def add_distribution(method_dict):
# Specify the distribution according to the uncertain variable
if method_dict['uncertain_var'] == 'speed':
dist = distributions.getWeibull()
method_dict['distribution'] = dist
elif method_dict['uncertain_var'] == 'direction':
dist = distributions.getWindRose()
method_dict['distribution'] = dist
else:
raise ValueError('unknown uncertain_var option "%s", valid options "speed" or "direction".' %method_dict['uncertain_var'])
return method_dict
# 'layout1', 'layout2', 'layout3'
# 'distribution' = a distribution object
# 'dakota_filename' = 'dakotaInput.in', applicable for dakota method
# Get arguments
args = get_args()
method_dict = vars(args) # Start a dictionary with the arguments specified in the command line
method_dict['method'] = 'dakota'
method_dict['uncertain_var'] = 'direction'
method_dict['layout'] = 'layout3'
if method_dict['uncertain_var'] == 'speed':
dist = distributions.getWeibull()
method_dict['distribution'] = dist
elif method_dict['uncertain_var'] == 'direction':
dist = distributions.getWindRose()
method_dict['distribution'] = dist
else:
raise ValueError('unknown uncertain_var option "%s", valid options "speed" or "direction".' %method_dict['uncertain_var'])
method_dict['dakota_filename'] = 'dakotageneral.in'
# n = int(sys.argv[1]) # number of points, i.e., number of winddirections and windspeeds pairs
n = 10
points, weights = windfarm_setup.getPoints(method_dict, n)
if method_dict['uncertain_var'] == 'speed':
# For wind speed
windspeeds = points
winddirections = np.ones(n)*225
elif method_dict['uncertain_var'] == 'direction':
method_dict['method'] = 'dakota'
# select model: floris, jensen, gauss, larsen (larsen not working yet) TODO get larsen model working
method_dict['wake_model'] = 'floris'
method_dict['uncertain_var'] = 'direction'
# method_dict['layout'] = 'optimized' # Now this is specified in the command line
method_dict['dakota_filename'] = 'dakotageneral.in'
# method_dict['dakota_filename'] = 'dakotageneralPy.in' # Interface with python support
# To Do specify the number of points (directions or speeds) as an option as well.
method_dict['coeff_method'] = 'quadrature'
# Specify the distribution according to the uncertain variable
if method_dict['uncertain_var'] == 'speed':
dist = distributions.getWeibull()
method_dict['distribution'] = dist
elif method_dict['uncertain_var'] == 'direction':
dist = distributions.getWindRose()
method_dict['distribution'] = dist
elif method_dict['uncertain_var'] == 'direction_and_speed':
dist1 = distributions.getWindRose()
dist2 = distributions.getWeibull()
dist = cp.J(dist1, dist2)
method_dict['distribution'] = dist
else:
raise ValueError('unknown uncertain_var option "%s", valid options "speed", "direction" or "direction_and_speed".' %method_dict['uncertain_var'])
# Run the problem multiple times for statistics convergence
mean = []
std = []
samples = []
if verbose:
winddir = []
p = p[order]
windspeed_approx = x_s
winddirection_approx = x_d
power_approx = p
else:
raise ValueError('unknown uncertain_var option "%s", valid options "speed" or "direction".' %uncertain_var)
return winddirection_approx, windspeed_approx, power_approx
if __name__ == "__main__":
# method_dict = {'uncertain_var': 'speed', 'distribution': distributions.getWeibull()}
method_dict = {'uncertain_var': 'direction', 'distribution': distributions.getWindRose(),
'Noffset': 10, 'offset': 0, 'windspeed_ref': 8,
'winddirection_ref': 225}
# Get the approximation points
d, s, p = get_approximation(method_dict)
# Get the reference for comparison
f = open('figure1.json', 'r')
r = json.load(f)
f.close()
if method_dict['uncertain_var'] == 'speed':
pref = np.array(r['speed_optimized']['power'])
xref = np.array(r['speed_optimized']['speed'])
