ftag.hdf5.h5utils#
Functions#
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Recursively compare two h5py.Groups or in-memory dicts. |
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Write a nested dictionary structure to an HDF5 group. |
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Extract the full contents of an HDF5 group into a nested dictionary. |
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Return a dtype based on an existing dataset and requested variables. |
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Cast float type to half or full precision. |
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Join a list of structured numpy arrays. |
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Convert a dict to a structured array. |
Module Contents#
- ftag.hdf5.h5utils.compare_groups(g1: h5py.Group | dict, g2: h5py.Group | dict, path: str = '')#
Recursively compare two h5py.Groups or in-memory dicts.
- Parameters:
g1 (h5py.Group | dict) – First group or dict to compare
g2 (h5py.Group | dict) – Second group or dict to compare
path (str, optional) – Path to the current group, by default “”
- Raises:
TypeError – If the types of the items do not match
- ftag.hdf5.h5utils.write_group_full(h5group: h5py.Group, data: dict)#
Write a nested dictionary structure to an HDF5 group.
This function recursively writes a dictionary containing datasets and subgroups to an HDF5 group. The dictionary should have the structure created by extract_group_full().
- Parameters:
h5group (h5py.Group) – The HDF5 group to write data to
data (dict) – Dictionary containing the data structure to write. Can contain: - ‘_group_attrs’: dict of group-level attributes - dataset entries: dict with ‘data’ and ‘attrs’ keys - subgroup entries: nested dictionaries
- Raises:
TypeError – If an unexpected value type is encountered in the data dict
- ftag.hdf5.h5utils.extract_group_full(group: h5py.Group) dict#
Extract the full contents of an HDF5 group into a nested dictionary.
This function recursively extracts all datasets, subgroups, and attributes from an HDF5 group into an in-memory dictionary structure. Group-level attributes are stored under the ‘_group_attrs’ key.
- Parameters:
group (h5py.Group) – The HDF5 group to extract data from
- Returns:
Nested dictionary containing: - ‘_group_attrs’: dict of group-level attributes (if any) - dataset entries: dict with ‘data’ (array) and ‘attrs’ (dict) keys - subgroup entries: nested dictionaries with same structure
- Return type:
dict
- Raises:
TypeError – If an unsupported HDF5 item type is encountered
- ftag.hdf5.h5utils.get_dtype(ds: h5py.Dataset, variables: list[str] | None = None, precision: str | None = None, transform: ftag.transform.Transform | None = None, full_precision_vars: list[str] | None = None) numpy.dtype#
Return a dtype based on an existing dataset and requested variables.
- Parameters:
ds (h5py.Dataset) – Input h5 dataset
variables (list[str] | None, optional) – List of variables to include in dtype, by default None
precision (str | None, optional) – Precision to cast floats to, “half” or “full”, by default None
transform (Transform | None, optional) – Transform to apply to variables names, by default None
full_precision_vars (list[str] | None, optional) – List of variables to keep in full precision, by default None
- Returns:
Output dtype
- Return type:
np.dtype
- Raises:
ValueError – If variables are not found in dataset
- ftag.hdf5.h5utils.cast_dtype(typestr: str, precision: str) numpy.dtype#
Cast float type to half or full precision.
- Parameters:
typestr (str) – Input type string
precision (str) – Precision to cast to, “half” or “full”
- Returns:
Output dtype
- Return type:
np.dtype
- Raises:
ValueError – If precision is not “half” or “full”
- ftag.hdf5.h5utils.join_structured_arrays(arrays: list)#
Join a list of structured numpy arrays.
See numpy/numpy#7811
- Parameters:
arrays (list) – List of structured numpy arrays to join
- Returns:
A merged structured array
- Return type:
np.array
- ftag.hdf5.h5utils.structured_from_dict(d: dict[str, numpy.ndarray]) numpy.ndarray#
Convert a dict to a structured array.
- Parameters:
d (dict[str, np.ndarray]) – Input dict of numpy arrays
- Returns:
Structured array
- Return type:
np.ndarray