Commit 11fb8714 authored by Nikola Vasiljevic's avatar Nikola Vasiljevic
Browse files

Updated examples to work with the latest version of pywasp.

Only kept the tutorial examples and removed bunch of folders containing smaller scripts.

all examples updated
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notes.md
# pywasp_examples
# **pywasp examples**
Examples for pytest and running PyWAsP in Jupyter Notebooks.
This repository contains [pywasp](https://gitlab-internal.windenergy.dtu.dk/WAsP/WAsP/pywasp) examples provided as [jupyter](https://jupyter.org/) notebooks. The repository is a submodule of [pywasp](https://gitlab-internal.windenergy.dtu.dk/WAsP/WAsP/pywasp). The purpose of the examples are to familiarize users with [pywasp](https://gitlab-internal.windenergy.dtu.dk/WAsP/WAsP/pywasp) and enable them to quickly build there own workflows with [pywasp](https://gitlab-internal.windenergy.dtu.dk/WAsP/WAsP/pywasp). The examples cover typical usage of [pywasp](https://gitlab-internal.windenergy.dtu.dk/WAsP/WAsP/pywasp) and they are provided as 4 tutorials.
The repository is a submodule of [pywasp](https://gitlab-internal.windenergy.dtu.dk/WAsP/WAsP/pywasp).
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## **Tutorial 1: mapping of wind resources**
This [tutorial](./tutorial_1) introduces the basic functionalities of `pywasp`, such as:
- Opening and inspecting wind climate files
- Opening and inspecting terrain data (i.e., subset of [SRTM](https://www2.jpl.nasa.gov/srtm/) dataset)
- Opening and inspecting roughness data (i.e., subset of [CORINE](https://land.copernicus.eu/pan-european/corine-land-cover) Land Cover dataset)
- Calculating and inspecting site effects from the terrain and roughness data
- Creating a generalized wind climate from the observed wind climate
- Creating a resource map by downscaling the generalized wind climate
The users will be also introduce to [xarray](http://xarray.pydata.org), a powerful high level package for labelled multi-dimensional arrays.
## **Tutorial 2: analysis of roughness change model response**
One of the aims of this [tutorial](./tutorial_2) is to show how straightforward is to configure and run series of simulations using `pywasp`. For that purpose, the tutorial explores the sensitivity of WAsP roughness change induced speedups to various parameters of a single roughness change line. The users will learn how to:
- Create a single roughness change line
- Create a series of test cases for the analysis of roughness change model response of a single roughness change line
- Execute the series of test cases
- Present the results of the test cases
In this tutorial we will touch upon [pandas](https://pandas.pydata.org/), python's main package for manipulation of tabular data. [pandas](https://pandas.pydata.org/) will be used to create the test case series.
## **Tutorial 3: comparison of predicted and measured power density**
In this [tutorial](./tutorial_3) users will get to know how to:
- Work with tab files from two different met masts
- Work with roughness maps ([CORINE](https://land.copernicus.eu/pan-european/corine-land-cover) Land Cover dataset)
- Provide more elaborate roughness description using a lookup table
- Work with terrain data from lidar scans ([Geodatastyrelsen](https://eng.gst.dk/) dataset)
- Create a generalized wind climate from the observed wind climate
- Downscale generalized wind climate
- Compare downscaled and measured power density
## **Tutorial 4: wind farm AEP calculation**
This [tutorial](./tutorial_4) represnts a `pywasp` adaptation of the classical `WAsP` step-by-step example from the `WAsP` help. In the tutorial users will work through a complete wind turbine siting operation, starting with some measured wind data and ending up with a prediction of the power yield from erecting a turbine at a specific site. Similar to the [first tutorial](./tutorial_1), the users will get to know how to :
- Load and work with observed wind climate (i.e., met mast data from a Portuguese site)
- Load vector map file which contains both the roughness and terrain data
- Load power curve of a wind turbine
- Calculate and inspect site effects from the terrain and roughness data
- Create a generalized wind climate from the observed wind climate
- Downscale the generalized wind climate to the wind turbine locations
- Calculate AEP of a single and multiple wind turbines
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