pvlib.solarposition.spa_python#

pvlib.solarposition.spa_python(time, latitude, longitude, altitude=0.0, pressure=101325.0, temperature=12.0, delta_t=67.0, atmos_refract=None, how='numpy', numthreads=4)[source]#

Calculate the solar position using a python implementation of the NREL SPA algorithm.

The details of the NREL SPA algorithm are described in [1], [2].

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) – Must be localized or UTC will be assumed.
latitude (float) – Latitude in decimal degrees. Positive north of equator, negative to south.
longitude (float) – Longitude in decimal degrees. Positive east of prime meridian, negative to west.
altitude (float, default 0.0) – Distance above sea level.
pressure (int or float, optional, default 101325.0) – avg. yearly air pressure in Pascals.
temperature (int or float, optional, default 12.0) – avg. yearly air temperature in degrees C.
delta_t (float or array, optional, default 67.0) – Difference between terrestrial time and UT1. If delta_t is None, uses spa.calculate_deltat using time.year and time.month from pandas.DatetimeIndex. For most simulations the default delta_t is sufficient. 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, 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