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solarposition.py
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solarposition.py
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"""
Calculate the solar position using a variety of methods/packages.
"""
# Contributors:
# Rob Andrews (@Calama-Consulting), Calama Consulting, 2014
# Will Holmgren (@wholmgren), University of Arizona, 2014
# Tony Lorenzo (@alorenzo175), University of Arizona, 2015
# Cliff hansen (@cwhanse), Sandia National Laboratories, 2018
import os
import datetime as dt
try:
from importlib import reload
except ImportError:
try:
from imp import reload
except ImportError:
pass
import numpy as np
import pandas as pd
import warnings
from pvlib import atmosphere
from pvlib.tools import datetime_to_djd, djd_to_datetime
NS_PER_HR = 1.e9 * 3600. # nanoseconds per hour
def get_solarposition(time, latitude, longitude,
altitude=None, pressure=None,
method='nrel_numpy',
temperature=12, **kwargs):
"""
A convenience wrapper for the solar position calculators.
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 : None or float, default None
If None, computed from pressure. Assumed to be 0 m
if pressure is also None.
pressure : None or float, default None
If None, computed from altitude. Assumed to be 101325 Pa
if altitude is also None.
method : string, default 'nrel_numpy'
'nrel_numpy' uses an implementation of the NREL SPA algorithm
described in [1] (default, recommended): :py:func:`spa_python`
'nrel_numba' uses an implementation of the NREL SPA algorithm
described in [1], but also compiles the code first:
:py:func:`spa_python`
'pyephem' uses the PyEphem package: :py:func:`pyephem`
'ephemeris' uses the pvlib ephemeris code: :py:func:`ephemeris`
'nrel_c' uses the NREL SPA C code [3]: :py:func:`spa_c`
temperature : float, default 12
Degrees C.
kwargs
Other keywords are passed to the solar position function
specified by the ``method`` argument.
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] NREL SPA code: http://rredc.nrel.gov/solar/codesandalgorithms/spa/
"""
if altitude is None and pressure is None:
altitude = 0.
pressure = 101325.
elif altitude is None:
altitude = atmosphere.pres2alt(pressure)
elif pressure is None:
pressure = atmosphere.alt2pres(altitude)
method = method.lower()
if isinstance(time, dt.datetime):
time = pd.DatetimeIndex([time, ])
if method == 'nrel_c':
ephem_df = spa_c(time, latitude, longitude, pressure, temperature,
**kwargs)
elif method == 'nrel_numba':
ephem_df = spa_python(time, latitude, longitude, altitude,
pressure, temperature,
how='numba', **kwargs)
elif method == 'nrel_numpy':
ephem_df = spa_python(time, latitude, longitude, altitude,
pressure, temperature,
how='numpy', **kwargs)
elif method == 'pyephem':
ephem_df = pyephem(time, latitude, longitude,
altitude=altitude,
pressure=pressure,
temperature=temperature, **kwargs)
elif method == 'ephemeris':
ephem_df = ephemeris(time, latitude, longitude, pressure, temperature,
**kwargs)
else:
raise ValueError('Invalid solar position method')
return ephem_df
def spa_c(time, latitude, longitude, pressure=101325, altitude=0,
temperature=12, delta_t=67.0,
raw_spa_output=False):
"""
Calculate the solar position using the C implementation of the NREL
SPA code.
The source files for this code are located in './spa_c_files/', along with
a README file which describes how the C code is wrapped in Python.
Due to license restrictions, the C code must be downloaded seperately
and used in accordance with it's license.
This function is slower and no more accurate than :py:func:`spa_python`.
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.
pressure : float, default 101325
Pressure in Pascals
altitude : float, default 0
Height above sea level. [m]
temperature : float, default 12
Temperature in C
delta_t : float, default 67.0
Difference between terrestrial time and UT1.
USNO has previous values and predictions.
raw_spa_output : bool, default False
If true, returns the raw SPA output.
Returns
-------
DataFrame
The DataFrame will have the following columns:
elevation,
azimuth,
zenith,
apparent_elevation,
apparent_zenith.
References
----------
.. [1] NREL SPA reference:
http://rredc.nrel.gov/solar/codesandalgorithms/spa/
NREL SPA C files: https://midcdmz.nrel.gov/spa/
Note: The ``timezone`` field in the SPA C files is replaced with
``time_zone`` to avoid a nameclash with the function ``__timezone`` that is
redefined by Python>=3.5. This issue is
`Python bug 24643 <https://bugs.python.org/issue24643>`_.
