我们从Python开源项目中，提取了以下6个代码示例，用于说明如何使用pylab.axvline()。
def edgescatter(self, ps): for ei,X in enumerate(self.edges): i,j = X[:2] matchdRA, matchdDec = X[10:12] mu = X[9] A = self.alignments[ei] plt.clf() if len(matchdRA) > 1000: plothist(matchdRA, matchdDec, 101) else: plt.plot(matchdRA, matchdDec, 'k.', alpha=0.5) plt.axvline(0, color='0.5') plt.axhline(0, color='0.5') plt.axvline(mu[0], color='b') plt.axhline(mu[1], color='b') for nsig in [1,2]: X,Y = A.getContours(nsigma=nsig) plt.plot(X, Y, 'b-') plt.xlabel('delta-RA (arcsec)') plt.ylabel('delta-Dec (arcsec)') plt.axis('scaled') ps.savefig()
def plot_2D_contour(states,p,labels,inter=False): import pylab as pl from pyme.statistics import expectation as EXP exp = EXP((states,p)) X = np.unique(states[0,:]) Y = np.unique(states[1,:]) X_len = len(X) Y_len = len(Y) Z = np.zeros((X.max()+1,Y.max()+1)) for i in range(len(p)): Z[states[0,i],states[1,i]] = p[i] Z = np.where(Z < 1e-8,0.0,Z) pl.clf() XX, YY = np.meshgrid(X,Y) pl.contour(range(X.max()+1),range(Y.max()+1),Z.T) pl.axhline(y=exp[1]) pl.axvline(x=exp[0]) pl.xlabel(labels[0]) pl.ylabel(labels[1]) if inter == True: pl.draw() else: pl.show()
def plotalignment(A, nbins=200, M=None, rng=None, doclf=True, docolorbar=True, docutcircle=True, docontours=True, dologhist=False, doaxlines=False, imshowargs={}): import pylab as plt from astrometry.util.plotutils import plothist, loghist if doclf: plt.clf() if M is None: M = A.match if dologhist: f = loghist else: f = plothist H,xe,ye = f(M.dra_arcsec*1000., M.ddec_arcsec*1000., nbins, range=rng, doclf=doclf, docolorbar=docolorbar, imshowargs=imshowargs) ax = plt.axis() if A is not None: # The EM fit is based on a subset of the matches; # draw the subset cut circle. if docutcircle: angle = np.linspace(0, 2.*pi, 360) plt.plot((A.cutcenter[0] + A.cutrange * np.cos(angle))*1000., (A.cutcenter[1] + A.cutrange * np.sin(angle))*1000., 'r-') if docontours: for i,c in enumerate(['b','c','g']*2): if i == A.ngauss: break for nsig in [1,2]: XY = A.getContours(nsig, c=i) if XY is None: break X,Y = XY plt.plot(X*1000., Y*1000., '-', color=c)#, alpha=0.5) if doaxlines: plt.axhline(0., color='b', alpha=0.5) plt.axvline(0., color='b', alpha=0.5) plt.axis(ax) plt.xlabel('dRA (mas)') plt.ylabel('dDec (mas)') return H,xe,ye
def plot_marginals(state_space,p,name,t,labels = False): import matplotlib #matplotlib.use("PDF") #matplotlib.rcParams['figure.figsize'] = 5,10 import matplotlib.pyplot as pl pl.suptitle("time: "+ str(t)+" units") print("time : "+ str(t)) D = state_space.shape[1] for i in range(D): marg_X = np.unique(state_space[:,i]) A = np.where(marg_X[:,np.newaxis] == state_space[:,i].T[np.newaxis,:],1,0) marg_p = np.dot(A,p) pl.subplot(int(D/2)+1,2,i+1) pl.plot(marg_X,marg_p) pl.axvline(np.sum(marg_X*marg_p),color= 'r') pl.axvline(marg_X[np.argmax(marg_p)],color='g') if labels == False: pl.xlabel("Specie: " + str(i+1)) else: pl.xlabel(labels[i]) #pl.savefig("Visuals/marginal_"+name+".pdf",format='pdf') pl.show() pl.clf() ##Simple Compress : best N-term approximation under the ell_1 norm #@param state_space the state space shape: (Number of Species X Number of states) #@param p probability vector #@param eps the ell_1 error to remove #@return -Compressed state space # -Compressed Probs
def plot_marginals(state_space,p,name,t,labels = False,interactive = False): import matplotlib import matplotlib.pyplot as pl if interactive == True: pl.ion() pl.clf() pl.suptitle("time: "+ str(t)+" units") #print("time : "+ str(t)) D = state_space.shape[1] for i in range(D): marg_X = np.unique(state_space[:,i]) A = np.where(marg_X[:,np.newaxis] == state_space[:,i].T[np.newaxis,:],1,0) marg_p = np.dot(A,p) pl.subplot(int(D/2)+1,2,i+1) pl.plot(marg_X,marg_p) pl.yticks(np.linspace(np.amin(marg_p), np.amax(marg_p), num=3)) pl.axvline(np.sum(marg_X*marg_p),color= 'r') pl.axvline(marg_X[np.argmax(marg_p)],color='g') if labels == False: pl.xlabel("Specie: " + str(i+1)) else: pl.xlabel(labels[i]) if interactive == True: pl.draw() else: pl.tight_layout() pl.show()
def plotAgainstGFP(self, extradataA = [], extradataG = [], intensity = [], seq = []): fig1 = pylab.figure(figsize = (25, 10)) print len(self.GFP) for i in xrange(min(len(data.cat), 3)): print len(self.GFP[self.categories == i]) vect = [] pylab.subplot(1,3,i+1) #pylab.hist(self.GFP[self.categories == i], bins = 20, color = data.colors[i]) pop = self.GFP[self.categories == i] pylab.plot(self.GFP[self.categories == i], self.angles[self.categories == i], data.colors[i]+'o', markersize = 8)#, label = data.cat[i]) print "cat", i, "n pop", len(self.GFP[(self.categories == i) & (self.GFP > -np.log(12.5))]) x = np.linspace(np.min(self.GFP[self.categories == i]), np.percentile(self.GFP[self.categories == i], 80),40) #fig1.canvas.mpl_connect('pick_event', onpick) for j in x: vect.append(np.median(self.angles[(self.GFP > j) & (self.categories == i)])) pylab.plot([-4.5, -0.5], [vect[0], vect[0]], data.colors[i], label = "mediane de la population entiere", linewidth = 5) print vect[0], vect[np.argmax(x > -np.log(12.5))] pylab.plot([-np.log(12.5), -0.5], [vect[np.argmax(x > -np.log(12.5))] for k in [0,1]], data.colors[i], label = "mediane de la population de droite", linewidth = 5, ls = '--') pylab.axvline(x = -np.log(12.5), color = 'm', ls = '--', linewidth = 3) pylab.xlim([-4.5, -0.5]) pylab.legend(loc = 2, prop = {'size':17}) pylab.title(data.cat[i].split(',')[0], fontsize = 24) pylab.xlabel('score GFP', fontsize = 20) pylab.ylabel('Angle (degre)', fontsize = 20) pylab.tick_params(axis='both', which='major', labelsize=20) pylab.ylim([-5, 105]) ##pylab.xscale('log') pylab.show()
