"""Stream link segmentation for MFD (Multiple 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 MFD 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 MFD allows a cell to flow to multiple downstream neighbors,
a non-junction downstream cell may receive link_id from more than one
upstream cell. In that case it uses the first one that arrives in BFS
order.
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 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_matching_shape,
_validate_mfd_fractions,
_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
# =====================================================================
# Memory guards
# =====================================================================
#
# CPU peak working set per pixel for ``_stream_link_mfd_cpu``:
# frac input copy : (8,H,W) float64 -> 64
# stream_mask : int8 -> 1
# link_id : float64 -> 8
# in_degree : int32 -> 4
# orig_indeg : int32 -> 4
# queue_r : int64 -> 8
# queue_c : int64 -> 8
# Total ~97 bytes/pixel. The caller-provided ``flow_accum`` array
# already lives in RAM before the kernel runs and is not double-counted.
_BYTES_PER_PIXEL = 97
# GPU peak working set per pixel for ``_stream_link_mfd_cupy``:
# fractions_f64 : (8,H,W) float64 -> 64
# stream_mask_i8 : int8 -> 1
# in_degree : int32 -> 4
# orig_indeg : int32 -> 4
# state : int32 -> 4
# link_id : float64 -> 8
# fa_cp : float64 -> 8
# Total ~93 B/px. Use 100 B/px as a conservative budget.
_GPU_BYTES_PER_PIXEL = 100
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_mfd 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_mfd 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_mfd_cpu(fractions, stream_mask, height, width):
"""Kahn's BFS link ID assignment among stream cells (MFD).
Parameters
----------
fractions : (8, H, W) float64 -- MFD flow fractions
stream_mask : (H, W) int8
height, width : int
Returns
-------
link_id : (H, W) float64
"""
dy = np.array([0, 1, 1, 1, 0, -1, -1, -1], dtype=np.int64)
dx = np.array([1, 1, 0, -1, -1, -1, 0, 1], dtype=np.int64)
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 (only among stream cells)
for r in range(height):
for c in range(width):
if stream_mask[r, c] == 0:
continue
for k in range(8):
if fractions[k, r, c] > 0.0:
nr = r + dy[k]
nc = c + dx[k]
if 0 <= nr < height and 0 <= nc < width:
if stream_mask[nr, nc] == 1:
in_degree[nr, nc] += 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
for k in range(8):
frac = fractions[k, r, c]
if frac <= 0.0:
continue
nr = r + dy[k]
nc = c + dx[k]
if not (0 <= nr < height and 0 <= nc < width
and stream_mask[nr, nc] == 1):
continue
if orig_indeg[nr, nc] >= 2:
# Junction: decrement in-degree but don't propagate link_id.
# Junction gets its own position-based ID when ready.
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:
# Non-junction: inherit upstream link_id (first arrival wins)
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
return link_id
# =====================================================================
# GPU kernels
# =====================================================================
@cuda.jit
def _stream_link_mfd_init_gpu(fractions, stream_mask, in_degree, state,
link_id, H, W):
"""Initialise GPU arrays for MFD stream link computation."""
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
# Compute in-degree from fractions
for k in range(8):
frac = fractions[k, i, j]
if frac <= 0.0:
continue
if k == 0:
dy, dx = 0, 1
elif k == 1:
dy, dx = 1, 1
elif k == 2:
dy, dx = 1, 0
elif k == 3:
dy, dx = 1, -1
elif k == 4:
dy, dx = 0, -1
elif k == 5:
dy, dx = -1, -1
elif k == 6:
dy, dx = -1, 0
else:
dy, dx = -1, 1
ni = i + dy
nj = j + dx
if 0 <= ni < H and 0 <= nj < W and stream_mask[ni, nj] == 1:
cuda.atomic.add(in_degree, (ni, nj), 1)
@cuda.jit
def _stream_link_mfd_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_mfd_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
# Link-start cells get position-based ID
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_mfd_pull(fractions, stream_mask, in_degree, orig_indeg,
state, link_id, H, W):
"""Active cells pull link_id from frontier neighbours (MFD)."""
