def _no_collapse_expect_out(num_times,expect_out):
##Calculates xpect.values at times tlist if no collapse ops. given
#
#------------------------------------
opt=odeconfig.options
if odeconfig.tflag in array([1,10,11]):
ODE=ode(odeconfig.tdfunc)
code = compile('ODE.set_f_params('+odeconfig.string+')', '<string>', 'exec')
exec(code)
elif odeconfig.tflag==2:
ODE=ode(_cRHStd)
elif odeconfig.tflag in array([20,22]):
ODE=ode(_tdRHStd)
elif odeconfig.tflag==3:
ODE=ode(_pyRHSc)
else:
ODE = ode(cyq_ode_rhs)
ODE.set_f_params(odeconfig.h_data, odeconfig.h_ind, odeconfig.h_ptr)
ODE.set_integrator('zvode',method=opt.method,order=opt.order,atol=opt.atol,rtol=opt.rtol,nsteps=opt.nsteps,first_step=opt.first_step,min_step=opt.min_step,max_step=opt.max_step) #initialize ODE solver for RHS
ODE.set_initial_value(odeconfig.psi0,odeconfig.tlist[0]) #set initial conditions
for jj in range(odeconfig.e_num):
expect_out[jj][0]=mc_expect(odeconfig.e_ops_data[jj],odeconfig.e_ops_ind[jj],odeconfig.e_ops_ptr[jj],odeconfig.e_ops_isherm[jj],odeconfig.psi0)
for k in range(1,num_times):
ODE.integrate(odeconfig.tlist[k],step=0) #integrate up to tlist[k]
if ODE.successful():
state=ODE.y/norm(ODE.y)
for jj in range(odeconfig.e_num):
expect_out[jj][k]=mc_expect(odeconfig.e_ops_data[jj],odeconfig.e_ops_ind[jj],odeconfig.e_ops_ptr[jj],odeconfig.e_ops_isherm[jj],state)
else:
raise ValueError('Error in ODE solver')
return expect_out #return times and expectiation values
def __init__(self, lat, lon, h, psi=0, x_earth=0, y_earth=0,
integrator='dopri5', use_jac=False, **integrator_params):
"""
Initialize the equations of the chosen model and selects the
integrator. Check `scipy.integrate.ode` to see available integrators.
If use_jac = True the jacobian of the equations is used for the
integration.
"""
super().__init__(lat, lon, h, psi, x_earth, y_earth)
# State vector must be initialized with set_initial_state_vector() method
self.state_vector = None
from pyfme.models.euler_flat_earth import lamceq, kaeq, kleq
self.lamceq = lamceq
if use_jac:
from pyfme.models.euler_flat_earth import lamceq_jac, kaeq_jac
jac_LM_and_AM = lamceq_jac
jac_att = kaeq_jac
jac_nav = None # not implemented
else:
jac_LM_and_AM = None
jac_att = None
jac_nav = None
self._ode_lamceq = ode(lamceq, jac=jac_LM_and_AM)
self._ode_kaqeq = ode(kaeq, jac=jac_att)
self._ode_kleq = ode(kleq, jac=jac_nav)
self._ode_lamceq.set_integrator(integrator, **integrator_params)
self._ode_kaqeq.set_integrator(integrator, **integrator_params)
self._ode_kleq.set_integrator(integrator, **integrator_params)
def shooting_function_full(self, r0):
r"""
The function used in the shooting algorithm.
This is the full algorithm from integrating over
:math:`0 \le \theta \le \pi`. The difference between the
solution and its derivative at the matching point is the
error to be minimized.
Parameters
----------
r0 : list of float
Initial guess for the horizon radius, as outlined above.
Returns
-------
list of float
The error at the matching point.
"""
# First half of the horizon
H0 = np.array([r0[0], 0.0])
solver1 = ode(self.expansion)
solver1.set_integrator("dopri5", atol=1.e-8, rtol=1.e-6)
solver1.set_initial_value(H0, 0.0)
solver1.integrate(np.pi / 2.0)
# Second half of the horizon
H0 = np.array([r0[1], 0.0])
solver2 = ode(self.expansion)
solver2.set_integrator("dopri5", atol=1.e-8, rtol=1.e-6)
solver2.set_initial_value(H0, np.pi)
solver2.integrate(np.pi / 2.0)
return solver1.y - solver2.y
def __init__(self, integrator="dopri5", model="euler_flat_earth", jac=False, **integrator_params):
"""
Initialize the equations of the chosen model and selects the
integrator. Check `scipy.integrate.ode` to see available integrators.
If jac = True the jacobian of the equations is used for the
integration.
"""
if model not in models:
raise ValueError(
"The specified model is not available, please \
check available models in ..."
)
if jac:
jac_LM_and_AM = euler_flat_earth.jac_linear_and_angular_momentum_eqs
jac_att = euler_flat_earth.jac_linear_and_angular_momentum_eqs
jac_nav = None # not implemented
else:
jac_LM_and_AM = None
jac_att = None
jac_nav = None
self._LM_and_AM_eqs = ode(euler_flat_earth.linear_and_angular_momentum_eqs, jac=jac_LM_and_AM)
self._attitude_eqs = ode(euler_flat_earth.kinematic_angular_eqs, jac=jac_att)
self._navigation_eqs = ode(euler_flat_earth.navigation_eqs, jac=jac_nav)
self._LM_and_AM_eqs.set_integrator(integrator, **integrator_params)
self._attitude_eqs.set_integrator(integrator, **integrator_params)
self._navigation_eqs.set_integrator(integrator, **integrator_params)
# State vector must be initialized with set_initial_values() method
self.state_vector = None
def test_concurrent_ok(self):
f = lambda t, y: 1.0
for k in xrange(3):
for sol in ('vode', 'zvode', 'dopri5', 'dop853'):
r = ode(f).set_integrator(sol)
r.set_initial_value(0, 0)
r2 = ode(f).set_integrator(sol)
r2.set_initial_value(0, 0)
r.integrate(r.t + 0.1)
r2.integrate(r2.t + 0.1)
r2.integrate(r2.t + 0.1)
assert_allclose(r.y, 0.1)
assert_allclose(r2.y, 0.2)
for sol in ('dopri5', 'dop853'):
r = ode(f).set_integrator(sol)
r.set_initial_value(0, 0)
r2 = ode(f).set_integrator(sol)
r2.set_initial_value(0, 0)
r.integrate(r.t + 0.1)
r.integrate(r.t + 0.1)
r2.integrate(r2.t + 0.1)
r.integrate(r.t + 0.1)
r2.integrate(r2.t + 0.1)
assert_allclose(r.y, 0.3)
assert_allclose(r2.y, 0.2)
def s_ode(self,params):
x0 = np.ones(3)
s0 = np.zeros(18)
solver_x = ode(self.dx).set_integrator('dopri5')
solver_x.set_initial_value(x0,0).set_f_params(params,)
solver_s = ode(self.ds).set_integrator('dopri5')
solver_s.set_initial_value(s0,0).set_f_params(params,x0)
dt = 1
t1 = 250
sensitivities = []
for column in self.cols:
for param in self.params:
sensitivities.append('{0},{1}'.format(column,param))
sol = pandas.DataFrame(np.empty((t1/dt,22)),columns=self.cols+sensitivities)
i = 0
#return solver_x,solver_s
while solver_x.successful() and solver_s.successful() and solver_x.t < t1:
solver_x.integrate(solver_x.t+dt)
sol.iloc[i][self.cols] = solver_x.y
solver_s.set_f_params(params,solver_x.y)
solver_s.integrate(solver_s.t+dt)
sol.iloc[i][sensitivities] = solver_s.y
i += 1
return sol
def run_trials(z, integrators, times, M):
# Set up ODE solver
for integrator in integrators:
if integrator == 'dop853' or integrator == 'dopri5':
solver = ode(system)
solver.set_integrator(integrator, atol=1E-1, rtol=1E-3)
elif integrator == 'bdf':
solver = ode(system)
solver.set_integrator('vode', method=integrator, atol=1E-1, rtol=1E-3, nsteps=2000)
name = integrator + ' ' + str(times[-1]) + ' ' + str(M)
try:
z, c1, c2, t, c1_40km, c2_40km = load_variables(name)
print "Loaded data for: " + name
except:
print "Starting solver: ", integrator, "with times", times
c1, c2, c1_40km, c2_40km, t = solve(solver, c, times, integrator)
save_variables(name, z, c1, c2, t, c1_40km, c2_40km)
# And plot some things
if times[-1] == 86400.0:
labels = [str(int(time / 3600.)) + " hours" for time in times[1:]]
plot_c1(z, c, c1, labels, name)
plot_c2(z, c, c2, labels, name)
elif times[-1] == 864000.0:
plot_40km(t, c1_40km, c2_40km, name)
def solve(si, y, infile):
"""Conducts the solution step, based on the dopri5 integrator in scipy
:param si: the simulation info object
:type si: SimInfo
:param y: the solution vector
:type y: np.ndarray
:param infile: the imported infile module
:type infile: imported module
"""
n = ode(f_n).set_integrator('dopri5')
n.set_initial_value(y0_n(si), si.timer.
t0.magnitude)
n.set_f_params(si)
th = ode(f_th).set_integrator('dopri5', nsteps=infile.nsteps)
th.set_initial_value(y0_th(si), si.timer.t0.magnitude)
th.set_f_params(si)
while (n.successful()
and n.t < si.timer.tf.magnitude
and th.t < si.timer.tf.magnitude):
si.timer.advance_one_timestep()
si.db.record_all()
n.integrate(si.timer.current_time().magnitude)
update_n(n.t, n.y, si)
th.integrate(si.timer.current_time().magnitude)
update_th(th.t, n.y, th.y, si)
return si.y
def _no_collapse_psi_out(num_times,psi_out):
##Calculates state vectors at times tlist if no collapse AND no expectation values are given.
#
opt=odeconfig.options
if odeconfig.tflag in array([1,10,11]):
ODE=ode(odeconfig.tdfunc)
code = compile('ODE.set_f_params('+odeconfig.string+')', '<string>', 'exec')
exec(code)
elif odeconfig.tflag==2:
ODE=ode(_cRHStd)
elif odeconfig.tflag in array([20,22]):
ODE=ode(_tdRHStd)
elif odeconfig.tflag==3:
ODE=ode(_pyRHSc)
else:
ODE = ode(cyq_ode_rhs)
ODE.set_f_params(odeconfig.h_data, odeconfig.h_ind, odeconfig.h_ptr)
ODE.set_integrator('zvode',method=opt.method,order=opt.order,atol=opt.atol,rtol=opt.rtol,nsteps=opt.nsteps,first_step=opt.first_step,min_step=opt.min_step,max_step=opt.max_step) #initialize ODE solver for RHS
ODE.set_initial_value(odeconfig.psi0,odeconfig.tlist[0]) #set initial conditions
psi_out[0]=Qobj(odeconfig.psi0,odeconfig.psi0_dims,odeconfig.psi0_shape,'ket')
for k in range(1,num_times):
ODE.integrate(odeconfig.tlist[k],step=0) #integrate up to tlist[k]
if ODE.successful():
psi_out[k]=Qobj(ODE.y/norm(ODE.y,2),odeconfig.psi0_dims,odeconfig.psi0_shape,'ket')
else:
raise ValueError('Error in ODE solver')
return psi_out
def Weightloss_Shooting():
age, sex = 38. , 'female'
H, BW = 1.73, 72.7
T = 12*7. # Time frame = 3 months
PALf, EIf = 1.7, 2025
def EI(t): return EIf
def PAL(t): return PALf
from solution import fat_mass, compute_weight_curve, weight_odes
F = fat_mass(BW,age,H,sex)
L = BW-F
# Fix activity level and determine Energy Intake to achieve Target weight of 145 lbs = 65.90909 kg
init_FL = np.array([F,L])
EI0, EI1 = 2000, 1950 # Variable Energy Intake
beta = 65.90909*2.2
reltol, abstol = 1e-9,1e-8
dim, iterate = 2, 15
print '\nj = 1', '\nt = ', EI0
for j in range(2,iterate):
print '\nj = ', j, '\nt = ', EI1
ode_f = lambda t,y: weight_odes(t,y,EI1,PAL(t))
example1 = ode(ode_f).set_integrator('dopri5',atol=abstol,rtol=reltol)
example1.set_initial_value(init_FL, 0.)
y1 = 2.2*sum(example1.integrate(T) )
if abs(y1-beta)<1e-8:
print '\n--Solution computed successfully--'
break
# Update
ode_f = lambda t,y: weight_odes(t,y,EI0,PAL(t))
example0 = ode(ode_f).set_integrator('dopri5',atol=abstol,rtol=reltol)
example0.set_initial_value(init_FL, 0.)
y0 = 2.2*sum(example0.integrate(T))
EI2 = EI1 - (y1 - beta)*(EI1-EI0)/(y1- y0)
EI0 = EI1
EI1 = EI2
# Here we plot the solution
ode_f = lambda t,y: weight_odes(t,y,EI1,PAL(t))
example = ode(ode_f).set_integrator('dopri5',atol=abstol,rtol=reltol)
example.set_initial_value(init_FL, 0.)
X = np.linspace(0.,T,801)
Y = np.zeros((len(X),dim))
Y[0,:] = init_FL
for j in range(1,len(X)): Y[j,:] = example.integrate(X[j])
print '\n|y(T) - Target Weight| = ', np.abs(beta-2.2*sum(Y[-1,:]) ),'\n'
plt.plot(X,2.2*(Y[:,0]+Y[:,1]),'-k',linewidth=2.0)
plt.axis([-1,T+1,0,170])
plt.show(); plt.clf()
return
def addMethods(self):
# override to add _solver function
ODEsystem.addMethods(self, usePsyco=False)
if self.haveJacobian():
self._solver = ode(getattr(self,self.funcspec.spec[1]),
getattr(self,self.funcspec.auxfns["Jacobian"][1]))
self._funcreg['_solver'] = ('self',
'ode(getattr(self,self.funcspec.spec[1]),' \
+ 'getattr(self,self.funcspec.auxfns["Jacobian"][1]))')
else:
self._solver = ode(getattr(self,self.funcspec.spec[1]))
self._funcreg['_solver'] = ('self', 'ode(getattr(self,' \
+ 'self.funcspec.spec[1]))')
def DimensionalForms(BoundConditions, NDParameters, X0=param.X0, d=param.d, rICp=param.rICp, \
rhoH=param.rhoH, alpha=param.alpha, rhoD=param.rhoD, g=param.g, hminus=param.hmin, \
hmaxi=param.hmax, dh=param.dt, geom="cart", AbsTole=param.AbsTol, RelTole=param.AbsTol):
""" Integration RK (de type Dormand Prince 4-5) of the equations system for an horizontal F-layer.
resolutionSystem(y0, t0, tmax, dt, *arg)
Default parameters for the others arguments are :
PeT=1.e6,
MX=0.17,
MP=0.016,
Veff=1.e7,
gamma=7.2,
Atol=1.e-10,
Rtol=1.e-12
The result is given in fully dimensional form.
