pyironflow_demo.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "e685173d-686c-489a-bbb4-eedef29fbba3",
   "metadata": {},
   "source": [
    "# PyironFlow demo"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e10399f6-5f0c-426c-9dfc-8ecc2fc59354",
   "metadata": {},
   "source": [
    "Uncomment the following line if you have edited the .js files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "6b50621c-5611-4869-a758-1404ec9ea67a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1G\u001b[0K⠙\u001b[1G\u001b[0K\n",
      "  \u001b[37mstatic/\u001b[0m\u001b[1mwidget.js\u001b[0m    \u001b[33m1.7mb ⚠️\u001b[0m\n",
      "  \u001b[37mstatic/\u001b[0m\u001b[1mwidget.css\u001b[0m  \u001b[36m15.1kb\u001b[0m\n",
      "\n",
      "⚡ \u001b[32mDone in 155ms\u001b[0m\n",
      "\u001b[1G\u001b[0K⠙\u001b[1G\u001b[0K"
     ]
    }
   ],
   "source": [
    "!npx esbuild js/widget.jsx --minify --format=esm --bundle --outdir=static"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "3be83a1e-f2b0-4d54-91b2-34b39a903c65",
   "metadata": {},
   "outputs": [],
   "source": [
    "%config IPCompleter.evaluation='unsafe'\n",
    "\n",
    "import sys\n",
    "from pathlib import Path\n",
    "sys.path.insert(0, str(Path(Path.cwd()).parent) + '/pyiron_nodes')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5e556f67-d269-47be-9d23-bb50710b50c4",
   "metadata": {},
   "source": [
    "## Elastic constants"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "dbc017d4-7c5f-48df-a105-e75904a4ed53",
   "metadata": {},
   "outputs": [],
   "source": [
    "from pyiron_workflow import Workflow   \n",
    "\n",
    "import pyiron_nodes as pn\n",
    "\n",
    "wf = Workflow('compute_elastic_constants')\n",
    "wf.engine = pn.atomistic.engine.ase.M3GNet()\n",
    "wf.bulk = pn.atomistic.structure.build.Bulk('Pb', cubic=True)\n",
    "wf.elastic = pn.atomistic.property.elastic.ElasticConstants(structure=wf.bulk, engine=wf.engine) #, parameters=parameters)\n",
    "\n",
    "# wf.elastic.pull()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "56c26ff4-f7a8-46a6-a99c-40256af96174",
   "metadata": {},
   "outputs": [],
   "source": [
    "wf.draw();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "4a3c6b40-4426-4479-a039-686cce4f923e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wf.bulk.position = (10, 0)\n",
    "'position' in dir(wf.bulk)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "813d8693-da0b-42ab-9534-0818cdf34411",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "9e6831c3f1014224a14be92d513a00f0",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(Accordion(children=(Tree(nodes=(Node(icon_style='warning', name='databases'), Node(icon_style='…"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from python.pyironflow import PyironFlow\n",
    "\n",
    "pf = PyironFlow([wf])\n",
    "pf.gui"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3c7cf4ac-7bdc-4cdf-9d7b-2d22ec4c5297",
   "metadata": {},
   "source": [
    "### Some extra features"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "17dd3816-b2bb-45e2-873a-ff49f4ce1fee",
   "metadata": {},
   "source": [
    "#### Get the nodes from the gui"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "fc5d0e67-bb6f-493b-a59d-2234522af360",
   "metadata": {},
   "outputs": [],
   "source": [
    "import json\n",
    "json.loads(pf.wf_widgets[0].gui.nodes);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "55fd11ed-1567-4a12-8054-c557cf289725",
   "metadata": {},
   "source": [
    "#### Show the workflow widget from tab_0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "a6047623-934c-4339-953c-42ec4728f6e7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "a1c8a8650c3e43419c138a5ba298486a",
       "version_major": 2,
       "version_minor": 1
      },
      "text/plain": [
       "ReactFlowWidget(commands='run: elastic - 1725825070119', edges='[{\"source\": \"engine\", \"sourceHandle\": \"engine\"…"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pf.wf_widgets[0].gui"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0d46cdb7-cc6a-45ea-84bc-11537afe43eb",
   "metadata": {},
   "source": [
    "#### Get the current workflow from the gui and visualize it (test completeness of switching between graphical and programmatic representation)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "9e7a9e47-ca0d-4976-b257-3533f1c33957",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "1ff5151e89ab4d14856abbd143a8b6f0",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(Accordion(children=(Tree(nodes=(Node(icon_style='warning', name='databases'), Node(icon_style='…"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wf = pf.get_workflow()\n",
    "\n",
    "pf = PyironFlow([wf])\n",
    "pf.gui"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6b478083-a50a-42c9-8556-a0fb556f9b4d",
   "metadata": {},
   "source": [
    "## Phonons"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "6e64336b-18c5-444e-a8a1-24357a2b46f2",
   "metadata": {},
   "outputs": [],
   "source": [
    "from pyiron_workflow import Workflow   \n",
    "\n",
    "import pyiron_nodes as pn\n",
    "\n",
    "wf = Workflow('phonons')\n",
    "wf.engine = pn.atomistic.engine.ase.M3GNet()\n",
    "wf.bulk = pn.atomistic.structure.build.CubicBulkCell('Pb', cell_size=3)\n",
    "wf.phonopy = pn.atomistic.property.phonons.CreatePhonopy(structure=wf.bulk, engine=wf.engine) #, parameters=parameters)\n",
    "wf.dos = pn.atomistic.property.phonons.GetTotalDos(phonopy=wf.phonopy.outputs.phonopy)\n",
    "\n",
    "# wf.run()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "051bed2f-6100-4ee4-95e5-f70aa38f56cb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "e3b46db548d645f183ba719daa5e6a1a",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(Accordion(children=(Tree(nodes=(Node(icon_style='warning', name='databases'), Node(icon_style='…"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pf = PyironFlow([wf])\n",
    "pf.gui"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b7de4906-4a23-4d40-8913-755e1d9ae862",
   "metadata": {},
   "source": [
    "## Built Lammps workflow from scratch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "a7f0ced8-a876-421c-b384-500fcc946103",
   "metadata": {},
   "outputs": [],
   "source": [
    "from pyiron_workflow import Workflow   \n",
    "import pyiron_nodes as pn\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "2846592e-3e95-4064-8c9f-fa21a455d3a2",
   "metadata": {},
   "outputs": [],
   "source": [
    "wf = Workflow('Lammps')\n",
    "wf.structure = pn.atomistic.structure.build.Bulk('Al', cubic=True)\n",
    "wf.repeat = pn.atomistic.structure.transform.Repeat(structure=wf.structure, repeat_scalar=3)\n",
    "\n",
    "wf.calculator = pn.lammps.CalcMD() # temperature=300, n_ionic_steps=10_000)\n",
    "wf.potential = pn.atomistic.engine.lammps.Potential(\n",
    "    structure=wf.structure, name='1995--Angelo-J-E--Ni-Al-H--LAMMPS--ipr1'\n",
    ")\n",
    "\n",
    "wf.init_lammps = pn.atomistic.engine.lammps.InitLammps(\n",
    "        structure=wf.repeat,\n",
    "        potential=wf.potential,\n",
    "        calculator=wf.calculator,\n",
    "        working_directory=\"test2\",\n",
    "    )\n",
    "\n",
    "wf.shell = pn.atomistic.engine.lammps.Shell(\n",
    "        # command=ExecutablePathResolver(module=\"lammps\", code=\"lammps\").path(),\n",
    "        working_directory=wf.init_lammps,\n",
    "    )\n",
    "\n",
    "wf.ParseLogFile = pn.atomistic.engine.lammps.ParseLogFile(\n",
    "    log_file=wf.shell.outputs.log\n",
    ")\n",
    "wf.ParseDumpFile = pn.atomistic.engine.lammps.ParseDumpFile(\n",
    "    dump_file=wf.shell.outputs.dump\n",
    ")\n",
    "wf.Collect = pn.atomistic.engine.lammps.Collect(\n",
    "    out_dump=wf.ParseDumpFile.outputs.dump,\n",
    "    out_log=wf.ParseLogFile.outputs.log,\n",
    "    calc_mode='md',\n",
    ")\n",
    "\n",
    "wf.get_energy_pot = pn.atomistic.engine.lammps.GetEnergyPot(generic=wf.Collect)\n",
    "\n",
    "out = wf.run()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "64a70d90-410c-4079-a7c4-b5d123e06306",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-362.87999968, -358.5129855 , -358.4579939 , -358.31296039,\n",
       "       -358.55784106, -359.02478623, -358.31887295, -358.83137511,\n",
