Python pylab.scatter() Examples

def plot_question7():
    '''
    graph of total resources generated as a function of time,
    for upgrade_cost_increment == 1
    '''
    data = resources_vs_time(1.0, 50)
    time = [item[0] for item in data]
    resource = [item[1] for item in data]
    a, b, c = pylab.polyfit(time, resource, 2)
    print 'polyfit with argument \'2\' fits the data, thus the degree of the polynomial is 2 (quadratic)'

    # plot in pylab on logarithmic scale (total resources over time for upgrade growth 0.0)
    #pylab.loglog(time, resource, 'o')

    # plot fitting function
    yp = pylab.polyval([a, b, c], time)
    pylab.plot(time, yp)
    pylab.scatter(time, resource)
    pylab.title('Silly Homework, Question 7')
    pylab.legend(('Resources for increment 1', 'Fitting function' + ', slope: ' + str(a)))
    pylab.xlabel('Current Time')
    pylab.ylabel('Total Resources Generated')
    pylab.grid()
    pylab.show() 

def test_lines_dists():
    import pylab
    ax = pylab.gca()

    xs, ys = (0,30), (20,150)
    pylab.plot(xs, ys)
    points = list(zip(xs, ys))
    p0, p1 = points

    xs, ys = (0,0,20,30), (100,150,30,200)
    pylab.scatter(xs, ys)

    dist = line2d_seg_dist(p0, p1, (xs[0], ys[0]))
    dist = line2d_seg_dist(p0, p1, np.array((xs, ys)))
    for x, y, d in zip(xs, ys, dist):
        c = Circle((x, y), d, fill=0)
        ax.add_patch(c)

    pylab.xlim(-200, 200)
    pylab.ylim(-200, 200)
    pylab.show() 

def test_proj():
    import pylab
    M = test_proj_make_M()

    ts = ['%d' % i for i in [0,1,2,3,0,4,5,6,7,4]]
    xs, ys, zs = [0,1,1,0,0, 0,1,1,0,0], [0,0,1,1,0, 0,0,1,1,0], \
            [0,0,0,0,0, 1,1,1,1,1]
    xs, ys, zs = [np.array(v)*300 for v in (xs, ys, zs)]
    #
    test_proj_draw_axes(M, s=400)
    txs, tys, tzs = proj_transform(xs, ys, zs, M)
    ixs, iys, izs = inv_transform(txs, tys, tzs, M)

    pylab.scatter(txs, tys, c=tzs)
    pylab.plot(txs, tys, c='r')
    for x, y, t in zip(txs, tys, ts):
        pylab.text(x, y, t)

    pylab.xlim(-0.2, 0.2)
    pylab.ylim(-0.2, 0.2)

    pylab.show() 

def plot_iris_knn():
    iris = datasets.load_iris()
    X = iris.data[:, :2]  # we only take the first two features. We could
                        # avoid this ugly slicing by using a two-dim dataset
    y = iris.target

    knn = neighbors.KNeighborsClassifier(n_neighbors=3)
    knn.fit(X, y)

    x_min, x_max = X[:, 0].min() - .1, X[:, 0].max() + .1
    y_min, y_max = X[:, 1].min() - .1, X[:, 1].max() + .1
    xx, yy = np.meshgrid(np.linspace(x_min, x_max, 100),
                         np.linspace(y_min, y_max, 100))
    Z = knn.predict(np.c_[xx.ravel(), yy.ravel()])

    # Put the result into a color plot
    Z = Z.reshape(xx.shape)
    pl.figure()
    pl.pcolormesh(xx, yy, Z, cmap=cmap_light)

    # Plot also the training points
    pl.scatter(X[:, 0], X[:, 1], c=y, cmap=cmap_bold)
    pl.xlabel('sepal length (cm)')
    pl.ylabel('sepal width (cm)')
    pl.axis('tight') 

def plot_iris_knn():
    iris = datasets.load_iris()
    X = iris.data[:, :2]  # we only take the first two features. We could
                        # avoid this ugly slicing by using a two-dim dataset
    y = iris.target

    knn = neighbors.KNeighborsClassifier(n_neighbors=3)
    knn.fit(X, y)

    x_min, x_max = X[:, 0].min() - .1, X[:, 0].max() + .1
    y_min, y_max = X[:, 1].min() - .1, X[:, 1].max() + .1
    xx, yy = np.meshgrid(np.linspace(x_min, x_max, 100),
                         np.linspace(y_min, y_max, 100))
    Z = knn.predict(np.c_[xx.ravel(), yy.ravel()])

