{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Quick start with aiida_castep\n",
    "\n",
    "## Introduction\n",
    "\n",
    "`aiida-castep` is a plugin to interface CASTEP with [AiiDA](www.aiida.net) (Automated Interactive Infrastructure and Database for Computational Science).  \n",
    "It is quite complicated to explain what AiiDA is, but here are some key benefits:\n",
    "\n",
    "* Prepare/generate inputs for CASTEP in python\n",
    "* Job submission/state monitoring/retrieving is handled by AiiDA - no more ssh/rsync/sbatch/squeue/qsub/qstat/....\n",
    "* **All** inputs and outputs of **every** calculation are stored in a graph-like database, preserving the provenance\n",
    "* Workflow automation\n",
    "\n",
    "In this example, we go through how to setup a `CastepCalculation`. A `CastepCalculation` is essentially a \"transaction\" with the underlying CASTEP executable,\n",
    "and is the basis for more complex workflows. \n",
    "In reality, you will seldom have the need to setup such calculation explicitly like in this notebook, since it is more convenient to setup higher level workflows directly (through `WorkChain`s).\n",
    "Nevertheless, it can be useful to know how each elementary calculation operates.\n",
    "\n",
    "This example does not require any installation of the actual CASTEP code. The output file is generated by a *mock* code that matches the input with existing repository of completed calculations, and generate the output files accordingly. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Profile<uuid='835822e76755476d9826ed697be0e2b7' name='myprofile'>"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%load_ext aiida\n",
    "\n",
    "from aiida import load_profile, engine, orm, plugins\n",
    "from aiida.storage.sqlite_temp import SqliteTempBackend\n",
    "\n",
    "profile = load_profile(\n",
    "    SqliteTempBackend.create_profile(\n",
    "        'myprofile',\n",
    "        sandbox_path='_sandbox',\n",
    "        options={\n",
    "            'warnings.development_version': False,\n",
    "            'runner.poll.interval': 1\n",
    "        },\n",
    "        debug=False\n",
    "    ),\n",
    "    allow_switch=True\n",
    ")\n",
    "profile"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Basic concepts\n",
    "AiiDA stores everything as `Node` and connect them by *link* (i.e a graph database).\n",
    "There are two type of nodes `Calculation` and `Data`. \n",
    "\n",
    "### Below is an example provenance graph for a `CasteplCalculation`\n",
    "The red-square represents the `CastepCalculation` node, and the rounded nodes going in and out to it are the input and output nodes. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# A exmaple of the provenance graph of a CastepCalculation\n",
    "from __future__ import print_function\n",
    "from IPython.display import Image\n",
    "Image(\"./Si_bs_example.png\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The calculations (square) take data nodes (circle) as the input and create output data nodes.\n",
    "There is also a `Code` node which represents the code that is used to conduct the calculation, i.e the CASTEP executable on the remote cluster.  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Preparing a CASTEP calculation\n",
    "Here are the essential parts of a CASTEP calculations and their representation in AiiDA:\n",
    "* A input structure - `StructureData`\n",
    "* A set of kpoints - `KpointsData`\n",
    "* A set of pseudopotentials  - `OTFGData` or `UspData` or even `UpfData`\n",
    "* The parameters for calculations, e.g keys in the `param` and `cell` files - `Dict`\n",
    "\n",
    "Below is a walk-through of the steps to create a single point calculation using the basic `CastepCalculation` class."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import aiida.orm as orm\n",
    "from pprint import pprint"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Example CASTEP calculation - silicon bandstructure\n",
    "This is taken from the online tutorial."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `cell` file contain the crystal structure and related setting."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "%block lattice_cart\n",
      "2.6954645 2.6954645 0.0 \n",
      "2.6954645 0.0       2.6954645\n",
      "0.0       2.6954645 2.6954645\n",
      "%endblock lattice_cart\n",
      "%block positions_frac\n",
      "Si 0.00 0.00 0.00\n",
      "Si 0.25 0.25 0.25\n",
      "%endblock positions_frac\n",
      "symmetry_generate\n",
      "%block species_pot\n",
      "Si Si_00.usp\n",
      "%endblock species_pot\n",
      "kpoint_mp_grid 4 4 4\n",
      "%block bs_kpoint_path \n",
      "0.5 0.25 0.75    ! W\n",
      "0.5 0.5 0.5      ! L\n",
      "0.0 0.0  0.0     ! Gamma\n",
      "0.5 0.0 0.5      ! X\n",
      "0.5 0.25 0.75    ! W\n",
      "0.375 0.375 0.75 ! K\n",
      "%endblock bs_kpoint_path \n"
     ]
    }
   ],
   "source": [
    "!cat 'bandstructure/silicon/Si2.cell' | grep -v -e \"^!\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `param` file contains a list of key-value pairs "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "task\t\tbandstructure ! The TASK keyword instructs CASTEP what to do\n",
      "xc_functional   LDA           ! Which exchange-correlation functional to use.\n",
      "basis_precision MEDIUM        ! Choose high cut-off COARSE/MEDIUM/FINE/PRECISE\n",
      "fix_occupancy   true          ! Treat the system as an insulator\n",
      "opt_strategy    speed         ! Choose algorithms for best speed at expense of memory.\n",
      "num_dump_cycles 0             ! Don't write unwanted \".wvfn\" files.\n",
      "write_formatted_density TRUE  ! Write out a density file that we can view using (e.g.) Jmol.\n"
     ]
    }
   ],
   "source": [
    "!cat 'bandstructure/silicon/Si2.param' | grep -v -e \"^!\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Setup the calculation with AiiDA\n",
    "We setup a similar calculation with Si here. Instead of going for the band structure, we just do a single point run."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Define the structure by creating the `StructureData` node"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define the structure\n",
    "from aiida.plugins import DataFactory\n",
    "StructureData = DataFactory('structure')\n",
    "silicon = StructureData()\n",
    "r_unit = 2.6954645\n",
    "silicon.set_cell(np.array([[1, 1, 0], [1, 0, 1], [0, 1, 1]]) * r_unit)\n",
    "silicon.append_atom(symbols=[\"Si\"], position=[0, 0, 0])\n",
