Python matplotlib.offsetbox.AnnotationBbox() Examples

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() 

def plot_embedding(X, y, imgs=None, title=None, name=None):
    # Adapted from http://scikit-learn.org/stable/auto_examples/manifold/plot_lle_digits.html
    x_min, x_max = np.min(X, 0), np.max(X, 0)
    X = (X - x_min) / (x_max - x_min)

    # Plot colors numbers
    plt.figure(figsize=(10,10))
    ax = plt.subplot(111)
    for i in range(X.shape[0]):
        # plot colored number
        plt.text(X[i, 0], X[i, 1], str(y[i]),
                 color=plt.cm.Set1(y[i] / 10.),
                 fontdict={'weight': 'bold', 'size': 9})

    # Add image overlays
    if imgs is not None and hasattr(offsetbox, 'AnnotationBbox'):
        # only print thumbnails with matplotlib > 1.0
        shown_images = np.array([[1., 1.]])  # just something big
        for i in range(X.shape[0]):
            dist = np.sum((X[i] - shown_images) ** 2, 1)
            if np.min(dist) < 4e-3:
                # don't show points that are too close
                continue
            shown_images = np.r_[shown_images, [X[i]]]
            imagebox = offsetbox.AnnotationBbox(
                offsetbox.OffsetImage(imgs[i], cmap=plt.cm.gray_r), X[i])
            ax.add_artist(imagebox)

    plt.xticks([]), plt.yticks([])
    if title is not None:
        plt.title(title)

    plt.savefig("results/" + str(name) + '.svg') 

def ab_plotter(xcoords, ycoords, images, labels):

    ax = plt.subplot(111)
    ax.set_xlim([-30, 30])
    ax.set_ylim([-30, 30])
    
    for x, y, i, l in zip(xcoords, ycoords, images, labels):
        arr_hand = i
        imagebox = OffsetImage(arr_hand, zoom=.1)
        xy = [x, y]               # coordinates to position this image
        
        ab = AnnotationBbox(imagebox, xy,
            xybox=(10., -10.),
            xycoords='data',
            boxcoords="offset points",
            pad=0.0)                                  
        ax.annotate(ab, xy = xy)
    
    # rest is just standard matplotlib boilerplate
    ax.grid(True)
    plt.show() 

def tsne_plot(labels, tokens):
    "Creates and TSNE model and plots it"
    
    tsne_model = TSNE(perplexity=40, n_components=2, init='pca', n_iter=2500, random_state=23)
    X_2d = tsne_model.fit_transform(tokens)
    X_2d -= X_2d.min(axis=0)
    X_2d /= X_2d.max(axis=0)

    width = 1200
    grid, to_plot = tsne_to_grid(X_2d)
    out_dim = int(width / np.sqrt(to_plot))
   
    fig, ax = plt.subplots(figsize=(width/100, width/100))
    plt.subplots_adjust(left=0, bottom=0, right=1, top=1, wspace=None, hspace=None)
    
    for pos, label in zip(grid, labels[0:to_plot]):
        ax.scatter(pos[0], pos[1])
        if False:
            ax.annotate(label,
                     xy=(pos[0], pos[1]),
                     xytext=(5, 2),
                     fontsize=9,
                     textcoords='offset points',
                     ha='right',
                     va='bottom')
        ab = AnnotationBbox(getImage(label, new_size = out_dim / 2), (pos[0], pos[1]), frameon=False)
        ax.add_artist(ab)

    plt.show() 

def plot_tsne(X, imgs, outFile):

    def imscatter(x, y, images, ax=None, zoom=1.0):
        if ax is None:
            ax = plt.gca()
        x, y = np.atleast_1d(x, y)
        artists = []
        for x0, y0, img0 in zip(x, y, images):
            im = OffsetImage(img0, zoom=zoom)
            ab = AnnotationBbox(im, (x0, y0), xycoords='data', frameon=True)
            artists.append(ax.add_artist(ab))
        ax.update_datalim(np.column_stack([x, y]))
        ax.autoscale()
        return artists

