# 2. Configuration and running of a simulation¶

The basic Simpact Cyan program is a standalone command-line program. To be able to set up a simulation, you’d need to prepare a configuration file and specify this file as a command-line argument. Preparing the configuration file manually is time-consuming work, as all event properties necessary in a simulation need to be set. To make it easier to prepare and run simulations, there’s a pysimpactcyan module that you can use to control Simpact Cyan from Python, or alternatively there’s a RSimpactCyan library that you can install in R that provides a similar interface. The Python module is included when you install the Simpact Cyan binaries, the R library must be installed separately from an R session.

You can also use a combined approach: first run a simulation or simply prepare a configuration file from R or Python, and subsequently use this configuration to start one or more simulations. This can be very helpful to first prepare a base configuration file in an easy way, and then to launch one or more simulations on a computer cluster for example. For this particular case, it can be very helpful to override e.g. a prefix on the output files as explained below.

In this section, we briefly look into starting the simpact cyan program on the command line, followed by explanations of how the Python interface or R interface works. Some insights into the configuration file are given next, since that is the actual input to the simulation. Typically, if you can specify a particular probability distribution in the configuration file, you can also specify others. At the end of this section we describe which distributions are supported and what their parameters are.

## 2.1. Running from command line¶

The Simpact Cyan executable that you’re likely to need is called simpact-cyan-release. There is a version with debugging information as well: this performs exactly the same calculations, but has more checks enabled. As a command line argument you can specify if the very basic mNRM (in which all event times are recalculated after triggering an event) is to be used, or the more advanced version (in which the program recalculates far less event fire times). While simpact-cyan-release with the advanced mNRM algorithm is the fastest, it may be a good idea to verify from time to time that the simple algorithm yields the same results (when using the same random number generator seed), as well as the debug versions.

The program needs three additional arguments, the first of which is the path to the configuration file that specifies what should be simulated. The configuration file is just a text file containing a list of key/value pairs, a part of which could look like this:

...
population.nummen    = 200
population.numwomen  = 200
population.simtime   = 40
...


You can also define variables and use environment variables of your system, later we’ll look into this config file in more detail. All configuration options and their possible values are described in the section with the simulation details. For the configuration file itself, all options that are needed in the simulation must be set, no default values are assumed by the command line program. When using the R or Python interface however, this helper system does know about default values thereby severely limiting the number of options that must be specified. It will combine the options you specify with the defaults to produce a full configuration file that the command line program can use.

The second argument that the simpact-cyan-release program needs is either 0 or 1 and specifies whether the single core version should be used (corresponds to 0), or the version using multiple cores (corresponds to 1). For the parallel version, i.e. the version using multiple cores, OpenMP is used as the underlying technology. By default the program will try to use all processor cores your system has, but this can be adjusted by setting the OMP_NUM_THREADS environment variable. In general, it is a good idea to specify 0 for this option, selecting the single-core version. The parallel version currently only offers a modest speedup, and only for very large population sizes. Especially if you need to do several runs of a simulation, starting several single-core versions at once will use your computer’s power more efficiently than starting several parallel versions in a sequential way.

With the third and final argument you can specify which mNRM algorithm to use: if you specify ‘simple’, the basic mNRM is used in which all event fire times will be recalculated after an event was triggered. Since this is a slow algorithm, you’ll probably want to specify ‘opt’ here, to use the more advanced algorithm. In this case, the procedure explained above is used, where each user stores a list of relevant events.

So, assuming we’ve created a configuration file called myconfig.txt that resides in the current directory, we could run the corresponding simulation with the following command:

simpact-cyan-release myconfig.txt 0 opt


This will produce some output on screen, such as which version of the program is being used and which random number generator seed was set. Since the random number generator seed is in there, it may be a good idea to save this to a file in case you’d like to reproduce the exact simulation later. To save it to a file called myoutput.txt, you can run

simpact-cyan-release myconfig.txt 0 opt 2>myoutput.txt


Note that it is not a redirection of the output using simply >, but using 2>. This has to do with the fact that the information that you see on screen is actually sent to stderr instead of stdout.

When running the Simpact Cyan program, the default behaviour is to initialize the random number generator with a (more or less) random seed value. For reproducibility it may be necessary to enforce a specific seed. To do so, set the environment variable MNRM_DEBUG_SEED to the value you want to use, and verify in the output of the program that the specified seed is in fact the one being used:

• for an MS-Windows system:

set MNRM_DEBUG_SEED=12345
simpact-cyan-release myconfig.txt 0 opt


Note that value of MNRM_DEBUG_SEED is still set, which is important when running additional simulations. To clear it, either exit the current command prompt, or execute

set MNRM_DEBUG_SEED=


(nothing may be specified after the = sign, not even a space)

• for a Linux or OS X system:

export MNRM_DEBUG_SEED=12345
simpact-cyan-release myconfig.txt 0 opt


Note that value of MNRM_DEBUG_SEED is still set, which is important when running additional simulations. To clear it, either exit the current terminal window, or execute

unset MNRM_DEBUG_SEED


On one of these operating systems, it is also possible to specify everything in one line:

MNRM_DEBUG_SEED=12345 simpact-cyan-release myconfig.txt 0 opt


In this case, the value of MNRM_DEBUG_SEED will be visible to the program, but will no longer be set once the program finishes. It will therefore not affect other programs that are started.

## 2.2. Running from within R¶

### 2.2.1. Getting started¶

Quick getting started and troubleshooting instructions for R users can be found in the RSimpactCyan github repository.

The R interface to Simpact Cyan will underneath still execute one of the Simpact Cyan programs, e.g. simpact-cyan-release, so the program relevant to your operating system must be installed first. Note that if you’re using MS-Windows, you’ll also need to install the Visual Studio 2015 redistributable package (use the x86 version).

