Python matplotlib.pyplot.show() Examples

The following are 30 code examples for showing how to use matplotlib.pyplot.show(). These examples are extracted from open source projects. You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example.

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Example 1
Project: Sound-Recognition-Tutorial   Author: JasonZhang156   File: data_augmentation.py    License: Apache License 2.0 8 votes vote down vote up
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() 
Example 2
Project: MomentumContrast.pytorch   Author: peisuke   File: test.py    License: MIT License 6 votes vote down vote up
def show(mnist, targets, ret):
    target_ids = range(len(set(targets)))
    
    colors = ['r', 'g', 'b', 'c', 'm', 'y', 'k', 'violet', 'orange', 'purple']
    
    plt.figure(figsize=(12, 10))
    
    ax = plt.subplot(aspect='equal')
    for label in set(targets):
        idx = np.where(np.array(targets) == label)[0]
        plt.scatter(ret[idx, 0], ret[idx, 1], c=colors[label], label=label)
    
    for i in range(0, len(targets), 250):
        img = (mnist[i][0] * 0.3081 + 0.1307).numpy()[0]
        img = OffsetImage(img, cmap=plt.cm.gray_r, zoom=0.5) 
        ax.add_artist(AnnotationBbox(img, ret[i]))
    
    plt.legend()
    plt.show() 
Example 3
Project: pepper-robot-programming   Author: maverickjoy   File: asthama_search.py    License: MIT License 6 votes vote down vote up
def _capture2dImage(self, cameraId):
        # Capture Image in RGB

        # WARNING : The same Name could be used only six time.
        strName = "capture2DImage_{}".format(random.randint(1,10000000000))

        clientRGB = self.video_service.subscribeCamera(strName, cameraId, AL_kVGA, 11, 10)
        imageRGB = self.video_service.getImageRemote(clientRGB)

        imageWidth   = imageRGB[0]
        imageHeight  = imageRGB[1]
        array        = imageRGB[6]
        image_string = str(bytearray(array))

        # Create a PIL Image from our pixel array.
        im = Image.frombytes("RGB", (imageWidth, imageHeight), image_string)

        # Save the image.
        image_name_2d = "images/img2d-" + str(self.imageNo2d) + ".png"
        im.save(image_name_2d, "PNG") # Stored in images folder in the pwd, if not present then create one
        self.imageNo2d += 1
        im.show()

        return 
Example 4
Project: pepper-robot-programming   Author: maverickjoy   File: asthama_search.py    License: MIT License 6 votes vote down vote up
def _capture3dImage(self):
        # Depth Image in RGB

        # WARNING : The same Name could be used only six time.
        strName = "capture3dImage_{}".format(random.randint(1,10000000000))


        clientRGB = self.video_service.subscribeCamera(strName, AL_kDepthCamera, AL_kQVGA, 11, 10)
        imageRGB = self.video_service.getImageRemote(clientRGB)

        imageWidth  = imageRGB[0]
        imageHeight = imageRGB[1]
        array       = imageRGB[6]
        image_string = str(bytearray(array))

        # Create a PIL Image from our pixel array.
        im = Image.frombytes("RGB", (imageWidth, imageHeight), image_string)
        # Save the image.
        image_name_3d = "images/img3d-" + str(self.imageNo3d) + ".png"
        im.save(image_name_3d, "PNG") # Stored in images folder in the pwd, if not present then create one
        self.imageNo3d += 1
        im.show()

        return 
Example 5
Project: Stock-Price-Prediction   Author: dhingratul   File: helper.py    License: MIT License 6 votes vote down vote up
def plot_mul(Y_hat, Y, pred_len):
    """
    PLots the predicted data versus true data

