Python numpy.savez_compressed() Examples

The following are 30 code examples for showing how to use numpy.savez_compressed(). 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: Adversarial_Video_Generation   Author: dyelax   File: process_data.py    License: MIT License 6 votes vote down vote up
def process_training_data(num_clips):
    """
    Processes random training clips from the full training data. Saves to TRAIN_DIR_CLIPS by
    default.

    @param num_clips: The number of clips to process. Default = 5000000 (set in __main__).

    @warning: This can take a couple of hours to complete with large numbers of clips.
    """
    num_prev_clips = len(glob(c.TRAIN_DIR_CLIPS + '*'))

    for clip_num in xrange(num_prev_clips, num_clips + num_prev_clips):
        clip = process_clip()

        np.savez_compressed(c.TRAIN_DIR_CLIPS + str(clip_num), clip)

        if (clip_num + 1) % 100 == 0: print 'Processed %d clips' % (clip_num + 1) 
Example 2
Project: yatsm   Author: ceholden   File: cache.py    License: MIT License 6 votes vote down vote up
def write_cache_file(cache_filename, Y, image_IDs):
    """ Writes data to a cache file using np.savez_compressed

    Args:
        cache_filename (str): cache filename
        Y (np.ndarray): data to write to cache file
        image_IDs (iterable): list of image IDs corresponding to data in cache
            file. If not specified, function will not check for correspondence

    """
    np.savez_compressed(cache_filename, **{
        'Y': Y, _image_ID_str: image_IDs
    })


# Cache file updating 
Example 3
Project: ffn   Author: google   File: storage.py    License: Apache License 2.0 6 votes vote down vote up
def save_subvolume(labels, origins, output_path, **misc_items):
  """Saves an FFN subvolume.

  Args:
    labels: 3d zyx number array with the segment labels
    origins: dictionary mapping segment ID to origin information
    output_path: path at which to save the segmentation in the form
        of a .npz file
    **misc_items: (optional) additional values to save
        in the output file
  """
  seg = segmentation.reduce_id_bits(labels)
  gfile.MakeDirs(os.path.dirname(output_path))
  with atomic_file(output_path) as fd:
    np.savez_compressed(fd,
                        segmentation=seg,
                        origins=origins,
                        **misc_items) 
Example 4
Project: ffn   Author: google   File: inference.py    License: Apache License 2.0 6 votes vote down vote up
def save_checkpoint(self, path):
    """Saves a inference checkpoint to `path`."""
    self.log_info('Saving inference checkpoint to %s.', path)
    with timer_counter(self.counters, 'save_checkpoint'):
      gfile.MakeDirs(os.path.dirname(path))
      with storage.atomic_file(path) as fd:
        seed_policy_state = None
        if self.seed_policy is not None:
          seed_policy_state = self.seed_policy.get_state()

