yambo/4_YamboConvergence_G0W0.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "b07f7d9f",
   "metadata": {},
   "source": [
    "# YamboConvergence: automated G0W0 convergence\n",
    "\n",
    "The highest-level workflow is represented by the ``YamboConvergence`` workchain, \n",
    "which implements the full automation of the convergence algorithm described in [Bonacci, M., Qiao, J., Spallanzani, N. et al. Towards high-throughput many-body perturbation theory: efficient algorithms and automated workflows. npj Comput Mater 9, 74 (2023)](https://doi.org/10.1038/s41524-023-01027-2). \n",
    "Simulations are organized on the fly, without any external user intervention. \n",
    "The purpose of this new proposed convergence algorithm is to obtain an accurate converged \n",
    "result doing the least possible number of calculations. This is possible if a reliable description of the convergence space is achieved, resulting also in a \n",
    "precise guess for the converged point, i.e. the converged parameters. The description of the space is performed by fitting some calculations that the workchain runs. \n",
    "A simple functional form of the space is assumed:\n",
    "\n",
    "$f(\\textbf{x}) = \\prod_i^N \\left( \\frac{A_i}{x_i^{\\alpha_i}} + b_i \\right)$\n",
    "\n",
    "In this way it is straightforward to compute first and second mixed derivatives, and impose constraints on them to find the converged region of the space. \n",
    "The algorithm is specifically designed to solve the coupled convergence between \n",
    "summation over empty states (``BndsRnXp`` or ``BndsRnXs`` and ``GbndRnge`` for example) and PW expansion (``NGsBlkXp`` or ``NGsBlkXs``), but it can be used also to \n",
    "accelerate convergence tests with respect to the ``k-point mesh`` or ``FFTGvecs``, as we shall see later.\n",
    "\n",
    "Each simulation is performed by calling the `YamboWorkflow` workchain, ensuring the automation at all levels.\n",
    "\n",
    "\n",
    "### Example of workflow results:\n",
    "The successful workflow will return the results of the convergence iterations, as well as a final converged calculation, from which we can parse the\n",
    "converged parameters (they can be also found in the `infos` outputs of the workflow), and a complete story of all the calculations of the workflow with all the information provided.\n",
    "\n",
    "To show how the convergence algorithm works, here we plot the convergences performed on 2D-hBN imposing a convergence threshold of 1% on the final gap. The convergence is \n",
    "performed with respect to ``NGsBlkXp`` (G_cut in the plot) and ``BndsRnXp`` = ``GbndRnge`` (Nb in the plot). \n",
    "\n",
    "![2D_hBN_conv](./images/2D_conv_hBN.png)\n",
    "\n",
    "We can observe that first simulations (which are the black squares) are performed on a starting grid, the blue one. The algorithm decides then to perform another set of calculations on \n",
    "a shifted grid, as the fit was not accurate enough. Next, a converged point is predicted, corresponding to the blue square, and it is explicitely computed. \n",
    "Using also the information on that point, the algorithm understands (recomputing the fit) that a new converged point can be the red one. This is then computed and verified to be the real converged one. In this \n",
    "way, convergence is efficiently achieved. \n",
    "\n",
    "All the calculations are automatically collected in a group, labeled automatically using the structure formula, or pre-existing if the input \n",
    "``builder.group_label`` is provided as Str datatype."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "dc9bce04",
   "metadata": {},
   "outputs": [],
   "source": [
    "from aiida import orm, load_profile\n",
    "load_profile()\n",
    "\n",
    "from aiida.plugins import WorkflowFactory\n",
    "from aiida.orm import QueryBuilder\n",
    "\n",
    "from aiida_quantumespresso.common.types import ElectronicType\n",
    "\n",
    "import yaml\n",
    "\n",
    "\n",
    "qb = QueryBuilder()\n",
    "qb.append(orm.Group, filters={'label': 'Silicon/bulk'}, tag='group')\n",
    "qb.append(orm.StructureData, with_group='group')\n",
    "\n",
    "loaded_structure_id = qb.all()[0][0].pk\n",
    "\n",
    "# Read YAML file\n",
    "with open(\"../configuration/codes_localhost.yaml\", 'r') as stream:\n",
    "    codes = yaml.safe_load(stream)\n",
    "    \n",
    "with open(\"../configuration/resources_localhost.yaml\", 'r') as stream:\n",
    "    resources = yaml.safe_load(stream)\n",
    "    \n",
    "options = {\n",
    "    'pseudo_family':\"PseudoDojo/0.4/PBE/SR/standard/upf\",\n",
    "    'protocol':'fast',\n",
    "    #'parent_id':274, #not necessary to set; if you want it, take ytheour previously nscf id (pk) to skip the DFT part.\n",
    "    'structure_id':loaded_structure_id,\n",
    "}\n",
    "options.update(codes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "b9b44aed",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Summary of the main inputs:\n",
      "BndsRnXp = 150\n",
      "GbndRnge = 150\n",
      "NGsBlkXp = 2 Ry\n",
      "FFTGvecs = 9 Ry\n",
      "\n",
      "\n",
      "kpoint mesh for nscf: [6, 6, 6]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/max/.conda/envs/aiida/lib/python3.9/site-packages/aiida_quantumespresso/calculations/pw.py:211: UserWarning: `parent_folder` not provided for `nscf` calculation. For work chains wrapping this calculation, you can disable this warning by excluding the `parent_folder` when exposing the inputs of the `PwCalculation`.\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "YamboConvergence = WorkflowFactory('yambo.yambo.yamboconvergence')\n",
    "builder = YamboConvergence.get_builder_from_protocol(\n",
    "    pw_code = options['pwcode_id'],\n",
    "    preprocessing_code = options['yamboprecode_id'],\n",
    "    code = options['yambocode_id'],\n",
    "    protocol=options['protocol'],\n",
    "    protocol_qe=options['protocol'],\n",
    "    structure=orm.load_node(options['structure_id']),\n",
    "    overrides={},\n",
    "    #parent_folder=load_node(options['parent_id']).outputs.remote_folder,\n",
    "    electronic_type=ElectronicType.INSULATOR, #default is METAL: smearing is used\n",
    "    calc_type='gw', #or 'bse'; default is 'gw'\n",
    ")\n",
    "\n",
    "builder.ywfl.scf.pw.metadata.options = resources\n",
    "\n",
    "builder.ywfl.nscf.pw.metadata.options = builder.ywfl.scf.pw.metadata.options\n",
    "builder.ywfl.yres.yambo.metadata.options = builder.ywfl.scf.pw.metadata.options"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "70437464",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'fast': {'description': 'Fast protocol for a GW convergence: grid -> poor; thresholds -> poor'},\n",
       " 'moderate': {'description': 'Moderate protocol for a GW convergence: grid -> enough good for standard materials; thresholds -> moderate (5 percent)'},\n",
       " 'precise': {'description': 'precise protocol for a GW convergence: grid -> same as moderate; thresholds -> precise (1 percent)'},\n",
       " 'molecule': {'description': 'Moderate protocol for a GW convergence in molecules'}}"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#You can also try different protocols:\n",
    "    \n",
    "YamboConvergence.get_available_protocols()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "62fb8c7d",
   "metadata": {},
   "source": [
    "### Overrides\n",
    "\n",
    "It is possible to modify the default inputs also during the builder creation phase, so not a posteriori. This can be done by using overrides:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "d4cc34f9",
   "metadata": {},
   "outputs": [],
   "source": [
    "overrides_scf = {\n",
    "        'pseudo_family': \"PseudoDojo/0.4/PBE/SR/standard/upf\", \n",
    "        'pw':{\n",
    "            'parameters':{\n",
    "                'CONTROL':{}, #not needed if you don't override something\n",
    "                'SYSTEM':{\"ecutwfc\":70,\"ecutrho\":70*4},\n",
    "            },\n",
    "           'metadata':{\"options\":resources},\n",
    "        },\n",
    "    }\n",
    "\n",
    "overrides_nscf = {\n",
    "        'pseudo_family': \"PseudoDojo/0.4/PBE/SR/standard/upf\", \n",
    "        'pw': {\n",
    "            'parameters':{\n",
    "                'CONTROL':{}, #not needed if you don't override something\n",
    "                'SYSTEM':{\"ecutwfc\":70,\"ecutrho\":70*4},\n",
    "                'ELECTRONS':{'diagonalization':'david'},\n",
    "            },\n",
    "             'metadata':{\"options\":resources},\n",
    "    },\n",
    "}\n",
    "\n",
    "overrides_yambo = {\n",
    "        \"yambo\": {\n",
    "            \"parameters\": {\n",
    "                \"arguments\": [\n",
    "                    \"rim_cut\",\n",
    "                ],\n",
    "                \"variables\": {\n",
    "                    \"NGsBlkXp\": [4, \"Ry\"],\n",
    "                    \"FFTGvecs\": [20, \"Ry\"],\n",
    "                },\n",
    "            },\n",
    "        'metadata':{\"options\":resources},\n",
    "        },\n",
    "    \n",
    "}\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2eeb111a",
   "metadata": {},
   "source": [
    "## providing additional convergence settings\n",
    "\n",
    "In the following we provide additional convergence settings, namely:\n",
    "\n",
    "- 'what': a list of quantities to be computed, following the same naming convention of the `YamboWorkflow` additional parsing list;\n",
    "- 'type': 'heavy', or 'cheap'; heavy keeps the converged parameters as obtained in previous iterations; for example, if I convergence k-mesh and then bands, in the bands convergence we will use the converged k-mesh. This will make the calculation more and more computational demanding, but in the end we will obtain the true converged results, not only the converged value of the parameters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "6195dea0",
   "metadata": {},
   "outputs": [],
   "source": [
    "overrides_wfl_settings = {\n",
    "    'what':['gap_'],\n",
    "    'type': 'heavy', #or cheap; heavy uses converged value for parameters that we are not converging in a given iteration.\n",
    "    }"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e7384790",
   "metadata": {},
   "source": [
    "the quantities that we can converge are the ones that can be parsed by the \n",
    "``YamboWorkflow`` workchain: quasiparticle levels/gaps, and excitonic energies:\n",
    "\n",
    "* 'gap_': gap as found from nscf calculation (may differ from the final GW band gap, in terms of value and k-point indexes);\n",
    "* 'gap_GG': gap at the Gamma point;\n",
    "* 'lowest_exciton': lowest exciton from BSE;\n",
    "* 'brightest_exciton': brightest exciton from BSE."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "cac1d622",
   "metadata": {},
   "outputs": [],
   "source": [
    "#setting the overall overrides dictionary.\n",
    "\n",
    "overrides = {\n",
    "    'ywfl':{'scf':overrides_scf,'nscf':overrides_nscf,'yres':overrides_yambo},\n",
    "    'workflow_settings':overrides_wfl_settings,\n",
    "    }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "57339397",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Summary of the main inputs:\n",
