python类tight_layout()的实例源码

def plot_density_map(x, y, xbins, ybins, Nlevels=4, cbar=True, weights=None):

    Z = np.histogram2d(x, y, bins=(xbins, ybins), weights=weights)[0].astype(float).T

    # central values
    lt = get_centers_from_bins(xbins)
    lm = get_centers_from_bins(ybins)
    cX, cY = np.meshgrid(lt, lm)
    X, Y = np.meshgrid(xbins, ybins)

    im = plt.pcolor(X, Y, Z, cmap=plt.cm.Blues)
    plt.contour(cX, cY, Z, levels=nice_levels(Z, Nlevels), cmap=plt.cm.Greys_r)

    if cbar:
        cb = plt.colorbar(im)
    else:
        cb = None
    plt.xlim(xbins[0], xbins[-1])
    plt.ylim(ybins[0], ybins[-1])

    try:
        plt.tight_layout()
    except Exception as e:
        print(e)
    return plt.gca(), cb

def predicted_vs_actual_y_xgb(self, xgb, best_nrounds, xgb_params, x_train_split, x_test_split, y_train_split,
                                  y_test_split, title_name):
        # Split the training data into an extra set of test
        # x_train_split, x_test_split, y_train_split, y_test_split = train_test_split(x_train, y_train)
        dtrain_split = xgb.DMatrix(x_train_split, label=y_train_split)
        dtest_split = xgb.DMatrix(x_test_split)
        print(np.shape(x_train_split), np.shape(x_test_split), np.shape(y_train_split), np.shape(y_test_split))
        gbdt = xgb.train(xgb_params, dtrain_split, best_nrounds)
        y_predicted = gbdt.predict(dtest_split)
        plt.figure(figsize=(10, 5))
        plt.scatter(y_test_split, y_predicted, s=20)
        rmse_pred_vs_actual = self.rmse(y_predicted, y_test_split)
        plt.title(''.join([title_name, ', Predicted vs. Actual.', ' rmse = ', str(rmse_pred_vs_actual)]))
        plt.xlabel('Actual y')
        plt.ylabel('Predicted y')
        plt.plot([min(y_test_split), max(y_test_split)], [min(y_test_split), max(y_test_split)])
        plt.tight_layout()

def plot(self, fontsize=16):
        """Create the barplot from the stats file"""
        from sequana.lazy import pylab
        from sequana.lazy import pandas as pd
        pylab.clf()
        df = pd.DataFrame(self._parse_data()['rules'])
        ts = df.ix['mean-runtime']
        total_time = df.ix['mean-runtime'].sum()
        #ts['total'] = self._parse_data()['total_runtime'] / float(self.N)
        ts['total'] = total_time
        ts.sort_values(inplace=True)

        ts.plot.barh(fontsize=fontsize)
        pylab.grid(True)
        pylab.xlabel("Seconds (s)", fontsize=fontsize)
        try:
            pylab.tight_layout()
        except:
            pass

def predicted_vs_actual_sale_price(self, x_train, y_train, title_name):
        # Split the training data into an extra set of test
        x_train_split, x_test_split, y_train_split, y_test_split = train_test_split(x_train, y_train)
        print(np.shape(x_train_split), np.shape(x_test_split), np.shape(y_train_split), np.shape(y_test_split))
        lasso = LassoCV(alphas=[0.0001, 0.0003, 0.0006, 0.001, 0.003, 0.006, 0.01, 0.03, 0.06, 0.1,
                                0.3, 0.6, 1],
                        max_iter=50000, cv=10)
        # lasso = RidgeCV(alphas=[0.0001, 0.0003, 0.0006, 0.001, 0.003, 0.006, 0.01, 0.03, 0.06, 0.1,
        #                         0.3, 0.6, 1], cv=10)

        lasso.fit(x_train_split, y_train_split)
        y_predicted = lasso.predict(X=x_test_split)
        plt.figure(figsize=(10, 5))
        plt.scatter(y_test_split, y_predicted, s=20)
        rmse_pred_vs_actual = self.rmse(y_predicted, y_test_split)
        plt.title(''.join([title_name, ', Predicted vs. Actual.', ' rmse = ', str(rmse_pred_vs_actual)]))
        plt.xlabel('Actual Sale Price')
        plt.ylabel('Predicted Sale Price')
        plt.plot([min(y_test_split), max(y_test_split)], [min(y_test_split), max(y_test_split)])
        plt.tight_layout()

