"""Snap pour points to the highest-accumulation cell within a search radius.
Users typically place pour points manually, but these often land a cell or
two off from the actual drainage channel. This module moves each pour point
to the highest flow-accumulation cell within a circular search radius so
that subsequent ``watershed()`` calls delineate correctly.
Algorithm
---------
For each non-NaN cell in ``pour_points``:
1. Search all cells within ``search_radius`` pixels (Euclidean distance).
2. Among valid (non-NaN) ``flow_accum`` cells in that radius, find the one
with maximum accumulation.
3. Move the pour point label to that cell.
If multiple pour points snap to the same cell, the last one in raster-scan
order wins (deterministic across all backends).
"""
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_raster,
cuda_args,
has_cuda_and_cupy,
is_cupy_array,
is_dask_cupy,
ngjit,
)
from xrspatial.dataset_support import supports_dataset
def _to_numpy_f64(arr):
"""Convert *arr* to a contiguous numpy float64 array (handles CuPy)."""
if hasattr(arr, 'get'):
arr = arr.get()
return np.asarray(arr, dtype=np.float64)
# =====================================================================
# CPU kernel
# =====================================================================
@ngjit
def _snap_pour_point_cpu(flow_accum, pour_points, search_radius, H, W):
"""Snap each pour point to the max-accumulation cell within *radius*."""
out = np.empty((H, W), dtype=np.float64)
out[:] = np.nan
radius_sq = search_radius * search_radius
for r in range(H):
for c in range(W):
v = pour_points[r, c]
if v != v: # NaN
continue
label = v
best_r = r
best_c = c
fa_val = flow_accum[r, c]
if fa_val == fa_val: # not NaN
best_accum = fa_val
else:
best_accum = -1e308 # ~-inf
r_lo = r - search_radius
r_hi = r + search_radius
c_lo = c - search_radius
c_hi = c + search_radius
if r_lo < 0:
r_lo = 0
if r_hi >= H:
r_hi = H - 1
if c_lo < 0:
c_lo = 0
if c_hi >= W:
c_hi = W - 1
for nr in range(r_lo, r_hi + 1):
for nc in range(c_lo, c_hi + 1):
dr = nr - r
dc = nc - c
if dr * dr + dc * dc > radius_sq:
continue
fa_n = flow_accum[nr, nc]
if fa_n != fa_n: # NaN
continue
if fa_n > best_accum:
best_accum = fa_n
best_r = nr
best_c = nc
out[best_r, best_c] = label
return out
# =====================================================================
# CuPy backend
# =====================================================================
@cuda.jit
def _snap_pour_point_gpu(flow_accum, pp_rows, pp_cols,
snap_rows, snap_cols, search_radius,
n_pp, H, W):
"""Each thread handles one pour point: windowed max search on GPU."""
k = cuda.grid(1)
if k >= n_pp:
return
r = pp_rows[k]
c = pp_cols[k]
best_r = r
best_c = c
fa_val = flow_accum[r, c]
if fa_val == fa_val: # not NaN
best_accum = fa_val
else:
best_accum = -1e308
radius_sq = search_radius * search_radius
r_lo = r - search_radius
if r_lo < 0:
r_lo = 0
r_hi = r + search_radius
if r_hi >= H:
r_hi = H - 1
c_lo = c - search_radius
if c_lo < 0:
c_lo = 0
c_hi = c + search_radius
if c_hi >= W:
c_hi = W - 1
for nr in range(r_lo, r_hi + 1):
for nc in range(c_lo, c_hi + 1):
dr = nr - r
dc = nc - c
if dr * dr + dc * dc > radius_sq:
continue
fa_n = flow_accum[nr, nc]
if fa_n != fa_n: # NaN
continue
if fa_n > best_accum:
best_accum = fa_n
best_r = nr
best_c = nc
snap_rows[k] = best_r
snap_cols[k] = best_c
def _snap_pour_point_cupy(flow_accum_data, pour_points_data, search_radius):
"""Native CuPy: CUDA kernel for windowed max search per pour point.
Flow accumulation data stays on GPU. Only pour point coordinates
(sparse, typically < 100) are transferred CPU/GPU.
"""
import cupy as cp
H, W = flow_accum_data.shape
fa = flow_accum_data.astype(cp.float64)
pp = pour_points_data.astype(cp.float64)
out = cp.full((H, W), cp.nan, dtype=cp.float64)
mask = ~cp.isnan(pp)
if not cp.any(mask):
return out
rows, cols = cp.where(mask)
labels = pp[rows, cols]
n_pp = len(rows)
snap_rows = cp.empty(n_pp, dtype=cp.int64)
snap_cols = cp.empty(n_pp, dtype=cp.int64)
threads = min(256, n_pp)
blocks = (n_pp + threads - 1) // threads
_snap_pour_point_gpu[blocks, threads](
fa, rows, cols, snap_rows, snap_cols,
search_radius, n_pp, H, W)
# Write labels in raster-scan order (last wins if overlap)
# Sort by raster-scan position to ensure deterministic ordering
raster_pos = rows * W + cols
sort_idx = cp.argsort(raster_pos)
for k in sort_idx.get():
out[int(snap_rows[k]), int(snap_cols[k])] = float(labels[k])
return out
# =====================================================================
# Dask backend
# =====================================================================
def _snap_pour_point_dask(flow_accum_data, pour_points_data, search_radius):
"""Dask: extract sparse pour points chunk-by-chunk, windowed search, lazy assembly.
