Python matplotlib.pyplot.subplot() Examples

The following are 30 code examples for showing how to use matplotlib.pyplot.subplot(). These examples are extracted from open source projects. You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example.

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Example 1
Project: Sound-Recognition-Tutorial   Author: JasonZhang156   File: data_augmentation.py    License: Apache License 2.0 8 votes vote down vote up
def demo_plot():
    audio = './data/esc10/audio/Dog/1-30226-A.ogg'
    y, sr = librosa.load(audio, sr=44100)
    y_ps = librosa.effects.pitch_shift(y, sr, n_steps=6)   # n_steps控制音调变化尺度
    y_ts = librosa.effects.time_stretch(y, rate=1.2)   # rate控制时间维度的变换尺度
    plt.subplot(311)
    plt.plot(y)
    plt.title('Original waveform')
    plt.axis([0, 200000, -0.4, 0.4])
    # plt.axis([88000, 94000, -0.4, 0.4])
    plt.subplot(312)
    plt.plot(y_ts)
    plt.title('Time Stretch transformed waveform')
    plt.axis([0, 200000, -0.4, 0.4])
    plt.subplot(313)
    plt.plot(y_ps)
    plt.title('Pitch Shift transformed waveform')
    plt.axis([0, 200000, -0.4, 0.4])
    # plt.axis([88000, 94000, -0.4, 0.4])
    plt.tight_layout()
    plt.show() 
Example 2
Project: MomentumContrast.pytorch   Author: peisuke   File: test.py    License: MIT License 6 votes vote down vote up
def show(mnist, targets, ret):
    target_ids = range(len(set(targets)))
    
    colors = ['r', 'g', 'b', 'c', 'm', 'y', 'k', 'violet', 'orange', 'purple']
    
    plt.figure(figsize=(12, 10))
    
    ax = plt.subplot(aspect='equal')
    for label in set(targets):
        idx = np.where(np.array(targets) == label)[0]
        plt.scatter(ret[idx, 0], ret[idx, 1], c=colors[label], label=label)
    
    for i in range(0, len(targets), 250):
        img = (mnist[i][0] * 0.3081 + 0.1307).numpy()[0]
        img = OffsetImage(img, cmap=plt.cm.gray_r, zoom=0.5) 
        ax.add_artist(AnnotationBbox(img, ret[i]))
    
    plt.legend()
    plt.show() 
Example 3
Project: pruning_yolov3   Author: zbyuan   File: utils.py    License: GNU General Public License v3.0 6 votes vote down vote up
def plot_images(imgs, targets, paths=None, fname='images.jpg'):
    # Plots training images overlaid with targets
    imgs = imgs.cpu().numpy()
    targets = targets.cpu().numpy()
    # targets = targets[targets[:, 1] == 21]  # plot only one class

    fig = plt.figure(figsize=(10, 10))
    bs, _, h, w = imgs.shape  # batch size, _, height, width
    bs = min(bs, 16)  # limit plot to 16 images
    ns = np.ceil(bs ** 0.5)  # number of subplots