    def plot_embedding(X, imgs, title=None):
        x_min, x_max = np.min(X, 0), np.max(X, 0)
        X = (X - x_min) / (x_max - x_min)

        plt.figure()
        ax = plt.subplot(111)
        for i in range(X.shape[0]):
            plt.text(X[i, 0], X[i, 1], ".", fontdict={'weight': 'bold', 'size': 9})
        if hasattr(offsetbox, 'AnnotationBbox'):
            imscatter(X[:,0], X[:,1], imgs, zoom=0.3, ax=ax)

        plt.xticks([]), plt.yticks([])
        if title is not None:
            plt.title(title, fontsize=18)

    tsne = manifold.TSNE(n_components=2, init='pca', random_state=0)
    X_tsne = tsne.fit_transform(X)
    plot_embedding(X_tsne, imgs, "t-SNE embeddings")
    if outFile is None:
        plt.show()
    else:
        plt.savefig(outFile, bbox_inches='tight')
    plt.close()

# Plot image reconstructions 

def plot_embedding(X, title=None):
    x_min, x_max = np.min(X, 0), np.max(X, 0)
    X = (X - x_min) / (x_max - x_min)

    plt.figure()
    ax = plt.subplot(111)
    for i in range(X.shape[0]):
        plt.text(X[i, 0], X[i, 1], str(y[i]),
                 color=plt.cm.Set1(y[i] / 10.),
                 fontdict={'weight': 'bold', 'size': 9})

    if hasattr(offsetbox, 'AnnotationBbox'):
        # only print thumbnails with matplotlib > 1.0
        shown_images = np.array([[1., 1.]])  # just something big
        for i in range(X.shape[0]):
            dist = np.sum((X[i] - shown_images) ** 2, 1)
            if np.min(dist) < 4e-3:
                # don't show points that are too close
                continue
            shown_images = np.r_[shown_images, [X[i]]]
            imagebox = offsetbox.AnnotationBbox(
                offsetbox.OffsetImage(digits.images[i], cmap=plt.cm.gray_r),
                X[i])
            ax.add_artist(imagebox)
    plt.xticks([]), plt.yticks([])
    if title is not None:
        plt.title(title)


# ----------------------------------------------------------------------
# Plot images of the digits 

def plot_embedding(X, title=None):
    x_min, x_max = np.min(X, 0), np.max(X, 0)
    X = (X - x_min) / (x_max - x_min)

    plt.figure()
    ax = plt.subplot(111)
    for i in range(X.shape[0]):
        plt.text(X[i, 0], X[i, 1], str(y[i]),
                 color=plt.cm.Set1(y[i] / 10.),
                 fontdict={'weight': 'bold', 'size': 9})

    if hasattr(offsetbox, 'AnnotationBbox'):
        # only print thumbnails with matplotlib > 1.0
        shown_images = np.array([[1., 1.]])  # just something big
        for i in range(X.shape[0]):
            dist = np.sum((X[i] - shown_images) ** 2, 1)
            if np.min(dist) < 4e-3:
                # don't show points that are too close
                continue
            shown_images = np.r_[shown_images, [X[i]]]
            imagebox = offsetbox.AnnotationBbox(
                offsetbox.OffsetImage(digits.images[i], cmap=plt.cm.gray_r),
                X[i])
            ax.add_artist(imagebox)
    plt.xticks([]), plt.yticks([])
    if title is not None:
        plt.title(title)


# ----------------------------------------------------------------------
# Plot images of the digits 

def plot_embedding(X, title=None):
    x_min, x_max = np.min(X, 0), np.max(X, 0)
    X = (X - x_min) / (x_max - x_min)

    plt.figure()
    ax = plt.subplot(111)
    
    for i in range(X.shape[0]):
        plt.text(X[i, 0], X[i, 1], str(digits.target[i]),
                 color=plt.cm.Set1(y[i] / 10.),
                 fontdict={'weight': 'bold', 'size': 9})
   
