def changeReferenceFrame():
# From Howard Curtis: pg218
# For a given earth orbit, the elements are h 1⁄4 80,000 km 2 /s, e 1⁄4 1.4, i 1⁄4 30 , U 1⁄4 40 , u 1⁄4 60 , and q 1⁄4 30 .
# Using Algorithm 4.5, find the state vectors r and v in the geocentric equatorial frame.
h = 80000 * 1e6 #m^2/s
e = 1.4
i = AL_BF.deg2rad(30)
RAAN = AL_BF.deg2rad(40)
omega = AL_BF.deg2rad(60)
theta = AL_BF.deg2rad(30)
# rv in perifocal
r_norm = h**2/Cts.mu_E_m / (1+e*np.cos(theta))
r = r_norm* np.array([np.cos(theta), np.sin(theta),0])
v = Cts.mu_E_m / h * np.array([-np.sin(theta), e+ np.cos(theta),0])
print(r, v) # Validated
# r, v in geocentric
Frame1 = AL_Eph.FramesOfReference([omega, i, RAAN], 'perif')
r2 = Frame1.transform(r, 'helioc')
v2 = Frame1.transform(v, 'helioc')
print(r2, v2) # Validated
# Back to perifocal
r3 = Frame1.transform(r2, 'perif')
v3 = Frame1.transform(v2, 'perif')
print(r3, v3) # Validated: same as initial
def objFunction(self, Error, m_fuel=False, typeError='vec'):
if typeError == 'vec':
fc1 = np.linalg.norm(Error[0:3] / AL_BF.AU) # Normalize with AU
fc2 = np.linalg.norm(Error[3:] / AL_BF.AU * AL_BF.year2sec(1))
fc1_SI = np.linalg.norm(Error[0:3])
fc2_SI = np.linalg.norm(Error[3:])
else:
fc1 = Error[0] / AL_BF.AU
fc2 = Error[1] / AL_BF.AU * AL_BF.year2sec(1)
fc1_SI = fc1
fc2_SI = fc2
self.Epnorm_norm = fc1
self.Evnorm_norm = fc2
self.Epnorm = fc1_SI
self.Evnorm = fc2_SI
factor_pos = CONF['FEASIB']['factor_pos']
factor_vel = CONF['FEASIB']['factor_vel']
factor_mass = CONF['FEASIB']['factor_mass']
# if CONF['FEASIB']['log'] == 1:
# fc1 = np.log10(fc1)
# fc2 = np.log10(fc2)
# factor_pos = np.log10(factor_pos)
# factor_vel = np.log10(factor_vel)
# print('------------------------')
# print(m_fuel, Error)
# print(fc1, fc2)
if type(m_fuel) == bool:
value = fc1 * factor_pos + \
fc2 * factor_vel
else:
fc0 = m_fuel / self.Spacecraft.m_dry
# else:
res = [fc0, fc1, fc2]
res2 = [fc0, fc1_SI, fc2_SI]
# print(res)
# print('###################')
# print(fc1,factor_pos*fc1)
# print(fc2, factor_vel*fc2)
# print(fc0, factor_mass*fc0)
# print(res)
value = res[0]* factor_mass +\
res[1]* factor_pos + \
res[2]* factor_vel # Has to be in AU for the coeff to work in balance
return value
def test_convertAngleForm():
vector0 = np.array([90,0,0])
vector1 = AL_BF.convert3dvector(vector0, "cartesian")
print(vector1) # validated
vector0 = np.array([90,90,90])
vector1 = AL_BF.convert3dvector(vector0, "cartesian")
print(vector1) # validated
vector0 = np.array([155, 0.78, 0.61])
vector1 = AL_BF.convert3dvector(vector0, "polar")
print(vector1) # validated
print(np.arcsin(-0.7071067811865475))
vector0 = np.array([-90,-90,-90])
vector1 = AL_BF.convert3dvector(vector0, "cartesian")
print(vector1*AL_BF.rad2deg(1))
def to_helioc(r, vx, vy):
v_body = np.array([vx, vy, 0])
# Convert to heliocentric
angle = np.arctan2(r[1], r[0])
v_h = AL_BF.rot_matrix(v_body, angle, 'z')
return v_h
def plot_tvsT(self):
t = np.linspace(self.t0,
self.t0 + AL_BF.sec2days(self.t_t),
num=self.Nimp + 2)
deltaV_i = [
np.linalg.norm(self.DeltaV_list[i, :]) * self.DeltaV_max
for i in range(len(self.DeltaV_list[:, 0]))
]
deltaV = np.zeros(self.Nimp + 2)
deltaV[1:-1] = deltaV_i
fig, ax = plt.subplots()
plt.plot(t, deltaV, 'o-', color='k')
plt.title("Epoch vs Delta V")
plt.ylabel("Delta V (m/s)")
plt.xlabel("JD0 (days)")
plt.grid(alpha=0.5)
# AL_Plot.set_axes_equal(ax)
dpi = 200
layoutSave = 'tight'
plt.savefig('OptSol/tvsT.png', dpi=dpi, bbox_inches=layoutSave)
plt.show()
def propagateLambert():
### Using ephemeris
# Lambert trajectory obtain terminal velocity vectors
date0 = np.array([27,1,2016,0])
t_0 = AL_Eph.DateConv(date0,'calendar') #To JD
transfertime = 250
# Create bodies
sun = AL_2BP.Body('sun', 'yellow', mu = Cts.mu_S_m)
earth = AL_2BP.Body('earth', 'blue', mu = Cts.mu_E_m)
mars = AL_2BP.Body('mars', 'red', mu = Cts.mu_M_m)
# Calculate trajectory of the bodies based on ephem
earthephem = pk.planet.jpl_lp('earth')
marsephem = pk.planet.jpl_lp('mars')
r_E, v_E = earthephem.eph(t_0.JD_0)
r_M, v_M = marsephem.eph(t_0.JD_0 + transfertime)
orbit_E = AL_2BP.BodyOrbit(np.append(r_E, v_E), 'Cartesian', sun)
earth.addOrbit(orbit_E)
orbit_M = AL_2BP.BodyOrbit(np.append(r_M, v_M), 'Cartesian', sun)
mars.addOrbit(orbit_M)
# Create transfer in the first moment
lambert = AL_TR.Lambert(np.array(r_E), np.array(r_M), AL_BF.days2sec(transfertime), sun.mu)
