Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

GEOPY-1448: Add option to export sensitivities for all inversions #55

Merged
merged 3 commits into from
Oct 9, 2024
Merged
Changes from all commits
Commits
File filter

Filter by extension

Filter by extension

Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
23 changes: 15 additions & 8 deletions simpeg/directives/directives.py
Original file line number Diff line number Diff line change
Expand Up @@ -2987,23 +2987,29 @@ class SaveIterationsGeoH5(InversionDirective):
Saves inversion results to a geoh5 file
"""

def __init__(self, h5_object, **kwargs):
def __init__(
self, h5_object, dmisfit=None, attribute_type: str = "model", **kwargs
):
self.data_type = {}
self._association = None
self.attribute_type = "model"
self.attribute_type = attribute_type
self._label = None
self.channels = [""]
self.components = [""]
self._h5_object = None
self._workspace = None
self._transforms: list = []
self.save_objective_function = False
self.sorting = None
self._reshape = None
self.h5_object = h5_object
self._joint_index = None

if attribute_type == "sensitivities" and dmisfit is None:
raise ValueError(
"To save sensitivities, the data misfit object must be provided."
)

super().__init__(
inversion=None, dmisfit=None, reg=None, verbose=False, **kwargs
inversion=None, dmisfit=dmisfit, reg=None, verbose=False, **kwargs
)

def initialize(self):
Expand Down Expand Up @@ -3085,9 +3091,10 @@ def get_values(self, values: list[np.ndarray] | None):

prop = self.stack_channels(dpred)
elif self.attribute_type == "sensitivities":
for directive in self.inversion.directiveList.dList:
if isinstance(directive, directives.UpdateSensitivityWeights):
prop = self.reshape(np.sum(directive.JtJdiag, axis=0) ** 0.5)

prop = np.zeros_like(self.invProb.model)
for fun in self.dmisfit.objfcts:
prop += fun.getJtJdiag(self.invProb.model)

return prop

Expand Down