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Experienced Modgen Developer
Home > Model Development Topics > Experienced Modgen Developer
This topic contains a grab bag of subtopics of possible interest to model developers who are experienced in using Modgen.
- The End of Start
- Simplified iteration of range, classification, partition
- Parameter and table groups can be populated by module names
hook
to self-scheduling or trigger attribute
Yes, that Start. Person::Start(a,b,c,d,e,f,…)
Modgen required that each entity had a single entity function named Start
and a single entity function named Finish
. In a complex model, that required a single Start
with all possible arguments to handle the various ways to start an entity, most of which were unused in a given call. It also led to a monolithic Start
function to handle all possible ways of starting an entity, in a ‘catch-all’ module. And it sometimes led to the creation of a special argument whose only purpose was to distinguish the different ways of starting an entity.
By design OpenM++ has no such requirement. An OpenM++ foundational design principle is to let the C++ language do its thing, as a powerful stand-alone language which is also fully integrated and understood by the IDE used by the model developer.
In ompp, there is nothing special about the name Start
(or Finish
). In fact, the ompp compiler omc
contains no references whatsoever to Start
or Finish
. Instead, three entity member functions generated by the ompp compiler are called by model code to handle the lifecycle of an entity: initialize_attributes()
, enter_simulation()
, and exit_simulation()
.
This design freedom allows you to code a model without being limited to a single Start
function, like Modgen was.
One way is to declare overloaded versions of Start
, each specialized to start an entity in a different way. For example, to start a Person entity using a microdata Observation, one could declare (in an entity Person {...};
syntactic island) and define (in C++ code) the member function
void Person::Start(Microdata_ptr microdata_record);
and to start a Person entity as a newborn during the simulation, declare and define the member function
void Person::Start(Person_ptr mother);
and to clone a Person entity as an immigrant
void Person::Start(Person_ptr donor, int year_of_immigration);
These examples use a C++ language feature called “function overloading”, where different versions of the same named function are distinguished by the number, order, and types of arguments (aka the 'function signature').
Alternatively, different function names can be used, either if necessary to distinguish Start variants sharing the same 'function signature' or as a coding style choice, e.g.
void Person::Start_microdata(Microdata_ptr microdata_record);
void Person::Start_newborn(Person_ptr mother);
void Person::Start_immigrant(Person_ptr donor, int year_of_immigration);
Either way, the C++ implementation code for these functions can be located in its appropriate substantive module, e.g. StartPop.mpp
, Fertility.mpp
, Immigration.mpp
.
OpenM++ implements enhancements which can simplify model code which iterates ranges, classifications, or partitions. As the title of this post suggests, this was implemented some time ago, when ranges, classifications, and partitions were originally implemented in OpenM++ circa 2015.
The enhancement is not Modgen-compatible, so was of limited interest at the time. But it’s of greater interest now, with Modgen in the rear-view mirror.
It is not currently documented in the wiki but should be, in a dedicated topic on enumerations (ranges, classifications, partitions). There was no wiki back then, and documentation of ompp-exclusive features was typically communicated via email.
Here’s a sketch and some examples:
In OpenM++, any range, classification, or partition has a special member function indices()
, which is designed to be used in a C++ “range-based for” statement. Here’s an example from GMM, where RISK_FACTOR
is a classification:
// Verify that SimulateRiskFactor is true if the risk factor is passed to Boadicea in RA_BoadiceaUseRiskFactor.
for (auto j : RISK_FACTOR::indices()) {
if (RA_BoadiceaUseRiskFactor[j] && !SimulateRiskFactor[j]) {
ModelExit("Risk factors passed to Boadicea in RA_BoadiceaUseRiskFactor must be simulated in SimulateRiskFactor");
break; // not reached
}
}
The auto
keyword is not required, one could use int
(or size_t
for the illuminati). I tend to use auto
wherever it works, for code simplicity and generality. auto
is a message from the coder to the C++ compiler which says: “You know what I mean, figure it out yourself”.
Here’s another example, where ONCOGENESIS_AGE_GROUP
is a partition, and ONCOGENESIS_LAG
is a range:
for (auto j : ONCOGENESIS_AGE_GROUP::indices()) {
for (auto k : ONCOGENESIS_LAG::indices()) {
double dValue = OncogenesisLags[j][k];
SetTableValue("IM_OncogenesisLags.VALUE", dValue, j, k);
}
}
For ranges, OpenM++ also includes the member function values()
, which iterates the values of the range rather than the indices. Here’s an example where YEAR
is a range:
for (auto year : YEAR::values()) {
if (year == 1970) {
theLog->logMsg("begin peak disco decade");
}
}
A possible enhancement might be a member function for ranges which iterates indices and values as a std::pair
, allowing one to access either inside the same range-based for, something like:
for (auto pr : YEAR::indices_values()) {
auto j = pr.first();
auto year = pr.second();
…
}
In Modgen models, parameter and table groups often duplicate the organization of modules. Creating and maintaining these groups can be tedious and clutter model code. It can also be error-prone because it's easy to forget to include a newly added parameter or table into its group, with the forgotten symbol becoming a top-level 'orphan' symbol in the UI.
