def plot_solution(population, ds):
# ds = generate_dataset(dx_dt, theta)
ti = [t / 10 for t in times]
theta1 = np.array([1, 1])
theta = []
# rint "================================"
# rint population
plt.figure(1)
for p in population:
m = math.fsum(p) / float(len(p))
print str(m) + " median " + str(p[len(population) / 2])
theta.append(m)
plt.hist(p)
plt.figure(2)
X0 = np.array([1, 0.5])
t = np.arange(0, 15, 0.1)
X = integrate.odeint(dx_dt, X0, t, args=(theta,))
Y = integrate.odeint(dx_dt, X0, t, args=(theta1,))
x, y = X.T
x1, y1 = Y.T
plt.figure(3)
plt.subplot(211)
plt.plot(t, x, "r-", label="x(t)")
plt.plot(t, x1, "g-", label="x(t))")
plt.subplot(212)
plt.plot(ti, ds[:, 0], marker="s", linestyle="", color="g")
plt.plot(t, y, "b-", label="y(t)")
plt.plot(t, y1, "g-", label="y(t)")
plt.plot(ti, ds[:, 1], marker="^", linestyle="", color="g")
plt.xlabel("time")
plt.show()
def Rhalf(Rp,Rn,a,b):
Rn /= Rp
c = (a*a + b*b)**0.5 / Rp
r = linspace(0.001,1,100)
def numer(f,r):
lo,hi = theta_lohi(Rn,c,0,r)
return r*r * (sin(lo)-sin(hi))
def denom(f,r):
lo,hi = theta_lohi(Rn,c,0,r)
return r * (hi - lo)
def arc(f,r):
lo,hi = theta_lohi(Rn,c,d,r)
return r * (hi - lo)
up = odeint(numer,[0],r)
dn = odeint(denom,[0],r)
d = up[-1,0]/dn[-1,0]
area = odeint(arc,[0],r)
for i in range(1,len(r)):
if area[i-1] < 0.5*area[-1] and area[i] > 0.5*area[-1]:
return Rp * r[i]
def compute_jacobian(star, differences, surface_guesses, core_guesses, core_masses, surface_masses, mass_step):
jacobian =(np.zeros((4,4)))
for i in range(0,4):
guess_star = copy(star)
if i == 0:
step_size = core_guesses[0]*0.01
guess_star.core_pressure = core_guesses[0] + step_size
elif i == 1:
step_size = core_guesses[1]*0.01
guess_star.core_temp = core_guesses[1] + step_size
elif i == 2:
step_size = surface_guesses[2]*0.01
guess_star.total_radius = surface_guesses[2] + step_size
elif i == 3:
step_size = surface_guesses[3]*0.01
guess_star.total_lum = surface_guesses[3] + step_size
new_surface = inward_start(guess_star)
new_core = outward_start(guess_star, mass_step)
new_differences = difference_is(odeint(derivatives, new_core, core_masses, args=(guess_star,)),
odeint(derivatives, new_surface, surface_masses, args=(guess_star,)))
jacobian[:,i] = np.asarray((new_differences - differences)/step_size)
return np.linalg.inv(jacobian)
def __init__(self, f, u0, s, t, dfdu=None):
self.f = f
self.t = np.array(t, float).copy()
self.s = np.array(s, float).copy()
if self.s.ndim == 0:
self.s = self.s[np.newaxis]
if dfdu is None:
dfdu = ddu(f)
self.dfdu = dfdu
u0 = np.array(u0, float)
if u0.ndim == 1:
# run up to t[0]
f = lambda u, t : self.f(u, s)
assert t[0] >= 0 and t.size > 1
N0 = int(t[0] / (t[-1] - t[0]) * t.size)
u0 = odeint(f, u0, np.linspace(0, t[0], N0+1))[-1]
# compute a trajectory
self.u = odeint(f, u0, t - t[0])
else:
assert (u0.shape[0],) == t.shape
self.u = u0.copy()
self.dt = t[1:] - t[:-1]
self.uMid = 0.5 * (self.u[1:] + self.u[:-1])
self.dudt = (self.u[1:] - self.u[:-1]) / self.dt[:,np.newaxis]
def Advect(self, flow_type = None, t0 = 0, dt = 0, extra_args = None):
if flow_type is not None:
# Number of particles
num_particles = len(self.Particles);
# Get the positions of all of the particles
x0, y0, z0 = self.GetCoordinates();
# Time vector
tf = t0 + dt;
# Time vector
t = np.array([t0, tf]);
# Initial positions as a numpy vector
# in the form compatible with the
# velocity functions
xy0 = np.array(x0 + y0);
# xy0 = np.array([x0, y0]);
# Choose between fields
if "hama" in flow_type.lower():
xy = (odeint(HamaVelocity, y0 = xy0, t = t, args = (extra_args,)))[-1, :];
elif "cpipe" in flow_type.lower():
xy = (odeint(cpipe, y0 = xy0, t = t, args = (extra_args,)))[-1, :];
# Extract the new positions
x_new, y_new = Parse_Vector_2d(xy);
# Set the new coordinates
self.SetCoordinates(x = x_new, y = y_new);
def exercise3part1(T=200, Deltat=0.1, epsilon=0.5):
"In this exercise we want to perturb the parameters of the Lorenz system to see how the shape of the Lorenz attractor change"
y0=np.random.randn(3)
t = np.arange(0.,T, Deltat)
data = integrate.odeint(vectorfield, y0, t=t,args=(10,28,8/3))
data_sigma=integrate.odeint(vectorfield, y0, t=t,args=(10+epsilon,28,8/3))
data_rho=integrate.odeint(vectorfield, y0, t=t,args=(10,28+epsilon,8/3))
data_beta=integrate.odeint(vectorfield, y0, t=t,args=(10,28,8/3+epsilon))
fig = plt.figure()
ax0 = fig.add_subplot(2, 2, 1, projection='3d')
ax1 = fig.add_subplot(2, 2, 2, projection='3d')
ax2 = fig.add_subplot(2, 2, 3, projection='3d')
ax3 = fig.add_subplot(2, 2, 4, projection='3d')
ax0.plot(data[:,0],data[:,1],data[:,2])
ax1.plot(data_sigma[:,0],data_sigma[:,1],data_sigma[:,2])
ax2.plot(data_rho[:,0],data_rho[:,1],data_rho[:,2])
ax3.plot(data_beta[:,0],data_beta[:,1],data_beta[:,2])
ax0.set_title('Standard parameters')
ax1.set_title('Sigma perturbed')
ax2.set_title('Rho perturbed')
ax3.set_title('Beta perturbed')
pylab.show()
print 'From the plot, the system seems to be more sensibile in beta perturbations'
def test_repeated_t_values():
"""Regression test for gh-8217."""
def func(x, t):
return -0.25*x
t = np.zeros(10)
sol = odeint(func, [1.], t)
assert_array_equal(sol, np.ones((len(t), 1)))
tau = 4*np.log(2)
t = [0]*9 + [tau, 2*tau, 2*tau, 3*tau]
sol = odeint(func, [1, 2], t, rtol=1e-12, atol=1e-12)
expected_sol = np.array([[1.0, 2.0]]*9 +
[[0.5, 1.0],
[0.25, 0.5],
[0.25, 0.5],
[0.125, 0.25]])
assert_allclose(sol, expected_sol)
# Edge case: empty t sequence.
sol = odeint(func, [1.], [])
assert_array_equal(sol, np.array([], dtype=np.float64).reshape((0, 1)))
