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Update solver.py #14

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48 changes: 41 additions & 7 deletions solver.py
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Original file line number Diff line number Diff line change
Expand Up @@ -96,6 +96,7 @@ def HJSolver(dynamics_obj, grid, multiple_value, tau, compMethod,
constraint = multiple_value[1]
else:
init_value = multiple_value
constraint = None
################### PARSING ARGUMENTS FROM USERS #####################

parser = ArgumentParser()
Expand All @@ -106,11 +107,21 @@ def HJSolver(dynamics_obj, grid, multiple_value, tau, compMethod,

hcl.init()
hcl.config.init_dtype = hcl.Float(32)

################# INITIALIZE DATA TO BE INPUT INTO EXECUTABLE ##########################

print("Initializing\n")

if constraint is None:
print("no obstacles!")
else:
print('defining obstacles')
constraint_dim = constraint.ndim
if constraint_dim > grid.dims:
constraint_i = constraint[...,0]
else:
constraint_i = constraint

V_0 = hcl.asarray(init_value)
V_1 = hcl.asarray(np.zeros(tuple(grid.pts_each_dim)))
l0 = hcl.asarray(init_value)
Expand Down Expand Up @@ -138,7 +149,7 @@ def HJSolver(dynamics_obj, grid, multiple_value, tau, compMethod,
if grid.dims >= 6:
list_x6 = hcl.asarray(list_x6)

# Get executable
# Get executable, obstacle check initial value function
if grid.dims == 3:
solve_pde = graph_3D(dynamics_obj, grid, compMethod["PrevSetsMode"], accuracy)

Expand All @@ -151,6 +162,26 @@ def HJSolver(dynamics_obj, grid, multiple_value, tau, compMethod,
if grid.dims == 6:
solve_pde = graph_6D(dynamics_obj, grid, compMethod["PrevSetsMode"], accuracy)


# saving value function at each timestep
if grid.dims == 3:
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@buinm buinm Sep 14, 2021

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It might be shorter to do the following:

valfuns = np.zeros(np.insert(tuple(grid.pts_each_dim), grid.dims, len(tau)))
valfuns[..., -1 ] = V_0.asnumpy()

valfuns = np.zeros(np.insert(tuple(grid.pts_each_dim), 3, len(tau)))
valfuns[:,:,:,-1] = V_0.asnumpy()
print(np.shape(valfuns))
if grid.dims == 4:
valfuns = np.zeros(np.insert(tuple(grid.pts_each_dim), 4, len(tau)))
valfuns[:,:,:,:,-1] = V_0.asnumpy()
print(np.shape(valfuns))
if grid.dims == 5:
valfuns = np.zeros(np.insert(tuple(grid.pts_each_dim), 5, len(tau)))
valfuns[:,:,:,:,:,-1] = V_0.asnumpy()
print(np.shape(valfuns))
if grid.dims == 6:
valfuns = np.zeros(np.insert(tuple(grid.pts_each_dim), 6, len(tau)))
valfuns[:,:,:,:,:,:,-1] = V_0.asnumpy()
print(np.shape(valfuns))


# Print out code for different backend
#print(solve_pde)

Expand All @@ -163,6 +194,9 @@ def HJSolver(dynamics_obj, grid, multiple_value, tau, compMethod,
for i in range (1, len(tau)):
#tNow = tau[i-1]
t_minh= hcl.asarray(np.array((tNow, tau[i])))
# taking obstacle at each timestep
if "TargetSetMode" in compMethod and constraint_dim > grid.dims:
constraint_i = constraint[...,i]
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@buinm buinm Sep 14, 2021

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Is it a bit too many indentations for the constraint_i = constraint[...,i]

while tNow <= tau[i] - 1e-4:
tmp_arr = V_0.asnumpy()
# Start timing
Expand All @@ -187,9 +221,9 @@ def HJSolver(dynamics_obj, grid, multiple_value, tau, compMethod,
# If TargetSetMode is specified by user
if "TargetSetMode" in compMethod:
if compMethod["TargetSetMode"] == "max":
tmp_val = np.maximum(V_0.asnumpy(), constraint)
tmp_val = np.maximum(V_0.asnumpy(), -constraint_i)
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@buinm buinm Sep 14, 2021

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I think it's better to keep it as constraint. The negate operation on the constraint value array should just be done by users before passing in the variables to HJSolver function call

elif compMethod["TargetSetMode"] == "min":
tmp_val = np.minimum(V_0.asnumpy(), constraint)
tmp_val = np.minimum(V_0.asnumpy(), -constraint_i)
# Update final result
V_1 = hcl.asarray(tmp_val)
# Update input for next iteration
Expand All @@ -198,17 +232,17 @@ def HJSolver(dynamics_obj, grid, multiple_value, tau, compMethod,
# Some information printing
print(t_minh)
print("Computational time to integrate (s): {:.5f}".format(time.time() - start))

valfuns[..., -1-i] = V_1.asnumpy()
# Time info printing
print("Total kernel time (s): {:.5f}".format(execution_time))
print("Finished solving\n")

##################### PLOTTING #####################
if args.plot:
# Only plots last value array for now
plot_isosurface(grid, V_1.asnumpy(), plot_option)
plot_isosurface(grid, valfuns[...,0], plot_option)

return V_1.asnumpy()
return valfuns

def TTRSolver(dynamics_obj, grid, init_value, epsilon, plot_option):
print("Welcome to optimized_dp \n")
Expand Down