    "silicon.append_atom(symbols=[\"Si\"], position=[r_unit * 0.5] * 3)\n",
    "silicon.label = \"Si\"\n",
    "silicon.description = \"A silicon structure\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Atomic Simulation Environment (ASE) has a rich set of tools for handling structures, center around the `ase.Atoms` class.\n",
    "They can be converted to `StructureData` that AiiDA understands and saves to the provenance graph."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# You can also use ase.Atoms object to create the StructureData\n",
    "from ase import Atoms\n",
    "silicon_atoms = Atoms('Si2', cell=silicon.cell, scaled_positions=((0, 0, 0), (0.25, 0.25, 0.25)))\n",
    "silicon_from_atoms = StructureData(ase=silicon_atoms)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`StructureData` can be converted back to `Atoms` for complex operation using `ase`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# You can also convent the StructureData back to ase.Atoms\n",
    "silicon_atoms_2 = silicon_from_atoms.get_ase()\n",
    "silicon_atoms_2 == silicon_atoms"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that a similar interface also exists to work with `pymatgen`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Define the parameters by creating the `Dict` node\n",
    "We use a nested dictionary to represent the input key-value pairs for CASTEP.\n",
    "The keys for `param` file goes under `PARAM` and that for `cell` file goes under `CELL`.\n",
    "Finally, we convert the dictionary to `Dict`, allowing AiiDA to store it in the database.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define a dictionary to store the parameters of the calculations\n",
    "Dict = DataFactory('dict')\n",
    "param_dict = {\n",
    "    # Notice that the keywords are group into two sub-dictionaries\n",
    "    # just like you would do when preparing the inputs by hand\n",
    "    \"CELL\":{\n",
    "        \"symmetry_generate\": True,\n",
    "        \"snap_to_symmetry\": True,\n",
    "        # Pass a list of string to set a BLOCK inputs\n",
    "        #\"cell_constraints\":\n",
    "        #[\"0 0 0\", \"0 0 0\"]\n",
    "    },\n",
    "    \"PARAM\":{\n",
    "        \"task\": \"singlepoint\",\n",
    "        \"basis_precision\": \"medium\",\n",
    "        \"fix_occupancy\": True,   # Use bool type to make it easy for querying\n",
    "        \"opt_strategy\": \"speed\",\n",
    "        \"num_dump_cycles\": 0,  \n",
    "        \"write_formatted_density\": True\n",
    "    }}\n",
    "# We need to create a Dict node that holds the dictionary\n",
    "param = Dict(dict=param_dict)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`Dict` type can be converted back to python dictionary."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'CELL': {'snap_to_symmetry': True, 'symmetry_generate': True},\n",
      " 'PARAM': {'basis_precision': 'medium',\n",
      "           'fix_occupancy': True,\n",
      "           'num_dump_cycles': 0,\n",
      "           'opt_strategy': 'speed',\n",
      "           'task': 'singlepoint',\n",
      "           'write_formatted_density': True}}\n",
      "{'CELL': {'symmetry_generate': True, 'snap_to_symmetry': True}, 'PARAM': {'task': 'singlepoint', 'basis_precision': 'medium', 'fix_occupancy': True, 'opt_strategy': 'memory', 'num_dump_cycles': 0, 'write_formatted_density': True}}\n"
     ]
    }
   ],
   "source": [
    "# You can acceess the stored data\n",
    "pprint(param.get_dict())\n",
    "\n",
    "# You can also reset the data in the Dict node\n",
    "param_dict[\"PARAM\"].update(opt_strategy=\"memory\")\n",
    "param.set_dict(param_dict)\n",
    "print(param.get_dict())\n",
    "# Get it back\n",
    "param_dict[\"PARAM\"].update(opt_strategy=\"memory\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Define the kpoints with the `KpointsData` node\n",
    "Plane-wave DFT calculations require a k-point grid. We need to define it and store the database. The `KpointData` is used for this purpose."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(array([[0.  , 0.  , 0.  ],\n",
      "       [0.25, 0.25, 0.25]]), array([0.5, 0.5]))\n"
     ]
    }
   ],
   "source": [
    "KpointsData = DataFactory('array.kpoints')\n",
    "# You can define kpoints explicitly\n",
    "kpoints = KpointsData()\n",
    "# Use gamma and 0.25, 0.25, 0.25\n",
    "kpoints.set_kpoints(((0., 0., 0), (0.25, 0.25, 0.25)), weights=[0.5, 0.5])\n",
    "pprint(kpoints.get_kpoints(also_weights=True))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Very often the a grid is used, the actual kpoints are computed by CASTEP taking symmetry into account.\n",
    "The `KpointsData` can also represent the grid instead of explicit points."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "([4, 4, 4], [0.0, 0.0, 0.0])\n"
     ]
    }
   ],
   "source": [
    "# More commonly, we want to use a grid (mesh) of the kpoints\n",
    "kpoints = KpointsData()\n",
    "kpoints.set_kpoints_mesh((4, 4, 4), offset=(0,0,0))\n",
    "print(kpoints.get_kpoints_mesh())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Define the pseudopotentials\n",
    "Pseuodpotentials are also stored as `Node` in AiiDA and we have to define them. For CASTEP, they can be library names, on-the-fly generation strings or files."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Define an OTFG string for Si"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "from aiida_castep.data.otfg import OTFGData\n",
    "from aiida_castep.data.usp import UspData\n",
    "# Explicitly define the OTFG string\n",
    "si_c18 = OTFGData(otfg_entry=\"Si 3|1.8|5|6|7|30:31:32\")\n",
    "# Alternatively you can define explicitly the file to be used\n",
    "#si_usp = UspData(file=\"/home/max/MS61_00/Si_00.usp\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Alternatively, we can define a string as the name of the library."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "c9 = OTFGData(otfg_entry=\"C9\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Put things together\n",
    "A calculation has a bunch of inputs which have been define above.  \n",
    "It also requires other settings such as which code to use, what resources are need and for how long e.t.c."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "from aiida.plugins import CalculationFactory\n",
    "\n",
    "#Load up the CASTEP calculation\n",
