probAna is used to derive simple probability maps for a set of simulations for one avalanche track. These maps show for each point in space the fraction of simulations where a chosen parameter has exceeded a given threshold. For example, it is possible to compute the probability map for an avalanche with respect to a peak pressure threshold of 1kPa, but it is also possible to chose other result variables and threshold values.
A set of multiple avalanche simulations is required to generate these maps. The simulations can be generated with :pycom1DFA using a parameter variation, different release-, entrainment- or resistance scenarios. An example run script is given by :pyrunAna4ProbAna: where firstly, :pycom1DFA is used to perform avalanche simulations varying parameters set in the ana4Stats/probAnaCfg.ini file. Using these simulations, a probability map is generated. The output is a raster file (.asc) with values ranging from 0-1. 0 meaning that no simulation exceeded the threshold in this point in space. 1 on the contrary means that all simulations exceeded the threshold. Details on this function, as for example required inputs can be found in: :pyana4Stats.probAna.
Since version 1.6 it is possible to generate probability maps via the QGis connector. This is provided in the experimental folder of the QGis processing plugin via Probability run. A standard setup is used in which mu and release thickness are varied. Please note: samosAT friction is used instead of the default samosATAuto one.
The input requirements are the same as for :pycom1DFA with one important difference: an additional ci95 attribute is needed for the release shapes. This describes the confidence intervall for the release thickness, using the same units. I.e. if you are using a release thickness of 1.5m, you need to provide the ci in [m] as well, for example ci=0.3m. The release thickness is then automatically varied within the release thickness plus/minus the confidence interval.
In :pyrunProbAnaCom1DFA.py, avalanche simulations are performed with the settings defined in the configuration file of :pycom1DFA and in addition a parameter variation is performed according to the parameters set in ana4Stats/probAnaCfg.ini in the section PROBRUN. The parameters to be varied are set in varParList, the type of variation in variationType (options are: percent, range, rangefromci) and the value of the variation in variationValue. Then there are two sampling strategies to choose from, for performing the parameter variation: (1) a latin hypercube sample of all the parameters to be varied (provided in varParList) is generated and simulations are performed using sets of parameters drawn from this sample. Using sampling strategy (2) all the parameters set in PROBRUN are varied on at a time, i.e. simulations are performed for the standard settings of all parameters, except the one parameter to be varied, subsequently the other variations are performed. One probability map is created for all the different simulations and in case of sampling strategy (2), also one map per parameter that is varied once at a time, is created in addition. In order to run this example:
- first go to
AvaFrame/avaframe - copy
avaframeCfg.initolocal_avaframeCfg.iniand set your desired avalancheDir - copy
ana4Stats/probAnaCfg.initoana4Stats/local_probAnaCfg.iniand optionally adjust variation settings - uncomment
'FILTER'section inlocal_probAnaCfg.iniand insert filter parameters if you want to first filter simulations run:
python3 runAna4ProbAna.py
Another example on how to generate probability maps for avalanche simulations performed with :pycom1DFA is given in :pyrunScripts.runProbAna, where for avaHockeyChannel simulations are performed with varying release thickness values ranging from 0.75 to 1.75 meters in steps of 0.05 meters. The resulting simulations are then used to generate the probability map with :pyout3Plot.statsPlots.plotProbMap. There is also the option to filter the simulations further - using the function :pyin3Utils.fileHandlerUtils.getFilterDict which generates a parameter dictionary for filtering according to the filter criteria set in the configuration file (ana4Stats/probAnaCfg.ini) of the :pyana4Stats.probAna function. In order to run this example:
- first go to
AvaFrame/avaframe - copy
ana4Stats/probAnaCfg.initoana4Stats/local_probAnaCfg.ini - uncomment
'FILTER'section inlocal_probAnaCfg.iniand insert filter parameters if you want to first filter simulations run:
python3 runScripts/runProbAna.py
This point-wise probability is expressed by the relative frequency of avalanche peak flow field exceeding a certain threshold for a set of deterministic avalanche simulations derived from a range of input parameters (see HyBeBu2019).
In :pyana4Stats.getStats, functions that help to compute statistical properties of simulation results are gathered. :pyana4Stats.getStats.extractMaxValues can be used to determine the maximum peak values of the simulation results. These values can then be plotted using the functions in :pyout3Plot.statsPlots in order to visualise the statistics of a set of avalanche simulations. For further details on the specific functions, have a look at: :pyana4Stats.getStats.
An example on how to use these statistical functions is given in :pyrunScripts.runStatsExample, where for avaHockeyChannel simulations are performed for two different release area scenarios and the release thickness is varied from 0.75 to 1.75 meters in steps of 0.05 meters. The resulting simulations are then analysed using the :pyana4Stats.getStats.extractMaxValues function and plots are generated using the plotting routines from :pyout3Plot.statsPlots . If in the configuration file ana4Stats/getStats.ini the flag aimec is set to True, additionally an moduleAna3AIMEC:ana3AIMEC: Aimec analysis is performed.
- first go to
AvaFrame/avaframe - copy
ana4Stats/getStats.initoana4Stats/local_getStatsCfg.ini - uncomment
'FILTER'section inana4Stats/local_getStatsCfg.iniand insert filter parameters if you want to first filter simulations run:
python3 runScripts/runStatsExample.py
