我们从Python开源项目中，提取了以下41个代码示例，用于说明如何使用pylab.text()。
def plot_rgb(image, name, label=None, label_color='w', label_size='large'): """ This will plot the r,g,b channels of an *image* of shape (N,M,3) or (N,M,4). *name* is the prefix of the file name, which will be supplemented with "_rgb.png." *label*, *label_color* and *label_size* may also be specified. """ import pylab Nvec = image.shape[0] image[np.isnan(image)] = 0.0 if image.shape[2] >= 4: image = image[:,:,:3] pylab.clf() pylab.gcf().set_dpi(100) pylab.gcf().set_size_inches((Nvec/100.0, Nvec/100.0)) pylab.gcf().subplots_adjust(left=0.0, right=1.0, bottom=0.0, top=1.0, wspace=0.0, hspace=0.0) pylab.imshow(image, interpolation='nearest') if label is not None: pylab.text(20, 20, label, color = label_color, size=label_size) pylab.savefig("%s_rgb.png" % name) pylab.clf()
def disp(iimg, label = "", gray=False): """ Display an image using pylab """ try: import pylab dimage = iimg.copy() if iimg.ndim==3: dimage[...,0] = iimg[...,2] dimage[...,2] = iimg[...,0] pylab.imshow(dimage, interpolation='none') if gray: pylab.gray() #pylab.gca().format_coord = format_coord pylab.text(1500, -30, label) pylab.axis('off') pylab.show() except ImportError: print "Module pylab not available"
def view_(_pred,_lable): fname = ['Captcha/lv3/%i.jpg' %i for i in range(20)] img = [] for fn in fname: img.append(Image.open(open(fn))) #img.append(misc.imread(fn).astype(np.float)) for i in range(len(img)): pylab.subplot(4,5,i+1); pylab.axis('off') pylab.imshow(img[i]) #pylab.imshow( np.dot(np.array(img[i])[...,:3],[0.299,0.587,0.114]) , cmap=plt.get_cmap("gray")) #pylab.text(40,60,_pred[i],color = 'b') if ( _pred[i] == _lable[i] ): pylab.text(40,65,_pred[i],color = 'b',size = 15) else: pylab.text(40,65,_pred[i],color = 'r',size = 15) pylab.text(40,92,_lable[i],color = 'g',size = 15) pylab.show()
def show(self): # pl.semilogy(self.theta, self.omega) # , label = '$L =%.1f m, $'%self.l + '$dt = %.2f s, $'%self.dt + '$\\theta_0 = %.2f radians, $'%self.theta[0] + '$q = %i, $'%self.q + '$F_D = %.2f, $'%self.F_D + '$\\Omega_D = %.1f$'%self.Omega_D) pl.plot(self.theta_phase ,self.omega_phase, '.', label = '$t \\approx 2\\pi n / \\Omega_D$') pl.xlabel('$\\theta$ (radians)') pl.ylabel('$\\omega$ (radians/s)') pl.legend() # pl.text(-1.4, 0.3, '$\\omega$ versus $\\theta$ $F_D = 1.2$', fontsize = 'x-large') pl.title('Chaotic Regime') # pl.show() # pl.semilogy(self.time_array, self.delta) # pl.legend(loc = 'upper center', fontsize = 'small') # pl.xlabel('$time (s)$') # pl.ylabel('$\\Delta\\theta (radians)$') # pl.xlim(0, self.T) # pl.ylim(float(input('ylim-: ')),float(input('ylim+: '))) # pl.ylim(1E-11, 0.01) # pl.text(4, -0.15, 'nonlinear pendulum - Euler-Cromer method') # pl.text(10, 1E-3, '$\\Delta\\theta versus time F_D = 0.5$') # pl.title('Simple Harmonic Motion') pl.title('Chaotic Regime')
def show_log(self): # pl.subplot(121) pl.semilogy(self.time_array, self.delta, 'c') pl.xlabel('$time (s)$') pl.ylabel('$\\Delta\\theta$ (radians)') pl.xlim(0, self.T) # pl.ylim(1E-11, 0.01) pl.text(42, 1E-7, '$\\Delta\\theta$ versus time $F_D = 1.2$', fontsize = 'x-large') pl.title('Chaotic Regime') pl.show() # def show_log_sub122(self): # pl.subplot(122) # pl.semilogy(self.time_array, self.delta, 'g') # pl.xlabel('$time (s)$') # pl.ylabel('$\\Delta\\theta$ (radians)') # pl.xlim(0, self.T) # pl.ylim(1E-6, 100) # pl.text(20, 1E-5, '$\\Delta\\theta$ versus time $F_D = 1.2$', fontsize = 'x-large') # pl.title('Chaotic Regime') # pl.show()
