Python matplotlib.pyplot.NullLocator() Examples

def hinton(matrix, max_weight=None, ax=None):
    """Draw Hinton diagram for visualizing a weight matrix."""
    ax = ax if ax is not None else plt.gca()

    if not max_weight:
        max_weight = 2 ** np.ceil(np.log(np.abs(matrix).max()) / np.log(2))

    ax.patch.set_facecolor('gray')
    ax.set_aspect('equal', 'box')
    ax.xaxis.set_major_locator(plt.NullLocator())
    ax.yaxis.set_major_locator(plt.NullLocator())

    for (x, y), w in np.ndenumerate(matrix):
        color = 'white' if w > 0 else 'black'
        size = np.sqrt(np.abs(w) / max_weight)
        rect = plt.Rectangle([x - size / 2, y - size / 2], size, size,
                             facecolor=color, edgecolor=color)
        ax.add_patch(rect)

    ax.autoscale_view()
    ax.invert_yaxis() 

def view(self):
        imgs = []
        for path in self.paths:
            img = plt.imread(path)
            imgs.append(img)

        fig, axs = plt.subplots(nrows=4, ncols=1, figsize=(16,8))
        fig.subplots_adjust(hspace=0.1)
        idx = 0
        for i in range(4):
                axs[i].xaxis.set_major_locator(plt.NullLocator())
                axs[i].yaxis.set_major_locator(plt.NullLocator())
                axs[i].imshow(imgs[idx], cmap='bone')
                axs[i].set_xlabel(r'$(\alpha_'+str(idx+1) + ')$', fontsize=16)
                plt.tight_layout()
                idx = idx+1
        # save as a high quality image
        self.pdf.savefig(bbox_inches = 'tight', dpi=600)
        # plt.show() 

def hinton(matrix, max_weight=None, ax=None):
    """Draw Hinton diagram for visualizing a weight matrix."""
    ax = ax if ax is not None else plt.gca()

    if not max_weight:
        max_weight = 2 ** np.ceil(np.log(np.abs(matrix).max()) / np.log(2))

    ax.patch.set_facecolor('gray')
    ax.set_aspect('equal', 'box')
    ax.xaxis.set_major_locator(plt.NullLocator())
    ax.yaxis.set_major_locator(plt.NullLocator())

    for (x, y), w in np.ndenumerate(matrix):
        color = 'white' if w > 0 else 'black'
        size = np.sqrt(np.abs(w) / max_weight)
        rect = plt.Rectangle([x - size / 2, y - size / 2], size, size,
                             facecolor=color, edgecolor=color)
        ax.add_patch(rect)

    ax.autoscale_view()
    ax.invert_yaxis() 

def view(self):
        imgs = []
        for path in self.paths:
            img = plt.imread(path)
            imgs.append(img)

        fig, axs = plt.subplots(nrows=2, ncols=2, figsize=(12,16))
        fig.subplots_adjust(hspace=0.1, wspace=0)
        idx = 0
        for i in range(2):
            for j in range(2):
                axs[i,j].xaxis.set_major_locator(plt.NullLocator())
                axs[i,j].yaxis.set_major_locator(plt.NullLocator())
                axs[i,j].imshow(imgs[idx], cmap='bone')
                axs[i,j].set_xlabel(r'$(\alpha_'+str(idx+1) + ')$', fontsize=22)
                plt.tight_layout()
                idx = idx+1
        # save as a high quality image
        self.pdf.savefig(bbox_inches = 'tight', dpi=600)
        # plt.savefig(bbox_inches = 'tight', format='png', dpi=600)
        # plt.show() 

def hinton(matrix, max_weight=None, ax=None):
    """Draw Hinton diagram for visualizing a weight matrix."""
    ax = ax if ax is not None else plt.gca()

    if not max_weight:
        max_weight = 2 ** np.ceil(np.log(np.abs(matrix).max()) / np.log(2))

    ax.patch.set_facecolor('gray')
    ax.set_aspect('equal', 'box')
    ax.xaxis.set_major_locator(plt.NullLocator())
    ax.yaxis.set_major_locator(plt.NullLocator())

    for (x, y), w in np.ndenumerate(matrix):
        color = 'white' if w > 0 else 'black'
        size = np.sqrt(np.abs(w) / max_weight)
        rect = plt.Rectangle([x - size / 2, y - size / 2], size, size,
                             facecolor=color, edgecolor=color)
        ax.add_patch(rect)

    ax.autoscale_view()
    ax.invert_yaxis()

    return ax 

def Weights_opt(self, matrix, max_weight=None, ax=None):

