theta != theta or theta < 0.0:
continue
k1, f1, k2, f2 = _dinf_downstream(theta)
if k1 < 0:
continue
for kk, frac in [(k1, f1), (k2, f2)]:
if frac <= 0.0:
continue
dy = int(nb_dy[kk])
dx = int(nb_dx[kk])
if dx == 1: # flows east into our tile
tr = i + dy
if 0 <= tr < tile_h:
seed_ext_left[tr] += 1.0
lid = nb_link[i]
if lid > 0 and (seed_id_left[tr] == 0
or lid < seed_id_left[tr]):
seed_id_left[tr] = lid
# Right edge: left column of tile to the right
if ix < n_tile_x - 1:
nb_fdir = flow_bdry.get('left', iy, ix + 1)
nb_mask = mask_bdry.get('left', iy, ix + 1)
nb_link = boundaries.get('left', iy, ix + 1)
h = len(nb_fdir)
for i in range(h):
if nb_mask[i] == 0:
continue
theta = nb_fdir[i]
if theta != theta or theta < 0.0:
continue
k1, f1, k2, f2 = _dinf_downstream(theta)
if k1 < 0:
continue
for kk, frac in [(k1, f1), (k2, f2)]:
if frac <= 0.0:
continue
dy = int(nb_dy[kk])
dx = int(nb_dx[kk])
if dx == -1: # flows west into our tile
tr = i + dy
if 0 <= tr < tile_h:
seed_ext_right[tr] += 1.0
lid = nb_link[i]
if lid > 0 and (seed_id_right[tr] == 0
or lid < seed_id_right[tr]):
seed_id_right[tr] = lid
# Corner seeds from diagonal neighbour tiles
def _corner_link(nb_side, nb_iy, nb_ix, nb_idx,
required_dy, required_dx,
s_id, s_ext, target):
fdir = flow_bdry.get(nb_side, nb_iy, nb_ix)[nb_idx]
mask = mask_bdry.get(nb_side, nb_iy, nb_ix)[nb_idx]
if mask == 0:
return
if fdir != fdir or fdir < 0.0:
return
k1, f1, k2, f2 = _dinf_downstream(fdir)
if k1 < 0:
return
for kk, frac in [(k1, f1), (k2, f2)]:
if frac <= 0.0:
continue
dy = int(nb_dy[kk])
dx = int(nb_dx[kk])
if dy == required_dy and dx == required_dx:
s_ext[target] += 1.0
lid = float(boundaries.get(nb_side, nb_iy, nb_ix)[nb_idx])
if lid > 0 and (s_id[target] == 0
or lid < s_id[target]):
s_id[target] = lid
# Top-left corner: bottom-right cell of (iy-1, ix-1)
if iy > 0 and ix > 0:
_corner_link('bottom', iy - 1, ix - 1, -1,
1, 1, seed_id_top, seed_ext_top, 0)
# Top-right corner: bottom-left cell of (iy-1, ix+1)
if iy > 0 and ix < n_tile_x - 1:
_corner_link('bottom', iy - 1, ix + 1, 0,
1, -1, seed_id_top, seed_ext_top, tile_w - 1)
# Bottom-left corner: top-right cell of (iy+1, ix-1)
if iy < n_tile_y - 1 and ix > 0:
_corner_link('top', iy + 1, ix - 1, -1,
-1, 1, seed_id_bottom, seed_ext_bottom, 0)
# Bottom-right corner: top-left cell of (iy+1, ix+1)
if iy < n_tile_y - 1 and ix < n_tile_x - 1:
_corner_link('top', iy + 1, ix + 1, 0,
-1, -1, seed_id_bottom, seed_ext_bottom, tile_w - 1)
return (seed_id_top, seed_id_bottom, seed_id_left, seed_id_right,
seed_ext_top, seed_ext_bottom, seed_ext_left, seed_ext_right)
# =====================================================================
# Dask iterative tile sweep
# =====================================================================
def _process_link_tile_dinf(iy, ix, flow_dir_da, accum_da, threshold,
boundaries, flow_bdry, mask_bdry,
chunks_y, chunks_x, n_tile_y, n_tile_x,
row_offsets, col_offsets, total_width):
"""Run seeded BFS on one D-inf tile; update boundary stores."""
