.. The getting-started should show some primary use cases in more detail. The reader will follow a step-by-step procedure to set-up a working prototype
pyDEXP aims to process Mise-à-la-masse (MALM) datasets for a variety of applications. pyDEXP has been initially developed for plant root imaging.
The simpliest processing can be achieved with the python API. You'll first need to import the pyDEXP package::
import lib.dEXP as dEXP
import lib.plot_dEXP as pEXP
Then after loading you data make basics operation such as upward continuation (Uieda et al., 2013, 2018)::
mesh, label_prop = dEXP.upwc(xp, yp, zp, U, shape,
zmin=0, zmax=max_elevation, nlayers=nlay,
qorder=qorder)
Searching for ridges (Fedi et al., 2012)::
dfI,dfII, dfIII = dEXP.ridges_minmax(xp, yp, mesh, p1, p2,
label=label_prop,
fix_peak_nb=2,
and finally plot the results::
fig = plt.figure()
ax = plt.gca()
pEXP.plot_xy(mesh, label=label_prop, ax=ax)
pEXP.plot_ridges_harmonic(dfI,dfII,dfIII,ax=ax)
More examples are available in the Example gallery section.
References
Uieda, L., V. C. Oliveira Jr, and V. C. F. Barbosa (2013), Modeling the Earth with Fatiando a Terra, Proceedings of the 12th Python in Science Conference, pp. 91 - 98.
Uieda, L. (2018). Verde: Processing and gridding spatial data using Green's functions. Journal of Open Source Software, 3(29), 957. doi:10.21105/joss.00957
Fedi, M., and M. Pilkington (2012), Understanding imaging methods for potential field data, Geophysics, 77(1), G13, doi:10.1190/geo2011-0078.1