universal_qe_to_jobflow.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "8676f0ca-5b34-4867-a32d-928f37fb0ed2",
   "metadata": {},
   "outputs": [],
   "source": [
    "from importlib import import_module"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "041f3882-d302-4a99-99a6-beea67f2a2a8",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import subprocess\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from ase.atoms import Atoms\n",
    "from ase.build import bulk\n",
    "from ase.io import write\n",
    "from adis_tools.parsers import parse_pw"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "58df36ba-8d6f-4748-8fd5-15f8a851af5f",
   "metadata": {},
   "outputs": [],
   "source": [
    "from inspect import isfunction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "1bd1f75c-a07f-48c9-ae56-22ba674d6d3a",
   "metadata": {},
   "outputs": [],
   "source": [
    "from jobflow import job, Flow\n",
    "from jobflow.managers.local import run_locally"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "c8490247-4071-4108-ba4c-47fa73b8f34e",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_kwargs(lst):\n",
    "    return {t['targetHandle']: {'source': t['source'], 'sourceHandle': t['sourceHandle']} for t in lst}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "88d74f68-589a-46d8-8c8b-702093289ff8",
   "metadata": {},
   "outputs": [],
   "source": [
    "def resort_total_lst(total_dict, nodes_dict):\n",
    "    nodes_with_dep_lst = list(sorted(total_dict.keys()))\n",
    "    nodes_without_dep_lst = [k for k in nodes_dict.keys() if k not in nodes_with_dep_lst]\n",
    "    ordered_lst = []\n",
    "    total_new_dict = {}\n",
    "    while len(total_new_dict) < len(total_dict):\n",
    "        for ind in sorted(total_dict.keys()):\n",
    "            connect = total_dict[ind]\n",
    "            if ind not in ordered_lst:\n",
    "                source_lst = [sd[\"source\"] for sd in connect.values()]\n",
    "                if all([s in ordered_lst or s in nodes_without_dep_lst for s in source_lst]):\n",
    "                    ordered_lst.append(ind)\n",
    "                    total_new_dict[ind] = connect\n",
    "    return total_new_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "5bc6fa2b-5faa-4db8-ba8b-b275fb61ba7d",
   "metadata": {},
   "outputs": [],
   "source": [
    "def group_edges(edges_lst):\n",
    "    # edges_sorted_lst = sorted(edges_lst, key=lambda x: x['target'], reverse=True)     \n",
    "    total_dict = {}\n",
    "    for ed_major in edges_lst:\n",
    "        target_id = ed_major[\"target\"]\n",
    "        tmp_lst = []\n",
    "        if target_id not in total_dict.keys():\n",
    "            for ed in edges_lst:\n",
    "                if target_id == ed[\"target\"]:\n",
    "                    tmp_lst.append(ed)\n",
    "            total_dict[target_id] = get_kwargs(lst=tmp_lst)\n",
    "    # total_dict[target_id] = get_kwargs(lst=tmp_lst)\n",
    "    return total_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "023382c1-e812-4023-b100-3fd7b7bc840a",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_input_dict(nodes_dict):\n",
    "    return {k:v for k, v in nodes_dict.items() if not isfunction(v)}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "a934964d-127a-49d1-9634-b3c8ee2e50b0",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_workflow(nodes_dict, input_dict, total_dict, source_handles_dict):\n",
    "    def get_attr_helper(obj, source_handle):\n",
    "        # print(\"attr_helper\", source_handle, getattr(obj, \"output\"), getattr(getattr(obj, \"output\"), source_handle))\n",
    "        if source_handle is None:\n",
    "            return getattr(obj, \"output\") \n",
    "        else:\n",
    "            return getattr(getattr(obj, \"output\"), source_handle)\n",
    "    \n",
    "    memory_dict = {}\n",
    "    for k in total_dict.keys():\n",
    "        v = nodes_dict[k]\n",
    "        if isfunction(v):\n",
    "            if k in source_handles_dict.keys():\n",
    "                # print(k, {el: el for el in source_handles_dict[k] if el is not None})\n",
    "                fn = job(method=v, data=[el for el in source_handles_dict[k] if el is not None])\n",
    "            else:\n",
    "                fn = job(method=v)\n",
    "            kwargs = {\n",
    "                kw: input_dict[vw['source']] if vw['source'] in input_dict else get_attr_helper(obj=memory_dict[vw['source']], source_handle=vw['sourceHandle']) \n",
    "                for kw, vw in total_dict[k].items()\n",
    "            }\n",
    "            print(k, kwargs)\n",
    "            memory_dict[k] = fn(**kwargs)\n",
