InferenceObjectsNetCDF.jl

from_netcdf(path::AbstractString; kwargs...) -> InferenceData

Load an InferenceData from an unopened NetCDF file.

Remaining kwargs are passed to NCDatasets.NCDataset. This method loads data eagerly. To instead load data lazily, pass an opened NCDataset to from_netcdf.

Examples

julia> idata = from_netcdf("centered_eight.nc")
InferenceData with groups:
  > posterior
  > posterior_predictive
  > sample_stats
  > prior
  > observed_data

from_netcdf(ds::NCDatasets.NCDataset; load_mode) -> InferenceData

Load an InferenceData from an opened NetCDF file.

load_mode defaults to :lazy, which avoids reading variables into memory. Operations on these arrays will be slow. load_mode can also be :eager, which copies all variables into memory. It is then safe to close ds. If load_mode is :lazy and ds is closed after constructing InferenceData, using the variable arrays will have undefined behavior.

Examples

Here is how we might load an InferenceData from an InferenceData lazily from a web-hosted NetCDF file.

julia> using HTTP, NCDatasets

julia> resp = HTTP.get("https://github.com/arviz-devs/arviz_example_data/blob/main/data/centered_eight.nc?raw=true");

julia> ds = NCDataset("centered_eight", "r"; memory = resp.body);

julia> idata = from_netcdf(ds)
InferenceData with groups:
  > posterior
  > posterior_predictive
  > sample_stats
  > prior
  > observed_data

julia> idata_copy = copy(idata); # disconnect from the loaded dataset

julia> close(ds);

to_netcdf(data, dest::AbstractString; group::Symbol=:posterior, kwargs...)
to_netcdf(data, dest::NCDatasets.NCDataset; group::Symbol=:posterior)

Write data to a NetCDF file.

data is any type that can be converted to an InferenceData using convert_to_inference_data. If not an InferenceData, then group specifies which group the data represents.

dest specifies either the path to the NetCDF file or an opened NetCDF file. If dest is a path, remaining kwargs are passed to NCDatasets.NCDataset.

Examples

julia> using NCDatasets

julia> idata = from_namedtuple((; x = randn(4, 100, 3), z = randn(4, 100)))
InferenceData with groups:
  > posterior

julia> to_netcdf(idata, "data.nc")
"data.nc"