    "CastepCalculation = CalculationFactory('castep.castep')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It is most convenient to use a `ProcessBuilder` to build calculation. It provides a namespace for all the inputs and allows interactive tab completion. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "builder = CastepCalculation.get_builder()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<aiida.orm.authinfos.AuthInfo at 0x7f1bc0ebd430>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Uncomment the below line to create a localhost Computer if you have not done so\n",
    "comp = orm.Computer('localhost', 'localhost', transport_type='core.local', scheduler_type='core.direct')\n",
    "comp.store()\n",
    "\n",
    "\n",
    "# Some configuration may be needed for first-time user\n",
    "comp.set_workdir('/tmp/aiida_run/')\n",
    "comp.configure()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It is essential that we tell AiiDA which CASTEP executable on the remote compute that we want to run.\n",
    "Hence, a `Code` is required for the builder."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define a mock code on the localhost computer\n",
    "code_path = !which castep.mock\n",
    "castep_mock = orm.Code((comp, code_path[0]), input_plugin_name='castep.castep')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, we put everything together in the `builder`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "builder.structure = silicon\n",
    "builder.parameters = param\n",
    "builder.kpoints = kpoints\n",
    "builder.code = castep_mock\n",
    "builder.pseudos = {'Si': c9}\n",
    "builder.metadata.options.withmpi = False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "builder.metadata.options.resources = {'num_machines': 1, 'tot_num_mpiprocs': 2}\n",
    "builder.metadata.options.max_wallclock_seconds = 600\n",
    "builder.metadata.label = \"Si SINGLEPOINT\"\n",
    "builder.metadata.description = 'A Example CASTEP calculation for silicon'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "def d2attr(d, b):\n",
    "    for k, v in d.items():\n",
    "        b.__setattr__(k, v)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "from aiida.engine import run_get_node\n",
    "from aiida.engine import Process"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "results, calcjob  = run_get_node(builder)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['_scheduler-stderr.txt',\n",
       " '_scheduler-stdout.txt',\n",
       " 'aiida.bands',\n",
       " 'aiida.castep',\n",
       " 'aiida.den_fmt']"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results['retrieved'].base.repository.list_object_names()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['.aiida', '_aiidasubmit.sh', 'aiida.cell', 'aiida.param']"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "calcjob.base.repository.list_object_names()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[2.6954645, 2.6954645, 0.0],\n",
       " [2.6954645, 0.0, 2.6954645],\n",
       " [0.0, 2.6954645, 2.6954645]]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "calcjob.inputs.structure.cell"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'is_local': False,\n",
       " 'remote_exec_path': '/home/bonan/miniconda3/envs/aiida-2.0-dev/bin/castep.mock',\n",
       " 'input_plugin': 'castep.castep'}"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "calcjob.inputs.code.attributes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "##### Generated by aiida_castep 22:25:36 26/05/2022 BST #####\n",
      "#         author: Bonan Zhu (zhubonan@outlook.com)\n",
      "# # AiiDA User: user@email.com\n",
      "# AiiDA profile: myprofile\n",
      "# Information of the calculation node\n",
      "# label: Si SINGLEPOINT\n",
      "# description:\n",
      "# A Example CASTEP calculation for silicon\n",
      "# \n",
      "## Information of input nodes used:\n",
      "# \n",
      "# type: uuid: 16b540c5-e510-4d8f-8f8f-c789b109a71e (pk: 3)\n",
      "# pk: 3\n",
      "# linkname: parameters\n",
      "# uuid: 16b540c5-e510-4d8f-8f8f-c789b109a71e\n",
      "# label: \n",
      "# description:\n",
      "# \n",
      "# END OF HEADER\n",
      "task                : singlepoint\n",
      "basis_precision     : medium\n",
      "fix_occupancy       : True\n",
      "opt_strategy        : memory\n",
      "num_dump_cycles     : 0\n",
      "write_formatted_density: True\n",
      "iprint              : 1\n",
      "run_time            : 540\n",
      "comment             : Si SINGLEPOINT\n"
     ]
    }
   ],
   "source": [
    "print(calcjob.base.repository.get_object_content('aiida.param'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Transverse the graph\n",
    "We demostrate how to follow the links to get the input and output of a calculation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "lns = calcjob.get_incoming()  # This is an link manager"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Link label: pseudos__Si\n",
      "Link node: uuid: e9cb672c-a8a5-4233-af2d-858f4d2ee3f1 (pk: 1)\n",
      "Link type: LinkType.INPUT_CALC\n"
     ]
    }
   ],
   "source": [
    "# A link triple is a object contains the link label, vertex of the link\n",
    "link = lns.first()\n",
    "print(f\"Link label: {link.link_label}\")\n",
    "print(f\"Link node: {link.node}\")\n",
    "print(f\"Link type: {link.link_type}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<StructureData: uuid: 2a534b89-3f0c-481b-957b-24e338f627ad (pk: 2)>\n",
      "<Dict: uuid: 16b540c5-e510-4d8f-8f8f-c789b109a71e (pk: 3)>\n"
     ]
    }
   ],
   "source": [
    "# Get the node by the link label\n",
    "print(lns.get_node_by_label('structure').__repr__())\n",
    "print(lns.get_node_by_label('parameters').__repr__())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[LinkPair(link_type=<LinkType.INPUT_CALC: 'input_calc'>, link_label='pseudos__Si'),\n",
       " LinkPair(link_type=<LinkType.INPUT_CALC: 'input_calc'>, link_label='structure'),\n",
       " LinkPair(link_type=<LinkType.INPUT_CALC: 'input_calc'>, link_label='parameters'),\n",
       " LinkPair(link_type=<LinkType.INPUT_CALC: 'input_calc'>, link_label='kpoints'),\n",
       " LinkPair(link_type=<LinkType.INPUT_CALC: 'input_calc'>, link_label='code')]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Link pair includes link type and link label\n",
    "lns.all_link_pairs()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[LinkTriple(node=<OTFGData: uuid: e9cb672c-a8a5-4233-af2d-858f4d2ee3f1 (pk: 1)>, link_type=<LinkType.INPUT_CALC: 'input_calc'>, link_label='pseudos__Si'),\n",
       " LinkTriple(node=<StructureData: uuid: 2a534b89-3f0c-481b-957b-24e338f627ad (pk: 2)>, link_type=<LinkType.INPUT_CALC: 'input_calc'>, link_label='structure'),\n",
