def plotjson(fn): """ plotjson: make plots from json output of fiedler.py fn: the filename of the json file """ fo=open(fn) data=json.load(fo) fo.close() if "adj" in data: (A,adj,Npts) = fiedler.adj_mat(data["adj"]) #scew symetricise A = (A.T - A)/2 A=A.tocoo() pos=A.data>0 skew = numpy.column_stack((A.row[pos],A.col[pos],A.data[pos])).tolist() # #method from hodge decomposition driver.py # sc = simplicial_complex(([[el] for el in range(0,A.shape[0])],numpy.column_stack((A.row[pos],A.col[pos])).tolist())) # omega = sc.get_cochain(1) # omega.v[:] = A.data[pos] # p = omega.k # alpha = sc.get_cochain(p - 1) # #beta = sc.get_cochain(p + 1) # # Solve for alpha # A2 = delta(d(sc.get_cochain_basis(p - 1))).v # b = delta(omega).v # rank=cg( A2, b, tol=1e-8 )[0] # method from ranking driver.py asc = abstract_simplicial_complex([numpy.column_stack((A.row[pos],A.col[pos])).tolist()]) B1 = asc.chain_complex()[1] # boundary matrix rank = lsqr(B1.T, A.data[pos])[0] # solve least squares problem sc = simplicial_complex(([[el] for el in range(0,A.shape[0])],numpy.column_stack((A.row[pos],A.col[pos])).tolist())) omega = sc.get_cochain(1) omega.v[:] = A.data[pos] p = omega.k alpha = sc.get_cochain(p - 1) alpha.v = rank v = A.data[pos]-d(alpha).v cyclic_adj_list=numpy.column_stack((A.row[pos],A.col[pos],v)).tolist() div_adj_list=numpy.column_stack((A.row[pos],A.col[pos],d(alpha).v)).tolist() data["hodge"]=list(rank) data["hodgerank"]=list(numpy.argsort(numpy.argsort(rank))) fo = open(fn,"w") json.dump(data,fo, indent=2) fo.close() fn=fn+".abstract" #fiedler.doPlots(numpy.array(data["f1"]),numpy.array(data["f2"]),numpy.array(data["d"]),cyclic_adj_list,fn+".decomp.cyclic.",widths=[6],vsdeg=False,nByi=data["nByi"],directed=True) #fiedler.doPlots(numpy.array(data["f1"]),numpy.array(data["f2"]),numpy.array(data["d"]),div_adj_list,fn+".decomp.acyclic.",widths=[6],vsdeg=False,nByi=data["nByi"],directed=True) #fiedler.doPlots(numpy.array(data["f1"]),numpy.array(data["f2"]),numpy.array(data["d"]),data["adj"],fn+".decomp.acyclic.over.all.",widths=[6],vsdeg=False,nByi=data["nByi"],adj_list2=div_adj_list,directed=True) #fiedler.doPlots(numpy.array(data["f1"]),-1*numpy.array(rank),numpy.array(data["d"]),cyclic_adj_list,fn+".decomp.harmonic.v.grad.",widths=[6],heights=[2],vsdeg=False,nByi=data["nByi"],directed=True) #fiedler.doPlots(numpy.array(data["f1"]),-1*numpy.array(rank),numpy.array(data["d"]),skew,fn+".decomp.skew.v.grad.",widths=[6],heights=[2],vsdeg=False,nByi=data["nByi"],directed=True) #fiedler.doPlots(numpy.array(data["f1"]),-1*numpy.array(rank),numpy.array(data["d"]),data["adj"],fn+".decomp.acyclic.over.all.v.grad.",widths=[6],heights=[2],vsdeg=False,nByi=data["nByi"],adj_list2=div_adj_list,directed=True) fiedler.doPlots(numpy.array(data["f1"]),-1*numpy.array(rank),numpy.array(data["d"]),data["adj"],fn+".all.v.grad.",widths=[24],heights=[6],vsdeg=False,nByi=data["nByi"],directed=False)