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NAME

       r.series   -  Makes  each  output  cell  value  a  function  of the values assigned to the
       corresponding cells in the input raster map layers.

KEYWORDS

       raster, aggregation, series, parallel

SYNOPSIS

       r.series
       r.series --help
       r.series    [-nz]     [input=name[,name,...]]      [file=name]      output=name[,name,...]
       method=string[,string,...]    [quantile=float[,float,...]]     [weights=float[,float,...]]
       [range=lo,hi]    [nprocs=integer]    [memory=memory  in  MB]     [--overwrite]    [--help]
       [--verbose]  [--quiet]  [--ui]

   Flags:
       -n
           Propagate NULLs

       -z
           Do not keep files open

       --overwrite
           Allow output files to overwrite existing files

       --help
           Print usage summary

       --verbose
           Verbose module output

       --quiet
           Quiet module output

       --ui
           Force launching GUI dialog

   Parameters:
       input=name[,name,...]
           Name of input raster map(s)

       file=name
           Input  file with one raster map name and optional one weight per line, field separator
           between name and weight is |

       output=name[,name,...] [required]
           Name for output raster map

       method=string[,string,...] [required]
           Aggregate operation
           Options: average, count,  median,  mode,  minimum,  min_raster,  maximum,  max_raster,
           stddev,  range,  sum,  variance,  diversity,  slope, offset, detcoeff, tvalue, quart1,
           quart3, perc90, quantile, skewness, kurtosis

       quantile=float[,float,...]
           Quantile to calculate for method=quantile
           Options: 0.0-1.0

       weights=float[,float,...]
           Weighting factor for each input map, default value is 1.0 for each input map

       range=lo,hi
           Ignore values outside this range

       nprocs=integer
           Number of threads for parallel computing
           Default: 1

       memory=memory in MB
           Maximum memory to be used (in MB)
           Cache size for raster rows
           Default: 300

DESCRIPTION

       r.series makes  each  output  cell  value  a  function  of  the  values  assigned  to  the
       corresponding cells in the input raster map layers.

       Figure: Illustration for an average of three input rasters

       Following methods are available:

           •   average: average value

           •   count: count of non-NULL cells

           •   median: median value

           •   mode: most frequently occurring value

           •   minimum: lowest value

           •   min_raster: raster map number with the minimum time-series value

           •   maximum: highest value

           •   max_raster: raster map number with the maximum time-series value

           •   stddev: standard deviation

           •   range: range of values (max - min)

           •   sum: sum of values

           •   variance: statistical variance

           •   diversity: number of different values

           •   slope: linear regression slope

           •   offset: linear regression offset

           •   detcoeff: linear regression coefficient of determination

           •   tvalue: linear regression t-value

           •   quart1: first quartile

           •   quart3: third quartile

           •   perc90: ninetieth percentile

           •   quantile: arbitrary quantile

           •   skewness: skewness

           •   kurtosis: kurtosis
       Note  that  most  parameters  accept  multiple answers, allowing multiple aggregates to be
       computed in a single run, e.g.:

       r.series input=map1,...,mapN \
                output=map.mean,map.stddev \
             method=average,stddev
       or:

       r.series input=map1,...,mapN \
                output=map.p10,map.p50,map.p90 \
                method=quantile,quantile,quantile \
                quantile=0.1,0.5,0.9
       The same number of values must be provided for all options.

NOTES

   No-data (NULL) handling
       With -n flag, any cell for which  any  of  the  corresponding  input  cells  are  NULL  is
       automatically  set  to  NULL (NULL propagation).  The aggregate function is not called, so
       all methods behave this way with respect to the -n flag.

       Without -n flag, the complete list of inputs for each cell (including NULLs) is passed  to
       the aggregate function. Individual aggregates can handle data as they choose. Mostly, they
       just compute the aggregate over the non-NULL values, producing a NULL result only  if  all
       inputs are NULL.

   Minimum and maximum analysis
       The  min_raster  and  max_raster  methods generate a map with the number of the raster map
       that holds the minimum/maximum value of the time-series. The numbering starts at 0 up to n
       for the first and the last raster listed in input=, respectively.

   Range analysis
       If the range= option is given, any values which fall outside that range will be treated as
       if they were NULL. The range parameter can be set to low,high thresholds:  values  outside
       of  this range are treated as NULL (i.e., they will be ignored by most aggregates, or will
       cause the result to be NULL if -n is given). The low,high thresholds are  floating  point,
       so use -inf or inf for a single threshold (e.g., range=0,inf to ignore negative values, or
       range=-inf,-200.4 to ignore values above -200.4).