fd_chunk = np.asarray(
flow_dir_da.blocks[iy, ix].compute(), dtype=np.float64)
ac_chunk = np.asarray(
accum_da.blocks[iy, ix].compute(), dtype=np.float64)
sm = _make_stream_mask_np_dinf(ac_chunk, fd_chunk, threshold)
h, w = fd_chunk.shape
seeds = _compute_link_seeds_dinf(
iy, ix, boundaries, flow_bdry, mask_bdry,
chunks_y, chunks_x, n_tile_y, n_tile_x)
link = _stream_link_dinf_tile_kernel(
fd_chunk, sm, h, w, *seeds,
row_offsets[iy], col_offsets[ix], total_width)
change = 0.0
for side, strip in [('top', link[0, :]),
('bottom', link[-1, :]),
('left', link[:, 0]),
('right', link[:, -1])]:
new_vals = strip.copy()
new_vals = np.where(np.isnan(new_vals), 0.0, new_vals)
old = boundaries.get(side, iy, ix).copy()
with np.errstate(invalid='ignore'):
diff = np.abs(new_vals - old)
diff = np.where(np.isnan(diff), 0.0, diff)
m = float(np.max(diff))
if m > change:
change = m
boundaries.set(side, iy, ix, new_vals)
return change
def _stream_link_dinf_dask(flow_dir_da, accum_da, threshold):
"""Iterative boundary-propagation for D-inf dask arrays."""
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)
total_width = sum(chunks_x)
row_offsets = np.zeros(n_tile_y, dtype=np.int64)
col_offsets = np.zeros(n_tile_x, dtype=np.int64)
if n_tile_y > 1:
np.cumsum(chunks_y[:-1], out=row_offsets[1:])
if n_tile_x > 1:
np.cumsum(chunks_x[:-1], out=col_offsets[1:])
flow_bdry, mask_bdry = _preprocess_stream_tiles_dinf(
flow_dir_da, accum_da, threshold, chunks_y, chunks_x)
flow_bdry = flow_bdry.snapshot()
mask_bdry = mask_bdry.snapshot()
boundaries = BoundaryStore(chunks_y, chunks_x, fill_value=0.0)
max_iterations = max(n_tile_y, n_tile_x) + 10
for _ in range(max_iterations):
max_change = 0.0
for iy in range(n_tile_y):
for ix in range(n_tile_x):
c = _process_link_tile_dinf(
iy, ix, flow_dir_da, accum_da, threshold,
boundaries, flow_bdry, mask_bdry,
chunks_y, chunks_x, n_tile_y, n_tile_x,
row_offsets, col_offsets, total_width)
if c > max_change:
max_change = c
for iy in reversed(range(n_tile_y)):
for ix in reversed(range(n_tile_x)):
c = _process_link_tile_dinf(
iy, ix, flow_dir_da, accum_da, threshold,
boundaries, flow_bdry, mask_bdry,
chunks_y, chunks_x, n_tile_y, n_tile_x,
row_offsets, col_offsets, total_width)
if c > max_change:
max_change = c
if max_change == 0.0:
break
_boundaries = boundaries.snapshot()
_flow_bdry = flow_bdry
_mask_bdry = mask_bdry
_threshold = threshold
def _tile_fn(block, accum_block, block_info=None):
if block_info is None or 0 not in block_info:
return np.full(block.shape, np.nan, dtype=np.float64)
iy, ix = block_info[0]['chunk-location']
fd = np.asarray(block, dtype=np.float64)
ac = np.asarray(accum_block, dtype=np.float64)
sm = _make_stream_mask_np_dinf(ac, fd, _threshold)
h, w = fd.shape
seeds = _compute_link_seeds_dinf(
iy, ix, _boundaries, _flow_bdry, _mask_bdry,
chunks_y, chunks_x, n_tile_y, n_tile_x)
return _stream_link_dinf_tile_kernel(
fd, sm, h, w, *seeds,
row_offsets[iy], col_offsets[ix], total_width)
return da.map_blocks(
_tile_fn, flow_dir_da, accum_da,
dtype=np.float64, meta=np.array((), dtype=np.float64))
# =====================================================================
# Dask + CuPy
# =====================================================================
def _process_link_tile_dinf_cupy(iy, ix, flow_dir_da, accum_da, threshold,
boundaries, flow_bdry, mask_bdry,
chunks_y, chunks_x, n_tile_y, n_tile_x,
row_offsets, col_offsets, total_width):
"""Run seeded GPU D-inf stream link on one tile; update boundaries."""
