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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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()