        ax = ax if ax is not None else plt.gca()

        if not max_weight:
            max_weight = 2 ** np.ceil(np.log(np.abs(matrix).max()) / np.log(2))

        ax.patch.set_facecolor('gray')
        ax.set_aspect('equal', 'box')
        ax.xaxis.set_major_locator(plt.NullLocator())
        ax.yaxis.set_major_locator(plt.NullLocator())

        for (x, y), w in np.ndenumerate(matrix):
            color = 'white' if w > 0 else 'black'
            size = np.sqrt(np.abs(w))
            rect = plt.Rectangle([x - size / 2, y - size / 2], size, size,
                                 facecolor=color, edgecolor=color)
            ax.add_patch(rect)

        ax.autoscale_view()
        ax.invert_yaxis() 

def frame(I=None, second=5, saveable=True, name='frame', cmap=None, fig_idx=12836):
    """Display a frame(image). Make sure OpenAI Gym render() is disable before using it.

    Parameters
    ----------
    I : numpy.array
        The image
    second : int
        The display second(s) for the image(s), if saveable is False.
    saveable : boolean
        Save or plot the figure.
    name : a string
        A name to save the image, if saveable is True.
    cmap : None or string
        'gray' for greyscale, None for default, etc.
    fig_idx : int
        matplotlib figure index.

    Examples
    --------
    >>> env = gym.make("Pong-v0")
    >>> observation = env.reset()
    >>> tl.visualize.frame(observation)
    """
    if saveable is False:
        plt.ion()
    fig = plt.figure(fig_idx)      # show all feature images

    if len(I.shape) and I.shape[-1]==1:     # (10,10,1) --> (10,10)
        I = I[:,:,0]

    plt.imshow(I, cmap)
    plt.title(name)
    # plt.gca().xaxis.set_major_locator(plt.NullLocator())    # distable tick
    # plt.gca().yaxis.set_major_locator(plt.NullLocator())

    if saveable:
        plt.savefig(name+'.pdf',format='pdf')
    else:
        plt.draw()
        plt.pause(second) 

def save(path, remove_axis=False, dpi=300, fig=None):
  if fig is None:
    fig = plt.gcf()
  dirname = os.path.dirname(path)
  if dirname != '' and not os.path.exists(dirname):
    os.makedirs(dirname)
  if remove_axis:
    for ax in fig.axes:
      ax.axis('off')
      ax.margins(0,0)
    fig.subplots_adjust(top=1, bottom=0, right=1, left=0, hspace=0, wspace=0)
    for ax in fig.axes:
      ax.xaxis.set_major_locator(plt.NullLocator())
      ax.yaxis.set_major_locator(plt.NullLocator())
  fig.savefig(path, dpi=dpi, bbox_inches='tight', pad_inches=0) 

def atomicPlot(self, cmap="hot_r", vmin=None, vmax=None, ax=None):
        """
    Just a handler to parametricPlot. Useful to plot energy levels.

    It adds a fake k-point. Shouldn't be invoked with more than one
    k-point
    ax not implemented here, not need
    """

        print("Atomic plot: bands.shape  :", self.bands.shape)
        print("Atomic plot: spd.shape    :", self.spd.shape)
        print("Atomic plot: kpoints.shape:", self.kpoints.shape)

        self.bands = np.hstack((self.bands, self.bands))
        self.spd = np.hstack((self.spd, self.spd))
        self.kpoints = np.vstack((self.kpoints, self.kpoints))
        self.kpoints[0][-1] += 1
        print("Atomic plot: bands.shape  :", self.bands.shape)
        print("Atomic plot: spd.shape    :", self.spd.shape)
        print("Atomic plot: kpoints.shape:", self.kpoints.shape)

        #        print(self.kpoints)

        fig, ax1 = self.parametricPlot(cmap, vmin, vmax, ax=ax)

        #        plt.gca().xaxis.set_major_locator(plt.NullLocator())
        ax1.xaxis.set_major_locator(plt.NullLocator())
        # labels on each band
        for i in range(len(self.bands[:, 0])):
            # print i, self.bands[i]
            ax1.text(0, self.bands[i, 0], str(i + 1))

        return fig, ax1 

def frame(I=None, second=5, saveable=True, name='frame', cmap=None, fig_idx=12836):
    """Display a frame(image). Make sure OpenAI Gym render() is disable before using it.