    '''
    if hasattr(offsetbox, 'AnnotationBbox'):
        # only print thumbnails with matplotlib > 1.0
        shown_images = np.array([[1., 1.]])  # just something big
        for i in range(digits.data.shape[0]):
            dist = np.sum((X[i] - shown_images) ** 2, 1)
            if np.min(dist) < 4e-3:
                # don't show points that are too close
                continue
            shown_images = np.r_[shown_images, [X[i]]]
            imagebox = offsetbox.AnnotationBbox(
                offsetbox.OffsetImage(digits.images[i], cmap=plt.cm.gray_r),
                X[i])
            ax.add_artist(imagebox)
    '''
    plt.xticks([]), plt.yticks([])
    if title is not None:
        plt.title(title)


#----------------------------------------------------------------------
# Plot images of the digits 

def plot_embedding(Xp, y, imgs, title=None, figsize=(12, 4)):
    x_min, x_max = numpy.min(Xp, 0), numpy.max(Xp, 0)
    X = (Xp - x_min) / (x_max - x_min)

    fig, ax = plt.subplots(1, 2, figsize=figsize)
    for i in range(X.shape[0]):
        ax[0].text(X[i, 0], X[i, 1], str(y[i]),
                   color=plt.cm.Set1(y[i] / 10.),
                   fontdict={'weight': 'bold', 'size': 9})

    if hasattr(offsetbox, 'AnnotationBbox'):
        # only print thumbnails with matplotlib > 1.0
        shown_images = numpy.array([[1., 1.]])  # just something big
        for i in range(X.shape[0]):
            dist = numpy.sum((X[i] - shown_images) ** 2, 1)
            if numpy.min(dist) < 4e-3:
                # don't show points that are too close
                continue
            shown_images = numpy.r_[shown_images, [X[i]]]
            imagebox = offsetbox.AnnotationBbox(
                offsetbox.OffsetImage(imgs[i], cmap=plt.cm.gray_r),
                X[i])
            ax[0].add_artist(imagebox)
    ax[0].set_xticks([]), ax[0].set_yticks([])
    ax[1].plot(Xp[:, 0], Xp[:, 1], '.')
    if title is not None:
        ax[0].set_title(title)
    return ax 

def plot_embedding(X, title=None):
    x_min, x_max = np.min(X, 0), np.max(X, 0)
    X = (X - x_min) / (x_max - x_min)

    plt.figure()
    ax = plt.subplot(111)
    for i in range(X.shape[0]):
        plt.text(X[i, 0], X[i, 1], str(digits.target[i]),
                 color=plt.cm.Set1(y[i] / 10.),
                 fontdict={'weight': 'bold', 'size': 9})

    if hasattr(offsetbox, 'AnnotationBbox'):
        # only print thumbnails with matplotlib > 1.0
        shown_images = np.array([[1., 1.]])  # just something big
        for i in range(digits.data.shape[0]):
            dist = np.sum((X[i] - shown_images) ** 2, 1)
            if np.min(dist) < 4e-3:
                # don't show points that are too close
                continue
            shown_images = np.r_[shown_images, [X[i]]]
            imagebox = offsetbox.AnnotationBbox(
                offsetbox.OffsetImage(digits.images[i], cmap=plt.cm.gray_r),
                X[i])
            ax.add_artist(imagebox)
    plt.xticks([]), plt.yticks([])
    if title is not None:
        plt.title(title) 

def plot_embedding(data_matrix, y,
                   labels=None,
                   image_file_name=None,
                   title=None,
                   cmap='rainbow',
                   density=False):
    """plot_embedding."""
    import matplotlib.pyplot as plt
    from matplotlib import offsetbox
    from PIL import Image
    from eden.embedding import embed_dat_matrix_two_dimensions

