def spa_python(time, latitude, longitude, altitude=0, pressure=101325, temperature=10, delta_t=67.0, atmos_refract=None, numthreads=4, **kwargs): global result lati = latitude longi = longitude elevi = altitude pressure = pressure / 100 atmos_refract = atmos_refract or 0.5667 #calculate the timezone of the given latitude and longitude tz = tzwhere.tzwhere() timezone_str = tz.tzNameAt(lati, longi) timezone_str global timezone timezone = pytz.timezone(timezone_str) #conerting the time into local time based on timezone ltime = time.tz_convert(timezone) #converting the time into unixtime unixtime = np.array(ltime.astype(np.int64) / 10**9) spa = _spa_python_import() delta_t = delta_t or spa.calculate_deltat(ltime.year, ltime.month) #calculate azimuth and zenith app_zenith, zenith, app_elevation, elevation, azimuth, eot = \ spa.solar_position(unixtime, lati, longi, elevi, pressure, temperature, delta_t, atmos_refract, numthreads) result = pd.DataFrame({ 'apparent_zenith': app_zenith, 'zenith': zenith, 'apparent_elevation': app_elevation, 'elevation': elevation, 'azimuth': azimuth, 'equation_of_time': eot }) return result
def test_declination(): times = pd.DatetimeIndex(start="1/1/2015 0:00", end="12/31/2015 23:00", freq="H") atmos_refract = 0.5667 delta_t = spa.calculate_deltat(times.year, times.month) unixtime = np.array([calendar.timegm(t.timetuple()) for t in times]) _, _, declination = spa.solar_position(unixtime, 37.8, -122.25, 100, 1013.25, 25, delta_t, atmos_refract, sst=True) declination = np.deg2rad(declination) declination_rng = declination.max() - declination.min() declination_1 = solarposition.declination_cooper69(times.dayofyear) declination_2 = solarposition.declination_spencer71(times.dayofyear) a, b = declination_1 / declination_rng, declination / declination_rng assert np.allclose(a, b, atol=0.03) # cooper a, b = declination_2 / declination_rng, declination / declination_rng assert np.allclose(a, b, atol=0.02) # spencer
def test_declination(): times = pd.DatetimeIndex(start="1/1/2015 0:00", end="12/31/2015 23:00", freq="H") atmos_refract = 0.5667 delta_t = spa.calculate_deltat(times.year, times.month) unixtime = np.array([calendar.timegm(t.timetuple()) for t in times]) _, _, declination = spa.solar_position(unixtime, 37.8, -122.25, 100, 1013.25, 25, delta_t, atmos_refract, sst=True) declination = np.deg2rad(declination) declination_rng = declination.max() - declination.min() declination_1 = solarposition.declination_cooper69(times.dayofyear) declination_2 = solarposition.declination_spencer71(times.dayofyear) a, b = declination_1 / declination_rng, declination / declination_rng assert np.allclose(a, b, atol=0.03) # cooper a, b = declination_2 / declination_rng, declination / declination_rng assert np.allclose(a, b, atol=0.02) # spencer
def spa_python(time, latitude, longitude, altitude=0, pressure=101325, temperature=12, delta_t=None, atmos_refract=None, how='numpy', numthreads=4): """ Calculate the solar position using a python implementation of the NREL SPA algorithm described in [1]. If numba is installed, the functions can be compiled to machine code and the function can be multithreaded. Without numba, the function evaluates via numpy with a slight performance hit. Parameters ---------- time : pandas.DatetimeIndex Localized or UTC. latitude : float longitude : float altitude : float pressure : int or float, optional avg. yearly air pressure in Pascals. temperature : int or float, optional avg. yearly air temperature in degrees C. delta_t : float, optional Difference between terrestrial time and UT1. The USNO has historical and forecasted delta_t [3]. atmos_refrac : float, optional The approximate atmospheric refraction (in degrees) at sunrise and sunset. how : str, optional Options are 'numpy' or 'numba'. If numba >= 0.17.0 is installed, how='numba' will