examples/workflow_tasks_example.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Workflows\n",
    "This notebook is an example on how to use the nasctn_workflow software to create complex sequences of tasks.\n",
    "The software consists of 3 main parts:\n",
    "1. A dependency system, that represents the depencies that tasks require to run \n",
    "2. Task, an abstract class that represents something to be done\n",
    "3. Workflow an abstact class that represents a number of tasks that need to be done. Workflows are tasks,so workflows of workflows can be assembled. \n",
    "\n",
    "The flow of design is:\n",
    "1. Define the tasks to be done. Each task can be a predefined one, or a new task defined through inheritance.\n",
    "2. Define the dependencies for each task. There several stock dependencies and checkers, if a new dependency is needed, it is just a dictionary that uses a function that returns true if met or false if not met.\n",
    "3. Add the dependencies to the tasks \n",
    "4. Add the tasks to the workflow"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys\n",
    "from asmg_workflow import *"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## A simple example\n",
    "Supose you need to calculate a chain of functions that depend on the previous answer. We define three functions and create a workflow."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Variable \n",
    "start_number  = 10.1e8\n",
    "#### Define the functions\n",
    "###############\n",
    "def f1(x):\n",
    "    \"\"\"linear function, y = 3x+4\"\"\"\n",
    "    return 3*x+4\n",
    "def f2(x):\n",
    "    \"\"\"Square function x**2\"\"\"\n",
    "    return x**2\n",
    "def f3(x):\n",
    "    \"\"\"cubic function .25*x**3\"\"\"\n",
    "    return .25*x**3\n",
    "####################\n",
    "### define the tasks\n",
    "####################\n",
    "# The first task does not depend on anything\n",
    "# so we use FunctionTask\n",
    "#######################\n",
    "task_1 = FunctionTask(function = f1,args = [start_number],name = \"linear\")\n",
    "#########################\n",
    "# The next task depends on the first one \n",
    "# being done so we use DependentFunctionTask\n",
    "# The input will be passed by the dependency\n",
    "task_2 = DependentFunctionTask(function = f2, name =\"square\" )\n",
    "################################################\n",
    "task_3 = DependentFunctionTask(function =f3, name = \"cubic\")\n",
    "###################\n",
    "### define the dependencies\n",
    "###################\n",
    "# The first one is that the first task is done\n",
    "dependency_1 = TaskDependency(input  = {\"task\":task_1})\n",
    "# The next one is that the second task is done\n",
    "dependency_2 = TaskDependency(input  = {\"task\":task_2})\n",
    "########################\n",
    "## Add the dependencies\n",
    "#######################\n",
    "task_2.add_dependency(dependency_1)\n",
    "task_3.add_dependency(dependency_2)\n",
    "#######################\n",
    "## Create the workflow and add the tasks\n",
    "#######################\n",
    "test_workflow = Workflow(name = \"test_workflow_1\")\n",
    "## The start task is special, it has to be run before everybody else. If you don't assign it then it is just empty and\n",
    "# runs nothing\n",
    "test_workflow.add_task(task_1)\n",
    "test_workflow.add_task(task_2)\n",
    "test_workflow.add_task(task_3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Now we run the workflow"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Executing the workflow, all the tasks will be arranged by their dependencies \n",
    "test_workflow.execute()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "- event: Task test_workflow_1 was created.\n",
      "  timestamp: 2025-04-07 14:58:04.791127\n",
      "- event: Task id is deed6e98-5c2e-4e90-be7a-2eb7605693df\n",
