我们从Python开源项目中，提取了以下32个代码示例，用于说明如何使用pylab.yticks()。
def plot_importance(names, model, savefig=True): featureNames = numpy.array(names) featureImportance = model.feature_importances_ featureImportance = featureImportance / featureImportance.max() sorted_idx = numpy.argsort(featureImportance) barPos = numpy.arange(sorted_idx.shape[0]) + .5 plot.barh(barPos, featureImportance[sorted_idx], align='center') plot.yticks(barPos, featureNames[sorted_idx]) plot.xlabel('Variable Importance') plot.subplots_adjust(left=0.2, right=0.9, top=0.9, bottom=0.1) if savefig: dt_ = datetime.datetime.now().strftime('%d%b%y_%H%M') plt.savefig("../graphs/featureImportance_" + dt_ + ".png") plot.show() # Plot prediction save the graph with a timestamp
def plot(self): """ Plot startup data. """ import pylab print("Plotting result...", end="") avg_data = self.average_data() avg_data = self.__sort_data(avg_data, False) if len(self.raw_data) > 1: err = self.stdev_data() sorted_err = [err[k] for k in list(zip(*avg_data))[0]] else: sorted_err = None pylab.barh(range(len(avg_data)), list(zip(*avg_data))[1], xerr=sorted_err, align='center', alpha=0.4) pylab.yticks(range(len(avg_data)), list(zip(*avg_data))[0]) pylab.xlabel("Average startup time (ms)") pylab.ylabel("Plugins") pylab.show() print(" done.")
def scatter_labeled_z(z_batch, label_batch, filename="labeled_z"): fig = pylab.gcf() fig.set_size_inches(20.0, 16.0) pylab.clf() colors = ["#2103c8", "#0e960e", "#e40402","#05aaa8","#ac02ab","#aba808","#151515","#94a169", "#bec9cd", "#6a6551"] for n in range(z_batch.shape[0]): result = pylab.scatter(z_batch[n, 0], z_batch[n, 1], c=colors[label_batch[n]], s=40, marker="o", edgecolors='none') classes = ["0", "1", "2", "3", "4", "5", "6", "7", "8", "9"] recs = [] for i in range(0, len(colors)): recs.append(mpatches.Rectangle((0, 0), 1, 1, fc=colors[i])) ax = pylab.subplot(111) box = ax.get_position() ax.set_position([box.x0, box.y0, box.width * 0.8, box.height]) ax.legend(recs, classes, loc="center left", bbox_to_anchor=(1.1, 0.5)) pylab.xticks(pylab.arange(-4, 5)) pylab.yticks(pylab.arange(-4, 5)) pylab.xlabel("z1") pylab.ylabel("z2") pylab.savefig(filename)
def visualize_reconstruction(xp, model, x, visualization_dir, epoch, gpu=False): x_variable = chainer.Variable(xp.asarray(x)) _x = model.decode(model.encode(x_variable), test=True) _x.to_cpu() _x = _x.data fig = pylab.gcf() fig.set_size_inches(8.0, 8.0) pylab.clf() pylab.gray() for m in range(50): i = m / 10 j = m % 10 pylab.subplot(10, 10, 20 * i + j + 1, xticks=[], yticks=[]) pylab.imshow(x[m].reshape((28, 28)), interpolation="none") pylab.subplot(10, 10, 20 * i + j + 10 + 1, xticks=[], yticks=[]) pylab.imshow(_x[m].reshape((28, 28)), interpolation="none") # pylab.imshow(np.clip((_x_batch.data[m] + 1.0) / 2.0, 0.0, 1.0).reshape( # (config.img_channel, config.img_width, config.img_width)), interpolation="none") pylab.axis("off") pylab.savefig("{}/reconstruction_{}.png".format(visualization_dir, epoch)) # pylab.show()
