Merge pull request ME-ICA#14 from handwerkerd/harmonizing_kunduDTM_wi…

…th_main

removed unnecessary function tedana.selection.selection_utils.get_new_meanmetricrank

tedana/selection/selection_nodes.py

@@ -8,7 +8,7 @@
 from scipy.stats import scoreatpercentile
 
 from tedana.metrics.dependence import generate_decision_table_score
-from tedana.selection.selection_utils import (  # get_new_meanmetricrank,
+from tedana.selection.selection_utils import (
     change_comptable_classifications,
     confirm_metrics_exist,
     get_extend_factor,

tedana/selection/selection_utils.py

@@ -6,7 +6,6 @@
 
 import numpy as np
 
-from tedana.metrics.dependence import generate_decision_table_score
 from tedana.stats import getfbounds
 
 LGR = logging.getLogger("GENERAL")
@@ -653,108 +652,3 @@ def get_extend_factor(n_vols=None, extend_factor=None):
         LGR.error(error_msg)
         raise ValueError(error_msg)
     return extend_factor
-
-
-def get_new_meanmetricrank(component_table, comps2use, decision_node_idx, calc_new_rank=False):
-    """
-    If a revised d_table_score was already calculated, use that.
-    If not, calculate a new d_table_score based on the components
-    identified in comps2use
-
-    Parameters
-    ----------
-    component_table
-    comps2use
-    decision_node_idx: :obj:`int`
-        The index for the current decision node
-    calc_new_rank: :obj:`bool`
-        calculate a new d_table_score even if revised scores with the same
-        labels were already calculated
-
-    Return
-    ------
-    meanmetricrank
-    comptable
-    """
-    rank_label = f"d_table_score_node{decision_node_idx}"
-    if not calc_new_rank and (rank_label in component_table.columns):
-        # return existing
-        LGR.info(
-            f"{rank_label} already calculated so not recalculating in node {decision_node_idx}"
-        )
-        return component_table[rank_label], component_table
-
-    # get the array of ranks
-    ranks = generate_decision_table_score(
-        component_table.loc[comps2use, "kappa"],
-        component_table.loc[comps2use, "dice_FT2"],
-        component_table.loc[comps2use, "signal-noise_t"],
-        component_table.loc[comps2use, "countnoise"],
-        component_table.loc[comps2use, "countsigFT2"],
-    )
-    # see if we need to make a new column
-    if rank_label not in component_table.columns:
-        component_table[rank_label] = np.zeros(component_table.shape[0]) * np.nan
-
-    # fill in the column with the components of interest
-    for c, rank in zip(comps2use, ranks):
-        component_table.loc[c, rank_label] = rank
-
-    return component_table[rank_label].copy(), component_table.copy()
-
-
-# Not currently being used and hopefully will never again be used
-# def prev_classified_comps(comptable, decision_node_idx, classification_label, prev_X_steps=0):
-#     """
-#     Output a list of components with a specific label during the current or
-#     previous X steps of the decision tree. For example, if
-#     classification_label = ['provisionalaccept'] and prev_X_steps = 0
-#     then this outputs the indices of components that are currenlty
-#     classsified as provisionalaccept. If prev_X_steps=2, then this will
-#     output components that are classified as provisionalaccept or were
-#     classified as such any time before the previous two decision tree steps
-
-#     Parameters
-#     ----------
-#     comptable
-#     n_echos: :obj:`int`
-#         The number of echos in the multi-echo data set
-#     decision_node_idx: :obj:`int`
-#         The index of the node in the decision tree that called this function
-#     classification_label: :obj:`list[str]`
-#         A list of strings containing classification labels to identify in components
-#         For example: ['provisionalaccept']
-#     prev_X_steps: :obj:`int`
-#         If 0, then just count the number of provisionally accepted or rejected
-#         or unclassified components in the current node. If this is a positive
-#         integer, then also check if a component was a in one of those three
-#         categories in ignore_prev_X_steps previous nodes. default=0
-
-#     Returns
-#     -------
-#     full_comps2use: :obj:`list[int]`
-#         A list of indices of components that have or add classification_lable
-#     """
-
-#     full_comps2use = selectcomps2use(comptable, classification_label)
-#     rationales = comptable["rationale"]
-
-#     if prev_X_steps > 0:  # if checking classifications in prevision nodes
-#         for compidx in range(len(comptable)):
-#             tmp_rationale = rationales.values[compidx]
-#             tmp_list = re.split(":|;| ", tmp_rationale)
-#             while "" in tmp_list:  # remove blank strings after splitting rationale
-#                 tmp_list.remove("")
-#             # Check the previous nodes
-#             # This is inefficient, but it should work
-#             for didx in range(max(0, decision_node_idx - prev_X_steps), decision_node_idx):
-#                 if str(didx) in tmp_list:
-#                     didx_loc = tmp_list.index(str(didx))
-#                     if didx_loc > 1:
-#                         tmp_classifier = tmp_list[didx_loc - 1]
-#                         if tmp_classifier in classification_label:
-#                             full_comps2use.append(compidx)
-
-#     full_comps2use = list(set(full_comps2use))
-
-#     return full_comps2use