PostProcess SubPackage
Using this subpackage, user can export easily some specific data or results or plots from the recorders and user do not need to write code for post processing. Currently there are ObjsRecorder and Recorder modules for recording. In the following, some commands that has been provided are described for exporting some results from recorded data.
Note
Using the results in the form of this section for post processing is not mandatory and users can use their own code on the resulted values from analysis of the software. Also, users can develope the current module for post processing or add their modules or methods to the source code for further usages.
First Step: Create PostProcess Object
At very fist step, user should create the object of post processing. By creating this object, its constructor load data and get the results. For Objs_recorder and Recorder there are a specific command for this purpose. These commands are described in the following:
To create post process object for recorded data via Objs_recorder use the following command. In this command filename is the name of recoded file using Objs_recorder recorder. All the statistical analysis and data will be in this created object. (In the following examples the created object is named Post_Process_Object)
import opensrane as opr Post_Process_Object=opr.PostProcess.ObjsRecorderPP(ObjsRecorer_filename=filename, Number_Of_LOC_Histogram_Bins=100)To create post process object for recorded data Recorder use the following command. When user is using Recorder to record data and results, there are one separated file for each desired result and so finally there will be more than one file for recorded results. In this command Recorder_FilenamesList is the list of the recoded files name using Recorder recorder. All the statistical analysis and data will be in this created object. In the following sample it is assumed that user recorded the desired results in the mentioned file names.
import opensrane as opr Post_Process_Object=opr.PostProcess.RecorderPP(Recorder_FilenamesList=['RecorderA.OPRrec','RecorderB.OPRrec','RecorderC.OPRrec','RecorderD.OPRrec','RecorderE.OPRrec'],Number_Of_LOC_Histogram_Bins=100)
Second Step: Methods
By creating the post process object, various methods will be available for user that contains various results or plots that are resulted from recorded results by recorder object/s. In the following tables these methods and their its results are described.
DamagedLevelList method
Method Name
How to call
Description
DamagedLevelList()
Post_Process_Object.DamagedLevelList()
By calling this method, a list of dictionaries will be shown that each dictionary is related to a scenario and shows tag of the elements and level of damage.
Sample for resultes:
[{1: None, 2: None, 3: None, 4: None, 5: None, 6: None, 7: None, 8: None}, {1: 0, 2: 0, 3: 0, 4: 0, 5: 0, 6: 1, 7: 0, 8: 1}, {1: None, 2: None, 3: None, 4: None, 5: None, 6: None, 7: None, 8: None}, {1: 0, 2: 0, 3: 3, 4: 4, 5: 1, 6: 0, 7: 1, 8: 2},…]FragilityTagList method
Method Name
How to call
Description
FragilityTagList
Post_Process_Object.FragilityTagList()
This method, returns a list of dictionaries that each dictionary is related to a scenario and each key refers to a plant unit tag and the its result shows the tag of defined fragility or probit that cause damage.
Results sample:
[{1: None, 2: None, 3: None, 4: None, 5: None, 6: None, 7: None, 8: None}, {1: None, 2: None, 3: None, 4: None, 5: None, 6: None, 7: None, 8: None}, {1: None, 2: None, 3: None, 4: None, 5: None, 6: None, 7: None, 8: None}, {1: None, 2: None, 3: None, 4: None, 5: None, 6: None, 7: None, 8: None}, {1: 2, 2: 2, 3: 1, 4: 1, 5: 1, 6: 3, 7: 1, 8: 3}, {1: None, 2: None, 3: 1, 4: None, 5: None, 6: None, 7: 1, 8: 1}, {1: None, 2: 3, 3: 1, 4: 3, 5: 2, 6: 1, 7: 1, 8: 1},…]LOCList method
Method Name
How to call
Description
LOCList
Post_Process_Object.LOCList()
This method, returns a list of dictionaries that each dictionary shows the released liquid mass value (Loss Of Containment) of the plant unit in each scenario.
Results sample:
[{1: 0, 2: 0, 3: 0, 4: 0, 5: 0, 6: 0, 7: 0, 8: 0}, {1: 0, 2: 0, 3: 0, 4: 0, 5: 0, 6: 0, 7: 0, 8: 0}, {1: 0, 2: 0, 3: 0, 4: 0, 5: 0, 6: 0, 7: 0, 8: 0}, {1: 2827433.3882308137, 2: 4208351.855042743, 3: 4208351.855042743, 4: 4208351.855042743, 5: 3674532.4313447657, 6: 3674532.4313447657, 7: 3674532.4313447657, 8: 3674532.4313447657},…]NodesGroupDamageList method
Method Name
How to call
Description
NodesGroupDamageList
Post_Process_Object.NodesGroupDamageList()
This method, return a list of dictionaries each dictionary is results of each scenario and its keys are the NodesGroups tag and its result shows the elements damage condition according defined probit functions (0 shows No damage and 1 shows damaged). It returns empty list for Not damaged case.
