本文整理汇总了Python中Filter.median_filter方法的典型用法代码示例。如果您正苦于以下问题：Python Filter.median_filter方法的具体用法？Python Filter.median_filter怎么用？Python Filter.median_filter使用的例子？那么, 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Filter的用法示例。
在下文中一共展示了Filter.median_filter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 Filter [as 别名]
# 或者: from Filter import median_filter [as 别名]
#.........这里部分代码省略.........
elif method == "cs":
print "%s: %.2f +/- %.2f eV @ Ec%+.2f eV (Red. chi^2 = %.1f/%d = %.2f)" \
% (line, width, width_error, dE, chi_squared, dof, chi_squared/dof)
return dE, width, width_error
def _line_spectrum(data, min, line, dE, width, width_error):
# Draw histogram
n, bins = Analysis.histogram(data, binsize=binsize)
if method in ("cs"):
gn, gbins = Analysis.group_bin(n, bins, min=min)
else:
# No grouping in mle and ls
gn, gbins = n, bins
ngn = gn/(np.diff(gbins))
ngn_sigma = np.sqrt(gn)/(np.diff(gbins))
cbins = (gbins[1:]+gbins[:-1])/2
if plotting:
figure()
if width_error is not None:
label = 'FWHM$=%.2f\pm %.2f$ eV' % (width, width_error)
else:
label = 'FWHM$=%.2f$ eV (Fixed)' % width
if method == "cs":
errorbar(cbins, ngn, yerr=ngn_sigma, xerr=np.diff(gbins)/2, capsize=0, ecolor='k', fmt=None, label=label)
else:
hist(data, bins=gbins, weights=np.ones(len(data))/binsize, histtype='step', ec='k', label=label)
E = np.linspace(bins.min(), bins.max(), 1000)
model = Analysis.normalization(ngn, gbins, dE, width, line=line, shift=shift) \
* Analysis.line_model(E, dE, width, line=line, shift=shift, full=True)
# Plot theoretical model
plot(E, model[0], 'r-')
# Plot fine structures
for m in model[1:]:
plot(E, m, 'b--')
xlabel('Energy$\quad$(eV)')
ylabel('Normalized Count$\quad$(count/eV)')
legend(frameon=False)
ymin, ymax = ylim()
ylim(ymin, ymax*1.1)
tight_layout()
savefig("%s-%s.pdf" % (session, line))
if savedat:
np.savetxt('%s-%s.dat' % (session, line), np.vstack((cbins, gn)).T,
header='Energy (keV), Count', delimiter='\t')
## Ka
ka = Analysis.ka(lc_pha_p, sigma=sigma)
dE, width, width_error = _line_fit(ka, ka_min, "%sKa" % atom)
_line_spectrum(ka, ka_min, "%sKa" % atom, dE, width, width_error)
## Kb
kb = Analysis.kb(lc_pha_p, sigma=sigma)
if kbfit:
dE, width, width_error = _line_fit(kb, kb_min, "%sKb" % atom)
else:
width_error = None
_line_spectrum(kb, kb_min, "%sKb" % atom, dE, width, width_error)
## Baseline
f_pha_n = lc_pha_n[Filter.median_filter(lc_pha_n, sigma=sigma)]
baseline = Analysis.sigma2fwhm(np.std(f_pha_n))
print "Baseline resolution: %.2f eV" % baseline
n, bins = Analysis.histogram(f_pha_n, binsize=binsize)
if savedat:
np.savetxt('%s-baseline.dat' % session, np.vstack(((bins[1:]+bins[:-1])/2, n)).T,
header='Energy (keV), Count', delimiter='\t')
if plotting:
figure()
label = 'FWHM$=%.2f$ eV' % baseline
hist(f_pha_n, bins=bins, weights=np.ones(len(f_pha_n))/binsize, histtype='step', ec='k', label=label)
mu, sigma = norm.fit(f_pha_n)
E = np.linspace(bins.min(), bins.max(), 1000)
plot(E, norm.pdf(E, loc=mu, scale=sigma)*len(f_pha_n), 'r-')
xlabel('Energy$\quad$(eV)')
ylabel('Normalized Count$\quad$(count/eV)')
legend(frameon=False)
tight_layout()
savefig('%s-baseline.pdf' % session)