"""
if geom == "cart":
sol = ode(systemdiff.cart).set_integrator(
'dopri5', atol=param.AbsTol, rtol=param.AbsTol, nsteps=1e7)
elif geom == "spher":
sol = ode(systemdiff.spher).set_integrator(
'dopri5', atol=param.AbsTol, rtol=param.AbsTol, nsteps=1e7)
sol.set_initial_value(BoundConditions, param.hmin)
sol.set_f_params(NDParameters) # (PeT, Mx, Mp, Veff, gamma)
X, dXdh, phiVs, h = BoundConditions[0] * np.ones(1), BoundConditions[1] * np.ones(1), \
BoundConditions[2] * np.ones(1), param.hmin * np.ones(1)
if param.dt > 0:
while sol.successful() and sol.t < param.hmax:
sol.integrate(sol.t + param.dt)
# print("%g %g" % (r.t, r.y))
X = np.append(X, sol.y[0])
dXdh = np.append(dXdh, sol.y[1])
phiVs = np.append(phiVs, sol.y[2])
h = np.append(h, sol.t)
else:
while sol.successful() and sol.t > param.hmax:
sol.integrate(sol.t + param.dt)
# print("%g %g" % (r.t, r.y))
X = np.append(X, sol.y[0])
dXdh = np.append(dXdh, sol.y[1])
phiVs = np.append(phiVs, sol.y[2])
h = np.append(h, sol.t)
# back to fully dimensional:
x = X0 * X
z = h * d
T = param.Tliq0 * (1 - NDParameters['Mp'] * h - X * NDParameters['Mx'])
return z, x, phiVs, T
def run():
data_x = []
data_y = []
dt = 0.1
tmax = 10
state = None
m = model.model_push()
ir = sciint.ode(m.step).set_integrator('vode', atol=0.01)
ir.set_initial_value(m.init, 0)
while state is None or state[2] < m.x_h and ir.t < tmax:
state = ir.integrate(ir.t + dt/1000)
data_x.append(ir.t)
data_y.append(m.M)
if ir.t >= tmax:
print('front failed')
plt.ylim(0, m.Mmax + 10)
plt.plot(data_x, data_y)
plt.xlabel('Время t [с]')
plt.ylabel('Момент М(t) [Н м]')
plt.show()
return None
t1 = ir.t
m = model.model_torque()
ir = sciint.ode(m.step).set_integrator('vode', atol=0.01)
m.init = [
state[0], 0,
state[2] - m.L * cos(asin(((state[3] - m.y_f)/m.L))), m.r_k + m.y_f
]
state2 = state[:]
ir.set_initial_value(m.init, 0)
state = None
while state is None or state[2] < m.x_h and ir.t + t1 < tmax:
state = ir.integrate(ir.t + dt/1000)
data_x.append(ir.t + t1)
data_y.append(m.M)
if ir.t + t1 >= tmax:
print('rear failed')
return None
print('all pass')
plt.ylim(0, m.Mmax + 10)
plt.plot(data_x, data_y)
plt.xlabel('Время t [с]')
plt.ylabel('Момент М(t) [Н м]')
plt.show()
return (m.init, state2)
def test_concurrent_fail(self):
for sol in ('vode', 'zvode'):
f = lambda t, y: 1.0
r = ode(f).set_integrator(sol)
r.set_initial_value(0, 0)
r2 = ode(f).set_integrator(sol)
r2.set_initial_value(0, 0)
r.integrate(r.t + 0.1)
r2.integrate(r2.t + 0.1)
assert_raises(RuntimeError, r.integrate, r.t + 0.1)
def Cannon_Shooting(t0,t1,za=0.,va=45,nu=.0003):
a, b = 0., 195
beta = 0.
# t0,t1 = np.pi/6., np.pi/7
dim, iterate = 3,40
reltol, abstol = 1e-9,1e-8
# Initial_Conditions = np.array([z=za,v=va,phi=t])
g = 9.8067
def ode_f(x,y):
# y = [z,v,phi]
return np.array([np.tan(y[2]), -(g*np.sin(y[2]) + nu*y[1]**2.)/(y[1]*np.cos(y[2])),
-g/y[1]**2.])
print '\nj = 1'
print 't = ', t0
for j in range(2,iterate):
print '\nj = ', j
print 't = ', t1
example1 = ode(ode_f).set_integrator('dopri5',atol=abstol,rtol=reltol)
example1.set_initial_value(np.array([za,va,t1]),a)
y1 = example1.integrate(b)[0]
if abs(y1-beta)<1e-8:
print '\n--Solution y computed successfully--'
break
# Update
example0 = ode(ode_f).set_integrator('dopri5',atol=abstol,rtol=reltol)
example0.set_initial_value(np.array([za,va,t0]),a)
y0 = example0.integrate(b)[0]
t2 = t1 - (y1 - beta)*(t1-t0)/(y1- y0)
t0 = t1
t1 = t2
# Here we plot the solution
example = ode(ode_f).set_integrator('dopri5',atol=abstol,rtol=reltol)
example.set_initial_value(np.array([za,va,t1]),a)
X = np.linspace(a,b,801)
Y = np.zeros((len(X),dim))
Y[0,:] = np.array([0.,0.5,t1])
for j in range(1,len(X)): Y[j,:] = example.integrate(X[j])
print '\n|y(b) - beta| = ', np.abs(beta-Y[-1,0]),'\n'
# plt.plot(X,Y[:,0],'-k')
return X,Y
def Exercise1():
# y'' +4y = -9sin(x), y(0) = 1., y(3*pi/4.) = -(1.+3*sqrt(2))/2., y'(0) = -2
# Exact Solution: y(x) = cos(2x) + (1/2)sin(2x) - 3sin(x)
a, b = 0., 3*np.pi/4.
alpha, beta = 1., -(1.+3*np.sqrt(2))/2.
t0,t1 = 3., 1.
dim, iterate = 2,10
reltol, abstol = 1e-9,1e-8
def ode_f(x,y): return np.array([y[1] , -4.*y[0] - 9.*np.sin(x)])
print '\nj = 1','\nt = ', t0
for j in range(2,iterate):
print '\nj = ', j,'\nt = ', t1
if j >2:
y0 = y1 # Update
else:
example = ode(ode_f).set_integrator('dopri5',atol=abstol,rtol=reltol)
example.set_initial_value(np.array([alpha,t0]),a)
y0 = example.integrate(b)[0]
example = ode(ode_f).set_integrator('dopri5',atol=abstol,rtol=reltol)
example.set_initial_value(np.array([alpha,t1]),a)
y1 = example.integrate(b)[0]
if abs(y1-beta)<1e-8:
print '\n--Solution y computed successfully--',\
'\n|y(b) - beta| = ',np.abs(beta-y1),'\n'
break
t2 = t1 - (y1 - beta)*(t1-t0)/(y1- y0)
t0 = t1
t1 = t2
# Plots the solution
example = ode(ode_f).set_integrator('dopri5',atol=abstol,rtol=reltol)
example.set_initial_value(np.array([alpha,t1]),a)
X = np.linspace(a,b,401)
Y = np.zeros((len(X),dim))
Y[0,:] = np.array([alpha, t1])
for j in range(1,len(X)):
Y[j,:] = example.integrate(X[j])
plt.plot(X,Y[:,0],'-k')
plt.show()
return
def implicit_black_box(propensities, V, X, w, h, deter_vector, stoc_positions, positions, valid, deriv):
# Adjustment for systems reaching steady state
temp = derivative_G(propensities, V, X, w, deter_vector, stoc_positions, positions, valid)
# pdb.set_trace()
valid_adjust_pos = np.where(np.sum(np.abs(temp), axis=0) < 1e-10, True, False)
valid_adjust = valid[:, :]
valid_adjust[valid_adjust_pos, :] = False
# print(" Reached Steady State %d"%(np.sum(valid_adjust_pos)))
from scipy.integrate import ode
# pdb.set_trace()
deter_ode = ode(f).set_integrator("lsoda", method="adams", with_jacobian=False)
deter_ode.set_initial_value(X[deter_vector, :].flatten(), 0).set_f_params(
[propensities, V, X, deter_vector, stoc_positions, positions, valid_adjust, w]
)
# pdb.set_trace()
while deter_ode.successful() and deter_ode.t < h:
deter_ode.integrate(h)
# print("Black Box: \n"+ str(deter_ode.y))
# print("iterator : \n:"+str(next_X[deter_vector,:]))
X[deter_vector, :] = deter_ode.y.reshape((np.sum(deter_vector), X.shape[1]))
# Another adjust to compensate for non negative
X = np.where(X < 0.0, 0.0, X)
return X
def solve_opt(n,k):
nsteps = 1000-1
t = np.zeros((nsteps))
y = np.zeros((nsteps))
w0 = 1.98
y0 = [w0]
t0 = 1.99
t1 = .001
dt = (t1-w0)/nsteps
r = ode(multi_winner_ode)
r.set_f_params(n, k)
r.set_initial_value(y0, t0)
r.set_integrator("dropri853", nsteps=5000)
i = 0
while r.successful() and r.t > t1:
r.integrate(r.t+dt)
print("%g %g" % (r.t, r.y))
t[i] = r.t
y[i] = r.y
i+=1
w = np.where(y<0, 0, y)
# plt.plot(t, w)
# plt.show()
bid_func = interp1d(t[::-1], w[::-1])
print(min(t), max(t))
return bid_func
def run_ODE(f, a_in, C, D, num_of_variables, T = 10, dt = 0.01, y0 = None):
'''Run the ODE for the given set of equations and record the outputs.
Args:
f (function): Evolution of the system.
a_in (function): inputs as a function of time.
C,D (matrices): matrices to use to obtain output from system state and
input.
num_of_variables (int): number of variables the system has.
T (optional[positive float]): length of simulation.
dt (optional[float]): time step used by the simulation.
Returns:
(array):
An array Y of outputs.
'''
if y0 is None:
y0 = np.asmatrix([0.]*num_of_variables).T
r = ode(f).set_integrator('zvode', method='bdf')
r.set_initial_value(y0, 0.)
Y = []
while r.successful() and r.t < T:
Y.append(C*r.y+D*a_in(r.t))
r.integrate(r.t+dt)
return Y
def run(self, start, velocity):
# state data structures
self.t=zeros(self.num_steps)
self.Y=zeros([self.num_steps, 4])
self.Y[0] = array([start[0],start[1],velocity[0],velocity[1]])
r = ode(self.deriv)
r.set_integrator('vode', rtol=1e-9, atol=1e-6)
r.set_initial_value(self.Y[0],t=0.0)
time_idx=1
while r.successful() and r.t < self.max_time:
r.integrate(time_idx*self.time_step)
self.t[time_idx] = r.t
self.Y[time_idx] = r.y
time_idx = time_idx + 1
if self.debug:
print ("time=%10.3f: x=%g y=%g vx=%g vy=%g" % \
(r.t, r.y[0], r.y[1], r.y[2], r.y[3]))
if r.y[1]<0:
break
# simulation done: truncate arrays
self.Y.resize([time_idx, self.Y.shape[1]])
self.t.resize([time_idx])
def __init__(self, params, capital, consumption, jacobian, utility):
"""Initializes a RamseyModel object with the following attributes:
params: (dict) Dictionary of model parameters.
capital: (callable) Equation of motion for capital (per
person/effective person).
euler: (callable) Equation describing consumption (per
person/effective person).
jacobian: (callable) Function returning the model's jacobian matrix
of partial derivatives.
utility: (callable) Utility function representing household
preferences over consumption.
"""
self.params = params
self.capital = capital
self.consumption = consumption
self.jacobian = jacobian
self.utility = utility
# initialize model steady state
self.steady_state = SteadyState(self)
# create an instance of the scipy.integrate.ode class
f = lambda t, y, params: [self.capital(y, params),
self.consumption(y, params)]
jac = lambda t, y, params: self.jacobian(y, params)
self.simulator = integrate.ode(f, jac)
def _simulate_with_scipy(self, initcon, tsim, switchpts,
varying_inputs, integrator,
integrator_options):
scipyint = \
scipy.ode(self._rhsfun).set_integrator(integrator,
**integrator_options)
scipyint.set_initial_value(initcon, tsim[0])
profile = np.array(initcon)
i = 1
while scipyint.successful() and scipyint.t < tsim[-1]:
# check if tsim[i-1] is a switching time and update value
if tsim[i - 1] in switchpts:
for v in self._siminputvars.keys():
if tsim[i - 1] in varying_inputs[v]:
p = self._templatemap[self._siminputvars[v]]
p.set_value(varying_inputs[v][tsim[i - 1]])
profilestep = scipyint.integrate(tsim[i])
profile = np.vstack([profile, profilestep])
i += 1
if not scipyint.successful():
raise DAE_Error("The Scipy integrator %s did not terminate "
"successfully." % integrator)
return [tsim, profile]
def PlotJourneyBoundaryOrbits(self, JourneyAxes):
for boundarystate in self.boundary_states:
BoundaryStateScaled = np.array(boundarystate) / self.LU
BoundaryStateScaled[3:6] *= self.TU
r = np.linalg.norm(BoundaryStateScaled[0:3])
v = np.linalg.norm(BoundaryStateScaled[3:6])
a = r / (2.0 - r*v*v)
T = 2*math.pi*math.sqrt(a**3)
StateIntegrateObject = ode(EOM.EOM_inertial_2body, jac=EOM.EOM_jacobian_intertial_2body).set_integrator('dop853', atol=1.0e-8, rtol=1.0e-8)
StateIntegrateObject.set_initial_value(BoundaryStateScaled).set_f_params(1.0).set_jac_params(1.0)
dt = T / 100
StateHistory = []
while StateIntegrateObject.successful() and StateIntegrateObject.t <= T * 1.01:
StateIntegrateObject.integrate(StateIntegrateObject.t + dt)
StateHistory.append(StateIntegrateObject.y * self.LU)
X = []
Y = []
Z = []
for StateLine in StateHistory:
X.append(StateLine[0])
Y.append(StateLine[1])
Z.append(StateLine[2])
JourneyAxes.plot(X, Y, Z, lw=2, c='0.75')
def integrateODE(self,state,thetavec,u0):
'''
Integrates ODE over the gridpoints theta
'''
if not self.Vf ==None:
Fs = self.Vf,self.wf,self.w2f
else:
Fs = None
def dy_dtheta(theta,y):
dy = self.Para.differentialEquation(y,theta,state,Fs)
return dy
lambda_1,lambda_2,theta_ = state
mu_tilde = -lambda_2/theta_
if thetavec[0] > self.theta_min:
thetavec = hstack((self.theta_min,thetavec))
y0 = self.getInitial_y(u0,mu_tilde,thetavec[0],state)
r = ode(dy_dtheta).set_integrator('vode',rtol=1e-10,atol = 1e-10,nsteps=1000)
r.set_initial_value(y0,thetavec[0])
y = ones((len(thetavec),2))
y[0] = y0
for i,theta in enumerate(thetavec[1:]):
y[i+1] = r.integrate(theta)
if y[i+1,1] >0:
break
if not r.successful():
print "ode failed"
break
if thetavec[0] == self.theta_min:
return y[1:]
else:
return y
def numerical_integrator(self, t0, tf, dt, initial_conditions, solver, verbose):
"""
the numerical_integrator() method integrates the ODE of the dynamic system using a numerical solver
"""
f = self._ode_RHS
if initial_conditions:
y0 = initial_conditions
else:
y0 = self.initial_conditions
MdFBA_ode = ode(f).set_integrator(solver)
MdFBA_ode.set_initial_value(y0, t0)
t = [t0]
y = [y0]
while MdFBA_ode.successful() and MdFBA_ode.t < tf:
MdFBA_ode.integrate(MdFBA_ode.t + dt)
t.append(MdFBA_ode.t)
y.append(MdFBA_ode.y)
if verbose:
print MdFBA_ode.t
t = numpy.array(t)
y = numpy.array(y)
return t, y
def CalculateAllDistancesFor(startPosition, minTime, maxTime, display=True, numSteps = 100):
""" Calculate all distances for every t and tau between min and maxtime,
with # of steps """
minT, minTau = minTime
maxT, maxTau = maxTime
allTs = np.linspace(minT, maxT, numSteps)
allTaus = np.linspace(minTau, maxTau, numSteps)
distDataType = np.dtype([('Distance', np.float64), ('DistanceTau', np.float64)])
allDistances = np.empty((numSteps,numSteps), dtype=distDataType)
doprIntegrator = ode(currentField.GetDataAt).set_integrator("dopri5")
distIterator = np.nditer(allDistances, flags=['multi_index'], op_flags=['readwrite'])
while not distIterator.finished:
t = allTs[distIterator.multi_index[0]]
tau = allTaus[distIterator.multi_index[1]]
doprIntegrator.set_initial_value(startPosition, t)
distance = linalg.norm(startPosition - doprIntegrator.integrate(t+tau))
print(" distance calculated (t: {}, tau: {}): {}".format(t, tau, distance))
distIterator[0] = (distance, distance / tau)
distIterator.iternext()
if display:
import mpl_toolkits.mplot3d.axes3d as axes3d
fig, ax = plt.subplots(subplot_kw=dict(projection='3d'))
ax.plot_surface(allTs, allTaus, allDistances['DistanceTau'], cmap=plt.get_cmap('jet'), rstride=1, cstride=1, vmin=-150, vmax=150, shade=True)
plt.show()
return allDistances
def solve_dydt(tSpan,y0,options,M):
# unpack option values
opt_rtol = options[0]
opt_atol = options[1]
dt = options[2]
# use initial and final time range
t0 = tSpan[0]
tmax = tSpan[1]
# set equation to solve
solver = ode(Fct_sys)
# initialize time and solution vectors
t = []
sol = []
sol.append(y0)
t.append(t0)
# solver initial value
solver.set_initial_value(y0, t0).set_f_params(M)
# solver options
solver.set_integrator('dopri5',atol=opt_atol,rtol=opt_rtol)
# Start solver
while solver.successful() and solver.t < tmax:
solver.integrate(solver.t + dt)
t.append(solver.t)
sol.append(solver.y)
# Array conversion and processing for sensitivity and plotting
t = array(t)
sol = array(sol)
return t, sol
def run(self, number_of_trajectories=1, seed=None):
""" Run one simulation of the model.
