def test_apply_labeled_dataset():
"""
test that apply correctly applies a simple function across starfish-generated synthetic data
"""
original = SyntheticData().spots()
image = original.apply(divide, value=2)
assert np.all(image.xarray == original.xarray / 2)
def test_apply_in_place():
"""
test that apply correctly applies a simple function across a starfish stack without modifying
original data
"""
image = SyntheticData().spots()
original = copy.deepcopy(image)
image.apply(divide, value=2, in_place=True)
assert np.all(image.xarray == original.xarray / 2)
def test_fov_order():
data = SyntheticData()
codebook = data.codebook()
tilesets = {"primary": get_aligned_tileset()}
fovs = [FieldOfView("stack2", tilesets), FieldOfView("stack1", tilesets)]
extras = {"synthetic": True}
experiment = Experiment(fovs, codebook, extras)
assert "stack1" == experiment.fov().name
assert ["stack1", "stack2"] == [x.name for x in experiment.fovs()]
def test_medium_synthetic_stack():
np.random.seed(0)
n_z = 40
height = 300
width = 400
sigma = 2
sd = SyntheticData(
n_round=4,
n_ch=4,
n_z=n_z,
height=height,
width=width,
n_spots=100,
n_codes=10,
point_spread_function=(sigma, sigma, sigma),
)
codebook = sd.codebook()
intensities = sd.intensities(codebook=codebook)
# some spots won't be detected properly because they will spread outside the image where
# blurred,
# so we'll remove those from intensities before we generate spots.
spot_radius = sigma * np.sqrt(
2) # this is the radius of the spot in pixels
valid_z = np.logical_and(intensities.z.values > spot_radius,
intensities.z.values < (n_z - spot_radius))
valid_y = np.logical_and(intensities.y.values > spot_radius,
intensities.y.values < (height - spot_radius))
valid_x = np.logical_and(intensities.x.values > spot_radius,
intensities.x.values < (width - spot_radius))
valid_locations = valid_z & valid_y & valid_x
intensities = intensities[np.where(valid_locations)]
spots = sd.spots(intensities=intensities)
gsd = BlobDetector(min_sigma=1, max_sigma=4, num_sigma=5, threshold=1e-4)
spots_max_projector = Filter.Reduce((Axes.ROUND, Axes.CH),
func="max",
module=FunctionSource.np)
spots_max = spots_max_projector.run(spots)
spot_results = gsd.run(image_stack=spots, reference_image=spots_max)
decoder = DecodeSpots.MetricDistance(codebook=codebook,
max_distance=1,
min_intensity=0,
norm_order=2)
decoded_intensities = decoder.run(spots=spot_results)
assert len(decoded_intensities.coords[Features.TARGET]) == 80
def test_metric_decoder_original_intensities_flag():
np.random.seed(0)
n_z = 40
height = 300
width = 400
sigma = 2
sd = SyntheticData(
n_round=4,
n_ch=4,
n_z=n_z,
height=height,
width=width,
n_spots=100,
n_codes=10,
point_spread_function=(sigma, sigma, sigma),
)
codebook = sd.codebook()
intensities = sd.intensities(codebook=codebook)
spots = sd.spots(intensities=intensities)
gsd = BlobDetector(min_sigma=1, max_sigma=4, num_sigma=5, threshold=1e-4)
spots_max_projector = Filter.Reduce((Axes.ROUND, Axes.CH),
func=FunctionSource.np("max"))
spots_max = spots_max_projector.run(spots)
spot_results = gsd.run(image_stack=spots, reference_image=spots_max)
og_intensities = build_spot_traces_exact_match(spot_results=spot_results)
decoder = DecodeSpots.MetricDistance(codebook=codebook,
max_distance=1,
min_intensity=0,
norm_order=2,
return_original_intensities=True)
decoded_intensities = decoder.run(spots=spot_results)
# assert that original intensities were returned in the decocded intensity file
assert np.array_equal(decoded_intensities.values, og_intensities.values)
# now run without flag, assert intensities change
decoder = DecodeSpots.MetricDistance(codebook=codebook,
max_distance=1,
min_intensity=0,
norm_order=2,
return_original_intensities=False)
decoded_intensities = decoder.run(spots=spot_results)
assert not np.array_equal(decoded_intensities.values,
og_intensities.values)
def test_round_trip_synthetic_data():
np.random.seed(0)
sd = SyntheticData(
n_ch=2,
n_round=3,
n_spots=1,
n_codes=4,
n_photons_background=0,
background_electrons=0,
camera_detection_efficiency=1.0,
gray_level=1,
ad_conversion_bits=16,
point_spread_function=(2, 2, 2),
)
codebook = sd.codebook()
intensities = sd.intensities(codebook=codebook)
spots = sd.spots(intensities=intensities)
spots_max_projector = Filter.Reduce((Axes.ROUND, Axes.CH),
func="max",
module=FunctionSource.np)
spots_max = spots_max_projector.run(spots)
gsd = BlobDetector(min_sigma=1, max_sigma=4, num_sigma=5, threshold=0)
spot_results = gsd.run(image_stack=spots, reference_image=spots_max)
decoder = DecodeSpots.MetricDistance(codebook=codebook,
max_distance=1,
min_intensity=0,
norm_order=2)
decoded_intensities = decoder.run(spots=spot_results)
# applying the gaussian blur to the intensities causes them to be reduced in magnitude, so
# they won't be the same size, but they should be in the same place, and decode the same
# way
spot1, ch1, round1 = np.where(intensities.values)
spot2, ch2, round2 = np.where(decoded_intensities.values)
assert np.array_equal(spot1, spot2)
assert np.array_equal(ch1, ch2)
assert np.array_equal(round1, round2)
assert len(decoded_intensities.coords[Features.TARGET]) == 1
import numpy as np
import pytest
from starfish import BinaryMaskCollection, display, LabelImage
from starfish.core.test.factories import SyntheticData
from starfish.types import Coordinates
sd = SyntheticData(
n_ch=2,
n_round=3,
n_spots=1,
n_codes=4,
n_photons_background=0,
background_electrons=0,
camera_detection_efficiency=1.0,
gray_level=1,
ad_conversion_bits=16,
point_spread_function=(2, 2, 2),
)
stack = sd.spots()
spots = sd.intensities()
label_image = LabelImage.from_label_array_and_ticks(
np.random.randint(0, 4, size=(128, 128), dtype=np.uint8),
None,
{
Coordinates.Y: np.arange(128),
Coordinates.X: np.arange(128)
},
None,
)