    for i in range(bs):
        boxes = xywh2xyxy(targets[targets[:, 0] == i, 2:6]).T
        boxes[[0, 2]] *= w
        boxes[[1, 3]] *= h
        plt.subplot(ns, ns, i + 1).imshow(imgs[i].transpose(1, 2, 0))
        plt.plot(boxes[[0, 2, 2, 0, 0]], boxes[[1, 1, 3, 3, 1]], '.-')
        plt.axis('off')
        if paths is not None:
            s = Path(paths[i]).name
            plt.title(s[:min(len(s), 40)], fontdict={'size': 8})  # limit to 40 characters
    fig.tight_layout()
    fig.savefig(fname, dpi=200)
    plt.close() 
Example 4
Project: pruning_yolov3   Author: zbyuan   File: utils.py    License: GNU General Public License v3.0 6 votes vote down vote up
def plot_evolution_results(hyp):  # from utils.utils import *; plot_evolution_results(hyp)
    # Plot hyperparameter evolution results in evolve.txt
    x = np.loadtxt('evolve.txt', ndmin=2)
    f = fitness(x)
    weights = (f - f.min()) ** 2  # for weighted results
    fig = plt.figure(figsize=(12, 10))
    matplotlib.rc('font', **{'size': 8})
    for i, (k, v) in enumerate(hyp.items()):
        y = x[:, i + 5]
        # mu = (y * weights).sum() / weights.sum()  # best weighted result
        mu = y[f.argmax()]  # best single result
        plt.subplot(4, 5, i + 1)
        plt.plot(mu, f.max(), 'o', markersize=10)
        plt.plot(y, f, '.')
        plt.title('%s = %.3g' % (k, mu), fontdict={'size': 9})  # limit to 40 characters
        print('%15s: %.3g' % (k, mu))
    fig.tight_layout()
    plt.savefig('evolve.png', dpi=200) 
Example 5
Project: Recipes   Author: Lasagne   File: massachusetts_road_segm.py    License: MIT License 6 votes vote down vote up
def plot_some_results(pred_fn, test_generator, n_images=10):
    fig_ctr = 0
    for data, seg in test_generator:
        res = pred_fn(data)
        for d, s, r in zip(data, seg, res):
            plt.figure(figsize=(12, 6))
            plt.subplot(1, 3, 1)
            plt.imshow(d.transpose(1,2,0))
            plt.title("input patch")
            plt.subplot(1, 3, 2)
            plt.imshow(s[0])
            plt.title("ground truth")
            plt.subplot(1, 3, 3)
            plt.imshow(r)
            plt.title("segmentation")
            plt.savefig("road_segmentation_result_%03.0f.png"%fig_ctr)
            plt.close()
            fig_ctr += 1
            if fig_ctr > n_images:
                break 
Example 6
Project: medicaldetectiontoolkit   Author: MIC-DKFZ   File: plotting.py    License: Apache License 2.0 6 votes vote down vote up
def __init__(self, cf):

        self.file_name = cf.plot_dir + '/monitor_{}'.format(cf.fold)
        self.exp_name = cf.fold_dir
        self.do_validation = cf.do_validation
        self.separate_values_dict = cf.assign_values_to_extra_figure
        self.figure_list = []
        for n in range(cf.n_monitoring_figures):
            self.figure_list.append(plt.figure(figsize=(10, 6)))
            self.figure_list[-1].ax1 = plt.subplot(111)
            self.figure_list[-1].ax1.set_xlabel('epochs')
            self.figure_list[-1].ax1.set_ylabel('loss / metrics')
            self.figure_list[-1].ax1.set_xlim(0, cf.num_epochs)
            self.figure_list[-1].ax1.grid()

        self.figure_list[0].ax1.set_ylim(0, 1.5)
        self.color_palette = ['b', 'c', 'r', 'purple', 'm', 'y', 'k', 'tab:gray'] 
Example 7
Project: spinn   Author: stanfordnlp   File: analyze_log.py    License: MIT License 6 votes vote down vote up
def ShowPlots(subplot=False):
  for log_ind, path in enumerate(FLAGS.path.split(":")):
    log = Log(path)
    if subplot:
      plt.subplot(len(FLAGS.path.split(":")), 1, log_ind + 1)
    for index in FLAGS.index.split(","):
      index = int(index)
      for attr in ["pred_acc", "parse_acc", "total_cost", "xent_cost", "l2_cost", "action_cost"]:
        if getattr(FLAGS, attr):
          if "cost" in attr:
            assert index == 0, "costs only associated with training log"
          steps, val = zip(*[(l.step, getattr(l, attr)) for l in log.corpus[index] if l.step < FLAGS.iters])
          dct = {}
          for k, v in zip(steps, val):
            dct[k] = max(v, dct[k]) if k in dct else v
          steps, val = zip(*sorted(dct.iteritems()))
          plt.plot(steps, val, label="Log%d:%s-%d" % (log_ind, attr, index))
    