There's a better way to group parameters and tables by module in OpenM++.
You can specify that all declarations in a module become elements of a named group.
For example, the following code fragment in RiskPaths
specifies that all tables declared in the Tables.mpp
module are elements of the table group TG0_AllTables
:
table_group TG0_AllTables //EN All tables in Tables.mpp
{
"Tables.mpp"
};
The double quotes around the module name are required.
Group declarations can contain multiple module names and mixtures of module names, other groups, and symbols. A module name is expanded into a list of the symbols declared in the module in lexicographical order, exactly as if they had been typed in manually. The elements of groups containing module names will automatically track the current declarations of parameters and tables in the modules of your model. For example, if you re-organize the table declarations in your model to move validation tables to new distinctly-named modules to split them from release tables, you can create a group for them by using the new module name. At the same time, when you move the declaration of a validation table to the new module, it will automatically be removed from the release group of tables, if the module syntax is used.
For more on groups, please see this wiki topic.
Self-scheduling and trigger derived attributes can be very convenient in model code.
For example, self_scheduling_int(time)
implicitly creates a hidden event which implements an annual calendar clock which updates the attribute.
OpenmM++ takes self-scheduling and trigger attributes to another level by allowing hooks to them.
So, for example, one can do
entity Person {
hook NewYear, self_scheduling_int(time);
};
to call the model-specific entity function Person::NewYear()
when time
crosses an integer boundary,
without having to explicitly code a clock-like event in model code.
Here's another example which outputs microdata at age 65 using the built-in entity function write_microdata
.
entity Person {
int integer_age = self_scheduling_int(age);
hook write_microdata, trigger_entrances(integer_age, 65);
};
For a bit more on this, see the Entity Function Hooks wiki topic.
- Windows: Quick Start for Model Users
- Windows: Quick Start for Model Developers
- Linux: Quick Start for Model Users
- Linux: Quick Start for Model Developers
- MacOS: Quick Start for Model Users
- MacOS: Quick Start for Model Developers
- Model Run: How to Run the Model
- MIT License, Copyright and Contribution
- Model Code: Programming a model
- Windows: Create and Debug Models
- Linux: Create and Debug Models
- MacOS: Create and Debug Models
- MacOS: Create and Debug Models using Xcode
- Modgen: Convert case-based model to openM++
- Modgen: Convert time-based model to openM++
- Modgen: Convert Modgen models and usage of C++ in openM++ code
- Model Localization: Translation of model messages
- How To: Set Model Parameters and Get Results
- Model Run: How model finds input parameters
- Model Output Expressions
- Model Run Options and ini-file
- OpenM++ Compiler (omc) Run Options
- OpenM++ ini-file format
- UI: How to start user interface
- UI: openM++ user interface
- UI: Create new or edit scenario
- UI: Upload input scenario or parameters
- UI: Run the Model
- UI: Use ini-files or CSV parameter files
- UI: Compare model run results
- UI: Aggregate and Compare Microdata
- UI: Filter run results by value
- UI: Disk space usage and cleanup
- UI Localization: Translation of openM++
-
Highlight: hook to self-scheduling or trigger attribute
-
Highlight: The End of Start
-
Highlight: Enumeration index validity and the
index_errors
option -
Highlight: Simplified iteration of range, classification, partition
-
Highlight: Parameter, table, and attribute groups can be populated by module declarations
- Oms: openM++ web-service
- Oms: openM++ web-service API
- Oms: How to prepare model input parameters
- Oms: Cloud and model runs queue
- Use R to save output table into CSV file
- Use R to save output table into Excel
- Run model from R: simple loop in cloud
- Run RiskPaths model from R: advanced run in cloud
- Run RiskPaths model in cloud from local PC
- Run model from R and save results in CSV file
- Run model from R: simple loop over model parameter
- Run RiskPaths model from R: advanced parameters scaling
- Run model from Python: simple loop over model parameter
- Run RiskPaths model from Python: advanced parameters scaling
- Windows: Use Docker to get latest version of OpenM++
- Linux: Use Docker to get latest version of OpenM++
- RedHat 8: Use Docker to get latest version of OpenM++
- Quick Start for OpenM++ Developers
- Setup Development Environment
- 2018, June: OpenM++ HPC cluster: Test Lab
- Development Notes: Defines, UTF-8, Databases, etc.
