Python pyplot.subplots_adjust方法代码示例

# 需要导入模块: from matplotlib import pyplot [as 别名]
# 或者: from matplotlib.pyplot import subplots_adjust [as 别名]
def plot_bad_images(images):
    """This takes a list of images misclassified by a pretty good
    neural network --- one achieving over 93 percent accuracy --- and
    turns them into a figure."""
    bad_image_indices = [8, 18, 33, 92, 119, 124, 149, 151, 193, 233, 241, 247, 259, 300, 313, 321, 324, 341, 349, 352, 359, 362, 381, 412, 435, 445, 449, 478, 479, 495, 502, 511, 528, 531, 547, 571, 578, 582, 597, 610, 619, 628, 629, 659, 667, 691, 707, 717, 726, 740, 791, 810, 844, 846, 898, 938, 939, 947, 956, 959, 965, 982, 1014, 1033, 1039, 1044, 1050, 1055, 1107, 1112, 1124, 1147, 1181, 1191, 1192, 1198, 1202, 1204, 1206, 1224, 1226, 1232, 1242, 1243, 1247, 1256, 1260, 1263, 1283, 1289, 1299, 1310, 1319, 1326, 1328, 1357, 1378, 1393, 1413, 1422, 1435, 1467, 1469, 1494, 1500, 1522, 1523, 1525, 1527, 1530, 1549, 1553, 1609, 1611, 1634, 1641, 1676, 1678, 1681, 1709, 1717, 1722, 1730, 1732, 1737, 1741, 1754, 1759, 1772, 1773, 1790, 1808, 1813, 1823, 1843, 1850, 1857, 1868, 1878, 1880, 1883, 1901, 1913, 1930, 1938, 1940, 1952, 1969, 1970, 1984, 2001, 2009, 2016, 2018, 2035, 2040, 2043, 2044, 2053, 2063, 2098, 2105, 2109, 2118, 2129, 2130, 2135, 2148, 2161, 2168, 2174, 2182, 2185, 2186, 2189, 2224, 2229, 2237, 2266, 2272, 2293, 2299, 2319, 2325, 2326, 2334, 2369, 2371, 2380, 2381, 2387, 2393, 2395, 2406, 2408, 2414, 2422, 2433, 2450, 2488, 2514, 2526, 2548, 2574, 2589, 2598, 2607, 2610, 2631, 2648, 2654, 2695, 2713, 2720, 2721, 2730, 2770, 2771, 2780, 2863, 2866, 2896, 2907, 2925, 2927, 2939, 2995, 3005, 3023, 3030, 3060, 3073, 3102, 3108, 3110, 3114, 3115, 3117, 3130, 3132, 3157, 3160, 3167, 3183, 3189, 3206, 3240, 3254, 3260, 3280, 3329, 3330, 3333, 3383, 3384, 3475, 3490, 3503, 3520, 3525, 3559, 3567, 3573, 3597, 3598, 3604, 3629, 3664, 3702, 3716, 3718, 3725, 3726, 3727, 3751, 3752, 3757, 3763, 3766, 3767, 3769, 3776, 3780, 3798, 3806, 3808, 3811, 3817, 3821, 3838, 3848, 3853, 3855, 3869, 3876, 3902, 3906, 3926, 3941, 3943, 3951, 3954, 3962, 3976, 3985, 3995, 4000, 4002, 4007, 4017, 4018, 4065, 4075, 4078, 4093, 4102, 4139, 4140, 4152, 4154, 4163, 4165, 4176, 4199, 4201, 4205, 4207, 4212, 4224, 4238, 4248, 4256, 4284, 4289, 4297, 4300, 4306, 4344, 4355, 4356, 4359, 4360, 4369, 4405, 4425, 4433, 4435, 4449, 4487, 4497, 4498, 4500, 4521, 4536, 4548, 4563, 4571, 4575, 4601, 4615, 4620, 4633, 4639, 4662, 4690, 4722, 4731, 4735, 4737, 4739, 4740, 4761, 4798, 4807, 4814, 4823, 4833, 4837, 4874, 4876, 4879, 4880, 4886, 4890, 4910, 4950, 4951, 4952, 4956, 4963, 4966, 4968, 4978, 4990, 5001, 5020, 5054, 5067, 5068, 5078, 5135, 5140, 5143, 5176, 5183, 5201, 5210, 5331, 5409, 5457, 5495, 5600, 5601, 5617, 5623, 5634, 5642, 5677, 5678, 5718, 5734, 5735, 5749, 5752, 5771, 5787, 5835, 5842, 5845, 5858, 5887, 5888, 5891, 5906, 5913, 5936, 5937, 5945, 5955, 5957, 5972, 5973, 5985, 5987, 5997, 6035, 6042, 6043, 6045, 6053, 6059, 6065, 6071, 6081, 6091, 6112, 6124, 6157, 6166, 6168, 6172, 6173, 6347, 6370, 6386, 6390, 6391, 6392, 6421, 6426, 6428, 6505, 6542, 6555, 6556, 6560, 6564, 6568, 6571, 6572, 6597, 6598, 6603, 6608, 6625, 6651, 6694, 6706, 6721, 6725, 6740, 6746, 6768, 6783, 6785, 6796, 6817, 6827, 6847, 6870, 6872, 6926, 6945, 7002, 7035, 7043, 7089, 7121, 7130, 7198, 7216, 7233, 7248, 7265, 7426, 7432, 7434, 7494, 7498, 7691, 7777, 7779, 7797, 7800, 7809, 7812, 7821, 7849, 7876, 7886, 7897, 7902, 7905, 7917, 7921, 7945, 7999, 8020, 8059, 8081, 8094, 8095, 8115, 8246, 8256, 8262, 8272, 8273, 8278, 8279, 8293, 8322, 8339, 8353, 8408, 8453, 8456, 8502, 8520, 8522, 8607, 9009, 9010, 9013, 9015, 9019, 9022, 9024, 9026, 9036, 9045, 9046, 9128, 9214, 9280, 9316, 9342, 9382, 9433, 9446, 9506, 9540, 9544, 9587, 9614, 9634, 9642, 9645, 9700, 9716, 9719, 9729, 9732, 9738, 9740, 9741, 9742, 9744, 9745, 9749, 9752, 9768, 9770, 9777, 9779, 9792, 9808, 9831, 9839, 9856, 9858, 9867, 9879, 9883, 9888, 9890, 9893, 9905, 9944, 9970, 9982]
    n = len(bad_image_indices)
    bad_images = [images[j] for j in bad_image_indices]
    fig = plt.figure(figsize=(10, 15))
    for j in xrange(1, n+1):
        ax = fig.add_subplot(25, 125, j)
        ax.matshow(bad_images[j-1], cmap = matplotlib.cm.binary)
        ax.set_title(str(bad_image_indices[j-1]))
        plt.xticks(np.array([]))
        plt.yticks(np.array([]))
    plt.subplots_adjust(hspace = 1.2)
    plt.show() 