  plt.xlabel("No. of training iteration")
  plt.ylabel(FLAGS.ylabel)
  if FLAGS.legend:
    plt.legend()
  plt.show() 
Example 8
Project: bayesian_bootstrap   Author: lmc2179   File: demos.py    License: MIT License 6 votes vote down vote up
def plot_mean_bootstrap_exponential_readme():
    X = np.random.exponential(7, 4)
    classical_samples = [np.mean(resample(X)) for _ in range(10000)]
    posterior_samples = mean(X, 10000)
    l, r = highest_density_interval(posterior_samples)
    classical_l, classical_r = highest_density_interval(classical_samples)
    plt.subplot(2, 1, 1)
    plt.title('Bayesian Bootstrap of mean')
    sns.distplot(posterior_samples, label='Bayesian Bootstrap Samples')
    plt.plot([l, r], [0, 0], linewidth=5.0, marker='o', label='95% HDI')
    plt.xlim(-1, 18)
    plt.legend()
    plt.subplot(2, 1, 2)
    plt.title('Classical Bootstrap of mean')
    sns.distplot(classical_samples, label='Classical Bootstrap Samples')
    plt.plot([classical_l, classical_r], [0, 0], linewidth=5.0, marker='o', label='95% HDI')
    plt.xlim(-1, 18)
    plt.legend()
    plt.savefig('readme_exponential.png', bbox_inches='tight') 
Example 9
Project: Attention-Gated-Networks   Author: ozan-oktay   File: visualise_att_maps_epoch.py    License: MIT License 6 votes vote down vote up
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 
Example 10
Project: Attention-Gated-Networks   Author: ozan-oktay   File: visualise_fmaps.py    License: MIT License 6 votes vote down vote up
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 
Example 11
Project: Attention-Gated-Networks   Author: ozan-oktay   File: visualise_attention.py    License: MIT License 6 votes vote down vote up
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) 
Example 12
Project: dataiku-contrib   Author: dataiku   File: visualize.py    License: Apache License 2.0 6 votes vote down vote up
def display_images(images, titles=None, cols=4, cmap=None, norm=None,
                   interpolation=None):
    """Display the given set of images, optionally with titles.
    images: list or array of image tensors in HWC format.
    titles: optional. A list of titles to display with each image.
    cols: number of images per row
    cmap: Optional. Color map to use. For example, "Blues".
    norm: Optional. A Normalize instance to map values to colors.
    interpolation: Optional. Image interpolation to use for display.
    """
    titles = titles if titles is not None else [""] * len(images)
    rows = len(images) // cols + 1
    plt.figure(figsize=(14, 14 * rows // cols))
    i = 1
    for image, title in zip(images, titles):
        plt.subplot(rows, cols, i)
        plt.title(title, fontsize=9)
        plt.axis('off')
        plt.imshow(image.astype(np.uint8), cmap=cmap,
                   norm=norm, interpolation=interpolation)
        i += 1
    plt.show() 
Example 13
Project: residual-flows   Author: rtqichen   File: visualize_flow.py    License: MIT License 6 votes vote down vote up
def visualize_transform(
    potential_or_samples, prior_sample, prior_density, transform=None, inverse_transform=None, samples=True, npts=100,
    memory=100, device="cpu"
):
    """Produces visualization for the model density and samples from the model."""
    plt.clf()
    ax = plt.subplot(1, 3, 1, aspect="equal")
    if samples:
        plt_samples(potential_or_samples, ax, npts=npts)
    else:
        plt_potential_func(potential_or_samples, ax, npts=npts)

    ax = plt.subplot(1, 3, 2, aspect="equal")
    if inverse_transform is None:
        plt_flow(prior_density, transform, ax, npts=npts, device=device)
    else:
        plt_flow_density(prior_density, inverse_transform, ax, npts=npts, memory=memory, device=device)

    ax = plt.subplot(1, 3, 3, aspect="equal")
    if transform is not None:
        plt_flow_samples(prior_sample, transform, ax, npts=npts, memory=memory, device=device) 
Example 14
Project: DSMnet   Author: wyf2017   File: utils.py    License: Apache License 2.0 6 votes vote down vote up
def imsplot_tensor(*imgs_tensor):
    """
    使用matplotlib.pyplot绘制多个tensor类型图片
    图片尺寸应为(bn, c, h, w)
    或是单个图片尺寸为(1, c, h, w)的序列
    """
    count = min(8, len(imgs_tensor))
    if(count==0): return
    col = min(2, count)
    row = count//col
    if(count%col > 0):
        row = row + 1
    for i in range(count):
        plt.subplot(row, col, i+1);imshow_tensor(imgs_tensor[i])
    
