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


       raster, aggregation, 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]   [--overwrite]  [--help]  [--verbose]  [--quiet]  [--ui]

           Propagate NULLs

           Do not keep files open

           Allow output files to overwrite existing files

           Print usage summary

           Verbose module output

           Quiet module output

           Force launching GUI dialog

           Name of input raster map(s)

           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 to calculate for method=quantile
           Options: 0.0-1.0

           Weighting factor for each input map, default value is 1.0 for each input map

           Ignore values outside this range


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

       Following methods are available:

           ·   average: average value

           ·   count: count of non-NULL cells

           ·   median: median value

           ·   mode: most frequently occurring value

           ·   minimum: lowest value

           ·   maximum: highest value

           ·   range: range of values (max - min)

           ·   stddev: standard deviation

           ·   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

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

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

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


   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).

       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

   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:
       # <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

       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.


       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
       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
       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" sep=,` \
                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


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

       Hints for large raster data processing


       Glynn Clements


       Available at: r.series source code (history)

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       © 2003-2019 GRASS Development Team, GRASS GIS 7.8.2 Reference Manual