diff --git a/.github/workflows/jobflow.yml b/.github/workflows/jobflow.yml index 7f95e02..0889295 100644 --- a/.github/workflows/jobflow.yml +++ b/.github/workflows/jobflow.yml @@ -25,4 +25,5 @@ jobs: pip install -e python_workflow_definition conda install -c conda-forge jupyter papermill export ESPRESSO_PSEUDO=$(pwd)/espresso/pseudo - papermill universal_qe_to_jobflow.ipynb universal_qe_to_jobflow_out.ipynb -k "python3" \ No newline at end of file + papermill universal_qe_to_jobflow.ipynb universal_qe_to_jobflow_out.ipynb -k "python3" + papermill jobflow_to_pyiron_base.ipynb jobflow_to_pyiron_base_out.ipynb -k "python3" \ No newline at end of file diff --git a/jobflow_to_pyiron_base.ipynb b/jobflow_to_pyiron_base.ipynb new file mode 100644 index 0000000..bd299c1 --- /dev/null +++ b/jobflow_to_pyiron_base.ipynb @@ -0,0 +1 @@ +{"metadata":{"kernelspec":{"display_name":"Python 3 (ipykernel)","language":"python","name":"python3"},"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":5,"nbformat":4,"cells":[{"id":"c9e32d6d-5a26-43b3-8455-fae305761a5d","cell_type":"code","source":"import numpy as np","metadata":{"trusted":true},"outputs":[],"execution_count":1},{"id":"000bbd4a-f53c-4eea-9d85-76f0aa2ca10b","cell_type":"code","source":"from jobflow import job, Flow","metadata":{"trusted":true},"outputs":[],"execution_count":2},{"id":"b4a78447-e87c-4fb4-8d17-d9a280eb7254","cell_type":"code","source":"from pyiron_base import Project","metadata":{"trusted":true},"outputs":[],"execution_count":3},{"id":"06c2bd9e-b2ac-4b88-9158-fa37331c3418","cell_type":"code","source":"from python_workflow_definition.jobflow import write_workflow_json","metadata":{"trusted":true},"outputs":[],"execution_count":4},{"id":"fb6dbdaa-8cab-48b2-8307-448003eca3f5","cell_type":"code","source":"from python_workflow_definition.pyiron_base import load_workflow_json","metadata":{"trusted":true},"outputs":[],"execution_count":5},{"id":"fb847d49-7bf9-4839-9b99-c116d1b0e9ee","cell_type":"code","source":"from quantum_espresso_workflow import (\n calculate_qe as _calculate_qe, \n generate_structures as _generate_structures, \n get_bulk_structure as _get_bulk_structure, \n plot_energy_volume_curve as _plot_energy_volume_curve,\n)","metadata":{"trusted":true},"outputs":[],"execution_count":6},{"id":"07598344-0f75-433b-8902-bea21a42088c","cell_type":"code","source":"calculate_qe = job(_calculate_qe, data=[\"energy\", \"volume\", \"structure\"])\ngenerate_structures = job(_generate_structures, data=[\"i\" for i in range(100)])\nplot_energy_volume_curve = job(_plot_energy_volume_curve)\nget_bulk_structure = job(_get_bulk_structure)","metadata":{"trusted":true},"outputs":[],"execution_count":7},{"id":"e1ce0a51-0ab9-456c-81f9-d39875c3b709","cell_type":"code","source":"pseudopotentials = {\"Al\": \"Al.pbe-n-kjpaw_psl.1.0.0.UPF\"}","metadata":{"trusted":true},"outputs":[],"execution_count":8},{"id":"c03753c7-9936-4a80-9e4e-2ac56a7fc114","cell_type":"code","source":"structure = get_bulk_structure(\n name=\"Al\",\n a=4.05,\n cubic=True,\n)","metadata":{"trusted":true},"outputs":[],"execution_count":9},{"id":"ecef1ed5-a8d3-48c3-9e01-4a40e55c1153","cell_type":"code","source":"calc_mini = calculate_qe(\n working_directory=\"mini\",\n input_dict={\n \"structure\": structure.output,\n \"pseudopotentials\": pseudopotentials,\n \"kpts\": (3, 3, 3),\n \"calculation\": \"vc-relax\",\n \"smearing\": 0.02,\n },\n)","metadata":{"trusted":true},"outputs":[],"execution_count":10},{"id":"2b88a30a-e26b-4802-89b7-79ca08cc0af9","cell_type":"code","source":"number_of_strains = 5\nstructure_lst = generate_structures(\n structure=calc_mini.output.structure,\n strain_lst=np.linspace(0.9, 1.1, number_of_strains),\n)","metadata":{"trusted":true},"outputs":[],"execution_count":11},{"id":"53e979ac-21db-4aa5-ae58-7cfc08dfa87b","cell_type":"code","source":"job_strain_lst = []\nfor i in range(number_of_strains):\n calc_strain = calculate_qe(\n working_directory=\"strain_\" + str(i),\n input_dict={\n \"structure\": getattr(structure_lst.output, str(i)),\n \"pseudopotentials\": pseudopotentials,\n \"kpts\": (3, 3, 3),\n \"calculation\": \"scf\",\n \"smearing\": 0.02,\n },\n )\n job_strain_lst.append(calc_strain)","metadata":{"trusted":true},"outputs":[],"execution_count":12},{"id":"fbc5285c-7cc5-4318-acf8-06a48a4e2031","cell_type":"code","source":"plot = plot_energy_volume_curve(\n volume_lst=[job.output.volume for job in job_strain_lst],\n energy_lst=[job.output.energy for job in job_strain_lst],\n)","metadata":{"trusted":true},"outputs":[],"execution_count":13},{"id":"299aef9c-7ae7-46f9-a66f-521b05b7fa1c","cell_type":"code","source":"flow = Flow([structure, calc_mini, structure_lst] + job_strain_lst + [plot])","metadata":{"trusted":true},"outputs":[],"execution_count":14},{"id":"e464da97-16a1-4772-9a07-0a47f152781d","cell_type":"code","source":"write_workflow_json(flow=flow, file_name=\"workflow_jobflow.json\")","metadata":{"trusted":true},"outputs":[],"execution_count":15},{"id":"bca646b2-0a9a-4271-966a-e5903a8c9031","cell_type":"code","source":"!cat workflow_jobflow.json","metadata":{"trusted":true},"outputs":[{"name":"stdout","output_type":"stream","text":"{\"nodes\": {\"0\": \"quantum_espresso_workflow.get_bulk_structure\", \"1\": \"quantum_espresso_workflow.calculate_qe\", \"2\": \"quantum_espresso_workflow.generate_structures\", \"3\": \"quantum_espresso_workflow.calculate_qe\", \"4\": \"quantum_espresso_workflow.calculate_qe\", \"5\": \"quantum_espresso_workflow.calculate_qe\", \"6\": \"quantum_espresso_workflow.calculate_qe\", \"7\": \"quantum_espresso_workflow.calculate_qe\", \"8\": \"quantum_espresso_workflow.plot_energy_volume_curve\", \"9\": \"Al\", \"10\": 4.05, \"11\": true, \"12\": \"mini\", \"13\": \"python_workflow_definition.jobflow.get_dict\", \"14\": {\"Al\": \"Al.pbe-n-kjpaw_psl.1.0.0.UPF\"}, \"15\": [3, 3, 3], \"16\": \"vc-relax\", \"17\": 0.02, \"18\": [0.9, 0.9500000000000001, 1.0, 1.05, 1.1], \"19\": \"strain_0\", \"20\": \"python_workflow_definition.jobflow.get_dict\", \"21\": \"scf\", \"22\": \"strain_1\", \"23\": \"python_workflow_definition.jobflow.get_dict\", \"24\": \"strain_2\", \"25\": \"python_workflow_definition.jobflow.get_dict\", \"26\": \"strain_3\", \"27\": \"python_workflow_definition.jobflow.get_dict\", \"28\": \"strain_4\", \"29\": \"python_workflow_definition.jobflow.get_dict\", \"30\": \"python_workflow_definition.jobflow.get_list\", \"31\": \"python_workflow_definition.jobflow.get_list\"}, \"edges\": [{\"target\": 0, \"targetHandle\": \"name\", \"source\": 9, \"sourceHandle\": null}, {\"target\": 0, \"targetHandle\": \"a\", \"source\": 10, \"sourceHandle\": null}, {\"target\": 0, \"targetHandle\": \"cubic\", \"source\": 11, \"sourceHandle\": null}, {\"target\": 1, \"targetHandle\": \"working_directory\", \"source\": 12, \"sourceHandle\": null}, {\"target\": 13, \"targetHandle\": \"structure\", \"source\": 0, \"sourceHandle\": null}, {\"target\": 13, \"targetHandle\": \"pseudopotentials\", \"source\": 14, \"sourceHandle\": null}, {\"target\": 13, \"targetHandle\": \"kpts\", \"source\": 