Documentation: https://engine.avwx.rest

Source Code: https://github.com/avwx-rest/avwx-engine

PyPI: https://pypi.org/project/avwx-engine/

AVWX is a global aviation weather fetching and parsing engine. It sources reports from a variety of government sources, parses individual elements, and calculates additional information like flight rules and time range interpolation.

AVWX currently supports:

• Station data and search
• METAR
• TAF
• PIREP
• AIRMET / SIGMET
• NOTAM
• NBM (NBH, NBS, NBE)
• GFS (MAV, MEX)

>>> import avwx
>>> jfk_metar = avwx.Metar('KJFK')
>>> jfk_metar.update()
True
>>> jfk_metar.data.flight_rules
'VFR'

These docs could use some love, and I am not a writer. You can help by making a pull request on GitHub

Installation

AVWX is available on PyPI and requires Python 3.10 and above. Note: the package name is avwx-engine, but the import is avwx

python -m pip install avwx-engine

Certain features may require additional libraries which most users won't need. For example, finding stations near a coordinate require scipy which is a large package. Attempting to run these methods without the necessary library will prompt you to install them. If you want to install all dependencies at once, run this instead:

python -m pip install avwx-engine[all]

Tutorial

Let's run through a quick example of fetching and parsing a METAR and TAF. There are other report types, but they follow the same basic API.

>>> import avwx
>>> jfk_metar = avwx.Metar('KJFK')
>>> jfk_metar.update()
True
>>> jfk_metar.raw
'KJFK 281651Z 33021G25KT 10SM FEW060 M08/M23 A3054 RMK AO2 SLP339 T10831228'
>>> jfk_metar.data.flight_rules
'VFR'
>>> jfk_metar.summary
'Winds NNW-330 at 21kt gusting to 25kt, Vis 10sm, Temp -08C, Dew -23C, Alt 30.54inHg, Few clouds at 6000ft'
>>> jfk_metar.station.name
'John F Kennedy International Airport'

Here, we create a METAR object and initialize it to pull data for JFK International airport. The update call fetches the current report, parses it into its individual components, and formats the translations. We then view the original report, the calculated flight rules, and a summary string from the translations. We can also see details of the station if available.

>>> hnl_taf = avwx.Taf('PHNL')
>>> hnl_taf.update()
True
>>> hnl_taf.raw
'PHNL 312058Z 3121/0124 07012G19KT P6SM FEW030 SCT050 FM010500 06007KT P6SM FEW025 SCT045 FM012000 07012G19KT P6SM OVC030 SCT050'
>>> len(hnl_taf.data.forecast)
3
>>> for line in hnl_taf.data.forecast:
...   print(f"{line.flight_rules} from {line.start_time.dt.strftime('%d-%H:%M')} to {line.end_time.dt.strftime('%d-%H:%M')}")
...
VFR from 31-21:00 to 01-05:00
VFR from 01-05:00 to 01-20:00
MVFR from 01-20:00 to 01-24:00

Here we start of the same with the Taf object, this time for Honolulu. Because TAFs are forecasts, they contain multiple time periods. Here, we have three: a base and two amendments. Our code shows the different forecasted flight rules for each time period (day-hour). Taf objects have most of the same attributes as Metar objects, so we could also grab the station info if we needed to.

Aviation Reports

• METAR - Current surface conditions
• TAF - Localized 24-hour forecast
• PIREP - Inflight pilot reports
• AIRMET / SIGMET - Weather advisories
• NOTAM - Special events or conditions

Model Forecasts

NBM

• NBH - Current hourly forecast (25 hours)
• NBS - Short-range forecast (6-72 hours)
• NBE - Extended-range forecast (24-192 hours)

GFS

• MOS MAV - Short-range forecast (6-72 hours)
• MOS MEX - Extended-range forecast (24-192 hours)

Utilities

• Station - Airport information
• Service - Report fetching services
• Data Updates - Local database updates
• Data Structures - Underlying data structures
• Static Values - Parsing controls and term glossary
• Exceptions - Custom exception handling