# Operations

HARP provides a simple expression syntax with which one can specify operations that need to be performed on a product. A list of operations is always provided as a single string where individual operations are separated by a semi-colon (;). Each operation can be either a comparison filter, a membership test filter, or a function call. Strings used in operations should be quoted with double quotes.

## Comparison filter

`variable operator value [unit]`

Filter a dimension for all variables in the product such that items for which the value of the provided variable does not match the expression get excluded. The variable should be one dimensional or two dimensional (with the first dimension being time). The dimension that gets filtered is the last dimension of the referenced variable.

Supported operators are:

`==`

`!=`

`<`

`<=`

`>=`

`>`

(for numerical values only)`=&`

`=|`

`!&`

(bitfield operators, for integer values only)

Bitfield operators work such that `a =& 5`

returns true if both bits 1 and 3 in `a`

are set,
`a =| 5`

returns true of either bits 1 or 3 or both in `a`

are set,
and `a !& 5`

returns true if neither bits 1 and 3 in `a`

are set.

If a unit is specified, the comparison will be performed in the specified unit. Otherwise, it will be performed in the unit that the variable currently has. Units can only be specified for numerical values.

Comparison operations can also be performed on the index of a dimension.

`index(dimension) operator value`

For instance, `index(vertical) == 0`

will keep only the lowest vertical level of a vertical profile.

## Membership test filter

`variable in (value, ...) [unit]`

`variable not in (value, ...) [unit]`

Exclude measurements that occur or do not occur in the specified list.

If a unit is specified, the comparison will be performed in the specified unit. Otherwise, it will be performed in the unit that the variable currently has. Units can only be specified for numerical variables.

## Function call

`function(argument, ...)`

Supported functions:

`area_covers_area((lat, ...) [unit], (lon, ...) [unit])`

Exclude measurements whose area does not fully cover the given polygon. Example:

`area_covers_area((51.98,51.98,52.02,52.02),(4.33,4.39,4.39,4.33))`

`area_covers_area(area-mask-file)`

Exclude measurements whose area does not fully cover one of the areas of the area mask file.

Example:

`area_covers_area("areas.csv")`

`area_covers_point(latitude [unit], longitude [unit])`

Exclude measurements whose area does not cover the given point. Example:

`area_covers_point(52.012, 4.357)`

`area_inside_area((lat, ...) [unit], (lon, ...) [unit])`

Exclude measurements whose area is not inside the given polygon. Example:

`area_inside_area((50,50,54,54),(3,8,8,3))`

`area_inside_area(area-mask-file)`

Exclude measurements whose area is not inside one of the areas of the area mask file.

`area_intersects_area((lat, ...) [unit], (lon, ...) [unit], minimum-overlap-fraction)`

Exclude measurements whose area does not overlap at least the specified fraction with the given polygon. The fraction is calculated as area(intersection)/min(area(x),area(y))

`area_intersects_area(area-mask-file, minimum-overlap-fraction)`

Exclude measurements whose area does not overlap at least the specified fraction with one the areas of the area mask file.

`area_intersects_area((lat, ...) [unit], (lon, ...) [unit])`

Exclude measurements whose area does not overlap with the given polygon.

`area_intersects_area(area-mask-file)`

Exclude measurements whose area does not overlap with one of the areas of the area mask file.

`bin()`

For all variables in a product perform an averaging in the time dimension such that all samples end up in a single bin.

`bin(variable)`

For all variables in a product perform an averaging in the time dimension such that all samples in the same bin get averaged. A bin is defined by all samples of the given variable that have the same value. Example:

`bin(index)`

`bin(validity)`

`bin((variable, ...))`

Same as above, but each bin is defined by the unique combinations of values from the variables in the list. Example:

`bin((year, month))`

`bin(collocation-result-file, a|b)`

For all variables in a product perform an averaging in the time dimension such that all samples in the same bin get averaged. A bin is defined by all samples having the same collocated sample from the dataset that is indicated by the second parameter. Example:

`bin("collocation-result.csv", b)`

(the product is part of dataset A and the collocated sample that defines the bin is part of dataset B)

`bin_spatial((lat_edge, lat_edge, ...), (lon_edge, lon_edge, ...))`

For all variables in a product map all time samples onto a spatial latitude/longitude grid. The latitude/longitude grid is defined by the list of edge values. Example:

`bin_spatial((-90,-60,-30,0,30,60,90),(-180,0,180))`

(bin data onto latitude bands, separated into aneastern and western hemisphere)`bin_spatial(lat_edge_length, lat_edge_offset, lat_edge_step, lon_edge_length, lon_edge_offset, lon_edge_step)`

For all variables in a product map all time samples onto a spatial latitude/longitude grid. The latitude/longitude grid is defined by the list of edge values. Example:

`bin_spatial(7, -90, 30, 3, -180, 180)`

(this is the same as`bin_spatial((-90,-60,-30,0,30,60,90),(-180,0,180))`

)

`clamp(dimension, axis-variable unit, (lower_bound, upper_bound))`

Reduce the given dimension such that values of the given axis-variable and associated <axis-variable>_bounds fall within the given lower and upper bounds. The operation will use a

`derive(axis-variable {[time,]dimension} unit)`

and`derive(<axis-variable>_bounds {[time,]dimension} unit)`

to determine the current grid and boundaries. These grid+boundaries are then updated to fall within the given lower and upper limits. The updated grid+boundaries are then used to regrid the product in the given dimension. The values`+inf`

and`-inf`

can be used to indicate an unbound edge. Example:`clamp(vertical, altitude [km], (-inf, 60)`

`clamp(vertical, pressure [hPa], (+inf, 200)`

`collocate_left(collocation-result-file)`

Apply the specified collocation result file as an index filter assuming the product is part of dataset A.

`collocate_left(collocation-result-file, min_collocation_index)`

Same as regular

`collocation_left`

operation but only include collocations where collocation_index >= min_collocation_index`collocate_left(collocation-result-file, min_collocation_index, max_collocation_index)`

Same as regular

`collocation_left`

operation but only include collocations where min_collocation_index <= collocation_index <= max_collocation_index

`collocate_right(collocation-result-file)`

Apply the specified collocation result file as an index filter assuming the product is part of dataset B.

`collocate_right(collocation-result-file, min_collocation_index)`

Same as regular

`collocate_right`

operation but only include collocations where collocation_index >= min_collocation_index`collocate_right(collocation-result-file, min_collocation_index, max_collocation_index)`

Same as regular

`collocate_right`

operation but only include collocations where min_collocation_index <= collocation_index <= max_collocation_index

`derive(variable [datatype] [unit])`

The derive operation

withouta dimension specification can be used to change the data type or unit of an already existing variable. A variable with the given name should therefore already be in the product (with any kind of dimensions). If a unit conversion is performed and no data type is specified the variable will be converted to`double`

values.Example:

`derive(altitude [km])`

`derive(latitude float)`

`derive(variable [datatype] {dimension-type, ...} [unit])`

The derive operation

witha dimension specification is used to derive the specified variable from other variables found in the product (i.e. a variable with that name and dimension does not have to exist yet). The`--list-derivations`

option of harpdump can be used to list available variable conversions. The algorithms behind all the conversions are described in the Algorithms section of the documentation. If the datatype is not provided then the default result data type for a conversion will be used (usually`double`

). If a variable with the given name and dimension specification already exists then this operation will just perform a data type and/or unit conversion on that variable.Example:

`derive(number_density {time,vertical} [molec/m3])`

`derive(latitude float {time})`

`derive_smoothed_column(variable {dimension-type, ...} [unit], axis-variable unit, collocation-result-file, a|b, dataset-dir)`

Derive the given integrated column value by first deriving a partial column profile variant of the variable and then smoothing/integrating this partial column profile using the column avaraging kernel (and a-priori, if available) from a collocated dataset. The fourth parameter indicates which dataset contains the avaraging kernel. Before smoothing the partial column profile is regridded to the grid of the column averaging kernel using the given axis-variable (see also