import cupy as cp
fd_chunk = cp.asarray(
flow_dir_da.blocks[iy, ix].compute(), dtype=cp.float64)
ac_chunk = _to_numpy_f64(accum_da.blocks[iy, ix].compute())
fd_np = fd_chunk.get()
sm_np = _make_stream_mask_np_dinf(ac_chunk, fd_np, threshold)
sm_cp = cp.asarray(sm_np)
seeds = _compute_link_seeds_dinf(
iy, ix, boundaries, flow_bdry, mask_bdry,
chunks_y, chunks_x, n_tile_y, n_tile_x)
link = _stream_link_dinf_tile_cupy(
fd_chunk, sm_cp, *seeds,
row_offsets[iy], col_offsets[ix], total_width)
change = 0.0
for side, strip_cp in [('top', link[0, :]),
('bottom', link[-1, :]),
('left', link[:, 0]),
('right', link[:, -1])]:
new_vals = strip_cp.get().copy()
new_vals = np.where(np.isnan(new_vals), 0.0, new_vals)
old = boundaries.get(side, iy, ix).copy()
with np.errstate(invalid='ignore'):
diff = np.abs(new_vals - old)
diff = np.where(np.isnan(diff), 0.0, diff)
m = float(np.max(diff))
if m > change:
change = m
boundaries.set(side, iy, ix, new_vals)
return change
def _stream_link_dinf_dask_cupy(flow_dir_da, accum_da, threshold):
"""Dask+CuPy: native GPU processing per tile for D-inf stream link."""
import cupy as cp
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)
total_width = sum(chunks_x)
row_offsets = np.zeros(n_tile_y, dtype=np.int64)
col_offsets = np.zeros(n_tile_x, dtype=np.int64)
if n_tile_y > 1:
np.cumsum(chunks_y[:-1], out=row_offsets[1:])
if n_tile_x > 1:
np.cumsum(chunks_x[:-1], out=col_offsets[1:])
flow_bdry, mask_bdry = _preprocess_stream_tiles_dinf(
flow_dir_da, accum_da, threshold, chunks_y, chunks_x)
flow_bdry = flow_bdry.snapshot()
mask_bdry = mask_bdry.snapshot()
boundaries = BoundaryStore(chunks_y, chunks_x, fill_value=0.0)
max_iterations = max(n_tile_y, n_tile_x) + 10
for _ in range(max_iterations):
max_change = 0.0
for iy in range(n_tile_y):
for ix in range(n_tile_x):
c = _process_link_tile_dinf_cupy(
iy, ix, flow_dir_da, accum_da, threshold,
boundaries, flow_bdry, mask_bdry,
chunks_y, chunks_x, n_tile_y, n_tile_x,
row_offsets, col_offsets, total_width)
if c > max_change:
max_change = c
for iy in reversed(range(n_tile_y)):
for ix in reversed(range(n_tile_x)):
c = _process_link_tile_dinf_cupy(
iy, ix, flow_dir_da, accum_da, threshold,
boundaries, flow_bdry, mask_bdry,
chunks_y, chunks_x, n_tile_y, n_tile_x,
row_offsets, col_offsets, total_width)
if c > max_change:
max_change = c
if max_change == 0.0:
break
_boundaries = boundaries.snapshot()
def _tile_fn(block, accum_block, block_info=None):
if block_info is None or 0 not in block_info:
return cp.full(block.shape, cp.nan, dtype=cp.float64)
iy, ix = block_info[0]['chunk-location']
fd = cp.asarray(block, dtype=cp.float64)
ac_np = _to_numpy_f64(accum_block)
fd_np = fd.get()
sm = cp.asarray(_make_stream_mask_np_dinf(ac_np, fd_np, threshold))
seeds = _compute_link_seeds_dinf(
iy, ix, _boundaries, flow_bdry, mask_bdry,
chunks_y, chunks_x, n_tile_y, n_tile_x)
