diff --git a/aiida_to_jobflow_qe.ipynb b/aiida_to_jobflow_qe.ipynb index 64b3dfc..4849069 100644 --- a/aiida_to_jobflow_qe.ipynb +++ b/aiida_to_jobflow_qe.ipynb @@ -1,245 +1 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/geiger_j/.aiida_venvs/adis/lib/python3.10/site-packages/paramiko/pkey.py:82: CryptographyDeprecationWarning: TripleDES has been moved to cryptography.hazmat.decrepit.ciphers.algorithms.TripleDES and will be removed from this module in 48.0.0.\n", - " \"cipher\": algorithms.TripleDES,\n", - "/home/geiger_j/.aiida_venvs/adis/lib/python3.10/site-packages/paramiko/transport.py:253: CryptographyDeprecationWarning: TripleDES has been moved to cryptography.hazmat.decrepit.ciphers.algorithms.TripleDES and will be removed from this module in 48.0.0.\n", - " \"class\": algorithms.TripleDES,\n" - ] - } - ], - "source": [ - "from python_workflow_definition.aiida import write_workflow_json\n", - "from python_workflow_definition.jobflow import load_workflow_json\n", - "from jobflow.managers.local import run_locally\n", - "\n", - "from aiida import load_profile\n", - "load_profile()\n", - "\n", - "workflow_json_filename = \"aiida_to_jobflow_qe.json\"" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "d2bfdbd5fdbc414ba764c0876b61aa8a", - "version_major": 2, - "version_minor": 1 - }, - "text/plain": [ - "NodeGraphWidget(settings={'minimap': True}, style={'width': '90%', 'height': '600px'}, value={'name': 'wg-qe',…" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from quantum_espresso_workflow import generate_structures as _generate_structures\n", - "from quantum_espresso_workflow import get_bulk_structure as _get_bulk_structure\n", - "from quantum_espresso_workflow import calculate_qe as _calculate_qe\n", - "from quantum_espresso_workflow import plot_energy_volume_curve as _plot_energy_volume_curve\n", - "\n", - "from python_workflow_definition.aiida import construct_wg_qe\n", - "\n", - "# from python_workflow_definition.pyiron_base import get_dict\n", - "from aiida_workgraph import task\n", - "from typing import Any\n", - "\n", - "load_profile()\n", - "\n", - "get_bulk_structure = task.pythonjob()(_get_bulk_structure)\n", - "generate_structures = task.pythonjob()(_generate_structures)\n", - "calculate_qe = task.pythonjob(outputs=[\"energy\", \"volume\", \"structure\"])(\n", - " _calculate_qe\n", - ")\n", - "plot_energy_volume_curve = task.pythonjob()(_plot_energy_volume_curve)\n", - "\n", - "strain_lst = [0.9, 0.95, 1.0, 1.05, 1.1]\n", - "\n", - "wg = construct_wg_qe(\n", - " get_bulk_structure=get_bulk_structure,\n", - " calculate_qe=calculate_qe,\n", - " generate_structures=generate_structures,\n", - " plot_energy_volume_curve=plot_energy_volume_curve,\n", - " strain_lst=strain_lst\n", - ")\n", - "\n", - "wg" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "_ = write_workflow_json(wg=wg, file_name=workflow_json_filename)\n" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "\n", - "flow = load_workflow_json(file_name=workflow_json_filename)" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2025-03-21 08:42:13,940 INFO Started executing jobs locally\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2025-03-21 08:42:14,038 INFO Starting job - get_bulk_structure (40617f17-868a-4803-965b-12e459e43938)\n", - "2025-03-21 08:42:14,041 INFO Finished job - get_bulk_structure (40617f17-868a-4803-965b-12e459e43938)\n", - "2025-03-21 08:42:14,042 INFO Starting job - get_dict (6353da14-33e5-4226-8f88-32bcd2f206c0)\n", - "2025-03-21 08:42:14,045 INFO Finished job - get_dict (6353da14-33e5-4226-8f88-32bcd2f206c0)\n", - "2025-03-21 08:42:14,045 INFO Starting job - calculate_qe (a3cebbed-f96c-47b1-9213-990dc16c2c1a)\n", - "FROM ASE\n", - "Atoms(symbols='Al4', pbc=True, cell=[4.05, 4.05, 4.05])\n", - "['Al', 'Al', 'Al', 'Al']\n", - "{'Al': 'Al.pbe-n-kjpaw_psl.1.0.0.UPF'}\n", - "2025-03-21 08:42:41,138 INFO Finished job - calculate_qe (a3cebbed-f96c-47b1-9213-990dc16c2c1a)\n", - "2025-03-21 08:42:41,139 INFO Starting job - generate_structures (a49392e5-4881-48ec-9fe5-7cc72b4605dd)\n", - "2025-03-21 08:42:41,145 INFO Finished job - generate_structures (a49392e5-4881-48ec-9fe5-7cc72b4605dd)\n", - "2025-03-21 08:42:41,146 INFO Starting job - get_dict (7bb8e3a7-e519-4b34-8eb6-221410752f4b)\n", - "2025-03-21 08:42:41,149 INFO Finished job - get_dict (7bb8e3a7-e519-4b34-8eb6-221410752f4b)\n", - "2025-03-21 08:42:41,150 INFO Starting job - get_dict (e35f0151-0eda-497a-8b09-b34e4c6075f8)\n", - "2025-03-21 08:42:41,158 INFO Finished job - get_dict (e35f0151-0eda-497a-8b09-b34e4c6075f8)\n", - "2025-03-21 08:42:41,159 INFO Starting job - get_dict (79ddd270-b0b7-4919-9ca8-6ac65cf452f6)\n", - "2025-03-21 08:42:41,164 INFO Finished job - get_dict (79ddd270-b0b7-4919-9ca8-6ac65cf452f6)\n", - "2025-03-21 08:42:41,164 INFO Starting job - get_dict (cafbb124-ddd8-4f0c-8131-2cfd78d513fe)\n", - "2025-03-21 08:42:41,167 INFO Finished job - get_dict (cafbb124-ddd8-4f0c-8131-2cfd78d513fe)\n", - "2025-03-21 08:42:41,167 INFO Starting job - get_dict (70433dbe-a587-4af8-94be-ef8ed268307e)\n", - "2025-03-21 08:42:41,170 INFO Finished job - get_dict (70433dbe-a587-4af8-94be-ef8ed268307e)\n", - "2025-03-21 08:42:41,171 INFO Starting job - calculate_qe (5a260cfb-8626-446f-a5b4-24dd5ebcd452)\n", - "FROM ASE\n", - "Atoms(symbols='Al4', pbc=True, cell=[3.9060199552583725, 3.9060199552583725, 3.9060199552583725])\n", - "['Al', 'Al', 'Al', 'Al']\n", - "{'Al': 'Al.pbe-n-kjpaw_psl.1.0.0.UPF'}\n", - "2025-03-21 08:42:47,274 INFO Finished job - calculate_qe (5a260cfb-8626-446f-a5b4-24dd5ebcd452)\n", - "2025-03-21 08:42:47,275 INFO Starting job - calculate_qe (54da6cd2-dfa5-4f5b-a582-b14eb45ca05f)\n", - "FROM ASE\n", - "Atoms(symbols='Al4', pbc=True, cell=[3.9770540166930584, 3.9770540166930584, 3.9770540166930584])\n", - "['Al', 'Al', 'Al', 'Al']\n", - "{'Al': 'Al.pbe-n-kjpaw_psl.1.0.0.UPF'}\n", - "2025-03-21 08:42:53,514 INFO Finished job - calculate_qe (54da6cd2-dfa5-4f5b-a582-b14eb45ca05f)\n", - "2025-03-21 08:42:53,515 INFO Starting job - calculate_qe (80ee4883-56c0-40ce-bd80-9320428acea7)\n", - "FROM ASE\n", - "Atoms(symbols='Al4', pbc=True, cell=[4.0456373912944175, 4.0456373912944175, 4.0456373912944175])\n", - "['Al', 'Al', 'Al', 'Al']\n", - "{'Al': 'Al.pbe-n-kjpaw_psl.1.0.0.UPF'}\n", - "2025-03-21 08:43:01,156 INFO Finished job - calculate_qe (80ee4883-56c0-40ce-bd80-9320428acea7)\n", - "2025-03-21 08:43:01,157 INFO Starting job - calculate_qe (9c4d462c-7309-43e6-a143-d84f28143019)\n", - "FROM ASE\n", - "Atoms(symbols='Al4', pbc=True, cell=[4.111971105505594, 4.111971105505594, 4.111971105505594])\n", - "['Al', 'Al', 'Al', 'Al']\n", - "{'Al': 'Al.pbe-n-kjpaw_psl.1.0.0.UPF'}\n", - "2025-03-21 08:43:09,113 INFO Finished job - calculate_qe (9c4d462c-7309-43e6-a143-d84f28143019)\n", - "2025-03-21 08:43:09,114 INFO Starting job - calculate_qe (5ad4436e-3493-4707-a82c-ad84534de6e0)\n", - "FROM ASE\n", - "Atoms(symbols='Al4', pbc=True, cell=[4.176231033379998, 4.176231033379998, 4.176231033379998])\n", - "['Al', 'Al', 'Al', 'Al']\n", - "{'Al': 'Al.pbe-n-kjpaw_psl.1.0.0.UPF'}\n", - "2025-03-21 08:43:17,244 INFO Finished job - calculate_qe (5ad4436e-3493-4707-a82c-ad84534de6e0)\n", - "2025-03-21 08:43:17,246 INFO Starting job - get_list (21a5d42a-9a50-4dcf-9aea-0a3f467b9be5)\n", - "2025-03-21 08:43:17,251 INFO Finished job - get_list (21a5d42a-9a50-4dcf-9aea-0a3f467b9be5)\n", - "2025-03-21 08:43:17,252 INFO Starting job - get_list (f42f5129-ecda-441a-a542-062bc7fd20d3)\n", - "2025-03-21 08:43:17,257 INFO Finished job - get_list (f42f5129-ecda-441a-a542-062bc7fd20d3)\n", - "2025-03-21 08:43:17,258 INFO Starting job - plot_energy_volume_curve (942f3adc-8196-4564-bc8f-a9f5cedafe6e)\n", - "2025-03-21 08:43:17,366 INFO Finished job - plot_energy_volume_curve (942f3adc-8196-4564-bc8f-a9f5cedafe6e)\n", - "2025-03-21 08:43:17,366 INFO Finished executing jobs locally\n" - ] - }, - { - "data": { - "text/plain": [ - "{'40617f17-868a-4803-965b-12e459e43938': {1: Response(output={'numbers': [13, 13, 13, 13], 'positions': [[0.0, 0.0, 0.0], [0.0, 2.025, 2.025], [2.025, 0.0, 2.025], [2.025, 2.025, 0.0]], 'cell': [[4.05, 0.0, 0.0], [0.0, 4.05, 0.0], [0.0, 0.0, 4.05]], 'pbc': [True, True, True]}, detour=None, addition=None, replace=None, stored_data=None, stop_children=False, stop_jobflow=False, 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "result = run_locally(flow)\n", - "result" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "ADIS", - "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": "3.10.12" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} +{"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":"3.12.8"}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"code","source":"from python_workflow_definition.aiida import write_workflow_json\nfrom python_workflow_definition.jobflow import load_workflow_json\nfrom jobflow.managers.local import run_locally\n\nfrom aiida import load_profile\nload_profile()\n\nworkflow_json_filename = \"aiida_to_pyiron_base_qe.json\"","metadata":{"trusted":true},"outputs":[{"name":"stderr","output_type":"stream","text":"/srv/conda/envs/notebook/lib/python3.12/site-packages/paramiko/pkey.py:82: CryptographyDeprecationWarning: TripleDES has been moved to cryptography.hazmat.decrepit.ciphers.algorithms.TripleDES and will be removed from cryptography.hazmat.primitives.ciphers.algorithms in 48.0.0.\n \"cipher\": algorithms.TripleDES,\n/srv/conda/envs/notebook/lib/python3.12/site-packages/paramiko/transport.py:253: CryptographyDeprecationWarning: TripleDES has been moved to cryptography.hazmat.decrepit.ciphers.algorithms.TripleDES and will be removed from cryptography.hazmat.primitives.ciphers.algorithms in 48.0.0.\n \"class\": algorithms.TripleDES,\n"}],"execution_count":1},{"cell_type":"code","source":"from aiida_workgraph import task, WorkGraph\nfrom typing import Any","metadata":{"trusted":true},"outputs":[],"execution_count":2},{"cell_type":"code","source":"# NOTE: `get_dict` is `get_input_dict`, to compile the input values for the calc tasks\n# NOTE: `add_link` must be from outputs to inputs\nfrom python_workflow_definition.shared import get_dict, get_list","metadata":{"trusted":true},"outputs":[],"execution_count":3},{"cell_type":"code","source":"@task.pythonjob()\ndef pickle_node(value):\n \"\"\"Handle data nodes\"\"\"\n return value","metadata":{"trusted":true},"outputs":[],"execution_count":4},{"cell_type":"code","source":"from quantum_espresso_workflow import generate_structures as _generate_structures\nfrom quantum_espresso_workflow import get_bulk_structure as _get_bulk_structure\nfrom quantum_espresso_workflow import calculate_qe as _calculate_qe\nfrom quantum_espresso_workflow import plot_energy_volume_curve as _plot_energy_volume_curve","metadata":{"trusted":true},"outputs":[],"execution_count":5},{"cell_type":"code","source":"strain_lst = [0.9, 0.95, 1.0, 1.05, 1.1]","metadata":{"trusted":true},"outputs":[],"execution_count":6},{"cell_type":"code","source":"get_bulk_structure = task.pythonjob()(_get_bulk_structure)\ngenerate_structures = task.pythonjob()(_generate_structures)\ncalculate_qe = task.pythonjob(outputs=[\"energy\", \"volume\", \"structure\"])(\n _calculate_qe\n)\nplot_energy_volume_curve = task.pythonjob()(_plot_energy_volume_curve)","metadata":{"trusted":true},"outputs":[],"execution_count":7},{"cell_type":"code","source":"wg = WorkGraph(\"wg-qe\")","metadata":{"trusted":true},"outputs":[],"execution_count":8},{"cell_type":"code","source":"pickle_element_task = wg.add_task(\n pickle_node,\n name=\"pickle_element\",\n