# 需要导入模块: from matplotlib import pyplot [as 别名]
# 或者: from matplotlib.pyplot import subplots_adjust [as 别名]
def __init__(self, df):
        self.df = df

        # Create a figure on screen and set the title
        self.fig = plt.figure()

        # Create top subplot for net worth axis
        self.net_worth_ax = plt.subplot2grid((6, 1), (0, 0), rowspan=2, colspan=1)

        # Create bottom subplot for shared price/volume axis
        self.price_ax = plt.subplot2grid((6, 1), (2, 0), rowspan=8,
                                         colspan=1, sharex=self.net_worth_ax)

        # Create a new axis for volume which shares its x-axis with price
        self.volume_ax = self.price_ax.twinx()

        # Add padding to make graph easier to view
        plt.subplots_adjust(left=0.11, bottom=0.24, right=0.90, top=0.90, wspace=0.2, hspace=0)

        # Show the graph without blocking the rest of the program
        plt.show(block=False) 

# 需要导入模块: from matplotlib import pyplot [as 别名]
# 或者: from matplotlib.pyplot import subplots_adjust [as 别名]
def plotNNFilter(units, figure_id, interp='bilinear', colormap=cm.jet, colormap_lim=None):
    plt.ion()
    filters = units.shape[2]
    n_columns = round(math.sqrt(filters))
    n_rows = math.ceil(filters / n_columns) + 1
    fig = plt.figure(figure_id, figsize=(n_rows*3,n_columns*3))
    fig.clf()

    for i in range(filters):
        ax1 = plt.subplot(n_rows, n_columns, i+1)
        plt.imshow(units[:,:,i].T, interpolation=interp, cmap=colormap)
        plt.axis('on')
        ax1.set_xticklabels([])
        ax1.set_yticklabels([])
        plt.colorbar()
        if colormap_lim:
            plt.clim(colormap_lim[0],colormap_lim[1])

    plt.subplots_adjust(wspace=0, hspace=0)
    plt.tight_layout()