return _stream_link_dinf_tile_cupy(
fd, sm, *seeds,
row_offsets[iy], col_offsets[ix], total_width)
return da.map_blocks(
_tile_fn, flow_dir_da, accum_da,
dtype=np.float64, meta=cp.array((), dtype=cp.float64))
# =====================================================================
# Public API
# =====================================================================
[docs]
@supports_dataset
def stream_link_dinf(flow_dir_dinf: xr.DataArray,
flow_accum: xr.DataArray,
threshold: float = 100,
name: str = 'stream_link_dinf') -> xr.DataArray:
"""Assign unique IDs to stream link segments (D-infinity).
Each contiguous stream segment between junctions, headwaters, and
outlets receives a distinct positive integer ID. Non-stream cells
are NaN.
Parameters
----------
flow_dir_dinf : xarray.DataArray or xr.Dataset
2D D-infinity flow direction grid. Values are continuous
angles in radians in the range ``[0, 2*pi)``. ``-1.0``
indicates a pit or flat. ``NaN`` indicates nodata.
flow_accum : xarray.DataArray
2D flow accumulation grid. Cells with
``flow_accum >= threshold`` are stream cells.
threshold : float, default 100
Minimum accumulation for stream classification.
name : str, default 'stream_link_dinf'
Name of output DataArray.
Returns
-------
xarray.DataArray or xr.Dataset
2D float64 grid where each stream link has a unique positive
integer ID. Non-stream cells are NaN.
References
----------
Tarboton, D.G. (1997). A new method for the determination of flow
directions and upslope areas in grid digital elevation models.
Water Resources Research, 33(2), 309-319.
"""
_validate_raster(flow_dir_dinf,
func_name='stream_link_dinf',
name='flow_dir_dinf')
_validate_raster(flow_accum,
func_name='stream_link_dinf',
name='flow_accum')
fd_data = flow_dir_dinf.data
fa_data = flow_accum.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)
stream_mask = np.where(fa >= threshold, 1, 0).astype(np.int8)
stream_mask = np.where(np.isnan(fa), 0, stream_mask).astype(np.int8)
stream_mask = np.where(np.isnan(fd), 0, stream_mask).astype(np.int8)
h, w = fd.shape
out = _stream_link_dinf_cpu(fd, stream_mask, h, w)
elif has_cuda_and_cupy() and is_cupy_array(fd_data):
_check_gpu_memory(*fd_data.shape)
import cupy as cp
fa_cp = cp.asarray(fa_data, dtype=cp.float64)
fd_cp = fd_data.astype(cp.float64)
stream_mask = cp.where(fa_cp >= threshold, 1, 0).astype(cp.int8)
stream_mask = cp.where(
cp.isnan(fa_cp), 0, stream_mask).astype(cp.int8)
stream_mask = cp.where(
cp.isnan(fd_cp), 0, stream_mask).astype(cp.int8)
out = _stream_link_dinf_cupy(fd_cp, stream_mask)
elif has_cuda_and_cupy() and is_dask_cupy(flow_dir_dinf):
out = _stream_link_dinf_dask_cupy(fd_data, fa_data, threshold)
elif da is not None and isinstance(fd_data, da.Array):
out = _stream_link_dinf_dask(fd_data, fa_data, 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)