def predicted_vs_actual_sale_price_xgb(self, xgb_params, x_train, y_train, seed, title_name):
        # Split the training data into an extra set of test
        x_train_split, x_test_split, y_train_split, y_test_split = train_test_split(x_train, y_train)
        dtrain_split = xgb.DMatrix(x_train_split, label=y_train_split)
        dtest_split = xgb.DMatrix(x_test_split)

        res = xgb.cv(xgb_params, dtrain_split, num_boost_round=1000, nfold=4, seed=seed, stratified=False,
                     early_stopping_rounds=25, verbose_eval=10, show_stdv=True)

        best_nrounds = res.shape[0] - 1
        print(np.shape(x_train_split), np.shape(x_test_split), np.shape(y_train_split), np.shape(y_test_split))
        gbdt = xgb.train(xgb_params, dtrain_split, best_nrounds)
        y_predicted = gbdt.predict(dtest_split)
        plt.figure(figsize=(10, 5))
        plt.scatter(y_test_split, y_predicted, s=20)
        rmse_pred_vs_actual = self.rmse(y_predicted, y_test_split)
        plt.title(''.join([title_name, ', Predicted vs. Actual.', ' rmse = ', str(rmse_pred_vs_actual)]))
        plt.xlabel('Actual Sale Price')
        plt.ylabel('Predicted Sale Price')
        plt.plot([min(y_test_split), max(y_test_split)], [min(y_test_split), max(y_test_split)])
        plt.tight_layout()

def plotAccuracyGraph(X, Y, Xlabel='Variable', Ylabel='Accuracy', graphTitle="Test Accuracy Graph", filename="graph.pdf"):
    """ Plots and saves accuracy graphs """
    try:
        timestamp = int(time.time())
        fig = P.figure(figsize=(8,5))
        # Set the graph's title
        P.title(graphTitle, fontname='monospace')
        # Set the axes labels
        P.xlabel(Xlabel, fontsize=12, fontname='monospace')
        P.ylabel(Ylabel, fontsize=12, fontname='monospace')
        # Add horizontal and vertical lines to the graph
        P.grid(color='DarkGray', linestyle='--', linewidth=0.1, axis='both')
        # Add the data to the graph
        P.plot(X, Y, 'r-*', linewidth=1.0)
        # Save figure
        prettyPrint("Saving figure to ./%s" % filename)#(graphTitle.replace(" ","_"), timestamp))
        P.tight_layout()
        fig.savefig("./%s" % filename)#(graphTitle.replace(" ", "_"), timestamp))

    except Exception as e:
        prettyPrint("Error encountered in \"plotAccuracyGraph\": %s" % e, "error")
        return False

    return True

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 predicted_vs_actual_y_input_model(self, model, x_train_split, x_test_split, y_train_split, y_test_split,
                                          title_name):
        # Split the training data into an extra set of test
        # x_train_split, x_test_split, y_train_split, y_test_split = train_test_split(x_train, y_train)
        print(np.shape(x_train_split), np.shape(x_test_split), np.shape(y_train_split), np.shape(y_test_split))
        model.fit(x_train_split, y_train_split)
        y_predicted = model.predict(x_test_split)
        plt.figure(figsize=(10, 5))
        plt.scatter(y_test_split, y_predicted, s=20)
        rmse_pred_vs_actual = self.rmse(y_predicted, y_test_split)
        plt.title(''.join([title_name, ', Predicted vs. Actual.', ' rmse = ', str(rmse_pred_vs_actual)]))
        plt.xlabel('Actual y')
        plt.ylabel('Predicted y')
        plt.plot([min(y_test_split), max(y_test_split)], [min(y_test_split), max(y_test_split)])
        plt.tight_layout()

