Python numpy.save方法代碼示例

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import save [as 別名]
def _serialize_data(self, data):

        # Default to raw bytes
        type_ = _BYTES

        if isinstance(data, np.ndarray):
        # When the data is a numpy array, use the more compact native
        # numpy format.
            buf = io.BytesIO()
            np.save(buf, data)
            data = buf.getvalue()
            type_ = _NUMPY

        elif not isinstance(data, (bytearray, bytes)):
        # Everything else except byte data is serialized in pickle format.
            data = pickle.dumps(data)
            type_ = _PICKLE

        if self.compress:
        # Optional compression
            data = lz4.frame.compress(data)

        return type_, data 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import save [as 別名]
def visualize(m, story_buckets, wordlist, answerlist, output_format, outputdir, batch_size=1, seq_len=5, debugmode=False, snap=False):
    cur_bucket = random.choice(story_buckets)
    sampled_batch = sample_batch(cur_bucket, batch_size, len(answerlist), output_format)
    part_sampled_batch = sampled_batch[:3]
    with open(os.path.join(outputdir,'stories.txt'),'w') as f:
        ggtnn_graph_parse.print_batch(part_sampled_batch, wordlist, answerlist, file=f)
    with open(os.path.join(outputdir,'answer_list.txt'),'w') as f:
        f.write('\n'.join(answerlist) + '\n')
    if debugmode:
        args = sampled_batch
        fn = m.debug_test_fn
    else:
        args = part_sampled_batch[:2] + ((seq_len,) if output_format == model.ModelOutputFormat.sequence else ())
        fn = m.snap_test_fn if snap else m.fuzzy_test_fn
    results = fn(*args)
    for i,result in enumerate(results):
        np.save(os.path.join(outputdir,'result_{}.npy'.format(i)), result) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import save [as 別名]
def serialize_ndarray_npy(o):
    """
    Serializes a :obj:`numpy.ndarray` using numpy's built-in :obj:`save` function.
    This produces totally unreadable (and very un-JSON-like) results (in "npy"
    format), but it's basically guaranteed to work in 100% of cases.

    Args:
        o (:obj:`numpy.ndarray`): :obj:`ndarray` to be serialized.

    Returns:
        A dictionary that can be passed to :obj:`json.dumps`.
    """
    with io.BytesIO() as f:
        np.save(f, o)
        f.seek(0)
        serialized = json.dumps(f.read().decode('latin-1'))
    return dict(
        _type='np.ndarray',
        npy=serialized) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import save [as 別名]
def deserialize_ndarray_npy(d):
    """
    Deserializes a JSONified :obj:`numpy.ndarray` that was created using numpy's
    :obj:`save` function.

    Args:
        d (:obj:`dict`): A dictionary representation of an :obj:`ndarray` object, created
            using :obj:`numpy.save`.

    Returns:
        An :obj:`ndarray` object.
    """
    with io.BytesIO() as f:
        f.write(json.loads(d['npy']).encode('latin-1'))
        f.seek(0)
        return np.load(f) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import save [as 別名]
def extract_mnist_data(filename, num_images, image_size, pixel_depth):
  """
  Extract the images into a 4D tensor [image index, y, x, channels].

  Values are rescaled from [0, 255] down to [-0.5, 0.5].
  """
  # if not os.path.exists(file):
  if not tf.gfile.Exists(filename+".npy"):
    with gzip.open(filename) as bytestream:
      bytestream.read(16)
      buf = bytestream.read(image_size * image_size * num_images)
      data = np.frombuffer(buf, dtype=np.uint8).astype(np.float32)
      data = (data - (pixel_depth / 2.0)) / pixel_depth
      data = data.reshape(num_images, image_size, image_size, 1)
      np.save(filename, data)
      return data
  else:
    with tf.gfile.Open(filename+".npy", mode='r') as file_obj:
      return np.load(file_obj) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import save [as 別名]
def extractMeanDataStats(size = [200, 200, 100], 
						postfix = '_200x200x100orig', 
						main_folder_path = '../../Data/MS2017b/', 
						):
	scan_folders = glob.glob(main_folder_path + 'scans/*')
	img_path = 'pre/FLAIR' + postfix + '.nii.gz'
	segm_path = 'wmh' + postfix + '.nii.gz'
	
	shape_ = [len(scan_folders), size[0], size[1], size[2]]
	arr = np.zeros(shape_)

	for i, sf in enumerate(scan_folders):
		arr[i, :,:,:] =  numpyFromScan(os.path.join(sf,img_path)).squeeze()

	arr /= len(scan_folders)

	means = np.mean(arr)
	stds = np.std(arr, axis = 0)