number_of_trajectories: How many trajectories should be run.
seed: the random number seed (incremented by one for multiple runs).
input_file: the filename of the solver input data file .
loaddata: boolean, should the result object load the data into memory on creation.
Returns:
URDMEResult object.
or, if number_of_trajectories > 1
a list of URDMEResult objects
"""
if not self.is_compiled:
self.compile()
solver = ode(self.derivative_fn).set_integrator('vode', method='bdf', order=15, atol=self.error_tolarance)
solver.set_initial_value(self.solver_initial_value, 0)
result = PDEResult(self.model)
ndofs = result.U.shape[1]
for tndx, t in enumerate(self.model.tspan):
while solver.successful() and solver.t < t:
solver.integrate(t)
result.U[tndx:tndx+1,:] = solver.y.reshape(1,ndofs)
if number_of_trajectories > 1:
return [result]*number_of_trajectories
else:
return result
def solve(self, **kwargs):
n, m = self.dims
sa, sb = (-self.ta, -self.tb)
Pdot = lambda s, P: self._Pdot(s, P)
solver = ode(Pdot)
solver.set_integrator('vode', max_step=1e-2, **kwargs)
solver.set_initial_value(self.Pb.ravel(), sb)
self._Ptj = Trajectory('P')
results = [(-sb, self.Pb)]
while solver.successful() and solver.t < sa + 1e-2:
solver.integrate(sa, step=True)
P = solver.y.reshape((n, n))
# find which jumps lie between this time step and the previous one
# add the corresponding term to b = solver.y + jumpterm
if self.jumps:
prevtime = results[-1][0] # replace with call to _bt.tmin
for (tj, fj) in self.jumps:
if prevtime > tj and tj > -solver.t:
# positive sign because backwards integration
P = P + matmult(fj.T, P) + matmult(P, fj)
solver.set_initial_value(P.ravel(), solver.t)
results.append((-solver.t, P))
for (t, P) in reversed(results):
self._Ptj.addpoint(t, P=P)
self._Ptj.interpolate()
self.P = lambda t: self._Ptj.P(t)
def _run_solout_break_test(self, integrator):
# Check correct usage of stopping via solout
ts = []
ys = []
t0 = 0.0
tend = 10.0
y0 = [1.0, 2.0]
def solout(t, y):
ts.append(t)
ys.append(y.copy())
if t > tend/2.0:
return -1
def rhs(t, y):
return [y[0] + y[1], -y[1]**2]
ig = ode(rhs).set_integrator(integrator)
ig.set_solout(solout)
ig.set_initial_value(y0, t0)
ret = ig.integrate(tend)
assert_array_equal(ys[0], y0)
assert_array_equal(ys[-1], ret)
assert_equal(ts[0], t0)
assert_(ts[-1] > tend/2.0)
assert_(ts[-1] < tend)
print('R=', str(R))
# target point
xT = np.array([-1, -1])
# target region around xT
Starget = np.array([[9.5616, 2.2650], [2.2650, 3.6483]])
t1 = 2
dt = 0.05
# Compute the xs
def ff(t, y):
return -f(y)
r = ode(ff).set_integrator('vode', method='bdf', with_jacobian=False)
r.set_initial_value(xT, 0)
ts = [r.t]
xs = [r.y]
while r.successful() and r.t < t1:
r.integrate(r.t + dt)
ts.append(r.t)
xs.append(r.y)
xs.reverse()
# Compute the As
N = len(ts)
As = []
Bs = []
Ks = []
for t in range(N):
#m = 10.0e19
m1 = 10.0e13
lna_step = 0.0001
A0 = 1.
A1 = 0.001
A_real, n_iter = Secante_1D(A0, A1, fs, m1, a_i, lna_step, tol=0.00001, N=200)
IC = ic_lsfdm(A_real, m1, a_i)
print "Trying with our best initial conditions:", IC
#Y = odeint(background_system_real,IC,lna,mxstep=1000000)
#o = ode(background_system_real).set_integrator('vode', method='bdf', order=15, nsteps=5000)
#o = ode(background_system_real).set_integrator('vode', method='adams', order=12, nsteps=3000)
#o = ode(background_system_real).set_integrator('lsoda',nsteps = 5000)
o = ode(background_system_real).set_integrator('dopri5', nsteps=4000)
#o = ode(background_system_real).set_integrator('dop853',nsteps = 3000)
o.set_initial_value(IC, math.log(a_i))
rs = []
ys = []
while o.successful() and o.t < 0:
o.integrate(o.t + lna_step)
rs.append(o.t)
ys.append(o.y)
lna = np.vstack(rs)
Y = np.vstack(ys).T
om_phi = lna * 0.
for i in range(len(lna)):
om_phi[i] = -math.cos(Y[1, i])
def __init__(self, patient_number=0):
"""
Initializing the simulation environment.
"""
np.random.seed(1) ### Fixing seed
self.previous_action = 0
## Loading variable parameters
reward_flag, bg_init_flag = self._update_parameters()
# Initialize bolus history
self.bolusHistoryIndex = 0
self.bolusHistoryValue = []
self.bolusHistoryTime = []
self.insulinOnBoard = np.zeros(1)
# Initialize sensor model
self.CGMlambda = 15.96 # Johnson parameter of recalibrated and synchronized sensor error.
self.CGMepsilon = -5.471 # Johnson parameter of recalibrated and synchronized sensor error.
self.CGMdelta = 1.6898 # Johnson parameter of recalibrated and synchronized sensor error.
self.CGMgamma = -0.5444 # Johnson parameter of recalibrated and synchronized sensor error.
self.CGMerror = 0
self.sensor_noise = np.random.randn(1)
# self.CGMaux = []
self.sensorNoiseValue = 0.07 # Set a value
# =======================
# Anas patient parameters
# =======================
P, init_basal, carb_factor, _ = matlab_to_python(patient_number)
init_basal_optimal = init_basal
self.P = P
self.init_basal_optimal = init_basal_optimal
self.bolus = carb_factor
self.action_space = spaces.Box(0, 2*self.init_basal_optimal[0], (1,), dtype=np.float32)
if bg_init_flag == 'random':
self.init_basal = np.random.choice(np.linspace(init_basal_optimal-2, init_basal_optimal, 10))
elif bg_init_flag == 'fixed':
self.init_basal = init_basal_optimal
# Flag for manually resetting the init
self.reset_basal_manually = None
self._seed()
self.viewer = None
# ==========================================
# Setting up the Hovorka simulator
# ==========================================
# Initial values for parameters
initial_pars = (self.init_basal, 0, P)
# Initial value
X0 = fsolve(hovorka_model_tuple, np.zeros(11), args=initial_pars)
self.X0 = X0
# Simulation setup
self.integrator = ode(hovorka_model)
self.integrator.set_integrator('vode', method='bdf', order=5)
self.integrator.set_initial_value(X0, 0)
# Simulation time in minutes
self.simulation_time = 30
self.n_solver_steps = 1
self.stepsize = int(self.simulation_time/self.n_solver_steps)
self.observation_space = spaces.Box(0, 500, (int(self.stepsize + 4 + 2),), dtype=np.float32)
# State is BG, simulation_state is parameters of hovorka model
initial_bg = X0[-1] * 18
initial_insulin = np.ones(4) * self.init_basal_optimal
initial_iob = np.zeros(1)
self.state = np.concatenate([np.repeat(initial_bg, self.simulation_time), initial_insulin, initial_iob, np.zeros(1)])
self.simulation_state = X0
# Keeping track of entire blood glucose level for each episode
self.bg_history = []
self.insulin_history = initial_insulin
# ====================
# Meal setup
# ====================
eating_time = 1
meals, meal_indicator = meal_generator(eating_time=eating_time, premeal_bolus_time=0)
self.meals = meals
self.meal_indicator = meal_indicator
self.eating_time = eating_time
# Counter for number of iterations
self.num_iters = 0
# If blood glucose is less than zero, the simulator is out of bounds.
self.bg_threshold_low = 0
self.bg_threshold_high = 500
# TODO: This number is arbitrary
self.max_iter = 2160
# Reward flag
self.reward_flag = reward_flag
self.steps_beyond_done = None
tend = 2e5 * year
tarr = (tcurrent, tend)
tstep = 1e5 * year
for r in rr:
afstand = r/AU
mi, mc, nc, ac = input_mass_distribution(a_min,a_max, a_maxp, n_a, r)
nc = np.asarray(nc)
sig_d0 = nc * mc
K,C = coagulation_kernel(ac, mc, r,'bm_pp_set_az')
ode = spi.ode(ndot_coagulation2,jacobian)
# BDF method suitable to stiff systems of ODE's
atol = 1e0 # absolute error
rtol = 1e-6 # relative error
with_jacobian = True
ode.set_integrator('vode', method='bdf', with_jacobian=with_jacobian, nsteps=5000, atol=atol, rtol=rtol)
ode.set_initial_value(nc, t)
while ode.successful() and ode.t < tend:
nf = ode.integrate(ode.t + tstep)
plt.figure(1)
plt.loglog()
plt.plot(ac*1e2,nf*mc,label="Radial distance: %s AU" %afstand)
plt.ylim(1e-19, 1e0)
def calculate_path(profile, particle, p, y0, delta_t):
"""
Calculate the trajectory of a particle
Calculate the trajectory of a particle by integrating its path using
the `scipy.integrate.ode` object and associated methods.
Parameters
----------
profile : `ambient.Profile` object
Ambient CTD data for the model simulation
particle : `LagrangianParticle` object
Object describing the properties and behavior of the particle.
p : `ModelParams` object
Collection of model parameters passed to `derivs`.
y0 : ndarray
Initial values of the state space (depth in m, masses in kg, and heat
content in J of the particle) at the release point
delta_t : float
Maximum step size (s) to take in the integration
Notes
-----
The differential equation in `derivs` is written with respect to time, so
the independent variable in this simulation is time. The vertical
coordinate; therefore, becomes a dependent variable, along with the masses
of each component in the particle and the particle temperature. Thus,
the state space is::
y = np.hstack((z0, m0, H0))
where `H0` is the initial heat content, `m_p * cp * T0`. The variables
in the state space can be returned by::
>>> import seawater
>>> z = y[2]
>>> m = y[3:-1]
>>> T = y[-1] / (np.sum(y[1:-1]) * particle.cp)
"""
# Create the integrator object: use "vode" with "backward
# differentiation formula" for stiff ODEs
r = integrate.ode(derivs).set_integrator('vode',
method='bdf',
atol=1.e-6,
rtol=1e-3,
order=5,
max_step=delta_t)
# Initialize the state space
t0 = 0.
r.set_initial_value(y0, t0)
# Set passing variables for derivs method
r.set_f_params(profile, particle, p)
# Create vectors (using the list data type) to store the solution
t = [t0]
y = [y0]
# Integrate to the free surface (profile.z_min)
k = 0
psteps = 10.
stop = False
while r.successful() and not stop:
# Print progress to the screen
m0 = np.sum(y[0][3:-1])
mt = np.sum(y[-1][3:-1])
f = mt / m0
if np.remainder(np.float(k), psteps) == 0.:
print(' Depth: %g (m), t: %g (s), k: %d, f: %g (--)' %
(r.y[2], t[-1], k, f))
# Perform one step of the integration
r.integrate(t[-1] + delta_t, step=True)
# Store the results
if particle.K_T == 0:
# Make the state-space heat correct
Ta = profile.get_values(r.y[2], 'temperature')
r.y[-1] = np.sum(r.y[3:-1]) * particle.cp * Ta
for i in range(len(r.y[3:-1])):
if r.y[i + 3] < 0.:
# Concentration should not overshoot zero
r.y[i + 3] = 0.