v_0, v_f = lambert.TerminalVelVect()
print(v_0)
# Propagate
orbit_sp = AL_2BP.BodyOrbit(np.append(r_E, v_0), 'Cartesian', sun)
x = orbit_sp.Propagation(AL_BF.days2sec(transfertime), 'Cartesian') # Coordinates after propagation
r0 = np.array(r_E)
rf = np.array(r_M)
print('Mars', r_M, 'Propagation', x, 'Error', abs(rf - x[0:3])) # Almost zero
fig = plt.figure()
ax = fig.gca(projection = '3d')
ax.scatter(0,0,0, color = 'yellow', s = 100)
ax.scatter(r0[0],r0[1],r0[2], color = 'blue')
ax.scatter(rf[0],rf[1],rf[2], color = 'red')
ax.scatter(x[0], x[1], x[2], color = 'green')
AL_Plot.set_axes_equal(ax)
plt.show() # Validated
def findValidLambert():
SF = CONFIG.SimsFlan_config()
earthephem = pk.planet.jpl_lp('earth')
marsephem = pk.planet.jpl_lp('mars')
counter = 0
valid = False
while valid == False:
decv = np.zeros(len(SF.bnds))
for i in range(6,8):
decv[i] = np.random.uniform(low = SF.bnds[i][0], \
high = SF.bnds[i][1], size = 1)
r_E, v_E = earthephem.eph(decv[6])
r_M, v_M = marsephem.eph(decv[6] + AL_BF.sec2days(decv[7]) )
# Create transfer in the first moment
nrevs = 2
l = pk.lambert_problem(r1 = r_E, r2 = r_M, tof = decv[7], \
cw = False, mu = Cts.mu_S_m, max_revs=nrevs)
v1 = np.array(l.get_v1())
v2 = np.array(l.get_v2())
v_i_prev = 1e12 # Excessive random value
for rev in range(len(v1)):
v_i = np.linalg.norm(v1[rev] - np.array(v_E)) # Relative velocities for the bounds
v_i2 = np.linalg.norm(v2[rev] - np.array(v_M))
# Change to polar for the bounds
if v_i >= SF.bnds[0][0] and v_i <= SF.bnds[0][1] and \
v_i2 >= SF.bnds[3][0] and v_i2 <= SF.bnds[3][1]:
print('decv')
print(v1[rev]-v_E,v2[rev]-v_M)
decv[0:3] = AL_BF.convert3dvector(v1[rev]-v_E, "cartesian")
decv[3:6] = AL_BF.convert3dvector(v2[rev]-v_M, "cartesian")
print(decv[0:6])
valid = True
print('rev', rev, v1[rev], v2[rev])
counter += 1
print(counter)
return decv, l
def propagateSimsFlanagan():
"Test the propagation of SimsFlanagan back and forth using the velocities from Lambert"
### Using ephemeris
# Lambert trajectory obtain terminal velocity vectors
SF = CONFIG.SimsFlan_config()
# Create bodies
sun = AL_2BP.Body('sun', 'yellow', mu = Cts.mu_S_m)
earth = AL_2BP.Body('earth', 'blue', mu = Cts.mu_E_m)
mars = AL_2BP.Body('mars', 'red', mu = Cts.mu_M_m)
# Calculate trajectory of the bodies based on ephem
earthephem = pk.planet.jpl_lp('earth')
marsephem = pk.planet.jpl_lp('mars')
decv, l = findValidLambert()
print(decv)
Fit = Fitness(Nimp = SF.Nimp)
Fit.calculateFitness(decv)
Fit.printResult()
# We plot
mpl.rcParams['legend.fontsize'] = 10
# Create the figure and axis
fig = plt.figure(figsize = (16,5))
ax1 = fig.add_subplot(1, 3, 1, projection='3d')
ax1.scatter([0], [0], [0], color=['y'])
ax2 = fig.add_subplot(1, 3, 2, projection='3d')
ax2.scatter([0], [0], [0], color=['y'])
ax2.view_init(90, 0)
ax3 = fig.add_subplot(1, 3, 3, projection='3d')
ax3.scatter([0], [0], [0], color=['y'])
ax3.view_init(0,0)
t1 = SF.t0.JD
t2 = t1 + AL_BF.sec2days(decv[7])
for ax in [ax1, ax2, ax3]:
# Plot the planet orbits
# plot_planet(earth, t0=t1, color=(0.8, 0.8, 1), legend=True, units=AU, axes=ax)
# plot_planet(mars, t0=t2, color=(0.8, 0.8, 1), legend=True, units=AU, axes=ax)
# Plot the Lambert solutions
axis = plot_lambert(l, color='b', legend=True, units=AU, axes=ax)
# axis = plot_lambert(l, sol=1, color='g', legend=True, units=AU, axes=ax)
# axis = plot_lambert(l, sol=2, color='g', legend=True, units=AU, axes=ax)
plt.show()
def propagateSimsFlanaganForward():
"Test the propagation of SimsFlanagan forward using the velocities from Lambert"
### Using ephemeris
# Lambert trajectory obtain terminal velocity vectors
SF = CONFIG.SimsFlan_config()
# Create bodies
sun = AL_2BP.Body('sun', 'yellow', mu = Cts.mu_S_m)
earth = AL_2BP.Body('earth', 'blue', mu = Cts.mu_E_m)
mars = AL_2BP.Body('mars', 'red', mu = Cts.mu_M_m)
# Calculate trajectory of the bodies based on ephem
earthephem = pk.planet.jpl_lp('earth')
marsephem = pk.planet.jpl_lp('mars')
decv, l = findValidLambert()
print(decv)
r_M, v_M = marsephem.eph(decv[6] + AL_BF.sec2days(decv[7]) )
Fit = Propagate(Nimp = SF.Nimp)
Fit.prop(decv, plot = True)
print("Final", Fit.rv_final)
print("Theoretical final", r_M, AL_BF.convert3dvector(decv[3:6], "polar")+v_M)
def __init__(self, *args, **kwargs):
#Constants used