# t values are not monotonic.
assert_raises(ValueError, odeint, func, [1.], [0, 1, 0.5, 0])
assert_raises(ValueError, odeint, func, [1, 2, 3], [0, -1, -2, 3])
def get_pressure(Planet,layers):
radii = Planet.get('radius')
density = Planet.get('density')
gravity = Planet.get('gravity')
num_mantle_layers, num_core_layers, number_h2o_layers = layers
# convert radii to depths
depths = radii[-1] - radii
depths_core = depths[:num_core_layers]
gravity_core = gravity[:num_core_layers]
density_core = density[:num_core_layers]
depths_mant = depths[num_core_layers:(num_core_layers+num_mantle_layers)]
gravity_mant = gravity[num_core_layers:(num_core_layers+num_mantle_layers)]
density_mant = density[num_core_layers:(num_core_layers+num_mantle_layers)]
# Make a spline fit of density as a function of depth
rhofunc_mant = interpolate.UnivariateSpline(depths_mant[::-1], density_mant[::-1])
# Make a spline fit of gravity as a function of depth
gfunc_mant = interpolate.UnivariateSpline(depths_mant[::-1], gravity_mant[::-1])
rhofunc_core = interpolate.UnivariateSpline(depths_core[::-1], density_core[::-1])
gfunc_core = interpolate.UnivariateSpline(depths_core[::-1], gravity_core[::-1])
if number_h2o_layers > 0:
depths_water = depths[(num_core_layers + num_mantle_layers):]
gravity_water = gravity[(num_core_layers + num_mantle_layers):]
density_water = density[(num_core_layers + num_mantle_layers):]
rhofunc_water = interpolate.UnivariateSpline(depths_water[::-1], density_water[::-1])
gfunc_water = interpolate.UnivariateSpline(depths_water[::-1], gravity_water[::-1])
#integrate from 1 bar
pressure_water = np.ravel(odeint((lambda p, x: gfunc_water(x) * rhofunc_water(x)),(1./10000.)*1.e9, depths_water[::-1]))
WMB_pres = pressure_water[-1] #does not account for very small water layers and ends up breaking code
WMB_pres = 5.e8
else:
WMB_pres = 5.e8
# integrate the hydrostatic equation
pressure_mant = np.ravel(odeint((lambda p, x: gfunc_mant(x) * rhofunc_mant(x)),WMB_pres, depths_mant[::-1]))
CMB_pres = pressure_mant[-1]
pressure_core = np.ravel(odeint((lambda p, x: gfunc_core(x) * rhofunc_core(x)),CMB_pres, depths_core[::-1]))
if number_h2o_layers > 0:
pressure = np.concatenate((pressure_water,pressure_mant,pressure_core),axis=0)
pressure = [((i/1.e9)*10000.) for i in pressure]
return np.asarray(pressure[::-1])
else:
pressure = np.concatenate((pressure_mant,pressure_core),axis=0)
pressure = [((i/1.e9)*10000.) for i in pressure]
return np.asarray(pressure[::-1])
def delta_r(self): """ returns the coeficient delta indicating the RADIAL stability of the movement """ time = np.linspace(0, 5, 1000) def sm1(x, t): if x[0] < 0: return np.array([x[1], 0]) else: return np.array([x[1], self.eta*self.Ez0(x[0])]) traj = odeint(sm1,[0,1.],time) if traj[-1,0] < 0: ind = np.where(traj[:,0] > 0)[0][-1] tm = [time[ind+1],time[ind]] zm = [traj[ind+1,0],traj[ind,0]] period = np.interp(0,zm,tm) def sm2(x, t): t = t - np.floor(t/period)*period pos = np.interp(t, time, traj[:,0]) psi = self.alpha + self.b**2/4. + (self.eta/2.)*self.d2V(pos) return np.array([x[1], psi*x[0], x[3], psi*x[2]]) x0 = [1, 0, 0, 1] sol = odeint(sm2, x0, np.linspace(0, period, 1000)) delta = np.abs((sol[-1,0] + sol[-1,3])/2) beta = np.arccos((sol[-1,0] + sol[-1,3])/2)/np.pi if self.b == 0.: return delta else: out = np.log(delta + np.sqrt(np.abs(delta**2-1)))/period - self.b return out else: return 1.0
def getEta(data,t,xi):
global samp, ETA, time, agent, XI
if t < len(data):
return data[t]
else:
XI = xi # update input function from paramteter
if len(data) == 0:
ETA0 = getInitialEta(agent.beta,agent.gamma,XI)
data.append(ETA0[:])
for T in range(len(data),t+1):
# TODO: should this be samp*t so that accuracy is not lost far from 0???
logging.info('solving ode @ t='+str(T)+', using '+str(samp)+' sub-samples')
time = linspace(0,T,samp) #(start,end,nSamples)
etadot_0 = [0,0,0,0,0] #assumption of 1st order model
#get arrays of data len=samp*t
ETA[0] = integrate.odeint(eta1Func,[data[0][0],etadot_0[0]],time)
ETA[1] = integrate.odeint(eta2Func,[data[0][1],etadot_0[1]],time)
ETA[2] = integrate.odeint(eta3Func,[data[0][2],etadot_0[2]],time)
ETA[3] = integrate.odeint(eta4Func,[data[0][3],etadot_0[3]],time)
ETA[4] = integrate.odeint(eta5Func,[data[0][4],etadot_0[4]],time)
logging.debug('len(result)='+str(len(ETA[0][:,0])))
# restructure ETA using [eta#][time , eta_or_dEta] )
E = [ETA[0][-1,0],\
ETA[1][-1,0],\
ETA[2][-1,0],\
ETA[3][-1,0],\
ETA[4][-1,0]]
data.append(E)
return data[t]
def integrate(state, N_t=20, T=1., pts=None):
"""
flow forward integration
Points pts are carried along the flow without affecting it
"""
assert(N)
assert(DIM)
assert(SIGMA)
gaussian.N = N
gaussian.DIM = DIM
gaussian.SIGMA = SIGMA
t_span = np.linspace(0. ,T , N_t )
#print 'forward integration: SIGMA = ' + str(SIGMA) + ', N = ' + str(N) + ', DIM = ' + str(DIM) + ', N_t = ' + str(N_t) + ', T = ' + str(T)
if parallel:
odef = ode_function
else:
odef = ode_function_single
if pts == None:
y_span = odeint( odef , state , t_span)
return (t_span, y_span)
else:
y_span = odeint( odef , np.hstack((state,pts.flatten())) , t_span)
return (t_span, y_span[:,0:get_dim_state()], y_span[:,get_dim_state():])
def crystal_embed(e,eta):
"""
Evaluates crystal embedding potential by solving the one-dimensional
Schroedinger equation through one unit cell in both directions.
"""
y0=[1.0,0.0,0.0,0.0]
z1=np.linspace(-z_left,-z_left+alat,101)
sol1=odeint(schroed,y0,z1,args=(e,eta))
z2=np.linspace(-z_left+alat,-z_left,101)
sol2=odeint(schroed,y0,z2,args=(e,eta))
psi1=complex(sol1[50,0],sol1[50,1])
psi1_prime=complex(sol1[50,2],sol1[50,3])
psi2=complex(sol2[50,0],sol2[50,1])
psi2_prime=complex(sol2[50,2],sol2[50,3])
wronskian=psi1*psi2_prime-psi2*psi1_prime
psi1=complex(sol1[100,0],sol1[100,1])
psi2=complex(sol2[100,0],sol2[100,1])
cos_ka=0.5*(psi1+psi2)
ka=arccos(cos_ka)
if ka.imag<0: ka=np.conj(ka)
exp_ka=cos_ka+1.0j*sqrt(1.0-cos_ka**2)
emb_cryst=0.5*wronskian/(exp_ka-psi2)
if emb_cryst.imag>0.0:
exp_ka=cos_ka-1.0j*sqrt(1.0-cos_ka**2)
emb_cryst=0.5*wronskian/(exp_ka-psi2)
return emb_cryst
def ode_wrapper(fun,x0,tsp):
# array of times at which dynamic uncertainty happens
tdisc = np.arange(tsp[0],tsp[-1]+DT,DT)
# determine (ad hoc) whether the called function takes 2 or 3 args
flag_stoch = False
try:
dy = fun(x0,tsp[0],0.0)
flag_stoch = True
except TypeError:
dy = fun(x0,tsp[0])
flag_stoch = False
yt = np.zeros((len(tdisc),len(x0)))
y0 = x0.copy()
for k in range(len(tdisc)-1):
if flag_stoch:
# compute the noise
v = np.random.normal(scale=q_w)
yp = sp.odeint(fun,y0,tdisc[k:k+2],args=(v,))
else:
yp = sp.odeint(fun,y0,tdisc[k:k+2])
yt[k,:] = y0.copy()
y0 = yp[-1,:].copy()
yt[-1,:] = y0.copy()
# interpolate in the new history to match the passed-in history tsp
ysp = np.zeros((len(tsp),len(x0)))
for k in range(len(x0)):
ysp[:,k] = np.interp(tsp,tdisc,yt[:,k])
return ysp
def main():
# Load the data
data = json.loads(open("problem_2.json").read())
# Make the time array
t = np.linspace(0, data["t_f"], 10000)
grid_number = data["grid_number"]
initial_conditions = []
for i in np.linspace(data["x_ic"][0], data["x_ic"][1], grid_number):
for j in np.linspace(data["y_ic"][0], data["y_ic"][1], grid_number):
initial_conditions.append([i, j])
solns = []
for i in xrange(0, grid_number**2):
solns.append(odeint(SYSTEM, initial_conditions[i], t))
for ic in data["other_ic"]:
solns.append(odeint(SYSTEM, ic, t))
x = np.linspace(data["x_ic"][0], data["x_ic"][1], 10000)
plt.figure()
plt.plot(x, f_1(x), "k")
plt.plot(x, f_2(x), "k")
for soln in solns:
plt.plot(soln[:,0], soln[:,1])
plt.xlabel(r"$x$")
plt.ylabel(r"$y$")
plt.xlim(data["x_ic"][0], data["x_ic"][1])
plt.ylim(data["y_ic"][0], data["y_ic"][1])
plt.title(r"$\dot{x} = 1 + y - e^{-x}$; $\dot{y} = x^3 - y$")
plt.savefig("2_simulation.png", format="png", dpi=300)
plt.close()
gc.collect()
def computeTrajectories(func, E0=np.zeros(3), **keywords):
"""Movement of electron and ion under a constant magnetic field.