i, j = cuda.grid(2)
if i >= H or j >= W:
return
if state[i, j] != 1:
return
for nbr in range(8):
if nbr == 0:
dy, dx = 0, 1
elif nbr == 1:
dy, dx = 1, 1
elif nbr == 2:
dy, dx = 1, 0
elif nbr == 3:
dy, dx = 1, -1
elif nbr == 4:
dy, dx = 0, -1
elif nbr == 5:
dy, dx = -1, -1
elif nbr == 6:
dy, dx = -1, 0
else:
dy, dx = -1, 1
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
# Check if the neighbor flows to us via the opposite direction
if nbr == 0:
opp = 4
elif nbr == 1:
opp = 5
elif nbr == 2:
opp = 6
elif nbr == 3:
opp = 7
elif nbr == 4:
opp = 0
elif nbr == 5:
opp = 1
elif nbr == 6:
opp = 2
else:
opp = 3
frac = fractions[opp, ni, nj]
if frac > 0.0:
in_degree[i, j] -= 1
# Non-junction cells inherit the upstream link_id
if orig_indeg[i, j] < 2 and link_id[i, j] == 0.0:
link_id[i, j] = link_id[ni, nj]
def _stream_link_mfd_cupy(fractions_data, stream_mask_data):
"""GPU driver for MFD stream link computation."""
import cupy as cp
_, H, W = fractions_data.shape
fractions_f64 = fractions_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_mfd_init_gpu[griddim, blockdim](
fractions_f64, stream_mask_i8, in_degree, state, link_id, H, W)
# Save orig_indeg after atomic adds complete
_stream_link_mfd_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_mfd_find_ready[griddim, blockdim](
in_degree, orig_indeg, state, link_id, changed, H, W)
if int(changed[0]) == 0:
break
_stream_link_mfd_pull[griddim, blockdim](
fractions_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_mfd_tile_kernel(fractions, 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 MFD tile.
Parameters
----------
fractions : (8, h, w) float64 -- MFD flow fractions 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.
"""
dy = np.array([0, 1, 1, 1, 0, -1, -1, -1], dtype=np.int64)
dx = np.array([1, 1, 0, -1, -1, -1, 0, 1], dtype=np.int64)
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 among stream cells
for r in range(h):
for c in range(w):
if stream_mask[r, c] == 0:
continue
for k in range(8):
if fractions[k, r, c] > 0.0:
nr = r + dy[k]
nc = c + dx[k]
if 0 <= nr < h and 0 <= nc < w:
if stream_mask[nr, nc] == 1:
in_degree[nr, nc] += 1
# orig_indeg = within-tile + external (for junction detection only).
# Do NOT add external counts to in_degree -- external inflows are
# already "delivered" via seed_id values.
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
# Headwater (orig_indeg == 0) or junction (orig_indeg >= 2):
# get position-based global ID
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 that receive
# a link_id from their external upstream neighbor.
# These cells have in_degree > 0 so they are NOT yet in the queue.
# We apply the seed_id so that when their in_degree drops to 0
# during BFS, they already have the right inherited link_id.
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
for k in range(8):
frac = fractions[k, r, c]
if frac <= 0.0:
continue
nr = r + dy[k]
nc = c + dx[k]
if not (0 <= nr < h and 0 <= nc < w
and stream_mask[nr, nc] == 1):
continue
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
return link_id
# =====================================================================
# Dask preprocessing: extract boundary fraction strips and stream masks
# =====================================================================
def _preprocess_mfd_stream_tiles(fractions_da, accum_da, threshold,
chunks_y, chunks_x):
"""Extract boundary fraction strips (8-band) and stream masks.