`regrid()`

).Example:

`derive_smoothed_column(O3_column_number_density {time} [molec/cm2], altitude [km], "collocation-result.csv", b, "./correlative_data/")`

`derive_smoothed_column(variable {dimension-type, ...} [unit], axis-variable unit, collocated-file)`

Derive the given integrated column value by first deriving a partial column profile variant of the variable and then smoothing/integrating this partial column profile using the column avaraging kernel (and a-priori, if available) from a single merged collocated product. Both the product and the collocated product need to have a

`collocation_index`

variable that will be used to associate the right collocated grid/avk/apriori to each sample. Before smoothing the partial column profile is regridded to the grid of the column averaging kernel using the given axis-variable (see also`regrid()`

).Example:

`derive_smoothed_column(O3_column_number_density {time} [molec/cm2], altitude [km], "./collocated_file.nc")`

`exclude(variable, ...)`

Mark the specified variable(s) for exclusion from the ingested product. All variables marked for exclusion will be excluded from the ingested product, all other variables will be kept. Variables that do not exist will be ignored. Instead of a variable name, a pattern using ‘*’ and ‘?’ can be provided.

Example:

`exclude(datetime, *uncertainty*)`

`flatten(dimension)`

Flatten a product for a certain dimension by collapsing the given dimension into the time dimension. The time dimension will thus grow by a factor equal to the length of the given dimension and none of the variables in the product will depend on the given dimension anymore. If the length of the flattened dimension does not equal 1 then: variables that depend more than once on the given dimension will be removed, the index and collocation_index variables will be removed, and time independent variables are made time dependent. Independent dimensions and the time dimension cannot be flattened. Example:

`flatten(latitude);flatten(longitude)`

(turn a 2D lat/lon grid into a a series of individual points)`regrid(vertical, altitude [km], (20));flatten(vertical)`

(vertically slice the product at 20 km altitude)

`keep(variable, ...)`

Mark the specified variable(s) for inclusion in the ingested product. All variables marked for inclusion will be kept in the ingested product, all other variables will be excluded. Trying to keep a variable that does not exist will result in an error. Instead of a variable name, a pattern using ‘*’ and ‘?’ can be provided (unmatched patterns will not result in an error).

Example:

`keep(datetime*, latitude, longitude)`

`longitude_range(minimum [unit], maximum [unit])`

Exclude measurements of which the longitude of the measurement location falls outside the specified range. This function correctly handles longitude ranges that cross the international date line. It checks whether

`wrap(longitude, minimum, minimum + 360) <= maximum`

.`longitude_range(179.0, 181.0)`

(select a 2 degree range around the international dateline)`longitude_range(-181.0, -179.0)`

(gives exact same result as the first example)

`point_distance(latitude [unit], longitude [unit], distance [unit])`

Exclude measurements whose point location is situated further than the specified distance from the given location. Example:

`point_distance(52.012, 4.357, 3 [km])`

`point_in_area((lat, ...) [unit], (lon, ...) [unit])`

Exclude measurements whose point location does not fall inside the measurement area. Example:

`point_in_area((50,50,54,54) [degN],(3,8,8,3) [degE])`

`point_in_area(area-mask-file)`

Exclude measurements whose point location does not fall inside one of the areas from the area mask file.

`rebin(dimension, axis-bounds-variable unit, (value, ...))`

Regrid all variables in the product for the given dimension using the given axis boundaries variable as target grid. The operation will use a

`derive(axis-variable {[time,]dimension,2} unit)`

to determine the current grid. The target grid is specified as a list of N+1 boundary edge values (for N adjacent intervals). Rebinning uses a weighted average of the overlapping intervals of the current grid with the interval of the target grid. Example:`rebin(longitude, longitude_bounds [degree_east], (-180, -90, 0, 90, 180))`