      "  timestamp: 2025-04-07 14:58:04.792217\n",
      "- event: Starting slot 0, tasks ['Start_test_workflow_1'] have started\n",
      "  timestamp: 2025-04-07 14:58:04.809232\n",
      "- event: The task Start_test_workflow_1 has completed\n",
      "  timestamp: 2025-04-07 14:58:04.810548\n",
      "- event: Starting slot 1, tasks ['linear'] have started\n",
      "  timestamp: 2025-04-07 14:58:04.815706\n",
      "- event: The task linear has completed\n",
      "  timestamp: 2025-04-07 14:58:04.816702\n",
      "- event: Starting slot 2, tasks ['square'] have started\n",
      "  timestamp: 2025-04-07 14:58:04.834882\n",
      "- event: The task square has completed\n",
      "  timestamp: 2025-04-07 14:58:04.836739\n",
      "- event: Starting slot 3, tasks ['cubic'] have started\n",
      "  timestamp: 2025-04-07 14:58:04.862889\n",
      "- event: The task cubic has completed\n",
      "  timestamp: 2025-04-07 14:58:04.865793\n",
      "- event: Starting slot 4, tasks ['Start_test_workflow_1'] have started\n",
      "  timestamp: 2025-04-07 14:58:04.901282\n",
      "- event: The task Start_test_workflow_1 has completed\n",
      "  timestamp: 2025-04-07 14:58:04.904741\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Each workflow has a log and each task has one of its own\n",
    "print(test_workflow.log)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# the show method creates a network graph\n",
    "# You can display by node name or by scheduled slot\n",
    "test_workflow.show(display = \"name\",verbose = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The slot index == 0\n",
      "The slot index == 1\n",
      "The task index == 0 and the task == linear\n",
      "The slot index == 1, The task index == 0 and the task == <asmg_workflow.tasks.FunctionTask object at 0x0000023C78069450>\n",
      "Adding the edge Start_test_workflow_1,linear \n",
      "The slot index == 2\n",
      "The task index == 0 and the task == square\n",
      "task == in the list of dependencies\n",
      "task == the dependency type \n",
      "The slot index == 3\n",
      "The task index == 0 and the task == cubic\n",
      "task == in the list of dependencies\n",
      "task == the dependency type \n",
      "The task index == 0 and the task == <asmg_workflow.tasks.DependentFunctionTask object at 0x0000023C75D06780>\n",
      "The slot index == 4\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# the show method creates a network graph\n",
    "# You can display by node name or by scheduled slot\n",
    "test_workflow.show(display = \"name\",verbose = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The ouput of task cubic is 1.934620489794049e+56\n",
      "The ouput of task square is 9.18090002424e+18\n",
      "The ouput of task linear is 3030000004.0\n"
     ]
    }
   ],
   "source": [
    "# here is a printed output of the executed tasks\n",
    "for task in test_workflow.tasks:\n",
    "    try:\n",
    "        print(f'The ouput of task {task.name} is {task.output}')\n",
    "    except:\n",
    "        pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "- event: Task linear was created.\n",
      "  timestamp: 2025-04-07 14:58:04.770878\n",
      "- event: Task id is ea0dee40-1b13-4a99-91c4-4227eb5f0dfe\n",
      "  timestamp: 2025-04-07 14:58:04.771578\n",
      "- event: Execution of function <function f1 at 0x0000023C7802DA80> has begun\n",
      "  timestamp: 2025-04-07 14:58:04.812473\n",
      "- event: Execution of function <function f1 at 0x0000023C7802DA80> has completed\n",
      "  timestamp: 2025-04-07 14:58:04.814545\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# An example of the task log\n",
    "print(task_1.log)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "UUID('19e845cd-2865-4130-b0fd-dd0233f9c9fe')"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Each task gets a UUID upon creation\n",
