https://github.com/daeh/comosoco-env

PPL environment for the course "Computational Models of Social Cognition"

https://github.com/daeh/comosoco-env

Science Score: 13.0%

This score indicates how likely this project is to be science-related based on various indicators:

  • CITATION.cff file
  • codemeta.json file
    Found codemeta.json file
  • .zenodo.json file
  • DOI references
  • Academic publication links
  • Academic email domains
  • Institutional organization owner
  • JOSS paper metadata
  • Scientific vocabulary similarity
    Low similarity (11.2%) to scientific vocabulary
Last synced: 9 months ago · JSON representation

Repository

PPL environment for the course "Computational Models of Social Cognition"

Basic Info
  • Host: GitHub
  • Owner: daeh
  • Language: Jupyter Notebook
  • Default Branch: main
  • Homepage:
  • Size: 439 KB
Statistics
  • Stars: 0
  • Watchers: 1
  • Forks: 3
  • Open Issues: 0
  • Releases: 0
Created over 1 year ago · Last pushed over 1 year ago
Metadata Files
Readme

README.md

Computational Models of Social Cognition (CoMoSoCo) - Python Environment

This repository contains a starter Python environment for running Jupyter notebooks used in the Computational Models of Social Cognition course.

Environment Setup

For this course, you need a Python environment that lets you run Jupyter Notebooks with memo models. While this guide suggests creating an environment that matches the one I'll be using for the tutorials, you can also use a more minimal setup. As long as you can successfully run notebooks like comosoco/installation-test.ipynb, your setup should be sufficient.

Install Homebrew (MacOS only)

Homebrew is a package manager for MacOS that simplifies software installation and management.

Follow the installation instructions on https://brew.sh

Verify the installation:

bash brew --version

I can't or don't want to use homebrew! No problem, it just makes it easier to install and remove things on MacOS.

Install git

git is a version control system that helps manage code. Here's how to install it:

With Homebrew:

bash brew install git

Without Homebrew:

Follow the installation instructions on https://git-scm.com/download/win

Verify the installation:

bash git --version

I can't or don't want to use git! But git is great! Ok, though, you can skip it for now and download the repository directly as a ZIP file (see "Getting the Repository" section below).

Install uv

Astral uv is a fast Python package installer that we'll use to set up the environment.

With Homebrew:

bash brew install uv

Without Homebrew:

Follow the install instructions on https://docs.astral.sh/uv/getting-started/installation/

Verify the installation:

bash uv --version

I can't or don't want to use uv! Ok, we can work around that.

Install Task (MacOS only)

Task is a task runner that simplifies common commands. While Task is not specific to MacOS, at the moment this repository only supports Task on MacOS.

With Homebrew: bash brew install go-task

Without Homebrew:

Follow the installation instructions on https://taskfile.dev/installation/

Verify the installation:

bash task --version

I can't or don't want to use Task! No problem, it's just for convenience.

Install Visual Studio Code

VS Code is the recommended editor for this course:

  1. Download VS Code from https://code.visualstudio.com
  2. Install the downloaded file
I can't or don't want to use VS Code! You can use any editor that supports Jupyter notebooks, but this README only gives instructions for VS Code.

Clone this repository

Choose one of these methods:

Option 1: Fork and Clone (Recommended) 1. Create a GitHub account if you don't have one 2. Go to this repository https://github.com/daeh/comosoco-env 3. Click the "Fork" button in the top-right corner 4. Clone your fork: bash git clone https://github.com/YOUR-USERNAME/comosoco-env.git cd comosoco-env

Option 2: Direct Clone bash git clone https://github.com/daeh/comosoco-env.git cd comosoco-env

Option 3: Direct Download

  1. Visit https://github.com/daeh/comosoco-env
  2. Click the green "Code" button
  3. Select "Download ZIP"
  4. Extract the ZIP file
  5. Navigate to the comosoco-env folder in your terminal

Installing the Environment

Make sure you're in the comosoco-env directory, then choose one method:

Option 1: Using Task (Easiest, MacOS)

bash task install

Option 2: Using uv

bash uv sync

Option 3: Using pip 1, Make a virtual environment: ```bash python -m venv .venv ``` 2. Activate the environment: MacOS/Linux: ```bash source .venv/bin/activate ``` Windows (Command Prompt): ```cmd .venv\Scripts\activate.bat ``` Windows (PowerShell): ```powershell .venv\Scripts\Activate.ps1 ``` 3. Install packages: ```bash pip install -r requirements-standard.txt ``` If you encounter issues, try using `requirements-minimal.txt` instead: ```bash pip install -r requirements-minimal.txt ```