15, \"sourceHandle\": null}, {\"target\": 13, \"targetHandle\": \"calculation\", \"source\": 16, \"sourceHandle\": null}, {\"target\": 13, \"targetHandle\": \"smearing\", \"source\": 17, \"sourceHandle\": null}, {\"target\": 1, \"targetHandle\": \"input_dict\", \"source\": 13, \"sourceHandle\": null}, {\"target\": 2, \"targetHandle\": \"structure\", \"source\": 1, \"sourceHandle\": \"structure\"}, {\"target\": 2, \"targetHandle\": \"strain_lst\", \"source\": 18, \"sourceHandle\": null}, {\"target\": 3, \"targetHandle\": \"working_directory\", \"source\": 19, \"sourceHandle\": null}, {\"target\": 20, \"targetHandle\": \"structure\", \"source\": 2, \"sourceHandle\": \"0\"}, {\"target\": 20, \"targetHandle\": \"pseudopotentials\", \"source\": 14, \"sourceHandle\": null}, {\"target\": 20, \"targetHandle\": \"kpts\", \"source\": 15, \"sourceHandle\": null}, {\"target\": 20, \"targetHandle\": \"calculation\", \"source\": 21, \"sourceHandle\": null}, {\"target\": 20, \"targetHandle\": \"smearing\", \"source\": 17, \"sourceHandle\": null}, {\"target\": 3, \"targetHandle\": \"input_dict\", \"source\": 20, \"sourceHandle\": null}, {\"target\": 4, \"targetHandle\": \"working_directory\", \"source\": 22, \"sourceHandle\": null}, {\"target\": 23, \"targetHandle\": \"structure\", \"source\": 2, \"sourceHandle\": \"1\"}, {\"target\": 23, \"targetHandle\": \"pseudopotentials\", \"source\": 14, \"sourceHandle\": null}, {\"target\": 23, \"targetHandle\": \"kpts\", \"source\": 15, \"sourceHandle\": null}, {\"target\": 23, \"targetHandle\": \"calculation\", \"source\": 21, \"sourceHandle\": null}, {\"target\": 23, \"targetHandle\": \"smearing\", \"source\": 17, \"sourceHandle\": null}, {\"target\": 4, \"targetHandle\": \"input_dict\", \"source\": 23, \"sourceHandle\": null}, {\"target\": 5, \"targetHandle\": \"working_directory\", \"source\": 24, \"sourceHandle\": null}, {\"target\": 25, \"targetHandle\": \"structure\", \"source\": 2, \"sourceHandle\": \"2\"}, {\"target\": 25, \"targetHandle\": \"pseudopotentials\", \"source\": 14, \"sourceHandle\": null}, {\"target\": 25, \"targetHandle\": \"kpts\", \"source\": 15, \"sourceHandle\": null}, {\"target\": 25, \"targetHandle\": \"calculation\", \"source\": 21, \"sourceHandle\": null}, {\"target\": 25, \"targetHandle\": \"smearing\", \"source\": 17, \"sourceHandle\": null}, {\"target\": 5, \"targetHandle\": \"input_dict\", \"source\": 25, \"sourceHandle\": null}, {\"target\": 6, \"targetHandle\": \"working_directory\", \"source\": 26, \"sourceHandle\": null}, {\"target\": 27, \"targetHandle\": \"structure\", \"source\": 2, \"sourceHandle\": \"3\"}, {\"target\": 27, \"targetHandle\": \"pseudopotentials\", \"source\": 14, \"sourceHandle\": null}, {\"target\": 27, \"targetHandle\": \"kpts\", \"source\": 15, \"sourceHandle\": null}, {\"target\": 27, \"targetHandle\": \"calculation\", \"source\": 21, \"sourceHandle\": null}, {\"target\": 27, \"targetHandle\": \"smearing\", \"source\": 17, \"sourceHandle\": null}, {\"target\": 6, \"targetHandle\": \"input_dict\", \"source\": 27, \"sourceHandle\": null}, {\"target\": 7, \"targetHandle\": \"working_directory\", \"source\": 28, \"sourceHandle\": null}, {\"target\": 29, \"targetHandle\": \"structure\", \"source\": 2, \"sourceHandle\": \"4\"}, {\"target\": 29, \"targetHandle\": \"pseudopotentials\", \"source\": 14, \"sourceHandle\": null}, {\"target\": 29, \"targetHandle\": \"kpts\", \"source\": 15, \"sourceHandle\": null}, {\"target\": 29, \"targetHandle\": \"calculation\", \"source\": 21, \"sourceHandle\": null}, {\"target\": 29, \"targetHandle\": \"smearing\", \"source\": 17, \"sourceHandle\": null}, {\"target\": 7, \"targetHandle\": \"input_dict\", \"source\": 29, \"sourceHandle\": null}, {\"target\": 30, \"targetHandle\": \"0\", \"source\": 3, \"sourceHandle\": \"volume\"}, {\"target\": 30, \"targetHandle\": \"1\", \"source\": 4, \"sourceHandle\": \"volume\"}, {\"target\": 30, \"targetHandle\": \"2\", \"source\": 5, \"sourceHandle\": \"volume\"}, {\"target\": 30, \"targetHandle\": \"3\", \"source\": 6, \"sourceHandle\": \"volume\"}, {\"target\": 30, \"targetHandle\": \"4\", \"source\": 7, \"sourceHandle\": \"volume\"}, {\"target\": 8, \"targetHandle\": \"volume_lst\", \"source\": 30, \"sourceHandle\": null}, {\"target\": 31, \"targetHandle\": \"0\", \"source\": 3, \"sourceHandle\": \"energy\"}, {\"target\": 31, \"targetHandle\": \"1\", \"source\": 4, \"sourceHandle\": \"energy\"}, {\"target\": 31, \"targetHandle\": \"2\", \"source\": 5, \"sourceHandle\": \"energy\"}, {\"target\": 31, \"targetHandle\": \"3\", \"source\": 6, \"sourceHandle\": \"energy\"}, {\"target\": 31, \"targetHandle\": \"4\", \"source\": 7, \"sourceHandle\": \"energy\"}, {\"target\": 8, \"targetHandle\": \"energy_lst\", \"source\": 31, \"sourceHandle\": null}]}"}],"execution_count":16},{"id":"f45684a8-2613-415a-ab0a-5cb2bafaffea","cell_type":"code","source":"pr = Project(\"test\")\npr.remove_jobs(recursive=True, silently=True)","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":"de4596a640bb4c67bc94810c7c541560"}},"metadata":{}}],"execution_count":17},{"id":"8f2a621d-b533-4ddd-8bcd-c22db2f922ec","cell_type":"code","source":"delayed_object = load_workflow_json(project=pr, file_name=\"workflow_jobflow.json\")\ndelayed_object.draw()","metadata":{"trusted":true},"outputs":[{"output_type":"display_data","data":{"text/plain":"","image/svg+xml":"\n\n\n\n\ncreate_function_job_892a2611ed9dc46d9882a7bd4310c136\n\ncreate_function_job=<pyiron_base.project.delayed.DelayedObject object at 0x7c31b4d1b7d0>\n\n\n\nvolume_lst_357c1052d0fbaf2e8c3d01f15f35b1da\n\nvolume_lst=<pyiron_base.project.delayed.DelayedObject object at 0x7c31b4d1b470>\n\n\n\nvolume_lst_357c1052d0fbaf2e8c3d01f15f35b1da->create_function_job_892a2611ed9dc46d9882a7bd4310c136\n\n\n\n\n\n0_19cff3259db47a960216b630d221ec36\n\n0=<pyiron_base.project.delayed.DelayedObject object at 0x7c31b4d1ae40>\n\n\n\n0_19cff3259db47a960216b630d221ec36->volume_lst_357c1052d0fbaf2e8c3d01f15f35b1da\n\n\n\n\n\nworking_directory_2e9abb255f1a31f7d29b4451ad422add\n\nworking_directory=strain_0\n\n\n\nworking_directory_2e9abb255f1a31f7d29b4451ad422add->0_19cff3259db47a960216b630d221ec36\n\n\n\n\n\n0_7a30b972299a757a2a79392af4e52117\n\n0=<pyiron_base.project.delayed.DelayedObject object at 0x7c31b4d1a810>\n\n\n\nworking_directory_2e9abb255f1a31f7d29b4451ad422add->0_7a30b972299a757a2a79392af4e52117\n\n\n\n\n\nenergy_lst_fa3531fbb879910a7fc8ee623fea3cf9\n\nenergy_lst=<pyiron_base.project.delayed.DelayedObject object at 0x7c31b4d1b320>\n\n\n\n0_7a30b972299a757a2a79392af4e52117->energy_lst_fa3531fbb879910a7fc8ee623fea3cf9\n\n\n\n\n\ninput_dict_6c5e754466c3b4de0f7ffe00a5fea611\n\ninput_dict=<pyiron_base.project.delayed.DelayedObject object at 0x7c31b4d1a4e0>\n\n\n\ninput_dict_6c5e754466c3b4de0f7ffe00a5fea611->0_19cff3259db47a960216b630d221ec36\n\n\n\n\n\ninput_dict_6c5e754466c3b4de0f7ffe00a5fea611->0_7a30b972299a757a2a79392af4e52117\n\n\n\n\n\nstructure_41b186110ddac1745e9eb0e720dc581b\n\nstructure=<pyiron_base.project.delayed.DelayedObject object at 