simco-utils

simco-utils provides utilities for the online multiplayer game Sim Companies (https://www.simcompanies.com/).

Installation

Clone this repository

git clone https://github.com/short-fuss/simco-utils.git

Create a conda environment using the supplied YAML specification:

conda env create --file environment.yml

Commodity Prices

To obtain commodity prices, you can call

python get_prices

This will pull down all exchange listings from the SimCompanies API and store the current best price for each quality level of each commodity. The file will be stored as a CSV in the top-level file prices.csv (always overwritten), and in a storage folder with date-stamp to keep your own history. By default, this is set to the folder CommodityPrices, which is on the git ignore list.

The plotting.py module provides plotting utilities, which are demonstrated in the Jupyter notebook price_history.ipynb. An example plot for the price history of Carbon Composite is shown here:

Price history for Carbon Composite

Note how the economy is also indicated in the axis abscissa. This is made possible by a manually-maintained list of economy changes in the Prices class defined in plotting.py.

Sales Office

Parsing

The parse_salesoffice.py utility enables you to extract your Sales Office orders. To use this utility, you have two options:

1. PNG screenshots

The first option is to save screenshots of your orders (as PNG files). E.g.

Sales Office orders example

Under the hood, the tesseract optical character recognition (OCR) system is used to extract the text from the image. This is then converted to an HDF file (via pandas) that contains the prices in the terms.

Then you can call

python parse_salesoffice.py --economy=N example_salesoffice.png

The economy argument is required. You should supply either N (normal), R (recession) or B (boom). This economy key is then stored alongside the prices.

There are several drawbacks of using this approach:

OCR is slow
Tesseract must be installed separately
You will need multiple screenshots for each sales office when you have more than ~5 levels.
Currently, this approach doesn't identify the quantity of each commodity required, because tesseract doesn't identify text within the orange striped bars

2. Text dump

The easier and better option is to copy the text from the page (Ctrl-A, Ctrl-C), and save to a .txt file. The same call as above works, with the internal logic distinguishing between .png and .txt files, and processing accordingly.

By default, the HDF files are stored in the folder SalesOfficePrices, but this can be changed by providing your own to the command-line option --output_dir.

Analysis

The analyse_salesoffice.py script then enables you to identify the current prices, which takes a weighted average of your prices data. Higher weights are given to more recent prices, and the degree of preference is controlled by the argument weight_decay.

Visualization

To visualize the prices and quantities, use the sales_office.ipynb notebook. This provides a plot of all your prices data which looks like this: