Python matplotlib.pyplot.title() Examples

def demo_plot():
    audio = './data/esc10/audio/Dog/1-30226-A.ogg'
    y, sr = librosa.load(audio, sr=44100)
    y_ps = librosa.effects.pitch_shift(y, sr, n_steps=6)   # n_steps控制音调变化尺度
    y_ts = librosa.effects.time_stretch(y, rate=1.2)   # rate控制时间维度的变换尺度
    plt.subplot(311)
    plt.plot(y)
    plt.title('Original waveform')
    plt.axis([0, 200000, -0.4, 0.4])
    # plt.axis([88000, 94000, -0.4, 0.4])
    plt.subplot(312)
    plt.plot(y_ts)
    plt.title('Time Stretch transformed waveform')
    plt.axis([0, 200000, -0.4, 0.4])
    plt.subplot(313)
    plt.plot(y_ps)
    plt.title('Pitch Shift transformed waveform')
    plt.axis([0, 200000, -0.4, 0.4])
    # plt.axis([88000, 94000, -0.4, 0.4])
    plt.tight_layout()
    plt.show() 

def plot_confusion_matrix(y_true, y_pred, size=None, normalize=False):
    """plot_confusion_matrix."""
    cm = confusion_matrix(y_true, y_pred)
    fmt = "%d"
    if normalize:
        cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]
        fmt = "%.2f"
    xticklabels = list(sorted(set(y_pred)))
    yticklabels = list(sorted(set(y_true)))
    if size is not None:
        plt.figure(figsize=(size, size))
    heatmap(cm, xlabel='Predicted label', ylabel='True label',
            xticklabels=xticklabels, yticklabels=yticklabels,
            cmap=plt.cm.Blues, fmt=fmt)
    if normalize:
        plt.title("Confusion matrix (norm.)")
    else:
        plt.title("Confusion matrix")
    plt.gca().invert_yaxis() 

def plot_tsne(self, save_eps=False):
        ''' Plot TSNE figure. Set save_eps=True if you want to save a .eps file.
        '''
        tsne = TSNE(n_components=2, init='pca', random_state=0)
        features = tsne.fit_transform(self.features)
        x_min, x_max = np.min(features, 0), np.max(features, 0)
        data = (features - x_min) / (x_max - x_min)
        del features
        for i in range(data.shape[0]):
            plt.text(data[i, 0], data[i, 1], str(self.labels[i]),
                     color=plt.cm.Set1(self.labels[i] / 10.),
                     fontdict={'weight': 'bold', 'size': 9})
        plt.xticks([])
        plt.yticks([])
        plt.title('T-SNE')
        if save_eps:
            plt.savefig('tsne.eps', dpi=600, format='eps')
        plt.show() 

def __init__(self, title, varieties, data_points, attrs,
                 anim=False, data_func=None, is_headless=False):
        global anim_func

        plt.close()
        self.legend = ["Type"]
        self.title = title
        # self.anim = anim
        # self.data_func = data_func
        for i in varieties:
            data_points = len(varieties[i]["data"])
            break
        self.headless = is_headless
        self.draw_graph(data_points, varieties, attrs)

        # if anim and not self.headless:
        #     anim_func = animation.FuncAnimation(self.fig,
        #                                         self.update_plot,
        #                                         frames=1000,
        #                                         interval=500,
        #                                         blit=False) 

def __init__(self, title, varieties, data_points,
                 anim=False, data_func=None, is_headless=False, legend_pos=4):
        global anim_func

        self.title = title
        self.anim = anim
        self.data_func = data_func
        for i in varieties:
            data_points = len(varieties[i]["data"])
            break
        self.draw_graph(data_points, varieties)
        self.headless = is_headless

        if anim and not self.headless:
            anim_func = animation.FuncAnimation(self.fig,
                                    self.update_plot,
                                    frames=1000,
                                    interval=500,
                                    blit=False) 