    "    return list(memory_dict.values())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "0ae0fde2-2851-484f-8d76-8e4ae3bfabc6",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_source_handles(edges_lst):\n",
    "    source_handle_dict = {}\n",
    "    for ed in edges_lst: \n",
    "        if ed['source'] not in source_handle_dict.keys():\n",
    "            source_handle_dict[ed['source']] = [ed['sourceHandle']]\n",
    "        else:\n",
    "            source_handle_dict[ed['source']].append(ed['sourceHandle'])\n",
    "    return source_handle_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "af51be8c-a43e-4ebb-9ad9-78f98f7c0a20",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_item_from_tuple(input_obj, index, index_lst):\n",
    "    if isinstance(input_obj, dict):  \n",
    "        return input_obj[index]\n",
    "    else:  # input_obj is a tuple\n",
    "        return list(input_obj)[index_lst.index(index)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "c1a8c351-0aac-43ab-8b26-941e80578552",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_source_handles(edges_lst):\n",
    "    source_handle_dict = {}\n",
    "    for ed in edges_lst: \n",
    "        if ed['source'] not in source_handle_dict.keys():\n",
    "            source_handle_dict[ed['source']] = [ed['sourceHandle']]\n",
    "        else:\n",
    "            source_handle_dict[ed['source']].append(ed['sourceHandle'])\n",
    "    return {\n",
    "        k: list(range(len(v))) if len(v) > 1 and all([el is None for el in v]) else v \n",
    "        for k, v in source_handle_dict.items()\n",
    "    }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "a0ec8e80-06eb-4d0e-b7cf-9b27682951dc",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_dict(**kwargs):\n",
    "    return {k: v for k, v in kwargs.items()}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "b7934782-e5fe-44b2-a3c8-ab7cd0e0118f",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_list(**kwargs):\n",
    "    return list(kwargs.values())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "c8651b93-00a9-4f84-8d89-6d6cb2dd85a3",
   "metadata": {},
   "outputs": [],
   "source": [
    "def write_input(input_dict, working_directory=\".\"):\n",
    "    filename = os.path.join(working_directory, \"input.pwi\")\n",
    "    os.makedirs(working_directory, exist_ok=True)\n",
    "    write(\n",
    "        filename=filename,\n",
    "        images=Atoms(**input_dict[\"structure\"]),\n",
    "        Crystal=True,\n",
    "        kpts=input_dict[\"kpts\"],\n",
    "        input_data={\n",
    "            \"calculation\": input_dict[\"calculation\"],\n",
    "            \"occupations\": \"smearing\",\n",
    "            \"degauss\": input_dict[\"smearing\"],\n",
    "        },\n",
    "        pseudopotentials=input_dict[\"pseudopotentials\"],\n",
    "        tstress=True,\n",
    "        tprnfor=True,\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "5fcf98e6-07ae-4f78-8731-83ba249dadbf",
   "metadata": {},
   "outputs": [],
   "source": [
    "def collect_output(working_directory=\".\"):\n",
    "    output = parse_pw(os.path.join(working_directory, \"pwscf.xml\"))\n",
    "    return {\n",
    "        \"structure\": output[\"ase_structure\"].todict(),\n",
    "        \"energy\": output[\"energy\"],\n",
    "        \"volume\": output[\"ase_structure\"].get_volume(),\n",
    "    }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "3ba41b0b-a4f4-40e3-9a62-b19cab737d28",
   "metadata": {},
   "outputs": [],
   "source": [
    "# @job(output_key_lst=[\"energy\", \"volume\", \"structure\"])\n",
    "def calculate_qe(working_directory, input_dict):\n",
    "    write_input(\n",
    "        input_dict=input_dict,\n",
    "        working_directory=working_directory,\n",
    "    )\n",
    "    subprocess.check_output(\n",
    "        \"mpirun -np 1 pw.x -in input.pwi > output.pwo\",\n",
    "        cwd=working_directory,\n",
    "        shell=True,\n",
    "    )\n",
    "    return collect_output(working_directory=working_directory)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "61e0a96f-455f-454f-a720-c378450c38c6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# @job\n",
    "def generate_structures(structure, strain_lst):\n",
    "    structure_lst = []\n",
    "    for strain in strain_lst:\n",
    "        structure_strain = Atoms(**structure)\n",
    "        structure_strain.set_cell(\n",
    "            structure_strain.cell * strain ** (1 / 3), scale_atoms=True\n",
    "        )\n",
    "        structure_lst.append(structure_strain)\n",