.. [2] USNO delta T:
http://www.usno.navy.mil/USNO/earth-orientation/eo-products/long-term
See also
--------
pyephem, spa_python, ephemeris
"""
# Added by Rob Andrews (@Calama-Consulting), Calama Consulting, 2014
# Edited by Will Holmgren (@wholmgren), University of Arizona, 2014
# Edited by Tony Lorenzo (@alorenzo175), University of Arizona, 2015
try:
from pvlib.spa_c_files.spa_py import spa_calc
except ImportError:
raise ImportError('Could not import built-in SPA calculator. ' +
'You may need to recompile the SPA code.')
# if localized, convert to UTC. otherwise, assume UTC.
try:
time_utc = time.tz_convert('UTC')
except TypeError:
time_utc = time
spa_out = []
for date in time_utc:
spa_out.append(spa_calc(year=date.year,
month=date.month,
day=date.day,
hour=date.hour,
minute=date.minute,
second=date.second,
time_zone=0, # date uses utc time
latitude=latitude,
longitude=longitude,
elevation=altitude,
pressure=pressure / 100,
temperature=temperature,
delta_t=delta_t
))
spa_df = pd.DataFrame(spa_out, index=time)
if raw_spa_output:
# rename "time_zone" from raw output from spa_c_files.spa_py.spa_calc()
# to "timezone" to match the API of pvlib.solarposition.spa_c()
return spa_df.rename(columns={'time_zone': 'timezone'})
else:
dfout = pd.DataFrame({'azimuth': spa_df['azimuth'],
'apparent_zenith': spa_df['zenith'],
'apparent_elevation': spa_df['e'],
'elevation': spa_df['e0'],
'zenith': 90 - spa_df['e0']})
return dfout
def _spa_python_import(how):
"""Compile spa.py appropriately"""
from pvlib import spa
# check to see if the spa module was compiled with numba
using_numba = spa.USE_NUMBA
if how == 'numpy' and using_numba:
# the spa module was compiled to numba code, so we need to
# reload the module without compiling
# the PVLIB_USE_NUMBA env variable is used to tell the module
# to not compile with numba
warnings.warn('Reloading spa to use numpy')
os.environ['PVLIB_USE_NUMBA'] = '0'
spa = reload(spa)
del os.environ['PVLIB_USE_NUMBA']
elif how == 'numba' and not using_numba:
# The spa module was not compiled to numba code, so set
# PVLIB_USE_NUMBA so it does compile to numba on reload.
warnings.warn('Reloading spa to use numba')
os.environ['PVLIB_USE_NUMBA'] = '1'
spa = reload(spa)
del os.environ['PVLIB_USE_NUMBA']
elif how != 'numba' and how != 'numpy':
raise ValueError("how must be either 'numba' or 'numpy'")
return spa
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
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
Distance above sea level.
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 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 _ephem_convert_to_seconds_and_microseconds(date):
# utility from unreleased PyEphem 3.6.7.1
"""Converts a PyEphem date into seconds"""
microseconds = int(round(24 * 60 * 60 * 1000000 * date))
seconds, microseconds = divmod(microseconds, 1000000)
seconds -= 2209032000 # difference between epoch 1900 and epoch 1970
return seconds, microseconds
def _ephem_to_timezone(date, tzinfo):
# utility from unreleased PyEphem 3.6.7.1
""""Convert a PyEphem Date into a timezone aware python datetime"""
seconds, microseconds = _ephem_convert_to_seconds_and_microseconds(date)
date = dt.datetime.fromtimestamp(seconds, tzinfo)
date = date.replace(microsecond=microseconds)
return date
def _ephem_setup(latitude, longitude, altitude, pressure, temperature,
horizon):
import ephem
# initialize a PyEphem observer
obs = ephem.Observer()
obs.lat = str(latitude)
obs.lon = str(longitude)
obs.elevation = altitude
obs.pressure = pressure / 100. # convert to mBar
obs.temp = temperature
obs.horizon = horizon
# the PyEphem sun
sun = ephem.Sun()
return obs, sun
def sun_rise_set_transit_ephem(times, latitude, longitude,
next_or_previous='next',
altitude=0,
pressure=101325,
temperature=12, horizon='0:00'):
"""
Calculate the next sunrise and sunset times using the PyEphem package.
