def analyze(self, adjust=False, plot=False, learn=False, adjustparams={}, learnparams={'feature_opt':'1storder', 'coeforder':1}):
dataman = DataIO(self.case)
fu, gridvars, ICparams = dataman.loadSolution(self.loadnamenpy, array_opt='marginal')
##Make fu smaller (in time)
if adjust:
fu, gridvars = self.adjust(fu, gridvars, adjustparams)
grid = PdfGrid(gridvars)
if plot:
V = Visualize(grid)
V.plot_fu3D(fu)
V.plot_fu(fu, dim='t', steps=5)
V.plot_fu(fu, dim='x', steps=5)
V.show()
if learn:
t0 = time.time()
print('fu dimension: ', fu.shape)
print('fu num elem.: ', np.prod(fu.shape))
feature_opt = learnparams['feature_opt']
coeforder = learnparams['coeforder']
sindy_alpha = learnparams['sindy_alpha']
RegCoef = learnparams['RegCoef']
nzthresh = learnparams['nzthresh']
# Learn
difflearn = PDElearn(grid=grid, fu=fu, ICparams=ICparams, scase=self.case, trainratio=0.8, debug=False, verbose=True)
difflearn.fit_sparse(feature_opt=feature_opt, variableCoef=True, variableCoefBasis='simple_polynomial', \
variableCoefOrder=coeforder, use_sindy=True, sindy_alpha=sindy_alpha, RegCoef=RegCoef, nzthresh=nzthresh)
print('learning took t = ', str(t0 - time.time()))
def advection():
#loadnamenpy = 'advection_marginal_7397.npy'
loadnamenpy = 'advection_marginal_6328.npy'
loadnamenpy = 'advection_marginal_8028.npy'
loadnamenpy = 'advection_marginal_5765.npy'
#loadnamenpy = 'advection_marginal_4527.npy'
case = '_'.join(loadnamenpy.split('_')[:2])
dataman = DataIO(case)
fuk, fu, gridvars, ICparams = dataman.loadSolution(loadnamenpy)
grid = PdfGrid(gridvars)
V = Visualize(grid)
V.plot_fuk3D(fuk)
V.plot_fu3D(fu)
V.plot_fu(fu, dim='t', steps=5)
V.plot_fu(fu, dim='x', steps=5)
V.show()
# Learn
difflearn = PDElearn(fuk,
grid,
fu=fu,
ICparams=ICparams,
scase=case,
trainratio=0.8,
debug=False,
verbose=True)
difflearn.fit_sparse(feature_opt='2ndorder',
variableCoef=True,
variableCoefBasis='simple_polynomial',
variableCoefOrder=3,
use_sindy=True,
sindy_alpha=0.001)
def plot(self):
dataman = DataIO(self.case)
fu, gridvars, ICparams = dataman.loadSolution(self.loadnamenpy,
array_opt='marginal')
grid = PdfGrid(gridvars)
V = Visualize(grid)
V.plot_fu3D(fu)
V.plot_fu(fu, dim='t', steps=5)
V.plot_fu(fu, dim='x', steps=5)
V.show()
if "savenamepdf" not in locals():
# Check if there is already a loadfile (if not load it)
savenamepdf = 'advection_reaction_analytical_388_128.npy'
dataman = DataIO(case)
fu, gridvars, ICparams = dataman.loadSolution(savenamepdf, array_opt='marginal')
grid = PdfGrid(gridvars)
fu = grid.adjust(fu, adjustgrid)
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=trainratio, verbose=True)
output = 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)
d = DataIO(case, directory=LEARNDIR)
learndata, pdfdata, mcdata = d.readLearningResults(savenamepdf.split('.')[0]+'.txt', PDFdata=True, MCdata=True, display=False)