{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "22d177dc-6cfb-4de2-9509-f1eb45e10cf2",
   "metadata": {},
   "source": [
    "# Bands Structure\n",
    "For the source code, see [bands](https://workgraph-collections.readthedocs.io/en/latest/qe/module.html#workgraph_collections.ase.espresso.bands.bands_workgraph).\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "efa095d0",
   "metadata": {},
   "source": [
    "## Example: Silicon band structure\n",
    "### Prepare the inputs and visualize the WorkGraph\n",
    "\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 0x71fda0253650>"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from copy import deepcopy\n",
    "from aiida import load_profile\n",
    "from aiida.orm import Dict, KpointsData, StructureData, load_code\n",
    "from ase.build import bulk\n",
    "from workgraph_collections.qe.bands import BandsWorkgraph\n",
    "\n",
    "load_profile()\n",
    "\n",
    "atoms = bulk('Si')\n",
    "structure_si = StructureData(ase=atoms)\n",
    "\n",
    "pw_code = load_code('pw-7.4@localhost')\n",
    "scf_paras = {\n",
    "    'CONTROL': {\n",
    "        'calculation': 'scf',\n",
    "    },\n",
    "    'SYSTEM': {\n",
    "        'ecutwfc': 30,\n",
    "        'ecutrho': 240,\n",
    "        'occupations': 'smearing',\n",
    "        'smearing': 'gaussian',\n",
    "        'degauss': 0.1,\n",
    "    },\n",
    "}\n",
    "relax_paras = deepcopy(scf_paras)\n",
    "relax_paras['CONTROL']['calculation'] = 'vc-relax'\n",
    "bands_paras = deepcopy(scf_paras)\n",
    "bands_paras['CONTROL']['calculation'] = 'bands'\n",
    "\n",
    "kpoints = KpointsData()\n",
    "kpoints.set_kpoints_mesh([3, 3, 3])\n",
    "#\n",
    "metadata = {\n",
    "    'options': {\n",
    "        'resources': {\n",
    "            'num_machines': 1,\n",
    "            'num_mpiprocs_per_machine': 4,\n",
    "        },\n",
    "    }\n",
    "}\n",
    "\n",
    "bands_inputs = {\n",
    "    'relax': {\n",
    "        'base': {\n",
    "            'pw': {\n",
    "                'parameters': Dict(relax_paras),\n",
    "                'metadata': metadata,\n",
    "            },\n",
    "            'kpoints': kpoints,\n",
    "        },\n",
    "    },\n",
    "    'scf': {\n",
    "        'pw': {\n",
    "            'parameters': Dict(scf_paras),\n",
    "            'metadata': metadata,\n",
    "        },\n",
    "        'kpoints': kpoints,\n",
    "    },\n",
    "    'bands': {\n",
    "        'pw': {\n",
    "            'parameters': Dict(bands_paras),\n",
    "            'metadata': metadata,\n",
    "        },\n",
    "        'kpoints': kpoints,\n",
    "    },\n",
    "}\n",
    "\n",
    "wg = BandsWorkgraph.build_graph(\n",
    "    structure=structure_si,\n",
    "    code=pw_code,\n",
    "    inputs=bands_inputs,\n",
    "    pseudo_family='SSSP/1.3/PBEsol/efficiency',\n",
    "    run_relax=True,\n",
    "    bands_kpoints_distance=0.1,\n",
    ")\n",
    "wg.to_html()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "83f8b2ad",
   "metadata": {},
   "source": [
    "## Run the WorkGraph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "9dbe9a7a",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "09/02/2025 01:00:02 PM <1272331> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [175111|WorkGraphEngine|continue_workgraph]: tasks ready to run: PwRelaxWorkChain\n",
      "09/02/2025 01:00:03 PM <1272331> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [175111|WorkGraphEngine|on_wait]: Process status: Waiting for child processes: 175117\n",
      "09/02/2025 01:00:09 PM <1272331> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [175117|PwRelaxWorkChain|run_relax]: launching PwBaseWorkChain<175120>\n",
      "09/02/2025 01:00:09 PM <1272331> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [175120|PwBaseWorkChain|run_process]: launching PwCalculation<175123> iteration #1\n",
      "09/02/2025 01:00:24 PM <1272331> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [175120|PwBaseWorkChain|results]: work chain completed after 1 iterations\n",
      "09/02/2025 01:00:24 PM <1272331> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [175120|PwBaseWorkChain|on_terminated]: remote folders will not be cleaned\n",
      "09/02/2025 01:00:24 PM <1272331> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [175117|PwRelaxWorkChain|inspect_relax]: after iteration 1 cell volume of relaxed structure is 40.9225595043556\n",
      "09/02/2025 01:00:25 PM <1272331> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [175117|PwRelaxWorkChain|run_relax]: launching PwBaseWorkChain<175132>\n",
      "09/02/2025 01:00:25 PM <1272331> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [175132|PwBaseWorkChain|run_process]: launching PwCalculation<175135> iteration #1\n",
      "09/02/2025 01:00:33 PM <1272331> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [175132|PwBaseWorkChain|results]: work chain completed after 1 iterations\n",
      "09/02/2025 01:00:33 PM <1272331> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [175132|PwBaseWorkChain|on_terminated]: remote folders will not be cleaned\n",
      "09/02/2025 01:00:33 PM <1272331> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [175117|PwRelaxWorkChain|inspect_relax]: after iteration 2 cell volume of relaxed structure is 40.92255896569323\n",