Parameters
----------
time : pandas.DatetimeIndex
Must be localized
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
next_or_previous : str
'next' or 'previous' sunrise and sunset relative to time
altitude : float, default 0
distance above sea level in meters.
pressure : int or float, optional, default 101325
air pressure in Pascals.
temperature : int or float, optional, default 12
air temperature in degrees C.
horizon : string, format +/-X:YY
arc degrees:arc minutes from geometrical horizon for sunrise and
sunset, e.g., horizon='+0:00' to use sun center crossing the
geometrical horizon to define sunrise and sunset,
horizon='-0:34' for when the sun's upper edge crosses the
geometrical horizon
Returns
-------
pandas.DataFrame
index is the same as input `time` argument
columns are 'sunrise', 'sunset', and 'transit'
See also
--------
pyephem
"""
try:
import ephem
except ImportError:
raise ImportError('PyEphem must be installed')
# times must be localized
if times.tz:
tzinfo = times.tz
else:
raise ValueError('times must be localized')
obs, sun = _ephem_setup(latitude, longitude, altitude,
pressure, temperature, horizon)
# create lists of sunrise and sunset time localized to time.tz
if next_or_previous.lower() == 'next':
rising = obs.next_rising
setting = obs.next_setting
transit = obs.next_transit
elif next_or_previous.lower() == 'previous':
rising = obs.previous_rising
setting = obs.previous_setting
transit = obs.previous_transit
else:
raise ValueError("next_or_previous must be either 'next' or" +
" 'previous'")
sunrise = []
sunset = []
trans = []
for thetime in times:
thetime = thetime.to_pydatetime()
# pyephem drops timezone when converting to its internal datetime
# format, so handle timezone explicitly here
obs.date = ephem.Date(thetime - thetime.utcoffset())
sunrise.append(_ephem_to_timezone(rising(sun), tzinfo))
sunset.append(_ephem_to_timezone(setting(sun), tzinfo))
trans.append(_ephem_to_timezone(transit(sun), tzinfo))
return pd.DataFrame(index=times, data={'sunrise': sunrise,
'sunset': sunset,
'transit': trans})
def pyephem(time, latitude, longitude, altitude=0, pressure=101325,
temperature=12, horizon='+0:00'):
"""
Calculate the solar position using the PyEphem package.
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
Height above sea level in meters. [m]
pressure : int or float, optional, default 101325
air pressure in Pascals.
temperature : int or float, optional, default 12
air temperature in degrees C.
horizon : string, optional, default '+0:00'
arc degrees:arc minutes from geometrical horizon for sunrise and
sunset, e.g., horizon='+0:00' to use sun center crossing the
geometrical horizon to define sunrise and sunset,
horizon='-0:34' for when the sun's upper edge crosses the
geometrical horizon
Returns
-------
pandas.DataFrame
index is the same as input `time` argument
The DataFrame will have the following columns:
apparent_elevation, elevation,
apparent_azimuth, azimuth,
apparent_zenith, zenith.
See also
--------
spa_python, spa_c, ephemeris
"""
# Written by Will Holmgren (@wholmgren), University of Arizona, 2014
try:
import ephem
except ImportError:
raise ImportError('PyEphem must be installed')
# if localized, convert to UTC. otherwise, assume UTC.
try:
time_utc = time.tz_convert('UTC')
except TypeError:
time_utc = time
sun_coords = pd.DataFrame(index=time)
obs, sun = _ephem_setup(latitude, longitude, altitude,
pressure, temperature, horizon)
# make and fill lists of the sun's altitude and azimuth
# this is the pressure and temperature corrected apparent alt/az.
alts = []
azis = []
for thetime in time_utc:
obs.date = ephem.Date(thetime)
sun.compute(obs)
alts.append(sun.alt)
azis.append(sun.az)
sun_coords['apparent_elevation'] = alts
sun_coords['apparent_azimuth'] = azis
# redo it for p=0 to get no atmosphere alt/az
obs.pressure = 0
alts = []
azis = []
for thetime in time_utc:
obs.date = ephem.Date(thetime)
sun.compute(obs)
alts.append(sun.alt)
azis.append(sun.az)
sun_coords['elevation'] = alts
sun_coords['azimuth'] = azis
# convert to degrees. add zenith
sun_coords = np.rad2deg(sun_coords)
sun_coords['apparent_zenith'] = 90 - sun_coords['apparent_elevation']
sun_coords['zenith'] = 90 - sun_coords['elevation']
return sun_coords
def ephemeris(time, latitude, longitude, pressure=101325, temperature=12):
"""
Python-native solar position calculator.
The accuracy of this code is not guaranteed.
Consider using the built-in spa_c code or the PyEphem library.