# 计算并存储参数当前值和平均值 
Example 15
Project: AL   Author: iitml   File: utils.py    License: GNU General Public License v2.0 6 votes vote down vote up
def draw_plots(strategy, accu_x, accu_y, auc_x, auc_y):
    """Draws the plot

    **Parameters**

    * strategy
    * accu_x (*list*)
    * accu_y (*list*)
    * auc_x (*list*)
    * auc_y (*list*)

    """
    plt.figure(1)
    plt.subplot(211)
    plt.plot(accu_x, accu_y, '-', label=strategy)
    plt.legend(loc='best')
    plt.title('Accuracy')

    plt.subplot(212)
    plt.plot(auc_x, auc_y, '-', label=strategy)
    plt.legend(loc='best')
    plt.title('AUC') 
Example 16
Project: EDeN   Author: fabriziocosta   File: __init__.py    License: MIT License 5 votes vote down vote up
def draw_graph_row(graphs,
                   index=0,
                   contract=True,
                   n_graphs_per_line=5,
                   size=4,
                   xlim=None,
                   ylim=None,
                   **args):
    """draw_graph_row."""
    dim = len(graphs)
    size_y = size
    size_x = size * n_graphs_per_line * args.get('size_x_to_y_ratio', 1)
    plt.figure(figsize=(size_x, size_y))

    if xlim is not None:
        plt.xlim(xlim)
        plt.ylim(ylim)
    else:
        plt.xlim(xmax=3)

    for i in range(dim):
        plt.subplot(1, n_graphs_per_line, i + 1)
        graph = graphs[i]
        draw_graph(graph,
                   size=None,
                   pos=graph.graph.get('pos_dict', None),
                   **args)
    if args.get('file_name', None) is None:
        plt.show()
    else:
        row_file_name = '%d_' % (index) + args['file_name']
        plt.savefig(row_file_name,
                    bbox_inches='tight',
                    transparent=True,
                    pad_inches=0)
        plt.close() 
Example 17
Project: EDeN   Author: fabriziocosta   File: __init__.py    License: MIT License 5 votes vote down vote up
def plot_confusion_matrices(y_true, y_pred, size=12):
    """plot_confusion_matrices."""
    plt.figure(figsize=(size, size))
    plt.subplot(121)
    plot_confusion_matrix(y_true, y_pred, normalize=False)
    plt.subplot(122)
    plot_confusion_matrix(y_true, y_pred, normalize=True)
    plt.tight_layout(w_pad=5)
    plt.show() 
Example 18
Project: EDeN   Author: fabriziocosta   File: __init__.py    License: MIT License 5 votes vote down vote up
def plot_aucs(y_true, y_score, size=12):
    """plot_confusion_matrices."""
    plt.figure(figsize=(size, size / 2.0))
    plt.subplot(121, aspect='equal')
    plot_roc_curve(y_true, y_score)
    plt.subplot(122, aspect='equal')
    plot_precision_recall_curve(y_true, y_score)
    plt.tight_layout(w_pad=5)
    plt.show() 
Example 19
Project: FRIDA   Author: LCAV   File: point_cloud.py    License: MIT License 5 votes vote down vote up
def plot(self, axes=None, show_labels=True, **kwargs):

        if self.dim == 2:

            # Create a figure if needed
            if axes is None:
                axes = plt.subplot(111)

            axes.plot(self.X[0,:], self.X[1,:], **kwargs)
            axes.axis(aspect='equal')
            plt.show()


        elif self.dim == 3:
            if axes is None:
                fig = plt.figure()
                axes = fig.add_subplot(111, projection='3d')
            axes.scatter(self.X[0,:], self.X[1,:], self.X[2,:], **kwargs)
             
            axes.set_xlabel('X')
            axes.set_ylabel('Y')
            axes.set_zlabel('Z')
            plt.show()

        if show_labels and self.labels is not None:
            eps = np.linalg.norm(self.X[:,0] - self.X[:,1])/100
            for i in xrange(self.m):
                if self.dim == 2:
                    axes.text(self.X[0,i]+eps, self.X[1,i]+eps, self.labels[i])
                elif self.dim == 3:
                    axes.text(self.X[0,i]+eps, self.X[1,i]+eps, self.X[2,i]+eps, self.labels[i], None)

