tropycal
Science Score: 36.0%
This score indicates how likely this project is to be science-related based on various indicators:
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○CITATION.cff file
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✓codemeta.json file
Found codemeta.json file -
✓.zenodo.json file
Found .zenodo.json file -
○DOI references
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○Academic publication links
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✓Committers with academic emails
2 of 14 committers (14.3%) from academic institutions -
○Institutional organization owner
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○JOSS paper metadata
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○Scientific vocabulary similarity
Low similarity (14.9%) to scientific vocabulary
Repository
Basic Info
- Host: GitHub
- Owner: tropycal
- License: mit
- Language: Python
- Default Branch: master
- Homepage: https://tropycal.github.io/tropycal/
- Size: 228 MB
Statistics
- Stars: 162
- Watchers: 9
- Forks: 42
- Open Issues: 28
- Releases: 23
Metadata Files
README.md
Tropycal
Tropycal is a Python package intended to simplify the process of retrieving and analyzing tropical cyclone data, both for past storms and in real time, and is geared towards the research and operational meteorology sectors.
Tropycal can read in HURDAT2 and IBTrACS reanalysis data and operational National Hurricane Center (NHC) Best Track data and conform them to the same format, which can be used to perform climatological, seasonal and individual storm analyses. For each individual storm, operational NHC and model forecasts, aircraft reconnaissance data, rainfall data, and any associated tornado activity can be retrieved and plotted.
The latest version of Tropycal is v1.4.
Installation
Conda
The currently recommended method of installation is via conda:
sh
conda install -c conda-forge tropycal
Pip
Installation is also available via pip:
sh
pip install tropycal
From source
Tropycal can also be installed from source by cloning the GitHub repository:
sh
git clone https://github.com/tropycal/tropycal
cd tropycal
python setup.py install
Dependencies
- matplotlib >= 2.2.2
- numpy >= 1.14.3
- scipy >= 1.1.0
- pandas >= 1.3.0
- xarray >= 0.10.7
- networkx >= 2.0.0
- requests >= 2.22.0
- pyshp >= 2.1
To fully leverage tropycal's plotting capabilities, it is strongly recommended to have cartopy >= 0.17.0 installed.
Documentation
For full documentation and examples, please refer to Tropycal Documentation.
As of v0.3, the documentation is up-to-date following a bug that started with v0.2.5 where the documentation was not updated with each release.
Sample Usage
As an example, read in the North Atlantic HURDAT2 reanalysis dataset, excluding Best Track (current year's storms):
```python import tropycal.tracks as tracks
basin = tracks.TrackDataset(basin='north_atlantic') ```
Individual Storm Analysis
Individual storms can be retrieved from the dataset by calling the "get_storm" function, which returns an instance of a Storm object. This can be done by either entering a tuple containing the storm name and year, or by the standard tropical cyclone ID (e.g., AL012019).
Let's retrieve an instance of Hurricane Michael from 2018:
python
storm = basin.get_storm(('michael',2018))
This instance of Storm contains several methods that return the storm data back in different data types. The following examples will show how to retrieve 3 different data types.
Retrieve Michael's data in different data formats:
python
storm.to_dict()
storm.to_xarray()
storm.to_dataframe()
Visualize Michael's observed track with the plot function:
Note that you can pass various arguments to the plot function, such as customizing the map and track aspects. The only cartopy projection currently offered is PlateCarree. Read through the documentation for more customization options.
python
storm.plot()
If this storm was ever in NHC's area of responsibility, you can retrieve operational forecast data for this event provided it is available. Forecast discussions date back to 1992, and forecast tracks date back to 1950.
Retrieve a single forecast discussion for Michael - both of these methods will yield an identical result:
```python
Method 1: Specify date closest to desired discussion
disco = storm.getnhcdiscussion(forecast=dt.datetime(2018,10,7,0)) print(disco['text'])
Method 2: Specify forecast discussion ID
disco = storm.getnhcdiscussion(forecast=2) print(disco['text']) ```
NHC also archives forecast tracks, albeit in a different format than the official advisory data, so the operational forecast IDs here differ from the discussion IDs. As such, the forecast cone is not directly retrieved from NHC, but is generated using an algorithm that yields a cone closely resembling the official NHC cone.
