gamma

gamma( double n ) → double

return the gamma function for numbers greater than 0. This will soon work for any number where gamma has a result (Apache Math v3 is needed for this).

Parameters

n - a double

Returns:

a double

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gamma

gamma( Object n ) → QDataSet

return the gamma function for numbers greater than 0. This will soon work for any number where gamma has a result (Apache Math v3 is needed for this).

Parameters

n - an Object

Returns:

a QDataSet

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ge

ge( QDataSet ds1, QDataSet ds2 ) → QDataSet

element-wise function returns 1 where ds1>=ds2.

Parameters

ds1 - a QDataSet
ds2 - a QDataSet

Returns:

a QDataSet

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ge( Object ds1, Object ds2 ) → QDataSet [view source]

getProperty

getProperty( QDataSet ds, String name ) → Object

retrieve a property from the dataset. This was introduced for use in the Data Mash Up tool.

Parameters

ds - the dataset
name - the property name

Returns:

the property or null (None) if the dataset doesn't have the property.

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getProperty

getProperty( QDataSet ds, String propertyName, java.lang.Class clazz ) → Object

return the property, issuing a warning and returning null when the property value is not of the correct type.

Parameters

ds - the dataset
propertyName - the property name
clazz - the class of the property.

Returns:

the property, guaranteed to be of the correct type, or null.

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getQubeDimsForArray

getQubeDimsForArray( Object arg0 ) → int[]

return the length of each index of a n-D array. In Java these are arrays of arrays, and no test is made to verify that the array is really a qube. This was introduced when it appeared that Python/jpype was producing arrays without the getClass method. For example, if we have an array of 3 arrays, each having 5 elements, then [ 3,5 ] is returned.

Parameters

arg0 - an array, or array of arrays, or array of array of arrays, etc.

Returns:

the n dimensions of each index of the array.

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greaterOf

greaterOf( QDataSet ds1, QDataSet ds2 ) → QDataSet

element-wise function returns the greater of ds1 and ds2. If an element of ds1 or ds2 is fill, then the result is fill.

Parameters

ds1 - a QDataSet
ds2 - a QDataSet

Returns:

the bigger of the two, in the units of ds1.

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greaterOf( Object ds1, Object ds2 ) → QDataSet [view source]

grid

grid( QDataSet ds ) → QDataSet

Opposite of the flatten function, takes rank 2 bundle (x,y,z) and makes a table from it z(x,y). This presumes that the rank 1 X and Y data contain repeating elements for the rows and columns of the grid.

Parameters

ds - rank 2 bundle of X,Y, and Z data.

Returns:

rank 2 table.

See Also:

flatten(QDataSet)


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gridIrregularY

gridIrregularY( QDataSet t, QDataSet y, QDataSet z, QDataSet ytags ) → QDataSet

This finds sweeps of Y and interpolates T->Y->Z to make a regular spectrogram T,yTags->Z[T,yTags] This function was once known as "LvT" because it was used to create a spectrogram of Flux(Time,Lshell) by interpolating along sweeps.

Parameters

t - the rank 1 x values (often time)
y - the rank 1 y values (for example, L)
z - the rank 1 z values at each y.
ytags - the rank 1 y tags for the result.

Returns:

the rank 2 spectrogram.

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gt

gt( QDataSet ds1, QDataSet ds2 ) → QDataSet

element-wise function returns 1 where ds1>ds2.

Parameters

ds1 - a QDataSet
ds2 - a QDataSet

Returns:

a QDataSet

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gt( Object ds1, Object ds2 ) → QDataSet [view source]

guessLabel

guessLabel( QDataSet ds ) → String

get the label, using the NAME when LABEL is not available.

Parameters

ds - the dataset

Returns:

the human-readable label.

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guessLabel

guessLabel( QDataSet ds, String deft ) → String

get the label, using the NAME when LABEL is not available.

Parameters

ds - the dataset
deft - the default label to use.

Returns:

the human-readable label.

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guessName

guessName( QDataSet ds ) → String

guess a name for the dataset, looking for NAME and then safeName(LABEL). The result will be a Java-style identifier suitable for the variable.

Parameters

ds - the dataset

Returns:

the name or null if there is no NAME or LABEL

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guessName

guessName( QDataSet ds, String deft ) → String

guess a name for the dataset, looking for NAME and then safeName(LABEL). The result will be a Java-style identifier suitable for the variable.

Parameters

ds - the dataset
deft - the default name to use.

Returns:

the name, or deft if there is no NAME or LABEL.

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