    "    return {str(i): s.todict() for i, s in enumerate(structure_lst)}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "99c73bfe-2b80-4a40-a3f7-177e77d325c1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# @job\n",
    "def plot_energy_volume_curve(volume_lst, energy_lst):\n",
    "    plt.plot(volume_lst, energy_lst)\n",
    "    plt.xlabel(\"Volume\")\n",
    "    plt.ylabel(\"Energy\")\n",
    "    plt.savefig(\"evcurve.png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "62a98127-2d21-40df-a47a-20d89f045628",
   "metadata": {},
   "outputs": [],
   "source": [
    "# @job\n",
    "def plot_energy_volume_curve(volume_lst, energy_lst):\n",
    "    plt.plot(volume_lst, energy_lst)\n",
    "    plt.xlabel(\"Volume\")\n",
    "    plt.ylabel(\"Energy\")\n",
    "    plt.savefig(\"evcurve.png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "09caf119-8bbd-46d3-9ddf-64eb2ac6b649",
   "metadata": {},
   "outputs": [],
   "source": [
    "# @job\n",
    "def get_bulk_structure(name, a, cubic):\n",
    "    return bulk(\n",
    "        name=name,\n",
    "        a=a,\n",
    "        cubic=cubic,\n",
    "    ).todict()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "319b363b-f017-4b2b-b10b-a71a5c31dfbe",
   "metadata": {},
   "outputs": [],
   "source": [
    "pseudopotentials = {\"Al\": \"Al.pbe-n-kjpaw_psl.1.0.0.UPF\"}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "87dff023-58b4-4ba3-a962-d037a5e7ccd4",
   "metadata": {},
   "outputs": [],
   "source": [
    "nodes_dict = {  # from jobflow\n",
    "    0: get_bulk_structure,\n",
    "    1: calculate_qe,\n",
    "    2: generate_structures,\n",
    "    3: calculate_qe,\n",
    "    4: calculate_qe,\n",
    "    5: calculate_qe,\n",
    "    6: calculate_qe,\n",
    "    7: calculate_qe,\n",
    "    8: plot_energy_volume_curve,\n",
    "    9: 'Al',\n",
    "    10: 4.05,\n",
    "    11: True,\n",
    "    12: 'mini',\n",
    "    13: get_dict,\n",
    "    14: {'Al': 'Al.pbe-n-kjpaw_psl.1.0.0.UPF'},\n",
    "    15: [3, 3, 3],\n",
    "    16: 'vc-relax',\n",
    "    17: 0.02,\n",
    "    18: [0.9, 0.9500000000000001, 1.0, 1.05, 1.1],\n",
    "    19: 'strain_0',\n",
    "    20: get_dict,\n",
    "    21: 'scf',\n",
    "    22: 'strain_1',\n",
    "    23: get_dict,\n",
    "    24: 'strain_2',\n",
    "    25: get_dict,\n",
    "    26: 'strain_3',\n",
    "    27: get_dict,\n",
    "    28: 'strain_4',\n",
    "    29: get_dict,\n",
    "    30: get_list,\n",
    "    31: get_list,\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "88233f17-a6d3-4fb2-960d-b6eaa1f615dc",
   "metadata": {},
   "outputs": [],
   "source": [
    "edges_lst = [\n",
    "    {'target': 0, 'targetHandle': 'name', 'source': 9, 'sourceHandle': None},\n",
    "    {'target': 0, 'targetHandle': 'a', 'source': 10, 'sourceHandle': None},\n",
    "    {'target': 0, 'targetHandle': 'cubic', 'source': 11, 'sourceHandle': None},\n",
    "    {'target': 1, 'targetHandle': 'working_directory', 'source': 12, 'sourceHandle': None},\n",
    "    {'target': 13, 'targetHandle': 'structure', 'source': 0, 'sourceHandle': None},\n",
    "    {'target': 13, 'targetHandle': 'pseudopotentials', 'source': 14, 'sourceHandle': None},\n",
    "    {'target': 13, 'targetHandle': 'kpts', 'source': 15, 'sourceHandle': None},\n",
    "    {'target': 13, 'targetHandle': 'calculation', 'source': 16, 'sourceHandle': None},\n",
    "    {'target': 13, 'targetHandle': 'smearing', 'source': 17, 'sourceHandle': None},\n",
    "    {'target': 1, 'targetHandle': 'input_dict', 'source': 13, 'sourceHandle': None},\n",
    "    {'target': 2, 'targetHandle': 'structure', 'source': 1, 'sourceHandle': 'structure'},\n",
    "    {'target': 2, 'targetHandle': 'strain_lst', 'source': 18, 'sourceHandle': None},\n",
    "    {'target': 3, 'targetHandle': 'working_directory', 'source': 19, 'sourceHandle': None},\n",
    "    {'target': 20, 'targetHandle': 'structure', 'source': 2, 'sourceHandle': '0'},\n",
    "    {'target': 20, 'targetHandle': 'pseudopotentials', 'source': 14, 'sourceHandle': None},\n",
    "    {'target': 20, 'targetHandle': 'kpts', 'source': 15, 'sourceHandle': None},\n",
    "    {'target': 20, 'targetHandle': 'calculation', 'source': 21, 'sourceHandle': None},\n",
    "    {'target': 20, 'targetHandle': 'smearing', 'source': 17, 'sourceHandle': None},\n",
    "    {'target': 3, 'targetHandle': 'input_dict', 'source': 20, 'sourceHandle': None},\n",
    "    {'target': 4, 'targetHandle': 'working_directory', 'source': 22, 'sourceHandle': None},\n",
    "    {'target': 23, 'targetHandle': 'structure', 'source': 2, 'sourceHandle': '1'},\n",
    "    {'target': 23, 'targetHandle': 'pseudopotentials', 'source': 14, 'sourceHandle': None},\n",
