我们从Python开源项目中，提取了以下9个代码示例，用于说明如何使用matplotlib.cm.gray()。
def plot_shotrecord(rec, model, t0, tn, colorbar=True): """ Plot a shot record (receiver values over time). :param rec: Receiver data with shape (time, points) :param model: :class:`Model` object that holds the velocity model. :param t0: Start of time dimension to plot :param tn: End of time dimension to plot """ scale = np.max(rec) / 10. extent = [model.origin[0], model.origin[0] + 1e-3*model.domain_size[0], 1e-3*tn, t0] plot = plt.imshow(rec, vmin=-scale, vmax=scale, cmap=cm.gray, extent=extent) plt.xlabel('X position (km)') plt.ylabel('Time (s)') # Create aligned colorbar on the right if colorbar: ax = plt.gca() divider = make_axes_locatable(ax) cax = divider.append_axes("right", size="5%", pad=0.05) plt.colorbar(plot, cax=cax) plt.show()
def plot_image(data, vmin=None, vmax=None, colorbar=True, cmap="gray"): """ Plot image data, such as RTM images or FWI gradients. :param data: Image data to plot :param cmap: Choice of colormap, default is gray scale for images as a seismic convention """ plot = plt.imshow(np.transpose(data), vmin=vmin or 0.9 * np.min(data), vmax=vmax or 1.1 * np.max(data), cmap=cmap) # Create aligned colorbar on the right if colorbar: ax = plt.gca() divider = make_axes_locatable(ax) cax = divider.append_axes("right", size="5%", pad=0.05) plt.colorbar(plot, cax=cax) plt.show()
def drawGraphFromSM2(SM, names, outFile, Cut): graph = pydot.Dot(graph_type='graph') # THRESHOLD SM: nonZeroMean = np.mean(SM[SM.nonzero()]) if Cut: T = 5.0 * nonZeroMean else: T = 0.0; for i in range(SM.shape[0]): for j in range(SM.shape[0]): if SM[i,j] <= T: SM[i,j] = 0.0 else: SM[i,j] = 1.0 numOfConnections = sum(SM, axis = 0) #fig = plt.figure(1) #plot1 = plt.imshow(SM, origin='upper', cmap=cm.gray, interpolation='nearest') #plt.show() numOfConnections = 9*numOfConnections / max(numOfConnections) for i,f in enumerate(names): if sum(SM[i,:])>0: fillColorCurrent = "{0:d}".format(int(ceil(numOfConnections[i]))) # NOTE: SEE http://www.graphviz.org/doc/info/colors.html for color schemes node = pydot.Node(f, style="filled", fontsize="8", shape="egg", fillcolor=fillColorCurrent, colorscheme = "reds9") graph.add_node(node) for i in range(len(names)): for j in range(len(names)): if i<j: if SM[i][j] > 0: #gr.add_edge((names[i], names[j])) edge = pydot.Edge(names[i], names[j]) graph.add_edge(edge) graph.write_png(outFile)
def drawGraphFromSM(SM, names, outFile): fig = plt.figure(1) plot1 = plt.imshow(SM, origin='upper', cmap=cm.gray, interpolation='nearest') plt.show() gr = graph() namesNew = [] for i,f in enumerate(names): if sum(SM[i,:])>0: gr.add_nodes([f]) namesNew.append(f) Max = SM.max() Mean = mean(SM) Threshold = Mean * 1.5 for i in range(len(names)): for j in range(len(names)): if i<j: if SM[i][j] > 0: gr.add_edge((names[i], names[j])) # Draw as PNG dot = write(gr) gvv = gv.readstring(dot) gv.layout(gvv,'dot') gv.render(gvv,'png', outFile)
def plot_segmentation(I,GT,Seg, fig=None): I = np.squeeze(I) GT = np.squeeze(GT) Seg = np.squeeze(Seg) GTC = np.logical_and(GT, np.logical_not(ndimage.binary_erosion(GT))) SegC = np.logical_and(Seg, np.logical_not(ndimage.binary_erosion(Seg))) plt.figure(fig) maskedGT = np.ma.masked_where(GTC == 0, GTC) maskedSeg = np.ma.masked_where(SegC == 0, SegC) plt.imshow(I, cmap=cm.gray) plt.imshow(maskedGT, cmap=cm.jet, interpolation='none') plt.imshow(maskedSeg*100, cmap=cm.hsv, interpolation='none')