Results sample:
[{1: []}, {1: []}, {1: []}, {1: []}, {1: []}, {1:[0,0,0,0,0,1,1,1,0,1,0,0,0,1,1,1,1,0,0,0,1,0]}, ... ]
NodesGroupTypeDict method
Method Name
How to call
Description
NodesGroupTypeDict
Post_Process_Object.NodesGroupTypeDict()
This metthod returns a dictionary that each key refers to a NodesGroup tag and the its result is the type of the NodesGroup.
Results sample:
[{1: []}, {1: []}, {1: []}, {1: []}, {1: []}, {1:'Social'}, ... ]
TotalLOCList method
Method Name
How to call
Description
TotalLOCList
Post_Process_Object.TotalLOCList()
This method returns list of total liquid mass (kg) that has released in each scenario.
Results sample:
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3674532.4313447657, 0, 0, 03674532.4313447657, 20593020.94647379, 0, 0, 0, 11557416.717732275, 5674351.416812722, 0, 0, 0, 14028296.729529412, 4208351.855042743, 0, 0, 0, 0, 0,…]
LOC_bins_hist_probloc method
Method Name
How to call
Description
LOC_bins_hist_probloc
[bins,hist,probloc]= Post_Process_Object.LOC_bins_hist_probloc(nbins=None)
This method returns 3 lists that are histogram data of the released liquids however they can be calculated from the previous described list. The first list is the bins data that its length should be one value greater than the two other lists. The second list is histogram data that shows the frequency of the bins and the last list is the probability of each bin value. nbins is the number of desired bins (number of equal ranges of LOC values).
Total_Number_Of_Scenarios method
Method Name
How to call
Description
Total_Number_Of_Scenarios
Post_Process_Object.Total_Number_Of_Scenarios()
This method simply returns total number of sampled scenarios.
Results sample:
1000000UnitsZeroDamageProb method
Method Name
How to call
Description
UnitsZeroDamageProb
Post_Process_Object.UnitsZeroDamageProb()
This method returns the damage probability of each unit in zero level as a dictionary. The keys are the units tag and their its results show the probability of damaging in the zero level.
Results sample:
{1: 0.0018952380952380952, 2: 0.001990476190476190, 3: 0.0021714285714285715, 4: 0.0019047619047619048, 5: 0.0019904761904761905, 6: 0.0021714285714285715, 7: 0.0021523809523809525, 8: 0.001961904761904762}
ProbOfFragilities method
Method Name
How to call
Description
ProbOfFragilities
Post_Process_Object.ProbOfFragilities()
This method returns the probability of happening of each defined fragility or probit function as a dictionary. The keys show the defined fragility or probit tag and the its results shows their governing probability among analysis. Probits that have defined for the Vulnerable areas (NodesGroup) will not consider in this part and their probability will be shown as zero (probits with tag 5 and 6).
Results sample:
{1: 0.003952380952380952, 2: 0.012285714285714285, 3: 0.005238095238095238, 4: 0.0009904761904761905, 5: 0.0, 6: 0.0}
Damagelevel_eLOC method
Method Name
How to call
Description
Damagelevel_eLOC
Post_Process_Object.Damagelevel_eLOC()
This method returns the expected released liquid (mass in kg) in each damage level as a dictionary. Each key refers to the damage level and the its result shows expected liquid released mass in that damage level.
Results sample:
{0: 56082.8170381438, 1: 8802.563549482884, 3: 925.6543716581589, 4: 395.0027138190845, 2: 3376.962598791185, 6: 26.927937030769655, 5: 116.56262508340092}
ScenariosAnalyzeNumbers method
Method Name
How to call
Description
ScenariosAnalyzeNumbers
Post_Process_Object.ScenariosAnalyzeNumbers()
This method returns scenarios name with the following format as key and list of the analyze number as its result as a dictionary.
