daaayonearth 0.75

Easy access to geographic, meteorologic and photovoltaic performance data/services.

The 'Daaayonearth' library contains three classes: Location, TimeZone and EnvConditions. These provide easy access to geographical, meteorological and photovoltaic performance data/services, namely:

• the (latitude, longitude) tuple belonging to a specific address / location string (geocoding)

• the timezone belonging to a specific location (at a given date)

• the typical environmental conditions belonging to a day at a specific location & date, including:

• air temperature

• air pressure

• air humidity

• wind speed

• wind direction

• sun height

• solar irradiance

• maximal photovoltaic performance

For each of said parameters there is a function as well as a belonging plot available, which provide the course of said parameter over the chosen day.

Examples

	#import from daaayonearth
	import daaayonearth
	from   daaayonearth import Location, TimeZone, EnvConditions


	#import from standard library
	from   datetime     import datetime as Datetime
	from   datetime     import date     as Date


	#set application name
	daaayonearth.UserAgentNameS = "DaaayExampleApp"


	#get (latitude, longitude) tuple from address string
	deu = Location(  "Willy-Brandt-Str., 10557 Berlin" )                             # ==> (52.5202262, 13.3704874)


	#get timezone (Python tzfile) belonging to location
	tzDeu = TimeZone( deu )                                                          # ==> tzfile('Europe/Berlin')


	#convert a date to another timezone
	date = Datetime(2021, 11, 1, 20, 40, 0, tzinfo=tzDeu)                            # ==> 2021-11-01 20:40:00+01:00
	date.astimezone( TimeZone("Pennsylvania Ave. NW, Washington, DC 20020, USA") )   # ==> 2021-11-01 15:40:00-04:00


	#get the typical temperature at 12 o'clock 
	#in the Willy Brandt Street, Berlin - on the 2th of November (year is ignored)
	envConds = EnvConditions( Location("Willy-Brandt-Str., 10557 Berlin"), Date(2021, 11, 2) )
	envConds.functionsD["T(2m) [°C]"](12 * 3600)                                     # ==> 5.03


	#get a matplotlib figure of the belonging course of temperature for said location/day
	envConds.getFigureOf( "T(2m) [°C]" )                                             # ==> plot (course of temperature)


	#get the list of available parameters (for envConds.functionsD[...] and envConds.getFigureOf(...))
	list( envConds.functionsD.keys() )   # ==> [ 'T(2m) [°C]', 'Humidity [%]', 'Irradiance [W/m^2]', 
							 #       'WindSpeed(10m) [m/s]', 'WindDirection(10m) [°]', 
							 #       'Pressure(0m) [Pa]', 'PvPower [W]', 'SunHeight [°]' ]

      #it is also possible to setup the environment conditions 'manually' - using the 'auto' parameter
      #as the Tmy-part is far less time consuming than the Pvp-part, this e.g. can be used to minimize 
      #waiting times
      envConds = EnvConditions( "Willy-Brandt-Str., 10557 Berlin", "11-02", auto=False )

      envConds.loadTmy()
      envConds.processTmy()    #after this, one already has access to all functions/figures 
                               #but 'SunHeight [°]' and 'PvPower [W]' 
      
      envConds.loadPvp()       #the Pvp-block needs about 5 times more time (e.g. some 20s!)
      envConds.processPvp()

      envConds.cleanUp()       #do not forget to clean up - also if you just use the Tmy-part

If you use this library you might ('under the hood') use data and/or services connected with 'OpenStreetMap', 'Nominatim' and/or 'PVGIS' - please find more informations concerning this topic in the LICENSE text!

Further infomations and links also can be found on my homepage https://www.blackward.de

Have Fun!