def ffcontour(grids, X0=None): # if X0==None: assume square if X0==None: X0=(len(grids))**.5 # # in this case, X0 is explicitly the width of a row, including borders. Xs=[] Ys=[] Z=scipy.array(grids) Z.shape=(X0, math.ceil(len(grids)/X0)) Xs=range(X0) Ys=range(X0) #for i in xrange(len(grids)): # Xs+=[i%X0] # Ys+=[int(i/X0)] # # Z+=[[i%X0, int(i/X0), grids[i]]] X,Y=numpy.meshgrid(Xs, Ys) #Z.shape=(X0, math.ceil(len(grids)/X0)) #print "lens: %d * %d = %d, %d" % (len(Xs), len(Ys), len(Xs)*len(Ys), len(grids)) Z.shape=(len(Xs), len(Ys)) plt.contourf(X,Y,Z, alpha=.35) #plt.colorbar(ticks=[-5,-4,-3,-2,-1]) plt.colorbar() plt.spectral()
def show_fdct(filename, channel=0, scales=4, angles=16): image = pyplot.imread(filename)[:, :, channel] transformation = fdct2(image.shape, scales, angles, True, norm=True) cl = transformation.fwd(image) #fig, axs = pyplot.subplots(angles, scales + 1) pyplot.spectral() fig = pyplot.figure(1) # display original image #img_orig = axs[0, 0].imshow(image) img_orig = pyplot.imshow(image) pyplot.colorbar(orientation="horizontal") #pyplot.colorbar(img_orig, ax=axs[0, 0], orientation="horizontal") #imgs = [] angles = [1, ] + [angles] * (scales - 1) for scale in range(scales): fig = pyplot.figure(scale + 100) grid = ImageGrid(fig, 111, (1, angles[scale])) for angle in range(angles[scale]): print("displaying",scale,angle) cl_img = cl(scale, angle) print(cl_img.shape) grid[angle].imshow(cl_img) #img = axs[0, scale + 1].imshow(cl_img) #pyplot.colorbar(img, ax=axs[0, scale + 1], orientation="horizontal") #imgs.append(img) pyplot.show()
def plotar_silhueta(silhueta_dados): fig = plt.figure() ax = fig.add_subplot(111) flat_silhueta_dados = [] for cluster in silhueta_dados: cluster = list(cluster) cluster.sort(key=lambda x: x) #sort ordem decrescente for dado in cluster: flat_silhueta_dados.append(dado) Y = np.arange(0, len(flat_silhueta_dados)) plt.spectral() ax.fill_betweenx(Y, flat_silhueta_dados) plt.show()
def GetExec(self, f, coverPanel, rec, geMat, colorList): self.rec = rec self.coverPanel = coverPanel self.geMat = geMat self.bPSize = self.coverPanel.GetSize() self.plotter = mpl.PlotNotebook(self.coverPanel, size = (self.bPSize[1], self.bPSize[1] * 1.08), pos = ((self.bPSize[0]-self.bPSize[1])/2, -self.bPSize[1] * .08)) self.plotter.Show(True) self.axes1 = self.plotter.add('figure 1').gca() colMat = GetExec(self.rec, geMat, 'tri') self.Z3 = np.transpose(np.array(colMat)) plt.spectral() self.DoDraw() return self.geMat
def plot_clustering(n_clusters, cluster_labels, X): plt.figure() sample_silhouette_values = silhouette_samples(X, cluster_labels) y_lower = 10 for i in range(n_clusters): # Aggregate the silhouette scores for samples belonging to # cluster i, and sort them ith_cluster_silhouette_values = \ sample_silhouette_values[cluster_labels == i] ith_cluster_silhouette_values.sort() size_cluster_i = ith_cluster_silhouette_values.shape[0] y_upper = y_lower + size_cluster_i color = plt.spectral(float(i) / n_clusters) plt.fill_betweenx(np.arange(y_lower, y_upper), 0, ith_cluster_silhouette_values, facecolor=color, edgecolor=color, alpha=0.7) # Label the silhouette plots with their cluster numbers at the middle plt.text(-0.05, y_lower + 0.5 * size_cluster_i, str(i)) # Compute the new y_lower for next plot y_lower = y_upper + 10 # 10 for the 0 samples plt.set_title("The silhouette plot for the various clusters.") plt.set_xlabel("The silhouette coefficient values") plt.set_ylabel("Cluster label") # The vertical line for average silhouette score of all the values plt.axvline(x=silhouette_avg, color="red", linestyle="--") plt.set_yticks([]) # Clear the yaxis labels / ticks plt.set_xticks([-0.1, 0, 0.2, 0.4, 0.6, 0.8, 1]) plt.show()
recvbuf=[counts, MPI.INTEGER], root=MPI.ROOT) worker.Gatherv(sendbuf=None, recvbuf=[indices, (counts, None), MPI.INTEGER], root=MPI.ROOT) rowtype = MPI.INTEGER.Create_contiguous(w).Commit() worker.Gatherv(sendbuf=None, recvbuf=[result, (counts, None), rowtype], root=MPI.ROOT) rowtype.Free() # disconnect worker worker.Disconnect() # reconstruct result C = numpy.empty([h, w], dtype='i') C[indices, :] = result try: from matplotlib import pyplot as plt plt.imshow(C, aspect='equal') plt.spectral() if not plt.isinteractive(): import signal def action(*args): raise SystemExit signal.signal(signal.SIGALRM, action) signal.alarm(2) plt.show() except: pass