The R module actually contains Python code so to be able to use this, you’ll need to have a working Python installation. On Linux or OS X, this is usually already available, but if you’re using MS-Windows you may need to install this separately. In this case, it is best to install it in the default directory, e.g. C:\Python27 or C:\Python34, so that the R package will be able to locate it easily.

Before being able to use the RSimpactCyan module, the library which contains the R interface to the Simpact Cyan program, you need to make sure that other libraries are available. The most straightforward way is to run

source("https://raw.githubusercontent.com/j0r1/RSimpactCyanBootstrap/master/initsimpact.R")


If you prefer not to run a script this way you can also add, either temporarily in your current R session or more permanently in your .Rprofile file, the following lines which add the package repository containing the Simpact Cyan library:

local({ x <- options()$repos if (!is.element("CRAN", x)) { x["CRAN"] = "@CRAN@" } x["SimpactCyan"] <- "http://research.edm.uhasselt.be/jori" options(repos = x) })  Then, you simply have to run install.packages("RSimpactCyan")  and packages on which RSimpactCyan depends will be downloaded and installed automatically. Without modifiying the list of repositories, you can also install the dependencies first manually, followed by the RSimpactCyan library: install.packages("RJSONIO") install.packages("findpython") install.packages("rPithon", repos="http://research.edm.uhasselt.be/jori") install.packages("RSimpactCyan", repos="http://research.edm.uhasselt.be/jori")  Finally, you can load the library with the command: library("RSimpactCyan")  ### 2.2.2. Running a simulation¶ To configure a simulation, you need to specify the options for which you want to use a value other than the default. This is done using a list, for example cfg <- list() cfg["population.nummen"] <- 200 cfg["population.numwomen"] <- 200 cfg["population.simtime"] <- 40  All values that are entered this way are converted to character strings when creating a configuration file for the simulation. This means that instead of a numeric value, you could also use a string that corresponds to the same number, for example cfg["population.nummen"] <- "200"  Together with the defaults for other options, these settings will be combined into a configuration file that the real Simpact Cyan program can understand. Taking a look at the full configuration file will show you what other values are in use; to see this configuration file, run simpact.showconfig(cfg)  Lines that start with a # sign are ignored when the configuration file is read. They may contain comments about certain options, or show which options are not in use currently. In case you’d want to use a simulation using all defaults, you can either use an empty list, or specify NULL. If you’ve got the configuration you’d like to use, you can start the simulation from within R with the command simpact.run. Two parameters must be specified: the first is the settings to use (the cfg list in our example) and the second is a directory where generated files and results can be stored. The R module will attempt to create this directory if it does not exist yet. To use the directory /tmp/simpacttest, the command would become res <- simpact.run(cfg, "/tmp/simpacttest")  The other parameters are: • agedist: With this parameter, you can specify the age distribution that should be used when generating an initial population. The default is the age distribution of South Africa from 2003. In R, you can specify an alternative age distribution in two ways. The first way to do this, is to specify the age distribution as an R data frame or list, which contains columns named Age, Percent.Male and Percent.Female. The Age column should be increasing, and the other columns specify the probability of selecting each gender between the corresponding age and the next. Before the first specified age, this probability is zero, and the last mentioned age should have zeroes as the corresponding probabilities. The term probability here is not strictly correct: it can be any positive number since the resulting distribution will be normed. As an example ad <- list(Age=c(0,50,100), Percent.Male=c(1,2,0), Percent.Female=c(2,1,0))  will correspond to an age distribution which limits the age to 100 for everyone. Furthermore, there will be twice as many men over 50 than under 50, while for the women it’s the other way around. The other way an age distribution can be specified, is as a CSV file with (at least) three columns. The header of this CSV file will not be taken into account, instead the first column is assumed to hold the Age column, the second is interpreted as the Percent.Male column and the third as Percent.Female. • intervention: With this simulation intervention setting it is possible to change configuration options that are being used at specific times during the simulation. More information about how this can be used can be found in the explanation of the simulation intervention event. • release, slowalg, parallel: These flags specify which precise version of the simulation program will be used, and whether the single-core or multi-core version is used. The release parameter is TRUE by default, yielding the fastest version of the selected algorithm. If set to FALSE, many extra checks are performed, all of which should pass if the algorithm works as expected. By default, slowalg is FALSE which selects the population-based procedure described above. In case this is set to TRUE, the very basic mNRM algorithm is used, where all event fire times are recalculated after each event is executed. If all works as expected, the two algorithms should produce the same results for the same seed (although very small differences are possible due to limited numeric precision). The basic algorithm is very slow, keep this in mind if you use it. The parallel parameter is FALSE by default, selecting the version of the algorithm that only uses a single processor core. To use the parallel version, i.e. to use several processor cores at the same time, this can be set to TRUE. The parallel version currently only offers a modest speedup, and only for very large population sizes. Especially if you need to do several runs of a simulation, starting several single-core versions at once will use your computer’s power more efficiently than starting several parallel versions in a sequential way. • seed: By default, a more or less random seed value will be used to initialize the random number generator that’s being using in the simulation. In case you’d like to use a specific value for the seed, for example to reproduce results found earlier, you can set it here. • dryrun: If this is set to TRUE, the necessary configuration files will be generated, but the actual simulation is not performed. This can come in handy to prepare a simulation’s settings on your local machine and run one or more actual simulations on another machine, e.g. on a computer cluster. In case you’d like to perform several runs with the same configuration file, overriding the output prefix can be very helpful, as is described in the section on the configuration file. If you’d like to perform a run that has been prepared this way from within R, you can use the simpact.run.direct function. • identifierFormat: Files that are created by the simulation will all start with the same identifier. The identifierFormat parameter specifies what this identifier should be. Special properties start with a percent (%) sign, other things are just copied. An overview of these special properties: • %T: will expand to the simulation