        np.savez_compressed(fd,
                            movement_policy=self.movement_policy.get_state(),
                            segmentation=self.segmentation,
                            seg_qprob=self.seg_prob,
                            seed=self.seed,
                            origins=self.origins,
                            overlaps=self.overlaps,
                            history=np.array(self.history),
                            history_deleted=np.array(self.history_deleted),
                            seed_policy_state=seed_policy_state,
                            counters=self.counters.dumps())
    self.log_info('Inference checkpoint saved.') 
Example 5
Project: MeshCNN   Author: ranahanocka   File: mesh_prepare.py    License: MIT License 6 votes vote down vote up
def fill_mesh(mesh2fill, file: str, opt):
    load_path = get_mesh_path(file, opt.num_aug)
    if os.path.exists(load_path):
        mesh_data = np.load(load_path, encoding='latin1', allow_pickle=True)
    else:
        mesh_data = from_scratch(file, opt)
        np.savez_compressed(load_path, gemm_edges=mesh_data.gemm_edges, vs=mesh_data.vs, edges=mesh_data.edges,
                            edges_count=mesh_data.edges_count, ve=mesh_data.ve, v_mask=mesh_data.v_mask,
                            filename=mesh_data.filename, sides=mesh_data.sides,
                            edge_lengths=mesh_data.edge_lengths, edge_areas=mesh_data.edge_areas,
                            features=mesh_data.features)
    mesh2fill.vs = mesh_data['vs']
    mesh2fill.edges = mesh_data['edges']
    mesh2fill.gemm_edges = mesh_data['gemm_edges']
    mesh2fill.edges_count = int(mesh_data['edges_count'])
    mesh2fill.ve = mesh_data['ve']
    mesh2fill.v_mask = mesh_data['v_mask']
    mesh2fill.filename = str(mesh_data['filename'])
    mesh2fill.edge_lengths = mesh_data['edge_lengths']
    mesh2fill.edge_areas = mesh_data['edge_areas']
    mesh2fill.features = mesh_data['features']
    mesh2fill.sides = mesh_data['sides'] 
Example 6
Project: safelife   Author: PartnershipOnAI   File: level_iterator.py    License: Apache License 2.0 6 votes vote down vote up
def combine_levels(directory):
    """
    Merge all files in a single directory.
    """
    files = sorted(glob.glob(os.path.join(directory, '*.npz')))
    all_data = []
    max_name_len = 0
    for file in files:
        with np.load(file) as data:
            name = os.path.split(file)[1]
            max_name_len = max(max_name_len, len(name))
            all_data.append(data.items() + [('name', name)])
    dtype = []
    for key, val in all_data[0][:-1]:
        dtype.append((key, val.dtype, val.shape))
    dtype.append(('name', str, max_name_len))
    combo_data = np.array([
        tuple([val for key, val in data]) for data in all_data
    ], dtype=dtype)
    np.savez_compressed(directory + '.npz', levels=combo_data) 
Example 7
Project: ocelot   Author: ocelot-collab   File: section_track.py    License: GNU General Public License v3.0 6 votes vote down vote up
def save_twiss_file(self, twiss_list):
        if self.tws_file is None:
            tws_file_name = self.output_beam_file.replace("particles", "tws")
        else:
            tws_file_name = self.tws_file

        self.folder_check_create(tws_file_name)

        bx = np.array([tw.beta_x for tw in twiss_list])
        by = np.array([tw.beta_y for tw in twiss_list])
        ax = np.array([tw.alpha_x for tw in twiss_list])
        ay = np.array([tw.alpha_x for tw in twiss_list])
        s = np.array([tw.s for tw in twiss_list])
        E = np.array([tw.E for tw in twiss_list])

        emit_x = np.array([tw.emit_x for tw in twiss_list])
        emit_y = np.array([tw.emit_y for tw in twiss_list])

        np.savez_compressed(tws_file_name, beta_x=bx, beta_y=by, alpha_x=ax, alpha_y=ay, E=E, s=s,
                            emit_x=emit_x, emit_y=emit_y) 
Example 8
Project: imitation   Author: HumanCompatibleAI   File: convert_traj.py    License: MIT License 6 votes vote down vote up
def main():
    parser = argparse.ArgumentParser()
    parser.add_argument("src_path", type=str)
    parser.add_argument("dst_path", type=str)
    args = parser.parse_args()

    src_path = Path(args.src_path)
    dst_path = Path(args.dst_path)

    assert src_path.is_file()
    src_trajs = types.load(str(src_path))
    dst_trajs = convert_trajs_to_sb(src_trajs)
    os.makedirs(dst_path.parent, exist_ok=True)
    with open(dst_path, "wb") as f:
        np.savez_compressed(f, **dst_trajs)

    print(f"Dumped rollouts to {dst_path}") 
Example 9
Project: PyTorch-Elmo-BiLSTMCRF   Author: yongyuwen   File: data_utils.py    License: MIT License 6 votes vote down vote up
def export_trimmed_glove_vectors(vocab, glove_filename, trimmed_filename, dim):
    """Saves glove vectors in numpy array

    Args:
        vocab: dictionary vocab[word] = index
        glove_filename: a path to a glove file
        trimmed_filename: a path where to store a matrix in npy
        dim: (int) dimension of embeddings

