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BUG: Report standard deviations, not variances, in tract_math #145

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BUG: Report standard deviations, not variances, in tract_math #145

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44 changes: 44 additions & 0 deletions tract_querier/tests/test_tract_math.py
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Original file line number Diff line number Diff line change
Expand Up @@ -17,3 +17,47 @@ def test_tract_math_count(script_runner):
ret = script_runner.run(["tract_math", tract_file, "count"])
assert re.search('[^0-9]6783[^0-9]', ret.stdout) is not None
assert ret.success


def test_compute_all_measures_std():
import numpy
from tract_querier.tractography import Tractography
from tract_querier.tract_math.tensor_operations import compute_all_measures

lengths = numpy.array([10., 20., 40., 70.])
values = numpy.array([.2, .4, .5, .9])
tracts = [
numpy.c_[numpy.linspace(0, length, 11), numpy.zeros((11, 2))]
for length in lengths
]
data = {'fa': [numpy.full((11, 1), value) for value in values]}
tractography = Tractography(tracts, data)

measures = compute_all_measures(
tractography,
['length mean (mm)', 'length std (mm)',
'per tract distance weighted mean %s',
'per tract distance weighted std %s'],
scalars=['fa']
)

numpy.testing.assert_allclose(measures['length std (mm)'], lengths.std())
mean = numpy.average(values, weights=lengths)
numpy.testing.assert_allclose(
measures['per tract distance weighted mean fa'], mean
)
numpy.testing.assert_allclose(
measures['per tract distance weighted std fa'],
numpy.sqrt(numpy.average((values - mean) ** 2, weights=lengths))
)

tractography = Tractography(tracts[:1] * 4, data)
measures = compute_all_measures(
tractography,
['per tract distance weighted mean %s',
'per tract distance weighted std %s'],
scalars=['fa']
)
numpy.testing.assert_allclose(
measures['per tract distance weighted std fa'], values.std()
)
4 changes: 2 additions & 2 deletions tract_querier/tract_math/operations.py
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Original file line number Diff line number Diff line change
Expand Up @@ -40,7 +40,7 @@ def count(optional_flags, tractographies):

@tract_math_operation(': calculates mean and std of tract length')
def length_mean_std(optional_flags, tractography):
return tensor_operations.compute_all_measures(tractography, ['length mean (mm)', 'length std (mm^2)'])
return tensor_operations.compute_all_measures(tractography, ['length mean (mm)', 'length std (mm)'])


@tract_math_operation('<volume unit>: calculates the volume of a tract based on voxel occupancy of a certain voxel volume')
Expand Down Expand Up @@ -93,7 +93,7 @@ def scalar_compute_most(optional_flags, tractographies, scalar):
['per tract distance weighted mean %s',
'per tract distance weighted std %s',
'tract volume',
'length mean (mm)', 'length std (mm^2)',
'length mean (mm)', 'length std (mm)',
'number of tracts'
],
scalars=scalars, resolution=1.)
Expand Down
8 changes: 4 additions & 4 deletions tract_querier/tract_math/tensor_operations.py
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Expand Up @@ -14,12 +14,12 @@ def compute_all_measures(tractography, desired_keys_list, scalars=None, resoluti
unordered_results['number of tracts'] = tract_operations.tract_count(
tractography.tracts())

if ('length mean (mm)' in desired_keys_list) or ('length std (mm^2)' in desired_keys_list):
if ('length mean (mm)' in desired_keys_list) or ('length std (mm)' in desired_keys_list):
lengths = numpy.empty(len(tractography.tracts()))
for i, one_tract in enumerate(tractography.tracts()):
lengths[i] = tract_operations.tract_length(one_tract)
unordered_results['length mean (mm)'] = lengths.mean()
unordered_results['length std (mm^2)'] = lengths.std()
unordered_results['length std (mm)'] = lengths.std()

if ('tract volume' in desired_keys_list) and (resolution is not None):
resolution = float(resolution)
Expand Down Expand Up @@ -66,8 +66,8 @@ def compute_all_measures(tractography, desired_keys_list, scalars=None, resoluti
weighted_scalars[line_index, 1] = length
mean = numpy.average(
weighted_scalars[:, 0], weights=weighted_scalars[:, 1])
std = numpy.average(
(weighted_scalars[:, 0] - mean) ** 2, weights=weighted_scalars[:, 1])
std = numpy.sqrt(numpy.average(
(weighted_scalars[:, 0] - mean) ** 2, weights=weighted_scalars[:, 1]))
unordered_results[mean_key] = mean
unordered_results[std_key] = std
mii = desired_keys_list.index('per tract distance weighted mean %s')
Expand Down
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