0x7c31b4d194f0>\n\n\n\nstructure_41b186110ddac1745e9eb0e720dc581b->input_dict_6c5e754466c3b4de0f7ffe00a5fea611\n\n\n\n\n\nstructure_cccfe44f34288a35a302b719db0624da\n\nstructure=<pyiron_base.project.delayed.DelayedObject object at 0x7c31b4d188c0>\n\n\n\nstructure_cccfe44f34288a35a302b719db0624da->structure_41b186110ddac1745e9eb0e720dc581b\n\n\n\n\n\nstructure_68a7fa6df8016cd84089066aaf331433\n\nstructure=<pyiron_base.project.delayed.DelayedObject object at 0x7c31b4d19190>\n\n\n\nstructure_cccfe44f34288a35a302b719db0624da->structure_68a7fa6df8016cd84089066aaf331433\n\n\n\n\n\nstructure_bc09b2beae387c6d7a0c58649decaee3\n\nstructure=<pyiron_base.project.delayed.DelayedObject object at 0x7c31b4d18e60>\n\n\n\nstructure_cccfe44f34288a35a302b719db0624da->structure_bc09b2beae387c6d7a0c58649decaee3\n\n\n\n\n\nstructure_3a09a08dc34834d213afbd9123029bf3\n\nstructure=<pyiron_base.project.delayed.DelayedObject object at 0x7c31b4d18b30>\n\n\n\nstructure_cccfe44f34288a35a302b719db0624da->structure_3a09a08dc34834d213afbd9123029bf3\n\n\n\n\n\nstructure_99c5b8eeb24c17afd1d39fff281ebbe7\n\nstructure=<pyiron_base.project.delayed.DelayedObject object at 0x7c31b4d18950>\n\n\n\nstructure_cccfe44f34288a35a302b719db0624da->structure_99c5b8eeb24c17afd1d39fff281ebbe7\n\n\n\n\n\ninput_dict_13eeb01ea87de0954991a6006bfb3590\n\ninput_dict=<pyiron_base.project.delayed.DelayedObject object at 0x7c31b4d1a1b0>\n\n\n\nstructure_68a7fa6df8016cd84089066aaf331433->input_dict_13eeb01ea87de0954991a6006bfb3590\n\n\n\n\n\ninput_dict_74c734e57d093feea4abfb381f8910ce\n\ninput_dict=<pyiron_base.project.delayed.DelayedObject object at 0x7c31b4d19e80>\n\n\n\nstructure_bc09b2beae387c6d7a0c58649decaee3->input_dict_74c734e57d093feea4abfb381f8910ce\n\n\n\n\n\ninput_dict_d39c6de0ed9bdc787627669d7470467e\n\ninput_dict=<pyiron_base.project.delayed.DelayedObject object at 0x7c31b4d19b50>\n\n\n\nstructure_3a09a08dc34834d213afbd9123029bf3->input_dict_d39c6de0ed9bdc787627669d7470467e\n\n\n\n\n\ninput_dict_6d29bd06497aa4c92a9d715cdf5ce098\n\ninput_dict=<pyiron_base.project.delayed.DelayedObject object at 0x7c31b4d19820>\n\n\n\nstructure_99c5b8eeb24c17afd1d39fff281ebbe7->input_dict_6d29bd06497aa4c92a9d715cdf5ce098\n\n\n\n\n\nworking_directory_a17ade9a563d8dcadb655fb2e1c743a7\n\nworking_directory=mini\n\n\n\nworking_directory_a17ade9a563d8dcadb655fb2e1c743a7->structure_cccfe44f34288a35a302b719db0624da\n\n\n\n\n\ninput_dict_5d828273ab558b28ed1cf20c827eff63\n\ninput_dict=<pyiron_base.project.delayed.DelayedObject object at 0x7c31b4d185f0>\n\n\n\ninput_dict_5d828273ab558b28ed1cf20c827eff63->structure_cccfe44f34288a35a302b719db0624da\n\n\n\n\n\nstructure_304e7c01c57843976aefed4a4c8f177f\n\nstructure=<pyiron_base.project.delayed.DelayedObject object at 0x7c31b4d182c0>\n\n\n\nstructure_304e7c01c57843976aefed4a4c8f177f->input_dict_5d828273ab558b28ed1cf20c827eff63\n\n\n\n\n\nname_467734216d9bd2497ffd28d5cd6daba0\n\nname=Al\n\n\n\nname_467734216d9bd2497ffd28d5cd6daba0->structure_304e7c01c57843976aefed4a4c8f177f\n\n\n\n\n\na_aea0574e321c6f75f923c059730e9537\n\na=4.05\n\n\n\na_aea0574e321c6f75f923c059730e9537->structure_304e7c01c57843976aefed4a4c8f177f\n\n\n\n\n\ncubic_bad787c53fa02a5559fe570238fdb23a\n\ncubic=True\n\n\n\ncubic_bad787c53fa02a5559fe570238fdb23a->structure_304e7c01c57843976aefed4a4c8f177f\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_6c5e754466c3b4de0f7ffe00a5fea611\n\n\n\n\n\npseudopotentials_453cdcc0d627a851e196cd899d956d10->input_dict_5d828273ab558b28ed1cf20c827eff63\n\n\n\n\n\npseudopotentials_453cdcc0d627a851e196cd899d956d10->input_dict_13eeb01ea87de0954991a6006bfb3590\n\n\n\n\n\npseudopotentials_453cdcc0d627a851e196cd899d956d10->input_dict_74c734e57d093feea4abfb381f8910ce\n\n\n\n\n\npseudopotentials_453cdcc0d627a851e196cd899d956d10->input_dict_d39c6de0ed9bdc787627669d7470467e\n\n\n\n\n\npseudopotentials_453cdcc0d627a851e196cd899d956d10->input_dict_6d29bd06497aa4c92a9d715cdf5ce098\n\n\n\n\n\n1_e45fa48f1aee1072de7aaf5dc0420c75\n\n1=<pyiron_base.project.delayed.DelayedObject object at 0x7c31b4d1ae10>\n\n\n\ninput_dict_13eeb01ea87de0954991a6006bfb3590->1_e45fa48f1aee1072de7aaf5dc0420c75\n\n\n\n\n\n1_f38e3740000666a0a4d189abe1ff0cc0\n\n1=<pyiron_base.project.delayed.DelayedObject object at 0x7c31b4d1a7e0>\n\n\n\ninput_dict_13eeb01ea87de0954991a6006bfb3590->1_f38e3740000666a0a4d189abe1ff0cc0\n\n\n\n\n\n2_d984e75874ee5a6e75c8acb7f3652742\n\n2=<pyiron_base.project.delayed.DelayedObject object at 0x7c31b4d1ac30>\n\n\n\ninput_dict_74c734e57d093feea4abfb381f8910ce->2_d984e75874ee5a6e75c8acb7f3652742\n\n\n\n\n\n2_5088cdd7d1c10333b416945176c66741\n\n2=<pyiron_base.project.delayed.DelayedObject object at 0x7c31b4d1a630>\n\n\n\ninput_dict_74c734e57d093feea4abfb381f8910ce->2_5088cdd7d1c10333b416945176c66741\n\n\n\n\n\n3_e68494c37d53249018101125d1c99b9d\n\n3=<pyiron_base.project.delayed.DelayedObject object at 0x7c31b4d1af60>\n\n\n\ninput_dict_d39c6de0ed9bdc787627669d7470467e->3_e68494c37d53249018101125d1c99b9d\n\n\n\n\n\n3_efe9dce4b17e724869f939fe5d62fb21\n\n3=<pyiron_base.project.delayed.DelayedObject object at 0x7c31b4d1a930>\n\n\n\ninput_dict_d39c6de0ed9bdc787627669d7470467e->3_efe9dce4b17e724869f939fe5d62fb21\n\n\n\n\n\n4_48594a541b0ea0f115780b4d19539794\n\n4=<pyiron_base.project.delayed.DelayedObject object at 0x7c31b4d1b050>\n\n\n\ninput_dict_6d29bd06497aa4c92a9d715cdf5ce098->4_48594a541b0ea0f115780b4d19539794\n\n\n\n\n\n4_449536ef295052e3678b58d433789671\n\n4=<pyiron_base.project.delayed.DelayedObject object at 0x7c31b4d1aa20>\n\n\n\ninput_dict_6d29bd06497aa4c92a9d715cdf5ce098->4_449536ef295052e3678b58d433789671\n\n\n\n\n\nkpts_e961a9390797b0f6f8887a402ea3e9aa\n\nkpts=[3, 3, 3]\n\n\n\nkpts_e961a9390797b0f6f8887a402ea3e9aa->input_dict_6c5e754466c3b4de0f7ffe00a5fea611\n\n\n\n\n\nkpts_e961a9390797b0f6f8887a402ea3e9aa->input_dict_5d828273ab558b28ed1cf20c827eff63\n\n\n\n\n\nkpts_e961a9390797b0f6f8887a402ea3e9aa->input_dict_13eeb01ea87de0954991a6006bfb3590\n\n\n\n\n\nkpts_e961a9390797b0f6f8887a402ea3e9aa->input_dict_74c734e57d093feea4abfb381f8910ce\n\n\n\n\n\nkpts_e961a9390797b0f6f8887a402ea3e9aa->input_dict_d39c6de0ed9bdc787627669d7470467e\n\n\n\n\n\nkpts_e961a9390797b0f6f8887a402ea3e9aa->input_dict_6d29bd06497aa4c92a9d715cdf5ce098\n\n\n\n\n\ncalculation_77b75a01e65d83962d14fa8a882d6c34\n\ncalculation=vc-relax\n\n\n\ncalculation_77b75a01e65d83962d14fa8a882d6c34->input_dict_5d828273ab558b28ed1cf20c827eff63\n\n\n\n\n\nsmearing_64a632a7e5bfbb7d0c6face9b82082a9\n\nsmearing=0.02\n\n\n\nsmearing_64a632a7e5bfbb7d0c6face9b82082a9->input_dict_6c5e754466c3b4de0f7ffe00a5fea611\n\n\n\n\n\nsmearing_64a632a7e5bfbb7d0c6face9b82082a9->input_dict_5d828273ab558b28ed1cf20c827eff63\n\n\n\n\n\nsmearing_64a632a7e5bfbb7d0c6face9b82082a9->input_dict_13eeb01ea87de0954991a6006bfb3590\n\n\n\n\n\nsmearing_64a632a7e5bfbb7d0c6face9b82082a9->input_dict_74c734e57d093feea4abfb381f8910ce\n\n\n\n\n\nsmearing_64a632a7e5bfbb7d0c6face9b82082a9->input_dict_d39c6de0ed9bdc787627669d7470467e\n\n\n\n\n\nsmearing_64a632a7e5bfbb7d0c6face9b82082a9->input_dict_6d29bd06497aa4c92a9d715cdf5ce098\n\n\n\n\n\nstrain_lst_17d5bcbc7579ab5e0f98577d05347b86\n\nstrain_lst=[0.9, 0.9500000000000001, 1.0, 1.05, 