178 lines
6.9 KiB
Python
178 lines
6.9 KiB
Python
'''
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Tutorial link: https://realpython.com/flask-connexion-rest-api-part-2/
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https://github.com/realpython/materials/blob/master/flask-connexion-rest-part-2/version_1/people.py
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Sqlalchemy version: 1.2.15
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Python version: 3.10
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'''
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import os
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import json
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import uuid
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from datetime import datetime
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from dotenv import load_dotenv, find_dotenv
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from sqlalchemy.orm import session
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from sqlalchemy import func, asc, desc
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# from db.pg_models import Platform
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from db.models import (
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ObservationSchema, Observation, create_pg_session,
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Dataset, Procedure, Phenomenon, Platform)
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from gschliefgraben_glasfaser.my_api import MyApi
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def main():
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''' main method '''
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pg_session: session = create_pg_session()
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platform_sta_identifier = "gschliefgraben_glasfaser"
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# sensor_list = ["inclino1_14", "inclino1_02"]
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#sensor_list = os.environ.get("GLASFASER_GSCHLIEFGRABEN_SENSORS")
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sensor_list = json.loads(os.environ['GLASFASER_GSCHLIEFGRABEN_SENSORS'])
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# this will print elements along with their index value
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for sensor in sensor_list:
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pg_query = pg_session.query(Dataset) \
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.join(Procedure) \
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.join(Phenomenon) \
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.filter(Procedure.sta_identifier == sensor.lower())
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slope_dataset: Dataset = pg_query.filter(
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Phenomenon.sta_identifier == "Slope").first()
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if not slope_dataset:
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print("Sensor " + sensor + " ist noch nicht angelegt!")
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# exit()
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continue
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if not slope_dataset.is_published:
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slope_dataset.is_published = 1
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slope_dataset.is_hidden = 0
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slope_dataset.dataset_type = "timeseries"
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slope_dataset.observation_type = "simple"
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slope_dataset.value_type = "quantity"
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pg_session.commit()
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platform_exists: bool = pg_session.query(Platform.id).filter_by(
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sta_identifier=platform_sta_identifier).scalar() is not None
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if platform_exists:
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sensor_platform = pg_session.query(Platform.id) \
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.filter(Platform.sta_identifier == platform_sta_identifier) \
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.first()
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slope_dataset.fk_platform_id = sensor_platform.id
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# create all the observation for the given sensor names
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create_observations(sensor, slope_dataset)
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first_slope_observation = pg_session.query(Observation) \
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.filter(Observation.fk_dataset_id == slope_dataset.id) \
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.order_by(asc('sampling_time_start')) \
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.first()
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if first_slope_observation is not None:
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slope_dataset.first_time = first_slope_observation.sampling_time_start
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slope_dataset.first_value = first_slope_observation.value_quantity
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slope_dataset.fk_first_observation_id = first_slope_observation.id
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last_slope_observation = pg_session.query(Observation) \
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.filter(Observation.fk_dataset_id == slope_dataset.id) \
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.order_by(desc('sampling_time_start')) \
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.first()
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if last_slope_observation is not None:
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slope_dataset.last_time = last_slope_observation.sampling_time_start
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slope_dataset.last_value = last_slope_observation.value_quantity
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slope_dataset.fk_last_observation_id = last_slope_observation.id
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pg_session.commit()
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pg_session.close()
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def create_observations(sensor: str, slope_dataset: Dataset):
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''' create_observations method for given sensor '''
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pg_session: session = create_pg_session()
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# create access token
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token_api = os.environ.get("TOKEN_API")
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test_api = MyApi(token_api)
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# The size of each step in days
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# consider the start date as 2021-february 1 st
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start_date = datetime.today()
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query_date = start_date.strftime('%Y-%m-%d')
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create_db_observations(sensor, query_date, test_api,
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pg_session, slope_dataset)
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pg_session.commit()
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def create_db_observations(sensor: str, query_date, test_api, pg_session, dataset: Dataset):
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''' to do '''
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query_date_obj = datetime.strptime(query_date, "%Y-%m-%d")
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data = test_api.getSensorData(sensor, query_date, query_date)
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observation_array = (data['FeatureCollection']
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['Features'][0]['geometry']['properties'][0])
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# print(observation_array)
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max_id = pg_session.query(func.max(Observation.id)).scalar()
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if max_id is None:
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max_id = -1
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# pg_session.bulk_save_objects(observations)
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for observation_json in observation_array:
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ob_date_time = observation_json.get('DateTime')
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datetime_obj = datetime.strptime(ob_date_time, "%Y-%m-%dT%H:%M:%S.%fZ")
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if datetime_obj.date() != query_date_obj.date():
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continue
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ob_value = observation_json.get('Value')
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if ob_value is None:
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continue
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# max_id = max_id + 1
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max_id = create_observation(
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observation_json, pg_session, max_id, dataset)
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# pg_session.commit()
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print("observations for date " + query_date + " and sensor " + sensor +
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" succesfully imported \n")
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def create_observation(observation_json: ObservationSchema, db_session, max_id, dataset: Dataset):
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"""
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This function creates a new observation in the people structure
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based on the passed-in observation data
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:param observation: person to create in people structure
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:return: 201 on success, observation on person exists
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"""
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ob_id: str = str(observation_json.get('id'))
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# db_session = create_pg_session()
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existing_observation: bool = (
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db_session.query(Observation)
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.filter(Observation.value_identifier == ob_id, Observation.fk_dataset_id == dataset.id)
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.one_or_none()
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)
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# Can we insert this observation?
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if existing_observation is None:
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max_id += 1
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# Create a person instance using the schema and the passed in person
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schema = ObservationSchema()
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# deserialize to python object
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new_observation: Observation = schema.load(observation_json)
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# new_observation.id = max_id
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new_observation.sta_identifier = str(uuid.uuid4())
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new_observation.sampling_time_start = new_observation.result_time
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new_observation.sampling_time_end = new_observation.result_time
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new_observation.fk_dataset_id = dataset.id
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# Add the person to the database
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db_session.add(new_observation)
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# dataset.observations.append(new_observation)
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# db_session.commit()
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# Serialize and return the newly created person in the response
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# data = schema.dump(new_observation)
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# return data, 201
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return max_id
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# Otherwise, nope, person exists already
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else:
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print(409, f'Observation {ob_id} exists already')
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return max_id
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if __name__ == "__main__":
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load_dotenv(find_dotenv())
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sensor_list1 = os.environ.get('GLASFASER_GSCHLIEFGRABEN_SENSORS', [])
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print(f'sensors: {sensor_list1} .')
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main()
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