       "       -358.65856012])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wf.get_energy_pot.pull()[0:-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "10975d1d-e834-40e3-9797-a94b7b5c31f8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "e54a5b5c5a9d4b228e2494b68dbf158f",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(Accordion(children=(Tree(nodes=(Node(icon_style='warning', name='databases'), Node(icon_style='…"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from python.pyironflow import PyironFlow\n",
    "\n",
    "pf = PyironFlow([wf])\n",
    "pf.gui"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f1cfc2a4-9251-4b26-8a04-70638cee8863",
   "metadata": {},
   "source": [
    "### Use Lammps Macro"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "65bb2de4-2d11-495b-9eab-87c4369f448f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/json": {
       "checkpoint": "null",
       "executor": "null",
       "failed": "false",
       "future": "null",
       "inputs": {
        "delta_press": {
         "connections": "[]",
         "default": "null",
         "owner": "\"/InputCalcMD\"",
         "strict_hints": "true",
         "type_hint": "\"Optional[float]\"",
         "value": "null"
        },
        "delta_temp": {
         "connections": "[]",
         "default": "null",
         "owner": "\"/InputCalcMD\"",
         "strict_hints": "true",
         "type_hint": "\"Optional[float]\"",
         "value": "null"
        },
        "initial_temperature": {
         "connections": "[]",
         "default": "null",
         "owner": "\"/InputCalcMD\"",
         "strict_hints": "true",
         "type_hint": "\"Optional[float]\"",
         "value": "null"
        },
        "langevin": {
         "connections": "[]",
         "default": "false",
         "owner": "\"/InputCalcMD\"",
         "strict_hints": "true",
         "type_hint": "\"bool\"",
         "value": "false"
        },
        "n_ionic_steps": {
         "connections": "[]",
         "default": "10000",
         "owner": "\"/InputCalcMD\"",
         "strict_hints": "true",
         "type_hint": "\"int\"",
         "value": "10000"
        },
        "n_print": {
         "connections": "[]",
         "default": "100",
         "owner": "\"/InputCalcMD\"",
         "strict_hints": "true",
         "type_hint": "\"int\"",
         "value": "100"
        },
        "pressure": {
         "connections": "[]",
         "default": "null",
         "owner": "\"/InputCalcMD\"",
         "strict_hints": "true",
         "type_hint": "\"Optional[int | float]\"",
         "value": "null"
        },
        "pressure_damping_timescale": {
         "connections": "[]",
         "default": "1000.0",
         "owner": "\"/InputCalcMD\"",
         "strict_hints": "true",
         "type_hint": "\"Optional[int | float]\"",
         "value": "1000.0"
        },
        "seed": {
         "connections": "[]",
         "default": "null",
         "owner": "\"/InputCalcMD\"",
         "strict_hints": "true",
         "type_hint": "\"Optional[int]\"",
         "value": "null"
        },
        "temperature": {
         "connections": "[]",
         "default": "300",
         "owner": "\"/InputCalcMD\"",
         "strict_hints": "true",
         "type_hint": "\"Optional[int | float]\"",
         "value": "400"
        },
        "temperature_damping_timescale": {
         "connections": "[]",
         "default": "100.0",
         "owner": "\"/InputCalcMD\"",
         "strict_hints": "true",
         "type_hint": "\"Optional[int | float]\"",
         "value": "100.0"
        },
        "time_step": {
         "connections": "[]",
         "default": "1.0",
         "owner": "\"/InputCalcMD\"",
         "strict_hints": "true",
         "type_hint": "\"Optional[int | float]\"",
         "value": "1.0"
        },
        "tloop": {
         "connections": "[]",
         "default": "null",
         "owner": "\"/InputCalcMD\"",
         "strict_hints": "true",
         "type_hint": "\"Optional[float]\"",
         "value": "null"
        }
       },
       "outputs": {
        "dataclass": {
         "connections": "[]",
         "default": "\"NOT_DATA\"",
         "owner": "\"/InputCalcMD\"",
         "strict_hints": "true",
         "type_hint": "\"<class 'pyiron_nodes.atomistic.calculator.data.InputCalcMD.dataclass'>\"",
         "value": "\"NOT_DATA\""
        }
       },
       "running": "false",
       "signals": {
        "input": {
         "accumulate_and_run": {