1.1]\n\n\n\nstrain_lst_17d5bcbc7579ab5e0f98577d05347b86->structure_41b186110ddac1745e9eb0e720dc581b\n\n\n\n\n\nstrain_lst_17d5bcbc7579ab5e0f98577d05347b86->structure_68a7fa6df8016cd84089066aaf331433\n\n\n\n\n\nstrain_lst_17d5bcbc7579ab5e0f98577d05347b86->structure_bc09b2beae387c6d7a0c58649decaee3\n\n\n\n\n\nstrain_lst_17d5bcbc7579ab5e0f98577d05347b86->structure_3a09a08dc34834d213afbd9123029bf3\n\n\n\n\n\nstrain_lst_17d5bcbc7579ab5e0f98577d05347b86->structure_99c5b8eeb24c17afd1d39fff281ebbe7\n\n\n\n\n\ncalculation_bc91e0ce7227762f507f47b85f2f0a83\n\ncalculation=scf\n\n\n\ncalculation_bc91e0ce7227762f507f47b85f2f0a83->input_dict_6c5e754466c3b4de0f7ffe00a5fea611\n\n\n\n\n\ncalculation_bc91e0ce7227762f507f47b85f2f0a83->input_dict_13eeb01ea87de0954991a6006bfb3590\n\n\n\n\n\ncalculation_bc91e0ce7227762f507f47b85f2f0a83->input_dict_74c734e57d093feea4abfb381f8910ce\n\n\n\n\n\ncalculation_bc91e0ce7227762f507f47b85f2f0a83->input_dict_d39c6de0ed9bdc787627669d7470467e\n\n\n\n\n\ncalculation_bc91e0ce7227762f507f47b85f2f0a83->input_dict_6d29bd06497aa4c92a9d715cdf5ce098\n\n\n\n\n\n1_e45fa48f1aee1072de7aaf5dc0420c75->volume_lst_357c1052d0fbaf2e8c3d01f15f35b1da\n\n\n\n\n\nworking_directory_5423d2cc67129a6d0383af6f347df5bd\n\nworking_directory=strain_1\n\n\n\nworking_directory_5423d2cc67129a6d0383af6f347df5bd->1_e45fa48f1aee1072de7aaf5dc0420c75\n\n\n\n\n\nworking_directory_5423d2cc67129a6d0383af6f347df5bd->1_f38e3740000666a0a4d189abe1ff0cc0\n\n\n\n\n\n1_f38e3740000666a0a4d189abe1ff0cc0->energy_lst_fa3531fbb879910a7fc8ee623fea3cf9\n\n\n\n\n\n2_d984e75874ee5a6e75c8acb7f3652742->volume_lst_357c1052d0fbaf2e8c3d01f15f35b1da\n\n\n\n\n\nworking_directory_cc646e064ddfc4b2811aba3d86d27992\n\nworking_directory=strain_2\n\n\n\nworking_directory_cc646e064ddfc4b2811aba3d86d27992->2_d984e75874ee5a6e75c8acb7f3652742\n\n\n\n\n\nworking_directory_cc646e064ddfc4b2811aba3d86d27992->2_5088cdd7d1c10333b416945176c66741\n\n\n\n\n\n2_5088cdd7d1c10333b416945176c66741->energy_lst_fa3531fbb879910a7fc8ee623fea3cf9\n\n\n\n\n\n3_e68494c37d53249018101125d1c99b9d->volume_lst_357c1052d0fbaf2e8c3d01f15f35b1da\n\n\n\n\n\nworking_directory_e27768d53df6cd8dc245c52054ecf31f\n\nworking_directory=strain_3\n\n\n\nworking_directory_e27768d53df6cd8dc245c52054ecf31f->3_e68494c37d53249018101125d1c99b9d\n\n\n\n\n\nworking_directory_e27768d53df6cd8dc245c52054ecf31f->3_efe9dce4b17e724869f939fe5d62fb21\n\n\n\n\n\n3_efe9dce4b17e724869f939fe5d62fb21->energy_lst_fa3531fbb879910a7fc8ee623fea3cf9\n\n\n\n\n\n4_48594a541b0ea0f115780b4d19539794->volume_lst_357c1052d0fbaf2e8c3d01f15f35b1da\n\n\n\n\n\nworking_directory_72bba39b22d2b7ce154d37c7e8c658b7\n\nworking_directory=strain_4\n\n\n\nworking_directory_72bba39b22d2b7ce154d37c7e8c658b7->4_48594a541b0ea0f115780b4d19539794\n\n\n\n\n\nworking_directory_72bba39b22d2b7ce154d37c7e8c658b7->4_449536ef295052e3678b58d433789671\n\n\n\n\n\n4_449536ef295052e3678b58d433789671->energy_lst_fa3531fbb879910a7fc8ee623fea3cf9\n\n\n\n\n\nenergy_lst_fa3531fbb879910a7fc8ee623fea3cf9->create_function_job_892a2611ed9dc46d9882a7bd4310c136\n\n\n\n\n"},"metadata":{}}],"execution_count":18},{"id":"cf80267d-c2b0-4236-bf1d-a57596985fc1","cell_type":"code","source":"delayed_object.pull()","metadata":{"trusted":true},"outputs":[{"name":"stdout","output_type":"stream","text":"The job get_bulk_structure_40d4be995c851afca48e5650f5e2d787 was saved and received the ID: 1\nThe job get_dict_bccb1cf45d545b4187a57ac7e53a7f00 was saved and received the ID: 2\nThe job calculate_qe_1b41e67724a7f43d770185783035d160 was saved and received the ID: 3\n"},{"name":"stderr","output_type":"stream","text":"[jupyter-jan-janssen-pyt-flow-definition-0gcm79y7:00271] 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_9587ea80d4ae39fde016d9679db04033 was saved and received the ID: 4\nThe job get_dict_9341dc849896355205022dc393c01d46 was saved and received the ID: 5\nThe job calculate_qe_865c57c1d9f63b47d007ff4e29a79bcf was saved and received the ID: 6\n"},{"name":"stderr","output_type":"stream","text":"[jupyter-jan-janssen-pyt-flow-definition-0gcm79y7:00286] 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_caeeba9c2e636a0a2e4f521c5c45a5ec was saved and received the ID: 7\nThe job calculate_qe_bac6ab05ad881ee3dd6f7b7bcc9733c0 was saved and received the ID: 8\n"},{"name":"stderr","output_type":"stream","text":"[jupyter-jan-janssen-pyt-flow-definition-0gcm79y7:00297] 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_3c1dc10e66c8ae32a3b4b27f0cba6c3f was saved and received the ID: 9\nThe job calculate_qe_71d64b1736003745767a4e3919a051b4 was saved and received the ID: 10\n"},{"name":"stderr","output_type":"stream","text":"[jupyter-jan-janssen-pyt-flow-definition-0gcm79y7:00308] 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_867e1f207d6fa437181062461e609943 was saved and received the ID: 11\nThe job calculate_qe_8712848b85e12a80ebdd9a666e63622d was saved and received the ID: 12\n"},{"name":"stderr","output_type":"stream","text":"[jupyter-jan-janssen-pyt-flow-definition-0gcm79y7:00319] 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_6fa378cba5c6963b82440d4afdca7e2e was saved and received the ID: 13\nThe job calculate_qe_fb13b133dee87016b8479c393a8c7d87 was saved and received the ID: 14\n"},{"name":"stderr","output_type":"stream","text":"[jupyter-jan-janssen-pyt-flow-definition-0gcm79y7:00335] 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_56ca34376ea987f3f7e914d63d5c4354 was saved and received the ID: 15\nThe job get_list_d4c43df1226fc8ef49ffcfef683c4c13 was saved and received the ID: 16\nThe job plot_energy_volume_curve_d29710bff91802a34165a810862c773e was saved and received the ID: 17\n"},{"output_type":"display_data","data":{"text/plain":"
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eHhLFq0CABvb28GDBjAtGnTWLFiBfv27WPu3Ll88sknjB07Fiid2ps2bRpvvvkmCxYsYPfu3Tz55JPs2rWLO++805T3ak9lJ3p/tyWVIzkF5oYRERGpo0wdWboQn3/+Offddx9DhgwBYPTo0cyePbvCNgkJCWRlZZV/PW/ePKZPn86tt97K8ePHCQ0N5fnnn+eee+4p3+aBBx4gPz+fqVOncvz4cbp27cqSJUu45JL6f0l991ZN6RbiQ9zBTL5Ye4D7B7UzO5KIiEid4xDrLNV1jrTO0p99G5fC/fPi8GvizqpHr8LNxSEGG0VERC5avVpnSWrP8E4t8G/izpGcAn7clmZ2HBERkTpHZamBc3NxIrpvKFC6jIAGGkVERCpSWRJu6dMKNxcnthzKYtOBTLPjiIiI1CkqS0Lzxu6M7hoEaJFKERGRP1NZEuB/ywj8tD2dtKxT5oYRERGpQ1SWBICOQVZ6t25Gic3gs9j9ZscRERGpM1SWpNwdp0eXvlh7gPyiEnPDiIiI1BEqS1JuUEQALX0acSKviG/jUsyOIyIiUieoLEk5F2cnJkSVLSOQrGUEREREUFmSP7np0lY0cnVmV3oOsXuPmx1HRETEdCpLUoHV05XrerQEtIyAiIgIqCxJJSb2CwNgaXwGB4/nmRtGRETEZCpLcoZ2AU24vJ0vNgM+WZNsdhwRERFTqSxJpcoWqZy3/iC5BcXmhhERETGRypJUamB7f8Kae5KTX8zCTYfMjiMiImIalSWplJOThdtPn7s0d3UyNpuWERARkYZJZUnO6oaewTR2d2HPkVx+333U7DgiIiKmUFmSs2ri4cq4XsGAlhEQEZGGS2VJqnR7VBgWC6xIOMKeIyfNjiMiImJ3KktSpTBfL67q4A/AJ6uTzQ0jIiJiApUlOadJl7UGYMHGQ2TnF5mcRkRExL5UluScLmvbnHb+jcktLOE/6w+aHUdERMSuVJbknCwWCxNPL1L58ZpkSrSMgIiINCAqS3JerusejLWRKwePn2LZrsNmxxEREbEblSU5L43cnLmpdwigZQRERKRhUVmS8zYhKgwnC6zec4xd6dlmxxEREbELlSU5by19GjG0YyAAc1clmxtGRETETlSW5IKULSOwaHMKJ3ILTU4jIiJS+1SW5IJcGtaUjkHeFBTb+HL9AbPjiIiI1DqVJbkgFoulfHTp0zX7KSqxmZxIRESkdqksyQUb2aUFzb3cSMvK5+cd6WbHERERqVUqS3LBPFydubVPK0AneouISP2nsiTVclvfUFydLWzYf4Jth7LMjiMiIlJrVJakWvy9PRjRuQWgRSpFRKR+U1mSapt4+kTv77amcjgn3+Q0IiIitUNlSaqtW4gP3Vv5UFRi8MVaLSMgIiL1k8qSXJSyZQQ+iz1AQXGJyWlERERqnsqSXJThnQIJ8Hbn6MkCftiaZnYcERGRGqeyJBfF1dmJ6L6hAMxZlYxhGCYnEhERqVkqS3LRbu7dCjcXJ7alZLHpwAmz44iIiNQolSW5aM0buzOmWxAAH2mRShERqWdUlqRGTOxXeqJ3zPZ0UjNPmZxGRESk5qgsSY2IDPKmT+tmlNgMPovdb3YcERGRGqOyJDWmbBmBL9cdIL9IywiIiMjFO3g8jzmr9mGzmXcBkcqS1JjBkQEEN23EibwivtmcYnYcERFxcDabwbQFW/jHdzt54cd403KoLEmNcXaycHtUGKBlBERE5OJ9tnY/sXuP08jVmeioUNNyqCxJjbqxVwiNXJ1JyMhhzd5jZscREREHdeBYHjN/3AXAY8PDCW3uZVoWlSWpUVZPV67v2RIoHV0SERG5UDabwcMLtnCqqIS+bZqVL35sFpUlqXFlywgsjc/gwLE8k9OIiIij+WRNMuv2HcfTzZmXru+Kk5PF1DwqS1Lj2vo35or2fhgGfLwm2ew4IiLiQJKP5jIrpnT6bfrwcFo19zQ5kcqS1JJJ/cIA+M/6g+QWFJsbRkREHILNZvDIgq3kF9mIatOcW/uYO/1WRmVJasWA9n608fUip6CYrzcdMjuOiIg4gLmrk1mXfBwvN2deuqGL6dNvZVSWpFY4OVm4/fTo0txVyaYuJiYiInXfvqO5vPTz6em3ayIIaWb+9FsZlSWpNdf3DKaJuwt7j+byW9IRs+OIiEgdVWIzmDZ/C/lFNvq39eXWPq3MjlSBypLUmsbuLozrFQKUji6JiIhUZs6qfWzYf4LG7i7Mur4zFkvdmH4ro7IktWpivzAsFvgt8Qi7D580O46IiNQxe46c5OWfEwB4/JoIgpvWnem3MipLUqtaNffk6vAAAD5enWxuGBERqVPKpt8Kim1c3s6Xm3uHmB2pUipLUusmXRYGwNebDpF1qsjcMCIiUmd8tHIfmw5knp5+61Lnpt/KqCxJret3SXM6BDQhr7CE+RsOmh1HRETqgN2HT/LyL6XTb0+MiKClTyOTE52dypLUOovFwsTTo0tzVydTomUEREQatBKbwcPzt1BYbOOK9n6Mv7RuTr+VUVkSuxjTrSU+nq4cOnGKpfEZZscRERETffD7XuIOZtLE3YUX6+DVb3+msiR20cjNmZsuLV03Q8sIiIg0XLsP5/DakkQAnhwVSQtr3Z1+K6OyJHYzISoUZycLa/YeIz4t2+w4IiJiZ8UlNh6av5XCYhtXdvBjXM9gsyOdF4cpSydOnCA6Ohqr1YrVaiU6OprMzMwq9zl58iRTpkwhODiYRo0aERERwbvvvlthm/T0dKKjowkMDMTLy4sePXqwYMGCWnwnDVeQTyOGdQwENLokItIQ/ev3vWw5mEkTDxdmXld3r377M4cpS7fccgtxcXHExMQQExNDXFwc0dHRVe4zdepUYmJi+Oyzz4iPj2fq1Knce++9fPvtt+XbREdHk5CQwOLFi9m2bRvXXXcd48ePZ/PmzbX9lhqksmUEvolL4XhuoblhRETEbhIzcnhjSRIAT4/qSKDVw+RE588hylJ8fDwxMTH8+9//JioqiqioKD744AO+//57EhISzrrfmjVruP322xk4cCBhYWH85S9/oWvXrmzYsKHCNvfeey+9e/emTZs2PPHEE/j4+LBp0yZ7vLUGp2doUzq19Kag2MaX6w6YHUdEROyguMRWevVbiY2rwv25vkdLsyNdEIcoS2vWrMFqtdKnT5/y5/r27YvVamX16tVn3a9///4sXryYlJQUDMNg+fLlJCYmMnTo0ArbfPXVVxw/fhybzca8efMoKChg4MCBtfmWGiyLxcKkfq0B+HTNfopKbCYnEhGR2vb+f/ey9VAW3h4uzLyu7l/99mcOUZbS09Px9/c/43l/f3/S09PPut+bb75JZGQkwcHBuLm5MWzYMN555x369+9fvs1XX31FcXExzZs3x93dnbvvvptFixZxySWXnPV1CwoKyM7OrvCQ8zeyawt8G7uTnp1PzPaz//cTERHHtys9mzeWll79NmN0RwK8HWf6rYypZWnGjBlYLJYqH2VTZpW1UMMwqmynb775JrGxsSxevJiNGzfy6quv8re//Y2lS5eWb/PEE09w4sQJli5dyoYNG3jwwQcZN24c27ZtO+vrzpw5s/xEc6vVSkhI3V5Mq65xd3Hm1j6lywjMWbXP5DQiIlJbik5PvxWVGAyK8Gdsd8eafitjMQzDtOWUjx49ytGjR6vcJiwsjC+++IIHH3zwjKvffHx8eP3115k0adIZ+506dQqr1cqiRYsYMWJE+fN33XUXhw4dIiYmhj179tC2bVu2b99Ox44dy7cZNGgQbdu25b333qs0U0FBAQUFBeVfZ2dnExISQlZWFt7e3ufz1hu8wzn5XDZrGUUlBt/+/TK6hviYHUlERGrYW78m8eqSRKyNXFky9Qr869ioUnZ2Nlar9Zx/v13smOkMvr6++Pr6nnO7qKgosrKyWLduHb179wZg7dq1ZGVl0a9fv0r3KSoqoqioCCenioNnzs7O2Gyl58nk5eUBVLlNZdzd3XF3dz9nbjk7/yYejOwSxKLNKcxdnczr47uZHUlERGpQfFo2by4rvfrtH6M71rmidCEc4pyliIgIhg0bxuTJk4mNjSU2NpbJkyczcuRIOnToUL5deHg4ixYtAsDb25sBAwYwbdo0VqxYwb59+5g7dy6ffPIJY8eOLd++bdu23H333axbt449e/bw6quvsmTJEsaMGWPGW21QypYR+H5rKoez880NIyIiNeaP02+DIwO4tluQ2ZEuikOUJYDPP/+czp07M2TIEIYMGUKXLl349NNPK2yTkJBAVlZW+dfz5s3j0ksv5dZbbyUyMpJZs2bx/PPPc8899wDg6urKjz/+iJ+fH6NGjaJLly588sknfPzxx1xzzTV2fX8NUZdgH3qGNqWoxOCztVpGQESkvnhn+R52pGbj4+nK82M7OdzVb39m6jlL9cX5znnKmb7bksq9X27Gt7Ebqx67CncXZ7MjiYjIRdiRmsW1s1dRbDP4503duLZb3T2p+3z/fjvMyJLUT8M6BRLo7cHRk4V8vyXN7DgiInIRCottPDx/K8U2g6EdAxjd1bGn38qoLImpXJ2diI4KBWDO6n1ooFNExHG9vXw38WnZNPV05bkxjrf45NmoLInpbu7dCncXJ7anZLNh/wmz44iISDVsT8ni7eW7AXjm2k74Nak/V42rLInpmnm5Meb0nPbcVcnmhhERkQtWOv22hWKbwfBOgYzs0sLsSDVKZUnqhEn9wwCI2ZFOauYpc8OIiMgFmb0siV3pOTTzcuPZMY5/9dufqSxJnRAe6E1Um+aU2Aw+WbPf7DgiInKeth3K4u0VewB49tpO+DauP9NvZVSWpM4oW6Tyy3UHOFVYYm4YERE5p4LiEh6ev4USm8GILi0YUc+m38qoLEmdcXVEACHNGpF1qohv4lLMjiMiIufw1q+7ScjIobmXG8+M7njuHRyUypLUGc5OFm6PCgNgziotIyAiUpdtPZTJu7+VTr89N6YTzevh9FsZlSWpU8b1CsHTzZnEjJOs3nPM7DgiIlKJP06/jeoaxPDO9XP6rYzKktQp1kau3NAzGIA5WkZARKRO+ufSJBIzTuLb2I1/1OPptzIqS1Ln3N4vDIBfd2Ww/1iuuWFERKSCuIOZvFc+/daZZl5uJieqfSpLUudc4teYAe39MAz4eLWWERARqSvyi0qn32wGXNstiGGdAs2OZBfVKku5ufrXvtSusmUE5m84yMmCYnPDiIgIAK8vTWT34ZP4NnZnxqj6P/1WplplKSAggDvuuIOVK1fWdB4RAK5o50cbXy9yCor5euMhs+OIiDR4mw6c4IP/7gXghbGdaNoApt/KVKssffnll2RlZXH11VfTvn17Zs2aRWpqak1nkwbMycnCxNOjS3NXJ2OzaRkBERGz5BeVMO309NvY7i0Z0rFhTL+VqVZZGjVqFF9//TWpqan89a9/5csvvyQ0NJSRI0eycOFCios1bSIX7/oewTTxcGHf0Vx+SzxidhwRkQbrtSWJ7DmSi18Td54eFWl2HLu7qBO8mzdvztSpU9myZQuvvfYaS5cu5YYbbiAoKIinnnqKvLy8msopDZCXuwvje4UA8NGqfSanERFpmDbuP8EHv5dOv80c2xkfz4Yz/VbmospSeno6L730EhERETz22GPccMMN/Prrr7z++ussWrSIMWPG1FBMaagmRIVhscDvSUfZfTjH7DgiIg1K2fSbYcB1PVoyKDLA7EimcKnOTgsXLmTOnDn8/PPPREZG8ve//53bbrsNHx+f8m26detG9+7dayqnNFCtmnsyKCKAJTszmLs6mefGdDY7kohIg/HKzwnsPZpLgLc7T49sOFe//Vm1RpYmTZpEUFAQq1atIi4ujilTplQoSgBt2rTh//7v/2oiozRwZcsIfL0xhay8InPDiIg0EBuSj/Ph6VMgZl7XGaunq8mJzFOtkaW0tDQ8PT2r3KZRo0Y8/fTT1Qol8kdRbZoTHtiEXek5fLXhAH+54hKzI4mI1GunCksXnzQMuKFnMFeFN8zptzLVGlkqLi4mOzv7jEdOTg6FhYU1nVEaOIvFwsTTt0D5ePV+SrSMgIhIrXr55wSSj+UR6O3BkyMb3tVvf1atsuTj40PTpk3PePj4+NCoUSNCQ0N5+umnsdlsNZ1XGqgx3VvS1NOVlMxTLNmZYXYcEZF6a92+48xZfXr67frOWBs13Om3MtUqS3PnziUoKIjHH3+cb775hkWLFvH444/TsmVL3n33Xf7yl7/w5ptvMmvWrJrOKw2Uh6szN/duBcAcLSMgIlIr8gqLmbagdPrtxl7BXNnB3+xIdUK1zln6+OOPefXVV7nxxhvLnxs9ejSdO3fm/fff59dff6VVq1Y8//zzPP744zUWVhq26KhQ3v/vXtbuO86O1Cw6BlnNjiQiUq+8FJPA/mN5tLB68ISm38pVa2RpzZo1lS4L0L17d9asWQNA//79OXDgwMWlE/mDFtZG5Xe4/nh1srlhRETqmdi9x5h7+rN11vVd8PbQ9FuZapWl4OBgPvzwwzOe//DDDwkJKV1x+dixYzRt2vTi0on8yR2nlxH4Ji6VYycLzA0jIlJP5BUW88iCrQDcdGkIA9r7mZyobqnWNNwrr7zCuHHj+Omnn7j00kuxWCysX7+eXbt2sWDBAgDWr1/P+PHjazSsSI9WTekSbGXroSy+XHeAKVe1MzuSiIjDe/GnXRw4nkeQ1YP/GxFhdpw6x2IYRrWuw96/fz/vvfceCQkJGIZBeHg4d999N2FhYTUcse7Lzs7GarWSlZWFt7e32XHqvUWbDzH1qy0EeLuz8tGrcHW+qLv2iIg0aKv3HOWWD9YC8Omdvbm8XcMZVTrfv98XPLJUVFTEkCFDeP/995k5c+ZFhRSpjms6t+D5H3aRkV3AT9vTGd01yOxIIiIOKbfgf9Nvt/Rp1aCK0oW44H+Su7q6sn37diwWS23kETkndxdnbuurZQRERC7WrJ92cejEKVr6NOLxazT9djbVmr+YMGFCpSd4i9jLrX1CcXN2YvOBTOIOZpodR0TE4azefZRPY/cD8NINXWjsXq3TmBuEah2ZwsJC/v3vf7NkyRJ69eqFl5dXhe+/9tprNRJO5Gz8mrgzsmsLFm5KYe6qfbxx05lLWYiISOVOFhQz7fT02219W3FZW1+TE9Vt1SpL27dvp0ePHgAkJiZW+J6m58ReJvVrzcJNKfywLY3Hr4nA39vD7EgiIg5h5o/xpGSeIrhpI6YP1/TbuVSrLC1fvrymc4hcsM7BVnqFNmXD/hN8FrufB4d0MDuSiEidtzLpKJ+vLV00+qUbuuCl6bdzuqhrrnfv3s3PP//MqVOnAKjmKgQi1TbpstYAfL72APlFJSanERGp23Lyi3j069LptwlRofS7RNNv56NaZenYsWNcffXVtG/fnmuuuYa0tDQA7rrrLh566KEaDShSlaEdA2hh9eBYbiHfb00zO46ISJ32wunpt5BmjXh0WLjZcRxGtcrS1KlTcXV15cCBA3h6epY/P378eGJiYmosnMi5uDg7ER0VCpQuI6DRTRGRyv038QhfrjsIwMs3dNX02wWoVln65ZdfePHFFwkODq7wfLt27di/f3+NBBM5Xzdf2goPVyd2pGazPvmE2XFEROqc7PwiHjs9/TaxXxh92zQ3OZFjqVZZys3NrTCiVObo0aO4u7tfdCiRC9HUy42x3VsCWqRSRKQyz38fT2pWPqHNPXlkmC6GuVDVKktXXHEFn3zySfnXFosFm83Gyy+/zJVXXllj4UTO18R+pSd6/7wjnZTMUyanERGpO1YkHOarDQexWEqn3zzdNP12oap1xF5++WUGDhzIhg0bKCws5JFHHmHHjh0cP36cVatW1XRGkXPqENiEfpc0Z/WeY3yyJlnrhoiIAFmninjs621A6fRb79bNTE7kmKo1shQZGcnWrVvp3bs3gwcPJjc3l+uuu47NmzdzySWX1HRGkfNStozAvHUHySssNjmNiIj5nvt+J+nZ+YQ19+SRobr6rbqqPRYXGBjIP/7xj5rMInJRrgr3p1UzTw4cz2PR5hRu7RNqdiQREdMs33WY+RsPlU6/jetKIzdnsyM5rGqXpczMTNatW8fhw4ex2WwVvjdhwoSLDiZyoZydLEyICuW5H+KZuyqZW3q30u13RKRBysor4rGFpVe/3XFZay4N0/TbxahWWfruu++49dZbyc3NpUmTJhX+IFksFpUlMc2Nl4bw+pJEkg6fZNXuY/Rvp9VpRaTheeb7nWRkF9Da14uHdSuoi1atc5Yeeugh7rjjDnJycsjMzOTEiRPlj+PHj9d0RpHz5u3hyg09S9f/0jICItIQ/RqfwdebSqffXhnXRdNvNaBaZSklJYX77ruv0rWWRMx2e78wAJYlHCb5aK65YURE7Cgrr4jpC0uvfrurf2t6hmr6rSZUqywNHTqUDRs21HQWkRrRxq8xAzv4YRjw8Zpks