Python matplotlib.pyplot.draw() Examples

def hzfunc(self,label):
        ax = self.hzdict[label]
        num = int(label.replace("plot ",""))
        #print "Selected axis number:", num
        #global mainnum
        self.mainnum = num
        # drawtype is 'box' or 'line' or 'none'
        toggle_selector.RS = RectangleSelector(ax, self.line_select_callback,
                                           drawtype='box', useblit=True,
                                           button=[1,3], # don't use middle button
                                           minspanx=5, minspany=5,
                                           spancoords='pixels',
                                           rectprops = dict(facecolor='red', edgecolor = 'black', alpha=0.2, fill=True))

        #plt.connect('key_press_event', toggle_selector)
        plt.draw() 

def draw(vmean, vlogstd):
        from scipy import stats
        plt.cla()
        xlimits = [-2, 2]
        ylimits = [-4, 2]

        def log_prob(z):
            z1, z2 = z[:, 0], z[:, 1]
            return stats.norm.logpdf(z2, 0, 1.35) + \
                stats.norm.logpdf(z1, 0, np.exp(z2))

        plot_isocontours(ax, lambda z: np.exp(log_prob(z)), xlimits, ylimits)

        def variational_contour(z):
            return stats.multivariate_normal.pdf(
                z, vmean, np.diag(np.exp(vlogstd)))

        plot_isocontours(ax, variational_contour, xlimits, ylimits)
        plt.draw()
        plt.pause(1.0 / 30.0) 

def label(self, feature):
        plt.imshow(feature, cmap=plt.cm.gray_r, interpolation='nearest')
        plt.draw()

        banner = "Enter the associated label with the image: "

        if self.label_name is not None:
            banner += str(self.label_name) + ' '

        lbl = input(banner)

        while (self.label_name is not None) and (lbl not in self.label_name):
            print('Invalid label, please re-enter the associated label.')
            lbl = input(banner)

        return self.label_name.index(lbl) 

def plotprojection(Z, pc, labels, class_labels):
    diff_labels = np.unique(labels)
    opacity = 0.8
    fig, ax = plt.subplots()
    color_map = {0: 'orangered', 1: 'royalblue', 2: 'lightgreen', 3: 'darkorchid', 4: 'teal', 5: 'darkslategrey',
                 6: 'darkgreen', 7: 'darkgrey'}
    for label in diff_labels:
        idx = labels == label
        ax.plot(Z[idx, pc], Z[idx, pc + 1], 'o', alpha=opacity, c=color_map[label], label='{label}'.format(label=class_labels[label]))
    # ax.plot(Z[idx_below, pc], Z[idx_below, pc + 1], 'o', alpha=opacity,
    #         label='{name} below mean'.format(name=attributeNames[att]))
    ax.set_ylabel('$v{0}$'.format(pc + 2))
    ax.set_xlabel('$v{0}$'.format(pc + 1))
    ax.legend()
    ax.set_title('Data projected on v{0} and v{1}'.format(pc+1, pc+2))
    # fig.savefig('v{0}_v{1}_{att}.png'.format(pc + 1, pc + 2, att=attributeNames[att]), dpi=300)
    plt.draw() 

def create_image(D, filename, filepath = '_static/'):
    # if any(D.size == 0):
    #     D = pg.text('?')
    qp(D)
    fig = plt.gcf()
    # ax = plt.gca()
    scale = 0.75
    fig.set_size_inches(10*scale, 4*scale, forward=True)
    # ax.autoscale()
    # plt.draw()
    # plt.show(block = False)
    filename +=  '.png'
    filepathfull = os.path.join(os.path.curdir, filepath, filename)
    print(filepathfull)
    fig.savefig(filepathfull, dpi=int(96/scale))


# example-rectangle 

def render(self, close=False):
        if self.fig is None:
            self.fig = plt.figure()
            self.ax = self.fig.add_subplot(111)
            plt.axis('equal')

        if self.fixed_plots is None:
            self.fixed_plots = self.plot_position_cost(self.ax)