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 predicted_vs_actual_sale_price_input_model(self, model, x_train, y_train, title_name):
        # Split the training data into an extra set of test
        x_train_split, x_test_split, y_train_split, y_test_split = train_test_split(x_train, y_train)
        print(np.shape(x_train_split), np.shape(x_test_split), np.shape(y_train_split), np.shape(y_test_split))
        model.fit(x_train_split, y_train_split)
        y_predicted = model.predict(x_test_split)
        plt.figure(figsize=(10, 5))
        plt.scatter(y_test_split, y_predicted, s=20)
        rmse_pred_vs_actual = self.rmse(y_predicted, y_test_split)
        plt.title(''.join([title_name, ', Predicted vs. Actual.', ' rmse = ', str(rmse_pred_vs_actual)]))
        plt.xlabel('Actual Sale Price')
        plt.ylabel('Predicted Sale Price')
        plt.plot([min(y_test_split), max(y_test_split)], [min(y_test_split), max(y_test_split)])
        plt.tight_layout()

def adjust_layout(self) :
        x0, x1, y0, y1 = plt.axis()
        plot_margin_x = 0.01 * float(x1)
        plot_margin_y = 0.01 * float(y1)
        plt.axis((x0 - plot_margin_x, x1 + plot_margin_x, y0, y1 + plot_margin_y))
        plt.tight_layout()

def tight_layout():
    from matplotlib import get_backend
    from pylab import gcf
    if get_backend().lower() in ['agg', 'macosx']:
        gcf().set_tight_layout(True)
    else:
        plt.tight_layout()

def post_callback(self, *args, **kwargs):
        self.kwargs.update(kwargs)
        tight_layout(**self.kwargs)

def plot_marginals(state_space,p,name,t,labels = False,interactive = False):
    import matplotlib

    import matplotlib.pyplot as pl
    if interactive == True: 
        pl.ion()
    pl.clf()
    pl.suptitle("time: "+ str(t)+" units")
    #print("time : "+ str(t))
    D = state_space.shape[1]

    for i in range(D):
        marg_X = np.unique(state_space[:,i])
        A = np.where(marg_X[:,np.newaxis] == state_space[:,i].T[np.newaxis,:],1,0)
        marg_p = np.dot(A,p)
        pl.subplot(int(D/2)+1,2,i+1)
        pl.plot(marg_X,marg_p)
        pl.yticks(np.linspace(np.amin(marg_p), np.amax(marg_p), num=3))
        pl.axvline(np.sum(marg_X*marg_p),color= 'r')
        pl.axvline(marg_X[np.argmax(marg_p)],color='g')
        if labels == False:
            pl.xlabel("Specie: " + str(i+1))
        else:
            pl.xlabel(labels[i])
    if interactive == True:
        pl.draw()
    else:
        pl.tight_layout()
        pl.show()

def show_comparison(expt_base, algorithms, subset, ymin=None, ymax=None):
    expt_names = ['/'.join([expt_base, alg]) for alg in algorithms]
    labels = [ALGORITHM_LABELS[alg] for alg in algorithms]
    plot_log_probs(expt_names, subset=subset, labels=labels)
    pylab.xlim(1e1, 1e5)
    if ymin is None:
        ymin, ymax = get_ylim(expt_base)
    pylab.ylim(ymin, ymax)
    pylab.xlabel('Wall clock time (seconds)', fontsize='x-large')
    pylab.ylabel('{} log-probabilities'.format({'train': 'Training', 'test': 'Test'}[subset]),
                 fontsize='x-large')
    pylab.tight_layout()

def view_synthetic_templates(self, indices=None, time=None, nn=100, hf_dist=45, a_dist=1.0):

        if indices is None:
            indices = range(self.nb_cells)

        if not numpy.iterable(indices):
            indices = [indices]

        scaling = None
        pylab.figure()

        for i in indices:

            template   = self._get_synthetic_template(i, time, nn, hf_dist, a_dist)
            template   = template.toarray()
            width      = template.shape[1]
            xmin, xmax = self.probe.field_of_view['x_min'], self.probe.field_of_view['x_max']
            ymin, ymax = self.probe.field_of_view['y_min'], self.probe.field_of_view['y_max']
            if scaling is None:
                scaling= 10*numpy.max(numpy.abs(template))
            colorVal   = self._scalarMap_synthetic.to_rgba(i)

            for count, i in enumerate(xrange(self.nb_channels)):
                x, y     = self.probe.positions[:, i]
                xpadding = ((x - xmin)/(float(xmax - xmin) + 1))*(2*width)
                ypadding = ((y - ymin)/(float(ymax - ymin) + 1))*scaling
                pylab.plot(xpadding + numpy.arange(width), ypadding + template[i, :], color=colorVal)

