我们从Python开源项目中，提取了以下14个代码示例，用于说明如何使用matplotlib.colors.BoundaryNorm()。
def displayBrain(brain, res=25): mapV, mapA = mapBrain(brain, res) plt.close() plt.show() fig = plt.figure(figsize=(5,7)) fig.add_subplot(211) plt.imshow(mapV) plt.colorbar(orientation='vertical') fig.add_subplot(212) cmap = colors.ListedColormap(['yellow', 'blue', 'white', 'red']) bounds=[-1.5,-0.5,0.5,1.5,2.5] norm = colors.BoundaryNorm(bounds, cmap.N) plt.imshow(mapA, cmap=cmap, norm=norm) cb = plt.colorbar(orientation='vertical', ticks=[-1,0,1,2]) plt.pause(0.001)
def save_image(folder='images'): """ Coroutine of image saving """ from matplotlib import pyplot as plt from matplotlib import colors if folder not in os.listdir('.'): os.mkdir(folder) frame_cnt = it.count() cmap = colors.ListedColormap(['#009688', '#E0F2F1', '#004D40']) bounds = [0, 0.25, 0.75, 1] norm = colors.BoundaryNorm(bounds, cmap.N) while True: screen = (yield) shape = screen.shape plt.imshow( screen, interpolation='none', cmap=cmap, norm=norm, aspect='equal', extent=(0, shape[1], 0, shape[0])) plt.grid(True) plt.axis('off') plt.savefig('%s/frame%06i.png' % (folder, frame_cnt.next()))
def add_color_bar(ax, cmap, labels): """Add a colorbar to an axis. Args: ax (AxesSubplot) cmap (Colormap): A prepaped colormap of size n. labels (list of str): A list of strings of size n. """ norm = clr.BoundaryNorm(list(range(cmap.N+1)), cmap.N) smap = cm.ScalarMappable(norm=norm, cmap=cmap) smap.set_array([]) cbar = plt.colorbar(smap, ax=ax) cbar.set_ticks([i + 0.5 for i in range(cmap.N)]) cbar.set_ticklabels(labels)
def show_all(gt, pred): import matplotlib.pyplot as plt from matplotlib import colors from mpl_toolkits.axes_grid1 import make_axes_locatable fig, axes = plt.subplots(1, 2) ax1, ax2 = axes classes = np.array(('background', # always index 0 'aeroplane', 'bicycle', 'bird', 'boat', 'bottle', 'bus', 'car', 'cat', 'chair', 'cow', 'diningtable', 'dog', 'horse', 'motorbike', 'person', 'pottedplant', 'sheep', 'sofa', 'train', 'tvmonitor')) colormap = [(0,0,0),(0.5,0,0),(0,0.5,0),(0.5,0.5,0),(0,0,0.5),(0.5,0,0.5),(0,0.5,0.5), (0.5,0.5,0.5),(0.25,0,0),(0.75,0,0),(0.25,0.5,0),(0.75,0.5,0),(0.25,0,0.5), (0.75,0,0.5),(0.25,0.5,0.5),(0.75,0.5,0.5),(0,0.25,0),(0.5,0.25,0),(0,0.75,0), (0.5,0.75,0),(0,0.25,0.5)] cmap = colors.ListedColormap(colormap) bounds=[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21] norm = colors.BoundaryNorm(bounds, cmap.N) ax1.set_title('gt') ax1.imshow(gt, cmap=cmap, norm=norm) ax2.set_title('pred') ax2.imshow(pred, cmap=cmap, norm=norm) plt.show()
def _proportional_y(self): ''' Return colorbar data coordinates for the boundaries of a proportional colorbar. ''' if isinstance(self.norm, colors.BoundaryNorm): y = (self._boundaries - self._boundaries[0]) y = y / (self._boundaries[-1] - self._boundaries[0]) else: y = self.norm(self._boundaries.copy()) if self.extend == 'min': # Exclude leftmost interval of y. clen = y[-1] - y[1] automin = (y[2] - y[1]) / clen automax = (y[-1] - y[-2]) / clen elif self.extend == 'max': # Exclude rightmost interval in y. clen = y[-2] - y[0] automin = (y[1] - y[0]) / clen automax = (y[-2] - y[-3]) / clen else: # Exclude leftmost and rightmost intervals in y. clen = y[-2] - y[1] automin = (y[2] - y[1]) / clen automax = (y[-2] - y[-3]) / clen extendlength = self._get_extension_lengths(self.extendfrac, automin, automax, default=0.05) if self.extend in ('both', 'min'): y[0] = 0. - extendlength[0] if self.extend in ('both', 'max'): y[-1] = 1. + extendlength[1] yi = y[self._inside] norm = colors.Normalize(yi[0], yi[-1]) y[self._inside] = norm(yi) return y
