def test_get_downslope_neighbors_large():
num_of_cols = 6
num_of_rows = 5
heights = np.array([5, 7, 8, 7, 6, 0, 7, 2, 10, 10, 7, 6, 7, 2, 4, 5, 5, 4, 7, 7, 3.9, 4, 0, 0, 6, 5, 4, 4, 0, 0])
result_downslope_neighbors = np.array([7, 7, 7, 4, 5, -1, 7, -1, 7, 15, 5, 5, 13, -1, 13, 22, 22,
23, 13, 13, 13, 22, -1, -1, 25, 26, 20, 28, -1, -1])
downslope_neighbors = util.get_downslope_indices(num_of_cols, num_of_rows, heights)
assert np.array_equal(downslope_neighbors, result_downslope_neighbors)
def test_get_downslope_neighbors():
num_of_cols = 3
num_of_rows = 3
indices = np.array([0, 1, 2, 3, 4, 5, 6, 7, 8])
heights = np.array([10, 9, 7, 9, 5, 3, 7, 3, 1])
result_downslope_neighbors = np.array([4, 5, 5, 7, 8, 8, 7, 8, -1])
downslope_neighbors = util.get_downslope_indices(num_of_cols, num_of_rows, heights)
assert np.array_equal(downslope_neighbors, result_downslope_neighbors)
def get_watersheds(heights, num_of_cols, num_of_rows):
"""
Returns all watersheds in an area given the heights of the landscape
:param heights: z-coordinate for all indices
:param num_of_cols: Number of grid points in x-direction
:param num_of_rows: Number of grid points in y-direction
:return nodes_in_watersheds: List of arrays where each array is a watershed and its nodes
"""
downslope_neighbors = util.get_downslope_indices(num_of_cols, num_of_rows, heights)
endpoints = get_node_endpoints(num_of_cols, num_of_rows, downslope_neighbors)
minimum_indices = np.where(downslope_neighbors == -1)[0]
minimums_in_watersheds = sorted(get_minimums_in_watersheds(minimum_indices, num_of_cols, num_of_rows))
nodes_in_watersheds = get_nodes_in_watersheds(endpoints, minimums_in_watersheds)
return nodes_in_watersheds
def get_nr_of_upstream_nodes(heights, nx, ny):
downslope_indices = util.get_downslope_indices(nx, ny, heights)
indices = np.arange(0, len(heights), 1)
minima = np.where(downslope_indices == -1)[0]
u, count = np.unique(downslope_indices, return_counts=True)
u = u[1:]
count = count[1:]
counter = np.zeros(len(heights), dtype=int)
counter[u] = count
non_min = np.setdiff1d(indices, minima)
index = non_min[np.where(counter[non_min] != 0)[0]]
counter[downslope_indices[index]] += sum(counter[index])
return counter
import util
import trap_analysis
import time
saved_files = '/home/shomea/a/anderovo/Dropbox/watershedLargeFiles/'
file_name = saved_files + 'anders_hoh.tiff'
"""
Save the watershed using pickle and numpy save.
"""
landscape = load_geotiff.get_landscape_tyrifjorden(file_name)
# Get downslope neighbors
downslope_neighbors = util.get_downslope_indices(landscape.num_of_nodes_x, landscape.num_of_nodes_y,
landscape.coordinates[:, 2])
# Get endpoints
endpoints = trap_analysis.get_node_endpoints(landscape.num_of_nodes_x, landscape.num_of_nodes_y, downslope_neighbors)
# Get minimums in each watershed
minimum_indices = np.where(downslope_neighbors == -1)[0]
minimums_in_each_watershed = sorted(trap_analysis.get_minimums_in_watersheds(minimum_indices, landscape.num_of_nodes_x,
landscape.num_of_nodes_y))
# Get indices leading to endpoints
indices_leading_to_endpoints = trap_analysis.get_indices_leading_to_endpoints(endpoints)
# Get the nodes in the watersheds. Save to file.
nodes_in_watersheds = trap_analysis.get_nodes_in_watersheds(endpoints, minimums_in_each_watershed)
cPickle.dump(nodes_in_watersheds, open('nodesInWatershedsStandard.pkl', 'wb'))
# landscape.coordinates[:, 2]) # Get endpoints #endpoints = trap_analysis.get_node_endpoints(landscape.num_of_nodes_x, landscape.num_of_nodes_y, downslope_neighbors) #nr_of_upstream_nodes = rivers.get_rivers(endpoints) #nx = 3 #ny = 3 #heights = np.array([0, 1, 2, 1, 2, 3, 2, 3, 1]) #indices = np.arange(0, len(heights), 1) nx = 6 ny = 5 heights = np.array([5, 7, 8, 7, 6, 0, 7, 2, 10, 10, 7, 6, 7, 2, 4, 5, 5, 4, 7, 7, 3.9, 4, 0, 0, 6, 5, 4, 4, 0, 0]) indices = np.arange(0, len(heights), 1) downslope_indices = util.get_downslope_indices(nx, ny, heights) #print downslope_indices # #minimas = np.where(downslope_indices == -1)[0] #print minimas #u, count = np.unique(downslope_indices, return_counts=True) #u = u[1:] #count = count[1:] #print u, count # #counter = np.zeros(len(heights), dtype=int) # #counter[u] = count #non_min = np.setdiff1d(indices, minimas) #print non_min #index = non_min[np.where(counter[non_min] != 0)[0]]
