Python matplotlib.pyplot.subplots_adjust() Examples

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) 

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 

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 

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) 

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 

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() 

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) 

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) 

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() 

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) 

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) 

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) 

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) 

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) 

def make_pairplot(self, num_components_to_plot=4, outpath=None, dpi=150):
        # Get columns
        components_to_plot = [self.principal_observations_df.columns[x] for x in range(num_components_to_plot)]

        # Plot
        plot = sns.pairplot(data=self.principal_observations_df, hue=self.observation_colname,
                                vars=components_to_plot, markers=self.markers, size=4)
        plt.subplots_adjust(top=.95)
        plt.suptitle(self.plot_title)

        if outpath:
            plot.fig.savefig(outpath, dpi=dpi)
        else:
            plt.show()
        plt.close() 

def _test_remove_from_figure(use_gridspec):
    """
    Test `remove_from_figure` with the specified ``use_gridspec`` setting
    """
    fig = plt.figure()
    ax = fig.add_subplot(111)
    sc = ax.scatter([1, 2], [3, 4], cmap="spring")
    sc.set_array(np.array([5, 6]))
    pre_figbox = np.array(ax.figbox)
    cb = fig.colorbar(sc, use_gridspec=use_gridspec)
    fig.subplots_adjust()
    cb.remove()
    fig.subplots_adjust()
    post_figbox = np.array(ax.figbox)
    assert (pre_figbox == post_figbox).all() 

def gen_fig_2(path):
    """
    The parameter is the path namedtuple returned by compute_paths().  See
    the docstring of that function for details.
    """

    T = len(path.c)

    # == Prepare axes == #
    num_rows, num_cols = 2, 1
    fig, axes = plt.subplots(num_rows, num_cols, figsize=(10, 10))
    plt.subplots_adjust(hspace=0.5)
    bbox = (0., 1.02, 1., .102)
    bbox = (0., 1.02, 1., .102)
    legend_args = {'bbox_to_anchor': bbox, 'loc': 3, 'mode': 'expand'}
    p_args = {'lw': 2, 'alpha': 0.7}

    # == Plot adjustment factor == #
    ax = axes[0]
    ax.plot(list(range(2, T+1)), path.ξ, label=r'$\xi_t$', **p_args)
    ax.grid()
    ax.set_xlabel('Time')
    ax.legend(ncol=1, **legend_args)

    # == Plot adjusted cumulative return == #
    ax = axes[1]
    ax.plot(list(range(2, T+1)), path.Π, label=r'$\Pi_t$', **p_args)
    ax.grid()
    ax.set_xlabel('Time')
    ax.legend(ncol=1, **legend_args)

    plt.show() 

def _colorbar_extension_shape(spacing):
    '''
    Produce 4 colorbars with rectangular extensions for either uniform
    or proportional spacing.

    Helper function for test_colorbar_extension_shape.
    '''
    # Get a colormap and appropriate norms for each extension type.
    cmap, norms = _get_cmap_norms()
    # Create a figure and adjust whitespace for subplots.
    fig = plt.figure()
    fig.subplots_adjust(hspace=4)
    for i, extension_type in enumerate(('neither', 'min', 'max', 'both')):
        # Get the appropriate norm and use it to get colorbar boundaries.
        norm = norms[extension_type]
        boundaries = values = norm.boundaries
        # Create a subplot.
        cax = fig.add_subplot(4, 1, i + 1)
        # Turn off text and ticks.
        for item in cax.get_xticklabels() + cax.get_yticklabels() +\
                cax.get_xticklines() + cax.get_yticklines():
            item.set_visible(False)
        # Generate the colorbar.
        cb = ColorbarBase(cax, cmap=cmap, norm=norm,
                boundaries=boundaries, values=values,
                extend=extension_type, extendrect=True,
                orientation='horizontal', spacing=spacing)
    # Return the figure to the caller.
    return fig 

def _colorbar_extension_length(spacing):
    '''
    Produce 12 colorbars with variable length extensions for either
    uniform or proportional spacing.