t.append(r.t)
y.append(r.y)
k += 1
# Evaluate stop criteria
if r.successful():
# Check if bubble dissolved (us = 0 or based on fdis) or reached
# the free surface
us = -(y[-2][2] - y[-1][2]) / (t[-2] - t[-1])
if r.y[2] <= profile.z_min or us <= 0. or f < particle.fdis:
stop = True
if k > 5000:
stop = True
# Remove any negative depths due to overshooting the free surface
t = np.array(t)
y = np.array(y)
rows = y[:, 2] >= 0
t = t[rows]
y = y[rows, :]
# Return the solution
print(' Depth: %g (m), t: %g (s), k: %d' % (y[-1, 2], t[-1], k))
return (t, y)
f = lambda x: relu(x - threshold)
def dXdt(X, t=0):
return (- X + gain * input_signal(t) - np.dot(f(X), feedback_gain))/tau
def dXdt_ode(t, X):
print X
return (- X + gain * input_signal(t) - np.dot(f(X), feedback_gain))/tau
X0 = np.zeros((layer_size,))
# X, infodict = integrate.odeint(dXdt, X0, Tvec, full_output=True)
# X_euler = integrate_euler(dXdt, X0, Tvec, dt)
Xseq = list()
r = integrate.ode(dXdt_ode).set_integrator("dopri5", method="bdf")
r.set_initial_value(X0)
while r.successful() and r.t < Tmax:
Xseq.append(r.integrate(r.t + dt))
Xseq = np.asarray(Xseq)
point = point + 1
tn = peak_pos[point]
tn_1 = peak_pos[point - 1]
# print(point, i)
theta.append((i - tn) / (tn - tn_1))
else:
theta.append(0)
return theta
# print(U_ss(x,z))
t = np.linspace(0, 3000, 10000)
delta_t = t[1] - t[0]
print(delta_t)
arr = []
r = ode(model).set_integrator(name="lsoda", method="BDF", nsteps=100000)
r.set_initial_value(y0, 0).set_f_params(n)
for i in range(len(t)):
print(i)
r.integrate(r.t + delta_t)
arr.append(r.y)
arr = np.array(arr)
thetaArr = []
for k in range(n, 2 * n):
print(k)
thetaArr.append(theta_func(t, arr[:, k]))
print(k)
thetaArr = np.array(thetaArr)
R_theta = (1 / n) * abs(np.sum(np.exp(2 * np.pi * 1j * thetaArr), axis=0))
plt.plot(t, R_theta)
def _poisson(self, potonly):
""" Solves Poisson equation """
# y = [phi, u_j, U, K_j], where u = -M(<r)/G
# Initialize
self.r = numpy.array([0])
self._y = numpy.r_[self.phi0, numpy.zeros(2 * self.nmbin + 1)]
if (not potonly): self._y = numpy.r_[self._y, numpy.zeros(self.nmbin)]
# Ode solving using Runge-Kutta integator of order 4(5) 'dopri5'
# (Hairor, Norsett& Wanner 1993)
max_step = self.maxr if (potonly) else self.max_step
sol = ode(self._odes)
sol.set_integrator('dopri5',
nsteps=1e6,
max_step=max_step,
atol=self.ode_atol,
rtol=self.ode_rtol)
sol.set_solout(self._logcheck)
sol.set_f_params(potonly)
sol.set_initial_value(self._y, 0)
sol.integrate(self.maxr)
# Extrapolate to rt
derivs = self._odes(self.r[-1], self._y[:, -1], self.potonly)
self.rt = self.r[-1] - self._y[0][-1] / derivs[0]
dr = self.rt - self.r[-1]
ylast = [0]
for i in range(len(derivs) - 1):
ylast.append(self._y[1 + i, -1] + derivs[i + 1] * dr)
self._y = numpy.c_[self._y, ylast]
# Set the converged flag to True if successful
if (self.rt < self.maxr) & (sol.successful()):
self.converged = True
else:
self.converged = False
# Fill arrays needed if potonly=True
self.r = numpy.r_[self.r, self.rt]
self.rhat = self.r * 1.0
self.phihat = numpy.r_[self._y[0, :]]
self.phi = self.phihat * 1.0
self.nbound = len(self.phihat)
self._Mjtot = -sol.y[1:1 + self.nmbin] / self.G
self.M = sum(self._Mjtot)
self.Mpe = -sol.y[1 + self.nmbin] / self.G
self.fpe = self.Mpe / self.M
# Save the derivative of the potential for the potential interpolater
dphidr = numpy.sum(self._y[1:1 + self.nmbin, 1:],
axis=0) / self.r[1:]**2
self.dphidrhat1 = numpy.r_[0, dphidr]
self.A = 1 / (2 * pi * self.s2j)**1.5 / self.rhoint0
self.mc = -self._y[1, :] / self.G
# Compute radii to be able to scale in case potonly=True
self.U = self._y[2 + self.nmbin,
-1] - 0.5 * self.G * self.M**2 / self.rt
# Get half-mass radius from cubic interpolation, because the half-mass
# radius can be used as a scale length, this needs to be done
# accurately, a linear interpolation does not suffice. Because the
# density array is not known yet at this point, we need a temporary
# evaluation of density in the vicinity of r_h
ih = numpy.searchsorted(self.mc, 0.5 * self.mc[-1]) - 1
rhotmp = numpy.zeros(2)
for j in range(self.nmbin):
phi = self.phihat[ih:ih + 2] / self.s2j[j]
rhotmp += self.alpha[j] * self._rhohat(phi, self.r[ih:ih + 2], j)
drdm = 1. / (4 * pi * self.r[ih:ih + 2]**2 * rhotmp)
rmc_and_derivs = numpy.vstack([[self.r[ih:ih + 2]], [drdm]]).T
self.rh = BPoly.from_derivatives(self.mc[ih:ih + 2],
rmc_and_derivs)(0.5 * self.mc[-1])
self.rv = -0.5 * self.G * self.M**2 / self.U
# Solve (mass-less) part outside rt
if (self.nrt > 1):
# Continue to solve until rlast
sol.set_solout(self._logcheck2)
sol.set_f_params(potonly)
sol.set_initial_value(self._y[:, -1], self.rt)
sol.integrate(self.nrt * self.rt)
self.rhat = self.r * 1.0
self.phihat = numpy.r_[self.phihat, self._y[0, self.nbound:]]
self.mc = numpy.r_[self.mc,
numpy.zeros(len(self._y[0, self.nbound:])) +
self.mc[-1]]
self.phi = self.phihat * 1.0
dphidr = numpy.sum(self._y[1:1 + self.nmbin, self.nbound:],
axis=0) / self.r[self.nbound:]**2
self.dphidrhat1 = numpy.r_[self.dphidrhat1, dphidr]
# Additional stuff
if (not potonly):
# Calculate kinetic energy
self.K = numpy.sum(sol.y[4:5])
# Calculate density and velocity dispersion components
if (not self.multi):
self.rhohat = self._rhohat(self.phihat, self.r, 0)
self.rhohatpe = self._rhohatpe(self.phihat, self.r, 0)
self.rho = self.rhohat * 1.0
self.rhope = self.rhohatpe * 1.0
self.v2 = self._get_v2(self.phihat, self.rhohat, 0)
def PlotEvent(self, GraphicsObject, LU, TU, mu, PlotOptions):
#switch between different event types
if self.EventType == "launch" or self.EventType == "departure":
GraphicsObject.scatter(self.SpacecraftState[0],
self.SpacecraftState[1],
self.SpacecraftState[2],
s=4,
c='g',
marker='^')
elif self.EventType == "begin_spiral" or self.EventType == "end_spiral":
GraphicsObject.scatter(self.SpacecraftState[0],
self.SpacecraftState[1],
self.SpacecraftState[2],
s=4,
c='orange',
marker='^')
elif self.EventType == "upwr_flyby":
GraphicsObject.scatter(self.SpacecraftState[0],
self.SpacecraftState[1],
self.SpacecraftState[2],
s=4,
c='b',
marker=r'$\circlearrowleft$')
elif self.EventType == "pwr_flyby":
GraphicsObject.scatter(self.SpacecraftState[0],
self.SpacecraftState[1],
self.SpacecraftState[2],
s=4,
c='r',
marker=r'$\circlearrowleft$')
elif self.EventType == "chem_burn":
GraphicsObject.scatter(self.SpacecraftState[0],
self.SpacecraftState[1],
self.SpacecraftState[2],
s=4,
c='r',
marker='o')
elif self.EventType == "SFthrust" or self.EventType == "PSBIthrust" or self.EventType == "coast" or self.EventType == "force-coast":
color = ''
if self.EventType == "coast":
linestyle = '--'
color = 'k'
weight = 1
elif self.EventType == "SFthrust" or self.EventType == "PSBIthrust":
linestyle = '-'
color = 'k'
weight = 2
if PlotOptions.ShowThrustVectors:
ControlVector = np.array(
self.Thrust) / self.AvailableThrust * LU * 0.1
GraphicsObject.plot(self.SpacecraftState[0] +
np.array([0.0, ControlVector[0]]),
self.SpacecraftState[1] +
np.array([0.0, ControlVector[1]]),
self.SpacecraftState[2] +
np.array([0.0, ControlVector[2]]),
c='r',
lw=2)
else:
linestyle = '--'
color = 'b'
weight = 1
GraphicsObject.scatter(self.SpacecraftState[0],
self.SpacecraftState[1],
self.SpacecraftState[2],
s=1,
c=color,
marker='o')
if PlotOptions.ShowPropagatedTrajectory:
CenterPointState = np.zeros(6)
CenterPointState[0:6] = np.array(self.SpacecraftState) / LU
CenterPointState[3:6] *= TU
CenterPointStateAfterManeuver = copy.deepcopy(CenterPointState)
CenterPointStateAfterManeuver[3:6] += np.array(
self.DeltaVorThrustVectorControl) * TU / LU
ForwardIntegrateObject = ode(
EOM.EOM_inertial_2body).set_integrator('dop853',
atol=1.0e-6,
rtol=1.0e-6)
ForwardIntegrateObject.set_initial_value(
CenterPointStateAfterManeuver).set_f_params(
1.0).set_jac_params(1.0)
dt = self.TimestepLength * 86400.0 / TU / 10
StateHistoryForward = []
while ForwardIntegrateObject.successful(
) and ForwardIntegrateObject.t < self.TimestepLength * 86400.0 / TU / 2.0:
ForwardIntegrateObject.integrate(ForwardIntegrateObject.t +
dt)
StateHistoryForward.append(ForwardIntegrateObject.y * LU)
BackwardIntegrateObject = ode(
EOM.EOM_inertial_2body).set_integrator('dop853',
atol=1.0e-6,
rtol=1.0e-6)
BackwardIntegrateObject.set_initial_value(
CenterPointState).set_f_params(1.0).set_jac_params(1.0)
dt = self.TimestepLength * 86400.0 / TU / 10
StateHistoryBackward = []
while BackwardIntegrateObject.successful(
) and BackwardIntegrateObject.t > -self.TimestepLength * 86400.0 / TU / 2.0:
BackwardIntegrateObject.integrate(
BackwardIntegrateObject.t - dt)
StateHistoryBackward.append(BackwardIntegrateObject.y * LU)
StateHistoryBackward.reverse()
X = []
Y = []
Z = []
for StateLine in StateHistoryBackward:
X.append(StateLine[0])
Y.append(StateLine[1])
Z.append(StateLine[2])
for StateLine in StateHistoryForward:
X.append(StateLine[0])
Y.append(StateLine[1])
Z.append(StateLine[2])
GraphicsObject.plot(X, Y, Z, lw=weight, c=color, ls=linestyle)
elif self.EventType == "FBLTthrust" or self.EventType == "FBLTSthrust":
GraphicsObject.scatter(self.SpacecraftState[0],
self.SpacecraftState[1],
self.SpacecraftState[2],
s=2,
c='k',
marker='o')
if PlotOptions.ShowThrustVectors:
ControlVector = np.array(
self.Thrust) / self.AvailableThrust * LU * 0.1
GraphicsObject.plot(self.SpacecraftState[0] +
np.array([0.0, ControlVector[0]]),
self.SpacecraftState[1] +
np.array([0.0, ControlVector[1]]),
self.SpacecraftState[2] +
np.array([0.0, ControlVector[2]]),
c='r',
lw=2)
if PlotOptions.ShowPropagatedTrajectory:
CenterPointState = np.zeros(7)
CenterPointState[0:6] = np.array(self.SpacecraftState) / LU
CenterPointState[3:6] *= TU
CenterPointState[6] = 1.0
ScaledThrust = np.array(
self.Thrust) * TU * TU / 1000.0 / self.Mass / LU
ScaledMdot = self.MassFlowRate / self.Mass * TU
ForwardIntegrateObject = ode(
EOM.EOM_inertial_2bodyconstant_thrust).set_integrator(
'dop853', atol=1.0e-6, rtol=1.0e-6)
ForwardIntegrateObject.set_initial_value(
CenterPointState).set_f_params(ScaledThrust, ScaledMdot,
1.0).set_jac_params(
ScaledThrust,
ScaledMdot, 1.0)
dt = self.TimestepLength * 86400.0 / TU / 10
StateHistoryForward = []
while ForwardIntegrateObject.successful(
) and ForwardIntegrateObject.t < self.TimestepLength * 86400.0 / TU / 2.0:
ForwardIntegrateObject.integrate(ForwardIntegrateObject.t +
dt)
StateHistoryForward.append(ForwardIntegrateObject.y * LU)
BackwardIntegrateObject = ode(
EOM.EOM_inertial_2bodyconstant_thrust).set_integrator(
'dop853', atol=1.0e-6, rtol=1.0e-6)
BackwardIntegrateObject.set_initial_value(
CenterPointState).set_f_params(ScaledThrust, ScaledMdot,
1.0).set_jac_params(
ScaledThrust,
ScaledMdot, 1.0)
dt = self.TimestepLength * 86400.0 / TU / 10
StateHistoryBackward = []
while BackwardIntegrateObject.successful(
) and BackwardIntegrateObject.t > -self.TimestepLength * 86400.0 / TU / 2.0:
BackwardIntegrateObject.integrate(
BackwardIntegrateObject.t - dt)
StateHistoryBackward.append(BackwardIntegrateObject.y * LU)
StateHistoryBackward.reverse()
X = []
Y = []
Z = []
for StateLine in StateHistoryBackward:
X.append(StateLine[0])
Y.append(StateLine[1])
Z.append(StateLine[2])
for StateLine in StateHistoryForward:
X.append(StateLine[0])
Y.append(StateLine[1])
Z.append(StateLine[2])
GraphicsObject.plot(X, Y, Z, lw=2, c='k')
elif self.EventType == "insertion":
GraphicsObject.scatter(self.SpacecraftState[0],
self.SpacecraftState[1],
self.SpacecraftState[2],
s=4,
c='r',
marker='v')
elif self.EventType == "LT_rndzvs":
GraphicsObject.scatter(self.SpacecraftState[0],
self.SpacecraftState[1],
self.SpacecraftState[2],
s=4,
c='m',
marker='o')
elif self.EventType == "intercept":
GraphicsObject.scatter(self.SpacecraftState[0],
self.SpacecraftState[1],
self.SpacecraftState[2],
s=4,
c='m',
marker='d')
elif self.EventType == "rendezvous":
GraphicsObject.scatter(self.SpacecraftState[0],
self.SpacecraftState[1],
self.SpacecraftState[2],
s=4,
c='r',
marker='d')
elif self.EventType == "match-vinf":
GraphicsObject.scatter(self.SpacecraftState[0],
self.SpacecraftState[1],
self.SpacecraftState[2],
s=4,
c='c',
marker='s')
elif self.EventType == "match_point":
GraphicsObject.scatter(self.SpacecraftState[0],
self.SpacecraftState[1],
self.SpacecraftState[2],
s=4,
c='k',
marker='s')
if PlotOptions.ShowTextDescriptions and not (
self.EventType == 'zeroflyby' or self.EventType == 'coast' or
self.EventType == 'force-coast' or self.EventType == "SFthrust"
or self.EventType == "FBLTthrust" or self.EventType
== "PSBIthrust" or self.EventType == "match_point"
or self.EventType == "waiting"):
self.LabelEvent(GraphicsObject, PlotOptions)
def move_rob(adj_Mat, num_Nodes, num_Edges, a, b, c, fileName, test=0):
global adjMat
global numNodes
global numEdges
global alpha
global beta
global gamma
global init
global score
global edgeList
global xdiff
global np_pos
global t1
adjMat = adj_Mat
numNodes = num_Nodes
numEdges = num_Edges
alpha = a
beta = b
gamma = c
count = 0
score = []
while count < 10:
init = []
edgeList = []
t_rob = []
nodes = []
near_axis = random.choice(range(numNodes))
for i in range(numNodes):
if (i == near_axis):
init.append(random.uniform(0.89, 0.99))
else:
init.append(random.uniform(0.01, 0.11))
for i in range(numNodes):
for j in range(numNodes):
if (adjMat[i][j] == 1):
edgeList.append((i, j))
init.append(random.uniform(0.01, 0.11))
init.append(random.random())
xdiff = random.uniform(-0.3, 0.3)
t1 = int(random.random() * 200 + 200)
