def nrel_earthsun_distance(time, how='numpy', delta_t=67.0, numthreads=4):
"""
Calculates the distance from the earth to the sun using the
NREL SPA algorithm.
The details of the NREL SPA algorithm are described in [1]_.
Parameters
----------
time : pandas.DatetimeIndex
Must be localized or UTC will be assumed.
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.
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.*
By default, use USNO historical data and predictions
numthreads : int, optional, default 4
Number of threads to use if how == 'numba'.
Returns
-------
dist : pd.Series
Earth-sun distance in AU.
References
----------
.. [1] Reda, I., Andreas, A., 2003. Solar position algorithm for solar
radiation applications. Technical report: NREL/TP-560- 34302. Golden,
USA, http://www.nrel.gov.
"""
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)
dist = spa.earthsun_distance(unixtime, delta_t, numthreads)
dist = pd.Series(dist, index=time)
return dist
def nrel_earthsun_distance(time, how='numpy', delta_t=67.0, numthreads=4):
"""
Calculates the distance from the earth to the sun using the
NREL SPA algorithm described in [1].
Parameters
----------
time : pd.DatetimeIndex
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.
delta_t : float, optional
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.
By default, use USNO historical data and predictions
numthreads : int, optional
Number of threads to use if how == 'numba'.
Returns
-------
dist : pd.Series
Earth-sun distance in AU.
References
----------
[1] Reda, I., Andreas, A., 2003. Solar position algorithm for solar
radiation applications. Technical report: NREL/TP-560- 34302. Golden,
USA, http://www.nrel.gov.
"""
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)
dist = spa.earthsun_distance(unixtime, delta_t, numthreads)
dist = pd.Series(dist, index=time)
return dist
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 get_sun_rise_set_transit(time,
latitude,
longitude,
how='numpy',
delta_t=67.0,
numthreads=4):
"""
Calculate the sunrise, sunset, and sun transit times using 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
Only the date part is used
latitude : float
longitude : float
delta_t : float, optional
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.
By default, use USNO historical data and predictions
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:
sunrise, sunset, transit
References
----------
[1] Reda, I., Andreas, A., 2003. Solar position algorithm for solar
radiation applications. Technical report: NREL/TP-560- 34302. Golden,
USA, http://www.nrel.gov.
"""
# Added by Tony Lorenzo (@alorenzo175), University of Arizona, 2015
lat = latitude
lon = longitude
if not isinstance(time, pd.DatetimeIndex):
try:
time = pd.DatetimeIndex(time)
except (TypeError, ValueError):
time = pd.DatetimeIndex([
time,
])
# must convert to midnight UTC on day of interest
utcday = pd.DatetimeIndex(time.date).tz_localize('UTC')
unixtime = np.array(utcday.astype(np.int64) / 10**9)
spa = _spa_python_import(how)
delta_t = delta_t or spa.calculate_deltat(time.year, time.month)
transit, sunrise, sunset = spa.transit_sunrise_sunset(
unixtime, lat, lon, delta_t, numthreads)
# arrays are in seconds since epoch format, need to conver to timestamps
transit = pd.to_datetime(transit * 1e9, unit='ns',
utc=True).tz_convert(time.tz).tolist()
sunrise = pd.to_datetime(sunrise * 1e9, unit='ns',
utc=True).tz_convert(time.tz).tolist()
sunset = pd.to_datetime(sunset * 1e9, unit='ns',
utc=True).tz_convert(time.tz).tolist()
result = pd.DataFrame(
{
'transit': transit,
'sunrise': sunrise,
'sunset': sunset
}, 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 sun_rise_set_transit_spa(times, latitude, longitude, how='numpy',
delta_t=67.0, numthreads=4):
"""
Calculate the sunrise, sunset, and sun transit times using the
NREL SPA algorithm.
The details of the NREL SPA algorithm are 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
----------
times : pandas.DatetimeIndex
Must be localized to the timezone for ``latitude`` and ``longitude``.
latitude : float
Latitude in degrees, positive north of equator, negative to south
longitude : float
Longitude in degrees, positive east of prime meridian, negative to west
delta_t : float, optional
If delta_t is None, uses spa.calculate_deltat
using times.year and times.month from pandas.DatetimeIndex.
For most simulations specifing delta_t is sufficient.
Difference between terrestrial time and UT1.
delta_t = None will break code using nrel_numba,
this will be fixed in a future version.
By default, use USNO historical data and predictions
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
-------
pandas.DataFrame
index is the same as input `times` argument
columns are 'sunrise', 'sunset', and 'transit'
References
----------
.. [1] Reda, I., Andreas, A., 2003. Solar position algorithm for solar
radiation applications. Technical report: NREL/TP-560- 34302. Golden,
USA, http://www.nrel.gov.
