Python matplotlib.pylab 模块，savefig() 实例源码

def save_images(self, X, imgfile, density=False):
        ax = plt.axes()
        x = X[:, 0]
        y = X[:, 1]
        if density:
            xy = np.vstack([x,y])
            z = scipy.stats.gaussian_kde(xy)(xy)
            ax.scatter(x, y, c=z, marker='o', edgecolor='')
        else:
            ax.scatter(x, y, marker='o', c=range(x.shape[0]),
                        cmap=plt.cm.coolwarm)

        if self.collection is not None:
            self.collection.set_transform(ax.transData)
            ax.add_collection(self.collection)


        ax.text(x[0], y[0], str('start'), transform=ax.transAxes)
        ax.axis([-0.2, 1.2, -0.2, 1.2])
        fig = plt.gcf()

        plt.savefig(imgfile)
        plt.close()

def plot_volcano(logFC,p_val,sample_name,saveName,logFC_thresh):
    fig=pl.figure()
    ## To plot and save
    pl.scatter(logFC[(p_val>0.05)|(abs(logFC)<logFC_thresh)],-np.log10(p_val[(p_val>0.05)|(abs(logFC)<logFC_thresh)]),color='blue',alpha=0.5);
    pl.scatter(logFC[(p_val<0.05)&(abs(logFC)>logFC_thresh)],-np.log10(p_val[(p_val<0.05)&(abs(logFC)>logFC_thresh)]),color='red');
    pl.hlines(-np.log10(0.05),min(logFC),max(logFC))
    pl.vlines(-logFC_thresh,min(-np.log10(p_val)),max(-np.log10(p_val)))
    pl.vlines(logFC_thresh,min(-np.log10(p_val)),max(-np.log10(p_val)))
    pl.xlim(-3,3)
    pl.xlabel('Log Fold Change')
    pl.ylabel('-log10(p-value)')
    pl.savefig(saveName)
    pl.close(fig)


# def plot_histograms(df_peaks,pntr_list):
#
#     for pntr in pntr_list:
#         colName =pntr[2]+'_Intragenic_position'
#         pl.hist(df_peaks[colName])
#         pl.xlabel(colName)
#         pl.ylabel()
#         pl.show()

def plot_prediction_MM(model, y_train, y_test, plot_title=''):
    T = y_test.shape[0]
    mx, vx, my, vy_noiseless, vy = model.predict_forward(T, prop_mode=PROP_MM)
    T_train = y_train.shape[0]
    fig = plt.figure()
    ax = fig.add_subplot(111)
    ax.plot(np.arange(T_train), y_train[:, 0], 'k+-')
    ttest = np.arange(T_train, T_train+T)
    # pdb.set_trace()
    ax.plot(ttest, my[:, 0], '-', color='b')
    ax.fill_between(
        ttest, 
        my[:, 0] + 2*np.sqrt(vy_noiseless[:, 0]),
        my[:, 0] - 2*np.sqrt(vy_noiseless[:, 0]),
        alpha=0.3, edgecolor='b', facecolor='b')
    ax.fill_between(
        ttest, 
        my[:, 0] + 2*np.sqrt(vy[:, 0]),
        my[:, 0] - 2*np.sqrt(vy[:, 0]),
        alpha=0.1, edgecolor='b', facecolor='b')
    ax.plot(ttest, y_test, 'ro')
    ax.set_xlim([T_train-5, T_train + T])
    plt.title(plot_title)
    plt.savefig('/tmp/kink_pred_MM_'+plot_title+'.pdf')
    # plt.savefig('/tmp/kink_pred_MM_'+plot_title+'.png')

def plot(params_dir):
    model_dirs = [name for name in os.listdir(params_dir)
                  if os.path.isdir(os.path.join(params_dir, name))]

    df = defaultdict(list)
    for model_dir in model_dirs:
        df[re.sub('_bin_scaled_mono_True_ratio', '', model_dir)] = [
            dd.io.load(path)['best_epoch']['validate_objective']
            for path in glob.glob(os.path.join(
                params_dir, model_dir) + '/*.h5')]

    df = pd.DataFrame(dict([(k, pd.Series(v)) for k, v in df.iteritems()]))
    df.to_csv(os.path.basename(os.path.normpath(params_dir)))
    plt.figure(figsize=(16, 4), dpi=300)
    g = sns.boxplot(df)
    g.set_xticklabels(df.columns, rotation=45)
    plt.tight_layout()
    plt.savefig('{}_errors_box_plot.png'.format(
        os.path.join(IMAGES_DIRECTORY,
                     os.path.basename(os.path.normpath(params_dir)))))

def plot_confusion_matrix(cm, label_list, title='Confusion matrix', cmap=None):
    from matplotlib import pylab
    cm = np.asarray(cm, dtype=np.float32)
    for i, row in enumerate(cm):
        cm[i] = cm[i] / np.sum(cm[i])
    #import matplotlib.pyplot as plt
    #plt.ion()
    pylab.clf()
    pylab.matshow(cm, fignum=False, cmap='Blues', vmin=0, vmax=1.0)
    ax = pylab.axes()
    ax.set_xticks(range(len(label_list)))
    ax.set_xticklabels(label_list, rotation='vertical')
    ax.xaxis.set_ticks_position('bottom')
    ax.set_yticks(range(len(label_list)))
    ax.set_yticklabels(label_list)
    pylab.title(title)
    pylab.colorbar()
    pylab.grid(False)
    pylab.xlabel('Predicted class')
    pylab.ylabel('True class')
    pylab.grid(False)
    pylab.savefig('test.jpg')
    pylab.show()

