{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "22d177dc-6cfb-4de2-9509-f1eb45e10cf2",
   "metadata": {},
   "source": [
    "# Bands Structure\n",
    "\n",
    "For the source code, see [bands](https://workgraph-collections.readthedocs.io/en/latest/ase/espresso/module.html#workgraph_collections.ase.espresso.bands.bands_workgraph).\n",
    "\n",
    "## Example: Silicon\n",
    "\n",
    "### Prepare the inputs and submit the workflow\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "8ee799d2-0b5b-4609-957f-6b3f2cd451f0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "        <iframe\n",
       "            width=\"100%\"\n",
       "            height=\"600px\"\n",
       "            src=\"html/BandsWorkgraph.html\"\n",
       "            frameborder=\"0\"\n",
       "            allowfullscreen\n",
       "            \n",
       "        ></iframe>\n",
       "        "
      ],
      "text/plain": [
       "<IPython.lib.display.IFrame at 0x7b1ccd819750>"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from ase.build import bulk\n",
    "from aiida import load_profile\n",
    "from copy import deepcopy\n",
    "\n",
    "from workgraph_collections.ase.espresso.bands import BandsWorkgraph\n",
    "from aiida_pythonjob.config import config\n",
    "# allow pickle\n",
    "config[\"allow_pickle\"] = True\n",
    "\n",
    "load_profile()\n",
    "\n",
    "atoms = bulk('Si')\n",
    "\n",
    "\n",
    "metadata = {\n",
    "    \"options\": {\n",
    "        'prepend_text' : \"\"\"eval \"$(conda shell.posix hook)\"\n",
    "        conda activate aiida\n",
    "        export OMP_NUM_THREADS=1\n",
    "        \"\"\",\n",
    "    }\n",
    "}\n",
    "pseudopotentials = {\"Si\": \"Si.pbe-nl-rrkjus_psl.1.0.0.UPF\"}\n",
    "pseudo_dir = \"/home/xing/data/ase/espresso_pseudo\"\n",
    "# pseudo_dir = \"/home/wang_x3/datas/pseudos/psl\"\n",
    "scf_input_data = {\n",
    "    \"control\": {\"calculation\": \"scf\",\n",
    "                },\n",
    "    \"system\": {\"ecutwfc\": 30, \"ecutrho\": 240,\n",
    "               \"occupations\": \"smearing\",\n",
    "                \"degauss\": 0.01,\n",
    "                \"smearing\": \"cold\"\n",
    "                },\n",
    "}\n",
    "relax_input_data = deepcopy(scf_input_data)\n",
    "relax_input_data[\"control\"].update({\"calculation\": \"vc-relax\"})\n",
    "bands_input_data = deepcopy(scf_input_data)\n",
    "bands_input_data[\"control\"].update({\"calculation\": \"bands\"})\n",
    "\n",
    "inputs = {\n",
    "    \"relax\": {\n",
    "        \"input_data\": relax_input_data,\n",
    "        \"kpts\": [4, 4, 4],\n",
    "        \"computer\": \"localhost\",\n",
    "        \"metadata\": metadata\n",
    "    },\n",
    "    \"scf\": {\n",
    "        \"input_data\": scf_input_data,\n",
    "        \"kpts\": [4, 4, 4],\n",
    "        \"computer\": \"localhost\",\n",
    "        \"metadata\": metadata\n",
    "    },\n",
    "    \"bands\": {\n",
    "        \"input_data\": bands_input_data,\n",
    "        \"computer\": \"localhost\",\n",
    "        \"metadata\": metadata\n",
    "    },\n",
    "}\n",
    "#------------------------- Set the inputs -------------------------\n",
    "wg = BandsWorkgraph.build_graph(atoms=atoms,\n",
    "                    pw_command=\"mpirun -np 1 pw.x\",\n",
    "                    pseudopotentials=pseudopotentials,\n",
    "                    pseudo_dir=pseudo_dir,\n",
    "                    inputs=inputs,\n",
    "                    density=10,\n",
    "                    run_relax=True,\n",
    "                    run_scf=True,\n",
    "                    )\n",
    "wg.to_html()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c37aa1d6",
   "metadata": {},
   "source": [
    "Run the workgraph:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "c0668cfd",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "09/03/2025 09:32:44 PM <374217> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [178886|WorkGraphEngine|continue_workgraph]: tasks ready to run: pw_calculator\n",
      "REPORT:aiida.orm.nodes.process.workflow.workchain.WorkChainNode:[178886|WorkGraphEngine|continue_workgraph]: tasks ready to run: pw_calculator\n",
      "09/03/2025 09:32:45 PM <374217> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [178886|WorkGraphEngine|on_wait]: Process status: Waiting for child processes: 178890\n",
      "REPORT:aiida.orm.nodes.process.workflow.workchain.WorkChainNode:[178886|WorkGraphEngine|on_wait]: Process status: Waiting for child processes: 178890\n",