    "    {'target': 23, 'targetHandle': 'kpts', 'source': 15, 'sourceHandle': None},\n",
    "    {'target': 23, 'targetHandle': 'calculation', 'source': 21, 'sourceHandle': None},\n",
    "    {'target': 23, 'targetHandle': 'smearing', 'source': 17, 'sourceHandle': None},\n",
    "    {'target': 4, 'targetHandle': 'input_dict', 'source': 23, 'sourceHandle': None},\n",
    "    {'target': 5, 'targetHandle': 'working_directory', 'source': 24, 'sourceHandle': None},\n",
    "    {'target': 25, 'targetHandle': 'structure', 'source': 2, 'sourceHandle': '2'},\n",
    "    {'target': 25, 'targetHandle': 'pseudopotentials', 'source': 14, 'sourceHandle': None},\n",
    "    {'target': 25, 'targetHandle': 'kpts', 'source': 15, 'sourceHandle': None},\n",
    "    {'target': 25, 'targetHandle': 'calculation', 'source': 21, 'sourceHandle': None},\n",
    "    {'target': 25, 'targetHandle': 'smearing', 'source': 17, 'sourceHandle': None},\n",
    "    {'target': 5, 'targetHandle': 'input_dict', 'source': 25, 'sourceHandle': None},\n",
    "    {'target': 6, 'targetHandle': 'working_directory', 'source': 26, 'sourceHandle': None},\n",
    "    {'target': 27, 'targetHandle': 'structure', 'source': 2, 'sourceHandle': '3'},\n",
    "    {'target': 27, 'targetHandle': 'pseudopotentials', 'source': 14, 'sourceHandle': None},\n",
    "    {'target': 27, 'targetHandle': 'kpts', 'source': 15, 'sourceHandle': None},\n",
    "    {'target': 27, 'targetHandle': 'calculation', 'source': 21, 'sourceHandle': None},\n",
    "    {'target': 27, 'targetHandle': 'smearing', 'source': 17, 'sourceHandle': None},\n",
    "    {'target': 6, 'targetHandle': 'input_dict', 'source': 27, 'sourceHandle': None},\n",
    "    {'target': 7, 'targetHandle': 'working_directory', 'source': 28, 'sourceHandle': None},\n",
    "    {'target': 29, 'targetHandle': 'structure', 'source': 2, 'sourceHandle': '4'},\n",
    "    {'target': 29, 'targetHandle': 'pseudopotentials', 'source': 14, 'sourceHandle': None},\n",
    "    {'target': 29, 'targetHandle': 'kpts', 'source': 15, 'sourceHandle': None},\n",
    "    {'target': 29, 'targetHandle': 'calculation', 'source': 21, 'sourceHandle': None},\n",
    "    {'target': 29, 'targetHandle': 'smearing', 'source': 17, 'sourceHandle': None},\n",
    "    {'target': 7, 'targetHandle': 'input_dict', 'source': 29, 'sourceHandle': None},\n",
    "    {'target': 30, 'targetHandle': \"0\", 'source': 3, 'sourceHandle': 'volume'},\n",
    "    {'target': 30, 'targetHandle': \"1\", 'source': 4, 'sourceHandle': 'volume'},\n",
    "    {'target': 30, 'targetHandle': \"2\", 'source': 5, 'sourceHandle': 'volume'},\n",
    "    {'target': 30, 'targetHandle': \"3\", 'source': 6, 'sourceHandle': 'volume'},\n",
    "    {'target': 30, 'targetHandle': \"4\", 'source': 7, 'sourceHandle': 'volume'},\n",
    "    {'target': 8, 'targetHandle': 'volume_lst', 'source': 30, 'sourceHandle': None},\n",
    "    {'target': 31, 'targetHandle': \"0\", 'source': 3, 'sourceHandle': 'energy'},\n",
    "    {'target': 31, 'targetHandle': \"1\", 'source': 4, 'sourceHandle': 'energy'},\n",
    "    {'target': 31, 'targetHandle': \"2\", 'source': 5, 'sourceHandle': 'energy'},\n",
    "    {'target': 31, 'targetHandle': \"3\", 'source': 6, 'sourceHandle': 'energy'},\n",
    "    {'target': 31, 'targetHandle': \"4\", 'source': 7, 'sourceHandle': 'energy'},\n",
    "    {'target': 8, 'targetHandle': 'energy_lst', 'source': 31, 'sourceHandle': None},\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "1934cbea-7ec7-47a6-8434-079dbdeff28d",
   "metadata": {},
   "outputs": [],
   "source": [
    "def fine_root_node(nodes_dict, edges_lst):\n",
    "    source_count_dict = {k: sum([ed[\"source\"] == k for ed in edges_lst]) for k in nodes_dict.keys()}\n",
    "    return min(source_count_dict, key=source_count_dict.get)\n",
    "            "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "6df1f72d-dc59-4360-9753-4fb1c90624a4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "root_node = fine_root_node(nodes_dict=nodes_dict, edges_lst=edges_lst)\n",
    "root_node"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "972efbf2-1e1f-4429-a443-b83ff9c76e07",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{9: [None],\n",
       " 10: [None],\n",
       " 11: [None],\n",
       " 12: [None],\n",
       " 0: [None],\n",
       " 14: [0, 1, 2, 3, 4, 5],\n",
       " 15: [0, 1, 2, 3, 4, 5],\n",
       " 16: [None],\n",
       " 17: [0, 1, 2, 3, 4, 5],\n",
       " 13: [None],\n",
       " 1: ['structure'],\n",
       " 18: [None],\n",
       " 19: [None],\n",
       " 2: ['0', '1', '2', '3', '4'],\n",
       " 21: [0, 1, 2, 3, 4],\n",
       " 20: [None],\n",
       " 22: [None],\n",