        [o.remove() for o in self.dynamic_plots]

        x, y = self.observation
        point = self.ax.plot(x, y, 'b*')
        self.dynamic_plots = point

        if close:
            self.fixed_plots = None

        plt.pause(0.001)
        plt.draw() 

def update(self, conf_mat, classes, normalize=False):
        """This function prints and plots the confusion matrix.
        Normalization can be applied by setting `normalize=True`.
        """
        plt.imshow(conf_mat, interpolation='nearest', cmap=self.cmap)
        plt.title(self.title)
        plt.colorbar()
        tick_marks = np.arange(len(classes))
        plt.xticks(tick_marks, classes, rotation=45)
        plt.yticks(tick_marks, classes)

        if normalize:
            conf_mat = conf_mat.astype('float') / conf_mat.sum(axis=1)[:, np.newaxis]

        thresh = conf_mat.max() / 2.
        for i, j in itertools.product(range(conf_mat.shape[0]), range(conf_mat.shape[1])):
            plt.text(j, i, conf_mat[i, j],                                          
                         horizontalalignment="center",
                         color="white" if conf_mat[i, j] > thresh else "black")
                                                                                                         
        plt.tight_layout()                                                    
        plt.ylabel('True label')                                              
        plt.xlabel('Predicted label')                                         
        plt.draw() 

def main():
    # Generate synthetic data
    x = 2 * npr.rand(N,D) - 1  # data features, an (N,D) array
    x[:, 0] = 1
    th_true = 10.0 * np.array([0, 1, 1])
    y = np.dot(x, th_true[:, None])[:, 0]
    t = npr.rand(N) > (1 / ( 1 + np.exp(y)))  # data targets, an (N) array of 0s and 1s

    # Obtain joint distributions over z and th
    model = ff.LogisticModel(x, t, th0=th0, y0=y0)

    # Set up step functions
    th = np.random.randn(D) * th0
    z = ff.BrightnessVars(N)
    th_stepper = ff.ThetaStepMH(model.log_p_joint, stepsize)
    z__stepper = ff.zStepMH(model.log_pseudo_lik, q)

    plt.ion()
    ax = plt.figure(figsize=(8, 6)).add_subplot(111)
    while True:
        th = th_stepper.step(th, z)  # Markov transition step for theta
        z  = z__stepper.step(th ,z)  # Markov transition step for z
        update_fig(ax, x, y, z, th, t)
        plt.draw()
        plt.pause(0.05) 

def key_press_event(event):
    global figures_i
    fig = event.canvas.figure

    if event.key == 'q' or event.key == 'escape':
        plt.close(event.canvas.figure)
        return

    if event.key == 'right':
        figures_i = (figures_i + 1) % figures_N
    elif event.key == 'left':
        figures_i = (figures_i - 1) % figures_N

    fig.clear()
    my_plot(fig, figures_i)
    plt.draw() 

def key_press_event(event):
    global figures_i, figures_N

    fig = event.canvas.figure

    if event.key == 'q' or event.key == 'escape':
        plt.close(event.canvas.figure)
        return
    if event.key == 'right':
        figures_i += 1
        figures_i %= figures_N
    elif event.key == 'left':
        figures_i -= 1
        figures_i %= figures_N
    fig.clear()
    my_plot(fig, figures_i)
    plt.draw() 

def key_press_event(event):
    global figures_i, figures_N

    fig = event.canvas.figure

    if event.key == 'q' or event.key == 'escape':
        plt.close(event.canvas.figure)
        return
    if event.key == 'right':
        figures_i += 1
        figures_i %= figures_N
    elif event.key == 'left':
        figures_i -= 1
        figures_i %= figures_N
    fig.clear()
    my_plot(fig, figures_i)
    plt.draw() 

def _create_subgraph_plot(event, dna_helix_graph: nx.DiGraph):
    mouseevent = event.mouseevent
    if not mouseevent.dblclick or mouseevent.button != 1:
        return

    logger = logging.getLogger(__name__)
    nucleotide_name = event.artist.get_label().split(":")[-1]
    nucleotide = _get_nucleotide_by_name(nucleotide_name, dna_helix_graph)
    logger.info("Create subgraph plot for %s", nucleotide_name)
    figure, subplot = _create_figure_with_subplot()
    figure.suptitle("Subgraph of nucleotide {}".format(nucleotide_name))

    nucleotide_with_neighbors_subgraph = _get_nucleotide_subgraph(
        dna_helix_graph, nucleotide)
    draw_dna_helix_on_subplot(
        nucleotide_with_neighbors_subgraph, subplot, verbosity=1)
    _draw_click_instructions(subplot, doubleclick=False)
    plt.draw()
    logger.info("Done!") 