def show(self): # pl.semilogy(self.theta, self.omega) # , label = '$L =%.1f m, $'%self.l + '$dt = %.2f s, $'%self.dt + '$\\theta_0 = %.2f radians, $'%self.theta[0] + '$q = %i, $'%self.q + '$F_D = %.2f, $'%self.F_D + '$\\Omega_D = %.1f$'%self.Omega_D) pl.plot(self.time_array,self.delta) # pl.show() # pl.semilogy(self.time_array, self.delta) # pl.legend(loc = 'upper center', fontsize = 'small') # pl.xlabel('$time (s)$') # pl.ylabel('$\\Delta\\theta (radians)$') # pl.xlim(0, self.T) # pl.ylim(float(input('ylim-: ')),float(input('ylim+: '))) # pl.ylim(1E-11, 0.01) # pl.text(4, -0.15, 'nonlinear pendulum - Euler-Cromer method') # pl.text(10, 1E-3, '$\\Delta\\theta versus time F_D = 0.5$') # pl.title('Simple Harmonic Motion') # pl.title('Chaotic Regime')
def show_complex(self): font = {'family': 'serif', 'color': 'k', 'weight': 'normal', 'size': 16, } pl.title('The Trajectory of Tageted Baseball\n with air flow in adiabatic model', fontdict = font) pl.plot(self.x, self.y, label = '$v_0 = %.5f m/s$'%self.v0 + ', ' + '$\\theta = %.4f \degree$'%self.theta) pl.xlabel('x $m$') pl.ylabel('y $m$') pl.xlim(0, 300) pl.ylim(-100, 20) pl.grid() pl.legend(loc = 'upper right', shadow = True, fontsize = 'small') pl.text(15, -90, 'scan to approach the minimum velocity and corresponding launching angle', fontdict = font) pl.show()
def show_results(self): font = {'family': 'serif', 'color': 'k', 'weight': 'normal', 'size': 14, } pl.plot(self.x, self.y, 'c', label='firing angle = 45°') pl.title('The Trajectory of a Cannon Shell', fontdict = font) pl.xlabel('x (k$m$)') pl.ylabel('y ($km$)') pl.xlim(0, 60) pl.ylim(0, 20) pl.grid(True) pl.legend(loc='upper right', shadow=True, fontsize='large') pl.text(41, 16, 'Only with air drag', fontdict = font) pl.show()
def show_results(self): font = {'family': 'serif', 'color': 'k', 'weight': 'normal', 'size': 12, } pl.plot(self.x, self.y, 'c', label='firing angle = 45°') pl.title('The Trajectory of a Cannon Shell', fontdict = font) pl.xlabel('x (k$m$)') pl.ylabel('y ($km$)') pl.xlim(0, 60) pl.ylim(0, 20) pl.grid(True) pl.legend(loc='upper right', shadow=True, fontsize='large') pl.text(34, 16, ' With both air drag and \n reduced air density-isothermal', fontdict = font) pl.show()
def show_results(self): font = {'family': 'serif', 'color': 'k', 'weight': 'normal', 'size': 12, } pl.plot(self.x, self.y, 'c', label='firing angle = 45°') pl.title('The Trajectory of a Cannon Shell', fontdict = font) pl.xlabel('x (k$m$)') pl.ylabel('y ($km$)') pl.xlim(0, 60) pl.ylim(0, 20) pl.grid(True) pl.legend(loc='upper right', shadow=True, fontsize='large') pl.text(34.5, 16, ' With air drag and the \n dependence of g on altitude', fontdict = font) pl.show()
def show_results(self): font = {'family': 'serif', 'color': 'k', 'weight': 'normal', 'size': 12, } pl.plot(self.x, self.y, 'c', label='firing angle = 45°') pl.title('The Trajectory of a Cannon Shell', fontdict = font) pl.xlabel('x (k$m$)') pl.ylabel('y ($km$)') pl.xlim(0, 60) pl.ylim(0, 20) pl.grid(True) pl.legend(loc='upper right', shadow=True, fontsize='large') pl.text(34.5, 16, ' With both air drag and \n reduced air density-adiabatic', fontdict = font) pl.show()