        return axes 
Example 20
Project: mmdetection   Author: open-mmlab   File: coco_error_analysis.py    License: Apache License 2.0 5 votes vote down vote up
def makeplot(rs, ps, outDir, class_name, iou_type):
    cs = np.vstack([
        np.ones((2, 3)),
        np.array([.31, .51, .74]),
        np.array([.75, .31, .30]),
        np.array([.36, .90, .38]),
        np.array([.50, .39, .64]),
        np.array([1, .6, 0])
    ])
    areaNames = ['allarea', 'small', 'medium', 'large']
    types = ['C75', 'C50', 'Loc', 'Sim', 'Oth', 'BG', 'FN']
    for i in range(len(areaNames)):
        area_ps = ps[..., i, 0]
        figure_tile = iou_type + '-' + class_name + '-' + areaNames[i]
        aps = [ps_.mean() for ps_ in area_ps]
        ps_curve = [
            ps_.mean(axis=1) if ps_.ndim > 1 else ps_ for ps_ in area_ps
        ]
        ps_curve.insert(0, np.zeros(ps_curve[0].shape))
        fig = plt.figure()
        ax = plt.subplot(111)
        for k in range(len(types)):
            ax.plot(rs, ps_curve[k + 1], color=[0, 0, 0], linewidth=0.5)
            ax.fill_between(
                rs,
                ps_curve[k],
                ps_curve[k + 1],
                color=cs[k],
                label=str(f'[{aps[k]:.3f}]' + types[k]))
        plt.xlabel('recall')
        plt.ylabel('precision')
        plt.xlim(0, 1.)
        plt.ylim(0, 1.)
        plt.title(figure_tile)
        plt.legend()
        # plt.show()
        fig.savefig(outDir + f'/{figure_tile}.png')
        plt.close(fig) 
Example 21
Project: Random-Erasing   Author: zhunzhong07   File: visualize.py    License: Apache License 2.0 5 votes vote down vote up
def show_mask_single(images, mask, Mean=(2, 2, 2), Std=(0.5,0.5,0.5)):
    im_size = images.size(2)

    # save for adding mask
    im_data = images.clone()
    for i in range(0, 3):
        im_data[:,i,:,:] = im_data[:,i,:,:] * Std[i] + Mean[i]    # unnormalize

    images = make_image(torchvision.utils.make_grid(images), Mean, Std)
    plt.subplot(2, 1, 1)
    plt.imshow(images)
    plt.axis('off')

    # for b in range(mask.size(0)):
    #     mask[b] = (mask[b] - mask[b].min())/(mask[b].max() - mask[b].min())
    mask_size = mask.size(2)
    # print('Max %f Min %f' % (mask.max(), mask.min()))
    mask = (upsampling(mask, scale_factor=im_size/mask_size))
    # mask = colorize(upsampling(mask, scale_factor=im_size/mask_size))
    # for c in range(3):
    #     mask[:,c,:,:] = (mask[:,c,:,:] - Mean[c])/Std[c]

    # print(mask.size())
    mask = make_image(torchvision.utils.make_grid(0.3*im_data+0.7*mask.expand_as(im_data)))
    # mask = make_image(torchvision.utils.make_grid(0.3*im_data+0.7*mask), Mean, Std)
    plt.subplot(2, 1, 2)
    plt.imshow(mask)
    plt.axis('off') 
Example 22
Project: Random-Erasing   Author: zhunzhong07   File: visualize.py    License: Apache License 2.0 5 votes vote down vote up
def show_mask(images, masklist, Mean=(2, 2, 2), Std=(0.5,0.5,0.5)):
    im_size = images.size(2)

    # save for adding mask
    im_data = images.clone()
    for i in range(0, 3):
        im_data[:,i,:,:] = im_data[:,i,:,:] * Std[i] + Mean[i]    # unnormalize

    images = make_image(torchvision.utils.make_grid(images), Mean, Std)
    plt.subplot(1+len(masklist), 1, 1)
    plt.imshow(images)
    plt.axis('off')

    for i in range(len(masklist)):
        mask = masklist[i].data.cpu()
        # for b in range(mask.size(0)):
        #     mask[b] = (mask[b] - mask[b].min())/(mask[b].max() - mask[b].min())
        mask_size = mask.size(2)
        # print('Max %f Min %f' % (mask.max(), mask.min()))
        mask = (upsampling(mask, scale_factor=im_size/mask_size))
        # mask = colorize(upsampling(mask, scale_factor=im_size/mask_size))
        # for c in range(3):
        #     mask[:,c,:,:] = (mask[:,c,:,:] - Mean[c])/Std[c]