type, e.g. simpact-cyan • %y: the current year • %m: the current month (number) • %d: the current day of the month • %H: the current hour • %M: the current minute • %S: the current second • %p: the process ID of the process starting the simulation • %r: a random character The default identifier format %T-%y-%m-%d-%H-%M-%S_%p_%r%r%r%r%r%r%r%r- would lead to an identifier like simpact-cyan-2015-01-15-08-28-10_2425_q85z7m1G-. • dataFiles: if specified, this should be a list where each named entry contains a matrix. These matrices will be written to CSV files, which can be referred to in the configuration entries. For example, we could to something like this: myMatrix <- matrix(c(1,2,3,4),2,2) data <- list() data[["csvMatrix"]] <- myMatrix  To refer to this csv file in the configuration settings, we can use the data: prefix, e.g. cfg <- list() cfg["person.geo.dist2d.type"] <- "discrete" cfg["person.geo.dist2d.discrete.densfile"] <- "data:csvMatrix"  The simulation would then be started as follows: simpact.run(cfg, "/tmp/simpacttest", dataFiles=data)  The return value of the simpact.run function contains the paths to generated files and output files, or in case the dryrun option was used, of files that will be written to. The output files that are produced are described in the corresponding section. ### 2.2.3. Other functions¶ Apart from simpact.showconfig and simpact.run, some other functions exist in the RSimpactCyan library as well: • simpact.available This function returns a boolean value, that indicates if the RSimpactCyan library is able to find and use the Simpact Cyan simulation program. • simpact.getconfig This takes a list with config values as input, similar to simpact.showconfig, merges it with default settings, and returns the extended configuration list. If the second parameter (show) is set to TRUE, then the full configuration file will be shown on-screen as well. • simpact.run.direct This function allows you to start a simulation based on a previously created (e.g. using the dryrun setting of simpact.run) configuration file. This config file must be set as the first argument, and is always required. Other arguments are optional: • outputFile: If set to NULL, the output of the Simpact Cyan simulation (containing information about the version of the program and the random number generator seed) will just appear on screen. If a filename is specified here, the output will be written to that file as well. • release, slowalg, parallel, seed: Same meaning as in the simpact.run function • destDir: By default, the simulation will be run in the directory that contains the config file. This is important if the config file itself specifies files without an absolute path name since the directory of the config file will then be used as a starting point. If you don’t want this behaviour and need to select another directory, this parameter can be used to set it. • simpact.set.datadir The RSimpactCyan library will try to figure out where the Simpact Cyan data files are located. If you want to specify another location, this function can be used to do so. • simpact.set.simulation The Simpact Cyan package is actually meant to support alternative simulations as well. To use such an alternative simulation, this function can be used. For example, if maxart is specified here, instead of running e.g. simpact-cyan-release as underlying program, maxart-release would be executed instead. ## 2.3. Running from within Python¶ ### 2.3.1. Getting started¶ The pysimpactcyan module to control Simpact Cyan from within Python, is automatically available once you’ve installed the program. Note that if you’re using MS-Windows, you’ll also need to install the Visual Studio 2015 redistributable package (use the x86 version). To load the Simpact Cyan module in a Python script or interactive session, just execute import pysimpactcyan  This allows you to create an instance of the PySimpactCyan class that’s defined in this module, let’s call it simpact: simpact = pysimpactcyan.PySimpactCyan()  ### 2.3.2. Running a simulation¶ To configure a simulation, you need to specify the options for which you want to use a value other than the default. This is done using a dictionary, for example cfg = { } cfg["population.nummen"] = 200 cfg["population.numwomen"] = 200 cfg["population.simtime"] = 40  All values that are entered this way are converted to character strings when creating a configuration file for the simulation. This means that instead of a numeric value, you could also use a string that corresponds to the same number, for example cfg["population.nummen"] = "200"  Together with the defaults for other options, these settings will be combined into a configuration file that the real Simpact Cyan program can understand. Taking a look at the full configuration file will show you what other values are in use; to see this configuration file, run simpact.showConfiguration(cfg)  Lines that start with a # sign are ignored when the configuration file is read. They may contain comments about certain options, or show which options are not in use currently. In case you’d want to use a simulation using all defaults, you can either use an empty dictionary, or specify None. If you’ve got the configuration you’d like to use, you can start the simulation from within Python using the run method of the Simpact Cyan object you’re using. Two parameters must be specified: the first is the settings to use (the cfg dictionary in our example) and the second is a directory where generated files and results can be stored. The Python module will attempt to create this directory if it does not exist yet. To use the directory /tmp/simpacttest, the command would become res = simpact.run(cfg, "/tmp/simpacttest")  The other parameters are: • agedist: With this parameter, you can specify the age distribution that should be used when generating an initial population. The default is the age distribution of South Africa from 2003. In Python, you can specify an alternative age distribution in two ways. The first way to do this, is to specify the age distribution as a dictionary which contains lists of numbers named Age, Percent.Male and Percent.Female. The Age list should be increasing, and the other lists specify the probability of selecting each gender between the corresponding age and the next. Before the first specified age, this probability is zero, and the last mentioned age should have zeroes as the corresponding probabilities. The term probability here is not strictly correct: it can be any positive number since the resulting distribution will be normed. As an example ad = { "Age": [0, 50, 100], "Percent.Male": [1, 2, 0], "Percent.Female": [2, 1, 0] }  will correspond to an age distribution which limits the age to 100 for everyone. Furthermore, there will be twice as many men over 50 than under 50, while for the women it’s the other way around. The other way an age distribution can be specified, is as a CSV file with (at least) three columns. The header of this CSV file will not be taken into account, instead the first column is assumed to hold the Age column, the second is interpreted as the Percent.Male column and the third as Percent.Female. • parallel, opt, release: These flags specify which precise version of the simulation program will be used, and whether the single-core or multi-core version is used. The release parameter is True by default, yielding the fastest version of the selected algorithm. If set to False, many extra checks are performed, all of which should pass if the algorithm works as expected. By default, opt is True which selects the population-based procedure described above. In case this is set to False, the very basic mNRM