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SDR_algorithm.py
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SDR_algorithm.py
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from gdal_func import coords2rc, rc2coords, ogr, gdal
import numpy as np
import fiona, os
from time import time
from SDR_algorithm_fetch_startpoints import get_starting_pts_from_maximum_inundation_extent
from SDR_algorithm_functions import dem_checking, directory_checking, aggregate_arr
class Traj_search:
"""
Class inputs:
result_work_dir: the directory where the results will be saved
starting_pts: numpy array of shape (N, 2), N number of coordinates (x, y)
demfile: a string indicating the DEM file (accept only .tif format)
stopping_categories: default=(-555, -666)
search_win_size: default=9
cluster_threshold: default=1, used for unclustering trajectories
General notes:
DEM grid values representations during searching:
default stopping_categories:
-555: Non-flood mainstream water body area
-666: Downstream boundary grids
Note: the first category will be used as main category for trajectory classification!
np.nan: outside of model/search domain
np.inf: searched grids for the current trajectory
555: temporarily exist, for indicating the current grid when decide next point
drainage_map:
Has the same shape/size as the DEM array, indicating grids of successfully found trajectories
Values are Integers:
0: not marked in all successful trajectories
1-x: the index of successful trajectories plus 1;
when used to choose trajectory, use trajs[success_pts[x-1]]
"""
def __init__(self, result_work_dir, starting_pts, demfile, stoping_categories=(-555, -666), search_win_size=9,
cluster_threshold=1):
self.work_dir = result_work_dir
self.starting_pts = starting_pts
self.dem_dataset = gdal.Open(demfile)
self.dem_transform = self.dem_dataset.GetGeoTransform()
self.demarr = self.dem_dataset.GetRasterBand(1).ReadAsArray()
self.countline = [0]
self.drainage_map = np.zeros(self.demarr.shape)
self.default_search_win_size = search_win_size
self.stopping_vals = stoping_categories
self.straighten_degree = cluster_threshold
# standardise starting coordinates
for pt_idx in range(len(starting_pts)):
self.starting_pts[pt_idx, :] = rc2coords(self.dem_transform,
coords2rc(self.dem_transform, starting_pts[pt_idx, :]))
del pt_idx
def initial_stpts_trajs(self, search_window_size=9, save_to_shp=False, outfilename=None):
assert search_window_size % 3 == 0, 'The searching window size must be a multiple of 3!'
trajs = self.construct_lines_map(self.starting_pts, search_window_size)
if save_to_shp:
if outfilename is None:
outfilename = f"{self.work_dir}trajs_initial_search_from_starting_points.shp"
self.save_to_shp_lines(trajs, outfilename)
print(f'The total number of initially successful trajectories: {self.countline[0]}/{len(list(trajs.keys()))}')
return trajs
def construct_lines_map(self, stpts, search_win_size):
traj_collection = dict()
for i in range(stpts.shape[0]):
# print(f"traj_id: {i}")
traj_collection[(stpts[i, 0], stpts[i, 1])] = self.single_line_search(stpts[i, :], search_win_size)
return traj_collection
def single_line_search(self, st_coords, search_win_size, modified_dem=None):
if modified_dem is None:
demarr_cl = self.demarr.copy()
else:
demarr_cl = modified_dem.copy()
line_ls = [tuple(st_coords)] # store pts along line
r, c = coords2rc(self.dem_transform, st_coords)
demarr_cl[r, c] = 555 # mark current point as 555
# define terminate criteria test window boundaries, always 3x3
up0, dwn0, lf0, rit0 = max(0, r - 1), min(demarr_cl.shape[0], r + 1 + 1), max(0, c - 1), min(demarr_cl.shape[1], c + 1 + 1)
step_size = search_win_size // 2
# define searching window boundaries
up, dwn, lf, rit = max(0, r - step_size), min(demarr_cl.shape[0], r + step_size + 1), \
max(0, c - step_size), min(demarr_cl.shape[1], c + step_size + 1)
curr_block = demarr_cl[up:dwn, lf:rit].copy() #search window, of 9x9 size by default, with 555 in current pt
demarr_cl[r, c] = np.inf # mark searched point as inf
while self.non_stopping_criteria(demarr_cl[up0:dwn0, lf0:rit0]):