    Parameters
    ----------
    I : numpy.array
        The image
    second : int
        The display second(s) for the image(s), if saveable is False.
    saveable : boolean
        Save or plot the figure.
    name : a string
        A name to save the image, if saveable is True.
    cmap : None or string
        'gray' for greyscale, None for default, etc.
    fig_idx : int
        matplotlib figure index.

    Examples
    --------
    >>> env = gym.make("Pong-v0")
    >>> observation = env.reset()
    >>> tl.visualize.frame(observation)
    """
    if saveable is False:
        plt.ion()
    fig = plt.figure(fig_idx)      # show all feature images

    if len(I.shape) and I.shape[-1]==1:     # (10,10,1) --> (10,10)
        I = I[:,:,0]

    plt.imshow(I, cmap)
    plt.title(name)
    # plt.gca().xaxis.set_major_locator(plt.NullLocator())    # distable tick
    # plt.gca().yaxis.set_major_locator(plt.NullLocator())

    if saveable:
        plt.savefig(name+'.pdf',format='pdf')
    else:
        plt.draw()
        plt.pause(second) 

def detick(ax=None, x=True, y=True):
    """helper function for removing tick labels from an axis"""
    if not ax:
        ax = plt.gca()
    if x:
        ax.xaxis.set_major_locator(plt.NullLocator())
    if y:
        ax.yaxis.set_major_locator(plt.NullLocator()) 

def continuous(x, y, c=None, ax=None, colorbar=True, randomize=True,
                   remove_ticks=False, **kwargs):
        """
        wrapper for scatter wherein the coordinates x and y are colored according to a
        continuous vector c
        :param x, y: np.ndarray, coordinate data
        :param c: np.ndarray, continuous vector by which to color data points
        :param remove_ticks: remove axis ticks and labels
        :param args: additional args for scatter
        :param kwargs: additional kwargs for scatter
        :return: ax
        """

        if ax is None:
            ax = plt.gca()

        if c is None:  # plot density if no color vector is provided
            x, y, c = scatter.density_2d(x, y)

        if randomize:
            ind = np.random.permutation(len(x))
        else:
            ind = np.argsort(c)

        sm = ax.scatter(x[ind], y[ind], c=c[ind], **kwargs)
        if remove_ticks:
            ax.xaxis.set_major_locator(plt.NullLocator())
            ax.yaxis.set_major_locator(plt.NullLocator())
        if colorbar:
            cb = plt.colorbar(sm)
            cb.ax.xaxis.set_major_locator(plt.NullLocator())
            cb.ax.yaxis.set_major_locator(plt.NullLocator())
        return ax 

def test_font_scaling():
    matplotlib.rcParams['pdf.fonttype'] = 42
    fig, ax = plt.subplots(figsize=(6.4, 12.4))
    ax.xaxis.set_major_locator(plt.NullLocator())
    ax.yaxis.set_major_locator(plt.NullLocator())
    ax.set_ylim(-10, 600)

    for i, fs in enumerate(range(4, 43, 2)):
        ax.text(0.1, i*30, "{fs} pt font size".format(fs=fs), fontsize=fs) 

def hinton(matrix, max_weight=1.0, ax=None):
    """Draw Hinton diagram for visualizing a weight matrix."""
    ax = ax if ax is not None else plt.gca()

    if not max_weight:
        max_weight = 2 ** np.ceil(np.log(np.abs(matrix).max()) / np.log(2))

    ax.patch.set_facecolor('lightgrey')
    ax.set_aspect('equal', 'box')
    ax.xaxis.set_major_locator(plt.NullLocator())
    ax.yaxis.set_major_locator(plt.NullLocator())

    for (x, y), w in np.ndenumerate(matrix):
        color = np.arctan2(w.real, w.imag)
        color = ANGLE_MAPPER.to_rgba(color)
        size = np.sqrt(np.abs(w) / max_weight)
        rect = plt.Rectangle([x - size / 2, y - size / 2], size, size,
                             facecolor=color, edgecolor=color)
        ax.add_patch(rect)

    ax.set_xlim((-max_weight / 2, matrix.shape[0] - max_weight / 2))
    ax.set_ylim((-max_weight / 2, matrix.shape[1] - max_weight / 2))
    ax.autoscale_view()
    ax.invert_yaxis()


