Module: grid

applyToRange(function, data):

Apply the function name to each timestep of the data

applyToRangeValues(function, data):

Apply the function name to each value in each timestep of the data

averageOverTime(field, makeTimes):

Average the values in each time step If makeTimes is true (1) then we return a field mapping all of the times to the average. Else we just return the average

combineFields():

combine several fields together

differenceFromBaseTime(field):

Set the value of each time step N: D(N)=D(N)-D(0)

exportGridToNetcdf(grid, filename):

Writes out the gridded data to a CF-compliant netCDF file

extractLatitudeFromNWPGrid(fieldimpl):

Get the latitude coordinate from a grid. Return a grid of the latitudes at each point

extractLongitudeFromNWPGrid(fieldimpl):

Get the longitude coordinate from a grid. Return a grid of the longitudes at each point

extractPressureFromNWPGrid(fieldimpl):

Get the pressure coordinate from a time series grid and return a grid of the pressure at all points. Input grid must have pressure or height (which is converted to pressure in the standard atmosphere). User must be sure input is a suitable FlatField

flowVector(field):

Make a vector from flow direction

getAltitude(z):

Change units from geopotential meters to meters

getNthTimeGrid(fieldimpl, Nth):

Get the Nth grid in time series of grids; User must be sure input is a suitable data field. returns a single time. Nth is an integer, >=0, <= max index of grid time series

getSliceAtAltitude(fieldimpl, alt, unit):

Extract a 2D horizontal slice from a 3D grid at the given altitude; level is a real number; if unit is supplied, it must be compatible with meters (ft, fathoms, etc) param fieldimpl is a grid which may have one or more time steps

getSliceAtLevel(fieldimpl, level):

Extract a 2D horizontal slice from a 3D grid at "Level." level is a real number; must be appropriate for the grid. param fieldimpl is a grid which may have one or more time steps

GP2Z(gp):

Convert Geopotential (GP) to Height (Z)

horizontalAdvection(param, u, v):

Horizontal advection

horizontalDivergence(param, u, v):

Horizontal flux divergence

layerAverage(grid, top, bottom):

Wrapper for calculating layer average

layerDiff(grid, top, bottom):

Wrapper for calculating layer difference

lonFlip(grid):

Flip the longitudes in a grid from -180-180 to 0-360 (or vice-versa). Only works for cyclic rectilinear grids

make2D(slice):

Make a 2D slice from a 3D slice at a single level

makeFlowField(a, b, c):

Make a vector from 3 components

makeTimeSequence(g):

Merge a set of single time grids/images into a time sequence

makeTopographyFromField(grid):

Make a topography field out of a grid

makeTrueVector(u, v):

True wind vectors

makeVector(a, b):

Make a vector from 2 components

maskGrid(grid, mask, value, resample):

Mask one grid by the values in the other. Value is the masking value

maxOverTime(field, makeTimes):

Take the max of the values in each time step if makeTimes is true (1) then return a field mapping of all times to the average. Else return the max

mergeTimeSequences(g):

Merge a set of time sequences of grids/images into a single time sequence. All grids/images must have the same parameter name

minOverTime(field, makeTimes):

Take the min of the values in each time step if makeTimes is true (1) then return a field mapping of all times to the average. Else return the min

newName(field, varname, copy):

Create a new field with a new parameter name

newUnit(field, varname, unitname):

Set the name and unit on a grid

noUnit(field):

Remove the units from a grid

resampleGrid(oldGrid, gridwithNewDomain):

Display gridded data on a new domain

runave(grid, nave, option):

Generate a running average. nave = number of steps to average over. option = option for unsmoothed end points (0: set to missing; 1: use symmetry; -1: assume cyclic)

substitute(data, low, high, newValue):

Change values in data between low/high to newValue

sumFromBaseTime(field):

Set the value of each time step N: D(N)=D(N)+D(0)

sumOverTime(field, makeTimes):

Take the sum of the values in each time step if makeTimes is true (1) then return a field mapping of all times to the average. Else return the sum

timeStepDifference(field, offset):

Set the value of each time step N: D(N)=D(N)-D(N+offset) where offset should be negative

timeStepSum(field, offset):

Set the value of each time step N: D(N)=D(N)+D(N+offset) where offset should be negative

uvFromWindDir(dir):

Create the vector field using the wind direction

wgt_runave(grid, wgts, option):

Generate a weighted running average. wgts = comma separated list of weights option = option for unsmoothed end points (0: set to missing; 1: use symmetry; -1: assume cyclic)

windShear(u, v, z, top, bottom):

Calculate the wind shear between discrete layers

windShearVector(u, v, top, bottom):

Calculate the u and v layer difference and return as vector

writeGridToXls(grid, filename):

Write out the grid data to an excel spreadsheet

Module: griddiag

In the following operators, scalar operands are named Si and vector operands are named Vi. Lowercase u and v refer to the grid relative components of a vector.

