我们从Python开源项目中，提取了以下8个代码示例，用于说明如何使用pylab.linspace()。
def test_discretization(nmpc, nb_steps): dT = nmpc.preview.dT pylab.ion() pylab.clf() ax = pylab.subplot(311) ax.set_color_cycle(['r', 'g', 'b']) pylab.plot( [sum(dT[:i]) for i in xrange(len(dT))], nmpc.preview.P, marker='o') pylab.plot( pylab.linspace(0., sum(dT), nb_steps + 1), [x[0:3] for x in nmpc.preview.discretize(nb_steps)], marker='s', linestyle='--') ax = pylab.subplot(312) ax.set_color_cycle(['r', 'g', 'b']) pylab.plot( [sum(dT[:i]) for i in xrange(len(dT))], nmpc.preview.V, marker='o') pylab.plot( pylab.linspace(0., sum(dT), nb_steps + 1), [x[3:6] for x in nmpc.preview.discretize(nb_steps)], marker='s', linestyle='--') ax = pylab.subplot(313) ax.set_color_cycle(['r', 'g', 'b']) pylab.plot( [sum(dT[:i]) for i in xrange(len(dT))], nmpc.preview.Z, marker='o') pylab.plot( pylab.linspace(0., sum(dT), nb_steps + 1), [x[6:9] for x in nmpc.preview.discretize(nb_steps)], marker='s', linestyle='--')
def drawHealpixMap(map, lon, lat, size=1.0, xsize=501, coord='GC', **kwargs): """ Draw local projection of healpix map. """ ax = plt.gca() x = np.linspace(-size,size,xsize) y = np.linspace(-size,size,xsize) xx, yy = np.meshgrid(x,y) coord = coord.upper() if coord == 'GC': #Assumes map and (lon,lat) are Galactic, but plotting celestial llon, llat = image2sphere(*gal2cel(lon,lat),x=xx.flat,y=yy.flat) pix = ang2pix(healpy.get_nside(map),*cel2gal(llon,llat)) elif coord == 'CG': #Assumes map and (lon,lat) are celestial, but plotting Galactic llon, llat = image2sphere(*cel2gal(lon,lat),x=xx.flat,y=yy.flat) pix = ang2pix(healpy.get_nside(map),*gal2cel(llon,llat)) else: #Assumes plotting the native coordinates llon, llat = image2sphere(lon,lat,xx.flat,yy.flat) pix = ang2pix(healpy.get_nside(map),llon,llat) values = map[pix].reshape(xx.shape) zz = np.ma.array(values,mask=(values==healpy.UNSEEN),fill_value=np.nan) return drawProjImage(xx,yy,zz,coord=coord,**kwargs)
def drawImage(self,ax=None,invert=True): if not ax: ax = plt.gca() if self.config['data']['survey']=='sdss': # Optical Image im = ugali.utils.plotting.getSDSSImage(**self.image_kwargs) # Flipping JPEG: # https://github.com/matplotlib/matplotlib/issues/101 im = im[::-1] ax.annotate("SDSS Image",**self.label_kwargs) else: im = ugali.utils.plotting.getDSSImage(**self.image_kwargs) im = im[::-1,::-1] ax.annotate("DSS Image",**self.label_kwargs) size=self.image_kwargs.get('radius',1.0) # Celestial coordinates x = np.linspace(-size,size,im.shape[0]) y = np.linspace(-size,size,im.shape[1]) xx, yy = np.meshgrid(x,y) #kwargs = dict(cmap='gray',interpolation='none') kwargs = dict(cmap='gray',coord='C') im = drawProjImage(xx,yy,im,**kwargs) try: plt.gcf().delaxes(ax.cax) except AttributeError: pass return im
def drawHessDiagram(self,catalog=None): ax = plt.gca() if not catalog: catalog = self.get_stars() r_peak = self.kernel.extension angsep = ugali.utils.projector.angsep(self.ra, self.dec, catalog.ra, catalog.dec) cut_inner = (angsep < r_peak) cut_annulus = (angsep > 0.5) & (angsep < 1.) # deg mmin, mmax = 16., 24. cmin, cmax = -0.5, 1.0 mbins = np.linspace(mmin, mmax, 150) cbins = np.linspace(cmin, cmax, 150) color = catalog.color[cut_annulus] mag = catalog.mag[cut_annulus] h, xbins, ybins = numpy.histogram2d(color, mag, bins=[cbins,mbins]) blur = nd.filters.gaussian_filter(h.T, 2) kwargs = dict(extent=[xbins.min(),xbins.max(),ybins.min(),ybins.max()], cmap='gray_r', aspect='auto', origin='lower', rasterized=True, interpolation='none') ax.imshow(blur, **kwargs) pylab.scatter(catalog.color[cut_inner], catalog.mag[cut_inner], c='red', s=7, edgecolor='none')# label=r'$r < %.2f$ deg'%(r_peak)) ugali.utils.plotting.drawIsochrone(self.isochrone, c='b', zorder=10) ax.set_xlim(-0.5, 1.) ax.set_ylim(24., 16.) plt.xlabel(r'$g - r$') plt.ylabel(r'$g$') plt.xticks([-0.5, 0., 0.5, 1.]) plt.yticks(numpy.arange(mmax - 1., mmin - 1., -1.)) radius_string = (r'${\rm r}<%.1f$ arcmin'%( 60 * r_peak)) pylab.text(0.05, 0.95, radius_string, fontsize=10, ha='left', va='top', color='red', transform=pylab.gca().transAxes, bbox=dict(facecolor='white', alpha=1., edgecolor='none'))
