def regression_tst(method, func, y0, t, y_ref, tol_boundary=(0,6), h0=0.5, mxstep=10e6,
adaptive="order", p=4, solout=(lambda t: t), nworkers=2):
tol = [1.e-3,1.e-5,1.e-7,1.e-9,1.e-11,1.e-13]
a, b = tol_boundary
tol = tol[a:b]
err = np.zeros(len(tol))
print ''
for i in range(len(tol)):
print tol[i]
ys, infodict = ex_parallel.extrapolation_parallel(method,func, None, y0, t, atol=tol[i],
rtol=tol[i], mxstep=mxstep, full_output=True, nworkers=nworkers)
y = solout(ys[1:len(ys)])
err[i] = relative_error(y, y_ref)
return err
def convergenceTest(method, i, test, allSteps, order, dense=False):
'''''
Perform a convergence test with the test problem (in test parameter) with
the given steps in parameter allSteps.
'''''
y_ref = np.loadtxt(tst.getReferenceFile(test.problemName))
denseOutput = test.denseOutput
if(not dense):
y_ref=y_ref[-1]
denseOutput=[denseOutput[0], denseOutput[-1]]
else:
nhalf = np.ceil(len(y_ref)/2)
y_ref = y_ref[nhalf]
print("dense output time " + str(denseOutput[nhalf]))
k=0
errorPerStep=[]
for step in allSteps:
#rtol and atol are not important as we are fixing the step size
ys, infodict = ex_parallel.extrapolation_parallel(method,test.RHSFunction, None, test.initialValue, denseOutput, atol=1e-1,
rtol=1e-1, mxstep=10000000, full_output=True, nworkers=4, adaptative='fixed', p=order, h0=step)
# print("number steps: " + str(infodict['nst']) + " (should be " + str(denseOutput[-1]/step) + ")")
ys=ys[1:len(ys)]
if(dense):
ys=ys[nhalf]
error = relative_error(ys, y_ref)
errorPerStep.append(error)
coefficients = np.polyfit(np.log10(allSteps), np.log10(errorPerStep), 1)
print("coefficients: " + str(coefficients) + " order is: " + str(order-methodsmoothing[i]))
if(plotConv):
plt.plot(np.log10(allSteps),np.log10(errorPerStep), marker="x")
plt.show()
return coefficients[0]