def plot_precision_recall_curve(y_true, y_score, size=None):
    """plot_precision_recall_curve."""
    precision, recall, thresholds = precision_recall_curve(y_true, y_score)
    if size is not None:
        plt.figure(figsize=(size, size))
        plt.axis('equal')
    plt.plot(recall, precision, lw=2, color='navy')
    plt.xlabel('Recall')
    plt.ylabel('Precision')
    plt.ylim([-0.05, 1.05])
    plt.xlim([-0.05, 1.05])
    plt.grid()
    plt.title('Precision-Recall AUC={0:0.2f}'.format(average_precision_score(
        y_true, y_score))) 

def plot_roc_curve(y_true, y_score, size=None):
    """plot_roc_curve."""
    false_positive_rate, true_positive_rate, thresholds = roc_curve(
        y_true, y_score)
    if size is not None:
        plt.figure(figsize=(size, size))
        plt.axis('equal')
    plt.plot(false_positive_rate, true_positive_rate, lw=2, color='navy')
    plt.plot([0, 1], [0, 1], color='gray', lw=1, linestyle='--')
    plt.xlabel('False positive rate')
    plt.ylabel('True positive rate')
    plt.ylim([-0.05, 1.05])
    plt.xlim([-0.05, 1.05])
    plt.grid()
    plt.title('Receiver operating characteristic AUC={0:0.2f}'.format(
        roc_auc_score(y_true, y_score))) 

def update(self, xPhys, u, title=None):
        """Plot to screen"""
        self.im.set_array(-xPhys.reshape((self.nelx, self.nely)).T)
        stress = self.stress_calculator.calculate_stress(xPhys, u, self.nu)
        # self.stress_calculator.calculate_fdiff_stress(xPhys, u, self.nu)
        self.myColorMap.set_norm(colors.Normalize(vmin=0, vmax=max(stress)))
        stress_rgba = self.myColorMap.to_rgba(stress)
        stress_rgba[:, :, 3] = xPhys.reshape(-1, 1)
        self.stress_im.set_array(np.swapaxes(
            stress_rgba.reshape((self.nelx, self.nely, 4)), 0, 1))
        self.fig.canvas.draw()
        self.fig.canvas.flush_events()
        if title is not None:
            plt.title(title)
        else:
            plt.xlabel("Max stress = {:.2f}".format(max(stress)[0]))
        plt.pause(0.01) 

def plot_alignment(alignment, gs, dir=hp.logdir):
    """Plots the alignment.

    Args:
      alignment: A numpy array with shape of (encoder_steps, decoder_steps)
      gs: (int) global step.
      dir: Output path.
    """
    if not os.path.exists(dir): os.mkdir(dir)

    fig, ax = plt.subplots()
    im = ax.imshow(alignment)

    fig.colorbar(im)
    plt.title('{} Steps'.format(gs))
    plt.savefig('{}/alignment_{}.png'.format(dir, gs), format='png')
    plt.close(fig) 

def compute_roc(y_true, y_pred, plot=False):
    """
    TODO
    :param y_true: ground truth
    :param y_pred: predictions
    :param plot:
    :return:
    """
    fpr, tpr, _ = roc_curve(y_true, y_pred)
    auc_score = auc(fpr, tpr)
    if plot:
        plt.figure(figsize=(7, 6))
        plt.plot(fpr, tpr, color='blue',
                 label='ROC (AUC = %0.4f)' % auc_score)
        plt.legend(loc='lower right')
        plt.title("ROC Curve")
        plt.xlabel("FPR")
        plt.ylabel("TPR")
        plt.show()

    return fpr, tpr, auc_score 

def compute_roc_rfeinman(probs_neg, probs_pos, plot=False):
    """
    TODO
    :param probs_neg:
    :param probs_pos:
    :param plot:
    :return:
    """
    probs = np.concatenate((probs_neg, probs_pos))
    labels = np.concatenate((np.zeros_like(probs_neg), np.ones_like(probs_pos)))
    fpr, tpr, _ = roc_curve(labels, probs)
    auc_score = auc(fpr, tpr)
    if plot:
        plt.figure(figsize=(7, 6))
        plt.plot(fpr, tpr, color='blue',
                 label='ROC (AUC = %0.4f)' % auc_score)
        plt.legend(loc='lower right')
        plt.title("ROC Curve")
        plt.xlabel("FPR")
        plt.ylabel("TPR")
        plt.show()