    "task_2.id"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## A slightly more complex example\n",
    "In this example we will calculate three numbers and then pass the results to a final function that uses all three."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# variables\n",
    "x = 1\n",
    "y = 2\n",
    "z = 3\n",
    "\n",
    "#############\n",
    "# Functions - we will use f1,f2,f3 from the previous example\n",
    "def f4(x,y,z):\n",
    "    \"\"\"Calculate x+y-z\"\"\"\n",
    "    return x+y-z\n",
    "\n",
    "# Task Defintion - notice the FunctionTask instead of Dependent Function Task\n",
    "# Also the class MultipleDependentFunctionTask takes input from multiple previous tasks\n",
    "example2_task_1 = FunctionTask(function = f1,args = [x],name = \"linear\")\n",
    "example2_task_2 = FunctionTask(function = f2,args = [y],name = \"square\")\n",
    "example2_task_3 = FunctionTask(function = f3,args = [z],name = \"cubic\")\n",
    "example2_task_4 = MultipleDependentFunctionTask(function = f4,name = \"combiner\")\n",
    "# Dependency defintion\n",
    "dependency_1 = TaskDependency(input={\"task\":example2_task_1,\"output_name\":\"x\"})\n",
    "dependency_2 = TaskDependency(input={\"task\":example2_task_2,\"output_name\":\"y\"})\n",
    "dependency_3 = TaskDependency(input={\"task\":example2_task_3,\"output_name\":\"z\"})\n",
    "# Add the dependecies to task_4\n",
    "example2_task_4.add_dependency(dependency_1)\n",
    "example2_task_4.add_dependency(dependency_2)\n",
    "example2_task_4.add_dependency(dependency_3)\n",
    "# Create worflow and add tasks\n",
    "workflow = Workflow(name = \"test_workflow_2\")\n",
    "workflow.add_task(example2_task_1)\n",
    "workflow.add_task(example2_task_2)\n",
    "workflow.add_task(example2_task_3)\n",
    "workflow.add_task(example2_task_4)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "#execute\n",
    "workflow.execute()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "workflow.show(display=\"name\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The ouput of task combiner is 4.25\n",
      "The ouput of task cubic is 6.75\n",
      "The ouput of task square is 4\n",
      "The ouput of task linear is 7\n"
     ]
    }
   ],
   "source": [
    "# here is a printed output of the executed tasks\n",
    "for task in workflow.tasks:\n",
    "    try:\n",
    "        print(f'The ouput of task {task.name} is {task.output}')\n",
    "    except:\n",
    "        pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "- event: Task test_workflow_2 was created.\n",
      "  timestamp: 2025-04-07 14:58:13.938897\n",
      "- event: Task id is c6cf6de3-a34e-49d9-ade8-6a80c3d30c34\n",
      "  timestamp: 2025-04-07 14:58:13.939831\n",
      "- event: Starting slot 0, tasks ['Start_test_workflow_2'] have started\n",
      "  timestamp: 2025-04-07 14:58:13.972092\n",
      "- event: The task Start_test_workflow_2 has completed\n",
      "  timestamp: 2025-04-07 14:58:13.973439\n",
      "- event: Starting slot 1, tasks ['cubic', 'square', 'linear'] have started\n",
      "  timestamp: 2025-04-07 14:58:13.980617\n",
      "- event: The task cubic has completed\n",
      "  timestamp: 2025-04-07 14:58:13.981639\n",
      "- event: The task square has completed\n",
      "  timestamp: 2025-04-07 14:58:13.983217\n",
      "- event: The task linear has completed\n",
      "  timestamp: 2025-04-07 14:58:13.985678\n",
      "- event: Starting slot 2, tasks ['combiner'] have started\n",
      "  timestamp: 2025-04-07 14:58:14.037950\n",
      "- event: The task combiner has completed\n",
      "  timestamp: 2025-04-07 14:58:14.039572\n",
      "- event: Starting slot 3, tasks ['Start_test_workflow_2'] have started\n",
      "  timestamp: 2025-04-07 14:58:14.041377\n",
      "- event: The task Start_test_workflow_2 has completed\n",