def plot_eigenspectrum(G, s, nvis, nhid): with misc.gnumpy_conversion_check('allow'): dim = G.shape[0] d, Q = scipy.linalg.eigh(G) d = d[::-1] Q = Q[:, ::-1] pts = np.unique(np.floor(np.logspace(0., np.log10(dim-1), 500)).astype(int)) - 1 cf = [fisher.correlation_fraction(Q[:, i], s, nvis, nhid) for i in pts] pylab.figure() pylab.subplot(2, 1, 1) pylab.loglog(range(1, dim+1), d, 'b-', lw=2.) pylab.xticks([]) pylab.yticks(fontsize='large') pylab.subplot(2, 1, 2) pylab.semilogx(pts+1, cf, 'r-', lw=2.) pylab.xticks(fontsize='x-large') pylab.yticks(fontsize='large')
def plot_z(z, dir=None, filename="z", xticks_range=None, yticks_range=None): if dir is None: raise Exception() try: os.mkdir(dir) except: pass fig = pylab.gcf() fig.set_size_inches(16.0, 16.0) pylab.clf() for n in xrange(z.shape[0]): result = pylab.scatter(z[n, 0], z[n, 1], s=40, marker="o", edgecolors='none') pylab.xlabel("z1") pylab.ylabel("z2") if xticks_range is not None: pylab.xticks(pylab.arange(-xticks_range, xticks_range + 1)) if yticks_range is not None: pylab.yticks(pylab.arange(-yticks_range, yticks_range + 1)) pylab.savefig("{}/{}.png".format(dir, filename))
def plot(self,title='',include_baseline=False,equal_aspect=True): """ Method that generates a plot of the ROC curve Parameters: title: Title of the chart include_baseline: Add the baseline plot line if it's True equal_aspect: Aspects to be equal for all plot """ pylab.clf() pylab.plot([x[0] for x in self.derived_points], [y[1] for y in self.derived_points], self.linestyle) if include_baseline: pylab.plot([0.0,1.0], [0.0,1.0],'k-.') pylab.ylim((0,1)) pylab.xlim((0,1)) pylab.xticks(pylab.arange(0,1.1,.1)) pylab.yticks(pylab.arange(0,1.1,.1)) pylab.grid(True) if equal_aspect: cax = pylab.gca() cax.set_aspect('equal') pylab.xlabel('1 - Specificity') pylab.ylabel('Sensitivity') pylab.title(title) pylab.show()
def visualize_labeled_z(z_batch, label_batch, dir=None): fig = pylab.gcf() fig.set_size_inches(20.0, 16.0) pylab.clf() colors = ["#2103c8", "#0e960e", "#e40402","#05aaa8","#ac02ab","#aba808","#151515","#94a169", "#bec9cd", "#6a6551"] for n in xrange(z_batch.shape[0]): result = pylab.scatter(z_batch[n, 0], z_batch[n, 1], c=colors[label_batch[n]], s=40, marker="o", edgecolors='none') classes = ["0", "1", "2", "3", "4", "5", "6", "7", "8", "9"] recs = [] for i in range(0, len(colors)): recs.append(mpatches.Rectangle((0, 0), 1, 1, fc=colors[i])) ax = pylab.subplot(111) box = ax.get_position() ax.set_position([box.x0, box.y0, box.width * 0.8, box.height]) ax.legend(recs, classes, loc="center left", bbox_to_anchor=(1.1, 0.5)) pylab.xticks(pylab.arange(-4, 5)) pylab.yticks(pylab.arange(-4, 5)) pylab.xlabel("z1") pylab.ylabel("z2") pylab.savefig("%s/labeled_z.png" % dir)
def align_subplots( N, M, xlim=None, ylim=None, ): """make all of the subplots have the same limits, turn off unnecessary ticks""" # find sensible xlim,ylim if xlim is None: xlim = [np.inf, -np.inf] for i in range(N * M): pb.subplot(N, M, i + 1) xlim[0] = min(xlim[0], pb.xlim()[0]) xlim[1] = max(xlim[1], pb.xlim()[1]) if ylim is None: ylim = [np.inf, -np.inf] for i in range(N * M): pb.subplot(N, M, i + 1) ylim[0] = min(ylim[0], pb.ylim()[0]) ylim[1] = max(ylim[1], pb.ylim()[1]) for i in range(N * M): pb.subplot(N, M, i + 1) pb.xlim(xlim) pb.ylim(ylim) if i % M: pb.yticks([]) else: removeRightTicks() if i < M * (N - 1): pb.xticks([]) else: removeUpperTicks()