      "BndsRnXp = 150\n",
      "GbndRnge = 150\n",
      "NGsBlkXp = 4 Ry\n",
      "FFTGvecs = 20 Ry\n",
      "\n",
      "\n",
      "kpoint mesh for nscf: [6, 6, 6]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/max/.conda/envs/aiida/lib/python3.9/site-packages/aiida_quantumespresso/calculations/pw.py:211: UserWarning: `parent_folder` not provided for `nscf` calculation. For work chains wrapping this calculation, you can disable this warning by excluding the `parent_folder` when exposing the inputs of the `PwCalculation`.\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "builder = YamboConvergence.get_builder_from_protocol(\n",
    "    pw_code = options['pwcode_id'],\n",
    "    preprocessing_code = options['yamboprecode_id'],\n",
    "    code = options['yambocode_id'],\n",
    "    protocol=options['protocol'],\n",
    "    protocol_qe=options['protocol'],\n",
    "    structure= orm.load_node(options['structure_id']),\n",
    "    overrides=overrides,\n",
    "    pseudo_family = options[\"pseudo_family\"],\n",
    "    #parent_folder=load_node(options['parent_id']).outputs.remote_folder,\n",
    "    electronic_type=ElectronicType.INSULATOR, #default is METAL: smearing is used\n",
    "    calc_type='gw', #or 'bse'; default is 'gw'\n",
    ")\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "655b8ca2",
   "metadata": {},
   "source": [
    "### Inspecting the convergence space:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "1a1ebb67",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'var': ['FFTGvecs'],\n",
       "  'start': 17,\n",
       "  'stop': 49,\n",
       "  'delta': 7,\n",
       "  'max': 70,\n",
       "  'steps': 4,\n",
       "  'max_iterations': 4,\n",
       "  'conv_thr': 10,\n",
       "  'conv_thr_units': '%',\n",
       "  'convergence_algorithm': 'new_algorithm_1D'},\n",
       " {'var': ['kpoint_mesh'],\n",
       "  'start': [4, 4, 4],\n",
       "  'stop': [12, 12, 12],\n",
       "  'delta': [1, 1, 1],\n",
       "  'max': [22, 22, 22],\n",
       "  'steps': 4,\n",
       "  'max_iterations': 4,\n",
       "  'conv_thr': 10,\n",
       "  'conv_thr_units': '%',\n",
       "  'convergence_algorithm': 'new_algorithm_1D'},\n",
       " {'var': ['BndsRnXp', 'GbndRnge', 'NGsBlkXp'],\n",
       "  'start': [200, 200, 6],\n",
       "  'stop': [800, 800, 18],\n",
       "  'delta': [100, 100, 2],\n",
       "  'max': [2000, 2000, 40],\n",
       "  'steps': 6,\n",
       "  'max_iterations': 8,\n",
       "  'conv_thr': 10,\n",
       "  'conv_thr_units': '%',\n",
       "  'convergence_algorithm': 'new_algorithm_2D'}]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "builder.parameters_space.get_list()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7a33a651",
   "metadata": {},
   "source": [
    "The workflow will follow such a list, from `FFTGvecs`, `kpoint_mesh` and then to `BndsRnXp, GbndRnge, NGsBlkXp`, performing each of the three iterations trying to convergence the corresponding parameters. Please note that the last step (`BndsRnXp, GbndRnge, NGsBlkXp`) will perform the convergence of the coupled parameters *BndsRnXp=GbndRnge* and *NGsBlkXp*. Depending on your system, a good convergence journey would be ['FFTGvecs'] -> ['BndsRnXp', 'GbndRnge', 'NGsBlkXp'] -> ['kpoint_mesh']. \n",
    "\n",
    "The number of `steps` for each iteration should be fixed (do not change it):\n",
    "- new_algorithm_2D -> steps:6\n",
    "- new_algorithm_1D -> steps:4\n",
    "\n",
    "We can provide an ad hoc convergence space:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "ca92fda9",
   "metadata": {},
   "outputs": [],
   "source": [
    "builder.parameters_space=orm.List(\n",
    "    [{'var': ['FFTGvecs'],\n",
    "      'start': 21,\n",
    "      'stop': 58,\n",
    "      'delta': 8,\n",
    "      'max': 84,\n",
    "      'steps': 4,    # Fixed to 4 if convergence_algorithm=new_algorithm_1D\n",
    "      'max_iterations': 4,\n",
    "      'conv_thr': 10,\n",
    "      'conv_thr_units': '%',\n",
    "      'convergence_algorithm': 'new_algorithm_1D'},   #we are converging 1 parameter\n",
    "     #{'var': ['kpoint_mesh'],\n",
    "     # 'start': [4, 4, 2],\n",
    "     # 'stop': [16, 16, 6],\n",
    "     # 'delta': [3, 3, 3],\n",
    "     # 'max': [30, 30, 10],\n",
    "     # 'steps': 4,\n",
    "     # 'max_iterations': 4,\n",
    "     # 'conv_thr': 10,\n",
    "     # 'conv_thr_units': '%',\n",
    "     # 'convergence_algorithm': 'new_algorithm_1D'},\n",
    "     {'var': ['BndsRnXp', 'GbndRnge', 'NGsBlkXp'],\n",
    "      'start': [50,50,2],                           #starting values\n",
    "      'stop': [400,400, 8],                          #maximum values for the first grid\n",
    "      'delta': [50, 50, 1],                           #grid spacing\n",
    "      'max': [1000, 1000, 16],                          #maximum values for the largest grid possible\n",
    "      'steps': 6,                                     #steps/calculation per iteration. For ['BndsRnXp', 'GbndRnge', 'NGsBlkXp'], always 6\n",
    "      'max_iterations': 8,                            #maximum attempts\n",
    "      'conv_thr': 10,                                 #converge threshold\n",
    "      'conv_thr_units': '%',                          #converge threshold units: '%' is the relative error with respect to the most converged value; can be also 'eV'\n",
    "      'convergence_algorithm': 'new_algorithm_2D'}],  #we are converging actually 2 parameters: bands\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9d39863c",
   "metadata": {},
   "source": [
    "### Parameter-dependent resources\n",
    "\n",
    "As you can imagine, increasing the parameters of the simulations may require also the change of the related computational resources, in order to be able to successfully perform them. \n",
    "Before the submission of the WorkChain, we can provide instructions on how to continously adapt the resources when parameters are changing.\n",
    "\n",
    "In particular, we provide two dictionaries:\n",
    "- parallelism instructions\n",
    "- explicit resources instructions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "438114f6",
   "metadata": {},
   "outputs": [],
   "source": [
    "dict_para_medium = {}\n",
    "dict_para_medium['X_and_IO_CPU'] = '1 1 1 1 1'\n",
    "dict_para_medium['X_and_IO_ROLEs'] = 'q k g c v'\n",
    "dict_para_medium['DIP_CPU'] = '1 1 1'\n",
    "dict_para_medium['DIP_ROLEs'] = 'k c v'\n",
    "dict_para_medium['SE_CPU'] = '1 1 1'\n",
    "dict_para_medium['SE_ROLEs'] = 'q qp b'\n",
    "\n",
    "dict_res_medium = {\n",
    "        \"num_machines\": 1,\n",
    "        \"num_mpiprocs_per_machine\":1,\n",
    "        \"num_cores_per_mpiproc\":1,\n",
    "    }\n",
    "\n",
    "dict_para_high = {}\n",
    "dict_para_high['X_and_IO_CPU'] = '1 1 1 2 1' \n",
    "dict_para_high['X_and_IO_ROLEs'] = 'q k g c v'\n",
    "dict_para_high['DIP_CPU'] = '1 2 1'\n",
    "dict_para_high['DIP_ROLEs'] = 'k c v'\n",
    "dict_para_high['SE_CPU'] = '1 1 2'\n",
    "dict_para_high['SE_ROLEs'] = 'q qp b'\n",
    "\n",
    "dict_res_high = {\n",
    "        \"num_machines\": 1,\n",
    "        \"num_mpiprocs_per_machine\":2,\n",
    "        \"num_cores_per_mpiproc\":1,\n",
    "    }\n",
    "\n",
    "parallelism_instructions_manual = orm.Dict(dict={'manual' : {                                                            \n",
    "                                                            'std_1':{\n",
    "                                                                    'BndsRnXp':[1,100], #range for bands where to use the dict_para_medium and dict_res_medium instructions.\n",
    "                                                                    'NGsBlkXp':[2,18],\n",
    "                                                                    'parallelism':dict_para_medium,\n",
    "                                                                    'resources':dict_res_medium,\n",
    "                                                                    },\n",
    "                                                            'std_2':{\n",
    "                                                                    'BndsRnXp':[101,1000],\n",
    "                                                                    'NGsBlkXp':[2,18],\n",
    "                                                                    'parallelism':dict_para_high,\n",
    "                                                                    'resources':dict_res_high,\n",
    "                                                                    },}})"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "51ed29d1",
   "metadata": {},
   "source": [
    "We can just provide, together with the resources, the `mode` which yambo will use to automatically set up its parallelism, if we are not sure on how to decide the explicit parallelism instructions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "e55c32d8",
   "metadata": {},
   "outputs": [],
   "source": [
    "parallelism_instructions_auto = orm.Dict(dict={'automatic' : {                                                            \n",
    "                                                            'std_1':{\n",
    "                                                                    'BndsRnXp':[1,100],\n",
    "                                                                    'NGsBlkXp':[1,18],\n",
    "                                                                    'mode':'balanced',\n",
    "                                                                    'resources':dict_res_medium,\n",
    "                                                                    },\n",
    "                                                            'std_2':{\n",
    "                                                                    'BndsRnXp':[101,1000],\n",
    "                                                                    'NGsBlkXp':[1,18],\n",
    "                                                                    'mode':'memory',\n",
    "                                                                    'resources':dict_res_high,\n",
    "                                                                    },}})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "63d81f05",
   "metadata": {},
   "outputs": [],
   "source": [
    "builder.parallelism_instructions = parallelism_instructions_auto"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3e27731e",
   "metadata": {},
   "source": [
    "### Providing an AiiDA group where to collect all the convergence simulations\n",
    "\n",
    "When `YamboConvergence` is submitted, it automatically creates a group where to put all the simulations. Each time a simulation is ready to be submitted, there is an internal check in the group, to understand\n",
    "if an identical simulation has been already performed. In that case, the submission is skipped and we reuse the results to perform our analysis. This is a sort of ad-hoc [caching](https://aiida.readthedocs.io/projects/aiida-core/en/latest/topics/provenance/caching.html), which however does not duplicate the involved nodes. \n",
    "\n",