        # print(mask.size())
        mask = make_image(torchvision.utils.make_grid(0.3*im_data+0.7*mask.expand_as(im_data)))
        # mask = make_image(torchvision.utils.make_grid(0.3*im_data+0.7*mask), Mean, Std)
        plt.subplot(1+len(masklist), 1, i+2)
        plt.imshow(mask)
        plt.axis('off')



# x = torch.zeros(1, 3, 3)
# out = colorize(x)
# out_im = make_image(out)
# plt.imshow(out_im)
# plt.show() 
Example 23
Project: deep-learning-note   Author: wdxtub   File: utils.py    License: MIT License 5 votes vote down vote up
def show_fashion_mnist(images, labels):
    _, figs = plt.subplot(1, len(images), figsize=(12, 12))
    for f, img, lbl in zip(figs, images, labels):
        f.imshow(img.view((28, 28)).numpy())
        f.set_title(lbl)
        f.axes.get_xaxis().set_visible(False)
        f.axes.get_yaxis().set_visible(False)
    plt.show() 
Example 24
Project: deep-learning-note   Author: wdxtub   File: 16_basic_kernels.py    License: MIT License 5 votes vote down vote up
def show_images(images, rgb=True):
    gs = gridspec.GridSpec(1, len(images))
    for i, image in enumerate(images):
        plt.subplot(gs[0, i])
        if rgb:
            plt.imshow(image)
        else:
            image = image.reshape(image.shape[0], image.shape[1])
            plt.imshow(image, cmap='gray')
        plt.axis('off')
    plt.show() 
Example 25
Project: Sound-Recognition-Tutorial   Author: JasonZhang156   File: data_analysis.py    License: Apache License 2.0 5 votes vote down vote up
def plot_wave(sound_files, sound_names):
    """plot wave"""
    i = 1
    fig = plt.figure(figsize=(20, 64))
    for f, n in zip(sound_files, sound_names):
        y, sr = librosa.load(os.path.join('./data/esc10/audio/', f))
        plt.subplot(10, 1, i)
        librosa.display.waveplot(y, sr, x_axis=None)
        plt.title(n + ' - ' + 'Wave')

        i += 1

    plt.tight_layout(pad=10)
    plt.show() 
Example 26
Project: Sound-Recognition-Tutorial   Author: JasonZhang156   File: data_analysis.py    License: Apache License 2.0 5 votes vote down vote up
def plot_spectrum(sound_files, sound_names):
    """plot log power spectrum"""
    i = 1
    fig = plt.figure(figsize=(20, 64))
    for f, n in zip(sound_files, sound_names):
        y, sr = librosa.load(os.path.join('./data/esc10/audio/', f))
        plt.subplot(10, 1, i)
        D = librosa.logamplitude(np.abs(librosa.stft(y)) ** 2, ref_power=np.max)
        librosa.display.specshow(D, sr=sr, y_axis='log')
        plt.title(n + ' - ' + 'Spectrum')

        i += 1

    plt.tight_layout(pad=10)
    plt.show() 
Example 27
Project: neural-pipeline   Author: toodef   File: mpl.py    License: MIT License 5 votes vote down vote up
def _place_plots(self):
        number_of_subplots = len(self._plots)
        idx = 1
        for n, v in self._plots.items():
            v.place_plot(plt.subplot(number_of_subplots, 1, idx))
            idx += 1 
Example 28
Project: DOTA_models   Author: ringringyi   File: plot_lfads.py    License: Apache License 2.0 5 votes vote down vote up
def plot_priors():
  g0s_prior_mean_bxn = train_modelvals['prior_g0_mean']
  g0s_prior_var_bxn = train_modelvals['prior_g0_var']
  g0s_post_mean_bxn = train_modelvals['posterior_g0_mean']
  g0s_post_var_bxn = train_modelvals['posterior_g0_var']