Setting up VS Code

  1. Open the VS Code application

  2. Open VSCProject.code-workspace

open-workspace

  • You should see that the window name is "comosoco"

    window-name

  1. When prompted, install recommended extensions

recommended-extensions-manual

  • Troubleshooting: If you don't see a prompt to install the recommended extensions, make sure you've correctly open the VSCProject.code-workspace file. You should see "comosoco" as the window name.

  • Troubleshooting: If you're sure you have the workspace open, you can manually install the extensions by searching for @recommended in the Extension Pane

    recommended-extensions-manual

  1. Open comosoco/installation-test.ipynb

  2. Set the Jupyter notebook to use the .venv python in comosoco-env

    • See below for screen shots.
  3. Run all cells in the notebook - if they complete without error, your setup is working!

1

2

3

4

Owner

  • Name: Dae
  • Login: daeh
  • Kind: user
  • Location: Cambridge, MA
  • Company: MIT

Neukom Computational Science Postdoc Fellow at Dartmouth. PhD from MIT Brain and Cognitive Sciences.

GitHub Events

Total
  • Push event: 30
  • Fork event: 3
  • Create event: 2
Last Year
  • Push event: 30
  • Fork event: 3
  • Create event: 2

Issues and Pull Requests

Last synced: about 1 year ago

All Time
  • Total issues: 0
  • Total pull requests: 0
  • Average time to close issues: N/A
  • Average time to close pull requests: N/A
  • Total issue authors: 0
  • Total pull request authors: 0
  • Average comments per issue: 0
  • Average comments per pull request: 0
  • Merged pull requests: 0
  • Bot issues: 0
  • Bot pull requests: 0
Past Year
  • Issues: 0
  • Pull requests: 0
  • Average time to close issues: N/A
  • Average time to close pull requests: N/A
  • Issue authors: 0
  • Pull request authors: 0
  • Average comments per issue: 0
  • Average comments per pull request: 0
  • Merged pull requests: 0
  • Bot issues: 0
  • Bot pull requests: 0
Top Authors
Issue Authors
Pull Request Authors
Top Labels
Issue Labels
Pull Request Labels

Dependencies

pyproject.toml pypi
  • graphviz >=0.20.3
  • ipykernel >=6.29.5
  • jax >=0.4.36
  • jupyterlab >=4.3.4
  • matplotlib >=3.10.0
  • memo-lang >=0.4.5
  • numpy >=2.0.2
  • optype [numpy]>=0.7.3
  • pandas >=2.2.3
  • pandas-stubs >=2.2.3.241126
  • pydantic >=2.10.4
  • python-dotenv >=1.0.1
  • pyyaml >=6.0.2
  • ruff >=0.8.6
  • scikit-learn >=1.6.0
  • scipy >=1.15.0
  • scipy-stubs >=1.15.0.0
  • seaborn >=0.13.2
  • toml >=0.10.2
  • tqdm >=4.67.1
  • xarray >=2025.1.0
requirements-minimal.txt pypi
  • ipykernel ==6.29.5
  • matplotlib ==3.10.0
  • memo-lang ==0.5.1
  • pandas ==2.2.3
  • scikit-learn ==1.6.0
  • scipy ==1.15.0
  • seaborn ==0.13.2
  • xarray ==2025.1.1
requirements-standard.txt pypi
  • graphviz ==0.20.3
  • ipykernel ==6.29.5
  • jax ==0.4.38
  • jupyterlab ==4.3.4
  • matplotlib ==3.10.0
  • memo-lang ==0.5.1
  • numpy ==2.2.1
  • optype ==0.8.0
  • pandas ==2.2.3
  • pandas-stubs ==2.2.3.241126
  • pydantic ==2.10.5
  • python-dotenv ==1.0.1
  • pyyaml ==6.0.2
  • ruff ==0.9.0
  • scikit-learn ==1.6.0
  • scipy ==1.15.0
  • scipy-stubs ==1.15.0.0
  • seaborn ==0.13.2
  • toml ==0.10.2
  • tqdm ==4.67.1
  • xarray ==2025.1.1