Results sample:
{'(0):[3]': [202, 5646, 16833, 17173, 20846, 23658, 29179, 30415, 41698, 42064, 42114], '(0):[3,5]-(1):[2]-(2):[1,6,7]': [316778], '(0):[1,3,6]-(1):[4,7]': [316830], '(0):[1,3,6]-(1):[4,7]-(2):[8]': [316830], '(0):[4,5]-(1):[3]-(2):[2]-(3):[1,6]': [316858]}
Note
- The rule of mentioning scenarios is(Damage level):[list of units tag that damaged in this level] for example:
‘(0):[3]’ shows a scenario with damaging plant unit 3 in damage level 0 ‘(0):[3,5]-(1):[2]’ shows a scenario with damaged plant units with tag 3 and 5 in damage level 0 and damaged plant unit with tag 2 at damage level 1
ScenariosProbability method
Method Name
How to call
Description
ScenariosProbability
Post_Process_Object.ScenariosProbability()
This method returns scenarios name as key and the corresponding probability as value.
Results sample:
{'(0):[3]': [202, 5646, 16833, 17173, 20846, 23658, 29179, 30415, 41698, 42064, 42114], '(0):[3,5]-(1):[2]-(2):[1,6,7]': [316778], '(0):[1,3,6]-(1):[4,7]': [316830], '(0):[1,3,6]-(1):[4,7]-(2):[8]': [316830], '(0):[4,5]-(1):[3]-(2):[2]-(3):[1,6]': [316858]}
Note
- The rule of mentioning scenarios is(Damage level):[list of units tag that damaged in this level] for example:
‘(0):[3]’ shows a scenario with damaging plant unit 3 in damage level 0 ‘(0):[3,5]-(1):[2]’ shows a scenario with damaged plant units with tag 3 and 5 in damage level 0 and damaged plant unit with tag 2 at damage level 1
ScanariosSubScenario method
Method Name
How to call
Description
ScanariosSubScenario
Post_Process_Object.ScanariosSubScenario()[Scenario name]
This method returns a dictionary that its key is the Scenario name and the its result is next damage level scenarios.
Results sample:
Post_Process_Object.ScanariosSubScenario()['(0):[3]'] ['(0):[3]-(1):[2]', '(0):[3]-(1):[4]', '(0):[3]-(1):[2,4]', '(0):[3]-(1):[7,8]', '(0):[3]-(1):[7]', '(0):[3]-(1):[2,6]', '(0):[3]-(1):[4,7]', '(0):[3]-(1):[4,7,8]', '(0):[3]-(1):[2,7]', '(0):[3]-(1):[1,2]', '(0):[3]-(1):[2,4,7]', '(0):[3]-(1):[5,6,7]', '(0):[3]-(1):[6,7]']
To see next level scenario:
Post_Process_Object.ScanariosSubScenario()['(0):[3]-(1):[2]'] ['(0):[3]-(1):[2]-(2):[1]', '(0):[3]-(1):[2]-(2):[5,6]', '(0):[3]-(1):[2]-(2):[6]', '(0):[3]-(1):[2]-(2):[7]', '(0):[3]-(1):[2]-(2):[1,6]', '(0):[3]-(1):[2]-(2):[4]', '(0):[3]-(1):[2]-(2):[4,7]', '(0):[3]-(1):[2]-(2):[6,7]']
Note
- The rule of mentioning scenarios is(Damage level):[list of units tag that damaged in this level] for example:
‘(0):[3]’ shows a scenario with damaging plant unit 3 in damage level 0 ‘(0):[3,5]-(1):[2]’ shows a scenario with damaged plant units with tag 3 and 5 in damage level 0 and damaged plant unit with tag 2 at damage level 1
Damagelevel_Scenario_Dict method
Method Name
How to call
Description
Damagelevel_Scenario_Dict
Post_Process_Object.Damagelevel_Scenario_Dict()
This method returns a dictionary that its keys are the damage level and its values are list of the Scenarios in the corresponding level.
HazardMagnitude method
Method Name
How to call
Description
HazardMagnitude
Post_Process_Object.HazardMagnitude()
This method returns a list that each cell is a dictionary that its key is the hazard tag and each value is the sampled value.
NodesGroupRadiationDict method
Method Name
How to call
Description
NodesGroupRadiationDict
Post_Process_Object.NodesGroupRadiationDict()
This method returns a dictionary that its keys are the NodesGroup tag and the its result is a list of each node radiation average values.
NodesGroupOverPressureDict method
Method Name
How to call
Description
NodesGroupOverPressureDict
Post_Process_Object.NodesGroupOverPressureDict()
This method returns a dictionary that its keys are the NodesGroup tag and the its result is a list of each node Overpressure average values.
NodesGroup_Rad_Probit_Dict method
Method Name
How to call
Description
NodesGroup_Rad_Probit_Dict
Post_Process_Object.NodesGroup_Rad_Probit_Dict()
This method returns a dictionary that its keys are the NodesGroup tag and the its result is a list of each node Radiation probit average values [Probit(Radiation)].