Let's plot Michael's second forecast cone:
python
storm.plot_nhc_forecast(forecast=2)
Now let's look at the 12th forecast for Michael.
Note that the observed track here differs from the HURDAT2 track plotted previously! This is because this plot displays the operationally analyzed location and intensity, rather than the post-storm analysis data. This is done to account for differences between HURDAT2 and operational data.
python
storm.plot_nhc_forecast(forecast=12)
Owner
- Name: TroPYcal
- Login: tropycal
- Kind: user
- Website: https://tropycal.readthedocs.io/en/latest/index.html
- Repositories: 1
- Profile: https://github.com/tropycal
GitHub Events
Total
- Issues event: 3
- Watch event: 23
- Issue comment event: 3
- Push event: 14
- Pull request event: 2
- Fork event: 4
- Create event: 1
Last Year
- Issues event: 3
- Watch event: 23
- Issue comment event: 3
- Push event: 14
- Pull request event: 2
- Fork event: 4
- Create event: 1
Committers
Last synced: 10 months ago
Top Committers
| Name | Commits | |
|---|---|---|
| Tomer Burg | t****g@o****u | 598 |
| tomerburg | t****g@a****u | 96 |
| Sam Lillo | s****o@g****m | 42 |
| Sam Lillo | 4****o@u****m | 9 |
| Sam Lillo | S****o@d****m | 5 |
| Tyler Mitchell | t****l@b****m | 3 |
| Ray Bell | r****l@d****m | 2 |
| tropycal | t****g@g****m | 2 |
| CyanideCN | 4****N@u****m | 1 |
| Guillaume Andreu Sabater | g****r@g****m | 1 |
| Lisa L. Lowe | l****e@g****m | 1 |
| Ray Bell | r****0@g****m | 1 |
| Tomer Aberbach | t****h@g****m | 1 |
| Tyler Mitchell | z****i@g****m | 1 |
Committer Domains (Top 20 + Academic)
Issues and Pull Requests
Last synced: 10 months ago
All Time
- Total issues: 78
- Total pull requests: 62
- Average time to close issues: 6 months
- Average time to close pull requests: 10 days
- Total issue authors: 55
- Total pull request authors: 9
- Average comments per issue: 1.72
- Average comments per pull request: 0.29
- Merged pull requests: 53
- Bot issues: 0
- Bot pull requests: 0
Past Year
- Issues: 5
- Pull requests: 2
- Average time to close issues: about 3 hours
- Average time to close pull requests: N/A
- Issue authors: 5
- Pull request authors: 1
- Average comments per issue: 0.6
- Average comments per pull request: 0.0
- Merged pull requests: 0
- Bot issues: 0
- Bot pull requests: 0
Top Authors
Issue Authors
- srmullens (4)
- winash12 (3)
- CongGao-CG (3)
- stucka (3)
- raybellwaves (3)
- ahuang11 (3)
- mguzelevich (2)
- tropicalblog (2)
- cynthiazeng (2)
- cybertosher (2)
- elierpf (2)
- NHL-B (2)
- gameskip (2)
- More-Grace (2)
- wxguy (2)
Pull Request Authors
- tomerburg (46)
- splillo (6)
- ddxv (2)
- mguzelevich (2)
- raybellwaves (2)
- g-as (2)
- leosaffin (1)
- lisalenorelowe (1)
- brianmapes (1)
Top Labels
Issue Labels
Pull Request Labels
Packages
- Total packages: 2
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Total downloads:
- pypi 8,360 last-month
-
Total dependent packages: 2
(may contain duplicates) -
Total dependent repositories: 26
(may contain duplicates) - Total versions: 52
- Total maintainers: 1
pypi.org: tropycal
Package for retrieving and analyzing tropical cyclone data
- Homepage: https://github.com/tropycal/tropycal
- Documentation: https://tropycal.github.io/tropycal/
- License: mit
-
Latest release: 1.2.1
published almost 3 years ago
Rankings
Maintainers (1)
conda-forge.org: tropycal
- Homepage: https://github.com/tropycal/tropycal
- License: MIT
-
Latest release: 0.5.2
published almost 4 years ago
Rankings
Dependencies
- matplotlib >=2.2.2
- networkx *
- numpy >=1.14.3
- pandas >=0.23.0
- scipy >=1.1.0