value=\"Al\",\n)","metadata":{"trusted":true},"outputs":[],"execution_count":9},{"cell_type":"code","source":"pickle_a_task = wg.add_task(\n pickle_node, name=\"pickle_a\", value=4.05\n)","metadata":{"trusted":true},"outputs":[],"execution_count":10},{"cell_type":"code","source":"pickle_cubic_task = wg.add_task(\n pickle_node, name=\"pickle_cubic\", value=True\n)","metadata":{"trusted":true},"outputs":[],"execution_count":11},{"cell_type":"code","source":"pickle_relax_workdir_task = wg.add_task(\n pickle_node,\n name=\"pickle_relax_workdir\",\n value=\"mini\",\n)","metadata":{"trusted":true},"outputs":[],"execution_count":12},{"cell_type":"code","source":"# ? relax or SCF, or general? -> Should be relax\nrelax_get_dict_task = wg.add_task(\n task.pythonjob(\n # outputs=[\"structure\", \"calculation\", \"kpts\", \"pseudopotentials\", \"smearing\"]\n # outputs=[\"dict\"]\n )(get_dict),\n name=\"relax_get_dict\",\n register_pickle_by_value=True,\n)","metadata":{"trusted":true},"outputs":[],"execution_count":13},{"cell_type":"code","source":"pickle_pp_task = wg.add_task(\n pickle_node,\n name=\"pseudopotentials\",\n value={\"Al\": \"Al.pbe-n-kjpaw_psl.1.0.0.UPF\"},\n)","metadata":{"trusted":true},"outputs":[],"execution_count":14},{"cell_type":"code","source":"pickle_kpts_task = wg.add_task(\n pickle_node, name=\"kpts_task\", value=[3, 3, 3] # FIXME: Back to [3, 3, 3]\n)","metadata":{"trusted":true},"outputs":[],"execution_count":15},{"cell_type":"code","source":"pickle_calc_type_relax_task = wg.add_task(\n pickle_node,\n name=\"calc_type_relax\",\n value=\"vc-relax\",\n)","metadata":{"trusted":true},"outputs":[],"execution_count":16},{"cell_type":"code","source":"pickle_smearing_task = wg.add_task(\n pickle_node, name=\"smearing\", value=0.02\n)","metadata":{"trusted":true},"outputs":[],"execution_count":17},{"cell_type":"code","source":"strain_lst_task = wg.add_task(\n pickle_node,\n name=\"pickle_strain_lst\",\n value=strain_lst,\n)","metadata":{"trusted":true},"outputs":[],"execution_count":18},{"cell_type":"code","source":"get_bulk_structure_task = wg.add_task(\n get_bulk_structure,\n name=\"get_bulk_structure\",\n register_pickle_by_value=True,\n)","metadata":{"trusted":true},"outputs":[],"execution_count":19},{"cell_type":"code","source":"relax_task = wg.add_task(\n calculate_qe,\n # ! I don't like the `mini` name...\n name=\"mini\",\n register_pickle_by_value=True,\n)","metadata":{"trusted":true},"outputs":[],"execution_count":20},{"cell_type":"code","source":"generate_structures_task = wg.add_task(\n generate_structures,\n name=\"generate_structures\",\n register_pickle_by_value=True,\n)","metadata":{"trusted":true},"outputs":[],"execution_count":21},{"cell_type":"code","source":"# here we add the structure outputs based on the number of strains\ndel wg.tasks.generate_structures.outputs[\"result\"]","metadata":{"trusted":true},"outputs":[],"execution_count":22},{"cell_type":"code","source":"scf_qe_tasks = []\nfor i, strain in enumerate(strain_lst):\n generate_structures_task.add_output(\"workgraph.any\", f\"s_{i}\")\n\n scf_qe_task = wg.add_task(\n calculate_qe,\n name=f\"qe_{i}\",\n register_pickle_by_value=True,\n )\n scf_qe_tasks.append(scf_qe_task)","metadata":{"trusted":true},"outputs":[],"execution_count":23},{"cell_type":"code","source":"plot_energy_volume_curve_task = wg.add_task(\n plot_energy_volume_curve,\n name=\"plot_energy_volume_curve\",\n register_pickle_by_value=True,\n)","metadata":{"trusted":true},"outputs":[],"execution_count":24},{"cell_type":"code","source":"strain_dir_tasks, scf_get_dict_tasks = [], []\nfor i, strain in enumerate(strain_lst):\n strain_dir = f\"strain_{i}\"\n\n strain_dir_task = wg.add_task(\n pickle_node,\n name=f\"pickle_{strain_dir}_dir\",\n value=strain_dir,\n register_pickle_by_value=True,\n )\n strain_dir_tasks.append(strain_dir_task)\n\n scf_get_dict_task = wg.add_task(\n task.pythonjob()(get_dict),\n name=f\"get_dict_{i}\",\n register_pickle_by_value=True,\n )\n scf_get_dict_tasks.append(scf_get_dict_task)\n\n if i == 0:\n pickle_calc_type_scf_task = wg.add_task(\n pickle_node,\n name=\"calc_type_scf\",\n value=\"scf\",\n )","metadata":{"trusted":true},"outputs":[],"execution_count":25},{"cell_type":"code","source":"get_volumes_task = wg.add_task(\n task.pythonjob()(get_list),\n name=\"get_volumes\",\n register_pickle_by_value=True,\n)","metadata":{"trusted":true},"outputs":[],"execution_count":26},{"cell_type":"code","source":"get_energies_task = wg.add_task(\n task.pythonjob()(get_list),\n name=\"get_energies\",\n register_pickle_by_value=True,\n)","metadata":{"trusted":true},"outputs":[],"execution_count":27},{"cell_type":"code","source":"# Add remaining links\nwg.add_link(\n pickle_element_task.outputs.result, get_bulk_structure_task.inputs.element\n)\nwg.add_link(pickle_a_task.outputs.result, get_bulk_structure_task.inputs.a)\nwg.add_link(pickle_cubic_task.outputs.result, get_bulk_structure_task.inputs.cubic)","metadata":{"trusted":true},"outputs":[{"execution_count":28,"output_type":"execute_result","data":{"text/plain":"NodeLink(from=\"pickle_cubic.result\", to=\"get_bulk_structure.cubic\")"},"metadata":{}}],"execution_count":28},{"cell_type":"code","source":"# `.set` rather than `.add_link`, as get_dict takes `**kwargs` as input\nrelax_get_dict_task.set(\n {\n \"structure\": get_bulk_structure_task.outputs.result,\n \"calculation\": pickle_calc_type_relax_task.outputs.result,\n \"kpts\": pickle_kpts_task.outputs.result,\n \"pseudopotentials\": pickle_pp_task.outputs.result,\n \"smearing\": pickle_smearing_task.outputs.result,\n }\n)","metadata":{"trusted":true},"outputs":[],"execution_count":29},{"cell_type":"code","source":"wg.add_link(relax_get_dict_task.outputs.result, relax_task.inputs.input_dict)\nwg.add_link(\n pickle_relax_workdir_task.outputs.result,\n relax_task.inputs.working_directory,\n)","metadata":{"trusted":true},"outputs":[{"execution_count":30,"output_type":"execute_result","data":{"text/plain":"NodeLink(from=\"pickle_relax_workdir.result\", to=\"mini.working_directory\")"},"metadata":{}}],"execution_count":30},{"cell_type":"code","source":"wg.add_link(relax_task.outputs.structure, generate_structures_task.inputs.structure)\nwg.add_link(\n strain_lst_task.outputs.result, generate_structures_task.inputs.strain_lst\n)","metadata":{"trusted":true},"outputs":[{"execution_count":31,"output_type":"execute_result","data":{"text/plain":"NodeLink(from=\"pickle_strain_lst.result\", to=\"generate_structures.strain_lst\")"},"metadata":{}}],"execution_count":31},{"cell_type":"code","source":"for i, (scf_get_dict_task, scf_qe_task, strain_dir_task) in enumerate(\n list(zip(scf_get_dict_tasks, scf_qe_tasks, strain_dir_tasks))\n):\n scf_get_dict_task.set(\n {\n \"structure\": generate_structures_task.outputs[f\"s_{i}\"],\n \"calculation\": pickle_calc_type_scf_task.outputs.result,\n \"kpts\": pickle_kpts_task.outputs.result,\n \"pseudopotentials\": pickle_pp_task.outputs.result,\n \"smearing\": pickle_smearing_task.outputs.result,\n }\n )\n wg.add_link(scf_get_dict_task.outputs.result, scf_qe_task.inputs.input_dict)\n wg.add_link(\n strain_dir_task.outputs.result, scf_qe_task.inputs.working_directory\n )\n\n # collect energy and volume\n # wg.add_link(scf_qe_task.outputs.energy, get_energies_task.inputs.kwargs)\n get_energies_task.set({f\"{i}\": scf_qe_task.outputs.energy})\n # wg.add_link(scf_qe_task.outputs.volume, get_volumes_task.inputs.kwargs)\n get_volumes_task.set({f\"{i}\": scf_qe_task.outputs.volume})","metadata":{"trusted":true},"outputs":[],"execution_count":32},{"cell_type":"code","source":"wg.add_link(\n get_volumes_task.outputs.result,\n plot_energy_volume_curve_task.inputs.volume_lst,\n)","metadata":{"trusted":true},"outputs":[{"execution_count":33,"output_type":"execute_result","data":{"text/plain":"NodeLink(from=\"get_volumes.result\", to=\"plot_energy_volume_curve.volume_lst\")"},"metadata":{}}],"execution_count":33},{"cell_type":"code","source":"wg.add_link(\n get_energies_task.outputs.result,\n plot_energy_volume_curve_task.inputs.energy_lst,\n)","metadata":{"trusted":true},"outputs":[{"execution_count":34,"output_type":"execute_result","data":{"text/plain":"NodeLink(from=\"get_energies.result\", to=\"plot_energy_volume_curve.energy_lst\")"},"metadata":{}}],"execution_count":34},{"cell_type":"code","source":"_ = write_workflow_json(wg=wg, file_name=workflow_json_filename)\n","metadata":{"trusted":true},"outputs":[],"execution_count":35},{"cell_type":"code","source":"flow = load_workflow_json(file_name=workflow_json_filename)","metadata":{"trusted":true},"outputs":[],"execution_count":36},{"cell_type":"code","source":"result = run_locally(flow)\nresult","metadata":{"trusted":true},"outputs":[{"name":"stdout","output_type":"stream","text":"2025-03-21 21:26:20,046 INFO Started executing jobs locally\n2025-03-21 21:26:20,183 INFO Starting job - get_bulk_structure (fabb0d01-2442-4557-bd4e-b5ead7cf98d0)\n2025-03-21 21:26:20,186 INFO Finished job - get_bulk_structure (fabb0d01-2442-4557-bd4e-b5ead7cf98d0)\n2025-03-21 21:26:20,187 INFO Starting job - get_dict (612b1bb6-d593-4435-9b3d-5bb269ad2843)\n2025-03-21 21:26:20,189 INFO Finished job - get_dict (612b1bb6-d593-4435-9b3d-5bb269ad2843)\n2025-03-21 21:26:20,190 INFO Starting job - calculate_qe (f21e58fb-0abd-4b50-bc40-b0ed2fe1f9ee)\n"},{"name":"stderr","output_type":"stream","text":"[jupyter-pyiron-dev-pyth-flow-definition-fqg29pgr:00221] mca_base_component_repository_open: unable to open mca_btl_openib: librdmacm.so.1: cannot open shared object file: No such file or directory (ignored)\nNote: The following floating-point exceptions are signalling: IEEE_INVALID_FLAG\n"},{"name":"stdout","output_type":"stream","text":"2025-03-21 21:27:11,297 INFO Finished job - calculate_qe (f21e58fb-0abd-4b50-bc40-b0ed2fe1f9ee)\n2025-03-21 21:27:11,299 INFO Starting job - generate_structures (5ad4c3c2-9398-448d-b647-97b367ee2cf5)\n2025-03-21 21:27:11,303 INFO Finished job - generate_structures (5ad4c3c2-9398-448d-b647-97b367ee2cf5)\n2025-03-21 21:27:11,304 INFO Starting job - get_dict (d7b594b9-6ac6-474d-9c52-c8ee5fcdd8b4)\n2025-03-21 21:27:11,305 INFO Finished job - get_dict (d7b594b9-6ac6-474d-9c52-c8ee5fcdd8b4)\n2025-03-21 21:27:11,307 INFO Starting job - get_dict (fb9a4ee5-331f-408d-880d-9cd0d91acf0c)\n2025-03-21 21:27:11,308 INFO Finished job - get_dict (fb9a4ee5-331f-408d-880d-9cd0d91acf0c)\n2025-03-21 21:27:11,309 INFO Starting job - get_dict (12b26f9e-fb15-4c25-a2fc-b2d6ffa530ee)\n2025-03-21 21:27:11,311 INFO Finished job - get_dict (12b26f9e-fb15-4c25-a2fc-b2d6ffa530ee)\n2025-03-21 21:27:11,311 INFO Starting job - get_dict (b41cc69e-4168-4d6c-ab7b-fd3a4cefc9d7)\n2025-03-21 21:27:11,313 INFO Finished job - get_dict (b41cc69e-4168-4d6c-ab7b-fd3a4cefc9d7)\n2025-03-21 21:27:11,313 INFO Starting job - get_dict (a5cadc37-ee3c-4dad-891d-2953e90e57d5)\n2025-03-21 21:27:11,315 INFO Finished job - get_dict (a5cadc37-ee3c-4dad-891d-2953e90e57d5)\n2025-03-21 21:27:11,365 INFO Starting job - calculate_qe (7f7ed5dd-7756-4185-bc23-3dc77775c365)\n"},{"name":"stderr","output_type":"stream","text":"[jupyter-pyiron-dev-pyth-flow-definition-fqg29pgr:00236] mca_base_component_repository_open: unable to open mca_btl_openib: librdmacm.so.1: cannot open shared object file: No such file or directory (ignored)\n"},{"name":"stdout","output_type":"stream","text":"2025-03-21 21:27:21,677 INFO Finished job - calculate_qe (7f7ed5dd-7756-4185-bc23-3dc77775c365)\n2025-03-21 21:27:21,678 INFO Starting job - calculate_qe (145ab651-febe-43af-97ef-310d48ce2d71)\n"},{"name":"stderr","output_type":"stream","text":"Note: The following floating-point exceptions are signalling: IEEE_INVALID_FLAG\n[jupyter-pyiron-dev-pyth-flow-definition-fqg29pgr:00247] mca_base_component_repository_open: unable to open mca_btl_openib: librdmacm.so.1: cannot open shared object file: No such file or directory (ignored)\n"},{"name":"stdout","output_type":"stream","text":"2025-03-21 21:27:32,158 INFO Finished job - calculate_qe (145ab651-febe-43af-97ef-310d48ce2d71)\n2025-03-21 21:27:32,159 INFO Starting job - calculate_qe (b2f6f779-980a-4b0e-be32-9fda29034214)\n"},{"name":"stderr","output_type":"stream","text":"Note: The following floating-point exceptions are signalling: IEEE_INVALID_FLAG\n[jupyter-pyiron-dev-pyth-flow-definition-fqg29pgr:00258] mca_base_component_repository_open: unable to open mca_btl_openib: librdmacm.so.1: cannot open shared object file: No such file or directory (ignored)\nNote: The following floating-point exceptions are signalling: IEEE_INVALID_FLAG\n"},{"name":"stdout","output_type":"stream","text":"2025-03-21 21:27:44,722 INFO Finished job - calculate_qe (b2f6f779-980a-4b0e-be32-9fda29034214)\n2025-03-21 21:27:44,723 INFO Starting job - calculate_qe (6856f4da-485b-4d5a-852e-ca70b7d333ea)\n"},{"name":"stderr","output_type":"stream","text":"[jupyter-pyiron-dev-pyth-flow-definition-fqg29pgr:00269] mca_base_component_repository_open: unable to open mca_btl_openib: librdmacm.so.1: cannot open shared object file: No such file or directory (ignored)\nNote: The following floating-point exceptions are signalling: IEEE_INVALID_FLAG\n"},{"name":"stdout","output_type":"stream","text":"2025-03-21 21:27:57,827 INFO Finished job - calculate_qe (6856f4da-485b-4d5a-852e-ca70b7d333ea)\n2025-03-21 21:27:57,829 INFO Starting job - calculate_qe (ed56e21c-8b54-40cb-928a-a36f9ca8346d)\n"},{"name":"stderr","output_type":"stream","text":"[jupyter-pyiron-dev-pyth-flow-definition-fqg29pgr:00281] mca_base_component_repository_open: unable to open mca_btl_openib: librdmacm.so.1: cannot open shared object file: No such file or directory (ignored)\n"},{"name":"stdout","output_type":"stream","text":"2025-03-21 21:28:11,262 INFO Finished job - calculate_qe (ed56e21c-8b54-40cb-928a-a36f9ca8346d)\n2025-03-21 21:28:11,264 INFO Starting job - get_list (1533cc3f-f189-40c0-9489-e6249e87f464)\n2025-03-21 21:28:11,267 INFO Finished job - get_list (1533cc3f-f189-40c0-9489-e6249e87f464)\n"},{"name":"stderr","output_type":"stream","text":"Note: The following floating-point exceptions are signalling: IEEE_INVALID_FLAG\n"},{"name":"stdout","output_type":"stream","text":"2025-03-21 21:28:11,268 INFO Starting job - get_list (033d92b6-3b26-4732-9c1e-d29317f562f0)\n2025-03-21 21:28:11,271 INFO Finished job - get_list (033d92b6-3b26-4732-9c1e-d29317f562f0)\n2025-03-21 21:28:11,272 INFO Starting job - plot_energy_volume_curve (25f4b44b-532d-4a35-a8ef-c0faa55091d7)\n2025-03-21 21:28:11,391 INFO Finished job - plot_energy_volume_curve (25f4b44b-532d-4a35-a8ef-c0faa55091d7)\n2025-03-21 21:28:11,392 INFO Finished executing jobs locally\n"},{"execution_count":37,"output_type":"execute_result","data":{"text/plain":"{'fabb0d01-2442-4557-bd4e-b5ead7cf98d0': {1: Response(output='{\"immutable_id\": null, \"last_modified\": null, \"elements\": [\"Al\"], \"nelements\": 1, \"elements_ratios\": [1.0], \"chemical_formula_descriptive\": \"Al4\", \"chemical_formula_reduced\": \"Al\", \"chemical_formula_hill\": null, \"chemical_formula_anonymous\": \"A\", \"dimension_types\": [1, 1, 1], \"nperiodic_dimensions\": 3, \"lattice_vectors\": [[4.05, 0.0, 0.0], [0.0, 4.05, 0.0], [0.0, 0.0, 4.05]], \"space_group_symmetry_operations_xyz\": null, \"space_group_symbol_hall\": null, \"space_group_symbol_hermann_mauguin\": null, \"space_group_symbol_hermann_mauguin_extended\": null, \"space_group_it_number\": null, \"cartesian_site_positions\": [[0.0, 0.0, 0.0], [0.0, 2.025, 2.025], [2.025, 0.0, 2.025], [2.025, 2.025, 0.0]], \"nsites\": 4, \"species\": [{\"name\": \"Al\", \"chemical_symbols\": [\"Al\"], \"concentration\": [1.0], \"mass\": null, \"original_name\": null, \"attached\": null, \"nattached\": null}], \"species_at_sites\": [\"Al\", \"Al\", \"Al\", \"Al\"], \"assemblies\": null, \"structure_features\": []}', detour=None, addition=None, replace=None, stored_data=None, stop_children=False, stop_jobflow=False, job_dir=PosixPath('/home/jovyan'))},\n '612b1bb6-d593-4435-9b3d-5bb269ad2843': {1: Response(output={'structure': '{\"immutable_id\": null, \"last_modified\": null, \"elements\": [\"Al\"], \"nelements\": 1, \"elements_ratios\": [1.0], \"chemical_formula_descriptive\": \"Al4\", \"chemical_formula_reduced\": \"Al\", \"chemical_formula_hill\": null, \"chemical_formula_anonymous\": \"A\", \"dimension_types\": [1, 1, 1], \"nperiodic_dimensions\": 3, \"lattice_vectors\": [[4.05, 0.0, 0.0], [0.0, 4.05, 0.0], [0.0, 0.0, 4.05]], \"space_group_symmetry_operations_xyz\": null, \"space_group_symbol_hall\": null, \"space_group_symbol_hermann_mauguin\": null, \"space_group_symbol_hermann_mauguin_extended\": null, \"space_group_it_number\": null, \"cartesian_site_positions\": [[0.0, 0.0, 0.0], [0.0, 2.025, 2.025], [2.025, 0.0, 2.025], [2.025, 2.025, 0.0]], \"nsites\": 4, \"species\": [{\"name\": \"Al\", \"chemical_symbols\": [\"Al\"], \"concentration\": [1.0], \"mass\": null, \"original_name\": null, \"attached\": null, \"nattached\": null}], \"species_at_sites\": [\"Al\", \"Al\", \"Al\", \"Al\"], \"assemblies\": null, \"structure_features\": []}', 'calculation': 'vc-relax', 'kpts': [3, 3, 3], 'pseudopotentials': {'Al': 'Al.pbe-n-kjpaw_psl.1.0.0.UPF'}, 'smearing': 0.02}, detour=None, addition=None, replace=None, stored_data=None, stop_children=False, stop_jobflow=False, job_dir=PosixPath('/home/jovyan'))},\n 'f21e58fb-0abd-4b50-bc40-b0ed2fe1f9ee': {1: Response(output={'structure': '{\"immutable_id\": null, \"last_modified\": null, \"elements\": [\"Al\"], \"nelements\": 1, \"elements_ratios\": [1.0], \"chemical_formula_descriptive\": \"Al4\", \"chemical_formula_reduced\": \"Al\", \"chemical_formula_hill\": null, \"chemical_formula_anonymous\": \"A\", \"dimension_types\": [1, 1, 1], \"nperiodic_dimensions\": 3, \"lattice_vectors\": [[4.045637215946884, 0.0, 0.0], [0.0, 4.045637215946884, 0.0], [0.0, 0.0, 4.045637215946884]], \"space_group_symmetry_operations_xyz\": null, \"space_group_symbol_hall\": null, \"space_group_symbol_hermann_mauguin\": null, \"space_group_symbol_hermann_mauguin_extended\": null, \"space_group_it_number\": null, \"cartesian_site_positions\": [[0.0, 0.0, 0.0], [0.0, 2.022818607973441, 2.022818607973441], [2.022818607973441, 0.0, 2.022818607973441], [2.022818607973441, 2.022818607973441, 0.0]], \"nsites\": 4, \"species\": [{\"name\": \"Al\", \"chemical_symbols\": [\"Al\"], \"concentration\": [1.0], \"mass\": null, \"original_name\": null, \"attached\": null, \"nattached\": null}], \"species_at_sites\": [\"Al\", \"Al\", \"Al\", \"Al\"], \"assemblies\": null, \"structure_features\": []}', 'energy': -1074.9365262253589, 'volume': 66.21567448236458}, detour=None, addition=None, replace=None, stored_data=None, stop_children=False, stop_jobflow=False, job_dir=PosixPath('/home/jovyan'))},\n '5ad4c3c2-9398-448d-b647-97b367ee2cf5': {1: Response(output={'s_0': '{\"immutable_id\": null, \"last_modified\": null, \"elements\": [\"Al\"], \"nelements\": 1, \"elements_ratios\": [1.0], \"chemical_formula_descriptive\": \"Al4\", \"chemical_formula_reduced\": \"Al\", \"chemical_formula_hill\": null, \"chemical_formula_anonymous\": \"A\", \"dimension_types\": [1, 1, 1], \"nperiodic_dimensions\": 3, \"lattice_vectors\": [[3.9060197859621897, 0.0, 0.0], [0.0, 3.9060197859621897, 0.0], [0.0, 0.0, 3.9060197859621897]], \"space_group_symmetry_operations_xyz\": null, \"space_group_symbol_hall\": null, \"space_group_symbol_hermann_mauguin\": null, \"space_group_symbol_hermann_mauguin_extended\": null, \"space_group_it_number\": null, \"cartesian_site_positions\": [[0.0, 0.0, 0.0], [0.0, 1.953009892981094, 1.953009892981094], [1.953009892981094, 0.0, 1.953009892981094], [1.953009892981094, 1.953009892981094, 0.0]], \"nsites\": 4, \"species\": [{\"name\": \"Al\", \"chemical_symbols\": [\"Al\"], \"concentration\": [1.0], \"mass\": null, \"original_name\": null, \"attached\": null, \"nattached\": null}], \"species_at_sites\": [\"Al\", \"Al\", \"Al\", \"Al\"], \"assemblies\": null, \"structure_features\": []}', 's_1': '{\"immutable_id\": null, \"last_modified\": null, \"elements\": [\"Al\"], \"nelements\": 1, \"elements_ratios\": [1.0], \"chemical_formula_descriptive\": \"Al4\", \"chemical_formula_reduced\": \"Al\", \"chemical_formula_hill\": null, \"chemical_formula_anonymous\": \"A\", \"dimension_types\": [1, 1, 1], \"nperiodic_dimensions\": 3, \"lattice_vectors\": [[3.977053844318091, 0.0, 0.0], [0.0, 3.977053844318091, 0.0], [0.0, 0.0, 3.977053844318091]], \"space_group_symmetry_operations_xyz\": null, \"space_group_symbol_hall\": null, \"space_group_symbol_hermann_mauguin\": null, \"space_group_symbol_hermann_mauguin_extended\": null, \"space_group_it_number\": null, \"cartesian_site_positions\": [[0.0, 0.0, 0.0], [0.0, 1.9885269221590447, 1.9885269221590447], [1.9885269221590447, 0.0, 1.9885269221590447], [1.9885269221590447, 1.9885269221590447, 0.0]], \"nsites\": 4, \"species\": [{\"name\": \"Al\", \"chemical_symbols\": [\"Al\"], \"concentration\": [1.0], \"mass\": null, \"original_name\": null, \"attached\": null, \"nattached\": null}], \"species_at_sites\": [\"Al\", \"Al\", \"Al\", \"Al\"], \"assemblies\": null, \"structure_features\": []}', 's_2': '{\"immutable_id\": null, \"last_modified\": null, \"elements\": [\"Al\"], \"nelements\": 1, \"elements_ratios\": [1.0], \"chemical_formula_descriptive\": \"Al4\", \"chemical_formula_reduced\": \"Al\", \"chemical_formula_hill\": null, \"chemical_formula_anonymous\": \"A\", \"dimension_types\": [1, 1, 1], \"nperiodic_dimensions\": 3, \"lattice_vectors\": [[4.045637215946884, 0.0, 0.0], [0.0, 4.045637215946884, 0.0], [0.0, 0.0, 4.045637215946884]], \"space_group_symmetry_operations_xyz\": null, \"space_group_symbol_hall\": null, \"space_group_symbol_hermann_mauguin\": null, \"space_group_symbol_hermann_mauguin_extended\": null, \"space_group_it_number\": null, \"cartesian_site_positions\": [[0.0, 0.0, 0.0], [0.0, 2.022818607973441, 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"},"metadata":{}}],"execution_count":37}]} \ No newline at end of file diff --git a/aiida_to_jobflow_simple.ipynb b/aiida_to_jobflow_simple.ipynb index 92a702a..36e7240 100644 --- a/aiida_to_jobflow_simple.ipynb +++ b/aiida_to_jobflow_simple.ipynb @@ -1,147 +1 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/geiger_j/.aiida_venvs/adis/lib/python3.10/site-packages/paramiko/pkey.py:82: CryptographyDeprecationWarning: TripleDES has been moved to cryptography.hazmat.decrepit.ciphers.algorithms.TripleDES and will be removed from this module in 48.0.0.\n", - " \"cipher\": algorithms.TripleDES,\n", - "/home/geiger_j/.aiida_venvs/adis/lib/python3.10/site-packages/paramiko/transport.py:253: CryptographyDeprecationWarning: TripleDES has been moved to cryptography.hazmat.decrepit.ciphers.algorithms.TripleDES and will be removed from this module in 48.0.0.