algorithm is used, where all event fire times are recalculated after each event is executed. If all works as expected, the two algorithms should produce the same results for the same seed (although very small differences are possible due to limited numeric precision). The basic algorithm is very slow, keep this in mind if you use it. The parallel parameter is False by default, selecting the version of the algorithm that only uses a single processor core. To use the parallel version, i.e. to use several processor cores at the same time, this can be set to True. The parallel version currently only offers a modest speedup, and only for very large population sizes. Especially if you need to do several runs of a simulation, starting several single-core versions at once will use your computer’s power more efficiently than starting several parallel versions in a sequential way. • seed: By default, a more or less random seed value will be used to initialize the random number generator that’s being using in the simulation. In case you’d like to use a specific value for the seed, for example to reproduce results found earlier, you can set it here. • interventionConfig: With this simulation intervention setting it is possible to change configuration options that are being used at specific times during the simulation. More information about how this can be used can be found in the explanation of the simulation intervention event. • dryRun: If this is set to True, the necessary configuration files will be generated, but the actual simulation is _not_ performed. This can come in handy to prepare a simulation’s settings on your local machine and run one or more actual simulations on another machine, e.g. on a computer cluster. In case you’d like to perform several runs with the same configuration file, overriding the output prefix can be very helpful, as is described in the section on the configuration file. If you’d like to perform a run that has been prepared this way from within Python, you can use the runDirect method of the PySimpactCyan class. • identifierFormat: Files that are created by the simulation will all start with the same identifier. The identifierFormat parameter specifies what this identifier should be. Special properties start with a percent (%) sign, other things are just copied. An overview of these special properties: • %T: will expand to the simulation type, e.g. simpact-cyan • %y: the current year • %m: the current month (number) • %d: the current day of the month • %H: the current hour • %M: the current minute • %S: the current second • %p: the process ID of the process starting the simulation • %r: a random character The default identifier format %T-%y-%m-%d-%H-%M-%S_%p_%r%r%r%r%r%r%r%r- would lead to an identifier like simpact-cyan-2015-01-15-08-28-10_2425_q85z7m1G-. • dataFiles: if specified, this should be a dictionary where each named entry contains a an array of arrays or a Pandas DataFrame. These matrices will be written to CSV files, which can be referred to in the configuration entries. For example, we could to something like this: myMatrix = [ [1, 3], [2, 4] ] data = { } data["csvMatrix"] = myMatrix  To refer to this csv file in the configuration settings, we can use the data: prefix, e.g. cfg = { } cfg["person.geo.dist2d.type"] = "discrete" cfg["person.geo.dist2d.discrete.densfile"] = "data:csvMatrix"  The simulation would then be started as follows: simpact.run(cfg, "/tmp/simpacttest", dataFiles=data)  The return value of the run method contains the paths to generated files and output files, or in case the dryRun option was used, of files that will be written to. The output files that are produced are described in the corresponding section. ### 2.3.3. Other functions¶ Apart from the PySimpactCyan methods showConfiguration and run, some other methods exist in this Python class as well: • getConfiguration This takes a dictionary with config values as input, similar to showConfiguration, merges it with default settings, and returns the extended configuration dictionary. If the second parameter (show) is set to True, then the full configuration file will be shown on-screen as well. • runDirect This function allows you to start a simulation based on a previously created (e.g. using the dryRun setting of run) configuration file. This config file must be set as the first argument, and is always required. Other arguments are optional: • outputFile: If set to None, the output of the Simpact Cyan simulation (containing information about the version of the program and the random number generator seed) will just appear on screen. If a filename is specified here, the output will be written to that file as well. • release, opt, parallel, seed: Same meaning as in the run method. • destDir: By default, the simulation will be run in the directory that contains the config file. This is important if the config file itself specifies files without an absolute path name since the directory of the config file will then be used as a starting point. If you don’t want this behaviour and need to select another directory, this parameter can be used to set it. • setSimpactDataDirectory The pysimpactcyan module will try to figure out where the Simpact Cyan data files are located. If you want to specify another location, this function can be used to do so. • setSimpactDirectory In case you want to specify that the Simpact Cyan executables are located in a specific directory, you can use this function. • setSimulationPrefix The Simpact Cyan package is actually meant to support alternative simulations as well. To use such an alternative simulation, this function can be used. For example, if maxart is specified here, instead of running e.g. simpact-cyan-release as underlying program, maxart-release would be executed instead. ## 2.4. Configuration file and variables¶ The actual program that executes the simulation reads its settings from a certain configuration file. This is also the case when running from R or Python, where the R or Python interface prepares the configuration file and executes the simulation program. While this approach makes it much easier to configure and run simulations, some knowledge of the way the configuration file works can be helpful. ### 2.4.1. The basics¶ In essence, the configuration file is just a text file containing key/value pairs. For example, the line population.simtime = 100  assigns the value 100 to the simulation setting population.simtime, indicating that the simulation should run for 100 years. Lines that start with a hash sign (#) are not processed, they can be used for comments. In the config file itself, mathematical operations are not possible, but if you’re using R or Python, you can perform the operation there, and only let the result appear in the config file. For example, if you’d do library("RSimpactCyan") cfg <- list() cfg["population.simtime"] = 100/4 simpact.showconfig(cfg)  in an R session, you’d find population.simtime = 25  in the configuration file. We could force ‘100/4’ to appear in the configuration file by changing the line to cfg["population.simtime"] = "100/4"  (so we added quotes), but when trying to run the simulation this would lead to the following error: FATAL ERROR: Can't interpret value for key 'population.simtime' as a floating point number  ### 2.4.2. Config file variables and environment variables¶ Keys that start with a dollar sign ($) are treated in a special way: they define a variable that can be used further on in the configuration file. To use a variable’s contents in the value part of a config line, the variable’s name should be placed between ${ and }. For example, we could first have set $SIMTIME = 100


