pyironflow_demo.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "e685173d-686c-489a-bbb4-eedef29fbba3",
   "metadata": {},
   "source": [
    "# PyironFlow demo"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a9e6595e-22fa-4640-aa01-755e4d16c717",
   "metadata": {},
   "source": [
    "PyironFlow provides a visual programming environment to implement and execute workflows. It uses _pyiron_workflows_ as workflow language. The graphical interface is based on the js library _xyflow_ in combination with the ipywidgets library. A workflow consists of function nodes defined as pure Python functions. Input and output of these functions can be connected to input and output channels of other nodes via edges. Examples of nodes can be found in the _pyiron_nodes_ library."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e10399f6-5f0c-426c-9dfc-8ecc2fc59354",
   "metadata": {},
   "source": [
    "For developers only: Uncomment the following line if you have edited the .js files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "6b50621c-5611-4869-a758-1404ec9ea67a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# !npx esbuild js/widget.jsx --minify --format=esm --bundle --outdir=static"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "3be83a1e-f2b0-4d54-91b2-34b39a903c65",
   "metadata": {},
   "outputs": [],
   "source": [
    "%config IPCompleter.evaluation='unsafe'"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4b1c2477-131a-4c49-a807-baf7b086a619",
   "metadata": {},
   "source": [
    "## Generic example"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8d374905-5b28-4845-b84b-ed7d42fa8aa1",
   "metadata": {},
   "source": [
    "The following example shows how to define nodes (functions) and how to connect them to construct a workflow. More details on how to construct nodes and workflows can be found in the documentation of _pyiron_workflows_."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "eb34d77b-b1aa-4210-bd06-3eb22c119d81",
   "metadata": {},
   "outputs": [],
   "source": [
    "from pyiron_workflow import Workflow, as_function_node"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "61b9f6dd-af6a-4394-95a2-1b8aa51f252c",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "@as_function_node\n",
    "def linspace(x_min: int = 0, x_max: float = 2 * np.pi, n: int = 50):\n",
    "\n",
    "    vec = np.linspace(x_min, x_max, n)\n",
    "    return vec\n",
    "\n",
    "@as_function_node\n",
    "def sin(x):\n",
    "    import numpy as np\n",
    "    \n",
    "    vec = np.sin(x)\n",
    "    return vec\n",
    "\n",
    "@as_function_node('plot')\n",
    "def plot(x, y):\n",
    "    import matplotlib.pylab as plt\n",
    "    \n",
    "    plt.plot(x, y)\n",
    "    return plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "fc2652a7-a832-4763-b67f-401a8bf66326",
   "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__plot': None}"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wf = Workflow('simple_workflow')\n",
    "wf.x = linspace(n=10)\n",
    "wf.sin = sin(x=wf.x)\n",
    "wf.plot = plot(x=wf.x, y=wf.sin)\n",
    "\n",
    "wf.run()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7618b07b-fb96-47aa-9f97-5a6fbc88d492",
   "metadata": {},
   "source": [
    "The workflow _wf_ can be transferred to the visual programming interface _PyironFlow_ as shown in the cell below. To run the workflow, click on the last node (plot) and then click on the _run_ button. If you want to debug the code, you can click on any node and click run to see the output of that node. If you want to view the source code of the node, click the _source_ button."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "abac5734-a832-4d41-9f23-aa2908717df2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "bf4137583f7148aea8a1aea6e1e60dc1",
       "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": "a770996b-6503-4318-a3bd-a686822727e9",
   "metadata": {},
   "source": [
    "## Employ the node library to build more complex workflows"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6fa98eda-608f-4c6c-a018-2efb5c6ec19c",
   "metadata": {},
   "source": [
    "The _pyiron_nodes_ library collects nodes/functions from different domains. If you are using git clone to add the pyiron_nodes library, modify the following commands to add the pyiron_nodes path to your PYIRONPATH environment variable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "7d73a062-3ca7-4c01-8adc-ddb4960c2e55",
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys\n",
    "from pathlib import Path\n",
    "\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": 8,
   "id": "dbc017d4-7c5f-48df-a105-e75904a4ed53",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "5e944b22b4cb430fb9fb232c3328befe",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "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.input_elastic = pn.atomistic.property.elastic.InputElasticTensor()\n",
    "wf.elastic = pn.atomistic.property.elastic.ElasticConstants(structure=wf.bulk, engine=wf.engine, parameters=wf.input_elastic)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "56c26ff4-f7a8-46a6-a99c-40256af96174",
   "metadata": {},
   "outputs": [],
   "source": [
    "wf.draw();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "4a3c6b40-4426-4479-a039-686cce4f923e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wf.bulk.position = (10, 0)\n",
    "'position' in dir(wf.bulk)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "813d8693-da0b-42ab-9534-0818cdf34411",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "eb579afc3bce4169b5bc9fe4d9734a8c",
       "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": 11,
     "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": "f85df1d085fa4c9eae6a5b28d3bfdf61",
       "version_major": 2,
       "version_minor": 1
      },
      "text/plain": [
       "ReactFlowWidget(edges='[{\"source\": \"engine\", \"sourceHandle\": \"engine\", \"target\": \"elastic\", \"targetHandle\": \"e…"
      ]
     },
     "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": 14,
   "id": "9e7a9e47-ca0d-4976-b257-3533f1c33957",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b9ae5c48cfa04612af0c45e52ae4c2e4",
       "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",
    "# wf = pf.get_workflow()\n",
    "\n",
    "pf = PyironFlow([])\n",
    "pf.gui"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6b478083-a50a-42c9-8556-a0fb556f9b4d",
   "metadata": {},
   "source": [
    "### Phonons"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "682297aa-9948-4da5-95fa-4b3e6776bbc3",
   "metadata": {},
   "source": [
    "The following example computes the phonon total density of states. The dos node returns a dataframe. To plot it use from the Node Library the _PlotDataFrame_ module *(Node Library -> plotting -> PlotDataFrame)*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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": 16,
   "id": "051bed2f-6100-4ee4-95e5-f70aa38f56cb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "6d063b71e28246a3a1e5e4f4125606f5",
       "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": 16,
     "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": "markdown",
   "id": "8c587b73-2482-43cc-a64e-8a8cdfe4f885",
   "metadata": {},
   "source": [
    "This example demonstrates how to construct a low-level workflow to run a file-based executable using Lammps as a prototype. The concepts can be extended to any type of executable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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": 18,
   "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": 19,
   "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": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wf.get_energy_pot.pull()[0:-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "10975d1d-e834-40e3-9797-a94b7b5c31f8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "bcff5c3b9eef4fcfb1ff7b78daa0deea",
       "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": [
    "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": "markdown",
   "id": "4126988c-ce19-4f60-89cf-c8ae2e7898fa",
   "metadata": {},
   "source": [
    "The low-level workflow above has been converted into a macro 'Code'. The application of such a high-level macro is shown below. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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": 21,
     "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.run()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "1cec4223-ff48-4f68-8017-c9f8bb0dbdd1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "77a3ad81bac2461bb619b6e01361155f",
       "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": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pf = PyironFlow([wf])\n",
    "pf.gui"
   ]
  }
 ],
 "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": "3.12.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}