def plot_1d(dataset, nbins, data):
    with sns.axes_style('white'):
        plt.rc('font', weight='bold')
        plt.rc('grid', lw=2)
        plt.rc('lines', lw=3)
        plt.figure(1)
        plt.hist(data, bins=np.arange(nbins+1), color='blue')
        plt.ylabel('Count', weight='bold', fontsize=24)
        xticks = list(plt.gca().get_xticks())
        while (nbins-1) / float(xticks[-1]) < 1.1:
            xticks = xticks[:-1]
        while xticks[0] < 0:
            xticks = xticks[1:]
        xticks.append(nbins-1)
        xticks = list(sorted(xticks))
        plt.gca().set_xticks(xticks)
        plt.xlim([int(np.ceil(-0.05*nbins)),int(np.ceil(nbins*1.05))])
        plt.legend(loc='upper right')
        plt.savefig('plots/marginals-{0}.pdf'.format(dataset.replace('_','-')), bbox_inches='tight')
        plt.clf()
        plt.close()

def plot_2d(dataset, nbins, data, extra=None):
    with sns.axes_style('white'):
        plt.rc('font', weight='bold')
        plt.rc('grid', lw=2)
        plt.rc('lines', lw=2)
        rows, cols = nbins
        im = np.zeros(nbins)
        for i in xrange(rows):
            for j in xrange(cols):
                im[i,j] = ((data[:,0] == i) & (data[:,1] == j)).sum()
        plt.imshow(im, cmap='gray_r', interpolation='none')
        if extra is not None:
            dataset += extra
        plt.savefig('plots/marginals-{0}.pdf'.format(dataset.replace('_','-')), bbox_inches='tight')
        plt.clf()
        plt.close()

def plot_1d(dataset, nbins):
    data = np.loadtxt('experiments/uci/data/splits/{0}_all.csv'.format(dataset), skiprows=1, delimiter=',')[:,-1]
    with sns.axes_style('white'):
        plt.rc('font', weight='bold')
        plt.rc('grid', lw=2)
        plt.rc('lines', lw=3)
        plt.figure(1)
        plt.hist(data, bins=np.arange(nbins+1), color='blue')
        plt.ylabel('Count', weight='bold', fontsize=24)
        xticks = list(plt.gca().get_xticks())
        while (nbins-1) / float(xticks[-1]) < 1.1:
            xticks = xticks[:-1]
        while xticks[0] < 0:
            xticks = xticks[1:]
        xticks.append(nbins-1)
        xticks = list(sorted(xticks))
        plt.gca().set_xticks(xticks)
        plt.xlim([int(np.ceil(-0.05*nbins)),int(np.ceil(nbins*1.05))])
        plt.legend(loc='upper right')
        plt.savefig('plots/marginals-{0}.pdf'.format(dataset.replace('_','-')), bbox_inches='tight')
        plt.clf()
        plt.close()

def plot_2d(dataset, nbins, data=None, extra=None):
    if data is None:
        data = np.loadtxt('experiments/uci/data/splits/{0}_all.csv'.format(dataset), skiprows=1, delimiter=',')[:,-2:]
    with sns.axes_style('white'):
        plt.rc('font', weight='bold')
        plt.rc('grid', lw=2)
        plt.rc('lines', lw=2)
        rows, cols = nbins
        im = np.zeros(nbins)
        for i in xrange(rows):
            for j in xrange(cols):
                im[i,j] = ((data[:,0] == i) & (data[:,1] == j)).sum()
        plt.imshow(im, cmap='gray_r', interpolation='none')
        if extra is not None:
            dataset += extra
        plt.savefig('plots/marginals-{0}.pdf'.format(dataset.replace('_','-')), bbox_inches='tight')
        plt.clf()
        plt.close()

def plotValueFunction(self, valueFunction, prefix):
        '''3d plot of a value function.'''
        fig, ax = plt.subplots(subplot_kw = dict(projection = '3d'))
        X, Y = np.meshgrid(np.arange(self.numCols), np.arange(self.numRows))
        Z = valueFunction.reshape(self.numRows, self.numCols)

        for i in xrange(len(X)):
            for j in xrange(len(X[i])/2):
                tmp = X[i][j]
                X[i][j] = X[i][len(X[i]) - j - 1]
                X[i][len(X[i]) - j - 1] = tmp

        my_col = cm.jet(np.random.rand(Z.shape[0],Z.shape[1]))

        ax.plot_surface(X, Y, Z, rstride = 1, cstride = 1,
            cmap = plt.get_cmap('jet'))
        plt.gca().view_init(elev=30, azim=30)
        plt.savefig(self.outputPath + prefix + 'value_function.png')
        plt.close()

def plotLine(self, x_vals, y_vals, x_label, y_label, title, filename=None):
        plt.clf()

        plt.xlabel(x_label)
        plt.xlim(((min(x_vals) - 0.5), (max(x_vals) + 0.5)))
        plt.ylabel(y_label)
        plt.ylim(((min(y_vals) - 0.5), (max(y_vals) + 0.5)))

        plt.title(title)
        plt.plot(x_vals, y_vals, c='k', lw=2)
        #plt.plot(x_vals, len(x_vals) * y_vals[0], c='r', lw=2)

        if filename == None:
            plt.show()
        else:
            plt.savefig(self.outputPath + filename)

def plot_entropy():
    pylab.clf()
    pylab.figure(num=None, figsize=(5, 4))

