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| dimensions = ["A", "B-S", "B-O", "C-S", "C-O", "D"] | |
| dimension_to_layer_name_raw = { # in raw annotation files (Ayal's outputs) | |
| "A": "Segment Patient Affect (A)", | |
| "B-S": "Segment Patient B-S", | |
| "B-O": "Segment Patient B-O", | |
| "C-S": "Segment Patient C-S", | |
| "C-O": "Segment Patient C-O", | |
| "D": "Segment Patient Desire (D)", | |
| } | |
| dimension_to_layer_name_cleaned = { # in cleaned annotation files (Yael's outputs) | |
| "A": "Segment_Patient_A", | |
| "B-S": "Segment_Patient_B-S", | |
| "B-O": "Segment_Patient_B-O", | |
| "C-S": "Segment_Patient_C-S", | |
| "C-O": "Segment_Patient_C-O", | |
| "D": "Segment_Patient_D", | |
| } | |
| def df_to_self_states_json(df, doc_name, annotator = None): | |
| """Convert a dataframe into a json object that can be more easily used for visualization.""" | |
| # df is the dataframe of annotations | |
| # doc_name is the name of the document | |
| # annotator is the name of the annotator (optional) | |
| # select dimension_to_layer_name | |
| if 'Segment_Patient_A' in df.layer.unique(): | |
| dimension_to_layer_name = dimension_to_layer_name_cleaned | |
| else: | |
| dimension_to_layer_name = dimension_to_layer_name_raw | |
| def get_evidence_obj(evidence_df): | |
| "Assume that the evidence_df is a partial dataframe including only annotation of a single evidence span." | |
| evidence_obj = {k: v.value.iloc[0] for k, v in evidence_df.groupby("feature")} | |
| # evidence_obj["text"] = evidence_df.span_text.iloc[0] | |
| # evidence_obj["span_index_begin"] = evidence_df.begin.iloc[0] | |
| # evidence_obj["span_index_end"] = evidence_df.end.iloc[0] | |
| return evidence_obj | |
| doc_object = {"document": doc_name, "annotator": annotator} | |
| doc_object["segments"] = [] | |
| doc_df = df[df.document == doc_name] | |
| if annotator: | |
| doc_df = doc_df[doc_df.annotator == annotator] | |
| # now add the segments | |
| for segment_index, segment_group in df[df.document == doc_name].groupby("segment"): | |
| # add Segment Summary features into segment object | |
| segment_object = {"segment": segment_index} | |
| segment_summary_df = segment_group[segment_group.layer == "Segment Summary"] | |
| # # if not post-summary, skip this post | |
| # if segment_summary_df.empty: | |
| # continue | |
| segment_object["Segment Summary"] = {k: v.value.iloc[0] for k, v in segment_summary_df.groupby("feature")} | |
| state1_df = segment_group[segment_group.self_state_index == 1] | |
| state2_df = segment_group[segment_group.self_state_index == 2] | |
| states_list = list() | |
| state1_obj = dict() | |
| state2_obj = dict() | |
| # set is_adaptive for each state | |
| if not state1_df.empty: | |
| state1_obj["is_adaptive"] = state1_df.is_adaptive.dropna().iloc[0] | |
| states_list.append(state1_obj) | |
| if not state2_df.empty: | |
| state2_obj["is_adaptive"] = state2_df.is_adaptive.dropna().iloc[0] | |
| states_list.append(state2_obj) | |
| # collect elements per dimension | |
| for dimension in dimensions: | |
| segment_dim_layer_name = dimension_to_layer_name[dimension] | |
| state1_dimension_df = state1_df[state1_df.layer == segment_dim_layer_name] | |
| state2_dimension_df = state2_df[state2_df.layer == segment_dim_layer_name] | |
| # search for evidence layers that match the same segment and dimension | |
| dim_evidence_rows = segment_group[segment_group.layer == f"Patient_{dimension}_evidence"] | |
| if not state1_dimension_df.empty: | |
| state1_obj[dimension] = {k: v.value.iloc[0] for k, v in state1_dimension_df.groupby("feature")} # "Category", "Adaptivity", "Presence" | |
| evidences_obj = [] | |
| # for _, evidence_df in dim_evidence_rows.groupby("span_text"): | |
| for _, evidence_df in dim_evidence_rows.groupby(["begin", "end"]): | |
| # take only the evidence that matches the category of the segment-level element of the same dimension | |
| if not evidence_df.empty and evidence_df[evidence_df.feature == "Category"].value.iloc[0] == state1_obj[dimension]["Category"]: | |
| evidences_obj.append(get_evidence_obj(evidence_df)) | |
| if evidences_obj: | |
| state1_obj[dimension]["evidences"] = evidences_obj | |
| if not state2_dimension_df.empty: | |
| state2_obj[dimension] = {k: v.value.iloc[0] for k, v in state2_dimension_df.groupby("feature")} # "Category", "Adaptivity", "Presence" | |
| evidences_obj = [] | |
| # for _, evidence_df in dim_evidence_rows.groupby("span_text"): | |
| for _, evidence_df in dim_evidence_rows.groupby(["begin", "end"]): | |
| # take only the evidence that matches the category of the segment-level element of the same dimension | |
| if not evidence_df.empty and evidence_df[evidence_df.feature == "Category"].value.iloc[0] == state2_obj[dimension]["Category"]: | |
| evidences_obj.append(get_evidence_obj(evidence_df)) | |
| if evidences_obj: | |
| state2_obj[dimension]["evidences"] = evidences_obj | |
| segment_object["self-states"] = states_list | |
| # add the post object to the document object | |
| doc_object["segments"].append(segment_object) | |
| return doc_object | |
| element_short_desc_map = { | |
| 'A:(11) Proud': 'A:(11) Proud', | |
| 'B-O:(1) Relating behavior': 'B-O:(1) Relating', | |
| 'C-S:(1) Self-acceptance and compassion': 'C-S:(1) Self-compassion', | |
| 'D:(1) Relatedness': 'D:(1) Relatedness', | |
| 'A:(4) Depressed, despair, hopeless': 'A:(4) Depressed', | |
| 'C-O:(4) Perception of the other as blocking autonomy needs': 'C-O:(4) Other blocks autonomy', | |
| 'C-S:(2) Self criticism': 'C-S:(2) Self-criticism', | |
| 'C-O:(2) Perception of the other as detached or over attached': 'C-O:(2) Other detached/overattached', | |
| 'C-O:(1) Perception of the other as related': 'C-O:(1) Other related', | |
| 'A:(3) Sad, emotional pain, grieving': 'A:(3) Sadness', | |
| 'B-O:(2) Fight or flight behavior': 'B-O:(2) Fight/flight', | |
| 'A:(14) Feel lonely': 'A:(14) Lonely', | |
| 'D:(2) Expectation that relatedness needs will not be met': 'D:(2) Relatedness (-)', | |
| 'B-S:(2) Self harm, neglect and avoidance': 'B-S:(2) Self-harm', | |
| 'A:(10) Angry (aggression), disgust, contempt': 'A:(10) Angry/Aggressive', | |
| 'A:(8) Apathic, don’t care, blunted': 'A:(8) Apathetic', | |
| 'B-S:(1) Self care and improvement': 'B-S:(1) Self-care', | |
| 'D:(5) Competence, self esteem, self-care': 'D:(5) Competence', | |
| 'D:(6) Expectation that competence needs will not be met': 'D:(6) Competence (-)', | |
| 'C-O:(3) Perception of the other as facilitating autonomy needs': 'C-O:(3) Other supports autonomy', | |
| 'A:(2) Anxious/ fearful/ tense': 'A:(2) Anxious', | |
| 'A:(12) Ashamed, guilty': 'A:(12) Ashamed/Guilty', | |
| 'B-O:(4) Over controlled or controlling behavior': 'B-O:(4) Controlling', | |
| 'A:(1) Calm/ laid back': 'A:(1) Calm', | |
| 'D:(4) Expectation that autonomy needs will not be met': 'D:(4) Autonomy (-)', | |
| 'D:(3) Autonomy and adaptive control': 'D:(3) Autonomy', | |
| 'A:(5) Content, happy, joy, hopeful': 'A:(5) Happy', | |
| 'B-O:(3) Autonomous or adaptive control behavior': 'B-O:(3) Adaptive control', | |
| 'A:(9) Justifiable anger/ assertive anger, justifiable outrage': 'A:(9) Justified anger', | |
| 'A:(13) Feel loved, belong': 'A:(13) Loved/Belonging', | |
| 'A:(7) Vigor / energetic': 'A:(7) Vigor' | |
| } | |