Returns
-------
frac_bdry : dict mapping (side, iy, ix) -> (8, length) float64 array
mask_bdry : BoundaryStore with stream mask boundary strips
"""
n_tile_y = len(chunks_y)
n_tile_x = len(chunks_x)
frac_bdry = {}
mask_bdry = BoundaryStore(chunks_y, chunks_x, fill_value=0.0)
for iy in range(n_tile_y):
for ix in range(n_tile_x):
y_start = sum(chunks_y[:iy])
y_end = y_start + chunks_y[iy]
x_start = sum(chunks_x[:ix])
x_end = x_start + chunks_x[ix]
chunk = np.asarray(
fractions_da[:, y_start:y_end, x_start:x_end].compute(),
dtype=np.float64)
ac_chunk = _to_numpy_f64(
accum_da[y_start:y_end, x_start:x_end].compute())
sm = np.where(ac_chunk >= threshold, 1.0, 0.0)
sm = np.where(np.isnan(ac_chunk), 0.0, sm)
# Also mark as non-stream if fractions are NaN
sm = np.where(np.isnan(chunk[0]), 0.0, sm)
# Store fraction strips: (8, length)
frac_bdry[('top', iy, ix)] = chunk[:, 0, :].copy()
frac_bdry[('bottom', iy, ix)] = chunk[:, -1, :].copy()
frac_bdry[('left', iy, ix)] = chunk[:, :, 0].copy()
frac_bdry[('right', iy, ix)] = chunk[:, :, -1].copy()
# Store stream mask strips
for side, strip in [('top', sm[0, :]),
('bottom', sm[-1, :]),
('left', sm[:, 0]),
('right', sm[:, -1])]:
mask_bdry.set(side, iy, ix,
np.asarray(strip, dtype=np.float64))
return frac_bdry, mask_bdry
# =====================================================================
# Dask seed computation
# =====================================================================
def _compute_link_seeds_mfd(iy, ix, boundaries, frac_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).
"""
# Neighbor offsets: E(0), SE(1), S(2), SW(3), W(4), NW(5), N(6), NE(7)
dy_arr = np.array([0, 1, 1, 1, 0, -1, -1, -1], dtype=np.int64)
dx_arr = np.array([1, 1, 0, -1, -1, -1, 0, 1], dtype=np.int64)
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_frac = frac_bdry[('bottom', iy - 1, ix)] # (8, tile_w)
nb_mask = mask_bdry.get('bottom', iy - 1, ix)
nb_link = boundaries.get('bottom', iy - 1, ix)
w = nb_frac.shape[1]
for c in range(w):
if nb_mask[c] == 0:
continue
for k in range(8):
if not (nb_frac[k, c] > 0.0):
continue
ndy = dy_arr[k]
ndx = dx_arr[k]
if ndy == 1: # flows south into our tile
tc = c + ndx
if 0 <= tc < tile_w:
seed_ext_top[tc] += 1.0
lid = nb_link[c]
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_frac = frac_bdry[('top', iy + 1, ix)] # (8, tile_w)
nb_mask = mask_bdry.get('top', iy + 1, ix)
nb_link = boundaries.get('top', iy + 1, ix)
w = nb_frac.shape[1]
for c in range(w):
if nb_mask[c] == 0:
continue
for k in range(8):
if not (nb_frac[k, c] > 0.0):
continue
ndy = dy_arr[k]
ndx = dx_arr[k]
if ndy == -1: # flows north into our tile
tc = c + ndx
if 0 <= tc < tile_w:
seed_ext_bottom[tc] += 1.0
lid = nb_link[c]
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_frac = frac_bdry[('right', iy, ix - 1)] # (8, tile_h)
nb_mask = mask_bdry.get('right', iy, ix - 1)
nb_link = boundaries.get('right', iy, ix - 1)
h = nb_frac.shape[1]
for r in range(h):
if nb_mask[r] == 0:
continue
for k in range(8):