    """
    embeddings = np.zeros([len(vocab), dim])
    with open(glove_filename, encoding="utf8") as f:
        for line in f:
            line = line.strip().split(' ')
            word = line[0]
            embedding = [float(x) for x in line[1:]]
            if word in vocab:
                word_idx = vocab[word]
                embeddings[word_idx] = np.asarray(embedding)

    np.savez_compressed(trimmed_filename, embeddings=embeddings) 
Example 10
Project: EEND   Author: hitachi-speech   File: model_averaging.py    License: MIT License 6 votes vote down vote up
def average_model_chainer(ifiles, ofile):
    omodel = {}
    # get keys from the first file
    model = np.load(ifiles[0])
    for x in model:
        if 'model' in x:
            print(x)
    keys = [x.split('main/')[1] for x in model if 'model' in x]
    print(keys)
    for path in ifiles:
        model = np.load(path)
        for key in keys:
            val = model['updater/model:main/{}'.format(key)]
            if key not in omodel:
                omodel[key] = val
            else:
                omodel[key] += val
    for key in keys:
        omodel[key] /= len(ifiles)
    np.savez_compressed(ofile, **omodel) 
Example 11
Project: ICML2019-TREX   Author: hiwonjoon   File: fetch_data_generation.py    License: MIT License 6 votes vote down vote up
def main():
    env = gym.make('FetchPickAndPlace-v0')
    numItr = 100
    initStateSpace = "random"
    env.reset()
    print("Reset!")
    while len(actions) < numItr:
        obs = env.reset()
        print("ITERATION NUMBER ", len(actions))
        goToGoal(env, obs)
        

    fileName = "data_fetch"
    fileName += "_" + initStateSpace
    fileName += "_" + str(numItr)
    fileName += ".npz"
    
    np.savez_compressed(fileName, acs=actions, obs=observations, info=infos) # save the file 
Example 12
Project: ICML2019-TREX   Author: hiwonjoon   File: fetch_data_generation.py    License: MIT License 6 votes vote down vote up
def main():
    env = gym.make('FetchPickAndPlace-v0')
    numItr = 100
    initStateSpace = "random"
    env.reset()
    print("Reset!")
    while len(actions) < numItr:
        obs = env.reset()
        print("ITERATION NUMBER ", len(actions))
        goToGoal(env, obs)
        

    fileName = "data_fetch"
    fileName += "_" + initStateSpace
    fileName += "_" + str(numItr)
    fileName += ".npz"
    
    np.savez_compressed(fileName, acs=actions, obs=observations, info=infos) # save the file 
Example 13
Project: kits19.MIScnn   Author: muellerdo   File: data_io.py    License: GNU General Public License v3.0 6 votes vote down vote up
def backup_batches(batches_vol, batches_seg, path, case_id):
    # Create model directory of not existent
    if not os.path.exists(path):
        os.mkdir(path)
    # Create subdirectory for the case if not existent
    case_dir = os.path.join(path, "tmp.case_" + str(case_id).zfill(5))
    if not os.path.exists(case_dir):
        os.mkdir(case_dir)
    # Backup volume batches
    if batches_vol is not None:
        for i, batch in enumerate(batches_vol):
            out_path = os.path.join(case_dir,
                                    "batch_vol." + str(i))
            np.savez(out_path, data=batch)

    # Backup segmentation batches
    if batches_seg is not None:
        for i, batch in enumerate(batches_seg):
            out_path = os.path.join(case_dir,
                                    "batch_seg." + str(i))
            np.savez_compressed(out_path, data=batch)

# Load a MRI object from a npz for fast access 
Example 14
Project: panns   Author: ryanrhymes   File: test_mmap.py    License: GNU General Public License v2.0 6 votes vote down vote up
def generate_large_matrix():
    rows, cols = 1000000, 500
    print 'Test serializing a %i x %i matrix ...' % (rows, cols)
    t = time.time()
    vecs = numpy.random.normal(0,1,(rows,cols))
    print 'Matrix constructed, spent %.2f s' % (time.time() - t)

    f1 = open('test_data1', 'wb')
    t = time.time()
    print 'saving as numpy npz format ...'
    numpy.savez_compressed(f1, vecs)
    print 'save done, spent %.2f s' % (time.time() - t)
    f1.close()

    f2 = open('test_data2', 'wb')
    t = time.time()
    print 'saving as self-defined format ...'
    for v in vecs:
        f2.write(pickle.dumps(v, -1))
    f2.close()
    print 'save done, spent %.2f s' % (time.time() - t)
    pass 
Example 15
Project: robotreviewer   Author: ijmarshall   File: ner_data_utils.py    License: GNU General Public License v3.0 6 votes vote down vote up
def export_trimmed_glove_vectors(vocab, glove_filename, trimmed_filename, dim):
    """Saves glove vectors in numpy array

