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refactor ineichen function (#199)
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* refactor ineichen. good riddance

* fix nans, fix tests

* more cleanup from ineichen refactor

* reorder kwargs. pep8

* update docs
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@wholmgren
wholmgren committed Jul 7, 2016
1 parent 62cf0ae commit 675454f
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8 changes: 5 additions & 3 deletions docs/sphinx/source/package_overview.rst
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Expand Up @@ -56,7 +56,7 @@ configuration at a handful of sites listed below.
# get the module and inverter specifications from SAM
sandia_modules = pvlib.pvsystem.retrieve_sam('SandiaMod')
sapm_inverters = pvlib.pvsystem.retrieve_sam('sandiainverter')
sapm_inverters = pvlib.pvsystem.retrieve_sam('cecinverter')
module = sandia_modules['Canadian_Solar_CS5P_220M___2009_']
inverter = sapm_inverters['ABB__MICRO_0_25_I_OUTD_US_208_208V__CEC_2014_']
Expand All @@ -80,17 +80,19 @@ to accomplish our system modeling goal:
'surface_azimuth': 180}
energies = {}
# localize datetime indices (pvlib>=0.3.0)
for latitude, longitude, name, altitude, timezone in coordinates:
times = naive_times.tz_localize(timezone)
system['surface_tilt'] = latitude
cs = pvlib.clearsky.ineichen(times, latitude, longitude, altitude=altitude)
solpos = pvlib.solarposition.get_solarposition(times, latitude, longitude)
dni_extra = pvlib.irradiance.extraradiation(times)
dni_extra = pd.Series(dni_extra, index=times)
airmass = pvlib.atmosphere.relativeairmass(solpos['apparent_zenith'])
pressure = pvlib.atmosphere.alt2pres(altitude)
am_abs = pvlib.atmosphere.absoluteairmass(airmass, pressure)
tl = pvlib.clearsky.lookup_linke_turbidity(times, latitude, longitude)
cs = pvlib.clearsky.ineichen(solpos['apparent_zenith', am_abs, tl,
dni_extra=dni_extra, altitude=altitude)
aoi = pvlib.irradiance.aoi(system['surface_tilt'], system['surface_azimuth'],
solpos['apparent_zenith'], solpos['azimuth'])
total_irrad = pvlib.irradiance.total_irrad(system['surface_tilt'],
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2 changes: 1 addition & 1 deletion docs/sphinx/source/whatsnew/v0.4.0.txt
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Expand Up @@ -13,7 +13,7 @@ API Changes

* Remove unneeded module argument from singlediode function. (:issue:`200`)
* In ``pvlib.irradiance.perez``, renamed argument ``modelt`` to ``model``.
(:issue:`196`)
(:issue:`196`)


Enhancements
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251 changes: 105 additions & 146 deletions pvlib/clearsky.py
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Expand Up @@ -5,84 +5,63 @@

from __future__ import division

import logging
logger = logging.getLogger('pvlib')

import os
from collections import OrderedDict

import numpy as np
import pandas as pd

from pvlib import tools
from pvlib import irradiance
from pvlib import atmosphere
from pvlib import solarposition


def ineichen(time, latitude, longitude, altitude=0, linke_turbidity=None,
solarposition_method='nrel_numpy', zenith_data=None,
airmass_model='young1994', airmass_data=None,
interp_turbidity=True):
def ineichen(apparent_zenith, airmass_absolute, linke_turbidity,
altitude=0, dni_extra=1364.):
'''
Determine clear sky GHI, DNI, and DHI from Ineichen/Perez model
Determine clear sky GHI, DNI, and DHI from Ineichen/Perez model.
Implements the Ineichen and Perez clear sky model for global horizontal
irradiance (GHI), direct normal irradiance (DNI), and calculates
the clear-sky diffuse horizontal (DHI) component as the difference
between GHI and DNI*cos(zenith) as presented in [1, 2]. A report on clear
sky models found the Ineichen/Perez model to have excellent performance
with a minimal input data set [3].
Implements the Ineichen and Perez clear sky model for global
horizontal irradiance (GHI), direct normal irradiance (DNI), and
calculates the clear-sky diffuse horizontal (DHI) component as the
difference between GHI and DNI*cos(zenith) as presented in [1, 2]. A
report on clear sky models found the Ineichen/Perez model to have
excellent performance with a minimal input data set [3].
Default values for montly Linke turbidity provided by SoDa [4, 5].
Default values for monthly Linke turbidity provided by SoDa [4, 5].