        pylab.tight_layout()
        pylab.setp(pylab.gca(), xticks=[], yticks=[])
        pylab.xlim(xmin, 3*width)
        pylab.show()

def view_circus_templates(self, indices=None):

        if indices is None:
            indices = range(self.nb_templates)

        if not numpy.iterable(indices):
            indices = [indices]

        data      = self.template_store.get(indices, ['templates', 'norms'])
        width     = self.template_store.width
        templates = data.pop('templates').T
        norms     = data.pop('norms')
        scaling   = None
        pylab.figure()

        for count, i in enumerate(indices):

            template   = templates[count].toarray().reshape(self.nb_channels, width) * norms[count]
            xmin, xmax = self.probe.field_of_view['x_min'], self.probe.field_of_view['x_max']
            ymin, ymax = self.probe.field_of_view['y_min'], self.probe.field_of_view['y_max']
            if scaling is None:
                scaling= 10*numpy.max(numpy.abs(template))
            colorVal   = self._scalarMap_circus.to_rgba(i)

            for count, i in enumerate(xrange(self.nb_channels)):
                x, y     = self.probe.positions[:, i]
                xpadding = ((x - xmin)/(float(xmax - xmin) + 1))*(2*width)
                ypadding = ((y - ymin)/(float(ymax - ymin) + 1))*scaling
                pylab.plot(xpadding + numpy.arange(width), ypadding + template[i, :], color=colorVal)

        pylab.tight_layout()
        pylab.setp(pylab.gca(), xticks=[], yticks=[])
        pylab.xlim(xmin, 3*width)
        pylab.show()

def main():
    from argparse import ArgumentParser
    p = ArgumentParser()
    p.add_argument('--grammar', choices=('both', 'medium', 'big'))
    p.add_argument('--rollout', choices=('CP', 'DP'))

    args = p.parse_args()

    CP = ('evalb_avg', 'pops')
    DP = ('expected_recall_avg', 'mask')

    GRAMMARS = ['medium', 'big'] if args.grammar == 'both' else [args.grammar]
    ACC, RUN = DP if args.rollout == 'DP' else CP
    pl.ion()

    fig1, ax1 = pl.subplots(nrows=3, #sharex=True,
                            ncols=2, figsize=(10,10))

    for i in range(3):
        for j in range(2):
            ax1[i,j].grid(False)

    fig2, ax2 = pl.subplots(nrows=1, #sharex=True,
                            ncols=2, figsize=(10,5))

    for i, GRAMMAR in enumerate(GRAMMARS):
        plot(GRAMMAR, ACC, RUN, ax=ax1[:,i], col=i)
        plot2(GRAMMAR, ACC, RUN, ax=ax2[i], col=i)

    fig1.tight_layout()
    fig2.tight_layout()

    pl.ioff()
    pl.show()

def show_group_frontiers(groups, name, XMAX, YMIN, baseline=None):
    colors = cycle(['m','g','b','y','c','k'])
    ax = pl.figure().add_subplot(111)
    groups = list(groups)

    print
    print yellow % name
    print yellow % '============'
    for color, (group_name, r) in zip(colors, groups):
        print '%s; color: %s; len %s' % (group_name, color, len(r))

    for color, (group_name, r) in zip(colors, groups):
        show_frontier(r.dev_runtime, r.dev_accuracy, label='%s (dev)' % group_name,
                      c=color, alpha=0.5, ax=ax, XMAX=XMAX, YMIN=YMIN, lw=LW)
#        show_frontier(r.train_runtime, r.train_accuracy, label='%s (train)' % group_name,
#                      c=color, linestyle=':', alpha=0.5, ax=ax, XMAX=XMAX, YMIN=YMIN, lw=LW)

    ax.set_xlabel('runtime')
    ax.set_ylabel('accuracy')

    if baseline is not None:
#        show_frontier(baseline.train_runtime, baseline.train_accuracy, ax=pl.gca(),
#                      c='r', linestyle=':', alpha=0.25, label='baseline (train)', XMAX=XMAX, YMIN=YMIN, lw=LW)
        show_frontier(baseline.dev_runtime, baseline.dev_accuracy, ax=pl.gca(),
                      c='r', alpha=0.25, label='baseline (dev)', XMAX=XMAX, YMIN=YMIN, lw=LW)

    ax.set_title('Frontiers grouped-by %s' % name)
    #ax.set_xlim(0.35, .85)
    #ax.set_ylim(.74, .83)

    if 0:
        ax.legend(loc='best')
        pl.tight_layout()
    else:
        # Shink current axis by 20%
        ax.legend(loc='center left', bbox_to_anchor=(1, 0.5))
        box = ax.get_position()
        ax.set_position([box.x0, box.y0, box.width * 0.85, box.height])

    ax.figure.canvas.draw()

