Keywords: point-in-box numpy python vectorized range-query quadtree
inside = points_in_boxes( Npt points, Nbox boxes ) # -> (Npt, Nbox) bool
returns an Npt x Nbox bool array, True where points[i] is in boxes[j].
This takes < 3 nanoseconds * Npt * Nbox on my old imac;
1000 points in 1000 boxes, or a million points in 1 box, both take < 3 ms --
Keywords: wind-power Bayern CERRA capacity-factor renewable-energy open-data python
CERRA
has open data on windspeeds across Europe,
3-hour averages on a 5.5 km grid.
See CERRA, the Copernicus European Regional Reanalysis system
Ridal et al., 2024, 27p.
This map of average windspeeds in 2016 was made from CERRA data, using python and numpy:
Keywords: wind-speed COSMO-REA6 NEWA Germany neweuropeanwindatlas reanalysis open-data python
I'm interested in windspeeds at 175m, the turbine height of many new wind turbines in Germany, Two sources of windspeed data in EUrope are
What are good places to compare the two ?
TSH, Thyroid-stimulating_hormone, is measured in blood to determine a suitable dosage of L-thyroxine.
Wikipedia TSH gives "reference ranges" of
Keywords: scipy solve_ivp callback ODE ordinary-differential-equation IVP initial-value-problem python
add a callback function to the scipy ODE solver solve_ivp, so that you can
h is too small, or KeyboardInterrupt, or ...
and return the sol so far.
Keywords: Cosmo_rea6 DWD wind-data python numpy
Purpose:
DWD, the German Meteorological Service,
has wind data for Europe on a 6 x 6 km grid, from 1995 to August 2019, hourly,
at heights 10 100 .. 200m.
These are in 2 gigabyte files like
https://opendata.dwd.de/climate_environment/REA/COSMO_REA6/converted/hourly/2D/WS_100m.2D.201908.nc4
-- windspeed data at 100m for 2019 August.
mst.py is simple old (2011) code for MSTs using
Kruskal's algorithm
-- see the animation there.
single-link k-clustering == MST k-1 most expensive edges
but min( max spacing ), Theorem 1, may be wrong or at least odd for k-clustering ?