    title = "Entropy $H(X)$"
    pylab.title(title)
    pylab.xlabel("$P(X=$coin will show heads up$)$")
    pylab.ylabel("$H(X)$")

    pylab.xlim(xmin=0, xmax=1.1)
    x = np.arange(0.001, 1, 0.001)
    y = -x * np.log2(x) - (1 - x) * np.log2(1 - x)
    pylab.plot(x, y)
    # pylab.xticks([w*7*24 for w in [0,1,2,3,4]], ['week %i'%(w+1) for w in
    # [0,1,2,3,4]])

    pylab.autoscale(tight=True)
    pylab.grid(True)

    filename = "entropy_demo.png"
    pylab.savefig(os.path.join(CHART_DIR, filename), bbox_inches="tight")

def plot_feat_importance(feature_names, clf, name):
    pylab.clf()
    coef_ = clf.coef_
    important = np.argsort(np.absolute(coef_.ravel()))
    f_imp = feature_names[important]
    coef = coef_.ravel()[important]
    inds = np.argsort(coef)
    f_imp = f_imp[inds]
    coef = coef[inds]
    xpos = np.array(range(len(coef)))
    pylab.bar(xpos, coef, width=1)

    pylab.title('Feature importance for %s' % (name))
    ax = pylab.gca()
    ax.set_xticks(np.arange(len(coef)))
    labels = ax.set_xticklabels(f_imp)
    for label in labels:
        label.set_rotation(90)
    filename = name.replace(" ", "_")
    pylab.savefig(os.path.join(
        CHART_DIR, "feat_imp_%s.png" % filename), bbox_inches="tight")

def plot_feat_importance(feature_names, clf, name):
    pylab.figure(num=None, figsize=(6, 5))
    coef_ = clf.coef_
    important = np.argsort(np.absolute(coef_.ravel()))
    f_imp = feature_names[important]
    coef = coef_.ravel()[important]
    inds = np.argsort(coef)
    f_imp = f_imp[inds]
    coef = coef[inds]
    xpos = np.array(list(range(len(coef))))
    pylab.bar(xpos, coef, width=1)

    pylab.title('Feature importance for %s' % (name))
    ax = pylab.gca()
    ax.set_xticks(np.arange(len(coef)))
    labels = ax.set_xticklabels(f_imp)
    for label in labels:
        label.set_rotation(90)
    filename = name.replace(" ", "_")
    pylab.savefig(os.path.join(
        CHART_DIR, "feat_imp_%s.png" % filename), bbox_inches="tight")

def plot_confusion_matrix(cm, genre_list, name, title):
    pylab.clf()
    pylab.matshow(cm, fignum=False, cmap='Blues', vmin=0, vmax=1.0)
    ax = pylab.axes()
    ax.set_xticks(range(len(genre_list)))
    ax.set_xticklabels(genre_list)
    ax.xaxis.set_ticks_position("bottom")
    ax.set_yticks(range(len(genre_list)))
    ax.set_yticklabels(genre_list)
    pylab.title(title)
    pylab.colorbar()
    pylab.grid(False)
    pylab.show()
    pylab.xlabel('Predicted class')
    pylab.ylabel('True class')
    pylab.grid(False)
    pylab.savefig(
        os.path.join(CHART_DIR, "confusion_matrix_%s.png" % name), bbox_inches="tight")

def plot_roc(auc_score, name, tpr, fpr, label=None):
    pylab.clf()
    pylab.figure(num=None, figsize=(5, 4))
    pylab.grid(True)
    pylab.plot([0, 1], [0, 1], 'k--')
    pylab.plot(fpr, tpr)
    pylab.fill_between(fpr, tpr, alpha=0.5)
    pylab.xlim([0.0, 1.0])
    pylab.ylim([0.0, 1.0])
    pylab.xlabel('False Positive Rate')
    pylab.ylabel('True Positive Rate')
    pylab.title('ROC curve (AUC = %0.2f) / %s' %
                (auc_score, label), verticalalignment="bottom")
    pylab.legend(loc="lower right")
    filename = name.replace(" ", "_")
    pylab.savefig(
        os.path.join(CHART_DIR, "roc_" + filename + ".png"), bbox_inches="tight")

def plotKChart(self, misClassDict, saveFigPath):
        kList = []
        misRateList = []
        for k, misClassNum in misClassDict.iteritems():
            kList.append(k)
            misRateList.append(1.0 - 1.0/k*misClassNum)

        fig = plt.figure(saveFigPath)
        plt.plot(kList, misRateList, 'r--')
        plt.title(saveFigPath)
        plt.xlabel('k Num.')
        plt.ylabel('Misclassified Rate')
        plt.legend(saveFigPath)
        plt.grid(True)
        plt.savefig(saveFigPath)
        plt.show()

################################### PART3 TEST ########################################
# ??

