我们从Python开源项目中，提取了以下11个代码示例，用于说明如何使用matplotlib.pylab.colorbar()。
def plot_confusion_matrix(cm, label_list, title='Confusion matrix', cmap=None): from matplotlib import pylab cm = np.asarray(cm, dtype=np.float32) for i, row in enumerate(cm): cm[i] = cm[i] / np.sum(cm[i]) #import matplotlib.pyplot as plt #plt.ion() pylab.clf() pylab.matshow(cm, fignum=False, cmap='Blues', vmin=0, vmax=1.0) ax = pylab.axes() ax.set_xticks(range(len(label_list))) ax.set_xticklabels(label_list, rotation='vertical') ax.xaxis.set_ticks_position('bottom') ax.set_yticks(range(len(label_list))) ax.set_yticklabels(label_list) pylab.title(title) pylab.colorbar() pylab.grid(False) pylab.xlabel('Predicted class') pylab.ylabel('True class') pylab.grid(False) pylab.savefig('test.jpg') pylab.show()
def plot_confusion_matrix(cm, genre_list, name, title): pylab.clf() pylab.matshow(cm, fignum=False, cmap='Blues', vmin=0, vmax=1.0) ax = pylab.axes() ax.set_xticks(range(len(genre_list))) ax.set_xticklabels(genre_list) ax.xaxis.set_ticks_position("bottom") ax.set_yticks(range(len(genre_list))) ax.set_yticklabels(genre_list) pylab.title(title) pylab.colorbar() pylab.grid(False) pylab.show() pylab.xlabel('Predicted class') pylab.ylabel('True class') pylab.grid(False) pylab.savefig( os.path.join(CHART_DIR, "confusion_matrix_%s.png" % name), bbox_inches="tight")
def plot_confusion_matrix(cm, plot_title, filename, genres=None): if not genres: genres = GENRES pylab.clf() pylab.matshow(cm, fignum=False, cmap='Blues', vmin=0, vmax=100.0) axes = pylab.axes() axes.set_xticks(range(len(genres))) axes.set_xticklabels(genres, rotation=45) axes.set_yticks(range(len(genres))) axes.set_yticklabels(genres) axes.xaxis.set_ticks_position("bottom") pylab.title(plot_title, fontsize=14) pylab.colorbar() pylab.xlabel('Predicted class', fontsize=12) pylab.ylabel('Correct class', fontsize=12) pylab.grid(False) #pylab.show() pylab.savefig(os.path.join(PLOTS_DIR, "cm_%s.eps" % filename), bbox_inches="tight")
def draw(m, name, extra=None): FIG.clf() matrix = m orig_shape = np.shape(matrix) # lose the channel shape in the end of orig_shape new_shape = orig_shape[:-1] matrix = np.reshape(matrix, new_shape) ax = FIG.add_subplot(1,1,1) ax.set_aspect('equal') plt.imshow(matrix, interpolation='nearest', cmap=plt.cm.gray) # plt.imshow(matrix, interpolation='nearest', cmap=plt.cm.ocean) plt.colorbar() if extra != None: greens, reds = extra grn_x, grn_y, = greens red_x, red_y = reds plt.scatter(x=grn_x, y=grn_y, c='g', s=40) plt.scatter(x=red_x, y=red_y, c='r', s=40) # # put a blue dot at (10, 20) # plt.scatter([10], [20]) # # put a red dot, size 40, at 2 locations: # plt.scatter(x=[3, 4], y=[5, 6], c='r', s=40) # # plt.plot() plt.savefig(name)
def plot_earth_model(self,type='perturbation'): if(type=='model'): plt.pcolor(self.theta, self.radius, self.vs_array) plt.colorbar() plt.show() elif(type=='perturbation'): plt.pcolor(self.theta, self.radius, self.dvs_array) plt.colorbar() plt.show()
def plot_model_no_control(model, plot_title='', name_suffix=''): # plot function mx, vx = model.get_posterior_x() mins = np.min(mx, axis=0) - 0.5 maxs = np.max(mx, axis=0) + 0.5 nGrid = 50 xspaced = np.linspace(mins[0], maxs[0], nGrid) yspaced = np.linspace(mins[1], maxs[1], nGrid) xx, yy = np.meshgrid(xspaced, yspaced) Xplot = np.vstack((xx.flatten(), yy.flatten())).T mf, vf = model.predict_f(Xplot) fig = plt.figure() plt.imshow((mf[:, 0]).reshape(*xx.shape), vmin=mf.min(), vmax=mf.max(), origin='lower', extent=[mins[0], maxs[0], mins[1], maxs[1]], aspect='auto') plt.colorbar() plt.contour( xx, yy, (mf[:, 0]).reshape(*xx.shape), colors='k', linewidths=2, zorder=100) zu = model.dyn_layer.zu plt.plot(zu[:, 