# From QuTiP which in turn modified the code from the SciPy Cookbook. 

def draw_density_estimation(self, axis, title, samples, cmap):
        axis.clear()
        axis.set_xlabel(title)
        density_estimation = numpy.zeros((self.l_kde, self.l_kde))
        for x, y in samples:
            if 0 < x < 1 and 0 < y < 1:
                density_estimation[int((1-y) / self.resolution)][int(x / self.resolution)] += 1
        density_estimation = filters.gaussian(density_estimation, self.bw_kde_)
        axis.imshow(density_estimation, cmap=cmap)
        axis.xaxis.set_major_locator(pyplot.NullLocator())
        axis.yaxis.set_major_locator(pyplot.NullLocator()) 

def plot_x(x, name='x', xlabel='Frames'):
    x = x.transpose(1, 0)
    fig, ax = plt.subplots(figsize=(10, 3))
    im = ax.imshow(x, aspect='auto', origin='lower',
                   interpolation='none')
    plt.colorbar(im, ax=ax)
    plt.xlabel(xlabel)
    plt.ylabel('Channels')
    plt.tight_layout()
    plt.margins(0,0)
    plt.gca().xaxis.set_major_locator(plt.NullLocator())
    plt.gca().yaxis.set_major_locator(plt.NullLocator())

    fig.canvas.draw()
    fig.savefig(os.path.join(out_dir, name + '.png'), bbox_inches='tight', pad_inches = 0) 

def test_font_scaling():
    matplotlib.rcParams['pdf.fonttype'] = 42
    fig, ax = plt.subplots(figsize=(6.4, 12.4))
    ax.xaxis.set_major_locator(plt.NullLocator())
    ax.yaxis.set_major_locator(plt.NullLocator())
    ax.set_ylim(-10, 600)

    for i, fs in enumerate(range(4, 43, 2)):
        ax.text(0.1, i*30, "{fs} pt font size".format(fs=fs), fontsize=fs) 

def atomicPlot(self, cmap='hot_r', vmin=None, vmax=None):
        """
        Just a handler to parametricPlot. Useful to plot energy levels.

        It adds a fake k-point. Shouldn't be invoked with more than one
        k-point
        """

        print("Atomic plot: bands.shape  :", self.bands.shape)
        print("Atomic plot: spd.shape    :", self.spd.shape)
        print("Atomic plot: kpoints.shape:", self.kpoints.shape)

        self.bands = np.hstack((self.bands, self.bands))
        self.spd = np.hstack((self.spd, self.spd))
        self.kpoints = np.vstack((self.kpoints, self.kpoints))
        self.kpoints[0][-1] += 1
        print("Atomic plot: bands.shape  :", self.bands.shape)
        print("Atomic plot: spd.shape    :", self.spd.shape)
        print("Atomic plot: kpoints.shape:", self.kpoints.shape)

        print(self.kpoints)

        fig = self.parametricPlot(cmap, vmin, vmax)
        plt.gca().xaxis.set_major_locator(plt.NullLocator())

        # labels on each band
        for i in range(len(self.bands[:, 0])):
            # print i, self.bands[i]
            plt.text(0, self.bands[i, 0], str(i + 1), fontsize=15)

        return fig 

def hinton(matrix, max_weight=None, ax=None, xtick=None, ytick=None, inverted_color=False):
    """Draw Hinton diagram for visualizing a weight matrix.

    Copied from: http://matplotlib.org/examples/specialty_plots/hinton_demo.html
    """
    ax = ax if ax is not None else plt.gca()
    if not max_weight:
        max_weight = 2**np.ceil(np.log(np.abs(matrix).max())/np.log(2))

    ax.patch.set_facecolor('gray')
    ax.set_aspect('equal', 'box')
    ax.xaxis.set_major_locator(plt.NullLocator())
    ax.yaxis.set_major_locator(plt.NullLocator())

    for (x, y), w in np.ndenumerate(matrix):
        if inverted_color:
            color = 'black' if w > 0 else 'white'
        else:
            color = 'white' if w > 0 else 'black'
        size = np.sqrt(np.abs(w))
        rect = plt.Rectangle([x - size / 2, y - size / 2], size, size,
                             facecolor=color, edgecolor=color)
        ax.add_patch(rect)

    ax.autoscale_view()
    ax.invert_yaxis()

    if xtick:
        ax.set_xticks(np.arange(matrix.shape[0]))
        ax.set_xticklabels(xtick)
    if ytick:
        ax.set_yticks(np.arange(matrix.shape[1]))
        ax.set_yticklabels(ytick)
    return ax 

def hinton(matrix, max_weight=None, ax=None, xtick=None, ytick=None, inverted_color=False):
    """Draw Hinton diagram for visualizing a weight matrix.

