معرفی شرکت ها

تبلیغات ما

مشتریان به طور فزاینده ای آنلاین هستند. تبلیغات می تواند به آنها کمک کند تا کسب و کار شما را پیدا کنند.

مشاهده بیشتر

تبلیغات ما

مشتریان به طور فزاینده ای آنلاین هستند. تبلیغات می تواند به آنها کمک کند تا کسب و کار شما را پیدا کنند.

مشاهده بیشتر

تبلیغات ما

مشتریان به طور فزاینده ای آنلاین هستند. تبلیغات می تواند به آنها کمک کند تا کسب و کار شما را پیدا کنند.

مشاهده بیشتر

تبلیغات ما

مشتریان به طور فزاینده ای آنلاین هستند. تبلیغات می تواند به آنها کمک کند تا کسب و کار شما را پیدا کنند.

مشاهده بیشتر

تبلیغات ما

مشتریان به طور فزاینده ای آنلاین هستند. تبلیغات می تواند به آنها کمک کند تا کسب و کار شما را پیدا کنند.

مشاهده بیشتر

توضیحات

Simple wrapper for the Met Office DataPoint API.

ویژگی	مقدار
سیستم عامل	OS Independent
نام فایل	MetOffer-2.0
نام	MetOffer
نسخه کتابخانه	2.0
نگهدارنده	[]
ایمیل نگهدارنده	[]
نویسنده	Stephen B. Murray
ایمیل نویسنده	sbm199@gmail.com
آدرس صفحه اصلی	https://github.com/sludgedesk/metoffer
آدرس اینترنتی	https://pypi.org/project/MetOffer/
مجوز	LGPLv3+

================ metoffer v.2.0 ================ metoffer is a simple wrapper for the API provided by the British `Met Office <http://www.metoffice.gov.uk>`_ 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. This project is now maintained at `<https://github.com/sludgedesk/metoffer>`_. Example ------- 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.Weather(x) >>> y.name 'HAMPTON COURT PALACE' >>> y.country 'ENGLAND' >>> y.continent 'EUROPE' >>> y.lat 51.4007 >>> y.lon -0.3337 >>> y.elevation 4.0 >>> y.ident # The Met Office site ident '351747' >>> y.data_date '2014-06-14T23:00:00Z' >>> y.dtype 'Forecast' >>> import pprint >>> pprint.pprint(y.data) [{'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 y.data: ... 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[y.data[0]["Visibility"][0]] 'Very good - Between 20-40 km' >>> metoffer.guidance_UV(y.data[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. It parses 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. The TextForecast Class ---------------------- A hold-all for returned textual regional forecasts, including associated meta- data, created by parsing the data returned by ``MetOffer.text_forecast``. 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``. * ``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 <sbm199@gmail.com> @sludgedesk Legal ----- Copyright 2012-2014, 2018 Stephen B Murray Distributed under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. You should have received a copy of the GNU General Public License along with this package. If not, see <http://www.gnu.org/licenses/>

نحوه نصب

نصب پکیج whl MetOffer-2.0:

pip install MetOffer-2.0.whl

نصب پکیج tar.gz MetOffer-2.0:

pip install MetOffer-2.0.tar.gz