a demo to try some techniques for analysis of opensha modular solution files.
- opensha modular documentation
- pandas, geopanda references
From a typical modular opensha Inversion Solution archive, we want to produce views that allow deep exploration of the solution and rupture set characteristics. Features:
- user can choose from regions already defined in the solution
- user can select ruptures matching
- parent fault
- named fault (fault system)
- constraint region (from TargetMFDs)
- user can create new region polygons
- user can compare selections (e.g. Wellington East vs Wellington CBD vs Hutt Valley)
- for a given query result show me dimensions...
- mag, length, area, rate, section count, parent fault count, jump-length, jump angles, slip (various), partication, nucleation
- filter, group on any of the dimensions
-
create a MFD histogram in 0.01 bins from 7.0 to 7.30 (3O bins) for the WHV fault system
-
list all ruptures between 7.75 and 8.25, involving the TVZ, ordered by rupture-length
-
given a user-defined-function udfRuptureComplexity(rupture) rank ruptures in Region X by complexity, then by magnitude
-
regional MFD
- participation (sum of rate) for every rupture though a point
- nucleation/blame/culpability rate summed over the region normalised by the area of an area (region, named fault)
git clone
pip3 install .
python3 -m demo
or python3 demo.py
f = plt.figure() #nx = int(f.get_figwidth() * f.dpi) #ny = int(f.get_figheight() * f.dpi) f.figimage(data) plt.show()