# Airflow dependencies
from asyncio import Task
from airflow import DAG
from airflow.operators.python_operator import PythonOperator
from airflow.utils.dates import days_ago
from airflow.models import Variable
from jinja2 import Template
from functools import reduce
from datetime import datetime, timedelta
from standardized_process.dags.functions.hardness_clustering_process import *
import json
import os
task_loaded = False
# Default arguments
default_args = {
'owner': 'pedro',
'start_date': datetime(2023, 4, 22, 0, 0, 0)
}
year = datetime.now().year
month = datetime.now().month
day = datetime.now().day
mine_site = "BHP Spence"
drilling_source = "Surface Manager"
# Aplica templating Jinja al archivo JSON
with open('dags/standardized_process/dags/configuration/hardness_clustering_dag.json') as f:
template_content = f.read()
# Crear una plantilla Jinja2
template = Template(template_content)
rendered_config = template.render(mine_site=mine_site, drilling_source=drilling_source, year=year, month=month, day=day)
data = json.loads(rendered_config)
# Diccionario con un mapa de todas las funciones
function_mapping = {
'generate_feature_clustering_model': generate_feature_clustering_model,
'generate_hardness_label':generate_hardness_label,
'add_drilling_times': add_drilling_times,
'update_input_data': update_input_data
}
def chain_tasks(x, y):
return x << y
dag = DAG("hardness_clustering_bhp_spence", schedule_interval='50 16,0 * * *', tags=['Standardized Process'], default_args=default_args, max_active_runs=1, is_paused_upon_creation=False)
list_task = []
for task in data['task_scheme']:
if task['active'] == True:
drill_report_task = PythonOperator(
task_id = task['task_id'],
python_callable = function_mapping[task['function']],
op_kwargs = task['op_kwargs'],
retries=3,
dag = dag
)
list_task.append(drill_report_task)
# Establecer dependencias
for i in range(len(list_task) - 1):
list_task[i] >> list_task[i + 1]