`rebin(vertical, altitude [km], (0.0, 1.5, 3.0, 7.0))`

`rebin(dimension, axis-bounds-variable unit, length, offset, step)`

Regrid all variables in the product for the given dimension using the given axis boundaries variable as target grid. The operation will use a

`derive(axis-variable {[time,]dimension,2} unit)`

to determine the current grid. The N+1 edges of the target grid are specified as length, offset, and step parameters. Rebinning uses a weighted average of the overlapping intervals of the current grid with the interval of the target grid. Example:`rebin(longitude, longitude_bounds [degree_east], 5, -180, 90)`

`rebin(vertical, altitude [km], 11, 0, 1.0)`

`regrid(dimension, axis-variable unit, (value, ...))`

Regrid all variables in the product for the given dimension using the given axis variable as target grid. The operation will use a

`derive(axis-variable {[time,]dimension} unit)`

to determine the current grid. The target grid is specified as a list of values. Example:

`regrid(vertical, altitude [km], (1.0, 2.0, 5.0, 10.0, 15.0, 20.0, 30.0))`

`regrid(dimension, axis-variable unit, (value, ...), (value, ...))`

Regrid all variables in the product for the given dimension using the given axis variable as target grid. The operation will use a

`derive(axis-variable {[time,]dimension} unit)`

and`derive(<axis-variable>_bounds {[time,]dimension} unit)`

to determine the current grid and boundaries. The target grid mid points are specified by the first list of values and the target grid boundaries by the second list of values. If there are N mid points, then the list of boundary values can either contain N+1 points if the boundaries are adjacent or 2N points to define each boundary pair separately. Example:`regrid(vertical, altitude [km], (1.0, 2.0, 5.0), (0.0, 1.5, 3.0, 7.0))`

`regrid(vertical, altitude [km], (1.0, 2.0, 5.0), (0.5, 1.5, 1.5, 2.5, 4.0, 6.0))`

`regrid(dimension, axis-variable unit, length, offset, step)`

Regrid all variables in the product for the given dimension using the given axis variable as target grid. The operation will use a

`derive(axis-variable {[time,]dimension} unit)`

to determine the current grid. The target grid is specified as using a length, offset, and step parameters. Example:`regrid(vertical, altitude [km], 10, 0.5, 1.0)`

(indicating a grid of altitudes 0.5, 1.5, …, 9.5)`regrid(time, datetime [hours since 2017-04-01], 23, 0.5, 1.0)`

`regrid(dimension, axis-variable unit, collocation-result-file, a|b, dataset-dir)`

Regrid all variables in the product for the given dimension using the target grid taken from a collocated dataset. The fourth parameter indicates which dataset contains the target grid. Example:

`regrid(vertical, altitude [km], "collocation-result.csv", b, "./correlative_data/")`

`regrid(dimension, axis-variable unit, collocated-file)`

Regrid all variables in the product for the given dimension using the target grid taken from a single merged collocated product. Both the product and the collocated product need to have a

`collocation_index`

variable that will be used to associate the right collocated grid to each sample. Example:

`regrid(vertical, altitude [km], "./collocated_file.nc")`

`rename(variable, new_name)`

Rename the variable to the new name. Note that this operation should be used with care since it will change the meaning of the data (potentially interpreting it incorrectly in further operations). It is primarilly meant to add/remove prefixes (such as surface/tropospheric/etc.) to allow the variable to be used in a more specific (with prefix) or generic (without prefix) way. If a product does not have a variable with the source name but already has a variable with the target name then the rename operation will do nothing (assuming that the target state is already satisfied). Example:

`rename(surface_temperature, temperature)`

`set(option, value)`

Set a specific option in HARP. Both the option and value parameters need to be provided as string values (using double quotes). Options will be set ‘globally’ in HARP and will persists for all further operations in the list. After termination of the list of operations, all HARP options will be reverted back to their initial values. Available options are:

`afgl86`

Possible values are:

`disabled`

(default) disable the use of AFGL86 climatology in variable conversions

`enabled`

enable the use of AFGL86 climatology in variable conversions (using seasonal and latitude band dependence)