    # Put the result into a color plot
    Z = Z.reshape(xx.shape)
    pl.figure()
    pl.pcolormesh(xx, yy, Z, cmap=cmap_light)

    # Plot also the training points
    pl.scatter(X[:, 0], X[:, 1], c=y, cmap=cmap_bold)
    pl.xlabel('sepal length (cm)')
    pl.ylabel('sepal width (cm)')
    pl.axis('tight') 

def plot_iris_knn():
    iris = datasets.load_iris()
    X = iris.data[:, :2]  # we only take the first two features. We could
                        # avoid this ugly slicing by using a two-dim dataset
    y = iris.target

    knn = neighbors.KNeighborsClassifier(n_neighbors=3)
    knn.fit(X, y)

    x_min, x_max = X[:, 0].min() - .1, X[:, 0].max() + .1
    y_min, y_max = X[:, 1].min() - .1, X[:, 1].max() + .1
    xx, yy = np.meshgrid(np.linspace(x_min, x_max, 100),
                         np.linspace(y_min, y_max, 100))
    Z = knn.predict(np.c_[xx.ravel(), yy.ravel()])

    # Put the result into a color plot
    Z = Z.reshape(xx.shape)
    pl.figure()
    pl.pcolormesh(xx, yy, Z, cmap=cmap_light)

    # Plot also the training points
    pl.scatter(X[:, 0], X[:, 1], c=y, cmap=cmap_bold)
    pl.xlabel('sepal length (cm)')
    pl.ylabel('sepal width (cm)')
    pl.axis('tight') 

def plot_CDR_correlation(self, doplot=True):
        """
        Displays correlation between sampling time points and CDR. It returns the two
        parameters of the linear fit, Pearson's r, p-value and standard error. If optional argument 'doplot' is
        False, the plot is not displayed.
        """
        pel2, tol = self.get_gene(self.rootlane, ignore_log=True)
        pel = numpy.array([pel2[m] for m in self.pl])*tol
        dr2 = self.get_gene('_CDR')[0]
        dr = numpy.array([dr2[m] for m in self.pl])
        po = scipy.stats.linregress(pel, dr)
        if doplot:
            pylab.scatter(pel, dr, s=9.0, alpha=0.7, c='r')
            pylab.xlim(min(pel), max(pel))
            pylab.ylim(0, max(dr)*1.1)
            pylab.xlabel(self.rootlane)
            pylab.ylabel('CDR')
            xk = pylab.linspace(min(pel), max(pel), 50)
            pylab.plot(xk, po[1]+po[0]*xk, 'k--', linewidth=2.0)
            pylab.show()
        return po 

def __init__(self, table, lens='mds', metric='correlation', precomputed=False, **kwargs):
        """
        Initializes the class by providing the mapper input table generated by Preprocess.save(). The parameter 'metric'
        specifies the metric distance to be used ('correlation', 'euclidean' or 'neighbor'). The parameter 'lens'
        specifies the dimensional reduction algorithm to be used ('mds' or 'pca'). The rest of the arguments are
        passed directly to sklearn.manifold.MDS or sklearn.decomposition.PCA. It plots the low-dimensional projection
        of the data.
        """
        self.df = pandas.read_table(table + '.mapper.tsv')
        if lens == 'neighbor':
            self.lens_data_mds = sakmapper.apply_lens(self.df, lens=lens, **kwargs)
        elif lens == 'mds':
            if precomputed:
                self.lens_data_mds = sakmapper.apply_lens(self.df, lens=lens, metric=metric,
                                                          dissimilarity='precomputed', **kwargs)
            else:
                self.lens_data_mds = sakmapper.apply_lens(self.df, lens=lens, metric=metric, **kwargs)
        else:
            self.lens_data_mds = sakmapper.apply_lens(self.df, lens=lens, **kwargs)
        pylab.figure()
        pylab.scatter(numpy.array(self.lens_data_mds)[:, 0], numpy.array(self.lens_data_mds)[:, 1], s=10, alpha=0.7)
        pylab.show() 