if not (nb_frac[k, r] > 0.0):
continue
ndy = dy_arr[k]
ndx = dx_arr[k]
if ndx == 1: # flows east into our tile
tr = r + ndy
if 0 <= tr < tile_h:
seed_ext_left[tr] += 1.0
lid = nb_link[r]
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_frac = frac_bdry[('left', iy, ix + 1)] # (8, tile_h)
nb_mask = mask_bdry.get('left', iy, ix + 1)
nb_link = boundaries.get('left', iy, ix + 1)
h = nb_frac.shape[1]
for r in range(h):
if nb_mask[r] == 0:
continue
for k in range(8):
if not (nb_frac[k, r] > 0.0):
continue
ndy = dy_arr[k]
ndx = dx_arr[k]
if ndx == -1: # flows west into our tile
tr = r + ndy
if 0 <= tr < tile_h:
seed_ext_right[tr] += 1.0
lid = nb_link[r]
if lid > 0 and (seed_id_right[tr] == 0
or lid < seed_id_right[tr]):
seed_id_right[tr] = lid
# --- Diagonal corner seeds ---
# TL corner: bottom-right cell of (iy-1, ix-1) flows SE (k=1)
if iy > 0 and ix > 0:
nb_frac = frac_bdry[('bottom', iy - 1, ix - 1)] # (8, w)
nb_mask = mask_bdry.get('bottom', iy - 1, ix - 1)
nb_link = boundaries.get('bottom', iy - 1, ix - 1)
if nb_mask[-1] == 1:
frac_se = nb_frac[1, -1] # SE direction
if frac_se > 0.0:
seed_ext_top[0] += 1.0
lid = float(nb_link[-1])
if lid > 0 and (seed_id_top[0] == 0
or lid < seed_id_top[0]):
seed_id_top[0] = lid
# TR corner: bottom-left cell of (iy-1, ix+1) flows SW (k=3)
if iy > 0 and ix < n_tile_x - 1:
nb_frac = frac_bdry[('bottom', iy - 1, ix + 1)] # (8, w)
nb_mask = mask_bdry.get('bottom', iy - 1, ix + 1)
nb_link = boundaries.get('bottom', iy - 1, ix + 1)
if nb_mask[0] == 1:
frac_sw = nb_frac[3, 0] # SW direction
if frac_sw > 0.0:
seed_ext_top[tile_w - 1] += 1.0
lid = float(nb_link[0])
if lid > 0 and (seed_id_top[tile_w - 1] == 0
or lid < seed_id_top[tile_w - 1]):
seed_id_top[tile_w - 1] = lid
# BL corner: top-right cell of (iy+1, ix-1) flows NE (k=7)
if iy < n_tile_y - 1 and ix > 0:
nb_frac = frac_bdry[('top', iy + 1, ix - 1)] # (8, w)
nb_mask = mask_bdry.get('top', iy + 1, ix - 1)
nb_link = boundaries.get('top', iy + 1, ix - 1)
if nb_mask[-1] == 1:
frac_ne = nb_frac[7, -1] # NE direction
if frac_ne > 0.0:
seed_ext_bottom[0] += 1.0
lid = float(nb_link[-1])
if lid > 0 and (seed_id_bottom[0] == 0
or lid < seed_id_bottom[0]):
seed_id_bottom[0] = lid
# BR corner: top-left cell of (iy+1, ix+1) flows NW (k=5)
if iy < n_tile_y - 1 and ix < n_tile_x - 1:
nb_frac = frac_bdry[('top', iy + 1, ix + 1)] # (8, w)
nb_mask = mask_bdry.get('top', iy + 1, ix + 1)
nb_link = boundaries.get('top', iy + 1, ix + 1)
if nb_mask[0] == 1:
frac_nw = nb_frac[5, 0] # NW direction
if frac_nw > 0.0:
seed_ext_bottom[tile_w - 1] += 1.0
lid = float(nb_link[0])
if lid > 0 and (seed_id_bottom[tile_w - 1] == 0
or lid < seed_id_bottom[tile_w - 1]):
seed_id_bottom[tile_w - 1] = lid
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_mfd(iy, ix, fractions_da, accum_da, threshold,
boundaries, frac_bdry, mask_bdry,
chunks_y, chunks_x, n_tile_y, n_tile_x,
row_offsets, col_offsets, total_width):
"""Run seeded BFS on one MFD tile; update boundary stores."""
