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desc format cleanup
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‎nipype/interfaces/afni/preprocess.py

Lines changed: 16 additions & 15 deletions
Original file line numberDiff line numberDiff line change
@@ -1866,23 +1866,24 @@ class ROIStatsInputSpec(CommandLineInputSpec):
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desc='statistics to compute. Options include: '
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' * mean = Compute the mean using only non_zero voxels.'
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' Implies the opposite for the mean computed '
1869-
' by default.'
1870-
' * median = Compute the median of nonzero voxels'
1871-
' * mode = Compute the mode of nonzero voxels. '
1872-
' (integral valued sets only)'
1873-
' * minmax = Compute the min/max of nonzero voxels'
1874-
' * sum = Compute the sum using only nonzero voxels.'
1875-
' * voxels = Compute the number of nonzero voxels'
1876-
' * sigma = Compute the standard deviation of nonzero '
1877-
' voxels'
1878-
'Statistics that include zero-valued voxels:'
1879-
' * zerominmax = Compute the min/max of all voxels.'
1880-
' * zerosigma = Compute the standard deviation of all voxels.'
1881-
' * zeromedian = Compute the median of all voxels.'
1882-
' * zeromode = Compute the mode of all voxels.'
1869+
' by default.\n'
1870+
' * median = Compute the median of nonzero voxels\n'
1871+
' * mode = Compute the mode of nonzero voxels.'
1872+
' (integral valued sets only)\n'
1873+
' * minmax = Compute the min/max of nonzero voxels\n'
1874+
' * sum = Compute the sum using only nonzero voxels.\n'
1875+
' * voxels = Compute the number of nonzero voxels\n'
1876+
' * sigma = Compute the standard deviation of nonzero'
1877+
' voxels\n'
1878+
'Statistics that include zero-valued voxels:\n'
1879+
' * zerominmax = Compute the min/max of all voxels.\n'
1880+
' * zerosigma = Compute the standard deviation of all'
1881+
' voxels.\n'
1882+
' * zeromedian = Compute the median of all voxels.\n'
1883+
' * zeromode = Compute the mode of all voxels.\n'
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' * summary = Only output a summary line with the grand '
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' mean across all briks in the input dataset.'
1885-
' This option cannot be used with nomeanout.'
1886+
' This option cannot be used with nomeanout.\n'
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'More that one option can be specified.',
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argstr='%s...')
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out_file = File(