def drawSmoothCatalog(self, catalog, label=None, **kwargs): ax = plt.gca() ra,dec = catalog.ra_dec x, y = sphere2image(self.ra,self.dec,ra,dec) delta_x = self.radius/100. smoothing = 2*delta_x bins = numpy.arange(-self.radius, self.radius + 1.e-10, delta_x) h, xbins, ybins = numpy.histogram2d(x, y, bins=[bins, bins]) blur = nd.filters.gaussian_filter(h.T, smoothing / delta_x) defaults = dict(cmap='gray_r',rasterized=True) kwargs = dict(defaults.items()+kwargs.items()) xx,yy = np.meshgrid(xbins,ybins) im = drawProjImage(xx,yy,blur,coord='C',**kwargs) if label: plt.text(0.05, 0.95, label, fontsize=10, ha='left', va='top', color='k', transform=pylab.gca().transAxes, bbox=dict(facecolor='white', alpha=1., edgecolor='none'))
def plot_multiple_rocs_separate(rocList,title='', labels = None, equal_aspect = True): """ Plot multiples ROC curves as separate at the same painting area. """ pylab.clf() pylab.title(title) for ix, r in enumerate(rocList): ax = pylab.subplot(4,4,ix+1) pylab.ylim((0,1)) pylab.xlim((0,1)) ax.set_yticklabels([]) ax.set_xticklabels([]) if equal_aspect: cax = pylab.gca() cax.set_aspect('equal') if not labels: labels = ['' for x in rocList] pylab.text(0.2,0.1,labels[ix],fontsize=8) pylab.plot([x[0] for x in r.derived_points],[y[1] for y in r.derived_points], 'r-',linewidth=2) pylab.show()
def annotateImg(img, color, size, position, text): cv2.putText(img, text, position, cv2.FONT_HERSHEY_PLAIN, size, color, thickness = 2) return img
def genside (img1, img2, height, width, name1, name2, txt1, txt2): """ create a side-by-side view img1, img2: images name1, name2: their names txt1, txt2: some text """ if len(img1.shape)==2: cimg1 = np.zeros((img1.shape[0], img1.shape[1], 3), dtype=np.uint8) cimg1[...,0] = img1 cimg1[...,1] = img1 cimg1[...,2] = img1 else: cimg1 = img1 if len(img2.shape)==2: cimg2 = np.zeros((img2.shape[0], img2.shape[1], 3), dtype=np.uint8) cimg2[...,0] = img2 cimg2[...,1] = img2 cimg2[...,2] = img2 else: cimg2 = img2 if annotated: cimg1=annotateImg(cimg1, (0,0,255), 2, (100, 70), 'Source: '+name1) #cimg1=annotateImg(cimg1, (0,0,255), 2, (100, 130), txt1) cimg2=annotateImg(cimg2, (0,0,255), 2, (100, 70), 'Target: '+name2) #cimg2=annotateImg(cimg2, (0,0,255), 2, (100, 130), txt2) cimg = mergeSide(cimg1, cimg2) if annotated: cimg=annotateImg(cimg, (0,255,0), 2, (100, 130), txt1) return cimg
def genoverlay(img1, title, name1, name2, stattxt, img2=None): """ create an overlayed view img1, img2: images title: kind of title to print name1, name2: their names txt: text to print below the title """ if img2 is None: outimg = 255*(1-img1) else: s=np.maximum(img1.shape,img2.shape) outimg=np.zeros((s[0], s[1], 3), dtype=np.uint8) #outimg[:img1.shape[0], :img1.shape[1],0] = (255*(1-img1)) #outimg[:img2.shape[0], :img2.shape[1],1] = (255*(1-img2)) #outimg[:img2.shape[0], :img2.shape[1],2] = (255*(1-img2)) outimg[:img1.shape[0], :img1.shape[1],0] = img1 outimg[:img2.shape[0], :img2.shape[1],1] = img2 outimg[:img2.shape[0], :img2.shape[1],2] = img2 outimg = 255*(1-outimg) if annotated: outimg = annotateImg(outimg, (0, 0, 255), 2, (100, 50), title) txt = "cyan: %s %s"%(sourceid,name1) outimg = annotateImg(outimg, (0, 255, 255), 2, (100, 80), txt) txt = "red: %s %s"%(targetid,name2) outimg = annotateImg(outimg, (255, 0, 0), 2, (100, 110), txt) #outimg=annotateImg(outimg, 'blue', mm2px(4), mm2px(4), txt) outimg = annotateImg(outimg, (0, 0, 255), 1.3, (100, 140), stattxt) return outimg
