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Jython Library

## Module: grid

This is the doc for the grid module. For more information, see Description of Formulas - Grids.

`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

makeVector(a,b) = [a,b,c]

`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

makeVector(a,b) = [a,b]

`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

shear = sqrt((u(top)-u(bottom))^2 + (v(top)-v(bottom))^2)/zdiff

`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

This is the doc for the Grid Diagnostics module. These functions are based on the grid diagnostics from the GEneral Meteorological PAcKage (GEMPAK). Note that the names are case sensitive and some are named slightly different from GEMPAK functions to avoid conflicts with Jython built-ins (e.g. str).

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

ADD (S1, S2) = S1 + S2

`adv(S, V):`

Horizontal Advection, negative by convention

ADV ( S, V ) = - ( u * DDX (S) + v * DDY (S) )

`age(obs, geo):`

Ageostrophic wind

AGE ( S ) = [ u (OBS) - u (GEO(S)), v (OBS) - v (GEO(S)) ]

`atn2(S1, S2):`

Wrapper for atan2 built-in

ATN2 (S1, S2) = ATAN ( S1 / S2 )

`avg(S1, S2):`

Average of 2 scalars

AVG (S1, S2) = ( S1 + S2 ) / 2

`avor(V):`

Absolute Vorticity

AVOR ( V ) = VOR ( V ) + CORL(V)

`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

CORL = TWO_OMEGA*sin(latr)

`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

CROS ( V1, V2 ) = u1 * v2 - u2 * v1

`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

DEF ( V ) = ( STRD (V) ** 2 + SHR (V) ** 2 ) ** .5

`dirn(V):`

North relative direction of a vector

DIRN ( V ) = DIRR ( un(v), vn(v))

`dirr(V):`

Grid relative direction of a vector

`div(V):`

Horizontal Divergence

DIV ( V ) = DDX ( u ) + DDY ( v )

`dot(V1, V2):`

Vector dot product

DOT ( V1, V2 ) = u1 * u2 + v1 * v2

`dvdx(V):`

Partial x derivative of a vector

DVDX ( V ) = [ DDX (u), DDX (v) ]

`dvdy(V):`

Partial x derivative of a vector

DVDY ( V ) = [ DDY (u), DDY (v) ]

`frnt(S, V):`

Frontogenesis function from theta and the wind

FRNT ( THTA, V ) = 1/2 * MAG ( GRAD (THTA) ) * ( DEF * COS (2 * BETA) - DIV ) Where: BETA = ASIN ( (-DDX (THTA) * COS (PSI) - DDY (THTA) * SIN (PSI))/ MAG ( GRAD (THTA) ) ) PSI = 1/2 ATAN2 ( SHR / STR )

`geo(z):`

geostrophic wind from height

GEO ( S ) = [ - DDY (S) * const / CORL, DDX (S) * const / CORL ]

`grad(S):`

Gradient of a scalar

GRAD ( S ) = [ DDX ( S ), DDY ( S ) ]

`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

INAD ( V1, V2 ) = [ DOT ( V1, GRAD (u2) ), DOT ( V1, GRAD (v2) ) ]

`jcbn(S1, S2):`

Jacobian Determinant

JCBN ( S1, S2 ) = DDX (S1) * DDY (S2) - DDY (S1) * DDX (S2)

`lap(S):`

Laplacian operator

LAP ( S ) = DIV ( GRAD (S) )

`latr(S):`

Latitude all points in a grid

`lav(S, level1, level2):`

Layer Average

LAV ( S ) = ( S (level1) + S (level2) ) / 2.

`ldf(S, level1, level2):`

Layer Average

LDF ( S ) = S (level1) - S (level2)

`mag():`

Magnitude of a vector

`mixr(temp, rh):`

Mixing Ratio from Temperature, RH (requires pressure domain)

`mul(S1, S2):`

Multiply

MUL (S1, S2) = S1 * S2

`pvor(S, V):`

Potential vorticity (usually from theta and wind)

`quo(S1, S2):`

Divide

QUO (S1, S2) = S1 / S2

`qvcl(THTA, V):`

Q-vector ( K / m / s )

QVCL ( THTA, V ) = ( 1/( D (THTA) / DP ) ) * [ ( DOT ( DVDX (V), GRAD (THTA) ) ), ( DOT ( DVDY (V), GRAD (THTA) ) ) ]

`qvec(S, V):`

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

QVEC ( S, V ) = [ - ( DOT ( DVDX (V), GRAD (S) ) ), - ( DOT ( DVDY (V), GRAD (S) ) ) ] where S can be any thermal parameter, usually THTA.

