def get_aligned_tileset():
alignedTileset = TileSet(
[Axes.X, Axes.Y, Axes.CH, Axes.ZPLANE, Axes.ROUND], {
Axes.CH: NUM_CH,
Axes.ROUND: NUM_ROUND,
Axes.ZPLANE: NUM_Z
}, {
Axes.Y: HEIGHT,
Axes.X: WIDTH
})
for r in range(NUM_ROUND):
for ch in range(NUM_CH):
for z in range(NUM_Z):
tile = Tile(
{
Coordinates.X: 1,
Coordinates.Y: 4,
Coordinates.Z: 3,
}, {
Axes.ROUND: r,
Axes.CH: ch,
Axes.ZPLANE: z,
})
tile.numpy_array = np.zeros((100, 100))
alignedTileset.add_tile(tile)
return alignedTileset
def get_un_aligned_tileset():
unAlignedTileset = TileSet(
[Axes.X, Axes.Y, Axes.CH, Axes.ZPLANE, Axes.ROUND], {
Axes.CH: NUM_CH,
Axes.ROUND: NUM_ROUND,
Axes.ZPLANE: NUM_Z
}, {
Axes.Y: HEIGHT,
Axes.X: WIDTH
})
for r in range(NUM_ROUND):
for ch in range(NUM_CH):
for z in range(NUM_Z):
tile = Tile(
{
# The round_to methods generate coordinates
# based on the r value, therefore the coords vary
# throughout the tileset
Coordinates.X:
round_to_x(r),
Coordinates.Y:
round_to_y(r),
Coordinates.Z:
round_to_z(r),
},
{
Axes.ROUND: r,
Axes.CH: ch,
Axes.ZPLANE: z,
})
tile.numpy_array = np.zeros((HEIGHT, WIDTH))
unAlignedTileset.add_tile(tile)
return unAlignedTileset
def build_image(fov_count, hyb_count, ch_count, z_count,
image_fetcher: ImageFetcher, default_shape: Tuple[int, int]):
"""
Build and returns an image set with the following characteristics:
Parameters
----------
fov_count : int
Number of fields of view in this image set.
hyb_count : int
Number for hybridizations in this image set.
ch_count : int
Number for channels in this image set.
z_count : int
Number of z-layers in this image set.
default_shape : Tuple[int, int]
Default shape of the individual tiles in this image set.
Returns
-------
The slicedimage collection representing the image.
"""
collection = Collection()
for fov_ix in range(fov_count):
fov_images = TileSet(
[Coordinates.X, Coordinates.Y, Indices.Z, Indices.HYB, Indices.CH],
{
Indices.HYB: hyb_count,
Indices.CH: ch_count,
Indices.Z: z_count
},
default_shape,
ImageFormat.TIFF,
)
for z_ix in range(z_count):
for hyb_ix in range(hyb_count):
for ch_ix in range(ch_count):
image = image_fetcher.get_image(fov_ix, hyb_ix, ch_ix,
z_ix)
tile = Tile(
{
Coordinates.X: (0.0, 0.0001),
Coordinates.Y: (0.0, 0.0001),
Coordinates.Z: (0.0, 0.0001),
},
{
Indices.Z: z_ix,
Indices.HYB: hyb_ix,
Indices.CH: ch_ix,
},
image.shape,
)
tile.set_source_fh_contextmanager(image.image_data_handle,
image.format)
fov_images.add_tile(tile)
collection.add_partition("fov_{:03}".format(fov_ix), fov_images)
return collection
def synthetic_stack(
cls,
num_hyb: int = 4,
num_ch: int = 4,
num_z: int = 12,
tile_height: int = 50,
tile_width: int = 40,
tile_data_provider: Callable[[int, int, int, int, int],
np.ndarray] = None,
tile_extras_provider: Callable[[int, int, int], Any] = None,
) -> "ImageStack":
"""generate a synthetic ImageStack
Returns
-------
ImageStack :
imagestack containing a tensor whose default shape is (2, 3, 4, 30, 20)
and whose default values are all 1.