  plt.figure(figsize=(10,4), tight_layout=True);
  plt.subplot(1,2,1)
  plt.hist(g0s_post_mean_bxn.flatten(), bins=20, color='b');
  plt.hist(g0s_prior_mean_bxn.flatten(), bins=20, color='g');

  plt.title('Histogram of Prior/Posterior Mean Values')
  plt.subplot(1,2,2)
  plt.hist((g0s_post_var_bxn.flatten()), bins=20, color='b');
  plt.hist((g0s_prior_var_bxn.flatten()), bins=20, color='g');
  plt.title('Histogram of Prior/Posterior Log Variance Values')

  plt.figure(figsize=(10,10), tight_layout=True)
  plt.subplot(2,2,1)
  plt.imshow(g0s_prior_mean_bxn.T, interpolation='nearest', cmap='jet')
  plt.colorbar(fraction=0.025, pad=0.04)
  plt.title('Prior g0 means')

  plt.subplot(2,2,2)
  plt.imshow(g0s_post_mean_bxn.T, interpolation='nearest', cmap='jet')
  plt.colorbar(fraction=0.025, pad=0.04)
  plt.title('Posterior g0 means');

  plt.subplot(2,2,3)
  plt.imshow(g0s_prior_var_bxn.T, interpolation='nearest', cmap='jet')
  plt.colorbar(fraction=0.025, pad=0.04)
  plt.title('Prior g0 variance Values')

  plt.subplot(2,2,4)
  plt.imshow(g0s_post_var_bxn.T, interpolation='nearest', cmap='jet')
  plt.colorbar(fraction=0.025, pad=0.04)
  plt.title('Posterior g0 variance Values')

  plt.figure(figsize=(10,5))
  plt.stem(np.sort(np.log(g0s_post_mean_bxn.std(axis=0))));
  plt.title('Log standard deviation of h0 means'); 
Example 29
Project: fast-MPN-COV   Author: jiangtaoxie   File: functions.py    License: MIT License 5 votes vote down vote up
def plot_curve(stats, path, iserr):
    trainObj = np.array(stats.trainObj)
    valObj = np.array(stats.valObj)
    if iserr:
        trainTop1 = 100 - np.array(stats.trainTop1)
        trainTop5 = 100 - np.array(stats.trainTop5)
        valTop1 = 100 - np.array(stats.valTop1)
        valTop5 = 100 - np.array(stats.valTop5)
        titleName = 'error'
    else:
        trainTop1 = np.array(stats.trainTop1)
        trainTop5 = np.array(stats.trainTop5)
        valTop1 = np.array(stats.valTop1)
        valTop5 = np.array(stats.valTop5)
        titleName = 'accuracy'
    epoch = len(trainObj)
    figure = plt.figure()
    obj = plt.subplot(1,3,1)
    obj.plot(range(1,epoch+1),trainObj,'o-',label = 'train')
    obj.plot(range(1,epoch+1),valObj,'o-',label = 'val')
    plt.xlabel('epoch')
    plt.title('objective')
    handles, labels = obj.get_legend_handles_labels()
    obj.legend(handles[::-1], labels[::-1])
    top1 = plt.subplot(1,3,2)
    top1.plot(range(1,epoch+1),trainTop1,'o-',label = 'train')
    top1.plot(range(1,epoch+1),valTop1,'o-',label = 'val')
    plt.title('top1'+titleName)
    plt.xlabel('epoch')
    handles, labels = top1.get_legend_handles_labels()
    top1.legend(handles[::-1], labels[::-1])
    top5 = plt.subplot(1,3,3)
    top5.plot(range(1,epoch+1),trainTop5,'o-',label = 'train')
    top5.plot(range(1,epoch+1),valTop5,'o-',label = 'val')
    plt.title('top5'+titleName)
    plt.xlabel('epoch')
    handles, labels = top5.get_legend_handles_labels()
    top5.legend(handles[::-1], labels[::-1])
    filename = os.path.join(path, 'net-train.pdf')
    figure.savefig(filename, bbox_inches='tight')
    plt.close() 
Example 30
Project: RingNet   Author: soubhiksanyal   File: demo.py    License: MIT License 5 votes vote down vote up
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()