    if title is not None:
        plt.title(title)
    if density:
        embed_dat_matrix_two_dimensions(data_matrix,
                                        y=y,
                                        instance_colormap=cmap)
    else:
        plt.scatter(data_matrix[:, 0], data_matrix[:, 1],
                    c=y,
                    cmap=cmap,
                    alpha=.7,
                    s=30,
                    edgecolors='black')
        plt.xticks([])
        plt.yticks([])
        plt.axis('off')
    if image_file_name is not None:
        num_instances = data_matrix.shape[0]
        ax = plt.subplot(111)
        for i in range(num_instances):
            img = Image.open(image_file_name + str(i) + '.png')
            imagebox = offsetbox.AnnotationBbox(
                offsetbox.OffsetImage(img, zoom=1),
                data_matrix[i],
                pad=0,
                frameon=False)
            ax.add_artist(imagebox)
    if labels is not None:
        for id in range(data_matrix.shape[0]):
            label = str(labels[id])
            x = data_matrix[id, 0]
            y = data_matrix[id, 1]
            plt.annotate(label,
                         xy=(x, y),
                         xytext=(0, 0),
                         textcoords='offset points') 

def update_coverage_regions(self):
        point_mobiles = []

        for ix,code_mobile in enumerate(self.sim.mobile_fog_entities.keys()):
            if code_mobile in self.track_code_last_position.keys():
                (lng, lat) = self.track_code_last_position[code_mobile]
                point_mobiles.append(np.array([lng, lat]))

        point_mobiles = np.array(point_mobiles)

        if len(point_mobiles)==0:
            self.pointsVOR = self.sim.endpoints
        else:
            self.pointsVOR = np.concatenate((self.sim.endpoints, point_mobiles), axis=0)

        self.sim.coverage.update_coverage_of_endpoints(self.sim.map, self.pointsVOR)
        self.axarr.clear()

        plt.xticks([])
        plt.yticks([])
        plt.grid(False)
        plt.xlim(0, self.sim.map.w)
        plt.ylim(self.sim.map.h, 0)
        plt.axis('off')
        plt.tight_layout()

        self.axarr.imshow(self.sim.map.img)

        # self.axarr.add_collection(
        #     mpl.collections.PolyCollection(
        #         self.sim.coverage.cells, facecolors=self.sim.coverage.colors_cells,
        #         edgecolors='k', alpha=.25))

        # p = PatchCollection(self.sim.coverage.get_polygon_to_map(),facecolors=self.sim.coverage.get_polygon_colors(),alpha=.25)
        # p.set_array(self.sim.coverage.colors_cells)

        self.axarr.add_collection(self.sim.coverage.get_polygons_on_map())


        # self.ppix = [self.sim.map.to_pixels(vp[0], vp[1]) for vp in self.pointsVOR]
        # self.ppix = np.array(self.ppix)
        # for point in self.ppix:
        #     ab = AnnotationBbox(self.car_icon, (point[0], point[1]),frameon=False)
        #     self.axarr.add_artist(ab)

        # Endpoints of the network
        self.ppix = [self.sim.map.to_pixels(vp[0], vp[1]) for vp in self.sim.endpoints]
        for point in self.ppix:
            ab = AnnotationBbox(self.endpoint_icon, (point[0], point[1]), frameon=False)
            self.axarr.add_artist(ab)

        # self.axarr.scatter(self.ppix[:, 0], self.ppix[:, 1]) 

def show_frequency(self,draw_connection_line=False):
        self.axarr.texts = []

        # Draw names
        for ix, vp in enumerate(self.ppix):
            t = plt.text(vp[0] - 3, vp[1] - 8, self.name_mobile[ix], dict(size=6, color='b'))

        # Draw last movement
        for code in self.track_code_last_position:

            (lng, lat) = self.track_code_last_position[code]
            new_point=[lng,lat]

            if code not in self.sim.mobile_fog_entities.keys():
                point_index = self.sim.coverage.connection(new_point)
                self.connection[point_index] += 1
                icon = self.car_icon
            else:
                icon = self.car_endpoint_icon

            lng, lat = self.sim.map.to_pixels(lng, lat)

            plt.annotate(str(code).replace("_0.0",""),
                        xy=(lng, lat),  # theta, radius
                        xytext=(lng-1, lat-5),  # fraction, fraction
                        # arrowprops=dict(facecolor='black', arrowstyle="-|>"),
                        horizontalalignment='center',
                        verticalalignment='bottom', size= 6)

            ab = AnnotationBbox(icon, (lng,lat), frameon=False)
            self.axarr.add_artist(ab)

