"""
.. versionadded:: 0.33.0
"""
from __future__ import annotations
from typing import Optional
import numpy as np
from ..definitions import feat_logic
from .core import RTDCBase
from .fmt_hierarchy import RTDC_Hierarchy, map_indices_child2root
_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: str):
"""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: str,
label: Optional[str] = None,
is_scalar: bool = 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: RTDCBase,
feature: str,
data: np.ndarray):
"""Set temporary feature data for a dataset
Parameters
----------
rtdc_ds: dclab.RTDCBase
Dataset for which to set the feature. Note that the
length of the feature `data` must match the number of events
in `rtdc_ds`. If the dataset is a hierarchy child, the data will also
be set in the parent dataset, but only for those events that are part
of the child. For all events in the parent dataset that are not part
of the child dataset, the temporary feature is set to np.nan.
feature: str
Feature name
data: np.ndarray
The data
"""
if not feat_logic.feature_exists(feature):
raise ValueError(
f"Temporary feature '{feature}' has not been registered!")
if len(data) != len(rtdc_ds):
raise ValueError(f"The temporary feature {feature} must have same "
f"length as the dataset. Expected length "
f"{len(rtdc_ds)}, got length {len(data)}!")
if isinstance(rtdc_ds, RTDC_Hierarchy):
root_ids = map_indices_child2root(rtdc_ds, np.arange(len(rtdc_ds)))
root_parent = rtdc_ds.get_root_parent()
root_feat_data = np.empty((len(root_parent)))
root_feat_data[:] = np.nan
root_feat_data[root_ids] = data
set_temporary_feature(root_parent, feature, root_feat_data)
rtdc_ds.rejuvenate()
else:
feat_logic.check_feature_shape(feature, data)
rtdc_ds._usertemp[feature] = data