Source code for dclab.rtdc_dataset.feat_temp

.. 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) data_ro = data.view() data_ro.setflags(write=False) rtdc_ds._usertemp[feature] = data_ro