Positional arguments:
func -- the name of the function computing dy/dt at time t0
Keyword arguments:
E0 -- Constant component of the electric field
All other keyword arguments are collected in a 'keywords' dictionary
and are specific to each func."""
from scipy.integrate import odeint
# Processes the func specific arguments
re0, rp0 = "ri" in keywords.keys() and keywords["ri"] or [np.zeros(3),np.zeros(3)]
if "vi" in keywords.keys(): # Initial velocity
v0 = keywords["vi"]
else:
v0 = np.array([0,1,0])
wce, wcp = "wc" in keywords.keys() and keywords["wc"] or [0,0]
tf = 350; NPts = 10*tf; t = np.linspace(0,tf,NPts) # Time points
# Integration of the equations of movement
Q0 = np.concatenate((re0,v0)) # Initial values
if "wc" in keywords.keys():
keywords["wc"] = wce
Qe = odeint(func, Q0, t, args=(-q/me,E0,B0,keywords)) # Trajectory for the "electron"
Q0 = np.concatenate((rp0,v0)) # Initial values
if "wc" in keywords.keys():
keywords["wc"] = wcp
Qp = odeint(func, Q0, t, args=(q/Mp,E0,B0,keywords)) # Trajectory for the "ion"
return Qe, Qp
def newton_ang_func(L_val,c2,m): L = L_val slope = (m*(m+1)+c2-L)/(m+1)/2. z0 = [1+step*slope,slope] z = odeint(f,z0,x_ang,args=(c2,L,m)) temp=1-pow(x_ang,2.0) temp=pow(temp,m/2.) zz=temp*z[:,0] first_zz = np.array([1]) zz=np.append(first_zz, zz) sloper = -(m*(m+1)+c2-L)/(m+1)/2. z0r = [1-step*sloper,sloper] zr = odeint(f,z0r,x_angr,args=(c2,L,m)) zzr=temp*zr[:,0] zzr=np.append(first_zz, zzr) return z[:,1][-1]
def draw(self,file=None):
"""
Trace l'ensemble des lignes de champ passant par les points de départ
stockés dans Startpoints. Si un nom de fichier est donné, enregistre
la figure dans le fichier mais n'affiche rien à l'écran
"""
def fun(P,t):
B = self.B(P)
Bx = B[0]
By = B[1]
B = np.sqrt(Bx*Bx+By*By)
return [Bx/pow(B,4./3.),By/pow(B,4./3.)]
t = np.linspace(0,self.k*self.maxint,self.numpoints/2)
t2 = - t
for P0 in self.startpoints:
sol = odeint(fun,P0,t)
x = sol[:,0]
y = sol[:,1]
pl.plot(x,y,'-',color='k')
sol = odeint(fun,P0,t2)
x = sol[1:,0]
y = sol[1:,1]
pl.plot(x,y,'-',color='k')
pl.arrow(x[1],y[1],x[0]-x[1],y[0]-y[1],color='k')
pl.title(self.title)
pl.xlim([-self.size,self.size])
pl.ylim([-self.size,self.size])
if file:
pl.savefig(file)
pl.close()
else:
pl.show()
def Gate(x,t):
global pars
xinf = ActivationCurve(V[:,1], pars[0], pars[1])
tau = Kinetic(V[:,1], pars[2], pars[3], pars[4], pars[5])
return (xinf-x)/tau
# the following function is the optimisation part of the algorithm. The two chanels are modeled with initial conditions
# and the result is compared to the data, taking account the parameter physiological ranges for identificationdef FullTrace(p,time,y):
pars = p[0:6]
rinit1 = ActivationCurve(V[:,0], pars[0], pars[1]) # initial values
r1 = odeint(Gate,rinit1,time)
pars = p[7:13]
rinit2 = ActivationCurve(V[:,0], pars[0], pars[1]) # initial values
r2 = odeint(Gate,rinit2,time)
I = p[6]*r1*(V[:,1]-E)+p[13]*r2*(V[:,1]-E)
# this plot is to see in real time the reconstructed curves trying to fit the data (very fun)
plot(time,y,'b'); hold(True)
plot(time,I,'--r');hold(False)
draw()
# this part is added to constrain the algorithm with physiological search ranges
A = 0
for a in arange(len(p)):
if p[a] > HB[a] :
A = A + (p[a] - HB[a])*1e8
if p[a] < LB[a] :
A = A + (LB[a]-p[a])*1e8
return sum(array(y - I)*array(y - I))+A
def modelrun(self):
"""
Method to integrate a specific size-based model variant.
The integration it is done with the module "integrate.odeint",
from the library scipy.
"""
try:
if self.Model == "Imm":
outarray = odeint(self.sizemodel_imm, self.initcond, self.timedays)
return outarray
elif self.Model == "TraitDif":
outarray = odeint(self.sizemodel_traitdif, self.initcond, self.timedays)
return outarray
elif self.Model == "FixVar":
outarray = odeint(self.sizemodel_fixvar, self.initcond, self.timedays)
return outarray
elif self.Model == "UnsustVar":
outarray = odeint(self.sizemodel_unvar, self.initcond, self.timedays)
return outarray
elif self.Model == "FullModel":
outarray = odeint(self.fullmodel, self.initcond, self.timedays, rtol=1e-12, atol=1e-12)
return outarray
raise Exception("InputError:", "it is not a valid size model variant. Please specify one model variant "
"from: Imm, TraitDif, Fixvar, UnsustVar or FullModel.\n")
except Exception as expt:
print expt.args[0], self.Model, expt.args[1]
raise
def solve_leg_transient(self):
"""Solves leg based on array of transient BC's."""
self.delta_x = self.x[1] - self.x[0]
self.y0 = self.T_x
try:
self.T_xt
except AttributeError:
self.odeint_output = odeint(
self.get_dTx_dt, y0=self.y0, t=self.t_array,
full_output=1
)
self.T_xt = self.odeint_output[0]
else:
self.y0 = self.T_xt[-1,:]
self.odeint_output = odeint(
self.get_dTx_dt, y0=self.y0, t=self.t_array,
full_output=1
)
self.T_xt = np.concatenate((self.T_xt, self.odeint_output[0]))
def optimalControl(D, A, b, c, x_zv, H, g, t0, t_zv):
N = 30
y = np.zeros(len(A))
h = (t_zv - t0) / N
tj = []
for i in xrange(1, int(N) + 1):
tj.append(t0 + i * h)
tt = [t0, t_zv]
B0 = odeint(func, x_zv, tt, args=(A, b))[1]
tt1 = [t0, tj[0]]
y1 = odeint(func1, y, tt1, args=(A, b))[1]
temp = odeint(func1, y1, tj, args=(A, b))
B = np.random.random((len(A), int(N)))
for i in xrange(int(N)):
B[:, i] = temp[int(N) - i - 1]
A_new, D_new, G, b_new, c_new, h = prepareQuad(B, B0, D, H, N, c, g)
u = cvx.solvers.qp(D_new, c_new, G, h, A_new, b_new)['x']
for i in xrange(0, int(N)):
tj[i] -= (t_zv - t0) / N
pylab.plot(tj, u)
pylab.show()
def runClock(dut, t_start, init_cond):
global ts, dt, sig_jit
global p0, z0
global t_ab, t_ba
alpha = random.normal(0, sig_jit)
beta = random.normal(0, sig_jit)
print (alpha, beta)
t1 = arange(0, t_ab + alpha, dt)
t2 = arange(0, t_ba + beta, dt)
concatenate((t1, [t_ab + alpha])) # 0 ~ t_ab + jitter
concatenate((t2, [t_ba + beta ])) # 0 ~ t_ba + jitter
t2 = map(lambda x: x + t1[-1], t2) # 0 ~ t_ab+jitter, t_ab+jitter ~ (t_ab+t_ba+2*jitter)
state_half = odeint(dut, init_cond, t1, args=(t_start, p0, z0))
eventAtA(state_half[-1,:]) # comparator / DAC
if(t_ba > dt): # when t_ba = 0 sec (NRZ DAC)
state_full = odeint(dut, state_half[-1], t2, args=(t_start, p0, z0))
eventAtB(state_full[-1,:]) # DAC (RZ)
t = concatenate((t1, t2 ))
state = concatenate((state_half, state_full))
else:
t = t1
state = state_half
return (t, state)
def poly(n,zi,dz,filename):
'''
Integrates the L-E equation and returns and writes to a file the important values.
n is the index, zi is the interval to use the polynomial solution, dz is the step size, filename is the prefix for the output file (filename+.fits)
'''
#set up equation and variables
LEeq = LEeqn(n)
zs = np.arange(0.,zi,dz)
nz = np.size(zs)
#integrate over initial interval using polynomial
y = intg.odeint(thPN,np.array([1.,0.]),zs)
#integrate until th<0
while y[-1,0]>0.:
zs2 = np.arange(zs[-1],zs[-1]+200.*dz,dz)
zs = np.append(zs,zs2)
y = np.append(y,intg.odeint(LEeq,y[-1],zs2),axis=0)
#write progress
sys.stdout.write("\rz={0},th={1}".format(zs[-1],y[-1,0]))
sys.stdout.flush()
sys.stdout.write("\n")
#only keep values where th>0
ngood= np.size(np.where(y[:,0]>0.))
zs = zs[:ngood]
y = y[:ngood]
#prepare output array
data = np.array([zs,y[:,0],-(zs**2)*y[:,1],(-3./zs)*y[:,1]])
#write and return data
fits.writeto(filename+'.fits',data)
return data
def test_nonlinear_store():
""" Test the behaviour of the NonLinearStore
NonLinearStore should emulate a storage volume with the outflow proportional to the square of the current volume,
\frac{dV(t)}{dt} = I(t) = \frac{V(t)^2}{C_q}
This test compares the performance of the analytical soltuions in NonLinearStore (from Wilby 1994) with a numerical
integration of the above equation.
