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 array
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('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 figure
plt.figure(0)
plt.plot(range(10))
plt.savefig('0.png')
# 2nd figure
plt.figure(1)
plt.plot(sorted(range(10), reverse = True))
plt.savefig('1.png')
- Use
plt.subplots:
# 1st figure
fig1, ax1 = plt.subplots()
ax1.plot(range(10))
fig1.savefig('1.png')
# 2nd figure
fig2, 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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