def scaledimage(W, pixwidth=1, ax=None, grayscale=True): """ Do intensity plot, similar to MATLAB imagesc() W = intensity matrix to visualize pixwidth = size of each W element ax = matplotlib Axes to draw on grayscale = use grayscale color map Rely on caller to .show() """ # N = rows, M = column (N, M) = W.shape # Need to create a new Axes? if(ax == None): ax = plt.figure().gca() # extents = Left Right Bottom Top exts = (0, pixwidth * M, 0, pixwidth * N) if(grayscale): ax.imshow(W, interpolation='nearest', cmap=CM.gray, extent=exts) else: ax.imshow(W, interpolation='nearest', extent=exts) ax.xaxis.set_major_locator(MT.NullLocator()) ax.yaxis.set_major_locator(MT.NullLocator()) return ax
def show_image(data, shape, order='f', cmap=cm.gray): """ display an image from a 1d vector :param data: 1d vector containing image information :param shape: actual image dimensions :param order: 'c' or 'f' :param cmap: colour map, defaults to grayscale :return: """ img = data.reshape(shape, order=order) plt.imshow(img, cmap=cmap) plt.show()
def example_3D(): import pkg_resources DATA_PATH = pkg_resources.resource_filename('pynufft', './src/data/') image = numpy.load(DATA_PATH +'phantom_3D_128_128_128.npz')['arr_0'][0::2, 0::2, 0::2] pyplot.imshow(numpy.abs(image[:,:,32]), label='original signal',cmap=gray) pyplot.show() Nd = (64,64,64) # time grid, tuple Kd = (64,64,64) # frequency grid, tuple Jd = (1,1,1) # interpolator # om= numpy.load(DATA_PATH+'om3D.npz')['arr_0'] om = numpy.random.randn(15120,3) print(om.shape) from ..pynufft import NUFFT_cpu, NUFFT_hsa NufftObj = NUFFT_cpu() NufftObj.plan(om, Nd, Kd, Jd) kspace =NufftObj.forward(image) restore_image = NufftObj.solve(kspace,'cg', maxiter=200) restore_image1 = NufftObj.solve(kspace,'L1TVLAD', maxiter=200,rho=0.1) # restore_image2 = NufftObj.solve(kspace,'L1TVOLS', maxiter=200,rho=0.1) pyplot.subplot(2,2,1) pyplot.imshow(numpy.abs(image[:,:,32]), label='original signal',cmap=gray) pyplot.title('original') pyplot.subplot(2,2,2) pyplot.imshow(numpy.abs(restore_image1[:,:,32]), label='L1TVLAD',cmap=gray) pyplot.title('L1TVLAD') pyplot.subplot(2,2,3) pyplot.imshow(numpy.abs(restore_image2[:,:,32]), label='L1TVOLS',cmap=gray) pyplot.title('L1TVOLS') pyplot.subplot(2,2,4) pyplot.imshow(numpy.abs(restore_image[:,:,32]), label='CG',cmap=gray) pyplot.title('CG') # pyplot.legend([im1, im im4]) pyplot.show()
def crop_resize(img,space): """ Crop center and resize. :param img: image to be cropped and resized. """ ws=float(space[0]) hs=float(space[1]) wsextend= ws/1.4 hsextend=hs/1.4 wsint= int(img.shape[0]*wsextend) if wsint%2!=0: wsint+=1 hsint=int(img.shape[1]*hsextend) if hsint%2!=0: hsint+=1 #if img.shape[0] < img.shape[1]: #img = img.T # we crop image from center image_shape=(wsint,hsint) img = imresize(img, image_shape) #plt.imshow(img,cmap=cm.gray) #short_edge = min(img.shape[:2]) yyy=img.shape[0] xxx=img.shape[1] yy = int((img.shape[0] - cropsize) / 2) xx = int((img.shape[1] - cropsize) / 2) if (yy<=0) and (xx<=0) : crop_img=np.zeros((cropsize, cropsize), dtype=np.float32) for i in range(-yy,-yy+yyy): for j in range(-xx,-xx+xxx): crop_img[i][j]=img[i+yy][j+xx] img=crop_img elif (yy<=0) and (xx>=0) : crop_img=np.zeros((cropsize, cropsize), dtype=np.float32) for i in range(-yy,-yy+yyy): for j in range(0,cropsize): crop_img[i][j]=img[i+yy][j+xx] img=crop_img elif (yy>=0) and (xx<=0) : crop_img=np.zeros((cropsize, cropsize), dtype=np.float32) for i in range(0,cropsize): for j in range(-xx,-xx+xxx): crop_img[i][j]=img[i+yy][j+xx] img=crop_img else: crop_img = img[yy: yy + cropsize, xx: xx + cropsize] img = crop_img img = imresize(img, img_shape) return img
