Python matplotlib.pyplot.Subplot() Examples

The following are 6 code examples of matplotlib.pyplot.Subplot(). 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. You may also want to check out all available functions/classes of the module matplotlib.pyplot , or try the search function .
Example #1
Source File: visualize.py    From pre-trained-keras-example with MIT License 6 votes vote down vote up 
def plot_prediction(pixels, model, data_encoder):
    fig = plt.figure()
    inner = gridspec.GridSpec(2, 1, wspace=0.05, hspace=0, height_ratios=[5, 1.2])
    image_ax = plt.Subplot(fig, inner[0])
    labels_ax = plt.Subplot(fig, inner[1])

    predicted_labels = model.predict(np.array([pixels]), batch_size=1)
    character_name_to_probability = data_encoder.one_hot_decode(predicted_labels[0].astype(np.float64))
    top_character_probability = sorted(character_name_to_probability.items(),
                                       key=lambda item_tup: item_tup[1],
                                       reverse=True)[:3]
    top_character_names, top_character_probabilities = zip(*top_character_probability)
    character_idx = data_encoder.one_hot_index(top_character_names[0])

    plot_row_item(image_ax, labels_ax, pixels, top_character_names, top_character_probabilities)

    fig.add_subplot(image_ax)
    fig.add_subplot(labels_ax)
    return fig
Example #2
Source File: spots.py    From allesfitter with MIT License 6 votes vote down vote up 
def setup_grid():
    fig = plt.figure(figsize=(8,3.8))
    
    gs0 = gridspec.GridSpec(1, 2)
    
    gs00 = gridspec.GridSpecFromSubplotSpec(4, 1, subplot_spec=gs0[0], hspace=0)
    ax1 = plt.Subplot(fig, gs00[:-1, :])
    ax1.set(xlabel='', xticks=[], ylabel='Flux')
    fig.add_subplot(ax1)
    ax2 = plt.Subplot(fig, gs00[-1, :])
    ax2.set(xlabel='Phase', ylabel='Res.')
    fig.add_subplot(ax2)
    
    
    gs01 = gridspec.GridSpecFromSubplotSpec(1, 1, subplot_spec=gs0[1])
    ax3 = plt.Subplot(fig, gs01[:, :])
    ax3.set(xlabel='Long. (deg)', ylabel='Lat. (deg.)')
    fig.add_subplot(ax3)
    
    plt.tight_layout()
    return fig, ax1, ax2, ax3
Example #3
Source File: cam_animation.py    From pre-trained-keras-example with MIT License 5 votes vote down vote up 
def make_cam_plot(model, weight, image_path, cam_path, data_generator):
    path_head, npz_name = os.path.split(image_path)
    _, character_name = os.path.split(path_head)

    model_name = os.path.basename(os.path.dirname(weight))

    character_idx = data_generator.encoder.one_hot_index(character_name)
    cam = cam_weighted_image(model, image_path, character_idx)

    fig = plt.figure()
    inner = gridspec.GridSpec(2, 1, wspace=0.05, hspace=0, height_ratios=[5, 1.2])
    image_ax = plt.Subplot(fig, inner[0])
    labels_ax = plt.Subplot(fig, inner[1])
    character_name_to_probability = get_model_predictions_for_npz(model,
                                                                  data_generator,
                                                                  character_name,
                                                                  npz_name)
    top_character_probability = sorted(character_name_to_probability.items(),
                                       key=lambda item_tup: item_tup[1],
                                       reverse=True)[:3]
    top_character_names, top_character_probabilities = zip(*top_character_probability)

    plot_row_item(image_ax, labels_ax, cam, top_character_names, top_character_probabilities)
    weight_idx = os.path.basename(weight).split('.')[1]
    labels_ax.set_xlabel(npz_name)
    image_ax.set_title(model_name + ', epoch ' + weight_idx)

    fig.add_subplot(image_ax)
    fig.add_subplot(labels_ax)

    plt.savefig(os.path.join(cam_path, 'cam_{}.png'.format(weight_idx)))
    plt.close(fig)
Example #4
Source File: plotARContour.py    From ar-pde-cnn with MIT License 5 votes vote down vote up 
def plotContourGrid(t, xT, uPred, uTarget):
    '''
    Creates grid of 4 different test cases, plots target, prediction and error for each
    '''
    mpl.rcParams['font.family'] = ['serif'] # default is sans-serif
    rc('text', usetex=False)

