\donttest{
fn <- tempfile(fileext = ".h5")
hdf5_create_matrix(fn, "data/matrix1", data = matrix(1:100, 10, 10))
hdf5_create_matrix(fn, "data/matrix2", data = matrix(101:200, 10, 10))
hdf5_create_matrix(fn, "data/matrix3", data = matrix(201:300, 10, 10))
bdReduce_hdf5_dataset(
filename = fn,
group = "data",
reducefunction = "+",
outgroup = "results",
outdataset = "sum_matrix",
overwrite = TRUE
)
hdf5_close_all()
unlink(fn)
}bdReduce_hdf5_dataset
bdReduce_hdf5_dataset
HDF5_IO_MANAGEMENT
1 Description
Reduces multiple datasets within an HDF5 group using arithmetic operations (addition or subtraction).
2 Usage
bdReduce_hdf5_dataset(filename, group, reducefunction, outgroup = NULL, outdataset = NULL, overwrite = FALSE, remove = FALSE)3 Arguments
| Parameter | Description |
|---|---|
filename |
Character string. Path to the HDF5 file. |
group |
Character string. Path to the group containing datasets. |
reducefunction |
Character. Operation to apply, either “+” or “-”. |
outgroup |
Character string (optional). Output group path. If NULL, uses input group. |
outdataset |
Character string (optional). Output dataset name. If NULL, uses input group name. |
overwrite |
Logical (optional). Whether to overwrite existing dataset. Default is FALSE. |
remove |
Logical (optional). Whether to remove source datasets after reduction. Default is FALSE. |
4 Value
List with components. If an error occurs, all string values are returned as empty strings (““):
fn: Character string with the HDF5 filenameds: Character string with the full dataset path to the reduced dataset (group/dataset)func: Character string with the reduction function applied
5 Details
This function provides efficient dataset reduction capabilities with: - Operation options: - Addition of datasets - Subtraction of datasets - Output options: - Custom output location - Configurable dataset name - Overwrite protection - Implementation features: - Memory-efficient processing - Safe file operations - Optional source cleanup - Comprehensive error handling
The function processes datasets efficiently while maintaining data integrity.