       " 23: [None],\n",
       " 24: [None],\n",
       " 25: [None],\n",
       " 26: [None],\n",
       " 27: [None],\n",
       " 28: [None],\n",
       " 29: [None],\n",
       " 3: ['volume', 'energy'],\n",
       " 4: ['volume', 'energy'],\n",
       " 5: ['volume', 'energy'],\n",
       " 6: ['volume', 'energy'],\n",
       " 7: ['volume', 'energy'],\n",
       " 30: [None],\n",
       " 31: [None]}"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "source_handles_dict = get_source_handles(edges_lst=edges_lst)\n",
    "source_handles_dict"
   ]
  },
  {
   "cell_type": "raw",
   "id": "00f8a616-5a7a-42f9-9885-49e2e14477a4",
   "metadata": {},
   "source": [
    "nodes_updated_dict, edges_updated_lst = universal_reduce(nodes_dict=nodes_dict, edges_lst=edges_lst)\n",
    "nodes_updated_dict, edges_updated_lst"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "d60390c3-b83a-4fdd-bd2e-48469d94dc65",
   "metadata": {},
   "outputs": [],
   "source": [
    "total_dict = group_edges(edges_lst=edges_lst)\n",
    "input_dict = get_input_dict(nodes_dict=nodes_dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "28fad5d9-9f27-415c-a3b2-667e2a2e8abf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "({0: {'name': {'source': 9, 'sourceHandle': None},\n",
       "   'a': {'source': 10, 'sourceHandle': None},\n",
       "   'cubic': {'source': 11, 'sourceHandle': None}},\n",
       "  1: {'working_directory': {'source': 12, 'sourceHandle': None},\n",
       "   'input_dict': {'source': 13, 'sourceHandle': None}},\n",
       "  13: {'structure': {'source': 0, 'sourceHandle': None},\n",
       "   'pseudopotentials': {'source': 14, 'sourceHandle': None},\n",
       "   'kpts': {'source': 15, 'sourceHandle': None},\n",
       "   'calculation': {'source': 16, 'sourceHandle': None},\n",
       "   'smearing': {'source': 17, 'sourceHandle': None}},\n",
       "  2: {'structure': {'source': 1, 'sourceHandle': 'structure'},\n",
       "   'strain_lst': {'source': 18, 'sourceHandle': None}},\n",
       "  3: {'working_directory': {'source': 19, 'sourceHandle': None},\n",
       "   'input_dict': {'source': 20, 'sourceHandle': None}},\n",
       "  20: {'structure': {'source': 2, 'sourceHandle': '0'},\n",
       "   'pseudopotentials': {'source': 14, 'sourceHandle': None},\n",
       "   'kpts': {'source': 15, 'sourceHandle': None},\n",
       "   'calculation': {'source': 21, 'sourceHandle': None},\n",
       "   'smearing': {'source': 17, 'sourceHandle': None}},\n",
       "  4: {'working_directory': {'source': 22, 'sourceHandle': None},\n",
       "   'input_dict': {'source': 23, 'sourceHandle': None}},\n",
       "  23: {'structure': {'source': 2, 'sourceHandle': '1'},\n",
       "   'pseudopotentials': {'source': 14, 'sourceHandle': None},\n",
       "   'kpts': {'source': 15, 'sourceHandle': None},\n",
       "   'calculation': {'source': 21, 'sourceHandle': None},\n",
       "   'smearing': {'source': 17, 'sourceHandle': None}},\n",
       "  5: {'working_directory': {'source': 24, 'sourceHandle': None},\n",
       "   'input_dict': {'source': 25, 'sourceHandle': None}},\n",
       "  25: {'structure': {'source': 2, 'sourceHandle': '2'},\n",
       "   'pseudopotentials': {'source': 14, 'sourceHandle': None},\n",
       "   'kpts': {'source': 15, 'sourceHandle': None},\n",
       "   'calculation': {'source': 21, 'sourceHandle': None},\n",
       "   'smearing': {'source': 17, 'sourceHandle': None}},\n",
       "  6: {'working_directory': {'source': 26, 'sourceHandle': None},\n",
       "   'input_dict': {'source': 27, 'sourceHandle': None}},\n",
       "  27: {'structure': {'source': 2, 'sourceHandle': '3'},\n",
       "   'pseudopotentials': {'source': 14, 'sourceHandle': None},\n",
       "   'kpts': {'source': 15, 'sourceHandle': None},\n",
       "   'calculation': {'source': 21, 'sourceHandle': None},\n",
       "   'smearing': {'source': 17, 'sourceHandle': None}},\n",
       "  7: {'working_directory': {'source': 28, 'sourceHandle': None},\n",
       "   'input_dict': {'source': 29, 'sourceHandle': None}},\n",
       "  29: {'structure': {'source': 2, 'sourceHandle': '4'},\n",
       "   'pseudopotentials': {'source': 14, 'sourceHandle': None},\n",
       "   'kpts': {'source': 15, 'sourceHandle': None},\n",
       "   'calculation': {'source': 21, 'sourceHandle': None},\n",
       "   'smearing': {'source': 17, 'sourceHandle': None}},\n",
       "  30: {'0': {'source': 3, 'sourceHandle': 'volume'},\n",
       "   '1': {'source': 4, 'sourceHandle': 'volume'},\n",
       "   '2': {'source': 5, 'sourceHandle': 'volume'},\n",
       "   '3': {'source': 6, 'sourceHandle': 'volume'},\n",