Cts = AL_BF.ConstantsBook() #Constants for planets
# Define bodies involved
self.Spacecraft = AL_2BP.Spacecraft()
self.earthephem = pk.planet.jpl_lp('earth')
self.marsephem = pk.planet.jpl_lp('mars')
self.jupiterephem = pk.planet.jpl_lp('jupiter')
self.venusephem = pk.planet.jpl_lp('venus')
self.sun = AL_2BP.Body('Sun', 'yellow', mu=Cts.mu_S_m)
self.earth = AL_2BP.Body('Earth', 'blue', mu=Cts.mu_E_m)
self.mars = AL_2BP.Body('Mars', 'red', mu=Cts.mu_M_m)
self.jupiter = AL_2BP.Body('Jupiter', 'green', mu=Cts.mu_J_m)
self.venus = AL_2BP.Body('Venus', 'brown', mu=Cts.mu_V_m)
departure = kwargs.get('departure', "Earth")
arrival = kwargs.get('arrival', "Mars")
if departure == 'Earth':
self.departure = self.earth
self.departureephem = self.earthephem
elif departure == 'Mars':
self.departure = self.mars
self.departureephem = self.marsephem
if arrival == 'Mars':
self.arrival = self.mars
self.arrivalephem = self.marsephem
elif arrival == 'Jupiter':
self.arrival = self.jupiter
self.arrivalephem = self.jupiterephem
elif arrival == 'Venus':
self.arrival = self.venus
self.arrivalephem = self.venusephem
elif arrival == 'Earth':
self.arrival = self.earth
self.arrivalephem = self.earthephem
# Settings
self.color = ['blue', 'green', 'black', 'red', 'yellow', 'orange']
# Choose odd number to compare easily in middle point
self.Nimp = kwargs.get('Nimp', 11)
def EvolAlgorithm_cons(f, bounds, *args, **kwargs):
"""
EvolAlgorithm: evolutionary algorithm
INPUTS:
f: function to be analyzed
x: decision variables
bounds: bounds of x to initialize the random function
x_add: additional parameters for the function. As a vector
ind: number of individuals.
cuts: number of cuts to the variable
tol: tolerance for convergence
max_iter: maximum number of iterations (generations)
max_iter_success
elitism: percentage of population elitism
mut: mutation rate
immig: migration rate (new individuals)
cons: list of functions to constrain a function. Function should return a vector. return[0] = 0 if unfeasible, return[1] returns penalty
"""
x_add = kwargs.get('x_add', False)
ind = kwargs.get('ind', 100)
cuts = kwargs.get('cuts', 1)
tol = kwargs.get('tol', 1e-4)
max_iter = kwargs.get('max_iter', 1e3)
max_iter_success = kwargs.get('max_iter_success', 1e2)
elitism = kwargs.get('elitism', 0.1)
mut = kwargs.get('mutation', 0.01)
immig = kwargs.get('immig', 0.01)
cons = kwargs.get('cons', None)
###############################################
###### GENERATION OF INITIAL POPULATION #######
###############################################
pop_0 = np.zeros([ind, len(bounds) + 1])
for i in range(len(bounds)):
pop_0[:, i + 1] = np.random.rand(ind) * (bounds[i][1] -
bounds[i][0]) + bounds[i][0]
###############################################
###### FITNESS EVALUATION #######
###############################################
if x_add == False: # No additional arguments needed
for i in range(ind):
pop_0[i, 0] = f(pop_0[i, 1:])
else:
for i in range(ind):
pop_0[i, 0] = f(pop_0[i, 1:], x_add)
for j in range(ind):
for i in range(len(cons)):
feas = cons[i](pop_0[j, 1:]) # evaluate constraint function
if feas[0] == 0: # it is unfeasible
pop_0[:, 0] += feas[1] # Add penalty to unfeasible ones
Sol = pop_0[pop_0[:, 0].argsort()]
minVal = min(Sol[:, 0])
x_minVal = Sol[0, :]
###############################################
###### NEXT GENERATION #######
###############################################
noImprove = 0
counter = 0
lastMin = minVal
Best = np.zeros([max_iter + 1, len(bounds) + 1])
while noImprove <= max_iter_success and counter <= max_iter:
###############################################
#Generate descendents
#Elitism
ind_elit = int(round(elitism * ind))
children = np.zeros(np.shape(pop_0))
children[:, 1:] = Sol[:, 1:]
#Separate into the number of parents
pop = np.zeros(np.shape(pop_0))
pop[:ind_elit, :] = children[:ind_elit, :] #Keep best ones
np.random.shuffle(children[:, :]) #shuffle the others
for j in range((len(children) - ind_elit) // 2):
if len(bounds) == 2:
cut = 1
else:
cut = np.random.randint(1, len(bounds) - 1)
pop[ind_elit + 2 * j, 1:] = np.concatenate(
(children[2 * j, 1:cut + 1], children[2 * j + 1, cut + 1:]),
axis=0)
pop[ind_elit + 2 * j + 1, 1:] = np.concatenate(
(children[2 * j + 1, 1:cut + 1], children[2 * j, cut + 1:]),
axis=0)
if (len(children) - ind_elit) % 2 != 0:
pop[-1, :] = children[-ind_elit, :]
