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458 | class SingleFileStrategy(SavingStrategy):
"""A concrete saving strategy that saves all data to a single file.
This strategy implements the `SavingStrategy` abstract methods to manage saving all variables
for the entire time range into a single output file. It is a simple, non-partitioning approach.
Supports multiple file formats including MATLAB (.mat), HDF5 (.h5), NetCDF4 (.nc), and CDF (.cdf).
Users can also register custom format writers for additional file formats.
Attributes:
file_path (Path): The path to the single output file where all data will be saved.
output_files (list[OutputFile]): List of output files to be managed.
Methods:
__init__(file_path, format_writers): Initializes the strategy with file path and optional custom writers.
get_time_intervals_to_save: Returns the entire time range as a single interval.
get_file_path: Always returns the pre-defined single file path.
standardize_variable: Passes the variable through without any standardization.
save_single_file: Saves data to a file in the specified format using the dispatch table.
register_writer: Registers a custom format writer for a file extension.
Supported Formats:
- .mat: MATLAB format using scipy.io.savemat
- .h5: HDF5 format using h5py with optional gzip compression
- .nc: NetCDF4 format using netCDF4 with optional compression
- .cdf: CDF (Common Data Format) using cdflib with gzip compression
- Custom: Any user-defined format via register_writer() or format_writers parameter
Example:
```python
def write_custom(file_path: Path, data_dict: dict[str, Any]) -> None:
# Custom writer implementation
pass
strategy = SingleFileStrategy("output.myformat",format_writers={".myformat": write_custom})
ep.save(variables, saving_strategy=strategy, ...)
```
"""
output_files: list[OutputFile]
file_path: Path
_writers: dict[str, SingleFileFormatWriter]
def __init__(
self,
file_path: str | Path,
format_writers: dict[str, SingleFileFormatWriter] | None = None,
) -> None:
"""Initializes the SingleFileStrategy with the specified file path and optional custom format writers.
Parameters:
file_path (str | Path): The full path to the output file. The file extension determines
the format unless a custom writer is registered.
format_writers (dict[str, SingleFileFormatWriter] | None): Optional dictionary mapping file extensions
(including the dot, e.g., ".myformat") to custom writer functions. Custom writers override
built-in writers for the same extension. Defaults to None.
Example:
```python
def write_custom(file_path: Path, data_dict: dict[str, Any]) -> None:
# Custom writer implementation
pass
strategy = SingleFileStrategy("output.myformat",format_writers={".myformat": write_custom})
ep.save(variables, saving_strategy=strategy, ...)
```
"""
self.file_path = Path(file_path)
self.output_files = [OutputFile(self.file_path.name, [])]
# Build the dispatch table with built-in writers
self._writers: dict[str, SingleFileFormatWriter] = {
".mat": self._write_mat_file,
".h5": self._write_h5_file,
".nc": self._write_netcdf_file,
".cdf": self._write_cdf_file,
}
# Register custom writers (these override built-in writers if same extension)
if format_writers:
self._writers.update(format_writers)
def get_file_path_stem(self) -> None: # ty:ignore[invalid-method-override]
pass
def get_file_name_stem(self) -> None: # ty:ignore[invalid-method-override]
pass
def get_time_intervals_to_save(self, start_time: datetime, end_time: datetime) -> list[TimeInterval]:
"""Returns the entire time range as a single interval.
This strategy does not split data by time; it saves everything in one go.
Parameters:
start_time (datetime): The start time of the data range.
end_time (datetime): The end time of the data range.
Returns:
list[TimeInterval]: A list containing a single tuple with the start and end times.
"""
return [(start_time, end_time)]
def get_file_path(
self,
interval_start: datetime, # noqa: ARG002
interval_end: datetime, # noqa: ARG002
output_file: OutputFile, # noqa: ARG002
) -> Path:
"""Returns the pre-defined single file path, ignoring the interval.
This method ensures all data is saved to the same file, regardless of the time interval.
Parameters:
interval_start (datetime): The start of the time interval (ignored).
interval_end (datetime): The end of the time interval (ignored).
output_file (OutputFile): The output file configuration (ignored).
Returns:
Path: The `file_path` of this strategy instance.
"""
return self.file_path
def standardize_variable(
self,
variable: Variable,
internal_name: InternalName, # noqa: ARG002
*,
first_call_of_interval: bool, # noqa: ARG002
) -> Variable:
"""Does not modify the variable.
This strategy does not perform any specific standardization on the variables before saving.
Parameters:
variable (Variable): The variable instance to be standardized.
name_in_file (str): The name of the variable as it appears in the file (ignored).
first_call_of_interval (bool): Flag to indicate if it is the first call of a time interval
Returns:
Variable: The original variable instance, unchanged.
"""
return variable
def register_writer(self, extension: str, writer: SingleFileFormatWriter) -> None:
"""Register a custom format writer for a file extension.
This method allows you to register custom writers for file formats not natively supported,
or to override built-in writers. Custom writers are called when a file with the matching
extension is saved.