def plot(t, plots, shot_ind):
    n = len(plots)

    for i in range(0,n):
        label, data = plots[i]

        plt = py.subplot(n, 1, i+1)
        plt.tick_params(labelsize=8)
        py.grid()
        py.xlim([t[0], t[-1]])
        py.ylabel(label)

        py.plot(t, data, 'k-')
        py.scatter(t[shot_ind], data[shot_ind], marker='*', c='g')

    py.xlabel("Time")
    py.show()
    py.close() 

def coinc_timeseries_plot(coinc_file, start, end):
    fig = pylab.figure()
    f = h5py.File(coinc_file, 'r')

    stat1 = f['foreground/stat1']
    stat2 = f['foreground/stat2']
    time1 = f['foreground/time1']
    time2 = f['foreground/time2']
    ifo1 = f.attrs['detector_1']
    ifo2 = f.attrs['detector_2']

    pylab.scatter(time1, stat1, label=ifo1, color=ifo_color[ifo1])
    pylab.scatter(time2, stat2, label=ifo2, color=ifo_color[ifo2])

    fmt = '.12g'
    mpld3.plugins.connect(fig, mpld3.plugins.MousePosition(fmt=fmt))
    pylab.legend()
    pylab.xlabel('Time (s)')
    pylab.ylabel('NewSNR')
    pylab.grid()
    return mpld3.fig_to_html(fig) 

def trigger_timeseries_plot(file_list, ifos, start, end):

    fig = pylab.figure()
    for ifo in ifos:
        trigs = columns_from_file_list(file_list,
                                       ['snr', 'end_time'],
                                       ifo, start, end)
        print(trigs)
        pylab.scatter(trigs['end_time'], trigs['snr'], label=ifo,
                      color=ifo_color[ifo])

        fmt = '.12g'
        mpld3.plugins.connect(fig, mpld3.plugins.MousePosition(fmt=fmt))
    pylab.legend()
    pylab.xlabel('Time (s)')
    pylab.ylabel('SNR')
    pylab.grid()
    return mpld3.fig_to_html(fig) 

def polyfitting():
    '''
    helper function to play around with polyfit from:
    http://www.wired.com/2011/01/linear-regression-with-pylab/
    '''
    x = [0.2, 1.3, 2.1, 2.9, 3.3]
    y = [3.3, 3.9, 4.8, 5.5, 6.9]
    slope, intercept = pylab.polyfit(x, y, 1)
    print 'slope:', slope, 'intercept:', intercept

    yp = pylab.polyval([slope, intercept], x)
    pylab.plot(x, yp)
    pylab.scatter(x, y)
    pylab.show()

#polyfitting() 

def test_proj():
    import pylab
    M = test_proj_make_M()

    ts = ['%d' % i for i in [0,1,2,3,0,4,5,6,7,4]]
    xs, ys, zs = [0,1,1,0,0, 0,1,1,0,0], [0,0,1,1,0, 0,0,1,1,0], \
            [0,0,0,0,0, 1,1,1,1,1]
    xs, ys, zs = [np.array(v)*300 for v in (xs, ys, zs)]
    #
    test_proj_draw_axes(M, s=400)
    txs, tys, tzs = proj_transform(xs, ys, zs, M)
    ixs, iys, izs = inv_transform(txs, tys, tzs, M)

    pylab.scatter(txs, tys, c=tzs)
    pylab.plot(txs, tys, c='r')
    for x, y, t in zip(txs, tys, ts):
        pylab.text(x, y, t)

    pylab.xlim(-0.2, 0.2)
    pylab.ylim(-0.2, 0.2)

    pylab.show() 

def test_lines_dists():
    import pylab
    ax = pylab.gca()

    xs, ys = (0,30), (20,150)
    pylab.plot(xs, ys)
    points = zip(xs, ys)
    p0, p1 = points