def test_plot_timeseries2():
    filename = abspath(join(testdir, 'plot_timeseries2.png'))
    if isfile(filename):
        os.remove(filename)

    periods = 5
    index = pd.date_range('1/1/2016', periods=periods, freq='H')
    data = np.array([[1,2,3], [4,5,6], [7,8,9], [10,11,12], [13,14,15]])
    df = pd.DataFrame(data=data, index=index, columns=['A', 'B', 'C'])
    tfilter = pd.Series(data = (df.index < index[3]), index = df.index)

    plt.figure()
    pecos.graphics.plot_timeseries(df,tfilter, yaxis_min=0, yaxis_max=20)
    plt.savefig(filename, format='png')
    plt.close()

    assert_true(isfile(filename))

def test_plot_heatmap1():
    filename = abspath(join(testdir, 'plot_heatmap1.png'))
    if isfile(filename):
        os.remove(filename)

    periods = 5
    index = pd.date_range('1/1/2016', periods=periods, freq='D')
    data = np.random.rand(periods, 4)
    df = pd.DataFrame(data=data, index=index, columns=['A', 'B', 'C', 'D'])

    plt.figure()
    pecos.graphics.plot_heatmap(df)
    plt.savefig(filename, format='png', bbox_inches='tight', pad_inches = 0)
    plt.close()

    assert_true(isfile(filename))

def test_plot_doy_heatmap1():
    filename = abspath(join(testdir, 'plot_doy_heatmap1.png'))
    if isfile(filename):
        os.remove(filename)

    periods = 5*24 # 5 days
    index = pd.date_range('3/1/2016', periods=periods, freq='H')
    data = np.random.rand(periods)
    df = pd.DataFrame(data=data, index=index, columns=['A'])

    plt.figure()
    pecos.graphics.plot_doy_heatmap(df['A'])
    plt.savefig(filename, format='png')
    plt.close()

    assert_true(isfile(filename))

def test_plot_doy_heatmap2():
    filename = abspath(join(testdir, 'plot_doy_heatmap2.png'))
    if isfile(filename):
        os.remove(filename)

    periods = 365*12
    index = pd.date_range('1/1/2016', periods=periods, freq='2H')
    data = np.random.rand(periods)
    df = pd.DataFrame(data=data, index=index, columns=['A'])
    overlay = pd.DataFrame(index=[1,100,200,300,365], 
                           data={'A': [40000,20000,60000,10000,5000],
                                 'B': [60000,70000,75000,50000,65000]})

    plt.figure()
    pecos.graphics.plot_doy_heatmap(df['A'], cmap='gray', overlay=overlay)
    plt.savefig(filename, format='png')
    plt.close()

    assert_true(isfile(filename))

def fit_data():
    data=np.loadtxt('data.dat')
    print(data)
    params = dict()
    params["c"] = {"min" : -np.inf,"max" : np.inf}
    result = qudi_fitting.make_lorentzian_fit(axis=data[:,0], data=data[:,3], add_parameters=params)
    print(result.fit_report())
    plt.plot(data[:,0],-data[:,3]+2,"b-o",label="data mean")
#    plt.plot(data[:,0],data[:,1],label="data")
#    plt.plot(data[:,0],data[:,2],label="data")
    plt.plot(data[:,0],-result.best_fit+2,"r-",linewidth=2.,label="fit")
#    plt.plot(data[:,0],result.init_fit,label="init")
    plt.xlabel("time (ns)")
    plt.ylabel("polarization transfer (arb. u.)")
    plt.legend(loc=1)
#    plt.savefig("pol20_24repetition_pol.pdf")
#    plt.savefig("pol20_24repetition_pol.png")
    plt.show()
    savedata=[[data[ii,0],-data[ii,3]+2,-result.best_fit[ii]+2] for ii in range(len(data[:,0]))]
    np.savetxt("pol_data_fit.csv",savedata)
#    print(result.params)

    print(result.params)

def plot_confusion_matrix(cm, plot_title, filename, genres=None):
    if not genres:
        genres = GENRES

    pylab.clf()
    pylab.matshow(cm, fignum=False, cmap='Blues', vmin=0, vmax=100.0)

    axes = pylab.axes()
    axes.set_xticks(range(len(genres)))
    axes.set_xticklabels(genres, rotation=45)

    axes.set_yticks(range(len(genres)))
    axes.set_yticklabels(genres)
    axes.xaxis.set_ticks_position("bottom")

    pylab.title(plot_title, fontsize=14)
    pylab.colorbar()
    pylab.xlabel('Predicted class', fontsize=12)
    pylab.ylabel('Correct class', fontsize=12)
    pylab.grid(False)
    #pylab.show()
    pylab.savefig(os.path.join(PLOTS_DIR, "cm_%s.eps" % filename), bbox_inches="tight")

def plot_confusion_matrix(cm, label_list, title='Confusion matrix', cmap=None):
    from matplotlib import pylab
    cm = np.asarray(cm, dtype=np.float32)
    for i, row in enumerate(cm):
        cm[i] = cm[i] / np.sum(cm[i])
    #import matplotlib.pyplot as plt
    #plt.ion()
    pylab.clf()
    pylab.matshow(cm, fignum=False, cmap='Blues', vmin=0, vmax=1.0)
    ax = pylab.axes()
    ax.set_xticks(range(len(label_list)))
    ax.set_xticklabels(label_list, rotation='vertical')
    ax.xaxis.set_ticks_position('bottom')
    ax.set_yticks(range(len(label_list)))
    ax.set_yticklabels(label_list)
    pylab.title(title)
    pylab.colorbar()
    pylab.grid(False)
    pylab.xlabel('Predicted class')
    pylab.ylabel('True class')
    pylab.grid(False)
    pylab.savefig('test.jpg')
    pylab.show()

def plot_feat_importance(feature_names, clf, name):
    pylab.figure(num=None, figsize=(6, 5))
    coef_ = clf.coef_
    important = np.argsort(np.absolute(coef_.ravel()))
    f_imp = feature_names[important]
    coef = coef_.ravel()[important]
    inds = np.argsort(coef)
    f_imp = f_imp[inds]
    coef = coef[inds]
    xpos = np.array(list(range(len(coef))))
    pylab.bar(xpos, coef, width=1)

