def test_sweep_serial():
# set-up trial options
options = awe.Options(True) # True refers to internal access switch
options['user_options']['system_model']['architecture'] = {1: 0}
options['user_options']['system_model']['kite_dof'] = 3
options['user_options']['kite_standard'] = awe.ampyx_data.data_dict()
options['user_options']['tether_drag_model'] = 'split'
options['user_options']['trajectory']['lift_mode']['windings'] = 1
options['user_options']['induction_model'] = 'not_in_use'
options['nlp']['n_k'] = 2
options['solver']['max_iter'] = 0
# set-up sweep options
sweep_opts = [(['nlp', 'discretization'],
['direct_collocation', 'multiple_shooting'])] # trial sweep
sweep_opts = [(['user_options', 'wind',
'u_ref'], [5., 5.5])] # parametric sweep
# build, run and save sweep
sweep = awe.Sweep(name='serial_test', options=options, seed=sweep_opts)
sweep.build()
sweep.run(final_homotopy_step='initial')
sweep.save('dict')
# load and test sweep
file_pi = open('serial_test.dict', 'rb')
dict_test = pickle.load(file_pi)
sweep_test = awe.Sweep(dict_test)
file_pi.close()
os.remove("serial_test.dict")
sweep_test.plot(['all', 'comp_all'])
def test_visualization():
options = awe.Options(True)
# basic options
options['user_options']['system_model']['architecture'] = {1: 0}
options['user_options']['trajectory']['lift_mode']['windings'] = 1
options['user_options']['kite_standard'] = awe.ampyx_data.data_dict()
options['user_options']['trajectory']['type'] = 'lift_mode'
options['user_options']['system_model']['kite_dof'] = 3
options['user_options']['induction_model'] = 'not_in_use'
options['user_options']['tether_drag_model'] = 'trivial'
options['nlp']['n_k'] = 2
options['solver']['max_iter'] = 0
# build trial and optimize
trial = awe.Trial(options, 'trial1')
trial.build()
trial.optimize(final_homotopy_step='initial', debug_flags='all')
# set flags and plot
trial_flags = ['all']
trial.plot(trial_flags)
# build sweep and run
sweep_opts = [(['user_options', 'wind', 'u_ref'], [5., 5.5])]
sweep = awe.Sweep(name='sweep_viz_test', options=options, seed=sweep_opts)
sweep.run(final_homotopy_step='initial', debug_flags='all')
# set flags and plot
sweep_flags = [
'all', 'comp_all', 'outputs:tether_length',
'comp_outputs:tether_length'
]
sweep.plot(sweep_flags)
options['user_options']['wind']['u_ref'] = 5.0 # m/s
options['user_options']['induction_model'] = 'not_in_use'
options['user_options']['tether_drag_model'] = 'split'
trial = awe.Trial(seed = options, name = 'opt_design')
trial.build()
trial.optimize()
# fix params for wind speed sweep
fixed_params = {}
for name in list(trial.model.variables_dict['theta'].keys()):
if ('diam' in name) or (name == 'l_s'):
fixed_params[name] = trial.optimization.V_final['theta',name].full()
options['user_options']['trajectory']['fixed_params'] = fixed_params
########################
# SET-UP SWEEP OPTIONS #
########################
sweep_opts = [(['user_options', 'wind', 'u_ref'], np.linspace(3,15,5, endpoint=True))]
##################
# OPTIMIZE SWEEP #
##################
sweep = awe.Sweep(name = 'dual_kites_power_curve', options = options, seed = sweep_opts)
sweep.build()
sweep.run()
sweep.plot('comp_stats', 'comp_convergence')
plt.show()