    return fpr, tpr, auc_score 

def visualize_sampling(self,permutations):
        max_length = len(permutations[0])
        grid = np.zeros([max_length,max_length]) # initialize heatmap grid to 0
        transposed_permutations = np.transpose(permutations)
        for t, cities_t in enumerate(transposed_permutations): # step t, cities chosen at step t
            city_indices, counts = np.unique(cities_t,return_counts=True,axis=0)
            for u,v in zip(city_indices, counts):
                grid[t][u]+=v # update grid with counts from the batch of permutations
        # plot heatmap
        fig = plt.figure()
        rcParams.update({'font.size': 22})
        ax = fig.add_subplot(1,1,1)
        ax.set_aspect('equal')
        plt.imshow(grid, interpolation='nearest', cmap='gray')
        plt.colorbar()
        plt.title('Sampled permutations')
        plt.ylabel('Time t')
        plt.xlabel('City i')
        plt.show()

    # Heatmap of attention (x=cities; y=steps) 

def visualize_sampling(self, permutations):
        max_length = len(permutations[0])
        grid = np.zeros([max_length,max_length]) # initialize heatmap grid to 0

        transposed_permutations = np.transpose(permutations)
        for t, cities_t in enumerate(transposed_permutations): # step t, cities chosen at step t
            city_indices, counts = np.unique(cities_t,return_counts=True,axis=0)
            for u,v in zip(city_indices, counts):
                grid[t][u]+=v # update grid with counts from the batch of permutations

        # plot heatmap
        fig = plt.figure()
        rcParams.update({'font.size': 22})
        ax = fig.add_subplot(1,1,1)
        ax.set_aspect('equal')
        plt.imshow(grid, interpolation='nearest', cmap='gray')
        plt.colorbar()
        plt.title('Sampled permutations')
        plt.ylabel('Time t')
        plt.xlabel('City i')
        plt.show() 

def plot(PDF, figName, imgpath, show=False, save=True):
    # plot
    output = PDF.get_constraint_value()
    plt.plot(PDF.experimentalDistances,PDF.experimentalPDF, 'ro', label="experimental", markersize=7.5, markevery=1 )
    plt.plot(PDF.shellsCenter, output["pdf"], 'k', linewidth=3.0,  markevery=25, label="total" )

    styleIndex = 0
    for key in output:
        val = output[key]
        if key in ("pdf_total", "pdf"):
            continue
        elif "inter" in key:
            plt.plot(PDF.shellsCenter, val, STYLE[styleIndex], markevery=5, label=key.split('rdf_inter_')[1] )
            styleIndex+=1
    plt.legend(frameon=False, ncol=1)
    # set labels
    plt.title("$\\chi^{2}=%.6f$"%PDF.squaredDeviations, size=20)
    plt.xlabel("$r (\AA)$", size=20)
    plt.ylabel("$g(r)$", size=20)
    # show plot
    if save: plt.savefig(figName)
    if show: plt.show()
    plt.close() 

def visualize_anomaly(y_true, reconstruction_error, threshold):
    error_df = pd.DataFrame({'reconstruction_error': reconstruction_error,
                             'true_class': y_true})
    print(error_df.describe())

    groups = error_df.groupby('true_class')
    fig, ax = plt.subplots()

    for name, group in groups:
        ax.plot(group.index, group.reconstruction_error, marker='o', ms=3.5, linestyle='',
                label="Fraud" if name == 1 else "Normal")

    ax.hlines(threshold, ax.get_xlim()[0], ax.get_xlim()[1], colors="r", zorder=100, label='Threshold')
    ax.legend()
    plt.title("Reconstruction error for different classes")
    plt.ylabel("Reconstruction error")
    plt.xlabel("Data point index")
    plt.show() 

def plot_time_series(vals_bxtxn, bidx=None, n_to_plot=np.inf, scale=1.0,
                     color='r', title=None):

  if bidx is None:
    vals_txn = np.mean(vals_bxtxn, axis=0)
  else:
    vals_txn = vals_bxtxn[bidx,:,:]