      "09/03/2025 09:33:04 PM <374217> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [178886|WorkGraphEngine|update_task_state]: Task: pw_calculator, type: PYTHONJOB, finished.\n",
      "REPORT:aiida.orm.nodes.process.workflow.workchain.WorkChainNode:[178886|WorkGraphEngine|update_task_state]: Task: pw_calculator, type: PYTHONJOB, finished.\n",
      "09/03/2025 09:33:06 PM <374217> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [178886|WorkGraphEngine|continue_workgraph]: tasks ready to run: pw_calculator1,find_kpoint_path\n",
      "REPORT:aiida.orm.nodes.process.workflow.workchain.WorkChainNode:[178886|WorkGraphEngine|continue_workgraph]: tasks ready to run: pw_calculator1,find_kpoint_path\n",
      "INFO:aiida_pythonjob.decorator:Executing process function, current stack status: 47 frames of 3000\n",
      "09/03/2025 09:33:08 PM <374217> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [178886|WorkGraphEngine|update_task_state]: Task: find_kpoint_path, type: PYFUNCTION, finished.\n",
      "REPORT:aiida.orm.nodes.process.workflow.workchain.WorkChainNode:[178886|WorkGraphEngine|update_task_state]: Task: find_kpoint_path, type: PYFUNCTION, finished.\n",
      "09/03/2025 09:33:08 PM <374217> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [178886|WorkGraphEngine|continue_workgraph]: tasks ready to run: \n",
      "REPORT:aiida.orm.nodes.process.workflow.workchain.WorkChainNode:[178886|WorkGraphEngine|continue_workgraph]: tasks ready to run: \n",
      "09/03/2025 09:33:08 PM <374217> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [178886|WorkGraphEngine|on_wait]: Process status: Waiting for child processes: 178904\n",
      "REPORT:aiida.orm.nodes.process.workflow.workchain.WorkChainNode:[178886|WorkGraphEngine|on_wait]: Process status: Waiting for child processes: 178904\n",
      "09/03/2025 09:33:17 PM <374217> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [178886|WorkGraphEngine|update_task_state]: Task: pw_calculator1, type: PYTHONJOB, finished.\n",
      "REPORT:aiida.orm.nodes.process.workflow.workchain.WorkChainNode:[178886|WorkGraphEngine|update_task_state]: Task: pw_calculator1, type: PYTHONJOB, finished.\n",
      "09/03/2025 09:33:20 PM <374217> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [178886|WorkGraphEngine|continue_workgraph]: tasks ready to run: pw_calculator2\n",
      "REPORT:aiida.orm.nodes.process.workflow.workchain.WorkChainNode:[178886|WorkGraphEngine|continue_workgraph]: tasks ready to run: pw_calculator2\n",
      "09/03/2025 09:33:21 PM <374217> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [178886|WorkGraphEngine|on_wait]: Process status: Waiting for child processes: 178924\n",
      "REPORT:aiida.orm.nodes.process.workflow.workchain.WorkChainNode:[178886|WorkGraphEngine|on_wait]: Process status: Waiting for child processes: 178924\n",
      "09/03/2025 09:33:34 PM <374217> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [178886|WorkGraphEngine|update_task_state]: Task: pw_calculator2, type: PYTHONJOB, finished.\n",
      "REPORT:aiida.orm.nodes.process.workflow.workchain.WorkChainNode:[178886|WorkGraphEngine|update_task_state]: Task: pw_calculator2, type: PYTHONJOB, finished.\n",
      "09/03/2025 09:33:36 PM <374217> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [178886|WorkGraphEngine|continue_workgraph]: tasks ready to run: \n",
      "REPORT:aiida.orm.nodes.process.workflow.workchain.WorkChainNode:[178886|WorkGraphEngine|continue_workgraph]: tasks ready to run: \n",
      "09/03/2025 09:33:36 PM <374217> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [178886|WorkGraphEngine|finalize]: Finalize workgraph.\n",
      "REPORT:aiida.orm.nodes.process.workflow.workchain.WorkChainNode:[178886|WorkGraphEngine|finalize]: Finalize workgraph.\n"
     ]
    }
   ],
   "source": [
    "wg.run()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "afe2b91d",
   "metadata": {},
   "source": [
    "## Plot the band structure"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "07ec40d4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: ylabel='energies [eV]'>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from ase.spectrum.band_structure import BandStructure\n",
    "import numpy as np\n",
    "\n",
    "data = wg.outputs.bands.band.value.value\n",
    "eigenvalues = np.array([np.array(data['bands']['energies'])])\n",
    "kpts = wg.outputs.kpoints_path.value.value\n",
    "bs = BandStructure(path=kpts, energies=eigenvalues)\n",
    "bs.plot()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "aiida",
   "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": "3.11.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}