       " LinkTriple(node=<Dict: uuid: 16b540c5-e510-4d8f-8f8f-c789b109a71e (pk: 3)>, link_type=<LinkType.INPUT_CALC: 'input_calc'>, link_label='parameters'),\n",
       " LinkTriple(node=<KpointsData: uuid: 301e225b-980c-4d98-bb32-4fcb64c13b55 (pk: 4)>, link_type=<LinkType.INPUT_CALC: 'input_calc'>, link_label='kpoints'),\n",
       " LinkTriple(node=<Code: Remote code '' on localhost, pk: 5, uuid: 73b511f7-a5f4-4396-97c5-8c248466f170>, link_type=<LinkType.INPUT_CALC: 'input_calc'>, link_label='code')]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Link triples also include nodes\n",
    "lns.link_triples"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Raw output file\n",
    "\n",
    "The raw output is stored in the `retrieved` output node."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " +-------------------------------------------------+\n",
      " |                                                 |\n",
      " |      CCC   AA    SSS  TTTTT  EEEEE  PPPP        |\n",
      " |     C     A  A  S       T    E      P   P       |\n",
      " |     C     AAAA   SS     T    EEE    PPPP        |\n",
      " |     C     A  A     S    T    E      P           |\n",
      " |      CCC  A  A  SSS     T    EEEEE  P           |\n",
      " |                                                 |\n",
      " +-------------------------------------------------+\n",
      " |                                                 |\n",
      " | Welcome to Academic Release CASTEP version 19.1 |\n",
      " | Ab Initio Total Energy Program                  |\n",
      " |                                                 |\n",
      " | Authors:                                        |\n",
      " | M. Segall, M. Probert, C. Pickard, P. Hasnip,   |\n",
      " | S. Clark, K. Refson, J. R. Yates, M. Payne      |\n",
      " |                                                 |\n",
      " | Contributors:                                   |\n",
      " | P. Lindan, P. Haynes, J. White, V. Milman,      |\n",
      " | N. Govind, M. Gibson, P. Tulip, V. Cocula,      |\n",
      " | B. Montanari, D. Quigley, M. Glover,            |\n",
      " | L. Bernasconi, A. Perlov, M. Plummer,           |\n",
      " | E. McNellis, J. Meyer, J. Gale, D. Jochym       |\n",
      " | J. Aarons, B. Walker, R. Gillen, D. Jones       |\n",
      " | T. Green, I. J. Bush, C. J. Armstrong,          |\n",
      " | E. J. Higgins, E. L. Brown, M. S. McFly,        |\n",
      " | J. Wilkins, B-C. Shih, P. J. P. Byrne           |\n",
      " |                                                 |\n",
      " | Copyright (c) 2000 - 2018                       |\n",
      " |                                                 |\n",
      " |     Distributed under the terms of an           |\n",
      " |     Agreement between the United Kingdom        |\n",
      " |     Car-Parrinello (UKCP) Consortium,           |\n",
      " |     Daresbury Laboratory and Accelrys, Inc.     |\n",
      " |                                                 |\n",
      " | Please cite                                     |\n",
      " |                                                 |\n",
      " |     \"First principles methods using CASTEP\"     |\n",
      " |                                                 |\n",
      " |         Zeitschrift fuer Kristallographie       |\n",
      " |           220(5-6) pp. 567-570 (2005)           |\n",
      " |                                                 |\n",
      " | S. J. Clark, M. D. Segall, C. J. Pickard,       |\n",
      " | P. J. Hasnip, M. J. Probert, K. Refson,         |\n",
      " | M. C. Payne                                     |\n",
      " |                                                 |\n",
      " |       in all publications arising from          |\n",
      " |              your use of CASTEP                 |\n",
      " |                                                 |\n",
      " +-------------------------------------------------+\n",
      " |                                                 |\n",
      " |              http://www.castep.org              |\n",
      " |                                                 |\n",
      " +-------------------------------------------------+\n",
      "\n",
      "\n",
      "\n",
      " Compiled for linux_x86_64_gfortran7.0 on Sat, 23 Mar 2019 17:55:25 +0000\n",
      " from code version 669252f55895+  Tue, 15 Jan 2019 17:23:14 +0000\n",
      " Compiler: GNU Fortran 7.3.0; Optimisation: fast\n",
      " MATHLIBS: Intel MKL(2018.0.4) (LAPACK version 3.7.0)\n",
      " FFT Lib : mkl\n",
      " Fundamental constants values: CODATA 2014\n",
      "\n",
      " Run started: Mon, 02 Dec 2019 12:59:04 +0000\n",
      "\n",
      " Atomic calculation performed for Si: 1s2 2s2 2p6 3s2 3p2\n",
      "\n",
      " Converged in 55 iterations to an ae energy of -7859.183 eV\n",
      "\n",
      "   ============================================================\n",
      "   | Pseudopotential Report - Date of generation  2-12-2019   |\n",
      "   ------------------------------------------------------------\n",
      "   | Element: Si Ionic charge:  4.00 Level of theory: LDA     |\n",
      "   | Atomic Solver: Koelling-Harmon                           |\n",
      "   |                                                          |\n",
      "   |               Reference Electronic Structure             |\n",
      "   |         Orbital         Occupation         Energy        |\n",
      "   |            3s              2.000           -0.400        |\n",
      "   |            3p              2.000           -0.153        |\n",
      "   |                                                          |\n",
      "   |                 Pseudopotential Definition               |\n",
      "   |        Beta     l      e      Rc     scheme   norm       |\n",
      "   |          1      0   -0.400   1.797     qc      0         |\n",
      "   |          2      0    0.250   1.797     qc      0         |\n",
      "   |          3      1   -0.153   1.797     qc      0         |\n",
      "   |          4      1    0.250   1.797     qc      0         |\n",
      "   |          5      2    0.000   1.797     qc      0         |\n",
      "   |          6      2    0.250   1.797     qc      0         |\n",
      "   |         loc     3    0.000   1.797     pn      0         |\n",
      "   |                                                          |\n",
      "   | Augmentation charge Rinner = 1.255                       |\n",
      "   | Partial core correction Rc = 1.255                       |\n",
      "   ------------------------------------------------------------\n",
