Reading and writing grid search results

A grid search

ds = grid_search(data, greens, misfit, origins, sources)

returns a data structure ds that contains both misfit values and grid points.

To write this data structure to disk:

ds.save(filename)

Depending on whether the grid points are regularly- or irregularly-spaced, a NetCDF or HDF5 file will be written. Subsequently, this file can be read back in using open_ds, which tries to automatically determine file format:

from mtuq import open_ds
ds = open_ds(filename)

Alternatively, users can specify file type through the format keyword argument, as in the examples below.

Details on regularly-spaced grids

A search over reguarly-spaced sources

da = grid_search(data, greens, misfit, origins, sources)

returns an xarray DataArray da.

To write the grid search output da to disk as a NetCDF file:

da.save('output.nc')

To read the NetCDF file back from disk:

from mtuq import open_ds
da = open_ds('output.nc', format='NetCDF')

Before or after writing to disk, da can be passed to visualization utilities or manipulated using xarray methods.

Details on irregularly-spaced grids

A search over irregularly-spaced sources

df = grid_search(data, greens, misfit, origins, sources)

returns a pandas DataFrame df.

To write the grid search output df to disk as an HDF5 file:

df.save('output.hf5')

To read the HDF5 file back from disk:

from mtuq import open_ds
df = open_ds('output.hf5', format='HDF5')

Before or after writing to disk, df can be passed to visualization utilities or manipulated using pandas methods.