  T, N = vals_txn.shape
  if n_to_plot > N:
    n_to_plot = N

  plt.plot(vals_txn[:,0:n_to_plot] + scale*np.array(range(n_to_plot)),
           color=color, lw=1.0)
  plt.axis('tight')
  if title:
    plt.title(title) 

def plot_images(imgs, targets, paths=None, fname='images.jpg'):
    # Plots training images overlaid with targets
    imgs = imgs.cpu().numpy()
    targets = targets.cpu().numpy()
    # targets = targets[targets[:, 1] == 21]  # plot only one class

    fig = plt.figure(figsize=(10, 10))
    bs, _, h, w = imgs.shape  # batch size, _, height, width
    bs = min(bs, 16)  # limit plot to 16 images
    ns = np.ceil(bs ** 0.5)  # number of subplots

    for i in range(bs):
        boxes = xywh2xyxy(targets[targets[:, 0] == i, 2:6]).T
        boxes[[0, 2]] *= w
        boxes[[1, 3]] *= h
        plt.subplot(ns, ns, i + 1).imshow(imgs[i].transpose(1, 2, 0))
        plt.plot(boxes[[0, 2, 2, 0, 0]], boxes[[1, 1, 3, 3, 1]], '.-')
        plt.axis('off')
        if paths is not None:
            s = Path(paths[i]).name
            plt.title(s[:min(len(s), 40)], fontdict={'size': 8})  # limit to 40 characters
    fig.tight_layout()
    fig.savefig(fname, dpi=200)
    plt.close() 

def plot_evolution_results(hyp):  # from utils.utils import *; plot_evolution_results(hyp)
    # Plot hyperparameter evolution results in evolve.txt
    x = np.loadtxt('evolve.txt', ndmin=2)
    f = fitness(x)
    weights = (f - f.min()) ** 2  # for weighted results
    fig = plt.figure(figsize=(12, 10))
    matplotlib.rc('font', **{'size': 8})
    for i, (k, v) in enumerate(hyp.items()):
        y = x[:, i + 5]
        # mu = (y * weights).sum() / weights.sum()  # best weighted result
        mu = y[f.argmax()]  # best single result
        plt.subplot(4, 5, i + 1)
        plt.plot(mu, f.max(), 'o', markersize=10)
        plt.plot(y, f, '.')
        plt.title('%s = %.3g' % (k, mu), fontdict={'size': 9})  # limit to 40 characters
        print('%15s: %.3g' % (k, mu))
    fig.tight_layout()
    plt.savefig('evolve.png', dpi=200) 

def plot_some_results(pred_fn, test_generator, n_images=10):
    fig_ctr = 0
    for data, seg in test_generator:
        res = pred_fn(data)
        for d, s, r in zip(data, seg, res):
            plt.figure(figsize=(12, 6))
            plt.subplot(1, 3, 1)
            plt.imshow(d.transpose(1,2,0))
            plt.title("input patch")
            plt.subplot(1, 3, 2)
            plt.imshow(s[0])
            plt.title("ground truth")
            plt.subplot(1, 3, 3)
            plt.imshow(r)
            plt.title("segmentation")
            plt.savefig("road_segmentation_result_%03.0f.png"%fig_ctr)
            plt.close()
            fig_ctr += 1
            if fig_ctr > n_images:
                break 

def classify(self, features, show=False):
        recs, _ = features.shape
        result_shape = (features.shape[0], len(self.root))
        scores = np.zeros(result_shape)
        print scores.shape
        R = Record(np.arange(recs, dtype=int), features)

        for i, T in enumerate(self.root):
            for idxs, result in classify(T, R):
                for idx in idxs.indexes():
                    scores[idx, i] = float(result[0]) / sum(result.values())


        if show:
            plt.cla()
            plt.clf()
            plt.close()

            plt.imshow(scores, cmap=plt.cm.gray)
            plt.title('Scores matrix')
            plt.savefig(r"../scratch/tree_scores.png", bbox_inches='tight')
        
        return scores 

def plot_alignment(alignment, gs, dir=hp.logdir):
    """Plots the alignment.

