def check_RungeKutta4_exact_multi(platform, func_src, func_pyf):
from runge_kutta import RungeKutta
#----------------------------------------------------
# Allocate
#----------------------------------------------------
ps = PreSetup()
nx, dt, tmax, yinit = ps.nx, ps.dt, ps.tmax, ps.yinit
y = ArrayAs(platform, yinit, 'y')
rk = RungeKutta(platform, nx, dt)
#----------------------------------------------------
# Core function
#----------------------------------------------------
lib = platform.source_compile(func_src, func_pyf)
func_core = platform.get_function(lib, 'func')
func_core.prepare('iDOO', nx) # (t, y, ret)
func = lambda t, y, ret: func_core.prepared_call(t,y,ret)
comm = lambda k: None
#----------------------------------------------------
# RK4
#----------------------------------------------------
t = 0
for tstep in xrange(tmax):
rk.update_rk4(t, y, func, comm)
t += dt
aa_equal(ps.exact_func(t), y.get(), 13)
def main():
print MENU
INPUT = int(raw_input("Option: "))
if INPUT == 1:
handle_simpsons_task()
elif INPUT == 2:
handle_gaussian_task()
elif INPUT == 3:
u0 = float(raw_input("Enter u0: "))
handle_runge_kutta(u0)
elif INPUT == 4:
u0 = float(raw_input("Enter u0: "))
ranges = float(raw_input("Enter ranges: "))
runge = RungeKutta(FUNCTION2, u0=u0, start=0.0, end=1.0, ranges=ranges)
result, h = runge.solve(output=True)
print "RESULT: {0} with h={1}".format(result, h)
def __init__(self, config: Config):
'''The function arguments represent the following polynomial terms:\n
Ly(x) = g(x)y"(x) + p(x)y'(x) + q(x)y(x) = f(x)\n
with the following boundary conditions:\n
a0*y(a) + a1*y'(a) = A,\n
b0*y(b) + b1*y'(b) = B'''
a = config['a']
b = config['b']
a0 = config['a0']
a1 = config['a1']
b0 = config['b0']
b1 = config['b1']
A = config['A']
B = config['B']
p = config['p(x)']
q = config['q(x)']
g = config['g(x)']
f = config['f(x)']
if abs(a0) + abs(a1) == 0 or abs(b0) + abs(b1) == 0:
raise RuntimeError('The boundary condition does not exist.')
else:
self.a = a
self.b = b
self.C = lambda v, dv, u, du: (B - b0 * v - b1 * dv) / (b0 * u + b1
* du)
self.Lu = f"0 - ({p})*y' / ({g}) - ({q})*y / ({g})"
self.Lv = f"({f}) / ({g}) - ({p})*y' / ({g}) - ({q})*y / ({g})"
self.u_func_val = -(a1 / a0) if a0 != 0 else 1
self.u_diff_val = 1 if a0 != 0 else -(a0 / a1)
self.v_func_val = A / a0 if a0 != 0 else 0
self.v_diff_val = 0 if a0 != 0 else A / a1
self.u_rk = RungeKutta(self.Lu, a, self.u_func_val,
self.u_diff_val) # First Cauchy problem
self.v_rk = RungeKutta(self.Lv, a, self.v_func_val,
self.v_diff_val) # Second Cauchy problem
def handle_runge_kutta(u0):
print "{0: >16}\t{1: <16} {2: <14}".format(
"H", "Result", "Runge")
ranges = 10
prev_result = 0.0
prev_runge = 0.0
runge = 0.0
while ranges <= TASK3.N_LIMIT:
runge_kutta = RungeKutta(FUNCTION2, u0=u0, start=0.0, end=1.0, ranges=ranges)
result, h = runge_kutta.solve()
if prev_result != 0:
runge = runge_error(prev_result, result, TASK3.PRECISION)
output = "{0: >16}\t{1: <16} {2: <14} {3: <14}".format(
h, result, runge, prev_runge*1.0/runge)
else:
output = "{0: >16}\t{1: <16}".format(
h, result)
print output
prev_result = result
prev_runge = runge
ranges *= 2
import os
import matplotlib.pyplot as plt
from problem import Problem1, Problem2
from runge_kutta import RungeKutta
if __name__ == '__main__':
out_dir = '../report_tex'
# Problem 1
for i, epsilon in enumerate([0.05, 0.1, 0.3, 1.0]):
h = 0.05
n_iter = 4000
p1 = Problem1(h, n_iter, epsilon, 0.1, 0.1)
rk = RungeKutta(p1.h, p1.n_iter, p1.dxdt, p1.dydt, p1.x0, p1.y0)
rk.solve()
x, y = rk.get_output()
plt.figure(figsize=(18, 6))
plt.subplot(131)
plt.plot(x, y)
plt.title(u'$x$-$\dot{x}$')
plt.xlabel(u'$x$')
plt.ylabel(u'$\dot{x}$')
plt.subplot(132)
plt.plot(x)
plt.title(u'$t$-$x$')
plt.xlabel(u'$t$')
plt.ylabel(u'$\dot{x}$')
velocity[0,gi,gj,0,ie] = 2*pi*(cos(lat)*cos(alpha) + sin(lat)*cos(lon)*sin(alpha))
velocity[1,gi,gj,0,ie] = - 2*pi*sin(lon)*sin(alpha)
'''
"""
#----------------------------------------------
# spectral element
#----------------------------------------------
interact = InteractBetweenElems()
inside = InsideElems(N, ngll, nelem, state, interact)
# minimum dx, dt
max_v = 2*pi
dt = cfl*min_dx/max_v
tloop = RungeKutta(dt, inside)
tloop.allocate(state.psi.shape, state.psi.dtype)
#----------------------------------------------
# print the setup information
#----------------------------------------------
print '-'*47
print 'N\t\t', N
print 'ngll\t\t', ngll
print 'nelem\t\t', nelem
print 'cfl\t\t', cfl
print 'min_dx\t\t', min_dx
print 'dt\t\t', dt
#tmax = int( numpy.ceil(1/dt) )
class ReductionMethod:
'''Solution of the boundary value problem by dividing it into 2 Cauchy problems.'''
