本文整理汇总了Python中Analysis.fit_linearity方法的典型用法代码示例。如果您正苦于以下问题：Python Analysis.fit_linearity方法的具体用法？Python Analysis.fit_linearity怎么用？Python Analysis.fit_linearity使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类Analysis的用法示例。
在下文中一共展示了Analysis.fit_linearity方法的1个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: tesana
# 需要导入模块: import Analysis [as 别名]
# 或者: from Analysis import fit_linearity [as 别名]
#.........这里部分代码省略.........
if coef > thre:
oc_pha_p = Analysis.offset_correction(pha_p, offset, b)
oc_pha_n = Analysis.offset_correction(pha_n, offset, b)
print "Offset correction with: PHA = %f * (1 + %f * Offset)" % (a, b)
if plotting:
figure()
ka = Analysis.ka(np.vstack((pha_p, offset)).T, sigma=sigma)
plot(ka.T[1], ka.T[0], '.', c='k')
x_min, x_max = xlim()
ofs = np.linspace(x_min, x_max)
label = '$\mathrm{PHA}=%.2f\\times(1+%.2f\\times\mathrm{Offset})$' % (a, b)
plot(ofs, a*(1+b*ofs), 'r-', label=label)
xlabel('Offset$\quad$(V)')
ylabel('PHA$\quad$(V)')
legend(frameon=False)
tight_layout()
savefig('%s-offset.pdf' % session)
else:
oc_pha_p = pha_p
oc_pha_n = pha_n
print "Skipped offset correction: correlation coefficient (%f) is too small" % coef
# Check line database
if "%sKa" % atom not in Constants.LE.keys() or "%sKb" % atom not in Constants.LE.keys():
raise ValueError('Unsupported atom: %s' % atom)
# Linearity correction
pha_line_center = np.asarray([ np.median(Analysis.ka(oc_pha_p, sigma=sigma)), np.median(Analysis.kb(oc_pha_p, sigma=sigma)) ])
line_energy = np.asarray([ Constants.LE['%sKa' % atom], Constants.LE['%sKb' % atom] ])
if ignorekb:
a, b = Analysis.fit_linearity([pha_line_center[0]], [line_energy[0]], deg=1)
print "Linearity correction with: PHA = %e * E" % (b)
else:
a, b = Analysis.fit_linearity(pha_line_center, line_energy, deg=2)
print "Linearity correction with: PHA = %e * E^2 + %e * E" % (a, b)
print "MnKb saturation ratio: %.2f %%" % ((pha_line_center[1]/pha_line_center[0])/(line_energy[1]/line_energy[0])*100)
lc_pha_p = Analysis.linearity_correction(oc_pha_p, a, b)
lc_pha_n = Analysis.linearity_correction(oc_pha_n, a, b)
if savedat:
np.savetxt('%s-linearity.dat' % session, array([pha_line_center[0]]) if ignorekb else pha_line_center[np.newaxis,:],
header='%sKa PHA' % atom if ignorekb else '%sKa PHA, %sKb PHA' % (atom, atom), delimiter='\t')
if plotting:
figure()
x = np.linspace(0, 7e3)
if ignorekb:
plot(line_energy[0]/1e3, pha_line_center[0], '+', color='b')
plot(x/1e3, x*b, 'r--')
else:
plot(line_energy/1e3, pha_line_center, '+', color='b')
plot(x/1e3, x**2*a+x*b, 'r--')
xlim((0, 7))
xlabel('Energy$\quad$(keV)')
ylabel('PHA$\quad$(a.u.)')
tight_layout()
savefig('%s-linearity.pdf' % session)
# Energy Spectrum
if plotting:
figure()
hcount, hbin, hpatch = hist(lc_pha_p[lc_pha_p==lc_pha_p]/1e3, bins=7000/binsize, histtype='stepfilled', color='y')