0], zu[:, 1], 'wo', mew=0, ms=4) for i in range(mx.shape[0] - 1): plt.plot(mx[i:i + 2, 0], mx[i:i + 2, 1], '-bo', ms=3, linewidth=2, zorder=101) plt.xlabel(r'$x_{t, 1}$') plt.ylabel(r'$x_{t, 2}$') plt.xlim([mins[0], maxs[0]]) plt.ylim([mins[1], maxs[1]]) plt.title(plot_title) plt.savefig('/tmp/hh_gpssm_dim_0' + name_suffix + '.pdf') fig = plt.figure() plt.imshow((mf[:, 1]).reshape(*xx.shape), vmin=mf.min(), vmax=mf.max(), origin='lower', extent=[mins[0], maxs[0], mins[1], maxs[1]], aspect='auto') plt.colorbar() plt.contour( xx, yy, (mf[:, 1]).reshape(*xx.shape), colors='k', linewidths=2, zorder=100) zu = model.dyn_layer.zu plt.plot(zu[:, 0], zu[:, 1], 'wo', mew=0, ms=4) for i in range(mx.shape[0] - 1): plt.plot(mx[i:i + 2, 0], mx[i:i + 2, 1], '-bo', ms=3, linewidth=2, zorder=101) plt.xlabel(r'$x_{t, 1}$') plt.ylabel(r'$x_{t, 2}$') plt.xlim([mins[0], maxs[0]]) plt.ylim([mins[1], maxs[1]]) plt.title(plot_title) plt.savefig('/tmp/hh_gpssm_dim_1' + name_suffix + '.pdf')
def plot_2d(params_dir): model_dirs = [name for name in os.listdir(params_dir) if os.path.isdir(os.path.join(params_dir, name))] if len(model_dirs) == 0: model_dirs = [params_dir] colors = plt.get_cmap('plasma') plt.figure(figsize=(20, 10)) ax = plt.subplot(111) ax.set_xlabel('Learning Rate') ax.set_ylabel('Error rate') i = 0 for model_dir in model_dirs: model_df = pd.DataFrame() for param_path in glob.glob(os.path.join(params_dir, model_dir) + '/*.h5'): param = dd.io.load(param_path) gd = {'learning rate': param['hyperparameters']['learning_rate'], 'momentum': param['hyperparameters']['momentum'], 'dropout': param['hyperparameters']['dropout'], 'val. objective': param['best_epoch']['validate_objective']} model_df = model_df.append(pd.DataFrame(gd, index=[0]), ignore_index=True) if i != len(model_dirs) - 1: ax.scatter(model_df['learning rate'], model_df['val. objective'], s=128, marker=(i+3, 0), edgecolor='black', linewidth=model_df['dropout'], label=model_dir, c=model_df['momentum'], cmap=colors) else: im = ax.scatter(model_df['learning rate'], model_df['val. objective'], s=128, marker=(i+3, 0), edgecolor='black', linewidth=model_df['dropout'], label=model_dir, c=model_df['momentum'], cmap=colors) i += 1 plt.colorbar(im, label='Momentum') plt.legend() plt.show() plt.savefig('{}.eps'.format(os.path.join(IMAGES_DIRECTORY, 'params2d')), format='eps', dpi=1000) plt.close()
def plot_waterfall(fil, f_start=None, f_stop=None, if_id=0, logged=True,cb=False,freq_label=False,MJD_time=False, **kwargs): """ Plot waterfall of data Args: f_start (float): start frequency, in MHz f_stop (float): stop frequency, in MHz logged (bool): Plot in linear (False) or dB units (True), cb (bool): for plotting the colorbar kwargs: keyword args to be passed to matplotlib imshow() """ matplotlib.rc('font', **font) plot_f, plot_data = fil.grab_data(f_start, f_stop, if_id) # Make sure waterfall plot is under 4k*4k dec_fac_x, dec_fac_y = 1, 1 if plot_data.shape[0] > MAX_IMSHOW_POINTS[0]: dec_fac_x = plot_data.shape[0] / MAX_IMSHOW_POINTS[0] if plot_data.shape[1] > MAX_IMSHOW_POINTS[1]: dec_fac_y = plot_data.shape[1] / MAX_IMSHOW_POINTS[1] plot_data = rebin(plot_data, dec_fac_x, dec_fac_y) if MJD_time: extent=(plot_f[0], plot_f[-1], fil.timestamps[-1], fil.timestamps[0]) else: extent=(plot_f[0], plot_f[-1], (fil.timestamps[-1]-fil.timestamps[0])*24.*60.*60, 0.0) this_plot = plt.imshow(plot_data, aspect='auto', rasterized=True, interpolation='nearest', extent=extent, cmap='viridis_r', **kwargs ) if cb: plt.colorbar() if freq_label: plt.xlabel("Frequency [Hz]",fontdict=font) if MJD_time: plt.ylabel("Time [MJD]",fontdict=font) else: plt.ylabel("Time [s]",fontdict=font) return this_plot