"""
if tile_data_provider is None:
tile_data_provider = cls._default_tile_data_provider
if tile_extras_provider is None:
tile_extras_provider = cls._default_tile_extras_provider
img = TileSet(
{Coordinates.X, Coordinates.Y, Indices.HYB, Indices.CH, Indices.Z},
{
Indices.HYB: num_hyb,
Indices.CH: num_ch,
Indices.Z: num_z,
},
default_tile_shape=(tile_height, tile_width),
)
for hyb in range(num_hyb):
for ch in range(num_ch):
for z in range(num_z):
tile = Tile(
{
Coordinates.X: (0.0, 0.001),
Coordinates.Y: (0.0, 0.001),
Coordinates.Z: (0.0, 0.001),
},
{
Indices.HYB: hyb,
Indices.CH: ch,
Indices.Z: z,
},
extras=tile_extras_provider(hyb, ch, z),
)
tile.numpy_array = tile_data_provider(
hyb, ch, z, tile_height, tile_width)
img.add_tile(tile)
stack = cls(img)
return stack
def synthetic_stack(
num_hyb: int=DEFAULT_NUM_HYB,
num_ch: int=DEFAULT_NUM_CH,
num_z: int=DEFAULT_NUM_Z,
tile_height: int=DEFAULT_HEIGHT,
tile_width: int=DEFAULT_WIDTH,
tile_data_provider: Callable[[int, int, int, int, int], np.ndarray]=default_tile_data_provider,
tile_extras_provider: Callable[[int, int, int], Any]=default_tile_extras_provider
) -> ImageStack:
"""generate a synthetic ImageStack
Returns
-------
ImageStack :
imagestack containing a tensor of (2, 3, 4, 30, 20) whose values are all 1.
"""
img = TileSet(
{Coordinates.X, Coordinates.Y, Indices.HYB, Indices.CH, Indices.Z},
{
Indices.HYB: num_hyb,
Indices.CH: num_ch,
Indices.Z: num_z,
},
default_tile_shape=(tile_height, tile_width),
)
for hyb in range(num_hyb):
for ch in range(num_ch):
for z in range(num_z):
tile = Tile(
{
Coordinates.X: (0.0, 0.001),
Coordinates.Y: (0.0, 0.001),
Coordinates.Z: (0.0, 0.001),
},
{
Indices.HYB: hyb,
Indices.CH: ch,
Indices.Z: z,
},
extras=tile_extras_provider(hyb, ch, z),
)
tile.numpy_array = tile_data_provider(hyb, ch, z, tile_height, tile_width)
img.add_tile(tile)
stack = ImageStack(img)
return stack
def test_tuple_to_tuple(self):
tile = Tile(
{
'coord': (0, 1),
},
{},
)
self.assertEqual(tile.coordinates['coord'], (0, 1))
def test_long_tuple(self):
with self.assertRaises(ValueError):
Tile(
{
'coord': (0, 1, 2),
},
{},
)
def test_single_scalar_in_tuple(self):
with self.assertRaises(ValueError):
Tile(
{
'coord': (0,),
},
{},
)
def test_scalar_to_tuple(self):
tile = Tile(
{
'coord': 0,
},
{},
)
self.assertEqual(tile.coordinates['coord'], (0, 0))
def set_aux(self, key, img):
if key in self.auxiliary_images:
old_img = self.auxiliary_images[key]
if old_img.shape != img.shape:
msg = "Shape mismatch. Current data shape: {}, new data shape: {}".format(
old_img.shape, img.shape)
raise AttributeError(msg)
self.auxiliary_images[key].numpy_array = img
else:
# TODO: (ttung) major hack alert. we don't have a convenient mechanism to build an ImageStack from a single
# numpy array, which we probably should.
tileset = TileSet(
{
Indices.HYB,
Indices.CH,
Indices.Z,
Coordinates.X,
Coordinates.Y,
}, {
Indices.HYB: 1,
Indices.CH: 1,
Indices.Z: 1,
})
tile = Tile(
{
Coordinates.X: (0.000, 0.001),
Coordinates.Y: (0.000, 0.001),
},
{
Indices.HYB: 0,
Indices.CH: 0,
Indices.Z: 0,
},
img.shape,
)
tile.numpy_array = img
tileset.add_tile(tile)
self.auxiliary_images[key] = ImageStack(tileset)
self.org['auxiliary_images'][key] = f"{key}.json"
def export(self,
filepath: str,
tile_opener=None,
tile_format: ImageFormat = ImageFormat.NUMPY) -> None:
"""write the image tensor to disk in spaceTx format
Parameters
----------
filepath : str
Path + prefix for the images and primary_images.json written by this function
tile_opener : TODO ttung: doc me.
tile_format : ImageFormat
Format in which each 2D plane should be written.