    xs, ys = (0,0,20,30), (100,150,30,200)
    pylab.scatter(xs, ys)

    dist = line2d_seg_dist(p0, p1, (xs[0], ys[0]))
    dist = line2d_seg_dist(p0, p1, np.array((xs, ys)))
    for x, y, d in zip(xs, ys, dist):
        c = Circle((x, y), d, fill=0)
        ax.add_patch(c)

    pylab.xlim(-200, 200)
    pylab.ylim(-200, 200)
    pylab.show() 

def test_proj():
    import pylab
    M = test_proj_make_M()

    ts = ['%d' % i for i in [0,1,2,3,0,4,5,6,7,4]]
    xs, ys, zs = [0,1,1,0,0, 0,1,1,0,0], [0,0,1,1,0, 0,0,1,1,0], \
            [0,0,0,0,0, 1,1,1,1,1]
    xs, ys, zs = [np.array(v)*300 for v in (xs, ys, zs)]
    #
    test_proj_draw_axes(M, s=400)
    txs, tys, tzs = proj_transform(xs, ys, zs, M)
    ixs, iys, izs = inv_transform(txs, tys, tzs, M)

    pylab.scatter(txs, tys, c=tzs)
    pylab.plot(txs, tys, c='r')
    for x, y, t in zip(txs, tys, ts):
        pylab.text(x, y, t)

    pylab.xlim(-0.2, 0.2)
    pylab.ylim(-0.2, 0.2)

    pylab.show() 

def test_lines_dists():
    import pylab
    ax = pylab.gca()

    xs, ys = (0,30), (20,150)
    pylab.plot(xs, ys)
    points = zip(xs, ys)
    p0, p1 = points

    xs, ys = (0,0,20,30), (100,150,30,200)
    pylab.scatter(xs, ys)

    dist = line2d_seg_dist(p0, p1, (xs[0], ys[0]))
    dist = line2d_seg_dist(p0, p1, np.array((xs, ys)))
    for x, y, d in zip(xs, ys, dist):
        c = Circle((x, y), d, fill=0)
        ax.add_patch(c)

    pylab.xlim(-200, 200)
    pylab.ylim(-200, 200)
    pylab.show() 

def embed(words, matrix, classes, usermodel, fname):
    perplexity = int(len(words) ** 0.5)  # We set perplexity to a square root of the words number
    embedding = TSNE(n_components=2, perplexity=perplexity, metric='cosine', n_iter=500, init='pca')
    y = embedding.fit_transform(matrix)

    print('2-d embedding finished', file=sys.stderr)

    class_set = [c for c in set(classes)]
    colors = plot.cm.rainbow(np.linspace(0, 1, len(class_set)))

    class2color = [colors[class_set.index(w)] for w in classes]

    xpositions = y[:, 0]
    ypositions = y[:, 1]
    seen = set()

    plot.clf()

    for color, word, class_label, x, y in zip(class2color, words, classes, xpositions, ypositions):
        plot.scatter(x, y, 20, marker='.', color=color,
                     label=class_label if class_label not in seen else "")
        seen.add(class_label)

        lemma = word.split('_')[0].replace('::', ' ')
        mid = len(lemma) / 2
        mid *= 4  # TODO Should really think about how to adapt this variable to the real plot size
        plot.annotate(lemma, xy=(x - mid, y), size='x-large', weight='bold', fontproperties=font,
                      color=color)

    plot.tick_params(axis='x', which='both', bottom=False, top=False, labelbottom=False)
    plot.tick_params(axis='y', which='both', left=False, right=False, labelleft=False)
    plot.legend(loc='best')

    plot.savefig(root + 'data/images/tsneplots/' + usermodel + '_' + fname + '.png', dpi=150,
                 bbox_inches='tight')
    plot.close()
    plot.clf() 