def plot(self, bgimage=None): import pylab as pl self._plot_background(bgimage) ax = pl.gca() y0, y1 = pl.ylim() # r is the width of the thick line we use to show the facade colors r = 5 patch = pl.Rectangle((self.facade_left + r, self.sky_line + r), self.width - 2 * r, self.door_line - self.sky_line - 2 * r, color=self.color, fill=False, lw=2 * r) ax.add_patch(patch) pl.text((self.facade_right + self.facade_left) / 2., (self.door_line + self.sky_line) / 2., '$\sigma^2={:0.2f}$'.format(self.uncertainty_for_windows())) patch = pl.Rectangle((self.facade_left + r, self.door_line + r), self.width - 2 * r, y0 - self.door_line - 2 * r, color=self.mezzanine_color, fill=False, lw=2 * r) ax.add_patch(patch) # Plot the left and right edges in yellow pl.vlines([self.facade_left, self.facade_right], self.sky_line, y0, colors='yellow') # Plot the door line and the roof line pl.hlines([self.door_line, self.sky_line], self.facade_left, self.facade_right, linestyles='dashed', colors='yellow') self.window_grid.plot()
def plot_facade_cuts(self): facade_sig = self.facade_edge_scores.sum(0) facade_cuts = find_facade_cuts(facade_sig, dilation_amount=self.facade_merge_amount) mu = np.mean(facade_sig) sigma = np.std(facade_sig) w = self.rectified.shape[1] pad=10 gs1 = pl.GridSpec(5, 5) gs1.update(wspace=0.5, hspace=0.0) # set the spacing between axes. pl.subplot(gs1[:3, :]) pl.imshow(self.rectified) pl.vlines(facade_cuts, *pl.ylim(), lw=2, color='black') pl.axis('off') pl.xlim(-pad, w+pad) pl.subplot(gs1[3:, :], sharex=pl.gca()) pl.fill_between(np.arange(w), 0, facade_sig, lw=0, color='red') pl.fill_between(np.arange(w), 0, np.clip(facade_sig, 0, mu+sigma), color='blue') pl.plot(np.arange(w), facade_sig, color='blue') pl.vlines(facade_cuts, facade_sig[facade_cuts], pl.xlim()[1], lw=2, color='black') pl.scatter(facade_cuts, facade_sig[facade_cuts]) pl.axis('off') pl.hlines(mu, 0, w, linestyle='dashed', color='black') pl.text(0, mu, '$\mu$ ', ha='right') pl.hlines(mu + sigma, 0, w, linestyle='dashed', color='gray',) pl.text(0, mu + sigma, '$\mu+\sigma$ ', ha='right') pl.xlim(-pad, w+pad)
def plot_channel(image, name, cmap='gist_heat', log=True, dex=3, zero_factor=1.0e-10, label=None, label_color='w', label_size='large'): """ This function will plot a single channel. *image* is an array shaped like (N,M), *name* is the pefix for the output filename. *cmap* is the name of the colormap to apply, *log* is whether or not the channel should be logged. Additionally, you may optionally specify the minimum-value cutoff for scaling as *dex*, which is taken with respect to the minimum value of the image. *zero_factor* applies a minimum value to all zero-valued elements. Optionally, *label*, *label_color* and *label_size* may be specified. """ import matplotlib import pylab Nvec = image.shape[0] image[np.isnan(image)] = 0.0 ma = image[image>0.0].max() image[image==0.0] = ma*zero_factor if log: mynorm = matplotlib.colors.LogNorm(ma/(10.**dex), ma) pylab.clf() pylab.gcf().set_dpi(100) pylab.gcf().set_size_inches((Nvec/100.0, Nvec/100.0)) pylab.gcf().subplots_adjust(left=0.0, right=1.0, bottom=0.0, top=1.0, wspace=0.0, hspace=0.0) mycm = pylab.cm.get_cmap(cmap) if log: pylab.imshow(image,cmap=mycm, norm=mynorm, interpolation='nearest') else: pylab.imshow(image,cmap=mycm, interpolation='nearest') if label is not None: pylab.text(20, 20,label, color = label_color, size=label_size) pylab.savefig("%s_%s.png" % (name,cmap)) pylab.clf()