      "   | \"3|1.8|5|6|7|30:31:32\"                                   |\n",
      "   ------------------------------------------------------------\n",
      "   |      Author: Chris J. Pickard, Cambridge University      |\n",
      "   ============================================================\n",
      "\n",
      " Pseudo atomic calculation performed for Si 3s2 3p2\n",
      "\n",
      " Converged in 16 iterations to a total energy of -162.9815 eV\n",
      " Calculation not parallelised.\n",
      "\n",
      " ************************************ Title ************************************\n",
      " Si SINGLEPOINT\n",
      "\n",
      " ***************************** General Parameters ******************************\n",
      "\n",
      " output verbosity                               : normal  (1)\n",
      " write checkpoint data to                       : aiida.check\n",
      " type of calculation                            : single point energy\n",
      " stress calculation                             : off\n",
      " density difference calculation                 : off\n",
      " electron localisation func (ELF) calculation   : off\n",
      " Hirshfeld analysis                             : off\n",
      " calculation limited to maximum                 :        540   seconds\n",
      " timing information                             : on\n",
      " memory usage estimate                          : on\n",
      " write extra output files                       : on\n",
      " write final potential to formatted file        : off\n",
      " write final density to formatted file          : on\n",
      " write BibTeX reference list                    : on\n",
      " write OTFG pseudopotential files               : on\n",
      " write electrostatic potential file             : on\n",
      " write bands file                               : on\n",
      " checkpoint writing                             : both castep_bin and check files\n",
      "\n",
      " output         length unit                     : A\n",
      " output           mass unit                     : amu\n",
      " output           time unit                     : ps\n",
      " output         charge unit                     : e\n",
      " output           spin unit                     : hbar/2\n",
      " output         energy unit                     : eV\n",
      " output          force unit                     : eV/A\n",
      " output       velocity unit                     : A/ps\n",
      " output       pressure unit                     : GPa\n",
      " output     inv_length unit                     : 1/A\n",
      " output      frequency unit                     : cm-1\n",
      " output force constant unit                     : eV/A**2\n",
      " output         volume unit                     : A**3\n",
      " output   IR intensity unit                     : (D/A)**2/amu\n",
      " output         dipole unit                     : D\n",
      " output         efield unit                     : eV/A/e\n",
      " output        entropy unit                     : J/mol/K\n",
      "\n",
      " wavefunctions paging                           : none\n",
      " random number generator seed                   : randomised (125904847)\n",
      " data distribution                              : optimal for this architecture\n",
      " optimization strategy                          : minimize memory(---)\n",
      "\n",
      " *********************** Exchange-Correlation Parameters ***********************\n",
      "\n",
      " using functional                               : Local Density Approximation\n",
      " relativistic treatment                         : Koelling-Harmon\n",
      " DFT+D: Semi-empirical dispersion correction    : off\n",
      "\n",
      " ************************* Pseudopotential Parameters **************************\n",
      "\n",
      " pseudopotential representation                 : reciprocal space\n",
      " <beta|phi> representation                      : reciprocal space\n",
      " spin-orbit coupling                            : off\n",
      "\n",
      " **************************** Basis Set Parameters *****************************\n",
      "\n",
      " basis set accuracy                             : MEDIUM\n",
      " plane wave basis set cut-off                   :   163.2683   eV\n",
      " size of standard grid                          :     1.7500\n",
      " size of   fine   gmax                          :    11.4559   1/A\n",
      " finite basis set correction                    : none\n",
      "\n",
      " **************************** Electronic Parameters ****************************\n",
      "\n",
      " number of  electrons                           :  8.000\n",
      " net charge of system                           :  0.000\n",
      " treating system as non-spin-polarized\n",
      " number of bands                                :          4\n",
      "\n",
      " ********************* Electronic Minimization Parameters **********************\n",
      "\n",
      " Method: Treating system as non-metallic,\n",
      "         and number of  SD  steps               :          1\n",
      "         and number of  CG  steps               :          4\n",
      "\n",
      " total energy / atom convergence tol.           : 0.1000E-04   eV\n",
      " max force / atom convergence tol.              : ignored\n",
      " convergence tolerance window                   :          3   cycles\n",
      " max. number of SCF cycles                      :         30\n",
      " periodic dipole correction                     : NONE\n",
      "\n",
      " *********************** Population Analysis Parameters ************************\n",
      "\n",
      " Population analysis with cutoff                :  3.000       A\n",
      " Population analysis output                     : summary and pdos components\n",
      "\n",
      " *******************************************************************************\n",
      "\n",
      "\n",
      "                           -------------------------------\n",
      "                                      Unit Cell\n",
      "                           -------------------------------\n",
      "        Real Lattice(A)                      Reciprocal Lattice(1/A)\n",
      "     2.6954645     2.6954645     0.0000000        1.165510677   1.165510677  -1.165510677\n",
      "     2.6954645     0.0000000     2.6954645        1.165510677  -1.165510677   1.165510677\n",
      "     0.0000000     2.6954645     2.6954645       -1.165510677   1.165510677   1.165510677\n",