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@adriesse

adriesse Jan 15, 2017

Member

There doesn't appear to be a default anymore...
Parameters
-----------
time : pandas.DatetimeIndex
latitude : float
longitude : float
altitude : float
linke_turbidity : None or float
If None, uses ``LinkeTurbidities.mat`` lookup table.
apparent_zenith: numeric
Refraction corrected solar zenith angle in degrees.
solarposition_method : string
Sets the solar position algorithm.
See solarposition.get_solarposition()
airmass_absolute: numeric
Pressure corrected airmass.
zenith_data : None or Series
If None, ephemeris data will be calculated using ``solarposition_method``.
linke_turbidity: numeric
Linke Turbidity.
airmass_model : string
See pvlib.airmass.relativeairmass().
altitude: numeric
Altitude above sea level in meters.
airmass_data : None or Series
If None, absolute air mass data will be calculated using
``airmass_model`` and location.alitude.
interp_turbidity : bool
If ``True``, interpolates the monthly Linke turbidity values
found in ``LinkeTurbidities.mat`` to daily values.
dni_extra: numeric
Extraterrestrial irradiance. The units of ``dni_extra``
determine the units of the output.
Returns
--------
DataFrame with the following columns: ``ghi, dni, dhi``.
-------
clearsky : DataFrame (if Series input) or OrderedDict of arrays
DataFrame/OrderedDict contains the columns/keys
``'dhi', 'dni', 'ghi'``.
Notes
-----
If you are using this function
in a loop, it may be faster to load LinkeTurbidities.mat outside of
the loop and feed it in as a keyword argument, rather than
having the function open and process the file each time it is called.
See also
--------
lookup_linke_turbidity
pvlib.location.Location.get_clearsky
References
----------
[1] P. Ineichen and R. Perez, "A New airmass independent formulation for
the Linke turbidity coefficient", Solar Energy, vol 73, pp. 151-157, 2002.
the Linke turbidity coefficient", Solar Energy, vol 73, pp. 151-157,
2002.
[2] R. Perez et. al., "A New Operational Model for Satellite-Derived
Irradiances: Description and Validation", Solar Energy, vol 73, pp.
Expand All @@ -98,97 +77,76 @@ def ineichen(time, latitude, longitude, altitude=0, linke_turbidity=None,
[5] J. Remund, et. al., "Worldwide Linke Turbidity Information", Proc.
ISES Solar World Congress, June 2003. Goteborg, Sweden.
'''
# Initial implementation of this algorithm by Matthew Reno.
# Ported to python by Rob Andrews
# Added functionality by Will Holmgren (@wholmgren)

I0 = irradiance.extraradiation(time.dayofyear)

if zenith_data is None:
ephem_data = solarposition.get_solarposition(time,
latitude=latitude,
longitude=longitude,
altitude=altitude,
method=solarposition_method)
time = ephem_data.index # fixes issue with time possibly not being tz-aware
try:
ApparentZenith = ephem_data['apparent_zenith']
except KeyError:
ApparentZenith = ephem_data['zenith']
logger.warning('could not find apparent_zenith. using zenith')
else:
ApparentZenith = zenith_data
#ApparentZenith[ApparentZenith >= 90] = 90 # can cause problems in edge cases


if linke_turbidity is None:
TL = lookup_linke_turbidity(time, latitude, longitude,
interp_turbidity=interp_turbidity)
else:
TL = linke_turbidity

# Get the absolute airmass assuming standard local pressure (per
# alt2pres) using Kasten and Young's 1989 formula for airmass.

if airmass_data is None:
AMabsolute = atmosphere.absoluteairmass(airmass_relative=atmosphere.relativeairmass(ApparentZenith, airmass_model),
pressure=atmosphere.alt2pres(altitude))
else:
AMabsolute = airmass_data
# Dan's note on the TL correction: By my reading of the publication
# on pages 151-157, Ineichen and Perez introduce (among other
# things) three things. 1) Beam model in eqn. 8, 2) new turbidity
# factor in eqn 9 and appendix A, and 3) Global horizontal model in
# eqn. 11. They do NOT appear to use the new turbidity factor (item
# 2 above) in either the beam or GHI models. The phrasing of
# appendix A seems as if there are two separate corrections, the
# first correction is used to correct the beam/GHI models, and the
# second correction is used to correct the revised turibidity
# factor. In my estimation, there is no need to correct the
# turbidity factor used in the beam/GHI models.