    "We prefer to just reuse the results as often even the retrieved files for yambo simulations are large (`ndb.QP` for example).\n",
    "\n",
    "It is possible also to provide a pre-existing group, by means of the `group_label` input String."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "46955289",
   "metadata": {},
   "outputs": [],
   "source": [
    "try:\n",
    "    g = orm.load_group('tutorial/Silicon/convergence/GW')\n",
    "except:\n",
    "    g = orm.Group('tutorial/Silicon/convergence/GW')\n",
    "    g.store()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "1142fcc8",
   "metadata": {},
   "outputs": [],
   "source": [
    "builder.group_label = orm.Str('tutorial/Silicon/convergence/GW') # verdi group create tutorial/Silicon/convergence; all calculationsc are added to the group"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dd1b9c62",
   "metadata": {},
   "source": [
    "### Run"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "9e1cf68e",
   "metadata": {},
   "outputs": [],
   "source": [
    "from aiida.engine import submit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "78614e86",
   "metadata": {},
   "outputs": [],
   "source": [
    "run = None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "196e4b6d",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/max/.conda/envs/aiida/lib/python3.9/site-packages/aiida_quantumespresso/calculations/pw.py:211: UserWarning: `parent_folder` not provided for `nscf` calculation. For work chains wrapping this calculation, you can disable this warning by excluding the `parent_folder` when exposing the inputs of the `PwCalculation`.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "uuid: a1146632-fefc-49d1-8a36-3bec38239f27 (pk: 890) (aiida.workflows:yambo.yambo.yamboconvergence)\n"
     ]
    }
   ],
   "source": [
    "if run:\n",
    "    print('run is already running -> {}'.format(run.pk))\n",
    "    print('sure that you want to run again?, if so, copy the else instruction in the cell below and run!')\n",
    "else:\n",
    "    run = submit(builder)\n",
    "\n",
    "print(run)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "2700857f",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[22m2024-02-19 15:51:00 [201 | REPORT]: [890|YamboConvergence|start_workflow]: group: tutorial/Silicon/convergence/GW\n",
      "2024-02-19 15:51:00 [202 | REPORT]: [890|YamboConvergence|start_workflow]: Workflow type: heavy; looking for convergence of ['gap_']\n",
      "2024-02-19 15:51:00 [203 | REPORT]: [890|YamboConvergence|start_workflow]: Workflow initilization step completed, the parameters will be: ['FFTGvecs'].\n",
      "2024-02-19 15:51:00 [204 | REPORT]: [890|YamboConvergence|has_to_continue]: Still iteration on ['FFTGvecs']\n",
      "2024-02-19 15:51:00 [205 | REPORT]: [890|YamboConvergence|pre_needed]: {'FFTGvecs': [21, 37, 45, 58], 'BndsRnXp': [50, 400, 50, 150, 300, 400], 'NGsBlkXp': [2, 2, 8, 6, 4, 8], 'GbndRnge': [50, 400, 50, 150, 300, 400]}\n",
      "2024-02-19 15:51:00 [206 | REPORT]: [890|YamboConvergence|pre_needed]: ['GW bands are: 150', 'scf inputs found', 'nscf inputs found']\n",
      "2024-02-19 15:51:00 [207 | REPORT]: [890|YamboConvergence|do_pre]: mesh check\n",
      "2024-02-19 15:51:00 [208 | REPORT]: [890|YamboConvergence|do_pre]: doing the calculation: p2y\n",
      "2024-02-19 15:51:01 [209 | REPORT]: [890|YamboConvergence|do_pre]: Submitted YamboWorkflow up to p2y, pk = 895\n",
      "2024-02-19 15:51:02 [210 | REPORT]:   [895|YamboWorkflow|start_workflow]: no previous pw calculation found, we will start from scratch\n",
      "2024-02-19 15:51:02 [211 | REPORT]:   [895|YamboWorkflow|start_workflow]:  workflow initilization step completed.\n",
      "2024-02-19 15:51:02 [212 | REPORT]:   [895|YamboWorkflow|can_continue]: the workflow continues with a scf calculation\n",
      "2024-02-19 15:51:02 [213 | REPORT]:   [895|YamboWorkflow|perform_next]: performing a scf calculation\n",
      "2024-02-19 15:51:04 [214 | REPORT]:     [897|PwBaseWorkChain|run_process]: launching PwCalculation<902> iteration #1\n",
      "2024-02-19 15:51:13 [215 | REPORT]:     [897|PwBaseWorkChain|results]: work chain completed after 1 iterations\n",
      "2024-02-19 15:51:13 [216 | REPORT]:     [897|PwBaseWorkChain|on_terminated]: remote folders will not be cleaned\n",
      "2024-02-19 15:51:13 [217 | REPORT]:   [895|YamboWorkflow|can_continue]: the workflow continues with a nscf calculation\n",
      "2024-02-19 15:51:13 [218 | REPORT]:   [895|YamboWorkflow|perform_next]: performing a nscf calculation\n",
      "2024-02-19 15:51:15 [219 | REPORT]:     [908|PwBaseWorkChain|run_process]: launching PwCalculation<911> iteration #1\n",
      "2024-02-19 15:51:48 [220 | REPORT]:     [908|PwBaseWorkChain|results]: work chain completed after 1 iterations\n",
      "2024-02-19 15:51:49 [221 | REPORT]:     [908|PwBaseWorkChain|on_terminated]: remote folders will not be cleaned\n",
      "2024-02-19 15:51:49 [222 | REPORT]:   [895|YamboWorkflow|can_continue]: the workflow continues with a yambo calculation\n",
      "2024-02-19 15:51:49 [223 | REPORT]:   [895|YamboWorkflow|perform_next]: performing a yambo calculation\n",
      "2024-02-19 15:51:50 [224 | REPORT]:     [917|YamboRestart|run_process]: launching YamboCalculation<918> iteration #1\n",
      "2024-02-19 15:51:55 [225 | REPORT]:     [917|YamboRestart|results]: work chain completed after 1 iterations\n",
      "2024-02-19 15:51:55 [226 | REPORT]:     [917|YamboRestart|on_terminated]: remote folders will not be cleaned\n",
      "2024-02-19 15:51:55 [227 | REPORT]:   [895|YamboWorkflow|can_continue]: workflow is finished\n",
      "2024-02-19 15:51:56 [228 | REPORT]:   [895|YamboWorkflow|post_processing_needed]: no post processing needed\n",
      "2024-02-19 15:51:56 [229 | REPORT]:   [895|YamboWorkflow|report_wf]: workflow completed successfully\n",
      "2024-02-19 15:51:56 [230 | REPORT]: [890|YamboConvergence|prepare_calculations]: setting the pre calc remote folder 919 as parent\n",
      "2024-02-19 15:51:57 [231 | REPORT]: [890|YamboConvergence|next_step]: New parameters are: {'FFTGvecs': 21}\n",
      "2024-02-19 15:51:59 [232 | REPORT]: [890|YamboConvergence|next_step]: New parameters are: {'FFTGvecs': 37}\n",
      "2024-02-19 15:52:01 [233 | REPORT]: [890|YamboConvergence|next_step]: New parameters are: {'FFTGvecs': 45}\n",
      "2024-02-19 15:52:03 [234 | REPORT]: [890|YamboConvergence|next_step]: New parameters are: {'FFTGvecs': 58}\n",
      "2024-02-19 15:52:05 [235 | REPORT]:   [923|YamboWorkflow|start_workflow]:  workflow initilization step completed.\n",
      "2024-02-19 15:52:06 [236 | REPORT]:   [923|YamboWorkflow|can_continue]: the workflow continues with a yambo calculation\n",
      "2024-02-19 15:52:06 [237 | REPORT]:   [923|YamboWorkflow|perform_next]: performing a yambo calculation\n",
      "2024-02-19 15:52:06 [238 | REPORT]:   [923|YamboWorkflow|perform_next]: updating yambo parameters to parse more results\n",
      "2024-02-19 15:52:06 [239 | REPORT]:   [923|YamboWorkflow|perform_next]: {'dft_predicted': 'semiconductor/insulator', 'valence': 4, 'conduction': 5, 'number_of_kpoints': 8, 'nscf_gap_eV': 0.665, 'homo_k': 1, 'lumo_k': 7, 'gap_type': 'indirect', 'gap_': [[1, 1, 4, 4], [7, 7, 5, 5]], 'soc': False, 'magnetic_calculation': False}\n",
      "2024-02-19 15:52:07 [240 | REPORT]:   [925|YamboWorkflow|start_workflow]:  workflow initilization step completed.\n",
      "2024-02-19 15:52:07 [241 | REPORT]:   [925|YamboWorkflow|can_continue]: the workflow continues with a yambo calculation\n",
      "2024-02-19 15:52:07 [242 | REPORT]:   [925|YamboWorkflow|perform_next]: performing a yambo calculation\n",
      "2024-02-19 15:52:07 [243 | REPORT]:   [925|YamboWorkflow|perform_next]: updating yambo parameters to parse more results\n",
      "2024-02-19 15:52:07 [244 | REPORT]:   [925|YamboWorkflow|perform_next]: {'dft_predicted': 'semiconductor/insulator', 'valence': 4, 'conduction': 5, 'number_of_kpoints': 8, 'nscf_gap_eV': 0.665, 'homo_k': 1, 'lumo_k': 7, 'gap_type': 'indirect', 'gap_': [[1, 1, 4, 4], [7, 7, 5, 5]], 'soc': False, 'magnetic_calculation': False}\n",
      "2024-02-19 15:52:09 [245 | REPORT]:   [927|YamboWorkflow|start_workflow]:  workflow initilization step completed.\n",
      "2024-02-19 15:52:09 [246 | REPORT]:   [927|YamboWorkflow|can_continue]: the workflow continues with a yambo calculation\n",
      "2024-02-19 15:52:09 [247 | REPORT]:   [927|YamboWorkflow|perform_next]: performing a yambo calculation\n",
      "2024-02-19 15:52:09 [248 | REPORT]:   [927|YamboWorkflow|perform_next]: updating yambo parameters to parse more results\n",
      "2024-02-19 15:52:09 [249 | REPORT]:   [927|YamboWorkflow|perform_next]: {'dft_predicted': 'semiconductor/insulator', 'valence': 4, 'conduction': 5, 'number_of_kpoints': 8, 'nscf_gap_eV': 0.665, 'homo_k': 1, 'lumo_k': 7, 'gap_type': 'indirect', 'gap_': [[1, 1, 4, 4], [7, 7, 5, 5]], 'soc': False, 'magnetic_calculation': False}\n",
      "2024-02-19 15:52:11 [250 | REPORT]:   [929|YamboWorkflow|start_workflow]:  workflow initilization step completed.\n",
      "2024-02-19 15:52:11 [251 | REPORT]:   [929|YamboWorkflow|can_continue]: the workflow continues with a yambo calculation\n",
      "2024-02-19 15:52:11 [252 | REPORT]:   [929|YamboWorkflow|perform_next]: performing a yambo calculation\n",
      "2024-02-19 15:52:11 [253 | REPORT]:   [929|YamboWorkflow|perform_next]: updating yambo parameters to parse more results\n",
      "2024-02-19 15:52:11 [254 | REPORT]:   [929|YamboWorkflow|perform_next]: {'dft_predicted': 'semiconductor/insulator', 'valence': 4, 'conduction': 5, 'number_of_kpoints': 8, 'nscf_gap_eV': 0.665, 'homo_k': 1, 'lumo_k': 7, 'gap_type': 'indirect', 'gap_': [[1, 1, 4, 4], [7, 7, 5, 5]], 'soc': False, 'magnetic_calculation': False}\n",
      "2024-02-19 15:52:12 [255 | REPORT]:     [931|YamboRestart|run_process]: launching YamboCalculation<938> iteration #1\n",
      "2024-02-19 15:52:13 [256 | REPORT]:     [933|YamboRestart|run_process]: launching YamboCalculation<939> iteration #1\n",
      "2024-02-19 15:52:14 [257 | REPORT]:     [935|YamboRestart|run_process]: launching YamboCalculation<940> iteration #1\n",
      "2024-02-19 15:52:15 [258 | REPORT]:     [937|YamboRestart|run_process]: launching YamboCalculation<941> iteration #1\n",
      "2024-02-19 15:52:53 [259 | REPORT]:     [931|YamboRestart|results]: work chain completed after 1 iterations\n",
      "2024-02-19 15:52:53 [260 | REPORT]:     [931|YamboRestart|on_terminated]: remote folders will not be cleaned\n",
      "2024-02-19 15:52:53 [261 | REPORT]:   [923|YamboWorkflow|can_continue]: workflow is finished\n",
      "2024-02-19 15:52:54 [262 | REPORT]:   [923|YamboWorkflow|post_processing_needed]: no post processing needed\n",
      "2024-02-19 15:52:54 [263 | REPORT]:   [923|YamboWorkflow|report_wf]: PARSED: {'gap_': 1.0526193216979505, 'homo': -0.2454703648686409, 'lumo': 0.8071489568293095, 'gap_dft': 0.6646806137740612, 'homo_dft': 0.0, 'lumo_dft': 0.6646806137740612}\n",
      "2024-02-19 15:52:54 [264 | REPORT]:   [923|YamboWorkflow|report_wf]: workflow completed successfully\n",
      "2024-02-19 15:53:03 [265 | REPORT]:     [933|YamboRestart|results]: work chain completed after 1 iterations\n",