    Copied from: http://matplotlib.org/examples/specialty_plots/hinton_demo.html
    """
    ax = ax if ax is not None else plt.gca()
    if not max_weight:
        max_weight = 2**np.ceil(np.log(np.abs(matrix).max())/np.log(2))

    ax.patch.set_facecolor('gray')
    ax.set_aspect('equal', 'box')
    ax.xaxis.set_major_locator(plt.NullLocator())
    ax.yaxis.set_major_locator(plt.NullLocator())

    for (x, y), w in np.ndenumerate(matrix):
        if inverted_color:
            color = 'black' if w > 0 else 'white'
        else:
            color = 'white' if w > 0 else 'black'
        size = np.sqrt(np.abs(w))
        rect = plt.Rectangle([x - size / 2, y - size / 2], size, size,
                             facecolor=color, edgecolor=color)
        ax.add_patch(rect)

    ax.autoscale_view()
    ax.invert_yaxis()

    if xtick:
        ax.set_xticks(np.arange(matrix.shape[0]))
        ax.set_xticklabels(xtick)
    if ytick:
        ax.set_yticks(np.arange(matrix.shape[1]))
        ax.set_yticklabels(ytick)
    return ax 

def test_font_scaling():
    matplotlib.rcParams['pdf.fonttype'] = 42
    fig, ax = plt.subplots(figsize=(6.4, 12.4))
    ax.xaxis.set_major_locator(plt.NullLocator())
    ax.yaxis.set_major_locator(plt.NullLocator())
    ax.set_ylim(-10, 600)

    for i, fs in enumerate(range(4, 43, 2)):
        ax.text(0.1, i*30, "{fs} pt font size".format(fs=fs), fontsize=fs) 

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 frame(I=None, second=5, saveable=True, name='frame', cmap=None, fig_idx=12836):
    """Display a frame(image). Make sure OpenAI Gym render() is disable before using it.

    Parameters
    ----------
    I : numpy.array
        The image
    second : int
        The display second(s) for the image(s), if saveable is False.
    saveable : boolean
        Save or plot the figure.
    name : a string
        A name to save the image, if saveable is True.
    cmap : None or string
        'gray' for greyscale, None for default, etc.
    fig_idx : int
        matplotlib figure index.

    Examples
    --------
    >>> env = gym.make("Pong-v0")
    >>> observation = env.reset()
    >>> tl.visualize.frame(observation)
    """
    if saveable is False:
        plt.ion()
    fig = plt.figure(fig_idx)      # show all feature images

    if len(I.shape) and I.shape[-1]==1:     # (10,10,1) --> (10,10)
        I = I[:,:,0]

    plt.imshow(I, cmap)
    plt.title(name)
    # plt.gca().xaxis.set_major_locator(plt.NullLocator())    # distable tick
    # plt.gca().yaxis.set_major_locator(plt.NullLocator())

    if saveable:
        plt.savefig(name+'.pdf',format='pdf')
    else:
        plt.draw()
        plt.pause(second) 

def W(W=None, second=10, saveable=True, shape=[28,28], name='mnist', fig_idx=2396512):
    """Visualize every columns of the weight matrix to a group of Greyscale img.

    Parameters
    ----------
    W : numpy.array
        The weight matrix
    second : int
        The display second(s) for the image(s), if saveable is False.
    saveable : boolean
        Save or plot the figure.
    shape : a list with 2 int
        The shape of feature image, MNIST is [28, 80].
    name : a string
        A name to save the image, if saveable is True.
    fig_idx : int
        matplotlib figure index.

    Examples
    --------
    >>> tl.visualize.W(network.all_params[0].eval(), second=10, saveable=True, name='weight_of_1st_layer', fig_idx=2012)
    """
    if saveable is False:
        plt.ion()
    fig = plt.figure(fig_idx)      # show all feature images
    size = W.shape[0]
    n_units = W.shape[1]

    num_r = int(np.sqrt(n_units))  # 每行显示的个数   若25个hidden unit -> 每行显示5个
    num_c = int(np.ceil(n_units/num_r))
    count = int(1)
    for row in range(1, num_r+1):
        for col in range(1, num_c+1):
            if count > n_units:
                break
            a = fig.add_subplot(num_r, num_c, count)
            # ------------------------------------------------------------
            # plt.imshow(np.reshape(W[:,count-1],(28,28)), cmap='gray')
            # ------------------------------------------------------------
            feature = W[:,count-1] / np.sqrt( (W[:,count-1]**2).sum())
            # feature[feature<0.0001] = 0   # value threshold
            # if count == 1 or count == 2:
            #     print(np.mean(feature))
            # if np.std(feature) < 0.03:      # condition threshold
            #     feature = np.zeros_like(feature)
            # if np.mean(feature) < -0.015:      # condition threshold
            #     feature = np.zeros_like(feature)
            plt.imshow(np.reshape(feature ,(shape[0],shape[1])),
                    cmap='gray', interpolation="nearest")#, vmin=np.min(feature), vmax=np.max(feature))
            # plt.title(name)
            # ------------------------------------------------------------
            # plt.imshow(np.reshape(W[:,count-1] ,(np.sqrt(size),np.sqrt(size))), cmap='gray', interpolation="nearest")
            plt.gca().xaxis.set_major_locator(plt.NullLocator())    # distable tick
            plt.gca().yaxis.set_major_locator(plt.NullLocator())
            count = count + 1
    if saveable:
        plt.savefig(name+'.pdf',format='pdf')
    else:
        plt.draw()
        plt.pause(second) 