def test_aliasedcorrelate(display=False):
    Fs_hi = 10.0
    Fs_lo = 1.0
    siginfo = [[1.0, 1.36129345], [0.33, 2.0]]
    modamp = 0.01
    inlenhi = 1000
    inlenlo = 100
    offset = 0.5
    width = 2.5
    rangepts = 101
    timerange = np.linspace(0.0, width, num=101) - width / 2.0
    hiaxis = np.linspace(0.0, 2.0 * np.pi * inlenhi / Fs_hi, num=inlenhi, endpoint=False)
    loaxis = np.linspace(0.0, 2.0 * np.pi * inlenlo / Fs_lo, num=inlenlo, endpoint=False)
    sighi = hiaxis * 0.0
    siglo = loaxis * 0.0
    for theinfo in siginfo:
        sighi += theinfo[0] * np.sin(theinfo[1] * hiaxis)
        siglo += theinfo[0] * np.sin(theinfo[1] * loaxis)
    aliasedcorrelate_result = aliasedcorrelate(sighi, Fs_hi, siglo, Fs_lo, timerange, padvalue=width)

    thecorrelator = aliasedcorrelator(sighi, Fs_hi, Fs_lo, timerange, padvalue=width)
    aliasedcorrelate_result2 = thecorrelator.apply(siglo, 0.0)
    
    if display:
        plt.figure()
        #plt.ylim([-1.0, 3.0])
        plt.plot(hiaxis, sighi, 'k')
        plt.scatter(loaxis, siglo, c='r')
        plt.legend(['sighi', 'siglo'])

        plt.figure()
        plt.plot(timerange, aliasedcorrelate_result, 'k')
        plt.plot(timerange, aliasedcorrelate_result2, 'r')
        print('maximum occurs at offset', timerange[np.argmax(aliasedcorrelate_result)])

        plt.show()

    #assert (fastcorrelate_result == stdcorrelate_result).all
    aethresh = 10
    #np.testing.assert_almost_equal(fastcorrelate_result, stdcorrelate_result, aethresh) 

def print_values(self, values):
        pylab.scatter(range(len(values)), values)
        pylab.xlabel('credit')
        pylab.ylabel('expected return')
        pylab.show() 

def print_policy(self, policy):
        pylab.scatter(range(len(policy)), policy)
        pylab.xlabel('credit')
        pylab.ylabel('bet')
        pylab.show() 

def plot_polynomial_regression():
    rng = np.random.RandomState(0)
    x = 2*rng.rand(100) - 1

    f = lambda t: 1.2 * t**2 + .1 * t**3 - .4 * t **5 - .5 * t ** 9
    y = f(x) + .4 * rng.normal(size=100)

    x_test = np.linspace(-1, 1, 100)

    pl.figure()
    pl.scatter(x, y, s=4)

    X = np.array([x**i for i in range(5)]).T
    X_test = np.array([x_test**i for i in range(5)]).T
    regr = linear_model.LinearRegression()
    regr.fit(X, y)
    pl.plot(x_test, regr.predict(X_test), label='4th order')

    X = np.array([x**i for i in range(10)]).T
    X_test = np.array([x_test**i for i in range(10)]).T
    regr = linear_model.LinearRegression()
    regr.fit(X, y)
    pl.plot(x_test, regr.predict(X_test), label='9th order')

    pl.legend(loc='best')
    pl.axis('tight')
    pl.title('Fitting a 4th and a 9th order polynomial')

    pl.figure()
    pl.scatter(x, y, s=4)
    pl.plot(x_test, f(x_test), label="truth")
    pl.axis('tight')
    pl.title('Ground truth (9th order polynomial)') 

def plot_polynomial_regression():
    rng = np.random.RandomState(0)
    x = 2*rng.rand(100) - 1

    f = lambda t: 1.2 * t**2 + .1 * t**3 - .4 * t **5 - .5 * t ** 9
    y = f(x) + .4 * rng.normal(size=100)

    x_test = np.linspace(-1, 1, 100)

    pl.figure()
    pl.scatter(x, y, s=4)

    X = np.array([x**i for i in range(5)]).T
    X_test = np.array([x_test**i for i in range(5)]).T
    regr = linear_model.LinearRegression()
    regr.fit(X, y)
    pl.plot(x_test, regr.predict(X_test), label='4th order')