      "2024-02-19 15:53:03 [266 | REPORT]:     [933|YamboRestart|on_terminated]: remote folders will not be cleaned\n",
      "2024-02-19 15:53:04 [267 | REPORT]:     [935|YamboRestart|results]: work chain completed after 1 iterations\n",
      "2024-02-19 15:53:04 [268 | REPORT]:     [935|YamboRestart|on_terminated]: remote folders will not be cleaned\n",
      "2024-02-19 15:53:04 [269 | REPORT]:   [925|YamboWorkflow|can_continue]: workflow is finished\n",
      "2024-02-19 15:53:04 [270 | REPORT]:   [925|YamboWorkflow|post_processing_needed]: no post processing needed\n",
      "2024-02-19 15:53:05 [271 | REPORT]:   [925|YamboWorkflow|report_wf]: PARSED: {'gap_': 1.0522369526833297, 'homo': -0.24602668542861938, 'lumo': 0.8062102672547102, 'gap_dft': 0.6646806137740612, 'homo_dft': 0.0, 'lumo_dft': 0.6646806137740612}\n",
      "2024-02-19 15:53:05 [272 | REPORT]:   [925|YamboWorkflow|report_wf]: workflow completed successfully\n",
      "2024-02-19 15:53:05 [273 | REPORT]:   [927|YamboWorkflow|can_continue]: workflow is finished\n",
      "2024-02-19 15:53:06 [274 | REPORT]:   [927|YamboWorkflow|post_processing_needed]: no post processing needed\n",
      "2024-02-19 15:53:06 [275 | REPORT]:   [927|YamboWorkflow|report_wf]: PARSED: {'gap_': 1.0522414129793645, 'homo': -0.24608020898103716, 'lumo': 0.8061612039983272, 'gap_dft': 0.6646806137740612, 'homo_dft': 0.0, 'lumo_dft': 0.6646806137740612}\n",
      "2024-02-19 15:53:06 [276 | REPORT]:   [927|YamboWorkflow|report_wf]: workflow completed successfully\n",
      "2024-02-19 15:53:37 [277 | REPORT]:     [937|YamboRestart|results]: work chain completed after 1 iterations\n",
      "2024-02-19 15:53:37 [278 | REPORT]:     [937|YamboRestart|on_terminated]: remote folders will not be cleaned\n",
      "2024-02-19 15:53:37 [279 | REPORT]:   [929|YamboWorkflow|can_continue]: workflow is finished\n",
      "2024-02-19 15:53:37 [280 | REPORT]:   [929|YamboWorkflow|post_processing_needed]: no post processing needed\n",
      "2024-02-19 15:53:38 [281 | REPORT]:   [929|YamboWorkflow|report_wf]: PARSED: {'gap_': 1.052226815646887, 'homo': -0.2460550691306591, 'lumo': 0.8061717465162278, 'gap_dft': 0.6646806137740612, 'homo_dft': 0.0, 'lumo_dft': 0.6646806137740612}\n",
      "2024-02-19 15:53:38 [282 | REPORT]:   [929|YamboWorkflow|report_wf]: workflow completed successfully\n",
      "2024-02-19 15:53:39 [283 | REPORT]: [890|YamboConvergence|data_analysis]: Data analysis, we will try to parse some result and decide what next.\n",
      "2024-02-19 15:53:40 [284 | REPORT]: [890|YamboConvergence|data_analysis]:    FFTGvecs  BndsRnXp  ...      gap_                                  uuid\n",
      "0        21       150  ...  1.052619  86672fa7-1f72-4015-969f-541ae4397d74\n",
      "1        37       150  ...  1.052237  104611a0-dc81-4176-ba36-0325cf036638\n",
      "2        45       150  ...  1.052241  bdae7620-16d8-45ac-8a5f-9853a89b4087\n",
      "3        58       150  ...  1.052227  e9b5696d-545f-4f32-b99e-a0806e42d33e\n",
      "\n",
      "[4 rows x 6 columns]\n",
      "2024-02-19 15:53:40 [285 | REPORT]: [890|YamboConvergence|data_analysis]: results ['gap_']\n",
      ":{'gap_': array([1.05261932, 1.05223695, 1.05224141, 1.05222682])}\n",
      "2024-02-19 15:53:40 [286 | REPORT]: [890|YamboConvergence|data_analysis]: HINTS: {'FFTGvecs': 21, 'gap_': 1.052619321698, 'already_computed': True, 'E_ref': 1.052215665769555, 'new_grid': False, 'extrapolation': 1.0521891410023987, 'extrapolation_units': 'eV'}\n",
      "2024-02-19 15:53:40 [287 | REPORT]: [890|YamboConvergence|data_analysis]: Success, updating the history... \n",
      "2024-02-19 15:53:40 [288 | REPORT]: [890|YamboConvergence|data_analysis]: Success on ['FFTGvecs'] reached in 4 calculations, the result is [[1.05261932]]\n",
      "2024-02-19 15:53:40 [289 | REPORT]: [890|YamboConvergence|data_analysis]: {'type': 'heavy', 'what': ['gap_'], 'skip_pre': False, 'max': 84, 'var': ['FFTGvecs'], 'stop': 58, 'delta': 8, 'start': 21, 'iter': 1, 'G_iter': 1, 'success': True, 'skipped': 0, 'conv_thr': 10, 'conv_thr_units': '%', 'ratio': 1.2, 'max_iterations': 4, 'steps': 4, 'conv_window': 4, 'functional_form': 'power_law', 'convergence_algorithm': 'new_algorithm_1D', 'thr_fx': 5e-05}\n",
      "2024-02-19 15:53:40 [290 | REPORT]: [890|YamboConvergence|data_analysis]: {'FFTGvecs': 21, 'gap_': 1.052619321698, 'already_computed': True, 'E_ref': 1.052215665769555, 'new_grid': False, 'extrapolation_units': 'eV'}\n",
      "2024-02-19 15:53:40 [291 | REPORT]: [890|YamboConvergence|has_to_continue]: Next parameters: ['BndsRnXp', 'GbndRnge', 'NGsBlkXp']\n",
      "2024-02-19 15:53:40 [292 | REPORT]: [890|YamboConvergence|has_to_continue]: Mode is \"heavy\", so we mantain the other parameters as the converged ones, if any.\n",
      "2024-02-19 15:53:40 [293 | REPORT]: [890|YamboConvergence|pre_needed]: {'FFTGvecs': [21], 'BndsRnXp': [50, 400, 50, 150, 300, 400], 'NGsBlkXp': [2, 2, 8, 6, 4, 8], 'GbndRnge': [50, 400, 50, 150, 300, 400]}\n",
      "2024-02-19 15:53:41 [294 | REPORT]: [890|YamboConvergence|pre_needed]: ['GW bands are: 150', 'found scf inputs from parent', 'found nscf inputs from parent\\n']\n",
      "2024-02-19 15:53:41 [295 | REPORT]: [890|YamboConvergence|do_pre]: mesh check\n",
      "2024-02-19 15:53:41 [296 | REPORT]: [890|YamboConvergence|do_pre]: doing the calculation: p2y\n",
      "2024-02-19 15:53:42 [297 | REPORT]: [890|YamboConvergence|do_pre]: Submitted YamboWorkflow up to p2y, pk = 973\n",
      "2024-02-19 15:53:44 [298 | REPORT]:   [973|YamboWorkflow|start_workflow]:  workflow initilization step completed.\n",
      "2024-02-19 15:53:44 [299 | REPORT]:   [973|YamboWorkflow|can_continue]: the workflow continues with a nscf calculation\n",
      "2024-02-19 15:53:44 [300 | REPORT]:   [973|YamboWorkflow|perform_next]: performing a nscf calculation\n",
      "2024-02-19 15:53:45 [301 | REPORT]:     [976|PwBaseWorkChain|run_process]: launching PwCalculation<979> iteration #1\n",
      "2024-02-19 15:55:44 [302 | REPORT]:     [976|PwBaseWorkChain|results]: work chain completed after 1 iterations\n",
      "2024-02-19 15:55:44 [303 | REPORT]:     [976|PwBaseWorkChain|on_terminated]: remote folders will not be cleaned\n",
      "2024-02-19 15:55:44 [304 | REPORT]:   [973|YamboWorkflow|can_continue]: the workflow continues with a yambo calculation\n",
      "2024-02-19 15:55:44 [305 | REPORT]:   [973|YamboWorkflow|perform_next]: performing a yambo calculation\n",
      "2024-02-19 15:55:46 [306 | REPORT]:     [985|YamboRestart|run_process]: launching YamboCalculation<986> iteration #1\n",
      "2024-02-19 15:55:51 [307 | REPORT]:     [985|YamboRestart|results]: work chain completed after 1 iterations\n",
      "2024-02-19 15:55:51 [308 | REPORT]:     [985|YamboRestart|on_terminated]: remote folders will not be cleaned\n",
      "2024-02-19 15:55:51 [309 | REPORT]:   [973|YamboWorkflow|can_continue]: workflow is finished\n",
      "2024-02-19 15:55:51 [310 | REPORT]:   [973|YamboWorkflow|post_processing_needed]: no post processing needed\n",
      "2024-02-19 15:55:52 [311 | REPORT]:   [973|YamboWorkflow|report_wf]: workflow completed successfully\n",
      "2024-02-19 15:55:52 [312 | REPORT]: [890|YamboConvergence|prepare_calculations]: setting the pre calc remote folder 987 as parent\n",
      "2024-02-19 15:55:54 [313 | REPORT]: [890|YamboConvergence|next_step]: New parameters are: {'BndsRnXp': 50, 'GbndRnge': 50, 'NGsBlkXp': 2}\n",
      "2024-02-19 15:55:56 [314 | REPORT]: [890|YamboConvergence|next_step]: New parameters are: {'BndsRnXp': 400, 'GbndRnge': 400, 'NGsBlkXp': 2}\n",
      "2024-02-19 15:55:59 [315 | REPORT]: [890|YamboConvergence|next_step]: New parameters are: {'BndsRnXp': 50, 'GbndRnge': 50, 'NGsBlkXp': 8}\n",
      "2024-02-19 15:56:02 [316 | REPORT]: [890|YamboConvergence|next_step]: New parameters are: {'BndsRnXp': 150, 'GbndRnge': 150, 'NGsBlkXp': 6}\n",
      "2024-02-19 15:56:04 [317 | REPORT]: [890|YamboConvergence|next_step]: New parameters are: {'BndsRnXp': 300, 'GbndRnge': 300, 'NGsBlkXp': 4}\n",
      "2024-02-19 15:56:07 [318 | REPORT]: [890|YamboConvergence|next_step]: New parameters are: {'BndsRnXp': 400, 'GbndRnge': 400, 'NGsBlkXp': 8}\n",
      "2024-02-19 15:56:09 [319 | REPORT]:   [991|YamboWorkflow|start_workflow]:  workflow initilization step completed.\n",
      "2024-02-19 15:56:10 [320 | REPORT]:   [991|YamboWorkflow|can_continue]: the workflow continues with a yambo calculation\n",
      "2024-02-19 15:56:10 [321 | REPORT]:   [991|YamboWorkflow|perform_next]: performing a yambo calculation\n",
      "2024-02-19 15:56:10 [322 | REPORT]:   [991|YamboWorkflow|perform_next]: updating yambo parameters to parse more results\n",
      "2024-02-19 15:56:10 [323 | REPORT]:   [991|YamboWorkflow|perform_next]: {'dft_predicted': 'semiconductor/insulator', 'valence': 4, 'conduction': 5, 'number_of_kpoints': 8, 'nscf_gap_eV': 0.665, 'homo_k': 1, 'lumo_k': 7, 'gap_type': 'indirect', 'gap_': [[1, 1, 4, 4], [7, 7, 5, 5]], 'soc': False, 'magnetic_calculation': False}\n",
      "2024-02-19 15:56:11 [324 | REPORT]:   [993|YamboWorkflow|start_workflow]:  workflow initilization step completed.\n",
      "2024-02-19 15:56:11 [325 | REPORT]:   [993|YamboWorkflow|can_continue]: the workflow continues with a yambo calculation\n",
      "2024-02-19 15:56:11 [326 | REPORT]:   [993|YamboWorkflow|perform_next]: performing a yambo calculation\n",
      "2024-02-19 15:56:11 [327 | REPORT]:   [993|YamboWorkflow|perform_next]: updating yambo parameters to parse more results\n",
      "2024-02-19 15:56:11 [328 | REPORT]:   [993|YamboWorkflow|perform_next]: {'dft_predicted': 'semiconductor/insulator', 'valence': 4, 'conduction': 5, 'number_of_kpoints': 8, 'nscf_gap_eV': 0.665, 'homo_k': 1, 'lumo_k': 7, 'gap_type': 'indirect', 'gap_': [[1, 1, 4, 4], [7, 7, 5, 5]], 'soc': False, 'magnetic_calculation': False}\n",
      "2024-02-19 15:56:12 [329 | REPORT]:   [995|YamboWorkflow|start_workflow]:  workflow initilization step completed.\n",
      "2024-02-19 15:56:13 [330 | REPORT]:   [995|YamboWorkflow|can_continue]: the workflow continues with a yambo calculation\n",
      "2024-02-19 15:56:13 [331 | REPORT]:   [995|YamboWorkflow|perform_next]: performing a yambo calculation\n",
      "2024-02-19 15:56:13 [332 | REPORT]:   [995|YamboWorkflow|perform_next]: updating yambo parameters to parse more results\n",
      "2024-02-19 15:56:13 [333 | REPORT]:   [995|YamboWorkflow|perform_next]: {'dft_predicted': 'semiconductor/insulator', 'valence': 4, 'conduction': 5, 'number_of_kpoints': 8, 'nscf_gap_eV': 0.665, 'homo_k': 1, 'lumo_k': 7, 'gap_type': 'indirect', 'gap_': [[1, 1, 4, 4], [7, 7, 5, 5]], 'soc': False, 'magnetic_calculation': False}\n",
      "2024-02-19 15:56:14 [334 | REPORT]:   [997|YamboWorkflow|start_workflow]:  workflow initilization step completed.\n",
      "2024-02-19 15:56:14 [335 | REPORT]:   [997|YamboWorkflow|can_continue]: the workflow continues with a yambo calculation\n",
      "2024-02-19 15:56:14 [336 | REPORT]:   [997|YamboWorkflow|perform_next]: performing a yambo calculation\n",
      "2024-02-19 15:56:14 [337 | REPORT]:   [997|YamboWorkflow|perform_next]: updating yambo parameters to parse more results\n",
