Python pylab 模块，xticks() 实例源码

def plot_word_frequencies(freq, user):
        samples = [item for item, _ in freq.most_common(50)]

        freqs = np.array([float(freq[sample]) for sample in samples])
        freqs /= np.max(freqs)

        ylabel = "Normalized word count"

        pylab.grid(True, color="silver")
        kwargs = dict()
        kwargs["linewidth"] = 2
        kwargs["label"] = user
        pylab.plot(freqs, **kwargs)
        pylab.xticks(range(len(samples)), [nltk.compat.text_type(s) for s in samples], rotation=90)
        pylab.xlabel("Samples")
        pylab.ylabel(ylabel)
        pylab.gca().set_yscale('log', basey=2)

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 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 view_raw_templates(file_name, n_temp=2, square=True):

    N_e, N_t, N_tm = templates.shape
    if not numpy.iterable(n_temp):
        if square:
            idx = numpy.random.permutation(numpy.arange(N_tm//2))[:n_temp**2]
        else:
            idx = numpy.random.permutation(numpy.arange(N_tm//2))[:n_temp]
    else:
        idx = n_temp

    import matplotlib.colors as colors
    my_cmap   = pylab.get_cmap('winter')
    cNorm     = colors.Normalize(vmin=0, vmax=N_e)
    scalarMap = pylab.cm.ScalarMappable(norm=cNorm, cmap=my_cmap)

    pylab.figure()
    for count, i in enumerate(idx):
        if square:
            pylab.subplot(n_temp, n_temp, count + 1)
            if (numpy.mod(count, n_temp) != 0):
                pylab.setp(pylab.gca(), yticks=[])
            if (count < n_temp*(n_temp - 1)):
                pylab.setp(pylab.gca(), xticks=[])
        else:
            pylab.subplot(len(idx), 1, count + 1)
            if count != (len(idx) - 1):
                pylab.setp(pylab.gca(), xticks=[])
        for j in xrange(N_e):
            colorVal = scalarMap.to_rgba(j)
            pylab.plot(templates[j, :, i], color=colorVal)

        pylab.title('Template %d' %i)
    pylab.tight_layout()
    pylab.show()

def view_whitening(data):
    pylab.subplot(121)
    pylab.imshow(data['spatial'], interpolation='nearest')
    pylab.title('Spatial')
    pylab.xlabel('# Electrode')
    pylab.ylabel('# Electrode')
    pylab.colorbar()
    pylab.subplot(122)
    pylab.title('Temporal')
    pylab.plot(data['temporal'])
    pylab.xlabel('Time [ms]')
    x, y = pylab.xticks()
    pylab.xticks(x, (x-x[-1]//2)//10)
    pylab.tight_layout()

def get_performance(file_name, name):

    a, b            = os.path.splitext(os.path.basename(file_name))
    file_name, ext  = os.path.splitext(file_name)
    file_out        = os.path.join(os.path.abspath(file_name), a)
    data            = {}
    result          = h5py.File(file_out + '.basis.hdf5')
    data['spatial']  = result.get('spatial')[:]
    data['temporal'] = numpy.zeros(61) #result.get('temporal')[:]

    pylab.figure()
    pylab.subplot(121)
    pylab.imshow(data['spatial'], interpolation='nearest')
    pylab.title('Spatial')
    pylab.xlabel('# Electrode')
    pylab.ylabel('# Electrode')
    pylab.colorbar()
    pylab.subplot(122)
    pylab.title('Temporal')
    pylab.plot(data['temporal'])
    pylab.xlabel('Time [ms]')
    x, y = pylab.xticks()
    pylab.xticks(x, (x-x[-1]//2)//10)
    pylab.tight_layout()
    plot_path = os.path.abspath(os.path.join(os.path.dirname(__file__), '.'))
    plot_path = os.path.join(plot_path, 'plots')
    plot_path = os.path.join(plot_path, 'whitening')
    if not os.path.exists(plot_path):
        os.makedirs(plot_path)
    output = os.path.join(plot_path, '%s.pdf' %name)
    pylab.savefig(output)

    return data

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 dendrogram(df, number_of_clusters, agglomerated_feature_labels):
        import seaborn as sns
        # Todo: Create Dendrogram
        # used networks are the labels occuring in agglomerated_features.labels_
        # which corresponds to np.arange(0, number_of_clusters)
        # number_of_clusters = int(df.shape[1] / 1.2)
        # used_networks = np.arange(0, number_of_clusters, dtype=int)
        used_networks = np.unique(agglomerated_feature_labels)
        # used_networks = [1, 5, 6, 7, 8, 11, 12, 13, 16, 17]

        # In our case all columns are clustered, which means used_columns is true in every element
        # used_columns = (df.columns.get_level_values(None)
                        # .astype(int)
                        # .isin(used_networks))
        # used_columns = (agglomerated_feature_labels.astype(int).isin(used_networks))
        # df = df.loc[:, used_columns]