    Args:
        vocab: dictionary vocab[word] = index
        glove_filename: a path to a glove file
        trimmed_filename: a path where to store a matrix in npy
        dim: (int) dimension of embeddings

    """
    embeddings = np.zeros([len(vocab), dim])
    with open(glove_filename) as f:
        for line in f:
            line = line.strip().split(' ')
            word = line[0]
            embedding = [float(x) for x in line[1:]]
            if word in vocab:
                word_idx = vocab[word]
                embeddings[word_idx] = np.asarray(embedding)

    np.savez_compressed(trimmed_filename, embeddings=embeddings) 
Example 16
Project: petastorm   Author: uber   File: codecs.py    License: Apache License 2.0 6 votes vote down vote up
def encode(self, unischema_field, value):
        expected_dtype = unischema_field.numpy_dtype
        if isinstance(value, np.ndarray):
            if expected_dtype != value.dtype.type:
                raise ValueError('Unexpected type of {} feature. '
                                 'Expected {}. Got {}'.format(unischema_field.name, expected_dtype, value.dtype))

            expected_shape = unischema_field.shape
            if not _is_compliant_shape(value.shape, expected_shape):
                raise ValueError('Unexpected dimensions of {} feature. '
                                 'Expected {}. Got {}'.format(unischema_field.name, expected_shape, value.shape))
        else:
            raise ValueError('Unexpected type of {} feature. '
                             'Expected ndarray of {}. Got {}'.format(unischema_field.name, expected_dtype, type(value)))

        memfile = BytesIO()
        np.savez_compressed(memfile, arr=value)
        return bytearray(memfile.getvalue()) 
Example 17
Project: chainer   Author: chainer   File: test_npz.py    License: MIT License 6 votes vote down vote up
def setUp(self):
        self.data = numpy.random.uniform(-1, 1, (2, 3)).astype(numpy.float32)

        fd, path = tempfile.mkstemp()
        os.close(fd)
        self.temp_file_path = path
        with open(path, 'wb') as f:
            savez = numpy.savez_compressed if self.compress else numpy.savez
            savez(
                f, **{'x/': None, 'y': self.data, 'z': numpy.asarray(10),
                      'zf32': numpy.array(-2**60, dtype=numpy.float32),
                      'zi64': numpy.array(-2**60, dtype=numpy.int64),
                      'w': None})

        try:
            self.npzfile = numpy.load(path, allow_pickle=True)
        except TypeError:
            self.npzfile = numpy.load(path)
        self.deserializer = npz.NpzDeserializer(self.npzfile) 
Example 18
Project: chainer   Author: chainer   File: _mnist_helper.py    License: MIT License 6 votes vote down vote up
def make_npz(path, urls):
    x_url, y_url = urls
    x_path = download.cached_download(x_url)
    y_path = download.cached_download(y_url)

    with gzip.open(x_path, 'rb') as fx, gzip.open(y_path, 'rb') as fy:
        fx.read(4)
        fy.read(4)
        N, = struct.unpack('>i', fx.read(4))
        if N != struct.unpack('>i', fy.read(4))[0]:
            raise RuntimeError('wrong pair of MNIST images and labels')
        fx.read(8)

        x = numpy.empty((N, 784), dtype=numpy.uint8)
        y = numpy.empty(N, dtype=numpy.uint8)

        for i in six.moves.range(N):
            y[i] = ord(fy.read(1))
            for j in six.moves.range(784):
                x[i, j] = ord(fx.read(1))

    numpy.savez_compressed(path, x=x, y=y)
    return {'x': x, 'y': y} 
Example 19
Project: PJ_NLP   Author: moxiu2012   File: sample_data.py    License: Apache License 2.0 6 votes vote down vote up
def extract_data_and_split(train_set, data_label_id, label2id):
    """1、处理数据和标签, 抽取title_word和content_word,将label和data对应起来 保存到文件中
        2、划分数据集"""
    datas = []
    for line in train_set:
        data_id, _, title_word, _, content_word = line.replace('\n', '').split('\t')
        labels = ','.join([str(label) for label in data_label_id[data_id]])
        info = '{}\t{}\t{}'.format(labels, title_word, content_word)
        datas.append(info)