      "2024-02-19 15:56:15 [338 | REPORT]:   [997|YamboWorkflow|perform_next]: {'dft_predicted': 'semiconductor/insulator', 'valence': 4, 'conduction': 5, 'number_of_kpoints': 8, 'nscf_gap_eV': 0.665, 'homo_k': 1, 'lumo_k': 7, 'gap_type': 'indirect', 'gap_': [[1, 1, 4, 4], [7, 7, 5, 5]], 'soc': False, 'magnetic_calculation': False}\n",
      "2024-02-19 15:56:16 [339 | REPORT]:   [999|YamboWorkflow|start_workflow]:  workflow initilization step completed.\n",
      "2024-02-19 15:56:16 [340 | REPORT]:   [999|YamboWorkflow|can_continue]: the workflow continues with a yambo calculation\n",
      "2024-02-19 15:56:16 [341 | REPORT]:   [999|YamboWorkflow|perform_next]: performing a yambo calculation\n",
      "2024-02-19 15:56:16 [342 | REPORT]:   [999|YamboWorkflow|perform_next]: updating yambo parameters to parse more results\n",
      "2024-02-19 15:56:16 [343 | REPORT]:   [999|YamboWorkflow|perform_next]: {'dft_predicted': 'semiconductor/insulator', 'valence': 4, 'conduction': 5, 'number_of_kpoints': 8, 'nscf_gap_eV': 0.665, 'homo_k': 1, 'lumo_k': 7, 'gap_type': 'indirect', 'gap_': [[1, 1, 4, 4], [7, 7, 5, 5]], 'soc': False, 'magnetic_calculation': False}\n",
      "2024-02-19 15:56:18 [344 | REPORT]:   [1001|YamboWorkflow|start_workflow]:  workflow initilization step completed.\n",
      "2024-02-19 15:56:18 [345 | REPORT]:   [1001|YamboWorkflow|can_continue]: the workflow continues with a yambo calculation\n",
      "2024-02-19 15:56:18 [346 | REPORT]:   [1001|YamboWorkflow|perform_next]: performing a yambo calculation\n",
      "2024-02-19 15:56:18 [347 | REPORT]:   [1001|YamboWorkflow|perform_next]: updating yambo parameters to parse more results\n",
      "2024-02-19 15:56:18 [348 | REPORT]:   [1001|YamboWorkflow|perform_next]: {'dft_predicted': 'semiconductor/insulator', 'valence': 4, 'conduction': 5, 'number_of_kpoints': 8, 'nscf_gap_eV': 0.665, 'homo_k': 1, 'lumo_k': 7, 'gap_type': 'indirect', 'gap_': [[1, 1, 4, 4], [7, 7, 5, 5]], 'soc': False, 'magnetic_calculation': False}\n",
      "2024-02-19 15:56:20 [349 | REPORT]:     [1003|YamboRestart|run_process]: launching YamboCalculation<1014> iteration #1\n",
      "2024-02-19 15:56:22 [350 | REPORT]:     [1005|YamboRestart|run_process]: launching YamboCalculation<1015> iteration #1\n",
      "2024-02-19 15:56:23 [351 | REPORT]:     [1007|YamboRestart|run_process]: launching YamboCalculation<1016> iteration #1\n",
      "2024-02-19 15:56:24 [352 | REPORT]:     [1009|YamboRestart|run_process]: launching YamboCalculation<1017> iteration #1\n",
      "2024-02-19 15:56:25 [353 | REPORT]:     [1011|YamboRestart|run_process]: launching YamboCalculation<1018> iteration #1\n",
      "2024-02-19 15:56:26 [354 | REPORT]:     [1013|YamboRestart|run_process]: launching YamboCalculation<1019> iteration #1\n",
      "2024-02-19 15:56:51 [355 | REPORT]:     [1003|YamboRestart|results]: work chain completed after 1 iterations\n",
      "2024-02-19 15:56:52 [356 | REPORT]:     [1003|YamboRestart|on_terminated]: remote folders will not be cleaned\n",
      "2024-02-19 15:56:52 [357 | REPORT]:   [991|YamboWorkflow|can_continue]: workflow is finished\n",
      "2024-02-19 15:56:52 [358 | REPORT]:   [991|YamboWorkflow|post_processing_needed]: no post processing needed\n",
      "2024-02-19 15:56:53 [359 | REPORT]:   [991|YamboWorkflow|report_wf]: PARSED: {'gap_': 0.9213098283588886, 'homo': 0.0045430142521858214, 'lumo': 0.9258528426110745, 'gap_dft': 0.6646806137740612, 'homo_dft': 0.0, 'lumo_dft': 0.6646806137740612}\n",
      "2024-02-19 15:56:53 [360 | REPORT]:   [991|YamboWorkflow|report_wf]: workflow completed successfully\n",
      "2024-02-19 15:57:13 [361 | REPORT]:     [1009|YamboRestart|results]: work chain completed after 1 iterations\n",
      "2024-02-19 15:57:14 [362 | REPORT]:     [1009|YamboRestart|on_terminated]: remote folders will not be cleaned\n",
      "2024-02-19 15:57:14 [363 | REPORT]:   [997|YamboWorkflow|can_continue]: workflow is finished\n",
      "2024-02-19 15:57:14 [364 | REPORT]:   [997|YamboWorkflow|post_processing_needed]: no post processing needed\n",
      "2024-02-19 15:57:15 [365 | REPORT]:   [997|YamboWorkflow|report_wf]: PARSED: {'gap_': 1.1317044252097608, 'homo': -0.350916628909111, 'lumo': 0.7807877963006497, 'gap_dft': 0.6646806137740612, 'homo_dft': 0.0, 'lumo_dft': 0.6646806137740612}\n",
      "2024-02-19 15:57:15 [366 | REPORT]:   [997|YamboWorkflow|report_wf]: workflow completed successfully\n",
      "2024-02-19 15:57:24 [367 | REPORT]:     [1007|YamboRestart|results]: work chain completed after 1 iterations\n",
      "2024-02-19 15:57:24 [368 | REPORT]:     [1007|YamboRestart|on_terminated]: remote folders will not be cleaned\n",
      "2024-02-19 15:57:25 [369 | REPORT]:   [995|YamboWorkflow|can_continue]: workflow is finished\n",
      "2024-02-19 15:57:25 [370 | REPORT]:   [995|YamboWorkflow|post_processing_needed]: no post processing needed\n",
      "2024-02-19 15:57:25 [371 | REPORT]:   [995|YamboWorkflow|report_wf]: PARSED: {'gap_': 1.1991546437740326, 'homo': -0.3619757301867008, 'lumo': 0.8371789135873318, 'gap_dft': 0.6646806137740612, 'homo_dft': 0.0, 'lumo_dft': 0.6646806137740612}\n",
      "2024-02-19 15:57:25 [372 | REPORT]:   [995|YamboWorkflow|report_wf]: workflow completed successfully\n",
      "2024-02-19 15:57:38 [373 | REPORT]:     [1005|YamboRestart|results]: work chain completed after 1 iterations\n",
      "2024-02-19 15:57:38 [374 | REPORT]:     [1005|YamboRestart|on_terminated]: remote folders will not be cleaned\n",
      "2024-02-19 15:57:38 [375 | REPORT]:   [993|YamboWorkflow|can_continue]: workflow is finished\n",
      "2024-02-19 15:57:38 [376 | REPORT]:   [993|YamboWorkflow|post_processing_needed]: no post processing needed\n",
      "2024-02-19 15:57:39 [377 | REPORT]:   [993|YamboWorkflow|report_wf]: PARSED: {'gap_': 0.9193939284712077, 'homo': -0.023072705906629562, 'lumo': 0.8963212225645781, 'gap_dft': 0.6646806137740612, 'homo_dft': 0.0, 'lumo_dft': 0.6646806137740612}\n",
      "2024-02-19 15:57:39 [378 | REPORT]:   [993|YamboWorkflow|report_wf]: workflow completed successfully\n",
      "2024-02-19 15:57:59 [379 | REPORT]:     [1011|YamboRestart|results]: work chain completed after 1 iterations\n",
      "2024-02-19 15:58:00 [380 | REPORT]:     [1011|YamboRestart|on_terminated]: remote folders will not be cleaned\n",
      "2024-02-19 15:58:00 [381 | REPORT]:   [999|YamboWorkflow|can_continue]: workflow is finished\n",
      "2024-02-19 15:58:00 [382 | REPORT]:   [999|YamboWorkflow|post_processing_needed]: no post processing needed\n",
      "2024-02-19 15:58:00 [383 | REPORT]:   [999|YamboWorkflow|report_wf]: PARSED: {'gap_': 1.0539119965851307, 'homo': -0.2482268278181553, 'lumo': 0.8056851687669755, 'gap_dft': 0.6646806137740612, 'homo_dft': 0.0, 'lumo_dft': 0.6646806137740612}\n",
      "2024-02-19 15:58:00 [384 | REPORT]:   [999|YamboWorkflow|report_wf]: workflow completed successfully\n",
      "2024-02-19 15:58:46 [385 | REPORT]:     [1013|YamboRestart|results]: work chain completed after 1 iterations\n",
      "2024-02-19 15:58:47 [386 | REPORT]:     [1013|YamboRestart|on_terminated]: remote folders will not be cleaned\n",
      "2024-02-19 15:58:47 [387 | REPORT]:   [1001|YamboWorkflow|can_continue]: workflow is finished\n",
      "2024-02-19 15:58:47 [388 | REPORT]:   [1001|YamboWorkflow|post_processing_needed]: no post processing needed\n",
      "2024-02-19 15:58:48 [389 | REPORT]:   [1001|YamboWorkflow|report_wf]: PARSED: {'gap_': 1.2279511259377003, 'homo': -0.5072735228538513, 'lumo': 0.720677603083849, 'gap_dft': 0.6646806137740612, 'homo_dft': 0.0, 'lumo_dft': 0.6646806137740612}\n",
      "2024-02-19 15:58:48 [390 | REPORT]:   [1001|YamboWorkflow|report_wf]: workflow completed successfully\n",
      "2024-02-19 15:58:48 [391 | REPORT]: [890|YamboConvergence|data_analysis]: Data analysis, we will try to parse some result and decide what next.\n",
      "2024-02-19 15:58:49 [392 | REPORT]: [890|YamboConvergence|data_analysis]:    FFTGvecs  BndsRnXp  ...      gap_                                  uuid\n",
      "0        21        50  ...  0.921310  38640663-13b0-44a6-8442-6d2c06bb4848\n",
      "1        21       400  ...  0.919394  a0cc1686-6ce2-474b-bc09-43b8d3e35459\n",
      "2        21        50  ...  1.199155  10616881-430a-4dd7-9bf6-ddbfd4aaa433\n",
      "3        21       150  ...  1.131704  17ee124c-5152-46bb-9bac-4caed41e8dc1\n",
      "4        21       300  ...  1.053912  8a7e983d-f690-4bd1-9c55-5763d30115ea\n",
      "5        21       400  ...  1.227951  38ab7c55-3538-4518-a4f6-bb35e4f8af78\n",
      "\n",
      "[6 rows x 6 columns]\n",
      "2024-02-19 15:58:52 [393 | REPORT]: [890|YamboConvergence|data_analysis]: results ['gap_']\n",
      ":{'gap_': array([0.92130983, 0.91939393, 1.19915464, 1.13170443, 1.053912  ,\n",
      "       1.22795113])}\n",
      "2024-02-19 15:58:52 [394 | REPORT]: [890|YamboConvergence|data_analysis]: HINTS: {'new_grid': True, 'extrapolation': 1.348151784794909, 'extrapolation_units': 'eV'}\n",
      "2024-02-19 15:58:52 [395 | REPORT]: [890|YamboConvergence|data_analysis]: Success on ['BndsRnXp', 'GbndRnge', 'NGsBlkXp'] not reached yet in 6 calculations\n",
      "2024-02-19 15:58:52 [396 | REPORT]: [890|YamboConvergence|data_analysis]: {'new_grid': True, 'extrapolation': 1.348151784794909, 'extrapolation_units': 'eV'}\n",
      "2024-02-19 15:58:53 [397 | REPORT]: [890|YamboConvergence|has_to_continue]: Still iteration on ['BndsRnXp', 'GbndRnge', 'NGsBlkXp']\n",
      "2024-02-19 15:58:53 [398 | REPORT]: [890|YamboConvergence|pre_needed]: {'FFTGvecs': [21], 'BndsRnXp': [50, 400, 50, 150, 300, 400], 'NGsBlkXp': [2, 2, 8, 6, 4, 8], 'GbndRnge': [50, 400, 50, 150, 300, 400]}\n",
      "2024-02-19 15:58:53 [399 | REPORT]: [890|YamboConvergence|pre_needed]: ['GW bands are: 400', 'found scf inputs from parent', 'found nscf inputs from parent\\n', 'found nscf inputs from parent\\n and setting nbnd of the nscf calculation to b = 400']\n",
      "2024-02-19 15:58:53 [400 | REPORT]: [890|YamboConvergence|do_pre]: mesh check\n",
      "2024-02-19 15:58:53 [401 | REPORT]: [890|YamboConvergence|do_pre]: doing the calculation: p2y\n",
      "2024-02-19 15:58:55 [402 | REPORT]: [890|YamboConvergence|do_pre]: Submitted YamboWorkflow up to p2y, pk = 1065\n",
      "2024-02-19 15:58:56 [403 | REPORT]:   [1065|YamboWorkflow|start_workflow]:  workflow initilization step completed.\n",
      "2024-02-19 15:58:56 [404 | REPORT]:   [1065|YamboWorkflow|can_continue]: the workflow continues with a nscf calculation\n",
      "2024-02-19 15:58:56 [405 | REPORT]:   [1065|YamboWorkflow|perform_next]: performing a nscf calculation\n",
      "2024-02-19 15:58:57 [406 | REPORT]:     [1068|PwBaseWorkChain|run_process]: launching PwCalculation<1071> iteration #1\n",
      "2024-02-19 16:01:30 [407 | REPORT]:     [1068|PwBaseWorkChain|results]: work chain completed after 1 iterations\n",
      "2024-02-19 16:01:30 [408 | REPORT]:     [1068|PwBaseWorkChain|on_terminated]: remote folders will not be cleaned\n",
      "2024-02-19 16:01:31 [409 | REPORT]:   [1065|YamboWorkflow|can_continue]: the workflow continues with a yambo calculation\n",
      "2024-02-19 16:01:31 [410 | REPORT]:   [1065|YamboWorkflow|perform_next]: performing a yambo calculation\n",
      "2024-02-19 16:01:32 [411 | REPORT]:     [1077|YamboRestart|run_process]: launching YamboCalculation<1078> iteration #1\n",
      "2024-02-19 16:01:38 [412 | REPORT]:     [1077|YamboRestart|results]: work chain completed after 1 iterations\n",