          "connections": "[]",
          "owner": "\"/InputCalcMD\"",
          "received_signals": "\"set()\""
         },
         "run": {
          "connections": "[]",
          "owner": "\"/InputCalcMD\""
         }
        },
        "output": {
         "ran": {
          "connections": "[]",
          "owner": "\"/InputCalcMD\""
         }
        }
       }
      },
      "text/plain": [
       "<pyiron_nodes.atomistic.calculator.data.InputCalcMD at 0x15038be90>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from pyiron_nodes.atomistic.calculator.data import InputCalcMD\n",
    "InputCalcMD(temperature=400)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "abdb76ab-f294-4918-a649-23b8639cc67d",
   "metadata": {},
   "outputs": [],
   "source": [
    "from dataclasses import asdict\n",
    "inp_calc_md = pn.atomistic.calculator.data.InputCalcMD(temperature=700)\n",
    "\n",
    "# Workflow.create.transformer.dataclass_node(inp_calc_md.__class__, label='my_node')() # (**asdict(inp_calc_md))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "1389fde6-8b41-401a-9a13-cb5f726661fc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "{'plot__axis': None}"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wf = Workflow('lammps_macro')\n",
    "wf.bulk = pn.atomistic.structure.build.CubicBulkCell('Pb', cell_size=3)\n",
    "wf.inp_calc_md = pn.atomistic.calculator.data.InputCalcMD()\n",
    "wf.lammps = pn.atomistic.engine.lammps.Code(structure=wf.bulk, calculator=wf.inp_calc_md) \n",
    "wf.energies = pn.atomistic.engine.lammps.GetEnergyPot(generic=wf.lammps)\n",
    "wf.plot = pn.plotting.Plot(y=wf.energies)\n",
    "\n",
    "# wf.bulk.pull()\n",
    "# wf.lammps.Collect.pull() # works\n",
    "wf.run()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "1cec4223-ff48-4f68-8017-c9f8bb0dbdd1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "de60b8cb364a4f0085c879c56a10e8fd",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(Accordion(children=(Tree(nodes=(Node(icon_style='warning', name='databases'), Node(icon_style='…"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pf = PyironFlow([wf])\n",
    "pf.gui"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "3f6b5180-b532-416b-a522-e3225d26d7b4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# json.loads(pf.wf_widgets[0].gui.nodes)[1];\n",
    "\n",
    "from python.reactflow import get_node_types, _get_type_name, _get_generic_type\n",
    "import typing\n",
    "\n",
    "def get_node_types(node_io):\n",
    "    node_io_types = list()\n",
    "    for k in node_io.channel_dict:\n",
    "        type_hint = node_io[k].type_hint\n",
    "        # print (k, type(type_hint))\n",
    "        if isinstance(type_hint, typing._UnionGenericAlias):\n",
    "            # print (k, type_hint)\n",
    "            type_hint = _get_generic_type(type_hint)\n",
    "\n",
    "        node_io_types.append(_get_type_name(type_hint))\n",
    "    return node_io_types\n",
    "\n",
    "# wf.inp_calc_md.inputs.channel_dict['temperature'].type_hint"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "65bc56ae-c00b-49bc-979e-329dfc075a46",
   "metadata": {},
   "outputs": [],
   "source": [
    "get_node_types(wf.bulk.inputs), get_node_types(wf.inp_calc_md.inputs);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "d43a553e-55c0-48dc-a497-58b0c7fa2939",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "typing.Optional[int]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wf.bulk.inputs.channel_dict['vacancy_index'].type_hint"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3720a967-0afb-4031-857e-d15323894f95",
   "metadata": {},
   "outputs": [],
   "source": [
    "def f(a:int=3):\n",
    "    return a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e3c52948-23bb-491a-bbfd-bc19ea6aed05",
   "metadata": {},
   "outputs": [],
   "source": [
    "f.__annotations__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "de50c8d0-15a6-4ab4-8e2d-e4508fc94cfe",
   "metadata": {},
   "outputs": [],
   "source": [
    "def f(a, b, c: int=3):\n",
    "    return a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6c51ff1d-b21a-4a1a-b931-c718603d0610",
   "metadata": {},
   "outputs": [],
   "source": [