# Epochs 

# 需要导入模块: from matplotlib import pyplot [as 别名]
# 或者: from matplotlib.pyplot import subplots_adjust [as 别名]
def plotNNFilter(units, figure_id, interp='bilinear', colormap=cm.jet, colormap_lim=None):
    plt.ion()
    filters = units.shape[2]
    n_columns = round(math.sqrt(filters))
    n_rows = math.ceil(filters / n_columns) + 1
    fig = plt.figure(figure_id, figsize=(n_rows*3,n_columns*3))
    fig.clf()

    for i in range(filters):
        ax1 = plt.subplot(n_rows, n_columns, i+1)
        plt.imshow(units[:,:,i].T, interpolation=interp, cmap=colormap)
        plt.axis('on')
        ax1.set_xticklabels([])
        ax1.set_yticklabels([])
        plt.colorbar()
        if colormap_lim:
            plt.clim(colormap_lim[0],colormap_lim[1])

    plt.subplots_adjust(wspace=0, hspace=0)
    plt.tight_layout()

# Load options 

# 需要导入模块: from matplotlib import pyplot [as 别名]
# 或者: from matplotlib.pyplot import subplots_adjust [as 别名]
def plotNNFilter(units, figure_id, interp='bilinear', colormap=cm.jet, colormap_lim=None, title=''):
    plt.ion()
    filters = units.shape[2]
    n_columns = round(math.sqrt(filters))
    n_rows = math.ceil(filters / n_columns) + 1
    fig = plt.figure(figure_id, figsize=(n_rows*3,n_columns*3))
    fig.clf()

    for i in range(filters):
        ax1 = plt.subplot(n_rows, n_columns, i+1)
        plt.imshow(units[:,:,i].T, interpolation=interp, cmap=colormap)
        plt.axis('on')
        ax1.set_xticklabels([])
        ax1.set_yticklabels([])
        plt.colorbar()
        if colormap_lim:
            plt.clim(colormap_lim[0],colormap_lim[1])

    plt.subplots_adjust(wspace=0, hspace=0)
    plt.tight_layout()
    plt.suptitle(title) 

# 需要导入模块: from matplotlib import pyplot [as 别名]
# 或者: from matplotlib.pyplot import subplots_adjust [as 别名]
def plotNNFilterOverlay(input_im, units, figure_id, interp='bilinear',
                        colormap=cm.jet, colormap_lim=None, title='', alpha=0.8):
    plt.ion()
    filters = units.shape[2]
    fig = plt.figure(figure_id, figsize=(5,5))
    fig.clf()

    for i in range(filters):
        plt.imshow(input_im[:,:,0], interpolation=interp, cmap='gray')
        plt.imshow(units[:,:,i], interpolation=interp, cmap=colormap, alpha=alpha)
        plt.axis('off')
        plt.colorbar()
        plt.title(title, fontsize='small')
        if colormap_lim:
            plt.clim(colormap_lim[0],colormap_lim[1])

    plt.subplots_adjust(wspace=0, hspace=0)
    plt.tight_layout()

    # plt.savefig('{}/{}.png'.format(dir_name,time.time()))




## Load options 

# 需要导入模块: from matplotlib import pyplot [as 别名]
# 或者: from matplotlib.pyplot import subplots_adjust [as 别名]
def __init__(self, df):
        self.df = df

        # Create a figure on screen and set the title
        self.fig = plt.figure()

        # Create top subplot for net worth axis
        self.net_worth_ax = plt.subplot2grid((6, 1), (0, 0), rowspan=2, colspan=1)

        # Create bottom subplot for shared price/volume axis
        self.price_ax = plt.subplot2grid((6, 1), (2, 0), rowspan=8, colspan=1, sharex=self.net_worth_ax)

        # Create a new axis for volume which shares its x-axis with price
        self.volume_ax = self.price_ax.twinx()

        # Add padding to make graph easier to view
        plt.subplots_adjust(left=0.11, bottom=0.24, right=0.90, top=0.90, wspace=0.2, hspace=0)