- 2012, December: OpenM++ Design
- 2012, December: OpenM++ Model Architecture, December 2012
- 2012, December: Roadmap, Phase 1
- 2013, May: Prototype version
- 2013, September: Alpha version
- 2014, March: Project Status, Phase 1 completed
- 2016, December: Task List
- 2017, January: Design Notes. Subsample As Parameter problem. Completed
GET Model Metadata
- GET model list
- GET model list including text (description and notes)
- GET model definition metadata
- GET model metadata including text (description and notes)
- GET model metadata including text in all languages
GET Model Extras
GET Model Run results metadata
- GET list of model runs
- GET list of model runs including text (description and notes)
- GET status of model run
- GET status of model run list
- GET status of first model run
- GET status of last model run
- GET status of last completed model run
- GET model run metadata and status
- GET model run including text (description and notes)
- GET model run including text in all languages
GET Model Workset metadata: set of input parameters
- GET list of model worksets
- GET list of model worksets including text (description and notes)
- GET workset status
- GET model default workset status
- GET workset including text (description and notes)
- GET workset including text in all languages
Read Parameters, Output Tables or Microdata values
- Read parameter values from workset
- Read parameter values from workset (enum id's)
- Read parameter values from model run
- Read parameter values from model run (enum id's)
- Read output table values from model run
- Read output table values from model run (enum id's)
- Read output table calculated values from model run
- Read output table calculated values from model run (enum id's)
- Read output table values and compare model runs
- Read output table values and compare model runs (enun id's)
- Read microdata values from model run
- Read microdata values from model run (enum id's)
- Read aggregated microdata from model run
- Read aggregated microdata from model run (enum id's)
- Read microdata run comparison
- Read microdata run comparison (enum id's)
GET Parameters, Output Tables or Microdata values
- GET parameter values from workset
- GET parameter values from model run
- GET output table expression(s) from model run
- GET output table calculated expression(s) from model run
- GET output table values and compare model runs
- GET output table accumulator(s) from model run
- GET output table all accumulators from model run
- GET microdata values from model run
- GET aggregated microdata from model run
- GET microdata run comparison
GET Parameters, Output Tables or Microdata as CSV
- GET csv parameter values from workset
- GET csv parameter values from workset (enum id's)
- GET csv parameter values from model run
- GET csv parameter values from model run (enum id's)
- GET csv output table expressions from model run
- GET csv output table expressions from model run (enum id's)
- GET csv output table accumulators from model run
- GET csv output table accumulators from model run (enum id's)
- GET csv output table all accumulators from model run
- GET csv output table all accumulators from model run (enum id's)
- GET csv calculated table expressions from model run
- GET csv calculated table expressions from model run (enum id's)
- GET csv model runs comparison table expressions
- GET csv model runs comparison table expressions (enum id's)
- GET csv microdata values from model run
- GET csv microdata values from model run (enum id's)
- GET csv aggregated microdata from model run
- GET csv aggregated microdata from model run (enum id's)
- GET csv microdata run comparison
- GET csv microdata run comparison (enum id's)
GET Modeling Task metadata and task run history
- GET list of modeling tasks
- GET list of modeling tasks including text (description and notes)
- GET modeling task input worksets
- GET modeling task run history
- GET status of modeling task run
- GET status of modeling task run list
- GET status of modeling task first run
- GET status of modeling task last run
- GET status of modeling task last completed run
- GET modeling task including text (description and notes)
- GET modeling task text in all languages
Update Model Profile: set of key-value options
- PATCH create or replace profile
- DELETE profile
- POST create or replace profile option
- DELETE profile option
Update Model Workset: set of input parameters
- POST update workset read-only status
- PUT create new workset
- PUT create or replace workset
- PATCH create or merge workset
- DELETE workset
- POST delete multiple worksets
- DELETE parameter from workset
- PATCH update workset parameter values
- PATCH update workset parameter values (enum id's)
- PATCH update workset parameter(s) value notes
- PUT copy parameter from model run into workset
- PATCH merge parameter from model run into workset
- PUT copy parameter from workset to another
- PATCH merge parameter from workset to another
Update Model Runs
- PATCH update model run text (description and notes)
- DELETE model run
- POST delete model runs
- PATCH update run parameter(s) value notes
Update Modeling Tasks
Run Models: run models and monitor progress
Download model, model run results or input parameters
- GET download log file
- GET model download log files
- GET all download log files
- GET download files tree
- POST initiate entire model download
- POST initiate model run download
- POST initiate model workset download
- DELETE download files
- DELETE all download files
Upload model runs or worksets (input scenarios)
- GET upload log file
- GET all upload log files for the model
- GET all upload log files
- GET upload files tree
- POST initiate model run upload
- POST initiate workset upload
- DELETE upload files
- DELETE all upload files
Download and upload user files
- GET user files tree
- POST upload to user files
- PUT create user files folder
- DELETE file or folder from user files
- DELETE all user files
User: manage user settings
Model run jobs and service state
- GET service configuration
- GET job service state
- GET disk usage state
- POST refresh disk space usage info
- GET state of active model run job
- GET state of model run job from queue
- GET state of model run job from history
- PUT model run job into other queue position
- DELETE state of model run job from history
Administrative: manage web-service state
- POST a request to refresh models catalog
- POST a request to close models catalog
- POST a request to close model database
- POST a request to delete the model
- POST a request to open database file
- POST a request to cleanup database file
- GET the list of database cleanup log(s)
- GET database cleanup log file(s)
- POST a request to pause model run queue
- POST a request to pause all model runs queue
- PUT a request to shutdown web-service