compile the spa functions to machine code and run them multithreaded. numthreads : int, optional Number of threads to use if how == 'numba'. Returns ------- DataFrame The DataFrame will have the following columns: apparent_zenith (degrees), zenith (degrees), apparent_elevation (degrees), elevation (degrees), azimuth (degrees), equation_of_time (minutes). References ---------- [1] I. Reda and A. Andreas, Solar position algorithm for solar radiation applications. Solar Energy, vol. 76, no. 5, pp. 577-589, 2004. [2] I. Reda and A. Andreas, Corrigendum to Solar position algorithm for solar radiation applications. Solar Energy, vol. 81, no. 6, p. 838, 2007. [3] USNO delta T: http://www.usno.navy.mil/USNO/earth-orientation/eo-products/long-term See also -------- pyephem, spa_c, ephemeris """ # Added by Tony Lorenzo (@alorenzo175), University of Arizona, 2015 pvl_logger.debug('Calculating solar position with spa_python code') lat = latitude lon = longitude elev = altitude pressure = pressure / 100 # pressure must be in millibars for calculation delta_t = delta_t or 67.0 atmos_refract = atmos_refract or 0.5667 if not isinstance(time, pd.DatetimeIndex): try: time = pd.DatetimeIndex(time) except (TypeError, ValueError): time = pd.DatetimeIndex([time, ]) unixtime = time.astype(np.int64)/10**9 spa = _spa_python_import(how) app_zenith, zenith, app_elevation, elevation, azimuth, eot = spa.solar_position( unixtime, lat, lon, elev, pressure, temperature, delta_t, atmos_refract, numthreads) result = pd.DataFrame({'apparent_zenith': app_zenith, 'zenith': zenith, 'apparent_elevation': app_elevation, 'elevation': elevation, 'azimuth': azimuth, 'equation_of_time': eot}, index=time) return result
def spa_python(time, latitude, longitude, altitude=0, pressure=101325, temperature=12, delta_t=67.0, atmos_refract=None, how='numpy', numthreads=4, **kwargs): """ Calculate the solar position using a python implementation of the NREL SPA algorithm described in [1]. If numba is installed, the functions can be compiled to machine code and the function can be multithreaded. Without numba, the function evaluates via numpy with a slight performance hit. Parameters ---------- time : pandas.DatetimeIndex Localized or UTC. latitude : float longitude : float altitude : float, default 0 pressure : int or float, optional, default 101325 avg. yearly air pressure in Pascals. temperature : int or float, optional, default 12 avg. yearly air temperature in degrees C. delta_t : float, optional, default 67.0 If delta_t is None, uses spa.calculate_deltat using time.year and time.month from pandas.DatetimeIndex. For most simulations specifing delta_t is sufficient. Difference between terrestrial time and UT1. *Note: delta_t = None will break code using nrel_numba, this will be fixed in a future version.* The USNO has historical and forecasted delta_t [3]. atmos_refrac : None or float, optional, default None The approximate atmospheric refraction (in degrees) at sunrise and sunset. how : str, optional, default 'numpy' Options are 'numpy' or 'numba'. If numba >= 0.17.0 is installed, how='numba' will compile the spa functions to machine code and run them multithreaded. numthreads : int, optional, default 4 Number of threads to use if how == 'numba'. Returns ------- DataFrame The DataFrame will have the following columns: apparent_zenith (degrees), zenith (degrees), apparent_elevation (degrees), elevation (degrees), azimuth (degrees), equation_of_time (minutes). References ---------- [1] I. Reda and A. Andreas, Solar position algorithm for solar radiation applications. Solar Energy, vol. 76, no. 5, pp. 577-589, 2004. [2] I. Reda and A. Andreas, Corrigendum to Solar position algorithm for solar radiation applications. Solar Energy, vol. 81, no. 6, p. 838, 2007. [3] USNO delta T: http://www.usno.navy.mil/USNO/earth-orientation/eo-products/long-term See also -------- pyephem, spa_c, ephemeris """ # Added by Tony Lorenzo (@alorenzo175), University of Arizona, 2015 lat = latitude lon = longitude elev = altitude pressure = pressure / 100 # pressure must be in millibars for calculation atmos_refract = atmos_refract or 0.5667 if not isinstance(time, pd.DatetimeIndex): try: time = pd.DatetimeIndex(time) except (TypeError, ValueError): time = pd.DatetimeIndex([ time, ]) unixtime = np.array(time.astype(np.int64) / 10**9) spa = _spa_python_import(how) delta_t = delta_t or spa.calculate_deltat(time.year, time.month) app_zenith, zenith, app_elevation, elevation, azimuth, eot = \ spa.solar_position(unixtime, lat, lon, elev, pressure, temperature, delta_t, atmos_refract, numthreads) result = pd.DataFrame( { 'apparent_zenith': app_zenith, 'zenith': zenith, 'apparent_elevation': app_elevation, 'elevation': elevation, 'azimuth': azimuth, 'equation_of_time': eot }, index=time) return result
def spa_python(time, location, pressure=101325, temperature=12, delta_t=None, atmos_refract=None, how='numpy', numthreads=4): """ Calculate the solar position using a python implementation of the NREL SPA algorithm described in [1]. If numba is installed, the functions can be compiled to machine code and the function can be multithreaded. Without numba, the function evaluates via numpy with a slight performance hit. Parameters ---------- time : pandas.DatetimeIndex location : pvlib.Location object pressure : int or float, optional avg. yearly air pressure in Pascals. temperature : int or float, optional avg. yearly air temperature in degrees C. delta_t : float, optional Difference between terrestrial time and UT1. The USNO has historical and forecasted delta_t [3]. atmos_refrac : float, optional The approximate atmospheric refraction (in degrees) at sunrise and sunset. how : str, optional Options are 'numpy' or 'numba'. If numba >= 0.17.0 is installed, how='numba' will compile the spa functions to machine code and run them multithreaded. numthreads : int, optional Number of threads to use if how == 'numba'. Returns ------- DataFrame The DataFrame will have the following columns: apparent_zenith (degrees), zenith (degrees), apparent_elevation (degrees), elevation (degrees), azimuth (degrees), equation_of_time (minutes). References ---------- [1] I. Reda and A. Andreas, Solar position algorithm for solar radiation applications. Solar Energy, vol. 76, no. 5, pp. 577-589, 2004. [2] I. Reda and A. Andreas, Corrigendum to Solar position algorithm for solar radiation applications. Solar Energy, vol. 81, no. 6, p. 838, 2007. [3] USNO delta T: http://www.usno.navy.mil/USNO/earth-orientation/eo-products/long-term See also -------- pyephem, spa_c, ephemeris """ # Added by Tony Lorenzo (@alorenzo175), University of Arizona, 2015 pvl_logger.debug('Calculating solar position with spa_python code') lat = location.latitude lon = location.longitude elev = location.altitude pressure = pressure / 100 # pressure must be in millibars for calculation delta_t = delta_t or 67.0 atmos_refract = atmos_refract or 0.5667 if not isinstance(time, pd.DatetimeIndex): try: time = pd.DatetimeIndex(time) except (TypeError, ValueError): time = pd.DatetimeIndex([ time, ]) unixtime = localize_to_utc(time, location).astype(np.int64) / 10**9 spa = _spa_python_import(how) app_zenith, zenith, app_elevation, elevation, azimuth, eot = spa.solar_position( unixtime, lat, lon, elev, pressure, temperature, delta_t, atmos_refract, numthreads) result = pd.DataFrame( { 'apparent_zenith': app_zenith, 'zenith': zenith, 'apparent_elevation': app_elevation, 'elevation': elevation, 'azimuth': azimuth, 'equation_of_time': eot }, index=time) try: result = result.tz_convert(location.tz) except TypeError: result = result.tz_localize(location.tz) return result