meanAverageDeviation
meanAverageDeviation( QDataSet ds ) → QDataSet
return the Mean Average Deviation (MAD) of the rank N dataset.
The result will contain the USER_PROPERTIES with a map containing
the mean and number of points.
Parameters
ds - the rank N dataset.
Returns:
the rank 0 mean average deviation of the dataset.
See Also:
mean(QDataSet)
Ops_i.mdBinAverage#binMeanAverageDeviation(QDataSet, QDataSet)
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medianFilter
medianFilter( QDataSet ds, int size ) → QDataSet
1-D median filter with a boxcar of the given size. The first size/2
elements, and the last size/2 elements are copied from the input.
Parameters
ds - rank 1 or rank 2 dataset. Future implementations may support higher rank data.
size - the boxcar size
Returns:
rank 1 or rank 2 dataset.
See Also:
Ops_s.md#smooth(QDataSet, int)
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merge
merge( QDataSet ds1, QDataSet ds2 ) → QDataSet
Merge the two sorted rank N datasets, using their DEPEND_0 datasets, into one rank N dataset.
If neither dataset has DEPEND_0, then this will use the datasets themselves. When ds1 occurs "before" ds2, then this
is the same as concatenate.
When there is a collision where two data points are coincident, use ds1[j]. This is fuzzy, based on the depend_0 cadence of ds1.
When ds1 is null (or None), use ds2.
Thanks to: http://stackoverflow.com/questions/5958169/how-to-merge-two-sorted-arrays-into-a-sorted-array
Parameters
ds1 - rank N dataset, or null.
ds2 - rank N dataset
Returns:
dataset of rank N with elements interleaved.
See Also:
Ops_c.md#concatenate(QDataSet, QDataSet)
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mod
mod( QDataSet ds1, QDataSet ds2 ) → QDataSet
element-wise mod of two datasets with compatible geometry.
This should support Units.t2000 mod "24 hours" to get result in hours.
Parameters
ds1 - the numerator
ds2 - the divisor
Returns:
the remainder after the division
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mode
mode( QDataSet ds ) → QDataSet
return the most frequently occurring element of the valid elements of a rank N dataset
Parameters
ds - rank N dataset.
Returns:
the rank 0 dataset
See Also:
mean
median
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