      "  timestamp: 2025-04-07 14:58:14.042888\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(workflow.log)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'timestamp': datetime.datetime(2022, 8, 11, 11, 36, 44, 74545),\n",
       "  'event': 'Log Creation'},\n",
       " {'timestamp': datetime.datetime(2022, 8, 11, 11, 36, 44, 85220),\n",
       "  'event': 'Task test_workflow_2 was created.'},\n",
       " {'timestamp': datetime.datetime(2022, 8, 11, 11, 36, 44, 92226),\n",
       "  'event': 'Task id is fddb306e-3c11-4dd1-a5f0-b163801c6ea8'},\n",
       " {'timestamp': datetime.datetime(2022, 8, 11, 11, 36, 44, 194159),\n",
       "  'event': \"Starting slot 0, tasks ['Start'] have started\"},\n",
       " {'timestamp': datetime.datetime(2022, 8, 11, 11, 36, 44, 202156),\n",
       "  'event': 'The task Start has completed'},\n",
       " {'timestamp': datetime.datetime(2022, 8, 11, 11, 36, 44, 243494),\n",
       "  'event': \"Starting slot 1, tasks ['cubic', 'square', 'linear'] have started\"},\n",
       " {'timestamp': datetime.datetime(2022, 8, 11, 11, 36, 44, 253491),\n",
       "  'event': 'The task cubic has completed'},\n",
       " {'timestamp': datetime.datetime(2022, 8, 11, 11, 36, 44, 261008),\n",
       "  'event': 'The task square has completed'},\n",
       " {'timestamp': datetime.datetime(2022, 8, 11, 11, 36, 44, 271026),\n",
       "  'event': 'The task linear has completed'},\n",
       " {'timestamp': datetime.datetime(2022, 8, 11, 11, 36, 44, 461028),\n",
       "  'event': \"Starting slot 2, tasks ['combiner'] have started\"},\n",
       " {'timestamp': datetime.datetime(2022, 8, 11, 11, 36, 44, 471027),\n",
       "  'event': 'The task combiner has completed'},\n",
       " {'timestamp': datetime.datetime(2022, 8, 11, 11, 36, 44, 491027),\n",
       "  'event': \"Starting slot 3, tasks ['Stop'] have started\"},\n",
       " {'timestamp': datetime.datetime(2022, 8, 11, 11, 36, 44, 501027),\n",
       "  'event': 'The task Stop has completed'}]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "workflow.log.log"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "workflow.reset_workflow()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "workflow.execute(exit_condition='concurrently')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "- event: Task test_workflow_2 was created.\n",
      "  timestamp: 2025-04-07 14:58:13.938897\n",
      "- event: Task id is c6cf6de3-a34e-49d9-ade8-6a80c3d30c34\n",
      "  timestamp: 2025-04-07 14:58:13.939831\n",
      "- event: Starting slot 0, tasks ['Start_test_workflow_2'] have started\n",
      "  timestamp: 2025-04-07 14:58:13.972092\n",
      "- event: The task Start_test_workflow_2 has completed\n",
      "  timestamp: 2025-04-07 14:58:13.973439\n",
      "- event: Starting slot 1, tasks ['cubic', 'square', 'linear'] have started\n",
      "  timestamp: 2025-04-07 14:58:13.980617\n",
      "- event: The task cubic has completed\n",
      "  timestamp: 2025-04-07 14:58:13.981639\n",
      "- event: The task square has completed\n",
      "  timestamp: 2025-04-07 14:58:13.983217\n",
      "- event: The task linear has completed\n",
      "  timestamp: 2025-04-07 14:58:13.985678\n",
      "- event: Starting slot 2, tasks ['combiner'] have started\n",
      "  timestamp: 2025-04-07 14:58:14.037950\n",
      "- event: The task combiner has completed\n",
      "  timestamp: 2025-04-07 14:58:14.039572\n",
      "- event: Starting slot 3, tasks ['Start_test_workflow_2'] have started\n",
      "  timestamp: 2025-04-07 14:58:14.041377\n",
      "- event: The task Start_test_workflow_2 has completed\n",
      "  timestamp: 2025-04-07 14:58:14.042888\n",
      "- event: Reset the workflow\n",
      "  timestamp: 2025-04-07 14:58:21.868931\n",
      "- event: Starting slot 0, tasks ['Start_test_workflow_2'] have started\n",
      "  timestamp: 2025-04-07 14:58:23.194176\n",
      "- event: The task Start_test_workflow_2 has completed\n",