        # Create a custom palette to identify the networks
        network_pal = sns.cubehelix_palette(len(used_networks),
                                            light=.9, dark=.1, reverse=True,
                                            start=1, rot=-2)
        network_lut = dict(zip(map(str, df.columns), network_pal))

        # Convert the palette to vectors that will be drawn on the side of the matrix
        networks = df.columns.get_level_values(None)
        # networks = agglomerated_feature_labels
        network_colors = pd.Series(networks, index=df.columns).map(network_lut)
        # plt.figure()
        # cg = sns.clustermap(df, metric="correlation")
        # plt.setp(cg.ax_heatmap.yaxis.get_majorticklabels(), rotation=0)
        sns.set(font="monospace")
        # Create custom colormap
        cmap = sns.diverging_palette(h_neg=210, h_pos=350, s=90, l=30, as_cmap=True)
        cg = sns.clustermap(df.astype(float).corr(), cmap=cmap, linewidths=.5, row_colors=network_colors,
                            col_colors=network_colors)
        plt.setp(cg.ax_heatmap.yaxis.get_majorticklabels(), rotation=0)
        plt.setp(cg.ax_heatmap.xaxis.get_majorticklabels(), rotation=90)
        # plt.xticks(rotation=90)
        plt.show()

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 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 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 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 feature_importance(rf, save_path=None):
    """Plots feature importance of sklearn RandomForest

    Parameters
    ----------
    rf : RandomForestClassifier
    save_path : str
    """
    importances = rf.feature_importances_
    nb = len(importances)
    tree_imp = [tree.feature_importances_ for tree in rf.estimators_]
    # print "Print feature importance of rf with %d trees." % len(tree_imp)
    std = np.std(tree_imp, axis=0) / np.sqrt(len(tree_imp))
    indices = np.argsort(importances)[::-1]
    # Print the feature ranking
    # print("Feature ranking:")
    # for f in range(nb):
    #     print("%d. feature %d (%f)" %
    #           (f + 1, indices[f], importances[indices[f]]))

    # Plot the feature importances of the forest
    pl.figure()
    pl.title("Feature importances")
    pl.bar(range(nb), importances[indices],
           color="r", yerr=std[indices], align="center")
    pl.xticks(range(nb), indices)
    pl.xlim([-1, nb])
    if save_path is not None:
        pl.savefig(save_path)
    pl.close()

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 view_templates(file_name, temp_id=0, best_elec=None, templates=None):

    params          = CircusParser(file_name)
    N_e             = params.getint('data', 'N_e')
    N_total         = params.getint('data', 'N_total')
    sampling_rate   = params.getint('data', 'sampling_rate')
    do_temporal_whitening = params.getboolean('whitening', 'temporal')
    do_spatial_whitening  = params.getboolean('whitening', 'spatial')
    spike_thresh     = params.getfloat('detection', 'spike_thresh')
    file_out_suff    = params.get('data', 'file_out_suff')
    N_t              = params.getint('detection', 'N_t')
    nodes, edges     = get_nodes_and_edges(params)
    chunk_size       = N_t
    N_total          = params.getint('data', 'N_total')
    inv_nodes        = numpy.zeros(N_total, dtype=numpy.int32)
    inv_nodes[nodes] = numpy.argsort(nodes)

    if templates is None:
        templates    = load_data(params, 'templates')
    clusters         = load_data(params, 'clusters')
    probe            = params.probe

    positions = {}
    for i in probe['channel_groups'].keys():
        positions.update(probe['channel_groups'][i]['geometry'])
    xmin = 0
    xmax = 0
    ymin = 0
    ymax = 0
    scaling = 10*numpy.max(numpy.abs(templates[:,temp_id].toarray().reshape(N_e, N_t)))
    for i in xrange(N_e):
        if positions[i][0] < xmin:
            xmin = positions[i][0]
        if positions[i][0] > xmax:
            xmax = positions[i][0]
        if positions[i][1] < ymin:
            ymin = positions[i][0]
        if positions[i][1] > ymax:
            ymax = positions[i][1]
    if best_elec is None:
        best_elec = clusters['electrodes'][temp_id]
    elif best_elec == 'auto':
        best_elec = numpy.argmin(numpy.min(templates[:, :, temp_id], 1))
    pylab.figure()
    for count, i in enumerate(xrange(N_e)):
        x, y     = positions[i]
        xpadding = ((x - xmin)/(float(xmax - xmin) + 1))*(2*N_t)
        ypadding = ((y - ymin)/(float(ymax - ymin) + 1))*scaling

        if i == best_elec:
            c='r'
        elif i in inv_nodes[edges[nodes[best_elec]]]:
            c='k'
        else: 
            c='0.5'
        pylab.plot(xpadding + numpy.arange(0, N_t), ypadding + templates[i, :, temp_id], color=c)
    pylab.tight_layout()
    pylab.setp(pylab.gca(), xticks=[], yticks=[])
    pylab.xlim(xmin, 3*N_t)
    pylab.show()    
    return best_elec

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 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 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 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()