      "09/02/2025 01:00:33 PM <1272331> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [175117|PwRelaxWorkChain|inspect_relax]: relative cell volume difference 1.3162968698541411e-08 smaller than threshold 0.01\n",
      "09/02/2025 01:00:33 PM <1272331> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [175117|PwRelaxWorkChain|results]: workchain completed after 2 iterations\n",
      "09/02/2025 01:00:34 PM <1272331> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [175117|PwRelaxWorkChain|on_terminated]: remote folders will not be cleaned\n",
      "09/02/2025 01:00:34 PM <1272331> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [175111|WorkGraphEngine|update_task_state]: Task: PwRelaxWorkChain, type: WORKCHAIN, finished.\n",
      "09/02/2025 01:00:42 PM <1272331> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [175111|WorkGraphEngine|continue_workgraph]: tasks ready to run: inspect_relax,seekpath_structure_analysis\n",
      "09/02/2025 01:00:43 PM <1272331> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [175111|WorkGraphEngine|update_task_state]: Task: inspect_relax, type: CALCFUNCTION, finished.\n",
      "09/02/2025 01:00:44 PM <1272331> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [175111|WorkGraphEngine|continue_workgraph]: tasks ready to run: seekpath_structure_analysis,update_scf_parameters\n",
      "09/02/2025 01:00:45 PM <1272331> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [175111|WorkGraphEngine|update_task_state]: Task: seekpath_structure_analysis, type: CALCFUNCTION, finished.\n",
      "09/02/2025 01:00:46 PM <1272331> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [175111|WorkGraphEngine|continue_workgraph]: tasks ready to run: update_scf_parameters\n",
      "09/02/2025 01:00:47 PM <1272331> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [175111|WorkGraphEngine|update_task_state]: Task: update_scf_parameters, type: CALCFUNCTION, finished.\n",
      "09/02/2025 01:00:48 PM <1272331> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [175111|WorkGraphEngine|continue_workgraph]: tasks ready to run: PwBaseWorkChain\n",
      "09/02/2025 01:00:50 PM <1272331> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [175111|WorkGraphEngine|on_wait]: Process status: Waiting for child processes: 175153\n",
      "09/02/2025 01:00:55 PM <1272331> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [175153|PwBaseWorkChain|run_process]: launching PwCalculation<175156> iteration #1\n",
      "09/02/2025 01:01:00 PM <1272331> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [175153|PwBaseWorkChain|results]: work chain completed after 1 iterations\n",
      "09/02/2025 01:01:00 PM <1272331> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [175153|PwBaseWorkChain|on_terminated]: remote folders will not be cleaned\n",
      "09/02/2025 01:01:01 PM <1272331> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [175111|WorkGraphEngine|update_task_state]: Task: PwBaseWorkChain, type: WORKCHAIN, finished.\n",
      "09/02/2025 01:01:07 PM <1272331> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [175111|WorkGraphEngine|continue_workgraph]: tasks ready to run: update_bands_parameters\n",
      "09/02/2025 01:01:08 PM <1272331> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [175111|WorkGraphEngine|update_task_state]: Task: update_bands_parameters, type: CALCFUNCTION, finished.\n",
      "09/02/2025 01:01:08 PM <1272331> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [175111|WorkGraphEngine|continue_workgraph]: tasks ready to run: PwBaseWorkChain1\n",
      "09/02/2025 01:01:10 PM <1272331> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [175111|WorkGraphEngine|on_wait]: Process status: Waiting for child processes: 175166\n",
      "09/02/2025 01:01:15 PM <1272331> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [175166|PwBaseWorkChain|run_process]: launching PwCalculation<175169> iteration #1\n",
      "09/02/2025 01:01:22 PM <1272331> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [175166|PwBaseWorkChain|results]: work chain completed after 1 iterations\n",
      "09/02/2025 01:01:22 PM <1272331> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [175166|PwBaseWorkChain|on_terminated]: remote folders will not be cleaned\n",
      "09/02/2025 01:01:23 PM <1272331> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [175111|WorkGraphEngine|update_task_state]: Task: PwBaseWorkChain1, type: WORKCHAIN, finished.\n",
      "09/02/2025 01:01:28 PM <1272331> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [175111|WorkGraphEngine|continue_workgraph]: tasks ready to run: \n",
      "09/02/2025 01:01:30 PM <1272331> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [175111|WorkGraphEngine|finalize]: Finalize workgraph.\n"
     ]
    }
   ],
   "source": [
    "wg.run()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "66e9bd48",
   "metadata": {},
   "source": [
    "## Plot the band structure"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "3defa20f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(<Figure size 640x480 with 1 Axes>, <Axes: ylabel='Dispersion (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 workgraph_collections.qe.utils import plot_bandstructure\n",
    "    \n",
    "bands = wg.outputs.output_band.value\n",
    "plot_bandstructure(bands)"
   ]
  }
 ],
 "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
}