title='$\\beta$射线吸收曲线',
clr='green',
issetrange=0,
save='3.png')
print(k1, k2)
h = 0 - (k1 * Al_m[0] + b1)
print(h)
Rx = ((-4 - h) - b2) / k2
print(Rx)
R = Rx / 49
print(R)
Emax = 1.85e-3 * Rx + 0.245
print(Emax)
gendocx('gen.docx', '1.png', '2.png', '3.png',
'slope1, intercept1: %f %f' % (k1, b1),
'slope2, intercept2: %f %f' % (k2, b2))
# linear regression and plot
# #1
# result = linear_regression(x, y)
# setrange(x, y)
# plt.scatter(x, y, marker='o', color='black', label='原始数据')
# plt.plot(x, result['intercept'] + result['slope'] * x, 'r', label='拟合直线')
# plt.xlabel('')
# plt.ylabel('')
# plt.legend(loc=4)
# plt.title('')
# plt.savefig('pic.png')
# plt.show()
# #2
# calculate the Re(Epsilon_m)
re_epsilon_m = np**2 * math.sin(math.radians(theta_sp0))**2 * ns0**2 / \
(ns0**2 - np**2 * math.sin(math.radians(theta_sp0))**2)
print(re_epsilon_m)
ns2 = np * math.sin(math.radians(theta_sp2)) * \
math.sqrt(re_epsilon_m / (re_epsilon_m - np**2 * math.sin(math.radians(theta_sp2))**2))
print(ns2)
ns3 = np * math.sin(math.radians(theta_sp3)) * \
math.sqrt(re_epsilon_m / (re_epsilon_m - np**2 * math.sin(math.radians(theta_sp3))**2))
print(ns3)
# check the calc is correct
ns2 = np * math.sin(math.radians(theta_sp0)) * \
math.sqrt(re_epsilon_m / (re_epsilon_m - np**2 * math.sin(math.radians(theta_sp0))**2))
print(ns2)
gendocx('gen.docx', '1.png', '2.png', '3.png')
# linear regression and plot
# #1
# result = linear_regression(x, y)
# setrange(x, y)
# plt.scatter(x, y, marker='o', color='black', label='原始数据')
# plt.plot(x, result['intercept'] + result['slope'] * x, 'r', label='拟合直线')
# plt.xlabel('')
# plt.ylabel('')
# plt.legend(loc=4)
# plt.title('')
# plt.savefig('pic.png')
# plt.show()
# #2
# use automate tool
Ir = IIdown[np.argmin(IIdown)]
I0 = 51
Ihalf = 2 * I0 * Ir / (I0 + Ir)
dB = finddeltaB(IIdown, Bdown, Ihalf)
print(Br, Ir, Ihalf, dB)
# linear regression and plot
# #1
# result = linear_regression(x, y)
# setrange(x, y)
# plt.scatter(x, y, marker='o', color='black', label='原始数据')
# plt.plot(x, result['intercept'] + result['slope'] * x, 'r', label='拟合直线')
# plt.xlabel('')
# plt.ylabel('')
# plt.legend(loc=4)
# plt.title('')
# plt.savefig('pic.png')
# plt.show()
# #2
# use automate tool
# simple_linear_plot(x, y, xlab='x axis', ylab='y axis', title='my pic', save='pic.png')
# generate docx #1
gendocx('gen.docx', '1.png')
# generate docx #2
# docu = Document()
# docuaddtitle(docu)
# docuappend(docu, './picfull.png', './figure_1.png', './pic.png', result['string'], './picx.png')
# docu.save('gen.docx')
simple_plot(redwl, redabs, show=0, issetrange=0, clr='red', islegend=0)
simple_plot(bluewl, blueabs, show=0, issetrange=0, clr='blue', islegend=0)
simple_plot(greenwl, greenabs, xlab='波长', ylab='吸收相对强度',
save='2.png', issetrange=0, clr='green', islegend=0, title='红绿蓝滤波片的吸收光谱')
redabs = [(purestg[i] - redstg[i]) / purestg[i] for i in range(len(purestg))]
blueabs = [(purestg[i] - bluestg[i]) / purestg[i] for i in range(len(purestg))]
greenabs = [(purestg[i] - greenstg[i]) / purestg[i] for i in range(len(purestg))]
simple_plot(redwl, redabs, show=0, issetrange=0, clr='red', islegend=0)
simple_plot(bluewl, blueabs, show=0, issetrange=0, clr='blue', islegend=0)
simple_plot(greenwl, greenabs, xlab='波长', ylab='吸收相对比例',
save='3.png', issetrange=0, clr='green', islegend=0, title='红绿蓝滤波片的吸收比例光谱')
gendocx('gen.docx',
'滤波片光谱分析', '1.png', '2.png', '3.png',
'钠灯各线系光谱', './17000002/1Na589-300V.bmp', './17000002/2Na497-798V.bmp', './17000002/3Na568-633V.bmp', './17000002/4Na616-766V.bmp',
'红宝石发射光谱', './17000002/Ruby-531V.bmp')