def keypoint_detection(img, detector, pose_net, ctx=mx.cpu(), axes=None):
    x, img = gcv.data.transforms.presets.yolo.transform_test(img, short=512, max_size=350)
    x = x.as_in_context(ctx)
    class_IDs, scores, bounding_boxs = detector(x)

    plt.cla()
    pose_input, upscale_bbox = detector_to_alpha_pose(img, class_IDs, scores, bounding_boxs,
                                                       output_shape=(128, 96), ctx=ctx)
    if len(upscale_bbox) > 0:
        predicted_heatmap = pose_net(pose_input)
        pred_coords, confidence = heatmap_to_coord_alpha_pose(predicted_heatmap, upscale_bbox)

        axes = plot_keypoints(img, pred_coords, confidence, class_IDs, bounding_boxs, scores,
                              box_thresh=0.5, keypoint_thresh=0.2, ax=axes)
        plt.draw()
        plt.pause(0.001)
    else:
        axes = plot_image(frame, ax=axes)
        plt.draw()
        plt.pause(0.001)

    return axes 

def ampl(a):
    global i
    vdp.set(pars={'a': a},
            ics={'x': 0, 'y': 0},
            tdata=[0,20])
    # let solution settle
    transient = vdp.compute('trans')
    vdp.set(ics=transient(20),
            tdata=[0,6])
    traj = vdp.compute('ampl')
    pts = traj.sample()
    if mod(i, 10) == 0 or 1-abs(a) < 0.02:
        plt.figure(3)
        plt.plot(pts['x'], pts['y'], 'k-')
        plt.draw()
    i += 1
    return np.linalg.norm([max(pts['x']) - min(pts['x']), max(pts['y']) - min(pts['y'])]) 

def vis_detections(im, class_name, dets, thresh=0.5):
    """Draw detected bounding boxes."""
    inds = np.where(dets[:, -1] >= thresh)[0]
    if len(inds) == 0:
        return

    im = im[:, :, (2, 1, 0)]
    fig, ax = plt.subplots(figsize=(12, 12))
    ax.imshow(im, aspect='equal')
    for i in inds:
        bbox = dets[i, :4]
        score = dets[i, -1]

        ax.add_patch(
            plt.Rectangle((bbox[0], bbox[1]),
                          bbox[2] - bbox[0],
                          bbox[3] - bbox[1], fill=False,
                          edgecolor='red', linewidth=3.5)
            )
        ax.text(bbox[0], bbox[1] - 2,
                '{:s} {:.3f}'.format(class_name, score),
                bbox=dict(facecolor='blue', alpha=0.5),
                fontsize=14, color='white')

    ax.set_title(('{} detections with '
                  'p({} | box) >= {:.1f}').format(class_name, class_name,
                                                  thresh),
                  fontsize=14)
    plt.axis('off')
    plt.tight_layout()
    plt.draw() 

def render(self):
        # create a grid
        states = [self.state/self.scale]
        indices = np.array([int(self.preprocess(s)) for s in states])
        a = np.zeros(self.grid_size)
        for i in indices:
            a[i] += 1
        max_freq = np.max(a)
        a/=float(max_freq)  # normalize
        a = np.reshape(a, (self.scale, self.scale))
        ax = sns.heatmap(a)
        plt.draw()
        plt.pause(0.001)
        plt.clf() 

def visualize(img, proc_param, verts, cam, img_name='test_image'):
    """
    Renders the result in original image coordinate frame.
    """
    cam_for_render, vert_shifted = vis_util.get_original(
        proc_param, verts, cam, img_size=img.shape[:2])