       "   '4': {'source': 7, 'sourceHandle': 'volume'}},\n",
       "  8: {'volume_lst': {'source': 30, 'sourceHandle': None},\n",
       "   'energy_lst': {'source': 31, 'sourceHandle': None}},\n",
       "  31: {'0': {'source': 3, 'sourceHandle': 'energy'},\n",
       "   '1': {'source': 4, 'sourceHandle': 'energy'},\n",
       "   '2': {'source': 5, 'sourceHandle': 'energy'},\n",
       "   '3': {'source': 6, 'sourceHandle': 'energy'},\n",
       "   '4': {'source': 7, 'sourceHandle': 'energy'}}},\n",
       " {9: 'Al',\n",
       "  10: 4.05,\n",
       "  11: True,\n",
       "  12: 'mini',\n",
       "  14: {'Al': 'Al.pbe-n-kjpaw_psl.1.0.0.UPF'},\n",
       "  15: [3, 3, 3],\n",
       "  16: 'vc-relax',\n",
       "  17: 0.02,\n",
       "  18: [0.9, 0.9500000000000001, 1.0, 1.05, 1.1],\n",
       "  19: 'strain_0',\n",
       "  21: 'scf',\n",
       "  22: 'strain_1',\n",
       "  24: 'strain_2',\n",
       "  26: 'strain_3',\n",
       "  28: 'strain_4'})"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total_dict, input_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "f6ef7f23-e7fe-4834-8e47-91a997bd27c2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{0: {'name': {'source': 9, 'sourceHandle': None},\n",
       "  'a': {'source': 10, 'sourceHandle': None},\n",
       "  'cubic': {'source': 11, 'sourceHandle': None}},\n",
       " 13: {'structure': {'source': 0, 'sourceHandle': None},\n",
       "  'pseudopotentials': {'source': 14, 'sourceHandle': None},\n",
       "  'kpts': {'source': 15, 'sourceHandle': None},\n",
       "  'calculation': {'source': 16, 'sourceHandle': None},\n",
       "  'smearing': {'source': 17, 'sourceHandle': None}},\n",
       " 1: {'working_directory': {'source': 12, 'sourceHandle': None},\n",
       "  'input_dict': {'source': 13, 'sourceHandle': None}},\n",
       " 2: {'structure': {'source': 1, 'sourceHandle': 'structure'},\n",
       "  'strain_lst': {'source': 18, 'sourceHandle': None}},\n",
       " 20: {'structure': {'source': 2, 'sourceHandle': '0'},\n",
       "  'pseudopotentials': {'source': 14, 'sourceHandle': None},\n",
       "  'kpts': {'source': 15, 'sourceHandle': None},\n",
       "  'calculation': {'source': 21, 'sourceHandle': None},\n",
       "  'smearing': {'source': 17, 'sourceHandle': None}},\n",
       " 23: {'structure': {'source': 2, 'sourceHandle': '1'},\n",
       "  'pseudopotentials': {'source': 14, 'sourceHandle': None},\n",
       "  'kpts': {'source': 15, 'sourceHandle': None},\n",
       "  'calculation': {'source': 21, 'sourceHandle': None},\n",
       "  'smearing': {'source': 17, 'sourceHandle': None}},\n",
       " 25: {'structure': {'source': 2, 'sourceHandle': '2'},\n",
       "  'pseudopotentials': {'source': 14, 'sourceHandle': None},\n",
       "  'kpts': {'source': 15, 'sourceHandle': None},\n",
       "  'calculation': {'source': 21, 'sourceHandle': None},\n",
       "  'smearing': {'source': 17, 'sourceHandle': None}},\n",
       " 27: {'structure': {'source': 2, 'sourceHandle': '3'},\n",
       "  'pseudopotentials': {'source': 14, 'sourceHandle': None},\n",
       "  'kpts': {'source': 15, 'sourceHandle': None},\n",
       "  'calculation': {'source': 21, 'sourceHandle': None},\n",
       "  'smearing': {'source': 17, 'sourceHandle': None}},\n",
       " 29: {'structure': {'source': 2, 'sourceHandle': '4'},\n",
       "  'pseudopotentials': {'source': 14, 'sourceHandle': None},\n",
       "  'kpts': {'source': 15, 'sourceHandle': None},\n",
       "  'calculation': {'source': 21, 'sourceHandle': None},\n",
       "  'smearing': {'source': 17, 'sourceHandle': None}},\n",
       " 3: {'working_directory': {'source': 19, 'sourceHandle': None},\n",
       "  'input_dict': {'source': 20, 'sourceHandle': None}},\n",
       " 4: {'working_directory': {'source': 22, 'sourceHandle': None},\n",
       "  'input_dict': {'source': 23, 'sourceHandle': None}},\n",
       " 5: {'working_directory': {'source': 24, 'sourceHandle': None},\n",
       "  'input_dict': {'source': 25, 'sourceHandle': None}},\n",
       " 6: {'working_directory': {'source': 26, 'sourceHandle': None},\n",
       "  'input_dict': {'source': 27, 'sourceHandle': None}},\n",
       " 7: {'working_directory': {'source': 28, 'sourceHandle': None},\n",
       "  'input_dict': {'source': 29, 'sourceHandle': None}},\n",
       " 30: {'0': {'source': 3, 'sourceHandle': 'volume'},\n",
       "  '1': {'source': 4, 'sourceHandle': 'volume'},\n",
       "  '2': {'source': 5, 'sourceHandle': 'volume'},\n",
       "  '3': {'source': 6, 'sourceHandle': 'volume'},\n",
       "  '4': {'source': 7, 'sourceHandle': 'volume'}},\n",
       " 31: {'0': {'source': 3, 'sourceHandle': 'energy'},\n",
