我们从Python开源项目中，提取了以下6个代码示例，用于说明如何使用matplotlib.pylab.get_cmap()。
def init(self, data_len): """ Initialize plots based off the length of the data array. """ self._t = 0 self._data_len = data_len self._data = np.empty((0, data_len)) cm = plt.get_cmap('spectral') self._plots = [] for i in range(data_len): color = cm(1.0 * i / data_len) alpha = self._alphas[i] if self._alphas is not None else 1.0 label = self._labels[i] if self._labels is not None else str(i) self._plots.append( self._ax.plot([], [], color=color, alpha=alpha, label=label)[0] ) self._ax.set_xlim(0, self._time_window) self._ax.set_ylim(0, 1) self._ax.legend(loc='upper left', bbox_to_anchor=(0, 1.15)) self._init = True
def plotValueFunction(self, valueFunction, prefix): '''3d plot of a value function.''' fig, ax = plt.subplots(subplot_kw = dict(projection = '3d')) X, Y = np.meshgrid(np.arange(self.numCols), np.arange(self.numRows)) Z = valueFunction.reshape(self.numRows, self.numCols) for i in xrange(len(X)): for j in xrange(len(X[i])/2): tmp = X[i][j] X[i][j] = X[i][len(X[i]) - j - 1] X[i][len(X[i]) - j - 1] = tmp my_col = cm.jet(np.random.rand(Z.shape[0],Z.shape[1])) ax.plot_surface(X, Y, Z, rstride = 1, cstride = 1, cmap = plt.get_cmap('jet')) plt.gca().view_init(elev=30, azim=30) plt.savefig(self.outputPath + prefix + 'value_function.png') plt.close()
def discrete_cmap(N, base_cmap=None): """Create an N-bin discrete colormap from the specified input map""" # Note that if base_cmap is a string or None, you can simply do # return plt.cm.get_cmap(base_cmap, N) # The following works for string, None, or a colormap instance: base = plt.cm.get_cmap(base_cmap) color_list = base(np.linspace(0, 1, N)) cmap_name = base.name + str(N) return base.from_list(cmap_name, color_list, N)
def plotBasisFunctions(self, eigenvalues, eigenvectors): '''3d plot of the basis function. Right now I am plotting eigenvectors, so each coordinate of the eigenvector correspond to the value to be plotted for the correspondent state.''' for i in xrange(len(eigenvalues)): fig, ax = plt.subplots(subplot_kw = dict(projection = '3d')) X, Y = np.meshgrid(np.arange(self.numRows), np.arange(self.numCols)) Z = eigenvectors[:,i].reshape(self.numCols, self.numRows) for ii in xrange(len(X)): for j in xrange(len(X[ii])/2): tmp = X[ii][j] X[ii][j] = X[ii][len(X[ii]) - j - 1] X[ii][len(X[ii]) - j - 1] = tmp my_col = cm.jet(np.random.rand(Z.shape[0],Z.shape[1])) ax.plot_surface(X, Y, Z, rstride = 1, cstride = 1, cmap = plt.get_cmap('jet')) plt.gca().view_init(elev=30, azim=30) plt.savefig(self.outputPath + str(i) + '_eig' + '.png') plt.close() plt.plot(eigenvalues, 'o') plt.savefig(self.outputPath + 'eigenvalues.png')
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()