`usstd76`

enable AFGL86 using US Standard profiles`collocation_datetime`

Determine whether to create a collocation_datetime variable when a collocate_left or collocation_right operation is performed. The collocation_datetime variable will contain the datetime of the sample from the other dataset for the collocated pair. Possible values are:

`disabled`

(default) variable will not be created

`enabled`

variable will not be created`propagate_uncertainty`

Determine how to propagate uncertainties for operations that support this (and where there is a choice). Possible values are:

`uncorrelated`

(default) to assume fully uncorrelated uncertainties

`correlated`

to assume fully correlated uncertainties`regrid_out_of_bounds`

Determine how to deal with interpolation of target grid values that fall outside the source grid range. Possible values are:

`nan`

(default) to set values outside the range to NaN

`edge`

to use the nearest edge value

`extrapolate`

to perform extrapolationExample:

`set("afgl86", "enabled")`

`set("regrid_out_of_bounds", "extrapolate")`

`smooth(variable, dimension, axis-variable unit, collocation-result-file, a|b, dataset-dir)`

Smooth the given variable in the product for the given dimension using the avaraging kernel (and a-priori profile, if available) from a collocated dataset. The fifth parameter indicates which dataset contains the avaraging kernel. Before smoothing the product is regridded to the grid of the averaging kernel using the given axis-variable (see also

`regrid()`

). Example:

`smooth(O3_number_density, vertical, altitude [km], "collocation-result.csv", b, "./correlative_data/")`

`smooth((variable, variable, ...), dimension, axis-variable unit, collocation-result-file, a|b, dataset-dir)`

Same as above, but then providing a list of variables that need to be smoothed. For each variable an associated averaging kernel (and associated a-priori, if applicable) needs to be present in the collocated dataset.

`smooth(variable, dimension, axis-variable unit, collocated-file)`

Smooth the given variable in the product for the given dimension using the avaraging kernel (and a-priori profile, if available) from a single merged collocated product. Both the product and the collocated product need to have a

`collocation_index`

variable that will be used to associate the right collocated grid/avk/apriori to each sample. Before smoothing the product is regridded to the grid of the averaging kernel using the given axis-variable (see also`regrid()`

). Example:

`smooth(O3_number_density, vertical, altitude [km], "./collocated_file.nc")`

`smooth((variable, variable, ...), dimension, axis-variable unit, collocated-file)`

Same as above, but then providing a list of variables that need to be smoothed. For each variable an associated averaging kernel (and associated a-priori, if applicable) needs to be present in the merged collocated file.

`sort(variable)`

Reorder a dimension for all variables in the product such that the variable provided as parameter ends up being sorted. The variable should be one dimensional and the dimension that gets reordered is this dimension of the referenced variable.

`sort((variable, ...))`

Same as above, but use a list of variables for sorting.

`squash(dimension, variable)`

Remove the given dimension for the variable, assuming that the content for all items in the given dimension is the same. If the content is not the same an error will be raised.

`squash(dimension, (variable, ...))`

Same as above, but then providing a list of variables that need to be squashed.

`valid(variable)`

Filter a dimension for all variables in the product such that invalid values for the variable provided as parameter get excluded (values outside the valid range of the variable, or NaN). This operation is executed similar to a comparison filter.

`wrap(variable [unit], minimum, maximum)`

Wrap the values of the variable to the range given by minimum and maximum. The result is: min + (value - min) % (max - min) Example:

`wrap(longitude [degree_east], -180, 180)`

## Collocation result file

The format of the collocation result file is described in the conventions section of the HARP documentation.

## Area mask file

A comma separated (csv) file is used as input for area filters.

It has the following format:

```
lat0,lon0,lat1,lon1,lat2,lon2,lat3,lon3
0.0,60.0,40.0,60.0,40.0,-60.0,0.0,-60.0
...
```

It starts with a header with latitude, longitude column headers (this header will be skipped by HARP). Then, each further line defines a polygon. Each polygon consists of the vertices as defined on that line.