‎nipype/interfaces/afni/utils.py

Lines changed: 40 additions & 39 deletions
Original file line numberDiff line numberDiff line change
@@ -1481,47 +1481,47 @@ class LocalstatInputSpec(AFNICommandInputSpec):
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traits.Tuple(traits.Enum('perc'),
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traits.Tuple(traits.Float, traits.Float, traits.Float))),
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mandatory=True,
1484-
desc='statistics to compute. Possible names are :'
1485-
' * mean = average of the values'
1486-
' * stdev = standard deviation'
1487-
' * var = variance (stdev*stdev)'
1488-
' * cvar = coefficient of variation = stdev/fabs(mean)'
1489-
' * median = median of the values'
1490-
' * MAD = median absolute deviation'
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' * min = minimum'
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' * max = maximum'
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' * absmax = maximum of the absolute values'
1494-
' * num = number of the values in the region:'
1484+
desc='statistics to compute. Possible names are :\n'
1485+
' * mean = average of the values\n'
1486+
' * stdev = standard deviation\n'
1487+
' * var = variance (stdev*stdev)\n'
1488+
' * cvar = coefficient of variation = stdev/fabs(mean)\n'
1489+
' * median = median of the values\n'
1490+
' * MAD = median absolute deviation\n'
1491+
' * min = minimum\n'
1492+
' * max = maximum\n'
1493+
' * absmax = maximum of the absolute values\n'
1494+
' * num = number of the values in the region:\n'
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' with the use of -mask or -automask,'
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' the size of the region around any given'
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' voxel will vary; this option lets you'
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' map that size. It may be useful if you'
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' plan to compute a t-statistic (say) from'
1500-
' the mean and stdev outputs.'
1501-
' * sum = sum of the values in the region:'
1500+
' the mean and stdev outputs.\n'
1501+
' * sum = sum of the values in the region\n'
15021502
' * FWHM = compute (like 3dFWHM) image smoothness'
15031503
' inside each voxel\'s neighborhood. Results'
15041504
' are in 3 sub-bricks: FWHMx, FHWMy, and FWHMz.'
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' Places where an output is -1 are locations'
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' where the FWHM value could not be computed'
1507-
' (e.g., outside the mask).'
1507+
' (e.g., outside the mask).\n'
15081508
' * FWHMbar= Compute just the average of the 3 FWHM values'
1509-
' (normally would NOT do this with FWHM also).'
1510-
' * perc:P0:P1:Pstep = '
1509+
' (normally would NOT do this with FWHM also).\n'
1510+
' * perc:P0:P1:Pstep = \n'
15111511
' Compute percentiles between P0 and P1 with a '
1512-
' step of Pstep.'
1513-
' Default P1 is equal to P0 and default P2 = 1'
1514-
' * rank = rank of the voxel\'s intensity'
1515-
' * frank = rank / number of voxels in neighborhood'
1512+
' step of Pstep.\n'
1513+
' Default P1 is equal to P0 and default P2 = 1\n'
1514+
' * rank = rank of the voxel\'s intensity\n'
1515+
' * frank = rank / number of voxels in neighborhood\n'
15161516
' * P2skew = Pearson\'s second skewness coefficient'
1517-
' 3 * (mean - median) / stdev '
1517+
' 3 * (mean - median) / stdev\n'
15181518
' * ALL = all of the above, in that order '
1519-
' (except for FWHMbar and perc).'
1519+
' (except for FWHMbar and perc).\n'
15201520
' * mMP2s = Exactly the same output as:'
1521-
' median, MAD, P2skew'
1522-
' but it a little faster'
1521+
' median, MAD, P2skew,'
1522+
' but a little faster\n'
15231523
' * mmMP2s = Exactly the same output as:'
1524-
' mean, median, MAD, P2skew'
1524+
' mean, median, MAD, P2skew\n'
15251525
'More than one option can be used.',
15261526
argstr='-stat %s...')
15271527
mask_file = traits.File(
@@ -1537,7 +1537,7 @@ class LocalstatInputSpec(AFNICommandInputSpec):
15371537
nonmask = traits.Bool(
15381538
desc='Voxels not in the mask WILL have their local statistics '
15391539
'computed from all voxels in their neighborhood that ARE in '
1540-
'the mask.'
1540+
'the mask.\n'
15411541
' * For instance, this option can be used to compute the '
15421542
' average local white matter time series, even at non-WM '
15431543
' voxels.',
@@ -2328,9 +2328,10 @@ class ReHoInputSpec(CommandLineInputSpec):
23282328
xor=['sphere', 'ellipsoid'],
23292329
argstr='-nneigh %s',
23302330
desc='voxels in neighborhood. can be: '
2331-
'faces (for voxel and 6 facewise neighbors, only),'
2332-
'edges (for voxel and 18 face- and edge-wise neighbors),'
2333-
'vertices (for voxel and 26 face-, edge-, and node-wise neighbors).')
2331+
'* faces (for voxel and 6 facewise neighbors, only),\n'
2332+
'* edges (for voxel and 18 face- and edge-wise neighbors),\n'
2333+
'* vertices (for voxel and 26 face-, edge-, and node-wise '
2334+
'neighbors).\n')
23342335
sphere = traits.Float(
23352336
argstr='-neigh_RAD %s',
23362337
xor=['neighborhood', 'ellipsoid'],
@@ -2339,14 +2340,14 @@ class ReHoInputSpec(CommandLineInputSpec):
23392340
'a floating point number, and must be >1. Examples of '
23402341
'the numbers of voxels in a given radius are as follows '
23412342
'(you can roughly approximate with the ol\' 4*PI*(R^3)/3 '
2342-
'thing):'
2343-
' R=2.0 -> V=33,'
2344-
' R=2.3 -> V=57, '
2345-
' R=2.9 -> V=93, '
2346-
' R=3.1 -> V=123, '
2347-
' R=3.9 -> V=251, '
2348-
' R=4.5 -> V=389, '
2349-
' R=6.1 -> V=949, '
2343+
'thing):\n'
2344+
' R=2.0 -> V=33,\n'
2345+
' R=2.3 -> V=57, \n'
2346+
' R=2.9 -> V=93, \n'
2347+
' R=3.1 -> V=123, \n'
2348+
' R=3.9 -> V=251, \n'
2349+
' R=4.5 -> V=389, \n'
2350+
' R=6.1 -> V=949, \n'
23502351
'but you can choose most any value.')
23512352
ellipsoid = traits.Tuple(
23522353
traits.Float,
@@ -2355,9 +2356,9 @@ class ReHoInputSpec(CommandLineInputSpec):
23552356
xor=['sphere', 'neighborhood'],
23562357
argstr='-neigh_X %s -neigh_Y %s -neigh_Z %s',
23572358
desc='Tuple indicating the x, y, and z radius of an ellipsoid '
2358-
'defining the neighbourhood of each voxel.'
2359+
'defining the neighbourhood of each voxel.\n'
23592360
'The \'hood is then made according to the following relation:'
2360-
'(i/A)^2 + (j/B)^2 + (k/C)^2 <=1.'
2361+
'(i/A)^2 + (j/B)^2 + (k/C)^2 <=1.\n'
23612362
'which will have approx. V=4*PI*A*B*C/3. The impetus for '
23622363
'this freedom was for use with data having anisotropic '
23632364
'voxel edge lengths.')

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