    pylab.title('Feature importance for %s' % (name))
    ax = pylab.gca()
    ax.set_xticks(np.arange(len(coef)))
    labels = ax.set_xticklabels(f_imp)
    for label in labels:
        label.set_rotation(90)
    filename = name.replace(" ", "_")
    pylab.savefig(os.path.join(
        CHART_DIR, "feat_imp_%s.png" % filename), bbox_inches="tight")

def plot_feat_importance(feature_names, clf, name):
    pylab.figure(num=None, figsize=(6, 5))
    coef_ = clf.coef_
    important = np.argsort(np.absolute(coef_.ravel()))
    f_imp = feature_names[important]
    coef = coef_.ravel()[important]
    inds = np.argsort(coef)
    f_imp = f_imp[inds]
    coef = coef[inds]
    xpos = np.array(list(range(len(coef))))
    pylab.bar(xpos, coef, width=1)

    pylab.title('Feature importance for %s' % (name))
    ax = pylab.gca()
    ax.set_xticks(np.arange(len(coef)))
    labels = ax.set_xticklabels(f_imp)
    for label in labels:
        label.set_rotation(90)
    filename = name.replace(" ", "_")
    pylab.savefig(os.path.join(
        CHART_DIR, "feat_imp_%s.png" % filename), bbox_inches="tight")

def nmf(fdoc, fvocab):
    T = 100

    nmf = NMF(fdoc, fvocab)
    nmf.train(T)
    nmf.get_words()
    # print(mf.R)

    plt.figure()
    plt.plot(range(1,T+1),nmf.objective)
    plt.xticks(np.linspace(1,T,10))
    plt.xlabel('Iterations')
    plt.ylabel('Objective')
    plt.title('Variation of objective with iterations')
    plt.savefig('hw5_2a.png')
    plt.show()

def gp_partd(Xtrain,ytrain,Xtest,ytest):
    gp = gaussian_process(Xtrain[:,3],ytrain,Xtrain[:,3],ytrain)


    gp.init_kernel_matrices(b=5,var=2)
    gp.predict_test()

    x = np.asarray(Xtrain[:,3]).flatten()
    xsortind = np.argsort(x)
    y1 = np.asarray(ytrain).flatten()
    y2 = np.asarray(gp.test_predictions).flatten()
    plt.figure()
    plt.scatter(x[xsortind],y1[xsortind])
    plt.plot(x[xsortind],y2[xsortind],'b-')
    plt.xlabel('Car Weight (Dimension 4)')
    plt.ylabel('Outcome')
    plt.title('Visualizing model through single dimension')
    plt.savefig('hw3_gaussian_dim4_viz')
    plt.show()

def test_plot_fragility_curve1():
    from scipy.stats import lognorm
    filename = abspath(join(testdir, 'plot_fragility_curve1.png'))
    if isfile(filename):
        os.remove(filename)

    FC = wntr.scenario.FragilityCurve()
    FC.add_state('Minor', 1, {'Default': lognorm(0.5,scale=0.3)})
    FC.add_state('Major', 2, {'Default': lognorm(0.5,scale=0.7)}) 

    plt.figure()
    wntr.graphics.plot_fragility_curve(FC)
    plt.savefig(filename, format='png')
    plt.close()

    assert_true(isfile(filename))

def plot_true_and_augmented_data(sample,noised_sample,label,n_examples):
    output_dir = os.path.split(FLAGS.output)[0]
    # Save augmented data
    plt.clf()
    fig, ax = plt.subplots(3,1)
    for t in range(noised_sample.shape[1]):
        ax[t].plot(noised_sample[:,t])
        ax[t].set_xlabel('time (samples)')
        ax[t].set_ylabel('amplitude')
    ax[0].set_title('window {:03d}, cluster_id: {}'.format(n_examples,label))
    plt.savefig(os.path.join(output_dir, "augmented_data",
                            'augmented_{:03d}.pdf'.format(n_examples)))
    plt.close()

    # Save true data
    plt.clf()
    fig, ax = plt.subplots(3,1)
    for t in range(sample.shape[1]):
        ax[t].plot(sample[:,t])
        ax[t].set_xlabel('time (samples)')
        ax[t].set_ylabel('amplitude')
    ax[0].set_title('window {:03d}, cluster_id: {}'.format(n_examples,label))
    plt.savefig(os.path.join(output_dir, "true_data",
                            'true__{:03d}.pdf'.format(n_examples)))
    plt.close()

def test_abu_evolution(self):
        from nugridpy import ppn, utils
        import matplotlib
        matplotlib.use('agg')
        import matplotlib.pylab as mpy
        import os

        # Perform tests within temporary directory
        with TemporaryDirectory() as tdir:
            # wget the data for a ppn run from the CADC VOspace
            os.system("wget -q --content-disposition --directory '" + tdir +  "' "\
                          + "'http://www.canfar.phys.uvic.ca/vospace/synctrans?TARGET="\
                          + "vos%3A%2F%2Fcadc.nrc.ca%21vospace%2Fnugrid%2Fdata%2Fprojects%2Fppn%2Fexamples%2F"\
                          + "ppn_Hburn_simple%2Fx-time.dat&DIRECTION=pullFromVoSpace&PROTOCOL"\
                          + "=ivo%3A%2F%2Fivoa.net%2Fvospace%2Fcore%23httpget'")