st_pos = (8.0 / 9) + xdiff
x_target = 0.5
x_rob = []
x_cur = 0
r = ode(model).set_integrator('vode', rtol=10e-3, atol=10e-6)
r.set_initial_value(init)
dt = 0.05
while r.successful() and r.t < t1:
x_cur = r.y[-1]
x_rob.append(x_cur % 1)
nodes.append(r.y[0:-1])
t_rob.append(r.t)
r.integrate(t1, step=True)
if (test == 1):
plt.figure()
plt.subplot(311)
for i in range(numNodes):
plt.plot(t_rob, [node[i] for node in nodes],
label="p" + str(i + 1))
plt.legend(loc="upper right")
plt.subplot(312)
for i in range(numEdges):
plt.plot(t_rob, [node[numNodes + i] for node in nodes],
label="t" + str(i + 1))
plt.legend(loc="upper right")
plt.subplot(313)
plt.scatter(t_rob, [x for x in x_rob],
s=0.1,
label="Robot position")
plt.xlabel("Alpha:" + str(alpha))
plt.legend(loc="upper right")
plt.show()
return
rob_dist = [
abs(pos - x_target) for pos in x_rob[int(0.8 * len(x_rob)):]
]
score_val = np.max(rob_dist)
score.append(score_val)
count += 1
mean_score = np.mean(score)
print "Mean of max distance:" + str(mean_score)
if (mean_score < 0.1):
file = open("value" + fileName + ".txt", 'a+')
file.write("Score=" + str(mean_score) + '\n')
file.write("alpha=" + str(alpha) + '\n')
file.write("beta=" + str(beta) + '\n')
file.write("gamma=" + str(gamma) + '\n')
file.write('\n')
file.close()
return mean_score
def find_rotational_ic(body_dict, print_time=False):
"""
"""
now = time.time()
from scipy.integrate import ode
# turn off aerodynamic forces
body_dict = body_dict.copy() # just to be safe
body_dict['aero_interp'] = None
ang0 = np.r_[0, 0, 0] # horizontal flight
omg0 = np.r_[0, 0, 0] # no angular velocity
# v0_non = 1.7 / np.sqrt(2 * 29 / rho) # .2409, Ws=29
soln0 = np.r_[omg0, ang0]
# pick a velocity in the VPD (ignored in rotation function)
dRo0 = np.r_[0, 0, 0]
# arguments to the rotation dynamics function
vel_body_dict = (dRo0, body_dict)
# phases in cycle to simulate rotational dynamics
ntime_rot = 200 # 200
tend = 1 / body_dict['theta_dict']['f_theta']
ts_rot = np.linspace(0, tend, ntime_rot + 1)[:-1]
solver = ode(rotational_dynamics_eom)
solver.set_integrator('dopri5')
solver.set_initial_value(soln0, ts_rot[0]) # x0, t0
solver.set_f_params(vel_body_dict)
# integrate over one undulation cycle
i = 1
soln_rot = [soln0]
while i < ntime_rot:
t1 = time.time()
if print_time and i % 10 == 0:
print('Phase {} of {} in find_rotational_ic'.format(i, ntime_rot))
solver.integrate(ts_rot[i])
out = solver.y
soln_rot.append(out)
i = i + 1
t2 = time.time()
if print_time and i % 10 == 0:
print('\t{0:.3f} sec'.format(t2 - t1))
if print_time:
print('Find rotational IC: {0:.3f} sec'.format(time.time() - now))
# unpack the solution
soln_rot = np.array(soln_rot)
wx, wy, wz, yaw, pitch, roll = soln_rot.T
ang = np.c_[yaw, pitch, roll]
# omg = np.c_[wx, wy, wz]
# initial yaw, pitch, and roll angles in radians
return -ang.mean(axis=0)
i = -1
k = 0
file = open('hamil.txt','r')
for j,line in enumerate(file):
if j % n == 0:
i += 1
k = 0
elem = float(line)
Ax[i] += elem*x[k]
k+=1
file.close()
return(-(x.T@x)*Ax + (x.T@Ax)*x)
r = ode(f)
r.set_initial_value(x,0) #langsos algorithm
t1=10
dt=0.1
start = time.time()
while r.successful() and r.t < t1:
r.integrate(r.t+dt)
end = time.time()
print("RNN eig: ",r.y.T@[email protected]/([email protected]))
print("calculation time with RNN: ", end - start)
return At
#integrate the preallocated FSP
def FSPCalc_ODE_prealloc(self,parameters,TimeVar,case,N,x0,t,error):
errorcheck = 0
while errorcheck == 0:
FSP = lambda g,x: np.ravel(np.dot(At[0][g],np.array([x]).T))
#ode15s.set_integrator('vode', method='bdf', order=15, nsteps=3000)
At = self.Test_A(parameters,N,TimeVar,case,t)
Jac = lambda x,t: At[t]
ode15s = ode(FSP,jac=Jac)
ode15s.set_integrator('dopri5',nsteps=3000)
ode15s.set_initial_value(x0, t[0])
solution = np.empty((len(t),3*N))
solution[0,:] = x0[:,0]
for g in range(1,len(t)):
ode15s.set_initial_value(solution[g-1,:],t[g-1])
solution[g,:] = ode15s.integrate(g)
errorcheck = 1
for i in range(0,len(t)):
if 1-sum(solution[i]) > error:
errorcheck = 0
if errorcheck == 0:
dx[2] = x[2]*(mupr*x[0]/(kpr/mr+x[0]) + \
mupc*x[1]/(kpc/mc+x[1])-dp) - D*x[2]
dx[3] = D*N0 - x[0]*munr*mr/ynr*x[3]/(knr+x[3]) - \
x[1]*munc*mc/ync*x[3]/(knc+x[3]) - D*x[3]
return dx
##### Solve procedure goes here #####
Rsol = []; Csol = []; Psol = []; Nsol = []
Rsol.append(x0[0])
Csol.append(x0[1])
Psol.append(x0[2])
Nsol.append(x0[3])
r = ode(becks4Eq).set_integrator('dopri5',nsteps=100000,verbosity=1)
r.set_initial_value(x0,0).set_f_params(N0,D)
for t in tpts[1:]:
if STOC_D:
D_t = sp.random.gamma(D**2/var_D,var_D/D)
else:
D_t = D
r.set_f_params(N0,D_t)
r.integrate(t)
assert(r.successful())
Rsol.append(r.y[0])
Csol.append(r.y[1])
Psol.append(r.y[2])
Nsol.append(r.y[3])
##### Plot solution #####
def main():
# grab delta and g from the command line
delta = float(argv[1])
g = float(argv[2])
particles = 4
# setup shared data
dim1B = 8
# this defines the reference state
# 1st state
holes = [0,1,2,3]
particles = [4,5,6,7]
# 2nd state
# holes = [0,1,4,5]
# particles = [2,3,6,7]
# 3rd state
# holes = [0,1,6,7]
# particles = [2,3,4,5]
# basis definitions
bas1B = range(dim1B)
bas2B = construct_basis_2B(holes, particles)
basph2B = construct_basis_ph2B(holes, particles)
idx2B = construct_index_2B(bas2B)
idxph2B = construct_index_2B(basph2B)
# occupation number matrices
occ1B = construct_occupation_1B(bas1B, holes, particles)
occA_2B = construct_occupationA_2B(bas2B, occ1B)
occB_2B = construct_occupationB_2B(bas2B, occ1B)
occC_2B = construct_occupationC_2B(bas2B, occ1B)
occphA_2B = construct_occupationA_2B(basph2B, occ1B)
# store shared data in a dictionary, so we can avoid passing the basis
# lookups etc. as separate parameters all the time
user_data = {
"dim1B": dim1B,
"holes": holes,
"particles": particles,
"bas1B": bas1B,
"bas2B": bas2B,
"basph2B": basph2B,
"idx2B": idx2B,
"idxph2B": idxph2B,
"occ1B": occ1B,
"occA_2B": occA_2B,
"occB_2B": occB_2B,
"occC_2B": occC_2B,
"occphA_2B": occphA_2B,
"eta_norm": 0.0, # variables for sharing data between ODE solver
"dE": 0.0, # and main routine
"calc_eta": eta_white, # specify the generator (function object)
"calc_rhs": flow_imsrg2 # specify the right-hand side and truncation
}
# set up initial Hamiltonian
H1B, H2B = pairing_hamiltonian(delta, g, user_data)
E, f, Gamma = normal_order(H1B, H2B, user_data)
# reshape Hamiltonian into a linear array (initial ODE vector)
y0 = np.append([E], np.append(reshape(f, -1), reshape(Gamma, -1)))
# integrate flow equations
solver = ode(derivative_wrapper,jac=None)
solver.set_integrator('vode', method='bdf', order=5, nsteps=1000)
solver.set_f_params(user_data)
solver.set_initial_value(y0, 0.)
sfinal = 50
ds = 0.1
print "%-8s %-14s %-14s %-14s %-14s %-14s %-14s %-14s %-14s"%(
"s", "E" , "DE(2)", "DE(3)", "E+DE", "dE/ds",
"||eta||", "||fod||", "||Gammaod||")
# print "-----------------------------------------------------------------------------------------------------------------"
print "-" * 148
while solver.successful() and solver.t < sfinal:
ys = solver.integrate(sfinal, step=True)
dim2B = dim1B*dim1B
E, f, Gamma = get_operator_from_y(ys, dim1B, dim2B)
DE2 = calc_mbpt2(f, Gamma, user_data)
DE3 = calc_mbpt3(f, Gamma, user_data)
norm_fod = calc_fod_norm(f, user_data)
norm_Gammaod = calc_Gammaod_norm(Gamma, user_data)
print "%8.5f %14.8f %14.8f %14.8f %14.8f %14.8f %14.8f %14.8f %14.8f"%(
solver.t, E , DE2, DE3, E+DE2+DE3, user_data["dE"], user_data["eta_norm"], norm_fod, norm_Gammaod)
if abs(DE2/E) < 10e-8: break
def integrate(self, dt, planets):
"""Update the planet masses and radii:
args:
dt : Time to integrate for
planets : Planets container
"""
if planets.N == 0: return
self.update()
chem = False
if planets.chem:
chem = True
f = self._f_gas
def dMdt(R_p, M_core, M_env):
Mdot_s = self._peb_acc.computeMdot(R_p, M_core + M_env)
Mdot_g = self._gas_acc.computeMdot(R_p, M_core, M_env)
return Mdot_s * (1 - f), Mdot_g + Mdot_s * f
def dRdt(R_p, M_core, M_env):
if self._migrate:
return self._migrate.migration_rate(R_p, M_core + M_env)
else:
return np.zeros_like(R_p)
N = planets.N
Rmin = self._disc.R[0]
def f_integ(_, y):
R_p = y[:N]
M_core = y[N:2 * N]
M_env = y[2 * N:3 * N]
Rdot = dRdt(R_p, M_core, M_env)
Mcdot, Medot = dMdt(R_p, M_core, M_env)
accreted = R_p <= Rmin
Rdot[accreted] = Mcdot[accreted] = Medot[accreted] = 0
dydt = np.empty_like(y)
dydt[:N] = Rdot
dydt[N:2 * N] = Mcdot
dydt[2 * N:3 * N] = Medot
if chem:
Xs, Xg = self._compute_chem(R_p)
#Ms = Mcdot * f / (1-f)
Ms = 0
Mg = np.maximum(Medot - Mcdot, 0)
Nspec = Xs.shape[0]
dydt[3 * N:(3 + Nspec) * N] = (Mcdot * Xs).ravel()
dydt[(3 + Nspec) * N:(3 + 2 * Nspec) * N] = (Ms * Xs +
Mg * Xg).ravel()
return dydt
integ = ode(f_integ).set_integrator('dopri5', rtol=1e-5, atol=1e-5)
if chem:
Chem_core = (planets.M_core * planets.X_core).flat
Chem_env = (planets.M_env * planets.X_env).flat
X0 = np.concatenate([
planets.R, planets.M_core, planets.M_env, Chem_core, Chem_env
])
else:
X0 = np.concatenate([planets.R, planets.M_core, planets.M_env])
integ.set_initial_value(X0, 0)
integ.integrate(dt)
# Compute the fraction of the core / envelope that was accreted in
# solids
planets.R = integ.y[:N]
planets.M_core = integ.y[N:2 * N]
planets.M_env = integ.y[2 * N:3 * N]
if chem:
Ns = np.prod(planets.X_core.shape)
Xc = integ.y[3 * N:3 * N + Ns].reshape(-1, N)
Xe = integ.y[3 * N + Ns:3 * N + 2 * Ns].reshape(-1, N)
planets.X_core = Xc / np.maximum(planets.M_core, 1e-300)
planets.X_env = Xe / np.maximum(planets.M_env, 1e-300)
def integrate_triple_system(ics,
timemin,
timemax,
Nevals,
body0,
body1,
body2,
octupole_potential=True,
short_range_forces_conservative=False,
short_range_forces_dissipative=False,
solve_for_spin_vector=False,
version='tides',
tol=1.0e-10,
integrator='scipy',
add_derivatives=False):
atol = tol
rtol = tol / 10.0
rtol = 1.0e-8
m0, m1, m2 = body0.mass, body1.mass, body2.mass
radius0, radius1 = body0.radius, body1.radius
dradius0_dt, dradius1_dt = body0.dradius_dt, body1.dradius_dt
gyroradius0, gyroradius1 = body0.gyroradius, body1.gyroradius
dgyroradius0_dt, dgyroradius1_dt = body0.dgyroradius_dt, body1.dgyroradius_dt
k2_0, k2_1 = body0.apsidal_constant, body1.apsidal_constant
tv0, tv1 = body0.viscous_time, body1.viscous_time
tauconv0, tauconv1 = body0.convective_time, body1.convective_time
tlag0, tlag1 = body0.tidal_lag_time, body1.tidal_lag_time
rtol, atol = np.zeros(len(ics)), np.zeros(len(ics))
for key, val in triples.triple_keys.items():
if val is not None:
rtol[val] = 10 * triple_precision[key]
atol[val] = triple_precision[key]
#rtol,atol = 1.e-9,1.e-08
#integrator='other'
#integrator = 'bsint'
time = np.linspace(timemin, timemax, Nevals)
params = m0,m1,m2,radius0,radius1,gyroradius0,gyroradius1,k2_0,k2_1,\
tv0,tv1,tauconv0,tauconv1,\
tlag0,tlag1,\
dradius0_dt, dradius1_dt,\
dgyroradius0_dt, dgyroradius1_dt,\
octupole_potential,\
short_range_forces_conservative,short_range_forces_dissipative,solve_for_spin_vector
if (integrator == 'scipy'):
if (version == 'tides'):
params = m0,m1,m2,radius0,radius1,gyroradius0,gyroradius1,k2_0,k2_1,tv0,tv1,\
octupole_potential,\
short_range_forces_conservative,short_range_forces_dissipative,solve_for_spin_vector
sol =integ.odeint(threebody_ode_vf_tides_modified,ics,time,\
args=params,\
atol=atol,rtol=rtol,mxstep=1000000,hmin=0.0000001,mxords=16,mxordn=12)
elif (version == 'full'):
sol =integ.odeint(threebody_ode_vf_full_modified,ics,time,\
args=params,\
atol=atol,rtol=rtol,mxstep=1000000,hmin=0.000000001,mxords=12,mxordn=10)
if (add_derivatives):
for kk, y in enumerate(sol):
if (kk == 0):
dsol_dt = threebody_ode_vf_full_modified(
y, time[kk],
*params)[:len(triples.triple_derivative_keys)]
else:
dsol_dt = np.vstack(
(dsol_dt,
threebody_ode_vf_full_modified(
y, time[kk],
*params)[:len(triples.triple_derivative_keys)]
))
else:
if (version == 'tides'):
params = [p for p in params if p is not None]
solver = integ.ode(threebody_ode_vf_tides).set_integrator(
'dopri', nsteps=3000000, atol=atol, rtol=rtol, method='bdf')
elif (version == 'full'):
#solver = integ.ode(threebody_ode_vf_full).set_integrator('lsoda',nsteps=3000000,atol=atol,rtol=rtol, method='bdf',min_step=0.00001,max_order_ns=10)
solver = integ.ode(threebody_ode_vf_full).set_integrator(
'dopri', nsteps=3000000, atol=atol, rtol=rtol, method='bdf')
solver.set_initial_value(ics, time[0]).set_f_params(*params)
kk = 1
sol = []
sol.append(ics)
while solver.successful() and solver.t < time[-1]:
solver.integrate(time[kk])
sol.append(solver.y)
kk += 1
sol = np.asarray(sol)
retval = np.column_stack((time, sol))
if add_derivatives: retval = retval, dsol_dt
return retval
2 * i] = -delta * X[3 + i * 2] - a * X[2 + i * 2] - 3 * b * X[
2 + i *
2] * X[0]**2 #xprime acceleration for orthagnormal vectors
xprime[2 + 2 * i] = X[3 +
i * 2] #xprime velocity for orthagnormal vectors
return xprime #returns matrix of velocitys and accelerations
orbits = 100 #number of orbits
dt = 0.5 #time step
"""Setting initial conditions for normalised linear system (remember python starts counting at 0)"""
x[2] = 1
x[5] = 1
backend = 'dopri5' #Integration method
r = ode(duffing).set_integrator(backend)
t = 0
r.set_initial_value(x, t)
counter = 0
t1 = orbits * 2 * np.pi * w
while r.successful(
) and tlist[-1] < t1: #will integrate up until t1 time is reached
r.integrate(
r.t + dt
) #step=True means that variable steps are used, with more steps where gradient is close to zero.
