Python matplotlib.pyplot.title() Examples
The following are 30 code examples for showing how to use matplotlib.pyplot.title(). 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: EDeN Author: fabriziocosta File: __init__.py License: MIT License | 7 votes |
def plot_confusion_matrix(y_true, y_pred, size=None, normalize=False): """plot_confusion_matrix.""" cm = confusion_matrix(y_true, y_pred) fmt = "%d" if normalize: cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis] fmt = "%.2f" xticklabels = list(sorted(set(y_pred))) yticklabels = list(sorted(set(y_true))) if size is not None: plt.figure(figsize=(size, size)) heatmap(cm, xlabel='Predicted label', ylabel='True label', xticklabels=xticklabels, yticklabels=yticklabels, cmap=plt.cm.Blues, fmt=fmt) if normalize: plt.title("Confusion matrix (norm.)") else: plt.title("Confusion matrix") plt.gca().invert_yaxis()
Example 3
Project: transferlearning Author: jindongwang File: feature_vis.py License: MIT License | 7 votes |
def plot_tsne(self, save_eps=False): ''' Plot TSNE figure. Set save_eps=True if you want to save a .eps file. ''' tsne = TSNE(n_components=2, init='pca', random_state=0) features = tsne.fit_transform(self.features) x_min, x_max = np.min(features, 0), np.max(features, 0) data = (features - x_min) / (x_max - x_min) del features for i in range(data.shape[0]): plt.text(data[i, 0], data[i, 1], str(self.labels[i]), color=plt.cm.Set1(self.labels[i] / 10.), fontdict={'weight': 'bold', 'size': 9}) plt.xticks([]) plt.yticks([]) plt.title('T-SNE') if save_eps: plt.savefig('tsne.eps', dpi=600, format='eps') plt.show()
Example 4
Project: indras_net Author: gcallah File: display_methods.py License: GNU General Public License v3.0 | 6 votes |
def __init__(self, title, varieties, data_points, attrs, anim=False, data_func=None, is_headless=False): global anim_func plt.close() self.legend = ["Type"] self.title = title # self.anim = anim # self.data_func = data_func for i in varieties: data_points = len(varieties[i]["data"]) break self.headless = is_headless self.draw_graph(data_points, varieties, attrs) # if anim and not self.headless: # anim_func = animation.FuncAnimation(self.fig, # self.update_plot, # frames=1000, # interval=500, # blit=False)
Example 5
Project: indras_net Author: gcallah File: display_methods.py License: GNU General Public License v3.0 | 6 votes |
def __init__(self, title, varieties, data_points, anim=False, data_func=None, is_headless=False, legend_pos=4): global anim_func self.title = title self.anim = anim self.data_func = data_func for i in varieties: data_points = len(varieties[i]["data"]) break self.draw_graph(data_points, varieties) self.headless = is_headless if anim and not self.headless: anim_func = animation.FuncAnimation(self.fig, self.update_plot, frames=1000, interval=500, blit=False)
Example 6
Project: EDeN Author: fabriziocosta File: __init__.py License: MIT License | 6 votes |
def plot_precision_recall_curve(y_true, y_score, size=None): """plot_precision_recall_curve.""" precision, recall, thresholds = precision_recall_curve(y_true, y_score) if size is not None: plt.figure(figsize=(size, size)) plt.axis('equal') plt.plot(recall, precision, lw=2, color='navy') plt.xlabel('Recall') plt.ylabel('Precision') plt.ylim([-0.05, 1.05]) plt.xlim([-0.05, 1.05]) plt.grid() plt.title('Precision-Recall AUC={0:0.2f}'.format(average_precision_score( y_true, y_score)))
Example 7
Project: EDeN Author: fabriziocosta File: __init__.py License: MIT License | 6 votes |
