PyWAsP tutorial 1 From observed wind climate to resource map

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A notebook by the PyWAsP team

Introduction

This is an interactive tutorial that will introduce you to the basic WAsP workflow using PyWAsP.

After completing this tutorial you will be familiar with basic pywasp functionality, including:

opening and inspecting wind climate files;
opening map files and inspecting terrain data;
calculating and inspecting terrain-induced effects on the wind at various sites;
creating a generalized wind climate from the observed wind climate;
creating a resource map by “downscaling” the generalized wind climate.

As you work your way through the notebook, make sure to run the python code in each cell, in the order that the cells appear. You run the code by clicking on the cell (outlines around the cell should appear) and pressing <shift> + <enter> on the keyboard.

notebook note if something looks wrong, or when errors occur, it can be helpful to restart the python kernel via the kernel tab in the top

Import packages

Usually the first step when writing (or using) a python program is to import the standard (external) packages we will need. For the analysis in this tutorial we will import numpy, matplotlib, xarray, windkit and pywasp itself.

numpy is python’s main numerical array package https://www.numpy.org/
matplotlib is python’s main plotting package https://matplotlib.org/
xarray is a powerful high level package for labelled multi-dimensional arrays http://xarray.pydata.org
pywasp docummentation is found at http://docs.wasp.dk/
windkit documentation is found at http://docs.wasp.dk/windkit

[1]:

import warnings
warnings.filterwarnings('ignore')  # We will ignore warnings to avoid cluttering the notebook

import numpy as np
import matplotlib as mpl
import matplotlib.pyplot as plt
import xarray as xr

import windkit as wk
import pywasp as pw

It is common to make a short alias of a package when importing it, using the syntax import [package] as [short alias for package]; for example you can see import pywasp as pw above. The functions and classes of imported packages must be accessed through explicitly typing the package name or alias; e.g., np.cos(3.14) will use the cosine function from numpy.

Next, we will use a python “magic” command; this particular one makes matplotlib figures appear in the notebook when a plot command is used.

[2]:

%matplotlib inline

Observed wind climate

Now we will import our (observed) binned wind climate from the Høvsøre mast. The data is stored in hovsore.tab in the data folder. It represents the observed wind climate for the period 2007-2015 at 100 m above ground level.

pywasp note pywasp includes functionality to both read and write wind climate files in many data formats, including ascii (.tab), xml (.owc), and netCDF (.nc).

The geospatial coordinate in the file is in the European grid projection (EPSG:3035), which is the recommended projection for high-resolution studies in Europe. We will tell pywasp this by explicitly passing a keyword argument srs="EPSG:3035" to the open_bwc call.

[3]:

bwc = wk.read_bwc('data/hovsore.tab', crs="EPSG:3035")
print(bwc)

<xarray.Dataset>
Dimensions:       (point: 1, sector: 12, wsbin: 30)
Coordinates:
    height        (point) float64 100.0
    south_north   (point) float64 3.706e+06
    west_east     (point) float64 4.207e+06
    crs           int8 0
  * wsbin         (wsbin) float64 0.5 1.5 2.5 3.5 4.5 ... 26.5 27.5 28.5 29.5
    wsceil        (wsbin) float64 1.0 2.0 3.0 4.0 5.0 ... 27.0 28.0 29.0 30.0
    wsfloor       (wsbin) float64 0.0 1.0 2.0 3.0 4.0 ... 26.0 27.0 28.0 29.0
  * sector        (sector) float64 0.0 30.0 60.0 90.0 ... 270.0 300.0 330.0
    sector_ceil   (sector) float64 15.0 45.0 75.0 105.0 ... 285.0 315.0 345.0
    sector_floor  (sector) float64 345.0 15.0 45.0 75.0 ... 255.0 285.0 315.0
Dimensions without coordinates: point
Data variables:
    wdfreq        (sector, point) float64 0.02869 0.04459 ... 0.09727 0.0156
    wsfreq        (wsbin, sector, point) float64 0.01167 0.01544 ... 0.0 0.0
Attributes:
    Conventions:      CF-1.8
    history:          2024-06-03T15:05:01+00:00:\twindkit==0.7.1.dev53+gcced3...
    wasp_header:      Høvsøre observed wind climate EPSG:3035
    Package name:     windkit
    Package version:  0.7.1.dev53+gcced321
    Creation date:    2024-06-03T15:05:01+00:00
    Object type:      Binned Wind Climate
    author:           Bjarke Tobias Olsen
    author_email:     btol@dtu.dk
    institution:      DTU Wind Energy

Notice that the bwc object is of type <xarray.Dataset> and that it contains four kinds of data:

Dimensions: core named dimensions
Coordinates: coordinate values along dimensions
Data variables: named arrays with data along 0-N named dimensions
Attributes: additional meta data attached to the dataset

xarray note the primitive datatype and dimensions of each variable are also shown, along with a small sample of the data. wsfreq is a four-dimensional float64 (double precision) variable along dimensions (wsbin, sector, height, point)

Xarray datasets wrap numpy arrays, annotating them with human-readable dimensions and coordinates, and allowing for easy subsetting, data manipulation, and plotting of the underlying data. An xarray.Dataset object is a collection of data variables, while each varible itself has type xarray.DataArray.

xarray note Use the .values object attribute to access the underlying numpy array

Beyond the wind speed and wind direction distributions, the wind climate contains information about the height of the measurements (height) and the geospatial location (west_east and south_north), which in this case hold the location information in the projected coordinates of the EPSG:3035 projection.

In the cell below, we will store the location of the Høvsøre mast in variables loc_x and loc_y for later use.

[4]:

loc_x = bwc['west_east']
loc_y = bwc['south_north']

The next step is to plot the wind rose and wind speed distributions in the binned wind climate. For convinience a plotting function plot_bwc has been implemented in pywasp_tutorial that will do this.

notebook note: you can view the documentation for a function in jupyter notebooks by placing a ? in front of the function, and you can get the entire function by using ??.

[5]:

wk.plot.histogram_lines(bwc)