patient_data.py

import os
import pandas as pd
from exe_functions import build_path
import sys

def import_pt_data(run_time):
    import_date = (run_time - pd.Timedelta("1 day")).date()
    fp = build_path("000_PatientData", str(import_date) + "_pt_data.csv")
    date_cols = ["start_date", "censor_date"]
    try:
        pt_data = pd.read_csv(fp, sep=',', parse_dates=date_cols)
    except FileNotFoundError:
        while True:
            first_day = input("\nIs today the trial initiation?\n" 
                      + "If today is the first day, type 'yes' then hit Enter.\n"
                      + "Otherwise type 'no' then hit Enter.\n"
                      + "Answer here: ").lower()
            if first_day in ["yes", "no"]:
                first_day = first_day == "yes"
                break
            else:
                print("Input was not 'yes' or 'no'. Please try again.")
        if not first_day:
            input("\n" + str(import_date) + "_pt_data.csv in the 000_PatientData folder not found.\n"
                  + "This file should always exist with yesterday's date in the name. Please contact Lily.\n"
                  + "Press Enter to exit the program and close this window.")
            sys.exit()
        pt_data = new_empty_pt_data()
        return pt_data
    return pt_data

def new_empty_pt_data():
    fp = build_path("000_PatientData", "empty_start.csv")
    date_cols = ["start_date", "censor_date"]
    pt_data = pd.read_csv(fp, sep=',', header=0, parse_dates=date_cols)
    return pt_data

# def export_pt_data(pt_data, runtime, purpose):
#     filesave = str(runtime.date()) + "_pt_data.csv"
#     if purpose.lower() == "reward":
#         filepath = build_path("RewardLog", filesave)
#     elif purpose.lower() == "rank":
#         filepath = os.path.join("..",  "RankLog", filesave)
#     elif purpose.lower() == "final":
#         filepath = os.path.join("..",  "PatientData", filesave)
#     else:
#         filepath = os.path.join("..",  "Trash", filesave)
#     pt_data.to_csv(filepath, index=False)


def get_study_ids(pt_data):
    try:
    # Subsets the firstname column to find the unique study_id's available in the Pillsy data to update adherence
        study_ids_df = pt_data['record_id'].copy()
        unique_study_ids_df = study_ids_df.drop_duplicates()
        unique_study_ids_list = unique_study_ids_df.values.tolist()
    except ValueError:
        unique_study_ids_list = []
    except TypeError:
        unique_study_ids_list = []
        
    return unique_study_ids_list