我们从Python开源项目中，提取了以下4个代码示例，用于说明如何使用pylab.errorbar()。
def plot_objfn(pos_term_info, log_Z_info, color, zoom=False, label=None): assert np.all(pos_term_info.counts == log_Z_info.counts) exact = not hasattr(log_Z_info, 'lower') mean = pos_term_info.values - log_Z_info.mean if not exact: lower = pos_term_info.values - log_Z_info.upper upper = pos_term_info.values - log_Z_info.lower pylab.semilogx(pos_term_info.counts, mean, color=color, label=label) if not exact: pylab.errorbar(pos_term_info.counts, (lower+upper)/2., yerr=(upper-lower)/2., fmt='', ls='None', ecolor=color) if zoom: pylab.ylim(mean.max() - 50., mean.max() + 5.)
def plot_results(self, results, xloc, color, ls, label): iter_counts = sorted(set([it for it, av in results.keys() if av == self.average])) sorted_results = [results[it, self.average] for it in iter_counts] avg = np.array([r.train_logprob() for r in sorted_results]) if hasattr(r, 'train_logprob_interval'): lower = np.array([r.train_logprob_interval()[0] for r in sorted_results]) upper = np.array([r.train_logprob_interval()[1] for r in sorted_results]) if self.logscale: plot_cmd = pylab.semilogx else: plot_cmd = pylab.plot xloc = xloc[:len(avg)] lw = 2. if label not in self.labels: plot_cmd(xloc, avg, color=color, ls=ls, lw=lw, label=label) else: plot_cmd(xloc, avg, color=color, ls=ls, lw=lw) self.labels.add(label) pylab.xticks(fontsize='xx-large') pylab.yticks(fontsize='xx-large') try: pylab.errorbar(xloc, (lower+upper)/2., yerr=(upper-lower)/2., fmt='', ls='None', ecolor=color) except: pass
def drawDensityProfile(self, catalog=None): rmax = 24. # arcmin bins = numpy.arange(0, rmax + 1.e-10, 2.) centers = 0.5 * (bins[1:] + bins[0:-1]) area = numpy.pi * (bins[1:]**2 - bins[0:-1]**2) r_peak = self.kernel.extension stars = self.get_stars() angsep = ugali.utils.projector.angsep(self.ra, self.dec, stars.ra, stars.dec) angsep_arcmin = angsep * 60 # arcmin cut_iso = self.isochrone_selection(stars) h = numpy.histogram(angsep_arcmin[(angsep_arcmin < rmax) & cut_iso], bins=bins)[0] h_out = numpy.histogram(angsep_arcmin[(angsep_arcmin < rmax) & (~cut_iso)], bins=bins)[0] gals = self.get_galaxies() if len(gals): angsep_gal = ugali.utils.projector.angsep(self.ra, self.dec, gals.ra, gals.dec) angsep_gal_arcmin = angsep_gal * 60 # arcmin cut_iso_gal = self.isochrone_selection(gals) h_gal = np.histogram(angsep_gal_arcmin[(angsep_gal_arcmin < rmax) & cut_iso_gal], bins=bins)[0] h_gal_out = np.histogram(angsep_gal_arcmin[(angsep_gal_arcmin < rmax) & (~cut_iso_gal)], bins=bins)[0] plt.plot(centers, h/area, c='red', label='Filtered Stars') plt.errorbar(centers, h/area, yerr=(numpy.sqrt(h) / area), ecolor='red', c='red') plt.scatter(centers, h/area, edgecolor='none', c='red', zorder=22) plt.plot(centers, h_out/area, c='gray', label='Unfiltered Stars') plt.errorbar(centers, h_out/area, yerr=(numpy.sqrt(h_out) / area), ecolor='gray', c='gray') plt.scatter(centers, h_out/area, edgecolor='none', c='gray', zorder=21) if len(gals): plt.plot(centers, h_gal/area, c='black', label='Filtered Galaxies') plt.errorbar(centers, h_gal/area, yerr=(numpy.sqrt(h_gal) / area), ecolor='black', c='black') plt.scatter(centers, h_gal/area, edgecolor='none', c='black', zorder=20) plt.xlabel('Angular Separation (arcmin)') plt.ylabel(r'Density (arcmin$^{-2}$)') plt.xlim(0., rmax) ymax = pylab.ylim()[1] #pylab.ylim(0, ymax) pylab.ylim(0, 12) pylab.legend(loc='upper right', frameon=False, fontsize=10)
def drawKernelHist(ax, kernel): ext = kernel.extension theta = kernel.theta lon, lat = kernel.lon, kernel.lat xmin,xmax = -5*ext,5*ext ymin,ymax = -5*ext,5*ext, x = np.linspace(xmin,xmax,100)+kernel.lon y = np.linspace(ymin,ymax,100)+kernel.lat xx,yy = np.meshgrid(x,y) zz = kernel.pdf(xx,yy) im = ax.imshow(zz)#,extent=[xmin,xmax,ymin,ymax]) hax,vax = draw_slices(ax,zz,color='k') mc_lon,mc_lat = kernel.sample(1e5) hist,xedges,yedges = np.histogram2d(mc_lon,mc_lat,bins=[len(x),len(y)], range=[[x.min(),x.max()],[y.min(),y.max()]]) xbins,ybins = np.arange(hist.shape[0])+0.5,np.arange(hist.shape[1])+0.5 vzz = zz.sum(axis=0) hzz = zz.sum(axis=1) vmc = hist.sum(axis=0) hmc = hist.sum(axis=1) vscale = vzz.max()/vmc.max() hscale = hzz.max()/hmc.max() kwargs = dict(marker='.',ls='',color='r') hax.errorbar(hmc*hscale, ybins, xerr=np.sqrt(hmc)*hscale,**kwargs) vax.errorbar(xbins, vmc*vscale,yerr=np.sqrt(vmc)*vscale,**kwargs) ax.set_ylim(0,len(y)) ax.set_xlim(0,len(x)) #try: ax.cax.colorbar(im) #except: pylab.colorbar(im) #a0 = np.array([0.,0.]) #a1 =kernel.a*np.array([np.sin(np.deg2rad(theta)),-np.cos(np.deg2rad(theta))]) #ax.plot([a0[0],a1[0]],[a0[1],a1[1]],'-ob') # #b0 = np.array([0.,0.]) #b1 =kernel.b*np.array([np.cos(np.radians(theta)),np.sin(np.radians(theta))]) #ax.plot([b0[0],b1[0]],[b0[1],b1[1]],'-or') label_kwargs = dict(xy=(0.05,0.05),xycoords='axes fraction', xytext=(0, 0), textcoords='offset points',ha='left', va='bottom',size=10, bbox={'boxstyle':"round",'fc':'1'}, zorder=10) norm = zz.sum() * (x[1]-x[0])**2 ax.annotate("Sum = %.2f"%norm,**label_kwargs) #ax.set_xlabel(r'$\Delta$ LON (deg)') #ax.set_ylabel(r'$\Delta$ LAT (deg)') ###################################################