    X = np.array([x**i for i in range(10)]).T
    X_test = np.array([x_test**i for i in range(10)]).T
    regr = linear_model.LinearRegression()
    regr.fit(X, y)
    pl.plot(x_test, regr.predict(X_test), label='9th order')

    pl.legend(loc='best')
    pl.axis('tight')
    pl.title('Fitting a 4th and a 9th order polynomial')

    pl.figure()
    pl.scatter(x, y, s=4)
    pl.plot(x_test, f(x_test), label="truth")
    pl.axis('tight')
    pl.title('Ground truth (9th order polynomial)') 

def plot_data_classif(X,y,Z=None):
    if not Z is None:
        pl.pcolormesh(xx, yy,np.argmax(Z,2),edgecolors='face',alpha=.1, vmin=0, vmax=2)
    pl.scatter(X[:,i1],X[:,i2],c=y,edgecolors='black')#,cmap='Pastel2') 

def simple_lineal_regression(file_path):
        records = ReviewETL.load_file(file_path)
        data = [[record['review_count']] for record in records]
        ratings = [record['stars'] for record in records]

        num_testing_records = int(len(ratings) * 0.8)
        training_data = data[:num_testing_records]
        testing_data = data[num_testing_records:]
        training_ratings = ratings[:num_testing_records]
        testing_ratings = ratings[num_testing_records:]

        # Create linear regression object
        regr = linear_model.LinearRegression()

        # Train the model using the training sets
        regr.fit(training_data, training_ratings)

        # The coefficients
        print('Coefficients: \n', regr.coef_)
        print('Intercept: \n', regr.intercept_)
        # The root mean square error
        print("RMSE: %.2f"
              % (np.mean(
            (regr.predict(testing_data) - testing_ratings) ** 2)) ** 0.5)

        print(
            'Variance score: %.2f' % regr.score(testing_data, testing_ratings))

        # Plot outputs
        import pylab as pl

        pl.scatter(testing_data, testing_ratings, color='black')
        pl.plot(testing_data, regr.predict(testing_data), color='blue',
                linewidth=3)

        pl.xticks(())
        pl.yticks(())

        pl.show() 

def plot_rootlane_correlation(self):
        """
        Displays correlation between sampling time points and graph distance to root node. It returns the two
        parameters of the linear fit, Pearson's r, p-value and standard error.
        """
        pylab.scatter(self.pel, self.dr, s=9.0, alpha=0.7, c='r')
        pylab.xlim(min(self.pel), max(self.pel))
        pylab.ylim(0, max(self.dr)+1)
        pylab.xlabel(self.rootlane)
        pylab.ylabel('Distance to root node')
        xk = pylab.linspace(min(self.pel), max(self.pel), 50)
        pylab.plot(xk, self.po[1]+self.po[0]*xk, 'k--', linewidth=2.0)
        pylab.show()
        return self.po 

def test_kmeans(K=5):
    """Test k-means with the synthetic data."""
    X = XMeans.generate_2d_data(K=4)
    wX = vq.whiten(X)
    dic, dist = vq.kmeans(wX, K, iter=100)

    plt.scatter(wX[:, 0], wX[:, 1])
    plt.scatter(dic[:, 0], dic[:, 1], color="m")
    plt.show() 

def plot_polynomial_regression():
    rng = np.random.RandomState(0)
    x = 2*rng.rand(100) - 1

    f = lambda t: 1.2 * t**2 + .1 * t**3 - .4 * t **5 - .5 * t ** 9
    y = f(x) + .4 * rng.normal(size=100)

    x_test = np.linspace(-1, 1, 100)

    pl.figure()
    pl.scatter(x, y, s=4)

    X = np.array([x**i for i in range(5)]).T
    X_test = np.array([x_test**i for i in range(5)]).T
    regr = linear_model.LinearRegression()
    regr.fit(X, y)
    pl.plot(x_test, regr.predict(X_test), label='4th order')