    Helper function for test_colorbar_extension_length.
    '''
    # Get a colormap and appropriate norms for each extension type.
    cmap, norms = _get_cmap_norms()
    # Create a figure and adjust whitespace for subplots.
    fig = plt.figure()
    fig.subplots_adjust(hspace=.6)
    for i, extension_type in enumerate(('neither', 'min', 'max', 'both')):
        # Get the appropriate norm and use it to get colorbar boundaries.
        norm = norms[extension_type]
        boundaries = values = norm.boundaries
        for j, extendfrac in enumerate((None, 'auto', 0.1)):
            # Create a subplot.
            cax = fig.add_subplot(12, 1, i*3 + j + 1)
            # Turn off text and ticks.
            for item in cax.get_xticklabels() + cax.get_yticklabels() +\
                    cax.get_xticklines() + cax.get_yticklines():
                item.set_visible(False)
            # Generate the colorbar.
            cb = ColorbarBase(cax, cmap=cmap, norm=norm,
                    boundaries=boundaries, values=values,
                    extend=extension_type, extendfrac=extendfrac,
                    orientation='horizontal', spacing=spacing)
    # Return the figure to the caller.
    return fig 

def test_gridspec_make_colorbar():
    plt.figure()
    data = np.arange(1200).reshape(30, 40)
    levels = [0, 200, 400, 600, 800, 1000, 1200]

    plt.subplot(121)
    plt.contourf(data, levels=levels)
    plt.colorbar(use_gridspec=True, orientation='vertical')

    plt.subplot(122)
    plt.contourf(data, levels=levels)
    plt.colorbar(use_gridspec=True, orientation='horizontal')

    plt.subplots_adjust(top=0.95, right=0.95, bottom=0.2, hspace=0.25) 

def show_images(images, holdon=False, title=None, nmax=49):
    # use matplotlib to display profile images
    n = len(images)
    if n > nmax:
        n = nmax
        n_col = 7
    else:
        n_col = 3
    if n % n_col == 0:
        n_row = n // n_col
    else:
        n_row = n // 3 + 1
    if title is None:
        plt.figure()
    else:
        plt.figure(title)
    plt.tight_layout()
    for j, i in enumerate(images):
        if j == nmax:
            print('\n\nToo many images to show... \n\n')
            break
        temp_image = imageio.imread(i)
        if len(temp_image.shape) < 3:
            # needs to be converted to rgb
            temp_image = to_rgb(temp_image)
        plt.subplot(n_row, n_col, j+1)
        plt.imshow(temp_image)
        plt.axis('off')
        plt.subplots_adjust(wspace=0, hspace=0)

    if holdon is False:
        plt.show(block=False)
        plt.pause(0.1) 

def draw_label(label, img, label_names, colormap=None):
    plt.subplots_adjust(left=0, right=1, top=1, bottom=0,
                        wspace=0, hspace=0)
    plt.margins(0, 0)
    plt.gca().xaxis.set_major_locator(plt.NullLocator())
    plt.gca().yaxis.set_major_locator(plt.NullLocator())

    if colormap is None:
        colormap = label_colormap(len(label_names))

    label_viz = label2rgb(label, img, n_labels=len(label_names))
    plt.imshow(label_viz)
    plt.axis('off')

    plt_handlers = []
    plt_titles = []
    for label_value, label_name in enumerate(label_names):
        fc = colormap[label_value]
        p = plt.Rectangle((0, 0), 1, 1, fc=fc)
        plt_handlers.append(p)
        plt_titles.append(label_name)
    plt.legend(plt_handlers, plt_titles, loc='lower right', framealpha=.5)

    f = io.BytesIO()
    plt.savefig(f, bbox_inches='tight', pad_inches=0)
    plt.cla()
    plt.close()

    out_size = (img.shape[1], img.shape[0])
    out = PIL.Image.open(f).resize(out_size, PIL.Image.BILINEAR).convert('RGB')
    out = np.asarray(out)
    return out 

def _visualize_filters(self, layer, filter_idxs, num_iter, num_subplots,
                           title):
        # Prepare the main plot

        num_cols = 4
        num_rows = int(np.ceil(num_subplots / num_cols))

        fig = plt.figure(figsize=(16, num_rows * 5))
        plt.title(title)
        plt.axis('off')

        self.output = []