Parameters:
extension (str): The file extension (including the dot), e.g., ".myformat" or ".bin".
writer (SingleFileFormatWriter): A callable with signature `(Path, dict[str, Any]) -> None` that
handles writing the data dictionary to the specified file path.
Example:
```python
def write_binary(path: Path, data: dict[str, Any]) -> None:
import struct
with open(path, 'wb') as f:
for key, value in data.items():
if key != "metadata":
f.write(value.tobytes())
strategy = SingleFileStrategy("output.dat")
strategy.register_writer(".dat", write_binary)
```
"""
if not extension.startswith("."):
extension = "." + extension
self._writers[extension.lower()] = writer
def _write_metadata_to_netcdf_variable(self, data_set: nC.Variable[Any], metadata: dict[str, Any]) -> None:
"""Attach metadata values that can be represented as NetCDF attributes."""
for key, value in metadata.items():
if isinstance(value, list):
value = ", ".join(str(item) for item in value)
if getattr(value, "size", None) == 0:
continue
setattr(data_set, key, value)
def _write_netcdf_file(self, file_path: Path, data_dict: dict[str, Any]) -> None:
"""Write data dictionary to NetCDF4 (.nc) format.
Creates hierarchical groups based on paths (e.g., "group1/group2/dataset" becomes nested groups).
Applies zlib compression, shuffle filter, and creates dimension variables automatically.
Writes metadata as variable attributes.
Parameters:
file_path (Path): Path to save the .nc file.
data_dict (dict[str, Any]): Dictionary with variable data and metadata.
Keys are path strings (e.g., "var_name" or "group/subgroup/var_name").
The "metadata" key is skipped; metadata is stored as variable attributes.
"""
with nC.Dataset(file_path, "w", format="NETCDF4") as file:
for path, value in data_dict.items():
if path == "metadata":
continue
if value.size == 0:
continue
path_parts = path.split("/")
groups = path_parts[:-1]
dataset_name = path_parts[-1]
curr_hierarchy: nC.Group | nC.Dataset = file
for group in groups:
if group not in curr_hierarchy.groups:
curr_hierarchy = curr_hierarchy.createGroup(group)
else:
curr_hierarchy = curr_hierarchy.groups[group]
dimensions = []
for axis, size in enumerate(value.shape):
dimension_name = f"{dataset_name}_dim_{axis}"
if dimension_name not in curr_hierarchy.dimensions:
curr_hierarchy.createDimension(dimension_name, size)
dimensions.append(dimension_name)
data_set = typing.cast(
"nC.Variable[Any]",
curr_hierarchy.createVariable(
dataset_name, value.dtype, dimensions, zlib=True, complevel=5, shuffle=True
),
)
data_set[...] = value
if path in data_dict.get("metadata", {}):
self._write_metadata_to_netcdf_variable(data_set, data_dict["metadata"][path])
def save_single_file(self, file_path: Path, dict_to_save: dict[str, Any], *, append: bool = False) -> None: # ty:ignore[invalid-method-override]
"""Saves variable data to a single file in one of the supported formats.
The file format is determined by the file extension. Built-in formats include .mat, .h5, .nc, and .cdf.
Custom format writers can be registered via the format_writers parameter during initialization or
via the register_writer() method.
It is primarily designed to be used with the `el_paso.save()` function, which handles the logic of determining
what data to save and when.
Parameters:
file_path (Path): The path to the file where the dictionary will be saved.
The file extension determines the format.
dict_to_save (dict[str, Any]): The dictionary containing variable data to save.
Keys are variable names (strings), values are NumPy arrays or other serializable data.
Should include a "metadata" key with metadata dictionary.
append (bool, optional): If True, attempts to append to an existing file.
Only supported for CDF format. For other formats, raises NotImplementedError.
Defaults to False.
Raises:
NotImplementedError: If the file format is not registered or supported,
or if append is requested for formats that don't support it.
Any exception raised by the format writer function.
Supported Built-in Formats:
- .mat: MATLAB format using scipy.io.savemat
- .h5: HDF5 format using h5py with gzip compression
- .nc: NetCDF4 format using netCDF4 with compression
- .cdf: CDF (Common Data Format) using cdflib with gzip compression
"""
logger.info(f"Saving file {file_path.name}...")
file_path.parent.mkdir(parents=True, exist_ok=True)
format_name = file_path.suffix.lower()
# Look up the writer in the dispatch table
writer = self._writers.get(format_name)
if writer is None:
msg = f"The '{format_name}' format is not implemented. Registered formats: {list(self._writers.keys())}"
logger.error(msg)
raise NotImplementedError(msg)
if append:
msg = f"Appending to existing files is not supported for '{format_name}' format."
logger.error(msg)
raise NotImplementedError(msg)
writer(file_path, dict_to_save)
def _write_mat_file(self, file_path: Path, data_dict: dict[str, Any]) -> None:
"""Write data dictionary to MATLAB .mat format.
Parameters:
file_path (Path): Path to save the .mat file.
data_dict (dict[str, Any]): Dictionary with variable data and metadata.