      "2024-02-19 16:01:38 [413 | REPORT]:     [1077|YamboRestart|on_terminated]: remote folders will not be cleaned\n",
      "2024-02-19 16:01:38 [414 | REPORT]:   [1065|YamboWorkflow|can_continue]: workflow is finished\n",
      "2024-02-19 16:01:38 [415 | REPORT]:   [1065|YamboWorkflow|post_processing_needed]: no post processing needed\n",
      "2024-02-19 16:01:39 [416 | REPORT]:   [1065|YamboWorkflow|report_wf]: workflow completed successfully\n",
      "2024-02-19 16:01:39 [417 | REPORT]: [890|YamboConvergence|prepare_calculations]: setting the pre calc remote folder 1079 as parent\n",
      "2024-02-19 16:01:42 [418 | REPORT]: [890|YamboConvergence|next_step]: New parameters are: {'BndsRnXp': 100, 'GbndRnge': 100, 'NGsBlkXp': 3}\n",
      "2024-02-19 16:01:47 [419 | REPORT]: [890|YamboConvergence|next_step]: New parameters are: {'BndsRnXp': 450, 'GbndRnge': 450, 'NGsBlkXp': 3}\n",
      "2024-02-19 16:01:51 [420 | REPORT]: [890|YamboConvergence|next_step]: New parameters are: {'BndsRnXp': 100, 'GbndRnge': 100, 'NGsBlkXp': 9}\n",
      "2024-02-19 16:01:55 [421 | REPORT]: [890|YamboConvergence|next_step]: New parameters are: {'BndsRnXp': 200, 'GbndRnge': 200, 'NGsBlkXp': 7}\n",
      "2024-02-19 16:02:00 [422 | REPORT]: [890|YamboConvergence|next_step]: New parameters are: {'BndsRnXp': 350, 'GbndRnge': 350, 'NGsBlkXp': 5}\n",
      "2024-02-19 16:02:04 [423 | REPORT]: [890|YamboConvergence|next_step]: New parameters are: {'BndsRnXp': 450, 'GbndRnge': 450, 'NGsBlkXp': 9}\n",
      "2024-02-19 16:02:08 [424 | REPORT]:   [1083|YamboWorkflow|start_workflow]:  workflow initilization step completed.\n",
      "2024-02-19 16:02:08 [425 | REPORT]:   [1083|YamboWorkflow|can_continue]: the workflow continues with a yambo calculation\n",
      "2024-02-19 16:02:08 [426 | REPORT]:   [1083|YamboWorkflow|perform_next]: performing a yambo calculation\n",
      "2024-02-19 16:02:08 [427 | REPORT]:   [1083|YamboWorkflow|perform_next]: updating yambo parameters to parse more results\n",
      "2024-02-19 16:02:08 [428 | REPORT]:   [1083|YamboWorkflow|perform_next]: {'dft_predicted': 'semiconductor/insulator', 'valence': 4, 'conduction': 5, 'number_of_kpoints': 8, 'nscf_gap_eV': 0.665, 'homo_k': 1, 'lumo_k': 7, 'gap_type': 'indirect', 'gap_': [[1, 1, 4, 4], [7, 7, 5, 5]], 'soc': False, 'magnetic_calculation': False}\n",
      "2024-02-19 16:02:10 [429 | REPORT]:   [1085|YamboWorkflow|start_workflow]:  workflow initilization step completed.\n",
      "2024-02-19 16:02:10 [430 | REPORT]:   [1085|YamboWorkflow|can_continue]: the workflow continues with a yambo calculation\n",
      "2024-02-19 16:02:10 [431 | REPORT]:   [1085|YamboWorkflow|perform_next]: performing a yambo calculation\n",
      "2024-02-19 16:02:10 [432 | REPORT]:   [1085|YamboWorkflow|perform_next]: updating yambo parameters to parse more results\n",
      "2024-02-19 16:02:10 [433 | REPORT]:   [1085|YamboWorkflow|perform_next]: {'dft_predicted': 'semiconductor/insulator', 'valence': 4, 'conduction': 5, 'number_of_kpoints': 8, 'nscf_gap_eV': 0.665, 'homo_k': 1, 'lumo_k': 7, 'gap_type': 'indirect', 'gap_': [[1, 1, 4, 4], [7, 7, 5, 5]], 'soc': False, 'magnetic_calculation': False}\n",
      "2024-02-19 16:02:12 [434 | REPORT]:   [1087|YamboWorkflow|start_workflow]:  workflow initilization step completed.\n",
      "2024-02-19 16:02:12 [435 | REPORT]:   [1087|YamboWorkflow|can_continue]: the workflow continues with a yambo calculation\n",
      "2024-02-19 16:02:12 [436 | REPORT]:   [1087|YamboWorkflow|perform_next]: performing a yambo calculation\n",
      "2024-02-19 16:02:12 [437 | REPORT]:   [1087|YamboWorkflow|perform_next]: updating yambo parameters to parse more results\n",
      "2024-02-19 16:02:12 [438 | REPORT]:   [1087|YamboWorkflow|perform_next]: {'dft_predicted': 'semiconductor/insulator', 'valence': 4, 'conduction': 5, 'number_of_kpoints': 8, 'nscf_gap_eV': 0.665, 'homo_k': 1, 'lumo_k': 7, 'gap_type': 'indirect', 'gap_': [[1, 1, 4, 4], [7, 7, 5, 5]], 'soc': False, 'magnetic_calculation': False}\n",
      "2024-02-19 16:02:14 [439 | REPORT]:   [1089|YamboWorkflow|start_workflow]:  workflow initilization step completed.\n",
      "2024-02-19 16:02:14 [440 | REPORT]:   [1089|YamboWorkflow|can_continue]: the workflow continues with a yambo calculation\n",
      "2024-02-19 16:02:14 [441 | REPORT]:   [1089|YamboWorkflow|perform_next]: performing a yambo calculation\n",
      "2024-02-19 16:02:14 [442 | REPORT]:   [1089|YamboWorkflow|perform_next]: updating yambo parameters to parse more results\n",
      "2024-02-19 16:02:14 [443 | REPORT]:   [1089|YamboWorkflow|perform_next]: {'dft_predicted': 'semiconductor/insulator', 'valence': 4, 'conduction': 5, 'number_of_kpoints': 8, 'nscf_gap_eV': 0.665, 'homo_k': 1, 'lumo_k': 7, 'gap_type': 'indirect', 'gap_': [[1, 1, 4, 4], [7, 7, 5, 5]], 'soc': False, 'magnetic_calculation': False}\n",
      "2024-02-19 16:02:16 [444 | REPORT]:   [1091|YamboWorkflow|start_workflow]:  workflow initilization step completed.\n",
      "2024-02-19 16:02:16 [445 | REPORT]:   [1091|YamboWorkflow|can_continue]: the workflow continues with a yambo calculation\n",
      "2024-02-19 16:02:16 [446 | REPORT]:   [1091|YamboWorkflow|perform_next]: performing a yambo calculation\n",
      "2024-02-19 16:02:16 [447 | REPORT]:   [1091|YamboWorkflow|perform_next]: updating yambo parameters to parse more results\n",
      "2024-02-19 16:02:16 [448 | REPORT]:   [1091|YamboWorkflow|perform_next]: {'dft_predicted': 'semiconductor/insulator', 'valence': 4, 'conduction': 5, 'number_of_kpoints': 8, 'nscf_gap_eV': 0.665, 'homo_k': 1, 'lumo_k': 7, 'gap_type': 'indirect', 'gap_': [[1, 1, 4, 4], [7, 7, 5, 5]], 'soc': False, 'magnetic_calculation': False}\n",
      "2024-02-19 16:02:17 [449 | REPORT]:   [1093|YamboWorkflow|start_workflow]:  workflow initilization step completed.\n",
      "2024-02-19 16:02:18 [450 | REPORT]:   [1093|YamboWorkflow|can_continue]: the workflow continues with a yambo calculation\n",
      "2024-02-19 16:02:18 [451 | REPORT]:   [1093|YamboWorkflow|perform_next]: performing a yambo calculation\n",
      "2024-02-19 16:02:18 [452 | REPORT]:   [1093|YamboWorkflow|perform_next]: updating yambo parameters to parse more results\n",
      "2024-02-19 16:02:18 [453 | REPORT]:   [1093|YamboWorkflow|perform_next]: {'dft_predicted': 'semiconductor/insulator', 'valence': 4, 'conduction': 5, 'number_of_kpoints': 8, 'nscf_gap_eV': 0.665, 'homo_k': 1, 'lumo_k': 7, 'gap_type': 'indirect', 'gap_': [[1, 1, 4, 4], [7, 7, 5, 5]], 'soc': False, 'magnetic_calculation': False}\n",
      "2024-02-19 16:02:19 [454 | REPORT]:     [1095|YamboRestart|run_process]: launching YamboCalculation<1106> iteration #1\n",
      "2024-02-19 16:02:20 [455 | REPORT]:     [1097|YamboRestart|run_process]: launching YamboCalculation<1107> iteration #1\n",
      "2024-02-19 16:02:21 [456 | REPORT]:     [1099|YamboRestart|run_process]: launching YamboCalculation<1108> iteration #1\n",
      "2024-02-19 16:02:22 [457 | REPORT]:     [1101|YamboRestart|run_process]: launching YamboCalculation<1109> iteration #1\n",
      "2024-02-19 16:02:23 [458 | REPORT]:     [1103|YamboRestart|run_process]: launching YamboCalculation<1110> iteration #1\n",
      "2024-02-19 16:02:24 [459 | REPORT]:     [1105|YamboRestart|run_process]: launching YamboCalculation<1111> iteration #1\n",
      "2024-02-19 16:03:01 [460 | REPORT]:     [1095|YamboRestart|results]: work chain completed after 1 iterations\n",
      "2024-02-19 16:03:01 [461 | REPORT]:     [1095|YamboRestart|on_terminated]: remote folders will not be cleaned\n",
      "2024-02-19 16:03:02 [462 | REPORT]:   [1083|YamboWorkflow|can_continue]: workflow is finished\n",
      "2024-02-19 16:03:02 [463 | REPORT]:   [1083|YamboWorkflow|post_processing_needed]: no post processing needed\n",
      "2024-02-19 16:03:02 [464 | REPORT]:   [1083|YamboWorkflow|report_wf]: PARSED: {'gap_': 0.9176304896116256, 'homo': -0.01849563121199608, 'lumo': 0.8991348583996296, 'gap_dft': 0.6646806137740612, 'homo_dft': 0.0, 'lumo_dft': 0.6646806137740612}\n",
      "2024-02-19 16:03:02 [465 | REPORT]:   [1083|YamboWorkflow|report_wf]: workflow completed successfully\n",
      "2024-02-19 16:03:44 [466 | REPORT]:     [1101|YamboRestart|results]: work chain completed after 1 iterations\n",
      "2024-02-19 16:03:44 [467 | REPORT]:     [1101|YamboRestart|on_terminated]: remote folders will not be cleaned\n",
      "2024-02-19 16:03:44 [468 | REPORT]:   [1089|YamboWorkflow|can_continue]: workflow is finished\n",
      "2024-02-19 16:03:44 [469 | REPORT]:   [1089|YamboWorkflow|post_processing_needed]: no post processing needed\n",
      "2024-02-19 16:03:45 [470 | REPORT]:   [1089|YamboWorkflow|report_wf]: PARSED: {'gap_': 1.1699283512651921, 'homo': -0.4132804880678654, 'lumo': 0.7566478631973267, 'gap_dft': 0.6646806137740612, 'homo_dft': 0.0, 'lumo_dft': 0.6646806137740612}\n",
      "2024-02-19 16:03:45 [471 | REPORT]:   [1089|YamboWorkflow|report_wf]: workflow completed successfully\n",
      "2024-02-19 16:03:47 [472 | REPORT]:     [1097|YamboRestart|results]: work chain completed after 1 iterations\n",
      "2024-02-19 16:03:47 [473 | REPORT]:     [1097|YamboRestart|on_terminated]: remote folders will not be cleaned\n",
      "2024-02-19 16:03:47 [474 | REPORT]:   [1085|YamboWorkflow|can_continue]: workflow is finished\n",
      "2024-02-19 16:03:47 [475 | REPORT]:   [1085|YamboWorkflow|post_processing_needed]: no post processing needed\n",
      "2024-02-19 16:03:48 [476 | REPORT]:   [1085|YamboWorkflow|report_wf]: PARSED: {'gap_': 0.9187747582852841, 'homo': -0.022464483720064165, 'lumo': 0.8963102745652199, 'gap_dft': 0.6646806137740612, 'homo_dft': 0.0, 'lumo_dft': 0.6646806137740612}\n",
      "2024-02-19 16:03:48 [477 | REPORT]:   [1085|YamboWorkflow|report_wf]: workflow completed successfully\n",
      "2024-02-19 16:03:59 [478 | REPORT]:     [1099|YamboRestart|results]: work chain completed after 1 iterations\n",
      "2024-02-19 16:03:59 [479 | REPORT]:     [1099|YamboRestart|on_terminated]: remote folders will not be cleaned\n",
      "2024-02-19 16:03:59 [480 | REPORT]:   [1087|YamboWorkflow|can_continue]: workflow is finished\n",
      "2024-02-19 16:03:59 [481 | REPORT]:   [1087|YamboWorkflow|post_processing_needed]: no post processing needed\n",
      "2024-02-19 16:04:00 [482 | REPORT]:   [1087|YamboWorkflow|report_wf]: PARSED: {'gap_': 1.2186745211482048, 'homo': -0.465156163841486, 'lumo': 0.7535183573067189, 'gap_dft': 0.6646806137740612, 'homo_dft': 0.0, 'lumo_dft': 0.6646806137740612}\n",
      "2024-02-19 16:04:00 [483 | REPORT]:   [1087|YamboWorkflow|report_wf]: workflow completed successfully\n",
      "2024-02-19 16:04:39 [484 | REPORT]:     [1103|YamboRestart|results]: work chain completed after 1 iterations\n",
      "2024-02-19 16:04:39 [485 | REPORT]:     [1103|YamboRestart|on_terminated]: remote folders will not be cleaned\n",
      "2024-02-19 16:04:39 [486 | REPORT]:   [1091|YamboWorkflow|can_continue]: workflow is finished\n",
      "2024-02-19 16:04:40 [487 | REPORT]:   [1091|YamboWorkflow|post_processing_needed]: no post processing needed\n",
      "2024-02-19 16:04:40 [488 | REPORT]:   [1091|YamboWorkflow|report_wf]: PARSED: {'gap_': 1.1310248382866384, 'homo': -0.3525945111811161, 'lumo': 0.7784303271055222, 'gap_dft': 0.6646806137740612, 'homo_dft': 0.0, 'lumo_dft': 0.6646806137740612}\n",