"""
from scipy.integrate import odeint
def dVdt(V, t, I, Cq):
""" RHS of NonLinearStore ODE """
Q = I - V**2/Cq
if V <= 0.0:
# Volume should not be allowed to go negative. Therefore no net outflow when V <= 0.0
return max(Q, 0.0)
return Q
C = 0.5
store = NonLinearStore(np.zeros(1), nonlinear_storage_constant=C)
I = np.array([10.0])
O = np.empty_like(I)
store.step(I, O)
# Solve system numerical for the first time-step. Unlike the LinearStore there is no analytical solution given
# for the mean outflow. Therefore we can use a single time-step in the numerical integration and average the result
# to test against the analytical mean value for the timestep.
t = np.linspace(0, 1.0, 2.0)
V = odeint(dVdt, 0.0, t, args=(I, C))
# Test mean outflow
np.testing.assert_allclose(np.mean(V**2/C), O, rtol=1e-3)
# and end of time-step outflow.
np.testing.assert_allclose(V[-1]**2/C, np.array(store.previous_outflow), rtol=1e-3)
# And repeat for a second step ...
store.step(I, O)
V = odeint(dVdt, V[-1], t+1.0, args=(I, C))
# Test mean outflow
np.testing.assert_allclose(np.mean(V**2/C), O, rtol=1e-3)
# and end of time-step outflow.
np.testing.assert_allclose(V[-1]**2/C, np.array(store.previous_outflow), rtol=1e-3)
# And repeat for a third step with zero inflow
I = np.array([0.0])
store.step(I, O)
V = odeint(dVdt, V[-1], t+1.0, args=(I, C))
# Test mean outflow
np.testing.assert_allclose(np.mean(V**2/C), O, rtol=1e-3)
# and end of time-step outflow.
np.testing.assert_allclose(V[-1]**2/C, np.array(store.previous_outflow), rtol=1e-3)
# And repeat for a fourth step with -ve inflow
I = np.array([-1.0])
store.step(I, O)
V = odeint(dVdt, V[-1], t+1.0, args=(I, C))
# Test mean outflow
np.testing.assert_allclose(np.mean(V**2/C), O, rtol=1e-3)
# and end of time-step outflow.
np.testing.assert_allclose(V[-1]**2/C, np.array(store.previous_outflow), rtol=1e-3, atol=1e-10)
def numerical_equilibrium(self):
res = self.deterministic()
f = lambdify(res.keys(), res.values())
g = lambda y, t: f(*y)
r = odeint(g, y0=np.ones(len(res.keys())), t=[0.0, 1e6])[-1]
r = odeint(g, y0=r, t=[0.0, 1e6])[-1]
return dict(zip(res.keys(), r))
def integrate(self):
atol=1.49012e-8
rtol=1.49012e-8
if len(self.dvar)==0: raise sodeError, "No equations defined."
if 't' not in self.parval.keys(): raise sodeError, "Integration time limit 't' is undefined."
#TODO: implement adaptive grid
t = np.linspace(0,self.parval['t'], 10) # initial
t.sort() # sort in case t value was negative
dx = self.getdx()
parvals = map(lambda v: eval(self.rhs[v], dict(math.__dict__.items() + self.parval.items())), self.dvar)
x = odeint(dx, parvals, t,atol=atol,rtol=rtol) # integrate ODEs
if np.isnan(x).sum(): raise NameError, "Integration failed" # check for NANs in output
D0 = 0 # curve length from previous iteration
refine = True # flag to refine grid
npadd = 2
print "\n\nStart grid refinement"
iref=0 # iteration counter
while t.shape[0]<10000 and refine: # limit number of grid points
iref += 1
ds = map(lambda i: np.linalg.norm(x[i+1,:] - x[i,:]), range(x.shape[0]-1)) # curve segments lengths
D = sum(ds) # curve length
if (D == 0): break # stationary point?
dmax = max(ds) # max segment length
# C = x.mean(0) # curve center
# rs = map(lambda i: np.linalg.norm(x[i,:] - C), range(x.shape[0])) # curve points distance from center
# S = max(rs) # curve radius
print "\nGrid points count: %d"%t.shape[0]
print "Track length: %f"%D
print "Track length change: %.1f%%"%((1-D0/D)*100)
print "Max segment length: %f"%max(ds)
# print "Track size: %f"%S
refine = False
for i in xrange(0,t.shape[0]-1):
a=x[i+1,:]-x[i,:]
b=dx(x[i,:], t[i])*(t[i+1]-t[i])
d = np.linalg.norm(a-b)/np.linalg.norm(a)
if d > 1e-1 and np.abs((a / (atol + rtol*x[i]))).max() > 1:
t.resize((t.shape[0]+npadd))
t[-npadd:] = np.linspace(t[i],t[i+1],npadd+2)[1:-1]
# t.resize((t.shape[0]+1))
# t[-1] = (t[i]+t[i+1])/2
# print "refine at %f (%e,%f)"%(t[-1], np.linalg.norm(b-a), d)
refine = True
if not refine and abs((D-D0)/D) > 5e-2:
l=t.shape[0]
t.resize((2*l-1))
t[-l+1:] = (t[1:l] +t[:l-1])/2
refine = True
D0 = D
t.sort()
x = odeint(dx, parvals, t, atol=atol,rtol=rtol)
print "New grid points count: %d"%t.shape[0]
if not refine: print "Grid is fine",
else: print "Grid points limit reached",
print " after %d iterations"%iref
self.t = t
self.tracks = x
self.tracks2dict()
def solveequation(params,times,Cai):
timepoints = np.where(times == 0)
yinitial = initialConditions(params,Cai)
yinitial.append(0)
yinitial.append(0)
Ca = 5e-6
k_1 = params[1]
k2 = params[2]
k_2 = params[3]
ksr = params[4]
krr=params[5]
Kd=params[6]
rmax=params[7]
k1=(rmax*Ca)/(Ca+Kd)
Caa=params[8]
CaS=params[9]
CaF=params[0]
CaFa = params[10]
CaFS = params[11]
CaFF = params[12]
def f(yinitial,times):
A = yinitial[0]
B = yinitial[1]
C = yinitial[2]
D = yinitial[3]
E = yinitial[4]
dA = -k1*CaFa*Ca**Caa*A + k_1*B
dB = -(k_1+CaFS*Ca**CaS*ksr+k2)*B+A*k1*CaFa*Ca**Caa+C*k_2
dC = -(krr*CaFa*Ca**CaF+k_2)*C + B*k2
dD = B*ksr*CaFS*Ca**CaS
dE = C*krr*CaFF*Ca**CaF
return [dA,dB,dC,dD,dE]
out = np.array(odeint(f,yinitial,times[timepoints[0][0]:timepoints[0][1]]))
output = out[:,3]+out[:,4]
Ca = 10e-6;
k1 =(rmax*Ca)/(Ca+Kd)
out = np.array(odeint(f,yinitial,times[timepoints[0][1]:timepoints[0][2]]))
output = np.append(output,(out[:,3]+out[:,4]))
Ca = 30e-6;
k1 =(rmax*Ca)/(Ca+Kd)
out = np.array(odeint(f,yinitial,times[timepoints[0][2]:timepoints[0][3]]))
output = np.append(output,(out[:,3]+out[:,4]))
Ca = 100e-6;
k1 =(rmax*Ca)/(Ca+Kd)
out = np.array(odeint(f,yinitial,times[timepoints[0][3]:]))
output = np.append(output,(out[:,3]+out[:,4]))
return output
def test():
theta = [2., 4.]
theta1 = [2., 20.]
t = np.arange(0, 5, 0.1)
X0 = 1.
X = integrate.odeint(gene_reg, X0, t, args=(theta,))
X1 = integrate.odeint(gene_reg_simple, X0, t, args=(theta1,))
plt.plot(t, X, 'r-')
plt.show()
def trajectories(Q0, QbyM, tf): """Computes the electron and proton trajectories""" NPts = 5*tf; t = np.linspace(0,tf,NPts) # Integration of the equations of movement Qe = odeint(EqMovement, Q0, t, args=(QbyM[0],)) Qp = odeint(EqMovement, Q0, t, args=(QbyM[1],)) return Qe, Qp
def devFromInput(x, mh, vev, model):
"""
Given x=[mu12,lam1] at input scale, compute next iteration of
these parameters using the Coleman-Weinberg potential.
"""
#mu12, mu22, l1, l2, l3, l4, l5 = x
mu12, l1 = x
v = vev
# Modify RGE initial conditions
y0 = model.y0
y0[6] = l1
y0[8] = mu12
# update the input values stored in the model object!
model.y0 = y0
inputScale = np.sqrt(model.inputRgScaleSq)
# Integrate RGEs to mu=v
tmin = 0; tmax = np.log(v/inputScale)
tvev = np.log(v/inputScale)
ti = np.array([0., tvev])
model.rgsys.twoLoop = True
y_at_t = integrate.odeint(model.rgsys.f,y0,ti)
# y_at_t: [0] initial, [1] at vev
y_at_v = y_at_t[1]
# Update the potential parameters, compute v and mh
model.renormScaleSq = v**2.
g1,g2,g3,yt,yb,ytau,l1,Zh,mu12 = y_at_v
model.setPara(l1,mu12)
model.setGauge(g1,g2,g3)
model.setYukawa(yt,yb,ytau)
model.setFieldRenorm(Zh)
Vlp = model.gradV([v],T=0.)[0] - mu12*v - l1*v**3
Vl2p = model.d2V([v],T=0)[0,0] - mu12 - 3.*l1*v**2
l1New = (mh**2 + Vlp/v - Vl2p)/(2.*v**2)
mu12New = - l1New*v**2 - Vlp/v
# RG evolve back to input scale
y_at_v[6] = l1New
y_at_v[8] = mu12New
ti_reverse = ti[::-1]
y_at_t = integrate.odeint(model.rgsys.f,y_at_v,ti_reverse)
l1New = y_at_t[1][6]
mu12New = y_at_t[1][8]
return [mu12New,l1New]
def lsim2(system, U=None, T=None, X0=None, **kwargs):
"""
Simulate output of a continuous-time linear system, by using
the ODE solver `scipy.integrate.odeint`.
Parameters
----------
system : an instance of the LTI class or a tuple describing the system.
The following gives the number of elements in the tuple and
the interpretation:
* 2: (num, den)
* 3: (zeros, poles, gain)
* 4: (A, B, C, D)
U : array_like (1D or 2D), optional
An input array describing the input at each time T. Linear
interpolation is used between given times. If there are
multiple inputs, then each column of the rank-2 array
represents an input. If U is not given, the input is assumed
to be zero.
T : array_like (1D or 2D), optional
The time steps at which the input is defined and at which the
output is desired. The default is 101 evenly spaced points on
the interval [0,10.0].