def showgrid(l,cols=None,n=400,titles=None,xlabels=None,ylabels=None,**kw): if "cmap" not in kw: kw["cmap"] = cm.gray if "interpolation" not in kw: kw["interpolation"] = "nearest" n = minimum(n,len(l)) if cols is None: cols = int(sqrt(n)) rows = (n+cols-1)//cols for i in range(n): pylab.xticks([]) ;pylab.yticks([]) pylab.subplot(rows,cols,i+1) pylab.imshow(l[i],**kw) if titles is not None: pylab.title(str(titles[i])) if xlabels is not None: pylab.xlabel(str(xlabels[i])) if ylabels is not None: pylab.ylabel(str(ylabels[i]))
def plotaffinegrid(affines, exag=1e3, affineOnly=True, R=0.025, tpre='', bboxes=None): import pylab as plt NR = 3 NC = int(ceil(len(affines)/3.)) #R = 0.025 # 1.5 arcmin #for (exag,affonly) in [(1e2, False), (1e3, True), (1e4, True)]: plt.clf() for i,aff in enumerate(affines): plt.subplot(NR, NC, i+1) dl = aff.refdec - R dh = aff.refdec + R rl = aff.refra - R / aff.rascale rh = aff.refra + R / aff.rascale RR,DD = np.meshgrid(np.linspace(rl, rh, 11), np.linspace(dl, dh, 11)) plotaffine(aff, RR.ravel(), DD.ravel(), exag=exag, affineOnly=affineOnly, doclf=False, units='dots', width=2, headwidth=2.5, headlength=3, headaxislength=3) if bboxes is not None: for bb in bboxes: plt.plot(*bb, linestyle='-', color='0.5') plt.plot(*bboxes[i], linestyle='-', color='k') setRadecAxes(rl,rh,dl,dh) plt.xlabel('') plt.ylabel('') plt.xticks([]) plt.yticks([]) plt.title('field %i' % (i+1)) plt.subplots_adjust(left=0.05, right=0.95, wspace=0.1) if affineOnly: tt = tpre + 'Affine part of transformations' else: tt = tpre + 'Transformations' plt.suptitle(tt + ' (x %g)' % exag)
def visualize_10_2d_gaussian_prior(n_z, y_label, visualization_dir=None): z_batch = sample_z_from_n_2d_gaussian_mixture(len(y_label), n_z, y_label, 10, False) z_batch = z_batch.data fig = pylab.gcf() fig.set_size_inches(15, 12) pylab.clf() colors = ["#2103c8", "#0e960e", "#e40402", "#05aaa8", "#ac02ab", "#aba808", "#151515", "#94a169", "#bec9cd", "#6a6551"] for n in xrange(z_batch.shape[0]): result = pylab.scatter(z_batch[n, 0], z_batch[n, 1], c=colors[y_label[n]], s=40, marker="o", edgecolors='none') classes = ["0", "1", "2", "3", "4", "5", "6", "7", "8", "9"] recs = [] for i in range(0, len(colors)): recs.append(mpatches.Rectangle((0, 0), 1, 1, fc=colors[i])) ax = pylab.subplot(111) box = ax.get_position() ax.set_position([box.x0, box.y0, box.width * 0.8, box.height]) ax.legend(recs, classes, loc="center left", bbox_to_anchor=(1.1, 0.5)) pylab.xticks(pylab.arange(-4, 5)) pylab.yticks(pylab.arange(-4, 5)) pylab.xlabel("z1") pylab.ylabel("z2") if visualization_dir is not None: pylab.savefig("%s/10_2d-gaussian.png" % visualization_dir) pylab.show()
def visualize_labeled_z(xp, model, x, y_label, visualization_dir, epoch, gpu=False): x = chainer.Variable(xp.asarray(x)) z_batch = model.encode(x, test=True) z_batch.to_cpu() z_batch = z_batch.data fig = pylab.gcf() fig.set_size_inches(8.0, 8.0) pylab.clf() colors = ["#2103c8", "#0e960e", "#e40402", "#05aaa8", "#ac02ab", "#aba808", "#151515", "#94a169", "#bec9cd", "#6a6551"] for n in xrange(z_batch.shape[0]): result = pylab.scatter(z_batch[n, 0], z_batch[n, 1], c=colors[y_label[n]], s=40, marker="o", edgecolors='none') classes = ["0", "1", "2", "3", "4", "5", "6", "7", "8", "9"] recs = [] for i in range(0, len(colors)): recs.append(mpatches.Rectangle((0, 0), 1, 1, fc=colors[i])) ax = pylab.subplot(111) box = ax.get_position() ax.set_position([box.x0, box.y0, box.width * 0.8, box.height]) ax.legend(recs, classes, loc="center left", bbox_to_anchor=(1.1, 0.5)) pylab.xticks(pylab.arange(-4, 5)) pylab.yticks(pylab.arange(-4, 5)) pylab.xlabel("z1") pylab.ylabel("z2") pylab.savefig("{}/labeled_z_{}.png".format(visualization_dir, epoch)) # pylab.show()
