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added standard error of the mean to statistics_of_labelled_pixels #267

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added standard error of the mean to statistics_of_labelled_pixels #267

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61c7a31
added standard error of the mean to statistics_of_labelled_pixels
thawn Feb 27, 2023
704dc9a
use np.sqrt() instead of power()
thawn Feb 28, 2023
eab9941
fixed SEM calculation
thawn Mar 7, 2023
cddfacc
added test for standard error
thawn Mar 8, 2023
5048b66
added standard_error_intensity to push_regionprops
thawn Mar 20, 2023
ac21dc6
added tests for push_regionprops with SEM
thawn Mar 20, 2023
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4 changes: 3 additions & 1 deletion pyclesperanto_prototype/_tier9/_push_regionprops.py
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Original file line number Diff line number Diff line change
Expand Up @@ -97,7 +97,9 @@ def push_regionprops(props : Union[dict, RegionProperties], first_row_index : in
# MAX_DISTANCE_TO_CENTROID = 34
props['max_distance_to_centroid'],
# MAX_MEAN_DISTANCE_TO_CENTROID_RATIO = 35
props['mean_max_distance_to_centroid_ratio']
props['mean_max_distance_to_centroid_ratio'],
# STANDARD_ERROR_INTENSITY = 36
props['standard_error_intensity']
]

matrix = np.asarray(list).T
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3 changes: 3 additions & 0 deletions pyclesperanto_prototype/_tier9/_statistics_of_labelled_pixels.py
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Expand Up @@ -276,6 +276,9 @@ def statistics_of_labelled_pixels(intensity_image : Image = None, label_image :
power(sum_dim, result_vector, 0.5)
region_props['standard_deviation_intensity'] = pull(result_vector)[0]

# standard error intensity
region_props['standard_error_intensity'] = region_props['standard_deviation_intensity'] / np.sqrt(region_props['area'])

crop(max_statistics, result_vector, measurements_start_x, 4, 0)
region_props['max_distance_to_centroid'] = pull(result_vector)[0]
crop(max_statistics, result_vector, measurements_start_x, 5, 0)
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17 changes: 12 additions & 5 deletions tests/test_statistics_of_labelled_pixels.py
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Expand Up @@ -90,8 +90,10 @@ def test_statistics_of_labelled_pixels():
[0.65403885, 0.65403885, 0.6666667],
# MAX_DISTANCE_TO_CENTROID(34),
[0.74535596, 0.745356, 1.],
# MAX_MEAN_DISTANCE_TO_CENTROID_RATIO(35);
[1.1396203, 1.1396204, 1.5]
# MAX_MEAN_DISTANCE_TO_CENTROID_RATIO(35),
[1.1396203, 1.1396204, 1.5],
# STANDARD_ERROR_INTENSITY(36);
[0.27216553, 0.27216551, 0.4714043]
]
))

Expand Down Expand Up @@ -205,8 +207,10 @@ def test_statistics_of_labelled_pixels_3d():
[0.65403885, 0.65403885, 0.6666667],
# MAX_DISTANCE_TO_CENTROID(34),
[0.74535596, 0.745356, 1.],
# MAX_MEAN_DISTANCE_TO_CENTROID_RATIO(35);
[1.1396203, 1.1396204, 1.5]
# MAX_MEAN_DISTANCE_TO_CENTROID_RATIO(35),
[1.1396203, 1.1396204, 1.5],
# STANDARD_ERROR_INTENSITY(36);
[0.27216553, 0.27216551, 0.4714043]
]
))

Expand All @@ -226,7 +230,7 @@ def test_statistics_of_labelled_pixels_3d():
assert (np.allclose(a, b, 0.0001))


def test_standard_deviation():
def test_standard_deviation_and_standard_error():
import numpy as np

image = np.asarray([
Expand All @@ -243,11 +247,14 @@ def test_standard_deviation():

print("numpy standard deviation", np.std(image))
print("numpy variance", np.var(image))
print("numpy standard error", np.std(image) / np.sqrt(image.size))

stats = cle.statistics_of_labelled_pixels(image, labels)

print("std", stats['standard_deviation_intensity'][0])
print("sterr", stats['standard_error_intensity'][0])

from numpy import testing
testing.assert_almost_equal(stats['standard_deviation_intensity'][0], 2.58, decimal=1)
testing.assert_almost_equal(stats['standard_error_intensity'][0], 0.86, decimal=1)