Pour points are sparse (typically < 100 in a multi-million-cell raster).
We never materialize the full pour_points grid: a ``map_blocks`` pass
reduces each chunk to a 1-byte flag, then only the (few) flagged chunks
are loaded to extract coordinates. Small windows of ``flow_accum`` are
sliced for each pour point, and the output is assembled lazily.
"""
H, W = flow_accum_data.shape
chunks_y = pour_points_data.chunks[0]
chunks_x = pour_points_data.chunks[1]
# --- Phase 1: identify which chunks contain pour points --------
# Single dask pass; each chunk is reduced to a scalar flag.
# The scheduler parallelizes reads and releases each chunk after
# the reduction, so peak memory is bounded by thread count × chunk size.
def _has_pp(block):
return np.array(
[[np.any(~np.isnan(np.asarray(block))).item()]],
dtype=np.int8,
)
flags = da.map_blocks(
_has_pp, pour_points_data,
dtype=np.int8,
chunks=tuple((1,) * len(c) for c in pour_points_data.chunks),
).compute() # tiny array: one byte per chunk
# --- Phase 2: load only flagged chunks, extract coordinates ----
points = [] # list of (global_row, global_col, label)
row_off = 0
for iy, cy in enumerate(chunks_y):
col_off = 0
for ix, cx in enumerate(chunks_x):
if flags[iy, ix]:
chunk = np.asarray(
pour_points_data.blocks[iy, ix].compute(),
dtype=np.float64,
)
rs, cs = np.where(~np.isnan(chunk))
for k in range(len(rs)):
points.append((
row_off + int(rs[k]),
col_off + int(cs[k]),
float(chunk[rs[k], cs[k]]),
))
col_off += cx
row_off += cy
# --- Phase 3: snap each pour point via windowed search ---------
snapped = [] # list of (snap_r, snap_c, label)
radius_sq = search_radius * search_radius
for r, c, label in points:
r_lo = max(0, r - search_radius)
r_hi = min(H - 1, r + search_radius)
c_lo = max(0, c - search_radius)
c_hi = min(W - 1, c + search_radius)
# Small window; dask handles cross-chunk slicing
window = np.asarray(
flow_accum_data[r_lo:r_hi + 1, c_lo:c_hi + 1].compute(),
dtype=np.float64,
)
best_r, best_c = r, c
fa_val = window[r - r_lo, c - c_lo]
best_accum = fa_val if not np.isnan(fa_val) else -np.inf
for wr in range(window.shape[0]):
for wc in range(window.shape[1]):
nr = r_lo + wr
nc = c_lo + wc
dr = nr - r
dc = nc - c
if dr * dr + dc * dc > radius_sq:
continue
fa_n = window[wr, wc]
if np.isnan(fa_n):
continue
if fa_n > best_accum:
best_accum = fa_n
best_r = nr
best_c = nc
snapped.append((best_r, best_c, label))
# --- Phase 4: lazy output assembly via map_blocks --------------
snap_rows = np.array([s[0] for s in snapped], dtype=np.int64) if snapped else np.array([], dtype=np.int64)
snap_cols = np.array([s[1] for s in snapped], dtype=np.int64) if snapped else np.array([], dtype=np.int64)
snap_labels = np.array([s[2] for s in snapped], dtype=np.float64) if snapped else np.array([], dtype=np.float64)
_snap_rows = snap_rows
_snap_cols = snap_cols
_snap_labels = snap_labels
def _assemble_block(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)
row_start, row_end = block_info[0]['array-location'][0]
col_start, col_end = block_info[0]['array-location'][1]
h, w = block.shape
out = np.full((h, w), np.nan, dtype=np.float64)
for k in range(len(_snap_rows)):
sr = _snap_rows[k]
sc = _snap_cols[k]
if row_start <= sr < row_end and col_start <= sc < col_end:
out[sr - row_start, sc - col_start] = _snap_labels[k]
return out
dummy = da.zeros((H, W), chunks=flow_accum_data.chunks, dtype=np.float64)
return da.map_blocks(
_assemble_block, dummy,
dtype=np.float64,
meta=np.array((), dtype=np.float64),
)
# =====================================================================
# Dask+CuPy backend
# =====================================================================
def _snap_pour_point_dask_cupy(flow_accum_data, pour_points_data, search_radius):
"""Dask+CuPy: sparse pour-point processing with GPU-resident flow_accum.
Extracts sparse pour point coordinates from CuPy chunks (small
transfers), performs windowed search via dask slicing, and assembles
output as dask+cupy.
