-
Notifications
You must be signed in to change notification settings - Fork 1
/
Copy pathexp5.py
127 lines (100 loc) · 6.65 KB
/
exp5.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
from head import * # 导入万能头
def name(): # 返回实验名称
return "用拉伸法测量钢丝的杨氏模量"
def handle(workpath,extension):
# 处理数据并生成文档，workpath为工作文件夹路径（本程序涉及到的所有文件只能保存在此文件夹内），extension为扩展名（csv/xls/xlsx）
try:
zhfont = matplotlib.font_manager.FontProperties(fname="SourceHanSansSC-Regular.otf") # 设置图像中的文字字体
excelpath=workpath+name()+'.'+extension # Excel文件名（含路径），文件名与name()函数返回值一致
if extension=='csv':
with open(excelpath,'rb') as f:
encode=chardet.detect(f.read())["encoding"] # 判断编码格式
data=pd.read_csv(excelpath, header=0, names=["D","l","L","d","n","b1","b2"], encoding=encode) # 读取csv文件
else:
data=pd.read_excel(excelpath, header=0, names=["D","l","L","d","n","b1","b2"]) # 读取xls/xlsx文件
os.remove(excelpath) # 读取Excel数据后删除文件
res_D=analyse(data["D"], 0.12, 0.05, "D", "cm")
res_l=analyse(data["l"], 0.12, 0.05, "l", "cm")
res_L=analyse(data["L"], 0.12, 0.05, "L", "cm")
res_d=analyse(data["d"], 0.004, 0.005, "d", "mm")
data["砝码总质量m/g"]=[n*500 for n in data["n"]]
data["金属丝受拉力F/N"]=[m/1000*9.8 for m in data["砝码总质量m/g"]]
data["标尺读数平均值b/cm"]=[(data["b1"][i]+data["b2"][i])/2 for i in range(len(data["b1"]))]
res_lsm=analyse_lsm(data["金属丝受拉力F/N"], data["标尺读数平均值b/cm"], "F", "b", "cm/N", "cm") # 线性回归
res_E=analyse_com("E=(8*D*L)/(pi*d**2*l*m)",(("D",res_D.average,res_D.unc),("L",res_L.average,res_L.unc),("d",res_d.average/10,res_d.unc/10),("l",res_l.average,res_l.unc),("m",abs(res_lsm.m),res_lsm.u_m)),(),"N/cm^2")
fig, ax = plt.subplots() # 新建绘图对象
ax.xaxis.set_minor_locator(matplotlib.ticker.AutoMinorLocator(2))
ax.yaxis.set_minor_locator(matplotlib.ticker.AutoMinorLocator(2))
# 设置副刻度为主刻度的一半
ax.plot(data["金属丝受拉力F/N"], data["标尺读数平均值b/cm"], "o", color='r', markersize=3) # 绘制数据点
ax.plot(data["金属丝受拉力F/N"], res_lsm.b + res_lsm.m*data["金属丝受拉力F/N"], color='b', linewidth=1.5) # 拟合直线
# 作图，详见 https://www.runoob.com/matplotlib/matplotlib-marker.html 和 https://www.runoob.com/matplotlib/matplotlib-line.html
ax.set_title("标尺读数与金属丝受拉力 b-F 关系曲线", fontproperties=zhfont) # 若有中文，需加fontproperties=zhfont
ax.set_xlabel("金属丝受拉力 F/N", fontproperties=zhfont)
ax.set_ylabel("标尺读数 b/cm", fontproperties=zhfont)
# 添加标题和轴标签，详见 https://www.runoob.com/matplotlib/matplotlib-label.html
imgpath=workpath+"img.jpg"
fig.savefig(imgpath, dpi=300, bbox_inches='tight')
plt.close()
docu=Document()
docu.styles['Normal'].font.name = '微软雅黑'
docu.styles['Normal']._element.rPr.rFonts.set(qn('w:eastAsia'), '微软雅黑') # 设置Word文档字体
docu.add_paragraph(name()) # 在Word文档中添加文字
docu.add_paragraph()
docu.add_paragraph("【Latex代码在下面，请向下翻阅】")
docu.add_paragraph()
insert_data(docu, "标尺到平面镜的距离D", res_D, "word")
insert_data(docu, "光杠杆的臂长l", res_l, "word")
insert_data(docu, "钢丝原长L", res_L, "word")
insert_data(docu, "钢丝直径d", res_d, "word")
docu.add_paragraph("金属丝受拉力F与标尺读数b的关系：")
table = docu.add_table(rows=len(data["砝码总质量m/g"])+1, cols=3, style="Table Grid") # 在Word文档中插入表格
table.cell(0,0).text = '砝码总质量m/g'
table.cell(0,1).text = '金属丝受拉力F/N'
table.cell(0,2).text = '标尺读数平均值b/cm'
for i in range(len(data["砝码总质量m/g"])):
table.cell(i+1,0).text = ('%.5g' % data["砝码总质量m/g"][i])
table.cell(i+1,1).text = ('%.5g' % data["金属丝受拉力F/N"][i])
table.cell(i+1,2).text = ('%.5g' % data["标尺读数平均值b/cm"][i])
docu.add_paragraph()
docu.add_paragraph("最小二乘法拟合：")
docu.add_picture(imgpath) # 在Word文档中添加图片
insert_data_lsm(docu, res_lsm, "word")
docu.add_paragraph("杨氏模量")
docu.add_paragraph()._element.append(latex_to_word(res_E.ansx2))
docu.add_paragraph("杨氏模量E的延伸不确定度")
docu.add_paragraph()._element.append(latex_to_word(res_E.uncx2))
docu.add_paragraph("杨氏模量最终结果")
docu.add_paragraph()._element.append(latex_to_word(res_E.finalx2))
docu.add_paragraph()
docu.add_paragraph("【Latex代码】")
insert_data(docu, "标尺到平面镜的距离D", res_D, "latex")
insert_data(docu, "光杠杆的臂长l", res_l, "latex")
insert_data(docu, "钢丝原长L", res_L, "latex")
insert_data(docu, "钢丝直径d", res_d, "latex")
docu.add_paragraph("金属丝受拉力F与标尺读数b的关系：")
table = docu.add_table(rows=len(data["砝码总质量m/g"])+1, cols=3, style="Table Grid") # 在Word文档中插入表格
table.cell(0,0).text = '砝码总质量m/g'
table.cell(0,1).text = '金属丝受拉力F/N'
table.cell(0,2).text = '标尺读数平均值b/cm'
for i in range(len(data["砝码总质量m/g"])):
table.cell(i+1,0).text = ('%.5g' % data["砝码总质量m/g"][i])
table.cell(i+1,1).text = ('%.5g' % data["金属丝受拉力F/N"][i])
table.cell(i+1,2).text = ('%.5g' % data["标尺读数平均值b/cm"][i])
docu.add_paragraph()
docu.add_paragraph("最小二乘法拟合：")
docu.add_picture(imgpath) # 在Word文档中添加图片
insert_data_lsm(docu, res_lsm, "latex")
docu.add_paragraph("杨氏模量")
docu.add_paragraph(res_E.ansx)
docu.add_paragraph("杨氏模量E的延伸不确定度")
docu.add_paragraph(res_E.uncx)
docu.add_paragraph("杨氏模量最终结果")
docu.add_paragraph(res_E.finalx)
docu.add_paragraph()
docu.save(workpath+name()+".docx") # 保存Word文档，注意文件名必须与name()函数返回值一致
os.remove(imgpath) # 删除刚才保存的图像
return 0 # 若成功，返回0
except:
traceback.print_exc() # 打印错误
return 1 # 若失败，返回1