      "  timestamp: 2025-04-07 14:58:23.197628\n",
      "- event: Starting slot 1, tasks ['cubic', 'square', 'linear'] have started\n",
      "  timestamp: 2025-04-07 14:58:23.207190\n",
      "- event: The task cubic has completed\n",
      "  timestamp: 2025-04-07 14:58:23.210198\n",
      "- event: The task square has completed\n",
      "  timestamp: 2025-04-07 14:58:23.213277\n",
      "- event: The task linear has completed\n",
      "  timestamp: 2025-04-07 14:58:23.216409\n",
      "- event: Starting slot 2, tasks ['combiner'] have started\n",
      "  timestamp: 2025-04-07 14:58:23.266111\n",
      "- event: The task combiner has completed\n",
      "  timestamp: 2025-04-07 14:58:23.268651\n",
      "- event: Starting slot 3, tasks ['Start_test_workflow_2'] have started\n",
      "  timestamp: 2025-04-07 14:58:23.271559\n",
      "- event: The task Start_test_workflow_2 has completed\n",
      "  timestamp: 2025-04-07 14:58:23.274238\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(workflow.log)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Two Combined Workflows\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_workflow.reset_workflow()\n",
    "workflow.reset_workflow()\n",
    "combined_workflow = Workflow()\n",
    "combined_workflow.add_task(test_workflow)\n",
    "combined_workflow.add_task(workflow)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "combined_workflow.execute(exit_condition='concurrently')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "- event: Task New_workflow_20250407_001 was created.\n",
      "  timestamp: 2025-04-07 14:58:26.958701\n",
      "- event: Task id is 20545d59-d246-46bd-b0dc-74061d324c10\n",
      "  timestamp: 2025-04-07 14:58:26.959479\n",
      "- event: Starting slot 0, tasks ['Start_New_workflow_20250407_001'] have started\n",
      "  timestamp: 2025-04-07 14:58:28.936538\n",
      "- event: The task Start_New_workflow_20250407_001 has completed\n",
      "  timestamp: 2025-04-07 14:58:28.937973\n",
      "- event: Starting slot 1, tasks ['test_workflow_2', 'test_workflow_1'] have started\n",
      "  timestamp: 2025-04-07 14:58:29.123445\n",
      "- event: The task test_workflow_2 has completed\n",
      "  timestamp: 2025-04-07 14:58:29.124502\n",
      "- event: The task test_workflow_1 has completed\n",
      "  timestamp: 2025-04-07 14:58:29.125951\n",
      "- event: Starting slot 2, tasks ['Start_New_workflow_20250407_001'] have started\n",
      "  timestamp: 2025-04-07 14:58:29.127782\n",
      "- event: The task Start_New_workflow_20250407_001 has completed\n",
      "  timestamp: 2025-04-07 14:58:29.129250\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(combined_workflow.log)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The slot index == 0\n",
      "The slot index == 1\n",
      "The task index == 0 and the task == test_workflow_2\n",
      "The task index == 1 and the task == test_workflow_1\n",
      "The task index == 0 and the task == <asmg_workflow.workflows.Workflow object at 0x0000023C77DA0050>\n",
      "The task index == 1 and the task == <asmg_workflow.workflows.Workflow object at 0x0000023C78069590>\n",
      "The slot index == 1, The task index == 0 and the task == <asmg_workflow.workflows.Workflow object at 0x0000023C77DA0050>\n",
      "Adding the edge Start_New_workflow_20250407_001,test_workflow_2 \n",
      "The slot index == 1, The task index == 1 and the task == <asmg_workflow.workflows.Workflow object at 0x0000023C78069590>\n",
      "Adding the edge Start_New_workflow_20250407_001,test_workflow_1 \n",
      "The slot index == 2\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "combined_workflow.show(type_=\"nx\",display = \"name\",verbose = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "- event: Task test_workflow_2 was created.\n",
      "  timestamp: 2025-04-07 14:58:13.938897\n",
      "- event: Task id is c6cf6de3-a34e-49d9-ade8-6a80c3d30c34\n",
