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Getting Started

Model development in OpenM++

Using OpenM++

How to Use OpenM++ from other software: JSON web-service and Go

Using OpenM++ from Python and R

OpenM++ Docker images

OpenM++ Development

OpenM++ Design, Roadmap and Status

OpenM++ web-service API

GET Model Metadata

GET Model Extras

GET Model Run results metadata

GET Model Workset metadata: set of input parameters

Read Parameters or Output Tables values

GET Parameters or Output Tables values

GET Parameters or Output Tables values as CSV

GET Modeling Task metadata and task run history

Update Model Profile: set of key-value options

Update Model Workset: set of input parameters

Update Model Runs

Update Modeling Tasks

Run Models: run models and monitor progress

User: manage user settings and data

Administrative: manage web-service state

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This is the home of the OpenM++ wiki. It consists mostly of links to other topics, organized into sections. For a brief description of what OpenM++ can bring to a micro-simulation or agent-based modelling project please see the Features section. Our Glossary contains brief explanations of some of the terms used in this wiki.

Quick links

Contents

Introduction to OpenM++

OpenM++ is an open source platform to develop, use, and deploy micro-simulation or agent-based models. OpenM++ was designed to enable non-programmers to develop simple or complex models. Click here for an overview of OpenM++ features.

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Getting started

This section describes how to get OpenM++ installed and working on Windows, Linux, or MacOS, for model users or for model developers. The installation kits include a collection of simple illustrative models. That same collection of models is also present in the cloud, where it can be accessed from any web browser, with no installation required. For more information on the OpenM++ cloud collection, please Contact us.

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Model development

Platform-independent information:

Platform-specific information:

Modgen-specific information:

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Model use

This section describes how to use a model once built.

Modgen-specific information:

  • Modgen: CsvToDat utility: Command-line utility to convert CSV parameters to DAT format

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Model API

The model API provides programmatic access to scenario management, model inputs, model runs, and model outputs. It is implemented by the OpenM++ oms web service, which uses standard JSON to communicate with a controlling application. The worked examples in Model scripting provide practical illustrations of how to use the model API and the oms service to automate an analysis. Incidentally, the browser-based OpenM++ user interface uses the model API and the oms service for all model-specific operations.

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Model scripting

The topics in this section illustrate model-based analysis in two different scripting environments: Python and R. The Model API is used in these environments to create scenarios, run the model iteratively, and retrieve results for graphical presentation in the scripting environment.

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Docker

Docker is a technology used here to quickly replicate preconfigured operating system environments containing OpenM++ functionality.

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Features

Here is a summary of some OpenM++ features:

General features:

  • open source: OpenM++ and all components are licensed under the very broad MIT license.
  • cross-platform: Model development and use on Windows, Linux, or MacOS.
  • standards-based: Uses industry standard formats and technologies.
  • zero-footprint: File-based installation requires no elevation of privileges.

Model developer features:

  • high-level language: Model types, parameters, entities, events, tables, etc. are specified using a compact domain-specific language targeted to microsimulation.
  • scalable complexity: From simple 'toy' models to highly complex models.
  • modularity: New events and processes can be added to a model in a new module, often with little or no modification to existing modules.
  • continuous or discrete time, or a mixture.
  • supports multiple versions: Multiple OpenM++ versions can be installed and a single environment variable used to choose among them.
  • result compare: Supports rapid comparison of all model outputs during incremental model development.

Computational features:

  • scalable computation: Designed to scale linearly with population size or replicates when possible, N log N scaling for typical interacting populations.
  • grid-enabled, cloud-enabled: Supports MPI for multi-processing to distribute execution of replicates to a small or large computational grid or to the cloud, with automatic result assembly.
  • multi-threaded: Supports multi-threading for parallel execution of replicates on desktop or server.
  • on-the-fly tabulation: Tables are computed during the simulation, eliminating the need to output voluminous microdata for subsequent tabulation.
  • computationally efficient: The model specification is transformed to C++ which is processed by an optimizing C++ compiler to produce a highly efficient executable program.

Usability features:

  • generated UI: A model-specific UI is generated from the model specification.
  • browser-based UI: The UI requires only a browser, and runs on almost any modern browser.
  • cloud-enabled: Models can be deployed to a cloud and accessed remotely over the web, from a browser.
  • multilingual support: For UI and for model, with real-time language switching

Analyst features:

  • continuous time tabulation: Powerful but easy to use language constructs to tabulate time-in-state, empirical hazards, transitions counts, state changes, etc.
  • replicate support: All tables can have underlying replicate simulations to assess the uncertainty of any cell of any output table. Statistical measures of uncertainty are computed for all cells of all tables.
  • automation: Models can be controlled by scripts, eg Python or R.
  • import/export: Models and runs can be moved between databases, or to standard formats for upstream preparation of inputs or for downstream analysis of outputs.
  • dynamic run control: A computational grid can process runs dynamically to enable whole-model estimation or calibration, with a controlling script reading run results and preparing new runs for execution.

The OpenM++ language is based on the Modgen↗ language developed at Statistics Canada. With minor modifications to model source code, existing Modgen models can work with either Modgen or OpenM++.

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OpenM++ development

This section contains technical information for programmers interested in OpenM++ itself, as opposed to model developers or model users. It describes how to set up a programming environment to build and modify OpenM++.

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OpenM++ design

This section contains technical and project information of interest to programmers or system architects. It dates from the inception and 'alpha' days of the OpenM++ project. The road map diagram remains somewhat relevant and may be useful for a broad overview of the major components of OpenM++ from the perspective of a programmer or system architect.

Project Status: production stable since February 2016

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OpenM++ source code

This section contains technical information for programmers interested in OpenM++ itself, as opposed to model developers or model users. It contains links to the OpenM++ source code and to the documentation of that source code.

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Contact Us

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