PostProcess Plots
Using the following methods user can plot some data using PostProcess results.
plot_DamageLevel_ExpectedLoss
Using this command the expected loss of containment in each damage level will be plotted.
- Post_Process_Object.plot_DamageLevel_ExpectedLoss(yaxistype='log', PlotMode=1, height=None, width=None)
Argument
Type
Description
yaxistype
str
Type of the yaxis [‘linear’, ‘log’, ‘date’, ‘category’,’multicategory’]
PlotMode
int
Options between 1,2 and 3 to plot on various editors.
height
int
height of plot.
width
int
width of plot.
Example:
The following demonstrates the use of the mentioned command.
Python Code
import opensrane as opr Post_Process_Object=opr.PostProcess.ObjsRecorderPP('Recorder',100) Post_Process_Object.plot_DamageLevel_ExpectedLoss('linear')
The result of above command is:
plot_Unit_ZeroLevel_DamageProb
Using this command each plant unit damage probability in zero level will be plotted.
- Post_Process_Object.plot_Unit_ZeroLevel_DamageProb(yaxistype='log', PlotMode=1, height=None, width=None)
Argument
Type
Description
yaxistype
str
Type of the yaxis [‘linear’, ‘log’, ‘date’, ‘category’,’multicategory’]
PlotMode
int
Options between 1,2 and 3 to plot on various editors.
height
int
height of plot.
width
int
width of plot.
Example:
The following demonstrates the use of the mentioned command.
Python Code
import opensrane as opr Post_Process_Object=opr.PostProcess.ObjsRecorderPP('Recorder',100) Post_Process_Object.plot_Unit_ZeroLevel_DamageProb(results,'linear')
The result of above command is:
plot_Fragilities_Probits_Probability
Using this command each fragility and probit happening probability will be plotted.
- Post_Process_Object.plot_Fragilities_Probits_Probability(yaxistype='log', PlotMode=1, height=None, width=None)
Argument
Type
Description
yaxistype
str
Type of the yaxis [‘linear’, ‘log’, ‘date’, ‘category’,’multicategory’]
PlotMode
int
Options between 1,2 and 3 to plot on various editors.
height
int
height of plot.
width
int
width of plot.
Example:
The following demonstrates the use of the mentioned command.
Python Code
import opensrane as opr Post_Process_Object=opr.PostProcess.ObjsRecorderPP('Recorder',100) Post_Process_Object.plot_Fragilities_Probits_Probability('log')
The result of above command is:
plot_Expected_Total_LOC
Using this command expected total loss of containment will be plotted.
- Post_Process_Object.plot_Expected_Total_LOC(nbins=None, yaxistype='log', cumulative=True, PlotMode=1, height=None, width=None)
Argument
Type
Description
nbins
int
Number of equal ranges of LOC values
cumulative
bool
Does it show cumulative results or not.
yaxistype
str
Type of the yaxis [‘linear’, ‘log’, ‘date’, ‘category’,’multicategory’]
PlotMode
int
Options between 1,2 and 3 to plot on various editors.
height
int
height of plot.
width
int
width of plot.
Example:
The following demonstrates the use of the mentioned command.
Python Code
import opensrane as opr Post_Process_Object=opr.PostProcess.ObjsRecorderPP('Recorder',100) Post_Process_Object.plot_Expected_Total_LOC('log')
The result of above command is:
plot_ScenarioProbability
Using this command probability of scenarios will be plotted.
- PPost_Process_Object.plot_ScenarioProbability(yaxistype='log', DamageLevel=[], ScenarioList=[], PlotMode=1, height=None, width=None)
Argument
Type
Description
yaxistype
str
Type of the yaxis [‘linear’, ‘log’, ‘date’, ‘category’,’multicategory’]
PlotMode
int
Options between 1,2 and 3 to plot on various editors.
DamageLevel
list of int
List of damage level that user want to watch the results
ScenarioList
list of str
List of scenarios that want to be shown in plot. (for Empty it means that plot all scenarios)
height
int
height of plot.
width
int
width of plot.
Example:
The following demonstrates the use of the mentioned command.
Python Code
import opensrane as opr Post_Process_Object=opr.PostProcess.ObjsRecorderPP('Recorder',100) Post_Process_Object.plot_ScenarioProbability('log',)
The result of above command is:
And to plot just for damage level 0 and 1
import opensrane as opr Post_Process_Object=opr.PostProcess.ObjsRecorderPP('Recorder',100) Post_Process_Object.plot_ScenarioProbability('log',DamageLevel=[0,1],)
And if user wants to plot for some specific scenarios:
Post_Process_Object.plot_ScenarioProbability('linear', ScenarioList=[f'(0):[{i}]' for i in range(1,9)],)