def direct_flow(self):
"""
Find flow directions, save to the model grid, and return receivers.
direct_flow() checks for updated boundary conditions, calculates
slopes on links, finds basself.surface_valuesel nodes based on the status at node,
calculates flow directions, saves results to the grid, and returns a
at-node array of receiver nodes. This array is stored in the grid at:
grid['node']['flow__receiver_nodes']
An alternative to direct_flow() is run_one_step() which does the same
things but also returns a at-node array of receiver nodes. This array
is stored in the grid at:
grid['node']['flow__receiver_nodes']
"""
self._check_updated_bc()
# Step 1. Find and save base level nodes.
(baselevel_nodes,) = numpy.where(
numpy.logical_or(
self._grid.status_at_node == FIXED_VALUE_BOUNDARY,
self._grid.status_at_node == FIXED_GRADIENT_BOUNDARY,
)
)
# Calculate flow directions
(
self.receivers,
self.proportions,
slopes_to_receivers,
steepest_slope,
steepest_receiver,
sink,
receiver_links,
steepest_link,
) = flow_direction_dinf.flow_directions_dinf(
self._grid, self.surface_values, baselevel_nodes=baselevel_nodes
)
# Save the four ouputs of this component.
self._grid["node"]["flow__receiver_node"][:] = self.receivers
self._grid["node"]["flow__receiver_proportions"][:] = self.proportions
self._grid["node"]["topographic__steepest_slope"][:] = slopes_to_receivers
self._grid["node"]["flow__link_to_receiver_node"][:] = receiver_links
self._grid["node"]["flow__sink_flag"][:] = False
self._grid["node"]["flow__sink_flag"][sink] = True
return (self.receivers, self.proportions)
def direct_flow(self):
"""
Find flow directions, save to the model grid, and return receivers.
direct_flow() checks for updated boundary conditions, calculates
slopes on links, finds basself.surface_valuesel nodes based on the status at node,
calculates flow directions, saves results to the grid, and returns a
at-node array of receiver nodes. This array is stored in the grid at:
grid['node']['flow__receiver_nodes']
An alternative to direct_flow() is run_one_step() which does the same
things but also returns a at-node array of receiver nodes. This array
is stored in the grid at:
grid['node']['flow__receiver_nodes']
"""
self._check_updated_bc()
# Step 1. Find and save base level nodes.
(baselevel_nodes, ) = numpy.where(
numpy.logical_or(
self._grid.status_at_node == FIXED_VALUE_BOUNDARY,
self._grid.status_at_node == FIXED_GRADIENT_BOUNDARY,
))
# Calculate flow directions
(
self.receivers,
self.proportions,
slopes_to_receivers,
steepest_slope,
steepest_receiver,
sink,
receiver_links,
steepest_link,
) = flow_direction_dinf.flow_directions_dinf(
self._grid, self.surface_values, baselevel_nodes=baselevel_nodes)
# Save the four ouputs of this component.
self._grid["node"]["flow__receiver_node"][:] = self.receivers
self._grid["node"]["flow__receiver_proportions"][:] = self.proportions
self._grid["node"][
"topographic__steepest_slope"][:] = slopes_to_receivers
self._grid["node"]["flow__link_to_receiver_node"][:] = receiver_links
self._grid["node"]["flow__sink_flag"][:] = False
self._grid["node"]["flow__sink_flag"][sink] = True
return (self.receivers, self.proportions)
def test_not_implemented_voroni():
x = [0, 0.1, 0.2, 0.3, 1, 1.1, 1.2, 1.3, 2, 2.1, 2.2, 2.3]
y = [0, 1, 2, 3, 0, 1, 2, 3, 0, 1, 2, 3]
vmg = VoronoiDelaunayGrid(x, y)
with pytest.raises(NotImplementedError):
flow_direction_dinf.flow_directions_dinf(vmg)
def test_not_implemented_voroni():
x = [0, 0.1, 0.2, 0.3, 1, 1.1, 1.2, 1.3, 2, 2.1, 2.2, 2.3]
y = [0, 1, 2, 3, 0, 1, 2, 3, 0, 1, 2, 3]
vmg = VoronoiDelaunayGrid(x, y)
with pytest.raises(NotImplementedError):
flow_direction_dinf.flow_directions_dinf(vmg)