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.
pressure : float or Series, default 101325
Ambient pressure (Pascals)
temperature : float or Series, default 12
Ambient temperature (C)
Returns
-------
DataFrame with the following columns:
* apparent_elevation : apparent sun elevation accounting for
atmospheric refraction.
* elevation : actual elevation (not accounting for refraction)
of the sun in decimal degrees, 0 = on horizon.
The complement of the zenith angle.
* azimuth : Azimuth of the sun in decimal degrees East of North.
This is the complement of the apparent zenith angle.
* apparent_zenith : apparent sun zenith accounting for atmospheric
refraction.
* zenith : Solar zenith angle
* solar_time : Solar time in decimal hours (solar noon is 12.00).
References
-----------
.. [1] Grover Hughes' class and related class materials on Engineering
Astronomy at Sandia National Laboratories, 1985.
See also
--------
pyephem, spa_c, spa_python
"""
# Added by Rob Andrews (@Calama-Consulting), Calama Consulting, 2014
# Edited by Will Holmgren (@wholmgren), University of Arizona, 2014
# Most comments in this function are from PVLIB_MATLAB or from
# pvlib-python's attempt to understand and fix problems with the
# algorithm. The comments are *not* based on the reference material.
# This helps a little bit:
# http://www.cv.nrao.edu/~rfisher/Ephemerides/times.html
# the inversion of longitude is due to the fact that this code was
# originally written for the convention that positive longitude were for
# locations west of the prime meridian. However, the correct convention (as
# of 2009) is to use negative longitudes for locations west of the prime
# meridian. Therefore, the user should input longitude values under the
# correct convention (e.g. Albuquerque is at -106 longitude), but it needs
# to be inverted for use in the code.
Latitude = latitude
Longitude = -1 * longitude
Abber = 20 / 3600.
LatR = np.radians(Latitude)
# the SPA algorithm needs time to be expressed in terms of
# decimal UTC hours of the day of the year.
# if localized, convert to UTC. otherwise, assume UTC.
try:
time_utc = time.tz_convert('UTC')
except TypeError:
time_utc = time
# strip out the day of the year and calculate the decimal hour
DayOfYear = time_utc.dayofyear
DecHours = (time_utc.hour + time_utc.minute/60. + time_utc.second/3600. +
time_utc.microsecond/3600.e6)
# np.array needed for pandas > 0.20
UnivDate = np.array(DayOfYear)
UnivHr = np.array(DecHours)
Yr = np.array(time_utc.year) - 1900
YrBegin = 365 * Yr + np.floor((Yr - 1) / 4.) - 0.5
Ezero = YrBegin + UnivDate
T = Ezero / 36525.
# Calculate Greenwich Mean Sidereal Time (GMST)
GMST0 = 6 / 24. + 38 / 1440. + (
45.836 + 8640184.542 * T + 0.0929 * T ** 2) / 86400.
GMST0 = 360 * (GMST0 - np.floor(GMST0))
GMSTi = np.mod(GMST0 + 360 * (1.0027379093 * UnivHr / 24.), 360)
# Local apparent sidereal time
LocAST = np.mod((360 + GMSTi - Longitude), 360)
EpochDate = Ezero + UnivHr / 24.
T1 = EpochDate / 36525.
ObliquityR = np.radians(
23.452294 - 0.0130125 * T1 - 1.64e-06 * T1 ** 2 + 5.03e-07 * T1 ** 3)
MlPerigee = 281.22083 + 4.70684e-05 * EpochDate + 0.000453 * T1 ** 2 + (
3e-06 * T1 ** 3)
MeanAnom = np.mod((358.47583 + 0.985600267 * EpochDate - 0.00015 *
T1 ** 2 - 3e-06 * T1 ** 3), 360)
Eccen = 0.01675104 - 4.18e-05 * T1 - 1.26e-07 * T1 ** 2
EccenAnom = MeanAnom
E = 0
while np.max(abs(EccenAnom - E)) > 0.0001:
E = EccenAnom
EccenAnom = MeanAnom + np.degrees(Eccen)*np.sin(np.radians(E))
TrueAnom = (
2 * np.mod(np.degrees(np.arctan2(((1 + Eccen) / (1 - Eccen)) ** 0.5 *
np.tan(np.radians(EccenAnom) / 2.), 1)), 360))
EcLon = np.mod(MlPerigee + TrueAnom, 360) - Abber
EcLonR = np.radians(EcLon)
DecR = np.arcsin(np.sin(ObliquityR)*np.sin(EcLonR))
RtAscen = np.degrees(np.arctan2(np.cos(ObliquityR)*np.sin(EcLonR),
np.cos(EcLonR)))
HrAngle = LocAST - RtAscen
HrAngleR = np.radians(HrAngle)
HrAngle = HrAngle - (360 * ((abs(HrAngle) > 180)))
SunAz = np.degrees(np.arctan2(-np.sin(HrAngleR),
np.cos(LatR)*np.tan(DecR) -
np.sin(LatR)*np.cos(HrAngleR)))
SunAz[SunAz < 0] += 360
SunEl = np.degrees(np.arcsin(
np.cos(LatR) * np.cos(DecR) * np.cos(HrAngleR) +
np.sin(LatR) * np.sin(DecR)))
SolarTime = (180 + HrAngle) / 15.