#Mutation
for i in range(ind):
for j in range(len(bounds)):
if np.random.rand(1) < mut: #probability of mut
pop[i, j +
1] = np.random.rand(1) * (bounds[j][1] -
bounds[j][0]) + bounds[j][0]
#Immigration
ind_immig = int(round(immig * ind))
for i in range(len(bounds)):
pop[-ind_immig:, i +
1] = np.random.rand(ind_immig) * (bounds[i][1] -
bounds[i][0]) + bounds[i][0]
###############################################
# Fitness
if x_add == False: # No additional arguments needed
for i in range(ind):
pop[i, 0] = f(pop[i, 1:])
else:
for i in range(ind):
pop[i, 0] = f(pop[i, 1:], x_add)
for j in range(ind):
for i in range(len(cons)):
feas = cons[i](pop[j, 1:]) # evaluate constraint function
if feas[0] == 0: # it is unfeasible
pop[:, 0] += feas[1] # Add penalty to unfeasible ones
Sol = pop[pop[:, 0].argsort()]
minVal = min(Sol[:, 0])
###############################################
#Check convergence
if minVal >= lastMin:
noImprove += 1
else:
lastMin = minVal
x_minVal = Sol[0, 1:]
noImprove = 0
Best[counter, :] = Sol[0, :]
print(counter, "Minimum: ", minVal)
counter += 1 #Count generations
if counter % 20 == 0:
AL_BF.writeData(x_minVal, 'w', 'SolutionEA.txt')
# print(counter)
print("minimum:", lastMin)
print("Iterations:", counter)
print("Iterations with no improvement:", noImprove)
return x_minVal, lastMin
def EvolAlgorithm_integerinput(f, bounds, *args, **kwargs):
"""
EvolAlgorithm_integerinput: evolutionary algorithm, some inputs will be fixed to integers if decided
INPUTS:
f: function to be analyzed
x: decision variables
bounds: bounds of x to initialize the random function
x_add: additional parameters for the function. As a vector
ind: number of individuals.
cuts: number of cuts to the variable
tol: tolerance for convergence
max_iter: maximum number of iterations (generations)
max_iter_success
elitism: percentage of population elitism
bulk_fitness: if True, the data has to be passed to the function all
at once as a matrix with each row being an individual
int_input: force some inputs to be integers. Vector with zero (not integer) or one if integer
"""
x_add = kwargs.get('x_add', False)
ind = kwargs.get('ind', 100)
cuts = kwargs.get('cuts', 1)
tol = kwargs.get('tol', 1e-4)
max_iter = kwargs.get('max_iter', 1e3)
max_iter_success = kwargs.get('max_iter_success', 1e2)
elitism = kwargs.get('elitism', 0.1)
mut = kwargs.get('mutation', 0.01)
bulk = kwargs.get('bulk_fitness', False)
int_input = kwargs.get('int_input', np.zeros(len(bounds)))
def f_evaluate(pop_0):
if bulk == True:
if x_add == False:
pop_0[:, 0] = f(pop_0[:, 1:])
else:
pop_0[:, 0] = f(pop_0[:, 1:], x_add)
else:
if x_add == False: # No additional arguments needed
for i in range(ind):
pop_0[i, 0] = f(pop_0[i, 1:])
else:
for i in range(ind):
pop_0[i, 0] = f(pop_0[i, 1:], x_add)
return pop_0
###############################################
###### GENERATION OF INITIAL POPULATION #######
###############################################
pop_0 = np.zeros([ind, len(bounds) + 1])
for i in range(len(bounds)):
pop_0[:, i + 1] = np.random.uniform(low=bounds[i][0],
high=bounds[i][1],
size=ind)
if int_input[i] != 0: # Force it to be an integer
pop_0[:, i + 1] = [int(pop_0[indx, i + 1]) for indx in range(ind)]
print(pop_0)
###############################################
###### FITNESS EVALUATION #######
###############################################
pop_0 = f_evaluate(pop_0)
Sol = pop_0[pop_0[:, 0].argsort()]
minVal = min(Sol[:, 0])
x_minVal = Sol[0, :]
###############################################
###### NEXT GENERATION #######
###############################################
noImprove = 0
counter = 0
lastMin = minVal
Best = np.zeros([max_iter + 1, len(bounds) + 1])
while noImprove <= max_iter_success and counter <= max_iter:
###############################################
#Generate descendents
#Elitism
ind_elit = int(round(elitism * ind))
children = np.zeros(np.shape(pop_0))
children[:, 1:] = Sol[:, 1:]
#Separate into the number of parents
pop = np.zeros(np.shape(pop_0))
pop[:ind_elit, :] = children[:ind_elit, :] #Keep best ones
np.random.shuffle(children[:, :]) #shuffle the others
for j in range((len(children) - ind_elit) // 2):
if len(bounds) == 2:
cut = 1
else:
cut = np.random.randint(1, len(bounds) - 1)
pop[ind_elit + 2 * j, 1:] = np.concatenate(
(children[2 * j, 1:cut + 1], children[2 * j + 1, cut + 1:]),