"""
# Add log data to extras
self._tile_data.extras[STARFISH_EXTRAS_KEY] = logging.LogEncoder(
).encode({LOG: self.log})
tileset = TileSet(
dimensions={
Axes.ROUND,
Axes.CH,
Axes.ZPLANE,
Axes.Y,
Axes.X,
},
shape={
Axes.ROUND: self.num_rounds,
Axes.CH: self.num_chs,
Axes.ZPLANE: self.num_zplanes,
},
default_tile_shape={
Axes.Y: self.tile_shape[0],
Axes.X: self.tile_shape[1]
},
extras=self._tile_data.extras,
)
for axis_val_map in self._iter_axes({Axes.ROUND, Axes.CH,
Axes.ZPLANE}):
tilekey = TileKey(round=axis_val_map[Axes.ROUND],
ch=axis_val_map[Axes.CH],
zplane=axis_val_map[Axes.ZPLANE])
round_, ch, zplane = tilekey.round, tilekey.ch, tilekey.z
extras: dict = self._tile_data[tilekey]
selector = {
Axes.ROUND: round_,
Axes.CH: ch,
Axes.ZPLANE: zplane,
}
coordinates: MutableMapping[Coordinates, Union[Tuple[Number,
Number],
Number]] = dict()
x_coordinates = (float(self.xarray[Coordinates.X.value][0]),
float(self.xarray[Coordinates.X.value][-1]))
y_coordinates = (float(self.xarray[Coordinates.Y.value][0]),
float(self.xarray[Coordinates.Y.value][-1]))
coordinates[Coordinates.X] = x_coordinates
coordinates[Coordinates.Y] = y_coordinates
if Coordinates.Z in self.xarray.coords:
# set the z coord to the calculated value from the associated z plane
z_coordinates = float(self.xarray[Coordinates.Z.value][zplane])
coordinates[Coordinates.Z] = z_coordinates
tile = Tile(
coordinates=coordinates,
indices=selector,
extras=extras,
)
tile.numpy_array, _ = self.get_slice(selector={
Axes.ROUND: round_,
Axes.CH: ch,
Axes.ZPLANE: zplane
})
tileset.add_tile(tile)
if tile_opener is None:
def tile_opener(tileset_path: Path, tile, ext):
base = tileset_path.parent / tileset_path.stem
if Axes.ZPLANE in tile.indices:
zval = tile.indices[Axes.ZPLANE]
zstr = "-Z{}".format(zval)
else:
zstr = ""
return open(
"{}-H{}-C{}{}.{}".format(
str(base),
tile.indices[Axes.ROUND],
tile.indices[Axes.CH],
zstr,
ext,
), "wb")
if not filepath.endswith('.json'):
filepath += '.json'
Writer.write_to_path(tileset,
filepath,
pretty=True,
tile_opener=tile_opener,
tile_format=tile_format)
def build_image(
fov_count: int,
round_count: int,
ch_count: int,
z_count: int,
image_fetcher: TileFetcher,
default_shape: Optional[Tuple[int, int]] = None,
dimension_order: Sequence[Indices] = DEFAULT_DIMENSION_ORDER,
) -> Collection:
"""
Build and returns an image set with the following characteristics:
Parameters
----------
fov_count : int
Number of fields of view in this image set.
round_count : int
Number for rounds in this image set.
ch_count : int
Number for channels in this image set.
z_count : int
Number of z-layers in this image set.
image_fetcher : TileFetcher
Instance of TileFetcher that provides the data for the tile.
default_shape : Optional[Tuple[int, int]]
Default shape of the individual tiles in this image set.
dimension_order : Sequence[Indices]
Ordering for which dimensions vary, in order of the slowest changing dimension to the
fastest. For instance, if the order is (ROUND, Z, CH) and each dimension has size 2, then
the sequence is:
(ROUND=0, CH=0, Z=0)
(ROUND=0, CH=1, Z=0)
(ROUND=0, CH=0, Z=1)
(ROUND=0, CH=1, Z=1)
(ROUND=1, CH=0, Z=0)
(ROUND=1, CH=1, Z=0)
(ROUND=1, CH=0, Z=1)
(ROUND=1, CH=1, Z=1)
(default = (Indices.Z, Indices.ROUND, Indices.CH))
Returns
-------
The slicedimage collection representing the image.