   Linear regression
       Linear regression (slope, offset, coefficient of  determination,  t-value)  assumes  equal
       time  intervals.  If  the  data  have  irregular  time  intervals, NULL raster maps can be
       inserted into time series to make time intervals equal (see example).

   Quantiles
       r.series can calculate arbitrary quantiles.

   Memory consumption
       Memory usage is not an issue, as r.series only needs to hold one row from each  map  at  a
       time.

   Management of open file limits
       The  maximum  number  of  raster  maps that can be processed is given by the user-specific
       limit of the operating system. For example, the soft limits for users are  typically  1024
       files.  The  soft  limit  can  be  changed with e.g.  ulimit -n 4096 (UNIX-based operating
       systems) but it cannot be higher than the hard limit. If the latter is too low, you can as
       superuser add an entry in:
       /etc/security/limits.conf
       # <domain>      <type>  <item>         <value>
       your_username  hard    nofile          4096
       This will raise the hard limit to 4096 files. Also have a look at the overall limit of the
       operating system
       cat /proc/sys/fs/file-max
       which on modern Linux systems is several 100,000 files.

       For each map a weighting factor can be specified using the weights option.  Using  weights
       can  be  meaningful  when  computing  the  sum  or average of maps with different temporal
       extent. The default weight is 1.0. The number of weights must be identical to  the  number
       of  input  maps  and  must have the same order. Weights can also be specified in the input
       file.

       Use the -z flag to analyze large amounts of raster maps without hitting open  files  limit
       and  the file option to avoid hitting the size limit of command line arguments.  Note that
       the computation using the file option is slower than with the  input  option.   For  every
       single  row  in  the output map(s) all input maps are opened and closed. The amount of RAM
       will rise linearly with the number of specified input maps. The input and file options are
       mutually  exclusive:  the  former  is  a  comma separated list of raster map names and the
       latter is a text file with a new line separated list of  raster  map  names  and  optional
       weights. As separator between the map name and the weight the character "|" must be used.

   Performance
       To  enable parallel processing, the user can specify the number of threads to be used with
       the nprocs parameter (default 1). The memory  parameter  (default  300  MB)  can  also  be
       provided to determine the size of the buffer in MB for computation.
       Figure:  Benchmark  on  the  left  shows  execution  time  for  different number of cells,
       benchmark on the right shows execution time for  different  memory  size  for  10000x10000
       raster. See benchmark scripts in source code.  (Intel Core i9-10940X CPU @ 3.30GHz x 28)

       To  reduce  the  memory  requirements  to  minimum,  set  option  memory to zero.  To take
       advantage of the parallelization, GRASS GIS needs to compiled with OpenMP enabled.

EXAMPLES

       Using r.series with wildcards:
       r.series input="`g.list pattern=’insitu_data.*’ sep=,`" \
                output=insitu_data.stddev method=stddev

       Note the g.list script also supports regular expressions for selecting map names.

       Using r.series with NULL raster maps (in order to consider a "complete" time series):
       r.mapcalc "dummy = null()"
       r.series in=map2001,map2002,dummy,dummy,map2005,map2006,dummy,map2008 \
                out=res_slope,res_offset,res_coeff meth=slope,offset,detcoeff

       Example for multiple aggregates to be computed in one run (3 resulting aggregates from two
       input maps):
       r.series in=one,two out=result_avg,res_slope,result_count meth=sum,slope,count

       Example to use the file option of r.series:
       cat > input.txt << EOF
       map1
       map2
       map3
       EOF
       r.series file=input.txt out=result_sum meth=sum

       Example  to  use  the file option of r.series including weights. The weight 0.75 should be
       assigned to map2. As the other maps do not have weights we can leave it out:
       cat > input.txt << EOF
       map1
       map2|0.75
       map3
       EOF
       r.series file=input.txt out=result_sum meth=sum

       Example for counting the number of days above a certain temperature  using  daily  average
       maps (’???’ as DOY wildcard):
       # Approach for shell based systems
       r.series input=`g.list rast pattern="temp_2003_???_avg" separator=comma` \
                output=temp_2003_days_over_25deg range=25.0,100.0 method=count
       # Approach in two steps (e.g., for Windows systems)
       g.list rast pattern="temp_2003_???_avg" output=mapnames.txt
       r.series file=mapnames.txt \
                output=temp_2003_days_over_25deg range=25.0,100.0 method=count

SEE ALSO

        g.list, g.region, r.quantile, r.series.accumulate, r.series.interp, r.univar

       Hints for large raster data processing

AUTHOR

       Glynn Clements

SOURCE CODE

       Available at: r.series source code (history)

       Accessed: Tuesday Jun 27 11:13:09 2023

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       © 2003-2023 GRASS Development Team, GRASS GIS 8.3.0 Reference Manual