def _locate(self, x): ''' Given a set of color data values, return their corresponding colorbar data coordinates. ''' if isinstance(self.norm, (colors.NoNorm, colors.BoundaryNorm)): b = self._boundaries xn = x else: # Do calculations using normalized coordinates so # as to make the interpolation more accurate. b = self.norm(self._boundaries, clip=False).filled() xn = self.norm(x, clip=False).filled() # The rest is linear interpolation with extrapolation at ends. y = self._y N = len(b) ii = np.searchsorted(b, xn) i0 = ii - 1 itop = (ii == N) ibot = (ii == 0) i0[itop] -= 1 ii[itop] -= 1 i0[ibot] += 1 ii[ibot] += 1 #db = b[ii] - b[i0] db = np.take(b, ii) - np.take(b, i0) #dy = y[ii] - y[i0] dy = np.take(y, ii) - np.take(y, i0) z = np.take(y, i0) + (xn - np.take(b, i0)) * dy / db return z
def adjblocks(Y, clusters=None, title=''): """ Make a colormap image of a matrix :key Y: the matrix to be used for the colormap. """ # Artefact #np.fill_diagonal(Y, 0) plt.figure() if clusters is None: plt.axis('off') cmap = 'Greys' norm = None y = Y else: plt.axis('on') y = reorder_mat(Y, clusters, reverse=True) hist, label = clusters_hist(clusters) # Colors Setup u_colors.reset() #cmap = Colors.ListedColormap(['white','black']+ u_colors.seq[:len(hist)**2]) cmap = Colors.ListedColormap(['#000000', '#FFFFFF']+ u_colors.seq[:len(hist)**2]) bounds = np.arange(len(hist)**2+2) norm = Colors.BoundaryNorm(bounds, cmap.N) # Iterate over blockmodel for k, count_k in enumerate(hist): for l, count_l in enumerate(hist): # Draw a colored square (not white and black) topleft = (hist[:k].sum(), hist[:l].sum()) w = int(0.01 * y.shape[0]) # write on place draw_square(y, k+l+2, topleft, count_k, count_l, w) implt = plt.imshow(y, cmap=cmap, norm=norm, clim=(np.amin(y), np.amax(y)), interpolation='nearest',) #origin='upper') # change shape ! #plt.colorbar() plt.title(title) #plt.savefig(filename, fig=fig, facecolor='white', edgecolor='black')
def percolation(matrice): # methode 2 """Dessine la propagation de l'eau, et indique s'il y a percolation.""" cmap = colors.ListedColormap(couleurs) # TODO: Relève du display, à metttre ailleurs. norm = colors.BoundaryNorm(valeurs + [max(valeurs)+1], cmap.N) pyplot.matshow([valeurs], 1, cmap=cmap, norm=norm) pyplot.pause(1) eau_mouvante = initialisation_eau_mouvante(matrice) while eau_mouvante != []: pyplot.matshow(matrice, 1, cmap=cmap, norm=norm) # TODO: Séparer la logique de display de la logique de génération (threads ?) pyplot.pause(.0001) eau_mouvante = propagation(matrice,eau_mouvante) return resultat(matrice)
def _ticker(self): ''' Return two sequences: ticks (colorbar data locations) and ticklabels (strings). ''' locator = self.locator formatter = self.formatter if locator is None: if self.boundaries is None: if isinstance(self.norm, colors.NoNorm): nv = len(self._values) base = 1 + int(nv / 10) locator = ticker.IndexLocator(base=base, offset=0) elif isinstance(self.norm, colors.BoundaryNorm): b = self.norm.boundaries locator = ticker.FixedLocator(b, nbins=10) elif isinstance(self.norm, colors.LogNorm): locator = ticker.LogLocator() else: locator = ticker.MaxNLocator() else: b = self._boundaries[self._inside] locator = ticker.FixedLocator(b, nbins=10) if isinstance(self.norm, colors.NoNorm): intv = self._values[0], self._values[-1] else: intv = self.vmin, self.vmax locator.create_dummy_axis(minpos=intv[0]) formatter.create_dummy_axis(minpos=intv[0]) locator.set_view_interval(*intv) locator.set_data_interval(*intv) formatter.set_view_interval(*intv) formatter.set_data_interval(*intv) b = np.array(locator()) ticks = self._locate(b) inrange = (ticks > -0.001) & (ticks < 1.001) ticks = ticks[inrange] b = b[inrange] formatter.set_locs(b) ticklabels = [formatter(t, i) for i, t in enumerate(b)] offset_string = formatter.get_offset() return ticks, ticklabels, offset_string
def _ticker(self): ''' Return the sequence of ticks (colorbar data locations), ticklabels (strings), and the corresponding offset string. ''' locator = self.locator formatter = self.formatter if locator is None: if self.boundaries is None: if isinstance(self.norm, colors.NoNorm): nv = len(self._values) base = 1 + int(nv / 10) locator = ticker.IndexLocator(base=base, offset=0) elif isinstance(self.norm, colors.BoundaryNorm): b = self.norm.boundaries locator = ticker.FixedLocator(b, nbins=10) elif isinstance(self.norm, colors.LogNorm): locator = ticker.LogLocator() else: locator = ticker.MaxNLocator() else: b = self._boundaries[self._inside] locator = ticker.FixedLocator(b, nbins=10) if isinstance(self.norm, colors.NoNorm): intv = self._values[0], self._values[-1] else: intv = self.vmin, self.vmax locator.create_dummy_axis(minpos=intv[0]) formatter.create_dummy_axis(minpos=intv[0]) locator.set_view_interval(*intv) locator.set_data_interval(*intv) formatter.set_view_interval(*intv) formatter.set_data_interval(*intv) b = np.array(locator()) ticks = self._locate(b) inrange = (ticks > -0.001) & (ticks < 1.001) ticks = ticks[inrange] b = b[inrange] formatter.set_locs(b) ticklabels = [formatter(t, i) for i, t in enumerate(b)] offset_string = formatter.get_offset() return ticks, ticklabels, offset_string
def plot_streamfunction_hovmollers(trans, name='simulated', basename='', obs=None): """ Plot overturning stream function hovmoller diagrams""" # Extract variables from data objects dts = utils.get_ncdates(trans) z = trans.variables['depth'][:] sf_rapid = trans.variables['sf_rapid'][:] sf_model = trans.variables['sf_model'][:] # Set up figure fig = plt.figure(figsize=(8,12)) cmap=plt.cm.viridis levels = np.arange(15) * 2 - 4 norm = BoundaryNorm(levels, ncolors=cmap.N, clip=True) cmin,cmax=-5,30 # Add model data to axis fig.add_subplot(3,1,1) plt.pcolormesh(dts, -z, sf_model.transpose(), vmin=cmin,vmax=cmax,cmap=cmap, norm=norm) plt.colorbar(orientation='vertical') plt.title('Overturning streamfunction at 26N in %s (model velocities)' % name) plt.xlabel('Dates') plt.ylabel('Depth (m)') # Add model data to axis (RAPID approx) fig.add_subplot(3,1,2) plt.pcolormesh(dts, -z, sf_rapid.transpose(), vmin=cmin,vmax=cmax,cmap=cmap, norm=norm) plt.colorbar(orientation='vertical') plt.title('Overturning streamfunction at 26N in %s (RAPID approx)' % name) plt.xlabel('Dates') plt.ylabel('Depth (m)') # Add optional observed data to axis if obs is not None: fig.add_subplot(3,1,3) plt.pcolormesh(obs.dates, -obs.z, obs.sf.transpose(), vmin=cmin,vmax=cmax, cmap=cmap, norm=norm) plt.colorbar(orientation='vertical') plt.title('Overturning streamfunction at 26N from RAPID array') plt.xlabel('Dates') plt.ylabel('Depth (m)') # Save plot plt.tight_layout() savef = basename + 'overturning_streamfunction_at_26n_hovmoller.png' print 'SAVING: %s' % savef fig.savefig(savef, resolution=300) plt.close()