## Examples

`derive(altitude {time} [km]); pressure > 3.0 [bar]`

`point_distance(-52.5 [degree], 1.0 [rad], 1e3 [km])`

`index in (0, 10, 20, 30, 40); valid(pressure)`

## Formal definition

```
digit = '0'|'1'|'2'|'3'|'4'|'5'|'6'|'7'|'8'|'9' ;
sign = '+'|'-' ;
alpha =
'a'|'b'|'c'|'d'|'e'|'f'|'g'|'h'|'i'|'j'|'k'|'l'|'m'|
'n'|'o'|'p'|'q'|'r'|'s'|'t'|'u'|'v'|'w'|'x'|'y'|'z'|
'A'|'B'|'C'|'D'|'E'|'F'|'G'|'H'|'I'|'J'|'K'|'L'|'M'|
'N'|'O'|'P'|'Q'|'R'|'S'|'T'|'U'|'V'|'W'|'X'|'Y'|'Z' ;
character = alpha | digit |
' '|'!'|'"'|'#'|'$'|'%'|'&'|"'"|'('|')'|'*'|'+'|','|
'-'|'.'|'/'|':'|';'|'<'|'='|'>'|'?'|'@'|'['|'\'|']'|
'^'|'_'|'`'|'{'|'|'|'}'|'~' ;
identifier = alpha, [{alpha | digit | '_'}] ;
variable = identifier ;
variablelist =
variable |
variablelist, ',', variable ;
intvalue = [sign], {digit} ;
floatvalue =
[sign], ('N' | 'n'), ('A' | 'a'), ('N' | 'n') |
[sign], ('I' | 'i'), ('N' | 'n'), ('F' | 'f') |
(intvalue, '.', [{digit}] | '.', {digit}), [('D' | 'd' | 'E' | 'e'), intvalue] ;
stringvalue = '"', [{character-('\', '"') | '\' character}], '"' ;
value = intvalue | floatvalue | stringvalue ;
intvaluelist =
intvalue |
intvaluelist, ',', intvalue;
floatvaluelist =
floatvalue |
floatvaluelist, ',', floatvalue;
stringvaluelist =
stringvalue |
stringvaluelist, ',', stringvalue;
valuelist = intvaluelist | floatvaluelist | stringvaluelist ;
unit = '[', [{character-(']')}], ']' ;
datatype = 'int8' | 'int16' | 'int32' | 'float' | 'double' | 'string' ;
dimension = 'time' | 'latitude' | 'longitude' | 'vertical' | 'spectral' | 'independent' ;
dimensionlist =
dimension |
dimensionlist, ',', dimension ;
dimensionspec = '{' dimensionlist '}' ;
bit_mask_operator = '=&' | '!&' ;
operator = '==' | '!=' | '>=' | '<=' | '<' | '>' ;
functioncall =
'area_covers_area', '(', '(', floatvaluelist, ')', [unit], '(', floatvaluelist, ')', [unit], ')' |
'area_covers_area', '(', stringvalue, ')' |
'area_covers_point', '(', floatvalue, [unit], ',', floatvalue, [unit], ')' |
'area_inside_area', '(', '(', floatvaluelist, ')', [unit], '(', floatvaluelist, ')', [unit], ')' |
'area_inside_area', '(', stringvalue, ')' |
'area_intersects_area', '(', '(', floatvaluelist, ')', [unit], '(', floatvaluelist, ')', [unit], ',', floatvalue, ')' |
'area_intersects_area', '(', stringvalue, ',', floatvalue, ')' |
'area_intersects_area', '(', '(', floatvaluelist, ')', [unit], '(', floatvaluelist, ')', [unit], ')' |
'area_intersects_area', '(', stringvalue, ')' |
'bin', '(', [variable], ')' |
'bin', '(', variablelist, ')' |
'bin', '(', stringvalue, ',', ( 'a' | 'b' ), ')' |
'bin_spatial', '(', '(', floatvaluelist, ')', '(', floatvaluelist, ')', ')' |
'bin_spatial', '(', intvalue, ',', floatvalue, ',', floatvalue, ',', intvalue, ',', floatvalue, ',', floatvalue, ',', ')' |
'clamp', '(', dimension, ',', variable, [unit], '(', floatvalue, ',', floatvalue, ')', ')' |
'collocate_left', '(', stringvalue, ')' |
