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'],
mg.at_node['flow_receiver'])
dists_upstr = prf.get_distances_upstream(
mg, len(mg.at_node['steepest_slope']), profile_IDs,
mg.at_node['links_to_flow_receiver'])
prf.plot_profiles(dists_upstr, profile_IDs,
mg.at_node['topographic_elevation'])
#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, input_stream=input_file)
#perform the loops:
for i in xrange(nt):
#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'],
mg.at_node['flow_receiver'])
dists_upstr = prf.get_distances_upstream(mg, len(mg.at_node['steepest_slope']),
profile_IDs, mg.at_node['links_to_flow_receiver'])
prf.plot_profiles(dists_upstr, profile_IDs, mg.at_node['planet_surface__elevation'])
print 'Completed loop ', i
print 'Completed the simulation. Plotting...'
#instantiate the components:
diffuse = PerronNLDiffuse(mg, input_file)
lin_diffuse = DiffusionComponent(grid=mg, input_stream=input_file)
#Perform the loops.
for i in xrange(nt):
#This line performs the actual functionality of the component:
#***NB: both diffusers contain an "automatic" element of uplift.
#You can suppress this for the linear diffuser with the *internal_uplift* keyword, =False
#See the docstrings for both classes for more details.
#Switch these lines to switch between diffusion styles:
#mg = diffuse.diffuse(mg, i*dt) #nonlinear diffusion
mg = lin_diffuse.diffuse(mg) #linear diffusion
#Plot a Xsection north-south through the middle of the data, once per loop
pylab.figure(1)
elev_r = mg.node_vector_to_raster(mg['node']['topographic_elevation'])
im = pylab.plot(mg.dx*np.arange(nrows), elev_r[:,int(ncols//2)])
print 'Completed loop ', i
print 'Completed the simulation. Plotting...'
#Finalize and plot:
#put a title on figure 1
pylab.figure(1)
pylab.title('N-S cross_section')
pylab.xlabel('Distance')
##Reset the elevation field in the grid:
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)
#lin_diffuse.initialize(input_file)
for i in xrange(nt):
#This line performs the actual functionality of the component:
#***NB: the nonlinear diffuser contains an "automatic" element of uplift. If you instead use the linear diffuser, you need to add the uplift manually...
mg['node']['planet_surface__elevation'][uplifted_nodes] += uplift_per_step
mg = lin_diffuse.diffuse(mg, internal_uplift=False) #linear diffusion
pylab.figure(4)
elev_r = mg.node_vector_to_raster(mg['node']['planet_surface__elevation'])
im = pylab.plot(mg.dx * np.arange(nrows), elev_r[:, int(ncols // 2)])
print 'Completed loop ', i
print 'Completed the simulation. Plotting...'
#Finalize and plot:
#put a title on figure 4
pylab.figure(4)
pylab.title('N-S cross_section, linear diffusion')
pylab.xlabel('Distance')
pylab.ylabel('Elevation')
##Reset the elevation field in the grid:
mg["node"]["planet_surface__elevation"] = z + np.random.rand(len(z)) / 100000.0
# Display a message
print "Running ..."
##instantiate the components:
# diffuse = PerronNLDiffuse(mg, input_file)
# lin_diffuse = DiffusionComponent(grid=mg)
# lin_diffuse.initialize(input_file)
for i in xrange(nt):
# This line performs the actual functionality of the component:
# ***NB: the nonlinear diffuser contains an "automatic" element of uplift. If you instead use the linear diffuser, you need to add the uplift manually...
mg["node"]["planet_surface__elevation"][uplifted_nodes] += uplift_per_step
mg = lin_diffuse.diffuse(mg, internal_uplift=False) # linear diffusion
pylab.figure(4)
elev_r = mg.node_vector_to_raster(mg["node"]["planet_surface__elevation"])
im = pylab.plot(mg.dx * np.arange(nrows), elev_r[:, int(ncols // 2)])
print "Completed loop ", i
print "Completed the simulation. Plotting..."
# Finalize and plot:
# put a title on figure 4
pylab.figure(4)
pylab.title("N-S cross_section, linear diffusion")
pylab.xlabel("Distance")
pylab.ylabel("Elevation")
#instantiate the components:
diffuse = PerronNLDiffuse(mg, input_file)
lin_diffuse = DiffusionComponent(grid=mg, input_stream=input_file)
#Perform the loops.
for i in xrange(nt):
#This line performs the actual functionality of the component:
#***NB: both diffusers contain an "automatic" element of uplift.
#You can suppress this for the linear diffuser with the *internal_uplift* keyword, =False
#See the docstrings for both classes for more details.
#Switch these lines to switch between diffusion styles:
#mg = diffuse.diffuse(mg, i*dt) #nonlinear diffusion
mg = lin_diffuse.diffuse(mg) #linear diffusion
#Plot a Xsection north-south through the middle of the data, once per loop
pylab.figure(1)
elev_r = mg.node_vector_to_raster(mg['node']['topographic_elevation'])
im = pylab.plot(mg.dx * np.arange(nrows), elev_r[:, int(ncols // 2)])
print 'Completed loop ', i
print 'Completed the simulation. Plotting...'
#Finalize and plot:
#put a title on figure 1
pylab.figure(1)
pylab.title('N-S cross_section')
pylab.xlabel('Distance')