Source code for dclab.rtdc_dataset.feat_temp

.. versionadded:: 0.33.0
from ..definitions import feat_logic

from .fmt_hierarchy import RTDC_Hierarchy

_registered_temporary_features = []

[docs]def deregister_all(): """Deregisters all temporary features""" for feat in list(_registered_temporary_features): deregister_temporary_feature(feat)
[docs]def deregister_temporary_feature(feature): """Convenience function for deregistering a temporary feature This method is mostly used during testing. It does not remove the actual feature data from any dataset; the data will stay in memory but is not accessible anymore through the public methods of the :class:`RTDCBase` user interface. """ if feature in _registered_temporary_features: _registered_temporary_features.remove(feature) feat_logic.feature_deregister(feature)
[docs]def register_temporary_feature(feature, label=None, is_scalar=True): """Register a new temporary feature Temporary features are custom features that can be defined ad hoc by the user. Temporary features are helpful when the integral features are not enough, e.g. for prototyping, testing, or collating with other data. Temporary features allow you to leverage the full functionality of :class:`RTDCBase` with your custom features (no need to go for a custom `pandas.Dataframe`). Parameters ---------- feature: str Feature name; allowed characters are lower-case letters, digits, and underscores label: str Feature label used e.g. for plotting is_scalar: bool Whether or not the feature is a scalar feature """ feat_logic.feature_register(feature, label, is_scalar) _registered_temporary_features.append(feature)
[docs]def set_temporary_feature(rtdc_ds, feature, data): """Set temporary feature data for a dataset Parameters ---------- rtdc_ds: dclab.RTDCBase Dataset for which to set the feature. Note that temporary features cannot be set for hierarchy children and that the length of the feature `data` must match the number of events in `rtdc_ds`. feature: str Feature name data: np.ndarray The data """ if not feat_logic.feature_exists(feature): raise ValueError( "Temporary feature '{}' has not been registered!".format(feature)) if isinstance(rtdc_ds, RTDC_Hierarchy): raise NotImplementedError("Setting temporary features for hierarchy " "children not implemented yet!") if len(data) != len(rtdc_ds): raise ValueError("The temporary feature `data` must have same length " "as the dataset. Expected length {}, got length " "{}!".format(len(rtdc_ds), len(data))) feat_logic.check_feature_shape(feature, data) rtdc_ds._usertemp[feature] = data