thereby assigning 100 to the variable with name SIMTIME. This could then later be used as follows:

population.simtime = ${SIMTIME}  You don’t even need to define the variable inside the configuration file: if you define an environment variable, you can use its contents in the same way as before. For example, if the HOME environment variable has been set to /Users/JohnDoe/, then the lines periodiclogging.interval = 2 periodiclogging.outfile.logperiodic =${HOME}periodiclog.csv


would enable the periodic logging event and write its output every other year to /Users/JohnDoe/periodiclog.csv.

One very important thing to remember is that if an environment variable with the same name as a config file variable exists, the environment variable will always take precedence over config file variables. While this might seem a bit odd, it actually allows you to more easily use config files prepared on one system, on another system. Furthermore, it allows you to use a single config file multiple times, which can be very handy if you need to perform many runs using the same settings (but different output files).

### 2.4.3. Using environment variables¶

When you let the R or Python interface prepare a configuration file, this file will start by defining two config file variables, for example:

$SIMPACT_OUTPUT_PREFIX = simpact-cyan-2015-05-27-08-28-13_27090_8Al7O6mD-$SIMPACT_DATA_DIR = /usr/local/share/simpact-cyan/


The first variable is used in the config file when specifying which files to write output to. As an example, you’d also find the line

logsystem.outfile.logevents = ${SIMPACT_OUTPUT_PREFIX}eventlog.csv  in that file, so the full output file name would be simpact-cyan-2015-05-27-08-28-13_27090_8Al7O6mD-eventlog.csv  The second variable specifies the location that R or Python thinks the Simpact Cyan data files are stored in, and is used in the line that specifies which age distribution to use when initializing the population: population.agedistfile =${SIMPACT_DATA_DIR}sa_2003.csv


In this case, the file /usr/local/share/simpact-cyan/sa_2003.csv would be used to read the initial age distribution from.

Because those config variables are defined inside the configuration file, such a file can be used on its own. If you’d first prepared the config file using the ‘dryrun’ setting, you could still use the created config file to start the simulation, either directly on the command line, using simpact.run.direct from R, or using the PySimpactCyan method runDirect in Python.

If you’re running from the command line, it’s very easy to reuse the same configuration file for multiple runs. Normally if you’d try this, you’d see an error message like

FATAL ERROR:
Unable to open event log file:
Specified log file simpact-cyan-2015-05-27-08-28-13_27090_8Al7O6mD-eventlog.csv already exists


To make sure that you don’t lose data from simulations you’ve already performed, the simulation will not start if it needs to overwrite an output file, which is what causes this message.

However, because we can easily override the value of SIMPACT_OUTPUT_PREFIX from the command line by using an environment variable with the same name, it becomes possible to reuse the configuration file multiple times. For example, assuming that our config file is called myconfig.txt, the simple bash script

for i in 1 2 3 4 5 ; do
SIMPACT_OUTPUT_PREFIX=newprefix_\${i}- simpact-cyan-release myconfig.txt 0 opt
done


would produce output files like newprefix_1-eventlog.csv and newprefix_5-eventlog.csv.

In a similar way, setting an environment variable called SIMPACT_DATA_DIR can be helpful when preparing simulations on one system and running them on another. For example, you could prepare the simulations on your laptop, using the ‘dryrun’ option to prevent the simulation from actually running, and execute them on e.g. a computer cluster where you set the SIMPACT_DATA_DIR environment variable to make sure that the necessary data files can still be found.

## 2.5. Supported probability distributions and their parameters¶

If a configuration option ends with .dist.type or .dist2d.type, for example option birth.pregnancyduration.dist.type of the birth event, you can specify a number of probability distributions there. By choosing a specific type of probability distribution, you also activate a number of other options to configure the parameters of this probability distribution.

For example, if birth.pregnancyduration.dist.type is set to normal, then parameters of the one dimensional normal distribution need to be configured. For example, we could set birth.pregnancyduration.dist.normal.mu to 0.7342 and birth.pregnancyduration.normal.sigma to 0.0191, and we’d get a birth event that on average takes place after 0.7342 years (is 268 days), with a standard deviation of roughly one week (0.0191 years).

Below you can find an overview of the currently supported one and two dimensional distributions and their parameters.

### 2.5.1. One dimensional distributions¶

Here is an overview of the relevant configuration options, their defaults (between parentheses), and their meaning:

• some.option.dist.type: (‘fixed’):
With such an option, you specify which specific distribution to choose. Allowed values are beta, discrete.csv.onecol, discrete.csv.twocol, discrete.inline, exponential, fixed, gamma, lognormal, normal, uniform, and the corresponding parameters are given in the subsections below. Unless otherwise specified, the default here is a fixed distribution, which is not really a distribution but just causes a fixed value to be used.

#### 2.5.1.1. beta¶

If this distribution is chosen, the (scaled) beta distribution with the following probability density is used:

${\rm prob}(x) = \frac{\Gamma(a+b)}{\Gamma(a)\Gamma(b)} \left(\frac{x-x_{\rm min}}{x_{\rm max}-x_{\rm min}}\right)^{a-1} \left(1-\frac{x-x_{\rm min}}{x_{\rm max}-x_{\rm min}}\right)^{b-1} \frac{1}{x_{\rm max}-x_{\rm min}}$

This corresponds to a beta distribution that, instead of being non-zero between 0 and 1, is now scaled and translated to be defined between $$x_{\rm min}$$ and $$x_{\rm max}$$.