def plot_roc_curve(y_true, y_score, size=None): """plot_roc_curve.""" false_positive_rate, true_positive_rate, thresholds = roc_curve( y_true, y_score) if size is not None: plt.figure(figsize=(size, size)) plt.axis('equal') plt.plot(false_positive_rate, true_positive_rate, lw=2, color='navy') plt.plot([0, 1], [0, 1], color='gray', lw=1, linestyle='--') plt.xlabel('False positive rate') plt.ylabel('True positive rate') plt.ylim([-0.05, 1.05]) plt.xlim([-0.05, 1.05]) plt.grid() plt.title('Receiver operating characteristic AUC={0:0.2f}'.format( roc_auc_score(y_true, y_score)))
Example 8
Project: fenics-topopt Author: zfergus File: stress_gui.py License: MIT License | 6 votes |
def update(self, xPhys, u, title=None): """Plot to screen""" self.im.set_array(-xPhys.reshape((self.nelx, self.nely)).T) stress = self.stress_calculator.calculate_stress(xPhys, u, self.nu) # self.stress_calculator.calculate_fdiff_stress(xPhys, u, self.nu) self.myColorMap.set_norm(colors.Normalize(vmin=0, vmax=max(stress))) stress_rgba = self.myColorMap.to_rgba(stress) stress_rgba[:, :, 3] = xPhys.reshape(-1, 1) self.stress_im.set_array(np.swapaxes( stress_rgba.reshape((self.nelx, self.nely, 4)), 0, 1)) self.fig.canvas.draw() self.fig.canvas.flush_events() if title is not None: plt.title(title) else: plt.xlabel("Max stress = {:.2f}".format(max(stress)[0])) plt.pause(0.01)
Example 9
Project: dc_tts Author: Kyubyong File: utils.py License: Apache License 2.0 | 6 votes |
def plot_alignment(alignment, gs, dir=hp.logdir): """Plots the alignment. Args: alignment: A numpy array with shape of (encoder_steps, decoder_steps) gs: (int) global step. dir: Output path. """ if not os.path.exists(dir): os.mkdir(dir) fig, ax = plt.subplots() im = ax.imshow(alignment) fig.colorbar(im) plt.title('{} Steps'.format(gs)) plt.savefig('{}/alignment_{}.png'.format(dir, gs), format='png') plt.close(fig)
Example 10
Project: neural-fingerprinting Author: StephanZheng File: util.py License: BSD 3-Clause "New" or "Revised" License | 6 votes |
def compute_roc(y_true, y_pred, plot=False): """ TODO :param y_true: ground truth :param y_pred: predictions :param plot: :return: """ fpr, tpr, _ = roc_curve(y_true, y_pred) auc_score = auc(fpr, tpr) if plot: plt.figure(figsize=(7, 6)) plt.plot(fpr, tpr, color='blue', label='ROC (AUC = %0.4f)' % auc_score) plt.legend(loc='lower right') plt.title("ROC Curve") plt.xlabel("FPR") plt.ylabel("TPR") plt.show() return fpr, tpr, auc_score
Example 11
Project: neural-fingerprinting Author: StephanZheng File: util.py License: BSD 3-Clause "New" or "Revised" License | 6 votes |
def compute_roc_rfeinman(probs_neg, probs_pos, plot=False): """ TODO :param probs_neg: :param probs_pos: :param plot: :return: """ probs = np.concatenate((probs_neg, probs_pos)) labels = np.concatenate((np.zeros_like(probs_neg), np.ones_like(probs_pos))) fpr, tpr, _ = roc_curve(labels, probs) auc_score = auc(fpr, tpr) if plot: plt.figure(figsize=(7, 6)) plt.plot(fpr, tpr, color='blue', label='ROC (AUC = %0.4f)' % auc_score) plt.legend(loc='lower right') plt.title("ROC Curve") plt.xlabel("FPR") plt.ylabel("TPR") plt.show() return fpr, tpr, auc_score
Example 12
Project: neural-combinatorial-optimization-rl-tensorflow Author: MichelDeudon File: dataset.py License: MIT License | 6 votes |
def visualize_sampling(self,permutations): max_length = len(permutations[0]) grid = np.zeros([max_length,max_length]) # initialize heatmap grid to 0 transposed_permutations = np.transpose(permutations) for t, cities_t in enumerate(transposed_permutations): # step t, cities chosen at step t city_indices, counts = np.unique(cities_t,return_counts=True,axis=0) for u,v in zip(city_indices, counts): grid[t][u]+=v # update grid with counts from the batch of permutations # plot heatmap fig = plt.figure() rcParams.update({'font.size': 22}) ax = fig.add_subplot(1,1,1) ax.set_aspect('equal') plt.imshow(grid, interpolation='nearest', cmap='gray') plt.colorbar() plt.title('Sampled permutations') plt.ylabel('Time t') plt.xlabel('City i') plt.show() # Heatmap of attention (x=cities; y=steps)