def show_image(img, strg = ""): plt.imshow(img, cmap = "Greys_r") plt.text(0, 0, strg, color = "r") plt.show()
def show_results_prepare(self): font = {'family': 'serif', 'color': 'k', 'weight': 'normal', 'size': 13, } pl.figure(1) pl.title('The Trajectory of Cannon Shells', fontdict = font) pl.xlabel('x / $km$') pl.ylabel('y / $km$') pl.xlim(0, 60) pl.ylim(0, 20) pl.grid(True) pl.text(2, 16.5, 'With air drag, the reduced air density \n and g varying with altitudes', fontdict = font) pl.show()
def show(self): pl.plot(self.t, self.theta, label = '$F_D =$' + str(self.F_D)) pl.xlim(0, 100) pl.ylim(-4, 4) pl.xlabel('time ($s$)') pl.ylabel('$\\theta$ (radians)') pl.legend() # pl.text(32, 2, '$\\theta$ versus time $F_D =$' + str(self.F_D)) #pl.subplot(311) #r1 = routes_to_chaos(amplitude = 1.35) #r1.calculate() #r1.show() #pl.subplot(312) #r2 = routes_to_chaos(amplitude = 1.44) #r2.calculate() #r2.show() #pl.subplot(313) #r3 = routes_to_chaos(amplitude = 1.465) #r3.calculate() #r3.show() #pl.show() #r= routes_to_chaos(amplitude = 1.465) #r.calculate() #r.show()
def plot(self): pl.figure(figsize = (8, 8)) pl.plot(self.t,self.theta, 'c') pl.ylim(-4, 4) pl.xlim(0, 8) pl.ylabel('$\\theta$ (radians)') pl.xlabel('time (yr)') pl.title('Hyperion $\\theta$ versus time') pl.text(3.3, 3.5, 'Circular orbit')
def plot_delta(self): pl.figure(figsize = (8, 8)) pl.semilogy(self.tprime, self.deltatheta, 'r') # pl.ylim(0.0001, 0.1) # pl.ylim(0.0001, 10) pl.xlim(0, 100) pl.ylabel('$\\Delta\\theta$ (radians)') pl.xlabel('time (yr)') pl.title('Hyperion $\\theta$ versus time') pl.text(4.1, 3e3, 'Ellipitical orbit')
def plot(self): pl.figure(figsize = (8, 8)) pl.plot(self.theta,self.omega, 'k.') # pl.ylim(-4, 4) # pl.xlim(0, 8) pl.ylabel('$\\omega$ (radians/yr)') pl.xlabel('$\\theta$ (radians)') pl.title('Hyperion $\\omega$ versus $\\theta$') pl.text(-0.7, 13.3, 'Circular orbit')
def plot(self): pl.figure(figsize = (8, 8)) pl.plot(self.theta,self.omega, 'k.') # pl.ylim(-4, 4) # pl.xlim(0, 8) pl.ylabel('$\\omega$ (radians/yr)') pl.xlabel('$\\theta$ (radians)') pl.title('Hyperion $\\omega$ versus $\\theta$') pl.text(-0.75, 56, 'Elliptical orbit')
def plot_delta(self): pl.figure(figsize = (8, 8)) pl.semilogy(self.tprime, self.deltatheta, 'r.') # pl.ylim(0.0001, 0.1) pl.ylim(0.0001, 10) pl.xlim(0, 10) pl.ylabel('$\\Delta\\theta$ (radians)') pl.xlabel('time (yr)') pl.title('Hyperion $\\theta$ versus time') pl.text(4.1, 2e-4, 'Ellipitical orbit')
def plot_delta(self): pl.figure(figsize = (8, 8)) pl.semilogy(self.tprime, self.deltatheta, 'r') # pl.ylim(0.0001, 0.1) # pl.ylim(0.0001, 0.1) pl.xlim(0, 100) pl.ylabel('$\\Delta\\theta$ (radians)') pl.xlabel('time (yr)') pl.title('Hyperion $\\theta$ versus time') pl.text(4.1, 0.05, 'Circular orbit')
def plot(self): pl.figure(figsize = (8, 8)) pl.plot(self.t,self.omega, 'c') pl.ylim(-20, 60) pl.xlim(0, 10) pl.ylabel('$\\omega$ (radians/yr)') pl.xlabel('time (yr)') pl.title('Hyperion $\\omega$ versus time') pl.text(4.1, 55, 'Elliptical orbit')
def plot(self): pl.figure(figsize = (8, 8)) pl.plot(self.t,self.theta) pl.plot(self.t,self.theta, 'c') pl.ylim(-4, 4) pl.xlim(0, 10) pl.ylabel('$\\theta$ (radians)') pl.xlabel('time (yr)') pl.title('Hyperion $\\theta$ versus time') pl.text(4.1, 3.5, 'Elliptical orbit')