# read data
# # 1
# data, data_orig, name = readdata('./data.txt', need=0b111)
# # 2
# fin = open('./data.txt', 'r', encoding='utf-8')
# x = readoneline(fin)
# y = readoneline(fin)
# z = readonenumber(fin)
# fin.close()
# data process
# simple_plot(Ul21, Il21, show=0, issetrange=0)
# simple_plot(Ul22, Il22, show=0, issetrange=0, xlab='U/V', ylab='I/uA', title='双探针伏安特性-P=40Pa, P=3.82W')
# plt.show()
# plt.savefig('42.png')
# simple_plot(Ul2, Il2, show=0)
# linear regression and plot
# #1
# result = linear_regression(x, y)
# setrange(x, y)
# plt.scatter(x, y, marker='o', color='black', label='原始数据')
# plt.plot(x, result['intercept'] + result['slope'] * x, 'r', label='拟合直线')
# plt.xlabel('')
# plt.ylabel('')
# plt.legend(loc=4)
# plt.title('')
# plt.savefig('pic.png')
# plt.show()
# #2
# use automate tool
# simple_linear_plot(x, y, xlab='x axis', ylab='y axis', title='my pic', save='pic.png')
# generate docx #1
gendocx('gen.docx', '11.png', '12.png', '2.png', '3.png', '41.png', '42.png')
# generate docx #2
# docu = Document()
# docuaddtitle(docu)
# docuappend(docu, './picfull.png', './figure_1.png', './pic.png', result['string'], './picx.png')
# docu.save('gen.docx')
# xlab='f/Mhz', ylab='最高点间距', title='第2,3个信号间距随频率变化', save='2.png')
# simple_plot(f, [math.cos(max2[i] - max1[i]) for i in range(len(f))], show=0, issetrange=0, lab='max2-max1', dot='+',
# xlab='f/Mhz', ylab='最高点间距', title='第1,2个信号间距随频率变化')
# simple_plot(f, [math.cos(max3[i] - max2[i]) for i in range(len(f))], show=1, issetrange=0, lab='max3-max2', dot='*',
# xlab='f/Mhz', ylab='最高点间距', title='第2,3个信号间距随频率变化', save='2.png')
# linear regression and plot
# #1
# result = linear_regression(x, y)
# setrange(x, y)
# plt.scatter(x, y, marker='o', color='black', label='原始数据')
# plt.plot(x, result['intercept'] + result['slope'] * x, 'r', label='拟合直线')
# plt.xlabel('')
# plt.ylabel('')
# plt.legend(loc=4)
# plt.title('')
# plt.savefig('pic.png')
# plt.show()
# #2
# use automate tool
# simple_linear_plot(x, y, xlab='x axis', ylab='y axis', title='my pic', save='pic.png')
# generate docx #1
gendocx('gen.docx', '11.png', '12.png', '21.png', '22.png', '31.png', '32.png')
# generate docx #2
# docu = Document()
# docuaddtitle(docu)
# docuappend(docu, './picfull.png', './figure_1.png', './pic.png', result['string'], './picx.png')
# docu.save('gen.docx')
save='2.png')
CntLn = np.log(Cnt)
# d = 50 mg / cm^2
d = 50
Al_Real = Al_num * d
slope, intercept = simple_linear_plot(Al_Real,
CntLn,
xlab='质量厚度$g/cm^{-2}$',
ylab='选区计数率对数(射线强度)',
title='半对数曲线曲线',
save='3.png')
print(-slope)
print(math.log(1e4) / (-slope))
print((math.log(Cnt[0]) - 4 * math.log(10) - intercept) / slope)
gendocx('gen.docx', '1.png', '2.png', '3.png',
'slope, intercept: %f %f' % (slope, intercept))
# linear regression and plot
# #1
# result = linear_regression(x, y)
# setrange(x, y)
# plt.scatter(x, y, marker='o', color='black', label='原始数据')
# plt.plot(x, result['intercept'] + result['slope'] * x, 'r', label='拟合直线')
# plt.xlabel('')
# plt.ylabel('')
# plt.legend(loc=4)
# plt.title('')
# plt.savefig('pic.png')
# plt.show()
# #2
# use automate tool
k1, b1 = simple_linear_plot(t1, np.log(P1), xlab='t/s', ylab='ln(P)', title='粗真空lnP-t图', save='11.png')
# the volume of bottle
V = 2
print(k1, b1)
print('Speed: ', - V * k1, ' L')
simple_plot(t2, P2, xlab='时间/s', ylab='压强/Pa', title='低真空P-t图', save='2.png')
simple_plot(t2, np.log(P2), xlab='t/s', ylab='ln(P)', title='低真空lnP-t图', show=0)
k2, b2 = simple_linear_plot(t2[:4], np.log(P2[:4]), xlab='t/s', ylab='ln(P)', title='低真空lnP-t图', dotlab='使用的数据', save='21.png')
# the volume of bottle
V = 2
print(k2, b2)
print('Speed(may be bad): ', - V * k2, ' L')
gendocx('gen.docx', '1.png', '11.png', 'k: %f, b: %f' % (k1, b1), '2.png', '21.png', 'k: %f, b: %f' % (k2, b2))
# linear regression and plot
# #1
# result = linear_regression(x, y)
# setrange(x, y)
# plt.scatter(x, y, marker='o', color='black', label='原始数据')
# plt.plot(x, result['intercept'] + result['slope'] * x, 'r', label='拟合直线')
# plt.xlabel('')
# plt.ylabel('')
# plt.legend(loc=4)
# plt.title('')
# plt.savefig('pic.png')
# plt.show()
# #2