python类xlim()的实例源码

def make_canonical_plot(NMP_lim=(39,76),LID_lim=(99,154),
                        c1AKE=config.angles['1AKE'],
                        c4AKE=config.angles['4AKE'],
                        xray=True):
    """Scale current figure to default limits and plot the positions of 1AKE and 4AKE.

    The points for the end states are taken from txt/x-ray_angles.txt.

    If xray=True then add locations of the X-ray structures; this is
    the same as running plot_xary_structures().
    """
    import pylab
    if xray:
        plot_xray_structures()
    pylab.plot([c1AKE[0],c4AKE[0]], [c1AKE[1],c4AKE[1]], 'sw', ms=12, alpha=0.8)
    pylab.xlim(NMP_lim)
    pylab.ylim(LID_lim)

def plot(self, outpath=''):
        pylab.figure(figsize = (17,10))
        diff = self.f2-self.f3
        pylab.subplot(2,1,1)
        pylab.plot(range(self.lengthSeq), self.f2, 'r-', label = "f2")
        pylab.plot(range(self.lengthSeq), self.f3, 'g-', label = "f3")
        pylab.xlim([0., self.lengthSeq])
        pylab.tick_params(axis='both', which='major', labelsize=25)
        pylab.subplot(2,1,2)

        diff2 = diff/self.f3
        diff2 /= np.max(diff2)
        pylab.plot(range(self.lengthSeq), diff2, 'b-', label = "Rescaled (by max) difference / f3")
        pylab.xlabel("Temps (en images)", fontsize = 25)
        pylab.tick_params(axis='both', which='major', labelsize=25)
        pylab.xlim([0., self.lengthSeq])
        #pylab.legend(loc= 2, prop = {'size':15})
        pylab.savefig(outpath)
        pylab.close()

def plotAgainstGFP_hist2d(self):
        fig1 = pylab.figure(figsize = (20, 15))
        print len(self.GFP)
        for i in xrange(min(len(data.cat), 4)):
            print len(self.GFP[self.categories == i])
            vect = []
            pylab.subplot(2,2,i+1)
            pop = self.GFP[self.categories == i]
            print "cat", i, "n pop", len(self.GFP[(self.categories == i) & (self.GFP > -np.log(12.5))])
            H, xedges, yedges = np.histogram2d(self.angles[self.categories == i], self.GFP[self.categories == i], bins = 10)
            hist = pylab.hist2d(self.GFP[self.categories == i], self.angles[self.categories == i], bins = 10, cmap = pylab.cm.Reds, normed = True)
            pylab.clim(0.,0.035)
            pylab.colorbar()
            pylab.title(data.cat[i])
            pylab.xlabel('GFP score')
            pylab.ylabel('Angle (degree)')
            pylab.xlim([-4.2, -1])
        pylab.show()

def bootstrap_extradata(self, nBoot, extradataA, nbins = 20):
        pops =[]
        meanpop = [[] for i in data.cat]
        pylab.figure(figsize = (14,14))
        for i in xrange(min(4, len(extradataA))):
            #pylab.subplot(2,2,i+1)
            if  i ==0:
                pylab.title("Bootstrap on means", fontsize = 20.)
            pop = extradataA[i]# & (self.GFP > 2000)]#
            for index in xrange(nBoot):
                newpop = np.random.choice(pop, size=len(pop), replace=True)

                #meanpop[i].append(np.mean(newpop))
            pops.append(newpop)
            pylab.legend()
        #pylab.title(cat[i])
            pylab.xlabel("Angle(degree)", fontsize = 15)
            pylab.xlim([0., 90.])
        for i in xrange(len(extradataA)):
            for j in xrange(i+1, len(extradataA)):
                statT, pvalue = scipy.stats.ttest_ind(pops[i], pops[j], equal_var=False)
                print "cat{0} & cat{1} get {2} ({3})".format(i,j, pvalue,statT)
        pylab.savefig("/users/biocomp/frose/frose/Graphics/FINALRESULTS-diff-f3/mean_nBootstrap{0}_bins{1}_GFPsup{2}_FLO_{3}.png".format(nBoot, nbins, 'all', randint(0,999)))

def view_waveforms_clusters(data, halo, threshold, templates, amps_lim, n_curves=200, save=False):