def plot(self): pl.figure(figsize = (8, 8)) pl.plot(self.t,self.omega, 'c') pl.ylim(0, 15) pl.xlim(0, 8) pl.ylabel('$\\omega$ (radians/yr)') pl.xlabel('time (yr)') pl.title('Hyperion $\\omega$ versus time') pl.text(3.3, 13.5, 'Circular orbit')
def DrawStr(s): pl.figure pl.text(0,0,s) pl.show() pl.close()
def text(x, y, s, fontdict=None, withdash=False, **kwargs): pl.text(x, y, s, fontdict, withdash, **kwargs)
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 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(self, output=None): """ Plot the statistics. INPUT: None OUTPUT: None SIDE EFFECTS: Generates a plot, depending on the output mode of pylab this may open a ne window. """ fig = pylab.figure() ax1 = fig.add_subplot(111) ax1.set_xlabel(self.mode[1]) ax1.set_ylabel('MB/s') ax1.set_xlim([0,self.loop+0.5]) ax1.bar(where(self.y>=0)[0]+0.1,self.y) ax1.xaxis.axes.set_autoscalex_on(False) ax1.plot([0,self.loop+0.5],[median(self.y[where(self.y > 0)]), median(self.y[where(self.y > 0)])]) ax1.plot([0,self.loop+0.5],[mean(self.y[where(self.y > 0)]), mean(self.y[where(self.y > 0)])]) pylab.text(0.02,0.95,'Median: %5.2f MB/s' % median(self.y[where(self.y > 0)]), transform = ax1.transAxes,ha='left', va='bottom', color='b', fontsize=10) pylab.text(0.02,0.95,'Mean: %5.2f MB/s' % mean(self.y[where(self.y > 0)]), transform = ax1.transAxes,ha='left', va='top', color='g', fontsize=10) ax2 = ax1.twinx() ax2.xaxis.axes.set_autoscalex_on(False) ax2.plot(where(self.n>=0)[0]+0.5,self.n,'r-', marker='o') for tl in ax2.get_yticklabels(): tl.set_color('r') ax2.set_ylabel('Number of files',{'color':'r'}) if self.mode[1] == 'Day': fig.canvas.set_window_title('%s: %s' % (self.db,self.date)) ax2.set_title('%s %s ingest rate: %s' % (self.db, self.mode[0], self.date)) else: fig.canvas.set_window_title('%s: %s' % (self.db,self.date)) ax2.set_title('%s %s ingest rate: %s' % (self.db, self.mode[0], self.date)) pylab.text(0.99,0.95,'Total: %5.2f TB' % self.tvol,transform = ax1.transAxes,ha='right', va='bottom') pylab.text(0.99,0.95,'Total # files: %8d' % self.tfils,transform = ax1.transAxes,ha='right', va='top') if (output is not None): fig.savefig(output) else: fig.show() #pl.close(fig)
def fast_load(self, file_name): file_name = file_name + "_extra" with open(file_name, 'rb') as f: (self.current_run, self.current_patient, self.patients[self.current_patient].current_voxel) = pickle.load(f) # Patient.find_patients() # b = Batch_balanced(Patient.patients_list[0:10], 17) # b.start_iteration() # # k = 10000 # (X, y) = b.get_batch(k, two_class=True) # b.stop_iteration() # # # print("done ") # print(X.shape) # print(y.shape) # # for i in range(0, k): # plt.imshow(X[i][0], cmap = "Greys_r") # plt.text(0, 0, "class : "+str(y[i]), color = "r") # plt.show() # # b.set_current_location(1, 140, 0, 0) # # b.start_iteration() # # # print(b.get_current_location()) # print(b.has_next_batch()) # x, y = b.get_batch(10000000) # print(x.shape) # print(b.patients[b.current_patient].get_current_input()) # print(b.patients[b.current_patient].get_current_location()) # print(b.get_current_location()) # print(b.has_next_batch()) # # b.stop_iteration()