        # Show the graph without blocking the rest of the program
        plt.show(block=False) 

# 需要导入模块: from matplotlib import pyplot [as 别名]
# 或者: from matplotlib.pyplot import subplots_adjust [as 别名]
def plot_alerts(_g, _bank_accts, _output_png):
    bank_ids = _bank_accts.keys()
    cmap = plt.get_cmap("tab10")
    pos = nx.nx_agraph.graphviz_layout(_g)

    plt.figure(figsize=(12.0, 8.0))
    plt.axis('off')

    for i, bank_id in enumerate(bank_ids):
        color = cmap(i)
        members = _bank_accts[bank_id]
        nx.draw_networkx_nodes(_g, pos, members, node_size=300, node_color=color, label=bank_id)
        nx.draw_networkx_labels(_g, pos, {n: n for n in members}, font_size=10)

    edge_labels = nx.get_edge_attributes(_g, "label")
    nx.draw_networkx_edges(_g, pos)
    nx.draw_networkx_edge_labels(_g, pos, edge_labels, font_size=6)

    plt.legend(numpoints=1)
    plt.subplots_adjust(left=0, right=1, bottom=0, top=1)
    plt.savefig(_output_png, dpi=120) 

# 需要导入模块: from matplotlib import pyplot [as 别名]
# 或者: from matplotlib.pyplot import subplots_adjust [as 别名]
def show_images(images, nrow=10, text=None, overlay=None, matplot=False):
    images = np.squeeze(images)
    if not matplot:
        images = np.stack([draw_overlay(images[j], None if text is None else text[j])
                           for j in range(len(images))], axis=0)
        vis.images(images, padding=4, nrow=nrow)
    else:
        fig, axes = plt.subplots(2, 4, figsize=(8, 4))
        plt.subplots_adjust(top=1.0, bottom=0.0,
                            left=0.1, right=0.9,
                            wspace=0.1, hspace=-0.15)
        for i, image in enumerate(images):
            cur_axes = axes[i // 4, i % 4]
            setup_axis(cur_axes)
            cur_axes.imshow(image, cmap='gray', interpolation='none')
            if overlay is not None:
                cur_overlay = scipy.misc.imresize(overlay[i], image.shape)
                cur_axes.imshow(cur_overlay, cmap='RdYlGn', alpha=0.5)
        vis.matplot(plt)
        plt.close(fig) 

# 需要导入模块: from matplotlib import pyplot [as 别名]
# 或者: from matplotlib.pyplot import subplots_adjust [as 别名]
def vis(embed, vis_alg='PCA', pool_alg='REDUCE_MEAN'):
    plt.close()
    fig = plt.figure()
    plt.rcParams['figure.figsize'] = [21, 7]
    for idx, ebd in enumerate(embed):
        ax = plt.subplot(2, 6, idx + 1)
        vis_x = ebd[:, 0]
        vis_y = ebd[:, 1]
        plt.scatter(vis_x, vis_y, c=subset_label, cmap=ListedColormap(["blue", "green", "yellow", "red"]), marker='.',
                    alpha=0.7, s=2)
        ax.set_title('pool_layer=-%d' % (idx + 1))
    plt.tight_layout()
    plt.subplots_adjust(bottom=0.1, right=0.95, top=0.9)
    cax = plt.axes([0.96, 0.1, 0.01, 0.3])
    cbar = plt.colorbar(cax=cax, ticks=range(num_label))
    cbar.ax.get_yaxis().set_ticks([])
    for j, lab in enumerate(['ent.', 'bus.', 'sci.', 'heal.']):
        cbar.ax.text(.5, (2 * j + 1) / 8.0, lab, ha='center', va='center', rotation=270)
    fig.suptitle('%s visualization of BERT layers using "bert-as-service" (-pool_strategy=%s)' % (vis_alg, pool_alg),
                 fontsize=14)
    plt.show() 

# 需要导入模块: from matplotlib import pyplot [as 别名]
# 或者: from matplotlib.pyplot import subplots_adjust [as 别名]
def test_skew_rectange():

    fix, axes = plt.subplots(5, 5, sharex=True, sharey=True, figsize=(16, 12))
    axes = axes.flat

    rotations = list(itertools.product([-3, -1, 0, 1, 3], repeat=2))

    axes[0].set_xlim([-4, 4])
    axes[0].set_ylim([-4, 4])
    axes[0].set_aspect('equal')

    for ax, (xrots, yrots) in zip(axes, rotations):
        xdeg, ydeg = 45 * xrots, 45 * yrots
        t = transforms.Affine2D().skew_deg(xdeg, ydeg)

        ax.set_title('Skew of {0} in X and {1} in Y'.format(xdeg, ydeg))
        ax.add_patch(mpatch.Rectangle([-1, -1], 2, 2,
                                      transform=t + ax.transData,
                                      alpha=0.5, facecolor='coral'))

    plt.subplots_adjust(wspace=0, left=0, right=1, bottom=0) 

