def synthetic_dataset_with_truth_values_and_called_spots(
synthetic_dataset_with_truth_values):
codebook, true_intensities, image = synthetic_dataset_with_truth_values
wth = WhiteTophat(masking_radius=15)
filtered = wth.run(image, in_place=False)
filtered_mp = filtered.max_proj(Indices.CH, Indices.ROUND)
filtered_mp_numpy = filtered_mp._squeezed_numpy(Indices.CH, Indices.ROUND)
min_sigma = 1.5
max_sigma = 4
num_sigma = 10
threshold = 1e-4
gsd = BlobDetector(min_sigma=min_sigma,
max_sigma=max_sigma,
num_sigma=num_sigma,
threshold=threshold,
measurement_type='max')
intensities = gsd.run(data_stack=filtered, blobs_image=filtered_mp_numpy)
assert intensities.shape[0] == 5
codebook.metric_decode(intensities,
max_distance=1,
min_intensity=0,
norm_order=2)
return codebook, true_intensities, image, intensities
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 when 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)
calculated_intensities = gsd.run(spots,
blobs_image=spots,
blobs_axes=(Axes.ROUND, Axes.CH))
calculated_intensities = codebook.metric_decode(calculated_intensities,
max_distance=1,
min_intensity=0,
norm_order=2)
# spots are detected in a different order that they're generated; sorting makes comparison easy
sorted_intensities = intensities.sortby(
[Axes.ZPLANE.value, Axes.Y.value, Axes.X.value])
sorted_calculated_intensities = calculated_intensities.sortby(
[Axes.ZPLANE.value, Axes.Y.value, Axes.X.value])
# verify that the spots are all detected, and decode to the correct targets
assert np.array_equal(
sorted_intensities[Features.TARGET].values,
sorted_calculated_intensities[Features.TARGET].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_mp = spots.max_proj(Axes.CH, Axes.ROUND)
spots_mp_numpy = spots_mp._squeezed_numpy(Axes.CH, Axes.ROUND)
gsd = BlobDetector(min_sigma=1, max_sigma=4, num_sigma=5, threshold=0)
calculated_intensities = gsd.run(spots, blobs_image=spots_mp_numpy)
decoded_intensities = codebook.metric_decode(
calculated_intensities,
max_distance=1,
min_intensity=0,
norm_order=2
)
# 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(calculated_intensities.values)
assert np.array_equal(spot1, spot2)
assert np.array_equal(ch1, ch2)
assert np.array_equal(round1, round2)
assert np.array_equal(
intensities.coords[Features.TARGET],
decoded_intensities.coords[Features.TARGET]
)