def plotPrecisionRecallDiagram(title="title", points=None, labels=None, loc="best",xy_ranges = [0.6, 1.0, 0.6, 1.0], save_file = None): """Plot (precision,recall) values with 10 f-Measure equipotential lines. Plots into the current canvas. Points is a list of (precision,recall) pairs. Optionally you can also provide labels (list of strings), which will be used to create a legend, which is located at loc. """ if labels: ax = pl.axes([0.1, 0.1, 0.7, 0.8]) # llc_x, llc_y, width, height else: ax = pl.gca() pl.title(title) pl.xlabel("Precision") pl.ylabel("Recall") _plotFMeasures(start = min(xy_ranges[0],xy_ranges[2]), end = max(xy_ranges[1],xy_ranges[3])) if points: getColor = it.cycle(colors).next getMarker = it.cycle(markers).next scps = [] # scatter points for i, (x, y) in enumerate(points): label = None if labels: label = labels[i] print i, x, y, label scp = ax.scatter(x, y, label=label, s=50, linewidths=0.75, facecolor=getColor(), alpha=0.75, marker=getMarker()) scps.append(scp) # pl.plot(x,y, label=label, marker=getMarker(), markeredgewidth=0.75, markerfacecolor=getColor()) # if labels: pl.text(x, y, label, fontsize="x-small") if labels: # pl.legend(scps, labels, loc=loc, scatterpoints=1, numpoints=1, fancybox=True) # passing scps & labels explicitly to work around a bug with legend seeming to miss out the 2nd scatterplot #pl.legend(scps, labels, loc=(1.01, 0), scatterpoints=1, numpoints=1, fancybox=True) # passing scps & labels explicitly to work around a bug with legend seeming to miss out the 2nd scatterplot pl.legend(scps, labels, loc= loc, scatterpoints=1, numpoints=1, fancybox=True,fontsize = 10) # passing scps & labels explicitly to work around a bug with legend seeming to miss out the 2nd scatterplot pl.axis(xy_ranges) # xmin, xmax, ymin, ymax if save_file: pl.savefig(save_file) pl.show() pl.close()
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 plotTriangle(srcfile,samples,burn=0,**kwargs): #import triangle import corner import ugali.analysis.source import ugali.analysis.mcmc #matplotlib.rcParams.update({'text.usetex': True}) source = ugali.analysis.source.Source() source.load(srcfile,section='source') params = source.get_params() results = yaml.load(open(srcfile))['results'] samples = ugali.analysis.mcmc.Samples(samples) names = samples.names labels = names truths = [params[n] for n in names] chain = samples.get(burn=burn,clip=5) ### Triangle plot #extents = [[0,15e3],[323.6,323.8],[-59.8,-59.7],[0,0.1],[19.5,20.5]] kwargs.setdefault('extents',None) kwargs.setdefault('plot_contours',True) kwargs.setdefault('plot_datapoints',True) kwargs.setdefault('verbose',False) kwargs.setdefault('quantiles',[0.16,0.84]) if len(names) > 1: fig = corner.corner(chain,labels=labels,truths=truths,**kwargs) else: fig = plt.figure() plt.hist(chain,bins=100) plt.xlabel(names[0]) try: text = 'RA,DEC = (%.2f,%.2f)\n'%(results['ra'][0],results['dec'][0]) text += '(m-M,D) = (%.1f, %.0f kpc)\n'%(results['distance_modulus'][0],results['distance'][0]) text += r'$r_h$ = %.1f arcmin'%(results['extension_arcmin'][0])+'\n' text += 'TS = %.1f\n'%results['ts'][0] text += 'NSamples = %i\n'%(len(chain)) #plt.figtext(0.65,0.90,text,ha='left',va='top') except KeyError as e: logger.warning(str(e)) pass label = map(str.capitalize,source.name.split('_')) label[-1] = label[-1].upper() title = '%s'%' '.join(label) plt.suptitle(title) ############################################################
