# += 0.05 #half block uplift
# pylab.figure(1)
# pylab.close()
#elev = mg['node']['topographic__elevation']
#elev_r = mg.node_vector_to_raster(elev)
# pylab.figure(1)
#im = pylab.imshow(elev_r, cmap=pylab.cm.RdBu)
# pylab.show()
# Display a message
print('Running ...')
start_time = time.time()
# instantiate the component:
diffusion_component = PerronNLDiffuse(mg, './drive_perron_params.txt')
# perform the loop:
elapsed_time = 0. # total time in simulation
while elapsed_time < time_to_run:
print(elapsed_time)
if elapsed_time + dt < time_to_run:
diffusion_component.input_timestep(dt)
mg.at_node['topographic__elevation'][mg.core_nodes] += uplift * dt
# mg.at_node['topographic__elevation'][mg.active_nodes[:(mg.active_nodes.shape[0]//2.)]] += uplift*dt #half block uplift
# mg.at_node['topographic__elevation'][mg.active_nodes] += (numpy.arange(len(mg.active_nodes))) #nodes are tagged with their ID
# pylab.figure(1)
# pylab.close()
#elev = mg['node']['topographic__elevation']
#elev_r = mg.node_vector_to_raster(elev)
# pylab.figure(1)
mg.create_node_array_zeros('topographic_elevation')
z = mg.create_node_array_zeros() + leftmost_elev
z += initial_slope * np.amax(mg.node_y) - initial_slope * mg.node_y
#put these values plus roughness into that field
mg.at_node['topographic_elevation'] = z + np.random.rand(len(z)) / 100000.
#set up grid's boundary conditions (bottom, right, top, left is inactive)
mg.set_closed_boundaries_at_grid_edges(False, True, False, True)
# Display a message
print 'Running ...'
#instantiate the components:
fr = FlowRouter(mg)
sp = SPEroder(mg, input_file)
diffuse = PerronNLDiffuse(mg, input_file)
lin_diffuse = DiffusionComponent(grid=mg, input_stream=input_file)
#perform the loops:
for i in xrange(nt):
#note the input arguments here are not totally standardized between modules
#mg = diffuse.diffuse(mg, i*dt)
mg = lin_diffuse.diffuse(mg, dt)
mg = fr.route_flow(grid=mg)
mg = sp.erode(mg)
##plot long profiles along channels
pylab.figure(6)
profile_IDs = prf.channel_nodes(mg, mg.at_node['steepest_slope'],
mg.at_node['drainage_area'],
mg.at_node['upstream_ID_order'],
# create the field
mg.add_zeros('topographic__elevation', at='node')
# in our case, slope is zero, so the leftmost_elev is the mean elev
z = mg.zeros(at='node') + leftmost_elev
# put these values plus roughness into that field
mg['node']['topographic__elevation'] = z + np.random.rand(len(z)) / 100000.
# set up its boundary conditions (bottom, left, top, right)
# The mechanisms for this are all automated within the grid object
mg.set_fixed_value_boundaries_at_grid_edges(True, True, True, True)
# Display a message
print('Running ...')
# instantiate the components:
diffuse = PerronNLDiffuse(mg, input_file)
lin_diffuse = LinearDiffuser(grid=mg, input_stream=input_file)
# lin_diffuse.initialize(input_file)
# Perform the loops.
# First, we do the nonlinear diffusion:
# We're going to perform a block uplift of the interior of the grid, but
# leave the boundary nodes at their original elevations.
# access this function of the grid, and store the output with a local name
uplifted_nodes = mg.get_core_nodes()
#(Note: Node numbering runs across from the bottom left of the grid.)
# nt is the number of timesteps we calculated above, i.e., loop nt times.
# We never actually use i within the loop, but we could do.
for i in range(nt):
mg = RasterModelGrid(nrows, ncols, dx)
# mg.set_looped_boundaries(True, True)
mg.set_closed_boundaries_at_grid_edges(True, True, True, True)
#create the fields in the grid
mg.create_node_array_zeros('topographic__elevation')
z = mg.create_node_array_zeros() + init_elev
mg.at_node['topographic__elevation'] = z + numpy.random.rand(len(z)) / 1000.