y_start = sum(chunks_y[:iy])
y_end = y_start + chunks_y[iy]
x_start = sum(chunks_x[:ix])
x_end = x_start + chunks_x[ix]
frac_chunk = np.asarray(
fractions_da[:, y_start:y_end, x_start:x_end].compute(),
dtype=np.float64)
ac_chunk = _to_numpy_f64(
accum_da[y_start:y_end, x_start:x_end].compute())
sm = np.where(ac_chunk >= threshold, 1, 0).astype(np.int8)
sm = np.where(np.isnan(ac_chunk), 0, sm).astype(np.int8)
sm = np.where(np.isnan(frac_chunk[0]), 0, sm).astype(np.int8)
_, h, w = frac_chunk.shape
seeds = _compute_link_seeds_mfd(
iy, ix, boundaries, frac_bdry, mask_bdry,
chunks_y, chunks_x, n_tile_y, n_tile_x)
link = _stream_link_mfd_tile_kernel(
frac_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_mfd_dask(fractions_da, accum_da, threshold):
"""Iterative boundary-propagation for MFD dask arrays."""
chunks_y = fractions_da.chunks[1]
chunks_x = fractions_da.chunks[2]
n_tile_y = len(chunks_y)
n_tile_x = len(chunks_x)
total_width = sum(chunks_x)
# Precompute global pixel offsets for each tile
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:])
# Phase 0: extract boundary fraction strips and stream masks
frac_bdry, mask_bdry = _preprocess_mfd_stream_tiles(
fractions_da, accum_da, threshold, chunks_y, chunks_x)
mask_bdry = mask_bdry.snapshot()
# Phase 1: initialise boundary link_ids
boundaries = BoundaryStore(chunks_y, chunks_x, fill_value=0.0)
# Phase 2: iterative forward/backward sweeps
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_mfd(
iy, ix, fractions_da, accum_da, threshold,
boundaries, frac_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_mfd(
iy, ix, fractions_da, accum_da, threshold,
boundaries, frac_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
# Snapshot converged boundaries before assembly
_boundaries = boundaries.snapshot()
_frac_bdry = frac_bdry
_mask_bdry = mask_bdry
_threshold = threshold
# Assemble result via da.block
rows = []
for iy in range(n_tile_y):
row = []
for ix in range(n_tile_x):
y_start = sum(chunks_y[:iy])
y_end = y_start + chunks_y[iy]
x_start = sum(chunks_x[:ix])
x_end = x_start + chunks_x[ix]
frac_chunk = np.asarray(
fractions_da[:, y_start:y_end, x_start:x_end].compute(),
dtype=np.float64)
ac_chunk = _to_numpy_f64(
accum_da[y_start:y_end, x_start:x_end].compute())
sm = np.where(ac_chunk >= _threshold, 1, 0).astype(np.int8)
sm = np.where(np.isnan(ac_chunk), 0, sm).astype(np.int8)
sm = np.where(np.isnan(frac_chunk[0]), 0, sm).astype(np.int8)
_, h, w = frac_chunk.shape
seeds = _compute_link_seeds_mfd(
iy, ix, _boundaries, _frac_bdry, _mask_bdry,
chunks_y, chunks_x, n_tile_y, n_tile_x)
tile_link = _stream_link_mfd_tile_kernel(
frac_chunk, sm, h, w, *seeds,
row_offsets[iy], col_offsets[ix], total_width)
row.append(da.from_array(tile_link, chunks=tile_link.shape))
rows.append(row)
return da.block(rows)
def _stream_link_mfd_dask_cupy(fractions_da, accum_da, threshold):
"""Dask+CuPy MFD: convert to numpy, run CPU dask path, convert back."""