X0 : array_like (1D), optional
The initial condition of the state vector. If `X0` is not
given, the initial conditions are assumed to be 0.
kwargs : dict
Additional keyword arguments are passed on to the function
odeint. See the notes below for more details.
Returns
-------
T : 1D ndarray
The time values for the output.
yout : ndarray
The response of the system.
xout : ndarray
The time-evolution of the state-vector.
Notes
-----
This function uses :func:`scipy.integrate.odeint` to solve the
system's differential equations. Additional keyword arguments
given to `lsim2` are passed on to `odeint`. See the documentation
for :func:`scipy.integrate.odeint` for the full list of arguments.
"""
if isinstance(system, lti):
sys = system
else:
sys = lti(*system)
if X0 is None:
X0 = zeros(sys.B.shape[0], sys.A.dtype)
if T is None:
# XXX T should really be a required argument, but U was
# changed from a required positional argument to a keyword,
# and T is after U in the argument list. So we either: change
# the API and move T in front of U; check here for T being
# None and raise an excpetion; or assign a default value to T
# here. This code implements the latter.
T = linspace(0, 10.0, 101)
T = atleast_1d(T)
if len(T.shape) != 1:
raise ValueError("T must be a rank-1 array.")
if U is not None:
U = atleast_1d(U)
if len(U.shape) == 1:
U = U.reshape(-1, 1)
sU = U.shape
if sU[0] != len(T):
raise ValueError("U must have the same number of rows "
"as elements in T.")
if sU[1] != sys.inputs:
raise ValueError("The number of inputs in U (%d) is not "
"compatible with the number of system "
"inputs (%d)" % (sU[1], sys.inputs))
# Create a callable that uses linear interpolation to
# calculate the input at any time.
ufunc = interpolate.interp1d(T,
U,
kind='linear',
axis=0,
bounds_error=False)
def fprime(x, t, sys, ufunc):
"""The vector field of the linear system."""
return dot(sys.A, x) + squeeze(dot(sys.B, nan_to_num(ufunc([t]))))
xout = integrate.odeint(fprime, X0, T, args=(sys, ufunc), **kwargs)
yout = dot(sys.C, transpose(xout)) + dot(sys.D, transpose(U))
else:
def fprime(x, t, sys):
"""The vector field of the linear system."""
return dot(sys.A, x)
xout = integrate.odeint(fprime, X0, T, args=(sys, ), **kwargs)
yout = dot(sys.C, transpose(xout))
return T, squeeze(transpose(yout)), xout
def calcul(Yinit, t1, t2, TabConst):
"""Calcule l'evolution des variables entre t1 et t2 à partir de la config Yinit"""
T = linspace(t1, t2, floor((t2 - t1) * ptsperh))
return T, odeint(eqs, Yinit, T, args=(TabConst, 0))
# ODE solver parameters
abserr = 1.0e-8
relerr = 1.0e-6
stoptime = 10.0
numpoints = 1001
# Create the time samples for the output of the ODE solver.
t = np.linspace(0, stoptime, numpoints)
# Pack up the parameters and initial conditions:
p = [m, kp, kd, L, StartTime, Umax]
x0 = [x_init, x_dot_init]
# Call the ODE solver.
resp = odeint(eq_of_motion, x0, t, args=(p, ), atol=abserr, rtol=relerr)
#----- Plot the response
# Many of these setting could also be made default by the .matplotlibrc file
fig = figure(figsize=(6, 4))
ax = gca()
subplots_adjust(bottom=0.17, left=0.17, top=0.96, right=0.96)
setp(ax.get_ymajorticklabels(), fontsize=18)
setp(ax.get_xmajorticklabels(), fontsize=18)
ax.spines['right'].set_color('none')
ax.spines['top'].set_color('none')
ax.xaxis.set_ticks_position('bottom')
ax.yaxis.set_ticks_position('left')
ax.grid(True, linestyle=':', color='0.75')
ax.set_axisbelow(True)
R_0 = 0 # R_0为治愈者的初始人数
S_0 = N - I_0 - R_0 # S_0为易感者的初始人数
T = 150 # T为传播时间
# INI为初始状态下的数组
INI = (S_0, I_0, R_0)
def funcSIR(inivalue, _):
Y = np.zeros(3)
X = inivalue
# 易感个体变化
Y[0] = - (beta * X[0] * X[1]) / N
# 感染个体变化
Y[1] = (beta * X[0] * X[1]) / N - gamma * X[1]
# 治愈个体变化
Y[2] = gamma * X[1]
return Y
T_range = np.arange(0, T + 1)
RES = spi.odeint(funcSIR, INI, T_range)
plt.plot(RES[:, 0], color='darkblue', label='Susceptible', marker='.')
plt.plot(RES[:, 1], color='red', label='Infection', marker='.')
plt.plot(RES[:, 2], color='green', label='Recovery', marker='.')
plt.title('SIR Model')
plt.legend()
plt.xlabel('Day')
plt.ylabel('Number')
plt.show()
plt.legend()
plt.xlim(left=0.0, right=0.02)
plt.ylim(top=1.6, bottom=0.6)
plt.xlabel('k1_inv')
plt.ylabel('k2')
plt.savefig('two_parameter.png')
# plt.show()
# numerical solution
k1_inv_val = 0.01
k2_val_numerical = 1.0
initial = [0.2, 0.3]
dt = 0.1
t_max = 4000
t = np.linspace(0, t_max, int(t_max / dt))
sol = odeint(equation, initial, t)
fig = plt.figure(figsize=(20, 10))
plt.plot(t, sol[:, 0], color='black', label='x')
plt.plot(t, sol[:, 1], color='black', linestyle='--', label='y')
plt.legend()
plt.xlabel('t')
plt.savefig('numerical.png')
additional_solutions = []
additional_initial = [(0.5, 0.15), (0.5, 0.4)]
for init in additional_initial:
sol_add = odeint(equation, init, t)
additional_solutions.append(sol_add)
sol_end = odeint(equation, [sol[len(t) - 1, 0], sol[len(t) - 1, 1]], t)
def mwg_ode(self,
n_samples,
Y_t,
start_ind,
tmin,
tmax,
n_eval,
w_mat,
x0,
theta0,
theta_true,
theta_sd,
gamma,
rwsd,
accept=False):
# Get problem dimensions and initialization
n_theta = len(theta0)
theta_curr = theta0.copy()
theta_prop = theta0.copy()
paccept = np.zeros(n_theta, dtype=int)
Theta = np.zeros((n_samples, n_theta))
n_obs = len(Y_t)
n_skip = n_eval // n_obs
# MCMC process
old_state1 = self._n_state1
old_state2 = self._n_state2
self._n_state1 = 1
self._n_state2 = 1
tseq = np.linspace(tmin, tmax, n_eval + 1)
X_curr = odeint(self._fitz, x0, tseq,
args=(theta_curr, ))[start_ind::n_skip]
lp_curr = self._logprior(Y_t, X_curr, gamma, theta_curr, theta_true,
theta_sd)
for i in range(n_samples):
for j in range(n_theta):
theta_prop[j] += rwsd[j] * np.random.randn()
if theta_prop[j] > 0:
X_prop = odeint(self._fitz, x0, tseq,
args=(theta_prop, ))[start_ind::n_skip]
lp_prop = self._logprior(Y_t, X_prop, gamma, theta_prop,
theta_true, theta_sd)
lacc = lp_prop - lp_curr
if lacc > 0 or np.random.uniform() < np.exp(lacc):
theta_curr[j] = theta_prop[j]
lp_curr = lp_prop
paccept[j] = paccept[j] + 1
else:
theta_prop[j] = theta_curr[j]
else:
theta_prop[j] = theta_curr[j]
# storage
Theta[i] = theta_curr
self._n_state1 = old_state1
self._n_state2 = old_state2
# output
if not accept:
return Theta
else:
paccept = paccept / n_samples
return Theta, paccept
ter_1 = np.sqrt((A * a**(3 * n + 3.) + B)**(1. / (1 + n)) - 3 * K * a)
dadt = ter_1 / np.sqrt(3. * a)
return dadt
######### parameters
n = 0.05 ### the alpha
A = 1 / 3.
K = 0.
a0 = 1.
####### constant of integration
B = np.array([0., 0.6, 1.8, 2.5, 5.0])
labels = ["B=0.0", "B=0.6", "B=1.8", "B=2.5", "B=5.0"]
####### time
t = np.linspace(0., 21, 201)
for i in range(len(B)):
sol1 = odeint(GCG, a0, t, args=(A, B[i], 0.))
plt.plot(t, sol1, label=labels[i])
plt.legend(loc="upper left")
plt.xlabel('time ($t$)')
plt.ylabel('scale factor ($a$)')
plt.grid()
plt.title(r'Varying $B$')
axes = plt.gca()
plt.show()
def solve_mean_field_first_order(
Model,
DYN_SYSTEM={
'RecExc': {
'aff_pops': ['AffExc'],
'x0': 1.
},
'RecInh': {
'aff_pops': ['AffExc'],
'x0': 1.