    nb_templates = templates.shape[1]
    n_panels     = numpy.ceil(numpy.sqrt(nb_templates))
    mask         = numpy.where(halo > -1)[0]
    clust_idx    = numpy.unique(halo[mask])
    fig          = pylab.figure()    
    square       = True
    center       = len(data[0] - 1)//2
    for count, i in enumerate(xrange(nb_templates)):
        if square:
            pylab.subplot(n_panels, n_panels, count + 1)
            if (numpy.mod(count, n_panels) != 0):
                pylab.setp(pylab.gca(), yticks=[])
            if (count < n_panels*(n_panels - 1)):
                pylab.setp(pylab.gca(), xticks=[])

        subcurves = numpy.where(halo == clust_idx[count])[0]
        for k in numpy.random.permutation(subcurves)[:n_curves]:
            pylab.plot(data[k], '0.5')

        pylab.plot(templates[:, count], 'r')        
        pylab.plot(amps_lim[count][0]*templates[:, count], 'b', alpha=0.5)
        pylab.plot(amps_lim[count][1]*templates[:, count], 'b', alpha=0.5)

        xmin, xmax = pylab.xlim()
        pylab.plot([xmin, xmax], [-threshold, -threshold], 'k--')
        pylab.plot([xmin, xmax], [threshold, threshold], 'k--')
        #pylab.ylim(-1.5*threshold, 1.5*threshold)
        ymin, ymax = pylab.ylim()
        pylab.plot([center, center], [ymin, ymax], 'k--')
        pylab.title('Cluster %d' %i)

    if nb_templates > 0:
        pylab.tight_layout()
    if save:
        pylab.savefig(os.path.join(save[0], 'waveforms_%s' %save[1]))
        pylab.close()
    else:
        pylab.show()
    del fig

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 align_subplot_array(axes, xlim=None, ylim=None):
    """
    Make all of the axes in the array hae the same limits, turn off unnecessary ticks
    use pb.subplots() to get an array of axes
    """

    # find sensible xlim,ylim

    if xlim is None:
        xlim = [np.inf, -np.inf]
        for ax in axes.flatten():
            xlim[0] = min(xlim[0], ax.get_xlim()[0])
            xlim[1] = max(xlim[1], ax.get_xlim()[1])
    if ylim is None:
        ylim = [np.inf, -np.inf]
        for ax in axes.flatten():
            ylim[0] = min(ylim[0], ax.get_ylim()[0])
            ylim[1] = max(ylim[1], ax.get_ylim()[1])

    (N, M) = axes.shape
    for (i, ax) in enumerate(axes.flatten()):
        ax.set_xlim(xlim)
        ax.set_ylim(ylim)
        if i % M:
            ax.set_yticks([])
        else:
            removeRightTicks(ax)
        if i < M * (N - 1):
            ax.set_xticks([])
        else:
            removeUpperTicks(ax)

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 generate_plot(self,filename,title='',xlabel='',ylabel='',xlim=None,ylim=None):

        logger = logging.getLogger("plotting")
        logger.debug('MultipleSeriesPlot.generate_plot')

        # a plot with one or more time series sharing a common x axis:
        # e.g., the training error and the validation error plotted against epochs

        # sort the data series and make sure they are consistent
        self.sort_and_validate()

        # if there is a plot already in existence, we will clear it and re-use it;
        # this avoids creating extraneous figures which will stay in memory
        # (even if we are no longer referencing them)
        if self.plot:
            self.plot.clf()
        else:
            # create a plot
            self.plot = plt.figure()

        splt = self.plot.add_subplot(1, 1, 1)
        splt.set_title(title)
        splt.set_xlabel(xlabel)
        splt.set_ylabel(ylabel)

        if xlim:
            pylab.xlim(xlim)
        if ylim:
            pylab.ylim(ylim)

        for series_name,data_points in self.data.items():
            xpoints=numpy.asarray([seq[0] for seq in data_points])
            ypoints=numpy.asarray([seq[1] for seq in data_points])
            line, = splt.plot(xpoints, ypoints, '-', linewidth=2)
            logger.debug('set_label for %s' % series_name)
            line.set_label(series_name)

        splt.legend()