tlist.append(r.t) #adds results to already created lists
x = r.y
#Normalise first vector
for j in range(0, n):
zvector[0] = zvector[0] + (x[n * (j + 1)])**2
zvector[0] = np.sqrt(zvector[0])
def dyn(R0, ModVar, UseOp, tobsEnd, tol):
import numpy as np
import matplotlib.pyplot as plt
from scipy.integrate import ode
from scipy import optimize
from gammamin_fzero import minimize_gammamin
from nextstep_ode import nextstep_func
if UseOp.save_params:
import os
if tol < 1e-5:
print_error = True
else:
print_error = False
c, pi, mp, me, kB = 2.9979e10, np.pi, 1.6726e-24, 9.1094e-28, 1.38065e-16
qe = 4.803204e-10
sigma_B = 5.6704e-5 ### Stephan Boltzann constant in cgs
rad_const = 4 * sigma_B / c #### Radiation constant
#Initial conditions
#ModVar.M0 = ModVar.E0*c**-2
if UseOp.reverseShock: grandNan = [float('nan')] * 19
else: grandNan = [float('nan')] * 12
useSpread = True #If you want to turn spread off, turn it off on this line!
UseOp.remix_radloss = True
UseOp.continuous_radloss = True
#################
### Constants ###
#################
mup, mue = 1., me / mp
sigmaT = 6.6524e-25
numeric_Eint = True ### numeric or analytical approach to solve Eint?
##############################
### Runge-Kutta 853 method ###
##############################
"""
0 - tburst
1 - tcomoving
2 - Gamma
3 - Eint2
4 - Eint3
5 - theta
6 - Erad2
7 - Erad3
8 - Esh2
9 - Esh3
10 - Ead2
11 - Ead3
12 - M2
13 - M3
14 - deltaR4
"""
### Allocating space and setting initial values
Rstop = 1e23
nsteps = int(np.ceil(100 * np.log10(Rstop / R0)))
beta0 = 1 - 0.5 / ModVar.Gamma0**2 - 1 / 8. / ModVar.Gamma0**4
beta0c = beta0 * c
M20 = 2 * pi * R0**3 * (1 - np.cos(ModVar.theta0)) * ModVar.A0 * (
mp + me) * R0**-ModVar.s / 3
tburst0, tcomoving0 = R0 / beta0c, R0 / beta0c / ModVar.Gamma0
tobs, rho, B = np.zeros(nsteps), np.zeros(nsteps), np.zeros(nsteps)
Eint20 = M20 * (ModVar.Gamma0 - 1) * c**2
gamma_c_w = np.zeros(nsteps)
gamma_c_w[0] = 6 * me * c / (
ModVar.A0 * R0**(-ModVar.s) * (mp + me)
) / sigmaT / 8 / tcomoving0 / ModVar.eB / Eint20 * M20 ### Mass weighted cooling Lorentz factor
if UseOp.reverseShock:
gamma_c_w_RS = np.ones(nsteps)
gamma_c_w_RS *= float('inf')
rho4, BRS, gamma43_minus_one = np.zeros(nsteps), np.zeros(
nsteps), np.zeros(nsteps)
alpha_of = ModVar.tprompt * beta0c
shutOff = False
if ModVar.theta0 < 1e-3:
one_minus_costheta0 = ModVar.theta0**2 / 2 - ModVar.theta0**4 / 24 + ModVar.theta0**6 / 120
else:
one_minus_costheta0 = 1 - np.cos(ModVar.theta0)
initial_values = [
tburst0, tcomoving0, ModVar.Gamma0,
(ModVar.Gamma0 - 1) * M20 / ModVar.M0, 0., ModVar.theta0, 0., 0., 0.,
0., 0., 0., M20 / ModVar.M0, 0., 0.
]
#initial_values = [tburst0,tcomoving0,ModVar.Gamma0,(ModVar.Gamma0-1)*M20/ModVar.M0,0.,ModVar.theta0,0.,0.,0.,0.,0.,0.,M20/ModVar.M0,0.,0.] ### Logarithmic
"""
### An initial very small step, to initialize reverse shock quantities
infmal_R0 = R0*1e-9
initial_values += nextstep_func(R0 , initial_values , s , ModVar.Gamma0 , z , ModVar.theta0 , ModVar.tprompt , ModVar.M0 , ModVar.A0/ModVar.M0 , ModVar.R_ISM , [UseOp.radiativeLosses,remix_radloss,continuous_radloss,fixed_epsilon,UseOp.reverseShock,UseOp.exponential_outflow] , [ModVar.epsilone,ModVar.eB,p,ModVar.epsilone3,ModVar.eB3,pRS],float('inf'),float('inf')) * infmal_R0
R0 = R0 + infmal_R0
"""
odeinstance = ode(nextstep_func)
#firststep = R0/10000
odeinstance.set_integrator("dop853",
rtol=1e-5,
nsteps=100000,
first_step=R0 * 1e-9)
if UseOp.reverseShock:
odeinstance.set_f_params(ModVar, UseOp, float('inf'), float('inf'),
False)
else:
odeinstance.set_f_params(ModVar, UseOp, float('inf'))
odeinstance.set_initial_value(initial_values, R0)
R = np.zeros(nsteps)
out = np.zeros([nsteps, len(initial_values)])
for i in range(nsteps):
###################
### Integrating ###
###################
nextR = R0 * (Rstop / R0)**(float(i + 1) / float(nsteps))
#try:
out[i] = odeinstance.integrate(nextR)
"""
except:
while firststep > 1:
if i == 0:
firststep /= 10
print 'reducing first step to %s'%(firststep)
try:
odeinstance.set_integrator("dop853", rtol=1e-3, nsteps=100000, first_step=firststep)
out[i] = odeinstance.integrate(nextR)
break
except:
firststep /= 10
else:
print '\n\n\n!!!\n\n\n'
raise NameError('i is not 0 and nextstep_ode.py still crashed')
"""
R[i] = np.copy(odeinstance.t)
tobs[i] = (1 + ModVar.z) * (out[i, 0] - R[i] / c)
beta = np.sqrt(1 - out[i, 2]**-2)
######################################
### Magnetic fields and densities ###
######################################
rho1_fac = ModVar.A0 * (mp + me)
if ModVar.s == 0: ### CM
rho[i] = ModVar.A0 * (mp + me)
else:
if R[i] < ModVar.R_ISM:
rho[i] = ModVar.A0 * R[i]**(-ModVar.s) * (mp + me)
else:
rho[i] = ModVar.A0 * R[i]**(-ModVar.s) * (mp + me)
rho2 = 4 * rho1_fac * out[i, 2] * R[i]**(-ModVar.s)
B_fac = np.sqrt(8 * pi * ModVar.eB)
B[i] = B_fac * np.sqrt(out[i, 3]) / np.sqrt(out[i, 12]) * np.sqrt(
rho2
) * c ### Factor c is because Eint is on the form E/ModVar.M0/c**2 and M on the form M/ModVar.M0
gamma_max = (6 * pi * qe / sigmaT / B[i])**.5
if UseOp.reverseShock:
if out[i, 4] < 0: ### Negative energy in the RS
out[i, 4] = 0.
if beta < 0.99:
gamma43_minus_one[i] = out[i, 2] * ModVar.Gamma0 * (
1 - beta * beta0)
else:
gamma43_minus_one[i] = out[i, 2] * ModVar.Gamma0 * (
1 / ModVar.Gamma0**2 + 1 / out[i, 2]**2 - 1 /
out[i, 2]**2 / ModVar.Gamma0**2) / (1 + beta * beta0) - 1
if out[i, 13] > 0:
rho4_fac_1 = ModVar.M0 / 2 / alpha_of / np.pi / one_minus_costheta0
rho4_fac = rho4_fac_1 / R[i]**2
rho4[i] = rho4_fac * np.exp(-out[i, 14] / alpha_of)
if rho4[i] < 1e-60:
rho4[i] = 0. ### Avoiding numerical overflows
BRS[i] = 0.
shutOff = True
odeinstance.set_f_params(ModVar, UseOp, gamma_c_w[i],
gamma_c_w_RS[i], shutOff)
else:
rho3 = 4 * out[i, 2] * rho4[i]
BRS_fac = np.sqrt(8 * pi * ModVar.eB3)
BRS[i] = BRS_fac * np.sqrt(out[i, 4]) / np.sqrt(
out[i, 13]
) * np.sqrt(
rho3
) * c ### Factor c is because Eint is on the form E/ModVar.M0/c**2 and M on the form M/ModVar.M0
if np.isnan(BRS[i]):
print 'E3 =', out[i, 4]
print 'M3 =', out[i, 13]
print 'rho3 =', rho3
if BRS[i] == 0:
BRS[i] = 1e-70
gamma_max_RS = np.sqrt(6 * pi * qe / sigmaT / BRS[i])
#############################
### Calculating gamma_c_w ###
#############################
if i > 0:
dM2 = (out[1:i + 1, 12] - out[:i, 12])
gamma_c_w_fac = 6 * pi * me * c / sigmaT
rho2_array = 4 * rho1_fac * out[:i, 2] * R[:i]**(-ModVar.s)
B_array = B_fac * np.sqrt(out[:i, 3]) / np.sqrt(
out[:i, 12]) * np.sqrt(rho2_array) * c
gamma_c_w_array = gamma_c_w_fac / B_array**2 / out[:i, 1]
gamma_c_w[i] = np.sum(
dM2 * gamma_c_w_array) / (out[i - 1, 12] - M20 / ModVar.M0)
if gamma_c_w[i] > gamma_max:
gamma_c_w[i] = np.copy(gamma_max)
if UseOp.reverseShock and BRS[i] > 0:
dM3 = (out[1:i + 1, 13] - out[:i, 13])
rho3_array = 4 * out[:i, 2] * rho4_fac_1 * np.exp(
-out[:i, 14] / alpha_of) * R[:i]**-2
BRS_array = BRS[:
i] #BRS_fac * np.sqrt(out[:i,4]) / np.sqrt(out[:i,13]) * np.sqrt(rho3_array) * c
gamma_c_w_RS_array = gamma_c_w_fac / BRS_array**2 / out[:i, 1]
gamma_c_w_RS[i] = np.sum(dM3 * gamma_c_w_RS_array) / out[i - 1,
13]
"""
if gamma_c_w_RS[i] > gamma_max_RS:
gamma_c_w_RS[i] = np.copy(gamma_max_RS)
"""
############################
### Calulating gamma_min ###
############################
"""
#gamma_min[i] = (p-2)/(p-1)*(ModVar.epsilone/mue*(out[i,2]-1)+1)
gamma_min[i] = minimize_gammamin(gamma_c_w[i] , gamma_max , p , ModVar.epsilone , out[i,2] - 1,mue)
gamma_min_inj = minimize_gammamin(gamma_max , gamma_max , p ,ModVar.epsilone , out[i,2] - 1,mue)
if UseOp.reverseShock:
if (out[i,13]>0) and (BRS[i]>0) and (gamma_c_w_RS[i] > 1) and (i > 0):
gamma_min_RS[i] = minimize_gammamin(gamma_c_w_RS[i] , gamma_max_RS , pRS , ModVar.epsilone3 , gamma43_minus_one[i] , mue)
gamma_min_RS_inj = minimize_gammamin(gamma_max_RS , gamma_max_RS , pRS , ModVar.epsilone3 , gamma43_minus_one[i] , mue)
else:
gamma_min_RS[i] = (pRS-2)/(pRS-1)*(ModVar.epsilone3/mue*gamma43_minus_one[i]+1)
gamma_min_RS_inj = np.copy(gamma_min_RS[i])
odeinstance.set_f_params(s , ModVar.Gamma0 , z , ModVar.theta0 , ModVar.tprompt , ModVar.M0 , ModVar.A0/ModVar.M0 , ModVar.R_ISM , [UseOp.radiativeLosses,remix_radloss,continuous_radloss,fixed_epsilon,UseOp.reverseShock,UseOp.exponential_outflow] , [ModVar.epsilone,ModVar.eB,p,ModVar.epsilone3,ModVar.eB3,pRS] , gamma_c_w[i] , gamma_min_inj , gamma_min[i] , gamma_c_w_RS[i], gamma_min_RS_inj , gamma_min_RS[i] , shutOff)
else:
odeinstance.set_f_params(s , ModVar.Gamma0 , z , ModVar.theta0 , ModVar.tprompt , ModVar.M0 , ModVar.A0/ModVar.M0 , ModVar.R_ISM , [UseOp.radiativeLosses,remix_radloss,continuous_radloss,fixed_epsilon,UseOp.reverseShock,UseOp.exponential_outflow] , [ModVar.epsilone,ModVar.eB,p,ModVar.epsilone3,ModVar.eB3,pRS] , gamma_c_w[i] , gamma_min_inj , gamma_min[i])
if ( not odeinstance.successful()):
raise NameError('stepsize')
if ( tobs[i-3] > tobsEnd):#*365.35*10 ):
break #Stop at 10 years
"""
out[:, 3] *= ModVar.M0 * c**2
out[:, 4] *= ModVar.M0 * c**2
out[:, 6] *= ModVar.M0 * c**2
out[:, 7] *= ModVar.M0 * c**2
out[:, 8] *= ModVar.M0 * c**2
out[:, 9] *= ModVar.M0 * c**2
out[:, 10] *= ModVar.M0 * c**2
out[:, 11] *= ModVar.M0 * c**2
out[:, 12] *= ModVar.M0
out[:, 13] *= ModVar.M0
"""
plt.plot(R[:i+1] , out[:i+1,2])
plt.ylabel(r'$\Gamma$')
plt.loglog()
plt.show()
plt.plot(R[:i] , (out[1:i+1,13]-out[:i,13]) / (R[1:i+1]-R[:i]))
plt.loglog()
plt.ylabel(r'$\frac{dM_3}{dR}$')
plt.show()
plt.plot(R[:i+1] , out[:i+1,12])
plt.ylabel(r'$M_2$')
plt.loglog()
plt.show()
plt.plot(R[:i+1] , out[:i+1,13])
plt.ylabel(r'$M_3$')
plt.loglog()
plt.show()
plt.plot(R[:i+1] , out[:i+1,3])
plt.ylabel(r'$E_{\rm int,2}$')
plt.loglog()
plt.show()
plt.plot(R[:i+1] , out[:i+1,4])
plt.ylabel(r'$E_{\rm int,3}$')
plt.loglog()
plt.show()
plt.plot(R[:i+1] , out[:i+1,14])
plt.ylabel(r'$\Delta R_4$')
plt.loglog()
plt.show()
"""
### Calculating gamma_min ###
gamma_min = (ModVar.p - 2) / (ModVar.p -
1) * (1 + mp / me * ModVar.epsilone *
(out[:i + 1, 2] - 1))
gamma_min[np.where(gamma_min < 1)] = 1.
