Corona Hakab Simulator

Install and run the simulator from PyCharm (recommended):

• Install Python 3.8.x for Win64
  • Download Windows x86-64 executable installer from https://www.python.org/downloads/windows/
  • Install it (i.e. to C:\Python38)
• Install GIT
  • Download and install 64-bit Git for Windows Setup from https://git-scm.com/
• Install PyCharm (Community edition is good enough)
  • https://www.jetbrains.com/pycharm/download/#section=windows
• VCS -> Checkout from version control -> git
  • https://github.com/CoronaHakab/CoronaHakabModel.git
  • choose directory as you please
• File -> Settings -> Project Interpreter ->
  • Click on the plus sign on the right
    • add pipenv
  • Right click on the cog on the top right -> add
    • Pipenv environment ->
      • Base Interpreter Python 3.8 (C:\Python38)
      • Check install packages from pipfile
      • Pipenv executable (C:\Python38\Scripts\pipenv.exe)
      • OK
• In order to make PyCharm recognize the project modules:
  • right click src folder -> Mark directory as -> Source Root
  • right click src/corona_hakab_model folder -> Mark directory as -> Source Root
• Double click src/corona_hakab_model folder/main.py and run it (Run -> Run)

Install and run the simulator from cmd (also possible):

• Install Python 3.8
• pip install pipenv
• run git clone https://github.com/CoronaHakab/CoronaHakabModel.git from your dev directory (i.e. c:\dev)
• Create a pipenv environment and install the dependencies
  • full dependencies (for developers): pipenv install --dev
  • minimal dependencies (for researches): pipenv install
• go to src/corona_hakab_model folder
  • run set_pythonpath.bat to add entire src directory to python_path, allowing importing of corona_hakab_model_data.
  • run: python main.py generate to generate the population and matrix data.
  • run: python main.py simulate to run the simulation with the previously generated population and circles.
  • run: python main.py all to run both the generation and simulation.
  • run: python main.py [simulate|generate] --help for more help about each option.

Linux

• Ubuntu VM

  • If running on a remote Ubuntu machine (such as created by Microsoft Azure), please us linux/vm_install.sh
  • When connecting (with the same user) to the machine, it will automatically setup the environment variables and start the pipenv for CoronaHakabModel.
  • Note that when connecting to a machine via SSH, the graphs cannot be displayed, only saved for later viewing. Run main.py --figure-path image_location to save the image.

• Debian (tested on Debian 10):

  • Please use linux/deb_run.sh (work in progress)

• Docker

  • Please refer to docker.md and Dockerfile (work in progress)

Optional - Export/Import matrices!

• Export: python main.py -o
• Import: python main.py -i

##Optional - Initial sick agent constraints:

• Usage: python main.py --agent-constraints-path
• Format: csv file with the following columns : geographic_circles,age,Work,School,Family,Other
• each row represents an agent, amount of rows must correspond to initial number of sick agents
• to specify an exact value (geo-circle name, age, or number of members in social circle), simply write it in the appropriate column and row
• to specify a range, use '' eg. "1070" indicates age 10 to 70, including both
• unspecified values will be free.
• If no agents corresponding to the constraints are found, the code will crash

Workflow -

• When working, work on a new git branch.
• run quality test: tox -e quality, if it fails you might need to reformat: tox -e reformat.
• if you use named expressions := then for now you have to add the comment # flake8: noqa which will exclude the file from flake8 checks because it doesn't support named expressions currently.
• if ** tox -e reformat** fails then we have to check it out...
• When done, push changes to your branch, and create a pull request.
• Always run the simulator after making changes and before merging, to make sure you didn't break anything.
• Especially if not sure, try to get at least 1 person's review before merging.

Multiple JSONs generator

• In some cases, we would like to run the simulation/generation multiple times, when all parameters are the same, except 1 or 2 parameters that move across a certain range, in certain steps.
• The module is found in src/corona_hakab_model/moving_parameters_generator.py
• The module parameters
  • Input file path - file path of the json containing the parameters
  • "Moving" parameters file path
    • The file should contain a List of "moving" parameters
    • A "moving" parameter contains
      • Parameter name (has to be the same name as in the json file)
      • Start range
      • End range
      • Step size
  • Output folder path - folder path for the json output files
    • Each "moving" parameter will have a separate folder
    • The name of the file is set to be the parameter's value
• Running the module
  • Go to src/corona_hakab_model folder
  • Run: python moving_parameters_generator.py --help for parameters syntax.
  • Run: python moving_parameters_generator.py with parameters
  • Run example: python moving_parameters_generator.py --input-params Parameters/circles_parameters_example.py --moving-params Parameters/moving_parameters_example.json --output-folder ../../output/generated_parameters/circles_parameters

Analyzers

• This module is a library that allows a researcher to analyze the output of one or multiple simulation
  • The module implements both specific and generic methods to allow the user flexibility
• To run the module first run the simulation few times, you can do that from the file main.py
• For examples of usage of the module run python src/corona_hakab_model/analyzers/example_script.py, the script plots a few graphs and demonstrates the usage of the functions.

