raster, aggregation, series
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]
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,...]Â [required]
Name for output raster map
- method=string[,string,...]Â [required]
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
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
- 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
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.
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.
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 (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 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
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.
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" 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
Available at: r.series source code (history)
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