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Solar wind in situ data suitable for machine learning (python numpy structured arrays): STEREO-A/B, Wind, Parker Solar Probe, Ulysses, Venus Express, MESSENGER

dataset
posted on 09.04.2020, 12:14 by Christian MoestlChristian Moestl, Andreas Weiss, Rachel Bailey, Alexey Isavnin
These are solar wind in situ data arrays in python pickle format suitable for machine learning, i.e. the arrays consist only of numbers, no strings and no datetime objects.

See AAREADME_insitu_ML.txt for more explanation.

If you use these data for peer reviewed scientific publications, please get in touch concerning usage and possible co-authorship by the authors (C. Möstl, A. J. Weiss, R. L. Bailey, A. Isavnin): christian.moestl@oeaw.ac.at or twitter @chrisoutofspace

Made with https://github.com/cmoestl/heliocats

Load in python with e.g. for Parker Solar Probe data:

> import pickle
> filepsp='psp_2018_2019_sceq_ndarray.p'
> [psp,hpsp]=pickle.load(open(filepsp, "rb" ) )

plot time vs total field
> import matplotlib.pyplot as plt
> plt.plot(psp['time'],psp['bt'])

Times psp[:,0 ] or psp['time'] are in matplotlib format.
Variable 'hpsp' contains a header with the variable names and units for each column. Coordinate systems for magnetic field components are RTN (Ulysses), SCEQ (Parker Solar Probe, STEREO-A/B, VEX, MESSENGER), HEEQ (Wind)

available parameters:

bt = total magnetic field
bxyz = magnetic field components
vt = total proton speed
vxyz = velocity components (only for PSP)
np = proton density
tp = proton temperature
xyz = spacecraft position in HEEQ
r, lat, lon = spherical coordinates of position in HEEQ




Funding

Austrian Science Fund (FWF): P31521-N27

Austrian Science Fund (FWF): P31659-N27

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