geomon/gschliefgraben_glasfaser/main.py

'''
Tutorial link: https://realpython.com/flask-connexion-rest-api-part-2/
https://github.com/realpython/materials/blob/master/flask-connexion-rest-part-2/version_1/people.py
Sqlalchemy version: 1.2.15
Python version: 3.7
'''

import os
import uuid
from typing import List
from itertools import chain
import json
# import sys, inspect
# currentdir = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe())))
# parentdir = os.path.dirname(currentdir)
# sys.path.insert(0, parentdir)
# import requests
from datetime import datetime, date, timedelta
from dotenv import load_dotenv, find_dotenv
from sqlalchemy.orm import session
from sqlalchemy import func, asc, desc
# from db.pg_models import Platform
from db.models import (
    ObservationSchema, Person, PersonSchema, Observation,
    create_pg_session, Dataset, Procedure, Phenomenon, Platform)
from gschliefgraben_glasfaser.my_api import MyApi


def main():
    ''' main method '''
    pg_session: session = create_pg_session()
    platform_sta_identifier = "gschliefgraben_glasfaser"
    # sensor_list = ["inclino1_14", "inclino1_02"]
    #sensor_list = os.environ.get("GLASFASER_GSCHLIEFGRABEN_SENSORS")
    sensor_list = json.loads(os.environ['GLASFASER_GSCHLIEFGRABEN_SENSORS'])

    # this will print elements along with their index value
    for sensor in sensor_list:

        pg_query = pg_session.query(Dataset) \
            .join(Procedure) \
            .join(Phenomenon) \
            .filter(Procedure.sta_identifier == sensor.lower())
        slope_dataset: Dataset = pg_query.filter(
            Phenomenon.sta_identifier == "Slope").first()
        if not slope_dataset:
            print("Sensor " + sensor + " ist noch nicht angelegt!")
            exit()
        if not slope_dataset.is_published:
            slope_dataset.is_published = 1
            slope_dataset.is_hidden = 0
            slope_dataset.dataset_type = "timeseries"
            slope_dataset.observation_type = "simple"
            slope_dataset.value_type = "quantity"
            pg_session.commit()

        platform_exists: bool = pg_session.query(Platform.id).filter_by(
            sta_identifier=platform_sta_identifier).scalar() is not None
        if platform_exists:
            sensor_platform = pg_session.query(Platform.id) \
                .filter(Platform.sta_identifier == platform_sta_identifier) \
                .first()
            slope_dataset.fk_platform_id = sensor_platform.id
        else:
            exit()

        # create all the observation for the given sensor names
        create_observations(sensor, slope_dataset)

        # update first and last observations for the dataset
        first_slope_observation = pg_session.query(Observation) \
            .filter(Observation.fk_dataset_id == slope_dataset.id) \
            .order_by(asc('sampling_time_start')) \
            .first()
        if first_slope_observation is not None:
            slope_dataset.first_time = first_slope_observation.sampling_time_start
            slope_dataset.first_value = first_slope_observation.value_quantity
            slope_dataset.fk_first_observation_id = first_slope_observation.id
        last_slope_observation = pg_session.query(Observation) \
            .filter(Observation.fk_dataset_id == slope_dataset.id) \
            .order_by(desc('sampling_time_start')) \
            .first()
        if last_slope_observation is not None:
            slope_dataset.last_time = last_slope_observation.sampling_time_start
            slope_dataset.last_value = last_slope_observation.value_quantity
            slope_dataset.fk_last_observation_id = last_slope_observation.id

        pg_session.commit()
        pg_session.close()


def create_observations(sensor: str, slope_dataset: Dataset):
    ''' create_observations method for given sensor '''

    pg_session: session = create_pg_session()

    # The size of each step in days
    # consider the start date as 2021-february 1 st
    start_date = date(2022, 1, 1)
    # consider the end date as 2021-march 1 st
    end_date = date(2022, 3, 6)

    # delta time
    delta = timedelta(days=7)
    token_api = os.environ.get("TOKEN_API")
    test_api = MyApi(token_api)

    # iterate over range of dates
    while start_date <= end_date:
        # print(start_date, end="\n")
        query_date_start: str = start_date.strftime('%Y-%m-%d')
        end_date_temp: date = start_date + delta  # (plus 7 days)
        if end_date_temp > end_date:
            end_date_temp = end_date
        query_date_end: str = end_date_temp.strftime('%Y-%m-%d')
        create_db_observations(
            sensor, query_date_start, query_date_end, test_api, pg_session, slope_dataset)
        # for next loop step set new start_date (1 day greate then last end_date)
        start_date = end_date_temp + timedelta(days=1)
    pg_session.commit()

    # query_date = "2022-02-28"
    # create_db_observations(query_date, test_api, pg_session)
    # query_date_obj = datetime.strptime(query_date, "%Y-%m-%d")
    # data = test_api.getSensorData("inclino1_14", query_date)
    # observation_array = (data['FeatureCollection']
    #                      ['Features'][0]['geometry']['properties'][0])
    # print(observation_array)

