imod.prepare.Voxelizer#

class imod.prepare.Voxelizer(method, use_relative_weights=False)[source]#

Object to repeatedly voxelize similar objects. Compiles once on first call, can then be repeatedly called without JIT compilation overhead.

method#

The method to use for regridding. Default available methods are: {"mean", "harmonic_mean", "geometric_mean", "sum", "minimum", "maximum", "mode", "median", "max_overlap"}

Type: str, function

Examples

Usage is similar to the regridding. Initialize the Voxelizer object:

>>> mean_voxelizer = imod.prepare.Voxelizer(method="mean")

Then call the voxelize method to transform a layered dataset into a voxel based one. The vertical coordinates of the layers must be provided by top and bottom.

>>> mean_voxelizer.voxelize(source, top, bottom, like)

If your data is already voxel based, i.e. the layers have tops and bottoms that do not differ with x or y, you should use a Regridder instead.

It’s possible to provide your own methods to the Regridder, provided that numba can compile them. They need to take the arguments values and weights. Make sure they deal with nan values gracefully!

>>> def p30(values, weights):
>>>     return np.nanpercentile(values, 30)

>>> p30_voxelizer = imod.prepare.Voxelizer(method=p30)
>>> p30_result = p30_voxelizer.regrid(source, top, bottom, like)

The Numba developers maintain a list of support Numpy features here: https://numba.pydata.org/numba-doc/dev/reference/numpysupported.html

In general, however, the provided methods should be adequate for your voxelizing needs.

__init__(method, use_relative_weights=False)[source]#

Methods

__init__(method[, use_relative_weights])

voxelize(source, top, bottom, like)

param source: The values of the layered model.