"""
dimension_sizes = join_dimension_sizes(
dimension_order,
size_for_round=round_count,
size_for_ch=ch_count,
size_for_z=z_count,
)
collection = Collection()
for fov_ix in range(fov_count):
fov_images = TileSet(
[
Coordinates.X,
Coordinates.Y,
Coordinates.Z,
Indices.Z,
Indices.ROUND,
Indices.CH,
Indices.X,
Indices.Y,
],
{
Indices.ROUND: round_count,
Indices.CH: ch_count,
Indices.Z: z_count
},
default_shape,
ImageFormat.TIFF,
)
for dimension_indices in ordered_iterator(dimension_sizes):
image = image_fetcher.get_tile(fov_ix,
dimension_indices[Indices.ROUND],
dimension_indices[Indices.CH],
dimension_indices[Indices.Z])
tile = Tile(
image.coordinates,
{
Indices.Z: (dimension_indices[Indices.Z]),
Indices.ROUND: (dimension_indices[Indices.ROUND]),
Indices.CH: (dimension_indices[Indices.CH]),
},
image.shape,
extras=image.extras,
)
tile.set_numpy_array_future(image.tile_data)
fov_images.add_tile(tile)
collection.add_partition("fov_{:03}".format(fov_ix), fov_images)
return collection
def build_image(
fovs: Sequence[int],
rounds: Sequence[int],
chs: Sequence[int],
zplanes: Sequence[int],
image_fetcher: TileFetcher,
default_shape: Optional[Mapping[Axes, int]]=None,
axes_order: Sequence[Axes]=DEFAULT_DIMENSION_ORDER,
) -> Collection:
"""
Build and returns an image set with the following characteristics:
Parameters
----------
fovs : Sequence[int]
Sequence of field of view ids in this image set.
rounds : Sequence[int]
Sequence of the round numbers in this image set.
chs : Sequence[int]
Sequence of the ch numbers in this image set.
zplanes : Sequence[int]
Sequence of the zplane numbers in this image set.
image_fetcher : TileFetcher
Instance of TileFetcher that provides the data for the tile.
default_shape : Optional[Tuple[int, int]]
Default shape of the individual tiles in this image set.
axes_order : Sequence[Axes]
Ordering for which axes vary, in order of the slowest changing axis to the fastest. For
instance, if the order is (ROUND, Z, CH) and each dimension has size 2, then the sequence
is:
(ROUND=0, CH=0, Z=0)
(ROUND=0, CH=1, Z=0)
(ROUND=0, CH=0, Z=1)
(ROUND=0, CH=1, Z=1)
(ROUND=1, CH=0, Z=0)
(ROUND=1, CH=1, Z=0)
(ROUND=1, CH=0, Z=1)
(ROUND=1, CH=1, Z=1)
(default = (Axes.Z, Axes.ROUND, Axes.CH))
Returns
-------
The slicedimage collection representing the image.
"""
axes_sizes = join_axes_labels(
axes_order, rounds=rounds, chs=chs, zplanes=zplanes)
collection = Collection()
for fov_id in fovs:
fov_images = TileSet(
[
Coordinates.X,
Coordinates.Y,
Coordinates.Z,
Axes.ZPLANE,
Axes.ROUND,
Axes.CH,
Axes.X,
Axes.Y,
],
{Axes.ROUND: len(rounds), Axes.CH: len(chs), Axes.ZPLANE: len(zplanes)},
default_shape,
ImageFormat.TIFF,
)
for selector in ordered_iterator(axes_sizes):
image = image_fetcher.get_tile(
fov_id,
selector[Axes.ROUND],
selector[Axes.CH],
selector[Axes.ZPLANE])
tile = Tile(
image.coordinates,
{
Axes.ZPLANE: (selector[Axes.ZPLANE]),
Axes.ROUND: (selector[Axes.ROUND]),
Axes.CH: (selector[Axes.CH]),
},
image.shape,
extras=image.extras,
)
tile.set_numpy_array_future(image.tile_data)
fov_images.add_tile(tile)
collection.add_partition("fov_{:03}".format(fov_id), fov_images)
return collection
def build_irregular_image(
tile_identifiers: Iterable[TileIdentifier],
image_fetcher: TileFetcher,
default_shape: Optional[Mapping[Axes, int]] = None,
) -> Collection:
"""
Build and returns an image set that can potentially be irregular (i.e., the cardinality of the
dimensions are not always consistent). It can also build a regular image.
