Historionomy Dataviz App

This App is a Streamlit Cloud App, aimed at illustrating the principles of Historionomy

To understand the deployment, please follow Stream Cloud Documenation here

Main structure

When you create a Streamlit Cloud App, you must choose which python file is the "main" file to be launched at the execution of the app

Current "main" file is historionomy.py. The app start executing commands after the APP START comment block.

Data

Historionomical data

The historionomical data comes from this Google Sheet

The import script from the data-tooling project creates the following tables in Supabase :

• countries : current historionomical status of all countries
• history : historionomical courses of all countries over time
• koinons : a list of potential historionomical "koinons"
• labels : a description of the historionomical status
• legend : a legend for a small section of the "countries" table

Our World in Data metrics

The import script from the data-tooling project also allows to upload the following datasets from Our World in Data (link here) into the Supabase backend:

• Historical Literacy Rates (link)
• GDP per capita, PPA and constant dollars, from the Penn Database (link)
• Government spending (link)
• Urbanization rates (link)

Functional logic

Data Loading and preprocessing

At runtime, the app :

• load the "world map" data with the function load_world_map
• load the graphical asset with the function load_image. Graphical assets are part of the app code (for the moment)
• load the backend data from a Supabase PostgresQL database with the function load_backend_data

Functions for loading the backend data are defined in backend.py

Data structure for storing the content of long text content is defined in text_content.py

The languages_map_labels dict in historionomy.py is used to store the multilingual texts of the labels used by the app. There are currently two languages supported, english and french (EN and FR keys in the dictionary).

Drawing the frontend

Once data are loaded, the app starts drawing the frontend, starting from the DRAWING FRONTEND comment block.

There are currently 5 tabs in the main display :

• introduction
• the historionomical world map, displaying a plotly map
• the data tab, displaying historionomical courses per country, combined with Our World in Data metrics
• the historionomical stages tab, displaying the chart of stages made by Redcrow
• the resources tab, displaying hypertext links to external resources

Specific features

World Map

The historionomical world map is produced by the function create_map in historionomy.py. The map is a Plotly Express Choropleth map

Courses chart

The datatab combined two charts, whose creation is managed by function history_chart in history_chart.py

• a Plotly Express Line Chart for the metrics from Our World In Data
• a Plotly Express Bar Chart for the historionomical stages per country

Dev mode

Python environment

Install conda, a environment manager for python :

On Linux

wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh -O ~/miniconda.sh
bash ~/miniconda.sh -b -p $HOME/miniconda
eval "$($HOME/miniconda/bin/conda shell.bash hook)"
conda init

On MacOS (you will be prompted for admin password at some moments):

mkdir -p ~/miniconda3
curl https://repo.anaconda.com/miniconda/Miniconda3-latest-MacOSX-x86_64.sh -o ~/miniconda3/miniconda.sh
bash ~/miniconda3/miniconda.sh -b -u -p ~/miniconda3
rm -rf ~/miniconda3/miniconda.sh
~/miniconda3/bin/conda init bash
~/miniconda3/bin/conda init zsh

Move to your work folder, and create a conda environment for the project :

cd $WORKFOLDER ### move to your workfolder
conda create -n histo python=3.11 ### this command create a local environment in python 3.11

Now, activate the python 3.11 environment you just created :

export CONDA_ENV=privacy
conda activate $CONDA_ENV

This command will add the name of the environment between parenthesis on your command line prompt.

If you need to exit from the conda environment, simply run :

conda deactivate

Launch app locally

In historionomy.py, at the top of the file, there is a global function MODE. Set it to debug when you are developing the app, it allows to skip the loading of the World Map and the Stages Chart, and will load backend data from csv files in a folder ../data-tooling relative to your current app folder.

Launch app locally with command :

streamlit run historionomy.py