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MetOffer 1.3.2

Simple wrapper for the Met Office DataPoint API.

metoffer is a simple wrapper for the API provided by the British Met Office known as DataPoint. It can be used to retrieve weather observations and forecasts. At its heart is the MetOffer class which has methods to retrieve data available through the API and make them available as Python objects. Also included are a couple of functions and classes useful for interpretting the data.


Get forecast for Met Office site closest to supplied latitude and longitude, the forecast to be given in three-hourly intervals:

>>> import metoffer
>>> api_key = '01234567-89ab-cdef-0123-456789abcdef'
>>> M = metoffer.MetOffer(api_key)
>>> x = M.nearest_loc_forecast(51.4033, -0.3375, metoffer.THREE_HOURLY)

It’s worth noting here that, if you expect many requests for forecast data to be made, it is probably better to use the functions called by this convenience function so that data that does not change often (e.g. data about Met Office sites) may be cached.

Parse this data into a metoffer.Weather instance:

>>> y = metoffer.parse_val(x)
>>> y.continent
>>> y.lon
>>> y.elevation
>>> y.ident # The Met Office site ident
>>> y.data_date
>>> y.dtype
>>> import pprint
>>> pprint.pprint(
[{'Feels Like Temperature': (17, 'C', 'F'),
  'Max UV Index': (1, '', 'U'),
  'Precipitation Probability': (7, '%', 'Pp'),
  'Screen Relative Humidity': (63, '%', 'H'),
  'Temperature': (19, 'C', 'T'),
  'Visibility': ('VG', '', 'V'),
  'Weather Type': (7, '', 'W'),
  'Wind Direction': ('NNE', 'compass', 'D'),
  'Wind Gust': (18, 'mph', 'G'),
  'Wind Speed': (11, 'mph', 'S'),
  'timestamp': (datetime.datetime(2014, 6, 14, 18, 0), '')},
 {'Feels Like Temperature': (15, 'C', 'F'),
  'Max UV Index': (0, '', 'U'),
  'Precipitation Probability': (0, '%', 'Pp'),
  'Screen Relative Humidity': (72, '%', 'H'),
  'Temperature': (16, 'C', 'T'),
  'Visibility': ('VG', '', 'V'),
  'Weather Type': (0, '', 'W'),
  'Wind Direction': ('NNE', 'compass', 'D'),
  'Wind Gust': (18, 'mph', 'G'),
  'Wind Speed': (9, 'mph', 'S'),
  'timestamp': (datetime.datetime(2014, 6, 14, 21, 0), '')},


 {'Feels Like Temperature': (16, 'C', 'F'),
  'Max UV Index': (0, '', 'U'),
  'Precipitation Probability': (2, '%', 'Pp'),
  'Screen Relative Humidity': (66, '%', 'H'),
  'Temperature': (16, 'C', 'T'),
  'Visibility': ('VG', '', 'V'),
  'Weather Type': (0, '', 'W'),
  'Wind Direction': ('NNE', 'compass', 'D'),
  'Wind Gust': (13, 'mph', 'G'),
  'Wind Speed': (7, 'mph', 'S'),
  'timestamp': (datetime.datetime(2014, 6, 18, 21, 0), '')}]

Interpret the data further:

>>> for i in
...     print("{} - {}".format(i["timestamp"][0].strftime("%d %b, %H:%M"), metoffer.WEATHER_CODES[i["Weather Type"][0]]))
14 Jun, 18:00 - Cloudy
14 Jun, 21:00 - Clear night
15 Jun, 00:00 - Clear night
15 Jun, 03:00 - Cloudy


18 Jun, 09:00 - Partly cloudy (day)
18 Jun, 12:00 - Partly cloudy (day)
18 Jun, 15:00 - Cloudy
18 Jun, 18:00 - Cloudy
18 Jun, 21:00 - Clear night
>>> metoffer.VISIBILITY[[0]["Visibility"][0]]
'Very good - Between 20-40 km'
>>> metoffer.guidance_UV([0]["Max UV Index"][0])
'Low exposure. No protection required. You can safely stay outside'

The MetOffer Class

Available methods:

  • loc_forecast. Return location-specific forecast data (including lists of available sites and time capabilities) for given time step.
  • nearest_loc_forecast. Work out nearest possible site to lat & lon coordinates and return its forecast data for the given time step.
  • loc_observations. Return location-specific observation data, including a list of available sites (time step will be HOURLY).
  • nearest_loc_obs. Work out nearest possible site to lat & lon coordinates and return observation data for it.
  • text_forecast. Return textual forecast data for regions, national parks or mountain areas.
  • text_uk_extremes. Return textual data of UK extremes.
  • stand_alone_imagery. Returns capabilities data for stand alone imagery and includes URIs for the images.
  • map_overlay_forecast. Returns capabilities data for forecast map overlays.
  • map_overlay_obs. Returns capabilities data for observation map overlays.

The Site Class

Describes object to hold site metadata. Also describes method (distance_to_coords) to return a Site instance’s ‘distance’ from any given lat & lon coordinates. This ‘distance’ is a value which is used to guide MetOffer.nearest_loc_forecast and MetOffer.nearest_loc_obs. It simply calculates the difference between the two sets of coordinates and arrives at a value through Pythagorean theorem.

The Weather Class

A hold-all for returned weather data, including associated metadata.

Useful Functions

  • parse_sitelist. Return list of Site instances from retrieved sitelist data.
  • get_nearest_site. Return a list of strings (site IDs) which can be used as ‘request’ in calls to loc_forecast and loc_observations.
  • parse_val. Parse returned dict of MetOffer location-specific data into a Weather instance. Works with single or multiple time steps. There are a couple of points to note:
    • All dict keys have a tuple, even where there is no obvious need, such as with ‘timestamp’ and ‘Weather Type’. ‘timestamp’ is a 2-tuple, all else is a 3-tuple. This is a feature.
    • When the Met Office does not have a recorded observation against a category, metoffer will return None.
    • For parsed DAILY forecasts, the hours and minutes of the ‘timestamp’ datetime.datetime object are superfluous. In fact, it would be misleading to follow them. Rather, this time there is a sensible entry in the second part of the tuple. This alternates between ‘Day’ and ‘Night’ with each successive dict. The categories are often specific to the time of day. This is how the API provides it. Take note as it may catch you out.
  • guidance_UV. Return Met Office guidance regarding UV exposure based on UV index.
  • extract_data_key. Returns a dict that maps measurement type to its description and measurement unit.

Feedback & Bug Reports

Get in touch:

Stephen B Murray <> @SteveRigmortard

File Type Py Version Uploaded on Size
MetOffer-1.3.2.tar.gz (md5, pgp) Source 2014-06-16 21KB
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