ri, ci = self.search_next_pt(curr_block, search_win_size) # current point = 555
# update current row, column no.
r, c = r + ri, c + ci
line_ls.append(rc2coords(self.dem_transform, (r, c)))
up0, dwn0, lf0, rit0 = max(0, r - 1), min(demarr_cl.shape[0], r + 1 + 1), \
max(0, c - 1), min(demarr_cl.shape[1], c + 1 + 1) # update block boundaries, 3x3
up, dwn, lf, rit = max(0, r - step_size), min(demarr_cl.shape[0], r + step_size + 1), \
max(0, c - step_size), min(demarr_cl.shape[1], c + step_size + 1) # update searching window boundaries
demarr_cl[r, c] = 555
curr_block = demarr_cl[up:dwn, lf:rit].copy() # with 555 in current pt
demarr_cl[r, c] = np.inf # mark searched point as inf
if np.any(np.isnan(demarr_cl[up0:dwn0, lf0:rit0])): # add the boundary cell at last if exists
id_bd = np.argwhere(np.isnan(demarr_cl[up0:dwn0, lf0:rit0]))[0]
line_ls.append(rc2coords(self.dem_transform, (id_bd[0]+up0, id_bd[1]+lf0)))
self.countline[0] += 1
else:
for stop_val in self.stopping_vals:
if np.any(demarr_cl[up0:dwn0, lf0:rit0] == stop_val): # add the iwl cell at last if exists
id_iwl = np.argwhere(demarr_cl[up0:dwn0, lf0:rit0] == stop_val)[0]
line_ls.append(rc2coords(self.dem_transform, (id_iwl[0]+up0, id_iwl[1]+lf0)))
self.countline[0] += 1
break
return line_ls
def non_stopping_criteria(self, blockarr):
criterion_1 = bool(~np.isinf(np.nanmin(blockarr))) # the min is not inf; there is at least one inf in block
criterion_2 = True # not stopping
for stop_val in self.stopping_vals:
criterion_2 = criterion_2 & (~np.any(blockarr == stop_val)) # not reaching the grids with stop signs
criterion_3 = ~np.any(np.isnan(blockarr)) # the block is hitting the boundary of model domain; Sea-side includes
return bool(criterion_1 & criterion_2 & criterion_3)
def search_next_pt(self, blockarr, search_win_size):
# calc 3x3 min
resample_shape = (3, 3)
sh = resample_shape[0], search_win_size // 3, resample_shape[1], search_win_size // 3
blockarr_resample = np.nanmin(np.nanmin(blockarr.reshape(sh), axis=-1), axis=1)
# locate min direction, determine candidate for next step
direc_idx = np.argwhere(blockarr_resample == np.nanmin(blockarr_resample))[-1]
candidates = np.array([[direc_idx[0]-1, direc_idx[1]],
[direc_idx[0]+1, direc_idx[1]],
[direc_idx[0], direc_idx[1]],
[direc_idx[0], direc_idx[1]-1],
[direc_idx[0], direc_idx[1]+1]])
candidates = candidates[~((candidates < 0) | (candidates > 2)).any(axis=1)]
blockarr_central = blockarr[search_win_size//2-1:search_win_size//2+2,
search_win_size//2-1:search_win_size//2+2].copy() # central block always 3x3
candidates_val = [blockarr_central[r, c] for r, c in candidates]
ids_min = candidates[np.argwhere(candidates_val == np.nanmin(candidates_val))[-1][0]] # -1: taking backward first min
ids_curr_pt = np.argwhere(blockarr_central == 555)[0] # array([r,c]) of the current point
ri = ids_min[0] - ids_curr_pt[0] # row no. increase; -1, 0, 1
ci = ids_min[1] - ids_curr_pt[1] # column no. increase; -1, 0, 1
if ((ri == 0) & (ci == 0)) | np.all(np.isinf(candidates_val)):
ids_min = np.argwhere(blockarr_central == np.nanmin(blockarr_central))[-1]
ri = ids_min[0] - ids_curr_pt[0] # row no. increase; -1, 0, 1
ci = ids_min[1] - ids_curr_pt[1] # column no. increase; -1, 0, 1
return ri, ci
def save_to_shp_lines(self, trajs, outfile):
shpDriver = ogr.GetDriverByName("ESRI Shapefile")
if os.path.exists(outfile):
shpDriver.DeleteDataSource(outfile)
outDataSource = shpDriver.CreateDataSource(outfile)
outLayer = outDataSource.CreateLayer(outfile, geom_type=ogr.wkbMultiLineString)
featureDefn = outLayer.GetLayerDefn()
for key in trajs.keys():
multiline = ogr.Geometry(ogr.wkbMultiLineString)
line = ogr.Geometry(ogr.wkbLineString)
line.AddPoint(key[0], key[1])
for pts in trajs[key]:
line.AddPoint(pts[0], pts[1])
multiline.AddGeometry(line)
outFeature = ogr.Feature(featureDefn)
outFeature.SetGeometry(multiline)
outLayer.CreateFeature(outFeature)
del multiline, line, outFeature
# main post-processing function
def read_shp_to_trajs(self, shpfile, need_failed_trajs=False):
trajs = dict()
failed_pts = list()
with fiona.open(shpfile) as copy_shp:
for feature in copy_shp:
geom = feature['geometry']['coordinates']
trajs[geom[0]] = geom[1:]
r, c = coords2rc(self.dem_transform, trajs[geom[0]][-1])
if self.is_not_stopping_grids(self.demarr[r, c]):
failed_pts.append(geom[0]) # [(x,y), (x,y), ...]