if UseOp.reverseShock:
### Calculating gamma_min_RS ###
gamma_min_RS = (ModVar.pRS - 2) / (ModVar.pRS - 1) * (
1 + mp / me * ModVar.epsilone3 * gamma43_minus_one[:i + 1])
if UseOp.save_params:
os.system('rm Parameters/*')
np.savetxt('Parameters/tobs.txt', tobs[:i + 1])
np.savetxt('Parameters/tburst.txt', out[:i + 1, 0])
np.savetxt('Parameters/tcomoving.txt', out[:i + 1, 1])
np.savetxt('Parameters/Gamma.txt', out[:i + 1, 2])
np.savetxt('Parameters/R.txt', R[:i + 1])
#np.savetxt('Parameters/ModVar.epsilon_rad.txt',ModVar.epsilon_rad[:i+1])
np.savetxt('Parameters/dM2dR.txt',
(out[1:i + 1, 12] - out[:i, 12]) / (R[1:i + 1] - R[:i]))
np.savetxt('Parameters/M2.txt', out[:i + 1, 12])
np.savetxt('Parameters/Eint2.txt', out[:i + 1, 3])
np.savetxt('Parameters/Esh2.txt', out[:i + 1, 8])
np.savetxt('Parameters/Ead2.txt', out[:i + 1, 10])
np.savetxt('Parameters/Erad2.txt', out[:i + 1, 6])
np.savetxt('Parameters/gamma_c_w.txt', gamma_c_w[:i + 1])
np.savetxt('Parameters/gamma_min.txt', gamma_min)
np.savetxt('Parameters/B.txt', B[:i + 1])
np.savetxt('Parameters/theta.txt', out[:i + 1, 5])
np.savetxt('Parameters/rho.txt', rho[:i + 1])
if UseOp.reverseShock:
np.savetxt('Parameters/dM3dR.txt',
(out[1:i + 1, 13] - out[:i, 13]) / (R[1:i + 1] - R[:i]))
np.savetxt('Parameters/M3.txt', out[:i + 1, 13])
#np.savetxt('Parameters/epsilon_rad_RS.txt',ModVar.epsilon_rad_RS[:i+1])
np.savetxt('Parameters/Eint3.txt', out[:i + 1, 4])
np.savetxt('Parameters/Esh3.txt', out[:i + 1, 9])
np.savetxt('Parameters/Ead3.txt', out[:i + 1, 11])
np.savetxt('Parameters/Erad3.txt', out[:i + 1, 7])
np.savetxt('Parameters/Gamma43_minus_one.txt',
gamma43_minus_one[:i + 1])
np.savetxt('Parameters/gamma_c_w_RS.txt', gamma_c_w_RS[:i + 1])
np.savetxt('Parameters/gamma_min_RS.txt', gamma_min_RS)
np.savetxt('Parameters/BRS.txt', BRS[:i + 1])
np.savetxt('Parameters/rho4.txt', rho4[:i + 1])
if UseOp.reverseShock:
### RS_elements_upper reduces RS arrays to where there is an RS component
RS_elements_upper = np.count_nonzero(rho4)
return out[:i + 1,
2], out[:i + 1,
0], tobs[:i +
1], out[:i + 1,
1], out[:i + 1,
5], out[:i + 1,
12], out[:
RS_elements_upper,
13], out[:
i
+
1,
3], out[:
RS_elements_upper,
4], B[:
i
+
1], BRS[:
RS_elements_upper], rho[:
i
+
1], rho4[:
RS_elements_upper], R[:
i
+
1], gamma43_minus_one[:
RS_elements_upper], gamma_min, gamma_min_RS, gamma_c_w[:
i
+
1], gamma_c_w_RS[:
RS_elements_upper], RS_elements_upper
else:
return out[:i + 1,
2], out[:i + 1,
0], tobs[:i +
1], out[:i + 1,
1], out[:i + 1,
5], out[:i + 1,
12], None, out[:i +
1,
3], None, B[:
i
+
1], None, rho[:
i
+
1], None, R[:
i
+
1], None, gamma_min, None, gamma_c_w[:
i
+
1], None, None
jdos = 0.00108248
def derivFcn(t, y):
return JTwo(t, y, MU, jdos, RE)
r_0 = [-5282.628, -4217.988, 296.511]
v_0 = [-4.541972, 5.885228, 2.043106]
T0 = 0.0
times = np.arange(0, 86400, 100)
tF = 86400
dT = 100
Y_0 = np.concatenate([r_0, v_0], axis=0)
rv = ode(derivFcn)
# The integrator type 'dopri5' is the same as MATLAB's ode45()!
# rtol and atol are the relative and absolute tolerances, respectively
rv.set_integrator('dopri5', rtol=1e-10, atol=1e-20)
rv.set_initial_value(Y_0, T0)
output = []
output.append(np.insert(Y_0, 0, T0))
# Run the integrator and populate output array with positions and velocities
while rv.successful() and rv.t < tF: # rv.successful() and
rv.integrate(rv.t + dT)
output.append(np.insert(rv.y, 0, rv.t))
# Convert the output a numpy array for later use
output = np.array(output)
V0= 8 #o volume inicial não se altera
Fe= 0.7 #L/h || caudal de entrada || 350 g/L glucose
Se= 350 #concentração do substrato de entrada g/L
#lista com os valores iniciais fornecida a func
y0= [X0, S0, A0, P0, V]
#lista com os parametros fornecida a func
params= [k1, k2, k3, k4, k5, k6, k7, k8, k9, k10, k11, V0, Fe, Se]
t0= 0 #tempo inicial
t= 20 #tempo final
dt= 0.001 #intervalo de tempo entre reads
# Call the ODE solver
r = ode(bl21_FB).set_integrator('lsoda', method='bdf', lband=0) #lband é o limite inferior -- para nao haver valores negativos
r.set_initial_value(y0, t0).set_f_params(params)
#storing variables
T, x, s, a, p, v= [], [], [], [], [], []
while r.successful() and r.t < t:
time= r.t + dt
T.append(r.t)
x.append(r.integrate(time)[0])
s.append(r.integrate(time)[1])
a.append(r.integrate(time)[2])
p.append(r.integrate(time)[3])
v.append(r.integrate(time)[4])
#print(time, r.integrate(time))
def target_orbit(target_crossing,x0,t0,tf,mu,backend_bigprop,backend_littleprop,tol_integrate,tol_step,tol_converge):
'''
target_crossing = nth crossing to target a perpendicular crossing
x0 = 42 element initial state; the first 6 elements contain the
non-dimensionalized, rotating position and velocity; the remaining
elements are the state transition matrix.
t0 = initial time
tf = final time (this is typically going to be larger than the actual
desired propagation time)
mu = mass ratio of the two primaries
backend_bigprop = integrator to use when taking large steps
backend_littleprop = integrator to use when bisecting the crossing (if
bigprop uses vode, this must use a different integrator)
tol_integrate = integration tolerance
tol_step = smallest step size that the integrator will use
tol_converge = tolerance to determine whether crossing is perpendicular
'''
bigprop = ode(cr3bp_eom).set_integrator(backend_bigprop,atol=tol_integrate,rtol=tol_integrate)
littleprop = ode(cr3bp_eom).set_integrator(backend_littleprop,atol=tol_integrate,rtol=tol_integrate)
error = 1
iteration = 0
while np.abs(error) > tol_converge:
#integrate
bigprop.set_initial_value(x0,t0).set_f_params(mu)
t = [t0]
x = [x0]
current_crossing = 0
last_y = np.sign(x0[4])
while bigprop.successful() and current_crossing < target_crossing:
#step
current_state = bigprop.integrate(tf,step=True)
#check for crossing
if np.sign(current_state[1]) != np.sign(last_y):
current_crossing += 1
#littleprop to find actual crossing
if current_crossing == target_crossing:
dt = (t[-1]-t[-2])
while np.abs(current_state[1]) > tol_converge and abs(dt) > tol_step:
dt = -dt/2
last_y = current_state[1]
littleprop.set_initial_value(current_state,bigprop.t).set_f_params(mu)
current_state = littleprop.integrate(littleprop.t+dt)
#take another step if we didn't cross on the first one
if np.sign(current_state[1]) == np.sign(last_y):
current_state = littleprop.integrate(littleprop.t+dt)
#save correct time
t.append(littleprop.t)
else:
#save correct time
t.append(bigprop.t)
#save data to arrays
x.append(current_state)
last_y = current_state[1]
#targeting algorithm to update initial conditions
error = current_state[3]
r1 = np.sqrt( (current_state[0]+mu)**2 + current_state[1]**2 + current_state[2]**2 )
r2 = np.sqrt( (current_state[0]-1+mu)**2 + current_state[1]**2 + current_state[2]**2 )
ax = 2*current_state[4] + current_state[0] - (1-mu)*(current_state[0]+mu)/r1**3 - mu*(current_state[0]-1+mu)/r2**3
X = np.array([[x0[4]],[t[len(t)-1]]])
FX = np.array([[current_state[1]],[current_state[3]]])
DF = np.array([[current_state[16],current_state[4]],[current_state[28],ax]])
update = np.zeros((2,1))
update = np.mat(X) - np.mat(np.transpose(DF))*np.linalg.inv(np.mat(DF)*np.mat(np.transpose(DF)))*np.mat(FX)
x0[4] = update[0]
#increase perpendicular crossing tolerance if stuck
if iteration > 10:
tol_converge = 10*tol_converge
iteration = -1
iteration += 1
return t,x
import warnings
def logistic(t, y, r):
return r * y * (1.0 - y)
r = .01
t0 = 0
y0 = 1e-5
t1 = 5000.0
#backend = 'vode'
backend = 'dopri5'
#backend = 'dop853'
solver = sp.ode(logistic).set_integrator(backend, nsteps=1)
solver.set_initial_value(y0, t0).set_f_params(r)
# suppress Fortran-printed warning
solver._integrator.iwork[2] = -1
sol = []
warnings.filterwarnings("ignore", category=UserWarning)
while solver.t < t1:
solver.integrate(t1, step=True)
sol.append([solver.t, solver.y])
warnings.resetwarnings()
sol = np.array(sol)
# plot
py.figure(4)
# -*- coding: utf-8 -*-
"""
Created on Fri Aug 17 15:04:39 2018
@author: mtkes
"""
from scipy.integrate import ode
y0, t0 = [1.0j, 2.0], 0
def f(t, y, arg1):
return [1j * arg1 * y[0] + y[1], -arg1 * y[1]**2]
def jac(t, y, arg1):
return [[1j * arg1, 1], [0, -arg1 * 2 * y[1]]]
r = ode(f, jac).set_integrator('zvode', method='bdf', with_jacobian=True)
r.set_initial_value(y0, t0).set_f_params(2.0).set_jac_params(2.0)
t1 = 10
dt = 1
while r.successful() and r.t < t1:
r.integrate(r.t + dt)
print("%g %g %g" % (r.t, abs(r.y[0]), abs(r.y[1])))
_K = [[0.0, 0.3, 0.1, 0.1, 0.5, 0.0], [0.3, 0.0, 0.9, 0.0, 0.1, 1.0],
[0.1, 0.9, 0.0, 0.4, 0.0, 0.2], [0.1, 0.0, 0.4, 0.0, 1.0, 0.4],
[0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0]]
_K = np.array(_K)
_K = (_K + _K.T) / 2
# Preparing oscillators with Kuramoto model
oscN = 3 # num of oscillators
Y0 = _Y0[:oscN]
W = _W[:oscN]
K = _K[:oscN, :oscN]
####################################################
# Setting up ODE integrator
kODE = ode(kuramoto_ODE)
kODE.set_integrator("vode")
# Set parameters into model
kODE.set_initial_value(Y0, t0)
kODE.set_f_params((W, K))
# Run ODE integrator
odeT, odePhi = [], []
while kODE.successful() and kODE.t < t1:
kODE.integrate(kODE.t + dt)
odeT.append(kODE.t)
odePhi.append(kODE.y)
# Convert to numpy array
def __init__(self, f, **kwargs):
self.f = f
self.h = kwargs.pop('h', 0.01)
self.r = ode(f).set_integrator('dopri5')
self.fig, self.ax = plt.subplots(1, 1)
current_pass['Date1'] = date
current_pass['Pass Length'] = (
date - current_pass['Date0']).total_seconds()
if max_el > 20:
pass_list.append(current_pass)
date += datetime.timedelta(days=30)
for _pass in pass_list:
pass_directory = Directory + '/' + str(_pass['Date0']).replace(':', '-')
if not os.path.exists(pass_directory):
os.makedirs(pass_directory)
r0, v0 = Satellite.propagate(*_pass['Date0'].timetuple()[0:6])
state0 = hstack([r0, v0])
solver = ode(propagate_orbit)
solver.set_integrator('lsoda')
solver.set_initial_value(state0, 0)
#times = asarray(arange(0, _pass['Pass Length'], DT))
positions = []
times = []
while solver.t < _pass['Pass Length']:
positions.append(solver.integrate(solver.t + DT))
times.append(solver.t)
times = hstack(times)
positions = vstack(positions)
obs_vecs = []
sun_vecs = []
sat_poss = []
def OutputEphemerisData(self, LU, TU, mu, timehistory, statehistory,
accelhistory):
if self.EventType == 'coast' or self.EventType == 'force-coast' or self.EventType == "SFthrust" or self.EventType == "FBLTthrust" or self.EventType == "PSBIthrust" or self.EventType == "match_point":
#propagate the state forward and back
CenterPointState = []
CenterPointState = np.zeros(7)
CenterPointState[0:6] = np.array(self.SpacecraftState) / LU
