blockmean(cmd0::String="", arg1=nothing; kwargs...)
Block average (x,y,z) data tables by mean estimation.
Reads arbitrarily located (x,y,z) triples [or optionally weighted quadruples (x,y,z,w)] and computes a mean position and value for every non-empty block in a grid region defined by the region and increment parameters.
Takes a Mx3 matrix, a GMTdataset, or a file name as input and returns either a table (a GMTdataset) or one or more grids (GMTgrid). Aternatively, save the result directly in a disk file.
3 (or 4, see weights) column data table file (or binary, see binary_in) holding (x,y,z[,w]) data values, where [ w] is an optional weight for the data.
I or inc or increment or spacing : – inc=x_inc | inc=(xinc, yinc) | inc="xinc[+e|n][/yinc[+e|n]]"
Specify the grid increments or the block sizes. More at spacing
R or region or limits : – limits=(xmin, xmax, ymin, ymax) | limits=(BB=(xmin, xmax, ymin, ymax),) | limits=(LLUR=(xmin, xmax, ymin, ymax),units="unit") | ...more
Specify the region of interest. More at limits. For perspective view view, optionally add zmin,zmax. This option may be used to indicate the range used for the 3-D axes. You may ask for a larger w/e/s/n region to have more room between the image and the axes.
A or field or fields: – field=mean|std|highest|lowest|weights
Output is a grid with one of the select fields.
field=meanwrites the mean of z. Other options are: std (standard deviation), lowest (lowest value), highest (highest value) and weights (the output weight; requires the weights option). The deafault is
field=mean. Alternatively, one can use a condensed form which uses the first character (except the mean) of the above options, separated by commas, to compute more than one grid. For example:
fields="z,s"computes two grids; one with the means and the other with the standard deviations.
C or center : – center=true
Use the center of the block as the output location [Default uses the mean location]. Not used whith fields.
E or extend : –- extend=true | extend="+p" | extend="+P"
Provide Extended report which includes s (the standard deviation about the mean), l, the lowest value, and h, the high value for each block. Output order becomes x,y,z,s,l,h[,w]. Default outputs x,y,z[ ,w]. See weights for enabling w output. If
extend="+P"is used then input data uncertainties are expected and s becomes the propagated error of the weighted (+p) or simple (+P) z mean.
G or save or outgrid or outfile : – save=file_name.grd
Write one or more fields directly to grids on disk or return them to the Julia REPL as grid objects. If more than one field is specified via fields then file_name must contain the format flag %s so that we can embed the field code in the file names.
S or statistic : – statistic=:m|:n|:s|:w
statistic=:mto report the mean value in each blaock,
statistic=:nto report the number of points inside each block,
statistic=:sto report the sum of all z-values inside a block,
statistic=:wto report the sum of weights [Default (or
statistic=:mreports mean value]. This option works both for returning the result in a table in a GMTdataset or in a grid. For this later case, however, one must use the grid=true option.
V or verbose : – verbose=true | verbose=level
Select verbosity level. More at verbose
W or weights : – weights=:i | weights=:o | weights="i+s" | weights="i|o+s|+w"
Weighted modifier[s]. Unweighted input and output have 3 columns x,y,z; Weighted i/o has 4 columns x,y,z,w. Weights can be used in input to construct weighted mean values for each block. Weight sums can be reported in output for later combining several runs, etc. Use weights for weighted i/o, weights=:i for weighted input only, and weights=:o for weighted output only. [Default uses unweighted i/o]. If your weights are actually uncertainties (one sigma) then append the string +s (as in weights="i+s") and we compute weight = 1/sigma. Otherwise (or via +w) we use the weights directly.
bi or binary_in : – binary_in=??
Select native binary format for primary table input. More at
bo or binary_out : – binary_out=??
Select native binary format for table output. More at
di or nodata_in : – nodata_in=??
Substitute specific values with NaN. More at
e or pattern : – pattern=??
Only accept ASCII data records that contain the specified pattern. More at
f or colinfo : – colinfo=??
Specify the data types of input and/or output columns (time or geographical data). More at
h or header : – header=??
Specify that input and/or output file(s) have n header records. More at
i or incol or incols : – incol=col_num | incol="opts"
Select input columns and transformations (0 is first column, t is trailing text, append word to read one word only). More at incol
o or outcol : – outcol=??
Select specific data columns for primary output, in arbitrary order. More at
q or inrows : – inrows=??
Select specific data rows to be read and/or written. More at
r or reg or registration : – reg=:p | reg=:g
Select gridline or pixel node registration. Used only when output is a grid. More at
w or wrap or cyclic : – wrap=??
Convert input records to a cyclical coordinate. More at
yx : – yx=true
Swap 1st and 2nd column on input and/or output. More at
To find 5 by 5 minute block mean values from the ASCII data in ship_15.txt, run
D = blockmean("@ship_15.txt", region=(245,255,20,30), inc="5m");
To determine how many values were found in each 5x5 minute bin, try
D = blockmean("@ship_15.txt", region=(245,255,20,30), inc="5m", count=true);
To determine the mean and standard deviation per 10 minute bin and save these to two separate grids called fieldz.nc and fields.nc, run
blockmean("@ship_15.txt", spacing="10m", region=(-115,-105,20,30), extend=true, save="field_%s.nc", fields="z,s")
The same as above but this time returns the two grids to the Julia REPL
Z,S = blockmean("@ship_15.txt", spacing="10m", region=(-115,-105,20,30), extend=true, fields="z,s")