        # TO DO - better filename configuration for plots
        self.plot.savefig(filename)

def __init__(self):
        pylab.ion()
        self.com_real = []
        self.com_ref = []
        self.support_areas = []
        self.xlabel = "$y$ (m)"
        self.ylabel = "$x$ (m)"
        self.xlim = (-0.6, 0.1)
        self.ylim = (0. - 0.05, 1.4 + 0.05)
        self.zmp_real = []
        self.zmp_ref = []

def plot_com(self):
        pylab.plot(
            [-p[1] for p in self.com_real], [p[0] for p in self.com_real],
            'g-', lw=2)
        pylab.plot(
            [-p[1] for p in self.com_ref], [p[0] for p in self.com_ref],
            'k--', lw=1)
        pylab.legend(('$p_G$', '$p_G^{ref}$'), loc='upper right')
        pylab.grid(False)
        pylab.xlim(self.xlim)
        pylab.ylim(self.ylim)
        pylab.xlabel(self.xlabel)
        pylab.ylabel(self.ylabel)
        pylab.title("COM trajectory")

def plot_zmp(self):
        pylab.plot(
            [-p[1] for p in self.zmp_real], [p[0] for p in self.zmp_real],
            'r-', lw=2)
        pylab.plot(
            [-p[1] for p in self.zmp_ref], [p[0] for p in self.zmp_ref],
            'k--', lw=1)
        pylab.legend(('$p_Z$', '$p_Z^{ref}$'), loc='upper right')
        pylab.grid(False)
        pylab.xlim(self.xlim)
        pylab.ylim(self.ylim)
        pylab.xlabel(self.xlabel)
        pylab.ylabel(self.ylabel)
        pylab.title("ZMP trajectory")

def plot_support_areas(self, indices=None):
        if indices is None:
            indices = range(len(self.support_areas))
        for i in indices:
            vertices, rays = self.support_areas[i]
            vertices_3d = _convert_cone2d_to_vertices(vertices, rays)
            vertices = [[-v[1], v[0]] for v in vertices_3d]
            plot_polygon(vertices, color='g', fill=None, lw=2)
        pylab.xlim(self.xlim)
        pylab.ylim(self.ylim)

def displayRetirementWithMonthsAndRates(monthlies, rates, terms):
    plt.figure('retireBoth')
    plt.clf()
    plt.xlim(30 * 12, 40 * 12)
    for monthly in monthlies:
        for rate in rates:
            xvals, yvals = retire(monthly, rate, terms)
            plt.plot(xvals, yvals,
                     label='retire:' + str(monthly) + ':' + str(int(rate * 100)))
            plt.legend(loc='upper left')

def test_dT_impact(xvals, f, nmpc, sim, start=0.1, end=0.8, step=0.02, ymax=200,
                   sample_size=100, label=None):
    """Used to generate Figure XX of the paper."""
    c = raw_input("Did you remove iter/time caps in IPOPT settings? [y/N] ")
    if c.lower() not in ['y', 'yes']:
        print "Then go ahead and do it."
        return
    stats = [Statistics() for _ in xrange(len(xvals))]
    fails = [0. for _ in xrange(len(xvals))]
    pylab.ion()
    pylab.clf()
    for (i, dT) in enumerate(xvals):
        f(dT)
        for _ in xrange(sample_size):
            nmpc.on_tick(sim)
            if 'Solve' in nmpc.nlp.return_status:
                stats[i].add(nmpc.nlp.solve_time)
            else:  # max CPU time exceeded, infeasible problem detected, ...
                fails[i] += 1.
    yvals = [1000 * ts.avg if ts.avg is not None else 0. for ts in stats]
    yerr = [1000 * ts.std if ts.std is not None else 0. for ts in stats]
    pylab.bar(
        xvals, yvals, width=step, yerr=yerr, color='y', capsize=5,
        align='center', error_kw={'capsize': 5, 'elinewidth': 5})
    pylab.xlim(start - step / 2, end + step / 2)
    pylab.ylim(0, ymax)
    pylab.grid(True)
    if label is not None:
        pylab.xlabel(label, fontsize=24)
    pylab.ylabel('Comp. time (ms)', fontsize=20)
    pylab.tick_params(labelsize=16)
    pylab.twinx()
    yfails = [100. * fails[i] / sample_size for i in xrange(len(xvals))]
    pylab.plot(xvals, yfails, 'ro', markersize=12)
    pylab.plot(xvals, yfails, 'r--', linewidth=3)
    pylab.xlim(start - step / 2, end + step / 2)
    pylab.ylabel("Failure rate [%]", fontsize=20)
    pylab.tight_layout()

def generate_plot(self,filename,title='',xlabel='',ylabel='',xlim=None,ylim=None):

        logger = logging.getLogger("plotting")
        logger.debug('MultipleSeriesPlot.generate_plot')

        # a plot with one or more time series sharing a common x axis:
        # e.g., the training error and the validation error plotted against epochs

