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mostaphaRoudsari 
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 h... (continued)

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Source Files on job 571.2