    Input: Predicted data, True Data, Length of prediction
    Output: return plot

    Note: Run from timeSeriesPredict.py
    """
    fig = plt.figure(facecolor='white')
    ax = fig.add_subplot(111)
    ax.plot(Y, label='Y')
    # Print the predictions in its respective series-length
    for i, j in enumerate(Y_hat):
        shift = [None for p in range(i * pred_len)]
        plt.plot(shift + j, label='Y_hat')
        plt.legend()
    plt.show() 
Example 6
Project: dustmaps   Author: gregreen   File: test_bayestar.py    License: GNU General Public License v2.0 6 votes vote down vote up
def atest_plot_samples(self):
        dm = np.linspace(4., 19., 1001)
        samples = []

        for dm_k in dm:
            d = 10.**(dm_k/5.-2.)
            samples.append(self._interp_ebv(self._test_data[0], d))

        samples = np.array(samples).T
        # print samples

        import matplotlib.pyplot as plt
        fig = plt.figure()
        ax = fig.add_subplot(1,1,1)
        for s in samples:
            ax.plot(dm, s, lw=2., alpha=0.5)

        plt.show() 
Example 7
Project: mmdetection   Author: open-mmlab   File: inference.py    License: Apache License 2.0 6 votes vote down vote up
def show_result_pyplot(model, img, result, score_thr=0.3, fig_size=(15, 10)):
    """Visualize the detection results on the image.

    Args:
        model (nn.Module): The loaded detector.
        img (str or np.ndarray): Image filename or loaded image.
        result (tuple[list] or list): The detection result, can be either
            (bbox, segm) or just bbox.
        score_thr (float): The threshold to visualize the bboxes and masks.
        fig_size (tuple): Figure size of the pyplot figure.
    """
    if hasattr(model, 'module'):
        model = model.module
    img = model.show_result(img, result, score_thr=score_thr, show=False)
    plt.figure(figsize=fig_size)
    plt.imshow(mmcv.bgr2rgb(img))
    plt.show() 
Example 8
Project: neural-fingerprinting   Author: StephanZheng   File: util.py    License: BSD 3-Clause "New" or "Revised" License 6 votes vote down vote up
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 
Example 9
Project: neural-fingerprinting   Author: StephanZheng   File: util.py    License: BSD 3-Clause "New" or "Revised" License 6 votes vote down vote up
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 
Example 10
Project: deep-learning-note   Author: wdxtub   File: 3_linear_regression_raw.py    License: MIT License 6 votes vote down vote up
def generate_dataset(true_w, true_b):
    num_examples = 1000

    features = torch.tensor(np.random.normal(0, 1, (num_examples, num_inputs)), dtype=torch.float)
    # 真实 label
    labels = true_w[0] * features[:, 0] + true_w[1] * features[:, 1] + true_b
    # 添加噪声
    labels += torch.tensor(np.random.normal(0, 0.01, size=labels.size()), dtype=torch.float)
    # 展示下分布
    plt.scatter(features[:, 1].numpy(), labels.numpy(), 1)
    plt.show()
    
    return features, labels


# batch 读取数据集 
Example 11
Project: deep-learning-note   Author: wdxtub   File: simulate_sin.py    License: MIT License 6 votes vote down vote up
def run_eval(sess, test_X, test_y):
    ds = tf.data.Dataset.from_tensor_slices((test_X, test_y))
    ds = ds.batch(1)
    X, y = ds.make_one_shot_iterator().get_next()

    with tf.variable_scope("model", reuse=True):
        prediction, _, _ = lstm_model(X, [0.0], False)
        predictions = []
        labels = []
        for i in range(TESTING_EXAMPLES):
            p, l = sess.run([prediction, y])
            predictions.append(p)
            labels.append(l)

    predictions = np.array(predictions).squeeze()
    labels = np.array(labels).squeeze()
    rmse = np.sqrt(((predictions-labels) ** 2).mean(axis=0))
    print("Mean Square Error is: %f" % rmse)

    plt.figure()
    plt.plot(predictions, label='predictions')
    plt.plot(labels, label='real_sin')
    plt.legend()
    plt.show() 
Example 12
Project: Sound-Recognition-Tutorial   Author: JasonZhang156   File: data_analysis.py    License: Apache License 2.0 6 votes vote down vote up
def data_stat():
    """data statistic"""
    audio_path = './data/esc10/audio/'
    class_list = [os.path.basename(i) for i in glob(audio_path + '*')]
    nums_each_class = [len(glob(audio_path + cl + '/*.ogg')) for cl in class_list]
    rects = plt.bar(range(len(nums_each_class)), nums_each_class)

