def getMetricsFromAparcStats(fn, metric):
print(
"Getting metrics from aparc stats - this is a flag that this function is ever called"
)
# Get the two aparc.stats filenames from the base filename
rhfn = fn.replace("aseg.stats", "rh.aparc.stats")
lhfn = fn.replace("aseg.stats", "lh.aparc.stats")
# Read the stats in for each hemisphere
rhStats = CorticalParcellationStats.read(rhfn)
lhStats = CorticalParcellationStats.read(lhfn)
# Depending on the metric, need to look at different values
if metric == "CSF":
# Get whole brain values from the stats
rhDf = rhStats.whole_brain_measurements
lhDf = lhStats.whole_brain_measurements
# Get left and right CSF
rCsf = rhDf['brain_segmentation_volume_mm^3'].values[0] - rhDf[
'brain_segmentation_volume_without_ventricles_mm^3'].values[0]
lCsf = lhDf['brain_segmentation_volume_mm^3'].values[0] - lhDf[
'brain_segmentation_volume_without_ventricles_mm^3'].values[0]
# sum values together
measure = rCsf + lCsf
elif "Cortical" in metric:
# Get all of the rows at the bottom of the aparg.stats file
rhDf = rhStats.structural_measurements
lhDf = lhStats.structural_measurements
if "SurfaceArea" in metric:
# Sum the values in the SurfArea column
measure = sum(rhDf['surface_area_mm^2']) + sum(
lhDf['surface_area_mm^2'])
elif "ThickAvg":
# Sum the values in the ThickAvg column
measure = sum(rhDf['average_thickness_mm']) + sum(
lhDf['average_thickness_mm'])
if metric.startswith("Avg"):
denom = len(rhDf['average_thickness_mm']) + len(
lhDf['average_thickness_mm'])
measure = measure / denom
else:
measure = -1
return measure
def test__parse_whole_brain_measurements_line(line, expected_column_name,
expected_value):
# pylint: disable=protected-access
column_name, value = CorticalParcellationStats._parse_whole_brain_measurements_line(
line)
assert column_name == expected_column_name
assert numpy.allclose(value, [expected_value])
def test_read(path, headers, hemisphere, whole_brain_measurements,
structural_measurements):
stats = CorticalParcellationStats.read(path)
assert headers == stats.headers
assert hemisphere == stats.hemisphere
pandas.util.testing.assert_frame_equal(
left=pandas.DataFrame([whole_brain_measurements]),
right=stats.whole_brain_measurements,
check_like=True, # ignore the order of index & columns
check_dtype=True,
check_names=True,
)
assert list(stats.structural_measurements.columns) == [
'structure_name',
'number_of_vertices',
'surface_area_mm^2',
'gray_matter_volume_mm^3',
'average_thickness_mm',
'thickness_stddev_mm',
'integrated_rectified_mean_curvature_mm^-1',
'integrated_rectified_gaussian_curvature_mm^-2',
'folding_index',
'intrinsic_curvature_index',
]
pandas.util.testing.assert_frame_equal(
left=pandas.DataFrame(structural_measurements),
right=stats.structural_measurements,
check_like=True, # ignore the order of index & columns
check_dtype=True,
check_names=True,
)
def getCorticalThicknessIfs(fn, side, statsBase):
# Get the two aparc.stats filenames from the base filename
newFn = fn.replace(statsBase, side + ".aparc.stats")
# Read the stats in for each hemisphere
stats = CorticalParcellationStats.read(newFn)
# Get all of the rows at the bottom of the aparg.stats file
df = stats.structural_measurements
# if "SurfaceArea" in metric:
# # Sum the values in the SurfArea column
# measure = sum(rhDf['surface_area_mm^2']) + sum(lhDf['surface_area_mm^2'])
# Sum the values in the ThickAvg column
measure = sum(df['average_thickness_mm'])
denom = len(df['average_thickness_mm'])
measure = measure / denom
return measure
def test_read(
path,
headers,
hemisphere,
whole_brain_measurements,