"""
import cupy as cp
H, W = flow_accum_data.shape
chunks_y = pour_points_data.chunks[0]
chunks_x = pour_points_data.chunks[1]
# Phase 1: identify chunks with pour points
def _has_pp(block):
b = block.get() if hasattr(block, 'get') else np.asarray(block)
return np.array([[np.any(~np.isnan(b)).item()]], dtype=np.int8)
flags = da.map_blocks(
_has_pp, pour_points_data,
dtype=np.int8,
chunks=tuple((1,) * len(c) for c in pour_points_data.chunks),
).compute()
# Phase 2: extract coordinates from flagged chunks
points = []
row_off = 0
for iy, cy in enumerate(chunks_y):
col_off = 0
for ix, cx in enumerate(chunks_x):
if flags[iy, ix]:
chunk = _to_numpy_f64(
pour_points_data.blocks[iy, ix].compute())
rs, cs = np.where(~np.isnan(chunk))
for k in range(len(rs)):
points.append((
row_off + int(rs[k]),
col_off + int(cs[k]),
float(chunk[rs[k], cs[k]]),
))
col_off += cx
row_off += cy
# Phase 3: windowed search
snapped = []
radius_sq = search_radius * search_radius
for r, c, label in points:
r_lo = max(0, r - search_radius)
r_hi = min(H - 1, r + search_radius)
c_lo = max(0, c - search_radius)
c_hi = min(W - 1, c + search_radius)
window = _to_numpy_f64(
flow_accum_data[r_lo:r_hi + 1, c_lo:c_hi + 1].compute())
best_r, best_c = r, c
fa_val = window[r - r_lo, c - c_lo]
best_accum = fa_val if not np.isnan(fa_val) else -np.inf
for wr in range(window.shape[0]):
for wc in range(window.shape[1]):
nr = r_lo + wr
nc = c_lo + wc
dr = nr - r
dc = nc - c
if dr * dr + dc * dc > radius_sq:
continue
fa_n = window[wr, wc]
if np.isnan(fa_n):
continue
if fa_n > best_accum:
best_accum = fa_n
best_r = nr
best_c = nc
snapped.append((best_r, best_c, label))
# Phase 4: lazy output assembly as dask+cupy
snap_rows = np.array([s[0] for s in snapped], dtype=np.int64) \
if snapped else np.array([], dtype=np.int64)
snap_cols = np.array([s[1] for s in snapped], dtype=np.int64) \
if snapped else np.array([], dtype=np.int64)
snap_labels = np.array([s[2] for s in snapped], dtype=np.float64) \
if snapped else np.array([], dtype=np.float64)
_snap_rows = snap_rows
_snap_cols = snap_cols
_snap_labels = snap_labels
def _assemble_block(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)
row_start, row_end = block_info[0]['array-location'][0]
col_start, col_end = block_info[0]['array-location'][1]
h, w = block.shape
out = np.full((h, w), np.nan, dtype=np.float64)
for k in range(len(_snap_rows)):
sr = _snap_rows[k]
sc = _snap_cols[k]
if row_start <= sr < row_end and col_start <= sc < col_end:
out[sr - row_start, sc - col_start] = _snap_labels[k]
return cp.asarray(out)
dummy = da.zeros((H, W), chunks=flow_accum_data.chunks, dtype=np.float64)
return da.map_blocks(
_assemble_block, dummy,
dtype=np.float64,
meta=cp.array((), dtype=cp.float64),
)
# =====================================================================
# Public API
# =====================================================================
[docs]
@supports_dataset
def snap_pour_point_d8(flow_accum: xr.DataArray,
pour_points: xr.DataArray,
search_radius: int = 5,
name: str = 'snapped_pour_points') -> xr.DataArray:
"""Snap pour points to the highest-accumulation cell within a radius.
Parameters
----------
flow_accum : xarray.DataArray or xr.Dataset
2D flow accumulation grid.
pour_points : xarray.DataArray
2D raster where non-NaN cells mark pour points (same format
as ``watershed()`` expects). Values are preserved as labels.
search_radius : int, default 5
Maximum search distance in pixels (Euclidean).
name : str, default 'snapped_pour_points'
Name of output DataArray.
Returns
-------
xarray.DataArray or xr.Dataset
Same-shape grid with pour point labels moved to their snapped
locations. Non-pour-point cells are NaN.
"""
_validate_raster(flow_accum, func_name='snap_pour_point', name='flow_accum')
fa_data = flow_accum.data
pp_data = pour_points.data
if isinstance(fa_data, np.ndarray):
fa = fa_data.astype(np.float64)
pp = np.asarray(pp_data, dtype=np.float64)
H, W = fa.shape
out = _snap_pour_point_cpu(fa, pp, search_radius, H, W)
elif has_cuda_and_cupy() and is_cupy_array(fa_data):
out = _snap_pour_point_cupy(fa_data, pp_data, search_radius)
elif has_cuda_and_cupy() and is_dask_cupy(flow_accum):
out = _snap_pour_point_dask_cupy(fa_data, pp_data, search_radius)
elif da is not None and isinstance(fa_data, da.Array):
out = _snap_pour_point_dask(fa_data, pp_data, search_radius)
else:
raise TypeError(f"Unsupported array type: {type(fa_data)}")
return xr.DataArray(out,
name=name,
coords=flow_accum.coords,
dims=flow_accum.dims,
attrs=flow_accum.attrs)