def CNN2d(CNN=None, second=10, saveable=True, name='cnn', fig_idx=3119362):
    """Display a group of RGB or Greyscale CNN masks.

    Parameters
    ----------
    CNN : numpy.array
        The image. e.g: 64 5x5 RGB images can be (5, 5, 3, 64).
    second : int
        The display second(s) for the image(s), if saveable is False.
    saveable : boolean
        Save or plot the figure.
    name : a string
        A name to save the image, if saveable is True.
    fig_idx : int
        matplotlib figure index.

    Examples
    --------
    >>> tl.visualize.CNN2d(network.all_params[0].eval(), second=10, saveable=True, name='cnn1_mnist', fig_idx=2012)
    """
    # print(CNN.shape)    # (5, 5, 3, 64)
    # exit()
    n_mask = CNN.shape[3]
    n_row = CNN.shape[0]
    n_col = CNN.shape[1]
    n_color = CNN.shape[2]
    row = int(np.sqrt(n_mask))
    col = int(np.ceil(n_mask/row))
    plt.ion()   # active mode
    fig = plt.figure(fig_idx)
    count = 1
    for ir in range(1, row+1):
        for ic in range(1, col+1):
            if count > n_mask:
                break
            a = fig.add_subplot(col, row, count)
            # print(CNN[:,:,:,count-1].shape, n_row, n_col)   # (5, 1, 32) 5 5
            # exit()
            # plt.imshow(
            #         np.reshape(CNN[count-1,:,:,:], (n_row, n_col)),
            #         cmap='gray', interpolation="nearest")     # theano
            if n_color == 1:
                plt.imshow(
                        np.reshape(CNN[:,:,:,count-1], (n_row, n_col)),
                        cmap='gray', interpolation="nearest")
            elif n_color == 3:
                plt.imshow(
                        np.reshape(CNN[:,:,:,count-1], (n_row, n_col, n_color)),
                        cmap='gray', interpolation="nearest")
            else:
                raise Exception("Unknown n_color")
            plt.gca().xaxis.set_major_locator(plt.NullLocator())    # distable tick
            plt.gca().yaxis.set_major_locator(plt.NullLocator())
            count = count + 1
    if saveable:
        plt.savefig(name+'.pdf',format='pdf')
    else:
        plt.draw()
        plt.pause(second) 

def W(W=None, second=10, saveable=True, shape=[28,28], name='mnist', fig_idx=2396512):
    """Visualize every columns of the weight matrix to a group of Greyscale img.

    Parameters
    ----------
    W : numpy.array
        The weight matrix
    second : int
        The display second(s) for the image(s), if saveable is False.
    saveable : boolean
        Save or plot the figure.
    shape : a list with 2 int
        The shape of feature image, MNIST is [28, 80].
    name : a string
        A name to save the image, if saveable is True.
    fig_idx : int
        matplotlib figure index.

    Examples
    --------
    >>> tl.visualize.W(network.all_params[0].eval(), second=10, saveable=True, name='weight_of_1st_layer', fig_idx=2012)
    """
    if saveable is False:
        plt.ion()
    fig = plt.figure(fig_idx)      # show all feature images
    size = W.shape[0]
    n_units = W.shape[1]

