feat(skymodel): Added Perez Luminous Efficacy Model

* Deleted Unnecessary Code

* feat(skymodel): Added Perez Luminous Efficacy Model

This commit adds  the Perez model for luminaious efficacy of the sky, which seems to be the state-of-the-art model for deriving illuminance values from irradiance values.  More info on the model can be found in [this paper](http://www.cuepe.ch/html/biblio/pdf/perez-ineichen%201990%20-%20modelling%20daylight%20(se).pdf) and credit is due to @hansukyang for pointing me towards a Python implementation of this model.

With this commit, the Ladybug core now possesses enough climate models to make fully-simulate-able EPWs (for both daylight and energy) with only the following:
* dry bulb temperature
* dew point temperature
* atmospheric pressure
* sky cover
* wind speed
* wind direction

* Fixing docstring of luminous efficacy

* 0.3.0

Automatically generated by python-semantic-release

* fix(deploy): use javascript semantic-release package for deployment

Using the "pure" javascript implementation of semantic-release makes it a bit more flexible to set
up custom scripts to run at different stages of the semantic release process. The packages used as
well as scripts to run at different stages are specified in the .releaserc.json file. In this case
we use standard semantic-release and add a custom deploy script on top of the normal github release
setup. The custom deploy script is the new 'deploy.sh' file which takes the new version number as an
argument to deploy the package to PyPi and build the documentation.

#125

build(pytest): add pytest-cov deps to dev-requirements.txt

build(euclid): add euclid to dev-requirements.txt

* build(requirements): cleanup dev-requirements.txt and make python2 compatible

* ci(travis): use python image to deploy with semantic-release

Seems it's easy enough to install node on a python image but the node image's python distro doesn't
play well with https.

#125

* fix(deploy): fix travis file for docs deployment

#125

* Create .nojekyll

see here: https://github.blog/2009-12-29-bypassing-jekyll-on-github-pages/

* fix(init): fix isplus assignment

* Update skymodel.py

* fix(legendparam): fix import for Python 3

* fix(init): fix conflicts

not really! GitHub being crazy. there is no conflicts.

* Clean git history (#136)

* feat(skymodel): Added Perez Luminous Efficacy Model

See  5e0a32c

* fix(travis): fix travis file for docs deployment and build

docs/conf.py

@@ -23,7 +23,7 @@
 author = 'Ladybug Tools'
 
 # The full version, including alpha/beta/rc tags
-release = ''
+release = version
 
 # for example take major/minor
 version = ''

ladybug/__init__.py

@@ -7,6 +7,8 @@
 import importlib
 import pkgutil
 
+# This is a variable to check if the library is a [+] library.
+setattr(sys.modules[__name__], 'isplus', False)
 
 # find and import ladybug plugins
 # this is a critical step to add additional functionalities to ladybug core library.
@@ -20,5 +22,3 @@
 for key in lb_plugins:
  print('Successfully imported Ladybug plugin: {}'.format(key))
 
-# This is a variable to check if the library is a [+] library.
-setattr(sys.modules[__name__], 'isplus', False)

ladybug/_datacollectionbase.py

@@ -9,8 +9,6 @@
 from collections import Iterable
 from string import ascii_lowercase
 import math
-from os.path import dirname, basename, join
-from importlib import import_module
 
 import sys
 if (sys.version_info >= (3, 0)):

ladybug/legendparameters.py

@@ -1,8 +1,8 @@
 # coding=utf-8
 from __future__ import division
 
-from color import ColorRange
-from listoperations import flatten, unflatten
+from .color import ColorRange
+from .listoperations import flatten, unflatten
 
 
 class LegendParameters(object):

ladybug/skymodel.py

@@ -224,6 +224,128 @@ def zhang_huang_solar_split(altitudes, doys, cloud_cover, relative_humidity,
  return dir_norm_rad, dif_horiz_rad
 