      "  timestamp: 2025-04-07 14:58:13.939831\n",
      "- event: Starting slot 0, tasks ['Start_test_workflow_2'] have started\n",
      "  timestamp: 2025-04-07 14:58:13.972092\n",
      "- event: The task Start_test_workflow_2 has completed\n",
      "  timestamp: 2025-04-07 14:58:13.973439\n",
      "- event: Starting slot 1, tasks ['cubic', 'square', 'linear'] have started\n",
      "  timestamp: 2025-04-07 14:58:13.980617\n",
      "- event: The task cubic has completed\n",
      "  timestamp: 2025-04-07 14:58:13.981639\n",
      "- event: The task square has completed\n",
      "  timestamp: 2025-04-07 14:58:13.983217\n",
      "- event: The task linear has completed\n",
      "  timestamp: 2025-04-07 14:58:13.985678\n",
      "- event: Starting slot 2, tasks ['combiner'] have started\n",
      "  timestamp: 2025-04-07 14:58:14.037950\n",
      "- event: The task combiner has completed\n",
      "  timestamp: 2025-04-07 14:58:14.039572\n",
      "- event: Starting slot 3, tasks ['Start_test_workflow_2'] have started\n",
      "  timestamp: 2025-04-07 14:58:14.041377\n",
      "- event: The task Start_test_workflow_2 has completed\n",
      "  timestamp: 2025-04-07 14:58:14.042888\n",
      "- event: Reset the workflow\n",
      "  timestamp: 2025-04-07 14:58:21.868931\n",
      "- event: Starting slot 0, tasks ['Start_test_workflow_2'] have started\n",
      "  timestamp: 2025-04-07 14:58:23.194176\n",
      "- event: The task Start_test_workflow_2 has completed\n",
      "  timestamp: 2025-04-07 14:58:23.197628\n",
      "- event: Starting slot 1, tasks ['cubic', 'square', 'linear'] have started\n",
      "  timestamp: 2025-04-07 14:58:23.207190\n",
      "- event: The task cubic has completed\n",
      "  timestamp: 2025-04-07 14:58:23.210198\n",
      "- event: The task square has completed\n",
      "  timestamp: 2025-04-07 14:58:23.213277\n",
      "- event: The task linear has completed\n",
      "  timestamp: 2025-04-07 14:58:23.216409\n",
      "- event: Starting slot 2, tasks ['combiner'] have started\n",
      "  timestamp: 2025-04-07 14:58:23.266111\n",
      "- event: The task combiner has completed\n",
      "  timestamp: 2025-04-07 14:58:23.268651\n",
      "- event: Starting slot 3, tasks ['Start_test_workflow_2'] have started\n",
      "  timestamp: 2025-04-07 14:58:23.271559\n",
      "- event: The task Start_test_workflow_2 has completed\n",
      "  timestamp: 2025-04-07 14:58:23.274238\n",
      "- event: Reset the workflow\n",
      "  timestamp: 2025-04-07 14:58:26.951838\n",
      "- event: Starting slot 0, tasks ['Start_test_workflow_2'] have started\n",
      "  timestamp: 2025-04-07 14:58:28.940944\n",
      "- event: The task Start_test_workflow_2 has completed\n",
      "  timestamp: 2025-04-07 14:58:28.947645\n",
      "- event: Starting slot 1, tasks ['cubic', 'square', 'linear'] have started\n",
      "  timestamp: 2025-04-07 14:58:28.967975\n",
      "- event: The task cubic has completed\n",
      "  timestamp: 2025-04-07 14:58:28.973582\n",
      "- event: The task square has completed\n",
      "  timestamp: 2025-04-07 14:58:28.981198\n",
      "- event: The task linear has completed\n",
      "  timestamp: 2025-04-07 14:58:28.987631\n",
      "- event: Starting slot 2, tasks ['combiner'] have started\n",
      "  timestamp: 2025-04-07 14:58:29.076412\n",
      "- event: The task combiner has completed\n",
      "  timestamp: 2025-04-07 14:58:29.087181\n",
      "- event: Starting slot 3, tasks ['Start_test_workflow_2'] have started\n",
      "  timestamp: 2025-04-07 14:58:29.099531\n",
      "- event: The task Start_test_workflow_2 has completed\n",
      "  timestamp: 2025-04-07 14:58:29.110692\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(combined_workflow.tasks[1].log)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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