\n", - " \"class\": algorithms.TripleDES,\n" - ] - } - ], - "source": [ - "from python_workflow_definition.aiida import write_workflow_json, construct_wg_simple\n", - "from python_workflow_definition.jobflow import load_workflow_json\n", - "from jobflow.managers.local import run_locally\n", - "\n", - "from aiida import load_profile\n", - "load_profile()\n", - "\n", - "workflow_json_filename = \"aiida_to_jobflow_simple.json\"" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "from simple_workflow import add_x_and_y, add_x_and_y_and_z" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "wg = construct_wg_simple(\n", - " add_x_and_y_func=add_x_and_y, add_x_and_y_and_z_func=add_x_and_y_and_z\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'nodes': {0: 1,\n", - " 1: 2,\n", - " 2: 'simple_workflow.add_x_and_y',\n", - " 3: 'simple_workflow.add_x_and_y_and_z'},\n", - " 'edges': [{'source': 0,\n", - " 'target': 2,\n", - " 'sourceHandle': None,\n", - " 'targetHandle': 'x'},\n", - " {'source': 1, 'target': 2, 'sourceHandle': None, 'targetHandle': 'y'},\n", - " {'source': 2, 'target': 3, 'sourceHandle': 'x', 'targetHandle': 'x'},\n", - " {'source': 2, 'target': 3, 'sourceHandle': 'y', 'targetHandle': 'y'},\n", - " {'source': 2, 'target': 3, 'sourceHandle': 'z', 'targetHandle': 'z'}]}" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "write_workflow_json(wg=wg, file_name=workflow_json_filename)\n" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "\n", - "flow = load_workflow_json(file_name=workflow_json_filename)" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2025-03-17 09:34:31,257 INFO Started executing jobs locally\n", - "2025-03-17 09:34:31,421 INFO Starting job - add_x_and_y (a59c5b32-bfb1-4696-85c0-5b850df6d774)\n", - "2025-03-17 09:34:31,424 INFO Finished job - add_x_and_y (a59c5b32-bfb1-4696-85c0-5b850df6d774)\n", - "2025-03-17 09:34:31,425 INFO Starting job - add_x_and_y_and_z (d321223d-6de2-42b9-8f75-4675733c78d6)\n", - "2025-03-17 09:34:31,426 INFO Finished job - add_x_and_y_and_z (d321223d-6de2-42b9-8f75-4675733c78d6)\n", - "2025-03-17 09:34:31,427 INFO Finished executing jobs locally\n" - ] - }, - { - "data": { - "text/plain": [ - "{'a59c5b32-bfb1-4696-85c0-5b850df6d774': {1: Response(output={'x': 1, 'y': 2, 'z': 3}, detour=None, addition=None, replace=None, stored_data=None, stop_children=False, stop_jobflow=False, job_dir=PosixPath('/home/geiger_j/aiida_projects/adis/git-repos/python-workflow-definition'))},\n", - " 'd321223d-6de2-42b9-8f75-4675733c78d6': {1: Response(output=6, detour=None, addition=None, replace=None, stored_data=None, stop_children=False, stop_jobflow=False, job_dir=PosixPath('/home/geiger_j/aiida_projects/adis/git-repos/python-workflow-definition'))}}" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "result = run_locally(flow)\n", - "result" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "ADIS", - "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": "3.10.12" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} +{"metadata":{"kernelspec":{"name":"python3","display_name":"Python 3 (ipykernel)","language":"python"},"language_info":{"name":"python","version":"3.12.8","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"code","source":"from python_workflow_definition.aiida import write_workflow_json\nfrom python_workflow_definition.jobflow import load_workflow_json\nfrom jobflow.managers.local import run_locally\n\nfrom aiida_workgraph import WorkGraph, task\nfrom aiida import load_profile\nload_profile()\n\nworkflow_json_filename = \"aiida_to_jobflow_simple.json\"","metadata":{"trusted":true},"outputs":[{"name":"stderr","output_type":"stream","text":"/srv/conda/envs/notebook/lib/python3.12/site-packages/paramiko/pkey.py:82: CryptographyDeprecationWarning: TripleDES has been moved to cryptography.hazmat.decrepit.ciphers.algorithms.TripleDES and will be removed from cryptography.hazmat.primitives.ciphers.algorithms in 48.0.0.\n \"cipher\": algorithms.TripleDES,\n/srv/conda/envs/notebook/lib/python3.12/site-packages/paramiko/transport.py:253: CryptographyDeprecationWarning: TripleDES has been moved to cryptography.hazmat.decrepit.ciphers.algorithms.TripleDES and will be removed from cryptography.hazmat.primitives.ciphers.algorithms in 48.0.0.\n \"class\": algorithms.TripleDES,\n"}],"execution_count":1},{"cell_type":"code","source":"from simple_workflow import (\n add_x_and_y as _add_x_and_y, \n add_x_and_y_and_z as _add_x_and_y_and_z,\n)","metadata":{"trusted":true},"outputs":[],"execution_count":2},{"cell_type":"code","source":"@task.pythonjob()\ndef pickle_node(value):\n \"\"\"Handle data nodes\"\"\"\n return value","metadata":{"trusted":true},"outputs":[],"execution_count":3},{"cell_type":"code","source":"add_x_and_y = task.pythonjob(outputs=[\"x\", \"y\", \"z\"])(_add_x_and_y)\nadd_x_and_y_and_z = task.pythonjob()(_add_x_and_y_and_z)","metadata":{"trusted":true},"outputs":[],"execution_count":4},{"cell_type":"code","source":"# TODO: Create inputs rather than tasks out of data nodes\nwg = WorkGraph(\"wg-simple\")","metadata":{"trusted":true},"outputs":[],"execution_count":5},{"cell_type":"code","source":"helper_task1 = wg.add_task(pickle_node, name=\"x\", value=1)\nhelper_task2 = wg.add_task(pickle_node, name=\"y\", value=2)","metadata":{"trusted":true},"outputs":[],"execution_count":6},{"cell_type":"code","source":"add_x_and_y_task = wg.add_task(\n add_x_and_y,\n name=\"add_x_and_y\",\n x=helper_task1.outputs.result,\n y=helper_task2.outputs.result,\n)","metadata":{"trusted":true},"outputs":[],"execution_count":7},{"cell_type":"code","source":"add_x_and_y_and_z_task = wg.add_task(\n add_x_and_y_and_z,\n name=\"add_x_and_y_and_z\",\n x=add_x_and_y_task.outputs.x,\n y=add_x_and_y_task.outputs.y,\n z=add_x_and_y_task.outputs.z,\n)","metadata":{"trusted":true},"outputs":[],"execution_count":8},{"cell_type":"code","source":"write_workflow_json(wg=wg, file_name=workflow_json_filename)","metadata":{"trusted":true},"outputs":[{"execution_count":9,"output_type":"execute_result","data":{"text/plain":"{'nodes': {'0': 1,\n '1': 2,\n '2': 'simple_workflow.add_x_and_y',\n '3': 'simple_workflow.add_x_and_y_and_z'},\n 'edges': [{'target': 2,\n 'targetHandle': 'x',\n 'source': 0,\n 'sourceHandle': None},\n {'target': 2, 'targetHandle': 'y', 'source': 1, 'sourceHandle': None},\n {'target': 3, 'targetHandle': 'x', 'source': 2, 'sourceHandle': 'x'},\n {'target': 3, 'targetHandle': 'y', 'source': 2, 'sourceHandle': 'y'},\n {'target': 3, 'targetHandle': 'z', 'source': 2, 'sourceHandle': 'z'}]}"},"metadata":{}}],"execution_count":9},{"cell_type":"code","source":"flow = load_workflow_json(file_name=workflow_json_filename)","metadata":{"trusted":true},"outputs":[],"execution_count":10},{"cell_type":"code","source":"result = run_locally(flow)\nresult","metadata":{"trusted":true},"outputs":[{"name":"stdout","output_type":"stream","text":"2025-03-21 20:45:40,878 INFO Started executing jobs locally\n2025-03-21 20:45:41,115 INFO Starting job - add_x_and_y (ef28440e-7d32-49d3-85f5-121da0fdf65f)\n2025-03-21 20:45:41,117 INFO Finished job - add_x_and_y (ef28440e-7d32-49d3-85f5-121da0fdf65f)\n2025-03-21 20:45:41,118 INFO Starting job - add_x_and_y_and_z (c0da46a8-a430-4d88-adb6-7d5b36b8fe52)\n2025-03-21 20:45:41,119 INFO Finished job - add_x_and_y_and_z (c0da46a8-a430-4d88-adb6-7d5b36b8fe52)\n2025-03-21 20:45:41,120 INFO Finished executing jobs locally\n"},{"execution_count":11,"output_type":"execute_result","data":{"text/plain":"{'ef28440e-7d32-49d3-85f5-121da0fdf65f': {1: Response(output={'x': 1, 'y': 2, 'z': 3}, detour=None, addition=None, replace=None, stored_data=None, stop_children=False, stop_jobflow=False, job_dir=PosixPath('/home/jovyan'))},\n 'c0da46a8-a430-4d88-adb6-7d5b36b8fe52': {1: Response(output=6, detour=None, addition=None, replace=None, stored_data=None, stop_children=False, stop_jobflow=False, job_dir=PosixPath('/home/jovyan'))}}"},"metadata":{}}],"execution_count":11}]} \ No newline at end of file diff --git a/aiida_to_pyiron_base_qe.ipynb b/aiida_to_pyiron_base_qe.ipynb index 2a0cb62..b53733c 100644 --- a/aiida_to_pyiron_base_qe.ipynb +++ b/aiida_to_pyiron_base_qe.ipynb @@ -1,936 +1 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from python_workflow_definition.aiida import write_workflow_json\n", - "from python_workflow_definition.pyiron_base import load_workflow_json\n", - "from pyiron_base import Project\n", - "\n", - "from aiida import load_profile\n", - "load_profile()\n", - "\n", - "workflow_json_filename = \"aiida_to_pyiron_base_qe.json\"" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "abd761f0b1864f568c8bd98810d17358", - "version_major": 2, - "version_minor": 1 - }, - "text/plain": [ - "NodeGraphWidget(settings={'minimap': True}, style={'width': '90%', 'height': '600px'}, value={'name': 'wg-qe',…" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from quantum_espresso_workflow import generate_structures as _generate_structures\n", - "from quantum_espresso_workflow import get_bulk_structure as _get_bulk_structure\n", - "from quantum_espresso_workflow import calculate_qe as _calculate_qe\n", - "from quantum_espresso_workflow import plot_energy_volume_curve as _plot_energy_volume_curve\n", - "\n", - "from python_workflow_definition.aiida import construct_wg_qe\n", - "\n", - "# from python_workflow_definition.pyiron_base import get_dict\n", - "from aiida_workgraph import task\n", - "from typing import Any\n", - "\n", - "load_profile()\n", - "\n", - "get_bulk_structure = task.pythonjob()(_get_bulk_structure)\n", - "generate_structures = task.pythonjob()(_generate_structures)\n", - "calculate_qe = task.pythonjob(outputs=[\"energy\", \"volume\", \"structure\"])(\n", - " _calculate_qe\n", - ")\n", - "plot_energy_volume_curve = task.pythonjob()(_plot_energy_volume_curve)\n", - "\n", - "strain_lst = [0.9, 0.95, 1.0, 1.05, 1.1]\n", - "\n", - "wg = construct_wg_qe(\n", - " get_bulk_structure=get_bulk_structure,\n", - " calculate_qe=calculate_qe,\n", - " generate_structures=generate_structures,\n", - " plot_energy_volume_curve=plot_energy_volume_curve,\n", - " strain_lst=strain_lst\n", - ")\n", - "\n", - "wg" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "_ = write_workflow_json(wg=wg, file_name=workflow_json_filename)\n" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "324d4305767146a8a82a84f08e362d56", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - " 0%| | 0/2 [00:00\n", - "\n", - "\n", - "\n", - "\n", - "create_function_job_5e6df0ad09b4ac7b469cbe2f1a7df8a5\n", - "\n", - "create_function_job=<pyiron_base.project.delayed.DelayedObject object at 0x7cc4be5389d0>\n", - "\n", - "\n", - "\n", - "volume_lst_aab20beefddb6297a59d1be76ee25229\n", - "\n", - "volume_lst=<pyiron_base.project.delayed.DelayedObject object at 0x7cc4be538430>\n", - "\n", - "\n", - "\n", - "volume_lst_aab20beefddb6297a59d1be76ee25229->create_function_job_5e6df0ad09b4ac7b469cbe2f1a7df8a5\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "0_e6eaa011b5f08f03f2bb966da8448726\n", - "\n", - "0=<pyiron_base.project.delayed.DelayedObject object at 0x7cc4bc793be0>\n", - 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get_dict_096775f9846855cac32157b2309bd198 was saved and received the ID: 6\n", - "The job calculate_qe_c47675a4a62f231828be15b62023914a was saved and received the ID: 7\n", - "FROM ASE\n", - "Atoms(symbols='Al4', pbc=True, cell=[3.906019955258453, 3.906019955258453, 3.906019955258453])\n", - "['Al', 'Al', 'Al', 'Al']\n", - "{'Al': 'Al.pbe-n-kjpaw_psl.1.0.0.UPF'}\n", - "The job get_dict_bf59a6263e6cc812b6fd356cda01aa98 was saved and received the ID: 8\n", - "The job calculate_qe_d03f41eac4f8130a3dcf6c2dfb742bd3 was saved and received the ID: 9\n", - "FROM ASE\n", - "Atoms(symbols='Al4', pbc=True, cell=[3.9770540166931405, 3.9770540166931405, 3.9770540166931405])\n", - "['Al', 'Al', 