Installation

The code in this repository is written in Python, using the anaconda distribution. The environment.yaml file contains a working environment to run the analysis and plot the code. Create a conda evironment from this file, for example by running conda env create --file environment.yml (or conda env create --file environment.yml).

Data Download

Start by downloading the data using python download_data.py from the scripts/analysis directory. This will download most of the data, but can't download the Benedix et al. (2020) data because of the way the journal stores the data, and doesn't currently download the irH1k data since the repository link isn't public yet.

Data processing

All of these functions are from scripts/analysis.

1. A later stage of the analysis uses a rescaled version of Benedix data, but the data is needed here to make sure it's part of the analaysis. Run the rescaling using python rescale.py which creates a new table in the data/external directory.
2. The first comparison step is to normalize the catalogs so that columns are labelled in the same way. Run python normalize.py to read each catalog and convert them into tables in the interim/catalogs.hdf file.
3. Next, compare the catalogs against the ground truth. Run python compare_with_iou.py to generate one file for each catalog-comparison that includes the top 10 (by default) matches for each crater in the ground-truth, ranked by largest Intersection-over-Union (IOU) for non-zero IOU, or smallest distance for zero IOU.
4. The previous step can be used to generate a catalog that allows duplicates. In order to remove duplicate matches (where multiple candidates match a single crater) the catalogs are iteratively pruned to remove the non-maximum matches, then regrown using remaining potential matches. The code outputs the size of the catalog as it prunes and grows. Remove the duplicates using python deduplicate_matched.py.

plots

Print the table and plot the figures using the following programs. They each access the config.py file to get colors and (in some cases) the tables to plot.

1. metric_table.py : Metric table (table 1)
2. csfd.py : Crater Size Frequency Distribution (figure 1)
3. errors_benedix.py : Relative errors using the B20 method (figure 2)
4. errors_lee.py : Relative errors using the L19 method (figure 3)
5. iou.py : IOU histogram (Figure 4)
6. tpr_lee.py : Precision and recall for the catalogs in the L19 method (figure 5)
7. cosine_bias.py : Bias plot showing bias in latitude and correction (figure 6)