       "  '1': {'source': 4, 'sourceHandle': 'energy'},\n",
       "  '2': {'source': 5, 'sourceHandle': 'energy'},\n",
       "  '3': {'source': 6, 'sourceHandle': 'energy'},\n",
       "  '4': {'source': 7, 'sourceHandle': 'energy'}},\n",
       " 8: {'volume_lst': {'source': 30, 'sourceHandle': None},\n",
       "  'energy_lst': {'source': 31, 'sourceHandle': None}}}"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_total_dict = resort_total_lst(total_dict=total_dict, nodes_dict=nodes_dict)\n",
    "new_total_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "ca88e187-1f36-4fd8-a5a7-0b8b97e71441",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 {'name': 'Al', 'a': 4.05, 'cubic': True}\n",
      "13 {'structure': OutputReference(b4a48a0d-8db2-4a7a-82cf-8bb62cef4fcc), 'pseudopotentials': {'Al': 'Al.pbe-n-kjpaw_psl.1.0.0.UPF'}, 'kpts': [3, 3, 3], 'calculation': 'vc-relax', 'smearing': 0.02}\n",
      "1 {'working_directory': 'mini', 'input_dict': OutputReference(c3c5fdbf-9d83-4517-be76-3c253fd19a68)}\n",
      "2 {'structure': OutputReference(08c347f2-4374-4148-ad63-cbc63123c9b5, .structure), 'strain_lst': [0.9, 0.9500000000000001, 1.0, 1.05, 1.1]}\n",
      "20 {'structure': OutputReference(045577d2-e868-4a0a-b67c-f02215ed437a, .0), 'pseudopotentials': {'Al': 'Al.pbe-n-kjpaw_psl.1.0.0.UPF'}, 'kpts': [3, 3, 3], 'calculation': 'scf', 'smearing': 0.02}\n",
      "23 {'structure': OutputReference(045577d2-e868-4a0a-b67c-f02215ed437a, .1), 'pseudopotentials': {'Al': 'Al.pbe-n-kjpaw_psl.1.0.0.UPF'}, 'kpts': [3, 3, 3], 'calculation': 'scf', 'smearing': 0.02}\n",
      "25 {'structure': OutputReference(045577d2-e868-4a0a-b67c-f02215ed437a, .2), 'pseudopotentials': {'Al': 'Al.pbe-n-kjpaw_psl.1.0.0.UPF'}, 'kpts': [3, 3, 3], 'calculation': 'scf', 'smearing': 0.02}\n",
      "27 {'structure': OutputReference(045577d2-e868-4a0a-b67c-f02215ed437a, .3), 'pseudopotentials': {'Al': 'Al.pbe-n-kjpaw_psl.1.0.0.UPF'}, 'kpts': [3, 3, 3], 'calculation': 'scf', 'smearing': 0.02}\n",
      "29 {'structure': OutputReference(045577d2-e868-4a0a-b67c-f02215ed437a, .4), 'pseudopotentials': {'Al': 'Al.pbe-n-kjpaw_psl.1.0.0.UPF'}, 'kpts': [3, 3, 3], 'calculation': 'scf', 'smearing': 0.02}\n",
      "3 {'working_directory': 'strain_0', 'input_dict': OutputReference(70d45a01-973a-42c6-abee-190a85036b67)}\n",
      "4 {'working_directory': 'strain_1', 'input_dict': OutputReference(da71d272-80bc-4399-a460-2d45caa0aa4a)}\n",
      "5 {'working_directory': 'strain_2', 'input_dict': OutputReference(90c318bf-ce10-4834-a0d2-254224d8cb7f)}\n",
      "6 {'working_directory': 'strain_3', 'input_dict': OutputReference(d47f5ce9-144b-4d01-87e6-ae9aa1cec060)}\n",
      "7 {'working_directory': 'strain_4', 'input_dict': OutputReference(0bf53da0-fed3-4207-8c23-7a917780e7ee)}\n",
      "30 {'0': OutputReference(2a7da7d8-f785-46a2-81c4-3276767573dc, .volume), '1': OutputReference(d90cda2e-bdef-4718-a98c-c1c8250bd091, .volume), '2': OutputReference(8a427e06-f9ae-4f30-915f-f590181d9591, .volume), '3': OutputReference(a25c7ca7-7413-4495-b16a-f8ca261bb816, .volume), '4': OutputReference(1fb42a89-1daf-4968-856f-9421a7bc1326, .volume)}\n",
      "31 {'0': OutputReference(2a7da7d8-f785-46a2-81c4-3276767573dc, .energy), '1': OutputReference(d90cda2e-bdef-4718-a98c-c1c8250bd091, .energy), '2': OutputReference(8a427e06-f9ae-4f30-915f-f590181d9591, .energy), '3': OutputReference(a25c7ca7-7413-4495-b16a-f8ca261bb816, .energy), '4': OutputReference(1fb42a89-1daf-4968-856f-9421a7bc1326, .energy)}\n",
      "8 {'volume_lst': OutputReference(9a938237-68a0-4ea2-a355-b558a1030073), 'energy_lst': OutputReference(42764edc-5596-4a37-aaea-8c9d595d769e)}\n"
     ]
    }
   ],
   "source": [
    "task_lst = get_workflow(\n",
    "    nodes_dict=nodes_dict, \n",
    "    input_dict=input_dict, \n",
    "    total_dict=new_total_dict,\n",
    "    source_handles_dict=source_handles_dict,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "c63922d4-81fb-4fc8-b52c-b26eec68811f",
   "metadata": {},
   "outputs": [],
   "source": [
    "flow = Flow(task_lst)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "12af9070-05af-4063-9013-1d34dde5d7bb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2025-01-17 14:52:13,854 INFO Started executing jobs locally\n",
      "2025-01-17 14:52:14,594 INFO Starting job - get_bulk_structure (b4a48a0d-8db2-4a7a-82cf-8bb62cef4fcc)\n",
      "2025-01-17 14:52:14,599 INFO Finished job - get_bulk_structure (b4a48a0d-8db2-4a7a-82cf-8bb62cef4fcc)\n",
      "2025-01-17 14:52:14,600 INFO Starting job - get_dict (c3c5fdbf-9d83-4517-be76-3c253fd19a68)\n",
      "2025-01-17 14:52:14,602 INFO Finished job - get_dict (c3c5fdbf-9d83-4517-be76-3c253fd19a68)\n",