            #nugrid_dir= os.path.dirname(os.path.dirname(ppn.__file__))
            #NuPPN_dir= nugrid_dir + "/NuPPN"
            #test_data_dir= NuPPN_dir + "/examples/ppn_Hburn_simple/RUN_MASTER"

            symbs=utils.symbol_list('lines2')
            x=ppn.xtime(tdir)
            specs=['PROT','HE  4','C  12','N  14','O  16']
            i=0
            for spec in specs:
                x.plot('time',spec,logy=True,logx=True,shape=utils.linestyle(i)[0],show=False,title='')
                i += 1
            mpy.ylim(-5,0.2)
            mpy.legend(loc=0)
            mpy.xlabel('$\log t / \mathrm{min}$')
            mpy.ylabel('$\log X \mathrm{[mass fraction]}$')
            abu_evol_file = 'abu_evolution.png'
            mpy.savefig(abu_evol_file)
            self.assertTrue(os.path.exists(abu_evol_file))

def save(self, out_path):
        '''Saves a figure for the monitor

        Args:
            out_path: str
        '''

        plt.clf()
        np.set_printoptions(precision=4)
        font = {
            'size': 7
        }
        matplotlib.rc('font', **font)
        y = 2
        x = ((len(self.d) - 1) // y) + 1
        fig, axes = plt.subplots(y, x)
        fig.set_size_inches(20, 8)

        for j, (k, v) in enumerate(self.d.iteritems()):
            ax = axes[j // x, j % x]
            ax.plot(v, label=k)
            if k in self.d_valid.keys():
                ax.plot(self.d_valid[k], label=k + '(valid)')
            ax.set_title(k)
            ax.legend()

        plt.tight_layout()
        plt.savefig(out_path, facecolor=(1, 1, 1))
        plt.close()

def draw(m, name, extra=None):
  FIG.clf()

  matrix = m
  orig_shape = np.shape(matrix)
  # lose the channel shape in the end of orig_shape
  new_shape = orig_shape[:-1] 
  matrix = np.reshape(matrix, new_shape)
  ax = FIG.add_subplot(1,1,1)
  ax.set_aspect('equal')
  plt.imshow(matrix, interpolation='nearest', cmap=plt.cm.gray)
  # plt.imshow(matrix, interpolation='nearest', cmap=plt.cm.ocean)
  plt.colorbar()

  if extra != None:
    greens, reds = extra
    grn_x, grn_y, = greens
    red_x, red_y = reds
    plt.scatter(x=grn_x, y=grn_y, c='g', s=40)
    plt.scatter(x=red_x, y=red_y, c='r', s=40)
#  # put a blue dot at (10, 20)
#  plt.scatter([10], [20])
#  # put a red dot, size 40, at 2 locations:
#  plt.scatter(x=[3, 4], y=[5, 6], c='r', s=40)
#  # plt.plot()

  plt.savefig(name)

def draw_annotate(x_cords, y_cords, anns, name):
  FIG.clf()
  y = x_cords
  z = y_cords
  n = anns
  fig = FIG
  ax = fig.add_subplot(1,1,1)
  ax.set_xlim([0,L])
  ax.set_ylim([0,L])
  ax.set_ylim(ax.get_ylim()[::-1])
  ax.scatter(z, y)

  for i, txt in enumerate(n):
    ax.annotate(txt, (z[i],y[i]))
  fig.savefig(name)

def draw(m, name, extra=None):
  FIG.clf()

  matrix = m
  orig_shape = np.shape(matrix)
  # lose the channel shape in the end of orig_shape
  new_shape = orig_shape[:-1] 
  matrix = np.reshape(matrix, new_shape)
  ax = FIG.add_subplot(1,1,1)
  ax.set_aspect('equal')
  plt.imshow(matrix, interpolation='nearest', cmap=plt.cm.gray)
  # plt.imshow(matrix, interpolation='nearest', cmap=plt.cm.ocean)
  plt.colorbar()

  if extra != None:
    greens, reds = extra
    grn_x, grn_y, = greens
    red_x, red_y = reds
    plt.scatter(x=grn_x, y=grn_y, c='g', s=40)
    plt.scatter(x=red_x, y=red_y, c='r', s=40)
#  # put a blue dot at (10, 20)
#  plt.scatter([10], [20])
#  # put a red dot, size 40, at 2 locations:
#  plt.scatter(x=[3, 4], y=[5, 6], c='r', s=40)
#  # plt.plot()

  plt.savefig(name)

def draw_annotate(x_cords, y_cords, anns, name):
  FIG.clf()
  y = x_cords
  z = y_cords
  n = anns
  fig = FIG
  ax = fig.add_subplot(1,1,1)
  ax.set_xlim([0,L])
  ax.set_ylim([0,L])
  ax.set_ylim(ax.get_ylim()[::-1])
  ax.scatter(z, y)

  for i, txt in enumerate(n):
    ax.annotate(txt, (z[i],y[i]))
  fig.savefig(name)

def draw(m, name, extra=None):
  FIG.clf()

  matrix = m
  orig_shape = np.shape(matrix)
  # lose the channel shape in the end of orig_shape
  new_shape = orig_shape[:-1] 
  matrix = np.reshape(matrix, new_shape)
  ax = FIG.add_subplot(1,1,1)
  ax.set_aspect('equal')
  plt.imshow(matrix, interpolation='nearest', cmap=plt.cm.gray)
  # plt.imshow(matrix, interpolation='nearest', cmap=plt.cm.ocean)
  plt.colorbar()