    num_r = int(np.sqrt(n_units))  # 每行显示的个数   若25个hidden unit -> 每行显示5个
    num_c = int(np.ceil(n_units/num_r))
    count = int(1)
    for row in range(1, num_r+1):
        for col in range(1, num_c+1):
            if count > n_units:
                break
            a = fig.add_subplot(num_r, num_c, count)
            # ------------------------------------------------------------
            # plt.imshow(np.reshape(W[:,count-1],(28,28)), cmap='gray')
            # ------------------------------------------------------------
            feature = W[:,count-1] / np.sqrt( (W[:,count-1]**2).sum())
            # feature[feature<0.0001] = 0   # value threshold
            # if count == 1 or count == 2:
            #     print(np.mean(feature))
            # if np.std(feature) < 0.03:      # condition threshold
            #     feature = np.zeros_like(feature)
            # if np.mean(feature) < -0.015:      # condition threshold
            #     feature = np.zeros_like(feature)
            plt.imshow(np.reshape(feature ,(shape[0],shape[1])),
                    cmap='gray', interpolation="nearest")#, vmin=np.min(feature), vmax=np.max(feature))
            # plt.title(name)
            # ------------------------------------------------------------
            # plt.imshow(np.reshape(W[:,count-1] ,(np.sqrt(size),np.sqrt(size))), cmap='gray', interpolation="nearest")
            plt.gca().xaxis.set_major_locator(plt.NullLocator())    # distable tick
            plt.gca().yaxis.set_major_locator(plt.NullLocator())
            count = count + 1
    if saveable:
        plt.savefig(name+'.pdf',format='pdf')
    else:
        plt.draw()
        plt.pause(second) 

def CNN2d(CNN=None, second=10, saveable=True, name='cnn', fig_idx=3119362):
    """Display a group of RGB or Greyscale CNN masks.

    Parameters
    ----------
    CNN : numpy.array
        The image. e.g: 64 5x5 RGB images can be (5, 5, 3, 64).
    second : int
        The display second(s) for the image(s), if saveable is False.
    saveable : boolean
        Save or plot the figure.
    name : a string
        A name to save the image, if saveable is True.
    fig_idx : int
        matplotlib figure index.

    Examples
    --------
    >>> tl.visualize.CNN2d(network.all_params[0].eval(), second=10, saveable=True, name='cnn1_mnist', fig_idx=2012)
    """
    # print(CNN.shape)    # (5, 5, 3, 64)
    # exit()
    n_mask = CNN.shape[3]
    n_row = CNN.shape[0]
    n_col = CNN.shape[1]
    n_color = CNN.shape[2]
    row = int(np.sqrt(n_mask))
    col = int(np.ceil(n_mask/row))
    plt.ion()   # active mode
    fig = plt.figure(fig_idx)
    count = 1
    for ir in range(1, row+1):
        for ic in range(1, col+1):
            if count > n_mask:
                break
            a = fig.add_subplot(col, row, count)
            # print(CNN[:,:,:,count-1].shape, n_row, n_col)   # (5, 1, 32) 5 5
            # exit()
            # plt.imshow(
            #         np.reshape(CNN[count-1,:,:,:], (n_row, n_col)),
            #         cmap='gray', interpolation="nearest")     # theano
            if n_color == 1:
                plt.imshow(
                        np.reshape(CNN[:,:,:,count-1], (n_row, n_col)),
                        cmap='gray', interpolation="nearest")
            elif n_color == 3:
                plt.imshow(
                        np.reshape(CNN[:,:,:,count-1], (n_row, n_col, n_color)),
                        cmap='gray', interpolation="nearest")
            else:
                raise Exception("Unknown n_color")
            plt.gca().xaxis.set_major_locator(plt.NullLocator())    # distable tick
            plt.gca().yaxis.set_major_locator(plt.NullLocator())
            count = count + 1
    if saveable:
        plt.savefig(name+'.pdf',format='pdf')
    else:
        plt.draw()
        plt.pause(second) 

def hinton(self, max_weight=None, ax=None, matrix = None, xlabel=None, ylabel=None):
        """Draw Hinton diagram for visualizing a weight matrix."""
        if matrix is None:
            matrix = self.corr()
        ax = ax if ax is not None else plt.gca()

        if not max_weight:
            max_weight = 2**np.ceil(np.log(np.abs(matrix).max())/np.log(2))

        ax.patch.set_facecolor('lightgray')
        ax.set_aspect('equal', 'box')
        ax.xaxis.set_major_locator(plt.NullLocator())
        ax.yaxis.set_major_locator(plt.NullLocator())

        for (x, y), w in np.ndenumerate(matrix):
            color = 'red' if w > 0 else 'blue'
            size = np.sqrt(np.abs(w))
            rect = plt.Rectangle([y - size / 2, x - size / 2], size, size,
                                 facecolor=color, edgecolor=color)
            ax.add_patch(rect)