self.countline[0] = len(list(trajs.keys())) - len(failed_pts)
print(f'The total number of initially successful trajectories: {self.countline[0]}/{len(list(trajs.keys()))}')
if need_failed_trajs:
return trajs, failed_pts
else:
return trajs
def is_not_stopping_grids(self, grid_val):
if np.isnan(grid_val):
return False
for stop_val in self.stopping_vals:
if grid_val == stop_val:
return False
return True
def update_drainage_map(self, trajs, success_pts):
""" Drainage_map: contain integers. 0 for not drainage, 1, + for the number+1 of successful trajs """
self.drainage_map = np.zeros(self.demarr.shape)
for success_pt_idx in range(len(success_pts)):
rcs = np.array(
[list(coords2rc(self.dem_transform, coords)) for coords in trajs[success_pts[success_pt_idx]]]).astype(
int)
self.drainage_map[rcs[:, 0], rcs[:, 1]] = success_pt_idx + 1
return 0
def sep_trajs(self, trajs):
success_pts = []
failed_pts = []
for key in trajs.keys():
r, c = coords2rc(self.dem_transform, trajs[key][-1])
if self.is_not_stopping_grids(self.demarr[r, c]):
failed_pts.append(key)
else: # hit IWL value or boundary
success_pts.append(key)
return success_pts, failed_pts
def eliminate_clusters_in_trajs(self, trajs, starting_pts):
""" backward search, connect with the most downstream point of over-3-step-upstream points within 3x3 area if exists. """
curving_threshold = self.straighten_degree
for stpt in starting_pts:
curr_traj = trajs[stpt].copy()
rcs_ls = [list(coords2rc(self.dem_transform, coords)) for coords in curr_traj]
pt_id = len(curr_traj) - 1
while pt_id > curving_threshold:
r, c = rcs_ls[pt_id]
rcs = np.array(rcs_ls[:pt_id - curving_threshold]).astype(
int) # restrict to upstream points; next, filter for 3x3 area
block_check = rcs[
(rcs[:, 0] >= r - 1) & (rcs[:, 0] <= r + 1) & (rcs[:, 1] >= c - 1) & (rcs[:, 1] <= c + 1),
:]
if block_check.size > 0:
next_pt_id = rcs_ls.index(list(block_check[0, :])) # [0] most upstream, [-1] most downstream
del curr_traj[next_pt_id + 1:pt_id]
pt_id = next_pt_id
else:
pt_id -= 1
trajs[stpt] = curr_traj
return trajs
def connect_failed_success_if_possible(self, trajs):
""" Connect failed trajectories to succeeded trajectories if they share points. """
success_pts, failed_pts = self.sep_trajs(trajs)
self.update_drainage_map(trajs, success_pts)
for failed_pt in failed_pts:
rcs = np.array([list(coords2rc(self.dem_transform, coords)) for coords in trajs[failed_pt]]).astype(int)
drainage_tags = self.drainage_map[rcs[:, 0], rcs[:, 1]].astype(int)
if np.any(drainage_tags): # traj sharing any point with successful trajs
shared_idx = np.nonzero(drainage_tags)[0][0]
# 1D array, first item, the index of the first shared pt (row) in the rcs list
shared_coords = rc2coords(self.dem_transform, rcs[shared_idx, :])
new_traj = trajs[failed_pt][:trajs[failed_pt].index(shared_coords)]
new_traj.extend(trajs[success_pts[drainage_tags[shared_idx] - 1]][
trajs[success_pts[drainage_tags[shared_idx] - 1]].index(shared_coords):])
trajs[failed_pt] = new_traj
trajs = self.eliminate_clusters_in_trajs(trajs, [failed_pt])
return trajs
def find_boundary_min(self, rcs):
boundary_min_dems = np.empty(rcs.shape[0])
boundary_min_dems.fill(np.nan)
for i in range(rcs.shape[0]):
r, c = rcs[i, :]
block_check = rcs[(rcs[:, 0] >= r - 1) & (rcs[:, 0] <= r + 1) & (rcs[:, 1] >= c - 1) & (rcs[:, 1] <= c + 1),
:].astype(int)
