def isel(self, indexers: Mapping[Axes, Union[int, tuple]]):
"""Given a dictionary mapping the index name to either a value or a range represented as a
tuple, return an Imagestack with each dimension indexed by position accordingly
Parameters
----------
indexers : Dict[Axes, (int/tuple)]
A dictionary of dim:index where index is the value or range to index the dimension
Examples
--------
Create an Imagestack using the ``synthetic_stack`` method
>>> from starfish import ImageStack
>>> from starfish.types import Axes
>>> stack = ImageStack.synthetic_stack(5, 5, 15, 200, 200)
>>> stack
<starfish.ImageStack (r: 5, c: 5, z: 15, y: 200, x: 200)>
>>> stack.isel({Axes.ROUND: (1, None), Axes.CH: 0, Axes.ZPLANE: 0})
<starfish.ImageStack (r: 4, c: 1, z: 1, y: 200, x: 200)>
>>> stack.isel({Axes.ROUND: 0, Axes.CH: 0, Axes.ZPLANE: 1,
...Axes.Y: 100, Axes.X: (None, 100)})
<starfish.ImageStack (r: 1, c: 1, z: 1, y: 1, x: 100)>
and the imagestack's physical coordinates
xarray also indexed and recalculated according to the x,y slicing.
Returns
-------
ImageStack :
a new image stack indexed by given value or range.
"""
stack = deepcopy(self)
selector = indexing_utils.convert_to_selector(indexers)
stack._data._data = indexing_utils.index_keep_dimensions(self.xarray, selector, by_pos=True)
return stack
def get_partial(self, indexers: Mapping[Axes, Union[int, slice, Sequence]]):
"""
Slice the codebook data according to the provided indexing parameters. Used in a composite
codebook scenario.
Parameters
----------
indexers : Mapping[Axes, Union[int, Sequence]]
A dictionary of dim:index where index is the value, values or range to index the
dimension
"""
selector = indexing_utils.convert_to_selector(indexers)
return indexing_utils.index_keep_dimensions(self, indexers=selector)
def sel(self, indexers: Mapping[Axes, Union[int, slice, Sequence]]):
"""Given a dictionary mapping the index name to either a value or a range represented as a
tuple, return an Imagestack with each dimension indexed accordingly
Parameters
----------
indexers : Mapping[Axes, Union[int, Union[int, Sequence]]
A dictionary of dim:index where index is the value, values, or range to index the
dimension
Examples
--------
Create an Imagestack :py:func:`~starfish.imagestack.imagestack.ImageStack.synthetic_stack`
>>> from starfish import ImageStack
>>> from starfish.core.imagestack.test.factories import synthetic_stack
>>> from starfish.types import Axes
>>> stack = synthetic_stack(5, 5, 15, 200, 200)
>>> stack
<starfish.ImageStack (r: 5, c: 5, z: 15, y: 200, x: 200)>
>>> stack.sel({Axes.ROUND: (1, None), Axes.CH: 0, Axes.ZPLANE: 0})
<starfish.ImageStack (r: 4, c: 1, z: 1, y: 200, x: 200)>
>>> stack.sel({Axes.ROUND: 0, Axes.CH: 0, Axes.ZPLANE: 1,
...Axes.Y: 100, Axes.X: (None, 100)})
<starfish.ImageStack (r: 1, c: 1, z: 1, y: 1, x: 100)>
and the imagestack's physical coordinates
xarray also indexed and recalculated according to the x,y slicing.
Returns
-------
ImageStack :
a new image stack indexed by given value or range.
"""
self._ensure_data_loaded()
stack = deepcopy(self)
selector = indexing_utils.convert_to_selector(indexers)
stack._data = indexing_utils.index_keep_dimensions(
self.xarray, selector)
return stack
def process_fov(fov_num: int, round_num: int, experiment_str: str):
"""Process a single field of view of ISS data
Parameters
----------
fov_num : int
The field of view to process
experiment_str : int
path of experiment json file
round_num : int
The round number within the given fov to process
Returns
-------
DecodedSpots :
tabular object containing the locations of detected spots.
"""
fov_str: str = f"fov_{int(fov_num):03d}"
# load experiment
experiment = starfish.Experiment.from_json(experiment_str)
fov = experiment[fov_str]
img = fov.get_images(starfish.FieldOfView.PRIMARY_IMAGES,
rounds=[round_num])
codebook = indexing_utils.index_keep_dimensions(experiment.codebook,
{Axes.ROUND: round_num})
# filter
clip1 = starfish.image.Filter.Clip(p_min=50, p_max=100)
clip1.run(img)
bandpass = starfish.image.Filter.Bandpass(lshort=.5,
llong=7,
threshold=0.0)
bandpass.run(img)
glp = starfish.image.Filter.GaussianLowPass(sigma=(1, 0, 0),
is_volume=True)
glp.run(img)
clip2 = starfish.image.Filter.Clip(p_min=99, p_max=100, is_volume=True)
clip2.run(img)
# find spots
tlmpf = starfish.spots.DetectSpots.TrackpyLocalMaxPeakFinder(
spot_diameter=5, # must be odd integer
min_mass=0.02,
max_size=2, # this is max radius
separation=7,
noise_size=0.65, # this is not used because preprocess is False
preprocess=False,
percentile=
10, # this is irrelevant when min_mass, spot_diameter, and max_size are set properly
verbose=True,
is_volume=True,
)
intensities = tlmpf.run(img)
# decode
decoded = codebook.decode_per_round_max(intensities)
# save results
df = decoded.to_decoded_spots()
return df