# scml-vis
[](https://github.com/yasserfarouk/scml-vis/actions/workflows/main.yml)
[](https://scml-vis.github.io/scml-vis/)
[](https://pypi.org/project/scml-vis/)
[](https://gitter.im/scml-vis/community)
A simple visualiser for SCML worlds and tournaments.
You can watch [a demo on YouTube](https://youtu.be/BCDjnnSmIsk)
[](https://youtu.be/BCDjnnSmIsk)
<!-- <iframe width="560" height="315" src="https://www.youtube.com/embed/BCDjnnSmIsk" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe> -->
<!-- -->
## Screenshots
## Main Features
- Displays any world/tournament run using the [SCML package](https://www.github.com/yasserfarouk/scml)
- Allows filtering using worlds, agent types, and agent instances
- Shows world statistics, agent type and instance statistics and contract
statistics as functions of simulation step/time
## Requirements
- scml-vis requires Python 3.8 or above.
- scml-vis can visualize worlds created by [scml](https://github.com/yasserfarouk/scml) 0.4.2 or later (simulations created using older versions of scml can be visualized in most cases but are not officially supported).
## Installation
With `pip`:
```bash
python3 -m pip install scml-vis
```
With `pipx`:
```bash
python3 -m pip install --user pipx
pipx install scml-vis
```
## Usage
The visualizer can be run using any of the following commands:
```bash
scmlv
scmlvis
scml-vis
```
Hereafter we will use the shorter version.
- To visualize any of the recently run worlds and tournaments just run:
```bash
scmlv show
```
This will open your browser and allow you to choose a world or a tournament to
display.
- If this is the first time you visualize logs in this folder, you will be asked
to compile visualization data
- If visualization data is available (or your pressed the `compile visualization data` button), you can now start visualizing the logs
- To visualize the logs in a specific folder, you can directly pass the folder as in:
```bash
scmlv show -f path-to-your-folder
```
- It is also possible to just compile visualization data without running the visualizer using:
```bash
scmlv compile path-to-your-folder
```
- The visualizer creates a database that it uses to create all the figures you see. You can directly explore this database using:
```bash
scmlv explore path-to-your-folder
```
This will open a [datasette](https://docs.datasette.io/en/stable/getting_started.html) page allowing you to explore this database
This dataset will contain 8 tables describing everything that was logged in the world or tournament.
Please consult [datasette documentation](https://docs.datasette.io/en/stable/getting_started.html) for all the ways you can interact with this dataset.
## Available visualizations
To visualize your logs, you need to follow three steps:
1. Filter the dataset using the `Data Selection` section of the sidebar.
2. Choose the **family** of figures you would like to show from the `Figure Selection` dropdown in the sidebar. Currently we provide `Time-series`, `Tables` and `others` that are always available as well as `Networks` that are only available when you visualize a single world or filter the tournament to focus on a single world.
3. Choose the specific graph you want to see from the family selected in the previous step.
You can watch [a demo of this process here](https://youtu.be/BCDjnnSmIsk)
## TODO List (Good Ideas for PRs)
- ~~Show negotiation logs (i.e. negotiation results)~~
- ~~Display all contracts (i.e. in a table) based on selection criteria~~
- ~~Zoom on negotiation details (i.e. exchanged offers)~~
- ~~Add dynamic figures using plotly/altair~~
- ~~Add networkx like graphs of contracts / negotiations / offers~~
- ~~Allow starting the app without specifying a folder.~~
- ~~Add new figure types that do not have time/step in the x-axis.~~
- ~~Correcting the placement of weights on edges in network views.~~
- Adding a graph showing negotiation history in the ufun-space of negotiators (will require a change in the scml package).
- Add saving and loading of the visualizer's state (i.e. what is visible).
- Resolving the strange behavior of CI bands in plotly in some cases.
Raw data
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