 
+"""LUMINOUS EFFICACY OF THE SKY"""
+
+
+def estimate_illuminance_from_irradiance(
+ altitude, ghi, dni, dhi, dew_point, rel_airmass=None):
+ """Estimate sky illuminance components from irradiance components.
+
+ This function uses actual zenith rather than apparent zenith.
+
+ Note:
+ [1] Perez R. (1990). 'Modeling Daylight Availability and Irradiance
+ Components from Direct and Global Irradiance'. Solar Energy.
+ Vol. 44. No. 5, pp. 271-289. USA.
+
+ Args:
+ altitude: Solar altitude angle in degrees.
+ ghi: Number for Global Horizontal Irradiance in W/m2.
+ dni: Number for Direct Normal Irradiance in W/m2.
+ dhi: Number for Diffuse Horizontal Irradiance in W/m2.
+ dew_point: Surface dewpoint in degrees C.
+ rel_airmass: A number between 1 and ~38 representing the ratio of air mass
+ between the sun and the observer and the air mass that is directly above
+ the observer. Default is None, which will simply use the input solar
+ altitude and the get_relative_airmass function in this module with
+ the kastenyoung1989 model to compute this value.
+
+ Returns:
+ gh_ill: Value for Global Horizontal Illuminance in lux.
+ dn_ill: Value for Direct Normal Illuminance in lux.
+ dh_ill: Value for Diffuse Horizontal Illuminance in lux.
+ z_lum: Value for Zenith Luminance in lux.
+
+ """
+ if altitude <= 0: # sun is below the horizon, return 0 for all results
+ return 0, 0, 0, 0
+
+ if rel_airmass is None:
+ rel_airmass = get_relative_airmass(altitude)
+ zenith = math.radians(90 - altitude)
+ dhi = 0.1 if dhi == 0 else dhi
+ kai = 1.041
+ eps = ((dhi + dni) / dhi + kai * zenith ** 3) / (1 + kai * zenith ** 3)
+ delta = dhi * rel_airmass / 1360
+ w = math.exp(0.08 * dew_point - 0.075)
+
+ # Perez Table 1: Discrete Sky Clearness Categories
+ if eps >= 1 and eps < 1.065:
+ e_category = 0
+ elif eps >= 1.065 and eps < 1.23:
+ e_category = 1
+ elif eps >= 1.23 and eps < 1.5:
+ e_category = 2
+ elif eps >= 1.5 and eps < 1.95:
+ e_category = 3
+ elif eps >= 1.95 and eps < 2.8:
+ e_category = 4
+ elif eps >= 2.8 and eps < 4.5:
+ e_category = 5
+ elif eps >= 4.5 and eps < 6.2:
+ e_category = 6
+ elif eps >= 6.2:
+ e_category = 7
+ else:
+ raise ValueError('Error in sky luminous efficacy calculation\n'
+ 'eps: %f altitude: %f' % (eps, altitude))
+
+ # Perez Table 4: Luminous Efficacy
+ glob_lum_eff_coeff = ((96.63, -0.47, 11.50, -9.16),
+ (107.54, 0.79, 1.79, -1.19),
+ (98.73, 0.70, 4.40, -6.95),
+ (92.72, 0.56, 8.36, -8.31),
+ (86.73, 0.98, 7.10, -10.94),
+ (88.34, 1.39, 6.06, -7.60),
+ (78.63, 1.47, 4.93, -11.37),
+ (99.65, 1.86, -4.46, -3.15))
+
+ dir_lum_eff_coeff = ((57.20, -4.55, -2.98, 117.12),
+ (98.99, -3.46, -1.21, 12.38),
+ (109.83, -4.90, -1.71, -8.81),
+ (110.34, -5.84, -1.99, -4.56),
+ (106.36, -3.97, -1.75, -6.16),
+ (107.19, -1.25, -1.51, -26.73),
+ (105.75, 0.77, -1.26, -34.44),
+ (101.18, 1.58, -1.10, -8.29))
+
+ diff_lum_eff_coeff = ((97.24, -0.46, 12.00, -8.91),
+ (107.22, 1.15, 0.59, -3.95),
+ (104.97, 2.96, -5.52, -8.77),
+ (102.39, 5.59, -13.95, -13.90),
+ (100.71, 5.94, -22.75, -23.74),
+ (106.42, 3.83, -36.15, -28.83),
+ (141.88, 1.90, -53.24, -14.03),
+ (152.23, 0.35, -45.27, -7.98))
+
+ zen_lum_eff_coeff = ((40.86, 26.77, -29.59, -45.75),
+ (26.58, 14.73, 58.46, -21.25),
+ (19.34, 2.28, 100.00, 0.25),
+ (13.25, -1.39, 124.79, 15.66),
+ (14.47, -5.09, 160.09, 9.13),
+ (19.76, -3.88, 154.61, -19.21),
+ (28.39, -9.67, 151.58, -69.39),
+ (42.91, -19.62, 130.80, -164.08))
+
+ # Eq 6
+ a, b, c, d = glob_lum_eff_coeff[e_category]
+ gh_ill = ghi * (a + b * w + c * math.cos(zenith) + d * math.log(delta))
+
+ # Eq 8
+ a, b, c, d = dir_lum_eff_coeff[e_category]
+ dn_ill = max(0, dni * (a + b * w + c * math.exp(5.73 * zenith - 5) + d * delta))
+
+ # Eq 7
+ a, b, c, d = diff_lum_eff_coeff[e_category]
+ dh_ill = dhi * (a + b * w + c * math.cos(zenith) + d * math.log(delta))
+
+ # Eq 9
+ a, b, c, d = zen_lum_eff_coeff[e_category]
+ z_lum = dhi * (a + b * math.cos(zenith) + c * math.exp(-3 * zenith) + d * delta)
+
+ return gh_ill, dn_ill, dh_ill, z_lum
+
+
 """HORIZONTAL INFRARED INTENSITY + SKY TEMPERATURE MODELS"""
 
 
@@ -475,10 +597,10 @@ def disc(ghi, altitude, doy, pressure=101325,
  This implementation limits the clearness index to 1 by default.
 