      "2024-02-19 16:04:40 [489 | REPORT]:   [1091|YamboWorkflow|report_wf]: workflow completed successfully\n",
      "2024-02-19 16:05:24 [490 | REPORT]:     [1105|YamboRestart|results]: work chain completed after 1 iterations\n",
      "2024-02-19 16:05:24 [491 | REPORT]:     [1105|YamboRestart|on_terminated]: remote folders will not be cleaned\n",
      "2024-02-19 16:05:25 [492 | REPORT]:   [1093|YamboWorkflow|can_continue]: workflow is finished\n",
      "2024-02-19 16:05:25 [493 | REPORT]:   [1093|YamboWorkflow|post_processing_needed]: no post processing needed\n",
      "2024-02-19 16:05:25 [494 | REPORT]:   [1093|YamboWorkflow|report_wf]: PARSED: {'gap_': 1.2272974898278715, 'homo': -0.5066717883706093, 'lumo': 0.7206257014572621, 'gap_dft': 0.6646806137740612, 'homo_dft': 0.0, 'lumo_dft': 0.6646806137740612}\n",
      "2024-02-19 16:05:25 [495 | REPORT]:   [1093|YamboWorkflow|report_wf]: workflow completed successfully\n",
      "2024-02-19 16:05:26 [496 | REPORT]: [890|YamboConvergence|data_analysis]: Data analysis, we will try to parse some result and decide what next.\n",
      "2024-02-19 16:05:27 [497 | REPORT]: [890|YamboConvergence|data_analysis]:    FFTGvecs  BndsRnXp  ...      gap_                                  uuid\n",
      "0        21       100  ...  0.917630  96305916-b185-4cd3-b165-bae7852ee43d\n",
      "1        21       450  ...  0.918775  39435ccb-82e9-45ad-ae19-6b470634cac0\n",
      "2        21       100  ...  1.218675  7204d559-00cd-4963-8c1d-09560c540cc7\n",
      "3        21       200  ...  1.169928  41871b1a-2dbc-4bdd-9371-8c75cd17df91\n",
      "4        21       350  ...  1.131025  42c84587-ecfc-4483-ad9f-326c7607630b\n",
      "5        21       450  ...  1.227297  259d2433-be76-476d-9357-b4afae14505b\n",
      "\n",
      "[6 rows x 6 columns]\n",
      "2024-02-19 16:05:33 [498 | REPORT]: [890|YamboConvergence|data_analysis]: results ['gap_']\n",
      ":{'gap_': array([0.91763049, 0.91877476, 1.21867452, 1.16992835, 1.13102484,\n",
      "       1.22729749])}\n",
      "2024-02-19 16:05:33 [499 | REPORT]: [890|YamboConvergence|data_analysis]: HINTS: {'BndsRnXp': 100, 'NGsBlkXp': 5.0, 'gap_': 1.1157138753462554, 'already_computed': False, 'GbndRnge': 100, 'E_ref': 1.2630336259818509, 'new_grid': False, 'extrapolation': 1.27100881884356, 'extrapolation_units': 'eV'}\n",
      "2024-02-19 16:05:33 [500 | REPORT]: [890|YamboConvergence|data_analysis]: Success on ['BndsRnXp', 'GbndRnge', 'NGsBlkXp'] not reached yet in 12 calculations\n",
      "2024-02-19 16:05:33 [501 | REPORT]: [890|YamboConvergence|data_analysis]: {'BndsRnXp': 100, 'NGsBlkXp': 5.0, 'gap_': 1.1157138753462554, 'already_computed': False, 'GbndRnge': 100, 'E_ref': 1.2630336259818509, 'new_grid': False, 'extrapolation': 1.27100881884356, 'extrapolation_units': 'eV'}\n",
      "2024-02-19 16:05:33 [502 | REPORT]: [890|YamboConvergence|has_to_continue]: Still iteration on ['BndsRnXp', 'GbndRnge', 'NGsBlkXp']\n",
      "2024-02-19 16:05:33 [503 | REPORT]: [890|YamboConvergence|pre_needed]: {'FFTGvecs': [21], 'BndsRnXp': [50, 400, 50, 150, 300, 400], 'NGsBlkXp': [2, 2, 8, 6, 4, 8], 'GbndRnge': [50, 400, 50, 150, 300, 400]}\n",
      "2024-02-19 16:05:34 [504 | REPORT]: [890|YamboConvergence|pre_needed]: ['GW bands are: 450', 'found scf inputs from parent', 'found nscf inputs from parent\\n', 'found nscf inputs from parent\\n and setting nbnd of the nscf calculation to b = 450']\n",
      "2024-02-19 16:05:38 [505 | REPORT]: [890|YamboConvergence|next_step]: New parameters are: {'BndsRnXp': 100, 'GbndRnge': 100, 'NGsBlkXp': 5.0}\n",
      "2024-02-19 16:05:40 [506 | REPORT]:   [1155|YamboWorkflow|start_workflow]:  workflow initilization step completed.\n",
      "2024-02-19 16:05:41 [507 | REPORT]:   [1155|YamboWorkflow|can_continue]: the workflow continues with a yambo calculation\n",
      "2024-02-19 16:05:41 [508 | REPORT]:   [1155|YamboWorkflow|perform_next]: performing a yambo calculation\n",
      "2024-02-19 16:05:41 [509 | REPORT]:   [1155|YamboWorkflow|perform_next]: updating yambo parameters to parse more results\n",
      "2024-02-19 16:05:41 [510 | REPORT]:   [1155|YamboWorkflow|perform_next]: {'dft_predicted': 'semiconductor/insulator', 'valence': 4, 'conduction': 5, 'number_of_kpoints': 8, 'nscf_gap_eV': 0.665, 'homo_k': 1, 'lumo_k': 7, 'gap_type': 'indirect', 'gap_': [[1, 1, 4, 4], [7, 7, 5, 5]], 'soc': False, 'magnetic_calculation': False}\n",
      "2024-02-19 16:05:42 [511 | REPORT]:     [1157|YamboRestart|run_process]: launching YamboCalculation<1158> iteration #1\n",
      "2024-02-19 16:05:58 [512 | REPORT]:     [1157|YamboRestart|results]: work chain completed after 1 iterations\n",
      "2024-02-19 16:05:58 [513 | REPORT]:     [1157|YamboRestart|on_terminated]: remote folders will not be cleaned\n",
      "2024-02-19 16:05:58 [514 | REPORT]:   [1155|YamboWorkflow|can_continue]: workflow is finished\n",
      "2024-02-19 16:05:58 [515 | REPORT]:   [1155|YamboWorkflow|post_processing_needed]: no post processing needed\n",
      "2024-02-19 16:05:59 [516 | REPORT]:   [1155|YamboWorkflow|report_wf]: PARSED: {'gap_': 1.1291255631387234, 'homo': -0.33853484711647036, 'lumo': 0.7905907160222531, 'gap_dft': 0.6646806137740612, 'homo_dft': 0.0, 'lumo_dft': 0.6646806137740612}\n",
      "2024-02-19 16:05:59 [517 | REPORT]:   [1155|YamboWorkflow|report_wf]: workflow completed successfully\n",
      "2024-02-19 16:06:00 [518 | REPORT]: [890|YamboConvergence|data_analysis]: Data analysis, we will try to parse some result and decide what next.\n",
      "2024-02-19 16:06:00 [519 | REPORT]: [890|YamboConvergence|data_analysis]:    FFTGvecs  BndsRnXp  ...      gap_                                  uuid\n",
      "0        21       100  ...  1.129126  5e3dd853-4932-4cb8-a023-dc319b4e5e81\n",
      "\n",
      "[1 rows x 6 columns]\n",
      "2024-02-19 16:06:04 [520 | REPORT]: [890|YamboConvergence|data_analysis]: results ['gap_']\n",
      ":{'gap_': array([0.91763049, 0.91877476, 1.21867452, 1.16992835, 1.13102484,\n",
      "       1.22729749, 1.12912556])}\n",
      "2024-02-19 16:06:04 [521 | REPORT]: [890|YamboConvergence|data_analysis]: HINTS: {'BndsRnXp': 100.0, 'NGsBlkXp': 5.0, 'gap_': 1.1291255631387, 'already_computed': True, 'GbndRnge': 100.0, 'E_ref': 1.262900004647505, 'new_grid': False, 'extrapolation': 1.2701566422540151, 'extrapolation_units': 'eV'}\n",
      "2024-02-19 16:06:04 [522 | REPORT]: [890|YamboConvergence|data_analysis]: Success, updating the history... \n",
      "2024-02-19 16:06:04 [523 | REPORT]: [890|YamboConvergence|data_analysis]: Success on ['BndsRnXp', 'GbndRnge', 'NGsBlkXp'] reached in 3 calculations, the result is [[1.12912556]]\n",
      "2024-02-19 16:06:04 [524 | REPORT]: [890|YamboConvergence|has_to_continue]: Workflow finished\n",
      "2024-02-19 16:06:05 [525 | REPORT]: [890|YamboConvergence|report_wf]: Final step. It is True that the workflow was successful\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "!verdi process report {run.pk}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0c466b05",
   "metadata": {},
   "source": [
    "# Output analysis.\n",
    "\n",
    "suppose that your calculation completed successfully, then you can access the outputs via the output method of the run instance: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "bff7942e",
   "metadata": {},
   "outputs": [],
   "source": [
    "run = orm.load_node(890)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "bf076ce6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "run.is_finished_ok"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f72ee3c6",
   "metadata": {},
   "source": [
    "The converged parameters can be obtained via the \"infos\" output Dict:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "0cc0b992",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'gap_': 1.1291255631387,\n",
       " 'E_ref': 1.2629000046475,\n",
       " 'BndsRnXp': 100.0,\n",
       " 'FFTGvecs': 21,\n",
       " 'GbndRnge': 100.0,\n",
       " 'NGsBlkXp': 5.0,\n",
       " 'extrapolation': 1.2710088188436}"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "run.outputs.infos.get_dict()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9afc2076",
   "metadata": {},
   "source": [
    "The full convergence history can be visualized in a table form using pandas:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "357b8819",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "815c0e01",
   "metadata": {},
   "outputs": [],
   "source": [
    "history = run.outputs.history.get_dict()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "ad366c53",
   "metadata": {},
   "outputs": [],
   "source": [
    "history_table = pd.DataFrame(history)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "f11066f5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>gap_</th>\n",
       "      <th>uuid</th>\n",
       "      <th>failed</th>\n",
       "      <th>useful</th>\n",
       "      <th>BndsRnXp</th>\n",
       "      <th>FFTGvecs</th>\n",
       "      <th>GbndRnge</th>\n",
       "      <th>NGsBlkXp</th>\n",
       "      <th>global_step</th>\n",
       "      <th>parameters_studied</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.052619</td>\n",
       "      <td>86672fa7-1f72-4015-969f-541ae4397d74</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>150</td>\n",
       "      <td>21</td>\n",
       "      <td>150</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1</td>\n",
       "      <td>FFTGvecs</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.052237</td>\n",
       "      <td>104611a0-dc81-4176-ba36-0325cf036638</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>150</td>\n",
       "      <td>37</td>\n",
       "      <td>150</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2</td>\n",
       "      <td>FFTGvecs</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.052241</td>\n",
       "      <td>bdae7620-16d8-45ac-8a5f-9853a89b4087</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>150</td>\n",
       "      <td>45</td>\n",
       "      <td>150</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3</td>\n",
       "      <td>FFTGvecs</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.052227</td>\n",
       "      <td>e9b5696d-545f-4f32-b99e-a0806e42d33e</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>150</td>\n",
       "      <td>58</td>\n",
       "      <td>150</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4</td>\n",
       "      <td>FFTGvecs</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.921310</td>\n",
       "      <td>38640663-13b0-44a6-8442-6d2c06bb4848</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>50</td>\n",
       "      <td>21</td>\n",
       "      <td>50</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5</td>\n",
       "      <td>BndsRnXp, GbndRnge, NGsBlkXp</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.919394</td>\n",
       "      <td>a0cc1686-6ce2-474b-bc09-43b8d3e35459</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>400</td>\n",
       "      <td>21</td>\n",
       "      <td>400</td>\n",
       "      <td>2.0</td>\n",
       "      <td>6</td>\n",