    index = list(range(len(nums_each_class)))
    plt.title('Numbers of each class for ESC-10 dataset')
    plt.ylim(ymax=60, ymin=0)
    plt.xticks(index, class_list, rotation=45)
    plt.ylabel("numbers")

    for rect in rects:
        height = rect.get_height()
        plt.text(rect.get_x() + rect.get_width() / 2, height, str(height), ha='center', va='bottom')

    plt.tight_layout()
    plt.show() 
Example 13
Project: neural-combinatorial-optimization-rl-tensorflow   Author: MichelDeudon   File: dataset.py    License: MIT License 6 votes vote down vote up
def visualize_2D_trip(self,trip,tw_open,tw_close):
        plt.figure(figsize=(30,30))
        rcParams.update({'font.size': 22})
        # Plot cities
        colors = ['red'] # Depot is first city
        for i in range(len(tw_open)-1):
            colors.append('blue')
        plt.scatter(trip[:,0], trip[:,1], color=colors, s=200)
        # Plot tour
        tour=np.array(list(range(len(trip))) + [0])
        X = trip[tour, 0]
        Y = trip[tour, 1]
        plt.plot(X, Y,"--", markersize=100)
        # Annotate cities with TW
        tw_open = np.rint(tw_open)
        tw_close = np.rint(tw_close)
        time_window = np.concatenate((tw_open,tw_close),axis=1)
        for tw, (x, y) in zip(time_window,(zip(X,Y))):
            plt.annotate(tw,xy=(x, y))  
        plt.xlim(0,60)
        plt.ylim(0,60)
        plt.show()


    # Heatmap of permutations (x=cities; y=steps) 
Example 14
Project: neural-combinatorial-optimization-rl-tensorflow   Author: MichelDeudon   File: dataset.py    License: MIT License 6 votes vote down vote up
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) 
Example 15
Project: neural-combinatorial-optimization-rl-tensorflow   Author: MichelDeudon   File: dataset.py    License: MIT License 6 votes vote down vote up
def visualize_2D_trip(self, trip):
        plt.figure(figsize=(30,30))
        rcParams.update({'font.size': 22})

        # Plot cities
        plt.scatter(trip[:,0], trip[:,1], s=200)

        # Plot tour
        tour=np.array(list(range(len(trip))) + [0])
        X = trip[tour, 0]
        Y = trip[tour, 1]
        plt.plot(X, Y,"--", markersize=100)

        # Annotate cities with order
        labels = range(len(trip))
        for i, (x, y) in zip(labels,(zip(X,Y))):
            plt.annotate(i,xy=(x, y))  

        plt.xlim(0,100)
        plt.ylim(0,100)
        plt.show()


    # Heatmap of permutations (x=cities; y=steps) 
Example 16
Project: neural-combinatorial-optimization-rl-tensorflow   Author: MichelDeudon   File: dataset.py    License: MIT License 6 votes vote down vote up
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() 
Example 17
Project: fullrmc   Author: bachiraoun   File: plotFigures.py    License: GNU Affero General Public License v3.0 6 votes vote down vote up
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() 
Example 18
Project: keras-anomaly-detection   Author: chen0040   File: h2o_ecg_pulse_detection.py    License: MIT License 6 votes vote down vote up
def plot_bidimensional(model, test, recon_error, layer, title):
    bidimensional_data = model.deepfeatures(test, layer).cbind(recon_error).as_data_frame()

    cmap = cm.get_cmap('Spectral')