Parameters
----------
tile_identifiers : Iterable[TileIdentifier]
Iterable of all the TileIdentifier that are valid in the image.
image_fetcher : TileFetcher
Instance of TileFetcher that provides the data for the tile.
default_shape : Optional[Tuple[int, int]]
Default shape of the individual tiles in this image set.
Returns
-------
The slicedimage collection representing the image.
"""
def reducer_to_sets(
accumulated: Sequence[MutableSet[int]], update: TileIdentifier,
) -> Sequence[MutableSet[int]]:
"""Reduces to a list of sets of tile identifiers, in the order of FOV, round, ch, and
zplane."""
result: MutableSequence[MutableSet[int]] = list()
for accumulated_elem, update_elem in zip(accumulated, astuple(update)):
accumulated_elem.add(update_elem)
result.append(accumulated_elem)
return result
initial_value: Sequence[MutableSet[int]] = tuple(set() for _ in range(4))
fovs, rounds, chs, zplanes = functools.reduce(
reducer_to_sets, tile_identifiers, initial_value)
collection = Collection()
for expected_fov in fovs:
fov_images = TileSet(
[
Coordinates.X,
Coordinates.Y,
Coordinates.Z,
Axes.ZPLANE,
Axes.ROUND,
Axes.CH,
Axes.X,
Axes.Y,
],
{Axes.ROUND: len(rounds), Axes.CH: len(chs), Axes.ZPLANE: len(zplanes)},
default_shape,
ImageFormat.TIFF,
)
for tile_identifier in tile_identifiers:
current_fov, current_round, current_ch, current_zplane = astuple(tile_identifier)
# filter out the fovs that are not the one we are currently processing
if expected_fov != current_fov:
continue
image = image_fetcher.get_tile(
current_fov,
current_round,
current_ch,
current_zplane
)
tile = Tile(
image.coordinates,
{
Axes.ZPLANE: current_zplane,
Axes.ROUND: current_round,
Axes.CH: current_ch,
},
image.shape,
extras=image.extras,
)
tile.set_numpy_array_future(image.tile_data)