}
},
dt=0.1,
tstop=100.,
INPUTS={
'AffExc_RecExc': np.ones(1000),
'AffExc_RecInh': np.ones(1000)
},
T=5e-3,
replace_x0=False,
verbose=False):
"""
if replace_x0 -> then you can directly use get_stat_props
"""
DYN_KEYS = [key for key in DYN_SYSTEM.keys()
] # for a quick access to the dynamical system variables
# initialize neuronal and synaptic params
for key in DYN_KEYS:
DYN_SYSTEM[key]['nrn_params'] = built_up_neuron_params(Model, key)
DYN_SYSTEM[key]['syn_input'] = build_up_afferent_synaptic_input(Model,\
DYN_KEYS+DYN_SYSTEM[key]['aff_pops'], key,
verbose=verbose)
# --- CONSTRUCT THE DIFFERENTIAL OPERATOR --- #
def dX_dt(X, t, dt, DYN_KEYS, DYN_SYSTEM, INPUTS):
dX_dt, RATES = [], {}
# we fill the X-defined recurent act:
for x, key in zip(X, DYN_KEYS):
RATES['F_' + key] = x
# then we compute it, key by key
for i, key in enumerate(DYN_KEYS):
for aff_key in DYN_SYSTEM[key]['aff_pops']:
RATES['F_'+aff_key] = INPUTS[aff_key+'_'+key][\
min([int(t/dt), len(INPUTS[aff_key+'_'+key])-1]) ]
Fout = input_output(DYN_SYSTEM[key]['nrn_params'],
DYN_SYSTEM[key]['syn_input'], RATES,
Model['COEFFS_' + key])
dX_dt.append((Fout - X[i]) / T) # Simple one-dimensional framework
return dX_dt
# ------------------------------------------- #
# starting point
X0 = []
for key in DYN_KEYS:
X0.append(DYN_SYSTEM[key]['x0'])
X = odeint(dX_dt,
X0,
np.arange(int(tstop / dt)) * dt,
args=(dt, DYN_KEYS, DYN_SYSTEM, INPUTS))
output = {}
for key, x in zip(DYN_KEYS, X.T):
output[key] = x
if replace_x0:
DYN_SYSTEM[key]['x0'] = x[-1]
return output
y_0 = np.array([t0, r0, theta0, phi0, p_r0, p_theta0])
# these are functions of _0:
# angular momentum (= r * p^phi)
b = p_phi
# energy at infinity
#E = 0.973101
E = energy(y_0, a, b)
# Carter's constant
_q = q(theta0, p_theta0, a, E, b)
zeta = np.linspace(0, T, nt + 1)
orbit = np.zeros((nt + 1, 6))
orbit = spi.odeint(deriv, y_0, zeta, (a, E, b, _q), atol=1e-10)
t = orbit[:, 0]
r = orbit[:, 1]
theta = orbit[:, 2]
phi = orbit[:, 3]
pr = orbit[:, 4]
ptheta = orbit[:, 5]
orbit_x = np.sqrt(r**2 + a * a) * \
np.sin(theta) * np.cos(phi)
orbit_y = np.sqrt(r**2 + a * a) * \
np.sin(theta) * np.sin(phi)
orbit_z = r * np.cos(theta)
imaxs = maxima(pr)
def run_oscil(x0,T,p):
t = np.linspace(0, T, 1000)
dxdt = lambda x,t : f(x, t, p)
x_t = integrate.odeint(dxdt, x0, t)
return(t,x_t)
plt.xlabel('Vrijeme (s)')
plt.ylabel('Kut (Radijani)')
plt.legend(['Nelinearno', 'Linearno'], loc='top right')
plt.show()
Egp = m * g * l
T = 2 * np.pi * (np.sqrt(l / g))
print(T)
#Graf
time = np.arange(0, 10, 0.025)
x0 = np.radians(0.0)
theta1 = odeint(formule, [theta0, x0], time)
w = np.sqrt(g / l)
theta2 = [theta0 * np.cos(w * t) for t in time]
running = True
t = 0
dt = 0.025
n = int(input("Koliko kuglica želiš pomaknuti? "))
pygame.init()
screen = pygame.display.set_mode((Xsize, Ysize))
dx = 80
for i in range(n):
)
#set joint torques to zero for first simulation
numerical_specified = zeros(3)
# numerical_specified[0] = 0.1 #changing this value will add constant torque to joint
args = {'constants': numerical_constants, 'specified': numerical_specified}
frames_per_sec = 60
final_time = 3
t = linspace(0.0, final_time, final_time * frames_per_sec)
#integrate equations of motion
right_hand_side(x0, 0.0, numerical_specified, numerical_constants)
#create variable to store trajectories of states as func of time
y = odeint(right_hand_side,
x0,
t,
args=(numerical_specified, numerical_constants))
#plot trajectory of each joint
# fig = plt.figure(1)
# ax = fig.add_subplot()
# plt.plot(t, rad2deg(y[:, :3])) #generalized positions-first 3 states
# plt.draw()
# plt.pause(30)
#visualization ----------------------------------------------------------------
#print(pydy.shapes.__all__)
#draw spheres at each joint
j0_shape = Sphere(color='black', radius=0.025)
j1_shape = Sphere(color='black', radius=0.025)
E = sig(JEE * sEE - JEI * sEI + iE)
# stim here makes E less like integrator under long stim
I = sig(JIE * sIE - JII * sII)
dEE = (-sEE + E) / tEE
dIE = (-sIE + E) / tIE
dEI = (-sEI + I) / tEI
dII = (-sII + I) / tII
diE = (-iE + JO * stim(t)) / tO
return [dEE, dEI, dIE, dII, diE]
#%% stim by external
y0 = [.5, .5, .5, .5, 0]
t = numpy.arange(1000)
for JO in [-200, 200, 2e3, 2e4, 2e5, 2e9, 2e12]:
y = odeint(NDF, y0, t, tfirst=True)
sEE, sEI, sIE, sII, iE = y[:, 0], y[:, 1], y[:, 2], y[:, 3], y[:, 4]
E = sig(JEE * sEE - JEI * sEI + iE)
pyplot.plot(E)
#%% stim by initial value
JO = 0
t = numpy.arange(1000)
for E0 in [-0.005, 0.005, 0.01, 0.02, 0.04]:
y0 = [E0 + 0.5, E0 + 0.5, .5, .5, 0]
y = odeint(NDF, y0, t, tfirst=True)
sEE, sEI, sIE, sII, iE = y[:, 0], y[:, 1], y[:, 2], y[:, 3], y[:, 4]
E = sig(JEE * sEE - JEI * sEI + iE)
pyplot.plot(E)
#%%
iE = y[:, 4]
x30 = 0
v_x30 = 5000
y30 = 5*149 * 10**9
v_y30 = 0
s0 = (x10, v_x10, y10, v_y10,
x20, v_x20, y20, v_y20,
x30, v_x30, y30, v_y30)
m1 = 2.10777 * 10**30
m2 = 1.193 * 10**30
m3 = 0.596 *10**30
G = 6.67 * 10**(-11)
sol = odeint(move_func, s0, t)
fig = plt.figure()
bodys = []
for i in range (0, len(t), 1):
body1, = plt.plot(sol[:i,0], sol[:i,2], '-', color='r')
body1_line, = plt.plot(sol[i, 0], sol[i,2], 'o', color='r')
body2, = plt.plot(sol[:i,4], sol[:i,6], '-', color='g')
body2_line, = plt.plot(sol[i, 4], sol[i,6], 'o', color='g')
body3, = plt.plot(sol[:i,8], sol[:i,10], '-', color='b')
body3_line, = plt.plot(sol[i, 8], sol[i,10], 'o', color='b')
bodys.append([body1, body1_line, body2, body2_line, body3, body3_line])
def CancerSim(y, t, rN, rT, Kn, Kt, aNT, aTN):
Nn = y[0]
Nt = y[1]
dNndt = rN * (1 - ((Nn + aNT * Nt) / Kn)) * Nn
dNtdt = rT * (1 - ((Nt + aTN * Nn) / Kt)) * Nt
return [dNndt, dNtdt]
params2 = (0.5, 0.5, 10, 10, 0.5, 2)
params3 = (0.5, 0.5, 10, 10, 0.5, 0.5)
params4 = (0.5, 0.5, 10, 10, 2, 0.5)
N0 = [0.01, 0.01]
times = range(0, 500)
modelSim2 = spint.odeint(func=CancerSim, y0=N0, t=times, args=params2)
modelOutput2 = pandas.DataFrame({
"t": times,
"Nn": modelSim2[:, 0],
"Nt": modelSim2[:, 1]
})
g2 = ggplot(modelOutput2, aes(x="t"))
g2 = g + geom_line(aes(y="Nn"))
g2 = g + geom_line(aes(y="Nt"))
g2
numberOfAngles = 10
fangle = p.figure()
p.title("Forcing angle")
for angleIndex in range(numberOfAngles):
leaderLocation = array([2*cos(angleIndex*2*pi/numberOfAngles),2*sin(angleIndex*2*pi/numberOfAngles)])
print "leaderLocation", leaderLocation
X0 = array([0,0,0,leaderLocation[0],leaderLocation[1],0])
f = f1(X0)
print "control input", f[2]
p.plot(array([angleIndex*2*pi/numberOfAngles*180/pi]), array([f[2]]),"bo")
# p.show()
# Integrate the system.
X0 = array([0,0,0,5,10,0])
t = linspace(0,runTime,200)
X, infodict = integrate.odeint(f1, X0, t, full_output=True)
print infodict['message']
print "leader locations", X[:,0:1]
# Plot the trajectories.
f1 = p.figure()
for ind in range(0,N):
p.plot(X[:,3*ind],X[:,3*ind+1],'-rx', label=str(ind))
p.grid()
p.legend(loc="best")
p.xlabel('x')
p.ylabel('y')
# p.xlim([0,.5])
# p.ylim([0,.5])
p.title('Trajectory of agents.')