+OIiNjNP77bweGcAtr4efGQpt9qTLXOWRoxYgTTpk1j586ddO7cGVdX1wrfHz16dI2EE6muSZe1ZkXCEeZvOMSDg9vTxMP13DuJiDiwJTszWLg5BScLvDKuKx6umn6rKdUqS5MnTwbgmWeeOeN7FouFkpKSi0slcpGuaOfLJX5e7DmSy4KNh8rXYBIRqY8y8wp5fFHp9Nvky9vQo1VTkxPVL9WahrPZbGd9qChJXWCxWJh4uiB9vDoZm80wOZGISO2ZsXgHR3IKuMTPi6mD25sdp965oLJ0zTXXkJWVVf71888/T2ZmZvnXx44dIzIyssbCiVyM67q3pImHC8nH8liReNjsOCIiteLnHel8E5eKkwVevbGbpt9qwQWVpZ9//pmCgoLyr1988cUKSwUUFxeTkJBQc+lELoKXuws3XRoCwJxVyeaGERGpBSdyC/m/RdsB+MsVl9AtxMfcQPXUBZUlwzCq/FqkrpkQFYaTBX5POkpSRo7ZcUREatTTi3dw9GQB7fwb88CgdmbHqbeqdc6SiKMIaebJ4MgAAOauTjY3jIhIDYrZnsbiLak4O1l09Vstu6CyZLFYzrjXlu69JXXdxH6lJ3ov3JRCVl6RyWlERC7e8dxCnvimdPrtngFt6Krpt1p1QUsHGIbBxIkTcXd3ByA/P5977rkHLy8vgArnM4nUFX3bNCM8sAm70nOYt/4Adw+4xOxIIiIX5alvt3P0ZCEdAppw39WafqttFzSydPvtt+Pv74/VasVqtXLbbbcRFBRU/rW/v79uoit1jsVi4Y7Tywh8smY/xSU2kxOJiFTfj9vS+H5rWvn0m7uLpt9q2wWNLM2ZM6e2cojUqtHdgpgVs4uUzFMsjc9gWKcWZkcSEblgx04W8OTp6be/DbyEzsFWkxM1DDrBWxoED1dnbu5duozAR1pGQEQc1FPf7uBYbiHhgU249ypNv9mLypI0GNF9w3BxsrBu33F2pGadewcRkTrk+62p/LDtf9Nvbi76E24vDnOkT5w4QXR0dPn5UdHR0RVWD69MRkYGEydOJCgoCE9PT4YNG0ZSUlKFbQoKCrj33nvx9fXFy8uL0aNHc+jQoVp8J2KWQKsHwzuXTr9pkUoRcSRHcv43/fb3K9vSqaWm3+zJYcrSLbfcQlxcHDExMcTExBAXF0d0dPRZtzcMgzFjxrB3716+/fZbNm/eTGhoKIMGDSI3N7d8uwceeIBFixYxb948Vq5cycmTJxk5cqTucVdPTbosDIDFcakcPamrN0Wk7jMMgye/2c6JvCIiWngz5cq2ZkdqcCyGAyzDHR8fT2RkJLGxsfTp0weA2NhYoqKi2LVrFx06dDhjn8TERDp06MD27dvp2LEjACUlJfj7+/Piiy9y1113kZWVhZ+fH59++injx48HIDU1lZCQEH788UeGDh16Xvmys7OxWq1kZWXh7e1dQ+9aaoNhGIx5exVbDmXx0OD23KtLbkWkjlu8JZX7vtyMi5OFb6dcRscgjSrVlPP9++0QI0tr1qzBarWWFyWAvn37YrVaWb16daX7lK355OHhUf6cs7Mzbm5urFy5EoCNGzdSVFTEkCFDyrcJCgqiU6dOZ31dcWwWi4VJp5cR+DR2P4XFWkZAROquwzn5PPVt6fTblKvaqiiZxCHKUnp6Ov7+/mc87+/vT3p6eqX7hIeHExoayvTp0zlx4gSFhYXMmjWL9PR00tLSyl/Xzc2Npk2bVtg3ICDgrK8LpUUsOzu7wkMcxzWdW+DfxJ3DOQX8tD3N7DgiIpUyDIMnFm0nM6+IyBbe/F3Tb6YxtSzNmDGj/BYqZ3ts2LABqPy2KoZhnPV2K66urnz99dckJibSrFkzPD09WbFiBcOHD8fZueoFvKp6XYCZM2eWn2hutVoJCQm5gHctZnNzceK2vqGATvQWkbpr8ZZUftmZgatz6dVvrs4OMb5RL13QopQ1bcqUKdx0001VbhMWFsbWrVvJyMg443tHjhwhICDgrPv27NmTuLg4srKyKCwsxM/Pjz59+tCrVy8AAgMDKSws5MSJExVGlw4fPky/fv3O+rrTp0/nwQcfLP86OztbhcnB3Ny7FbOX7SbuYCabD5yge6um595JRMRODmfn89S3OwC496p2RAbpfFgzmVqWfH198fX1Ped2UVFRZGVlsW7dOnr37g3A2rVrycrKqrLUlLFaS+d4k5KS2LBhA88++yxQWqZcXV1ZsmQJN954IwBpaWls376dl1566ayv5+7uXn5/PHFMfk3cGdU1iK83HWLOqmSVJRGpMwzD4PFF28k6VUSnlt78daDuZ2k2hxjTi4iIYNiwYUyePJnY2FhiY2OZPHkyI0eOrHAlXHh4OIsWLSr/ev78+axYsaJ8+YDBgwczZsyY8hO6rVYrd955Jw899BC//vormzdv5rbbbqNz584MGjTI7u9T7KtsGYEft6WRnpVvbhgRkdO+iUthabym3+oSh/kv8Pnnn9O5c2eGDBnCkCFD6NKlC59++mmFbRISEsjK+t/KzGlpaURHRxMeHs59991HdHQ0X375ZYV9Xn/9dcaMGcONN97IZZddhqenJ9999905z2sSx9eppZXeYc0othl8vna/2XFERMjIzmfG4p0A3H91O8IDNf1WFzjEOkt1ndZZclw/bkvjb59vormXG6seuwoPV5VkETGHYRjc9fEGft11mM4trSz6Wz9cNKpUq+rVOksitWVIZAAtfRpxLLeQxVtSzY4jIg3Ywk0p/LrrMG7OTrx6Y1cVpTpE/yWkQXNxdiI66n/LCGigVUTMkJ6Vz4zvSq9+u39QO9oHNDE5kfyRypI0eDddGoKHqxPxadms23fc7Dgi0sAYhsH0hVvJyS+ma7CVu69oY3Yk+ROVJWnwfDzdGNs9GNAilSJifws2HmJ5whHcnJ14ZZym3+oi/RcR4X/LCPyyM52Dx/PMDSMiDUZa1ime+a706repg9vTTtNvdZLKkgjQPqAJ/dv6YjNKb7ArIlLbDMPgsa+3kVNQTLcQHyZf3trsSHIWKksip03sFwbAvHUHyCssNjeMiNR78zcc4rfEI7i5aPqtrtN/GZHTrgr3J7S5J9n5xSzclGJ2HBGpx1IzT/Hs96XTbw8Nbk9b/8YmJ5KqqCyJnObkZOH2qDAA5q7WMgIiUjsMw+DRr7eSU1BM91Y+3HW5rn6r61SWRP5gXK9gvNyc2X34JCt3HzU7jojUQ/PWH+T3pKO4n55+c3aymB1JzkFlSeQPmni4Mq5XCKBlBESk5qVknuL5H+IBmDa0A5f4afrNEagsifzJ7f3CsFhg2a7D7Duaa3YcEaknDMPg0QVbOVlQTK/Qpky6TFe/OQqVJZE/ae3rxZUd/AH4eHWyuWFEpN74Yt0BVu4unX576YYumn5zICpLIpUoW6Ry/oaD5OQXmRtGRBzeweN5vHB6+u2RYeG00fSbQ1FZEqlE/7a+tPVvTG5hCfM3HDI7jog4MJut9Oq33MISLg1ryqTTa7qJ41BZEqmExWIpX6Ty4zXJlNi0jICIVM/n6w6wes8xPFydePmGrjhp+s3hqCyJnMV1PVri7eHC/mN5LN912Ow4IuKADh7PY+aPpdNvjw4LJ8zXy+REUh0qSyJn4enmws29WwGli1SKiFwIm81g2oIt5BWW0Lt1s/JFb8XxqCyJVCE6KhQnC6zcfZTEjByz44iIA/ls7X5i9x6nkaszL9/QRdNvDkxlSaQKwU09GRIZCGiRShE5fweO5THzx10APDY8nNDmmn5zZCpLIudQtozAos2HyMwrNDeMiNR5NpvBwwu2cKqohL5tmhHdN9TsSHKRVJZEzqF362ZEtvAmv8jGvPUHzY4jInXcJ2uSWbfvOJ5uzrx0va5+qw9UlkTOwWKxMPH06NInq5MpLrGZG0hE6qzko7m8GJMAwPTh4bRq7mlyIqkJKksi52F01yCae7mRmpXPLzszzI4jInWQzWbwyIKtnCoqIapNc27to+m3+kJlSeQ8eLg6c0uf0mUE5qzaZ3IaEamL5q5OZl3ycbzcnHlJV7/VKypLIufptr6huDhZWJ98gu0pWWbHEZE6ZN/RXF76ufTqt+nXRBDSTNNv9YnKksh5CvD24JrOLQAtIyAi/1NiM5g2fwv5RTb6t/Xl1tOj0FJ/qCyJXICyZQS+25LKkZwCc8OISJ0wZ9U+Nuw/QWN3F2Zd3xmLRdNv9Y3KksgF6N6qKd1CfCgssfHF2gNmxxERk+05cpKXfy69+u3xayIIbqrpt/pIZUnkApWNLn22dj+FxVpGQKShKpt+Kyi2cXk7X27uHWJ2JKklKksiF2h4pxb4N3HnSE4BP25LMzuOiJjko5X72HQg8/T0WxdNv9VjKksiF8jNxan89gVzVu3DMAyTE4mIve0+fJKXfymdfntiRAQtfRqZnEhqk8qSSDXc0qcVbi5ObDmUxeaDmWbHERE7KrEZTFuwhcJiG1e092P8pZp+q+9UlkSqoXljd67tGgRoGQGRhubfv+9l84FMmri78KKufmsQVJZEqqnsfnE/bUsjPSvf3DAiYhe7D+fw6pJEAJ4cFUkLq6bfGgKVJZFq6hhkpXfrZhTbDD6NTTY7jojUsuISGw/N30phsY0rO/gxrmew2ZHETlSWRC7CHadHl75Ye4D8ohJzw4hIrfrX73vZcjCTJh4uzLxOV781JCpLIhdhUEQALX0acSKviMVxqWbHEZFakpiRwxtLkgB4elRHAq0eJicSe1JZErkILs5OTIgqXUbgIy0jIFIvFZfYeHj+FgpLbFwV7s/1PVqaHUnsTGVJ5CLddGkrGrk6sys9h9i9x82OIyI17P3/7mXroSy8PVyYeZ2ufmuIVJZELpLV05XrTv9Lc+7qfSanEZGalJCewxtLS69+mzG6IwHemn5riFSWRGrAxH5hACzZmcHB43nmhhGRGlF0evqtqMRgUIQ/Y7tr+q2hUlkSqQHtAppweTtfbAZ8sibZ7DgiUgPeW7GHbSlZWBu58sJYTb81ZCpLIjVk0ullBOatP0huQbG5YUTkosSnZfPmstKr3/4xuiP+mn5r0FSWRGrIwPb+hDX3JCe/mIWbU8yOIyLV9Mfpt8GRAVzbLcjsSGIylSWRGuLkZOH20+cuzV21D5tNywiIOKJ3lu9hR2o2Pp6uPD+2k6bfRGVJpCbd0DOYxu4u7DmSy++7j5odR0Qu0I7ULN764/RbE02/icqSSI1q4uHKuF6l94uas0rLCIg4ksJiGw/P30qxzWBoxwBGd9X0m5RSWRKpYbdHhWGxwIqEI+w9ctLsOCJynt5evpv4tGyaerry3Bhd/Sb/o7IkUsPCfL24qoM/AB+vTjY3jIicl+0pWby9fDcAz1zbCb8m7iYnkrpEZUmkFky6rDUACzYeIju/yOQ0IlKV0um3LRTbDIZ3CmRklxZmR5I6RmVJpBZc1rY57QMak1tYwn/WHzQ7johUYfayJHal59DMy41nx+jqNzmTypJILbBYLEzsVzq69PGaZPKLSkxOJCKV2Z6Sxdsr9gDw7LWd8G2s6Tc5k8qSSC0Z270lPp6uHDx+ilFvrWTLwUyzI4nIHxQUl/DQf7ZQYjMY0aUFIzT9JmehsiRSSxq5OTP75h74NnYn6fBJrnt3NS/F7KKgWKNMInXBW7/uJiEjh+ZebjwzuqPZcaQOU1kSqUX92/myZOoVjO4aRInN4J0Vexj11kq2Hso0O5pIg7b1UCbv/lY6/fbcmE401/SbVEFlSaSWNfVy482bu/PebT3wbexGYsZJxr6zmld+TtAok4gJCopLeHh+6fTbqK5BDO+s6TepmsqSiJ0M69SCX6YOYGSXFpTYDGYv383ot1axPSXL7GgiDco/lyaRmHES38Zu/EPTb3IeVJZE7KiZlxuzb+nBu7f2oLmXGwkZOVz79ipe+yWBwmKb2fFE6r24g5m8Vz791plmXm4mJxJHoLIkYoLhnVvwy9QrGNG5dJTpzWW7GT17pUaZRGpRflHp9JvNgGu7BTGsU6DZkcRBqCyJmKR5Y3fevrUHb9/Sg2ZebuxKz2HM26t4bUmiRplEasEbS5PYffgkvo3dmTFK029y/lSWREw2okvpKNPwToEU2wze/DWJa99exc7UbLOjidQbmw6c4F//LZ1+e2FsJ5pq+k0ugMqSSB3g29idd27twVs3d6eppyvxadmMnr2SN5YmUlSiUSaRi5FfVMK009NvY7u3ZEhHTb/JhXGYsnTixAmio6OxWq1YrVaio6PJzMyscp+MjAwmTpxIUFAQnp6eDBs2jKSkpPLvHz9+nHvvvZcOHTrg6elJq1atuO+++8jK0nkjYn8Wi4VRXYP4ZeoAhnUsHWV6Y2kSY95eRXyaRplEquu1JYnsOZKLXxN3nh4VaXYccUAOU5ZuueUW4uLiiImJISYmhri4OKKjo8+6vWEYjBkzhr179/Ltt9+yefNmQkNDGTRoELm5uQCkpqaSmprKK6+8wrZt25g7dy4xMTHceeed9npbImfwa+LOu7f14M2bu+Pj6cqO1NJRpjd/TdIok8gFKCy28e/f9/Lv3/cCMHNsZ3w8Nf0mF85iGIZhdohziY+PJzIyktjYWPr06QNAbGwsUVFR7Nq1iw4dOpyxT2JiIh06dGD79u107Fh6Il9JSQn+/v68+OKL3HXXXZX+rPnz53PbbbeRm5uLi4vLeeXLzs7GarWSlZWFt7d3Nd+lyJkO5+TzxKLt/LIzA4BOLb15ZVxXwgP1eyZyNoZhsGRnBjN/2sW+o6X/OL6xVzAv3dDV5GRS15zv32+HGFlas2YNVqu1vCgB9O3bF6vVyurVqyvdp6CgAAAPD4/y55ydnXFzc2PlypVn/VllB+x8i5JIbfJv4sH70T35503dsDZyZXtKNqPeWsnsZUkUa5RJ5Aw7UrO45YO1/OXTjew7motvY3dmXdeZmdd1MTuaODCHaATp6en4+/uf8by/vz/p6emV7hMeHk5oaCjTp0/n/fffx8vLi9dee4309HTS0tIq3efYsWM8++yz3H333VXmKSgoKC9jUNpMRWqLxWLh2m4tiWrTnMcXbWdpfAav/JLIzzsyeGVcVzoENjE7oojpDmfn8+ovifxn40EMA9xcnLirf2v+dmVbGrs7xJ86qcNMHVmaMWMGFoulyseGDRuA0j8Yf2YYRqXPA7i6uvL111+TmJhIs2bN8PT0ZMWKFQwfPhxnZ+czts/OzmbEiBFERkby9NNPV5l75syZ5SeaW61WQkJCqvHuRS6Mv7cHH0zoyevju2Jt5Mq2lCxGvbWSt5fv1iiTNFj5RSXMXpbEwFdW8NWG0qI0sksLfn1wAI8MC1dRkhph6jlLR48e5ejRo1VuExYWxhdffMGDDz54xtVvPj4+vP7660yaNKnK18jKyqKwsBA/Pz/69OlDr169ePvtt8u/n5OTw9ChQ/H09OT777+vMHVXmcpGlkJCQnTOktjN4ex8Hl+0jaXxhwHoGmzllXFdaRegUSZpGAzDYPGWVF6KSSAl8xQAXUN8eGpkBD1Dm5mcThzF+Z6z5FAneK9du5bevXsDsHbtWvr27XvWE7wrk5SURHh4OD/99BNDhgwBSg/U0KFDcXd358cff8TT0/OC8+kEbzGDYRgs3JTCP77bQXZ+MW7OTkwd3J7Jl7fGxdkhTkcUqZZNB07w7Pc72XwgE4AgqwePDg9nVJcgnJwqn20QqUy9KksAw4cPJzU1lffffx+Av/zlL4SGhvLdd9+VbxMeHs7MmTMZO3YsUHplm5+fH61atWLbtm3cf//99OzZk6+//hooHVEaPHgweXl5LFq0CC8vr/LX8vPzq3S6rjIqS2Km9KzSUaZlu06PMoX48Oq4LrT11yiT1C8pmad48addLN6SCoCnmzN/HXAJd13ehkZu5/d5LfJH5/v322Emcz///HPuu+++8hGh0aNHM3v27ArbJCQkVFhQMi0tjQcffJCMjAxatGjBhAkTePLJJ8u/v3HjRtauXQtA27ZtK7zWvn37CAsLq6V3I1JzAq0efHh7LxZsPMQz3+9ky8FMrnlzJQ8Obs/ky9vgrH9pi4PLLSjm3RV7+OD3vRQU27BY4IYewTw8tAMB3lWfNiFSExxmZKku08iS1BXpWfk8tnArKxKOANC9lQ8v39CVtv6NTU4mcuFKbAYLNh7klV8SOZJTep5on9bNeHJkJJ1aWk1OJ/VBvZuGq8tUlqQuMQyD+RsO8ez3O8kpKMbNxYmHh7Tnzv4aZRLHsXrPUZ77Pp6dp2/1E9rck8eviWBIZMBZr4IWuVAqS3aksiR1UWrmKR5buI3/JpaOMvVo5cPL47pyiZ9GmaTu2nc0lxd+jGfJ6VXrm3i4cP/V7ZgQFYabiy5ckJqlsmRHKktSVxmGwX82HOTZ7+M5WVCMu4sT04Z2YNJlrTXKJHVKVl4Rby5L4pM1yRSVGDg7Wbi1TyseGNSeZl66n5vUDpUlO1JZkrouJfMUj329ld+TStc16xXalJdu6EIbjTKJyYpKbHweu583fk0iM68IgIEd/Pi/ayK0bpjUOpUlO1JZEkdgGAbz1h/k+R80yiTmMwyD5QmHef6HePYcKb3ZbTv/xjwxMpIB7f1MTicNhcqSHaksiSNJyTzFowu2snJ36SjTpWFNefmGroT5ep1jT5GakZCew3M/7Cwf6Wzm5caDg9tz06UhWlBV7EplyY5UlsTRGIbBF+sO8MIP8eQWluDh6sQjQ8OZ2C9MKyBLrTl6soDXliQyb90BbAa4OTsx6bIw/n5VW7w9XM2OJw2QypIdqSyJozp4PI/HFm5l1e5jAPRu3YyXb+hCaHONMknNKSguYc6qZN5etpucgmIAhncK5LHh4fpdE1OpLNmRypI4MsMw+HztAV74MZ68whIauTrz6LAOTIjSKJNcHMMw+Gl7OjN/iufg8dKb3XZuaeWJERH0adPc5HQiKkt2pbIk9cHB43k8smAra/aWjjL1ad2Ml2/oSqvmF35zaZGthzJ59vudrE8+AUCAtzvThoZzXfeWKuFSZ6gs2ZHKktQXNpvB52v388KPuzhVVIKnmzOPDQ/ntj6h+gMn5yUt6xQvxySwcHMKAB6uTvzliku4Z0AbPN0c5nak0kCoLNmRypLUNweO5TFtwRbW7jsOQN82paNMIc00yiSVyyss5v3f9vL+f/eQX2QD4LruLZk2rAMtrI1MTidSOZUlO1JZkvrIZjP4NHY/s3763yjT9GsiuLV3K40ySTmbzWDR5hRe/jmB9Ox8oHTR0ydHRtI1xMfccCLnoLJkRypLUp/tP5bLtPlbWZdcOsrU75LmvHh9F40yCev2Hee5H3ay9VAWAMFNGzF9eATXdA7UzW7FIags2ZHKktR3NpvBx2uSeTFmF/lFNrzcnHl8RAS39G6lP4oN0IFjecyKiefHbekANHZ34e9XtmXSZWF4uDqbnE7k/Kks2ZHKkjQUyUdzmbZgS/kVTv3b+jLr+s4EN9UoU0OQnV/E28t2M2dVMoUlNpwscFPvVjw4uD2+jd3NjidywVSW7EhlSRoSm81gzupkXv65dJSpsbsLj18Twc29QzTKVE8Vl9iYt/4gry9J5FhuIQCXt/Pl/0ZEEB6ozzxxXCpLdqSyJA3RvqO5TJu/hQ37S0eZLm/ny6zru9DSR1c+1Sf/TTzCcz/sJDHjJABt/Lx4YkQEV3bwVzkWh6eyZEcqS9JQldgM5qzax8s/J1BQXDrK9MSICMZfqlEmR7f7cA7P/xDP8oQjAPh4uvLA1e24tW8orrrZrdQTKkt2pLIkDd2eIyeZNn8Lmw5kAnBFez9mXdeZII0yOZzjuYW8sTSRz9ceoMRm4OJkYUJUGPdf3Q6rp252K/WLypIdqSyJlI4yfbhyL6/8kkhhsY0m7i48OTKScb2CNcrkAAqLbXyyJpk3f00iO7/0ZreDIwOYPjycNn6NTU4nUjtUluxIZUnkf3YfPsm0BVvYfHqUaWAHP2Ze11mrONdRhmHwy84MZv4YT/KxPAAiWnjz5IgI+rX1NTmdSO1SWbIjlSWRikpsBv/+fS+vLjk9yuRxepSpp0aZ6pIdqVk8+/1OYveWLjjq29idaUPbc0PPEJy1Srs0ACpLdqSyJFK53YdzeGj+VrYczATgyg5+zLyuC4FWD3ODNXCHs/N55ZcE5m88hGGAm4sTky9vzV8HtqWxu252Kw2HypIdqSyJnF1xiY0Pft/H60sSKSyx4e3hwlOjOnJ9j5YaZbKz/KIS/v37Xt5ZsYe8whIARnUN4tFhHbSwqDRIKkt2pLIkcm5JGTk8PH8LW07fR+zqcH9euK4zAd4aZapthmGweEsqL/60i9Ss0pvddgvx4cmRkfQMbWpyOhHzqCzZkcqSyPkpLrHx/n/38s+lSeWjTDNGd2Rsd40y1ZaN+0/w7Pc7iTs9FRpk9eDR4eGM7hqkYy4NnsqSHaksiVyYhPTSUaZtKaWjTIMiAnhhbCf8NcpUYw6dyOPFmAS+25IKgKebM38beAl3Xd5GN7sVOU1lyY5UlkQuXNko0xtLEykqMbA2cuUfoztybTeNeFyMkwXFvLtiN//+fR8FxTYsFhjXM5iHh3RQGRX5E5UlO1JZEqm+XenZPDx/C9tTsoHShRCfH9sJ/yb6w34hSmwG8zcc5JVfEjl6sgCAvm2a8cSISDq1tJqcTqRuUlmyI5UlkYtTVGLjvRV7eHNZEkUlBj6epaNMOq/m/KzefZRnf4gnPq20cIY19+TxayIYHBmg4ydSBZUlO1JZEqkZ8WnZPPSfLew8/Ud/aMcAnhvTGb8m7iYnq5v2HjnJCz/uYml8BgDeHi7cd3U7JkSF4eaim92KnIvKkh2pLInUnKISG+8s38Nby5Iothk09XTlH9d2YlSXFholOS0rr4h//prEJ2uSKbYZODtZuK1PK+4f1J5mXm5mxxNxGCpLdqSyJFLzdqaWnstUNso0vFMgz47phG/jhjvKVFRi4/PY/bzxaxKZeUVA6aro/zcigrb+TUxOJ+J4VJbsSGVJpHYUFtt4e/lu3l6+m2KbQTMvN565tiMjuwSZHc2uDMNgecJhnv8hnj1HcgFoH9CYJ0ZEckV7P5PTiTgulSU7UlkSqV3bU7J4eP4WdqXnAHBN50CevbYTzRvAKNOu9Gye+z6elbuPAtDcy40Hh7RnfK8QXJx1XpLIxVBZsiOVJZHaV1hsY/ayJN5esYcSm0FzLzeeHdOJazq3MDtarTh6soBXf0nkq/UHsBng5uzEpP5h/P3Ktnh7uJodT6ReUFmyI5UlEfv58yjTiC4tePbaTvXmxOb8ohLmrErm7eW7OVlQDJSOpD02LIJWzXWzW5GapLJkRypLIvZVUFzCW7/u5t3f/jfK9NyYTgx34FEmwzD4cVs6M3+K59CJUwB0CbbyxIhIerduZnI6kfpJZcmOVJZEzLH1UCYPz99CYsZJAEZ1DeKZ0R1p6mCjTFsOZvLcDztZn3wCgEBvDx4Z1oEx3Vri5KTlEkRqi8qSHaksiZinoLiEN39N4t0Ve7AZ4NvYjefGdGZYp0Czo51TWtYpXo5JYOHmFAAauTpz94A2/OWKNni6uZicTqT+U1myI5UlEfNtOVg6ypR0uHSU6dpuQcwYVTdHmfIKi3nvt7386797yC+yAXBdj5Y8MjScQKvuiSdiLypLdqSyJFI35BeV8M9fk3j/t7JRJndeGNuJIR3rxiiTzWawcHMKL/+8i4zs0pvdXhrWlCdHRtIl2MfccCINkMqSHaksidQtcQczeeg/ceULOI7t3pKnR0Xi42neKNO6fcd59vudbEvJAiCkWSOmD49geKdA3cZFxCQqS3aksiRS9+QXlfD60kQ++O9ebAb4NXFn5tjODIoMsGuOA8fymPlTPD9tTwegibsLU65qy+39wvBwdbZrFhGpSGXJjlSWROquTQdOMG3+lvJRpuu6t+TpUR2xetbuwo7Z+UXMXrabuauSKSyx4WSBm3u3Yurg9g36/nYidYnKkh2pLInUbflFJby+JJF//b4Xw4AAb3dmXteZq8JrfpSpuMTGl+sP8vqSRI7nFgJweTtfnhgRSYdA3exWpC5RWbIjlSURx7Bxf+ko096jpaNM1/cI5qlRkVgb1cwo02+JR3j+h53l6z5d4ufFEyMiGdjBT+clidRBKkt2pLIk4jjyi0p49ZcE/r1yX/ko06zrunBluH+1X3P34Rye+yGeFQlHAPDxdGXqoPbc0qcVrrrZrUidpbJkRypLIo5nQ/Jxpi3Yyr7To0zjegbzxMgLG2U6nlvIG0sT+XztAUpsBq7OFiZEhXHfVe1q/ZwoEbl4Kkt2pLIk4phOFZbwyi8JfLSqdJQp0NuDWdd3ZmCHqkeZCottfLImmX/+mkROfunNbodEBjD9mgha+3rZI7qI1ACVJTtSWRJxbOuTjzNt/haSj+UBML5XCP83MgJvj4qjQ4Zh8POODGb+FM/+09tGtvDmiZER9LvE1+65ReTiqCzZkcqSiOM7VVjCSz/vYu7qZAwDWlg9ePH6LlzR3g+A7SlZPPfDTmL3HgdK122aNqQD1/cMxlk3uxVxSCpLdqSyJFJ/rN17jGkLtnLgeOnI0U2XhlBiM1iw6RCGAe4uTky+vA33DLyExu662a2II1NZsiOVJZH6Ja+wmJdiEpi7OrnC89d2C+KRYeG09GlkTjARqVHn+/db/ywSEfkTTzcXZozuyLBOgTz97Q6snq48NjycHq2amh1NREygkaUaoJElERERx3O+f7+1WpqIiIhIFVSWRERERKqgsiQiIiJSBZUlERERkSqoLImIiIhUQWVJREREpAoOU5ZOnDhBdHQ0VqsVq9VKdHQ0mZmZVe6TkZHBxIkTCQoKwtPTk2HDhpGUlFTptoZhMHz4cCwWC998803NvwERERFxSA5Tlm655Rbi4uKIiYkhJiaGuLg4oqOjz7q9YRiMGTOGvXv38u2337J582ZCQ0MZNGgQubm5Z2z/xhtvYLHo/k4iIiJSkUOs4B0fH09MTAyxsbH06dMHgA8++ICoqCgSEhLo0KHDGfskJSURGxvL9u3b6dixIwDvvPMO/v7+fPnll9x1113l227ZsoXXXnuN9evX06JFC/u8KREREXEIDjGytGbNGqxWa3lRAujbty9Wq5XVq1dXuk9BQQEAHh4e5c85Ozvj5ubGypUry5/Ly8vj5ptvZvbs2QQGBtbSOxARERFH5RBlKT09HX9//zOe9/f3Jz09vdJ9wsPDCQ0NZfr06Zw4cYLCwkJmzZpFeno6aWlp5dtNnTqVfv36ce211553noKCArKzsys8REREpH4ytSzNmDEDi8VS5WPDhg0AlZ5PZBjGWc8zcnV15euvvyYxMZFmzZrh6enJihUrGD58OM7OzgAsXryYZcuW8cYbb1xQ7pkzZ5afaG61WgkJCbmwNy4iIiIOw9RzlqZMmcJNN91U5TZhYWFs3bqVjIyMM7535MgRAgICzrpvz549iYuLIysri8LCQvz8/OjTpw+9evUCYNmyZezZswcfH58K+11//fVcfvnlrFixotLXnT59Og8++GD519nZ2SpMIiIi9ZTFMAzD7BDnEh8fT2RkJGvXrqV3794ArF27lr59+7Jr165KT/CuTFJSEuHh4fz0008MGTKE9PR0jh49WmGbzp07889//pNRo0bRunXr83rd871rsYiIiNQd5/v32yGuhouIiGDYsGFMnjyZ999/H4C//OUvjBw5skJRCg8PZ+bMmYwdOxaA+fPn4+fnR6tWrdi2bRv3338/Y8aMYciQIQAEBgZWelJ3q1atzrsoQel0IKBzl0RERBxI2d/tc40bOURZAvj888+57777yovO6NGjmT17doVtEhISyMrKKv86LS2NBx98kIyMDFq0aMGECRN48sknazxbTk4OgKbiREREHFBOTg5Wq/Ws33eIabi6zmazkZqaSpMmTerEwpZl51AdPHhQ04LoePyZjseZdEwq0vGoSMejovp0PAzDICcnh6CgIJyczn7Nm8OMLNVlTk5OBAcHmx3jDN7e3g7/i1yTdDwq0vE4k45JRToeFel4VFRfjkdVI0plHGKdJRERERGzqCyJiIiIVEFlqR5yd3fn6aefxt3d3ewodYKOR0U6HmfSMalIx6MiHY+KGuLx0AneIiIiIlXQyJKIiIhIFVSWRERERKqgsiQiIiJSBZUlB5aSksJtt91