3D viewing

This notebook will show how to display 3D results with resipy in a notebook. Note that 3D viewing is based on the Python package: pyvista that needs to be installed (pip install pyvista).

import sys
sys.path.append('../src')
from resipy import Project
import pyvista as pv
testdir = '../src/examples/'

API path =  /media/jkl/data/phd/tmp/resipy/src/resipy
ResIPy version =  3.2.0
cR2.exe found and up to date.
R3t.exe found and up to date.
cR3t.exe found and up to date.

Create a Survey and Mesh, then display the mesh using a pyvista plotter object (which displays inline with the notebook).

k = Project(typ='R3t')
k.createSurvey(testdir + 'dc-3d/protocol.dat', ftype='ProtocolDC')
k.importElec(testdir + 'dc-3d/elec.csv')
k.createMesh()
pl = pv.Plotter() # init pyvista plotter object
k.showMesh(ax=pl)

should_run_async will not call transform_cell automatically in the future. Please pass the result to transformed_cell argument and any exception that happen during thetransform in preprocessing_exc_tuple in IPython 7.17 and above.
Working directory is: /media/jkl/data/phd/tmp/resipy/src/resipy
clearing dirname
0/644 reciprocal measurements found.
Creating tetrahedral mesh...fmd in gmshWrap.py: 16.343534
writing .geo to file completed, save location:
/media/jkl/data/phd/tmp/resipy/src/resipy/invdir

Reading mesh3d.msh
Gmsh version == 3.x
reading node coordinates...
Determining element type...Tetrahedra
Reading connection matrix...
ignoring 7581 elements in the mesh file, as they are not required for R2/R3t
Finished reading .msh file
interpolating topography onto mesh using triangulate interpolation...done
Done
ResIPy Estimated RAM usage = 0.164347 Gb
done

Converting np.character to a dtype is deprecated. The current result is np.dtype(np.str_) which is not strictly correct. Note that np.character is generally deprecated and 'S1' should be used.
Converting np.character to a dtype is deprecated. The current result is np.dtype(np.str_) which is not strictly correct. Note that np.character is generally deprecated and 'S1' should be used.
../_images/nb_3d-visualisation_0.png

Now invert the data

k.invert()

Writing .in file and protocol.dat... done!
--------------------- MAIN INVERSION ------------------


 >> R 3 t     E R T    M o d e l    v2.01 <<

 >> Date: 05-11-2020
 >> My beautiful 3D survey
 >> I n v e r s e   S o l u t i o n   S e l e c t e d <<
 >> T e t r a h e d r a l   E l e m e n t   M e s h <<

 >> Reading mesh file
 >> Determining storage needed for finite element conductance matrix
 >> Generating index array for finite element conductance matrix
 >> Reading resistivity model from res0.dat

 >> L o g - D a t a   I n v e r s i o n <<
 >> N o r m a l   R e g u l a r i s a t i o n <<

 >> Memory estimates:
    For   1000 measurements the memory needed is:          0.250 Gb
    For   2000 measurements the memory needed is:          0.491 Gb
    For   5000 measurements the memory needed is:          1.213 Gb
    For  10000 measurements the memory needed is:          2.417 Gb

 >> Forming roughness matrix

 >> Number of measurements read:  644

 >> Total Memory required is:          0.164 Gb


 Processing frame   1 - output to file f001.dat

   Iteration   1
     Initial RMS Misfit:         2.41      Number of data ignored:     0
     Alpha:          67.696   RMS Misfit:        0.54  Roughness:        3.185
     Step length set to      1.000
     Final RMS Misfit:        0.54
     Final RMS Misfit:        1.01

 Solution converged - Outputing results to file

 Calculating sensitivity map

 End of data:  Terminating


 >> Program ended normally

1/1 results parsed (1 ok; 0 failed)

Options available

  • pvslices: a tuple of list, each list represent the coordinates of an slice orthogonal to X, Y and Z respectively

  • pvthreshold : a list of two values representing the minimum and maximum values between which to keep the cells

  • pvcontour : a list of values at which draw isosurfaces

  • pvgrid: a bool, if True, the grid is plotted

pl = pv.Plotter()
k.showResults(ax=pl, color_map='jet')
../_images/nb_3d-visualisation_1.png 
pl = pv.Plotter()
k.showResults(ax=pl, pvslices=([5,15,25,35,45],[0],[]), pvgrid=True, vmin=1.95, vmax=2.2, color_map='plasma')
../_images/nb_3d-visualisation_2.png 
pl = pv.Plotter()
k.showResults(ax=pl, pvthreshold=[2.1, 2.2], pvgrid=True)
../_images/nb_3d-visualisation_3.png 
pl = pv.Plotter()
k.showResults(ax=pl, pvcontour=[2, 2.1], pvgrid=True)
../_images/nb_3d-visualisation_4.png

Download python script: nb_3d-visualisation.py

Download Jupyter notebook: nb_3d-visualisation.ipynb

View the notebook in the Jupyter nbviewer

Run this example interactively: binder