# Execute until all tasks are done or require manual intervention.
# For the sake of this tutorial, we ignore the "manual" flag on the
# tasks. In practice, you probably don't want to do that.
for i in range(20):
print(i)
workflow.complete_all(False)
if workflow.is_completed():
break
time.sleep(0.5)
#workflow.complete_all(halt_on_manual=False)
#workflow.complete_next()
#tasks = workflow.get_tasks(Task.WAITING)
#for t in tasks:
# print(t.get_name())
# t.complete()
#time.sleep(10)
# 类似get_dump（返回当前内部任务树的完整转储以进行调试），但是将输出打印到终端，而不是返回输出
workflow.dump()
# get_tasks_from_spec_name：返回其规范具有给定名称的所有任务。
# get_data：返回具有给定名称的数据字段的值，如果数据字段不存在，则返回给定的默认值。
print(workflow.get_tasks_from_spec_name('synch_1')[0].get_data('Result'))
print(workflow.get_tasks_from_spec_name('synch_1')[1].get_data('Result'))
print(workflow.get_tasks_from_spec_name('multi_choice_1')[0].get_data('result'))
print(workflow.get_tasks_from_spec_name('End')[0].get_data('Result'))
print(workflow.get_tasks_from_spec_name('Ping')[0].get_data('result'))
# print(workflow.get_tasks_from_spec_name('Ping')[0].results)
user_selct = {'confirmation':'yes'}
task_data_dict = user_selct.copy()
for t in tasks:
print("Ready:",t.task_spec.name)
if hasattr(t.task_spec,"cond_task_specs"):
for cond,name in t.task_spec.cond_task_specs:
for cond_unit in cond.args:
print("cond_unit",cond_unit)
if hasattr(cond_unit,"name"):
condition_keys.append(cond_unit.name)
for t in tasks:
t.set_data(**task_data_dict)
t.complete()
print(condition_keys)
print(task_data_dict)
workflow.get_tasks_from_spec_name('nuclear_strike')[0].set_data(**vartest)
workflow.complete_next()
print("NAME:",workflow.get_tasks_from_spec_name('nuclear_strike')[0].get_data('name'))
workflow.dump()
data = workflow.serialize(serializer)
pretty = json.dumps(json.loads(data), indent=4, separators=(',', ': '))
open('workflow-test-complete.json', 'w').write(pretty)