      "\n",
      "                       Lattice parameters(A)       Cell Angles\n",
      "                    a =      3.811962          alpha =   60.000000\n",
      "                    b =      3.811962          beta  =   60.000000\n",
      "                    c =      3.811962          gamma =   60.000000\n",
      "\n",
      "                       Current cell volume =            39.167950       A**3\n",
      "                                   density =             1.434106   AMU/A**3\n",
      "                                           =             2.381389     g/cm^3\n",
      "\n",
      "                           -------------------------------\n",
      "                                     Cell Contents\n",
      "                           -------------------------------\n",
      "                         Total number of ions in cell =    2\n",
      "                      Total number of species in cell =    1\n",
      "                        Max number of any one species =    2\n",
      "\n",
      "            xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx\n",
      "            x  Element    Atom        Fractional coordinates of atoms  x\n",
      "            x            Number           u          v          w      x\n",
      "            x----------------------------------------------------------x\n",
      "            x  Si           1        -0.000000  -0.000000  -0.000000   x\n",
      "            x  Si           2         0.250000   0.250000   0.250000   x\n",
      "            xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx\n",
      "\n",
      "\n",
      "                         No user defined ionic velocities\n",
      "\n",
      "                           -------------------------------\n",
      "                                   Details of Species\n",
      "                           -------------------------------\n",
      "\n",
      "                               Mass of species in AMU\n",
      "                                    Si   28.0855000\n",
      "\n",
      "                          Electric Quadrupole Moment (Barn)\n",
      "                                    Si    1.0000000 No Isotope Defined\n",
      "\n",
      "                          Files used for pseudopotentials:\n",
      "                                    Si 3|1.8|5|6|7|30:31:32\n",
      "\n",
      "                           -------------------------------\n",
      "                              k-Points For BZ Sampling\n",
      "                           -------------------------------\n",
      "                       MP grid size for SCF calculation is  4  4  4\n",
      "                            with an offset of   0.000  0.000  0.000\n",
      "                       Number of kpoints used =            10\n",
      "\n",
      "                           -------------------------------\n",
      "                               Symmetry and Constraints\n",
      "                           -------------------------------\n",
      "\n",
      "                      Maximum deviation from symmetry = 0.748142E-16     ANG\n",
      "\n",
      "                      Number of symmetry operations   =          48\n",
      "                      Number of ionic constraints     =           3\n",
      "                      Point group of crystal =    32: Oh, m-3m, 4/m -3 2/m\n",
      "                      Space group of crystal =   227: Fd-3m, F 4d 2 3 -1d\n",
      "\n",
      "             Set iprint > 1 for details on symmetry rotations/translations\n",
      "\n",
      "                         Centre of mass is constrained\n",
      "             Set iprint > 1 for details of linear ionic constraints\n",
      "\n",
      "                         Number of cell constraints= 5\n",
      "                         Cell constraints are:  1 1 1 0 0 0\n",
      "\n",
      "                         External pressure/stress (GPa)\n",
      "                          0.00000   0.00000   0.00000\n",
      "                                    0.00000   0.00000\n",
      "                                              0.00000\n",
      "\n",
      "+---------------- MEMORY AND SCRATCH DISK ESTIMATES PER PROCESS --------------+\n",
      "|                                                     Memory          Disk    |\n",
      "| Baseline code, static data and system overhead       33.0 MB         0.0 MB |\n",
      "| BLAS internal memory storage                          0.0 MB         0.0 MB |\n",
      "| Model and support data                               15.5 MB         4.4 MB |\n",
      "| Electronic energy minimisation requirements           0.4 MB         0.6 MB |\n",
      "| Force calculation requirements                        0.4 MB         0.0 MB |\n",
      "|                                               ----------------------------- |\n",
      "| Approx. total storage required per process           48.9 MB         5.0 MB |\n",
      "|                                                                             |\n",
      "| Requirements will fluctuate during execution and may exceed these estimates |\n",
      "+-----------------------------------------------------------------------------+\n",
      "Calculating total energy with cut-off of  163.268 eV.\n",
      "------------------------------------------------------------------------ <-- SCF\n",
      "SCF loop      Energy                           Energy gain       Timer   <-- SCF\n",
      "                                               per atom          (sec)   <-- SCF\n",
      "------------------------------------------------------------------------ <-- SCF\n",
      "Initial   5.38885016E+001                                          3.73  <-- SCF\n",
      "      1  -3.23029209E+002                    1.88458855E+002       4.07  <-- SCF\n",
      "      2  -3.36680788E+002                    6.82578942E+000       4.39  <-- SCF\n",
      "      3  -3.37655316E+002                    4.87264051E-001       4.72  <-- SCF\n",
      "      4  -3.37909301E+002                    1.26992416E-001       5.07  <-- SCF\n",
      "      5  -3.37909560E+002                    1.29231306E-004       5.40  <-- SCF\n",
      "      6  -3.37909560E+002                    2.51802717E-007       5.79  <-- SCF\n",
      "      7  -3.37909560E+002                    7.15553895E-009       6.14  <-- SCF\n",
      "------------------------------------------------------------------------ <-- SCF\n",
      "\n",
      "Final energy =  -337.9095600892     eV\n",
      "(energy not corrected for finite basis set)\n",
      "\n",