       "      <td>BndsRnXp, GbndRnge, NGsBlkXp</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1.199155</td>\n",
       "      <td>10616881-430a-4dd7-9bf6-ddbfd4aaa433</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>50</td>\n",
       "      <td>21</td>\n",
       "      <td>50</td>\n",
       "      <td>8.0</td>\n",
       "      <td>7</td>\n",
       "      <td>BndsRnXp, GbndRnge, NGsBlkXp</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1.131704</td>\n",
       "      <td>17ee124c-5152-46bb-9bac-4caed41e8dc1</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>150</td>\n",
       "      <td>21</td>\n",
       "      <td>150</td>\n",
       "      <td>6.0</td>\n",
       "      <td>8</td>\n",
       "      <td>BndsRnXp, GbndRnge, NGsBlkXp</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1.053912</td>\n",
       "      <td>8a7e983d-f690-4bd1-9c55-5763d30115ea</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>300</td>\n",
       "      <td>21</td>\n",
       "      <td>300</td>\n",
       "      <td>4.0</td>\n",
       "      <td>9</td>\n",
       "      <td>BndsRnXp, GbndRnge, NGsBlkXp</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1.227951</td>\n",
       "      <td>38ab7c55-3538-4518-a4f6-bb35e4f8af78</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>400</td>\n",
       "      <td>21</td>\n",
       "      <td>400</td>\n",
       "      <td>8.0</td>\n",
       "      <td>10</td>\n",
       "      <td>BndsRnXp, GbndRnge, NGsBlkXp</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.917630</td>\n",
       "      <td>96305916-b185-4cd3-b165-bae7852ee43d</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>100</td>\n",
       "      <td>21</td>\n",
       "      <td>100</td>\n",
       "      <td>3.0</td>\n",
       "      <td>11</td>\n",
       "      <td>BndsRnXp, GbndRnge, NGsBlkXp</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.918775</td>\n",
       "      <td>39435ccb-82e9-45ad-ae19-6b470634cac0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>450</td>\n",
       "      <td>21</td>\n",
       "      <td>450</td>\n",
       "      <td>3.0</td>\n",
       "      <td>12</td>\n",
       "      <td>BndsRnXp, GbndRnge, NGsBlkXp</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>1.218675</td>\n",
       "      <td>7204d559-00cd-4963-8c1d-09560c540cc7</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>100</td>\n",
       "      <td>21</td>\n",
       "      <td>100</td>\n",
       "      <td>9.0</td>\n",
       "      <td>13</td>\n",
       "      <td>BndsRnXp, GbndRnge, NGsBlkXp</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>1.169928</td>\n",
       "      <td>41871b1a-2dbc-4bdd-9371-8c75cd17df91</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>200</td>\n",
       "      <td>21</td>\n",
       "      <td>200</td>\n",
       "      <td>7.0</td>\n",
       "      <td>14</td>\n",
       "      <td>BndsRnXp, GbndRnge, NGsBlkXp</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>1.131025</td>\n",
       "      <td>42c84587-ecfc-4483-ad9f-326c7607630b</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>350</td>\n",
       "      <td>21</td>\n",
       "      <td>350</td>\n",
       "      <td>5.0</td>\n",
       "      <td>15</td>\n",
       "      <td>BndsRnXp, GbndRnge, NGsBlkXp</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>1.227297</td>\n",
       "      <td>259d2433-be76-476d-9357-b4afae14505b</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>450</td>\n",
       "      <td>21</td>\n",
       "      <td>450</td>\n",
       "      <td>9.0</td>\n",
       "      <td>16</td>\n",
       "      <td>BndsRnXp, GbndRnge, NGsBlkXp</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>1.129126</td>\n",
       "      <td>5e3dd853-4932-4cb8-a023-dc319b4e5e81</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>100</td>\n",
       "      <td>21</td>\n",
       "      <td>100</td>\n",
       "      <td>5.0</td>\n",
       "      <td>17</td>\n",
       "      <td>BndsRnXp, GbndRnge, NGsBlkXp</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        gap_                                  uuid  failed  useful  BndsRnXp   \n",
       "0   1.052619  86672fa7-1f72-4015-969f-541ae4397d74   False   False       150  \\\n",
       "1   1.052237  104611a0-dc81-4176-ba36-0325cf036638   False   False       150   \n",
       "2   1.052241  bdae7620-16d8-45ac-8a5f-9853a89b4087   False   False       150   \n",
       "3   1.052227  e9b5696d-545f-4f32-b99e-a0806e42d33e   False   False       150   \n",
       "4   0.921310  38640663-13b0-44a6-8442-6d2c06bb4848   False   False        50   \n",
       "5   0.919394  a0cc1686-6ce2-474b-bc09-43b8d3e35459   False   False       400   \n",
       "6   1.199155  10616881-430a-4dd7-9bf6-ddbfd4aaa433   False   False        50   \n",
       "7   1.131704  17ee124c-5152-46bb-9bac-4caed41e8dc1   False   False       150   \n",
       "8   1.053912  8a7e983d-f690-4bd1-9c55-5763d30115ea   False   False       300   \n",
       "9   1.227951  38ab7c55-3538-4518-a4f6-bb35e4f8af78   False   False       400   \n",
       "10  0.917630  96305916-b185-4cd3-b165-bae7852ee43d   False   False       100   \n",
       "11  0.918775  39435ccb-82e9-45ad-ae19-6b470634cac0   False   False       450   \n",
       "12  1.218675  7204d559-00cd-4963-8c1d-09560c540cc7   False   False       100   \n",
       "13  1.169928  41871b1a-2dbc-4bdd-9371-8c75cd17df91   False   False       200   \n",
       "14  1.131025  42c84587-ecfc-4483-ad9f-326c7607630b   False   False       350   \n",
       "15  1.227297  259d2433-be76-476d-9357-b4afae14505b   False   False       450   \n",
       "16  1.129126  5e3dd853-4932-4cb8-a023-dc319b4e5e81   False    True       100   \n",
       "\n",
       "    FFTGvecs  GbndRnge  NGsBlkXp  global_step            parameters_studied  \n",
       "0         21       150       4.0            1                      FFTGvecs  \n",
       "1         37       150       4.0            2                      FFTGvecs  \n",
       "2         45       150       4.0            3                      FFTGvecs  \n",
       "3         58       150       4.0            4                      FFTGvecs  \n",
       "4         21        50       2.0            5  BndsRnXp, GbndRnge, NGsBlkXp  \n",
       "5         21       400       2.0            6  BndsRnXp, GbndRnge, NGsBlkXp  \n",
       "6         21        50       8.0            7  BndsRnXp, GbndRnge, NGsBlkXp  \n",
       "7         21       150       6.0            8  BndsRnXp, GbndRnge, NGsBlkXp  \n",
       "8         21       300       4.0            9  BndsRnXp, GbndRnge, NGsBlkXp  \n",
       "9         21       400       8.0           10  BndsRnXp, GbndRnge, NGsBlkXp  \n",
       "10        21       100       3.0           11  BndsRnXp, GbndRnge, NGsBlkXp  \n",
       "11        21       450       3.0           12  BndsRnXp, GbndRnge, NGsBlkXp  \n",
       "12        21       100       9.0           13  BndsRnXp, GbndRnge, NGsBlkXp  \n",
       "13        21       200       7.0           14  BndsRnXp, GbndRnge, NGsBlkXp  \n",
       "14        21       350       5.0           15  BndsRnXp, GbndRnge, NGsBlkXp  \n",
       "15        21       450       9.0           16  BndsRnXp, GbndRnge, NGsBlkXp  \n",
       "16        21       100       5.0           17  BndsRnXp, GbndRnge, NGsBlkXp  "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "history_table"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f83760c6",
   "metadata": {},
   "source": [
    "The converged calculations can be easily observed using:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "bffc7445",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>gap_</th>\n",
       "      <th>uuid</th>\n",
       "      <th>failed</th>\n",
       "      <th>useful</th>\n",
       "      <th>BndsRnXp</th>\n",
       "      <th>FFTGvecs</th>\n",
       "      <th>GbndRnge</th>\n",
       "      <th>NGsBlkXp</th>\n",
       "      <th>global_step</th>\n",
       "      <th>parameters_studied</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>1.129126</td>\n",
       "      <td>5e3dd853-4932-4cb8-a023-dc319b4e5e81</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>100</td>\n",
       "      <td>21</td>\n",
       "      <td>100</td>\n",
       "      <td>5.0</td>\n",
       "      <td>17</td>\n",
       "      <td>BndsRnXp, GbndRnge, NGsBlkXp</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        gap_                                  uuid  failed  useful  BndsRnXp   \n",
       "16  1.129126  5e3dd853-4932-4cb8-a023-dc319b4e5e81   False    True       100  \\\n",
       "\n",
       "    FFTGvecs  GbndRnge  NGsBlkXp  global_step            parameters_studied  \n",
       "16        21       100       5.0           17  BndsRnXp, GbndRnge, NGsBlkXp  "
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "history_table[history_table['useful']==True]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "53a3541b",
   "metadata": {},
   "source": [
    "Result on the convergence path can be plotted. Here below a code snippet to print our results: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "955638a5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f8047236970>"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# following: https://matplotlib.org/stable/gallery/images_contours_and_fields/irregulardatagrid.html\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "b_G_history = history_table[history_table['parameters_studied']==\"BndsRnXp, GbndRnge, NGsBlkXp\"]\n",
    "\n",
    "np.random.seed(19680801)\n",
    "npts = 200\n",
    "ngridx = 100\n",
    "ngridy = 200\n",
    "x = b_G_history.NGsBlkXp\n",
    "y = b_G_history.BndsRnXp\n",
    "z = b_G_history.gap_\n",
    "\n",
    "fig, ax1 = plt.subplots()\n",
    "\n",
    "# -----------------------\n",
    "# Interpolation on a grid\n",
    "# -----------------------\n",
    "# A contour plot of irregularly spaced data coordinates\n",
    "# via interpolation on a grid.\n",
    "\n",
    "# Create grid values first.\n",
    "xi = np.linspace(2,9, ngridx)\n",
    "yi = np.linspace(50,450, ngridy)\n",
    "\n",
    "from scipy.interpolate import griddata\n",
    "zi = griddata((x, y), z, (xi[None, :], yi[:, None]), method='cubic')\n",
    "\n",
    "ax1.contour(xi, yi, zi, levels=14, linewidths=0.5, colors='k')\n",
    "cntr1 = ax1.contourf(xi, yi, zi, levels=14, cmap=\"RdBu_r\")\n",
    "\n",
    "fig.colorbar(cntr1, ax=ax1, label=\"gap (eV)\")\n",
    "ax1.plot(x[:6], y[:6], 'o', ms=6, label=\"grid #1\")\n",
    "ax1.plot(x[6:12], y[6:12], 'o', ms=6, label=\"grid #2\")\n",
    "ax1.plot(x[12], y[12], 'ro', ms=10, label=\"E$_{conv}$ = \"+f\"{np.round(z[12],2)} eV\")\n",
    "\n",
    "ax1.set(xlim=(1,10), ylim=(10,500))\n",
    "ax1.set_title('Convergence of Silicon quasiparticle gap')\n",
    "ax1.set_xlabel(\"NGsBlkXp\")\n",
    "ax1.set_ylabel(\"BndsRnXp\")\n",
    "\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e9f8e7e4",
   "metadata": {},
   "source": [
    "The interpolation of the convergence space, as done above, is just for having an idea of the convergence region; indeed, the accuracy of the interpolation is not completly reliable with such few points."
   ]
  }
 ],
 "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": "3.9.16"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}