      "2025-01-17 14:52:14,602 INFO Starting job - calculate_qe (08c347f2-4374-4148-ad63-cbc63123c9b5)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Note: The following floating-point exceptions are signalling: IEEE_INVALID_FLAG\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2025-01-17 14:52:27,833 INFO Finished job - calculate_qe (08c347f2-4374-4148-ad63-cbc63123c9b5)\n",
      "2025-01-17 14:52:27,833 INFO Starting job - generate_structures (045577d2-e868-4a0a-b67c-f02215ed437a)\n",
      "2025-01-17 14:52:27,836 INFO Finished job - generate_structures (045577d2-e868-4a0a-b67c-f02215ed437a)\n",
      "2025-01-17 14:52:27,837 INFO Starting job - get_dict (70d45a01-973a-42c6-abee-190a85036b67)\n",
      "2025-01-17 14:52:27,839 INFO Finished job - get_dict (70d45a01-973a-42c6-abee-190a85036b67)\n",
      "2025-01-17 14:52:27,839 INFO Starting job - get_dict (da71d272-80bc-4399-a460-2d45caa0aa4a)\n",
      "2025-01-17 14:52:27,841 INFO Finished job - get_dict (da71d272-80bc-4399-a460-2d45caa0aa4a)\n",
      "2025-01-17 14:52:27,841 INFO Starting job - get_dict (90c318bf-ce10-4834-a0d2-254224d8cb7f)\n",
      "2025-01-17 14:52:27,843 INFO Finished job - get_dict (90c318bf-ce10-4834-a0d2-254224d8cb7f)\n",
      "2025-01-17 14:52:27,843 INFO Starting job - get_dict (d47f5ce9-144b-4d01-87e6-ae9aa1cec060)\n",
      "2025-01-17 14:52:27,845 INFO Finished job - get_dict (d47f5ce9-144b-4d01-87e6-ae9aa1cec060)\n",
      "2025-01-17 14:52:27,845 INFO Starting job - get_dict (0bf53da0-fed3-4207-8c23-7a917780e7ee)\n",
      "2025-01-17 14:52:27,847 INFO Finished job - get_dict (0bf53da0-fed3-4207-8c23-7a917780e7ee)\n",
      "2025-01-17 14:52:27,847 INFO Starting job - calculate_qe (2a7da7d8-f785-46a2-81c4-3276767573dc)\n",
      "2025-01-17 14:52:30,858 INFO Finished job - calculate_qe (2a7da7d8-f785-46a2-81c4-3276767573dc)\n",
      "2025-01-17 14:52:30,859 INFO Starting job - calculate_qe (d90cda2e-bdef-4718-a98c-c1c8250bd091)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Note: The following floating-point exceptions are signalling: IEEE_INVALID_FLAG\n",
      "Note: The following floating-point exceptions are signalling: IEEE_INVALID_FLAG\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2025-01-17 14:52:34,081 INFO Finished job - calculate_qe (d90cda2e-bdef-4718-a98c-c1c8250bd091)\n",
      "2025-01-17 14:52:34,081 INFO Starting job - calculate_qe (8a427e06-f9ae-4f30-915f-f590181d9591)\n",
      "2025-01-17 14:52:37,498 INFO Finished job - calculate_qe (8a427e06-f9ae-4f30-915f-f590181d9591)\n",
      "2025-01-17 14:52:37,498 INFO Starting job - calculate_qe (a25c7ca7-7413-4495-b16a-f8ca261bb816)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Note: The following floating-point exceptions are signalling: IEEE_INVALID_FLAG\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2025-01-17 14:52:41,281 INFO Finished job - calculate_qe (a25c7ca7-7413-4495-b16a-f8ca261bb816)\n",
      "2025-01-17 14:52:41,282 INFO Starting job - calculate_qe (1fb42a89-1daf-4968-856f-9421a7bc1326)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Note: The following floating-point exceptions are signalling: IEEE_INVALID_FLAG\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2025-01-17 14:52:45,163 INFO Finished job - calculate_qe (1fb42a89-1daf-4968-856f-9421a7bc1326)\n",
      "2025-01-17 14:52:45,164 INFO Starting job - get_list (9a938237-68a0-4ea2-a355-b558a1030073)\n",
      "2025-01-17 14:52:45,166 INFO Finished job - get_list (9a938237-68a0-4ea2-a355-b558a1030073)\n",
      "2025-01-17 14:52:45,167 INFO Starting job - get_list (42764edc-5596-4a37-aaea-8c9d595d769e)\n",
      "2025-01-17 14:52:45,169 INFO Finished job - get_list (42764edc-5596-4a37-aaea-8c9d595d769e)\n",
      "2025-01-17 14:52:45,169 INFO Starting job - plot_energy_volume_curve (fa13a862-7064-4b11-8f0b-99a308c2f79a)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Note: The following floating-point exceptions are signalling: IEEE_INVALID_FLAG\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2025-01-17 14:52:45,288 INFO Finished job - plot_energy_volume_curve (fa13a862-7064-4b11-8f0b-99a308c2f79a)\n",
      "2025-01-17 14:52:45,289 INFO Finished executing jobs locally\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'b4a48a0d-8db2-4a7a-82cf-8bb62cef4fcc': {1: Response(output={'numbers': array([13, 13, 13, 13]), 'positions': array([[0.   , 0.   , 0.   ],\n",
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     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "result = run_locally(flow)\n",
    "result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fde82477-d967-40d2-ac77-3ec35f27650d",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}