plaid.containers.utils

Utility functions for PLAID containers.

Attributes

path_to_location
retrocompatibility

Functions

get_sample_ids(→ list[int])	Return list of sample ids from a dataset on disk.
get_number_of_samples(→ int)	Return number of samples in a dataset on disk.
get_feature_type_and_details_from(→ tuple[str, ...)	Extract and validate the feature type and its associated metadata from a feature identifier.
check_features_type_homogeneity(→ None)	Check type homogeneity of features, for tabular conversion.
check_features_size_homogeneity(→ int)	Check size homogeneity of features, for tabular conversion.
has_duplicates_feature_ids(feature_identifiers)	Check whether a list of feature identifier contains duplicates.
get_feature_details_from_path(→ dict[str, str])	Retrieve semantic details from a CGNS-style path.
validate_required_infos(→ None)	Validate that required infos categories and keys are present.

Module Contents

path_to_location

retrocompatibility

get_sample_ids(savedir: str | pathlib.Path) → list[int]

Return list of sample ids from a dataset on disk.

Parameters:

savedir (Union[str,Path]) – The path to the directory where sample files are stored.

Returns:

List of sample ids.

Return type:

list[int]

get_number_of_samples(savedir: str | pathlib.Path) → int

Return number of samples in a dataset on disk.

Parameters:

savedir (Union[str,Path]) – The path to the directory where sample files are stored.

Returns:

number of samples.

Return type:

int

get_feature_type_and_details_from(feature_identifier: plaid.containers.feature_identifier.FeatureIdentifier) → tuple[str, plaid.containers.feature_identifier.FeatureIdentifier]

Extract and validate the feature type and its associated metadata from a feature identifier.

This utility function ensures that the feature_identifier dictionary contains a valid “type” key (e.g., “scalar”, “field”, “node”) and returns the type along with the remaining identifier keys, which are specific to the feature type.

Parameters:

feature_identifier (dict) – A dictionary with a “type” key, and other keys (some optional) depending on the feature type. For example: - {“type”: “scalar”, “name”: “Mach”} - {“type”: “field”, “name”: “pressure”} - {“type”: “field”, “name”: “pressure”, “time”:0.} - {“type”: “nodes”, “base_name”: “Base_2_2”}

Returns:

A tuple (feature_type, feature_details) where:

• feature_type is the value of the “type” key (e.g., “scalar”).

• feature_details is a dictionary of the remaining keys.

Return type:

tuple[str, dict]

Raises:

AssertionError –

• If “type” is missing.

• If the type is not in AUTHORIZED_FEATURE_TYPES.

• If any unexpected keys are present for the given type.

check_features_type_homogeneity(feature_identifiers: list[plaid.containers.feature_identifier.FeatureIdentifier]) → None

Check type homogeneity of features, for tabular conversion.

Parameters:

feature_identifiers (list[dict]) – dict with a “type” key, and other keys (some optional) depending on the feature type. For example: - {“type”: “scalar”, “name”: “Mach”} - {“type”: “field”, “name”: “pressure”}

Raises:

AssertionError – if types are not consistent

check_features_size_homogeneity(feature_identifiers: list[plaid.containers.feature_identifier.FeatureIdentifier], features: dict[int, list[plaid.types.Feature]]) → int

Check size homogeneity of features, for tabular conversion.

Size homogeneity is check through samples for each feature, and through features for each sample. To be converted to tabular data, each sample must have the same number of features and each feature must have the same dimension

Parameters:

• feature_identifiers (list[dict]) – dict with a “type” key, and other keys (some optional) depending on the feature type. For example: - {“type”: “scalar”, “name”: “Mach”} - {“type”: “field”, “name”: “pressure”}

• features (dict) – dict with sample index as keys and one or more features as values.

Returns:

the common feature dimension

Return type:

int

Raises:

AssertionError – if sizes are not consistent

has_duplicates_feature_ids(feature_identifiers: list[plaid.containers.feature_identifier.FeatureIdentifier])

Check whether a list of feature identifier contains duplicates.

Parameters:

feature_identifiers (list[FeatureIdentifier]) – A list of dictionaries representing feature identifiers.

Returns:

True if a duplicate is found in the list, False otherwise.

Return type:

bool

get_feature_details_from_path(path: str) → dict[str, str]

Retrieve semantic details from a CGNS-style path.

validate_required_infos(infos: dict[str, Any]) → None

Validate that required infos categories and keys are present.

Parameters:

infos – Dataset infos dictionary loaded from disk.

Raises:

ValueError – If a required infos category or key is missing.