        # sort the data series and make sure they are consistent
        self.sort_and_validate()

        # if there is a plot already in existence, we will clear it and re-use it;
        # this avoids creating extraneous figures which will stay in memory
        # (even if we are no longer referencing them)
        if self.plot:
            self.plot.clf()
        else:
            # create a plot
            self.plot = plt.figure()

        splt = self.plot.add_subplot(1, 1, 1)
        splt.set_title(title)
        splt.set_xlabel(xlabel)
        splt.set_ylabel(ylabel)

        if xlim:
            pylab.xlim(xlim)
        if ylim:
            pylab.ylim(ylim)

        for series_name,data_points in self.data.iteritems():
            xpoints=numpy.asarray([seq[0] for seq in data_points])
            ypoints=numpy.asarray([seq[1] for seq in data_points])
            line, = splt.plot(xpoints, ypoints, '-', linewidth=2)
            logger.debug('set_label for %s' % series_name)
            line.set_label(series_name)

        splt.legend()

        # TO DO - better filename configuration for plots
        self.plot.savefig(filename)

def generate_plot(self,filename,title='',xlabel='',ylabel='',xlim=None,ylim=None):

        logger = logging.getLogger("plotting")
        logger.debug('MultipleSeriesPlot.generate_plot')

        # a plot with one or more time series sharing a common x axis:
        # e.g., the training error and the validation error plotted against epochs

        # sort the data series and make sure they are consistent
        self.sort_and_validate()

        # if there is a plot already in existence, we will clear it and re-use it;
        # this avoids creating extraneous figures which will stay in memory
        # (even if we are no longer referencing them)
        if self.plot:
            self.plot.clf()
        else:
            # create a plot
            self.plot = plt.figure()

        splt = self.plot.add_subplot(1, 1, 1)
        splt.set_title(title)
        splt.set_xlabel(xlabel)
        splt.set_ylabel(ylabel)

        if xlim:
            pylab.xlim(xlim)
        if ylim:
            pylab.ylim(ylim)

        for series_name,data_points in self.data.iteritems():
            xpoints=numpy.asarray([seq[0] for seq in data_points])
            ypoints=numpy.asarray([seq[1] for seq in data_points])
            line, = splt.plot(xpoints, ypoints, '-', linewidth=2)
            logger.debug('set_label for %s' % series_name)
            line.set_label(series_name)

        splt.legend()

        # TO DO - better filename configuration for plots
        self.plot.savefig(filename)

def show_results(self):
        pl.plot(self.t, self.n_A1, 'b--', label='A1: Time Constant = 1')
        pl.plot(self.t, self.n_B1, 'g', label='B1: Time Constant = 2')
        pl.plot(self.t, self.n_A2, 'k--', label='A2: Time Constant = 1')
        pl.plot(self.t, self.n_B2, 'c', label='B2: Time Constant = 2')
        pl.plot(self.t, self.n_A3, 'm--', label='A3: Time Constant = 1')
        pl.plot(self.t, self.n_B3, 'y', label='B3: Time Constant = 2')
        pl.title('Double Decay Probelm - Nuclei with Different Time Constans')
        pl.xlim(0.0, 5.0)
        pl.ylim(0.0, 100.0)
        pl.xlabel('time ($s$)')
        pl.ylabel('Number of Nuclei')
        pl.legend(loc='upper right', shadow=True, fontsize='small')

def show_results(self):
        pl.plot(self.t, self.n_A, 'b--', label='Number of Nuclei A: Time Constant = 1')
        pl.plot(self.t, self.n_B, 'g', label='Number of Nuclei B: Time Constant = 2')
        pl.title('Double Decay Probelm - Nuclei with Different Time Constans')
        pl.xlim(0.0, 5.0)
        pl.ylim(0.0, 100.0)
        pl.xlabel('time ($s$)')
        pl.ylabel('Number of Nuclei')
        pl.legend(loc='best', shadow=True)

def show_results(self):
        pl.plot(self.t, self.n_A, 'b--', label='Number of Nuclei A')
        pl.plot(self.t, self.n_B, 'g', label='Number of Nuclei B')
        pl.title('Double Decay Probelm-Situation 1')
        pl.xlim(0.0, 5.0)
        pl.ylim(0.0, 100.0)
        pl.xlabel('time ($s$)')
        pl.ylabel('Number of Nuclei')
        pl.legend(loc='best', shadow=True)