allfed-integrated-model

An integrated economic+food supply model for resilient foods in nuclear winter

Run the model

• You can create a varierety of different scenarios with this model. A collection of possible scenarios are already available in the scenarios folder (e.g. run_model_with_resilient_foods.py).
• A scenario is created by creating a new instance of the Scenario class in scenario.py. This class contains a collection of methods that provide your model with the parameter value it needs to run. Here you can also change the parameter values if you want to change the model to your specifications.
• Once you got all your parameter values ready you create an Instance of the Parameter class from parameter.py. This class allows you to initialize the model with the parameter values you defined.
• Finally to create an instance of the Optimizer class from optimizer.py and provide it with your parameters. This will run the model itself and optimize it.

Using Colab (most users use this):

The interactive model runs off colab. It doesn't require any downloading or code, you just need to hit the right buttons as they show up. Here's a demo:

First click on the "notebooks" folder in the allfed-integrated-model directory.

Open 'example_optimize.ipynb'

Click 'Open in Colab'.

Select 'Authorize with github'.

Select 'OK'.

For the repository, select 'allfed/allfed-integrated-model' branch 'main', then click on the item labelled under Path, 'notebooks/example_optimize.ipynb'.

Now follow the instructions in the Jupyter notebook. Run each line of the Jupyter notebook in succession to view results of the paper.

Please let me know if any of these steps go awry! (contact morgan [at] allfed [dot] info)

From the command line (requires cloned repo):

The integrated model is written in python 3, ensure you have some version of python3, although it has only been tested with python 3.7 or later. Then, install the required packages using pip if not already installed:

pip install PuLP
pip install numpy
pip install matplotlib
pip install seaborn
pip install scipy
pip install geopandas

Dependency management with Anaconda

If you would like to run the entire by-country model with no trade, you will need to install geopandas and geoplot.

unfortunately geoplot also requires conda, so you'll need to install conda if you want to plot these.

See https://docs.anaconda.com/anaconda/install/index.html for installation instructions.

Once the program is installed on your device, set up a separate environment for the project (do not use the base environment). This step and the following can be done in two ways:

• using the GUI or
• using the Anaconda Prompt. For people new to coding the GUI is more intuitive.

GUI

1. Open the Anaconda Navigator.
2. Select the tap "Environments".
3. Click "Import" and select the "tintmodel.yml" file from the repository and name the new environment. All dependencies will be installed automatically.

Anaconda Prompt

1. Open Anaconda Prompt.
2. Type in the following line:

conda env create -f intmodel.yml

The dependencies will be installed automatically and the environment will be name intmodel.

For both versions: Code from this project will only run smoothly when opened in the new environment and when the working directory is set to the path location of the repository on your machine.

Running on command line

results from the paper can be rerun using the following commands in the src/scenarios folder

python run_model_with_resilient_foods.py
python run_model_no_resilient_foods.py
python run_model_before_catastrophe.py
python plot_available_food.py

for the country-by-country no food trade model, run

python run_baseline_by_country_no_trade.py

Using Jupyter (requires cloned repo):

In the notebooks/ folder run:

jupyter notebook example_optimize.ipynb

Then skip the colab section and execute every other cell in order.

Project Tree

├── README.md          <- The top-level README for developers using this project.
├── data               <- Data from Monte Carlo runs.
├── notebooks          <- Jupyter notebooks. Naming convention is a number (for ordering),
│                         the creator's initials, and a short `-` delimited description, e.g.
│                         `1.0-jqp-initial-data-exploration`.
├── figures            <- Generated graphics and figures to be used in reporting
├── src                <- Source code for use in this project.
    └── __init__.py    <- Makes src a Python module

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