Python numpy.max() Examples
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Example #1
Source File: spectrum_painter.py From spectrum_painter with MIT License | 7 votes |
def convert_image(self, filename): pic = img.imread(filename) # Set FFT size to be double the image size so that the edge of the spectrum stays clear # preventing some bandfilter artifacts self.NFFT = 2*pic.shape[1] # Repeat image lines until each one comes often enough to reach the desired line time ffts = (np.flipud(np.repeat(pic[:, :, 0], self.repetitions, axis=0) / 16.)**2.) / 256. # Embed image in center bins of the FFT fftall = np.zeros((ffts.shape[0], self.NFFT)) startbin = int(self.NFFT/4) fftall[:, startbin:(startbin+pic.shape[1])] = ffts # Generate random phase vectors for the FFT bins, this is important to prevent high peaks in the output # The phases won't be visible in the spectrum phases = 2*np.pi*np.random.rand(*fftall.shape) rffts = fftall * np.exp(1j*phases) # Perform the FFT per image line, then concatenate them to form the final signal timedata = np.fft.ifft(np.fft.ifftshift(rffts, axes=1), axis=1) / np.sqrt(float(self.NFFT)) linear = timedata.flatten() linear = linear / np.max(np.abs(linear)) return linear
Example #2
Source File: core.py From neuropythy with GNU Affero General Public License v3.0 | 6 votes |
def cortex_cmap_plot_2D(the_map, zs, cmap, vmin=None, vmax=None, axes=None, triangulation=None): ''' cortex_cmap_plot_2D(map, zs, cmap, axes) plots the given cortical map values zs on the given axes using the given given color map and yields the resulting polygon collection object. cortex_cmap_plot_2D(map, zs, cmap) uses matplotlib.pyplot.gca() for the axes. The following options may be passed: * triangulation (None) may specify the triangularion object for the mesh if it has already been created; otherwise it is generated fresh. * axes (None) specify the axes on which to plot; if None, then matplotlib.pyplot.gca() is used. If Ellipsis, then a tuple (triangulation, z, cmap) is returned; to recreate the plot, one would call: axes.tripcolor(triangulation, z, cmap, shading='gouraud', vmin=vmin, vmax=vmax) * vmin (default: None) specifies the minimum value for scaling the property when one is passed as the color option. None means to use the min value of the property. * vmax (default: None) specifies the maximum value for scaling the property when one is passed as the color option. None means to use the max value of the property. ''' if triangulation is None: triangulation = matplotlib.tri.Triangulation(the_map.coordinates[0], the_map.coordinates[1], triangles=the_map.tess.indexed_faces.T) if axes is Ellipsis: return (triangulation, zs, cmap) return axes.tripcolor(triangulation, zs, cmap=cmap, shading='gouraud', vmin=vmin, vmax=vmax)
Example #3
Source File: gtf_utils.py From models with MIT License | 6 votes |
def get_exon_max_num(self): exonMax = 0 for _tran in self.trans: exonMax = max(exonMax, _tran.exonNum) return exonMax
Example #4
Source File: dataset_tool.py From disentangling_conditional_gans with MIT License | 6 votes |
def create_mnist(tfrecord_dir, mnist_dir): print('Loading MNIST from "%s"' % mnist_dir) import gzip with gzip.open(os.path.join(mnist_dir, 'train-images-idx3-ubyte.gz'), 'rb') as file: images = np.frombuffer(file.read(), np.uint8, offset=16) with gzip.open(os.path.join(mnist_dir, 'train-labels-idx1-ubyte.gz'), 'rb') as file: labels = np.frombuffer(file.read(), np.uint8, offset=8) images = images.reshape(-1, 1, 28, 28) images = np.pad(images, [(0,0), (0,0), (2,2), (2,2)], 'constant', constant_values=0) assert images.shape == (60000, 1, 32, 32) and images.dtype == np.uint8 assert labels.shape == (60000,) and labels.dtype == np.uint8 assert np.min(images) == 0 and np.max(images) == 255 assert np.min(labels) == 0 and np.max(labels) == 9 onehot = np.zeros((labels.size, np.max(labels) + 1), dtype=np.float32) onehot[np.arange(labels.size), labels] = 1.0 with TFRecordExporter(tfrecord_dir, images.shape[0]) as tfr: order = tfr.choose_shuffled_order() for idx in range(order.size): tfr.add_image(images[order[idx]]) tfr.add_labels(onehot[order]) #----------------------------------------------------------------------------