    Args:
      alignment: A numpy array with shape of (encoder_steps, decoder_steps)
      gs: (int) global step.
      dir: Output path.
    """
    if not os.path.exists(dir): os.mkdir(dir)

    fig, ax = plt.subplots()
    im = ax.imshow(alignment)

    fig.colorbar(im)
    plt.title('{} Steps'.format(gs))
    plt.savefig('{}/alignment_{}.png'.format(dir, gs), format='png') 

def draw_graph(graph, title, hierarchy=False, root=None):
    """
    Drawing networkx graphs.
    graph is the graph to draw.
    hierarchy is whether we should draw it as a tree.
    """
    # pos = None
    plt.title(title)
    # if hierarchy:
    #     pos = hierarchy_pos(graph, root)
    # out for now:
    # nx.draw(graph, pos=pos, with_labels=True)
    plt.show() 

def draw_graph(graph, title, hierarchy=False, root=None):
    """
    Drawing networkx graphs.
    graph is the graph to draw.
    hierarchy is whether we should draw it as a tree.
    """
    pos = None
    plt.title(title)
    if hierarchy:
        pos = hierarchy_pos(graph, root)
    nx.draw(graph, pos=pos, with_labels=True)
    plt.show() 

def __init__(self, title, varieties, width, height,
                 anim=True, data_func=None, is_headless=False, legend_pos=4):
        """
        Setup a scatter plot.
        varieties contains the different types of
        entities to show in the plot, which
        will get assigned different colors
        """
        global anim_func

        self.scats = None
        self.anim = anim
        self.data_func = data_func
        self.s = ceil(4096 / width)
        self.headless = is_headless

        fig, ax = plt.subplots()
        ax.set_xlim(0, width)
        ax.set_ylim(0, height)
        self.create_scats(varieties)
        ax.legend(loc = legend_pos)
        ax.set_title(title)
        plt.grid(True)

        if anim and not self.headless:
            anim_func = animation.FuncAnimation(fig,
                                    self.update_plot,
                                    frames=1000,
                                    interval=500,
                                    blit=False) 

def __call__(self, args, env):

        import numpy as np
        import matplotlib.pyplot as plt
        from sklearn.metrics import average_precision_score
        from sklearn.metrics import precision_recall_curve
        from vergeml.plots import load_labels, load_predictions

        try:
            labels = load_labels(env)
        except FileNotFoundError:
            raise VergeMLError("Can't plot PR curve - not supported by model.")

        nclasses = len(labels)
        if args['class'] not in labels:
            raise VergeMLError("Unknown class: " + args['class'])

        try:
            y_test, y_score = load_predictions(env, nclasses)
        except FileNotFoundError:
            raise VergeMLError("Can't plot PR curve - not supported by model.")

        # From:
        # https://scikit-learn.org/stable/auto_examples/model_selection/plot_precision_recall.html#sphx-glr-auto-examples-model-selection-plot-precision-recall-py

        ix = labels.index(args['class'])
        y_test = y_test[:,ix].astype(np.int)
        y_score = y_score[:,ix]

        precision, recall, _ = precision_recall_curve(y_test, y_score)
        average_precision = average_precision_score(y_test, y_score)

        plt.step(recall, precision, color='b', alpha=0.2, where='post')
        plt.fill_between(recall, precision, alpha=0.2, color='b', step='post')