axis=0)
pop[ind_elit + 2 * j + 1, 1:] = np.concatenate(
(children[2 * j + 1, 1:cut + 1], children[2 * j, cut + 1:]),
axis=0)
if (len(children) - ind_elit) % 2 != 0:
pop[-1, :] = children[-ind_elit, :]
#Mutation
for i in range(ind):
for j in range(len(bounds)):
if np.random.rand(1) < mut: #probability of mut
pop[i, j +
1] = np.random.rand(1) * (bounds[j][1] -
bounds[j][0]) + bounds[j][0]
if int_input[i] != 0: # Force to be an int
pop[i, j + 1] = int(pop[i, j + 1])
###############################################
# Fitness
pop = f_evaluate(pop)
Sol = pop[pop[:, 0].argsort()]
minVal = min(Sol[:, 0])
###############################################
#Check convergence
if minVal >= lastMin:
noImprove += 1
# elif abs(lastMin-minVal)/lastMin > tol:
# noImprove += 1
else:
# print('here')
lastMin = minVal
x_minVal = Sol[0, 1:]
noImprove = 0
Best[counter, :] = Sol[0, :]
print(counter, "Minimum: ", minVal)
counter += 1 #Count generations
if counter % 20 == 0:
AL_BF.writeData(x_minVal, 'w', 'SolutionEA.txt')
# print(counter)
print("minimum:", lastMin)
print("Iterations:", counter)
print("Iterations with no improvement:", noImprove)
return x_minVal, lastMin
def test_convertRange():
angle = -3.02
print(AL_BF.convertRange(angle, 'deg', 0, 360))
def test_Exposin():
# Verify with example
print("VERIFICATION")
r1 = 1
r2 = 5
psi = np.pi/2
k2 = 1/4
# eSin = AL_Sh.shapingMethod(sun.mu / Cts.AU_m**3)
# gammaOptim_v = eSin.start(r1, r2, psi, 365*1.5, k2) # verified
# Ni = 1
# print(gammaOptim_v)
# eSin.plot_sphere(r1, r2, psi, gammaOptim_v[Ni], Ni) # verified
# Real life case
sun = AL_2BP.Body('sun', 'yellow', mu = Cts.mu_S_m)
earth = AL_2BP.Body('earth', 'blue', mu = Cts.mu_E_m)
mars = AL_2BP.Body('mars', 'red', mu = Cts.mu_M_m)
# Calculate trajectory of the bodies based on ephem
earthephem = pk.planet.jpl_lp('earth')
marsephem = pk.planet.jpl_lp('mars')
date0 = np.array([27,1,2018,0])
t0 = AL_Eph.DateConv(date0,'calendar') #To JD
t_t = 350
r_E, v_E = earthephem.eph( t0.JD_0 )
r_M, v_M = marsephem.eph(t0.JD_0+ t_t )
r_1 = r_E
r_2 = r_M
r_1_norm = np.linalg.norm( r_1 )
r_2_norm = np.linalg.norm( r_2 )
dot = np.dot(r_1[0:2], r_2[0:2]) # dot product between [x1, y1] and [x2, y2]
det = r_1[0]*r_2[1] - r_2[0]*r_1[1] # determinant
psi = np.arctan2(det, dot)
psi = AL_BF.convertRange(psi, 'rad', 0 ,2*np.pi)
k2 = 1/12
eSin = AL_Sh.shapingMethod(sun.mu / AL_BF.AU**3)
gammaOptim_v = eSin.calculategamma1(r_1_norm / AL_BF.AU, r_2_norm / AL_BF.AU, psi, \
t_t, k2, plot = False)
Ni = 1
eSin.calculateExposin(Ni, gammaOptim_v[Ni],r_1_norm / AL_BF.AU, r_2_norm / AL_BF.AU, psi )
eSin.plot_sphere(r_1_norm / AL_BF.AU, r_2_norm / AL_BF.AU, psi)
v1, v2 = eSin.terminalVel(r_1_norm / AL_BF.AU, r_2_norm / AL_BF.AU, psi)
t, a_T = eSin.calculateThrustProfile(r_1_norm / AL_BF.AU, r_2_norm / AL_BF.AU, psi)
# print('a', a_T*AL_BF.AU)
print("body coord", v1, v2)
# print(v1[0]*Cts.AU_m, v1[1], v2[0]*Cts.AU_m, v2[1])
# To heliocentric coordinates (2d approximation)
def to_helioc(r, vx, vy):
v_body = np.array([vx, vy, 0])
# Convert to heliocentric
angle = np.arctan2(r[1], r[0])
v_h = AL_BF.rot_matrix(v_body, angle, 'z')
return v_h
v_1 = to_helioc(r_1, v1[0]*AL_BF.AU, v1[1]*AL_BF.AU)
v_2 = to_helioc(r_2, v2[0]*AL_BF.AU, v2[1]*AL_BF.AU)
# def to_helioc(r, v, gamma):
# v_body = np.array([v*np.sin(gamma), \
# v*np.cos(gamma),\
# 0])
# # Convert to heliocentric
# angle = np.arctan2(r[1], r[0])
# v_h = AL_BF.rot_matrix(v_body, angle, 'z')
# return v_h
# v_1 = to_helioc(r_1, v1[0]*Cts.AU_m, v1[1])
# v_2 = to_helioc(r_2, v2[0]*Cts.AU_m, v2[1])
print("Helioc vel", v_1, v_2)
print("with respect to body ", v_1-v_E, v_2-v_M)
v_1_E = np.linalg.norm(v_1-v_E)
v_2_M = np.linalg.norm(v_2-v_M)
print("Final vel", v_1_E, v_2_M)
################################################3
# Propagate with terminal velocity vectors
SF = CONFIG.SimsFlan_config()
Fit = Fitness(Nimp = SF.Nimp)
decv = np.zeros(len(SF.bnds))
decv[6] = t0.JD_0
decv[7] = AL_BF.days2sec(t_t)
decv[0:3] = AL_BF.convert3dvector(v_1-v_E,'cartesian')
decv[3:6] = AL_BF.convert3dvector(v_2- v_M,'cartesian')
print('decv', decv[0:9])
for i in range(SF.Nimp):
# Acceleration on segment is average of extreme accelerations
t_i = AL_BF.days2sec(t_t) / (SF.Nimp+1) *i
t_i1 = AL_BF.days2sec(t_t) / (SF.Nimp+1) *(i+1)
#find acceleration at a certain time