    "from inspect import signature"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d7ebe47a-ad06-437e-a61b-55f488ab9225",
   "metadata": {},
   "outputs": [],
   "source": [
    "f'f{str(signature(f))}'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "13d65cb0-ab8c-4cf8-9d6f-ae4a6ade7ad0",
   "metadata": {},
   "outputs": [],
   "source": [
    "a = pn.atomistic.structure.build.CubicBulkCell"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6a42e293-fd79-4d3a-a998-17cdb2b4a7bc",
   "metadata": {},
   "outputs": [],
   "source": [
    "signature(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bbd7e4f6-2564-45e5-b3e0-13b03a61d86e",
   "metadata": {},
   "outputs": [],
   "source": [
    "pf = PyironFlow([])\n",
    "pf.gui"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0523a057-4e1f-4644-92dd-b7603a63c4db",
   "metadata": {},
   "outputs": [],
   "source": [
    "wf = pf.get_workflow()\n",
    "wf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2da70c93-17b7-44a1-a14f-4389558aa8e0",
   "metadata": {},
   "outputs": [],
   "source": [
    "pf = PyironFlow([wf])\n",
    "pf.gui"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "a4931165-be58-4355-9b9e-e7490633a326",
   "metadata": {},
   "outputs": [],
   "source": [
    "from python.reactflow import get_node_types, _get_type_name, _get_generic_type\n",
    "import typing\n",
    "\n",
    "def get_node_types(node_io):\n",
    "    node_io_types = list()\n",
    "    for k in node_io.channel_dict:\n",
    "        type_hint = node_io[k].type_hint\n",
    "        # print (k, type(type_hint))\n",
    "        if isinstance(type_hint, typing._UnionGenericAlias):\n",
    "            # print (k, type_hint)\n",
    "            type_hint = _get_generic_type(type_hint)\n",
    "\n",
    "        node_io_types.append(_get_type_name(type_hint))\n",
    "    return node_io_types"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "e5dbae22-6488-467b-9153-d944b69d80ef",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\u001b[0;31mInit signature:\u001b[0m \u001b[0mInputCalcMD\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
       "\u001b[0;31mDocstring:\u001b[0m      <no docstring>\n",
       "\u001b[0;31mType:\u001b[0m           type\n",
       "\u001b[0;31mSubclasses:\u001b[0m     "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from pyiron_workflow import as_dataclass_node\n",
    "from typing import Optional\n",
    "\n",
    "# @as_dataclass_node\n",
    "class InputCalcMD:\n",
    "    temperature: Optional[int | float] = 300\n",
    "    n_ionic_steps: int = 10_000\n",
    "    n_print: int = 100\n",
    "    pressure: Optional[int | float] = None\n",
    "    time_step: Optional[int | float] = 1.0\n",
    "    temperature_damping_timescale: Optional[int | float] = 100.0\n",
    "    pressure_damping_timescale: Optional[int | float] = 1000.0\n",
    "    seed: Optional[int] = None\n",
    "    tloop: Optional[float] = None\n",
    "    initial_temperature: Optional[float] = None\n",
    "    langevin: bool = False\n",
    "    delta_temp: Optional[float] = None\n",
    "    delta_press: Optional[float] = None\n",
    "\n",
    "InputCalcMD??"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "30e6551f-aea7-46ea-b682-83e7f48fc564",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['float',\n",
       " 'int',\n",
       " 'int',\n",
       " 'float',\n",
       " 'float',\n",
       " 'float',\n",
       " 'float',\n",
       " 'int',\n",
       " 'float',\n",
       " 'float',\n",
       " 'bool',\n",
       " 'float',\n",
       " 'float']"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ic_md = InputCalcMD()\n",
    "get_node_types(ic_md.inputs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "5672af9f-9565-4a77-8f08-a060bc0ca546",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['NonPrimitive',\n",
       " 'NonPrimitive',\n",
       " 'NonPrimitive',\n",
       " 'NonPrimitive',\n",
       " 'NonPrimitive',\n",
       " 'NonPrimitive',\n",
       " 'NonPrimitive',\n",
       " 'NonPrimitive',\n",
       " 'NonPrimitive',\n",
       " 'NonPrimitive',\n",
       " 'NonPrimitive',\n",
       " 'NonPrimitive',\n",
       " 'NonPrimitive']"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from pyiron_nodes.atomistic.calculator.data import InputCalcMD\n",
