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)