        # Plot subplots

        for i, filter_idx in enumerate(filter_idxs):
            output = self.optimize(layer, filter_idx, num_iter=num_iter)

            self.output.append(output)

            ax = fig.add_subplot(num_rows, num_cols, i+1)
            ax.set_xticks([])
            ax.set_yticks([])
            ax.set_title(f'filter {filter_idx}')

            ax.imshow(format_for_plotting(
                standardize_and_clip(output[-1],
                                     saturation=0.15,
                                     brightness=0.7)))

        plt.subplots_adjust(wspace=0, hspace=0); # noqa 

def plot(self, fig=None, handles=None, figsize=(6,4), color="#377eb8",
             data_index=0, T_slice=None):
        """
        Plot the rates, events, and weights
        :param fig:
        :return:
        """
        import matplotlib.pyplot as plt
        if handles is None:
            if fig is None:
                fig = plt.figure(figsize=figsize)

            # Plot network on left
            rate_width = 3
            ax_net = plt.subplot2grid((self.K, 1+rate_width), (0,0), rowspan=self.K, colspan=1)
            # im = self.plot_adjacency_matrix(ax=ax_net)
            net_lns = self.plot_network(ax=ax_net, color=color)

            # Plot the rates on the right
            axs_rate = [plt.subplot2grid((self.K,4), (k,1), rowspan=1, colspan=rate_width)
                        for k in range(self.K)]
            rate_lns = self.plot_rates(axs=axs_rate, data_index=data_index, T_slice=T_slice, color=color)

            plt.subplots_adjust(wspace=1.0)

        else:
            # Update given handles
            net_lns, rate_lns = handles
            # self.plot_adjacency_matrix(im=im)
            self.plot_network(lns=net_lns)
            self.plot_rates(lns=rate_lns, data_index=data_index)
            plt.pause(0.001)

        return fig, (net_lns, rate_lns) 

def make_figure_a(S, F, C):
    """
    Plot fluorescence traces, filtered fluorescence, and spike times
    for three neurons
    """
    col = harvard_colors()
    dt = 0.02
    T_start = 0
    T_stop = 1 * 50 * 60
    t = dt * np.arange(T_start, T_stop)

    ks = [0,1]
    nk = len(ks)
    fig = create_figure((3,3))
    for ind,k in enumerate(ks):
        ax = fig.add_subplot(nk,1,ind+1)
        ax.plot(t, F[T_start:T_stop, k], color=col[1], label="$F$")    # Plot the raw flourescence in blue
        ax.plot(t, C[T_start:T_stop, k], color=col[0], lw=1.5, label="$\widehat{F}$")    # Plot the filtered flourescence in red
        spks  = np.where(S[T_start:T_stop, k])[0]
        ax.plot(t[spks], C[spks,k], 'ko', label="S")            # Plot the spike times in black

        # Make a legend
        if ind == 0:
            # Put a legend above
            plt.legend(bbox_to_anchor=(0., 1.02, 1., .102), loc=3,
                       ncol=3, mode="expand", borderaxespad=0.,
                       prop={'size':9})

        # Add labels
        ax.set_ylabel("$F_%d(t)$" % (k+1))
        if ind == nk-1:
            ax.set_xlabel("Time $t$ [sec]")