    train_data, val_data = train_test_split(datas, test_size=0.05, random_state=2019)
    print('label num: {} - data num:{}'.format(len(label2id), len(datas)))
    print("train num: {} - dev num: {}".format(len(train_data), len(val_data)))

    np.savez_compressed(conf.label2id_path, data_label_id=data_label_id, label2id=label2id)
    with open(conf.train_file, 'w', encoding='utf-8') as fw:
        fw.write('\n'.join(train_data))
    with open(conf.dev_file, 'w', encoding='utf-8') as fw:
        fw.write('\n'.join(val_data)) 
Example 20
Project: DOTA_models   Author: ringringyi   File: gen_synthetic_single.py    License: Apache License 2.0 5 votes vote down vote up
def GenerateSample(filename, code_shape, layer_depth):
  # {0, +1} binary codes.
  # No conversion since the output file is expected to store
  # codes using {0, +1} codes (and not {-1, +1}).
  code = synthetic_model.GenerateSingleCode(code_shape)
  code = np.round(code)

  # Reformat the code so as to be compatible with what is generated
  # by the image encoder.
  # The image encoder generates a tensor of size:
  # iteration_count x batch_size x height x width x iteration_depth.
  # Here: batch_size = 1
  if code_shape[-1] % layer_depth != 0:
    raise ValueError('Number of layers is not an integer')
  height = code_shape[0]
  width = code_shape[1]
  code = code.reshape([1, height, width, -1, layer_depth])
  code = np.transpose(code, [3, 0, 1, 2, 4])

  int_codes = code.astype(np.int8)
  exported_codes = np.packbits(int_codes.reshape(-1))

  output = io.BytesIO()
  np.savez_compressed(output, shape=int_codes.shape, codes=exported_codes)
  with tf.gfile.FastGFile(filename, 'wb') as code_file:
    code_file.write(output.getvalue()) 
Example 21
Project: medicaldetectiontoolkit   Author: MIC-DKFZ   File: pack_dataset.py    License: Apache License 2.0 5 votes vote down vote up
def mp_pack(inputs):
    ix , f = inputs
    file_path, source_dir, target_dir = f
    print('packing file number: {}'.format(ix))
    if 'npy' in file_path:
        source_path = os.path.join(source_dir, file_path)
        target_path = os.path.join(target_dir, file_path.split('.')[0] + '.npz')
        arr = np.load(source_path, mmap_mode='r')
        np.savez_compressed(target_path, data=arr)
        print('target_path', target_path) 
Example 22
Project: dataflow   Author: tensorpack   File: serialize.py    License: Apache License 2.0 5 votes vote down vote up
def save(df, path):
        """
        Args:
            df (DataFlow): the DataFlow to serialize.
            path (str): output npz file.
        """
        buffer = []
        size = _reset_df_and_get_size(df)
        with get_tqdm(total=size) as pbar:
            for dp in df:
                buffer.append(dp)
                pbar.update()
        np.savez_compressed(path, buffer=np.asarray(buffer, dtype=np.object)) 
Example 23
Project: yatsm   Author: ceholden   File: test_cache.py    License: MIT License 5 votes vote down vote up
def test_update_cache_file_add_obs(cachefile, example_cache,
                                   example_timeseries):
    """ Grab a subset of test data and see if we get more data back """
    stack_images = example_timeseries['images']
    stack_image_IDs = example_timeseries['image_IDs']

    # Presort and subset for comparison
    sort_idx = np.argsort(example_cache['image_IDs'])
    test_Y = example_cache['Y'][:, sort_idx, :]
    test_IDs = example_cache['image_IDs'][sort_idx]

    size_1 = 100
    size_2 = 200

    sort_idx = np.argsort(stack_image_IDs)[:size_2]
    stack_images = stack_images[sort_idx]
    stack_IDs = stack_image_IDs[sort_idx]

    # Create reduced dataset to add to
    np.savez_compressed('test.npz',
                        Y=test_Y[:, :size_1, :],
                        image_IDs=test_IDs[:size_1])

    # Write update and read back
    cache.update_cache_file(stack_images, stack_IDs,
                            'test.npz', 'test_new.npz',
                            0, io.gdal_reader)
    updated = np.load('test_new.npz')