Compare outputs of simulation to real life

• This still does not compare real life example to simulation.
• This module shifts the real time csv to best fit inputed simulation output.
• To use run python main.py shift-real-life with first argument as csv to compare to, second argument simulation output

Population Analyser

• The population analyser reads a population data file (generated with the simulator, usually "population_data.pickle") and outputs a histogram of population ages, and social circles sizes by type.
• to use, run python analyzers\population_analysis.py
• Most commonly, you will use the [-d|--directory] option to specify the directory to read. The directory is expected to contain a file named "population_data.pickle"
• You can also specify the input population data file using [-p|--population], and output files using --circle and --age.
• As with all runnables, additional help can be found by running python analyzers\population_analysis.py --help

State Machine Analyser

• The state machine analyzer get a population size, and uses it as a way to average results of the state machine of all the agents.
• To use run python main.py analyze-state-machine
• Optional flags includes:
  • --population_size to set population size.
  • --consts_file
  • --circle_consts_file
  • ages_and_probs - list of age1, pro of age1, age2, prob of age2, ...
• The result of the run creates a json file with suffix of "state_machine_analysis_" and prefix of the time we ran the analyzer.
• The result file has several fields:
  • population_size. Defaults to 50,000
  • days_passed - The number of days the simulation ended
  • time_in_each_state - dictionary whose keys are states and values are total time spent at that state
  • visitors_in_each_state - dictionary whose keys are states and values are number of people who were at this state of the infection
  • average_duration_in_state - dictionary whose keys are states and values are the expected time to be at that state provided that we visited it at least once
  • state_duration_expected_time - dictionary whose keys are states and values are expected time spent in that state of the illness
  • average_time_to_terminal - The average time it took agent to end at terminal state

Simulation Analyser

• TODO: Wrap simulation analyser in runnable (argparse, main, the works), and add documentation.

Matrix Analyzer

• This module calculates histograms and statistics about the connections represented in the matrix.

INPUTS:

--matrix < path_to_matrix > (optional) : This input specifies the path to the matrix which will be analyzed. If not given, assuming the matrix file is at the project's output folder, under the name matrix_data.
--show (optional) : This input determines whether or not histogram plots will be shown (they are always being saved).
**NOTICE: When plots are being shown, you need to close them in order for the program to finish.

OUTPUTS

• The module creates multiple files. The files created are raw-data '.csv' file of the matrix's different connection type (e.g. work, family) and an histogram analysis of those connections (both '.csv' file and '.png' of the histogram). The files are being saved to '/output/matrix_analysis/' folder.
  The created folder is divided to 2 sub-folders: histogram_analysis - This folder is where the histogram plots and .csv files are saved.
  raw_matrices - This folder is where the matrix's raw data is saved.
• The file names indicate what type of connection they are related to.

EXAMPLES

1. Run the analysis on default matrix file and don't show plots: python ./main analyze-matrix
2. Run with specified matrix file:
   python ./main analyze-matrix --matrix /example/to/matrix/path
3. Run with specified matrix file and show histograms:
   python ./main analyze-matrix --matrix /example/to/matrix/path --show

Random Connections Analysis

• This module calculates histograms and statistics about the random connections.

INPUTS:

--population-data < path_to_population_data > : This input specifies the path to the population data which will be analyzed.
--show (optional) : This input determines whether or not histogram plots will be shown (they are always being saved).
**NOTICE: When plots are being shown, you need to close them in order for the program to finish.

OUTPUTS

• The module creates multiple files. The files created are raw-data '.csv' file of the different connection type (e.g. work, family) and an histogram analysis of those connections (both '.csv' file and '.png' of the histogram). The files are being saved to the same folder as the given population data.
  The created folder is divided to 2 sub-folders: histograms - This folder is where the histogram plots and .csv files are saved.
  raw - This folder is where the raw data is saved.
• The file names indicate what type of connection they are related to.

EXAMPLES

2. Run without displaying histograms:
   python ./main analyze-random-connections --population-data /example/to/population-data/path
3. Run and display histograms:
   python ./main analyze-random-connections --population-data /example/to/population-data/path --show

New to git/github?

See the "How to set up a git environment" guide in the docs folder.

For Pull-Requests, look at this guide, from step 7 - https://product.hubspot.com/blog/git-and-github-tutorial-for-beginners.