Here is an overview of the relevant configuration options, their defaults (between parentheses), and their meaning:

• some.option.dist.beta.a (no default):
Corresponds to the value of $$a$$ in the formula for the probability density above.
• some.option.dist.beta.b (no default):
Corresponds to the value of $$b$$ in the formula for the probability density above.
• some.option.dist.beta.max (no default):
Corresponds to the value of $$x_{\rm min}$$ in the formula for the probability density above.
• some.option.dist.beta.min (no default):
Corresponds to the value of $$x_{\rm max}$$ in the formula for the probability density above.

#### 2.5.1.2. discrete.csv.onecol¶

This distribution allows you to draw random numbers based on the values in a single column (ycolumn) of a CSV file (file). The distribution will return values between xmin and xmax, with probabilities according to the entries in the column of the CSV file. If floor is set to no, then any value that lies within the bin is possible; if yes, then only start values of each bin can be returned.

For example, suppose we have a CSV file that corresponds to this table:

Prob
10
30

If we specify xmin to be 0 and xmax to be 2, then there’s 25% chance that the generated random number will lie between 0 and 1, and 75% chance that it will lie between 1 and 2. If we set floor to yes, then 25% of the generated random numbers will be 0, while 75% will be 1.

Here is an overview of the relevant configuration options, their defaults (between parentheses), and their meaning:

• some.option.dist.discrete.csv.onecol.file (no default):
This is the filename of the CSV file containing the column to use for the probability density.
• some.option.dist.discrete.csv.onecol.floor (‘no’):
By default, any value within a bin is allowed. If set to yes, then only the bin start values can be generated.
• some.option.dist.discrete.csv.onecol.xmin (0):
The minimum value that’s possibly generated by the distribution. Maps to the start of the CSV column.
• some.option.dist.discrete.csv.onecol.xmax (1):
The maximum value that’s possibly generated by the distribution. Maps to the end of the CSV column.
• some.option.dist.discrete.csv.onecol.ycolumn (1):
The number of the column to use from the CSV file.

#### 2.5.1.3. discrete.csv.twocol¶

This distribution allows you to generate random numbers based on the values in two columns (xcolumn and ycolumn) of a CSV file (file). The xcolumn of the CSV file specifies the start of each bin, the ycolumn corresponds to the probability of generating a random number in that bin. To be able to determine when the last bin ends, the last entry in the ycolumn must be zero. If floor is set to no, then any value that lies within the bin is possible; if yes, then only start values of each bin can be returned.

For example, support we have a CSV file that corresponds to the table below, and we’ve set xcolumn to 1 and ycolumn to 2. In this case, 25% of the generated random numbers will have a value between 0 and 3, while 75% of the random numbers will lie between 3 and 4. If floor is set to yes, then 25% of the random numbers will be 0, and 75% will be 3.

X Prob
0 10
3 30
4 0

Here is an overview of the relevant configuration options, their defaults (between parentheses), and their meaning:

• some.option.dist.discrete.csv.twocol.file (no default):
This is the filename of the CSV file containing the columns to use for the probability density.
• some.option.dist.discrete.csv.twocol.floor (‘no’):
By default, any value within a bin is allowed. If set to yes, then only the bin start values can be generated.
• some.option.dist.discrete.csv.twocol.xcolumn (1):
The number of the column to use from the CSV file that contains the bin start values.
• some.option.dist.discrete.csv.twocol.ycolumn (2):
The number of the column to use from the CSV file that contains the probabilities.

#### 2.5.1.4. discrete.inline¶

This distribution allows you to generate random numbers based on the values in two lists (xvalues and yvalues). The xvalues list specifies the start of each bin, the yvalues entries correspond to the probability of generating a random number in that bin. To be able to determine when the last bin ends, the last entry in yvalues must be zero. If floor is set to no, then any value that lies within the bin is possible; if yes, then only start values of each bin can be returned.

For example, support we have a the following settings:

xvalues = 0,3,4
yvalues = 10,30,0


In this case, 25% of the generated random numbers will have a value between 0 and 3, while 75% of the random numbers will lie between 3 and 4. If floor is set to yes, then 25% of the random numbers will be 0, and 75% will be 3.

Here is an overview of the relevant configuration options, their defaults (between parentheses), and their meaning:

• some.option.dist.discrete.inline.floor (‘no’):
By default, any value within a bin is allowed. If set to yes, then only the bin start values can be generated.
• some.option.dist.discrete.inline.xvalues (no default):
A list of increasing values corresponding to the bin start values.
• some.option.dist.discrete.inline.yvalues (no default):
A list of equal length as xvalues, specifying the probability of generating a random number in the corresponding bin. The last value must be zero.

#### 2.5.1.5. exponential¶

If the exponential distribution is selected, the probability density for a negative value is zero, while the probability density for positive values is given by:

${\rm prob}(x) = \lambda \exp(-\lambda x)$

Here is an overview of the relevant configuration options, their defaults (between parentheses), and their meaning:

• some.option.dist.exponential.lambda (no default):
This specifies the value of $$\lambda$$ in the expression of the probability density above.

#### 2.5.1.6. fixed¶

This does not really correspond to a distribution, instead a predefined value is always used.

Here is an overview of the relevant configuration options, their defaults (between parentheses), and their meaning:

• some.option.dist.fixed.value (0):
When a number is chosen from this ‘distribution’, this value is always returned.

#### 2.5.1.7. gamma¶

In this case, the gamma distribution will be used to choose random numbers. The probability density is

${\rm prob}(x) = \frac{x^{a-1} \exp\left(-\frac{x}{b}\right)}{b^a \Gamma(a)}$

for positive numbers, and zero for negative ones.

Here is an overview of the relevant configuration options, their defaults (between parentheses), and their meaning:

• some.option.dist.gamma.a (no default):
This corresponds to the value of $$a$$ in the expression of the probability distribution.
• some.option.dist.gamma.b (no default):
This corresponds to the value of $$b$$ in the expression of the probability distribution.