"""
# Added by Tony Lorenzo (@alorenzo175), University of Arizona, 2015
lat = latitude
lon = longitude
# times must be localized
if times.tz:
tzinfo = times.tz
else:
raise ValueError('times must be localized')
# must convert to midnight UTC on day of interest
utcday = pd.DatetimeIndex(times.date).tz_localize('UTC')
unixtime = np.array(utcday.astype(np.int64)/10**9)
spa = _spa_python_import(how)
delta_t = delta_t or spa.calculate_deltat(times.year, times.month)
transit, sunrise, sunset = spa.transit_sunrise_sunset(
unixtime, lat, lon, delta_t, numthreads)
# arrays are in seconds since epoch format, need to conver to timestamps
transit = pd.to_datetime(transit*1e9, unit='ns', utc=True).tz_convert(
tzinfo).tolist()
sunrise = pd.to_datetime(sunrise*1e9, unit='ns', utc=True).tz_convert(
tzinfo).tolist()
sunset = pd.to_datetime(sunset*1e9, unit='ns', utc=True).tz_convert(
tzinfo).tolist()
return pd.DataFrame(index=times, data={'sunrise': sunrise,
'sunset': sunset,
'transit': transit})
def get_sun_rise_set_transit(time, latitude, longitude, how='numpy',
delta_t=67.0,
numthreads=4):
"""
Calculate the sunrise, sunset, and sun transit times using 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
Only the date part is used
latitude : float
longitude : float
delta_t : float, optional
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.
By default, use USNO historical data and predictions
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:
sunrise, sunset, transit
References
----------
[1] Reda, I., Andreas, A., 2003. Solar position algorithm for solar
radiation applications. Technical report: NREL/TP-560- 34302. Golden,
USA, http://www.nrel.gov.
"""
# Added by Tony Lorenzo (@alorenzo175), University of Arizona, 2015
pvl_logger.debug('Calculating sunrise, set, transit with spa_python code')
lat = latitude
lon = longitude
if not isinstance(time, pd.DatetimeIndex):
try:
time = pd.DatetimeIndex(time)
except (TypeError, ValueError):
time = pd.DatetimeIndex([time, ])
# must convert to midnight UTC on day of interest
utcday = pd.DatetimeIndex(time.date).tz_localize('UTC')
unixtime = np.array(utcday.astype(np.int64)/10**9)
spa = _spa_python_import(how)
delta_t = delta_t or spa.calculate_deltat(time.year, time.month)
transit, sunrise, sunset = spa.transit_sunrise_sunset(
unixtime, lat, lon, delta_t, numthreads)
# arrays are in seconds since epoch format, need to conver to timestamps
transit = pd.to_datetime(transit*1e9, unit='ns', utc=True).tz_convert(
time.tz).tolist()
sunrise = pd.to_datetime(sunrise*1e9, unit='ns', utc=True).tz_convert(
time.tz).tolist()
sunset = pd.to_datetime(sunset*1e9, unit='ns', utc=True).tz_convert(
time.tz).tolist()
result = pd.DataFrame({'transit': transit,
'sunrise': sunrise,
'sunset': sunset}, 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
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
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 : 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
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 sun_rise_set_transit_spa(times, latitude, longitude, how='numpy',
delta_t=67.0, numthreads=4):
"""
Calculate the sunrise, sunset, and sun transit times using 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
----------
times : pandas.DatetimeIndex
Must be localized to the timezone for ``latitude`` and ``longitude``.
latitude : float
Latitude in degrees, positive north of equator, negative to south
longitude : float
Longitude in degrees, positive east of prime meridian, negative to west
delta_t : float, optional
If delta_t is None, uses spa.calculate_deltat
using times.year and times.month from pandas.DatetimeIndex.
For most simulations specifing delta_t is sufficient.
Difference between terrestrial time and UT1.
delta_t = None will break code using nrel_numba,
this will be fixed in a future version.
By default, use USNO historical data and predictions
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
-------
pandas.DataFrame
index is the same as input `times` argument
columns are 'sunrise', 'sunset', and 'transit'
References
----------
[1] Reda, I., Andreas, A., 2003. Solar position algorithm for solar
radiation applications. Technical report: NREL/TP-560- 34302. Golden,
USA, http://www.nrel.gov.
"""
# Added by Tony Lorenzo (@alorenzo175), University of Arizona, 2015
lat = latitude
lon = longitude
# times must be localized
if times.tz:
tzinfo = times.tz
else:
raise ValueError('times must be localized')
# must convert to midnight UTC on day of interest
utcday = pd.DatetimeIndex(times.date).tz_localize('UTC')
unixtime = np.array(utcday.astype(np.int64)/10**9)
spa = _spa_python_import(how)
delta_t = delta_t or spa.calculate_deltat(times.year, times.month)
transit, sunrise, sunset = spa.transit_sunrise_sunset(
unixtime, lat, lon, delta_t, numthreads)
# arrays are in seconds since epoch format, need to conver to timestamps
transit = pd.to_datetime(transit*1e9, unit='ns', utc=True).tz_convert(
tzinfo).tolist()
sunrise = pd.to_datetime(sunrise*1e9, unit='ns', utc=True).tz_convert(
tzinfo).tolist()
sunset = pd.to_datetime(sunset*1e9, unit='ns', utc=True).tz_convert(
tzinfo).tolist()
return pd.DataFrame(index=times, data={'sunrise': sunrise,
'sunset': sunset,
'transit': transit})