def compute_stability(gene, bootstrap=500, force2D=True):
total_mads = []
imageset = ImageSet(analysis_repo, ["mrna/" + gene + "/"], force2D=force2D)
spots_peripheral_distances = imageset.compute_spots_peripheral_distance()
peripheral_profiles = np.zeros((len(spots_peripheral_distances), 10))
for j in range(len(spots_peripheral_distances)):
for i in range(0, 10):
peripheral_profiles[j, i] = float(
len(
np.where((spots_peripheral_distances[j] >= (
(i * 10) + 1)) & (spots_peripheral_distances[j] <=
(i + 1) * 10))[0]) /
float(len(spots_peripheral_distances[j])))
logger.info(
"Compute mean_absolute_deviation for randomly selected images in {} dataset",
gene)
for j in tqdm.tqdm(range(bootstrap), desc="Simulation"):
mads = []
for i in range(1, len(imageset.images) - 1):
arr = peripheral_profiles[np.random.choice(
peripheral_profiles.shape[0], i, replace=True)]
rand_idx = randint(0, peripheral_profiles.shape[0] - 1)
mean_arr = np.mean(arr, axis=0)
arr_diff = mean_arr - peripheral_profiles[rand_idx, :]
mse = helpers.mean_absolute_deviation(arr_diff)
mads.append(mse)
total_mads.append(mads)
return total_mads
def test_compute_spots_peripheral_distance(self):
image_set = ImageSet(self.repo, path_list=['mrna/arhgdia/2h/'])
result = image_set.compute_spots_peripheral_distance()
total_sum=0
for res in result:
total_sum += np.sum(res)
self.assertEqual(total_sum, 20030.0)