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 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 showFourier(self): psd2D = np.log(np.abs(self.four)**2+1) (height,width) = psd2D.shape py.figure(figsize=(10,10*height/width),facecolor='white') py.clf() py.rc('text',usetex=True) py.xlabel(r'$\omega_1$',fontsize=24) py.ylabel(r'$\omega_2$',fontsize=24) py.xticks(fontsize=16) py.yticks(fontsize=16) py.imshow( psd2D, cmap='Greys_r',extent=[-pi,pi,-pi,pi],aspect='auto') py.show()
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 view_rates(self, indices=None, spacing=1): res = self.synthetic_store.get(indices=indices, variables='r') pylab.figure() for key in res.keys(): colorVal = self._scalarMap_synthetic.to_rgba(int(key)) pylab.plot(res[key]['r'] + int(key)*spacing, color=colorVal) pylab.xlabel('Time [chunks]') pylab.yticks([], []) pylab.show()
def view_synthetic_templates(self, indices=None, time=None, nn=100, hf_dist=45, a_dist=1.0): if indices is None: indices = range(self.nb_cells) if not numpy.iterable(indices): indices = [indices] scaling = None pylab.figure() for i in indices: template = self._get_synthetic_template(i, time, nn, hf_dist, a_dist) template = template.toarray() width = template.shape[1] xmin, xmax = self.probe.field_of_view['x_min'], self.probe.field_of_view['x_max'] ymin, ymax = self.probe.field_of_view['y_min'], self.probe.field_of_view['y_max'] if scaling is None: scaling= 10*numpy.max(numpy.abs(template)) colorVal = self._scalarMap_synthetic.to_rgba(i) for count, i in enumerate(xrange(self.nb_channels)): x, y = self.probe.positions[:, i] xpadding = ((x - xmin)/(float(xmax - xmin) + 1))*(2*width) ypadding = ((y - ymin)/(float(ymax - ymin) + 1))*scaling pylab.plot(xpadding + numpy.arange(width), ypadding + template[i, :], color=colorVal) pylab.tight_layout() pylab.setp(pylab.gca(), xticks=[], yticks=[]) pylab.xlim(xmin, 3*width) pylab.show()
def view_circus_templates(self, indices=None): if indices is None: indices = range(self.nb_templates) if not numpy.iterable(indices): indices = [indices] data = self.template_store.get(indices, ['templates', 'norms']) width = self.template_store.width templates = data.pop('templates').T norms = data.pop('norms') scaling = None pylab.figure() for count, i in enumerate(indices): template = templates[count].toarray().reshape(self.nb_channels, width) * norms[count] xmin, xmax = self.probe.field_of_view['x_min'], self.probe.field_of_view['x_max'] ymin, ymax = self.probe.field_of_view['y_min'], self.probe.field_of_view['y_max'] if scaling is None: scaling= 10*numpy.max(numpy.abs(template)) colorVal = self._scalarMap_circus.to_rgba(i) for count, i in enumerate(xrange(self.nb_channels)): x, y = self.probe.positions[:, i] xpadding = ((x - xmin)/(float(xmax - xmin) + 1))*(2*width) ypadding = ((y - ymin)/(float(ymax - ymin) + 1))*scaling pylab.plot(xpadding + numpy.arange(width), ypadding + template[i, :], color=colorVal) pylab.tight_layout() pylab.setp(pylab.gca(), xticks=[], yticks=[]) pylab.xlim(xmin, 3*width) pylab.show()