        nxticks = matrix.shape[1]
        nyticks = matrix.shape[0]
        if xlabel is None:
            xlabel = []
            for x in list(matrix.columns):
                x = re.sub(r'\^([0-9]+)',r'^{\1}', x)
                x = re.sub(r'_([0-9]+)',r'_{\1}', x)
                if x[-1] in ['+','-']:
                    x = x[:-1]+'^'+x[-1]
                xlabel.append('$'+x+'$')
        if ylabel is None:
            ylabel = list(matrix.index)
        ax.xaxis.tick_top()
        ax.set_xticks(range(nxticks))
        ax.set_xticklabels(xlabel, rotation=90)
        ax.set_yticks(range(nyticks))
        ax.set_yticklabels(ylabel)
        ax.grid(False)

        ax.autoscale_view()
        ax.invert_yaxis() 

def categorical(
            x, y, c, ax=None, cmap=plt.get_cmap(), legend=True, legend_kwargs=None,
            randomize=True, remove_ticks=False, *args, **kwargs):
        """
        wrapper for scatter wherein the output should be colored by a categorical vector
        c

        :param x, y: np.ndarray, coordinate data to be scattered
        :param c: categories for data
        :param ax: axis on which to scatter data
        :param cmap: color map
        :param legend: bool, if True, plot legend
        :param legend_kwargs: additional kwargs for legend
        :param randomize: if True, randomize order of plotting
        :param remove_ticks: if True, removes axes ticks and labels
        :param args: additional args for scatter
        :param kwargs: additional kwargs for scatter
        :return: ax
        """
        if not ax:  # todo replace with plt.gridspec() method
            ax = plt.gca()

        if legend_kwargs is None:
            legend_kwargs = dict()

        color_vector, category_to_color = map_categorical_to_cmap(c, cmap)

        if randomize:
            ind = np.random.permutation(len(x))
        else:
            ind = np.argsort(np.ravel(c))

        ax.scatter(np.ravel(x)[ind], np.ravel(y)[ind], c=color_vector[ind], *args,
                   **kwargs)
        if remove_ticks:
            ax.xaxis.set_major_locator(plt.NullLocator())
            ax.yaxis.set_major_locator(plt.NullLocator())

        labels, colors = zip(*sorted(category_to_color.items()))
        if legend:
            add_legend_to_categorical_vector(colors, labels, ax, markerscale=2,
                                             **legend_kwargs)
        return ax 

def CNN2d(CNN=None, second=10, saveable=True, name='cnn', fig_idx=3119362):
    """Display a group of RGB or Greyscale CNN masks.

    Parameters
    ----------
    CNN : numpy.array
        The image. e.g: 64 5x5 RGB images can be (5, 5, 3, 64).
    second : int
        The display second(s) for the image(s), if saveable is False.
    saveable : boolean
        Save or plot the figure.
    name : a string
        A name to save the image, if saveable is True.
    fig_idx : int
        matplotlib figure index.

    Examples
    --------
    >>> tl.visualize.CNN2d(network.all_params[0].eval(), second=10, saveable=True, name='cnn1_mnist', fig_idx=2012)
    """
    # print(CNN.shape)    # (5, 5, 3, 64)
    # exit()
    n_mask = CNN.shape[3]
    n_row = CNN.shape[0]
    n_col = CNN.shape[1]
    n_color = CNN.shape[2]
    row = int(np.sqrt(n_mask))
    col = int(np.ceil(n_mask/row))
    plt.ion()   # active mode
    fig = plt.figure(fig_idx)
    count = 1
    for ir in range(1, row+1):
        for ic in range(1, col+1):
            if count > n_mask:
                break
            a = fig.add_subplot(col, row, count)
            # print(CNN[:,:,:,count-1].shape, n_row, n_col)   # (5, 1, 32) 5 5
            # exit()
            # plt.imshow(
            #         np.reshape(CNN[count-1,:,:,:], (n_row, n_col)),
            #         cmap='gray', interpolation="nearest")     # theano
            if n_color == 1:
                plt.imshow(
                        np.reshape(CNN[:,:,:,count-1], (n_row, n_col)),
                        cmap='gray', interpolation="nearest")
            elif n_color == 3:
                plt.imshow(
                        np.reshape(CNN[:,:,:,count-1], (n_row, n_col, n_color)),
                        cmap='gray', interpolation="nearest")
            else:
                raise Exception("Unknown n_color")
            plt.gca().xaxis.set_major_locator(plt.NullLocator())    # distable tick
            plt.gca().yaxis.set_major_locator(plt.NullLocator())
            count = count + 1
    if saveable:
        plt.savefig(name+'.pdf',format='pdf')
    else:
        plt.draw()
        plt.pause(second)