from taskhuddler import TaskGraph

# All tasks will be cached in memory when TaskGraph is called
# But this means data may get stale.
graph = TaskGraph('M5hSue6oRSu_klunMRHolg')
for task in graph.tasks():
    print(task.taskId)
# Fetch the set of tasks again.
graph.fetch_tasks()

# On-disk caching:
os.environ['TC_CACHE_DIR'] = '/tmp/cache/'
# All future TaskGraph calls from now on will write the
# json to TC_CACHE_DIR and will avoid a call to taskcluster


# Are all the tasks in the 'completed' state?
print(graph.completed)

if graph.completed:
    started = graph.earliest_start_time
    finished = graph.latest_finished_time
    print("Graph took {} to run".format(finished-started))

from taskhuddler import Task, TaskDefinition, TaskStatus
from dataclasses import asdict

mytask = Task.from_task_id('M5hSue6oRSu_klunMRHolg')
print(task.status.state)

print(asdict(task))

my_task_def = TaskDefinition.from_task_id('M5hSue6oRSu_klunMRHolg')
my_task_def = TaskDefinition.from_dict(mytask.task)

my_task_status = TaskStatus.from_task_id('M5hSue6oRSu_klunMRHolg')
my_task_status = TaskStatus.from_dict(mytask.status)

from taskhuddler import TaskGraph

graph = TaskGraph('M5hSue6oRSu_klunMRHolg')

df = graph.to_dataframe()