# Display a message
print('Running ...')
start_time = time.time()
#instantiate the component:
diffusion_component = PerronNLDiffuse(mg, './drive_perron_params.txt')
#perform the loop:
elapsed_time = 0. #total time in simulation
while elapsed_time < time_to_run:
print elapsed_time
if elapsed_time + dt < time_to_run:
diffusion_component.input_timestep(dt)
mg.at_node['topographic__elevation'][mg.active_nodes[:(
mg.active_nodes.shape[0] // 2.)]] += uplift * dt #half block uplift
mg = diffusion_component.diffuse(mg, elapsed_time)
elapsed_time += dt
print('Total run time = ' + str(time.time() - start_time) + ' seconds.')
##create the elevation field in the grid:
#create the field
mg.create_node_array_zeros('planet_surface__elevation')
z = mg.create_node_array_zeros() + leftmost_elev
z += initial_slope * np.amax(mg.node_y) - initial_slope * mg.node_y
#put these values plus roughness into that field
mg['node']['planet_surface__elevation'] = z + np.random.rand(len(z)) / 100000.
# Display a message
print 'Running ...'
#instantiate the components:
fr = FlowRouter(mg)
sp = SPEroder(mg, input_file)
diffuse = PerronNLDiffuse(mg, input_file)
lin_diffuse = DiffusionComponent(grid=mg)
lin_diffuse.initialize(input_file)
#perform the loops:
for i in xrange(nt):
mg['node']['planet_surface__elevation'][
mg.get_interior_nodes()] += uplift_per_step
mg = fr.route_flow(grid=mg)
mg = sp.erode(mg)
mg = diffuse.diffuse(mg, i * dt)
#mg = lin_diffuse.diffuse(mg, dt)
##plot long profiles along channels
pylab.figure(6)
profile_IDs = prf.channel_nodes(mg, mg.at_node['steepest_slope'],
##create the elevation field in the grid:
#create the field
mg.create_node_array_zeros('planet_surface__elevation')
z = mg.create_node_array_zeros() + leftmost_elev
z += initial_slope*np.amax(mg.node_y) - initial_slope*mg.node_y
#put these values plus roughness into that field
mg['node'][ 'planet_surface__elevation'] = z + np.random.rand(len(z))/100000.
# Display a message
print 'Running ...'
#instantiate the components:
fr = FlowRouter(mg)
sp = SPEroder(mg, input_file)
diffuse = PerronNLDiffuse(mg, input_file)
lin_diffuse = DiffusionComponent(grid=mg)
lin_diffuse.initialize(input_file)
#perform the loops:
for i in xrange(nt):
mg['node']['planet_surface__elevation'][mg.get_interior_nodes()] += uplift_per_step
mg = fr.route_flow(grid=mg)
mg = sp.erode(mg)
mg = diffuse.diffuse(mg, i*dt)
#mg = lin_diffuse.diffuse(mg, dt)
##plot long profiles along channels
pylab.figure(6)
profile_IDs = prf.channel_nodes(mg, mg.at_node['steepest_slope'],
#The mechanisms for this are all automated within the grid object
mg.set_inactive_boundaries(False, False, False, False)
##create the elevation field in the grid:
#create the field
mg.create_node_array_zeros('planet_surface__elevation')
z = mg.create_node_array_zeros(
) + leftmost_elev #in our case, slope is zero, so the leftmost_elev is the mean elev
#put these values plus roughness into that field
mg['node']['planet_surface__elevation'] = z + np.random.rand(len(z)) / 100000.
# Display a message
print 'Running ...'
#instantiate the components:
diffuse = PerronNLDiffuse(mg, input_file)
lin_diffuse = DiffusionComponent(grid=mg, input_stream=input_file)
#lin_diffuse.initialize(input_file)
#Perform the loops.
#First, we do the nonlinear diffusion:
#We're going to perform a block uplift of the interior of the grid, but leave the boundary nodes at their original elevations.
uplifted_nodes = mg.get_core_nodes(
) #access this function of the grid, and store the output with a local name
#(Note: Node numbering runs across from the bottom left of the grid.)
for i in xrange(
nt
): #nt is the number of timesteps we calculated above, i.e., loop nt times. We never actually use i within the loop, but we could do.
#("xrange" is a clever memory-saving way of producing consecutive integers to govern a loop)