a_i = eSin.accelerationAtTime(t_i)
a_i1 = eSin.accelerationAtTime(t_i1)
a = (a_i+a_i1)/2 # find the acceleration at a certain time
print("a", a_i, a_i1, a)
deltav_i = AL_BF.days2sec(t_t) / (SF.Nimp+1) * a*AL_BF.AU
print(deltav_i)
decv[8+3*i] = deltav_i /100
decv[8+3*i+1] = 0
decv[8+3*i+2] = 0
Fit.calculateFeasibility(decv, plot = True, thrust = 'tangential')
Fit.printResult()
def adaptDecisionVector(self, DecV, optMode=True):
"""
adaptDecisionVector: modify decision vector to input in the problem
"""
self.DecV = DecV
v0 = np.array(DecV[0:3]) # vector, [magnitude, angle, angle]
vf = np.array(DecV[3:6]) # vector, [magnitude, angle, angle]
self.t0, self.t_t = DecV[6:8]
# Delta V
if optMode == True:
DeltaV_list = np.array(DecV[8:]).reshape(
-1, 3) # make a Nimp x 3 matrix
else:
DeltaV_list = DecV[8:][0]
########################################################################
# INITIAL CALCULATION OF VARIABLES
########################################################################
# Modify from magnitude angle angle to cartesian
self.DeltaV_list = np.zeros(np.shape(DeltaV_list))
DeltaV_sum = np.zeros(len(
self.DeltaV_list)) # Magnitude of the impulses
for i in range(len(self.DeltaV_list)):
self.DeltaV_list[i, :] = AL_BF.convert3dvector(
DeltaV_list[i, :], "polar")
DeltaV_sum[i] = np.linalg.norm(self.DeltaV_list[i])
# Write velocity as x,y,z vector
v0_cart = AL_BF.convert3dvector(v0, "polar")
vf_cart = AL_BF.convert3dvector(vf, "polar")
# Sum of all the Delta V = DeltaV max * sum Delta V_list
# Assumption: the DeltaV_max is calculated as if mass is constant and
# equal to the dry mass as it is the largest contribution. This means
# that the DelaV_max is actually smaller than it will be obtained in this
# problem
# = Thrust for segment
self.DeltaV_max = self.Spacecraft.T / self.Spacecraft.m_dry * \
self.t_t / (self.Nimp + 1)
# Total DeltaV
DeltaV_total = sum(DeltaV_sum) * self.DeltaV_max
#Calculate total mass of fuel for the given impulses
self.m0 = \
self.Spacecraft.MassChangeInverse(self.Spacecraft.m_dry, DeltaV_total)
self.m_fuel = self.m0 - self.Spacecraft.m_dry
# Times and ephemeris
# t_0 = AL_Eph.DateConv(self.date0,'calendar') #To JD
self.t_1 = AL_Eph.DateConv(self.t0 + AL_BF.sec2days(self.t_t), 'JD_0')
self.r_p0, self.v_p0 = self.departureephem.eph(self.t0)
self.r_p1, self.v_p1 = self.arrivalephem.eph(self.t_1.JD_0)
# Change from relative to heliocentric velocity
self.v0 = v0_cart + self.v_p0
self.vf = vf_cart + self.v_p1
# Create state vector for initial and final point
self.SV_0 = np.append(self.r_p0, self.v0)
self.SV_f = np.append(self.r_p1, self.vf) # - to propagate backwards
self.SV_f_corrected = np.append(self.r_p1,
-self.vf) # - to propagate backwards
def validateSimsFlanagan():
SF = CONFIG.SimsFlan_config()
# Create bodies
sun = AL_2BP.Body('sun', 'yellow', mu = Cts.mu_S_m)
earth = AL_2BP.Body('earth', 'blue', mu = Cts.mu_E_m)
mars = AL_2BP.Body('mars', 'red', mu = Cts.mu_M_m)
Spacecraft = AL_2BP.Spacecraft( )
# Calculate trajectory of the bodies based on ephem
earthephem = pk.planet.jpl_lp('earth')
marsephem = pk.planet.jpl_lp('mars')
# Create a random decision vector
DecV = np.zeros(len(SF.bnds))
samples_rand = 1
np.random.seed(1)
for decv in range(len(SF.bnds)):
DecV[decv] = np.random.rand(samples_rand) * (SF.bnds[decv][1]-SF.bnds[decv][0]) + SF.bnds[decv][0]
DecV[8:] = np.zeros(3*SF.Nimp)
Fit = Fitness(Nimp = 21)
Fit.adaptDecisionVector(DecV)
r = Fit.r_p0
r2 = Fit.r_p1
v = Fit.v0
v2 = Fit.vf
imp = Fit.DeltaV_list.flatten()
# imp = (0,0,0,0,0,0,0,0,0,0,0,0)
m0 = Fit.m0
# Pykep Sims-Flanagan
start = pk.epoch(DecV[6])
end = pk.epoch(DecV[6] + AL_BF.sec2days(DecV[7]) )
sc = pk.sims_flanagan.spacecraft(m0, Spacecraft.T, Spacecraft.Isp)
x0 = pk.sims_flanagan.sc_state(r,v,sc.mass)
xe = pk.sims_flanagan.sc_state(r2,v2,sc.mass)
l = pk.sims_flanagan.leg(start, x0, imp, end, xe, sc,pk.MU_SUN)
ceq = l.mismatch_constraints()
c = l.throttles_constraints()
times,r,v,m = l.get_states()
r_vec = np.zeros((len(r),3))
for i in range(len(r)):
r_vec[i, :] = r[i]
plt.scatter(r[i][0], r[i][1], color = 'black')
if i == 0:
plt.scatter(r[i][0], r[i][1], color = 'blue')
elif i == len(r)-1:
plt.scatter(r[i][0], r[i][1], color = 'red')
elif i == len(r)//2+1:
plt.scatter(r[i][0], r[i][1], color = 'orange')
elif i == len(r)//2:
plt.scatter(r[i][0], r[i][1], color = 'green')