    # Render results
    rend_img_overlay = renderer(
        vert_shifted*1.0, cam=cam_for_render, img=img, do_alpha=True)
    rend_img = renderer(
        vert_shifted*1.0, cam=cam_for_render, img_size=img.shape[:2])
    rend_img_vp1 = renderer.rotated(
        vert_shifted, 30, cam=cam_for_render, img_size=img.shape[:2])

    import matplotlib.pyplot as plt
    fig = plt.figure(1)
    plt.clf()
    plt.subplot(221)
    plt.imshow(img)
    plt.title('input')
    plt.axis('off')
    plt.subplot(222)
    plt.imshow(rend_img_overlay)
    plt.title('3D Mesh overlay')
    plt.axis('off')
    plt.subplot(223)
    plt.imshow(rend_img)
    plt.title('3D mesh')
    plt.axis('off')
    plt.subplot(224)
    plt.imshow(rend_img_vp1)
    plt.title('diff vp')
    plt.axis('off')
    plt.draw()
    plt.show(block=False)
    fig.savefig(img_name + '.png')
    # import ipdb
    # ipdb.set_trace() 

def add_overlay(self, vol, cmap='gray', clim=None, alpha=1., draw=True):
        '''
        volume : array-like
            The data that will be overlaid. Must have the same dimensions as the original
            plot
        cmap: matplotlib colormap, optional
            Colormap to use for ploting. Default: 'gray'
        clim: [min, max] or None
            Limits to use for plotting. Default: 1 and 99th percentiles
        alpha: float
            transparency value
        '''
        if len(self._volumes) > 0 and vol.shape != self._volumes[0].shape:
            raise ValueError('Cannot add overlay, different shape')
        # add volume
        self._volumes.append(vol)
        if clim is None:
            clim = np.percentile(vol, (1., 99.))
        # create new images
        ims = []
        for ii, xax, yax in zip([0, 1, 2], [1, 0, 0], [2, 2, 1]):
            d = np.zeros((self._sizes[yax], self._sizes[xax]))
            im = self._axes[ii].imshow(
                d, aspect=1, cmap=cmap, clim=clim, alpha=alpha,
                interpolation='nearest', origin='lower'
            )
            ims.append(im)
        self._ims.append(ims)
        if draw:
            self.set_position() 

def draw(self):
        """Redraw the current image"""
        for fig in self._figs:
            fig.canvas.draw() 

def _draw(self):
        """Update all four (or three) plots"""
        if self._closed:  # make sure we don't draw when we shouldn't
            return
        for ii in range(3):
            ax = self._axes[ii]
            for im in self._ims:
                ax.draw_artist(im[ii])
            if self._cross:
                for line in self._crosshairs[ii].values():
                    ax.draw_artist(line)
            ax.figure.canvas.blit(ax.bbox) 

def plot(cg):
    """
    Plot the call graph using matplotlib
    For larger graphs, this should not be used, as it is very slow
    and probably you can not see anything on it.

    :param cg: A networkx call graph to plot
    """
    from androguard.core.analysis.analysis import ExternalMethod
    import matplotlib.pyplot as plt
    import networkx as nx
    pos = nx.spring_layout(cg)

    internal = []
    external = []

    for n in cg.node:
        if isinstance(n, ExternalMethod):
            external.append(n)
        else:
            internal.append(n)

    nx.draw_networkx_nodes(cg, pos=pos, node_color='r', nodelist=internal)
    nx.draw_networkx_nodes(cg, pos=pos, node_color='b', nodelist=external)
    nx.draw_networkx_edges(cg, pos, arrow=True)
    nx.draw_networkx_labels(cg, pos=pos,
                            labels={x: "{} {}".format(x.get_class_name(),
                                                      x.get_name())
                                    for x in cg.edge})
    plt.draw()
    plt.show() 

def UpdateImage(x, y):
    if(type(x) == None.__class__ or type(x) == None.__class__):
        return
    x = int(round(x))
    y = int(round(y))
    image_y = -(y + 1)
    if (image_y - 2) < 0 or image_y >= image_height or (x + 2) >= image_width:
        return