Example #5
Source File: dataset_tool.py From disentangling_conditional_gans with MIT License | 6 votes |
def create_mnistrgb(tfrecord_dir, mnist_dir, num_images=1000000, random_seed=123): print('Loading MNIST from "%s"' % mnist_dir) import gzip with gzip.open(os.path.join(mnist_dir, 'train-images-idx3-ubyte.gz'), 'rb') as file: images = np.frombuffer(file.read(), np.uint8, offset=16) images = images.reshape(-1, 28, 28) images = np.pad(images, [(0,0), (2,2), (2,2)], 'constant', constant_values=0) assert images.shape == (60000, 32, 32) and images.dtype == np.uint8 assert np.min(images) == 0 and np.max(images) == 255 with TFRecordExporter(tfrecord_dir, num_images) as tfr: rnd = np.random.RandomState(random_seed) for idx in range(num_images): tfr.add_image(images[rnd.randint(images.shape[0], size=3)]) #----------------------------------------------------------------------------
Example #6
Source File: dataset.py From Deep_VoiceChanger with MIT License | 6 votes |
def wave2input_image(wave, window, pos=0, pad=0): wave_image = np.hstack([wave[pos+i*sride:pos+(i+pad*2)*sride+dif].reshape(height+pad*2, sride) for i in range(256//sride)])[:,:254] wave_image *= window spectrum_image = np.fft.fft(wave_image, axis=1) input_image = np.abs(spectrum_image[:,:128].reshape(1, height+pad*2, 128), dtype=np.float32) np.clip(input_image, 1000, None, out=input_image) np.log(input_image, out=input_image) input_image += bias input_image /= scale if np.max(input_image) > 0.95: print('input image max bigger than 0.95', np.max(input_image)) if np.min(input_image) < 0.05: print('input image min smaller than 0.05', np.min(input_image)) return input_image
Example #7
Source File: dataset_tool.py From disentangling_conditional_gans with MIT License | 6 votes |
def create_cifar100(tfrecord_dir, cifar100_dir): print('Loading CIFAR-100 from "%s"' % cifar100_dir) import pickle with open(os.path.join(cifar100_dir, 'train'), 'rb') as file: data = pickle.load(file, encoding='latin1') images = data['data'].reshape(-1, 3, 32, 32) labels = np.array(data['fine_labels']) assert images.shape == (50000, 3, 32, 32) and images.dtype == np.uint8 assert labels.shape == (50000,) and labels.dtype == np.int32 assert np.min(images) == 0 and np.max(images) == 255 assert np.min(labels) == 0 and np.max(labels) == 99 onehot = np.zeros((labels.size, np.max(labels) + 1), dtype=np.float32) onehot[np.arange(labels.size), labels] = 1.0 with TFRecordExporter(tfrecord_dir, images.shape[0]) as tfr: order = tfr.choose_shuffled_order() for idx in range(order.size): tfr.add_image(images[order[idx]]) tfr.add_labels(onehot[order]) #----------------------------------------------------------------------------
Example #8
Source File: vertcoord.py From aospy with Apache License 2.0 | 6 votes |
def to_radians(arr, is_delta=False): """Force data with units either degrees or radians to be radians.""" # Infer the units from embedded metadata, if it's there. try: units = arr.units except AttributeError: pass else: if units.lower().startswith('degrees'): warn_msg = ("Conversion applied: degrees -> radians to array: " "{}".format(arr)) logging.debug(warn_msg) return np.deg2rad(arr) # Otherwise, assume degrees if the values are sufficiently large. threshold = 0.1*np.pi if is_delta else 4*np.pi if np.max(np.abs(arr)) > threshold: warn_msg = ("Conversion applied: degrees -> radians to array: " "{}".format(arr)) logging.debug(warn_msg) return np.deg2rad(arr) return arr
Example #9
