def __next__(self):
"""
Return in each iteration the data from a single file
"""
if self.__curr_index < len(channel_files):
newfp = np.memmap(channel_files[self.__curr_index],
dtype='float64', mode='r', shape=(self.num_steps,))
curr_data = newfp[:]
i = self.__curr_index
self.__curr_index += 1
del newfp
return DataChunk(data=curr_data,
selection=np.s_[:, i])
else:
raise StopIteration
def __next__(self):
"""
Return in each iteration a fully occupied data chunk of self.chunk_shape values at a random
location within the matrix. Chunks are non-overlapping. REMEMBER: h5py does not support all
fancy indexing that numpy does so we need to make sure our selection can be
handled by the backend.
"""
if self.__chunks_created < self.num_chunks:
data = np.random.rand(np.prod(self.chunk_shape)).reshape(self.chunk_shape)
xmin = np.random.randint(0, int(self.shape[0] / self.chunk_shape[0]), 1)[0] * self.chunk_shape[0]
xmax = xmin + self.chunk_shape[0]
ymin = np.random.randint(0, int(self.shape[1] / self.chunk_shape[1]), 1)[0] * self.chunk_shape[1]
ymax = ymin + self.chunk_shape[1]
self.__chunks_created += 1
return DataChunk(data=data,
selection=np.s_[xmin:xmax, ymin:ymax])
else:
raise StopIteration
def test_len_operator_with_data(self):
temp = DataChunk(np.arange(10).reshape(5, 2))
self.assertEqual(len(temp), 5)
def test_len_operator_no_data(self):
temp = DataChunk()
self.assertEqual(len(temp), 0)