;+
; NAME:
; ARRAY_TOTAL
;
; PURPOSE:
; This function does more flexible array integration than IDL's total.
;
; CATEGORY:
; Array
;
; CALLING SEQUENCE:
; Result = array_total( Data [, Instruct] )
;
; INPUTS:
; Data: A numerical array of data to be processed, of any dimension.
; Instruct: An array of type string containing processing instructions,
; performed sequentially in the given order. Each instruction is
; of the form 'INSTRUCT_TYPE=INSTRUCT_DIM'. Possible values for
; INSTRUCT_TYPE are:
; 'total': calculate the total along the INSTRUCT_DIMdimension(s)
; 'mean': calculate the mean along the INSTRUCT_DIMdimension(s).
; INSTRUCT_DIM is a sequence of dimension indices separated by
; commas. These are the same as for IDL's total function, i.e.
; the left-most dimension is '1'. If multiple dimensions are
; listed in a single instruction, then those dimensions are
; treated as one and the operation is performed simultaneously on
; all of them. So 'mean=1,2' will average over dimensions 1 and
; 2 simulataneously, while ['mean=1','mean=2'] will first average
; over dimension 1 and average then over dimension 2.
; FRAC_COVERAGE_THRESH, NAN, REFORM, WEIGHT
;
; KEYWORD PARAMETERS:
; COVERAGE: Returns an array of the same dimensions of Result containing
; the fractional coverage of values in data, relative to the
; total possible (i.e. the (weighted) fraction of values that are
; not NaN), involved in the calculation of the final instruction
; in Instruct which produces Result.
; FRAC_COVERAGE_THRESH: The minimum fraction of (weighted) data used in
; each operation that must be defined (i.e. not NaN) in order to
; proceed with the calculation. If the criterion is not met
; then a NaN is returned from that calculation. The default is 0.
; NAN: If set then NaNs (not-a-number values) are ignored in the
; calculations. The default is not set.
; REFORM: If set then the reform function is performed on Result,
; COVERAGE, DATA_MEAN, and MEAN COVERAGE to remove all 1-element
; dimensions. The default is for these output to maintain the
; original number of dimensions, with some dimensions reduced to
; 1-element size according to Instruct and REFERENCE.
; WEIGHT: An array of the same size as DATA containing relative
; weightings for the corresponding elements in DATA. All
; calculations producing Result, COVERAGE, DATA_MEAN, and
; MEAN_COVERAGE take account of these weightings. If not set
; then identical weightings are assumed for all elements.
;
; OUTPUTS:
; Result: Returns the processed version of Data. This is of the same
; size as Data except that the dimensions with instructions
; specified in Instruct have only one element. If REFORM is set
; then these 1-element dimensions are removed.
; COVERAGE
;
; USES:
;
; PROCEDURE:
;
; EXAMPLE:
;
; MODIFICATION HISTORY:
; Written by: Daithi A. Stone (stoned@atm.ox.ac.uk), 2008-03-11,
; adapted from mask_lonlatmonth.pro
;-
FUNCTION ARRAY_TOTAL, $
Data, $
Instruct, $
FRAC_COVERAGE_THRESH=frac_coverage_thresh, $
NAN=nan_opt, $
REFORM=reform_opt, $
WEIGHT=weight, $
COVERAGE=coverage
;***********************************************************************
; Constants and Options
; Missing data flag
nan = !values.f_nan
; Note the dimensions of data
len = size( data )
n_len = len[0]
len = len[1:n_len]
; The default minimum coverage (0)
if n_elements( frac_coverage_thresh ) eq 0 then frac_coverage_thresh = 0
; Ensure normalised weightings
if keyword_set( weight ) then weight = weight / total( weight )
; The number of instructions
n_instruct = n_elements( instruct )
; Copy input array to output
result = data
;***********************************************************************
; Integrate Along Dimensions of Data
; Weight data array if requested
if keyword_set( weight ) then result = weight * result
; Iterate through commands
for i = 0, n_instruct - 1 do begin
; Initialise coverage array and weight if requested
coverage = finite( result )
if keyword_set( weight ) then coverage = weight * coverage
; Parse instruction
instruct_type = strsplit( instruct[i], '=', extract=1 )
instruct_dim = fix( strsplit( instruct_type[1], ',', extract=1, $
count=n_instruct_dim ) )
instruct_type = instruct_type[0]
; Perform integration in this dimension
for j = 0, n_instruct_dim - 1 do begin
result = total( result, instruct_dim[j], nan=nan_opt )
coverage = total( coverage, instruct_dim[j] )
if keyword_set( weight ) then weight = total( weight, instruct_dim[j] )
; Reform data to maintain dimensions
len[instruct_dim[j]-1] = 1
result = reform( result, len )
coverage = reform( coverage, len )
if keyword_set( weight ) then weight = reform( weight, len )
endfor
; Perform averaging
if instruct_type eq 'mean' then begin
result = result / coverage
if keyword_set( weight ) then result = result * weight
endif
endfor
; Normalise weighting on data and coverage
if keyword_set( weight ) then begin
result = result / weight
coverage = coverage / weight
endif
; Flag insufficient coverage as missing
id = where( coverage lt frac_coverage_thresh, n_id )
if n_id gt 0 then result[id] = nan
id = -1
;***********************************************************************
; Post-Processing
; Reform output to requested format
if keyword_set( reform_opt ) then begin
result = reform( result )
coverage = reform( coverage )
endif
;***********************************************************************
; The End
return, result
END