# Create the corrected TL for TL < 2
# TLcorr = TL;
# TLcorr(TL < 2) = TLcorr(TL < 2) - 0.25 .* (2-TLcorr(TL < 2)) .^ (0.5);

# This equation is found in Solar Energy 73, pg 311. Full ref: Perez
# et. al., Vol. 73, pp. 307-317 (2002). It is slightly different
# than the equation given in Solar Energy 73, pg 156. We used the
# equation from pg 311 because of the existence of known typos in
# the pg 156 publication (notably the fh2-(TL-1) should be fh2 *
# (TL-1)).

# The NaN handling is a little subtle. The AM input is likely to
# have NaNs that we'll want to map to 0s in the output. However, we
# want NaNs in other inputs to propagate through to the output. This
# is accomplished by judicious use and placement of np.maximum,
# np.minimum, and np.fmax

# use max so that nighttime values will result in 0s instead of
# negatives. propagates nans.
cos_zenith = np.maximum(tools.cosd(apparent_zenith), 0)

tl = linke_turbidity

fh1 = np.exp(-altitude/8000.)
fh2 = np.exp(-altitude/1250.)
cg1 = 5.09e-05 * altitude + 0.868
cg2 = 3.92e-05 * altitude + 0.0387
logger.debug('fh1=%s, fh2=%s, cg1=%s, cg2=%s', fh1, fh2, cg1, cg2)

# Dan's note on the TL correction: By my reading of the publication on
# pages 151-157, Ineichen and Perez introduce (among other things) three
# things. 1) Beam model in eqn. 8, 2) new turbidity factor in eqn 9 and
# appendix A, and 3) Global horizontal model in eqn. 11. They do NOT appear
# to use the new turbidity factor (item 2 above) in either the beam or GHI
# models. The phrasing of appendix A seems as if there are two separate
# corrections, the first correction is used to correct the beam/GHI models,
# and the second correction is used to correct the revised turibidity
# factor. In my estimation, there is no need to correct the turbidity
# factor used in the beam/GHI models.

# Create the corrected TL for TL < 2
# TLcorr = TL;
# TLcorr(TL < 2) = TLcorr(TL < 2) - 0.25 .* (2-TLcorr(TL < 2)) .^ (0.5);

# This equation is found in Solar Energy 73, pg 311.
# Full ref: Perez et. al., Vol. 73, pp. 307-317 (2002).
# It is slightly different than the equation given in Solar Energy 73, pg 156.
# We used the equation from pg 311 because of the existence of known typos
# in the pg 156 publication (notably the fh2-(TL-1) should be fh2 * (TL-1)).

cos_zenith = tools.cosd(ApparentZenith)

clearsky_GHI = ( cg1 * I0 * cos_zenith *
np.exp(-cg2*AMabsolute*(fh1 + fh2*(TL - 1))) *
np.exp(0.01*AMabsolute**1.8) )
clearsky_GHI[clearsky_GHI < 0] = 0

# BncI == "normal beam clear sky radiation"

ghi = (np.exp(-cg2*airmass_absolute*(fh1 + fh2*(tl - 1))) *
np.exp(0.01*airmass_absolute**1.8))
# use fmax to map airmass nans to 0s. multiply and divide by tl to
# reinsert tl nans
ghi = cg1 * dni_extra * cos_zenith * tl / tl * np.fmax(ghi, 0)

# BncI = "normal beam clear sky radiation"
b = 0.664 + 0.163/fh1
BncI = b * I0 * np.exp( -0.09 * AMabsolute * (TL - 1) )
logger.debug('b=%s', b)
bnci = b * np.exp(-0.09 * airmass_absolute * (tl - 1))
bnci = dni_extra * np.fmax(bnci, 0)