    # Test and clean update
    np.testing.assert_equal(test_Y[:, :size_2, :], updated['Y'])
    np.testing.assert_equal(
        test_IDs[:size_2].astype(updated['image_IDs'].dtype),
        updated['image_IDs']
    )

    os.remove('test.npz')
    os.remove('test_new.npz') 
Example 24
Project: recruit   Author: Frank-qlu   File: test_io.py    License: Apache License 2.0 5 votes vote down vote up
def test_savez_compressed_load(self):
        # Test that pathlib.Path instances can be used with savez.
        with temppath(suffix='.npz') as path:
            path = Path(path)
            np.savez_compressed(path, lab='place holder')
            data = np.load(path)
            assert_array_equal(data['lab'], 'place holder')
            data.close() 
Example 25
Project: recruit   Author: Frank-qlu   File: test_format.py    License: Apache License 2.0 5 votes vote down vote up
def test_compressed_roundtrip():
    arr = np.random.rand(200, 200)
    npz_file = os.path.join(tempdir, 'compressed.npz')
    np.savez_compressed(npz_file, arr=arr)
    arr1 = np.load(npz_file)['arr']
    assert_array_equal(arr, arr1)


# aligned 
Example 26
Project: typhon   Author: atmtools   File: dataset.py    License: MIT License 5 votes vote down vote up
def save_npz(self, path, M):
        """Save to compressed npz

        Arguments:

            path (pathlib.Path): Path to store to

            M (ndarray): Contents of what to store.
        """
        p = pathlib.Path(path)
        p.parent.mkdir(parents=True, exist_ok=True)
        numpy.savez_compressed(str(path), M) 
Example 27
Project: supair   Author: stelzner   File: datasets.py    License: MIT License 5 votes vote down vote up
def make_sprites(n=50000, path='./data'):
    path = os.path.expanduser(path)
    cache_filename = 'sprites_{}_{}.npz'.format(n, 50)
    if os.path.exists(os.path.join(path, cache_filename)):
        data = np.load(os.path.join(path, cache_filename), allow_pickle=True)
        return (data['x_train'], data['count_train'], None),\
               (data['x_test'], data['count_test'], None)

    images = np.zeros((n, 50, 50, 3))
    counts = np.zeros((n,))
    for i in range(n):
        if i < 100:
            num_sprites = i % 3
        else:
            num_sprites = np.random.random_integers(0, 2)
        counts[i] = num_sprites
        for j in range(num_sprites):
            images[i] = add_sprite(images[i])

    np.clip(images, 0.0, 1.0)
    x_train, count_train = images[:4 * n // 5], counts[:4 * n // 5]
    x_test, count_test = images[4 * n // 5:], counts[4 * n // 5:]

    with open(os.path.join(path, cache_filename), 'wb') as f:
        np.savez_compressed(f, x_train=x_train, count_train=count_train,
                            x_test=x_test, count_test=count_test)

    return (x_train, count_train, None), (x_test, count_test, None) 
Example 28
Project: PyTorchText   Author: chenyuntc   File: embedding2matrix.py    License: MIT License 5 votes vote down vote up
def main(em_file, em_result):
    '''
    embedding ->numpy
    '''
    em = word2vec.load(em_file)
    vec = (em.vectors)
    word2id = em.vocab_hash
    # d = dict(vector = vec, word2id = word2id)
    # t.save(d,em_result)
    np.savez_compressed(em_result,vector=vec,word2id=word2id) 
Example 29
Project: lambda-packs   Author: ryfeus   File: test_io.py    License: MIT License 5 votes vote down vote up
def test_savez_compressed_load(self):
        # Test that pathlib.Path instances can be used with savez.
        with temppath(suffix='.npz') as path:
            path = Path(path)
            np.savez_compressed(path, lab='place holder')
            data = np.load(path)
            assert_array_equal(data['lab'], 'place holder')
            data.close() 
Example 30
Project: lambda-packs   Author: ryfeus   File: test_format.py    License: MIT License 5 votes vote down vote up
def test_compressed_roundtrip():
    arr = np.random.rand(200, 200)
    npz_file = os.path.join(tempdir, 'compressed.npz')
    np.savez_compressed(npz_file, arr=arr)
    arr1 = np.load(npz_file)['arr']
    assert_array_equal(arr, arr1)