# 需要导入模块: from matplotlib import pyplot [as 别名]
# 或者: from matplotlib.pyplot import subplots_adjust [as 别名]
def init_figure(self):
        self.init_fig = True
        if self.args.figure == True:# and self.obj_fig==None:
            self.obj_fig = plt.figure(figsize=(16,12))
            plt.set_cmap('viridis')

            self.gridspec = gridspec.GridSpec(3,5)
            self.ax_map = plt.subplot(self.gridspec[0,0])
            self.ax_scan = plt.subplot(self.gridspec[1,0])
            self.ax_pose =  plt.subplot(self.gridspec[2,0])

            self.ax_bel =  plt.subplot(self.gridspec[0,1])
            self.ax_lik =  plt.subplot(self.gridspec[1,1])
            self.ax_gtl =  plt.subplot(self.gridspec[2,1])


            self.ax_pbel =  plt.subplot(self.gridspec[0,2:4])
            self.ax_plik =  plt.subplot(self.gridspec[1,2:4])
            self.ax_pgtl =  plt.subplot(self.gridspec[2,2:4])

            self.ax_act = plt.subplot(self.gridspec[0,4])
            self.ax_rew = plt.subplot(self.gridspec[1,4])
            self.ax_err = plt.subplot(self.gridspec[2,4])

            plt.subplots_adjust(hspace = 0.4, wspace=0.4, top=0.95, bottom=0.05) 

# 需要导入模块: from matplotlib import pyplot [as 别名]
# 或者: from matplotlib.pyplot import subplots_adjust [as 别名]
def test_skew_rectangle():

    fix, axes = plt.subplots(5, 5, sharex=True, sharey=True, figsize=(8, 8))
    axes = axes.flat

    rotations = list(itertools.product([-3, -1, 0, 1, 3], repeat=2))

    axes[0].set_xlim([-3, 3])
    axes[0].set_ylim([-3, 3])
    axes[0].set_aspect('equal', share=True)

    for ax, (xrots, yrots) in zip(axes, rotations):
        xdeg, ydeg = 45 * xrots, 45 * yrots
        t = transforms.Affine2D().skew_deg(xdeg, ydeg)

        ax.set_title('Skew of {0} in X and {1} in Y'.format(xdeg, ydeg))
        ax.add_patch(mpatch.Rectangle([-1, -1], 2, 2,
                                      transform=t + ax.transData,
                                      alpha=0.5, facecolor='coral'))

    plt.subplots_adjust(wspace=0, left=0.01, right=0.99, bottom=0.01, top=0.99) 

# 需要导入模块: from matplotlib import pyplot [as 别名]
# 或者: from matplotlib.pyplot import subplots_adjust [as 别名]
def showImgAtts(instance):
    img = imread(inImgName(instance["imageId"]))

    length = len(instance["attentions"]["kb"])
    
    # show images
    for j in range(length):
        fig, ax = plt.subplots()
        fig.set_figheight(figureImageDims[0])
        fig.set_figwidth(figureImageDims[1])              
        
        showImgAtt(img, instance, j, ax)
        
        plt.subplots_adjust(bottom = 0, top = 1, left = 0, right = 1)
        savePlot(fig, outImgAttName(instance, j)) 

# 需要导入模块: from matplotlib import pyplot [as 别名]
# 或者: from matplotlib.pyplot import subplots_adjust [as 别名]
def show_checkbox_predictions(checkboxes, rows, cols, captcha_query, correct):

    fig, axes = plt.subplots(rows, cols)
    for checkbox in checkboxes:
        position = checkbox['position']
        path = checkbox['path']
        predictions = checkbox['predictions']
        matching = checkbox['matching']

        if path:
            path = os.path.join('../', path)
            x = position[0]-1
            y = position[1]-1
            axes[x, y].imshow(skimage.io.imread(path))
            if matching:
                axes[x, y].set_title("Picked \n {0}".format(predictions))
            else:
                axes[x, y].set_title(predictions)

            axes[x, y].set_xticks([])
            axes[x, y].set_yticks([])

    fig.suptitle("{0}, Correct".format(captcha_query)) if correct else fig.suptitle("{0}, Incorrect".format(captcha_query))
    plt.subplots_adjust(hspace=0.5)

    plt.show()