    "\n",
    "ic_md = as_dataclass_node(InputCalcMD)()\n",
    "get_node_types(ic_md.inputs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "ab73a64c-e460-422c-a5ad-babf39b9efa1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\u001b[0;31mInit signature:\u001b[0m\n",
       "\u001b[0mInputCalcMD\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mtemperature\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m'Optional[int | float]'\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m300\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mn_ionic_steps\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m'int'\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m10000\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mn_print\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m'int'\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m100\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mpressure\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m'Optional[int | float]'\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mtime_step\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m'Optional[int | float]'\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m1.0\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mtemperature_damping_timescale\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m'Optional[int | float]'\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m100.0\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mpressure_damping_timescale\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m'Optional[int | float]'\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m1000.0\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mseed\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m'Optional[int]'\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mtloop\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m'Optional[float]'\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0minitial_temperature\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m'Optional[float]'\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mlangevin\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m'bool'\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mdelta_temp\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m'Optional[float]'\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mdelta_press\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m'Optional[float]'\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
       "\u001b[0;31mDocstring:\u001b[0m      InputCalcMD(temperature: 'Optional[int | float]' = 300, n_ionic_steps: 'int' = 10000, n_print: 'int' = 100, pressure: 'Optional[int | float]' = None, time_step: 'Optional[int | float]' = 1.0, temperature_damping_timescale: 'Optional[int | float]' = 100.0, pressure_damping_timescale: 'Optional[int | float]' = 1000.0, seed: 'Optional[int]' = None, tloop: 'Optional[float]' = None, initial_temperature: 'Optional[float]' = None, langevin: 'bool' = False, delta_temp: 'Optional[float]' = None, delta_press: 'Optional[float]' = None)\n",
       "\u001b[0;31mFile:\u001b[0m           ~/python_projects/git_libs/pyiron_nodes/pyiron_nodes/atomistic/calculator/data.py\n",
       "\u001b[0;31mType:\u001b[0m           type\n",
       "\u001b[0;31mSubclasses:\u001b[0m     "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "InputCalcMD??"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fb6ddd9c-0d11-49d7-9a68-1c8af9fb0fd8",
   "metadata": {},
   "outputs": [],
   "source": [
    "pn.atomistic.engine.lammps.SetInputCalcMD().color"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7106d1fc-dd5d-4502-84ad-46fa7e93dee5",
   "metadata": {},
   "outputs": [],
   "source": [
    "type(pn.atomistic.calculator.data.InputCalcMD().run())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dd7afe61-547a-4c3b-8c41-443efa405d32",
   "metadata": {},
   "outputs": [],
   "source": [
    "isinstance(pn.atomistic.calculator.data.InputCalcMD().run(), pyiron_nodes.atomistic.calculator.data.InputCalcMD.dataclass)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1df26dc5-3d25-4ccf-b9f8-53eedbd0f1b5",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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