        plt.xlabel('Recall ({})'.format(args['class']))
        plt.ylabel('Precision ({})'.format(args['class']))
        plt.ylim([0.0, 1.05])
        plt.xlim([0.0, 1.0])
        plt.title('Precision-Recall curve for @{0}: AP={1:0.2f}'.format(args['@AI'], average_precision))
        plt.show() 

def show_graph(g, vertex_color='typeof', size=15, vertex_label=None):
    """show_graph."""
    degrees = [len(g.neighbors(u)) for u in g.nodes()]

    print(('num nodes=%d' % len(g)))
    print(('num edges=%d' % len(g.edges())))
    print(('num non edges=%d' % len(list(nx.non_edges(g)))))
    print(('max degree=%d' % max(degrees)))
    print(('median degree=%d' % np.percentile(degrees, 50)))

    draw_graph(g, size=size,
               vertex_color=vertex_color, vertex_label=vertex_label,
               vertex_size=200, edge_label=None)

    # display degree distribution
    size = int((max(degrees) - min(degrees)) / 1.5)
    plt.figure(figsize=(size, 3))
    plt.title('Degree distribution')
    _bins = np.arange(min(degrees), max(degrees) + 2) - .5
    n, bins, patches = plt.hist(degrees, _bins,
                                alpha=0.3,
                                facecolor='navy', histtype='bar',
                                rwidth=0.8, edgecolor='k')
    labels = np.array([str(int(i)) for i in n])
    for xi, yi, label in zip(bins, n, labels):
        plt.text(xi + 0.5, yi, label, ha='center', va='bottom')

    plt.xticks(bins + 0.5)
    plt.xlim((min(degrees) - 1, max(degrees) + 1))
    plt.ylim((0, max(n) * 1.1))
    plt.xlabel('Node degree')
    plt.ylabel('Counts')
    plt.grid(linestyle=":")
    plt.show() 

def __init__(self, nelx, nely, stress_calculator, nu, title=""):
        """Initialize plot and plot the initial design"""
        super(StressGUI, self).__init__(nelx, nely, title)
        self.stress_im = self.ax.imshow(
            np.swapaxes(np.zeros((nelx, nely, 4)), 0, 1),
            norm=colors.Normalize(vmin=0, vmax=1), cmap='jet')
        self.fig.colorbar(self.stress_im)
        self.stress_calculator = stress_calculator
        self.nu = nu
        self.myColorMap = colormaps.ScalarMappable(
            norm=colors.Normalize(vmin=0, vmax=1), cmap=colormaps.jet) 

def __init__(self, nelx, nely, title=""):
        """Initialize plot and plot the initial design"""
        plt.ion()  # Ensure that redrawing is possible
        self.fig, self.ax = plt.subplots()
        self.im = self.ax.imshow(-np.zeros((nelx, nely)).T, cmap='gray',
            interpolation='none', norm=colors.Normalize(vmin=-1, vmax=0))
        plt.xlabel(title)
        # self.fig.tight_layout()
        self.fig.show()
        self.nelx, self.nely = nelx, nely 

def update(self, xPhys, title=None):
        """Plot to screen"""
        self.im.set_array(-xPhys.reshape((self.nelx, self.nely)).T)
        self.fig.canvas.draw()
        self.fig.canvas.flush_events()
        if title is not None:
            plt.title(title)
        plt.pause(0.01) 

def __init__(self, nelx, nely, stress_calculator, nu, title=""):
        """Initialize plot and plot the initial design"""
        super(StressGUI, self).__init__(nelx, nely, title)
        self.stress_im = self.ax.imshow(
            np.swapaxes(np.zeros((nelx, nely, 4)), 0, 1),
            norm=colors.Normalize(vmin=0, vmax=1), cmap='jet')
        self.fig.colorbar(self.stress_im)
        self.stress_calculator = stress_calculator
        self.nu = nu
        self.myColorMap = colormaps.ScalarMappable(
            norm=colors.Normalize(vmin=0, vmax=1), cmap=colormaps.jet)