G8+bN8fT0pFu3bmzcuLH8+4ZhMGPGDIKCgmjUqBEDBw5kx44dJiauXVUdj6KiIh599FE6d+6Ml5cXQUFBTJgwgdTUVJNT165z/Y780d13343FYuGNN96wb0g7Op/jER8fz+jRo7FarTRp0oS+ffty4MABkxLXrnMdj5MnTzJlyhSCg4Np1KgRERERvPvuuyYmrj1hYWFYLJYzHn//+9+Bhvd5WtXxaIifpypLDurEiRNcdtlluLq68tNPP7Fz505effVVfHx8yrd56aWXeO2115g9ezbr168nMDCQwYMHl9+epT451/HIy8tj06ZNPPnkk2zatImFCxeSmJjI6NGjzQ1ei87nd6TMN998w9q1awkKCrJ/UDs5n+OxZ88e+vfvT3h4OCtWrGDLli08+eSTeHh4mBe8lpzP8Zg6dSoxMTF89tlnxMfHM3XqVO69916+/fZb84LXkvXr15OWllb+WLJkCQDjxo0DGtbnKVR9PBri5ymGOKRHH33U6N+//1m/b7PZjMDAQGPWrFnlz+Xn5xtWq9V477337BHRrs51PCqzbt06AzD2799fS6nMdb7H5NChQ0bLli2N7du3G6Ghocbrr79e++FMcD7HY/z48cZtt91mp0TmOp/j0bFjR+OZZ56p8FyPHj2MJ554ojaj1Qn333+/cckllxg2m63BfZ5W5o/HozL1/fNUI0sOavHixfTq1Ytx48bh7+9P9+7d+eCDD8q/v2/fPtLT08tvPAyla2MMGDCA1atXmxG5Vp3reFQmKysLi8VS6UhLfXA+x8RmsxEdHc20adPo2LGjSUnt41zHw2az8cMPP9C+fXuGDh2Kv78/ffr04ZtvvjEvdC06n9+P/v37s3jxYlJSUjAMg+XLl5OYmMjQoUNNSm0fhYWFfPbZZ9xxxx1YLJYG93n6Z38+HpWp75+nGllyUO7u7oa7u7sxffp0Y9OmTcZ7771neHh4GB9//LFhGIaxatUqAzBSUlIq7Dd58mRjyJAhZkSuVec6Hn926tQpo2fPnsatt95q56T2cz7H5IUXXjAGDx5c/q/F+jyydK7jkZaWZgCGp6en8dprrxmbN282Zs6caVgsFmPFihUmp6955/P7UVBQYEyYMMEADBcXF8PNzc345JNPTExtH1999ZXh7Oxc/vnZ0D5P/+zPx+PPGsLnqcqSg3J1dTWioqIqPHfvvfcaffv2NQzjf//jTk1NrbDNXXfdZQwdOtRuOe3lXMfjjwoLC41rr73W6N69u5GVlWWviHZ3rmOyYcMGIyAgoMIHYH0uS+c6HikpKQZg3HzzzRW2GTVqlHHTTTfZLae9nM//Zl5++WWjffv2xuLFi40tW7YYb731ltG4cWNjyZIl9o5rV0OGDDFGjhxZ/nVD+zz9sz8fjz9qKJ+nmoZzUC1atCAyMrLCcxEREeVX7QQGBgKQnp5eYZvDhw8TEBBgn5B2dK7jUaaoqIgbb7yRffv2sWTJknpxx+yzOdcx+f333zl8+DCtWrXCxcUFFxcX9u/fz0MPPURYWJgJiWvXuY6Hr68vLi4u5/V7VB+c63icOnWKxx9/nNdee41Ro0bRpUsXpkyZwvjx43nllVfMiGwX+/fvZ+nSpdx1113lzzW0z9M/qux4lGlIn6cqSw7qsssuIyEhocJziYmJhIaGAtC6dWsCAwPLr2CA0nnn3377jX79+tk1qz2c63jA//6HnZSUxNKlS2nevLm9Y9rVuY5JdHQ0W7duJS4urvwRFBTEtGnT+Pnnn82IXKvOdTzc3Ny49NJLz/l7VF+c63gUFRVRVFSEk1PFPxPOzs7YbDa75bS3OXPm4O/vz4gRI8qfa2ifp39U2fGAhvd5qmk4B7Vu3TrDxcXFeP75542kpCTj888/Nzw9PY3PPvusfJtZs2YZVqvVWLhwobFt2zbj5ptvNlq0aGFkZ2ebmLx2nOt4FBUVGaNHjzaCg4ONuLg4Iy0trfxRUFBgcvracT6/I39Wn6fhzud4LFy40HB1dTX+9a9/GUlJScZbb71lODs7G7///ruJyWvH+RyPAQMGGB07djSWL19u7N2715gzZ47h4eFhvPPOOyYmrz0lJSVGq1atjEcfffSM7zWkz9MyZzseDfHzVGXJgX333XdGp06dDHd3dyM8PNz417/+VeH7NpvNePrpp43AwEDD3d3duOKKK4xt27aZlLb2VXU89u3bZwCVPpYvX25e6Fp2rt+RP6vPZckwzu94fPjhh0bbtm0NDw8Po2vXrsY333xjQlL7ONfxSEtLMyZOnGgEBQUZHh4eRocOHYxXX331rJePO7qff/7ZAIyEhIQzvtfQPk8N4+zHoyF+nloMwzDMGNESERERcQQ6Z0lERESkCipLIiIiIlVQWRIRERGpgsqSiIiISBVUlkRERESqoLIkIiIiUgWVJREREZEqqCyJiIiIVEFlSUQatLCwMN544w2zY4hIHaayJCIOa9SoUQwaNKjS761ZswaLxcKmTZvsnEpE6huVJRFxWHfeeSfLli1j//79Z3zvo48+olu3bvTo0cOEZCJSn6gsiYjDGjlyJP7+/sydO7fC83l5eXz11VfceeedfP3113Ts2BF3d3fCwsJ49dVXz/p6ycnJWCwW4uLiyp/LzMzEYrGwYsUKAFasWIHFYuHnn3+me/fuNGrUiKuuuorDhw/z008/ERERgbe3NzfffDN5eXnlr2MYBi+99BJt2rShUaNGdO3alQULFtTk4RCRWqKyJCIOy8XFhQkTJjB37lz+eE/w+fPnU1hYSFRUFDfeeCM33XQT27ZtY8aMGTz55JNnlKvqmDFjBrNnz2b16tUcPHiQG2+8kTfeeIMvvviCH374gSVLlvDWW2+Vb//EE08wZ84c3n33XXbs2MHUqVO57bbb+O233y46i4jULovxx08YEREHs2vXLiIiIli2bBlXXnklAAMGDKBly5ZYLBaOHDnCL7/8Ur79I488wg8//MCOHTuA0hO8H3jgAR544AGSk5Np3bo1mzdvplu3bkDpyFLTpk1Zvnw5AwcOZMWKFVx55ZUsXbqUq6++GoBZs2Yxffp09uzZQ5s2bQC45557SE5OJiYmhtzcXHx9fVm2bBlRUVHlWe666y7y8vL44osv7HGoRKSaNLIkIg4tPDycfv368dFHHwGwZ88efv/9d+644w7i4+O57LLLKmx/2WWXkZSURElJyUX93C5dupT//wEBAXh6epYXpbLnDh8+DMDOnTvJz89n8ODBNG7cuPzxySefsGfPnovKISK1z8XsACIiF+vOO+9kypQpvP3228yZM4fQ0FCuvvpqDMPAYrFU2LaqwXQnJ6cztikqKqp0W1dX1/L/32KxVPi67DmbzQZQ/n9/+OEHWrZsWWE7d3f3c709ETGZRpZExOHdeOONODs788UXX/Dxxx8zadIkLBYLkZGRrFy5ssK2q1evpn379jg7O5/xOn5+fgCkpaWVP/fHk72rKzIyEnd3dw4cOEDbtm0rPEJCQi769UWkdmlkSUQcXuPGjRk/fjyPP/44WVlZTJw4EYCHHnqISy+9lGeffZbx48ezZs0aZs+ezTvvvFPp6zRq1Ii+ffsya9YswsLCOHr0KE888cRF52vSpAkPP/wwU6dOxWaz0b9/f7Kzs1m9ejWNGzfm9ttvv+ifISK1RyNLIlIv3HnnnZw4cYJBgwbRqlUrAHr06MF//vMf5s2bR6dOnXjqqad45plnystUZT766COKioro1asX999/P88991yN5Hv22Wd56qmnmDlzJhEREQwdOpTvvvuO1q1b18jri0jt0dVwIiIiIlXQyJKIiIhIFVSWRERERKqgsiQiIiJSBZUlERERkSqoLImIiIhUQWVJREREpAoqSyIiIiJVUFkSERERqYLKkoiIiEgVVJZEREREqqCyJCIiIlIFlSURERGRKvw/FsCLm/9axZsAAAAASUVORK5CYII="},"metadata":{}}],"execution_count":19}]} \ No newline at end of file diff --git a/python_workflow_definition/src/python_workflow_definition/jobflow.py b/python_workflow_definition/src/python_workflow_definition/jobflow.py index 063281a..9f7d530 100644 --- a/python_workflow_definition/src/python_workflow_definition/jobflow.py +++ b/python_workflow_definition/src/python_workflow_definition/jobflow.py @@ -2,6 +2,7 @@ from importlib import import_module from inspect import isfunction +import numpy as np from jobflow import job, Flow @@ -215,3 +216,26 @@ def load_workflow_json(file_name): source_handles_dict=source_handles_dict, ) return Flow(task_lst) + + +def write_workflow_json(flow, file_name="workflow.json"): + flow_dict = flow.as_dict() + function_dict = get_function_dict(flow=flow) + nodes_dict, nodes_mapping_dict = get_nodes_dict(function_dict=function_dict) + edges_lst, nodes_dict = get_edges_and_extend_nodes( + flow_dict=flow_dict, + nodes_mapping_dict=nodes_mapping_dict, + nodes_dict=nodes_dict, + ) + + nodes_store_dict = {} + for k, v in nodes_dict.items(): + if isfunction(v): + nodes_store_dict[k] = v.__module__ + "." + v.__name__ + elif isinstance(v, np.ndarray): + nodes_store_dict[k] = v.tolist() + else: + nodes_store_dict[k] = v + + with open(file_name, "w") as f: + json.dump({"nodes": nodes_store_dict, "edges": edges_lst}, f) diff --git a/python_workflow_definition/src/python_workflow_definition/pyiron_base.py b/python_workflow_definition/src/python_workflow_definition/pyiron_base.py index 5ab11e5..f921e98 100644 --- a/python_workflow_definition/src/python_workflow_definition/pyiron_base.py +++ b/python_workflow_definition/src/python_workflow_definition/pyiron_base.py @@ -1,6 +1,8 @@ -import json from importlib import import_module +from inspect import isfunction +import json +import numpy as np from pyiron_base import job from pyiron_base.project.delayed import DelayedObject @@ -216,3 +218,24 @@ def load_workflow_json(project, file_name): pyiron_project=project, ) return delayed_object_dict[list(delayed_object_dict.keys())[-1]] + + +def write_workflow_json(delayed_object, file_name="workflow.json"): + nodes_dict, edges_lst = delayed_object.get_graph() + nodes_dict, edges_lst = remove_server_obj(nodes_dict=nodes_dict, edges_lst=edges_lst) + delayed_object_updated_dict, match_dict = get_unique_objects(nodes_dict=nodes_dict, edges_lst=edges_lst) + connection_dict, lookup_dict = get_connection_dict(delayed_object_updated_dict=delayed_object_updated_dict, match_dict=match_dict) + nodes_new_dict = get_nodes(connection_dict=connection_dict, delayed_object_updated_dict=delayed_object_updated_dict) + edges_new_lst = get_edges_dict(edges_lst=edges_lst, nodes_dict=nodes_dict, connection_dict=connection_dict, lookup_dict=lookup_dict) + + nodes_store_dict = {} + for k, v in nodes_new_dict.items(): + if isfunction(v): + nodes_store_dict[k] = v.__module__ + "." + v.__name__ + elif isinstance(v, np.ndarray): + nodes_store_dict[k] = v.tolist() + else: + nodes_store_dict[k] = v + + with open(file_name, "w") as f: + json.dump({"nodes": nodes_store_dict, "edges": edges_new_lst}, f)