t = np.linspace(0, 60, 60)
# The SIR model differential equations.
def deriv(y, t, N, beta, gamma):
S, I, R = y
dSdt = -beta * S * I / N
dIdt = beta * S * I / N - gamma * I
dRdt = gamma * I
return dSdt, dIdt, dRdt
# Initial conditions vector
y0 = S0, I0, R0
# Integrate the SIR equations over the time grid, t.
ret = odeint(deriv, y0, t, args=(N, beta, gamma))
S, I, R = ret.T
# Plot the data on three separate curves for S(t), I(t) and R(t)
fig = plt.figure(facecolor='w')
ax = fig.add_subplot(111, axisbelow=True)
ax.plot(t, S / 1000, 'b', alpha=0.5, lw=2, label='Susceptible')
ax.plot(t, I / 1000, 'r', alpha=0.5, lw=2, label='Infected')
ax.plot(t, R / 1000, 'g', alpha=0.5, lw=2, label='Recovered with immunity')
ax.set_xlabel('Time /days')
ax.set_ylabel('Number (1000s)')
ax.set_ylim(0, 1.2)
ax.yaxis.set_tick_params(length=0)
ax.xaxis.set_tick_params(length=0)
ax.grid(b=True, which='major', c='w', lw=2, ls='-')
legend = ax.legend()
sigma = 5.67e-8
Ta = 23
Cp = 500
m = 0.004
TaK = Ta + 273.15
def labsim(TC, t):
TK = TC + 273.15
dTCdt = (U * A * (Ta - TC) + sigma * eps * A *
(TaK**4 - TK**4) + alpha * 50) / (m * Cp)
return dTCdt
tm = np.linspace(0, n, n + 1) # Time values
Tsim = odeint(labsim, 23, tm)
# calculate losses from conv and rad
conv = U * A * (Ta - Tsim)
rad = sigma * eps * A * (TaK**4 - (Tsim + 273.15)**4)
loss = conv + rad
gain = alpha * 50
# Plot results
plt.figure()
plt.subplot(2, 1, 1)
plt.plot(tm, Tsim, 'b-', label='Simulated')
if connected:
plt.plot(tm, T1, 'r.', label='Measured')
plt.ylabel(r'Temperature ($^oC$)')
plt.legend()
def model(y, t):
w = 2 * math.pi * 60
Vrms = 120
L = 0.006
dydt = (heaviside(t) * (2**0.5) * Vrms * math.cos(w * t)) * (1/L) - y*(0.05/L)
return dydt
# initial condition
y0 = 5
# time points
t = np.linspace(0,20)
# solve ODE
y = odeint(model,y0,t)
# plot results
plt.plot(t,y)
plt.xlabel('t')
plt.ylabel('y(t)')
plt.show()
## ---------------------
# Multiple graphs
# def f(s,t):
# a = 4
# b = 7
def value(c_var, dspan, vreme, vreme_c, Lambda, mu_val, broj_mesta, g_function, Cc):
Out = odeint(model, var_init, dspan,
args = (vreme_c, vreme, c_var, Lambda, mu_val, broj_mesta))
function = [function_evaluate(g_function, Out[i,:], Cc, c_var, vreme_c, self.time_control[i]) for i in range(len(self.time_control))]
return function, Out
# Function that evaluates the RHS of the ODE. It has two components,
# representing the changes in prey and predator populations.
def f(y, t):
return np.array([alpha1*y[0]-beta1*y[0]*y[1], \
-alpha2*y[1]+beta2*y[0]*y[1]])
# Specify the range of time values where the ODE should be solved at
time = np.linspace(0, 70, 500)
# Initial conditions, set to the initial populations of prey and predators
yinit = np.array([10, 5])
# Solve ODE using the "odeint" library in SciPy
y = odeint(f, yinit, time)
# Print the solutions
#for i in range(0,500):
# print(time[i],x[i,0],x[i,1])
# Plot the solutions
plt.figure()
p0, = plt.plot(time, y[:, 0])
p1, = plt.plot(time, y[:, 1])
plt.legend([p0, p1], ["Prey", "Predators"])
plt.xlabel('t')
plt.ylabel('Population')
plt.show()
def fit_odeint(x, beta, gamma):
return integrate.odeint(SIR_model_t, SIR0, t, args=(beta, gamma))[:, 1]
llg.alpha=0.1
llg.H_app=(0,0,0)
llg.A = 1.3e-11
llg.D = 4e-3
#llg.set_m((
# 'MS * (2*x[0]/L - 1)',
# 'sqrt(MS*MS - MS*MS*(2*x[0]/L - 1)*(2*x[0]/L - 1))',
# '0'), L=length, MS=llg.Ms)
llg.set_m((
'MS',
'0',
'0'), MS=llg.Ms)
llg.setup(use_dmi=True,use_exchange=True)
llg.pins = []
print "point 0:",mesh.coordinates()[0]
print "Solving problem..."
y = llg.m[:]
y.shape=(3,len(llg.m)/3)
ts = numpy.linspace(0, 7e-10, 1000)
tol = 1e-4
for i in range(len(ts)-1):
print i
ys,infodict = odeint(llg.solve_for, llg.m, [ts[i],ts[i+1]], full_output=True,printmessg=True,rtol=tol,atol=tol)
df.plot(llg._m)
df.interactive()
print "Done"
import numpy as np
def CG(a, t, A, B):
dadt = np.sqrt(np.sqrt(A * a**6 + B)) / np.sqrt(3. * a)
return dadt
A = 1. / 3
B = 0.3
a0 = 0.1
t = np.linspace(0, 16, 101)
from scipy.integrate import odeint
sol1 = odeint(CG, a0, t, args=(A, B))
import matplotlib.pyplot as plt
def d1(a):
den = np.sqrt(B / a**6. + A)
return den
plt.plot(t, d1(sol1[:]))
plt.xlabel(r'time ($t$)')
plt.ylabel(r'density ($\rho$)')
plt.legend()
#plt.grid()
def simulate_wt_experiment(inits, total_protein, sig, learned_params, time):
odes = odeint(model,
inits,
time,
args=(total_protein, sig, learned_params))
return odes
def pooled_plotter_data_model(k=[],save = False):
if k == []:
k = np.loadtxt('data/pooled_param_drug_cpt11.txt')
# print(k)
tot_dose = pk.CPT11_tot_dose
pat_cp_data = pk.CPT11
pat_sn_data = pk.SN38
vol = pk.vol
data_time = pk.CPT11_time
pat_range = pk.pat_nums_cpt
Title_str = 'CPT11'
tmax = 36
x0 = [0]*9
a = int(np.ceil(len(pat_range)/2))
fig0, ax0 = plt.subplots(2,int(np.ceil(len(pat_range)/2)),sharex = True, sharey = True)
# fig0.suptitle('CPT11 model vs data')
fig1, ax1 = plt.subplots(2,int(np.ceil(len(pat_range)/2)),sharex = True, sharey = True)
# fig1.suptitle('SN38 model vs data')
plt.setp(ax0, xticks=np.arange(0,tmax+1,12),xticklabels = [24,12,24,12])
plt.setp(ax1, xticks=np.arange(0,tmax+1,12),xticklabels = [24,12,24,12])
cmax_cp = [None]*len(pat_range)
cmax_sn = [None]*len(pat_range)
cmax_cp_time = [None]*len(pat_range)
cmax_sn_time = [None]*len(pat_range)
cost = [None]*len(pat_range)
SS_tot = [None]*len(pat_range)
SS_res = [None]*len(pat_range)
for i in pat_range:
tspan = data_time[i]
t = np.sort(list(np.linspace(0.1,36,1000))+list(tspan))
t = np.sort(list(set(t)))
pos = np.where(np.in1d(t,tspan))[0]
x = odeint(non_phys_ode_CPT,x0,t,(k,tot_dose[i],vol[i]),hmax=1)
x = np.multiply(1000,x)
ax0[int(np.floor(i/a)),i%a].plot(t,x[:,2],'k--')
ax0[int(np.floor(i/a)),i%a].plot(tspan,np.multiply(1000,pat_cp_data[i]),'.',color='0.4',mew=2)
ax0[int(np.floor(i/a)),i%a].spines['right'].set_visible(False)
ax0[int(np.floor(i/a)),i%a].spines['top'].set_visible(False)
ax0[int(np.floor(i/a)),i%a].text(24,1.2,'('+str(pat_range[i]+1)+')')
ax1[int(np.floor(i/a)),i%a].plot(t,x[:,3],'k--')
ax1[int(np.floor(i/a)),i%a].plot(tspan,np.multiply(1000,pat_sn_data[i]),'.',color='0.4',mew=2)
ax1[int(np.floor(i/a)),i%a].spines['right'].set_visible(False)
ax1[int(np.floor(i/a)),i%a].spines['top'].set_visible(False)
ax1[int(np.floor(i/a)),i%a].text(24,0.03,'('+str(pat_range[i]+1)+')')
plt.setp( ax0[int(np.floor(i/a)),i%a].xaxis.get_majorticklabels(), rotation=90 )
plt.setp( ax1[int(np.floor(i/a)),i%a].xaxis.get_majorticklabels(), rotation=90 )
D_cp = np.multiply(1000,pat_cp_data[i])
D_sn = np.multiply(1000,pat_sn_data[i])
cmax_cp[i] = np.max(x[:,2])
cmax_sn[i] = np.max(x[:,3])