if block_check.size / 2 < 9: # at boundary
dem_arr = self.demarr[r - 1:r + 2, c - 1:c + 2].copy()
dem_arr[block_check[:, 0] - (r - 1), block_check[:, 1] - (c - 1)] = np.nan
boundary_min_dems[i] = np.nanmin(dem_arr)
min_rc_idx = np.where(boundary_min_dems == np.nanmin(boundary_min_dems))[0][0]
return rcs[min_rc_idx, :]
def continue_failed_trajs(self, trajs):
success_pts, failed_pts = self.sep_trajs(trajs)
self.update_drainage_map(trajs, success_pts)
for failed_pt in failed_pts: # only self-grounded trajs exist
# saddle-point-driven reviving trajs
rcs = np.array([list(coords2rc(self.dem_transform, coords)) for coords in trajs[failed_pt]])
r, c = coords2rc(self.dem_transform, trajs[failed_pt][-1])
masked_dem_arr = self.demarr.copy()
masked_dem_arr[rcs[:, 0].astype(int), rcs[:, 1].astype(int)] = np.inf # mark as 'searched'
# Hard loop until successfully constructed all the failed lines; loop_count for maximum looping time control
loop_count = 0
while self.is_not_stopping_grids(self.demarr[r, c]):
loop_count += 1
# if loop_count > 10: # Manual termination setting: Maximum 10 times of looping
# break
r, c = self.find_boundary_min(rcs)
curr_coords = rc2coords(self.dem_transform, (r, c))
extended_line = self.single_line_search(curr_coords,
self.default_search_win_size, modified_dem=masked_dem_arr)
extended_rcs = np.array(
[list(coords2rc(self.dem_transform, coords)) for coords in extended_line]).astype(int)
masked_dem_arr[extended_rcs[:, 0], extended_rcs[:, 1]] = np.inf # mark as 'searched'
# trajs[failed_pt] = trajs[failed_pt][:trajs[failed_pt].index(curr_coords) + 1] + extended_line
trajs[failed_pt] = trajs[failed_pt] + extended_line
rcs = np.array([list(coords2rc(self.dem_transform, coords)) for coords in trajs[failed_pt]])
r, c = coords2rc(self.dem_transform, trajs[failed_pt][-1])
else: # successful, check with existing successful trajs & post-process clusters
# try connect to previously succeeded trajs
drainage_tags = self.drainage_map[rcs[:, 0], rcs[:, 1]].astype(int)
if np.any(drainage_tags): # traj sharing any point with successful trajs
shared_idx = np.nonzero(drainage_tags)[0][0]
# 1D array, first item, the index of the first shared pt (row) in the rcs list
shared_coords = rc2coords(self.dem_transform, rcs[shared_idx, :])
new_traj = trajs[failed_pt][:trajs[failed_pt].index(shared_coords)]
new_traj.extend(trajs[success_pts[drainage_tags[shared_idx] - 1]][
trajs[success_pts[drainage_tags[shared_idx] - 1]].index(shared_coords):])
trajs[failed_pt] = new_traj
trajs = self.eliminate_clusters_in_trajs(trajs, [failed_pt])
return trajs
def continue_failed_river_trajs(self, trajs):
success_pts, failed_pts = self.sep_trajs(trajs)
for failed_pt in failed_pts: # only self-grounded trajs exist
# saddle-point-driven reviving trajs
rcs = np.array([list(coords2rc(self.dem_transform, coords)) for coords in trajs[failed_pt]])
r, c = coords2rc(self.dem_transform, trajs[failed_pt][-1])
masked_dem_arr = self.demarr.copy()
masked_dem_arr[rcs[:, 0].astype(int), rcs[:, 1].astype(int)] = np.inf # mark as 'searched'
# Hard loop until successfully constructed all the failed lines; loop_count for maximum looping time control
loop_count = 0
while self.is_not_stopping_grids(self.demarr[r, c]):
loop_count += 1
# if loop_count > 10: # Manual termination setting: Maximum 10 times of looping
# break
r, c = self.find_boundary_min(rcs)