def drawMembersSpatial(self,data): ax = plt.gca() if isinstance(data,basestring): filename = data data = pyfits.open(filename)[1].data xmin, xmax = -0.25,0.25 ymin, ymax = -0.25,0.25 xx,yy = np.meshgrid(np.linspace(xmin,xmax),np.linspace(ymin,ymax)) x_prob, y_prob = sphere2image(self.ra, self.dec, data['RA'], data['DEC']) sel = (x_prob > xmin)&(x_prob < xmax) & (y_prob > ymin)&(y_prob < ymax) sel_prob = data['PROB'][sel] > 5.e-2 index_sort = numpy.argsort(data['PROB'][sel][sel_prob]) plt.scatter(x_prob[sel][~sel_prob], y_prob[sel][~sel_prob], marker='o', s=2, c='0.75', edgecolor='none') sc = plt.scatter(x_prob[sel][sel_prob][index_sort], y_prob[sel][sel_prob][index_sort], c=data['PROB'][sel][sel_prob][index_sort], marker='o', s=10, edgecolor='none', cmap='jet', vmin=0., vmax=1.) # Spectral_r drawProjImage(xx,yy,None,coord='C') #ax.set_xlim(xmax, xmin) #ax.set_ylim(ymin, ymax) #plt.xlabel(r'$\Delta \alpha_{2000}\,(\deg)$') #plt.ylabel(r'$\Delta \delta_{2000}\,(\deg)$') plt.xticks([-0.2, 0., 0.2]) plt.yticks([-0.2, 0., 0.2]) divider = make_axes_locatable(ax) ax_cb = divider.new_horizontal(size="7%", pad=0.1) plt.gcf().add_axes(ax_cb) pylab.colorbar(sc, cax=ax_cb, orientation='vertical', ticks=[0, 0.2, 0.4, 0.6, 0.8, 1.0], label='Membership Probability') ax_cb.yaxis.tick_right()
def drawMembersCMD(self,data): ax = plt.gca() if isinstance(data,basestring): filename = data data = pyfits.open(filename)[1].data xmin, xmax = -0.25,0.25 ymin, ymax = -0.25,0.25 mmin, mmax = 16., 24. cmin, cmax = -0.5, 1.0 mbins = np.linspace(mmin, mmax, 150) cbins = np.linspace(cmin, cmax, 150) mag_1 = data[self.config['catalog']['mag_1_field']] mag_2 = data[self.config['catalog']['mag_2_field']] x_prob, y_prob = sphere2image(self.ra, self.dec, data['RA'], data['DEC']) sel = (x_prob > xmin)&(x_prob < xmax) & (y_prob > ymin)&(y_prob < ymax) sel_prob = data['PROB'][sel] > 5.e-2 index_sort = numpy.argsort(data['PROB'][sel][sel_prob]) plt.scatter(data['COLOR'][sel][~sel_prob], mag_1[sel][~sel_prob], marker='o',s=2,c='0.75',edgecolor='none') sc = pylab.scatter(data['COLOR'][sel][sel_prob][index_sort], mag_1[sel][sel_prob][index_sort], c=data['PROB'][sel][sel_prob][index_sort], marker='o', s=10, edgecolor='none', cmap='jet', vmin=0., vmax=1) pylab.xlim(cmin, cmax) pylab.ylim(mmax, mmin) pylab.xlabel(r'$g - r$') pylab.ylabel(r'$g$') #axes[1].yaxis.set_major_locator(MaxNLocator(prune='lower')) pylab.xticks([-0.5, 0., 0.5, 1.]) pylab.yticks(numpy.arange(mmax - 1., mmin - 1., -1.)) ugali.utils.plotting.drawIsochrone(self.isochrone, c='k', zorder=10) pylab.text(0.05, 0.95, r'$\Sigma p_{i} = %i$'%(data['PROB'].sum()), fontsize=10, horizontalalignment='left', verticalalignment='top', color='k', transform=pylab.gca().transAxes, bbox=dict(facecolor='white', alpha=1., edgecolor='none')) divider = make_axes_locatable(pylab.gca()) ax_cb = divider.new_horizontal(size="7%", pad=0.1) plt.gcf().add_axes(ax_cb) pylab.colorbar(sc, cax=ax_cb, orientation='vertical', ticks=[0, 0.2, 0.4, 0.6, 0.8, 1.0], label='Membership Probability') ax_cb.yaxis.tick_right()
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)') ###################################################
def drawKernel(kernel, contour=False, coords='C', **kwargs): ax = plt.gca() if 'colors' not in kwargs: kwargs.setdefault('cmap',matplotlib.cm.jet) kwargs.setdefault('origin','lower') ext = kernel.extension theta = kernel.theta xmin,xmax = -kernel.edge,kernel.edge ymin,ymax = -kernel.edge,kernel.edge if coords[-1] == 'G': lon, lat = kernel.lon, kernel.lat elif coords[-1] == 'C': lon,lat = gal2cel(kernel.lon, kernel.lat) else: msg = 'Unrecognized coordinate: %s'%coords raise Exception(msg) x = np.linspace(xmin,xmax,500)+lon y = np.linspace(ymin,ymax,500)+lat xx,yy = np.meshgrid(x,y) extent = [x[0],x[-1],y[0],y[-1]] kwargs.setdefault('extent',extent) if coords[-1] == 'C': xx,yy = cel2gal(xx,yy) zz = kernel.pdf(xx.flat,yy.flat).reshape(xx.shape) zmax = zz.max() if contour: levels = kwargs.pop('levels',10) #levels = np.logspace(np.log10(zmax)-1,np.log10(zmax),7) ret = ax.contour(zz,levels,**kwargs) else: val = np.ma.array(zz,mask=zz<zz.max()/100.) ret = ax.imshow(val,**kwargs) return ret ###################################################