    X = np.array([x**i for i in range(10)]).T
    X_test = np.array([x_test**i for i in range(10)]).T
    regr = linear_model.LinearRegression()
    regr.fit(X, y)
    pl.plot(x_test, regr.predict(X_test), label='9th order')

    pl.legend(loc='best')
    pl.axis('tight')
    pl.title('Fitting a 4th and a 9th order polynomial')

    pl.figure()
    pl.scatter(x, y, s=4)
    pl.plot(x_test, f(x_test), label="truth")
    pl.axis('tight')
    pl.title('Ground truth (9th order polynomial)') 

def plot(Y, labels):
    pylab.scatter(Y[:, 0], Y[:, 1], s=30, c=labels, cmap=colors, linewidth=0)
    pylab.show() 

def plot_facade_cuts(self):

        facade_sig = self.facade_edge_scores.sum(0)
        facade_cuts = find_facade_cuts(facade_sig, dilation_amount=self.facade_merge_amount)
        mu = np.mean(facade_sig)
        sigma = np.std(facade_sig)

        w = self.rectified.shape[1]
        pad=10

        gs1 = pl.GridSpec(5, 5)
        gs1.update(wspace=0.5, hspace=0.0)  # set the spacing between axes.

        pl.subplot(gs1[:3, :])
        pl.imshow(self.rectified)
        pl.vlines(facade_cuts, *pl.ylim(), lw=2, color='black')
        pl.axis('off')
        pl.xlim(-pad, w+pad)

        pl.subplot(gs1[3:, :], sharex=pl.gca())
        pl.fill_between(np.arange(w), 0, facade_sig, lw=0, color='red')
        pl.fill_between(np.arange(w), 0, np.clip(facade_sig, 0, mu+sigma), color='blue')
        pl.plot(np.arange(w), facade_sig, color='blue')

        pl.vlines(facade_cuts, facade_sig[facade_cuts], pl.xlim()[1], lw=2, color='black')
        pl.scatter(facade_cuts, facade_sig[facade_cuts])

        pl.axis('off')

        pl.hlines(mu, 0, w, linestyle='dashed', color='black')
        pl.text(0, mu, '$\mu$ ', ha='right')

        pl.hlines(mu + sigma, 0, w, linestyle='dashed', color='gray',)
        pl.text(0, mu + sigma, '$\mu+\sigma$ ', ha='right')
        pl.xlim(-pad, w+pad) 

def vis_face(im_array, dets, landmarks=None):
    """Visualize detection results before and after calibration

    Parameters:
    ----------
    im_array: numpy.ndarray, shape(1, c, h, w)
        test image in rgb
    dets1: numpy.ndarray([[x1 y1 x2 y2 score]])
        detection results before calibration
    dets2: numpy.ndarray([[x1 y1 x2 y2 score]])
        detection results after calibration
    thresh: float
        boxes with scores > thresh will be drawn in red otherwise yellow

    Returns:
    -------
    """
    import matplotlib.pyplot as plt
    import random
    import pylab

    figure = pylab.figure()
    # plt.subplot(121)
    pylab.imshow(im_array)
    figure.suptitle('DFace Detector', fontsize=20)



    for i in range(dets.shape[0]):
        bbox = dets[i, :4]

        rect = pylab.Rectangle((bbox[0], bbox[1]),
                             bbox[2] - bbox[0],
                             bbox[3] - bbox[1], fill=False,
                             edgecolor='yellow', linewidth=0.9)
        pylab.gca().add_patch(rect)

    if landmarks is not None:
        for i in range(landmarks.shape[0]):
            landmarks_one = landmarks[i, :]
            landmarks_one = landmarks_one.reshape((5, 2))
            for j in range(5):
                # pylab.scatter(landmarks_one[j, 0], landmarks_one[j, 1], c='yellow', linewidths=0.1, marker='x', s=5)

                cir1 = Circle(xy=(landmarks_one[j, 0], landmarks_one[j, 1]), radius=2, alpha=0.4, color="red")
                pylab.gca().add_patch(cir1)
                # plt.gca().text(bbox[0], bbox[1] - 2,
                #                '{:.3f}'.format(score),
                #                bbox=dict(facecolor='blue', alpha=0.5), fontsize=12, color='white')
                # else:
                #     rect = plt.Rectangle((bbox[0], bbox[1]),
                #                          bbox[2] - bbox[0],
                #                          bbox[3] - bbox[1], fill=False,
                #                          edgecolor=color, linewidth=0.5)
                #     plt.gca().add_patch(rect)

        pylab.show()