#def __init__(self, a: float, b: float, a0: float, a1: float, A: float, b0: float, b1: float, B: float, p: str, q: str, f: str, g: str = '1'):
def __init__(self, config: Config):
'''The function arguments represent the following polynomial terms:\n
Ly(x) = g(x)y"(x) + p(x)y'(x) + q(x)y(x) = f(x)\n
with the following boundary conditions:\n
a0*y(a) + a1*y'(a) = A,\n
b0*y(b) + b1*y'(b) = B'''
a = config['a']
b = config['b']
a0 = config['a0']
a1 = config['a1']
b0 = config['b0']
b1 = config['b1']
A = config['A']
B = config['B']
p = config['p(x)']
q = config['q(x)']
g = config['g(x)']
f = config['f(x)']
if abs(a0) + abs(a1) == 0 or abs(b0) + abs(b1) == 0:
raise RuntimeError('The boundary condition does not exist.')
else:
self.a = a
self.b = b
self.C = lambda v, dv, u, du: (B - b0 * v - b1 * dv) / (b0 * u + b1
* du)
self.Lu = f"0 - ({p})*y' / ({g}) - ({q})*y / ({g})"
self.Lv = f"({f}) / ({g}) - ({p})*y' / ({g}) - ({q})*y / ({g})"
self.u_func_val = -(a1 / a0) if a0 != 0 else 1
self.u_diff_val = 1 if a0 != 0 else -(a0 / a1)
self.v_func_val = A / a0 if a0 != 0 else 0
self.v_diff_val = 0 if a0 != 0 else A / a1
self.u_rk = RungeKutta(self.Lu, a, self.u_func_val,
self.u_diff_val) # First Cauchy problem
self.v_rk = RungeKutta(self.Lv, a, self.v_func_val,
self.v_diff_val) # Second Cauchy problem
def get_values(self, step: float) -> float:
'''Calculates the approximate values of the needed funciton and yields them.'''
un, vn, dun, dvn = None, None, None, None
crutch_u = copy(self.u_rk)
crutch_v = copy(self.v_rk)
for u_zip, v_zip in zip(
crutch_u.get_values(
self.a, self.b, step
), # Just a crutch for getting the values of v(b) and u(b).
crutch_v.get_values(self.a, self.b, step)
): # Year, I've made the separate loop for getting these values.
un, dun = u_zip[1], u_zip[
2] # Year, I've gone through this loop twice.
vn, dvn = v_zip[1], v_zip[
2] # I just don't know how to get it without this loop, so this will be depricated in future.
C = self.C(vn, dvn, un, dun)
for u_zip, v_zip in zip(self.u_rk.get_values(self.a, self.b, step),
self.v_rk.get_values(self.a, self.b, step)):
x, u, _ = u_zip # We don't need the derivate here, so it's ommited.
x, v, _ = v_zip
yield x, C * u + v
lat1, lon1 = latlons[0,:]
lat2, lon2 = latlons[1,:]
min_dx = arccos( sin(lat1)*sin(lat2) + cos(lat1)*cos(lat2)*cos( abs(lon1-lon2) ) )
# minimum dt
max_v = 2*pi
dt = cfl*min_dx/max_v
#----------------------------------------------
# spectral element
#----------------------------------------------
print 'a'
se = SpectralElement2D(platform, cubegrid, velocity)
print 'b'
rk = RungeKutta(platform, ep_size, dt)
print 'c'
#----------------------------------------------
# print the setup information
#----------------------------------------------
print '-'*47
print 'ne\t\t', ne
print 'ngq\t\t', ngq
print 'cfl\t\t', cfl
print 'min_dx\t\t', min_dx
print 'dt\t\t', dt
#tmax = int( numpy.ceil(1/dt) )
tmax = 1000