#### 2.5.1.8. lognormal¶

If this log-normal distribution is chosen, the probability density for negative numbers is zero, while for positive numbers it is:

${\rm prob}(x) = \frac{1}{x \sigma \sqrt{2\pi}} \exp\left(-\frac{(\log{x}-\zeta)^2}{2\sigma^2}\right)$

Here is an overview of the relevant configuration options, their defaults (between parentheses), and their meaning:

• some.option.dist.lognormal.sigma (no default):
This corresponds to the value of $$\sigma$$ in the formula for the probability distribution.
• some.option.dist.lognormal.zeta (no default):
This corresponds to the value of $$\zeta$$ in the formula for the probability distribution.

#### 2.5.1.9. normal¶

The base probability distribution used when the normal distribution is selected is the following:

${\rm prob}(x) = \frac{1}{\sigma\sqrt{2\pi}} \exp\left(- \frac{(x-\mu)^2}{2\sigma^2}\right)$

It is possible to specify a minimum and maximum value as well, which causes the probability density to be zero outside of these bounds, and somewhat higher in between. A very straightforward rejection sampling method is used for this at the moment, so it is best not to use this to sample from a narrow interval (or in general an interval with a low probability) since this can require many iterations.

Here is an overview of the relevant configuration options, their defaults (between parentheses), and their meaning:

• some.option.dist.normal.max (+infinity):
This can be used to specify the maximum value beyond which the probability density is zero. By default no truncation is used, and this maximum is set to positive infinity.
• some.option.dist.normal.min (-infinity):
This can be used to specify the minimum value below which the probability density is zero. By default no trunctation is used, and this minimum is set to negative infinity.
• some.option.dist.normal.mu (no default):
This corresponds to the value of $$\mu$$ in the expression of the probability density above.
• some.option.dist.normal.sigma (no default):
This corresponds to the value of $$\sigma$$ in the expression of the probability density above.

#### 2.5.1.10. uniform¶

When this probability density is selected, each number has an equal probability density between a certain minimum and maximum value. Outside of these bounds, the probability density is zero.

Here is an overview of the relevant configuration options, their defaults (between parentheses), and their meaning:

• some.option.dist.uniform.min (0):
This specifies the start of the interval with the same, constant probability density.
• some.option.dist.uniform.max (1):
This specifies the end of the interval with the same, constant probability density.

### 2.5.2. Two dimensional distributions¶

Here is an overview of the relevant configuration options, their defaults (between parentheses), and their meaning:

• some.option.dist2d.type (‘fixed’):
binormal, binormalsymm, discrete, fixed, uniform

#### 2.5.2.1. binormal¶

This corresponds to the two dimensional multivariate normal distribution, which has the following probability density:

${\rm prob}(x,y) = \frac{1}{2\pi\sigma_x\sigma_y\sqrt{1-\rho^2}} \exp\left[-\frac{ \frac{(x-\mu_x)^2}{\sigma_x^2} + \frac{(y-\mu_y)^2}{\sigma_y^2} - \frac{2\rho (x-\mu_x)(y-\mu_y)}{\sigma_x\sigma_y} }{2(1-\rho^2)} \right]$

If desired, this probability density can be truncated to specific bounds, by setting the minx, maxx, miny and maxy parameters. These default to negative and positive infinity causing truncation to be disabled. To enforce these bounds a straightforward rejection sampling method is used, so to avoid a large number of iterations to find a valid random number, it is best not to restrict the acceptable region to one with a low probability.

Here is an overview of the relevant configuration options, their defaults (between parentheses), and their meaning:

• some.option.dist2d.binormal.meanx (0):
Corresponds to $$\mu_x$$ in the expression for the probability density above.
• some.option.dist2d.binormal.meany (0):
Corresponds to $$\mu_y$$ in the expression for the probability density above.
• some.option.dist2d.binormal.rho (0):
Corresponds to $$\rho$$ in the expression for the probability density above.
• some.option.dist2d.binormal.sigmax (1):
Corresponds to $$\sigma_x$$ in the expression for the probability density above.
• some.option.dist2d.binormal.sigmay (1):
Corresponds to $$\sigma_y$$ in the expression for the probability density above.
• some.option.dist2d.binormal.minx (-infinity):
This can be used to truncate the probability distribution in the x-direction.
• some.option.dist2d.binormal.maxx (+infinity):
This can be used to truncate the probability distribution in the x-direction.
• some.option.dist2d.binormal.miny (-infinity):
This can be used to truncate the probability distribution in the y-direction.
• some.option.dist2d.binormal.maxy (+infinity):
This can be used to truncate the probability distribution in the y-direction.

#### 2.5.2.2. binormalsymm¶

This is similar to the binormal distribution above, but using the same parameters for the x-direction as for the y-direction. This means it is also a two dimensional multivariate normal distribution, but with a less general probability distribution:

${\rm prob}(x,y) = \frac{1}{2\pi\sigma^2\sqrt{1-\rho^2}} \exp\left[-\frac{(x-\mu)^2 + (y-\mu)^2 - 2\rho (x-\mu)(y-\mu)}{2\sigma^2 (1-\rho^2)}\right]$

If desired, this probability density can be truncated to specific bounds, by setting the min and max parameters. These default to negative and positive infinity causing truncation to be disabled. To enforce these bounds a straightforward rejection sampling method is used, so to avoid a large number of iterations to find a valid random number, it is best not to restrict the acceptable region to one with a low probability.