Source File: layers.py From deep-learning-note with MIT License | 6 votes |
def forward(self, x): N, C, H, W = x.shape out_h = int(1 + (H - self.pool_h) / self.stride) out_w = int(1 + (W - self.pool_w) / self.stride) col = im2col(x, self.pool_h, self.pool_w, self.stride, self.pad) col = col.reshape(-1, self.pool_h * self.pool_w) arg_max = np.argmax(col, axis=1) out = np.max(col, axis=1) out = out.reshape(N, out_h, out_w, C).transpose(0, 3, 1, 2) self.x = x self.arg_max = arg_max return out
Example #10
Source File: feature_extraction.py From Sound-Recognition-Tutorial with Apache License 2.0 | 6 votes |
def extract_logmel(y, sr, size=3): """ extract log mel spectrogram feature :param y: the input signal (audio time series) :param sr: sample rate of 'y' :param size: the length (seconds) of random crop from original audio, default as 3 seconds :return: log-mel spectrogram feature """ # normalization y = y.astype(np.float32) normalization_factor = 1 / np.max(np.abs(y)) y = y * normalization_factor # random crop start = random.randint(0, len(y) - size * sr) y = y[start: start + size * sr] # extract log mel spectrogram ##### melspectrogram = librosa.feature.melspectrogram(y=y, sr=sr, n_fft=2048, hop_length=1024, n_mels=60) logmelspec = librosa.power_to_db(melspectrogram) return logmelspec
Example #11
Source File: feature_extraction.py From Sound-Recognition-Tutorial with Apache License 2.0 | 6 votes |
def extract_mfcc(y, sr, size=3): """ extract MFCC feature :param y: np.ndarray [shape=(n,)], real-valued the input signal (audio time series) :param sr: sample rate of 'y' :param size: the length (seconds) of random crop from original audio, default as 3 seconds :return: MFCC feature """ # normalization y = y.astype(np.float32) normalization_factor = 1 / np.max(np.abs(y)) y = y * normalization_factor # random crop start = random.randint(0, len(y) - size * sr) y = y[start: start + size * sr] # extract log mel spectrogram ##### melspectrogram = librosa.feature.melspectrogram(y=y, sr=sr, n_fft=2048, hop_length=1024) mfcc = librosa.feature.mfcc(S=librosa.power_to_db(melspectrogram), n_mfcc=20) mfcc_delta = librosa.feature.delta(mfcc) mfcc_delta_delta = librosa.feature.delta(mfcc_delta) mfcc_comb = np.concatenate([mfcc, mfcc_delta, mfcc_delta_delta], axis=0) return mfcc_comb
Example #12
Source File: utils.py From pytorch_NER_BiLSTM_CNN_CRF with Apache License 2.0 | 5 votes |
def torch_max(output): """ :param output: batch * seq_len * label_num :return: """ # print(output) batch_size = output.size(0) _, arg_max = torch.max(output, dim=2) # print(arg_max) label = [] for i in range(batch_size): label.append(arg_max[i].cpu().data.numpy()) return label
Example #13
Source File: utils.py From pytorch_NER_BiLSTM_CNN_CRF with Apache License 2.0 | 5 votes |
def getMaxindex_batch(model_out): """ :param model_out: model output :return: max index for predict """ model_out_list = model_out.data.tolist() maxIndex_batch = [] for l in model_out_list: maxIndex_batch.append(l.index(np.max(l))) return maxIndex_batch
Example #14
Source File: data_analysis.py From Sound-Recognition-Tutorial with 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 #15
Source File: utils.py From pytorch_NER_BiLSTM_CNN_CRF with Apache License 2.0 | 5 votes |
def getMaxindex_np(model_out): """ :param model_out: model output :return: max index for predict """ model_out_list = model_out.data.tolist() maxIndex = model_out_list.index(np.max(model_out_list)) return maxIndex
Example #16
Source File: monitoring.py From neural-pipeline with MIT License | 5 votes |
def update_losses(self, losses: {}) -> None: def on_loss(name: str, values: np.ndarray, string) -> None: string.append(" {}: [{:4f}, {:4f}, {:4f}];".format(name, np.min(values), np.mean(values), np.max(values))) res_string = self.ResStr("Epoch: [{}];".format(self.epoch_num)) self._iterate_by_losses(losses, lambda m, v: on_loss(m, v, res_string)) print(res_string)