# Astute readers might wonder why we set this variable. This is to support in-place
# experiment construction. We monkey-patch slicedimage's Tile class such that checksum
# computation is done by finding the FetchedTile object, which allows us to calculate
# the checksum of the original file.
tile.provider = image
fov_images.add_tile(tile)
collection.add_partition("fov_{:03}".format(expected_fov), fov_images)
return collection
def synthesize() -> Tuple[Stack, list]:
"""Synthesize synthetic spatial image-based transcriptomics data
Returns
-------
Stack :
starfish Stack containing synthetic spots
list :
codebook matching the synthetic data
"""
# set random seed so that data is consistent across tests
random.seed(2)
np.random.seed(2)
NUM_HYB = 4
NUM_CH = 2
NUM_Z = 1
HEIGHT = 100
WIDTH = 100
assert WIDTH == HEIGHT # for compatibility with the parameterization of the code
def choose(n, k):
if n == k:
return [[1] * k]
subsets = [[0] + a for a in choose(n - 1, k)]
if k > 0:
subsets += [[1] + a for a in choose(n - 1, k - 1)]
return subsets
def graham_sloane_codes(n):
# n is length of codeword
# number of on bits is 4
def code_sum(codeword):
return sum([i * c for i, c in enumerate(codeword)]) % n
return [c for c in choose(n, 4) if code_sum(c) == 0]
p = {
# number of on bits (not used with current codebook)
'N_high': 4,
# length of barcode
'N_barcode': NUM_CH * NUM_HYB,
# mean number of flourophores per transcripts - depends on amplification strategy (e.g HCR, bDNA)
'N_flour': 200,
# mean number of photons per flourophore - depends on exposure time, bleaching rate of dye
'N_photons_per_flour': 50,
# mean number of background photons per pixel - depends on tissue clearing and autoflourescence
'N_photon_background': 1000,
# quantum efficiency of the camera detector units number of electrons per photon
'detection_efficiency': .25,
# camera read noise per pixel in units electrons
'N_background_electrons': 1,
# number of RNA puncta; keep this low to reduce overlap probability
'N_spots': 20,
# height and width of image in pixel units
'N_size': WIDTH,
# standard devitation of gaussian in pixel units
'psf': 2,
# dynamic range of camera sensor 37,000 assuming a 16-bit AD converter
'graylevel': 37000.0 / 2 ** 16,
# 16-bit AD converter
'bits': 16
}
codebook = graham_sloane_codes(p['N_barcode'])
def generate_spot(p):
position = rand(2)
gene = random.choice(range(len(codebook)))
barcode = array(codebook[gene])
photons = [poisson(p['N_photons_per_flour']) * poisson(p['N_flour']) * b for b in barcode]
return DataFrame({'position': [position], 'barcode': [barcode], 'photons': [photons], 'gene': gene})
# right now there is no jitter on x-y positions of the spots, we might want to make it a vector
spots = concat([generate_spot(p) for _ in range(p['N_spots'])]) # type: ignore
image = zeros((p['N_barcode'], p['N_size'], p['N_size'],))
for s in spots.itertuples():
image[:, int(p['N_size'] * s.position[0]), int(p['N_size'] * s.position[1])] = s.photons
image_with_background = image + poisson(p['N_photon_background'], size=image.shape)
filtered = array([gaussian(im, p['psf']) for im in image_with_background])
filtered = filtered * p['detection_efficiency'] + normal(scale=p['N_background_electrons'], size=filtered.shape)
signal = np.array([(x / p['graylevel']).astype(int).clip(0, 2 ** p['bits']) for x in filtered])
def select_uint_dtype(array):
"""choose appropriate dtype based on values of an array"""
max_val = np.max(array)
for dtype in [np.uint8, np.uint16, np.uint32, np.uint64]:
if max_val <= dtype(-1):
return array.astype(dtype)
raise ValueError('value exceeds dynamic range of largest numpy type')
corrected_signal = select_uint_dtype(signal)
rescaled_signal: np.ndarray = rescale_intensity(corrected_signal)
# set up the tile set
image_data = TileSet(
{Coordinates.X, Coordinates.Y, Indices.HYB, Indices.CH, Indices.Z},
{
Indices.HYB: NUM_HYB,
Indices.CH: NUM_CH,
Indices.Z: NUM_Z,
},
default_tile_shape=(HEIGHT, WIDTH),
)
# fill the TileSet
experiment_indices = list(product(range(NUM_HYB), range(NUM_CH), range(NUM_Z)))
for i, (hyb, ch, z) in enumerate(experiment_indices):
tile = Tile(
{
Coordinates.X: (0.0, 0.001),
Coordinates.Y: (0.0, 0.001),
Coordinates.Z: (0.0, 0.001),
},
{
Indices.HYB: hyb,
Indices.CH: ch,
Indices.Z: z,
}
)
tile.numpy_array = rescaled_signal[i]
image_data.add_tile(tile)
data_stack = ImageStack(image_data)
# make a max projection and pretend that's the dots image, which we'll create another ImageStack for this
dots_data = TileSet(
{Coordinates.X, Coordinates.Y, Indices.HYB, Indices.CH, Indices.Z},
{
Indices.HYB: 1,
Indices.CH: 1,
Indices.Z: 1,
},
default_tile_shape=(HEIGHT, WIDTH),
)
tile = Tile(
{
Coordinates.X: (0.0, 0.001),
Coordinates.Y: (0.0, 0.001),
Coordinates.Z: (0.0, 0.001),
},
{
Indices.HYB: 0,
Indices.CH: 0,
Indices.Z: 0,
}
)
tile.numpy_array = np.max(rescaled_signal, 0)
dots_data.add_tile(tile)
dots_stack = ImageStack(dots_data)