GRAVITY():

Gravity constant

add(S1, S2):

Addition

adv(S, V):

Horizontal Advection, negative by convention

age(obs, geo):

Ageostrophic wind

atn2(S1, S2):

Wrapper for atan2 built-in

avg(S1, S2):

Average of 2 scalars

avor(V):

Absolute Vorticity

circs(S, D):

Apply a circular aperature smoothing to the grid points. The weighting function is the circular aperature diffraction function. D is the radius of influence in grid increments, increasing D increases the smoothing (default D=2)

corl(S):

Coriolis Parameter for all points in a grid

cress(S, D):

Apply a Cressman smoothing to the grid points. The smoothed value is given by a weighted average of surrounding grid points. D is the radius of influence in grid increments, increasing D increases the smoothing (default D=2)

cros(V1, V2):

Vector cross product magnitude

ddx(S):

Take the derivative with respect to the domain's X coordinate

ddy(S):

Take the derivative with respect to the domain's Y coordinate

defr(V):

Total deformation

dirn(V):

North relative direction of a vector

dirr(V):

Grid relative direction of a vector

div(V):

Horizontal Divergence

dot(V1, V2):

Vector dot product

dvdx(V):

Partial x derivative of a vector

dvdy(V):

Partial x derivative of a vector

frnt(S, V):

Frontogenesis function from theta and the wind

geo(z):

geostrophic wind from height

grad(S):

Gradient of a scalar

gwfs(S, N):

Horizontal smoothing using normally distributed weights with theoretical response of 1/e for N * delta-x wave. Increasing N increases the smoothing (default N=6)

gwfv(V, N):

Horizontal smoothing using normally distributed weights with theoretical response of 1/e for N * delta-x wave. Increasing N increases the smoothing (default N=6)

inad(V1, V2):

Inertial advective wind

jcbn(S1, S2):

Jacobian Determinant

lap(S):

Laplacian operator

latr(S):

Latitude all points in a grid

lav(S, level1, level2):

Layer Average

ldf(S, level1, level2):

Layer Average

mag():

Magnitude of a vector

mixr(temp, rh):

Mixing Ratio from Temperature, RH (requires pressure domain)

mul(S1, S2):

Multiply

pvor(S, V):

Potential vorticity (usually from theta and wind)

quo(S1, S2):

Divide

qvcl(THTA, V):

Q-vector ( K / m / s )

qvec(S, V):

Q-vector at a level ( K / m / s )

rects(S, D):

Apply a rectangular aperature smoothing to the grid points. The weighting function is the product of the rectangular aperature diffraction function in the x and y directions. D is the radius of influence in grid increments, increasing D increases the smoothing (default D=2)

rehl(temp, mixr):

Create relative humidity from temperature and mixing ratio (requires pressure domain)

savg(S):

Average over whole grid

savs(S):

Average over grid subset

sdiv(S, V):

Horizontal Flux Divergence

shr(V):

Shear Deformation

sm5s(S):

Smooth a scalar grid using a 5-point smoother

sm9s(S):

Smooth a scalar grid using a 9-point smoother

sm5v(V):

Smooth a scalar grid using a 5-point smoother (see sm5s)

sm9v(V):

Smooth a scalar grid using a 9-point smoother (see sm9s)

strd(V):

Stretching Deformation

sub(S1, S2):

Subtract

thrm(S, level1, level2):

Thermal wind

thta(temp):

Potential Temperature from Temperature (requires pressure domain)

thte(temp, rh):

Equivalent Potential Temperature from Temperature and Relative humidity (requires pressure domain)

un(V):

North relative u component

ur(V):

Grid relative u component

vadd(V1, V2):

add the components of 2 vectors

vecn(S1, S2):

Make a true north vector from two components

vecr(S1, S2):

Make a vector from two components

vlav(V, level1, level2):

calculate the vector layer average

vldf(V, level1, level2):

calculate the vector layer difference

vmul(V1, V2):

Multiply the components of 2 vectors

vn(V):

North relative v component

vor(V):

Relative Vorticity

vquo(V1, V2):

Divide the components of 2 vectors

vr(V):

Grid relative v component

vsub(V1, V2):

subtract the components of 2 vectors

wshr(V, Z, top, bottom):

Magnitude of the vertical wind shear in a layer

xav(S):

Average along a grid row. KXD = number of points in row; KNT = number of non-missing points in row; XAV for a row is stored at every point in that row

xsum(S):

Sum along a grid row. KXD = number of points in row; XSUM for a row is stored at every point in that row

yav(S):

Average along a grid column. KYD = number of points in column; KNT = number of non-missing points in column

ysum(S):

Sum along a grid column. KYD = number of points in row; YSUM for a column is stored at every point in that column

zav(S):

Average across the levels of a grid at all points. KZD = number of levels; KNT = number of non-missing points in column

zsum(S):

Sum across the levels of a grid at all points. KZD = number of levels ZSUM for a vertical column is stored at every point

Module: ensemble

ens_mode(grid):

Mode value

ens_prcntl(grid, percent):

Percentile value

ens_savg(grid):

Basic ensemble average

ens_smax(grid):

Max value of all members

ens_smin(grid):

Minimum value of all members

ens_srng(grid):

