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Train Coding

This example can be used as, which is namely reference in this documentation.

Example Train

Calculate average age based on a fhir query

The query to be used in this train is the JSON version of the minimal example found in the next section. What this train will do ist calculate the average age of patients matching the query across multiple stations.
The stations will pass the query results to the train as volumes and also set the environment variable TRAIN_DATA_PATH inside the train container, which is used by the train to load the passed json file.

import pandas as pd
import os
import json
import datetime

RESULTS_PATH = "/opt/pht_results/average_age.json"

def load_previous_data(path):
    if os.path.exists(path):
        with open(path, "r") as f:
            average_age_dict = json.load(f)

        return average_age_dict

        return None

def age_from_dob(dob):
    today =
    return today.year - dob.year - ((today.month, < (dob.month,

def parse_fhir_response(data_path) -> pd.DataFrame:
    Load and parse provided FHIR resources to a pandas dataframe
    with open(data_path, "r") as f:
        results = json.load(f)
    parsed_resources = []
    for patient in results["entry"]:
        resource = patient["resource"]

    df = pd.DataFrame(parsed_resources)
    return df

def parse_resource(resource):
    Parse a FHIR resource returned from a FHIR server in a desired format
    :param resource:
    :return: dictionary of parsed resource
    sequence_dict = {
        "givenName": resource['name'][0]['given'],
        "familyName": resource['name'][0]['family'],
        "birthDate": resource["birthDate"],
        "gender": resource["gender"]
    return sequence_dict

def calculate_new_average(average_age_dict, data_path, results_path):
    # load the data and ensure that birthdate is a datetime column
    data = parse_fhir_response(data_path)
    data["birthDate"] = pd.to_datetime(data["birthDate"])

    ages = data["birthDate"].apply(lambda x: age_from_dob(x))

    local_average = ages.mean()

    # previous results exist load them otherwise create a new dictionary containing the results
    if average_age_dict:
        prev_average = average_age_dict["average_age"]
        new_average = (prev_average + local_average) / 2 if prev_average else local_average
        average_age_dict["average_age"] = new_average
        new_average = local_average
        average_age_dict = {"average_age": new_average}


    # store the updated results
    with open(results_path, "w") as f:
        json.dump(average_age_dict, fp=f, indent=2)

def main():
    data_path = os.getenv("TRAIN_DATA_PATH", "/opt/train_data/patients.json")
    print(f"Loading data at {data_path}")
    prev_results = load_previous_data(RESULTS_PATH)
    calculate_new_average(prev_results, data_path, RESULTS_PATH)

if __name__ == '__main__':