# TODO can we mock up a nuclei image somehow?
# put the data together into a top-level Stack
results = Stack.from_data(data_stack, aux_dict={'dots': dots_stack})
# make the codebook(s)
codebook = []
for _, code_record in spots.iterrows():
codeword = []
for code_value, (hyb, ch, z) in zip(code_record['barcode'], experiment_indices):
if code_value != 0:
codeword.append({
Indices.HYB: hyb,
Indices.CH: ch,
Indices.Z: z,
"v": code_value
})
codebook.append(
{
'codeword': codeword,
'gene_name': code_record['gene']
}
)
return results, codebook
def export(self,
filepath: str,
tile_opener=None,
tile_format: ImageFormat = ImageFormat.NUMPY) -> None:
"""write the image tensor to disk in spaceTx format
Parameters
----------
filepath : str
Path + prefix for the images and primary_images.json written by this function
tile_opener : TODO ttung: doc me.
tile_format : ImageFormat
Format in which each 2D plane should be written.
"""
tileset = TileSet(
dimensions={
Indices.ROUND,
Indices.CH,
Indices.Z,
Indices.Y,
Indices.X,
},
shape={
Indices.ROUND: self.num_rounds,
Indices.CH: self.num_chs,
Indices.Z: self.num_zlayers,
},
default_tile_shape=self._tile_shape,
extras=self._tile_metadata.extras,
)
for round_ in range(self.num_rounds):
for ch in range(self.num_chs):
for zlayer in range(self.num_zlayers):
tilekey = TileKey(round=round_, ch=ch, z=zlayer)
extras: dict = self._tile_metadata[tilekey]
tile_indices = {
Indices.ROUND: round_,
Indices.CH: ch,
Indices.Z: zlayer,
}
coordinates: MutableMapping[Coordinates,
Tuple[Number,
Number]] = dict()
x_coordinates = self.tile_coordinates(
tile_indices, Coordinates.X)
y_coordinates = self.tile_coordinates(
tile_indices, Coordinates.Y)
z_coordinates = self.tile_coordinates(
tile_indices, Coordinates.Z)
coordinates[Coordinates.X] = x_coordinates
coordinates[Coordinates.Y] = y_coordinates
if z_coordinates[0] != np.nan and z_coordinates[
1] != np.nan:
coordinates[Coordinates.Z] = z_coordinates
tile = Tile(
coordinates=coordinates,
indices=tile_indices,
extras=extras,
)
tile.numpy_array, _ = self.get_slice(indices={
Indices.ROUND: round_,
Indices.CH: ch,
Indices.Z: zlayer
})
tileset.add_tile(tile)
if tile_opener is None:
def tile_opener(tileset_path, tile, ext):
tile_basename = os.path.splitext(tileset_path)[0]
if Indices.Z in tile.indices:
zval = tile.indices[Indices.Z]
zstr = "-Z{}".format(zval)
else:
zstr = ""
return open(
"{}-H{}-C{}{}.{}".format(
tile_basename,
tile.indices[Indices.ROUND],
tile.indices[Indices.CH],
zstr,
ext,
), "wb")
if not filepath.endswith('.json'):
filepath += '.json'
Writer.write_to_path(tileset,
filepath,
pretty=True,
tile_opener=tile_opener,
tile_format=tile_format)
def export(self,
filepath: str,
tile_opener: Optional[Callable[[PurePath, Tile, str], BinaryIO]] = None,
tile_format: ImageFormat=ImageFormat.NUMPY) -> None:
"""write the image tensor to disk in spaceTx format
Parameters
----------
filepath : str
Path + prefix for the images and primary_images.json written by this function
tile_opener : Optional[Callable[[PurePath, Tile, str], BinaryIO]]
A callable responsible for opening the file that a tile's data is to be written to. The
callable should accept three arguments -- the path of the tileset, the tile data, and
the expected file extension. If this is not specified, a reasonable default is provided.
tile_format : ImageFormat
Format in which each 2D plane should be written.