  The original report describing the DISC model [1]_ uses the
- relative airmass rather than the absolute (pressure-corrected)
- airmass. However, the NREL implementation of the DISC model [2]_
- uses absolute airmass. PVLib Matlab also uses the absolute airmass.
- pvlib python defaults to absolute airmass, but the relative airmass
+ relative air mass rather than the absolute (pressure-corrected)
+ air mass. However, the NREL implementation of the DISC model [2]_
+ uses absolute air mass. PVLib Matlab also uses the absolute air mass.
+ pvlib python defaults to absolute air mass, but the relative airmass
  can be used by supplying `pressure=None`.
 
  Note:
@@ -498,18 +620,18 @@ def disc(ghi, altitude, doy, pressure=101325,
  degrees.
  doy : An integer representing the day of the year.
  pressure : None or numeric, default 101325
- Site pressure in Pascal. If None, relative airmass is used
- instead of absolute (pressure-corrected) airmass.
+ Site pressure in Pascal. If None, relative air mass is used
+ instead of absolute (pressure-corrected) air mass.
  min_sin_altitude : numeric, default 0.065
  Minimum value of sin(altitude) to allow when calculating global
  clearness index `kt`. Equivalent to altitude = 3.727 degrees.
  min_altitude : numeric, default 87
  Minimum value of altitude to allow in DNI calculation. DNI will be
  set to 0 for times with altitude values smaller than `min_altitude`.
  max_airmass : numeric, default 12
- Maximum value of the airmass to allow in Kn calculation.
+ Maximum value of the air mass to allow in Kn calculation.
  Default value (12) comes from range over which Kn was fit
- to airmass in the original paper.
+ to air mass in the original paper.
 
  Returns:
  dni: The modeled direct normal irradiance
@@ -698,15 +820,14 @@ def clearness_index_zenith_independent(clearness_index, airmass,
 def get_absolute_airmass(airmass_relative, pressure=101325.):
  """
  Determine absolute (pressure corrected) airmass from relative
- airmass and pressure.
+ airmass and atmospheirc pressure.
 
  Gives the airmass for locations not at sea-level (i.e. not at
  standard pressure). The input argument airmass_relative is the relative
- airmass. The input argument pressure is the pressure (in Pascals)
+ air mass. The input argument pressure is the pressure (in Pascals)
  at the location of interest and must be greater than 0. The
- calculation for absolute airmass is
- .. math::
- absolute airmass = (relative airmass)*pressure/101325
+ calculation for absolute air mass is
+ `absolute airmass = (relative airmass)*pressure/101325`
 
  Note:
  [1] C. Gueymard, "Critical analysis and performance assessment of
@@ -715,13 +836,13 @@ def get_absolute_airmass(airmass_relative, pressure=101325.):
 
  Args:
  airmass_relative: numeric
- The airmass at sea-level.
+ The air mass at sea-level.
  pressure: numeric, default 101325
  The site pressure in Pascal.
 
  Returns:
  airmass_absolute: numeric
- Absolute (pressure corrected) airmass
+ Absolute (pressure corrected) air mass
  """
  if airmass_relative is not None:
  return airmass_relative * pressure / 101325.
@@ -734,8 +855,8 @@ def get_relative_airmass(altitude, model='kastenyoung1989'):
  Gives the relative (not pressure-corrected) airmass.
 
  Gives the airmass at sea-level when given a sun altitude angle (in
- degrees). The ``model`` variable allows selection of different
- airmass models (described below). If ``model`` is not included or is
+ degrees). The `model` variable allows selection of different
+ airmass models (described below). If `model` is not included or is
  not valid, the default model is 'kastenyoung1989'.
 
  Note:
@@ -782,8 +903,7 @@ def get_relative_airmass(altitude, model='kastenyoung1989'):
  requires apparent sun altitude
 
  Returns:
- airmass_relative: numeric
- Relative airmass at sea level. Will return None for any
+ airmass_relative: Relative airmass at sea level. Will return None for any
  altitude angle smaller than 0 degrees.
  """
  if altitude < 0: