added demographics.py for open house to put code to load demographics…

… into a background script'

src/b2aiprep/demographics.py

@@ -0,0 +1,100 @@
+import pandas as pd
+from pandas import DataFrame
+from enum import Enum
+import json
+import pickle
+
+
+class RepeatInstrument(Enum):
+    SESSION = 'Session'
+    ACOUSTIC_TASK = 'Acoustic Task'
+    RECORDING = 'Recording'
+    GENERIC_DEMOGRAPHICS = 'Q Generic Demographics'
+    GENERIC_CONFOUNDERS = 'Q Generic Confounders'
+    GENERIC_VOICE_PERCEPTION = 'Q Generic Voice Perception'
+    GENERIC_VOICE_HANDICAP = 'Q Generic Voice Handicap Index Vhi10'
+    GENERIC_PHQ9_DEPRESSION = 'Q Generic Patient Health Questionnaire9'
+    GENERIC_GAD7_ANXIETY = 'Q - Generic - Gad7 Anxiety'
+    RESP_DYSPNEA_INDEX = 'Q - Resp - Dyspnea Index Di'
+    RESP_LEICESTER_COUGH = 'Q - Resp - Leicester Cough Questionnaire Lcq'
+    VOICE_VOICE_PROBLEM_SEVERITY = 'Q - Voice - Voice Problem Severity'
+    MOOD_PANAS = 'Q - Mood - Panas'
+    MOOD_CUSTOM_AFFECT = 'Q - Mood - Custom Affect Scale'
+    MOOD_DSM5_ADULT = 'Q - Mood - Dsm5 Adult'
+    MOOD_PTSD_ADULT = 'Q - Mood - Ptsd Adult'
+    MOOD_ADHD_ADULT = 'Q - Mood - Adhd Adult'
+    NEURO_WINOGRAD_SCHEMAS = 'Q - Neuro Winograd Schemas'
+    NEURO_WORDCOLOR_STROOP = 'Q - Neuro - Wordcolor Stroop'
+    NEURO_PRODUCTIVE_VOCABULARY = 'Q - Neuro - Productive Vocabulary'
+    NEURO_RANDOM_ITEM_GENERATION = 'Q - Neuro - Random Item Generation'
+
+def load_csv_file(file_path):
+    try:
+        data = pd.read_csv(file_path, low_memory=False, na_values='')
+        return data
+    except FileNotFoundError:
+        print('File not found.')
+        return None
+    except pd.errors.ParserError:
+        print('Error parsing CSV file.')
+        return None
+
+def pickle_new_data_columns(category: str, column_names: list):
+
+    out_file = f'{category}.pkl'
+    with open(out_file, "wb") as f:
+        pickle.dump(column_names, f)
+    print(f'New column names saved to: {out_file}')
+    return
+
+def load_data_columns():       
+    data_columns = {}
+    with open('participant_columns.pkl', "rb") as f:
+        data_columns['participant_columns'] = pickle.load(f)
+
+    with open('demographics_columns.pkl', "rb") as f:
+        data_columns['demographics_columns'] = pickle.load(f)
+
+    with open('confounders_columns.pkl', "rb") as f:
+        data_columns['confounders_columns'] = pickle.load(f)
+
+    with open('phq9_columns.pkl', "rb") as f:
+        data_columns['phq9_columns'] = pickle.load(f)
+
+    with open('gad7_columns.pkl', "rb") as f:
+        data_columns['gad7_columns'] = pickle.load(f)
+
+    return data_columns
+
+def get_columns_of_repeat_instrument(df: DataFrame, repeat_instrument: RepeatInstrument):
+    columns = pd.Index(['record_id'])
+
+    data_columns = load_data_columns()
+    participant_columns=data_columns['participant_columns']
+    demographics_columns=data_columns['demographics_columns']
+    confounders_columns=data_columns['confounders_columns']
+    phq9_columns=data_columns['phq9_columns']
+    gad7_columns=data_columns['gad7_columns']
+
+    if repeat_instrument == RepeatInstrument.SESSION:
+        columns = columns.append(df.columns[pd.Series(df.columns).str.startswith('session_')])
+    elif repeat_instrument == RepeatInstrument.ACOUSTIC_TASK:
+        columns = columns.append(df.columns[pd.Series(df.columns).str.startswith('acoustic_task_')])
+    elif repeat_instrument == RepeatInstrument.RECORDING:
+        columns = columns.append(df.columns[pd.Series(df.columns).str.startswith('recording_')])
+    elif repeat_instrument == RepeatInstrument.GENERIC_DEMOGRAPHICS:
+        columns = columns.append(pd.Index(demographics_columns))
+    elif repeat_instrument == RepeatInstrument.GENERIC_CONFOUNDERS:
+        columns = columns.append(pd.Index(confounders_columns))
+    elif repeat_instrument == RepeatInstrument.GENERIC_PHQ9_DEPRESSION:
+        columns = columns.append(pd.Index(phq9_columns))
+    elif repeat_instrument == RepeatInstrument.GENERIC_GAD7_ANXIETY:
+        columns = columns.append(pd.Index(gad7_columns))
+    else:
+        # add rest of columns for other repeat instruments
+        raise NotImplementedError('Repeat Instrument not implemented.')
+    return columns
+
+def get_df_of_repeat_instrument(df: DataFrame, repeat_instrument: RepeatInstrument):
+    return df[df['redcap_repeat_instrument'] == repeat_instrument.value][get_columns_of_repeat_instrument(df, repeat_instrument)]
+