def graph(text,text2=''): pl.xticks(()) pl.yticks(()) pl.xlim(0,30) pl.ylim(0,20) pl.plot([x,x],[0,3]) pl.text(x,-2,"X"); pl.text(0,x,"X") pl.text(x,x*1.7, text, ha='center', va='center',size=10, alpha=.5) pl.text(-5,10,text2,size=25)
def plot_multiple_roc(rocList,title='',labels=None, include_baseline=False, equal_aspect=True): """ Plots multiple ROC curves on the same chart. Parameters: rocList: the list of ROCData objects title: The tile of the chart labels: The labels of each ROC curve include_baseline: if it's True include the random baseline equal_aspect: keep equal aspect for all roc curves """ pylab.clf() pylab.ylim((0,1)) pylab.xlim((0,1)) pylab.xticks(pylab.arange(0,1.1,.1)) pylab.yticks(pylab.arange(0,1.1,.1)) pylab.grid(True) if equal_aspect: cax = pylab.gca() cax.set_aspect('equal') pylab.xlabel("1 - Specificity") pylab.ylabel("Sensitivity") pylab.title(title) if not labels: labels = [ '' for x in rocList] _remove_duplicate_styles(rocList) for ix, r in enumerate(rocList): pylab.plot([x[0] for x in r.derived_points], [y[1] for y in r.derived_points], r.linestyle, linewidth=1, label=labels[ix]) if include_baseline: pylab.plot([0.0,1.0], [0.0, 1.0], 'k-', label= 'random') if labels: pylab.legend(loc='lower right') pylab.show()
def plotPrecisionRecall(precision, recall, outFileName, Fig=None, drawCol=1, textLabel = None, title = None, fontsize1 = 24, fontsize2 = 20, linewidth = 3): ''' :param precision: :param recall: :param outFileName: :param Fig: :param drawCol: :param textLabel: :param fontsize1: :param fontsize2: :param linewidth: ''' clearFig = False if Fig == None: Fig = pylab.figure() clearFig = True #tableString = 'Algo avgprec Fmax prec recall accuracy fpr Q(TonITS)\n' linecol = ['g','m','b','c'] #if we are evaluating SP, then BL is available #sectionName = 'Evaluation_'+tag+'PxProb' #fullEvalFile = os.path.join(eval_dir,evalName) #Precision,Recall,evalString = readEvaluation(fullEvalFile,sectionName,AlgoLabel) pylab.plot(100*recall, 100*precision, linewidth=linewidth, color=linecol[drawCol], label=textLabel) #writing out PrecRecall curves as graphic setFigLinesBW(Fig) if textLabel!= None: pylab.legend(loc='lower left',prop={'size':fontsize2}) if title!= None: pylab.title(title, fontsize=fontsize1) #pylab.title(title,fontsize=24) pylab.ylabel('PRECISION [%]',fontsize=fontsize1) pylab.xlabel('RECALL [%]',fontsize=fontsize1) pylab.xlim(0,100) pylab.xticks( [0, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100], ('0','','20','','40','','60','','80','','100'), fontsize=fontsize2 ) pylab.ylim(0,100) pylab.yticks( [0, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100], ('0','','20','','40','','60','','80','','100'), fontsize=fontsize2 ) pylab.grid(True) # if type(outFileName) != list: pylab.savefig( outFileName ) else: for outFn in outFileName: pylab.savefig( outFn ) if clearFig: pylab.close() Fig.clear()
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()
