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Rename heap functions; add more speed tests to readme
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@mdbartos
mdbartos committed Jan 27, 2022
1 parent b0f8bf5 commit c683077
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Showing 4 changed files with 8 additions and 74 deletions.
3 changes: 3 additions & 0 deletions README.md
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Expand Up @@ -478,10 +478,13 @@ Performance benchmarks on a 2015 MacBook Pro (M: million, K: thousand):
| `resolve_flats` | N/A | 36M | 9.56 [s] |
| `cell_dh` | D8 | 36M | 2.34 [s] |
| `cell_dh` | DINF | 36M | 4.92 [s] |
| `cell_dh` | MFD | 36M | 30.1 [s] |
| `cell_distances` | D8 | 36M | 1.11 [s] |
| `cell_distances` | DINF | 36M | 2.16 [s] |
| `cell_distances` | MFD | 36M | 26.8 [s] |
| `cell_slopes` | D8 | 36M | 4.01 [s] |
| `cell_slopes` | DINF | 36M | 10.2 [s] |
| `cell_slopes` | MFD | 36M | 58.7 [s] |

Speed tests were run on a conditioned DEM from the HYDROSHEDS DEM repository
(linked above as `elevation.tiff`).
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3 changes: 3 additions & 0 deletions docs/index.md
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Expand Up @@ -482,10 +482,13 @@ Performance benchmarks on a 2015 MacBook Pro (M: million, K: thousand):
| `resolve_flats` | N/A | 36M | 9.56 [s] |
| `cell_dh` | D8 | 36M | 2.34 [s] |
| `cell_dh` | DINF | 36M | 4.92 [s] |
| `cell_dh` | MFD | 36M | 30.1 [s] |
| `cell_distances` | D8 | 36M | 1.11 [s] |
| `cell_distances` | DINF | 36M | 2.16 [s] |
| `cell_distances` | MFD | 36M | 26.8 [s] |
| `cell_slopes` | D8 | 36M | 4.01 [s] |
| `cell_slopes` | DINF | 36M | 10.2 [s] |
| `cell_slopes` | MFD | 36M | 58.7 [s] |

Speed tests were run on a conditioned DEM from the HYDROSHEDS DEM repository
(linked above as `elevation.tiff`).
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72 changes: 0 additions & 72 deletions pysheds/_sgrid.py
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Expand Up @@ -826,44 +826,6 @@ def _dinf_flow_distance_recur_numba(fdir_0, fdir_1, weights_0, weights_1,
def _dinf_flow_distance_iter_numba(fdir_0, fdir_1, weights_0, weights_1,
pour_point, dirmap):
dist = np.full(fdir_0.shape, np.inf, dtype=np.float64)
r_dirmap = np.array([dirmap[4], dirmap[5], dirmap[6],
dirmap[7], dirmap[0], dirmap[1],
dirmap[2], dirmap[3]])
m, n = fdir_0.shape
offsets = np.array([-n, 1 - n, 1,
1 + n, n, - 1 + n,
- 1, - 1 - n])
i, j = pour_point
ix = (i * n) + j
dist.flat[ix] = 0.
queue = [ix]
while queue:
parent = queue.pop()
parent_dist = dist.flat[parent]
neighbors = offsets + parent
for k in range(8):
neighbor = neighbors[k]
current_neighbor_dist = dist.flat[neighbor]
points_to_0 = (fdir_0.flat[neighbor] == r_dirmap[k])
points_to_1 = (fdir_1.flat[neighbor] == r_dirmap[k])
if points_to_0:
neighbor_dist_0 = parent_dist + weights_0.flat[neighbor]
if (neighbor_dist_0 < current_neighbor_dist):
dist.flat[neighbor] = neighbor_dist_0
queue.append(neighbor)
elif points_to_1:
neighbor_dist_1 = parent_dist + weights_1.flat[neighbor]
if (neighbor_dist_1 < current_neighbor_dist):
dist.flat[neighbor] = neighbor_dist_1
queue.append(neighbor)
return dist