import cupy as cp
fractions_np = fractions_da.map_blocks(
lambda b: b.get(), dtype=fractions_da.dtype,
meta=np.array((), dtype=fractions_da.dtype),
)
accum_np = accum_da.map_blocks(
lambda b: b.get(), dtype=accum_da.dtype,
meta=np.array((), dtype=accum_da.dtype),
)
result = _stream_link_mfd_dask(fractions_np, accum_np, threshold)
return result.map_blocks(
cp.asarray, dtype=result.dtype,
meta=cp.array((), dtype=result.dtype),
)
# =====================================================================
# Public API
# =====================================================================
[docs]
@supports_dataset
def stream_link_mfd(fractions: xr.DataArray,
flow_accum: xr.DataArray,
threshold: float = 100,
name: str = 'stream_link_mfd') -> xr.DataArray:
"""Assign unique IDs to stream link segments (MFD).
Each contiguous stream segment between junctions, headwaters, and
outlets receives a distinct positive integer ID. Non-stream cells
are NaN.
Parameters
----------
fractions : xarray.DataArray or xr.Dataset
3-D MFD flow direction array of shape ``(8, H, W)`` as returned
by ``flow_direction_mfd``. Values are flow fractions in
``[0, 1]`` that sum to 1.0 at each cell (NaN at nodata cells).
The neighbor order is:
E(0), SE(1), S(2), SW(3), W(4), NW(5), N(6), NE(7).
flow_accum : xarray.DataArray
2-D 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_mfd'
Name of output DataArray.
Returns
-------
xarray.DataArray or xr.Dataset
2-D float64 grid where each stream link has a unique positive
integer ID. Non-stream cells are NaN.
"""
_validate_raster(fractions, func_name='stream_link_mfd',
name='fractions', ndim=3)
_validate_raster(flow_accum, func_name='stream_link_mfd',
name='flow_accum')
data = fractions.data
if data.ndim != 3 or data.shape[0] != 8:
raise ValueError(
"fractions must be a 3-D array of shape (8, H, W), "
f"got shape {data.shape}"
)
_validate_matching_shape(
flow_accum, data.shape[1:], func_name='stream_link_mfd',
name='flow_accum', expected_name='fractions')
_validate_mfd_fractions(data, func_name='stream_link_mfd',
name='fractions')
fa_data = flow_accum.data
if isinstance(data, np.ndarray):
_check_memory(data.shape[1], data.shape[2])
frac = 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(frac[0]), 0, stream_mask).astype(np.int8)
h, w = frac.shape[1], frac.shape[2]
out = _stream_link_mfd_cpu(frac, stream_mask, h, w)
elif has_cuda_and_cupy() and is_cupy_array(data):
_check_gpu_memory(data.shape[1], data.shape[2])
import cupy as cp
fa_cp = cp.asarray(fa_data, dtype=cp.float64)
frac_cp = 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(frac_cp[0]), 0, stream_mask).astype(cp.int8)
out = _stream_link_mfd_cupy(frac_cp, stream_mask)
elif has_cuda_and_cupy() and is_dask_cupy(fractions):
out = _stream_link_mfd_dask_cupy(data, fa_data, threshold)
elif da is not None and isinstance(data, da.Array):
out = _stream_link_mfd_dask(data, fa_data, threshold)
else:
raise TypeError(f"Unsupported array type: {type(data)}")
# Build 2-D output coords (drop 'neighbor' dim)
spatial_dims = fractions.dims[1:]
coords = {k: v for k, v in fractions.coords.items()
if k != 'neighbor' and k not in fractions.dims[:1]}
for d in spatial_dims:
if d in fractions.coords:
coords[d] = fractions.coords[d]
return xr.DataArray(out,
name=name,
coords=coords,
dims=spatial_dims,
attrs=fractions.attrs)