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 view_masks(file_name, t_start=0, t_stop=1, n_elec=0):

    params          = CircusParser(file_name)
    data_file       = params.get_data_file()
    data_file.open()
    N_e             = params.getint('data', 'N_e')
    N_t             = params.getint('detection', 'N_t')
    N_total         = params.nb_channels
    sampling_rate   = params.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')
    nodes, edges     = get_nodes_and_edges(params)
    chunk_size       = (t_stop - t_start)*sampling_rate
    padding          = (t_start*sampling_rate, t_start*sampling_rate)
    inv_nodes        = numpy.zeros(N_total, dtype=numpy.int32)
    inv_nodes[nodes] = numpy.argsort(nodes)
    safety_time      = params.getint('clustering', 'safety_time')

    if do_spatial_whitening:
        spatial_whitening  = load_data(params, 'spatial_whitening')
    if do_temporal_whitening:
        temporal_whitening = load_data(params, 'temporal_whitening')

    thresholds       = load_data(params, 'thresholds')
    data = data_file.get_data(0, chunk_size, padding=padding, nodes=nodes)
    data_shape = len(data)
    data_file.close()
    peaks            = {}
    indices          = inv_nodes[edges[nodes[n_elec]]]

    if do_spatial_whitening:
        data = numpy.dot(data, spatial_whitening)
    if do_temporal_whitening: 
        data = scipy.ndimage.filters.convolve1d(data, temporal_whitening, axis=0, mode='constant')

    for i in xrange(N_e):
        peaks[i]   = algo.detect_peaks(data[:, i], thresholds[i], valley=True, mpd=0)


    pylab.figure()

    for count, i in enumerate(indices):

        pylab.plot(count*5 + data[:, i], '0.25')
        #xmin, xmax = pylab.xlim()
        pylab.scatter(peaks[i], count*5 + data[peaks[i], i], s=10, c='r')

    for count, i in enumerate(peaks[n_elec]):
        pylab.axvspan(i - safety_time, i + safety_time, facecolor='r', alpha=0.5)

    pylab.ylim(-5, len(indices)*5 )
    pylab.xlabel('Time [ms]')
    pylab.ylabel('Electrode')
    pylab.tight_layout()
    pylab.setp(pylab.gca(), yticks=[])
    pylab.show()
    return peaks

def plot_rels(data, labels=None, colors=None, outfile="rels", latent=None, alpha=0.8, title=''):
    ns, n = data.shape
    if labels is None:
        labels = map(str, range(n))
    ncol = 5
    nrow = int(np.ceil(float(n * (n - 1) / 2) / ncol))

    fig, axs = pylab.subplots(nrow, ncol)
    fig.set_size_inches(5 * ncol, 5 * nrow)
    pairs = list(combinations(range(n), 2))
    if colors is not None:
        colors = (colors - np.min(colors)) / (np.max(colors) - np.min(colors))

    for ax, pair in zip(axs.flat, pairs):
        diff_x = max(data[:, pair[0]]) - min(data[:, pair[0]])
        diff_y = max(data[:, pair[1]]) - min(data[:, pair[1]])
        ax.set_xlim([min(data[:, pair[0]]) - 0.05 * diff_x, max(data[:, pair[0]]) + 0.05 * diff_x])
        ax.set_ylim([min(data[:, pair[1]]) - 0.05 * diff_y, max(data[:, pair[1]]) + 0.05 * diff_y])
        ax.scatter(data[:, pair[0]], data[:, pair[1]], c=colors, cmap=pylab.get_cmap("jet"),
                       marker='.', alpha=alpha, edgecolors='none', vmin=0, vmax=1)

        ax.set_xlabel(shorten(labels[pair[0]]))
        ax.set_ylabel(shorten(labels[pair[1]]))

    for ax in axs.flat[axs.size - 1:len(pairs) - 1:-1]:
        ax.scatter(data[:, 0], data[:, 1], marker='.')

