r.series(1)

📦
version 8.4.2 · 8 versions
, in fedora 44

💖 r.series — the GRASS GIS manual, but make it cute 💖

okay bestie, so this is r.series(1) from the GRASS GIS User's Manual and it is genuinely one of the most useful little icons in the whole raster toolbox, no cap. She takes a whole series of raster map layers and squishes them down into one output cell value per cell — like a group chat poll where everyone votes and you get one answer. 🔥

✨ 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. (she's strict, she says if any input is NULL the whole thing is NULL, more on that below 👇)
  • -z — Do not keep files open. (big-brain move for when you have SO many maps)
  • --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). Comma-separated list, bestie.
  • 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 are: 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. That's literally the whole vibe. She stacks your maps like a little deck of cards 🃏 and looks at the same spot on every single one.

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

Not me about to tell you that most parameters accept multiple answers, which means you can compute multiple aggregates in a single run. it's giving multitasking queen. 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. ⚠️ like, she WILL notice if your counts don't match, and it will not be cute.

📝 NOTES 📝

🫥 No-data (NULL) handling 🫥

With the -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 the -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.

so tl;dr: -n = one NULL ruins the whole vibe. no -n = she's forgiving, works with what she's got. 💅

🔺 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. yeah. any of them. iconic.

🧠 Memory consumption 🧠

Memory usage is not an issue, as r.series only needs to hold one row from each map at a time. she's low maintenance like that 💅

🗂️ 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.

🚨 bestie listen: input and file do NOT go together. pick one. she's not doing both.

⚡ 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: Saturday Jan 17 05:40:59 2026

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