Here is an overview of the relevant configuration options, their defaults (between parentheses), and their meaning:

• some.option.dist2d.binormalsymm.mean (0):
This corresponds to the value of $$\mu$$ in the formula for the probability density.
• some.option.dist2d.binormalsymm.rho (0:
This corresponds to the value of $$\rho$$ in the formula for the probability density.
• some.option.dist2d.binormalsymm.sigma (1):
This corresponds to the value of $$\sigma$$ in the formula for the probability density.
• some.option.dist2d.binormalsymm.max (+infinity):
This can be used to truncate the probability density, to set a maximum in both the x- and y-directions.
• some.option.dist2d.binormalsymm.min (-infinity):
This can be used to truncate the probability density, to set a minimum in both the x- and y-directions.

#### 2.5.2.3. discrete¶

With the discrete distribution, you can use a TIFF image file or a CSV file to specify a probability distribution. This can come in handy if you’d like to use population density data for example. The TIFF file format is very general, and can support several sample representations and color channels. Since the specified file will be used for a probability distribution, only one value per pixel is allowed (as opposed to separate values for red, green and blue for example), and a 32-bit or 64-bit floating point representation should be used. Negative values are set to zero, while positive values will be normalized and used for the probability distribution. Instead of reading the probabilities for each pixel/bin from a TIFF file, they can also be read from a CSV file. Again, negative values will be clipped to zero, while positive values will be used for the probability distribution.

If desired, a ‘mask file’ can be specified as well (using maskfile). Such a file should also be a TIFF or CSV file, with the same number of entries in each dimension. If the value of a certain pixel in the mask file is zero or negative, the value read from the real input file (densfile) will be set to zero, otherwise the value from the real input file is left unmodified. This can be used to easily restrict the original file to a certain region.

Suppose we have a 320x240 image that we’d like to use to base a probability density on. In the TIFF file format, as with many other image formats, the upper-left pixel is the (0, 0) pixel, while the bottom-right pixel will be (319, 239). Usually, we’d like the y-axis to point up however, which is why the default value of the flipy parameter is set to yes. To illustrate, suppose we use the up32.tiff file, which corresponds to the following image. If we use this as a discrete probability density, a histogram of the samples should show the text ‘UP’.

In the discretedistribution.ipynb example, we abuse the setting of the population density to sample from this distribution: each person will have a location that is sampled from this discrete distribution. In case the flipy parameter is yes (the default), we obtain the following histogram, which is probably what we’d expect.

On the other hand, if we explicitly set the flipy parameter to no, the mismatch between the y-axes becomes apparent:

The same reasoning applies for the CSV file: because the row number increases each time a line is read from the CSV file, the y-axis of the file points down. If the flipy parameter is set to yes (the default), this axis is inverted. So for example, suppose you have the following CSV file

"C1","C2","C3"
1,2,3
4,5,6


which corresponds to the following table:

C1 C2 C3
1 2 3
4 5 6

In this case, with flipy set to yes, this would correspond to the following 2D distribution:

The image file itself just specifies the shape of the probability distribution. The actual size and position in the x-y plane can be set using the width, height, xoffset and yoffset parameters.

There’s also a floor parameter, which defaults to no. If this is the case, then in each pixel/bin a constant probability is assumed, making it possible for the 2D probability distribution to generate any value pair within the bin. If however, the value of the parameter is set to yes, then only the bin corner values can be returned, nothing in between. This is useful if you’d like a distribution to generate only a fixed set of values. The following notebook illustrates this: discretedistfloor.ipynb

Here is an overview of the relevant configuration options, their defaults (between parentheses), and their meaning:

• some.option.dist2d.discrete.densfile (no default):
This should be used to specify the TIFF or CSV file that contains the discrete probability density to be used.
• some.option.dist2d.discrete.maskfile (no default):
As explained above, a TIFF or CSV mask file can be used to restrict the previous file to a certain region. Set to an empty string in case you don’t need this.
• some.option.dist2d.discrete.flipy (‘yes’):
By default, the image will be flipped in the y-direction. This has to do with the y-axis in images being different from what we’d expect (see explanation above).
• some.option.dist2d.discrete.floor (‘no’):
By default, any value inside a bin/pixel is possible. If set to ‘yes’, the 2D distribution will only generate values that correspond to the corner of a bin.
• some.option.dist2d.discrete.width (1):
The TIFF or CSV file itself just specifies the shape of the distribution. With this parameter you can set the actual width (scale in x-direction) in the x-y plane.
• some.option.dist2d.discrete.height (1):
The TIFF or CSV file itself just specifies the shape of the distribution. With this parameter you can set the actual height (scale in y-direction) in the x-y plane.
• some.option.dist2d.discrete.xoffset (0):
The TIFF or CSV file itself just specifies the shape of the distribution. With this parameter you can set the x-offset in the x-y plane.
• some.option.dist2d.discrete.yoffset (0):
The TIFF or CSV file itself just specifies the shape of the distribution. With this parameter you can set the y-offset in the x-y plane.

#### 2.5.2.4. fixed¶

This does not really correspond to a distribution, instead a predefined (x, y) value is always used.

Here is an overview of the relevant configuration options, their defaults (between parentheses), and their meaning:

• some.option.dist2d.fixed.xvalue (0):
The x-value of the (x, y) coordinate that is always returned.
• some.option.dist2d.fixed.yvalue (0):
The y-value of the (x, y) coordinate that is always returned.

#### 2.5.2.5. uniform¶

By specifying this probability density, a point shall be chosen from a rectangular region in the x-y plane. All points within this region have an equal probability density, while points outside the region have a probably density of zero.

Here is an overview of the relevant configuration options, their defaults (between parentheses), and their meaning:

• some.option.dist2d.uniform.xmin (0):
This specifies the start of the region along the x-axis.
• some.option.dist2d.uniform.xmax (1):
This specifies the end of the region along the x-axis.
• some.option.dist2d.uniform.ymin (0):
This specifies the start of the region along the y-axis.
• some.option.dist2d.uniform.ymax (1):
This specifies the end of the region along the y-axis.