	np.save(main_folder_path + 'extra_data/std' + postfix, stds)
	np.save(main_folder_path + 'extra_data/mean' + postfix, means) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import save [as 別名]
def generateImgSlicesFolder(data_folder = '../Data/MS2017a/scans/'):
	scan_folders = glob.glob(data_folder + '*')

	for sf in scan_folders:
		slice_dir_path = os.path.join(sf, 'slices/')
		if not os.path.exists(slice_dir_path):
			print('Creating directory at:' , slice_dir_path)
			os.makedirs(slice_dir_path)

		img = nib.load(os.path.join(sf, 'pre/FLAIR.nii.gz'))
		img_np = img.get_data()
		img_affine = img.affine
		print(sf)
		print('The img shape', img_np.shape[2])
		for i in range(img_np.shape[2]):
			slice_img_np = img_np[:,:,i]
			nft_img = nib.Nifti1Image(slice_img_np, img_affine)
			nib.save(nft_img, slice_dir_path + 'FLAIR_' + str(i) + '.nii.gz')

			if os.path.basename(sf) == '0':
				slice_img = nib.load(slice_dir_path + 'FLAIR_' + str(i) + '.nii.gz').get_data() / 5
				print('DID I GET HERE?')
				print('Writing to', str(i) + '.jpg') 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import save [as 別名]
def generateGTSlicesFolder(data_folder = '../Data/MS2017a/scans/'):
	scan_folders = glob.glob(data_folder + '*')

	for sf in scan_folders:
		slice_dir_path = os.path.join(sf, 'gt_slices/')
		if not os.path.exists(slice_dir_path):
			print('Creating directory at:' , slice_dir_path)
			os.makedirs(slice_dir_path)

		img = nib.load(os.path.join(sf, 'wmh.nii.gz'))
		img_np = img.get_data()
		img_affine = img.affine
		print(sf)
		print('The img shape', img_np.shape[2])
		for i in range(img_np.shape[2]):
			slice_img_np = img_np[:,:,i]
			nft_img = nib.Nifti1Image(slice_img_np, img_affine)
			nib.save(nft_img, slice_dir_path + 'wmh_' + str(i) + '.nii.gz')

			if os.path.basename(sf) == '0':
				slice_img = nib.load(slice_dir_path + 'wmh_' + str(i) + '.nii.gz').get_data() * 256
				#cv2.imwrite('temp/' + str(i) + '.jpg', slice_img) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import save [as 別名]
def generateTrainValFile_Slices(train_fraction, main_folder = '../Data/MS2017a/'):
	train_folders, val_folders = splitTrainVal_Slices(0.8)

	train_folder_names = [train_folders[i].split(main_folder)[1] for i in range(len(train_folders))]
	val_folder_names = [val_folders[i].split(main_folder)[1] for i in range(len(val_folders))]

	f_train = open(main_folder + 'train_slices.txt', 'w+')
	f_val = open(main_folder + 'val_slices.txt', 'w+')

	for fn in train_folder_names:
		f_train.write(fn + '\n')

	for fn in val_folder_names:
		f_val.write(fn + '\n')

	f_train.close()
	f_val.close()

#Use this to save the images quickly (for testing purposes) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import save [as 別名]
def next_batch(self, whichSet='train'):
		if whichSet == 'train':
			self.trainBatchCnt += 1
			assert self.trainBatchCnt < self.trainMaxBatch
			return self.train[self.trainBatchCnt * self.batch_size: (self.trainBatchCnt + 1) * self.batch_size]
		elif whichSet == 'validation':
			self.validationBatchCnt += 1
			assert self.validationBatchCnt < self.validationMaxBatch
			return self.validation[self.validationBatchCnt * self.batch_size: (self.validationBatchCnt + 1) * self.batch_size]
		elif whichSet == 'test':
			self.testBatchCnt += 1
			assert self.testBatchCnt < self.testMaxBatch
			return self.test[self.testBatchCnt * self.batch_size: (self.testBatchCnt + 1) * self.batch_size]
		else:
			msg = 'Wrong set name!\n'+ \
				  'Should be train / validation / test.'
			raise Exception(msg)
	# Following code copied here:
	# https://stackoverflow.com/questions/17219481/save-to-file-and-load-an-instance-of-a-python-class-with-its-attributes 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import save [as 別名]
def pred_test_fold(predictor, fold, test_data):
    fold_prediction_dir = PREDICTION_DIR / f'fold_{fold}' / 'test'
    fold_prediction_dir.mkdir(parents=True, exist_ok=True)

    fname_lst, images_lst = test_data
    pred_lst = []
    for fname, image in zip(fname_lst, images_lst):
        pred = predictor.predict(image)

        pred_path = fold_prediction_dir / f'{fname}.npy'
        np.save(pred_path, pred)