    fig.suptitle(title, fontsize=16)
    pylab.rcParams['font.size'] = 12  #6
    # pylab.draw()
    # fig.set_tight_layout(True)
    pylab.tight_layout()
    pylab.subplots_adjust(top=0.95)
    for ax in axs.flat[axs.size - 1:len(pairs) - 1:-1]:
        ax.set_visible(False)
    filename = outfile + '.png'
    if not os.path.exists(os.path.dirname(filename)):
        os.makedirs(os.path.dirname(filename))
    fig.savefig(outfile + '.png')
    pylab.close('all')
    return True


# Hierarchical graph visualization utilities

def _plot_features(out_dir, signal, sampling_rate, logmel, delta, delta_delta, specgram, filename):
    try:
        os.makedirs(out_dir)
    except:
        pass

    sampling_interval = 1.0 / sampling_rate
    times = np.arange(len(signal)) * sampling_interval
    pylab.clf()
    plt.rcParams['font.size'] = 18
    pylab.figure(figsize=(len(signal) / 2000, 16)) 

    ax1 = pylab.subplot(511)
    pylab.plot(times, signal)
    pylab.title("Waveform")
    pylab.xlabel("Time [sec]")
    pylab.ylabel("Amplitude")
    pylab.xlim([0, len(signal) * sampling_interval])

    ax2 = pylab.subplot(512)
    specgram = np.log(specgram)
    pylab.pcolormesh(np.arange(0, specgram.shape[0]), np.arange(0, specgram.shape[1]) * 8000 / specgram.shape[1], specgram.T, cmap=pylab.get_cmap("jet"))
    pylab.title("Spectrogram")
    pylab.xlabel("Time [sec]")
    pylab.ylabel("Frequency [Hz]")
    pylab.colorbar()

    ax3 = pylab.subplot(513)
    pylab.pcolormesh(np.arange(0, logmel.shape[0]), np.arange(1, 41), logmel.T, cmap=pylab.get_cmap("jet"))
    pylab.title("Log mel filter bank features")
    pylab.xlabel("Frame")
    pylab.ylabel("Filter number")
    pylab.colorbar()

    ax4 = pylab.subplot(514)
    pylab.pcolormesh(np.arange(0, delta.shape[0]), np.arange(1, 41), delta.T, cmap=pylab.get_cmap("jet"))
    pylab.title("Deltas")
    pylab.xlabel("Frame")
    pylab.ylabel("Filter number")
    pylab.colorbar()

    ax5 = pylab.subplot(515)
    pylab.pcolormesh(np.arange(0, delta_delta.shape[0]), np.arange(1, 41), delta_delta.T, cmap=pylab.get_cmap("jet"))
    pylab.title("Delta-deltas")
    pylab.xlabel("Frame")
    pylab.ylabel("Filter number")
    pylab.colorbar()

    pylab.tight_layout()
    pylab.savefig(os.path.join(out_dir, filename), bbox_inches="tight")

def _plotVirtualTime(accessList, archName, fgname, rd_bin_width = 250):
    """
    Plot data access based on virtual time
    """
    print "converting to num arrary for _plotVirtualTime"
    stt = time.time()
    x, y, id, ad, yd = _raListToVTimeNA(accessList)
    print ("Converting to num array takes %d seconds" % (time.time() - stt))
    fig = pl.figure()
    ax = fig.add_subplot(211)
    ax.set_xlabel('Access sequence number (%s to %s)' % (id.split('T')[0], ad.split('T')[0]), fontsize = 9)
    ax.set_ylabel('Observation sequence number', fontsize = 9)
    ax.set_title('%s archive activity ' % (archName), fontsize=10)
    ax.tick_params(axis='both', which='major', labelsize=8)
    ax.tick_params(axis='both', which='minor', labelsize=6)

    ax.plot(x, y, color = 'b', marker = 'x', linestyle = '',
                        label = 'access', markersize = 3)