Example 13
Project: neural-combinatorial-optimization-rl-tensorflow Author: MichelDeudon File: dataset.py License: MIT License | 6 votes |
def visualize_sampling(self, permutations): max_length = len(permutations[0]) grid = np.zeros([max_length,max_length]) # initialize heatmap grid to 0 transposed_permutations = np.transpose(permutations) for t, cities_t in enumerate(transposed_permutations): # step t, cities chosen at step t city_indices, counts = np.unique(cities_t,return_counts=True,axis=0) for u,v in zip(city_indices, counts): grid[t][u]+=v # update grid with counts from the batch of permutations # plot heatmap fig = plt.figure() rcParams.update({'font.size': 22}) ax = fig.add_subplot(1,1,1) ax.set_aspect('equal') plt.imshow(grid, interpolation='nearest', cmap='gray') plt.colorbar() plt.title('Sampled permutations') plt.ylabel('Time t') plt.xlabel('City i') plt.show()
Example 14
Project: fullrmc Author: bachiraoun File: plotFigures.py License: GNU Affero General Public License v3.0 | 6 votes |
def plot(PDF, figName, imgpath, show=False, save=True): # plot output = PDF.get_constraint_value() plt.plot(PDF.experimentalDistances,PDF.experimentalPDF, 'ro', label="experimental", markersize=7.5, markevery=1 ) plt.plot(PDF.shellsCenter, output["pdf"], 'k', linewidth=3.0, markevery=25, label="total" ) styleIndex = 0 for key in output: val = output[key] if key in ("pdf_total", "pdf"): continue elif "inter" in key: plt.plot(PDF.shellsCenter, val, STYLE[styleIndex], markevery=5, label=key.split('rdf_inter_')[1] ) styleIndex+=1 plt.legend(frameon=False, ncol=1) # set labels plt.title("$\\chi^{2}=%.6f$"%PDF.squaredDeviations, size=20) plt.xlabel("$r (\AA)$", size=20) plt.ylabel("$g(r)$", size=20) # show plot if save: plt.savefig(figName) if show: plt.show() plt.close()
Example 15
Project: keras-anomaly-detection Author: chen0040 File: plot_utils.py License: MIT License | 6 votes |
def visualize_anomaly(y_true, reconstruction_error, threshold): error_df = pd.DataFrame({'reconstruction_error': reconstruction_error, 'true_class': y_true}) print(error_df.describe()) groups = error_df.groupby('true_class') fig, ax = plt.subplots() for name, group in groups: ax.plot(group.index, group.reconstruction_error, marker='o', ms=3.5, linestyle='', label="Fraud" if name == 1 else "Normal") ax.hlines(threshold, ax.get_xlim()[0], ax.get_xlim()[1], colors="r", zorder=100, label='Threshold') ax.legend() plt.title("Reconstruction error for different classes") plt.ylabel("Reconstruction error") plt.xlabel("Data point index") plt.show()
Example 16
Project: DOTA_models Author: ringringyi File: plot_lfads.py License: Apache License 2.0 | 6 votes |
def plot_time_series(vals_bxtxn, bidx=None, n_to_plot=np.inf, scale=1.0, color='r', title=None): if bidx is None: vals_txn = np.mean(vals_bxtxn, axis=0) else: vals_txn = vals_bxtxn[bidx,:,:] T, N = vals_txn.shape if n_to_plot > N: n_to_plot = N plt.plot(vals_txn[:,0:n_to_plot] + scale*np.array(range(n_to_plot)), color=color, lw=1.0) plt.axis('tight') if title: plt.title(title)
Example 17
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 18
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 19
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 20
Project: trees Author: gdanezis File: malware.py License: Apache License 2.0 | 6 votes |
