This repository contains code for GenPlot: Increasing the Scale and Diversity of Chart Derendering Data.
Before running these steps, ensure that you have Anaconda or Miniconda installed.
- Create environment
conda create --prefix ../genplot_env python=3.10
- Activate environment
conda activate ../genplot_env
- Install required packages
conda install matplotlib numpy scipy pandas tqdm scikit-learn
- Clone the GenPlot repository
git clone https://github.com/brendanartley/GenPlot.git
- Navigate to repository
cd GenPlot
- Generate plots. See more examples in the next section.
python generate.py --generate_n_imgs=100_000 --ctype="v" --seed=9090 --out_file="./graphs_v"
There are a number of command line arguments that can be use to customize the generation process. These are shown below, or simply run python generate.py -h to view them in the command line.
options:
-h, --help show this help message and exit
--out_file OUT_FILE Directory to save charts in. (default: ./graphs)
--generate_n_imgs GENERATE_N_IMGS
Number of charts to generate. (default: 100000)
--seed SEED Seed used to generate data. (default: 9090)
--ctype CTYPE Flag to determine which graph to generate. (v, h, l, s, d) (default: v)
Here are some sample commands which can be used to generate 100,000 plots of each type.
python generate.py --generate_n_imgs=100_000 --ctype="v" --seed=9090 --out_file="./graphs_v"
python generate.py --generate_n_imgs=100_000 --ctype="h" --seed=9090 --out_file="./graphs_h"
python generate.py --generate_n_imgs=100_000 --ctype="l" --seed=9090 --out_file="./graphs_l"
python generate.py --generate_n_imgs=100_000 --ctype="s" --seed=9090 --out_file="./graphs_s"
python generate.py --generate_n_imgs=100_000 --ctype="d" --seed=9090 --out_file="./graphs_d"