README.md

huracanpy

A python package for working with various forms of feature tracking data

Installation

To install the package, you can use pip: pip install huracanpy.

This can fail with older python versions due to issues with installing cartopy through pip. If this happens, use conda to install cartopy first (e.g. conda install -c conda-forge cartopy), then install huracanpy as normal

Usage

The idea of this package is to be a standard way for working with cyclone track data. We were all working on track data, but in slightly different ways which makes sharing code more difficult. The method chosen here is to treat a set of tracks as an xarray Dataset with a minimal number of assumed variables (track_id, lon, lat, time). e.g. running

import huracanpy

tracks = huracanpy.load(huracanpy.example_csv_file)
minimal_tracks = tracks[["track_id", "lon", "lat", "time"]]

print(minimal_tracks)

gives

<xarray.Dataset>
Dimensions:   (obs: 99)
Coordinates:
  * obs       (obs) int64 0 1 2 3 4 5 6 7 8 9 ... 89 90 91 92 93 94 95 96 97 98
Data variables:
    track_id  (obs) int64 0 0 0 0 0 0 0 0 0 0 0 0 0 ... 2 2 2 2 2 2 2 2 2 2 2 2
    lon       (obs) float64 120.5 119.0 119.0 119.2 ... 57.5 58.5 60.25 62.25
    lat       (obs) float64 -14.25 -14.75 -15.0 -15.0 ... -39.25 -42.0 -45.5
    time      (obs) datetime64[ns] 1980-01-06T06:00:00 ... 1980-01-30T18:00:00

where each "obs" corresponds to a TC point (time, lon, lat). All variables that were present in your file are variables in the loaded dataset.

Note that the data is one dimensional but represents multiple tracks. This is done rather than having track_id as an additional dimension to avoid having to add a bunch of extra blank data to each track when they are not the same length. The groupby function allows us to easily loop over or index tracks in this format.

import huracanpy

tracks = huracanpy.load(huracanpy.example_csv_file)

track_groups = tracks.groupby("track_id")

# Selecting track by ID
# The track_id is not necessarily an integer, it follows whatever you have loaded
# e.g. could be a string for IBTrACS
track_id1 = track_groups[1]

# Iterating over all tracks
# Each track will be a subset of the xarray Dataset with a unique track_id
for n, track in track_groups:
    # Do something with the track

csv tracks data

If you tracks are stored in csv (including if they were outputed from TempestExtremes' StitchNodes), you can specify the tracker="csv" argument, or, if your filename ends with csv, it will be detected automatically.

TRACK tracks data

If your tracks are in TRACK format, use the tracker="TRACK" option

Contributing

We welcome suggestions and contributions to huracanpy. If you have existing code for working with track data, we would definitely welcome contributions/suggestions for modifying it to work with the xarray format used here. To add suggestions, feel free to open an issue or contact us. To contribute code, please make a fork of this repository, follow the instructions below, and open a pull request.

To install your copy locally run

pip install -e .[dev]

The "[dev]" argument installs the following optional packages that are useful for contributing to development

1. pytest

   We use pytest to run automated tests. If you add a new feature, it would be good to also add tests to check that feature is working and keeps working in the future. You can also run pytest from the top level directory of the package to check that your changes haven't broken anything.

2. ruff

   We use ruff to automatically keep the style of the code consistent so we don't have to worry about it. To check that your code passes you can run ruff check and ruff format --check. To automatically fix differences run ruff check --fix and ruff format.

3. pre-commit

   You can use pre-commit to automate the formatting done by ruff. After running pre-commit install at the top level directory, any future git commits will automatically run the ruff formatting on any files you have changes.