# Create graph G, colors = build_regular_structure(width_basis=15, basis_type="star", nb_shapes=5, shape=["house"], start=0, add_random_edges=0) W = nx.adjacency_matrix(G) W.eliminate_zeros() taus = [0.5, 0.6, 0.7, 0.8, 0.9, 1.0, 1.1] # Apply kernel at every node signal = np.eye(W.shape[0]) heat_kernel = heat.Heat(W=W, taus=taus) feats = heat_kernel.featurize(signal) print('simple-example.py: saving feats to %s.npy' % args.outpath) np.save(args.outpath, feats) # -- # Cluster resulting features # Normalize features nfeats = feats - feats.mean(axis=0, keepdims=True) nfeats /= (1e-10 + nfeats.std(axis=0, keepdims=True)) nfeats[np.isnan(nfeats)] = 0 # Reduce dimension pca_feats = PCA(n_components=10).fit_transform(nfeats)
np.random.seed(args.seed) taus = map(float, args.taus.split(',')) print("main.py: loading %s" % args.inpath, file=sys.stderr) edgelist = np.array(pd.read_csv(args.inpath, sep='\t', header=None, dtype=int)) W = sparse.csr_matrix((np.arange(edgelist.shape[0]), (edgelist[:,0], edgelist[:,1]))) W = ((W + W.T) > 0).astype(int) if not (np.sum(W, axis=0) > 0).all(): print("main.py: dropping isolated nodes", file=sys.stderr) keep = np.asarray(W.sum(axis=0) > 0).squeeze() W = W[keep] W = W[:,keep] print("main.py: running on graph w/ %d edges" % W.nnz, file=sys.stderr) t = time() hk = heat.Heat(W=W, taus=taus) if args.n_queries < 0: args.n_queries = hk.num_nodes print("main.py: running %d queries" % args.n_queries, file=sys.stderr) pfeats = par_graphwave(hk, n_queries=args.n_queries, n_chunks=args.n_chunks, n_jobs=args.n_jobs, verbose=10) run_time = time() - t print("main.py: took %f seconds -- saving %s.npy" % (run_time, args.outpath), file=sys.stderr) np.save(args.outpath, pfeats)
# Program that solve the heat equation using finite differences import heat import test_function # Factors r = 0.3 dx = 0.1 dt = r*dx*dx x_end = 3. t_end = 6. b = [0., 0.] coef = 1. # Creating and solving the problem H = heat.Heat(dx, dt, x_end, t_end, coef, b, test_function.sinx) H.heat_solve() # Ploting the result using matplotlui H.heat_plot()
# from heat import Heat # from plot import Plot # read data df = pd.read_csv( '/Users/lukasgehrke/Documents/temp/chatham/crd_gaze_phys-LOW_work-HIGH_equip-Bike_all_good_s.csv' ) # select a subject s1 = df[df['pID'] == 40] s1[['X', 'Y']] # read background image for heatmap img = mpimg.imread('/Users/lukasgehrke/Documents/temp/matb.png') h = heat.Heat('workhigh', s1, None, img) # specify resolution x_edges = np.linspace(0, 1280, 100) y_edges = np.linspace(0, 720, 100) bins = [x_edges, y_edges] h.heatmap = h.histogram2d(bins) h.heatmap = h.zscore(h.heatmap) h.heatmap = h.gaussian(h.heatmap, 1) p = plot.Plot(h) p.cm = p.make_cm_transparent(p.cm) p.heat_xy() # # create plot and add plot data