cmax_cp_time[i] = t[np.argmax(x[:,2])]
cmax_sn_time[i] = t[np.argmax(x[:,3])]
M_cp = x[pos,2]
M_sn = x[pos,3]
cost[i] = np.nansum(np.square((M_cp - D_cp)/(2.19317759e-02)) + np.square((M_sn - D_sn)/(6.81021571e-04)))
SS_tot = np.nansum((D_cp - np.nanmean(D_cp))**2) + np.nansum((D_sn - np.nanmean(D_sn))**2)
SS_res = np.nansum(np.square((M_cp - D_cp))) + np.nansum(np.square((M_sn - D_sn)))
Cost = np.sum(cost)
r2 = 1 - np.sum(SS_res)/np.sum(SS_tot)
if int(np.floor(len(pat_range)/2))%2 != 0:
fig0.delaxes(ax0[-1,-1])
fig1.delaxes(ax1[-1,-1])
ax0[int(np.floor(i/a)),i%a].legend(labels=['model','data'],bbox_to_anchor=(1.3,1))
fig0.text(0.5,-0.05,'Time (h)',ha='right')
fig0.text(0.05,0.65,'Concentration (ng/ml)',ha='right',rotation='vertical')
ax1[int(np.floor(i/a)),i%a].legend(labels=['model','data'],bbox_to_anchor=(2.9,1))
fig1.text(0.5,-0.05,'Time (h)',ha='right')
fig1.text(0.05,0.65,'Concentration (ng/ml)',ha='right',rotation='vertical')
plt.setp(ax0,ylim=[0,np.max(cmax_cp)*1.1])
plt.setp(ax1,ylim=[0,np.max(cmax_sn)*1.15])
plt.show()
if save:
np.savetxt('cmax_cpt_patients.txt',cmax_cp)
np.savetxt('cmax_sn_patients.txt',cmax_sn)
np.savetxt('cmax_cp_time.txt',cmax_cp_time)
np.savetxt('cmax_sn_time.txt',cmax_sn_time)
folder_path = uf.new_folder('Model_fitting')
with PdfPages(folder_path+'model_fit_for_'+Title_str+'.pdf') as pdf:
pdf.savefig(fig0, bbox_inches='tight')
with PdfPages(folder_path+'model_fit_for_SN38.pdf') as pdf:
pdf.savefig(fig1, bbox_inches='tight')
return x,Cost, r2
((kd + Y[2])**2 / ((kd + Y[2])**2 + kd * bt)))
])
# to generate the x-axes
t = np.linspace(0, 10, 1000)
#initial values
func0 = [0, 0, 0] # [N,V,CA]
pars = (-0.0275, 0.025, 0.0145, 0.008, -0.015, 4.0e-7, 1.5e-7, 2.664, -0.07,
-0.09, 0.08, 3.1416e-13, 7.854e-14, 1.57e-13, 1.9635e-14, 1.0e-6,
1.0e-4, 7.9976e7, 1.3567537e2)
Y = integrate.odeint(func, func0, t, pars)
n, v, ca = Y.T
# the plots
plt.subplot(4, 1, 1)
plt.plot(t, n, 'r', linewidth=2, label='n')
plt.xlabel('t')
plt.ylabel('n(t)')
plt.legend(loc='best')
plt.subplot(4, 1, 2)
plt.plot(t, v, 'b', linewidth=2, label='v')
plt.xlabel('t')
plt.ylabel('v(t)')
def non_phys_plotter_data_model_colour(k=[],save = False):
if k == []:
k = np.loadtxt('data/pat_param_drug_cpt11.txt')
tot_dose = pk.CPT11_tot_dose
pat_cp_data = pk.CPT11
pat_sn_data = pk.SN38
data_time = pk.CPT11_time
pat_range = pk.pat_nums_cpt
Title_str = 'CPT11'
tmax = 36
x0 = [0]*9
a = int(np.ceil(len(pat_range)/2))
fig0, ax0 = plt.subplots(2,int(np.ceil(len(pat_range)/2)),sharex = True, sharey = True)
fig0.suptitle('CPT11 model fit to data')
fig1, ax1 = plt.subplots(2,int(np.ceil(len(pat_range)/2)),sharex = True, sharey = True)
fig1.suptitle('SN38 model fit to data')
plt.setp(ax0, xticks=np.arange(0,tmax+1,12))
plt.setp(ax1, xticks=np.arange(0,tmax+1,12))
cmax_cp = [None]*len(pat_range)
cmax_sn = [None]*len(pat_range)
cmax_cp_time = [None]*len(pat_range)
cmax_sn_time = [None]*len(pat_range)
cost = [None]*len(pat_range)
for i in pat_range:
tspan = data_time[i]
t = np.sort(list(np.linspace(0.1,36,1000))+list(tspan))
t = np.sort(list(set(t)))
pos = np.where(np.in1d(t,tspan))[0]
x = odeint(non_phys_ode_CPT,x0,t,(k[i],tot_dose[i]),hmax=1)
ax0[int(np.floor(i/a)),i%a].plot(t,x[:,2],'--',color=(0,0,0.5))
ax0[int(np.floor(i/a)),i%a].plot(tspan,pat_cp_data[i],'.',color=(0.7,0,0),mew=2)
ax0[int(np.floor(i/a)),i%a].spines['right'].set_visible(False)
ax0[int(np.floor(i/a)),i%a].spines['top'].set_visible(False)
ax0[int(np.floor(i/a)),i%a].text(24,0.0012,'('+str(pat_range[i]+1)+')')
ax1[int(np.floor(i/a)),i%a].plot(t,x[:,3] + x[:,7],'--',color=(0,0,0.5))
ax1[int(np.floor(i/a)),i%a].plot(tspan,pat_sn_data[i],'.',color=(0.7,0,0),mew=2)
ax1[int(np.floor(i/a)),i%a].spines['right'].set_visible(False)
ax1[int(np.floor(i/a)),i%a].spines['top'].set_visible(False)
ax1[int(np.floor(i/a)),i%a].text(24,0.00003,'('+str(pat_range[i]+1)+')')
plt.setp( ax0[int(np.floor(i/a)),i%a].xaxis.get_majorticklabels(), rotation=90 )
plt.setp( ax1[int(np.floor(i/a)),i%a].xaxis.get_majorticklabels(), rotation=90 )
cmax_cp[i] = np.max(x[:,2])
cmax_sn[i] = np.max(x[:,3] + x[:,7])
cmax_cp_time[i] = t[np.argmax(x[:,2])]
cmax_sn_time[i] = t[np.argmax(x[:,3])]
M_cp = x[pos,2]
M_sn = x[pos,3] + x[pos,7]
D_cp = pat_cp_data[i]
D_sn = pat_sn_data[i]
cost[i] = np.nansum(np.square(M_cp - D_cp)/M_cp + np.square(M_sn - D_sn)/M_sn)
if int(np.floor(len(pat_range)/2))%2 != 0:
fig0.delaxes(ax0[-1,-1])
fig1.delaxes(ax1[-1,-1])
ax0[int(np.floor(i/a)),i%a].legend(labels=['model','data'],bbox_to_anchor=(1.3,1))
fig0.text(0.5,-0.05,'Time (h)',ha='right')
fig0.text(-0.05,0.65,'Concentration (mg/ml)',ha='right',rotation='vertical')
ax1[int(np.floor(i/a)),i%a].legend(labels=['model','data'],bbox_to_anchor=(2.9,1))
fig1.text(0.5,-0.05,'Time (h)',ha='right')
fig1.text(-0.05,0.65,'Concentration (mg/ml)',ha='right',rotation='vertical')
plt.setp(ax0,ylim=[0,np.max(cmax_cp)*1.1])
plt.setp(ax1,ylim=[0,np.max(cmax_sn)*1.1])
if save:
np.savetxt('cmax_cpt_patients.txt',cmax_cp)
np.savetxt('cmax_sn_patients.txt',cmax_sn)
np.savetxt('cmax_cp_time.txt',cmax_cp_time)
np.savetxt('cmax_sn_time.txt',cmax_sn_time)
folder_path = uf.new_folder('Model_fitting')
with PdfPages(folder_path+'model_fit_for_'+Title_str+'_colour.pdf') as pdf:
pdf.savefig(fig0, bbox_inches='tight')
with PdfPages(folder_path+'model_fit_for_SN38_colour.pdf') as pdf:
pdf.savefig(fig1, bbox_inches='tight')
return x,cost
g_l = 0.1
v_k = -100.0
v_na = 50.0
v_l = -67.0
i_ext = 1.5
t_final = 100.0
dt = 0.01
v = -70.0
if __name__ == "__main__":
tau_d = 2.0
tau_r = 0.2
x0 = initial_condition(v)
t = np.arange(0, t_final, dt)
sol = odeint(derivative, x0, t)
V = sol[:, 0]
S1 = sol[:, -1]
# --------------------------------------------------------------#
tau_r = 1.0
sol = odeint(derivative, x0, t)
S2 = sol[:, -1]
fig, ax = pl.subplots(3, figsize=(7, 5), sharex=True)
ax[0].plot(t, V, lw=2, c="k")
ax[1].plot(t, S1, lw=2, c="k")
ax[2].plot(t, S2, lw=2, c="k")
ax[0].set_xlim(min(t), max(t))
ax[0].set_ylim(-100, 100)
zp = np.zeros(6)
zp[0:3] = z[3:6]
z2 = ((-g * m_t) /
(r**3)) * z[0:3] - R.T @ (dR_dt2 @ z[0:3] +
2 * dR_dt @ z[3:6]) + J_2[0:3] + J_3[0:3]
zp[3:6] = z2
return zp
t, x, y, z, vx, vy, vz = leer_eof(
"S1B_OPER_AUX_POEORB_OPOD_20200827T111142_V20200806T225942_20200808T005942.EOF"
)
z0 = np.array([x[0], y[0], z[0], vx[0], vy[0], vz[0]])
sol = odeint(satelite, z0, t)
x_s = sol[:, 0]
y_s = sol[:, 1]
z_s = sol[:, 2]
y_1 = [-5e6, 0, 5e6]
eje_y = ["-5000", "0", "5000"]
x_1 = [0, 18000, 36000, 54000, 72000, 90000]
eje_x = ["0", "5", "10", "15", "20", "25"]
plt.figure()
plt.subplot(3, 1, 1)
plt.title("Posición")
plt.ylabel("X (KM)")