print('---------------------')
print('\tMCcount = ', MCcount)
print('---------------------')
# BUILD PDF
MCprocess = MCprocessing(savenameMC, case=case)
savenamepdf = MCprocess.buildKDE(nu, distribution=distribution, MCcount=MCcount, save=save, u_margin=u_margin, bandwidth=bandwidth)
# LEARN
dataman = DataIO(case, directory=PDFDIR)
fu, gridvars, ICparams = dataman.loadSolution(savenamepdf, array_opt='marginal')
adjustgrid = {'mu':mu, 'mx':mx, 'mt':mt, 'pu':pu, 'px':px, 'pt':pt}
grid = PdfGrid(gridvars)
fu = grid.adjust(fu, adjustgrid)
difflearn = PDElearn(grid=grid, fu=fu, ICparams=ICparams, scase=case, trainratio=trainratio, verbose=printlearning)
filename = difflearn.fit_sparse(feature_opt=feature_opt, variableCoef=variableCoef, variableCoefBasis=variableCoefBasis, \
variableCoefOrder=coeforder, use_rfe=use_rfe, rfe_alpha=rfe_alpha, nzthresh=nzthresh, maxiter=maxiter, \
LassoType=LassoType, RegCoef=RegCoef, cv=cv, criterion=criterion, print_rfeiter=print_rfeiter, shuffle=shuffle, \
basefile=savenamepdf, adjustgrid=adjustgrid, save=save, normalize=normalize, comments=comments)
# Save Learning
D = DataIO(case, directory=LEARNDIR)
output, metadata = D.readLearningResults(filename)
output_vec.append(output)
metadata_vec.append(metadata)
filename_vec.append(filename)
feature_opt = '1storder'
coeforder = 2
sindy_alpha = 0.01
nzthresh = 1e-190
RegCoef = 0.000004
maxiter = 10000
if "savenamepdf" not in locals():
# Check if there is already a loadfile (if not load it)
savenamepdf = 'advection_reaction_analytical_717_944.npy'
dataman = DataIO(case)
fu, gridvars, ICparams = dataman.loadSolution(savenamepdf,
array_opt='marginal')
grid = PdfGrid(gridvars)
fu = grid.adjust(fu, aparams)
if plot:
s = 10
V = Visualize(grid)
V.plot_fu3D(fu)
V.plot_fu(fu, dim='t', steps=s)
V.plot_fu(fu, dim='x', steps=s)
V.show()
difflearn = PDElearn(grid=grid,
fu=fu,
ICparams=ICparams,
scase=case,
trainratio=0.8,
debug=False,