    fig, ax = plt.subplots()
    bidimensional_data.plot(kind='scatter',
                            x='DF.L{}.C1'.format(layer + 1),
                            y='DF.L{}.C2'.format(layer + 1),
                            s=500,
                            c='Reconstruction.MSE',
                            title=title,
                            ax=ax,
                            colormap=cmap)
    layer_column = 'DF.L{}.C'.format(layer + 1)
    columns = [layer_column + '1', layer_column + '2']
    for k, v in bidimensional_data[columns].iterrows():
        ax.annotate(k, v, size=20, verticalalignment='bottom', horizontalalignment='left')
    fig.canvas.draw()
    plt.show() 
Example 19
Project: keras-anomaly-detection   Author: chen0040   File: plot_utils.py    License: MIT License 6 votes vote down vote up
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() 
Example 20
Project: indras_net   Author: gcallah   File: display_methods.py    License: GNU General Public License v3.0 5 votes vote down vote up
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() 
Example 21
Project: indras_net   Author: gcallah   File: display_methods.py    License: GNU General Public License v3.0 5 votes vote down vote up
def show(self):
        """
        Display the plot.
        """
        if not self.headless:
            plt.show()
        else:
            file = io.BytesIO()
            plt.savefig(file, format="png")
            return file 
Example 22
Project: indras_net   Author: gcallah   File: display_methods.py    License: GNU General Public License v3.0 5 votes vote down vote up
def update_plot(self, i):
        """
        This is our animation function.
        For line graphs, redraw the whole thing.
        """
        plt.clf()
        (data_points, varieties) = self.data_func()
        self.draw_graph(data_points, varieties)
        self.show() 
Example 23
Project: indras_net   Author: gcallah   File: display_methods.py    License: GNU General Public License v3.0 5 votes vote down vote up
def show(self):
        """
        Display the plot.
        """
        if not self.headless:
            plt.show()
        else:
            file = io.BytesIO()
            plt.savefig(file, format="png")
            return file 
Example 24
Project: indras_net   Author: gcallah   File: display_methods.py    License: GNU General Public License v3.0 5 votes vote down vote up
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() 
Example 25
Project: indras_net   Author: gcallah   File: display_methods.py    License: GNU General Public License v3.0 5 votes vote down vote up
def update_plot(self, i):
        """
        This is our animation function.
        For line graphs, redraw the whole thing.
        """
        plt.clf()
        (data_points, varieties) = self.data_func()
        self.draw_graph(data_points, varieties)
        self.show() 
Example 26
Project: indras_net   Author: gcallah   File: display_methods.py    License: GNU General Public License v3.0 5 votes vote down vote up
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) 
Example 27
Project: indras_net   Author: gcallah   File: display_methods.py    License: GNU General Public License v3.0 5 votes vote down vote up
def show(self):
        """
        Display the plot.
        """
        if not self.headless:
            plt.show()
        else:
            file = io.BytesIO()
            plt.savefig(file, format="png")
            return file 
Example 28
Project: simulated-annealing-tsp   Author: chncyhn   File: anneal.py    License: MIT License 5 votes vote down vote up
def plot_learning(self):
        """
        Plot the fitness through iterations.
        """
        plt.plot([i for i in range(len(self.fitness_list))], self.fitness_list)
        plt.ylabel("Fitness")
        plt.xlabel("Iteration")
        plt.show() 
Example 29
Project: vergeml   Author: mme   File: pr.py    License: MIT License 5 votes vote down vote up
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() 
Example 30
Project: pepper-robot-programming   Author: maverickjoy   File: asthama_search.py    License: MIT License 5 votes vote down vote up
def run(self):
        self._printLogs("Waiting for the robot to be in wake up position", "OKBLUE")

        self.motion_service.wakeUp()
        self.posture_service.goToPosture("StandInit", 0.1)

        self.create_callbacks()
        # self.startDLServer()
        self._addTopic()

        # graphplots
        self._initialisePlot()
        ani = animation.FuncAnimation(self.fig, self._animate, blit=False, interval=500 ,repeat=False)


        # loop on, wait for events until manual interruption
        try:
            # while True:
            #     time.sleep(1)
            # starting graph plot
            plt.show() # blocking call hence no need for while(True)

        except KeyboardInterrupt:
            self._printLogs("Interrupted by user, shutting down", "FAIL")
            self._cleanUp()

            self._printLogs("Waiting for the robot to be in rest position", "FAIL")
            # self.motion_service.rest()
            sys.exit(0)

        return