def findL(S):
n = len(S)
IoC = IOC(S)
ioc = IoC.calcIOC()
ill = (0.027 * n) / ((ioc * (n - 1)) + 0.065 - (0.038 * n))
if (ill == 0):
ill = 1
return ((ill))
def _calcIOC(self, string):
"""this calulates the IOC from a passed string"""
ob = IOC(string)
ioc = ob.calcIOC()
if (self.verbose == 1):
print(ioc)
return (ioc)
def __init__(self, url_site=None, verbose=False):
# Estructura del diccionario -> {"raiz_cms": [Objeto_Analizer (, url_donde_se_encontro_websh)]}
self.cms_dict = {}
self.current_site = url_site
self.lock = Lock()
self.verbose = verbose
self.ioc = IOC(verbose=verbose)
def calcIOC(string, verbose=0):
"""this calulates the IOC from a passed string"""
ob = IOC(string)
ioc = ob.calcIOC
if (verbose == 1):
print(ioc)
return (ioc)
goal_q = toQuaternion(0, [0, 0, 1])
goal_w_B = [0, 0, 0]
target = goal_p_I + goal_v_I + goal_q + goal_w_B
############################### Perform optimal control ###############################
T = 5 # number of control intervals
dt = 0.1
nT = int(10 / 0.1)
OCsys = OC()
OCsys.getTraj(dyn, init, target, T)
x_his = OCsys.x_his
u_his = OCsys.u_his
############################### IOC ###############################
IOCsys = IOC()
IOCsys.getdPhi(OCsys.x_his, OCsys.u_his, OCsys.PHI)
IOCsys.getdf(OCsys.x_his, OCsys.u_his, OCsys.DYN)
IOCsys.IOC_main(IOCsys.dfdx_his, IOCsys.dfdu_his, IOCsys.dphidx_his,
IOCsys.dphidu_his)
omega = IOCsys.omega / min(IOCsys.omega) #normalize omega
print('Omega =', omega)
############################### Pendulum and LTI trajectory ###############################
if dyn == 'LTI' or dyn == 'Pendulum':
x_his = np.concatenate(x_his, axis=1)
u_his = np.concatenate(u_his, axis=1)
fig, axs = plt.subplots(2)