'Al', 'Al']\n", - "{'Al': 'Al.pbe-n-kjpaw_psl.1.0.0.UPF'}\n", - "The job get_dict_492928e83c59f7de21c2f43af686be11 was saved and received the ID: 10\n", - "The job calculate_qe_ae8b841a8c2539a92c4cc697dd8ae4bb was saved and received the ID: 11\n", - "FROM ASE\n", - "Atoms(symbols='Al4', pbc=True, cell=[4.045637391294501, 4.045637391294501, 4.045637391294501])\n", - "['Al', 'Al', 'Al', 'Al']\n", - "{'Al': 'Al.pbe-n-kjpaw_psl.1.0.0.UPF'}\n", - "The job get_dict_1e347d7f73d5515b2b54efcd80930a8f was saved and received the ID: 12\n", - "The job calculate_qe_2aa46a3007c45e1d89df22cb891c46bd was saved and received the ID: 13\n", - "FROM ASE\n", - "Atoms(symbols='Al4', pbc=True, cell=[4.1119711055056785, 4.1119711055056785, 4.1119711055056785])\n", - "['Al', 'Al', 'Al', 'Al']\n", - "{'Al': 'Al.pbe-n-kjpaw_psl.1.0.0.UPF'}\n", - "The job get_dict_d1a7fc8d50dbecd07c32c5da29d9fa94 was saved and received the ID: 14\n", - "The job calculate_qe_2acb8cf0baf4ac2b9aff6d7665170bdb was saved and received the ID: 15\n", - "FROM ASE\n", - "Atoms(symbols='Al4', pbc=True, cell=[4.176231033380084, 4.176231033380084, 4.176231033380084])\n", - "['Al', 'Al', 'Al', 'Al']\n", - "{'Al': 'Al.pbe-n-kjpaw_psl.1.0.0.UPF'}\n", - "The job get_list_03b175780cc97fc8921731ee255b886d was saved and received the ID: 16\n", - "The job get_list_07c646b7483c508d53d1bce2980d700d was saved and received the ID: 17\n", - "The job plot_energy_volume_curve_612ce5c26dfa78510f23d013823ab3a3 was saved and received the ID: 18\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "\n", - "delayed_object.pull()\n" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "ADIS", - "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": "3.10.12" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} +{"metadata":{"kernelspec":{"name":"python3","display_name":"Python 3 (ipykernel)","language":"python"},"language_info":{"name":"python","version":"3.12.8","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"code","source":"from python_workflow_definition.aiida import write_workflow_json\nfrom python_workflow_definition.pyiron_base import load_workflow_json\nfrom pyiron_base import Project\n\nfrom aiida import load_profile\nload_profile()\n\nworkflow_json_filename = \"aiida_to_pyiron_base_qe.json\"","metadata":{"trusted":true},"outputs":[],"execution_count":1},{"cell_type":"code","source":"from aiida_workgraph import task, WorkGraph\nfrom typing import Any","metadata":{"trusted":true},"outputs":[],"execution_count":2},{"cell_type":"code","source":"# NOTE: `get_dict` is `get_input_dict`, to compile the input values for the calc tasks\n# NOTE: `add_link` must be from outputs to inputs\nfrom python_workflow_definition.shared import get_dict, get_list","metadata":{"trusted":true},"outputs":[],"execution_count":3},{"cell_type":"code","source":"@task.pythonjob()\ndef pickle_node(value):\n \"\"\"Handle data nodes\"\"\"\n return value","metadata":{"trusted":true},"outputs":[],"execution_count":4},{"cell_type":"code","source":"from quantum_espresso_workflow import generate_structures as _generate_structures\nfrom quantum_espresso_workflow import get_bulk_structure as _get_bulk_structure\nfrom quantum_espresso_workflow import calculate_qe as _calculate_qe\nfrom quantum_espresso_workflow import plot_energy_volume_curve as _plot_energy_volume_curve","metadata":{"trusted":true},"outputs":[],"execution_count":5},{"cell_type":"code","source":"strain_lst = [0.9, 0.95, 1.0, 1.05, 1.1]","metadata":{"trusted":true},"outputs":[],"execution_count":6},{"cell_type":"code","source":"get_bulk_structure = task.pythonjob()(_get_bulk_structure)\ngenerate_structures = task.pythonjob()(_generate_structures)\ncalculate_qe = task.pythonjob(outputs=[\"energy\", \"volume\", \"structure\"])(\n _calculate_qe\n)\nplot_energy_volume_curve = task.pythonjob()(_plot_energy_volume_curve)","metadata":{"trusted":true},"outputs":[],"execution_count":7},{"cell_type":"code","source":"wg = WorkGraph(\"wg-qe\")","metadata":{"trusted":true},"outputs":[],"execution_count":8},{"cell_type":"code","source":"pickle_element_task = wg.add_task(\n pickle_node,\n name=\"pickle_element\",\n value=\"Al\",\n)","metadata":{"trusted":true},"outputs":[],"execution_count":9},{"cell_type":"code","source":"pickle_a_task = wg.add_task(\n pickle_node, name=\"pickle_a\", value=4.05\n)","metadata":{"trusted":true},"outputs":[],"execution_count":10},{"cell_type":"code","source":"pickle_cubic_task = wg.add_task(\n pickle_node, name=\"pickle_cubic\", value=True\n)","metadata":{"trusted":true},"outputs":[],"execution_count":11},{"cell_type":"code","source":"pickle_relax_workdir_task = wg.add_task(\n pickle_node,\n name=\"pickle_relax_workdir\",\n value=\"mini\",\n)","metadata":{"trusted":true},"outputs":[],"execution_count":12},{"cell_type":"code","source":"# ? relax or SCF, or general? -> Should be relax\nrelax_get_dict_task = wg.add_task(\n task.pythonjob(\n # outputs=[\"structure\", \"calculation\", \"kpts\", \"pseudopotentials\", \"smearing\"]\n # outputs=[\"dict\"]\n )(get_dict),\n name=\"relax_get_dict\",\n register_pickle_by_value=True,\n)","metadata":{"trusted":true},"outputs":[],"execution_count":13},{"cell_type":"code","source":"pickle_pp_task = wg.add_task(\n pickle_node,\n name=\"pseudopotentials\",\n value={\"Al\": \"Al.pbe-n-kjpaw_psl.1.0.0.UPF\"},\n)","metadata":{"trusted":true},"outputs":[],"execution_count":14},{"cell_type":"code","source":"pickle_kpts_task = wg.add_task(\n pickle_node, name=\"kpts_task\", value=[3, 3, 3] # FIXME: Back to [3, 3, 3]\n)","metadata":{"trusted":true},"outputs":[],"execution_count":15},{"cell_type":"code","source":"pickle_calc_type_relax_task = wg.add_task(\n pickle_node,\n name=\"calc_type_relax\",\n value=\"vc-relax\",\n)","metadata":{"trusted":true},"outputs":[],"execution_count":16},{"cell_type":"code","source":"pickle_smearing_task = wg.add_task(\n pickle_node, name=\"smearing\", value=0.02\n)","metadata":{"trusted":true},"outputs":[],"execution_count":17},{"cell_type":"code","source":"strain_lst_task = wg.add_task(\n pickle_node,\n name=\"pickle_strain_lst\",\n value=strain_lst,\n)","metadata":{"trusted":true},"outputs":[],"execution_count":18},{"cell_type":"code","source":"get_bulk_structure_task = wg.add_task(\n get_bulk_structure,\n name=\"get_bulk_structure\",\n register_pickle_by_value=True,\n)","metadata":{"trusted":true},"outputs":[],"execution_count":19},{"cell_type":"code","source":"relax_task = wg.add_task(\n calculate_qe,\n # ! I don't like the `mini` name...\n name=\"mini\",\n register_pickle_by_value=True,\n)","metadata":{"trusted":true},"outputs":[],"execution_count":20},{"cell_type":"code","source":"generate_structures_task = wg.add_task(\n generate_structures,\n name=\"generate_structures\",\n register_pickle_by_value=True,\n)","metadata":{"trusted":true},"outputs":[],"execution_count":21},{"cell_type":"code","source":"# here we add the structure outputs based on the number of strains\ndel wg.tasks.generate_structures.outputs[\"result\"]","metadata":{"trusted":true},"outputs":[],"execution_count":22},{"cell_type":"code","source":"scf_qe_tasks = []\nfor i, strain in enumerate(strain_lst):\n generate_structures_task.add_output(\"workgraph.any\", f\"s_{i}\")\n\n scf_qe_task = wg.add_task(\n calculate_qe,\n name=f\"qe_{i}\",\n register_pickle_by_value=True,\n )\n scf_qe_tasks.append(scf_qe_task)","metadata":{"trusted":true},"outputs":[],"execution_count":23},{"cell_type":"code","source":"plot_energy_volume_curve_task = wg.add_task(\n plot_energy_volume_curve,\n name=\"plot_energy_volume_curve\",\n register_pickle_by_value=True,\n)","metadata":{"trusted":true},"outputs":[],"execution_count":24},{"cell_type":"code","source":"strain_dir_tasks, scf_get_dict_tasks = [], []\nfor i, strain in enumerate(strain_lst):\n strain_dir = f\"strain_{i}\"\n\n strain_dir_task = wg.add_task(\n pickle_node,\n name=f\"pickle_{strain_dir}_dir\",\n value=strain_dir,\n register_pickle_by_value=True,\n )\n strain_dir_tasks.append(strain_dir_task)\n\n scf_get_dict_task = wg.add_task(\n task.pythonjob()(get_dict),\n name=f\"get_dict_{i}\",\n register_pickle_by_value=True,\n )\n scf_get_dict_tasks.append(scf_get_dict_task)\n\n if i == 0:\n pickle_calc_type_scf_task = wg.add_task(\n pickle_node,\n name=\"calc_type_scf\",\n value=\"scf\",\n )","metadata":{"trusted":true},"outputs":[],"execution_count":25},{"cell_type":"code","source":"get_volumes_task = wg.add_task(\n task.pythonjob()(get_list),\n name=\"get_volumes\",\n register_pickle_by_value=True,\n)","metadata":{"trusted":true},"outputs":[],"execution_count":26},{"cell_type":"code","source":"get_energies_task = wg.add_task(\n task.pythonjob()(get_list),\n name=\"get_energies\",\n register_pickle_by_value=True,\n)","metadata":{"trusted":true},"outputs":[],"execution_count":27},{"cell_type":"code","source":"# Add remaining links\nwg.add_link(\n pickle_element_task.outputs.result, get_bulk_structure_task.inputs.element\n)\nwg.add_link(pickle_a_task.outputs.result, get_bulk_structure_task.inputs.a)\nwg.add_link(pickle_cubic_task.outputs.result, get_bulk_structure_task.inputs.cubic)","metadata":{"trusted":true},"outputs":[{"execution_count":28,"output_type":"execute_result","data":{"text/plain":"NodeLink(from=\"pickle_cubic.result\", to=\"get_bulk_structure.cubic\")"},"metadata":{}}],"execution_count":28},{"cell_type":"code","source":"# `.set` rather than `.add_link`, as get_dict takes `**kwargs` as input\nrelax_get_dict_task.set(\n {\n \"structure\": get_bulk_structure_task.outputs.result,\n \"calculation\": pickle_calc_type_relax_task.outputs.result,\n \"kpts\": pickle_kpts_task.outputs.result,\n \"pseudopotentials\": pickle_pp_task.outputs.result,\n \"smearing\": pickle_smearing_task.outputs.result,\n }\n)","metadata":{"trusted":true},"outputs":[],"execution_count":29},{"cell_type":"code","source":"","metadata":{"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"wg.add_link(relax_get_dict_task.outputs.result, relax_task.inputs.input_dict)\nwg.add_link(\n pickle_relax_workdir_task.outputs.result,\n relax_task.inputs.working_directory,\n)","metadata":{"trusted":true},"outputs":[{"execution_count":30,"output_type":"execute_result","data":{"text/plain":"NodeLink(from=\"pickle_relax_workdir.result\", to=\"mini.working_directory\")"},"metadata":{}}],"execution_count":30},{"cell_type":"code","source":"wg.add_link(relax_task.outputs.structure, generate_structures_task.inputs.structure)\nwg.add_link(\n strain_lst_task.outputs.result, generate_structures_task.inputs.strain_lst\n)","metadata":{"trusted":true},"outputs":[{"execution_count":31,"output_type":"execute_result","data":{"text/plain":"NodeLink(from=\"pickle_strain_lst.result\", to=\"generate_structures.strain_lst\")"},"metadata":{}}],"execution_count":31},{"cell_type":"code","source":"for i, (scf_get_dict_task, scf_qe_task, strain_dir_task) in enumerate(\n list(zip(scf_get_dict_tasks, scf_qe_tasks, strain_dir_tasks))\n):\n scf_get_dict_task.set(\n {\n \"structure\": generate_structures_task.outputs[f\"s_{i}\"],\n \"calculation\": pickle_calc_type_scf_task.outputs.result,\n \"kpts\": pickle_kpts_task.outputs.result,\n \"pseudopotentials\": pickle_pp_task.outputs.result,\n \"smearing\": pickle_smearing_task.outputs.result,\n }\n )\n wg.add_link(scf_get_dict_task.outputs.result, scf_qe_task.inputs.input_dict)\n wg.add_link(\n strain_dir_task.outputs.result, scf_qe_task.inputs.working_directory\n )\n\n # collect energy and volume\n # wg.add_link(scf_qe_task.outputs.energy, get_energies_task.inputs.kwargs)\n get_energies_task.set({f\"{i}\": scf_qe_task.outputs.energy})\n # wg.add_link(scf_qe_task.outputs.volume, get_volumes_task.inputs.kwargs)\n get_volumes_task.set({f\"{i}\": scf_qe_task.outputs.volume})","metadata":{"trusted":true},"outputs":[],"execution_count":32},{"cell_type":"code","source":"wg.add_link(\n get_volumes_task.outputs.result,\n plot_energy_volume_curve_task.inputs.volume_lst,\n)","metadata":{"trusted":true},"outputs":[{"execution_count":33,"output_type":"execute_result","data":{"text/plain":"NodeLink(from=\"get_volumes.result\", to=\"plot_energy_volume_curve.volume_lst\")"},"metadata":{}}],"execution_count":33},{"cell_type":"code","source":"wg.add_link(\n get_energies_task.outputs.result,\n plot_energy_volume_curve_task.inputs.energy_lst,\n)","metadata":{"trusted":true},"outputs":[{"execution_count":34,"output_type":"execute_result","data":{"text/plain":"NodeLink(from=\"get_energies.result\", to=\"plot_energy_volume_curve.energy_lst\")"},"metadata":{}}],"execution_count":34},{"cell_type":"code","source":"_ = write_workflow_json(wg=wg, file_name=workflow_json_filename)\n","metadata":{"trusted":true},"outputs":[],"execution_count":35},{"cell_type":"code","source":"pr = Project(\"test\")\npr.remove_jobs(recursive=True, silently=True)\ndelayed_object = load_workflow_json(project=pr, file_name=workflow_json_filename)","metadata":{"trusted":true},"outputs":[{"output_type":"display_data","data":{"text/plain":"0it [00:00, ?it/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"03df3f64020f45d3bc3a82639d9a9c8a"}},"metadata":{}}],"execution_count":36},{"cell_type":"code","source":"delayed_object.draw()\n","metadata":{"trusted":true},"outputs":[{"output_type":"display_data","data":{"text/plain":"","image/svg+xml":"\n\n\n\n\ncreate_function_job_fcd8fcdc89b69afab330ef8968f8ba88\n\ncreate_function_job=<pyiron_base.project.delayed.DelayedObject object at 0x71f6ef9cb740>\n\n\n\nvolume_lst_156840dbe97e5f6066213571cdc12647\n\nvolume_lst=<pyiron_base.project.delayed.DelayedObject object at 0x71f6ef9cb3e0>\n\n\n\nvolume_lst_156840dbe97e5f6066213571cdc12647->create_function_job_fcd8fcdc89b69afab330ef8968f8ba88\n\n\n\n\n\n0_7cde3aa2bb39828b5b154dfa93b17df8\n\n0=<pyiron_base.project.delayed.DelayedObject object at 0x71f6ef9cadb0>\n\n\n\n0_7cde3aa2bb39828b5b154dfa93b17df8->volume_lst_156840dbe97e5f6066213571cdc12647\n\n\n\n\n\ninput_dict_9f1eb324ee1200169a52ccf48f4e03c5\n\ninput_dict=<pyiron_base.project.delayed.DelayedObject object at 0x71f6ef9ca450>\n\n\n\ninput_dict_9f1eb324ee1200169a52ccf48f4e03c5->0_7cde3aa2bb39828b5b154dfa93b17df8\n\n\n\n\n\n0_27ba9ca33868eec9f240e296f25d2bf5\n\n0=<pyiron_base.project.delayed.DelayedObject object at 0x71f6ef9ca780>\n\n\n\ninput_dict_9f1eb324ee1200169a52ccf48f4e03c5->0_27ba9ca33868eec9f240e296f25d2bf5\n\n\n\n\n\nenergy_lst_a351e70573369fad8fd0689ccf95a3c9\n\nenergy_lst=<pyiron_base.project.delayed.DelayedObject object at 0x71f6ef9cb290>\n\n\n\n0_27ba9ca33868eec9f240e296f25d2bf5->energy_lst_a351e70573369fad8fd0689ccf95a3c9\n\n\n\n\n\nstructure_688acb81d323e1e5208535179f2f7e79\n\nstructure=<pyiron_base.project.delayed.DelayedObject object at 0x71f6ef9c9460>\n\n\n\nstructure_688acb81d323e1e5208535179f2f7e79->input_dict_9f1eb324ee1200169a52ccf48f4e03c5\n\n\n\n\n\nstructure_58e2123ded099e38e365d9d2c3108367\n\nstructure=<pyiron_base.project.delayed.DelayedObject object at 0x71f6ef9c8440>\n\n\n\nstructure_58e2123ded099e38e365d9d2c3108367->structure_688acb81d323e1e5208535179f2f7e79\n\n\n\n\n\nstructure_0d47d331360e24bcf6b3f0f7040193f5\n\nstructure=<pyiron_base.project.delayed.DelayedObject object at 0x71f6ef9c9100>\n\n\n\nstructure_58e2123ded099e38e365d9d2c3108367->structure_0d47d331360e24bcf6b3f0f7040193f5\n\n\n\n\n\nstructure_a0dc16678d79e4bbce66124f05af2b31\n\nstructure=<pyiron_base.project.delayed.DelayedObject object at 0x71f6ef9c8dd0>\n\n\n\nstructure_58e2123ded099e38e365d9d2c3108367->structure_a0dc16678d79e4bbce66124f05af2b31\n\n\n\n\n\nstructure_638d2f7bdb962fde50699a9c9bf4f64e\n\nstructure=<pyiron_base.project.delayed.DelayedObject object at 0x71f6ef9c8aa0>\n\n\n\nstructure_58e2123ded099e38e365d9d2c3108367->structure_638d2f7bdb962fde50699a9c9bf4f64e\n\n\n\n\n\nstructure_dd7843a681b0e60589aee6ac322ecfd6\n\nstructure=<pyiron_base.project.delayed.DelayedObject object at 0x71f6ef9c8770>\n\n\n\nstructure_58e2123ded099e38e365d9d2c3108367->structure_dd7843a681b0e60589aee6ac322ecfd6\n\n\n\n\n\ninput_dict_72c62c23520600b58298a014a8392d82\n\ninput_dict=<pyiron_base.project.delayed.DelayedObject object at 0x71f6ef9ca120>\n\n\n\nstructure_0d47d331360e24bcf6b3f0f7040193f5->input_dict_72c62c23520600b58298a014a8392d82\n\n\n\n\n\ninput_dict_2daac2b89f0cbe2afa9e13680d7310c0\n\ninput_dict=<pyiron_base.project.delayed.DelayedObject object at 0x71f6ef9c9df0>\n\n\n\nstructure_a0dc16678d79e4bbce66124f05af2b31->input_dict_2daac2b89f0cbe2afa9e13680d7310c0\n\n\n\n\n\ninput_dict_e00b47e8ee21ba8ea677ac6e77856557\n\ninput_dict=<pyiron_base.project.delayed.DelayedObject object at 0x71f6ef9c9ac0>\n\n\n\nstructure_638d2f7bdb962fde50699a9c9bf4f64e->input_dict_e00b47e8ee21ba8ea677ac6e77856557\n\n\n\n\n\ninput_dict_1e5c74531c5d758e792761650a77a978\n\ninput_dict=<pyiron_base.project.delayed.DelayedObject object at 0x71f6ef9c9790>\n\n\n\nstructure_dd7843a681b0e60589aee6ac322ecfd6->input_dict_1e5c74531c5d758e792761650a77a978\n\n\n\n\n\ninput_dict_5833d3563fbb118187c208d2dea8bad2\n\ninput_dict=<pyiron_base.project.delayed.DelayedObject object at 0x71f6ef96be60>\n\n\n\ninput_dict_5833d3563fbb118187c208d2dea8bad2->structure_58e2123ded099e38e365d9d2c3108367\n\n\n\n\n\nstructure_2598506a768b28e8d82621579e1e670b\n\nstructure=<pyiron_base.project.delayed.DelayedObject object at 0x71f6ef96ac30>\n\n\n\nstructure_2598506a768b28e8d82621579e1e670b->input_dict_5833d3563fbb118187c208d2dea8bad2\n\n\n\n\n\nelement_467734216d9bd2497ffd28d5cd6daba0\n\nelement=Al\n\n\n\nelement_467734216d9bd2497ffd28d5cd6daba0->structure_2598506a768b28e8d82621579e1e670b\n\n\n\n\n\na_aea0574e321c6f75f923c059730e9537\n\na=4.05\n\n\n\na_aea0574e321c6f75f923c059730e9537->structure_2598506a768b28e8d82621579e1e670b\n\n\n\n\n\ncubic_bad787c53fa02a5559fe570238fdb23a\n\ncubic=True\n\n\n\ncubic_bad787c53fa02a5559fe570238fdb23a->structure_2598506a768b28e8d82621579e1e670b\n\n\n\n\n\ncalculation_77b75a01e65d83962d14fa8a882d6c34\n\ncalculation=vc-relax\n\n\n\ncalculation_77b75a01e65d83962d14fa8a882d6c34->input_dict_5833d3563fbb118187c208d2dea8bad2\n\n\n\n\n\nkpts_e961a9390797b0f6f8887a402ea3e9aa\n\nkpts=[3, 3, 3]\n\n\n\nkpts_e961a9390797b0f6f8887a402ea3e9aa->input_dict_9f1eb324ee1200169a52ccf48f4e03c5\n\n\n\n\n\nkpts_e961a9390797b0f6f8887a402ea3e9aa->input_dict_5833d3563fbb118187c208d2dea8bad2\n\n\n\n\n\nkpts_e961a9390797b0f6f8887a402ea3e9aa->input_dict_72c62c23520600b58298a014a8392d82\n\n\n\n\n\nkpts_e961a9390797b0f6f8887a402ea3e9aa->input_dict_2daac2b89f0cbe2afa9e13680d7310c0\n\n\n\n\n\nkpts_e961a9390797b0f6f8887a402ea3e9aa->input_dict_e00b47e8ee21ba8ea677ac6e77856557\n\n\n\n\n\nkpts_e961a9390797b0f6f8887a402ea3e9aa->input_dict_1e5c74531c5d758e792761650a77a978\n\n\n\n\n\n1_aa1347fdf985eea68011f244b6452284\n\n1=<pyiron_base.project.delayed.DelayedObject object at 0x71f6ef9cad80>\n\n\n\ninput_dict_72c62c23520600b58298a014a8392d82->1_aa1347fdf985eea68011f244b6452284\n\n\n\n\n\n1_ebc72f1e15a7fd582d2ab255d76e37e7\n\n1=<pyiron_base.project.delayed.DelayedObject object at 0x71f6ef9ca750>\n\n\n\ninput_dict_72c62c23520600b58298a014a8392d82->1_ebc72f1e15a7fd582d2ab255d76e37e7\n\n\n\n\n\n2_c32c324143ca0cdf6a33ba7a82f43283\n\n2=<pyiron_base.project.delayed.DelayedObject object at 0x71f6ef9caba0>\n\n\n\ninput_dict_2daac2b89f0cbe2afa9e13680d7310c0->2_c32c324143ca0cdf6a33ba7a82f43283\n\n\n\n\n\n2_a8bf2595eb69ade530568c387db38e38\n\n2=<pyiron_base.project.delayed.DelayedObject object at 0x71f6ef9ca5a0>\n\n\n\ninput_dict_2daac2b89f0cbe2afa9e13680d7310c0->2_a8bf2595eb69ade530568c387db38e38\n\n\n\n\n\n3_df40f366cd91aab14140d0cf9970c4e3\n\n3=<pyiron_base.project.delayed.DelayedObject object at 0x71f6ef9caed0>\n\n\n\ninput_dict_e00b47e8ee21ba8ea677ac6e77856557->3_df40f366cd91aab14140d0cf9970c4e3\n\n\n\n\n\n3_ea56c46b45b4491d4943b7793ed7799f\n\n3=<pyiron_base.project.delayed.DelayedObject object at 0x71f6ef9ca8a0>\n\n\n\ninput_dict_e00b47e8ee21ba8ea677ac6e77856557->3_ea56c46b45b4491d4943b7793ed7799f\n\n\n\n\n\n4_817cad9f45654904c6817f42dff5b612\n\n4=<pyiron_base.project.delayed.DelayedObject object at 0x71f6ef9cafc0>\n\n\n\ninput_dict_1e5c74531c5d758e792761650a77a978->4_817cad9f45654904c6817f42dff5b612\n\n\n\n\n\n4_0ce2bb658e04b560cb30e5eb419f57e4\n\n4=<pyiron_base.project.delayed.DelayedObject object at 0x71f6ef9ca990>\n\n\n\ninput_dict_1e5c74531c5d758e792761650a77a978->4_0ce2bb658e04b560cb30e5eb419f57e4\n\n\n\n\n\npseudopotentials_453cdcc0d627a851e196cd899d956d10\n\npseudopotentials={'Al': 'Al.pbe-n-kjpaw_psl.1.0.0.UPF'}\n\n\n\npseudopotentials_453cdcc0d627a851e196cd899d956d10->input_dict_9f1eb324ee1200169a52ccf48f4e03c5\n\n\n\n\n\npseudopotentials_453cdcc0d627a851e196cd899d956d10->input_dict_5833d3563fbb118187c208d2dea8bad2\n\n\n\n\n\npseudopotentials_453cdcc0d627a851e196cd899d956d10->input_dict_72c62c23520600b58298a014a8392d82\n\n\n\n\n\npseudopotentials_453cdcc0d627a851e196cd899d956d10->input_dict_2daac2b89f0cbe2afa9e13680d7310c0\n\n\n\n\n\npseudopotentials_453cdcc0d627a851e196cd899d956d10->input_dict_e00b47e8ee21ba8ea677ac6e77856557\n\n\n\n\n\npseudopotentials_453cdcc0d627a851e196cd899d956d10->input_dict_1e5c74531c5d758e792761650a77a978\n\n\n\n\n\nsmearing_64a632a7e5bfbb7d0c6face9b82082a9\n\nsmearing=0.02\n\n\n\nsmearing_64a632a7e5bfbb7d0c6face9b82082a9->input_dict_9f1eb324ee1200169a52ccf48f4e03c5\n\n\n\n\n\nsmearing_64a632a7e5bfbb7d0c6face9b82082a9->input_dict_5833d3563fbb118187c208d2dea8bad2\n\n\n\n\n\nsmearing_64a632a7e5bfbb7d0c6face9b82082a9->input_dict_72c62c23520600b58298a014a8392d82\n\n\n\n\n\nsmearing_64a632a7e5bfbb7d0c6face9b82082a9->input_dict_2daac2b89f0cbe2afa9e13680d7310c0\n\n\n\n\n\nsmearing_64a632a7e5bfbb7d0c6face9b82082a9->input_dict_e00b47e8ee21ba8ea677ac6e77856557\n\n\n\n\n\nsmearing_64a632a7e5bfbb7d0c6face9b82082a9->input_dict_1e5c74531c5d758e792761650a77a978\n\n\n\n\n\nworking_directory_a17ade9a563d8dcadb655fb2e1c743a7\n\nworking_directory=mini\n\n\n\nworking_directory_a17ade9a563d8dcadb655fb2e1c743a7->structure_58e2123ded099e38e365d9d2c3108367\n\n\n\n\n\nstrain_lst_2d8c38dac8f1aeeeecfa3c0b1cc8a781\n\nstrain_lst=[0.9, 0.95, 1.0, 1.05, 