# Calculate refraction correction
Elevation = SunEl
TanEl = pd.Series(np.tan(np.radians(Elevation)), index=time_utc)
Refract = pd.Series(0, index=time_utc)
Refract[(Elevation > 5) & (Elevation <= 85)] = (
58.1/TanEl - 0.07/(TanEl**3) + 8.6e-05/(TanEl**5))
Refract[(Elevation > -0.575) & (Elevation <= 5)] = (
Elevation *
(-518.2 + Elevation*(103.4 + Elevation*(-12.79 + Elevation*0.711))) +
1735)
Refract[(Elevation > -1) & (Elevation <= -0.575)] = -20.774 / TanEl
Refract *= (283/(273. + temperature)) * (pressure/101325.) / 3600.
ApparentSunEl = SunEl + Refract
# make output DataFrame
DFOut = pd.DataFrame(index=time_utc)
DFOut['apparent_elevation'] = ApparentSunEl
DFOut['elevation'] = SunEl
DFOut['azimuth'] = SunAz
DFOut['apparent_zenith'] = 90 - ApparentSunEl
DFOut['zenith'] = 90 - SunEl
DFOut['solar_time'] = SolarTime
DFOut.index = time
return DFOut
def calc_time(lower_bound, upper_bound, latitude, longitude, attribute, value,
altitude=0, pressure=101325, temperature=12, horizon='+0:00',
xtol=1.0e-12):
"""
Calculate the time between lower_bound and upper_bound
where the attribute is equal to value. Uses PyEphem for
solar position calculations.
Parameters
----------
lower_bound : datetime.datetime
upper_bound : datetime.datetime
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.
attribute : str
The attribute of a pyephem.Sun object that
you want to solve for. Likely options are 'alt'
and 'az' (which must be given in radians).
value : int or float
The value of the attribute to solve for
altitude : float, default 0
Distance above sea level.
pressure : int or float, optional, default 101325
Air pressure in Pascals. Set to 0 for no
atmospheric correction.
temperature : int or float, optional, default 12
Air temperature in degrees C.
horizon : string, optional, default '+0:00'
arc degrees:arc minutes from geometrical horizon for sunrise and
sunset, e.g., horizon='+0:00' to use sun center crossing the
geometrical horizon to define sunrise and sunset,
horizon='-0:34' for when the sun's upper edge crosses the
geometrical horizon
xtol : float, optional, default 1.0e-12
The allowed error in the result from value
Returns
-------
datetime.datetime
Raises
------
ValueError
If the value is not contained between the bounds.
AttributeError
If the given attribute is not an attribute of a
PyEphem.Sun object.
"""
try:
import scipy.optimize as so
except ImportError:
raise ImportError('The calc_time function requires scipy')
obs, sun = _ephem_setup(latitude, longitude, altitude,
pressure, temperature, horizon)
def compute_attr(thetime, target, attr):
obs.date = thetime
sun.compute(obs)
return getattr(sun, attr) - target
lb = datetime_to_djd(lower_bound)
ub = datetime_to_djd(upper_bound)
djd_root = so.brentq(compute_attr, lb, ub,
(value, attribute), xtol=xtol)
return djd_to_datetime(djd_root)
def pyephem_earthsun_distance(time):
"""
Calculates the distance from the earth to the sun using pyephem.
Parameters
----------
time : pandas.DatetimeIndex
Must be localized or UTC will be assumed.
Returns
-------
pd.Series. Earth-sun distance in AU.
"""
import ephem
sun = ephem.Sun()
earthsun = []
for thetime in time:
sun.compute(ephem.Date(thetime))
earthsun.append(sun.earth_distance)
return pd.Series(earthsun, index=time)
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 : 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):