            # if code not in self.sim.mobile_fog_entities and \
            #         draw_connection_line:
            #     pointA = self.ppix[point_index]
            #     pointB = [lng,lat]
            #
            #     plt.plot([pointA[0], pointB[0]], [pointA[1], pointB[1]], color="gray")

        # draw number of connections by node
        # for k in self.connection:
            # plt.text(20, 20 + (k * 30), "%s : %i" % (self.name_mobile[k], self.connection[k]), dict(size=10, color='black')) 

def _tsne_plot_embedding(self, x, y, inputs, path_result_image, title=""):
        x_min, x_max = np.min(x, 0), np.max(x, 0)
        x_normalized = (x - x_min) / (x_max - x_min)

        tableau20 = style.generate_tableau20_colors()
        figure = plt.figure()
        figure.set_size_inches(18.5, 10.5)
        ax = figure.add_subplot(111)
        ax.axis('off')
        for i in xrange(x.shape[0]):
            plt.text(x_normalized[i, 0], x_normalized[i, 1], str(y[i]),
                     color=tableau20[y[i]],
                     fontdict={'weight': 'bold', 'size': 12})

        labels = [mpatches.Patch(color=tableau20[output_descriptor.value],
                                 label="[{0}] {1}".format(output_descriptor.value, output_descriptor.name)) for output_descriptor in list(self.output_descriptor_enum)]
        legend = ax.legend(handles=labels, bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0., frameon=False)

        if hasattr(offsetbox, 'AnnotationBbox'): # only print thumbnails with matplotlib > 1.0
            shown_images = np.array([[1., 1.]])
            for i in xrange(len(x_normalized)):
                distance_between_points = np.sum((x_normalized[i] - shown_images) ** 2, 1)
                if np.min(distance_between_points) < self.MIN_DISTANCE_BETWEEN_IMAGES:
                    continue
                shown_images = np.r_[shown_images, [x_normalized[i]]]
                rendered_image = offsetbox.OffsetImage(self._state_into_grid_of_screenshots(inputs[i]),
                                                       cmap=plt.get_cmap('gray'))
                image_position = x_normalized[i]
                annotation_box_relative_position = (-70, 250) if x_normalized[i][1] > 0.5 else (-70, -250)
                imagebox = offsetbox.AnnotationBbox(rendered_image, image_position,
                                                    xybox=annotation_box_relative_position,
                                                    xycoords='data',
                                                    boxcoords="offset points",
                                                    arrowprops=dict(arrowstyle="->"))
                ax.add_artist(imagebox)

        plt.xticks([]), plt.yticks([])
        if title is not None:
            plt.title(title)

        plt.savefig(path_result_image, bbox_extra_artists=(legend,), bbox_inches='tight', pad_inches=4)
        print("Visualization written to {0}".format(path_result_image)) 

def digits_plot():
    digits = datasets.load_digits(n_class=6)
    n_digits = 500
    X = digits.data[:n_digits]
    y = digits.target[:n_digits]
    n_samples, n_features = X.shape
    n_neighbors = 30

    def plot_embedding(X, title=None):
        x_min, x_max = np.min(X, 0), np.max(X, 0)
        X = (X - x_min) / (x_max - x_min)

        plt.figure()
        ax = plt.subplot(111)
        for i in range(X.shape[0]):
            plt.text(X[i, 0], X[i, 1], str(digits.target[i]),
                    color=plt.cm.Set1(y[i] / 10.),
                    fontdict={'weight': 'bold', 'size': 9})