1.1]\n\n\n\nstrain_lst_2d8c38dac8f1aeeeecfa3c0b1cc8a781->structure_688acb81d323e1e5208535179f2f7e79\n\n\n\n\n\nstrain_lst_2d8c38dac8f1aeeeecfa3c0b1cc8a781->structure_0d47d331360e24bcf6b3f0f7040193f5\n\n\n\n\n\nstrain_lst_2d8c38dac8f1aeeeecfa3c0b1cc8a781->structure_a0dc16678d79e4bbce66124f05af2b31\n\n\n\n\n\nstrain_lst_2d8c38dac8f1aeeeecfa3c0b1cc8a781->structure_638d2f7bdb962fde50699a9c9bf4f64e\n\n\n\n\n\nstrain_lst_2d8c38dac8f1aeeeecfa3c0b1cc8a781->structure_dd7843a681b0e60589aee6ac322ecfd6\n\n\n\n\n\ncalculation_bc91e0ce7227762f507f47b85f2f0a83\n\ncalculation=scf\n\n\n\ncalculation_bc91e0ce7227762f507f47b85f2f0a83->input_dict_9f1eb324ee1200169a52ccf48f4e03c5\n\n\n\n\n\ncalculation_bc91e0ce7227762f507f47b85f2f0a83->input_dict_72c62c23520600b58298a014a8392d82\n\n\n\n\n\ncalculation_bc91e0ce7227762f507f47b85f2f0a83->input_dict_2daac2b89f0cbe2afa9e13680d7310c0\n\n\n\n\n\ncalculation_bc91e0ce7227762f507f47b85f2f0a83->input_dict_e00b47e8ee21ba8ea677ac6e77856557\n\n\n\n\n\ncalculation_bc91e0ce7227762f507f47b85f2f0a83->input_dict_1e5c74531c5d758e792761650a77a978\n\n\n\n\n\nworking_directory_2e9abb255f1a31f7d29b4451ad422add\n\nworking_directory=strain_0\n\n\n\nworking_directory_2e9abb255f1a31f7d29b4451ad422add->0_7cde3aa2bb39828b5b154dfa93b17df8\n\n\n\n\n\nworking_directory_2e9abb255f1a31f7d29b4451ad422add->0_27ba9ca33868eec9f240e296f25d2bf5\n\n\n\n\n\n1_aa1347fdf985eea68011f244b6452284->volume_lst_156840dbe97e5f6066213571cdc12647\n\n\n\n\n\n1_ebc72f1e15a7fd582d2ab255d76e37e7->energy_lst_a351e70573369fad8fd0689ccf95a3c9\n\n\n\n\n\nworking_directory_5423d2cc67129a6d0383af6f347df5bd\n\nworking_directory=strain_1\n\n\n\nworking_directory_5423d2cc67129a6d0383af6f347df5bd->1_aa1347fdf985eea68011f244b6452284\n\n\n\n\n\nworking_directory_5423d2cc67129a6d0383af6f347df5bd->1_ebc72f1e15a7fd582d2ab255d76e37e7\n\n\n\n\n\n2_c32c324143ca0cdf6a33ba7a82f43283->volume_lst_156840dbe97e5f6066213571cdc12647\n\n\n\n\n\n2_a8bf2595eb69ade530568c387db38e38->energy_lst_a351e70573369fad8fd0689ccf95a3c9\n\n\n\n\n\nworking_directory_cc646e064ddfc4b2811aba3d86d27992\n\nworking_directory=strain_2\n\n\n\nworking_directory_cc646e064ddfc4b2811aba3d86d27992->2_c32c324143ca0cdf6a33ba7a82f43283\n\n\n\n\n\nworking_directory_cc646e064ddfc4b2811aba3d86d27992->2_a8bf2595eb69ade530568c387db38e38\n\n\n\n\n\n3_df40f366cd91aab14140d0cf9970c4e3->volume_lst_156840dbe97e5f6066213571cdc12647\n\n\n\n\n\n3_ea56c46b45b4491d4943b7793ed7799f->energy_lst_a351e70573369fad8fd0689ccf95a3c9\n\n\n\n\n\nworking_directory_e27768d53df6cd8dc245c52054ecf31f\n\nworking_directory=strain_3\n\n\n\nworking_directory_e27768d53df6cd8dc245c52054ecf31f->3_df40f366cd91aab14140d0cf9970c4e3\n\n\n\n\n\nworking_directory_e27768d53df6cd8dc245c52054ecf31f->3_ea56c46b45b4491d4943b7793ed7799f\n\n\n\n\n\n4_817cad9f45654904c6817f42dff5b612->volume_lst_156840dbe97e5f6066213571cdc12647\n\n\n\n\n\n4_0ce2bb658e04b560cb30e5eb419f57e4->energy_lst_a351e70573369fad8fd0689ccf95a3c9\n\n\n\n\n\nworking_directory_72bba39b22d2b7ce154d37c7e8c658b7\n\nworking_directory=strain_4\n\n\n\nworking_directory_72bba39b22d2b7ce154d37c7e8c658b7->4_817cad9f45654904c6817f42dff5b612\n\n\n\n\n\nworking_directory_72bba39b22d2b7ce154d37c7e8c658b7->4_0ce2bb658e04b560cb30e5eb419f57e4\n\n\n\n\n\nenergy_lst_a351e70573369fad8fd0689ccf95a3c9->create_function_job_fcd8fcdc89b69afab330ef8968f8ba88\n\n\n\n\n"},"metadata":{}}],"execution_count":37},{"cell_type":"code","source":"\ndelayed_object.pull()\n","metadata":{"trusted":true},"outputs":[{"name":"stdout","output_type":"stream","text":"The job get_bulk_structure_f1e730ed97e30e5439e855d2ac41396f was saved and received the ID: 1\nThe job get_dict_20400c1655d51731f9f5ffb50c2b401f was saved and received the ID: 2\nThe job calculate_qe_22b5d0934d89d8f08761e744e4612c30 was saved and received the ID: 3\n"},{"name":"stderr","output_type":"stream","text":"[jupyter-pyiron-dev-pyth-flow-definition-8esr8q3h:00369] mca_base_component_repository_open: unable to open mca_btl_openib: librdmacm.so.1: cannot open shared object file: No such file or directory (ignored)\nNote: The following floating-point exceptions are signalling: IEEE_INVALID_FLAG\n"},{"name":"stdout","output_type":"stream","text":"The job generate_structures_27aeab6880c34540a9c765deda80d784 was saved and received the ID: 4\nThe job get_dict_56392dab5a2d38555828b1468d5e8287 was saved and received the ID: 5\nThe job calculate_qe_bd9b696e66da921fa544f1ad2b69aa33 was saved and received the ID: 6\n"},{"name":"stderr","output_type":"stream","text":"[jupyter-pyiron-dev-pyth-flow-definition-8esr8q3h:00411] mca_base_component_repository_open: unable to open mca_btl_openib: librdmacm.so.1: cannot open shared object file: No such file or directory (ignored)\nNote: The following floating-point exceptions are signalling: IEEE_INVALID_FLAG\n"},{"name":"stdout","output_type":"stream","text":"The job get_dict_c227b2e1715bd0f80fbe24b632771b94 was saved and received the ID: 7\nThe job calculate_qe_ba8d6500a3e3f0408c6ca19ff59fdfa4 was saved and received the ID: 8\n"},{"name":"stderr","output_type":"stream","text":"[jupyter-pyiron-dev-pyth-flow-definition-8esr8q3h:00442] mca_base_component_repository_open: unable to open mca_btl_openib: librdmacm.so.1: cannot open shared object file: No such file or directory (ignored)\nNote: The following floating-point exceptions are signalling: IEEE_INVALID_FLAG\n"},{"name":"stdout","output_type":"stream","text":"The job get_dict_83d16dc4d098b2e035fca90333aca031 was saved and received the ID: 9\nThe job calculate_qe_f7b748fd111920b3fb969ee2587b536c was saved and received the ID: 10\n"},{"name":"stderr","output_type":"stream","text":"[jupyter-pyiron-dev-pyth-flow-definition-8esr8q3h:00458] mca_base_component_repository_open: unable to open mca_btl_openib: librdmacm.so.1: cannot open shared object file: No such file or directory (ignored)\nNote: The following floating-point exceptions are signalling: IEEE_INVALID_FLAG\n"},{"name":"stdout","output_type":"stream","text":"The job get_dict_603887d10c637f43b4f90d6ddbb33013 was saved and received the ID: 11\nThe job calculate_qe_891be2cd796cb0adc95d358fa4133ed3 was saved and received the ID: 12\n"},{"name":"stderr","output_type":"stream","text":"[jupyter-pyiron-dev-pyth-flow-definition-8esr8q3h:00469] mca_base_component_repository_open: unable to open mca_btl_openib: librdmacm.so.1: cannot open shared object file: No such file or directory (ignored)\nNote: The following floating-point exceptions are signalling: IEEE_INVALID_FLAG\n"},{"name":"stdout","output_type":"stream","text":"The job get_dict_afcfad2368e2171e3180b69033224e43 was saved and received the ID: 13\nThe job calculate_qe_bc9abb6cf6d595892e6f855d1082e68a was saved and received the ID: 14\n"},{"name":"stderr","output_type":"stream","text":"[jupyter-pyiron-dev-pyth-flow-definition-8esr8q3h:00486] mca_base_component_repository_open: unable to open mca_btl_openib: librdmacm.so.1: cannot open shared object file: No such file or directory (ignored)\nNote: The following floating-point exceptions are signalling: IEEE_INVALID_FLAG\n"},{"name":"stdout","output_type":"stream","text":"The job get_list_ca41097c0a5332599f7e520b68408ebd was saved and received the ID: 15\nThe job get_list_d61010ba7362bd7a5b1051e56d40f4cd was saved and received the ID: 16\nThe job plot_energy_volume_curve_219cb7246e0c237f48d2298204ff8273 was saved and received the ID: 17\n"},{"output_type":"display_data","data":{"text/plain":"
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"},"metadata":{}}],"execution_count":38}]} diff --git a/aiida_to_pyiron_base_simple.ipynb b/aiida_to_pyiron_base_simple.ipynb index e27ce21..18181d0 100644 --- a/aiida_to_pyiron_base_simple.ipynb +++ b/aiida_to_pyiron_base_simple.ipynb @@ -1,263 +1 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from python_workflow_definition.aiida import write_workflow_json, construct_wg_simple\n", - "from python_workflow_definition.pyiron_base import load_workflow_json\n", - "\n", - "from aiida import load_profile\n", - "load_profile()\n", - "\n", - "workflow_json_filename = \"aiida_to_pyiron_base_simple.json\"" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "from simple_workflow import add_x_and_y, add_x_and_y_and_z" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "wg = construct_wg_simple(\n", - " add_x_and_y_func=add_x_and_y, add_x_and_y_and_z_func=add_x_and_y_and_z\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'nodes': {0: 1,\n", - " 1: 2,\n", - " 2: 'simple_workflow.add_x_and_y',\n", - " 3: 'simple_workflow.add_x_and_y_and_z'},\n", - " 'edges': [{'source': 0,\n", - " 'target': 2,\n", - " 'sourceHandle': None,\n", - " 'targetHandle': 'x'},\n", - " {'source': 1, 'target': 2, 'sourceHandle': None, 'targetHandle': 'y'},\n", - " {'source': 2, 'target': 3, 'sourceHandle': 'x', 'targetHandle': 'x'},\n", - " {'source': 2, 'target': 3, 'sourceHandle': 'y', 'targetHandle': 'y'},\n", - " {'source': 2, 'target': 3, 'sourceHandle': 'z', 'targetHandle': 'z'}]}" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "write_workflow_json(wg=wg, file_name=workflow_json_filename)\n" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "from pyiron_base import Project\n" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "e6ffcc6726e74a4cbf6d14109cf2a3d9", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - " 0%| | 0/2 [00:00\n", - "\n", - "\n", - "\n", - "\n", - "create_function_job_c7d5a2eaf006faed5e048478186e5006\n", - "\n", - "create_function_job=<pyiron_base.project.delayed.DelayedObject object at 0x723b62b84b80>\n", - "\n", - "\n", - "\n", - "x_72c4a551025d57f72dca17727aef044e\n", - "\n", - "x=<pyiron_base.project.delayed.DelayedObject object at 0x723b62b84550>\n", - "\n", - "\n", - "\n", - "x_72c4a551025d57f72dca17727aef044e->create_function_job_c7d5a2eaf006faed5e048478186e5006\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "x_1d847da32ecaabf6731c38f798c3d4ce\n", - "\n", - "x=1\n", - "\n", - "\n", - "\n", - "x_1d847da32ecaabf6731c38f798c3d4ce->x_72c4a551025d57f72dca17727aef044e\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "y_454d7f4e4b54021d3386dcdafb7344ee\n", - "\n", - "y=<pyiron_base.project.delayed.DelayedObject object at 0x723b62b84580>\n", - "\n", - "\n", - "\n", - "x_1d847da32ecaabf6731c38f798c3d4ce->y_454d7f4e4b54021d3386dcdafb7344ee\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "z_7b1d0ad3687984f980950fa9864be0fd\n", - "\n", - "z=<pyiron_base.project.delayed.DelayedObject object at 0x723b62b84370>\n", - "\n", - "\n", - "\n", - "x_1d847da32ecaabf6731c38f798c3d4ce->z_7b1d0ad3687984f980950fa9864be0fd\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "y_454d7f4e4b54021d3386dcdafb7344ee->create_function_job_c7d5a2eaf006faed5e048478186e5006\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "z_7b1d0ad3687984f980950fa9864be0fd->create_function_job_c7d5a2eaf006faed5e048478186e5006\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "y_a9ec4f5f33f0d64e74ed5d9900bceac6\n", - "\n", - "y=2\n", - "\n", - "\n", - "\n", - "y_a9ec4f5f33f0d64e74ed5d9900bceac6->x_72c4a551025d57f72dca17727aef044e\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "y_a9ec4f5f33f0d64e74ed5d9900bceac6->y_454d7f4e4b54021d3386dcdafb7344ee\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "y_a9ec4f5f33f0d64e74ed5d9900bceac6->z_7b1d0ad3687984f980950fa9864be0fd\n", - "\n", - "\n", - "\n", - "\n", - "" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "pr = Project(\"test\")\n", - "pr.remove_jobs(recursive=True, silently=True)\n", - "\n", - "\n", - "delayed_object = load_workflow_json(project=pr, file_name=workflow_json_filename)\n", - "delayed_object.draw()" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "The job add_x_and_y_68901482a2c5221cc845f828aabebd27 was saved and received the ID: 2\n", - "The job add_x_and_y_and_z_b671e81aaa4670d81d7eee509650af8d was saved and received the ID: 3\n" - ] - }, - { - "data": { - "text/plain": [ - "6" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "delayed_object.pull()" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "ADIS", - "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": "3.10.12" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} +{"metadata":{"kernelspec":{"name":"python3","display_name":"Python 3 (ipykernel)","language":"python"},"language_info":{"name":"python","version":"3.12.8","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"code","source":"from python_workflow_definition.aiida import write_workflow_json\nfrom python_workflow_definition.pyiron_base import load_workflow_json\n\nfrom aiida_workgraph import WorkGraph, task\nfrom aiida import load_profile\nload_profile()\n\nworkflow_json_filename = \"aiida_to_pyiron_base_simple.json\"","metadata":{"trusted":true},"outputs":[],"execution_count":1},{"cell_type":"code","source":"from