我想根據pycharm中的以下代碼繪制pca組件圖。import numpy as npimport matplotlib.pyplot as pltfrom sklearn import linear_model, decomposition, datasetsfrom sklearn.pipeline import Pipelinefrom sklearn.model_selection import GridSearchCVlogistic = linear_model.LogisticRegression()pca = decomposition.PCA()pipe = Pipeline(steps = [('pca',pca), ('logistic', logistic)])digits = datasets.load_digits()x_digits = digits.datay_digits = digits.target# plot pca spectrumpca.fit(x_digits)plt.figure(1, figsize=(4,3))# clear the current figureplt.clf()# add axesplt.axes([.2,.2,.7,.7])plt.plot(pca.explained_variance_, linewidth = 2)plt.xlabel('n_components')plt.ylabel('explained_variance_')# predictionn_comp = [20, 40, 64]# logspace default is base 10, this is 10^-4 to 10^4cs = np.logspace(-4, 4, 3)# parameters of pipelines can be set using '__' separated parameter names:estimator = GridSearchCV(pipe,                         dict(pca__n_components = n_comp,                              logistic__C = cs))estimator.fit(x_digits, y_digits)plt.axvline(estimator.best_estimator_.named_steps['pca'].n_components,            linestyle = ':',label = 'n_compoenents chosen')plt.legend(prop = dict(size = 12))plt.axis('tight')plt.show()我在spyder中嘗試了相同的代碼，但效果卻令人吃驚。pycharm plot設置有什么問題？spyder和pycharm都使用python 3.5。