magnitude
magnitude( QDataSet ds ) → QDataSet
return the magnitudes of vectors in a rank 1 or greater dataset (typically
rank 2). The last index should be the cartesian dimension. For example,
ds= getDataSet('http://autoplot.org/data/autoplot.cdf?BGSM') # BGSM[Epoch=24,cart=3]
m= magnitude(ds)
For rank 0, this just returns the absolute value, but with the same units.
Parameters
ds - dataset of Rank N.
Returns:
dataset of Rank N-1.
See Also:
abs(QDataSet)
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magnitude( Object ds1 ) → QDataSet
matrixFormat
matrixFormat( QDataSet mm, String style ) → String
format the matrix values, using %.3f and tabs to
make it legible.
Parameters
mm - the 3x3 matrix.
style - "code" or "newlines". None/null is newlines.
Returns:
formatted matrix
See Also:
matrixParse(java.lang.String) will parse when style is "code"
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matrixFromEuler
matrixFromEuler( String seq, Datum angle ) → QDataSet
return the matrix rotating about one axis.
Parameters
seq - the string "x", "y", or "z"
angle - the angle in degrees or radians.
Returns:
a QDataSet
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matrixMultiply
matrixMultiply( QDataSet m, QDataSet v ) → QDataSet
matrix multiply the components of vector v
by matrix m
. Argument v
can also be a
rank 2 series of vectors (vv[n,3]), and argument m
can be an array of matrices (mm[n,3,3]).
Parameters
m - matrix or array of matrices
v - vector or array of vectors
Returns:
vectors in the same form as b
See Also:
bundle(QDataSet, QDataSet, QDataSet) bundle for creating vectors from
components.
slice1(QDataSet, int) for extracting components from the result.
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matrixParse
matrixParse( String smat ) → QDataSet
parse the string into a rank 2 matrix
Parameters
smat - a string like '[[1,0,0],[0,1,0],[0,0,1]]'
Returns:
rank 2 matrix.
See Also:
matrixFormat(QDataSet, java.lang.String)
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maybeCopy
maybeCopy( QDataSet ads0 ) → org.das2.qds.WritableDataSet
Copy the dataset to an ArrayDataSet only if the dataset is not already an ArrayDataSet
or BufferDataSet.
Note this does not consider the mutability of the data. If the dataset is not mutable, then the
original data could be returned (probably).
Parameters
ads0 - a dataset.
Returns:
an ArrayDataSet or BufferDataSet
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mean
mean( QDataSet ds ) → QDataSet
Mean function that returns the average of the valid elements of a rank N dataset
Parameters
ds - rank N dataset
Returns:
rank 0 dataset
See Also:
mode
median
variance(QDataSet)
meanAverageDeviation(QDataSet)
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mean( Object o ) → QDataSet
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)
BinAverage#binMeanAverageDeviation(QDataSet, QDataSet)
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median
median( Object o ) → QDataSet
Median function that sorts a rank N dataset and returns its median.
If lists are equal in size (even number of elements), always choose
first element of 'more' list
Parameters
o - object which can be interpreted as a dataset.
Returns:
rank 0 dataset
See Also:
mean
mode
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median( QDataSet ds ) → QDataSet
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:
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:
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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mod( Object ds1, Object ds2 ) → QDataSet
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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modp
modp( QDataSet ds1, QDataSet ds2 ) → QDataSet
element-wise mod of two datasets with compatible geometry. This returns
a positive number for -18 % 10. This is Python's behavior.
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
See Also:
sawtooth(QDataSet)
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modp( Object ds1, Object ds2 ) → QDataSet
monotonicSubset
monotonicSubset( QDataSet ds ) → org.das2.qds.MutablePropertyDataSet
ensure that there are no non-monotonic or repeat records, by removing
the first N-1 records of N repeated records.
Warning: this was extracted from AggregatingDataSource to support BufferDataSets,
and is minimally implemented.
When ds has property QDataSet.DEPEND_0, then this is used to identify the
monotonic subset. When ds is a set of timetags, then these are used.
Parameters
ds - MutablePropertyDataSet, which must be writable.
Returns:
dataset, possibly with records removed.
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multiply
multiply( QDataSet ds1, QDataSet ds2 ) → QDataSet
element-wise multiply of two datasets with compatible geometry.
Presently, either ds1 or ds2 should be dimensionless.
TODO: units improvements.
Parameters
ds1 - a QDataSet
ds2 - a QDataSet
Returns:
a QDataSet
See Also:
multiplyUnits
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multiply( Object ds1, Object ds2 ) → QDataSet