`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

SAVG (S) = average of all non-missing grid point values

`savs(S):`

Average over grid subset

SAVS (S) = average of all non-missing grid point values in the subset area

`sdiv(S, V):`

Horizontal Flux Divergence

SDIV ( S, V ) = S * DIV ( V ) + DOT ( V, GRAD ( S ) )

`shr(V):`

Shear Deformation

SHR ( V ) = DDX ( v ) + DDY ( u )

`sm5s(S):`

Smooth a scalar grid using a 5-point smoother

SM5S ( S ) = .5 * S (i,j) + .125 * ( S (i+1,j) + S (i,j+1) + S (i-1,j) + S (i,j-1) )

`sm9s(S):`

Smooth a scalar grid using a 9-point smoother

SM9S ( S ) = .25 * S (i,j) + .125 * ( S (i+1,j) + S (i,j+1) + S (i-1,j) + S (i,j-1) ) + .0625 * ( S (i+1,j+1) + S (i+1,j-1) + S (i-1,j+1) + S (i-1,j-1) )

`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

STRD ( V ) = DDX ( u ) - DDY ( v )

`sub(S1, S2):`

Subtract

SUB (S1, S2) = S1 - S2

`thrm(S, level1, level2):`

Thermal wind

THRM ( S ) = [ u (GEO(S)) (level1) - u (GEO(S)) (level2), v (GEO(S)) (level1) - v (GEO(S)) (level2) ]

`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

VADD (V1, V2) = [ u1+u2, v1+v2 ]

`vecn(S1, S2):`

Make a true north vector from two components

VECR ( S1, S2 ) = [ S1, S2 ]

`vecr(S1, S2):`

Make a vector from two components

VECR ( S1, S2 ) = [ S1, S2 ]

`vlav(V, level1, level2):`

calculate the vector layer average

VLDF(V) = [(u(level1) - u(level2))/2, (v(level1) - v(level2))/2]

`vldf(V, level1, level2):`

calculate the vector layer difference

VLDF(V) = [u(level1) - u(level2), v(level1) - v(level2)]

`vmul(V1, V2):`

Multiply the components of 2 vectors

VMUL (V1, V2) = [ u1*u2, v1*v2 ]

`vn(V):`

North relative v component

`vor(V):`

Relative Vorticity

VOR ( V ) = DDX ( v ) - DDY ( u )

`vquo(V1, V2):`

Divide the components of 2 vectors

VQUO (V1, V2) = [ u1/u2, v1/v2 ]

`vr(V):`

Grid relative v component

`vsub(V1, V2):`

subtract the components of 2 vectors

VSUB (V1, V2) = [ u1-u2, v1-v2 ]

`wshr(V, Z, top, bottom):`

Magnitude of the vertical wind shear in a layer

WSHR ( V ) = MAG [ VLDF (V) ] / LDF (Z)

`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

XAV (S) = ( S (X1) + S (X2) + ... + S (KXD) ) / KNT

`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

XSUM (S) = ( S (X1) + S (X2) + ... + S (KXD) )

`yav(S):`

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

YAV (S) = ( S (Y1) + S (Y2) + ... + S (KYD) ) / KNT

`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

YSUM (S) = ( S (Y1) + S (Y2) + ... + S (KYD) )

`zav(S):`

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

ZAV (S) = ( S (Z1) + S (Z2) + ... + S (KZD) ) / KNT

`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

ZSUM (S) = ( S (Z1) + S (Z2) + ... + S (KZD) )

## Module: ensemble

This is the doc for the grid ensemble module. For more information, see Description of Formulas - Grids.

`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

A set of utilities to apply to a map field. For more information, see Description of Formulas - 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

A set of utilities for selecting data and creating displays. For use from the Jython 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:

dataList.get(0).getData(None)

`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

A set of miscellaneous utilities.

`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

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