curr_coords = rc2coords(self.dem_transform, (r, c))
extended_line = self.single_line_search(curr_coords,
self.default_search_win_size, modified_dem=masked_dem_arr)
extended_rcs = np.array(
[list(coords2rc(self.dem_transform, coords)) for coords in extended_line]).astype(int)
masked_dem_arr[extended_rcs[:, 0], extended_rcs[:, 1]] = np.inf # mark as 'searched'
# trajs[failed_pt] = trajs[failed_pt][:trajs[failed_pt].index(curr_coords) + 1] + extended_line
trajs[failed_pt] = trajs[failed_pt] + extended_line
rcs = np.array([list(coords2rc(self.dem_transform, coords)) for coords in trajs[failed_pt]])
r, c = coords2rc(self.dem_transform, trajs[failed_pt][-1])
else:
trajs = self.eliminate_clusters_in_trajs(trajs, [failed_pt])
return trajs
def post_process_initial_trajs(self, read_from_shp=False, shpfile=None, print_counting_report=True):
if read_from_shp:
if shpfile is None:
shpfile = f"{self.work_dir}trajs_initial_search_from_starting_points.shp"
trajs = self.read_shp_to_trajs(shpfile)
else:
trajs = self.initial_stpts_trajs(save_to_shp=True)
success_trajs, _ = self.sep_trajs(trajs)
trajs = self.eliminate_clusters_in_trajs(trajs, success_trajs)
trajs = self.connect_failed_success_if_possible(
trajs) # newly succeeded trajs are cluster-free (eliminated inside)
trajs = self.continue_failed_trajs(trajs)
trajs = self.connect_failed_success_if_possible(
trajs) # newly succeeded trajs are cluster-free (eliminated inside)
if print_counting_report:
success_trajs, failed_trajs = self.sep_trajs(trajs)
self.countline.append(len(success_trajs))
self.countline.append(len(failed_trajs))
print('The increased number of successful trajectories after post-process: ', self.countline[1])
print('The total number of failed trajectories after post-process: ', self.countline[2])
return trajs
def define_main_river(self, print_counting_report=True):
trajs = self.initial_stpts_trajs(save_to_shp=False)
trajs = self.continue_failed_river_trajs(trajs)
if print_counting_report:
success_trajs, failed_trajs = self.sep_trajs(trajs)
self.countline.append(len(success_trajs))
print('The number of successful trajectories: ', self.countline[1])
return trajs
# external calls
def generate_drainage_shp(self, outshpfile, initial_trajs_saved=False):
trajs = self.post_process_initial_trajs(read_from_shp=initial_trajs_saved)
self.save_to_shp_lines(trajs, outshpfile)
return print('Successfully saved drainage network shapefile.')
def generate_drainage_trajs(self, initial_trajs_saved=False):
trajs = self.post_process_initial_trajs(read_from_shp=initial_trajs_saved)
return trajs
def generate_main_river_shp(self, outshpfile, dem_aggregate_factor=3, dem_aggregate_func=np.nanmean):
self.demarr, self.dem_transform = aggregate_arr(self.demarr, self.dem_transform,
dem_aggregate_factor, agg_func=dem_aggregate_func)
self.demarr[np.isnan(self.demarr)] = 999
trajs = self.define_main_river()
self.save_to_shp_lines(trajs, outshpfile)
return print('Successfully saved main river shapefile.')
def perform_SDR(work_dir, starting_pts, dem_tif_file, out_line_shp_file, stopping_categories,
sdr_search_window_size=9, continueRun=False):
directory_checking(work_dir)
Traj_search(work_dir, starting_pts, dem_tif_file, stoping_categories=stopping_categories,
search_win_size=sdr_search_window_size).generate_drainage_shp(out_line_shp_file,
initial_trajs_saved=continueRun)
return 0