        pred = pred.mean(axis=0)
        pred_lst.append(pred)

    preds = np.stack(pred_lst, axis=0)
    subm_df = pd.DataFrame(data=preds,
                           index=fname_lst,
                           columns=config.classes)
    subm_df.index.name = 'fname'
    subm_df.to_csv(fold_prediction_dir / 'probs.csv') 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import save [as 別名]
def calculate_weigths_labels(dataset, dataloader, num_classes):
    # Create an instance from the data loader
    z = np.zeros((num_classes,))
    # Initialize tqdm
    tqdm_batch = tqdm(dataloader)
    print('Calculating classes weights')
    for sample in tqdm_batch:
        y = sample['label']
        y = y.detach().cpu().numpy()
        mask = (y >= 0) & (y < num_classes)
        labels = y[mask].astype(np.uint8)
        count_l = np.bincount(labels, minlength=num_classes)
        z += count_l
    tqdm_batch.close()
    total_frequency = np.sum(z)
    class_weights = []
    for frequency in z:
        class_weight = 1 / (np.log(1.02 + (frequency / total_frequency)))
        class_weights.append(class_weight)
    ret = np.array(class_weights)
    classes_weights_path = os.path.join(Path.db_root_dir(dataset), dataset+'_classes_weights.npy')
    np.save(classes_weights_path, ret)

    return ret 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import save [as 別名]
def MakeBaseIntegrals(self, Smh=True, MakeS=False):
      """Invoke bfint to calculate CoreEnergy (scalar), CoreH (nOrb x nOrb),
      Int2e_Frs (nFit x nOrb x nOrb), and overlap matrix (nOrb x nOrb)"""

      # assemble arguments to integral generation program
      Args = []
      if Smh:
         Args.append("--orb-trafo=Smh")
         # ^- calculate integrals in symmetrically orthogonalized AO basis
      Outputs = []
      Outputs.append(("--save-coreh", "INT1E"))
      Outputs.append(("--save-fint2e", "INT2E"))
      Outputs.append(("--save-overlap", "OVERLAP"))

      CoreH, Int2e, Overlap = self._InvokeBfint(Args, Outputs)

      nOrb = CoreH.shape[0]
      Int2e = Int2e.reshape((Int2e.shape[0], nOrb, nOrb))
      CoreEnergy = self.Atoms.fCoreRepulsion()

      if MakeS:
         return CoreEnergy, CoreH, Int2e, Overlap
      else:
         return CoreEnergy, CoreH, Int2e 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import save [as 別名]
def MakeOverlap(self, OrbBasis2=None):
      """calculate overlap within main orbital basis, and, optionally, between main
      orbital basis and a second basis, as described in OrbBasis2.
      Returns <1|1>, <1|2>, and <2|2> matrices."""
      Args = []
      Outputs = []
      Outputs.append(("--save-overlap", "OVERLAP_1"))
      if OrbBasis2 is not None:
         MoreBases = {'--basis-orb-2': OrbBasis2}
         Outputs.append(("--save-overlap-12", "OVERLAP_12"))
         Outputs.append(("--save-overlap-2", "OVERLAP_2"))
         return self._InvokeBfint(Args, Outputs, MoreBases=MoreBases)
      else:
         MoreBases = None
         Overlap, = self._InvokeBfint(Args, Outputs, MoreBases=MoreBases)
         return Overlap 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import save [as 別名]
def MakeRaw2eIntegrals(self, Smh=True, Kernel2e="coulomb"):
      """compute Int2e_Frs (nFit x nOrb x nOrb) and fitting metric Int2e_FG (nFit x nFit),
      where the fitting metric is *not* absorbed into the 2e integrals."""

      # assemble arguments to integral generation program
      Args = []
      if Smh:
         Args.append("--orb-trafo=Smh")
         # ^- calculate integrals in symmetrically orthogonalized AO basis
      Args.append("--kernel2e='%s'" % Kernel2e)
      Args.append("--solve-fitting-eq=false")
      Outputs = []
      Outputs.append(("--save-fint2e", "INT2E_3IX"))
      Outputs.append(("--save-fitting-metric", "INT2E_METRIC"))

      Int2e_Frs, Int2e_FG = self._InvokeBfint(Args, Outputs)

      nOrb = int(Int2e_Frs.shape[1]**.5 + .5)
      assert(nOrb**2 == Int2e_Frs.shape[1])
      Int2e_Frs = Int2e_Frs.reshape((Int2e_Frs.shape[0], nOrb, nOrb))
      assert(Int2e_Frs.shape[0] == Int2e_FG.shape[0])
      assert(Int2e_FG.shape[0] == Int2e_FG.shape[1])
      return Int2e_FG, Int2e_Frs