        if hasattr(offsetbox, 'AnnotationBbox'):
            # only print thumbnails with matplotlib > 1.0
            shown_images = np.array([[1., 1.]])  # just something big
            for i in range(X.shape[0]):
                dist = np.sum((X[i] - shown_images) ** 2, 1)
                if np.min(dist) < 1e5:
                    # don't show points that are too close
                    # set a high threshold to basically turn this off
                    continue
                shown_images = np.r_[shown_images, [X[i]]]
                imagebox = offsetbox.AnnotationBbox(
                    offsetbox.OffsetImage(digits.images[i], cmap=plt.cm.gray_r),
                    X[i])
                ax.add_artist(imagebox)
        plt.xticks([]), plt.yticks([])
        if title is not None:
            plt.title(title)

    n_img_per_row = 10
    img = np.zeros((10 * n_img_per_row, 10 * n_img_per_row))
    for i in range(n_img_per_row):
        ix = 10 * i + 1
        for j in range(n_img_per_row):
            iy = 10 * j + 1
            img[ix:ix + 8, iy:iy + 8] = X[i * n_img_per_row + j].reshape((8, 8))

    plt.imshow(img, cmap=plt.cm.binary)
    plt.xticks([])
    plt.yticks([])
    plt.title('A selection from the 64-dimensional digits dataset')
    print("Computing PCA projection")
    pca = decomposition.PCA(n_components=2).fit(X)
    X_pca = pca.transform(X)
    plot_embedding(X_pca, "Principal Components projection of the digits")
    plt.figure()
    plt.matshow(pca.components_[0, :].reshape(8, 8), cmap="gray")
    plt.axis('off')
    plt.figure()
    plt.matshow(pca.components_[1, :].reshape(8, 8), cmap="gray")
    plt.axis('off')
    plt.show() 

def visualize(model_path, model, algo_name):
    if not MATPLOTLIB_AVAIBLABLE:
        logger.error("visualize requires matplotlib package ...")
        return
    pred = OfflinePredictor(PredictConfig(
        session_init=SmartInit(model_path),
        model=model(),
        input_names=['input'],
        output_names=['emb']))

    NUM_BATCHES = 6
    BATCH_SIZE = 128
    images = np.zeros((BATCH_SIZE * NUM_BATCHES, 28, 28))  # the used digits
    embed = np.zeros((BATCH_SIZE * NUM_BATCHES, 2))  # the actual embeddings in 2-d

    # get only the embedding model data (MNIST test)
    ds = get_test_data()
    ds.reset_state()

    for offset, dp in enumerate(ds):
        digit, label = dp
        prediction = pred(digit)[0]
        embed[offset * BATCH_SIZE:offset * BATCH_SIZE + BATCH_SIZE, ...] = prediction
        images[offset * BATCH_SIZE:offset * BATCH_SIZE + BATCH_SIZE, ...] = digit
        offset += 1
        if offset == NUM_BATCHES:
            break

    plt.figure()
    ax = plt.subplot(111)
    ax_min = np.min(embed, 0)
    ax_max = np.max(embed, 0)

    ax_dist_sq = np.sum((ax_max - ax_min)**2)
    ax.axis('off')
    shown_images = np.array([[1., 1.]])
    for i in range(embed.shape[0]):
        dist = np.sum((embed[i] - shown_images)**2, 1)
        if np.min(dist) < 3e-4 * ax_dist_sq:     # don't show points that are too close
            continue
        shown_images = np.r_[shown_images, [embed[i]]]
        imagebox = offsetbox.AnnotationBbox(offsetbox.OffsetImage(np.reshape(images[i, ...], [28, 28]),
                                            zoom=0.6, cmap=plt.cm.gray_r), xy=embed[i], frameon=False)
        ax.add_artist(imagebox)

    plt.axis([ax_min[0], ax_max[0], ax_min[1], ax_max[1]])
    plt.xticks([]), plt.yticks([])
    plt.title('Embedding using %s-loss' % algo_name)
    plt.savefig('%s.jpg' % algo_name)