      "\n",
      "Writing analysis data to aiida.castep_bin\n",
      "\n",
      "Writing model to aiida.check\n",
      "\n",
      " ******************** Symmetrised Forces ********************\n",
      " *                                                          *\n",
      " *               Cartesian components (eV/A)                *\n",
      " * -------------------------------------------------------- *\n",
      " *                         x            y            z      *\n",
      " *                                                          *\n",
      " * Si              1      0.00000      0.00000      0.00000 *\n",
      " * Si              2      0.00000      0.00000      0.00000 *\n",
      " *                                                          *\n",
      " ************************************************************\n",
      "\n",
      " Pseudo atomic calculation performed for Si 3s2 3p2\n",
      "\n",
      " Converged in 16 iterations to a total energy of -162.9815 eV\n",
      "Charge spilling parameter for spin component 1 = 0.84%\n",
      "\n",
      "            Orbital Populations\n",
      "     Ion    Atom   Orbital             Charge\n",
      "  -------------------------------------------\n",
      "     Si     1      S                    1.357\n",
      "     Si     1      Px                   0.924\n",
      "     Si     1      Py                   0.884\n",
      "     Si     1      Pz                   0.834\n",
      "     Si     2      S                    1.357\n",
      "     Si     2      Px                   0.924\n",
      "     Si     2      Py                   0.884\n",
      "     Si     2      Pz                   0.835\n",
      "  -------------------------------------------\n",
      "                           Total:       8.000\n",
      "  -------------------------------------------\n",
      "\n",
      "     Atomic Populations (Mulliken)\n",
      "     -----------------------------\n",
      "Species          Ion     s      p      d      f     Total  Charge (e)\n",
      "=====================================================================\n",
      "  Si              1     1.36   2.64   0.00   0.00   4.00     0.00\n",
      "  Si              2     1.36   2.64   0.00   0.00   4.00     0.00\n",
      "=====================================================================\n",
      "\n",
      "                 Bond                   Population      Length (A)\n",
      "======================================================================\n",
      "              Si 1 -- Si 2                   2.98        2.33434\n",
      "======================================================================\n",
      "\n",
      "\n",
      "Writing analysis data to aiida.castep_bin\n",
      "\n",
      "Writing model to aiida.check\n",
      "\n",
      " A BibTeX formatted list of references used in this run has been written to\n",
      " aiida.bib\n",
      "\n",
      "Initialisation time =      3.44 s\n",
      "Calculation time    =      2.92 s\n",
      "Finalisation time   =      0.02 s\n",
      "Total time          =      6.38 s\n",
      "Peak Memory Use     = 77736 kB\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# The to access the output\n",
    "print(calcjob.outputs.retrieved.base.repository.get_object_content(\"aiida.castep\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "An result of calculation is reflected by the exit status. Like processes, zero return code means it completed without error."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "calcjob.exit_status"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To can the list of possible `exit_status` and they meanings, we can use `verdi plugin list`. This also shows list of inputs of which the required ones are in bold."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": []
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[31m\u001b[1mDescription:\n",
      "\u001b[0m\n",
      "\u001b[22m    Class representing a generic CASTEP calculation -\u001b[0m\n",
      "\u001b[22m    This class should work for all types of calculations.\u001b[0m\n",
      "\u001b[22m\u001b[0m\n",
      "\u001b[31m\u001b[1mInputs:\u001b[0m\n",
      "\u001b[1m           parameters:  required  Dict             A node that defines the input parameters\u001b[0m\n",
      "\u001b[1m              pseudos:  required                   Use nodes for the pseudopotentails of one ofthe element in the structure. Y ...\u001b[0m\n",
      "\u001b[1m            structure:  required  StructureData    The input structure\u001b[0m\n",
      "\u001b[22m           bs_kpoints:  optional  KpointsData      Extra kpoints input for task: bandstructure\u001b[0m\n",
      "\u001b[22m                 code:  optional  Code             The `Code` to use for this job. This input is required, unless the `remote_ ...\u001b[0m\n",
      "\u001b[22m        elnes_kpoints:  optional  KpointsData      Extra kpoints input for task: elnes\u001b[0m\n",
      "\u001b[22m              kpoints:  optional  KpointsData      Use a node defining the kpoints for the calculation\u001b[0m\n",
      "\u001b[22m       magres_kpoints:  optional  KpointsData      Extra kpoints input for task: magres\u001b[0m\n",
      "\u001b[22m             metadata:  optional                   \u001b[0m\n",
      "\u001b[22m       optics_kpoints:  optional  KpointsData      Extra kpoints input for task: optics\u001b[0m\n",
      "\u001b[22m   parent_calc_folder:  optional  RemoteData       Use a remote folder as the parent folder. Useful for restarts.\u001b[0m\n",
      "\u001b[22m  phonon_fine_kpoints:  optional  KpointsData      Extra kpoints input for task: phonon, phonon+efield\u001b[0m\n",
      "\u001b[22m       phonon_kpoints:  optional  KpointsData      Extra kpoints input for task: phonon, phonon+efield\u001b[0m\n",
      "\u001b[22m        remote_folder:  optional  RemoteData       Remote directory containing the results of an already completed calculation ...\u001b[0m\n",
      "\u001b[22m             settings:  optional  Dict             A node for additional settings\u001b[0m\n",
      "\u001b[22m     spectral_kpoints:  optional  KpointsData      Extra kpoints input for task: spectral\u001b[0m\n",
      "\u001b[22m    supercell_kpoints:  optional  KpointsData      Extra kpoints input for task: phonon\u001b[0m\n",
      "\u001b[31m\u001b[1mOutputs:\u001b[0m\n",
      "\u001b[1m    output_parameters:  required  Dict             Parsed results in a dictionary format.\u001b[0m\n",
      "\u001b[1m        remote_folder:  required  RemoteData       Input files necessary to run the process will be stored in this folder node ...\u001b[0m\n",