Example #17
Source File: 6_nn_basis.py From deep-learning-note with MIT License | 5 votes |
def softmax(a): c = np.max(a) exp_a = np.exp(a - c) # 溢出对策 sum_exp_a = np.sum(exp_a) y = exp_a / sum_exp_a return y
Example #18
Source File: test_attacks.py From neural-fingerprinting with BSD 3-Clause "New" or "Revised" License | 5 votes |
def test_generate_np_gives_clipped_adversarial_examples(self): x_val = np.random.rand(100, 2) x_val = np.array(x_val, dtype=np.float32) x_adv = self.attack.generate_np(x_val, over_shoot=0.02, max_iter=50, nb_candidate=2, clip_min=-0.2, clip_max=0.3) self.assertTrue(-0.201 < np.min(x_adv)) self.assertTrue(np.max(x_adv) < .301)
Example #19
Source File: utils.py From pytorch_NER_BiLSTM_CNN_CRF with Apache License 2.0 | 5 votes |
def getMaxindex(model_out, label_size, args): """ :param model_out: model output :param label_size: label size :param args: argument :return: max index for predict """ max = model_out.data[0] maxIndex = 0 for idx in range(1, label_size): if model_out.data[idx] > max: max = model_out.data[idx] maxIndex = idx return maxIndex
Example #20
Source File: gtf_utils.py From models with MIT License | 5 votes |
def gene_ends_update(self): for t in self.trans: self.start = min(self.start, np.min(t.exons)) self.stop = max(self.stop, np.max(t.exons))
Example #21
Source File: gtf_utils.py From models with MIT License | 5 votes |
def get_exon_max_num(self): exonMax = 0 for _tran in self.trans: exonMax = max(exonMax, _tran.exonNum) return exonMax
Example #22
Source File: utils.py From nmp_qc with MIT License | 5 votes |
def collate_g(batch): batch_sizes = np.max(np.array([[len(input_b[1]), len(input_b[1][0]), len(input_b[2]), len(list(input_b[2].values())[0])] if input_b[2] else [len(input_b[1]), len(input_b[1][0]), 0,0] for (input_b, target_b) in batch]), axis=0) g = np.zeros((len(batch), batch_sizes[0], batch_sizes[0])) h = np.zeros((len(batch), batch_sizes[0], batch_sizes[1])) e = np.zeros((len(batch), batch_sizes[0], batch_sizes[0], batch_sizes[3])) target = np.zeros((len(batch), len(batch[0][1]))) for i in range(len(batch)): num_nodes = len(batch[i][0][1]) # Adjacency matrix g[i, 0:num_nodes, 0:num_nodes] = batch[i][0][0] # Node features h[i, 0:num_nodes, :] = batch[i][0][1] # Edges for edge in batch[i][0][2].keys(): e[i, edge[0], edge[1], :] = batch[i][0][2][edge] e[i, edge[1], edge[0], :] = batch[i][0][2][edge] # Target target[i, :] = batch[i][1] g = torch.FloatTensor(g) h = torch.FloatTensor(h) e = torch.FloatTensor(e) target = torch.FloatTensor(target) return g, h, e, target
Example #23
Source File: utils.py From nmp_qc with MIT License | 5 votes |
def accuracy(output, target, topk=(1,)): """Computes the precision@k for the specified values of k""" maxk = max(topk) batch_size = target.size(0) _, pred = output.topk(maxk, 1, True, True) pred = pred.t() pred = pred.type_as(target) target = target.type_as(pred) correct = pred.eq(target.view(1, -1).expand_as(pred)) res = [] for k in topk: correct_k = correct[:k].view(-1).float().sum(0) res.append(correct_k.mul_(100.0 / batch_size)) return res
Example #24
Source File: audio.py From Griffin_lim with MIT License | 5 votes |
def save_wav(wav, path): wav *= 32767 / max(0.01, np.max(np.abs(wav))) wavfile.write(path, hparams.sample_rate, wav.astype(np.int16))
Example #25
Source File: set_loader.py From neural-fingerprinting with BSD 3-Clause "New" or "Revised" License | 5 votes |
