英文:
Matplotlib Save Figures Write Over Each Other
问题
这是你提供的代码的翻译部分：
我有两个函数用于创建保存的图表，但第二个图表总是覆盖在第一个图表上 - 这是用于重现问题的代码。在这种情况下，graph.png 将是 dendo.png 和本来应该是单独的 graph.png 的组合。这只是 Matplotlib 的工作方式，还是我遗漏了什么？def dendo():# 创建一个数组x = np.array([100., 200., 300., 400., 500., 250.,450., 280., 450., 750.])Z = linkage(x, 'ward')dendrogram(Z, leaf_rotation=45., leaf_font_size=12.)plt.title('层次聚类树状图')plt.xlabel('聚类大小')plt.ylabel('距离')plt.rcParams["figure.figsize"] = (9, 8)plt.savefig('dendo.png')def stats():df = pd.DataFrame({'gmat': [580.0, 660.0, 740.0, 590.0, 660.0, 540.0, 690.0, 550.0, 580.0, 620.0, 710.0, 660.0, 780.0, 680.0, 680.0, 550.0, 580.0, 650.0, 620.0, 750.0, 730.0, 690.0, 570.0, 660.0, 690.0, 650.0, 670.0, 690.0, 690.0, 590.0],'gpa': [2.7, 3.3, 3.3, 1.7, 4.0, 2.7, 2.3, 2.7, 2.3, 2.7, 3.7, 3.3, 4.0, 3.3, 3.9, 2.3, 3.3, 3.7, 3.3, 3.9, 3.7, 1.7, 3.0, 3.7, 3.3, 3.7, 3.3, 3.7, 3.7, 2.3],'工作经验': [4.0, 6.0, 5.0, 4.0, 4.0, 2.0, 1.0, 1.0, 2.0, 2.0, 5.0, 5.0, 3.0, 4.0, 4.0, 4.0, 1.0, 6.0, 2.0, 4.0, 6.0, 1.0, 2.0, 4.0, 3.0, 6.0, 6.0, 5.0, 5.0, 3.0],'录取': [0.0, 1.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 0.0]})Xtrain = df[['gmat', 'gpa', '工作经验']]ytrain = df[['录取']]log_reg = sm.Logit(ytrain, Xtrain).fit()stats1 = log_reg.summary()plt.rc('figure', figsize=(12, 7))plt.text(0.01, 0.05, str(stats1), {'fontsize': 10}, fontproperties='等宽')plt.axis('off')plt.tight_layout()plt.savefig('graph.png')dendo()stats()
英文:
I have two functions that create saved graphs, but the second graph always has the first graph laid over it - here is the code to reproduce the problem. In this case graph.png will be a combination of dendo.png and what was supposed to be graph.png by itself. Is this just way Matplotlib works or is there something I'm missing?
def dendo():# Create an arrayx = np.array([100., 200., 300., 400., 500., 250.,450., 280., 450., 750.])Z=linkage(x,'ward')dendrogram(Z, leaf_rotation=45., leaf_font_size=12.)plt.title('Hierarchical Clustering Dendrogram')plt.xlabel('Cluster Size')plt.ylabel('Distance')plt.rcParams["figure.figsize"] = (9,8)plt.savefig('dendo.png')def stats():df = pd.DataFrame({'gmat':[ 580.0, 660.0, 740.0, 590.0, 660.0, 540.0, 690.0, 550.0, 580.0, 620.0, 710.0, 660.0, 780.0, 680.0, 680.0, 550.0, 580.0, 650.0, 620.0, 750.0, 730.0, 690.0, 570.0, 660.0, 690.0, 650.0, 670.0, 690.0, 690.0, 590.0],'gpa': [2.7, 3.3, 3.3, 1.7, 4.0, 2.7, 2.3, 2.7, 2.3, 2.7, 3.7, 3.3, 4.0, 3.3, 3.9, 2.3, 3.3, 3.7, 3.3, 3.9, 3.7, 1.7, 3.0, 3.7, 3.3, 3.7, 3.3, 3.7, 3.7, 2.3],'work_experience': [4.0, 6.0, 5.0, 4.0, 4.0, 2.0, 1.0, 1.0, 2.0, 2.0, 5.0, 5.0, 3.0, 4.0, 4.0, 4.0, 1.0, 6.0, 2.0, 4.0, 6.0, 1.0, 2.0, 4.0, 3.0, 6.0, 6.0, 5.0, 5.0, 3.0],'admitted': [0.0, 1.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 0.0]})Xtrain = df[['gmat', 'gpa', 'work_experience']]ytrain = df[['admitted']]log_reg = sm.Logit(ytrain, Xtrain).fit()stats1=log_reg.summary()plt.rc('figure', figsize=(12, 7))plt.text(0.01, 0.05, str(stats1), {'fontsize': 10}, fontproperties = 'monospace')plt.axis('off')plt.tight_layout()plt.savefig('graph.png')dendo()stats()
答案1
得分: 2
以下是翻译好的部分：
- 使用
plt.figure在绘制新图之前：
# 第一个图plt.figure(0)plt.plot(range(10))plt.savefig('0.png')# 第二个图plt.figure(1)plt.plot(sorted(range(10), reverse = True))plt.savefig('1.png')
- 使用
plt.subplots：
# 第一个图fig1, ax1 = plt.subplots()ax1.plot(range(10))fig1.savefig('1.png')# 第二个图fig2, ax2 = plt.subplots()ax2.plot(sorted(range(10), reverse = True))fig2.savefig('2.png')
更多详情请参阅文档或matplotlib示例。
英文:
There are two ways (can't use your code since it's not reproducible):
- Use
plt.figurebefore drawing a new figure:
# 1st figureplt.figure(0)plt.plot(range(10))plt.savefig('0.png')# 2nd figureplt.figure(1)plt.plot(sorted(range(10), reverse = True))plt.savefig('1.png')
- Use
plt.subplots:
# 1st figurefig1, ax1 = plt.subplots()ax1.plot(range(10))fig1.savefig('1.png')# 2nd figurefig2, ax2 = plt.subplots()ax2.plot(sorted(range(10), reverse = True))fig2.savefig('2.png')
For more details see documentation or matplotlib examples.
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