'collocate_left', '(', stringvalue, ',', intvalue, ')' |
'collocate_left', '(', stringvalue, ',', intvalue, ',', intvalue, ')' |
'collocate_right', '(', stringvalue, ')' |
'collocate_right', '(', stringvalue, ',', intvalue, ')' |
'collocate_right', '(', stringvalue, ',', intvalue, ',', intvalue, ')' |
'derive', '(', variable, [datatype], [dimensionspec], [unit], ')' |
'derive_smoothed_column', '(', variable, dimensionspec, [unit], ',', variable, unit, ',', stringvalue, ',', ( 'a' | 'b' ), ',', stringvalue, ')' |
'derive_smoothed_column', '(', variable, dimensionspec, [unit], ',', variable, unit, ',', stringvalue, ')' |
'exclude', '(', variablelist, ')' |
'flatten', '(', dimension, ')' |
'keep', '(', variablelist, ')' |
'longitude_range', '(', floatvalue, [unit], ',', floatvalue, [unit], ')' |
'point_distance', '(', floatvalue, [unit], ',', floatvalue, [unit], ',', floatvalue, [unit], ')' |
'point_in_area', '(', '(', floatvaluelist, ')', [unit], '(', floatvaluelist, ')', [unit], ')' |
'point_in_area', '(', stringvalue, ')' |
'rebin', '(', dimension, ',', variable, unit, ',', '(', floatvaluelist, ')', ')' |
'rebin', '(', dimension, ',', variable, unit, ',', intvalue, ',', floatvalue, ',', floatvalue, ')' |
'regrid', '(', dimension, ',', variable, unit, ',', '(', floatvaluelist, ')', ')' |
'regrid', '(', dimension, ',', variable, unit, ',', '(', floatvaluelist, ')', ',', '(', floatvaluelist, ')', ')' |
'regrid', '(', dimension, ',', variable, unit, ',', intvalue, ',', floatvalue, ',', floatvalue, ')' |
'regrid', '(', dimension, ',', variable, unit, ',', stringvalue, ',', ( 'a' | 'b' ), ',', stringvalue, ')' |
'regrid', '(', dimension, ',', variable, unit, ',', stringvalue, ')' |
'rename', '(', variable, ',', variable, ')' |
'set', '(', stringvalue, ',', stringvalue, ')' |
'smooth', '(', variable, ',', dimension, ',', variable, unit, ',', stringvalue, ',', ( 'a' | 'b' ), ',', stringvalue, ')' |
'smooth', '(', '(', variablelist, ')', ',', dimension, ',', variable, unit, ',', stringvalue, ',', ( 'a' | 'b' ), ',', stringvalue, ')' |
'smooth', '(', variable, ',', dimension, ',', variable, unit, ',', stringvalue, ')' |
'smooth', '(', '(', variablelist, ')', ',', dimension, ',', variable, unit, ',', stringvalue, ')' |
'sort', '(', variable, ')' |
'sort', '(', variablelist, ')' |
'squash', '(', dimension, ',', variable, ')' |
'squash', '(', dimension, ',', variablelist, ')' |
'valid', '(', variable, ')' |
'wrap', '(', variable, [unit], ',', floatvalue, ',', floatvalue, ')' ;
operationexpr =
variable, bit_mask_operator, intvalue |
variable, operator, value, [unit] |
variable, ['not'], 'in', '(', valuelist, ')', [unit] |
'index', '(', dimension, ')', operator, intvalue |
'index', '(', dimension, ')', ['not'], 'in', '(', intvaluelist, ')' |
functioncall |
operationexpr, ';', operationexpr ;
operations =
operationexpr ';' |
operationexpr ;
```