def load_testset(size): # Load images paths and labels pairs = lfw.read_pairs(pairs_path) paths, labels = lfw.get_paths(testset_path, pairs, file_extension) # Random choice permutation = np.random.choice(len(labels), size, replace=False) paths_batch_1 = [] paths_batch_2 = [] for index in permutation: paths_batch_1.append(paths[index * 2]) paths_batch_2.append(paths[index * 2 + 1]) labels = np.asarray(labels)[permutation] paths_batch_1 = np.asarray(paths_batch_1) paths_batch_2 = np.asarray(paths_batch_2) # Load images faces1 = facenet.load_data(paths_batch_1, False, False, image_size) faces2 = facenet.load_data(paths_batch_2, False, False, image_size) # Change pixel values to 0 to 1 values min_pixel = min(np.min(faces1), np.min(faces2)) max_pixel = max(np.max(faces1), np.max(faces2)) faces1 = (faces1 - min_pixel) / (max_pixel - min_pixel) faces2 = (faces2 - min_pixel) / (max_pixel - min_pixel) # Convert labels to one-hot vectors onehot_labels = [] for index in range(len(labels)): if labels[index]: onehot_labels.append([1, 0]) else: onehot_labels.append([0, 1]) return faces1, faces2, np.array(onehot_labels)
Example #26
Source File: work_data.py From neural-fingerprinting with BSD 3-Clause "New" or "Revised" License | 5 votes |
def compute_work_statistics(self): """Computes statistics from all work pieces stored in this class.""" result = {} for v in itervalues(self.work): submission_id = v['submission_id'] if submission_id not in result: result[submission_id] = { 'completed': 0, 'num_errors': 0, 'error_messages': set(), 'eval_times': [], 'min_eval_time': None, 'max_eval_time': None, 'mean_eval_time': None, 'median_eval_time': None, } if not v['is_completed']: continue result[submission_id]['completed'] += 1 if 'error' in v and v['error']: result[submission_id]['num_errors'] += 1 result[submission_id]['error_messages'].add(v['error']) else: result[submission_id]['eval_times'].append(float(v['elapsed_time'])) for v in itervalues(result): if v['eval_times']: v['min_eval_time'] = np.min(v['eval_times']) v['max_eval_time'] = np.max(v['eval_times']) v['mean_eval_time'] = np.mean(v['eval_times']) v['median_eval_time'] = np.median(v['eval_times']) return result
Example #27
Source File: test_attacks.py From neural-fingerprinting with BSD 3-Clause "New" or "Revised" License | 5 votes |
def test_generate_np_gives_clipped_adversarial_examples(self): x_val = np.random.rand(100, 2) x_val = np.array(x_val, dtype=np.float32) feed_labs = np.zeros((100, 2)) feed_labs[np.arange(100), np.random.randint(0, 1, 100)] = 1 x_adv = self.attack.generate_np(x_val, max_iterations=10, binary_search_steps=1, initial_const=1, clip_min=-0.2, clip_max=0.3, batch_size=100, y_target=feed_labs) self.assertTrue(-0.201 < np.min(x_adv)) self.assertTrue(np.max(x_adv) < .301)
Example #28
Source File: test_attacks.py From neural-fingerprinting with BSD 3-Clause "New" or "Revised" License | 5 votes |
def test_generate_np_gives_clipped_adversarial_examples(self): x_val = np.random.rand(100, 2) x_val = np.array(x_val, dtype=np.float32) x_adv = self.attack.generate_np(x_val, eps=1.0, eps_iter=0.1, nb_iter=5, clip_min=-0.2, clip_max=0.3) self.assertTrue(-0.201 < np.min(x_adv)) self.assertTrue(np.max(x_adv) < .301)
Example #29
Source File: test_attacks.py From neural-fingerprinting with BSD 3-Clause "New" or "Revised" License | 5 votes |
def test_clip_eta(self): x_val = np.random.rand(100, 2) x_val = np.array(x_val, dtype=np.float32) x_adv = self.attack.generate_np(x_val, eps=1.0, eps_iter=0.1, nb_iter=5) delta = np.max(np.abs(x_adv - x_val), axis=1) self.assertTrue(np.all(delta <= 1.))
Example #30
Source File: run_audio_attack.py From Black-Box-Audio with MIT License | 5 votes |
def get_new_pop(elite_pop, elite_pop_scores, pop_size): scores_logits = np.exp(elite_pop_scores - elite_pop_scores.max()) elite_pop_probs = scores_logits / scores_logits.sum() cand1 = elite_pop[np.random.choice(len(elite_pop), p=elite_pop_probs, size=pop_size)] cand2 = elite_pop[np.random.choice(len(elite_pop), p=elite_pop_probs, size=pop_size)] mask = np.random.rand(pop_size, elite_pop.shape[1]) < 0.5 next_pop = mask * cand1 + (1 - mask) * cand2 return next_pop