python类text()的实例源码

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)


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