      "\u001b[1m            retrieved:  required  FolderData       Files that are retrieved by the daemon will be stored in this node. By defa ...\u001b[0m\n",
      "\u001b[22m         remote_stash:  optional  RemoteStashData  Contents of the `stash.source_list` option are stored in this remote folder ...\u001b[0m\n",
      "\u001b[31m\u001b[1mExit codes:\u001b[0m\n",
      "\u001b[22m                    0:  Calculation terminated gracefully, end found\u001b[0m\n",
      "\u001b[22m                    1:  The process has failed with an unspecified error.\u001b[0m\n",
      "\u001b[22m                    2:  The process failed with legacy failure mode.\u001b[0m\n",
      "\u001b[22m                   10:  The process returned an invalid output.\u001b[0m\n",
      "\u001b[22m                   11:  The process did not register a required output.\u001b[0m\n",
      "\u001b[22m                  100:  The process did not have the required `retrieved` output.\u001b[0m\n",
      "\u001b[22m                  101:  SCF Cycles failed to reach convergence\u001b[0m\n",
      "\u001b[22m                  103:  Stopped execuation due to detection of 'stop ' keyword in param file.\u001b[0m\n",
      "\u001b[22m                  104:  CASTEP generate error files. Check them for details\u001b[0m\n",
      "\u001b[22m                  105:  Cannot find the end of calculation\u001b[0m\n",
      "\u001b[22m                  106:  No output .castep files found\u001b[0m\n",
      "\u001b[22m                  107:  Calculation self-terminated due to time limit\u001b[0m\n",
      "\u001b[22m                  108:  No retrieve folder is found\u001b[0m\n",
      "\u001b[22m                  110:  The job ran out of memory.\u001b[0m\n",
      "\u001b[22m                  120:  The job ran out of walltime.\u001b[0m\n",
      "\u001b[22m                  200:  UNKOWN ERROR\u001b[0m\n",
      "\u001b[22m                  501:  At least one kpoints/spin has no empty bands - please rerun with increased nextra_bands.\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "!verdi plugin list aiida.calculations castep.castep"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Access the results\n",
    "Results of calculation can be access in python"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'warnings': [],\n",
       " 'castep_version': '19.1',\n",
       " 'unit_length': 'A',\n",
       " 'unit_time': 'ps',\n",
       " 'unit_energy': 'eV',\n",
       " 'unit_force': 'eV/A',\n",
       " 'num_ions': 2,\n",
       " 'n_kpoints': '10',\n",
       " 'point_group': '32: Oh, m-3m, 4/m -3 2/m',\n",
       " 'space_group': '227: Fd-3m, F 4d 2 3 -1d',\n",
       " 'cell_constraints': '1 1 1 0 0 0',\n",
       " 'pseudo_pots': {'Si': '3|1.8|5|6|7|30:31:32'},\n",
       " 'initialisation_time': 3.44,\n",
       " 'calculation_time': 2.92,\n",
       " 'finalisation_time': 0.02,\n",
       " 'total_time': 6.38,\n",
       " 'geom_unconverged': None,\n",
       " 'parser_warnings': [],\n",
       " 'parser_info': 'AiiDA CASTEP basic Parser v1.1.1',\n",
       " 'total_energy': -337.9095600892,\n",
       " 'error_messages': []}"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# A Dict node is created containing some of the parsed results\n",
    "calcjob.outputs.output_parameters.get_dict()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'19.1'"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Alternatively, you can access the parsed results using\n",
    "calcjob.res.castep_version   # hit tab for completion after `res`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[LinkTriple(node=<RemoteData: uuid: 36167374-0f02-467e-9ccb-25a6c62f27a0 (pk: 7)>, link_type=<LinkType.CREATE: 'create'>, link_label='remote_folder'),\n",
       " LinkTriple(node=<FolderData: uuid: d610ec30-f095-4018-b403-5c4ddb5aa4cb (pk: 8)>, link_type=<LinkType.CREATE: 'create'>, link_label='retrieved'),\n",
       " LinkTriple(node=<BandsData: uuid: c05a309e-706d-499c-8558-3cbe958f61da (pk: 9)>, link_type=<LinkType.CREATE: 'create'>, link_label='output_bands'),\n",
       " LinkTriple(node=<ArrayData: uuid: bae83129-6fee-44df-a8a4-d895406e6c0b (pk: 10)>, link_type=<LinkType.CREATE: 'create'>, link_label='output_array'),\n",
       " LinkTriple(node=<Dict: uuid: 2adc9560-6b1e-47d4-817a-7d6ba740b84d (pk: 11)>, link_type=<LinkType.CREATE: 'create'>, link_label='output_parameters')]"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# To get all the outgoing links and nodes\n",
    "calcjob.get_outgoing().all()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<RemoteData: uuid: 36167374-0f02-467e-9ccb-25a6c62f27a0 (pk: 7)>,\n",
       " <FolderData: uuid: d610ec30-f095-4018-b403-5c4ddb5aa4cb (pk: 8)>,\n",
       " <BandsData: uuid: c05a309e-706d-499c-8558-3cbe958f61da (pk: 9)>,\n",
       " <ArrayData: uuid: bae83129-6fee-44df-a8a4-d895406e6c0b (pk: 10)>,\n",
       " <Dict: uuid: 2adc9560-6b1e-47d4-817a-7d6ba740b84d (pk: 11)>]"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Or just the nodes\n",
    "calcjob.get_outgoing().all_nodes()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The actual files retrived from remote computer is stored in a `FolderData` node."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['_scheduler-stderr.txt',\n",
       " '_scheduler-stdout.txt',\n",
       " 'aiida.bands',\n",
       " 'aiida.castep',\n",
       " 'aiida.den_fmt']"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# The output .castep files and others are stored in the repository\n",
    "calcjob.outputs.retrieved.base.repository.list_object_names()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We check if the correct energy was parsed from the output `.castep` file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Final energy =  -337.9095600892     eV\n",
      "-337.9095600892\n"
     ]
    }
   ],
   "source": [
    "# To quickly check the output file\n",
    "!echo '{calcjob.outputs.retrieved.base.repository.get_object_content('aiida.castep')}' | grep 'Final energy' \n",
    "\n",
    "# The parsed the total energy\n",
    "print(calcjob.res.total_energy)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So the energy matches 👏!"
   ]
  }
 ],
 "metadata": {
  "interpreter": {
   "hash": "cf5bfbb275efa34724f83034d97eac1f6cad0e6c7edb82ca36de8357056ef910"
  },
  "kernelspec": {
   "display_name": "Python [conda env:aiida-2.0-dev]",
   "language": "python",
   "name": "conda-env-aiida-2.0-dev-py"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.10"
  }
 },
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