simple_workflow import (\n add_x_and_y as _add_x_and_y, \n add_x_and_y_and_z as _add_x_and_y_and_z,\n)","metadata":{"trusted":true},"outputs":[],"execution_count":2},{"cell_type":"code","source":"@task.pythonjob()\ndef pickle_node(value):\n \"\"\"Handle data nodes\"\"\"\n return value","metadata":{"trusted":true},"outputs":[],"execution_count":3},{"cell_type":"code","source":"add_x_and_y = task.pythonjob(outputs=[\"x\", \"y\", \"z\"])(_add_x_and_y)\nadd_x_and_y_and_z = task.pythonjob()(_add_x_and_y_and_z)","metadata":{"trusted":true},"outputs":[],"execution_count":4},{"cell_type":"code","source":"# TODO: Create inputs rather than tasks out of data nodes\nwg = WorkGraph(\"wg-simple\")","metadata":{"trusted":true},"outputs":[],"execution_count":5},{"cell_type":"code","source":"helper_task1 = wg.add_task(pickle_node, name=\"x\", value=1)\nhelper_task2 = wg.add_task(pickle_node, name=\"y\", value=2)","metadata":{"trusted":true},"outputs":[],"execution_count":6},{"cell_type":"code","source":"add_x_and_y_task = wg.add_task(\n add_x_and_y,\n name=\"add_x_and_y\",\n x=helper_task1.outputs.result,\n y=helper_task2.outputs.result,\n)","metadata":{"trusted":true},"outputs":[],"execution_count":7},{"cell_type":"code","source":"add_x_and_y_and_z_task = wg.add_task(\n add_x_and_y_and_z,\n name=\"add_x_and_y_and_z\",\n x=add_x_and_y_task.outputs.x,\n y=add_x_and_y_task.outputs.y,\n z=add_x_and_y_task.outputs.z,\n)","metadata":{"trusted":true},"outputs":[],"execution_count":8},{"cell_type":"code","source":"write_workflow_json(wg=wg, file_name=workflow_json_filename)\n","metadata":{"trusted":true},"outputs":[{"execution_count":9,"output_type":"execute_result","data":{"text/plain":"{'nodes': {'0': 1,\n '1': 2,\n '2': 'simple_workflow.add_x_and_y',\n '3': 'simple_workflow.add_x_and_y_and_z'},\n 'edges': [{'target': 2,\n 'targetHandle': 'x',\n 'source': 0,\n 'sourceHandle': None},\n {'target': 2, 'targetHandle': 'y', 'source': 1, 'sourceHandle': None},\n {'target': 3, 'targetHandle': 'x', 'source': 2, 'sourceHandle': 'x'},\n {'target': 3, 'targetHandle': 'y', 'source': 2, 'sourceHandle': 'y'},\n {'target': 3, 'targetHandle': 'z', 'source': 2, 'sourceHandle': 'z'}]}"},"metadata":{}}],"execution_count":9},{"cell_type":"code","source":"from pyiron_base import Project\n","metadata":{"trusted":true},"outputs":[],"execution_count":10},{"cell_type":"code","source":"pr = Project(\"test\")\npr.remove_jobs(recursive=True, silently=True)\n\n\ndelayed_object = load_workflow_json(project=pr, file_name=workflow_json_filename)\ndelayed_object.draw()","metadata":{"trusted":true},"outputs":[{"output_type":"display_data","data":{"text/plain":"0it [00:00, ?it/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"2fd0ba5dafde43de9aaf5f81977b9d48"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"","image/svg+xml":"\n\n\n\n\ncreate_function_job_2c7c2fda085e8f388e4f08e1c9d38369\n\ncreate_function_job=<pyiron_base.project.delayed.DelayedObject object at 0x766e2335b770>\n\n\n\nx_f208a8c01bcd74935a14cc9659bb85c7\n\nx=<pyiron_base.project.delayed.DelayedObject object at 0x766e38533bc0>\n\n\n\nx_f208a8c01bcd74935a14cc9659bb85c7->create_function_job_2c7c2fda085e8f388e4f08e1c9d38369\n\n\n\n\n\nx_1d847da32ecaabf6731c38f798c3d4ce\n\nx=1\n\n\n\nx_1d847da32ecaabf6731c38f798c3d4ce->x_f208a8c01bcd74935a14cc9659bb85c7\n\n\n\n\n\ny_4d8d0e211b942004d9aff58f0c436d65\n\ny=<pyiron_base.project.delayed.DelayedObject object at 0x766e2335a8d0>\n\n\n\nx_1d847da32ecaabf6731c38f798c3d4ce->y_4d8d0e211b942004d9aff58f0c436d65\n\n\n\n\n\nz_b091b9a2fb9060eb0480915f16381781\n\nz=<pyiron_base.project.delayed.DelayedObject object at 0x766e2335ab10>\n\n\n\nx_1d847da32ecaabf6731c38f798c3d4ce->z_b091b9a2fb9060eb0480915f16381781\n\n\n\n\n\ny_4d8d0e211b942004d9aff58f0c436d65->create_function_job_2c7c2fda085e8f388e4f08e1c9d38369\n\n\n\n\n\nz_b091b9a2fb9060eb0480915f16381781->create_function_job_2c7c2fda085e8f388e4f08e1c9d38369\n\n\n\n\n\ny_a9ec4f5f33f0d64e74ed5d9900bceac6\n\ny=2\n\n\n\ny_a9ec4f5f33f0d64e74ed5d9900bceac6->x_f208a8c01bcd74935a14cc9659bb85c7\n\n\n\n\n\ny_a9ec4f5f33f0d64e74ed5d9900bceac6->y_4d8d0e211b942004d9aff58f0c436d65\n\n\n\n\n\ny_a9ec4f5f33f0d64e74ed5d9900bceac6->z_b091b9a2fb9060eb0480915f16381781\n\n\n\n\n"},"metadata":{}}],"execution_count":11},{"cell_type":"code","source":"delayed_object.pull()","metadata":{"trusted":true},"outputs":[{"name":"stdout","output_type":"stream","text":"The job add_x_and_y_68901482a2c5221cc845f828aabebd27 was saved and received the ID: 1\nThe job add_x_and_y_and_z_b671e81aaa4670d81d7eee509650af8d was saved and received the ID: 2\n"},{"execution_count":12,"output_type":"execute_result","data":{"text/plain":"6"},"metadata":{}}],"execution_count":12}]} \ No newline at end of file diff --git a/python_workflow_definition/src/python_workflow_definition/aiida.py b/python_workflow_definition/src/python_workflow_definition/aiida.py index 6f0c79f..97809c4 100644 --- a/python_workflow_definition/src/python_workflow_definition/aiida.py +++ b/python_workflow_definition/src/python_workflow_definition/aiida.py @@ -123,247 +123,3 @@ def write_workflow_json(wg, file_name): json.dump(data, f, indent=2) return data - - -def construct_wg_simple(add_x_and_y_func, add_x_and_y_and_z_func) -> WorkGraph: - helper_1 = pickle_node - helper_2 = pickle_node - - add_x_and_y = task.pythonjob(outputs=["x", "y", "z"])(add_x_and_y_func) - add_x_and_y_and_z = task.pythonjob()(add_x_and_y_and_z_func) - - # TODO: Create inputs rather than tasks out of data nodes - wg = WorkGraph("wg-simple") - - helper_task1 = wg.add_task(helper_1, name="x", value=1) - - helper_task2 = wg.add_task(helper_2, name="y", value=2) - - add_x_and_y_task = wg.add_task( - add_x_and_y, - name="add_x_and_y", - x=helper_task1.outputs.result, - y=helper_task2.outputs.result, - ) - - add_x_and_y_and_z_task = wg.add_task( - add_x_and_y_and_z, - name="add_x_and_y_and_z", - x=add_x_and_y_task.outputs.x, - y=add_x_and_y_task.outputs.y, - z=add_x_and_y_task.outputs.z, - ) - - return wg - - -def construct_wg_qe( - get_bulk_structure, - calculate_qe, - generate_structures, - plot_energy_volume_curve, - strain_lst, -): - - from .shared import get_dict - from .shared import get_list - - # NOTE: `get_dict` is `get_input_dict`, to compile the input values for the calc tasks - # NOTE: `add_link` must be from outputs to inputs - wg = WorkGraph("wg-qe") - - get_bulk_structure_task = wg.add_task( - get_bulk_structure, - name="get_bulk_structure", - register_pickle_by_value=True, - ) - - relax_task = wg.add_task( - calculate_qe, - # ! I don't like the `mini` name... - name="mini", - register_pickle_by_value=True, - ) - - generate_structures_task = wg.add_task( - generate_structures, - name="generate_structures", - register_pickle_by_value=True, - ) - - # here we add the structure outputs based on the number of strains - del wg.tasks.generate_structures.outputs["result"] - - scf_qe_tasks = [] - for i, strain in enumerate(strain_lst): - generate_structures_task.add_output("workgraph.any", f"s_{i}") - - scf_qe_task = wg.add_task( - calculate_qe, - name=f"qe_{i}", - register_pickle_by_value=True, - ) - scf_qe_tasks.append(scf_qe_task) - - plot_energy_volume_curve_task = wg.add_task( - plot_energy_volume_curve, - name="plot_energy_volume_curve", - register_pickle_by_value=True, - ) - - pickle_element_task = wg.add_task( - pickle_node, - name="pickle_element", - value="Al", - ) - - pickle_a_task = wg.add_task( - pickle_node, name="pickle_a", value=4.05 - ) - - pickle_cubic_task = wg.add_task( - pickle_node, name="pickle_cubic", value=True - ) - - pickle_relax_workdir_task = wg.add_task( - pickle_node, - name="pickle_relax_workdir", - value="mini", - ) - - # ? relax or SCF, or general? -> Should be relax - relax_get_dict_task = wg.add_task( - task.pythonjob( - # outputs=["structure", "calculation", "kpts", "pseudopotentials", "smearing"] - # outputs=["dict"] - )(get_dict), - name="relax_get_dict", - register_pickle_by_value=True, - ) - - pickle_pp_task = wg.add_task( - pickle_node, - name="pseudopotentials", - value={"Al": "Al.pbe-n-kjpaw_psl.1.0.0.UPF"}, - ) - - pickle_kpts_task = wg.add_task( - pickle_node, name="kpts_task", value=[3, 3, 3] # FIXME: Back to [3, 3, 3] - ) - - pickle_calc_type_relax_task = wg.add_task( - pickle_node, - name="calc_type_relax", - value="vc-relax", - ) - - pickle_smearing_task = wg.add_task( - pickle_node, name="smearing", value=0.02 - ) - - strain_lst_task = wg.add_task( - pickle_node, - name="pickle_strain_lst", - value=strain_lst, - ) - - strain_dir_tasks, scf_get_dict_tasks = [], [] - for i, strain in enumerate(strain_lst): - strain_dir = f"strain_{i}" - - strain_dir_task = wg.add_task( - pickle_node, - name=f"pickle_{strain_dir}_dir", - value=strain_dir, - register_pickle_by_value=True, - ) - strain_dir_tasks.append(strain_dir_task) - - scf_get_dict_task = wg.add_task( - task.pythonjob()(get_dict), - name=f"get_dict_{i}", - register_pickle_by_value=True, - ) - scf_get_dict_tasks.append(scf_get_dict_task) - - if i == 0: - pickle_calc_type_scf_task = wg.add_task( - pickle_node, - name="calc_type_scf", - value="scf", - ) - - get_volumes_task = wg.add_task( - task.pythonjob()(get_list), - name="get_volumes", - register_pickle_by_value=True, - ) - - get_energies_task = wg.add_task( - task.pythonjob()(get_list), - name="get_energies", - register_pickle_by_value=True, - ) - - # Add remaining links - wg.add_link( - pickle_element_task.outputs.result, get_bulk_structure_task.inputs.element - ) - wg.add_link(pickle_a_task.outputs.result, get_bulk_structure_task.inputs.a) - wg.add_link(pickle_cubic_task.outputs.result, get_bulk_structure_task.inputs.cubic) - - # `.set` rather than `.add_link`, as get_dict takes `**kwargs` as input - relax_get_dict_task.set( - { - "structure": get_bulk_structure_task.outputs.result, - "calculation": pickle_calc_type_relax_task.outputs.result, - "kpts": pickle_kpts_task.outputs.result, - "pseudopotentials": pickle_pp_task.outputs.result, - "smearing": pickle_smearing_task.outputs.result, - } - ) - - wg.add_link(relax_get_dict_task.outputs.result, relax_task.inputs.input_dict) - wg.add_link( - pickle_relax_workdir_task.outputs.result, - relax_task.inputs.working_directory, - ) - - wg.add_link(relax_task.outputs.structure, generate_structures_task.inputs.structure) - wg.add_link( - strain_lst_task.outputs.result, generate_structures_task.inputs.strain_lst - ) - - for i, (scf_get_dict_task, scf_qe_task, strain_dir_task) in enumerate( - list(zip(scf_get_dict_tasks, scf_qe_tasks, strain_dir_tasks)) - ): - scf_get_dict_task.set( - { - "structure": generate_structures_task.outputs[f"s_{i}"], - "calculation": pickle_calc_type_scf_task.outputs.result, - "kpts": pickle_kpts_task.outputs.result, - "pseudopotentials": pickle_pp_task.outputs.result, - "smearing": pickle_smearing_task.outputs.result, - } - ) - wg.add_link(scf_get_dict_task.outputs.result, scf_qe_task.inputs.input_dict) - wg.add_link( - strain_dir_task.outputs.result, scf_qe_task.inputs.working_directory - ) - - # collect energy and volume - # wg.add_link(scf_qe_task.outputs.energy, get_energies_task.inputs.kwargs) - get_energies_task.set({f"{i}": scf_qe_task.outputs.energy}) - # wg.add_link(scf_qe_task.outputs.volume, get_volumes_task.inputs.kwargs) - get_volumes_task.set({f"{i}": scf_qe_task.outputs.volume}) - - wg.add_link( - get_volumes_task.outputs.result, - plot_energy_volume_curve_task.inputs.volume_lst, - ) - wg.add_link( - get_energies_task.outputs.result, - plot_energy_volume_curve_task.inputs.energy_lst, - ) - - return wg