CenterPointState[3:6] *= TU
CenterPointState[6] = 1.0
if self.EventType == 'coast' or self.EventType == 'force-coast' or self.EventType == "SFthrust" or self.EventType == "PSBIthrust":
CenterPointState = np.array(copy.deepcopy(
self.SpacecraftState))
if self.EventType == "SFthrust" or event.EventType == "PSBIthrust":
CenterPointState[3:6] += np.array(
self.DeltaVorThrustVectorControl)
CenterPointState /= LU
CenterPointState[3:6] *= TU
ForwardIntegrateObject = ode(
EOM.EOM_inertial_2body).set_integrator('dop853',
atol=1.0e-8,
rtol=1.0e-8)
ForwardIntegrateObject.set_initial_value(
CenterPointState).set_f_params(1.0)
StateHistoryForward = []
TimeHistoryForward = []
while ForwardIntegrateObject.successful(
) and ForwardIntegrateObject.t < (self.TimestepLength / 2.0 -
1) * 86400 / TU:
ForwardIntegrateObject.integrate(ForwardIntegrateObject.t +
86400 / TU)
StateHistoryForward.append(ForwardIntegrateObject.y * LU)
TimeHistoryForward.append(ForwardIntegrateObject.t * TU)
#backward integration
CenterPointState = np.array(copy.deepcopy(
self.SpacecraftState))
CenterPointState /= LU
CenterPointState[3:6] *= TU
BackwardIntegrateObject = ode(
EOM.EOM_inertial_2body).set_integrator('dop853',
atol=1.0e-8,
rtol=1.0e-8)
BackwardIntegrateObject.set_initial_value(
CenterPointState).set_f_params(1.0)
StateHistoryBackward = []
TimeHistoryBackward = []
while BackwardIntegrateObject.successful(
) and BackwardIntegrateObject.t > -(self.TimestepLength / 2.0 -
1) * 86400 / TU:
BackwardIntegrateObject.integrate(
BackwardIntegrateObject.t - 86400 / TU)
StateHistoryBackward.append(BackwardIntegrateObject.y * LU)
TimeHistoryBackward.append(BackwardIntegrateObject.t * TU)
StateHistoryBackward.reverse()
TimeHistoryBackward.reverse()
for StateLine in StateHistoryBackward:
statehistory.append(
np.array([
StateLine[0], StateLine[1], StateLine[2],
StateLine[3] / TU, StateLine[4] / TU,
StateLine[5] / TU
]) * 1000)
accelhistory.append(np.array([0.0, 0.0, 0.0]))
for TimeLine in TimeHistoryBackward:
timehistory.append(TimeLine + self.JulianDate * 86400)
statehistory.append(
np.array([
self.SpacecraftState[0], self.SpacecraftState[1],
self.SpacecraftState[2], self.SpacecraftState[3],
self.SpacecraftState[4], self.SpacecraftState[5]
]) * 1000)
accelhistory.append(np.array([0.0, 0.0, 0.0]))
timehistory.append(self.JulianDate * 86400)
for StateLine in StateHistoryForward:
statehistory.append(
np.array([
StateLine[0], StateLine[1], StateLine[2],
StateLine[3] / TU, StateLine[4] / TU,
StateLine[5] / TU
]) * 1000)
accelhistory.append(np.array([0.0, 0.0, 0.0]))
for TimeLine in TimeHistoryForward:
timehistory.append(TimeLine + self.JulianDate * 86400)
elif self.EventType == 'FBLTthrust':
CenterPointState = np.zeros(7)
CenterPointState[0:6] = np.array(self.SpacecraftState) / LU
CenterPointState[3:6] *= TU
CenterPointState[6] = 1.0
ScaledThrust = np.array(
self.Thrust) / self.Mass / LU / 1000 * TU * TU
ScaledMdot = self.MassFlowRate / self.Mass * TU
ForwardIntegrateObject = ode(
EOM.EOM_inertial_2bodyconstant_thrust).set_integrator(
'dop853', atol=1.0e-8, rtol=1.0e-8)
ForwardIntegrateObject.set_initial_value(
CenterPointState).set_f_params(ScaledThrust, ScaledMdot,
1.0)
StateHistoryForward = []
TimeHistoryForward = []
while ForwardIntegrateObject.successful(
) and ForwardIntegrateObject.t < (self.TimestepLength / 2.0 -
1) * 86400 / TU:
ForwardIntegrateObject.integrate(ForwardIntegrateObject.t +
86400 / TU)
UnscaledState = copy.deepcopy(ForwardIntegrateObject.y)
UnscaledState[0:3] *= LU
UnscaledState[4:6] *= LU / TU
UnscaledState[6] *= self.Mass
StateHistoryForward.append(UnscaledState)
TimeHistoryForward.append(ForwardIntegrateObject.t * TU)
BackwardIntegrateObject = ode(
EOM.EOM_inertial_2bodyconstant_thrust).set_integrator(
'dop853', atol=1.0e-8, rtol=1.0e-8)
BackwardIntegrateObject.set_initial_value(
CenterPointState).set_f_params(ScaledThrust, ScaledMdot,
1.0)
StateHistoryBackward = []
TimeHistoryBackward = []
while BackwardIntegrateObject.successful(
) and BackwardIntegrateObject.t > -(self.TimestepLength / 2.0 -
1) * 86400 / TU:
BackwardIntegrateObject.integrate(
BackwardIntegrateObject.t - 86400 / TU)
UnscaledState = copy.deepcopy(BackwardIntegrateObject.y)
UnscaledState[0:3] *= LU
UnscaledState[4:6] *= LU / TU
UnscaledState[6] *= self.Mass
StateHistoryBackward.append(UnscaledState)
TimeHistoryBackward.append(BackwardIntegrateObject.t * TU)
StateHistoryBackward.reverse()
TimeHistoryBackward.reverse()
for StateLine in StateHistoryBackward:
statehistory.append(
np.array([
StateLine[0], StateLine[1], StateLine[2],
StateLine[3], StateLine[4], StateLine[5]
]) * 1000)
accelhistory.append(
np.array([
self.Thrust[0] / StateLine[6],
self.Thrust[1] / StateLine[6],
self.Thrust[2] / StateLine[6]
]))
for TimeLine in TimeHistoryBackward:
timehistory.append(TimeLine + self.JulianDate * 86400)
statehistory.append(
np.array([
self.SpacecraftState[0], self.SpacecraftState[1],
self.SpacecraftState[2], self.SpacecraftState[3],
self.SpacecraftState[4], self.SpacecraftState[5]
]) * 1000)
accelhistory.append(
np.array([
self.Thrust[0] / self.Mass, self.Thrust[1] / self.Mass,
self.Thrust[2] / self.Mass
]))
timehistory.append(self.JulianDate * 86400)
for StateLine in StateHistoryForward:
statehistory.append(
np.array([
StateLine[0], StateLine[1], StateLine[2],
StateLine[3], StateLine[4], StateLine[5]
]) * 1000)
accelhistory.append(
np.array([
self.Thrust[0] / StateLine[6],
self.Thrust[1] / StateLine[6],
self.Thrust[2] / StateLine[6]
]))
for TimeLine in TimeHistoryForward:
timehistory.append(TimeLine + self.JulianDate * 86400)
else:
statehistory.append(np.array(self.SpacecraftState[0:6]) * 1000.0)
accelhistory.append(np.array([0.0, 0.0, 0.0]))
timehistory.append(self.JulianDate * 86400)
return timehistory, statehistory, accelhistory
return dy
k = 9
_m = 1
T = 2*m.pi*m.sqrt(_m/k)
omega = 2*m.pi/T
N = 1e4
R0 = [0.5, 1.]
t0, t1 = 0, 5*T
t = np.linspace(t0, t1, 10000)
R = np.zeros((len(t), len(R0)), dtype=np.float64)
R[0, :] = R0
r = integrate.ode(oscillator).set_integrator("dopri5")
r.set_initial_value(R0, t0)
for i in range(1, t.size):
R[i, :] = r.integrate(t[i])
if not r.successful():
raise RuntimeError("Could not integrate")
E = np.zeros((len(t), len(R0)), dtype = np.float64)
delta = np.zeros(len(t), dtype = np.float64)
for i in range (len(t)):
E[i,:] = (0.5*(k*R[i,0]**2 + _m*R[i,1]**2), 0.5*k*R[i,0]**2 )
delta[i] = (E[i,1] - E[i,0])/E[i,0]
fig = plt.figure()
ax = fig.add_subplot(111)
def propagate_orbit(spacecraftConfig,
forcesCoeff,
surfaces,
eop_alldata,
ndays,
dt,
ode_flag=True):
# Integrator
int_tol = 1e-12
intfcn = spacecraftConfig['intfcn']
integrator = spacecraftConfig['integrator']
# If flag is set use ode
if ode_flag:
start = time.time()
y0 = spacecraftConfig['X'].flatten()
tvec = np.arange(0., 86400. * ndays + (0.1 * dt), dt)
solver = ode(intfcn)
solver.set_integrator(integrator, atol=int_tol, rtol=int_tol)
solver.set_f_params(
[spacecraftConfig, forcesCoeff, surfaces, eop_alldata])
solver.set_initial_value(y0, tvec[0])
state = np.zeros((len(tvec), len(y0)))
state[0] = y0
k = 1
while solver.successful() and solver.t < tvec[-1]:
solver.integrate(tvec[k])
state[k] = solver.y
k += 1
# print(k)
# print(len(tvec))
# solver = ode(self.FuncDyn)
# solver.set_integrator(self.integrator)
# self.t0 = 0.0
# if (self.state.type=='3DoF'):
# solver.set_f_params(self.forces)
# self.lenForces = len(self.forces)
# y0 = [self.state.scStateOrekit.getPVCoordinates().getPosition().getX(),
# self.state.scStateOrekit.getPVCoordinates().getPosition().getY(),
# self.state.scStateOrekit.getPVCoordinates().getPosition().getZ(),
# self.state.scStateOrekit.getPVCoordinates().getVelocity().getX(),
# self.state.scStateOrekit.getPVCoordinates().getVelocity().getY(),
# self.state.scStateOrekit.getPVCoordinates().getVelocity().getZ()]
# lenY0 = 6
# elif (self.state.type=='6DoF'):
# solver.set_f_params([self.forces,self.torques])
# self.lenForces = len(self.forces)
# self.lenTorques = len(self.torques)
# # Below states should be defined in some inertial frame - e.g. J2000
# y0 = [self.state.scStateOrekit.getPVCoordinates().getPosition().getX(),
# self.state.scStateOrekit.getPVCoordinates().getPosition().getY(),
# self.state.scStateOrekit.getPVCoordinates().getPosition().getZ(),
# self.state.scStateOrekit.getPVCoordinates().getVelocity().getX(),
# self.state.scStateOrekit.getPVCoordinates().getVelocity().getY(),
# self.state.scStateOrekit.getPVCoordinates().getVelocity().getZ(),
# self.state.scStateOrientationIne2Body[0],
# self.state.scStateOrientationIne2Body[1],
# self.state.scStateOrientationIne2Body[2],
# self.state.scStateOrientationIne2Body[3],
# self.state.scStateOrientationRateBodywrtIne[0],
# self.state.scStateOrientationRateBodywrtIne[1],
# self.state.scStateOrientationRateBodywrtIne[2]]
# lenY0 = 13
# else:
# print('Please select a 3DoF or 6DoF dynamics for the type')
# solver.set_initial_value(y0, self.t0)
# N = int(self.tShift/self.tStep)
# t = np.linspace(self.t0, self.tShift, N)
# self.sol = {}
# self.sol['time'] = []
# self.sol['time'].append(str(self.state.scStateOrekit.getDate()))
# self.sol['state'] = np.empty((N, lenY0))
# self.sol['state'][0] = y0
# if (self.visibility!=None):
# self.sol['visibility'] = np.zeros((N, 6))
# visibilityState = self.visibility.ComputeVisibilityState()
# if visibilityState[0]!=0.0:
# appMagnitude = self.visibility.ComputeAppMagnitude()
# self.sol['visibility'][0] = [appMagnitude,self.visibility.sumFobs\
# ,visibilityState[0],visibilityState[1],visibilityState[2]\
# ,visibilityState[3]]
# k = 1
# while solver.successful() and solver.t < self.tShift:
# solver.integrate(t[k])
# self.sol['state'][k] = solver.y
# self.sol['time'].append(str(self.state.scStateOrekit.getDate()))
# if (self.visibility!=None):
# visibilityState = self.visibility.ComputeVisibilityState()
# if visibilityState[0]!=0.0:
# appMagnitude = self.visibility.ComputeAppMagnitude()
# self.sol['visibility'][k] = [appMagnitude,self.visibility.sumFobs\
# ,visibilityState[0],visibilityState[1],visibilityState[2]\
# ,visibilityState[3]]
# k += 1
# Otherwise use odeint
else:
start = time.time()
# Setup propagator
tvec = np.arange(0., 86400. * ndays + (0.1 * dt), dt)
args = (spacecraftConfig, forcesCoeff, surfaces)
int0 = spacecraftConfig['X'].flatten()
print(int0)
state = odeint(intfcn, int0, tvec, args, rtol=int_tol, atol=int_tol)
# Print time
print('Propagation Time:', time.time() - start)
# Output time
UTC0 = spacecraftConfig['time']
UTC_times = [UTC0 + timedelta(seconds=ti) for ti in tvec]
return UTC_times, state