Setup¶
Recommended: Set up conda environment with provided .yml file¶
Updated September 2020
We recommend seting up a fresh Python virutal environment in which to
use solar-data-tools. We recommend using the
Conda
package management system, and creating an environment with the
environment configuration file named pvi-user.yml, provided in the
top level of this repository. This will install the
statistical-clear-sky package as well.
Please see the Conda documentation page, “Creating an environment from an environment.yml file” for more information.
Installing this project as PIP package¶
$ pip install solar-data-tools
As of March 6, 2019, it fails because scs package installed as a dependency of cxvpy expects numpy to be already installed. scs issue 85 says, it is fixed. However, it doesn’t seem to be reflected in its pip package. Also, cvxpy doesn’t work with numpy version less than 1.16. As a work around, install numpy separatly first and then install this package. i.e.
$ pip install 'numpy>=1.16'
$ pip install statistical-clear-sky
Solvers¶
By default, ECOS solver is used, which is supported by cvxpy because it is Open Source.
However, it is found that Mosek solver is more stable. Thus, we encourage you to install it separately as below and obtain the license on your own.
mosek - For using MOSEK solver.
$ pip install -f https://download.mosek.com/stable/wheel/index.html Mosek
Installing this project as Anaconda package¶
$ conda install -c slacgismo solar-data-tools
If you are using Anaconda, the problem described in the section for PIP package above doesn’t occur since numpy is already installed. And during solar-data-tools installation, numpy is upgraded above 1.16.
Solvers¶
By default, ECOS solver is used, which is supported by cvxpy because it is Open Source.
However, it is found that Mosek solver is more stable. Thus, we encourage you to install it separately as below and obtain the license on your own.
mosek - For using MOSEK solver.
$ conda install -c mosek mosek
Using this project by cloning this GIT repository¶
From a fresh python environment, run the following from the base
project folder:
$ pip install -r requirements.txt
As of March 6, 2019, it fails because scs package installed as a dependency of cxvpy expects numpy to be already installed. scs issue 85 says, it is fixed. However, it doesn’t seem to be reflected in its pip package. Also, cvxpy doesn’t work with numpy version less than 1.16. As a work around, install numpy separatly first and then install this package. i.e.
$ pip install 'numpy>=1.16'
$ pip install -r requirements.txt
To test that everything is working correctly, launch
$ jupyter notebook
and run the two notebooks in the notebooks/ folder.