def classify(self, features, show=False): recs, _ = features.shape result_shape = (features.shape[0], len(self.root)) scores = np.zeros(result_shape) print scores.shape R = Record(np.arange(recs, dtype=int), features) for i, T in enumerate(self.root): for idxs, result in classify(T, R): for idx in idxs.indexes(): scores[idx, i] = float(result[0]) / sum(result.values()) if show: plt.cla() plt.clf() plt.close() plt.imshow(scores, cmap=plt.cm.gray) plt.title('Scores matrix') plt.savefig(r"../scratch/tree_scores.png", bbox_inches='tight') return scores
Example 21
Project: kss Author: Kyubyong File: utils.py License: Apache License 2.0 | 6 votes |
def plot_alignment(alignment, gs, dir=hp.logdir): """Plots the alignment. Args: alignment: A numpy array with shape of (encoder_steps, decoder_steps) gs: (int) global step. dir: Output path. """ if not os.path.exists(dir): os.mkdir(dir) fig, ax = plt.subplots() im = ax.imshow(alignment) fig.colorbar(im) plt.title('{} Steps'.format(gs)) plt.savefig('{}/alignment_{}.png'.format(dir, gs), format='png')
Example 22
Project: indras_net Author: gcallah File: display_methods.py License: GNU General Public License v3.0 | 5 votes |
def draw_graph(graph, title, hierarchy=False, root=None): """ Drawing networkx graphs. graph is the graph to draw. hierarchy is whether we should draw it as a tree. """ # pos = None plt.title(title) # if hierarchy: # pos = hierarchy_pos(graph, root) # out for now: # nx.draw(graph, pos=pos, with_labels=True) plt.show()
Example 23
Project: indras_net Author: gcallah File: display_methods.py License: GNU General Public License v3.0 | 5 votes |
def draw_graph(graph, title, hierarchy=False, root=None): """ Drawing networkx graphs. graph is the graph to draw. hierarchy is whether we should draw it as a tree. """ pos = None plt.title(title) if hierarchy: pos = hierarchy_pos(graph, root) nx.draw(graph, pos=pos, with_labels=True) plt.show()
Example 24
Project: indras_net Author: gcallah File: display_methods.py License: GNU General Public License v3.0 | 5 votes |
def __init__(self, title, varieties, width, height, anim=True, data_func=None, is_headless=False, legend_pos=4): """ Setup a scatter plot. varieties contains the different types of entities to show in the plot, which will get assigned different colors """ global anim_func self.scats = None self.anim = anim self.data_func = data_func self.s = ceil(4096 / width) self.headless = is_headless fig, ax = plt.subplots() ax.set_xlim(0, width) ax.set_ylim(0, height) self.create_scats(varieties) ax.legend(loc = legend_pos) ax.set_title(title) plt.grid(True) if anim and not self.headless: anim_func = animation.FuncAnimation(fig, self.update_plot, frames=1000, interval=500, blit=False)
Example 25
Project: vergeml Author: mme File: pr.py License: MIT License | 5 votes |
def __call__(self, args, env): import numpy as np import matplotlib.pyplot as plt from sklearn.metrics import average_precision_score from sklearn.metrics import precision_recall_curve from vergeml.plots import load_labels, load_predictions try: labels = load_labels(env) except FileNotFoundError: raise VergeMLError("Can't plot PR curve - not supported by model.") nclasses = len(labels) if args['class'] not in labels: raise VergeMLError("Unknown class: " + args['class']) try: y_test, y_score = load_predictions(env, nclasses) except FileNotFoundError: raise VergeMLError("Can't plot PR curve - not supported by model.") # From: # https://scikit-learn.org/stable/auto_examples/model_selection/plot_precision_recall.html#sphx-glr-auto-examples-model-selection-plot-precision-recall-py ix = labels.index(args['class']) y_test = y_test[:,ix].astype(np.int) y_score = y_score[:,ix] precision, recall, _ = precision_recall_curve(y_test, y_score) average_precision = average_precision_score(y_test, y_score) plt.step(recall, precision, color='b', alpha=0.2, where='post') plt.fill_between(recall, precision, alpha=0.2, color='b', step='post') plt.xlabel('Recall ({})'.format(args['class'])) plt.ylabel('Precision ({})'.format(args['class'])) plt.ylim([0.0, 1.05]) plt.xlim([0.0, 1.0]) plt.title('Precision-Recall curve for @{0}: AP={1:0.2f}'.format(args['@AI'], average_precision)) plt.show()
Example 26
Project: EDeN Author: fabriziocosta File: link_prediction_utils.py License: MIT License | 5 votes |
def show_graph(g, vertex_color='typeof', size=15, vertex_label=None): """show_graph.""" degrees = [len(g.neighbors(u)) for u in g.nodes()] print(('num nodes=%d' % len(g))) print(('num edges=%d' % len(g.edges()))) print(('num non edges=%d' % len(list(nx.non_edges(g))))) print(('max degree=%d' % max(degrees))) print(('median degree=%d' % np.percentile(degrees, 50))) draw_graph(g, size=size, vertex_color=vertex_color, vertex_label=vertex_label, vertex_size=200, edge_label=None) # display degree distribution size = int((max(degrees) - min(degrees)) / 1.5) plt.figure(figsize=(size, 3)) plt.title('Degree distribution') _bins = np.arange(min(degrees), max(degrees) + 2) - .5 n, bins, patches = plt.hist(degrees, _bins, alpha=0.3, facecolor='navy', histtype='bar', rwidth=0.8, edgecolor='k') labels = np.array([str(int(i)) for i in n]) for xi, yi, label in zip(bins, n, labels): plt.text(xi + 0.5, yi, label, ha='center', va='bottom') plt.xticks(bins + 0.5) plt.xlim((min(degrees) - 1, max(degrees) + 1)) plt.ylim((0, max(n) * 1.1)) plt.xlabel('Node degree') plt.ylabel('Counts') plt.grid(linestyle=":") plt.show()
Example 27
Project: fenics-topopt Author: zfergus File: stress_gui.py License: MIT License | 5 votes |
def __init__(self, nelx, nely, stress_calculator, nu, title=""): """Initialize plot and plot the initial design""" super(StressGUI, self).__init__(nelx, nely, title) self.stress_im = self.ax.imshow( np.swapaxes(np.zeros((nelx, nely, 4)), 0, 1), norm=colors.Normalize(vmin=0, vmax=1), cmap='jet') self.fig.colorbar(self.stress_im) self.stress_calculator = stress_calculator self.nu = nu self.myColorMap = colormaps.ScalarMappable( norm=colors.Normalize(vmin=0, vmax=1), cmap=colormaps.jet)
Example 28
Project: fenics-topopt Author: zfergus File: gui.py License: MIT License | 5 votes |
def __init__(self, nelx, nely, title=""): """Initialize plot and plot the initial design""" plt.ion() # Ensure that redrawing is possible self.fig, self.ax = plt.subplots() self.im = self.ax.imshow(-np.zeros((nelx, nely)).T, cmap='gray', interpolation='none', norm=colors.Normalize(vmin=-1, vmax=0)) plt.xlabel(title) # self.fig.tight_layout() self.fig.show() self.nelx, self.nely = nelx, nely
Example 29
Project: fenics-topopt Author: zfergus File: gui.py License: MIT License | 5 votes |
def update(self, xPhys, title=None): """Plot to screen""" self.im.set_array(-xPhys.reshape((self.nelx, self.nely)).T) self.fig.canvas.draw() self.fig.canvas.flush_events() if title is not None: plt.title(title) plt.pause(0.01)
Example 30
Project: fenics-topopt Author: zfergus File: stress_gui.py License: MIT License | 5 votes |
def __init__(self, nelx, nely, stress_calculator, nu, title=""): """Initialize plot and plot the initial design""" super(StressGUI, self).__init__(nelx, nely, title) self.stress_im = self.ax.imshow( np.swapaxes(np.zeros((nelx, nely, 4)), 0, 1), norm=colors.Normalize(vmin=0, vmax=1), cmap='jet') self.fig.colorbar(self.stress_im) self.stress_calculator = stress_calculator self.nu = nu self.myColorMap = colormaps.ScalarMappable( norm=colors.Normalize(vmin=0, vmax=1), cmap=colormaps.jet)