        # Format the ticks
        ax.set_ylim([-0.1,1.0])
        plt.locator_params(nbins=5, axis="y")


    plt.subplots_adjust(left=0.2, bottom=0.2)
    fig.savefig("figure3a.pdf")
    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 plots(imgs, figsize=(12, 12), rows=None, cols=None,
          interp=None, titles=None, cmap='gray',
          fig=None):
    if not isinstance(imgs, list):
        imgs = [imgs]
    imgs = [np.array(img) for img in imgs]
    if not isinstance(cmap, list):
        if imgs[0].ndim == 2:
            cmap = 'gray'
        cmap = [cmap] * len(imgs)
    if not isinstance(interp, list):
        interp = [interp] * len(imgs)
    n = len(imgs)
    if not rows and not cols:
        cols = n
        rows = 1
    elif not rows:
        rows = cols
    elif not cols:
        cols = rows
    if not fig:
        rows = int(np.ceil(len(imgs) / cols))
        w = 12
        h = rows * (w / cols + 1)
        figsize = (w, h)
        fig = plt.figure(figsize=figsize)
    fontsize = 13 if cols == 5 else 16
    fig.set_figheight(figsize[1], forward=True)
    fig.clear()
    for i in range(len(imgs)):
        sp = fig.add_subplot(rows, cols, i+1)
        if titles:
            sp.set_title(titles[i], fontsize=fontsize)
        plt.imshow(imgs[i], interpolation=interp[i], cmap=cmap[i])
        plt.axis('off')
        plt.subplots_adjust(0, 0, 1, 1, .1, 0)
        #  plt.tight_layout()
    if fig:
        fig.canvas.draw() 

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_prior =  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) 

def create_pie_chart(elements, suptitle, png, figure_id):
    """
    Create pie chart

    :param elements: dict with elements (dict)
    :param suptitle: name of chart (str)
    :param png: name of output file (str)
    :param figure_id: id of current plot (started with 1) (int)
    :return: None
    """
    values = [value for value in elements.values()]
    keys = [key for key in elements.keys()]
    plt.figure(figure_id)
    plt.subplots_adjust(bottom=.05, left=.01, right=.99, top=.90, hspace=.35)

    explode = [0 for x in range(len(keys))]
    max_value = max(values)
    explode[list(values).index(max_value)] = 0.1

    plt.pie(values, labels=keys,
            autopct=make_autopct(values), explode=explode,
            textprops={'fontsize': PIE_LABEL_FONT_SIZE})
    plt.axis("equal")
    plt.suptitle(suptitle, fontsize=PIE_SUPTITLE_FONT_SIZE)

    plt.gcf().set_dpi(PIE_DPI)
    plt.savefig("{dest}/{png}/{result_file}".format(dest=RESULTS_DIR,
                                                    png=PNG_DIR,
                                                    result_file=png)) 

def update(self, matrix_list, name_list):
        # draw first line
        axes_input = plt.subplot2grid((3, 1), (0, 0), colspan=1)
        axes_input.set_aspect('equal')
        plt.imshow(matrix_list[0], interpolation='none')
        axes_input.set_xticks([])
        axes_input.set_yticks([])

        # draw second line
        axes_output = plt.subplot2grid((3, 1), (1, 0), colspan=1)
        plt.imshow(matrix_list[1], interpolation='none')
        axes_output.set_xticks([])
        axes_output.set_yticks([])

        # draw third line
        axes_predict = plt.subplot2grid((3, 1), (2, 0), colspan=1)
        plt.imshow(matrix_list[2], interpolation='none')
        axes_predict.set_xticks([])
        axes_predict.set_yticks([])

        # # add text
        # plt.text(-2, -19.5, name_list[0], ha='right')
        # plt.text(-2, -7.5, name_list[1], ha='right')
        # plt.text(-2, 4.5, name_list[2], ha='right')
        # plt.text(6, 10, 'Time $\longrightarrow$', ha='right')

        # set tick labels invisible
        make_tick_labels_invisible(plt.gcf())
        # adjust spaces
        plt.subplots_adjust(hspace=0.05, wspace=0.05, bottom=0.1, right=0.8, top=0.9)
        # add color bars
        # *rect* = [left, bottom, width, height]
        cax = plt.axes([0.85, 0.125, 0.015, 0.75])
        plt.colorbar(cax=cax)

        # show figure
        # plt.show()
        plt.draw()
        plt.pause(0.025)
        # plt.pause(15)