# plt.show()
# plt.plot(r_vec[:,0], r_vec[:,1])
plt.grid()
print("State middle point")
print(r[len(r)//2], v[len(r)//2])
print(r[len(r)//2+1], v[len(r)//2+1])
print("Mismatch middle point")
mis_r = -(np.array(r[len(r)//2]) - np.array(r[len(r)//2+1]))
mis_v = -(np.array(v[len(r)//2] )- np.array(v[len(r)//2+1]))
print(mis_r, mis_v)
mis = np.append(mis_r, mis_v)
Fit = Fitness(Nimp = SF.Nimp)
Fit.calculateFitness(DecV, plot=True)
cep2 = Fit.Error
# print(Fit.SV_0, Fit.SV_f)
for i in range(6):
print(i, ceq[i], '%.4E'%cep2[i], '%.4E'%mis[i])
from FitnessFunction import Fitness
import AstroLibraries.AstroLib_Basic as AL_BF
from AstroLibraries import AstroLib_2BP as AL_2BP
from AstroLibraries import AstroLib_Ephem as AL_Eph
from AstroLibraries import AstroLib_Plots as AL_Plot
from AstroLibraries import AstroLib_Trajectories as AL_TR
from AstroLibraries import AstroLib_ShapingMethod as AL_Sh
import LoadConfigFiles as CONFIG
# # In[]
# ##############################################
# ############# VALIDATION #####################
# ##############################################
Cts = AL_BF.ConstantsBook()
def pykepephem():
# earth = pk.planet.jpl_lp('earth')
# marsephem = pk.planet.jpl_lp('mars')
earth = pk.planet.jpl_lp('earth')
K_0 = earth.eph(0)
# print(K_0)
earth.mu_central_body
print(earth.mu_self)
elem = earth.osculating_elements(pk.epoch(2345.3, 'mjd2000'))
print(elem)
print(np.array(elem))
def MonotonicBasinHopping(f, x, take_step, *args, **kwargs):
"""
Step for jump is small, as minimum cluster together.
Jump from current min until n_no improve reaches the lim, then the jump is random again.
"""
niter = kwargs.get('niter', 100)
niter_success = kwargs.get('niter_success', 50)
niter_local = kwargs.get('niter_local', 50)
bnds = kwargs.get('bnds', None)
cons = kwargs.get('cons', 0)
jumpMagnitude_default = kwargs.get(
'jumpMagnitude', 0.1) # Small jumps to search around the minimum
tolLocal = kwargs.get('tolLocal', 1e2)
tolGlobal = kwargs.get('tolGobal', 1e-5)
n_itercounter = 1
n_noimprove = 0
Best = x
bestMin = f(x)
previousMin = f(x)
jumpMagnitude = jumpMagnitude_default
while n_itercounter < niter:
n_itercounter += 1
# Change decision vector to find one within the bounds
feasible = False
while feasible == False and bnds != None:
x_test = take_step.call(x, jumpMagnitude)
feasible = check_feasibility(x_test, bnds)
# if feasible == True:
# feasible = check_constraints(x, cons)
# Local optimization
# solutionLocal = spy.minimize(f, x, method = 'COBYLA', constraints = cons, options = {'maxiter': niter_local} )
if type(cons) == int:
solutionLocal = spy.minimize(f, x_test, method = 'SLSQP', \
tol = tolLocal, bounds = bnds, options = {'maxiter': niter_local} )
else:
solutionLocal = spy.minimize(f, x_test, method = 'SLSQP', \
tol = tolLocal, bounds = bnds, options = {'maxiter': niter_local},\
constraints = cons )
currentMin = f(solutionLocal.x)
feasible = check_feasibility(solutionLocal.x, bnds)
# if feasible == True: # jump from current point even if it is not optimum
# x = solutionLocal.x
# Check te current point from which to jump: after doing a long jump or
# when the solution is improved
if jumpMagnitude == 1 or currentMin < previousMin:
x = solutionLocal.x
# Check improvement
if currentMin < bestMin and feasible == True: # Improvement
Best = x
bestMin = currentMin
accepted = True
# If the improvement is not large, assume it is not improvement
if (previousMin - currentMin) < tolGlobal:
n_noimprove += 1
else:
n_noimprove = 0
jumpMagnitude = jumpMagnitude_default
elif n_noimprove == niter_success: # Not much improvement
accepted = False
jumpMagnitude = 1
n_noimprove = 0 # Restart count so that it performs jumps around a point
else:
accepted = False
jumpMagnitude = jumpMagnitude_default
n_noimprove += 1
previousMin = currentMin
# Save results every 5 iter
if n_itercounter % 20 == 0:
AL_BF.writeData(Best, 'w', 'SolutionMBH_self.txt')
print("iter", n_itercounter)
print("Current min vs best one", currentMin, bestMin)
print_fun(f, x, accepted)
# Print solution
# print_sol(Best, bestMin, n_itercounter, niter_success)
return Best, bestMin
def MonotonicBasinHopping_batch(f, x, take_step, *args, **kwargs):
"""
Step for jump is small, as minimum cluster together.
Jump from current min until n_no improve reaches the lim, then the jump is random again.