    # max_id = pg_session.query(func.max(Observation.id)).scalar()
    # if max_id is None:
    #     max_id = -1
    # # pg_session.bulk_save_objects(observations)
    # for observation_json in observation_array:
    #     ob_date_time = observation_json.get('DateTime')
    #     datetime_obj = datetime.strptime(ob_date_time, "%Y-%m-%dT%H:%M:%S.%fZ")
    #     if datetime_obj.date() != query_date_obj.date():
    #         continue
    #     max_id = max_id + 1
    #     create_observation(observation_json, pg_session, max_id)

    # pg_session.commit()


def create_db_observations(sensor, query_date_start, query_date_end,  test_api,
                           pg_session, dataset: Dataset):
    ''' to do '''
    query_date_start_obj = datetime.strptime(query_date_start, "%Y-%m-%d")
    query_date_end_obj = datetime.strptime(query_date_end, "%Y-%m-%d")
    data = test_api.getSensorData(sensor, query_date_start, query_date_end)
    observation_array = (data['FeatureCollection']
                         ['Features'][0]['geometry']['properties'][0])
    # print(observation_array)
    result = (
        pg_session.query(Observation.value_identifier)
        .filter(Observation.fk_dataset_id == dataset.id)
        .all()
    )
    value_identifier_db_list: List[str] = list(chain(*result))

    max_id = pg_session.query(func.max(Observation.id)).scalar()
    if max_id is None:
        max_id = -1
    # pg_session.bulk_save_objects(observations)
    for observation_json in observation_array:
        ob_date_time = observation_json.get('DateTime')
        datetime_obj = datetime.strptime(ob_date_time, "%Y-%m-%dT%H:%M:%S.%fZ")
        if datetime_obj.date() < query_date_start_obj.date():
            continue
        if datetime_obj.date() > query_date_end_obj.date():
            continue
        ob_value = observation_json.get('Value')
        if ob_value is None:
            continue
        # max_id = max_id + 1
        max_id = create_observation(
            observation_json, pg_session, max_id, dataset, value_identifier_db_list)
    # pg_session.commit()
    print("observations for date " + query_date_start +
          " to " + query_date_end + " for sensor " + sensor + " succesfully imported \n")


def create_observation(observation_json: ObservationSchema, db_session,
                       max_id, dataset: Dataset, value_identifier_db_list):
    """
    This function creates a new observation in the people structure
    based on the passed-in observation data
    :param observation:  observation to create in people structure
    :return:        201 on success, observation on person exists
    """

    ob_id: str = str(observation_json.get('id'))
    # db_session = create_pg_session()

    # existing_observation: bool = (
    #     db_session.query(Observation)
    #     .filter(Observation.value_identifier == ob_id)
    #     .one_or_none()
    # )
    existing_observation: bool = ob_id in value_identifier_db_list

    # Can we insert this observation?
    if existing_observation is False:
        max_id += 1
        # Create a person instance using the schema and the passed in person
        schema = ObservationSchema()
        # deserialize to python object
        new_observation: Observation = schema.load(observation_json)
        # new_observation.id = max_id
        new_observation.sta_identifier = str(uuid.uuid4())
        new_observation.sampling_time_start = new_observation.result_time
        new_observation.sampling_time_end = new_observation.result_time
        new_observation.fk_dataset_id = dataset.id

        # Add the person to the database
        db_session.add(new_observation)
        # dataset.observations.append(new_observation)
        # db_session.commit()

        # Serialize and return the newly created person in the response
        # data = schema.dump(new_observation)
        # return data, 201
        return max_id
    # Otherwise, nope, person exists already
    else:
        # print(409, f'Observation {ob_id} exists already')
        return max_id


def create(person_json: PersonSchema):
    """
    This function creates a new person in the people structure
    based on the passed-in person data
    :param person:  person to create in people structure
    :return:        201 on success, 406 on person exists
    """

    login = person_json.get('login')
    #lname = person.get('lname')
    db_session = create_pg_session()

    # existing_person = Person.query \
    #     .filter(Person.login == login) \
    #     .one_or_none()
    existing_person: bool = (
        db_session.query(Person)
        .filter(Person.login == login)
        .one_or_none()
    )

    # Can we insert this person?
    if existing_person is None:
        # Create a person instance using the schema and the passed in person
        schema = PersonSchema()
        # deserialize to object
        new_person: Person = schema.load(person_json)

        # Add the person to the database
        db_session.add(new_person)
        db_session.commit()

        # Serialize and return the newly created person in the response
        data = schema.dump(new_person)
        return data, 201
    # Otherwise, nope, person exists already
    else:
        print(409, f'Person {login} exists already')


if __name__ == "__main__":
    load_dotenv(find_dotenv())
    sensor_list1 = os.environ.get('GLASFASER_GSCHLIEFGRABEN_SENSORS', [])
    print(f'sensors: {sensor_list1} .')
    main()