    axes.add_patch(patches.Rectangle((x , y), 3, 3))
    plt.draw()

    image[image_y][x] = image[image_y][x + 1] = image[image_y][x + 2] = gray
    image[image_y - 1][x] = image[image_y - 1][x + 1] = image[image_y - 1][x + 2] = gray    
    image[image_y - 2 ][x] = image[image_y - 2][x + 1] = image[image_y - 2][x + 2] = gray 

def update_figure(result):
    if result == -1:
        axes.set_xlabel("")
    else:
        axes.set_xlabel("Predict: {0}".format(result))
    plt.draw() 

def UpdateImage(x, y):
    if(type(x) == None.__class__ or type(x) == None.__class__):
        return
    x = int(round(x))
    y = int(round(y))
    image_y = -(y + 1)
    if (image_y - 2) < 0 or image_y >= image_height or (x + 2) >= image_width:
        return

    axes.add_patch(patches.Rectangle((x , y), 3, 3))
    plt.draw()

    image[image_y][x] = image[image_y][x + 1] = image[image_y][x + 2] = gray
    image[image_y - 1][x] = image[image_y - 1][x + 1] = image[image_y - 1][x + 2] = gray    
    image[image_y - 2 ][x] = image[image_y - 2][x + 1] = image[image_y - 2][x + 2] = gray 

def update_figure(result):
    if result == -1:
        axes.set_xlabel("")
    else:
        axes.set_xlabel("Predict: {0}".format(result))
    plt.draw() 

def plot_draw(self, x_axis: list, y_axis: list, latest_result):
        y_axis_parsed = []
        for y in y_axis:
            try:
                y_axis_parsed.append(y['best'])
            except:
                break
        if y_axis_parsed:
            y_axis = y_axis_parsed

        plt.figure(1)
        # self.subplot.set_xlim([self.plot_x_axis[0], self.plot_x_axis[-1]])
        self.subplot.set_ylim([min(y_axis) - 5, max(y_axis) + 5])
        plt.suptitle('Best solution so far: ' + re.sub("(.{64})", "\\1\n", str(latest_result), 0, re.DOTALL),
                     fontsize=10)
        print(latest_result)
        self.subplot.plot(x_axis, y_axis)

        plt.figure(2)
        for route_x, route_y in latest_result.plot_get_route_cords():
            plt.plot(route_x, route_y)

        plt.draw()
        self.fig.savefig("plot-output.png")
        self.fig_node_connector.savefig("plot2-output.png")
        plt.pause(0.000001) 

def drawNewImage(ax, image_dict, label):
    ax.imshow(image_dict[label], origin='lower')
    plt.draw() 

def get_graphics(self, gc, polygons, points_line, rgbFace, closed=False):
        '''Return an instruction group which contains the necessary graphics
           instructions to draw the respective graphics.
        '''
        instruction_group = InstructionGroup()
        if isinstance(gc.line['dash_list'], tuple):
            gc.line['dash_list'] = list(gc.line['dash_list'])
        if rgbFace is not None:
            if len(polygons.meshes) != 0:
                instruction_group.add(Color(*rgbFace))
                for vertices, indices in polygons.meshes:
                    instruction_group.add(Mesh(
                        vertices=vertices,
                        indices=indices,
                        mode=str("triangle_fan")
                    ))
        instruction_group.add(Color(*gc.get_rgb()))
        if _mpl_ge_1_5 and (not _mpl_ge_2_0) and closed:
            points_poly_line = points_line[:-2]
        else:
            points_poly_line = points_line
        if gc.line['width'] > 0:
            instruction_group.add(Line(points=points_poly_line,
                width=int(gc.line['width'] / 2),
                dash_length=gc.line['dash_length'],
                dash_offset=gc.line['dash_offset'],
                dash_joint=gc.line['join_style'],
                dash_list=gc.line['dash_list']))
        return instruction_group 

def draw(self):
        '''Draw the figure using the KivyRenderer
        '''
        self.clear_widgets()
        self.canvas.clear()
        self._renderer = RendererKivy(self)
        self.figure.draw(self._renderer) 

def _on_pos_changed(self, *args):
        self.draw()