"""
f_batch = kwargs.get('f_batch', False) # Function allows for batch eval
f_opt = kwargs.get('f_opt', None) # function to optimze locally
nind = kwargs.get('nind', 100)
niter = kwargs.get('niter', 100)
niter_success = kwargs.get('niter_success', 50)
bnds = kwargs.get('bnds', None)
jumpMagnitude_default = kwargs.get(
'jumpMagnitude', 0.1) # Small jumps to search around the minimum
tolGlobal = kwargs.get('tolGobal', 1e-5)
niter_local = kwargs.get('niter_local', 50)
tolLocal = kwargs.get('tolLocal', 1e2)
n_itercounter = 1
n_noimprove = np.zeros((nind))
Best = x
bestMin = seqEval(f, x)
previousMin = seqEval(f, x)
jumpMagnitude = np.ones((nind)) * jumpMagnitude_default
accepted = np.zeros((nind))
feasibility = np.zeros((nind))
while n_itercounter < niter:
n_itercounter += 1
# Change decision vector to find one within the bounds
x_test = np.zeros(np.shape(x))
for ind in range(nind):
feasible = False
while feasible == False and bnds != None:
x_test[ind] = take_step.call(x[ind], jumpMagnitude[ind])
feasible = check_feasibility(x_test[ind], bnds)
# Local optimization
if f_opt != None:
currentMin = f_opt(x_test)
solutionLocal = x_test
else:
currentMin = np.zeros((nind))
solutionLocal = np.zeros(np.shape(x))
for ind in range(nind):
solLocal = spy.minimize(f, x_test[ind], method = 'SLSQP', \
tol = tolLocal, bounds = bnds, options = {'maxiter': niter_local} )
solutionLocal[ind] = solLocal.x
feasibility[ind] = check_feasibility(solLocal.x, bnds)
if feasibility[ind] == False:
print("Warning: Point out of limits")
currentMin = seqEval(f, solutionLocal)
for ind in range(nind):
# Check te current point from which to jump: after doing a long jump or
# when the solution is improved
if jumpMagnitude[ind] == 1 or currentMin[ind] < previousMin[ind]:
x[ind] = solutionLocal[ind]
# Check improvement
if currentMin[ind] < bestMin[ind] and feasibility[
ind] == True: # Improvement
Best[ind] = x[ind]
bestMin[ind] = currentMin[ind]
accepted[ind] = True
# If the improvement is not large, assume it is not improvement
if (previousMin[ind] - currentMin[ind]) < tolGlobal:
n_noimprove[ind] += 1
else:
n_noimprove[ind] = 0
jumpMagnitude[ind] = jumpMagnitude_default
elif n_noimprove[ind] == niter_success: # Go to a bigger jump
accepted[ind] = False
jumpMagnitude[ind] = 1
n_noimprove[
ind] = 0 # Restart count so that it performs jumps around a point
else:
accepted[ind] = False
jumpMagnitude[ind] = jumpMagnitude_default
n_noimprove[ind] += 1
previousMin[ind] = currentMin[ind]
# Save results every 5 iter
if n_itercounter % 20 == 0:
AL_BF.writeData(Best, 'w', './OptSol/allMBH_batch.txt')
print("iter", n_itercounter)
print("Current min vs best one", min(currentMin), min(bestMin))
print_fun(min(currentMin), min(bestMin),
np.count_nonzero(accepted == True))
# Print solution
# print_sol(Best, bestMin, n_itercounter, niter_success)
return Best, bestMin
def DecV2inputV(self, typeinputs, newDecV=0):
if type(newDecV) != int:
self.adaptDecisionVector(newDecV)
inputs = np.zeros(8)
inputs[0] = self.t_t
inputs[1] = self.m0
if typeinputs == "deltakeplerian":
# Elements of the spacecraft
earth_elem = AL_2BP.BodyOrbit(self.SV_0, "Cartesian", self.sun)
elem_0 = earth_elem.KeplerElem
mars_elem = AL_2BP.BodyOrbit(self.SV_f, "Cartesian", self.sun)
elem_f = mars_elem.KeplerElem
K_0 = np.array(elem_0)
K_f = np.array(elem_f)
# Mean anomaly to true anomaly
K_0[-1] = AL_2BP.Kepler(K_0[-1], K_0[1], 'Mean')[0]
K_f[-1] = AL_2BP.Kepler(K_f[-1], K_f[1], 'Mean')[0]
inputs[2:] = K_f - K_0
inputs[2] = abs(inputs[2]) # absolute value
inputs[3] = abs(inputs[3]) # absolute value
inputs[4] = np.cos(inputs[4]) # cosine
labels = [
r'$t_t$', r'$m_0$', r'$\vert \Delta a \vert$',
r'$\vert \Delta e \vert$', r'$cos( \Delta i) $',
r'$ \Delta \Omega$', r'$ \Delta \omega$', r'$ \Delta \theta$'
]
elif typeinputs == "deltakeplerian_planet":
t_1 = self.t0 + AL_BF.sec2days(self.t_t)
# Elements of the planets
elem_0 = self.departureephem.osculating_elements(
pk.epoch(self.t0, 'mjd2000'))
elem_f = self.arrivalephem.osculating_elements(
pk.epoch(t_1, 'mjd2000'))
K_0 = np.array(elem_0)
K_f = np.array(elem_f)
# Mean anomaly to true anomaly
K_0[-1] = AL_2BP.Kepler(K_0[-1], K_0[1], 'Mean')[0]
K_f[-1] = AL_2BP.Kepler(K_f[-1], K_f[1], 'Mean')[0]
inputs[2:] = K_f - K_0
inputs[2] = abs(inputs[2]) # absolute value
inputs[3] = abs(inputs[3]) # absolute value
inputs[4] = np.cos(inputs[4]) # cosine
labels = [
r'$t_t$', r'$m_0$', r'$\vert \Delta a \vert$',
r'$\vert \Delta e \vert$', r'$cos( \Delta i) $',
r'$ \Delta \Omega$', r'$ \Delta \omega$', r'$ \Delta \theta$'
]
elif typeinputs == "cartesian":
delta_r = np.array(self.SV_f[0:3]) - np.array(self.SV_0[0:3])
delta_v = np.array(self.SV_f[3:]) - np.array(self.SV_0[3:])
inputs[2:5] = np.abs(delta_r)
inputs[5:] = np.abs(delta_v)
labels = [
r'$t_t$', r'$m_0$', r'$\vert \Delta x \vert$',
r'$\vert \Delta y \vert$', r'$\vert \Delta z \vert$',
r'$\vert \Delta v_x \vert$', r'$\vert \Delta v_y \vert$',
r'$\vert \Delta v_z \vert$'
]
return inputs, labels