structural_measurements_length,
structural_measurements_subset,
):
stats = CorticalParcellationStats.read(path)
assert headers == stats.headers
assert hemisphere == stats.hemisphere
pandas.util.testing.assert_frame_equal(
left=pandas.DataFrame([whole_brain_measurements]),
right=stats.whole_brain_measurements,
check_like=True, # ignore the order of index & columns
check_dtype=True,
check_names=True,
)
assert list(stats.structural_measurements.columns) == [
"structure_name",
"number_of_vertices",
"surface_area_mm^2",
"gray_matter_volume_mm^3",
"average_thickness_mm",
"thickness_stddev_mm",
"integrated_rectified_mean_curvature_mm^-1",
"integrated_rectified_gaussian_curvature_mm^-2",
"folding_index",
"intrinsic_curvature_index",
]
assert len(stats.structural_measurements) == structural_measurements_length
pandas.util.testing.assert_frame_equal(
left=pandas.DataFrame(structural_measurements_subset),
right=stats.structural_measurements.iloc[:3],
check_like=True, # ignore the order of index & columns
check_dtype=True,
check_names=True,
)
tot = lines_var[16]
tot = tot.replace(',', '')
lh_wm_vol = float(lh.split()[10])
rh_wm_vol = float(rh.split()[10])
tot_wm_vol = float(tot.split()[9])
return [lh_wm_vol, rh_wm_vol, tot_wm_vol]
with open('config.json') as config_f:
config = json.load(config_f)
output_dir = config["freesurfer"]
parc = config["parcellation"]
# left hemisphere
lh_stats = CorticalParcellationStats.read(output_dir + '/stats/lh.' + parc +
'.stats')
dfl = lh_stats.structural_measurements
dfl.to_csv('lh.cortex.csv')
# right hemisphere
rh_stats = CorticalParcellationStats.read(output_dir + '/stats/rh.' + parc +
'.stats')
dfr = rh_stats.structural_measurements
dfr.to_csv('rh.cortex.csv')
# whole brain
white_matter_stats = open(output_dir + '/stats/wmparc.stats')
[lh_wm_vol, rh_wm_vol, tot_wm_vol] = extract_wm_stats(white_matter_stats)
whole_brain = lh_stats.whole_brain_measurements[[
'brain_segmentation_volume_mm^3',
"l": 175,
"b": 30,
}
}
}
results["brainlife"].append(graph)
for parc in [
"aparc", "aparc.a2009s", "aparc.DKTatlas", "aparc.DKTatlas40"
]:
if os.path.exists(freesurfer_dir + '/stats/lh.' + parc + '.stats'):
#convert stats to csv
lh_stats = CorticalParcellationStats.read(freesurfer_dir +
'/stats/lh.' + parc +
'.stats')
dfl = lh_stats.structural_measurements
dfl.to_csv("secondary/" + parc + '_lh-cortex.csv')
rh_stats = CorticalParcellationStats.read(freesurfer_dir +
'/stats/rh.' + parc +
'.stats')
dfr = rh_stats.structural_measurements
dfr.to_csv("secondary/" + parc + '_rh-cortex.csv')
#these value are very close between different parcellation, so let's just output for aparc
if parc == "aparc":
######################### volume ###################################################
x = []
y = []
def test_read_https(url: str):
stats = CorticalParcellationStats.read(url)
assert stats.headers["generating_program"] == "mris_anatomical_stats"
def test_read_pathlib(path_str: str):
stats_str = CorticalParcellationStats.read(path_str)
stats_pathlib = CorticalParcellationStats.read(pathlib.Path(path_str))
assert stats_str.headers == stats_pathlib.headers
def test__parse_whole_brain_measurements_line_parse_error(line):
# pylint: disable=protected-access
with pytest.raises(ValueError):
CorticalParcellationStats._parse_whole_brain_measurements_line(line)
def test_read_structural_measurements_length(path,
structural_measurements_length):
# simple test to verify no exception gets raised, see test_read for comprehensive test
stats = CorticalParcellationStats.read(path)
assert len(stats.structural_measurements) == structural_measurements_length