"""
# Add log data to extras
tileset_extras = self._tile_data.extras if self._tile_data else {}
tileset_extras[STARFISH_EXTRAS_KEY] = self.log.encode()
tileset = TileSet(
dimensions={
Axes.ROUND,
Axes.CH,
Axes.ZPLANE,
Axes.Y,
Axes.X,
},
shape={
Axes.ROUND: self.num_rounds,
Axes.CH: self.num_chs,
Axes.ZPLANE: self.num_zplanes,
},
default_tile_shape={Axes.Y: self.tile_shape[0], Axes.X: self.tile_shape[1]},
extras=tileset_extras,
)
for selector in self._iter_axes({Axes.ROUND, Axes.CH, Axes.ZPLANE}):
tilekey = TileKey(
round=selector[Axes.ROUND],
ch=selector[Axes.CH],
zplane=selector[Axes.ZPLANE])
extras: dict = self._tile_data[tilekey] if self._tile_data else {}
coordinates: MutableMapping[Coordinates, Union[Tuple[Number, Number], Number]] = dict()
x_coordinates = (float(self.xarray[Coordinates.X.value][0]),
float(self.xarray[Coordinates.X.value][-1]))
y_coordinates = (float(self.xarray[Coordinates.Y.value][0]),
float(self.xarray[Coordinates.Y.value][-1]))
coordinates[Coordinates.X] = x_coordinates
coordinates[Coordinates.Y] = y_coordinates
if Coordinates.Z in self.xarray.coords:
# set the z coord to the calculated value from the associated z plane
z_coordinates = float(self.xarray[Coordinates.Z.value][selector[Axes.ZPLANE]])
coordinates[Coordinates.Z] = z_coordinates
tile = Tile(
coordinates=coordinates,
indices=selector,
extras=extras,
)
tile.numpy_array, _ = self.get_slice(selector)
tileset.add_tile(tile)
if tile_opener is None:
def tile_opener(tileset_path: PurePath, tile: Tile, ext: str):
base = tileset_path.parent / tileset_path.stem
if Axes.ZPLANE in tile.indices:
zval = tile.indices[Axes.ZPLANE]
zstr = "-Z{}".format(zval)
else:
zstr = ""
return open(
"{}-H{}-C{}{}.{}".format(
str(base),
tile.indices[Axes.ROUND],
tile.indices[Axes.CH],
zstr,
ext,
),
"wb")
if not filepath.endswith('.json'):
filepath += '.json'
Writer.write_to_path(
tileset,
filepath,
pretty=True,
tile_opener=tile_opener,
tile_format=tile_format)
def build_image(
fov_count: int,
round_count: int,
ch_count: int,
z_count: int,
image_fetcher: TileFetcher,
default_shape: Optional[Tuple[int, int]] = None,
) -> Collection:
"""
Build and returns an image set with the following characteristics:
Parameters
----------
fov_count : int
Number of fields of view in this image set.
round_count : int
Number for rounds in this image set.
ch_count : int
Number for channels in this image set.
z_count : int
Number of z-layers in this image set.
image_fetcher : TileFetcher
Instance of TileFetcher that provides the data for the tile.
default_shape : Optional[Tuple[int, int]]
Default shape of the individual tiles in this image set.
Returns
-------
The slicedimage collection representing the image.
"""
collection = Collection()
for fov_ix in range(fov_count):
fov_images = TileSet(
[
Coordinates.X,
Coordinates.Y,
Coordinates.Z,
Indices.Z,
Indices.ROUND,
Indices.CH,
Indices.X,
Indices.Y,
],
{
Indices.ROUND: round_count,
Indices.CH: ch_count,
Indices.Z: z_count
},
default_shape,
ImageFormat.TIFF,
)
for z_ix in range(z_count):
for round_ix in range(round_count):
for ch_ix in range(ch_count):
image = image_fetcher.get_tile(fov_ix, round_ix, ch_ix,
z_ix)
tile = Tile(
image.coordinates,
{
Indices.Z: z_ix,
Indices.ROUND: round_ix,
Indices.CH: ch_ix,
},
image.shape,
extras=image.extras,
)
tile.set_numpy_array_future(image.tile_data)
fov_images.add_tile(tile)
collection.add_partition("fov_{:03}".format(fov_ix), fov_images)
return collection