  if extra != None:
    greens, reds = extra
    grn_x, grn_y, = greens
    red_x, red_y = reds
    plt.scatter(x=grn_x, y=grn_y, c='g', s=40)
    plt.scatter(x=red_x, y=red_y, c='r', s=40)
#  # put a blue dot at (10, 20)
#  plt.scatter([10], [20])
#  # put a red dot, size 40, at 2 locations:
#  plt.scatter(x=[3, 4], y=[5, 6], c='r', s=40)
#  # plt.plot()

  plt.savefig(name)

def plot_latent(model, y, plot_title=''):
    # make prediction on some test inputs
    N_test = 300
    C = model.get_hypers()['C_emission'][0, 0]
    x_test = np.linspace(-10, 8, N_test) / C
    x_test = np.reshape(x_test, [N_test, 1])
    if isinstance(model, aep.SGPSSM) or isinstance(model, vfe.SGPSSM):
        zu = model.dyn_layer.zu
    else:
        zu = model.sgp_layer.zu
    mu, vu = model.predict_f(zu)
    # mu, Su = model.dyn_layer.mu, model.dyn_layer.Su
    mf, vf = model.predict_f(x_test)
    my, vy = model.predict_y(x_test)
    # plot function
    fig = plt.figure()
    ax = fig.add_subplot(111)
    # ax.plot(x_test[:,0], kink_true(x_test[:,0]), '-', color='k')
    ax.plot(C*x_test[:,0], my[:,0], '-', color='r', label='y')
    ax.fill_between(
        C*x_test[:,0], 
        my[:,0] + 2*np.sqrt(vy[:, 0]), 
        my[:,0] - 2*np.sqrt(vy[:, 0]), 
        alpha=0.2, edgecolor='r', facecolor='r')
    ax.plot(
        y[0:model.N-1], 
        y[1:model.N], 
        'r+', alpha=0.5)
    mx, vx = model.get_posterior_x()
    ax.set_xlabel(r'$x_{t-1}$')
    ax.set_ylabel(r'$x_{t}$')
    plt.title(plot_title)
    plt.savefig('/tmp/lincos_'+plot_title+'.png')

# generate a dataset from the lincos function above

def plot_latent(model, y, plot_title=''):
    # make prediction on some test inputs
    N_test = 200
    C = model.get_hypers()['C_emission'][0, 0]
    x_test = np.linspace(-4, 6, N_test) / C
    x_test = np.reshape(x_test, [N_test, 1])
    zu = model.dyn_layer.zu
    mu, vu = model.predict_f(zu)
    # mu, Su = model.dyn_layer.mu, model.dyn_layer.Su
    mf, vf = model.predict_f(x_test)
    my, vy = model.predict_y(x_test)
    # plot function
    fig = plt.figure()
    ax = fig.add_subplot(111)
    # ax.plot(x_test[:,0], kink_true(x_test[:,0]), '-', color='k')
    ax.plot(C*x_test[:,0], my[:,0], '-', color='r', label='y')
    ax.fill_between(
        C*x_test[:,0], 
        my[:,0] + 2*np.sqrt(vy[:, 0]), 
        my[:,0] - 2*np.sqrt(vy[:, 0]), 
        alpha=0.2, edgecolor='r', facecolor='r')
    # ax.plot(zu, mu, 'ob')
    # ax.errorbar(zu, mu, yerr=3*np.sqrt(vu), fmt='ob')
    # ax.plot(x_test[:,0], mf[:,0], '-', color='b')
    # ax.fill_between(
    #     x_test[:,0], 
    #     mf[:,0] + 2*np.sqrt(vf[:,0]), 
    #     mf[:,0] - 2*np.sqrt(vf[:,0]), 
    #     alpha=0.2, edgecolor='b', facecolor='b')
    ax.plot(
        y[0:model.N-1], 
        y[1:model.N], 
        'r+', alpha=0.5)
    mx, vx = model.get_posterior_x()
    ax.set_xlabel(r'$x_{t-1}$')
    ax.set_ylabel(r'$x_{t}$')
    ax.set_xlim([-4, 6])
    # ax.set_ylim([-7, 7])
    plt.title(plot_title)
    # plt.savefig('/tmp/kink_'+plot_title+'.pdf')
    plt.savefig('/tmp/kink_'+plot_title+'.png')

def plot_prediction_MC(model, y_train, y_test, plot_title=''):
    T = y_test.shape[0]
    x_samples, my, vy = model.predict_forward(T, prop_mode=PROP_MC)
    T_train = y_train.shape[0]
    fig = plt.figure()
    ax = fig.add_subplot(111)
    ax.plot(np.arange(T_train), y_train[:, 0], 'k+-')
    ttest = np.arange(T_train, T_train+T)
    ttest = np.reshape(ttest, [T, 1])
    loglik, ranks = compute_log_lik(np.exp(2*model.sn), y_test, my[:, :, 0].T)
    red = 0.1
    green = 0. * red
    blue = 1. - red
    color = np.array([red, green, blue]).T
    for k in np.argsort(ranks):
        ax.plot(ttest, my[:, k, 0], '-', color=color*ranks[k], alpha=0.5)
    # ax.plot(np.tile(ttest, [1, my.shape[1]]), my[:, :, 0], '-x', color='r', alpha=0.3)
    # ax.plot(np.tile(ttest, [1, my.shape[1]]), x_samples[:, :, 0], 'x', color='m', alpha=0.3)
    ax.plot(ttest, y_test, 'ro')
    ax.set_xlim([T_train-5, T_train + T])
    plt.title(plot_title)
    plt.savefig('/tmp/kink_pred_MC_'+plot_title+'.pdf')
    # plt.savefig('/tmp/kink_pred_MC_'+plot_title+'.png')