    fig = plt.figure(figsize=(15, 9), dpi=150)
    outer = gridspec.GridSpec(2, 2, wspace=0.45, hspace=0.2) # Outer grid
    for i in range(4):
        # Inner grid
        inner = gridspec.GridSpecFromSubplotSpec(3, 1, 
            subplot_spec=outer[i], wspace=0, hspace=0.2)
        ax = []
        for j in range(3):
            ax0 = plt.Subplot(fig, inner[j])
            fig.add_subplot(ax0)
            ax.append(ax0)
        # Plot specific test case
        plotPred(fig, ax, t, xT, uPred[i], uTarget[i])

    file_dir = '.'
    # If directory does not exist create it
    if not os.path.exists(file_dir):
        os.makedirs(file_dir)
    file_name = file_dir+"/burger_AR_pred"
    plt.savefig(file_name+".png", bbox_inches='tight')
    plt.savefig(file_name+".pdf", bbox_inches='tight')

    plt.show()
Example #5
Source File: plotBARContour.py    From ar-pde-cnn with MIT License 5 votes vote down vote up 
def plotContourGrid(t, xT, uPred, betas, uTarget):
    '''
    Creates grid of 4 different test cases, plots target, prediction, variance and error for each
    '''
    mpl.rcParams['font.family'] = ['serif'] # default is sans-serif
    rc('text', usetex=False)

    fig = plt.figure(figsize=(15, 13), dpi=150)
    outer = gridspec.GridSpec(2, 2, wspace=0.45, hspace=0.2) # Outer grid
    for i in range(4):
        # Inner grid
        inner = gridspec.GridSpecFromSubplotSpec(4, 1, 
            subplot_spec=outer[i], wspace=0, hspace=0.25)
        ax = []
        for j in range(4):
            ax0 = plt.Subplot(fig, inner[j])
            fig.add_subplot(ax0)
            ax.append(ax0)
        # Plot specific test case
        plotPred(fig, ax, t, xT, uPred[i], betas, uTarget[i])

    file_dir = '.'
    # If directory does not exist create it
    if not os.path.exists(file_dir):
        os.makedirs(file_dir)
    file_name = file_dir+"/burger_BAR_pred"
    plt.savefig(file_name+".png", bbox_inches='tight')
    plt.savefig(file_name+".pdf", bbox_inches='tight')

    plt.show()
Example #6
Source File: util.py    From bayes_nn with MIT License 4 votes vote down vote up 
def plot_preds(preds, batch):
    """
    Lots of matplotlib magic to plot the predictions
    :param preds:
    :param batch: tuple of images, labels
    :return:
    """
    images, labels = batch
    if isinstance(preds, list):
        preds = np.stack(preds)

    num_samples, num_batch, num_classes = preds.shape

    ave_preds = np.mean(preds, 0)
    pred_class = np.argmax(ave_preds, 1)

    entropy, variance, _, _ = calc_risk(preds)

    # Do all the plotting

    for n in range(num_batch):
        fig = plt.figure(figsize=(10, 8))
        outer = gridspec.GridSpec(1, 2, wspace=0.2, hspace=0.2)

        half = gridspec.GridSpecFromSubplotSpec(4, 4, subplot_spec=outer[0], wspace=0.1, hspace=0.1)
        colors = get_color(pred_class[n], labels[n])
        for num_sample in range(half._ncols * half._nrows):
            ax = plt.Subplot(fig, half[num_sample])
            ax.bar(range(10), preds[num_sample, n], color=colors)
            ax.set_ylim(0, np.max(preds))
            ax.set_xticks([])
            ax.set_yticks([])
            fig.add_subplot(ax)

        half = gridspec.GridSpecFromSubplotSpec(3, 1, subplot_spec=outer[1], wspace=0.1, hspace=0.1)

        ax = plt.Subplot(fig, half[0])
        ax.imshow(np.squeeze(images[n]))
        fig.add_subplot(ax)

        ax = plt.Subplot(fig, half[1])
        ax.bar(range(10), ave_preds[n], color=colors)
        ax.set_ylim(0, np.max(preds))
        ax.set_xticks([])
        fig.add_subplot(ax)

        ax = plt.Subplot(fig, half[2])
        t = ax.text(0.5, 0.5, 'Entropy %7.3f \n Std %7.3f' % (entropy[n], variance[n]))
        t.set_ha('center')
        fig.add_subplot(ax)

        # fig.show()
        plt.savefig('im/plot%i.png' % n)