import matplotlib.pyplot as plt import numpy as np plt.spectral() #setting the default color map to "spectral" plt.rcParams['contour.negative_linestyle'] = 'solid' def getfile(ts=None, dtype=None, filename=None, fmt="%s.%06d_p000000.h5"): ''' Return the hdf5 file pointer. "filename" is either given explicitly, or formatted from "ts"(timestep) and "dtype"('pfd'/'tfd') according to "fmt". ''' import h5py, os if filename == None: if ts != None and dtype != None: filename = fmt % (dtype, ts) else: print "ERROR: either ts & dtype together, or filename alone should be given" return try: f = h5py.File(filename, 'r') except IOError: print "ERROR: Cannot open %s" % filename return return f def getfld_file(fld, f, dims='xz', n=0, i1b=0, i1e=None,
local_height = height - default_local_height * (size - 1) local_array_len = local_height * width local_result = np.zeros(shape=[local_array_len], dtype='i') dx = a / height dy = b / width for row in range(local_height): shift = default_local_height * rank y = -b / 3 + (row + shift) * dy for col in range(width): x = -a + col * dx local_result[col + row * width] = mandelbrot(x, y, max_iter) print('{0} process for {1} seconds'.format(rank, time.time() - t0)) local_result.shape = (local_height, width) pyplot.imshow(local_result, aspect='equal') pyplot.spectral() pyplot.show() result = None if rank == 0: result = np.empty([height * width], dtype='i') comm.Gather(sendbuf=local_result, recvbuf=result, root=0) if rank == 0: result.shape = (height, width) print('all time for execution {}'.format(time.time() - t0)) pyplot.imshow(result, aspect='equal') pyplot.spectral() pyplot.show()
# without load balancing # for i in range(rank*strip_size,rank*strip_size+strip_size): # y = y1 + i*dx # for j in range(ny): # x = x1 + j*dy # c[i,j] = mandlebrot(x,y,127) # with load balancing by cyclic distribution i = rank while i < nx: y = y1 + i*dx for j in range(ny): x = x1 + j*dy c[i,j] = mandlebrot(x,y,127) i += size end_local = MPI.Wtime() print "Total time taken for local calculation by process "+str(rank)+" : "+str(end_local-start) comm.Allreduce(MPI.IN_PLACE,c,op=MPI.MAX) if rank == 0: # print c end_comm = time.time() print "Total time taken for all communications : "+str(end_comm-start) pyplot.imshow(c, aspect='equal') pyplot.spectral() pyplot.show()
warnings = ['No','Low','Guarded','Elevated','High','Severe'] #-----------------------------------------------------------------------------# Z_i = numpy.loadtxt('tmp/Zarray.dat') Y_i = 37.0 + .1*numpy.arange(Z_i.shape[0]) X_i = -127.5 + .1*numpy.arange(Z_i.shape[1]) print(Z_i.shape, len(Y_i), len(X_i)) #-----------------------------------------------------------------------------# # Create the KML file cs = plt.contourf(X_i, Y_i, Z_i, LevelsNumber, cm=plt.spectral()).collections file = open('doc.kml','w') file.write('<?xml version="1.0" encoding="UTF-8"?>\n') file.write('<kml xmlns="http://www.opengis.net/kml/2.2">\n') file.write('<Document>\n') for i in range(0,len(cs)): bgra_array = 255*cs[i].get_facecolor()[0] file.write('<Style id="l%d">\n' % i) file.write('<LineStyle><color>00000000</color></LineStyle>\n') file.write('<PolyStyle><color>7d%02x%02x%02x</color></PolyStyle>\n' %\ ( bgra_array[2] , bgra_array[1] , bgra_array[0] ) ) file.write('</Style>\n') file.write('<ScreenOverlay id="scale">\n')
import matplotlib.pyplot as plt import numpy as np plt.spectral()#setting the default color map to "spectral" plt.rcParams['contour.negative_linestyle'] = 'solid' def getfile(ts=None,dtype=None,filename=None,fmt="%s.%06d_p000000.h5"): ''' Return the hdf5 file pointer. "filename" is either given explicitly, or formatted from "ts"(timestep) and "dtype"('pfd'/'tfd') according to "fmt". ''' import h5py,os if filename==None: if ts!=None and dtype!=None: filename=fmt%(dtype,ts) else: print "ERROR: either ts & dtype together, or filename alone should be given" return try: f=h5py.File(filename,'r') except IOError: print "ERROR: Cannot open %s"%filename return return f def getfld_file(fld,f,dims='xz',n=0,i1b=0,i1e=None,i2b=0,i2e=None,stride1=None,stride2=None,stride=1): ''' Retrieve field named by "fld" from a hdf5 file pointer. For the first dimension(e.g., 'x' in 'xz'), indices ;i1b'/'i1e' specify a range from which data is retrieved. Data array will also be strided every 'stride1' step. If 'stride1' is not set, the value of 'stride' will be used, which is 1 (no striding) by default. Similarly, for the second dimension, 'i2b', 'i2e', 'stride2' can be used. ''' if stride1==None: stride1=stride if stride2==None: stride2=stride