Max - min grid values

ens_ssprd(grid):

Standard deviation of all members

ens_uprob(grid, logicalOp1, pValue1, and_or, logicalOp2, pValue2, exptdLoBound, exptdUpBound):

Ensemble univariate probability calculation

Module: image

combineRGB(red, green, blue):

combine 3 images as an RGB image

makeNavigatedImage(d, ulLat, ulLon, lrLat, lrLon):

This takes a image data object and a lat/lon bounding box and adds a lat/lon domain to the data. Use it in conjunction with a formula:

Module: maps

applyFunctionToValuesInField(function, field, min, max, inside):

applyFunctionToValuesInRange(function, range, timeStep, min, max, inside):

applyToIndices(function, range, timeStep, indices):

averageFromMap(field, mapSets):

mapSets defines a set of polygons. This procedure fills the areas in the field are enclosed by each polygon with the average value within that area

averageFromMapAndClip(field, mapSets):

averageRangeFromMap(range, timeStep, mapSets):

mapSets defines a set of polygons. This procedure fills the areas in the field are enclosed by each polygon with the average value within that area

filterMaps(mapSets, propName, operator, value):

Return a new set of maps whose property propName satisfies the given operator/value. The operators can be ==,!=, <,>,<=,>=, match, !match

getMapProperty(polygon, propName):

Get the named property from the given mapData

getMapsWithProperty(mapSets, propName, value):

Return a new set of maps that have the given property value

make3DMap(map, topo):

Make a 3d map. map - map line data - topo - topography dataset

makeFieldFromMapBounds(mapSets, length1, length2, fill, unit):

Make a field whose lat/lon area is the bounds of the given mapSet. It has length1 points in the x and length2 in the y. Fill it with the fill value and the given unit

mapsAbasoluteValue(originalValues, newValues, indexArray):

mapsApplyToField(function, field, mapSets, inside):

Fills the areas in the field are enclosed by each polygon with the average value within that area. mapSets defines a set of polygons

mapsApplyToRange(function, range, timeStep, mapSets, inside):

mapsAverage(originalValues, newValues, indexArray):

mapsMax(originalValues, newValues, indexArray, value):

mapsMin(originalValues, newValues, indexArray, value):

mapsSet(originalValues, newValues, indexArray, value):

subsetFromMap(field, mapSets, fillValue, inverse):

mapSets defines a set of polygons. This procedure fills the areas in the field that are not enclosed by the polygons with the fill value. If inverse is 1 then it fills the areas that are enclosed

subsetRangeFromMap(range, timeStep, mapSets, fillValue, inverse):

mapSets defines a set of polygons. This procedure fills the areas in the field that are not enclosed by the polygons with the fill value. If inverse is 1 then it fills the areas that are enclosed

subsetRangeWithProperty(range, mapSets):

test code

subsetWithProperty(field, mapSets):

test code

Module: shell

clear():

Clear the shell

createDisplay(displayType, data, dataName):

create a display of type displayType. Right click in input field to select particular displayType. The data is can be a data object, a datachoice or a list of data or datachoices The dataName is used to name the data, i.e., its the parameter name

findDataSource(name):

Find the data source object with the given name. If no name is given then this will return the first (non-formula) data source

getData(dataSourceName, dataChoiceName):

Find the data source with the given name and the data choice on that data source with the given name. If no dataSourceName is given then use the first one in the list If no dataChoiceName is given then use the first one held by the data source Return the data for the data choice. If no data source or data choice is found then return null

getDataChoice(dataSourceName, dataChoiceName):

Find the data source with the given name and the data choice on that data source with the given name. If no dataSourceName is given then use the first one in the list If no dataChoiceName is given then use the first one held by the data source Return the data choice If no data source or data choice is found then return null

listVars():

List all of the variables defined in the shell's interpreter

makeDataSource(path, type):

Create a datasource from the given file name or url. The optional type parameter is used to specify the type of data

printType(data):

Print out the math type of the given data

selectData(name1, name2, name3, name4, name5):

Select up to 5 data fields. This returns a List of the actual Data objects

selectDataChoice(name1, name2, name3, name4, name5):

Select up to 5 data choices. This returns a List of the data choices, not the actual Data To get the data do:

setDataChoices(dataSource):

The given dataSource can be an actual data source or the name of a data source. This procedure will define a set of jython variables that correspond to the data choices held by the given data source

setDataSources():

This procedure will define a set of jython variables, 'dataSource0, dataSource1, ...' that correspond to loaded data sources

showLib():

Bring up the jython library dialog

Module: misc

cloneArray(array):

Make a 2 dimensional float array filled with the given value

idveval(formula):

Evaluate a formula

makeFloatArray(rows, cols, value):

A utility to make a 2 dimensional float array filled with the given value

printSounding(sounding):

Print out the values of the sounding data

printSoundings(data):

Print out the values of the set of sounding data

Module: sandwich

sandwich(imgIR, imgVIS, minIR, maxIR, colorTable, useNaN):

Creates a 3-color RGB sandwich product image from infrared and visible data