def get_image_by_attributes(self, frame_number, field_of_view, channel_name, z_level, height, width):
"""
Attempts to get Image based on attributes alone.
:type frame_number: int
:type field_of_view: int
:type channel_name: str
:type z_level: int
:type height: int
:type width: int
:rtype: Image or None
"""
image_group_number = self._calculate_image_group_number(frame_number, field_of_view, z_level)
try:
timestamp, raw_image_data = self._get_raw_image_data(image_group_number,
self._channel_offset[channel_name],
height,
width)
image = Image(raw_image_data)
image.add_params(image_group_number, timestamp, frame_number, field_of_view, channel_name, z_level)
except (TypeError, NoImageError):
return None
else:
return image
class ImageTests(unittest.TestCase):
"""
Basically just tests that the Image API works and that Images act as Numpy arrays. There's very little going on
here other than simply storing data.
"""
def setUp(self):
array = np.array([[0, 1, 254], [45, 12, 9], [12, 12, 99]])
self.image = Image(array)
self.image.add_params(1200.314, 17, 2, 'GFP', 1)
def test_size(self):
self.assertEqual(self.image.height, 3)
self.assertEqual(self.image.width, 3)
def test_timestamp(self):
self.assertEqual(self.image.timestamp, 1.200314)
def test_frame_number(self):
self.assertEqual(self.image.frame_number, 17)
def test_fov(self):
self.assertEqual(self.image.field_of_view, 2)
def test_channel(self):
self.assertEqual(self.image.channel, 'GFP')
def test_z_level(self):
self.assertEqual(self.image.z_level, 1)
def test_slice(self):
subimage = self.image[:2, :2]
expected = np.array([[0, 1], [45, 12]])
self.assertTrue(np.array_equal(subimage, expected))
def get_image_by_attributes(self, frame_number, field_of_view,
channel_name, z_level, height, width):
"""
Attempts to get Image based on attributes alone.
:type frame_number: int
:type field_of_view: int
:type channel_name: str
:type z_level: int
:type height: int
:type width: int
:rtype: Image or None
"""
image_group_number = self._calculate_image_group_number(
frame_number, field_of_view, z_level)
try:
timestamp, raw_image_data = self._get_raw_image_data(
image_group_number, self._channel_offset[channel_name], height,
width)
image = Image(raw_image_data)
image.add_params(image_group_number, timestamp, frame_number,
field_of_view, channel_name, z_level)
except (TypeError, NoImageError):
return None
else:
return image
def _get_raw_image_data(self, image_group_number, channel_offset, height, width):
"""
Reads the raw bytes and the timestamp of an image.
:param image_group_number: groups are made of images with the same time index, field of view and z-level.
:type image_group_number: int
:param channel_offset: the offset in the array where the bytes for this image are found.
:type channel_offset: int
:return: (int, array.array()) or None
"""
chunk = self._label_map[six.b("ImageDataSeq|%d!" % image_group_number)]
data = read_chunk(self._file_handle, chunk)
# All images in the same image group share the same timestamp! So if you have complicated image data,
# your timestamps may not be entirely accurate. Practically speaking though, they'll only be off by a few
# seconds unless you're doing something super weird.
timestamp = struct.unpack("d", data[:8])[0]
image_group_data = array.array("H", data)
image_data_start = 4 + channel_offset
# The images for the various channels are interleaved within the same array. For example, the second image
# of a four image group will be composed of bytes 2, 6, 10, etc. If you understand why someone would design
# a data structure that way, please send the author of this library a message.
image_data = np.reshape(image_group_data[image_data_start::len(self._metadata.channels)], (height, width))
# Skip images that are all zeros! This is important, since NIS Elements creates blank "gap" images if you
# don't have the same number of images each cycle. We discovered this because we only took GFP images every
# other cycle to reduce phototoxicity, but NIS Elements still allocated memory as if we were going to take
# them every cycle.
if np.any(image_data):
return timestamp, Image(image_data)
raise NoImageError
class ImageTests(unittest.TestCase):
"""
Basically just tests that the Image API works and that Images act as Numpy arrays. There's very little going on
here other than simply storing data.
"""
def setUp(self):
array = np.array([[0, 1, 254],
[45, 12, 9],
[12, 12, 99]])
self.image = Image(array)
self.image.add_params(1200.314, 17, 2, 'GFP', 1)
def test_size(self):
self.assertEqual(self.image.height, 3)
self.assertEqual(self.image.width, 3)
def test_timestamp(self):
self.assertEqual(self.image.timestamp, 1.200314)
def test_frame_number(self):
self.assertEqual(self.image.frame_number, 17)
def test_fov(self):
self.assertEqual(self.image.field_of_view, 2)
def test_channel(self):
self.assertEqual(self.image.channel, 'GFP')
def test_z_level(self):
self.assertEqual(self.image.z_level, 1)
def test_slice(self):
subimage = self.image[:2, :2]
expected = np.array([[0, 1],
[45, 12]])
self.assertTrue(np.array_equal(subimage, expected))
def setUp(self):
array = np.array([[0, 1, 254],
[45, 12, 9],
[12, 12, 99]])
self.image = Image(array)
self.image.add_params(1200.314, 17, 2, 'GFP', 1)
def setUp(self):
array = np.array([[0, 1, 254], [45, 12, 9], [12, 12, 99]])
self.image = Image(array)
self.image.add_params(1200.314, 17, 2, 'GFP', 1)