# "empirical correction" SE 73, 157 & SE 73, 312.
BncI_2 = ( clearsky_GHI *
( 1 - (0.1 - 0.2*np.exp(-TL))/(0.1 + 0.882/fh1) ) /
cos_zenith )
bnci_2 = ((1 - (0.1 - 0.2*np.exp(-tl))/(0.1 + 0.882/fh1)) /
cos_zenith)
bnci_2 = ghi * np.fmin(np.fmax(bnci_2, 0), 1e20)

clearsky_DNI = np.minimum(BncI, BncI_2)
dni = np.minimum(bnci, bnci_2)

clearsky_DHI = clearsky_GHI - clearsky_DNI*cos_zenith
dhi = ghi - dni*cos_zenith

irrads = OrderedDict()
irrads['ghi'] = ghi
irrads['dni'] = dni
irrads['dhi'] = dhi

df_out = pd.DataFrame({'ghi':clearsky_GHI, 'dni':clearsky_DNI,
'dhi':clearsky_DHI})
df_out.fillna(0, inplace=True)
if isinstance(dni, pd.Series):
irrads = pd.DataFrame.from_dict(irrads)

return df_out
return irrads


def lookup_linke_turbidity(time, latitude, longitude, filepath=None,
Expand Down Expand Up @@ -242,14 +200,17 @@ def lookup_linke_turbidity(time, latitude, longitude, filepath=None,
mat = scipy.io.loadmat(filepath)
linke_turbidity_table = mat['LinkeTurbidity']

latitude_index = np.around(_linearly_scale(latitude, 90, -90, 1, 2160)).astype(np.int64)
longitude_index = np.around(_linearly_scale(longitude, -180, 180, 1, 4320)).astype(np.int64)
latitude_index = (
np.around(_linearly_scale(latitude, 90, -90, 1, 2160))
.astype(np.int64))
longitude_index = (
np.around(_linearly_scale(longitude, -180, 180, 1, 4320))
.astype(np.int64))

g = linke_turbidity_table[latitude_index][longitude_index]

if interp_turbidity:
logger.info('interpolating turbidity to the day')
# Cata covers 1 year.
# Data covers 1 year.
# Assume that data corresponds to the value at
# the middle of each month.
# This means that we need to add previous Dec and next Jan
Expand All @@ -262,10 +223,9 @@ def lookup_linke_turbidity(time, latitude, longitude, filepath=None,
linke_turbidity = pd.Series(np.interp(time.dayofyear, days, g2),
index=time)
else:
logger.info('using monthly turbidity')
apply_month = lambda x: g[x[0]-1]
linke_turbidity = pd.DataFrame(time.month, index=time)
linke_turbidity = linke_turbidity.apply(apply_month, axis=1)
# apply monthly data
linke_turbidity = linke_turbidity.apply(lambda x: g[x[0]-1], axis=1)

linke_turbidity /= 20.

Expand Down Expand Up @@ -312,11 +272,11 @@ def haurwitz(apparent_zenith):

cos_zenith = tools.cosd(apparent_zenith)

clearsky_GHI = 1098.0 * cos_zenith * np.exp(-0.059/cos_zenith)
clearsky_ghi = 1098.0 * cos_zenith * np.exp(-0.059/cos_zenith)

clearsky_GHI[clearsky_GHI < 0] = 0
clearsky_ghi[clearsky_ghi < 0] = 0

df_out = pd.DataFrame({'ghi':clearsky_GHI})
df_out = pd.DataFrame({'ghi': clearsky_ghi})

return df_out

Expand All @@ -326,8 +286,8 @@ def _linearly_scale(inputmatrix, inputmin, inputmax, outputmin, outputmax):

inputrange = inputmax - inputmin
outputrange = outputmax - outputmin
OutputMatrix = (inputmatrix-inputmin) * outputrange/inputrange + outputmin
return OutputMatrix
outputmatrix = (inputmatrix-inputmin) * outputrange/inputrange + outputmin
return outputmatrix


def simplified_solis(apparent_elevation, aod700=0.1, precipitable_water=1.,
Expand Down Expand Up @@ -360,16 +320,15 @@ def simplified_solis(apparent_elevation, aod700=0.1, precipitable_water=1.,
or 101325 and 41000 Pascals.
dni_extra: numeric
Extraterrestrial irradiance.
Extraterrestrial irradiance. The units of ``dni_extra``
determine the units of the output.
Returns
--------
-------
clearsky : DataFrame (if Series input) or OrderedDict of arrays
DataFrame/OrderedDict contains the columns/keys
``'dhi', 'dni', 'ghi'``.
The units of ``dni_extra`` determine the units of the output.
References
----------
.. [1] P. Ineichen, "A broadband simplified version of the
Expand Down

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