denis-bz

Windspeeds and capacity factors in the Netherlands from wikipedia, KNMI and globalwindatlas

Keywords: wind, wind farm, wind speed, capacity factor, onshore, Netherlands, globalwindatlas, GWA, KNMI

What I want to do is show my neighbors around Munich that windfarms make more sense where there's more wind. GWA has a nice GUI, which everyone can understand. But are their wind speed and capacity factors accurate enough to convince anyone ?

nl.wikipedia Windturbines_in_Nederland,

denis-bz

Introduction to Netherlands wind data with xarray

Keywords: Netherlands, wind, wind speed, KNMI, python, xarray, data-carpentry

Purpose: introduce Netherlands wind data from KNMI, the Royal Netherlands$ Meteorological Institute, with the steps:

1. download 2 \.nc files by hand, one day and one gridpoint
2. look at them in xarray

denis-bz

Windspeeds and windfarms in Bayern

Keywords: wind power, Germany, Bayern, globalwindatlas, GWA

Purpose: I'm trying to show my neighbors around Munich that windfarms make more sense where there's more wind. To this end, here's a map of GWA windspeeds at 150m, together with the sites of 202 windfarms in Bayern:

denis-bz

Windfarms in Germany from de.wikipedia

Keywords: wind power, Germany, python, globalwindatlas

Purpose: collect data on wind farms in Germany in a single .csv file, from https://de.wikipedia.org/wiki/Liste_von_Windkraftanlagen_in_Bayern etc.

The file windfarms-in-Germany.csv looks like this:

MW MW/Rad Nrad Year N E Land Lkr Typ Ort

denis-bz

Poisson2 - 4 I: nice test matrices for linear solvers and eigensolvers

Purpose: a simple generator of test matrices for linear solvers and eigensolvers

Keywords: sparse-matrix, linear-solver, eigensolver, arpack, nullspace, python

poisson2( n ) below generates sparse $n^2 \ x \ n^2$ matrices $A$ with $A$ symmetric (aka Hermitian) and positive-definite; solving $A \ x = b$ is then relatively easy. $A_4 = A - 4 I$ is more interesting: there are $n$ vectors $z_i$

denis-bz

#!/usr/bin/env python3
""" Usage:
python3 ssquery.py  '(posdef == 1)  &  (1000 <= rows <= 20000)'  posdef.csv
reads http://sparse.tamu.edu/files/ssstats.csv 
queries it
writes "posdef.csv" like

# SuiteSparse  posdef  &  (1000 <= rows <= 20000) 
id,group,name,rows,cols,nnz,real,bool,is2d3d,posdef,symm,nsymm,kind
29156,Boeing,msc01050,1050,1050,26198,1,0,1,1,1.0,1.0,structural

denis-bz

Comparing time series in Python

The task: you have a hundred pairs of time series, say A1 .. A100 and B1 .. B100, e.g. temperature data at 100 cities in two different years. All the time series are say 365 long, but many days are missing (NA or NaN). Some pairs are quite similar, some not. You want to rank them from most similar .. least similar, 0 .. 100, in order to describe and understand how A and B differ.

(The $64 question: does it matter ?

denis-bz

HUSL rgb_to_lch is for CIELuv, not CIELab

Keywords: color-space HUSL HSLuv CIELCh CIELuv CIELab max-chroma everyone-knows

_{Whenever something can be done in two ways, someone will be confused.}

There are two quite different color spaces: \

denis-bz

Fitting 1 / x with polynomials in relative error

Keywords, topics: approximation, relative error, inverse, Chebyshev polynomial, Krylov, python

This plot shows polynomials of degrees 10, 15, 20 that fit 1 / x on the range 0.1 .. 2.1, shifted to fits of 1 / (1.1 + x) on -1 .. 1.

denis-bz

Warping maps or images

Keywords: image raster resize rescale reproject Manhattan-grid regular-grid

We have a picture (image, raster) A, an array of colored pixels, and want to warp it to a new picture O of a different size or shape. Examples: Map projection, flat maps <-> globe; Image scaling.
There are hundreds of cases of these, and zillions of programs