# generate a dataset from the kink function above

def run_regression_1D():
    np.random.seed(42)

    print "create dataset ..."
    N = 200
    X = np.random.rand(N, 1)
    Y = np.sin(12 * X) + 0.5 * np.cos(25 * X) + np.random.randn(N, 1) * 0.2
    # plt.plot(X, Y, 'kx', mew=2)

    def plot(m):
        xx = np.linspace(-0.5, 1.5, 100)[:, None]
        # mean, var = m.predict_f(xx)
        samples, mf, vf = m.predict_f(xx, config.PROP_MC)
        zu = m.sgp_layers[0].zu
        mean_u, var_u = m.predict_f(zu)
        plt.figure()
        plt.plot(X, Y, 'kx', mew=2)
        # plt.plot(xx, mean, 'b', lw=2)
        # plt.fill_between(
        #     xx[:, 0],
        #     mean[:, 0] - 2 * np.sqrt(var[:, 0]),
        #     mean[:, 0] + 2 * np.sqrt(var[:, 0]),
        #     color='blue', alpha=0.2)
        plt.plot(np.tile(xx[np.newaxis, :], [200, 1]))
        plt.errorbar(zu, mean_u, yerr=2 * np.sqrt(var_u), fmt='ro')
        plt.xlim(-0.1, 1.1)

    # inference
    print "create model and optimize ..."
    M = 20
    hidden_size = [2]
    model = aep.SDGPR(X, Y, M, hidden_size, lik='Gaussian')
    model.optimise(method='L-BFGS-B', alpha=1, maxiter=2000)
    plot(model)
    # plt.show()
    plt.savefig('/tmp/aep_dgpr_1D.pdf')

def run_regression_1D_stoc():
    np.random.seed(42)

    print "create dataset ..."
    N = 200
    X = np.random.rand(N, 1)
    Y = np.sin(12 * X) + 0.5 * np.cos(25 * X) + np.random.randn(N, 1) * 0.2
    # plt.plot(X, Y, 'kx', mew=2)

    def plot(m):
        xx = np.linspace(-0.5, 1.5, 100)[:, None]
        mean, var = m.predict_f(xx)
        zu = m.sgp_layers[0].zu
        mean_u, var_u = m.predict_f(zu)
        plt.figure()
        plt.plot(X, Y, 'kx', mew=2)
        plt.plot(xx, mean, 'b', lw=2)
        plt.fill_between(
            xx[:, 0],
            mean[:, 0] - 2 * np.sqrt(var[:, 0]),
            mean[:, 0] + 2 * np.sqrt(var[:, 0]),
            color='blue', alpha=0.2)
        plt.errorbar(zu, mean_u, yerr=2 * np.sqrt(var_u), fmt='ro')
        plt.xlim(-0.1, 1.1)

    # inference
    print "create model and optimize ..."
    M = 20
    hidden_size = [2]
    model = aep.SDGPR(X, Y, M, hidden_size, lik='Gaussian')
    model.optimise(method='adam', alpha=1.0,
                   maxiter=50000, mb_size=M, adam_lr=0.001)
    plot(model)
    plt.show()
    plt.savefig('/tmp/aep_dgpr_1D_stoc.pdf')

def run_cluster_MM(nat_param=True):
    import GPy
    # create dataset
    print "creating dataset..."
    N = 100
    k1 = GPy.kern.RBF(5, variance=1, lengthscale=1. /
                      np.random.dirichlet(np.r_[10, 10, 10, 0.1, 0.1]), ARD=True)
    k2 = GPy.kern.RBF(5, variance=1, lengthscale=1. /
                      np.random.dirichlet(np.r_[10, 0.1, 10, 0.1, 10]), ARD=True)
    k3 = GPy.kern.RBF(5, variance=1, lengthscale=1. /
                      np.random.dirichlet(np.r_[0.1, 0.1, 10, 10, 10]), ARD=True)
    X = np.random.normal(0, 1, (N, 5))
    A = np.random.multivariate_normal(np.zeros(N), k1.K(X), 10).T
    B = np.random.multivariate_normal(np.zeros(N), k2.K(X), 10).T
    C = np.random.multivariate_normal(np.zeros(N), k3.K(X), 10).T

    Y = np.vstack((A, B, C))
    labels = np.hstack((np.zeros(A.shape[0]), np.ones(
        B.shape[0]), np.ones(C.shape[0]) * 2))

    # inference
    np.random.seed(42)
    print "inference ..."
    M = 30
    D = 5
    lvm = vfe.SGPLVM(Y, D, M, lik='Gaussian', nat_param=nat_param)
    lvm.optimise(method='L-BFGS-B', maxiter=20)
    # lvm.optimise(method='adam', adam_lr=0.05, maxiter=2000)

    ls = np.exp(lvm.sgp_layer.ls)
    print ls
    inds = np.argsort(ls)
    plt.figure()
    mx, vx = lvm.get_posterior_x()
    plt.scatter(mx[:, inds[0]], mx[:, inds[1]], c=labels)
    zu = lvm.sgp_layer.zu
    plt.plot(zu[:, inds[0]], zu[:, inds[1]], 'ko')
    # plt.show()
    plt.savefig('/tmp/gplvm_cluster_MM.pdf')