    #legend = ax.legend(loc = 'upper left', shadow=True, prop={'size':7})

    ax1 = fig.add_subplot(212)
    ax1.set_xlabel('Access sequence number (%s to %s)' % (id.split('T')[0], ad.split('T')[0]), fontsize = 9)
    ax1.set_ylabel('Reuse distance (in-between accesses)', fontsize = 9)

    ax1.tick_params(axis='both', which='major', labelsize=8)
    ax1.tick_params(axis='both', which='minor', labelsize=6)

    ax1.plot(x, yd, color = 'k', marker = '+', linestyle = '',
                        label = 'reuse distance', markersize = 3)

    pl.tight_layout()
    fig.savefig(fgname)
    pl.close(fig)

    y1d = yd[~np.isnan(yd)]
    num_bin = (max(y1d) - min(y1d)) / rd_bin_width
    hist, bins = np.histogram(y1d, bins = num_bin)

    width = 0.7 * (bins[1] - bins[0])
    center = (bins[:-1] + bins[1:]) / 2
    fig1 = pl.figure()
    #fig1.suptitle('Histogram of data transfer rate from Pawsey to MIT', fontsize=14)
    ax2 = fig1.add_subplot(111)
    ax2.set_title('Reuse distance Histogram for %s' % archName, fontsize = 10)
    ax2.set_ylabel('Frequency', fontsize = 9)
    ax2.set_xlabel('Reuse distance (# of observation)', fontsize = 9)

    ax2.tick_params(axis='both', which='major', labelsize=8)
    ax2.tick_params(axis='both', which='minor', labelsize=6)

    pl.bar(center, hist, align='center', width=width)

    fileName, fileExtension = os.path.splitext(fgname)
    fig1.savefig('%s_rud_hist%s' % (fileName, fileExtension))

    pl.close(fig1)

def plotReductionGraph(dataSamples, dataLabels, classNames, dimension=2, graphTitle="Test Graph", filename="reduction.pdf"):
    """ Plots data sample visualization graphs """
    try:
        timestamp = int(time.time())
        colors = ['DarkRed', 'DarkGreen', 'DarkBlue', 'DarkOrange', 'DarkMagenta', 'DarkCyan', 'Gray', 'Black']
        randomColor = lambda: random.randint(0,255)
        markers = ['*', 'o', 'v', '^', 's', 'd', 'D', 'p', 'h', 'H', '<', '>', '.', ',', '|', '_']

        fig = P.figure(figsize=(8,5))
        if dimension == 3:
            ax = fig.add_subplot(111, projection='3d')
        P.title(graphTitle, fontname='monospace')
        if dimension == 2:
            P.xlabel('x1', fontsize=12, fontname='monospace')
            P.ylabel('x2', fontsize=12, fontname='monospace')
        else:
            ax.set_xlabel('x1', fontsize=12, fontname='monospace')
            ax.set_ylabel('x2', fontsize=12, fontname='monospace')
            ax.set_zlabel('x3', fontsize=12, fontname='monospace')

        P.grid(color='DarkGray', linestyle='--', linewidth=0.1, axis='both')

        for c in range(len(classNames)):
            X,Y,Z = [], [], []
            for labelIndex in range(len(dataLabels)):
                if c == dataLabels[labelIndex]:
                    X.append(dataSamples[labelIndex,:].tolist()[0])
                    Y.append(dataSamples[labelIndex,:].tolist()[1])
                    if dimension == 3:
                        Z.append(dataSamples[labelIndex,:].tolist()[2])

            # Plot points of that class
            #P.plot(Y, X, color='#%02X%02X%02X' % (randomColor(), randomColor(), randomColor()), marker=markers[c], markeredgecolor='None', markersize=4.0, linestyle='None', label=classNames[c])
            if dimension == 2:
                P.plot(Y, X, color=colors[c % len(colors)], marker=markers[c % len(markers)], markersize=5.0, linestyle='None', label=classNames[c])
            else:
                ax.scatter(X,Y,Z,c=colors[c % len(colors)], marker=markers[c % len(markers)])

        if dimension == 2:
            #P.legend([x.split(",")[-1] for x in classNames], fontsize='xx-small', numpoints=1, fancybox=True)
            P.legend([x for x in classNames], fontsize='xx-small', numpoints=1, fancybox=True)
        else:
            ax.legend([x for x in classNames], fontsize='xx-small', numpoints=1, fancybox=True)

        prettyPrint("Saving results to ./%s" % filename)#(graphTitle, timestamp))
        P.tight_layout()
        fig.savefig("./%s" % filename)#(graphTitle, timestamp))

    except Exception as e:
        prettyPrint("Error encountered in \"plotReductionGraph\": %s" % e, "error")
        return False

    return True