"""
savemat(str(file_path), data_dict)
def _write_h5_file(self, file_path: Path, data_dict: dict[str, Any]) -> None:
"""Write data dictionary to HDF5 (.h5) format.
Creates hierarchical groups based on paths (e.g., "group1/group2/dataset" becomes nested groups).
Applies gzip compression and shuffling to all datasets. Writes metadata as dataset attributes.
Parameters:
file_path (Path): Path to save the .h5 file.
data_dict (dict[str, Any]): Dictionary with variable data and metadata.
Keys are path strings (e.g., "var_name" or "group/subgroup/var_name").
The "metadata" key is skipped; metadata is stored as dataset attributes.
"""
with h5py.File(file_path, "w") as file:
for path, value in data_dict.items():
if path == "metadata":
continue
path_parts = path.split("/")
groups = path_parts[:-1]
dataset_name = path_parts[-1]
curr_hierachy = file
for group in groups:
if group not in curr_hierachy:
curr_hierachy = curr_hierachy.create_group(group)
else:
curr_hierachy = typing.cast("h5py.Group", curr_hierachy[group])
data_set = curr_hierachy.create_dataset(dataset_name, data=value, compression="gzip", shuffle=True)
if path in data_dict["metadata"]:
for key, metadata in data_dict["metadata"][path].items():
data_set.attrs[key] = metadata
def _write_cdf_file(self, file_path: Path, data_dict: dict[str, Any]) -> None:
"""Write data dictionary to CDF (Common Data Format) format.
Converts NumPy arrays to appropriate CDF data types and writes them as zVariables.
Supports global attributes and per-variable attributes from the metadata dictionary.
Applies gzip compression (Compress=6) to all variables.
Parameters:
file_path (Path): Path to save the .cdf file.
data_dict (dict[str, Any]): Dictionary with variable data and metadata.
Keys are variable names. The "metadata" key contains global and variable attributes.
Metadata should follow the format: {var_name: {attr_name: attr_value, ...}, ...}
"""
try:
cdf_file = cdflib.cdfwrite.CDF(str(file_path), delete=True)
try:
metadata = data_dict.get("metadata")
if isinstance(metadata, dict):
global_attrs: dict[str, dict[int, Any]] = {}
for attr_name, attr_value in metadata.items():
attr_name_str = str(attr_name)
if isinstance(attr_value, dict):
keys = list(attr_value.keys())
if all(isinstance(k, (int, np.integer)) or str(k).isdigit() for k in keys):
global_attrs[attr_name_str] = {int(k): v for k, v in attr_value.items()}
else:
for sub_key, sub_val in attr_value.items():
if isinstance(sub_val, (list, tuple)) and len(sub_val) == 0:
logger.warning(f"Skipping empty global attribute {attr_name_str}_{sub_key}")
continue
flat_name = f"{attr_name_str}_{sub_key}"
global_attrs[flat_name] = {0: sub_val}
elif isinstance(attr_value, (list, tuple)):
if len(attr_value) == 0:
logger.warning(f"Skipping empty global attribute {attr_name_str}")
continue
global_attrs[attr_name_str] = dict(enumerate(attr_value))
else:
global_attrs[attr_name_str] = {0: attr_value}
if global_attrs:
cdf_file.write_globalattrs(global_attrs)
for var_name, var_data in data_dict.items():
if var_name == "metadata":
continue
if getattr(var_data, "size", 0) == 0:
logger.warning(f"Skipping empty variable {var_name}")
continue
var_data_array = np.asarray(var_data)
if np.issubdtype(var_data_array.dtype, np.integer):
if var_data_array.dtype == np.int8:
cdf_dtype = cdflib.cdfwrite.CDF.CDF_INT1
elif var_data_array.dtype == np.int16:
cdf_dtype = cdflib.cdfwrite.CDF.CDF_INT2
elif var_data_array.dtype == np.int32:
cdf_dtype = cdflib.cdfwrite.CDF.CDF_INT4
else:
cdf_dtype = cdflib.cdfwrite.CDF.CDF_INT8
elif np.issubdtype(var_data_array.dtype, np.floating):
if var_data_array.dtype == np.float32:
cdf_dtype = cdflib.cdfwrite.CDF.CDF_FLOAT
else:
cdf_dtype = cdflib.cdfwrite.CDF.CDF_DOUBLE
else:
var_data_array = var_data_array.astype(np.float64)
cdf_dtype = cdflib.cdfwrite.CDF.CDF_DOUBLE
var_spec: dict[str, Any] = {
"Variable": var_name,
"Data_Type": cdf_dtype,
"Num_Elements": 1,
"Rec_Vary": True,
"Dim_Sizes": (list(var_data_array.shape[1:]) if var_data_array.ndim > 1 else []),
}
var_attrs: dict[str, Any] = {
"Compress": 6,
}
cdf_file.write_var(
var_spec,
var_attrs=var_attrs,
var_data=var_data_array,
)
finally:
cdf_file.close()
except Exception as e:
msg = f"Failed to write CDF file {file_path}: {e}"
logger.exception(msg)
raise RuntimeError(msg) from e
|