@njit(float64[:,:](int64[:,:], int64[:,:], float64[:,:], float64[:,:],
UniTuple(int64, 2), UniTuple(int64, 8)),
cache=True)
def _dinf_flow_distance_heap_numba(fdir_0, fdir_1, weights_0, weights_1,
pour_point, dirmap):
dist = np.full(fdir_0.shape, np.inf, dtype=np.float64)
visited = np.zeros(fdir_0.shape, dtype=np.bool8)
r_dirmap = np.array([dirmap[4], dirmap[5], dirmap[6],
dirmap[7], dirmap[0], dirmap[1],
Expand Down Expand Up @@ -905,40 +867,6 @@ def _dinf_flow_distance_heap_numba(fdir_0, fdir_1, weights_0, weights_1,
@njit(float64[:,:](float64[:,:,:], UniTuple(int64, 2), float64[:,:]),
cache=True)
def _mfd_flow_distance_iter_numba(fdir, pour_point, weights):
_, m, n = fdir.shape
mn = m * n
dist = np.full((m, n), np.inf, dtype=np.float64)
i, j = pour_point
ix = (i * n) + j
offsets = np.array([-n, 1 - n, 1,
1 + n, n, - 1 + n,
- 1, - 1 - n])
r_dirmap = np.array([4, 5, 6, 7,
0, 1, 2, 3])
dist.flat[ix] = 0.
queue = [ix]
while queue:
parent = queue.pop()
parent_dist = dist.flat[parent]
neighbors = offsets + parent
for k in range(8):
neighbor = neighbors[k]
neighbor_dir = r_dirmap[k]
current_neighbor_dist = dist.flat[neighbor]
kix = neighbor + (neighbor_dir * mn)
points_to = fdir.flat[kix] > 0.
if points_to:
neighbor_dist = parent_dist + weights.flat[neighbor]
if (neighbor_dist < current_neighbor_dist):
dist.flat[neighbor] = neighbor_dist
queue.append(neighbor)
return dist

# TODO: Weights should actually by (8, m, n)
# neighbor_dist = parent_dist + weights.flat[kix]
@njit(float64[:,:](float64[:,:,:], UniTuple(int64, 2), float64[:,:]),
cache=True)
def _mfd_flow_distance_heap_numba(fdir, pour_point, weights):
_, m, n = fdir.shape
mn = m * n
dist = np.full((m, n), np.inf, dtype=np.float64)
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4 changes: 2 additions & 2 deletions pysheds/sgrid.py
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Expand Up @@ -1189,7 +1189,7 @@ def _dinf_flow_distance(self, x, y, fdir, weights=None, dirmap=(64, 128, 1, 2, 4
weights_1 = weights_0
if method.lower() == 'shortest':
if algorithm.lower() == 'iterative':
dist = _self._dinf_flow_distance_heap_numba(fdir_0, fdir_1, weights_0,
dist = _self._dinf_flow_distance_iter_numba(fdir_0, fdir_1, weights_0,
weights_1, (y, x), dirmap)
elif algorithm.lower() == 'recursive':
dist = _self._dinf_flow_distance_recur_numba(fdir_0, fdir_1, weights_0,
Expand Down Expand Up @@ -1218,7 +1218,7 @@ def _mfd_flow_distance(self, x, y, fdir, weights=None, dirmap=(64, 128, 1, 2, 4,
# Compute flow distances
if method.lower() == 'shortest':
if algorithm.lower() == 'iterative':
dist = _self._mfd_flow_distance_heap_numba(fdir, (y, x), weights)
dist = _self._mfd_flow_distance_iter_numba(fdir, (y, x), weights)
elif algorithm.lower() == 'recursive':
raise NotImplementedError('Recursive algorithm not implemented.')
else:
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